From b6f1c991b3bc79c7e5cb81bc22d559f4cae1ab11 Mon Sep 17 00:00:00 2001
From: Dmytro Chechenets <demavend12345@gmail.com>
Date: Sun, 22 Jun 2025 14:29:08 +0300
Subject: [PATCH 1/4] First submissions

---
 assets/BCCP/sample_submission.csv             | 10001 +++++++++++
 assets/BCCP/test.csv                          | 10001 +++++++++++
 assets/BCCP/train.csv                         | 15001 ++++++++++++++++
 .../catboost_info/catboost_training.json      |  9004 ++++++++++
 .../catboost_info/learn/events.out.tfevents   |   Bin 0 -> 494870 bytes
 .../catboost_info/learn_error.tsv             |  9001 ++++++++++
 .../catboost_info/test/events.out.tfevents    |   Bin 0 -> 917740 bytes
 .../catboost_info/test_error.tsv              |  9001 ++++++++++
 .../catboost_info/time_left.tsv               |  9001 ++++++++++
 .../kaggle_competition.ipynb                  |  5361 ++++++
 .../submission_cat.csv                        | 10001 +++++++++++
 .../submission_lgbm.csv                       | 10001 +++++++++++
 .../submission_rf.csv                         | 10001 +++++++++++
 .../submission_xgb.csv                        | 10001 +++++++++++
 14 files changed, 116375 insertions(+)
 create mode 100644 assets/BCCP/sample_submission.csv
 create mode 100644 assets/BCCP/test.csv
 create mode 100644 assets/BCCP/train.csv
 create mode 100644 bank_customer_churn_prediction/catboost_info/catboost_training.json
 create mode 100644 bank_customer_churn_prediction/catboost_info/learn/events.out.tfevents
 create mode 100644 bank_customer_churn_prediction/catboost_info/learn_error.tsv
 create mode 100644 bank_customer_churn_prediction/catboost_info/test/events.out.tfevents
 create mode 100644 bank_customer_churn_prediction/catboost_info/test_error.tsv
 create mode 100644 bank_customer_churn_prediction/catboost_info/time_left.tsv
 create mode 100644 bank_customer_churn_prediction/kaggle_competition.ipynb
 create mode 100644 bank_customer_churn_prediction/submission_cat.csv
 create mode 100644 bank_customer_churn_prediction/submission_lgbm.csv
 create mode 100644 bank_customer_churn_prediction/submission_rf.csv
 create mode 100644 bank_customer_churn_prediction/submission_xgb.csv

diff --git a/assets/BCCP/sample_submission.csv b/assets/BCCP/sample_submission.csv
new file mode 100644
index 0000000..57ca445
--- /dev/null
+++ b/assets/BCCP/sample_submission.csv
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diff --git a/assets/BCCP/test.csv b/assets/BCCP/test.csv
new file mode 100644
index 0000000..bff1926
--- /dev/null
+++ b/assets/BCCP/test.csv
@@ -0,0 +1,10001 @@
+id,CustomerId,Surname,CreditScore,Geography,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary
+15000,15594796.0,Chu,584.0,Germany,Male,30.0,2.0,146053.66,1.0,1.0,1.0,157891.86
+15001,15642821.0,Mazzi,551.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,67431.28
+15002,15716284.0,Onyekachi,706.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,156768.45
+15003,15785078.0,Martin,717.0,Spain,Male,45.0,3.0,0.0,1.0,1.0,1.0,166909.87
+15004,15662955.0,Kenechukwu,592.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,143681.97
+15005,15759066.0,Genovesi,611.0,France,Male,36.0,7.0,61811.23,1.0,1.0,0.0,145796.49
+15006,15754926.0,Haddon,684.0,France,Female,36.0,0.0,0.0,2.0,1.0,0.0,161671.15
+15007,15664802.0,Chiang,588.0,France,Male,39.0,4.0,0.0,2.0,0.0,0.0,57862.88
+15008,15757537.0,Iweobiegbulam,612.0,Germany,Male,27.0,3.0,155413.06,2.0,1.0,0.0,140166.95
+15009,15674928.0,Hoolan,736.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,75969.19
+15010,15609510.0,Ts'ao,584.0,France,Male,34.0,5.0,125860.2,1.0,0.0,0.0,143393.24
+15011,15686964.0,Yudina,724.0,Germany,Female,57.0,5.0,107960.46,1.0,1.0,0.0,53954.24
+15012,15793949.0,Mazzanti,675.0,France,Male,36.0,8.0,89086.31,1.0,1.0,0.0,122218.61
+15013,15747168.0,Chukwualuka,628.0,Germany,Male,38.0,6.0,143330.56,1.0,1.0,1.0,146723.72
+15014,15635277.0,T'ien,543.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,85890.75
+15015,15758856.0,Chukwuhaenye,703.0,Spain,Female,34.0,2.0,0.0,2.0,0.0,1.0,79071.89
+15016,15744044.0,Ch'ien,638.0,Germany,Male,28.0,6.0,125909.85,2.0,1.0,0.0,155701.91
+15017,15729201.0,Chukwualuka,695.0,France,Female,36.0,2.0,0.0,2.0,0.0,1.0,183566.78
+15018,15595324.0,Hao,656.0,Germany,Female,39.0,3.0,108235.02,1.0,1.0,1.0,134442.9
+15019,15594556.0,Chiabuotu,687.0,France,Female,37.0,4.0,0.0,2.0,0.0,1.0,15859.95
+15020,15791842.0,Nnaife,633.0,France,Female,29.0,2.0,0.0,2.0,0.0,1.0,70974.13
+15021,15782454.0,Dore,673.0,France,Male,34.0,2.0,165610.41,2.0,1.0,1.0,50368.63
+15022,15647782.0,Trevisani,560.0,Germany,Female,51.0,0.0,124485.98,1.0,0.0,0.0,175853.64
+15023,15572374.0,Cremonesi,693.0,France,Male,39.0,2.0,0.0,1.0,1.0,0.0,72993.65
+15024,15692761.0,Lai,687.0,Germany,Female,32.0,8.0,80780.16,2.0,0.0,1.0,137985.69
+15025,15595588.0,Chukwufumnanya,741.0,Germany,Male,53.0,7.0,81732.88,1.0,0.0,1.0,169921.12
+15026,15792668.0,Chin,584.0,Germany,Female,46.0,6.0,116711.06,1.0,1.0,1.0,79957.95
+15027,15709861.0,Fomin,755.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,164825.07
+15028,15770225.0,T'ien,652.0,Spain,Male,38.0,5.0,0.0,1.0,1.0,1.0,88958.49
+15029,15651272.0,Chukwujamuike,609.0,Germany,Male,43.0,7.0,124095.54,1.0,1.0,0.0,54474.48
+15030,15686776.0,Genovese,627.0,Spain,Female,43.0,8.0,0.0,1.0,1.0,1.0,141035.65
+15031,15603883.0,Genovesi,727.0,Germany,Female,37.0,6.0,121399.98,1.0,1.0,1.0,164038.07
+15032,15717010.0,Houghton,648.0,Germany,Female,37.0,10.0,142321.72,2.0,0.0,1.0,113079.19
+15033,15780579.0,Milne,542.0,Spain,Male,45.0,7.0,0.0,2.0,1.0,0.0,123982.14
+15034,15636572.0,McGregor,552.0,France,Female,41.0,6.0,0.0,1.0,1.0,0.0,129964.94
+15035,15787071.0,Nwankwo,529.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,161854.98
+15036,15567557.0,Kisch,605.0,France,Male,36.0,4.0,0.0,2.0,0.0,1.0,182650.15
+15037,15702566.0,McGregor,748.0,Germany,Male,55.0,3.0,155519.59,1.0,1.0,1.0,131710.49
+15038,15608968.0,O'Loughlin,678.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,114522.68
+15039,15648800.0,Ch'ien,567.0,Spain,Male,41.0,2.0,0.0,2.0,0.0,1.0,57762.0
+15040,15804822.0,Chidiebere,730.0,Germany,Male,43.0,8.0,132044.6,2.0,0.0,0.0,79997.14
+15041,15763515.0,Chiabuotu,724.0,Germany,Male,29.0,2.0,82909.14,2.0,1.0,1.0,170593.87
+15042,15683553.0,Hsia,678.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,143315.57
+15043,15769783.0,Chukwuemeka,589.0,France,Female,35.0,2.0,0.0,2.0,0.0,1.0,159742.33
+15044,15706587.0,L?,682.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,147955.91
+15045,15751057.0,Li Fonti,731.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,105570.44
+15046,15752626.0,Pagnotto,745.0,France,Female,35.0,8.0,0.0,1.0,1.0,0.0,88890.05
+15047,15730579.0,Kambinachi,602.0,Germany,Male,47.0,1.0,97544.29,1.0,1.0,0.0,173498.45
+15048,15663917.0,Nnamutaezinwa,599.0,Spain,Female,28.0,1.0,0.0,2.0,1.0,1.0,61793.47
+15049,15750121.0,H?,526.0,France,Female,38.0,9.0,0.0,1.0,1.0,0.0,121428.4
+15050,15606855.0,Nucci,569.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,161241.65
+15051,15759872.0,Ebelechukwu,575.0,Germany,Male,37.0,3.0,131101.04,2.0,1.0,1.0,96875.52
+15052,15647975.0,Clements,637.0,France,Male,32.0,4.0,121260.19,1.0,1.0,1.0,143783.28
+15053,15745355.0,Nwabugwu,749.0,Germany,Female,41.0,0.0,108736.52,1.0,1.0,1.0,53394.12
+15054,15644501.0,White,632.0,Germany,Male,35.0,6.0,149961.99,1.0,1.0,0.0,93478.96
+15055,15650288.0,Flemming,516.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,145528.22
+15056,15782530.0,Li Fonti,710.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,121451.21
+15057,15788025.0,T'ang,577.0,France,Female,54.0,1.0,0.0,1.0,1.0,0.0,131301.74
+15058,15686588.0,Chia,643.0,France,Male,47.0,5.0,0.0,1.0,0.0,0.0,96472.34
+15059,15596088.0,P'eng,629.0,France,Female,36.0,4.0,151242.99,1.0,1.0,0.0,91433.88
+15060,15662698.0,McElhone,591.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,91673.6
+15061,15679048.0,Clements,686.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,118862.08
+15062,15627697.0,Fiorentini,752.0,Spain,Female,58.0,1.0,0.0,1.0,1.0,1.0,143683.75
+15063,15803689.0,Toscani,652.0,France,Female,46.0,1.0,0.0,1.0,0.0,1.0,180598.28
+15064,15587562.0,Hs?eh,693.0,Spain,Male,31.0,8.0,117152.3,1.0,1.0,1.0,113645.4
+15065,15645778.0,Y?,713.0,Spain,Female,42.0,2.0,0.0,2.0,1.0,1.0,90964.54
+15066,15805637.0,Blesing,707.0,France,Male,36.0,4.0,99165.84,1.0,1.0,1.0,113553.43
+15067,15652693.0,Chiu,636.0,France,Female,51.0,6.0,0.0,1.0,0.0,0.0,127543.81
+15068,15772573.0,Chimezie,611.0,France,Female,46.0,4.0,0.0,1.0,1.0,0.0,166957.82
+15069,15794849.0,Manna,704.0,France,Male,38.0,9.0,0.0,2.0,0.0,0.0,143628.31
+15070,15773322.0,Lucchese,730.0,Germany,Female,55.0,2.0,113657.83,1.0,1.0,1.0,57553.02
+15071,15732268.0,Chiazagomekpele,598.0,France,Female,36.0,2.0,0.0,2.0,0.0,1.0,180712.52
+15072,15742824.0,Lombardi,765.0,Germany,Female,44.0,2.0,96471.3,1.0,1.0,1.0,140937.98
+15073,15750181.0,Ting,756.0,Germany,Male,48.0,1.0,108862.76,1.0,0.0,1.0,91652.43
+15074,15803378.0,Zaytseva,535.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,59668.81
+15075,15681705.0,Trevisani,598.0,Spain,Male,37.0,7.0,0.0,2.0,1.0,1.0,53817.23
+15076,15642099.0,Chukwumaobim,681.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,167984.72
+15077,15592387.0,Uspenskaya,850.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,75884.65
+15078,15624293.0,Chu,595.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,88967.48
+15079,15624993.0,Davide,619.0,Germany,Male,27.0,1.0,144450.58,1.0,1.0,1.0,108872.45
+15080,15748673.0,Tung,713.0,Spain,Female,45.0,1.0,0.0,1.0,1.0,0.0,122925.4
+15081,15676937.0,Lombardi,706.0,France,Female,36.0,4.0,111257.59,1.0,1.0,1.0,73882.49
+15082,15776596.0,Tsai,679.0,Germany,Male,37.0,6.0,109949.05,2.0,1.0,0.0,179128.69
+15083,15798960.0,Ch'ien,521.0,France,Male,45.0,8.0,0.0,2.0,1.0,1.0,180598.86
+15084,15583681.0,Mazzanti,717.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,118711.75
+15085,15670602.0,Tien,675.0,Spain,Male,28.0,9.0,121313.88,1.0,1.0,1.0,103560.98
+15086,15752312.0,Kerr,748.0,France,Female,42.0,8.0,0.0,1.0,1.0,0.0,88729.22
+15087,15783526.0,Zikoranachidimma,730.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,184954.11
+15088,15581871.0,Milne,563.0,France,Female,36.0,2.0,0.0,2.0,0.0,1.0,109057.56
+15089,15770995.0,Genovese,680.0,France,Male,28.0,2.0,145578.28,1.0,1.0,0.0,93769.89
+15090,15709447.0,Chukwuemeka,634.0,Spain,Female,35.0,9.0,0.0,2.0,1.0,1.0,123587.85
+15091,15780144.0,Chinweuba,634.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,0.0,130500.65
+15092,15615473.0,Nwachukwu,646.0,France,Male,30.0,10.0,0.0,2.0,1.0,1.0,165562.84
+15093,15693337.0,T'ien,586.0,France,Male,38.0,4.0,130622.34,1.0,1.0,0.0,141364.62
+15094,15761047.0,Brient,729.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,91817.32
+15095,15632538.0,Hsia,619.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,166650.4
+15096,15626806.0,Milne,708.0,France,Female,47.0,9.0,130274.22,1.0,1.0,1.0,91573.19
+15097,15613180.0,Tien,556.0,Spain,Male,27.0,5.0,0.0,2.0,1.0,1.0,116978.19
+15098,15792868.0,Udinese,769.0,France,Male,41.0,5.0,0.0,2.0,0.0,1.0,99595.67
+15099,15661380.0,Nucci,724.0,France,Female,36.0,5.0,0.0,1.0,1.0,1.0,27345.18
+15100,15778861.0,Hoolan,637.0,France,Female,21.0,1.0,71264.02,2.0,0.0,1.0,176868.89
+15101,15783029.0,Ch'ang,614.0,France,Female,43.0,5.0,0.0,1.0,1.0,0.0,126475.79
+15102,15602572.0,Ch'in,661.0,France,Male,43.0,10.0,0.0,1.0,1.0,0.0,139161.64
+15103,15592571.0,Lo Duca,586.0,Spain,Male,42.0,0.0,0.0,1.0,1.0,1.0,70899.27
+15104,15706128.0,Cattaneo,639.0,France,Male,43.0,9.0,122417.17,1.0,1.0,0.0,136028.97
+15105,15716164.0,Hoolan,709.0,Germany,Female,36.0,7.0,82729.47,2.0,1.0,0.0,62421.97
+15106,15782578.0,H?,669.0,Spain,Male,38.0,1.0,0.0,1.0,1.0,1.0,161241.65
+15107,15626141.0,Chiganu,633.0,Germany,Female,51.0,5.0,115924.89,1.0,1.0,0.0,57896.27
+15108,15777743.0,Chukwualuka,559.0,Spain,Female,29.0,8.0,0.0,2.0,0.0,0.0,101382.86
+15109,15746729.0,Biryukova,622.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,28667.56
+15110,15674840.0,Dettmann,661.0,France,Male,38.0,5.0,0.0,1.0,1.0,0.0,139048.0
+15111,15751175.0,Hsiao,589.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,146585.29
+15112,15609618.0,Nwora,619.0,France,Female,39.0,7.0,130170.82,1.0,1.0,0.0,51548.14
+15113,15749583.0,Ch'iu,682.0,France,Female,38.0,7.0,0.0,2.0,0.0,1.0,170886.43
+15114,15689344.0,Chinedum,628.0,France,Female,35.0,8.0,0.0,2.0,0.0,1.0,177065.24
+15115,15707596.0,Tsao,447.0,France,Female,41.0,3.0,0.0,2.0,1.0,1.0,177028.0
+15116,15593969.0,Onyekachi,695.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,92898.14
+15117,15609562.0,Tuan,711.0,Germany,Female,41.0,7.0,124577.33,3.0,0.0,0.0,46824.08
+15118,15715597.0,Martin,661.0,Germany,Male,34.0,9.0,102535.35,2.0,1.0,0.0,176692.87
+15119,15649858.0,Scott,572.0,France,Male,44.0,1.0,0.0,2.0,1.0,0.0,140579.17
+15120,15622083.0,Ch'ang,757.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,115525.13
+15121,15585901.0,Calabresi,592.0,Spain,Male,40.0,3.0,130806.99,1.0,1.0,0.0,140801.64
+15122,15693683.0,T'ao,685.0,France,Male,37.0,4.0,0.0,2.0,0.0,0.0,57558.95
+15123,15791279.0,Obiajulu,669.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,1.0,158845.49
+15124,15682355.0,Pisano,667.0,Germany,Male,38.0,3.0,122724.45,1.0,1.0,0.0,146955.71
+15125,15705512.0,Goliwe,681.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,121061.89
+15126,15762351.0,Ngozichukwuka,595.0,Spain,Male,49.0,2.0,0.0,1.0,0.0,0.0,134435.75
+15127,15663987.0,Chin,619.0,France,Male,26.0,7.0,0.0,2.0,0.0,1.0,164967.21
+15128,15736420.0,Ugochukwutubelum,724.0,France,Male,36.0,1.0,104784.23,1.0,1.0,0.0,164966.17
+15129,15666043.0,Kenechukwu,703.0,Germany,Male,29.0,4.0,142417.97,1.0,1.0,1.0,152417.97
+15130,15591286.0,Hsu,683.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,114020.08
+15131,15803378.0,Mazzi,581.0,Germany,Female,42.0,2.0,107784.65,1.0,1.0,0.0,138700.94
+15132,15676810.0,Li Fonti,667.0,France,Female,25.0,9.0,103516.87,1.0,0.0,1.0,878.0
+15133,15651020.0,P'eng,774.0,France,Female,45.0,2.0,0.0,2.0,1.0,1.0,158420.14
+15134,15757911.0,Maclean,718.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,176945.96
+15135,15794895.0,Martin,703.0,Spain,Female,45.0,8.0,121355.99,1.0,1.0,0.0,43931.51
+15136,15746726.0,Pagnotto,639.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,1528.4
+15137,15601351.0,Y?an,634.0,Germany,Male,40.0,2.0,104433.08,2.0,1.0,0.0,170518.83
+15138,15778025.0,Chukwuemeka,619.0,France,Male,32.0,1.0,62974.64,1.0,1.0,1.0,156774.75
+15139,15682454.0,Onwudiwe,742.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,80619.17
+15140,15703763.0,Ch'ien,706.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,174800.31
+15141,15656300.0,Uwakwe,667.0,France,Female,47.0,9.0,0.0,1.0,1.0,0.0,174027.3
+15142,15778025.0,H?,685.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,151335.24
+15143,15605826.0,Trevisano,528.0,Germany,Female,44.0,7.0,136423.42,1.0,1.0,0.0,151887.16
+15144,15719352.0,Pisano,722.0,Germany,Male,43.0,10.0,127738.25,2.0,1.0,0.0,176925.69
+15145,15592999.0,Kao,632.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,121061.89
+15146,15616172.0,Hsing,595.0,Germany,Male,31.0,0.0,151709.33,2.0,1.0,1.0,137980.58
+15147,15593900.0,Lai,707.0,France,Male,59.0,7.0,0.0,2.0,0.0,0.0,140765.25
+15148,15572735.0,Hs?eh,608.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,138887.86
+15149,15582591.0,Manna,727.0,Germany,Female,54.0,6.0,150844.94,1.0,1.0,0.0,113410.21
+15150,15572398.0,T'ien,597.0,Spain,Male,31.0,8.0,97408.03,1.0,1.0,0.0,143628.31
+15151,15645323.0,Chukwueloka,677.0,France,Female,41.0,10.0,99610.92,1.0,1.0,0.0,179908.71
+15152,15777901.0,T'ien,479.0,Germany,Female,41.0,7.0,151659.52,3.0,1.0,1.0,141364.62
+15153,15625023.0,Onuora,567.0,France,Male,50.0,5.0,0.0,2.0,1.0,1.0,93719.85
+15154,15711889.0,Nnonso,686.0,France,Male,57.0,10.0,152390.29,1.0,1.0,0.0,146785.44
+15155,15637644.0,Chukwukere,727.0,Germany,Female,43.0,2.0,130229.14,2.0,1.0,1.0,149991.32
+15156,15679770.0,Chukwujamuike,653.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,121789.3
+15157,15752588.0,Ch'en,659.0,Germany,Female,33.0,2.0,107712.57,2.0,0.0,0.0,130189.72
+15158,15679622.0,Aksenov,714.0,Germany,Male,41.0,2.0,129901.21,1.0,0.0,1.0,93739.71
+15159,15772337.0,Nnamutaezinwa,583.0,France,Male,45.0,9.0,0.0,1.0,0.0,1.0,139953.94
+15160,15732265.0,Chineze,790.0,Spain,Female,47.0,5.0,0.0,2.0,0.0,0.0,81442.6
+15161,15583480.0,Ndubueze,749.0,France,Male,24.0,6.0,0.0,2.0,0.0,1.0,143544.48
+15162,15744044.0,De Salis,708.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,129803.08
+15163,15645593.0,Mazzanti,742.0,France,Male,32.0,6.0,0.0,2.0,0.0,0.0,82222.86
+15164,15766017.0,Pirozzi,678.0,France,Male,29.0,2.0,0.0,2.0,0.0,1.0,71179.31
+15165,15640074.0,Yobachi,613.0,France,Male,36.0,1.0,126473.33,1.0,0.0,0.0,95611.47
+15166,15594408.0,Ch'ang,490.0,Germany,Female,62.0,6.0,102365.49,2.0,1.0,0.0,168816.15
+15167,15581286.0,Smith,665.0,France,Male,41.0,3.0,172325.67,1.0,0.0,0.0,27474.81
+15168,15747097.0,Okwudilichukwu,637.0,France,Male,37.0,4.0,112949.71,1.0,1.0,0.0,101319.76
+15169,15658969.0,Chiazagomekpere,629.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,59645.24
+15170,15724863.0,Maclean,651.0,France,Male,41.0,2.0,95957.88,1.0,1.0,0.0,156774.75
+15171,15665963.0,Ch'eng,567.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,88729.22
+15172,15806049.0,Hsieh,602.0,France,Male,35.0,3.0,123372.3,1.0,1.0,0.0,93959.96
+15173,15578722.0,Lucchese,676.0,France,Female,52.0,1.0,0.0,1.0,1.0,0.0,122268.75
+15174,15791114.0,Nwabugwu,564.0,Germany,Female,46.0,3.0,106731.58,1.0,1.0,1.0,92811.2
+15175,15692570.0,Ch'iu,775.0,Germany,Female,36.0,2.0,122314.39,2.0,1.0,0.0,153036.97
+15176,15762583.0,Ch'iu,757.0,Spain,Female,30.0,7.0,0.0,2.0,0.0,1.0,163550.58
+15177,15757535.0,Walker,613.0,Germany,Male,25.0,6.0,131394.38,1.0,0.0,0.0,54495.82
+15178,15609618.0,Baresi,585.0,Germany,Female,36.0,9.0,135984.65,2.0,1.0,1.0,151677.31
+15179,15635177.0,Milano,559.0,France,Female,35.0,7.0,0.0,2.0,1.0,1.0,156910.18
+15180,15632210.0,Naylor,749.0,Germany,Female,43.0,8.0,96798.79,2.0,0.0,0.0,149665.65
+15181,15781574.0,Maclean,549.0,Germany,Male,60.0,3.0,122449.38,1.0,1.0,0.0,165038.43
+15182,15694879.0,Hsiung,592.0,France,Female,33.0,9.0,91291.1,1.0,1.0,0.0,139048.0
+15183,15806957.0,Hsiung,506.0,France,Female,63.0,2.0,92930.92,1.0,1.0,1.0,53534.8
+15184,15791842.0,Nwebube,569.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,151325.24
+15185,15691396.0,Ts'ui,717.0,France,Male,51.0,4.0,0.0,1.0,1.0,1.0,65864.4
+15186,15782591.0,Uspenskaya,668.0,France,Male,34.0,7.0,91246.53,1.0,1.0,0.0,76515.79
+15187,15774696.0,Akhtar,663.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,52337.97
+15188,15605916.0,Ch'en,692.0,Germany,Female,28.0,6.0,101641.14,2.0,1.0,1.0,67830.2
+15189,15758031.0,Lo,718.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,96515.02
+15190,15689953.0,Uspenskaya,628.0,Germany,Male,30.0,1.0,148853.29,1.0,1.0,1.0,35367.19
+15191,15725024.0,Biryukov,616.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,0.0,168034.9
+15192,15689061.0,Chukwudi,678.0,Germany,Male,35.0,1.0,114737.26,1.0,1.0,0.0,179937.38
+15193,15736112.0,Teng,717.0,France,Female,36.0,8.0,0.0,1.0,0.0,1.0,38960.59
+15194,15624293.0,Trevisano,679.0,Germany,Female,45.0,1.0,118889.66,1.0,1.0,1.0,161479.19
+15195,15703978.0,Shih,739.0,France,Male,41.0,4.0,77910.23,1.0,1.0,0.0,152790.92
+15196,15661249.0,Okechukwu,679.0,France,Male,49.0,9.0,131834.76,1.0,1.0,1.0,158043.11
+15197,15605020.0,Maclean,671.0,France,Male,26.0,9.0,0.0,2.0,1.0,1.0,88958.49
+15198,15604792.0,Nwabugwu,586.0,France,Male,55.0,2.0,108004.64,1.0,0.0,1.0,118024.1
+15199,15651396.0,Onyemauchechukwu,692.0,Germany,Male,37.0,1.0,142072.02,2.0,0.0,0.0,159830.58
+15200,15621140.0,Davide,621.0,France,Female,38.0,8.0,144428.99,1.0,1.0,0.0,56787.8
+15201,15585835.0,Clements,788.0,France,Male,43.0,5.0,0.0,2.0,0.0,1.0,78854.57
+15202,15597919.0,Ts'ai,727.0,France,Male,26.0,8.0,0.0,2.0,0.0,1.0,64323.24
+15203,15802823.0,P'eng,699.0,France,Female,27.0,8.0,0.0,2.0,1.0,1.0,170968.99
+15204,15803689.0,Dickson,604.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,155876.06
+15205,15591747.0,Chukwueloka,850.0,France,Male,46.0,5.0,0.0,2.0,0.0,0.0,158336.36
+15206,15600720.0,Hsing,600.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,115115.32
+15207,15802501.0,Ponomaryova,616.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,166677.35
+15208,15691817.0,Hao,756.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,25887.89
+15209,15772412.0,Lu,788.0,Spain,Male,40.0,3.0,98899.91,1.0,1.0,0.0,166929.43
+15210,15773520.0,Ukaegbunam,681.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,38960.59
+15211,15626233.0,Trevisani,709.0,Germany,Male,35.0,8.0,99848.86,2.0,0.0,0.0,134732.99
+15212,15711901.0,Martin,757.0,France,Female,41.0,5.0,0.0,1.0,1.0,0.0,126848.01
+15213,15615176.0,Yuryeva,733.0,France,Female,28.0,9.0,0.0,2.0,0.0,0.0,153566.97
+15214,15775124.0,Chinomso,662.0,France,Male,29.0,6.0,125167.02,1.0,1.0,0.0,152982.79
+15215,15629793.0,Ch'iu,544.0,France,Female,39.0,8.0,0.0,2.0,0.0,1.0,138700.75
+15216,15665834.0,Udobata,576.0,France,Female,45.0,9.0,0.0,1.0,1.0,1.0,67830.2
+15217,15602731.0,Hsieh,579.0,France,Male,35.0,0.0,156972.91,1.0,1.0,1.0,141210.5
+15218,15768282.0,Mazzanti,678.0,France,Female,43.0,4.0,0.0,2.0,1.0,1.0,82393.08
+15219,15791342.0,Teng,624.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,172526.9
+15220,15686974.0,Hsiung,646.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,122925.44
+15221,15803965.0,Pugh,741.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,1.0,102547.56
+15222,15689614.0,Hsiao,681.0,France,Male,35.0,6.0,0.0,1.0,1.0,1.0,125961.48
+15223,15569249.0,Ives,649.0,Spain,Male,36.0,5.0,0.0,2.0,0.0,0.0,103792.53
+15224,15621815.0,Biryukova,637.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,80701.72
+15225,15795874.0,Chu,684.0,France,Female,40.0,4.0,0.0,1.0,0.0,0.0,136985.08
+15226,15585067.0,Nwokeocha,597.0,France,Female,48.0,2.0,126534.6,1.0,1.0,1.0,179866.01
+15227,15627848.0,K'ung,594.0,France,Male,29.0,8.0,123971.27,2.0,1.0,0.0,152499.8
+15228,15606841.0,Thompson,599.0,Germany,Male,45.0,0.0,104784.23,1.0,1.0,1.0,142944.09
+15229,15641389.0,Onwumelu,730.0,France,Male,46.0,0.0,0.0,2.0,1.0,1.0,143022.32
+15230,15585047.0,Ogochukwu,850.0,Germany,Female,46.0,6.0,121898.58,1.0,0.0,0.0,109794.31
+15231,15751175.0,P'eng,675.0,Germany,Female,68.0,4.0,108477.27,1.0,0.0,1.0,179521.84
+15232,15692991.0,Efremov,746.0,France,Male,37.0,9.0,114939.75,1.0,0.0,1.0,71420.16
+15233,15782531.0,Onyekachukwu,590.0,Spain,Female,46.0,7.0,0.0,2.0,1.0,1.0,35367.19
+15234,15631868.0,Milanesi,809.0,Germany,Female,54.0,5.0,145081.73,3.0,1.0,0.0,123951.86
+15235,15744606.0,Tien,706.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,138021.36
+15236,15701109.0,Hs?eh,743.0,Germany,Female,32.0,7.0,141581.08,1.0,1.0,1.0,47729.33
+15237,15778936.0,Teng,754.0,France,Male,58.0,7.0,121254.65,2.0,1.0,1.0,53478.02
+15238,15788367.0,Ndubuagha,571.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,82322.86
+15239,15791114.0,Iadanza,719.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,168816.18
+15240,15598884.0,Hsia,635.0,France,Male,38.0,0.0,0.0,1.0,1.0,0.0,55725.04
+15241,15808958.0,Nkemjika,664.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,1.0,111307.98
+15242,15733705.0,Macleod,678.0,Germany,Female,27.0,1.0,138341.51,1.0,0.0,0.0,171707.93
+15243,15649523.0,Lettiere,567.0,France,Male,54.0,3.0,0.0,1.0,0.0,1.0,115818.51
+15244,15643770.0,Chuang,676.0,France,Female,31.0,8.0,0.0,2.0,0.0,0.0,93844.86
+15245,15730602.0,Hsueh,690.0,Germany,Female,56.0,3.0,141078.57,1.0,0.0,1.0,113931.57
+15246,15667620.0,Nkemdirim,587.0,France,Male,35.0,2.0,0.0,1.0,1.0,1.0,166526.26
+15247,15742607.0,Hsia,619.0,Spain,Female,43.0,1.0,0.0,2.0,0.0,0.0,109742.55
+15248,15641411.0,Nwankwo,710.0,France,Female,39.0,7.0,185966.64,1.0,0.0,0.0,78560.64
+15249,15784923.0,Chukwumaobim,724.0,France,Male,30.0,1.0,136093.74,1.0,1.0,0.0,61290.99
+15250,15788151.0,Chukwuemeka,583.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,1.0,141404.07
+15251,15576723.0,Chizoba,516.0,Spain,Female,30.0,2.0,0.0,2.0,1.0,0.0,51362.82
+15252,15670172.0,Chiekwugo,655.0,Germany,Female,20.0,4.0,91522.04,1.0,1.0,0.0,147955.91
+15253,15683625.0,Ch'eng,775.0,Spain,Male,45.0,4.0,0.0,2.0,1.0,0.0,176883.42
+15254,15809826.0,Chukwuebuka,521.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,165576.55
+15255,15672912.0,Ch'eng,624.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,122433.09
+15256,15584785.0,Chukwualuka,714.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,113946.67
+15257,15667934.0,Yeh,718.0,Spain,Female,31.0,1.0,99842.5,1.0,1.0,1.0,131647.31
+15258,15797733.0,Toscani,642.0,Germany,Male,46.0,3.0,124445.58,1.0,1.0,0.0,823.96
+15259,15768095.0,Chinedum,662.0,France,Male,38.0,8.0,0.0,1.0,0.0,1.0,104469.06
+15260,15730161.0,Udobata,741.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,1.0,115509.59
+15261,15634792.0,Galkin,707.0,France,Female,48.0,8.0,0.0,4.0,1.0,0.0,133871.83
+15262,15679062.0,Yermakov,516.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,146715.07
+15263,15742778.0,Nkemakonam,622.0,Germany,Male,65.0,2.0,127290.61,2.0,0.0,0.0,163427.18
+15264,15593736.0,Toscani,613.0,Germany,Male,29.0,3.0,110783.28,1.0,1.0,0.0,147545.65
+15265,15795571.0,Maclean,596.0,Spain,Female,48.0,8.0,98955.03,2.0,1.0,1.0,134417.31
+15266,15686957.0,Hsueh,711.0,France,Male,24.0,4.0,0.0,1.0,1.0,1.0,166370.81
+15267,15578980.0,Ch'ang,624.0,Germany,Male,41.0,8.0,168078.83,2.0,0.0,0.0,57589.29
+15268,15807312.0,Zaytseva,716.0,Spain,Male,39.0,7.0,0.0,2.0,1.0,1.0,94562.88
+15269,15735221.0,Ts'ao,706.0,France,Female,30.0,5.0,0.0,2.0,0.0,0.0,78436.36
+15270,15789560.0,Kaleski,583.0,France,Female,31.0,8.0,109941.51,1.0,1.0,0.0,151902.78
+15271,15651533.0,Ts'ui,632.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,4654.28
+15272,15770039.0,Hs?eh,635.0,France,Male,36.0,5.0,116880.53,1.0,1.0,0.0,164768.82
+15273,15634501.0,Milano,754.0,France,Female,24.0,4.0,131852.81,2.0,1.0,0.0,144870.28
+15274,15641633.0,Ku,657.0,Germany,Female,45.0,3.0,109958.12,1.0,0.0,0.0,181224.56
+15275,15589660.0,Olisaemeka,535.0,France,Male,46.0,2.0,0.0,1.0,0.0,0.0,176450.19
+15276,15783859.0,Lombardo,850.0,France,Female,39.0,1.0,91291.2,1.0,0.0,1.0,166370.81
+15277,15583595.0,Hsiung,705.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,51565.98
+15278,15694566.0,Onuchukwu,712.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,158059.95
+15279,15697480.0,Ch'iu,753.0,Spain,Male,43.0,5.0,0.0,2.0,0.0,0.0,187811.71
+15280,15734588.0,Chiedozie,675.0,France,Female,54.0,8.0,0.0,1.0,1.0,0.0,164083.72
+15281,15684769.0,Mott,678.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,182867.84
+15282,15726589.0,K?,605.0,Germany,Female,36.0,4.0,116498.22,2.0,1.0,0.0,53511.74
+15283,15756680.0,Kolesnikova,581.0,France,Male,60.0,0.0,0.0,1.0,1.0,1.0,113931.57
+15284,15663888.0,Hsia,720.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,126475.79
+15285,15672894.0,Yefremov,595.0,France,Female,47.0,5.0,0.0,1.0,1.0,0.0,126469.09
+15286,15649060.0,Ts'ui,826.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,83342.73
+15287,15598044.0,Bibi,646.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,176433.7
+15288,15781665.0,K'ung,749.0,Spain,Female,32.0,2.0,0.0,2.0,1.0,0.0,48606.71
+15289,15685329.0,Miah,538.0,France,Male,35.0,3.0,133934.4,1.0,1.0,1.0,117038.96
+15290,15571059.0,Hs?eh,741.0,France,Male,25.0,4.0,0.0,2.0,1.0,1.0,143628.31
+15291,15800295.0,Padovesi,603.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,123982.14
+15292,15572428.0,Chukwukere,659.0,Spain,Male,34.0,9.0,0.0,1.0,1.0,1.0,82526.92
+15293,15584899.0,Obidimkpa,656.0,Germany,Female,59.0,9.0,143680.47,1.0,1.0,0.0,81624.09
+15294,15808566.0,Nkemdirim,593.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,61766.76
+15295,15627212.0,Burt,663.0,Spain,Male,35.0,8.0,0.0,1.0,1.0,1.0,91830.75
+15296,15644833.0,Nnamutaezinwa,753.0,France,Male,33.0,1.0,91328.71,1.0,1.0,0.0,97939.3
+15297,15772601.0,Nkemjika,706.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,90821.72
+15298,15685500.0,Hargreaves,683.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,1.0,120540.83
+15299,15602572.0,Milanesi,762.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,158444.61
+15300,15591747.0,Nkemdirim,632.0,France,Male,42.0,2.0,0.0,2.0,0.0,1.0,82422.91
+15301,15792360.0,Chukwualuka,649.0,Spain,Male,63.0,7.0,116886.59,1.0,1.0,1.0,81622.52
+15302,15684296.0,Hsueh,758.0,Germany,Male,35.0,1.0,120911.75,1.0,1.0,0.0,96444.88
+15303,15717185.0,Ts'ui,639.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,94790.34
+15304,15765311.0,Ch'eng,749.0,France,Female,34.0,7.0,92616.64,2.0,1.0,0.0,181224.56
+15305,15714586.0,Chinweike,756.0,France,Male,29.0,0.0,0.0,1.0,1.0,0.0,153955.38
+15306,15750638.0,Chinagorom,627.0,France,Male,32.0,10.0,154506.75,1.0,1.0,1.0,143538.51
+15307,15750121.0,Tuan,739.0,Germany,Female,50.0,8.0,142028.36,1.0,1.0,1.0,90896.04
+15308,15585985.0,Y?,725.0,France,Male,27.0,9.0,0.0,2.0,1.0,1.0,163714.92
+15309,15592077.0,DeRose,721.0,Germany,Male,43.0,10.0,118613.26,3.0,0.0,0.0,145551.6
+15310,15772262.0,Iweobiegbunam,479.0,Germany,Male,45.0,4.0,126473.33,1.0,1.0,1.0,57549.65
+15311,15807312.0,Chinwendu,754.0,Germany,Female,27.0,8.0,118899.45,1.0,1.0,0.0,54816.57
+15312,15623053.0,Chinomso,667.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,189392.66
+15313,15724076.0,Nwankwo,526.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,76637.38
+15314,15642996.0,Chiang,682.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,66469.2
+15315,15582591.0,Larkin,598.0,France,Male,30.0,6.0,0.0,2.0,1.0,0.0,110273.84
+15316,15767414.0,Macleod,727.0,France,Male,31.0,1.0,0.0,1.0,1.0,0.0,129964.94
+15317,15752601.0,Thompson,637.0,Germany,Female,34.0,5.0,97750.07,2.0,1.0,1.0,123951.19
+15318,15735078.0,Ni,593.0,Spain,Female,46.0,0.0,105907.42,1.0,1.0,1.0,96961.0
+15319,15631721.0,Uwaezuoke,682.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,129912.76
+15320,15783714.0,Uspenskaya,572.0,France,Male,30.0,4.0,127864.85,1.0,1.0,1.0,160976.83
+15321,15659656.0,Ts'ao,646.0,France,Female,37.0,2.0,0.0,2.0,1.0,1.0,129107.97
+15322,15691792.0,Ch'ang,554.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,139431.0
+15323,15809227.0,Mancini,743.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,183872.88
+15324,15592091.0,Fanucci,549.0,France,Female,51.0,6.0,141581.08,1.0,1.0,0.0,74510.65
+15325,15728480.0,Chukwujamuike,774.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,145796.49
+15326,15798885.0,P'eng,693.0,France,Female,42.0,2.0,0.0,2.0,0.0,1.0,119374.74
+15327,15679743.0,Ting,545.0,France,Male,44.0,10.0,0.0,1.0,0.0,1.0,59961.81
+15328,15800524.0,Ch'iu,651.0,France,Female,29.0,3.0,0.0,1.0,1.0,1.0,121277.78
+15329,15763065.0,Mazzi,594.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,175070.58
+15330,15739292.0,Hsia,562.0,Spain,Female,32.0,2.0,167795.6,2.0,1.0,1.0,175071.29
+15331,15709604.0,Olisanugo,710.0,Spain,Male,37.0,9.0,105229.73,1.0,1.0,1.0,158973.45
+15332,15776733.0,Hs?eh,652.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,94450.97
+15333,15615176.0,Chukwujekwu,724.0,France,Male,30.0,9.0,122314.5,1.0,0.0,1.0,115818.17
+15334,15764073.0,Kibby,595.0,Germany,Male,38.0,2.0,126534.6,1.0,1.0,1.0,177896.92
+15335,15652667.0,Mazzanti,582.0,France,Male,31.0,7.0,0.0,2.0,0.0,0.0,178779.52
+15336,15598044.0,Chikezie,581.0,Spain,Female,31.0,9.0,115062.81,1.0,0.0,0.0,71926.28
+15337,15645323.0,Ts'ui,639.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,0.0,129562.74
+15338,15756950.0,Hsueh,736.0,Germany,Female,37.0,4.0,131394.56,2.0,0.0,0.0,16928.4
+15339,15717290.0,Ebelegbulam,638.0,Spain,Male,30.0,5.0,83772.95,1.0,1.0,1.0,158976.75
+15340,15752294.0,Lo,561.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,103557.85
+15341,15669501.0,Milne,748.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,121592.58
+15342,15781298.0,Ch'ien,542.0,France,Male,53.0,6.0,0.0,1.0,1.0,0.0,125962.02
+15343,15710295.0,Ch'ien,613.0,France,Male,31.0,4.0,105405.97,1.0,1.0,0.0,117323.31
+15344,15779497.0,Bales,646.0,France,Male,36.0,1.0,103764.95,1.0,1.0,0.0,72318.77
+15345,15772896.0,Maclean,638.0,Spain,Male,54.0,5.0,0.0,2.0,1.0,1.0,140941.47
+15346,15764409.0,Wang,633.0,Germany,Female,44.0,7.0,102773.2,1.0,1.0,0.0,169963.2
+15347,15756920.0,Hargreaves,667.0,France,Female,34.0,7.0,83348.89,1.0,1.0,0.0,174032.0
+15348,15598892.0,Lucchese,571.0,France,Female,31.0,7.0,140614.15,1.0,1.0,1.0,53581.18
+15349,15803801.0,Hsieh,690.0,France,Female,40.0,9.0,0.0,2.0,0.0,1.0,79919.13
+15350,15664339.0,Hsing,659.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,126726.33
+15351,15661526.0,Chiazagomekpere,677.0,Spain,Female,29.0,8.0,0.0,2.0,1.0,0.0,133831.37
+15352,15782530.0,Ejimofor,708.0,France,Female,36.0,4.0,118547.72,1.0,1.0,1.0,172792.43
+15353,15652934.0,Okwudiliolisa,646.0,France,Male,39.0,10.0,0.0,1.0,1.0,0.0,114448.77
+15354,15750549.0,Hsing,637.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,163327.27
+15355,15616280.0,Lo,762.0,Germany,Male,40.0,1.0,133658.24,2.0,1.0,1.0,186942.49
+15356,15690164.0,Smith,605.0,France,Female,40.0,4.0,0.0,1.0,1.0,0.0,171364.18
+15357,15603741.0,Ch'iu,661.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,110783.28
+15358,15583764.0,Ts'ui,605.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,144584.29
+15359,15798964.0,Dike,684.0,France,Male,24.0,2.0,0.0,2.0,1.0,1.0,79577.38
+15360,15685314.0,Toscani,636.0,Germany,Male,37.0,6.0,73348.56,2.0,1.0,1.0,129818.59
+15361,15674922.0,Hill,691.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,79450.61
+15362,15642835.0,Hargreaves,735.0,France,Female,36.0,2.0,0.0,2.0,0.0,1.0,187926.6
+15363,15614187.0,Ozerova,476.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,112465.19
+15364,15786026.0,Ch'eng,667.0,Spain,Male,38.0,6.0,119496.87,1.0,1.0,0.0,155915.79
+15365,15772383.0,Chigozie,589.0,Germany,Male,45.0,3.0,127261.35,1.0,1.0,1.0,74564.41
+15366,15591552.0,Milani,593.0,France,Male,60.0,1.0,126817.65,1.0,1.0,1.0,82273.82
+15367,15623478.0,Nwachukwu,642.0,France,Female,50.0,1.0,121496.63,1.0,1.0,0.0,79146.36
+15368,15763613.0,Chukwufumnanya,753.0,France,Male,29.0,7.0,127570.93,1.0,1.0,1.0,109394.62
+15369,15774285.0,T'ien,642.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,10963.04
+15370,15641675.0,Hao,645.0,France,Male,63.0,10.0,0.0,1.0,1.0,1.0,67622.46
+15371,15578738.0,Fiorentino,675.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,35342.33
+15372,15597951.0,Mazzanti,743.0,France,Female,33.0,10.0,0.0,2.0,1.0,0.0,141035.65
+15373,15679770.0,Achebe,648.0,Spain,Female,35.0,7.0,0.0,1.0,0.0,1.0,138871.12
+15374,15750407.0,Nwankwo,683.0,France,Male,43.0,5.0,154863.15,1.0,1.0,0.0,150802.41
+15375,15750569.0,Ch'eng,733.0,France,Male,24.0,7.0,0.0,1.0,1.0,1.0,121505.65
+15376,15652955.0,Chiang,627.0,Germany,Female,25.0,6.0,109327.65,1.0,1.0,1.0,199493.38
+15377,15763625.0,Chukwuebuka,696.0,France,Female,48.0,6.0,0.0,1.0,1.0,1.0,88890.05
+15378,15615004.0,Liao,712.0,Germany,Female,37.0,8.0,93569.86,2.0,0.0,0.0,131919.48
+15379,15746970.0,Ch'ien,703.0,Germany,Male,47.0,7.0,144757.02,1.0,0.0,0.0,62979.93
+15380,15783338.0,Hsu,627.0,Germany,Female,54.0,1.0,108935.85,1.0,1.0,1.0,174570.55
+15381,15711571.0,Li Fonti,687.0,France,Male,27.0,2.0,121498.11,2.0,1.0,0.0,71830.1
+15382,15769955.0,Y?an,577.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,56908.41
+15383,15589949.0,Chien,467.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,121765.0
+15384,15804937.0,Maclean,671.0,France,Male,64.0,5.0,0.0,1.0,1.0,1.0,82867.55
+15385,15781495.0,Genovese,743.0,France,Male,36.0,7.0,0.0,1.0,1.0,0.0,64872.33
+15386,15591036.0,Pirozzi,643.0,France,Male,43.0,6.0,125724.95,1.0,1.0,1.0,189143.94
+15387,15762543.0,L?,565.0,Spain,Male,37.0,7.0,101827.07,1.0,1.0,0.0,186841.71
+15388,15595537.0,Chibueze,613.0,Spain,Male,37.0,10.0,0.0,2.0,1.0,0.0,134954.85
+15389,15771569.0,Maclean,593.0,Germany,Female,39.0,7.0,77949.69,2.0,1.0,1.0,158973.77
+15390,15752816.0,Goliwe,568.0,France,Male,46.0,8.0,0.0,2.0,1.0,0.0,88783.63
+15391,15738220.0,Milne,597.0,France,Male,36.0,2.0,0.0,1.0,1.0,0.0,94875.03
+15392,15689328.0,Brown,708.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,96885.19
+15393,15810444.0,Nwora,572.0,Spain,Female,28.0,5.0,0.0,2.0,1.0,1.0,95941.55
+15394,15598884.0,Chukwuebuka,749.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,121440.8
+15395,15587584.0,Ch'eng,593.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,121681.19
+15396,15721487.0,Okeke,684.0,France,Male,44.0,9.0,0.0,2.0,1.0,0.0,93844.87
+15397,15652981.0,Tsou,714.0,Germany,Male,33.0,2.0,93796.42,2.0,1.0,1.0,136910.18
+15398,15673929.0,Shih,682.0,France,Female,29.0,9.0,0.0,2.0,0.0,0.0,162719.69
+15399,15722599.0,Chiazagomekpere,571.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,72993.67
+15400,15699340.0,Eipper,775.0,France,Female,49.0,4.0,170557.91,2.0,1.0,0.0,113557.4
+15401,15721433.0,Dickson,619.0,Spain,Female,25.0,2.0,0.0,1.0,1.0,1.0,157959.02
+15402,15622182.0,Iadanza,711.0,Spain,Male,33.0,6.0,0.0,2.0,0.0,0.0,1485.27
+15403,15780254.0,Onyekachi,641.0,France,Male,31.0,7.0,0.0,1.0,1.0,1.0,83475.44
+15404,15756475.0,Ts'ui,719.0,France,Female,61.0,2.0,0.0,2.0,1.0,1.0,71672.86
+15405,15806941.0,Chiazagomekpele,712.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,80178.54
+15406,15593623.0,Ch'ang,614.0,Germany,Female,35.0,5.0,107106.8,1.0,1.0,1.0,53437.1
+15407,15637169.0,P'eng,644.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,0.0,163534.6
+15408,15793723.0,Ch'ang,587.0,Spain,Male,25.0,5.0,0.0,1.0,1.0,1.0,100137.5
+15409,15736302.0,Tien,683.0,Germany,Female,39.0,6.0,127523.75,1.0,1.0,0.0,181224.56
+15410,15790750.0,Chineze,741.0,Germany,Male,39.0,4.0,141787.31,1.0,1.0,0.0,73881.68
+15411,15794849.0,Yefremov,723.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,97468.44
+15412,15780359.0,Hargreaves,662.0,Spain,Male,34.0,4.0,118032.53,1.0,1.0,0.0,70774.97
+15413,15622774.0,Toscani,662.0,Spain,Female,47.0,4.0,0.0,1.0,0.0,1.0,162858.29
+15414,15573741.0,Chiu,643.0,Germany,Female,56.0,6.0,148074.79,1.0,1.0,0.0,179655.87
+15415,15582892.0,Hsueh,699.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,159576.75
+15416,15797960.0,Lai,749.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,179445.24
+15417,15732266.0,Maclean,615.0,France,Male,40.0,9.0,0.0,1.0,1.0,1.0,157916.49
+15418,15643942.0,Toscani,583.0,France,Male,32.0,8.0,116121.84,1.0,0.0,0.0,116495.22
+15419,15656086.0,L?,644.0,France,Female,38.0,7.0,0.0,1.0,1.0,0.0,67622.84
+15420,15750731.0,Hsing,656.0,France,Male,42.0,4.0,0.0,2.0,0.0,1.0,152855.96
+15421,15799357.0,Toscani,567.0,Spain,Male,39.0,2.0,148962.76,2.0,1.0,1.0,91346.03
+15422,15652694.0,Chiang,696.0,France,Female,38.0,2.0,95377.13,1.0,1.0,0.0,179634.69
+15423,15693637.0,Smith,807.0,Germany,Male,41.0,6.0,71083.98,1.0,1.0,1.0,96832.82
+15424,15804072.0,Sholes,571.0,Germany,Male,32.0,1.0,127132.96,2.0,1.0,1.0,102908.96
+15425,15790314.0,Cattaneo,611.0,Spain,Female,49.0,3.0,0.0,1.0,1.0,0.0,180427.24
+15426,15760093.0,McGregor,595.0,Germany,Male,39.0,4.0,125920.98,1.0,1.0,0.0,152956.33
+15427,15690772.0,Okwudiliolisa,691.0,Spain,Male,37.0,2.0,118889.35,1.0,1.0,1.0,149946.43
+15428,15802478.0,Kao,565.0,Spain,Male,44.0,6.0,0.0,2.0,1.0,0.0,56760.71
+15429,15677796.0,Hsia,579.0,France,Female,31.0,8.0,114835.43,1.0,1.0,0.0,91817.38
+15430,15719348.0,Chien,682.0,France,Male,23.0,7.0,0.0,2.0,1.0,1.0,152884.85
+15431,15803415.0,Brown,652.0,Germany,Male,38.0,7.0,80780.16,2.0,1.0,1.0,132736.32
+15432,15713578.0,Chikezie,684.0,France,Male,40.0,0.0,0.0,2.0,0.0,0.0,109783.29
+15433,15737404.0,Ts'ai,686.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,121299.14
+15434,15600110.0,De Luca,641.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,81425.61
+15435,15576022.0,Hsiao,651.0,France,Male,23.0,5.0,0.0,2.0,1.0,0.0,180344.69
+15436,15643361.0,Y?an,726.0,France,Male,46.0,7.0,0.0,2.0,1.0,1.0,102638.35
+15437,15756070.0,Nkemdirim,753.0,Germany,Male,42.0,9.0,77919.78,2.0,1.0,0.0,35300.08
+15438,15684597.0,Tsao,547.0,Spain,Male,39.0,4.0,0.0,2.0,1.0,0.0,177670.57
+15439,15652981.0,Hsiung,627.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,84460.18
+15440,15570192.0,Hs?eh,673.0,France,Male,34.0,7.0,85845.43,1.0,1.0,1.0,54530.51
+15441,15725302.0,Manna,629.0,France,Female,40.0,1.0,0.0,3.0,1.0,1.0,127022.87
+15442,15787035.0,Chiang,697.0,Germany,Male,51.0,9.0,117020.08,1.0,1.0,1.0,133871.83
+15443,15568573.0,Chidiegwu,564.0,Germany,Female,36.0,3.0,123835.52,1.0,1.0,0.0,131718.12
+15444,15691446.0,Lai,699.0,Germany,Male,29.0,8.0,99745.95,2.0,0.0,1.0,62390.59
+15445,15583857.0,Lucchese,611.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,166513.49
+15446,15665416.0,Shih,646.0,France,Male,38.0,5.0,109978.83,1.0,0.0,0.0,62390.67
+15447,15761047.0,Lai,710.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,84920.49
+15448,15800620.0,Lucchesi,684.0,France,Male,53.0,8.0,0.0,1.0,1.0,0.0,91652.43
+15449,15773469.0,Pirozzi,686.0,Germany,Male,39.0,5.0,121513.31,2.0,1.0,1.0,49594.02
+15450,15779915.0,Rowntree,596.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,47880.71
+15451,15744127.0,Onyeorulu,721.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,164975.82
+15452,15802478.0,Pagnotto,603.0,France,Female,44.0,5.0,0.0,1.0,1.0,1.0,174825.57
+15453,15783715.0,Hsueh,721.0,Spain,Male,48.0,1.0,0.0,2.0,1.0,1.0,171716.81
+15454,15779915.0,Ugochukwutubelum,632.0,Germany,Male,22.0,6.0,168920.75,1.0,0.0,0.0,166677.53
+15455,15643496.0,Ejimofor,745.0,France,Male,54.0,2.0,0.0,1.0,1.0,0.0,85746.52
+15456,15593470.0,Onyemere,637.0,Spain,Male,35.0,3.0,122314.5,1.0,1.0,0.0,97623.42
+15457,15750874.0,Aksenov,728.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,108543.21
+15458,15582033.0,T'ang,837.0,Spain,Female,38.0,8.0,113625.94,1.0,1.0,0.0,132418.59
+15459,15693381.0,Nkemdilim,706.0,Germany,Male,40.0,4.0,112372.96,1.0,0.0,0.0,93718.01
+15460,15605327.0,Pirozzi,578.0,France,Male,66.0,1.0,0.0,1.0,1.0,1.0,81862.81
+15461,15585599.0,Lucchesi,637.0,France,Female,35.0,1.0,174937.64,1.0,1.0,1.0,149807.01
+15462,15686963.0,Hsueh,685.0,France,Female,41.0,7.0,0.0,2.0,1.0,0.0,149170.92
+15463,15751788.0,Milne,710.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,107839.03
+15464,15775192.0,Ch'iu,652.0,France,Male,51.0,2.0,0.0,1.0,1.0,0.0,182055.36
+15465,15793688.0,Macleod,571.0,Germany,Male,37.0,7.0,141918.09,1.0,1.0,0.0,80619.09
+15466,15614215.0,Chinwenma,635.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,165675.01
+15467,15691828.0,Hsiung,738.0,Germany,Male,46.0,2.0,132558.26,1.0,1.0,0.0,82422.97
+15468,15663939.0,De Luca,677.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,0.0,127059.04
+15469,15677550.0,Milani,556.0,France,Male,28.0,2.0,129743.75,1.0,0.0,1.0,123587.97
+15470,15735263.0,Oster,629.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,73977.23
+15471,15792722.0,Hsueh,664.0,France,Female,33.0,3.0,161591.11,2.0,1.0,1.0,109201.53
+15472,15579781.0,Ch'ien,663.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,94888.6
+15473,15686835.0,Hsu,687.0,France,Female,37.0,9.0,112935.87,1.0,0.0,0.0,182867.84
+15474,15798895.0,Ting,589.0,France,Male,32.0,5.0,0.0,1.0,0.0,0.0,167984.72
+15475,15683053.0,Chukwujamuike,823.0,Germany,Female,39.0,5.0,115532.52,1.0,1.0,1.0,94499.06
+15476,15795934.0,Fanucci,774.0,Germany,Female,35.0,4.0,137761.55,1.0,1.0,0.0,110359.68
+15477,15728006.0,Ch'in,461.0,Spain,Female,38.0,5.0,0.0,2.0,0.0,0.0,142416.35
+15478,15806911.0,Genovese,599.0,France,Male,28.0,5.0,0.0,2.0,1.0,0.0,53511.78
+15479,15637644.0,Cremonesi,633.0,France,Male,34.0,4.0,131283.11,1.0,1.0,0.0,182725.6
+15480,15621433.0,Fokina,593.0,France,Female,56.0,9.0,0.0,2.0,1.0,0.0,121408.55
+15481,15603925.0,Manna,634.0,Germany,Female,54.0,1.0,144849.74,1.0,1.0,0.0,177655.68
+15482,15743293.0,Trevisani,663.0,Spain,Male,43.0,8.0,0.0,2.0,0.0,0.0,10096.25
+15483,15677173.0,Nnachetam,585.0,France,Female,54.0,1.0,0.0,1.0,1.0,0.0,138526.96
+15484,15592645.0,Fiorentino,739.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,83978.86
+15485,15584899.0,Chiang,586.0,Germany,Female,26.0,1.0,125534.51,1.0,1.0,0.0,131473.31
+15486,15781129.0,O'Callaghan,757.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,142377.69
+15487,15620505.0,Chiang,597.0,France,Female,48.0,3.0,0.0,1.0,0.0,0.0,106977.8
+15488,15742404.0,Tsao,684.0,France,Female,38.0,3.0,0.0,2.0,0.0,1.0,44756.25
+15489,15802478.0,Chukwukere,739.0,France,Female,34.0,0.0,127785.17,1.0,1.0,0.0,82796.29
+15490,15668894.0,Smith,681.0,France,Female,49.0,7.0,0.0,1.0,1.0,0.0,173837.54
+15491,15685500.0,Y?an,579.0,Germany,Female,43.0,10.0,106770.82,1.0,1.0,1.0,126131.9
+15492,15751084.0,McIntyre,746.0,France,Male,47.0,9.0,0.0,1.0,1.0,0.0,62944.75
+15493,15639131.0,Lucchese,681.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,136812.04
+15494,15626900.0,Yefremova,597.0,France,Male,31.0,7.0,121063.8,1.0,1.0,0.0,134531.96
+15495,15759913.0,Ch'ien,660.0,France,Female,47.0,6.0,91711.59,2.0,1.0,0.0,99061.75
+15496,15786463.0,Ch'ien,721.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,152491.22
+15497,15592379.0,Chiemenam,713.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,155879.57
+15498,15759499.0,Hsieh,849.0,Germany,Female,55.0,8.0,100240.2,1.0,1.0,0.0,78650.95
+15499,15772528.0,Soto,632.0,Germany,Male,37.0,2.0,105957.44,1.0,1.0,1.0,56780.97
+15500,15643359.0,Shih,556.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,94774.12
+15501,15786463.0,Shih,696.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,1.0,187866.21
+15502,15589544.0,Ugochukwu,628.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,93959.96
+15503,15642937.0,Nkemakonam,584.0,France,Female,58.0,8.0,0.0,1.0,1.0,0.0,47775.39
+15504,15640059.0,Buchi,537.0,France,Male,27.0,5.0,0.0,1.0,1.0,1.0,111312.96
+15505,15618562.0,Yermakova,596.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,80425.65
+15506,15595067.0,Goliwe,678.0,Germany,Female,40.0,9.0,133658.24,2.0,1.0,1.0,35600.25
+15507,15583076.0,Nwachukwu,711.0,Spain,Male,27.0,2.0,0.0,2.0,1.0,1.0,115136.51
+15508,15692226.0,Ukaegbunam,752.0,Germany,Male,41.0,9.0,113922.81,1.0,0.0,1.0,176712.59
+15509,15628885.0,Chien,714.0,France,Male,30.0,10.0,0.0,2.0,0.0,0.0,142516.35
+15510,15763874.0,Kao,738.0,France,Female,33.0,10.0,0.0,1.0,1.0,1.0,110899.3
+15511,15694237.0,Liao,651.0,France,Male,36.0,9.0,167864.4,2.0,1.0,1.0,50488.91
+15512,15682447.0,Onodugoadiegbemma,597.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,93901.61
+15513,15598097.0,Chiganu,628.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,93536.38
+15514,15583480.0,Frolov,683.0,France,Male,36.0,2.0,89900.24,1.0,1.0,1.0,142200.15
+15515,15658670.0,Clements,565.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,132973.17
+15516,15694376.0,T'ien,496.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,86622.56
+15517,15747345.0,Yuryeva,775.0,Germany,Female,37.0,2.0,132876.23,1.0,1.0,1.0,64448.36
+15518,15592761.0,Hsia,592.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,162699.7
+15519,15589488.0,Yuriev,593.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,0.0,183584.14
+15520,15777060.0,Ch'en,584.0,France,Female,38.0,4.0,0.0,1.0,1.0,1.0,164083.72
+15521,15675501.0,Fiorentino,752.0,Germany,Male,39.0,2.0,143930.92,2.0,1.0,0.0,8751.59
+15522,15673837.0,Ritchie,584.0,Spain,Male,31.0,0.0,0.0,1.0,1.0,1.0,170142.09
+15523,15660602.0,Hsu,487.0,Spain,Male,29.0,0.0,125860.2,1.0,1.0,1.0,74853.94
+15524,15602845.0,Mazzanti,485.0,Spain,Female,43.0,4.0,0.0,2.0,1.0,1.0,62002.04
+15525,15800229.0,Chukwueloka,691.0,France,Male,28.0,4.0,0.0,2.0,1.0,1.0,85901.09
+15526,15598097.0,Hsing,735.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,82565.01
+15527,15604536.0,Tung,593.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,149517.71
+15528,15654489.0,Palerma,662.0,France,Male,26.0,3.0,0.0,2.0,1.0,0.0,176518.35
+15529,15595036.0,Macfarlan,695.0,France,Female,41.0,7.0,0.0,2.0,1.0,0.0,62733.05
+15530,15622442.0,Kenechukwu,561.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,173776.5
+15531,15692570.0,Chiang,579.0,Germany,Female,34.0,7.0,129401.87,2.0,1.0,1.0,153747.76
+15532,15593601.0,Fanucci,644.0,Germany,Male,35.0,2.0,101160.99,2.0,1.0,1.0,182720.07
+15533,15590828.0,Nnachetam,516.0,France,Female,33.0,2.0,106871.81,1.0,1.0,0.0,166476.95
+15534,15620204.0,Ch'ang,673.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,131097.9
+15535,15794048.0,White,602.0,France,Female,22.0,3.0,0.0,1.0,1.0,0.0,58974.0
+15536,15614778.0,Tien,683.0,France,Male,43.0,2.0,139532.34,1.0,1.0,0.0,91291.2
+15537,15719606.0,Ch'in,689.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,49594.02
+15538,15695078.0,O'Donnell,628.0,Germany,Male,39.0,1.0,105387.89,2.0,1.0,1.0,136675.22
+15539,15762573.0,Hsia,585.0,Spain,Male,37.0,7.0,0.0,2.0,0.0,0.0,112664.24
+15540,15777923.0,Ponomaryov,616.0,France,Male,44.0,4.0,0.0,2.0,0.0,1.0,124964.82
+15541,15731463.0,Ts'ui,589.0,France,Female,37.0,3.0,0.0,2.0,0.0,0.0,166586.99
+15542,15703394.0,Chinomso,588.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,141756.32
+15543,15620398.0,Galkin,676.0,France,Female,32.0,3.0,0.0,2.0,1.0,1.0,179689.07
+15544,15661249.0,Kenechukwu,694.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,128815.25
+15545,15665861.0,Chukwueloka,646.0,France,Male,34.0,2.0,122951.92,1.0,1.0,0.0,81537.85
+15546,15746564.0,Tseng,596.0,France,Male,42.0,9.0,0.0,1.0,0.0,0.0,130404.53
+15547,15695499.0,Padovano,613.0,France,Male,54.0,2.0,0.0,2.0,1.0,1.0,141300.53
+15548,15570192.0,Pinto,589.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,49594.02
+15549,15723339.0,Nnamutaezinwa,781.0,France,Female,44.0,2.0,0.0,1.0,0.0,0.0,86937.2
+15550,15790442.0,L?,694.0,France,Female,31.0,2.0,0.0,2.0,0.0,1.0,98640.15
+15551,15757537.0,H?,732.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,43496.36
+15552,15613292.0,Hsiung,667.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,168465.85
+15553,15675896.0,Pirozzi,543.0,Spain,Male,31.0,7.0,76548.6,1.0,0.0,1.0,146511.2
+15554,15681316.0,Chiawuotu,627.0,Spain,Male,56.0,7.0,144976.5,2.0,1.0,1.0,177737.07
+15555,15780650.0,Oguejiofor,617.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,137136.29
+15556,15610140.0,Manna,662.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,49833.5
+15557,15775294.0,Tien,850.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,91510.71
+15558,15659962.0,Shih,554.0,France,Male,33.0,1.0,125712.2,1.0,1.0,0.0,57876.05
+15559,15644878.0,Onyemachukwu,718.0,France,Male,31.0,9.0,141665.63,1.0,1.0,0.0,38798.79
+15560,15758845.0,T'ao,709.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,157937.15
+15561,15636700.0,P'eng,585.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,0.0,103726.71
+15562,15688876.0,Chukwujamuike,707.0,Spain,Female,32.0,0.0,0.0,2.0,0.0,1.0,170518.83
+15563,15774847.0,Pagnotto,681.0,France,Female,41.0,3.0,0.0,2.0,1.0,1.0,145565.25
+15564,15749130.0,Hs?,558.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,118959.64
+15565,15707038.0,Yobanna,645.0,France,Female,42.0,9.0,0.0,2.0,1.0,0.0,72350.47
+15566,15635277.0,Teng,541.0,Spain,Male,32.0,7.0,131778.08,1.0,1.0,1.0,88566.9
+15567,15592326.0,Tseng,682.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,83056.22
+15568,15668830.0,Nnamdi,725.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,180553.48
+15569,15610972.0,Onyekachukwu,635.0,France,Female,29.0,8.0,0.0,2.0,0.0,1.0,49492.15
+15570,15723008.0,Wright,611.0,France,Male,38.0,10.0,0.0,1.0,1.0,0.0,38308.34
+15571,15598892.0,Nwabugwu,627.0,Spain,Male,54.0,1.0,0.0,2.0,1.0,0.0,80425.65
+15572,15659105.0,Kuo,706.0,Spain,Female,50.0,8.0,0.0,1.0,1.0,0.0,59103.13
+15573,15627232.0,Liao,694.0,Spain,Female,27.0,6.0,86055.17,1.0,1.0,0.0,95611.47
+15574,15798975.0,Chien,669.0,France,Male,32.0,6.0,0.0,2.0,0.0,0.0,158973.77
+15575,15771570.0,Hsiung,497.0,France,Male,37.0,9.0,0.0,2.0,0.0,1.0,120115.5
+15576,15768104.0,Yobanna,637.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,163427.18
+15577,15717290.0,Yermakova,756.0,France,Female,46.0,3.0,0.0,1.0,1.0,1.0,129890.94
+15578,15602736.0,Dickson,599.0,Germany,Female,55.0,8.0,124577.33,1.0,0.0,1.0,104722.42
+15579,15756481.0,Trevisano,663.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,65765.22
+15580,15769974.0,Rizzo,542.0,Spain,Male,31.0,7.0,0.0,2.0,0.0,0.0,176712.59
+15581,15592979.0,Kao,573.0,France,Male,30.0,6.0,0.0,1.0,0.0,1.0,100862.36
+15582,15646632.0,Baresi,773.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,102899.16
+15583,15642725.0,Calabresi,771.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,83478.12
+15584,15750299.0,Y?,710.0,France,Female,41.0,8.0,0.0,2.0,0.0,0.0,72643.95
+15585,15726058.0,Cremonesi,775.0,France,Female,43.0,1.0,0.0,3.0,1.0,1.0,142331.85
+15586,15592387.0,Olisaemeka,712.0,Spain,Male,27.0,2.0,0.0,2.0,1.0,1.0,105599.82
+15587,15806447.0,Miah,678.0,Germany,Male,32.0,10.0,76915.4,2.0,1.0,1.0,118024.1
+15588,15581276.0,Yermakova,729.0,Spain,Female,44.0,3.0,0.0,2.0,1.0,1.0,67622.46
+15589,15605447.0,Onyinyechukwuka,628.0,France,Male,30.0,0.0,0.0,2.0,1.0,0.0,1710.89
+15590,15662533.0,Calabresi,571.0,France,Female,33.0,10.0,0.0,2.0,1.0,0.0,115468.84
+15591,15762927.0,Chiang,724.0,Spain,Female,38.0,5.0,0.0,2.0,0.0,0.0,78566.64
+15592,15682533.0,Ts'ao,653.0,France,Female,31.0,9.0,151307.11,1.0,1.0,1.0,140941.47
+15593,15571261.0,Ch'iu,545.0,France,Female,63.0,6.0,0.0,1.0,1.0,0.0,56583.88
+15594,15706463.0,Fomin,651.0,France,Male,49.0,6.0,0.0,1.0,1.0,1.0,103832.58
+15595,15771750.0,Dickson,655.0,Spain,Male,33.0,5.0,132057.92,1.0,1.0,1.0,173837.54
+15596,15807167.0,Nkemakonam,684.0,Germany,Female,49.0,6.0,139875.2,1.0,0.0,1.0,105969.05
+15597,15673591.0,Shih,611.0,France,Male,54.0,4.0,0.0,1.0,1.0,1.0,184312.88
+15598,15621227.0,Okwudilichukwu,674.0,Germany,Male,39.0,5.0,117294.12,1.0,1.0,1.0,88391.97
+15599,15771580.0,Balsillie,635.0,Spain,Male,31.0,1.0,0.0,1.0,1.0,0.0,113926.31
+15600,15775131.0,Nkemjika,775.0,Spain,Female,41.0,1.0,0.0,1.0,1.0,0.0,56767.67
+15601,15799128.0,O'Brien,667.0,Spain,Male,45.0,9.0,123783.25,1.0,1.0,0.0,179705.13
+15602,15676909.0,Chukwueloka,659.0,France,Female,32.0,6.0,0.0,2.0,0.0,1.0,115462.27
+15603,15794048.0,Brown,627.0,Spain,Male,41.0,2.0,0.0,2.0,1.0,1.0,164886.64
+15604,15775235.0,Zetticci,520.0,Spain,Male,44.0,5.0,111760.4,1.0,0.0,1.0,125877.22
+15605,15719606.0,Milanesi,850.0,Germany,Male,31.0,8.0,165896.0,1.0,0.0,0.0,71139.31
+15606,15771543.0,Walker,646.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,1.0,24275.32
+15607,15790442.0,Walker,832.0,France,Male,41.0,4.0,131283.11,1.0,1.0,1.0,152843.53
+15608,15623053.0,Tuan,709.0,France,Male,38.0,1.0,0.0,2.0,0.0,0.0,51554.58
+15609,15606472.0,T'ang,573.0,Spain,Female,35.0,2.0,0.0,2.0,0.0,0.0,60905.51
+15610,15594878.0,Chibueze,598.0,Germany,Female,53.0,6.0,117438.92,1.0,0.0,1.0,182531.74
+15611,15694188.0,Calabresi,717.0,France,Male,25.0,6.0,0.0,2.0,1.0,1.0,180626.68
+15612,15692577.0,Genovese,674.0,Spain,Male,36.0,2.0,0.0,1.0,1.0,0.0,129890.94
+15613,15680998.0,Chidumaga,632.0,Germany,Female,29.0,3.0,102515.37,1.0,1.0,1.0,125877.22
+15614,15705301.0,Ukaegbunam,489.0,France,Male,36.0,10.0,164948.98,1.0,0.0,1.0,127827.18
+15615,15646091.0,Genovese,598.0,France,Male,37.0,1.0,62400.48,1.0,0.0,0.0,71175.94
+15616,15676513.0,Mazzi,643.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,133042.9
+15617,15756118.0,Hao,615.0,France,Female,49.0,6.0,0.0,1.0,1.0,0.0,148564.76
+15618,15603741.0,Onio,716.0,Germany,Female,25.0,7.0,102535.57,1.0,0.0,0.0,119377.36
+15619,15691483.0,Chinedum,624.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,166476.95
+15620,15593882.0,Calabresi,731.0,France,Female,38.0,7.0,121702.73,1.0,1.0,0.0,93953.84
+15621,15732778.0,Shih,719.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,149913.84
+15622,15757559.0,Ibragimova,583.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,103914.4
+15623,15692991.0,Lucciano,629.0,Spain,Female,40.0,4.0,0.0,2.0,0.0,0.0,109881.71
+15624,15764105.0,Nnonso,686.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,176714.46
+15625,15776433.0,McGregor,648.0,Germany,Female,46.0,8.0,116939.06,1.0,1.0,0.0,143859.66
+15626,15610755.0,Onyemauchechi,523.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,55725.04
+15627,15779743.0,Ndubuagha,586.0,Germany,Female,33.0,8.0,98173.02,1.0,1.0,0.0,157871.55
+15628,15793070.0,Brierly,530.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,131736.43
+15629,15772423.0,Onwuamaeze,807.0,France,Female,43.0,6.0,0.0,2.0,0.0,0.0,80792.83
+15630,15742442.0,Ejikemeifeuwa,669.0,France,Male,35.0,9.0,109564.1,2.0,1.0,1.0,37266.43
+15631,15651737.0,Ch'eng,605.0,France,Female,37.0,1.0,0.0,1.0,1.0,1.0,182738.0
+15632,15570141.0,Ch'in,677.0,France,Male,31.0,1.0,0.0,2.0,0.0,0.0,161407.48
+15633,15793901.0,Nnamutaezinwa,613.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,1276.87
+15634,15719606.0,Ukaegbunam,746.0,France,Male,36.0,6.0,0.0,1.0,1.0,1.0,119969.99
+15635,15667634.0,Nnachetam,652.0,Germany,Male,47.0,3.0,103421.49,2.0,1.0,1.0,7900.46
+15636,15732438.0,Milne,648.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,187980.44
+15637,15769582.0,She,573.0,Germany,Female,26.0,6.0,154733.97,1.0,1.0,1.0,139712.79
+15638,15743293.0,Chiu,583.0,France,Male,41.0,5.0,0.0,2.0,0.0,0.0,115286.68
+15639,15622532.0,Ch'in,653.0,Spain,Female,38.0,2.0,0.0,1.0,1.0,0.0,126518.0
+15640,15747795.0,Chuang,619.0,Spain,Male,44.0,9.0,89385.92,1.0,1.0,0.0,85937.52
+15641,15775892.0,Ch'eng,726.0,Germany,Male,67.0,1.0,124450.36,1.0,1.0,1.0,177622.38
+15642,15662662.0,Chiazagomekpere,682.0,Germany,Male,50.0,8.0,106192.1,2.0,1.0,1.0,167033.83
+15643,15701175.0,Lu,717.0,France,Male,39.0,1.0,0.0,2.0,0.0,0.0,122949.71
+15644,15649450.0,Chukwueloka,568.0,France,Female,34.0,6.0,0.0,1.0,0.0,1.0,131389.39
+15645,15790750.0,Nnaife,714.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,112687.57
+15646,15750921.0,Ch'en,585.0,France,Male,28.0,5.0,0.0,1.0,1.0,1.0,92568.07
+15647,15794414.0,Chien,749.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,179267.94
+15648,15779497.0,Nucci,537.0,France,Male,50.0,3.0,0.0,2.0,1.0,1.0,93835.64
+15649,15611961.0,Kline,752.0,Spain,Male,38.0,2.0,0.0,1.0,1.0,1.0,115818.55
+15650,15771540.0,Hsiung,850.0,France,Male,32.0,2.0,0.0,2.0,0.0,0.0,179843.33
+15651,15782530.0,Shih,717.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,84927.42
+15652,15733387.0,Hsing,662.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,159899.97
+15653,15793881.0,Toscani,682.0,France,Male,46.0,7.0,123082.08,1.0,0.0,0.0,125898.26
+15654,15683375.0,Kornilova,743.0,France,Female,32.0,7.0,0.0,1.0,1.0,1.0,128881.87
+15655,15655341.0,Li Fonti,619.0,France,Female,49.0,6.0,0.0,1.0,1.0,1.0,102930.46
+15656,15775490.0,Yefimova,633.0,Germany,Female,29.0,1.0,116933.27,1.0,1.0,0.0,188343.05
+15657,15806941.0,McIntosh,657.0,France,Male,45.0,7.0,0.0,1.0,1.0,0.0,70810.6
+15658,15802758.0,Onio,665.0,Germany,Male,23.0,8.0,167795.5,2.0,0.0,1.0,182354.46
+15659,15651022.0,Ts'ui,591.0,Spain,Female,39.0,6.0,0.0,2.0,0.0,0.0,49361.84
+15660,15752622.0,Onwumelu,616.0,France,Male,36.0,0.0,0.0,2.0,1.0,0.0,183572.39
+15661,15589380.0,Walker,579.0,France,Female,33.0,2.0,0.0,1.0,1.0,0.0,38772.65
+15662,15769293.0,Zito,644.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,14073.11
+15663,15576683.0,Cattaneo,719.0,France,Male,40.0,4.0,0.0,2.0,0.0,0.0,130132.41
+15664,15662085.0,Onwuamaeze,694.0,Germany,Female,39.0,9.0,125190.86,2.0,0.0,1.0,174027.3
+15665,15773709.0,Hsia,721.0,Germany,Male,37.0,6.0,153360.02,1.0,1.0,0.0,29358.57
+15666,15722583.0,Nwachinemelu,587.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,77837.63
+15667,15621685.0,Calabresi,535.0,France,Male,36.0,4.0,0.0,2.0,0.0,0.0,61230.83
+15668,15612043.0,Ch'ien,634.0,France,Male,20.0,8.0,0.0,2.0,1.0,0.0,118383.18
+15669,15637315.0,Ts'ui,675.0,France,Female,36.0,11.0,0.0,1.0,1.0,0.0,38190.78
+15670,15594450.0,Chiedozie,688.0,Germany,Female,50.0,1.0,121286.27,2.0,0.0,0.0,121440.8
+15671,15591169.0,Chu,803.0,France,Female,71.0,5.0,0.0,2.0,1.0,1.0,157862.82
+15672,15742378.0,T'ao,691.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,122449.18
+15673,15644296.0,Nnamdi,635.0,Germany,Female,40.0,2.0,139840.36,2.0,1.0,1.0,185211.28
+15674,15613787.0,Nwora,638.0,France,Female,40.0,7.0,151914.93,1.0,1.0,1.0,11302.7
+15675,15686913.0,Chukwuebuka,638.0,France,Female,41.0,1.0,0.0,1.0,1.0,0.0,144593.4
+15676,15691910.0,Niu,703.0,Spain,Male,25.0,7.0,0.0,2.0,0.0,0.0,57553.02
+15677,15633112.0,Baresi,699.0,Spain,Male,43.0,4.0,0.0,1.0,0.0,0.0,50485.7
+15678,15625126.0,Ku,671.0,France,Male,31.0,8.0,99786.07,1.0,1.0,1.0,134790.74
+15679,15763063.0,Tsou,774.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,138880.24
+15680,15691792.0,Lu,684.0,France,Female,32.0,9.0,63488.7,1.0,1.0,1.0,126734.96
+15681,15799350.0,Chin,755.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,94003.11
+15682,15694860.0,Ponomaryova,697.0,Spain,Male,37.0,9.0,0.0,2.0,0.0,1.0,118045.98
+15683,15641655.0,Toscani,679.0,Germany,Female,31.0,3.0,154495.79,2.0,1.0,1.0,177524.19
+15684,15642933.0,Kambinachi,535.0,Spain,Female,29.0,5.0,0.0,2.0,0.0,0.0,151325.24
+15685,15681671.0,Y?an,712.0,France,Male,31.0,1.0,110527.17,1.0,1.0,0.0,101168.3
+15686,15662337.0,Mazzanti,590.0,Germany,Male,47.0,2.0,125801.03,1.0,1.0,1.0,147093.84
+15687,15592763.0,Iweobiegbunam,679.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,103379.31
+15688,15731553.0,Chukwujekwu,565.0,Germany,Female,40.0,4.0,117864.85,1.0,1.0,1.0,173498.45
+15689,15798850.0,Nwankwo,446.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,82422.91
+15690,15593468.0,Lai,713.0,France,Female,39.0,10.0,111396.97,1.0,1.0,0.0,93901.61
+15691,15732774.0,Lucciano,679.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,143913.17
+15692,15666884.0,Hsueh,722.0,Germany,Male,33.0,5.0,88770.71,1.0,1.0,1.0,175792.87
+15693,15654851.0,Tung,645.0,Spain,Female,42.0,9.0,0.0,2.0,0.0,0.0,170893.85
+15694,15667085.0,Chinedum,554.0,France,Female,22.0,2.0,0.0,2.0,1.0,1.0,91514.62
+15695,15727281.0,Chidubem,646.0,France,Female,39.0,9.0,0.0,2.0,1.0,1.0,161396.95
+15696,15586494.0,Chizoba,602.0,France,Female,33.0,0.0,0.0,2.0,1.0,0.0,149665.65
+15697,15591388.0,Fanucci,695.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,105760.01
+15698,15620507.0,Teng,632.0,France,Female,32.0,7.0,0.0,1.0,1.0,0.0,88890.05
+15699,15643908.0,Nwachukwu,506.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,1.0,187900.12
+15700,15676526.0,Ibeabuchi,659.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,0.0,118974.77
+15701,15597915.0,Brient,731.0,Germany,Female,28.0,6.0,154970.54,1.0,1.0,0.0,73066.73
+15702,15644765.0,Macleod,616.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,189366.04
+15703,15753549.0,Ibezimako,613.0,France,Female,38.0,1.0,0.0,1.0,1.0,1.0,177092.16
+15704,15786800.0,Onyekachukwu,704.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,126495.57
+15705,15732672.0,Lo Duca,596.0,Germany,Male,52.0,5.0,114940.67,1.0,1.0,0.0,123475.88
+15706,15588986.0,Nnachetam,594.0,Spain,Female,34.0,9.0,135559.9,1.0,1.0,0.0,105568.4
+15707,15700511.0,Ch'ien,714.0,Spain,Male,31.0,3.0,87541.06,1.0,1.0,0.0,179360.76
+15708,15677243.0,Chiazagomekpele,706.0,Spain,Female,45.0,4.0,106891.71,1.0,1.0,0.0,131736.23
+15709,15738954.0,Ch'ang,651.0,France,Male,27.0,5.0,0.0,2.0,0.0,1.0,164804.02
+15710,15704844.0,Ibeamaka,775.0,Germany,Female,45.0,1.0,172646.82,1.0,1.0,0.0,83679.46
+15711,15629002.0,Y?an,792.0,Germany,Female,42.0,8.0,124577.33,2.0,1.0,0.0,102260.16
+15712,15671591.0,Bitter,659.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,55859.27
+15713,15673481.0,Onyeorulu,667.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,91346.03
+15714,15631927.0,T'ang,713.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,162697.93
+15715,15627220.0,Ch'en,635.0,France,Male,54.0,2.0,0.0,1.0,0.0,1.0,93820.49
+15716,15757811.0,Tien,742.0,France,Male,33.0,9.0,159863.9,1.0,1.0,0.0,19799.26
+15717,15586029.0,P'an,723.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,79623.28
+15718,15716491.0,Clements,773.0,Spain,Female,29.0,8.0,157781.24,1.0,1.0,1.0,181204.4
+15719,15769169.0,Chinomso,602.0,Spain,Male,50.0,7.0,0.0,3.0,1.0,1.0,153080.4
+15720,15642570.0,Palerma,573.0,Spain,Male,43.0,8.0,112994.65,1.0,0.0,0.0,184843.77
+15721,15672983.0,Chukwualuka,684.0,France,Male,31.0,1.0,0.0,1.0,1.0,1.0,139162.15
+15722,15659677.0,Chizuoke,706.0,Spain,Male,28.0,6.0,0.0,2.0,1.0,1.0,78854.58
+15723,15756475.0,H?,693.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,119924.84
+15724,15731519.0,Pirozzi,682.0,Spain,Male,37.0,8.0,0.0,1.0,1.0,1.0,126728.27
+15725,15754796.0,Chia,718.0,Germany,Male,33.0,2.0,98899.91,2.0,1.0,0.0,80552.36
+15726,15595872.0,Ugoji,635.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,166909.95
+15727,15726310.0,Hoolan,655.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,62454.01
+15728,15693732.0,Fanucci,683.0,Spain,Female,21.0,1.0,0.0,2.0,0.0,0.0,136329.96
+15729,15782899.0,Nkemdilim,595.0,France,Female,45.0,9.0,0.0,1.0,0.0,0.0,110784.42
+15730,15679075.0,Ch'ien,569.0,France,Female,35.0,7.0,141787.27,1.0,0.0,1.0,177844.06
+15731,15664214.0,Ugochukwu,648.0,Spain,Male,32.0,8.0,0.0,2.0,0.0,1.0,162719.69
+15732,15570967.0,Ch'eng,581.0,France,Female,52.0,9.0,0.0,1.0,0.0,0.0,118711.75
+15733,15594041.0,Chukwuemeka,644.0,Germany,Male,39.0,5.0,128243.03,1.0,1.0,0.0,80582.34
+15734,15668081.0,Ch'ang,736.0,Spain,Female,39.0,4.0,0.0,2.0,0.0,0.0,53358.73
+15735,15619608.0,Chukwualuka,744.0,France,Female,39.0,5.0,0.0,1.0,1.0,0.0,177616.17
+15736,15656683.0,Baresi,652.0,Spain,Male,36.0,6.0,0.0,2.0,0.0,0.0,124045.65
+15737,15592386.0,H?,636.0,France,Female,41.0,5.0,80926.02,1.0,1.0,0.0,97640.2
+15738,15765650.0,Nwachinemelu,614.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,86950.72
+15739,15749679.0,White,636.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,1.0,38252.25
+15740,15803378.0,T'ien,583.0,France,Female,39.0,5.0,0.0,2.0,0.0,0.0,109096.71
+15741,15800233.0,Ch'iu,634.0,France,Male,64.0,7.0,0.0,2.0,1.0,1.0,163570.25
+15742,15643487.0,Smith,595.0,Spain,Female,43.0,1.0,0.0,1.0,0.0,0.0,133699.82
+15743,15747168.0,Lettiere,719.0,Spain,Female,40.0,2.0,0.0,1.0,1.0,0.0,144584.29
+15744,15602851.0,Kao,682.0,France,Female,32.0,8.0,0.0,2.0,0.0,1.0,52711.55
+15745,15771543.0,L?,619.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,132801.57
+15746,15802823.0,Dettmann,554.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,0.0,102681.32
+15747,15703793.0,Chukwuemeka,632.0,France,Female,39.0,5.0,0.0,1.0,1.0,0.0,71830.1
+15748,15745295.0,Chukwudi,605.0,Spain,Male,42.0,0.0,137811.75,1.0,0.0,0.0,176471.79
+15749,15699446.0,Ch'eng,662.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,92914.67
+15750,15761149.0,Hao,591.0,Spain,Male,32.0,9.0,134177.06,1.0,1.0,0.0,160529.71
+15751,15792922.0,Fitts,651.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,108419.41
+15752,15598510.0,Hargreaves,613.0,Germany,Male,36.0,7.0,113657.41,1.0,1.0,0.0,91510.71
+15753,15583552.0,Chuang,525.0,France,Female,38.0,4.0,0.0,2.0,0.0,1.0,169862.01
+15754,15567422.0,Ch'ien,684.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,93717.55
+15755,15663888.0,Pisano,640.0,Germany,Male,35.0,2.0,95502.11,1.0,1.0,1.0,176570.55
+15756,15739592.0,Ukaegbunam,590.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,1.0,189428.75
+15757,15781702.0,Ndubuagha,697.0,France,Female,24.0,1.0,0.0,2.0,1.0,0.0,6795.61
+15758,15602934.0,Hs?,758.0,Spain,Female,23.0,9.0,0.0,1.0,1.0,1.0,136028.97
+15759,15758027.0,Hs?eh,724.0,France,Female,24.0,4.0,117554.26,1.0,1.0,0.0,100722.72
+15760,15664339.0,Udegbulam,582.0,France,Female,40.0,4.0,0.0,2.0,0.0,0.0,174431.01
+15761,15703544.0,Udegbulam,667.0,France,Male,34.0,5.0,0.0,1.0,1.0,0.0,58883.91
+15762,15770174.0,Chia,516.0,France,Female,41.0,10.0,0.0,1.0,0.0,0.0,107825.08
+15763,15644796.0,Okwudilichukwu,753.0,Germany,Female,35.0,5.0,109632.85,3.0,1.0,0.0,137793.35
+15764,15726354.0,Lu,634.0,France,Female,31.0,5.0,0.0,2.0,0.0,0.0,184514.01
+15765,15795963.0,Nwachinemelu,708.0,France,Female,41.0,1.0,0.0,2.0,1.0,0.0,97468.44
+15766,15573599.0,Nkemakonam,577.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,138882.98
+15767,15672798.0,Cremonesi,578.0,Spain,Female,27.0,8.0,0.0,2.0,1.0,1.0,38296.21
+15768,15671209.0,Onyemauchechukwu,846.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,72990.62
+15769,15751206.0,Hsueh,733.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,68849.65
+15770,15665784.0,H?,645.0,Germany,Female,27.0,3.0,120596.93,1.0,1.0,1.0,171056.65
+15771,15574072.0,Chinweike,578.0,France,Male,33.0,5.0,0.0,1.0,1.0,1.0,93833.38
+15772,15605918.0,Genovese,804.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,123954.15
+15773,15685314.0,Chukwuhaenye,619.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,149614.17
+15774,15772892.0,Tung,576.0,Spain,Female,36.0,0.0,132576.25,1.0,1.0,1.0,176576.62
+15775,15620204.0,Biryukov,635.0,Spain,Male,26.0,3.0,145974.79,2.0,0.0,0.0,174921.91
+15776,15648800.0,Yuryeva,679.0,France,Male,35.0,2.0,0.0,1.0,0.0,0.0,26257.01
+15777,15761693.0,Smith,637.0,Spain,Female,47.0,6.0,95877.74,1.0,1.0,0.0,62678.53
+15778,15596915.0,Tikhonov,732.0,France,Female,51.0,8.0,0.0,1.0,1.0,0.0,139431.0
+15779,15584518.0,Ts'ai,719.0,France,Male,39.0,7.0,0.0,1.0,1.0,1.0,167909.52
+15780,15656319.0,Ibeabuchi,713.0,Germany,Male,42.0,1.0,132835.56,1.0,1.0,0.0,105720.09
+15781,15790596.0,Tuan,653.0,France,Male,36.0,10.0,0.0,2.0,0.0,0.0,6057.81
+15782,15662908.0,Oguejiofor,755.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,63687.06
+15783,15690796.0,Chidiebere,678.0,France,Male,26.0,1.0,100524.69,1.0,0.0,1.0,60721.4
+15784,15762729.0,Hsu,558.0,France,Male,31.0,2.0,146580.69,1.0,0.0,0.0,48924.73
+15785,15702641.0,Genovese,607.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,173632.92
+15786,15683637.0,Genovese,619.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,143635.55
+15787,15615020.0,Ndubueze,721.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,131281.22
+15788,15639552.0,Ts'ui,526.0,France,Female,46.0,8.0,0.0,1.0,1.0,1.0,80552.11
+15789,15682048.0,Lombardi,775.0,France,Male,44.0,2.0,0.0,1.0,0.0,0.0,185821.41
+15790,15759913.0,Pirozzi,704.0,France,Male,33.0,7.0,89122.3,1.0,0.0,0.0,61931.21
+15791,15807993.0,Nnachetam,821.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,152992.04
+15792,15626900.0,Gorman,659.0,Germany,Female,46.0,3.0,117070.21,1.0,1.0,0.0,27330.27
+15793,15604588.0,Padovesi,604.0,France,Female,29.0,3.0,0.0,2.0,0.0,1.0,129964.94
+15794,15632010.0,Chiang,730.0,Spain,Female,26.0,6.0,0.0,2.0,1.0,0.0,106785.7
+15795,15641850.0,Chukwufumnanya,710.0,France,Male,40.0,3.0,136858.42,1.0,0.0,0.0,58883.91
+15796,15753895.0,H?,752.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,121751.04
+15797,15593365.0,Chiang,756.0,Germany,Male,36.0,9.0,101160.99,1.0,1.0,1.0,174770.14
+15798,15651460.0,Ch'iu,600.0,Germany,Female,53.0,9.0,139529.93,1.0,1.0,0.0,115916.55
+15799,15691387.0,Chiemenam,595.0,France,Female,37.0,9.0,0.0,2.0,0.0,1.0,103787.28
+15800,15705544.0,Ch'iu,722.0,France,Male,41.0,5.0,0.0,1.0,1.0,0.0,88729.22
+15801,15761692.0,Onyemauchechukwu,726.0,Spain,Male,46.0,7.0,0.0,1.0,0.0,0.0,53710.23
+15802,15724150.0,Monds,678.0,Germany,Female,44.0,8.0,128524.19,2.0,1.0,0.0,177783.04
+15803,15756472.0,Chibueze,732.0,Germany,Male,39.0,7.0,121325.84,1.0,1.0,1.0,147822.81
+15804,15664504.0,Aitken,579.0,Germany,Female,31.0,7.0,151739.65,2.0,1.0,1.0,146637.45
+15805,15726167.0,T'an,585.0,France,Male,33.0,5.0,95741.75,1.0,1.0,0.0,96444.88
+15806,15752300.0,H?,704.0,Germany,Male,25.0,2.0,132951.92,2.0,1.0,0.0,122433.07
+15807,15777893.0,Iloerika,573.0,France,Female,56.0,9.0,133950.19,1.0,1.0,1.0,62634.94
+15808,15667046.0,Onwumelu,621.0,Germany,Female,39.0,4.0,186445.31,2.0,0.0,0.0,116969.89
+15809,15762331.0,Hs?,675.0,France,Female,27.0,2.0,0.0,2.0,1.0,0.0,131389.39
+15810,15740900.0,Yudina,688.0,Spain,Female,50.0,9.0,0.0,1.0,0.0,0.0,93901.61
+15811,15746065.0,Ts'ui,705.0,Spain,Male,72.0,8.0,0.0,1.0,1.0,1.0,83052.03
+15812,15662403.0,Genovesi,554.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,109614.57
+15813,15763579.0,Ch'iu,767.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,1.0,181490.42
+15814,15685320.0,Martin,635.0,Germany,Male,26.0,1.0,96143.47,2.0,1.0,1.0,147955.91
+15815,15622171.0,Baresi,526.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,0.0,75888.65
+15816,15672467.0,Chien,641.0,France,Female,37.0,1.0,113907.58,1.0,1.0,0.0,159835.82
+15817,15668057.0,Uvarov,622.0,France,Male,40.0,2.0,0.0,2.0,0.0,0.0,139048.0
+15818,15634359.0,Grieve,569.0,France,Female,32.0,9.0,122471.09,1.0,1.0,1.0,38131.77
+15819,15785819.0,Pisani,833.0,Spain,Female,41.0,9.0,0.0,2.0,1.0,0.0,44265.28
+15820,15793896.0,Eipper,634.0,Spain,Male,42.0,2.0,0.0,2.0,0.0,1.0,645.61
+15821,15791030.0,Onuora,685.0,France,Female,42.0,7.0,0.0,1.0,1.0,0.0,151349.35
+15822,15581229.0,Ch'ang,488.0,France,Female,32.0,7.0,114370.41,1.0,1.0,0.0,104730.52
+15823,15679743.0,Onwumelu,611.0,France,Male,67.0,8.0,125913.93,2.0,1.0,1.0,124749.08
+15824,15638774.0,Manna,615.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,143086.09
+15825,15809722.0,De Luca,629.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,141896.74
+15826,15801417.0,Fomin,559.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,38252.25
+15827,15665956.0,L?,682.0,France,Male,34.0,9.0,0.0,1.0,1.0,1.0,151581.79
+15828,15768006.0,Onyemachukwu,686.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,94811.85
+15829,15645136.0,Chiedozie,583.0,France,Female,35.0,2.0,0.0,1.0,1.0,0.0,88890.05
+15830,15695499.0,H?,704.0,Germany,Female,45.0,4.0,133658.24,1.0,1.0,0.0,185510.63
+15831,15783338.0,Nwabugwu,790.0,Germany,Female,32.0,4.0,122486.47,2.0,0.0,0.0,143357.07
+15832,15627212.0,T'ien,516.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,164696.28
+15833,15796790.0,Chukwudi,614.0,France,Female,42.0,0.0,0.0,1.0,1.0,0.0,82522.61
+15834,15657778.0,Lucciano,850.0,France,Male,40.0,5.0,107764.75,2.0,1.0,0.0,199409.19
+15835,15659305.0,Pirozzi,559.0,Spain,Female,29.0,8.0,0.0,2.0,1.0,1.0,48131.92
+15836,15629885.0,Onuchukwu,705.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,121798.52
+15837,15684769.0,Chidumaga,673.0,France,Male,45.0,9.0,113873.23,1.0,1.0,1.0,135925.72
+15838,15667046.0,Folliero,632.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,1.0,90162.35
+15839,15689671.0,Ts'ui,559.0,Spain,Male,23.0,4.0,0.0,2.0,1.0,1.0,121061.89
+15840,15672967.0,Onyemauchechukwu,634.0,Spain,Male,34.0,10.0,0.0,2.0,0.0,1.0,96670.2
+15841,15684999.0,Nwachinemelu,721.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,172096.08
+15842,15721719.0,Hao,819.0,Germany,Female,38.0,7.0,108924.33,2.0,1.0,0.0,61761.77
+15843,15611644.0,Fiorentino,699.0,France,Male,48.0,8.0,0.0,2.0,1.0,1.0,147964.99
+15844,15692761.0,Chinonyelum,742.0,Spain,Female,38.0,7.0,80958.39,1.0,1.0,1.0,169964.12
+15845,15645136.0,Ch'en,621.0,France,Female,43.0,10.0,129440.3,1.0,1.0,0.0,7861.68
+15846,15803523.0,McIntosh,587.0,Spain,Female,43.0,2.0,0.0,1.0,1.0,1.0,93336.3
+15847,15752508.0,Shih,598.0,France,Male,37.0,9.0,0.0,2.0,0.0,0.0,162697.93
+15848,15793854.0,Tung,592.0,France,Male,33.0,9.0,116967.68,1.0,1.0,0.0,116503.92
+15849,15800703.0,Okwudilichukwu,606.0,France,Female,23.0,1.0,118879.35,1.0,1.0,0.0,43821.56
+15850,15790282.0,Chibueze,705.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,135071.12
+15851,15595592.0,T'ien,591.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,0.0,123850.58
+15852,15771674.0,Chukwujamuike,741.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,79866.73
+15853,15648067.0,Cattaneo,682.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,144290.25
+15854,15736578.0,Ts'ao,773.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,93826.63
+15855,15748360.0,P'eng,758.0,France,Female,36.0,0.0,0.0,1.0,1.0,1.0,133897.32
+15856,15690731.0,Hsing,749.0,Spain,Female,25.0,7.0,160599.06,1.0,1.0,0.0,141364.62
+15857,15594878.0,Okeke,809.0,Spain,Female,42.0,6.0,0.0,2.0,1.0,0.0,152740.16
+15858,15794868.0,Yermakova,594.0,Germany,Male,34.0,7.0,161671.16,2.0,0.0,1.0,171457.06
+15859,15731751.0,Chiang,581.0,Spain,Female,67.0,8.0,0.0,2.0,1.0,0.0,102908.63
+15860,15757829.0,T'ien,678.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,87412.24
+15861,15722212.0,Horsley,622.0,Spain,Female,37.0,6.0,0.0,1.0,1.0,0.0,150836.98
+15862,15593468.0,Ts'ao,601.0,Germany,Female,30.0,2.0,108806.96,2.0,1.0,1.0,58854.88
+15863,15671137.0,Fiorentino,709.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,26589.56
+15864,15790810.0,Martin,651.0,Spain,Female,62.0,2.0,0.0,3.0,1.0,0.0,101485.72
+15865,15684769.0,Houghton,589.0,Germany,Female,27.0,1.0,155060.88,1.0,1.0,0.0,101039.53
+15866,15769353.0,Chinedum,670.0,Spain,Female,32.0,7.0,109570.21,1.0,1.0,1.0,131768.98
+15867,15727198.0,Ojiofor,684.0,Spain,Male,34.0,1.0,0.0,1.0,0.0,0.0,177719.88
+15868,15612444.0,Kornilova,697.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,142223.94
+15869,15767414.0,Ugoji,491.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,121789.3
+15870,15691808.0,Hsueh,704.0,France,Male,33.0,4.0,0.0,2.0,0.0,0.0,87566.97
+15871,15780572.0,Pinto,639.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,131283.6
+15872,15570134.0,Ndubueze,659.0,France,Male,28.0,6.0,0.0,1.0,1.0,1.0,55763.66
+15873,15672027.0,Chibueze,652.0,France,Male,40.0,6.0,0.0,1.0,1.0,0.0,93629.66
+15874,15654060.0,Ozerova,738.0,France,Male,26.0,0.0,0.0,2.0,1.0,1.0,143317.42
+15875,15640133.0,Lu,593.0,Spain,Female,42.0,0.0,0.0,2.0,1.0,1.0,172749.4
+15876,15672242.0,Chiemenam,678.0,France,Male,37.0,3.0,0.0,2.0,0.0,1.0,179521.24
+15877,15777873.0,Ch'in,554.0,France,Female,26.0,5.0,0.0,2.0,0.0,1.0,164066.46
+15878,15730793.0,Baresi,627.0,France,Female,31.0,2.0,0.0,1.0,1.0,0.0,154364.66
+15879,15780128.0,Nnonso,633.0,France,Male,45.0,10.0,0.0,1.0,0.0,0.0,47318.75
+15880,15682600.0,O'Brien,583.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,146785.44
+15881,15731166.0,Kao,669.0,Spain,Male,35.0,7.0,124548.99,1.0,1.0,1.0,147530.06
+15882,15588955.0,Walker,667.0,France,Male,41.0,7.0,123345.65,1.0,1.0,1.0,83342.73
+15883,15792922.0,Mancini,683.0,France,Male,29.0,6.0,125977.81,1.0,0.0,1.0,159475.08
+15884,15646720.0,Aksenov,580.0,Germany,Female,32.0,9.0,119452.03,2.0,0.0,0.0,125986.18
+15885,15779985.0,Okagbue,721.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,146041.45
+15886,15763662.0,Ch'in,709.0,Spain,Male,45.0,5.0,143938.27,1.0,1.0,0.0,131804.86
+15887,15681887.0,Chijindum,753.0,Spain,Female,37.0,1.0,0.0,2.0,0.0,0.0,66463.62
+15888,15661629.0,Chukwufumnanya,775.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,131807.14
+15889,15703588.0,Aksenov,629.0,France,Male,44.0,2.0,0.0,2.0,1.0,1.0,112157.31
+15890,15651389.0,Okwudilichukwu,579.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,157564.75
+15891,15721582.0,Ch'iu,638.0,France,Female,51.0,10.0,0.0,1.0,0.0,0.0,103099.55
+15892,15581280.0,Hsing,603.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,102083.78
+15893,15584518.0,Hsueh,588.0,France,Female,30.0,4.0,0.0,1.0,1.0,0.0,705.18
+15894,15732643.0,Tsao,695.0,Spain,Female,28.0,8.0,0.0,2.0,1.0,0.0,76264.27
+15895,15589969.0,Chidozie,555.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,121138.77
+15896,15786463.0,Mazzanti,676.0,France,Male,37.0,4.0,0.0,1.0,1.0,1.0,171457.06
+15897,15591036.0,Niu,535.0,Spain,Male,45.0,9.0,0.0,1.0,1.0,1.0,114869.35
+15898,15792726.0,Mancini,697.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,0.0,59129.72
+15899,15759066.0,Uwakwe,627.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,92223.59
+15900,15610686.0,Rizzo,684.0,France,Male,42.0,1.0,152496.82,1.0,1.0,0.0,176843.53
+15901,15612043.0,H?,520.0,France,Male,42.0,1.0,127543.13,1.0,1.0,0.0,108258.22
+15902,15806467.0,Y?an,731.0,France,Female,33.0,9.0,0.0,2.0,0.0,0.0,170968.99
+15903,15620579.0,Hsueh,632.0,France,Male,47.0,0.0,0.0,1.0,1.0,1.0,140189.72
+15904,15777896.0,Wright,651.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,131097.9
+15905,15607874.0,Scott,682.0,Germany,Female,41.0,1.0,138089.97,2.0,1.0,1.0,94875.04
+15906,15787907.0,Wright,705.0,France,Female,43.0,9.0,0.0,2.0,1.0,1.0,62850.87
+15907,15620341.0,Manna,652.0,France,Male,37.0,8.0,0.0,1.0,1.0,1.0,170764.08
+15908,15773520.0,Onwuamaeze,633.0,Spain,Male,44.0,2.0,0.0,2.0,1.0,0.0,131281.29
+15909,15718773.0,K'ung,549.0,France,Male,30.0,8.0,0.0,1.0,0.0,0.0,164081.42
+15910,15621974.0,Lucciano,649.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,141926.43
+15911,15593773.0,Chiebuka,724.0,France,Male,45.0,3.0,77526.66,2.0,1.0,0.0,131660.31
+15912,15662263.0,Chia,637.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,51362.82
+15913,15648075.0,T'ao,741.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,57323.18
+15914,15643677.0,Tung,479.0,Germany,Female,37.0,0.0,109542.25,1.0,1.0,1.0,26249.86
+15915,15629010.0,Hsia,644.0,France,Female,35.0,8.0,160903.2,1.0,1.0,1.0,93081.87
+15916,15647725.0,Padovano,646.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,0.0,52319.26
+15917,15747757.0,Tuan,633.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,96833.0
+15918,15749265.0,Ting,558.0,France,Female,27.0,9.0,0.0,2.0,1.0,1.0,25684.93
+15919,15788025.0,Ndubueze,623.0,France,Female,37.0,6.0,0.0,1.0,1.0,1.0,69320.97
+15920,15799479.0,Hs?,607.0,Germany,Female,43.0,6.0,119618.42,2.0,1.0,1.0,102073.46
+15921,15695792.0,Ch'iu,477.0,Germany,Male,33.0,0.0,118829.03,1.0,0.0,0.0,171345.02
+15922,15733491.0,Chibuzo,543.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,47941.84
+15923,15775892.0,Fiorentini,668.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,86927.53
+15924,15793331.0,Niu,601.0,Germany,Female,39.0,6.0,124976.19,1.0,1.0,0.0,88827.33
+15925,15715199.0,Chien,745.0,France,Female,32.0,10.0,0.0,2.0,1.0,1.0,89822.48
+15926,15779903.0,T'ang,559.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,121537.91
+15927,15751131.0,Kao,705.0,Spain,Female,47.0,6.0,0.0,1.0,1.0,0.0,142935.83
+15928,15732903.0,Pirozzi,656.0,France,Female,39.0,7.0,146053.66,2.0,1.0,0.0,121840.2
+15929,15630053.0,Mancini,573.0,Spain,Female,45.0,2.0,0.0,1.0,1.0,1.0,159800.16
+15930,15807473.0,Ch'ien,703.0,Germany,Female,45.0,4.0,116520.78,1.0,0.0,0.0,81778.72
+15931,15668270.0,Baresi,547.0,France,Female,39.0,8.0,141136.62,1.0,1.0,0.0,156792.89
+15932,15671165.0,Hsieh,749.0,Spain,Male,67.0,8.0,0.0,2.0,0.0,1.0,128815.29
+15933,15616280.0,Chukwufumnanya,828.0,France,Female,46.0,6.0,0.0,1.0,1.0,1.0,170821.43
+15934,15639265.0,T'ien,733.0,Spain,Female,32.0,1.0,0.0,2.0,0.0,1.0,147913.56
+15935,15782530.0,Tung,554.0,Spain,Male,28.0,9.0,0.0,2.0,1.0,1.0,149912.28
+15936,15737173.0,Maclean,612.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,81617.5
+15937,15702669.0,Iloerika,675.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,1.0,84300.4
+15938,15579826.0,Hsu,656.0,Germany,Male,37.0,1.0,124401.9,1.0,1.0,1.0,121053.19
+15939,15793949.0,Chinonyelum,582.0,Spain,Male,41.0,2.0,0.0,2.0,1.0,1.0,93777.81
+15940,15655410.0,Rizzo,643.0,France,Male,33.0,9.0,0.0,1.0,1.0,1.0,63736.16
+15941,15607690.0,Nkemakolam,725.0,France,Male,37.0,10.0,0.0,1.0,1.0,1.0,178067.33
+15942,15780954.0,Onyemachukwu,656.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,62368.33
+15943,15623525.0,Lucciano,588.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,47856.82
+15944,15672826.0,Hargreaves,686.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,97623.42
+15945,15751057.0,Zuev,686.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,0.0,67789.99
+15946,15756481.0,Ch'in,627.0,France,Female,31.0,8.0,117899.95,1.0,1.0,0.0,152571.61
+15947,15778897.0,Ts'ui,807.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,98623.78
+15948,15627995.0,Kao,729.0,Spain,Male,49.0,5.0,95858.37,1.0,1.0,0.0,78955.8
+15949,15781574.0,Mistry,719.0,France,Male,51.0,9.0,0.0,1.0,0.0,0.0,114563.66
+15950,15635244.0,White,543.0,France,Male,36.0,2.0,0.0,2.0,0.0,1.0,120790.02
+15951,15794413.0,Lucchesi,675.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,162599.51
+15952,15585039.0,K'ung,534.0,Germany,Female,56.0,7.0,73054.37,1.0,0.0,0.0,135662.17
+15953,15593854.0,Miller,639.0,Germany,Male,37.0,3.0,122550.05,1.0,0.0,1.0,73857.94
+15954,15684868.0,Hsieh,687.0,Spain,Male,40.0,9.0,102541.1,2.0,1.0,1.0,152843.23
+15955,15595874.0,Chukwumaobim,659.0,France,Male,28.0,6.0,181656.54,1.0,1.0,0.0,183572.39
+15956,15652469.0,Chu,730.0,France,Male,36.0,6.0,151818.51,2.0,1.0,1.0,64595.25
+15957,15702416.0,Ting,619.0,France,Male,66.0,7.0,0.0,2.0,1.0,1.0,72796.29
+15958,15732266.0,Onwumelu,567.0,Germany,Female,52.0,9.0,120916.52,2.0,1.0,0.0,136837.97
+15959,15687828.0,Tsou,656.0,France,Male,36.0,3.0,0.0,1.0,0.0,0.0,152992.04
+15960,15653762.0,Ch'ien,685.0,France,Male,34.0,4.0,0.0,2.0,0.0,0.0,146636.44
+15961,15675896.0,Chinweike,579.0,Spain,Female,42.0,3.0,0.0,1.0,1.0,0.0,141558.04
+15962,15708505.0,Tung,651.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,0.0,135650.21
+15963,15734365.0,Bancroft,732.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,40889.14
+15964,15649744.0,Uspenskaya,692.0,Spain,Female,21.0,6.0,0.0,1.0,1.0,1.0,116848.79
+15965,15657771.0,Pinto,706.0,Germany,Male,34.0,4.0,123870.07,1.0,1.0,1.0,147797.54
+15966,15772363.0,Wilson,836.0,Germany,Female,53.0,1.0,143566.12,1.0,1.0,0.0,172668.39
+15967,15689322.0,T'ien,685.0,Germany,Male,35.0,9.0,126815.97,2.0,0.0,1.0,94440.45
+15968,15638247.0,Ch'en,705.0,Germany,Female,31.0,4.0,84557.82,1.0,0.0,0.0,143981.31
+15969,15791877.0,Yevdokimova,697.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,137916.49
+15970,15722533.0,Kao,711.0,France,Male,48.0,7.0,0.0,2.0,0.0,1.0,123954.15
+15971,15731144.0,Uchenna,646.0,France,Female,28.0,3.0,123856.53,1.0,0.0,0.0,170528.63
+15972,15600739.0,Mazzanti,589.0,France,Female,49.0,0.0,0.0,3.0,1.0,0.0,132527.6
+15973,15750769.0,Chiu,669.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,107866.22
+15974,15572291.0,Johnstone,642.0,France,Male,36.0,5.0,116322.27,1.0,1.0,0.0,142331.85
+15975,15752312.0,H?,585.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,121189.3
+15976,15731522.0,Chien,646.0,France,Male,22.0,5.0,0.0,2.0,1.0,1.0,85983.54
+15977,15614878.0,Kambinachi,439.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,103955.31
+15978,15597946.0,Hsia,545.0,Germany,Male,37.0,6.0,116800.25,1.0,1.0,1.0,166370.81
+15979,15597951.0,Chiekwugo,496.0,France,Male,27.0,3.0,0.0,2.0,1.0,0.0,142956.33
+15980,15761950.0,Folliero,593.0,France,Female,30.0,9.0,79951.9,2.0,0.0,1.0,121408.55
+15981,15572390.0,Frolova,612.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,172782.69
+15982,15724863.0,Ahern,677.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,141954.11
+15983,15661349.0,Hsiao,717.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,60552.44
+15984,15741719.0,Trevisano,738.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,143357.07
+15985,15721779.0,Chu,576.0,France,Female,37.0,7.0,0.0,1.0,1.0,1.0,645.61
+15986,15766552.0,Chukwufumnanya,621.0,Germany,Male,38.0,10.0,137781.65,1.0,1.0,1.0,184954.11
+15987,15728505.0,Toscani,678.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,19264.33
+15988,15700255.0,Ch'ang,567.0,France,Female,27.0,7.0,0.0,2.0,0.0,0.0,180318.6
+15989,15780359.0,Chukwukere,579.0,Spain,Male,33.0,2.0,88827.53,1.0,1.0,0.0,158032.5
+15990,15635141.0,Chia,627.0,Spain,Male,52.0,6.0,0.0,1.0,1.0,1.0,142838.64
+15991,15806922.0,Chuang,705.0,Spain,Male,32.0,7.0,0.0,2.0,1.0,1.0,180829.99
+15992,15719276.0,Chiawuotu,663.0,Germany,Male,55.0,1.0,104930.38,1.0,1.0,0.0,98820.09
+15993,15704081.0,Ch'eng,479.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,104902.68
+15994,15597951.0,P'eng,705.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,61315.37
+15995,15580146.0,Lo Duca,678.0,France,Female,35.0,6.0,0.0,2.0,0.0,1.0,63889.1
+15996,15690541.0,Chidimma,752.0,Germany,Female,46.0,3.0,93925.4,1.0,0.0,0.0,94951.27
+15997,15569867.0,Onwumelu,539.0,Spain,Male,32.0,9.0,0.0,1.0,1.0,0.0,131177.22
+15998,15777060.0,Hsu,527.0,Germany,Male,31.0,3.0,158623.14,2.0,0.0,1.0,94870.23
+15999,15651802.0,Teng,678.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,133699.82
+16000,15803908.0,Chiang,687.0,France,Female,39.0,0.0,0.0,1.0,1.0,0.0,94670.77
+16001,15671351.0,Obiajulu,714.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,52319.26
+16002,15590730.0,Yuriev,712.0,Spain,Female,30.0,8.0,0.0,2.0,0.0,0.0,160546.58
+16003,15669326.0,Young,755.0,France,Male,33.0,7.0,0.0,2.0,0.0,0.0,28806.32
+16004,15646513.0,Onyekachi,593.0,Germany,Female,34.0,7.0,121542.57,2.0,1.0,0.0,100722.72
+16005,15781530.0,Hsing,629.0,France,Male,40.0,10.0,0.0,1.0,1.0,1.0,95611.47
+16006,15669414.0,Yermakova,709.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,0.0,101319.76
+16007,15761047.0,Ts'ai,637.0,France,Male,41.0,2.0,165413.57,1.0,0.0,1.0,70810.8
+16008,15691817.0,Nwabugwu,634.0,France,Female,31.0,10.0,0.0,2.0,1.0,1.0,46857.52
+16009,15764174.0,Iadanza,699.0,Germany,Male,44.0,9.0,131390.75,1.0,1.0,0.0,137791.18
+16010,15579892.0,Walker,627.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,173844.89
+16011,15576623.0,Nwabugwu,717.0,France,Female,42.0,2.0,0.0,1.0,1.0,0.0,77965.44
+16012,15641675.0,Ndubueze,730.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,1.0,131609.48
+16013,15699772.0,Ts'ui,663.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,70550.0
+16014,15593365.0,Chukwujamuike,548.0,Spain,Male,38.0,8.0,0.0,2.0,0.0,1.0,54871.02
+16015,15662884.0,Chukwuemeka,707.0,Germany,Male,38.0,6.0,91893.32,1.0,1.0,0.0,131189.34
+16016,15593795.0,Oluchukwu,707.0,Germany,Female,40.0,2.0,141535.52,1.0,1.0,0.0,141400.51
+16017,15694237.0,Uvarov,807.0,France,Female,24.0,3.0,0.0,1.0,0.0,0.0,82870.75
+16018,15591130.0,Ejimofor,732.0,France,Female,51.0,1.0,0.0,2.0,1.0,1.0,72990.62
+16019,15789874.0,Nwachukwu,634.0,France,Male,29.0,3.0,0.0,1.0,1.0,1.0,137519.92
+16020,15643826.0,Nwabugwu,678.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,126106.4
+16021,15692991.0,Chibugo,705.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,98820.09
+16022,15798532.0,Nnaife,714.0,France,Female,27.0,5.0,0.0,2.0,1.0,1.0,143393.24
+16023,15720371.0,Fanucci,643.0,France,Male,38.0,4.0,124442.58,1.0,0.0,0.0,100598.98
+16024,15682928.0,Onyemauchechukwu,692.0,France,Female,43.0,6.0,0.0,2.0,1.0,1.0,123982.14
+16025,15787035.0,Hao,679.0,Germany,Male,31.0,8.0,135842.41,2.0,1.0,1.0,47125.11
+16026,15652700.0,Hao,592.0,Germany,Male,48.0,2.0,121237.65,1.0,1.0,0.0,91810.71
+16027,15572762.0,Biryukova,786.0,Spain,Male,38.0,3.0,0.0,2.0,0.0,1.0,67611.36
+16028,15792868.0,Chidiegwu,649.0,France,Male,38.0,1.0,87637.28,1.0,1.0,1.0,140134.43
+16029,15668767.0,K'ung,683.0,France,Female,36.0,10.0,0.0,2.0,1.0,0.0,158623.94
+16030,15771299.0,Hsieh,621.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,65936.28
+16031,15761606.0,Hs?,731.0,Spain,Female,71.0,9.0,0.0,2.0,1.0,1.0,178267.07
+16032,15773421.0,Iadanza,774.0,Spain,Male,44.0,0.0,142658.04,1.0,1.0,0.0,163566.85
+16033,15630661.0,Chukwualuka,596.0,France,Male,26.0,2.0,0.0,1.0,1.0,1.0,110953.33
+16034,15762218.0,Lo Duca,729.0,Spain,Female,40.0,7.0,0.0,1.0,1.0,0.0,109626.01
+16035,15752409.0,Goliwe,733.0,Spain,Male,42.0,8.0,0.0,2.0,1.0,1.0,133535.29
+16036,15785547.0,Ch'ien,722.0,France,Female,26.0,6.0,0.0,1.0,1.0,1.0,90876.95
+16037,15641688.0,Biryukova,732.0,France,Male,34.0,7.0,0.0,1.0,0.0,0.0,86935.08
+16038,15765048.0,Zikoranachidimma,693.0,France,Female,34.0,3.0,0.0,1.0,1.0,0.0,122021.33
+16039,15790810.0,T'ang,748.0,France,Female,32.0,4.0,137963.07,1.0,1.0,0.0,133831.37
+16040,15778410.0,Ts'ui,753.0,France,Female,34.0,2.0,0.0,1.0,1.0,1.0,115268.86
+16041,15606471.0,Fanucci,717.0,Spain,Female,56.0,9.0,0.0,3.0,0.0,0.0,159638.77
+16042,15778915.0,White,611.0,Germany,Female,35.0,10.0,132571.48,2.0,0.0,0.0,71179.31
+16043,15566378.0,Li Fonti,584.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,109291.22
+16044,15714485.0,Chiawuotu,755.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,158918.02
+16045,15674840.0,Nwachukwu,683.0,Germany,Female,52.0,2.0,101583.11,1.0,0.0,0.0,142516.35
+16046,15585067.0,Pagnotto,763.0,Germany,Female,43.0,3.0,131519.49,1.0,1.0,1.0,174032.0
+16047,15752300.0,White,611.0,France,Male,50.0,4.0,0.0,2.0,1.0,0.0,146875.86
+16048,15792660.0,Shih,581.0,Spain,Male,38.0,4.0,117468.67,1.0,0.0,0.0,110454.46
+16049,15632575.0,Mancini,627.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,133091.32
+16050,15652401.0,Iloerika,678.0,Spain,Male,38.0,8.0,0.0,2.0,0.0,0.0,141679.73
+16051,15589147.0,McVey,681.0,Spain,Female,29.0,8.0,0.0,2.0,1.0,0.0,180551.76
+16052,15697948.0,Rizzo,653.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,138871.12
+16053,15611338.0,Tien,714.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,167984.72
+16054,15676909.0,Hsu,674.0,France,Male,43.0,8.0,0.0,1.0,1.0,0.0,38614.69
+16055,15673737.0,Hsieh,754.0,France,Male,31.0,5.0,0.0,1.0,1.0,0.0,162523.66
+16056,15642787.0,Macleod,678.0,Germany,Female,33.0,6.0,79962.92,1.0,0.0,1.0,72008.61
+16057,15592461.0,Udinesi,546.0,Germany,Female,45.0,1.0,137482.19,1.0,1.0,1.0,161479.19
+16058,15726487.0,Ch'ang,744.0,Spain,Female,44.0,4.0,123544.0,1.0,0.0,0.0,71047.51
+16059,15697095.0,Chia,721.0,France,Male,29.0,1.0,0.0,1.0,1.0,0.0,81409.0
+16060,15792650.0,McGregor,597.0,Germany,Female,38.0,5.0,113657.83,1.0,0.0,0.0,95857.18
+16061,15709928.0,Onwuamaeze,531.0,France,Female,39.0,3.0,0.0,2.0,1.0,0.0,78310.04
+16062,15797960.0,Hsing,539.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,120107.1
+16063,15683560.0,Okonkwo,632.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,122488.59
+16064,15794414.0,Efremova,592.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,184474.72
+16065,15807593.0,Rowntree,643.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,123499.28
+16066,15756538.0,Chiu,571.0,France,Female,27.0,3.0,0.0,2.0,1.0,0.0,120830.73
+16067,15689955.0,Teng,706.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,175070.94
+16068,15756071.0,Cameron,687.0,Germany,Female,37.0,4.0,166824.59,1.0,1.0,1.0,58814.41
+16069,15796470.0,Chin,535.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,108008.65
+16070,15733138.0,Chiazagomekpele,593.0,Germany,Female,45.0,7.0,111798.06,1.0,0.0,1.0,142963.29
+16071,15779711.0,White,656.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,109563.28
+16072,15747222.0,Baresi,769.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,0.0,97465.88
+16073,15631603.0,Chuang,758.0,Spain,Male,42.0,6.0,0.0,1.0,1.0,0.0,181224.56
+16074,15687852.0,Fisk,575.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,113089.43
+16075,15585490.0,Tien,571.0,France,Male,71.0,8.0,172463.45,1.0,1.0,1.0,119882.86
+16076,15814209.0,Ting,807.0,Spain,Female,39.0,2.0,0.0,1.0,1.0,0.0,172576.43
+16077,15687913.0,Ch'ien,657.0,France,Male,52.0,1.0,120549.21,1.0,0.0,1.0,144875.18
+16078,15584785.0,Ch'in,715.0,Spain,Female,55.0,3.0,0.0,1.0,0.0,1.0,173365.89
+16079,15730673.0,Ch'in,707.0,Germany,Male,59.0,4.0,131082.89,1.0,1.0,1.0,161428.25
+16080,15664543.0,Chinweike,562.0,Germany,Male,33.0,6.0,123544.0,2.0,1.0,1.0,91573.19
+16081,15685829.0,Yobanna,638.0,Spain,Female,24.0,5.0,0.0,2.0,0.0,0.0,163830.64
+16082,15637646.0,Folliero,730.0,Germany,Male,33.0,0.0,122034.4,1.0,1.0,0.0,161653.5
+16083,15584477.0,Hsing,651.0,Germany,Male,40.0,7.0,85845.43,2.0,1.0,0.0,59601.15
+16084,15637366.0,Hsueh,579.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,97794.71
+16085,15602909.0,Chia,677.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,175310.38
+16086,15765258.0,Fanucci,556.0,Spain,Male,50.0,9.0,166933.89,1.0,1.0,0.0,64448.36
+16087,15620372.0,Frolova,544.0,Spain,Female,22.0,2.0,0.0,2.0,0.0,0.0,122518.5
+16088,15573077.0,Fokina,684.0,France,Male,30.0,9.0,0.0,2.0,0.0,0.0,78854.57
+16089,15668971.0,Ch'ang,705.0,France,Male,33.0,9.0,61825.5,2.0,1.0,1.0,83912.24
+16090,15609913.0,Maclean,674.0,France,Male,52.0,2.0,0.0,1.0,1.0,0.0,64716.08
+16091,15648725.0,Nwachinemelu,689.0,France,Male,40.0,8.0,136623.19,1.0,1.0,0.0,174825.57
+16092,15773520.0,Chiu,635.0,Spain,Male,38.0,8.0,171932.26,1.0,1.0,0.0,70778.63
+16093,15683563.0,Ch'eng,743.0,Germany,Male,29.0,9.0,119688.81,1.0,0.0,1.0,133535.29
+16094,15643658.0,Chidimma,752.0,Germany,Male,42.0,5.0,113858.95,1.0,1.0,0.0,131301.74
+16095,15629133.0,Chinedum,581.0,France,Male,63.0,4.0,152039.4,1.0,1.0,1.0,178038.17
+16096,15598883.0,Yevdokimova,648.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,151843.54
+16097,15645316.0,Maughan,682.0,Germany,Male,40.0,5.0,115221.36,1.0,1.0,1.0,96498.14
+16098,15690744.0,L?,652.0,Germany,Male,33.0,3.0,125919.35,1.0,1.0,0.0,152996.89
+16099,15651022.0,Manna,758.0,France,Female,44.0,9.0,99836.47,1.0,1.0,1.0,53478.02
+16100,15688963.0,De Garis,718.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,61531.86
+16101,15642570.0,Rounsevell,714.0,France,Male,31.0,5.0,103377.81,1.0,1.0,1.0,183427.14
+16102,15775082.0,Uchenna,605.0,France,Male,38.0,10.0,121629.22,1.0,1.0,0.0,47777.15
+16103,15705001.0,Zikoranachidimma,581.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,97416.34
+16104,15744240.0,Onyekachi,526.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,177092.16
+16105,15644724.0,Ogochukwu,813.0,Spain,Female,45.0,8.0,0.0,1.0,0.0,0.0,157862.82
+16106,15803764.0,Onyekaozulu,537.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,133020.9
+16107,15602312.0,K'ung,671.0,France,Male,37.0,7.0,101238.24,1.0,1.0,1.0,175071.29
+16108,15701354.0,McWilliams,838.0,France,Male,59.0,9.0,0.0,1.0,1.0,0.0,102397.22
+16109,15746824.0,Tsao,612.0,Spain,Male,43.0,5.0,0.0,1.0,1.0,1.0,163666.85
+16110,15672945.0,Chiabuotu,604.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,1.0,91947.71
+16111,15805212.0,Ejimofor,671.0,Germany,Male,39.0,10.0,111097.87,1.0,1.0,0.0,172646.92
+16112,15649450.0,Chiu,696.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,141946.92
+16113,15603840.0,Lucchesi,602.0,Germany,Male,33.0,5.0,73088.06,1.0,1.0,1.0,129938.38
+16114,15791925.0,Fanucci,807.0,Germany,Male,26.0,3.0,129120.64,2.0,1.0,1.0,178827.3
+16115,15795519.0,Hsing,613.0,Spain,Male,52.0,8.0,0.0,3.0,0.0,1.0,152499.8
+16116,15740283.0,Chinedum,655.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,137793.35
+16117,15602844.0,Tuan,685.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,92915.84
+16118,15643496.0,Eluemuno,752.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,115022.94
+16119,15771086.0,K'ung,683.0,Germany,Female,45.0,10.0,115182.84,2.0,1.0,0.0,74836.64
+16120,15628893.0,Flemming,602.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,158043.11
+16121,15642344.0,Chizoba,637.0,Spain,Male,25.0,9.0,0.0,2.0,1.0,0.0,125877.22
+16122,15632899.0,Okechukwu,653.0,France,Male,34.0,2.0,130306.49,1.0,1.0,1.0,142967.68
+16123,15651533.0,Ts'ai,684.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,106977.8
+16124,15661275.0,Palermo,850.0,France,Male,31.0,9.0,132670.53,1.0,1.0,1.0,62402.38
+16125,15680597.0,Ch'ien,743.0,France,Male,27.0,9.0,0.0,2.0,1.0,1.0,61602.42
+16126,15681206.0,Nwankwo,721.0,France,Male,40.0,2.0,153663.77,1.0,1.0,1.0,97930.29
+16127,15665629.0,Genovesi,593.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,99800.87
+16128,15625247.0,Onyekachi,697.0,France,Female,50.0,7.0,0.0,2.0,1.0,1.0,125877.22
+16129,15781457.0,Chin,642.0,Spain,Female,32.0,6.0,128551.36,1.0,0.0,0.0,177519.39
+16130,15685500.0,Genovesi,635.0,France,Female,46.0,4.0,172646.82,1.0,1.0,0.0,55313.51
+16131,15653715.0,Trevisano,756.0,Spain,Male,33.0,9.0,109387.9,1.0,0.0,1.0,140134.42
+16132,15676937.0,T'an,539.0,Germany,Female,38.0,6.0,138825.34,1.0,1.0,0.0,157891.11
+16133,15700263.0,Tung,632.0,Germany,Female,47.0,4.0,104449.8,3.0,0.0,0.0,122518.5
+16134,15721582.0,Cawker,704.0,France,Female,38.0,1.0,0.0,1.0,0.0,0.0,175743.92
+16135,15792934.0,Hs?,632.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,1.0,51117.09
+16136,15794566.0,Calabresi,739.0,Germany,Male,31.0,9.0,90778.76,2.0,1.0,0.0,52629.73
+16137,15707559.0,Clements,627.0,France,Male,26.0,6.0,135422.33,1.0,0.0,0.0,161241.65
+16138,15638806.0,Chukwubuikem,578.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,89048.46
+16139,15585768.0,Tu,735.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,40318.33
+16140,15768449.0,H?,683.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,183490.99
+16141,15784890.0,Chidiegwu,605.0,Germany,Male,34.0,9.0,123575.51,1.0,1.0,0.0,177524.19
+16142,15612667.0,Bledsoe,669.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,121408.56
+16143,15777873.0,Mazzanti,667.0,France,Male,36.0,4.0,0.0,2.0,0.0,0.0,143393.24
+16144,15772412.0,Pirozzi,679.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,83056.22
+16145,15785024.0,Iweobiegbunam,646.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,74564.41
+16146,15765283.0,Chukwudi,678.0,Spain,Male,33.0,10.0,0.0,1.0,0.0,0.0,156768.45
+16147,15681075.0,Naylor,741.0,Germany,Male,35.0,7.0,116324.53,1.0,0.0,1.0,121866.81
+16148,15583480.0,Ch'ang,582.0,France,Male,46.0,9.0,112291.34,2.0,0.0,0.0,145562.4
+16149,15705968.0,Galkin,556.0,Germany,Female,44.0,5.0,141559.5,1.0,1.0,0.0,156953.52
+16150,15607537.0,Chukwuemeka,591.0,France,Male,35.0,7.0,0.0,1.0,1.0,1.0,174311.3
+16151,15589017.0,Hsing,648.0,Spain,Male,27.0,6.0,76548.6,1.0,1.0,0.0,119808.64
+16152,15604482.0,Yuryeva,604.0,France,Female,51.0,8.0,0.0,2.0,1.0,0.0,77500.81
+16153,15654870.0,Tsao,686.0,Spain,Male,47.0,6.0,108632.48,1.0,0.0,0.0,63737.19
+16154,15577078.0,Kao,657.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,126451.14
+16155,15788556.0,Okechukwu,523.0,France,Female,45.0,7.0,0.0,2.0,1.0,0.0,91560.69
+16156,15663885.0,Hsia,715.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,64839.23
+16157,15589969.0,Nkemjika,748.0,France,Female,32.0,1.0,0.0,1.0,1.0,1.0,47322.05
+16158,15737705.0,Hsueh,704.0,Spain,Female,46.0,3.0,0.0,2.0,1.0,0.0,187740.04
+16159,15651868.0,Nwankwo,703.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,137510.41
+16160,15750055.0,Cremonesi,602.0,France,Female,43.0,6.0,128743.55,1.0,0.0,1.0,134851.12
+16161,15763898.0,Napolitani,605.0,France,Female,37.0,2.0,0.0,1.0,1.0,0.0,161344.13
+16162,15792726.0,Kao,602.0,France,Female,25.0,2.0,0.0,2.0,0.0,1.0,52337.97
+16163,15739271.0,Onyekachi,644.0,Spain,Female,29.0,7.0,0.0,2.0,1.0,0.0,155879.57
+16164,15657818.0,Chukwueloka,703.0,France,Female,45.0,9.0,0.0,1.0,0.0,1.0,163471.01
+16165,15593973.0,Martin,744.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,172063.09
+16166,15648032.0,Milanesi,683.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,184544.59
+16167,15672598.0,Martin,691.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,107876.2
+16168,15694349.0,Hsing,610.0,France,Male,38.0,7.0,0.0,2.0,0.0,0.0,97794.71
+16169,15791513.0,Nwachinemelu,679.0,France,Female,42.0,8.0,0.0,2.0,0.0,1.0,168816.22
+16170,15572051.0,Hs?eh,572.0,France,Female,37.0,6.0,0.0,2.0,0.0,1.0,40496.72
+16171,15689516.0,Shih,598.0,Germany,Female,29.0,5.0,88963.31,1.0,0.0,0.0,143619.52
+16172,15592451.0,Dennys,690.0,Spain,Male,42.0,7.0,0.0,2.0,1.0,0.0,71139.31
+16173,15602844.0,Trevisano,649.0,Germany,Male,43.0,5.0,111128.04,1.0,1.0,0.0,182784.11
+16174,15609510.0,Chiedozie,725.0,France,Female,50.0,6.0,0.0,2.0,0.0,1.0,63736.17
+16175,15648735.0,Ch'iu,686.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,0.0,52894.01
+16176,15592877.0,Chijindum,597.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,142410.94
+16177,15716276.0,Onwuamaeze,687.0,Spain,Male,55.0,2.0,0.0,1.0,1.0,1.0,94978.1
+16178,15573741.0,Kao,531.0,France,Female,33.0,10.0,0.0,2.0,1.0,1.0,134916.41
+16179,15809837.0,Chin,599.0,Spain,Male,45.0,6.0,0.0,2.0,1.0,0.0,172668.39
+16180,15795861.0,Ch'iu,705.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,1.0,131653.31
+16181,15804864.0,Cattaneo,679.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,159899.97
+16182,15574879.0,Ts'ui,773.0,France,Female,39.0,8.0,136858.42,1.0,0.0,1.0,90174.54
+16183,15706186.0,Chia,745.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,87711.61
+16184,15704746.0,Trevisan,579.0,Germany,Male,32.0,3.0,123870.07,2.0,1.0,1.0,145700.22
+16185,15736287.0,Omeokachie,564.0,France,Male,32.0,5.0,89750.21,1.0,1.0,0.0,184399.76
+16186,15639277.0,T'ang,652.0,Germany,Female,46.0,1.0,170557.91,1.0,1.0,0.0,150468.28
+16187,15699335.0,Chukwufumnanya,590.0,France,Male,32.0,7.0,181627.14,1.0,1.0,0.0,176680.16
+16188,15806511.0,Chin,631.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,172363.41
+16189,15728078.0,Tuan,592.0,Spain,Female,34.0,4.0,161613.94,1.0,1.0,1.0,146891.07
+16190,15795586.0,O'Meara,678.0,Spain,Female,28.0,0.0,0.0,2.0,1.0,1.0,180439.75
+16191,15605039.0,Chiedozie,656.0,France,Male,48.0,5.0,0.0,1.0,1.0,0.0,126561.07
+16192,15650026.0,Pisano,743.0,France,Female,56.0,10.0,0.0,1.0,1.0,0.0,161344.13
+16193,15781710.0,Ch'ang,705.0,France,Female,42.0,9.0,0.0,1.0,1.0,1.0,173870.39
+16194,15684269.0,Ch'ang,593.0,Spain,Female,46.0,6.0,0.0,3.0,1.0,1.0,102930.46
+16195,15634141.0,Sacco,712.0,Germany,Male,28.0,5.0,119496.25,1.0,1.0,0.0,61636.22
+16196,15663438.0,Pinto,785.0,France,Female,30.0,6.0,0.0,1.0,1.0,0.0,101986.16
+16197,15776733.0,Lipton,586.0,France,Female,31.0,4.0,125724.95,1.0,1.0,1.0,92827.49
+16198,15808851.0,Chukwuemeka,728.0,France,Male,36.0,7.0,130666.2,1.0,0.0,0.0,72876.65
+16199,15715184.0,Chukwualuka,576.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,0.0,179540.73
+16200,15703923.0,Genovesi,758.0,France,Male,33.0,10.0,0.0,2.0,0.0,1.0,176750.81
+16201,15750772.0,Onyekachi,590.0,Germany,Female,29.0,0.0,160132.75,1.0,1.0,1.0,180134.88
+16202,15592761.0,T'ien,703.0,France,Male,33.0,6.0,0.0,1.0,1.0,0.0,183318.79
+16203,15669411.0,Smith,731.0,Germany,Female,44.0,2.0,115130.34,1.0,1.0,1.0,171587.9
+16204,15740356.0,Onyemauchechukwu,703.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,182609.97
+16205,15622532.0,Nkemakonam,678.0,France,Female,42.0,0.0,130169.23,1.0,1.0,0.0,58854.88
+16206,15702631.0,Chiazagomekpere,681.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,65466.93
+16207,15795429.0,Tuan,785.0,Spain,Male,37.0,3.0,104015.54,1.0,1.0,0.0,145562.4
+16208,15674671.0,Nnamutaezinwa,572.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,0.0,165675.01
+16209,15651389.0,Wallace,624.0,Germany,Female,37.0,0.0,108393.35,2.0,1.0,0.0,71173.03
+16210,15682533.0,Milne,662.0,Spain,Female,34.0,4.0,0.0,1.0,1.0,0.0,91482.5
+16211,15750055.0,H?,648.0,Germany,Female,42.0,10.0,181166.44,2.0,1.0,0.0,89048.46
+16212,15585047.0,Chibugo,667.0,Germany,Female,26.0,10.0,140197.18,2.0,1.0,0.0,28257.63
+16213,15770498.0,Walker,671.0,Spain,Female,63.0,5.0,0.0,2.0,0.0,1.0,155915.24
+16214,15769312.0,Hs?,657.0,France,Male,38.0,7.0,0.0,1.0,1.0,0.0,135851.3
+16215,15673372.0,Whitehead,516.0,France,Female,38.0,0.0,0.0,2.0,1.0,0.0,125848.88
+16216,15628600.0,Pagnotto,652.0,France,Male,22.0,2.0,0.0,1.0,1.0,1.0,161671.15
+16217,15617507.0,Hsiung,697.0,Germany,Female,43.0,8.0,106941.08,1.0,1.0,1.0,175836.03
+16218,15682757.0,Lettiere,619.0,Spain,Male,67.0,2.0,0.0,2.0,1.0,1.0,129964.94
+16219,15768295.0,Hsia,594.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,127734.16
+16220,15676895.0,Fan,710.0,Germany,Male,33.0,3.0,152606.82,2.0,1.0,1.0,121525.48
+16221,15736578.0,Mironov,653.0,France,Male,26.0,10.0,0.0,1.0,1.0,0.0,173899.42
+16222,15746805.0,Lucciano,689.0,Germany,Female,49.0,4.0,163894.35,2.0,0.0,1.0,95985.64
+16223,15761775.0,Zikoranachidimma,633.0,Germany,Male,54.0,4.0,110483.86,1.0,1.0,0.0,106655.88
+16224,15582591.0,Cremonesi,685.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,163482.7
+16225,15763544.0,T'ang,744.0,Spain,Male,35.0,7.0,0.0,1.0,0.0,1.0,79450.61
+16226,15594766.0,Genovese,592.0,France,Male,31.0,8.0,137936.94,1.0,0.0,0.0,176750.81
+16227,15735782.0,Ts'ui,689.0,France,Female,28.0,7.0,0.0,2.0,1.0,1.0,60907.49
+16228,15672390.0,Hsia,850.0,France,Female,36.0,8.0,152582.3,1.0,1.0,1.0,164038.07
+16229,15652658.0,Lucchese,589.0,Germany,Female,39.0,9.0,132591.36,2.0,1.0,0.0,131173.9
+16230,15808760.0,Yefimova,619.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,0.0,138526.96
+16231,15672027.0,Manna,731.0,Germany,Female,41.0,9.0,84557.82,1.0,0.0,1.0,103934.12
+16232,15728006.0,Smith,654.0,Spain,Male,59.0,2.0,0.0,2.0,1.0,0.0,56784.11
+16233,15660146.0,Biryukova,648.0,Spain,Female,58.0,3.0,144552.05,1.0,0.0,1.0,102057.86
+16234,15574886.0,Chidubem,743.0,Germany,Female,47.0,7.0,128927.93,1.0,1.0,0.0,143859.66
+16235,15792669.0,Hs?eh,692.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,149629.13
+16236,15582797.0,Ts'ai,758.0,Germany,Male,37.0,7.0,171325.98,2.0,1.0,1.0,169287.99
+16237,15665811.0,Tseng,807.0,Spain,Female,71.0,2.0,0.0,2.0,1.0,0.0,98820.09
+16238,15794875.0,Tretiakova,774.0,Germany,Male,40.0,7.0,150685.79,1.0,1.0,1.0,148564.76
+16239,15584114.0,Milanesi,683.0,France,Male,31.0,5.0,129743.75,1.0,1.0,1.0,60905.51
+16240,15654025.0,Hs?,662.0,France,Male,27.0,9.0,0.0,1.0,1.0,0.0,143882.25
+16241,15591169.0,Udinesi,678.0,France,Male,34.0,9.0,146622.35,1.0,1.0,1.0,104889.2
+16242,15806941.0,Tien,545.0,France,Female,36.0,1.0,0.0,2.0,1.0,1.0,106847.51
+16243,15751203.0,T'ang,549.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,0.0,163714.92
+16244,15691817.0,Shao,834.0,France,Female,35.0,9.0,62212.29,1.0,1.0,0.0,113048.79
+16245,15664523.0,Okwuadigbo,661.0,Germany,Male,36.0,2.0,87541.06,1.0,0.0,0.0,122964.18
+16246,15588928.0,Onwuka,730.0,Germany,Male,27.0,4.0,144432.04,2.0,1.0,1.0,156574.79
+16247,15793947.0,T'ao,732.0,France,Male,26.0,6.0,0.0,2.0,0.0,0.0,180427.24
+16248,15744044.0,Okechukwu,534.0,Germany,Female,48.0,5.0,127734.92,1.0,1.0,0.0,132418.59
+16249,15586931.0,Ch'iu,545.0,Germany,Female,31.0,4.0,91216.0,1.0,1.0,1.0,180595.02
+16250,15618255.0,Chiang,603.0,France,Female,45.0,4.0,0.0,1.0,1.0,1.0,93739.71
+16251,15715078.0,Fisk,712.0,Germany,Female,42.0,9.0,117852.26,1.0,1.0,0.0,174916.85
+16252,15737705.0,Obiajulu,711.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,134878.19
+16253,15625905.0,Tsao,637.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,72970.93
+16254,15652981.0,Tsao,633.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,93713.15
+16255,15779414.0,Chukwubuikem,733.0,France,Male,59.0,1.0,0.0,2.0,1.0,1.0,82298.59
+16256,15662063.0,Rizzo,695.0,France,Male,27.0,6.0,109848.6,1.0,1.0,1.0,63218.85
+16257,15707634.0,Lo Duca,521.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,40257.79
+16258,15623479.0,Eremenko,682.0,Germany,Female,39.0,10.0,79954.61,2.0,1.0,1.0,126565.84
+16259,15690796.0,T'ien,721.0,Spain,Female,39.0,3.0,107592.89,1.0,0.0,1.0,155711.64
+16260,15799031.0,Pisano,721.0,France,Female,26.0,8.0,0.0,2.0,1.0,0.0,142548.64
+16261,15582892.0,Onwuamaeze,614.0,France,Male,31.0,10.0,0.0,1.0,1.0,0.0,87412.24
+16262,15769272.0,Ulyanova,653.0,France,Male,31.0,1.0,115532.52,1.0,1.0,1.0,90174.84
+16263,15802362.0,Brownless,815.0,Spain,Male,41.0,3.0,129669.32,1.0,1.0,1.0,79475.3
+16264,15674929.0,Chidubem,577.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,162022.67
+16265,15718289.0,Ch'ang,709.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,138340.75
+16266,15703364.0,Onyemere,616.0,France,Female,48.0,9.0,0.0,1.0,0.0,1.0,26606.28
+16267,15733797.0,Onochie,816.0,France,Female,29.0,10.0,0.0,2.0,0.0,0.0,85522.84
+16268,15646936.0,Ch'ang,587.0,Spain,Female,29.0,3.0,90217.07,1.0,0.0,1.0,174770.14
+16269,15670668.0,Chien,756.0,France,Female,29.0,2.0,0.0,2.0,0.0,0.0,151325.24
+16270,15802857.0,Nwachinemelu,752.0,Spain,Female,30.0,8.0,0.0,1.0,0.0,1.0,136054.45
+16271,15570087.0,Shih,582.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,60552.44
+16272,15757521.0,Ch'eng,586.0,France,Male,35.0,3.0,0.0,1.0,0.0,1.0,149614.61
+16273,15771668.0,Onyekachukwu,547.0,France,Male,29.0,1.0,83061.26,1.0,0.0,0.0,159800.16
+16274,15575679.0,Chiemenam,577.0,France,Female,38.0,1.0,0.0,1.0,0.0,1.0,149991.32
+16275,15757911.0,Chien,785.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,26116.82
+16276,15805312.0,Mazzanti,718.0,France,Male,28.0,5.0,0.0,1.0,1.0,0.0,82565.01
+16277,15802362.0,Hsueh,735.0,France,Female,52.0,3.0,0.0,1.0,1.0,0.0,157937.35
+16278,15674833.0,Ohearn,628.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,0.0,67830.2
+16279,15574782.0,Toscani,543.0,France,Female,29.0,9.0,0.0,1.0,1.0,0.0,172524.51
+16280,15597901.0,Yermolayev,834.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,142797.5
+16281,15679810.0,Pisano,682.0,Germany,Male,43.0,3.0,131955.07,1.0,1.0,0.0,94415.3
+16282,15754929.0,Chukwuemeka,738.0,France,Male,41.0,5.0,0.0,2.0,1.0,0.0,143357.07
+16283,15572626.0,Kosisochukwu,790.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,168816.22
+16284,15625944.0,Dore,809.0,Germany,Female,44.0,7.0,123845.36,2.0,1.0,1.0,199249.29
+16285,15641158.0,Palerma,614.0,France,Female,34.0,2.0,131765.72,2.0,1.0,0.0,71139.31
+16286,15791851.0,Calabresi,687.0,Spain,Male,37.0,0.0,0.0,2.0,0.0,0.0,152882.95
+16287,15692761.0,Onyekachukwu,589.0,France,Female,41.0,9.0,0.0,2.0,1.0,1.0,178331.36
+16288,15725762.0,Ginn,564.0,Germany,Female,28.0,1.0,122314.5,1.0,1.0,0.0,143913.32
+16289,15634245.0,Milano,793.0,France,Male,32.0,5.0,106044.72,1.0,1.0,1.0,96444.88
+16290,15689955.0,Chinweuba,614.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,104079.62
+16291,15725776.0,Olisanugo,696.0,France,Female,31.0,5.0,0.0,2.0,0.0,1.0,61531.86
+16292,15643658.0,O'Donnell,738.0,Spain,Female,51.0,3.0,0.0,1.0,1.0,1.0,35600.25
+16293,15603328.0,Shih,749.0,Germany,Female,48.0,5.0,107856.57,1.0,1.0,1.0,57764.65
+16294,15574584.0,Ch'ang,775.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,164018.98
+16295,15790093.0,Shih,581.0,Spain,Male,37.0,7.0,0.0,1.0,1.0,1.0,168002.31
+16296,15800814.0,Genovese,644.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,0.0,83629.6
+16297,15620505.0,Hsia,678.0,France,Male,48.0,3.0,0.0,1.0,1.0,0.0,132029.95
+16298,15649744.0,Palermo,648.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,121798.52
+16299,15770309.0,Pirozzi,632.0,France,Male,49.0,0.0,0.0,1.0,1.0,0.0,24267.28
+16300,15714981.0,Shih,730.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,92810.6
+16301,15785042.0,Yermakov,638.0,France,Female,39.0,6.0,99097.33,1.0,1.0,0.0,176843.53
+16302,15795527.0,Ch'ien,558.0,France,Female,42.0,0.0,118199.97,1.0,1.0,0.0,60905.51
+16303,15807335.0,Hs?eh,521.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,1.0,89566.64
+16304,15584812.0,Ch'iu,687.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,138051.19
+16305,15752912.0,Nkemdirim,591.0,Spain,Male,35.0,10.0,0.0,1.0,0.0,1.0,177068.24
+16306,15642933.0,Olisaemeka,678.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,167033.83
+16307,15752388.0,Toscani,634.0,France,Male,51.0,4.0,123374.46,1.0,1.0,0.0,171774.5
+16308,15716605.0,Uvarov,731.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,117505.07
+16309,15625021.0,Lombardi,588.0,France,Female,46.0,5.0,111962.99,3.0,1.0,0.0,8751.59
+16310,15593973.0,Hsing,676.0,Spain,Male,35.0,8.0,0.0,1.0,1.0,1.0,172049.84
+16311,15699507.0,Chin,571.0,Spain,Male,34.0,4.0,121063.41,1.0,1.0,0.0,159629.64
+16312,15602797.0,Rischbieth,677.0,Germany,Female,50.0,1.0,109346.13,1.0,1.0,1.0,129964.94
+16313,15761554.0,Chidozie,597.0,France,Male,48.0,9.0,0.0,3.0,1.0,0.0,90979.64
+16314,15729018.0,Chijioke,749.0,France,Male,24.0,5.0,0.0,2.0,1.0,1.0,57876.05
+16315,15745080.0,Mazzanti,462.0,Germany,Female,39.0,9.0,141784.02,1.0,0.0,0.0,5862.53
+16316,15641794.0,Chigozie,529.0,France,Female,45.0,2.0,0.0,1.0,1.0,0.0,168034.9
+16317,15663888.0,Onwuamaeze,756.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,198490.33
+16318,15761298.0,Trevisano,653.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,181698.34
+16319,15690731.0,Onio,658.0,France,Male,43.0,5.0,119658.49,1.0,1.0,0.0,65816.53
+16320,15703437.0,Chidozie,605.0,Germany,Male,35.0,9.0,111739.4,1.0,1.0,0.0,75832.53
+16321,15743976.0,P'eng,591.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,123954.11
+16322,15692581.0,Okoli,653.0,Spain,Female,56.0,6.0,0.0,2.0,1.0,1.0,130553.47
+16323,15806981.0,Okwudilichukwu,595.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,1.0,143938.28
+16324,15792064.0,Ch'in,689.0,France,Female,26.0,6.0,0.0,2.0,1.0,1.0,91574.92
+16325,15662532.0,Shih,622.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,181449.74
+16326,15621550.0,Manna,718.0,France,Male,36.0,6.0,130170.82,1.0,1.0,0.0,141533.19
+16327,15586517.0,Milanesi,752.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,104469.06
+16328,15799031.0,Shao,707.0,France,Male,51.0,5.0,124023.24,2.0,1.0,0.0,62280.91
+16329,15807335.0,Hixson,755.0,Spain,Male,32.0,1.0,0.0,1.0,1.0,1.0,175043.69
+16330,15790782.0,Chidiegwu,551.0,France,Female,46.0,2.0,0.0,2.0,1.0,1.0,181429.87
+16331,15694237.0,Chu,599.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,1.0,158973.45
+16332,15662091.0,Frolova,676.0,Spain,Female,31.0,2.0,136680.23,1.0,0.0,0.0,64321.02
+16333,15711759.0,Chidiebere,812.0,France,Male,33.0,9.0,0.0,2.0,0.0,0.0,171708.66
+16334,15592762.0,Hsueh,609.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,168895.06
+16335,15605327.0,Nnonso,551.0,France,Male,44.0,8.0,0.0,2.0,1.0,1.0,126475.79
+16336,15621435.0,Pagnotto,622.0,France,Female,33.0,10.0,0.0,1.0,1.0,0.0,70954.41
+16337,15652218.0,Milanesi,581.0,Spain,Male,25.0,6.0,0.0,2.0,1.0,0.0,151411.3
+16338,15761141.0,Hsueh,699.0,France,Male,24.0,8.0,116084.22,1.0,1.0,1.0,81898.81
+16339,15807989.0,Genovese,725.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,102799.14
+16340,15605965.0,Y?an,679.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,1.0,143783.46
+16341,15702566.0,Okwudilichukwu,711.0,Germany,Male,45.0,9.0,130142.79,1.0,1.0,1.0,149043.78
+16342,15680141.0,Teng,640.0,France,Female,23.0,5.0,0.0,2.0,1.0,0.0,138380.11
+16343,15702788.0,T'ien,702.0,France,Male,36.0,7.0,0.0,1.0,1.0,0.0,170638.98
+16344,15750547.0,Onyekachi,599.0,France,Female,31.0,1.0,102723.35,1.0,1.0,1.0,151335.24
+16345,15716284.0,Sun,732.0,France,Male,35.0,9.0,0.0,2.0,0.0,0.0,176694.87
+16346,15793046.0,Ku,565.0,France,Female,60.0,7.0,118400.91,4.0,0.0,1.0,187816.59
+16347,15713774.0,Chiazagomekpere,644.0,France,Female,34.0,7.0,0.0,2.0,0.0,0.0,182705.05
+16348,15655298.0,Chiu,703.0,France,Male,36.0,0.0,0.0,2.0,1.0,0.0,147720.27
+16349,15654067.0,Chukwukadibia,599.0,Germany,Male,38.0,8.0,133050.92,1.0,1.0,1.0,38652.23
+16350,15773972.0,Gadsden,665.0,Spain,Male,29.0,7.0,115897.12,1.0,1.0,1.0,70783.61
+16351,15602851.0,Mancini,681.0,France,Female,38.0,7.0,0.0,2.0,0.0,1.0,59650.44
+16352,15765311.0,Teng,709.0,Spain,Male,35.0,2.0,0.0,2.0,0.0,0.0,51387.1
+16353,15765311.0,Chiazagomekpele,689.0,Spain,Female,36.0,9.0,0.0,2.0,1.0,1.0,88965.46
+16354,15712584.0,Ch'ang,559.0,Spain,Male,33.0,3.0,0.0,1.0,1.0,1.0,110783.28
+16355,15585853.0,Trentini,667.0,Germany,Female,41.0,10.0,94442.11,1.0,1.0,0.0,131804.86
+16356,15812071.0,Ch'ang,712.0,Germany,Female,43.0,2.0,115095.88,3.0,1.0,0.0,57517.71
+16357,15656426.0,Lombardo,561.0,Spain,Male,51.0,7.0,0.0,1.0,1.0,0.0,86701.4
+16358,15600982.0,Kao,613.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,142516.35
+16359,15777599.0,Genovese,649.0,France,Male,39.0,2.0,133970.24,1.0,0.0,0.0,77965.44
+16360,15646592.0,Nwora,651.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,174729.3
+16361,15804962.0,McLean,649.0,France,Female,49.0,6.0,0.0,1.0,1.0,1.0,144874.67
+16362,15742229.0,Calabresi,695.0,France,Male,29.0,1.0,0.0,1.0,1.0,1.0,182055.36
+16363,15686963.0,Pinto,584.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,52795.15
+16364,15765257.0,Bellucci,574.0,France,Male,35.0,10.0,0.0,2.0,1.0,0.0,134878.19
+16365,15627002.0,Chien,641.0,Spain,Female,36.0,2.0,0.0,1.0,1.0,0.0,164815.59
+16366,15659428.0,Nnonso,632.0,Spain,Female,40.0,9.0,0.0,1.0,1.0,0.0,116893.87
+16367,15735782.0,Lucchese,804.0,France,Male,32.0,7.0,123675.69,1.0,1.0,1.0,65612.01
+16368,15677387.0,Hsieh,678.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,173870.39
+16369,15686885.0,Rizzo,727.0,France,Male,45.0,9.0,0.0,2.0,1.0,0.0,68860.3
+16370,15706463.0,Onyekachi,676.0,France,Female,43.0,5.0,113599.74,1.0,0.0,0.0,110899.3
+16371,15708504.0,Kao,586.0,Spain,Male,31.0,8.0,151510.17,1.0,1.0,1.0,162668.33
+16372,15709861.0,DeRose,665.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,152054.33
+16373,15742668.0,Lucchesi,632.0,Spain,Male,35.0,4.0,0.0,1.0,1.0,1.0,160593.48
+16374,15693376.0,Tsai,703.0,France,Female,26.0,7.0,115309.75,1.0,1.0,0.0,48249.88
+16375,15733111.0,Chukwuemeka,696.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,160810.85
+16376,15760662.0,T'ang,634.0,Germany,Male,38.0,1.0,109730.22,1.0,1.0,1.0,87767.55
+16377,15587735.0,Biryukov,622.0,France,Male,44.0,4.0,116695.27,1.0,1.0,1.0,143357.02
+16378,15761149.0,Tretiakov,645.0,France,Male,24.0,7.0,0.0,2.0,1.0,0.0,145796.49
+16379,15657540.0,Lombardi,539.0,Germany,Male,43.0,4.0,117640.42,1.0,0.0,1.0,88837.15
+16380,15608408.0,P'eng,615.0,France,Male,42.0,2.0,0.0,1.0,0.0,0.0,59864.96
+16381,15702541.0,Pisano,495.0,Germany,Male,33.0,6.0,86843.61,1.0,1.0,0.0,169921.11
+16382,15654577.0,Ibeabuchi,627.0,Germany,Male,33.0,1.0,132439.75,2.0,1.0,1.0,167878.5
+16383,15626684.0,Chukwumaobim,806.0,Spain,Male,28.0,9.0,0.0,2.0,0.0,0.0,136054.45
+16384,15672374.0,Toscani,603.0,France,Female,29.0,5.0,166501.17,1.0,1.0,1.0,113049.72
+16385,15603096.0,Ch'ien,709.0,France,Male,43.0,4.0,0.0,2.0,1.0,0.0,127939.19
+16386,15807900.0,Chukwujekwu,667.0,Germany,Female,50.0,2.0,102708.97,2.0,0.0,0.0,96885.19
+16387,15627859.0,Trevisan,635.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,81884.8
+16388,15792868.0,Tien,577.0,Spain,Female,29.0,8.0,0.0,2.0,0.0,0.0,140168.36
+16389,15772262.0,Frolova,569.0,France,Female,50.0,4.0,0.0,1.0,1.0,0.0,173912.29
+16390,15602551.0,Ch'ang,583.0,Germany,Male,38.0,4.0,122384.22,1.0,0.0,0.0,153015.42
+16391,15682468.0,Chukwunonso,749.0,France,Female,33.0,0.0,0.0,1.0,1.0,0.0,140166.95
+16392,15765457.0,Chukwudi,634.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,0.0,198134.9
+16393,15708003.0,Ndubuagha,776.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,112333.22
+16394,15589017.0,Tien,666.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,72643.95
+16395,15691792.0,Hsing,667.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,152790.92
+16396,15775572.0,Yobanna,607.0,France,Female,37.0,8.0,0.0,1.0,1.0,0.0,7861.68
+16397,15686963.0,DeRose,654.0,Spain,Female,43.0,2.0,0.0,2.0,0.0,1.0,159742.33
+16398,15600710.0,Ndubuagha,659.0,Spain,Male,56.0,3.0,0.0,1.0,0.0,1.0,175043.69
+16399,15673837.0,McFarland,687.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,112442.63
+16400,15736249.0,Hovell,724.0,France,Male,31.0,7.0,0.0,1.0,0.0,0.0,92888.52
+16401,15798532.0,Yobachi,743.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,113597.64
+16402,15732266.0,T'ao,537.0,Spain,Male,50.0,6.0,0.0,1.0,1.0,0.0,48478.47
+16403,15600746.0,Uwaezuoke,826.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,36249.76
+16404,15642063.0,Kao,758.0,France,Male,24.0,5.0,0.0,2.0,0.0,1.0,162960.05
+16405,15658032.0,K?,678.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,159800.16
+16406,15744695.0,Macleod,496.0,France,Female,44.0,2.0,0.0,1.0,1.0,1.0,84872.66
+16407,15658364.0,Mazzanti,682.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,133501.94
+16408,15794868.0,K?,636.0,Spain,Female,42.0,8.0,112281.6,1.0,0.0,0.0,155721.15
+16409,15768282.0,Genovesi,724.0,Spain,Male,45.0,4.0,98775.23,2.0,1.0,1.0,36308.93
+16410,15690134.0,Ch'in,613.0,Spain,Male,34.0,10.0,0.0,1.0,0.0,1.0,160162.42
+16411,15756884.0,Y?an,676.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,72993.65
+16412,15636454.0,Chikelu,705.0,France,Female,55.0,3.0,0.0,1.0,1.0,0.0,168515.61
+16413,15666096.0,McGregor,757.0,France,Male,53.0,3.0,0.0,1.0,1.0,0.0,94498.82
+16414,15641250.0,Ndubueze,646.0,France,Female,43.0,5.0,0.0,2.0,0.0,1.0,171364.18
+16415,15767391.0,Hsia,594.0,France,Male,29.0,3.0,0.0,2.0,1.0,0.0,133617.57
+16416,15682369.0,Akhtar,628.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,141180.66
+16417,15658852.0,Kosisochukwu,704.0,France,Female,43.0,5.0,0.0,2.0,1.0,1.0,125939.22
+16418,15575119.0,Chidiebere,646.0,France,Male,33.0,8.0,0.0,2.0,0.0,0.0,91957.39
+16419,15581626.0,Folliero,738.0,France,Female,40.0,9.0,0.0,2.0,1.0,0.0,101168.9
+16420,15765694.0,Chukwukadibia,730.0,Germany,Male,34.0,6.0,146445.54,1.0,1.0,1.0,178827.3
+16421,15689514.0,Chukwumaobim,667.0,Spain,Male,34.0,2.0,120867.18,1.0,0.0,0.0,129964.94
+16422,15569917.0,P'eng,678.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,186869.58
+16423,15658946.0,Clements,585.0,France,Female,46.0,8.0,123835.52,1.0,1.0,1.0,187740.04
+16424,15747808.0,Milanesi,639.0,France,Male,27.0,6.0,0.0,2.0,1.0,0.0,173516.71
+16425,15783029.0,Onyemachukwu,646.0,France,Female,40.0,7.0,0.0,2.0,0.0,0.0,125979.36
+16426,15646563.0,Rizzo,649.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,97939.3
+16427,15619343.0,Akeroyd,681.0,France,Male,25.0,7.0,0.0,2.0,1.0,1.0,163746.09
+16428,15780954.0,Chinonyelum,697.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,151871.84
+16429,15809800.0,Chiang,564.0,Germany,Male,48.0,8.0,135518.76,1.0,1.0,0.0,182566.76
+16430,15677522.0,Chia,725.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,136863.36
+16431,15636700.0,Chidozie,633.0,France,Male,33.0,2.0,106947.26,1.0,0.0,1.0,60906.12
+16432,15757541.0,Trentino,809.0,France,Female,28.0,6.0,0.0,1.0,0.0,0.0,138344.7
+16433,15677052.0,Onyemauchechukwu,694.0,Spain,Male,31.0,6.0,0.0,1.0,1.0,0.0,18082.06
+16434,15631070.0,Iadanza,589.0,France,Male,42.0,4.0,0.0,1.0,1.0,0.0,52884.02
+16435,15692463.0,Ritchie,665.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,81884.8
+16436,15645200.0,De Luca,679.0,France,Male,32.0,7.0,0.0,2.0,0.0,0.0,173870.39
+16437,15583724.0,Piazza,696.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,125518.32
+16438,15769005.0,Chukwuemeka,526.0,France,Female,34.0,3.0,130812.91,1.0,1.0,1.0,143684.2
+16439,15798964.0,Uspensky,834.0,France,Female,35.0,5.0,99811.44,1.0,1.0,1.0,125920.54
+16440,15710365.0,McElhone,640.0,France,Male,26.0,4.0,0.0,2.0,1.0,1.0,59905.29
+16441,15590828.0,Chukwujamuike,641.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,1.0,168816.22
+16442,15605918.0,Kao,471.0,France,Female,38.0,1.0,136855.94,1.0,1.0,1.0,27355.8
+16443,15644878.0,Yobanna,594.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,103611.66
+16444,15739592.0,Chukwuemeka,634.0,France,Female,45.0,9.0,0.0,2.0,1.0,0.0,142513.5
+16445,15667932.0,Onwumelu,612.0,France,Female,34.0,8.0,114959.12,1.0,1.0,0.0,131736.23
+16446,15586523.0,Hsieh,741.0,Germany,Female,37.0,5.0,138053.67,2.0,0.0,1.0,196257.67
+16447,15784700.0,Tsou,676.0,France,Male,50.0,7.0,0.0,2.0,0.0,1.0,176576.71
+16448,15702464.0,Kao,583.0,Spain,Male,35.0,9.0,99816.46,1.0,1.0,1.0,102999.33
+16449,15609637.0,Yefimova,773.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,160968.44
+16450,15752508.0,Hargreaves,697.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,25407.59
+16451,15697035.0,Onyekachukwu,699.0,France,Male,27.0,9.0,115409.6,1.0,1.0,1.0,129562.74
+16452,15721569.0,Chiu,663.0,France,Female,46.0,8.0,71264.02,1.0,1.0,0.0,127951.81
+16453,15583857.0,Hsia,727.0,France,Female,44.0,4.0,0.0,2.0,1.0,1.0,175314.87
+16454,15641110.0,Tien,645.0,France,Female,45.0,5.0,0.0,2.0,1.0,0.0,138882.98
+16455,15782467.0,Fanucci,561.0,France,Female,33.0,8.0,133607.75,1.0,0.0,0.0,122580.48
+16456,15777893.0,Yobanna,763.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,172363.41
+16457,15585255.0,Trevisano,694.0,France,Male,46.0,4.0,121342.24,1.0,0.0,1.0,116848.79
+16458,15757912.0,Ts'ao,652.0,France,Female,36.0,2.0,80780.16,2.0,1.0,0.0,91943.94
+16459,15644976.0,Lai,633.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,58879.11
+16460,15609998.0,Iweobiegbunam,769.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,152992.04
+16461,15628292.0,Ch'ien,622.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,100556.98
+16462,15570087.0,Manna,705.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,114775.53
+16463,15806941.0,Hs?eh,564.0,Germany,Male,46.0,5.0,104584.16,1.0,1.0,0.0,131660.31
+16464,15680643.0,Chiu,694.0,France,Female,31.0,6.0,0.0,2.0,1.0,1.0,123882.47
+16465,15592707.0,Chukwumaobim,605.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,145551.6
+16466,15691840.0,Ch'iu,648.0,Germany,Male,31.0,9.0,178058.06,1.0,1.0,0.0,180712.52
+16467,15775490.0,Calabresi,529.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,179521.24
+16468,15709920.0,Chijioke,549.0,France,Male,45.0,7.0,108862.76,1.0,1.0,1.0,63717.49
+16469,15619407.0,Macintyre,581.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,142400.27
+16470,15572356.0,H?,663.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,155721.15
+16471,15668893.0,Ch'eng,683.0,Germany,Female,44.0,1.0,121411.9,2.0,1.0,0.0,16281.94
+16472,15641110.0,L?,697.0,France,Male,36.0,4.0,126473.99,1.0,1.0,1.0,75574.48
+16473,15632143.0,Palermo,604.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,172810.51
+16474,15769246.0,Trevisano,582.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,182867.83
+16475,15605425.0,Udegbunam,572.0,France,Female,46.0,7.0,0.0,1.0,1.0,0.0,110252.53
+16476,15719276.0,Chidiegwu,705.0,France,Male,64.0,5.0,0.0,1.0,1.0,0.0,131807.14
+16477,15727177.0,Ikedinachukwu,571.0,France,Male,35.0,4.0,0.0,2.0,0.0,1.0,60906.12
+16478,15585067.0,P'an,586.0,France,Female,33.0,9.0,0.0,2.0,1.0,1.0,166677.35
+16479,15608916.0,Ch'ang,706.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,68701.73
+16480,15701280.0,Grieve,695.0,France,Male,36.0,0.0,0.0,2.0,1.0,0.0,80701.72
+16481,15660679.0,Scott,624.0,France,Female,31.0,4.0,93791.38,1.0,1.0,1.0,79633.38
+16482,15795874.0,Bischof,697.0,France,Male,23.0,8.0,0.0,2.0,1.0,1.0,49085.0
+16483,15756535.0,Palerma,653.0,France,Female,44.0,9.0,103033.83,1.0,1.0,0.0,142995.32
+16484,15789563.0,Maconochie,775.0,Spain,Male,36.0,0.0,0.0,1.0,1.0,1.0,162267.91
+16485,15590820.0,Chien,663.0,France,Male,27.0,9.0,0.0,2.0,0.0,1.0,85733.33
+16486,15664539.0,Nwankwo,613.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,0.0,72929.96
+16487,15642725.0,Ch'en,577.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,121408.55
+16488,15671351.0,Kornilova,564.0,France,Male,38.0,9.0,0.0,2.0,0.0,1.0,74878.23
+16489,15781589.0,Milne,652.0,Germany,Male,61.0,2.0,143421.78,1.0,1.0,0.0,185547.76
+16490,15679307.0,Hsia,571.0,France,Female,43.0,8.0,0.0,2.0,1.0,1.0,71512.8
+16491,15707488.0,Onwudiwe,634.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,1710.89
+16492,15634896.0,Ch'ien,673.0,France,Female,43.0,5.0,0.0,1.0,1.0,1.0,64477.25
+16493,15771742.0,O'Donnell,634.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,141406.43
+16494,15607746.0,Ch'en,592.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,171045.41
+16495,15731829.0,Genovesi,699.0,France,Female,22.0,6.0,0.0,2.0,1.0,1.0,103799.71
+16496,15714220.0,Y?an,622.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,129975.25
+16497,15737178.0,Y?,727.0,Spain,Male,35.0,4.0,0.0,1.0,0.0,1.0,62722.44
+16498,15803790.0,Gilleland,708.0,France,Male,40.0,6.0,0.0,1.0,1.0,1.0,128875.86
+16499,15708713.0,Maclean,711.0,France,Male,27.0,0.0,127415.85,1.0,1.0,1.0,170968.99
+16500,15649744.0,Ts'ui,603.0,Spain,Male,29.0,4.0,75893.11,1.0,1.0,0.0,87400.49
+16501,15629338.0,Ch'ang,645.0,France,Male,37.0,6.0,0.0,2.0,0.0,0.0,110114.19
+16502,15809909.0,McIntosh,632.0,Germany,Male,31.0,4.0,110864.38,1.0,1.0,1.0,176984.34
+16503,15592387.0,K'ung,744.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,123978.86
+16504,15781234.0,Harker,554.0,Germany,Male,26.0,3.0,123345.85,2.0,1.0,1.0,147055.87
+16505,15704746.0,Ojiofor,622.0,France,Male,37.0,7.0,0.0,2.0,0.0,1.0,44220.5
+16506,15592751.0,Chidi,611.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,109626.64
+16507,15765537.0,Chibueze,745.0,Spain,Male,32.0,7.0,125512.98,1.0,0.0,1.0,178034.9
+16508,15717489.0,Whitehead,616.0,France,Female,48.0,2.0,0.0,2.0,0.0,1.0,134901.58
+16509,15602731.0,Shih,589.0,Germany,Male,38.0,6.0,136596.15,2.0,1.0,1.0,84760.32
+16510,15746674.0,Lo Duca,711.0,Germany,Female,37.0,8.0,121556.53,3.0,1.0,0.0,181221.09
+16511,15723169.0,Chidubem,781.0,Spain,Female,28.0,8.0,0.0,1.0,1.0,1.0,54776.64
+16512,15611786.0,Ijendu,714.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,104079.62
+16513,15762392.0,Pagnotto,763.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,151316.37
+16514,15704819.0,Iadanza,516.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,55725.04
+16515,15593622.0,Ch'ang,590.0,Spain,Male,29.0,7.0,116114.93,1.0,1.0,1.0,145936.81
+16516,15576615.0,Ch'ang,684.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,179674.83
+16517,15703778.0,Chien,663.0,France,Male,33.0,6.0,0.0,1.0,0.0,0.0,122996.69
+16518,15756875.0,Chiu,573.0,France,Male,59.0,1.0,0.0,2.0,0.0,1.0,155870.82
+16519,15792726.0,Chiebuka,539.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,47775.34
+16520,15777045.0,Chimaobim,586.0,Germany,Male,31.0,8.0,129974.79,2.0,0.0,0.0,120830.63
+16521,15576629.0,Lucchese,774.0,France,Female,26.0,5.0,0.0,2.0,1.0,0.0,111868.23
+16522,15772798.0,Chia,545.0,Spain,Female,41.0,2.0,0.0,2.0,1.0,1.0,140159.95
+16523,15633274.0,Onuchukwu,587.0,Spain,Male,42.0,5.0,0.0,1.0,1.0,0.0,171681.86
+16524,15776725.0,P'eng,631.0,France,Female,43.0,3.0,0.0,2.0,0.0,1.0,61995.57
+16525,15572547.0,De Salis,587.0,France,Female,47.0,2.0,0.0,2.0,1.0,0.0,48990.5
+16526,15658889.0,Shih,769.0,Spain,Male,31.0,8.0,90723.36,1.0,1.0,0.0,131906.44
+16527,15762392.0,Ts'ui,713.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,107876.2
+16528,15643696.0,Liao,633.0,Germany,Male,49.0,10.0,110829.52,1.0,1.0,0.0,172618.52
+16529,15787550.0,Hsing,681.0,Germany,Female,39.0,2.0,112650.89,2.0,1.0,0.0,151349.35
+16530,15687913.0,Chiu,667.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,53353.37
+16531,15652469.0,Pinto,755.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,1.0,164825.04
+16532,15731519.0,Ngozichukwuka,619.0,Spain,Male,46.0,2.0,0.0,1.0,1.0,0.0,176433.6
+16533,15688865.0,Ch'iu,655.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,140947.05
+16534,15593499.0,Toscani,739.0,France,Female,36.0,6.0,139914.7,1.0,1.0,1.0,71154.1
+16535,15806796.0,Hs?,537.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,136487.86
+16536,15667588.0,Ch'eng,678.0,Germany,Female,29.0,8.0,87070.23,2.0,1.0,0.0,173783.38
+16537,15794493.0,Chukwuemeka,707.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,186418.85
+16538,15690175.0,O'Brien,676.0,Germany,Male,29.0,6.0,133598.4,2.0,1.0,0.0,147822.54
+16539,15746490.0,P'eng,597.0,Germany,Female,37.0,2.0,117307.53,1.0,1.0,0.0,166929.43
+16540,15750141.0,Pipes,635.0,Spain,Female,35.0,2.0,0.0,1.0,1.0,0.0,53412.54
+16541,15689410.0,Iweobiegbunam,629.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,1.0,141400.51
+16542,15652914.0,Mironova,741.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,48426.98
+16543,15672216.0,Hsiung,632.0,France,Male,46.0,1.0,0.0,2.0,1.0,0.0,68860.3
+16544,15732299.0,Chukwualuka,648.0,Germany,Male,43.0,2.0,141947.67,1.0,1.0,1.0,126726.33
+16545,15701175.0,Brient,622.0,France,Female,40.0,4.0,140197.18,1.0,1.0,1.0,92840.67
+16546,15597909.0,Ch'ien,559.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,1.0,65986.48
+16547,15628303.0,Toscani,669.0,Germany,Female,37.0,8.0,106920.91,1.0,1.0,1.0,158958.9
+16548,15704918.0,Chukwualuka,641.0,France,Male,51.0,6.0,0.0,1.0,1.0,1.0,134878.19
+16549,15581840.0,Yeates,715.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,170518.83
+16550,15777599.0,Hsueh,661.0,France,Female,30.0,4.0,0.0,1.0,1.0,1.0,142331.85
+16551,15705544.0,Mazzanti,595.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,140168.36
+16552,15722548.0,Manna,603.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,181204.5
+16553,15658693.0,McElhone,704.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,59961.81
+16554,15679963.0,Moore,667.0,Spain,Male,36.0,8.0,132468.24,1.0,1.0,1.0,166677.54
+16555,15585733.0,O'Brien,633.0,France,Male,42.0,6.0,0.0,2.0,0.0,1.0,159820.92
+16556,15798895.0,Martin,636.0,Spain,Female,45.0,1.0,0.0,2.0,1.0,0.0,94498.82
+16557,15627848.0,Obiajulu,668.0,Germany,Female,45.0,7.0,121286.27,1.0,1.0,0.0,59325.21
+16558,15723654.0,P'eng,589.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,1.0,16250.61
+16559,15662658.0,She,586.0,France,Female,31.0,1.0,81564.1,1.0,1.0,0.0,141652.2
+16560,15793901.0,DeRose,562.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,0.0,126761.2
+16561,15570194.0,Ritchie,644.0,France,Male,32.0,2.0,105229.71,1.0,0.0,0.0,129562.04
+16562,15647572.0,Lucciano,730.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,0.0,165082.25
+16563,15798964.0,Shao,662.0,France,Female,36.0,7.0,0.0,2.0,0.0,1.0,62232.6
+16564,15792525.0,Onyinyechukwuka,592.0,Germany,Male,29.0,8.0,119315.75,1.0,0.0,1.0,180427.24
+16565,15624293.0,Genovese,599.0,Germany,Female,51.0,7.0,125512.98,1.0,1.0,0.0,171482.56
+16566,15604813.0,T'ien,678.0,France,Male,44.0,8.0,0.0,1.0,1.0,1.0,155879.57
+16567,15785831.0,O'Loughlin,752.0,France,Female,41.0,7.0,0.0,1.0,1.0,1.0,142223.94
+16568,15710671.0,Hsueh,607.0,France,Female,42.0,1.0,151843.51,1.0,1.0,0.0,97623.42
+16569,15631310.0,Lucciano,632.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,69011.66
+16570,15603665.0,Mazzanti,669.0,France,Female,36.0,7.0,152347.01,1.0,1.0,0.0,898.51
+16571,15701392.0,Hsia,717.0,France,Male,39.0,3.0,0.0,1.0,1.0,1.0,28461.29
+16572,15800517.0,Pisano,803.0,France,Male,35.0,0.0,0.0,2.0,1.0,0.0,166650.32
+16573,15758775.0,Chukwudi,677.0,Spain,Male,28.0,1.0,0.0,2.0,0.0,1.0,164038.07
+16574,15805884.0,Ch'en,741.0,Germany,Male,43.0,2.0,147933.07,1.0,1.0,0.0,162858.29
+16575,15734649.0,Chukwufumnanya,762.0,France,Male,33.0,10.0,0.0,2.0,1.0,0.0,189428.75
+16576,15692744.0,Ch'ang,739.0,Germany,Male,39.0,6.0,137492.35,2.0,1.0,1.0,93728.47
+16577,15794566.0,Chimezie,633.0,France,Male,38.0,5.0,138858.07,1.0,0.0,1.0,159742.33
+16578,15757811.0,Brown,850.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,161479.19
+16579,15793331.0,Chinedum,774.0,France,Male,36.0,1.0,125193.96,1.0,1.0,0.0,124486.28
+16580,15607629.0,Ch'in,753.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,5983.7
+16581,15665064.0,Shen,705.0,Germany,Male,34.0,7.0,122031.55,2.0,1.0,1.0,49815.62
+16582,15614778.0,Okwudiliolisa,616.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,0.0,109626.13
+16583,15603565.0,Lai,668.0,France,Male,39.0,3.0,0.0,2.0,0.0,0.0,93718.86
+16584,15764033.0,Nucci,571.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,136506.86
+16585,15626233.0,Folliero,653.0,France,Male,37.0,2.0,0.0,2.0,0.0,1.0,180932.46
+16586,15777904.0,Chukwukere,756.0,France,Female,43.0,8.0,0.0,1.0,1.0,0.0,141827.33
+16587,15657937.0,Yefimova,794.0,France,Male,36.0,10.0,61319.63,1.0,0.0,0.0,164039.51
+16588,15607560.0,Hsiung,554.0,France,Male,48.0,2.0,0.0,1.0,1.0,0.0,73977.23
+16589,15758513.0,Milne,621.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,92357.21
+16590,15809838.0,Milano,495.0,Spain,Female,45.0,3.0,137841.53,1.0,1.0,0.0,146878.89
+16591,15769353.0,Nwachinemelu,803.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,106655.88
+16592,15702300.0,Lucciano,588.0,Spain,Female,52.0,8.0,0.0,1.0,0.0,0.0,164247.51
+16593,15752885.0,Onwuamaeze,712.0,Spain,Female,34.0,7.0,0.0,1.0,1.0,0.0,86410.28
+16594,15748068.0,Chukwufumnanya,711.0,France,Female,50.0,5.0,0.0,1.0,1.0,1.0,116977.89
+16595,15598892.0,Chukwuemeka,593.0,Spain,Female,39.0,10.0,0.0,2.0,1.0,1.0,123882.73
+16596,15610226.0,Zaytseva,704.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,141069.88
+16597,15600821.0,Ch'in,554.0,Germany,Male,43.0,10.0,115062.61,1.0,1.0,0.0,71133.12
+16598,15573851.0,Sievier,546.0,Germany,Male,47.0,1.0,144837.79,2.0,0.0,0.0,82438.13
+16599,15659949.0,Nwankwo,680.0,Spain,Male,32.0,6.0,92899.27,1.0,1.0,1.0,79691.4
+16600,15689886.0,Zikoranachidimma,794.0,Germany,Male,43.0,7.0,132558.26,1.0,0.0,0.0,109291.39
+16601,15628112.0,Brown,751.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,74048.1
+16602,15576728.0,Ch'ien,724.0,Germany,Female,37.0,7.0,166501.17,1.0,1.0,0.0,189085.07
+16603,15776921.0,Olisanugo,669.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,104786.59
+16604,15671610.0,Yudin,694.0,Germany,Male,37.0,5.0,141462.13,2.0,0.0,0.0,94790.34
+16605,15736126.0,Chidi,603.0,France,Male,41.0,5.0,0.0,2.0,0.0,0.0,78700.02
+16606,15592854.0,Fiorentino,729.0,France,Female,23.0,0.0,78535.25,1.0,1.0,0.0,141946.92
+16607,15635277.0,Yobanna,732.0,France,Female,35.0,3.0,145396.49,1.0,0.0,1.0,60778.11
+16608,15670266.0,Iloerika,562.0,Spain,Male,48.0,6.0,0.0,1.0,0.0,0.0,90860.97
+16609,15682533.0,McGregor,760.0,Germany,Male,44.0,7.0,105422.61,1.0,0.0,0.0,80552.11
+16610,15770101.0,Fiorentini,573.0,France,Female,35.0,5.0,0.0,1.0,0.0,0.0,116467.35
+16611,15591336.0,T'ien,628.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,147040.25
+16612,15641136.0,White,681.0,France,Female,27.0,3.0,0.0,2.0,0.0,1.0,54409.48
+16613,15772363.0,Ch'ang,582.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,53793.1
+16614,15720649.0,Calabrese,645.0,Germany,Female,40.0,2.0,136494.85,1.0,1.0,1.0,598.8
+16615,15608528.0,Chigolum,572.0,France,Female,31.0,6.0,0.0,2.0,0.0,0.0,144870.28
+16616,15661723.0,Chukwumaobim,624.0,France,Female,38.0,4.0,0.0,1.0,0.0,1.0,76773.53
+16617,15649450.0,Nwachukwu,721.0,France,Male,37.0,4.0,136492.92,1.0,1.0,1.0,24255.21
+16618,15742272.0,Ts'ao,751.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,63736.17
+16619,15791767.0,Eluemuno,589.0,France,Male,37.0,0.0,180716.1,1.0,1.0,0.0,82662.73
+16620,15623668.0,Linger,599.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,140136.1
+16621,15575676.0,Teng,524.0,France,Male,50.0,1.0,139979.07,1.0,0.0,0.0,100127.71
+16622,15684868.0,Nwankwo,717.0,France,Female,55.0,2.0,105204.01,1.0,0.0,1.0,41970.72
+16623,15738662.0,Knepper,679.0,Germany,Male,37.0,1.0,126473.99,2.0,0.0,0.0,122925.44
+16624,15758831.0,Ebelechukwu,689.0,France,Male,52.0,9.0,122552.34,1.0,1.0,1.0,131167.98
+16625,15713490.0,Chien,591.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,163666.85
+16626,15573741.0,Fanucci,790.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,167023.95
+16627,15565879.0,Lettiere,580.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,103560.98
+16628,15788448.0,Smith,621.0,Spain,Male,28.0,1.0,0.0,2.0,0.0,1.0,83056.22
+16629,15643359.0,Ch'ang,675.0,Spain,Male,32.0,1.0,152347.01,1.0,0.0,1.0,124486.28
+16630,15669611.0,Onuchukwu,682.0,Germany,Female,31.0,7.0,150956.71,2.0,1.0,0.0,16150.34
+16631,15758051.0,Shih,652.0,Germany,Female,47.0,5.0,108431.85,1.0,0.0,1.0,55313.51
+16632,15806963.0,Tien,673.0,Germany,Male,36.0,2.0,152390.21,2.0,1.0,0.0,137986.45
+16633,15589715.0,Chiang,531.0,Germany,Male,32.0,8.0,109876.82,1.0,1.0,0.0,161811.23
+16634,15615444.0,Y?,710.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,71592.8
+16635,15789014.0,Ch'ang,730.0,Germany,Female,44.0,5.0,115024.5,1.0,1.0,0.0,26249.86
+16636,15702656.0,Hsueh,610.0,France,Female,39.0,9.0,0.0,2.0,1.0,0.0,167026.71
+16637,15731463.0,Ch'ien,667.0,Spain,Male,39.0,1.0,0.0,2.0,1.0,1.0,103421.91
+16638,15694860.0,Iloerika,753.0,Germany,Female,43.0,6.0,93522.2,2.0,1.0,1.0,187985.77
+16639,15809847.0,Nwabugwu,667.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,133501.94
+16640,15754569.0,Ch'eng,637.0,France,Male,40.0,8.0,0.0,1.0,1.0,0.0,81439.4
+16641,15745324.0,Onyekachukwu,663.0,Spain,Male,39.0,3.0,86055.17,2.0,1.0,1.0,120225.62
+16642,15746035.0,Iadanza,627.0,France,Female,29.0,4.0,0.0,2.0,1.0,1.0,132979.43
+16643,15802758.0,Loewenthal,554.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,97932.68
+16644,15682369.0,Iloerika,638.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,172792.43
+16645,15682778.0,Ch'ien,626.0,Germany,Female,32.0,1.0,145860.98,2.0,1.0,0.0,110498.1
+16646,15711489.0,Hsing,592.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,173464.43
+16647,15793723.0,Chiang,660.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,18082.06
+16648,15753841.0,Scott,613.0,France,Male,33.0,8.0,171922.72,1.0,0.0,0.0,92612.88
+16649,15702561.0,O'Donnell,769.0,Germany,Male,41.0,8.0,117866.92,1.0,1.0,1.0,582.53
+16650,15790630.0,Chia,682.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,142903.44
+16651,15735078.0,L?,578.0,Spain,Male,19.0,3.0,0.0,2.0,1.0,0.0,161242.99
+16652,15714260.0,Chidiebere,586.0,France,Male,37.0,3.0,131184.67,1.0,1.0,0.0,129890.94
+16653,15651460.0,Iqbal,704.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,117939.17
+16654,15577313.0,Zuyev,558.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,105886.85
+16655,15658032.0,Pisano,681.0,France,Male,21.0,9.0,0.0,2.0,0.0,0.0,96875.52
+16656,15587066.0,Palerma,584.0,Spain,Male,62.0,1.0,0.0,1.0,1.0,1.0,151324.19
+16657,15807432.0,Horsley,711.0,Germany,Male,36.0,1.0,108914.17,1.0,1.0,0.0,2498.67
+16658,15703614.0,Y?,547.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,96847.91
+16659,15775307.0,Hsiao,817.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,70529.0
+16660,15705918.0,Chinomso,748.0,France,Female,45.0,8.0,0.0,1.0,0.0,0.0,138021.36
+16661,15789425.0,Ku,652.0,France,Female,26.0,1.0,101070.69,1.0,1.0,0.0,114932.62
+16662,15697034.0,Hsu,727.0,France,Female,35.0,2.0,156570.86,1.0,1.0,1.0,137028.51
+16663,15586674.0,Nwachukwu,683.0,France,Female,39.0,3.0,0.0,2.0,0.0,1.0,141558.04
+16664,15590091.0,White,850.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,123866.28
+16665,15715003.0,Ch'in,583.0,France,Male,45.0,7.0,0.0,2.0,1.0,1.0,159677.46
+16666,15665783.0,Onyeorulu,571.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,26920.43
+16667,15671934.0,Palermo,628.0,Germany,Male,32.0,7.0,128770.88,2.0,0.0,1.0,93713.92
+16668,15665460.0,Whitehead,731.0,France,Female,54.0,6.0,0.0,2.0,1.0,0.0,187905.12
+16669,15641490.0,Yudina,634.0,Spain,Female,30.0,4.0,0.0,2.0,1.0,1.0,176780.39
+16670,15809847.0,Yobanna,611.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,153064.82
+16671,15631800.0,Chiemenam,847.0,France,Female,27.0,9.0,0.0,2.0,1.0,1.0,167916.35
+16672,15634282.0,Chukwueloka,635.0,Germany,Male,40.0,1.0,107963.38,1.0,1.0,1.0,79775.19
+16673,15768887.0,Hs?,582.0,Germany,Female,46.0,1.0,119624.6,1.0,1.0,1.0,43889.41
+16674,15650018.0,Doyne,714.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,129912.74
+16675,15691792.0,Pinto,590.0,France,Male,40.0,4.0,124577.33,1.0,1.0,1.0,114448.77
+16676,15647842.0,Dancy,663.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,60775.71
+16677,15597951.0,Walker,535.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,88890.05
+16678,15777054.0,Macleod,774.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,175310.38
+16679,15749123.0,Chidiegwu,607.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,117622.8
+16680,15731781.0,Hargreaves,667.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,143980.29
+16681,15651389.0,Yefimova,711.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,88852.47
+16682,15781451.0,Ojiofor,611.0,Spain,Female,38.0,8.0,0.0,1.0,1.0,0.0,177524.19
+16683,15578251.0,Onyemauchechukwu,662.0,France,Male,45.0,5.0,0.0,2.0,1.0,1.0,56594.36
+16684,15571081.0,Ch'iu,679.0,Spain,Female,32.0,0.0,0.0,2.0,1.0,1.0,182775.08
+16685,15793871.0,Chuang,591.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,1.0,107825.08
+16686,15593468.0,Chiganu,775.0,France,Male,42.0,7.0,0.0,1.0,1.0,0.0,142513.5
+16687,15732832.0,Young,653.0,France,Male,37.0,1.0,0.0,2.0,0.0,0.0,86410.28
+16688,15696852.0,Hinton,613.0,Spain,Male,45.0,1.0,109127.54,1.0,0.0,1.0,137793.35
+16689,15612776.0,Obiajulu,706.0,Germany,Male,29.0,4.0,148962.46,1.0,1.0,1.0,88827.67
+16690,15763798.0,Pirozzi,681.0,France,Male,62.0,8.0,0.0,3.0,1.0,0.0,129891.66
+16691,15691785.0,Toscani,676.0,Germany,Female,32.0,7.0,139301.68,1.0,1.0,1.0,109626.03
+16692,15737240.0,O'Toole,712.0,France,Female,31.0,0.0,0.0,2.0,1.0,1.0,88724.92
+16693,15591286.0,Cameron,748.0,Germany,Female,28.0,9.0,147974.16,1.0,0.0,1.0,141896.74
+16694,15609899.0,Doyne,753.0,France,Female,28.0,0.0,0.0,2.0,1.0,0.0,123978.86
+16695,15770101.0,Chinwemma,662.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,158043.11
+16696,15761600.0,Young,593.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,67840.81
+16697,15702912.0,Hsia,627.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,0.0,86622.82
+16698,15737792.0,Brown,684.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,0.0,136985.08
+16699,15641250.0,Chukwudi,654.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,94767.77
+16700,15610343.0,Chukwujamuike,809.0,Spain,Female,43.0,1.0,0.0,2.0,1.0,1.0,115509.32
+16701,15795963.0,Ch'en,553.0,France,Female,46.0,6.0,0.0,1.0,1.0,0.0,93844.88
+16702,15645593.0,Lo,654.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,143049.72
+16703,15698932.0,Shih,741.0,France,Female,37.0,8.0,0.0,2.0,0.0,1.0,119712.78
+16704,15584518.0,L?,651.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,86575.46
+16705,15647570.0,Ch'iu,624.0,France,Male,26.0,6.0,0.0,1.0,0.0,0.0,86079.67
+16706,15672798.0,Hsieh,632.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,1.0,82276.62
+16707,15723250.0,Onyemauchechukwu,656.0,Germany,Male,42.0,9.0,132796.04,1.0,0.0,0.0,171552.02
+16708,15577514.0,Chizuoke,702.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,51721.52
+16709,15779744.0,Pisano,755.0,France,Female,28.0,5.0,0.0,2.0,1.0,0.0,81602.02
+16710,15691822.0,Onyekaozulu,721.0,Germany,Male,42.0,2.0,146329.57,2.0,1.0,1.0,148811.52
+16711,15672798.0,Macleod,509.0,Germany,Male,42.0,4.0,136525.99,1.0,1.0,0.0,176730.02
+16712,15773868.0,Hs?,801.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,0.0,140745.91
+16713,15651802.0,Ibeabuchi,607.0,Germany,Female,46.0,2.0,103421.49,1.0,1.0,1.0,86490.09
+16714,15698031.0,Chukwufumnanya,582.0,Germany,Male,28.0,3.0,121376.15,2.0,0.0,0.0,124874.23
+16715,15574554.0,Frolova,708.0,France,Female,45.0,2.0,99824.45,1.0,1.0,1.0,177747.72
+16716,15697042.0,T'ang,679.0,Spain,Male,42.0,7.0,0.0,2.0,1.0,0.0,127429.56
+16717,15756481.0,Nwebube,442.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,166511.94
+16718,15585580.0,L?,724.0,Spain,Male,45.0,0.0,0.0,1.0,1.0,0.0,171657.57
+16719,15693376.0,Chidubem,713.0,Spain,Female,49.0,4.0,125456.07,1.0,1.0,0.0,95611.47
+16720,15653715.0,Y?an,578.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,102747.02
+16721,15593795.0,Chinomso,779.0,France,Female,50.0,3.0,0.0,2.0,1.0,0.0,88729.22
+16722,15586976.0,Chukwudi,681.0,Spain,Male,38.0,5.0,0.0,1.0,1.0,0.0,134625.09
+16723,15778936.0,K?,732.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,60920.03
+16724,15654408.0,Lo,628.0,Germany,Female,34.0,3.0,160979.68,2.0,1.0,1.0,16081.62
+16725,15672481.0,Nwankwo,710.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,177613.19
+16726,15663410.0,Onyemachukwu,710.0,Spain,Male,33.0,7.0,0.0,2.0,0.0,1.0,65612.12
+16727,15716134.0,Calabresi,849.0,France,Male,50.0,2.0,70927.55,4.0,1.0,1.0,83090.35
+16728,15768777.0,Ch'ang,639.0,France,Female,43.0,4.0,88011.4,1.0,0.0,1.0,72993.65
+16729,15601184.0,Cremonesi,683.0,Germany,Female,37.0,7.0,141581.08,2.0,0.0,1.0,176843.53
+16730,15809302.0,Manna,607.0,France,Male,25.0,8.0,0.0,2.0,0.0,1.0,119087.25
+16731,15706764.0,Teng,694.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,93959.96
+16732,15586931.0,Ch'eng,687.0,Germany,Female,54.0,1.0,114212.6,1.0,0.0,1.0,19287.06
+16733,15706464.0,Chizuoke,674.0,Spain,Female,39.0,2.0,0.0,2.0,0.0,1.0,152499.8
+16734,15792228.0,Ch'in,714.0,France,Male,34.0,3.0,159732.02,1.0,0.0,0.0,149188.41
+16735,15586425.0,Dickson,547.0,Germany,Male,39.0,4.0,104430.07,2.0,0.0,0.0,178074.04
+16736,15746333.0,McGregor,655.0,Spain,Female,54.0,1.0,0.0,2.0,1.0,0.0,103379.31
+16737,15602797.0,Chukwuemeka,628.0,Germany,Male,35.0,9.0,117469.92,2.0,1.0,1.0,81482.7
+16738,15684319.0,Frolov,567.0,Spain,Female,30.0,2.0,0.0,2.0,1.0,1.0,161845.81
+16739,15751788.0,Lucchese,678.0,France,Female,40.0,5.0,0.0,3.0,0.0,1.0,126241.4
+16740,15648967.0,Ts'ao,676.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,1.0,74564.41
+16741,15731760.0,Nnaife,583.0,France,Male,35.0,1.0,0.0,2.0,0.0,0.0,119232.33
+16742,15588928.0,Onyeoruru,743.0,Spain,Female,45.0,8.0,0.0,1.0,1.0,1.0,160529.71
+16743,15680201.0,Maclean,526.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,136902.01
+16744,15697574.0,White,633.0,France,Male,30.0,8.0,0.0,1.0,1.0,0.0,90876.95
+16745,15762927.0,Chukwudi,806.0,France,Female,26.0,6.0,0.0,2.0,0.0,1.0,177747.72
+16746,15708534.0,Tsou,722.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,164886.64
+16747,15741988.0,Onyekaozulu,651.0,France,Male,33.0,7.0,0.0,2.0,0.0,1.0,175843.68
+16748,15785831.0,Lo,694.0,France,Male,45.0,9.0,0.0,2.0,1.0,0.0,129569.92
+16749,15641733.0,McWilliams,633.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,139469.11
+16750,15641655.0,Chukwujekwu,683.0,France,Male,29.0,1.0,0.0,2.0,1.0,1.0,60905.51
+16751,15765520.0,Maclean,531.0,Spain,Male,25.0,5.0,152582.3,2.0,1.0,0.0,88890.05
+16752,15668818.0,Chikelu,706.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,122964.18
+16753,15683375.0,Ch'iu,850.0,France,Male,31.0,0.0,0.0,2.0,1.0,0.0,1112.76
+16754,15583681.0,Hsiung,733.0,Germany,Male,35.0,10.0,141806.0,2.0,1.0,1.0,82222.86
+16755,15620507.0,Ch'ang,585.0,France,Female,27.0,7.0,0.0,2.0,1.0,0.0,126585.2
+16756,15667460.0,Ponomaryov,739.0,Spain,Male,33.0,7.0,180520.21,1.0,1.0,0.0,85522.84
+16757,15763665.0,Milne,632.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,172437.12
+16758,15652674.0,Chimaobim,774.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,19998.31
+16759,15718454.0,Ting,709.0,France,Female,44.0,5.0,0.0,2.0,1.0,1.0,145562.4
+16760,15780256.0,Hsueh,681.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,150720.59
+16761,15779059.0,Fanucci,657.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,171706.66
+16762,15796617.0,Manners,694.0,Germany,Male,31.0,8.0,108881.73,2.0,1.0,1.0,155905.79
+16763,15785290.0,Chien,682.0,France,Female,42.0,9.0,0.0,2.0,1.0,0.0,154598.58
+16764,15752534.0,Hs?,579.0,Germany,Male,49.0,2.0,137781.65,1.0,1.0,1.0,180800.66
+16765,15789339.0,Zetticci,732.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,109393.19
+16766,15662662.0,Efimov,613.0,Germany,Male,35.0,7.0,96652.86,2.0,1.0,0.0,105887.04
+16767,15573599.0,Chukwufumnanya,703.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,97938.59
+16768,15781542.0,Genovesi,544.0,France,Male,35.0,9.0,120124.35,1.0,1.0,0.0,9241.83
+16769,15799042.0,Palerma,639.0,Germany,Male,38.0,9.0,92889.91,2.0,1.0,0.0,53817.23
+16770,15641604.0,Tuan,656.0,Germany,Male,47.0,4.0,115024.5,1.0,1.0,1.0,182081.45
+16771,15768282.0,Mancini,733.0,France,Male,27.0,2.0,0.0,2.0,1.0,0.0,94450.97
+16772,15664772.0,Chiang,633.0,France,Female,31.0,5.0,0.0,1.0,1.0,0.0,164162.44
+16773,15679668.0,Chijindum,613.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,131919.48
+16774,15584545.0,Chijioke,680.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,121277.78
+16775,15674929.0,T'ao,733.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,0.0,124625.08
+16776,15571281.0,P'eng,709.0,France,Female,27.0,7.0,132512.26,1.0,0.0,1.0,86587.37
+16777,15645544.0,Maclean,645.0,France,Male,48.0,7.0,122314.5,2.0,1.0,1.0,6975.02
+16778,15575214.0,Onyekachi,669.0,France,Female,46.0,4.0,0.0,2.0,1.0,0.0,98820.09
+16779,15810444.0,Genovese,554.0,Spain,Male,34.0,1.0,0.0,2.0,0.0,1.0,102391.06
+16780,15585823.0,Shih,678.0,France,Male,35.0,5.0,0.0,1.0,0.0,0.0,111241.37
+16781,15763907.0,Lettiere,704.0,Germany,Female,33.0,6.0,111096.91,1.0,1.0,1.0,108469.2
+16782,15593900.0,Ch'in,683.0,Germany,Female,45.0,5.0,132932.14,1.0,0.0,1.0,163783.0
+16783,15671766.0,Hsieh,598.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,173599.38
+16784,15735339.0,Chiemenam,611.0,France,Male,50.0,1.0,0.0,2.0,1.0,1.0,167066.95
+16785,15784890.0,Ch'ien,754.0,France,Male,37.0,9.0,0.0,2.0,0.0,1.0,125961.48
+16786,15627665.0,T'ien,651.0,France,Male,31.0,3.0,122314.5,1.0,1.0,1.0,159840.51
+16787,15757425.0,Y?an,558.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,94790.34
+16788,15737542.0,Aparicio,755.0,Spain,Female,41.0,2.0,0.0,2.0,1.0,1.0,130610.78
+16789,15707567.0,Kisch,605.0,France,Female,42.0,4.0,0.0,1.0,1.0,0.0,136859.55
+16790,15797165.0,Hsiao,746.0,France,Male,34.0,6.0,0.0,1.0,1.0,1.0,91326.72
+16791,15684960.0,Ekechukwu,650.0,Spain,Female,25.0,2.0,0.0,2.0,0.0,1.0,182355.21
+16792,15681956.0,Chiu,751.0,Spain,Female,42.0,5.0,0.0,2.0,0.0,0.0,138524.31
+16793,15620398.0,Okwudilichukwu,712.0,France,Female,41.0,0.0,0.0,2.0,1.0,1.0,90162.35
+16794,15597951.0,Ch'in,663.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,123870.81
+16795,15575883.0,Ts'ao,653.0,France,Male,43.0,5.0,0.0,1.0,1.0,0.0,130853.03
+16796,15802462.0,Ewers,581.0,Germany,Male,29.0,7.0,131082.06,2.0,0.0,0.0,86044.98
+16797,15667438.0,Hs?,573.0,France,Male,38.0,4.0,0.0,2.0,0.0,0.0,124532.79
+16798,15743759.0,Chinweuba,567.0,Germany,Male,29.0,6.0,139979.06,1.0,0.0,1.0,172629.67
+16799,15736302.0,Ch'ang,635.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,87422.15
+16800,15591279.0,Ts'ai,447.0,France,Male,37.0,9.0,122084.63,1.0,1.0,0.0,155786.39
+16801,15699446.0,Whitehead,708.0,France,Female,33.0,4.0,116022.73,1.0,1.0,0.0,142400.27
+16802,15804787.0,Iadanza,582.0,France,Female,31.0,4.0,125879.29,1.0,1.0,1.0,161845.81
+16803,15761141.0,Ch'ang,752.0,France,Male,29.0,1.0,0.0,2.0,1.0,1.0,128880.23
+16804,15586069.0,Houghton,558.0,France,Male,53.0,4.0,0.0,2.0,0.0,1.0,102908.91
+16805,15730657.0,Zaytseva,572.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,173016.46
+16806,15580149.0,Tsao,554.0,France,Female,25.0,4.0,0.0,2.0,1.0,1.0,72462.25
+16807,15654025.0,Lai,549.0,France,Female,48.0,1.0,0.0,2.0,0.0,0.0,116957.6
+16808,15623478.0,Macdonald,850.0,France,Male,24.0,4.0,0.0,2.0,0.0,0.0,121681.19
+16809,15757981.0,Iadanza,705.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,175843.68
+16810,15762418.0,Ch'eng,705.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,0.0,81441.8
+16811,15689492.0,Y?,687.0,Spain,Female,24.0,1.0,0.0,2.0,1.0,1.0,114876.65
+16812,15732628.0,Chukwueloka,673.0,Spain,Female,24.0,8.0,0.0,1.0,1.0,1.0,129891.66
+16813,15600688.0,T'ang,644.0,Spain,Female,37.0,8.0,181166.44,1.0,1.0,0.0,162599.51
+16814,15680370.0,Nnonso,613.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,175314.87
+16815,15663438.0,Hargreaves,756.0,Germany,Male,34.0,7.0,90560.3,1.0,1.0,1.0,171045.3
+16816,15774586.0,Omeokachie,717.0,France,Male,32.0,2.0,132796.04,1.0,1.0,1.0,173365.89
+16817,15760244.0,Lettiere,727.0,France,Male,34.0,3.0,139729.3,1.0,1.0,0.0,162053.92
+16818,15682533.0,Obidimkpa,707.0,Germany,Female,43.0,8.0,123959.97,1.0,1.0,0.0,62700.08
+16819,15611058.0,Martin,627.0,Spain,Female,49.0,7.0,0.0,3.0,1.0,0.0,133059.15
+16820,15589431.0,Obidimkpa,684.0,France,Female,37.0,7.0,182705.05,1.0,1.0,0.0,79479.74
+16821,15771086.0,Nwora,582.0,France,Female,55.0,9.0,148884.47,1.0,1.0,0.0,147600.54
+16822,15581279.0,Ch'ang,594.0,Spain,Male,23.0,9.0,0.0,2.0,1.0,0.0,176921.0
+16823,15637644.0,Burtch,627.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,119327.75
+16824,15757811.0,Macleod,590.0,France,Female,46.0,8.0,0.0,1.0,1.0,0.0,178122.71
+16825,15634896.0,Greco,603.0,Germany,Male,30.0,10.0,161628.66,2.0,1.0,0.0,130446.65
+16826,15605791.0,Ch'ien,693.0,France,Female,35.0,4.0,0.0,2.0,0.0,1.0,177747.72
+16827,15682563.0,Linton,597.0,France,Male,28.0,0.0,0.0,2.0,1.0,1.0,6761.84
+16828,15625092.0,Bibi,634.0,France,Female,25.0,4.0,0.0,2.0,1.0,1.0,120853.83
+16829,15693737.0,Chu,657.0,Spain,Female,56.0,0.0,94598.48,1.0,1.0,0.0,164040.94
+16830,15705676.0,Onyemauchechi,605.0,France,Male,46.0,9.0,157761.56,1.0,1.0,1.0,111243.09
+16831,15739357.0,Hsiao,545.0,France,Male,52.0,2.0,0.0,1.0,0.0,0.0,105965.25
+16832,15751175.0,Chidumaga,671.0,Germany,Female,56.0,7.0,121898.82,1.0,1.0,1.0,63795.8
+16833,15656484.0,Niu,704.0,Germany,Male,44.0,1.0,123711.73,1.0,1.0,0.0,71460.68
+16834,15581548.0,Findlay,596.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,1.0,120830.73
+16835,15649733.0,Lucchese,753.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,72846.57
+16836,15589224.0,T'ien,535.0,France,Male,55.0,9.0,0.0,2.0,1.0,1.0,178341.33
+16837,15707968.0,Calabresi,674.0,France,Female,32.0,0.0,0.0,1.0,1.0,0.0,82614.89
+16838,15706587.0,Y?an,733.0,Germany,Female,35.0,5.0,121545.43,1.0,1.0,0.0,96832.82
+16839,15706232.0,P'eng,753.0,Germany,Male,69.0,0.0,95984.69,1.0,0.0,1.0,72927.71
+16840,15613141.0,Kerr,663.0,France,Female,57.0,1.0,0.0,1.0,1.0,0.0,170829.87
+16841,15728005.0,Ting,738.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,125877.22
+16842,15811389.0,Golovanov,707.0,Germany,Male,43.0,7.0,151839.26,1.0,0.0,1.0,96670.2
+16843,15782454.0,Ejimofor,635.0,Spain,Male,35.0,7.0,0.0,1.0,1.0,0.0,96524.11
+16844,15579714.0,Zikoranachidimma,669.0,Spain,Female,47.0,9.0,0.0,1.0,0.0,0.0,176883.42
+16845,15731555.0,Nwebube,716.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,103560.98
+16846,15773876.0,Oluchukwu,645.0,France,Female,48.0,4.0,143500.82,1.0,0.0,0.0,166677.35
+16847,15605072.0,Echezonachukwu,638.0,France,Male,71.0,6.0,0.0,1.0,1.0,1.0,62810.91
+16848,15641640.0,Chialuka,598.0,France,Female,46.0,9.0,0.0,1.0,1.0,1.0,81753.92
+16849,15607837.0,Ch'iu,683.0,Spain,Male,44.0,3.0,0.0,2.0,1.0,0.0,64375.4
+16850,15772383.0,Nnachetam,581.0,France,Female,71.0,8.0,0.0,1.0,0.0,0.0,141023.01
+16851,15777599.0,Chigolum,738.0,France,Female,37.0,8.0,0.0,2.0,0.0,0.0,183598.77
+16852,15777076.0,Scott,821.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,0.0,145562.4
+16853,15591036.0,McIntyre,721.0,Spain,Female,38.0,4.0,0.0,1.0,1.0,1.0,92357.21
+16854,15687558.0,Tsai,632.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,170634.49
+16855,15680141.0,Ibezimako,646.0,France,Female,35.0,1.0,0.0,2.0,1.0,1.0,26019.59
+16856,15633666.0,Ozerova,725.0,Spain,Female,49.0,8.0,0.0,2.0,1.0,0.0,159899.97
+16857,15649024.0,Aksakova,582.0,France,Male,43.0,1.0,0.0,1.0,1.0,0.0,58469.75
+16858,15651352.0,T'ao,730.0,Germany,Female,43.0,2.0,90721.52,1.0,1.0,0.0,164924.11
+16859,15762543.0,Chia,637.0,Spain,Male,39.0,2.0,0.0,2.0,1.0,1.0,72549.65
+16860,15658364.0,Chiu,677.0,France,Male,36.0,2.0,0.0,1.0,0.0,0.0,48228.1
+16861,15619360.0,Chidubem,594.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,1.0,174435.48
+16862,15607986.0,Chibuzo,592.0,France,Female,29.0,3.0,0.0,2.0,0.0,0.0,96792.15
+16863,15722404.0,Toscani,567.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,171733.22
+16864,15765643.0,Tseng,706.0,France,Male,36.0,7.0,0.0,1.0,1.0,1.0,68860.3
+16865,15582841.0,Milne,538.0,Spain,Male,33.0,1.0,113882.31,1.0,1.0,1.0,50759.4
+16866,15744044.0,Obijiaku,590.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,5791.85
+16867,15612507.0,Bledsoe,656.0,France,Female,33.0,5.0,0.0,2.0,0.0,0.0,55775.84
+16868,15779985.0,Ch'eng,521.0,Spain,Male,40.0,4.0,122314.39,1.0,0.0,0.0,168834.39
+16869,15667438.0,Hsieh,545.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,82868.34
+16870,15690670.0,Pagnotto,602.0,France,Male,41.0,0.0,0.0,1.0,1.0,1.0,128881.87
+16871,15713644.0,Onyemauchechukwu,632.0,Germany,Female,41.0,4.0,105837.86,1.0,0.0,1.0,96875.52
+16872,15803378.0,Zuyeva,593.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,57380.13
+16873,15804075.0,Nucci,691.0,Spain,Female,30.0,9.0,84906.45,2.0,1.0,0.0,59887.15
+16874,15615126.0,Nkemjika,684.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,182765.14
+16875,15585892.0,Chizuoke,686.0,France,Male,48.0,5.0,0.0,1.0,1.0,0.0,51548.14
+16876,15752650.0,Ibezimako,599.0,France,Male,35.0,10.0,0.0,2.0,1.0,1.0,96985.58
+16877,15665460.0,Yobachi,811.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,147600.54
+16878,15728706.0,Olisaemeka,775.0,France,Male,32.0,3.0,0.0,2.0,0.0,1.0,166031.08
+16879,15812184.0,H?,724.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,77550.18
+16880,15693737.0,Teng,659.0,France,Male,37.0,9.0,0.0,1.0,0.0,1.0,139919.38
+16881,15661409.0,Zikoranachidimma,543.0,France,Female,33.0,1.0,0.0,2.0,1.0,0.0,186942.49
+16882,15800515.0,Chidozie,564.0,France,Female,36.0,10.0,0.0,2.0,1.0,1.0,60420.07
+16883,15702715.0,Mazzanti,593.0,France,Male,37.0,8.0,0.0,1.0,1.0,1.0,157937.35
+16884,15761694.0,Mazzi,633.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,118913.53
+16885,15573741.0,Ijendu,624.0,Spain,Female,34.0,1.0,0.0,2.0,1.0,0.0,144290.97
+16886,15627212.0,Ponce,614.0,Spain,Male,31.0,3.0,118829.03,2.0,1.0,1.0,144880.81
+16887,15736413.0,Onyemere,438.0,France,Male,40.0,8.0,0.0,1.0,0.0,1.0,143783.46
+16888,15654700.0,Y?,645.0,France,Male,31.0,7.0,0.0,2.0,0.0,1.0,151336.96
+16889,15675360.0,Liao,683.0,Germany,Female,36.0,7.0,104498.79,2.0,1.0,0.0,75927.35
+16890,15711889.0,Yuriev,587.0,France,Female,43.0,6.0,181821.96,1.0,1.0,1.0,103480.69
+16891,15713346.0,T'ao,637.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,170518.83
+16892,15640491.0,Y?,651.0,France,Female,39.0,10.0,0.0,2.0,1.0,0.0,126153.24
+16893,15671172.0,Chukwuemeka,648.0,Spain,Female,43.0,0.0,0.0,2.0,1.0,0.0,61290.99
+16894,15740223.0,Okonkwo,583.0,Germany,Male,35.0,9.0,135919.33,2.0,1.0,1.0,16491.64
+16895,15722692.0,Hsing,696.0,France,Female,26.0,6.0,0.0,1.0,1.0,1.0,63795.8
+16896,15593795.0,Okwuadigbo,775.0,Spain,Male,41.0,5.0,0.0,1.0,1.0,1.0,132038.65
+16897,15654760.0,Calabresi,431.0,Spain,Female,37.0,2.0,102870.93,2.0,1.0,1.0,167984.61
+16898,15571917.0,Ch'eng,743.0,France,Male,28.0,10.0,61297.05,1.0,1.0,0.0,174800.31
+16899,15615016.0,Lucciano,675.0,France,Male,31.0,9.0,134740.19,1.0,1.0,0.0,175795.87
+16900,15768887.0,Cawood,587.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,0.0,106040.97
+16901,15617078.0,Toscani,850.0,Spain,Female,24.0,2.0,0.0,2.0,1.0,0.0,50771.16
+16902,15699467.0,Nebechi,679.0,France,Female,51.0,7.0,0.0,1.0,0.0,0.0,131625.14
+16903,15668893.0,Chukwufumnanya,607.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,182988.94
+16904,15725028.0,Chidumaga,590.0,France,Female,42.0,3.0,0.0,1.0,1.0,1.0,56984.58
+16905,15777892.0,Young,685.0,France,Male,26.0,2.0,0.0,2.0,1.0,0.0,152290.28
+16906,15689955.0,Chimezie,597.0,France,Male,40.0,5.0,143637.58,1.0,1.0,1.0,103557.85
+16907,15656300.0,Ch'in,659.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,126192.54
+16908,15642835.0,P'eng,686.0,France,Female,36.0,8.0,0.0,2.0,1.0,0.0,92547.01
+16909,15696733.0,Fanucci,686.0,France,Male,27.0,7.0,0.0,2.0,0.0,1.0,138350.74
+16910,15752578.0,Chukwuhaenye,678.0,France,Male,31.0,4.0,0.0,2.0,0.0,1.0,141798.98
+16911,15586522.0,Liao,595.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,124489.88
+16912,15682472.0,Lucchesi,630.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,131283.6
+16913,15735246.0,Oguejiofor,686.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,141031.01
+16914,15708650.0,Ndubueze,684.0,France,Male,33.0,9.0,0.0,2.0,0.0,0.0,59195.05
+16915,15711299.0,Ugoji,671.0,Spain,Female,26.0,5.0,0.0,1.0,1.0,0.0,116537.96
+16916,15789941.0,Ts'ui,581.0,France,Male,26.0,0.0,0.0,2.0,1.0,0.0,163471.01
+16917,15686588.0,Cremonesi,850.0,Spain,Male,28.0,7.0,0.0,1.0,0.0,0.0,48849.89
+16918,15671148.0,Ponomaryov,613.0,Germany,Male,43.0,10.0,123793.18,1.0,0.0,0.0,134625.09
+16919,15785869.0,Ch'ang,667.0,France,Female,36.0,10.0,0.0,2.0,1.0,1.0,140941.47
+16920,15815560.0,Chidozie,740.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,131259.6
+16921,15704910.0,Genovesi,632.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,598.8
+16922,15793425.0,Ch'ang,648.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,132450.18
+16923,15773042.0,P'an,754.0,France,Male,32.0,3.0,0.0,1.0,1.0,0.0,142903.44
+16924,15718281.0,Hs?eh,662.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,79919.97
+16925,15690796.0,Ni,729.0,France,Male,31.0,1.0,0.0,1.0,0.0,1.0,156906.15
+16926,15727291.0,Ts'ui,697.0,Spain,Male,35.0,10.0,0.0,1.0,1.0,1.0,38962.94
+16927,15803406.0,Okwudiliolisa,716.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,0.0,113428.77
+16928,15770214.0,Chin,705.0,Spain,Male,49.0,3.0,0.0,1.0,1.0,1.0,24113.91
+16929,15803801.0,Chuang,613.0,France,Female,44.0,5.0,91286.51,1.0,1.0,0.0,181270.84
+16930,15594576.0,T'ien,718.0,France,Male,34.0,1.0,0.0,2.0,1.0,1.0,133537.53
+16931,15726289.0,Ch'ang,565.0,France,Female,48.0,2.0,0.0,1.0,0.0,1.0,105986.01
+16932,15681554.0,Mazzanti,646.0,France,Female,38.0,9.0,0.0,1.0,1.0,1.0,118570.56
+16933,15772601.0,Calabrese,573.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,64593.04
+16934,15762048.0,Yefimova,589.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,61164.45
+16935,15808930.0,O'Donnell,662.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,78670.46
+16936,15699340.0,Hsueh,705.0,France,Male,55.0,3.0,129968.67,1.0,1.0,0.0,174918.46
+16937,15666884.0,Chinedum,572.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,101311.95
+16938,15773098.0,Ukaegbunam,679.0,France,Female,22.0,6.0,0.0,2.0,1.0,1.0,140780.8
+16939,15763874.0,Chibugo,687.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,162501.83
+16940,15664339.0,Ibezimako,516.0,Germany,Female,33.0,8.0,133040.32,1.0,1.0,0.0,52484.01
+16941,15589323.0,Yeh,637.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,9087.81
+16942,15689633.0,Tien,709.0,France,Female,38.0,8.0,0.0,1.0,1.0,1.0,83342.82
+16943,15756475.0,Yeh,719.0,Germany,Male,55.0,9.0,107812.57,1.0,1.0,0.0,142223.94
+16944,15773972.0,Genovese,656.0,Spain,Male,36.0,6.0,0.0,2.0,0.0,0.0,86587.37
+16945,15636023.0,Ts'ao,557.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,0.0,177624.01
+16946,15678058.0,Lu,749.0,Germany,Male,41.0,1.0,116662.96,2.0,0.0,1.0,93959.96
+16947,15777893.0,Dyer,602.0,France,Male,33.0,9.0,0.0,1.0,1.0,1.0,124954.58
+16948,15700540.0,Onyekachi,664.0,Spain,Male,34.0,5.0,0.0,1.0,1.0,1.0,49374.82
+16949,15642579.0,Ch'ang,585.0,France,Male,54.0,1.0,0.0,3.0,1.0,0.0,176779.46
+16950,15593683.0,Genovese,627.0,France,Female,29.0,7.0,113000.92,1.0,1.0,0.0,77977.69
+16951,15663625.0,Hughes,719.0,Germany,Female,51.0,0.0,132730.17,1.0,1.0,0.0,161889.84
+16952,15594409.0,Walker,757.0,Spain,Male,38.0,8.0,125822.21,1.0,1.0,1.0,143628.31
+16953,15751491.0,Yobanna,749.0,France,Female,38.0,9.0,0.0,2.0,0.0,0.0,48606.92
+16954,15740406.0,Chigozie,724.0,France,Female,67.0,6.0,0.0,2.0,1.0,1.0,131301.74
+16955,15768474.0,Ch'en,731.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,104889.2
+16956,15672238.0,Zikoranachidimma,744.0,France,Male,30.0,4.0,0.0,1.0,1.0,0.0,71450.96
+16957,15733581.0,Rizzo,744.0,France,Female,46.0,9.0,0.0,2.0,1.0,0.0,90537.97
+16958,15663883.0,Hsia,705.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,126192.54
+16959,15689351.0,Nwachinemelu,651.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,117834.92
+16960,15752622.0,Nwora,554.0,Spain,Male,72.0,1.0,0.0,2.0,1.0,1.0,128875.86
+16961,15775750.0,Ch'ien,741.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,117523.41
+16962,15793324.0,Fisk,738.0,Spain,Female,33.0,8.0,0.0,2.0,0.0,0.0,141426.78
+16963,15756941.0,Chiang,636.0,France,Female,34.0,7.0,0.0,2.0,0.0,0.0,109742.55
+16964,15758056.0,Cattaneo,575.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,0.0,27758.36
+16965,15792868.0,Chiu,579.0,France,Female,28.0,0.0,0.0,2.0,0.0,1.0,59864.96
+16966,15756875.0,Hargreaves,683.0,France,Female,54.0,8.0,0.0,1.0,1.0,0.0,96528.0
+16967,15661745.0,Genovesi,684.0,France,Male,62.0,4.0,0.0,2.0,0.0,1.0,131097.9
+16968,15737571.0,Hsiung,714.0,Germany,Female,46.0,8.0,97588.6,1.0,1.0,0.0,125979.36
+16969,15778418.0,Chidimma,679.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,45909.87
+16970,15642063.0,Ting,769.0,France,Male,29.0,8.0,0.0,2.0,0.0,0.0,44390.38
+16971,15768387.0,Ekechukwu,643.0,Germany,Male,46.0,1.0,108698.96,1.0,0.0,1.0,116471.43
+16972,15807593.0,Rubeo,544.0,France,Female,47.0,1.0,0.0,1.0,1.0,0.0,102655.31
+16973,15653773.0,Maclean,683.0,Spain,Female,64.0,9.0,70655.48,1.0,1.0,1.0,161579.85
+16974,15602929.0,Maclean,758.0,France,Female,40.0,1.0,0.0,2.0,0.0,0.0,143534.28
+16975,15629338.0,Chu,590.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,86699.98
+16976,15687431.0,Brown,468.0,Germany,Female,29.0,6.0,138089.97,2.0,1.0,0.0,85746.82
+16977,15585067.0,Hs?,591.0,France,Female,29.0,7.0,109346.13,2.0,0.0,0.0,148564.76
+16978,15747168.0,Hs?,628.0,France,Female,36.0,7.0,0.0,1.0,1.0,1.0,72873.33
+16979,15593973.0,White,611.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,130514.78
+16980,15598084.0,Mistry,792.0,Spain,Male,50.0,8.0,143964.36,1.0,1.0,0.0,97623.42
+16981,15672374.0,Kao,529.0,France,Male,47.0,7.0,0.0,1.0,0.0,0.0,142223.94
+16982,15802625.0,Smith,574.0,France,Male,56.0,4.0,152265.43,1.0,1.0,0.0,116696.77
+16983,15652658.0,Ch'ang,716.0,France,Female,55.0,5.0,0.0,1.0,1.0,0.0,171706.66
+16984,15684801.0,Yoo,691.0,France,Female,40.0,2.0,0.0,2.0,0.0,1.0,180002.42
+16985,15747808.0,Ozerova,663.0,Spain,Female,35.0,9.0,0.0,2.0,1.0,1.0,61977.82
+16986,15638231.0,Y?an,729.0,Spain,Male,31.0,10.0,0.0,2.0,1.0,1.0,65816.53
+16987,15610686.0,K'ung,678.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,48988.86
+16988,15777315.0,Ch'ang,633.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,112928.74
+16989,15640034.0,Chinwendu,564.0,France,Female,43.0,5.0,142405.52,1.0,1.0,0.0,18082.06
+16990,15697035.0,Pirozzi,590.0,Spain,Male,38.0,10.0,0.0,2.0,0.0,1.0,146652.01
+16991,15732266.0,Bogle,587.0,Germany,Male,32.0,2.0,121093.44,2.0,1.0,1.0,153776.6
+16992,15640865.0,Chidozie,728.0,France,Female,42.0,5.0,132603.36,1.0,1.0,1.0,72350.47
+16993,15646936.0,T'ien,741.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,183515.12
+16994,15664802.0,Chinweike,591.0,France,Male,44.0,8.0,0.0,2.0,1.0,0.0,90967.48
+16995,15768282.0,Kao,703.0,Spain,Male,36.0,6.0,0.0,2.0,0.0,0.0,122810.53
+16996,15572022.0,Chin,725.0,France,Male,36.0,6.0,0.0,2.0,0.0,1.0,132649.85
+16997,15804864.0,Gell,650.0,Germany,Male,32.0,4.0,105245.34,2.0,1.0,0.0,158917.83
+16998,15705953.0,Udinese,681.0,France,Male,56.0,6.0,0.0,1.0,0.0,1.0,93833.38
+16999,15806486.0,Ch'ang,659.0,Spain,Female,34.0,1.0,0.0,2.0,1.0,0.0,189175.71
+17000,15781674.0,Nwokezuike,553.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,194998.34
+17001,15793070.0,Nwora,695.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,1.0,166370.81
+17002,15585287.0,Chinedum,705.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,176925.69
+17003,15625641.0,Ch'eng,632.0,Germany,Male,46.0,2.0,93522.3,1.0,1.0,1.0,124571.09
+17004,15732963.0,Kuznetsova,596.0,France,Male,35.0,9.0,0.0,1.0,1.0,1.0,54117.03
+17005,15757821.0,Udinese,724.0,France,Female,38.0,7.0,152324.66,1.0,0.0,0.0,151623.71
+17006,15655436.0,Iloerika,643.0,Spain,Male,32.0,8.0,0.0,1.0,1.0,1.0,92888.52
+17007,15764405.0,Gboliwe,678.0,France,Female,36.0,8.0,0.0,1.0,1.0,0.0,155061.97
+17008,15608541.0,Hsueh,648.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,131389.39
+17009,15634373.0,Nkemakonam,669.0,Spain,Male,44.0,2.0,0.0,1.0,1.0,0.0,172586.89
+17010,15791767.0,Nnaife,651.0,Germany,Male,37.0,9.0,118082.89,2.0,1.0,0.0,140197.42
+17011,15775116.0,Yefremova,521.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,0.0,56908.41
+17012,15679963.0,Smith,642.0,Spain,Female,36.0,1.0,0.0,2.0,0.0,1.0,156792.89
+17013,15628992.0,Tseng,690.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,161559.12
+17014,15583748.0,Chikwado,579.0,Germany,Male,42.0,9.0,93680.43,1.0,1.0,0.0,49667.28
+17015,15636595.0,Mironova,738.0,Germany,Female,39.0,6.0,107949.35,1.0,1.0,0.0,148814.54
+17016,15777639.0,Clements,697.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,161671.15
+17017,15682070.0,Fanucci,659.0,France,Female,35.0,8.0,133736.04,1.0,1.0,0.0,178028.96
+17018,15795895.0,Fiorentini,542.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,141756.32
+17019,15604792.0,Aitken,662.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,161479.19
+17020,15694404.0,Chidimma,662.0,France,Female,35.0,5.0,160838.13,1.0,1.0,1.0,27509.52
+17021,15589210.0,H?,620.0,France,Male,33.0,1.0,111739.4,1.0,1.0,1.0,153064.87
+17022,15654877.0,Lo,597.0,Germany,Female,37.0,8.0,131853.13,2.0,1.0,1.0,88703.04
+17023,15638355.0,L?,562.0,Germany,Female,48.0,4.0,114421.55,1.0,1.0,0.0,100727.68
+17024,15670080.0,Onyemauchechukwu,716.0,France,Female,35.0,1.0,0.0,2.0,1.0,1.0,166650.8
+17025,15663820.0,Milne,554.0,France,Female,38.0,9.0,0.0,2.0,0.0,1.0,111243.09
+17026,15695078.0,Chinagorom,569.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,57553.02
+17027,15681075.0,Chidimma,821.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,81251.24
+17028,15772067.0,Milani,656.0,Germany,Male,55.0,6.0,132906.88,1.0,0.0,1.0,127413.0
+17029,15696744.0,Tuan,689.0,France,Female,28.0,8.0,0.0,2.0,0.0,0.0,131363.57
+17030,15613180.0,Chukwudi,607.0,Spain,Male,31.0,3.0,0.0,2.0,1.0,1.0,133653.38
+17031,15616471.0,Hs?,692.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,130260.11
+17032,15686936.0,Obiajulu,521.0,Germany,Male,45.0,1.0,116520.73,1.0,0.0,1.0,85470.45
+17033,15751158.0,H?,589.0,France,Male,45.0,4.0,0.0,2.0,1.0,1.0,80552.36
+17034,15800524.0,Onyeoruru,677.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,86043.27
+17035,15644878.0,Hsing,539.0,Spain,Female,36.0,8.0,0.0,2.0,1.0,1.0,129936.53
+17036,15626794.0,Aitken,751.0,France,Male,38.0,7.0,0.0,2.0,0.0,0.0,119419.37
+17037,15698928.0,Nnamutaezinwa,593.0,Spain,Male,23.0,9.0,0.0,2.0,1.0,0.0,133871.83
+17038,15792388.0,Nwora,711.0,France,Male,26.0,7.0,121729.42,1.0,1.0,1.0,69643.31
+17039,15775490.0,Shao,622.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,153605.75
+17040,15592854.0,Chidumaga,526.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,111096.97
+17041,15732640.0,Yefimova,703.0,Spain,Female,28.0,7.0,0.0,2.0,1.0,1.0,140168.36
+17042,15697035.0,Mazzanti,576.0,Germany,Male,54.0,7.0,137811.75,1.0,1.0,0.0,141501.45
+17043,15641877.0,Ch'iu,778.0,France,Female,28.0,9.0,0.0,1.0,1.0,1.0,168816.38
+17044,15754526.0,Ch'ang,569.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,88669.89
+17045,15605622.0,Chu,637.0,France,Female,31.0,8.0,0.0,1.0,1.0,0.0,69409.47
+17046,15592994.0,Yermakova,643.0,Germany,Male,47.0,8.0,115465.98,1.0,0.0,1.0,172749.65
+17047,15604576.0,Li Fonti,658.0,France,Male,29.0,10.0,0.0,2.0,1.0,0.0,174969.94
+17048,15688865.0,H?,679.0,France,Male,38.0,3.0,0.0,1.0,1.0,1.0,132034.13
+17049,15737605.0,Trevisani,645.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,143613.74
+17050,15792029.0,Aksenov,579.0,Germany,Male,39.0,10.0,133970.22,2.0,1.0,1.0,185543.67
+17051,15800784.0,Chidiebele,675.0,France,Female,47.0,10.0,0.0,1.0,1.0,0.0,146680.86
+17052,15744983.0,Tsai,697.0,France,Female,34.0,3.0,131691.33,1.0,0.0,0.0,82868.33
+17053,15687310.0,Nwachinemelu,696.0,Spain,Male,52.0,3.0,0.0,1.0,1.0,0.0,142763.51
+17054,15589948.0,Ch'iu,591.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,149458.81
+17055,15740283.0,Chiu,611.0,France,Male,39.0,0.0,0.0,2.0,1.0,0.0,28123.99
+17056,15646921.0,Hsu,684.0,France,Male,35.0,9.0,117866.92,1.0,1.0,0.0,66580.13
+17057,15651389.0,Kao,602.0,Spain,Female,27.0,7.0,0.0,2.0,1.0,0.0,95500.98
+17058,15648858.0,Macleod,590.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,143835.52
+17059,15579892.0,Hao,715.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,123954.22
+17060,15628303.0,T'ien,543.0,Spain,Female,47.0,7.0,0.0,2.0,1.0,0.0,131772.51
+17061,15697948.0,Milne,646.0,Germany,Male,56.0,0.0,123575.51,1.0,1.0,0.0,125334.16
+17062,15691627.0,De Luca,653.0,France,Female,31.0,6.0,0.0,2.0,1.0,1.0,54865.92
+17063,15575581.0,Frolova,479.0,Germany,Female,22.0,3.0,124619.33,2.0,1.0,0.0,131764.52
+17064,15682563.0,Yevdokimova,439.0,France,Female,42.0,3.0,167735.69,1.0,1.0,1.0,129050.67
+17065,15771383.0,Hsueh,636.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,0.0,161807.21
+17066,15591036.0,Udinese,549.0,France,Female,46.0,10.0,0.0,1.0,0.0,1.0,138880.24
+17067,15686963.0,Uspensky,676.0,France,Female,40.0,8.0,0.0,1.0,1.0,0.0,169920.92
+17068,15613282.0,Pirozzi,692.0,Spain,Female,53.0,8.0,93680.43,1.0,1.0,1.0,64771.61
+17069,15733387.0,Ch'en,717.0,Germany,Female,39.0,9.0,123825.56,2.0,0.0,0.0,158049.9
+17070,15779627.0,Ch'in,577.0,Germany,Female,35.0,6.0,117337.54,1.0,1.0,0.0,96833.0
+17071,15800845.0,DeRose,587.0,Spain,Male,48.0,3.0,0.0,1.0,0.0,0.0,171774.5
+17072,15776467.0,Bibi,635.0,France,Female,33.0,6.0,0.0,2.0,0.0,0.0,67830.2
+17073,15716357.0,Ritchie,637.0,France,Male,41.0,2.0,0.0,2.0,0.0,1.0,140937.91
+17074,15722404.0,Chizoba,682.0,Germany,Female,35.0,2.0,95698.79,1.0,1.0,1.0,123475.78
+17075,15686917.0,Chukwujamuike,649.0,Spain,Male,36.0,7.0,118962.58,1.0,1.0,0.0,58320.06
+17076,15690670.0,Kao,757.0,France,Female,30.0,0.0,0.0,2.0,1.0,0.0,185541.2
+17077,15637178.0,Chigbogu,628.0,Germany,Male,41.0,4.0,95826.49,1.0,1.0,0.0,126192.54
+17078,15589257.0,Chineze,611.0,Germany,Female,34.0,7.0,172646.82,1.0,0.0,0.0,121555.51
+17079,15630106.0,Harker,676.0,Spain,Female,34.0,4.0,0.0,1.0,0.0,0.0,181419.25
+17080,15585324.0,Chibugo,733.0,Germany,Female,31.0,2.0,137841.53,1.0,1.0,1.0,182784.11
+17081,15802390.0,Ozioma,681.0,France,Female,31.0,6.0,0.0,2.0,0.0,0.0,142547.58
+17082,15641411.0,Li Fonti,656.0,Spain,Male,39.0,10.0,0.0,2.0,0.0,0.0,158958.2
+17083,15571928.0,Okechukwu,828.0,France,Male,34.0,8.0,0.0,1.0,1.0,1.0,116972.91
+17084,15596962.0,Hsia,695.0,Spain,Male,39.0,9.0,0.0,1.0,1.0,0.0,145562.4
+17085,15692583.0,Chiganu,687.0,France,Female,47.0,8.0,0.0,1.0,1.0,0.0,188486.94
+17086,15776631.0,Manna,645.0,Spain,Male,44.0,7.0,0.0,2.0,0.0,1.0,131919.48
+17087,15711251.0,Ch'in,598.0,Spain,Female,33.0,6.0,0.0,1.0,1.0,1.0,88920.68
+17088,15574761.0,P'eng,679.0,France,Female,28.0,4.0,0.0,2.0,1.0,1.0,177740.58
+17089,15601184.0,Yermakova,613.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,57898.58
+17090,15615091.0,Lucchese,738.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,1.0,161579.85
+17091,15705352.0,Ch'eng,635.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,180932.46
+17092,15659937.0,Chidimma,725.0,Spain,Male,43.0,4.0,0.0,1.0,1.0,1.0,108469.2
+17093,15587581.0,Ch'iu,727.0,Spain,Male,31.0,6.0,123845.36,1.0,1.0,1.0,71672.86
+17094,15622494.0,Lo,705.0,France,Male,38.0,8.0,161592.76,1.0,0.0,1.0,197111.99
+17095,15794493.0,Y?an,662.0,France,Female,45.0,0.0,0.0,1.0,1.0,0.0,177065.24
+17096,15593694.0,Hsia,592.0,Germany,Male,39.0,9.0,114422.85,1.0,1.0,0.0,118024.1
+17097,15726153.0,Pinto,648.0,Germany,Female,46.0,4.0,117438.92,2.0,1.0,1.0,116086.22
+17098,15767391.0,Chukwuemeka,684.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,82662.73
+17099,15653773.0,Ch'ang,704.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,79450.61
+17100,15739292.0,Ulyanova,479.0,Germany,Female,42.0,2.0,132436.86,1.0,0.0,1.0,180932.46
+17101,15788367.0,Ch'in,687.0,Germany,Male,39.0,4.0,125845.66,2.0,1.0,0.0,182650.15
+17102,15640377.0,Fanucci,649.0,Germany,Male,45.0,5.0,142987.46,1.0,1.0,0.0,61513.87
+17103,15768451.0,Chiu,706.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,0.0,129964.94
+17104,15685320.0,Lucciano,683.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,170968.99
+17105,15623107.0,McIntosh,648.0,France,Male,25.0,5.0,0.0,1.0,1.0,0.0,133518.76
+17106,15576774.0,Lo Duca,694.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,79577.4
+17107,15591698.0,Ozerova,845.0,France,Male,44.0,7.0,108269.2,1.0,1.0,1.0,55816.4
+17108,15768455.0,Toscani,835.0,Germany,Male,40.0,7.0,132351.29,2.0,1.0,0.0,88592.8
+17109,15663421.0,Okwukwe,730.0,France,Female,40.0,7.0,0.0,2.0,1.0,1.0,147040.25
+17110,15737104.0,Ugorji,548.0,Spain,Male,31.0,10.0,0.0,2.0,1.0,1.0,121316.07
+17111,15665590.0,Ts'ai,591.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,100941.57
+17112,15583681.0,Fomin,648.0,France,Male,38.0,6.0,0.0,1.0,1.0,1.0,187943.6
+17113,15782530.0,Chukwudi,766.0,France,Female,32.0,10.0,108465.65,1.0,1.0,0.0,187905.12
+17114,15583552.0,H?,661.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,118959.64
+17115,15585066.0,Chukwujekwu,611.0,France,Male,45.0,2.0,101514.89,1.0,1.0,1.0,91514.62
+17116,15772073.0,Mazzanti,633.0,Spain,Female,31.0,2.0,0.0,1.0,1.0,0.0,151843.54
+17117,15793425.0,Chukwujamuike,712.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,167784.28
+17118,15787907.0,Hsiung,585.0,Germany,Female,31.0,6.0,111094.05,1.0,0.0,0.0,122580.48
+17119,15656319.0,Yobanna,773.0,France,Female,42.0,8.0,0.0,2.0,0.0,1.0,163584.29
+17120,15727703.0,Ch'in,584.0,France,Male,30.0,1.0,0.0,1.0,1.0,0.0,70810.6
+17121,15654700.0,Hsueh,627.0,Germany,Male,29.0,3.0,135923.11,2.0,1.0,1.0,127439.16
+17122,15772632.0,Goliwe,691.0,France,Male,43.0,7.0,0.0,1.0,0.0,0.0,19370.73
+17123,15626793.0,Ch'eng,722.0,Spain,Female,38.0,2.0,168066.87,1.0,0.0,0.0,141822.5
+17124,15682757.0,De Salis,708.0,France,Female,56.0,6.0,0.0,1.0,0.0,0.0,103931.49
+17125,15635367.0,Hs?,850.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,127095.14
+17126,15648800.0,Pagnotto,593.0,France,Male,34.0,3.0,0.0,1.0,1.0,0.0,119313.74
+17127,15802088.0,Yuryeva,806.0,France,Male,26.0,8.0,151223.59,1.0,0.0,0.0,163558.42
+17128,15761364.0,Manna,549.0,Germany,Male,49.0,2.0,141642.92,1.0,1.0,0.0,51565.99
+17129,15761717.0,Chia,691.0,France,Male,41.0,1.0,0.0,2.0,1.0,0.0,122996.76
+17130,15722692.0,Kaeppel,635.0,Germany,Female,28.0,3.0,142987.46,1.0,1.0,0.0,126464.94
+17131,15766427.0,Shih,583.0,France,Male,24.0,7.0,128940.24,1.0,1.0,0.0,119903.2
+17132,15776936.0,T'ien,724.0,France,Male,49.0,8.0,0.0,2.0,1.0,0.0,63889.1
+17133,15802758.0,Chiang,553.0,France,Female,32.0,9.0,142905.51,1.0,1.0,1.0,53817.23
+17134,15608968.0,Chukwudi,641.0,Spain,Male,34.0,0.0,0.0,1.0,1.0,0.0,82526.92
+17135,15680370.0,Chukwuhaenye,644.0,Spain,Female,37.0,6.0,121411.9,1.0,1.0,0.0,147075.69
+17136,15724444.0,Chukwumaobim,712.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,158527.55
+17137,15708063.0,P'eng,628.0,France,Male,32.0,8.0,140553.46,1.0,1.0,0.0,94483.06
+17138,15650098.0,Eluemuno,489.0,Germany,Female,60.0,10.0,121151.05,1.0,1.0,0.0,164117.69
+17139,15583681.0,Mackay,639.0,France,Male,41.0,1.0,80931.75,1.0,0.0,0.0,148814.54
+17140,15638463.0,Chukwuemeka,839.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,123954.15
+17141,15761047.0,H?,675.0,Germany,Female,31.0,4.0,123374.46,1.0,1.0,0.0,18826.06
+17142,15687558.0,Ukaegbunam,685.0,France,Female,43.0,5.0,0.0,2.0,1.0,0.0,132418.59
+17143,15691792.0,Ch'iu,678.0,Germany,Male,39.0,3.0,103421.49,1.0,0.0,0.0,162719.69
+17144,15605059.0,Walker,637.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,91830.75
+17145,15603683.0,Fanucci,709.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,1.0,151344.09
+17146,15671534.0,Lettiere,574.0,France,Male,43.0,3.0,0.0,1.0,1.0,1.0,79240.9
+17147,15723481.0,Fomina,595.0,France,Male,62.0,2.0,0.0,2.0,1.0,1.0,147055.87
+17148,15684930.0,K'ung,634.0,France,Female,35.0,5.0,127290.61,1.0,1.0,1.0,187988.01
+17149,15700826.0,Okwudilichukwu,699.0,France,Female,38.0,4.0,97560.16,1.0,1.0,1.0,128528.83
+17150,15696564.0,Tobeolisa,589.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,140947.05
+17151,15576680.0,Yocum,673.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,1.0,67599.69
+17152,15694300.0,Milanesi,695.0,France,Male,44.0,8.0,0.0,2.0,0.0,1.0,89524.83
+17153,15717489.0,Fisk,694.0,Spain,Female,30.0,4.0,0.0,2.0,1.0,0.0,149535.72
+17154,15802486.0,Zikoranachidimma,558.0,France,Male,40.0,7.0,0.0,2.0,0.0,0.0,74838.64
+17155,15726167.0,Manna,624.0,France,Male,35.0,3.0,0.0,2.0,0.0,1.0,187597.98
+17156,15727197.0,Nwachukwu,681.0,Germany,Male,35.0,6.0,131341.46,1.0,0.0,0.0,179258.68
+17157,15807961.0,Brown,721.0,Germany,Female,33.0,7.0,133658.24,2.0,1.0,0.0,100538.8
+17158,15734365.0,Chiu,632.0,Spain,Male,38.0,5.0,0.0,1.0,0.0,0.0,85411.01
+17159,15691910.0,Hsing,685.0,Germany,Male,33.0,7.0,151675.65,1.0,1.0,1.0,123954.15
+17160,15793854.0,Onyemauchechukwu,699.0,France,Female,39.0,5.0,0.0,2.0,1.0,0.0,147878.05
+17161,15794479.0,H?,850.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,153392.28
+17162,15692929.0,De Bernales,540.0,France,Female,24.0,10.0,0.0,2.0,1.0,0.0,90821.48
+17163,15772528.0,Pisano,696.0,Germany,Female,43.0,0.0,104488.19,1.0,1.0,0.0,81299.17
+17164,15704081.0,Chineze,775.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,1.0,132673.23
+17165,15718779.0,Hsia,759.0,Spain,Male,26.0,7.0,0.0,2.0,1.0,1.0,90162.35
+17166,15641353.0,Bogle,633.0,Germany,Male,27.0,4.0,132405.52,1.0,0.0,1.0,143913.33
+17167,15737310.0,Chiu,559.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,152956.33
+17168,15780128.0,Trevisani,751.0,Germany,Female,34.0,4.0,104449.8,2.0,0.0,1.0,46422.46
+17169,15656319.0,Cremonesi,807.0,France,Female,42.0,2.0,0.0,1.0,0.0,0.0,100717.54
+17170,15761854.0,Fetherstonhaugh,679.0,France,Female,24.0,6.0,0.0,2.0,1.0,0.0,110317.39
+17171,15771742.0,Lucchesi,632.0,France,Female,34.0,8.0,0.0,2.0,0.0,1.0,92888.52
+17172,15702416.0,Chinagorom,559.0,France,Male,34.0,8.0,0.0,1.0,0.0,1.0,91565.61
+17173,15809800.0,Ch'ien,551.0,Germany,Male,37.0,6.0,146098.43,2.0,1.0,0.0,181605.85
+17174,15684319.0,Cattaneo,595.0,Germany,Male,40.0,3.0,120962.13,2.0,1.0,1.0,132424.69
+17175,15703394.0,Nnamutaezinwa,668.0,France,Female,42.0,2.0,0.0,1.0,1.0,1.0,147955.91
+17176,15592645.0,Hsiung,661.0,France,Male,32.0,6.0,141243.65,1.0,1.0,0.0,188516.45
+17177,15575955.0,Trevisani,579.0,France,Female,40.0,5.0,123694.42,1.0,1.0,0.0,144532.85
+17178,15655961.0,T'ien,715.0,Spain,Male,45.0,4.0,0.0,2.0,0.0,1.0,179521.24
+17179,15608726.0,Yobanna,724.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,152298.84
+17180,15686914.0,Nwabugwu,694.0,France,Male,30.0,1.0,123681.32,1.0,0.0,1.0,72350.21
+17181,15649738.0,T'ien,559.0,Spain,Male,46.0,8.0,0.0,2.0,0.0,0.0,179267.94
+17182,15627876.0,Nwankwo,693.0,France,Female,30.0,7.0,145984.92,1.0,0.0,0.0,6090905.51
+17183,15675888.0,Cattaneo,624.0,Germany,Female,36.0,7.0,131835.52,2.0,1.0,0.0,162452.65
+17184,15756655.0,Uwaezuoke,583.0,Germany,Female,33.0,5.0,101438.0,1.0,0.0,1.0,93739.71
+17185,15566139.0,Teng,744.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,74858.3
+17186,15795963.0,Lucciano,635.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,150803.44
+17187,15748797.0,Kao,724.0,Germany,Male,31.0,7.0,100433.8,2.0,1.0,0.0,102999.23
+17188,15762799.0,Chidiebele,649.0,Germany,Male,59.0,3.0,102708.77,1.0,1.0,1.0,134417.31
+17189,15672027.0,Lu,731.0,France,Female,39.0,9.0,0.0,2.0,1.0,1.0,137791.18
+17190,15777922.0,Chukwuemeka,744.0,Spain,Male,67.0,7.0,120877.24,1.0,1.0,1.0,68400.14
+17191,15589428.0,Chukwuemeka,567.0,France,Female,27.0,2.0,76915.4,1.0,1.0,1.0,142940.39
+17192,15571201.0,Yevdokimova,565.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,161668.13
+17193,15768233.0,Chukwufumnanya,684.0,France,Male,33.0,0.0,0.0,2.0,1.0,1.0,125557.95
+17194,15725036.0,K?,841.0,Spain,Female,35.0,9.0,0.0,2.0,1.0,1.0,60954.45
+17195,15733489.0,L?,619.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,152992.04
+17196,15803976.0,Ugochukwu,672.0,Germany,Male,35.0,6.0,81571.05,2.0,1.0,1.0,123870.07
+17197,15673584.0,Kepley,678.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,94498.82
+17198,15755223.0,Sargent,593.0,Spain,Female,27.0,5.0,0.0,1.0,1.0,0.0,101617.64
+17199,15654975.0,Okonkwo,545.0,France,Female,52.0,1.0,0.0,1.0,1.0,0.0,65636.92
+17200,15742019.0,Tseng,593.0,France,Male,31.0,4.0,0.0,1.0,0.0,1.0,73977.23
+17201,15589589.0,Lu,739.0,France,Male,40.0,8.0,0.0,1.0,0.0,1.0,151733.43
+17202,15576774.0,Hsing,751.0,France,Female,26.0,1.0,0.0,1.0,1.0,1.0,176713.47
+17203,15617401.0,Maclean,729.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,122920.69
+17204,15788218.0,Chiazagomekpere,598.0,Spain,Male,49.0,6.0,0.0,3.0,1.0,1.0,172495.81
+17205,15774179.0,De Salis,627.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,123614.01
+17206,15757952.0,Mironova,586.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,158045.49
+17207,15641366.0,Martin,776.0,Germany,Male,24.0,3.0,129743.75,1.0,1.0,1.0,168053.4
+17208,15593470.0,Chukwualuka,732.0,Spain,Male,33.0,3.0,102016.22,2.0,1.0,0.0,76621.49
+17209,15665956.0,Fomin,653.0,France,Male,30.0,3.0,0.0,2.0,0.0,0.0,144584.29
+17210,15774931.0,Mironova,633.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,0.0,103603.65
+17211,15705735.0,Chidozie,787.0,France,Female,24.0,2.0,0.0,2.0,1.0,1.0,168085.97
+17212,15716994.0,Toscani,688.0,Germany,Male,43.0,2.0,86911.8,2.0,1.0,1.0,120512.08
+17213,15740494.0,Yobachi,712.0,France,Female,33.0,3.0,0.0,1.0,1.0,1.0,159823.16
+17214,15777046.0,T'ien,624.0,France,Female,44.0,2.0,0.0,2.0,1.0,0.0,128882.66
+17215,15685564.0,Chukwudi,727.0,Germany,Female,55.0,3.0,113599.74,1.0,1.0,0.0,189558.44
+17216,15616555.0,Chukwujamuike,682.0,Spain,Male,31.0,0.0,0.0,2.0,1.0,0.0,172450.04
+17217,15620268.0,McLean,649.0,Spain,Male,41.0,9.0,0.0,1.0,0.0,0.0,82438.13
+17218,15617252.0,Chukwudi,581.0,Spain,Female,34.0,9.0,0.0,1.0,1.0,0.0,115287.99
+17219,15578878.0,Palerma,749.0,Spain,Male,39.0,2.0,144432.04,2.0,1.0,1.0,78896.59
+17220,15585738.0,Nwabugwu,628.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,173870.39
+17221,15706815.0,Yobachi,613.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,61995.57
+17222,15665416.0,Hs?,667.0,France,Male,53.0,10.0,0.0,2.0,0.0,1.0,19233.41
+17223,15799350.0,T'ien,668.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,143960.72
+17224,15776596.0,Chuang,745.0,France,Male,36.0,5.0,0.0,1.0,1.0,1.0,167636.15
+17225,15667934.0,H?,719.0,France,Female,36.0,1.0,66483.32,3.0,0.0,0.0,1375.0
+17226,15627854.0,Groom,658.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,149184.15
+17227,15707042.0,Ozioma,714.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,142935.83
+17228,15622494.0,Mancini,668.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,168034.9
+17229,15689351.0,Chukwualuka,749.0,Germany,Female,39.0,4.0,121152.05,1.0,1.0,0.0,166957.82
+17230,15721582.0,Ts'ai,714.0,France,Male,34.0,3.0,157531.82,1.0,1.0,1.0,146752.67
+17231,15724443.0,Lettiere,667.0,Germany,Male,27.0,1.0,160777.9,2.0,0.0,1.0,177099.71
+17232,15745455.0,Maclean,645.0,Spain,Male,31.0,6.0,0.0,2.0,0.0,0.0,89893.6
+17233,15700325.0,Nwoye,721.0,France,Female,32.0,1.0,101238.24,1.0,1.0,1.0,168002.31
+17234,15672930.0,Brazenor,477.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,131043.2
+17235,15761141.0,Mazzanti,616.0,France,Male,36.0,8.0,141665.63,1.0,1.0,1.0,88878.15
+17236,15765461.0,Nucci,678.0,France,Male,24.0,0.0,0.0,2.0,0.0,0.0,70783.61
+17237,15761717.0,Ch'ien,677.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,130862.11
+17238,15674899.0,Chukwumaobim,699.0,France,Female,45.0,5.0,73494.21,1.0,1.0,1.0,91573.19
+17239,15803941.0,Hsiung,637.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,0.0,90189.72
+17240,15798024.0,Bischof,599.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,1.0,102280.29
+17241,15730590.0,Onwuamaeze,686.0,France,Female,33.0,2.0,0.0,2.0,0.0,1.0,80194.83
+17242,15616365.0,Clements,584.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,176750.81
+17243,15783338.0,Mazzanti,646.0,France,Female,31.0,5.0,121685.31,1.0,1.0,1.0,111285.85
+17244,15764491.0,Chidimma,755.0,Spain,Male,43.0,4.0,0.0,1.0,1.0,0.0,176713.47
+17245,15581893.0,McLean,651.0,France,Female,23.0,2.0,153040.62,2.0,1.0,1.0,38190.78
+17246,15697948.0,Ngozichukwuka,738.0,Spain,Female,26.0,8.0,0.0,2.0,1.0,0.0,142730.27
+17247,15766355.0,Ndubueze,653.0,France,Male,30.0,5.0,0.0,2.0,1.0,0.0,168184.62
+17248,15682778.0,Y?,706.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,109742.8
+17249,15658696.0,Henty,681.0,France,Male,41.0,7.0,0.0,2.0,0.0,0.0,58440.41
+17250,15631838.0,Chinedum,595.0,France,Male,45.0,6.0,0.0,1.0,0.0,0.0,152845.99
+17251,15608845.0,Hsia,715.0,Spain,Female,47.0,9.0,122552.34,1.0,0.0,1.0,148564.76
+17252,15649101.0,Onyekachi,663.0,France,Female,35.0,5.0,153855.52,1.0,1.0,0.0,162699.7
+17253,15754952.0,Okwuadigbo,672.0,Spain,Female,30.0,8.0,122917.71,1.0,1.0,0.0,117832.88
+17254,15581229.0,Kosisochukwu,627.0,Germany,Male,32.0,5.0,127847.86,1.0,0.0,1.0,74836.64
+17255,15600478.0,Tsou,709.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,123825.83
+17256,15591036.0,Okwudilichukwu,684.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,1.0,142963.28
+17257,15700460.0,Chien,710.0,Germany,Female,49.0,5.0,108815.56,1.0,1.0,0.0,152882.79
+17258,15576723.0,Hsia,636.0,Spain,Female,47.0,5.0,0.0,2.0,1.0,0.0,152992.04
+17259,15603883.0,Mazzanti,732.0,Spain,Female,46.0,8.0,0.0,1.0,1.0,0.0,184980.23
+17260,15653776.0,Lombardi,693.0,Germany,Male,39.0,10.0,131468.44,2.0,1.0,0.0,171657.57
+17261,15649060.0,Pai,468.0,France,Female,29.0,3.0,94486.47,1.0,0.0,1.0,63908.86
+17262,15624703.0,Onyemauchechukwu,592.0,France,Female,36.0,3.0,127552.85,1.0,1.0,0.0,125953.83
+17263,15671334.0,Chidiegwu,743.0,France,Male,39.0,3.0,0.0,2.0,0.0,1.0,60420.07
+17264,15758056.0,H?,539.0,France,Female,41.0,9.0,141540.63,1.0,0.0,0.0,137985.69
+17265,15809800.0,Toscani,622.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,106655.88
+17266,15701555.0,Chiazagomekpere,553.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,74846.67
+17267,15782404.0,Nwankwo,756.0,France,Male,31.0,9.0,175575.95,1.0,0.0,0.0,176779.46
+17268,15654067.0,Nnaife,473.0,France,Female,55.0,9.0,0.0,2.0,1.0,1.0,137028.51
+17269,15798960.0,Tseng,625.0,Spain,Female,32.0,1.0,0.0,1.0,1.0,0.0,139844.04
+17270,15573971.0,Obidimkpa,662.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,92320.37
+17271,15716134.0,Onwughara,667.0,France,Male,28.0,5.0,0.0,1.0,0.0,1.0,146680.25
+17272,15653547.0,Ch'eng,643.0,Spain,Male,42.0,6.0,0.0,1.0,0.0,1.0,132473.17
+17273,15627365.0,Lucciano,579.0,France,Female,48.0,9.0,0.0,1.0,1.0,0.0,86622.56
+17274,15738588.0,Nnonso,743.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,0.0,167984.61
+17275,15646566.0,Ch'in,773.0,France,Female,44.0,2.0,0.0,2.0,0.0,0.0,49826.68
+17276,15662098.0,Onyeoruru,716.0,Germany,Male,35.0,3.0,107716.35,1.0,1.0,1.0,105887.04
+17277,15734044.0,Ch'eng,705.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,122433.09
+17278,15780409.0,P'eng,667.0,France,Male,37.0,10.0,126534.9,2.0,1.0,0.0,145536.32
+17279,15640161.0,Tsou,652.0,France,Female,47.0,3.0,0.0,1.0,1.0,0.0,186775.25
+17280,15577359.0,Chia,569.0,France,Female,37.0,1.0,0.0,2.0,0.0,0.0,67740.08
+17281,15592761.0,Ts'ui,619.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,130189.53
+17282,15569590.0,T'ien,773.0,France,Male,36.0,4.0,134571.5,1.0,1.0,0.0,150548.5
+17283,15676521.0,Tuan,603.0,Spain,Female,43.0,2.0,0.0,1.0,0.0,0.0,154767.34
+17284,15720583.0,Chinwendu,732.0,France,Female,46.0,4.0,0.0,2.0,1.0,1.0,186976.6
+17285,15607746.0,Iweobiegbunam,719.0,Spain,Male,33.0,3.0,153861.1,1.0,1.0,1.0,26318.73
+17286,15656865.0,K'ung,543.0,France,Male,37.0,6.0,146623.36,1.0,1.0,1.0,158531.01
+17287,15743819.0,Smith,584.0,France,Female,45.0,9.0,0.0,1.0,1.0,1.0,181429.87
+17288,15631739.0,Ch'ang,679.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,75562.47
+17289,15594827.0,Yeh,749.0,Spain,Male,31.0,5.0,0.0,1.0,1.0,0.0,172063.09
+17290,15572657.0,Pipes,732.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,152855.96
+17291,15572767.0,Chijindum,769.0,France,Male,36.0,8.0,0.0,2.0,0.0,1.0,141533.19
+17292,15729110.0,Tung,665.0,France,Female,34.0,5.0,0.0,1.0,1.0,0.0,149814.68
+17293,15641389.0,Ma,516.0,France,Female,43.0,6.0,0.0,1.0,1.0,1.0,141756.32
+17294,15778025.0,Chijioke,716.0,Germany,Female,41.0,7.0,121542.26,1.0,0.0,0.0,114442.66
+17295,15770474.0,Chukwufumnanya,807.0,France,Female,27.0,2.0,137555.66,1.0,1.0,1.0,124487.28
+17296,15672966.0,K'ung,713.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,160968.44
+17297,15709920.0,Genovesi,721.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,0.0,173495.77
+17298,15745080.0,De Salis,681.0,Germany,Male,34.0,7.0,102800.65,2.0,0.0,0.0,92840.67
+17299,15595036.0,McElhone,703.0,Germany,Female,32.0,7.0,125712.2,1.0,1.0,1.0,157549.6
+17300,15731117.0,Ijendu,729.0,France,Male,42.0,4.0,0.0,2.0,0.0,1.0,121866.87
+17301,15658716.0,Nwokezuike,546.0,France,Female,57.0,9.0,0.0,1.0,1.0,1.0,185547.71
+17302,15569423.0,Cattaneo,598.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,161479.19
+17303,15779947.0,Udinesi,691.0,France,Male,41.0,1.0,132759.9,1.0,0.0,1.0,123969.64
+17304,15616172.0,Ch'in,744.0,France,Male,35.0,4.0,0.0,1.0,0.0,1.0,137794.63
+17305,15763515.0,Ch'in,733.0,France,Female,28.0,5.0,0.0,2.0,0.0,1.0,82438.13
+17306,15603035.0,Liao,553.0,Germany,Male,33.0,6.0,106570.37,1.0,1.0,0.0,173899.42
+17307,15650476.0,Fiorentino,667.0,France,Male,32.0,8.0,88980.32,1.0,1.0,0.0,74415.2
+17308,15588928.0,Ponomarev,673.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,1.0,140650.19
+17309,15711572.0,Lo Duca,663.0,Germany,Female,50.0,0.0,133866.22,1.0,1.0,0.0,26578.69
+17310,15779581.0,Efremov,682.0,Spain,Male,40.0,6.0,72012.76,1.0,1.0,0.0,161479.19
+17311,15681671.0,Ch'iu,663.0,France,Male,33.0,2.0,0.0,1.0,1.0,0.0,151822.66
+17312,15609913.0,Chiazagomekpere,686.0,Spain,Male,50.0,2.0,0.0,1.0,1.0,0.0,128521.17
+17313,15802088.0,Maclean,663.0,Germany,Female,62.0,4.0,92895.56,2.0,1.0,0.0,187616.16
+17314,15620981.0,Udinesi,805.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,122899.01
+17315,15679299.0,Ts'ao,627.0,Spain,Female,46.0,9.0,151963.26,2.0,1.0,0.0,77830.06
+17316,15714493.0,Genovese,571.0,France,Male,37.0,5.0,0.0,1.0,1.0,1.0,143913.32
+17317,15641822.0,Walker,653.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,82463.69
+17318,15662397.0,Uwaezuoke,703.0,Germany,Male,27.0,7.0,114590.43,2.0,1.0,0.0,175823.2
+17319,15588923.0,Brownless,754.0,France,Female,36.0,1.0,0.0,2.0,0.0,0.0,143681.83
+17320,15593365.0,Mazzanti,632.0,Germany,Female,45.0,9.0,118897.1,3.0,1.0,1.0,92748.16
+17321,15642824.0,Trevisani,687.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,153920.28
+17322,15791321.0,K'ung,578.0,France,Male,31.0,9.0,142872.36,1.0,1.0,0.0,184564.67
+17323,15669922.0,Onyekachi,636.0,France,Female,59.0,2.0,0.0,1.0,1.0,0.0,160968.44
+17324,15791045.0,Y?an,646.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,145562.4
+17325,15776985.0,Onyemauchechukwu,718.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,180344.69
+17326,15778025.0,Lucchesi,625.0,France,Female,29.0,10.0,150635.3,1.0,1.0,0.0,131768.98
+17327,15572291.0,Yuryeva,710.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,162812.16
+17328,15590810.0,Nwabugwu,638.0,Germany,Male,41.0,9.0,83727.68,1.0,0.0,0.0,103570.8
+17329,15753861.0,Walker,594.0,France,Male,31.0,9.0,0.0,2.0,0.0,0.0,134483.08
+17330,15769169.0,Lucchesi,644.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,161479.19
+17331,15645045.0,Tsou,591.0,Spain,Male,58.0,2.0,0.0,2.0,1.0,1.0,166677.35
+17332,15682912.0,Li,624.0,France,Female,30.0,3.0,0.0,1.0,0.0,1.0,148516.79
+17333,15779985.0,Dickson,652.0,Germany,Female,52.0,5.0,146547.78,2.0,1.0,1.0,8991.17
+17334,15572657.0,Cremonesi,719.0,France,Female,41.0,5.0,91328.71,1.0,1.0,0.0,53584.03
+17335,15703682.0,Chiu,697.0,Germany,Male,32.0,8.0,121241.57,1.0,0.0,1.0,177025.79
+17336,15603665.0,Chibugo,638.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,53477.76
+17337,15777893.0,Yermakov,646.0,France,Male,40.0,6.0,127120.62,1.0,0.0,0.0,121061.89
+17338,15721740.0,Yobanna,733.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,157871.55
+17339,15782418.0,Chidozie,638.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,0.0,149991.32
+17340,15769412.0,Pirozzi,677.0,France,Female,45.0,7.0,0.0,2.0,1.0,1.0,171463.83
+17341,15682070.0,Tuan,706.0,France,Female,29.0,3.0,0.0,1.0,1.0,1.0,55893.37
+17342,15581548.0,Tuan,609.0,Spain,Female,48.0,1.0,110148.56,1.0,1.0,1.0,81652.02
+17343,15698716.0,Efremova,597.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,161051.75
+17344,15704763.0,O'Brien,703.0,Germany,Male,52.0,9.0,111257.59,1.0,1.0,0.0,164040.94
+17345,15591628.0,Tsai,683.0,France,Female,33.0,9.0,0.0,1.0,0.0,1.0,129975.54
+17346,15665879.0,Maclean,535.0,France,Male,34.0,9.0,0.0,1.0,0.0,1.0,183656.12
+17347,15650538.0,Hsia,639.0,France,Male,49.0,0.0,0.0,1.0,0.0,0.0,161579.85
+17348,15799128.0,T'ien,718.0,France,Male,43.0,9.0,0.0,1.0,1.0,0.0,97916.49
+17349,15795586.0,Maconochie,690.0,Spain,Male,24.0,0.0,0.0,2.0,1.0,0.0,102260.16
+17350,15602851.0,Brient,591.0,France,Female,37.0,1.0,0.0,2.0,0.0,1.0,145936.28
+17351,15667932.0,De Luca,680.0,France,Female,35.0,7.0,136525.99,1.0,0.0,1.0,123583.85
+17352,15703793.0,Hsia,707.0,Spain,Female,34.0,3.0,122917.71,1.0,0.0,1.0,172097.4
+17353,15788151.0,Lai,758.0,Spain,Male,36.0,6.0,99773.85,2.0,1.0,1.0,161809.95
+17354,15570060.0,Chukwuemeka,607.0,France,Female,50.0,3.0,0.0,2.0,1.0,1.0,100101.06
+17355,15673820.0,Omeokachie,715.0,France,Female,38.0,4.0,0.0,1.0,0.0,1.0,142838.64
+17356,15735782.0,Ulyanova,691.0,Spain,Male,47.0,2.0,0.0,1.0,1.0,0.0,170886.01
+17357,15629985.0,Chiebuka,632.0,Spain,Female,38.0,8.0,0.0,2.0,0.0,0.0,153012.13
+17358,15680643.0,T'ien,678.0,France,Female,28.0,4.0,0.0,2.0,1.0,1.0,131301.74
+17359,15776733.0,Maclean,667.0,Spain,Male,41.0,8.0,128551.36,1.0,1.0,1.0,87412.24
+17360,15681075.0,Tien,592.0,Spain,Male,50.0,5.0,0.0,2.0,1.0,1.0,184842.08
+17361,15624592.0,Maclean,634.0,France,Female,41.0,2.0,0.0,2.0,0.0,0.0,175399.14
+17362,15624397.0,Fyodorov,648.0,France,Female,40.0,10.0,162702.35,1.0,1.0,1.0,138490.03
+17363,15615020.0,K'ung,656.0,France,Female,51.0,6.0,0.0,3.0,1.0,0.0,114448.77
+17364,15672437.0,Chiagoziem,651.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,141210.5
+17365,15750055.0,Niu,633.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,183569.25
+17366,15783378.0,Okechukwu,447.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,121365.45
+17367,15782939.0,Uspenskaya,516.0,France,Female,29.0,0.0,0.0,2.0,1.0,0.0,151336.96
+17368,15603481.0,Onyekachi,624.0,France,Female,38.0,5.0,0.0,2.0,0.0,0.0,161428.25
+17369,15662532.0,Folliero,663.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,75578.67
+17370,15788676.0,Liao,758.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,124862.57
+17371,15732438.0,McIntyre,607.0,France,Female,71.0,9.0,0.0,1.0,1.0,0.0,182738.0
+17372,15764654.0,Y?an,708.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,136521.82
+17373,15673837.0,Scott,598.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,68815.05
+17374,15791767.0,Pirozzi,718.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,1.0,141804.86
+17375,15674727.0,Brown,565.0,France,Male,31.0,6.0,121161.57,1.0,1.0,1.0,121904.14
+17376,15728605.0,Chidubem,774.0,Germany,Male,57.0,4.0,112079.58,1.0,0.0,1.0,82662.73
+17377,15581840.0,Mao,528.0,France,Female,40.0,9.0,0.0,1.0,0.0,0.0,77830.36
+17378,15599433.0,Onuoha,609.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,105986.01
+17379,15729224.0,Chukwujekwu,804.0,France,Female,55.0,2.0,0.0,1.0,0.0,0.0,49480.66
+17380,15651713.0,Burdekin,607.0,Spain,Female,28.0,6.0,0.0,2.0,1.0,0.0,102908.91
+17381,15643396.0,Tsai,687.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,63889.1
+17382,15757867.0,Brown,588.0,Spain,Male,37.0,2.0,0.0,2.0,0.0,0.0,82709.8
+17383,15591607.0,Nkemjika,578.0,France,Male,28.0,6.0,96926.12,1.0,1.0,1.0,89520.75
+17384,15707025.0,Ch'iu,651.0,France,Male,33.0,0.0,0.0,2.0,1.0,0.0,129892.93
+17385,15698932.0,O'Toole,721.0,Germany,Female,46.0,3.0,129855.32,2.0,0.0,0.0,71811.9
+17386,15772412.0,Palermo,571.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,127528.84
+17387,15693764.0,Ch'iu,556.0,France,Female,35.0,2.0,0.0,1.0,1.0,0.0,196257.67
+17388,15645355.0,Ch'ien,757.0,Germany,Female,38.0,6.0,84006.88,1.0,1.0,1.0,183584.14
+17389,15715079.0,Zaytseva,629.0,Spain,Male,32.0,8.0,0.0,2.0,0.0,0.0,167984.72
+17390,15591489.0,Chukwudi,685.0,Spain,Male,42.0,4.0,0.0,3.0,1.0,0.0,78650.95
+17391,15794323.0,Eluemuno,585.0,France,Male,46.0,9.0,0.0,1.0,1.0,0.0,82463.69
+17392,15571928.0,Trevisano,607.0,Spain,Male,34.0,8.0,0.0,1.0,1.0,0.0,169915.02
+17393,15566494.0,Hsia,622.0,Spain,Female,27.0,5.0,0.0,1.0,1.0,0.0,122762.95
+17394,15698819.0,Chiang,705.0,France,Male,45.0,5.0,0.0,1.0,1.0,0.0,184839.7
+17395,15773591.0,Onyemauchechi,547.0,France,Male,35.0,9.0,0.0,1.0,1.0,0.0,65816.53
+17396,15747772.0,Ch'ang,762.0,France,Female,53.0,9.0,0.0,2.0,1.0,1.0,176902.83
+17397,15803689.0,Chiu,661.0,France,Male,47.0,8.0,0.0,2.0,0.0,1.0,63795.7
+17398,15801942.0,P'eng,646.0,Germany,Male,52.0,7.0,130142.79,1.0,1.0,0.0,52341.15
+17399,15644962.0,Calabrese,572.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,103382.79
+17400,15748597.0,Hsu,602.0,France,Female,34.0,0.0,142644.64,1.0,1.0,0.0,126554.96
+17401,15642635.0,Ts'ui,619.0,France,Male,25.0,6.0,0.0,2.0,1.0,0.0,181208.47
+17402,15760294.0,Nwankwo,705.0,Spain,Female,40.0,8.0,0.0,2.0,1.0,1.0,85890.75
+17403,15676521.0,H?,549.0,Germany,Female,43.0,7.0,137655.24,1.0,0.0,1.0,53440.43
+17404,15579892.0,Maclean,755.0,France,Female,39.0,5.0,0.0,2.0,1.0,0.0,117060.2
+17405,15761023.0,H?,573.0,France,Female,43.0,2.0,0.0,1.0,0.0,1.0,178268.36
+17406,15762762.0,Ch'in,717.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,102397.22
+17407,15720353.0,Nwankwo,663.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,140763.07
+17408,15722409.0,Okwuadigbo,699.0,Spain,Female,33.0,7.0,0.0,1.0,1.0,0.0,138051.19
+17409,15810563.0,Chikwendu,594.0,Germany,Male,26.0,2.0,125921.37,2.0,1.0,0.0,25650.04
+17410,15716449.0,Pinto,547.0,France,Male,34.0,10.0,126773.43,1.0,1.0,1.0,121497.61
+17411,15762391.0,Maclean,582.0,Spain,Male,30.0,3.0,0.0,2.0,1.0,1.0,71323.23
+17412,15641091.0,Ch'eng,648.0,France,Female,38.0,0.0,81589.04,1.0,1.0,0.0,141946.92
+17413,15614220.0,Onyeorulu,632.0,France,Female,42.0,5.0,0.0,2.0,1.0,1.0,173843.21
+17414,15631927.0,Dickson,529.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,164038.07
+17415,15707289.0,Frolov,663.0,Spain,Female,35.0,7.0,94464.81,1.0,0.0,0.0,166031.07
+17416,15764170.0,Ch'ang,633.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,173870.39
+17417,15723320.0,Ndubueze,572.0,France,Male,32.0,7.0,104960.46,1.0,1.0,0.0,163711.01
+17418,15651460.0,Hsing,637.0,Germany,Male,46.0,1.0,125437.86,1.0,0.0,1.0,82970.69
+17419,15802570.0,Tien,605.0,Germany,Female,35.0,6.0,128927.93,1.0,1.0,1.0,131644.41
+17420,15707974.0,Onyemauchechukwu,668.0,France,Female,34.0,9.0,0.0,2.0,0.0,1.0,142223.94
+17421,15664720.0,Fiorentini,597.0,France,Female,50.0,8.0,106328.08,1.0,1.0,0.0,51959.82
+17422,15679963.0,Chukwueloka,627.0,France,Male,35.0,4.0,121250.04,1.0,1.0,1.0,138399.67
+17423,15628992.0,Tien,602.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,175082.2
+17424,15689953.0,Nkemdirim,562.0,France,Female,41.0,1.0,142550.28,1.0,1.0,0.0,80552.36
+17425,15761158.0,Loyau,586.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,135034.43
+17426,15748068.0,Pisani,648.0,France,Female,32.0,1.0,0.0,1.0,0.0,1.0,90189.72
+17427,15765806.0,Tretiakova,645.0,France,Male,31.0,2.0,0.0,2.0,0.0,0.0,171575.54
+17428,15593954.0,Chigolum,556.0,France,Male,36.0,9.0,122034.4,2.0,0.0,1.0,121537.19
+17429,15632050.0,Ugochukwu,636.0,France,Female,35.0,7.0,110942.9,1.0,0.0,0.0,143598.7
+17430,15774872.0,Ch'ien,713.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,151871.84
+17431,15762268.0,Chiu,687.0,France,Female,34.0,10.0,113829.45,1.0,1.0,0.0,55832.6
+17432,15726313.0,Ch'iu,542.0,France,Male,34.0,3.0,0.0,1.0,1.0,1.0,26687.21
+17433,15653547.0,Ch'eng,685.0,Spain,Female,44.0,3.0,0.0,3.0,1.0,0.0,120892.96
+17434,15654574.0,Tuan,670.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,7861.68
+17435,15794413.0,Hsueh,613.0,France,Male,30.0,6.0,114005.94,1.0,1.0,1.0,126469.09
+17436,15571940.0,Omeokachie,744.0,Spain,Female,45.0,4.0,0.0,1.0,1.0,0.0,121451.21
+17437,15683363.0,Nkemakolam,651.0,France,Female,44.0,7.0,0.0,1.0,1.0,0.0,79792.3
+17438,15793723.0,Ch'ien,611.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,1.0,135077.01
+17439,15809100.0,Yobachukwu,663.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,146680.28
+17440,15690743.0,Trevisano,611.0,Spain,Male,36.0,2.0,120911.75,1.0,1.0,0.0,119377.36
+17441,15662758.0,Brierly,716.0,Spain,Male,22.0,4.0,0.0,2.0,1.0,0.0,172450.02
+17442,15606097.0,Chienezie,749.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,1.0,129984.2
+17443,15676921.0,Ch'ang,644.0,Spain,Male,50.0,6.0,0.0,2.0,1.0,1.0,142223.94
+17444,15744517.0,Onio,628.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,81723.8
+17445,15668894.0,Nwankwo,769.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,184514.01
+17446,15656627.0,Fiorentino,543.0,Germany,Female,49.0,5.0,131519.49,1.0,1.0,0.0,179540.73
+17447,15672915.0,Mazzi,663.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,136497.28
+17448,15782210.0,Ibeamaka,651.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,1.0,102640.53
+17449,15690164.0,Ndukaku,656.0,France,Male,49.0,6.0,132739.8,1.0,1.0,0.0,186489.99
+17450,15806447.0,Yermakova,628.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,0.0,69531.22
+17451,15721935.0,Nwankwo,679.0,Spain,Male,36.0,10.0,0.0,2.0,1.0,0.0,172049.84
+17452,15661290.0,K?,578.0,France,Female,39.0,6.0,0.0,2.0,0.0,0.0,107415.02
+17453,15681075.0,De Neeve,667.0,Spain,Male,42.0,5.0,0.0,2.0,0.0,1.0,120540.83
+17454,15614687.0,Hsia,678.0,Spain,Female,45.0,5.0,0.0,1.0,1.0,1.0,182059.28
+17455,15704763.0,Hsia,769.0,France,Female,50.0,8.0,0.0,2.0,1.0,1.0,137961.41
+17456,15632472.0,Li Fonti,521.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,142400.27
+17457,15711489.0,Palerma,706.0,Spain,Female,36.0,4.0,0.0,2.0,0.0,1.0,142963.29
+17458,15592999.0,Chinweuba,672.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,120115.5
+17459,15775042.0,Ting,703.0,Spain,Female,37.0,3.0,107467.56,1.0,1.0,0.0,113086.08
+17460,15640131.0,Shih,613.0,Germany,Female,31.0,0.0,108915.85,2.0,0.0,1.0,136985.08
+17461,15640507.0,Tseng,718.0,Germany,Male,44.0,7.0,110949.41,2.0,1.0,1.0,38308.34
+17462,15803764.0,Tretiakov,681.0,Spain,Female,45.0,6.0,0.0,1.0,1.0,1.0,134875.31
+17463,15704954.0,Pinto,757.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,136983.77
+17464,15695872.0,Eipper,751.0,Spain,Male,48.0,10.0,0.0,1.0,1.0,0.0,75578.67
+17465,15797960.0,Folliero,696.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,166650.32
+17466,15787835.0,Liao,642.0,Germany,Male,33.0,6.0,126426.22,2.0,1.0,1.0,128521.16
+17467,15672330.0,Onio,745.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,64872.33
+17468,15794142.0,Maclean,641.0,France,Male,33.0,10.0,0.0,2.0,1.0,0.0,199358.58
+17469,15593778.0,Walker,754.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,0.0,95551.22
+17470,15632079.0,Lucchese,593.0,France,Male,45.0,2.0,0.0,2.0,1.0,0.0,181605.85
+17471,15808831.0,Genovese,655.0,Germany,Female,44.0,3.0,138053.79,1.0,0.0,1.0,161407.48
+17472,15782231.0,Ch'ien,605.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,64595.25
+17473,15802869.0,Nnamutaezinwa,633.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,123866.28
+17474,15775295.0,Y?,679.0,France,Male,31.0,6.0,112949.71,1.0,1.0,1.0,120853.83
+17475,15576517.0,Ts'ui,599.0,France,Female,41.0,1.0,0.0,1.0,0.0,0.0,171345.02
+17476,15797733.0,Hsiung,593.0,France,Male,24.0,1.0,0.0,2.0,1.0,0.0,112256.57
+17477,15579969.0,Onyekachi,506.0,France,Male,31.0,4.0,137781.65,1.0,1.0,1.0,121151.1
+17478,15614687.0,Hs?,709.0,France,Female,36.0,5.0,0.0,2.0,0.0,1.0,75888.65
+17479,15732299.0,Hsieh,691.0,France,Male,42.0,8.0,99472.76,1.0,1.0,1.0,26920.43
+17480,15690670.0,Ch'iu,666.0,France,Male,32.0,4.0,0.0,2.0,0.0,0.0,136909.87
+17481,15751131.0,Ch'iu,714.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,51345.39
+17482,15635078.0,Bazarova,614.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,147074.67
+17483,15777784.0,Macleod,624.0,France,Female,35.0,3.0,0.0,1.0,1.0,0.0,184312.88
+17484,15707120.0,Chigozie,665.0,France,Female,34.0,8.0,125890.72,1.0,1.0,0.0,94460.97
+17485,15581552.0,Uchenna,648.0,France,Male,31.0,0.0,92887.13,2.0,1.0,1.0,92621.9
+17486,15602312.0,T'ien,592.0,Spain,Male,41.0,8.0,119817.35,1.0,1.0,0.0,166944.89
+17487,15725429.0,Yuryeva,578.0,Spain,Male,32.0,6.0,0.0,2.0,0.0,0.0,175894.24
+17488,15757559.0,Pagnotto,786.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,1.0,138862.98
+17489,15684314.0,Hsiung,706.0,Germany,Female,55.0,3.0,118469.38,1.0,1.0,0.0,55775.84
+17490,15742834.0,Yobachi,696.0,France,Female,67.0,5.0,0.0,1.0,1.0,1.0,134901.57
+17491,15626507.0,Chidubem,712.0,France,Male,28.0,2.0,93960.35,1.0,1.0,0.0,105837.38
+17492,15705298.0,Y?an,589.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,146663.38
+17493,15606901.0,White,677.0,France,Female,31.0,9.0,0.0,2.0,0.0,1.0,82522.61
+17494,15608916.0,Hs?eh,738.0,France,Male,32.0,5.0,0.0,2.0,0.0,1.0,67788.49
+17495,15642710.0,P'an,677.0,France,Male,71.0,2.0,0.0,2.0,1.0,1.0,85547.33
+17496,15579223.0,Chidimma,802.0,France,Female,38.0,2.0,0.0,2.0,0.0,1.0,121151.46
+17497,15585853.0,McEwan,609.0,France,Male,41.0,1.0,151218.98,1.0,0.0,0.0,158071.69
+17498,15618182.0,T'ien,640.0,France,Female,31.0,10.0,0.0,2.0,1.0,0.0,172524.51
+17499,15662533.0,Tuan,604.0,Spain,Female,40.0,2.0,0.0,1.0,0.0,0.0,52629.73
+17500,15589323.0,Palerma,641.0,Spain,Male,23.0,9.0,0.0,2.0,0.0,1.0,98640.15
+17501,15632256.0,Macleod,593.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,64516.8
+17502,15624995.0,P'eng,683.0,France,Male,45.0,0.0,97473.87,1.0,0.0,0.0,131043.9
+17503,15589715.0,Ts'ai,648.0,Spain,Female,36.0,4.0,0.0,2.0,0.0,0.0,172498.79
+17504,15652914.0,Fyodorova,841.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,180551.76
+17505,15632256.0,Chiu,648.0,France,Female,48.0,4.0,0.0,3.0,1.0,0.0,160129.99
+17506,15584785.0,Shao,585.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,177099.71
+17507,15643024.0,Bibi,717.0,France,Male,43.0,8.0,0.0,1.0,0.0,0.0,55829.25
+17508,15620341.0,Ch'ien,579.0,Germany,Female,46.0,5.0,98141.57,1.0,1.0,1.0,155870.82
+17509,15614137.0,Folliero,634.0,France,Male,35.0,8.0,0.0,2.0,0.0,1.0,135077.01
+17510,15626795.0,Chiganu,627.0,France,Female,42.0,9.0,0.0,1.0,0.0,1.0,79704.12
+17511,15783875.0,Hsieh,725.0,France,Male,38.0,3.0,0.0,1.0,1.0,1.0,38652.96
+17512,15762268.0,Dyer,805.0,Spain,Male,44.0,2.0,133800.98,1.0,1.0,1.0,80320.3
+17513,15682355.0,Walker,745.0,Germany,Female,37.0,5.0,134796.85,2.0,1.0,1.0,135853.62
+17514,15665062.0,Ting,506.0,Germany,Male,43.0,5.0,106603.74,1.0,1.0,0.0,89938.08
+17515,15791321.0,Onwuamaeze,692.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,166957.82
+17516,15661412.0,Milanesi,716.0,Spain,Male,48.0,3.0,0.0,2.0,1.0,1.0,129980.93
+17517,15686957.0,Chinweike,657.0,Germany,Female,58.0,7.0,102994.04,1.0,1.0,1.0,177397.49
+17518,15692671.0,Genovese,685.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,9085.0
+17519,15644882.0,Chijioke,763.0,Germany,Female,39.0,1.0,128663.9,1.0,1.0,0.0,143357.08
+17520,15747014.0,Ch'iu,681.0,France,Male,48.0,6.0,0.0,2.0,1.0,0.0,166677.54
+17521,15641480.0,Udinesi,704.0,Germany,Female,41.0,4.0,145747.67,1.0,1.0,0.0,147964.99
+17522,15642824.0,Pirozzi,651.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,110887.14
+17523,15609824.0,Walker,609.0,France,Female,33.0,8.0,135515.76,1.0,1.0,0.0,78940.59
+17524,15702541.0,Ts'ao,673.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,83419.15
+17525,15772573.0,Maclean,653.0,Germany,Female,33.0,4.0,139825.51,2.0,1.0,1.0,168816.22
+17526,15585855.0,T'an,705.0,France,Male,26.0,7.0,0.0,2.0,0.0,1.0,103832.58
+17527,15753955.0,Nwachinemelu,659.0,Spain,Male,40.0,10.0,0.0,2.0,1.0,0.0,122937.75
+17528,15694518.0,Hsueh,684.0,France,Male,35.0,7.0,0.0,2.0,0.0,1.0,172495.81
+17529,15792360.0,O'Brien,584.0,France,Female,55.0,0.0,0.0,1.0,1.0,0.0,130789.6
+17530,15602551.0,L?,794.0,France,Female,48.0,8.0,0.0,2.0,1.0,1.0,176925.69
+17531,15765322.0,Pagnotto,660.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,171681.86
+17532,15691808.0,Kuo,764.0,Germany,Female,58.0,1.0,148249.54,1.0,1.0,0.0,154483.54
+17533,15676835.0,Rizzo,545.0,France,Female,36.0,2.0,129855.63,2.0,1.0,0.0,76640.29
+17534,15764153.0,Onyekachi,588.0,France,Male,39.0,7.0,0.0,1.0,1.0,1.0,103560.98
+17535,15636731.0,Ts'ai,805.0,France,Male,40.0,9.0,0.0,2.0,0.0,1.0,155876.06
+17536,15783883.0,Ch'ang,744.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,138528.96
+17537,15691808.0,Nkemakolam,662.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,92708.1
+17538,15750487.0,Kenechukwu,721.0,Spain,Male,30.0,9.0,0.0,2.0,0.0,1.0,69767.14
+17539,15692977.0,Hsing,608.0,Germany,Male,32.0,8.0,161613.94,2.0,1.0,0.0,119374.74
+17540,15672374.0,Nwankwo,711.0,France,Male,49.0,1.0,130385.82,1.0,1.0,0.0,157871.55
+17541,15789413.0,Ibezimako,679.0,France,Female,43.0,3.0,93680.43,1.0,1.0,0.0,97936.38
+17542,15641835.0,Palerma,695.0,France,Female,32.0,10.0,0.0,2.0,0.0,0.0,166678.28
+17543,15606861.0,Luffman,703.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,96985.58
+17544,15723032.0,Fanucci,661.0,France,Male,25.0,8.0,0.0,2.0,1.0,1.0,180622.02
+17545,15604588.0,Hodge,564.0,Germany,Female,47.0,3.0,118628.4,1.0,1.0,0.0,170829.87
+17546,15585192.0,Chidiebere,637.0,Germany,Male,51.0,3.0,122265.22,2.0,1.0,1.0,59571.5
+17547,15676937.0,Chukwukadibia,595.0,France,Male,43.0,7.0,0.0,1.0,1.0,1.0,104769.96
+17548,15732774.0,T'ien,648.0,France,Female,39.0,9.0,131390.75,1.0,1.0,0.0,171706.66
+17549,15717995.0,O'Donnell,721.0,France,Female,43.0,3.0,0.0,1.0,0.0,1.0,175295.89
+17550,15725772.0,Hsia,759.0,France,Female,46.0,1.0,0.0,3.0,1.0,0.0,148564.76
+17551,15673372.0,Ch'ang,754.0,Germany,Female,31.0,6.0,142663.46,1.0,1.0,1.0,171681.86
+17552,15702801.0,Chukwukere,753.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,123866.43
+17553,15643359.0,Chiazagomekpere,644.0,France,Male,36.0,1.0,0.0,1.0,1.0,0.0,176984.34
+17554,15658169.0,Soto,648.0,Germany,Male,50.0,9.0,133598.4,1.0,1.0,0.0,171345.02
+17555,15637993.0,Onuora,576.0,Germany,Male,36.0,1.0,127545.75,2.0,1.0,0.0,88838.14
+17556,15672930.0,Lassetter,849.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,55893.37
+17557,15757541.0,Hsueh,614.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,159830.58
+17558,15657535.0,Nucci,675.0,France,Male,23.0,6.0,123981.31,1.0,1.0,0.0,46864.0
+17559,15632069.0,Watson,634.0,Germany,Female,28.0,7.0,152264.81,2.0,1.0,0.0,177065.24
+17560,15675450.0,Hsia,646.0,Spain,Male,44.0,7.0,0.0,2.0,1.0,0.0,91340.09
+17561,15758769.0,Liao,813.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,176713.47
+17562,15746664.0,Yuriev,677.0,Germany,Male,43.0,7.0,151233.62,2.0,0.0,0.0,93843.21
+17563,15697035.0,T'an,697.0,Germany,Male,40.0,1.0,122314.39,1.0,1.0,0.0,179655.87
+17564,15581840.0,Ch'iu,731.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,152845.99
+17565,15754926.0,Chinomso,496.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,0.0,86981.45
+17566,15793883.0,P'an,619.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,0.0,145558.42
+17567,15572547.0,Chiang,599.0,Germany,Female,31.0,8.0,125827.43,1.0,1.0,1.0,178722.24
+17568,15800554.0,Chidumaga,581.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,47251.79
+17569,15660147.0,Y?an,434.0,France,Male,39.0,2.0,118560.0,1.0,1.0,0.0,131501.72
+17570,15765283.0,Kao,646.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,61792.76
+17571,15793311.0,Yudina,641.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,0.0,111590.35
+17572,15604891.0,Hsing,850.0,Spain,Female,22.0,1.0,0.0,2.0,1.0,1.0,97939.4
+17573,15568573.0,T'ien,678.0,Germany,Male,35.0,4.0,135795.31,2.0,1.0,1.0,155711.64
+17574,15596863.0,Mancini,662.0,Germany,Male,33.0,6.0,90218.9,1.0,1.0,1.0,132973.17
+17575,15642093.0,Piazza,581.0,France,Male,43.0,7.0,0.0,1.0,1.0,0.0,151902.78
+17576,15672798.0,Toscani,687.0,France,Female,42.0,9.0,0.0,2.0,0.0,1.0,17095.49
+17577,15691817.0,Ch'iu,573.0,Germany,Male,38.0,4.0,98899.91,1.0,1.0,0.0,59645.24
+17578,15742442.0,Milanesi,581.0,France,Female,50.0,1.0,0.0,1.0,0.0,1.0,141796.39
+17579,15786035.0,Horsfall,593.0,Spain,Male,28.0,6.0,0.0,2.0,0.0,0.0,170195.4
+17580,15684516.0,Onyemachukwu,581.0,France,Female,43.0,9.0,165526.71,1.0,1.0,0.0,172576.43
+17581,15773971.0,Ngozichukwuka,615.0,Spain,Female,35.0,0.0,0.0,1.0,1.0,0.0,166513.49
+17582,15739997.0,Hsiao,648.0,France,Female,31.0,1.0,0.0,2.0,1.0,1.0,171840.24
+17583,15571577.0,Chukwuemeka,678.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,141827.33
+17584,15668767.0,Chikwado,616.0,Germany,Male,31.0,7.0,102025.32,2.0,1.0,1.0,198134.9
+17585,15783883.0,K'ung,556.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,1.0,161326.73
+17586,15806327.0,Ma,685.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,0.0,56562.57
+17587,15694450.0,Ch'iu,596.0,Germany,Male,31.0,10.0,96652.86,2.0,1.0,0.0,184474.15
+17588,15811588.0,Mazzi,648.0,France,Male,33.0,6.0,0.0,2.0,0.0,0.0,170950.58
+17589,15638646.0,Fanucci,709.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,79919.13
+17590,15805523.0,P'eng,651.0,Germany,Female,44.0,6.0,144855.34,2.0,1.0,1.0,94767.77
+17591,15815552.0,Ts'ui,757.0,France,Female,55.0,7.0,149297.18,1.0,1.0,1.0,138375.54
+17592,15738181.0,Teng,599.0,Germany,Male,35.0,3.0,79932.41,2.0,1.0,1.0,116663.47
+17593,15792726.0,Lu,716.0,Spain,Male,50.0,7.0,104944.1,1.0,0.0,1.0,145548.64
+17594,15568562.0,Onio,673.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,1.0,182076.97
+17595,15788045.0,Galkin,647.0,Germany,Male,26.0,3.0,148045.58,1.0,1.0,1.0,143668.44
+17596,15617078.0,Burt,538.0,France,Female,35.0,2.0,0.0,2.0,1.0,1.0,73464.88
+17597,15769504.0,Chibugo,709.0,France,Female,25.0,2.0,139529.93,1.0,1.0,0.0,72846.58
+17598,15808544.0,Mancini,692.0,Germany,Male,50.0,7.0,130142.79,2.0,1.0,1.0,179908.71
+17599,15738785.0,Kornilova,602.0,France,Female,40.0,3.0,167974.35,1.0,0.0,1.0,176580.46
+17600,15639882.0,Ts'ui,684.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,86786.41
+17601,15771750.0,Cavenagh,663.0,France,Male,45.0,5.0,129748.54,1.0,0.0,1.0,132873.23
+17602,15672692.0,Genovese,537.0,Germany,Male,43.0,4.0,54901.01,1.0,0.0,1.0,140763.18
+17603,15572390.0,Chia,718.0,France,Female,43.0,7.0,0.0,2.0,0.0,0.0,89566.64
+17604,15698324.0,Fomin,652.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,176913.42
+17605,15768600.0,Calabresi,645.0,France,Male,36.0,3.0,0.0,2.0,0.0,0.0,86413.11
+17606,15766289.0,Tuan,539.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,86044.98
+17607,15707042.0,Ugochukwu,565.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,1.0,142331.85
+17608,15686913.0,Fiorentini,559.0,Germany,Male,44.0,4.0,143054.56,2.0,1.0,1.0,70954.41
+17609,15628893.0,Chiedozie,632.0,France,Female,53.0,3.0,0.0,1.0,1.0,0.0,84908.2
+17610,15713379.0,Olisanugo,591.0,France,Female,35.0,3.0,0.0,2.0,0.0,1.0,139356.3
+17611,15646936.0,Chukwudi,667.0,France,Female,38.0,3.0,0.0,1.0,1.0,0.0,75884.65
+17612,15671934.0,Ts'ao,635.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,142548.33
+17613,15734438.0,P'eng,636.0,Spain,Male,42.0,3.0,135096.77,1.0,0.0,0.0,123954.15
+17614,15646513.0,Chinweuba,676.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,61994.2
+17615,15642063.0,Chuang,648.0,Germany,Female,35.0,0.0,146053.66,1.0,1.0,1.0,140941.47
+17616,15572390.0,Hsia,682.0,Germany,Male,35.0,10.0,132759.8,1.0,1.0,1.0,116977.89
+17617,15644878.0,Mancini,634.0,France,Male,34.0,5.0,0.0,1.0,0.0,1.0,152992.04
+17618,15721426.0,Zuyeva,577.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,187900.12
+17619,15807433.0,Udinesi,705.0,Germany,Male,56.0,1.0,104822.45,1.0,1.0,0.0,120801.65
+17620,15777772.0,Nnaife,729.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,57549.65
+17621,15762728.0,Shih,712.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,142377.69
+17622,15701392.0,Hsueh,691.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,59961.81
+17623,15692406.0,T'ao,674.0,France,Male,24.0,8.0,0.0,2.0,0.0,1.0,85547.33
+17624,15592765.0,Chukwuemeka,647.0,Germany,Male,33.0,3.0,152827.99,2.0,1.0,1.0,149970.59
+17625,15589449.0,Hsueh,581.0,Spain,Male,51.0,2.0,0.0,1.0,1.0,0.0,123437.05
+17626,15727361.0,Chidubem,850.0,France,Male,32.0,7.0,0.0,1.0,1.0,0.0,162515.17
+17627,15701354.0,Chidiegwu,479.0,Germany,Male,51.0,6.0,111827.27,1.0,1.0,0.0,161579.85
+17628,15771299.0,Hao,667.0,Spain,Male,39.0,4.0,0.0,2.0,1.0,0.0,145792.01
+17629,15687049.0,Chiagoziem,682.0,Spain,Female,52.0,4.0,0.0,2.0,0.0,1.0,48673.18
+17630,15644849.0,Macleod,653.0,France,Male,42.0,9.0,0.0,1.0,0.0,0.0,141230.07
+17631,15703778.0,Chu,850.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,131177.22
+17632,15769216.0,Tsao,584.0,France,Female,49.0,9.0,0.0,1.0,1.0,0.0,138700.94
+17633,15662294.0,Nwachukwu,645.0,France,Female,24.0,5.0,0.0,2.0,0.0,1.0,160523.33
+17634,15682639.0,Pisano,635.0,Spain,Male,47.0,5.0,135559.8,1.0,1.0,0.0,74849.48
+17635,15583680.0,T'ao,756.0,France,Male,36.0,5.0,147910.3,1.0,1.0,1.0,135925.72
+17636,15776985.0,L?,615.0,France,Female,44.0,3.0,0.0,2.0,1.0,1.0,143357.07
+17637,15677785.0,P'eng,676.0,Germany,Female,56.0,6.0,105961.68,1.0,1.0,0.0,172930.28
+17638,15597950.0,Shih,545.0,France,Male,58.0,4.0,131659.59,1.0,1.0,1.0,179565.57
+17639,15799720.0,Galkin,696.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,122964.18
+17640,15640161.0,Nucci,694.0,Germany,Male,38.0,2.0,83347.25,1.0,1.0,1.0,43956.83
+17641,15795519.0,Frolov,656.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,134901.58
+17642,15767781.0,Smith,718.0,Germany,Female,37.0,7.0,129755.99,2.0,1.0,0.0,175072.57
+17643,15685564.0,Fanucci,841.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,181879.56
+17644,15718773.0,Yermakova,683.0,France,Female,39.0,0.0,0.0,2.0,1.0,0.0,72861.94
+17645,15659568.0,Chukwumaobim,547.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,102513.69
+17646,15588535.0,Chiawuotu,679.0,Germany,Male,29.0,7.0,132057.78,1.0,1.0,0.0,175749.36
+17647,15774882.0,Mamelu,644.0,France,Male,41.0,1.0,0.0,1.0,1.0,0.0,174815.54
+17648,15793954.0,Blesing,763.0,France,Female,57.0,3.0,0.0,1.0,1.0,1.0,84863.85
+17649,15683544.0,Hsieh,669.0,France,Female,32.0,5.0,0.0,2.0,0.0,0.0,193995.37
+17650,15590320.0,Ch'ien,678.0,Spain,Male,28.0,4.0,0.0,2.0,1.0,0.0,95957.09
+17651,15635367.0,Milani,697.0,Germany,Male,31.0,2.0,81677.22,1.0,1.0,0.0,108010.78
+17652,15709256.0,Nkemakolam,647.0,Germany,Male,50.0,7.0,121215.9,3.0,1.0,0.0,80120.19
+17653,15802570.0,Lettiere,641.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,82870.75
+17654,15720203.0,Ch'iu,796.0,Spain,Male,39.0,9.0,0.0,1.0,0.0,1.0,87908.01
+17655,15778463.0,Nwankwo,714.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,107890.69
+17656,15783398.0,Macvitie,753.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,1.0,132870.15
+17657,15729377.0,Rizzo,678.0,Spain,Female,44.0,4.0,0.0,2.0,1.0,1.0,91957.39
+17658,15771535.0,Ch'en,643.0,Germany,Female,31.0,9.0,101012.77,2.0,1.0,1.0,61937.6
+17659,15583748.0,T'ang,758.0,Germany,Female,46.0,4.0,127837.54,1.0,0.0,0.0,179964.55
+17660,15793331.0,Toscani,543.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,126702.41
+17661,15805413.0,Wood,781.0,France,Male,39.0,9.0,124976.19,1.0,1.0,0.0,96832.82
+17662,15735339.0,Zikoranachidimma,597.0,France,Female,30.0,3.0,98955.84,1.0,1.0,0.0,166682.57
+17663,15592300.0,Ndubuisi,684.0,France,Male,42.0,2.0,127772.1,1.0,0.0,1.0,74854.97
+17664,15593969.0,Chuang,697.0,France,Male,37.0,5.0,0.0,1.0,0.0,1.0,81313.51
+17665,15708505.0,Chukwuemeka,714.0,France,Male,22.0,7.0,125782.89,1.0,1.0,0.0,149535.79
+17666,15711398.0,Chiu,587.0,France,Female,32.0,1.0,151421.44,2.0,0.0,0.0,109895.16
+17667,15682048.0,Ch'in,651.0,France,Male,37.0,2.0,0.0,1.0,1.0,1.0,173498.45
+17668,15624703.0,Efimov,597.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,161671.15
+17669,15591512.0,Smith,706.0,France,Female,34.0,3.0,81684.97,1.0,1.0,1.0,60905.51
+17670,15631868.0,Nnaife,597.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,127939.26
+17671,15582762.0,Chuang,684.0,France,Male,22.0,9.0,0.0,2.0,1.0,1.0,121866.87
+17672,15749489.0,Yudina,543.0,Germany,Female,33.0,9.0,151855.33,2.0,1.0,1.0,28226.15
+17673,15682533.0,Chiang,712.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,183515.85
+17674,15571261.0,Chiemenam,706.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,171457.06
+17675,15706810.0,Chukwujekwu,599.0,Spain,Female,29.0,7.0,57568.94,1.0,1.0,0.0,151335.24
+17676,15583074.0,Martin,644.0,Germany,Female,50.0,8.0,141947.67,2.0,1.0,0.0,134638.97
+17677,15654060.0,Hsia,697.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,115818.51
+17678,15796463.0,Milano,724.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,97939.3
+17679,15749964.0,Dickson,705.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,113481.02
+17680,15793331.0,Nnachetam,590.0,France,Female,36.0,10.0,0.0,2.0,0.0,1.0,90876.95
+17681,15582714.0,Huddart,651.0,France,Male,71.0,5.0,145081.73,1.0,1.0,1.0,161229.84
+17682,15745324.0,Zikoranachidimma,626.0,France,Female,44.0,0.0,89079.41,1.0,1.0,0.0,173495.77
+17683,15693637.0,Manna,676.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,95857.18
+17684,15683375.0,Chidubem,605.0,Germany,Female,50.0,2.0,129901.21,1.0,1.0,0.0,40315.27
+17685,15633608.0,Hsia,704.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,52615.62
+17686,15750258.0,Chigozie,599.0,Spain,Male,47.0,3.0,0.0,2.0,1.0,1.0,85421.48
+17687,15793723.0,Hsieh,793.0,Germany,Male,41.0,5.0,137964.01,2.0,1.0,1.0,105965.25
+17688,15750466.0,Y?,748.0,France,Male,38.0,1.0,0.0,2.0,0.0,0.0,133091.32
+17689,15589563.0,Teng,571.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,1.0,159800.16
+17690,15749472.0,Onwughara,678.0,France,Male,34.0,8.0,0.0,2.0,0.0,1.0,88958.49
+17691,15722479.0,Ch'eng,589.0,Spain,Male,33.0,8.0,0.0,2.0,0.0,0.0,122449.15
+17692,15583090.0,Ukaegbunam,595.0,Spain,Male,38.0,4.0,127307.32,1.0,0.0,0.0,88827.67
+17693,15746035.0,Fanucci,636.0,Spain,Female,31.0,4.0,0.0,1.0,1.0,0.0,173486.39
+17694,15758831.0,Onwuatuegwu,757.0,Spain,Female,34.0,9.0,0.0,2.0,0.0,1.0,81776.84
+17695,15779743.0,Chukwumaobim,468.0,France,Female,46.0,8.0,0.0,1.0,1.0,0.0,182336.45
+17696,15695846.0,Genovesi,643.0,France,Male,44.0,4.0,0.0,2.0,0.0,1.0,86937.2
+17697,15727041.0,Ma,713.0,France,Male,31.0,8.0,0.0,2.0,0.0,0.0,170514.21
+17698,15593501.0,Chuang,579.0,Germany,Female,38.0,3.0,141642.92,1.0,1.0,1.0,52462.25
+17699,15794056.0,Pirozzi,669.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,175082.2
+17700,15810978.0,Ch'ien,605.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,82938.75
+17701,15731790.0,Zetticci,599.0,Germany,Female,33.0,5.0,107879.04,2.0,1.0,1.0,143538.52
+17702,15643359.0,Manna,582.0,Spain,Female,28.0,9.0,141749.68,1.0,1.0,1.0,64831.36
+17703,15727059.0,Pirozzi,627.0,Germany,Male,48.0,9.0,133906.22,1.0,0.0,1.0,157098.28
+17704,15804009.0,Chiemenam,707.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,187866.21
+17705,15722404.0,Aksenov,696.0,France,Female,36.0,0.0,0.0,2.0,1.0,1.0,138051.19
+17706,15740283.0,P'eng,703.0,France,Female,50.0,1.0,61581.97,3.0,0.0,0.0,179435.21
+17707,15759133.0,Chukwueloka,663.0,Spain,Female,33.0,3.0,0.0,2.0,0.0,1.0,94499.06
+17708,15615140.0,Maclean,730.0,France,Male,34.0,8.0,109730.22,2.0,1.0,1.0,121210.09
+17709,15637523.0,Debellis,706.0,Spain,Male,27.0,1.0,0.0,2.0,1.0,0.0,121765.0
+17710,15785899.0,Yobanna,720.0,France,Female,30.0,9.0,0.0,2.0,1.0,1.0,60920.03
+17711,15794868.0,Lombardi,668.0,France,Male,24.0,5.0,132403.56,1.0,1.0,1.0,122084.32
+17712,15672397.0,Pisani,609.0,Spain,Male,42.0,9.0,0.0,2.0,0.0,0.0,115136.51
+17713,15747772.0,Zikoranachidimma,724.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,158610.89
+17714,15667632.0,Nnamutaezinwa,710.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,127022.87
+17715,15753888.0,Chia,613.0,Germany,Male,32.0,6.0,129895.19,2.0,1.0,0.0,81778.72
+17716,15588537.0,T'ien,598.0,Spain,Male,28.0,8.0,0.0,2.0,1.0,1.0,82967.97
+17717,15753840.0,H?,554.0,France,Male,30.0,1.0,0.0,1.0,1.0,0.0,137037.97
+17718,15803976.0,Frolova,537.0,France,Female,28.0,8.0,0.0,1.0,0.0,1.0,79082.62
+17719,15800326.0,Trejo,758.0,Spain,Female,34.0,1.0,0.0,1.0,1.0,0.0,99183.09
+17720,15797905.0,Nnachetam,598.0,Germany,Female,41.0,6.0,121235.05,1.0,1.0,0.0,56908.41
+17721,15728683.0,Chibueze,597.0,Spain,Male,39.0,0.0,0.0,1.0,1.0,0.0,84421.34
+17722,15585047.0,Ch'ien,773.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,176576.62
+17723,15698732.0,Ch'in,696.0,France,Female,31.0,6.0,0.0,2.0,0.0,1.0,159742.55
+17724,15789560.0,Smith,667.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,26215.85
+17725,15605037.0,Hsia,669.0,Spain,Male,50.0,10.0,0.0,1.0,1.0,0.0,75664.56
+17726,15611947.0,Milne,749.0,Germany,Male,45.0,9.0,96936.81,1.0,1.0,0.0,141180.66
+17727,15604314.0,Nkemakolam,754.0,France,Female,40.0,6.0,0.0,1.0,1.0,1.0,166677.53
+17728,15809837.0,Tung,649.0,France,Male,34.0,3.0,0.0,1.0,1.0,1.0,151871.84
+17729,15664802.0,Nwachinemelu,679.0,France,Male,39.0,5.0,131394.42,1.0,0.0,1.0,125062.02
+17730,15694192.0,Ts'ui,593.0,Spain,Male,42.0,6.0,0.0,1.0,1.0,0.0,79886.06
+17731,15803682.0,Chuang,537.0,Germany,Male,46.0,6.0,84438.57,2.0,1.0,0.0,56518.0
+17732,15746674.0,Nkemdirim,719.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,110421.31
+17733,15677317.0,Chukwuemeka,757.0,France,Female,36.0,8.0,120825.7,1.0,1.0,1.0,91947.74
+17734,15777904.0,Ts'ui,706.0,France,Female,43.0,8.0,113543.33,1.0,1.0,1.0,162719.69
+17735,15791326.0,Tsou,752.0,France,Female,39.0,8.0,0.0,1.0,1.0,0.0,174027.3
+17736,15665460.0,K'ung,661.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,57876.05
+17737,15765311.0,Chien,641.0,Germany,Male,39.0,9.0,78939.15,2.0,0.0,1.0,122933.73
+17738,15581388.0,Lombardi,645.0,Germany,Female,44.0,7.0,105845.26,2.0,0.0,0.0,149503.54
+17739,15593365.0,D'Albertis,724.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,71139.8
+17740,15785654.0,Chukwuhaenye,634.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,140745.91
+17741,15757559.0,Kao,648.0,France,Male,38.0,1.0,108895.93,2.0,1.0,1.0,158816.03
+17742,15800703.0,Nebechukwu,705.0,Spain,Male,31.0,3.0,0.0,1.0,1.0,1.0,121241.37
+17743,15643865.0,Maclean,681.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,113469.65
+17744,15642063.0,Chukwueloka,679.0,Germany,Female,32.0,1.0,143680.47,2.0,1.0,0.0,124795.84
+17745,15743582.0,Y?,714.0,France,Male,29.0,3.0,0.0,2.0,0.0,0.0,187905.12
+17746,15570947.0,Dore,611.0,France,Male,41.0,2.0,71497.79,1.0,1.0,1.0,52795.27
+17747,15672598.0,Uspenskaya,600.0,France,Female,40.0,5.0,174937.64,2.0,1.0,0.0,187597.98
+17748,15732967.0,Hsiung,717.0,Spain,Male,28.0,1.0,170890.05,1.0,1.0,0.0,5791.85
+17749,15582616.0,Tien,707.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,51695.41
+17750,15716609.0,Hs?eh,662.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,82870.75
+17751,15677908.0,Toth,543.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,35600.25
+17752,15723216.0,Chinonyelum,686.0,France,Male,36.0,8.0,129893.56,1.0,0.0,0.0,126837.72
+17753,15691387.0,P'an,672.0,Spain,Female,42.0,5.0,0.0,1.0,1.0,1.0,54474.48
+17754,15581840.0,Ritchie,714.0,Germany,Male,25.0,10.0,97560.16,1.0,1.0,1.0,126396.01
+17755,15766683.0,Y?,562.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,140763.18
+17756,15650591.0,Hsueh,679.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,0.0,182718.25
+17757,15623072.0,Oluchukwu,772.0,Germany,Male,44.0,4.0,107556.07,1.0,1.0,1.0,125445.65
+17758,15782941.0,P'an,613.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,44653.5
+17759,15643496.0,Manna,596.0,Germany,Male,39.0,8.0,113657.83,2.0,1.0,0.0,158973.85
+17760,15735270.0,Chuang,584.0,Spain,Female,39.0,9.0,0.0,1.0,1.0,1.0,52318.26
+17761,15693337.0,Chukwujamuike,652.0,France,Male,35.0,2.0,119948.09,1.0,1.0,0.0,645.61
+17762,15796496.0,P'eng,682.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,51703.09
+17763,15766185.0,Chinwendu,659.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,164064.44
+17764,15683625.0,P'eng,685.0,France,Female,35.0,3.0,118004.26,1.0,1.0,0.0,74322.75
+17765,15795540.0,Obialo,652.0,France,Female,50.0,7.0,162532.17,1.0,0.0,0.0,172524.51
+17766,15773876.0,Kaleski,732.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,98894.64
+17767,15797389.0,McIntyre,663.0,Germany,Female,48.0,9.0,142658.04,1.0,1.0,0.0,129964.94
+17768,15803457.0,L?,673.0,Germany,Female,45.0,2.0,126066.13,1.0,1.0,1.0,166317.71
+17769,15783883.0,Yeh,850.0,Germany,Female,63.0,9.0,126588.81,1.0,1.0,0.0,49656.24
+17770,15781530.0,Mazzanti,731.0,France,Female,27.0,6.0,0.0,2.0,0.0,0.0,28461.29
+17771,15726403.0,Ch'iu,669.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,99398.36
+17772,15701352.0,Ndubueze,621.0,Spain,Male,60.0,5.0,0.0,2.0,1.0,1.0,40339.9
+17773,15617476.0,Akabueze,671.0,Spain,Male,30.0,1.0,0.0,2.0,1.0,0.0,152740.16
+17774,15604891.0,Ugochukwutubelum,678.0,Spain,Male,40.0,9.0,0.0,1.0,1.0,1.0,93478.96
+17775,15782390.0,Lucchese,640.0,France,Male,43.0,8.0,0.0,1.0,1.0,0.0,96875.52
+17776,15724863.0,Uspenskaya,673.0,Germany,Female,41.0,9.0,114779.35,2.0,1.0,0.0,83419.15
+17777,15618661.0,Chidubem,611.0,France,Female,27.0,0.0,0.0,2.0,1.0,1.0,151871.84
+17778,15690164.0,Nwagugheuzo,716.0,France,Male,34.0,2.0,0.0,2.0,0.0,1.0,177772.03
+17779,15737173.0,Chizuoke,678.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,120892.96
+17780,15793331.0,Chizoba,652.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,53412.24
+17781,15642726.0,Hsu,673.0,Germany,Female,41.0,2.0,104488.17,1.0,1.0,0.0,124749.08
+17782,15753955.0,Tsai,633.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,117547.76
+17783,15707291.0,T'ien,577.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,53784.22
+17784,15709916.0,Martin,679.0,France,Male,33.0,7.0,101609.01,1.0,1.0,0.0,143665.44
+17785,15747677.0,T'ien,678.0,France,Female,28.0,8.0,0.0,2.0,1.0,1.0,167035.07
+17786,15763747.0,Chinweuba,554.0,France,Male,34.0,7.0,171913.87,1.0,1.0,0.0,199808.1
+17787,15709734.0,Yashina,835.0,Spain,Female,49.0,5.0,0.0,1.0,1.0,0.0,49337.84
+17788,15674573.0,Zikoranachidimma,568.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,38186.85
+17789,15605826.0,Fiorentini,715.0,Germany,Male,37.0,5.0,102895.1,2.0,1.0,0.0,120858.13
+17790,15749669.0,Kibble,769.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,180962.32
+17791,15587712.0,Hsieh,794.0,Germany,Male,37.0,5.0,95900.02,2.0,1.0,0.0,34948.76
+17792,15806822.0,Chiemenam,579.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,54448.41
+17793,15635141.0,Milanesi,730.0,France,Female,50.0,5.0,101506.66,1.0,1.0,1.0,170819.01
+17794,15659820.0,K?,595.0,Germany,Male,27.0,7.0,90560.3,2.0,1.0,0.0,116548.02
+17795,15641675.0,Chia,573.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,67979.35
+17796,15702480.0,K'ung,561.0,Spain,Male,29.0,2.0,125921.36,1.0,0.0,1.0,71173.64
+17797,15658969.0,H?,705.0,Spain,Female,54.0,1.0,137285.26,1.0,1.0,0.0,183963.34
+17798,15741652.0,Groom,576.0,France,Female,46.0,1.0,0.0,1.0,1.0,1.0,103712.05
+17799,15792463.0,Okwudilichukwu,677.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,181605.85
+17800,15788151.0,H?,631.0,Germany,Female,38.0,7.0,116933.89,2.0,1.0,1.0,124071.71
+17801,15621436.0,Chizoba,551.0,Spain,Female,37.0,2.0,115076.06,1.0,1.0,0.0,38962.54
+17802,15598084.0,Lombardo,834.0,Germany,Male,31.0,4.0,132576.25,2.0,1.0,1.0,136620.64
+17803,15763612.0,Folliero,629.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,138113.71
+17804,15572361.0,Tuan,554.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,75762.0
+17805,15689425.0,Chukwuhaenye,573.0,Germany,Female,29.0,4.0,132044.6,2.0,1.0,1.0,77864.41
+17806,15588517.0,Uspensky,606.0,Germany,Female,42.0,1.0,97792.21,1.0,1.0,0.0,95625.03
+17807,15597901.0,Li,556.0,France,Male,38.0,3.0,0.0,2.0,0.0,0.0,88890.75
+17808,15664547.0,Okechukwu,573.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,55775.84
+17809,15614215.0,Ch'eng,850.0,France,Female,58.0,1.0,0.0,1.0,1.0,1.0,130113.78
+17810,15772412.0,Hsieh,699.0,France,Male,28.0,7.0,0.0,2.0,0.0,0.0,102021.39
+17811,15631222.0,Chukwuemeka,752.0,Germany,Female,57.0,9.0,113657.83,3.0,1.0,0.0,72909.75
+17812,15594577.0,Iweobiegbunam,586.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,0.0,141478.06
+17813,15576623.0,Maclean,699.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,141856.81
+17814,15623082.0,Kao,537.0,France,Male,35.0,0.0,0.0,2.0,1.0,0.0,154071.27
+17815,15765952.0,Ugoji,583.0,Spain,Male,60.0,8.0,116536.43,2.0,0.0,1.0,28368.37
+17816,15643819.0,Ch'iu,608.0,Germany,Female,32.0,1.0,113873.45,1.0,0.0,1.0,72849.29
+17817,15795586.0,T'ang,670.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,184524.74
+17818,15804950.0,Hickey,697.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,113581.85
+17819,15696812.0,Dickson,752.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,138051.19
+17820,15661162.0,Fenton,651.0,Spain,Female,34.0,9.0,148666.99,1.0,1.0,0.0,64833.08
+17821,15750929.0,K?,603.0,France,Male,46.0,3.0,101609.01,1.0,1.0,1.0,173629.78
+17822,15681554.0,Pisani,711.0,France,Male,22.0,10.0,0.0,2.0,1.0,1.0,125870.5
+17823,15581295.0,Trevisani,703.0,France,Female,53.0,4.0,122359.5,2.0,1.0,0.0,114092.87
+17824,15589431.0,Chinagorom,743.0,France,Male,27.0,8.0,0.0,2.0,1.0,0.0,101319.76
+17825,15806901.0,Palerma,769.0,France,Female,29.0,3.0,123723.24,1.0,1.0,0.0,129964.94
+17826,15588928.0,H?,553.0,France,Male,31.0,1.0,156815.71,1.0,1.0,1.0,62674.11
+17827,15587584.0,Hsia,636.0,Germany,Female,34.0,8.0,121558.42,2.0,1.0,1.0,141896.74
+17828,15658057.0,Ch'iu,577.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,151581.79
+17829,15790744.0,H?,775.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,124071.71
+17830,15698528.0,Hsia,581.0,Spain,Female,33.0,8.0,0.0,1.0,1.0,0.0,1002.39
+17831,15681671.0,Calabresi,652.0,Spain,Female,42.0,10.0,0.0,2.0,0.0,0.0,48249.88
+17832,15733032.0,Cawthorne,724.0,France,Male,26.0,6.0,0.0,2.0,1.0,0.0,180002.42
+17833,15649024.0,Eluemuno,679.0,Spain,Female,61.0,7.0,0.0,1.0,1.0,1.0,78670.52
+17834,15791341.0,T'an,712.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,151739.67
+17835,15638269.0,Ch'ang,644.0,Germany,Male,45.0,2.0,140931.82,1.0,1.0,0.0,82782.08
+17836,15792649.0,Tsao,622.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,36405.21
+17837,15595588.0,Toscani,667.0,France,Female,42.0,5.0,148843.59,1.0,1.0,1.0,130553.47
+17838,15587562.0,Ozerova,704.0,Spain,Female,45.0,3.0,0.0,1.0,1.0,0.0,85890.75
+17839,15695097.0,Chiagoziem,644.0,Germany,Male,30.0,10.0,143637.52,1.0,0.0,0.0,76510.46
+17840,15609823.0,T'ien,648.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,185599.34
+17841,15782454.0,Manna,715.0,Germany,Female,31.0,5.0,163549.63,1.0,1.0,0.0,175894.24
+17842,15657779.0,P'eng,645.0,Germany,Male,32.0,1.0,133681.98,2.0,1.0,0.0,113581.85
+17843,15647838.0,Shih,713.0,Germany,Male,42.0,9.0,132351.29,2.0,1.0,1.0,85547.33
+17844,15605037.0,Tuan,842.0,France,Female,29.0,6.0,0.0,1.0,0.0,1.0,176692.65
+17845,15713378.0,Udinese,621.0,France,Female,38.0,2.0,0.0,1.0,1.0,0.0,80178.54
+17846,15654959.0,Chia,708.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,171463.83
+17847,15679801.0,Toscani,469.0,Germany,Male,33.0,8.0,137059.05,2.0,0.0,1.0,111241.37
+17848,15583849.0,Whitehead,749.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,146680.25
+17849,15748231.0,Watson,633.0,Germany,Female,52.0,4.0,105701.91,1.0,0.0,0.0,154844.22
+17850,15782802.0,Nwachinemelu,651.0,Germany,Male,45.0,8.0,125829.13,2.0,1.0,0.0,25650.04
+17851,15636024.0,Okechukwu,686.0,France,Male,33.0,7.0,130170.82,1.0,1.0,0.0,55775.84
+17852,15578865.0,T'ien,707.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,177065.24
+17853,15703763.0,Chigolum,637.0,Germany,Male,32.0,2.0,99845.04,2.0,1.0,1.0,166586.99
+17854,15687413.0,King,684.0,France,Male,29.0,8.0,134956.02,1.0,1.0,0.0,121311.12
+17855,15620341.0,Ts'ao,704.0,France,Male,30.0,3.0,97202.32,1.0,1.0,1.0,90928.48
+17856,15602851.0,Chikelu,622.0,Germany,Male,46.0,2.0,122034.4,1.0,0.0,1.0,177622.38
+17857,15790489.0,Linton,709.0,France,Female,28.0,0.0,0.0,1.0,1.0,0.0,86460.28
+17858,15640953.0,Chiebuka,624.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,138780.89
+17859,15731513.0,K'ung,689.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,75937.38
+17860,15684868.0,Calabrese,631.0,Germany,Female,44.0,1.0,106901.94,1.0,1.0,1.0,155746.55
+17861,15803689.0,Hsueh,704.0,France,Male,52.0,2.0,0.0,2.0,1.0,1.0,14081.64
+17862,15686963.0,Chidiegwu,658.0,Germany,Female,45.0,10.0,113348.6,1.0,0.0,0.0,90839.61
+17863,15613102.0,T'ien,635.0,Spain,Male,32.0,10.0,149961.99,1.0,0.0,1.0,94888.6
+17864,15632324.0,Hsiao,686.0,Spain,Male,41.0,6.0,0.0,2.0,1.0,1.0,175743.92
+17865,15640866.0,McGregor,757.0,Germany,Female,45.0,1.0,157960.49,1.0,1.0,0.0,103570.8
+17866,15692408.0,Tuan,706.0,Spain,Female,36.0,5.0,134498.54,1.0,1.0,1.0,91673.6
+17867,15621075.0,Chukwukere,723.0,Spain,Male,45.0,10.0,0.0,1.0,0.0,0.0,89566.64
+17868,15598884.0,Ch'ien,586.0,France,Male,37.0,7.0,0.0,2.0,0.0,1.0,141023.01
+17869,15583736.0,Yobanna,653.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,129936.53
+17870,15649858.0,Ch'iu,577.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,186841.71
+17871,15594864.0,Chiu,632.0,France,Male,25.0,6.0,126442.59,1.0,1.0,1.0,160976.87
+17872,15798895.0,T'ang,850.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,143681.97
+17873,15807989.0,Maclean,669.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,172629.67
+17874,15605827.0,Ibeabuchi,598.0,Spain,Female,41.0,0.0,171325.98,1.0,1.0,1.0,135034.43
+17875,15613787.0,Nnamdi,675.0,Spain,Female,43.0,0.0,0.0,2.0,1.0,1.0,91347.76
+17876,15792250.0,Nwora,597.0,France,Female,32.0,2.0,122338.21,1.0,1.0,0.0,166677.35
+17877,15769959.0,Iadanza,673.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,88947.62
+17878,15671351.0,Oluchukwu,584.0,France,Female,32.0,10.0,0.0,2.0,1.0,0.0,81436.68
+17879,15641595.0,Chibugo,681.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,162668.33
+17880,15596713.0,Onyekachukwu,645.0,France,Male,26.0,2.0,0.0,2.0,1.0,1.0,131303.1
+17881,15600882.0,Nock,696.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,93073.04
+17882,15667046.0,Onwuamaeze,569.0,France,Male,37.0,3.0,151855.33,1.0,1.0,0.0,56905.38
+17883,15779915.0,Onwuamaeze,667.0,France,Male,33.0,7.0,0.0,1.0,1.0,0.0,76582.95
+17884,15791326.0,Ch'iu,677.0,France,Female,31.0,4.0,161546.88,1.0,1.0,0.0,131647.31
+17885,15605067.0,Lucciano,738.0,France,Female,40.0,2.0,139561.94,1.0,1.0,0.0,121866.87
+17886,15793331.0,Teng,562.0,Germany,Male,35.0,4.0,139825.37,2.0,0.0,0.0,168886.18
+17887,15659949.0,Lo Duca,753.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,180962.32
+17888,15651664.0,Nwebube,710.0,Germany,Female,49.0,2.0,123971.51,1.0,1.0,1.0,125381.02
+17889,15617790.0,Toscani,699.0,Germany,Female,58.0,7.0,101242.98,1.0,1.0,0.0,84871.99
+17890,15686917.0,K?,774.0,Germany,Female,32.0,5.0,71269.89,2.0,0.0,1.0,185543.74
+17891,15589329.0,Tien,663.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,69578.49
+17892,15583067.0,Palerma,652.0,Germany,Male,40.0,0.0,128605.32,1.0,0.0,0.0,170518.31
+17893,15587562.0,Lucchese,679.0,Germany,Female,52.0,6.0,89182.09,1.0,1.0,0.0,70563.9
+17894,15654964.0,Chidimma,649.0,Germany,Male,46.0,1.0,127544.84,2.0,1.0,0.0,57320.92
+17895,15753847.0,Lu,587.0,France,Male,48.0,2.0,0.0,2.0,1.0,0.0,96832.82
+17896,15757811.0,Hsieh,682.0,Germany,Female,31.0,0.0,98955.87,1.0,1.0,0.0,164018.97
+17897,15572022.0,Lo Duca,583.0,France,Female,48.0,4.0,0.0,2.0,1.0,1.0,72970.93
+17898,15748597.0,Chuang,607.0,France,Male,42.0,1.0,138515.02,1.0,1.0,0.0,87834.24
+17899,15700946.0,Cawood,811.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,1.0,123427.98
+17900,15621205.0,Onyekachukwu,539.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,88890.75
+17901,15650018.0,Echezonachukwu,521.0,France,Female,37.0,3.0,0.0,2.0,0.0,1.0,162960.05
+17902,15653572.0,Hsueh,667.0,Spain,Female,22.0,5.0,88141.1,1.0,1.0,1.0,64422.81
+17903,15709441.0,Pirozzi,724.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,85890.75
+17904,15656319.0,Chibugo,699.0,Germany,Male,25.0,1.0,107192.38,1.0,1.0,0.0,52332.85
+17905,15795586.0,Ch'eng,718.0,Germany,Female,49.0,6.0,88902.21,1.0,1.0,0.0,103901.31
+17906,15739931.0,Hargreaves,758.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,141827.83
+17907,15725141.0,Zetticci,641.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,79495.01
+17908,15609824.0,Ch'ang,711.0,Germany,Male,51.0,5.0,113905.48,1.0,1.0,0.0,74564.41
+17909,15661409.0,Bischof,697.0,France,Male,27.0,9.0,141604.76,1.0,1.0,1.0,26793.82
+17910,15693814.0,Ch'ien,656.0,France,Female,36.0,4.0,84421.62,1.0,1.0,1.0,80591.16
+17911,15654519.0,Folliero,565.0,Germany,Male,36.0,5.0,120197.42,1.0,1.0,0.0,181490.42
+17912,15720353.0,Chiang,583.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,47722.79
+17913,15815295.0,K'ung,592.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,172490.78
+17914,15795895.0,H?,669.0,Germany,Female,46.0,9.0,156848.13,1.0,0.0,1.0,17095.49
+17915,15771942.0,Ch'eng,565.0,Spain,Female,35.0,3.0,0.0,2.0,0.0,1.0,85982.48
+17916,15741658.0,Nnachetam,727.0,France,Male,37.0,6.0,0.0,2.0,0.0,0.0,127059.04
+17917,15631457.0,Ch'ang,751.0,Germany,Male,33.0,6.0,117153.0,2.0,1.0,0.0,172668.39
+17918,15683363.0,Palermo,651.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,141872.05
+17919,15681705.0,Calabrese,756.0,France,Male,32.0,10.0,0.0,2.0,0.0,0.0,133871.83
+17920,15579130.0,Onyekachi,652.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,170886.01
+17921,15654463.0,Lai,681.0,France,Female,44.0,7.0,0.0,2.0,1.0,0.0,167984.72
+17922,15693326.0,Hsiung,651.0,Germany,Male,38.0,2.0,128927.93,1.0,1.0,1.0,94499.06
+17923,15614220.0,Gboliwe,681.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,172576.43
+17924,15757559.0,Chigolum,650.0,Germany,Male,31.0,1.0,113858.95,1.0,1.0,0.0,88890.05
+17925,15787830.0,Chiu,642.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,184544.59
+17926,15736397.0,Clements,687.0,France,Female,32.0,8.0,0.0,2.0,0.0,1.0,171519.06
+17927,15649738.0,T'ien,705.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,165562.84
+17928,15783097.0,Ch'ang,579.0,France,Male,44.0,5.0,0.0,2.0,1.0,0.0,136071.05
+17929,15727299.0,De Luca,664.0,France,Female,37.0,4.0,107716.93,1.0,1.0,1.0,26019.59
+17930,15576352.0,Maclean,584.0,Spain,Male,51.0,10.0,0.0,1.0,1.0,1.0,102655.31
+17931,15605916.0,Kornilova,722.0,Spain,Female,42.0,4.0,0.0,1.0,1.0,1.0,79623.28
+17932,15782418.0,T'ien,699.0,Germany,Male,56.0,5.0,125193.96,1.0,0.0,1.0,59981.62
+17933,15579112.0,Shih,739.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,74872.46
+17934,15674851.0,Nwankwo,598.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,127825.25
+17935,15655659.0,Hsueh,622.0,France,Female,63.0,1.0,140576.11,1.0,1.0,1.0,116474.35
+17936,15780056.0,Iadanza,582.0,France,Male,24.0,5.0,0.0,2.0,0.0,1.0,109626.73
+17937,15679963.0,Nnonso,633.0,Spain,Male,46.0,6.0,0.0,1.0,0.0,1.0,176943.59
+17938,15585595.0,Pirozzi,757.0,France,Male,33.0,5.0,136487.86,1.0,1.0,1.0,96883.42
+17939,15615896.0,Iadanza,620.0,Spain,Male,28.0,9.0,0.0,1.0,1.0,1.0,114675.75
+17940,15693906.0,Mazzanti,612.0,Germany,Female,37.0,4.0,121461.87,2.0,1.0,1.0,72993.65
+17941,15632446.0,Ch'iu,571.0,France,Male,22.0,8.0,0.0,1.0,1.0,0.0,149535.97
+17942,15778959.0,Rizzo,762.0,France,Female,30.0,7.0,0.0,2.0,1.0,1.0,180712.52
+17943,15649024.0,Mbanefo,850.0,Spain,Male,32.0,6.0,0.0,1.0,1.0,0.0,4861.72
+17944,15664543.0,Onwumelu,769.0,Germany,Female,46.0,6.0,116037.52,1.0,1.0,0.0,133814.28
+17945,15705860.0,Ch'in,636.0,Spain,Male,42.0,5.0,0.0,1.0,1.0,0.0,54439.64
+17946,15790689.0,Fomin,633.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,152996.89
+17947,15581525.0,P'an,658.0,France,Male,54.0,0.0,0.0,2.0,1.0,1.0,51637.67
+17948,15732660.0,Hs?,556.0,France,Female,42.0,8.0,83772.95,1.0,1.0,1.0,181893.31
+17949,15643361.0,Ting,693.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,88890.75
+17950,15807989.0,Hsiao,572.0,Germany,Male,32.0,2.0,92474.05,2.0,1.0,0.0,61257.83
+17951,15792566.0,Nnachetam,707.0,France,Male,29.0,7.0,0.0,1.0,1.0,1.0,168816.73
+17952,15593979.0,T'ien,705.0,Spain,Male,28.0,8.0,0.0,2.0,1.0,1.0,178373.43
+17953,15603563.0,Lo,556.0,Germany,Male,43.0,2.0,121542.57,1.0,1.0,0.0,113481.02
+17954,15589435.0,Rowntree,645.0,France,Female,32.0,9.0,121405.04,1.0,0.0,1.0,141400.51
+17955,15714993.0,Macdonald,698.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,86767.48
+17956,15611786.0,Chinedum,494.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,1.0,135077.01
+17957,15577274.0,Lucchesi,762.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,50899.91
+17958,15793854.0,Mollison,748.0,France,Female,35.0,3.0,0.0,1.0,1.0,1.0,142670.61
+17959,15778975.0,Liao,742.0,France,Male,42.0,7.0,0.0,3.0,1.0,0.0,65675.48
+17960,15586517.0,H?,755.0,Spain,Female,51.0,1.0,0.0,3.0,1.0,0.0,75860.01
+17961,15690829.0,Mazzi,729.0,Spain,Male,32.0,5.0,0.0,2.0,0.0,1.0,149614.17
+17962,15768095.0,Chien,730.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,24402.87
+17963,15745012.0,Chigolum,634.0,France,Male,46.0,2.0,135078.41,1.0,1.0,0.0,158750.43
+17964,15604813.0,Chibugo,687.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,73322.0
+17965,15727281.0,Onyemauchechukwu,677.0,Spain,Female,26.0,9.0,0.0,2.0,0.0,1.0,109659.12
+17966,15632826.0,Onyekachi,557.0,France,Female,37.0,1.0,0.0,1.0,0.0,1.0,105716.05
+17967,15809846.0,K'ung,705.0,France,Male,58.0,5.0,0.0,2.0,1.0,1.0,95719.73
+17968,15687634.0,Hsing,667.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,1.0,98820.09
+17969,15633650.0,Chiazagomekpere,727.0,France,Female,37.0,2.0,0.0,2.0,0.0,1.0,152733.89
+17970,15621063.0,Lucciano,622.0,France,Female,30.0,4.0,0.0,1.0,0.0,1.0,166678.28
+17971,15572265.0,Fiorentino,586.0,France,Male,25.0,7.0,0.0,2.0,1.0,0.0,141946.92
+17972,15626806.0,Ch'ang,622.0,France,Male,45.0,5.0,0.0,2.0,1.0,0.0,179634.69
+17973,15790744.0,K?,597.0,Spain,Male,43.0,8.0,0.0,2.0,0.0,1.0,168544.85
+17974,15764491.0,Chiu,734.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,109523.88
+17975,15577078.0,Yoo,696.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,118913.53
+17976,15793311.0,Ch'ang,612.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,153917.41
+17977,15733705.0,Ugochukwu,607.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,101455.07
+17978,15672950.0,Nweke,553.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,122925.44
+17979,15591747.0,Chien,777.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,133086.32
+17980,15628283.0,Tuan,681.0,France,Female,33.0,4.0,0.0,2.0,0.0,1.0,104889.3
+17981,15737778.0,Hs?eh,762.0,France,Male,49.0,5.0,0.0,1.0,0.0,1.0,136810.18
+17982,15686963.0,Onwughara,726.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,106826.92
+17983,15593897.0,Ch'in,520.0,France,Female,31.0,1.0,0.0,2.0,0.0,1.0,140889.32
+17984,15781409.0,Biryukov,785.0,France,Female,40.0,3.0,0.0,2.0,1.0,1.0,175043.69
+17985,15585978.0,Li Fonti,741.0,France,Male,40.0,2.0,98894.39,1.0,1.0,0.0,127939.19
+17986,15605424.0,Kenechukwu,748.0,Spain,Female,30.0,1.0,0.0,2.0,1.0,0.0,82565.01
+17987,15721383.0,Calabresi,588.0,Spain,Male,42.0,6.0,0.0,2.0,1.0,1.0,120892.63
+17988,15798964.0,Okechukwu,687.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,70810.6
+17989,15607988.0,Genovesi,681.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,132973.17
+17990,15761976.0,Chienezie,687.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,140765.55
+17991,15762953.0,Uchenna,755.0,Spain,Male,28.0,6.0,0.0,2.0,1.0,1.0,60901.72
+17992,15762803.0,Yancy,648.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,93091.02
+17993,15630244.0,Hs?,648.0,Spain,Female,37.0,2.0,114435.47,1.0,1.0,0.0,167030.51
+17994,15709621.0,O'Donnell,697.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,109545.56
+17995,15623450.0,Yashina,648.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,151411.2
+17996,15600813.0,Ch'iu,675.0,Germany,Male,37.0,7.0,121093.39,1.0,1.0,1.0,76679.6
+17997,15593973.0,Iadanza,724.0,Germany,Male,29.0,1.0,126670.37,1.0,1.0,1.0,72628.41
+17998,15595537.0,Y?,721.0,France,Male,33.0,6.0,0.0,2.0,0.0,1.0,176471.79
+17999,15589969.0,Pirozzi,652.0,Germany,Female,44.0,6.0,129855.32,1.0,1.0,0.0,106630.51
+18000,15796074.0,Maclean,850.0,France,Male,27.0,5.0,0.0,2.0,1.0,1.0,94003.11
+18001,15775153.0,Nnachetam,553.0,Spain,Female,60.0,3.0,0.0,1.0,0.0,1.0,159654.0
+18002,15581280.0,Pirozzi,731.0,France,Female,41.0,8.0,117413.95,1.0,0.0,1.0,171050.43
+18003,15585580.0,Maclean,627.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,85523.24
+18004,15607884.0,Fennell,714.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,173870.39
+18005,15757577.0,Onuoha,807.0,Spain,Female,41.0,1.0,121681.91,1.0,1.0,1.0,150242.44
+18006,15665760.0,Fyodorova,817.0,France,Male,40.0,4.0,130812.91,1.0,1.0,0.0,81861.1
+18007,15674442.0,Chigolum,542.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,141798.98
+18008,15589488.0,Hsia,680.0,Germany,Male,29.0,1.0,115142.9,1.0,1.0,1.0,104982.39
+18009,15717489.0,Smith,678.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,171681.86
+18010,15673539.0,H?,606.0,France,Male,31.0,1.0,0.0,1.0,1.0,1.0,61937.6
+18011,15703437.0,H?,708.0,France,Male,35.0,5.0,0.0,2.0,0.0,0.0,126796.69
+18012,15727138.0,Padovesi,593.0,Spain,Female,37.0,0.0,0.0,2.0,1.0,0.0,70523.92
+18013,15730287.0,Chuang,664.0,Germany,Male,32.0,1.0,123675.69,1.0,1.0,1.0,70563.9
+18014,15595440.0,Smith,682.0,France,Male,33.0,4.0,115897.73,1.0,1.0,1.0,157564.75
+18015,15792222.0,Ch'ang,582.0,France,Female,31.0,8.0,112713.34,2.0,1.0,1.0,67979.35
+18016,15716236.0,Chiang,663.0,Spain,Male,42.0,7.0,0.0,2.0,1.0,1.0,93624.55
+18017,15787014.0,Ch'ien,676.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,104889.3
+18018,15775153.0,Tien,593.0,France,Female,46.0,7.0,0.0,2.0,1.0,1.0,87908.01
+18019,15696834.0,Chukwuebuka,632.0,France,Female,29.0,3.0,0.0,1.0,1.0,1.0,171657.57
+18020,15655774.0,Babbage,743.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,167036.94
+18021,15693732.0,Tseng,584.0,Spain,Female,52.0,6.0,79563.46,1.0,1.0,1.0,79577.4
+18022,15671345.0,Nwachukwu,850.0,France,Male,40.0,8.0,153639.11,1.0,1.0,0.0,64849.74
+18023,15622230.0,Chigozie,684.0,France,Female,29.0,4.0,0.0,1.0,1.0,1.0,79919.13
+18024,15783030.0,Nwachinemelu,645.0,Spain,Female,47.0,4.0,0.0,1.0,1.0,0.0,102908.91
+18025,15608845.0,Bischof,686.0,Germany,Male,28.0,4.0,134896.04,2.0,1.0,0.0,165562.84
+18026,15754072.0,Ch'eng,706.0,Spain,Female,39.0,6.0,155973.88,1.0,1.0,0.0,164064.44
+18027,15814772.0,Onuchukwu,697.0,France,Female,34.0,10.0,161546.68,1.0,1.0,1.0,176791.27
+18028,15603846.0,Lu,703.0,Germany,Female,24.0,7.0,106920.91,2.0,1.0,0.0,109496.45
+18029,15571689.0,Calabrese,553.0,Germany,Female,55.0,1.0,137510.34,2.0,1.0,0.0,156476.36
+18030,15581295.0,Chiu,677.0,Spain,Male,42.0,10.0,0.0,2.0,1.0,1.0,180551.76
+18031,15747265.0,Ch'ien,549.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,0.0,67830.2
+18032,15790448.0,Nnachetam,605.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,38190.78
+18033,15686743.0,Y?,678.0,Spain,Male,44.0,1.0,156091.97,1.0,1.0,1.0,137793.39
+18034,15682021.0,Okwuadigbo,732.0,Germany,Male,38.0,5.0,144848.74,2.0,0.0,1.0,127547.18
+18035,15768295.0,Uvarov,684.0,France,Female,34.0,9.0,0.0,1.0,1.0,1.0,70550.0
+18036,15762045.0,Shao,646.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,101835.24
+18037,15592994.0,Chukwuemeka,839.0,Spain,Male,43.0,1.0,0.0,2.0,1.0,1.0,142956.48
+18038,15650266.0,Ritchie,835.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,87822.14
+18039,15652981.0,Chukwufumnanya,569.0,France,Female,31.0,10.0,0.0,1.0,1.0,1.0,132450.19
+18040,15652218.0,Ch'ien,639.0,France,Female,44.0,7.0,0.0,2.0,1.0,0.0,149814.68
+18041,15605037.0,Toscani,706.0,Germany,Female,41.0,1.0,127545.94,2.0,0.0,1.0,161519.66
+18042,15592550.0,K'ung,568.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,157553.98
+18043,15673908.0,Milne,473.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,102297.15
+18044,15715707.0,Ives,735.0,France,Male,32.0,3.0,163630.76,1.0,1.0,1.0,158032.5
+18045,15719302.0,T'ien,652.0,France,Male,27.0,4.0,0.0,2.0,0.0,0.0,64443.75
+18046,15700845.0,L?,673.0,Spain,Male,32.0,8.0,146587.3,1.0,0.0,0.0,186612.47
+18047,15641366.0,Chiazagomekpele,675.0,Germany,Male,37.0,2.0,151836.36,2.0,1.0,1.0,118059.19
+18048,15583681.0,Kao,659.0,Spain,Male,26.0,7.0,0.0,2.0,1.0,0.0,136812.09
+18049,15645778.0,Brown,668.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,91810.71
+18050,15803526.0,Ch'ang,616.0,Germany,Female,38.0,3.0,121537.71,1.0,1.0,1.0,185599.12
+18051,15787699.0,Ch'ien,569.0,Germany,Female,41.0,4.0,95984.69,2.0,1.0,1.0,171096.2
+18052,15764343.0,Tung,645.0,Spain,Female,30.0,2.0,0.0,2.0,1.0,0.0,66893.28
+18053,15702801.0,Toscani,729.0,France,Female,54.0,9.0,105420.18,1.0,0.0,0.0,137028.51
+18054,15756625.0,L?,708.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,138526.96
+18055,15782778.0,Folliero,705.0,France,Male,44.0,7.0,0.0,1.0,1.0,1.0,105533.97
+18056,15742824.0,Chien,729.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,153810.41
+18057,15589428.0,Chukwualuka,758.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,135431.72
+18058,15656005.0,Obialo,714.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,145562.4
+18059,15693906.0,Maclean,706.0,Germany,Male,42.0,6.0,112367.34,2.0,1.0,1.0,131772.47
+18060,15672534.0,Rizzo,625.0,Spain,Male,39.0,0.0,0.0,1.0,1.0,0.0,121311.12
+18061,15764314.0,Larkin,733.0,Spain,Female,35.0,7.0,0.0,2.0,1.0,0.0,142548.33
+18062,15576022.0,Hsiao,628.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,172033.57
+18063,15583764.0,Fanucci,543.0,France,Male,37.0,5.0,113308.77,1.0,0.0,0.0,137508.42
+18064,15617705.0,Ch'iu,710.0,Spain,Male,32.0,1.0,135645.01,1.0,0.0,0.0,175544.02
+18065,15770498.0,Li Fonti,656.0,France,Female,51.0,2.0,0.0,1.0,1.0,1.0,91673.6
+18066,15653849.0,Yashina,753.0,Spain,Female,40.0,1.0,0.0,2.0,0.0,1.0,170528.63
+18067,15770159.0,Chibugo,596.0,Germany,Female,31.0,1.0,102994.04,1.0,1.0,0.0,61290.99
+18068,15810978.0,P'eng,752.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,73656.38
+18069,15677310.0,Lucciano,785.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,1.0,186841.71
+18070,15652667.0,Shao,634.0,Spain,Male,24.0,2.0,0.0,1.0,1.0,1.0,145937.99
+18071,15803037.0,Mamelu,652.0,France,Female,31.0,1.0,96891.46,2.0,1.0,0.0,97923.24
+18072,15796864.0,Hsieh,595.0,France,Female,45.0,10.0,0.0,1.0,1.0,0.0,57572.38
+18073,15578098.0,Hsia,569.0,France,Female,47.0,9.0,0.0,1.0,1.0,0.0,140724.64
+18074,15618661.0,Genovesi,754.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,1.0,121208.77
+18075,15797293.0,Mancini,559.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,129994.63
+18076,15772423.0,De Luca,706.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,60917.24
+18077,15690620.0,Chukwudi,554.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,0.0,141872.05
+18078,15722599.0,Ts'ui,577.0,Spain,Male,39.0,5.0,0.0,2.0,0.0,1.0,122996.76
+18079,15718779.0,Macleod,592.0,Spain,Female,29.0,6.0,0.0,2.0,1.0,0.0,145562.4
+18080,15804064.0,Scott,752.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,5791.85
+18081,15662501.0,Nucci,605.0,France,Female,42.0,8.0,0.0,2.0,1.0,0.0,145528.22
+18082,15649738.0,Chukwuemeka,561.0,France,Male,35.0,9.0,155872.5,1.0,1.0,1.0,163558.36
+18083,15659486.0,Chienezie,686.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,1.0,9201.53
+18084,15674551.0,Cattaneo,531.0,Spain,Male,45.0,5.0,112211.61,1.0,1.0,1.0,75888.65
+18085,15699467.0,Ts'ai,616.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,0.0,83058.14
+18086,15589431.0,Ni,667.0,France,Female,54.0,8.0,0.0,1.0,1.0,0.0,186489.95
+18087,15798964.0,Nnachetam,695.0,France,Female,41.0,1.0,141749.68,1.0,1.0,0.0,88930.2
+18088,15573077.0,Tuan,663.0,France,Female,32.0,2.0,0.0,3.0,1.0,1.0,141946.92
+18089,15763515.0,Nnamutaezinwa,632.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,122518.5
+18090,15599410.0,Ch'iu,577.0,France,Female,27.0,0.0,0.0,2.0,1.0,1.0,148811.52
+18091,15807597.0,Chinweuba,648.0,France,Female,56.0,7.0,79450.09,1.0,1.0,0.0,40385.61
+18092,15707473.0,Tseng,605.0,Germany,Female,37.0,3.0,122550.05,1.0,1.0,0.0,185599.12
+18093,15693543.0,Ejikemeifeuwa,589.0,Germany,Female,35.0,6.0,121863.61,2.0,1.0,1.0,136521.82
+18094,15590094.0,Trevisani,639.0,Germany,Male,61.0,3.0,124577.33,1.0,1.0,1.0,161579.85
+18095,15668747.0,Trevisani,604.0,France,Male,28.0,1.0,136496.12,1.0,1.0,1.0,107841.77
+18096,15617482.0,Li Fonti,716.0,France,Female,36.0,8.0,0.0,2.0,1.0,0.0,90896.04
+18097,15803976.0,Chidimma,677.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,106977.8
+18098,15730590.0,Chuang,735.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,160129.99
+18099,15646632.0,L?,579.0,France,Female,32.0,2.0,118336.95,1.0,1.0,1.0,77830.25
+18100,15670039.0,Genovese,622.0,Spain,Male,40.0,2.0,0.0,1.0,1.0,0.0,176750.81
+18101,15627365.0,Shao,565.0,France,Female,40.0,4.0,0.0,4.0,1.0,0.0,60765.79
+18102,15675328.0,Maccallum,634.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,0.0,52795.56
+18103,15762332.0,Pirozzi,678.0,France,Male,40.0,4.0,164948.98,1.0,0.0,1.0,27758.36
+18104,15795519.0,Calabresi,683.0,France,Female,32.0,5.0,0.0,2.0,0.0,0.0,199693.84
+18105,15679294.0,Ch'iu,678.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,108891.3
+18106,15694408.0,Eluemuno,581.0,Spain,Female,49.0,3.0,80542.81,1.0,1.0,1.0,177065.24
+18107,15592222.0,H?,597.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,112157.31
+18108,15697948.0,Chiang,592.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,161653.5
+18109,15773338.0,McLean,637.0,France,Female,45.0,4.0,0.0,2.0,1.0,0.0,121763.69
+18110,15642344.0,Y?,743.0,France,Female,33.0,7.0,99564.22,1.0,0.0,0.0,124720.68
+18111,15763033.0,De Luca,807.0,Germany,Male,37.0,8.0,92887.06,1.0,1.0,1.0,79580.13
+18112,15763898.0,Gorman,641.0,France,Female,63.0,6.0,0.0,2.0,0.0,1.0,133091.32
+18113,15690164.0,P'eng,691.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,123866.43
+18114,15589475.0,Echezonachukwu,635.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,1.0,92794.61
+18115,15803801.0,Brierly,646.0,France,Male,47.0,1.0,0.0,2.0,1.0,1.0,185543.67
+18116,15793331.0,Milne,663.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,157567.37
+18117,15691150.0,Macleod,684.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,124489.88
+18118,15652789.0,Liao,634.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,182076.97
+18119,15777045.0,Ch'iu,739.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,138350.74
+18120,15738785.0,Bischof,679.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,187597.98
+18121,15775886.0,Ch'eng,718.0,Spain,Male,34.0,0.0,0.0,2.0,1.0,0.0,51112.8
+18122,15780561.0,Chukwualuka,627.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,163666.85
+18123,15584580.0,Hsieh,658.0,France,Male,36.0,0.0,0.0,2.0,1.0,0.0,103570.8
+18124,15599591.0,Uchechukwu,590.0,Germany,Female,40.0,7.0,120718.28,2.0,1.0,1.0,131629.82
+18125,15720353.0,Chiedozie,653.0,France,Female,34.0,2.0,170184.99,1.0,1.0,1.0,43889.41
+18126,15640407.0,Yobanna,675.0,France,Male,39.0,8.0,141441.36,1.0,1.0,0.0,126812.63
+18127,15793424.0,Ch'eng,831.0,Germany,Male,40.0,4.0,141325.56,1.0,1.0,0.0,186778.89
+18128,15647895.0,Chiemela,556.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,71557.12
+18129,15672983.0,Clements,676.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,115668.82
+18130,15611786.0,Maclean,659.0,France,Male,36.0,6.0,0.0,1.0,0.0,0.0,86935.08
+18131,15687558.0,Scott,694.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,86733.61
+18132,15740356.0,De Luca,583.0,Spain,Male,39.0,3.0,0.0,2.0,0.0,1.0,51670.82
+18133,15610271.0,Nnonso,603.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,645.61
+18134,15797710.0,Onyemauchechukwu,703.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,156906.15
+18135,15798964.0,Ts'ai,663.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,103511.35
+18136,15656822.0,Chien,666.0,France,Male,50.0,3.0,0.0,1.0,1.0,0.0,180598.66
+18137,15783691.0,Teng,635.0,Germany,Female,46.0,1.0,143452.74,1.0,0.0,0.0,124874.23
+18138,15643658.0,Trevisani,703.0,France,Male,44.0,5.0,0.0,2.0,1.0,0.0,169303.65
+18139,15783063.0,Y?an,786.0,France,Female,41.0,0.0,0.0,2.0,1.0,0.0,166677.35
+18140,15742229.0,Pirozzi,675.0,Spain,Female,37.0,4.0,123971.16,1.0,1.0,0.0,143681.83
+18141,15722583.0,Fiorentini,539.0,Spain,Male,34.0,3.0,0.0,2.0,0.0,1.0,121551.56
+18142,15701175.0,Ch'en,698.0,Spain,Male,26.0,2.0,0.0,2.0,1.0,0.0,131177.22
+18143,15652952.0,Shubin,489.0,Germany,Female,61.0,3.0,124577.15,1.0,1.0,0.0,90879.13
+18144,15654531.0,Ch'eng,627.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,126144.96
+18145,15724691.0,Tretiakova,775.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,176883.42
+18146,15599131.0,Chiu,692.0,France,Female,38.0,3.0,0.0,1.0,1.0,0.0,96420.58
+18147,15641640.0,Chukwuemeka,695.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,175894.24
+18148,15641114.0,Hsiao,721.0,France,Female,39.0,2.0,0.0,1.0,1.0,0.0,95957.09
+18149,15658435.0,Ch'in,648.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,52876.41
+18150,15600462.0,Ch'ien,721.0,France,Male,31.0,7.0,0.0,2.0,0.0,0.0,141872.05
+18151,15692408.0,Chiemezie,748.0,France,Female,69.0,1.0,0.0,2.0,0.0,1.0,102908.91
+18152,15709991.0,T'ien,672.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,176768.59
+18153,15585067.0,Chidiebere,705.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,140800.66
+18154,15592994.0,Ugochukwutubelum,602.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,82647.65
+18155,15662091.0,Macleod,624.0,France,Female,40.0,2.0,0.0,1.0,1.0,0.0,180598.28
+18156,15696733.0,Okwudilichukwu,697.0,France,Male,20.0,9.0,0.0,2.0,0.0,1.0,179227.16
+18157,15739476.0,Ndubueze,604.0,France,Male,35.0,9.0,0.0,1.0,1.0,0.0,131712.53
+18158,15763415.0,Chiganu,682.0,Germany,Male,48.0,1.0,106731.58,1.0,1.0,0.0,109523.88
+18159,15654891.0,Ch'ang,588.0,Spain,Male,32.0,3.0,147813.05,1.0,1.0,1.0,166677.35
+18160,15778936.0,Oluchukwu,616.0,France,Female,44.0,9.0,0.0,1.0,0.0,1.0,177614.17
+18161,15694829.0,Manna,686.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,168816.35
+18162,15757981.0,Nwankwo,506.0,France,Female,39.0,6.0,0.0,2.0,0.0,0.0,99800.87
+18163,15694450.0,Gant,588.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,169161.46
+18164,15670354.0,Nnonso,586.0,Spain,Male,48.0,7.0,121206.96,1.0,0.0,1.0,161253.08
+18165,15674541.0,K?,590.0,France,Male,36.0,6.0,168066.87,1.0,1.0,0.0,115818.51
+18166,15611786.0,Chidiebele,583.0,France,Female,37.0,9.0,72494.69,1.0,1.0,1.0,47322.05
+18167,15775709.0,Hsing,652.0,France,Female,46.0,1.0,0.0,1.0,1.0,1.0,153059.62
+18168,15641733.0,Li Fonti,635.0,Spain,Female,48.0,7.0,0.0,2.0,1.0,0.0,139431.0
+18169,15695872.0,Pirozzi,576.0,France,Male,40.0,7.0,0.0,2.0,0.0,1.0,177614.17
+18170,15584091.0,Okechukwu,651.0,Germany,Female,46.0,1.0,108738.92,1.0,1.0,0.0,131710.59
+18171,15607746.0,P'eng,622.0,Germany,Female,55.0,1.0,119024.75,1.0,1.0,0.0,103760.53
+18172,15692599.0,Ts'ui,828.0,France,Male,37.0,7.0,0.0,1.0,0.0,1.0,55867.38
+18173,15604544.0,Nwankwo,597.0,France,Male,33.0,0.0,0.0,2.0,1.0,0.0,90967.77
+18174,15585192.0,Chiganu,579.0,Spain,Male,33.0,8.0,121063.8,1.0,1.0,0.0,180934.67
+18175,15691446.0,Aksenov,587.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,49848.86
+18176,15732299.0,Ch'ang,745.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,187929.43
+18177,15785782.0,Cavenagh,576.0,France,Male,48.0,2.0,0.0,1.0,0.0,1.0,162260.93
+18178,15781598.0,Mancini,605.0,Spain,Female,31.0,3.0,141806.0,1.0,1.0,0.0,171868.08
+18179,15761132.0,Rizzo,706.0,France,Male,35.0,1.0,0.0,1.0,1.0,1.0,71159.84
+18180,15792077.0,Genovese,697.0,France,Female,64.0,6.0,0.0,3.0,1.0,0.0,152790.92
+18181,15595036.0,Lo Duca,676.0,France,Male,39.0,6.0,0.0,1.0,1.0,0.0,142911.99
+18182,15591932.0,K?,699.0,France,Male,21.0,1.0,0.0,2.0,1.0,1.0,82222.86
+18183,15576517.0,Udegbunam,763.0,France,Female,61.0,2.0,0.0,2.0,0.0,1.0,133020.9
+18184,15645463.0,Yermakov,712.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,148814.54
+18185,15740160.0,Onyemauchechukwu,592.0,Germany,Male,38.0,3.0,91285.21,1.0,1.0,0.0,57876.05
+18186,15637138.0,Hsueh,689.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,114869.55
+18187,15804937.0,Cattaneo,592.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,48228.1
+18188,15585595.0,Chuang,657.0,Germany,Female,33.0,10.0,95845.6,3.0,1.0,0.0,93717.46
+18189,15774285.0,Ch'ang,516.0,Spain,Male,37.0,10.0,0.0,1.0,0.0,1.0,91514.61
+18190,15791114.0,Sagese,678.0,France,Female,47.0,1.0,0.0,1.0,0.0,0.0,70778.63
+18191,15774393.0,Chukwualuka,644.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,137537.22
+18192,15588535.0,McLean,644.0,France,Female,55.0,4.0,0.0,1.0,1.0,1.0,173599.38
+18193,15632977.0,Chikwado,571.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,180712.28
+18194,15625592.0,Folliero,741.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,101418.05
+18195,15590736.0,Okagbue,648.0,France,Male,56.0,8.0,0.0,1.0,1.0,0.0,56908.41
+18196,15696054.0,Chinwendu,751.0,Spain,Male,39.0,10.0,0.0,2.0,1.0,1.0,151903.6
+18197,15662291.0,Scott,603.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,112976.67
+18198,15758856.0,Thompson,545.0,France,Male,45.0,0.0,97588.6,1.0,1.0,0.0,162683.06
+18199,15621520.0,Burt,662.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,0.0,68701.73
+18200,15662662.0,Bancks,669.0,Spain,Female,35.0,7.0,0.0,1.0,1.0,0.0,162960.05
+18201,15703059.0,Ting,676.0,France,Female,43.0,10.0,0.0,1.0,0.0,1.0,143598.7
+18202,15592078.0,Liao,604.0,France,Male,44.0,9.0,0.0,1.0,1.0,1.0,184474.15
+18203,15603554.0,Ch'ang,739.0,Spain,Female,38.0,4.0,0.0,1.0,0.0,0.0,141400.51
+18204,15611186.0,Johnson,691.0,Spain,Female,31.0,1.0,162039.59,1.0,1.0,1.0,177719.88
+18205,15611189.0,Hsieh,581.0,Germany,Male,24.0,2.0,123969.92,2.0,0.0,1.0,131073.9
+18206,15698528.0,Chidubem,596.0,Spain,Female,23.0,5.0,0.0,1.0,1.0,1.0,79329.7
+18207,15709928.0,Trentino,684.0,Germany,Female,40.0,10.0,134740.24,2.0,1.0,1.0,57589.29
+18208,15586629.0,Nwabugwu,681.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,171056.65
+18209,15803801.0,Hs?,662.0,France,Male,36.0,3.0,0.0,2.0,0.0,1.0,92664.09
+18210,15702463.0,Lucchese,539.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,72993.65
+18211,15641640.0,Genovese,675.0,Spain,Male,51.0,3.0,0.0,2.0,0.0,1.0,80582.34
+18212,15744368.0,Nnachetam,758.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,89938.08
+18213,15747043.0,Smith,716.0,Spain,Male,47.0,7.0,0.0,2.0,0.0,0.0,163830.64
+18214,15763613.0,Mao,651.0,Germany,Male,48.0,5.0,123761.68,3.0,1.0,1.0,142916.43
+18215,15781589.0,Ch'in,642.0,France,Female,39.0,9.0,0.0,2.0,1.0,1.0,57762.0
+18216,15805909.0,Ch'iu,594.0,Spain,Female,35.0,2.0,0.0,1.0,1.0,0.0,189085.07
+18217,15658768.0,Pinto,604.0,Spain,Female,56.0,6.0,69974.66,1.0,1.0,1.0,109296.18
+18218,15815443.0,Chukwudi,684.0,France,Female,31.0,8.0,0.0,1.0,0.0,1.0,56583.88
+18219,15673995.0,Nieves,596.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,158973.77
+18220,15781330.0,Nucci,628.0,France,Female,56.0,2.0,98630.31,1.0,1.0,1.0,137028.51
+18221,15694188.0,Lombardi,691.0,France,Male,31.0,9.0,0.0,1.0,1.0,0.0,65801.44
+18222,15585595.0,Onwudiwe,617.0,France,Male,26.0,0.0,0.0,2.0,1.0,0.0,67311.12
+18223,15606861.0,Onwughara,766.0,France,Female,40.0,0.0,0.0,1.0,1.0,1.0,52367.29
+18224,15690164.0,Niu,584.0,Spain,Female,47.0,3.0,125845.66,1.0,1.0,0.0,113469.65
+18225,15674932.0,Hsia,574.0,Germany,Male,26.0,1.0,100548.67,2.0,1.0,1.0,119348.28
+18226,15604217.0,Trentino,544.0,Germany,Male,48.0,8.0,121537.71,1.0,1.0,1.0,58883.91
+18227,15676571.0,Wright,701.0,France,Male,37.0,10.0,0.0,2.0,0.0,0.0,151887.16
+18228,15806922.0,Oguejiofor,716.0,Spain,Male,27.0,2.0,0.0,2.0,1.0,0.0,151902.78
+18229,15782210.0,Walker,705.0,France,Female,44.0,6.0,113882.31,1.0,1.0,0.0,183566.78
+18230,15778515.0,Cremonesi,623.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,64716.08
+18231,15722404.0,Zetticci,741.0,Spain,Female,45.0,9.0,0.0,1.0,1.0,0.0,57876.05
+18232,15782659.0,Smith,570.0,Spain,Female,47.0,8.0,0.0,1.0,1.0,0.0,119348.28
+18233,15617982.0,Okechukwu,613.0,Spain,Female,29.0,5.0,0.0,1.0,1.0,0.0,25310.82
+18234,15591512.0,Frolov,745.0,France,Male,48.0,4.0,0.0,1.0,1.0,0.0,121866.87
+18235,15777907.0,Nkemdilim,656.0,Germany,Female,52.0,4.0,102238.01,1.0,1.0,1.0,133831.37
+18236,15791114.0,T'ien,683.0,France,Male,35.0,9.0,87944.39,2.0,1.0,0.0,173568.3
+18237,15783541.0,Hsiung,652.0,Germany,Male,42.0,8.0,148051.81,1.0,1.0,1.0,163550.58
+18238,15762238.0,Nnaife,482.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,124532.9
+18239,15592979.0,Tien,556.0,France,Male,35.0,0.0,154969.79,1.0,1.0,0.0,110900.43
+18240,15634281.0,Hsieh,559.0,France,Male,26.0,5.0,0.0,1.0,1.0,0.0,61666.87
+18241,15769955.0,Chukwufumnanya,719.0,France,Female,40.0,8.0,0.0,1.0,0.0,0.0,120834.13
+18242,15729771.0,Onyemauchechukwu,545.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,25684.89
+18243,15731327.0,Ch'eng,731.0,France,Male,26.0,1.0,0.0,1.0,1.0,1.0,72927.68
+18244,15603065.0,Chigozie,699.0,Germany,Female,48.0,5.0,123356.63,2.0,0.0,0.0,134732.99
+18245,15567802.0,Nwoye,741.0,France,Male,39.0,9.0,104077.19,1.0,1.0,0.0,146785.44
+18246,15632576.0,Ch'ien,562.0,France,Female,44.0,6.0,0.0,2.0,1.0,1.0,61961.57
+18247,15646168.0,H?,774.0,Spain,Male,31.0,5.0,0.0,1.0,0.0,0.0,122268.75
+18248,15645323.0,Teng,594.0,Spain,Male,43.0,1.0,176913.52,1.0,1.0,0.0,134417.31
+18249,15642570.0,Manna,624.0,France,Female,44.0,2.0,0.0,2.0,1.0,1.0,58813.41
+18250,15572398.0,Hs?,750.0,France,Male,31.0,1.0,0.0,2.0,0.0,0.0,143317.19
+18251,15602479.0,Okechukwu,715.0,Spain,Male,37.0,7.0,0.0,2.0,0.0,0.0,169278.51
+18252,15747558.0,Chu,648.0,Spain,Female,35.0,0.0,0.0,2.0,1.0,1.0,125961.48
+18253,15690743.0,O'Donnell,745.0,Germany,Female,42.0,2.0,146053.66,1.0,1.0,0.0,147055.87
+18254,15764294.0,Yuryeva,619.0,Germany,Female,36.0,3.0,116576.45,1.0,1.0,1.0,199403.37
+18255,15659325.0,Lu,461.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,179833.45
+18256,15631063.0,Ebelegbulam,714.0,France,Female,33.0,4.0,0.0,2.0,1.0,1.0,85522.84
+18257,15799785.0,Ch'iu,699.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,51548.14
+18258,15724153.0,Maclean,649.0,Germany,Male,30.0,1.0,126588.81,1.0,1.0,0.0,92647.94
+18259,15789413.0,Chidumaga,662.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,91652.43
+18260,15602934.0,Lajoie,696.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,136983.77
+18261,15643042.0,Ch'eng,645.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,123977.86
+18262,15588933.0,Ting,712.0,Spain,Male,32.0,4.0,151532.4,1.0,1.0,1.0,72830.46
+18263,15624388.0,Fanucci,693.0,France,Male,38.0,6.0,0.0,1.0,1.0,0.0,171482.56
+18264,15738662.0,Cawood,645.0,Spain,Female,32.0,1.0,0.0,2.0,0.0,1.0,176750.81
+18265,15773934.0,Chiu,613.0,France,Male,45.0,9.0,123544.0,1.0,1.0,0.0,93795.86
+18266,15692929.0,Nott,696.0,France,Female,41.0,8.0,0.0,1.0,1.0,1.0,149550.62
+18267,15776847.0,Ebelegbulam,558.0,Germany,Female,39.0,8.0,97460.1,1.0,1.0,0.0,46811.77
+18268,15583976.0,Ch'ang,667.0,France,Male,27.0,10.0,0.0,2.0,1.0,0.0,169964.12
+18269,15778861.0,Gboliwe,628.0,Germany,Male,48.0,8.0,121263.62,3.0,1.0,0.0,137763.83
+18270,15790744.0,Nnamutaezinwa,850.0,Spain,Female,35.0,4.0,139825.34,1.0,1.0,0.0,163570.25
+18271,15804482.0,Napolitano,685.0,France,Male,42.0,7.0,0.0,2.0,0.0,0.0,85901.09
+18272,15602844.0,Pinto,762.0,Germany,Female,38.0,1.0,122029.15,1.0,1.0,0.0,131283.6
+18273,15574324.0,Nnonso,759.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,103379.31
+18274,15757566.0,Chukwudi,646.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,97640.2
+18275,15798850.0,T'ang,556.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,126796.69
+18276,15793893.0,Ts'ao,681.0,Spain,Male,45.0,3.0,0.0,1.0,1.0,0.0,133031.96
+18277,15751561.0,Nwankwo,696.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,160948.87
+18278,15753362.0,Chidiebele,501.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,82298.89
+18279,15802483.0,Trevisani,569.0,France,Female,28.0,3.0,0.0,2.0,1.0,1.0,140650.19
+18280,15660608.0,Chukwudi,748.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,182336.45
+18281,15646764.0,Shih,727.0,France,Male,61.0,1.0,0.0,2.0,1.0,1.0,9984.4
+18282,15751748.0,Chiu,729.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,0.0,171716.81
+18283,15640034.0,Onyemauchechukwu,656.0,Spain,Female,31.0,1.0,0.0,1.0,1.0,1.0,168816.38
+18284,15752499.0,Ts'ai,591.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,143628.31
+18285,15752488.0,Marshall,686.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,177670.57
+18286,15658670.0,Smith,726.0,France,Male,33.0,7.0,0.0,1.0,1.0,1.0,169964.12
+18287,15796790.0,Manna,703.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,127528.84
+18288,15745455.0,Ekechukwu,754.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,62868.33
+18289,15582335.0,Ch'eng,683.0,France,Female,38.0,4.0,0.0,1.0,0.0,0.0,81778.72
+18290,15583049.0,Moseley,581.0,France,Female,26.0,5.0,0.0,2.0,0.0,1.0,131919.48
+18291,15757537.0,Ch'iu,644.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,104072.53
+18292,15771750.0,Kornilova,643.0,France,Female,31.0,3.0,135470.9,1.0,1.0,1.0,117038.96
+18293,15749489.0,Pirozzi,673.0,Spain,Female,34.0,2.0,122453.97,1.0,0.0,1.0,123548.25
+18294,15609637.0,Y?an,635.0,Germany,Male,30.0,7.0,126588.81,2.0,1.0,1.0,49848.86
+18295,15651092.0,Onwumelu,596.0,France,Male,57.0,6.0,0.0,2.0,0.0,1.0,163830.64
+18296,15796370.0,Nebeolisa,636.0,Germany,Female,44.0,4.0,125845.66,1.0,0.0,0.0,182336.43
+18297,15682442.0,Hs?,668.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,80178.54
+18298,15603674.0,Ch'ang,603.0,France,Female,35.0,7.0,0.0,2.0,0.0,0.0,95009.8
+18299,15798895.0,P'eng,714.0,France,Female,34.0,7.0,0.0,1.0,1.0,1.0,67622.46
+18300,15702434.0,Y?an,621.0,Germany,Female,39.0,6.0,93499.82,2.0,0.0,0.0,117024.38
+18301,15686885.0,Bales,704.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,155743.48
+18302,15776844.0,Onuoha,634.0,Germany,Female,40.0,2.0,143330.56,1.0,1.0,0.0,63687.06
+18303,15690175.0,Genovese,567.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,141152.28
+18304,15706767.0,H?,634.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,176750.81
+18305,15592222.0,Onyekachi,565.0,Spain,Female,41.0,9.0,0.0,1.0,0.0,0.0,105760.01
+18306,15795571.0,Hao,586.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,155700.15
+18307,15679622.0,Yudina,628.0,Spain,Male,35.0,9.0,109558.36,1.0,0.0,1.0,121440.8
+18308,15654495.0,Chiang,637.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,26136.89
+18309,15750141.0,K?,615.0,France,Female,23.0,8.0,0.0,2.0,1.0,0.0,92647.95
+18310,15605425.0,Ch'en,681.0,France,Female,40.0,9.0,0.0,2.0,1.0,0.0,136071.05
+18311,15813659.0,Ch'ien,699.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,0.0,97640.5
+18312,15773789.0,O'Sullivan,718.0,France,Male,36.0,6.0,0.0,2.0,0.0,1.0,44335.54
+18313,15643496.0,Maclean,559.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,77977.69
+18314,15624293.0,Scott,770.0,Germany,Male,39.0,8.0,127660.46,2.0,1.0,1.0,132467.54
+18315,15694444.0,Nnachetam,691.0,Germany,Male,36.0,0.0,124577.33,2.0,1.0,1.0,110932.24
+18316,15592761.0,Nkemjika,704.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,68872.33
+18317,15613463.0,Yegorova,638.0,France,Female,29.0,7.0,125977.81,1.0,0.0,1.0,141030.08
+18318,15653547.0,Ch'ien,732.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,9984.4
+18319,15693864.0,White,678.0,Spain,Female,27.0,9.0,0.0,2.0,0.0,1.0,62232.6
+18320,15684248.0,Tuan,559.0,Germany,Female,33.0,9.0,148846.39,2.0,1.0,0.0,171060.01
+18321,15594898.0,Chukwueloka,656.0,Spain,Female,32.0,7.0,0.0,1.0,1.0,0.0,124795.84
+18322,15790829.0,Hsieh,749.0,France,Female,42.0,5.0,0.0,1.0,1.0,0.0,79005.95
+18323,15736299.0,Onyemere,584.0,France,Female,39.0,9.0,0.0,2.0,0.0,1.0,131281.28
+18324,15605826.0,Onyemauchechukwu,597.0,Germany,Female,42.0,8.0,96748.89,1.0,1.0,0.0,126554.96
+18325,15792453.0,Chukwueloka,588.0,Spain,Male,46.0,1.0,0.0,3.0,1.0,0.0,122925.5
+18326,15664037.0,T'ao,652.0,Spain,Female,48.0,6.0,0.0,2.0,1.0,1.0,161241.65
+18327,15571947.0,Ch'iu,671.0,France,Male,38.0,3.0,145933.27,1.0,1.0,1.0,189392.66
+18328,15595842.0,Chiemela,718.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,180800.42
+18329,15761976.0,T'ang,752.0,Germany,Female,39.0,6.0,136623.19,2.0,1.0,0.0,90177.3
+18330,15777904.0,Hao,501.0,Spain,Female,30.0,9.0,0.0,2.0,0.0,0.0,106636.89
+18331,15739502.0,Ch'ien,847.0,Germany,Female,37.0,5.0,122925.5,1.0,1.0,1.0,74425.41
+18332,15733797.0,Nwachukwu,624.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,143859.66
+18333,15752508.0,Lucciano,646.0,Spain,Female,26.0,6.0,104088.59,1.0,1.0,1.0,57949.72
+18334,15617304.0,Nwankwo,769.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,1.0,176570.38
+18335,15658716.0,Ch'ien,668.0,France,Female,72.0,7.0,0.0,2.0,1.0,1.0,57844.26
+18336,15686957.0,Ch'in,641.0,France,Male,32.0,6.0,76665.71,1.0,0.0,0.0,135875.51
+18337,15744517.0,Onyemauchechukwu,596.0,France,Male,46.0,3.0,0.0,1.0,1.0,0.0,114675.75
+18338,15692235.0,Onwuatuegwu,577.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,145089.64
+18339,15794566.0,Mazzanti,706.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,98894.64
+18340,15791045.0,Chibuzo,751.0,Spain,Male,40.0,7.0,106059.4,1.0,1.0,1.0,90928.02
+18341,15778838.0,Ch'ang,652.0,France,Male,38.0,6.0,112465.06,2.0,1.0,1.0,177655.68
+18342,15768887.0,Maclean,684.0,Spain,Female,36.0,2.0,107720.64,1.0,1.0,1.0,162697.93
+18343,15661528.0,Maclean,674.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,93901.61
+18344,15784890.0,Ch'eng,724.0,France,Male,51.0,4.0,0.0,3.0,1.0,0.0,105982.79
+18345,15658969.0,Onwuamaeze,693.0,France,Male,47.0,0.0,0.0,1.0,1.0,0.0,182336.45
+18346,15763327.0,Aksenov,741.0,France,Female,39.0,5.0,0.0,2.0,1.0,0.0,97792.02
+18347,15773520.0,O'Donnell,705.0,France,Male,38.0,10.0,0.0,2.0,0.0,0.0,157891.86
+18348,15646539.0,Smith,590.0,France,Male,27.0,7.0,0.0,2.0,1.0,0.0,125877.22
+18349,15665634.0,Ijendu,577.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,105570.39
+18350,15729773.0,Ch'en,732.0,France,Male,48.0,9.0,0.0,1.0,0.0,1.0,183487.98
+18351,15799217.0,Eluemuno,678.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,1.0,120830.73
+18352,15603683.0,Dickson,686.0,Spain,Male,54.0,5.0,0.0,2.0,1.0,1.0,126728.27
+18353,15637593.0,Palerma,719.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,120893.85
+18354,15656937.0,Chin,703.0,Spain,Female,32.0,1.0,0.0,2.0,1.0,0.0,167773.55
+18355,15701206.0,Tuan,791.0,France,Male,33.0,1.0,148848.54,2.0,1.0,1.0,79259.77
+18356,15620750.0,Ch'ang,661.0,France,Female,25.0,2.0,123857.51,1.0,1.0,1.0,46774.94
+18357,15586870.0,Nkemakonam,742.0,Spain,Female,57.0,7.0,0.0,1.0,1.0,1.0,1528.4
+18358,15590810.0,Pinto,699.0,France,Male,34.0,7.0,0.0,1.0,0.0,0.0,187800.63
+18359,15645463.0,Ch'ang,591.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,179436.6
+18360,15622578.0,Lai,733.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,136983.77
+18361,15688880.0,Zikoranachidimma,748.0,Germany,Male,48.0,3.0,123372.3,1.0,0.0,0.0,18094.96
+18362,15797065.0,Chidozie,599.0,France,Female,35.0,8.0,0.0,2.0,0.0,1.0,167984.72
+18363,15678725.0,Hsia,644.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,74593.84
+18364,15778830.0,Lombardo,516.0,France,Female,43.0,6.0,0.0,1.0,1.0,1.0,146585.77
+18365,15740383.0,Laidley,738.0,Spain,Male,50.0,7.0,0.0,1.0,1.0,0.0,164017.89
+18366,15571571.0,Uchenna,694.0,Spain,Male,44.0,9.0,0.0,1.0,1.0,0.0,182436.81
+18367,15807568.0,Chukwualuka,567.0,Spain,Female,28.0,1.0,0.0,2.0,1.0,1.0,97640.2
+18368,15615456.0,Zetticci,763.0,Spain,Female,28.0,3.0,0.0,2.0,0.0,1.0,125518.31
+18369,15690935.0,Ch'en,583.0,Germany,Female,32.0,1.0,51522.75,2.0,1.0,0.0,123956.83
+18370,15765311.0,Tien,590.0,Spain,Male,39.0,4.0,0.0,1.0,1.0,1.0,73679.59
+18371,15803337.0,Ch'en,656.0,Germany,Male,45.0,9.0,138341.51,1.0,1.0,0.0,116766.79
+18372,15780779.0,Yefimova,765.0,France,Male,35.0,3.0,0.0,2.0,0.0,0.0,160579.15
+18373,15658969.0,Tuan,716.0,France,Male,36.0,8.0,141078.37,1.0,0.0,0.0,14081.64
+18374,15754569.0,Milanesi,807.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,0.0,181461.46
+18375,15602354.0,Chinweuba,675.0,Spain,Female,31.0,2.0,0.0,1.0,0.0,0.0,91817.31
+18376,15576623.0,Maclean,682.0,Spain,Male,39.0,9.0,149762.08,1.0,1.0,0.0,121365.45
+18377,15617482.0,Nwachinemelu,745.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,82463.69
+18378,15682600.0,Tseng,565.0,France,Male,32.0,6.0,0.0,1.0,1.0,0.0,79082.62
+18379,15641359.0,Nnamdi,663.0,France,Female,31.0,6.0,0.0,2.0,0.0,1.0,88852.47
+18380,15646513.0,Chukwueloka,682.0,Germany,Male,45.0,1.0,126772.57,1.0,1.0,1.0,70954.41
+18381,15662291.0,Hsing,659.0,Germany,Female,52.0,1.0,148061.89,1.0,1.0,0.0,97640.2
+18382,15786800.0,Nnanna,600.0,Germany,Male,52.0,6.0,97445.49,2.0,1.0,1.0,182728.8
+18383,15571869.0,Ch'iu,464.0,France,Male,41.0,7.0,106913.43,1.0,1.0,0.0,64869.32
+18384,15661409.0,Chibugo,648.0,France,Female,28.0,7.0,0.0,2.0,1.0,1.0,184474.72
+18385,15741032.0,Ndubueze,707.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,1.0,140889.13
+18386,15631481.0,Chiu,645.0,Germany,Male,51.0,6.0,137794.18,1.0,1.0,0.0,166370.81
+18387,15782454.0,Echezonachukwu,586.0,Spain,Female,53.0,3.0,0.0,1.0,1.0,1.0,95936.84
+18388,15651144.0,Chukwualuka,686.0,Spain,Female,39.0,4.0,80780.16,1.0,1.0,0.0,182612.38
+18389,15785078.0,Nkemdilim,587.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,115733.16
+18390,15686461.0,Chimezie,479.0,Germany,Male,41.0,8.0,81570.06,1.0,1.0,1.0,103898.78
+18391,15711572.0,Tien,611.0,France,Male,46.0,5.0,112808.18,1.0,0.0,1.0,1375.46
+18392,15572038.0,Ts'ai,718.0,Spain,Male,36.0,5.0,0.0,1.0,1.0,1.0,181757.19
+18393,15577514.0,Ibezimako,645.0,Germany,Female,29.0,1.0,106521.49,1.0,1.0,0.0,147055.87
+18394,15707596.0,Cattaneo,711.0,Spain,Male,43.0,10.0,0.0,2.0,1.0,0.0,98931.5
+18395,15682355.0,Pisano,637.0,Germany,Male,40.0,4.0,118788.57,1.0,0.0,1.0,93769.89
+18396,15572398.0,Chimezie,595.0,France,Male,50.0,10.0,0.0,1.0,0.0,1.0,181419.29
+18397,15783541.0,Kao,714.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,120893.07
+18398,15742091.0,Iweobiegbunam,605.0,France,Male,56.0,3.0,0.0,1.0,1.0,0.0,126551.65
+18399,15681075.0,Trevisani,657.0,France,Male,37.0,2.0,0.0,1.0,1.0,0.0,111241.37
+18400,15607560.0,Kaeppel,717.0,France,Male,36.0,7.0,0.0,1.0,1.0,1.0,141806.44
+18401,15702631.0,Tung,683.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,121277.78
+18402,15807107.0,Chukwuemeka,656.0,France,Female,64.0,3.0,108377.75,1.0,1.0,1.0,103921.41
+18403,15777923.0,Biryukov,678.0,Germany,Male,45.0,1.0,133970.21,2.0,1.0,0.0,176750.81
+18404,15676521.0,Young,689.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,1.0,106826.92
+18405,15813095.0,Fiorentino,655.0,France,Female,30.0,7.0,137794.18,1.0,1.0,0.0,184800.27
+18406,15610105.0,Zaytseva,535.0,Spain,Male,61.0,8.0,0.0,2.0,0.0,0.0,169415.61
+18407,15803681.0,Onyemachukwu,556.0,France,Female,38.0,9.0,0.0,2.0,1.0,0.0,172049.84
+18408,15753361.0,Hsia,646.0,Spain,Female,52.0,8.0,0.0,1.0,1.0,1.0,143357.07
+18409,15581551.0,Ewing,721.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,1.0,141946.92
+18410,15604345.0,Folliero,526.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,10963.04
+18411,15617206.0,Ponomaryov,648.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,0.0,150422.35
+18412,15771417.0,Ch'iu,687.0,Spain,Female,32.0,3.0,0.0,2.0,1.0,0.0,52795.15
+18413,15672640.0,Okechukwu,587.0,France,Male,42.0,1.0,0.0,3.0,1.0,0.0,43982.41
+18414,15730705.0,Hovell,669.0,Germany,Male,33.0,9.0,162482.76,1.0,1.0,1.0,186884.04
+18415,15615126.0,Hsueh,675.0,France,Female,35.0,0.0,0.0,2.0,1.0,1.0,172096.08
+18416,15605826.0,Napolitano,549.0,Spain,Female,33.0,4.0,86614.06,1.0,1.0,0.0,133961.19
+18417,15632503.0,Martin,681.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,67926.18
+18418,15802741.0,T'ang,567.0,France,Female,56.0,8.0,0.0,3.0,1.0,1.0,136910.18
+18419,15745196.0,T'ao,684.0,France,Male,40.0,4.0,0.0,1.0,0.0,0.0,86786.41
+18420,15743817.0,Young,624.0,France,Male,33.0,6.0,0.0,2.0,0.0,1.0,143635.36
+18421,15747591.0,Brierly,718.0,France,Male,50.0,10.0,0.0,1.0,0.0,0.0,171097.39
+18422,15656029.0,Ch'iu,727.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,163471.01
+18423,15809837.0,Iadanza,601.0,France,Male,48.0,7.0,0.0,2.0,1.0,1.0,177670.57
+18424,15734246.0,Tung,663.0,France,Female,40.0,2.0,0.0,1.0,0.0,1.0,162858.29
+18425,15790763.0,Naylor,704.0,Germany,Male,48.0,2.0,151958.19,3.0,1.0,0.0,52876.41
+18426,15656812.0,Tuan,653.0,Germany,Female,37.0,6.0,87926.24,2.0,0.0,0.0,116969.73
+18427,15786418.0,Ndubueze,607.0,Spain,Male,50.0,2.0,113736.26,1.0,1.0,0.0,189392.66
+18428,15668894.0,Ch'iu,706.0,France,Male,29.0,2.0,0.0,2.0,0.0,1.0,130500.65
+18429,15740383.0,Hao,568.0,France,Female,43.0,4.0,0.0,2.0,1.0,0.0,143783.29
+18430,15617166.0,Okwudilichukwu,729.0,Germany,Male,48.0,5.0,115074.02,1.0,1.0,0.0,180622.02
+18431,15781534.0,Smith,712.0,France,Female,24.0,7.0,0.0,2.0,0.0,0.0,110114.19
+18432,15669611.0,Chukwufumnanya,616.0,France,Male,30.0,10.0,0.0,2.0,1.0,1.0,84760.32
+18433,15658982.0,Lai,627.0,France,Male,30.0,9.0,123593.22,1.0,1.0,1.0,134946.43
+18434,15578744.0,Ts'ai,545.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,56908.41
+18435,15633064.0,Ch'iu,706.0,Spain,Female,31.0,3.0,0.0,1.0,1.0,0.0,100365.51
+18436,15585738.0,Ngozichukwuka,707.0,Spain,Male,40.0,4.0,0.0,1.0,0.0,1.0,136983.77
+18437,15736146.0,Kao,601.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,61324.87
+18438,15783530.0,Fyans,715.0,France,Female,32.0,8.0,126473.99,1.0,1.0,0.0,92381.01
+18439,15704583.0,Lucchesi,754.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,63737.19
+18440,15668309.0,Iadanza,645.0,Germany,Male,31.0,1.0,106070.89,1.0,1.0,1.0,102930.46
+18441,15676937.0,T'ien,535.0,France,Male,31.0,4.0,0.0,1.0,0.0,0.0,182867.84
+18442,15583392.0,Ch'ang,676.0,Spain,Female,38.0,6.0,115409.6,1.0,1.0,1.0,71405.17
+18443,15751561.0,Ch'iu,588.0,France,Male,33.0,8.0,122325.53,1.0,1.0,1.0,63795.8
+18444,15651958.0,Chuang,554.0,France,Female,43.0,6.0,0.0,2.0,1.0,0.0,171457.06
+18445,15772363.0,Tsao,632.0,Germany,Female,44.0,9.0,121276.32,1.0,1.0,0.0,80698.54
+18446,15784323.0,Mancini,667.0,France,Female,44.0,5.0,0.0,1.0,0.0,0.0,174969.91
+18447,15701354.0,Hsu,594.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,95957.09
+18448,15576085.0,Hsia,731.0,Germany,Female,42.0,9.0,74430.1,1.0,1.0,0.0,184524.74
+18449,15691808.0,Toscani,616.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,126812.63
+18450,15783029.0,Ch'ien,736.0,Spain,Female,62.0,8.0,0.0,2.0,1.0,1.0,136909.87
+18451,15776947.0,Hsiung,582.0,Spain,Male,23.0,7.0,0.0,2.0,1.0,0.0,74850.84
+18452,15733705.0,Ch'iu,622.0,France,Female,45.0,7.0,0.0,1.0,1.0,0.0,153917.38
+18453,15630195.0,Sun,695.0,France,Female,36.0,4.0,0.0,2.0,0.0,0.0,180318.6
+18454,15742408.0,Hsiao,683.0,France,Female,47.0,9.0,0.0,1.0,1.0,0.0,23146.62
+18455,15589147.0,Nnamdi,683.0,Germany,Female,50.0,10.0,148045.45,2.0,1.0,1.0,149994.68
+18456,15592999.0,Mazzanti,771.0,France,Male,26.0,8.0,94416.78,1.0,1.0,0.0,82336.73
+18457,15598044.0,Dickson,624.0,Spain,Male,35.0,9.0,76665.71,1.0,1.0,1.0,122518.5
+18458,15706464.0,Toscani,611.0,France,Male,50.0,9.0,0.0,1.0,0.0,0.0,92381.01
+18459,15693376.0,K?,642.0,Germany,Female,44.0,4.0,132405.52,1.0,1.0,1.0,88890.05
+18460,15651389.0,Ch'eng,634.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,0.0,94767.17
+18461,15717185.0,Smith,594.0,France,Female,51.0,0.0,0.0,2.0,0.0,1.0,150835.21
+18462,15610379.0,Hs?,705.0,France,Male,39.0,2.0,115897.73,1.0,1.0,1.0,57553.02
+18463,15617166.0,Maclean,771.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,76614.06
+18464,15593939.0,Tseng,685.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,55867.38
+18465,15607301.0,Lucchese,682.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,106663.46
+18466,15785559.0,Yuryeva,710.0,France,Male,31.0,3.0,137761.55,1.0,0.0,1.0,159800.16
+18467,15754926.0,Clements,745.0,Spain,Female,42.0,8.0,0.0,2.0,1.0,0.0,57962.41
+18468,15712409.0,Onwuamaeze,573.0,Spain,Female,38.0,9.0,0.0,1.0,1.0,0.0,84920.49
+18469,15585853.0,Toscani,627.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,142425.07
+18470,15777047.0,Maclean,619.0,France,Female,40.0,0.0,93616.35,1.0,1.0,0.0,121277.38
+18471,15714227.0,Nkemdirim,488.0,Germany,Female,45.0,1.0,131834.76,1.0,1.0,0.0,126143.96
+18472,15759874.0,Ch'in,678.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,142516.35
+18473,15655389.0,Padovano,632.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,143315.57
+18474,15631460.0,Ch'eng,706.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,84760.32
+18475,15727299.0,Ch'iu,751.0,France,Female,39.0,9.0,0.0,2.0,1.0,0.0,160979.66
+18476,15675787.0,P'eng,741.0,France,Female,32.0,6.0,0.0,1.0,1.0,1.0,124749.08
+18477,15673020.0,Ts'ao,683.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,103760.53
+18478,15680399.0,Chidalu,685.0,France,Female,56.0,7.0,0.0,1.0,1.0,0.0,133624.56
+18479,15594902.0,Uspensky,621.0,France,Male,40.0,0.0,0.0,2.0,0.0,1.0,151315.76
+18480,15729110.0,Shih,561.0,France,Female,49.0,9.0,0.0,1.0,1.0,0.0,138380.12
+18481,15568487.0,McElhone,646.0,Germany,Female,26.0,7.0,87098.91,2.0,1.0,1.0,62972.13
+18482,15568106.0,Genovese,674.0,France,Female,47.0,8.0,166594.78,1.0,1.0,0.0,74854.97
+18483,15722532.0,T'ien,632.0,France,Male,27.0,8.0,124729.96,2.0,1.0,0.0,114775.53
+18484,15579891.0,Hs?eh,746.0,France,Female,64.0,4.0,0.0,2.0,1.0,1.0,74836.64
+18485,15572073.0,Milanesi,663.0,France,Male,63.0,9.0,0.0,1.0,0.0,0.0,157557.71
+18486,15576683.0,Onyemauchechi,673.0,Spain,Male,38.0,10.0,122471.09,1.0,0.0,0.0,47886.44
+18487,15807900.0,Yermolayev,717.0,France,Female,27.0,7.0,0.0,2.0,0.0,0.0,178373.43
+18488,15808846.0,Maclean,713.0,France,Female,47.0,9.0,0.0,1.0,1.0,0.0,92712.97
+18489,15801351.0,Ch'iu,705.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,1676.92
+18490,15786035.0,Cattaneo,711.0,Germany,Female,23.0,10.0,71497.79,2.0,1.0,1.0,84436.67
+18491,15642725.0,Ts'ui,709.0,Spain,Male,30.0,4.0,0.0,2.0,0.0,0.0,95957.09
+18492,15771750.0,Mazzanti,683.0,France,Female,44.0,6.0,0.0,1.0,1.0,0.0,91775.51
+18493,15600974.0,Hs?,568.0,Germany,Male,27.0,3.0,140197.18,1.0,0.0,0.0,173386.95
+18494,15795519.0,Cattaneo,637.0,France,Male,56.0,9.0,137688.71,1.0,1.0,1.0,132540.33
+18495,15682355.0,P'eng,607.0,Germany,Female,45.0,6.0,134006.77,1.0,1.0,0.0,175072.47
+18496,15627162.0,Yobachi,628.0,Germany,Male,42.0,4.0,126645.13,2.0,1.0,1.0,140938.95
+18497,15735572.0,Chukwukadibia,567.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,0.0,67552.71
+18498,15623102.0,Kao,637.0,Germany,Female,41.0,3.0,102749.72,1.0,1.0,0.0,163570.25
+18499,15618245.0,Hovell,683.0,France,Male,23.0,8.0,0.0,2.0,1.0,1.0,163484.8
+18500,15674727.0,Manna,716.0,France,Female,64.0,5.0,126473.33,1.0,1.0,1.0,44001.11
+18501,15635143.0,Hs?,579.0,France,Female,41.0,6.0,0.0,1.0,1.0,0.0,71926.28
+18502,15600792.0,Lucciano,773.0,France,Female,30.0,9.0,106414.57,1.0,1.0,1.0,124592.84
+18503,15792729.0,Nwagugheuzo,642.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,1.0,189307.7
+18504,15783883.0,Onio,738.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,0.0,152566.75
+18505,15592707.0,Onyemachukwu,699.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,0.0,81602.02
+18506,15592645.0,P'eng,526.0,Germany,Female,33.0,2.0,75801.74,2.0,0.0,0.0,27472.07
+18507,15584518.0,Ts'ao,781.0,France,Male,38.0,7.0,126473.33,1.0,1.0,0.0,180829.99
+18508,15671591.0,Scott,653.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,124071.71
+18509,15748473.0,Nnamutaezinwa,716.0,France,Male,31.0,6.0,0.0,2.0,0.0,0.0,143598.7
+18510,15618255.0,Yermakov,587.0,Germany,Female,34.0,2.0,129855.64,1.0,1.0,1.0,122639.73
+18511,15756539.0,Ebelegbulam,809.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,177719.88
+18512,15618018.0,Chuang,687.0,Spain,Male,37.0,4.0,167545.32,1.0,1.0,1.0,157862.82
+18513,15738785.0,Chiu,792.0,Germany,Male,49.0,9.0,125903.22,1.0,1.0,0.0,134822.04
+18514,15761148.0,Yermolayeva,679.0,France,Male,28.0,2.0,0.0,2.0,1.0,1.0,145562.4
+18515,15593968.0,Nwabugwu,658.0,Germany,Female,28.0,9.0,118560.0,2.0,1.0,0.0,62810.91
+18516,15674811.0,Teng,619.0,Spain,Male,38.0,10.0,99812.88,1.0,1.0,0.0,119313.74
+18517,15729201.0,Chibueze,792.0,France,Male,26.0,3.0,0.0,2.0,1.0,1.0,143783.46
+18518,15781678.0,White,679.0,Spain,Female,42.0,5.0,0.0,2.0,1.0,1.0,54113.38
+18519,15657778.0,Ts'ai,639.0,France,Male,39.0,10.0,0.0,2.0,0.0,0.0,162884.85
+18520,15778936.0,Ch'ien,753.0,France,Male,28.0,10.0,0.0,2.0,1.0,0.0,126796.69
+18521,15596972.0,Ch'in,585.0,Spain,Female,37.0,2.0,0.0,2.0,0.0,0.0,138526.96
+18522,15591766.0,P'eng,679.0,Spain,Female,29.0,2.0,0.0,2.0,0.0,0.0,96670.2
+18523,15645849.0,Brierly,573.0,Spain,Male,26.0,0.0,0.0,2.0,1.0,0.0,61315.37
+18524,15756743.0,Chukwufumnanya,741.0,France,Female,37.0,7.0,0.0,2.0,0.0,0.0,163587.9
+18525,15758726.0,Ch'iu,590.0,Germany,Male,29.0,1.0,107879.03,2.0,1.0,1.0,141180.66
+18526,15671779.0,L?,727.0,Spain,Male,38.0,3.0,0.0,1.0,1.0,0.0,102297.15
+18527,15707974.0,Chinomso,653.0,Germany,Female,56.0,6.0,135296.33,1.0,1.0,0.0,160574.09
+18528,15600813.0,Mistry,644.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,143022.31
+18529,15731790.0,Cattaneo,731.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,136079.64
+18530,15754068.0,Ritchie,699.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,89519.47
+18531,15616168.0,Chinomso,540.0,France,Male,38.0,4.0,0.0,2.0,0.0,1.0,61811.1
+18532,15633194.0,Hsieh,663.0,France,Female,43.0,9.0,0.0,2.0,1.0,0.0,118862.08
+18533,15767681.0,Chinwenma,774.0,France,Female,27.0,9.0,0.0,2.0,1.0,1.0,50679.81
+18534,15750778.0,Mazzanti,516.0,France,Female,34.0,0.0,0.0,1.0,1.0,0.0,136985.08
+18535,15691808.0,Iqbal,684.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,151317.27
+18536,15740383.0,Milano,686.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,143357.02
+18537,15757559.0,Shih,697.0,Spain,Male,35.0,6.0,0.0,3.0,1.0,1.0,38196.24
+18538,15572626.0,Chiu,640.0,Spain,Female,37.0,5.0,0.0,1.0,1.0,0.0,85746.52
+18539,15678058.0,Chin,598.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,71862.79
+18540,15722072.0,Ch'iu,571.0,France,Female,39.0,8.0,122388.34,1.0,0.0,0.0,176692.65
+18541,15594827.0,Yermakova,725.0,France,Female,28.0,4.0,0.0,2.0,0.0,0.0,176780.69
+18542,15640507.0,Onyemere,641.0,Spain,Male,38.0,7.0,0.0,1.0,1.0,0.0,161671.15
+18543,15717010.0,Tung,744.0,Spain,Male,39.0,2.0,0.0,2.0,1.0,0.0,94790.34
+18544,15581871.0,Calabrese,652.0,Germany,Female,35.0,6.0,103391.38,2.0,1.0,0.0,160532.88
+18545,15689886.0,Chiu,706.0,France,Female,35.0,8.0,0.0,2.0,0.0,0.0,103832.58
+18546,15642824.0,Nnonso,662.0,France,Male,34.0,4.0,0.0,1.0,0.0,1.0,51358.66
+18547,15681887.0,Okwudilichukwu,773.0,France,Male,30.0,0.0,0.0,1.0,1.0,0.0,102260.16
+18548,15667896.0,Chien,686.0,France,Female,44.0,9.0,0.0,1.0,0.0,0.0,143665.44
+18549,15762682.0,Olisanugo,653.0,France,Male,60.0,10.0,0.0,1.0,1.0,0.0,187800.63
+18550,15684801.0,Hs?,602.0,France,Female,48.0,4.0,124053.03,3.0,1.0,0.0,148948.17
+18551,15593897.0,Hs?,641.0,France,Male,30.0,7.0,128663.9,1.0,1.0,0.0,134600.94
+18552,15752084.0,Olisaemeka,646.0,France,Male,51.0,8.0,0.0,1.0,1.0,1.0,40920.59
+18553,15772638.0,Chiazagomekpele,645.0,Germany,Male,40.0,2.0,124043.8,1.0,1.0,0.0,184464.7
+18554,15661827.0,Tuan,731.0,France,Male,31.0,8.0,0.0,1.0,1.0,0.0,74532.04
+18555,15730796.0,T'an,753.0,Spain,Female,24.0,7.0,139825.3,1.0,1.0,0.0,118959.64
+18556,15598044.0,Cattaneo,779.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,91347.76
+18557,15748797.0,Chiang,593.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,170819.01
+18558,15777873.0,Ndubueze,664.0,Germany,Female,27.0,2.0,98765.84,2.0,1.0,1.0,24482.16
+18559,15742798.0,Horsley,603.0,France,Male,45.0,3.0,0.0,1.0,1.0,0.0,158840.99
+18560,15708046.0,Ch'ang,590.0,Spain,Male,47.0,10.0,0.0,2.0,1.0,0.0,90928.48
+18561,15727041.0,Martin,676.0,Germany,Male,30.0,9.0,102962.62,2.0,1.0,0.0,115387.4
+18562,15758035.0,Ch'eng,645.0,France,Female,42.0,9.0,0.0,1.0,1.0,1.0,180593.45
+18563,15780256.0,Ch'eng,599.0,Germany,Male,34.0,2.0,178058.06,2.0,0.0,1.0,177528.92
+18564,15633840.0,Li Fonti,584.0,France,Female,50.0,7.0,128262.72,1.0,1.0,0.0,75825.57
+18565,15732299.0,Dickson,671.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,1.0,179128.69
+18566,15572390.0,Pugh,507.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,1.0,170968.99
+18567,15813140.0,Chia,597.0,France,Male,43.0,3.0,0.0,2.0,0.0,1.0,120880.72
+18568,15587394.0,Nwokezuike,693.0,France,Male,25.0,7.0,117231.63,2.0,1.0,1.0,151401.33
+18569,15593959.0,Pirozzi,730.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,153537.57
+18570,15681843.0,Yefimova,653.0,France,Female,40.0,3.0,101952.96,1.0,0.0,0.0,135655.29
+18571,15624293.0,Toscani,699.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,114448.77
+18572,15692991.0,H?,726.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,67979.35
+18573,15803479.0,Pirozzi,724.0,France,Male,41.0,4.0,0.0,2.0,0.0,1.0,181694.44
+18574,15572792.0,Chidozie,758.0,Germany,Female,40.0,10.0,123374.46,2.0,1.0,1.0,177279.41
+18575,15646936.0,Lo Duca,644.0,France,Male,39.0,4.0,143340.44,2.0,0.0,0.0,147040.44
+18576,15668886.0,Hill,665.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,0.0,64716.08
+18577,15762392.0,Mironova,712.0,France,Male,42.0,5.0,0.0,2.0,0.0,0.0,91673.6
+18578,15700749.0,Ch'iu,645.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,1.0,105886.77
+18579,15748691.0,Zetticci,543.0,Spain,Male,57.0,9.0,0.0,2.0,1.0,1.0,92797.23
+18580,15775104.0,Onwuamaeze,706.0,France,Male,31.0,8.0,0.0,1.0,1.0,1.0,138380.11
+18581,15631512.0,Ch'ang,706.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,151903.6
+18582,15809837.0,Hsiung,704.0,Germany,Male,28.0,9.0,130142.79,1.0,1.0,1.0,93901.61
+18583,15655232.0,Chiang,749.0,Germany,Female,29.0,2.0,150944.94,2.0,1.0,1.0,161519.77
+18584,15773869.0,Nwebube,628.0,Germany,Female,30.0,6.0,88084.13,1.0,1.0,0.0,92220.12
+18585,15775892.0,Chidiebere,549.0,France,Male,32.0,7.0,88594.14,1.0,0.0,1.0,133692.88
+18586,15623695.0,Chia,756.0,Germany,Female,35.0,5.0,161889.79,2.0,1.0,0.0,99017.34
+18587,15762268.0,Hsia,794.0,France,Female,38.0,4.0,0.0,2.0,0.0,1.0,86043.27
+18588,15759066.0,Kao,666.0,Germany,Female,38.0,5.0,115924.8,1.0,1.0,0.0,167553.06
+18589,15681755.0,Buchi,562.0,France,Female,37.0,9.0,0.0,2.0,0.0,0.0,174434.06
+18590,15671915.0,Scott,731.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,168816.22
+18591,15597957.0,Lu,742.0,Spain,Female,46.0,4.0,143542.36,1.0,1.0,1.0,64345.61
+18592,15711219.0,Ku,733.0,France,Female,36.0,6.0,0.0,1.0,0.0,0.0,158735.1
+18593,15743759.0,Ch'ien,706.0,France,Female,28.0,7.0,132598.34,1.0,1.0,0.0,129964.94
+18594,15671172.0,Sun,574.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,121747.96
+18595,15804853.0,Nnamutaezinwa,668.0,France,Female,51.0,3.0,0.0,1.0,0.0,0.0,93717.55
+18596,15694566.0,Chikelu,675.0,France,Female,36.0,5.0,0.0,1.0,1.0,1.0,66810.48
+18597,15648067.0,Yeh,615.0,Germany,Male,25.0,10.0,129837.72,1.0,1.0,1.0,121537.91
+18598,15708046.0,Ts'ui,627.0,Spain,Male,50.0,9.0,0.0,1.0,0.0,1.0,47799.15
+18599,15711901.0,Hsu,753.0,France,Male,62.0,5.0,0.0,1.0,1.0,1.0,178342.63
+18600,15728010.0,P'an,637.0,France,Female,40.0,9.0,0.0,1.0,1.0,0.0,122518.5
+18601,15732967.0,Onyemere,642.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,1.0,108313.34
+18602,15658946.0,P'eng,699.0,Spain,Male,23.0,9.0,0.0,2.0,1.0,0.0,93874.87
+18603,15769346.0,Nucci,571.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,73686.75
+18604,15760873.0,Okwudiliolisa,719.0,Germany,Male,43.0,2.0,136576.02,1.0,1.0,0.0,75825.57
+18605,15646513.0,Hs?,640.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,53817.23
+18606,15774882.0,Hs?,624.0,France,Male,47.0,8.0,0.0,2.0,1.0,1.0,82696.84
+18607,15584518.0,Liao,542.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,158071.69
+18608,15774857.0,Onyekachi,669.0,France,Female,43.0,2.0,0.0,1.0,0.0,0.0,108863.38
+18609,15796313.0,Chidubem,682.0,Germany,Female,37.0,2.0,123959.97,1.0,0.0,1.0,134878.19
+18610,15741921.0,Teng,696.0,Spain,Female,39.0,8.0,0.0,1.0,1.0,1.0,86044.98
+18611,15670343.0,Kennedy,543.0,Spain,Male,31.0,3.0,0.0,2.0,0.0,1.0,170634.49
+18612,15681075.0,Lai,636.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,132973.39
+18613,15573319.0,Chukwuemeka,569.0,Germany,Female,50.0,7.0,114737.43,1.0,1.0,0.0,172939.3
+18614,15701176.0,Onwuamaeze,614.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,55857.48
+18615,15730038.0,Chukwukadibia,746.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,167984.61
+18616,15619494.0,Macleod,634.0,Spain,Male,32.0,10.0,0.0,2.0,0.0,1.0,111241.37
+18617,15633944.0,Frolov,850.0,France,Female,49.0,3.0,0.0,1.0,0.0,0.0,28707.69
+18618,15691867.0,Ch'ien,687.0,Germany,Female,29.0,2.0,119278.01,2.0,0.0,0.0,131097.9
+18619,15694376.0,K'ung,513.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,164864.67
+18620,15699415.0,Hsieh,684.0,France,Female,34.0,7.0,151027.56,2.0,0.0,1.0,189430.86
+18621,15787827.0,P'eng,691.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,54474.48
+18622,15652693.0,Tien,473.0,France,Male,26.0,8.0,82441.75,1.0,1.0,1.0,143952.24
+18623,15661936.0,Hsia,595.0,France,Female,41.0,2.0,0.0,2.0,0.0,0.0,96734.46
+18624,15809722.0,Udobata,602.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,141872.05
+18625,15765402.0,Kenechukwu,693.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,141069.88
+18626,15776596.0,Ugoji,679.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,167984.72
+18627,15662494.0,H?,675.0,Spain,Female,28.0,7.0,0.0,1.0,1.0,0.0,173486.39
+18628,15743016.0,Teng,535.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,129912.74
+18629,15605968.0,Shao,568.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,93769.89
+18630,15588537.0,Ch'iu,597.0,Spain,Female,64.0,1.0,0.0,2.0,1.0,1.0,140889.02
+18631,15592089.0,Milne,658.0,Germany,Male,42.0,0.0,93816.2,1.0,1.0,0.0,145558.42
+18632,15773798.0,Chigolum,694.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,1.0,123611.94
+18633,15656592.0,P'eng,696.0,France,Male,29.0,1.0,148846.39,1.0,1.0,1.0,155879.57
+18634,15665129.0,Milani,686.0,France,Male,44.0,9.0,51949.95,1.0,1.0,0.0,141756.32
+18635,15806822.0,Maclean,604.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,1.0,141069.88
+18636,15702293.0,Smith,551.0,France,Male,36.0,6.0,0.0,1.0,1.0,0.0,123882.73
+18637,15656937.0,Ch'ien,667.0,France,Male,53.0,4.0,124577.33,1.0,1.0,1.0,126570.79
+18638,15571244.0,Chuang,664.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,63737.19
+18639,15593468.0,Ukaegbunam,755.0,France,Male,55.0,9.0,0.0,2.0,0.0,1.0,173784.04
+18640,15750476.0,Chiazagomekpele,705.0,Germany,Female,37.0,2.0,76495.04,1.0,1.0,1.0,172668.38
+18641,15697311.0,Manna,538.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,141210.5
+18642,15698932.0,Hao,711.0,Spain,Male,63.0,6.0,0.0,1.0,1.0,1.0,185510.34
+18643,15569222.0,Onyinyechukwuka,639.0,Spain,Female,33.0,1.0,0.0,2.0,1.0,0.0,151738.54
+18644,15591723.0,Chiawuotu,763.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,87566.97
+18645,15742329.0,Hs?,724.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,142838.64
+18646,15686611.0,T'ang,732.0,France,Female,41.0,9.0,95823.3,1.0,1.0,0.0,82565.01
+18647,15662238.0,Ibezimako,595.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,103603.65
+18648,15657540.0,Chin,456.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,143397.28
+18649,15597591.0,Nkemdirim,753.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,123968.14
+18650,15580146.0,Onyemere,581.0,France,Male,33.0,9.0,0.0,1.0,1.0,1.0,185561.89
+18651,15665047.0,Chiang,755.0,France,Male,67.0,1.0,0.0,2.0,1.0,1.0,170528.63
+18652,15674840.0,Udegbulam,641.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,0.0,181224.56
+18653,15708713.0,P'eng,599.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,72862.94
+18654,15804787.0,Scott,682.0,France,Female,46.0,8.0,0.0,2.0,1.0,0.0,88834.41
+18655,15668575.0,Aitken,627.0,Spain,Female,37.0,8.0,0.0,2.0,0.0,0.0,180716.1
+18656,15795511.0,Chiawuotu,437.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,0.0,199242.65
+18657,15769504.0,Yefimova,722.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,131772.51
+18658,15657779.0,Tsou,678.0,France,Male,35.0,4.0,152347.01,1.0,0.0,0.0,59645.43
+18659,15791323.0,Calabresi,661.0,France,Male,24.0,8.0,0.0,1.0,1.0,0.0,77965.67
+18660,15776433.0,Ritchie,644.0,Spain,Male,27.0,3.0,0.0,2.0,1.0,0.0,60728.89
+18661,15661219.0,Tung,537.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,64726.65
+18662,15588537.0,Ch'ien,699.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,123663.63
+18663,15738219.0,Chibueze,730.0,France,Female,22.0,1.0,0.0,1.0,0.0,1.0,80134.65
+18664,15790594.0,Chidumaga,669.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,1.0,28257.89
+18665,15775203.0,T'ien,657.0,France,Female,28.0,4.0,115462.02,1.0,0.0,0.0,120893.07
+18666,15687329.0,Olisanugo,469.0,France,Male,29.0,3.0,0.0,1.0,1.0,0.0,93874.87
+18667,15680405.0,Zetticci,651.0,France,Male,39.0,0.0,0.0,2.0,1.0,0.0,166677.35
+18668,15785782.0,O'Sullivan,550.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,0.0,82537.01
+18669,15761713.0,Liao,717.0,France,Male,38.0,6.0,0.0,2.0,0.0,0.0,59249.83
+18670,15805523.0,Palerma,704.0,France,Female,29.0,1.0,0.0,1.0,1.0,1.0,174570.55
+18671,15583692.0,Nwankwo,793.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,152422.91
+18672,15635388.0,Ebelechukwu,653.0,Germany,Male,51.0,3.0,109733.2,1.0,1.0,0.0,57589.29
+18673,15688880.0,Ch'in,657.0,Germany,Female,38.0,1.0,118994.84,1.0,1.0,0.0,133600.89
+18674,15732430.0,Uspensky,749.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,140780.8
+18675,15675190.0,Trevisano,679.0,Germany,Female,46.0,4.0,92716.96,1.0,1.0,1.0,74800.99
+18676,15690744.0,P'an,595.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,113581.85
+18677,15761158.0,T'ao,667.0,France,Female,29.0,3.0,0.0,2.0,0.0,1.0,53478.02
+18678,15603594.0,Nwabugwu,710.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,91347.76
+18679,15637354.0,Genovese,597.0,Spain,Male,33.0,6.0,108862.76,1.0,1.0,1.0,162889.1
+18680,15616088.0,Kosisochukwu,553.0,France,Female,59.0,2.0,127862.93,1.0,1.0,1.0,100862.54
+18681,15666982.0,Ozerova,602.0,France,Male,51.0,5.0,79871.02,2.0,1.0,1.0,44406.17
+18682,15608688.0,Toscani,652.0,France,Female,39.0,3.0,123042.0,1.0,1.0,1.0,153373.71
+18683,15752915.0,Mistry,584.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,0.0,56582.95
+18684,15765537.0,Hsu,537.0,Germany,Male,56.0,3.0,111568.01,1.0,1.0,0.0,57553.98
+18685,15585312.0,Yegorova,727.0,France,Male,29.0,8.0,0.0,2.0,0.0,1.0,174094.24
+18686,15723400.0,Palerma,639.0,France,Female,36.0,2.0,115280.49,1.0,1.0,0.0,98820.09
+18687,15642530.0,Chukwuemeka,586.0,France,Female,46.0,0.0,0.0,1.0,1.0,0.0,179445.24
+18688,15730076.0,Tuan,599.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,156768.45
+18689,15729246.0,Nkemdirim,647.0,Spain,Female,39.0,6.0,123760.68,1.0,1.0,1.0,187900.12
+18690,15669414.0,Chukwuemeka,735.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,108841.83
+18691,15576517.0,Toscani,553.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,141035.65
+18692,15600724.0,Zhirov,682.0,Spain,Female,40.0,9.0,0.0,2.0,0.0,0.0,112104.55
+18693,15661290.0,Zaytseva,538.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,152491.03
+18694,15794048.0,Cattaneo,628.0,France,Male,29.0,1.0,0.0,1.0,0.0,1.0,94739.2
+18695,15701164.0,Chukwufumnanya,683.0,Germany,Female,58.0,4.0,120599.38,4.0,1.0,0.0,60721.4
+18696,15696812.0,Uwaezuoke,687.0,Germany,Male,43.0,5.0,150777.72,1.0,0.0,1.0,172363.41
+18697,15778915.0,L?,543.0,Spain,Female,42.0,8.0,156847.29,1.0,1.0,0.0,169963.2
+18698,15662494.0,Pagnotto,714.0,Germany,Male,50.0,9.0,85438.21,1.0,1.0,0.0,58854.88
+18699,15726058.0,Hsia,663.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,83058.14
+18700,15773447.0,Ebelechukwu,732.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,155846.69
+18701,15691792.0,Folliero,537.0,Germany,Male,36.0,2.0,141252.18,2.0,1.0,1.0,88842.8
+18702,15753874.0,Smith,571.0,France,Female,35.0,7.0,0.0,1.0,0.0,1.0,167973.63
+18703,15710390.0,Aitken,667.0,Spain,Male,25.0,3.0,0.0,2.0,1.0,0.0,75969.19
+18704,15587913.0,Ch'ang,577.0,Spain,Female,44.0,5.0,128760.32,1.0,1.0,0.0,172810.51
+18705,15804787.0,Nnachetam,716.0,France,Male,44.0,6.0,0.0,2.0,1.0,0.0,141533.19
+18706,15738751.0,T'ien,573.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,134778.01
+18707,15700801.0,Obidimkpa,751.0,France,Female,34.0,1.0,0.0,1.0,0.0,1.0,35600.25
+18708,15737998.0,T'ien,641.0,France,Female,31.0,6.0,0.0,2.0,0.0,0.0,141230.07
+18709,15702631.0,Smith,592.0,France,Female,42.0,4.0,0.0,1.0,1.0,0.0,139946.68
+18710,15679812.0,Macleod,604.0,France,Male,40.0,7.0,0.0,2.0,0.0,0.0,171681.86
+18711,15645103.0,Tung,575.0,France,Male,34.0,2.0,138843.7,1.0,1.0,1.0,139953.94
+18712,15613786.0,Okwudiliolisa,691.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,0.0,4989.33
+18713,15791334.0,Uchechukwu,635.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,99645.04
+18714,15682467.0,Mazzanti,703.0,France,Female,32.0,5.0,0.0,1.0,1.0,1.0,144874.67
+18715,15788659.0,Chukwueloka,667.0,Spain,Male,33.0,3.0,0.0,2.0,0.0,1.0,52885.23
+18716,15806081.0,Ch'in,790.0,Germany,Male,41.0,3.0,121461.63,1.0,0.0,0.0,180712.28
+18717,15714485.0,Kolesnikova,751.0,Spain,Male,33.0,4.0,142296.13,1.0,1.0,0.0,145551.6
+18718,15799031.0,Chiabuotu,581.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,0.0,102021.39
+18719,15655961.0,Young,645.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,158545.25
+18720,15673372.0,Ubanwa,567.0,France,Female,31.0,9.0,166501.16,1.0,1.0,0.0,128841.31
+18721,15578878.0,Kincaid,682.0,France,Male,24.0,4.0,0.0,1.0,0.0,1.0,146652.11
+18722,15762605.0,L?,685.0,France,Male,29.0,10.0,0.0,2.0,1.0,0.0,156792.89
+18723,15784761.0,Brown,773.0,Germany,Female,49.0,8.0,116439.65,1.0,1.0,1.0,38171.62
+18724,15655238.0,Udinese,603.0,Germany,Female,43.0,4.0,132287.92,1.0,0.0,1.0,82565.01
+18725,15766289.0,Lucchese,587.0,Spain,Female,46.0,4.0,0.0,1.0,1.0,0.0,156908.45
+18726,15664753.0,Tsai,667.0,Spain,Male,39.0,1.0,64345.21,1.0,1.0,0.0,82709.8
+18727,15591711.0,Hsia,709.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,180551.76
+18728,15772412.0,Chien,579.0,Germany,Male,36.0,1.0,98899.91,1.0,1.0,0.0,132973.21
+18729,15737265.0,Cawthorne,535.0,Germany,Male,40.0,4.0,119556.1,1.0,0.0,0.0,89972.25
+18730,15659428.0,Chiemenam,619.0,France,Male,33.0,3.0,109978.83,2.0,1.0,1.0,166476.95
+18731,15712551.0,Zetticci,656.0,Germany,Male,52.0,1.0,151855.33,1.0,1.0,0.0,16287.34
+18732,15695301.0,Chiazagomekpere,850.0,France,Female,25.0,8.0,0.0,2.0,1.0,0.0,98894.64
+18733,15656592.0,Ndubuagha,605.0,France,Female,52.0,1.0,0.0,3.0,1.0,1.0,126644.98
+18734,15654783.0,Macleod,584.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,4395.45
+18735,15698031.0,Yobachi,648.0,France,Male,46.0,6.0,0.0,2.0,1.0,0.0,74965.61
+18736,15699450.0,Chienezie,601.0,France,Male,44.0,7.0,0.0,2.0,1.0,0.0,180598.66
+18737,15602572.0,Iloerika,642.0,Germany,Female,32.0,2.0,77967.5,1.0,1.0,1.0,55313.51
+18738,15636595.0,Onochie,595.0,France,Female,42.0,5.0,0.0,2.0,0.0,0.0,108419.41
+18739,15792329.0,Pirozzi,726.0,France,Female,30.0,8.0,0.0,2.0,1.0,1.0,86911.49
+18740,15603088.0,Chineze,596.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,79623.28
+18741,15644878.0,Nkemdilim,593.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,160162.42
+18742,15692406.0,K?,850.0,Spain,Female,41.0,5.0,0.0,1.0,1.0,1.0,175877.7
+18743,15751158.0,Chukwuemeka,588.0,Germany,Male,22.0,5.0,107957.28,1.0,1.0,1.0,165035.17
+18744,15675963.0,Tsao,669.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,91763.67
+18745,15665067.0,Onuora,667.0,Spain,Female,27.0,2.0,0.0,2.0,1.0,0.0,115719.08
+18746,15690164.0,Calabrese,622.0,Spain,Male,32.0,3.0,0.0,1.0,1.0,0.0,144880.81
+18747,15582033.0,Hsueh,528.0,Spain,Female,27.0,4.0,80615.42,4.0,1.0,1.0,117622.8
+18748,15650026.0,Pinto,656.0,France,Female,51.0,3.0,0.0,1.0,1.0,0.0,175893.53
+18749,15607598.0,Milani,582.0,Germany,Female,67.0,7.0,89994.71,2.0,0.0,1.0,142903.44
+18750,15575486.0,Cremonesi,705.0,Spain,Male,42.0,1.0,0.0,2.0,1.0,1.0,97734.3
+18751,15661349.0,Chien,745.0,France,Male,52.0,7.0,0.0,2.0,1.0,1.0,160132.65
+18752,15670103.0,Ugorji,794.0,France,Male,24.0,3.0,0.0,2.0,1.0,1.0,196261.64
+18753,15660505.0,Onyemauchechukwu,703.0,Spain,Female,39.0,1.0,0.0,1.0,0.0,1.0,95625.03
+18754,15772381.0,Chia,635.0,France,Female,42.0,2.0,0.0,2.0,0.0,1.0,149471.13
+18755,15756475.0,Bibi,662.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,1.0,137519.92
+18756,15589431.0,Onwuemelie,742.0,France,Male,30.0,0.0,0.0,1.0,0.0,0.0,176576.71
+18757,15659324.0,Chinomso,699.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,126829.72
+18758,15624595.0,Ch'iu,678.0,France,Male,31.0,1.0,0.0,1.0,1.0,0.0,110916.15
+18759,15770420.0,Maclean,624.0,France,Male,59.0,5.0,62400.48,1.0,1.0,1.0,123536.52
+18760,15588535.0,Cremonesi,662.0,France,Female,29.0,10.0,0.0,2.0,1.0,0.0,73329.08
+18761,15690589.0,Mancini,598.0,France,Female,28.0,7.0,0.0,2.0,1.0,0.0,170634.49
+18762,15809901.0,Houghton,619.0,France,Male,35.0,5.0,0.0,1.0,0.0,0.0,153566.97
+18763,15582519.0,Okwudiliolisa,479.0,Germany,Female,41.0,8.0,129387.94,1.0,0.0,1.0,60538.28
+18764,15769412.0,Chienezie,629.0,Germany,Male,27.0,10.0,157085.98,2.0,1.0,1.0,103901.27
+18765,15582940.0,Iloerika,746.0,France,Female,32.0,8.0,149525.55,1.0,1.0,1.0,142223.94
+18766,15694946.0,Scott,678.0,France,Female,38.0,6.0,139810.34,1.0,1.0,0.0,4791.4
+18767,15595350.0,Mazzanti,590.0,France,Female,37.0,2.0,99844.68,1.0,1.0,1.0,78700.03
+18768,15611058.0,Hsiung,751.0,France,Male,35.0,9.0,171913.87,1.0,1.0,1.0,175310.41
+18769,15581550.0,Hsieh,604.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,51113.14
+18770,15772996.0,Walker,691.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,115779.48
+18771,15746422.0,Chukwujamuike,678.0,Spain,Female,34.0,4.0,0.0,2.0,1.0,1.0,142963.29
+18772,15693764.0,Ch'ang,697.0,Germany,Male,39.0,0.0,126670.37,2.0,1.0,1.0,94499.06
+18773,15611105.0,Chukwumaobim,754.0,Spain,Male,30.0,7.0,0.0,1.0,1.0,1.0,57553.02
+18774,15790298.0,Chibueze,678.0,Germany,Male,31.0,4.0,154483.98,1.0,0.0,0.0,167522.19
+18775,15754068.0,Chia,651.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,1.0,166957.82
+18776,15680597.0,Fanucci,547.0,France,Male,26.0,6.0,0.0,2.0,1.0,1.0,107841.77
+18777,15641794.0,Lai,682.0,Spain,Male,33.0,6.0,141223.68,1.0,0.0,1.0,75860.01
+18778,15809901.0,Lo,598.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,56908.41
+18779,15762573.0,Nnachetam,773.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,1.0,134423.66
+18780,15744368.0,Hsia,639.0,France,Male,33.0,1.0,147948.52,1.0,1.0,0.0,161653.5
+18781,15752488.0,Hsiao,659.0,Spain,Male,39.0,3.0,0.0,2.0,0.0,1.0,158086.33
+18782,15751185.0,Milani,719.0,France,Male,37.0,10.0,0.0,2.0,0.0,1.0,171060.01
+18783,15654625.0,Ozoemena,576.0,Spain,Female,43.0,8.0,146566.01,1.0,0.0,0.0,146821.42
+18784,15646720.0,Toscani,686.0,Germany,Male,48.0,4.0,143542.36,1.0,1.0,0.0,51548.14
+18785,15671390.0,Onwuamaeze,681.0,Germany,Female,25.0,4.0,107784.65,2.0,1.0,0.0,109538.39
+18786,15722599.0,Chukwuemeka,590.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,151336.96
+18787,15702464.0,Chimaobim,663.0,France,Male,29.0,3.0,133616.39,1.0,1.0,1.0,149517.72
+18788,15638231.0,Mironova,647.0,Germany,Female,33.0,3.0,73023.17,1.0,1.0,0.0,183868.01
+18789,15684925.0,Lajoie,708.0,France,Female,44.0,4.0,135655.66,1.0,1.0,1.0,138536.09
+18790,15793881.0,Udinesi,638.0,Germany,Male,43.0,9.0,141078.57,1.0,1.0,0.0,80587.41
+18791,15744327.0,Hs?,624.0,Spain,Male,27.0,2.0,0.0,2.0,1.0,1.0,99792.82
+18792,15672798.0,T'ang,644.0,Germany,Male,32.0,2.0,101744.84,2.0,1.0,0.0,171045.31
+18793,15665956.0,Omeokachie,674.0,Germany,Male,33.0,9.0,99027.61,2.0,0.0,1.0,120830.73
+18794,15614782.0,Mazzanti,714.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,177065.24
+18795,15574830.0,Chidubem,724.0,France,Female,34.0,2.0,0.0,1.0,1.0,0.0,176984.34
+18796,15703394.0,Hsieh,679.0,France,Female,32.0,9.0,112230.38,1.0,1.0,1.0,120892.96
+18797,15581282.0,Hickey,684.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,1.0,178229.04
+18798,15614365.0,Hsia,660.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,139297.71
+18799,15783398.0,Padovano,713.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,158853.98
+18800,15740356.0,Chukwueloka,651.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,1.0,125870.5
+18801,15774192.0,Ch'en,561.0,Spain,Male,37.0,1.0,0.0,2.0,0.0,1.0,65864.4
+18802,15684461.0,Ni,663.0,France,Female,59.0,8.0,0.0,1.0,1.0,1.0,166586.99
+18803,15791114.0,De Salis,619.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,107876.2
+18804,15636407.0,Onwuamaeze,807.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,139435.36
+18805,15678886.0,Nnachetam,466.0,France,Female,46.0,6.0,0.0,2.0,1.0,1.0,138526.96
+18806,15631848.0,Nnaife,539.0,Germany,Male,37.0,9.0,116195.58,2.0,1.0,1.0,123587.83
+18807,15592029.0,Chukwualuka,575.0,France,Female,44.0,3.0,0.0,2.0,0.0,1.0,141926.43
+18808,15787597.0,Hsia,648.0,Germany,Male,52.0,3.0,103291.25,2.0,0.0,1.0,51358.66
+18809,15711455.0,Chia,696.0,France,Male,42.0,10.0,0.0,2.0,1.0,1.0,162876.92
+18810,15806912.0,Lombardo,678.0,Germany,Male,37.0,6.0,124525.52,1.0,1.0,1.0,164741.5
+18811,15748327.0,Iloerika,634.0,France,Female,37.0,3.0,0.0,2.0,0.0,1.0,49732.9
+18812,15728006.0,Babbage,691.0,France,Female,34.0,2.0,0.0,2.0,0.0,1.0,36320.87
+18813,15618258.0,Cattaneo,769.0,France,Male,34.0,0.0,129847.75,1.0,1.0,1.0,86937.2
+18814,15688880.0,Chizoba,605.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,120540.83
+18815,15757521.0,Chidubem,632.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,65413.41
+18816,15583736.0,Lucchese,635.0,France,Female,31.0,4.0,0.0,2.0,1.0,0.0,167553.06
+18817,15570379.0,Dike,696.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,117031.2
+18818,15756026.0,Chifley,684.0,France,Male,25.0,9.0,0.0,1.0,0.0,1.0,131177.23
+18819,15648967.0,Ch'ien,725.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,180712.28
+18820,15573851.0,Milanesi,850.0,Germany,Female,29.0,8.0,126598.99,1.0,1.0,0.0,132424.69
+18821,15595588.0,Iweobiegbunam,563.0,France,Male,36.0,9.0,0.0,3.0,1.0,1.0,73581.05
+18822,15750410.0,D'Albertis,635.0,France,Male,38.0,2.0,155423.61,1.0,1.0,1.0,80190.36
+18823,15605531.0,Hsiung,751.0,France,Male,31.0,2.0,0.0,1.0,1.0,1.0,53534.8
+18824,15691823.0,Echezonachukwu,652.0,France,Female,48.0,4.0,0.0,1.0,1.0,0.0,115676.61
+18825,15654574.0,Chukwudi,587.0,Germany,Female,35.0,3.0,141796.63,2.0,1.0,1.0,61666.87
+18826,15652674.0,T'ien,638.0,Germany,Female,42.0,5.0,109162.82,1.0,1.0,1.0,59981.62
+18827,15781589.0,Shih,479.0,France,Male,44.0,8.0,0.0,1.0,1.0,0.0,152883.95
+18828,15576723.0,Nnamutaezinwa,554.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,166317.71
+18829,15628992.0,Chien,539.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,81409.0
+18830,15641114.0,O'Brien,716.0,Spain,Male,27.0,0.0,0.0,1.0,1.0,0.0,199409.19
+18831,15585599.0,Onwumelu,681.0,France,Female,56.0,1.0,81042.42,1.0,1.0,0.0,140931.57
+18832,15624188.0,Echezonachukwu,570.0,Germany,Male,46.0,1.0,111096.91,1.0,1.0,0.0,43932.54
+18833,15774743.0,Y?,650.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,52327.57
+18834,15571059.0,Yefremova,537.0,France,Male,42.0,10.0,0.0,1.0,1.0,0.0,88815.25
+18835,15804064.0,Milanesi,728.0,France,Female,38.0,1.0,0.0,1.0,0.0,1.0,67431.28
+18836,15803526.0,Chukwukere,735.0,Germany,Male,48.0,3.0,110822.9,1.0,1.0,1.0,86950.72
+18837,15733032.0,Mazzanti,727.0,Germany,Male,37.0,7.0,87772.52,1.0,1.0,0.0,103660.31
+18838,15602811.0,Chukwuemeka,703.0,France,Male,32.0,5.0,0.0,1.0,1.0,0.0,87822.14
+18839,15743192.0,Shao,697.0,Spain,Female,44.0,1.0,0.0,2.0,1.0,0.0,149665.65
+18840,15741417.0,O'Loughlin,589.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,164815.59
+18841,15582944.0,Hsia,731.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,0.0,177613.19
+18842,15676958.0,P'an,554.0,France,Male,40.0,6.0,0.0,2.0,1.0,1.0,142763.51
+18843,15693690.0,Obialo,590.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,123969.64
+18844,15761141.0,Ts'ao,717.0,France,Male,46.0,4.0,0.0,2.0,0.0,0.0,146651.99
+18845,15723488.0,Trevisani,696.0,Spain,Male,45.0,2.0,0.0,2.0,1.0,0.0,160976.83
+18846,15694829.0,Chiu,667.0,France,Male,31.0,5.0,144880.34,1.0,0.0,1.0,116973.42
+18847,15643679.0,Biryukov,633.0,France,Male,43.0,4.0,0.0,1.0,1.0,0.0,85411.01
+18848,15769346.0,De Salis,709.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,82970.14
+18849,15631739.0,Hsing,591.0,France,Male,29.0,0.0,0.0,2.0,1.0,0.0,172576.43
+18850,15591360.0,Chinomso,682.0,Spain,Male,48.0,8.0,0.0,1.0,1.0,0.0,153918.17
+18851,15659475.0,Rubeo,679.0,Germany,Male,26.0,6.0,123372.3,2.0,1.0,0.0,125963.14
+18852,15684865.0,Biryukov,597.0,France,Female,28.0,7.0,0.0,1.0,1.0,1.0,165574.54
+18853,15751022.0,Iloerika,586.0,Spain,Female,52.0,9.0,0.0,3.0,1.0,0.0,93336.3
+18854,15586699.0,K'ung,705.0,Spain,Male,32.0,7.0,0.0,1.0,1.0,0.0,162734.31
+18855,15800961.0,Ch'iu,613.0,Spain,Male,60.0,6.0,0.0,2.0,1.0,1.0,127558.26
+18856,15802560.0,Efremova,773.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,0.0,70529.0
+18857,15723669.0,Ugochukwu,732.0,France,Male,33.0,3.0,0.0,1.0,1.0,0.0,173844.89
+18858,15661827.0,Ojiofor,635.0,Spain,Male,29.0,1.0,0.0,2.0,0.0,1.0,167030.51
+18859,15733476.0,Chiedozie,651.0,Spain,Female,28.0,7.0,0.0,2.0,0.0,0.0,122433.09
+18860,15672940.0,Hsia,641.0,France,Male,51.0,8.0,0.0,1.0,0.0,1.0,54469.54
+18861,15704315.0,Toscani,643.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,107615.43
+18862,15628219.0,Calabrese,565.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,1.0,105122.85
+18863,15683524.0,Hsia,850.0,France,Female,37.0,5.0,116805.02,1.0,0.0,0.0,138540.08
+18864,15700353.0,Mironova,678.0,France,Male,32.0,4.0,0.0,1.0,1.0,0.0,131647.31
+18865,15784700.0,Binder,622.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,27244.35
+18866,15681316.0,Tretiakov,645.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,174971.64
+18867,15643658.0,Nucci,589.0,Spain,Female,26.0,8.0,0.0,2.0,0.0,1.0,60420.07
+18868,15793901.0,Hsing,557.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,0.0,86937.2
+18869,15756668.0,Lai,707.0,Spain,Female,29.0,1.0,121276.32,1.0,1.0,1.0,98820.09
+18870,15754578.0,Fanucci,754.0,France,Female,33.0,4.0,103471.52,1.0,1.0,0.0,131804.86
+18871,15792986.0,Scott,751.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,14081.64
+18872,15570681.0,Yefremova,706.0,France,Male,49.0,4.0,0.0,1.0,1.0,0.0,109891.6
+18873,15800062.0,Onuoha,682.0,Germany,Male,41.0,5.0,90218.9,1.0,1.0,0.0,125962.02
+18874,15809515.0,Chigbogu,584.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,122518.5
+18875,15651264.0,Walker,719.0,France,Male,29.0,7.0,161889.79,1.0,0.0,1.0,86767.48
+18876,15714586.0,Hsiung,767.0,Germany,Female,53.0,4.0,113674.2,1.0,1.0,0.0,169583.18
+18877,15605067.0,Ibeamaka,682.0,France,Male,35.0,2.0,0.0,2.0,0.0,1.0,58853.08
+18878,15745001.0,Hsu,600.0,Spain,Male,48.0,9.0,0.0,1.0,1.0,1.0,61315.37
+18879,15798860.0,K?,651.0,France,Male,32.0,3.0,0.0,1.0,1.0,1.0,138051.19
+18880,15678057.0,Chukwukadibia,775.0,France,Female,46.0,2.0,132932.13,1.0,1.0,0.0,179453.66
+18881,15752488.0,T'ien,729.0,Spain,Female,47.0,5.0,0.0,2.0,1.0,1.0,35786.76
+18882,15658364.0,Ch'eng,669.0,France,Female,39.0,1.0,0.0,1.0,1.0,1.0,167035.07
+18883,15588904.0,Ting,693.0,Germany,Male,37.0,6.0,121406.96,1.0,1.0,0.0,137079.86
+18884,15671333.0,Sagese,543.0,Germany,Male,50.0,5.0,125534.51,1.0,1.0,0.0,156598.23
+18885,15685920.0,Lucchesi,581.0,France,Male,43.0,1.0,0.0,4.0,1.0,0.0,146585.78
+18886,15694852.0,Chinedum,636.0,Germany,Female,62.0,9.0,91241.85,1.0,0.0,0.0,124777.14
+18887,15800620.0,Okwudilichukwu,516.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,122950.53
+18888,15583840.0,Lu,581.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,146785.44
+18889,15658935.0,Chidiebele,782.0,France,Female,38.0,9.0,0.0,2.0,1.0,0.0,52795.15
+18890,15676909.0,Shoebridge,634.0,France,Male,42.0,4.0,136914.6,1.0,1.0,0.0,158976.85
+18891,15598751.0,T'ao,629.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,175893.53
+18892,15743532.0,Ts'ui,698.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,140889.32
+18893,15707918.0,Laidley,684.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,135067.02
+18894,15782231.0,Ch'iu,673.0,France,Male,46.0,8.0,0.0,2.0,0.0,1.0,175544.02
+18895,15635277.0,Hs?,711.0,France,Male,42.0,0.0,186788.96,1.0,1.0,0.0,56583.88
+18896,15762218.0,Hs?,596.0,Germany,Female,44.0,5.0,128743.55,2.0,0.0,0.0,103557.95
+18897,15731569.0,Aksenov,714.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,86981.45
+18898,15686696.0,Iweobiegbunam,605.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,108764.69
+18899,15742671.0,Lucchese,516.0,France,Male,27.0,7.0,160599.06,1.0,0.0,0.0,124532.9
+18900,15807709.0,Hsia,724.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,168544.86
+18901,15676519.0,Hargreaves,678.0,Germany,Male,33.0,4.0,168735.74,1.0,1.0,0.0,4175.68
+18902,15628886.0,Chukwumaobim,667.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,75860.01
+18903,15726487.0,Goliwe,693.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,123587.83
+18904,15691817.0,Ch'ien,669.0,France,Male,42.0,7.0,0.0,2.0,0.0,1.0,94498.82
+18905,15652069.0,Onyemachukwu,774.0,France,Female,28.0,1.0,0.0,2.0,1.0,0.0,162428.05
+18906,15734886.0,Chukwudi,468.0,France,Female,45.0,6.0,0.0,2.0,1.0,1.0,85547.33
+18907,15803479.0,Uspenskaya,543.0,France,Female,23.0,8.0,0.0,2.0,0.0,1.0,168034.83
+18908,15651144.0,Ndubuagha,593.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,60375.18
+18909,15686917.0,Hsing,627.0,France,Male,50.0,6.0,156773.78,1.0,0.0,0.0,86490.09
+18910,15747323.0,Tien,613.0,Spain,Female,37.0,5.0,0.0,1.0,1.0,1.0,88550.28
+18911,15586870.0,Tuan,710.0,France,Male,40.0,4.0,0.0,1.0,1.0,1.0,106977.8
+18912,15769254.0,Lo Duca,733.0,Germany,Female,39.0,5.0,124576.65,1.0,1.0,0.0,169963.2
+18913,15730137.0,Fenton,659.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,107499.63
+18914,15643426.0,Ch'ang,624.0,France,Female,29.0,9.0,0.0,1.0,0.0,0.0,131283.6
+18915,15670266.0,Chuang,656.0,Germany,Male,40.0,1.0,133800.98,2.0,1.0,1.0,170968.99
+18916,15705309.0,McGregor,516.0,Germany,Female,45.0,5.0,122552.51,1.0,0.0,1.0,166513.49
+18917,15763515.0,Chidiebere,850.0,France,Female,29.0,1.0,0.0,2.0,1.0,1.0,91406.62
+18918,15765093.0,Lo Duca,633.0,Germany,Female,51.0,4.0,102800.72,1.0,1.0,0.0,157791.18
+18919,15754574.0,Ebelechukwu,577.0,Germany,Female,34.0,1.0,121166.89,1.0,1.0,0.0,163558.36
+18920,15806926.0,Okechukwu,602.0,Spain,Female,34.0,9.0,0.0,1.0,1.0,1.0,131302.74
+18921,15758031.0,Grant,624.0,Spain,Male,46.0,4.0,0.0,2.0,1.0,0.0,171096.2
+18922,15742282.0,Chukwualuka,796.0,Germany,Female,46.0,2.0,161592.76,2.0,1.0,0.0,126106.4
+18923,15769902.0,Nnaife,537.0,Spain,Male,53.0,9.0,0.0,1.0,1.0,0.0,61538.43
+18924,15771742.0,Okeke,619.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,83473.82
+18925,15747168.0,Eremenko,656.0,Germany,Female,37.0,4.0,113913.57,1.0,1.0,0.0,149535.97
+18926,15701200.0,Chukwumaobim,661.0,France,Male,38.0,5.0,135984.66,2.0,1.0,0.0,103396.08
+18927,15643679.0,Chien,613.0,Germany,Female,19.0,6.0,133723.43,2.0,1.0,1.0,107499.8
+18928,15667944.0,Chinonyelum,637.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,118863.15
+18929,15697318.0,Ch'in,706.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,142331.39
+18930,15762707.0,Mamelu,578.0,Spain,Male,66.0,4.0,124577.33,2.0,0.0,1.0,103430.46
+18931,15777060.0,Chinwemma,642.0,Spain,Female,58.0,5.0,170833.46,1.0,0.0,0.0,124749.08
+18932,15620443.0,Chinomso,603.0,France,Male,46.0,8.0,0.0,2.0,1.0,1.0,582.59
+18933,15690492.0,Gresswell,714.0,Spain,Male,54.0,6.0,102773.15,1.0,0.0,0.0,106969.98
+18934,15798888.0,Fomin,703.0,France,Male,26.0,4.0,122717.56,1.0,1.0,0.0,170980.86
+18935,15772995.0,Cawood,556.0,France,Female,47.0,8.0,0.0,2.0,1.0,0.0,75732.25
+18936,15773868.0,Mancini,585.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,0.0,92787.16
+18937,15614302.0,Pisano,537.0,France,Male,27.0,8.0,0.0,2.0,0.0,0.0,151903.6
+18938,15652981.0,Tsao,631.0,Spain,Female,31.0,3.0,0.0,1.0,0.0,0.0,134732.99
+18939,15744553.0,Hsiao,641.0,Spain,Female,58.0,4.0,0.0,2.0,0.0,0.0,181204.4
+18940,15756625.0,Fiorentino,707.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,180712.28
+18941,15659475.0,Ugoji,708.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,0.0,175893.53
+18942,15653595.0,Macleod,577.0,France,Female,38.0,6.0,0.0,2.0,0.0,1.0,131534.98
+18943,15783338.0,T'ao,675.0,France,Male,22.0,6.0,0.0,2.0,1.0,1.0,79871.16
+18944,15756475.0,Tsao,556.0,Spain,Female,47.0,7.0,0.0,1.0,0.0,0.0,93826.43
+18945,15762745.0,Genovese,598.0,France,Female,39.0,4.0,135645.76,1.0,1.0,1.0,120531.31
+18946,15752884.0,L?,714.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,131389.39
+18947,15627190.0,Chukwufumnanya,706.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,170908.95
+18948,15574873.0,Tsou,669.0,France,Female,26.0,9.0,0.0,2.0,1.0,0.0,57558.95
+18949,15745375.0,Chidubem,695.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,145796.49
+18950,15784323.0,Watson,564.0,France,Male,39.0,5.0,0.0,1.0,0.0,0.0,54530.51
+18951,15747534.0,Ch'iu,628.0,France,Female,64.0,3.0,132311.71,2.0,0.0,0.0,169161.46
+18952,15730657.0,Lucciano,766.0,Spain,Male,52.0,6.0,139972.18,1.0,1.0,1.0,84872.66
+18953,15648858.0,Padovano,774.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,64869.32
+18954,15654562.0,K'ung,686.0,France,Male,21.0,9.0,0.0,2.0,1.0,1.0,165035.17
+18955,15658675.0,Chukwujamuike,508.0,France,Male,42.0,7.0,119013.86,1.0,1.0,1.0,74858.3
+18956,15782231.0,Hsia,615.0,France,Male,53.0,1.0,0.0,1.0,1.0,0.0,26116.82
+18957,15581548.0,Chizuoke,595.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,134626.3
+18958,15771636.0,Ch'ien,544.0,France,Female,38.0,1.0,0.0,2.0,0.0,1.0,111590.35
+18959,15666095.0,Lucciano,748.0,France,Male,31.0,7.0,0.0,2.0,0.0,1.0,146775.75
+18960,15626679.0,P'eng,721.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,102908.91
+18961,15682472.0,Pirozzi,627.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,67976.05
+18962,15691703.0,Chukwukere,675.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,1.0,66893.28
+18963,15751788.0,Chukwuhaenye,646.0,France,Male,30.0,7.0,0.0,1.0,1.0,0.0,52831.13
+18964,15589660.0,H?,583.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,48673.18
+18965,15683055.0,Chukwuemeka,614.0,Spain,Male,39.0,6.0,0.0,3.0,1.0,1.0,91810.71
+18966,15672930.0,K?,645.0,Spain,Male,48.0,8.0,0.0,2.0,1.0,1.0,74945.11
+18967,15683993.0,Hsieh,719.0,Germany,Female,43.0,9.0,145536.7,1.0,1.0,0.0,19116.97
+18968,15738785.0,Hsiao,717.0,Germany,Female,47.0,7.0,114493.79,1.0,0.0,0.0,145536.28
+18969,15642636.0,Blesing,587.0,Germany,Male,33.0,2.0,132759.8,1.0,0.0,1.0,90591.15
+18970,15631392.0,Ts'ui,609.0,France,Female,52.0,3.0,0.0,1.0,1.0,1.0,92647.94
+18971,15603323.0,Maclean,756.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,92914.67
+18972,15769596.0,Ponomaryova,664.0,France,Female,29.0,5.0,140916.81,1.0,1.0,1.0,51117.06
+18973,15571081.0,P'eng,727.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,1.0,87834.24
+18974,15690589.0,Olisaemeka,741.0,France,Female,22.0,1.0,126495.57,1.0,0.0,1.0,59604.45
+18975,15708710.0,Ch'ang,773.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,172063.09
+18976,15783030.0,Chinweuba,774.0,Spain,Male,41.0,4.0,0.0,2.0,1.0,1.0,52301.15
+18977,15607537.0,Walker,558.0,France,Female,46.0,2.0,0.0,2.0,1.0,1.0,160523.33
+18978,15705512.0,Palerma,512.0,Germany,Female,38.0,8.0,114940.67,2.0,1.0,0.0,143628.31
+18979,15685826.0,Ibezimako,715.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,76510.49
+18980,15693737.0,K'ung,652.0,France,Male,33.0,8.0,0.0,3.0,1.0,1.0,94875.05
+18981,15572022.0,Kosisochukwu,683.0,Spain,Male,45.0,2.0,0.0,2.0,1.0,0.0,125918.17
+18982,15631063.0,Yermakova,731.0,Spain,Female,46.0,7.0,160566.11,1.0,1.0,1.0,176768.59
+18983,15642824.0,Kornilova,642.0,France,Male,32.0,2.0,112652.08,1.0,1.0,0.0,60552.44
+18984,15646615.0,Y?,605.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,1.0,130755.34
+18985,15757559.0,Genovesi,621.0,France,Male,26.0,9.0,0.0,2.0,0.0,1.0,112687.57
+18986,15679966.0,Chinedum,703.0,France,Female,56.0,9.0,0.0,1.0,1.0,0.0,93713.93
+18987,15748752.0,Y?,551.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,5791.85
+18988,15578908.0,Mazzanti,669.0,France,Male,24.0,1.0,0.0,2.0,0.0,1.0,112366.98
+18989,15604551.0,Onyemauchechukwu,547.0,France,Male,40.0,3.0,72594.0,1.0,1.0,1.0,131649.46
+18990,15620243.0,T'ien,721.0,Germany,Female,36.0,7.0,130461.02,1.0,0.0,1.0,82547.01
+18991,15785831.0,Maclean,679.0,France,Female,34.0,10.0,0.0,2.0,1.0,1.0,158887.09
+18992,15602551.0,Gough,654.0,Spain,Female,47.0,8.0,0.0,1.0,1.0,0.0,158937.55
+18993,15574558.0,Mazzanti,593.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,83056.22
+18994,15792229.0,Chiemenam,731.0,France,Male,33.0,2.0,0.0,1.0,0.0,0.0,82222.86
+18995,15654574.0,Houghton,659.0,France,Female,30.0,4.0,0.0,1.0,1.0,1.0,90878.13
+18996,15593976.0,Nweke,746.0,Germany,Female,51.0,3.0,80728.15,1.0,1.0,1.0,142963.17
+18997,15792986.0,Kambinachi,591.0,Germany,Female,40.0,1.0,146720.98,1.0,1.0,0.0,98686.4
+18998,15732265.0,Hsiung,581.0,France,Male,33.0,5.0,0.0,1.0,1.0,1.0,60917.24
+18999,15690925.0,Chiu,714.0,France,Male,28.0,5.0,56214.85,1.0,1.0,0.0,91406.62
+19000,15598046.0,Teng,682.0,Spain,Male,35.0,1.0,0.0,1.0,0.0,1.0,94433.08
+19001,15807609.0,Ch'iu,607.0,Spain,Male,44.0,7.0,0.0,2.0,1.0,1.0,40807.27
+19002,15581229.0,Chien,584.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,0.0,82276.62
+19003,15589715.0,Ts'ui,806.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,49374.82
+19004,15592386.0,Eames,775.0,Germany,Female,46.0,4.0,105419.73,1.0,0.0,0.0,161479.19
+19005,15806438.0,Scott,753.0,Germany,Female,29.0,5.0,125903.22,1.0,1.0,0.0,161069.73
+19006,15807900.0,Okwudilichukwu,643.0,France,Male,41.0,2.0,162448.69,1.0,0.0,0.0,130404.53
+19007,15583090.0,Iweobiegbunam,571.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,134526.87
+19008,15659843.0,Hsiao,594.0,Germany,Female,46.0,1.0,103267.8,1.0,1.0,0.0,80553.87
+19009,15707120.0,Chuang,678.0,Spain,Female,60.0,7.0,0.0,2.0,0.0,1.0,142730.27
+19010,15618363.0,Clements,652.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,172576.43
+19011,15728082.0,Chidozie,743.0,Germany,Male,34.0,4.0,124495.98,2.0,1.0,0.0,166586.99
+19012,15650437.0,Chien,545.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,1.0,72926.31
+19013,15693947.0,Ritchie,815.0,Spain,Male,37.0,0.0,0.0,2.0,1.0,1.0,94888.5
+19014,15662578.0,P'eng,705.0,France,Female,50.0,8.0,0.0,1.0,0.0,1.0,173495.77
+19015,15696900.0,Maclean,576.0,France,Male,39.0,2.0,0.0,2.0,0.0,0.0,121311.12
+19016,15781802.0,Folliero,565.0,France,Female,31.0,6.0,0.0,1.0,1.0,1.0,139919.38
+19017,15646091.0,Onwughara,637.0,Spain,Male,30.0,3.0,151963.26,1.0,1.0,0.0,185543.34
+19018,15659694.0,T'ien,815.0,France,Male,29.0,4.0,142862.21,2.0,1.0,1.0,87951.45
+19019,15789753.0,Tien,694.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,104469.06
+19020,15677739.0,Kao,717.0,Spain,Female,29.0,9.0,81564.1,1.0,1.0,1.0,112256.98
+19021,15656592.0,Chidimma,704.0,France,Female,60.0,3.0,0.0,2.0,1.0,1.0,67972.45
+19022,15773876.0,Ts'ai,653.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,156476.05
+19023,15750839.0,Koo,712.0,France,Male,42.0,6.0,0.0,2.0,0.0,1.0,26136.13
+19024,15737408.0,H?,693.0,France,Female,40.0,3.0,0.0,1.0,1.0,0.0,151903.6
+19025,15703682.0,Kao,850.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,143463.28
+19026,15750099.0,Trevisano,703.0,France,Female,19.0,2.0,0.0,1.0,1.0,0.0,95611.47
+19027,15798895.0,Chukwudi,724.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,166650.32
+19028,15693864.0,Ch'ien,583.0,Germany,Female,34.0,6.0,122570.87,1.0,0.0,0.0,167023.95
+19029,15636572.0,T'ien,835.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,86634.3
+19030,15792388.0,Manna,543.0,Spain,Female,37.0,5.0,129668.43,1.0,0.0,0.0,35179.46
+19031,15651943.0,Ndubueze,759.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,62449.07
+19032,15702741.0,Folliero,593.0,France,Female,32.0,4.0,0.0,2.0,0.0,0.0,149188.41
+19033,15682356.0,Ch'iu,695.0,Spain,Female,33.0,9.0,124495.98,1.0,1.0,1.0,180712.28
+19034,15670066.0,Lattimore,645.0,Germany,Male,25.0,2.0,152685.4,2.0,0.0,0.0,166038.13
+19035,15768777.0,Rizzo,719.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,181430.91
+19036,15804009.0,Kao,604.0,Spain,Male,24.0,5.0,118628.6,1.0,1.0,1.0,143783.46
+19037,15734987.0,Onyemauchechukwu,579.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,166650.9
+19038,15652914.0,Milanesi,684.0,France,Male,37.0,8.0,116251.24,2.0,1.0,1.0,59816.41
+19039,15635367.0,Ch'ang,710.0,Spain,Male,33.0,1.0,0.0,2.0,1.0,0.0,123475.78
+19040,15710203.0,Hsia,682.0,Germany,Female,45.0,7.0,127818.52,2.0,0.0,1.0,53478.02
+19041,15708917.0,Chiazagomekpele,629.0,France,Male,32.0,6.0,89772.3,1.0,1.0,0.0,142963.29
+19042,15758051.0,Teng,678.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,106036.94
+19043,15682442.0,Manna,738.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,94739.2
+19044,15665590.0,Chidozie,581.0,France,Male,36.0,7.0,121411.9,1.0,1.0,1.0,26318.73
+19045,15626474.0,Sagese,557.0,France,Female,70.0,4.0,105264.88,1.0,1.0,0.0,113481.02
+19046,15585362.0,Ch'ang,712.0,France,Female,27.0,7.0,0.0,2.0,1.0,1.0,134901.58
+19047,15726178.0,Chukwuebuka,505.0,Spain,Female,41.0,1.0,0.0,1.0,1.0,0.0,117431.1
+19048,15757541.0,Onochie,521.0,France,Male,33.0,8.0,92787.93,2.0,1.0,0.0,173486.39
+19049,15582903.0,Maclean,758.0,Germany,Female,31.0,5.0,114370.41,1.0,1.0,1.0,199293.01
+19050,15627697.0,Onyekaozulu,614.0,France,Male,34.0,4.0,133800.98,1.0,1.0,1.0,111180.4
+19051,15803685.0,T'ien,681.0,Spain,Male,27.0,6.0,0.0,1.0,0.0,0.0,62449.07
+19052,15756901.0,Oluchi,587.0,France,Female,48.0,7.0,0.0,3.0,1.0,1.0,135925.72
+19053,15692406.0,Li,599.0,Spain,Male,35.0,3.0,0.0,2.0,0.0,1.0,171039.56
+19054,15800620.0,Hsia,583.0,Germany,Female,39.0,7.0,74483.97,2.0,1.0,1.0,170621.43
+19055,15583681.0,Hsieh,683.0,Germany,Male,44.0,5.0,125889.3,2.0,1.0,0.0,113482.2
+19056,15700650.0,Nwachukwu,765.0,Germany,Male,32.0,5.0,122314.5,1.0,1.0,0.0,76510.46
+19057,15651336.0,Lu,525.0,France,Male,29.0,5.0,143390.51,2.0,1.0,0.0,180595.02
+19058,15798824.0,Chinweuba,632.0,France,Male,40.0,8.0,0.0,1.0,1.0,0.0,100717.54
+19059,15597901.0,Y?an,667.0,France,Female,42.0,3.0,0.0,2.0,0.0,0.0,153792.87
+19060,15642824.0,Kosisochukwu,653.0,France,Male,29.0,3.0,115290.32,1.0,0.0,0.0,157072.91
+19061,15639576.0,O'Sullivan,633.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,152956.33
+19062,15667938.0,Y?an,697.0,France,Female,35.0,9.0,0.0,2.0,0.0,1.0,180553.48
+19063,15800736.0,Chukwubuikem,646.0,France,Male,27.0,7.0,0.0,2.0,1.0,1.0,131284.93
+19064,15713853.0,Ting,613.0,France,Male,52.0,4.0,0.0,2.0,1.0,1.0,55829.25
+19065,15772632.0,Fanucci,576.0,France,Male,24.0,4.0,0.0,1.0,1.0,1.0,180800.42
+19066,15803886.0,Biryukov,629.0,Spain,Male,56.0,9.0,0.0,1.0,1.0,1.0,93777.61
+19067,15807593.0,Onyemauchechukwu,751.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,144584.29
+19068,15572038.0,Hs?,729.0,Germany,Male,32.0,1.0,105701.91,2.0,1.0,1.0,58320.06
+19069,15617320.0,Ch'en,738.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,128528.83
+19070,15631339.0,Teng,479.0,France,Male,36.0,7.0,0.0,1.0,1.0,1.0,165035.17
+19071,15783082.0,Ch'ang,699.0,Spain,Female,50.0,5.0,0.0,1.0,1.0,1.0,71095.41
+19072,15808760.0,Hsiung,597.0,Germany,Male,38.0,8.0,129745.3,1.0,1.0,1.0,139439.31
+19073,15572461.0,L?,729.0,France,Male,51.0,4.0,0.0,3.0,1.0,1.0,118024.1
+19074,15702298.0,Hsu,665.0,France,Male,25.0,6.0,0.0,2.0,1.0,1.0,171056.65
+19075,15738220.0,Pirozzi,571.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,127528.79
+19076,15742285.0,T'an,557.0,France,Male,34.0,4.0,0.0,1.0,1.0,1.0,133535.29
+19077,15681446.0,Nkemdilim,842.0,France,Female,46.0,9.0,136622.55,2.0,0.0,1.0,171840.24
+19078,15731519.0,Ugochukwu,635.0,France,Male,35.0,0.0,0.0,2.0,1.0,0.0,176902.83
+19079,15762228.0,Ch'ien,751.0,France,Male,44.0,9.0,0.0,1.0,1.0,1.0,114732.62
+19080,15790935.0,Chukwumaobim,584.0,France,Male,23.0,1.0,0.0,2.0,1.0,0.0,80190.36
+19081,15639277.0,Ch'ien,782.0,France,Female,31.0,4.0,114732.23,1.0,0.0,1.0,80181.12
+19082,15581229.0,Lo Duca,699.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,1.0,24255.21
+19083,15588944.0,Efremov,661.0,France,Female,47.0,5.0,118287.01,1.0,0.0,0.0,146636.44
+19084,15668830.0,Palerma,807.0,France,Male,32.0,2.0,121063.9,2.0,1.0,1.0,51380.9
+19085,15754926.0,Uvarova,731.0,Spain,Male,32.0,8.0,116644.29,1.0,1.0,1.0,108152.75
+19086,15570134.0,Chukwufumnanya,667.0,France,Female,45.0,1.0,0.0,1.0,0.0,0.0,112465.19
+19087,15648735.0,Chin,584.0,France,Female,39.0,3.0,0.0,1.0,0.0,0.0,136610.02
+19088,15696564.0,Chidozie,743.0,France,Male,36.0,3.0,0.0,1.0,1.0,1.0,118959.64
+19089,15597905.0,Ch'in,622.0,France,Male,42.0,8.0,0.0,2.0,0.0,0.0,91943.94
+19090,15709387.0,Chukwueloka,774.0,Germany,Male,35.0,4.0,98263.14,1.0,0.0,0.0,126013.58
+19091,15673747.0,Nnamutaezinwa,619.0,France,Female,38.0,2.0,0.0,1.0,1.0,0.0,166586.99
+19092,15753081.0,Fiorentino,763.0,France,Female,59.0,8.0,95994.98,1.0,1.0,0.0,62746.03
+19093,15772422.0,H?,652.0,France,Male,46.0,3.0,0.0,2.0,1.0,1.0,177734.49
+19094,15600399.0,Lockington,602.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,0.0,58879.31
+19095,15692452.0,Cawker,633.0,Spain,Female,37.0,4.0,142643.54,1.0,1.0,0.0,115789.25
+19096,15702912.0,Cattaneo,583.0,Spain,Female,32.0,1.0,0.0,2.0,0.0,0.0,183387.12
+19097,15767129.0,Hs?,684.0,Germany,Male,34.0,4.0,170935.94,2.0,1.0,0.0,177740.58
+19098,15777784.0,Sorenson,706.0,Germany,Male,49.0,6.0,136689.66,1.0,1.0,0.0,84420.5
+19099,15626474.0,Ch'iu,732.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,60988.21
+19100,15765283.0,T'ang,636.0,Germany,Male,46.0,8.0,126589.12,1.0,1.0,0.0,146785.44
+19101,15632264.0,Nwokeocha,641.0,Spain,Male,43.0,1.0,0.0,1.0,1.0,1.0,73881.68
+19102,15690743.0,Ch'ien,586.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,140941.47
+19103,15652693.0,De Luca,703.0,France,Male,31.0,6.0,128966.13,1.0,1.0,1.0,161076.92
+19104,15775124.0,Chiazagomekpere,516.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,49848.86
+19105,15701200.0,Yashina,793.0,France,Male,41.0,8.0,0.0,2.0,0.0,1.0,82290.81
+19106,15809837.0,Onuchukwu,627.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,177852.57
+19107,15605375.0,Omeokachie,627.0,France,Male,40.0,6.0,167735.83,1.0,0.0,0.0,159773.14
+19108,15806983.0,Chien,607.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,129912.74
+19109,15692226.0,Ch'in,697.0,France,Male,50.0,8.0,0.0,2.0,1.0,1.0,135079.62
+19110,15651144.0,Mazzi,739.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,182270.88
+19111,15771211.0,Onuchukwu,687.0,France,Female,28.0,1.0,61691.19,1.0,1.0,0.0,138504.28
+19112,15704771.0,Ritchie,550.0,Spain,Male,32.0,7.0,0.0,2.0,1.0,1.0,60905.51
+19113,15612455.0,Ts'ai,717.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,66515.87
+19114,15658813.0,Chuang,842.0,Germany,Male,37.0,6.0,125877.65,2.0,1.0,1.0,105563.41
+19115,15770174.0,O'Loghlin,669.0,Germany,Male,32.0,2.0,91622.42,1.0,1.0,1.0,71865.7
+19116,15585324.0,Mello,559.0,France,Male,36.0,5.0,140576.11,1.0,1.0,0.0,79577.38
+19117,15617301.0,Hsiung,679.0,Spain,Female,48.0,5.0,0.0,2.0,1.0,1.0,99843.98
+19118,15756538.0,Ch'iu,558.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,149912.28
+19119,15653050.0,Y?an,718.0,Germany,Male,39.0,1.0,117294.12,1.0,1.0,1.0,176763.27
+19120,15592379.0,Ch'ien,583.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,145700.22
+19121,15631392.0,P'eng,703.0,France,Female,29.0,1.0,116318.23,1.0,0.0,1.0,129964.94
+19122,15782454.0,Ch'en,696.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,57572.38
+19123,15681755.0,Yermakova,819.0,Spain,Female,39.0,9.0,0.0,2.0,0.0,1.0,170968.99
+19124,15737647.0,Dreyer,724.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,146585.53
+19125,15589544.0,Pinto,597.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,0.0,164815.59
+19126,15792934.0,Eluemuno,569.0,France,Male,25.0,3.0,0.0,1.0,1.0,1.0,151317.48
+19127,15807305.0,Walker,574.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,0.0,95007.8
+19128,15658364.0,Tuan,721.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,160696.72
+19129,15635143.0,Chibueze,767.0,Germany,Male,30.0,9.0,128728.96,2.0,1.0,1.0,1187.88
+19130,15589449.0,Shih,745.0,Germany,Female,48.0,8.0,139542.82,1.0,1.0,0.0,83617.53
+19131,15782899.0,Scott,771.0,Germany,Male,35.0,8.0,115897.12,1.0,0.0,1.0,153662.88
+19132,15723008.0,Yuryeva,705.0,Germany,Female,40.0,2.0,110939.3,1.0,1.0,1.0,172669.39
+19133,15641833.0,Okorie,757.0,France,Female,31.0,10.0,0.0,2.0,1.0,0.0,79082.76
+19134,15675184.0,Ts'ai,850.0,France,Male,25.0,5.0,0.0,2.0,0.0,1.0,121311.12
+19135,15742809.0,Zito,613.0,Spain,Male,46.0,9.0,0.0,1.0,1.0,0.0,82565.01
+19136,15732644.0,Ch'eng,651.0,France,Female,35.0,8.0,0.0,1.0,0.0,0.0,57876.05
+19137,15732672.0,Baresi,714.0,Spain,Female,31.0,6.0,0.0,2.0,0.0,1.0,114092.52
+19138,15677049.0,Uspenskaya,613.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,1.0,74850.84
+19139,15797065.0,Kaur,694.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,141946.92
+19140,15789413.0,Creswell,562.0,Germany,Male,38.0,8.0,119963.45,2.0,1.0,1.0,105570.39
+19141,15606386.0,Pinto,727.0,France,Female,37.0,4.0,0.0,1.0,1.0,1.0,82709.8
+19142,15752953.0,Ibezimako,669.0,Germany,Male,45.0,1.0,121161.57,1.0,1.0,0.0,70532.53
+19143,15689955.0,Fanucci,516.0,Spain,Male,38.0,8.0,0.0,1.0,1.0,0.0,164768.82
+19144,15642996.0,Yewen,516.0,France,Female,44.0,4.0,0.0,2.0,1.0,0.0,141558.04
+19145,15792360.0,T'ao,572.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,126728.27
+19146,15624239.0,Chidiebere,610.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,83090.35
+19147,15724334.0,Ch'in,619.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,100101.06
+19148,15734461.0,Folliero,524.0,Germany,Female,32.0,8.0,108914.17,1.0,0.0,1.0,134878.19
+19149,15778936.0,Ijendu,679.0,France,Female,32.0,2.0,153639.11,1.0,1.0,1.0,155743.48
+19150,15632789.0,Ku,729.0,France,Male,33.0,8.0,0.0,2.0,0.0,0.0,145558.42
+19151,15724443.0,Chu,539.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,182627.25
+19152,15677906.0,Tretiakova,675.0,France,Male,47.0,4.0,0.0,1.0,0.0,0.0,129563.86
+19153,15622003.0,Ku,607.0,France,Female,51.0,6.0,0.0,1.0,1.0,0.0,93825.83
+19154,15734886.0,Ch'en,677.0,Spain,Female,45.0,3.0,0.0,2.0,0.0,1.0,138537.03
+19155,15583692.0,Nwankwo,579.0,Germany,Male,38.0,2.0,103377.81,2.0,1.0,0.0,94774.12
+19156,15789425.0,Hsieh,643.0,France,Male,45.0,8.0,123356.63,1.0,1.0,1.0,96823.32
+19157,15591344.0,Okwuadigbo,579.0,France,Female,38.0,6.0,83693.34,1.0,0.0,1.0,72993.65
+19158,15618750.0,Chukwuebuka,600.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,161229.84
+19159,15643825.0,Ives,735.0,France,Female,28.0,8.0,0.0,1.0,1.0,1.0,133535.29
+19160,15707132.0,McGregor,653.0,Spain,Female,44.0,4.0,0.0,1.0,1.0,0.0,142670.61
+19161,15635942.0,Obiora,678.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,170819.01
+19162,15754713.0,Shih,727.0,France,Male,44.0,9.0,0.0,2.0,1.0,1.0,88947.56
+19163,15680197.0,Chiu,710.0,Germany,Female,32.0,3.0,121205.15,2.0,1.0,1.0,77965.67
+19164,15609998.0,Hsiung,683.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,0.0,112256.57
+19165,15742404.0,Udegbulam,662.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,91903.9
+19166,15772383.0,Chikwado,697.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,94862.93
+19167,15758035.0,Pinto,730.0,France,Male,35.0,1.0,143390.51,1.0,1.0,0.0,182262.66
+19168,15572631.0,Ts'ao,587.0,Germany,Female,50.0,4.0,123692.0,1.0,1.0,1.0,82967.93
+19169,15682533.0,Chukwudi,667.0,Spain,Male,37.0,8.0,0.0,1.0,1.0,0.0,81251.24
+19170,15772529.0,Chukwubuikem,744.0,Spain,Female,42.0,6.0,0.0,1.0,0.0,0.0,179861.79
+19171,15788659.0,Loyau,779.0,Spain,Male,44.0,1.0,0.0,2.0,1.0,1.0,139994.68
+19172,15764661.0,Hsia,589.0,France,Male,34.0,5.0,0.0,1.0,0.0,0.0,162820.5
+19173,15665963.0,Genovese,746.0,Spain,Male,32.0,8.0,125919.35,1.0,1.0,0.0,122021.33
+19174,15646521.0,Ndubueze,579.0,Spain,Female,33.0,0.0,0.0,2.0,1.0,1.0,155876.06
+19175,15765952.0,Yobanna,619.0,France,Male,37.0,1.0,0.0,2.0,0.0,0.0,122949.71
+19176,15697480.0,Chia,547.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,151317.27
+19177,15641389.0,Nnamutaezinwa,705.0,Germany,Female,21.0,5.0,131033.76,2.0,1.0,1.0,168035.62
+19178,15737476.0,Ts'ui,678.0,France,Male,30.0,3.0,117866.92,1.0,1.0,1.0,144880.81
+19179,15652401.0,Hargreaves,616.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,62733.05
+19180,15659098.0,Hs?,708.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,101728.46
+19181,15686983.0,Nwachukwu,639.0,France,Female,34.0,3.0,151218.98,1.0,1.0,0.0,93835.64
+19182,15641490.0,Hs?,581.0,Spain,Female,37.0,9.0,0.0,1.0,1.0,0.0,103931.49
+19183,15603594.0,Ch'eng,680.0,France,Male,42.0,4.0,0.0,2.0,1.0,0.0,86044.98
+19184,15667085.0,Nucci,754.0,Germany,Male,37.0,6.0,118879.35,2.0,1.0,0.0,122073.23
+19185,15804064.0,Genovese,695.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,61937.6
+19186,15797900.0,Chiganu,677.0,France,Female,29.0,8.0,0.0,2.0,0.0,0.0,181419.41
+19187,15651958.0,Mazzanti,678.0,Germany,Female,37.0,9.0,165798.1,1.0,0.0,1.0,54448.14
+19188,15648069.0,Hs?,686.0,France,Female,23.0,2.0,0.0,1.0,1.0,0.0,170518.83
+19189,15654870.0,Liao,602.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,88721.84
+19190,15585047.0,Kao,635.0,France,Male,35.0,3.0,123971.16,1.0,1.0,1.0,167030.51
+19191,15644765.0,Hsia,719.0,Spain,Female,39.0,1.0,137482.19,1.0,1.0,0.0,138021.36
+19192,15606945.0,Calabrese,572.0,Germany,Female,49.0,2.0,93522.2,2.0,1.0,0.0,151822.66
+19193,15614778.0,Okwuoma,850.0,Spain,Female,36.0,4.0,0.0,2.0,0.0,0.0,77965.67
+19194,15579892.0,Oluchi,698.0,France,Female,28.0,2.0,0.0,1.0,1.0,1.0,165576.55
+19195,15642570.0,Ts'ui,586.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,40385.61
+19196,15692819.0,Chiawuotu,678.0,Spain,Female,45.0,3.0,0.0,4.0,0.0,1.0,180693.61
+19197,15652069.0,Hsueh,679.0,Spain,Female,43.0,1.0,0.0,2.0,1.0,1.0,28750.67
+19198,15665327.0,Chimezie,710.0,France,Male,36.0,3.0,0.0,1.0,1.0,1.0,168816.22
+19199,15691906.0,Wilson,636.0,France,Male,59.0,8.0,0.0,1.0,0.0,0.0,153776.6
+19200,15622578.0,Ch'iu,594.0,Germany,Female,44.0,2.0,116070.58,1.0,1.0,0.0,72970.93
+19201,15591036.0,Shih,542.0,France,Female,43.0,9.0,0.0,2.0,1.0,0.0,173464.43
+19202,15749679.0,Hsia,676.0,France,Female,52.0,6.0,0.0,1.0,1.0,1.0,50783.1
+19203,15783025.0,P'eng,632.0,Germany,Male,23.0,8.0,140720.93,2.0,1.0,0.0,52462.25
+19204,15781989.0,Onyemauchechukwu,757.0,Germany,Male,28.0,5.0,121325.84,2.0,1.0,1.0,96498.14
+19205,15597945.0,Onwudiwe,572.0,France,Female,32.0,8.0,69434.4,1.0,1.0,1.0,163471.01
+19206,15656592.0,Fiorentini,711.0,Spain,Male,35.0,3.0,0.0,2.0,0.0,0.0,121232.51
+19207,15613197.0,Yashina,628.0,France,Female,47.0,3.0,0.0,1.0,0.0,0.0,176903.35
+19208,15698031.0,Ngozichukwuka,757.0,Spain,Male,30.0,3.0,0.0,2.0,1.0,0.0,161388.22
+19209,15661349.0,Hsueh,741.0,France,Male,50.0,8.0,0.0,1.0,1.0,0.0,132939.64
+19210,15605918.0,Baresi,610.0,France,Female,45.0,5.0,0.0,1.0,0.0,0.0,104079.62
+19211,15752601.0,McGregor,652.0,France,Male,35.0,1.0,0.0,1.0,0.0,0.0,83978.86
+19212,15578515.0,Martin,754.0,Germany,Female,59.0,5.0,122471.09,1.0,1.0,0.0,166694.93
+19213,15731751.0,Aiken,573.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,141364.27
+19214,15809772.0,Lucciano,707.0,Spain,Male,36.0,7.0,144961.97,1.0,1.0,0.0,182627.25
+19215,15596800.0,Pendergrass,564.0,France,Female,44.0,0.0,127616.56,1.0,0.0,1.0,49656.24
+19216,15639277.0,Pinto,667.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,131736.23
+19217,15667932.0,Ch'ang,682.0,Germany,Male,33.0,7.0,107648.94,2.0,0.0,1.0,45610.63
+19218,15762799.0,Chiu,683.0,Spain,Male,44.0,8.0,0.0,1.0,1.0,1.0,60429.43
+19219,15752953.0,Chinomso,730.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,67622.36
+19220,15653715.0,T'ien,683.0,Germany,Female,35.0,8.0,140745.33,1.0,1.0,0.0,179861.79
+19221,15744695.0,Chiazagomekpele,561.0,Germany,Female,32.0,7.0,119023.28,2.0,1.0,1.0,164806.89
+19222,15778395.0,Trevisan,538.0,Germany,Female,42.0,5.0,157624.85,2.0,1.0,1.0,16081.62
+19223,15686696.0,Li,561.0,France,Male,28.0,4.0,0.0,2.0,0.0,1.0,157891.86
+19224,15688071.0,H?,718.0,Spain,Male,39.0,6.0,0.0,1.0,0.0,0.0,179861.79
+19225,15621423.0,Pisani,639.0,Germany,Female,42.0,4.0,142765.24,1.0,1.0,1.0,136846.75
+19226,15768270.0,Nkemakolam,709.0,France,Female,43.0,7.0,0.0,1.0,1.0,1.0,50783.1
+19227,15809309.0,Chidubem,644.0,Spain,Female,37.0,2.0,0.0,1.0,1.0,1.0,141364.62
+19228,15624141.0,Kao,715.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,154844.22
+19229,15662021.0,Ch'ang,719.0,France,Male,42.0,1.0,0.0,2.0,1.0,1.0,116503.92
+19230,15627305.0,Chidumaga,671.0,France,Male,38.0,2.0,0.0,1.0,0.0,1.0,60552.44
+19231,15697546.0,Ch'iu,645.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,136535.75
+19232,15666032.0,Scott,611.0,Spain,Female,33.0,3.0,125537.38,1.0,0.0,1.0,100862.36
+19233,15708917.0,Y?,579.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,136985.08
+19234,15573769.0,Kao,671.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,37662.49
+19235,15679966.0,Chia,747.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,121681.19
+19236,15808960.0,Onwumelu,656.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,0.0,167984.72
+19237,15746199.0,Tuan,678.0,Spain,Female,32.0,6.0,0.0,1.0,1.0,0.0,38019.74
+19238,15797900.0,Genovese,613.0,France,Female,33.0,2.0,0.0,2.0,0.0,1.0,145986.35
+19239,15628893.0,Ogochukwu,603.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,182784.11
+19240,15613787.0,Chiazagomekpere,695.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,1.0,146652.11
+19241,15803689.0,Hsueh,683.0,France,Female,40.0,5.0,0.0,2.0,0.0,1.0,83084.33
+19242,15802220.0,Chibugo,684.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,100598.98
+19243,15791925.0,Smith,653.0,France,Male,54.0,5.0,0.0,1.0,1.0,0.0,90896.04
+19244,15631359.0,Tuan,714.0,France,Male,32.0,6.0,123863.64,1.0,1.0,1.0,169583.18
+19245,15644796.0,Chiazagomekpere,596.0,Germany,Female,28.0,1.0,143421.78,1.0,1.0,1.0,107876.2
+19246,15612095.0,Chukwufumnanya,751.0,France,Male,21.0,8.0,0.0,2.0,0.0,1.0,151310.16
+19247,15602312.0,Zetticci,632.0,France,Female,43.0,8.0,0.0,1.0,1.0,0.0,155722.73
+19248,15610147.0,Chizoba,682.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,66526.01
+19249,15703588.0,Chiu,754.0,Spain,Male,32.0,3.0,0.0,2.0,0.0,1.0,92826.49
+19250,15705512.0,Zetticci,687.0,France,Female,22.0,6.0,0.0,1.0,1.0,0.0,82647.65
+19251,15776526.0,Chukwujamuike,749.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,1.0,172576.43
+19252,15757981.0,Nnonso,694.0,Spain,Female,29.0,3.0,0.0,2.0,1.0,0.0,131644.41
+19253,15764284.0,Uspensky,712.0,Spain,Male,25.0,3.0,0.0,2.0,1.0,1.0,130132.54
+19254,15632899.0,Chukwufumnanya,714.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,110114.19
+19255,15778049.0,Bancks,753.0,Spain,Female,36.0,1.0,109805.4,1.0,1.0,1.0,152843.53
+19256,15583576.0,Ch'ien,694.0,Spain,Male,40.0,8.0,0.0,2.0,0.0,0.0,134778.01
+19257,15791045.0,Genovese,718.0,Germany,Male,32.0,10.0,141782.57,2.0,1.0,0.0,177028.0
+19258,15712608.0,Okorie,646.0,France,Female,48.0,4.0,0.0,1.0,0.0,0.0,185561.89
+19259,15702464.0,Hs?,710.0,France,Male,52.0,4.0,0.0,1.0,1.0,0.0,116081.97
+19260,15803609.0,Johnstone,752.0,France,Female,43.0,6.0,0.0,1.0,1.0,0.0,175722.71
+19261,15658972.0,Genovese,590.0,France,Female,33.0,1.0,0.0,2.0,1.0,0.0,167984.72
+19262,15669491.0,Mazzi,583.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,128881.87
+19263,15670266.0,Chuang,644.0,France,Male,23.0,9.0,0.0,2.0,1.0,1.0,10350.74
+19264,15662494.0,Iloerika,628.0,France,Female,40.0,3.0,129674.89,1.0,1.0,0.0,126812.63
+19265,15643359.0,Chibueze,649.0,Germany,Female,37.0,8.0,96471.2,2.0,0.0,1.0,64345.61
+19266,15678058.0,Hsing,705.0,France,Male,30.0,8.0,90218.9,1.0,1.0,1.0,71175.94
+19267,15589323.0,Onwuamaeze,695.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,76611.33
+19268,15629133.0,Chukwuebuka,738.0,Germany,Female,36.0,5.0,134542.73,1.0,1.0,0.0,133692.88
+19269,15699225.0,Baresi,639.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,161407.48
+19270,15785547.0,Onwuamaeze,652.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,132973.63
+19271,15631838.0,H?,697.0,France,Male,39.0,5.0,110822.9,1.0,1.0,0.0,127095.14
+19272,15624892.0,Hsiung,554.0,France,Male,38.0,9.0,0.0,1.0,1.0,0.0,151815.76
+19273,15794266.0,Clements,773.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,85801.77
+19274,15694518.0,Y?an,775.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,1.0,96794.46
+19275,15723488.0,T'ien,614.0,France,Male,41.0,1.0,0.0,1.0,1.0,1.0,132730.07
+19276,15602034.0,Hsiung,650.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,183492.9
+19277,15774854.0,Chikwado,642.0,France,Female,24.0,3.0,0.0,2.0,1.0,1.0,132973.18
+19278,15587913.0,Milne,757.0,France,Male,60.0,6.0,0.0,1.0,1.0,1.0,180593.45
+19279,15657535.0,Chiang,643.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,63795.62
+19280,15769312.0,Fyodorova,727.0,France,Female,39.0,9.0,0.0,2.0,1.0,0.0,36754.13
+19281,15697567.0,Liao,679.0,France,Female,28.0,5.0,0.0,2.0,0.0,0.0,142331.85
+19282,15688069.0,Scott,684.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,121210.09
+19283,15740476.0,Lucchese,671.0,Spain,Male,33.0,6.0,0.0,2.0,0.0,0.0,151316.37
+19284,15667417.0,Yermolayeva,586.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,182720.07
+19285,15704581.0,Nucci,697.0,France,Female,56.0,5.0,99513.87,1.0,0.0,1.0,64831.36
+19286,15663939.0,DeRose,573.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,147055.87
+19287,15694879.0,Hsia,636.0,France,Male,31.0,4.0,154969.79,1.0,0.0,1.0,134794.02
+19288,15566894.0,Rizzo,571.0,France,Male,40.0,9.0,0.0,2.0,0.0,0.0,52894.01
+19289,15597951.0,Hsiung,762.0,France,Male,24.0,10.0,123347.5,1.0,1.0,1.0,152499.8
+19290,15668894.0,Pisano,715.0,France,Male,48.0,4.0,0.0,2.0,0.0,0.0,86678.48
+19291,15737173.0,Folliero,677.0,France,Female,41.0,7.0,0.0,2.0,0.0,0.0,86699.98
+19292,15586429.0,Chukwudi,633.0,Germany,Male,34.0,1.0,148897.02,3.0,1.0,0.0,112649.22
+19293,15591036.0,Walker,599.0,Germany,Female,46.0,8.0,150856.79,1.0,1.0,0.0,140941.47
+19294,15735270.0,Clements,590.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,64595.25
+19295,15591428.0,Calabrese,709.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,52484.01
+19296,15765650.0,Hs?,501.0,Spain,Female,25.0,1.0,0.0,2.0,1.0,0.0,82634.41
+19297,15585599.0,Ebelechukwu,595.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,1.0,177624.01
+19298,15720509.0,Eluemuno,678.0,Spain,Male,60.0,4.0,0.0,1.0,1.0,0.0,106977.53
+19299,15635367.0,Chidiebele,667.0,Spain,Male,34.0,2.0,156091.97,1.0,1.0,1.0,143598.7
+19300,15759717.0,Teng,695.0,Germany,Male,37.0,1.0,125997.22,2.0,0.0,1.0,46635.11
+19301,15658935.0,Hsieh,554.0,Germany,Female,40.0,2.0,123936.54,2.0,0.0,1.0,125898.39
+19302,15584766.0,Sagese,608.0,Germany,Female,38.0,8.0,92676.3,2.0,1.0,0.0,80593.49
+19303,15743590.0,Lappin,638.0,France,Male,44.0,1.0,0.0,2.0,1.0,0.0,103901.58
+19304,15697042.0,Nnamdi,682.0,France,Male,46.0,3.0,0.0,1.0,1.0,0.0,83052.03
+19305,15679961.0,Ch'ien,579.0,Spain,Female,31.0,4.0,0.0,1.0,1.0,0.0,193014.26
+19306,15668818.0,Nnamutaezinwa,528.0,France,Male,30.0,3.0,91219.29,1.0,1.0,1.0,68759.57
+19307,15643574.0,Hsia,642.0,France,Female,40.0,1.0,0.0,2.0,1.0,1.0,90908.95
+19308,15694566.0,Tien,646.0,France,Female,31.0,1.0,0.0,1.0,1.0,0.0,57862.88
+19309,15642655.0,Ch'ang,652.0,France,Male,39.0,3.0,0.0,1.0,0.0,0.0,160594.85
+19310,15746333.0,Kambinachi,713.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,187826.11
+19311,15806945.0,Tung,714.0,Spain,Female,37.0,2.0,0.0,1.0,0.0,1.0,125877.22
+19312,15680752.0,Iloerika,717.0,Spain,Male,36.0,10.0,0.0,1.0,1.0,0.0,134851.12
+19313,15783029.0,Onyemauchechukwu,549.0,Germany,Male,38.0,8.0,105249.71,2.0,1.0,0.0,47880.71
+19314,15595728.0,Chukwubuikem,679.0,Spain,Female,40.0,6.0,0.0,1.0,1.0,0.0,189175.71
+19315,15579914.0,Ch'iu,683.0,Spain,Male,34.0,7.0,128927.93,1.0,0.0,0.0,125079.65
+19316,15596074.0,Mazzi,635.0,Spain,Female,40.0,7.0,150186.55,1.0,1.0,1.0,148516.79
+19317,15788775.0,Chuang,757.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,181208.47
+19318,15732774.0,Macdonald,535.0,Germany,Female,31.0,3.0,118588.83,1.0,0.0,1.0,166677.54
+19319,15740476.0,Maclean,613.0,Spain,Male,47.0,1.0,0.0,2.0,0.0,0.0,125962.02
+19320,15792489.0,Cattaneo,640.0,Spain,Male,27.0,2.0,0.0,2.0,0.0,0.0,131718.12
+19321,15756535.0,Li Fonti,637.0,France,Male,35.0,7.0,0.0,1.0,1.0,1.0,182436.81
+19322,15598892.0,Yobachukwu,681.0,France,Male,41.0,9.0,0.0,1.0,1.0,1.0,131283.6
+19323,15727198.0,Hsiung,710.0,Germany,Female,31.0,6.0,121513.88,1.0,1.0,0.0,114448.77
+19324,15719265.0,Lu,757.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,186489.95
+19325,15779176.0,Lo,724.0,Germany,Female,41.0,4.0,95128.86,2.0,1.0,0.0,94499.06
+19326,15632256.0,Chukwubuikem,687.0,Spain,Male,43.0,1.0,0.0,1.0,1.0,0.0,112157.31
+19327,15640407.0,Iloerika,592.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,101728.46
+19328,15656865.0,Chizuoke,675.0,France,Male,34.0,4.0,0.0,2.0,0.0,0.0,140937.97
+19329,15635572.0,H?,611.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,171575.54
+19330,15714057.0,Zikoranachidimma,641.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,107712.42
+19331,15770030.0,Ugochukwu,602.0,France,Female,57.0,9.0,0.0,1.0,1.0,0.0,57962.41
+19332,15794345.0,Lettiere,683.0,Spain,Male,44.0,6.0,0.0,2.0,1.0,1.0,105818.51
+19333,15659305.0,Chiazagomekpele,496.0,Germany,Male,26.0,4.0,100315.1,2.0,1.0,0.0,171457.06
+19334,15582914.0,Trevisani,553.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,138851.03
+19335,15794472.0,Pisano,716.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,17095.49
+19336,15702631.0,Chukwumaobim,678.0,Germany,Female,32.0,8.0,134442.81,1.0,1.0,1.0,146652.01
+19337,15754577.0,P'eng,667.0,France,Female,52.0,2.0,0.0,1.0,1.0,0.0,103940.68
+19338,15659962.0,Ting,535.0,France,Female,38.0,6.0,0.0,1.0,1.0,0.0,55324.49
+19339,15691792.0,Chiedozie,603.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,121151.1
+19340,15750778.0,Ogochukwu,595.0,France,Male,38.0,7.0,0.0,1.0,1.0,1.0,131710.59
+19341,15692571.0,Okwudiliolisa,538.0,France,Female,37.0,6.0,113744.72,1.0,1.0,1.0,52337.97
+19342,15757916.0,Okwudiliolisa,731.0,France,Male,30.0,3.0,0.0,2.0,0.0,1.0,175399.14
+19343,15684469.0,Kambinachi,588.0,France,Male,30.0,4.0,115488.52,1.0,1.0,0.0,116748.14
+19344,15752884.0,Ch'iu,586.0,France,Male,45.0,3.0,0.0,2.0,1.0,1.0,157527.6
+19345,15634359.0,Ejimofor,709.0,France,Male,40.0,1.0,127638.35,1.0,0.0,1.0,109742.8
+19346,15696733.0,Ch'iu,636.0,France,Male,21.0,5.0,0.0,2.0,1.0,1.0,77979.35
+19347,15593636.0,Ibeamaka,613.0,Germany,Male,35.0,10.0,130334.35,2.0,1.0,1.0,103921.43
+19348,15655229.0,Hsia,751.0,France,Male,35.0,5.0,141136.62,1.0,1.0,1.0,9221.78
+19349,15744044.0,Gadsdon,665.0,Germany,Male,46.0,1.0,105858.68,1.0,1.0,1.0,126535.57
+19350,15693737.0,Chiang,708.0,Germany,Male,34.0,5.0,68881.59,2.0,0.0,0.0,92938.74
+19351,15646720.0,Onyekaozulu,635.0,France,Male,44.0,7.0,0.0,2.0,0.0,1.0,185561.89
+19352,15575060.0,Hsieh,537.0,France,Female,67.0,5.0,0.0,1.0,1.0,1.0,125870.5
+19353,15771299.0,H?,733.0,France,Male,38.0,4.0,0.0,1.0,0.0,1.0,131239.65
+19354,15725882.0,Chukwuemeka,577.0,France,Female,33.0,7.0,125724.95,1.0,1.0,0.0,122964.18
+19355,15760456.0,Ts'ai,642.0,France,Female,32.0,2.0,141786.78,1.0,0.0,1.0,28367.21
+19356,15644977.0,Whitehead,741.0,Spain,Female,34.0,5.0,151857.81,1.0,1.0,1.0,134889.95
+19357,15681550.0,Ozerova,829.0,Spain,Female,40.0,2.0,0.0,2.0,0.0,1.0,57517.71
+19358,15739271.0,Hsing,684.0,Germany,Male,38.0,9.0,126147.84,2.0,1.0,0.0,146041.45
+19359,15761706.0,Hsiung,579.0,France,Female,51.0,1.0,0.0,2.0,1.0,1.0,111340.36
+19360,15781495.0,Chien,565.0,France,Female,35.0,3.0,0.0,1.0,1.0,1.0,187638.14
+19361,15616152.0,Y?an,741.0,Spain,Male,27.0,5.0,158713.08,1.0,1.0,1.0,105969.05
+19362,15778885.0,Nnachetam,539.0,France,Female,38.0,3.0,0.0,1.0,1.0,0.0,64420.5
+19363,15580988.0,Chukwudi,751.0,France,Male,37.0,1.0,0.0,1.0,1.0,1.0,142797.5
+19364,15673529.0,Kambinachi,545.0,France,Female,44.0,8.0,126597.89,1.0,1.0,1.0,111312.96
+19365,15673591.0,Nkemdirim,553.0,France,Female,47.0,9.0,0.0,2.0,0.0,0.0,93777.15
+19366,15804822.0,Hsueh,678.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,1.0,149614.17
+19367,15752507.0,Mazzanti,538.0,France,Female,41.0,5.0,0.0,1.0,1.0,0.0,141364.62
+19368,15672930.0,Martin,638.0,Germany,Male,43.0,6.0,116244.14,2.0,1.0,0.0,55893.33
+19369,15763431.0,Chiu,663.0,France,Female,40.0,2.0,0.0,2.0,0.0,0.0,85411.01
+19370,15628272.0,Cawood,586.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,48131.92
+19371,15584928.0,K?,718.0,France,Female,48.0,9.0,118448.15,1.0,1.0,0.0,35356.38
+19372,15783541.0,Ngozichukwuka,682.0,Spain,Female,32.0,1.0,0.0,2.0,0.0,1.0,47775.34
+19373,15741745.0,K?,632.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,134878.19
+19374,15621644.0,Lettiere,683.0,France,Male,34.0,9.0,116498.22,1.0,1.0,1.0,180712.52
+19375,15739248.0,Ch'ien,571.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,92381.01
+19376,15771855.0,Onwuamaeze,606.0,France,Male,27.0,5.0,0.0,2.0,1.0,0.0,109538.75
+19377,15654574.0,Udegbunam,711.0,Germany,Female,46.0,9.0,145541.56,1.0,1.0,0.0,46415.09
+19378,15754929.0,Okwuoma,637.0,France,Female,36.0,3.0,155739.76,1.0,1.0,0.0,87834.23
+19379,15644896.0,Onyemauchechukwu,634.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,1.0,137784.25
+19380,15771580.0,Cremonesi,648.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,146056.29
+19381,15748625.0,Onio,684.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,162699.7
+19382,15581539.0,Lo Duca,481.0,Spain,Male,31.0,9.0,148853.29,1.0,1.0,1.0,91092.84
+19383,15761706.0,Liao,607.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,66795.78
+19384,15605835.0,Kistler,706.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,143393.24
+19385,15691871.0,Scott,599.0,France,Female,42.0,2.0,0.0,2.0,0.0,1.0,141023.01
+19386,15743478.0,Chukwudi,637.0,Spain,Female,29.0,10.0,154878.79,1.0,1.0,1.0,185827.74
+19387,15651359.0,Ch'ien,535.0,France,Male,29.0,2.0,0.0,2.0,0.0,1.0,132719.8
+19388,15746708.0,Ejimofor,619.0,France,Male,45.0,0.0,141573.55,2.0,0.0,0.0,82815.93
+19389,15572022.0,Ch'eng,661.0,France,Male,49.0,1.0,0.0,3.0,1.0,0.0,132467.54
+19390,15568360.0,Onwubiko,605.0,France,Male,42.0,7.0,0.0,1.0,1.0,0.0,171657.57
+19391,15809847.0,Yobanna,595.0,France,Female,49.0,6.0,110610.41,1.0,0.0,0.0,106936.09
+19392,15589210.0,Y?,790.0,France,Female,40.0,1.0,0.0,2.0,1.0,1.0,152499.8
+19393,15604544.0,Ch'eng,619.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,178667.7
+19394,15783030.0,Clements,729.0,Germany,Male,34.0,8.0,153972.89,1.0,1.0,0.0,158036.72
+19395,15613292.0,Chidimma,471.0,Spain,Male,33.0,3.0,0.0,1.0,1.0,1.0,177627.33
+19396,15757916.0,Nwabugwu,586.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,148527.56
+19397,15575679.0,Hs?,755.0,France,Male,39.0,5.0,0.0,3.0,1.0,1.0,137959.0
+19398,15803907.0,Hs?,710.0,Germany,Male,32.0,7.0,91547.84,1.0,1.0,0.0,83022.75
+19399,15709368.0,Chigolum,611.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,82709.8
+19400,15633516.0,Chidozie,549.0,Germany,Female,37.0,2.0,106022.61,1.0,1.0,0.0,115566.57
+19401,15579189.0,Hs?,756.0,Spain,Female,47.0,4.0,0.0,2.0,1.0,1.0,70778.6
+19402,15754577.0,Palerma,535.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,183318.79
+19403,15792388.0,Lu,687.0,France,Male,48.0,10.0,0.0,2.0,1.0,0.0,128918.42
+19404,15707637.0,Trevisano,731.0,France,Male,38.0,4.0,96473.59,2.0,0.0,1.0,72970.67
+19405,15774931.0,Trentini,707.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,176962.31
+19406,15809901.0,Zetticci,682.0,Germany,Female,38.0,1.0,92510.9,1.0,1.0,0.0,7063.6
+19407,15631603.0,De Salis,738.0,France,Female,32.0,2.0,0.0,1.0,0.0,0.0,162812.16
+19408,15750731.0,Ch'en,634.0,France,Female,35.0,1.0,0.0,2.0,1.0,0.0,28157.34
+19409,15731519.0,Hao,648.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,61164.45
+19410,15624013.0,Pirozzi,644.0,France,Male,31.0,7.0,0.0,2.0,0.0,1.0,136985.08
+19411,15806922.0,Wood,745.0,France,Male,25.0,8.0,0.0,2.0,1.0,1.0,96847.91
+19412,15756960.0,Onwumelu,717.0,Spain,Female,34.0,6.0,0.0,2.0,0.0,1.0,87400.53
+19413,15806438.0,Iweobiegbunam,619.0,France,Male,29.0,6.0,0.0,2.0,0.0,1.0,97734.3
+19414,15641411.0,Hsu,706.0,Germany,Female,46.0,8.0,98592.88,1.0,0.0,1.0,120531.31
+19415,15688880.0,Nkemakolam,751.0,France,Female,26.0,8.0,0.0,2.0,1.0,0.0,132468.76
+19416,15727299.0,Martin,594.0,Germany,Female,41.0,9.0,110148.58,2.0,1.0,1.0,155061.97
+19417,15770185.0,Onyemachukwu,727.0,France,Male,54.0,3.0,0.0,3.0,1.0,0.0,96420.58
+19418,15808044.0,Chiang,646.0,France,Female,31.0,1.0,0.0,2.0,1.0,0.0,102975.72
+19419,15696852.0,Chukwudi,738.0,Germany,Female,38.0,7.0,115930.87,2.0,1.0,0.0,131886.46
+19420,15592229.0,Pape,637.0,Spain,Male,40.0,9.0,160139.59,1.0,1.0,1.0,187905.12
+19421,15623369.0,Smith,645.0,France,Female,62.0,5.0,0.0,2.0,1.0,0.0,141359.11
+19422,15606901.0,Genovese,725.0,Germany,Male,38.0,8.0,96542.16,2.0,1.0,0.0,156024.01
+19423,15696852.0,Tuan,547.0,France,Female,40.0,1.0,117466.03,1.0,1.0,1.0,126495.39
+19424,15754569.0,Rizzo,684.0,Germany,Female,41.0,3.0,118879.35,1.0,1.0,0.0,183542.08
+19425,15768295.0,Dickson,651.0,France,Female,28.0,10.0,0.0,2.0,1.0,1.0,182436.81
+19426,15706799.0,Lai,581.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,131736.44
+19427,15641298.0,Palerma,596.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,167984.72
+19428,15757867.0,Palerma,479.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,1.0,151325.24
+19429,15622750.0,Hsiao,613.0,France,Female,43.0,9.0,0.0,2.0,1.0,1.0,152499.8
+19430,15596671.0,Kenechi,661.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,176924.21
+19431,15649523.0,Johnson,714.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,158958.2
+19432,15576928.0,Ts'ui,756.0,France,Female,25.0,4.0,0.0,2.0,0.0,0.0,132467.54
+19433,15757911.0,Folliero,651.0,France,Female,41.0,5.0,0.0,2.0,1.0,1.0,103934.12
+19434,15761158.0,Mazzanti,710.0,France,Female,41.0,10.0,0.0,1.0,1.0,0.0,89524.83
+19435,15650432.0,Fiorentino,646.0,France,Male,36.0,7.0,115062.61,1.0,1.0,1.0,166520.96
+19436,15733597.0,Chidubem,754.0,France,Male,28.0,5.0,0.0,2.0,0.0,1.0,122580.48
+19437,15595036.0,Ts'ui,682.0,Germany,Female,57.0,2.0,122522.34,1.0,1.0,0.0,63795.8
+19438,15658969.0,Shao,850.0,Spain,Male,24.0,9.0,0.0,2.0,0.0,0.0,166957.82
+19439,15772381.0,Nieves,792.0,Spain,Female,52.0,0.0,0.0,2.0,0.0,0.0,179655.87
+19440,15654760.0,Hsing,645.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,172810.51
+19441,15575060.0,Chukwumaobim,595.0,Spain,Male,32.0,9.0,83807.86,1.0,0.0,0.0,145524.36
+19442,15668830.0,Nucci,648.0,Germany,Female,41.0,5.0,80923.56,2.0,1.0,0.0,131300.68
+19443,15631222.0,Yobanna,714.0,France,Female,35.0,2.0,0.0,2.0,0.0,1.0,35786.76
+19444,15798975.0,Hsu,583.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,162719.69
+19445,15648075.0,K'ung,579.0,France,Female,38.0,1.0,99745.95,1.0,1.0,1.0,56794.73
+19446,15682472.0,Ndukaku,612.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,158973.45
+19447,15751175.0,Frolova,758.0,Germany,Male,34.0,5.0,108432.88,2.0,1.0,1.0,186418.85
+19448,15578251.0,Trevisani,763.0,Germany,Female,29.0,1.0,93680.43,1.0,0.0,1.0,121798.52
+19449,15694890.0,T'ao,679.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,176925.69
+19450,15691823.0,Iheanacho,508.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,0.0,159899.97
+19451,15668893.0,Trevisan,523.0,France,Female,42.0,1.0,0.0,1.0,1.0,1.0,79178.31
+19452,15585367.0,Chiekwugo,607.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,100433.83
+19453,15776725.0,Obiajulu,613.0,Spain,Male,37.0,5.0,0.0,2.0,0.0,0.0,187811.71
+19454,15780215.0,T'ien,537.0,France,Female,48.0,5.0,0.0,1.0,1.0,1.0,47880.71
+19455,15796834.0,Chiu,592.0,Germany,Male,38.0,3.0,161884.99,1.0,1.0,0.0,141137.53
+19456,15733597.0,Chidiebele,554.0,Germany,Female,39.0,4.0,129745.1,1.0,1.0,0.0,181850.74
+19457,15669922.0,Olisaemeka,771.0,France,Male,44.0,1.0,0.0,2.0,1.0,0.0,91036.74
+19458,15751180.0,H?,579.0,France,Male,40.0,8.0,92887.13,1.0,1.0,1.0,71581.79
+19459,15576022.0,Ukaegbunam,663.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,183584.17
+19460,15684951.0,Chiazagomekpere,565.0,Germany,Female,60.0,2.0,146580.69,1.0,1.0,0.0,162599.51
+19461,15774854.0,Chiekwugo,677.0,France,Male,37.0,4.0,0.0,2.0,0.0,0.0,52404.47
+19462,15621063.0,D'Albertis,678.0,France,Female,25.0,3.0,0.0,2.0,1.0,0.0,150685.52
+19463,15621353.0,Pinto,587.0,Germany,Male,25.0,6.0,109922.61,2.0,1.0,0.0,60721.4
+19464,15586931.0,Iredale,713.0,France,Male,33.0,9.0,0.0,1.0,1.0,0.0,122284.38
+19465,15702789.0,Yobachukwu,745.0,Germany,Male,33.0,9.0,114435.47,1.0,1.0,0.0,141021.22
+19466,15711759.0,Fane,610.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,55313.44
+19467,15787597.0,Iweobiegbunam,709.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,134732.99
+19468,15747014.0,Ma,686.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,157862.82
+19469,15634218.0,Milne,623.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,109638.78
+19470,15772383.0,Chiemela,693.0,Spain,Male,60.0,1.0,0.0,2.0,1.0,1.0,91094.46
+19471,15797905.0,Lo,619.0,Germany,Female,39.0,10.0,111018.98,1.0,1.0,1.0,73562.05
+19472,15587573.0,Niu,757.0,Spain,Male,57.0,3.0,0.0,1.0,1.0,1.0,130862.11
+19473,15593694.0,Okonkwo,593.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,180329.83
+19474,15666856.0,Moore,569.0,France,Female,32.0,4.0,146466.13,1.0,1.0,1.0,92712.97
+19475,15586824.0,Genovese,775.0,France,Male,39.0,1.0,106234.02,1.0,1.0,0.0,121964.46
+19476,15772996.0,Teng,678.0,France,Male,29.0,7.0,0.0,2.0,0.0,0.0,10883.52
+19477,15693906.0,Hsueh,609.0,Germany,Female,45.0,3.0,132876.23,1.0,1.0,0.0,143544.48
+19478,15735246.0,Chukwufumnanya,619.0,France,Male,45.0,10.0,0.0,2.0,1.0,0.0,142238.54
+19479,15663917.0,Lai,663.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,56767.67
+19480,15677243.0,Nwokezuike,637.0,France,Male,30.0,8.0,0.0,1.0,1.0,1.0,51112.8
+19481,15766776.0,Dyer,638.0,Spain,Male,43.0,3.0,0.0,1.0,1.0,0.0,140941.47
+19482,15591915.0,Chiang,559.0,France,Male,24.0,0.0,0.0,1.0,1.0,0.0,121789.3
+19483,15627365.0,Ahern,613.0,France,Male,33.0,3.0,0.0,2.0,0.0,1.0,193309.58
+19484,15715750.0,Hsing,850.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,86039.27
+19485,15773796.0,De Luca,681.0,Spain,Male,32.0,7.0,0.0,2.0,0.0,1.0,79078.91
+19486,15788215.0,Fokina,596.0,France,Female,59.0,6.0,162390.61,1.0,0.0,0.0,146715.07
+19487,15781802.0,Ch'ang,684.0,France,Female,27.0,3.0,0.0,1.0,1.0,0.0,53584.03
+19488,15745355.0,Iweobiegbulam,651.0,France,Male,53.0,7.0,0.0,2.0,1.0,1.0,184312.88
+19489,15783097.0,Chidumaga,565.0,Spain,Female,36.0,9.0,0.0,1.0,1.0,1.0,106977.53
+19490,15692730.0,Ibezimako,703.0,France,Male,32.0,10.0,0.0,2.0,0.0,1.0,126475.79
+19491,15592387.0,Toscani,674.0,France,Male,38.0,1.0,0.0,1.0,1.0,1.0,151375.46
+19492,15773869.0,Ch'ang,606.0,Germany,Male,42.0,6.0,150126.44,2.0,1.0,1.0,86786.41
+19493,15671165.0,Okwudiliolisa,697.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,161845.81
+19494,15763063.0,Uwaezuoke,497.0,Germany,Female,56.0,9.0,96479.81,2.0,1.0,1.0,88779.06
+19495,15604345.0,Milano,662.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,53477.76
+19496,15797960.0,Liao,727.0,Germany,Male,59.0,5.0,121405.04,1.0,1.0,1.0,147720.27
+19497,15678916.0,Shao,753.0,France,Male,36.0,7.0,154953.94,1.0,1.0,1.0,76510.46
+19498,15602497.0,Dike,700.0,France,Female,30.0,9.0,0.0,1.0,1.0,1.0,64872.55
+19499,15652935.0,Ejikemeifeuwa,661.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,141148.21
+19500,15604782.0,Manna,659.0,France,Female,47.0,5.0,127475.29,1.0,1.0,0.0,142797.5
+19501,15744200.0,Moseley,699.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,131886.46
+19502,15792232.0,Nnaife,685.0,France,Female,56.0,6.0,0.0,1.0,1.0,0.0,89520.25
+19503,15721935.0,Folliero,632.0,Spain,Female,51.0,4.0,0.0,2.0,0.0,0.0,167984.72
+19504,15644724.0,Eluemuno,706.0,Spain,Female,53.0,7.0,0.0,1.0,1.0,0.0,143981.27
+19505,15643967.0,Chidiebere,652.0,Spain,Male,59.0,1.0,0.0,2.0,0.0,1.0,164741.7
+19506,15592028.0,Ritchie,527.0,France,Female,30.0,7.0,0.0,1.0,0.0,1.0,72876.43
+19507,15621893.0,Manna,804.0,Germany,Female,48.0,3.0,131078.42,1.0,1.0,1.0,54926.51
+19508,15809800.0,Pipes,627.0,Spain,Female,24.0,3.0,0.0,1.0,1.0,1.0,103832.58
+19509,15601387.0,Onyemere,742.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,78650.95
+19510,15772777.0,Milani,535.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,62989.82
+19511,15586523.0,Chiu,741.0,Germany,Male,33.0,9.0,137948.51,1.0,1.0,1.0,181224.56
+19512,15772997.0,Ts'ui,681.0,Spain,Male,38.0,4.0,0.0,1.0,1.0,0.0,52820.13
+19513,15680405.0,Lettiere,662.0,France,Male,39.0,10.0,0.0,2.0,1.0,0.0,142223.94
+19514,15674840.0,Ch'en,616.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,169781.45
+19515,15636407.0,Onyekachi,584.0,France,Female,49.0,5.0,0.0,1.0,1.0,1.0,67979.35
+19516,15706365.0,Trevisano,714.0,Germany,Female,44.0,5.0,104783.81,1.0,1.0,1.0,4395.45
+19517,15776824.0,Macleod,587.0,Germany,Male,42.0,0.0,123873.27,2.0,1.0,1.0,143981.27
+19518,15789560.0,Hsiung,629.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,88890.05
+19519,15632365.0,Ts'ui,850.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,120852.85
+19520,15593973.0,Iheanacho,774.0,France,Male,30.0,10.0,0.0,2.0,0.0,0.0,67734.79
+19521,15744691.0,Nkemdirim,639.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,94845.54
+19522,15796790.0,Hsiao,538.0,Germany,Male,43.0,8.0,106045.92,1.0,1.0,0.0,181224.56
+19523,15598086.0,Ni,661.0,Germany,Female,40.0,8.0,94762.89,2.0,1.0,0.0,1247772.61
+19524,15649414.0,Hsing,646.0,France,Female,36.0,7.0,121498.11,1.0,0.0,0.0,4861.72
+19525,15794580.0,Ch'ang,715.0,France,Male,34.0,2.0,129401.87,1.0,0.0,1.0,136079.64
+19526,15603088.0,Ijendu,812.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,157939.21
+19527,15711602.0,Ch'ien,774.0,France,Female,43.0,4.0,105770.14,1.0,0.0,1.0,120801.65
+19528,15675318.0,DeRose,641.0,Spain,Male,43.0,9.0,0.0,1.0,1.0,0.0,62868.33
+19529,15646513.0,Binder,718.0,France,Male,33.0,6.0,137755.76,1.0,1.0,1.0,6925.36
+19530,15689953.0,Ch'iu,652.0,Germany,Female,56.0,7.0,110827.49,1.0,0.0,1.0,102299.81
+19531,15782659.0,Yefremova,632.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,117616.29
+19532,15717706.0,Tung,731.0,France,Female,41.0,1.0,131647.01,1.0,1.0,0.0,126797.28
+19533,15787693.0,Ts'ai,722.0,France,Male,43.0,2.0,0.0,2.0,0.0,0.0,140723.05
+19534,15777892.0,Mazzanti,727.0,Spain,Female,59.0,4.0,0.0,2.0,1.0,1.0,151401.33
+19535,15785183.0,Chien,719.0,France,Male,30.0,7.0,136659.74,1.0,1.0,0.0,132719.8
+19536,15757837.0,Chukwuemeka,579.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,0.0,153535.56
+19537,15673529.0,Chidiegwu,850.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,101839.77
+19538,15692575.0,Chidiebere,661.0,France,Male,46.0,1.0,0.0,2.0,1.0,0.0,106937.06
+19539,15682884.0,Ch'en,553.0,Germany,Female,49.0,10.0,130854.59,2.0,1.0,0.0,89892.75
+19540,15690744.0,Hsing,730.0,Germany,Male,40.0,8.0,116560.57,1.0,1.0,0.0,139048.0
+19541,15761670.0,Iadanza,721.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,1.0,126477.41
+19542,15807003.0,Ch'ang,708.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,52484.01
+19543,15699598.0,Ch'ien,602.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,119333.95
+19544,15790782.0,Onyemauchechukwu,619.0,Germany,Female,52.0,2.0,139914.7,1.0,1.0,1.0,16402.11
+19545,15781884.0,Nkemjika,684.0,Germany,Male,56.0,9.0,103522.75,1.0,1.0,1.0,148564.76
+19546,15647842.0,Frolova,672.0,Spain,Female,54.0,9.0,0.0,1.0,1.0,1.0,36124.98
+19547,15775116.0,Chia,696.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,72990.62
+19548,15696964.0,Kao,676.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,108887.44
+19549,15691808.0,Liao,624.0,Spain,Male,33.0,6.0,102025.88,2.0,1.0,1.0,124596.63
+19550,15637178.0,Yobachi,628.0,Spain,Female,38.0,8.0,0.0,1.0,1.0,1.0,127543.81
+19551,15573456.0,Onyinyechukwuka,663.0,Germany,Female,37.0,3.0,112930.28,2.0,1.0,1.0,166896.01
+19552,15782530.0,Hsieh,719.0,France,Female,34.0,4.0,0.0,2.0,0.0,0.0,54448.41
+19553,15572038.0,Mazzi,706.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,158043.11
+19554,15632576.0,Ch'ien,559.0,France,Female,39.0,7.0,0.0,2.0,0.0,0.0,106920.57
+19555,15802088.0,Okwukwe,645.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,115509.59
+19556,15619340.0,Ch'in,449.0,France,Female,30.0,9.0,0.0,2.0,1.0,1.0,139919.38
+19557,15678067.0,Hsiao,629.0,Germany,Female,59.0,6.0,159316.64,1.0,1.0,0.0,153662.88
+19558,15694566.0,Hao,542.0,France,Male,26.0,8.0,0.0,2.0,0.0,1.0,86173.92
+19559,15585280.0,Chinweuba,681.0,France,Male,33.0,9.0,130806.99,1.0,1.0,0.0,167554.86
+19560,15572073.0,P'eng,726.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,134600.94
+19561,15681562.0,P'eng,616.0,Germany,Female,51.0,6.0,127122.79,1.0,0.0,0.0,55313.51
+19562,15746410.0,Chiganu,682.0,Spain,Male,45.0,7.0,0.0,2.0,0.0,1.0,146652.11
+19563,15584812.0,Ugoji,683.0,France,Female,43.0,9.0,0.0,2.0,1.0,0.0,54949.73
+19564,15769504.0,Lucchesi,725.0,France,Female,39.0,4.0,0.0,2.0,1.0,1.0,88729.08
+19565,15652400.0,Shih,722.0,France,Male,44.0,3.0,0.0,2.0,0.0,0.0,96420.58
+19566,15641582.0,Chukwukere,676.0,Germany,Female,32.0,2.0,114023.64,1.0,1.0,0.0,105424.02
+19567,15654611.0,Nwankwo,521.0,France,Female,46.0,8.0,0.0,1.0,1.0,0.0,162719.69
+19568,15693526.0,Kudryashova,640.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,199493.38
+19569,15765457.0,Pagnotto,754.0,France,Female,32.0,8.0,0.0,1.0,1.0,0.0,120892.83
+19570,15671766.0,Ch'ien,645.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,0.0,142331.82
+19571,15686983.0,Chuang,738.0,Spain,Female,23.0,3.0,0.0,2.0,1.0,0.0,167553.06
+19572,15717286.0,Kline,602.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,78896.59
+19573,15691627.0,Ch'in,685.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,97985.4
+19574,15792736.0,Zetticci,742.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,124796.84
+19575,15616240.0,Ojiofor,748.0,France,Female,33.0,4.0,0.0,1.0,1.0,0.0,122580.48
+19576,15588517.0,Manna,649.0,Germany,Female,62.0,1.0,113874.15,1.0,0.0,1.0,149593.61
+19577,15757981.0,Lu,642.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,153747.71
+19578,15731522.0,Y?an,569.0,France,Female,28.0,3.0,0.0,2.0,1.0,1.0,130486.57
+19579,15667932.0,Ts'ui,765.0,Germany,Female,34.0,1.0,142703.4,1.0,1.0,0.0,57862.88
+19580,15757811.0,Yermakov,583.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,102299.81
+19581,15792232.0,Milne,599.0,Germany,Female,41.0,0.0,134692.4,1.0,1.0,1.0,131312.56
+19582,15788367.0,Yermakov,634.0,Spain,Female,33.0,2.0,152367.21,1.0,0.0,0.0,131283.6
+19583,15691823.0,White,751.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,1.0,82868.34
+19584,15814405.0,Okwudilichukwu,582.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,145936.28
+19585,15737571.0,Lucchese,651.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,139475.0
+19586,15637354.0,Lucchese,633.0,France,Male,41.0,4.0,118068.93,1.0,1.0,0.0,182725.9
+19587,15602551.0,Yashina,634.0,France,Female,42.0,2.0,0.0,2.0,1.0,0.0,51380.9
+19588,15655952.0,Nwankwo,682.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,93081.87
+19589,15809826.0,Lucchesi,612.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,97629.31
+19590,15741180.0,Macartney,584.0,Spain,Male,40.0,1.0,154776.42,1.0,1.0,0.0,134778.01
+19591,15618647.0,Olisaemeka,678.0,Germany,Male,35.0,8.0,121581.56,1.0,1.0,1.0,62222.81
+19592,15651581.0,Ch'eng,681.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,50899.91
+19593,15729582.0,Nnamutaezinwa,535.0,France,Female,52.0,7.0,0.0,1.0,1.0,0.0,131281.28
+19594,15792360.0,Nkemdilim,656.0,Germany,Male,39.0,6.0,77354.37,2.0,1.0,0.0,64872.55
+19595,15650322.0,Ts'ui,557.0,Spain,Female,34.0,5.0,0.0,1.0,1.0,1.0,164975.82
+19596,15731989.0,Chijindum,531.0,France,Female,37.0,3.0,77526.66,1.0,1.0,0.0,161854.98
+19597,15665963.0,Yobachi,596.0,Spain,Male,66.0,2.0,0.0,2.0,1.0,1.0,136301.1
+19598,15615016.0,Onio,730.0,France,Male,26.0,2.0,0.0,2.0,1.0,0.0,173495.77
+19599,15642834.0,Biryukov,627.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,96444.88
+19600,15666884.0,Nwachukwu,544.0,Spain,Female,38.0,9.0,0.0,2.0,0.0,1.0,82969.61
+19601,15764170.0,Ch'ien,578.0,Germany,Female,44.0,8.0,106901.42,1.0,1.0,0.0,136497.28
+19602,15797748.0,Chiu,588.0,Germany,Female,46.0,8.0,123802.98,1.0,0.0,0.0,47880.71
+19603,15783028.0,Chigbogu,709.0,France,Male,52.0,4.0,0.0,1.0,0.0,0.0,181419.29
+19604,15782591.0,Cattaneo,687.0,France,Female,41.0,5.0,0.0,2.0,0.0,1.0,182787.17
+19605,15668958.0,Chidubem,718.0,Spain,Male,54.0,8.0,83568.55,1.0,1.0,1.0,142670.61
+19606,15694404.0,Hsueh,842.0,Germany,Male,31.0,4.0,122486.11,1.0,0.0,0.0,134732.99
+19607,15652218.0,Ch'eng,741.0,France,Male,43.0,3.0,0.0,2.0,1.0,1.0,171707.93
+19608,15608406.0,Onyemauchechukwu,693.0,France,Male,38.0,3.0,97541.24,1.0,1.0,1.0,163530.22
+19609,15813444.0,Aitken,592.0,Germany,Male,36.0,7.0,135984.15,2.0,1.0,1.0,123439.65
+19610,15742668.0,Nkemdilim,607.0,Germany,Female,48.0,9.0,106397.75,2.0,1.0,1.0,169469.3
+19611,15641640.0,Fiorentino,724.0,France,Female,39.0,6.0,0.0,1.0,1.0,0.0,122478.19
+19612,15677906.0,Ejimofor,604.0,France,Male,37.0,6.0,0.0,1.0,0.0,0.0,82662.73
+19613,15735782.0,Hsiung,627.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,93881.75
+19614,15641411.0,Yashina,602.0,France,Female,32.0,6.0,88915.37,1.0,0.0,1.0,121151.1
+19615,15643362.0,Ch'en,794.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,96542.46
+19616,15697424.0,Ukaegbulam,710.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,148087.62
+19617,15783863.0,P'eng,614.0,France,Male,27.0,9.0,0.0,2.0,0.0,0.0,181208.47
+19618,15578211.0,Zetticci,705.0,Spain,Male,56.0,5.0,0.0,2.0,1.0,0.0,120790.02
+19619,15590094.0,Chukwufumnanya,758.0,Germany,Male,37.0,2.0,113657.83,2.0,1.0,0.0,115468.91
+19620,15655436.0,Cremonesi,722.0,France,Male,47.0,7.0,0.0,2.0,1.0,1.0,162597.15
+19621,15750014.0,Chukwudi,714.0,France,Male,69.0,1.0,0.0,2.0,1.0,1.0,140197.42
+19622,15682533.0,P'eng,721.0,France,Male,46.0,8.0,0.0,3.0,1.0,0.0,166471.79
+19623,15773520.0,Nnonso,727.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,82696.84
+19624,15613197.0,Sagese,738.0,France,Male,37.0,4.0,0.0,1.0,1.0,1.0,181598.25
+19625,15782530.0,T'ien,754.0,Germany,Female,30.0,4.0,129974.79,2.0,1.0,1.0,105279.74
+19626,15585490.0,Okonkwo,707.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,82559.77
+19627,15729689.0,Tuan,714.0,France,Male,31.0,7.0,107749.03,1.0,0.0,1.0,141152.28
+19628,15586039.0,Chuang,598.0,Spain,Male,43.0,9.0,0.0,1.0,1.0,1.0,119071.56
+19629,15752588.0,Hsu,745.0,Germany,Male,32.0,6.0,107926.02,2.0,1.0,1.0,131772.48
+19630,15692761.0,Kisch,714.0,France,Female,40.0,8.0,0.0,2.0,0.0,1.0,52795.56
+19631,15677317.0,Pagnotto,663.0,Germany,Male,48.0,3.0,121326.42,3.0,1.0,1.0,181429.87
+19632,15802479.0,Genovese,679.0,France,Female,31.0,9.0,0.0,1.0,1.0,0.0,113860.81
+19633,15746203.0,Chiemenam,556.0,France,Male,43.0,4.0,61636.97,2.0,1.0,0.0,141872.05
+19634,15619407.0,Chimezie,527.0,Spain,Male,33.0,5.0,139840.36,1.0,1.0,1.0,105983.09
+19635,15806913.0,Nwankwo,775.0,Spain,Male,43.0,6.0,0.0,2.0,0.0,0.0,147055.87
+19636,15749489.0,Cattaneo,651.0,Germany,Female,39.0,4.0,131659.58,2.0,1.0,0.0,83679.46
+19637,15734491.0,Nkemakolam,652.0,Germany,Female,48.0,1.0,111098.85,1.0,0.0,1.0,113483.96
+19638,15683540.0,Y?,584.0,France,Female,39.0,4.0,0.0,2.0,1.0,1.0,125445.8
+19639,15695499.0,Chuang,749.0,Spain,Female,25.0,7.0,0.0,2.0,1.0,1.0,134878.19
+19640,15658675.0,Okonkwo,822.0,Spain,Male,29.0,9.0,97473.87,1.0,1.0,0.0,157874.79
+19641,15806917.0,Eiland,708.0,France,Female,26.0,7.0,0.0,2.0,0.0,1.0,59981.62
+19642,15569590.0,Nnachetam,664.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,0.0,143981.27
+19643,15672432.0,De Luca,661.0,France,Female,23.0,7.0,0.0,2.0,1.0,1.0,176743.51
+19644,15686936.0,Tien,697.0,Spain,Male,38.0,4.0,0.0,2.0,0.0,1.0,134531.96
+19645,15672756.0,Chuang,587.0,Spain,Female,71.0,3.0,0.0,2.0,0.0,1.0,91514.62
+19646,15793311.0,Macleod,593.0,Spain,Female,29.0,7.0,147069.69,1.0,1.0,0.0,143774.06
+19647,15578073.0,Chiemezie,596.0,France,Female,37.0,6.0,0.0,2.0,0.0,1.0,165082.25
+19648,15637366.0,Hao,745.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,172363.41
+19649,15720354.0,T'ao,586.0,Germany,Female,41.0,10.0,134760.21,2.0,1.0,0.0,94767.77
+19650,15769974.0,DeRose,613.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,127875.1
+19651,15685300.0,Iheanacho,645.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,108790.95
+19652,15645956.0,Frolova,648.0,Spain,Female,36.0,8.0,146186.28,1.0,0.0,0.0,133672.46
+19653,15794895.0,Ch'eng,619.0,France,Female,22.0,9.0,0.0,2.0,1.0,0.0,61792.76
+19654,15794468.0,Ch'ien,648.0,Germany,Female,46.0,2.0,129753.69,1.0,1.0,0.0,124571.09
+19655,15695034.0,Lai,678.0,Germany,Female,28.0,7.0,149372.55,2.0,0.0,1.0,123663.63
+19656,15782096.0,Onyemauchechukwu,521.0,France,Female,36.0,4.0,124495.98,1.0,0.0,1.0,162812.16
+19657,15792493.0,Onio,686.0,Spain,Male,42.0,2.0,0.0,2.0,1.0,1.0,152740.16
+19658,15642870.0,Udegbulam,747.0,France,Male,61.0,1.0,0.0,1.0,0.0,0.0,99818.41
+19659,15683553.0,K?,535.0,Spain,Male,25.0,7.0,0.0,2.0,1.0,1.0,199392.14
+19660,15696744.0,Onwuamaeze,501.0,France,Male,39.0,10.0,0.0,1.0,1.0,0.0,85411.01
+19661,15794566.0,Yobachukwu,739.0,France,Male,45.0,8.0,109949.05,1.0,1.0,1.0,148811.48
+19662,15768219.0,Palerma,543.0,Germany,Male,28.0,1.0,109724.38,1.0,0.0,1.0,188997.05
+19663,15725028.0,Zhirov,667.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,161479.19
+19664,15632185.0,McLean,628.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,119882.86
+19665,15643679.0,Y?an,730.0,France,Female,35.0,8.0,0.0,2.0,0.0,0.0,122518.5
+19666,15687913.0,Hs?,722.0,France,Female,37.0,1.0,0.0,2.0,1.0,0.0,59650.42
+19667,15757559.0,H?an,692.0,France,Female,42.0,8.0,0.0,2.0,1.0,0.0,187776.49
+19668,15672966.0,Yudin,651.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,179436.6
+19669,15692599.0,P'eng,643.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,170893.85
+19670,15748068.0,Nnaife,633.0,Germany,Female,31.0,6.0,133628.09,1.0,1.0,0.0,106977.53
+19671,15705890.0,Hsia,673.0,France,Male,39.0,2.0,0.0,1.0,1.0,0.0,95611.47
+19672,15652400.0,Iheatu,775.0,France,Female,37.0,1.0,131465.37,1.0,1.0,1.0,173516.71
+19673,15699005.0,Kao,535.0,France,Female,34.0,8.0,0.0,2.0,0.0,0.0,151903.6
+19674,15587562.0,Nnachetam,624.0,Spain,Male,42.0,1.0,116980.23,1.0,1.0,1.0,183868.01
+19675,15736146.0,Ukaegbunam,624.0,Spain,Male,45.0,7.0,0.0,1.0,1.0,1.0,81884.8
+19676,15641782.0,Folliero,647.0,Germany,Male,36.0,8.0,120952.74,2.0,0.0,1.0,11158.18
+19677,15734573.0,Hsieh,683.0,France,Male,22.0,1.0,0.0,2.0,1.0,1.0,88890.05
+19678,15731744.0,Chukwudi,683.0,France,Female,20.0,3.0,0.0,2.0,1.0,1.0,131736.23
+19679,15758021.0,Ni,697.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,143433.33
+19680,15767358.0,Ch'in,727.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,82336.73
+19681,15640106.0,Okwuoma,678.0,Germany,Female,52.0,2.0,141749.68,1.0,0.0,1.0,180150.49
+19682,15792360.0,Yeh,645.0,Germany,Female,56.0,6.0,101985.81,1.0,0.0,0.0,133535.29
+19683,15731951.0,Babbage,605.0,France,Male,48.0,8.0,0.0,2.0,1.0,1.0,166677.53
+19684,15763515.0,Ndukaku,521.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,85983.54
+19685,15626114.0,Lo Duca,753.0,Germany,Male,48.0,10.0,106486.66,1.0,1.0,1.0,91936.1
+19686,15642063.0,Chidiebere,673.0,Spain,Female,33.0,4.0,0.0,2.0,1.0,1.0,84760.32
+19687,15662488.0,Piazza,628.0,Spain,Female,26.0,7.0,0.0,1.0,0.0,1.0,118429.02
+19688,15665760.0,Whitehead,634.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,54439.73
+19689,15690733.0,Yobachi,641.0,France,Female,31.0,10.0,111589.33,1.0,1.0,0.0,93524.19
+19690,15791321.0,Gilleland,710.0,France,Male,47.0,7.0,106234.02,1.0,1.0,0.0,181419.35
+19691,15605742.0,Chiemenam,845.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,1.0,170829.87
+19692,15597913.0,K'ung,584.0,Germany,Male,51.0,3.0,107467.56,1.0,1.0,0.0,166513.49
+19693,15646936.0,Chiang,697.0,Spain,Male,33.0,0.0,0.0,2.0,0.0,0.0,126832.63
+19694,15700255.0,Hao,685.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,164074.89
+19695,15617052.0,Brown,705.0,France,Male,60.0,6.0,0.0,2.0,0.0,1.0,95421.0
+19696,15684958.0,Y?,714.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,173498.45
+19697,15771856.0,Okechukwu,619.0,France,Male,61.0,5.0,65812.2,1.0,1.0,1.0,46824.08
+19698,15682454.0,Ts'ui,603.0,France,Female,43.0,1.0,0.0,2.0,1.0,1.0,54474.48
+19699,15706904.0,Ulyanov,652.0,Germany,Male,39.0,9.0,140720.93,1.0,1.0,0.0,103570.52
+19700,15598046.0,Omeokachie,850.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,169915.02
+19701,15665963.0,Blackwood,706.0,France,Male,22.0,0.0,0.0,2.0,1.0,1.0,119818.17
+19702,15782735.0,Hsiung,684.0,Germany,Female,46.0,5.0,131765.72,1.0,0.0,1.0,152698.24
+19703,15693906.0,Chibugo,632.0,Spain,Female,24.0,8.0,0.0,2.0,0.0,1.0,175072.47
+19704,15651740.0,Y?an,659.0,France,Male,35.0,10.0,0.0,1.0,1.0,1.0,136079.64
+19705,15775490.0,Chu,651.0,Spain,Male,38.0,9.0,0.0,1.0,1.0,0.0,127569.8
+19706,15804787.0,Olisanugo,703.0,France,Female,44.0,5.0,0.0,1.0,1.0,1.0,84421.34
+19707,15780362.0,Ts'ui,716.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,1.0,161479.19
+19708,15798895.0,Ts'ui,697.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,158610.89
+19709,15748986.0,Ch'iu,689.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,55763.66
+19710,15703394.0,Ch'eng,590.0,Spain,Male,26.0,4.0,0.0,2.0,1.0,0.0,124872.42
+19711,15665460.0,Nwokezuike,691.0,Germany,Male,55.0,1.0,116880.53,1.0,0.0,0.0,176450.19
+19712,15786035.0,Chidiegwu,682.0,France,Female,40.0,4.0,89364.91,2.0,1.0,0.0,93829.17
+19713,15652674.0,T'ien,644.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,167636.15
+19714,15667458.0,Kao,633.0,France,Female,37.0,5.0,0.0,1.0,1.0,0.0,117034.32
+19715,15794868.0,Okwuadigbo,811.0,France,Male,58.0,7.0,0.0,2.0,1.0,1.0,107961.11
+19716,15618446.0,Folliero,717.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,146636.86
+19717,15652084.0,Hsieh,689.0,France,Male,33.0,4.0,0.0,1.0,1.0,0.0,131772.51
+19718,15801559.0,Manna,611.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,1.0,67364.42
+19719,15802220.0,Chuang,594.0,France,Female,24.0,4.0,0.0,2.0,1.0,1.0,56767.67
+19720,15672894.0,Bess,747.0,Germany,Female,37.0,5.0,94464.81,1.0,1.0,1.0,160968.44
+19721,15793049.0,Gebhart,642.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,129563.86
+19722,15678067.0,Tsao,610.0,France,Male,46.0,4.0,0.0,1.0,0.0,0.0,87985.2
+19723,15733234.0,Milani,656.0,Germany,Female,47.0,1.0,91592.06,1.0,1.0,0.0,23305.01
+19724,15642530.0,Tuan,588.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,91561.57
+19725,15602851.0,Hsieh,592.0,Germany,Male,35.0,6.0,98903.06,1.0,1.0,0.0,126153.24
+19726,15733032.0,Chiang,731.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,1.0,180829.99
+19727,15592078.0,Cremonesi,641.0,France,Male,48.0,3.0,0.0,2.0,1.0,1.0,113645.4
+19728,15707918.0,Nwabugwu,545.0,Spain,Female,48.0,7.0,0.0,2.0,1.0,1.0,53426.02
+19729,15798024.0,Nnachetam,583.0,France,Female,37.0,0.0,0.0,2.0,0.0,0.0,94977.3
+19730,15792934.0,Hsiao,685.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,185561.89
+19731,15800620.0,Ibezimako,635.0,Germany,Male,37.0,6.0,168085.38,1.0,1.0,0.0,26260.98
+19732,15691396.0,Chiang,559.0,France,Female,28.0,6.0,0.0,1.0,1.0,1.0,117622.8
+19733,15665943.0,Ts'ui,762.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,0.0,166830.71
+19734,15579566.0,Ritchie,593.0,France,Male,45.0,6.0,0.0,2.0,1.0,0.0,70783.61
+19735,15762799.0,Yermakova,676.0,France,Female,36.0,6.0,0.0,2.0,0.0,0.0,125658.28
+19736,15684440.0,Y?an,646.0,France,Female,44.0,3.0,0.0,2.0,1.0,1.0,177655.68
+19737,15771580.0,Mazzi,722.0,Germany,Female,65.0,2.0,90791.69,1.0,0.0,0.0,171060.01
+19738,15751455.0,Mancini,542.0,Germany,Female,43.0,5.0,95052.29,1.0,1.0,0.0,163798.41
+19739,15672056.0,Ch'eng,674.0,Spain,Male,52.0,3.0,122570.69,1.0,1.0,1.0,95985.64
+19740,15637644.0,Haddon,644.0,France,Male,34.0,6.0,0.0,2.0,0.0,0.0,149853.89
+19741,15667424.0,Oguejiofor,663.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,121085.72
+19742,15641416.0,Li,590.0,France,Female,43.0,5.0,0.0,2.0,0.0,1.0,120853.83
+19743,15607330.0,Maclean,538.0,France,Male,40.0,4.0,0.0,1.0,1.0,0.0,77981.54
+19744,15786800.0,P'an,711.0,France,Male,23.0,5.0,0.0,2.0,0.0,1.0,176750.81
+19745,15573452.0,Chinedum,679.0,France,Female,32.0,4.0,156811.74,1.0,1.0,1.0,100615.14
+19746,15637240.0,T'ien,707.0,France,Male,37.0,8.0,152296.84,1.0,0.0,1.0,62449.07
+19747,15766458.0,Chukwudi,616.0,Spain,Female,24.0,4.0,162390.52,1.0,1.0,0.0,168753.34
+19748,15739123.0,Fanucci,627.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,19515.48
+19749,15716164.0,Y?an,586.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,112554.68
+19750,15594577.0,Chigozie,678.0,Spain,Male,31.0,3.0,99842.6,1.0,0.0,1.0,156878.51
+19751,15609521.0,Yobanna,627.0,Germany,Female,36.0,5.0,135096.05,1.0,1.0,0.0,143981.67
+19752,15676521.0,Yefimova,635.0,France,Male,23.0,2.0,104356.54,1.0,1.0,0.0,156495.57
+19753,15718443.0,Chukwuemeka,640.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,88695.19
+19754,15769254.0,Kao,659.0,Spain,Male,46.0,7.0,85891.55,2.0,1.0,1.0,141533.19
+19755,15750545.0,Ch'ang,686.0,Spain,Female,26.0,9.0,0.0,2.0,1.0,1.0,48260.19
+19756,15720110.0,Nwebube,758.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,28805.09
+19757,15694321.0,Chuang,556.0,France,Male,33.0,8.0,94126.67,1.0,1.0,1.0,181490.42
+19758,15698927.0,Onwughara,641.0,France,Female,46.0,0.0,0.0,1.0,1.0,1.0,144849.1
+19759,15742886.0,Maclean,705.0,Germany,Female,30.0,5.0,124577.33,1.0,1.0,0.0,127569.8
+19760,15617482.0,Lettiere,686.0,Spain,Male,36.0,3.0,113348.5,1.0,1.0,0.0,178106.34
+19761,15702337.0,Fanucci,662.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,60728.89
+19762,15589924.0,Chidiebele,653.0,France,Male,33.0,5.0,0.0,2.0,0.0,1.0,170829.87
+19763,15572777.0,Hsieh,602.0,France,Female,22.0,0.0,0.0,2.0,1.0,1.0,141533.19
+19764,15583073.0,Chuang,582.0,France,Female,43.0,4.0,0.0,1.0,1.0,0.0,159638.77
+19765,15590177.0,Ch'en,585.0,Spain,Female,31.0,9.0,0.0,2.0,1.0,0.0,86099.23
+19766,15596575.0,Liao,567.0,Spain,Male,55.0,5.0,122917.71,1.0,1.0,0.0,115700.2
+19767,15662533.0,Ozioma,673.0,France,Male,42.0,7.0,0.0,1.0,1.0,1.0,147720.27
+19768,15697035.0,Nebechukwu,669.0,Germany,Male,54.0,7.0,102955.14,3.0,1.0,0.0,23135.65
+19769,15689671.0,Chidubem,477.0,France,Female,30.0,5.0,0.0,2.0,0.0,0.0,132436.71
+19770,15798960.0,Chukwudi,516.0,France,Male,34.0,4.0,106511.85,1.0,1.0,1.0,122477.38
+19771,15589969.0,Chuang,653.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,99818.31
+19772,15726494.0,H?,593.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,126551.65
+19773,15762682.0,Fomin,705.0,Spain,Male,41.0,7.0,143054.85,1.0,0.0,1.0,49418.87
+19774,15592765.0,Genovese,651.0,France,Female,52.0,6.0,0.0,1.0,1.0,0.0,172572.64
+19775,15632841.0,Chiang,705.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,160979.66
+19776,15739850.0,Ch'iu,769.0,France,Male,26.0,6.0,0.0,2.0,1.0,1.0,59595.66
+19777,15802857.0,Nnaife,634.0,Germany,Male,42.0,8.0,107362.52,1.0,1.0,1.0,121428.4
+19778,15729763.0,Yegorova,738.0,Germany,Male,41.0,4.0,99811.48,2.0,1.0,0.0,90877.76
+19779,15605916.0,Baresi,687.0,France,Female,30.0,2.0,141462.13,1.0,1.0,0.0,71672.86
+19780,15684956.0,Lucchese,676.0,France,Female,37.0,8.0,0.0,2.0,0.0,1.0,116748.14
+19781,15652999.0,Walker,637.0,Spain,Female,49.0,4.0,0.0,2.0,1.0,0.0,73322.0
+19782,15763662.0,Fadden,774.0,Spain,Male,31.0,2.0,121907.97,1.0,1.0,0.0,67552.98
+19783,15805884.0,T'an,652.0,France,Female,25.0,2.0,0.0,2.0,0.0,1.0,122580.48
+19784,15693817.0,Manna,781.0,Germany,Male,22.0,9.0,142391.08,1.0,0.0,1.0,154882.79
+19785,15674868.0,Kuo,678.0,Spain,Female,31.0,9.0,0.0,2.0,1.0,1.0,141180.66
+19786,15806926.0,Onwuatuegwu,581.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,61624.19
+19787,15790763.0,Chibueze,696.0,France,Female,31.0,2.0,0.0,2.0,0.0,1.0,124411.08
+19788,15686933.0,Ch'iu,492.0,France,Female,28.0,3.0,109196.67,1.0,1.0,1.0,53394.12
+19789,15617331.0,K'ung,637.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,1.0,93716.38
+19790,15801767.0,Ch'iu,657.0,Spain,Female,47.0,9.0,0.0,1.0,1.0,0.0,52615.62
+19791,15644572.0,Chukwueloka,605.0,Germany,Female,37.0,2.0,117300.02,1.0,1.0,0.0,176680.16
+19792,15627484.0,Uwaezuoke,652.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,165562.84
+19793,15674541.0,Ku,611.0,France,Male,41.0,6.0,106648.98,1.0,0.0,1.0,71672.86
+19794,15672894.0,Ikedinachukwu,727.0,Spain,Female,50.0,6.0,148167.84,1.0,1.0,0.0,113483.96
+19795,15662532.0,Y?,695.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,56767.67
+19796,15760861.0,Chioke,516.0,France,Male,38.0,8.0,129499.42,1.0,1.0,0.0,106977.8
+19797,15795954.0,Okwudilichukwu,762.0,Germany,Male,33.0,5.0,134032.05,1.0,0.0,1.0,171457.06
+19798,15736154.0,Nwabugwu,633.0,France,Male,27.0,5.0,0.0,2.0,1.0,0.0,102908.91
+19799,15701354.0,Trevisani,628.0,Germany,Female,57.0,6.0,116085.2,1.0,1.0,0.0,79734.23
+19800,15780492.0,Y?an,752.0,Spain,Male,36.0,7.0,0.0,2.0,0.0,0.0,158043.11
+19801,15609928.0,Hsia,617.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,139410.08
+19802,15671209.0,Pinto,632.0,France,Female,38.0,0.0,0.0,2.0,1.0,1.0,91574.92
+19803,15567114.0,Trevisano,478.0,Germany,Female,31.0,2.0,135067.02,1.0,1.0,0.0,184524.74
+19804,15595883.0,Hsia,577.0,Germany,Female,36.0,3.0,140553.46,4.0,0.0,0.0,121866.87
+19805,15692583.0,Chidi,528.0,France,Female,39.0,7.0,93844.69,1.0,1.0,1.0,4493.12
+19806,15648067.0,Smith,705.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,152577.62
+19807,15758510.0,Eluemuno,675.0,France,Female,30.0,3.0,107596.22,1.0,1.0,0.0,184514.01
+19808,15761497.0,Chin,632.0,Spain,Male,42.0,0.0,0.0,2.0,0.0,0.0,138527.56
+19809,15779103.0,Ozerova,671.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,172576.43
+19810,15578866.0,L?,585.0,Germany,Female,35.0,1.0,147974.19,2.0,1.0,1.0,176932.59
+19811,15653764.0,Lucchese,616.0,France,Male,24.0,8.0,0.0,2.0,1.0,0.0,79495.01
+19812,15592716.0,Nnamutaezinwa,662.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,88721.84
+19813,15593365.0,Ch'iu,669.0,France,Female,23.0,6.0,0.0,2.0,1.0,0.0,151401.33
+19814,15752953.0,Oguejiofor,543.0,France,Male,31.0,7.0,131834.8,1.0,0.0,0.0,162597.15
+19815,15652469.0,Teng,699.0,Spain,Female,44.0,9.0,0.0,2.0,0.0,1.0,143681.83
+19816,15667871.0,H?,733.0,Spain,Male,45.0,7.0,0.0,2.0,0.0,1.0,90876.95
+19817,15673929.0,Hsiao,576.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,1.0,133537.53
+19818,15623791.0,Maclean,626.0,France,Male,41.0,6.0,0.0,2.0,1.0,0.0,179291.89
+19819,15761606.0,Chiang,538.0,France,Male,26.0,4.0,0.0,2.0,1.0,1.0,77951.84
+19820,15780954.0,Chiemezie,681.0,Spain,Male,43.0,0.0,0.0,2.0,1.0,0.0,63736.17
+19821,15628203.0,Uwakwe,594.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,105291.22
+19822,15761168.0,Hsia,714.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,134483.85
+19823,15682355.0,Kao,713.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,156425.57
+19824,15751549.0,Ch'iu,619.0,Spain,Male,44.0,10.0,0.0,2.0,1.0,1.0,79414.0
+19825,15677906.0,Okechukwu,694.0,Spain,Male,37.0,7.0,0.0,1.0,0.0,1.0,185823.41
+19826,15794468.0,Hsieh,581.0,Germany,Male,31.0,3.0,130142.79,1.0,1.0,1.0,54947.51
+19827,15662578.0,Hsia,561.0,Germany,Male,31.0,7.0,123943.18,1.0,1.0,0.0,167023.95
+19828,15775318.0,Martin,671.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,94739.2
+19829,15654519.0,Ojiofor,463.0,Germany,Male,50.0,3.0,171922.72,1.0,1.0,1.0,136497.28
+19830,15595728.0,L?,653.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,0.0,158853.98
+19831,15663410.0,Frolov,706.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,1.0,44265.28
+19832,15637315.0,Lo,730.0,France,Female,46.0,2.0,0.0,2.0,1.0,1.0,139911.58
+19833,15753955.0,Omeokachie,711.0,Spain,Female,42.0,2.0,0.0,1.0,0.0,1.0,163587.9
+19834,15637650.0,Ch'ang,667.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,94775.54
+19835,15574830.0,Ch'en,652.0,Germany,Female,37.0,1.0,92659.64,1.0,1.0,0.0,81778.72
+19836,15653547.0,Tisdall,578.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,5791.85
+19837,15716276.0,Macdonald,628.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,38196.24
+19838,15691906.0,Onwumelu,678.0,Spain,Male,41.0,8.0,0.0,2.0,1.0,1.0,81753.92
+19839,15771409.0,Onyinyechukwuka,516.0,Spain,Male,59.0,4.0,0.0,2.0,1.0,1.0,83087.13
+19840,15589969.0,Bufkin,712.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,92381.01
+19841,15670889.0,Chinomso,827.0,Germany,Female,37.0,8.0,137648.41,1.0,0.0,0.0,142839.64
+19842,15608968.0,Obiajulu,699.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,93777.61
+19843,15690540.0,Hsing,724.0,Spain,Male,31.0,7.0,0.0,1.0,1.0,0.0,55775.84
+19844,15768455.0,McLean,714.0,France,Male,29.0,6.0,139859.54,1.0,1.0,1.0,187816.59
+19845,15572735.0,Iheanacho,590.0,Germany,Female,37.0,7.0,137648.41,1.0,1.0,0.0,64849.74
+19846,15600399.0,Nnachetam,668.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,189366.04
+19847,15641715.0,Manna,573.0,Spain,Female,32.0,7.0,0.0,2.0,0.0,0.0,171515.84
+19848,15769783.0,Nkemakolam,598.0,France,Male,28.0,4.0,0.0,1.0,1.0,1.0,146821.42
+19849,15676521.0,Chieloka,735.0,France,Male,31.0,9.0,121669.93,1.0,0.0,0.0,81673.5
+19850,15686743.0,Long,627.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,126307.25
+19851,15582033.0,Fanucci,683.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,180862.26
+19852,15705313.0,Ponomarev,743.0,Germany,Female,36.0,3.0,137761.55,2.0,1.0,0.0,171515.84
+19853,15754068.0,T'ang,711.0,France,Male,27.0,7.0,0.0,1.0,0.0,1.0,162683.06
+19854,15788025.0,Ts'ui,742.0,Germany,Female,56.0,2.0,120825.14,1.0,1.0,0.0,56905.38
+19855,15669994.0,Uspenskaya,521.0,France,Male,37.0,9.0,146766.72,1.0,1.0,1.0,139712.72
+19856,15752578.0,Hsia,524.0,Spain,Male,39.0,3.0,81677.22,1.0,1.0,0.0,105986.01
+19857,15659937.0,Trevisano,727.0,Germany,Female,55.0,7.0,81733.74,1.0,1.0,1.0,149550.95
+19858,15581840.0,Ch'ang,658.0,France,Female,38.0,1.0,0.0,1.0,1.0,1.0,163558.42
+19859,15572398.0,Mazzanti,718.0,Germany,Female,48.0,4.0,127758.09,2.0,1.0,0.0,86490.29
+19860,15751549.0,T'ien,434.0,France,Male,47.0,10.0,113569.43,1.0,1.0,0.0,155721.15
+19861,15762045.0,Hs?,562.0,Germany,Female,52.0,9.0,120867.18,3.0,1.0,0.0,49637.65
+19862,15702561.0,Gentry,622.0,Germany,Female,37.0,3.0,141819.02,2.0,1.0,1.0,94951.27
+19863,15757895.0,T'ien,567.0,France,Female,42.0,4.0,75915.75,1.0,1.0,1.0,182867.84
+19864,15750867.0,Mironova,632.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,127543.83
+19865,15748673.0,Chizuoke,537.0,France,Male,43.0,9.0,0.0,2.0,0.0,1.0,95937.53
+19866,15796834.0,Mazzi,771.0,Germany,Male,38.0,4.0,122570.87,2.0,1.0,0.0,140765.57
+19867,15585855.0,Zuyeva,714.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,122998.26
+19868,15690796.0,Chukwudi,575.0,Spain,Male,40.0,0.0,0.0,2.0,0.0,0.0,140941.47
+19869,15633274.0,Onyekachi,629.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,146785.44
+19870,15796218.0,Chinedum,642.0,France,Female,41.0,9.0,113882.31,2.0,1.0,0.0,176883.42
+19871,15776631.0,Ritchie,542.0,Germany,Male,32.0,4.0,161371.5,1.0,1.0,0.0,175082.2
+19872,15761047.0,Ts'ui,711.0,Germany,Male,35.0,7.0,115532.52,2.0,1.0,0.0,166694.93
+19873,15643630.0,Tsai,652.0,Spain,Female,40.0,2.0,109740.62,1.0,1.0,0.0,88890.05
+19874,15804962.0,Onyemauchechukwu,724.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,1.0,149558.83
+19875,15638355.0,Hsieh,681.0,Germany,Female,29.0,8.0,140694.78,1.0,1.0,1.0,43486.73
+19876,15737795.0,Chukwumaobim,746.0,France,Male,24.0,4.0,0.0,2.0,1.0,0.0,131283.6
+19877,15776936.0,T'ien,835.0,Spain,Female,45.0,2.0,0.0,2.0,1.0,1.0,85901.09
+19878,15672826.0,Nnaife,661.0,France,Male,28.0,3.0,0.0,2.0,0.0,1.0,1408.68
+19879,15672640.0,Trevisano,718.0,Spain,Female,32.0,6.0,0.0,2.0,1.0,0.0,126728.27
+19880,15658339.0,Lucciano,498.0,Spain,Female,42.0,4.0,0.0,2.0,1.0,1.0,143628.31
+19881,15715117.0,Mazzanti,721.0,France,Female,27.0,8.0,142360.62,1.0,0.0,1.0,172749.72
+19882,15758072.0,H?,696.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,74898.8
+19883,15602456.0,Gell,590.0,France,Male,35.0,8.0,116220.5,1.0,1.0,1.0,131764.57
+19884,15753092.0,Obialo,675.0,France,Female,55.0,7.0,0.0,2.0,1.0,1.0,100809.99
+19885,15581036.0,Frolov,599.0,Germany,Male,57.0,4.0,139848.17,1.0,0.0,0.0,124749.08
+19886,15664506.0,Hsiung,526.0,France,Male,33.0,1.0,113829.45,1.0,1.0,1.0,162443.05
+19887,15702277.0,Chukwubuikem,529.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,167023.95
+19888,15700854.0,Chukwumaobim,646.0,Germany,Female,43.0,5.0,146720.98,3.0,1.0,0.0,152821.15
+19889,15738785.0,Obidimkpa,687.0,France,Female,34.0,6.0,0.0,1.0,0.0,0.0,176943.59
+19890,15803479.0,Chukwujekwu,652.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,184524.74
+19891,15682355.0,Chia,632.0,Germany,Male,38.0,3.0,135096.77,1.0,1.0,0.0,142516.35
+19892,15677906.0,Pirozzi,659.0,Spain,Male,41.0,8.0,130142.79,1.0,1.0,1.0,145562.4
+19893,15714391.0,Hargreaves,696.0,France,Male,26.0,5.0,92472.1,1.0,1.0,0.0,61251.25
+19894,15644878.0,Chiu,604.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,70613.52
+19895,15726494.0,P'an,850.0,Spain,Male,48.0,2.0,0.0,1.0,1.0,1.0,117832.88
+19896,15777893.0,Chukwueloka,613.0,Spain,Male,27.0,9.0,0.0,2.0,1.0,0.0,196134.44
+19897,15793883.0,Chidozie,718.0,France,Male,54.0,5.0,0.0,1.0,1.0,1.0,122218.81
+19898,15797960.0,Mazzanti,588.0,France,Female,41.0,9.0,0.0,2.0,0.0,1.0,156424.4
+19899,15641870.0,Yobachukwu,528.0,France,Male,33.0,3.0,138521.47,1.0,1.0,0.0,57898.58
+19900,15684173.0,Pisani,756.0,Germany,Male,44.0,9.0,132570.59,2.0,0.0,1.0,157959.02
+19901,15775991.0,Mazzi,599.0,France,Female,32.0,4.0,0.0,1.0,0.0,1.0,120138.27
+19902,15676937.0,Thompson,642.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,145562.4
+19903,15773904.0,Hs?eh,556.0,France,Male,61.0,5.0,0.0,2.0,1.0,1.0,116520.07
+19904,15675256.0,Ch'iu,561.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,162960.05
+19905,15795881.0,Ch'ien,579.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,168034.9
+19906,15606077.0,Nkemakonam,683.0,France,Female,38.0,7.0,141441.75,1.0,1.0,0.0,75732.25
+19907,15795878.0,Chiawuotu,619.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,9209.21
+19908,15585748.0,Onyemachukwu,622.0,France,Male,28.0,4.0,0.0,2.0,1.0,1.0,185599.24
+19909,15582910.0,Chien,709.0,France,Male,30.0,2.0,0.0,1.0,1.0,1.0,135961.9
+19910,15646720.0,T'ien,579.0,Spain,Female,43.0,3.0,0.0,1.0,0.0,0.0,134790.74
+19911,15771442.0,Pinto,699.0,France,Male,40.0,1.0,0.0,1.0,0.0,0.0,116696.77
+19912,15793331.0,Chiazagomekpere,659.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,38798.79
+19913,15756538.0,Frolov,627.0,France,Male,34.0,6.0,0.0,2.0,0.0,0.0,19500.1
+19914,15732442.0,Rizzo,589.0,France,Male,37.0,2.0,104857.19,2.0,1.0,1.0,162858.28
+19915,15675380.0,Ebelechukwu,695.0,Germany,Male,43.0,2.0,121898.98,1.0,1.0,1.0,10488.44
+19916,15682540.0,Chibugo,708.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,95625.03
+19917,15675320.0,Cattaneo,629.0,France,Female,37.0,1.0,0.0,2.0,1.0,0.0,140198.18
+19918,15790810.0,Chia,583.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,1.0,78730.23
+19919,15593778.0,Kao,595.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,109783.29
+19920,15672955.0,Ts'ui,620.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,67622.34
+19921,15745804.0,Eluemuno,535.0,France,Male,31.0,9.0,127776.42,1.0,1.0,0.0,132034.13
+19922,15742585.0,Hsing,645.0,France,Female,41.0,4.0,107949.39,1.0,1.0,0.0,91036.74
+19923,15761728.0,Chukwudi,733.0,France,Female,34.0,7.0,108760.27,1.0,1.0,0.0,37490.84
+19924,15650751.0,Chuang,601.0,Germany,Male,41.0,4.0,84445.68,1.0,1.0,1.0,167564.82
+19925,15587914.0,Chiu,711.0,France,Male,46.0,6.0,0.0,2.0,1.0,0.0,113436.05
+19926,15605067.0,Chuang,721.0,Germany,Male,40.0,2.0,133384.66,1.0,1.0,1.0,25292.53
+19927,15792868.0,McFarland,751.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,0.0,131177.17
+19928,15688872.0,Genovese,656.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,111241.37
+19929,15744327.0,Piazza,684.0,Germany,Male,37.0,3.0,121729.42,1.0,0.0,1.0,70783.61
+19930,15612505.0,Cattaneo,591.0,France,Male,32.0,4.0,123475.04,1.0,1.0,0.0,142513.5
+19931,15747808.0,Hsiung,696.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,49684.09
+19932,15642530.0,Mazzanti,711.0,Spain,Male,57.0,8.0,0.0,2.0,0.0,1.0,173486.39
+19933,15711789.0,Lai,646.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,131281.29
+19934,15775791.0,Ch'iu,599.0,Germany,Female,32.0,3.0,108736.52,2.0,1.0,1.0,142903.44
+19935,15806049.0,Tien,706.0,Germany,Male,37.0,4.0,142926.64,2.0,1.0,1.0,88827.67
+19936,15616954.0,Yobachi,713.0,Spain,Male,29.0,10.0,0.0,2.0,1.0,1.0,178268.36
+19937,15656840.0,Ibezimako,633.0,France,Male,38.0,2.0,0.0,1.0,0.0,1.0,94780.8
+19938,15745375.0,Chiazagomekpere,551.0,France,Female,29.0,6.0,0.0,2.0,0.0,0.0,114775.76
+19939,15607312.0,Yermakov,565.0,Spain,Female,38.0,7.0,0.0,2.0,0.0,1.0,64539.64
+19940,15710206.0,Chinweuba,641.0,France,Male,35.0,10.0,0.0,2.0,1.0,1.0,168886.39
+19941,15707596.0,Chiekwugo,741.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,170638.98
+19942,15780650.0,Chiedozie,634.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,93835.64
+19943,15805769.0,Y?an,794.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,132973.15
+19944,15569247.0,Onyemauchechukwu,775.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,181224.56
+19945,15592387.0,Ozioma,511.0,Spain,Male,38.0,7.0,144326.09,1.0,1.0,1.0,115818.51
+19946,15572428.0,Shih,712.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,101728.46
+19947,15806901.0,Chien,667.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,100601.73
+19948,15727041.0,Smith,540.0,France,Male,57.0,4.0,0.0,2.0,0.0,1.0,88890.05
+19949,15783026.0,Palermo,679.0,Spain,Female,30.0,7.0,0.0,2.0,1.0,0.0,179421.84
+19950,15573851.0,Cremonesi,794.0,France,Female,35.0,2.0,0.0,1.0,1.0,1.0,61312.15
+19951,15793331.0,Hsu,694.0,France,Male,53.0,2.0,168085.98,1.0,0.0,1.0,159584.81
+19952,15658980.0,Manna,705.0,France,Female,36.0,9.0,0.0,2.0,0.0,0.0,141069.88
+19953,15622442.0,Li Fonti,581.0,Spain,Female,47.0,1.0,0.0,2.0,1.0,0.0,172437.12
+19954,15664343.0,Ting,675.0,France,Male,42.0,2.0,0.0,2.0,1.0,1.0,183492.9
+19955,15793042.0,Chukwujamuike,678.0,France,Male,27.0,7.0,74989.58,1.0,1.0,1.0,123982.14
+19956,15771668.0,Nwokike,710.0,France,Female,35.0,10.0,0.0,1.0,0.0,0.0,179634.49
+19957,15573599.0,Fan,634.0,France,Male,36.0,7.0,0.0,2.0,0.0,1.0,161479.19
+19958,15659736.0,Yobachi,633.0,France,Male,35.0,10.0,0.0,2.0,0.0,1.0,60552.44
+19959,15618171.0,O'Brien,594.0,France,Female,43.0,3.0,96951.21,2.0,1.0,1.0,92797.23
+19960,15677785.0,Pisano,714.0,France,Female,37.0,2.0,132876.23,1.0,1.0,0.0,86410.29
+19961,15654296.0,Chibugo,745.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,176902.83
+19962,15687079.0,Chu,714.0,Germany,Male,40.0,1.0,131184.67,2.0,1.0,1.0,54726.43
+19963,15581893.0,Chia,679.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,131834.75
+19964,15696852.0,Hsia,675.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,91346.03
+19965,15593601.0,Tsou,685.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,149583.67
+19966,15792649.0,T'ien,516.0,Germany,Female,38.0,7.0,114931.35,1.0,1.0,1.0,83700.66
+19967,15631244.0,Obiajulu,718.0,Germany,Female,31.0,7.0,127864.4,1.0,1.0,1.0,146821.42
+19968,15723169.0,Hargreaves,574.0,Germany,Female,52.0,4.0,152663.77,1.0,1.0,1.0,118913.53
+19969,15750476.0,Ch'ang,651.0,France,Female,72.0,2.0,0.0,1.0,1.0,1.0,153342.38
+19970,15749557.0,Chidubem,543.0,Germany,Male,38.0,3.0,132725.1,1.0,1.0,0.0,176768.59
+19971,15588535.0,Hsieh,564.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,120834.48
+19972,15665766.0,Uspenskaya,699.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,163504.33
+19973,15602741.0,Chukwueloka,644.0,France,Female,53.0,5.0,76318.64,1.0,1.0,0.0,139923.38
+19974,15800736.0,Tseng,632.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,102566.76
+19975,15641655.0,Pinto,707.0,Germany,Female,55.0,2.0,131647.01,1.0,1.0,0.0,54871.02
+19976,15584899.0,Toscani,586.0,France,Female,37.0,8.0,167866.87,1.0,1.0,1.0,67599.71
+19977,15796721.0,T'ien,718.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,84932.4
+19978,15682453.0,Onyemauchechukwu,521.0,Spain,Male,38.0,8.0,0.0,2.0,0.0,1.0,89017.38
+19979,15585041.0,Lucchese,575.0,France,Female,42.0,1.0,0.0,1.0,1.0,0.0,149575.59
+19980,15734588.0,Nwachukwu,729.0,France,Female,34.0,4.0,0.0,1.0,1.0,0.0,9241.83
+19981,15714087.0,Chiang,708.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,102908.91
+19982,15592461.0,Ch'ien,786.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,54726.43
+19983,15744691.0,Ch'ang,850.0,Spain,Male,44.0,7.0,0.0,1.0,1.0,0.0,16250.61
+19984,15676966.0,Chinwemma,611.0,France,Female,41.0,6.0,0.0,1.0,1.0,0.0,179341.83
+19985,15576065.0,T'ien,579.0,Germany,Male,32.0,0.0,139542.58,1.0,1.0,0.0,175380.77
+19986,15793883.0,K?,743.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,130795.52
+19987,15777076.0,McGregor,638.0,Germany,Male,43.0,1.0,125845.66,3.0,0.0,0.0,54865.92
+19988,15720559.0,Liao,590.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,160790.02
+19989,15631815.0,Chibuzo,677.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,1.0,172097.95
+19990,15758775.0,O'Brien,579.0,France,Female,44.0,1.0,0.0,2.0,0.0,0.0,107874.39
+19991,15783875.0,Chikezie,521.0,Spain,Female,29.0,3.0,0.0,2.0,1.0,1.0,83700.66
+19992,15790730.0,Rizzo,578.0,Germany,Male,52.0,7.0,119839.69,1.0,1.0,0.0,47775.29
+19993,15783691.0,Skelton,589.0,Spain,Male,36.0,2.0,132767.66,1.0,0.0,0.0,106841.12
+19994,15604829.0,P'eng,654.0,Germany,Female,50.0,2.0,124620.01,2.0,1.0,1.0,125518.32
+19995,15734161.0,Pirozzi,631.0,Spain,Male,33.0,3.0,0.0,1.0,1.0,1.0,113860.81
+19996,15809847.0,Hsia,741.0,Germany,Male,37.0,7.0,141428.99,2.0,1.0,0.0,73873.65
+19997,15585867.0,Ch'in,559.0,Spain,Female,26.0,9.0,0.0,2.0,1.0,1.0,71740.59
+19998,15794549.0,Chiang,593.0,France,Male,27.0,3.0,0.0,2.0,0.0,1.0,91096.76
+19999,15574886.0,Ch'ang,633.0,Spain,Male,25.0,3.0,0.0,2.0,0.0,0.0,64436.16
+20000,15703763.0,Ibragimova,634.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,0.0,57844.79
+20001,15694453.0,Folliero,588.0,Spain,Female,25.0,7.0,0.0,2.0,1.0,1.0,91433.92
+20002,15800583.0,Eipper,652.0,France,Male,27.0,10.0,0.0,1.0,1.0,1.0,115068.99
+20003,15623595.0,P'eng,590.0,France,Female,47.0,3.0,0.0,2.0,0.0,1.0,110142.34
+20004,15642823.0,Lucciano,547.0,France,Male,66.0,2.0,131142.53,2.0,1.0,1.0,91291.2
+20005,15739592.0,Nwankwo,580.0,Germany,Male,42.0,6.0,102773.2,1.0,1.0,1.0,61290.99
+20006,15705890.0,Calabrese,473.0,Germany,Female,54.0,8.0,107949.45,1.0,1.0,0.0,53353.51
+20007,15798895.0,Okeke,699.0,France,Female,43.0,8.0,121166.89,1.0,1.0,1.0,87060.77
+20008,15694188.0,Lucciano,645.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,107499.59
+20009,15709441.0,Hsieh,633.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,180134.88
+20010,15618255.0,Chukwuemeka,607.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,116097.26
+20011,15814675.0,Okwukwe,717.0,Germany,Female,34.0,8.0,106328.08,2.0,1.0,0.0,101694.67
+20012,15769980.0,Chukwuemeka,574.0,France,Female,29.0,0.0,0.0,2.0,0.0,1.0,111698.0
+20013,15681316.0,Shao,633.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,0.0,128527.83
+20014,15780572.0,Nwachukwu,667.0,France,Male,28.0,9.0,0.0,1.0,0.0,0.0,79071.89
+20015,15607098.0,Ch'ien,642.0,France,Female,38.0,7.0,0.0,2.0,0.0,0.0,84760.32
+20016,15746664.0,Chukwumaobim,721.0,Germany,Female,31.0,1.0,124640.41,2.0,1.0,1.0,120801.65
+20017,15756026.0,Shih,594.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,0.0,65612.12
+20018,15765518.0,Tien,727.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,64350.8
+20019,15742204.0,Onyemauchechukwu,756.0,France,Female,30.0,4.0,185714.28,2.0,1.0,1.0,113436.08
+20020,15594917.0,Nnachetam,786.0,France,Female,33.0,5.0,0.0,2.0,0.0,0.0,93717.55
+20021,15690540.0,Chibi,678.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,160129.99
+20022,15762762.0,Mazzi,638.0,France,Female,49.0,1.0,0.0,2.0,1.0,1.0,128882.66
+20023,15779985.0,Onwubiko,657.0,France,Male,37.0,4.0,165878.76,1.0,1.0,0.0,44384.57
+20024,15690664.0,Hsiao,675.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,86767.48
+20025,15752502.0,Chidimma,684.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,90876.95
+20026,15795540.0,Mazzanti,771.0,France,Male,40.0,6.0,0.0,2.0,0.0,0.0,24267.28
+20027,15684548.0,Toscani,634.0,France,Female,53.0,5.0,0.0,1.0,1.0,0.0,120852.85
+20028,15693322.0,Ch'ien,584.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,143317.42
+20029,15691792.0,O'Donnell,726.0,France,Female,49.0,8.0,0.0,1.0,1.0,1.0,60920.03
+20030,15634218.0,Chukwuebuka,576.0,Spain,Male,43.0,6.0,0.0,2.0,0.0,1.0,60920.03
+20031,15705512.0,Chiu,687.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,0.0,78622.46
+20032,15778861.0,Ch'en,623.0,Germany,Male,28.0,8.0,124483.53,1.0,1.0,1.0,91830.75
+20033,15686539.0,Toscani,652.0,France,Male,42.0,5.0,119349.69,1.0,1.0,0.0,182262.66
+20034,15631681.0,Chidubem,706.0,Germany,Male,31.0,7.0,137390.11,2.0,1.0,1.0,166677.35
+20035,15642099.0,Chinedum,744.0,France,Female,38.0,1.0,0.0,1.0,1.0,0.0,97416.34
+20036,15626008.0,Chiu,585.0,France,Female,29.0,6.0,0.0,2.0,0.0,1.0,180551.76
+20037,15775116.0,Yeh,639.0,Spain,Male,30.0,4.0,0.0,1.0,1.0,1.0,58854.88
+20038,15592765.0,Chiemenam,683.0,Germany,Female,57.0,2.0,128284.45,3.0,1.0,0.0,139844.04
+20039,15794916.0,Hsia,675.0,France,Male,40.0,5.0,0.0,1.0,1.0,0.0,93524.19
+20040,15615177.0,Tsai,592.0,Spain,Female,26.0,8.0,0.0,2.0,0.0,1.0,178373.93
+20041,15694530.0,H?,683.0,France,Male,30.0,4.0,0.0,2.0,0.0,0.0,144290.97
+20042,15653857.0,T'ien,633.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,121525.48
+20043,15776054.0,Omeokachie,614.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,1.0,149613.14
+20044,15806967.0,Ts'ui,632.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,76637.38
+20045,15731751.0,Ch'ien,760.0,Spain,Male,48.0,5.0,127290.61,1.0,1.0,1.0,152698.24
+20046,15779581.0,McIntyre,589.0,Spain,Female,57.0,8.0,0.0,1.0,1.0,1.0,178291.09
+20047,15809558.0,Chiang,651.0,France,Female,60.0,5.0,0.0,3.0,0.0,0.0,52301.15
+20048,15704954.0,Ts'ui,748.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,0.0,132770.11
+20049,15777599.0,Mazzi,637.0,Germany,Male,55.0,1.0,121365.39,1.0,0.0,1.0,72993.65
+20050,15642736.0,Yermakov,639.0,France,Male,69.0,9.0,119859.52,1.0,1.0,1.0,103557.93
+20051,15641490.0,Hsiung,599.0,France,Female,36.0,5.0,0.0,2.0,0.0,1.0,90905.26
+20052,15643394.0,Onyekaozulu,697.0,France,Female,41.0,4.0,0.0,2.0,0.0,1.0,61994.2
+20053,15661528.0,O'Donnell,636.0,France,Female,49.0,4.0,0.0,3.0,1.0,0.0,113581.86
+20054,15805769.0,P'eng,699.0,Spain,Male,35.0,10.0,0.0,2.0,0.0,0.0,131300.68
+20055,15774872.0,Ch'ien,571.0,France,Male,35.0,2.0,0.0,1.0,1.0,0.0,95625.03
+20056,15648032.0,Chiekwugo,662.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,75837.58
+20057,15680141.0,Lucciano,696.0,Spain,Female,34.0,4.0,160541.0,1.0,0.0,1.0,82565.01
+20058,15809558.0,Folliero,667.0,France,Male,41.0,7.0,0.0,1.0,0.0,1.0,171037.63
+20059,15752588.0,O'Brien,628.0,France,Female,33.0,7.0,0.0,1.0,1.0,1.0,93901.61
+20060,15739502.0,Ozioma,624.0,Spain,Female,40.0,4.0,0.0,2.0,1.0,0.0,142910.18
+20061,15621893.0,Young,579.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,163714.92
+20062,15626882.0,Ch'eng,554.0,France,Female,29.0,8.0,91536.93,1.0,1.0,1.0,135969.19
+20063,15784890.0,Chiemenam,694.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,92381.01
+20064,15724307.0,Mazzanti,739.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,140937.91
+20065,15589329.0,Ndubueze,613.0,Germany,Male,39.0,4.0,114722.05,1.0,1.0,0.0,150465.93
+20066,15598070.0,Chinomso,774.0,Germany,Female,36.0,4.0,135096.05,1.0,0.0,0.0,143952.24
+20067,15640161.0,Chukwuhaenye,683.0,France,Female,32.0,5.0,103544.11,1.0,1.0,1.0,120512.08
+20068,15734649.0,Trevisano,663.0,Germany,Male,36.0,1.0,134760.21,1.0,1.0,0.0,104353.26
+20069,15621815.0,Ch'ang,642.0,Germany,Male,43.0,1.0,152576.25,1.0,1.0,0.0,179655.85
+20070,15806447.0,Tretiakova,759.0,Germany,Male,34.0,8.0,131647.01,2.0,1.0,1.0,143980.29
+20071,15692575.0,Trevisani,589.0,Germany,Male,33.0,3.0,119919.12,2.0,0.0,1.0,134875.37
+20072,15774857.0,Genovese,727.0,Spain,Male,37.0,6.0,0.0,1.0,1.0,0.0,158750.53
+20073,15711571.0,Ch'in,794.0,France,Male,27.0,5.0,0.0,1.0,1.0,0.0,58426.81
+20074,15783477.0,Onwudiwe,635.0,France,Female,29.0,4.0,0.0,2.0,1.0,1.0,109523.88
+20075,15595766.0,Martin,757.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,73489.15
+20076,15704763.0,Chibugo,589.0,France,Female,42.0,6.0,77326.6,1.0,1.0,1.0,101108.85
+20077,15579892.0,Chukwuemeka,695.0,France,Male,35.0,4.0,0.0,1.0,1.0,1.0,9984.4
+20078,15705521.0,Ch'iu,629.0,France,Female,34.0,2.0,157780.84,2.0,1.0,0.0,147055.87
+20079,15811594.0,Fyodorova,850.0,France,Female,30.0,2.0,0.0,2.0,1.0,0.0,171045.48
+20080,15615473.0,Kistler,588.0,Germany,Female,38.0,6.0,120599.38,1.0,1.0,1.0,163796.01
+20081,15642098.0,Manna,469.0,France,Male,54.0,4.0,0.0,1.0,1.0,1.0,90908.95
+20082,15729954.0,Genovese,697.0,France,Male,35.0,8.0,167539.97,1.0,0.0,0.0,145562.4
+20083,15692977.0,Chukwuemeka,706.0,France,Male,38.0,9.0,0.0,1.0,1.0,0.0,175750.31
+20084,15701765.0,Lucchese,507.0,France,Male,53.0,6.0,0.0,1.0,1.0,0.0,56937.43
+20085,15598838.0,Bock,673.0,France,Female,26.0,0.0,0.0,2.0,1.0,0.0,172363.41
+20086,15735270.0,Chukwukere,619.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,146738.83
+20087,15802088.0,Obidimkpa,598.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,142400.27
+20088,15775293.0,Hsing,579.0,Germany,Male,38.0,10.0,97445.49,2.0,1.0,0.0,188795.85
+20089,15804864.0,Pinto,749.0,France,Female,34.0,7.0,0.0,2.0,0.0,0.0,71450.92
+20090,15672598.0,Onuora,715.0,France,Male,47.0,7.0,0.0,2.0,1.0,0.0,187821.84
+20091,15755077.0,Ch'ien,613.0,France,Male,40.0,3.0,0.0,1.0,1.0,1.0,180345.44
+20092,15682322.0,Mazzanti,597.0,Spain,Female,32.0,8.0,0.0,2.0,0.0,1.0,82662.73
+20093,15754577.0,Onwumelu,684.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,55796.83
+20094,15681755.0,Chidiebele,692.0,France,Male,40.0,9.0,97585.32,1.0,1.0,0.0,19720.08
+20095,15815271.0,Chien,693.0,Germany,Female,39.0,2.0,123757.52,1.0,0.0,0.0,82526.92
+20096,15736431.0,Maclean,549.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,128892.66
+20097,15781409.0,Onyemauchechukwu,598.0,Spain,Male,48.0,1.0,97560.16,1.0,1.0,1.0,75884.65
+20098,15691808.0,Nnamutaezinwa,705.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,132973.21
+20099,15677243.0,Ts'ao,815.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,75837.58
+20100,15574071.0,Ugochukwu,738.0,Spain,Male,50.0,1.0,0.0,1.0,0.0,0.0,158313.87
+20101,15674765.0,Chukwuebuka,725.0,France,Female,39.0,10.0,145984.92,1.0,0.0,0.0,123982.42
+20102,15648800.0,Kao,592.0,Germany,Male,33.0,2.0,72647.64,1.0,1.0,1.0,187985.85
+20103,15709135.0,Nkemdirim,758.0,France,Female,34.0,4.0,82940.25,2.0,1.0,0.0,176713.47
+20104,15694821.0,Chukwujamuike,554.0,Spain,Female,56.0,9.0,0.0,1.0,1.0,1.0,114448.13
+20105,15690134.0,Ponce,652.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,175043.69
+20106,15768359.0,Genovesi,593.0,France,Female,40.0,7.0,0.0,2.0,1.0,1.0,107013.59
+20107,15583718.0,Tuan,678.0,Germany,Female,55.0,9.0,137783.65,1.0,1.0,1.0,52795.56
+20108,15598536.0,Macknight,778.0,Spain,Female,36.0,1.0,0.0,2.0,0.0,1.0,171707.93
+20109,15631681.0,Tsou,753.0,Spain,Female,48.0,4.0,0.0,3.0,1.0,0.0,89891.4
+20110,15584897.0,Ts'ui,547.0,Germany,Female,40.0,7.0,121260.32,1.0,0.0,0.0,84760.32
+20111,15689517.0,Y?,678.0,France,Male,26.0,1.0,99812.88,1.0,1.0,0.0,172618.52
+20112,15583680.0,Hao,683.0,France,Female,40.0,2.0,0.0,2.0,0.0,1.0,111573.3
+20113,15751180.0,T'ang,637.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,55313.44
+20114,15682834.0,Li Fonti,543.0,Germany,Male,48.0,9.0,126570.86,1.0,0.0,0.0,152290.28
+20115,15598510.0,Hao,593.0,Germany,Female,47.0,6.0,113537.73,1.0,0.0,0.0,175373.46
+20116,15686538.0,Lucchese,605.0,France,Male,40.0,9.0,0.0,1.0,0.0,1.0,121894.31
+20117,15769216.0,Ch'ien,781.0,Spain,Male,31.0,3.0,0.0,2.0,1.0,1.0,48559.19
+20118,15659194.0,T'ang,721.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,180622.02
+20119,15694556.0,Mamelu,718.0,France,Female,39.0,4.0,123601.56,1.0,1.0,1.0,177065.24
+20120,15682355.0,Chidi,609.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,91514.62
+20121,15691808.0,Nnaife,678.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,83679.46
+20122,15594577.0,Chigolum,727.0,Germany,Female,28.0,7.0,134498.54,1.0,1.0,0.0,141533.19
+20123,15697574.0,Hsing,549.0,France,Male,38.0,5.0,85982.04,1.0,1.0,1.0,49848.86
+20124,15683522.0,Onyemauchechukwu,679.0,France,Male,36.0,1.0,0.0,1.0,1.0,0.0,98624.04
+20125,15777892.0,Chiang,731.0,Spain,Male,43.0,3.0,0.0,2.0,1.0,0.0,121455.81
+20126,15573851.0,Shih,605.0,France,Female,43.0,9.0,0.0,2.0,1.0,1.0,56865.62
+20127,15795936.0,Onyekachukwu,572.0,France,Male,45.0,7.0,0.0,2.0,1.0,1.0,51980.25
+20128,15576085.0,Martin,686.0,France,Male,56.0,4.0,0.0,1.0,0.0,0.0,178361.48
+20129,15756840.0,Nwachinemelu,656.0,France,Female,38.0,9.0,0.0,1.0,1.0,1.0,181461.46
+20130,15762573.0,H?,585.0,France,Female,38.0,6.0,0.0,1.0,1.0,0.0,176680.16
+20131,15777076.0,Smith,637.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,126419.9
+20132,15651352.0,Pinto,612.0,France,Female,35.0,4.0,119024.75,1.0,1.0,1.0,73881.68
+20133,15687421.0,Pisano,584.0,Spain,Male,24.0,8.0,0.0,2.0,1.0,1.0,75969.19
+20134,15627262.0,Ts'ai,675.0,Germany,Male,28.0,1.0,90696.78,1.0,1.0,1.0,180595.02
+20135,15803749.0,Uspenskaya,774.0,Spain,Female,55.0,6.0,0.0,2.0,1.0,0.0,187811.71
+20136,15680430.0,Chiekwugo,652.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,184843.77
+20137,15679770.0,Ch'ien,635.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,1276.87
+20138,15664555.0,Milanesi,511.0,Germany,Female,58.0,0.0,102520.37,1.0,1.0,0.0,136869.31
+20139,15798885.0,Ch'ien,704.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,0.0,161888.63
+20140,15795571.0,Lai,665.0,France,Female,45.0,4.0,134752.08,1.0,1.0,1.0,28367.19
+20141,15803764.0,Nkemdilim,682.0,Spain,Male,21.0,7.0,0.0,2.0,1.0,1.0,71862.79
+20142,15647842.0,Hsiung,537.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,121239.65
+20143,15769005.0,Chiang,757.0,Germany,Male,30.0,4.0,149961.99,2.0,1.0,0.0,48778.46
+20144,15589948.0,Pisani,665.0,Spain,Male,37.0,5.0,85957.88,1.0,1.0,0.0,166468.21
+20145,15656901.0,Hsieh,537.0,France,Female,24.0,7.0,0.0,2.0,1.0,1.0,55313.51
+20146,15697424.0,Nwabugwu,576.0,France,Male,26.0,9.0,161887.81,1.0,1.0,1.0,93959.17
+20147,15600974.0,Chukwufumnanya,539.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,143859.66
+20148,15791533.0,Uspensky,605.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,145593.85
+20149,15609510.0,Cattaneo,633.0,Germany,Male,46.0,9.0,117879.35,1.0,1.0,1.0,10488.44
+20150,15672640.0,Pinto,628.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,152843.53
+20151,15657535.0,Zito,544.0,Germany,Female,54.0,0.0,127785.16,1.0,1.0,0.0,170968.99
+20152,15791045.0,Tuan,774.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,167984.81
+20153,15759718.0,Hsiung,720.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,145936.28
+20154,15686957.0,Yobanna,667.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,0.0,159618.17
+20155,15602034.0,Ch'ang,695.0,France,Male,22.0,7.0,182781.74,1.0,1.0,1.0,168035.62
+20156,15581197.0,Zikoranachidimma,769.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,86767.48
+20157,15788025.0,Onwudiwe,703.0,Germany,Female,39.0,6.0,132944.53,2.0,1.0,0.0,152054.33
+20158,15772363.0,Aksyonova,758.0,Spain,Male,39.0,2.0,0.0,2.0,0.0,0.0,55775.72
+20159,15765952.0,K?,629.0,France,Female,38.0,4.0,95441.27,1.0,1.0,0.0,142400.27
+20160,15573520.0,Chibugo,590.0,France,Female,55.0,5.0,0.0,1.0,1.0,0.0,121567.91
+20161,15787825.0,Genovese,781.0,France,Male,23.0,3.0,124577.33,2.0,1.0,0.0,97629.31
+20162,15791342.0,Chidubem,702.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,136902.01
+20163,15703628.0,T'ien,642.0,France,Female,41.0,9.0,0.0,2.0,0.0,1.0,143980.28
+20164,15652069.0,Tung,637.0,Spain,Male,39.0,6.0,0.0,1.0,0.0,0.0,53483.21
+20165,15641312.0,Chukwujamuike,559.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,95936.84
+20166,15654067.0,Chukwumaobim,678.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,79919.13
+20167,15738448.0,Chidubem,663.0,Germany,Female,44.0,3.0,109528.16,1.0,1.0,1.0,171463.83
+20168,15808228.0,Lai,743.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,49667.28
+20169,15796468.0,Chijioke,790.0,France,Male,36.0,2.0,0.0,2.0,0.0,1.0,57929.9
+20170,15716994.0,Chibuzo,590.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,169952.82
+20171,15686588.0,Hsieh,639.0,France,Female,48.0,9.0,156660.72,1.0,1.0,0.0,173870.39
+20172,15738497.0,Hsia,543.0,France,Female,67.0,5.0,0.0,1.0,1.0,1.0,176883.42
+20173,15743075.0,Chidiebele,644.0,Germany,Female,61.0,8.0,120962.13,1.0,0.0,0.0,97734.3
+20174,15761453.0,Chukwuemeka,613.0,France,Female,33.0,2.0,118626.05,1.0,0.0,1.0,81251.24
+20175,15625592.0,Niu,677.0,Spain,Male,40.0,2.0,0.0,1.0,1.0,0.0,162501.83
+20176,15608387.0,Hsueh,724.0,Germany,Female,23.0,4.0,116471.43,2.0,1.0,0.0,180598.86
+20177,15632143.0,Onuchukwu,584.0,Spain,Male,28.0,6.0,0.0,2.0,1.0,0.0,183492.9
+20178,15705953.0,Tuan,651.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,0.0,10771.46
+20179,15650014.0,Chiu,678.0,Spain,Female,27.0,1.0,138476.41,1.0,1.0,1.0,62678.53
+20180,15739046.0,Cremonesi,685.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,60721.4
+20181,15636395.0,Chuang,598.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,63736.17
+20182,15653776.0,Y?an,706.0,France,Male,46.0,2.0,134969.13,2.0,0.0,1.0,159800.16
+20183,15604891.0,Biryukova,694.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,122949.71
+20184,15642719.0,Mancini,619.0,Spain,Female,49.0,7.0,0.0,1.0,0.0,0.0,93820.49
+20185,15778089.0,Manna,567.0,France,Female,33.0,7.0,0.0,1.0,1.0,0.0,60533.96
+20186,15668323.0,Ts'ui,583.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,162760.96
+20187,15762882.0,Ch'ien,721.0,Spain,Male,25.0,9.0,0.0,2.0,0.0,1.0,40315.27
+20188,15656822.0,Baresi,528.0,Spain,Female,40.0,1.0,101609.01,1.0,1.0,1.0,160546.58
+20189,15594409.0,Ugorji,730.0,France,Male,28.0,4.0,0.0,1.0,1.0,0.0,140134.43
+20190,15612092.0,Chiu,613.0,Germany,Female,51.0,1.0,120787.18,1.0,0.0,1.0,74425.41
+20191,15765537.0,Yermolayeva,577.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,161653.5
+20192,15768163.0,Pagnotto,696.0,Germany,Female,46.0,4.0,124976.19,1.0,1.0,0.0,172749.4
+20193,15736302.0,Chidiebere,769.0,France,Male,34.0,6.0,0.0,1.0,1.0,1.0,127543.81
+20194,15648800.0,Ch'iu,619.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,160828.98
+20195,15629002.0,Nwebube,709.0,Spain,Female,48.0,7.0,133964.3,1.0,1.0,0.0,180197.74
+20196,15700772.0,Liao,613.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,65769.3
+20197,15781298.0,Kornilova,678.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,0.0,94715.24
+20198,15686936.0,Nkemakonam,662.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,62746.54
+20199,15646921.0,Ch'iu,651.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,136910.21
+20200,15806926.0,Ch'iu,687.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,177099.71
+20201,15694409.0,Chibueze,597.0,France,Male,43.0,0.0,0.0,1.0,1.0,1.0,182266.01
+20202,15673747.0,Ting,597.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,0.0,96961.0
+20203,15728693.0,Yefremova,682.0,France,Male,22.0,10.0,0.0,1.0,0.0,0.0,150944.31
+20204,15779620.0,Nkemdilim,561.0,France,Female,28.0,7.0,84440.1,1.0,1.0,1.0,177020.0
+20205,15574558.0,Y?,679.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,134778.02
+20206,15596088.0,Ch'ang,835.0,Spain,Male,34.0,2.0,134177.06,1.0,1.0,0.0,173599.38
+20207,15682618.0,Oluchukwu,516.0,Spain,Male,57.0,5.0,0.0,3.0,1.0,1.0,161854.98
+20208,15719348.0,Yefremova,837.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,1.0,180934.67
+20209,15584091.0,Chiang,754.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,151648.66
+20210,15777893.0,Fanucci,705.0,Spain,Male,36.0,7.0,0.0,2.0,0.0,0.0,89543.25
+20211,15793723.0,Chiu,633.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,197226.32
+20212,15703394.0,Ch'ang,721.0,Spain,Female,55.0,1.0,116936.81,1.0,1.0,0.0,171364.18
+20213,15725429.0,Yefremova,521.0,Spain,Female,30.0,7.0,0.0,2.0,1.0,1.0,182354.46
+20214,15600337.0,Mironova,523.0,Spain,Male,46.0,9.0,0.0,2.0,1.0,0.0,160829.98
+20215,15795959.0,Martin,678.0,France,Female,41.0,9.0,121325.84,1.0,0.0,1.0,171668.2
+20216,15792934.0,Teng,599.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,158049.9
+20217,15682778.0,Dyer,651.0,Germany,Male,37.0,4.0,90432.92,2.0,0.0,1.0,133692.88
+20218,15789924.0,Ch'en,806.0,France,Male,54.0,7.0,0.0,2.0,1.0,1.0,131283.6
+20219,15782390.0,Chiu,551.0,Germany,Female,52.0,6.0,140931.57,1.0,0.0,0.0,1713.1
+20220,15567367.0,Palermo,633.0,France,Male,31.0,6.0,0.0,1.0,1.0,0.0,146738.83
+20221,15671172.0,Hsia,684.0,Germany,Female,41.0,5.0,127552.85,1.0,1.0,0.0,101693.73
+20222,15643908.0,Chia,569.0,France,Female,32.0,7.0,0.0,2.0,0.0,1.0,63795.62
+20223,15609637.0,Brown,613.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,140166.95
+20224,15653547.0,Nwankwo,849.0,France,Female,24.0,2.0,120599.21,1.0,1.0,0.0,166586.99
+20225,15797381.0,T'ien,738.0,France,Male,34.0,4.0,0.0,1.0,1.0,1.0,131807.14
+20226,15668971.0,King,730.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,9984.4
+20227,15791169.0,Nnamdi,616.0,Germany,Male,50.0,10.0,133970.21,2.0,1.0,0.0,52876.41
+20228,15765322.0,Hales,624.0,France,Female,52.0,2.0,151844.64,1.0,0.0,0.0,124592.84
+20229,15638247.0,Okwudilichukwu,496.0,Germany,Male,31.0,3.0,120822.86,1.0,1.0,1.0,176883.42
+20230,15592751.0,Kosisochukwu,698.0,France,Male,33.0,5.0,0.0,2.0,0.0,0.0,65769.3
+20231,15756975.0,Ponce,660.0,Germany,Female,48.0,8.0,108797.63,1.0,1.0,1.0,161671.15
+20232,15752344.0,T'ien,681.0,France,Male,49.0,4.0,117685.31,1.0,1.0,1.0,115441.66
+20233,15574859.0,Chiu,697.0,Germany,Male,26.0,6.0,131325.56,2.0,1.0,0.0,184954.11
+20234,15709846.0,Chukwumaobim,727.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,108764.69
+20235,15771668.0,Trevisano,648.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,1.0,92647.94
+20236,15657535.0,Chien,686.0,Spain,Female,50.0,5.0,145965.33,2.0,1.0,0.0,93524.19
+20237,15592412.0,Kepley,651.0,France,Female,49.0,4.0,71786.9,1.0,0.0,1.0,145562.4
+20238,15686885.0,Chia,686.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,147552.06
+20239,15591257.0,Scott,641.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,83087.13
+20240,15589754.0,Chukwualuka,697.0,France,Male,25.0,10.0,0.0,2.0,1.0,1.0,189392.66
+20241,15762799.0,T'an,591.0,France,Female,22.0,8.0,0.0,2.0,1.0,1.0,147090.9
+20242,15654060.0,Chukwuhaenye,679.0,France,Female,21.0,6.0,0.0,2.0,1.0,1.0,126996.67
+20243,15592089.0,Chinedum,644.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,171056.65
+20244,15671800.0,Chiazagomekpele,721.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,104786.59
+20245,15809823.0,Chien,734.0,Germany,Male,68.0,7.0,132405.51,1.0,1.0,1.0,60905.51
+20246,15581388.0,Ndubueze,583.0,France,Male,51.0,4.0,0.0,1.0,1.0,0.0,172501.38
+20247,15604813.0,Chia,557.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,0.0,132467.54
+20248,15772337.0,Tsou,619.0,France,Female,34.0,0.0,0.0,2.0,1.0,0.0,163798.41
+20249,15737795.0,Lu,679.0,France,Female,41.0,3.0,0.0,1.0,1.0,0.0,183617.33
+20250,15783585.0,Hsieh,632.0,France,Male,38.0,2.0,128929.88,1.0,1.0,1.0,140745.91
+20251,15573851.0,Ch'eng,572.0,France,Female,35.0,7.0,0.0,2.0,0.0,1.0,176580.63
+20252,15704769.0,Ch'ien,811.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,104469.06
+20253,15664521.0,Chidiebere,687.0,France,Male,26.0,3.0,0.0,2.0,1.0,0.0,155823.2
+20254,15665064.0,T'ao,732.0,Germany,Male,27.0,1.0,142767.39,1.0,1.0,1.0,55829.25
+20255,15798895.0,Yobachi,632.0,France,Female,39.0,2.0,154970.54,1.0,1.0,1.0,158853.98
+20256,15789865.0,Martin,567.0,Germany,Male,43.0,2.0,106871.81,1.0,1.0,0.0,113943.17
+20257,15609356.0,Chiang,695.0,Germany,Female,46.0,8.0,125510.82,1.0,0.0,0.0,183540.22
+20258,15648067.0,O'Donnell,558.0,France,Female,35.0,3.0,0.0,1.0,1.0,0.0,155846.69
+20259,15726684.0,Chien,589.0,Germany,Male,44.0,2.0,123971.51,2.0,1.0,0.0,173870.69
+20260,15654067.0,Bage,624.0,France,Male,29.0,4.0,0.0,2.0,0.0,0.0,66926.83
+20261,15731751.0,Kao,609.0,France,Female,37.0,10.0,0.0,1.0,0.0,0.0,162697.93
+20262,15626457.0,Iadanza,559.0,France,Male,38.0,5.0,101514.89,1.0,1.0,1.0,137784.25
+20263,15668943.0,Hao,715.0,France,Female,45.0,4.0,102395.79,3.0,1.0,1.0,140780.8
+20264,15593694.0,Shih,628.0,France,Female,29.0,6.0,0.0,1.0,1.0,1.0,81491.7
+20265,15583841.0,Mazzi,646.0,Germany,Male,32.0,6.0,131078.42,1.0,1.0,0.0,74846.67
+20266,15776743.0,Tuan,738.0,Germany,Female,48.0,5.0,121647.54,1.0,0.0,0.0,175310.38
+20267,15653773.0,Hsiung,769.0,Germany,Male,40.0,9.0,170840.08,1.0,1.0,0.0,147055.87
+20268,15647453.0,Chizuoke,547.0,France,Female,28.0,7.0,126873.52,2.0,1.0,0.0,79134.78
+20269,15672955.0,Ozerova,705.0,France,Male,35.0,2.0,0.0,1.0,1.0,1.0,100950.48
+20270,15752409.0,Onyekaozulu,663.0,France,Male,31.0,5.0,145536.7,1.0,1.0,0.0,152992.03
+20271,15776518.0,Liao,624.0,Spain,Female,37.0,7.0,0.0,1.0,0.0,1.0,181429.87
+20272,15769254.0,Iadanza,679.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,151925.25
+20273,15652527.0,P'eng,591.0,France,Female,37.0,9.0,127616.56,1.0,1.0,1.0,177834.79
+20274,15765650.0,Y?,850.0,Germany,Female,37.0,2.0,153656.85,2.0,0.0,0.0,119333.95
+20275,15779627.0,Houghton,678.0,France,Male,27.0,0.0,0.0,2.0,1.0,1.0,38962.94
+20276,15761854.0,Hao,726.0,Germany,Male,41.0,3.0,121532.17,1.0,1.0,0.0,102076.03
+20277,15586039.0,Lo,678.0,Germany,Female,42.0,8.0,123544.0,1.0,1.0,0.0,129891.66
+20278,15780359.0,Hao,577.0,France,Male,33.0,4.0,138053.67,1.0,1.0,0.0,122964.18
+20279,15761158.0,Lo Duca,581.0,Germany,Female,39.0,4.0,122570.87,1.0,1.0,1.0,50743.83
+20280,15664523.0,T'ien,757.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,105915.01
+20281,15581871.0,Ozerova,646.0,Spain,Male,69.0,8.0,0.0,2.0,1.0,1.0,40889.14
+20282,15727281.0,Chiang,684.0,Germany,Male,38.0,2.0,94416.78,2.0,0.0,1.0,73977.23
+20283,15587535.0,Efremov,577.0,Spain,Male,51.0,7.0,0.0,1.0,1.0,1.0,187921.5
+20284,15800173.0,Tsao,547.0,Spain,Female,37.0,3.0,0.0,2.0,1.0,1.0,171515.84
+20285,15679048.0,Cremonesi,762.0,France,Female,45.0,8.0,125537.38,1.0,0.0,0.0,172782.69
+20286,15807595.0,H?,733.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,27330.59
+20287,15578825.0,Eluemuno,669.0,France,Female,26.0,8.0,0.0,2.0,1.0,1.0,113428.77
+20288,15576774.0,Nkemdirim,741.0,France,Female,26.0,8.0,0.0,2.0,1.0,0.0,131804.86
+20289,15725355.0,Ma,648.0,France,Female,46.0,10.0,0.0,1.0,1.0,0.0,151335.24
+20290,15583692.0,Onyekachi,730.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,84760.32
+20291,15601594.0,Scott,559.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,97938.59
+20292,15636407.0,Okwudiliolisa,439.0,Germany,Male,45.0,5.0,141581.71,1.0,1.0,0.0,179521.24
+20293,15709142.0,Chuang,635.0,Spain,Male,46.0,5.0,0.0,1.0,1.0,0.0,176984.34
+20294,15777045.0,Pirozzi,636.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,164768.82
+20295,15580493.0,Aksyonova,569.0,France,Female,36.0,9.0,132954.64,2.0,1.0,0.0,161479.19
+20296,15581252.0,Ch'ien,652.0,Germany,Male,33.0,4.0,102238.01,1.0,1.0,0.0,199505.52
+20297,15587066.0,Uchenna,699.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,100423.88
+20298,15737571.0,Ch'ien,725.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,181461.46
+20299,15658364.0,P'eng,674.0,France,Female,47.0,4.0,0.0,1.0,0.0,0.0,187866.21
+20300,15772381.0,Milano,786.0,Germany,Female,31.0,9.0,121874.89,1.0,0.0,1.0,57589.29
+20301,15569452.0,Mazzanti,662.0,France,Male,27.0,8.0,0.0,2.0,1.0,0.0,92816.86
+20302,15746662.0,Pisano,675.0,Germany,Female,55.0,4.0,129731.64,1.0,1.0,0.0,43982.41
+20303,15681327.0,Toscani,652.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,158054.49
+20304,15698792.0,Mazzanti,678.0,France,Male,44.0,3.0,0.0,2.0,0.0,0.0,101319.76
+20305,15572735.0,Lipton,732.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,72945.29
+20306,15792767.0,Ts'ao,758.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,16897.19
+20307,15588854.0,Ch'in,712.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,1.0,176984.34
+20308,15798975.0,Ch'ang,850.0,France,Male,36.0,9.0,0.0,1.0,1.0,0.0,45422.13
+20309,15605424.0,Ch'ien,648.0,France,Male,41.0,1.0,129728.54,1.0,0.0,1.0,127847.25
+20310,15592854.0,Ogochukwu,683.0,France,Female,39.0,3.0,0.0,2.0,1.0,0.0,62980.96
+20311,15665238.0,Mazzanti,589.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,1.0,120107.1
+20312,15796569.0,Liao,619.0,France,Male,40.0,1.0,0.0,2.0,0.0,1.0,79019.65
+20313,15788483.0,De Salis,651.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,0.0,172459.39
+20314,15761600.0,Milne,543.0,Germany,Female,48.0,3.0,109941.56,1.0,0.0,1.0,140941.47
+20315,15721585.0,Lucchesi,567.0,France,Female,51.0,1.0,0.0,1.0,1.0,0.0,150156.82
+20316,15644295.0,Chukwuemeka,656.0,Spain,Male,33.0,4.0,162560.32,1.0,1.0,0.0,86767.48
+20317,15762927.0,Ch'iu,704.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,122433.09
+20318,15758750.0,Chukwubuikem,577.0,Spain,Female,28.0,1.0,145965.24,1.0,0.0,0.0,167772.06
+20319,15763415.0,Gomes,583.0,Spain,Female,47.0,2.0,0.0,1.0,1.0,0.0,115994.52
+20320,15589428.0,Chukwujekwu,667.0,Germany,Female,49.0,3.0,127637.92,1.0,0.0,0.0,61324.87
+20321,15717155.0,Chuang,659.0,Spain,Male,36.0,0.0,143734.01,1.0,0.0,1.0,164967.21
+20322,15728013.0,Eluemuno,622.0,France,Male,29.0,7.0,0.0,1.0,1.0,0.0,176570.55
+20323,15642093.0,Ugochukwu,676.0,France,Female,23.0,7.0,0.0,2.0,1.0,1.0,133692.88
+20324,15641783.0,Yeh,628.0,Spain,Female,48.0,7.0,147904.31,1.0,0.0,0.0,141501.45
+20325,15585599.0,O'Brien,706.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,143666.48
+20326,15792323.0,Chidubem,649.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,164966.18
+20327,15661670.0,Lueck,581.0,France,Male,33.0,3.0,123881.31,1.0,1.0,1.0,116471.43
+20328,15784958.0,Genovese,652.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,109805.17
+20329,15691543.0,Chukwukadibia,611.0,Germany,Male,29.0,1.0,139496.35,1.0,1.0,1.0,113931.57
+20330,15792228.0,Mancini,711.0,Germany,Female,40.0,9.0,112419.92,1.0,1.0,0.0,163558.36
+20331,15673971.0,Tuan,648.0,Spain,Male,36.0,9.0,0.0,4.0,1.0,0.0,110782.88
+20332,15701376.0,Lucchese,730.0,France,Male,45.0,5.0,123385.74,1.0,1.0,0.0,91097.46
+20333,15771087.0,Ch'ang,706.0,Germany,Female,38.0,9.0,120165.3,1.0,1.0,1.0,174825.57
+20334,15786063.0,Ch'ien,558.0,Spain,Male,51.0,8.0,0.0,1.0,0.0,0.0,94912.78
+20335,15631267.0,Hsing,645.0,Spain,Male,44.0,7.0,0.0,1.0,1.0,0.0,102628.98
+20336,15813067.0,Chienezie,850.0,Spain,Male,36.0,9.0,134740.24,1.0,1.0,0.0,120880.72
+20337,15573741.0,Tsao,652.0,Spain,Male,31.0,0.0,0.0,2.0,1.0,0.0,168816.07
+20338,15734461.0,Ni,632.0,Germany,Male,24.0,4.0,124976.19,1.0,1.0,0.0,120830.73
+20339,15689612.0,Tseng,657.0,Germany,Female,49.0,1.0,119501.88,1.0,1.0,1.0,167984.72
+20340,15672798.0,Teng,597.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,105433.53
+20341,15662494.0,Ch'iu,586.0,France,Female,31.0,3.0,0.0,1.0,1.0,1.0,126475.79
+20342,15600280.0,Oguejiofor,733.0,Spain,Male,39.0,0.0,0.0,2.0,1.0,1.0,177613.19
+20343,15710111.0,Zikoranaudodimma,539.0,France,Male,60.0,3.0,0.0,2.0,1.0,0.0,139712.16
+20344,15702601.0,Walker,812.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,143665.44
+20345,15686885.0,Fanucci,706.0,Germany,Male,62.0,7.0,132725.1,2.0,1.0,0.0,113481.02
+20346,15615352.0,Nwankwo,559.0,France,Female,28.0,6.0,0.0,1.0,1.0,1.0,97794.71
+20347,15727177.0,Onyemachukwu,632.0,Spain,Female,28.0,3.0,0.0,2.0,0.0,0.0,177738.98
+20348,15751561.0,P'eng,646.0,Germany,Female,40.0,5.0,105599.67,1.0,1.0,0.0,105784.85
+20349,15800773.0,Scott,594.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,102260.28
+20350,15725028.0,Ts'ao,651.0,Germany,Male,23.0,3.0,132454.45,2.0,0.0,1.0,184842.21
+20351,15804822.0,Folliero,592.0,France,Male,28.0,7.0,0.0,1.0,1.0,0.0,91510.71
+20352,15652981.0,Ts'ui,629.0,Germany,Female,37.0,9.0,121389.06,2.0,1.0,0.0,72994.62
+20353,15741416.0,T'ao,581.0,France,Female,34.0,7.0,0.0,2.0,0.0,1.0,140745.91
+20354,15783025.0,Trevisani,619.0,Spain,Female,32.0,5.0,0.0,1.0,1.0,0.0,91903.9
+20355,15793896.0,Ch'iu,567.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,73977.23
+20356,15622985.0,Trevisani,699.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,180622.02
+20357,15598338.0,Hsia,595.0,Germany,Male,64.0,7.0,95231.91,1.0,1.0,1.0,159823.16
+20358,15732621.0,Smith,713.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,172576.43
+20359,15598840.0,Nwebube,790.0,France,Male,38.0,5.0,118089.97,1.0,1.0,0.0,139710.24
+20360,15798084.0,Pacheco,603.0,Germany,Male,36.0,2.0,84474.62,1.0,1.0,0.0,41800.18
+20361,15730579.0,Chuang,724.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,83022.92
+20362,15786042.0,T'ien,663.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,88729.22
+20363,15592300.0,H?,732.0,France,Male,33.0,10.0,0.0,2.0,1.0,0.0,121798.53
+20364,15582762.0,H?,632.0,Spain,Male,51.0,5.0,0.0,1.0,1.0,0.0,90876.97
+20365,15704771.0,Hsu,632.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,121151.1
+20366,15670552.0,Nnachetam,567.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,25684.93
+20367,15756475.0,Onwuamaeze,622.0,France,Male,33.0,9.0,169312.13,1.0,0.0,1.0,80619.17
+20368,15761763.0,P'eng,695.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,101515.76
+20369,15691823.0,O'Donnell,706.0,Germany,Male,33.0,7.0,122214.0,2.0,1.0,1.0,177747.03
+20370,15809265.0,Burt,591.0,Spain,Male,37.0,10.0,164902.66,1.0,1.0,0.0,157552.08
+20371,15704586.0,Hsueh,766.0,Spain,Male,48.0,10.0,0.0,2.0,1.0,1.0,65731.53
+20372,15684960.0,Lu,606.0,Germany,Male,29.0,9.0,134980.74,2.0,1.0,1.0,178587.36
+20373,15764343.0,Ts'ai,679.0,France,Male,56.0,2.0,0.0,2.0,0.0,1.0,121537.85
+20374,15787655.0,Kao,705.0,Spain,Male,31.0,7.0,0.0,2.0,0.0,1.0,177740.58
+20375,15785042.0,Chidiebere,597.0,France,Female,36.0,4.0,0.0,2.0,0.0,0.0,111190.53
+20376,15707681.0,Nnamdi,814.0,Germany,Female,34.0,3.0,141782.57,2.0,1.0,1.0,166468.21
+20377,15803685.0,Onyekachi,679.0,France,Female,25.0,3.0,0.0,4.0,1.0,0.0,82438.64
+20378,15591577.0,Fyodorova,743.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,79577.4
+20379,15572758.0,Chidiebere,587.0,Germany,Male,34.0,5.0,128718.84,1.0,0.0,0.0,103713.93
+20380,15694409.0,Pinto,703.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,937.15
+20381,15627385.0,Ch'in,681.0,Germany,Male,47.0,6.0,154970.54,1.0,1.0,0.0,198446.91
+20382,15585985.0,O'Loghlin,718.0,Spain,Female,33.0,10.0,0.0,2.0,1.0,0.0,181893.31
+20383,15682541.0,K?,605.0,France,Female,50.0,8.0,0.0,1.0,1.0,0.0,183566.78
+20384,15748625.0,Shao,684.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,90162.35
+20385,15785367.0,Walker,651.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,109613.14
+20386,15743759.0,Pomeroy,712.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,169921.11
+20387,15646082.0,Hsieh,724.0,Germany,Female,28.0,2.0,121172.97,2.0,1.0,0.0,116520.07
+20388,15645571.0,Chukwukadibia,520.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,161388.22
+20389,15674811.0,Hargreaves,642.0,France,Female,37.0,2.0,130147.98,2.0,1.0,0.0,49778.79
+20390,15751131.0,Tung,663.0,Germany,Male,47.0,8.0,125396.24,1.0,0.0,1.0,185561.89
+20391,15683483.0,Hsiung,611.0,France,Male,47.0,0.0,0.0,2.0,0.0,0.0,145894.9
+20392,15751137.0,Burtch,659.0,France,Male,59.0,9.0,0.0,2.0,0.0,1.0,120862.46
+20393,15802088.0,Zito,671.0,France,Male,44.0,6.0,0.0,2.0,1.0,1.0,179913.98
+20394,15723210.0,Lo,579.0,Spain,Female,40.0,9.0,0.0,3.0,1.0,0.0,97613.96
+20395,15663888.0,Fang,676.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,75860.01
+20396,15769703.0,Ozerova,684.0,France,Male,37.0,6.0,0.0,1.0,1.0,1.0,96670.2
+20397,15592461.0,Tien,704.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,92320.37
+20398,15752884.0,Onyekachi,660.0,France,Female,55.0,4.0,0.0,1.0,1.0,0.0,101704.19
+20399,15725028.0,Macleod,585.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,126554.96
+20400,15772650.0,Ts'ui,745.0,France,Female,37.0,8.0,0.0,1.0,1.0,1.0,79547.39
+20401,15652289.0,T'ao,727.0,Spain,Male,50.0,7.0,120916.52,3.0,0.0,1.0,149913.94
+20402,15765952.0,Gant,678.0,France,Male,50.0,10.0,0.0,1.0,1.0,0.0,176763.27
+20403,15605059.0,Walker,653.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,61569.07
+20404,15596710.0,P'eng,753.0,France,Male,34.0,7.0,0.0,2.0,0.0,1.0,122925.4
+20405,15655232.0,Longo,671.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,102260.16
+20406,15686936.0,Calabresi,806.0,Spain,Male,29.0,7.0,118402.25,1.0,1.0,1.0,44904.26
+20407,15578866.0,T'ien,538.0,Spain,Male,39.0,8.0,0.0,1.0,1.0,0.0,124489.88
+20408,15734461.0,Baresi,598.0,France,Female,51.0,1.0,123086.39,1.0,1.0,1.0,185830.72
+20409,15790717.0,Efremov,586.0,France,Female,43.0,9.0,0.0,1.0,0.0,1.0,103560.98
+20410,15652266.0,Udinese,678.0,France,Male,49.0,9.0,0.0,1.0,0.0,1.0,79874.72
+20411,15715117.0,Chineze,706.0,France,Female,29.0,5.0,142862.21,1.0,1.0,0.0,167773.55
+20412,15713774.0,Ch'ien,577.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,88333.74
+20413,15682955.0,Okechukwu,628.0,France,Female,28.0,5.0,141334.95,1.0,1.0,0.0,162858.29
+20414,15777784.0,Uchenna,564.0,Germany,Female,59.0,7.0,172646.74,1.0,1.0,0.0,99490.01
+20415,15587562.0,Ch'ang,809.0,France,Female,47.0,4.0,0.0,1.0,0.0,0.0,131753.97
+20416,15628112.0,Chidimma,598.0,Spain,Female,33.0,6.0,0.0,2.0,0.0,0.0,166984.44
+20417,15783955.0,Ch'iu,673.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,161653.5
+20418,15593365.0,Ch'in,723.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,52615.62
+20419,15587581.0,Calabresi,614.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,1.0,104722.47
+20420,15767954.0,Y?an,656.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,101828.39
+20421,15735025.0,McLean,710.0,France,Male,46.0,10.0,0.0,2.0,1.0,0.0,142956.48
+20422,15642787.0,Yermolayev,686.0,Spain,Male,26.0,9.0,0.0,2.0,1.0,0.0,187734.49
+20423,15746802.0,Yegorova,663.0,France,Female,30.0,10.0,137494.28,1.0,1.0,1.0,108841.83
+20424,15770420.0,Chidumaga,680.0,Germany,Female,35.0,9.0,114268.85,2.0,1.0,0.0,54474.48
+20425,15764073.0,DeRose,583.0,France,Male,27.0,4.0,0.0,2.0,0.0,1.0,66825.33
+20426,15610686.0,Mazzi,662.0,France,Male,47.0,1.0,0.0,1.0,1.0,0.0,199398.68
+20427,15606841.0,Chukwubuikem,622.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,177747.72
+20428,15804822.0,Lo Duca,729.0,France,Female,52.0,1.0,0.0,1.0,1.0,0.0,183682.09
+20429,15799785.0,Ibeamaka,648.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,164084.82
+20430,15692594.0,Chidimma,559.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,167984.72
+20431,15576723.0,Tuan,684.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,135083.28
+20432,15762331.0,Bair,678.0,Spain,Male,35.0,7.0,0.0,1.0,1.0,1.0,94912.78
+20433,15659234.0,Ts'ai,685.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,113086.08
+20434,15594815.0,Chiang,699.0,Spain,Male,34.0,4.0,145071.24,1.0,1.0,0.0,133871.83
+20435,15571689.0,Mazzi,544.0,Spain,Male,27.0,1.0,0.0,2.0,1.0,1.0,183358.21
+20436,15677307.0,Yobanna,551.0,France,Male,28.0,1.0,0.0,2.0,1.0,1.0,135875.51
+20437,15790322.0,Chien,707.0,Germany,Female,47.0,8.0,97925.11,1.0,0.0,0.0,169952.82
+20438,15791321.0,O'Sullivan,696.0,Germany,Female,49.0,9.0,135470.9,1.0,0.0,0.0,66893.28
+20439,15795895.0,Chieloka,567.0,France,Male,31.0,6.0,142767.39,1.0,1.0,0.0,66810.48
+20440,15624781.0,Baresi,586.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,152290.29
+20441,15794875.0,T'ang,583.0,Spain,Female,36.0,1.0,84944.58,1.0,1.0,1.0,127939.19
+20442,15795519.0,Hsiung,669.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,1.0,181221.09
+20443,15617206.0,Lucciano,646.0,France,Female,45.0,6.0,0.0,2.0,1.0,0.0,61931.21
+20444,15794941.0,Trevisani,704.0,France,Female,45.0,9.0,0.0,2.0,1.0,0.0,151871.84
+20445,15651020.0,Iweobiegbunam,653.0,France,Female,34.0,3.0,0.0,1.0,1.0,1.0,166310.82
+20446,15682868.0,Ts'ai,652.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,93524.22
+20447,15703923.0,Ch'iu,648.0,Germany,Male,43.0,1.0,119794.67,1.0,0.0,0.0,73585.18
+20448,15651022.0,Ch'eng,595.0,France,Female,55.0,5.0,0.0,2.0,1.0,1.0,94415.2
+20449,15759717.0,Lo Duca,516.0,Spain,Male,41.0,7.0,0.0,4.0,1.0,0.0,87987.06
+20450,15777830.0,Piazza,684.0,Germany,Male,45.0,7.0,135070.78,1.0,1.0,0.0,131662.47
+20451,15684319.0,Ch'en,736.0,France,Female,55.0,2.0,0.0,1.0,0.0,0.0,43921.36
+20452,15638028.0,Calabrese,731.0,Spain,Male,48.0,7.0,0.0,2.0,1.0,1.0,183487.98
+20453,15602373.0,Ch'ang,574.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,90591.15
+20454,15611579.0,Chikwendu,711.0,France,Female,31.0,7.0,136416.82,1.0,0.0,0.0,158610.89
+20455,15696989.0,Chiang,696.0,Spain,Female,27.0,5.0,0.0,2.0,1.0,0.0,121084.27
+20456,15643396.0,Teng,716.0,Spain,Male,34.0,5.0,0.0,2.0,0.0,1.0,199336.63
+20457,15777053.0,Pinto,666.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,16081.62
+20458,15763544.0,Yobanna,712.0,France,Female,39.0,1.0,151553.19,1.0,1.0,0.0,76637.38
+20459,15736271.0,Chukwuemeka,638.0,Germany,Female,43.0,5.0,139706.31,1.0,0.0,0.0,53394.28
+20460,15642530.0,Manna,588.0,France,Male,63.0,8.0,88977.39,1.0,1.0,1.0,101178.85
+20461,15657321.0,P'eng,639.0,France,Male,41.0,8.0,0.0,2.0,0.0,1.0,129933.5
+20462,15785519.0,K'ung,627.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,0.0,88728.47
+20463,15693018.0,Walker,673.0,France,Female,39.0,8.0,121250.04,1.0,0.0,0.0,59129.72
+20464,15622003.0,Trevisani,716.0,France,Male,24.0,7.0,0.0,2.0,0.0,1.0,110525.6
+20465,15796370.0,Chidubem,559.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,88783.59
+20466,15794849.0,Yobanna,556.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,175894.24
+20467,15693771.0,Lombardi,644.0,Germany,Female,45.0,6.0,144386.32,1.0,1.0,0.0,167564.82
+20468,15637461.0,Chia,744.0,France,Male,34.0,8.0,85906.65,1.0,1.0,1.0,159800.16
+20469,15762216.0,Maughan,581.0,Germany,Female,39.0,2.0,178820.28,1.0,1.0,1.0,172063.09
+20470,15815271.0,Ch'eng,684.0,Spain,Male,34.0,1.0,0.0,2.0,0.0,1.0,179705.13
+20471,15730447.0,Ritchie,597.0,Germany,Female,38.0,4.0,121498.58,1.0,1.0,1.0,172096.08
+20472,15591720.0,McIntosh,792.0,Germany,Female,31.0,9.0,122950.94,2.0,1.0,1.0,164248.33
+20473,15652789.0,Iloerika,663.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,35113.08
+20474,15772423.0,Pagnotto,739.0,Spain,Female,31.0,4.0,0.0,2.0,1.0,1.0,70563.9
+20475,15576774.0,T'an,678.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,54487.43
+20476,15571928.0,Palerma,667.0,Spain,Male,21.0,6.0,0.0,1.0,1.0,1.0,77469.38
+20477,15732963.0,Chidumaga,662.0,Germany,Female,55.0,3.0,113625.83,2.0,1.0,0.0,170195.4
+20478,15693906.0,Onio,581.0,France,Female,41.0,6.0,0.0,2.0,1.0,0.0,1276.87
+20479,15651883.0,Onyekaozulu,810.0,Germany,Male,44.0,6.0,102870.93,1.0,1.0,1.0,92887.06
+20480,15614893.0,Ch'ang,641.0,France,Female,34.0,4.0,0.0,1.0,1.0,1.0,138527.56
+20481,15679622.0,O'Sullivan,727.0,France,Male,39.0,4.0,0.0,1.0,1.0,1.0,44965.44
+20482,15624641.0,Nwabugwu,682.0,France,Male,42.0,6.0,0.0,1.0,1.0,1.0,148727.42
+20483,15792489.0,Bellucci,671.0,France,Male,41.0,1.0,0.0,2.0,0.0,1.0,158411.65
+20484,15652883.0,Chukwubuikem,687.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,69011.66
+20485,15769246.0,Pugh,596.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,104774.67
+20486,15762268.0,Pisani,706.0,France,Female,43.0,5.0,0.0,2.0,1.0,1.0,176713.47
+20487,15758750.0,Folliero,586.0,Germany,Male,40.0,3.0,109730.22,1.0,1.0,0.0,61290.99
+20488,15682533.0,Kao,584.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,91482.5
+20489,15617301.0,Toscani,669.0,Germany,Male,28.0,9.0,89800.14,3.0,0.0,1.0,172572.64
+20490,15752409.0,Yeh,557.0,France,Female,47.0,5.0,0.0,3.0,1.0,0.0,52327.57
+20491,15644765.0,Mazzanti,623.0,France,Male,33.0,7.0,0.0,1.0,1.0,1.0,52319.26
+20492,15681075.0,K'ung,605.0,France,Male,38.0,5.0,114835.04,1.0,0.0,1.0,145568.28
+20493,15676571.0,Cattaneo,576.0,France,Male,42.0,1.0,0.0,2.0,1.0,1.0,114448.77
+20494,15656592.0,Calabresi,775.0,France,Male,51.0,7.0,0.0,2.0,1.0,1.0,103372.79
+20495,15753874.0,Genovese,725.0,France,Male,30.0,6.0,0.0,2.0,1.0,0.0,134878.19
+20496,15672960.0,Nwebube,694.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,133051.64
+20497,15671342.0,Hs?eh,673.0,Spain,Male,24.0,9.0,0.0,2.0,1.0,0.0,88724.49
+20498,15570141.0,Pinto,724.0,Germany,Male,34.0,2.0,100549.43,1.0,0.0,1.0,155711.64
+20499,15570134.0,Cremonesi,653.0,Germany,Male,44.0,9.0,121161.57,1.0,0.0,0.0,47911.03
+20500,15592994.0,DeRose,683.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,121681.19
+20501,15635097.0,Riggs,720.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,1.0,160941.78
+20502,15595881.0,Mazzi,696.0,Germany,Female,35.0,3.0,139825.34,1.0,1.0,0.0,172321.21
+20503,15672754.0,Hovell,593.0,Germany,Male,36.0,8.0,72535.45,1.0,1.0,0.0,106920.57
+20504,15782875.0,Young,527.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,56784.11
+20505,15622993.0,Chiu,718.0,Germany,Female,48.0,1.0,141665.63,1.0,1.0,0.0,115719.08
+20506,15697546.0,Gorman,562.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,166516.84
+20507,15637593.0,Maclean,708.0,France,Male,48.0,3.0,0.0,2.0,1.0,1.0,96832.82
+20508,15602844.0,Hsing,712.0,Germany,Female,39.0,7.0,107864.85,1.0,1.0,1.0,93336.3
+20509,15572728.0,Maclean,716.0,France,Female,45.0,6.0,0.0,1.0,1.0,0.0,166586.99
+20510,15782899.0,Yefremova,721.0,France,Female,33.0,8.0,137936.94,1.0,1.0,0.0,73881.68
+20511,15768887.0,Maclean,576.0,France,Male,23.0,5.0,0.0,2.0,1.0,1.0,138880.24
+20512,15641411.0,Ni,730.0,Germany,Male,54.0,1.0,123086.39,1.0,1.0,0.0,74580.8
+20513,15798903.0,Yobachukwu,565.0,Germany,Female,43.0,6.0,145965.24,1.0,0.0,1.0,161653.5
+20514,15602354.0,Iadanza,815.0,France,Male,39.0,0.0,0.0,2.0,1.0,0.0,136095.94
+20515,15772030.0,Chigolum,775.0,Germany,Female,41.0,3.0,103700.69,1.0,1.0,0.0,166370.81
+20516,15573851.0,Nwokike,574.0,Spain,Female,31.0,1.0,0.0,2.0,1.0,0.0,158840.99
+20517,15763662.0,Mazzi,523.0,Spain,Female,36.0,3.0,0.0,2.0,0.0,0.0,111342.66
+20518,15801125.0,Ts'ui,556.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,9041.71
+20519,15626383.0,Chien,744.0,France,Male,32.0,5.0,122314.5,1.0,1.0,1.0,64855.58
+20520,15790782.0,Ch'ien,749.0,France,Female,32.0,10.0,0.0,2.0,1.0,0.0,82696.84
+20521,15641585.0,Chimezie,730.0,Germany,Female,50.0,6.0,138476.41,1.0,0.0,1.0,88187.81
+20522,15662238.0,Chukwualuka,710.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,1.0,94978.1
+20523,15614103.0,Brown,604.0,Germany,Male,39.0,3.0,110114.42,2.0,0.0,0.0,82870.75
+20524,15654870.0,Teng,605.0,France,Male,67.0,7.0,0.0,2.0,1.0,1.0,161579.85
+20525,15589019.0,Hsia,588.0,Germany,Male,37.0,2.0,131406.56,2.0,1.0,1.0,46415.09
+20526,15731751.0,Hs?,735.0,Spain,Female,21.0,9.0,0.0,2.0,1.0,0.0,121408.55
+20527,15589924.0,Palerma,576.0,France,Female,30.0,5.0,142871.27,1.0,1.0,1.0,181224.56
+20528,15690164.0,Chukwujamuike,757.0,Spain,Male,29.0,3.0,98800.0,1.0,1.0,1.0,86490.09
+20529,15808971.0,Uchechukwu,567.0,Germany,Male,33.0,9.0,136444.25,1.0,1.0,1.0,126192.54
+20530,15791342.0,Hs?,581.0,Germany,Male,30.0,3.0,145984.92,2.0,0.0,0.0,166476.95
+20531,15740383.0,Nnachetam,582.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,141946.92
+20532,15796834.0,Kornilova,679.0,France,Female,39.0,3.0,90594.55,1.0,1.0,0.0,134889.95
+20533,15739592.0,Frolov,811.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,45438.38
+20534,15782089.0,Chiemela,556.0,Spain,Male,33.0,1.0,0.0,2.0,1.0,1.0,105986.01
+20535,15762228.0,Onyekaozulu,786.0,France,Male,40.0,6.0,133628.89,1.0,1.0,1.0,142995.32
+20536,15786035.0,Ts'ai,592.0,France,Male,31.0,9.0,0.0,2.0,0.0,1.0,182855.21
+20537,15641366.0,Lombardi,743.0,France,Female,46.0,1.0,110132.55,1.0,1.0,0.0,65951.65
+20538,15659475.0,Milano,636.0,Germany,Female,33.0,6.0,142400.77,2.0,1.0,0.0,96881.32
+20539,15761067.0,T'ao,667.0,Germany,Female,29.0,6.0,131694.97,2.0,1.0,1.0,54487.43
+20540,15661626.0,Zuyeva,729.0,France,Female,42.0,2.0,125837.76,1.0,1.0,0.0,140941.47
+20541,15721592.0,Chiu,647.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,103557.85
+20542,15809872.0,Trevisani,616.0,Germany,Female,28.0,6.0,152264.81,2.0,0.0,0.0,110953.33
+20543,15576680.0,Martin,678.0,Germany,Male,41.0,5.0,116474.35,2.0,1.0,0.0,26149.25
+20544,15738174.0,Hsiao,595.0,Spain,Female,62.0,4.0,0.0,1.0,1.0,1.0,96792.15
+20545,15766163.0,Ugochukwutubelum,693.0,France,Male,62.0,9.0,0.0,2.0,0.0,1.0,26265.18
+20546,15777616.0,Chukwukere,619.0,Spain,Male,26.0,10.0,0.0,2.0,1.0,0.0,125877.22
+20547,15780256.0,O'Sullivan,679.0,Spain,Male,45.0,9.0,0.0,2.0,0.0,0.0,102930.46
+20548,15775339.0,Chukwueloka,777.0,France,Male,49.0,9.0,0.0,1.0,1.0,0.0,118319.5
+20549,15754929.0,Chiang,724.0,France,Male,41.0,4.0,128876.71,1.0,1.0,1.0,61666.87
+20550,15790846.0,Chukwujamuike,774.0,France,Female,43.0,1.0,0.0,2.0,1.0,0.0,160131.58
+20551,15766887.0,Biryukova,553.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,115387.4
+20552,15671358.0,Yermakova,697.0,France,Female,38.0,2.0,57530.06,1.0,0.0,0.0,151902.78
+20553,15784918.0,Chukwuemeka,596.0,France,Male,33.0,4.0,0.0,2.0,0.0,0.0,180622.43
+20554,15687079.0,Zetticci,593.0,France,Female,43.0,8.0,0.0,2.0,1.0,1.0,133535.29
+20555,15780492.0,Hsueh,683.0,France,Female,38.0,3.0,0.0,2.0,0.0,0.0,124095.69
+20556,15691808.0,Nnaife,567.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,112331.19
+20557,15702293.0,Onuchukwu,578.0,France,Male,46.0,8.0,0.0,2.0,1.0,0.0,123663.63
+20558,15649160.0,Chukwualuka,709.0,France,Male,28.0,7.0,0.0,2.0,0.0,1.0,4791.8
+20559,15589948.0,Ritchie,559.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,141501.45
+20560,15668818.0,Eluemuno,711.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,158590.25
+20561,15614847.0,Yermakov,724.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,91574.92
+20562,15750335.0,Chiawuotu,639.0,Spain,Female,18.0,3.0,0.0,2.0,0.0,1.0,144874.67
+20563,15644295.0,Ch'ien,667.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,82531.91
+20564,15604072.0,Y?an,718.0,France,Male,32.0,7.0,0.0,1.0,0.0,1.0,131283.6
+20565,15587562.0,Yobanna,678.0,France,Male,59.0,8.0,0.0,2.0,1.0,1.0,134790.74
+20566,15606841.0,Chidiebere,669.0,France,Female,37.0,0.0,0.0,1.0,1.0,1.0,38570.13
+20567,15651336.0,Maughan,600.0,France,Female,22.0,7.0,84719.13,1.0,1.0,1.0,54517.25
+20568,15598838.0,Clements,678.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,144650.18
+20569,15655229.0,Ch'ang,779.0,Spain,Male,31.0,5.0,129717.3,1.0,1.0,0.0,124872.58
+20570,15673747.0,Y?,556.0,Spain,Male,43.0,9.0,0.0,2.0,1.0,0.0,151325.24
+20571,15707132.0,Chukwuma,679.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,106981.05
+20572,15641359.0,Brown,619.0,France,Male,29.0,2.0,0.0,1.0,1.0,1.0,40313.47
+20573,15793331.0,Chijindum,597.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,181543.67
+20574,15655875.0,P'eng,615.0,Spain,Female,41.0,1.0,102983.93,1.0,1.0,1.0,170984.97
+20575,15702631.0,Nwankwo,667.0,France,Male,40.0,4.0,71996.09,2.0,1.0,1.0,121277.78
+20576,15768106.0,Hsia,717.0,France,Male,38.0,2.0,0.0,1.0,1.0,0.0,177747.03
+20577,15616555.0,Nnachetam,716.0,France,Female,36.0,5.0,0.0,1.0,1.0,0.0,105784.85
+20578,15762238.0,Trevisani,564.0,France,Female,47.0,6.0,127415.85,1.0,1.0,0.0,144880.81
+20579,15618363.0,Trevisano,691.0,Spain,Female,39.0,4.0,0.0,2.0,1.0,1.0,56875.76
+20580,15668958.0,Ch'iu,769.0,France,Female,28.0,8.0,0.0,2.0,1.0,1.0,81298.09
+20581,15600724.0,Tsao,682.0,Spain,Female,38.0,5.0,79039.84,1.0,1.0,0.0,114323.66
+20582,15693376.0,K?,587.0,France,Male,48.0,8.0,0.0,2.0,0.0,1.0,171575.54
+20583,15632521.0,Kuo,645.0,Germany,Male,40.0,9.0,92716.96,1.0,1.0,0.0,118749.58
+20584,15792722.0,Walker,635.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,92915.84
+20585,15772262.0,Uvarova,715.0,France,Male,39.0,9.0,0.0,2.0,0.0,1.0,10350.74
+20586,15696849.0,Tuan,602.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,112928.74
+20587,15690670.0,Onyemauchechukwu,774.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,115789.25
+20588,15793346.0,Okechukwu,594.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,131283.6
+20589,15717185.0,Ijendu,602.0,France,Female,39.0,3.0,106116.5,3.0,1.0,1.0,84475.13
+20590,15681446.0,Tobeolisa,745.0,France,Male,47.0,3.0,122028.65,1.0,1.0,1.0,176728.28
+20591,15633141.0,Onwuamaeze,678.0,Germany,Male,35.0,9.0,122447.86,2.0,1.0,1.0,13018.96
+20592,15773039.0,Maclean,641.0,Germany,Female,63.0,3.0,118560.0,1.0,1.0,1.0,86022.09
+20593,15693637.0,Uwaezuoke,628.0,Germany,Female,39.0,5.0,92961.61,2.0,0.0,0.0,74850.84
+20594,15808876.0,Obialo,597.0,Germany,Male,35.0,8.0,93547.23,2.0,1.0,0.0,131736.23
+20595,15757777.0,Chiu,579.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,68621.98
+20596,15669301.0,Fennell,635.0,Germany,Male,40.0,9.0,117468.67,2.0,1.0,0.0,172646.88
+20597,15783029.0,Nwora,655.0,France,Female,27.0,4.0,117412.19,1.0,1.0,1.0,146411.6
+20598,15693332.0,Lombardo,610.0,Spain,Female,55.0,9.0,125789.69,1.0,1.0,1.0,106040.97
+20599,15773779.0,Hsueh,579.0,France,Female,61.0,3.0,0.0,2.0,1.0,1.0,4460.2
+20600,15768120.0,Chia,636.0,Germany,Female,47.0,6.0,126473.33,2.0,1.0,0.0,6076.62
+20601,15768201.0,Binder,631.0,Spain,Female,23.0,7.0,0.0,2.0,0.0,1.0,198208.0
+20602,15722585.0,Chukwujamuike,611.0,Germany,Male,40.0,2.0,91286.51,1.0,1.0,0.0,167522.19
+20603,15587584.0,Smith,597.0,France,Male,39.0,9.0,127289.28,2.0,0.0,0.0,143357.08
+20604,15763612.0,Chukwuemeka,628.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,79734.23
+20605,15755084.0,Yermakova,693.0,France,Male,35.0,2.0,0.0,2.0,0.0,0.0,88827.67
+20606,15754926.0,Fanucci,586.0,France,Female,35.0,8.0,0.0,1.0,1.0,0.0,64815.05
+20607,15645103.0,Lucchese,645.0,France,Female,28.0,1.0,0.0,2.0,1.0,0.0,161971.42
+20608,15729557.0,Ritchie,628.0,Germany,Female,61.0,3.0,118087.68,1.0,1.0,0.0,161253.08
+20609,15667438.0,Naylor,495.0,Spain,Male,49.0,8.0,0.0,1.0,1.0,0.0,88724.49
+20610,15652955.0,Chimaobim,646.0,France,Male,71.0,2.0,0.0,1.0,1.0,1.0,102299.82
+20611,15761477.0,Chiang,633.0,France,Female,44.0,4.0,0.0,2.0,1.0,1.0,152417.79
+20612,15652914.0,Maclean,597.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,151902.78
+20613,15658169.0,Clements,641.0,France,Female,37.0,6.0,0.0,1.0,1.0,1.0,137028.51
+20614,15752622.0,Cattaneo,538.0,Germany,Male,44.0,1.0,136659.66,3.0,0.0,0.0,164886.64
+20615,15659325.0,Ch'ang,687.0,France,Male,49.0,2.0,0.0,1.0,0.0,1.0,173870.39
+20616,15663917.0,Hsia,642.0,Germany,Male,35.0,10.0,142871.27,1.0,1.0,0.0,120135.58
+20617,15611331.0,Ch'en,556.0,France,Female,30.0,9.0,0.0,2.0,0.0,1.0,138021.41
+20618,15779915.0,Hsia,686.0,France,Male,29.0,3.0,106415.57,1.0,1.0,0.0,72402.0
+20619,15659736.0,Ch'iu,611.0,France,Male,27.0,9.0,0.0,2.0,0.0,1.0,88852.11
+20620,15728012.0,Davide,646.0,France,Female,37.0,3.0,0.0,1.0,1.0,1.0,86079.67
+20621,15690079.0,Ch'ien,641.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,170634.49
+20622,15769412.0,Nwachukwu,588.0,Spain,Male,32.0,0.0,0.0,2.0,1.0,1.0,122268.75
+20623,15771270.0,Ch'ang,671.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,47880.71
+20624,15756056.0,Pisano,713.0,Germany,Male,41.0,6.0,92316.31,1.0,0.0,0.0,111448.92
+20625,15706537.0,Chu,652.0,Germany,Male,37.0,1.0,96863.13,1.0,1.0,1.0,64831.36
+20626,15680430.0,Chinweuba,769.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,176580.63
+20627,15742238.0,H?,727.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,163911.32
+20628,15731781.0,Chiganu,653.0,France,Male,39.0,5.0,0.0,1.0,1.0,1.0,86414.41
+20629,15605918.0,Chukwukere,629.0,Germany,Female,53.0,6.0,113811.78,1.0,1.0,1.0,74532.02
+20630,15691323.0,Yermakova,661.0,Spain,Female,35.0,7.0,124423.65,1.0,1.0,1.0,77866.92
+20631,15775153.0,Chukwufumnanya,671.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,86678.48
+20632,15662085.0,P'eng,619.0,France,Female,42.0,0.0,0.0,2.0,1.0,1.0,88890.05
+20633,15593736.0,Ch'iu,663.0,France,Male,24.0,1.0,0.0,2.0,1.0,0.0,179403.04
+20634,15728885.0,Yashina,629.0,France,Female,49.0,0.0,0.0,1.0,1.0,1.0,160979.66
+20635,15596914.0,Olisaemeka,642.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,170360.11
+20636,15706637.0,Ugorji,651.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,166310.82
+20637,15757911.0,Trevisano,586.0,Spain,Female,33.0,4.0,0.0,1.0,0.0,0.0,151481.86
+20638,15679991.0,Onyekachukwu,516.0,Germany,Female,33.0,3.0,148363.38,1.0,1.0,0.0,134794.02
+20639,15572792.0,Hsia,641.0,France,Male,29.0,2.0,92636.96,1.0,1.0,0.0,83084.33
+20640,15668818.0,Lucchesi,581.0,France,Male,46.0,2.0,151958.19,1.0,1.0,1.0,72350.17
+20641,15752388.0,Tung,645.0,France,Male,27.0,5.0,0.0,1.0,1.0,1.0,78965.61
+20642,15667944.0,Pagnotto,773.0,France,Female,35.0,3.0,87098.91,2.0,1.0,1.0,121798.93
+20643,15775294.0,Ch'ang,676.0,France,Female,38.0,1.0,120599.38,1.0,1.0,1.0,176768.59
+20644,15754929.0,T'ao,535.0,Germany,Male,41.0,5.0,133467.77,1.0,1.0,0.0,94767.77
+20645,15690164.0,Hs?,754.0,Germany,Male,37.0,0.0,137262.71,1.0,1.0,1.0,71167.17
+20646,15752300.0,Pinto,699.0,France,Male,38.0,4.0,0.0,2.0,0.0,1.0,121451.21
+20647,15634262.0,Nkemakonam,732.0,France,Male,36.0,8.0,132835.56,1.0,1.0,0.0,14081.64
+20648,15677871.0,Nwachinemelu,710.0,France,Male,36.0,9.0,66220.17,1.0,1.0,1.0,111241.37
+20649,15681220.0,Calabresi,516.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,1.0,126585.9
+20650,15792989.0,Kao,725.0,Germany,Female,43.0,6.0,165901.59,2.0,1.0,1.0,60788.61
+20651,15690695.0,Ch'ang,599.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,93881.75
+20652,15791942.0,Pisano,584.0,Germany,Male,32.0,9.0,91522.04,1.0,0.0,0.0,132870.15
+20653,15787597.0,Ch'iu,707.0,France,Male,44.0,4.0,154575.76,1.0,1.0,1.0,163989.66
+20654,15765297.0,Kao,717.0,Spain,Female,52.0,4.0,132623.6,1.0,1.0,1.0,174094.24
+20655,15621451.0,Tung,602.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,54448.41
+20656,15685500.0,Chinomso,718.0,Spain,Female,50.0,7.0,0.0,1.0,1.0,1.0,136859.55
+20657,15663489.0,Chia,554.0,Spain,Male,46.0,8.0,0.0,2.0,1.0,0.0,55853.38
+20658,15729954.0,Hsieh,644.0,Germany,Male,36.0,7.0,93716.72,2.0,1.0,0.0,80120.19
+20659,15781298.0,Hsu,705.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,142937.97
+20660,15682454.0,Macintyre,620.0,Spain,Male,45.0,1.0,0.0,2.0,1.0,0.0,91844.18
+20661,15669927.0,Chienezie,676.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,82662.73
+20662,15798885.0,Yobanna,662.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,175092.4
+20663,15727198.0,Bogle,632.0,France,Male,30.0,3.0,0.0,1.0,1.0,1.0,43889.03
+20664,15652401.0,Liao,645.0,France,Female,23.0,7.0,0.0,2.0,1.0,0.0,160110.65
+20665,15570141.0,Chinwendu,638.0,France,Female,38.0,7.0,0.0,2.0,0.0,1.0,55868.97
+20666,15650591.0,Walker,646.0,France,Male,46.0,1.0,0.0,2.0,1.0,1.0,28257.89
+20667,15641280.0,Fanucci,638.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,94738.54
+20668,15737173.0,Ch'ien,653.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,118740.37
+20669,15662955.0,Shao,682.0,France,Female,48.0,5.0,0.0,2.0,1.0,1.0,121151.1
+20670,15692406.0,Eluemuno,554.0,Germany,Female,49.0,6.0,123845.36,3.0,0.0,1.0,70954.41
+20671,15793046.0,Tien,753.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,166513.49
+20672,15572735.0,Maclean,663.0,France,Female,47.0,7.0,0.0,1.0,0.0,0.0,45414.12
+20673,15641480.0,Nwankwo,656.0,Germany,Male,27.0,8.0,132351.29,1.0,0.0,1.0,111285.85
+20674,15783541.0,Ts'ao,510.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,85982.47
+20675,15673929.0,Chinwemma,707.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,182738.0
+20676,15776947.0,Chiang,710.0,Germany,Female,33.0,5.0,113569.23,2.0,1.0,0.0,95611.47
+20677,15602497.0,Tseng,683.0,France,Female,32.0,3.0,121580.41,1.0,0.0,1.0,107874.39
+20678,15602282.0,Ch'iu,583.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,1.0,160529.71
+20679,15585066.0,Hsiao,638.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,123583.85
+20680,15646726.0,Cremonesi,715.0,France,Male,60.0,7.0,0.0,1.0,1.0,1.0,152992.04
+20681,15675650.0,Lucchesi,596.0,France,Male,28.0,0.0,0.0,2.0,1.0,1.0,90174.84
+20682,15780144.0,Nwankwo,721.0,Spain,Male,52.0,6.0,122351.29,1.0,0.0,1.0,116471.43
+20683,15799790.0,Chukwualuka,624.0,France,Female,29.0,5.0,0.0,3.0,1.0,1.0,27472.07
+20684,15595037.0,Nkemakolam,774.0,France,Male,40.0,5.0,0.0,2.0,0.0,1.0,54871.02
+20685,15690164.0,Milani,576.0,France,Female,25.0,9.0,165896.0,1.0,1.0,0.0,84566.96
+20686,15586880.0,Iadanza,619.0,Spain,Male,39.0,4.0,0.0,2.0,1.0,0.0,160976.85
+20687,15806822.0,K'ung,648.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,176902.83
+20688,15757981.0,Hsing,551.0,Germany,Female,51.0,8.0,104784.23,1.0,1.0,1.0,118545.49
+20689,15721740.0,Kao,605.0,France,Male,54.0,7.0,0.0,2.0,1.0,0.0,5566.6
+20690,15632848.0,Onyeorulu,738.0,France,Female,35.0,2.0,0.0,2.0,1.0,1.0,159863.28
+20691,15808023.0,Yuryeva,753.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,162960.05
+20692,15585823.0,Hs?,538.0,Spain,Female,33.0,0.0,0.0,1.0,1.0,0.0,131283.6
+20693,15727059.0,Nwabugwu,588.0,Spain,Male,33.0,1.0,0.0,1.0,1.0,1.0,94790.34
+20694,15768574.0,Ch'ien,632.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,70790.8
+20695,15738148.0,Chu,682.0,Germany,Male,32.0,8.0,81828.22,2.0,0.0,0.0,119096.63
+20696,15741336.0,Pirozzi,667.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,59645.24
+20697,15646566.0,Rizzo,579.0,France,Female,56.0,6.0,0.0,1.0,1.0,0.0,164741.5
+20698,15783526.0,Tsao,636.0,Germany,Male,40.0,6.0,79460.98,2.0,1.0,1.0,187925.75
+20699,15737354.0,Chuang,653.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,167032.49
+20700,15686963.0,Udegbulam,632.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,182784.11
+20701,15592531.0,Folliero,649.0,France,Male,42.0,7.0,0.0,1.0,1.0,0.0,131718.48
+20702,15662066.0,T'ao,743.0,France,Male,29.0,3.0,95441.27,1.0,1.0,1.0,52894.01
+20703,15652981.0,Yuriev,708.0,France,Female,51.0,8.0,0.0,1.0,0.0,1.0,70778.62
+20704,15758056.0,Kao,486.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,146949.74
+20705,15682868.0,Pagnotto,634.0,France,Male,40.0,8.0,0.0,1.0,0.0,1.0,167984.72
+20706,15674928.0,P'an,775.0,France,Female,37.0,9.0,0.0,2.0,0.0,1.0,121210.09
+20707,15642394.0,Chukwuemeka,749.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,180197.74
+20708,15583090.0,Onwuamaeze,652.0,France,Male,36.0,7.0,0.0,1.0,0.0,1.0,152210.81
+20709,15574068.0,Y?,850.0,Spain,Female,31.0,5.0,123949.86,1.0,1.0,1.0,78854.57
+20710,15675316.0,Ts'ui,753.0,France,Male,41.0,2.0,83714.41,1.0,1.0,0.0,177519.39
+20711,15686909.0,Chukwudi,704.0,France,Male,25.0,2.0,0.0,1.0,1.0,1.0,110114.19
+20712,15715796.0,P'eng,588.0,France,Male,48.0,10.0,0.0,2.0,1.0,0.0,143913.62
+20713,15676937.0,Ting,539.0,Spain,Male,47.0,2.0,0.0,1.0,1.0,0.0,175696.16
+20714,15667460.0,H?,729.0,Spain,Female,51.0,1.0,0.0,1.0,1.0,1.0,193502.9
+20715,15781530.0,Chidumaga,646.0,France,Male,27.0,8.0,0.0,2.0,0.0,0.0,143980.29
+20716,15659234.0,Brown,645.0,France,Female,37.0,8.0,152532.3,1.0,1.0,0.0,97976.75
+20717,15803368.0,Chukwuraenye,635.0,France,Male,33.0,5.0,0.0,1.0,1.0,0.0,70954.41
+20718,15685829.0,Shih,516.0,France,Female,38.0,0.0,0.0,1.0,0.0,1.0,131043.9
+20719,15645371.0,Chikere,696.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,169963.2
+20720,15662532.0,Gorman,619.0,France,Female,28.0,1.0,0.0,1.0,1.0,1.0,127825.25
+20721,15576263.0,Onyemauchechukwu,743.0,Germany,Female,36.0,1.0,141359.27,2.0,1.0,0.0,110253.1
+20722,15693902.0,Toscani,686.0,Germany,Male,33.0,10.0,136576.02,1.0,1.0,0.0,114899.13
+20723,15595537.0,Nwankwo,678.0,France,Male,41.0,2.0,0.0,2.0,0.0,1.0,153387.28
+20724,15583576.0,Yashina,665.0,Spain,Male,30.0,5.0,139852.71,1.0,1.0,1.0,44965.44
+20725,15589420.0,Ibeamaka,641.0,Spain,Male,28.0,4.0,0.0,2.0,1.0,0.0,175324.74
+20726,15676966.0,Ch'ang,718.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,135088.58
+20727,15691792.0,Onyemere,721.0,Spain,Male,51.0,2.0,0.0,1.0,0.0,0.0,83052.03
+20728,15754588.0,Ch'iu,817.0,Spain,Male,40.0,6.0,84445.68,1.0,0.0,1.0,115767.93
+20729,15734005.0,Chidubem,562.0,France,Male,39.0,10.0,0.0,2.0,1.0,1.0,64849.74
+20730,15702499.0,Hsiao,579.0,France,Male,38.0,5.0,0.0,2.0,0.0,0.0,88890.05
+20731,15752816.0,Naylor,591.0,Germany,Male,39.0,7.0,113034.31,2.0,1.0,0.0,171021.44
+20732,15792229.0,Ch'iu,516.0,France,Female,31.0,4.0,98759.89,1.0,1.0,1.0,106660.46
+20733,15706899.0,Nkemdilim,648.0,France,Female,53.0,5.0,107592.23,1.0,1.0,0.0,155876.06
+20734,15687431.0,Yewen,581.0,France,Female,32.0,8.0,98548.62,1.0,0.0,0.0,82709.8
+20735,15657535.0,Onyemauchechukwu,652.0,France,Male,41.0,0.0,0.0,1.0,0.0,0.0,96881.32
+20736,15783875.0,Chizuoke,540.0,France,Male,35.0,1.0,0.0,2.0,0.0,1.0,166677.54
+20737,15720893.0,Chimaobim,549.0,France,Female,46.0,6.0,149961.11,2.0,1.0,0.0,159737.56
+20738,15804771.0,T'ien,655.0,Germany,Male,51.0,6.0,117899.95,1.0,0.0,1.0,140889.31
+20739,15700864.0,Ugochukwutubelum,705.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,0.0,81753.92
+20740,15732758.0,Baresi,624.0,France,Male,40.0,3.0,0.0,2.0,1.0,1.0,156495.56
+20741,15783029.0,Martin,685.0,Germany,Male,47.0,4.0,123783.25,2.0,1.0,0.0,92812.88
+20742,15688880.0,Tsai,775.0,Spain,Female,26.0,4.0,0.0,2.0,1.0,0.0,83681.2
+20743,15709511.0,Udegbulam,526.0,France,Female,40.0,9.0,0.0,2.0,0.0,1.0,143317.37
+20744,15651575.0,Maclean,818.0,France,Female,46.0,5.0,116251.24,1.0,1.0,1.0,156738.82
+20745,15763414.0,Chimezie,706.0,France,Male,49.0,3.0,0.0,1.0,1.0,0.0,62402.38
+20746,15762708.0,Onyekachukwu,556.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,172097.4
+20747,15791877.0,Scott,794.0,Germany,Male,39.0,7.0,93547.23,2.0,0.0,0.0,148887.69
+20748,15668057.0,Tung,712.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,153955.38
+20749,15771728.0,Udegbunam,730.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,166650.9
+20750,15643359.0,Nkemdilim,687.0,France,Female,24.0,10.0,0.0,1.0,1.0,0.0,79888.78
+20751,15592736.0,Chia,637.0,Germany,Female,54.0,4.0,119492.88,1.0,1.0,0.0,170980.86
+20752,15648362.0,Ting,648.0,France,Female,33.0,0.0,0.0,2.0,1.0,1.0,98810.78
+20753,15606097.0,Uchenna,704.0,Spain,Female,38.0,5.0,0.0,1.0,1.0,0.0,60552.44
+20754,15590199.0,Ch'ien,649.0,France,Male,34.0,1.0,0.0,1.0,1.0,1.0,147055.87
+20755,15769955.0,Chinomso,632.0,Germany,Female,38.0,6.0,144855.34,2.0,1.0,1.0,183494.87
+20756,15748589.0,Dyer,734.0,Germany,Male,31.0,9.0,96674.56,1.0,1.0,1.0,64849.74
+20757,15806975.0,Martin,730.0,Germany,Female,32.0,3.0,126679.69,2.0,1.0,1.0,82647.61
+20758,15675450.0,Ponomarev,757.0,France,Male,37.0,4.0,131834.76,1.0,1.0,0.0,65720.92
+20759,15703394.0,Onyemachukwu,592.0,Germany,Male,30.0,9.0,132944.34,1.0,1.0,0.0,117028.6
+20760,15647975.0,Chia,599.0,France,Male,37.0,1.0,0.0,2.0,0.0,0.0,79919.13
+20761,15677020.0,Kolesnikov,637.0,Spain,Male,44.0,1.0,0.0,2.0,0.0,1.0,143598.7
+20762,15643658.0,Chiawuotu,745.0,Germany,Male,36.0,7.0,76510.52,1.0,1.0,0.0,127528.84
+20763,15576526.0,Ibezimako,648.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,46786.92
+20764,15707007.0,Chikelu,579.0,Spain,Female,45.0,0.0,102945.01,1.0,1.0,0.0,182336.45
+20765,15800515.0,Ch'eng,619.0,France,Female,32.0,8.0,144428.99,1.0,1.0,0.0,35687.43
+20766,15781298.0,Hargreaves,776.0,Germany,Male,41.0,4.0,139872.81,2.0,1.0,1.0,164074.89
+20767,15793764.0,Ting,678.0,Germany,Male,49.0,1.0,132088.59,2.0,1.0,0.0,166677.35
+20768,15722448.0,Hsiao,695.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,185827.74
+20769,15717307.0,Fennell,646.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,1.0,91573.19
+20770,15789563.0,Chukwualuka,645.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,152643.48
+20771,15803750.0,K'ung,790.0,France,Male,37.0,2.0,0.0,1.0,1.0,1.0,125870.5
+20772,15762984.0,Cremonesi,682.0,France,Female,39.0,4.0,0.0,2.0,1.0,1.0,162719.69
+20773,15686983.0,Okechukwu,726.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,158071.69
+20774,15664793.0,Ch'ang,622.0,France,Male,32.0,9.0,106116.5,1.0,1.0,1.0,60906.12
+20775,15809100.0,Chibuzo,661.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,59249.83
+20776,15775803.0,Groom,748.0,France,Female,48.0,8.0,0.0,2.0,1.0,1.0,53814.71
+20777,15681406.0,Nwachinemelu,605.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,119782.89
+20778,15757913.0,Hsia,609.0,France,Male,36.0,6.0,141547.26,1.0,1.0,1.0,132673.23
+20779,15793322.0,P'eng,584.0,France,Female,55.0,4.0,0.0,1.0,1.0,1.0,46786.92
+20780,15602551.0,Hsiung,699.0,France,Female,58.0,1.0,143966.3,1.0,1.0,0.0,152210.81
+20781,15775293.0,Pisano,667.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,121440.8
+20782,15623595.0,Lucciano,598.0,France,Male,37.0,6.0,121520.37,2.0,1.0,0.0,102930.46
+20783,15642098.0,Hargreaves,714.0,Germany,Male,48.0,3.0,129667.77,1.0,1.0,1.0,138350.74
+20784,15622182.0,Ting,592.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,1.0,60552.44
+20785,15756975.0,Nnaife,691.0,France,Male,31.0,9.0,0.0,2.0,0.0,1.0,19500.1
+20786,15726234.0,Ch'eng,530.0,France,Female,56.0,3.0,0.0,1.0,1.0,0.0,107948.72
+20787,15706810.0,McElhone,567.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,36308.93
+20788,15632446.0,Li Fonti,685.0,France,Female,42.0,6.0,0.0,1.0,1.0,1.0,140166.95
+20789,15710257.0,H?,719.0,Germany,Male,50.0,9.0,148962.76,1.0,1.0,1.0,158887.09
+20790,15771054.0,Ponomaryov,708.0,Spain,Male,28.0,1.0,171671.9,1.0,1.0,0.0,41967.03
+20791,15642636.0,Martin,675.0,France,Female,44.0,8.0,0.0,1.0,1.0,1.0,75927.35
+20792,15758031.0,Toscani,596.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,87060.77
+20793,15680643.0,Okagbue,598.0,Spain,Male,30.0,1.0,0.0,2.0,1.0,1.0,9984.4
+20794,15777045.0,Zuev,710.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,180427.24
+20795,15589296.0,Whitehead,567.0,France,Male,25.0,9.0,0.0,1.0,1.0,1.0,57876.05
+20796,15634141.0,Tien,678.0,Germany,Male,43.0,7.0,120688.61,1.0,0.0,0.0,125240.8
+20797,15751549.0,Hs?,590.0,Spain,Male,50.0,2.0,0.0,2.0,1.0,0.0,120511.03
+20798,15598782.0,T'ien,625.0,France,Male,29.0,10.0,0.0,2.0,0.0,0.0,85890.75
+20799,15682600.0,Ch'eng,619.0,France,Female,67.0,1.0,0.0,2.0,1.0,1.0,120108.72
+20800,15597951.0,Nnonso,549.0,Germany,Female,49.0,3.0,117325.53,2.0,1.0,0.0,131712.52
+20801,15667093.0,Chimaoke,645.0,France,Female,55.0,9.0,141234.98,1.0,1.0,1.0,187597.98
+20802,15618661.0,Tuan,613.0,France,Male,35.0,4.0,0.0,1.0,0.0,0.0,61623.19
+20803,15723216.0,Soto,681.0,Germany,Female,51.0,2.0,109712.85,1.0,1.0,1.0,50610.79
+20804,15682472.0,Palermo,695.0,Spain,Female,22.0,3.0,0.0,1.0,0.0,1.0,145894.9
+20805,15617486.0,Iadanza,679.0,France,Female,26.0,10.0,113567.94,1.0,1.0,0.0,115916.55
+20806,15753874.0,Fiorentino,605.0,France,Male,44.0,4.0,125719.69,1.0,1.0,0.0,57844.69
+20807,15745012.0,Mazzanti,682.0,France,Female,33.0,10.0,0.0,2.0,0.0,0.0,132673.23
+20808,15764537.0,Trevisani,712.0,Germany,Male,34.0,4.0,170890.05,2.0,1.0,0.0,134822.04
+20809,15617507.0,Chukwuemeka,537.0,France,Female,32.0,3.0,0.0,1.0,1.0,0.0,178268.36
+20810,15791279.0,Tien,644.0,Germany,Male,33.0,2.0,78892.23,1.0,1.0,1.0,121402.53
+20811,15651022.0,Onyemauchechukwu,605.0,France,Female,28.0,1.0,127343.4,1.0,1.0,0.0,106761.47
+20812,15628205.0,Ch'eng,704.0,Spain,Male,40.0,2.0,152400.51,1.0,1.0,1.0,99843.98
+20813,15694849.0,Chia,651.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,184524.65
+20814,15760090.0,Trevisano,648.0,France,Female,30.0,5.0,141786.78,1.0,1.0,0.0,171060.01
+20815,15772798.0,Brient,684.0,Spain,Female,41.0,6.0,0.0,1.0,0.0,0.0,184954.11
+20816,15742886.0,Fanucci,667.0,France,Male,34.0,8.0,0.0,1.0,0.0,0.0,75664.56
+20817,15695097.0,Eiland,775.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,125962.02
+20818,15806918.0,Chukwuebuka,685.0,Spain,Male,19.0,8.0,0.0,2.0,1.0,1.0,115767.93
+20819,15613787.0,T'ien,611.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,103713.93
+20820,15694829.0,Ch'eng,683.0,Germany,Female,44.0,1.0,155413.07,2.0,1.0,0.0,136028.99
+20821,15589420.0,Y?an,632.0,France,Female,62.0,7.0,0.0,1.0,1.0,1.0,133086.48
+20822,15806945.0,K?,668.0,Germany,Female,28.0,9.0,148045.45,1.0,1.0,1.0,179908.71
+20823,15783097.0,Hsu,695.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,166650.9
+20824,15603743.0,Frolov,696.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,94822.47
+20825,15684865.0,Nwankwo,684.0,Germany,Female,38.0,0.0,98373.26,1.0,1.0,1.0,132770.15
+20826,15663883.0,Shubin,567.0,France,Male,39.0,8.0,0.0,2.0,0.0,0.0,150661.42
+20827,15775235.0,Hsia,575.0,Germany,Male,38.0,7.0,92340.69,1.0,1.0,1.0,60728.89
+20828,15605918.0,McGregor,674.0,France,Female,41.0,5.0,0.0,2.0,0.0,0.0,147059.16
+20829,15802372.0,Ch'ien,709.0,France,Female,29.0,6.0,0.0,1.0,1.0,0.0,156910.46
+20830,15657535.0,Chikelu,678.0,Germany,Female,41.0,9.0,71264.02,1.0,1.0,0.0,164075.51
+20831,15659931.0,Milani,583.0,Spain,Male,39.0,2.0,0.0,1.0,1.0,1.0,106761.47
+20832,15606841.0,Lucchesi,705.0,France,Female,36.0,8.0,0.0,2.0,0.0,0.0,86044.98
+20833,15805413.0,Lucciano,645.0,Spain,Female,44.0,8.0,0.0,1.0,1.0,0.0,143960.72
+20834,15797919.0,Yudina,767.0,Germany,Male,44.0,8.0,91285.22,1.0,1.0,0.0,64907.48
+20835,15731835.0,Chidiebere,474.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,102577.92
+20836,15636592.0,Chijioke,590.0,Germany,Male,47.0,6.0,107508.93,1.0,0.0,0.0,120801.65
+20837,15793861.0,Lai,749.0,France,Male,34.0,1.0,126817.65,2.0,1.0,1.0,126812.63
+20838,15687852.0,Fanucci,573.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,182528.44
+20839,15585599.0,Palermo,715.0,France,Male,46.0,3.0,0.0,2.0,1.0,1.0,126812.63
+20840,15785576.0,T'ien,701.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,125877.22
+20841,15622774.0,Ch'iu,683.0,France,Male,32.0,2.0,0.0,1.0,1.0,0.0,49594.02
+20842,15717290.0,Onyekachi,681.0,France,Male,58.0,9.0,123971.51,1.0,1.0,1.0,181449.74
+20843,15640106.0,Tuan,679.0,Spain,Male,30.0,9.0,131287.36,1.0,1.0,1.0,61513.67
+20844,15692599.0,Pisani,687.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,125435.81
+20845,15619116.0,Y?an,697.0,France,Female,47.0,9.0,90560.3,1.0,1.0,0.0,139266.48
+20846,15595842.0,Toscani,624.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,172967.87
+20847,15802220.0,Onyekaozulu,730.0,France,Male,37.0,6.0,125724.95,1.0,1.0,0.0,161453.62
+20848,15609510.0,P'eng,647.0,Spain,Female,22.0,8.0,0.0,2.0,0.0,1.0,61761.77
+20849,15703763.0,Chien,695.0,France,Female,32.0,7.0,0.0,2.0,0.0,1.0,61538.43
+20850,15582841.0,Trevisani,687.0,France,Male,38.0,8.0,141616.55,1.0,1.0,1.0,137028.51
+20851,15605918.0,Nwabugwu,732.0,Germany,Female,37.0,2.0,127864.4,2.0,0.0,1.0,144880.81
+20852,15775663.0,Maclean,679.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,131919.48
+20853,15583724.0,Nnamutaezinwa,653.0,Spain,Male,39.0,3.0,125909.85,1.0,1.0,1.0,149740.22
+20854,15603088.0,Ni,714.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,159418.1
+20855,15692226.0,Udinesi,730.0,France,Male,39.0,1.0,73070.18,1.0,0.0,0.0,106922.42
+20856,15806937.0,Milne,561.0,Germany,Male,39.0,9.0,154737.88,1.0,0.0,1.0,185537.76
+20857,15773731.0,Hsia,589.0,France,Male,44.0,5.0,0.0,1.0,1.0,0.0,138519.71
+20858,15789563.0,Kumm,686.0,France,Female,56.0,7.0,0.0,1.0,1.0,0.0,43982.41
+20859,15791268.0,Ugorji,678.0,France,Male,42.0,6.0,0.0,2.0,0.0,1.0,112605.9
+20860,15688085.0,Ndubuagha,711.0,Germany,Male,33.0,9.0,138523.2,1.0,0.0,1.0,133653.62
+20861,15639303.0,Yobachi,717.0,France,Female,35.0,4.0,0.0,2.0,1.0,0.0,142223.94
+20862,15784099.0,Yudin,627.0,Germany,Female,26.0,6.0,122314.5,1.0,1.0,1.0,64849.74
+20863,15794345.0,Ch'ien,614.0,Spain,Male,39.0,5.0,0.0,1.0,1.0,0.0,161465.31
+20864,15682600.0,Pisano,576.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,1.0,4272.0
+20865,15761950.0,Mancini,633.0,France,Female,30.0,1.0,132876.55,1.0,0.0,0.0,152698.24
+20866,15693904.0,T'ao,584.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,0.0,96660.76
+20867,15683483.0,Chuang,611.0,France,Male,30.0,3.0,0.0,2.0,0.0,0.0,149807.01
+20868,15731751.0,Nnaife,684.0,France,Female,48.0,7.0,0.0,1.0,1.0,0.0,171378.77
+20869,15761158.0,K?,476.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,71047.51
+20870,15791533.0,Chidubem,634.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,91574.92
+20871,15766427.0,Onwuamaeze,850.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,176925.69
+20872,15687526.0,Mazzanti,714.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,1.0,62845.32
+20873,15796969.0,Trevisano,731.0,France,Male,33.0,8.0,128940.24,1.0,1.0,1.0,138191.81
+20874,15665096.0,Chiu,605.0,Germany,Male,52.0,8.0,111096.91,2.0,1.0,0.0,137898.57
+20875,15576723.0,Hsieh,661.0,Spain,Female,43.0,3.0,116993.43,1.0,1.0,1.0,145537.32
+20876,15792388.0,Gadsden,694.0,Spain,Male,42.0,8.0,95984.21,1.0,1.0,1.0,111868.23
+20877,15592761.0,Tuan,746.0,Germany,Female,34.0,2.0,118626.55,1.0,1.0,1.0,173776.5
+20878,15592999.0,Zito,591.0,France,Male,33.0,2.0,143835.52,1.0,1.0,1.0,187810.74
+20879,15619016.0,Cattaneo,584.0,Spain,Male,23.0,1.0,0.0,2.0,0.0,0.0,172668.39
+20880,15722533.0,Nkemdilim,609.0,France,Female,29.0,6.0,0.0,2.0,0.0,0.0,110520.48
+20881,15670163.0,Chizuoke,745.0,Spain,Female,38.0,6.0,133707.18,1.0,1.0,1.0,113481.02
+20882,15581295.0,Mactier,635.0,Germany,Male,46.0,4.0,113873.22,1.0,1.0,1.0,148515.61
+20883,15676521.0,Ch'iu,615.0,France,Male,34.0,3.0,121669.23,1.0,1.0,1.0,122925.44
+20884,15776824.0,Macleod,683.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,164061.6
+20885,15629390.0,Ibezimako,655.0,France,Male,36.0,3.0,115341.19,1.0,1.0,1.0,9684.52
+20886,15646539.0,Olisaemeka,688.0,France,Male,41.0,4.0,88977.39,1.0,1.0,0.0,71179.31
+20887,15721793.0,Milne,684.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,61936.1
+20888,15668323.0,Chidiebere,649.0,France,Male,42.0,3.0,117307.58,1.0,1.0,1.0,120107.1
+20889,15583770.0,Onwuka,635.0,France,Male,33.0,3.0,165438.26,1.0,1.0,1.0,199392.14
+20890,15590097.0,Bogle,627.0,France,Male,23.0,4.0,0.0,1.0,1.0,0.0,170829.99
+20891,15674573.0,Pinto,678.0,France,Male,33.0,1.0,139973.65,1.0,1.0,0.0,166476.95
+20892,15705918.0,Kao,754.0,France,Male,24.0,8.0,0.0,1.0,1.0,0.0,94544.88
+20893,15592999.0,Yermakova,743.0,Germany,Male,40.0,2.0,153972.89,1.0,1.0,0.0,110776.01
+20894,15703394.0,Genovesi,678.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,66721.98
+20895,15690958.0,Genovesi,730.0,France,Female,37.0,1.0,111061.01,1.0,1.0,1.0,51117.06
+20896,15688071.0,Maclean,679.0,France,Female,50.0,4.0,0.0,1.0,1.0,1.0,150659.35
+20897,15598044.0,Ch'ien,850.0,Spain,Male,22.0,1.0,0.0,2.0,0.0,1.0,57851.8
+20898,15638231.0,Tisdall,850.0,France,Female,39.0,8.0,0.0,1.0,1.0,1.0,168034.9
+20899,15672594.0,Aksenov,644.0,France,Female,44.0,7.0,0.0,2.0,0.0,0.0,163317.5
+20900,15641413.0,Iweobiegbunam,807.0,France,Male,32.0,4.0,116320.68,1.0,1.0,0.0,167984.72
+20901,15796790.0,Kao,642.0,Germany,Male,35.0,4.0,91070.34,2.0,1.0,1.0,193502.93
+20902,15797919.0,Onwuamaeze,817.0,Germany,Female,34.0,6.0,81523.38,1.0,1.0,0.0,59981.62
+20903,15758031.0,K?,587.0,Germany,Female,48.0,7.0,123969.96,1.0,1.0,1.0,94872.45
+20904,15589428.0,Hazon,652.0,Spain,Female,30.0,2.0,0.0,2.0,1.0,0.0,151336.96
+20905,15589660.0,Onyemauchechukwu,628.0,France,Female,38.0,7.0,0.0,2.0,0.0,0.0,110371.84
+20906,15572461.0,Ts'ui,735.0,France,Male,36.0,4.0,0.0,1.0,0.0,1.0,89891.4
+20907,15576644.0,Fancher,652.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,152422.91
+20908,15644896.0,Kao,594.0,France,Male,28.0,9.0,0.0,2.0,1.0,1.0,137739.02
+20909,15587914.0,Ch'ang,609.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,80425.65
+20910,15690130.0,Aksenov,662.0,France,Female,46.0,10.0,0.0,2.0,1.0,0.0,80552.36
+20911,15759913.0,Lucciano,686.0,France,Female,43.0,5.0,0.0,1.0,1.0,1.0,94845.45
+20912,15733444.0,Kao,648.0,Spain,Male,29.0,7.0,0.0,2.0,1.0,1.0,140889.39
+20913,15590820.0,Tuan,641.0,France,Male,52.0,8.0,107907.34,1.0,1.0,0.0,177670.57
+20914,15591711.0,Brown,755.0,France,Male,42.0,3.0,125529.55,1.0,1.0,0.0,174032.0
+20915,15686963.0,Aksenov,614.0,Germany,Male,38.0,8.0,181671.14,2.0,0.0,1.0,106894.34
+20916,15795874.0,L?,743.0,France,Male,37.0,1.0,0.0,2.0,0.0,1.0,179655.87
+20917,15720644.0,Palerma,589.0,Spain,Male,28.0,10.0,122265.18,1.0,1.0,0.0,60536.56
+20918,15793331.0,Nnonso,678.0,France,Female,29.0,6.0,108541.04,1.0,1.0,1.0,128892.66
+20919,15753837.0,Ch'iu,667.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,148527.56
+20920,15758775.0,Chinweuba,762.0,Spain,Female,25.0,9.0,125692.03,1.0,1.0,0.0,147090.9
+20921,15767594.0,Liao,652.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,1.0,104930.46
+20922,15682778.0,Lombardo,741.0,Germany,Male,46.0,5.0,109632.85,1.0,1.0,0.0,136079.64
+20923,15649523.0,Genovese,719.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,48869.67
+20924,15572706.0,Chukwudi,603.0,Spain,Male,53.0,7.0,0.0,1.0,0.0,0.0,115116.35
+20925,15754929.0,Hsiung,564.0,Spain,Male,35.0,9.0,107569.96,1.0,1.0,0.0,53931.05
+20926,15655274.0,Lo Duca,668.0,Germany,Female,50.0,5.0,112727.06,3.0,1.0,0.0,159463.8
+20927,15693637.0,Lucchesi,743.0,Germany,Female,22.0,4.0,121207.66,2.0,0.0,1.0,142903.44
+20928,15687913.0,Calabresi,790.0,Germany,Male,31.0,5.0,127480.57,1.0,0.0,0.0,174729.3
+20929,15596713.0,Pinto,639.0,France,Male,43.0,6.0,0.0,1.0,1.0,1.0,185827.74
+20930,15773876.0,Chidi,646.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,183494.87
+20931,15571221.0,Manna,706.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,0.0,135875.51
+20932,15796074.0,Nnachetam,679.0,France,Female,29.0,7.0,0.0,2.0,1.0,1.0,176779.46
+20933,15597901.0,Fries,543.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,180553.48
+20934,15593969.0,Frolov,714.0,Spain,Male,44.0,3.0,0.0,2.0,1.0,1.0,120847.13
+20935,15724339.0,Chidimma,707.0,France,Male,37.0,4.0,99169.67,1.0,1.0,0.0,60920.03
+20936,15665416.0,Obiajulu,714.0,Germany,Female,47.0,4.0,115988.86,1.0,1.0,0.0,72402.0
+20937,15645571.0,Chukwuemeka,713.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,38252.25
+20938,15694566.0,Hsiao,645.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,53477.76
+20939,15589401.0,Nnaife,712.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,1.0,100538.8
+20940,15652048.0,Ch'iu,704.0,France,Female,38.0,5.0,0.0,1.0,1.0,0.0,140880.75
+20941,15651955.0,Hargreaves,632.0,Spain,Male,39.0,2.0,0.0,2.0,1.0,1.0,117832.88
+20942,15592091.0,Yeh,581.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,63736.17
+20943,15667216.0,P'an,602.0,France,Female,51.0,9.0,0.0,2.0,1.0,1.0,139946.68
+20944,15576723.0,Chukwujekwu,635.0,Germany,Male,52.0,1.0,137482.19,1.0,1.0,1.0,27308.58
+20945,15751167.0,Ting,711.0,France,Male,42.0,2.0,0.0,2.0,0.0,1.0,74510.65
+20946,15684960.0,Chiang,730.0,Spain,Female,51.0,4.0,167983.17,1.0,1.0,0.0,163570.25
+20947,15670345.0,Onuora,586.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,79577.4
+20948,15586300.0,Aksenova,559.0,France,Female,30.0,3.0,133800.98,1.0,1.0,1.0,88958.49
+20949,15750476.0,Tseng,597.0,France,Male,32.0,4.0,0.0,1.0,0.0,0.0,99843.98
+20950,15611189.0,Trevisani,521.0,Germany,Male,32.0,4.0,128206.79,2.0,1.0,1.0,93718.16
+20951,15792646.0,Toscani,739.0,France,Male,30.0,6.0,148481.09,1.0,1.0,0.0,131097.9
+20952,15693322.0,Chialuka,689.0,Spain,Female,51.0,6.0,0.0,1.0,1.0,1.0,80963.92
+20953,15664720.0,Y?,584.0,France,Male,40.0,7.0,82547.94,1.0,1.0,1.0,92647.94
+20954,15695585.0,Illingworth,651.0,France,Male,28.0,2.0,133658.24,1.0,1.0,0.0,176712.51
+20955,15709531.0,Onio,674.0,Spain,Male,60.0,1.0,0.0,2.0,1.0,1.0,108528.72
+20956,15764708.0,O'Donnell,613.0,France,Female,27.0,10.0,0.0,2.0,1.0,0.0,96823.32
+20957,15770291.0,Hao,573.0,France,Male,39.0,10.0,125193.96,1.0,1.0,1.0,46811.77
+20958,15641877.0,Chiedozie,683.0,Germany,Male,35.0,2.0,122486.47,2.0,1.0,0.0,99017.34
+20959,15652400.0,Yeh,753.0,Spain,Female,45.0,4.0,0.0,1.0,0.0,1.0,107499.66
+20960,15583724.0,Ngozichukwuka,597.0,France,Male,31.0,5.0,0.0,1.0,0.0,1.0,156048.64
+20961,15791780.0,P'an,564.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,174435.48
+20962,15793813.0,White,677.0,France,Female,33.0,5.0,91680.1,1.0,1.0,0.0,131662.47
+20963,15690620.0,Chukwualuka,792.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,91903.9
+20964,15794479.0,Bazarova,730.0,Germany,Male,30.0,4.0,151191.31,1.0,1.0,1.0,12128.69
+20965,15706904.0,Walker,629.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,71830.1
+20966,15640034.0,Chinomso,763.0,Germany,Male,39.0,10.0,51122.64,1.0,1.0,0.0,57871.34
+20967,15690772.0,Chu,591.0,Spain,Male,37.0,1.0,0.0,1.0,1.0,0.0,85421.48
+20968,15802560.0,K?,649.0,France,Male,35.0,7.0,88915.85,1.0,0.0,0.0,121592.5
+20969,15813144.0,Ojiofor,773.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,9984.4
+20970,15784597.0,Tsao,669.0,Spain,Female,27.0,9.0,0.0,2.0,1.0,1.0,64539.64
+20971,15784536.0,Folliero,678.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,181224.56
+20972,15606887.0,Ritchie,621.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,1.0,53630.9
+20973,15723006.0,Eluemuno,641.0,France,Female,29.0,9.0,0.0,2.0,0.0,0.0,161519.77
+20974,15802585.0,Li,744.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,27380.99
+20975,15749489.0,Hsueh,739.0,France,Male,38.0,4.0,0.0,1.0,0.0,0.0,89891.4
+20976,15656912.0,Tuan,733.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,64448.36
+20977,15803790.0,Nwankwo,721.0,Germany,Male,39.0,5.0,141252.18,1.0,1.0,1.0,131736.23
+20978,15671591.0,Chigolum,677.0,France,Male,34.0,8.0,0.0,1.0,0.0,0.0,69052.87
+20979,15659002.0,Pirozzi,633.0,France,Male,31.0,7.0,106301.85,1.0,1.0,1.0,120853.83
+20980,15593897.0,H?,687.0,France,Male,43.0,7.0,122088.67,1.0,1.0,0.0,174311.3
+20981,15633260.0,Nwora,707.0,France,Female,32.0,6.0,105858.71,1.0,1.0,1.0,143317.18
+20982,15809654.0,Chizuoke,648.0,France,Male,38.0,0.0,0.0,2.0,1.0,1.0,129964.94
+20983,15651389.0,H?,750.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,133571.23
+20984,15586870.0,Nucci,644.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,180962.32
+20985,15795586.0,Ritchie,731.0,Spain,Female,36.0,10.0,0.0,2.0,0.0,1.0,64323.24
+20986,15674583.0,Chiang,609.0,Spain,Female,42.0,4.0,0.0,2.0,0.0,0.0,140941.47
+20987,15679394.0,Onwuamaeze,585.0,France,Female,55.0,7.0,0.0,1.0,1.0,0.0,143393.24
+20988,15571843.0,Ch'iu,739.0,Spain,Male,49.0,9.0,0.0,1.0,1.0,0.0,73881.68
+20989,15681843.0,Lo,718.0,France,Female,29.0,6.0,0.0,1.0,1.0,1.0,35199.21
+20990,15659399.0,Hsieh,706.0,Germany,Male,44.0,6.0,123374.46,1.0,1.0,0.0,85733.33
+20991,15646513.0,Baresi,661.0,Spain,Female,42.0,7.0,0.0,3.0,1.0,1.0,171519.06
+20992,15762048.0,Hs?,850.0,France,Female,32.0,3.0,0.0,1.0,0.0,1.0,172557.77
+20993,15609623.0,T'ao,739.0,France,Male,32.0,10.0,0.0,2.0,1.0,0.0,129912.74
+20994,15727408.0,Ch'en,641.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,142548.33
+20995,15572626.0,Kao,584.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,175082.2
+20996,15588928.0,Yobachukwu,611.0,Spain,Male,36.0,7.0,121558.46,1.0,1.0,1.0,199392.14
+20997,15777045.0,O'Brien,594.0,France,Male,37.0,5.0,0.0,1.0,1.0,1.0,184796.84
+20998,15739194.0,Pisani,756.0,Germany,Male,30.0,3.0,125877.65,1.0,0.0,0.0,175894.24
+20999,15806918.0,Mazzanti,781.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,181893.31
+21000,15685920.0,Onwubiko,725.0,Germany,Female,60.0,2.0,145580.61,1.0,0.0,0.0,14379.01
+21001,15695034.0,Clements,593.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,88878.15
+21002,15594878.0,Ponomaryova,682.0,France,Female,49.0,9.0,185891.54,1.0,1.0,0.0,1173.23
+21003,15785078.0,Nwachukwu,720.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,111190.53
+21004,15773591.0,Chia,619.0,Germany,Female,38.0,9.0,109052.59,1.0,1.0,0.0,136435.75
+21005,15762928.0,Chukwukadibia,669.0,Germany,Male,35.0,9.0,97473.87,2.0,1.0,1.0,168840.33
+21006,15790299.0,Nnaife,669.0,Germany,Female,46.0,5.0,104449.8,1.0,1.0,0.0,166516.84
+21007,15690741.0,Ch'eng,749.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,163558.36
+21008,15652674.0,Lo Duca,648.0,France,Female,39.0,3.0,0.0,2.0,1.0,1.0,182038.6
+21009,15693326.0,Dore,628.0,Germany,Female,29.0,3.0,141819.02,2.0,0.0,0.0,74846.67
+21010,15799128.0,Hsieh,535.0,France,Female,46.0,10.0,0.0,1.0,1.0,0.0,146891.07
+21011,15729489.0,Lai,641.0,Germany,Male,40.0,5.0,82931.85,2.0,1.0,0.0,139093.88
+21012,15591552.0,Fanucci,667.0,Spain,Male,35.0,7.0,133964.3,1.0,0.0,1.0,189678.7
+21013,15721460.0,Chiemenam,575.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,58879.31
+21014,15705050.0,H?,663.0,France,Male,33.0,4.0,0.0,1.0,1.0,0.0,168816.22
+21015,15720354.0,Nwora,678.0,France,Male,35.0,9.0,0.0,1.0,1.0,0.0,38587.7
+21016,15674929.0,Bitter,705.0,France,Male,37.0,9.0,54901.01,1.0,1.0,1.0,124571.09
+21017,15804822.0,Ch'ang,809.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,151401.33
+21018,15801247.0,Mazzanti,732.0,France,Male,27.0,4.0,0.0,2.0,1.0,0.0,152298.28
+21019,15673971.0,L?,544.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,147075.69
+21020,15737647.0,Chiazagomekpele,549.0,France,Male,37.0,3.0,157559.7,1.0,1.0,1.0,149614.17
+21021,15679991.0,Teng,632.0,France,Male,29.0,5.0,123845.36,1.0,0.0,0.0,55893.33
+21022,15671241.0,T'an,658.0,Germany,Male,44.0,1.0,118907.6,1.0,0.0,0.0,147822.44
+21023,15730759.0,Chiganu,592.0,France,Male,42.0,1.0,0.0,2.0,1.0,1.0,89543.25
+21024,15590011.0,Hs?eh,684.0,Germany,Female,41.0,1.0,105864.6,1.0,1.0,0.0,119333.95
+21025,15662091.0,Hs?,681.0,Germany,Male,25.0,7.0,91537.88,1.0,1.0,0.0,116973.48
+21026,15766417.0,Scott,582.0,France,Female,35.0,8.0,0.0,1.0,1.0,1.0,182784.11
+21027,15623566.0,Lucchesi,655.0,France,Male,52.0,2.0,0.0,1.0,0.0,0.0,132467.54
+21028,15718789.0,Hsia,683.0,France,Female,47.0,1.0,0.0,1.0,1.0,1.0,184312.88
+21029,15612103.0,Ch'in,685.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,161579.85
+21030,15752507.0,Rolon,593.0,France,Female,24.0,9.0,0.0,1.0,0.0,1.0,48233.75
+21031,15636016.0,Okwuadigbo,569.0,Germany,Male,49.0,7.0,133616.39,1.0,0.0,1.0,177670.57
+21032,15670163.0,Shih,597.0,Spain,Female,40.0,6.0,0.0,2.0,1.0,0.0,121210.09
+21033,15701200.0,Kibble,583.0,France,Female,44.0,10.0,0.0,1.0,1.0,0.0,161559.12
+21034,15607986.0,Ts'ui,733.0,France,Female,40.0,0.0,0.0,3.0,1.0,0.0,153383.54
+21035,15758031.0,Y?,635.0,Spain,Female,35.0,9.0,0.0,2.0,1.0,0.0,176743.51
+21036,15783338.0,McIntyre,656.0,France,Female,47.0,7.0,0.0,1.0,1.0,0.0,94872.45
+21037,15578515.0,Hsing,593.0,France,Female,35.0,4.0,0.0,1.0,1.0,0.0,55867.38
+21038,15684960.0,Ch'ang,678.0,Spain,Female,28.0,2.0,0.0,2.0,1.0,0.0,142331.85
+21039,15658889.0,Chiu,521.0,Germany,Female,49.0,5.0,116968.91,1.0,1.0,0.0,102073.96
+21040,15588944.0,Ch'eng,603.0,Spain,Female,36.0,3.0,0.0,2.0,1.0,1.0,175722.31
+21041,15705001.0,Riggs,554.0,France,Male,35.0,2.0,0.0,1.0,1.0,1.0,189414.74
+21042,15727177.0,Chukwudi,696.0,France,Male,45.0,5.0,0.0,2.0,1.0,0.0,166588.76
+21043,15583736.0,Chinedum,758.0,Spain,Male,41.0,6.0,0.0,2.0,0.0,1.0,136079.64
+21044,15713578.0,Hsiao,619.0,France,Female,61.0,0.0,0.0,1.0,1.0,1.0,85891.02
+21045,15579820.0,Hsia,557.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,128521.16
+21046,15781702.0,Chinomso,595.0,Germany,Male,38.0,8.0,131394.56,1.0,1.0,0.0,85522.84
+21047,15783541.0,Lettiere,609.0,France,Female,48.0,8.0,0.0,1.0,0.0,0.0,151401.33
+21048,15740264.0,Mistry,718.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,84760.32
+21049,15701291.0,Chidumaga,717.0,Germany,Male,58.0,1.0,115341.19,1.0,1.0,0.0,94790.34
+21050,15741032.0,Hsueh,653.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,84584.67
+21051,15815490.0,Chukwukadibia,713.0,France,Female,41.0,3.0,131753.11,1.0,0.0,0.0,121551.93
+21052,15737782.0,Ch'eng,652.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,98896.06
+21053,15771535.0,Calabresi,707.0,Spain,Female,61.0,2.0,0.0,2.0,0.0,1.0,60533.96
+21054,15781598.0,Efremov,725.0,France,Female,51.0,5.0,0.0,2.0,1.0,0.0,1055.66
+21055,15701605.0,Y?an,758.0,France,Female,40.0,3.0,0.0,2.0,1.0,1.0,106785.4
+21056,15662865.0,Frolova,749.0,France,Female,30.0,4.0,141541.25,1.0,1.0,0.0,167538.66
+21057,15595588.0,Hsing,591.0,France,Female,58.0,4.0,0.0,1.0,0.0,0.0,140889.12
+21058,15664793.0,Lettiere,729.0,France,Female,37.0,10.0,0.0,2.0,1.0,1.0,106663.46
+21059,15595588.0,Fiorentini,645.0,France,Female,54.0,3.0,0.0,1.0,1.0,1.0,138350.74
+21060,15606435.0,Hsing,699.0,Germany,Female,44.0,2.0,115625.07,1.0,1.0,0.0,70523.92
+21061,15684722.0,Onyekaozulu,730.0,Germany,Male,29.0,4.0,91857.66,1.0,0.0,0.0,42012.55
+21062,15602551.0,Chinomso,583.0,France,Female,27.0,9.0,0.0,2.0,1.0,1.0,99654.13
+21063,15771750.0,Pirozzi,729.0,Spain,Male,38.0,2.0,90547.48,1.0,1.0,1.0,134526.87
+21064,15773421.0,Okechukwu,695.0,Germany,Male,46.0,1.0,123694.42,1.0,0.0,1.0,80618.47
+21065,15672640.0,Shih,663.0,Spain,Female,28.0,8.0,0.0,1.0,0.0,0.0,94878.1
+21066,15594084.0,Kao,731.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,60533.96
+21067,15631406.0,Genovese,663.0,Spain,Male,31.0,4.0,0.0,2.0,0.0,1.0,174311.3
+21068,15656484.0,Nnonso,699.0,France,Female,37.0,3.0,171490.57,1.0,1.0,0.0,121592.5
+21069,15753831.0,Hsueh,535.0,France,Male,33.0,2.0,101579.71,1.0,0.0,0.0,182855.21
+21070,15813504.0,Olisaemeka,744.0,France,Female,35.0,1.0,0.0,2.0,1.0,0.0,143859.66
+21071,15601383.0,Yermolayev,644.0,France,Female,42.0,1.0,0.0,1.0,1.0,1.0,152054.33
+21072,15796961.0,Clements,667.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,75969.19
+21073,15780561.0,Yudin,587.0,France,Male,49.0,5.0,0.0,2.0,1.0,0.0,62403.44
+21074,15789379.0,Mazzanti,554.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,174729.3
+21075,15735388.0,Chinomso,738.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,0.0,44965.54
+21076,15686588.0,Pagnotto,714.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,93874.87
+21077,15696744.0,Onyemauchechukwu,738.0,Germany,Male,32.0,8.0,123860.18,1.0,1.0,0.0,140934.87
+21078,15663410.0,Grieve,593.0,France,Female,32.0,5.0,0.0,1.0,1.0,0.0,145558.42
+21079,15667417.0,Yeates,598.0,Germany,Male,48.0,9.0,104474.62,1.0,1.0,0.0,153747.72
+21080,15781465.0,Tsao,593.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,121789.3
+21081,15588928.0,Mazzanti,644.0,France,Male,48.0,9.0,0.0,3.0,0.0,0.0,129569.92
+21082,15591035.0,Chukwudi,585.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,181850.74
+21083,15792922.0,Tung,581.0,France,Male,34.0,8.0,172325.67,1.0,1.0,1.0,153786.47
+21084,15765258.0,Okechukwu,554.0,Germany,Male,43.0,8.0,117726.33,1.0,1.0,1.0,52404.47
+21085,15691823.0,Ch'iu,545.0,France,Female,26.0,7.0,0.0,2.0,1.0,1.0,142223.94
+21086,15813503.0,L?,592.0,France,Female,47.0,5.0,0.0,2.0,1.0,1.0,62484.01
+21087,15742404.0,Ts'ui,602.0,France,Male,41.0,1.0,83714.41,1.0,1.0,0.0,181429.87
+21088,15707962.0,Shih,743.0,France,Male,37.0,7.0,0.0,1.0,1.0,0.0,174536.17
+21089,15611247.0,Hsu,628.0,Germany,Male,33.0,1.0,127637.92,1.0,1.0,0.0,97640.2
+21090,15590011.0,Genovesi,701.0,France,Male,39.0,5.0,133801.01,2.0,0.0,1.0,63795.62
+21091,15765806.0,Pinto,653.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,152883.95
+21092,15798895.0,Kuo,607.0,Spain,Female,28.0,1.0,0.0,1.0,1.0,0.0,131043.9
+21093,15727384.0,Tung,706.0,France,Male,27.0,5.0,0.0,2.0,1.0,0.0,132673.15
+21094,15656829.0,Mazzi,695.0,Spain,Male,40.0,8.0,0.0,2.0,0.0,1.0,130664.79
+21095,15684319.0,Dyer,694.0,Spain,Male,39.0,5.0,121827.43,1.0,1.0,1.0,67976.57
+21096,15781702.0,Onyemachukwu,694.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,161807.81
+21097,15626900.0,Chien,627.0,France,Male,28.0,4.0,128653.59,1.0,0.0,1.0,117948.0
+21098,15595036.0,Trevisani,743.0,Germany,Male,33.0,5.0,124067.32,2.0,0.0,0.0,115767.93
+21099,15636407.0,Kao,676.0,France,Female,34.0,2.0,122029.15,1.0,1.0,0.0,86424.57
+21100,15792679.0,Nnaife,695.0,France,Male,38.0,6.0,92316.31,1.0,1.0,0.0,94450.97
+21101,15598089.0,Scott,731.0,France,Male,38.0,4.0,0.0,2.0,0.0,1.0,172718.35
+21102,15642833.0,Onyekaozulu,622.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,62722.44
+21103,15646969.0,Hsiao,682.0,Germany,Male,32.0,8.0,162560.32,1.0,1.0,0.0,117622.8
+21104,15656822.0,Piazza,653.0,France,Female,40.0,4.0,103033.83,1.0,1.0,1.0,176487.05
+21105,15638803.0,Pirozzi,638.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,86575.46
+21106,15715079.0,Hsiao,681.0,Germany,Female,55.0,5.0,161532.14,1.0,1.0,1.0,109078.35
+21107,15775335.0,Kaleski,746.0,France,Female,39.0,4.0,0.0,2.0,0.0,0.0,9984.4
+21108,15785559.0,T'ao,718.0,France,Female,33.0,3.0,0.0,1.0,1.0,0.0,149550.95
+21109,15662494.0,Ozioma,678.0,Germany,Male,51.0,9.0,125870.5,1.0,1.0,0.0,198490.33
+21110,15595153.0,Ch'en,647.0,Germany,Female,43.0,0.0,121286.47,1.0,0.0,1.0,119056.76
+21111,15679760.0,Lo,592.0,Germany,Female,59.0,5.0,104944.1,4.0,0.0,0.0,121151.1
+21112,15707042.0,T'ien,850.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,140131.08
+21113,15585777.0,Onwughara,678.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,142548.33
+21114,15694879.0,Chu,724.0,Germany,Female,52.0,7.0,151532.4,1.0,1.0,0.0,135662.17
+21115,15654074.0,Onio,640.0,Germany,Male,32.0,8.0,140720.93,1.0,0.0,0.0,144593.3
+21116,15790689.0,Shubin,708.0,France,Female,51.0,6.0,0.0,1.0,1.0,0.0,168822.01
+21117,15685314.0,Brown,704.0,France,Male,32.0,10.0,0.0,1.0,0.0,0.0,138531.01
+21118,15788189.0,Eluemuno,683.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,126477.41
+21119,15721935.0,Macgroarty,516.0,Spain,Female,49.0,3.0,0.0,1.0,0.0,0.0,121681.19
+21120,15648881.0,Ts'ui,620.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,28257.89
+21121,15671609.0,Titheradge,651.0,Germany,Male,48.0,3.0,113905.48,2.0,1.0,1.0,103557.91
+21122,15709136.0,Lucchese,776.0,Germany,Female,27.0,9.0,102585.88,2.0,1.0,0.0,157559.06
+21123,15783589.0,Trevisano,718.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,98613.34
+21124,15649379.0,Chinomso,565.0,France,Female,40.0,7.0,0.0,2.0,1.0,1.0,92888.52
+21125,15697020.0,Genovese,581.0,Spain,Female,37.0,1.0,142724.48,1.0,1.0,1.0,163714.92
+21126,15706537.0,Chibueze,539.0,France,Male,24.0,1.0,0.0,2.0,1.0,0.0,80587.39
+21127,15574554.0,Frolova,709.0,France,Female,32.0,2.0,0.0,2.0,0.0,0.0,38962.94
+21128,15732644.0,De Luca,757.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,168924.64
+21129,15777639.0,Uspensky,730.0,France,Male,36.0,0.0,115409.6,1.0,1.0,1.0,130404.53
+21130,15775238.0,Obiajulu,664.0,France,Female,45.0,10.0,0.0,1.0,1.0,0.0,70563.9
+21131,15793331.0,Chidiegwu,850.0,Germany,Female,43.0,5.0,155062.8,1.0,1.0,1.0,159888.1
+21132,15651581.0,Chiemenam,632.0,France,Male,52.0,6.0,0.0,2.0,1.0,1.0,51380.9
+21133,15754084.0,Bischof,603.0,France,Male,33.0,2.0,148564.66,1.0,1.0,1.0,175843.68
+21134,15601503.0,Y?an,679.0,France,Male,39.0,9.0,0.0,2.0,0.0,1.0,116977.89
+21135,15737310.0,Liao,663.0,France,Female,49.0,5.0,0.0,1.0,1.0,0.0,57553.02
+21136,15694160.0,Ts'ui,652.0,Germany,Female,20.0,6.0,135470.9,2.0,1.0,1.0,171922.72
+21137,15782879.0,H?,635.0,Spain,Male,34.0,2.0,0.0,2.0,0.0,1.0,100919.19
+21138,15580462.0,Chiemenam,703.0,France,Female,22.0,9.0,0.0,2.0,1.0,0.0,175070.94
+21139,15711378.0,Shao,625.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,100447.53
+21140,15709861.0,K'ung,667.0,France,Female,47.0,3.0,0.0,1.0,1.0,1.0,103726.71
+21141,15775886.0,Tien,656.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,119377.36
+21142,15573045.0,Ts'ui,665.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,48963.59
+21143,15720353.0,Chidimma,850.0,Germany,Male,33.0,6.0,86911.7,1.0,0.0,1.0,138881.02
+21144,15737792.0,Ch'in,621.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,67611.36
+21145,15739248.0,Cawker,593.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,0.0,151481.86
+21146,15642710.0,L?,661.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,67769.4
+21147,15807961.0,Ts'ai,733.0,Germany,Female,35.0,6.0,127818.52,1.0,1.0,0.0,168816.22
+21148,15703541.0,Chiedozie,596.0,Spain,Female,33.0,4.0,0.0,1.0,1.0,0.0,164741.81
+21149,15641877.0,Uspensky,574.0,France,Male,39.0,6.0,0.0,2.0,0.0,1.0,84760.32
+21150,15783526.0,Chien,673.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,65769.2
+21151,15589017.0,L?,687.0,Germany,Female,44.0,1.0,132406.86,1.0,0.0,0.0,72876.43
+21152,15737571.0,Onwumelu,718.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,116748.14
+21153,15807525.0,Nwachukwu,635.0,Germany,Female,35.0,5.0,133634.02,1.0,1.0,0.0,120830.73
+21154,15726776.0,Yermakova,652.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,103914.4
+21155,15727177.0,Yobanna,567.0,France,Female,36.0,10.0,0.0,2.0,1.0,0.0,82276.62
+21156,15641410.0,Calabrese,653.0,Germany,Female,43.0,4.0,130142.79,1.0,1.0,1.0,121551.93
+21157,15643575.0,Pinto,687.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,53817.23
+21158,15681406.0,Tsou,631.0,France,Male,41.0,3.0,0.0,1.0,1.0,1.0,14073.11
+21159,15661526.0,Eluemuno,653.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,1.0,142903.44
+21160,15587913.0,Tuan,687.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,173365.89
+21161,15665861.0,T'ien,752.0,Spain,Male,31.0,4.0,0.0,2.0,0.0,1.0,1375.46
+21162,15589425.0,Chiang,598.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,139410.04
+21163,15792360.0,O'Donnell,684.0,Spain,Female,51.0,10.0,0.0,1.0,1.0,1.0,51454.06
+21164,15627195.0,Pino,715.0,France,Male,36.0,6.0,0.0,2.0,0.0,0.0,125986.18
+21165,15569503.0,Fanucci,535.0,Spain,Female,35.0,2.0,0.0,1.0,0.0,1.0,67979.35
+21166,15709741.0,Ch'ien,652.0,Germany,Male,52.0,8.0,126683.8,1.0,1.0,0.0,108764.69
+21167,15693817.0,Ch'iu,651.0,Spain,Male,60.0,4.0,0.0,2.0,1.0,1.0,38798.79
+21168,15783029.0,Chuang,712.0,Spain,Female,32.0,6.0,0.0,2.0,0.0,1.0,122662.98
+21169,15576517.0,Ts'ai,705.0,France,Male,47.0,9.0,108348.66,1.0,1.0,0.0,100557.03
+21170,15603839.0,T'ao,738.0,Spain,Female,39.0,8.0,0.0,1.0,1.0,0.0,118913.53
+21171,15791216.0,Ch'iu,636.0,France,Female,34.0,6.0,0.0,1.0,1.0,0.0,68860.3
+21172,15752801.0,Mazzanti,575.0,France,Male,37.0,8.0,0.0,1.0,0.0,1.0,138328.24
+21173,15612095.0,Hs?,622.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,180693.61
+21174,15791836.0,Nnamutaezinwa,554.0,Spain,Female,43.0,3.0,0.0,3.0,1.0,0.0,139065.54
+21175,15572412.0,Ibezimako,712.0,France,Female,32.0,6.0,0.0,1.0,1.0,0.0,82836.73
+21176,15672594.0,Ts'ai,695.0,France,Male,36.0,4.0,0.0,2.0,0.0,1.0,62674.38
+21177,15663410.0,Hs?,753.0,Spain,Female,45.0,1.0,0.0,1.0,1.0,1.0,176763.27
+21178,15769703.0,T'ang,582.0,Germany,Female,40.0,8.0,91241.85,1.0,1.0,0.0,98301.61
+21179,15665760.0,Chukwubuikem,676.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,54865.92
+21180,15665956.0,Ozioma,613.0,Germany,Female,52.0,2.0,133658.24,2.0,1.0,0.0,117622.8
+21181,15676937.0,Chukwuebuka,531.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,113557.7
+21182,15677052.0,Ch'in,729.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,36557.55
+21183,15787204.0,Chukwudi,654.0,Germany,Female,36.0,1.0,150348.37,2.0,1.0,1.0,176768.59
+21184,15693326.0,Shih,718.0,Germany,Male,53.0,0.0,163943.89,1.0,1.0,1.0,173464.43
+21185,15626787.0,T'ang,850.0,Spain,Female,36.0,1.0,0.0,1.0,1.0,1.0,46772.36
+21186,15653952.0,Lu,637.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,1.0,78955.8
+21187,15724444.0,Nwabugwu,634.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,159823.16
+21188,15631162.0,Chizuoke,583.0,France,Female,26.0,6.0,0.0,2.0,1.0,1.0,53412.08
+21189,15803790.0,Nnaife,633.0,Spain,Male,31.0,1.0,0.0,2.0,0.0,1.0,50783.1
+21190,15793901.0,Chukwuemeka,598.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,0.0,93783.0
+21191,15665062.0,Ch'ang,645.0,France,Female,42.0,0.0,0.0,1.0,1.0,0.0,152883.95
+21192,15589698.0,Chuang,543.0,Spain,Female,46.0,1.0,0.0,2.0,1.0,1.0,183584.14
+21193,15594409.0,Onyekachi,568.0,France,Female,46.0,2.0,0.0,2.0,1.0,0.0,171039.56
+21194,15701206.0,Tien,659.0,France,Female,43.0,4.0,0.0,3.0,0.0,0.0,129870.92
+21195,15807525.0,Trevisano,707.0,France,Male,38.0,2.0,116498.22,1.0,1.0,1.0,24677.54
+21196,15806945.0,Chialuka,825.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,51362.82
+21197,15811127.0,O'Donnell,649.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,146723.72
+21198,15604130.0,O'Donnell,738.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,0.0,187826.11
+21199,15750335.0,Yevseyev,565.0,Germany,Male,41.0,4.0,98877.95,1.0,1.0,0.0,140133.43
+21200,15665956.0,T'ien,579.0,France,Female,34.0,3.0,0.0,2.0,0.0,1.0,80593.31
+21201,15732963.0,Pagnotto,850.0,France,Female,31.0,5.0,109730.2,1.0,1.0,1.0,149641.58
+21202,15634295.0,Y?,703.0,France,Female,31.0,6.0,0.0,2.0,0.0,1.0,131083.72
+21203,15806922.0,Chibugo,593.0,France,Male,39.0,7.0,115587.49,1.0,1.0,0.0,173365.9
+21204,15631927.0,Ch'eng,593.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,167984.72
+21205,15689614.0,Cawthorne,682.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,104719.66
+21206,15792729.0,T'ien,666.0,Germany,Male,54.0,4.0,151922.94,1.0,1.0,0.0,96528.0
+21207,15639946.0,Yoo,691.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,158021.39
+21208,15701160.0,Yermakov,641.0,France,Male,23.0,3.0,127187.86,1.0,0.0,0.0,66799.69
+21209,15761706.0,Onyekachukwu,633.0,Germany,Female,47.0,4.0,102703.62,1.0,1.0,0.0,106851.6
+21210,15643966.0,Macleod,645.0,France,Male,42.0,9.0,118899.45,2.0,0.0,0.0,163550.58
+21211,15690540.0,Chibueze,686.0,Germany,Female,50.0,4.0,110949.41,1.0,1.0,0.0,97640.2
+21212,15752388.0,Chiang,850.0,France,Female,35.0,2.0,0.0,2.0,1.0,1.0,131473.31
+21213,15602841.0,Lettiere,634.0,Germany,Female,44.0,9.0,94598.48,1.0,1.0,0.0,88592.8
+21214,15632256.0,Tung,739.0,France,Male,27.0,5.0,0.0,1.0,0.0,0.0,86678.48
+21215,15752841.0,Hsu,594.0,France,Male,48.0,1.0,0.0,2.0,0.0,1.0,123982.42
+21216,15645496.0,Iadanza,602.0,France,Female,63.0,6.0,144757.02,1.0,1.0,1.0,17992.94
+21217,15578722.0,Nnaife,577.0,Spain,Female,38.0,8.0,0.0,1.0,0.0,1.0,83090.35
+21218,15698872.0,Udegbulam,706.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,88463.63
+21219,15673837.0,Lucciano,645.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,152843.53
+21220,15668854.0,Chukwukere,651.0,Germany,Male,33.0,6.0,133903.12,1.0,1.0,0.0,86791.9
+21221,15574741.0,Chineze,677.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,182336.45
+21222,15585595.0,Ts'ai,739.0,Spain,Female,34.0,7.0,0.0,2.0,0.0,1.0,8991.17
+21223,15786042.0,Pinto,678.0,Germany,Female,37.0,6.0,131938.92,2.0,1.0,1.0,94875.03
+21224,15687431.0,Shipton,703.0,Spain,Female,42.0,1.0,95293.86,1.0,1.0,0.0,160523.33
+21225,15757911.0,Walker,821.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,1.0,60427.3
+21226,15793424.0,Gboliwe,636.0,France,Female,39.0,9.0,83568.26,1.0,1.0,0.0,177670.57
+21227,15806901.0,Milanesi,715.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,120107.1
+21228,15690563.0,Udinesi,703.0,Germany,Female,45.0,5.0,97762.16,1.0,1.0,0.0,175893.53
+21229,15680597.0,Thompson,637.0,Germany,Female,47.0,5.0,121152.05,1.0,1.0,0.0,122021.33
+21230,15588449.0,Chiazagomekpele,712.0,Germany,Female,37.0,8.0,152806.74,1.0,1.0,1.0,13946.66
+21231,15722090.0,Kao,806.0,France,Male,47.0,7.0,0.0,1.0,1.0,0.0,187915.7
+21232,15672970.0,Chukwualuka,658.0,France,Male,41.0,7.0,79857.64,1.0,1.0,1.0,129892.93
+21233,15778830.0,Onwudiwe,686.0,France,Female,39.0,7.0,0.0,1.0,1.0,1.0,86699.98
+21234,15763980.0,Milanesi,605.0,Spain,Male,31.0,9.0,125924.35,1.0,0.0,1.0,127059.05
+21235,15578865.0,Nucci,730.0,France,Male,41.0,0.0,0.0,1.0,1.0,0.0,47880.71
+21236,15792668.0,Chidiegwu,560.0,France,Female,42.0,6.0,0.0,1.0,1.0,0.0,90591.15
+21237,15705343.0,Chukwuemeka,826.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,60427.3
+21238,15766064.0,Ch'in,641.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,64849.74
+21239,15743076.0,Lucchese,635.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,133086.45
+21240,15776051.0,Li Fonti,635.0,France,Female,24.0,1.0,0.0,2.0,0.0,1.0,85424.34
+21241,15697567.0,Iweobiegbunam,729.0,France,Female,40.0,1.0,0.0,1.0,0.0,1.0,167036.94
+21242,15677785.0,Uvarov,638.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,163331.65
+21243,15585041.0,Bischof,728.0,France,Male,39.0,7.0,0.0,1.0,0.0,0.0,96833.0
+21244,15793317.0,Toscani,678.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,152698.24
+21245,15654975.0,Udinesi,679.0,France,Female,51.0,9.0,0.0,1.0,1.0,0.0,86587.37
+21246,15747043.0,Onyemauchechukwu,731.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,69607.1
+21247,15605826.0,P'eng,749.0,France,Female,30.0,5.0,0.0,2.0,0.0,0.0,56984.53
+21248,15803840.0,Palermo,676.0,France,Male,41.0,9.0,91219.29,1.0,1.0,1.0,169952.82
+21249,15750649.0,Chiedozie,599.0,Germany,Female,51.0,8.0,122949.14,1.0,1.0,0.0,169920.92
+21250,15688963.0,Mazzanti,645.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,1.0,47428.73
+21251,15673539.0,Macleod,659.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,0.0,143635.36
+21252,15634295.0,Chiang,691.0,Spain,Female,27.0,6.0,136953.49,1.0,1.0,0.0,88827.67
+21253,15735080.0,Ch'en,582.0,France,Female,30.0,2.0,0.0,2.0,0.0,1.0,138527.56
+21254,15601383.0,Ch'en,691.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,174434.04
+21255,15589224.0,Hao,670.0,Spain,Male,41.0,2.0,112803.92,1.0,1.0,1.0,135088.58
+21256,15806938.0,Onyekachukwu,685.0,France,Male,37.0,10.0,142406.43,1.0,0.0,0.0,83056.22
+21257,15677369.0,Li Fonti,569.0,Spain,Male,54.0,7.0,0.0,1.0,1.0,1.0,120115.5
+21258,15611669.0,Mazzanti,663.0,Spain,Male,32.0,3.0,0.0,1.0,1.0,0.0,141827.83
+21259,15762331.0,Palermo,716.0,France,Male,34.0,5.0,121355.99,1.0,1.0,0.0,187841.71
+21260,15685500.0,Chin,607.0,Spain,Male,25.0,2.0,0.0,1.0,0.0,0.0,60533.96
+21261,15762855.0,Yuryeva,605.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,0.0,142210.94
+21262,15587735.0,Maclean,637.0,Spain,Male,41.0,8.0,0.0,2.0,1.0,1.0,182531.74
+21263,15593555.0,Tung,739.0,Germany,Male,34.0,4.0,131038.57,1.0,1.0,1.0,63831.36
+21264,15706593.0,Sun,716.0,France,Male,31.0,10.0,0.0,2.0,1.0,0.0,52709.55
+21265,15727499.0,Y?an,640.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,136507.69
+21266,15742834.0,Tseng,712.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,91346.03
+21267,15790322.0,Onyekachi,687.0,Spain,Male,38.0,3.0,148080.43,2.0,0.0,1.0,47414.15
+21268,15685327.0,Ch'en,633.0,France,Male,58.0,4.0,0.0,1.0,1.0,0.0,160110.65
+21269,15652979.0,Monds,586.0,Spain,Female,41.0,7.0,119336.29,1.0,0.0,0.0,145936.28
+21270,15667085.0,Skelton,731.0,France,Female,25.0,3.0,0.0,1.0,1.0,0.0,67944.09
+21271,15783097.0,Fiorentino,592.0,France,Male,41.0,4.0,129055.13,1.0,0.0,0.0,156868.21
+21272,15772995.0,P'eng,574.0,Spain,Female,50.0,10.0,0.0,2.0,1.0,1.0,82970.69
+21273,15656522.0,Okwudiliolisa,526.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,107876.2
+21274,15806913.0,Hargraves,627.0,Spain,Female,31.0,0.0,0.0,2.0,1.0,1.0,84584.69
+21275,15679813.0,Tseng,660.0,Germany,Female,40.0,5.0,107716.57,2.0,0.0,1.0,152298.28
+21276,15650864.0,Ch'ang,679.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,131644.41
+21277,15674840.0,Ritchie,699.0,France,Male,41.0,1.0,0.0,2.0,1.0,0.0,133535.29
+21278,15591135.0,Kerr,591.0,Germany,Male,49.0,6.0,122351.29,1.0,1.0,1.0,94898.1
+21279,15663932.0,Bellucci,684.0,France,Male,49.0,9.0,0.0,2.0,0.0,0.0,88992.05
+21280,15679024.0,Chukwuebuka,681.0,France,Male,33.0,6.0,0.0,1.0,0.0,1.0,175743.92
+21281,15700511.0,Nkemjika,589.0,Spain,Female,22.0,4.0,0.0,2.0,0.0,1.0,180800.42
+21282,15667588.0,Chidiebele,656.0,France,Female,33.0,5.0,0.0,1.0,1.0,0.0,102665.92
+21283,15683955.0,Chukwumaobim,619.0,Germany,Male,34.0,2.0,99838.71,1.0,1.0,0.0,91624.11
+21284,15738148.0,Nkemdilim,586.0,Spain,Male,55.0,6.0,0.0,1.0,0.0,0.0,86784.42
+21285,15671148.0,Hsing,644.0,France,Female,41.0,1.0,0.0,2.0,1.0,1.0,102280.29
+21286,15571873.0,Nnanna,588.0,France,Male,42.0,5.0,0.0,1.0,1.0,1.0,145593.85
+21287,15712176.0,Y?an,705.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,115818.51
+21288,15749807.0,Hsu,629.0,Spain,Male,36.0,9.0,0.0,2.0,0.0,0.0,134878.19
+21289,15622478.0,Cattaneo,762.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,135925.72
+21290,15654489.0,Ch'en,810.0,Germany,Male,40.0,9.0,124447.58,1.0,1.0,0.0,116046.22
+21291,15682048.0,Kashiwagi,813.0,Spain,Male,27.0,3.0,0.0,2.0,1.0,0.0,174098.28
+21292,15641640.0,Ts'ui,731.0,France,Male,52.0,5.0,0.0,3.0,1.0,1.0,93439.75
+21293,15593345.0,Chidubem,636.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,142783.98
+21294,15652400.0,Onyekachukwu,620.0,France,Male,38.0,3.0,0.0,1.0,1.0,1.0,143913.32
+21295,15743759.0,Chukwukere,718.0,Germany,Male,46.0,9.0,106901.42,2.0,1.0,0.0,82469.06
+21296,15664754.0,Iadanza,544.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,102799.22
+21297,15677052.0,Chukwuemeka,697.0,France,Female,45.0,4.0,0.0,3.0,1.0,0.0,49326.04
+21298,15749300.0,Hsueh,516.0,France,Male,39.0,8.0,0.0,1.0,1.0,1.0,143783.46
+21299,15736495.0,Ku,798.0,Germany,Male,50.0,5.0,177619.71,1.0,1.0,1.0,62634.87
+21300,15805909.0,Chia,645.0,Germany,Male,40.0,8.0,96781.39,2.0,1.0,0.0,172490.78
+21301,15678057.0,McIntosh,639.0,France,Female,48.0,4.0,0.0,1.0,0.0,0.0,96660.76
+21302,15752312.0,Tuan,744.0,France,Female,61.0,1.0,0.0,1.0,0.0,0.0,134767.13
+21303,15798850.0,Onuora,850.0,France,Male,34.0,4.0,0.0,2.0,0.0,1.0,165008.97
+21304,15800295.0,Pisano,687.0,France,Male,25.0,5.0,0.0,2.0,1.0,0.0,176713.47
+21305,15700772.0,Hsueh,686.0,Germany,Male,29.0,7.0,131038.14,2.0,1.0,0.0,175312.59
+21306,15677739.0,Maclean,708.0,France,Female,30.0,7.0,0.0,2.0,0.0,0.0,86410.29
+21307,15619360.0,Cawker,646.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,91560.63
+21308,15786539.0,Ts'ai,619.0,Spain,Male,41.0,7.0,111798.06,1.0,1.0,0.0,119882.7
+21309,15759913.0,Oluchukwu,551.0,France,Female,46.0,0.0,0.0,2.0,1.0,1.0,107815.31
+21310,15658852.0,Trevisani,646.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,146051.72
+21311,15642530.0,Pisano,613.0,France,Male,44.0,5.0,0.0,2.0,1.0,0.0,143345.69
+21312,15637993.0,Onwudiwe,584.0,France,Male,50.0,1.0,154483.98,1.0,1.0,1.0,179521.24
+21313,15605050.0,Ponomarev,531.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,105732.9
+21314,15791674.0,Chukwubuikem,679.0,France,Female,35.0,2.0,0.0,1.0,1.0,1.0,70956.75
+21315,15750778.0,Kenechukwu,662.0,France,Male,37.0,7.0,0.0,1.0,1.0,1.0,51620.94
+21316,15696989.0,Chinomso,644.0,France,Female,38.0,7.0,0.0,1.0,1.0,0.0,168080.53
+21317,15752914.0,Chukwufumnanya,726.0,Germany,Female,39.0,8.0,181671.14,1.0,0.0,1.0,5983.7
+21318,15734999.0,Milano,710.0,Spain,Male,51.0,7.0,133384.66,1.0,1.0,0.0,66548.58
+21319,15789953.0,Ugorji,634.0,Spain,Male,36.0,0.0,0.0,2.0,1.0,0.0,168834.41
+21320,15665088.0,Mironova,614.0,Spain,Male,39.0,4.0,0.0,2.0,0.0,1.0,143980.29
+21321,15634968.0,Chukwukadibia,699.0,France,Female,27.0,7.0,0.0,2.0,1.0,1.0,159899.97
+21322,15698902.0,Chukwualuka,633.0,Spain,Female,38.0,9.0,0.0,2.0,0.0,0.0,152959.02
+21323,15777033.0,Ts'ai,675.0,Germany,Female,52.0,1.0,107764.75,1.0,1.0,0.0,26387.71
+21324,15613787.0,Fyodorov,681.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,182059.28
+21325,15618243.0,Ts'ao,489.0,Spain,Female,28.0,2.0,0.0,2.0,0.0,0.0,148516.79
+21326,15786717.0,Milani,591.0,Germany,Female,39.0,5.0,90560.3,1.0,0.0,0.0,199293.01
+21327,15650488.0,Biryukova,850.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,175894.24
+21328,15700864.0,Mazzi,687.0,Germany,Female,34.0,5.0,86055.17,2.0,1.0,1.0,140941.47
+21329,15702061.0,P'an,627.0,Germany,Male,32.0,7.0,134543.7,2.0,1.0,0.0,96498.14
+21330,15690744.0,T'ao,805.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,93881.75
+21331,15630698.0,Ts'ui,696.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,49594.02
+21332,15651022.0,Maclean,639.0,France,Male,51.0,9.0,0.0,2.0,0.0,1.0,56562.57
+21333,15785831.0,Liao,695.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,1.0,139048.0
+21334,15665221.0,Tsao,661.0,France,Female,45.0,2.0,0.0,1.0,1.0,0.0,25662.27
+21335,15736299.0,Chukwuemeka,684.0,Spain,Male,40.0,5.0,0.0,1.0,1.0,0.0,155876.06
+21336,15762762.0,Onyemere,633.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,9984.4
+21337,15626882.0,Mancini,599.0,France,Male,39.0,8.0,0.0,1.0,1.0,0.0,151903.6
+21338,15782899.0,Nwachukwu,649.0,France,Female,42.0,4.0,0.0,1.0,0.0,0.0,166513.47
+21339,15603436.0,Chiang,829.0,Spain,Female,31.0,2.0,0.0,1.0,1.0,1.0,93817.34
+21340,15783526.0,Y?an,627.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,0.0,131473.31
+21341,15571940.0,Chukwumaobim,656.0,Spain,Male,37.0,7.0,0.0,2.0,1.0,1.0,142516.35
+21342,15691952.0,Yeh,588.0,France,Male,42.0,6.0,127864.4,2.0,1.0,1.0,74800.99
+21343,15677310.0,Cremonesi,635.0,Spain,Male,40.0,9.0,0.0,2.0,1.0,0.0,121408.46
+21344,15737489.0,Cavenagh,757.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,72993.65
+21345,15636590.0,Niu,708.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,59631.7
+21346,15808023.0,Manna,516.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,105720.09
+21347,15709511.0,Nnachetam,682.0,Spain,Male,43.0,1.0,0.0,2.0,1.0,1.0,114083.73
+21348,15773869.0,Nnaife,710.0,Germany,Male,57.0,1.0,121206.96,3.0,1.0,0.0,161800.77
+21349,15626383.0,Tien,742.0,France,Male,25.0,7.0,0.0,2.0,0.0,1.0,120591.19
+21350,15643630.0,Ndukaku,763.0,Spain,Male,35.0,6.0,148863.31,1.0,1.0,0.0,172363.41
+21351,15724281.0,Ch'eng,633.0,France,Male,46.0,0.0,0.0,1.0,1.0,0.0,88866.92
+21352,15641091.0,Gidney,624.0,France,Male,33.0,7.0,0.0,2.0,0.0,1.0,102681.32
+21353,15677908.0,Molineux,554.0,Spain,Female,48.0,9.0,0.0,1.0,0.0,0.0,95611.47
+21354,15582841.0,Ts'ai,712.0,France,Male,20.0,4.0,0.0,2.0,1.0,0.0,94898.1
+21355,15581548.0,Zikoranachidimma,629.0,France,Female,41.0,6.0,0.0,2.0,0.0,0.0,180427.24
+21356,15772412.0,Chukwumaobim,705.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,57600.66
+21357,15749489.0,Ch'ien,777.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,84421.34
+21358,15598070.0,Olague,628.0,Spain,Male,58.0,7.0,0.0,1.0,1.0,1.0,74900.9
+21359,15715951.0,Chu,607.0,France,Male,47.0,0.0,135536.4,1.0,1.0,0.0,135096.78
+21360,15636423.0,Milanesi,644.0,France,Female,46.0,1.0,0.0,2.0,1.0,0.0,93783.0
+21361,15594409.0,McIntyre,686.0,Spain,Male,56.0,9.0,0.0,3.0,1.0,0.0,90558.24
+21362,15639303.0,P'eng,628.0,Spain,Female,39.0,8.0,0.0,2.0,0.0,1.0,107876.2
+21363,15705034.0,Pisano,678.0,France,Male,35.0,7.0,0.0,1.0,0.0,1.0,119259.34
+21364,15640491.0,Chidubem,613.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,447.73
+21365,15785831.0,Dyer,719.0,France,Male,29.0,10.0,86911.8,1.0,0.0,1.0,111241.37
+21366,15696852.0,Chukwueloka,593.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,0.0,83700.66
+21367,15798850.0,Smith,674.0,Spain,Male,52.0,6.0,0.0,2.0,1.0,0.0,145562.4
+21368,15794874.0,Nwachinemelu,545.0,Germany,Male,63.0,4.0,128135.99,1.0,0.0,0.0,169920.92
+21369,15748473.0,Chukwudi,635.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,169952.82
+21370,15737354.0,Onyemachukwu,732.0,France,Female,47.0,8.0,155973.88,1.0,1.0,0.0,184536.01
+21371,15646513.0,Chu,669.0,Germany,Male,51.0,0.0,109528.16,1.0,1.0,0.0,169161.46
+21372,15595588.0,Y?an,577.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,180553.48
+21373,15768282.0,Hargreaves,652.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,1.0,170893.85
+21374,15778975.0,Ch'iu,599.0,Germany,Male,32.0,3.0,122874.6,2.0,1.0,1.0,182787.16
+21375,15643865.0,Trevisan,651.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,0.0,181490.42
+21376,15807663.0,Ch'in,577.0,Germany,Male,45.0,7.0,139432.35,1.0,0.0,1.0,91884.5
+21377,15757912.0,Ch'ien,777.0,Spain,Female,52.0,6.0,0.0,2.0,1.0,1.0,187826.11
+21378,15776827.0,Nnonso,707.0,France,Male,54.0,10.0,0.0,1.0,1.0,0.0,116097.26
+21379,15622585.0,Houghton,686.0,Germany,Male,48.0,2.0,98186.7,2.0,0.0,1.0,72993.67
+21380,15691627.0,Mazzanti,687.0,Spain,Female,40.0,5.0,0.0,1.0,1.0,0.0,54495.86
+21381,15726773.0,Chikelu,682.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,142995.32
+21382,15631685.0,Ting,682.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,119830.08
+21383,15652674.0,T'ien,731.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,73365.04
+21384,15741078.0,Uvarov,811.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,161854.98
+21385,15762793.0,Scott,753.0,France,Female,39.0,7.0,143391.07,1.0,0.0,0.0,170905.94
+21386,15571958.0,Hsu,675.0,France,Female,59.0,0.0,0.0,1.0,1.0,0.0,52820.13
+21387,15746012.0,Grieve,729.0,France,Male,40.0,7.0,123544.0,1.0,1.0,0.0,58974.0
+21388,15569764.0,Okwudilichukwu,644.0,France,Female,34.0,3.0,0.0,1.0,0.0,0.0,130132.49
+21389,15782758.0,Liao,681.0,Germany,Male,40.0,8.0,84438.57,2.0,1.0,0.0,136859.55
+21390,15732644.0,Maclean,724.0,France,Female,28.0,9.0,0.0,2.0,1.0,1.0,35786.76
+21391,15583857.0,Nkemakolam,581.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,65632.33
+21392,15714240.0,Rowntree,741.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,68293.93
+21393,15688880.0,Lo,587.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,182728.8
+21394,15588517.0,Chukwujamuike,662.0,France,Female,32.0,0.0,91246.53,1.0,1.0,0.0,153605.75
+21395,15642467.0,Iadanza,675.0,France,Female,60.0,5.0,0.0,1.0,1.0,1.0,113639.67
+21396,15751058.0,Iadanza,607.0,France,Male,34.0,1.0,0.0,1.0,1.0,0.0,9241.78
+21397,15682541.0,Ch'en,838.0,France,Female,48.0,3.0,0.0,1.0,0.0,0.0,175071.33
+21398,15699507.0,Galkin,745.0,France,Female,25.0,2.0,0.0,2.0,1.0,1.0,188007.99
+21399,15659931.0,Iloerika,683.0,France,Male,37.0,5.0,0.0,1.0,1.0,1.0,123882.73
+21400,15783859.0,Yuriev,805.0,Germany,Female,44.0,8.0,144591.96,1.0,1.0,0.0,125445.8
+21401,15806438.0,Bibi,669.0,Spain,Male,33.0,6.0,0.0,2.0,1.0,1.0,103557.91
+21402,15776051.0,H?,748.0,Spain,Female,39.0,8.0,0.0,2.0,1.0,1.0,129912.74
+21403,15723032.0,Manna,582.0,Germany,Female,43.0,2.0,99152.73,2.0,0.0,1.0,132424.69
+21404,15624471.0,Fanucci,648.0,Germany,Female,33.0,8.0,102540.61,2.0,1.0,1.0,130483.56
+21405,15572467.0,Liao,543.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,127939.18
+21406,15776985.0,White,705.0,France,Male,28.0,4.0,0.0,2.0,0.0,1.0,171706.66
+21407,15717185.0,Pinto,595.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,131712.52
+21408,15789924.0,Ch'eng,557.0,France,Male,41.0,5.0,0.0,2.0,0.0,1.0,133692.88
+21409,15681402.0,Uspenskaya,663.0,France,Female,38.0,1.0,0.0,2.0,0.0,0.0,168834.38
+21410,15773394.0,Maclean,620.0,France,Male,34.0,3.0,0.0,1.0,1.0,1.0,173486.39
+21411,15777907.0,Fiorentino,616.0,France,Female,32.0,2.0,0.0,1.0,1.0,0.0,94738.54
+21412,15741275.0,Chu,850.0,Germany,Female,32.0,3.0,143054.86,2.0,1.0,0.0,84469.13
+21413,15748481.0,Teng,605.0,Spain,Male,30.0,5.0,0.0,2.0,0.0,0.0,164741.5
+21414,15802071.0,Piazza,675.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,52795.15
+21415,15751531.0,Pirozzi,627.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,93769.89
+21416,15581575.0,Lai,677.0,Spain,Male,33.0,8.0,0.0,1.0,1.0,1.0,88838.14
+21417,15640648.0,Okechukwu,633.0,France,Male,36.0,3.0,138053.67,1.0,1.0,0.0,185557.66
+21418,15757837.0,Yobachukwu,699.0,Spain,Female,30.0,2.0,0.0,2.0,0.0,0.0,110253.2
+21419,15709604.0,Fenton,711.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,131285.92
+21420,15572093.0,Ch'en,586.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,82868.34
+21421,15778915.0,Hao,632.0,France,Male,36.0,6.0,0.0,1.0,1.0,0.0,199409.19
+21422,15790782.0,Yudina,685.0,Spain,Female,62.0,4.0,0.0,2.0,0.0,1.0,80177.8
+21423,15669342.0,Lucciano,591.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,92816.86
+21424,15662232.0,Hs?,545.0,Spain,Female,31.0,7.0,0.0,1.0,1.0,1.0,127847.25
+21425,15761348.0,Chukwuemeka,632.0,Germany,Male,41.0,8.0,140950.92,1.0,1.0,0.0,172938.29
+21426,15771087.0,Ch'in,665.0,France,Female,31.0,6.0,0.0,1.0,1.0,0.0,186948.35
+21427,15668775.0,Akabueze,653.0,France,Female,41.0,0.0,0.0,2.0,0.0,0.0,46811.77
+21428,15644551.0,Niu,678.0,Spain,Female,31.0,3.0,129745.1,1.0,0.0,1.0,88890.75
+21429,15591723.0,Fyodorov,806.0,Spain,Female,46.0,0.0,130114.17,1.0,0.0,1.0,79623.28
+21430,15748473.0,P'eng,744.0,France,Female,28.0,1.0,0.0,2.0,1.0,1.0,129891.66
+21431,15692991.0,Mazzi,651.0,France,Male,33.0,5.0,115532.52,1.0,0.0,1.0,172501.38
+21432,15706061.0,L?,756.0,Germany,Male,47.0,7.0,182888.08,2.0,1.0,0.0,107712.42
+21433,15774738.0,Pirozzi,640.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,74048.1
+21434,15578515.0,Baddeley,592.0,France,Male,33.0,7.0,0.0,2.0,0.0,0.0,38761.61
+21435,15622494.0,Nwokezuike,585.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,55857.48
+21436,15686917.0,Hao,623.0,Spain,Female,37.0,8.0,136658.61,1.0,0.0,1.0,63687.06
+21437,15793881.0,Uwaezuoke,686.0,Germany,Male,26.0,0.0,120549.29,1.0,1.0,0.0,118570.56
+21438,15766017.0,Chia,603.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,179521.24
+21439,15615020.0,Hsu,684.0,Germany,Female,35.0,6.0,98723.67,1.0,1.0,0.0,101617.64
+21440,15705050.0,Uwakwe,742.0,France,Male,38.0,5.0,0.0,2.0,0.0,0.0,126012.57
+21441,15622578.0,Ejimofor,850.0,France,Male,34.0,2.0,148863.31,1.0,1.0,0.0,181204.5
+21442,15793901.0,Uvarova,831.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,126728.27
+21443,15603665.0,Ratten,671.0,Spain,Male,37.0,2.0,166594.78,1.0,0.0,1.0,161465.31
+21444,15725355.0,Pirozzi,738.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,125870.5
+21445,15795519.0,Kuo,598.0,Spain,Male,29.0,1.0,0.0,1.0,1.0,1.0,152054.33
+21446,15762268.0,Udegbunam,572.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,175743.92
+21447,15681276.0,Ngozichukwuka,698.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,163789.49
+21448,15745399.0,Y?,625.0,Spain,Male,36.0,2.0,0.0,1.0,1.0,1.0,76515.79
+21449,15793491.0,Iweobiegbunam,686.0,France,Male,31.0,3.0,91612.91,1.0,0.0,0.0,49463.44
+21450,15728352.0,Calabrese,774.0,Germany,Female,28.0,2.0,161592.76,1.0,1.0,1.0,161854.98
+21451,15602312.0,Ch'in,594.0,Germany,Female,40.0,3.0,148046.39,1.0,0.0,0.0,125062.02
+21452,15757897.0,Ponomaryov,568.0,France,Female,41.0,8.0,0.0,1.0,1.0,0.0,181224.56
+21453,15742369.0,Dickson,556.0,France,Male,28.0,2.0,0.0,1.0,1.0,1.0,115441.66
+21454,15593468.0,Ch'iu,714.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,141359.11
+21455,15745404.0,Chidiegwu,711.0,Germany,Male,42.0,4.0,106012.2,1.0,1.0,0.0,134878.34
+21456,15727197.0,Zikoranachidimma,756.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,58632.37
+21457,15783030.0,P'eng,584.0,France,Male,33.0,5.0,0.0,2.0,0.0,0.0,112928.74
+21458,15773013.0,Hsueh,638.0,France,Female,49.0,9.0,0.0,2.0,0.0,0.0,173586.63
+21459,15647898.0,Chien,590.0,France,Female,36.0,5.0,143390.51,1.0,1.0,0.0,60584.33
+21460,15572467.0,Chiemezie,651.0,Spain,Male,34.0,3.0,95675.62,2.0,0.0,1.0,181694.44
+21461,15687828.0,L?,684.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,0.0,67364.41
+21462,15764033.0,T'ao,692.0,France,Female,48.0,8.0,0.0,1.0,0.0,0.0,186489.95
+21463,15727811.0,Calabrese,632.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,73066.72
+21464,15789548.0,Onwuamaeze,661.0,France,Male,38.0,3.0,146587.3,1.0,1.0,0.0,9854.72
+21465,15746199.0,Chukwuemeka,739.0,Germany,Male,41.0,3.0,106836.67,1.0,1.0,1.0,154360.97
+21466,15810845.0,Manna,703.0,Germany,Male,35.0,7.0,107418.28,2.0,1.0,0.0,174094.24
+21467,15582892.0,Chukwuemeka,746.0,France,Female,43.0,5.0,0.0,1.0,0.0,0.0,132979.69
+21468,15773098.0,Scott,594.0,Spain,Female,23.0,6.0,0.0,2.0,1.0,0.0,106977.5
+21469,15742297.0,Bogle,710.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,163550.58
+21470,15652999.0,DeRose,713.0,France,Female,45.0,9.0,0.0,2.0,1.0,0.0,96833.0
+21471,15718779.0,T'ien,756.0,France,Female,25.0,5.0,108269.2,2.0,1.0,0.0,60920.03
+21472,15594577.0,Hsueh,677.0,France,Male,46.0,4.0,161588.89,1.0,1.0,0.0,134794.02
+21473,15777825.0,McLean,633.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,148087.88
+21474,15606901.0,Milne,719.0,Germany,Male,50.0,0.0,128981.07,2.0,1.0,0.0,126434.61
+21475,15657565.0,Dickson,648.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,102260.16
+21476,15673367.0,Fadden,568.0,France,Male,23.0,2.0,0.0,2.0,1.0,1.0,93568.77
+21477,15631838.0,P'eng,629.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,123663.63
+21478,15742798.0,Gant,663.0,Germany,Male,29.0,9.0,97544.84,2.0,1.0,1.0,187826.11
+21479,15809847.0,Ch'iu,495.0,Spain,Female,46.0,8.0,0.0,1.0,1.0,0.0,133537.02
+21480,15792360.0,Ejikemeifeuwa,667.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,1.0,184520.0
+21481,15593959.0,Ch'ang,671.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,51454.06
+21482,15593601.0,Lo,641.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,1.0,1276.87
+21483,15680706.0,Lu,542.0,Germany,Male,46.0,5.0,130575.26,1.0,1.0,1.0,103932.58
+21484,15583849.0,Shao,682.0,France,Female,37.0,9.0,0.0,2.0,0.0,0.0,175310.78
+21485,15642824.0,Uwakwe,678.0,Germany,Male,38.0,3.0,127585.98,2.0,1.0,1.0,113469.65
+21486,15784320.0,H?,681.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,138878.54
+21487,15797329.0,McLean,684.0,Spain,Male,50.0,3.0,0.0,2.0,1.0,1.0,61761.66
+21488,15658158.0,Fomina,530.0,France,Female,47.0,9.0,110939.3,2.0,1.0,0.0,113428.77
+21489,15679743.0,Hovell,684.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,142331.39
+21490,15672499.0,Olisanugo,584.0,France,Male,33.0,6.0,0.0,2.0,0.0,1.0,136491.72
+21491,15672912.0,Chidumaga,562.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,10553.31
+21492,15579914.0,Milano,642.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,0.0,158054.49
+21493,15656322.0,Ch'ien,697.0,France,Female,47.0,9.0,0.0,2.0,1.0,0.0,131718.48
+21494,15692430.0,Shao,595.0,France,Male,30.0,8.0,138341.51,1.0,0.0,0.0,154775.7
+21495,15691808.0,Ugorji,596.0,France,Male,38.0,5.0,0.0,1.0,1.0,1.0,81298.09
+21496,15695078.0,Chukwuebuka,683.0,Spain,Female,37.0,7.0,0.0,2.0,0.0,1.0,52827.51
+21497,15589019.0,Chinagorom,695.0,France,Female,29.0,1.0,0.0,2.0,1.0,1.0,163327.27
+21498,15765311.0,Whitelegge,555.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,55859.27
+21499,15668830.0,Boni,656.0,France,Male,62.0,2.0,82321.88,1.0,1.0,1.0,142223.84
+21500,15702656.0,Maclean,671.0,France,Female,31.0,9.0,0.0,1.0,1.0,0.0,48876.84
+21501,15589312.0,Ugochukwutubelum,638.0,Germany,Female,35.0,8.0,87946.39,2.0,1.0,1.0,194808.51
+21502,15775797.0,Ch'in,561.0,France,Male,26.0,4.0,0.0,1.0,1.0,1.0,91574.94
+21503,15571666.0,Chukwukere,686.0,Germany,Female,35.0,7.0,131955.07,2.0,0.0,0.0,171021.44
+21504,15691483.0,Tobeolisa,577.0,Germany,Female,35.0,5.0,163700.78,2.0,1.0,0.0,114566.47
+21505,15585855.0,Onyemauchechukwu,806.0,Spain,Female,40.0,5.0,0.0,1.0,1.0,0.0,90860.97
+21506,15673747.0,Chu,699.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,128815.33
+21507,15571666.0,Chijindum,731.0,France,Male,37.0,1.0,0.0,2.0,0.0,0.0,93332.64
+21508,15631693.0,Chimaobim,695.0,France,Female,42.0,0.0,170833.46,1.0,1.0,0.0,184544.59
+21509,15607988.0,K'ung,543.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,74077.91
+21510,15665834.0,Chidumaga,636.0,France,Male,47.0,6.0,0.0,1.0,1.0,1.0,138526.96
+21511,15776596.0,Onyekaozulu,743.0,Germany,Female,39.0,2.0,134760.21,1.0,1.0,0.0,163534.75
+21512,15748381.0,Y?,591.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,0.0,147720.27
+21513,15754068.0,Lai,667.0,France,Male,44.0,7.0,0.0,2.0,1.0,0.0,194808.51
+21514,15615207.0,Lo Duca,707.0,Spain,Male,31.0,4.0,100628.0,2.0,1.0,0.0,133831.27
+21515,15630661.0,Liao,699.0,Spain,Female,42.0,9.0,0.0,2.0,1.0,0.0,74850.84
+21516,15587562.0,Ku,578.0,Germany,Male,37.0,9.0,125537.38,1.0,1.0,0.0,180595.02
+21517,15777784.0,Trevisano,745.0,France,Male,38.0,5.0,0.0,1.0,0.0,1.0,69381.05
+21518,15804822.0,H?,724.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,137916.49
+21519,15643818.0,Chien,648.0,France,Female,21.0,4.0,0.0,2.0,1.0,0.0,60905.51
+21520,15791373.0,Maclean,587.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,1.0,95625.03
+21521,15593694.0,Cattaneo,666.0,France,Female,39.0,5.0,0.0,2.0,1.0,0.0,181893.31
+21522,15584515.0,Dike,637.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,138510.01
+21523,15592999.0,Ch'ang,632.0,France,Male,26.0,7.0,77967.5,1.0,1.0,0.0,105817.63
+21524,15677382.0,Manna,751.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,1.0,71159.84
+21525,15781882.0,DeRose,703.0,France,Female,31.0,2.0,103471.49,1.0,1.0,0.0,93881.75
+21526,15757865.0,Chigolum,663.0,Germany,Male,52.0,4.0,107980.37,1.0,0.0,1.0,169920.92
+21527,15593601.0,Hsia,713.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,126517.43
+21528,15687310.0,Fang,559.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,105983.09
+21529,15728010.0,Chukwufumnanya,619.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,84760.32
+21530,15747591.0,Rizzo,676.0,Germany,Male,43.0,1.0,131468.44,2.0,1.0,0.0,4993.94
+21531,15617301.0,Tien,569.0,Germany,Male,33.0,0.0,106687.76,1.0,1.0,0.0,94499.06
+21532,15757952.0,Nwachinemelu,559.0,Germany,Female,60.0,6.0,130854.59,1.0,1.0,1.0,124411.08
+21533,15744327.0,Ch'ang,741.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,96444.89
+21534,15692761.0,Mancini,697.0,France,Female,34.0,8.0,97403.18,1.0,0.0,0.0,130590.35
+21535,15583576.0,Hsiung,648.0,Germany,Female,44.0,6.0,115062.02,2.0,0.0,1.0,140891.07
+21536,15735270.0,Shih,588.0,France,Male,47.0,8.0,0.0,1.0,1.0,0.0,161465.21
+21537,15709861.0,Hsueh,678.0,Germany,Female,36.0,5.0,113882.23,1.0,1.0,1.0,28204.98
+21538,15758939.0,Lettiere,745.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,73669.94
+21539,15644849.0,H?,704.0,France,Female,57.0,2.0,144962.74,1.0,0.0,1.0,118749.12
+21540,15683339.0,Ndubuagha,695.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,91810.71
+21541,15698619.0,Tsou,729.0,France,Female,48.0,7.0,0.0,2.0,1.0,1.0,164081.42
+21542,15686958.0,Lai,644.0,France,Female,37.0,7.0,0.0,1.0,1.0,1.0,121405.02
+21543,15782311.0,Eiland,748.0,France,Male,60.0,7.0,0.0,1.0,1.0,1.0,88890.05
+21544,15605067.0,Calabresi,739.0,Spain,Female,37.0,2.0,131691.75,1.0,1.0,1.0,84574.89
+21545,15804075.0,Onyekachi,849.0,Germany,Male,57.0,4.0,116220.5,1.0,1.0,0.0,163558.36
+21546,15741417.0,Rizzo,679.0,Spain,Female,33.0,3.0,0.0,1.0,1.0,0.0,87067.73
+21547,15684469.0,Lucciano,679.0,Spain,Male,26.0,10.0,0.0,2.0,1.0,1.0,199336.63
+21548,15658577.0,Fanucci,679.0,France,Female,39.0,7.0,0.0,2.0,0.0,1.0,72993.65
+21549,15621063.0,Liao,705.0,Germany,Female,29.0,8.0,162482.76,1.0,1.0,1.0,77965.67
+21550,15786167.0,Teng,676.0,France,Male,51.0,5.0,0.0,1.0,0.0,0.0,107874.39
+21551,15667633.0,Onwuatuegwu,653.0,Spain,Male,36.0,9.0,0.0,1.0,0.0,1.0,74849.46
+21552,15582325.0,Tien,613.0,France,Female,41.0,3.0,0.0,2.0,0.0,0.0,68730.91
+21553,15762861.0,Tsou,697.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,158887.09
+21554,15621140.0,Chukwufumnanya,735.0,France,Male,35.0,7.0,0.0,2.0,0.0,0.0,85424.34
+21555,15721426.0,Hsia,577.0,France,Male,25.0,0.0,0.0,1.0,1.0,0.0,180712.28
+21556,15726679.0,Nucci,748.0,Spain,Male,35.0,8.0,0.0,1.0,1.0,1.0,87422.15
+21557,15739972.0,Shih,677.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,124592.84
+21558,15771749.0,Hsueh,722.0,France,Male,29.0,8.0,0.0,1.0,1.0,0.0,158610.89
+21559,15646725.0,Hs?,578.0,France,Male,54.0,5.0,0.0,1.0,1.0,1.0,24472.23
+21560,15740283.0,Smith,751.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,48962.58
+21561,15752588.0,Chiemezie,679.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,1.0,129964.56
+21562,15669301.0,Toscani,699.0,Germany,Female,34.0,8.0,150561.03,2.0,1.0,1.0,122949.71
+21563,15650288.0,Ch'ang,589.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,147055.87
+21564,15716000.0,Li Fonti,714.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,86956.2
+21565,15721781.0,T'ao,554.0,France,Male,35.0,10.0,0.0,2.0,1.0,1.0,164768.82
+21566,15773520.0,Genovese,639.0,Germany,Male,28.0,1.0,121874.89,1.0,1.0,1.0,54439.53
+21567,15773098.0,Okonkwo,563.0,France,Female,23.0,8.0,0.0,2.0,1.0,1.0,89566.64
+21568,15750921.0,Dyson,667.0,France,Female,39.0,4.0,124693.39,1.0,1.0,0.0,165082.25
+21569,15806915.0,Tien,545.0,France,Female,34.0,9.0,0.0,2.0,0.0,0.0,133086.45
+21570,15681021.0,Calabresi,651.0,France,Male,46.0,5.0,137986.01,1.0,1.0,0.0,88701.74
+21571,15739850.0,Onyemauchechukwu,684.0,France,Male,32.0,2.0,105921.52,1.0,1.0,1.0,152992.04
+21572,15794276.0,Okeke,762.0,Germany,Female,50.0,2.0,154970.54,2.0,1.0,0.0,90964.54
+21573,15775492.0,Nwankwo,696.0,France,Male,37.0,2.0,85915.52,1.0,0.0,0.0,101866.39
+21574,15572294.0,Pirozzi,686.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,0.0,82463.69
+21575,15756625.0,Kincaid,445.0,Germany,Female,49.0,4.0,148045.68,1.0,1.0,0.0,132547.02
+21576,15665879.0,Udegbunam,758.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,138700.75
+21577,15581229.0,Li Fonti,598.0,France,Male,46.0,8.0,0.0,2.0,1.0,0.0,111868.23
+21578,15663438.0,Nwachukwu,743.0,Spain,Male,30.0,1.0,77919.78,1.0,1.0,0.0,64539.64
+21579,15651627.0,Ts'ui,753.0,France,Male,34.0,10.0,126473.99,1.0,1.0,0.0,146723.72
+21580,15787907.0,Ndubueze,605.0,France,Male,37.0,7.0,0.0,2.0,0.0,1.0,175314.87
+21581,15612103.0,Hsieh,622.0,France,Male,39.0,1.0,0.0,1.0,1.0,0.0,116851.6
+21582,15665879.0,Hsiung,667.0,France,Female,36.0,9.0,123544.0,1.0,0.0,1.0,44384.57
+21583,15634373.0,Folliero,527.0,Spain,Male,45.0,5.0,0.0,2.0,1.0,0.0,101918.96
+21584,15698872.0,Yermakova,691.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,0.0,98820.09
+21585,15573045.0,Hsing,545.0,France,Male,32.0,8.0,0.0,1.0,0.0,1.0,168080.53
+21586,15652289.0,Hs?,506.0,France,Female,43.0,6.0,0.0,1.0,1.0,0.0,67552.71
+21587,15628950.0,Ch'ien,569.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,44406.17
+21588,15640059.0,Nwebube,663.0,France,Female,42.0,0.0,112712.17,1.0,1.0,0.0,160532.88
+21589,15604482.0,Nnachetam,590.0,France,Male,26.0,7.0,152462.79,1.0,0.0,1.0,61761.77
+21590,15663883.0,Onyemauchechukwu,545.0,Spain,Female,35.0,7.0,148610.11,1.0,1.0,0.0,62634.94
+21591,15682585.0,Calabresi,649.0,France,Female,34.0,6.0,102540.61,1.0,1.0,0.0,103421.91
+21592,15711457.0,Rizzo,636.0,Germany,Male,34.0,5.0,140733.74,2.0,1.0,1.0,91830.75
+21593,15750772.0,Shih,727.0,Germany,Female,48.0,4.0,115341.55,1.0,1.0,0.0,44244.49
+21594,15641877.0,Lu,583.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,0.0,78616.92
+21595,15803378.0,Uspenskaya,641.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,126761.2
+21596,15586963.0,Hsia,706.0,France,Male,22.0,7.0,0.0,1.0,1.0,1.0,164038.07
+21597,15752912.0,Ch'eng,616.0,Spain,Female,37.0,3.0,122453.97,2.0,1.0,0.0,168085.97
+21598,15744273.0,Pagnotto,661.0,Germany,Male,48.0,5.0,121729.42,1.0,1.0,0.0,110265.24
+21599,15702801.0,Luffman,815.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,167040.94
+21600,15757617.0,Onio,607.0,France,Female,39.0,3.0,0.0,1.0,1.0,0.0,128881.87
+21601,15571917.0,Fanucci,648.0,Germany,Male,39.0,4.0,123544.0,2.0,1.0,1.0,177524.19
+21602,15700511.0,Onyemauchechukwu,687.0,Spain,Female,42.0,1.0,123699.48,2.0,0.0,0.0,140197.42
+21603,15589924.0,Chiazagomekpere,658.0,Spain,Female,54.0,8.0,0.0,1.0,0.0,1.0,142838.64
+21604,15605037.0,Dyson,641.0,France,Female,40.0,9.0,0.0,2.0,1.0,1.0,125870.5
+21605,15600781.0,Fiorentini,564.0,France,Female,62.0,7.0,0.0,2.0,1.0,1.0,116520.09
+21606,15810864.0,Chigolum,697.0,Spain,Male,37.0,10.0,0.0,2.0,1.0,1.0,121239.65
+21607,15733974.0,Nnanna,602.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,162858.29
+21608,15682600.0,Lucchese,553.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,89801.9
+21609,15692718.0,Chiazagomekpere,632.0,Spain,Female,38.0,5.0,0.0,1.0,1.0,0.0,143301.53
+21610,15572467.0,T'ao,705.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,56562.57
+21611,15671383.0,Onyemauchechukwu,679.0,Spain,Female,38.0,7.0,0.0,1.0,1.0,1.0,82698.84
+21612,15646513.0,Pinto,717.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,130489.37
+21613,15728362.0,Milne,682.0,Germany,Male,33.0,7.0,142905.51,2.0,1.0,0.0,136983.77
+21614,15785782.0,Ch'in,607.0,France,Female,38.0,0.0,0.0,1.0,1.0,0.0,52869.71
+21615,15583392.0,Nwachukwu,706.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,120108.56
+21616,15763898.0,Uchenna,584.0,Germany,Male,29.0,5.0,131468.44,1.0,1.0,0.0,102085.35
+21617,15658935.0,K?,646.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,162960.03
+21618,15727556.0,Onyekachi,651.0,France,Male,41.0,4.0,0.0,2.0,1.0,1.0,87412.24
+21619,15777797.0,Ndubueze,662.0,France,Male,42.0,7.0,0.0,1.0,1.0,0.0,146875.86
+21620,15780779.0,Yobachi,633.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,135962.42
+21621,15743192.0,Y?,730.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,172501.38
+21622,15689514.0,Okeke,682.0,Germany,Female,41.0,1.0,138052.81,1.0,1.0,0.0,7063.6
+21623,15599403.0,White,709.0,France,Male,41.0,3.0,110340.68,1.0,1.0,1.0,180626.36
+21624,15574554.0,Zikoranachidimma,696.0,Spain,Female,49.0,4.0,0.0,2.0,1.0,0.0,151954.39
+21625,15689168.0,Yobachukwu,635.0,France,Female,48.0,2.0,0.0,2.0,0.0,0.0,152491.31
+21626,15627262.0,Ch'in,850.0,Germany,Female,32.0,5.0,136658.61,1.0,0.0,1.0,92887.06
+21627,15684440.0,Lai,523.0,France,Female,46.0,1.0,0.0,2.0,1.0,0.0,143681.83
+21628,15758845.0,Dickson,537.0,Spain,Male,38.0,10.0,132088.59,1.0,1.0,0.0,161435.02
+21629,15750765.0,Lucchese,617.0,France,Male,26.0,1.0,131287.36,1.0,0.0,0.0,64516.8
+21630,15649450.0,Palerma,637.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,1.0,87951.83
+21631,15744515.0,Nnaife,531.0,France,Female,26.0,7.0,0.0,2.0,1.0,0.0,166677.54
+21632,15665956.0,Trevisano,478.0,Germany,Female,56.0,10.0,127864.4,1.0,1.0,1.0,162889.1
+21633,15673372.0,T'ang,741.0,France,Male,44.0,6.0,0.0,2.0,1.0,1.0,143096.49
+21634,15604813.0,Chiawuotu,498.0,Germany,Male,36.0,1.0,76510.52,1.0,1.0,1.0,168469.65
+21635,15633648.0,Chijindum,699.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,129964.94
+21636,15646936.0,Ch'eng,656.0,France,Male,26.0,9.0,0.0,1.0,1.0,1.0,166317.71
+21637,15780409.0,Mackenzie,667.0,Spain,Male,25.0,2.0,141806.0,1.0,1.0,1.0,184564.67
+21638,15705009.0,Chikezie,576.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,25261.27
+21639,15721592.0,Ts'ui,561.0,Germany,Female,46.0,6.0,149961.99,3.0,1.0,0.0,187925.75
+21640,15579781.0,Goliwe,638.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,87071.18
+21641,15664897.0,Hao,627.0,Germany,Female,34.0,2.0,154872.08,1.0,1.0,1.0,137959.0
+21642,15790067.0,Ch'iu,585.0,Germany,Female,29.0,9.0,100194.44,2.0,1.0,1.0,52331.15
+21643,15665088.0,Haugh,614.0,France,Male,37.0,6.0,137811.75,1.0,1.0,1.0,171457.06
+21644,15672945.0,Okwudilichukwu,682.0,France,Male,33.0,3.0,121823.4,1.0,1.0,1.0,130789.6
+21645,15575581.0,Pinto,645.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,129690.5
+21646,15612103.0,Chukwuebuka,565.0,France,Male,31.0,6.0,0.0,1.0,1.0,0.0,117948.0
+21647,15656596.0,Ch'in,652.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,44390.38
+21648,15766378.0,Onyemere,562.0,Germany,Female,60.0,9.0,130428.78,4.0,1.0,0.0,103511.26
+21649,15606229.0,Lo Duca,756.0,Spain,Male,34.0,10.0,0.0,1.0,1.0,1.0,136079.64
+21650,15756820.0,H?,627.0,France,Male,37.0,7.0,87739.08,1.0,0.0,0.0,122580.48
+21651,15796958.0,Nwankwo,708.0,France,Female,39.0,8.0,0.0,1.0,1.0,1.0,151316.37
+21652,15687540.0,Nwokezuike,697.0,Spain,Female,34.0,8.0,0.0,2.0,0.0,1.0,153662.28
+21653,15643523.0,Wei,678.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,1.0,97767.38
+21654,15623695.0,White,745.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,97938.54
+21655,15786469.0,Fiorentino,612.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,0.0,898.51
+21656,15793671.0,Kolesnikov,763.0,France,Female,37.0,1.0,134498.54,1.0,1.0,1.0,109742.8
+21657,15611800.0,Niu,539.0,France,Male,43.0,1.0,0.0,2.0,1.0,1.0,132468.76
+21658,15765537.0,Hsiung,699.0,France,Female,36.0,10.0,0.0,2.0,1.0,1.0,171762.87
+21659,15690079.0,Chung,781.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,63737.19
+21660,15572735.0,Nwokike,682.0,Spain,Male,47.0,2.0,0.0,2.0,1.0,0.0,105986.01
+21661,15785831.0,Shubin,559.0,France,Male,38.0,2.0,133618.01,1.0,1.0,1.0,26019.59
+21662,15731553.0,Y?,581.0,Germany,Female,36.0,8.0,137911.57,1.0,1.0,0.0,96463.25
+21663,15765257.0,Macleod,633.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,164018.98
+21664,15681316.0,Ch'ang,648.0,Germany,Female,31.0,5.0,96408.98,1.0,1.0,0.0,131718.12
+21665,15687913.0,Gadsden,611.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,105448.74
+21666,15776847.0,He,704.0,Germany,Female,29.0,6.0,125227.84,1.0,0.0,1.0,171037.63
+21667,15569807.0,Tsai,724.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,100556.98
+21668,15733447.0,Ch'ien,699.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,9984.4
+21669,15667046.0,Hs?,597.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,17974.08
+21670,15790564.0,H?,724.0,France,Male,48.0,6.0,0.0,1.0,1.0,1.0,141756.32
+21671,15602497.0,Scott,651.0,France,Female,29.0,4.0,0.0,2.0,0.0,0.0,80963.92
+21672,15567839.0,Chu,659.0,Germany,Male,32.0,10.0,84460.71,1.0,1.0,0.0,143668.58
+21673,15641675.0,Rizzo,708.0,Spain,Female,49.0,2.0,0.0,2.0,1.0,0.0,166984.44
+21674,15709441.0,Udegbunam,707.0,Spain,Male,38.0,5.0,125860.2,1.0,1.0,1.0,80552.36
+21675,15752342.0,P'an,651.0,Germany,Male,43.0,6.0,117036.52,1.0,1.0,0.0,160976.85
+21676,15795878.0,Chiang,594.0,Spain,Female,33.0,0.0,133800.98,1.0,1.0,1.0,58854.88
+21677,15658693.0,Chigozie,561.0,France,Male,37.0,9.0,0.0,2.0,0.0,0.0,94774.12
+21678,15683908.0,Chiemenam,521.0,France,Female,54.0,2.0,0.0,2.0,0.0,1.0,155786.39
+21679,15796434.0,Chidiebele,758.0,France,Male,30.0,5.0,0.0,2.0,1.0,0.0,133535.29
+21680,15632079.0,Kenechukwu,477.0,France,Female,30.0,1.0,0.0,1.0,1.0,0.0,116471.43
+21681,15638487.0,Mironova,753.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,1.0,125427.42
+21682,15694765.0,Calabrese,633.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,142513.5
+21683,15773013.0,Uwaezuoke,775.0,France,Female,54.0,2.0,112544.45,2.0,1.0,1.0,117704.65
+21684,15592300.0,Lucciano,686.0,France,Male,42.0,7.0,0.0,1.0,0.0,0.0,112946.67
+21685,15735330.0,Chiedozie,593.0,Germany,Male,44.0,1.0,123906.22,1.0,1.0,1.0,122937.57
+21686,15662488.0,Brown,696.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,162697.93
+21687,15782778.0,Ugonnatubelum,750.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,167557.71
+21688,15676715.0,Shih,521.0,France,Female,30.0,0.0,0.0,2.0,0.0,1.0,131363.57
+21689,15772073.0,Chukwuemeka,561.0,France,Female,41.0,2.0,0.0,1.0,1.0,0.0,180622.43
+21690,15709643.0,Nwebube,630.0,France,Female,69.0,3.0,0.0,1.0,1.0,1.0,24472.23
+21691,15578211.0,Zetticci,669.0,France,Male,26.0,4.0,121669.23,1.0,1.0,0.0,181258.76
+21692,15800233.0,Okwukwe,653.0,Germany,Male,39.0,8.0,131834.8,1.0,1.0,0.0,145547.71
+21693,15646726.0,H?,638.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,121440.8
+21694,15621021.0,Ch'en,689.0,France,Female,43.0,6.0,137933.18,1.0,1.0,1.0,106977.8
+21695,15689514.0,Tsao,717.0,Germany,Female,28.0,1.0,102562.23,2.0,1.0,1.0,38170.04
+21696,15660144.0,Koehler,635.0,France,Female,32.0,10.0,0.0,2.0,1.0,1.0,106920.57
+21697,15702499.0,T'ao,592.0,Spain,Male,31.0,3.0,85216.61,1.0,1.0,1.0,59351.68
+21698,15733661.0,Li,707.0,Spain,Female,34.0,8.0,0.0,2.0,1.0,0.0,8908.0
+21699,15763097.0,Sagese,537.0,Spain,Male,29.0,2.0,119912.46,2.0,1.0,1.0,72993.65
+21700,15631392.0,Soto,662.0,Germany,Female,40.0,9.0,110646.54,1.0,0.0,0.0,143301.49
+21701,15771086.0,Wright,685.0,Germany,Male,31.0,8.0,137851.31,1.0,1.0,1.0,26261.42
+21702,15603665.0,Yefimova,659.0,Spain,Male,36.0,2.0,0.0,1.0,1.0,1.0,125696.26
+21703,15758513.0,Hsu,584.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,1.0,94844.87
+21704,15658946.0,Trevisani,677.0,Germany,Female,37.0,9.0,114510.85,2.0,1.0,0.0,26342.33
+21705,15685768.0,Ts'ui,660.0,France,Male,38.0,9.0,0.0,1.0,1.0,1.0,134878.19
+21706,15712264.0,Chiang,645.0,France,Female,31.0,8.0,0.0,1.0,1.0,0.0,131283.6
+21707,15586976.0,Uwaezuoke,585.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,162683.06
+21708,15728505.0,Onyemere,605.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,143635.36
+21709,15787026.0,Yermolayev,567.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,81409.0
+21710,15774936.0,Hsia,738.0,Spain,Female,37.0,3.0,0.0,2.0,1.0,0.0,162452.79
+21711,15583090.0,H?,659.0,France,Female,61.0,9.0,0.0,2.0,1.0,1.0,168034.9
+21712,15628863.0,Olisaemeka,682.0,France,Male,38.0,2.0,111677.88,1.0,1.0,1.0,107876.2
+21713,15635502.0,Nweke,600.0,France,Male,54.0,4.0,0.0,2.0,0.0,0.0,126472.39
+21714,15689425.0,Ts'ao,641.0,Spain,Female,42.0,1.0,102723.35,1.0,1.0,1.0,121763.61
+21715,15742824.0,Chinwendu,592.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,174738.2
+21716,15762803.0,Hao,599.0,Spain,Female,49.0,9.0,0.0,1.0,1.0,0.0,170886.01
+21717,15572390.0,Iloerika,686.0,France,Female,31.0,1.0,0.0,2.0,1.0,1.0,92816.86
+21718,15663987.0,Hsiao,646.0,France,Female,30.0,5.0,0.0,1.0,1.0,1.0,598.8
+21719,15733234.0,Hsing,703.0,Germany,Male,51.0,5.0,125860.2,1.0,1.0,0.0,100835.19
+21720,15632793.0,Nkemakolam,678.0,Spain,Male,40.0,8.0,130786.92,1.0,0.0,0.0,189678.7
+21721,15653760.0,Ch'iu,592.0,France,Male,32.0,6.0,121823.7,1.0,1.0,1.0,154767.34
+21722,15631512.0,Ch'iu,652.0,France,Male,31.0,2.0,96769.04,1.0,1.0,1.0,1852.58
+21723,15771742.0,Chuang,635.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,84536.72
+21724,15682424.0,Chukwuemeka,757.0,France,Male,54.0,7.0,0.0,2.0,1.0,0.0,149614.17
+21725,15761733.0,Tuan,819.0,Spain,Male,33.0,6.0,131286.46,1.0,1.0,1.0,117626.9
+21726,15655213.0,Sells,638.0,Germany,Male,44.0,5.0,90218.9,1.0,1.0,1.0,47729.33
+21727,15771580.0,Maclean,684.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,1.0,180736.24
+21728,15667932.0,Iloerika,656.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,98810.78
+21729,15579826.0,Ch'iu,685.0,France,Male,21.0,7.0,67972.19,1.0,1.0,1.0,121551.93
+21730,15750014.0,Yermakov,486.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,114859.19
+21731,15598046.0,Sousa,633.0,France,Male,43.0,4.0,0.0,1.0,1.0,0.0,188343.05
+21732,15789863.0,Onio,645.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,24677.54
+21733,15654700.0,Hsing,646.0,France,Male,40.0,10.0,0.0,2.0,0.0,1.0,152491.22
+21734,15689955.0,Chinwenma,613.0,Germany,Male,44.0,8.0,99325.03,2.0,0.0,0.0,147964.99
+21735,15714993.0,Chiazagomekpere,736.0,France,Male,39.0,3.0,111095.85,1.0,1.0,0.0,135851.3
+21736,15679760.0,Scott,743.0,France,Female,45.0,4.0,123086.39,1.0,1.0,0.0,73857.94
+21737,15589437.0,Ch'ang,571.0,France,Female,28.0,7.0,0.0,2.0,1.0,1.0,166929.43
+21738,15697028.0,Manna,707.0,Germany,Female,31.0,5.0,116805.82,2.0,1.0,0.0,77475.61
+21739,15741094.0,Chukwubuikem,722.0,Spain,Female,42.0,0.0,0.0,2.0,1.0,0.0,166677.35
+21740,15689955.0,Chin,682.0,Spain,Female,48.0,1.0,0.0,1.0,1.0,0.0,66810.48
+21741,15602845.0,Ts'ui,696.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,116885.59
+21742,15581893.0,Chien,712.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,170819.01
+21743,15777529.0,T'ien,528.0,Spain,Female,46.0,7.0,0.0,1.0,1.0,1.0,65675.01
+21744,15644551.0,Nkemjika,689.0,France,Female,47.0,1.0,0.0,2.0,0.0,0.0,187943.6
+21745,15673619.0,Cattaneo,621.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,134878.24
+21746,15678727.0,Hsiao,675.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,88958.49
+21747,15657535.0,Yefremova,583.0,Spain,Male,40.0,3.0,0.0,1.0,1.0,0.0,182337.98
+21748,15771569.0,Chinwendu,645.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,96670.2
+21749,15585067.0,Walker,722.0,France,Male,28.0,3.0,0.0,2.0,1.0,1.0,28367.21
+21750,15756475.0,Kambinachi,638.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,140937.97
+21751,15602844.0,Ts'ao,671.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,9984.4
+21752,15605037.0,Ch'eng,676.0,Germany,Female,29.0,9.0,84909.12,1.0,1.0,0.0,114442.66
+21753,15662865.0,Onwuamaeze,545.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,141872.05
+21754,15594041.0,Onyekaozulu,703.0,Germany,Male,52.0,2.0,135936.1,1.0,0.0,0.0,159742.33
+21755,15784280.0,Onyemachukwu,669.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,151677.31
+21756,15593784.0,Nwabugwu,624.0,Germany,Male,38.0,4.0,75225.84,1.0,1.0,0.0,81491.7
+21757,15727408.0,Ch'eng,713.0,France,Male,29.0,8.0,0.0,2.0,0.0,0.0,161241.65
+21758,15658946.0,Chigozie,716.0,Germany,Male,45.0,10.0,99004.24,1.0,1.0,0.0,88773.04
+21759,15646764.0,Chidimma,635.0,France,Female,41.0,9.0,0.0,2.0,0.0,1.0,58854.88
+21760,15624013.0,Y?,771.0,Germany,Female,32.0,9.0,153360.02,1.0,1.0,1.0,60721.4
+21761,15806945.0,McIntyre,695.0,Spain,Female,49.0,10.0,0.0,1.0,1.0,0.0,120880.72
+21762,15747795.0,Ugochukwu,668.0,France,Female,43.0,1.0,0.0,2.0,1.0,1.0,58879.31
+21763,15776733.0,Folliero,694.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,163558.26
+21764,15772073.0,Calabresi,653.0,France,Female,52.0,1.0,0.0,1.0,0.0,0.0,83056.22
+21765,15617507.0,Udinese,696.0,France,Male,26.0,1.0,106328.08,1.0,1.0,1.0,121525.48
+21766,15598894.0,Hsueh,644.0,Spain,Male,55.0,7.0,119496.87,1.0,1.0,0.0,90928.02
+21767,15739576.0,Chidiebere,724.0,France,Female,36.0,7.0,90544.0,1.0,1.0,0.0,140745.96
+21768,15584090.0,Manna,585.0,Germany,Female,56.0,1.0,163760.82,1.0,1.0,0.0,71672.86
+21769,15691808.0,Chuang,576.0,Germany,Male,36.0,9.0,140956.99,1.0,1.0,1.0,60728.89
+21770,15782531.0,Hsueh,729.0,France,Male,36.0,8.0,80764.03,1.0,1.0,0.0,152490.02
+21771,15792650.0,Tung,587.0,Germany,Male,41.0,6.0,120586.56,2.0,0.0,1.0,80552.36
+21772,15794868.0,Pisano,573.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,0.0,151871.84
+21773,15754569.0,Chidumaga,542.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,1.0,121846.81
+21774,15782574.0,Ch'iu,516.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,122925.4
+21775,15607598.0,Martin,756.0,Germany,Female,54.0,1.0,123906.22,2.0,0.0,1.0,79146.36
+21776,15792360.0,Zuyeva,748.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,142377.69
+21777,15755084.0,Chiabuotu,694.0,France,Male,37.0,7.0,139825.53,1.0,1.0,1.0,63889.1
+21778,15732299.0,Ts'ui,678.0,France,Male,39.0,1.0,140170.61,2.0,0.0,1.0,135067.02
+21779,15809248.0,Y?an,590.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,147913.56
+21780,15693906.0,Chibueze,673.0,Germany,Male,44.0,1.0,126732.85,1.0,1.0,0.0,62282.99
+21781,15637354.0,Uwaezuoke,646.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,148098.95
+21782,15762216.0,K'ung,583.0,France,Female,55.0,8.0,0.0,1.0,0.0,1.0,141709.67
+21783,15709621.0,Ch'iu,684.0,France,Female,41.0,10.0,0.0,2.0,1.0,1.0,120586.32
+21784,15776433.0,Onwughara,807.0,France,Male,41.0,10.0,0.0,2.0,1.0,0.0,80178.54
+21785,15706812.0,Cremonesi,595.0,France,Male,42.0,0.0,0.0,2.0,0.0,1.0,167554.86
+21786,15679963.0,Nwankwo,556.0,France,Female,31.0,5.0,0.0,1.0,1.0,0.0,150475.14
+21787,15592994.0,Chukwualuka,537.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,50679.48
+21788,15592761.0,Onwuamaeze,710.0,France,Male,25.0,3.0,0.0,2.0,1.0,0.0,161069.73
+21789,15572428.0,Hsia,742.0,France,Male,39.0,10.0,151607.56,1.0,1.0,1.0,45985.52
+21790,15790448.0,Chidubem,683.0,Spain,Female,29.0,3.0,0.0,2.0,1.0,1.0,105715.01
+21791,15673908.0,Tuan,667.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,58420.21
+21792,15617454.0,Udegbunam,579.0,Germany,Male,41.0,1.0,155973.88,1.0,0.0,0.0,131807.14
+21793,15701364.0,Teng,565.0,Germany,Female,51.0,5.0,141334.98,1.0,1.0,1.0,185561.89
+21794,15724200.0,McIntyre,743.0,Spain,Female,37.0,1.0,0.0,1.0,0.0,0.0,158887.09
+21795,15661629.0,Lucchesi,684.0,France,Female,25.0,7.0,0.0,2.0,1.0,1.0,2423.9
+21796,15701264.0,Chukwudi,538.0,Spain,Male,41.0,1.0,0.0,1.0,1.0,0.0,85411.01
+21797,15633666.0,Chiu,722.0,Germany,Male,55.0,4.0,142987.46,1.0,1.0,0.0,118862.08
+21798,15636700.0,Tung,747.0,France,Male,44.0,7.0,0.0,2.0,1.0,0.0,126726.33
+21799,15788189.0,Calabresi,686.0,Germany,Female,43.0,9.0,127728.25,4.0,1.0,0.0,141827.83
+21800,15662294.0,Hsiao,624.0,Germany,Female,36.0,5.0,108705.52,1.0,1.0,0.0,167564.82
+21801,15799652.0,Ch'ang,756.0,France,Male,22.0,5.0,153656.85,1.0,1.0,0.0,167553.06
+21802,15686963.0,Lai,611.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,102970.69
+21803,15567802.0,Tuan,707.0,France,Female,43.0,6.0,0.0,2.0,1.0,0.0,26578.69
+21804,15750141.0,H?,751.0,Germany,Male,50.0,8.0,118812.5,2.0,0.0,1.0,114092.74
+21805,15641043.0,Tuan,704.0,France,Female,46.0,4.0,0.0,1.0,1.0,0.0,151733.43
+21806,15622443.0,Kline,757.0,France,Male,67.0,3.0,0.0,1.0,1.0,1.0,146738.83
+21807,15729763.0,Tsou,652.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,94774.12
+21808,15649853.0,Ch'ien,751.0,Germany,Male,52.0,5.0,136855.94,3.0,1.0,1.0,142911.99
+21809,15775761.0,Onyekachi,595.0,Germany,Male,53.0,7.0,104784.23,1.0,1.0,1.0,10070.4
+21810,15603594.0,Onyemere,543.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,126469.09
+21811,15642099.0,Bischof,605.0,France,Male,51.0,1.0,110976.23,1.0,1.0,0.0,73564.44
+21812,15654060.0,K'ung,775.0,Spain,Male,25.0,7.0,0.0,2.0,1.0,0.0,103931.49
+21813,15661526.0,Lettiere,722.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,86767.48
+21814,15771580.0,Chukwudi,520.0,Spain,Female,48.0,3.0,0.0,1.0,1.0,0.0,134878.19
+21815,15773779.0,Ts'ui,529.0,Spain,Female,46.0,5.0,0.0,1.0,1.0,1.0,181258.76
+21816,15683524.0,Hsia,748.0,Germany,Female,51.0,3.0,82559.42,4.0,1.0,1.0,72678.93
+21817,15673929.0,Nucci,605.0,Spain,Male,39.0,5.0,0.0,1.0,1.0,0.0,101168.9
+21818,15794560.0,Yermakov,792.0,Spain,Male,44.0,10.0,0.0,1.0,0.0,0.0,160979.66
+21819,15770174.0,Ts'ai,676.0,France,Male,30.0,7.0,0.0,2.0,0.0,1.0,81402.2
+21820,15724889.0,Ting,711.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,96517.97
+21821,15639669.0,Smith,686.0,France,Female,30.0,9.0,0.0,1.0,1.0,1.0,73329.08
+21822,15570002.0,Chidi,672.0,Spain,Male,38.0,6.0,138053.79,1.0,1.0,1.0,113860.79
+21823,15582892.0,Chiabuotu,703.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,0.0,24983.39
+21824,15671097.0,Y?an,679.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,132973.18
+21825,15649744.0,Ngozichukwuka,609.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,143983.99
+21826,15762218.0,Ch'eng,579.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,71047.51
+21827,15593555.0,T'ang,669.0,Spain,Female,44.0,7.0,0.0,1.0,1.0,0.0,79084.1
+21828,15572021.0,Chu,718.0,Germany,Male,58.0,3.0,122227.87,4.0,0.0,0.0,53394.12
+21829,15798084.0,Biryukov,710.0,France,Male,27.0,3.0,0.0,2.0,1.0,1.0,107841.77
+21830,15682778.0,Ch'iu,581.0,Germany,Male,31.0,3.0,119782.72,2.0,1.0,1.0,28274.6
+21831,15803358.0,Fanucci,619.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,51636.7
+21832,15646936.0,P'eng,694.0,Spain,Male,37.0,3.0,128298.74,1.0,1.0,1.0,71178.09
+21833,15727291.0,Milanesi,588.0,France,Female,31.0,6.0,0.0,1.0,0.0,0.0,100556.98
+21834,15747808.0,Shao,717.0,Spain,Male,49.0,3.0,106059.4,1.0,1.0,0.0,1187.88
+21835,15596962.0,Chiazagomekpere,578.0,France,Female,29.0,3.0,0.0,1.0,1.0,1.0,132973.17
+21836,15794278.0,Hsieh,637.0,France,Female,43.0,4.0,0.0,1.0,1.0,0.0,99377.27
+21837,15775082.0,Ch'iu,578.0,Spain,Female,30.0,6.0,84497.47,1.0,1.0,0.0,164825.07
+21838,15746065.0,Onwughara,669.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,82970.69
+21839,15602551.0,Lettiere,611.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,143357.02
+21840,15736656.0,Nucci,587.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,180712.28
+21841,15727696.0,Uspensky,706.0,Germany,Female,38.0,6.0,157848.57,1.0,1.0,1.0,173899.42
+21842,15668575.0,Hsieh,667.0,France,Female,40.0,10.0,0.0,1.0,1.0,0.0,176763.27
+21843,15654870.0,Uspenskaya,621.0,Germany,Female,45.0,4.0,77647.6,2.0,1.0,1.0,120852.85
+21844,15720644.0,Chiang,716.0,Germany,Female,36.0,9.0,116083.07,1.0,1.0,0.0,142331.85
+21845,15621093.0,Yermakova,850.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,65618.01
+21846,15593555.0,Eluemuno,819.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,175544.02
+21847,15709780.0,Goliwe,538.0,France,Female,41.0,3.0,0.0,2.0,1.0,1.0,85439.73
+21848,15749905.0,Genovese,674.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,172490.78
+21849,15573741.0,Milne,675.0,Spain,Male,51.0,7.0,0.0,3.0,0.0,1.0,142033.07
+21850,15807340.0,Lo,530.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,79082.76
+21851,15778410.0,Ch'in,521.0,Spain,Female,38.0,1.0,0.0,1.0,1.0,0.0,79082.62
+21852,15693526.0,Swayne,498.0,Germany,Male,50.0,5.0,117738.97,1.0,1.0,0.0,163141.18
+21853,15671244.0,Chia,637.0,Germany,Male,31.0,4.0,157848.57,1.0,1.0,1.0,181605.85
+21854,15602312.0,Hsia,582.0,Spain,Female,40.0,9.0,0.0,2.0,1.0,0.0,62282.99
+21855,15662494.0,Biryukov,850.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,1.0,166830.71
+21856,15644788.0,Okechukwu,648.0,France,Female,41.0,6.0,0.0,2.0,1.0,1.0,153727.32
+21857,15713594.0,Mazzanti,678.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,187816.59
+21858,15718216.0,Chukwuemeka,697.0,Spain,Female,43.0,2.0,0.0,2.0,1.0,1.0,91346.03
+21859,15778838.0,Ch'iu,468.0,Germany,Male,32.0,9.0,102741.15,2.0,1.0,1.0,67949.27
+21860,15574072.0,Tien,682.0,Germany,Female,34.0,8.0,120811.89,1.0,1.0,0.0,1055.66
+21861,15761348.0,Chukwuemeka,696.0,Spain,Male,34.0,8.0,0.0,2.0,0.0,0.0,45985.52
+21862,15646566.0,Hao,554.0,Spain,Male,25.0,8.0,0.0,2.0,1.0,1.0,139946.68
+21863,15698716.0,Chidiebere,529.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,81430.61
+21864,15704581.0,Hs?eh,741.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,128881.87
+21865,15730397.0,Chidiebere,477.0,Spain,Male,34.0,1.0,0.0,2.0,0.0,0.0,46786.92
+21866,15642826.0,Y?an,652.0,France,Male,46.0,0.0,0.0,1.0,1.0,0.0,86622.86
+21867,15789544.0,Ko,792.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,122964.18
+21868,15699507.0,Nnaife,599.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,51703.09
+21869,15782236.0,Calabresi,516.0,France,Male,31.0,6.0,0.0,1.0,1.0,0.0,165574.54
+21870,15642991.0,Fan,614.0,France,Male,19.0,1.0,0.0,2.0,1.0,1.0,94767.77
+21871,15658852.0,T'ang,434.0,Spain,Male,33.0,0.0,112333.63,1.0,1.0,0.0,136983.77
+21872,15655252.0,Padovesi,581.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,172601.62
+21873,15701265.0,Milani,598.0,Spain,Male,67.0,8.0,145965.33,1.0,0.0,1.0,180693.61
+21874,15792868.0,Y?,684.0,France,Male,33.0,5.0,128298.74,1.0,0.0,1.0,74898.8
+21875,15737169.0,Hsiao,652.0,France,Male,35.0,1.0,0.0,2.0,0.0,1.0,173365.89
+21876,15719579.0,Chiu,645.0,Spain,Female,50.0,5.0,0.0,1.0,1.0,1.0,80618.47
+21877,15778838.0,Tuan,619.0,Germany,Male,43.0,1.0,141078.37,2.0,1.0,1.0,176760.79
+21878,15671148.0,Hsiung,588.0,Germany,Male,38.0,3.0,135095.47,1.0,1.0,0.0,106977.8
+21879,15799932.0,Chukwueloka,592.0,France,Female,34.0,2.0,0.0,3.0,1.0,1.0,186976.6
+21880,15592025.0,Chiekwugo,662.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,84870.23
+21881,15783883.0,Chukwualuka,651.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,180598.86
+21882,15796787.0,Onyekachi,739.0,France,Male,20.0,7.0,152368.21,1.0,1.0,1.0,131772.51
+21883,15693690.0,Udobata,712.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,157871.55
+21884,15662494.0,Udinese,713.0,Germany,Female,49.0,9.0,121376.15,1.0,1.0,0.0,161069.73
+21885,15603328.0,Lucchese,632.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,136028.97
+21886,15752626.0,Yefremova,641.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,71154.1
+21887,15688876.0,Chia,633.0,France,Male,44.0,5.0,0.0,1.0,0.0,0.0,101300.94
+21888,15803947.0,Hsing,607.0,Germany,Female,30.0,0.0,161613.94,1.0,1.0,0.0,110359.68
+21889,15700657.0,Chia,683.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,0.0,35279.74
+21890,15607993.0,Ch'eng,611.0,France,Male,47.0,5.0,0.0,1.0,1.0,0.0,151415.81
+21891,15650889.0,Lu,579.0,Germany,Male,32.0,3.0,121542.57,2.0,1.0,1.0,166526.26
+21892,15604813.0,Ch'iu,637.0,Germany,Female,44.0,4.0,121669.93,1.0,0.0,1.0,182728.8
+21893,15781298.0,Ch'eng,682.0,Spain,Male,44.0,8.0,0.0,2.0,1.0,1.0,112269.67
+21894,15667460.0,H?,697.0,Germany,Male,62.0,3.0,113657.83,1.0,1.0,0.0,104079.53
+21895,15589420.0,Sun,661.0,Spain,Male,33.0,5.0,136067.82,1.0,1.0,1.0,102908.91
+21896,15690526.0,Shih,742.0,Germany,Male,30.0,7.0,141541.25,2.0,1.0,0.0,177844.06
+21897,15678916.0,Tuan,540.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,175893.53
+21898,15665129.0,Omeokachie,766.0,Spain,Female,40.0,1.0,0.0,1.0,1.0,0.0,121846.81
+21899,15642937.0,Hsiung,703.0,Germany,Female,49.0,5.0,113858.95,2.0,1.0,0.0,172501.38
+21900,15740557.0,Chibuzo,555.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,27239.28
+21901,15698816.0,T'ien,667.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,140745.96
+21902,15678916.0,Hsiao,645.0,Spain,Female,39.0,8.0,0.0,2.0,0.0,0.0,146785.44
+21903,15611786.0,Mazzi,673.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,180622.02
+21904,15775888.0,Chiu,586.0,Germany,Male,30.0,3.0,81733.74,1.0,0.0,1.0,120853.83
+21905,15631604.0,Hsia,584.0,Spain,Male,26.0,7.0,0.0,2.0,0.0,1.0,52321.22
+21906,15573741.0,Ch'iu,727.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,167984.72
+21907,15680197.0,Nwankwo,663.0,Germany,Male,47.0,1.0,113525.53,2.0,1.0,1.0,72549.27
+21908,15768471.0,Folliero,594.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,170638.98
+21909,15751082.0,Ndubuagha,607.0,France,Male,47.0,6.0,0.0,1.0,1.0,0.0,175722.31
+21910,15666163.0,Hs?eh,689.0,Germany,Male,36.0,1.0,120145.96,2.0,1.0,0.0,61315.37
+21911,15661734.0,Hsia,605.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,142937.57
+21912,15633059.0,Ch'iu,562.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,80428.48
+21913,15777904.0,Mazzanti,715.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,107429.72
+21914,15794566.0,Pinto,653.0,Spain,Male,36.0,9.0,0.0,2.0,0.0,0.0,130113.78
+21915,15602841.0,Chukwualuka,769.0,Spain,Female,41.0,3.0,0.0,2.0,1.0,0.0,143357.07
+21916,15751057.0,Onyekaozulu,633.0,Germany,Male,38.0,5.0,135962.4,1.0,1.0,1.0,123427.98
+21917,15730360.0,Ikedinachukwu,683.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,123883.52
+21918,15772383.0,Maclean,554.0,France,Male,33.0,0.0,118207.69,1.0,1.0,0.0,78854.57
+21919,15729763.0,Chiemenam,807.0,France,Male,27.0,4.0,0.0,1.0,1.0,0.0,109057.59
+21920,15740557.0,Hsieh,616.0,France,Male,42.0,1.0,0.0,1.0,1.0,1.0,184842.21
+21921,15784594.0,Gant,774.0,France,Female,38.0,1.0,0.0,1.0,1.0,1.0,137973.45
+21922,15762423.0,T'ang,662.0,Germany,Male,37.0,3.0,157842.37,1.0,1.0,1.0,51587.04
+21923,15632264.0,Ts'ui,707.0,Spain,Male,42.0,7.0,0.0,1.0,1.0,1.0,128815.23
+21924,15785865.0,Trevisano,652.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,162599.51
+21925,15591915.0,McIntyre,696.0,Spain,Male,36.0,7.0,0.0,1.0,1.0,1.0,62810.91
+21926,15579892.0,Chien,462.0,France,Female,25.0,9.0,0.0,2.0,0.0,1.0,92982.61
+21927,15574324.0,Fanucci,585.0,Spain,Male,32.0,3.0,160541.0,1.0,1.0,1.0,142223.94
+21928,15610147.0,Hs?eh,569.0,Germany,Female,35.0,8.0,121498.58,2.0,1.0,1.0,64448.36
+21929,15730273.0,Estrada,583.0,France,Male,50.0,4.0,0.0,2.0,0.0,1.0,93777.61
+21930,15752626.0,Aitken,516.0,France,Male,35.0,0.0,0.0,2.0,1.0,0.0,47322.05
+21931,15581229.0,Zetticci,756.0,Spain,Male,37.0,9.0,93716.72,1.0,0.0,0.0,121789.3
+21932,15802220.0,Milne,586.0,Germany,Female,48.0,8.0,122724.35,1.0,1.0,0.0,159132.83
+21933,15777892.0,Kenechi,706.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,36706.56
+21934,15579969.0,Hsieh,717.0,Spain,Male,42.0,2.0,0.0,2.0,1.0,1.0,125762.95
+21935,15768120.0,Bogle,689.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,103560.98
+21936,15732672.0,Nnachetam,755.0,France,Male,54.0,3.0,71281.29,1.0,1.0,0.0,124052.97
+21937,15594556.0,Liao,663.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,1.0,145792.01
+21938,15698528.0,Bales,590.0,Spain,Male,27.0,5.0,0.0,2.0,1.0,1.0,140745.91
+21939,15658486.0,Mazzi,573.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,171378.77
+21940,15753092.0,Hs?,516.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,1.0,36242.19
+21941,15642063.0,Folliero,757.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,91347.76
+21942,15589793.0,Ndubueze,531.0,Germany,Female,31.0,3.0,113743.37,1.0,1.0,0.0,80552.11
+21943,15641782.0,T'ao,716.0,France,Female,25.0,7.0,0.0,2.0,1.0,1.0,56780.97
+21944,15806926.0,Yuryeva,735.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,45424.13
+21945,15734659.0,Chia,584.0,Spain,Male,31.0,3.0,0.0,2.0,0.0,0.0,35367.19
+21946,15686957.0,Yobanna,592.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,160810.85
+21947,15608845.0,Trevisani,764.0,France,Male,36.0,3.0,0.0,2.0,0.0,0.0,66580.13
+21948,15663410.0,Ts'ui,605.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,169952.82
+21949,15571666.0,Hsieh,633.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,116581.1
+21950,15763065.0,Hsueh,628.0,France,Female,31.0,1.0,166518.63,1.0,1.0,0.0,62347.71
+21951,15648876.0,L?,743.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,119969.99
+21952,15687071.0,Chidubem,703.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,0.0,145765.83
+21953,15663917.0,White,607.0,France,Female,52.0,8.0,0.0,1.0,1.0,1.0,132798.65
+21954,15785519.0,Okwudiliolisa,748.0,Germany,Male,40.0,10.0,130142.79,1.0,1.0,1.0,142963.31
+21955,15812198.0,Pirozzi,686.0,France,Female,40.0,3.0,0.0,1.0,1.0,0.0,168816.22
+21956,15588580.0,Shih,711.0,Germany,Male,41.0,3.0,94773.11,1.0,1.0,0.0,121210.09
+21957,15652693.0,Iweobiegbunam,710.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,85746.52
+21958,15713347.0,Fitch,727.0,Spain,Female,35.0,5.0,0.0,2.0,0.0,0.0,10054.05
+21959,15777896.0,Sagese,622.0,Germany,Female,48.0,1.0,139265.31,1.0,1.0,0.0,62449.07
+21960,15771501.0,Ch'ang,604.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,159508.52
+21961,15690744.0,Iheatu,612.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,79919.13
+21962,15572762.0,Chidimma,724.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,182627.25
+21963,15795519.0,Chia,656.0,France,Female,40.0,5.0,120911.75,1.0,1.0,1.0,123969.64
+21964,15703793.0,Onyemauchechukwu,585.0,France,Male,33.0,6.0,0.0,1.0,1.0,1.0,66893.28
+21965,15783883.0,Chiu,596.0,France,Male,36.0,1.0,0.0,2.0,0.0,1.0,183492.9
+21966,15726289.0,Chikere,687.0,France,Male,40.0,5.0,0.0,1.0,1.0,1.0,150390.57
+21967,15581871.0,Hsiung,677.0,France,Female,27.0,4.0,90124.64,2.0,1.0,0.0,96875.52
+21968,15782778.0,Martin,706.0,Germany,Male,35.0,9.0,108876.91,1.0,0.0,0.0,155823.2
+21969,15761793.0,Ts'ui,579.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,170142.09
+21970,15751788.0,Pinto,683.0,France,Female,50.0,4.0,107949.45,3.0,1.0,0.0,119996.95
+21971,15627162.0,Palermo,577.0,France,Male,44.0,1.0,0.0,1.0,1.0,0.0,18082.06
+21972,15752846.0,Ch'en,635.0,Spain,Male,36.0,5.0,0.0,1.0,1.0,0.0,67611.36
+21973,15731865.0,P'eng,716.0,France,Male,27.0,4.0,0.0,1.0,0.0,0.0,142223.94
+21974,15692991.0,Kao,685.0,France,Male,42.0,1.0,0.0,1.0,0.0,0.0,166929.43
+21975,15784526.0,Nkemjika,627.0,France,Male,26.0,1.0,82547.11,1.0,1.0,1.0,172576.43
+21976,15671769.0,Kambinachi,721.0,Spain,Male,23.0,4.0,0.0,2.0,1.0,0.0,89804.83
+21977,15809901.0,Chidiegwu,573.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,0.0,60901.72
+21978,15731760.0,Chiu,679.0,Germany,Male,30.0,9.0,115251.64,1.0,1.0,1.0,182059.28
+21979,15583553.0,Onwuamaeze,739.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,81482.6
+21980,15572626.0,Calabrese,637.0,France,Female,28.0,10.0,0.0,2.0,0.0,0.0,143980.28
+21981,15612193.0,T'ang,677.0,France,Female,23.0,6.0,0.0,1.0,1.0,1.0,73322.0
+21982,15722425.0,Chukwubuikem,633.0,Germany,Female,43.0,5.0,79858.13,1.0,0.0,1.0,83556.65
+21983,15775490.0,Chidubem,607.0,Spain,Male,32.0,4.0,121404.64,1.0,1.0,1.0,60721.4
+21984,15591915.0,Ekechukwu,577.0,France,Female,39.0,7.0,162657.64,1.0,1.0,0.0,121765.0
+21985,15585853.0,Tien,794.0,Germany,Female,43.0,5.0,131254.81,3.0,1.0,0.0,107839.03
+21986,15771573.0,Ebelechukwu,763.0,Germany,Female,44.0,5.0,140733.74,3.0,1.0,0.0,163989.66
+21987,15682576.0,Chiemela,772.0,Germany,Female,61.0,1.0,116084.32,1.0,1.0,0.0,90977.48
+21988,15640491.0,Nwebube,714.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,113428.77
+21989,15595588.0,Liao,648.0,France,Male,44.0,9.0,0.0,2.0,0.0,1.0,136915.15
+21990,15655961.0,Zikoranachidimma,574.0,France,Female,39.0,0.0,123760.68,1.0,1.0,1.0,160553.87
+21991,15573628.0,T'ang,549.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,167033.83
+21992,15769216.0,Shih,611.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,1.0,118324.75
+21993,15582841.0,Ch'iu,696.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,105817.53
+21994,15679048.0,Lo Duca,632.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,63687.06
+21995,15767129.0,Nkemakolam,744.0,Spain,Female,25.0,8.0,85891.55,1.0,1.0,0.0,161571.79
+21996,15804962.0,Chiu,757.0,France,Male,29.0,7.0,0.0,1.0,1.0,0.0,105760.01
+21997,15579548.0,Chukwuemeka,645.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,160529.71
+21998,15767894.0,Palerma,706.0,France,Female,42.0,3.0,117052.82,1.0,0.0,1.0,161465.31
+21999,15757837.0,Yefremova,592.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,10963.04
+22000,15717191.0,Nnaife,709.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,145796.49
+22001,15698932.0,Ch'iu,598.0,Germany,Male,63.0,7.0,133636.04,1.0,1.0,1.0,170968.99
+22002,15591179.0,Shao,738.0,France,Male,45.0,2.0,126473.33,1.0,0.0,0.0,79577.38
+22003,15694860.0,White,693.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,125253.46
+22004,15648067.0,Yashina,738.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,168835.11
+22005,15584090.0,Liao,739.0,Spain,Female,32.0,9.0,0.0,1.0,1.0,0.0,149970.59
+22006,15619238.0,Nkemakolam,610.0,Spain,Male,54.0,8.0,0.0,1.0,1.0,0.0,53477.76
+22007,15661219.0,Chukwudi,662.0,Germany,Female,36.0,7.0,116939.35,1.0,1.0,0.0,51345.39
+22008,15778089.0,Chigolum,712.0,Germany,Female,38.0,3.0,137452.09,1.0,1.0,1.0,94898.1
+22009,15624592.0,Yermakova,584.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,159585.61
+22010,15666884.0,Lu,730.0,France,Female,40.0,3.0,167034.96,1.0,1.0,1.0,151623.91
+22011,15673747.0,Okwuadigbo,528.0,Spain,Male,49.0,0.0,0.0,1.0,1.0,0.0,184843.77
+22012,15607988.0,Hsiung,652.0,France,Male,29.0,5.0,0.0,2.0,0.0,0.0,177739.4
+22013,15684769.0,Hs?,645.0,Germany,Male,34.0,6.0,107784.65,1.0,1.0,0.0,151317.27
+22014,15729574.0,Rowntree,598.0,Spain,Male,31.0,6.0,0.0,2.0,0.0,0.0,161076.92
+22015,15625592.0,Hao,645.0,Germany,Male,36.0,2.0,104763.41,2.0,1.0,1.0,81298.09
+22016,15698664.0,Kosisochukwu,583.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,5791.85
+22017,15691170.0,Onyemauchechukwu,653.0,France,Female,32.0,4.0,125785.23,1.0,1.0,0.0,71738.56
+22018,15685920.0,O'Sullivan,592.0,France,Female,37.0,3.0,102936.72,1.0,1.0,1.0,158816.03
+22019,15636673.0,T'ao,687.0,France,Female,47.0,4.0,0.0,2.0,1.0,1.0,93718.16
+22020,15806467.0,Hs?,681.0,France,Male,45.0,2.0,0.0,2.0,0.0,0.0,98820.09
+22021,15761076.0,Shih,596.0,France,Female,38.0,6.0,164951.56,1.0,1.0,1.0,170968.99
+22022,15632050.0,Ch'ang,564.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,141168.22
+22023,15783816.0,Calabresi,725.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,83087.13
+22024,15685329.0,Lo Duca,631.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,92381.01
+22025,15748068.0,Onyekaozulu,695.0,France,Female,35.0,4.0,0.0,2.0,1.0,0.0,170634.49
+22026,15754569.0,T'ao,692.0,France,Female,36.0,6.0,0.0,3.0,1.0,0.0,176768.59
+22027,15595047.0,Burt,694.0,France,Male,47.0,3.0,0.0,2.0,1.0,0.0,128882.66
+22028,15673908.0,Ts'ui,683.0,France,Male,43.0,1.0,139432.37,1.0,1.0,1.0,35644.79
+22029,15757777.0,Ch'ang,719.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,157577.71
+22030,15589325.0,Lombardi,762.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,134794.02
+22031,15764033.0,Maclean,739.0,France,Female,24.0,10.0,0.0,2.0,1.0,1.0,67976.57
+22032,15785519.0,Chiang,592.0,France,Female,44.0,9.0,0.0,2.0,0.0,0.0,73686.75
+22033,15695792.0,Pisano,719.0,France,Female,32.0,6.0,74989.58,1.0,1.0,0.0,118913.53
+22034,15603846.0,Ponomarev,538.0,Germany,Male,41.0,9.0,92350.47,1.0,1.0,0.0,127875.1
+22035,15709639.0,Lu,740.0,France,Male,38.0,1.0,0.0,1.0,1.0,0.0,93439.75
+22036,15731781.0,Mazzi,714.0,France,Female,35.0,0.0,0.0,1.0,1.0,1.0,100153.43
+22037,15581748.0,K?,811.0,France,Female,28.0,4.0,0.0,1.0,1.0,0.0,167984.72
+22038,15794396.0,Genovesi,634.0,France,Male,40.0,4.0,0.0,2.0,0.0,0.0,38794.71
+22039,15605968.0,Onyemauchechi,493.0,France,Female,37.0,10.0,126663.51,1.0,1.0,1.0,184514.01
+22040,15736154.0,Cremonesi,734.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,100686.11
+22041,15777896.0,K'ung,705.0,Spain,Female,32.0,5.0,0.0,1.0,1.0,0.0,141827.33
+22042,15577274.0,Ch'eng,721.0,Spain,Male,32.0,3.0,0.0,2.0,0.0,1.0,182612.38
+22043,15796218.0,Chukwualuka,556.0,Spain,Female,39.0,4.0,0.0,2.0,0.0,0.0,60775.76
+22044,15737521.0,Pagnotto,599.0,Spain,Female,38.0,9.0,143988.0,2.0,1.0,1.0,107815.31
+22045,15798824.0,Chinwemma,714.0,Spain,Male,42.0,9.0,183579.54,2.0,1.0,1.0,175067.08
+22046,15788068.0,Pirozzi,521.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,0.0,60427.3
+22047,15590888.0,Nkemdilim,578.0,Germany,Female,35.0,7.0,133476.04,1.0,1.0,1.0,105760.01
+22048,15661626.0,Chuang,696.0,Spain,Female,28.0,2.0,161209.66,1.0,0.0,1.0,104076.51
+22049,15692406.0,O'Toole,751.0,Germany,Female,32.0,5.0,117833.3,2.0,1.0,1.0,82469.06
+22050,15659327.0,Obielumani,567.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,141501.45
+22051,15745324.0,Zetticci,607.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,0.0,129912.74
+22052,15736078.0,Ojiofor,704.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,151146.67
+22053,15652289.0,Nwachinemelu,556.0,Spain,Female,29.0,8.0,0.0,2.0,0.0,1.0,121568.37
+22054,15656086.0,Chiu,663.0,Germany,Female,37.0,3.0,98567.1,2.0,1.0,0.0,103945.58
+22055,15585748.0,Nkemakonam,628.0,France,Male,35.0,5.0,0.0,1.0,1.0,1.0,172718.35
+22056,15684958.0,Ting,599.0,Germany,Female,40.0,7.0,132211.85,1.0,1.0,1.0,79577.39
+22057,15742285.0,H?,730.0,France,Male,40.0,3.0,0.0,1.0,1.0,0.0,162683.06
+22058,15643496.0,Tung,669.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,0.0,49424.6
+22059,15756472.0,P'eng,709.0,France,Male,54.0,9.0,0.0,1.0,0.0,1.0,124598.59
+22060,15804822.0,Teng,595.0,France,Female,27.0,8.0,0.0,2.0,1.0,1.0,79414.0
+22061,15640034.0,Rizzo,669.0,France,Female,28.0,5.0,0.0,1.0,1.0,0.0,138526.96
+22062,15699335.0,Udinese,759.0,France,Female,29.0,8.0,104760.82,1.0,1.0,1.0,139953.94
+22063,15744105.0,Trevisano,634.0,Spain,Female,38.0,3.0,0.0,1.0,1.0,0.0,122925.4
+22064,15627859.0,Calabrese,682.0,France,Male,35.0,5.0,142082.07,2.0,1.0,0.0,143954.99
+22065,15571059.0,Ts'ai,707.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,89822.48
+22066,15683522.0,Zetticci,850.0,Spain,Female,40.0,9.0,0.0,1.0,1.0,1.0,181598.25
+22067,15783883.0,Ch'in,641.0,France,Female,42.0,9.0,0.0,2.0,0.0,1.0,62674.05
+22068,15731553.0,Lo Duca,627.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,142513.5
+22069,15724307.0,Hsia,717.0,France,Male,26.0,3.0,0.0,2.0,0.0,0.0,180622.03
+22070,15707681.0,Chinomso,675.0,France,Female,28.0,1.0,0.0,1.0,1.0,0.0,85516.37
+22071,15691624.0,Chialuka,684.0,France,Male,40.0,4.0,0.0,2.0,0.0,1.0,103396.08
+22072,15634792.0,Chukwueloka,662.0,France,Male,37.0,4.0,112666.67,1.0,1.0,0.0,26767.85
+22073,15697948.0,Tsai,749.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,121485.1
+22074,15717231.0,Chikelu,613.0,Germany,Female,40.0,1.0,95858.37,1.0,0.0,0.0,51112.8
+22075,15615254.0,Eames,596.0,France,Male,52.0,9.0,0.0,3.0,1.0,0.0,183572.39
+22076,15684930.0,Ch'ien,735.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,0.0,184524.74
+22077,15782418.0,Tsou,570.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,140763.18
+22078,15692631.0,Dore,685.0,France,Male,47.0,4.0,0.0,2.0,1.0,1.0,126794.56
+22079,15761497.0,Ch'iu,731.0,France,Male,28.0,4.0,0.0,2.0,1.0,1.0,122964.14
+22080,15654574.0,Hsia,722.0,Germany,Male,31.0,1.0,121206.96,1.0,0.0,1.0,91652.43
+22081,15783541.0,Fanucci,706.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,1.0,141872.05
+22082,15690670.0,Fetherstonhaugh,496.0,Germany,Female,45.0,5.0,117791.06,3.0,1.0,1.0,51620.94
+22083,15698028.0,Ch'ien,752.0,Germany,Male,45.0,6.0,131834.8,1.0,1.0,0.0,184954.11
+22084,15803886.0,Doyne,686.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,92917.54
+22085,15654495.0,Yobachukwu,644.0,Spain,Female,34.0,5.0,0.0,3.0,1.0,1.0,118570.05
+22086,15802758.0,Lai,639.0,France,Female,29.0,5.0,0.0,2.0,0.0,1.0,97623.28
+22087,15802570.0,Chukwuebuka,710.0,Spain,Male,29.0,4.0,0.0,2.0,0.0,1.0,134790.61
+22088,15571958.0,Bage,646.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,132418.59
+22089,15690175.0,Chukwualuka,738.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,173486.39
+22090,15782868.0,Iqbal,593.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,123866.43
+22091,15631451.0,Chiu,633.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,93439.75
+22092,15690169.0,Horsley,705.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,122398.54
+22093,15604576.0,Chin,516.0,France,Male,44.0,4.0,0.0,2.0,1.0,0.0,103557.85
+22094,15581552.0,Teng,659.0,Germany,Male,44.0,2.0,97565.74,2.0,1.0,1.0,178373.93
+22095,15799790.0,Ts'ao,649.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,1.0,143681.85
+22096,15639357.0,Tung,684.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,1.0,79734.23
+22097,15777784.0,Hsia,771.0,Germany,Male,49.0,4.0,115409.6,1.0,0.0,1.0,70849.47
+22098,15708505.0,Obialo,592.0,France,Male,47.0,3.0,142688.57,1.0,1.0,1.0,175820.91
+22099,15729201.0,Onyekaozulu,597.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,91514.62
+22100,15732113.0,Shih,651.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,93835.73
+22101,15726289.0,Nkemdilim,653.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,77965.44
+22102,15746805.0,Eluemuno,645.0,Germany,Male,34.0,9.0,120825.7,2.0,1.0,1.0,44946.29
+22103,15749693.0,Ives,821.0,France,Male,64.0,3.0,0.0,1.0,1.0,1.0,149614.61
+22104,15659327.0,Chia,678.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,49374.82
+22105,15614491.0,Lu,637.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,178067.33
+22106,15586974.0,Chuang,717.0,France,Male,33.0,0.0,0.0,2.0,0.0,0.0,152429.67
+22107,15693376.0,Martin,547.0,France,Female,31.0,7.0,132876.23,1.0,0.0,0.0,79871.16
+22108,15745196.0,Cattaneo,558.0,France,Female,45.0,6.0,138577.34,1.0,1.0,0.0,100015.79
+22109,15761686.0,Okwudiliolisa,584.0,Germany,Female,26.0,4.0,152212.88,1.0,0.0,1.0,120512.08
+22110,15632551.0,Chukwuemeka,777.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,148527.56
+22111,15739168.0,Ch'in,731.0,Spain,Female,46.0,2.0,0.0,2.0,1.0,0.0,172363.41
+22112,15747174.0,Zito,523.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,45985.52
+22113,15698028.0,Yobachi,649.0,Spain,Male,54.0,1.0,127864.4,1.0,1.0,0.0,141533.19
+22114,15762927.0,Chifley,657.0,Spain,Female,42.0,4.0,0.0,2.0,0.0,1.0,106781.59
+22115,15687828.0,Dike,619.0,Germany,Female,40.0,7.0,118098.62,1.0,0.0,1.0,67789.99
+22116,15794204.0,Onyemauchechukwu,556.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,84927.42
+22117,15604813.0,P'an,850.0,Spain,Male,35.0,9.0,0.0,1.0,1.0,0.0,136491.72
+22118,15646784.0,Lai,577.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,0.0,35770.84
+22119,15728773.0,Outhwaite,537.0,France,Female,23.0,7.0,0.0,2.0,1.0,1.0,106977.53
+22120,15634267.0,Chukwuebuka,597.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,171657.57
+22121,15781598.0,Yobachi,510.0,Spain,Male,39.0,4.0,0.0,1.0,1.0,1.0,122925.4
+22122,15621083.0,Manna,705.0,France,Male,34.0,9.0,0.0,1.0,1.0,0.0,121151.1
+22123,15686973.0,Ku,590.0,France,Male,49.0,1.0,112465.06,1.0,1.0,0.0,47911.03
+22124,15784320.0,Skelton,528.0,Germany,Male,36.0,2.0,147989.79,2.0,1.0,0.0,173989.66
+22125,15667554.0,Bibi,608.0,France,Male,34.0,6.0,141525.55,1.0,1.0,0.0,131521.72
+22126,15582090.0,Ch'en,659.0,France,Male,55.0,3.0,0.0,1.0,1.0,1.0,103832.58
+22127,15807524.0,O'Donnell,619.0,Germany,Female,44.0,8.0,116471.43,1.0,1.0,0.0,167873.49
+22128,15635125.0,Ch'ien,711.0,France,Male,42.0,6.0,0.0,2.0,1.0,1.0,28238.25
+22129,15674929.0,Kolesnikov,744.0,France,Male,39.0,6.0,129968.67,2.0,1.0,0.0,166038.27
+22130,15658435.0,Ts'ai,640.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,180551.76
+22131,15695475.0,Lo,778.0,Germany,Male,29.0,1.0,135470.8,1.0,1.0,1.0,62810.91
+22132,15777772.0,P'an,660.0,Germany,Female,25.0,8.0,126773.43,2.0,1.0,0.0,51695.41
+22133,15716085.0,Manna,655.0,France,Female,38.0,6.0,0.0,2.0,1.0,0.0,88481.32
+22134,15806922.0,Harker,673.0,Germany,Female,37.0,3.0,123737.04,2.0,1.0,0.0,162501.83
+22135,15592578.0,Hs?,582.0,France,Male,35.0,1.0,0.0,1.0,1.0,0.0,38190.78
+22136,15757577.0,Tien,678.0,France,Male,23.0,2.0,0.0,2.0,1.0,1.0,177099.71
+22137,15570194.0,Watson,724.0,Spain,Female,47.0,9.0,0.0,1.0,1.0,0.0,180622.02
+22138,15586870.0,Chiazagomekpele,645.0,Germany,Male,42.0,2.0,92877.33,2.0,1.0,0.0,179521.24
+22139,15763625.0,Lu,653.0,Germany,Male,40.0,1.0,112070.86,1.0,1.0,0.0,73464.88
+22140,15752912.0,Palermo,705.0,France,Male,40.0,8.0,121729.42,1.0,1.0,0.0,145551.6
+22141,15718773.0,T'ao,538.0,France,Female,41.0,8.0,0.0,2.0,1.0,1.0,152883.95
+22142,15691513.0,Iloerika,744.0,France,Male,36.0,8.0,0.0,1.0,1.0,0.0,180969.55
+22143,15767358.0,P'eng,644.0,France,Male,32.0,6.0,0.0,1.0,1.0,1.0,130789.15
+22144,15796958.0,Hsiung,686.0,Spain,Female,37.0,7.0,151933.63,1.0,1.0,0.0,72945.95
+22145,15677243.0,Hs?,610.0,France,Male,58.0,4.0,122446.61,1.0,1.0,0.0,53379.52
+22146,15790717.0,T'ao,588.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,52367.29
+22147,15632210.0,Iweobiegbunam,691.0,Spain,Female,34.0,9.0,128736.39,1.0,1.0,0.0,92664.09
+22148,15723299.0,Ch'ien,638.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,114448.77
+22149,15795963.0,Chinweike,576.0,France,Male,27.0,9.0,0.0,2.0,0.0,0.0,161243.09
+22150,15662091.0,Lai,721.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,138780.86
+22151,15606755.0,Hsieh,703.0,France,Male,43.0,2.0,0.0,1.0,0.0,1.0,91881.29
+22152,15794580.0,Onwumelu,678.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,181461.46
+22153,15753874.0,Ts'ao,558.0,Spain,Female,37.0,6.0,0.0,2.0,1.0,0.0,158973.77
+22154,15602851.0,Ch'iu,642.0,France,Male,36.0,8.0,126725.25,1.0,1.0,1.0,149614.81
+22155,15612109.0,T'an,686.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,160548.12
+22156,15763093.0,Maclean,578.0,Spain,Male,41.0,4.0,99844.68,2.0,1.0,0.0,71175.94
+22157,15620981.0,Hsueh,659.0,Germany,Male,31.0,9.0,96659.64,2.0,1.0,1.0,40321.87
+22158,15606901.0,Obiajulu,567.0,France,Female,46.0,5.0,0.0,2.0,0.0,0.0,64345.61
+22159,15696859.0,Okoli,516.0,France,Male,55.0,3.0,0.0,1.0,1.0,1.0,93478.96
+22160,15652626.0,Lai,617.0,France,Male,37.0,3.0,0.0,2.0,0.0,0.0,60917.24
+22161,15742285.0,Trevisano,543.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,82298.59
+22162,15687959.0,Toscani,850.0,France,Female,42.0,10.0,0.0,2.0,1.0,1.0,91354.41
+22163,15572792.0,T'ien,646.0,France,Female,40.0,1.0,0.0,2.0,0.0,1.0,91574.92
+22164,15641411.0,Iadanza,520.0,France,Male,31.0,0.0,106521.31,1.0,0.0,1.0,128881.87
+22165,15573452.0,H?,581.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,77951.84
+22166,15772892.0,Ch'iu,635.0,France,Female,38.0,3.0,97435.85,1.0,1.0,0.0,142331.85
+22167,15646563.0,Echezonachukwu,648.0,Germany,Male,41.0,3.0,141749.68,1.0,1.0,1.0,88890.75
+22168,15717995.0,Hsueh,609.0,France,Female,47.0,5.0,0.0,2.0,1.0,1.0,161388.22
+22169,15612108.0,Y?an,589.0,France,Male,30.0,0.0,0.0,1.0,1.0,0.0,183872.88
+22170,15680167.0,Chigozie,662.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,180360.34
+22171,15684319.0,Ch'iu,731.0,France,Female,40.0,3.0,0.0,1.0,1.0,0.0,119259.44
+22172,15750014.0,Scott,613.0,France,Male,38.0,4.0,113317.1,1.0,1.0,0.0,55313.51
+22173,15682533.0,Tung,713.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,134778.02
+22174,15692599.0,Hsia,520.0,France,Female,46.0,3.0,0.0,1.0,0.0,0.0,25310.82
+22175,15655274.0,Ugochukwu,703.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,77414.45
+22176,15671769.0,Hsueh,731.0,France,Male,27.0,1.0,0.0,2.0,1.0,0.0,26796.73
+22177,15646859.0,Smith,733.0,Germany,Male,47.0,9.0,138032.15,1.0,1.0,1.0,75825.57
+22178,15752502.0,Onyemauchechukwu,672.0,Germany,Male,28.0,9.0,120782.7,2.0,1.0,1.0,126761.2
+22179,15813681.0,Hsieh,850.0,France,Female,35.0,6.0,0.0,2.0,0.0,1.0,77830.78
+22180,15695792.0,Onyemachukwu,613.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,71047.51
+22181,15736146.0,De Mestre,746.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,115676.61
+22182,15798850.0,Hs?,590.0,Spain,Female,39.0,0.0,0.0,2.0,1.0,0.0,52629.73
+22183,15673570.0,Onio,549.0,Spain,Female,47.0,8.0,113858.95,1.0,1.0,0.0,82298.59
+22184,15635752.0,Scott,743.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,129936.53
+22185,15709511.0,Scott,757.0,France,Female,27.0,8.0,96415.57,1.0,1.0,1.0,26920.43
+22186,15754919.0,Teng,637.0,France,Male,56.0,8.0,0.0,1.0,0.0,0.0,104730.52
+22187,15578098.0,Chinwendu,679.0,Germany,Male,49.0,7.0,148074.79,1.0,1.0,0.0,114469.55
+22188,15600379.0,Milanesi,709.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,0.0,170195.4
+22189,15662232.0,Hs?,632.0,France,Male,46.0,8.0,137780.74,1.0,1.0,1.0,181757.19
+22190,15640409.0,Lai,570.0,France,Male,25.0,1.0,0.0,2.0,1.0,1.0,113677.53
+22191,15727499.0,Chukwualuka,539.0,Germany,Male,42.0,9.0,120911.75,1.0,1.0,0.0,85890.75
+22192,15677336.0,Chibugo,569.0,Germany,Male,29.0,1.0,139840.36,1.0,1.0,0.0,26389.92
+22193,15666055.0,Ugoji,594.0,Germany,Male,41.0,5.0,116037.76,1.0,0.0,0.0,131301.74
+22194,15798885.0,Galkin,614.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,152992.04
+22195,15615473.0,Nkemdirim,704.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,172782.69
+22196,15772381.0,Whitehead,648.0,France,Male,32.0,6.0,146584.19,1.0,1.0,1.0,176984.34
+22197,15668943.0,Martin,641.0,Spain,Female,50.0,4.0,0.0,2.0,1.0,1.0,120892.96
+22198,15772995.0,Ch'ang,738.0,France,Female,44.0,1.0,0.0,2.0,1.0,0.0,147963.07
+22199,15636023.0,Pinto,608.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,0.0,50610.62
+22200,15782899.0,Trevisano,627.0,Spain,Male,48.0,8.0,0.0,1.0,0.0,0.0,61290.99
+22201,15783875.0,Pirozzi,753.0,Germany,Female,28.0,7.0,118098.62,1.0,1.0,1.0,158049.9
+22202,15679048.0,Ts'ui,710.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,65765.22
+22203,15572461.0,Chukwukere,699.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,180329.83
+22204,15686963.0,Nwankwo,669.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,0.0,158420.14
+22205,15794560.0,Kao,582.0,France,Female,47.0,1.0,0.0,2.0,1.0,1.0,184842.21
+22206,15675650.0,O'Donnell,655.0,France,Male,41.0,9.0,125845.65,2.0,0.0,0.0,91429.74
+22207,15573077.0,Hsiao,850.0,Germany,Female,48.0,3.0,133656.93,1.0,1.0,1.0,64831.36
+22208,15635285.0,Lo,633.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,136610.01
+22209,15792726.0,DeRose,705.0,France,Male,49.0,3.0,0.0,2.0,1.0,0.0,72849.29
+22210,15690898.0,Calabresi,590.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,86414.24
+22211,15791131.0,Hsueh,609.0,Spain,Female,44.0,6.0,136526.26,1.0,1.0,0.0,168886.73
+22212,15803479.0,Hsia,581.0,Germany,Female,36.0,2.0,73348.56,2.0,1.0,1.0,152992.04
+22213,15656937.0,Palermo,676.0,France,Male,36.0,8.0,83116.68,1.0,1.0,1.0,103381.47
+22214,15807003.0,Ting,526.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,74890.58
+22215,15791767.0,Onuchukwu,516.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,5983.7
+22216,15617348.0,Milne,825.0,Germany,Female,51.0,3.0,128306.7,1.0,1.0,1.0,172601.62
+22217,15687959.0,Iweobiegbulam,763.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,40313.47
+22218,15686463.0,Udinesi,703.0,France,Female,27.0,3.0,148045.45,2.0,1.0,0.0,87816.86
+22219,15732963.0,Tsao,742.0,France,Female,29.0,8.0,0.0,2.0,0.0,0.0,131919.48
+22220,15750937.0,Hsing,638.0,Germany,Male,44.0,9.0,125537.38,1.0,1.0,0.0,172501.38
+22221,15808930.0,Palermo,618.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,44406.17
+22222,15738751.0,Rizzo,597.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,167564.82
+22223,15772383.0,Hs?,597.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,158071.69
+22224,15594812.0,Frolova,690.0,France,Female,33.0,4.0,123894.35,1.0,0.0,1.0,103914.4
+22225,15574743.0,Chukwukere,649.0,Germany,Female,33.0,1.0,97473.87,2.0,0.0,1.0,187597.98
+22226,15664504.0,McWilliams,585.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,82222.86
+22227,15658850.0,Hs?eh,592.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,169915.02
+22228,15593601.0,Hsia,619.0,Germany,Female,42.0,5.0,142387.31,1.0,1.0,0.0,80120.19
+22229,15668580.0,Tuan,805.0,France,Male,72.0,4.0,0.0,2.0,0.0,1.0,176924.21
+22230,15779985.0,Aksenov,554.0,Spain,Female,43.0,5.0,0.0,1.0,1.0,0.0,158043.11
+22231,15674880.0,Milani,641.0,Germany,Female,39.0,3.0,147726.98,1.0,1.0,0.0,171587.9
+22232,15669957.0,Yobanna,745.0,France,Male,46.0,5.0,0.0,2.0,1.0,0.0,149170.92
+22233,15777892.0,Chukwufumnanya,624.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,94498.82
+22234,15779904.0,L?,615.0,France,Female,33.0,7.0,142882.49,1.0,1.0,0.0,60906.12
+22235,15785042.0,Tikhonov,663.0,France,Female,27.0,5.0,0.0,2.0,0.0,1.0,89972.25
+22236,15763665.0,Tung,849.0,France,Male,31.0,2.0,0.0,1.0,1.0,0.0,138889.15
+22237,15629200.0,Ting,688.0,France,Male,40.0,7.0,0.0,2.0,0.0,1.0,163550.58
+22238,15593969.0,Hsiung,771.0,Germany,Female,42.0,9.0,92787.93,1.0,1.0,0.0,153419.62
+22239,15661629.0,Lo Duca,717.0,Germany,Male,37.0,6.0,93680.43,2.0,1.0,0.0,103945.58
+22240,15591932.0,Yefimov,590.0,France,Male,27.0,5.0,0.0,2.0,1.0,0.0,93551.36
+22241,15791501.0,Nnachetam,569.0,France,Female,39.0,6.0,0.0,2.0,1.0,0.0,94872.55
+22242,15664754.0,Aksenov,743.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,0.0,19370.73
+22243,15790683.0,Chidiebere,586.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,72350.47
+22244,15668575.0,Nucci,695.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,0.0,183656.12
+22245,15585928.0,Maclean,670.0,France,Male,35.0,0.0,151242.54,2.0,1.0,1.0,136497.28
+22246,15596972.0,Y?an,649.0,Germany,Male,40.0,8.0,121405.04,1.0,1.0,1.0,161217.18
+22247,15648064.0,Pinto,647.0,France,Female,38.0,7.0,0.0,2.0,0.0,1.0,103349.74
+22248,15732674.0,Ch'ien,586.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,62449.07
+22249,15682355.0,Ts'ui,731.0,France,Male,26.0,0.0,126729.41,1.0,1.0,0.0,172490.78
+22250,15776939.0,Tsou,607.0,Spain,Female,42.0,10.0,0.0,2.0,1.0,1.0,61312.15
+22251,15775625.0,Chia,681.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,61977.82
+22252,15772798.0,Chiemenam,644.0,Germany,Female,46.0,3.0,117899.95,1.0,1.0,0.0,186942.49
+22253,15697460.0,Uchenna,725.0,France,Male,40.0,7.0,141134.67,2.0,1.0,1.0,46811.77
+22254,15806420.0,Nwebube,651.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,161845.81
+22255,15588854.0,Maclean,627.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,92888.52
+22256,15702361.0,Nworie,559.0,France,Male,32.0,4.0,163733.82,1.0,0.0,0.0,126434.61
+22257,15583076.0,Folliero,627.0,Spain,Male,32.0,4.0,0.0,1.0,1.0,1.0,142331.85
+22258,15597909.0,T'ien,731.0,Spain,Male,38.0,9.0,114655.58,1.0,1.0,0.0,91098.76
+22259,15774727.0,Ugochukwu,724.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,38962.94
+22260,15807593.0,Tseng,634.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,110457.99
+22261,15793337.0,Ch'ang,630.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,93777.61
+22262,15706799.0,Ives,531.0,France,Female,35.0,9.0,0.0,1.0,1.0,0.0,132798.78
+22263,15588019.0,Ozoemena,746.0,France,Female,56.0,1.0,0.0,1.0,1.0,0.0,184843.77
+22264,15624781.0,White,724.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,116046.22
+22265,15679968.0,Manna,707.0,Spain,Male,21.0,1.0,0.0,2.0,1.0,1.0,66463.62
+22266,15707011.0,Chinwenma,685.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,145562.4
+22267,15697574.0,Nnamutaezinwa,593.0,France,Female,31.0,8.0,0.0,2.0,0.0,0.0,134901.56
+22268,15762928.0,Chukwuemeka,703.0,Spain,Male,42.0,5.0,116967.68,1.0,0.0,0.0,166471.79
+22269,15732628.0,Ch'ang,516.0,France,Male,28.0,5.0,0.0,2.0,0.0,1.0,68815.05
+22270,15665629.0,Ch'ang,757.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,44965.44
+22271,15632275.0,P'eng,711.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,59864.88
+22272,15782311.0,Ejimofor,705.0,France,Female,32.0,8.0,166833.86,1.0,0.0,0.0,124592.84
+22273,15583732.0,Tung,553.0,Germany,Male,46.0,1.0,141581.08,2.0,1.0,0.0,179631.85
+22274,15643438.0,Chia,581.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,91981.85
+22275,15619238.0,Chinonyelum,754.0,France,Female,48.0,8.0,0.0,2.0,1.0,0.0,121592.5
+22276,15760063.0,Hsiung,598.0,France,Male,44.0,1.0,130147.98,1.0,0.0,1.0,167636.15
+22277,15724296.0,Chiu,642.0,Germany,Female,49.0,6.0,143321.97,1.0,1.0,0.0,143317.42
+22278,15686964.0,Ibezimako,689.0,France,Female,67.0,1.0,104267.7,1.0,1.0,1.0,183490.99
+22279,15626008.0,DeRose,674.0,France,Male,31.0,1.0,82729.49,2.0,1.0,0.0,82662.73
+22280,15721251.0,Chukwuebuka,520.0,Germany,Male,45.0,6.0,123906.55,1.0,0.0,0.0,179655.85
+22281,15589924.0,Chigolum,521.0,Spain,Female,37.0,6.0,0.0,1.0,1.0,0.0,149517.71
+22282,15578251.0,Kosisochukwu,592.0,France,Male,48.0,0.0,110928.51,1.0,1.0,1.0,71420.16
+22283,15604576.0,Mazzanti,697.0,France,Female,47.0,7.0,121451.29,1.0,0.0,0.0,79866.73
+22284,15777922.0,Nnaife,637.0,Germany,Male,36.0,7.0,148061.89,1.0,0.0,1.0,141946.92
+22285,15681115.0,Smith,697.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,166476.95
+22286,15642715.0,DeRose,687.0,Spain,Female,36.0,9.0,0.0,2.0,1.0,1.0,97938.59
+22287,15702277.0,Fanucci,686.0,Germany,Male,33.0,1.0,121159.65,1.0,1.0,1.0,102260.16
+22288,15651740.0,Hsueh,774.0,France,Female,28.0,6.0,0.0,1.0,1.0,0.0,138871.12
+22289,15677052.0,Calabresi,551.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,143357.07
+22290,15622370.0,Onyinyechukwuka,554.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,143357.07
+22291,15576022.0,T'ang,779.0,Germany,Female,34.0,7.0,144895.04,2.0,1.0,1.0,148087.62
+22292,15572022.0,Chien,689.0,France,Male,42.0,8.0,0.0,2.0,0.0,0.0,136915.15
+22293,15614847.0,Chukwukere,692.0,Germany,Female,48.0,10.0,149318.55,4.0,1.0,0.0,113677.53
+22294,15780654.0,Yobachukwu,726.0,France,Male,32.0,5.0,117704.73,1.0,0.0,1.0,155711.64
+22295,15570134.0,Tuan,567.0,France,Male,37.0,2.0,0.0,1.0,1.0,1.0,114722.42
+22296,15605827.0,Dickson,649.0,Germany,Female,46.0,3.0,109052.04,1.0,1.0,0.0,51393.1
+22297,15632264.0,Hao,645.0,Spain,Female,45.0,7.0,0.0,1.0,1.0,0.0,177524.19
+22298,15663438.0,Chibuzo,547.0,France,Male,52.0,10.0,0.0,1.0,1.0,0.0,140763.18
+22299,15600510.0,T'ien,581.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,101240.08
+22300,15791326.0,Walker,709.0,Germany,Male,35.0,3.0,119266.69,2.0,1.0,1.0,179833.45
+22301,15800517.0,Tsao,543.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,121525.48
+22302,15775124.0,Trevisan,645.0,France,Female,32.0,3.0,101985.81,1.0,1.0,1.0,176925.69
+22303,15653776.0,Milne,769.0,France,Male,49.0,5.0,0.0,1.0,1.0,0.0,28698.17
+22304,15793424.0,Olague,709.0,France,Male,31.0,1.0,121365.38,1.0,1.0,1.0,143317.31
+22305,15571244.0,Sazonova,710.0,Germany,Female,30.0,1.0,125095.94,2.0,1.0,0.0,143481.85
+22306,15777060.0,Omeokachie,614.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,105720.09
+22307,15774931.0,Scott,614.0,France,Male,26.0,7.0,0.0,2.0,1.0,1.0,121751.04
+22308,15721462.0,Hs?eh,622.0,Germany,Female,49.0,1.0,152212.44,2.0,1.0,0.0,114066.77
+22309,15643916.0,Pagnotto,732.0,France,Female,35.0,10.0,0.0,1.0,1.0,1.0,9984.4
+22310,15710689.0,Shih,651.0,Germany,Female,47.0,0.0,102773.2,1.0,1.0,0.0,63736.68
+22311,15705009.0,Onyekachukwu,775.0,Spain,Male,36.0,8.0,127760.07,1.0,1.0,1.0,161069.73
+22312,15736302.0,Teng,754.0,Germany,Male,40.0,4.0,113386.36,3.0,1.0,0.0,58467.08
+22313,15765322.0,Rizzo,850.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,124749.04
+22314,15772363.0,T'ao,711.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,0.0,100556.98
+22315,15804937.0,Calabrese,628.0,Spain,Female,40.0,6.0,0.0,2.0,1.0,1.0,91981.85
+22316,15718057.0,Ch'ien,685.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,105720.09
+22317,15764284.0,Y?,703.0,France,Male,39.0,5.0,0.0,1.0,0.0,0.0,136050.44
+22318,15570134.0,Nworie,716.0,Germany,Female,51.0,3.0,92474.05,1.0,1.0,0.0,121525.48
+22319,15770041.0,Ch'ang,615.0,Germany,Female,42.0,1.0,99897.59,2.0,1.0,0.0,167036.94
+22320,15723989.0,McIntosh,715.0,France,Female,29.0,2.0,0.0,1.0,1.0,0.0,163736.09
+22321,15798084.0,Maclean,633.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,60905.51
+22322,15576526.0,Aksyonova,682.0,France,Male,69.0,8.0,0.0,2.0,1.0,1.0,48378.4
+22323,15701352.0,Ndubueze,660.0,Spain,Male,30.0,5.0,0.0,2.0,1.0,1.0,142223.94
+22324,15605968.0,Skelton,699.0,Germany,Male,27.0,10.0,127823.93,2.0,1.0,1.0,161344.13
+22325,15594812.0,Ts'ui,663.0,Germany,Female,39.0,4.0,131764.96,2.0,0.0,1.0,108469.2
+22326,15801277.0,Nweke,619.0,France,Female,48.0,9.0,0.0,1.0,1.0,1.0,108577.97
+22327,15581282.0,Okwuoma,729.0,France,Male,47.0,4.0,0.0,2.0,1.0,0.0,80582.34
+22328,15693543.0,Aitken,619.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,148769.08
+22329,15785819.0,Tuan,727.0,Spain,Male,39.0,1.0,0.0,1.0,0.0,0.0,173365.9
+22330,15676521.0,Chidiegwu,751.0,Germany,Female,46.0,8.0,121411.9,2.0,1.0,0.0,126812.63
+22331,15769216.0,T'ang,635.0,France,Male,49.0,8.0,0.0,2.0,1.0,0.0,166317.71
+22332,15592707.0,Iadanza,746.0,France,Male,28.0,9.0,102290.57,1.0,1.0,0.0,67789.99
+22333,15726153.0,Ch'iu,587.0,France,Male,37.0,4.0,110340.68,1.0,1.0,0.0,143585.29
+22334,15658813.0,Cremonesi,595.0,France,Female,34.0,9.0,0.0,1.0,0.0,1.0,141756.32
+22335,15623989.0,Oguejiofor,642.0,Spain,Male,44.0,6.0,0.0,2.0,0.0,0.0,145490.85
+22336,15672798.0,Ch'in,718.0,France,Male,41.0,9.0,0.0,1.0,0.0,1.0,60728.89
+22337,15651883.0,Krichauff,556.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,0.0,60905.51
+22338,15774738.0,Shih,718.0,Spain,Female,31.0,1.0,121581.56,1.0,1.0,0.0,126794.56
+22339,15703399.0,Calabrese,553.0,Germany,Male,40.0,9.0,122453.27,2.0,1.0,0.0,118429.02
+22340,15588517.0,Onyekachi,545.0,France,Male,34.0,6.0,0.0,1.0,1.0,1.0,76773.53
+22341,15631451.0,H?,589.0,France,Female,20.0,6.0,0.0,2.0,1.0,0.0,63736.17
+22342,15593094.0,Ts'ui,685.0,France,Male,32.0,1.0,83568.26,1.0,1.0,1.0,116766.79
+22343,15746664.0,Pirozzi,697.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,170984.87
+22344,15612043.0,Cawood,815.0,France,Male,44.0,5.0,0.0,2.0,1.0,0.0,145551.6
+22345,15766064.0,Folliero,707.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,115217.59
+22346,15591036.0,T'ao,565.0,France,Female,39.0,1.0,0.0,1.0,1.0,0.0,132038.65
+22347,15756848.0,McElhone,681.0,France,Female,43.0,7.0,0.0,1.0,1.0,0.0,70954.41
+22348,15700937.0,Tien,699.0,Spain,Female,48.0,7.0,0.0,1.0,1.0,0.0,59957.83
+22349,15592461.0,Ch'ang,684.0,France,Male,53.0,0.0,0.0,1.0,1.0,0.0,141803.77
+22350,15656300.0,Chidimma,695.0,France,Male,44.0,4.0,0.0,2.0,1.0,0.0,109057.56
+22351,15607786.0,Ugochukwu,583.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,133091.32
+22352,15775108.0,O'Donnell,678.0,Germany,Male,31.0,7.0,124098.54,1.0,1.0,1.0,181605.85
+22353,15749103.0,H?,590.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,66561.4
+22354,15663927.0,Chiang,717.0,France,Female,29.0,6.0,96152.68,1.0,1.0,1.0,169621.69
+22355,15667938.0,Onwubiko,642.0,Germany,Male,25.0,4.0,103033.83,1.0,1.0,0.0,115068.99
+22356,15602851.0,Ukaegbunam,521.0,France,Male,55.0,5.0,122550.05,1.0,1.0,1.0,177747.03
+22357,15631217.0,Hsiao,663.0,Germany,Male,29.0,4.0,90217.07,2.0,1.0,1.0,69244.24
+22358,15642824.0,Hs?eh,627.0,Germany,Male,28.0,2.0,112727.06,2.0,1.0,0.0,38596.34
+22359,15694456.0,T'ien,676.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,102628.98
+22360,15641723.0,Houghton,712.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,125062.02
+22361,15656613.0,Chukwukere,675.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,116973.19
+22362,15736302.0,Ch'ien,590.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,104889.3
+22363,15615254.0,Tuan,547.0,France,Female,51.0,1.0,114655.58,1.0,1.0,1.0,9209.21
+22364,15605020.0,Oluchukwu,850.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,0.0,88965.46
+22365,15770185.0,Frolova,596.0,France,Male,59.0,6.0,0.0,2.0,1.0,1.0,133086.45
+22366,15669326.0,Yancy,471.0,France,Male,37.0,4.0,122227.71,1.0,1.0,1.0,75832.53
+22367,15634896.0,Onyekachukwu,648.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,147090.9
+22368,15802585.0,Chiekwugo,706.0,France,Female,64.0,4.0,0.0,2.0,0.0,1.0,100557.03
+22369,15754569.0,Fiorentino,593.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,79329.7
+22370,15742272.0,Hsia,652.0,Spain,Female,27.0,6.0,0.0,2.0,1.0,0.0,134463.24
+22371,15626143.0,Maclean,463.0,Germany,Male,58.0,2.0,131563.88,1.0,1.0,0.0,131301.74
+22372,15589435.0,McLean,648.0,Spain,Female,53.0,7.0,0.0,1.0,1.0,1.0,143635.01
+22373,15795927.0,Niu,691.0,France,Female,37.0,9.0,93578.68,1.0,1.0,0.0,160546.58
+22374,15756901.0,Ch'ien,651.0,France,Female,40.0,6.0,0.0,4.0,1.0,0.0,120852.85
+22375,15724334.0,K'ung,724.0,France,Female,41.0,6.0,122329.26,1.0,1.0,0.0,125877.22
+22376,15730397.0,Okechukwu,775.0,France,Male,50.0,4.0,0.0,1.0,0.0,1.0,131807.14
+22377,15631681.0,Shao,703.0,France,Female,34.0,8.0,0.0,2.0,0.0,0.0,78834.41
+22378,15759499.0,H?,642.0,Germany,Female,43.0,9.0,147167.25,2.0,1.0,0.0,100941.57
+22379,15594815.0,Mazzanti,526.0,France,Female,44.0,2.0,0.0,1.0,1.0,0.0,96546.55
+22380,15778936.0,Ch'in,651.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,63747.51
+22381,15706458.0,Yobachukwu,706.0,France,Female,55.0,8.0,127269.75,1.0,1.0,0.0,75762.0
+22382,15654891.0,T'ang,850.0,Germany,Male,38.0,9.0,134740.19,2.0,1.0,0.0,86414.41
+22383,15798975.0,Onwumelu,681.0,France,Female,61.0,2.0,0.0,1.0,1.0,0.0,72852.74
+22384,15792922.0,P'eng,608.0,Spain,Male,38.0,5.0,123906.55,1.0,1.0,0.0,143829.54
+22385,15644296.0,Hsiao,583.0,Germany,Female,39.0,4.0,144875.34,1.0,0.0,0.0,103315.72
+22386,15769125.0,Scott,656.0,France,Male,51.0,7.0,0.0,2.0,1.0,1.0,93769.89
+22387,15707556.0,Okwudiliolisa,675.0,France,Female,53.0,9.0,0.0,1.0,1.0,0.0,168034.9
+22388,15743075.0,Hsia,607.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,161894.4
+22389,15757829.0,Onyemauchechukwu,677.0,Spain,Female,38.0,4.0,0.0,1.0,1.0,0.0,115818.17
+22390,15603880.0,Fennell,671.0,France,Female,29.0,6.0,0.0,2.0,1.0,1.0,76637.38
+22391,15734565.0,McLean,704.0,France,Male,25.0,3.0,0.0,2.0,1.0,0.0,122381.02
+22392,15681075.0,Hao,592.0,France,Female,42.0,6.0,0.0,2.0,1.0,1.0,88890.05
+22393,15777772.0,Tuan,598.0,Spain,Male,43.0,7.0,99106.73,1.0,1.0,0.0,17949.41
+22394,15687094.0,Mao,535.0,France,Female,53.0,9.0,0.0,1.0,1.0,0.0,136985.08
+22395,15595036.0,Hsieh,629.0,France,Male,29.0,2.0,137523.02,1.0,1.0,1.0,4050.57
+22396,15585068.0,Milani,709.0,France,Male,37.0,7.0,133537.1,1.0,1.0,0.0,146715.07
+22397,15583499.0,Ts'ai,731.0,France,Male,47.0,7.0,0.0,1.0,1.0,0.0,40313.47
+22398,15736287.0,T'ao,528.0,Spain,Female,41.0,1.0,0.0,2.0,1.0,1.0,107837.43
+22399,15631815.0,Hs?eh,667.0,France,Male,49.0,8.0,152303.8,1.0,0.0,0.0,149772.61
+22400,15730137.0,Ts'ai,724.0,France,Female,42.0,6.0,106301.85,1.0,1.0,0.0,4798.12
+22401,15709920.0,Echezonachukwu,635.0,France,Female,40.0,9.0,125845.66,1.0,0.0,1.0,60988.21
+22402,15651022.0,Ch'ang,744.0,France,Male,27.0,2.0,134562.9,1.0,0.0,0.0,117972.45
+22403,15643574.0,Chikezie,636.0,Germany,Male,31.0,7.0,146033.62,1.0,0.0,0.0,114448.77
+22404,15657566.0,Bitter,665.0,France,Male,38.0,3.0,0.0,2.0,0.0,1.0,59893.8
+22405,15641091.0,Chiang,627.0,France,Male,40.0,3.0,0.0,1.0,1.0,1.0,167773.55
+22406,15611171.0,Hsiao,773.0,Spain,Male,41.0,2.0,0.0,2.0,1.0,0.0,87537.32
+22407,15594878.0,Fang,755.0,France,Female,34.0,8.0,0.0,2.0,0.0,0.0,173365.89
+22408,15766272.0,Chiang,576.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,85982.47
+22409,15696733.0,Ch'in,710.0,France,Male,41.0,9.0,91536.93,1.0,1.0,1.0,118045.98
+22410,15786680.0,Lassetter,719.0,Germany,Female,36.0,9.0,153360.02,1.0,1.0,0.0,55313.51
+22411,15806447.0,Ponomaryov,682.0,France,Male,47.0,9.0,0.0,1.0,0.0,1.0,123954.15
+22412,15637354.0,Milani,646.0,Spain,Female,40.0,8.0,0.0,1.0,0.0,0.0,109742.8
+22413,15697318.0,Nickson,663.0,France,Female,38.0,8.0,0.0,2.0,0.0,1.0,122823.24
+22414,15765014.0,Nnaife,763.0,France,Female,30.0,4.0,143054.85,1.0,1.0,0.0,53710.23
+22415,15687765.0,Onyemauchechi,668.0,Spain,Male,39.0,9.0,0.0,1.0,0.0,1.0,170818.28
+22416,15703037.0,O'Donnell,587.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,139947.17
+22417,15583595.0,O'Toole,735.0,France,Female,33.0,2.0,0.0,1.0,0.0,1.0,143954.99
+22418,15714789.0,Chidubem,684.0,Germany,Male,49.0,4.0,116622.73,2.0,1.0,0.0,161671.15
+22419,15625941.0,Ch'en,636.0,Spain,Male,33.0,3.0,152265.43,1.0,1.0,1.0,143598.7
+22420,15784923.0,Iqbal,746.0,Germany,Female,41.0,7.0,105473.74,1.0,1.0,0.0,144290.97
+22421,15776631.0,Martin,632.0,Germany,Female,46.0,2.0,111756.5,1.0,1.0,0.0,172576.43
+22422,15643487.0,Shih,684.0,Germany,Male,43.0,5.0,145260.23,2.0,1.0,0.0,187905.12
+22423,15573077.0,Yobachi,711.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,49108.23
+22424,15811486.0,Chinwendu,693.0,France,Female,31.0,4.0,0.0,2.0,1.0,0.0,54871.02
+22425,15641298.0,Chigbogu,635.0,France,Female,46.0,7.0,0.0,1.0,1.0,0.0,60954.45
+22426,15674720.0,Liao,564.0,France,Female,37.0,5.0,0.0,1.0,1.0,0.0,122381.02
+22427,15809302.0,O'Meara,554.0,Spain,Female,31.0,7.0,0.0,2.0,0.0,0.0,93719.87
+22428,15771299.0,Hao,850.0,Spain,Female,38.0,3.0,0.0,1.0,0.0,0.0,169915.02
+22429,15782530.0,Trevisani,743.0,France,Female,27.0,8.0,121854.45,1.0,1.0,0.0,81256.29
+22430,15689633.0,Ch'ang,550.0,Germany,Male,51.0,1.0,104598.19,3.0,1.0,0.0,125777.28
+22431,15777772.0,Genovese,598.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,187616.16
+22432,15602845.0,Nwachinemelu,590.0,France,Female,45.0,2.0,0.0,1.0,1.0,1.0,125658.28
+22433,15791533.0,Manna,648.0,France,Male,32.0,1.0,137933.07,1.0,1.0,0.0,149550.97
+22434,15792650.0,Tien,549.0,Germany,Male,34.0,4.0,132598.38,2.0,1.0,1.0,51554.58
+22435,15798868.0,Chukwudi,599.0,Germany,Male,35.0,1.0,111095.85,1.0,1.0,1.0,122899.01
+22436,15677310.0,Ritchie,835.0,France,Female,45.0,6.0,108883.29,1.0,1.0,1.0,172063.09
+22437,15734762.0,Bibi,588.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,181918.26
+22438,15658048.0,Fang,582.0,France,Female,29.0,9.0,0.0,1.0,1.0,1.0,152733.89
+22439,15757912.0,Yobachukwu,604.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,1.0,64516.8
+22440,15788367.0,Y?,593.0,Spain,Female,32.0,2.0,0.0,2.0,1.0,1.0,45419.08
+22441,15773779.0,Olisanugo,615.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,25339.29
+22442,15643908.0,Teng,771.0,Spain,Female,35.0,8.0,0.0,1.0,0.0,1.0,70774.97
+22443,15669922.0,Chidimma,710.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,1710.89
+22444,15632578.0,Tsou,763.0,Spain,Female,28.0,1.0,0.0,2.0,1.0,1.0,73564.44
+22445,15585855.0,K?,781.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,121073.23
+22446,15596713.0,Maclean,697.0,Germany,Male,35.0,5.0,131729.17,2.0,1.0,0.0,123954.45
+22447,15636407.0,Hs?,730.0,Spain,Female,23.0,2.0,0.0,2.0,0.0,1.0,109296.18
+22448,15589475.0,Yeh,656.0,France,Male,45.0,1.0,0.0,1.0,0.0,1.0,144413.92
+22449,15718281.0,H?,682.0,Germany,Male,35.0,7.0,84309.57,2.0,1.0,0.0,79323.61
+22450,15756071.0,Onyekachi,850.0,France,Male,41.0,10.0,0.0,1.0,1.0,0.0,141533.19
+22451,15636595.0,Aitken,678.0,France,Female,34.0,7.0,0.0,1.0,1.0,1.0,97633.93
+22452,15700854.0,Maclean,659.0,Spain,Female,42.0,2.0,79450.09,1.0,0.0,1.0,175025.27
+22453,15682618.0,Teng,592.0,Spain,Male,37.0,6.0,0.0,2.0,0.0,0.0,162511.55
+22454,15656414.0,Nnachetam,699.0,France,Male,26.0,2.0,121404.64,1.0,1.0,1.0,161845.81
+22455,15800295.0,Yermakov,597.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,171045.31
+22456,15763617.0,P'an,714.0,Spain,Female,54.0,1.0,0.0,1.0,0.0,0.0,53817.23
+22457,15798839.0,P'an,479.0,France,Female,33.0,4.0,0.0,1.0,0.0,0.0,117036.38
+22458,15721740.0,Chukwuemeka,571.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,82647.65
+22459,15776743.0,Chien,595.0,France,Male,31.0,6.0,123960.97,1.0,0.0,1.0,175843.68
+22460,15752578.0,Aitken,561.0,France,Male,35.0,8.0,0.0,2.0,0.0,0.0,168527.56
+22461,15729377.0,O'Brien,537.0,Spain,Male,19.0,2.0,0.0,2.0,1.0,0.0,144423.17
+22462,15793726.0,Ewing,727.0,Germany,Female,35.0,3.0,139592.98,2.0,1.0,0.0,121407.48
+22463,15591512.0,Hsia,586.0,France,Male,35.0,2.0,74989.58,2.0,1.0,0.0,79623.28
+22464,15635244.0,Zito,778.0,Spain,Male,52.0,0.0,117565.03,1.0,1.0,1.0,167032.67
+22465,15795872.0,Ts'ai,615.0,Spain,Female,55.0,6.0,0.0,1.0,1.0,1.0,162599.51
+22466,15609704.0,Kerr,685.0,France,Female,41.0,1.0,137853.0,2.0,0.0,0.0,179436.6
+22467,15752388.0,Chiu,703.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,96660.76
+22468,15711352.0,Nwagugheuzo,738.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,149807.01
+22469,15571059.0,Bovee,850.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,0.0,102076.03
+22470,15585041.0,Nwachinemelu,745.0,France,Female,40.0,8.0,0.0,1.0,1.0,0.0,83681.2
+22471,15728352.0,Genovese,703.0,Spain,Female,60.0,6.0,178827.3,1.0,1.0,1.0,159576.75
+22472,15754849.0,Baresi,624.0,France,Female,44.0,0.0,0.0,1.0,1.0,0.0,146891.07
+22473,15791216.0,Trevisani,709.0,Spain,Female,33.0,2.0,0.0,2.0,0.0,1.0,84635.64
+22474,15770091.0,Hsiung,719.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,106564.88
+22475,15658548.0,Hsing,707.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,82951.12
+22476,15795540.0,Istomin,714.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,87071.18
+22477,15801559.0,Chienezie,602.0,France,Male,34.0,6.0,0.0,2.0,0.0,1.0,96833.0
+22478,15624703.0,Chidiebele,651.0,Spain,Female,46.0,5.0,114519.57,2.0,0.0,0.0,147040.25
+22479,15761497.0,Tsou,577.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,179780.74
+22480,15607988.0,Dickson,648.0,Spain,Male,33.0,7.0,0.0,1.0,1.0,0.0,180345.44
+22481,15746190.0,Yobanna,681.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,141533.19
+22482,15718507.0,Chukwuhaenye,571.0,Germany,Male,38.0,3.0,140745.33,2.0,1.0,0.0,173486.39
+22483,15793890.0,Ts'ao,682.0,France,Female,29.0,6.0,0.0,2.0,0.0,0.0,179421.84
+22484,15651022.0,Ch'ien,622.0,Germany,Female,34.0,2.0,132865.56,1.0,1.0,0.0,104722.47
+22485,15794870.0,Shih,571.0,France,Female,24.0,3.0,0.0,2.0,1.0,1.0,123880.19
+22486,15596979.0,Smith,678.0,France,Female,54.0,4.0,0.0,1.0,1.0,1.0,132770.11
+22487,15782390.0,Hsia,637.0,Germany,Female,45.0,8.0,141819.02,1.0,1.0,0.0,135088.58
+22488,15716134.0,Ni,667.0,Spain,Male,53.0,3.0,0.0,1.0,1.0,0.0,178291.09
+22489,15706268.0,Yuriev,651.0,France,Female,50.0,7.0,0.0,3.0,1.0,0.0,131644.41
+22490,15783882.0,Ubanwa,706.0,France,Male,47.0,4.0,0.0,2.0,0.0,1.0,92898.17
+22491,15674671.0,Chiang,746.0,Spain,Male,26.0,4.0,0.0,2.0,1.0,1.0,161069.73
+22492,15685920.0,Pirozzi,705.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,176858.5
+22493,15756920.0,Yermolayeva,850.0,France,Female,34.0,7.0,120718.28,1.0,1.0,1.0,143357.07
+22494,15782364.0,Ugochukwu,707.0,France,Male,46.0,7.0,132351.29,2.0,1.0,1.0,54865.92
+22495,15768293.0,Kolesnikov,588.0,Germany,Male,47.0,4.0,161608.81,2.0,1.0,0.0,126518.0
+22496,15579914.0,Chinwendu,705.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,140941.47
+22497,15656471.0,Kambinachi,683.0,France,Male,38.0,2.0,0.0,2.0,0.0,1.0,113796.15
+22498,15682536.0,Chukwualuka,633.0,Spain,Female,45.0,10.0,0.0,2.0,1.0,0.0,75632.78
+22499,15749167.0,Chin,667.0,France,Female,27.0,5.0,0.0,2.0,0.0,1.0,103921.43
+22500,15803976.0,Yobanna,604.0,Germany,Male,32.0,4.0,90723.36,2.0,1.0,1.0,166476.95
+22501,15591035.0,Tretiakova,704.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,160579.19
+22502,15662291.0,Kosisochukwu,667.0,France,Male,26.0,7.0,121172.97,1.0,1.0,1.0,124749.08
+22503,15713463.0,Chukwukadibia,586.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,134767.34
+22504,15686984.0,Chidumaga,719.0,Spain,Male,46.0,3.0,0.0,2.0,0.0,1.0,101694.67
+22505,15692991.0,Tretiakova,656.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,94774.12
+22506,15779915.0,Zikoranachidimma,652.0,France,Female,51.0,3.0,0.0,1.0,1.0,0.0,181543.67
+22507,15576131.0,Nkemdilim,691.0,France,Female,62.0,2.0,107541.06,1.0,1.0,1.0,126551.65
+22508,15807008.0,Ch'ien,579.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,138527.56
+22509,15772798.0,Iloerika,628.0,Germany,Female,46.0,6.0,138664.57,1.0,1.0,0.0,149822.04
+22510,15660155.0,Chigolum,710.0,Germany,Female,27.0,6.0,162702.35,2.0,1.0,1.0,121553.19
+22511,15604217.0,Shao,693.0,Spain,Female,57.0,9.0,0.0,1.0,1.0,1.0,160696.72
+22512,15779267.0,Yobanna,616.0,Spain,Male,37.0,4.0,118022.06,1.0,1.0,1.0,166694.93
+22513,15737408.0,Ch'iu,481.0,Spain,Male,34.0,2.0,0.0,2.0,0.0,0.0,125848.88
+22514,15642099.0,Onyekaozulu,576.0,France,Female,31.0,3.0,110689.07,1.0,1.0,0.0,172810.52
+22515,15656937.0,Pirozzi,685.0,France,Female,41.0,7.0,0.0,1.0,1.0,0.0,160548.12
+22516,15667632.0,Kudryashova,627.0,Spain,Female,27.0,5.0,0.0,2.0,0.0,1.0,55857.48
+22517,15673372.0,Dike,682.0,France,Female,43.0,5.0,0.0,1.0,1.0,0.0,60375.18
+22518,15699238.0,Tsao,749.0,Germany,Male,35.0,10.0,120158.74,2.0,1.0,0.0,77977.69
+22519,15809462.0,Hs?,573.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,131629.71
+22520,15714883.0,Eluemuno,659.0,France,Female,62.0,6.0,0.0,1.0,1.0,0.0,139469.11
+22521,15668894.0,McIntyre,684.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,133086.45
+22522,15570970.0,Hsiao,669.0,Germany,Male,27.0,7.0,147043.89,2.0,1.0,0.0,137028.51
+22523,15668575.0,Onyeorulu,581.0,France,Female,31.0,4.0,0.0,1.0,1.0,0.0,89566.74
+22524,15794849.0,Chibueze,645.0,Spain,Male,44.0,9.0,0.0,2.0,1.0,0.0,121239.65
+22525,15641091.0,Chinedum,613.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,28276.83
+22526,15576094.0,Uvarova,679.0,France,Female,42.0,2.0,0.0,2.0,1.0,1.0,176913.42
+22527,15735782.0,Nkemjika,656.0,Germany,Female,54.0,10.0,124580.69,1.0,0.0,0.0,178045.97
+22528,15785865.0,Onwuamaeze,773.0,Spain,Male,26.0,5.0,79562.98,1.0,0.0,1.0,99490.01
+22529,15775750.0,Chiazagomekpere,713.0,Germany,Female,52.0,5.0,150148.51,1.0,1.0,0.0,92888.52
+22530,15705663.0,Yefremova,635.0,France,Male,39.0,1.0,87842.98,1.0,0.0,1.0,168840.22
+22531,15581229.0,Ugochukwu,593.0,Spain,Female,34.0,4.0,0.0,1.0,1.0,1.0,51536.99
+22532,15665629.0,Chien,567.0,France,Male,41.0,3.0,131326.47,1.0,0.0,1.0,173321.65
+22533,15806447.0,Smith,706.0,Spain,Male,46.0,7.0,0.0,2.0,1.0,1.0,48249.88
+22534,15770309.0,Ch'iu,591.0,France,Female,29.0,8.0,0.0,1.0,1.0,1.0,178045.97
+22535,15594404.0,Lettiere,538.0,Germany,Female,41.0,3.0,135962.2,1.0,0.0,1.0,12796.43
+22536,15761053.0,Liao,678.0,France,Male,54.0,2.0,0.0,2.0,1.0,0.0,128247.03
+22537,15784761.0,Tsai,559.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,120790.02
+22538,15598884.0,Hsing,773.0,France,Female,40.0,2.0,0.0,2.0,0.0,1.0,97769.04
+22539,15740406.0,P'eng,555.0,Germany,Male,37.0,4.0,91893.32,1.0,1.0,0.0,71173.64
+22540,15651052.0,Chien,741.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,125870.5
+22541,15782360.0,Hsiao,731.0,France,Male,34.0,5.0,114715.71,2.0,1.0,1.0,78915.68
+22542,15787035.0,Ch'ang,708.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,60901.72
+22543,15681180.0,Ch'iu,753.0,France,Male,25.0,2.0,152314.66,1.0,1.0,1.0,114442.66
+22544,15789425.0,Lu,618.0,Spain,Male,32.0,4.0,0.0,1.0,1.0,1.0,85983.54
+22545,15762615.0,Hsueh,710.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,112554.68
+22546,15665438.0,Nwachukwu,612.0,Germany,Female,50.0,2.0,126152.84,2.0,1.0,0.0,96660.76
+22547,15750731.0,Chukwumaobim,554.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,182784.11
+22548,15648069.0,Chung,558.0,Germany,Female,44.0,8.0,132559.4,1.0,1.0,1.0,59938.81
+22549,15671209.0,H?,678.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,97939.4
+22550,15696534.0,Hsieh,669.0,France,Male,59.0,9.0,129855.32,1.0,1.0,1.0,54869.52
+22551,15684801.0,Chidozie,675.0,Germany,Female,40.0,10.0,102823.34,1.0,0.0,0.0,91936.1
+22552,15735688.0,Ts'ui,718.0,France,Female,39.0,5.0,181671.16,1.0,1.0,1.0,70550.0
+22553,15660390.0,Young,621.0,Spain,Male,38.0,6.0,0.0,2.0,0.0,0.0,132468.76
+22554,15796370.0,Iloerika,628.0,France,Female,50.0,3.0,116623.31,2.0,0.0,0.0,172586.89
+22555,15631310.0,Nwachukwu,573.0,Germany,Female,35.0,5.0,123815.86,1.0,0.0,0.0,52332.85
+22556,15603082.0,Ch'iu,614.0,France,Female,27.0,9.0,0.0,2.0,0.0,1.0,86786.41
+22557,15725698.0,Chia,698.0,Spain,Female,37.0,8.0,131389.06,1.0,0.0,1.0,57549.65
+22558,15605673.0,Milani,595.0,France,Male,38.0,2.0,0.0,1.0,1.0,0.0,87816.86
+22559,15692408.0,Dominguez,558.0,Germany,Male,24.0,2.0,153058.64,1.0,1.0,1.0,49480.66
+22560,15743067.0,Ch'eng,671.0,France,Male,46.0,3.0,0.0,2.0,1.0,0.0,54290.62
+22561,15585533.0,T'ien,705.0,France,Female,43.0,9.0,0.0,2.0,0.0,1.0,81773.82
+22562,15707962.0,Lai,679.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,123882.73
+22563,15741181.0,Lucchese,704.0,Spain,Female,27.0,5.0,116969.28,1.0,1.0,0.0,80701.72
+22564,15793049.0,Hsu,663.0,Germany,Female,57.0,6.0,132796.04,1.0,1.0,0.0,125518.32
+22565,15793046.0,Martin,714.0,Germany,Male,35.0,9.0,118788.57,1.0,1.0,0.0,52332.85
+22566,15790782.0,Hs?,686.0,Spain,Male,42.0,9.0,0.0,1.0,1.0,1.0,153955.38
+22567,15794941.0,P'eng,543.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,187616.16
+22568,15621546.0,Hsueh,715.0,France,Female,44.0,8.0,0.0,2.0,1.0,0.0,120225.62
+22569,15578006.0,Trevisani,643.0,Germany,Female,34.0,3.0,124788.18,2.0,1.0,1.0,118059.19
+22570,15643824.0,Ibeamaka,679.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,0.0,109538.35
+22571,15807595.0,Lucchesi,744.0,France,Male,31.0,9.0,68213.99,1.0,1.0,0.0,79355.87
+22572,15655238.0,Chikwendu,539.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,88890.05
+22573,15643359.0,Ponomaryova,652.0,Spain,Male,33.0,4.0,0.0,1.0,1.0,1.0,82951.12
+22574,15661670.0,Ch'in,748.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,107875.1
+22575,15604818.0,Kable,678.0,Spain,Female,52.0,9.0,151709.33,2.0,1.0,1.0,52327.57
+22576,15689955.0,Hsing,659.0,Germany,Male,42.0,7.0,102520.37,1.0,1.0,0.0,116748.14
+22577,15686957.0,Teng,592.0,France,Female,46.0,3.0,0.0,2.0,1.0,0.0,93959.96
+22578,15749083.0,Chibuzo,714.0,Spain,Male,45.0,2.0,137811.75,1.0,1.0,1.0,164815.59
+22579,15758031.0,King,850.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,172501.38
+22580,15751032.0,Shen,710.0,Germany,Male,44.0,8.0,151855.33,1.0,1.0,1.0,94875.03
+22581,15593776.0,Tsao,597.0,Spain,Male,30.0,1.0,0.0,2.0,0.0,1.0,90863.73
+22582,15583748.0,Iweobiegbunam,629.0,France,Female,42.0,4.0,0.0,1.0,1.0,1.0,60552.44
+22583,15667417.0,Nkemdilim,833.0,Germany,Female,37.0,3.0,144961.97,1.0,1.0,0.0,108469.2
+22584,15631639.0,Trevisani,588.0,Germany,Female,54.0,3.0,128486.11,1.0,1.0,0.0,120852.85
+22585,15671390.0,Lai,756.0,Spain,Female,35.0,5.0,0.0,2.0,0.0,0.0,102233.73
+22586,15794868.0,Chinweuba,656.0,Spain,Female,32.0,1.0,0.0,2.0,1.0,1.0,187632.22
+22587,15782210.0,Okonkwo,581.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,95583.45
+22588,15658577.0,Lo,749.0,Germany,Female,38.0,9.0,134796.87,2.0,1.0,1.0,93844.88
+22589,15647474.0,Lucchesi,648.0,Spain,Male,46.0,5.0,0.0,2.0,0.0,1.0,40320.3
+22590,15567335.0,Frolova,652.0,France,Female,61.0,4.0,0.0,1.0,1.0,0.0,141872.05
+22591,15635388.0,Romani,706.0,Germany,Male,28.0,3.0,105792.49,2.0,1.0,0.0,152290.28
+22592,15660809.0,Folliero,611.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,170886.88
+22593,15637428.0,Uwakwe,576.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,187811.71
+22594,15785547.0,H?,645.0,Germany,Male,37.0,3.0,81210.61,1.0,1.0,1.0,120893.85
+22595,15657085.0,Walker,545.0,Germany,Female,44.0,5.0,126835.32,1.0,1.0,1.0,82900.25
+22596,15636595.0,Onyemauchechukwu,638.0,Spain,Female,47.0,9.0,0.0,2.0,1.0,1.0,118749.58
+22597,15612455.0,Ch'iu,588.0,France,Female,37.0,7.0,0.0,1.0,1.0,0.0,137908.05
+22598,15680201.0,Mancini,721.0,France,Male,44.0,9.0,126442.59,1.0,1.0,0.0,79323.61
+22599,15793813.0,Onyekachukwu,529.0,Spain,Male,35.0,5.0,154475.74,1.0,1.0,1.0,146875.86
+22600,15572762.0,Chukwuebuka,554.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,138841.59
+22601,15671610.0,Teng,587.0,Spain,Male,26.0,8.0,0.0,1.0,1.0,0.0,131827.15
+22602,15737025.0,Hs?,714.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,84863.85
+22603,15670080.0,Obijiaku,627.0,France,Female,55.0,5.0,0.0,1.0,1.0,0.0,93873.13
+22604,15783659.0,Chiu,697.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,84863.85
+22605,15691543.0,Trevisani,735.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,55893.37
+22606,15586699.0,Maclean,718.0,France,Female,33.0,1.0,117989.73,1.0,0.0,1.0,113581.83
+22607,15760090.0,Pagnotto,596.0,France,Female,37.0,1.0,135936.1,1.0,0.0,1.0,199290.68
+22608,15572762.0,Hs?,727.0,France,Male,46.0,2.0,0.0,1.0,1.0,0.0,170893.85
+22609,15772383.0,Onyeorulu,671.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,64853.51
+22610,15684958.0,Chukwufumnanya,621.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,93844.82
+22611,15700937.0,Zhirov,706.0,France,Female,33.0,9.0,0.0,2.0,0.0,1.0,93777.63
+22612,15802625.0,Chidi,725.0,France,Female,48.0,8.0,0.0,1.0,1.0,0.0,135875.51
+22613,15795954.0,Teng,707.0,France,Female,36.0,8.0,126872.6,1.0,1.0,1.0,48682.1
+22614,15722758.0,Ch'iu,545.0,Germany,Female,46.0,7.0,116674.72,1.0,0.0,0.0,176712.59
+22615,15676571.0,O'Sullivan,652.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,72970.93
+22616,15607040.0,T'ang,773.0,Spain,Male,42.0,9.0,132558.26,1.0,1.0,0.0,129980.93
+22617,15761047.0,Chia,635.0,France,Female,40.0,3.0,0.0,2.0,1.0,1.0,184524.65
+22618,15691396.0,Chukwubuikem,554.0,France,Female,27.0,3.0,0.0,2.0,1.0,0.0,62762.12
+22619,15765258.0,Ch'en,563.0,France,Female,68.0,3.0,0.0,1.0,1.0,0.0,124489.88
+22620,15779947.0,Chidumaga,651.0,Germany,Male,52.0,7.0,153340.83,1.0,1.0,0.0,160532.88
+22621,15652883.0,Chin,637.0,France,Female,28.0,9.0,0.0,2.0,1.0,0.0,109523.88
+22622,15615296.0,Nwachukwu,582.0,Germany,Male,26.0,8.0,91301.72,2.0,1.0,0.0,71862.79
+22623,15754569.0,Nnonso,684.0,France,Female,42.0,7.0,0.0,2.0,1.0,1.0,79495.01
+22624,15747762.0,Toscani,665.0,France,Male,38.0,1.0,0.0,1.0,1.0,0.0,130500.65
+22625,15603035.0,Ch'iu,745.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,47911.04
+22626,15692406.0,Nwankwo,474.0,Spain,Male,32.0,5.0,169445.4,1.0,0.0,1.0,161465.31
+22627,15723644.0,Pirozzi,619.0,Germany,Male,50.0,1.0,125163.62,1.0,0.0,0.0,160593.32
+22628,15761487.0,Ts'ai,795.0,France,Female,24.0,7.0,0.0,1.0,1.0,0.0,78744.39
+22629,15738018.0,P'eng,758.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,158958.9
+22630,15730044.0,Ndubueze,577.0,France,Male,49.0,1.0,0.0,1.0,1.0,0.0,101694.67
+22631,15687634.0,Wright,646.0,France,Female,32.0,2.0,0.0,2.0,0.0,1.0,71179.31
+22632,15681705.0,Fetherstonhaugh,624.0,Spain,Female,49.0,1.0,0.0,1.0,1.0,0.0,170514.21
+22633,15588924.0,Martin,523.0,France,Male,43.0,5.0,0.0,2.0,1.0,1.0,171716.81
+22634,15768282.0,Calabrese,595.0,Germany,Male,42.0,6.0,82931.85,1.0,0.0,0.0,49826.68
+22635,15806913.0,Tretiakov,621.0,France,Female,46.0,2.0,0.0,2.0,0.0,1.0,97926.24
+22636,15609637.0,Mazzanti,615.0,Germany,Female,35.0,0.0,121389.78,1.0,1.0,1.0,74042.0
+22637,15783027.0,Buchi,732.0,Germany,Male,49.0,6.0,131762.94,1.0,1.0,0.0,177362.45
+22638,15609913.0,Onwuamaeze,681.0,Spain,Male,33.0,7.0,0.0,1.0,1.0,0.0,174098.28
+22639,15792868.0,Zito,678.0,France,Male,30.0,0.0,129938.07,1.0,1.0,0.0,144880.81
+22640,15614778.0,Nielson,663.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,0.0,99645.04
+22641,15618661.0,Chukwubuikem,652.0,France,Female,30.0,0.0,0.0,1.0,1.0,1.0,139048.0
+22642,15803947.0,Lucchesi,708.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,180626.68
+22643,15697028.0,Maclean,683.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,1276.87
+22644,15691792.0,Hs?eh,638.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,65323.11
+22645,15748673.0,T'ien,769.0,Germany,Male,35.0,7.0,121890.05,2.0,1.0,1.0,49865.04
+22646,15778861.0,Binder,832.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,148087.88
+22647,15641158.0,Chukwualuka,652.0,France,Male,41.0,8.0,116561.05,1.0,1.0,0.0,65765.55
+22648,15686963.0,Trevisano,754.0,Spain,Male,33.0,4.0,144976.5,1.0,1.0,1.0,88890.75
+22649,15724889.0,Kosisochukwu,667.0,Germany,Male,51.0,9.0,150540.43,1.0,1.0,0.0,160853.06
+22650,15568487.0,Chiu,553.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,102908.96
+22651,15716609.0,Chiu,661.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,116951.1
+22652,15732987.0,Okwudiliolisa,696.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,141180.66
+22653,15783882.0,Chibugo,568.0,Spain,Male,53.0,8.0,0.0,2.0,1.0,1.0,180427.24
+22654,15605534.0,Soto,622.0,Germany,Male,41.0,1.0,103522.64,1.0,1.0,0.0,77866.91
+22655,15736112.0,Okwukwe,587.0,Germany,Female,34.0,10.0,99538.3,2.0,1.0,1.0,147794.63
+22656,15806049.0,Kambinachi,708.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,85901.09
+22657,15593973.0,T'ien,585.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,180769.55
+22658,15751561.0,DeRose,662.0,France,Female,26.0,7.0,0.0,2.0,1.0,0.0,147551.09
+22659,15605804.0,Onyemauchechukwu,474.0,Germany,Female,46.0,9.0,131834.8,1.0,1.0,1.0,151902.78
+22660,15806967.0,Frolova,559.0,France,Female,35.0,1.0,0.0,2.0,1.0,1.0,171657.57
+22661,15589428.0,Genovese,632.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,54926.51
+22662,15761158.0,Y?an,667.0,Germany,Female,46.0,4.0,142072.02,1.0,0.0,0.0,104786.59
+22663,15743075.0,Nwachukwu,805.0,France,Male,34.0,2.0,0.0,1.0,0.0,0.0,140941.47
+22664,15792726.0,Onyinyechukwuka,579.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,123866.43
+22665,15669414.0,Walker,850.0,France,Male,32.0,7.0,107712.57,1.0,0.0,1.0,62282.99
+22666,15686917.0,Trevisano,738.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,144870.28
+22667,15651740.0,T'ang,643.0,France,Female,44.0,9.0,0.0,2.0,1.0,1.0,149575.59
+22668,15648766.0,Chibueze,528.0,Germany,Male,37.0,7.0,121231.39,2.0,0.0,1.0,158974.79
+22669,15593795.0,Chiu,581.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,114732.62
+22670,15677522.0,Li Fonti,524.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,129892.93
+22671,15631053.0,T'ao,607.0,France,Female,34.0,6.0,0.0,2.0,1.0,0.0,120801.65
+22672,15652667.0,K'ung,494.0,Spain,Male,38.0,7.0,143966.3,1.0,1.0,0.0,89566.64
+22673,15791513.0,Chukwujamuike,581.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,1.0,143683.75
+22674,15788025.0,Kaeppel,682.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,93901.61
+22675,15683585.0,Onwuamaegbu,642.0,France,Male,40.0,5.0,99713.76,1.0,0.0,1.0,54487.43
+22676,15585888.0,Kao,727.0,France,Male,38.0,3.0,0.0,2.0,0.0,1.0,101418.05
+22677,15679760.0,Nwagugheuzo,578.0,France,Male,39.0,3.0,0.0,3.0,1.0,0.0,161845.81
+22678,15613141.0,Aiken,663.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,166650.32
+22679,15809800.0,K?,543.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,193008.52
+22680,15741295.0,Ch'eng,730.0,Germany,Male,32.0,4.0,140614.15,1.0,1.0,1.0,16281.94
+22681,15656592.0,P'eng,572.0,France,Male,24.0,6.0,0.0,2.0,1.0,0.0,28391.52
+22682,15749557.0,Chidiebere,632.0,Germany,Female,38.0,7.0,95853.39,2.0,1.0,0.0,173837.54
+22683,15682868.0,Yermakova,696.0,France,Male,33.0,3.0,109632.85,2.0,1.0,1.0,147554.26
+22684,15798850.0,Fyodorova,644.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,171060.01
+22685,15757577.0,Hs?,526.0,France,Male,45.0,0.0,0.0,2.0,0.0,0.0,133692.88
+22686,15610105.0,Yobanna,634.0,France,Male,37.0,5.0,0.0,3.0,1.0,1.0,177524.19
+22687,15722350.0,Chidubem,481.0,France,Female,37.0,3.0,108711.57,1.0,1.0,1.0,116467.35
+22688,15607546.0,Brown,756.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,121299.14
+22689,15593773.0,Chukwudi,633.0,Germany,Male,34.0,2.0,116324.53,1.0,1.0,1.0,122925.4
+22690,15786418.0,Maclean,634.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,0.0,85421.47
+22691,15641158.0,Fang,850.0,Spain,Female,26.0,6.0,178713.98,1.0,0.0,1.0,28789.94
+22692,15616454.0,Chidubem,549.0,Germany,Male,59.0,9.0,114422.85,1.0,1.0,0.0,155061.97
+22693,15809462.0,Tuan,598.0,France,Male,40.0,8.0,0.0,1.0,1.0,0.0,126012.57
+22694,15573456.0,Shih,715.0,France,Female,45.0,7.0,0.0,1.0,1.0,0.0,85516.37
+22695,15717498.0,Chin,714.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,187905.12
+22696,15567367.0,Onio,669.0,Spain,Female,37.0,8.0,0.0,1.0,0.0,0.0,114522.68
+22697,15754919.0,Udinesi,623.0,France,Male,36.0,2.0,137847.41,1.0,0.0,1.0,163331.65
+22698,15700937.0,Chiazagomekpere,714.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,1.0,167554.86
+22699,15745804.0,Aksenov,771.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,142331.85
+22700,15803976.0,Ch'in,684.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,0.0,104889.3
+22701,15693764.0,Hao,593.0,Spain,Male,42.0,8.0,101641.14,1.0,1.0,0.0,74858.3
+22702,15690164.0,Ch'ien,724.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,176984.34
+22703,15648067.0,Yudina,667.0,Spain,Female,48.0,3.0,0.0,2.0,1.0,1.0,118313.38
+22704,15793897.0,Chiang,724.0,Spain,Female,28.0,3.0,0.0,2.0,1.0,1.0,118528.16
+22705,15594812.0,Fanucci,598.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,45956.05
+22706,15697046.0,Yobanna,707.0,Spain,Male,45.0,9.0,0.0,2.0,0.0,0.0,160523.33
+22707,15752507.0,H?,609.0,Germany,Female,35.0,7.0,142028.88,2.0,0.0,1.0,131284.62
+22708,15583764.0,Chia,765.0,France,Female,37.0,9.0,0.0,2.0,0.0,0.0,91673.6
+22709,15646810.0,Fanucci,648.0,France,Male,39.0,9.0,0.0,2.0,0.0,1.0,62634.94
+22710,15699467.0,Ugochukwu,596.0,France,Female,42.0,8.0,0.0,1.0,1.0,0.0,131736.23
+22711,15716781.0,Echezonachukwu,670.0,France,Male,45.0,2.0,0.0,2.0,1.0,0.0,26606.28
+22712,15688868.0,Pirozzi,699.0,France,Male,25.0,7.0,0.0,2.0,1.0,0.0,107961.11
+22713,15684999.0,Chiazagomekpere,667.0,Spain,Male,46.0,8.0,174588.8,1.0,1.0,0.0,178291.09
+22714,15642821.0,Lu,645.0,France,Male,33.0,7.0,144773.29,1.0,1.0,0.0,65816.53
+22715,15609824.0,Tseng,678.0,Spain,Male,45.0,7.0,0.0,2.0,1.0,0.0,187839.9
+22716,15716491.0,Chikwado,583.0,France,Male,35.0,0.0,0.0,2.0,1.0,1.0,91830.75
+22717,15791533.0,Ritchie,668.0,France,Male,31.0,5.0,0.0,1.0,1.0,0.0,108887.44
+22718,15752841.0,Omeokachie,753.0,Spain,Female,39.0,9.0,0.0,2.0,1.0,0.0,176762.91
+22719,15637644.0,Shao,607.0,Germany,Female,36.0,1.0,126732.85,2.0,1.0,1.0,107815.31
+22720,15697028.0,Hao,628.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,138834.94
+22721,15704795.0,Tien,639.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,166038.27
+22722,15639278.0,Chinweike,682.0,France,Male,42.0,7.0,111537.23,1.0,0.0,1.0,91514.62
+22723,15621768.0,Genovese,810.0,France,Female,41.0,9.0,0.0,2.0,0.0,1.0,73564.44
+22724,15749583.0,Oguejiofor,569.0,Germany,Male,37.0,9.0,129974.79,1.0,0.0,0.0,164804.02
+22725,15644796.0,O'Loghlen,694.0,France,Male,34.0,4.0,148962.76,1.0,1.0,1.0,78899.15
+22726,15622578.0,Ts'ui,559.0,Germany,Female,45.0,6.0,135120.56,2.0,1.0,1.0,137791.18
+22727,15626474.0,Tretiakova,633.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,0.0,143022.32
+22728,15651713.0,Frolov,641.0,Spain,Male,33.0,5.0,113220.3,1.0,1.0,1.0,126761.2
+22729,15662488.0,Ritchie,751.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,143668.44
+22730,15808978.0,Lucchese,684.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,26170.04
+22731,15779915.0,Chigozie,573.0,Germany,Female,39.0,4.0,120599.21,2.0,1.0,0.0,93881.75
+22732,15792649.0,Li Fonti,599.0,France,Male,37.0,9.0,106520.73,1.0,1.0,1.0,132970.57
+22733,15689425.0,Uspensky,577.0,France,Male,51.0,0.0,0.0,1.0,1.0,1.0,171096.2
+22734,15662291.0,McIver,671.0,France,Female,31.0,5.0,0.0,2.0,0.0,0.0,99490.01
+22735,15719606.0,Pagnotto,687.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,76515.79
+22736,15680430.0,Chukwukadibia,604.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,1.0,151336.96
+22737,15597591.0,Walker,588.0,Spain,Male,34.0,4.0,136492.92,1.0,0.0,1.0,136492.18
+22738,15635972.0,Chuang,679.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,71167.17
+22739,15797960.0,Yermolayeva,686.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,181461.46
+22740,15783026.0,Onyekachi,704.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,63736.16
+22741,15702909.0,Ch'ang,605.0,Spain,Female,23.0,6.0,0.0,2.0,0.0,0.0,65816.53
+22742,15595391.0,Manna,686.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,168515.61
+22743,15656613.0,Kao,588.0,France,Male,29.0,1.0,0.0,2.0,1.0,1.0,94912.78
+22744,15667460.0,Hsiao,498.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,106663.46
+22745,15677550.0,Lettiere,622.0,Germany,Female,22.0,3.0,102278.81,2.0,1.0,0.0,53534.8
+22746,15792489.0,Chijindum,679.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,92814.31
+22747,15690526.0,Nwachukwu,521.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,120853.83
+22748,15661380.0,Trevisani,628.0,France,Female,49.0,1.0,89588.35,2.0,1.0,0.0,169137.24
+22749,15798824.0,Obijiaku,648.0,Germany,Female,35.0,8.0,122793.96,1.0,1.0,0.0,59457.0
+22750,15782454.0,Tsou,590.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,171707.93
+22751,15680278.0,Tseng,678.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,146680.28
+22752,15671450.0,Ting,637.0,France,Female,29.0,7.0,0.0,2.0,1.0,1.0,168035.62
+22753,15777742.0,Hsueh,591.0,France,Female,41.0,6.0,82697.13,1.0,1.0,0.0,79704.12
+22754,15694946.0,Pagnotto,715.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,152698.24
+22755,15656901.0,Cattaneo,615.0,Spain,Male,43.0,5.0,144801.97,1.0,0.0,1.0,100101.06
+22756,15716347.0,Tuan,850.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,72994.71
+22757,15769451.0,Palermo,671.0,Spain,Male,41.0,7.0,0.0,2.0,1.0,1.0,87071.18
+22758,15680449.0,Ch'eng,729.0,France,Female,56.0,2.0,0.0,1.0,1.0,1.0,149614.81
+22759,15576774.0,DeRose,684.0,France,Female,41.0,4.0,0.0,2.0,0.0,0.0,118788.59
+22760,15724848.0,Genovese,556.0,France,Male,27.0,10.0,0.0,2.0,1.0,1.0,129936.56
+22761,15775590.0,Onwuamaeze,641.0,France,Male,35.0,1.0,122531.86,1.0,1.0,1.0,184839.7
+22762,15695819.0,O'Sullivan,710.0,Germany,Female,34.0,7.0,88815.56,2.0,0.0,0.0,113436.08
+22763,15715208.0,Ch'in,699.0,France,Female,45.0,5.0,137687.72,1.0,1.0,0.0,79414.0
+22764,15643679.0,Tsao,583.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,57549.65
+22765,15672930.0,Macleod,676.0,Germany,Male,51.0,9.0,144453.75,1.0,0.0,0.0,127630.88
+22766,15617401.0,Palermo,769.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,177025.79
+22767,15756875.0,Y?,705.0,France,Male,57.0,1.0,0.0,2.0,1.0,1.0,180716.1
+22768,15758813.0,Chidiebere,731.0,France,Male,43.0,6.0,0.0,2.0,1.0,0.0,83419.15
+22769,15761477.0,Onyekachi,724.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,157862.82
+22770,15791053.0,Chijioke,596.0,France,Male,40.0,3.0,0.0,1.0,1.0,0.0,116081.93
+22771,15724200.0,Ebelechukwu,607.0,Germany,Male,28.0,9.0,101827.07,1.0,1.0,1.0,55868.97
+22772,15802781.0,Hs?,850.0,Germany,Male,29.0,2.0,109974.01,2.0,0.0,1.0,108841.83
+22773,15656592.0,Hsing,751.0,France,Female,43.0,2.0,129299.28,1.0,1.0,0.0,66526.01
+22774,15644878.0,Mazzanti,712.0,Spain,Male,41.0,1.0,0.0,1.0,0.0,0.0,140134.43
+22775,15798824.0,Hs?,640.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,110593.62
+22776,15809309.0,Nwokezuike,589.0,Germany,Male,58.0,6.0,93722.73,1.0,1.0,1.0,68814.23
+22777,15653776.0,L?,639.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,186612.45
+22778,15605968.0,Mott,850.0,Germany,Female,31.0,8.0,108632.48,1.0,1.0,1.0,66721.98
+22779,15754926.0,Tien,681.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,168035.6
+22780,15761717.0,Hs?eh,588.0,France,Male,31.0,3.0,0.0,1.0,1.0,1.0,60425.03
+22781,15752835.0,Shoebridge,652.0,Germany,Male,39.0,0.0,130682.11,1.0,0.0,0.0,52404.47
+22782,15615032.0,Chiu,695.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,18082.06
+22783,15632669.0,Ch'in,645.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,135088.58
+22784,15646594.0,Chukwufumnanya,659.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,116951.1
+22785,15630195.0,Pinto,769.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,105570.44
+22786,15700212.0,Scott,643.0,Germany,Male,31.0,6.0,96643.32,1.0,1.0,1.0,133692.88
+22787,15759067.0,Hsing,695.0,France,Female,50.0,5.0,0.0,2.0,1.0,0.0,161241.65
+22788,15727489.0,K?,624.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,89566.64
+22789,15712403.0,Ts'ui,632.0,Germany,Female,52.0,2.0,125445.04,1.0,1.0,1.0,185561.89
+22790,15793331.0,Galkin,706.0,France,Male,26.0,7.0,0.0,1.0,1.0,0.0,48606.92
+22791,15744517.0,Zaytseva,607.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,166468.21
+22792,15589320.0,L?,663.0,Germany,Female,32.0,8.0,150329.15,2.0,1.0,0.0,199293.01
+22793,15786078.0,Brown,538.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,162858.29
+22794,15758048.0,Udinese,706.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,71173.64
+22795,15702541.0,Golubev,596.0,Germany,Female,37.0,7.0,121537.71,1.0,1.0,1.0,151811.01
+22796,15776743.0,Ndukaku,639.0,Germany,Male,41.0,9.0,102721.49,1.0,1.0,0.0,10472.31
+22797,15691627.0,Chuang,678.0,Spain,Male,27.0,5.0,0.0,2.0,1.0,0.0,163736.09
+22798,15786469.0,Genovese,596.0,Spain,Male,37.0,7.0,0.0,2.0,1.0,1.0,156903.44
+22799,15583764.0,Clements,724.0,Spain,Male,32.0,1.0,0.0,1.0,1.0,0.0,71692.8
+22800,15587587.0,Chinedum,624.0,Spain,Female,31.0,3.0,139810.34,1.0,1.0,1.0,183682.09
+22801,15792228.0,Shih,792.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,78854.57
+22802,15726588.0,Nwabugwu,710.0,France,Female,69.0,1.0,0.0,2.0,1.0,1.0,93524.19
+22803,15791958.0,Whitehead,631.0,France,Female,40.0,7.0,0.0,2.0,1.0,0.0,61977.82
+22804,15736124.0,Chidozie,758.0,France,Female,32.0,10.0,84719.13,1.0,1.0,1.0,71178.09
+22805,15709639.0,Ch'ang,633.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,91347.76
+22806,15765461.0,Hsu,582.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,1.0,143954.99
+22807,15597951.0,Hs?,495.0,France,Female,32.0,4.0,0.0,2.0,0.0,1.0,162699.7
+22808,15731166.0,Obiajulu,565.0,France,Male,38.0,6.0,88758.84,1.0,1.0,1.0,177097.24
+22809,15632436.0,Chimaobim,727.0,France,Male,41.0,3.0,125487.89,1.0,1.0,1.0,86983.53
+22810,15791767.0,Ugochukwutubelum,656.0,France,Female,31.0,4.0,160515.37,1.0,0.0,0.0,145562.4
+22811,15803683.0,Burt,679.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,105433.53
+22812,15704053.0,Chukwumaobim,637.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,157057.82
+22813,15671766.0,Hsueh,649.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,79734.23
+22814,15748231.0,Ch'ang,645.0,Germany,Male,47.0,3.0,127738.75,1.0,1.0,0.0,183572.39
+22815,15612071.0,Lucchese,648.0,France,Female,41.0,9.0,0.0,1.0,1.0,0.0,74425.41
+22816,15622442.0,Lettiere,552.0,Spain,Female,30.0,4.0,103862.31,1.0,1.0,1.0,142963.28
+22817,15758510.0,Chidumaga,712.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,96864.36
+22818,15691871.0,H?,553.0,France,Male,37.0,1.0,147069.78,1.0,1.0,0.0,83911.33
+22819,15639665.0,Martin,704.0,France,Male,32.0,5.0,142581.08,1.0,0.0,1.0,162511.55
+22820,15750487.0,Chukwukadibia,594.0,Germany,Male,37.0,2.0,117280.23,2.0,1.0,1.0,103563.23
+22821,15642570.0,T'ao,636.0,France,Female,30.0,6.0,0.0,2.0,0.0,0.0,183358.21
+22822,15679642.0,Hargreaves,667.0,France,Male,41.0,5.0,0.0,1.0,1.0,0.0,180593.45
+22823,15746190.0,Chukwuebuka,686.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,57552.02
+22824,15690744.0,Hsing,589.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,51705.01
+22825,15702300.0,Dyer,588.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,115724.78
+22826,15623062.0,Ts'ai,639.0,Spain,Female,46.0,3.0,84442.6,2.0,0.0,0.0,83052.06
+22827,15717282.0,Y?,628.0,Spain,Male,48.0,4.0,0.0,2.0,0.0,1.0,66799.28
+22828,15724145.0,Ch'iu,726.0,France,Male,32.0,8.0,142721.52,1.0,1.0,0.0,99449.86
+22829,15746662.0,Chiang,581.0,France,Female,39.0,9.0,0.0,2.0,1.0,0.0,85421.48
+22830,15808389.0,Ch'ien,753.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,176770.28
+22831,15734461.0,Chien,576.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,109041.53
+22832,15588515.0,Pinto,553.0,Germany,Male,28.0,7.0,123372.3,2.0,1.0,1.0,59354.24
+22833,15603749.0,Ts'ui,641.0,France,Female,39.0,9.0,0.0,1.0,1.0,1.0,147055.87
+22834,15585865.0,Nwabugwu,539.0,France,Female,46.0,5.0,0.0,1.0,0.0,0.0,104469.06
+22835,15622578.0,Ch'ang,677.0,Germany,Male,53.0,3.0,120549.29,1.0,1.0,0.0,82463.69
+22836,15673820.0,T'ien,678.0,France,Female,44.0,9.0,0.0,2.0,1.0,1.0,63736.36
+22837,15749947.0,P'eng,545.0,Spain,Female,35.0,10.0,147832.15,1.0,1.0,1.0,143022.31
+22838,15594577.0,Ch'eng,632.0,Germany,Male,37.0,2.0,91538.51,1.0,1.0,0.0,187985.85
+22839,15778955.0,Olisanugo,613.0,France,Male,46.0,0.0,141792.61,1.0,1.0,0.0,131281.28
+22840,15693381.0,Obielumani,663.0,France,Male,60.0,2.0,123681.32,2.0,0.0,0.0,99444.02
+22841,15789425.0,Onwuamaeze,729.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,0.0,88890.05
+22842,15580149.0,Young,656.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,177065.24
+22843,15575492.0,Chien,676.0,Spain,Female,35.0,9.0,0.0,2.0,1.0,1.0,123954.15
+22844,15583681.0,Aikenhead,576.0,France,Female,30.0,5.0,161588.89,1.0,0.0,1.0,141148.21
+22845,15571529.0,Rowntree,584.0,France,Female,44.0,9.0,0.0,1.0,1.0,1.0,132034.13
+22846,15752588.0,P'an,612.0,France,Female,43.0,5.0,0.0,1.0,1.0,0.0,146516.86
+22847,15662085.0,Onwuamaeze,699.0,France,Male,28.0,0.0,0.0,2.0,0.0,1.0,94440.45
+22848,15752488.0,Genovese,731.0,Spain,Male,44.0,2.0,0.0,1.0,1.0,0.0,73564.44
+22849,15633574.0,Chiemela,628.0,France,Female,29.0,6.0,0.0,2.0,0.0,0.0,158887.09
+22850,15720662.0,P'an,721.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,1.0,175082.2
+22851,15772896.0,Ibezimako,652.0,France,Female,39.0,6.0,144889.18,1.0,1.0,1.0,74532.02
+22852,15660541.0,Nwachinemelu,756.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,99503.19
+22853,15681446.0,McIntyre,751.0,France,Female,40.0,1.0,0.0,1.0,0.0,0.0,107787.28
+22854,15667588.0,Eluemuno,766.0,Spain,Female,37.0,6.0,69712.74,1.0,1.0,1.0,149814.68
+22855,15665629.0,Hsiao,695.0,France,Female,27.0,10.0,122453.37,1.0,1.0,0.0,79005.95
+22856,15626141.0,Chinedum,599.0,France,Male,24.0,6.0,137761.55,1.0,0.0,1.0,118711.75
+22857,15657812.0,Nwachukwu,657.0,France,Female,43.0,9.0,104784.23,1.0,1.0,0.0,165574.54
+22858,15676571.0,Ritchie,624.0,France,Female,58.0,10.0,162657.64,1.0,1.0,0.0,61602.42
+22859,15780779.0,Nwabugwu,707.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,110253.2
+22860,15585853.0,Tseng,635.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,168816.22
+22861,15770291.0,Onyemauchechukwu,732.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,109394.62
+22862,15802009.0,Babbage,627.0,France,Female,49.0,3.0,0.0,3.0,1.0,0.0,75562.47
+22863,15632476.0,Mactier,597.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,105780.18
+22864,15648069.0,Zikoranaudodimma,762.0,Germany,Female,30.0,7.0,113690.81,1.0,0.0,1.0,68730.91
+22865,15614215.0,Onyeoruru,592.0,Germany,Female,32.0,9.0,122446.61,1.0,1.0,1.0,180934.67
+22866,15631392.0,Manna,573.0,France,Male,26.0,7.0,0.0,1.0,1.0,1.0,90876.95
+22867,15759066.0,Okeke,597.0,Spain,Male,24.0,8.0,0.0,2.0,1.0,0.0,95625.03
+22868,15770345.0,Tretiakova,684.0,France,Female,29.0,8.0,134498.54,1.0,1.0,1.0,140947.05
+22869,15582848.0,Manners,597.0,Germany,Female,44.0,7.0,113858.95,1.0,1.0,0.0,166586.99
+22870,15751549.0,Ch'ien,724.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,171378.77
+22871,15574795.0,Manna,529.0,Germany,Male,32.0,2.0,100912.19,2.0,0.0,1.0,58879.31
+22872,15690772.0,Hsueh,694.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,106761.47
+22873,15641180.0,Folliero,627.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,51871.95
+22874,15598884.0,Ts'ui,729.0,France,Female,54.0,6.0,0.0,2.0,1.0,1.0,103379.31
+22875,15696744.0,Martin,758.0,Germany,Female,34.0,6.0,124580.69,1.0,1.0,0.0,97640.2
+22876,15737506.0,Chidubem,711.0,France,Female,52.0,2.0,0.0,2.0,1.0,0.0,108732.96
+22877,15571917.0,Okwudiliolisa,678.0,Spain,Male,59.0,1.0,0.0,1.0,1.0,0.0,169415.3
+22878,15691817.0,T'ang,628.0,France,Male,39.0,7.0,0.0,2.0,0.0,1.0,131647.31
+22879,15644878.0,P'eng,592.0,France,Male,49.0,6.0,0.0,2.0,1.0,1.0,116951.1
+22880,15578096.0,Ts'ui,679.0,Spain,Female,61.0,3.0,0.0,1.0,1.0,0.0,94949.84
+22881,15769980.0,O'Brien,684.0,Spain,Male,36.0,8.0,0.0,1.0,1.0,1.0,73679.59
+22882,15662091.0,Ch'in,545.0,France,Male,40.0,3.0,0.0,1.0,1.0,1.0,86937.2
+22883,15671769.0,Genovese,694.0,France,Female,35.0,2.0,92474.05,1.0,1.0,1.0,107875.69
+22884,15585287.0,Okwuadigbo,850.0,France,Female,31.0,10.0,115350.63,1.0,1.0,0.0,101314.98
+22885,15775294.0,Ch'ien,648.0,France,Female,43.0,3.0,0.0,1.0,1.0,0.0,86634.3
+22886,15583352.0,Brown,659.0,Germany,Male,29.0,9.0,96951.21,1.0,1.0,0.0,72852.74
+22887,15732628.0,Hs?,633.0,Spain,Female,51.0,7.0,0.0,2.0,1.0,1.0,141364.62
+22888,15692226.0,Iadanza,714.0,Spain,Male,36.0,5.0,162448.69,1.0,1.0,0.0,142911.99
+22889,15676526.0,Monds,591.0,France,Male,26.0,9.0,0.0,2.0,1.0,1.0,91094.46
+22890,15736154.0,Chibugo,596.0,France,Male,35.0,7.0,111565.28,1.0,0.0,0.0,130404.53
+22891,15758750.0,Nnanna,850.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,181224.56
+22892,15603840.0,Iqbal,592.0,Spain,Female,36.0,8.0,166070.23,1.0,1.0,0.0,100335.55
+22893,15717489.0,Nwora,663.0,Germany,Female,38.0,8.0,115315.04,1.0,1.0,0.0,125445.8
+22894,15790782.0,Tien,593.0,Spain,Female,40.0,1.0,72535.45,1.0,0.0,1.0,157097.09
+22895,15673529.0,P'eng,636.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,120531.31
+22896,15771477.0,Mazzanti,581.0,Spain,Male,48.0,2.0,154485.74,1.0,1.0,1.0,66732.63
+22897,15649858.0,Walker,695.0,Spain,Male,29.0,0.0,102821.45,2.0,1.0,0.0,79547.39
+22898,15632967.0,Hsiung,681.0,France,Male,38.0,9.0,0.0,1.0,1.0,1.0,58961.49
+22899,15573926.0,Goliwe,715.0,Spain,Female,45.0,4.0,0.0,1.0,1.0,0.0,174777.98
+22900,15772412.0,Ch'ien,621.0,Germany,Female,32.0,2.0,88775.23,2.0,0.0,1.0,76510.46
+22901,15664539.0,Tien,439.0,Germany,Female,34.0,9.0,130334.35,2.0,1.0,0.0,180427.24
+22902,15643574.0,Onyekachi,575.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,126726.33
+22903,15729238.0,Udinese,719.0,Germany,Male,48.0,5.0,129312.84,1.0,1.0,0.0,60552.44
+22904,15601184.0,Scott,633.0,Germany,Male,44.0,8.0,82909.14,2.0,1.0,0.0,81409.0
+22905,15654772.0,T'ien,727.0,Spain,Male,36.0,6.0,0.0,1.0,1.0,1.0,56767.67
+22906,15777045.0,Hsiung,601.0,Spain,Female,33.0,5.0,123372.3,1.0,1.0,1.0,149991.32
+22907,15609928.0,Onwuamaeze,616.0,France,Female,35.0,4.0,151233.62,1.0,1.0,0.0,61538.43
+22908,15780643.0,Brownless,609.0,France,Female,34.0,1.0,0.0,2.0,0.0,1.0,59249.83
+22909,15815534.0,Chinedum,619.0,France,Female,67.0,3.0,0.0,2.0,1.0,1.0,93820.49
+22910,15689514.0,K?,565.0,Germany,Male,38.0,6.0,142296.13,1.0,1.0,0.0,136902.01
+22911,15599152.0,Onwumelu,572.0,Spain,Female,31.0,4.0,160736.63,1.0,1.0,1.0,142377.69
+22912,15585595.0,Hsiung,815.0,France,Female,32.0,3.0,106521.49,1.0,1.0,1.0,176945.96
+22913,15569682.0,Okonkwo,653.0,Spain,Male,30.0,7.0,0.0,2.0,1.0,0.0,52827.51
+22914,15644765.0,Maclean,547.0,Spain,Female,30.0,4.0,0.0,2.0,1.0,1.0,3173.31
+22915,15661290.0,T'ien,705.0,Spain,Male,35.0,7.0,121172.97,1.0,1.0,0.0,109608.81
+22916,15672798.0,Pirozzi,593.0,Spain,Female,40.0,6.0,0.0,2.0,0.0,1.0,131281.29
+22917,15712176.0,Chiang,526.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,109805.17
+22918,15646563.0,Ch'iu,686.0,France,Female,30.0,9.0,0.0,2.0,1.0,0.0,120853.83
+22919,15609913.0,Knepper,774.0,Germany,Male,51.0,6.0,108862.76,1.0,0.0,0.0,86935.73
+22920,15683522.0,Hs?eh,622.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,154598.56
+22921,15793723.0,Chidiebere,646.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,63736.17
+22922,15581840.0,Ch'in,639.0,France,Male,46.0,5.0,0.0,1.0,1.0,1.0,171457.06
+22923,15585067.0,Chukwudi,629.0,Spain,Male,44.0,2.0,0.0,2.0,0.0,0.0,89048.46
+22924,15676521.0,T'ang,685.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,151325.24
+22925,15592999.0,Pisano,704.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,55725.04
+22926,15603319.0,Ch'in,665.0,France,Female,35.0,6.0,0.0,2.0,0.0,1.0,122284.38
+22927,15641640.0,Chinonyelum,678.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,63889.1
+22928,15723858.0,Nnonso,687.0,France,Male,39.0,1.0,0.0,2.0,0.0,0.0,48357.9
+22929,15661528.0,Ch'ang,589.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,143357.02
+22930,15768777.0,Onyemere,619.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,53630.9
+22931,15591169.0,Genovese,678.0,France,Male,35.0,7.0,0.0,2.0,0.0,0.0,82290.81
+22932,15669611.0,Ch'eng,731.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,143598.7
+22933,15570087.0,Iadanza,735.0,France,Female,48.0,8.0,0.0,1.0,1.0,0.0,131919.48
+22934,15691543.0,Chimezie,587.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,125870.5
+22935,15763817.0,Dore,705.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,182059.28
+22936,15763612.0,Obidimkpa,598.0,France,Female,60.0,2.0,0.0,1.0,1.0,1.0,71175.94
+22937,15679024.0,Nkemakolam,682.0,Germany,Male,38.0,8.0,141525.52,2.0,1.0,1.0,121451.21
+22938,15787318.0,Chukwuemeka,696.0,France,Female,30.0,8.0,118897.1,1.0,1.0,0.0,180712.28
+22939,15692406.0,Chidiegwu,793.0,Germany,Female,26.0,0.0,141099.43,1.0,1.0,0.0,142963.28
+22940,15694852.0,Chukwueloka,584.0,Germany,Male,38.0,7.0,84421.62,2.0,0.0,1.0,72990.62
+22941,15584897.0,H?,633.0,Germany,Male,60.0,5.0,108239.11,1.0,1.0,0.0,126469.09
+22942,15737275.0,Uchenna,738.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,92357.21
+22943,15586931.0,Nnamutaezinwa,701.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,114474.98
+22944,15798895.0,Pape,659.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,27448.33
+22945,15723216.0,Ts'ui,646.0,France,Male,38.0,6.0,129968.67,1.0,1.0,1.0,175025.27
+22946,15795564.0,Manna,635.0,Spain,Male,29.0,8.0,0.0,2.0,0.0,0.0,109895.16
+22947,15772632.0,P'eng,637.0,Germany,Male,36.0,6.0,135843.66,1.0,1.0,1.0,94760.32
+22948,15583692.0,Folliero,705.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,1.0,173365.89
+22949,15586880.0,Hsia,757.0,France,Male,45.0,2.0,0.0,2.0,1.0,0.0,115116.35
+22950,15681075.0,Yashina,594.0,France,Female,40.0,10.0,0.0,2.0,1.0,1.0,158580.05
+22951,15776939.0,Uwaezuoke,637.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,135655.29
+22952,15780124.0,Pirozzi,682.0,Spain,Female,42.0,9.0,0.0,2.0,1.0,0.0,142903.44
+22953,15636428.0,Hsueh,708.0,France,Female,46.0,0.0,0.0,1.0,1.0,0.0,149369.41
+22954,15662534.0,Nkemakolam,660.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,153080.4
+22955,15576219.0,Ch'ang,673.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,163989.66
+22956,15643908.0,Chukwumaobim,669.0,France,Male,43.0,1.0,113873.53,1.0,0.0,1.0,137510.41
+22957,15750638.0,Young,710.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,40807.27
+22958,15699461.0,Onyekaozulu,551.0,Spain,Female,35.0,0.0,99816.46,2.0,1.0,0.0,129964.94
+22959,15668893.0,Chigbogu,558.0,Spain,Male,31.0,6.0,0.0,2.0,0.0,0.0,29761.29
+22960,15810203.0,Chikezie,732.0,Germany,Male,38.0,10.0,114430.18,1.0,1.0,0.0,104272.04
+22961,15576723.0,Chiawuotu,638.0,Spain,Male,30.0,7.0,0.0,2.0,1.0,1.0,160129.99
+22962,15755239.0,Okwudilichukwu,699.0,France,Male,43.0,6.0,0.0,1.0,1.0,1.0,103099.22
+22963,15605534.0,Chizoba,705.0,Germany,Female,31.0,9.0,137910.45,1.0,1.0,1.0,149381.32
+22964,15622519.0,Zetticci,652.0,Spain,Female,30.0,7.0,0.0,2.0,1.0,1.0,95611.47
+22965,15805565.0,Maclean,582.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,1.0,28690.9
+22966,15683526.0,Ch'ien,529.0,France,Male,49.0,6.0,0.0,2.0,1.0,1.0,125870.5
+22967,15659149.0,Nnaemeka,679.0,France,Male,32.0,8.0,72783.42,2.0,1.0,1.0,39488.04
+22968,15585892.0,H?,598.0,France,Female,47.0,5.0,0.0,1.0,1.0,0.0,177092.16
+22969,15672598.0,P'eng,576.0,Spain,Male,33.0,6.0,0.0,2.0,1.0,0.0,19233.41
+22970,15802466.0,Hsing,618.0,Germany,Male,31.0,5.0,131694.97,1.0,1.0,1.0,91936.1
+22971,15763029.0,Scott,692.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,0.0,166476.95
+22972,15706021.0,Yobachi,632.0,Spain,Female,28.0,8.0,0.0,2.0,1.0,0.0,50783.1
+22973,15593250.0,Baresi,691.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,180598.28
+22974,15792646.0,P'eng,748.0,France,Female,48.0,9.0,0.0,2.0,1.0,0.0,140941.47
+22975,15583692.0,Hsiung,598.0,Germany,Male,39.0,2.0,121455.65,2.0,1.0,1.0,198103.32
+22976,15588449.0,Folliero,592.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,119357.93
+22977,15719606.0,H?,721.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,143049.72
+22978,15699340.0,Tsao,611.0,France,Female,33.0,1.0,0.0,2.0,1.0,0.0,182865.09
+22979,15787597.0,Nwabugwu,524.0,France,Male,42.0,3.0,0.0,1.0,1.0,0.0,69414.13
+22980,15785576.0,Toscani,648.0,France,Male,41.0,2.0,90727.79,1.0,1.0,0.0,131924.45
+22981,15596870.0,Frolova,670.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,118959.64
+22982,15676869.0,Chienezie,711.0,Spain,Male,44.0,3.0,0.0,1.0,1.0,1.0,131473.31
+22983,15674558.0,Chukwufumnanya,602.0,France,Male,25.0,8.0,115062.02,1.0,0.0,1.0,163666.85
+22984,15581388.0,Chibuzo,659.0,France,Male,40.0,1.0,144895.04,2.0,1.0,0.0,169286.74
+22985,15672598.0,Y?,607.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,99800.87
+22986,15578045.0,Young,559.0,France,Female,31.0,0.0,0.0,2.0,1.0,1.0,147097.26
+22987,15621818.0,Young,614.0,Spain,Male,35.0,8.0,0.0,2.0,0.0,1.0,125553.51
+22988,15769959.0,Chukwuemeka,628.0,Spain,Female,32.0,9.0,0.0,1.0,1.0,0.0,141449.86
+22989,15626042.0,Li,694.0,France,Female,70.0,5.0,0.0,1.0,1.0,1.0,152577.62
+22990,15626578.0,Tien,721.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,159638.77
+22991,15605284.0,Hsia,693.0,France,Male,32.0,6.0,140931.57,1.0,1.0,1.0,75562.47
+22992,15642997.0,Yermakov,663.0,France,Male,31.0,0.0,168001.34,1.0,1.0,0.0,131300.68
+22993,15648032.0,Cattaneo,710.0,France,Male,40.0,3.0,0.0,2.0,1.0,1.0,141872.05
+22994,15806771.0,Kao,611.0,France,Female,29.0,9.0,0.0,2.0,1.0,1.0,71333.44
+22995,15678727.0,Aksenov,669.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,106841.12
+22996,15735025.0,Maclean,652.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,1.0,185821.41
+22997,15656426.0,Ozioma,619.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,80701.72
+22998,15709487.0,Iadanza,581.0,Spain,Female,34.0,7.0,0.0,2.0,0.0,1.0,115068.99
+22999,15623369.0,T'ien,675.0,Germany,Female,29.0,9.0,97764.41,2.0,1.0,0.0,26170.08
+23000,15645600.0,Chinomso,591.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,61761.77
+23001,15773876.0,Chukwukere,721.0,France,Male,41.0,5.0,0.0,1.0,0.0,1.0,159618.17
+23002,15717962.0,Kao,677.0,France,Female,51.0,4.0,0.0,2.0,1.0,0.0,71865.31
+23003,15594576.0,Trevisani,739.0,France,Female,52.0,4.0,0.0,2.0,1.0,1.0,142903.44
+23004,15769216.0,Fanucci,554.0,Germany,Male,40.0,3.0,97720.35,1.0,1.0,1.0,156598.23
+23005,15751549.0,Cremonesi,639.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,61994.2
+23006,15768449.0,Chukwukere,684.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,161479.19
+23007,15776824.0,Hsueh,679.0,France,Male,45.0,8.0,0.0,2.0,1.0,0.0,61513.87
+23008,15661745.0,H?,683.0,Germany,Male,28.0,1.0,107712.57,1.0,1.0,1.0,90876.95
+23009,15748696.0,Hsiung,516.0,France,Female,65.0,4.0,0.0,1.0,1.0,0.0,26116.82
+23010,15649738.0,Chia,757.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,149474.69
+23011,15752694.0,Chukwujamuike,692.0,France,Female,35.0,7.0,0.0,2.0,1.0,1.0,122964.18
+23012,15651336.0,Chiawuotu,749.0,France,Female,37.0,2.0,0.0,1.0,0.0,1.0,48961.49
+23013,15627848.0,Kaeppel,487.0,Germany,Male,52.0,4.0,102800.65,1.0,1.0,0.0,166957.82
+23014,15792668.0,Ch'ang,727.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,171070.55
+23015,15801395.0,Lo,579.0,France,Male,41.0,6.0,0.0,1.0,1.0,0.0,141796.73
+23016,15702789.0,O'Donnell,776.0,Germany,Male,38.0,4.0,124098.54,1.0,1.0,0.0,62972.13
+23017,15712121.0,Fyodorova,753.0,Spain,Female,43.0,2.0,0.0,3.0,1.0,0.0,142670.61
+23018,15603319.0,Toscani,569.0,France,Male,35.0,6.0,0.0,1.0,1.0,1.0,72628.56
+23019,15717498.0,Chiagoziem,743.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,93777.15
+23020,15807167.0,Shih,727.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,164162.44
+23021,15688963.0,Hs?,621.0,Germany,Male,55.0,9.0,120170.07,1.0,1.0,0.0,131281.28
+23022,15773973.0,Hsieh,565.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,0.0,143315.07
+23023,15595588.0,P'an,763.0,Spain,Male,33.0,7.0,0.0,1.0,1.0,0.0,143960.72
+23024,15752588.0,Chukwuraenye,598.0,France,Female,46.0,7.0,0.0,1.0,0.0,0.0,170518.83
+23025,15693904.0,Zetticci,568.0,France,Female,26.0,6.0,0.0,1.0,1.0,1.0,64442.35
+23026,15609998.0,Yobachi,735.0,Germany,Female,37.0,2.0,104430.08,1.0,1.0,0.0,141926.43
+23027,15714986.0,Hs?,697.0,France,Female,50.0,0.0,0.0,2.0,1.0,1.0,141860.62
+23028,15572467.0,Hsia,551.0,Spain,Male,37.0,3.0,141573.55,1.0,1.0,1.0,131284.93
+23029,15597901.0,Biryukov,751.0,France,Female,46.0,8.0,0.0,2.0,0.0,0.0,70849.47
+23030,15730447.0,Molineux,705.0,France,Male,29.0,4.0,0.0,1.0,1.0,0.0,132798.78
+23031,15746995.0,Ts'ui,530.0,France,Male,45.0,4.0,0.0,2.0,0.0,0.0,167036.94
+23032,15710397.0,Nwebube,629.0,France,Female,35.0,2.0,0.0,1.0,1.0,1.0,86022.09
+23033,15704315.0,Toscani,699.0,Germany,Male,39.0,9.0,168883.08,2.0,1.0,1.0,120853.83
+23034,15734850.0,Okechukwu,679.0,France,Female,29.0,3.0,0.0,2.0,0.0,1.0,109895.16
+23035,15647800.0,Uspensky,575.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,83700.66
+23036,15782530.0,Fiorentino,663.0,Germany,Male,29.0,10.0,108891.7,1.0,0.0,0.0,154844.36
+23037,15810845.0,Ubanwa,752.0,France,Male,39.0,7.0,178713.98,1.0,1.0,0.0,95957.09
+23038,15747591.0,Ugorji,718.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,9085.0
+23039,15750141.0,Martin,567.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,84872.66
+23040,15641609.0,Chukwukadibia,709.0,Germany,Female,57.0,5.0,133432.59,1.0,1.0,0.0,173898.58
+23041,15646817.0,Ch'eng,552.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,598.8
+23042,15753861.0,Ting,565.0,Germany,Male,40.0,0.0,106942.08,2.0,1.0,0.0,152742.0
+23043,15768342.0,Horsley,841.0,France,Female,36.0,3.0,165896.2,1.0,0.0,0.0,131834.87
+23044,15633840.0,Kudryashova,705.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,60721.4
+23045,15697691.0,Harker,680.0,Germany,Female,47.0,8.0,126803.34,3.0,1.0,1.0,177613.19
+23046,15788151.0,Hsia,751.0,France,Male,31.0,1.0,150186.22,1.0,1.0,1.0,170835.32
+23047,15692671.0,T'ien,605.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,1.0,62733.05
+23048,15758057.0,Chidiebere,659.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,186948.35
+23049,15576526.0,Ch'in,678.0,France,Male,35.0,1.0,146093.39,2.0,1.0,0.0,115733.16
+23050,15782210.0,Nwachinemelu,605.0,Germany,Male,46.0,8.0,98899.91,3.0,1.0,0.0,106977.53
+23051,15695301.0,Nkemjika,595.0,France,Male,51.0,4.0,0.0,2.0,1.0,1.0,131312.56
+23052,15796787.0,Chukwufumnanya,775.0,Spain,Female,36.0,5.0,120911.75,1.0,1.0,1.0,177832.93
+23053,15663883.0,Echezonachukwu,627.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,152821.12
+23054,15588907.0,Ulyanov,678.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,103480.69
+23055,15617304.0,Hsiung,712.0,Spain,Male,50.0,6.0,148897.02,1.0,1.0,0.0,90905.26
+23056,15588928.0,Frolova,686.0,Spain,Female,38.0,8.0,0.0,2.0,0.0,0.0,83053.08
+23057,15759566.0,Chukwudi,638.0,France,Male,64.0,6.0,0.0,1.0,1.0,1.0,108887.44
+23058,15797065.0,Yeh,728.0,France,Male,60.0,2.0,0.0,2.0,1.0,1.0,97468.44
+23059,15626452.0,Tien,561.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,124052.97
+23060,15593365.0,Nnaife,466.0,Spain,Female,36.0,9.0,0.0,2.0,1.0,0.0,159823.16
+23061,15657540.0,Aksenov,709.0,France,Male,26.0,3.0,113574.61,1.0,1.0,0.0,162599.51
+23062,15694879.0,Chialuka,551.0,France,Male,46.0,1.0,0.0,2.0,1.0,1.0,150468.35
+23063,15775235.0,Chien,653.0,France,Male,30.0,9.0,0.0,1.0,1.0,0.0,92612.88
+23064,15582944.0,P'eng,815.0,Spain,Female,47.0,4.0,0.0,1.0,1.0,0.0,146895.58
+23065,15795571.0,Hsieh,669.0,France,Female,25.0,9.0,131284.37,1.0,1.0,0.0,163655.01
+23066,15690169.0,Gorman,662.0,France,Male,61.0,1.0,132571.36,1.0,0.0,1.0,171867.08
+23067,15794964.0,Chukwuemeka,622.0,France,Male,41.0,1.0,0.0,1.0,1.0,1.0,135851.3
+23068,15781530.0,Chuang,587.0,Germany,Female,51.0,9.0,117212.18,1.0,0.0,1.0,172269.21
+23069,15690164.0,Ritchie,669.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,1.0,129890.94
+23070,15749103.0,Chidubem,638.0,Spain,Male,26.0,3.0,0.0,2.0,0.0,0.0,82463.69
+23071,15671934.0,Tung,749.0,Germany,Female,46.0,6.0,157955.83,1.0,0.0,0.0,160532.88
+23072,15611058.0,Gadsdon,564.0,Spain,Male,21.0,8.0,0.0,1.0,1.0,1.0,183358.21
+23073,15671930.0,De Salis,681.0,France,Female,43.0,8.0,0.0,1.0,1.0,1.0,57764.65
+23074,15749300.0,Chukwufumnanya,652.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,93718.45
+23075,15585888.0,Kline,758.0,Germany,Male,33.0,8.0,131778.66,2.0,1.0,1.0,129562.74
+23076,15642530.0,Ch'en,713.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,157567.38
+23077,15790093.0,Liao,571.0,Spain,Male,33.0,3.0,125801.03,1.0,1.0,1.0,15650.73
+23078,15624592.0,Chukwudi,590.0,France,Male,41.0,3.0,0.0,2.0,1.0,0.0,70400.86
+23079,15722409.0,Clements,673.0,France,Female,29.0,2.0,152436.56,1.0,0.0,0.0,162452.65
+23080,15717286.0,Chu,703.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,172601.62
+23081,15750181.0,Teng,727.0,France,Female,37.0,4.0,120599.21,1.0,0.0,0.0,179834.45
+23082,15576935.0,Zetticci,594.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,147963.07
+23083,15687421.0,Lu,641.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,133535.29
+23084,15573854.0,Echezonachukwu,739.0,Germany,Female,45.0,8.0,118899.45,2.0,1.0,0.0,198355.28
+23085,15583049.0,Trevisano,591.0,Germany,Male,34.0,6.0,148843.56,2.0,0.0,1.0,28391.52
+23086,15762543.0,Ch'ang,817.0,France,Male,34.0,7.0,0.0,2.0,0.0,1.0,96517.97
+23087,15586039.0,Hsiung,710.0,France,Male,42.0,1.0,0.0,1.0,1.0,0.0,92888.52
+23088,15616471.0,Hs?,762.0,France,Female,29.0,5.0,172231.93,1.0,0.0,1.0,138380.12
+23089,15580579.0,Ugochukwu,645.0,France,Female,32.0,10.0,0.0,1.0,1.0,1.0,187597.98
+23090,15694381.0,McEwan,719.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,73069.3
+23091,15792679.0,Ch'ien,565.0,Spain,Male,38.0,8.0,86824.09,1.0,1.0,0.0,149772.61
+23092,15693690.0,Ch'ang,614.0,France,Male,25.0,2.0,0.0,2.0,1.0,1.0,141926.43
+23093,15781804.0,Yermolayev,679.0,Spain,Female,31.0,5.0,0.0,1.0,1.0,0.0,121537.91
+23094,15591386.0,Chukwujekwu,684.0,Germany,Female,39.0,3.0,93616.35,2.0,0.0,0.0,104222.8
+23095,15602845.0,Scott,684.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,123969.64
+23096,15803965.0,L?,599.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,1.0,67788.49
+23097,15659937.0,Hargraves,710.0,France,Female,44.0,4.0,0.0,2.0,1.0,1.0,165035.17
+23098,15750602.0,Chukwuebuka,646.0,France,Female,32.0,6.0,0.0,1.0,1.0,0.0,119808.64
+23099,15653784.0,Fyodorov,683.0,France,Male,22.0,0.0,125877.65,2.0,1.0,0.0,75585.48
+23100,15803689.0,Genovese,618.0,France,Male,38.0,6.0,0.0,2.0,0.0,1.0,51637.67
+23101,15677052.0,T'ao,723.0,Spain,Female,49.0,3.0,0.0,1.0,0.0,0.0,141023.01
+23102,15593900.0,Nebechukwu,646.0,Germany,Male,31.0,9.0,86006.21,2.0,1.0,0.0,77500.81
+23103,15774318.0,Aitken,588.0,Germany,Male,27.0,5.0,115141.18,2.0,1.0,0.0,146949.72
+23104,15688865.0,Onyemauchechukwu,611.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,90980.04
+23105,15783875.0,H?,576.0,France,Female,37.0,5.0,0.0,1.0,1.0,0.0,164083.72
+23106,15782688.0,Udegbunam,526.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,143450.61
+23107,15596761.0,Milano,645.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,3510.18
+23108,15664339.0,Chukwuemeka,714.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,145551.6
+23109,15792358.0,T'ien,638.0,France,Female,48.0,7.0,0.0,1.0,1.0,1.0,180932.46
+23110,15644962.0,Boulger,850.0,Germany,Female,40.0,1.0,106086.42,2.0,0.0,0.0,115818.51
+23111,15600882.0,Nkemjika,681.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,161519.77
+23112,15736420.0,Brown,753.0,Germany,Male,36.0,6.0,131778.08,1.0,1.0,1.0,181794.86
+23113,15632439.0,Hsia,591.0,France,Female,28.0,2.0,123978.96,1.0,1.0,1.0,81778.72
+23114,15654489.0,Yewen,704.0,France,Male,32.0,0.0,0.0,2.0,1.0,1.0,52861.89
+23115,15625126.0,Hsiung,711.0,Germany,Male,42.0,3.0,96353.8,1.0,1.0,1.0,141148.21
+23116,15643824.0,Chiawuotu,713.0,Spain,Male,34.0,4.0,99564.22,1.0,1.0,0.0,102021.39
+23117,15574830.0,De Luca,718.0,Germany,Female,46.0,1.0,113958.55,1.0,1.0,0.0,177611.57
+23118,15660524.0,Shih,620.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,150694.42
+23119,15809847.0,Chiu,581.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,61240.83
+23120,15802501.0,Nnamutaezinwa,556.0,France,Female,25.0,1.0,0.0,2.0,0.0,1.0,133571.14
+23121,15686870.0,Ts'ai,550.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,1.0,139946.68
+23122,15795895.0,Chidiebere,743.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,17978.68
+23123,15806926.0,Hsiung,627.0,France,Female,33.0,9.0,115897.12,1.0,0.0,0.0,47675.29
+23124,15736399.0,Zito,637.0,Germany,Female,54.0,4.0,140950.97,1.0,1.0,0.0,170968.99
+23125,15582944.0,McIntyre,635.0,Germany,Female,33.0,7.0,141952.07,2.0,1.0,0.0,180932.46
+23126,15756820.0,Trevisano,587.0,France,Male,41.0,0.0,142644.64,1.0,1.0,1.0,49618.37
+23127,15777639.0,Yudin,587.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,1.0,57975.8
+23128,15701291.0,Chukwujamuike,653.0,France,Male,33.0,3.0,133701.07,1.0,0.0,0.0,111324.41
+23129,15656029.0,Nwankwo,638.0,Germany,Male,50.0,6.0,118235.93,1.0,1.0,0.0,159418.1
+23130,15622171.0,White,692.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,125961.48
+23131,15676091.0,Okechukwu,543.0,France,Male,35.0,7.0,124577.33,1.0,1.0,1.0,106636.89
+23132,15783629.0,Toscani,662.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,1.0,36754.13
+23133,15673372.0,Nnamutaezinwa,531.0,Germany,Female,38.0,4.0,137781.65,1.0,1.0,1.0,134901.34
+23134,15716041.0,Manna,641.0,Germany,Male,47.0,7.0,116098.82,1.0,1.0,1.0,51380.9
+23135,15774586.0,Zetticci,674.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,91406.62
+23136,15687558.0,Pinto,705.0,Spain,Female,34.0,7.0,87099.85,1.0,1.0,0.0,141798.98
+23137,15692991.0,Onyemachukwu,774.0,France,Female,40.0,1.0,107868.14,1.0,1.0,1.0,171519.06
+23138,15773421.0,Iqbal,655.0,Spain,Female,33.0,9.0,0.0,1.0,1.0,0.0,90928.02
+23139,15738785.0,Tsou,794.0,France,Male,33.0,10.0,0.0,2.0,1.0,1.0,161671.15
+23140,15764226.0,Okwudiliolisa,637.0,France,Female,39.0,5.0,0.0,1.0,0.0,0.0,182650.15
+23141,15627995.0,Chu,596.0,France,Female,32.0,4.0,120107.71,1.0,1.0,1.0,24402.87
+23142,15641664.0,Chuang,727.0,France,Female,42.0,6.0,115569.21,1.0,1.0,0.0,158973.77
+23143,15792064.0,Biryukov,622.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,1.0,92914.67
+23144,15582335.0,Tien,678.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,92664.09
+23145,15758856.0,Fomin,602.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,101057.95
+23146,15651369.0,Tretiakova,629.0,France,Female,55.0,2.0,0.0,1.0,1.0,1.0,55313.44
+23147,15769902.0,Brazenor,679.0,Germany,Male,39.0,5.0,122524.61,2.0,1.0,0.0,130752.01
+23148,15711571.0,Chukwudi,696.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,83084.33
+23149,15642824.0,Fanucci,749.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,85522.84
+23150,15677522.0,P'eng,573.0,Spain,Male,45.0,2.0,0.0,2.0,1.0,1.0,98896.06
+23151,15701176.0,Chidiebere,694.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,140881.2
+23152,15689344.0,Diggs,713.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,91574.92
+23153,15804862.0,Hs?,589.0,France,Male,23.0,6.0,0.0,1.0,0.0,1.0,58854.88
+23154,15760090.0,Onio,707.0,France,Male,42.0,2.0,0.0,1.0,1.0,0.0,95421.0
+23155,15768574.0,Ch'en,838.0,Spain,Female,32.0,6.0,157577.28,1.0,1.0,0.0,100408.31
+23156,15735703.0,Ch'ien,687.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,142516.35
+23157,15774872.0,Hsia,529.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,74835.65
+23158,15682928.0,Chidiebele,695.0,France,Male,39.0,9.0,125191.69,1.0,1.0,0.0,124489.88
+23159,15680167.0,Cattaneo,696.0,Spain,Male,37.0,2.0,88977.39,1.0,1.0,0.0,180622.02
+23160,15622578.0,Nwachukwu,554.0,France,Male,40.0,3.0,95297.11,1.0,1.0,1.0,62674.38
+23161,15772601.0,Goliwe,710.0,Germany,Male,36.0,7.0,124099.13,2.0,1.0,0.0,62402.38
+23162,15724892.0,Chijioke,567.0,France,Female,57.0,2.0,0.0,1.0,1.0,0.0,151902.78
+23163,15592781.0,Hsueh,596.0,France,Male,46.0,6.0,0.0,2.0,1.0,0.0,85736.26
+23164,15797919.0,Mazzanti,757.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,152290.28
+23165,15570192.0,Nkemdilim,667.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,152298.28
+23166,15764284.0,Tien,616.0,Spain,Female,46.0,1.0,137781.65,3.0,1.0,0.0,187987.01
+23167,15737605.0,Hargreaves,577.0,France,Male,67.0,3.0,0.0,2.0,1.0,1.0,126419.8
+23168,15711378.0,Toscani,651.0,France,Male,44.0,8.0,88977.39,1.0,0.0,0.0,102982.79
+23169,15744044.0,Fiorentini,607.0,France,Female,39.0,3.0,109724.38,1.0,1.0,1.0,60728.89
+23170,15595557.0,Onwuamaegbu,529.0,France,Male,39.0,1.0,0.0,2.0,0.0,1.0,163798.41
+23171,15697042.0,Iadanza,692.0,France,Female,44.0,9.0,154983.98,1.0,1.0,0.0,104240.77
+23172,15591091.0,Ch'eng,656.0,Germany,Female,37.0,10.0,104744.89,1.0,0.0,1.0,136876.47
+23173,15755874.0,Shih,741.0,France,Male,41.0,6.0,0.0,2.0,1.0,0.0,170528.63
+23174,15705379.0,Chukwukere,645.0,France,Male,34.0,2.0,131244.65,2.0,1.0,1.0,166650.32
+23175,15704910.0,Atherton,681.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,131083.56
+23176,15594786.0,Galkin,776.0,France,Male,40.0,2.0,0.0,1.0,1.0,1.0,163736.09
+23177,15762762.0,Yobanna,678.0,France,Male,42.0,4.0,0.0,2.0,1.0,0.0,125979.36
+23178,15698932.0,Chidimma,651.0,Spain,Male,57.0,2.0,0.0,1.0,0.0,0.0,86480.29
+23179,15681446.0,Teng,584.0,France,Male,29.0,10.0,0.0,2.0,0.0,1.0,130789.6
+23180,15763460.0,Walker,683.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,90875.94
+23181,15572051.0,Yobanna,756.0,Germany,Male,47.0,4.0,154779.35,2.0,0.0,0.0,145789.71
+23182,15697318.0,Clements,597.0,France,Male,38.0,7.0,0.0,1.0,1.0,0.0,134626.3
+23183,15648032.0,Hsia,727.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,126535.97
+23184,15790717.0,Ch'iu,586.0,Germany,Male,33.0,6.0,134571.5,1.0,1.0,1.0,147955.91
+23185,15806967.0,Lai,613.0,France,Male,44.0,9.0,0.0,2.0,1.0,1.0,99398.36
+23186,15803685.0,P'eng,721.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,51711.57
+23187,15734892.0,P'eng,726.0,Spain,Female,51.0,0.0,0.0,1.0,0.0,0.0,52301.15
+23188,15582910.0,Obidimkpa,599.0,France,Male,36.0,1.0,0.0,2.0,0.0,1.0,75860.01
+23189,15574910.0,Nwankwo,771.0,France,Female,44.0,4.0,0.0,2.0,0.0,1.0,52471.36
+23190,15766649.0,Lettiere,758.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,69055.45
+23191,15677522.0,Onyemauchechukwu,530.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,76582.95
+23192,15806822.0,Ch'eng,622.0,Germany,Female,32.0,6.0,177619.71,2.0,1.0,0.0,177844.06
+23193,15692991.0,Hsieh,678.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,1.0,121232.51
+23194,15685920.0,Kambinachi,583.0,France,Male,29.0,10.0,0.0,2.0,1.0,1.0,44008.91
+23195,15648721.0,Nucci,731.0,France,Male,34.0,5.0,0.0,1.0,0.0,1.0,76510.46
+23196,15799785.0,Chia,757.0,France,Male,41.0,3.0,0.0,2.0,1.0,0.0,132973.21
+23197,15745804.0,Lucchesi,727.0,France,Female,48.0,5.0,0.0,2.0,1.0,0.0,143913.32
+23198,15810839.0,Ewing,724.0,France,Female,41.0,7.0,0.0,2.0,1.0,0.0,123663.63
+23199,15682467.0,Tseng,652.0,Germany,Male,36.0,1.0,127560.46,1.0,1.0,1.0,179227.52
+23200,15654574.0,Kuo,498.0,Germany,Male,47.0,5.0,127763.24,1.0,0.0,0.0,63736.17
+23201,15722593.0,Onyekachi,675.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,182728.8
+23202,15748327.0,Ch'iu,605.0,France,Male,51.0,8.0,0.0,1.0,1.0,1.0,139919.38
+23203,15608916.0,Chinwenma,545.0,France,Female,36.0,8.0,0.0,1.0,1.0,1.0,187632.22
+23204,15783883.0,Chukwuma,646.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,0.0,100538.8
+23205,15809823.0,Kaeppel,644.0,Germany,Male,30.0,2.0,121898.82,2.0,1.0,1.0,180598.28
+23206,15615477.0,Onyeorulu,555.0,Germany,Female,48.0,1.0,129895.19,1.0,0.0,0.0,152944.39
+23207,15737408.0,Chiazagomekpere,663.0,France,Male,57.0,3.0,0.0,2.0,0.0,1.0,67944.09
+23208,15751137.0,Chia,684.0,Spain,Female,35.0,8.0,0.0,3.0,1.0,1.0,168544.85
+23209,15657535.0,Hodge,648.0,France,Male,22.0,6.0,0.0,2.0,1.0,1.0,74048.1
+23210,15691808.0,Mott,667.0,France,Male,51.0,2.0,0.0,2.0,1.0,0.0,161388.22
+23211,15705737.0,H?,730.0,France,Male,32.0,4.0,0.0,1.0,1.0,1.0,126469.09
+23212,15794142.0,Walker,756.0,Germany,Female,32.0,4.0,124415.54,1.0,0.0,0.0,57844.69
+23213,15681697.0,Brown,762.0,France,Female,44.0,3.0,0.0,2.0,0.0,0.0,51554.58
+23214,15690695.0,H?,692.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,0.0,164975.82
+23215,15786617.0,Uspensky,675.0,France,Female,29.0,5.0,0.0,2.0,1.0,0.0,184514.01
+23216,15591035.0,Chiang,644.0,France,Female,36.0,3.0,121685.31,1.0,0.0,1.0,182867.84
+23217,15803801.0,Mamelu,687.0,France,Male,44.0,9.0,108449.23,1.0,1.0,1.0,53478.02
+23218,15749123.0,Ulyanov,762.0,Germany,Female,40.0,5.0,140576.11,1.0,1.0,0.0,91291.2
+23219,15745307.0,Yobachukwu,741.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,162699.7
+23220,15721658.0,Chibuzo,695.0,Spain,Female,42.0,7.0,0.0,2.0,1.0,1.0,120892.96
+23221,15753861.0,Chia,663.0,France,Female,42.0,3.0,0.0,2.0,1.0,1.0,86786.41
+23222,15769412.0,Chidiebere,667.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,171707.93
+23223,15578045.0,Folliero,691.0,France,Male,38.0,2.0,99824.45,1.0,1.0,0.0,158313.87
+23224,15601184.0,Calabrese,705.0,France,Male,42.0,3.0,0.0,1.0,0.0,0.0,158887.09
+23225,15671582.0,Chin,559.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,126797.9
+23226,15591036.0,Chidiegwu,663.0,France,Female,29.0,6.0,113348.5,1.0,1.0,1.0,147090.9
+23227,15682639.0,Ts'ao,619.0,Spain,Male,29.0,3.0,0.0,2.0,0.0,1.0,158840.99
+23228,15573077.0,Trevisano,651.0,Spain,Male,35.0,9.0,0.0,2.0,0.0,0.0,71865.31
+23229,15702442.0,Tsao,668.0,France,Male,38.0,5.0,141803.77,1.0,1.0,1.0,170528.63
+23230,15603065.0,Trevisan,699.0,France,Male,44.0,6.0,0.0,2.0,1.0,0.0,86937.2
+23231,15764409.0,Tuan,706.0,France,Male,40.0,6.0,125921.37,1.0,1.0,1.0,92916.53
+23232,15589435.0,Tien,627.0,France,Female,28.0,5.0,0.0,2.0,0.0,1.0,166370.83
+23233,15621205.0,Ijendu,714.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,0.0,60721.4
+23234,15804064.0,Ch'ang,652.0,France,Female,29.0,8.0,106511.85,2.0,1.0,0.0,129964.94
+23235,15794895.0,Hsiao,592.0,Spain,Male,64.0,9.0,112281.7,1.0,1.0,1.0,141069.88
+23236,15746871.0,Nwachinemelu,717.0,Spain,Female,34.0,6.0,0.0,1.0,1.0,1.0,62703.38
+23237,15615016.0,Fanucci,573.0,France,Male,41.0,6.0,0.0,2.0,0.0,0.0,185580.89
+23238,15775192.0,Hao,726.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,104786.59
+23239,15673599.0,Chiang,599.0,France,Female,41.0,3.0,0.0,2.0,1.0,0.0,158918.03
+23240,15711299.0,Hsiao,758.0,France,Female,46.0,4.0,69328.47,1.0,0.0,0.0,131473.31
+23241,15740442.0,Onyekachukwu,757.0,Spain,Male,33.0,7.0,0.0,2.0,0.0,0.0,79366.98
+23242,15775108.0,Nkemdirim,757.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,113868.2
+23243,15657868.0,Hsia,549.0,Spain,Male,69.0,4.0,121092.6,1.0,1.0,1.0,80593.31
+23244,15673747.0,Hsia,646.0,France,Male,48.0,6.0,0.0,2.0,1.0,1.0,151733.43
+23245,15572408.0,Kudryashova,704.0,France,Female,50.0,5.0,152323.66,1.0,1.0,0.0,164018.98
+23246,15800116.0,Chukwubuikem,648.0,France,Female,45.0,7.0,129893.56,1.0,1.0,0.0,26687.22
+23247,15736431.0,Chia,628.0,France,Male,38.0,7.0,0.0,1.0,1.0,0.0,181543.67
+23248,15675190.0,Ch'en,705.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,135083.73
+23249,15752694.0,Farrar,724.0,Germany,Female,52.0,1.0,118889.66,2.0,0.0,1.0,57541.06
+23250,15792668.0,Lai,622.0,France,Female,44.0,3.0,148061.46,1.0,1.0,0.0,141860.62
+23251,15658375.0,Kaur,659.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,94774.12
+23252,15591552.0,Lucchesi,699.0,Spain,Male,29.0,9.0,95196.26,1.0,0.0,1.0,150836.22
+23253,15714485.0,Tsao,842.0,Germany,Male,30.0,7.0,132623.12,1.0,1.0,0.0,60942.0
+23254,15751177.0,Nwabugwu,652.0,France,Female,40.0,4.0,0.0,2.0,0.0,0.0,161887.16
+23255,15791842.0,Ch'in,613.0,France,Male,36.0,4.0,0.0,2.0,0.0,0.0,85411.01
+23256,15765014.0,Shih,555.0,France,Female,32.0,7.0,0.0,2.0,0.0,0.0,67622.34
+23257,15673372.0,Lo,633.0,Spain,Female,33.0,8.0,0.0,2.0,1.0,1.0,131644.37
+23258,15768282.0,Maclean,671.0,France,Male,31.0,8.0,0.0,1.0,1.0,1.0,63736.17
+23259,15647574.0,Bess,751.0,Germany,Female,37.0,9.0,98710.89,1.0,1.0,1.0,161064.29
+23260,15658577.0,Davison,763.0,France,Female,52.0,7.0,0.0,1.0,1.0,0.0,64511.44
+23261,15639265.0,Ch'ang,679.0,Germany,Female,45.0,1.0,125879.27,1.0,1.0,0.0,158043.11
+23262,15807525.0,Brown,656.0,France,Male,32.0,3.0,161807.18,1.0,1.0,1.0,94876.65
+23263,15643865.0,Yefremova,691.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,114879.64
+23264,15778936.0,White,685.0,France,Male,24.0,5.0,125265.61,1.0,1.0,1.0,81313.51
+23265,15708063.0,Liao,685.0,France,Female,32.0,8.0,0.0,2.0,0.0,0.0,83470.4
+23266,15591286.0,Folliero,545.0,Spain,Male,50.0,1.0,141675.65,2.0,0.0,1.0,141175.18
+23267,15720671.0,Palerma,648.0,Germany,Male,33.0,3.0,91897.8,1.0,1.0,1.0,118313.87
+23268,15679760.0,Soto,473.0,Germany,Male,43.0,8.0,101257.46,1.0,1.0,0.0,131097.9
+23269,15758769.0,H?,697.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,103931.49
+23270,15596972.0,Chibuzo,699.0,Spain,Male,44.0,9.0,0.0,1.0,1.0,0.0,163714.92
+23271,15596649.0,Hsia,637.0,Germany,Male,35.0,2.0,157780.84,1.0,1.0,0.0,48673.18
+23272,15664214.0,Chimezie,714.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,66977.68
+23273,15626900.0,Ch'ang,593.0,France,Male,41.0,3.0,118537.47,1.0,0.0,1.0,132034.13
+23274,15810444.0,Chia,632.0,Germany,Male,28.0,2.0,147904.31,1.0,1.0,0.0,153604.44
+23275,15603065.0,Onuchukwu,545.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,181893.31
+23276,15680197.0,Ch'eng,577.0,France,Male,48.0,4.0,0.0,3.0,0.0,0.0,161811.23
+23277,15642710.0,Onyekachi,561.0,France,Male,28.0,7.0,0.0,1.0,1.0,0.0,121542.33
+23278,15573077.0,Ch'en,641.0,France,Female,31.0,2.0,113709.28,1.0,1.0,0.0,51711.57
+23279,15788775.0,Chia,663.0,France,Female,46.0,4.0,0.0,1.0,1.0,1.0,25136.95
+23280,15690079.0,Eluemuno,599.0,Germany,Female,45.0,5.0,123939.36,1.0,1.0,1.0,55868.97
+23281,15626144.0,Chukwueloka,526.0,Germany,Female,33.0,1.0,141814.64,1.0,1.0,0.0,72929.96
+23282,15758845.0,Calabresi,721.0,Spain,Female,52.0,5.0,0.0,3.0,1.0,0.0,153662.88
+23283,15651740.0,Fiorentini,812.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,88866.92
+23284,15781679.0,L?,676.0,Germany,Female,55.0,6.0,92676.3,1.0,1.0,1.0,172967.87
+23285,15762216.0,Genovese,636.0,France,Female,22.0,7.0,0.0,2.0,1.0,1.0,135851.3
+23286,15706464.0,DeRose,656.0,France,Male,31.0,0.0,0.0,2.0,1.0,1.0,184520.0
+23287,15663987.0,Tuan,646.0,Spain,Male,21.0,2.0,0.0,1.0,0.0,1.0,91943.94
+23288,15693526.0,T'ien,694.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,0.0,145562.4
+23289,15738219.0,Okwuadigbo,699.0,France,Female,31.0,2.0,119370.11,1.0,1.0,1.0,72924.56
+23290,15697283.0,Palerma,631.0,Germany,Male,20.0,1.0,126679.69,2.0,1.0,0.0,119996.95
+23291,15687558.0,Ts'ui,705.0,Germany,Male,29.0,6.0,127728.24,1.0,1.0,0.0,176913.42
+23292,15581548.0,Chinwenma,545.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,74854.97
+23293,15718247.0,Tsou,656.0,France,Male,33.0,7.0,0.0,2.0,0.0,1.0,98640.78
+23294,15678916.0,Miah,759.0,France,Male,41.0,5.0,136658.61,1.0,1.0,1.0,172501.38
+23295,15628992.0,Chukwukadibia,613.0,France,Female,31.0,7.0,0.0,2.0,0.0,0.0,125451.01
+23296,15572073.0,McLean,756.0,Germany,Male,41.0,1.0,118626.55,1.0,1.0,1.0,183494.87
+23297,15611682.0,Onwughara,692.0,France,Female,32.0,8.0,0.0,1.0,1.0,0.0,80190.36
+23298,15681562.0,Maclean,645.0,France,Female,32.0,2.0,0.0,1.0,1.0,1.0,121175.86
+23299,15680683.0,Ts'ui,588.0,Spain,Female,34.0,10.0,0.0,2.0,0.0,1.0,64872.55
+23300,15667938.0,Mazzanti,544.0,France,Female,28.0,7.0,0.0,2.0,0.0,1.0,91943.94
+23301,15718247.0,Shih,733.0,Germany,Female,40.0,3.0,97792.16,1.0,1.0,0.0,158313.87
+23302,15648032.0,Zito,748.0,France,Female,45.0,1.0,127305.5,1.0,1.0,0.0,107876.85
+23303,15661629.0,L?,675.0,France,Male,54.0,4.0,0.0,1.0,1.0,1.0,56323.21
+23304,15727391.0,Smith,663.0,Germany,Female,44.0,3.0,74988.59,1.0,1.0,0.0,94498.82
+23305,15752956.0,Lo Duca,669.0,France,Female,39.0,8.0,0.0,1.0,1.0,1.0,151336.96
+23306,15789563.0,Mazzanti,699.0,France,Female,42.0,5.0,0.0,1.0,1.0,1.0,75578.67
+23307,15768104.0,Lai,821.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,170968.99
+23308,15792250.0,Hsing,602.0,France,Female,62.0,2.0,0.0,1.0,1.0,1.0,138021.36
+23309,15757628.0,Lettiere,651.0,Spain,Male,43.0,9.0,169824.46,1.0,1.0,1.0,172501.38
+23310,15650098.0,Chiemenam,809.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,114957.22
+23311,15684769.0,De Salis,773.0,France,Female,42.0,5.0,91857.66,1.0,1.0,0.0,108402.54
+23312,15668580.0,Trevisani,682.0,France,Female,32.0,7.0,0.0,1.0,0.0,1.0,152571.33
+23313,15773421.0,Nwokeocha,731.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,135077.01
+23314,15641312.0,Cremonesi,699.0,Germany,Male,37.0,8.0,131734.96,1.0,0.0,0.0,163798.41
+23315,15721921.0,Hsiao,572.0,France,Male,35.0,9.0,0.0,2.0,0.0,1.0,121894.31
+23316,15574795.0,Onwuamaeze,596.0,Spain,Female,43.0,1.0,0.0,1.0,0.0,1.0,167984.61
+23317,15733114.0,Hsing,587.0,France,Male,39.0,6.0,0.0,1.0,1.0,0.0,176692.65
+23318,15601184.0,Mironova,581.0,Germany,Female,32.0,1.0,87927.45,2.0,1.0,1.0,162347.05
+23319,15714642.0,Hs?,717.0,France,Female,36.0,8.0,0.0,1.0,0.0,0.0,141796.79
+23320,15767238.0,Chukwukere,569.0,Germany,Female,40.0,1.0,121206.96,1.0,1.0,0.0,18924.92
+23321,15769346.0,Manna,652.0,France,Female,26.0,10.0,0.0,1.0,1.0,1.0,19370.73
+23322,15776824.0,Hsieh,762.0,France,Male,55.0,3.0,0.0,1.0,1.0,1.0,76621.99
+23323,15695301.0,Onwumelu,778.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,187811.71
+23324,15615473.0,T'ien,705.0,France,Male,44.0,9.0,0.0,2.0,0.0,0.0,145593.85
+23325,15605665.0,Shand,652.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,155700.15
+23326,15590146.0,Lucciano,709.0,Spain,Male,39.0,7.0,0.0,2.0,1.0,1.0,171378.77
+23327,15687828.0,Nwankwo,642.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,121883.87
+23328,15790782.0,Chibueze,753.0,France,Male,49.0,7.0,99792.82,3.0,0.0,0.0,107712.42
+23329,15788126.0,Calabresi,723.0,Spain,Female,44.0,5.0,0.0,2.0,0.0,0.0,88920.68
+23330,15777060.0,Fiorentini,751.0,Spain,Female,36.0,9.0,0.0,2.0,1.0,0.0,152843.53
+23331,15692291.0,Milani,675.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,54926.51
+23332,15808182.0,O'Brien,694.0,France,Male,38.0,2.0,0.0,1.0,1.0,1.0,139048.0
+23333,15628274.0,Hsueh,550.0,Germany,Male,29.0,4.0,106453.53,1.0,1.0,0.0,173617.09
+23334,15595036.0,Okonkwo,711.0,Germany,Female,32.0,2.0,126773.43,2.0,1.0,1.0,89874.82
+23335,15632050.0,Chiazagomekpele,695.0,France,Female,42.0,9.0,0.0,2.0,0.0,0.0,93524.19
+23336,15755722.0,Nnaife,634.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,187905.12
+23337,15661591.0,Chidiebele,599.0,France,Female,33.0,3.0,113328.65,1.0,0.0,0.0,129526.57
+23338,15718454.0,Ch'ang,714.0,France,Male,45.0,7.0,0.0,1.0,0.0,0.0,48244.64
+23339,15726167.0,T'ien,602.0,France,Female,59.0,5.0,0.0,1.0,1.0,1.0,58855.85
+23340,15654085.0,Nwebube,811.0,France,Female,37.0,1.0,0.0,1.0,1.0,0.0,114899.13
+23341,15811927.0,Hsing,709.0,Spain,Female,46.0,7.0,0.0,2.0,0.0,1.0,131534.96
+23342,15692599.0,Chinwendu,687.0,France,Male,25.0,0.0,115095.88,1.0,1.0,1.0,112585.19
+23343,15636658.0,Teng,712.0,Spain,Male,38.0,2.0,77846.9,1.0,1.0,1.0,178709.98
+23344,15682600.0,Milano,692.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,0.0,88566.9
+23345,15589572.0,Biryukov,681.0,France,Female,33.0,8.0,0.0,1.0,0.0,0.0,156910.38
+23346,15665760.0,Ts'ui,703.0,France,Female,67.0,2.0,129901.21,1.0,1.0,1.0,146585.57
+23347,15783501.0,Ma,569.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,143598.7
+23348,15769168.0,Hsiao,687.0,France,Female,35.0,1.0,0.0,2.0,1.0,0.0,187632.22
+23349,15605037.0,Hsieh,661.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,82867.55
+23350,15663421.0,Chukwudi,525.0,France,Male,39.0,4.0,0.0,1.0,1.0,1.0,93524.19
+23351,15770041.0,Hales,496.0,Germany,Male,51.0,5.0,96942.06,1.0,1.0,0.0,151903.6
+23352,15762929.0,Ch'en,593.0,Spain,Male,36.0,2.0,0.0,2.0,0.0,1.0,62368.33
+23353,15742216.0,Ikedinachukwu,715.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,93959.96
+23354,15583736.0,Nwabugwu,850.0,France,Female,44.0,9.0,90217.07,1.0,0.0,1.0,163989.66
+23355,15804482.0,Ch'ang,709.0,France,Female,40.0,8.0,0.0,1.0,0.0,0.0,180800.42
+23356,15773469.0,Shipton,695.0,Spain,Female,37.0,3.0,0.0,2.0,1.0,0.0,138399.67
+23357,15705735.0,Hsiao,581.0,Germany,Male,34.0,7.0,147127.81,1.0,1.0,1.0,64716.08
+23358,15666096.0,Palermo,591.0,Germany,Female,48.0,8.0,97023.21,1.0,1.0,0.0,172372.63
+23359,15766887.0,Hsiao,622.0,Spain,Female,41.0,9.0,0.0,2.0,1.0,1.0,141023.01
+23360,15751167.0,Sun,650.0,Germany,Female,28.0,5.0,165350.61,2.0,0.0,1.0,142903.44
+23361,15681014.0,Chukwufumnanya,622.0,Germany,Male,27.0,7.0,104944.1,2.0,1.0,0.0,121151.1
+23362,15607988.0,Hs?,681.0,Germany,Male,39.0,5.0,146098.43,2.0,1.0,1.0,152290.28
+23363,15698028.0,Chiang,753.0,France,Male,45.0,8.0,0.0,2.0,1.0,0.0,94978.1
+23364,15593976.0,Maclean,537.0,Germany,Female,35.0,4.0,140720.93,1.0,1.0,1.0,62830.97
+23365,15793890.0,Macleod,605.0,France,Female,43.0,8.0,114327.66,1.0,1.0,0.0,92982.61
+23366,15761706.0,Fanucci,667.0,Spain,Male,39.0,2.0,0.0,2.0,1.0,1.0,91830.75
+23367,15666884.0,Pagnotto,646.0,France,Male,27.0,1.0,132951.92,2.0,1.0,0.0,142377.69
+23368,15743192.0,Ibezimako,719.0,France,Female,35.0,4.0,124023.19,1.0,1.0,0.0,143668.44
+23369,15627665.0,Chukwufumnanya,703.0,France,Male,44.0,1.0,0.0,2.0,1.0,0.0,147964.99
+23370,15575581.0,P'eng,716.0,France,Male,33.0,6.0,0.0,1.0,1.0,1.0,139946.68
+23371,15567367.0,Chiang,696.0,Germany,Female,31.0,1.0,136855.94,1.0,1.0,1.0,100727.68
+23372,15597909.0,Ch'eng,651.0,Germany,Male,24.0,3.0,113873.27,1.0,1.0,0.0,131718.12
+23373,15650098.0,Shao,689.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,1.0,27011.8
+23374,15757829.0,Maccallum,638.0,France,Male,28.0,3.0,124547.13,1.0,1.0,0.0,170638.98
+23375,15589435.0,Hao,633.0,France,Male,36.0,2.0,127924.25,2.0,1.0,1.0,14081.64
+23376,15583090.0,Hsiung,578.0,Spain,Male,46.0,2.0,0.0,3.0,1.0,0.0,152577.62
+23377,15686538.0,Ch'in,649.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,131924.38
+23378,15761720.0,Chukwudi,724.0,Germany,Female,54.0,6.0,162657.64,4.0,0.0,0.0,90928.48
+23379,15778861.0,Chukwuemeka,637.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,46811.77
+23380,15605827.0,Folliero,711.0,Spain,Male,33.0,6.0,0.0,2.0,1.0,1.0,69011.66
+23381,15711457.0,Brown,687.0,Germany,Female,34.0,2.0,130306.49,1.0,1.0,1.0,87901.83
+23382,15759913.0,Chidozie,678.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,131283.6
+23383,15674583.0,T'ang,697.0,Germany,Female,47.0,8.0,109341.87,2.0,0.0,0.0,160941.78
+23384,15579996.0,Chia,678.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,114968.99
+23385,15719479.0,Pirozzi,678.0,France,Male,33.0,6.0,0.0,1.0,0.0,1.0,137961.41
+23386,15797733.0,Y?an,695.0,France,Female,37.0,8.0,144974.55,1.0,1.0,1.0,78858.07
+23387,15697318.0,Genovese,605.0,France,Male,51.0,1.0,121152.05,1.0,0.0,1.0,162668.33
+23388,15657105.0,Lu,727.0,France,Male,44.0,5.0,0.0,1.0,1.0,0.0,176743.51
+23389,15656912.0,Ugochukwu,554.0,France,Female,36.0,8.0,0.0,1.0,0.0,0.0,182867.84
+23390,15603594.0,Onyemauchechukwu,684.0,Germany,Female,56.0,3.0,102540.61,1.0,1.0,0.0,95857.18
+23391,15740404.0,Macleod,721.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,162260.93
+23392,15781298.0,T'ien,573.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,81492.8
+23393,15648735.0,Pirozzi,615.0,Germany,Female,33.0,6.0,109786.82,2.0,1.0,0.0,110230.4
+23394,15581748.0,Kuo,652.0,France,Male,29.0,0.0,0.0,1.0,1.0,1.0,149593.61
+23395,15777772.0,Hsieh,781.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,1.0,72909.75
+23396,15809826.0,Chienezie,478.0,France,Female,44.0,3.0,0.0,2.0,0.0,0.0,92621.9
+23397,15665083.0,Chukwufumnanya,718.0,France,Female,40.0,8.0,0.0,1.0,0.0,0.0,146785.44
+23398,15642099.0,Ch'in,631.0,Spain,Male,42.0,3.0,0.0,2.0,1.0,1.0,72350.47
+23399,15763898.0,Palerma,614.0,France,Female,51.0,10.0,0.0,2.0,1.0,1.0,19500.1
+23400,15750728.0,Macleod,637.0,France,Female,42.0,0.0,0.0,2.0,1.0,0.0,185466.85
+23401,15698872.0,Uchenna,667.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,823.96
+23402,15583748.0,Hsing,597.0,Germany,Male,37.0,3.0,112808.18,2.0,0.0,1.0,89801.9
+23403,15684868.0,Zito,749.0,Germany,Male,41.0,4.0,126619.27,1.0,1.0,1.0,135077.01
+23404,15700383.0,Cawthorne,678.0,France,Female,29.0,4.0,160838.13,1.0,1.0,0.0,43480.43
+23405,15585853.0,Iadanza,675.0,Spain,Male,49.0,8.0,0.0,1.0,1.0,1.0,136902.01
+23406,15776921.0,Martin,553.0,Germany,Female,51.0,8.0,121345.88,1.0,1.0,0.0,78854.57
+23407,15742404.0,Yashina,727.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,119882.7
+23408,15715796.0,Ch'ang,731.0,Germany,Female,34.0,5.0,146547.78,2.0,0.0,0.0,75585.48
+23409,15737705.0,Hsiung,643.0,Germany,Male,40.0,1.0,133968.96,2.0,1.0,0.0,172363.41
+23410,15663489.0,Hsieh,724.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,139537.43
+23411,15641670.0,Sells,714.0,Germany,Male,34.0,8.0,148897.02,1.0,1.0,1.0,143357.03
+23412,15684319.0,Ts'ui,720.0,France,Male,31.0,3.0,0.0,1.0,1.0,0.0,123663.63
+23413,15731830.0,Teng,588.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,125962.02
+23414,15690175.0,Chia,619.0,Germany,Male,29.0,9.0,165978.01,2.0,1.0,0.0,162858.29
+23415,15795895.0,Yobanna,599.0,France,Male,29.0,1.0,0.0,2.0,0.0,0.0,82526.92
+23416,15700801.0,Smith,555.0,France,Male,33.0,3.0,140197.18,2.0,0.0,1.0,166475.44
+23417,15642996.0,Hsueh,554.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,71830.1
+23418,15615060.0,O'Donnell,714.0,Germany,Female,33.0,3.0,92474.05,2.0,1.0,1.0,131312.56
+23419,15689671.0,Teng,718.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,168886.66
+23420,15742229.0,Chijindum,729.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,115611.97
+23421,15776939.0,Ebelechukwu,704.0,Germany,Male,46.0,6.0,90963.49,1.0,0.0,1.0,180593.45
+23422,15766166.0,Hsieh,670.0,Spain,Male,39.0,5.0,0.0,1.0,1.0,0.0,113581.85
+23423,15775339.0,Tsao,627.0,France,Female,34.0,9.0,0.0,2.0,0.0,0.0,132468.76
+23424,15635178.0,Milani,836.0,Germany,Male,29.0,8.0,144961.97,2.0,1.0,1.0,60721.4
+23425,15582591.0,Ch'eng,584.0,Spain,Male,43.0,7.0,97588.6,1.0,1.0,1.0,187632.22
+23426,15791700.0,Chin,632.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,158973.77
+23427,15697574.0,Akabueze,648.0,Spain,Male,35.0,7.0,0.0,2.0,0.0,1.0,103931.49
+23428,15790743.0,Ch'ien,635.0,France,Male,41.0,3.0,0.0,2.0,1.0,0.0,94875.03
+23429,15777596.0,Kornilova,812.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,0.0,198798.44
+23430,15790594.0,Ch'in,633.0,France,Female,36.0,1.0,113657.83,1.0,1.0,1.0,154598.58
+23431,15683337.0,Houghton,712.0,Germany,Female,40.0,3.0,128743.8,1.0,0.0,0.0,104902.68
+23432,15680141.0,Shao,644.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,84711.03
+23433,15592645.0,De Salis,528.0,Germany,Female,41.0,8.0,122351.29,1.0,1.0,1.0,110253.2
+23434,15771580.0,Pirozzi,691.0,France,Female,27.0,6.0,118742.71,1.0,1.0,0.0,64872.55
+23435,15578908.0,Walker,683.0,France,Male,38.0,2.0,121260.64,2.0,1.0,1.0,126726.33
+23436,15681476.0,Chiang,705.0,Spain,Female,40.0,3.0,0.0,1.0,1.0,0.0,161076.92
+23437,15733247.0,Fan,635.0,France,Female,72.0,8.0,0.0,2.0,1.0,1.0,133535.29
+23438,15652218.0,Onyekachi,691.0,Spain,Female,35.0,8.0,0.0,1.0,1.0,1.0,165574.54
+23439,15703763.0,Calabrese,662.0,France,Female,45.0,5.0,0.0,2.0,1.0,0.0,184524.65
+23440,15662294.0,Macintyre,724.0,Germany,Female,35.0,9.0,82696.09,1.0,1.0,1.0,176743.51
+23441,15768151.0,Uspensky,731.0,France,Male,34.0,0.0,0.0,2.0,0.0,0.0,94790.34
+23442,15640133.0,Tsai,678.0,Spain,Male,53.0,1.0,171913.87,1.0,0.0,1.0,144875.31
+23443,15572706.0,Okechukwu,572.0,France,Female,36.0,9.0,0.0,2.0,0.0,0.0,81861.1
+23444,15643794.0,Mazzanti,628.0,Germany,Male,45.0,3.0,100249.41,2.0,1.0,1.0,143629.72
+23445,15768777.0,Chiemenam,714.0,France,Female,35.0,4.0,0.0,2.0,0.0,0.0,63736.16
+23446,15572415.0,Lucchese,648.0,Spain,Male,38.0,6.0,141349.68,1.0,0.0,1.0,161249.07
+23447,15625021.0,Clements,731.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,136915.15
+23448,15642515.0,Lo,695.0,France,Male,42.0,4.0,121161.57,1.0,1.0,0.0,52794.71
+23449,15745307.0,Tseng,595.0,Germany,Male,36.0,5.0,87541.06,1.0,1.0,0.0,88827.67
+23450,15573851.0,Hsu,648.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,142331.85
+23451,15651140.0,Folliero,495.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,59981.62
+23452,15592762.0,Ndubuagha,662.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,182765.14
+23453,15695846.0,Trevisani,631.0,Germany,Female,62.0,5.0,73059.38,1.0,0.0,1.0,134878.19
+23454,15702571.0,Mazzanti,710.0,Spain,Female,56.0,1.0,0.0,1.0,0.0,0.0,95562.81
+23455,15772995.0,Chiu,590.0,France,Male,35.0,2.0,0.0,2.0,0.0,1.0,121061.89
+23456,15767339.0,Hsieh,652.0,France,Male,32.0,5.0,0.0,2.0,0.0,0.0,161435.02
+23457,15592765.0,Lucciano,662.0,France,Male,62.0,7.0,0.0,1.0,0.0,0.0,136071.05
+23458,15583076.0,Pinto,633.0,Spain,Male,36.0,2.0,0.0,1.0,1.0,0.0,173576.71
+23459,15803790.0,Mazzi,695.0,France,Female,38.0,0.0,102575.05,1.0,1.0,1.0,166677.54
+23460,15746564.0,Pagnotto,498.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,138882.98
+23461,15803790.0,Chukwudi,705.0,France,Male,33.0,5.0,0.0,1.0,0.0,1.0,51703.09
+23462,15720649.0,Lo Duca,706.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,140979.68
+23463,15570086.0,Ts'ai,646.0,Spain,Female,40.0,4.0,165798.1,1.0,1.0,0.0,155700.15
+23464,15758856.0,Chidiebere,634.0,France,Female,30.0,0.0,0.0,2.0,1.0,0.0,52876.41
+23465,15792868.0,Onuora,652.0,France,Female,40.0,8.0,125877.65,1.0,1.0,0.0,164924.11
+23466,15726676.0,Chuang,528.0,Germany,Male,41.0,6.0,163549.61,2.0,1.0,0.0,99251.24
+23467,15658364.0,Chimezie,850.0,Spain,Female,34.0,1.0,0.0,2.0,0.0,1.0,93783.0
+23468,15632439.0,Monds,748.0,France,Male,38.0,4.0,152063.59,1.0,0.0,0.0,161064.29
+23469,15682638.0,Ch'in,671.0,Spain,Female,39.0,7.0,0.0,2.0,0.0,0.0,146660.21
+23470,15758073.0,O'Donnell,730.0,France,Male,35.0,1.0,115888.04,1.0,1.0,0.0,77330.35
+23471,15645819.0,Aikenhead,591.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,90896.04
+23472,15568487.0,Smith,637.0,France,Female,43.0,10.0,153313.66,1.0,1.0,0.0,131363.57
+23473,15790379.0,Ugorji,712.0,France,Female,43.0,5.0,0.0,1.0,1.0,0.0,162697.93
+23474,15722692.0,Ch'ang,713.0,France,Female,44.0,6.0,0.0,2.0,1.0,1.0,142626.49
+23475,15644551.0,Padovano,667.0,Spain,Male,27.0,6.0,0.0,2.0,1.0,1.0,160529.71
+23476,15612893.0,Y?an,604.0,France,Female,47.0,4.0,103907.28,1.0,1.0,0.0,133501.94
+23477,15706232.0,Shih,684.0,France,Male,47.0,2.0,142360.61,1.0,1.0,1.0,166370.81
+23478,15654975.0,Baresi,589.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,102908.91
+23479,15653857.0,Nnaife,756.0,Germany,Male,37.0,3.0,109346.13,1.0,1.0,1.0,143711.02
+23480,15724453.0,Ozoemena,597.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,71926.28
+23481,15592461.0,Kao,637.0,Germany,Male,31.0,8.0,138577.34,1.0,1.0,1.0,100200.4
+23482,15721469.0,Chiedozie,725.0,France,Male,43.0,6.0,0.0,2.0,0.0,1.0,74580.8
+23483,15637646.0,Ts'ui,648.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,180622.02
+23484,15589754.0,Hs?,724.0,France,Female,54.0,4.0,138664.24,1.0,0.0,0.0,143960.72
+23485,15773469.0,Walker,619.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,180693.45
+23486,15752300.0,L?,591.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,120892.96
+23487,15581811.0,Ozioma,695.0,France,Female,25.0,5.0,0.0,2.0,1.0,1.0,152210.81
+23488,15582797.0,Ts'ui,695.0,France,Male,29.0,5.0,0.0,2.0,0.0,1.0,62634.94
+23489,15757310.0,Ch'iu,652.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,171378.77
+23490,15743817.0,Iheatu,763.0,Germany,Female,32.0,8.0,96486.57,1.0,0.0,1.0,107841.77
+23491,15663489.0,T'ao,649.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,123954.15
+23492,15576022.0,T'ao,746.0,Spain,Female,46.0,2.0,0.0,1.0,1.0,1.0,108747.12
+23493,15593601.0,Calabrese,663.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,105886.77
+23494,15702561.0,Fiorentino,678.0,Spain,Male,43.0,4.0,124723.92,2.0,0.0,1.0,87071.18
+23495,15693322.0,Nwabugwu,850.0,Spain,Female,49.0,1.0,0.0,2.0,0.0,1.0,71709.12
+23496,15596397.0,Fanucci,668.0,France,Female,41.0,6.0,0.0,2.0,0.0,1.0,164741.5
+23497,15622443.0,Nwachinemelu,633.0,France,Female,45.0,10.0,80764.03,1.0,1.0,0.0,72927.67
+23498,15592736.0,Mancini,667.0,Germany,Female,31.0,1.0,143595.87,2.0,1.0,0.0,153698.65
+23499,15591279.0,Hsing,708.0,France,Male,37.0,9.0,0.0,2.0,0.0,0.0,72929.96
+23500,15773973.0,Chien,627.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,91829.17
+23501,15573926.0,Ch'eng,712.0,France,Female,36.0,8.0,0.0,2.0,0.0,1.0,169921.11
+23502,15702300.0,Iadanza,697.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,135635.32
+23503,15663917.0,Milanesi,678.0,France,Female,31.0,8.0,125608.72,1.0,1.0,1.0,152577.62
+23504,15777576.0,Gentry,544.0,Germany,Male,43.0,10.0,185714.28,1.0,1.0,0.0,142566.75
+23505,15579706.0,Chien,692.0,Spain,Female,33.0,3.0,0.0,2.0,1.0,0.0,125986.18
+23506,15654495.0,Tung,748.0,France,Female,37.0,3.0,103516.87,2.0,1.0,1.0,1140.02
+23507,15569438.0,Babbage,573.0,Germany,Female,34.0,4.0,109346.13,2.0,1.0,1.0,64716.08
+23508,15643908.0,Calabresi,683.0,Germany,Male,37.0,2.0,131834.4,2.0,1.0,0.0,86920.57
+23509,15751203.0,Gboliwe,646.0,France,Female,29.0,4.0,115074.02,1.0,1.0,0.0,120540.83
+23510,15597914.0,Chiabuotu,592.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,167035.07
+23511,15704954.0,Udinesi,723.0,Germany,Male,41.0,9.0,119741.77,1.0,1.0,0.0,130113.78
+23512,15596797.0,Chiazagomekpele,710.0,France,Male,47.0,6.0,0.0,1.0,1.0,0.0,46811.77
+23513,15682442.0,Y?,833.0,France,Male,40.0,7.0,0.0,2.0,0.0,1.0,96834.15
+23514,15762169.0,Sagese,616.0,Germany,Male,43.0,9.0,113672.91,3.0,1.0,1.0,166694.93
+23515,15663932.0,Miah,655.0,Spain,Male,37.0,3.0,0.0,1.0,1.0,0.0,71492.28
+23516,15762803.0,Toscani,615.0,Spain,Male,33.0,0.0,0.0,2.0,1.0,0.0,40313.47
+23517,15666055.0,P'eng,586.0,France,Female,33.0,8.0,0.0,2.0,0.0,1.0,155047.11
+23518,15605968.0,Ch'iu,573.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,151924.9
+23519,15716347.0,Ma,535.0,France,Female,32.0,4.0,0.0,2.0,0.0,1.0,90928.02
+23520,15645778.0,Ch'iu,732.0,France,Male,41.0,2.0,0.0,1.0,1.0,1.0,139947.17
+23521,15626383.0,Kaur,550.0,Germany,Male,45.0,1.0,105303.74,1.0,0.0,0.0,187925.75
+23522,15702293.0,Dickson,757.0,Spain,Female,31.0,9.0,166501.17,1.0,1.0,1.0,106061.47
+23523,15745250.0,Liao,637.0,Spain,Male,35.0,0.0,135923.17,1.0,0.0,0.0,86937.2
+23524,15769308.0,Ch'ien,714.0,Spain,Male,30.0,8.0,0.0,2.0,0.0,0.0,67789.99
+23525,15724223.0,Shih,711.0,Spain,Male,43.0,6.0,0.0,1.0,0.0,0.0,113597.64
+23526,15766776.0,Mazzi,667.0,Germany,Male,45.0,5.0,122552.34,1.0,1.0,0.0,167984.72
+23527,15572398.0,Maclean,686.0,France,Female,23.0,10.0,0.0,1.0,1.0,1.0,170528.63
+23528,15747222.0,Hsu,707.0,Germany,Female,43.0,2.0,123372.3,2.0,1.0,1.0,123850.58
+23529,15727177.0,Okwukwe,705.0,France,Female,31.0,2.0,135020.21,1.0,1.0,0.0,185543.33
+23530,15662238.0,Hsia,709.0,Germany,Female,55.0,6.0,89750.21,1.0,1.0,1.0,134964.85
+23531,15773394.0,Tuan,505.0,France,Male,46.0,1.0,123938.6,1.0,0.0,1.0,40980.87
+23532,15585047.0,Fanucci,794.0,Germany,Male,41.0,8.0,96834.78,2.0,1.0,0.0,933.38
+23533,15809800.0,Yefremova,706.0,France,Male,38.0,2.0,103516.87,1.0,1.0,0.0,89872.33
+23534,15800773.0,Fang,588.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,55829.25
+23535,15671322.0,Bischof,597.0,France,Male,34.0,6.0,106089.3,1.0,1.0,0.0,93536.38
+23536,15783875.0,Chukwubuikem,704.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,91830.75
+23537,15627377.0,Chinomso,588.0,Spain,Male,33.0,6.0,0.0,2.0,1.0,1.0,54474.48
+23538,15729279.0,Onyekachi,792.0,Germany,Male,48.0,9.0,116498.22,1.0,0.0,1.0,131729.52
+23539,15722350.0,Macleod,741.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,78591.15
+23540,15664808.0,H?,594.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,131167.98
+23541,15772423.0,Lai,763.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,183575.01
+23542,15756901.0,Nwabugwu,676.0,France,Male,20.0,3.0,0.0,1.0,1.0,1.0,129892.93
+23543,15739502.0,Tuan,703.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,112946.67
+23544,15650437.0,Chiang,707.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,91830.75
+23545,15672970.0,Ch'in,543.0,Spain,Female,50.0,7.0,0.0,2.0,1.0,1.0,141364.56
+23546,15607301.0,Chukwumaobim,766.0,Spain,Male,37.0,1.0,0.0,3.0,1.0,1.0,82868.34
+23547,15786539.0,Ch'eng,849.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,1.0,90908.95
+23548,15725028.0,Hsu,659.0,Germany,Female,38.0,0.0,115625.07,2.0,1.0,1.0,163714.92
+23549,15674880.0,Chiazagomekpere,681.0,Germany,Male,37.0,7.0,97720.35,2.0,0.0,1.0,159483.76
+23550,15677324.0,Chu,683.0,France,Male,50.0,9.0,183768.47,1.0,1.0,1.0,142680.64
+23551,15624451.0,Chukwudi,602.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,181208.47
+23552,15806546.0,O'Loghlen,645.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,132547.02
+23553,15751208.0,Brownless,710.0,Germany,Female,58.0,7.0,125861.74,1.0,1.0,1.0,158918.03
+23554,15664759.0,Chukwudi,682.0,France,Female,31.0,1.0,0.0,2.0,0.0,0.0,77965.67
+23555,15803886.0,Zuev,663.0,France,Male,55.0,3.0,0.0,3.0,1.0,0.0,51907.72
+23556,15642997.0,Ko,656.0,Germany,Male,32.0,2.0,100375.39,2.0,1.0,0.0,147822.56
+23557,15771535.0,Bischof,535.0,France,Male,38.0,5.0,117294.12,1.0,0.0,0.0,101319.76
+23558,15659327.0,Okwudiliolisa,774.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,64539.64
+23559,15628950.0,Smith,691.0,France,Male,35.0,8.0,99883.16,1.0,1.0,1.0,141148.21
+23560,15774510.0,T'ao,673.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,105817.63
+23561,15791836.0,Tsou,735.0,Spain,Female,52.0,1.0,0.0,1.0,0.0,0.0,167773.55
+23562,15626144.0,Zetticci,653.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,86937.2
+23563,15664897.0,Achebe,543.0,Germany,Male,37.0,5.0,102708.77,1.0,1.0,1.0,172668.31
+23564,15688865.0,Smith,469.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,81602.02
+23565,15748123.0,Chukwukere,554.0,France,Female,38.0,1.0,0.0,1.0,1.0,1.0,57320.92
+23566,15757897.0,White,653.0,Spain,Female,33.0,3.0,81231.85,1.0,1.0,1.0,131662.47
+23567,15615016.0,Tuan,607.0,Spain,Female,40.0,6.0,0.0,1.0,0.0,1.0,130726.96
+23568,15768120.0,Chidubem,579.0,Germany,Male,43.0,3.0,93960.35,2.0,1.0,1.0,145551.6
+23569,15628674.0,H?,678.0,France,Female,45.0,9.0,0.0,1.0,0.0,1.0,51454.06
+23570,15723720.0,Iloerika,577.0,Germany,Female,49.0,1.0,108308.0,1.0,1.0,0.0,152740.16
+23571,15585360.0,Nkemdilim,568.0,Spain,Male,49.0,2.0,0.0,1.0,0.0,0.0,47722.79
+23572,15569209.0,Zhirov,745.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,1.0,152992.04
+23573,15728505.0,Nwachinemelu,705.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,158750.53
+23574,15701605.0,Ch'iu,671.0,France,Male,33.0,5.0,0.0,2.0,0.0,0.0,120531.31
+23575,15602934.0,Ch'iu,620.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,171021.44
+23576,15738672.0,Hsia,686.0,Spain,Female,34.0,4.0,0.0,2.0,0.0,0.0,52367.29
+23577,15670481.0,P'eng,710.0,Spain,Female,28.0,9.0,0.0,2.0,0.0,0.0,177670.57
+23578,15780492.0,Chukwudi,758.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,140130.22
+23579,15572728.0,Onwumelu,439.0,France,Male,51.0,7.0,0.0,1.0,1.0,1.0,62762.12
+23580,15632816.0,McIntyre,634.0,Germany,Male,39.0,7.0,141927.1,1.0,1.0,0.0,103349.74
+23581,15709135.0,Ch'in,637.0,Spain,Male,32.0,7.0,122486.11,1.0,1.0,0.0,99341.87
+23582,15596710.0,Maughan,634.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,138780.89
+23583,15644849.0,Trevisano,699.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,170819.01
+23584,15591766.0,De Luca,592.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,90591.15
+23585,15583480.0,Chukwukere,683.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,28269.86
+23586,15773394.0,Chidalu,556.0,France,Male,35.0,2.0,97445.49,1.0,1.0,1.0,115468.81
+23587,15586029.0,K'ung,594.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,176750.81
+23588,15732265.0,Chukwufumnanya,739.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,137037.97
+23589,15702912.0,P'eng,564.0,France,Female,32.0,0.0,0.0,1.0,0.0,0.0,184524.65
+23590,15662736.0,Yobanna,638.0,France,Male,29.0,10.0,0.0,2.0,1.0,1.0,91514.62
+23591,15788448.0,Tsao,588.0,France,Male,33.0,8.0,134936.94,1.0,1.0,0.0,172495.81
+23592,15793883.0,O'Donnell,760.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,152843.53
+23593,15775572.0,Manna,660.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,52301.15
+23594,15757981.0,Chiazagomekpere,746.0,Spain,Female,32.0,3.0,108914.17,1.0,0.0,1.0,182059.28
+23595,15775335.0,Milanesi,584.0,Spain,Female,47.0,6.0,0.0,1.0,0.0,0.0,116747.76
+23596,15680167.0,Chukwukere,729.0,France,Male,40.0,2.0,94464.81,1.0,1.0,0.0,28806.32
+23597,15658935.0,Milani,726.0,France,Male,52.0,0.0,138023.79,3.0,1.0,1.0,87412.24
+23598,15711386.0,Nwachukwu,695.0,France,Male,36.0,5.0,0.0,1.0,1.0,0.0,166677.35
+23599,15744942.0,Chikezie,794.0,Germany,Male,30.0,9.0,87099.85,2.0,1.0,1.0,91514.69
+23600,15766185.0,Aiken,663.0,France,Male,36.0,1.0,129901.91,1.0,0.0,0.0,146511.2
+23601,15744517.0,Tung,607.0,Spain,Female,46.0,8.0,0.0,2.0,1.0,1.0,141446.01
+23602,15783026.0,Ch'eng,850.0,France,Male,28.0,1.0,0.0,1.0,1.0,1.0,82222.86
+23603,15641640.0,Biryukov,653.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,135655.29
+23604,15659475.0,Onyekaozulu,743.0,France,Male,35.0,3.0,116318.23,1.0,1.0,0.0,35600.25
+23605,15772798.0,Ch'iu,581.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,185555.63
+23606,15730673.0,Ch'eng,725.0,Germany,Male,51.0,7.0,105103.33,2.0,0.0,0.0,143475.01
+23607,15770562.0,McGregor,682.0,Germany,Male,59.0,4.0,141533.55,2.0,1.0,0.0,120892.85
+23608,15777892.0,White,595.0,Spain,Male,39.0,4.0,0.0,2.0,0.0,0.0,89935.46
+23609,15667934.0,Chien,681.0,France,Male,42.0,6.0,0.0,2.0,0.0,0.0,105969.05
+23610,15667042.0,Nwankwo,554.0,France,Female,29.0,1.0,102918.38,1.0,1.0,0.0,147040.25
+23611,15761707.0,Uwaezuoke,581.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,161242.99
+23612,15771543.0,Ch'ien,836.0,France,Female,57.0,2.0,0.0,2.0,1.0,1.0,71179.31
+23613,15798885.0,P'eng,758.0,Spain,Female,38.0,2.0,0.0,1.0,0.0,1.0,152843.53
+23614,15640074.0,Scott,674.0,Spain,Male,26.0,3.0,0.0,2.0,1.0,0.0,161887.16
+23615,15591552.0,Ting,529.0,France,Female,61.0,7.0,0.0,1.0,0.0,1.0,100566.81
+23616,15634267.0,Onyekachi,714.0,Spain,Female,33.0,7.0,0.0,1.0,1.0,1.0,131710.59
+23617,15684395.0,Hsieh,667.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,1.0,121537.91
+23618,15772559.0,Yobachukwu,714.0,France,Male,52.0,4.0,0.0,1.0,1.0,0.0,71154.1
+23619,15702461.0,Hsia,516.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,141756.32
+23620,15763029.0,Chimaobim,641.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,160548.12
+23621,15665760.0,Nnamutaezinwa,606.0,France,Female,32.0,8.0,0.0,1.0,0.0,1.0,61820.41
+23622,15783875.0,Ch'en,817.0,France,Male,67.0,9.0,0.0,2.0,1.0,1.0,152290.28
+23623,15640491.0,Yudin,637.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,101102.39
+23624,15751043.0,Genovese,562.0,France,Male,41.0,3.0,102088.67,1.0,1.0,1.0,138380.12
+23625,15735263.0,Hs?eh,619.0,Spain,Female,63.0,6.0,0.0,1.0,1.0,0.0,179908.71
+23626,15623647.0,Chidubem,669.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,100995.68
+23627,15803522.0,Chu,659.0,France,Male,27.0,6.0,161606.56,2.0,0.0,0.0,54019.93
+23628,15752588.0,Onwumelu,651.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,137961.41
+23629,15807595.0,K?,652.0,Spain,Female,32.0,0.0,0.0,2.0,1.0,1.0,70778.63
+23630,15582761.0,Palermo,579.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,27330.59
+23631,15758813.0,T'ang,732.0,France,Female,48.0,7.0,0.0,3.0,1.0,0.0,141035.65
+23632,15671137.0,Mironov,646.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,16081.62
+23633,15697034.0,Chien,582.0,France,Male,51.0,8.0,162657.64,1.0,1.0,0.0,176750.81
+23634,15732643.0,Chiang,628.0,France,Female,36.0,3.0,0.0,2.0,0.0,1.0,166677.35
+23635,15605037.0,Ch'ien,599.0,France,Male,34.0,0.0,81811.71,1.0,0.0,1.0,129983.5
+23636,15714493.0,Pisano,691.0,France,Male,42.0,6.0,0.0,2.0,0.0,0.0,67611.36
+23637,15570051.0,Chiu,652.0,Spain,Male,26.0,3.0,0.0,2.0,1.0,1.0,129562.74
+23638,15709780.0,Chigozie,695.0,France,Female,39.0,7.0,178058.06,1.0,0.0,1.0,49089.46
+23639,15585599.0,Shih,707.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,152740.16
+23640,15649487.0,Clements,695.0,France,Male,22.0,5.0,0.0,2.0,1.0,1.0,161256.18
+23641,15690733.0,White,628.0,Spain,Female,24.0,6.0,0.0,2.0,0.0,0.0,180626.68
+23642,15573730.0,Wang,669.0,Germany,Female,37.0,4.0,132796.04,2.0,1.0,1.0,99490.01
+23643,15589493.0,Shih,598.0,France,Male,29.0,5.0,0.0,1.0,1.0,1.0,79555.08
+23644,15652266.0,Chinwendu,646.0,Germany,Female,23.0,9.0,167557.12,1.0,1.0,0.0,126743.33
+23645,15667460.0,Teng,729.0,Spain,Female,26.0,8.0,0.0,2.0,1.0,1.0,50610.78
+23646,15782659.0,Milani,650.0,France,Male,41.0,5.0,0.0,1.0,1.0,0.0,162812.16
+23647,15723105.0,Milne,677.0,France,Male,23.0,7.0,0.0,2.0,1.0,0.0,80701.72
+23648,15783883.0,T'ang,633.0,France,Female,33.0,1.0,0.0,1.0,1.0,0.0,28750.67
+23649,15764072.0,H?,718.0,France,Male,32.0,9.0,0.0,1.0,1.0,0.0,113926.31
+23650,15592025.0,Hs?,539.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,90876.95
+23651,15716347.0,Onuchukwu,714.0,France,Female,37.0,9.0,0.0,2.0,0.0,0.0,147059.16
+23652,15692463.0,Fiorentino,633.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,72549.27
+23653,15777896.0,Martin,733.0,Germany,Male,52.0,6.0,117799.28,1.0,1.0,0.0,143315.57
+23654,15715888.0,Chien,682.0,France,Female,36.0,8.0,0.0,2.0,0.0,1.0,27019.59
+23655,15722409.0,Fan,739.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,124625.08
+23656,15578098.0,Ts'ao,721.0,France,Male,37.0,3.0,134497.27,1.0,0.0,0.0,176902.83
+23657,15791767.0,Folliero,625.0,France,Male,45.0,1.0,0.0,2.0,1.0,0.0,182566.76
+23658,15586674.0,Toscani,667.0,Germany,Female,40.0,10.0,113599.42,1.0,1.0,0.0,92647.94
+23659,15602979.0,Ch'en,652.0,Germany,Male,35.0,8.0,93796.42,2.0,1.0,0.0,143357.02
+23660,15660263.0,Nkemakonam,561.0,Spain,Female,33.0,2.0,117879.32,1.0,1.0,1.0,138191.81
+23661,15718779.0,Krawczyk,662.0,France,Male,37.0,6.0,142862.21,1.0,1.0,0.0,82662.73
+23662,15702462.0,Ozoemena,588.0,Germany,Male,34.0,6.0,116537.6,1.0,0.0,1.0,183566.78
+23663,15740442.0,H?,663.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,1.0,27286.1
+23664,15759066.0,Ch'iu,579.0,France,Female,50.0,0.0,0.0,1.0,1.0,0.0,107948.72
+23665,15700813.0,Hargreaves,590.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,139024.31
+23666,15680918.0,Mazzanti,669.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,124874.23
+23667,15591139.0,Ch'iu,624.0,Germany,Male,30.0,8.0,113923.57,1.0,1.0,0.0,116442.42
+23668,15642991.0,Zikoranachidimma,714.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,115700.2
+23669,15632789.0,Hovell,749.0,Spain,Female,46.0,8.0,0.0,1.0,1.0,0.0,67431.28
+23670,15661528.0,Pisano,847.0,Germany,Male,34.0,3.0,174588.8,2.0,1.0,0.0,131177.22
+23671,15585892.0,Hsia,628.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,127587.22
+23672,15630113.0,Chibueze,684.0,France,Male,43.0,5.0,0.0,2.0,1.0,1.0,142516.35
+23673,15721582.0,Onyekachi,756.0,France,Female,37.0,2.0,0.0,2.0,1.0,1.0,183572.39
+23674,15662238.0,P'eng,584.0,Germany,Female,56.0,9.0,90963.49,2.0,1.0,0.0,93821.95
+23675,15793881.0,Pisano,648.0,Spain,Male,29.0,7.0,0.0,1.0,1.0,0.0,169505.83
+23676,15651802.0,Hs?eh,809.0,Spain,Male,36.0,0.0,0.0,2.0,1.0,1.0,143960.72
+23677,15579787.0,Ch'ang,779.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,151871.84
+23678,15706764.0,Hs?,678.0,Germany,Male,47.0,3.0,82228.67,1.0,1.0,0.0,199674.83
+23679,15613292.0,Ts'ui,593.0,Germany,Male,45.0,6.0,90560.3,2.0,0.0,1.0,75578.67
+23680,15767891.0,Yeh,715.0,Spain,Female,40.0,10.0,0.0,2.0,1.0,0.0,166677.53
+23681,15631848.0,Ndubuagha,632.0,France,Female,34.0,4.0,0.0,1.0,1.0,0.0,51620.94
+23682,15809838.0,Chinwendu,754.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,76569.64
+23683,15767358.0,Biryukov,659.0,Spain,Female,41.0,9.0,144237.91,1.0,1.0,1.0,176713.47
+23684,15725076.0,Fanucci,663.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,183566.78
+23685,15652658.0,Tien,681.0,Germany,Male,44.0,10.0,108691.95,1.0,1.0,1.0,70810.6
+23686,15675983.0,Tuan,629.0,France,Female,30.0,5.0,161889.79,1.0,1.0,1.0,183872.88
+23687,15758531.0,Genovese,567.0,France,Female,47.0,8.0,0.0,1.0,1.0,0.0,89801.9
+23688,15706815.0,P'eng,632.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,176570.28
+23689,15667588.0,Cattaneo,559.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,51907.72
+23690,15574523.0,Chukwukadibia,663.0,France,Male,41.0,0.0,103564.83,1.0,1.0,1.0,78670.52
+23691,15707634.0,Genovesi,599.0,Spain,Male,42.0,5.0,0.0,1.0,1.0,1.0,46635.47
+23692,15732061.0,K'ung,709.0,France,Male,24.0,10.0,0.0,2.0,0.0,1.0,126419.8
+23693,15658935.0,Uspenskaya,587.0,Germany,Female,42.0,1.0,119624.6,3.0,1.0,1.0,5983.8
+23694,15662494.0,Okwudilichukwu,685.0,France,Male,56.0,0.0,0.0,1.0,1.0,0.0,167778.62
+23695,15641733.0,Y?,659.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,25684.93
+23696,15734203.0,Watson,712.0,Germany,Male,40.0,4.0,117641.43,1.0,1.0,1.0,179445.28
+23697,15747649.0,Chikezie,744.0,France,Female,30.0,3.0,128743.55,2.0,1.0,0.0,71481.79
+23698,15739046.0,Udinesi,619.0,Germany,Male,40.0,7.0,102773.2,1.0,1.0,0.0,131283.6
+23699,15812279.0,Aitken,718.0,Germany,Male,39.0,5.0,131569.73,2.0,1.0,1.0,54949.73
+23700,15681410.0,Hsu,684.0,Spain,Male,39.0,4.0,142296.13,1.0,1.0,1.0,113553.43
+23701,15635598.0,Yefimova,667.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,108764.69
+23702,15642530.0,Mazzanti,559.0,Spain,Female,43.0,3.0,122316.31,1.0,1.0,0.0,115789.25
+23703,15807555.0,K'ung,681.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,158527.56
+23704,15796910.0,Ponomarev,763.0,France,Male,39.0,8.0,0.0,1.0,0.0,0.0,91943.94
+23705,15596797.0,Folliero,592.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,107815.31
+23706,15587978.0,Lucchesi,573.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,149535.97
+23707,15682271.0,Chigolum,732.0,France,Male,42.0,2.0,0.0,2.0,1.0,1.0,65731.53
+23708,15624781.0,Ts'ai,662.0,Germany,Male,48.0,9.0,105736.32,2.0,0.0,1.0,171021.44
+23709,15690829.0,Hsueh,724.0,France,Male,38.0,6.0,141462.13,1.0,0.0,0.0,74850.84
+23710,15746490.0,Folliero,850.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,81491.7
+23711,15693893.0,Smith,565.0,France,Male,44.0,8.0,0.0,2.0,1.0,1.0,161076.92
+23712,15603082.0,Okonkwo,697.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,141426.78
+23713,15656912.0,Maclean,685.0,France,Male,39.0,2.0,126817.65,1.0,1.0,0.0,77951.84
+23714,15756056.0,Ebelegbulam,850.0,Germany,Female,51.0,6.0,119027.28,2.0,0.0,0.0,51721.52
+23715,15689322.0,Chin,603.0,France,Female,61.0,2.0,0.0,1.0,1.0,0.0,103934.12
+23716,15570681.0,Ch'ien,537.0,France,Female,41.0,5.0,0.0,2.0,1.0,0.0,162858.29
+23717,15682070.0,Lo,703.0,France,Female,44.0,3.0,0.0,2.0,1.0,0.0,147822.81
+23718,15791045.0,Chizoba,648.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,100615.14
+23719,15694129.0,Ngozichukwuka,754.0,Spain,Female,33.0,7.0,139022.74,2.0,1.0,0.0,62370.67
+23720,15799653.0,Chukwuemeka,678.0,France,Female,40.0,3.0,71264.02,1.0,1.0,1.0,161344.09
+23721,15807923.0,Ch'eng,706.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,121789.3
+23722,15676909.0,Chinwemma,741.0,France,Male,38.0,5.0,0.0,2.0,0.0,1.0,149705.25
+23723,15705403.0,Onwumelu,712.0,France,Female,54.0,10.0,0.0,3.0,1.0,0.0,166692.02
+23724,15716236.0,Smith,621.0,Germany,Female,34.0,2.0,123544.0,2.0,1.0,1.0,175893.53
+23725,15740404.0,Chidimma,567.0,France,Female,25.0,8.0,0.0,2.0,1.0,0.0,122580.48
+23726,15641640.0,P'an,711.0,France,Male,35.0,9.0,0.0,2.0,0.0,0.0,52337.97
+23727,15586931.0,K?,682.0,Spain,Male,26.0,1.0,0.0,1.0,1.0,0.0,171587.9
+23728,15778915.0,Tuan,719.0,Germany,Female,41.0,1.0,111432.13,1.0,1.0,1.0,82696.84
+23729,15729377.0,Hsia,709.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,123880.19
+23730,15651737.0,Chiemela,537.0,Germany,Female,48.0,5.0,84719.98,1.0,1.0,0.0,166677.35
+23731,15789955.0,Walker,695.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,126471.13
+23732,15656086.0,T'ao,731.0,Spain,Female,34.0,1.0,68799.72,2.0,0.0,0.0,61931.21
+23733,15779915.0,Johnstone,710.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,184350.78
+23734,15603794.0,Houghton,683.0,Germany,Female,48.0,8.0,109799.55,1.0,0.0,0.0,167984.72
+23735,15714789.0,Tsou,659.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,132418.59
+23736,15677249.0,Yuriev,693.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,105983.09
+23737,15621550.0,Zikoranachidimma,687.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,121846.81
+23738,15745354.0,Tien,753.0,France,Male,39.0,3.0,152663.77,1.0,0.0,1.0,67789.99
+23739,15745080.0,Ndubuagha,678.0,Germany,Female,36.0,7.0,124495.98,2.0,1.0,0.0,141175.18
+23740,15756481.0,Maclean,641.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,152790.56
+23741,15658969.0,Fanucci,735.0,France,Female,45.0,6.0,0.0,1.0,1.0,1.0,96929.24
+23742,15597901.0,Udokamma,588.0,Germany,Female,42.0,8.0,146033.62,2.0,1.0,1.0,94774.12
+23743,15576269.0,Nwankwo,669.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,151903.6
+23744,15626012.0,Lo Duca,569.0,Germany,Male,36.0,10.0,82940.25,2.0,1.0,0.0,132298.59
+23745,15699247.0,Hs?,705.0,Germany,Male,56.0,5.0,103515.39,1.0,1.0,0.0,76611.33
+23746,15626141.0,Trevisani,718.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,82298.59
+23747,15812706.0,Chimaobim,661.0,France,Male,35.0,7.0,143773.07,1.0,1.0,1.0,57896.27
+23748,15794566.0,Fan,594.0,France,Male,51.0,2.0,0.0,1.0,1.0,0.0,141946.92
+23749,15814035.0,Chigozie,637.0,Germany,Male,40.0,2.0,131908.35,1.0,1.0,1.0,115037.94
+23750,15794560.0,Niu,705.0,France,Male,40.0,5.0,0.0,1.0,1.0,1.0,90928.02
+23751,15642530.0,Mazzi,762.0,Spain,Male,42.0,1.0,0.0,2.0,0.0,1.0,94888.6
+23752,15782659.0,Chiganu,755.0,Germany,Male,58.0,2.0,103033.83,1.0,1.0,0.0,176680.32
+23753,15749365.0,Iadanza,685.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,176730.02
+23754,15580149.0,Maclean,719.0,France,Male,35.0,8.0,0.0,2.0,0.0,0.0,120853.83
+23755,15788480.0,Ting,682.0,Spain,Male,36.0,3.0,0.0,1.0,1.0,1.0,168834.85
+23756,15662494.0,Chikwendu,663.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,140763.17
+23757,15629299.0,Milanesi,681.0,Germany,Female,35.0,5.0,103688.19,1.0,1.0,1.0,102260.16
+23758,15633194.0,Shao,585.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,140937.91
+23759,15803842.0,Fiorentini,632.0,France,Female,37.0,4.0,0.0,1.0,1.0,0.0,50695.93
+23760,15772896.0,Smith,591.0,Spain,Female,33.0,0.0,0.0,2.0,1.0,0.0,109614.57
+23761,15775235.0,Zhirov,823.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,56780.97
+23762,15697391.0,Ngozichukwuka,726.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,1.0,58440.43
+23763,15704053.0,Hsueh,611.0,France,Male,49.0,2.0,0.0,1.0,0.0,0.0,160529.71
+23764,15742229.0,Dike,669.0,Spain,Male,31.0,8.0,0.0,2.0,0.0,0.0,109843.16
+23765,15593776.0,Mazzanti,738.0,Germany,Male,33.0,0.0,133871.05,2.0,0.0,1.0,171706.66
+23766,15674583.0,Bibi,611.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,54776.55
+23767,15579777.0,Yermakova,636.0,France,Female,40.0,7.0,0.0,2.0,0.0,1.0,109794.31
+23768,15776733.0,Fanucci,648.0,Germany,Female,45.0,4.0,132436.39,1.0,1.0,0.0,93336.3
+23769,15711396.0,Chubb,675.0,Germany,Male,28.0,1.0,96471.2,1.0,1.0,1.0,177065.24
+23770,15740160.0,Hsing,619.0,France,Male,28.0,9.0,0.0,1.0,0.0,0.0,100720.02
+23771,15791700.0,Nwabugwu,677.0,Spain,Male,41.0,4.0,0.0,2.0,1.0,1.0,61553.51
+23772,15650034.0,Onyekachukwu,638.0,Germany,Male,36.0,1.0,123070.89,1.0,1.0,1.0,133041.15
+23773,15792668.0,Onwudiwe,645.0,France,Female,29.0,8.0,82456.95,2.0,0.0,1.0,147090.9
+23774,15781598.0,Hsiung,659.0,Spain,Male,42.0,2.0,137781.65,1.0,1.0,1.0,141558.04
+23775,15638231.0,Nwabugwu,602.0,France,Female,53.0,2.0,111096.91,1.0,0.0,1.0,67789.99
+23776,15716994.0,Chubb,806.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,79602.86
+23777,15791780.0,Udinese,713.0,France,Male,31.0,1.0,135296.33,1.0,1.0,0.0,121428.2
+23778,15766066.0,T'ien,647.0,France,Male,52.0,5.0,0.0,1.0,1.0,0.0,180598.66
+23779,15585241.0,Chigozie,727.0,Germany,Male,33.0,0.0,141547.26,1.0,0.0,0.0,59325.21
+23780,15690958.0,Lai,758.0,France,Female,50.0,2.0,0.0,1.0,1.0,0.0,78436.36
+23781,15706021.0,T'ao,681.0,Germany,Male,34.0,6.0,125927.51,2.0,1.0,1.0,168816.23
+23782,15603088.0,Pisani,703.0,France,Male,53.0,5.0,0.0,2.0,1.0,1.0,149587.92
+23783,15640686.0,Macleod,697.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,141152.28
+23784,15652674.0,Pisano,574.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,85470.45
+23785,15754919.0,Young,629.0,France,Male,21.0,9.0,0.0,2.0,1.0,0.0,70530.1
+23786,15624592.0,Yermolayev,664.0,France,Female,44.0,10.0,0.0,1.0,1.0,1.0,151146.67
+23787,15694829.0,Palerma,659.0,France,Male,37.0,4.0,0.0,1.0,1.0,0.0,141446.01
+23788,15654574.0,L?,687.0,France,Male,24.0,2.0,0.0,1.0,1.0,1.0,166677.35
+23789,15762729.0,Manna,757.0,Germany,Female,33.0,4.0,79715.36,1.0,1.0,0.0,143681.83
+23790,15809872.0,Nwankwo,580.0,Germany,Male,53.0,9.0,140694.78,1.0,1.0,1.0,156768.45
+23791,15792372.0,Yevdokimova,598.0,France,Female,24.0,6.0,0.0,2.0,1.0,1.0,145796.49
+23792,15619116.0,Lucchese,684.0,France,Female,33.0,1.0,0.0,2.0,0.0,1.0,82298.59
+23793,15768219.0,Calabrese,550.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,115611.97
+23794,15752601.0,Chidimma,620.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,101617.64
+23795,15803689.0,Milanesi,531.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,0.0,100423.88
+23796,15726695.0,Milne,682.0,France,Female,28.0,3.0,0.0,2.0,0.0,1.0,127543.83
+23797,15607748.0,Onwuamaeze,732.0,Germany,Female,45.0,9.0,108862.76,1.0,1.0,0.0,64872.55
+23798,15804009.0,Hsiung,781.0,France,Female,27.0,8.0,0.0,2.0,1.0,1.0,51907.72
+23799,15776824.0,T'ien,689.0,France,Female,50.0,2.0,116967.68,1.0,0.0,1.0,141400.51
+23800,15785863.0,Nwachinemelu,576.0,Germany,Male,36.0,4.0,132057.92,1.0,1.0,1.0,88838.14
+23801,15589435.0,Olisaemeka,569.0,France,Male,29.0,6.0,0.0,1.0,1.0,1.0,162858.29
+23802,15783338.0,T'ao,722.0,Germany,Male,35.0,1.0,141816.25,1.0,1.0,0.0,27149.27
+23803,15572735.0,Palermo,717.0,France,Male,46.0,8.0,116322.27,1.0,1.0,0.0,49833.5
+23804,15677105.0,T'ang,597.0,Germany,Male,35.0,9.0,154737.88,2.0,0.0,1.0,47764.99
+23805,15809823.0,Rizzo,639.0,France,Male,46.0,8.0,0.0,1.0,1.0,0.0,93768.94
+23806,15809837.0,Zetticci,569.0,Spain,Male,52.0,4.0,0.0,1.0,1.0,1.0,59129.72
+23807,15656829.0,P'eng,735.0,Spain,Female,37.0,3.0,0.0,1.0,1.0,1.0,89935.73
+23808,15643359.0,Chinomso,681.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,86764.46
+23809,15588854.0,Hs?,611.0,France,Male,51.0,1.0,0.0,1.0,1.0,1.0,43982.41
+23810,15671514.0,H?,596.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,149912.28
+23811,15777892.0,Lo,591.0,France,Female,41.0,6.0,165350.69,1.0,1.0,0.0,143635.33
+23812,15593501.0,Milani,671.0,France,Female,48.0,3.0,0.0,1.0,1.0,0.0,149043.78
+23813,15689201.0,Chidubem,669.0,Spain,Male,31.0,6.0,0.0,1.0,1.0,1.0,73977.23
+23814,15803801.0,Hsieh,712.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,116781.71
+23815,15776939.0,Lucciano,642.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,90927.48
+23816,15613886.0,Chiedozie,725.0,France,Male,32.0,2.0,110949.41,1.0,1.0,1.0,141476.56
+23817,15642815.0,H?,732.0,France,Male,47.0,8.0,0.0,1.0,0.0,0.0,171463.83
+23818,15641822.0,Iqbal,766.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,67772.52
+23819,15743760.0,Onyekaozulu,542.0,Germany,Female,36.0,8.0,108891.5,1.0,1.0,0.0,133042.33
+23820,15793896.0,Padovano,712.0,Spain,Female,30.0,6.0,0.0,2.0,1.0,1.0,61789.12
+23821,15592091.0,Y?,559.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,1276.87
+23822,15681708.0,Burt,699.0,France,Male,38.0,0.0,0.0,2.0,1.0,0.0,160968.44
+23823,15674929.0,Maclean,683.0,France,Female,31.0,4.0,151858.98,1.0,0.0,1.0,126812.63
+23824,15751203.0,Nnanna,645.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,52876.41
+23825,15673372.0,Yobachi,646.0,France,Male,33.0,4.0,99046.31,1.0,1.0,1.0,105732.9
+23826,15580203.0,T'ang,635.0,France,Female,41.0,3.0,0.0,1.0,0.0,1.0,131647.31
+23827,15631339.0,Lucciano,645.0,Spain,Female,37.0,4.0,0.0,1.0,1.0,1.0,170634.49
+23828,15651369.0,Lu,730.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,1.0,146891.08
+23829,15598162.0,Obiajulu,695.0,Germany,Female,34.0,2.0,149592.14,2.0,0.0,0.0,115186.97
+23830,15716078.0,Milano,739.0,Spain,Female,25.0,6.0,145580.61,1.0,1.0,0.0,99792.82
+23831,15741032.0,Chioke,590.0,Germany,Male,40.0,2.0,161807.18,2.0,1.0,0.0,179032.68
+23832,15591257.0,Walker,850.0,Spain,Male,52.0,8.0,117696.67,1.0,1.0,0.0,157937.35
+23833,15586848.0,Lu,696.0,France,Female,33.0,1.0,0.0,2.0,0.0,1.0,138889.15
+23834,15665802.0,Lucchese,756.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,125083.29
+23835,15682421.0,T'ien,642.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,52347.71
+23836,15757867.0,Ch'ien,642.0,France,Female,27.0,2.0,127450.54,2.0,1.0,0.0,196142.39
+23837,15576044.0,Onyemachukwu,486.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,120852.85
+23838,15737051.0,Liao,577.0,France,Male,25.0,4.0,97565.74,1.0,0.0,1.0,120893.07
+23839,15605918.0,K?,673.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,85890.75
+23840,15605926.0,Hsiao,736.0,Germany,Female,34.0,5.0,110717.84,1.0,1.0,0.0,94718.13
+23841,15745804.0,Chukwudi,586.0,Germany,Female,44.0,9.0,126619.27,1.0,1.0,0.0,75832.53
+23842,15583736.0,Trevisano,586.0,Germany,Male,41.0,2.0,127120.07,1.0,1.0,1.0,164038.07
+23843,15623668.0,Chiawuotu,559.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,175071.29
+23844,15729574.0,Zaitsev,657.0,Spain,Male,39.0,6.0,0.0,1.0,1.0,1.0,131301.74
+23845,15809057.0,Kambinachi,616.0,France,Female,39.0,9.0,178095.55,1.0,1.0,0.0,64421.02
+23846,15733661.0,Hsiao,686.0,France,Female,27.0,9.0,0.0,2.0,1.0,1.0,92647.94
+23847,15596800.0,Nwachukwu,757.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,146680.25
+23848,15715184.0,Y?an,682.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,0.0,91565.57
+23849,15585823.0,T'ien,602.0,France,Female,47.0,8.0,0.0,1.0,0.0,0.0,114083.73
+23850,15795878.0,Nnonso,677.0,Spain,Male,38.0,4.0,0.0,2.0,0.0,1.0,59748.19
+23851,15774166.0,Ch'ien,707.0,Germany,Male,41.0,7.0,99713.87,1.0,1.0,0.0,16081.62
+23852,15719309.0,Chukwudi,659.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,156774.75
+23853,15703793.0,Chiu,721.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,179565.57
+23854,15643826.0,Ch'ang,695.0,France,Male,48.0,7.0,0.0,1.0,0.0,1.0,95562.92
+23855,15806360.0,Onio,691.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,93332.64
+23856,15803801.0,Lucchesi,743.0,Germany,Female,40.0,9.0,132057.92,2.0,1.0,0.0,51565.96
+23857,15631512.0,Nwabugwu,813.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,81723.8
+23858,15606836.0,Folliero,633.0,France,Female,32.0,5.0,0.0,1.0,0.0,0.0,162861.89
+23859,15652835.0,Smith,485.0,France,Male,29.0,10.0,0.0,2.0,1.0,1.0,162683.06
+23860,15779913.0,T'ang,535.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,142911.99
+23861,15623220.0,Shao,637.0,France,Female,36.0,7.0,116968.91,2.0,0.0,1.0,178291.89
+23862,15650322.0,Maclean,753.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,86764.46
+23863,15732660.0,Efimov,749.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,105599.65
+23864,15677906.0,H?,678.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,132673.23
+23865,15649423.0,Hazon,703.0,France,Female,52.0,3.0,0.0,2.0,1.0,1.0,145528.22
+23866,15778025.0,Onwuamaeze,711.0,Germany,Male,38.0,3.0,163700.78,2.0,1.0,1.0,123954.32
+23867,15812351.0,Tuan,721.0,Germany,Male,54.0,1.0,118886.55,1.0,1.0,0.0,132973.21
+23868,15633194.0,Ijendu,667.0,Germany,Female,48.0,3.0,126428.38,2.0,0.0,1.0,105155.66
+23869,15750929.0,Chuang,678.0,France,Female,37.0,7.0,0.0,2.0,0.0,0.0,168034.9
+23870,15635078.0,Yevseyev,738.0,France,Male,37.0,3.0,81732.88,2.0,0.0,0.0,141023.01
+23871,15783383.0,Folliero,657.0,France,Female,48.0,10.0,108528.07,1.0,0.0,1.0,157097.09
+23872,15654562.0,Nwachukwu,659.0,France,Female,42.0,7.0,0.0,2.0,0.0,0.0,180969.55
+23873,15750181.0,T'ien,634.0,Spain,Female,36.0,9.0,91547.84,2.0,1.0,0.0,94450.97
+23874,15629750.0,Zaytseva,498.0,France,Female,43.0,4.0,0.0,1.0,1.0,0.0,81492.8
+23875,15683375.0,Chukwudi,811.0,Spain,Male,32.0,2.0,0.0,2.0,0.0,0.0,198490.33
+23876,15601659.0,Shih,637.0,France,Male,42.0,3.0,0.0,2.0,0.0,0.0,119924.8
+23877,15776947.0,Lo Duca,613.0,Germany,Female,31.0,10.0,129743.75,2.0,1.0,0.0,54495.82
+23878,15805764.0,Chukwudi,696.0,Germany,Female,54.0,9.0,123763.49,1.0,1.0,0.0,115068.99
+23879,15576824.0,Kambinachi,763.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,89891.4
+23880,15571529.0,Chinomso,755.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,1.0,54448.15
+23881,15641585.0,Shih,778.0,Spain,Female,49.0,10.0,0.0,1.0,0.0,0.0,87071.18
+23882,15784099.0,Lo,762.0,France,Female,45.0,10.0,0.0,1.0,1.0,0.0,62810.91
+23883,15786063.0,White,706.0,France,Female,28.0,1.0,0.0,1.0,0.0,1.0,59571.5
+23884,15753847.0,Boniwell,652.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,0.0,149536.62
+23885,15763327.0,Okechukwu,752.0,Germany,Female,59.0,8.0,119539.3,1.0,1.0,0.0,176773.59
+23886,15769254.0,Lettiere,717.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,103736.15
+23887,15588854.0,Shaver,682.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,1.0,6554.97
+23888,15763895.0,K?,741.0,France,Male,35.0,9.0,0.0,1.0,1.0,0.0,81491.7
+23889,15737506.0,Ch'iu,683.0,Germany,Female,44.0,9.0,109844.81,2.0,1.0,0.0,145536.28
+23890,15697460.0,Ijendu,653.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,119644.59
+23891,15792668.0,Hovell,545.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,146517.71
+23892,15672374.0,Mazzi,679.0,France,Female,33.0,4.0,0.0,1.0,1.0,0.0,124411.08
+23893,15737506.0,Chiganu,701.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,151375.57
+23894,15579566.0,P'an,550.0,France,Male,28.0,9.0,0.0,1.0,0.0,1.0,27330.59
+23895,15632264.0,Chimaobim,696.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,77878.62
+23896,15811947.0,Ch'en,669.0,France,Female,41.0,2.0,128264.03,1.0,1.0,1.0,153392.28
+23897,15693018.0,Smith,703.0,Germany,Female,33.0,3.0,106022.73,1.0,1.0,1.0,175837.06
+23898,15622305.0,Chinwendu,667.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,95686.4
+23899,15764237.0,Ch'iu,611.0,France,Male,33.0,0.0,133550.67,1.0,1.0,0.0,69531.22
+23900,15641582.0,Lucchese,637.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,163427.18
+23901,15595440.0,Onyemauchechukwu,642.0,Spain,Male,41.0,3.0,98765.84,2.0,1.0,0.0,82820.85
+23902,15721730.0,Yermakov,686.0,France,Male,38.0,1.0,0.0,2.0,0.0,0.0,184524.74
+23903,15654964.0,Onio,627.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,138350.74
+23904,15682533.0,Ugoji,593.0,France,Male,43.0,4.0,98202.76,2.0,0.0,0.0,44965.44
+23905,15736327.0,Ch'iu,686.0,France,Male,28.0,4.0,121854.45,1.0,1.0,1.0,141175.18
+23906,15743582.0,Macleod,686.0,France,Male,29.0,5.0,121250.04,2.0,1.0,0.0,171757.76
+23907,15687913.0,Yashina,756.0,France,Female,39.0,7.0,0.0,2.0,0.0,0.0,104889.3
+23908,15718454.0,Chinweike,696.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,184514.01
+23909,15570073.0,Chidubem,678.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,151822.68
+23910,15656627.0,Hsia,679.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,69056.26
+23911,15600974.0,Ch'ang,677.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,0.0,100615.14
+23912,15771580.0,Shipton,668.0,France,Female,67.0,4.0,0.0,2.0,0.0,1.0,60907.49
+23913,15759480.0,K'ung,679.0,Spain,Female,34.0,8.0,0.0,2.0,1.0,0.0,84487.62
+23914,15780359.0,Ch'iu,652.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,30772.86
+23915,15721460.0,Milne,706.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,116046.22
+23916,15593897.0,Ting,611.0,Germany,Female,39.0,1.0,106901.94,1.0,0.0,1.0,180712.28
+23917,15598838.0,Onyekachi,717.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,152843.53
+23918,15582847.0,Onwumelu,646.0,Germany,Female,48.0,6.0,119558.35,1.0,1.0,1.0,114675.75
+23919,15809872.0,Yefimova,573.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,84972.9
+23920,15629885.0,Ch'en,706.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,115217.59
+23921,15643378.0,Ch'ien,769.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,0.0,72876.41
+23922,15627395.0,Onyemauchechukwu,572.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,91055.46
+23923,15693737.0,Chiang,600.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,1710.89
+23924,15671353.0,Chukwudi,727.0,Spain,Female,33.0,9.0,0.0,2.0,1.0,0.0,132770.11
+23925,15809736.0,Hsiung,592.0,France,Male,47.0,5.0,0.0,3.0,1.0,0.0,88669.89
+23926,15585067.0,Bibi,646.0,Spain,Male,27.0,5.0,0.0,2.0,1.0,1.0,87906.83
+23927,15756513.0,Onyemauchechukwu,516.0,Germany,Female,39.0,2.0,82729.49,1.0,1.0,1.0,167984.72
+23928,15727299.0,Chiebuka,605.0,Germany,Female,33.0,9.0,107556.06,2.0,1.0,0.0,142995.32
+23929,15583090.0,Ch'in,589.0,Germany,Male,48.0,4.0,95039.12,2.0,0.0,0.0,176945.96
+23930,15651533.0,Udinese,671.0,France,Male,33.0,4.0,126817.65,2.0,1.0,1.0,157891.11
+23931,15748752.0,Kao,555.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,172501.38
+23932,15620750.0,Hsia,659.0,Germany,Female,39.0,4.0,138490.37,2.0,1.0,0.0,69244.24
+23933,15682454.0,Hsia,622.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,0.0,82438.13
+23934,15662483.0,Chukwujekwu,729.0,Spain,Female,38.0,7.0,102278.65,1.0,1.0,1.0,146895.86
+23935,15705639.0,Okechukwu,758.0,Spain,Male,31.0,5.0,116536.43,1.0,1.0,1.0,111249.62
+23936,15754072.0,H?,668.0,Spain,Female,39.0,10.0,0.0,2.0,0.0,0.0,48486.25
+23937,15775678.0,Nucci,639.0,Spain,Female,26.0,6.0,89750.21,1.0,0.0,1.0,158043.11
+23938,15606901.0,Ch'eng,516.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,64516.5
+23939,15774551.0,Ts'ai,743.0,France,Female,43.0,6.0,139825.53,1.0,0.0,1.0,156868.21
+23940,15775750.0,Zuyeva,581.0,Spain,Female,35.0,6.0,107960.46,1.0,1.0,0.0,179964.55
+23941,15812191.0,Nwabugwu,639.0,France,Female,48.0,4.0,0.0,1.0,1.0,0.0,80963.92
+23942,15578980.0,Pugh,632.0,Germany,Female,30.0,1.0,123816.5,2.0,1.0,0.0,77832.82
+23943,15779247.0,Nkemdilim,755.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,149814.68
+23944,15804787.0,Chidubem,526.0,Spain,Female,28.0,8.0,0.0,1.0,0.0,0.0,56984.58
+23945,15742504.0,Ibezimako,593.0,France,Male,66.0,6.0,0.0,2.0,1.0,1.0,92914.67
+23946,15656300.0,Ch'ang,667.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,94820.85
+23947,15656522.0,Maclean,635.0,Spain,Male,33.0,7.0,124099.13,1.0,0.0,1.0,65951.65
+23948,15717498.0,Kambinachi,615.0,France,Female,48.0,5.0,173340.83,1.0,1.0,1.0,123885.88
+23949,15734005.0,Nkemjika,684.0,France,Male,38.0,8.0,0.0,2.0,0.0,0.0,92621.9
+23950,15596962.0,Okoli,584.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,10063.75
+23951,15781702.0,H?,671.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,102515.42
+23952,15575679.0,Lo,708.0,France,Male,38.0,7.0,121551.58,1.0,1.0,1.0,71139.8
+23953,15627203.0,Palerma,686.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,109626.03
+23954,15720644.0,Tien,767.0,France,Male,54.0,6.0,0.0,2.0,1.0,1.0,168053.4
+23955,15586699.0,Nwabugwu,663.0,Germany,Male,33.0,5.0,133550.67,2.0,1.0,0.0,173629.78
+23956,15734008.0,Li,652.0,Spain,Male,50.0,7.0,0.0,2.0,1.0,1.0,133501.94
+23957,15633922.0,Yobachukwu,592.0,France,Male,32.0,7.0,0.0,2.0,0.0,0.0,132436.71
+23958,15585595.0,Ch'ien,711.0,Germany,Male,43.0,3.0,112419.92,1.0,1.0,0.0,149912.28
+23959,15572022.0,Lo,646.0,France,Male,29.0,3.0,0.0,2.0,1.0,0.0,24255.21
+23960,15712596.0,Aksenov,585.0,Spain,Female,58.0,5.0,0.0,2.0,0.0,1.0,143614.01
+23961,15783724.0,Nielson,658.0,Germany,Male,37.0,3.0,92350.85,1.0,1.0,1.0,155853.52
+23962,15772573.0,Chukwudi,775.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,176570.28
+23963,15651336.0,O'Loghlen,705.0,Germany,Female,39.0,4.0,122031.55,1.0,1.0,1.0,598.8
+23964,15699335.0,Babbage,710.0,France,Female,63.0,7.0,0.0,2.0,1.0,1.0,149912.28
+23965,15662641.0,Chukwudi,683.0,France,Female,31.0,2.0,130565.02,2.0,1.0,1.0,113846.59
+23966,15589496.0,Ch'in,613.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,95583.45
+23967,15656029.0,DeRose,583.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,48228.1
+23968,15693817.0,Hs?eh,663.0,France,Female,40.0,8.0,0.0,2.0,0.0,1.0,170950.58
+23969,15773869.0,Wright,634.0,France,Female,41.0,5.0,0.0,2.0,0.0,0.0,10656.89
+23970,15588933.0,Ting,568.0,Germany,Female,43.0,7.0,144590.92,1.0,0.0,0.0,119381.41
+23971,15662955.0,Lettiere,732.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,124532.9
+23972,15794187.0,Chukwudi,699.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,1.0,132467.54
+23973,15801488.0,Fomin,602.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,59893.84
+23974,15772896.0,Chibugo,726.0,Spain,Male,45.0,1.0,98899.91,1.0,1.0,1.0,126131.9
+23975,15762762.0,Ch'iu,683.0,Germany,Female,51.0,2.0,153325.1,1.0,1.0,0.0,53445.17
+23976,15725429.0,Okonkwo,777.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,171768.76
+23977,15571958.0,Shih,809.0,France,Female,53.0,7.0,0.0,1.0,0.0,0.0,183584.14
+23978,15662901.0,Yefremova,547.0,France,Female,24.0,3.0,0.0,2.0,1.0,1.0,86737.16
+23979,15649853.0,Jamieson,563.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,64421.02
+23980,15595883.0,Tretiakova,584.0,France,Female,39.0,7.0,0.0,1.0,0.0,0.0,123825.83
+23981,15796074.0,Mazzi,678.0,France,Male,34.0,6.0,0.0,2.0,0.0,1.0,159123.82
+23982,15586522.0,Nworie,663.0,France,Male,45.0,5.0,0.0,2.0,0.0,1.0,87941.85
+23983,15752984.0,Bibi,582.0,Germany,Female,57.0,9.0,118022.06,1.0,1.0,0.0,182728.8
+23984,15793896.0,Onwumelu,773.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,115818.51
+23985,15803965.0,Onwuamaeze,562.0,France,Female,34.0,2.0,0.0,2.0,0.0,1.0,72927.68
+23986,15741921.0,T'ien,607.0,Spain,Male,39.0,3.0,154483.98,1.0,0.0,1.0,199409.19
+23987,15679801.0,Toscani,652.0,France,Female,34.0,1.0,0.0,1.0,0.0,0.0,83629.6
+23988,15807524.0,Bazarova,683.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,100919.19
+23989,15769191.0,Ts'ao,603.0,Germany,Male,36.0,4.0,166824.59,1.0,0.0,1.0,145765.83
+23990,15803840.0,Chigozie,694.0,Germany,Male,33.0,1.0,166501.17,1.0,1.0,1.0,172490.78
+23991,15760063.0,Hsiung,632.0,Spain,Female,43.0,6.0,156811.74,1.0,1.0,0.0,90863.81
+23992,15635078.0,Ozioma,535.0,Germany,Female,24.0,9.0,119782.76,1.0,1.0,0.0,91573.19
+23993,15799494.0,Rischbieth,630.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,83983.54
+23994,15769402.0,Onyeorulu,603.0,Spain,Female,40.0,1.0,0.0,1.0,1.0,0.0,116471.43
+23995,15760090.0,T'ien,703.0,France,Male,26.0,7.0,0.0,1.0,1.0,1.0,52876.41
+23996,15646936.0,Chukwumaobim,724.0,France,Male,39.0,10.0,0.0,1.0,1.0,1.0,54871.02
+23997,15782741.0,Cawthorne,704.0,Spain,Female,33.0,1.0,147523.03,1.0,1.0,1.0,113048.79
+23998,15572051.0,Chidimma,727.0,France,Female,56.0,3.0,0.0,1.0,1.0,0.0,52327.47
+23999,15757588.0,Nebechi,506.0,Germany,Female,25.0,5.0,123971.51,1.0,0.0,0.0,127059.03
+24000,15658716.0,H?,669.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,145731.83
+24001,15596070.0,Chien,686.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,141180.66
+24002,15622578.0,Ibezimako,771.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,93835.64
+24003,15578747.0,Ch'ang,762.0,France,Female,43.0,1.0,0.0,2.0,1.0,1.0,126494.82
+24004,15796790.0,Cremonesi,586.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,65675.47
+24005,15654296.0,Hargreaves,607.0,Germany,Female,39.0,9.0,123378.2,1.0,1.0,0.0,134901.58
+24006,15783629.0,Hsiung,551.0,Spain,Male,36.0,10.0,0.0,1.0,1.0,0.0,121408.55
+24007,15711299.0,Iweobiegbunam,583.0,France,Male,24.0,9.0,0.0,2.0,1.0,1.0,62678.53
+24008,15762799.0,Chukwufumnanya,682.0,France,Female,36.0,8.0,0.0,2.0,1.0,0.0,184524.74
+24009,15778304.0,Hsia,757.0,France,Female,30.0,9.0,0.0,2.0,1.0,1.0,153771.8
+24010,15752956.0,Onwuamaegbu,713.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,105817.18
+24011,15684516.0,Macleod,745.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,181419.29
+24012,15727251.0,Mazzi,715.0,Germany,Male,29.0,3.0,92578.14,2.0,1.0,1.0,158610.89
+24013,15593973.0,T'ien,713.0,France,Male,66.0,2.0,98894.39,1.0,1.0,1.0,130132.54
+24014,15679622.0,Nwankwo,686.0,France,Male,39.0,8.0,0.0,3.0,1.0,0.0,167058.75
+24015,15686588.0,Mazzanti,489.0,Germany,Male,47.0,0.0,129974.79,2.0,1.0,0.0,1118.31
+24016,15671249.0,Ch'ien,607.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,181419.29
+24017,15662641.0,Ponomaryov,565.0,Germany,Female,39.0,3.0,132446.08,1.0,1.0,0.0,171718.52
+24018,15773876.0,Chiemezie,627.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,125307.82
+24019,15752300.0,Nwankwo,714.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,116816.53
+24020,15655666.0,Liao,697.0,France,Female,40.0,2.0,0.0,2.0,0.0,1.0,181694.44
+24021,15788189.0,Nwachukwu,539.0,France,Male,34.0,4.0,138033.55,1.0,1.0,1.0,26327.42
+24022,15572356.0,Nkemjika,693.0,Germany,Male,32.0,4.0,115680.81,2.0,0.0,0.0,85421.48
+24023,15766163.0,Ku,554.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,175072.47
+24024,15663917.0,Ku,656.0,France,Female,40.0,8.0,0.0,1.0,1.0,0.0,168580.73
+24025,15660301.0,Ogochukwu,609.0,France,Female,35.0,2.0,0.0,2.0,0.0,0.0,180329.83
+24026,15646810.0,Chukwueloka,732.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,113089.43
+24027,15693382.0,Pugh,751.0,France,Female,36.0,10.0,0.0,2.0,1.0,0.0,92612.88
+24028,15680430.0,Romano,648.0,Germany,Male,32.0,7.0,93522.2,2.0,1.0,1.0,89935.73
+24029,15679743.0,Ojiofor,751.0,France,Female,40.0,8.0,160599.08,1.0,1.0,0.0,126475.79
+24030,15733581.0,Young,619.0,France,Female,22.0,10.0,0.0,2.0,0.0,0.0,131736.23
+24031,15711759.0,Tsou,582.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,162889.1
+24032,15596710.0,Chinwenma,695.0,France,Female,37.0,5.0,94464.81,1.0,1.0,0.0,72929.84
+24033,15782210.0,Chuang,841.0,France,Female,36.0,1.0,0.0,2.0,0.0,1.0,161407.48
+24034,15769312.0,Chia,850.0,France,Male,58.0,7.0,0.0,2.0,1.0,1.0,64872.55
+24035,15780805.0,Onyemauchechukwu,708.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,82900.25
+24036,15651460.0,Hs?,850.0,Spain,Female,37.0,2.0,137003.68,1.0,1.0,0.0,54726.43
+24037,15571928.0,Ts'ao,644.0,Spain,Female,33.0,4.0,0.0,2.0,0.0,0.0,182624.51
+24038,15793331.0,Onyinyechukwuka,678.0,France,Male,39.0,8.0,0.0,1.0,1.0,1.0,143783.28
+24039,15795586.0,Wright,679.0,Germany,Female,45.0,2.0,116822.8,1.0,1.0,0.0,111573.3
+24040,15696900.0,Clements,753.0,France,Male,29.0,9.0,0.0,2.0,0.0,1.0,148811.14
+24041,15579223.0,Nnachetam,577.0,France,Female,49.0,5.0,0.0,1.0,1.0,0.0,151822.66
+24042,15661723.0,Sagese,850.0,Germany,Female,45.0,1.0,139799.63,2.0,1.0,0.0,157072.91
+24043,15751185.0,Chinwemma,544.0,France,Female,61.0,0.0,0.0,2.0,1.0,1.0,176778.76
+24044,15589643.0,Okwudilichukwu,629.0,Spain,Female,42.0,0.0,0.0,1.0,1.0,1.0,146875.86
+24045,15571059.0,Chukwuemeka,719.0,Spain,Male,49.0,5.0,0.0,3.0,1.0,0.0,187616.16
+24046,15749103.0,Ch'ang,579.0,France,Male,42.0,6.0,0.0,1.0,0.0,1.0,129964.94
+24047,15689061.0,Hsiung,634.0,France,Female,31.0,5.0,0.0,2.0,0.0,0.0,125240.8
+24048,15567114.0,Iloerika,484.0,France,Male,29.0,2.0,0.0,1.0,1.0,0.0,171045.46
+24049,15678720.0,Onyemere,646.0,France,Male,28.0,6.0,0.0,1.0,0.0,1.0,183569.25
+24050,15604792.0,Ch'ien,585.0,France,Male,66.0,2.0,0.0,2.0,1.0,1.0,93777.81
+24051,15722409.0,Ch'iu,472.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,181461.46
+24052,15754569.0,Maclean,685.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,121316.07
+24053,15663421.0,P'an,592.0,France,Male,44.0,1.0,0.0,2.0,1.0,0.0,56908.41
+24054,15680666.0,Ch'in,547.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,0.0,96670.2
+24055,15761043.0,Kuykendall,634.0,Spain,Male,42.0,8.0,141581.08,1.0,1.0,0.0,167036.92
+24056,15774857.0,Liao,590.0,Spain,Female,37.0,9.0,0.0,2.0,1.0,0.0,134600.94
+24057,15791927.0,Scott,581.0,France,Female,45.0,4.0,0.0,2.0,1.0,1.0,152298.28
+24058,15798024.0,K?,551.0,Germany,Male,35.0,8.0,87946.39,1.0,1.0,1.0,116977.89
+24059,15763898.0,T'ao,616.0,Germany,Female,47.0,8.0,147910.3,1.0,1.0,0.0,141896.74
+24060,15646535.0,Chinagorom,718.0,France,Male,48.0,0.0,0.0,1.0,1.0,0.0,102528.46
+24061,15677958.0,Ch'iu,659.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,95562.81
+24062,15782530.0,Onyemere,554.0,Germany,Male,31.0,2.0,130565.02,2.0,1.0,1.0,161243.09
+24063,15783030.0,Chia,567.0,France,Female,42.0,9.0,0.0,2.0,1.0,0.0,155870.82
+24064,15658577.0,Nnonso,616.0,Germany,Female,37.0,8.0,90194.34,2.0,1.0,1.0,137037.97
+24065,15598521.0,Chiu,656.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,138880.24
+24066,15778884.0,Ch'ien,772.0,Germany,Female,31.0,6.0,109949.05,2.0,0.0,0.0,72993.67
+24067,15750049.0,Baresi,679.0,France,Male,24.0,4.0,0.0,2.0,1.0,0.0,181850.74
+24068,15589475.0,Onyemachukwu,743.0,France,Male,47.0,1.0,0.0,2.0,1.0,1.0,147533.09
+24069,15583718.0,T'ao,599.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,111190.53
+24070,15720774.0,Niu,642.0,Spain,Female,52.0,2.0,0.0,2.0,1.0,1.0,102908.91
+24071,15809227.0,Ch'iu,543.0,Spain,Female,69.0,9.0,0.0,2.0,1.0,1.0,180197.74
+24072,15665524.0,Hsueh,590.0,France,Male,30.0,9.0,0.0,1.0,1.0,0.0,144593.3
+24073,15583718.0,Nwokezuike,717.0,Spain,Female,46.0,4.0,145984.92,1.0,1.0,0.0,56787.8
+24074,15766422.0,Chukwufumnanya,729.0,Germany,Female,34.0,7.0,127095.14,2.0,1.0,1.0,151902.78
+24075,15762799.0,Iloerika,563.0,France,Male,53.0,4.0,0.0,1.0,1.0,0.0,184581.68
+24076,15746139.0,Nucci,758.0,France,Male,32.0,9.0,118100.59,1.0,0.0,1.0,97938.59
+24077,15806945.0,Ch'ang,516.0,France,Female,39.0,10.0,118715.86,1.0,1.0,1.0,141807.51
+24078,15671351.0,Onwumelu,579.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,142839.71
+24079,15570086.0,Chibueze,636.0,Germany,Male,45.0,9.0,97041.16,1.0,1.0,0.0,182718.25
+24080,15768124.0,Nkemjika,732.0,France,Male,55.0,2.0,0.0,2.0,1.0,1.0,38019.74
+24081,15809663.0,Chu,794.0,France,Male,27.0,1.0,0.0,2.0,1.0,0.0,91943.94
+24082,15671610.0,Chukwuemeka,619.0,France,Female,42.0,3.0,90957.81,1.0,1.0,0.0,76611.26
+24083,15796956.0,Hsing,559.0,Germany,Female,43.0,9.0,93547.23,1.0,1.0,0.0,142514.71
+24084,15808526.0,Yashina,662.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,80552.36
+24085,15692599.0,Chibugo,592.0,Germany,Male,42.0,8.0,94762.16,1.0,1.0,1.0,185599.34
+24086,15590098.0,K'ung,633.0,France,Male,34.0,2.0,0.0,2.0,0.0,1.0,86935.08
+24087,15652948.0,Nwachukwu,691.0,France,Female,31.0,2.0,150500.64,1.0,1.0,1.0,50664.07
+24088,15587978.0,Miller,581.0,Germany,Female,40.0,3.0,135756.96,2.0,1.0,1.0,136909.87
+24089,15621818.0,Manna,612.0,Germany,Female,43.0,5.0,131411.24,1.0,1.0,1.0,163830.63
+24090,15605050.0,Yevseyev,757.0,Spain,Male,39.0,10.0,152663.77,1.0,1.0,0.0,125079.65
+24091,15731522.0,Chinedum,754.0,Germany,Female,43.0,8.0,137851.31,2.0,1.0,1.0,91937.21
+24092,15662093.0,Scott,581.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,111442.71
+24093,15705735.0,Calabresi,512.0,Spain,Male,36.0,8.0,127974.06,1.0,1.0,1.0,43486.73
+24094,15681446.0,Chiu,678.0,France,Male,31.0,2.0,0.0,1.0,1.0,0.0,61761.77
+24095,15772995.0,Dickson,648.0,Spain,Male,27.0,1.0,0.0,2.0,1.0,1.0,91086.73
+24096,15605916.0,Fanucci,686.0,France,Female,36.0,9.0,0.0,1.0,1.0,0.0,71179.31
+24097,15654074.0,Chiu,682.0,France,Male,35.0,10.0,0.0,1.0,1.0,0.0,145796.49
+24098,15609913.0,Hsiao,746.0,France,Female,38.0,10.0,0.0,1.0,1.0,0.0,86767.48
+24099,15716000.0,Pisano,543.0,France,Male,39.0,2.0,0.0,1.0,0.0,1.0,113796.15
+24100,15658716.0,Chiang,719.0,Spain,Female,31.0,4.0,0.0,2.0,0.0,0.0,38276.84
+24101,15589948.0,Lucchesi,660.0,Germany,Female,36.0,5.0,125175.56,2.0,1.0,1.0,175893.35
+24102,15682533.0,Hao,711.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,169921.11
+24103,15738672.0,Macleod,652.0,Germany,Male,58.0,2.0,102785.16,3.0,0.0,1.0,45978.39
+24104,15625247.0,Chibugo,726.0,France,Male,31.0,3.0,92833.89,1.0,0.0,0.0,137674.01
+24105,15597951.0,Uwaezuoke,711.0,France,Female,59.0,2.0,0.0,2.0,0.0,1.0,177613.19
+24106,15672798.0,Ritchie,696.0,France,Male,36.0,4.0,100524.69,2.0,1.0,1.0,95583.45
+24107,15622017.0,Padovano,779.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,147955.91
+24108,15757541.0,Chukwuemeka,704.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,1.0,158580.05
+24109,15777826.0,Onwumelu,653.0,France,Male,49.0,5.0,0.0,1.0,0.0,0.0,159463.8
+24110,15775306.0,Piazza,709.0,Germany,Female,49.0,5.0,148074.79,1.0,0.0,0.0,100538.8
+24111,15662403.0,Chien,609.0,France,Female,37.0,2.0,178056.54,3.0,1.0,0.0,122379.54
+24112,15681316.0,Cattaneo,633.0,France,Female,37.0,1.0,0.0,1.0,1.0,0.0,184954.11
+24113,15768104.0,Chukwufumnanya,596.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,122580.48
+24114,15736274.0,Hs?,762.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,1.0,131283.6
+24115,15652069.0,Kornilova,774.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,62347.71
+24116,15610912.0,H?,663.0,Spain,Female,56.0,7.0,0.0,1.0,1.0,0.0,158887.09
+24117,15698932.0,Smith,613.0,France,Male,37.0,7.0,0.0,1.0,1.0,1.0,106920.57
+24118,15665340.0,Chinedum,578.0,France,Female,39.0,3.0,0.0,2.0,1.0,0.0,92888.52
+24119,15717290.0,Ch'iu,584.0,France,Male,35.0,1.0,0.0,1.0,1.0,1.0,101274.17
+24120,15573926.0,Onyemere,661.0,France,Male,34.0,1.0,120107.71,1.0,1.0,0.0,126518.9
+24121,15578722.0,Chiang,756.0,France,Female,32.0,6.0,0.0,1.0,1.0,1.0,84908.2
+24122,15690164.0,Lucchese,584.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,0.0,52337.97
+24123,15777614.0,Yefimova,685.0,Spain,Male,38.0,6.0,0.0,2.0,0.0,1.0,90876.95
+24124,15792232.0,Chiemenam,534.0,Germany,Female,32.0,3.0,123034.43,1.0,1.0,0.0,140889.64
+24125,15693877.0,Folliero,707.0,France,Female,27.0,3.0,0.0,2.0,1.0,0.0,127939.19
+24126,15639357.0,Hsia,725.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,51553.43
+24127,15770091.0,McElhone,691.0,France,Female,27.0,0.0,0.0,2.0,0.0,1.0,115719.08
+24128,15762799.0,Ritchie,733.0,Spain,Female,33.0,9.0,0.0,2.0,1.0,1.0,73329.08
+24129,15758031.0,Nnamutaezinwa,669.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,131662.47
+24130,15700174.0,Kao,583.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,95857.18
+24131,15660646.0,Ch'ang,745.0,France,Female,31.0,1.0,92676.2,1.0,1.0,0.0,174918.46
+24132,15579223.0,Tsai,629.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,44220.4
+24133,15583049.0,Onyinyechukwuka,705.0,France,Female,22.0,9.0,99712.74,1.0,1.0,0.0,138527.56
+24134,15587712.0,Chukwudi,762.0,Germany,Female,53.0,5.0,101060.25,1.0,1.0,0.0,140746.13
+24135,15583857.0,Walker,528.0,Germany,Female,40.0,1.0,124439.18,1.0,1.0,0.0,177025.79
+24136,15658668.0,Aksenov,721.0,France,Female,61.0,5.0,0.0,2.0,1.0,1.0,18082.06
+24137,15643659.0,DeRose,648.0,France,Female,32.0,4.0,0.0,2.0,0.0,1.0,83052.03
+24138,15794468.0,Tuan,516.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,16081.62
+24139,15800515.0,Ch'iu,790.0,France,Male,32.0,4.0,0.0,2.0,0.0,0.0,131886.46
+24140,15605067.0,Smith,714.0,France,Female,28.0,4.0,0.0,2.0,1.0,1.0,158612.08
+24141,15646764.0,Sneddon,763.0,Spain,Male,52.0,6.0,0.0,1.0,1.0,0.0,163554.85
+24142,15773469.0,H?,718.0,France,Male,64.0,6.0,0.0,2.0,0.0,1.0,145558.42
+24143,15636999.0,Chia,544.0,France,Male,48.0,2.0,0.0,3.0,0.0,1.0,823.96
+24144,15721202.0,Onyemauchechukwu,651.0,Spain,Male,50.0,1.0,0.0,1.0,1.0,1.0,48963.59
+24145,15612071.0,Chukwukadibia,644.0,France,Female,37.0,2.0,0.0,2.0,0.0,1.0,69052.87
+24146,15702669.0,Milani,605.0,France,Female,61.0,7.0,0.0,2.0,1.0,1.0,160110.65
+24147,15754574.0,Hsieh,614.0,Germany,Male,26.0,2.0,142905.51,1.0,1.0,0.0,115022.94
+24148,15752312.0,Ch'ang,589.0,France,Male,38.0,7.0,120916.52,1.0,1.0,1.0,184856.89
+24149,15643826.0,Chiganu,590.0,Spain,Male,39.0,4.0,0.0,2.0,1.0,0.0,90877.76
+24150,15769959.0,Nwankwo,681.0,France,Female,35.0,4.0,114327.86,1.0,1.0,0.0,78854.57
+24151,15787051.0,Lucciano,628.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,106977.53
+24152,15752731.0,Martin,551.0,Spain,Female,48.0,0.0,0.0,1.0,0.0,1.0,163331.65
+24153,15625942.0,Hs?,717.0,Germany,Male,35.0,8.0,107363.31,2.0,1.0,0.0,116581.1
+24154,15721383.0,Ch'en,516.0,Germany,Female,48.0,2.0,133702.89,2.0,0.0,1.0,80552.11
+24155,15577598.0,Onwuatuegwu,581.0,France,Male,40.0,1.0,141136.62,1.0,1.0,1.0,81402.68
+24156,15726354.0,Hs?,757.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,91055.46
+24157,15596303.0,Mazzanti,561.0,France,Female,37.0,7.0,0.0,2.0,0.0,0.0,38190.78
+24158,15796370.0,Ebelechukwu,731.0,Germany,Female,57.0,4.0,100107.99,2.0,1.0,1.0,146598.28
+24159,15803526.0,Hao,605.0,Germany,Female,32.0,6.0,151027.56,2.0,1.0,1.0,179655.87
+24160,15573318.0,Chinedum,581.0,Germany,Male,28.0,3.0,153611.83,1.0,0.0,1.0,168034.9
+24161,15637979.0,Zikoranachidimma,575.0,Germany,Female,54.0,2.0,143903.11,1.0,1.0,0.0,85902.39
+24162,15591169.0,Johnson,713.0,France,Male,21.0,3.0,103818.47,2.0,1.0,1.0,163427.18
+24163,15687913.0,Aksenov,599.0,Germany,Female,38.0,7.0,112291.34,2.0,0.0,1.0,140579.17
+24164,15726776.0,Yobachi,599.0,France,Male,35.0,6.0,95826.49,1.0,0.0,1.0,104719.66
+24165,15726588.0,Hsiao,662.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,180932.46
+24166,15626012.0,Ch'ien,554.0,France,Male,35.0,10.0,148843.56,1.0,1.0,1.0,152992.04
+24167,15684920.0,Chiu,679.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,71433.88
+24168,15742329.0,Chukwukere,683.0,Germany,Male,34.0,9.0,107435.83,2.0,1.0,0.0,70532.53
+24169,15753840.0,Chukwuemeka,711.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,116893.87
+24170,15595043.0,Chukwumaobim,639.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,143859.66
+24171,15696054.0,Lo Duca,679.0,Germany,Male,21.0,1.0,118079.47,1.0,1.0,0.0,49506.82
+24172,15794870.0,Teng,633.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,120511.03
+24173,15678916.0,L?,715.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,84920.49
+24174,15813144.0,Hsieh,635.0,France,Female,46.0,8.0,0.0,1.0,0.0,0.0,19287.06
+24175,15802070.0,Ch'iu,709.0,Germany,Male,29.0,8.0,139603.22,1.0,1.0,1.0,51364.56
+24176,15699238.0,Kao,711.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,88842.8
+24177,15718779.0,Okechukwu,676.0,France,Male,34.0,7.0,0.0,1.0,0.0,1.0,53584.03
+24178,15656592.0,Ch'in,619.0,Germany,Male,54.0,1.0,116932.58,1.0,1.0,0.0,53581.17
+24179,15677796.0,Hsia,661.0,France,Male,38.0,6.0,160903.2,1.0,1.0,1.0,27802.0
+24180,15757837.0,Shao,599.0,Spain,Male,37.0,6.0,0.0,1.0,1.0,1.0,141364.62
+24181,15634310.0,Sagese,632.0,Germany,Male,38.0,7.0,118400.91,1.0,1.0,1.0,137510.41
+24182,15648933.0,Ogochukwu,598.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,129563.86
+24183,15617507.0,Nwachukwu,639.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,135089.57
+24184,15616471.0,Fan,663.0,France,Female,41.0,3.0,132088.59,1.0,1.0,0.0,12796.43
+24185,15569526.0,Linton,556.0,Germany,Female,44.0,1.0,140720.93,1.0,1.0,0.0,124571.09
+24186,15629448.0,Chukwufumnanya,683.0,Germany,Female,30.0,5.0,82228.67,2.0,1.0,1.0,175314.87
+24187,15644788.0,O'Donnell,613.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,167023.95
+24188,15663917.0,Lu,708.0,Spain,Male,37.0,0.0,0.0,2.0,1.0,0.0,124486.28
+24189,15738448.0,Biryukov,699.0,France,Male,37.0,5.0,113625.93,1.0,1.0,0.0,170893.85
+24190,15659495.0,Ch'eng,762.0,France,Male,35.0,1.0,0.0,1.0,1.0,0.0,84790.59
+24191,15588560.0,Hao,724.0,France,Male,35.0,10.0,0.0,2.0,1.0,0.0,113860.81
+24192,15705001.0,Cremonesi,721.0,Spain,Female,55.0,4.0,0.0,1.0,0.0,0.0,57929.8
+24193,15754926.0,Li Fonti,708.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,182960.46
+24194,15586931.0,Ch'eng,850.0,France,Male,37.0,5.0,0.0,2.0,0.0,1.0,143980.27
+24195,15603328.0,Kao,694.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,169183.66
+24196,15583576.0,Hs?,709.0,France,Female,40.0,5.0,0.0,2.0,0.0,0.0,142223.94
+24197,15628274.0,Martin,481.0,Germany,Male,40.0,5.0,122334.26,1.0,1.0,1.0,178994.13
+24198,15611947.0,Chukwudi,686.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,180932.46
+24199,15643361.0,Olisanugo,642.0,France,Male,46.0,2.0,0.0,1.0,1.0,0.0,85421.48
+24200,15705113.0,Chukwufumnanya,605.0,France,Female,35.0,4.0,109876.82,1.0,1.0,1.0,164925.11
+24201,15639277.0,Kao,584.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,10054.51
+24202,15716082.0,Lombardi,653.0,France,Female,31.0,3.0,130935.56,1.0,1.0,0.0,79734.23
+24203,15604020.0,Bitter,597.0,Germany,Female,43.0,5.0,121161.57,2.0,1.0,0.0,139435.11
+24204,15744327.0,Yefimova,632.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,0.0,82951.12
+24205,15589715.0,Chidozie,581.0,France,Male,29.0,3.0,96858.35,1.0,1.0,0.0,71139.31
+24206,15791742.0,Nwokike,771.0,France,Male,36.0,3.0,0.0,1.0,0.0,1.0,52795.56
+24207,15705544.0,Chibugo,645.0,France,Male,44.0,2.0,0.0,2.0,0.0,0.0,101866.39
+24208,15708513.0,Ebelegbulam,645.0,Spain,Female,36.0,1.0,0.0,2.0,0.0,0.0,58879.31
+24209,15686964.0,Chiang,671.0,France,Male,38.0,10.0,133616.39,1.0,1.0,0.0,70523.92
+24210,15791040.0,Hsueh,638.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,85746.52
+24211,15586674.0,Eluemuno,699.0,France,Female,37.0,1.0,138341.51,1.0,1.0,1.0,141926.43
+24212,15656829.0,Onwuamaeze,624.0,Spain,Male,27.0,9.0,0.0,2.0,0.0,0.0,177848.02
+24213,15692819.0,Hsueh,597.0,Spain,Female,32.0,9.0,0.0,1.0,1.0,1.0,66463.62
+24214,15674750.0,Nnachetam,668.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,75732.25
+24215,15752984.0,Wallace,496.0,Germany,Female,36.0,1.0,135927.06,1.0,1.0,0.0,135077.01
+24216,15614893.0,Shih,850.0,Germany,Female,29.0,5.0,120711.73,1.0,1.0,1.0,62390.59
+24217,15603065.0,Uspenskaya,709.0,Spain,Male,41.0,7.0,0.0,2.0,1.0,1.0,161465.31
+24218,15741921.0,Chukwujekwu,753.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,93736.69
+24219,15658956.0,Cremonesi,593.0,Spain,Male,40.0,5.0,0.0,1.0,1.0,0.0,88659.07
+24220,15726484.0,K'ung,528.0,France,Female,34.0,0.0,0.0,2.0,1.0,1.0,46774.94
+24221,15584462.0,Ch'in,743.0,France,Female,40.0,6.0,121537.73,1.0,0.0,0.0,146738.83
+24222,15705001.0,Aitken,850.0,Germany,Male,25.0,8.0,118433.94,1.0,1.0,1.0,136050.44
+24223,15681671.0,Dickson,712.0,Germany,Male,22.0,1.0,175575.95,1.0,0.0,1.0,56865.62
+24224,15742428.0,Hsueh,586.0,Germany,Male,35.0,1.0,108269.2,1.0,0.0,0.0,52327.57
+24225,15753362.0,Lucciano,693.0,France,Female,31.0,1.0,0.0,1.0,1.0,0.0,82647.65
+24226,15572467.0,Yudina,759.0,Spain,Male,38.0,8.0,0.0,1.0,1.0,1.0,177092.16
+24227,15799479.0,Kaeppel,484.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,0.0,103349.72
+24228,15649430.0,Hsia,657.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,82662.73
+24229,15806837.0,Hsia,704.0,France,Male,50.0,8.0,0.0,2.0,0.0,1.0,191198.92
+24230,15637324.0,Ch'in,616.0,France,Female,40.0,3.0,0.0,1.0,1.0,0.0,85901.09
+24231,15809909.0,Tien,725.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,153662.88
+24232,15579617.0,Zikoranachidimma,716.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,28269.86
+24233,15604020.0,Ts'ui,603.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,172618.52
+24234,15586974.0,Nwachukwu,639.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,111698.0
+24235,15793331.0,Uvarova,681.0,Spain,Female,33.0,5.0,0.0,1.0,1.0,1.0,131389.39
+24236,15597951.0,Hsueh,607.0,France,Female,57.0,0.0,0.0,1.0,1.0,1.0,93832.33
+24237,15658195.0,Olisaemeka,694.0,Spain,Male,31.0,3.0,0.0,1.0,1.0,0.0,156660.31
+24238,15758510.0,Tretiakova,671.0,France,Male,36.0,1.0,114514.73,1.0,0.0,1.0,80410.68
+24239,15572735.0,Ch'en,679.0,France,Male,39.0,6.0,0.0,1.0,1.0,0.0,128882.66
+24240,15775886.0,P'an,667.0,France,Female,41.0,2.0,0.0,2.0,0.0,1.0,180553.48
+24241,15731519.0,Fanucci,635.0,Germany,Female,56.0,9.0,122359.5,2.0,1.0,1.0,63736.17
+24242,15654562.0,Nnaife,646.0,France,Male,24.0,1.0,0.0,2.0,1.0,1.0,185599.34
+24243,15784097.0,Chu,614.0,Spain,Female,38.0,4.0,0.0,1.0,1.0,1.0,163714.92
+24244,15771054.0,Onwughara,491.0,Germany,Male,32.0,7.0,121866.89,2.0,1.0,0.0,120314.75
+24245,15603594.0,Lucciano,777.0,Spain,Female,35.0,2.0,0.0,2.0,0.0,0.0,83470.9
+24246,15593943.0,Udokamma,619.0,Spain,Male,38.0,7.0,123971.51,1.0,1.0,1.0,90174.83
+24247,15757912.0,Chibueze,755.0,France,Female,31.0,8.0,80728.15,1.0,1.0,1.0,140159.95
+24248,15643908.0,De Luca,619.0,France,Male,29.0,1.0,119266.69,2.0,0.0,0.0,48930.8
+24249,15681332.0,T'ang,741.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,177065.24
+24250,15743490.0,Onyekachukwu,621.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,160976.87
+24251,15733299.0,Tien,588.0,France,Female,21.0,10.0,0.0,2.0,1.0,0.0,109685.61
+24252,15736254.0,Ts'ui,748.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,168515.61
+24253,15692408.0,T'ien,683.0,France,Female,34.0,9.0,176511.08,1.0,1.0,0.0,150401.53
+24254,15783629.0,Gilleland,653.0,Germany,Male,46.0,2.0,142296.13,1.0,1.0,0.0,53394.24
+24255,15611338.0,Houghton,752.0,France,Male,38.0,8.0,0.0,2.0,0.0,0.0,81482.4
+24256,15775124.0,Folliero,717.0,France,Female,33.0,0.0,0.0,2.0,0.0,1.0,132673.23
+24257,15709920.0,Ch'eng,751.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,143952.24
+24258,15690743.0,Ch'en,640.0,Spain,Male,33.0,1.0,0.0,1.0,0.0,0.0,151903.6
+24259,15777906.0,Chinedum,634.0,Germany,Female,38.0,5.0,130142.79,2.0,0.0,0.0,136875.31
+24260,15803790.0,T'ang,614.0,Spain,Female,25.0,9.0,117412.19,1.0,1.0,0.0,116081.97
+24261,15658577.0,Hao,720.0,France,Male,49.0,6.0,0.0,2.0,0.0,1.0,153920.22
+24262,15702561.0,Tuan,646.0,France,Male,28.0,6.0,121161.57,1.0,1.0,1.0,82662.73
+24263,15726773.0,Hsieh,619.0,France,Male,42.0,6.0,75893.11,1.0,0.0,1.0,142977.05
+24264,15709441.0,Aksenov,727.0,France,Male,35.0,8.0,0.0,2.0,0.0,1.0,164083.72
+24265,15604792.0,Hsueh,717.0,Spain,Female,42.0,5.0,0.0,2.0,1.0,0.0,145796.49
+24266,15770255.0,Ting,709.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,168515.61
+24267,15722583.0,Mazzi,699.0,Germany,Male,41.0,9.0,129299.79,2.0,1.0,1.0,99490.01
+24268,15609623.0,T'ang,823.0,Spain,Female,37.0,4.0,0.0,2.0,1.0,0.0,160162.42
+24269,15704844.0,Chidimma,642.0,Spain,Male,35.0,2.0,112262.84,1.0,0.0,0.0,155853.62
+24270,15685829.0,Maclean,691.0,Germany,Female,44.0,3.0,107949.39,1.0,1.0,0.0,162452.65
+24271,15593894.0,Hs?eh,751.0,Spain,Male,34.0,9.0,0.0,2.0,0.0,0.0,62722.44
+24272,15662294.0,Mazzanti,686.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,0.0,61315.37
+24273,15665783.0,Wright,748.0,France,Male,35.0,3.0,0.0,2.0,0.0,0.0,185510.34
+24274,15791172.0,Hs?,678.0,France,Male,38.0,1.0,126670.37,1.0,1.0,1.0,106564.88
+24275,15753362.0,Chien,598.0,Germany,Female,43.0,0.0,107747.57,1.0,1.0,0.0,44147.95
+24276,15789055.0,Chin,711.0,France,Male,58.0,1.0,0.0,2.0,1.0,1.0,142513.5
+24277,15769353.0,Iweobiegbunam,573.0,Spain,Female,29.0,5.0,0.0,1.0,1.0,0.0,48988.28
+24278,15792679.0,Smith,598.0,Germany,Male,38.0,3.0,104930.38,2.0,1.0,1.0,177866.87
+24279,15713559.0,Kincaid,591.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,108764.69
+24280,15777616.0,Ts'ao,633.0,Spain,Female,63.0,1.0,0.0,2.0,1.0,1.0,186489.95
+24281,15603550.0,Uspenskaya,646.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,80630.32
+24282,15794895.0,Maclean,616.0,Germany,Female,36.0,7.0,118082.89,1.0,1.0,0.0,139024.31
+24283,15607877.0,T'ang,849.0,France,Male,36.0,2.0,118287.01,1.0,1.0,1.0,70956.75
+24284,15581229.0,Chukwudi,613.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,170529.63
+24285,15646936.0,Nkemakolam,738.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,82870.75
+24286,15570073.0,Chigolum,684.0,France,Male,41.0,9.0,129895.57,1.0,0.0,0.0,84487.62
+24287,15578211.0,Onwuamaeze,623.0,Spain,Female,46.0,1.0,91216.0,1.0,1.0,1.0,174825.57
+24288,15646764.0,Fomin,833.0,France,Male,40.0,2.0,0.0,1.0,0.0,0.0,193009.21
+24289,15795586.0,Ch'ang,644.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,67944.09
+24290,15684936.0,Kaeppel,678.0,France,Female,38.0,7.0,135096.05,1.0,1.0,0.0,79458.12
+24291,15601787.0,Hsu,543.0,France,Male,40.0,2.0,82344.84,2.0,0.0,0.0,77867.23
+24292,15693637.0,Chiang,553.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,0.0,73551.44
+24293,15590327.0,Cattaneo,613.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,1.0,129964.94
+24294,15771086.0,K'ung,637.0,France,Male,34.0,8.0,0.0,1.0,0.0,0.0,153727.32
+24295,15761606.0,P'eng,647.0,Germany,Male,34.0,2.0,102962.62,2.0,1.0,1.0,116051.44
+24296,15626474.0,Ch'iu,726.0,France,Female,34.0,3.0,0.0,2.0,0.0,0.0,131284.92
+24297,15635388.0,Trevisano,738.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,142730.27
+24298,15758023.0,Hsieh,651.0,Spain,Female,41.0,9.0,0.0,1.0,1.0,1.0,180598.66
+24299,15705544.0,Ch'ang,642.0,Germany,Male,37.0,5.0,125445.04,1.0,0.0,1.0,52332.85
+24300,15756056.0,Onwuamaeze,709.0,Spain,Male,39.0,10.0,0.0,2.0,1.0,0.0,143980.29
+24301,15781689.0,Milano,755.0,France,Male,40.0,8.0,120171.58,1.0,0.0,1.0,142331.85
+24302,15723488.0,Lucchese,579.0,France,Male,36.0,8.0,0.0,2.0,0.0,1.0,161822.66
+24303,15795878.0,K?,777.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,160968.48
+24304,15582325.0,T'ien,545.0,France,Male,26.0,4.0,0.0,2.0,1.0,1.0,94977.3
+24305,15682467.0,Hsueh,686.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,187905.12
+24306,15621093.0,Brown,714.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,140800.62
+24307,15724181.0,Mancini,703.0,Germany,Female,45.0,9.0,101168.3,1.0,1.0,0.0,101168.3
+24308,15653572.0,Goliwe,663.0,Spain,Female,47.0,3.0,0.0,1.0,1.0,1.0,140763.18
+24309,15701300.0,Y?,633.0,France,Male,32.0,1.0,132387.91,1.0,1.0,1.0,164864.66
+24310,15712403.0,Yobachi,756.0,France,Female,40.0,8.0,0.0,2.0,0.0,1.0,61602.42
+24311,15756668.0,Chukwukere,614.0,France,Male,33.0,1.0,94771.61,1.0,1.0,0.0,131301.74
+24312,15740383.0,Tien,751.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,78670.52
+24313,15796790.0,Liao,632.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,70533.96
+24314,15736413.0,Chia,485.0,Spain,Female,29.0,3.0,0.0,2.0,0.0,0.0,176962.31
+24315,15790750.0,Onyemauchechi,712.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,135077.01
+24316,15650266.0,Knepper,649.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,95583.45
+24317,15702464.0,Ch'ien,551.0,France,Female,41.0,3.0,139180.2,1.0,1.0,1.0,90878.13
+24318,15742269.0,Maclean,568.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,93336.3
+24319,15682355.0,Nkemjika,657.0,Germany,Female,53.0,4.0,91286.51,1.0,1.0,1.0,116848.79
+24320,15798975.0,Pisani,543.0,France,Male,23.0,1.0,0.0,2.0,0.0,0.0,142730.27
+24321,15802274.0,Tsou,669.0,France,Male,33.0,1.0,121411.9,1.0,1.0,1.0,64375.4
+24322,15696733.0,Y?an,535.0,Spain,Male,37.0,5.0,143566.12,1.0,1.0,0.0,108872.45
+24323,15579996.0,Okonkwo,535.0,Spain,Male,33.0,7.0,109646.83,1.0,1.0,1.0,90977.48
+24324,15672357.0,Hsia,582.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,176943.59
+24325,15612095.0,Chigozie,642.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,15650.89
+24326,15731463.0,Manna,567.0,Spain,Female,49.0,5.0,0.0,3.0,1.0,0.0,183490.99
+24327,15683522.0,Chukwuebuka,779.0,France,Male,33.0,5.0,119496.25,1.0,0.0,1.0,120853.83
+24328,15589312.0,Chidiebere,556.0,France,Male,27.0,7.0,0.0,2.0,1.0,0.0,123882.73
+24329,15616666.0,Eluemuno,596.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,0.0,141501.45
+24330,15632275.0,Obinna,595.0,Germany,Male,43.0,6.0,152603.45,1.0,0.0,1.0,71830.1
+24331,15587914.0,Zito,598.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,59961.81
+24332,15699257.0,Chien,588.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,64448.36
+24333,15667633.0,Cattaneo,709.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,127825.25
+24334,15693906.0,Chukwuemeka,710.0,France,Female,38.0,0.0,0.0,1.0,1.0,1.0,152843.53
+24335,15670172.0,Hsiao,850.0,Germany,Female,30.0,7.0,123601.56,1.0,1.0,0.0,197305.91
+24336,15593468.0,Gadsdon,637.0,France,Female,49.0,10.0,0.0,3.0,0.0,0.0,174434.06
+24337,15645544.0,Mironova,710.0,France,Male,36.0,5.0,0.0,1.0,1.0,0.0,93718.86
+24338,15721433.0,Dyer,605.0,France,Male,43.0,5.0,121898.82,1.0,1.0,1.0,143635.33
+24339,15592736.0,Ch'en,573.0,Spain,Female,40.0,1.0,0.0,1.0,1.0,0.0,48924.73
+24340,15721460.0,Manna,627.0,Germany,Male,39.0,7.0,97796.04,2.0,1.0,1.0,122964.14
+24341,15690744.0,Chukwuebuka,702.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,1.0,94670.77
+24342,15794345.0,Piazza,708.0,Germany,Female,42.0,6.0,142072.02,2.0,1.0,1.0,29333.01
+24343,15762331.0,Ch'ang,687.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,162511.55
+24344,15624695.0,Yuryeva,696.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,169498.61
+24345,15635367.0,Kao,634.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,74898.8
+24346,15652084.0,Uvarova,622.0,France,Female,41.0,5.0,0.0,2.0,1.0,1.0,147074.67
+24347,15638882.0,Mazzi,712.0,Spain,Female,35.0,5.0,61687.33,1.0,1.0,1.0,104769.89
+24348,15593778.0,Nebeolisa,577.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,182059.28
+24349,15807167.0,Onio,829.0,France,Female,46.0,5.0,0.0,1.0,0.0,0.0,50783.1
+24350,15754569.0,Chineze,706.0,Germany,Male,37.0,8.0,118894.22,2.0,0.0,0.0,89566.64
+24351,15588928.0,Ogochukwu,633.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,120531.31
+24352,15677288.0,Chiawuotu,615.0,France,Female,46.0,8.0,0.0,2.0,1.0,0.0,111324.41
+24353,15607098.0,Cremonesi,807.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,27286.1
+24354,15803526.0,Mbanefo,675.0,Spain,Male,26.0,3.0,137714.66,1.0,1.0,0.0,73066.71
+24355,15695879.0,Nnachetam,844.0,France,Female,27.0,5.0,132446.08,1.0,1.0,1.0,71405.17
+24356,15777892.0,Nwankwo,748.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,159840.94
+24357,15761487.0,Maclean,614.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,173365.9
+24358,15670822.0,Hsia,675.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,143614.01
+24359,15808223.0,Chukwujamuike,697.0,France,Male,30.0,3.0,113957.01,1.0,1.0,1.0,183399.12
+24360,15661936.0,Chukwuemeka,526.0,Spain,Female,27.0,8.0,0.0,2.0,0.0,0.0,108008.65
+24361,15592854.0,Chiu,663.0,Spain,Male,38.0,3.0,0.0,1.0,1.0,0.0,120893.07
+24362,15664547.0,Chiang,598.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,185561.89
+24363,15581323.0,Hsieh,560.0,France,Female,49.0,1.0,0.0,1.0,0.0,0.0,121210.09
+24364,15672374.0,Onyemauchechi,646.0,France,Male,33.0,2.0,0.0,1.0,0.0,1.0,183598.77
+24365,15694188.0,Chibugo,629.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,79919.13
+24366,15697028.0,Lai,781.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,92914.67
+24367,15628292.0,Lueck,554.0,France,Female,34.0,4.0,98545.4,1.0,0.0,0.0,168034.9
+24368,15749177.0,Li,605.0,Spain,Male,40.0,4.0,121513.31,1.0,1.0,1.0,164825.07
+24369,15701376.0,Ukaegbunam,667.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,80178.54
+24370,15673971.0,Aksenov,584.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,130755.34
+24371,15765283.0,Ch'ien,520.0,France,Male,36.0,7.0,153861.1,1.0,1.0,0.0,184856.89
+24372,15603065.0,Chidi,537.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,125877.22
+24373,15786063.0,K?,695.0,Germany,Male,51.0,1.0,127864.4,1.0,1.0,0.0,59816.41
+24374,15758513.0,Ts'ui,663.0,France,Female,33.0,7.0,0.0,1.0,0.0,1.0,179631.85
+24375,15761047.0,P'eng,729.0,Spain,Female,47.0,6.0,0.0,1.0,1.0,0.0,60420.07
+24376,15672912.0,Tretiakova,629.0,Germany,Female,29.0,5.0,150679.71,1.0,1.0,1.0,168886.31
+24377,15717995.0,Hsiao,714.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,149369.41
+24378,15602647.0,Nwebube,678.0,France,Male,20.0,10.0,0.0,2.0,1.0,0.0,121747.96
+24379,15626452.0,Milano,628.0,Germany,Female,43.0,2.0,125927.51,1.0,1.0,1.0,142903.44
+24380,15809558.0,Pirozzi,481.0,Germany,Female,46.0,3.0,118087.89,1.0,1.0,0.0,79178.37
+24381,15657535.0,Houghton,591.0,Germany,Male,35.0,9.0,117833.3,3.0,1.0,1.0,182691.31
+24382,15702656.0,O'Brien,703.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,142903.44
+24383,15682868.0,Ch'ien,603.0,Germany,Female,27.0,9.0,72494.69,1.0,1.0,0.0,175072.47
+24384,15572767.0,Aitken,589.0,Spain,Female,44.0,7.0,0.0,1.0,1.0,1.0,138841.81
+24385,15714689.0,Onyeoruru,758.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,151317.48
+24386,15718572.0,Nwankwo,598.0,Germany,Male,32.0,4.0,115764.32,1.0,1.0,0.0,48382.6
+24387,15798521.0,Lai,743.0,France,Female,61.0,1.0,0.0,1.0,1.0,1.0,104721.5
+24388,15769955.0,Walker,707.0,France,Female,26.0,3.0,0.0,2.0,0.0,0.0,171045.35
+24389,15777892.0,Ts'ui,545.0,France,Female,39.0,3.0,0.0,2.0,1.0,1.0,126419.8
+24390,15586674.0,Onyekachukwu,580.0,France,Female,35.0,7.0,100116.82,1.0,1.0,1.0,121798.52
+24391,15745452.0,Nkemakolam,725.0,France,Female,43.0,5.0,0.0,1.0,0.0,1.0,77522.61
+24392,15613471.0,Onwumelu,689.0,France,Female,27.0,1.0,0.0,1.0,1.0,1.0,84475.13
+24393,15785024.0,Ts'ao,657.0,France,Male,38.0,4.0,142412.14,1.0,0.0,1.0,181879.56
+24394,15708534.0,O'Sullivan,567.0,Spain,Male,33.0,4.0,102989.83,1.0,0.0,0.0,66463.62
+24395,15592716.0,Mancini,634.0,France,Female,38.0,5.0,0.0,2.0,0.0,0.0,131283.6
+24396,15704771.0,O'Brien,681.0,France,Female,48.0,1.0,0.0,1.0,1.0,0.0,116692.8
+24397,15658946.0,Chiedozie,721.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,93524.19
+24398,15781534.0,Chinomso,587.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,125880.53
+24399,15732644.0,Hao,773.0,France,Male,32.0,7.0,0.0,2.0,0.0,1.0,80698.54
+24400,15628292.0,Onyeorulu,608.0,Germany,Male,29.0,6.0,107915.94,2.0,1.0,1.0,180622.43
+24401,15581323.0,Ch'iu,695.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,151822.66
+24402,15770101.0,Y?,636.0,Spain,Female,37.0,10.0,0.0,2.0,1.0,1.0,176750.81
+24403,15581388.0,H?,605.0,France,Male,62.0,2.0,0.0,3.0,1.0,1.0,148811.71
+24404,15789563.0,Calabresi,576.0,Spain,Female,25.0,4.0,0.0,2.0,1.0,0.0,143315.57
+24405,15680706.0,Manna,709.0,Germany,Female,39.0,7.0,104960.46,2.0,0.0,1.0,177844.06
+24406,15649858.0,Nnamdi,787.0,Germany,Male,25.0,7.0,82909.14,2.0,0.0,0.0,84917.4
+24407,15653773.0,Chiagoziem,485.0,Spain,Male,54.0,7.0,119380.37,1.0,0.0,1.0,138380.12
+24408,15675176.0,Oguejiofor,584.0,France,Female,42.0,7.0,0.0,1.0,1.0,0.0,51907.72
+24409,15665963.0,Chiang,677.0,France,Male,34.0,9.0,113907.58,2.0,1.0,1.0,82709.8
+24410,15771668.0,Rolon,542.0,France,Male,50.0,4.0,0.0,1.0,0.0,1.0,66561.6
+24411,15720371.0,T'ien,615.0,France,Female,27.0,7.0,0.0,2.0,0.0,0.0,54495.82
+24412,15785831.0,Genovesi,729.0,France,Male,35.0,7.0,144875.74,1.0,1.0,0.0,124872.66
+24413,15744553.0,Chinonyelum,717.0,France,Female,31.0,3.0,0.0,2.0,0.0,0.0,74898.8
+24414,15571144.0,Naylor,605.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,172668.39
+24415,15806420.0,P'eng,604.0,France,Male,28.0,9.0,0.0,2.0,1.0,1.0,51548.14
+24416,15586870.0,Ukaegbunam,543.0,France,Female,23.0,1.0,0.0,2.0,1.0,1.0,124720.68
+24417,15723339.0,Tretyakova,593.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,136497.28
+24418,15698522.0,Fiorentino,714.0,Germany,Male,45.0,4.0,122486.11,1.0,1.0,0.0,62768.8
+24419,15591550.0,Hs?eh,689.0,France,Male,32.0,6.0,116244.14,2.0,1.0,0.0,103901.35
+24420,15636016.0,Tung,645.0,France,Female,31.0,2.0,0.0,2.0,0.0,0.0,110468.51
+24421,15736250.0,Hsiao,593.0,Spain,Female,26.0,7.0,0.0,2.0,1.0,1.0,164864.67
+24422,15739271.0,Mazzi,704.0,Spain,Female,41.0,3.0,0.0,1.0,1.0,0.0,134901.34
+24423,15732266.0,Yermakov,593.0,France,Male,26.0,8.0,0.0,2.0,0.0,0.0,100862.36
+24424,15788131.0,Hsueh,535.0,Spain,Female,32.0,10.0,0.0,2.0,1.0,1.0,105898.79
+24425,15588928.0,Kao,850.0,France,Female,47.0,9.0,0.0,2.0,1.0,1.0,131083.73
+24426,15744200.0,Trevisani,754.0,France,Male,45.0,7.0,115029.4,1.0,0.0,1.0,10034.77
+24427,15733883.0,Zuyev,682.0,France,Female,32.0,8.0,0.0,2.0,0.0,0.0,102628.98
+24428,15701404.0,Ugochukwu,717.0,France,Male,34.0,2.0,0.0,1.0,1.0,0.0,132673.23
+24429,15687946.0,Nwabugwu,706.0,Germany,Female,40.0,3.0,127864.4,1.0,1.0,0.0,180551.76
+24430,15653937.0,Fiorentino,841.0,France,Female,45.0,1.0,0.0,2.0,1.0,1.0,86787.34
+24431,15595592.0,Yuriev,635.0,Germany,Male,32.0,9.0,102773.2,2.0,1.0,0.0,179833.45
+24432,15690526.0,Onwubiko,711.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,118545.49
+24433,15708714.0,Onyemauchechi,613.0,Spain,Female,39.0,4.0,0.0,2.0,1.0,0.0,110782.88
+24434,15676521.0,White,651.0,Spain,Female,27.0,7.0,0.0,2.0,1.0,0.0,130113.58
+24435,15635078.0,Onyemachukwu,582.0,France,Male,38.0,2.0,0.0,1.0,1.0,1.0,66926.83
+24436,15775430.0,Yermakova,719.0,Spain,Female,37.0,0.0,85304.29,1.0,1.0,0.0,59259.44
+24437,15670584.0,Hsu,545.0,Spain,Male,43.0,9.0,0.0,1.0,1.0,0.0,104533.88
+24438,15621974.0,Onyinyechukwuka,773.0,France,Male,24.0,3.0,0.0,2.0,1.0,1.0,147600.54
+24439,15772892.0,Fomin,730.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,85470.45
+24440,15689661.0,Ch'eng,678.0,Spain,Male,35.0,9.0,0.0,2.0,0.0,1.0,61623.19
+24441,15752508.0,Ch'ien,535.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,151623.71
+24442,15652693.0,Hsiao,707.0,France,Male,30.0,4.0,133656.95,1.0,1.0,1.0,51565.99
+24443,15785519.0,Hsieh,712.0,Germany,Male,48.0,7.0,113715.36,1.0,1.0,1.0,121568.37
+24444,15643908.0,Fanucci,835.0,France,Male,38.0,1.0,0.0,1.0,1.0,0.0,138526.96
+24445,15593365.0,Onwughara,703.0,Germany,Female,48.0,1.0,117419.35,1.0,1.0,0.0,51565.98
+24446,15685829.0,Kaleski,695.0,Spain,Male,32.0,8.0,0.0,1.0,1.0,0.0,85891.02
+24447,15686696.0,Nwachinemelu,646.0,France,Female,42.0,6.0,118897.1,1.0,1.0,0.0,86696.77
+24448,15583480.0,Ch'ang,790.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,177670.57
+24449,15682757.0,T'ien,636.0,France,Female,34.0,0.0,0.0,1.0,1.0,0.0,158750.58
+24450,15763613.0,Genovese,648.0,Spain,Male,27.0,2.0,0.0,2.0,0.0,1.0,69079.85
+24451,15811032.0,Nnachetam,535.0,France,Male,45.0,2.0,0.0,2.0,1.0,1.0,182738.0
+24452,15686964.0,Kumm,558.0,France,Male,49.0,7.0,93569.86,1.0,1.0,0.0,71830.1
+24453,15586029.0,Ritchie,593.0,Germany,Male,35.0,1.0,112013.81,1.0,0.0,0.0,134878.19
+24454,15643908.0,Shih,594.0,France,Female,38.0,10.0,0.0,2.0,1.0,0.0,160968.44
+24455,15642996.0,Yancy,571.0,France,Female,42.0,6.0,0.0,2.0,1.0,1.0,118029.09
+24456,15631681.0,Chukwukere,753.0,France,Female,37.0,10.0,128940.24,1.0,1.0,1.0,121451.21
+24457,15595228.0,Rizzo,704.0,France,Male,38.0,3.0,119658.49,1.0,1.0,0.0,108162.13
+24458,15588854.0,Ch'in,671.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,85901.09
+24459,15701301.0,Maclean,582.0,Spain,Female,37.0,5.0,145081.73,1.0,1.0,1.0,59905.29
+24460,15568562.0,Folliero,586.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,79555.08
+24461,15665790.0,Johnson,731.0,France,Female,28.0,7.0,0.0,2.0,1.0,0.0,124045.65
+24462,15807595.0,Shih,703.0,Germany,Female,51.0,3.0,123345.49,1.0,1.0,1.0,88890.75
+24463,15800856.0,Walker,632.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,133007.34
+24464,15776936.0,Uspenskaya,751.0,France,Male,41.0,1.0,141726.88,1.0,1.0,0.0,60917.24
+24465,15588904.0,Obiora,678.0,Spain,Female,40.0,1.0,0.0,1.0,0.0,0.0,151401.33
+24466,15768340.0,O'Donnell,731.0,Germany,Female,44.0,1.0,152628.98,1.0,1.0,0.0,176925.69
+24467,15741094.0,Udinese,624.0,France,Female,34.0,0.0,151766.71,1.0,1.0,1.0,149550.94
+24468,15802570.0,Nwankwo,834.0,France,Male,28.0,5.0,0.0,2.0,1.0,0.0,67789.99
+24469,15626985.0,Ch'ang,707.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,172459.39
+24470,15662488.0,Teng,620.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,0.0,179883.04
+24471,15584515.0,Eluemuno,686.0,Germany,Female,40.0,9.0,122570.69,3.0,1.0,0.0,122925.4
+24472,15671917.0,Cattaneo,755.0,Spain,Male,45.0,6.0,0.0,2.0,1.0,0.0,99800.87
+24473,15603594.0,Curnow,545.0,France,Female,51.0,1.0,0.0,1.0,0.0,1.0,101491.23
+24474,15593736.0,Hsiung,730.0,France,Male,29.0,8.0,76665.71,1.0,1.0,0.0,136079.64
+24475,15591747.0,Chukwualuka,709.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,179521.24
+24476,15711287.0,Calabresi,710.0,France,Male,39.0,5.0,0.0,2.0,0.0,1.0,85901.09
+24477,15782311.0,Hao,682.0,Spain,Female,44.0,8.0,0.0,2.0,1.0,1.0,106977.8
+24478,15694450.0,Chiawuotu,717.0,France,Female,36.0,4.0,0.0,1.0,1.0,1.0,102908.91
+24479,15711478.0,Nkemakolam,715.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,1.0,178106.34
+24480,15728505.0,Zito,645.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,162889.1
+24481,15571529.0,K'ung,591.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,180736.24
+24482,15740104.0,Tien,634.0,Germany,Male,48.0,9.0,115301.31,1.0,0.0,1.0,120106.5
+24483,15707968.0,Chukwuemeka,619.0,Germany,Female,40.0,0.0,141796.43,2.0,1.0,1.0,93829.17
+24484,15748625.0,Bibi,666.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,111179.84
+24485,15731478.0,K'ung,790.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,132870.15
+24486,15589493.0,Liao,661.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,128815.23
+24487,15768359.0,Chialuka,634.0,Germany,Male,35.0,5.0,125977.81,1.0,0.0,1.0,46824.08
+24488,15624180.0,Kao,709.0,Spain,Male,39.0,9.0,0.0,1.0,1.0,0.0,71926.36
+24489,15796370.0,Hsu,651.0,France,Female,38.0,2.0,138705.02,2.0,1.0,1.0,116973.48
+24490,15782688.0,Onwuamaeze,545.0,France,Female,39.0,5.0,89039.9,2.0,1.0,1.0,155799.86
+24491,15787459.0,Ch'ang,825.0,Germany,Male,42.0,1.0,142765.24,1.0,0.0,0.0,28157.34
+24492,15616700.0,Hsia,665.0,Germany,Male,33.0,6.0,118962.58,1.0,0.0,1.0,86950.72
+24493,15714391.0,H?,672.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,66795.78
+24494,15699430.0,Nnonso,497.0,Spain,Female,34.0,2.0,133616.39,1.0,1.0,0.0,88827.67
+24495,15624293.0,Chiemezie,589.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,105898.79
+24496,15709295.0,Chukwuhaenye,769.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,145544.48
+24497,15588928.0,Shen,697.0,Germany,Male,35.0,5.0,133105.47,2.0,1.0,0.0,35337.31
+24498,15683544.0,Chien,807.0,France,Female,33.0,3.0,0.0,1.0,1.0,1.0,108804.28
+24499,15635277.0,Onwumelu,683.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,85746.52
+24500,15704250.0,Rizzo,678.0,Spain,Male,23.0,5.0,0.0,2.0,0.0,1.0,80178.54
+24501,15682455.0,Ku,646.0,France,Male,38.0,2.0,0.0,1.0,1.0,1.0,141148.21
+24502,15789955.0,Walker,743.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,80425.65
+24503,15693864.0,Iheatu,665.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,0.0,102750.6
+24504,15771299.0,Mazzanti,794.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,121151.46
+24505,15718893.0,Kao,695.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,169921.11
+24506,15581531.0,K?,704.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,151843.54
+24507,15677117.0,Chiu,684.0,France,Male,26.0,10.0,0.0,2.0,0.0,0.0,27472.07
+24508,15753362.0,T'ao,637.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,144880.18
+24509,15676966.0,Nnamutaezinwa,717.0,France,Male,44.0,4.0,0.0,2.0,1.0,0.0,53581.18
+24510,15684461.0,Shao,692.0,France,Male,40.0,10.0,117325.02,1.0,1.0,1.0,93524.22
+24511,15742210.0,Yobanna,537.0,Germany,Male,40.0,6.0,100549.42,1.0,1.0,1.0,160979.66
+24512,15601263.0,McEncroe,591.0,Spain,Male,41.0,9.0,141616.55,1.0,0.0,1.0,155062.02
+24513,15581548.0,Kao,706.0,France,Female,29.0,8.0,0.0,2.0,1.0,1.0,147600.54
+24514,15719479.0,Ch'ien,636.0,France,Female,53.0,9.0,0.0,1.0,1.0,1.0,53784.22
+24515,15802857.0,Uvarov,577.0,Germany,Male,24.0,6.0,130788.71,1.0,1.0,0.0,99503.19
+24516,15667871.0,Ch'ang,611.0,Germany,Male,32.0,8.0,129335.65,2.0,1.0,1.0,76611.33
+24517,15799932.0,Ch'ien,731.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,183399.12
+24518,15795881.0,Ozerova,642.0,Germany,Female,51.0,9.0,116968.91,1.0,1.0,0.0,177065.24
+24519,15803750.0,K?,517.0,Germany,Female,55.0,4.0,148143.84,1.0,1.0,0.0,52795.56
+24520,15775886.0,Yobanna,663.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,40339.9
+24521,15752894.0,Fiorentino,709.0,France,Male,38.0,1.0,112595.85,1.0,1.0,0.0,182855.42
+24522,15623478.0,Ts'ai,756.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,145936.28
+24523,15662901.0,Mao,628.0,France,Female,56.0,1.0,61688.61,1.0,1.0,1.0,127569.2
+24524,15778915.0,Ch'eng,733.0,France,Male,42.0,2.0,0.0,2.0,1.0,1.0,53477.76
+24525,15594576.0,Ozerova,543.0,Germany,Female,34.0,4.0,152462.78,1.0,1.0,1.0,61513.87
+24526,15662641.0,Ts'ui,696.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,91406.62
+24527,15711352.0,Ts'ai,624.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,125451.01
+24528,15777772.0,Sagese,699.0,France,Female,32.0,5.0,107467.35,1.0,1.0,1.0,124964.82
+24529,15754053.0,Whitelegge,645.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,166031.08
+24530,15606861.0,Okwukwe,684.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,26136.13
+24531,15768495.0,Tien,707.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,141069.88
+24532,15769596.0,Hightower,696.0,Germany,Female,54.0,3.0,133656.91,1.0,0.0,0.0,166440.32
+24533,15604020.0,Ts'ai,834.0,Germany,Male,42.0,9.0,114446.84,1.0,0.0,0.0,141756.32
+24534,15792668.0,Bellucci,594.0,France,Female,40.0,5.0,155413.08,1.0,1.0,1.0,179436.6
+24535,15658195.0,Ch'ien,724.0,France,Female,35.0,5.0,0.0,1.0,1.0,1.0,184564.67
+24536,15622017.0,Yevdokimova,705.0,France,Male,42.0,2.0,160680.41,1.0,0.0,0.0,74945.11
+24537,15793881.0,Macleod,670.0,Germany,Male,34.0,5.0,151769.23,1.0,0.0,1.0,9983.88
+24538,15757571.0,Chialuka,727.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,0.0,129562.74
+24539,15691387.0,H?,588.0,France,Male,47.0,5.0,0.0,2.0,1.0,0.0,171482.56
+24540,15725772.0,Y?an,605.0,Spain,Female,45.0,1.0,0.0,1.0,1.0,0.0,133871.83
+24541,15762543.0,Ch'ang,656.0,France,Male,31.0,9.0,128842.56,2.0,1.0,0.0,182266.01
+24542,15794468.0,Teng,683.0,Germany,Male,35.0,10.0,153610.61,1.0,0.0,0.0,92727.41
+24543,15734886.0,Palermo,535.0,France,Female,43.0,10.0,0.0,2.0,1.0,1.0,113931.57
+24544,15614491.0,Folliero,602.0,France,Male,46.0,5.0,0.0,2.0,0.0,0.0,182913.95
+24545,15574859.0,Chukwudi,756.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,126812.63
+24546,15776947.0,Maclean,850.0,Germany,Female,36.0,4.0,145219.3,1.0,0.0,1.0,182055.36
+24547,15718572.0,Ch'iu,675.0,Germany,Male,39.0,6.0,126431.54,2.0,0.0,0.0,100950.48
+24548,15576774.0,Chidumaga,769.0,France,Female,56.0,3.0,0.0,1.0,1.0,0.0,49373.97
+24549,15685576.0,Lai,529.0,France,Male,35.0,1.0,138296.94,1.0,0.0,0.0,117038.96
+24550,15690021.0,Trevisano,579.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,146700.22
+24551,15722409.0,T'ang,678.0,France,Female,29.0,7.0,0.0,2.0,1.0,1.0,177834.79
+24552,15751185.0,Nwabugwu,651.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,0.0,120853.83
+24553,15730657.0,Ch'iu,716.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,111590.35
+24554,15687828.0,Achebe,726.0,France,Female,42.0,6.0,0.0,2.0,1.0,1.0,69079.85
+24555,15665460.0,Yobachi,632.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,167032.49
+24556,15604792.0,Dickson,694.0,Spain,Male,25.0,6.0,0.0,2.0,1.0,0.0,131281.29
+24557,15629885.0,Watson,697.0,France,Female,36.0,6.0,135096.05,2.0,1.0,0.0,141822.5
+24558,15711889.0,Lo Duca,479.0,Germany,Male,60.0,0.0,114948.76,1.0,1.0,0.0,184954.11
+24559,15805671.0,Onochie,681.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,26019.59
+24560,15651737.0,Ch'en,656.0,Spain,Female,46.0,2.0,0.0,1.0,0.0,0.0,103557.93
+24561,15672894.0,Tuan,763.0,France,Female,39.0,7.0,0.0,1.0,1.0,0.0,182822.5
+24562,15583736.0,Okwuoma,584.0,Spain,Male,53.0,2.0,0.0,1.0,1.0,0.0,158973.77
+24563,15757867.0,Chukwudi,633.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,0.0,153036.97
+24564,15749365.0,Pinto,507.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,176984.34
+24565,15642996.0,Smith,556.0,Germany,Female,40.0,10.0,109196.66,2.0,1.0,1.0,45978.39
+24566,15702631.0,K?,644.0,France,Female,38.0,7.0,119496.88,2.0,1.0,1.0,152290.28
+24567,15644501.0,Nnamutaezinwa,710.0,Spain,Female,36.0,4.0,0.0,2.0,1.0,0.0,183487.98
+24568,15761763.0,Genovesi,573.0,Spain,Male,50.0,4.0,95927.04,1.0,1.0,1.0,149648.41
+24569,15640268.0,Echezonachukwu,631.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,153015.42
+24570,15663438.0,Nwokike,712.0,France,Female,46.0,3.0,0.0,2.0,1.0,0.0,167032.94
+24571,15808023.0,Pagnotto,688.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,143960.72
+24572,15781598.0,Lettiere,595.0,Germany,Male,31.0,3.0,152063.59,1.0,1.0,0.0,103557.85
+24573,15757911.0,Chiu,774.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,62944.75
+24574,15616709.0,Ch'ang,659.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,173952.5
+24575,15754929.0,T'ien,686.0,Germany,Male,33.0,6.0,122446.61,2.0,1.0,1.0,78854.57
+24576,15684516.0,Okwuoma,711.0,Germany,Female,51.0,9.0,130933.74,1.0,1.0,0.0,142377.69
+24577,15605447.0,Chukwudi,593.0,Spain,Female,27.0,7.0,0.0,2.0,1.0,0.0,143598.7
+24578,15660390.0,Y?an,678.0,France,Female,23.0,0.0,0.0,2.0,1.0,1.0,119647.6
+24579,15570192.0,Ugochukwu,571.0,France,Female,46.0,6.0,0.0,1.0,1.0,0.0,134417.31
+24580,15684512.0,Hsia,605.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,156618.35
+24581,15802351.0,Calabresi,605.0,Spain,Female,54.0,8.0,0.0,1.0,1.0,0.0,175310.78
+24582,15634262.0,Iweobiegbulam,685.0,Spain,Female,42.0,7.0,82344.84,1.0,0.0,0.0,68701.23
+24583,15694556.0,Goliwe,746.0,Germany,Female,47.0,2.0,121534.43,1.0,1.0,0.0,119087.25
+24584,15599131.0,Chuter,627.0,Germany,Female,49.0,5.0,135937.86,1.0,1.0,0.0,181208.47
+24585,15796787.0,Shih,718.0,France,Female,30.0,8.0,0.0,2.0,1.0,1.0,140937.91
+24586,15653572.0,Yobachi,705.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,106785.5
+24587,15709639.0,Nnamutaezinwa,624.0,France,Male,42.0,2.0,102962.62,1.0,1.0,1.0,51907.72
+24588,15596870.0,Onio,712.0,France,Male,27.0,3.0,99462.8,2.0,0.0,0.0,143953.96
+24589,15724453.0,Chibugo,742.0,France,Male,27.0,2.0,92674.97,1.0,0.0,1.0,56650.47
+24590,15569410.0,Chin,699.0,Germany,Female,32.0,5.0,121215.9,1.0,1.0,0.0,129964.94
+24591,15756056.0,O'Donnell,665.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,61961.57
+24592,15627485.0,Mancini,683.0,France,Male,41.0,8.0,0.0,1.0,1.0,1.0,121151.1
+24593,15691840.0,Obidimkpa,727.0,France,Male,45.0,7.0,0.0,2.0,0.0,1.0,136054.45
+24594,15679991.0,Nnamutaezinwa,721.0,Spain,Female,46.0,2.0,144591.96,1.0,1.0,1.0,176883.42
+24595,15631063.0,Ch'in,678.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,4459.5
+24596,15773876.0,Houghton,730.0,Spain,Female,53.0,9.0,0.0,1.0,1.0,0.0,120770.75
+24597,15583392.0,Nwora,581.0,France,Male,40.0,8.0,0.0,2.0,0.0,0.0,143683.75
+24598,15655590.0,Li,641.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,43821.56
+24599,15664772.0,Ritchie,746.0,Spain,Female,30.0,10.0,0.0,2.0,1.0,0.0,138849.08
+24600,15661380.0,Chukwumaobim,850.0,France,Male,47.0,9.0,0.0,2.0,1.0,0.0,154767.34
+24601,15682778.0,Hsiao,682.0,Germany,Female,41.0,4.0,161592.76,2.0,0.0,0.0,127528.86
+24602,15634281.0,Genovese,705.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,58855.85
+24603,15809777.0,Aksenov,685.0,France,Male,29.0,0.0,0.0,2.0,0.0,0.0,157871.55
+24604,15693732.0,Ponomaryova,684.0,Spain,Male,29.0,8.0,0.0,2.0,0.0,1.0,60552.44
+24605,15795895.0,Young,646.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,57905.05
+24606,15642725.0,Kenechukwu,656.0,France,Female,45.0,4.0,0.0,1.0,0.0,0.0,63795.8
+24607,15747097.0,L?,696.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,160594.85
+24608,15782551.0,K?,718.0,Germany,Male,37.0,7.0,123826.07,2.0,0.0,1.0,109523.88
+24609,15800845.0,Ni,707.0,Germany,Female,24.0,9.0,100116.67,1.0,1.0,0.0,129964.94
+24610,15704795.0,Yobanna,745.0,Spain,Male,30.0,1.0,0.0,2.0,1.0,1.0,60552.44
+24611,15798960.0,Kao,682.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,1.0,98894.64
+24612,15796071.0,Lucchesi,716.0,France,Female,46.0,4.0,0.0,1.0,1.0,1.0,88958.49
+24613,15674724.0,Yegorov,586.0,France,Male,32.0,10.0,108924.33,1.0,1.0,1.0,142400.27
+24614,15650864.0,Onyemachukwu,684.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,160529.71
+24615,15598884.0,H?,589.0,France,Male,32.0,6.0,0.0,1.0,1.0,1.0,127429.56
+24616,15704081.0,Manna,632.0,Germany,Female,46.0,1.0,124577.33,1.0,1.0,0.0,104889.2
+24617,15766417.0,De Luca,481.0,France,Male,33.0,5.0,82228.67,1.0,1.0,0.0,88592.8
+24618,15767064.0,Hsueh,738.0,Germany,Female,46.0,3.0,110527.17,1.0,1.0,0.0,92887.06
+24619,15746333.0,Y?,773.0,France,Male,24.0,1.0,0.0,1.0,0.0,1.0,78955.8
+24620,15638487.0,Ewing,755.0,Spain,Female,44.0,7.0,0.0,2.0,1.0,0.0,189428.75
+24621,15676835.0,Chukwumaobim,646.0,France,Male,46.0,7.0,0.0,1.0,1.0,1.0,156858.2
+24622,15673372.0,Nwankwo,589.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,169291.7
+24623,15781347.0,Nwankwo,693.0,Spain,Male,35.0,5.0,0.0,1.0,1.0,0.0,158071.69
+24624,15608541.0,Bellucci,778.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,177747.72
+24625,15705309.0,Kosisochukwu,658.0,France,Male,37.0,9.0,0.0,3.0,0.0,0.0,95551.36
+24626,15759717.0,Ch'ang,678.0,Germany,Female,39.0,9.0,122949.14,1.0,1.0,1.0,43486.73
+24627,15636562.0,Chukwujamuike,573.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,1.0,122268.33
+24628,15736126.0,Cremonesi,544.0,France,Male,35.0,10.0,0.0,1.0,1.0,0.0,88890.05
+24629,15672822.0,De Luca,667.0,Spain,Female,29.0,5.0,146766.72,1.0,1.0,1.0,143450.61
+24630,15688612.0,Ch'iu,684.0,Germany,Female,43.0,4.0,120599.21,2.0,1.0,0.0,5985.36
+24631,15578219.0,Tien,686.0,France,Female,41.0,9.0,0.0,2.0,1.0,1.0,66518.0
+24632,15681554.0,Hsiao,709.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,135841.35
+24633,15727408.0,Chu,682.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,162820.6
+24634,15656901.0,Ts'ai,729.0,France,Male,42.0,7.0,0.0,1.0,0.0,1.0,143665.44
+24635,15732448.0,Bovee,669.0,Germany,Male,29.0,7.0,94486.47,1.0,1.0,1.0,74870.99
+24636,15687094.0,Trevisano,681.0,Germany,Female,29.0,9.0,113657.83,1.0,0.0,1.0,60906.12
+24637,15643679.0,Okwudiliolisa,727.0,France,Male,45.0,9.0,0.0,2.0,1.0,0.0,100809.99
+24638,15583729.0,Chiu,691.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,123775.15
+24639,15805025.0,Yeh,633.0,France,Male,31.0,7.0,111284.53,1.0,1.0,1.0,57549.65
+24640,15606914.0,Walker,748.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,78899.22
+24641,15758449.0,Y?,730.0,Spain,Male,49.0,3.0,123826.07,1.0,1.0,0.0,151859.98
+24642,15643903.0,Genovese,637.0,France,Male,33.0,8.0,0.0,3.0,1.0,1.0,100127.71
+24643,15752650.0,H?,597.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,82565.01
+24644,15609735.0,Chinedum,669.0,Germany,Male,36.0,1.0,88902.21,1.0,1.0,1.0,123866.43
+24645,15751774.0,Palerma,646.0,France,Female,40.0,9.0,145974.79,1.0,0.0,1.0,84760.32
+24646,15642816.0,Nwabugwu,743.0,France,Female,40.0,5.0,0.0,1.0,0.0,0.0,56585.2
+24647,15704014.0,Chizuoke,639.0,Germany,Male,37.0,5.0,120530.13,1.0,1.0,1.0,119400.08
+24648,15690134.0,Folliero,521.0,France,Male,59.0,4.0,118680.57,1.0,1.0,1.0,162022.48
+24649,15756425.0,Nnaife,592.0,France,Female,28.0,6.0,0.0,2.0,1.0,0.0,142223.94
+24650,15727177.0,Chinwenma,633.0,Germany,Male,37.0,3.0,95984.69,2.0,0.0,1.0,161559.12
+24651,15791877.0,Ch'ien,676.0,Spain,Male,35.0,9.0,153711.57,1.0,0.0,1.0,183494.87
+24652,15656592.0,Okechukwu,554.0,France,Male,45.0,7.0,0.0,2.0,0.0,0.0,176903.35
+24653,15660608.0,Lo Duca,731.0,Germany,Male,35.0,10.0,118022.06,1.0,1.0,0.0,167538.66
+24654,15636105.0,Chien,569.0,Germany,Male,56.0,5.0,140418.81,1.0,0.0,0.0,124592.84
+24655,15778947.0,Onyemachukwu,619.0,Germany,Male,32.0,6.0,114027.7,2.0,1.0,1.0,91346.03
+24656,15792668.0,Chiedozie,667.0,France,Male,34.0,1.0,0.0,2.0,0.0,0.0,96615.23
+24657,15735784.0,Onyekachi,568.0,France,Male,49.0,4.0,0.0,1.0,0.0,0.0,53353.37
+24658,15698522.0,Lucciano,593.0,France,Female,42.0,2.0,71996.09,1.0,1.0,0.0,129333.69
+24659,15686983.0,Piazza,628.0,France,Male,29.0,3.0,0.0,2.0,0.0,1.0,158054.49
+24660,15616380.0,Horsley,679.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,141756.32
+24661,15808971.0,Fanucci,493.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,142513.5
+24662,15629117.0,Chukwuebuka,598.0,France,Female,54.0,10.0,0.0,1.0,0.0,1.0,48606.92
+24663,15782688.0,Yobanna,751.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,154844.22
+24664,15777892.0,Rizzo,668.0,France,Male,33.0,8.0,71264.02,1.0,0.0,0.0,88846.92
+24665,15657535.0,Ch'ang,600.0,France,Male,34.0,8.0,101486.96,2.0,1.0,1.0,62390.59
+24666,15717191.0,DeRose,729.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,126143.26
+24667,15695034.0,Chukwufumnanya,774.0,France,Female,32.0,5.0,132730.17,1.0,0.0,0.0,8991.17
+24668,15783859.0,Onwuamaeze,738.0,France,Female,55.0,4.0,0.0,1.0,1.0,1.0,81898.81
+24669,15570141.0,Yevdokimova,584.0,France,Male,41.0,7.0,0.0,1.0,1.0,0.0,140947.05
+24670,15651001.0,Nnonso,809.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,141364.27
+24671,15579112.0,Milano,635.0,France,Female,54.0,5.0,0.0,1.0,1.0,0.0,171045.65
+24672,15761158.0,Chinweuba,590.0,France,Male,30.0,8.0,0.0,1.0,0.0,1.0,92712.97
+24673,15714493.0,Nwachukwu,731.0,Spain,Male,27.0,9.0,0.0,2.0,1.0,0.0,112554.68
+24674,15592379.0,Onyekaozulu,581.0,Germany,Male,48.0,1.0,155973.88,2.0,1.0,1.0,109523.88
+24675,15711889.0,Mazzanti,697.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,161076.92
+24676,15788659.0,Chukwumaobim,678.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,0.0,146636.44
+24677,15585888.0,Nucci,610.0,Germany,Female,45.0,3.0,163894.35,1.0,0.0,0.0,27330.27
+24678,15746422.0,Trevisani,600.0,Germany,Male,60.0,7.0,101633.03,1.0,1.0,0.0,27468.72
+24679,15793723.0,Ebelegbulam,713.0,France,Male,52.0,3.0,0.0,2.0,1.0,1.0,51907.72
+24680,15652289.0,H?,739.0,Germany,Male,49.0,8.0,149940.04,1.0,1.0,0.0,115115.32
+24681,15777901.0,Mazzanti,638.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,53793.1
+24682,15705034.0,Eluemuno,663.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,127587.22
+24683,15663885.0,Ts'ai,678.0,France,Female,43.0,4.0,0.0,2.0,1.0,0.0,99398.36
+24684,15617348.0,Ch'ang,590.0,France,Female,51.0,6.0,0.0,1.0,1.0,0.0,129149.06
+24685,15646764.0,Cremonesi,718.0,France,Female,43.0,7.0,0.0,2.0,1.0,0.0,63736.17
+24686,15671610.0,Li Fonti,605.0,France,Male,48.0,3.0,0.0,2.0,0.0,1.0,116547.31
+24687,15804844.0,Fiorentini,748.0,Spain,Male,32.0,7.0,0.0,1.0,1.0,1.0,88838.14
+24688,15651581.0,Iadanza,479.0,France,Male,46.0,1.0,0.0,1.0,1.0,1.0,75825.57
+24689,15726791.0,Ch'in,579.0,France,Male,46.0,6.0,0.0,1.0,1.0,1.0,103934.22
+24690,15756901.0,Watson,709.0,France,Male,31.0,9.0,0.0,2.0,0.0,0.0,79723.78
+24691,15642098.0,Chiang,576.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,163550.58
+24692,15815660.0,Chiu,757.0,Spain,Female,50.0,3.0,171982.95,1.0,0.0,0.0,167973.63
+24693,15731553.0,Tuan,662.0,France,Male,42.0,8.0,121365.39,1.0,1.0,1.0,148087.88
+24694,15615184.0,Mazzanti,705.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,93360.35
+24695,15750141.0,Nwachukwu,758.0,France,Female,29.0,7.0,123870.07,1.0,1.0,1.0,84760.32
+24696,15603594.0,Ulyanov,663.0,Germany,Male,42.0,10.0,145984.92,1.0,1.0,1.0,174729.3
+24697,15773868.0,K?,748.0,Spain,Male,22.0,5.0,0.0,2.0,1.0,1.0,66586.56
+24698,15643523.0,T'ang,628.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,57553.02
+24699,15662483.0,Ebelechukwu,585.0,France,Female,44.0,7.0,136576.02,1.0,1.0,0.0,146056.29
+24700,15648766.0,Pethard,757.0,France,Female,31.0,2.0,178713.98,1.0,1.0,1.0,94440.45
+24701,15782899.0,Scott,695.0,France,Female,33.0,9.0,0.0,2.0,1.0,1.0,126812.63
+24702,15735295.0,Onwuamaeze,646.0,France,Male,35.0,7.0,0.0,1.0,1.0,1.0,95794.98
+24703,15727391.0,Ts'ui,622.0,France,Female,38.0,3.0,0.0,2.0,0.0,1.0,18082.06
+24704,15601594.0,Lucciano,615.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,174431.01
+24705,15696812.0,Onyekachukwu,711.0,Germany,Female,31.0,9.0,127763.25,1.0,1.0,0.0,172363.41
+24706,15658946.0,Ts'ui,651.0,Spain,Female,39.0,6.0,0.0,1.0,1.0,1.0,141031.08
+24707,15792993.0,Onuora,639.0,Germany,Male,63.0,8.0,114370.91,1.0,1.0,1.0,174738.2
+24708,15671534.0,Chibugo,498.0,Spain,Male,31.0,6.0,0.0,2.0,0.0,0.0,168544.85
+24709,15690939.0,Chiemenam,774.0,Germany,Male,29.0,4.0,122552.51,1.0,0.0,0.0,64443.75
+24710,15737104.0,Li,616.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,178667.7
+24711,15592761.0,Lo,748.0,Spain,Male,42.0,2.0,0.0,2.0,0.0,1.0,145796.49
+24712,15671779.0,Mancini,617.0,France,Female,46.0,3.0,174400.36,2.0,1.0,0.0,73334.43
+24713,15648967.0,Lucchesi,778.0,France,Male,37.0,5.0,0.0,1.0,1.0,1.0,72678.75
+24714,15623357.0,Lucciano,581.0,Spain,Male,38.0,2.0,0.0,1.0,0.0,1.0,159800.16
+24715,15626491.0,Bancroft,714.0,France,Male,42.0,2.0,0.0,2.0,0.0,0.0,143913.32
+24716,15661036.0,Hao,760.0,France,Female,62.0,8.0,165947.99,1.0,1.0,1.0,108876.75
+24717,15626485.0,Tsai,682.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,141533.19
+24718,15714240.0,Ch'eng,588.0,Spain,Male,26.0,7.0,0.0,2.0,0.0,1.0,139410.08
+24719,15716706.0,Smith,636.0,France,Female,37.0,9.0,0.0,1.0,1.0,1.0,145789.71
+24720,15772798.0,Clements,559.0,Germany,Female,34.0,8.0,124525.52,1.0,1.0,1.0,152843.53
+24721,15812356.0,Teng,705.0,Germany,Female,35.0,9.0,133467.77,1.0,0.0,0.0,145089.64
+24722,15726234.0,Nwachukwu,733.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,55893.37
+24723,15625080.0,Chukwudi,686.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,198193.75
+24724,15733476.0,Yermakova,565.0,France,Male,33.0,5.0,116537.6,1.0,0.0,0.0,142911.99
+24725,15585867.0,Ugoji,683.0,France,Female,44.0,9.0,0.0,1.0,1.0,1.0,130664.79
+24726,15624343.0,Chiu,706.0,France,Female,33.0,7.0,0.0,1.0,1.0,0.0,142963.29
+24727,15655274.0,Biryukov,714.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,147668.64
+24728,15571244.0,McIntyre,648.0,Germany,Female,48.0,3.0,133903.12,1.0,1.0,1.0,177852.57
+24729,15631162.0,Hs?,597.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,62449.07
+24730,15607560.0,T'ien,679.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,133051.64
+24731,15593949.0,Bancks,602.0,Spain,Male,35.0,4.0,96173.17,1.0,1.0,0.0,62002.04
+24732,15717995.0,Yobachi,545.0,Spain,Female,29.0,0.0,119919.12,1.0,1.0,1.0,110454.46
+24733,15758775.0,Johnstone,562.0,Spain,Male,23.0,1.0,156864.2,1.0,1.0,0.0,134593.23
+24734,15746664.0,Groom,582.0,France,Female,45.0,6.0,0.0,2.0,0.0,0.0,107875.5
+24735,15783025.0,Macleod,676.0,France,Female,24.0,7.0,0.0,2.0,1.0,1.0,119927.81
+24736,15694946.0,Nnaife,598.0,France,Female,32.0,8.0,0.0,1.0,0.0,0.0,120512.09
+24737,15806945.0,Onio,741.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,1.0,36108.5
+24738,15585580.0,Iweobiegbunam,594.0,France,Male,36.0,1.0,0.0,1.0,1.0,0.0,184524.74
+24739,15759387.0,White,712.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,95009.6
+24740,15745375.0,Ebelegbulam,724.0,France,Female,31.0,8.0,0.0,1.0,1.0,0.0,183872.88
+24741,15651943.0,Y?,652.0,Germany,Female,38.0,3.0,144895.05,2.0,0.0,1.0,128875.86
+24742,15747174.0,Chukwumaobim,757.0,France,Male,47.0,3.0,0.0,1.0,0.0,1.0,126477.41
+24743,15756875.0,Onwuamaeze,648.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,67769.94
+24744,15602841.0,Manna,748.0,Germany,Male,32.0,1.0,102800.65,1.0,1.0,1.0,131283.6
+24745,15696900.0,McNeill,639.0,France,Male,31.0,5.0,0.0,1.0,1.0,0.0,118959.64
+24746,15658890.0,L?,718.0,Germany,Female,24.0,7.0,123861.5,2.0,1.0,1.0,92865.92
+24747,15730038.0,Ch'eng,774.0,France,Male,55.0,9.0,0.0,2.0,1.0,1.0,115800.1
+24748,15702968.0,Lo Duca,651.0,Germany,Female,42.0,6.0,125724.95,1.0,1.0,1.0,125909.95
+24749,15639368.0,Mancini,628.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,88728.47
+24750,15589401.0,Ch'in,713.0,France,Male,39.0,7.0,96659.64,1.0,1.0,0.0,129870.93
+24751,15591036.0,Chukwuemeka,641.0,Spain,Male,25.0,6.0,90218.9,1.0,1.0,0.0,92984.78
+24752,15795586.0,Ch'iu,663.0,France,Female,34.0,8.0,0.0,2.0,0.0,1.0,67840.81
+24753,15769312.0,Pisani,615.0,France,Female,43.0,3.0,0.0,2.0,1.0,0.0,141364.62
+24754,15616152.0,Chukwudi,748.0,Germany,Female,42.0,4.0,97960.03,2.0,0.0,0.0,132979.43
+24755,15677288.0,Nkemdirim,577.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,49831.7
+24756,15782360.0,Olisaemeka,682.0,France,Male,23.0,7.0,0.0,2.0,1.0,1.0,106977.53
+24757,15702380.0,Genovese,663.0,France,Male,50.0,8.0,0.0,2.0,1.0,0.0,181177.9
+24758,15752312.0,Folliero,676.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,158580.05
+24759,15610972.0,Smith,714.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,1.0,10963.04
+24760,15795519.0,Hsiao,545.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,1.0,171070.44
+24761,15693817.0,Y?an,682.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,185561.89
+24762,15593973.0,Oluchi,697.0,Spain,Male,39.0,5.0,0.0,1.0,1.0,0.0,172668.39
+24763,15798532.0,Chukwuemeka,573.0,France,Female,45.0,2.0,0.0,2.0,1.0,1.0,88890.05
+24764,15587913.0,Ojiofor,595.0,France,Male,37.0,2.0,0.0,2.0,0.0,1.0,92357.21
+24765,15570912.0,Onyekachukwu,662.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,101728.46
+24766,15674840.0,Ch'ang,569.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,115950.96
+24767,15787196.0,Genovese,691.0,France,Male,34.0,3.0,97338.19,1.0,0.0,0.0,107825.08
+24768,15757911.0,Onio,694.0,Germany,Male,22.0,2.0,101579.28,2.0,1.0,0.0,76640.29
+24769,15652626.0,Yefremova,583.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,102681.32
+24770,15654489.0,Trevisani,707.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,71420.16
+24771,15653715.0,Hazon,571.0,France,Male,34.0,2.0,0.0,2.0,0.0,1.0,169920.92
+24772,15777045.0,Kolesnikov,645.0,France,Female,27.0,7.0,0.0,1.0,1.0,1.0,151822.61
+24773,15701166.0,Iloerika,705.0,France,Male,29.0,5.0,127649.64,1.0,1.0,0.0,47675.36
+24774,15598883.0,Rizzo,684.0,Germany,Female,27.0,3.0,135553.5,1.0,1.0,1.0,151902.78
+24775,15664503.0,Hs?,579.0,France,Male,31.0,3.0,0.0,2.0,0.0,1.0,134790.74
+24776,15760865.0,Iadanza,589.0,Germany,Male,33.0,9.0,107467.56,1.0,1.0,0.0,52319.26
+24777,15598046.0,T'ang,705.0,Germany,Female,33.0,2.0,121541.56,2.0,1.0,1.0,162668.33
+24778,15775891.0,Hsing,558.0,Germany,Female,40.0,5.0,131834.8,1.0,1.0,0.0,163746.09
+24779,15785541.0,Ch'in,752.0,Spain,Female,35.0,1.0,0.0,2.0,1.0,0.0,153080.4
+24780,15705512.0,Tsao,681.0,France,Female,34.0,9.0,0.0,2.0,0.0,0.0,82870.75
+24781,15576928.0,Ts'ui,679.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,93716.38
+24782,15730059.0,H?,439.0,Germany,Female,44.0,3.0,135071.12,1.0,0.0,0.0,177480.54
+24783,15625759.0,Teng,562.0,France,Male,39.0,7.0,137262.71,1.0,0.0,0.0,94949.84
+24784,15759140.0,Onyemachukwu,709.0,Germany,Female,37.0,9.0,116711.06,1.0,0.0,1.0,131710.59
+24785,15652266.0,Trevisani,731.0,France,Female,48.0,3.0,0.0,2.0,1.0,0.0,66462.79
+24786,15739248.0,Yermolayev,681.0,Germany,Male,29.0,5.0,119782.72,1.0,0.0,0.0,97628.08
+24787,15801277.0,Y?an,631.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,90928.02
+24788,15597945.0,Obidimkpa,652.0,Spain,Female,46.0,8.0,0.0,1.0,1.0,1.0,122221.71
+24789,15687946.0,Tsai,632.0,Germany,Female,32.0,6.0,160566.11,2.0,1.0,0.0,77469.38
+24790,15658057.0,Pirozzi,710.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,1.0,65816.53
+24791,15609083.0,Pinto,596.0,Spain,Female,28.0,6.0,0.0,1.0,1.0,0.0,12905.89
+24792,15703778.0,Ting,585.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,98309.27
+24793,15662483.0,Hsueh,559.0,Germany,Male,48.0,7.0,131765.72,2.0,1.0,0.0,125986.18
+24794,15796414.0,Onyeorulu,590.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,79278.22
+24795,15756472.0,Hsiao,661.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,96961.0
+24796,15593501.0,Ch'ien,730.0,Germany,Female,58.0,9.0,111096.91,1.0,0.0,0.0,43486.73
+24797,15706464.0,Onyemauchechukwu,605.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,1.0,73066.72
+24798,15697546.0,Hsueh,674.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,186612.47
+24799,15806901.0,Lo,593.0,Spain,Female,42.0,1.0,0.0,2.0,1.0,1.0,138526.96
+24800,15598883.0,Hsieh,710.0,Germany,Male,40.0,8.0,127772.1,2.0,1.0,1.0,103557.85
+24801,15746076.0,Chukwudi,651.0,Spain,Female,27.0,2.0,0.0,2.0,1.0,1.0,109626.18
+24802,15782598.0,Palerma,590.0,Spain,Female,41.0,8.0,0.0,1.0,1.0,0.0,158918.02
+24803,15781702.0,Ch'in,699.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,1.0,166495.2
+24804,15761348.0,Chigozie,748.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,26752.56
+24805,15691387.0,Aitken,773.0,France,Male,29.0,9.0,0.0,1.0,1.0,1.0,57896.27
+24806,15604551.0,Ch'en,748.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,122949.71
+24807,15747222.0,Babbage,676.0,Germany,Female,27.0,9.0,140576.11,2.0,0.0,0.0,90591.15
+24808,15697480.0,Chia,635.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,173576.71
+24809,15589475.0,Ts'ui,544.0,Germany,Female,31.0,3.0,101470.29,1.0,1.0,0.0,43496.36
+24810,15727281.0,Wang,493.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,164064.44
+24811,15642098.0,Lombardo,559.0,France,Female,28.0,0.0,0.0,2.0,0.0,0.0,84927.42
+24812,15677184.0,Fan,706.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,107839.03
+24813,15734886.0,Iqbal,521.0,Spain,Female,37.0,7.0,132005.77,1.0,1.0,0.0,120830.73
+24814,15631756.0,Nwokeocha,571.0,France,Male,33.0,5.0,0.0,1.0,1.0,1.0,73881.68
+24815,15763515.0,Y?an,661.0,Spain,Male,36.0,3.0,0.0,2.0,0.0,1.0,79495.01
+24816,15577771.0,Trevisano,598.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,52301.15
+24817,15759034.0,Mazzanti,757.0,France,Male,42.0,4.0,0.0,2.0,0.0,0.0,62851.6
+24818,15678412.0,Tuan,652.0,Germany,Male,33.0,8.0,122384.22,2.0,1.0,1.0,133059.15
+24819,15803790.0,Ni,593.0,France,Female,44.0,8.0,57784.22,1.0,1.0,1.0,137028.51
+24820,15666055.0,McIntyre,586.0,Spain,Male,48.0,3.0,117153.0,1.0,1.0,0.0,112973.23
+24821,15809847.0,Efremov,531.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,95611.47
+24822,15716609.0,Scott,715.0,France,Male,67.0,3.0,0.0,2.0,1.0,1.0,95583.45
+24823,15584533.0,K?,773.0,France,Male,34.0,5.0,0.0,1.0,1.0,1.0,147090.9
+24824,15759480.0,Chikelu,587.0,Germany,Male,33.0,7.0,141078.57,1.0,1.0,1.0,112979.6
+24825,15676895.0,Kline,581.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,96429.29
+24826,15750447.0,Nwebube,588.0,France,Female,40.0,9.0,0.0,1.0,1.0,0.0,180134.88
+24827,15737173.0,Chu,678.0,France,Male,33.0,6.0,0.0,2.0,0.0,0.0,149599.71
+24828,15625759.0,Obialo,628.0,Germany,Male,38.0,2.0,150777.2,2.0,1.0,1.0,52709.55
+24829,15609562.0,Chialuka,596.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,107499.8
+24830,15592761.0,Chukwuebuka,535.0,France,Female,34.0,9.0,0.0,1.0,1.0,0.0,171706.66
+24831,15752694.0,Onio,614.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,1.0,183487.98
+24832,15713374.0,Chukwuemeka,709.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,109626.13
+24833,15602841.0,K'ung,743.0,France,Male,34.0,1.0,0.0,1.0,0.0,1.0,124626.83
+24834,15658935.0,Onwumelu,579.0,France,Male,36.0,8.0,131406.56,1.0,1.0,1.0,158071.69
+24835,15574783.0,Ugochukwu,718.0,France,Male,47.0,2.0,0.0,2.0,1.0,1.0,141453.62
+24836,15628674.0,Trevisani,594.0,France,Female,36.0,7.0,127543.11,1.0,1.0,1.0,94719.66
+24837,15665629.0,Pirozzi,469.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,72350.17
+24838,15629078.0,Y?,597.0,Spain,Male,46.0,4.0,0.0,1.0,1.0,1.0,153400.24
+24839,15682868.0,Lucciano,568.0,Germany,Male,38.0,5.0,113899.92,1.0,0.0,1.0,199505.53
+24840,15588535.0,Ch'en,706.0,France,Female,55.0,5.0,136525.98,1.0,0.0,0.0,93777.53
+24841,15787884.0,Ch'ang,781.0,Germany,Male,58.0,4.0,84474.62,1.0,1.0,0.0,108675.01
+24842,15779059.0,Hsieh,586.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,121866.87
+24843,15689786.0,Chukwubuikem,753.0,Germany,Male,28.0,9.0,142388.61,1.0,1.0,0.0,38596.34
+24844,15793070.0,Smith,727.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,171345.02
+24845,15574886.0,Ch'iu,512.0,France,Male,26.0,5.0,0.0,2.0,0.0,1.0,107815.31
+24846,15666095.0,L?,564.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,0.0,154486.01
+24847,15648725.0,Hsieh,589.0,France,Male,52.0,6.0,0.0,1.0,1.0,1.0,121365.45
+24848,15617603.0,Ch'ien,581.0,Spain,Female,34.0,3.0,0.0,2.0,0.0,1.0,97734.3
+24849,15717489.0,Hs?eh,551.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,170518.17
+24850,15596962.0,Genovese,712.0,France,Female,30.0,2.0,0.0,2.0,0.0,0.0,93478.96
+24851,15793491.0,Onyemauchechi,718.0,Germany,Female,40.0,5.0,105736.32,1.0,1.0,1.0,180622.44
+24852,15589428.0,McIntosh,667.0,Germany,Male,37.0,10.0,139859.54,1.0,1.0,1.0,118959.64
+24853,15690829.0,Nnamdi,678.0,Spain,Male,42.0,2.0,0.0,2.0,1.0,1.0,139911.58
+24854,15604782.0,Chiu,619.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,1.0,121789.3
+24855,15711889.0,Chiazagomekpere,634.0,France,Female,54.0,4.0,0.0,1.0,1.0,0.0,1375.46
+24856,15750547.0,Yobachukwu,654.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,95655.16
+24857,15643330.0,Chijindum,678.0,Spain,Male,40.0,9.0,0.0,2.0,1.0,1.0,141454.79
+24858,15676835.0,Y?an,521.0,Spain,Male,28.0,5.0,0.0,1.0,1.0,1.0,77550.18
+24859,15813067.0,T'ang,717.0,France,Female,33.0,2.0,122314.5,1.0,1.0,0.0,143301.49
+24860,15697360.0,Walker,646.0,Germany,Male,38.0,3.0,141078.37,1.0,1.0,1.0,143973.38
+24861,15795519.0,Udobata,589.0,Spain,Male,41.0,5.0,131394.39,1.0,1.0,0.0,169920.92
+24862,15593601.0,Ch'iu,703.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,1.0,171707.93
+24863,15605698.0,Hsiung,667.0,Germany,Female,41.0,5.0,139874.79,1.0,1.0,1.0,54290.62
+24864,15800366.0,Milne,595.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,119374.74
+24865,15658889.0,McNeil,615.0,France,Male,38.0,9.0,0.0,1.0,1.0,0.0,126517.46
+24866,15569248.0,Genovese,611.0,Spain,Male,55.0,3.0,0.0,3.0,1.0,0.0,155700.15
+24867,15793881.0,Ch'in,605.0,Germany,Male,51.0,2.0,91285.17,1.0,1.0,1.0,104353.26
+24868,15703588.0,Ch'ien,684.0,France,Female,31.0,2.0,185513.67,1.0,1.0,1.0,153662.88
+24869,15679048.0,Chukwumaobim,584.0,France,Male,46.0,5.0,0.0,2.0,0.0,1.0,60920.03
+24870,15673367.0,Chidi,671.0,Germany,Male,52.0,6.0,161598.98,1.0,1.0,1.0,44756.25
+24871,15709474.0,Nwachinemelu,682.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,55775.84
+24872,15793434.0,Yobanna,706.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,96444.88
+24873,15705082.0,Lu,762.0,Spain,Male,38.0,0.0,0.0,2.0,0.0,0.0,140763.17
+24874,15588580.0,Chukwualuka,744.0,France,Male,27.0,2.0,0.0,1.0,1.0,0.0,160532.88
+24875,15670562.0,Ch'ien,711.0,France,Male,48.0,9.0,0.0,2.0,1.0,1.0,96823.32
+24876,15673971.0,Nnachetam,589.0,Spain,Male,37.0,6.0,178058.06,1.0,0.0,0.0,171770.58
+24877,15641114.0,De Salis,749.0,Spain,Male,47.0,0.0,0.0,2.0,0.0,0.0,92621.9
+24878,15749905.0,Obialo,669.0,Germany,Male,28.0,2.0,74596.15,2.0,0.0,0.0,3560.0
+24879,15592773.0,Hsiao,589.0,Germany,Male,25.0,6.0,123871.3,1.0,0.0,0.0,169920.92
+24880,15693376.0,Yeh,546.0,Spain,Male,58.0,1.0,0.0,2.0,1.0,1.0,111605.9
+24881,15802060.0,Hsia,710.0,Germany,Female,42.0,5.0,153611.83,2.0,1.0,1.0,163587.9
+24882,15787830.0,Cattaneo,806.0,Germany,Female,36.0,1.0,107569.93,2.0,1.0,1.0,93629.66
+24883,15698028.0,Lai,583.0,Germany,Female,41.0,2.0,120721.52,1.0,1.0,0.0,176450.19
+24884,15655007.0,Chien,537.0,France,Male,34.0,6.0,0.0,1.0,1.0,0.0,79734.23
+24885,15582910.0,Trentino,616.0,Germany,Male,37.0,7.0,128966.13,1.0,1.0,1.0,70810.8
+24886,15669987.0,Li Fonti,763.0,France,Female,27.0,4.0,0.0,1.0,0.0,1.0,86044.98
+24887,15680804.0,Palerma,649.0,France,Female,26.0,7.0,0.0,2.0,1.0,0.0,103570.8
+24888,15571586.0,Rios,645.0,France,Female,32.0,1.0,0.0,2.0,0.0,0.0,179634.69
+24889,15796721.0,Lo,594.0,France,Female,49.0,8.0,0.0,1.0,1.0,1.0,175843.68
+24890,15626452.0,Hsing,663.0,France,Female,40.0,3.0,0.0,2.0,0.0,1.0,121592.5
+24891,15752312.0,Chidiebere,639.0,Germany,Male,39.0,0.0,102290.81,2.0,1.0,1.0,177614.17
+24892,15626983.0,Udegbulam,632.0,France,Female,35.0,5.0,0.0,2.0,0.0,1.0,155711.64
+24893,15681476.0,Efremov,710.0,France,Female,39.0,7.0,155047.56,1.0,1.0,0.0,166588.76
+24894,15594796.0,Fancher,678.0,Spain,Male,35.0,9.0,123070.89,1.0,0.0,1.0,141872.05
+24895,15596671.0,Tien,683.0,Germany,Female,57.0,3.0,136948.09,1.0,1.0,0.0,73679.29
+24896,15585867.0,Chibueze,648.0,Germany,Female,58.0,4.0,122570.69,1.0,1.0,0.0,83093.25
+24897,15633574.0,Maclean,526.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,82565.01
+24898,15691543.0,Shih,667.0,Germany,Female,51.0,9.0,138341.51,1.0,0.0,0.0,100433.83
+24899,15750765.0,Nkemdilim,633.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,108008.65
+24900,15803790.0,Chimezie,694.0,France,Female,42.0,2.0,0.0,2.0,1.0,0.0,117832.88
+24901,15815552.0,Iadanza,624.0,France,Male,33.0,7.0,141819.02,1.0,1.0,1.0,60721.4
+24902,15720644.0,Ch'ang,628.0,France,Male,44.0,3.0,0.0,1.0,0.0,1.0,135077.01
+24903,15643496.0,Y?,645.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,16081.62
+24904,15569430.0,Brient,752.0,France,Male,31.0,2.0,140187.1,1.0,1.0,1.0,94888.6
+24905,15579915.0,Chidimma,599.0,Germany,Male,51.0,3.0,105473.74,1.0,1.0,1.0,72496.05
+24906,15716609.0,Chiu,540.0,France,Male,38.0,6.0,123866.22,1.0,1.0,0.0,88721.84
+24907,15684364.0,T'an,603.0,Germany,Male,45.0,9.0,125879.73,1.0,1.0,1.0,134600.94
+24908,15809654.0,Chukwuemeka,561.0,Spain,Male,47.0,1.0,104508.1,1.0,1.0,1.0,88721.84
+24909,15793883.0,Chiu,587.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,55313.51
+24910,15579969.0,Lucchese,633.0,France,Male,31.0,2.0,0.0,2.0,0.0,0.0,181224.24
+24911,15791040.0,Nwankwo,756.0,France,Female,30.0,7.0,0.0,2.0,1.0,1.0,73679.59
+24912,15738487.0,Manna,583.0,France,Male,39.0,5.0,120911.75,1.0,0.0,1.0,176450.19
+24913,15637354.0,Tikhonov,751.0,Spain,Male,42.0,2.0,0.0,2.0,1.0,1.0,172363.52
+24914,15790594.0,Ni,562.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,132673.23
+24915,15650437.0,Pinto,656.0,France,Male,74.0,5.0,0.0,2.0,1.0,1.0,187985.85
+24916,15734461.0,Onio,524.0,Germany,Female,55.0,9.0,140197.71,1.0,1.0,1.0,118522.73
+24917,15659710.0,Smith,526.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,53581.19
+24918,15585853.0,O'Donnell,632.0,Spain,Male,54.0,9.0,0.0,1.0,1.0,1.0,131173.9
+24919,15575060.0,Y?,638.0,France,Female,30.0,5.0,0.0,2.0,1.0,1.0,171706.66
+24920,15742971.0,Palerma,694.0,Germany,Male,34.0,7.0,123974.96,1.0,0.0,1.0,176713.47
+24921,15590177.0,Hsia,580.0,France,Female,45.0,5.0,0.0,1.0,1.0,1.0,9087.81
+24922,15676937.0,Nwachukwu,596.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,71926.28
+24923,15601383.0,Ch'iu,607.0,France,Male,28.0,8.0,0.0,1.0,1.0,1.0,111241.37
+24924,15697997.0,Frolov,712.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,64907.48
+24925,15688865.0,Yegorova,602.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,38315.04
+24926,15705313.0,Ch'en,679.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,89048.46
+24927,15771535.0,Macfarlan,733.0,Spain,Male,40.0,5.0,115378.94,1.0,0.0,1.0,87537.32
+24928,15574886.0,Okwudilichukwu,535.0,Spain,Male,46.0,8.0,0.0,2.0,1.0,1.0,105898.79
+24929,15702300.0,Chidozie,607.0,France,Female,26.0,1.0,120867.18,1.0,0.0,0.0,177737.03
+24930,15592078.0,Onyekaozulu,627.0,Germany,Female,40.0,3.0,174852.89,1.0,1.0,1.0,52796.31
+24931,15682355.0,Hixson,813.0,Spain,Female,50.0,7.0,0.0,1.0,1.0,0.0,148087.62
+24932,15700696.0,Pirozzi,595.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,127875.1
+24933,15746995.0,Pagnotto,743.0,France,Female,43.0,0.0,0.0,2.0,1.0,1.0,164038.07
+24934,15767757.0,Chuang,567.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,122588.33
+24935,15644765.0,Zetticci,642.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,137932.99
+24936,15611961.0,Udinese,710.0,France,Male,39.0,5.0,98431.37,1.0,0.0,0.0,60533.96
+24937,15751549.0,Chukwufumnanya,704.0,Germany,Female,45.0,4.0,144889.18,1.0,0.0,0.0,66463.62
+24938,15709621.0,Lo,749.0,Germany,Female,57.0,8.0,101514.89,1.0,1.0,0.0,49872.33
+24939,15696054.0,Ts'ui,678.0,France,Male,36.0,3.0,132287.92,1.0,1.0,0.0,88837.11
+24940,15794870.0,Chukwufumnanya,506.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,105965.05
+24941,15581036.0,Whitehead,706.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,78854.58
+24942,15798916.0,Nwachinemelu,730.0,France,Male,30.0,5.0,0.0,2.0,1.0,0.0,167636.15
+24943,15628205.0,Ch'ang,738.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,87067.73
+24944,15593973.0,White,727.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,71830.1
+24945,15573326.0,Chiemenam,721.0,France,Female,32.0,3.0,0.0,2.0,1.0,1.0,88992.05
+24946,15772363.0,Toscani,695.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,133535.29
+24947,15702801.0,Smith,544.0,France,Male,34.0,3.0,141806.0,1.0,0.0,1.0,164074.89
+24948,15806922.0,Lu,651.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,0.0,149946.43
+24949,15771750.0,Tien,632.0,France,Female,36.0,1.0,0.0,2.0,1.0,1.0,123587.93
+24950,15665416.0,Toscani,653.0,France,Male,63.0,10.0,0.0,2.0,1.0,1.0,126082.29
+24951,15612771.0,Mollison,757.0,France,Male,43.0,2.0,0.0,2.0,0.0,0.0,62347.71
+24952,15628007.0,T'ien,569.0,Spain,Male,28.0,3.0,153058.64,1.0,1.0,0.0,119400.08
+24953,15571747.0,K?,645.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,173683.0
+24954,15677020.0,Ijendu,619.0,Spain,Male,36.0,9.0,115576.44,1.0,1.0,0.0,151902.78
+24955,15641640.0,Chibugo,731.0,France,Female,46.0,4.0,0.0,2.0,1.0,1.0,183572.39
+24956,15756070.0,Mironova,535.0,France,Female,30.0,0.0,135969.26,1.0,0.0,1.0,88890.05
+24957,15771086.0,K'ung,598.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,84569.13
+24958,15777586.0,Hsiao,707.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,1.0,139431.0
+24959,15622454.0,Chukwubuikem,749.0,Spain,Male,35.0,8.0,0.0,1.0,0.0,0.0,136812.02
+24960,15610403.0,Macnamara,521.0,Spain,Female,39.0,3.0,0.0,1.0,0.0,0.0,143681.85
+24961,15711446.0,Hsueh,718.0,France,Male,30.0,7.0,98640.01,1.0,0.0,1.0,116442.42
+24962,15694518.0,P'eng,579.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,113645.4
+24963,15661526.0,Ting,641.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,93718.16
+24964,15596972.0,Macdonald,687.0,France,Male,36.0,6.0,0.0,2.0,0.0,1.0,141400.51
+24965,15739576.0,Ch'en,559.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,123613.81
+24966,15782630.0,Ch'ien,548.0,France,Female,43.0,5.0,0.0,1.0,1.0,0.0,145593.85
+24967,15682048.0,O'Kane,707.0,Spain,Female,28.0,2.0,0.0,2.0,1.0,1.0,160523.33
+24968,15626383.0,P'eng,641.0,Spain,Female,32.0,6.0,107418.27,1.0,1.0,1.0,86981.45
+24969,15704770.0,Niu,598.0,France,Female,42.0,2.0,0.0,1.0,1.0,0.0,102925.76
+24970,15726250.0,Chukwudi,643.0,Germany,Female,43.0,9.0,117469.91,1.0,1.0,0.0,96473.5
+24971,15754926.0,Chibugo,558.0,Germany,Male,38.0,9.0,126473.99,2.0,1.0,1.0,84760.7
+24972,15659234.0,Hsia,587.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,128882.66
+24973,15579994.0,Chioke,588.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,186489.95
+24974,15682355.0,Tsai,582.0,France,Male,41.0,8.0,0.0,2.0,0.0,1.0,159418.1
+24975,15721207.0,Chidiebere,633.0,France,Male,40.0,10.0,0.0,1.0,0.0,1.0,151375.24
+24976,15600724.0,Aksenov,684.0,France,Female,39.0,3.0,0.0,1.0,1.0,1.0,130132.41
+24977,15648067.0,Chibueze,568.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,121894.31
+24978,15583576.0,Kao,753.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,9984.4
+24979,15634792.0,T'ang,708.0,Germany,Male,37.0,0.0,140720.93,1.0,1.0,0.0,117060.2
+24980,15648725.0,Chia,671.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,99800.87
+24981,15708710.0,Lo,599.0,France,Female,40.0,5.0,116922.92,1.0,1.0,0.0,46632.87
+24982,15762799.0,Baresi,683.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,71405.17
+24983,15747772.0,Chidubem,592.0,Spain,Male,37.0,10.0,0.0,2.0,1.0,0.0,79498.02
+24984,15589296.0,Liao,678.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,134420.75
+24985,15693526.0,Maclean,559.0,France,Male,44.0,9.0,103914.17,1.0,0.0,0.0,137028.51
+24986,15603764.0,Gboliwe,628.0,Germany,Female,46.0,8.0,131659.57,1.0,0.0,0.0,171770.58
+24987,15773098.0,Tsao,605.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,93336.3
+24988,15738785.0,Fanucci,775.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,177747.02
+24989,15781574.0,Hs?,683.0,Germany,Female,41.0,3.0,131354.25,1.0,1.0,0.0,153955.38
+24990,15583568.0,Kao,675.0,France,Female,51.0,3.0,114543.26,1.0,0.0,0.0,142903.44
+24991,15809100.0,Chiang,682.0,Germany,Male,37.0,10.0,76495.04,1.0,0.0,0.0,130638.53
+24992,15779985.0,Houghton,598.0,France,Male,34.0,1.0,0.0,2.0,1.0,1.0,168816.22
+24993,15759499.0,L?,689.0,France,Female,61.0,9.0,0.0,2.0,1.0,1.0,96429.29
+24994,15737542.0,Ts'ui,729.0,Germany,Female,47.0,7.0,90560.48,3.0,1.0,0.0,123663.63
+24995,15642997.0,Chukwumaobim,639.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,1.0,49637.65
+24996,15739271.0,Clements,678.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,1.0,142513.5
+24997,15756743.0,Chidiebere,774.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,4861.72
+24998,15680167.0,Yermakova,595.0,France,Male,38.0,6.0,144875.79,1.0,1.0,0.0,126469.09
+24999,15662901.0,Ting,660.0,France,Male,30.0,3.0,0.0,1.0,1.0,0.0,105784.85
diff --git a/assets/BCCP/train.csv b/assets/BCCP/train.csv
new file mode 100644
index 0000000..b410559
--- /dev/null
+++ b/assets/BCCP/train.csv
@@ -0,0 +1,15001 @@
+id,CustomerId,Surname,CreditScore,Geography,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Exited
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+1,15650086.0,Ch'in,687.0,France,Female,34.0,1.0,0.0,2.0,0.0,1.0,63736.17,0.0
+2,15733602.0,Thompson,682.0,France,Female,52.0,6.0,0.0,3.0,0.0,0.0,179655.87,1.0
+3,15645794.0,Macleod,753.0,Germany,Male,44.0,6.0,83347.25,2.0,1.0,0.0,161407.48,0.0
+4,15633840.0,Hsia,544.0,Germany,Female,55.0,0.0,107747.57,1.0,1.0,0.0,176580.86,1.0
+5,15781598.0,Hsieh,602.0,Spain,Male,33.0,1.0,120599.38,1.0,0.0,1.0,72950.14,0.0
+6,15763093.0,Kao,739.0,France,Female,36.0,1.0,0.0,1.0,1.0,1.0,134626.3,0.0
+7,15631715.0,Hargreaves,763.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,0.0,19283.14,0.0
+8,15632275.0,Hs?,620.0,Germany,Female,56.0,4.0,113743.37,1.0,1.0,0.0,121440.8,1.0
+9,15577598.0,Chukwuemeka,525.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,160670.58,0.0
+10,15809302.0,Hsueh,667.0,Germany,Male,41.0,5.0,106116.56,2.0,1.0,0.0,69676.55,0.0
+11,15639357.0,P'an,711.0,France,Female,71.0,8.0,0.0,1.0,1.0,0.0,176924.21,1.0
+12,15598088.0,Nwachukwu,649.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,173498.45,0.0
+13,15622083.0,Lucciano,644.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,166678.28,0.0
+14,15576676.0,Ugochukwutubelum,645.0,Spain,Female,22.0,5.0,0.0,2.0,1.0,1.0,35644.79,0.0
+15,15667460.0,Akobundu,731.0,Germany,Female,23.0,3.0,130788.81,2.0,1.0,1.0,105720.09,0.0
+16,15702095.0,Pagnotto,814.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,28707.69,0.0
+17,15806049.0,Ku,628.0,Germany,Female,41.0,2.0,115532.52,1.0,0.0,0.0,77965.67,1.0
+18,15594862.0,Hs?,685.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,0.0,171378.77,0.0
+19,15756901.0,Lombardi,635.0,France,Female,46.0,6.0,0.0,3.0,1.0,0.0,143683.75,1.0
+20,15584899.0,H?,712.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,136837.97,0.0
+21,15769499.0,Onochie,716.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,62944.75,0.0
+22,15803801.0,Kao,663.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,54372.37,0.0
+23,15654574.0,Tuan,659.0,France,Female,34.0,3.0,0.0,2.0,0.0,1.0,147553.06,0.0
+24,15787204.0,Chukwudi,668.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,114522.68,0.0
+25,15777922.0,Ch'ang,644.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,175749.36,0.0
+26,15715117.0,McLean,769.0,France,Male,55.0,6.0,76915.4,1.0,1.0,1.0,123906.19,1.0
+27,15802351.0,Teng,694.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,101491.23,0.0
+28,15777045.0,Nnachetam,667.0,Spain,Male,30.0,5.0,124576.65,1.0,1.0,0.0,120145.65,0.0
+29,15701116.0,P'an,745.0,France,Male,29.0,6.0,0.0,1.0,1.0,1.0,145792.01,0.0
+30,15681332.0,Aksakova,529.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,108764.69,0.0
+31,15733429.0,Zetticci,592.0,France,Female,48.0,4.0,0.0,1.0,0.0,1.0,100137.5,1.0
+32,15708193.0,Ndubuagha,593.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,103921.43,0.0
+33,15679963.0,Mazzanti,628.0,France,Female,50.0,2.0,0.0,3.0,1.0,0.0,106799.29,1.0
+34,15796370.0,Onyemachukwu,705.0,France,Female,35.0,1.0,134692.3,1.0,0.0,1.0,177783.04,0.0
+35,15684960.0,Genovese,642.0,France,Female,35.0,2.0,0.0,3.0,1.0,1.0,171413.66,1.0
+36,15695097.0,Ejimofor,633.0,France,Female,39.0,1.0,0.0,1.0,1.0,1.0,81299.17,0.0
+37,15778838.0,Mamelu,684.0,France,Male,28.0,7.0,141541.25,1.0,1.0,0.0,64831.36,0.0
+38,15651369.0,Ch'ien,549.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,175870.59,0.0
+39,15659962.0,Ts'ao,812.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,158437.73,0.0
+40,15652693.0,Chukwufumnanya,730.0,France,Female,25.0,2.0,0.0,2.0,0.0,1.0,95941.55,0.0
+41,15594577.0,Cattaneo,593.0,Spain,Male,28.0,9.0,0.0,2.0,1.0,0.0,5194.95,0.0
+42,15784594.0,Maclean,743.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,96444.88,0.0
+43,15744517.0,Ts'ao,671.0,France,Female,32.0,9.0,0.0,2.0,0.0,1.0,166520.96,0.0
+44,15698732.0,Echezonachukwu,668.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,97939.4,0.0
+45,15639277.0,Chiang,596.0,Germany,Male,27.0,6.0,135020.21,1.0,1.0,0.0,9087.81,1.0
+46,15589329.0,Macleod,786.0,Spain,Male,34.0,3.0,110765.68,2.0,1.0,0.0,187955.87,0.0
+47,15692406.0,Tsou,651.0,Germany,Male,25.0,5.0,80926.02,2.0,1.0,0.0,175235.99,0.0
+48,15743964.0,Ch'ien,686.0,Germany,Male,28.0,2.0,101238.24,2.0,0.0,0.0,141827.83,0.0
+49,15702669.0,Biryukov,593.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,0.0,141069.88,0.0
+50,15792453.0,Chidubem,684.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,108411.66,0.0
+51,15584899.0,Fisk,577.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,125877.22,0.0
+52,15636016.0,Chukwubuikem,807.0,France,Female,51.0,3.0,0.0,3.0,1.0,0.0,169781.45,1.0
+53,15631392.0,Hsiao,588.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,172459.39,0.0
+54,15743817.0,Ch'iu,735.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,142331.85,0.0
+55,15718779.0,Maughan,639.0,France,Male,25.0,2.0,0.0,2.0,1.0,1.0,90558.24,0.0
+56,15591747.0,Onyinyechukwuka,584.0,Germany,Female,37.0,5.0,74989.58,1.0,0.0,0.0,131807.14,1.0
+57,15600911.0,Hsiung,707.0,Germany,Female,43.0,3.0,101247.06,2.0,1.0,0.0,73329.08,0.0
+58,15690021.0,Maclean,662.0,France,Male,46.0,2.0,0.0,2.0,0.0,0.0,176903.35,1.0
+59,15653050.0,Macdonald,437.0,France,Male,39.0,4.0,0.0,1.0,0.0,0.0,125877.22,0.0
+60,15803764.0,Aksyonova,528.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,19720.08,0.0
+61,15641794.0,H?,682.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,95583.48,0.0
+62,15693381.0,Pirozzi,615.0,France,Male,32.0,9.0,134446.04,1.0,1.0,1.0,94978.1,0.0
+63,15771483.0,Fomin,743.0,France,Male,36.0,5.0,0.0,2.0,0.0,1.0,89893.6,0.0
+64,15792360.0,Chiekwugo,679.0,Spain,Male,38.0,4.0,137593.07,2.0,0.0,0.0,83700.66,1.0
+65,15565706.0,Ch'ang,607.0,Spain,Female,33.0,5.0,0.0,2.0,0.0,1.0,71405.17,0.0
+66,15767339.0,Hirst,724.0,Spain,Male,27.0,4.0,0.0,2.0,1.0,0.0,162743.53,0.0
+67,15632272.0,Hargreaves,589.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,1.0,71844.3,0.0
+68,15584897.0,Okechukwu,694.0,France,Male,54.0,1.0,0.0,1.0,0.0,1.0,181850.74,0.0
+69,15771573.0,T'ao,586.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,1.0,153066.97,0.0
+70,15756070.0,Maclean,756.0,France,Female,53.0,2.0,0.0,3.0,1.0,1.0,104721.5,1.0
+71,15654851.0,Ross,751.0,France,Female,26.0,2.0,0.0,2.0,1.0,0.0,51345.41,0.0
+72,15671781.0,Iweobiegbunam,633.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,88604.89,0.0
+73,15671244.0,Ch'ien,744.0,France,Male,29.0,7.0,0.0,2.0,0.0,0.0,160523.33,0.0
+74,15705918.0,P'eng,477.0,France,Male,66.0,7.0,92786.66,1.0,0.0,1.0,50906.65,0.0
+75,15744789.0,Shih,523.0,Spain,Male,27.0,7.0,0.0,2.0,0.0,0.0,122518.5,0.0
+76,15813363.0,Fenton,704.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,85891.02,0.0
+77,15640161.0,Ch'eng,582.0,France,Female,47.0,8.0,0.0,1.0,1.0,1.0,102057.86,0.0
+78,15730673.0,Lettiere,706.0,France,Female,41.0,1.0,121159.65,1.0,1.0,1.0,71862.79,0.0
+79,15711396.0,Hsiung,537.0,France,Female,45.0,5.0,0.0,1.0,1.0,0.0,16081.62,1.0
+80,15774586.0,Ch'in,705.0,Germany,Male,35.0,6.0,116320.68,2.0,1.0,0.0,174431.01,0.0
+81,15762902.0,T'ien,681.0,Spain,Female,22.0,6.0,0.0,2.0,0.0,0.0,65733.53,0.0
+82,15732563.0,Lettiere,613.0,Germany,Female,28.0,2.0,137980.23,1.0,1.0,1.0,158887.09,0.0
+83,15672357.0,Yermolayev,575.0,Germany,Female,33.0,9.0,143124.87,2.0,1.0,0.0,57898.58,0.0
+84,15720662.0,Kornilova,632.0,France,Female,23.0,6.0,0.0,2.0,1.0,0.0,95625.03,0.0
+85,15772996.0,Onyekachi,627.0,Spain,Male,45.0,7.0,0.0,2.0,1.0,1.0,167916.35,0.0
+86,15681755.0,Ts'ao,680.0,Germany,Female,29.0,5.0,119657.53,1.0,1.0,1.0,177655.68,0.0
+87,15764314.0,Uvarov,645.0,Spain,Male,33.0,4.0,141162.08,1.0,1.0,0.0,142238.54,0.0
+88,15793331.0,Okwukwe,774.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,57896.24,0.0
+89,15588928.0,Hsiung,583.0,Germany,Female,54.0,0.0,121558.46,1.0,0.0,0.0,140765.57,1.0
+90,15761043.0,Bogle,737.0,France,Female,41.0,6.0,0.0,2.0,1.0,0.0,67445.85,0.0
+91,15577515.0,Ch'eng,696.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,86657.52,0.0
+92,15572547.0,Palerma,679.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,0.0,96670.2,0.0
+93,15716781.0,Li,687.0,Spain,Female,34.0,7.0,0.0,1.0,0.0,1.0,96670.2,1.0
+94,15607874.0,Dike,611.0,France,Male,47.0,9.0,0.0,2.0,0.0,0.0,122518.5,0.0
+95,15690744.0,Onyekachi,741.0,France,Male,33.0,1.0,0.0,2.0,0.0,1.0,118862.08,0.0
+96,15601172.0,Milne,753.0,France,Female,30.0,0.0,138171.1,1.0,0.0,0.0,157891.86,0.0
+97,15576680.0,Trevisani,686.0,Germany,Male,42.0,8.0,111827.27,1.0,0.0,1.0,126469.09,0.0
+98,15738785.0,Mironova,583.0,Spain,Female,45.0,2.0,0.0,1.0,1.0,1.0,114876.82,1.0
+99,15606472.0,Bazarova,589.0,Spain,Female,35.0,2.0,135984.15,1.0,1.0,1.0,130862.11,0.0
+100,15658716.0,Onwumelu,711.0,Germany,Male,48.0,2.0,122726.56,1.0,0.0,0.0,51117.06,1.0
+101,15679307.0,Hsiao,535.0,France,Male,37.0,1.0,0.0,2.0,0.0,0.0,123663.63,0.0
+102,15605426.0,Chibueze,535.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,0.0,65906.07,0.0
+103,15785547.0,Uspensky,554.0,France,Female,40.0,10.0,0.0,2.0,1.0,0.0,67734.79,0.0
+104,15723484.0,Milanesi,632.0,Germany,Male,52.0,9.0,117300.02,1.0,1.0,0.0,154598.56,1.0
+105,15756475.0,Palermo,535.0,Germany,Female,29.0,8.0,115315.04,1.0,0.0,1.0,175094.63,0.0
+106,15703394.0,Hsieh,652.0,Germany,Male,31.0,3.0,124576.65,1.0,1.0,0.0,118570.55,0.0
+107,15644724.0,Ch'ien,707.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,71862.79,0.0
+108,15724127.0,Olisaemeka,682.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,199290.68,0.0
+109,15646539.0,Nnamdi,705.0,Spain,Female,31.0,9.0,92599.55,1.0,1.0,0.0,61820.41,0.0
+110,15718231.0,Nkemdilim,592.0,Spain,Male,34.0,10.0,0.0,2.0,1.0,0.0,168034.83,0.0
+111,15733602.0,Brown,679.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,136916.54,0.0
+112,15639265.0,Chiazagomekpere,681.0,Germany,Male,40.0,3.0,108881.73,1.0,0.0,0.0,17095.49,1.0
+113,15741385.0,Ch'in,706.0,France,Female,46.0,9.0,0.0,1.0,1.0,0.0,143086.09,1.0
+114,15761023.0,Chukwuemeka,663.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,140723.05,0.0
+115,15656426.0,Teng,578.0,France,Female,41.0,7.0,0.0,1.0,0.0,0.0,180197.74,1.0
+116,15596979.0,Ritchie,692.0,France,Male,35.0,9.0,0.0,1.0,1.0,1.0,142903.44,0.0
+117,15670172.0,Chin,678.0,France,Female,50.0,5.0,0.0,2.0,1.0,1.0,146504.35,0.0
+118,15684951.0,Toscani,583.0,Spain,Female,40.0,6.0,113328.93,1.0,1.0,0.0,64375.4,0.0
+119,15733303.0,Tien,679.0,France,Male,36.0,1.0,0.0,1.0,1.0,0.0,48778.46,0.0
+120,15750458.0,Chinomso,679.0,Germany,Female,47.0,0.0,118628.8,1.0,0.0,0.0,184842.21,1.0
+121,15583681.0,Smith,597.0,France,Male,57.0,2.0,0.0,1.0,1.0,1.0,84760.32,1.0
+122,15736431.0,Ruggiero,586.0,France,Female,27.0,2.0,0.0,2.0,1.0,1.0,86791.9,0.0
+123,15704763.0,Ponomarev,752.0,France,Female,34.0,7.0,132387.91,1.0,1.0,1.0,154486.01,0.0
+124,15803562.0,Ch'iu,850.0,Spain,Female,34.0,4.0,0.0,2.0,1.0,0.0,144870.28,0.0
+125,15728167.0,Onyekachi,682.0,France,Male,34.0,8.0,90425.15,1.0,0.0,0.0,122580.48,0.0
+126,15735246.0,Yermakova,724.0,Spain,Female,56.0,1.0,0.0,1.0,0.0,0.0,137519.92,1.0
+127,15801817.0,Kao,763.0,Spain,Male,29.0,6.0,0.0,1.0,1.0,1.0,151887.16,0.0
+128,15580149.0,Folliero,588.0,Spain,Male,61.0,10.0,0.0,2.0,1.0,1.0,90883.12,0.0
+129,15733261.0,Ts'ui,684.0,France,Male,35.0,1.0,91547.84,1.0,1.0,1.0,115733.16,0.0
+130,15618243.0,Whitehead,678.0,France,Male,24.0,2.0,0.0,2.0,0.0,1.0,127059.05,0.0
+131,15757559.0,Chukwumaobim,699.0,Germany,Female,36.0,9.0,110148.49,1.0,1.0,1.0,177065.24,0.0
+132,15768151.0,Tuan,619.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,143585.29,0.0
+133,15736112.0,Yobachi,712.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,95611.47,0.0
+134,15750772.0,Lo Duca,677.0,France,Male,36.0,5.0,0.0,1.0,1.0,1.0,120801.65,1.0
+135,15644692.0,Ch'in,752.0,Germany,Male,51.0,2.0,114435.52,3.0,1.0,0.0,173495.77,1.0
+136,15666095.0,Clements,646.0,France,Male,29.0,1.0,127763.24,1.0,1.0,1.0,95611.47,0.0
+137,15748995.0,P'eng,599.0,Germany,Female,42.0,8.0,118520.41,1.0,1.0,1.0,141175.21,1.0
+138,15692259.0,Chifley,547.0,Germany,Female,44.0,4.0,120107.56,2.0,1.0,0.0,172576.43,1.0
+139,15750181.0,Calabresi,619.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,108008.65,0.0
+140,15605275.0,Tsai,674.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,120512.08,0.0
+141,15795537.0,Ts'ui,564.0,Germany,Male,38.0,1.0,131563.88,1.0,1.0,0.0,133465.57,1.0
+142,15621818.0,Zikoranachidimma,646.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,120892.96,0.0
+143,15662232.0,Trevisano,678.0,France,Female,39.0,7.0,0.0,2.0,0.0,0.0,88890.05,0.0
+144,15694566.0,Hsu,665.0,France,Male,39.0,9.0,144801.97,1.0,1.0,0.0,132798.65,0.0
+145,15589969.0,Lai,597.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,169386.81,0.0
+146,15623369.0,Onwuka,709.0,Germany,Male,36.0,4.0,79962.92,2.0,1.0,1.0,18082.06,0.0
+147,15624667.0,Maclean,671.0,France,Female,39.0,2.0,0.0,2.0,0.0,1.0,162889.1,0.0
+148,15680234.0,T'ien,595.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,84972.9,0.0
+149,15651389.0,Lucchese,763.0,France,Male,47.0,3.0,123374.46,2.0,1.0,0.0,61569.07,0.0
+150,15794566.0,Ch'ang,725.0,Germany,Female,50.0,1.0,126444.25,1.0,1.0,0.0,158853.98,1.0
+151,15690620.0,Zikoranachidimma,755.0,Germany,Male,31.0,7.0,110997.76,1.0,1.0,1.0,118862.08,0.0
+152,15796721.0,T'ao,729.0,France,Male,32.0,1.0,0.0,2.0,0.0,1.0,189428.75,0.0
+153,15592761.0,Hsing,754.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,120596.83,0.0
+154,15602909.0,Ndubuagha,590.0,France,Female,32.0,5.0,0.0,2.0,0.0,1.0,61977.82,0.0
+155,15780961.0,Ch'iu,762.0,France,Male,38.0,2.0,0.0,1.0,1.0,0.0,77906.87,0.0
+156,15642725.0,Yashina,575.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,131301.74,0.0
+157,15636562.0,Clements,773.0,France,Male,38.0,8.0,0.0,2.0,0.0,1.0,81861.1,0.0
+158,15782390.0,Shih,850.0,Spain,Male,26.0,9.0,123971.16,1.0,1.0,0.0,176902.83,0.0
+159,15659420.0,Kao,564.0,France,Male,30.0,4.0,72541.48,1.0,1.0,0.0,90857.76,0.0
+160,15756570.0,Hsieh,581.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,93524.19,0.0
+161,15691817.0,Eluemuno,714.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,67740.08,0.0
+162,15741728.0,Iweobiegbunam,696.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,51820.87,0.0
+163,15583857.0,T'ien,651.0,France,Male,56.0,3.0,135067.02,3.0,1.0,0.0,147963.07,1.0
+164,15650391.0,Chukwualuka,678.0,France,Male,35.0,5.0,0.0,1.0,1.0,1.0,114442.66,0.0
+165,15773469.0,Larkin,704.0,Spain,Male,40.0,1.0,123694.0,1.0,1.0,1.0,134901.58,0.0
+166,15662294.0,Ch'in,639.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,107625.08,0.0
+167,15736154.0,Okwudiliolisa,648.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,161848.03,0.0
+168,15659949.0,Chukwumaobim,727.0,France,Male,43.0,1.0,0.0,2.0,1.0,1.0,81861.1,0.0
+169,15661526.0,Hsia,567.0,Germany,Female,32.0,9.0,120599.21,4.0,1.0,1.0,159418.1,1.0
+170,15638963.0,Ch'ang,597.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,167023.95,1.0
+171,15640078.0,Hsia,665.0,Germany,Female,43.0,5.0,106328.08,1.0,1.0,1.0,117622.8,1.0
+172,15691874.0,McElhone,714.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,0.0,102297.15,0.0
+173,15783883.0,Walker,678.0,Spain,Female,32.0,3.0,134714.3,1.0,1.0,1.0,48137.08,0.0
+174,15692575.0,Fancher,567.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,122925.4,0.0
+175,15772383.0,Mazzi,693.0,France,Male,26.0,4.0,0.0,1.0,1.0,0.0,90928.04,0.0
+176,15721251.0,Chinomso,641.0,France,Female,35.0,8.0,0.0,2.0,1.0,0.0,164741.5,0.0
+177,15743193.0,Azubuike,699.0,Spain,Male,33.0,1.0,155060.41,1.0,1.0,1.0,73977.23,0.0
+178,15663438.0,Yudina,763.0,Spain,Female,39.0,2.0,0.0,2.0,0.0,0.0,144870.28,0.0
+179,15689781.0,De Neeve,581.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,106781.59,1.0
+180,15775339.0,Mazzanti,730.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,167873.49,0.0
+181,15759733.0,Tung,537.0,Germany,Female,33.0,3.0,126592.47,1.0,1.0,0.0,48382.4,0.0
+182,15731569.0,Chukwuemeka,681.0,Germany,Female,56.0,8.0,123971.51,1.0,0.0,0.0,178291.09,1.0
+183,15643826.0,McWilliams,578.0,France,Female,38.0,9.0,0.0,2.0,0.0,1.0,161229.84,0.0
+184,15796834.0,Pugh,656.0,France,Female,34.0,5.0,0.0,1.0,1.0,1.0,176902.83,0.0
+185,15684461.0,Yobachi,521.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,121409.06,0.0
+186,15600792.0,Chiang,636.0,France,Female,32.0,5.0,0.0,1.0,1.0,1.0,178067.33,0.0
+187,15589431.0,Ugochukwu,694.0,France,Male,32.0,4.0,0.0,2.0,0.0,1.0,127825.25,0.0
+188,15808566.0,Milne,748.0,France,Male,46.0,3.0,0.0,2.0,1.0,0.0,82696.84,0.0
+189,15627854.0,O'Kane,523.0,France,Male,62.0,7.0,68892.77,1.0,1.0,1.0,189366.04,0.0
+190,15809838.0,Nnanna,757.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,161579.85,0.0
+191,15806837.0,Yobanna,733.0,Spain,Male,45.0,3.0,0.0,2.0,1.0,1.0,80410.68,0.0
+192,15573319.0,Aksenov,695.0,France,Male,27.0,3.0,0.0,2.0,1.0,0.0,121565.51,0.0
+193,15803801.0,Biryukov,706.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,113079.19,0.0
+194,15623595.0,Efremov,607.0,France,Female,38.0,5.0,0.0,1.0,1.0,0.0,57844.26,0.0
+195,15773322.0,Chidubem,682.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,83473.82,0.0
+196,15772601.0,Ndubuagha,696.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,102655.31,0.0
+197,15662085.0,Chinweike,667.0,Germany,Male,37.0,9.0,126670.37,2.0,1.0,0.0,131729.48,0.0
+198,15600992.0,Mazzi,573.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,150548.5,0.0
+199,15786539.0,Lu,594.0,France,Female,57.0,1.0,0.0,2.0,1.0,1.0,102655.31,0.0
+200,15677828.0,Chukwujamuike,726.0,Spain,Male,54.0,6.0,0.0,1.0,0.0,0.0,144880.81,1.0
+201,15723786.0,Chukwubuikem,712.0,France,Male,47.0,9.0,122359.5,1.0,1.0,0.0,186778.89,0.0
+202,15801994.0,L?,565.0,France,Female,54.0,3.0,0.0,1.0,1.0,0.0,122284.38,1.0
+203,15599792.0,Chibueze,619.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,146875.86,0.0
+204,15754574.0,Chinomso,635.0,France,Male,38.0,6.0,81231.85,1.0,1.0,1.0,79178.31,0.0
+205,15696733.0,Onyemauchechukwu,651.0,France,Female,37.0,5.0,0.0,2.0,1.0,1.0,149614.17,0.0
+206,15758081.0,Ts'ui,576.0,France,Female,38.0,3.0,116870.12,1.0,1.0,0.0,43982.41,0.0
+207,15699772.0,Sarratt,554.0,Germany,Female,33.0,9.0,152390.52,4.0,1.0,0.0,138552.74,1.0
+208,15591711.0,Fiorentino,539.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,168035.62,0.0
+209,15742809.0,Chiazagomekpere,635.0,France,Female,50.0,5.0,0.0,2.0,1.0,1.0,131301.74,1.0
+210,15672432.0,McFarland,599.0,Spain,Female,46.0,7.0,99208.46,1.0,0.0,0.0,47414.15,1.0
+211,15776922.0,Iloerika,596.0,Germany,Female,50.0,5.0,116993.43,1.0,1.0,1.0,60905.51,1.0
+212,15726087.0,Bales,713.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,143681.85,0.0
+213,15748797.0,Haugh,678.0,France,Female,43.0,9.0,0.0,1.0,0.0,0.0,177738.98,1.0
+214,15627697.0,Iloerika,714.0,Germany,Male,47.0,7.0,80780.16,1.0,0.0,1.0,177670.57,0.0
+215,15742238.0,Chukwukadibia,779.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,1.0,168834.38,0.0
+216,15686977.0,Okoli,663.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,0.0,51907.72,0.0
+217,15596060.0,Chien,727.0,France,Male,36.0,9.0,94741.74,2.0,0.0,1.0,141970.02,0.0
+218,15769980.0,Lu,732.0,Spain,Male,39.0,4.0,0.0,1.0,0.0,1.0,142377.69,0.0
+219,15634180.0,Chukwudi,602.0,France,Female,37.0,7.0,0.0,2.0,0.0,1.0,51548.14,0.0
+220,15749964.0,Ts'ui,850.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,1.0,180712.28,0.0
+221,15788151.0,Calabrese,569.0,France,Male,42.0,8.0,94486.47,1.0,1.0,0.0,15120.86,0.0
+222,15792388.0,Mott,597.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,147040.25,0.0
+223,15785373.0,Kao,694.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,1.0,111285.85,0.0
+224,15596713.0,Chinweuba,555.0,France,Male,28.0,3.0,0.0,2.0,1.0,1.0,88566.9,0.0
+225,15680201.0,Kao,682.0,France,Male,44.0,9.0,0.0,1.0,1.0,0.0,28188.96,0.0
+226,15662494.0,Ch'in,559.0,France,Female,30.0,4.0,0.0,1.0,1.0,0.0,131473.31,1.0
+227,15603594.0,Ugochukwu,731.0,France,Male,40.0,8.0,0.0,2.0,0.0,1.0,119446.64,0.0
+228,15673877.0,Ts'ai,810.0,France,Male,27.0,4.0,0.0,1.0,1.0,1.0,128841.31,0.0
+229,15606901.0,Ponomaryov,653.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,92811.2,0.0
+230,15771483.0,Fanucci,641.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,73881.68,0.0
+231,15763381.0,Chukwuemeka,571.0,Germany,Female,35.0,9.0,142862.21,1.0,1.0,1.0,5669.31,0.0
+232,15646513.0,Mazzanti,678.0,Germany,Female,48.0,9.0,95853.33,1.0,1.0,1.0,179689.07,1.0
+233,15797748.0,Lucciano,779.0,Spain,Male,27.0,2.0,0.0,2.0,1.0,0.0,141023.01,0.0
+234,15592914.0,K?,628.0,France,Female,37.0,1.0,0.0,2.0,1.0,0.0,97633.73,0.0
+235,15810944.0,Hsueh,705.0,France,Male,41.0,4.0,118627.1,1.0,1.0,1.0,60427.3,0.0
+236,15751208.0,Tsou,727.0,Germany,Male,34.0,8.0,127706.33,1.0,1.0,1.0,119333.95,0.0
+237,15667417.0,Tien,612.0,Germany,Male,54.0,6.0,116322.22,1.0,1.0,1.0,164815.59,1.0
+238,15756481.0,Li Fonti,642.0,France,Female,36.0,8.0,0.0,1.0,1.0,1.0,141180.66,1.0
+239,15658935.0,McLean,588.0,France,Male,39.0,0.0,127864.4,1.0,1.0,1.0,62370.67,0.0
+240,15664668.0,Trevisani,754.0,Spain,Male,36.0,5.0,0.0,2.0,0.0,1.0,57675.88,0.0
+241,15756820.0,Smith,652.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,1.0,92712.97,0.0
+242,15736069.0,Hsiao,609.0,Germany,Male,36.0,3.0,123870.07,2.0,1.0,1.0,167973.63,0.0
+243,15732963.0,Lombardi,572.0,France,Male,29.0,1.0,106939.08,1.0,0.0,0.0,170819.01,0.0
+244,15694946.0,Chialuka,632.0,Spain,Male,40.0,10.0,0.0,2.0,1.0,1.0,40368.65,0.0
+245,15797389.0,Fanucci,716.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,183358.21,0.0
+246,15674950.0,Clements,709.0,Spain,Male,34.0,4.0,103818.47,1.0,1.0,1.0,79314.1,0.0
+247,15781802.0,Chiang,602.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,162960.05,0.0
+248,15782418.0,P'eng,745.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,163558.43,0.0
+249,15651062.0,Nwachukwu,775.0,Spain,Female,36.0,2.0,0.0,1.0,0.0,0.0,91359.79,0.0
+250,15782758.0,Okwudilichukwu,632.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,177528.92,0.0
+251,15605665.0,Nnamutaezinwa,646.0,Spain,Female,26.0,9.0,113328.65,1.0,1.0,0.0,149991.32,1.0
+252,15804962.0,Fiorentino,668.0,Germany,Male,37.0,1.0,125877.65,1.0,1.0,0.0,69113.14,0.0
+253,15791216.0,Omeokachie,662.0,France,Male,42.0,0.0,0.0,1.0,1.0,0.0,199693.84,1.0
+254,15753969.0,Lucciano,678.0,France,Female,24.0,9.0,0.0,1.0,1.0,0.0,159800.16,0.0
+255,15573876.0,H?,689.0,Germany,Male,35.0,4.0,133681.98,1.0,0.0,1.0,176580.93,0.0
+256,15796957.0,Ch'eng,634.0,Germany,Male,45.0,7.0,123894.42,1.0,0.0,1.0,182650.15,1.0
+257,15585388.0,Tsai,676.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,86733.61,0.0
+258,15789432.0,L?,714.0,Spain,Female,41.0,2.0,0.0,2.0,1.0,0.0,141533.19,0.0
+259,15750055.0,Maclean,686.0,Germany,Female,38.0,7.0,141806.0,1.0,1.0,1.0,93551.36,0.0
+260,15635367.0,Black,584.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,129964.94,0.0
+261,15726186.0,Shubin,677.0,France,Female,28.0,5.0,0.0,2.0,0.0,1.0,149911.99,0.0
+262,15585901.0,Martin,682.0,France,Male,44.0,8.0,0.0,1.0,1.0,0.0,139024.31,1.0
+263,15733581.0,Onwumelu,599.0,France,Female,27.0,3.0,0.0,2.0,0.0,0.0,57844.26,0.0
+264,15681254.0,De Luca,627.0,France,Female,45.0,6.0,111018.98,1.0,1.0,1.0,107541.07,0.0
+265,15776629.0,Udegbulam,597.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,1.0,151739.67,0.0
+266,15613102.0,Macleod,624.0,France,Male,27.0,10.0,122726.56,1.0,1.0,1.0,77414.45,0.0
+267,15720509.0,Chiu,610.0,Spain,Male,35.0,3.0,0.0,2.0,0.0,1.0,81251.24,0.0
+268,15627232.0,Maclean,573.0,France,Male,34.0,1.0,72535.45,1.0,1.0,1.0,158937.55,0.0
+269,15696588.0,Chidimma,577.0,France,Male,30.0,6.0,108736.52,2.0,1.0,1.0,172618.52,0.0
+270,15815070.0,Chukwuemeka,709.0,France,Male,30.0,2.0,0.0,2.0,0.0,1.0,80619.09,0.0
+271,15644446.0,Tisdall,619.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,126761.2,0.0
+272,15642824.0,Folliero,703.0,France,Female,45.0,9.0,0.0,2.0,1.0,1.0,59354.24,0.0
+273,15646535.0,Ch'ang,678.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,176680.32,0.0
+274,15721426.0,Chiu,726.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,179908.71,0.0
+275,15752844.0,Mazzanti,646.0,France,Male,34.0,3.0,102562.23,1.0,1.0,1.0,114514.64,0.0
+276,15732963.0,Aitken,656.0,Germany,Female,43.0,5.0,122781.51,1.0,1.0,1.0,122488.59,0.0
+277,15780124.0,L?,597.0,Spain,Male,42.0,6.0,0.0,2.0,0.0,0.0,157097.09,0.0
+278,15724876.0,Hsia,763.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,0.0,165576.55,0.0
+279,15771086.0,Lo,706.0,France,Female,30.0,8.0,0.0,1.0,1.0,0.0,172450.04,1.0
+280,15632079.0,Chiang,718.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,185543.35,0.0
+281,15795895.0,Onuoha,719.0,France,Male,45.0,6.0,0.0,2.0,0.0,1.0,182728.8,0.0
+282,15759874.0,Nwokike,755.0,France,Male,35.0,2.0,0.0,2.0,0.0,0.0,167066.95,0.0
+283,15802585.0,Hsiung,668.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,96444.88,0.0
+284,15639946.0,Maclean,644.0,France,Female,39.0,6.0,0.0,2.0,1.0,0.0,161074.85,0.0
+285,15585599.0,Chiazagomekpere,663.0,France,Male,39.0,5.0,143340.44,1.0,0.0,0.0,71333.44,0.0
+286,15663438.0,Mao,630.0,France,Female,42.0,4.0,0.0,1.0,1.0,0.0,157543.11,1.0
+287,15742369.0,Hsia,731.0,Germany,Female,35.0,2.0,134564.41,1.0,1.0,0.0,106538.99,1.0
+288,15602373.0,McLean,596.0,France,Female,34.0,1.0,0.0,2.0,0.0,1.0,158973.65,0.0
+289,15598846.0,Ritchie,581.0,France,Male,38.0,3.0,0.0,2.0,0.0,1.0,126812.63,0.0
+290,15586674.0,Ch'ang,539.0,France,Female,46.0,8.0,0.0,3.0,1.0,0.0,121834.81,1.0
+291,15776824.0,Chiemenam,596.0,Spain,Female,37.0,5.0,0.0,2.0,1.0,1.0,99818.46,0.0
+292,15570947.0,Chinweike,730.0,France,Female,42.0,3.0,129440.3,1.0,1.0,0.0,91036.74,0.0
+293,15787035.0,Tien,704.0,France,Female,44.0,4.0,0.0,2.0,1.0,0.0,85890.75,0.0
+294,15794473.0,Lucchesi,602.0,France,Female,34.0,5.0,0.0,1.0,1.0,0.0,182632.64,0.0
+295,15568487.0,Ogochukwu,754.0,France,Male,72.0,8.0,98765.84,1.0,1.0,1.0,147635.33,0.0
+296,15679743.0,Ch'ang,668.0,Germany,Male,32.0,3.0,145605.44,1.0,1.0,1.0,179565.57,0.0
+297,15632669.0,Hsiao,604.0,Spain,Female,28.0,4.0,0.0,1.0,1.0,1.0,88658.73,1.0
+298,15782941.0,Uspensky,755.0,Spain,Male,39.0,2.0,0.0,2.0,0.0,0.0,144593.3,0.0
+299,15797919.0,Akobundu,602.0,Spain,Female,31.0,10.0,0.0,1.0,1.0,0.0,156868.21,1.0
+300,15763028.0,Nwankwo,757.0,Spain,Male,45.0,6.0,161613.94,1.0,0.0,0.0,164038.07,1.0
+301,15572051.0,Bell,581.0,France,Female,21.0,4.0,0.0,2.0,1.0,0.0,170634.49,0.0
+302,15777893.0,Hsieh,639.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,1.0,130910.78,0.0
+303,15702541.0,Ch'iu,668.0,France,Male,41.0,10.0,0.0,2.0,1.0,0.0,91086.73,0.0
+304,15788683.0,H?,586.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,61531.86,0.0
+305,15768244.0,Chiazagomekpere,572.0,France,Male,32.0,8.0,0.0,1.0,1.0,1.0,104991.28,0.0
+306,15607877.0,Udokamma,756.0,France,Male,49.0,10.0,0.0,1.0,1.0,0.0,177719.88,1.0
+307,15750181.0,Cremonesi,684.0,France,Male,25.0,9.0,0.0,1.0,1.0,1.0,159830.58,0.0
+308,15671769.0,Shih,576.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,154844.22,0.0
+309,15726186.0,Ting,712.0,France,Female,41.0,4.0,0.0,3.0,1.0,0.0,182495.7,1.0
+310,15768120.0,Fanucci,542.0,France,Male,45.0,8.0,0.0,2.0,1.0,0.0,145593.85,1.0
+311,15595883.0,Yeh,602.0,Spain,Female,44.0,4.0,0.0,1.0,1.0,0.0,152843.53,1.0
+312,15616709.0,Lombardi,627.0,Germany,Male,51.0,9.0,121355.99,1.0,1.0,0.0,76510.46,1.0
+313,15728474.0,Fanucci,769.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,1.0,168035.63,0.0
+314,15585047.0,Ch'iu,645.0,France,Female,35.0,8.0,0.0,1.0,1.0,0.0,142039.67,0.0
+315,15692406.0,Mamelu,670.0,France,Female,34.0,2.0,139859.55,1.0,1.0,1.0,35338.96,0.0
+316,15779915.0,Okwudiliolisa,667.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,107874.39,0.0
+317,15571586.0,Chiemenam,614.0,France,Male,35.0,6.0,124586.93,2.0,1.0,0.0,142400.27,0.0
+318,15775335.0,Mazzanti,581.0,Germany,Female,39.0,6.0,121207.65,2.0,1.0,1.0,114020.08,0.0
+319,15583462.0,T'ien,558.0,Germany,Male,39.0,2.0,122570.87,2.0,1.0,1.0,61538.43,0.0
+320,15684526.0,Tien,659.0,Germany,Female,43.0,9.0,121827.43,2.0,1.0,1.0,121074.33,0.0
+321,15633227.0,T'ao,637.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,0.0,125440.11,0.0
+322,15747168.0,Olisaemeka,730.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,106977.52,0.0
+323,15593882.0,Ukaegbunam,651.0,Spain,Male,34.0,7.0,0.0,2.0,0.0,1.0,158036.72,0.0
+324,15785042.0,P'an,738.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,148087.88,0.0
+325,15731865.0,Ch'en,728.0,Spain,Male,43.0,6.0,0.0,3.0,1.0,1.0,158750.58,1.0
+326,15641723.0,Mazzanti,646.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,152876.13,0.0
+327,15608760.0,H?,748.0,Germany,Female,47.0,10.0,102025.32,1.0,1.0,0.0,136079.64,1.0
+328,15682467.0,Milani,597.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,10963.04,0.0
+329,15794580.0,Trevisani,656.0,France,Male,41.0,9.0,0.0,2.0,1.0,1.0,175324.74,0.0
+330,15801488.0,Smith,731.0,France,Male,31.0,8.0,0.0,2.0,0.0,0.0,100862.54,0.0
+331,15632439.0,Lo Duca,569.0,Germany,Male,52.0,7.0,125889.3,1.0,0.0,0.0,60212.81,1.0
+332,15713578.0,Mazzanti,554.0,France,Male,43.0,5.0,121555.55,1.0,0.0,0.0,118913.53,0.0
+333,15583595.0,Hargreaves,653.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,1.0,82463.69,0.0
+334,15605276.0,Hsiung,633.0,France,Male,39.0,2.0,57871.18,1.0,0.0,1.0,63889.1,1.0
+335,15632324.0,Nkemjika,632.0,Spain,Male,44.0,8.0,0.0,1.0,1.0,1.0,151871.84,1.0
+336,15661219.0,Trevisano,686.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,179655.85,0.0
+337,15628697.0,Onochie,762.0,Germany,Female,26.0,9.0,142658.04,2.0,0.0,1.0,154775.7,0.0
+338,15807989.0,P'eng,579.0,Spain,Female,31.0,8.0,0.0,1.0,0.0,0.0,148814.54,0.0
+339,15766017.0,Iloerika,646.0,France,Male,35.0,5.0,0.0,2.0,0.0,1.0,80701.72,0.0
+340,15664204.0,Hsueh,542.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,142516.35,0.0
+341,15775294.0,Ts'ui,632.0,Germany,Male,28.0,7.0,103909.86,1.0,1.0,1.0,185547.71,0.0
+342,15779913.0,Hs?,686.0,France,Male,44.0,7.0,0.0,1.0,0.0,0.0,118914.4,0.0
+343,15651533.0,De Salis,516.0,France,Female,51.0,6.0,0.0,2.0,1.0,1.0,123587.83,0.0
+344,15732442.0,Nwachinemelu,634.0,France,Male,38.0,7.0,0.0,1.0,1.0,1.0,125646.82,0.0
+345,15658852.0,Lucchesi,596.0,France,Female,44.0,5.0,0.0,1.0,0.0,0.0,106977.8,1.0
+346,15592222.0,Ts'ai,706.0,France,Female,40.0,3.0,122314.5,1.0,1.0,0.0,87985.2,0.0
+347,15584364.0,Chukwueloka,651.0,France,Female,37.0,1.0,0.0,2.0,1.0,0.0,43932.54,0.0
+348,15695846.0,Onyekaozulu,678.0,Spain,Female,49.0,8.0,0.0,1.0,1.0,0.0,38773.74,1.0
+349,15728474.0,Maclean,658.0,France,Male,41.0,4.0,0.0,2.0,0.0,1.0,163570.25,0.0
+350,15729689.0,Milanesi,748.0,Spain,Male,42.0,2.0,0.0,1.0,1.0,0.0,169915.02,0.0
+351,15724153.0,Ewing,686.0,France,Female,32.0,9.0,0.0,1.0,1.0,0.0,97416.34,0.0
+352,15633114.0,Bazarova,709.0,Spain,Male,39.0,4.0,0.0,2.0,1.0,0.0,123870.28,0.0
+353,15701392.0,Onyemauchechi,581.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,71323.23,0.0
+354,15641250.0,Udobata,645.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,141364.62,0.0
+355,15592773.0,Manna,662.0,Germany,Male,41.0,9.0,139528.95,2.0,1.0,1.0,189607.71,0.0
+356,15775307.0,Chu,538.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,138510.01,0.0
+357,15800811.0,Hargreaves,778.0,France,Male,32.0,10.0,148061.89,2.0,0.0,1.0,165082.25,0.0
+358,15793949.0,Lucchesi,757.0,France,Male,49.0,5.0,0.0,1.0,1.0,0.0,142514.71,1.0
+359,15600103.0,Nkemjika,646.0,France,Male,26.0,7.0,0.0,1.0,1.0,1.0,149536.15,0.0
+360,15772383.0,Ugochukwu,681.0,France,Male,34.0,2.0,0.0,1.0,0.0,0.0,147059.14,0.0
+361,15702434.0,Chia,567.0,Germany,Male,26.0,6.0,127678.8,2.0,1.0,0.0,126012.57,0.0
+362,15763063.0,Ndubueze,769.0,France,Female,48.0,9.0,0.0,1.0,1.0,0.0,88827.67,1.0
+363,15662063.0,Ts'ui,696.0,Germany,Female,44.0,8.0,132351.29,1.0,1.0,1.0,141706.43,0.0
+364,15627298.0,Nwankwo,687.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,103557.91,0.0
+365,15662494.0,Hs?eh,683.0,Spain,Female,40.0,2.0,119624.6,1.0,1.0,1.0,157567.37,0.0
+366,15813363.0,Hsiao,709.0,France,Female,28.0,3.0,0.0,1.0,1.0,0.0,147545.65,0.0
+367,15631267.0,Nwankwo,781.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,169920.92,0.0
+368,15736778.0,Ch'iu,634.0,Spain,Female,33.0,4.0,105435.73,1.0,1.0,0.0,134600.94,0.0
+369,15648067.0,Baresi,704.0,France,Male,44.0,4.0,177092.39,2.0,0.0,1.0,53373.61,1.0
+370,15638751.0,Chiu,686.0,France,Female,51.0,2.0,151510.17,1.0,1.0,0.0,72402.0,1.0
+371,15702541.0,Sun,708.0,France,Female,35.0,9.0,89045.25,1.0,1.0,1.0,115767.93,0.0
+372,15640648.0,Chimaobim,696.0,Germany,Male,36.0,3.0,151766.71,1.0,1.0,1.0,111421.77,0.0
+373,15762927.0,Hsueh,676.0,France,Male,46.0,3.0,178820.91,1.0,1.0,0.0,118749.62,0.0
+374,15605275.0,Chiemezie,732.0,Germany,Male,48.0,6.0,109949.05,1.0,1.0,1.0,170360.11,1.0
+375,15750545.0,Hsiao,659.0,France,Female,49.0,9.0,0.0,1.0,1.0,0.0,105760.01,1.0
+376,15728669.0,Nnamutaezinwa,719.0,Spain,Female,42.0,3.0,123948.54,1.0,1.0,1.0,184856.89,0.0
+377,15642093.0,Kuo,535.0,Germany,Female,56.0,5.0,63492.94,1.0,1.0,0.0,161241.65,1.0
+378,15584899.0,Chijindum,609.0,France,Female,39.0,2.0,138171.1,1.0,1.0,1.0,121408.06,0.0
+379,15667932.0,Walker,606.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,126679.69,0.0
+380,15672594.0,P'eng,588.0,Germany,Female,39.0,7.0,102238.01,2.0,1.0,1.0,85987.35,0.0
+381,15693893.0,Okwudilichukwu,648.0,Spain,Male,24.0,7.0,0.0,2.0,0.0,1.0,128881.87,0.0
+382,15782939.0,Nwankwo,706.0,France,Male,61.0,9.0,0.0,2.0,1.0,1.0,158937.57,0.0
+383,15790829.0,Teng,675.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,0.0,49374.82,0.0
+384,15809999.0,Chukwukadibia,628.0,France,Male,28.0,0.0,0.0,2.0,1.0,0.0,57905.25,0.0
+385,15742269.0,O'Callaghan,724.0,Germany,Female,45.0,5.0,123969.96,1.0,1.0,0.0,1099.95,1.0
+386,15690546.0,Ch'ien,590.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,1.0,47428.73,0.0
+387,15798834.0,Kao,706.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,38410.74,0.0
+388,15692977.0,Napolitani,685.0,France,Female,41.0,5.0,0.0,2.0,0.0,1.0,85522.88,0.0
+389,15732265.0,Toscani,673.0,Germany,Female,57.0,6.0,113865.23,1.0,0.0,1.0,102908.91,1.0
+390,15676937.0,Ojiofor,516.0,France,Male,43.0,5.0,0.0,1.0,1.0,0.0,146652.11,1.0
+391,15705281.0,Chiang,637.0,Spain,Female,44.0,5.0,0.0,2.0,0.0,1.0,103760.53,0.0
+392,15762218.0,Chiu,711.0,France,Male,39.0,6.0,97734.79,1.0,1.0,1.0,51972.92,0.0
+393,15791302.0,Tseng,810.0,Spain,Female,32.0,6.0,0.0,1.0,1.0,0.0,134732.99,0.0
+394,15699195.0,Nwebube,682.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,1.0,106040.97,0.0
+395,15766659.0,Chiemezie,598.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,0.0,121138.77,0.0
+396,15678058.0,Trevisano,706.0,France,Male,45.0,2.0,122917.71,1.0,1.0,0.0,151871.84,1.0
+397,15585036.0,Kaur,596.0,Spain,Female,35.0,5.0,86891.84,1.0,1.0,0.0,175893.53,0.0
+398,15570601.0,Brazenor,769.0,France,Female,26.0,8.0,0.0,2.0,1.0,1.0,56793.02,0.0
+399,15588517.0,Ch'ang,724.0,France,Female,31.0,1.0,0.0,2.0,0.0,1.0,81602.02,0.0
+400,15752488.0,Chineze,584.0,France,Male,35.0,4.0,148021.12,1.0,0.0,0.0,93336.3,0.0
+401,15672516.0,Kerr,586.0,Spain,Female,33.0,9.0,0.0,1.0,0.0,0.0,162053.92,1.0
+402,15581548.0,Genovesi,633.0,France,Female,33.0,9.0,0.0,3.0,1.0,0.0,136610.02,1.0
+403,15756981.0,Kornilova,678.0,Germany,Male,39.0,5.0,125470.81,2.0,1.0,1.0,169781.45,0.0
+404,15705918.0,Lettiere,647.0,France,Female,43.0,4.0,0.0,2.0,1.0,1.0,51548.14,0.0
+405,15759872.0,Maclean,605.0,Spain,Female,44.0,5.0,0.0,1.0,0.0,0.0,172501.38,1.0
+406,15751022.0,Kornilova,656.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,1.0,116885.59,0.0
+407,15751336.0,Creswell,648.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,40318.33,0.0
+408,15604020.0,Ch'ien,663.0,France,Male,36.0,4.0,127475.39,1.0,1.0,1.0,159629.64,0.0
+409,15677946.0,Mazzanti,679.0,Spain,Male,41.0,9.0,0.0,1.0,1.0,1.0,92982.61,1.0
+410,15600478.0,Ejimofor,627.0,Spain,Male,54.0,5.0,143350.41,3.0,1.0,0.0,101314.65,1.0
+411,15763665.0,Hsiung,686.0,France,Male,35.0,3.0,0.0,2.0,0.0,0.0,159835.91,0.0
+412,15597930.0,Hsia,790.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,171706.66,1.0
+413,15661827.0,Lucciano,732.0,Germany,Male,32.0,8.0,135219.57,2.0,1.0,0.0,170819.01,0.0
+414,15576085.0,Okwudilichukwu,757.0,France,Female,45.0,4.0,0.0,2.0,1.0,0.0,62280.91,0.0
+415,15690695.0,Nnachetam,696.0,France,Male,31.0,10.0,0.0,2.0,1.0,0.0,51548.14,0.0
+416,15793725.0,Chidiebere,671.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,113943.17,0.0
+417,15704583.0,Ibragimova,668.0,Germany,Female,28.0,2.0,131834.8,1.0,1.0,1.0,6076.62,1.0
+418,15752578.0,Trevisan,597.0,France,Female,39.0,5.0,0.0,1.0,1.0,1.0,176882.08,0.0
+419,15610557.0,Genovese,650.0,France,Male,37.0,7.0,0.0,1.0,0.0,1.0,97734.3,0.0
+420,15596972.0,Akabueze,754.0,France,Male,40.0,1.0,0.0,1.0,1.0,0.0,64872.55,1.0
+421,15607746.0,Okwudiliolisa,456.0,Germany,Female,41.0,8.0,102773.2,1.0,1.0,0.0,124095.69,1.0
+422,15692406.0,Chuang,647.0,France,Female,36.0,5.0,121376.15,1.0,1.0,1.0,53437.1,0.0
+423,15802466.0,Hsia,706.0,Spain,Female,29.0,8.0,141784.02,1.0,0.0,0.0,70774.97,0.0
+424,15745307.0,Mancini,651.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,189428.75,0.0
+425,15802478.0,Chukwudi,573.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,79919.13,0.0
+426,15626679.0,Shen,667.0,Germany,Female,50.0,7.0,157989.13,2.0,1.0,0.0,9087.81,0.0
+427,15696989.0,Ch'in,625.0,France,Female,57.0,8.0,0.0,1.0,1.0,0.0,121747.96,1.0
+428,15695475.0,Li,711.0,France,Male,54.0,7.0,0.0,1.0,1.0,0.0,51721.52,0.0
+429,15727041.0,Yuryeva,704.0,France,Male,38.0,1.0,0.0,1.0,1.0,1.0,136497.28,0.0
+430,15575060.0,Chukwuemeka,683.0,Spain,Male,40.0,6.0,0.0,1.0,0.0,0.0,166677.53,1.0
+431,15798895.0,Chukwujamuike,576.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,1.0,158918.02,0.0
+432,15590097.0,Chiedozie,652.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,166475.44,0.0
+433,15766686.0,Hsiao,605.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,51572.08,0.0
+434,15717185.0,Onyemere,648.0,Spain,Male,33.0,5.0,0.0,2.0,0.0,1.0,143683.75,0.0
+435,15656683.0,Chidimma,648.0,France,Male,61.0,2.0,0.0,2.0,1.0,1.0,79577.4,0.0
+436,15772250.0,Ukaegbunam,738.0,Spain,Male,41.0,9.0,0.0,1.0,1.0,0.0,53534.8,0.0
+437,15786063.0,Pisani,589.0,Germany,Female,36.0,1.0,108736.52,2.0,1.0,1.0,71823.15,0.0
+438,15700601.0,Cattaneo,649.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,52314.71,0.0
+439,15732266.0,Ch'eng,726.0,France,Male,39.0,0.0,142598.47,1.0,1.0,1.0,143598.7,0.0
+440,15628203.0,Chiu,661.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,82969.61,0.0
+441,15770174.0,Hao,586.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,63747.51,0.0
+442,15772412.0,Uchenna,605.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,166652.02,0.0
+443,15806925.0,Palermo,687.0,Germany,Female,43.0,9.0,119027.28,2.0,1.0,1.0,51554.58,0.0
+444,15574879.0,Chidiebele,707.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,111241.37,0.0
+445,15640648.0,Chiang,682.0,France,Female,31.0,5.0,0.0,1.0,1.0,0.0,16081.62,0.0
+446,15571778.0,Ugochukwutubelum,597.0,France,Male,33.0,9.0,0.0,1.0,1.0,0.0,106977.8,0.0
+447,15767757.0,Lucciano,567.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,153787.42,0.0
+448,15737133.0,Chukwueloka,631.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,57962.41,0.0
+449,15809837.0,Nwoye,673.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,65316.5,0.0
+450,15772896.0,Nucci,694.0,France,Male,26.0,7.0,0.0,1.0,1.0,1.0,102950.79,0.0
+451,15690733.0,K'ung,663.0,Germany,Female,28.0,9.0,127763.24,2.0,1.0,1.0,92888.52,0.0
+452,15596074.0,Chukwufumnanya,602.0,Germany,Male,27.0,2.0,117524.72,2.0,1.0,0.0,25395.83,0.0
+453,15702298.0,Sagese,528.0,Spain,Female,43.0,9.0,0.0,2.0,1.0,0.0,15959.01,1.0
+454,15793881.0,T'ang,735.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,170142.09,0.0
+455,15701885.0,P'an,682.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,51548.14,0.0
+456,15752463.0,Y?,554.0,France,Male,47.0,4.0,0.0,2.0,1.0,0.0,4791.8,0.0
+457,15792232.0,Chieloka,582.0,France,Male,40.0,2.0,0.0,2.0,0.0,1.0,86657.48,0.0
+458,15753043.0,Chinwenma,645.0,Germany,Female,48.0,10.0,155519.59,2.0,1.0,1.0,35640.12,0.0
+459,15777896.0,Hsieh,753.0,Germany,Female,40.0,8.0,133686.52,2.0,0.0,1.0,171070.59,0.0
+460,15664808.0,Lo,584.0,Spain,Male,23.0,8.0,0.0,2.0,0.0,0.0,105570.44,0.0
+461,15656592.0,Teng,636.0,Spain,Male,46.0,1.0,0.0,2.0,1.0,1.0,109145.2,0.0
+462,15708003.0,Fanucci,598.0,Spain,Male,20.0,9.0,0.0,2.0,1.0,1.0,114020.06,0.0
+463,15703778.0,Ch'ien,682.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,143882.25,0.0
+464,15589715.0,Nwankwo,750.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,0.0,149593.61,0.0
+465,15679961.0,Tien,705.0,France,Male,61.0,3.0,0.0,2.0,1.0,1.0,170950.58,0.0
+466,15616555.0,Chin,721.0,Spain,Female,43.0,1.0,0.0,3.0,1.0,0.0,136521.82,1.0
+467,15656121.0,Hsueh,654.0,Spain,Female,61.0,5.0,0.0,1.0,0.0,0.0,129870.93,1.0
+468,15725028.0,Udegbulam,648.0,France,Male,45.0,4.0,0.0,1.0,1.0,1.0,91560.63,0.0
+469,15723488.0,Shih,596.0,Germany,Male,25.0,8.0,133598.4,1.0,1.0,0.0,171922.72,0.0
+470,15683544.0,Nwankwo,735.0,Spain,Male,57.0,6.0,0.0,2.0,1.0,1.0,183542.08,0.0
+471,15754577.0,Baresi,596.0,France,Female,38.0,9.0,0.0,2.0,1.0,0.0,103570.8,0.0
+472,15808621.0,Chineze,732.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,83343.73,0.0
+473,15797767.0,Eluemuno,516.0,Spain,Male,45.0,0.0,0.0,1.0,1.0,0.0,90966.79,1.0
+474,15743545.0,Chukwuemeka,643.0,France,Male,34.0,6.0,81732.88,1.0,1.0,0.0,199392.14,0.0
+475,15732329.0,T'ang,656.0,Spain,Female,44.0,2.0,0.0,2.0,0.0,1.0,66795.78,0.0
+476,15759034.0,Iweobiegbunam,653.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,134589.58,0.0
+477,15693902.0,Bazarova,568.0,Spain,Male,70.0,2.0,0.0,1.0,1.0,0.0,146785.44,1.0
+478,15757837.0,Folliero,636.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,181879.56,1.0
+479,15624993.0,Chiu,703.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,182822.5,0.0
+480,15674932.0,Milanesi,637.0,France,Male,36.0,0.0,0.0,1.0,1.0,0.0,167030.51,0.0
+481,15729574.0,Shao,564.0,France,Male,36.0,10.0,0.0,1.0,0.0,0.0,144593.5,0.0
+482,15663438.0,Trevisani,605.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,108699.59,0.0
+483,15780650.0,Onwuamaeze,582.0,France,Male,29.0,8.0,89900.24,2.0,1.0,1.0,58854.88,0.0
+484,15656592.0,Liao,584.0,Spain,Male,33.0,6.0,0.0,2.0,1.0,1.0,145524.36,0.0
+485,15742204.0,Ch'ang,656.0,Spain,Male,34.0,9.0,0.0,2.0,0.0,1.0,1187.88,0.0
+486,15656829.0,Ogochukwu,704.0,Germany,Male,50.0,1.0,106301.85,1.0,0.0,0.0,171378.77,1.0
+487,15719304.0,Onwumelu,539.0,France,Female,40.0,0.0,0.0,2.0,0.0,0.0,75937.48,0.0
+488,15618581.0,Iweobiegbunam,605.0,Germany,Female,37.0,7.0,72783.42,1.0,0.0,0.0,131259.6,0.0
+489,15645316.0,L?,709.0,France,Female,31.0,9.0,83008.31,1.0,1.0,0.0,54474.48,0.0
+490,15773792.0,Bischof,677.0,Germany,Female,34.0,8.0,106045.92,2.0,1.0,1.0,131043.1,0.0
+491,15722010.0,Tuan,582.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,167564.82,0.0
+492,15792649.0,T'ien,637.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,103560.98,0.0
+493,15605916.0,Hsueh,581.0,Germany,Male,28.0,5.0,111835.94,1.0,1.0,0.0,123207.17,0.0
+494,15681562.0,Olisaemeka,587.0,France,Male,40.0,1.0,0.0,1.0,1.0,0.0,82872.64,1.0
+495,15602979.0,Liao,661.0,Spain,Male,38.0,4.0,0.0,1.0,0.0,0.0,140937.97,0.0
+496,15760450.0,Ch'eng,743.0,France,Male,43.0,7.0,137592.96,1.0,1.0,1.0,155876.06,0.0
+497,15778936.0,Ch'in,633.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,173365.89,0.0
+498,15777060.0,Ekechukwu,632.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,148527.56,0.0
+499,15765322.0,Yegorov,523.0,France,Female,57.0,1.0,0.0,1.0,1.0,1.0,79919.13,0.0
+500,15758032.0,Chiu,579.0,France,Male,41.0,5.0,0.0,2.0,0.0,0.0,105898.69,0.0
+501,15723484.0,Sagese,576.0,Spain,Female,40.0,10.0,0.0,1.0,1.0,1.0,171706.66,1.0
+502,15798975.0,Ozerova,631.0,Spain,Male,28.0,3.0,0.0,2.0,1.0,1.0,127543.81,0.0
+503,15681956.0,Hsing,665.0,Germany,Female,54.0,3.0,171740.69,1.0,1.0,0.0,164866.87,1.0
+504,15669957.0,Milanesi,709.0,Germany,Female,31.0,4.0,141749.68,2.0,0.0,1.0,107841.77,0.0
+505,15616666.0,Piazza,712.0,France,Male,33.0,8.0,0.0,1.0,0.0,1.0,127543.81,0.0
+506,15793317.0,Fanucci,695.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,50368.63,0.0
+507,15651737.0,Ting,589.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,134878.19,0.0
+508,15616700.0,Milanesi,730.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,180800.42,0.0
+509,15641043.0,Ch'en,741.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,178458.86,0.0
+510,15634602.0,Calabrese,584.0,France,Female,42.0,8.0,152622.34,1.0,1.0,0.0,61820.41,1.0
+511,15691150.0,Ch'iu,680.0,Germany,Female,33.0,5.0,122726.56,1.0,1.0,0.0,175893.53,0.0
+512,15687959.0,Hsieh,691.0,France,Male,45.0,5.0,0.0,2.0,1.0,1.0,121592.5,0.0
+513,15625023.0,Teng,706.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,140586.08,0.0
+514,15761417.0,Chigozie,545.0,France,Male,45.0,5.0,0.0,2.0,1.0,1.0,183515.13,0.0
+515,15586914.0,Yudina,619.0,France,Female,51.0,9.0,152462.79,1.0,1.0,1.0,166472.81,1.0
+516,15668894.0,P'eng,542.0,Germany,Female,32.0,8.0,142644.64,1.0,1.0,0.0,162760.96,0.0
+517,15758031.0,Hsieh,678.0,France,Male,24.0,9.0,0.0,2.0,1.0,1.0,93839.3,0.0
+518,15681075.0,Chukwuemeka,558.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,56905.38,0.0
+519,15724444.0,Chukwuemeka,609.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,159718.16,0.0
+520,15593854.0,K'ung,683.0,Germany,Female,48.0,3.0,134446.04,2.0,1.0,1.0,179861.79,0.0
+521,15646513.0,Ch'eng,850.0,France,Female,28.0,10.0,0.0,2.0,1.0,0.0,171096.2,0.0
+522,15764708.0,Folliero,645.0,Spain,Male,24.0,9.0,141078.57,1.0,1.0,0.0,141501.45,0.0
+523,15793046.0,Ch'iu,645.0,Spain,Female,39.0,2.0,0.0,2.0,0.0,1.0,187638.34,0.0
+524,15576723.0,Ritchie,758.0,Spain,Male,40.0,5.0,145747.67,1.0,1.0,0.0,181258.76,0.0
+525,15670066.0,Lombardo,579.0,Spain,Male,48.0,1.0,0.0,1.0,0.0,1.0,125557.95,1.0
+526,15803456.0,Chu,528.0,Germany,Female,29.0,4.0,135096.05,1.0,1.0,1.0,164038.07,0.0
+527,15572767.0,Ugochukwu,757.0,France,Female,50.0,0.0,0.0,1.0,1.0,0.0,113868.4,1.0
+528,15641181.0,Ch'ang,639.0,France,Male,29.0,5.0,118862.76,1.0,1.0,0.0,82970.69,0.0
+529,15711287.0,Chinwemma,675.0,France,Male,43.0,2.0,0.0,2.0,0.0,1.0,120586.32,0.0
+530,15797767.0,Y?,848.0,France,Male,32.0,2.0,0.0,1.0,1.0,1.0,91098.76,0.0
+531,15621611.0,Trevisani,748.0,France,Male,31.0,3.0,0.0,1.0,1.0,1.0,95655.16,0.0
+532,15692235.0,Nock,665.0,Spain,Male,38.0,8.0,123544.0,1.0,1.0,1.0,189414.74,0.0
+533,15765173.0,Fitch,697.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,120790.02,0.0
+534,15726354.0,Rizzo,545.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,1.0,112491.96,0.0
+535,15774847.0,Lucchesi,716.0,France,Female,60.0,9.0,0.0,1.0,1.0,0.0,180716.1,1.0
+536,15750099.0,Ratten,713.0,France,Female,30.0,6.0,119961.48,1.0,1.0,1.0,147822.54,0.0
+537,15589435.0,Eluemuno,609.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,60920.03,0.0
+538,15726588.0,Chia,653.0,France,Male,33.0,7.0,133489.12,1.0,0.0,1.0,125961.48,0.0
+539,15632551.0,Ulyanova,684.0,France,Male,31.0,3.0,0.0,2.0,0.0,1.0,161465.31,0.0
+540,15773971.0,DeRose,756.0,Germany,Male,36.0,3.0,144961.97,1.0,0.0,0.0,185543.68,1.0
+541,15794868.0,Paling,721.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,159823.16,0.0
+542,15681075.0,Fisk,633.0,Germany,Female,38.0,1.0,113715.36,2.0,0.0,0.0,170980.86,0.0
+543,15804052.0,Chia,632.0,France,Male,37.0,1.0,93791.38,1.0,0.0,1.0,72993.65,0.0
+544,15643865.0,Nwachetam,721.0,Spain,Female,35.0,5.0,147493.89,3.0,1.0,0.0,129980.93,1.0
+545,15751137.0,Hs?,652.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,91560.63,0.0
+546,15804950.0,Chukwuebuka,792.0,Spain,Male,36.0,7.0,97565.74,1.0,1.0,1.0,133897.32,0.0
+547,15635893.0,Teng,743.0,Germany,Male,37.0,3.0,77846.9,1.0,1.0,1.0,84467.38,0.0
+548,15747358.0,Hsia,749.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,90883.96,0.0
+549,15659800.0,Chiganu,794.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,0.0,110916.15,0.0
+550,15569503.0,H?,651.0,France,Male,37.0,6.0,140614.15,1.0,1.0,0.0,160129.99,0.0
+551,15783526.0,Iloerika,591.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,97985.4,0.0
+552,15591512.0,Macleod,728.0,Germany,Female,30.0,7.0,111837.78,1.0,1.0,1.0,163736.09,0.0
+553,15678497.0,Chiu,445.0,France,Male,45.0,9.0,0.0,2.0,0.0,0.0,162501.83,1.0
+554,15663883.0,Liao,667.0,France,Male,31.0,6.0,106011.59,1.0,1.0,0.0,153396.05,0.0
+555,15614962.0,Chukwueloka,850.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,153727.32,0.0
+556,15576676.0,Chikwendu,699.0,Germany,Female,56.0,6.0,132088.59,1.0,1.0,1.0,92914.67,1.0
+557,15675337.0,Harker,731.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,114935.21,0.0
+558,15756026.0,Nkemakonam,709.0,Spain,Male,30.0,3.0,0.0,1.0,1.0,0.0,9241.78,0.0
+559,15585928.0,Toscani,613.0,France,Male,28.0,7.0,0.0,1.0,0.0,1.0,51387.1,0.0
+560,15764033.0,Biryukov,682.0,Spain,Male,42.0,9.0,0.0,1.0,1.0,1.0,87822.14,0.0
+561,15615352.0,Chiang,627.0,France,Male,31.0,7.0,152303.8,1.0,1.0,0.0,112503.24,0.0
+562,15772337.0,Chukwuebuka,686.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,143450.61,0.0
+563,15595440.0,Trevisano,818.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,176913.42,0.0
+564,15673790.0,Kao,661.0,Germany,Male,39.0,3.0,113590.73,1.0,1.0,0.0,62703.38,1.0
+565,15662063.0,Ch'iu,738.0,Spain,Male,50.0,8.0,0.0,2.0,1.0,1.0,82298.59,0.0
+566,15680405.0,Chukwualuka,721.0,Spain,Male,27.0,2.0,0.0,2.0,0.0,0.0,187900.12,0.0
+567,15583718.0,Lucciano,713.0,France,Female,43.0,3.0,0.0,2.0,1.0,1.0,152992.04,0.0
+568,15598510.0,Chikezie,571.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,82822.34,0.0
+569,15632050.0,K'ung,559.0,France,Male,23.0,3.0,0.0,2.0,1.0,1.0,70532.53,0.0
+570,15673529.0,Y?an,636.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,133535.29,0.0
+571,15690829.0,Nnanna,629.0,France,Male,29.0,1.0,73070.18,1.0,0.0,0.0,132798.65,0.0
+572,15771742.0,Liao,683.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,1.0,145792.01,0.0
+573,15653780.0,Pirozzi,682.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,49707.85,0.0
+574,15804052.0,P'eng,622.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,86935.08,0.0
+575,15568106.0,Macdonald,745.0,France,Female,39.0,5.0,111094.05,1.0,1.0,0.0,133051.64,0.0
+576,15646521.0,Chin,729.0,France,Female,41.0,1.0,0.0,1.0,1.0,0.0,103480.69,1.0
+577,15811588.0,Mazzi,641.0,Spain,Male,30.0,2.0,0.0,1.0,1.0,0.0,109145.2,0.0
+578,15647898.0,Ch'in,699.0,Spain,Male,30.0,8.0,0.0,1.0,0.0,0.0,91514.62,0.0
+579,15666884.0,Milanesi,749.0,France,Male,29.0,8.0,0.0,2.0,0.0,1.0,88890.05,0.0
+580,15656937.0,Linger,651.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,86099.23,0.0
+581,15724150.0,Ibeamaka,691.0,Spain,Male,33.0,3.0,102645.48,2.0,1.0,0.0,135925.72,0.0
+582,15691823.0,Chukwudi,627.0,France,Female,41.0,2.0,0.0,1.0,1.0,0.0,182784.11,0.0
+583,15771856.0,T'ien,595.0,France,Male,24.0,8.0,0.0,2.0,1.0,1.0,132038.65,0.0
+584,15591286.0,Iloerika,669.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,146891.07,0.0
+585,15783718.0,Shih,673.0,Germany,Female,44.0,1.0,124099.13,1.0,1.0,0.0,91573.19,1.0
+586,15784761.0,Ukaegbunam,582.0,Spain,Female,48.0,1.0,0.0,1.0,0.0,1.0,182738.0,1.0
+587,15654096.0,Pinto,607.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,1354.25,0.0
+588,15813140.0,Chiu,468.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,172049.84,0.0
+589,15600974.0,Tuan,526.0,Spain,Female,36.0,8.0,141573.55,1.0,1.0,0.0,75832.53,0.0
+590,15682533.0,Young,717.0,Germany,Female,31.0,5.0,157791.06,1.0,1.0,1.0,153662.88,0.0
+591,15741875.0,Ugochukwu,563.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,47832.82,0.0
+592,15720354.0,Ch'eng,696.0,France,Male,33.0,8.0,134446.04,1.0,1.0,1.0,126396.01,0.0
+593,15769254.0,Onuoha,758.0,Germany,Male,32.0,7.0,102703.62,2.0,0.0,1.0,188244.4,0.0
+594,15643487.0,Zuyev,608.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,187943.6,0.0
+595,15598044.0,Martin,646.0,France,Female,39.0,2.0,151242.99,1.0,1.0,1.0,62722.44,0.0
+596,15776629.0,Ts'ai,681.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,149665.65,0.0
+597,15672798.0,Onyemauchechukwu,592.0,France,Male,28.0,1.0,0.0,2.0,1.0,1.0,93835.64,0.0
+598,15681696.0,Fiorentini,521.0,Germany,Female,23.0,4.0,110822.9,1.0,1.0,1.0,160979.66,0.0
+599,15668747.0,Mazzanti,623.0,Germany,Male,30.0,3.0,106036.52,2.0,1.0,0.0,171733.22,0.0
+600,15802859.0,Maclean,790.0,France,Female,31.0,6.0,110864.38,1.0,1.0,1.0,61636.22,0.0
+601,15783029.0,Yeh,633.0,Germany,Male,31.0,8.0,141528.88,2.0,1.0,0.0,55859.27,0.0
+602,15711015.0,Nkemdilim,613.0,Germany,Male,37.0,1.0,116498.22,1.0,1.0,1.0,88866.92,0.0
+603,15640855.0,Fennell,569.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,73564.44,0.0
+604,15783026.0,T'ien,685.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,140881.2,0.0
+605,15656592.0,Ch'iu,661.0,Spain,Male,32.0,6.0,0.0,2.0,0.0,1.0,85411.01,0.0
+606,15589210.0,Hs?,738.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,163798.41,0.0
+607,15624592.0,K?,595.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,9241.83,0.0
+608,15773776.0,Ozioma,609.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,103944.2,0.0
+609,15796969.0,Macleod,687.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,1.0,79633.38,0.0
+610,15776939.0,Chu,667.0,Germany,Female,40.0,9.0,79340.95,1.0,1.0,1.0,133573.29,0.0
+611,15627395.0,Young,684.0,France,Female,27.0,8.0,0.0,2.0,0.0,0.0,64350.8,0.0
+612,15724296.0,P'eng,633.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,111698.0,0.0
+613,15743016.0,Hsia,635.0,Spain,Male,44.0,3.0,124532.4,1.0,1.0,0.0,120853.83,0.0
+614,15718773.0,Y?,645.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,83617.53,0.0
+615,15626485.0,Ch'ien,603.0,France,Male,24.0,1.0,125837.42,1.0,1.0,1.0,138540.24,0.0
+616,15646615.0,Yefremova,683.0,Spain,Male,61.0,8.0,0.0,1.0,1.0,1.0,180716.1,0.0
+617,15726695.0,Chidozie,850.0,Germany,Male,39.0,4.0,115282.3,2.0,1.0,1.0,94820.85,0.0
+618,15799653.0,Chukwubuikem,605.0,Germany,Male,29.0,6.0,120787.68,1.0,1.0,0.0,129890.93,0.0
+619,15687431.0,Maclean,662.0,France,Male,22.0,9.0,0.0,2.0,1.0,1.0,134612.41,0.0
+620,15631267.0,Okorie,603.0,France,Female,50.0,6.0,59532.18,1.0,1.0,0.0,87071.18,1.0
+621,15568748.0,Onuoha,716.0,France,Male,52.0,7.0,78653.05,1.0,0.0,0.0,164717.95,1.0
+622,15576526.0,Clements,538.0,Spain,Male,44.0,9.0,111723.08,1.0,0.0,0.0,143463.28,0.0
+623,15692226.0,Ch'ien,667.0,France,Female,41.0,6.0,0.0,2.0,1.0,0.0,70774.97,0.0
+624,15668032.0,Hsia,651.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,167909.52,0.0
+625,15601012.0,Ts'ai,726.0,Spain,Female,39.0,4.0,0.0,2.0,1.0,1.0,158973.77,0.0
+626,15756901.0,Burt,590.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,0.0,167553.06,0.0
+627,15652030.0,Nnachetam,556.0,France,Female,30.0,10.0,0.0,2.0,1.0,1.0,179445.28,0.0
+628,15679760.0,Nwabugwu,684.0,Germany,Male,29.0,9.0,150826.99,1.0,0.0,1.0,132038.65,0.0
+629,15703614.0,Shih,644.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,184748.23,1.0
+630,15686884.0,Ikedinachukwu,714.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,147964.99,0.0
+631,15787597.0,Kao,628.0,Germany,Male,46.0,1.0,140187.1,1.0,1.0,1.0,155876.06,0.0
+632,15801994.0,Onwuamaeze,663.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,141872.05,0.0
+633,15799652.0,Udegbunam,739.0,Spain,Male,35.0,3.0,0.0,2.0,0.0,0.0,121763.68,0.0
+634,15661821.0,L?,653.0,Spain,Male,32.0,9.0,141749.74,1.0,1.0,1.0,94875.03,0.0
+635,15692575.0,L?,683.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,141180.66,0.0
+636,15623210.0,Ritchie,644.0,France,Male,45.0,7.0,0.0,2.0,1.0,1.0,171483.82,0.0
+637,15579566.0,Mancini,659.0,France,Male,24.0,4.0,0.0,2.0,1.0,0.0,163331.65,0.0
+638,15754569.0,T'ien,641.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,86022.09,0.0
+639,15752731.0,L?,704.0,France,Female,60.0,7.0,0.0,1.0,0.0,0.0,95957.09,1.0
+640,15638806.0,Maclean,711.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,82438.13,0.0
+641,15594577.0,Ting,717.0,France,Male,36.0,0.0,126473.99,1.0,1.0,0.0,182867.84,0.0
+642,15686963.0,Chiang,749.0,Spain,Male,47.0,5.0,0.0,2.0,1.0,1.0,62845.64,0.0
+643,15625126.0,Y?,703.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,140745.91,0.0
+644,15793046.0,Li Fonti,675.0,France,Male,27.0,3.0,143566.12,1.0,1.0,1.0,164040.94,0.0
+645,15750728.0,Yermakova,707.0,France,Male,39.0,3.0,0.0,1.0,1.0,1.0,129561.87,0.0
+646,15775238.0,Mironova,641.0,France,Male,35.0,7.0,121537.71,1.0,1.0,0.0,25803.59,0.0
+647,15703588.0,Nwachinemelu,719.0,Germany,Female,50.0,6.0,116576.45,1.0,1.0,0.0,172629.67,1.0
+648,15753895.0,Ting,723.0,France,Male,30.0,0.0,0.0,2.0,1.0,0.0,121063.9,0.0
+649,15790235.0,Nwokeocha,544.0,France,Male,35.0,4.0,0.0,1.0,1.0,0.0,57844.26,1.0
+650,15812191.0,Chiang,676.0,Germany,Female,40.0,9.0,111065.93,2.0,1.0,0.0,81898.81,0.0
+651,15712121.0,Martin,648.0,France,Female,62.0,9.0,0.0,1.0,1.0,1.0,172668.39,1.0
+652,15803378.0,Onio,673.0,France,Female,43.0,4.0,155739.76,1.0,0.0,1.0,111622.76,0.0
+653,15682454.0,Nucci,727.0,France,Female,37.0,5.0,0.0,1.0,0.0,1.0,120106.1,0.0
+654,15585595.0,T'an,602.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,0.0,177614.17,0.0
+655,15782941.0,Onwumelu,586.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,60954.45,0.0
+656,15718289.0,Hsiao,669.0,Germany,Female,50.0,5.0,131834.76,1.0,0.0,0.0,70774.97,1.0
+657,15696900.0,Ch'ien,699.0,France,Male,37.0,7.0,98548.62,1.0,0.0,0.0,63736.17,0.0
+658,15572291.0,Pethard,604.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,52409.87,0.0
+659,15604497.0,Milne,597.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,123981.36,0.0
+660,15773789.0,Kao,656.0,France,Female,34.0,1.0,0.0,2.0,0.0,0.0,173498.45,0.0
+661,15653547.0,Ts'ui,742.0,France,Female,38.0,2.0,107784.65,1.0,1.0,0.0,174729.3,0.0
+662,15705521.0,Hsiung,850.0,Germany,Male,25.0,3.0,103656.82,2.0,1.0,1.0,91070.58,0.0
+663,15703763.0,Onyinyechukwuka,751.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,120511.04,0.0
+664,15649024.0,Genovesi,521.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,53478.02,0.0
+665,15771054.0,Hsia,686.0,France,Female,53.0,6.0,0.0,1.0,1.0,1.0,137028.51,0.0
+666,15597915.0,Ch'ien,684.0,Spain,Female,28.0,6.0,126817.65,1.0,1.0,1.0,148527.56,0.0
+667,15631813.0,P'eng,721.0,Germany,Female,42.0,10.0,124427.33,1.0,1.0,0.0,132973.15,1.0
+668,15702541.0,Pirozzi,646.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,176712.59,0.0
+669,15736112.0,Ukaegbunam,592.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,131300.68,0.0
+670,15703923.0,Palermo,641.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,180551.76,0.0
+671,15776051.0,Yefimova,721.0,France,Male,35.0,7.0,0.0,1.0,1.0,0.0,96660.76,1.0
+672,15780128.0,Nwabugwu,612.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,132649.85,0.0
+673,15792868.0,Onyemauchechukwu,554.0,France,Male,46.0,3.0,0.0,1.0,1.0,1.0,706.5,0.0
+674,15641640.0,Kuo,554.0,Spain,Male,29.0,10.0,0.0,2.0,1.0,1.0,109843.16,0.0
+675,15770039.0,Tien,581.0,France,Male,36.0,8.0,0.0,2.0,0.0,0.0,132424.69,0.0
+676,15621644.0,Maclean,573.0,France,Female,39.0,10.0,175314.72,1.0,1.0,1.0,92647.94,0.0
+677,15786717.0,De Luca,714.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,130883.9,0.0
+678,15651022.0,Fanucci,538.0,France,Male,19.0,1.0,0.0,2.0,0.0,0.0,125445.8,0.0
+679,15595427.0,Ch'in,773.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,94003.11,0.0
+680,15746461.0,Nwachukwu,725.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,0.0,100433.83,0.0
+681,15586680.0,Kosisochukwu,716.0,Germany,Male,32.0,9.0,122724.51,1.0,1.0,1.0,74800.99,0.0
+682,15762799.0,Onwubiko,682.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,186489.95,0.0
+683,15569249.0,Bibi,563.0,Germany,Male,36.0,3.0,119657.53,1.0,1.0,1.0,96444.88,0.0
+684,15697567.0,Chiemela,805.0,Germany,Female,45.0,4.0,108239.11,2.0,1.0,1.0,180598.28,0.0
+685,15577587.0,DeRose,586.0,France,Female,42.0,6.0,0.0,2.0,1.0,1.0,141763.27,0.0
+686,15758813.0,McIntyre,621.0,Germany,Male,47.0,9.0,123971.27,2.0,0.0,0.0,78650.95,1.0
+687,15780654.0,Chin,635.0,France,Male,35.0,8.0,0.0,2.0,0.0,1.0,64872.55,0.0
+688,15778959.0,Manna,646.0,France,Male,44.0,2.0,0.0,2.0,1.0,1.0,181419.29,0.0
+689,15775231.0,Ch'ien,775.0,Spain,Male,44.0,3.0,0.0,2.0,1.0,0.0,170634.49,0.0
+690,15761053.0,Onwubiko,706.0,France,Male,39.0,4.0,119336.29,1.0,0.0,0.0,55732.9,0.0
+691,15651955.0,O'Sullivan,676.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,1.0,145789.71,0.0
+692,15774551.0,Babbage,641.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,89286.4,0.0
+693,15777784.0,Lo Duca,741.0,France,Male,42.0,6.0,0.0,2.0,1.0,1.0,166694.93,0.0
+694,15716276.0,Lo,669.0,Spain,Male,43.0,4.0,0.0,2.0,0.0,1.0,176902.83,0.0
+695,15743538.0,Lucciano,633.0,Germany,Male,37.0,10.0,131935.56,1.0,0.0,0.0,146511.2,0.0
+696,15664555.0,Tretiakov,642.0,Germany,Male,39.0,9.0,131326.47,1.0,1.0,0.0,83058.14,0.0
+697,15691840.0,Ku,693.0,Germany,Female,39.0,1.0,118628.8,1.0,0.0,0.0,142963.29,0.0
+698,15705860.0,Chidimma,662.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,59325.21,0.0
+699,15769917.0,De Luca,637.0,France,Male,40.0,4.0,0.0,2.0,0.0,1.0,176843.53,0.0
+700,15605698.0,Ch'ien,585.0,Spain,Female,48.0,9.0,0.0,1.0,1.0,1.0,111285.85,1.0
+701,15603749.0,K'ung,676.0,Germany,Male,27.0,7.0,165445.04,2.0,0.0,1.0,73334.04,0.0
+702,15585961.0,Nwora,744.0,France,Male,31.0,10.0,94748.76,1.0,1.0,1.0,167023.95,0.0
+703,15768682.0,Chibugo,803.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,100998.5,0.0
+704,15702801.0,Zikoranachidimma,684.0,Spain,Male,42.0,6.0,0.0,2.0,1.0,0.0,81884.8,0.0
+705,15582914.0,Ts'ai,663.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,141501.45,0.0
+706,15680197.0,Ku,586.0,Spain,Female,36.0,9.0,0.0,1.0,0.0,1.0,56905.38,0.0
+707,15609637.0,Hsiung,685.0,Germany,Male,36.0,8.0,96679.69,2.0,1.0,0.0,52462.25,0.0
+708,15623989.0,Tsao,621.0,Spain,Female,37.0,3.0,0.0,1.0,1.0,0.0,54474.48,0.0
+709,15717225.0,Kepley,708.0,France,Male,37.0,6.0,113909.67,1.0,1.0,0.0,175749.36,0.0
+710,15662091.0,Goliwe,703.0,France,Male,41.0,4.0,0.0,1.0,1.0,0.0,112269.67,1.0
+711,15812191.0,Chibugo,681.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,90839.61,0.0
+712,15751561.0,Iadanza,717.0,Spain,Male,51.0,4.0,0.0,2.0,1.0,1.0,52487.98,0.0
+713,15663438.0,P'eng,691.0,Spain,Female,32.0,3.0,110182.54,1.0,1.0,0.0,134878.19,0.0
+714,15782736.0,Mazzanti,543.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,170821.43,0.0
+715,15720354.0,Hsieh,757.0,Germany,Male,53.0,5.0,137453.43,1.0,1.0,0.0,171708.06,1.0
+716,15746035.0,Liao,675.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,125986.18,0.0
+717,15751175.0,Onyekaozulu,707.0,France,Male,29.0,1.0,0.0,2.0,0.0,0.0,128892.66,0.0
+718,15667633.0,Chung,592.0,Spain,Male,43.0,4.0,0.0,1.0,1.0,0.0,63908.86,1.0
+719,15682355.0,Chibugo,737.0,Spain,Male,24.0,3.0,0.0,2.0,1.0,0.0,143783.46,0.0
+720,15598510.0,Lucchesi,686.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,84908.2,0.0
+721,15654463.0,H?,468.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,171378.77,0.0
+722,15693376.0,Chukwuemeka,599.0,Germany,Female,53.0,10.0,117561.49,1.0,1.0,1.0,127558.26,1.0
+723,15732293.0,Nkemakonam,664.0,France,Male,37.0,1.0,94464.81,1.0,1.0,0.0,54474.48,0.0
+724,15768282.0,Walker,584.0,Germany,Male,32.0,6.0,103819.43,1.0,1.0,1.0,62347.71,0.0
+725,15606097.0,Nnaemeka,567.0,Spain,Male,24.0,5.0,0.0,2.0,1.0,1.0,166476.95,0.0
+726,15771409.0,Onwumelu,725.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,55313.51,0.0
+727,15658969.0,Calabresi,644.0,France,Female,35.0,10.0,0.0,2.0,1.0,1.0,54487.43,0.0
+728,15641158.0,Ch'in,705.0,France,Male,51.0,9.0,0.0,1.0,1.0,0.0,109057.56,1.0
+729,15811490.0,Padovano,581.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,0.0,83022.09,0.0
+730,15716041.0,Ch'ien,839.0,France,Female,36.0,9.0,116886.59,1.0,1.0,0.0,60905.51,0.0
+731,15586699.0,Trevisani,752.0,Germany,Male,38.0,9.0,126573.33,2.0,1.0,1.0,136497.28,0.0
+732,15672397.0,Chiu,486.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,176868.89,0.0
+733,15739168.0,Atherton,582.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,95957.09,0.0
+734,15578908.0,Nnaife,681.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,162523.66,0.0
+735,15778975.0,Maclean,637.0,France,Female,31.0,4.0,140133.08,1.0,1.0,0.0,12483.56,0.0
+736,15578788.0,Uwaezuoke,586.0,Germany,Male,39.0,0.0,127616.56,1.0,0.0,0.0,149913.84,0.0
+737,15773776.0,Chukwualuka,762.0,France,Male,41.0,10.0,0.0,2.0,1.0,1.0,66469.2,0.0
+738,15572467.0,Chukwujamuike,568.0,France,Male,42.0,1.0,0.0,1.0,1.0,1.0,49826.68,1.0
+739,15649150.0,Shih,684.0,France,Female,60.0,1.0,137714.7,1.0,0.0,1.0,71179.31,1.0
+740,15713774.0,Chien,706.0,France,Female,44.0,8.0,0.0,2.0,1.0,0.0,176903.35,0.0
+741,15792631.0,Chia,691.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,52462.25,0.0
+742,15634143.0,Hsing,689.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,131662.47,0.0
+743,15650026.0,Rischbieth,569.0,Germany,Male,28.0,5.0,77002.2,2.0,0.0,0.0,35337.31,0.0
+744,15660385.0,Pugh,829.0,France,Male,53.0,5.0,0.0,1.0,1.0,0.0,123882.73,1.0
+745,15783659.0,Hsing,775.0,France,Female,38.0,5.0,121355.99,1.0,1.0,1.0,64477.25,0.0
+746,15773394.0,Tsou,669.0,France,Male,39.0,6.0,0.0,1.0,1.0,0.0,63889.1,0.0
+747,15724181.0,H?,651.0,Germany,Male,38.0,6.0,122417.17,2.0,1.0,1.0,146511.2,0.0
+748,15740356.0,Chijioke,755.0,Spain,Male,43.0,5.0,122449.18,1.0,0.0,0.0,142783.98,0.0
+749,15679760.0,Yobachukwu,678.0,France,Female,50.0,9.0,0.0,1.0,1.0,0.0,88827.67,1.0
+750,15729557.0,K'ung,682.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,115779.48,0.0
+751,15688865.0,Yobachi,586.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,26793.82,0.0
+752,15585853.0,Piazza,632.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,79919.13,0.0
+753,15806926.0,Genovese,706.0,Spain,Female,51.0,3.0,106687.76,1.0,1.0,0.0,64593.07,1.0
+754,15592994.0,Ch'ang,704.0,France,Female,23.0,8.0,107594.11,1.0,0.0,1.0,132467.76,0.0
+755,15798850.0,Hsing,666.0,Spain,Female,38.0,4.0,0.0,1.0,1.0,1.0,17095.49,0.0
+756,15778025.0,Y?an,587.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,1.0,157959.02,1.0
+757,15802593.0,Hsia,684.0,France,Male,41.0,1.0,153696.97,1.0,0.0,1.0,117572.59,0.0
+758,15716781.0,Chigozie,687.0,Germany,Female,44.0,6.0,102290.56,1.0,1.0,0.0,172576.43,1.0
+759,15570087.0,Zetticci,699.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,126812.63,0.0
+760,15624072.0,Skelton,619.0,Germany,Female,31.0,2.0,134006.77,2.0,1.0,1.0,96864.36,0.0
+761,15729238.0,Chiagoziem,543.0,Spain,Male,42.0,10.0,178032.53,1.0,1.0,1.0,88837.15,0.0
+762,15652693.0,Baresi,577.0,France,Male,25.0,5.0,0.0,2.0,1.0,1.0,60917.24,0.0
+763,15789863.0,Hsueh,637.0,Germany,Male,34.0,6.0,81564.1,1.0,1.0,0.0,158027.57,0.0
+764,15617166.0,Onyekaozulu,679.0,Germany,Male,49.0,4.0,109740.53,1.0,1.0,0.0,117832.88,1.0
+765,15705968.0,Pinto,769.0,France,Male,42.0,2.0,0.0,1.0,0.0,0.0,3185.67,0.0
+766,15743826.0,Nwokezuike,648.0,France,Male,54.0,6.0,139872.81,1.0,1.0,1.0,175072.47,1.0
+767,15750447.0,Hsieh,746.0,Germany,Male,29.0,10.0,144772.69,2.0,1.0,1.0,71830.1,0.0
+768,15739931.0,Palerma,692.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,0.0,83052.03,0.0
+769,15608528.0,Onyemere,678.0,France,Female,25.0,5.0,0.0,2.0,1.0,0.0,163534.6,0.0
+770,15584364.0,Chinwemma,640.0,France,Male,49.0,6.0,0.0,2.0,0.0,0.0,91573.19,0.0
+771,15718057.0,T'an,686.0,Germany,Male,33.0,8.0,122295.17,1.0,1.0,0.0,173632.92,0.0
+772,15718289.0,Nkemdirim,604.0,France,Male,31.0,4.0,120623.21,1.0,1.0,0.0,102515.42,0.0
+773,15668958.0,Trevisano,633.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,107964.36,0.0
+774,15791373.0,Tsao,632.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,180553.48,0.0
+775,15700654.0,Iadanza,587.0,France,Female,58.0,2.0,0.0,2.0,1.0,1.0,11783.1,0.0
+776,15806964.0,Smith,663.0,France,Female,38.0,9.0,0.0,1.0,1.0,1.0,170621.55,0.0
+777,15667460.0,Clements,726.0,France,Female,34.0,7.0,105473.74,2.0,1.0,0.0,49136.29,0.0
+778,15710397.0,Nwankwo,583.0,France,Male,34.0,2.0,0.0,1.0,1.0,0.0,141872.05,1.0
+779,15684861.0,Genovese,708.0,France,Male,32.0,3.0,0.0,1.0,0.0,1.0,150580.88,0.0
+780,15676835.0,Ch'ang,559.0,Spain,Male,32.0,3.0,136689.66,1.0,1.0,1.0,135083.73,1.0
+781,15772383.0,Teng,699.0,Germany,Female,55.0,2.0,118613.26,1.0,0.0,1.0,93795.86,1.0
+782,15721917.0,Oluchukwu,624.0,France,Female,36.0,2.0,141642.92,1.0,0.0,1.0,187811.71,1.0
+783,15703588.0,Nnaemeka,636.0,Germany,Male,36.0,3.0,113034.31,1.0,1.0,1.0,57558.95,1.0
+784,15593501.0,Ritchie,677.0,France,Female,45.0,8.0,0.0,2.0,0.0,0.0,141798.98,0.0
+785,15627000.0,Onochie,726.0,France,Female,30.0,9.0,0.0,2.0,1.0,0.0,126469.09,0.0
+786,15589488.0,Lettiere,659.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,88815.25,0.0
+787,15590094.0,Lo,614.0,Germany,Female,49.0,7.0,120530.34,1.0,1.0,0.0,148087.62,1.0
+788,15668057.0,Ch'ang,688.0,France,Female,34.0,3.0,0.0,1.0,0.0,1.0,69381.05,0.0
+789,15743076.0,McLean,468.0,France,Male,36.0,2.0,0.0,1.0,0.0,0.0,136886.86,1.0
+790,15613787.0,Milanesi,559.0,Spain,Female,33.0,1.0,0.0,2.0,1.0,0.0,72375.03,0.0
+791,15602354.0,Ritchie,632.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,166677.54,0.0
+792,15793883.0,Goliwe,634.0,Germany,Female,54.0,0.0,123331.36,1.0,1.0,0.0,96660.76,1.0
+793,15579915.0,Yeh,663.0,Germany,Male,37.0,6.0,105435.74,2.0,1.0,0.0,106977.53,0.0
+794,15715951.0,Iweobiegbunam,683.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,52896.31,0.0
+795,15593365.0,Chin,575.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,102517.83,0.0
+796,15706761.0,Genovese,669.0,Spain,Female,33.0,3.0,0.0,2.0,1.0,0.0,10488.44,0.0
+797,15596939.0,Nucci,729.0,France,Female,31.0,6.0,0.0,2.0,0.0,1.0,165576.55,0.0
+798,15588854.0,Ch'ien,612.0,France,Female,39.0,3.0,0.0,1.0,0.0,0.0,125518.32,1.0
+799,15671256.0,De Luca,516.0,France,Female,48.0,8.0,82696.09,1.0,1.0,0.0,103372.79,1.0
+800,15591512.0,Genovese,710.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,67789.99,0.0
+801,15609928.0,Hs?,721.0,France,Male,49.0,3.0,0.0,2.0,1.0,1.0,145551.6,1.0
+802,15782404.0,Calabresi,658.0,Spain,Female,31.0,6.0,123544.0,1.0,1.0,0.0,121451.21,1.0
+803,15769293.0,Davide,586.0,France,Female,41.0,1.0,0.0,1.0,1.0,1.0,51919.04,1.0
+804,15656086.0,Tsao,685.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,141533.19,0.0
+805,15786800.0,Ts'ai,719.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,155743.48,0.0
+806,15663410.0,Trevisani,627.0,France,Male,43.0,8.0,104867.18,1.0,1.0,0.0,145560.82,0.0
+807,15774727.0,Ch'ien,678.0,France,Female,28.0,1.0,0.0,1.0,1.0,1.0,85891.02,0.0
+808,15717290.0,Lettiere,699.0,France,Male,38.0,4.0,0.0,1.0,0.0,0.0,168034.9,1.0
+809,15575247.0,Ch'iu,634.0,France,Male,31.0,1.0,146126.75,1.0,1.0,1.0,90212.38,0.0
+810,15780498.0,Horsley,662.0,France,Male,34.0,1.0,0.0,1.0,1.0,1.0,56984.58,0.0
+811,15583748.0,Zetticci,559.0,France,Male,35.0,0.0,133681.98,1.0,0.0,1.0,178045.97,0.0
+812,15645593.0,Ch'iu,710.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,1.0,178458.86,0.0
+813,15809837.0,Chiu,605.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,172668.39,0.0
+814,15796787.0,Hsiao,664.0,France,Male,30.0,3.0,171770.84,1.0,1.0,0.0,52796.31,0.0
+815,15793346.0,Pagnotto,692.0,Germany,Male,51.0,7.0,158621.04,1.0,1.0,0.0,140941.47,0.0
+816,15693326.0,Onyekachi,479.0,Germany,Female,28.0,6.0,132835.56,1.0,0.0,1.0,175893.53,1.0
+817,15791323.0,T'ang,653.0,Spain,Male,48.0,5.0,0.0,2.0,1.0,0.0,100618.17,0.0
+818,15769246.0,Chiang,595.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,152992.04,0.0
+819,15679810.0,Ch'ien,607.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,141827.33,0.0
+820,15578747.0,Chukwuemeka,629.0,Germany,Male,39.0,7.0,79610.92,2.0,1.0,1.0,84760.32,0.0
+821,15735388.0,Hsu,711.0,Germany,Male,40.0,5.0,131563.88,3.0,1.0,0.0,59604.45,1.0
+822,15670822.0,Hsieh,607.0,Germany,Female,50.0,6.0,91955.61,1.0,0.0,1.0,187985.7,1.0
+823,15723216.0,Pirozzi,710.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,61324.87,0.0
+824,15611157.0,Chimezie,758.0,Spain,Male,38.0,7.0,0.0,2.0,0.0,0.0,143980.28,0.0
+825,15776931.0,Lucciano,593.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,170122.22,0.0
+826,15621075.0,Clements,746.0,Germany,Female,33.0,4.0,121730.49,2.0,1.0,1.0,73881.68,0.0
+827,15809006.0,Chia,579.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,147090.9,0.0
+828,15793883.0,Fan,632.0,Spain,Female,34.0,9.0,0.0,2.0,0.0,0.0,174431.01,0.0
+829,15583548.0,T'ien,637.0,France,Male,45.0,9.0,0.0,1.0,1.0,0.0,53353.36,1.0
+830,15606097.0,Yobachi,562.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,161203.6,0.0
+831,15757837.0,Chiemenam,676.0,France,Female,32.0,9.0,165947.99,1.0,1.0,1.0,112973.33,0.0
+832,15632002.0,Frolova,617.0,Spain,Male,29.0,5.0,97565.74,1.0,1.0,0.0,113557.9,0.0
+833,15631693.0,Lucchese,553.0,Germany,Male,60.0,9.0,118772.71,1.0,1.0,0.0,150850.29,1.0
+834,15786800.0,Zito,673.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,136521.82,0.0
+835,15662291.0,Nwabugwu,553.0,Spain,Female,40.0,6.0,0.0,1.0,0.0,1.0,91098.76,0.0
+836,15806945.0,Palerma,512.0,France,Female,52.0,0.0,0.0,1.0,1.0,1.0,51454.06,1.0
+837,15728005.0,Chigolum,682.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,96734.46,0.0
+838,15668894.0,White,738.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,66979.06,0.0
+839,15815236.0,Manna,850.0,France,Male,48.0,10.0,0.0,1.0,1.0,0.0,13898.31,1.0
+840,15721383.0,Chibugo,755.0,Germany,Female,34.0,8.0,101514.89,1.0,1.0,0.0,180551.76,0.0
+841,15681562.0,Lai,548.0,France,Female,39.0,5.0,117103.26,1.0,0.0,1.0,180800.42,0.0
+842,15699325.0,Okwudilichukwu,805.0,Germany,Male,25.0,8.0,115141.18,1.0,1.0,1.0,149614.81,0.0
+843,15604818.0,Hs?,644.0,Spain,Female,45.0,4.0,0.0,2.0,1.0,1.0,151316.37,0.0
+844,15692406.0,Chifley,724.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,172939.3,0.0
+845,15749381.0,Okwudilichukwu,539.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,141872.05,0.0
+846,15757867.0,O'Neill,637.0,France,Male,29.0,0.0,0.0,2.0,1.0,1.0,179521.84,0.0
+847,15682533.0,Ts'ai,699.0,France,Male,29.0,2.0,132450.24,1.0,0.0,1.0,100433.83,0.0
+848,15611409.0,Lupton,731.0,Germany,Female,42.0,10.0,137781.65,1.0,1.0,0.0,140929.98,0.0
+849,15651052.0,Nwebube,604.0,Spain,Female,30.0,10.0,0.0,1.0,0.0,0.0,125553.51,1.0
+850,15662662.0,Ritchie,588.0,France,Male,52.0,6.0,0.0,1.0,1.0,0.0,171575.54,1.0
+851,15581871.0,Tien,707.0,France,Female,39.0,7.0,0.0,1.0,1.0,0.0,73371.65,1.0
+852,15748709.0,Shih,551.0,Germany,Female,35.0,2.0,141078.37,2.0,1.0,0.0,104936.88,0.0
+853,15729689.0,Wang,661.0,France,Male,46.0,5.0,114631.23,1.0,1.0,1.0,169642.13,0.0
+854,15702300.0,Hsieh,705.0,Germany,Female,29.0,2.0,108546.16,1.0,1.0,1.0,76946.6,0.0
+855,15652069.0,Teng,708.0,Spain,Female,61.0,2.0,0.0,2.0,1.0,1.0,74850.84,0.0
+856,15738219.0,Tretiakova,628.0,Spain,Female,45.0,10.0,0.0,1.0,1.0,0.0,115136.51,1.0
+857,15654074.0,Biryukov,653.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,166495.2,0.0
+858,15783030.0,Pagnotto,607.0,France,Female,38.0,4.0,0.0,1.0,0.0,1.0,125918.17,0.0
+859,15759499.0,Ch'in,726.0,Germany,Male,47.0,3.0,114435.47,1.0,1.0,0.0,169287.99,1.0
+860,15684592.0,L?,597.0,France,Male,31.0,10.0,123870.07,1.0,1.0,1.0,161519.77,0.0
+861,15662501.0,Ch'in,572.0,Germany,Female,55.0,8.0,127772.1,1.0,1.0,0.0,179718.17,1.0
+862,15751177.0,Lucchese,580.0,France,Male,46.0,1.0,126576.62,1.0,0.0,0.0,172450.04,1.0
+863,15812071.0,Milne,682.0,Germany,Female,43.0,10.0,84327.77,2.0,0.0,1.0,186826.22,0.0
+864,15622033.0,Chidubem,537.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,177613.19,0.0
+865,15761986.0,H?,682.0,Germany,Male,48.0,5.0,92716.96,3.0,0.0,0.0,28797.32,1.0
+866,15727361.0,Yobanna,684.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,19233.41,0.0
+867,15663650.0,Trevisano,641.0,Germany,Male,39.0,9.0,143500.82,2.0,1.0,1.0,5907.11,0.0
+868,15736327.0,Onyenachiya,645.0,France,Female,32.0,4.0,116927.89,1.0,1.0,1.0,87412.24,0.0
+869,15641480.0,Palerma,628.0,France,Male,28.0,8.0,0.0,2.0,0.0,0.0,93839.3,0.0
+870,15716164.0,Hsieh,850.0,France,Female,35.0,5.0,122314.5,1.0,1.0,0.0,162697.93,0.0
+871,15725794.0,Yobanna,652.0,France,Female,45.0,5.0,0.0,2.0,1.0,1.0,60905.51,0.0
+872,15697311.0,Milanesi,614.0,France,Female,50.0,8.0,100476.46,1.0,1.0,0.0,121277.78,0.0
+873,15783865.0,Walker,644.0,Germany,Male,42.0,9.0,111837.78,2.0,1.0,0.0,120853.83,0.0
+874,15762793.0,Hao,560.0,France,Female,28.0,9.0,0.0,2.0,1.0,1.0,90876.95,0.0
+875,15636023.0,Sun,675.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,131772.51,0.0
+876,15779738.0,Udinese,790.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,0.0,163558.18,0.0
+877,15741180.0,Mazzanti,619.0,Germany,Male,27.0,5.0,152462.79,1.0,1.0,0.0,171519.06,0.0
+878,15757827.0,Young,607.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,106981.02,0.0
+879,15654074.0,McIntyre,645.0,Germany,Female,53.0,9.0,105575.06,1.0,0.0,0.0,178045.97,1.0
+880,15671165.0,T'ien,649.0,France,Female,48.0,7.0,0.0,2.0,1.0,0.0,123954.15,1.0
+881,15687913.0,Ponomarev,642.0,France,Male,28.0,7.0,0.0,1.0,1.0,1.0,60552.44,0.0
+882,15763798.0,Cremonesi,671.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,67830.2,0.0
+883,15654562.0,Lucchesi,678.0,Germany,Male,51.0,6.0,117879.32,3.0,0.0,0.0,163558.42,1.0
+884,15776947.0,Ebelegbulam,648.0,France,Male,31.0,8.0,113386.36,1.0,1.0,0.0,187985.85,0.0
+885,15782899.0,Chukwudi,751.0,Germany,Male,45.0,3.0,126473.99,1.0,0.0,1.0,15650.89,0.0
+886,15657778.0,Chien,656.0,France,Female,38.0,8.0,117852.26,1.0,0.0,1.0,96985.58,0.0
+887,15616270.0,Ch'ang,564.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,73656.38,0.0
+888,15662736.0,H?,596.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,114093.73,0.0
+889,15699446.0,Dike,652.0,France,Female,37.0,7.0,0.0,2.0,0.0,1.0,102297.15,0.0
+890,15658675.0,Tung,678.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,183980.56,0.0
+891,15600997.0,P'eng,700.0,France,Male,45.0,8.0,0.0,2.0,1.0,1.0,121098.65,0.0
+892,15659475.0,Lucciano,589.0,Spain,Male,40.0,6.0,0.0,1.0,0.0,1.0,158027.45,1.0
+893,15749401.0,Kosisochukwu,724.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,157891.86,0.0
+894,15761663.0,P'eng,641.0,France,Female,25.0,5.0,0.0,2.0,1.0,0.0,142453.07,0.0
+895,15782231.0,Goliwe,596.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,157891.86,0.0
+896,15595071.0,Goliwe,773.0,France,Female,39.0,2.0,0.0,1.0,1.0,0.0,140579.19,1.0
+897,15683657.0,Scott,677.0,France,Male,39.0,3.0,113657.41,1.0,1.0,1.0,176925.69,0.0
+898,15715078.0,Chiawuotu,637.0,France,Female,39.0,5.0,0.0,1.0,1.0,1.0,71173.83,0.0
+899,15703784.0,Chiemezie,634.0,France,Female,42.0,3.0,0.0,2.0,1.0,0.0,102076.03,0.0
+900,15595726.0,Ch'ang,603.0,France,Female,29.0,1.0,120811.89,1.0,1.0,1.0,176580.23,0.0
+901,15813917.0,Maclean,765.0,France,Female,33.0,6.0,141267.67,1.0,1.0,0.0,165038.41,0.0
+902,15761670.0,Pagnotto,731.0,France,Male,40.0,1.0,123081.42,1.0,1.0,0.0,60905.51,0.0
+903,15594720.0,Neumayer,657.0,Germany,Female,29.0,2.0,156864.2,1.0,1.0,0.0,122379.56,1.0
+904,15768072.0,Ch'ang,649.0,Germany,Male,60.0,2.0,118851.05,1.0,1.0,0.0,92612.88,1.0
+905,15711378.0,DeRose,636.0,Germany,Female,46.0,3.0,150186.82,1.0,0.0,0.0,59981.62,1.0
+906,15620579.0,Ikedinachukwu,694.0,France,Female,31.0,3.0,0.0,1.0,1.0,1.0,128882.79,0.0
+907,15592386.0,Lombardo,703.0,France,Female,43.0,5.0,0.0,3.0,1.0,1.0,174825.57,1.0
+908,15596074.0,Lo,625.0,France,Male,67.0,9.0,0.0,2.0,1.0,1.0,58814.41,0.0
+909,15804919.0,Chikelu,551.0,Spain,Male,66.0,2.0,0.0,2.0,1.0,1.0,83949.68,0.0
+910,15644765.0,Ku,730.0,Germany,Male,56.0,9.0,106036.52,1.0,1.0,1.0,149614.17,1.0
+911,15643359.0,Uwaezuoke,678.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,97939.3,0.0
+912,15592761.0,Ts'ai,743.0,France,Male,38.0,5.0,127585.98,1.0,1.0,1.0,145578.25,0.0
+913,15778861.0,Chukwukadibia,690.0,France,Female,30.0,9.0,0.0,2.0,0.0,1.0,70954.41,0.0
+914,15693326.0,Maclean,449.0,France,Female,39.0,4.0,0.0,2.0,0.0,0.0,153316.16,0.0
+915,15593365.0,Ritchie,719.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,159899.97,0.0
+916,15589924.0,Okwuoma,746.0,France,Female,39.0,10.0,0.0,1.0,1.0,0.0,26136.13,1.0
+917,15792679.0,Hs?,554.0,Germany,Male,25.0,10.0,90450.0,2.0,1.0,1.0,59195.05,0.0
+918,15750772.0,Genovese,757.0,Germany,Female,40.0,1.0,111855.03,1.0,1.0,0.0,156911.35,0.0
+919,15602312.0,Lucciano,675.0,France,Female,31.0,0.0,0.0,2.0,1.0,0.0,160589.18,0.0
+920,15759717.0,Pagnotto,743.0,Germany,Female,32.0,7.0,133658.24,1.0,1.0,0.0,151401.33,0.0
+921,15736990.0,Olisaemeka,685.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,0.0,152643.48,0.0
+922,15714485.0,Nwabugwu,705.0,Spain,Male,40.0,3.0,0.0,2.0,1.0,1.0,184977.2,0.0
+923,15594576.0,Fancher,639.0,France,Female,40.0,9.0,0.0,1.0,1.0,0.0,82462.25,1.0
+924,15796496.0,Lucchesi,681.0,France,Female,45.0,5.0,0.0,2.0,1.0,1.0,106977.8,0.0
+925,15740164.0,Ugochukwu,724.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,184839.7,0.0
+926,15676513.0,Chidiebele,644.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,126307.25,0.0
+927,15737571.0,Chiemenam,578.0,Spain,Male,23.0,4.0,0.0,2.0,1.0,1.0,48249.88,0.0
+928,15634591.0,Chiang,694.0,France,Female,45.0,5.0,0.0,2.0,0.0,0.0,159463.7,1.0
+929,15798824.0,Lucchese,850.0,France,Female,25.0,3.0,0.0,2.0,1.0,0.0,65816.53,0.0
+930,15722533.0,Brown,728.0,France,Female,31.0,9.0,113146.98,2.0,0.0,0.0,120511.03,0.0
+931,15773013.0,P'eng,712.0,France,Female,31.0,9.0,0.0,2.0,0.0,0.0,134901.41,0.0
+932,15681957.0,Brown,615.0,France,Male,34.0,2.0,123031.56,2.0,1.0,1.0,125518.29,0.0
+933,15649024.0,Nwachukwu,727.0,Spain,Male,57.0,7.0,115725.24,1.0,1.0,1.0,180829.99,0.0
+934,15732643.0,P'eng,828.0,France,Female,31.0,10.0,114318.94,1.0,0.0,0.0,92357.21,0.0
+935,15652883.0,Trevisani,676.0,Spain,Female,59.0,3.0,0.0,2.0,1.0,1.0,120511.01,0.0
+936,15656901.0,Mazzanti,598.0,France,Male,25.0,5.0,118311.76,1.0,1.0,0.0,145558.49,0.0
+937,15603741.0,Ts'ui,762.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,123946.13,0.0
+938,15685500.0,Scott,693.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,75888.65,0.0
+939,15713354.0,Lo Duca,658.0,Germany,Female,47.0,3.0,152264.81,2.0,1.0,1.0,119927.81,1.0
+940,15641655.0,T'ien,581.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,24983.39,0.0
+941,15690664.0,Yudina,632.0,France,Male,41.0,2.0,0.0,3.0,1.0,1.0,185821.41,0.0
+942,15747014.0,Chia,762.0,Spain,Female,37.0,1.0,0.0,1.0,1.0,1.0,82709.8,1.0
+943,15707362.0,Hsueh,659.0,France,Female,35.0,1.0,120811.89,1.0,1.0,0.0,158610.89,0.0
+944,15750921.0,Chia,648.0,Spain,Female,22.0,4.0,0.0,2.0,0.0,1.0,102908.91,0.0
+945,15663917.0,Yefremova,640.0,France,Male,35.0,9.0,0.0,1.0,1.0,1.0,97908.01,1.0
+946,15632051.0,Maclean,712.0,Spain,Female,39.0,9.0,0.0,2.0,0.0,0.0,126131.9,0.0
+947,15742523.0,Chimaoke,677.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,1.0,102747.02,0.0
+948,15753861.0,Onio,712.0,Germany,Female,32.0,8.0,108407.34,1.0,0.0,1.0,88669.89,0.0
+949,15723488.0,Hsia,697.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,170528.63,0.0
+950,15669922.0,Pinto,709.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,105746.94,0.0
+951,15727059.0,Tsou,637.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,170968.99,0.0
+952,15592736.0,Biryukova,584.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,177524.19,0.0
+953,15794479.0,Onyemachukwu,632.0,France,Female,46.0,9.0,0.0,2.0,1.0,1.0,124615.59,0.0
+954,15644878.0,Lo Duca,728.0,Germany,Male,43.0,8.0,110997.76,1.0,1.0,0.0,163798.41,1.0
+955,15640648.0,Chien,712.0,Spain,Male,54.0,4.0,0.0,1.0,0.0,0.0,170893.85,1.0
+956,15702277.0,Kepley,520.0,France,Male,28.0,0.0,0.0,2.0,1.0,0.0,121098.65,0.0
+957,15808689.0,Maclean,568.0,France,Male,38.0,4.0,87894.26,1.0,0.0,1.0,49833.5,0.0
+958,15692406.0,Ts'ui,535.0,France,Male,36.0,1.0,0.0,1.0,1.0,1.0,126761.2,0.0
+959,15694366.0,Chukwukere,714.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,75562.47,0.0
+960,15629133.0,Chikezie,670.0,France,Female,39.0,2.0,88980.32,1.0,1.0,0.0,136983.77,0.0
+961,15591747.0,Hsueh,712.0,Germany,Male,40.0,5.0,71667.74,1.0,1.0,1.0,152450.96,0.0
+962,15600882.0,Chien,589.0,France,Female,26.0,3.0,0.0,2.0,1.0,1.0,57553.02,0.0
+963,15746664.0,Yudina,659.0,France,Male,22.0,6.0,0.0,2.0,1.0,1.0,142331.85,0.0
+964,15705639.0,Chikwado,659.0,France,Female,30.0,8.0,0.0,2.0,1.0,1.0,57549.65,0.0
+965,15640409.0,Milanesi,661.0,France,Male,35.0,7.0,145081.73,1.0,1.0,1.0,176984.34,0.0
+966,15677123.0,Nkemakolam,675.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,153481.85,0.0
+967,15653773.0,Nwankwo,551.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,126565.92,0.0
+968,15757577.0,O'Donnell,754.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,109805.52,0.0
+969,15585985.0,Mazzi,508.0,Germany,Male,31.0,6.0,139432.37,1.0,1.0,1.0,133692.88,1.0
+970,15701376.0,McBurney,648.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,180862.26,0.0
+971,15779586.0,Hsu,549.0,Germany,Female,39.0,0.0,115074.02,1.0,1.0,0.0,122518.5,1.0
+972,15658946.0,Chiang,564.0,Germany,Male,49.0,6.0,113590.73,1.0,1.0,0.0,163550.58,1.0
+973,15623595.0,Ch'ang,537.0,France,Female,54.0,2.0,0.0,2.0,0.0,1.0,90905.26,0.0
+974,15681697.0,Ch'iu,614.0,France,Male,37.0,7.0,0.0,2.0,0.0,0.0,52471.36,0.0
+975,15624397.0,Fanucci,577.0,France,Female,42.0,3.0,0.0,1.0,1.0,0.0,173940.59,1.0
+976,15698872.0,Ijendu,696.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,142911.99,0.0
+977,15778830.0,Ch'ang,635.0,France,Male,31.0,3.0,0.0,2.0,0.0,1.0,56767.67,0.0
+978,15754849.0,McElhone,711.0,France,Male,33.0,6.0,0.0,2.0,0.0,0.0,82222.86,0.0
+979,15792729.0,Hsing,538.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,48778.46,0.0
+980,15668289.0,Trevisano,604.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,79792.3,0.0
+981,15784958.0,Milne,593.0,Germany,Male,39.0,9.0,127290.61,1.0,0.0,1.0,64375.3,0.0
+982,15573045.0,Martin,597.0,Spain,Female,29.0,6.0,0.0,2.0,1.0,0.0,125870.79,0.0
+983,15736250.0,Trevisan,704.0,France,Male,31.0,3.0,0.0,2.0,0.0,1.0,118959.64,0.0
+984,15732265.0,Toscani,693.0,Spain,Female,39.0,9.0,0.0,2.0,1.0,0.0,123954.16,0.0
+985,15600813.0,Ikedinachukwu,634.0,Germany,Female,59.0,4.0,136652.01,1.0,1.0,0.0,107964.36,1.0
+986,15815040.0,Y?an,683.0,Spain,Male,21.0,5.0,0.0,2.0,1.0,1.0,143882.25,0.0
+987,15579166.0,Pirozzi,645.0,Spain,Male,29.0,5.0,111827.27,1.0,1.0,0.0,71433.88,0.0
+988,15756539.0,T'ien,707.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,93873.8,0.0
+989,15691627.0,Mazzanti,474.0,Spain,Male,39.0,8.0,0.0,2.0,0.0,1.0,83093.25,0.0
+990,15751177.0,Ting,707.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,173851.11,0.0
+991,15591698.0,O'Connor,635.0,Germany,Male,51.0,0.0,114440.38,1.0,1.0,1.0,131553.82,0.0
+992,15669611.0,Biryukov,599.0,France,Female,31.0,5.0,0.0,2.0,1.0,0.0,53477.76,0.0
+993,15807989.0,Ch'ang,638.0,France,Male,36.0,8.0,0.0,1.0,1.0,0.0,132973.21,0.0
+994,15684440.0,Tung,744.0,Spain,Male,31.0,5.0,0.0,1.0,1.0,1.0,73329.08,0.0
+995,15740406.0,Chinomso,538.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,124595.95,0.0
+996,15766289.0,Hsieh,673.0,Spain,Female,46.0,9.0,0.0,2.0,1.0,1.0,88890.03,0.0
+997,15643770.0,Shih,850.0,Spain,Male,31.0,3.0,0.0,2.0,0.0,0.0,112554.68,0.0
+998,15646566.0,Genovese,718.0,France,Female,44.0,5.0,0.0,2.0,1.0,1.0,149599.62,0.0
+999,15751788.0,Nnamdi,602.0,Germany,Female,52.0,6.0,131834.76,1.0,1.0,0.0,84584.69,1.0
+1000,15751057.0,Cattaneo,641.0,France,Male,37.0,5.0,142987.46,1.0,1.0,0.0,156774.94,0.0
+1001,15723028.0,Ch'ien,774.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,0.0,79377.45,0.0
+1002,15626012.0,Onyemauchechukwu,672.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,127553.79,0.0
+1003,15641411.0,Kao,649.0,France,Female,36.0,1.0,0.0,2.0,0.0,0.0,52335.15,0.0
+1004,15600106.0,Chukwuemeka,806.0,Spain,Male,26.0,4.0,0.0,2.0,1.0,0.0,143711.02,0.0
+1005,15662698.0,Cattaneo,699.0,Spain,Male,28.0,10.0,0.0,2.0,1.0,0.0,48571.14,0.0
+1006,15681115.0,Pipes,703.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,180598.66,0.0
+1007,15642785.0,Hsing,746.0,Germany,Female,40.0,5.0,108895.93,1.0,1.0,1.0,55868.97,1.0
+1008,15593583.0,Ch'ang,667.0,France,Male,40.0,10.0,0.0,2.0,0.0,0.0,82815.14,0.0
+1009,15741643.0,T'ao,727.0,Spain,Male,43.0,4.0,99836.47,1.0,1.0,0.0,153535.17,0.0
+1010,15696835.0,Lu,581.0,Spain,Male,30.0,5.0,0.0,2.0,0.0,0.0,50368.63,0.0
+1011,15607632.0,Ejimofor,673.0,Spain,Male,46.0,6.0,0.0,1.0,1.0,0.0,152996.89,1.0
+1012,15700710.0,Ch'ang,595.0,Spain,Female,23.0,3.0,0.0,2.0,1.0,1.0,119996.95,0.0
+1013,15587562.0,Yao,713.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,103787.29,0.0
+1014,15581840.0,Chien,687.0,Germany,Male,32.0,3.0,143500.82,1.0,0.0,0.0,87400.53,0.0
+1015,15689517.0,McLean,696.0,France,Female,35.0,6.0,0.0,1.0,1.0,0.0,111180.3,0.0
+1016,15697480.0,Milani,671.0,Germany,Male,25.0,0.0,107747.57,2.0,0.0,0.0,170142.09,0.0
+1017,15758050.0,Nwora,605.0,Spain,Male,54.0,8.0,0.0,1.0,0.0,0.0,125451.01,1.0
+1018,15682468.0,Baresi,731.0,France,Male,31.0,9.0,0.0,2.0,0.0,0.0,69531.22,0.0
+1019,15654096.0,Hsia,763.0,Germany,Female,39.0,1.0,108586.86,1.0,1.0,0.0,134794.02,0.0
+1020,15690164.0,Ch'ang,699.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,1.0,85746.52,0.0
+1021,15700654.0,Obidimkpa,581.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,115186.97,0.0
+1022,15667424.0,Ch'ang,617.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,131609.48,0.0
+1023,15597949.0,Onwumelu,597.0,Spain,Male,41.0,8.0,0.0,2.0,1.0,0.0,158840.99,0.0
+1024,15598884.0,Teng,586.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,180134.88,0.0
+1025,15675141.0,Martin,661.0,Germany,Female,51.0,9.0,106415.57,1.0,1.0,1.0,126812.63,0.0
+1026,15589212.0,Neitenstein,686.0,Germany,Male,42.0,2.0,133467.77,1.0,1.0,0.0,28276.83,1.0
+1027,15726186.0,Folliero,651.0,Germany,Female,38.0,6.0,151858.98,2.0,1.0,1.0,96429.29,0.0
+1028,15782530.0,Pye,657.0,France,Female,44.0,9.0,0.0,2.0,1.0,0.0,152698.24,0.0
+1029,15571081.0,Ch'eng,636.0,Spain,Female,23.0,7.0,0.0,2.0,1.0,1.0,87985.2,0.0
+1030,15651144.0,De Salis,646.0,France,Male,31.0,3.0,103711.56,1.0,0.0,0.0,75969.19,0.0
+1031,15682533.0,Chinagorom,579.0,Spain,Male,43.0,7.0,0.0,2.0,1.0,0.0,10553.31,0.0
+1032,15736287.0,Milanesi,664.0,Germany,Male,40.0,1.0,76319.64,1.0,1.0,0.0,63736.17,1.0
+1033,15812198.0,Nwebube,564.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,142331.85,0.0
+1034,15724281.0,Chiazagomekpere,584.0,France,Male,38.0,1.0,125512.98,1.0,1.0,1.0,153662.88,0.0
+1035,15677522.0,Clements,582.0,France,Female,29.0,8.0,81844.81,1.0,1.0,1.0,107811.28,1.0
+1036,15642815.0,Chibugo,850.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,180345.44,0.0
+1037,15662501.0,Tien,710.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,150694.42,0.0
+1038,15681755.0,Cattaneo,603.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,110317.39,0.0
+1039,15768282.0,Tsao,584.0,France,Male,23.0,5.0,0.0,1.0,1.0,1.0,160548.12,0.0
+1040,15700212.0,Udegbunam,644.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,121894.31,0.0
+1041,15777825.0,Ch'in,705.0,France,Male,49.0,4.0,0.0,3.0,0.0,0.0,81481.9,1.0
+1042,15781598.0,Nwora,607.0,Spain,Female,28.0,7.0,0.0,2.0,1.0,0.0,172618.52,0.0
+1043,15623695.0,Yevseyev,596.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,139024.31,0.0
+1044,15684769.0,Ch'iu,645.0,France,Male,39.0,9.0,0.0,1.0,1.0,0.0,167036.94,1.0
+1045,15671766.0,Chinedum,706.0,France,Male,41.0,2.0,0.0,2.0,0.0,1.0,91036.74,0.0
+1046,15594812.0,Chibuzo,727.0,Germany,Female,42.0,1.0,121325.84,1.0,1.0,0.0,72993.65,1.0
+1047,15764174.0,Bazarova,634.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,0.0,123880.19,0.0
+1048,15675888.0,Palerma,586.0,France,Female,25.0,9.0,0.0,2.0,1.0,0.0,65906.07,0.0
+1049,15641662.0,Chien,648.0,France,Female,25.0,8.0,0.0,2.0,1.0,0.0,180326.83,0.0
+1050,15709451.0,Chukwumaobim,543.0,France,Male,44.0,3.0,0.0,2.0,0.0,0.0,97629.31,0.0
+1051,15664311.0,Shao,706.0,France,Male,45.0,6.0,0.0,2.0,1.0,0.0,60906.12,1.0
+1052,15686957.0,Chuang,644.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,158545.25,0.0
+1053,15746986.0,Palermo,679.0,France,Male,28.0,5.0,97268.1,1.0,0.0,1.0,46630.47,0.0
+1054,15774841.0,Calabresi,634.0,France,Female,53.0,1.0,0.0,3.0,1.0,0.0,160968.44,1.0
+1055,15628059.0,Ni,682.0,Spain,Female,51.0,1.0,0.0,1.0,0.0,0.0,173576.71,1.0
+1056,15803032.0,Y?an,634.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,94499.06,0.0
+1057,15810347.0,Maclean,637.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,153036.97,0.0
+1058,15688612.0,Hales,559.0,Spain,Female,32.0,8.0,0.0,1.0,1.0,1.0,70550.0,0.0
+1059,15585100.0,Efimov,684.0,France,Female,25.0,2.0,0.0,2.0,0.0,0.0,7861.68,0.0
+1060,15658977.0,Toscani,603.0,France,Female,39.0,9.0,97588.8,1.0,1.0,0.0,122379.73,1.0
+1061,15628697.0,Nkemakolam,619.0,Spain,Male,32.0,9.0,0.0,1.0,1.0,1.0,150694.42,1.0
+1062,15784099.0,Chukwuemeka,511.0,France,Female,41.0,2.0,0.0,1.0,1.0,1.0,51387.1,1.0
+1063,15661528.0,Trevisano,795.0,Germany,Male,39.0,8.0,133705.74,2.0,1.0,1.0,71905.77,0.0
+1064,15667944.0,Ritchie,661.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,129569.92,0.0
+1065,15635197.0,Hsieh,706.0,France,Male,38.0,3.0,0.0,2.0,0.0,1.0,177852.57,0.0
+1066,15691875.0,Ewing,578.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,93835.64,0.0
+1067,15793046.0,Chidiegwu,730.0,Spain,Female,43.0,8.0,0.0,2.0,1.0,0.0,83981.02,0.0
+1068,15781530.0,Aikenhead,726.0,Germany,Female,31.0,4.0,141571.5,2.0,1.0,0.0,180862.26,0.0
+1069,15795571.0,Ndukaku,553.0,France,Female,34.0,0.0,96654.72,1.0,0.0,1.0,167973.65,0.0
+1070,15583090.0,Bitter,710.0,France,Female,35.0,9.0,0.0,3.0,0.0,0.0,69414.13,1.0
+1071,15614827.0,Walker,733.0,Spain,Female,41.0,3.0,0.0,2.0,1.0,0.0,83629.4,0.0
+1072,15716134.0,Mazzi,635.0,Germany,Male,54.0,7.0,129585.19,1.0,1.0,0.0,124874.23,1.0
+1073,15604818.0,Tuan,657.0,Germany,Male,35.0,9.0,91286.51,2.0,1.0,0.0,153917.41,0.0
+1074,15605020.0,Chukwudi,543.0,France,Male,29.0,7.0,0.0,1.0,1.0,0.0,94925.62,0.0
+1075,15614832.0,Pagnotto,664.0,Spain,Female,22.0,9.0,126492.24,2.0,0.0,1.0,149614.61,0.0
+1076,15648067.0,Uvarova,719.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,162858.29,0.0
+1077,15726588.0,Lo,642.0,Spain,Male,28.0,1.0,0.0,2.0,0.0,0.0,153810.41,0.0
+1078,15676571.0,Tretiakova,774.0,Germany,Female,35.0,7.0,123711.73,2.0,1.0,0.0,137028.51,0.0
+1079,15641733.0,Tsai,683.0,France,Female,49.0,2.0,0.0,1.0,0.0,1.0,89972.25,1.0
+1080,15740161.0,Fan,661.0,Spain,Male,31.0,9.0,125445.04,1.0,1.0,1.0,184350.78,0.0
+1081,15649033.0,Nnachetam,684.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,182988.94,1.0
+1082,15629793.0,Y?,769.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,0.0,138527.56,0.0
+1083,15766378.0,Shih,667.0,France,Male,43.0,3.0,0.0,2.0,1.0,1.0,85891.09,0.0
+1084,15788676.0,Yobanna,619.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,1.0,131662.47,0.0
+1085,15682758.0,MacDonnell,645.0,France,Male,25.0,5.0,125510.82,1.0,1.0,0.0,147822.81,0.0
+1086,15632029.0,Y?an,709.0,France,Male,38.0,1.0,160858.13,1.0,1.0,1.0,37240.11,0.0
+1087,15806945.0,Shubin,498.0,Germany,Male,30.0,6.0,86911.7,2.0,1.0,1.0,151843.54,0.0
+1088,15733768.0,Bogle,534.0,France,Female,56.0,2.0,0.0,1.0,0.0,1.0,171045.31,1.0
+1089,15624391.0,Brown,850.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,161579.85,0.0
+1090,15632185.0,Trevisano,724.0,France,Male,35.0,0.0,0.0,1.0,0.0,1.0,137791.18,0.0
+1091,15701687.0,Lu,662.0,France,Male,55.0,7.0,99844.81,1.0,0.0,1.0,131736.23,0.0
+1092,15634218.0,Chukwukere,701.0,France,Female,32.0,9.0,90727.79,1.0,1.0,1.0,133516.51,0.0
+1093,15735346.0,McLean,729.0,Germany,Male,33.0,5.0,107963.38,1.0,1.0,0.0,108003.38,1.0
+1094,15782895.0,Iloerika,824.0,France,Female,48.0,3.0,0.0,1.0,1.0,0.0,107847.02,1.0
+1095,15760085.0,Brown,633.0,Spain,Male,29.0,2.0,0.0,1.0,0.0,1.0,82868.34,0.0
+1096,15777315.0,Onyemauchechukwu,652.0,France,Female,36.0,3.0,109493.62,2.0,1.0,1.0,86786.41,0.0
+1097,15715047.0,Liao,729.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,145789.71,0.0
+1098,15654025.0,T'ao,641.0,France,Female,69.0,5.0,0.0,2.0,1.0,1.0,52629.73,0.0
+1099,15798885.0,Yeh,576.0,Spain,Male,57.0,7.0,0.0,2.0,1.0,1.0,140941.47,1.0
+1100,15605375.0,P'an,641.0,Germany,Male,32.0,4.0,143396.54,2.0,1.0,0.0,123882.73,0.0
+1101,15748473.0,Hsiung,646.0,France,Male,40.0,6.0,0.0,2.0,1.0,1.0,180551.76,0.0
+1102,15643438.0,Nnamutaezinwa,753.0,France,Male,21.0,4.0,129804.17,1.0,1.0,1.0,65801.44,0.0
+1103,15589323.0,Chiemenam,595.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,122964.18,0.0
+1104,15712807.0,Udegbunam,762.0,France,Male,50.0,5.0,0.0,2.0,0.0,1.0,151316.37,0.0
+1105,15781598.0,Ch'ien,667.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,142539.71,0.0
+1106,15783097.0,Yudin,656.0,France,Female,40.0,6.0,0.0,1.0,1.0,0.0,133871.83,0.0
+1107,15574558.0,Bitter,669.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,186612.47,1.0
+1108,15571666.0,Nwabugwu,758.0,Spain,Male,28.0,10.0,0.0,2.0,1.0,1.0,82709.8,0.0
+1109,15783589.0,Chidubem,656.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,129912.74,0.0
+1110,15597951.0,O'Connor,850.0,France,Female,32.0,1.0,0.0,1.0,1.0,1.0,104079.62,1.0
+1111,15698031.0,Chidiegwu,591.0,France,Female,33.0,9.0,0.0,1.0,0.0,1.0,174434.06,0.0
+1112,15708710.0,Chiemela,590.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,84484.13,0.0
+1113,15643359.0,Lo,679.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,1.0,55725.04,0.0
+1114,15769499.0,Chukwubuikem,693.0,France,Female,24.0,8.0,0.0,2.0,1.0,1.0,60954.45,0.0
+1115,15587038.0,Chiedozie,663.0,France,Male,62.0,8.0,0.0,2.0,1.0,1.0,103970.58,0.0
+1116,15753092.0,Nnaife,631.0,Germany,Male,33.0,5.0,115557.0,1.0,1.0,0.0,76510.46,0.0
+1117,15592461.0,Smith,624.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,147785.83,0.0
+1118,15807524.0,Nwokezuike,732.0,Germany,Male,35.0,3.0,115725.24,1.0,1.0,1.0,110248.88,0.0
+1119,15782404.0,T'ang,699.0,France,Male,39.0,8.0,123761.68,1.0,1.0,0.0,145900.89,0.0
+1120,15757981.0,Ts'ui,590.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,143628.31,0.0
+1121,15694860.0,Ts'ai,554.0,France,Female,42.0,1.0,0.0,2.0,1.0,1.0,132547.02,0.0
+1122,15694556.0,Toscani,662.0,Germany,Male,35.0,9.0,115350.63,1.0,1.0,1.0,60905.51,0.0
+1123,15624793.0,Nkemakolam,754.0,Germany,Female,34.0,4.0,147832.15,2.0,1.0,1.0,181605.85,1.0
+1124,15769682.0,Chukwueloka,724.0,Germany,Female,39.0,2.0,107848.71,2.0,1.0,0.0,50488.91,0.0
+1125,15678915.0,Chialuka,708.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,152549.62,0.0
+1126,15652789.0,Lo Duca,819.0,France,Male,25.0,4.0,0.0,2.0,1.0,0.0,58601.61,0.0
+1127,15727197.0,Tseng,676.0,France,Female,28.0,7.0,0.0,2.0,1.0,0.0,98810.78,0.0
+1128,15759499.0,Y?an,748.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,176943.59,0.0
+1129,15746986.0,K?,837.0,France,Female,29.0,1.0,0.0,1.0,1.0,1.0,83983.39,0.0
+1130,15674750.0,K?,741.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,48242.19,0.0
+1131,15642655.0,Olague,611.0,France,Female,35.0,5.0,0.0,1.0,1.0,1.0,176943.59,0.0
+1132,15641835.0,H?,571.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,132770.11,0.0
+1133,15579287.0,Chukwumaobim,642.0,Spain,Male,44.0,9.0,0.0,2.0,0.0,0.0,47921.22,0.0
+1134,15676810.0,Milanesi,589.0,France,Female,44.0,2.0,0.0,1.0,1.0,1.0,124908.52,1.0
+1135,15629793.0,Onyeoruru,573.0,Spain,Female,31.0,1.0,0.0,2.0,1.0,1.0,175324.74,0.0
+1136,15736371.0,Okechukwu,755.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,93874.87,0.0
+1137,15674928.0,Yobanna,579.0,Spain,Female,40.0,9.0,0.0,1.0,1.0,0.0,162599.51,1.0
+1138,15684958.0,Tien,642.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,1.0,177065.24,0.0
+1139,15658670.0,Kambinachi,516.0,Germany,Female,25.0,7.0,116932.59,1.0,1.0,1.0,109291.39,0.0
+1140,15751412.0,Toscani,682.0,Spain,Female,32.0,3.0,144855.34,1.0,1.0,1.0,51536.99,0.0
+1141,15633059.0,Tseng,662.0,Germany,Male,33.0,8.0,138341.51,1.0,1.0,0.0,163440.8,0.0
+1142,15800062.0,Nnonso,696.0,Spain,Male,35.0,4.0,123593.22,1.0,0.0,0.0,142548.33,0.0
+1143,15797900.0,Chiang,562.0,Germany,Male,34.0,5.0,122227.71,2.0,0.0,0.0,91086.73,0.0
+1144,15652048.0,P'eng,559.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,176778.89,0.0
+1145,15694300.0,Ch'ang,576.0,Spain,Female,46.0,10.0,154495.74,1.0,1.0,1.0,136519.58,1.0
+1146,15768340.0,Calabresi,619.0,Germany,Male,41.0,4.0,141547.13,2.0,1.0,0.0,107876.2,0.0
+1147,15626141.0,Uvarova,641.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,60721.4,0.0
+1148,15581525.0,Teng,709.0,Germany,Male,30.0,2.0,99897.76,1.0,0.0,0.0,4389.4,0.0
+1149,15679760.0,K'ung,651.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,1.0,103379.31,0.0
+1150,15654760.0,Lombardo,850.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,120893.07,0.0
+1151,15780256.0,Onyekachukwu,708.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,142238.54,0.0
+1152,15754577.0,Nnaife,669.0,Germany,Male,36.0,6.0,99760.85,2.0,1.0,0.0,79734.23,1.0
+1153,15782778.0,Chukwudi,471.0,Germany,Female,52.0,0.0,133870.01,1.0,1.0,0.0,152996.89,1.0
+1154,15742204.0,Ch'iu,679.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,11663.09,0.0
+1155,15602354.0,P'eng,696.0,Spain,Male,32.0,1.0,133658.24,1.0,1.0,1.0,185830.72,0.0
+1156,15728352.0,Bair,718.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,121053.19,0.0
+1157,15612207.0,Nwachinemelu,668.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,141679.73,0.0
+1158,15651369.0,Chiawuotu,602.0,Spain,Male,39.0,4.0,0.0,2.0,0.0,1.0,94498.82,0.0
+1159,15665416.0,Onuchukwu,678.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,138871.12,0.0
+1160,15790846.0,Hs?,632.0,France,Female,37.0,8.0,134980.23,1.0,1.0,1.0,180002.42,0.0
+1161,15592025.0,Chukwuhaenye,602.0,France,Male,23.0,3.0,0.0,2.0,1.0,1.0,64448.36,0.0
+1162,15645572.0,Chukwufumnanya,604.0,France,Female,57.0,5.0,141806.0,1.0,1.0,0.0,182867.84,1.0
+1163,15658577.0,Ch'ang,558.0,France,Male,44.0,6.0,145536.28,1.0,0.0,0.0,101986.16,0.0
+1164,15681316.0,Goliwe,669.0,Spain,Male,42.0,7.0,109974.22,1.0,1.0,1.0,149853.89,0.0
+1165,15614893.0,Hs?eh,439.0,France,Female,36.0,4.0,96679.71,1.0,0.0,1.0,85516.37,0.0
+1166,15769312.0,Chuang,736.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,51919.04,0.0
+1167,15748589.0,Lu,683.0,Spain,Male,34.0,2.0,0.0,1.0,1.0,0.0,167784.28,0.0
+1168,15784597.0,Yermakov,778.0,Germany,Female,31.0,9.0,121874.89,2.0,1.0,1.0,189607.71,0.0
+1169,15806975.0,Yeh,769.0,France,Female,39.0,1.0,134598.47,1.0,1.0,1.0,163798.41,0.0
+1170,15595728.0,Chizoba,641.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,123547.28,0.0
+1171,15647800.0,Chinwendu,658.0,Spain,Male,37.0,10.0,131659.59,2.0,1.0,1.0,109718.44,0.0
+1172,15616700.0,Chiebuka,663.0,France,Male,46.0,8.0,0.0,1.0,1.0,0.0,141242.57,0.0
+1173,15778515.0,Kuykendall,486.0,Spain,Male,41.0,10.0,0.0,2.0,1.0,1.0,48226.76,0.0
+1174,15633574.0,Chukwukadibia,637.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,0.0,151336.96,0.0
+1175,15792486.0,Fan,707.0,France,Female,44.0,1.0,0.0,1.0,0.0,0.0,179908.71,1.0
+1176,15602312.0,Chidumaga,676.0,Spain,Male,32.0,5.0,134740.24,1.0,0.0,0.0,170122.22,0.0
+1177,15708713.0,Uspensky,665.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,145700.22,0.0
+1178,15581252.0,Hsia,521.0,Germany,Female,51.0,5.0,128829.25,2.0,1.0,0.0,171096.2,1.0
+1179,15765643.0,Chukwueloka,602.0,France,Female,49.0,3.0,0.0,1.0,0.0,0.0,171922.72,1.0
+1180,15589428.0,Milano,661.0,France,Male,41.0,4.0,0.0,1.0,1.0,0.0,146851.74,1.0
+1181,15773789.0,Chiwetelu,739.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,164967.98,0.0
+1182,15699467.0,Chien,598.0,France,Female,32.0,8.0,0.0,2.0,0.0,0.0,163581.85,0.0
+1183,15813645.0,Oster,850.0,France,Male,39.0,3.0,0.0,1.0,1.0,0.0,172646.88,0.0
+1184,15588854.0,Hsiung,637.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,27330.59,0.0
+1185,15746986.0,T'ang,569.0,Spain,Male,30.0,6.0,91070.18,1.0,1.0,1.0,145562.4,0.0
+1186,15684962.0,Yobanna,850.0,France,Female,20.0,4.0,0.0,2.0,1.0,1.0,114442.66,0.0
+1187,15586069.0,Iadanza,681.0,Spain,Female,49.0,9.0,0.0,2.0,0.0,1.0,172495.81,1.0
+1188,15674678.0,Shih,679.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,26687.22,0.0
+1189,15668767.0,Ch'in,581.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,90876.95,0.0
+1190,15698872.0,Mactier,699.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,172646.88,0.0
+1191,15750808.0,Chikezie,586.0,France,Female,29.0,5.0,164850.54,1.0,1.0,1.0,95421.0,0.0
+1192,15662337.0,Nkemjika,641.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,0.0,128882.66,0.0
+1193,15584897.0,Tretiakov,587.0,France,Male,51.0,4.0,0.0,1.0,1.0,0.0,80587.37,1.0
+1194,15734588.0,T'ang,648.0,Spain,Male,39.0,7.0,153548.13,1.0,1.0,1.0,137476.09,0.0
+1195,15644796.0,Chukwualuka,682.0,Spain,Female,43.0,4.0,0.0,2.0,0.0,1.0,126809.15,0.0
+1196,15686964.0,Nkemdirim,645.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,173899.42,0.0
+1197,15651460.0,Chukwualuka,706.0,Spain,Female,37.0,3.0,0.0,2.0,1.0,1.0,152987.42,0.0
+1198,15707505.0,Trevisano,667.0,Spain,Male,26.0,10.0,0.0,2.0,0.0,0.0,172674.36,0.0
+1199,15765257.0,McIntosh,633.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,179258.68,0.0
+1200,15693526.0,Cattaneo,648.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,105732.9,0.0
+1201,15708003.0,K'ung,502.0,Spain,Female,39.0,5.0,141038.57,1.0,1.0,1.0,130474.03,0.0
+1202,15636551.0,Iweobiegbunam,715.0,Germany,Male,31.0,8.0,96774.55,2.0,0.0,1.0,165035.17,0.0
+1203,15775886.0,Nkemakonam,633.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,151645.96,1.0
+1204,15656471.0,Rieke,633.0,France,Male,42.0,1.0,0.0,1.0,0.0,0.0,199390.45,1.0
+1205,15803750.0,Chidubem,691.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,94738.54,0.0
+1206,15803801.0,Chiedozie,553.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,1.0,49815.62,0.0
+1207,15677510.0,Chukwufumnanya,725.0,France,Male,48.0,5.0,0.0,1.0,0.0,1.0,166038.28,1.0
+1208,15643770.0,Onyekachi,678.0,France,Male,39.0,1.0,0.0,2.0,0.0,1.0,132719.8,0.0
+1209,15590011.0,Chiazagomekpele,596.0,Germany,Male,32.0,0.0,162532.14,2.0,1.0,0.0,143598.7,0.0
+1210,15701074.0,Whitehead,582.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,121084.27,0.0
+1211,15687913.0,Uspenskaya,773.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,86786.41,0.0
+1212,15725068.0,Onio,706.0,France,Male,36.0,2.0,117438.92,1.0,0.0,0.0,198402.37,0.0
+1213,15772423.0,Omeokachie,645.0,France,Male,29.0,7.0,0.0,1.0,1.0,1.0,115733.16,0.0
+1214,15572390.0,Nkemakolam,683.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,161479.19,0.0
+1215,15603319.0,Chieloka,564.0,Spain,Male,33.0,2.0,107966.39,2.0,1.0,1.0,162599.51,0.0
+1216,15585867.0,Hs?,727.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,90537.47,0.0
+1217,15694518.0,Shih,743.0,France,Male,45.0,2.0,123544.21,2.0,0.0,1.0,134889.95,0.0
+1218,15788775.0,Yobanna,748.0,France,Female,39.0,9.0,133866.22,1.0,0.0,0.0,104889.3,0.0
+1219,15742798.0,Ch'iu,735.0,France,Male,42.0,1.0,0.0,1.0,1.0,1.0,107822.39,0.0
+1220,15592089.0,Shih,544.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,83910.4,0.0
+1221,15573741.0,Chukwualuka,774.0,France,Female,28.0,9.0,0.0,1.0,1.0,0.0,7921.57,0.0
+1222,15706647.0,P'eng,473.0,Germany,Female,31.0,4.0,111432.77,2.0,1.0,1.0,147545.65,0.0
+1223,15745307.0,Chizuoke,756.0,Germany,Female,41.0,2.0,127545.75,2.0,0.0,0.0,95719.73,1.0
+1224,15783883.0,Yevseyev,611.0,France,Female,39.0,3.0,135470.9,1.0,1.0,1.0,158887.09,0.0
+1225,15687903.0,Maclean,693.0,France,Female,34.0,7.0,0.0,1.0,1.0,1.0,128880.69,0.0
+1226,15603088.0,Nwebube,586.0,Spain,Female,44.0,5.0,0.0,2.0,1.0,1.0,163769.89,0.0
+1227,15595228.0,Zikoranachidimma,651.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,159475.08,0.0
+1228,15705885.0,Mault,769.0,France,Female,67.0,6.0,0.0,2.0,1.0,1.0,168034.83,0.0
+1229,15745804.0,Ch'eng,775.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,65765.22,0.0
+1230,15630698.0,Rubeo,545.0,France,Male,41.0,7.0,0.0,2.0,0.0,0.0,151843.54,0.0
+1231,15799064.0,Chiazagomekpere,719.0,France,Male,22.0,5.0,120912.98,1.0,0.0,1.0,134720.51,0.0
+1232,15667620.0,Maclean,850.0,Spain,Male,33.0,10.0,0.0,2.0,1.0,0.0,70532.53,0.0
+1233,15570194.0,Chu,602.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,168034.9,0.0
+1234,15631481.0,Trevisan,774.0,Spain,Female,28.0,2.0,152309.21,1.0,1.0,1.0,114448.77,0.0
+1235,15750055.0,Yermolayeva,667.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,138526.96,0.0
+1236,15769308.0,Ukaegbunam,590.0,Spain,Female,28.0,3.0,123699.28,1.0,1.0,0.0,102391.06,0.0
+1237,15598883.0,Tien,705.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,149583.67,0.0
+1238,15583555.0,Clements,715.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,1.0,48170.57,0.0
+1239,15699309.0,Uspensky,678.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,67944.09,0.0
+1240,15796370.0,Kuykendall,594.0,Spain,Male,23.0,5.0,0.0,2.0,1.0,1.0,133086.45,0.0
+1241,15734312.0,Okwuadigbo,807.0,Spain,Male,40.0,6.0,138521.87,1.0,1.0,0.0,180551.76,0.0
+1242,15705512.0,Hsia,696.0,France,Female,46.0,9.0,0.0,1.0,1.0,0.0,61759.33,0.0
+1243,15652789.0,Hsieh,774.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,81298.09,0.0
+1244,15782941.0,Nkemakonam,614.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,80619.09,0.0
+1245,15803801.0,Teng,642.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,96875.52,0.0
+1246,15768137.0,Shih,674.0,France,Male,48.0,1.0,117866.92,1.0,1.0,1.0,145986.35,1.0
+1247,15673599.0,Ch'en,758.0,Spain,Male,31.0,8.0,116937.05,1.0,1.0,1.0,130567.02,0.0
+1248,15698528.0,Chien,633.0,France,Female,41.0,4.0,0.0,1.0,1.0,1.0,84749.89,0.0
+1249,15752300.0,Chiwetelu,753.0,France,Female,38.0,1.0,0.0,1.0,1.0,1.0,177519.39,0.0
+1250,15798521.0,Chinonwu,588.0,Germany,Male,42.0,2.0,79460.96,2.0,0.0,0.0,177747.03,0.0
+1251,15669957.0,Udinese,667.0,Spain,Female,27.0,9.0,0.0,1.0,1.0,1.0,73371.65,1.0
+1252,15771483.0,Naylor,653.0,France,Female,34.0,5.0,126670.37,1.0,1.0,1.0,158973.46,0.0
+1253,15749093.0,Nwachinemelu,542.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,44965.44,0.0
+1254,15715510.0,Kao,712.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,152790.92,0.0
+1255,15743067.0,Chiu,757.0,Spain,Male,45.0,5.0,148962.76,1.0,1.0,1.0,4791.8,0.0
+1256,15627995.0,Pinto,697.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,91366.42,0.0
+1257,15611682.0,Shao,686.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,131647.31,0.0
+1258,15742009.0,Liao,605.0,France,Female,43.0,2.0,0.0,2.0,0.0,0.0,77330.35,0.0
+1259,15809817.0,Echezonachukwu,613.0,France,Female,29.0,4.0,0.0,2.0,1.0,1.0,172501.38,0.0
+1260,15712264.0,Chigozie,597.0,Germany,Female,47.0,6.0,107784.65,1.0,1.0,0.0,132436.71,1.0
+1261,15595842.0,Onwuamaeze,602.0,France,Female,48.0,3.0,0.0,1.0,1.0,0.0,51705.01,1.0
+1262,15736371.0,Calabresi,673.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,1.0,125083.29,0.0
+1263,15771483.0,Nkemjika,689.0,Spain,Female,44.0,4.0,0.0,2.0,0.0,1.0,79408.17,0.0
+1264,15658969.0,Palermo,690.0,France,Male,30.0,4.0,0.0,2.0,0.0,1.0,71139.31,0.0
+1265,15605968.0,Chidiegwu,663.0,France,Female,31.0,3.0,165350.61,1.0,1.0,1.0,53534.8,0.0
+1266,15622182.0,P'eng,773.0,Spain,Female,66.0,2.0,0.0,2.0,1.0,1.0,99843.98,0.0
+1267,15592230.0,Ts'ai,578.0,Spain,Male,34.0,3.0,104760.82,1.0,0.0,0.0,130883.8,0.0
+1268,15643671.0,Lombardo,668.0,Germany,Female,52.0,9.0,146098.43,3.0,1.0,0.0,118570.53,1.0
+1269,15632848.0,Lo,561.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,182270.88,0.0
+1270,15705739.0,Hsiao,748.0,Spain,Female,40.0,5.0,166501.17,1.0,0.0,1.0,156424.4,0.0
+1271,15600813.0,Yermakova,508.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,49424.6,0.0
+1272,15629133.0,Tung,694.0,Spain,Male,58.0,7.0,166594.78,1.0,1.0,1.0,136859.47,0.0
+1273,15685536.0,Ting,598.0,Germany,Male,41.0,3.0,123863.64,1.0,1.0,0.0,176868.89,0.0
+1274,15590344.0,McLean,636.0,France,Female,30.0,5.0,0.0,2.0,0.0,1.0,175324.74,0.0
+1275,15592386.0,Nwokeocha,616.0,Germany,Female,38.0,3.0,122220.19,1.0,0.0,0.0,179445.24,1.0
+1276,15792650.0,Fanucci,678.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,94790.34,0.0
+1277,15596939.0,Chukwumaobim,573.0,Spain,Male,38.0,9.0,0.0,2.0,0.0,0.0,74077.91,0.0
+1278,15714087.0,Chu,663.0,France,Female,62.0,6.0,0.0,2.0,1.0,1.0,131281.29,0.0
+1279,15787026.0,Lucchesi,627.0,France,Male,40.0,5.0,133658.24,1.0,1.0,0.0,48963.73,0.0
+1280,15576723.0,Manna,586.0,Germany,Male,63.0,1.0,125547.43,1.0,1.0,1.0,8123.11,1.0
+1281,15585853.0,Chukwubuikem,679.0,Germany,Female,52.0,7.0,107141.27,1.0,1.0,0.0,129980.93,1.0
+1282,15694879.0,Kambinachi,637.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,55893.37,0.0
+1283,15585066.0,Y?,706.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,160129.99,0.0
+1284,15661380.0,Chiganu,719.0,France,Male,34.0,8.0,0.0,1.0,1.0,1.0,173576.71,1.0
+1285,15572631.0,Y?an,697.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,131644.41,0.0
+1286,15690164.0,Onyemere,682.0,France,Female,25.0,9.0,147832.13,1.0,1.0,1.0,96833.0,0.0
+1287,15691703.0,Nwokeocha,667.0,France,Male,30.0,1.0,88363.03,1.0,1.0,0.0,57553.98,0.0
+1288,15801934.0,T'ien,694.0,Spain,Male,37.0,9.0,71097.23,2.0,0.0,0.0,143783.28,0.0
+1289,15659656.0,Li Fonti,637.0,France,Female,40.0,0.0,0.0,2.0,1.0,1.0,88724.49,0.0
+1290,15703337.0,Biryukov,729.0,Germany,Female,42.0,5.0,108805.42,1.0,1.0,0.0,109742.8,1.0
+1291,15586970.0,Ch'en,538.0,Germany,Male,31.0,7.0,151802.29,1.0,1.0,0.0,127875.1,0.0
+1292,15709920.0,Chiang,749.0,France,Male,34.0,3.0,143321.97,1.0,1.0,0.0,1187.88,1.0
+1293,15658057.0,Okwuadigbo,733.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,645.61,0.0
+1294,15687079.0,Hsing,696.0,France,Male,29.0,7.0,152302.72,1.0,1.0,0.0,153955.38,0.0
+1295,15773709.0,Hs?eh,659.0,France,Female,42.0,3.0,0.0,2.0,1.0,1.0,126223.84,1.0
+1296,15639669.0,T'ang,738.0,Spain,Female,41.0,5.0,121669.23,1.0,0.0,1.0,147534.16,0.0
+1297,15696812.0,De Luca,596.0,Spain,Male,25.0,7.0,0.0,1.0,1.0,1.0,107876.2,0.0
+1298,15602797.0,Okwudiliolisa,497.0,Germany,Female,24.0,2.0,76920.86,1.0,1.0,0.0,116973.48,1.0
+1299,15645839.0,Hsieh,637.0,France,Female,34.0,10.0,0.0,2.0,1.0,0.0,72945.62,0.0
+1300,15714586.0,T'ao,712.0,France,Male,40.0,1.0,125547.53,1.0,0.0,1.0,78707.97,0.0
+1301,15795878.0,Obiajulu,567.0,Germany,Female,45.0,0.0,124493.03,2.0,1.0,0.0,52629.71,1.0
+1302,15672894.0,Ibezimako,645.0,France,Female,37.0,8.0,0.0,3.0,1.0,0.0,82498.09,1.0
+1303,15672598.0,De Luca,561.0,France,Male,37.0,3.0,0.0,2.0,0.0,1.0,73669.94,0.0
+1304,15736271.0,Chinonyelum,634.0,France,Male,33.0,8.0,120777.88,1.0,0.0,0.0,152298.28,0.0
+1305,15605827.0,Niu,633.0,Germany,Male,47.0,9.0,125608.72,2.0,1.0,0.0,153955.38,1.0
+1306,15657540.0,T'ao,537.0,Germany,Female,47.0,3.0,141541.25,1.0,1.0,0.0,85890.75,1.0
+1307,15690772.0,Hs?,578.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,0.0,82868.34,0.0
+1308,15771299.0,Chukwumaobim,590.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,96985.58,0.0
+1309,15771742.0,Ch'in,573.0,France,Female,46.0,8.0,0.0,1.0,1.0,1.0,171463.83,1.0
+1310,15762799.0,Cattaneo,584.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,78854.58,0.0
+1311,15662641.0,Chukwumaobim,653.0,Spain,Male,31.0,1.0,0.0,1.0,1.0,0.0,102640.52,0.0
+1312,15751628.0,H?,778.0,France,Female,29.0,5.0,125265.61,1.0,1.0,1.0,67468.67,0.0
+1313,15672798.0,K'ung,726.0,Spain,Female,26.0,1.0,0.0,2.0,1.0,1.0,79577.4,0.0
+1314,15774507.0,Hsing,645.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,184524.65,0.0
+1315,15594896.0,Chiang,692.0,France,Male,43.0,3.0,139972.18,1.0,1.0,1.0,129983.2,0.0
+1316,15671930.0,Wright,642.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,143007.49,0.0
+1317,15681671.0,Ch'in,611.0,Spain,Female,29.0,6.0,0.0,2.0,1.0,0.0,9085.0,0.0
+1318,15572762.0,Pisano,733.0,Germany,Male,30.0,10.0,91611.12,1.0,1.0,0.0,158845.49,0.0
+1319,15736271.0,Ts'ao,561.0,France,Female,50.0,4.0,0.0,1.0,1.0,0.0,181419.29,1.0
+1320,15813894.0,Shao,667.0,France,Female,37.0,9.0,0.0,1.0,1.0,1.0,180553.48,0.0
+1321,15707559.0,Obialo,644.0,Spain,Male,35.0,9.0,85982.05,1.0,0.0,0.0,104469.05,0.0
+1322,15739271.0,T'ien,682.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,50213.81,0.0
+1323,15710365.0,Hsu,545.0,Germany,Male,39.0,7.0,129490.33,2.0,1.0,1.0,168834.39,1.0
+1324,15809847.0,Chu,534.0,France,Male,45.0,7.0,0.0,2.0,1.0,0.0,67468.67,1.0
+1325,15752626.0,Chidiebele,611.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,121098.65,0.0
+1326,15638003.0,Shao,687.0,Spain,Male,58.0,2.0,0.0,2.0,1.0,1.0,171837.06,0.0
+1327,15719606.0,Teng,679.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,158420.14,0.0
+1328,15590199.0,Yip,705.0,Germany,Male,58.0,4.0,117989.76,1.0,0.0,1.0,172097.95,0.0
+1329,15656096.0,Okwudiliolisa,679.0,France,Female,47.0,7.0,79811.72,1.0,0.0,0.0,172749.65,1.0
+1330,15674940.0,Dickson,687.0,Spain,Female,46.0,9.0,0.0,1.0,1.0,0.0,116081.93,0.0
+1331,15788215.0,Tuan,718.0,France,Female,33.0,7.0,135020.21,1.0,0.0,0.0,26136.13,1.0
+1332,15679991.0,T'ien,585.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,103604.65,0.0
+1333,15641655.0,Milanesi,685.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,57524.71,0.0
+1334,15752320.0,Nickson,656.0,Germany,Male,35.0,9.0,144470.77,1.0,1.0,1.0,179631.85,0.0
+1335,15671784.0,Walker,677.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,163830.64,0.0
+1336,15656029.0,Udegbulam,762.0,Germany,Male,29.0,0.0,127258.79,1.0,1.0,0.0,158845.49,0.0
+1337,15716134.0,Moore,850.0,Spain,Male,36.0,6.0,144953.31,1.0,0.0,0.0,177747.71,0.0
+1338,15783875.0,Ijendu,721.0,France,Female,44.0,8.0,0.0,1.0,0.0,1.0,116467.35,1.0
+1339,15782974.0,Ch'ien,609.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,174094.24,0.0
+1340,15623346.0,P'eng,639.0,France,Male,40.0,7.0,0.0,2.0,0.0,0.0,104170.48,0.0
+1341,15752694.0,Nwachinemelu,662.0,France,Male,66.0,2.0,0.0,2.0,1.0,1.0,125658.28,0.0
+1342,15664881.0,Lucchesi,567.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,150390.57,0.0
+1343,15778934.0,Hsia,567.0,Germany,Male,42.0,9.0,93925.3,2.0,1.0,0.0,118045.97,0.0
+1344,15692575.0,Brown,850.0,Germany,Male,56.0,1.0,128173.9,1.0,1.0,1.0,94949.84,0.0
+1345,15650018.0,Chukwujamuike,706.0,France,Male,32.0,8.0,0.0,2.0,0.0,1.0,180598.28,0.0
+1346,15570073.0,Ch'ien,656.0,Spain,Male,32.0,3.0,0.0,2.0,0.0,1.0,180622.02,0.0
+1347,15658532.0,Chin,602.0,France,Male,35.0,1.0,93955.83,1.0,1.0,0.0,93717.55,0.0
+1348,15783378.0,T'ien,687.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,121840.2,0.0
+1349,15582910.0,Tien,748.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,96442.86,0.0
+1350,15782231.0,Hsing,684.0,France,Female,46.0,8.0,0.0,2.0,1.0,0.0,163477.22,0.0
+1351,15567630.0,Milne,622.0,France,Female,28.0,5.0,0.0,2.0,1.0,0.0,131283.6,0.0
+1352,15708714.0,Lu,699.0,Spain,Male,39.0,4.0,0.0,2.0,0.0,0.0,159823.16,0.0
+1353,15806918.0,Yobanna,543.0,France,Female,41.0,6.0,0.0,2.0,1.0,1.0,121277.78,0.0
+1354,15793723.0,Pinto,643.0,Spain,Male,22.0,6.0,165438.26,1.0,1.0,1.0,47848.86,0.0
+1355,15789863.0,Smith,663.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,51380.9,1.0
+1356,15698031.0,Chukwudi,684.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,122218.62,0.0
+1357,15742971.0,P'eng,579.0,Spain,Female,70.0,2.0,0.0,4.0,1.0,1.0,170819.01,1.0
+1358,15636023.0,Iweobiegbulam,582.0,Germany,Male,29.0,4.0,122661.39,1.0,1.0,0.0,172576.56,0.0
+1359,15782688.0,Iweobiegbunam,714.0,Germany,Male,34.0,5.0,122314.5,1.0,1.0,1.0,121592.5,0.0
+1360,15624595.0,Liao,706.0,Spain,Male,34.0,2.0,125860.2,2.0,1.0,0.0,104889.3,0.0
+1361,15801417.0,Hsia,561.0,Spain,Male,29.0,10.0,0.0,1.0,1.0,1.0,75927.35,0.0
+1362,15610337.0,Nwoye,621.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,171587.9,0.0
+1363,15653620.0,H?,705.0,Germany,Male,38.0,6.0,146798.81,2.0,1.0,1.0,130883.9,0.0
+1364,15726588.0,Zaytseva,684.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,88659.07,0.0
+1365,15690182.0,Clements,689.0,France,Female,33.0,6.0,98903.06,1.0,1.0,1.0,71709.12,0.0
+1366,15688865.0,Chia,684.0,Spain,Female,36.0,4.0,0.0,1.0,1.0,1.0,138880.24,0.0
+1367,15715394.0,Nwokezuike,695.0,France,Female,43.0,10.0,0.0,2.0,1.0,0.0,7900.46,0.0
+1368,15751180.0,Chidumaga,778.0,France,Male,21.0,1.0,0.0,2.0,1.0,1.0,181224.56,0.0
+1369,15793455.0,Onyekaozulu,685.0,France,Female,36.0,7.0,0.0,1.0,1.0,1.0,155782.89,0.0
+1370,15766047.0,Martin,705.0,Germany,Female,41.0,7.0,172448.77,2.0,1.0,1.0,60954.45,0.0
+1371,15773520.0,Shih,753.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,167636.15,0.0
+1372,15691483.0,Ch'in,599.0,France,Male,29.0,1.0,0.0,2.0,0.0,1.0,91086.73,0.0
+1373,15799435.0,Shih,707.0,France,Female,27.0,4.0,0.0,2.0,1.0,0.0,131043.2,0.0
+1374,15645355.0,Nwachukwu,628.0,Germany,Female,41.0,8.0,151855.33,4.0,1.0,0.0,51705.01,1.0
+1375,15750055.0,Ts'ai,716.0,Spain,Female,25.0,0.0,0.0,2.0,0.0,0.0,164768.82,0.0
+1376,15598884.0,Hsiao,535.0,France,Female,34.0,1.0,136526.26,1.0,1.0,1.0,175743.92,0.0
+1377,15703588.0,Pagnotto,578.0,France,Male,26.0,8.0,0.0,1.0,1.0,1.0,113481.02,0.0
+1378,15603378.0,Kerr,678.0,France,Female,49.0,10.0,0.0,1.0,1.0,0.0,140723.02,1.0
+1379,15663917.0,Iweobiegbulam,542.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,96444.88,0.0
+1380,15770406.0,Iadanza,683.0,Spain,Female,41.0,7.0,0.0,1.0,1.0,0.0,137041.15,0.0
+1381,15693381.0,Chibugo,625.0,Germany,Female,34.0,5.0,142862.21,1.0,1.0,0.0,93959.96,1.0
+1382,15702561.0,Maughan,726.0,Germany,Female,44.0,3.0,105795.9,4.0,1.0,0.0,141446.01,1.0
+1383,15586680.0,Chukwumaobim,646.0,Spain,Male,39.0,7.0,0.0,1.0,1.0,1.0,93883.53,0.0
+1384,15588854.0,Hsiao,589.0,Spain,Female,38.0,6.0,0.0,2.0,0.0,1.0,141364.27,1.0
+1385,15622442.0,Chizoba,683.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,117948.0,0.0
+1386,15780128.0,Pankhurst,794.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,153917.41,0.0
+1387,15680346.0,Pinto,549.0,France,Female,42.0,1.0,0.0,2.0,1.0,1.0,54726.43,0.0
+1388,15773098.0,Chikwado,722.0,France,Female,35.0,0.0,0.0,1.0,1.0,0.0,192505.62,0.0
+1389,15700710.0,Ngozichukwuka,593.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,53584.03,0.0
+1390,15643487.0,Mazzi,616.0,Germany,Female,27.0,10.0,107304.39,1.0,1.0,1.0,75927.35,0.0
+1391,15690925.0,Ugoji,648.0,France,Male,32.0,7.0,0.0,1.0,1.0,0.0,171575.54,0.0
+1392,15572408.0,Iloerika,693.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,173486.39,0.0
+1393,15583595.0,Ritchie,622.0,Spain,Female,56.0,7.0,0.0,1.0,0.0,0.0,126381.6,1.0
+1394,15598884.0,Y?,592.0,Spain,Male,42.0,3.0,84944.58,1.0,1.0,1.0,153662.88,0.0
+1395,15593976.0,Uchechukwu,715.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,71139.31,0.0
+1396,15682912.0,Yeh,671.0,Germany,Female,46.0,4.0,134497.27,1.0,1.0,1.0,120892.96,1.0
+1397,15602405.0,Ch'ang,641.0,France,Male,43.0,6.0,0.0,2.0,1.0,0.0,167784.28,0.0
+1398,15691840.0,Kao,649.0,Spain,Male,37.0,8.0,0.0,2.0,0.0,1.0,138886.08,0.0
+1399,15772216.0,Sagese,683.0,France,Male,58.0,7.0,0.0,1.0,1.0,1.0,53584.04,1.0
+1400,15797905.0,Ch'ien,707.0,France,Male,28.0,4.0,169824.46,2.0,1.0,1.0,158973.77,0.0
+1401,15572767.0,Baresi,684.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,47850.1,0.0
+1402,15626135.0,Ritchie,621.0,France,Male,30.0,5.0,119624.6,2.0,1.0,0.0,55893.33,0.0
+1403,15800366.0,Linger,651.0,Germany,Female,37.0,9.0,132948.51,2.0,1.0,0.0,161253.08,0.0
+1404,15731511.0,Chikelu,561.0,Germany,Female,24.0,2.0,86891.84,2.0,1.0,1.0,121537.91,0.0
+1405,15649418.0,Olisaemeka,663.0,France,Male,48.0,4.0,0.0,2.0,1.0,0.0,99800.87,0.0
+1406,15684460.0,Obiajulu,594.0,Germany,Male,33.0,9.0,106854.21,2.0,1.0,0.0,91366.42,0.0
+1407,15698031.0,Yevdokimova,728.0,France,Male,37.0,6.0,0.0,2.0,0.0,0.0,142783.98,0.0
+1408,15691483.0,Genovese,714.0,France,Male,41.0,2.0,0.0,1.0,1.0,0.0,67979.35,0.0
+1409,15798844.0,Chijindum,716.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,63737.19,0.0
+1410,15794895.0,Lo Duca,684.0,France,Male,27.0,3.0,145578.28,1.0,1.0,0.0,166516.84,0.0
+1411,15794236.0,Onwuamaeze,438.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,74593.84,0.0
+1412,15632848.0,Ts'ai,643.0,France,Male,38.0,3.0,0.0,2.0,0.0,0.0,146851.76,0.0
+1413,15570086.0,Tien,636.0,Germany,Male,46.0,1.0,102025.32,1.0,0.0,1.0,125870.5,1.0
+1414,15640852.0,Nwachinemelu,758.0,France,Male,38.0,1.0,99836.47,1.0,1.0,1.0,64477.25,0.0
+1415,15651264.0,De Luca,642.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,86950.72,0.0
+1416,15582519.0,Pagnotto,629.0,Germany,Male,29.0,8.0,142406.43,2.0,1.0,1.0,177747.72,0.0
+1417,15783029.0,Lucchese,693.0,France,Male,37.0,7.0,0.0,1.0,1.0,0.0,163550.58,1.0
+1418,15671345.0,Chibugo,637.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,131768.98,0.0
+1419,15758035.0,Tsao,496.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,141446.01,0.0
+1420,15754574.0,Nwora,850.0,Spain,Female,39.0,5.0,63763.49,1.0,0.0,0.0,115442.16,0.0
+1421,15756680.0,Chifley,705.0,France,Male,43.0,4.0,126679.73,1.0,0.0,0.0,77866.91,0.0
+1422,15716041.0,Yobanna,709.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,1.0,164844.37,0.0
+1423,15583692.0,Chukwudi,583.0,France,Male,33.0,10.0,0.0,2.0,1.0,1.0,105780.18,0.0
+1424,15713378.0,Chinweike,645.0,France,Female,27.0,5.0,91893.32,1.0,1.0,1.0,125079.65,0.0
+1425,15751167.0,Mironova,572.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,142331.85,0.0
+1426,15635078.0,Pirozzi,664.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,88815.25,0.0
+1427,15771059.0,Manna,656.0,Germany,Female,37.0,3.0,119948.09,2.0,1.0,1.0,102515.43,0.0
+1428,15809279.0,T'ien,685.0,Spain,Male,44.0,8.0,0.0,2.0,1.0,1.0,93874.87,0.0
+1429,15699450.0,Hsiao,622.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,1.0,122810.53,0.0
+1430,15806447.0,Chinagorom,627.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,86043.27,0.0
+1431,15734987.0,Gadsdon,708.0,France,Female,56.0,5.0,0.0,1.0,1.0,0.0,100423.88,1.0
+1432,15780124.0,Cremonesi,716.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,112349.51,0.0
+1433,15756986.0,Martin,617.0,Spain,Male,45.0,8.0,95772.13,1.0,1.0,0.0,157072.91,0.0
+1434,15697318.0,Kistler,659.0,France,Male,37.0,7.0,132932.15,1.0,1.0,1.0,59961.81,0.0
+1435,15707974.0,Genovese,556.0,France,Female,65.0,3.0,0.0,1.0,1.0,0.0,103349.74,1.0
+1436,15803801.0,Ebelegbulam,584.0,Spain,Female,27.0,1.0,0.0,2.0,0.0,1.0,93551.36,0.0
+1437,15706021.0,Clements,738.0,France,Male,32.0,2.0,125512.98,1.0,1.0,1.0,59601.15,0.0
+1438,15758531.0,Toscani,679.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,132973.34,0.0
+1439,15761286.0,Onio,595.0,Spain,Female,27.0,2.0,109052.04,1.0,1.0,1.0,85522.84,0.0
+1440,15746553.0,Obidimkpa,590.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,1.0,149614.61,0.0
+1441,15620557.0,Chidalu,573.0,Spain,Female,36.0,8.0,99842.6,1.0,1.0,1.0,73329.08,0.0
+1442,15670266.0,Trentino,749.0,Germany,Male,37.0,4.0,108888.56,1.0,1.0,1.0,176902.83,0.0
+1443,15682355.0,Genovese,687.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,84421.34,0.0
+1444,15810457.0,Pinto,667.0,France,Female,42.0,3.0,0.0,2.0,1.0,0.0,162452.65,0.0
+1445,15677317.0,Ch'iu,539.0,Germany,Male,29.0,4.0,121172.97,2.0,1.0,0.0,48131.92,0.0
+1446,15627002.0,Shao,679.0,Germany,Male,36.0,5.0,98630.31,1.0,1.0,0.0,73669.94,0.0
+1447,15573851.0,Onyemauchechi,642.0,Germany,Female,23.0,6.0,89799.47,3.0,1.0,1.0,141679.66,0.0
+1448,15803764.0,Okeke,556.0,Germany,Female,46.0,9.0,128940.24,3.0,1.0,1.0,61936.1,1.0
+1449,15605872.0,T'ien,758.0,Germany,Female,59.0,1.0,114959.12,3.0,0.0,0.0,83629.6,1.0
+1450,15652700.0,Yobanna,651.0,Germany,Male,33.0,5.0,152582.2,2.0,1.0,1.0,1112.76,0.0
+1451,15730038.0,Chibugo,516.0,Spain,Male,22.0,5.0,0.0,2.0,1.0,0.0,168035.6,1.0
+1452,15572022.0,Diggs,567.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,1.0,176750.81,0.0
+1453,15623791.0,Chiu,652.0,Spain,Female,33.0,6.0,85845.43,1.0,1.0,1.0,138759.15,0.0
+1454,15792650.0,Hsu,573.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,173870.39,0.0
+1455,15739248.0,Scott,682.0,France,Male,35.0,3.0,120623.21,1.0,0.0,0.0,149517.72,0.0
+1456,15798975.0,Ch'en,565.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,1.0,107839.03,0.0
+1457,15660316.0,Hsing,586.0,France,Male,45.0,4.0,130170.82,1.0,1.0,0.0,93839.4,0.0
+1458,15681446.0,Mancini,686.0,Spain,Female,48.0,5.0,0.0,1.0,1.0,1.0,96864.36,1.0
+1459,15605968.0,Chibueze,586.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,97623.42,0.0
+1460,15697035.0,Yeh,603.0,Spain,Female,36.0,5.0,0.0,1.0,1.0,0.0,84935.49,1.0
+1461,15690546.0,Chialuka,678.0,Spain,Female,28.0,9.0,0.0,2.0,1.0,0.0,168034.9,0.0
+1462,15589320.0,Tsai,676.0,France,Female,47.0,2.0,0.0,2.0,1.0,1.0,140941.47,0.0
+1463,15692761.0,Hs?,642.0,Germany,Female,27.0,6.0,108698.96,1.0,1.0,1.0,180598.32,1.0
+1464,15750049.0,Iweobiegbunam,619.0,Germany,Female,34.0,3.0,168066.87,2.0,1.0,0.0,110273.84,0.0
+1465,15662063.0,Chukwueloka,724.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,1.0,93717.55,0.0
+1466,15620751.0,Chidimma,649.0,Spain,Female,39.0,1.0,0.0,1.0,1.0,1.0,95857.18,0.0
+1467,15616270.0,Obialo,544.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,63737.19,0.0
+1468,15685329.0,Hs?,614.0,France,Male,30.0,7.0,0.0,1.0,1.0,0.0,121866.87,0.0
+1469,15778696.0,Chigozie,679.0,Spain,Female,29.0,2.0,0.0,2.0,0.0,1.0,187638.34,0.0
+1470,15586029.0,Chiganu,539.0,Spain,Female,27.0,7.0,0.0,2.0,0.0,0.0,90928.02,1.0
+1471,15780128.0,Onyemauchechukwu,714.0,France,Male,26.0,4.0,0.0,2.0,1.0,1.0,85890.75,0.0
+1472,15769902.0,Nickson,645.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,153064.87,0.0
+1473,15646037.0,Kashiwagi,489.0,Germany,Male,30.0,9.0,119496.25,2.0,1.0,1.0,157891.11,0.0
+1474,15605968.0,Martin,648.0,Germany,Female,39.0,4.0,120911.75,2.0,1.0,1.0,89048.46,1.0
+1475,15692461.0,Ejimofor,645.0,Spain,Female,55.0,7.0,0.0,1.0,1.0,1.0,177528.92,1.0
+1476,15741402.0,Mazzanti,679.0,France,Male,37.0,1.0,119278.01,1.0,1.0,0.0,86950.72,0.0
+1477,15585928.0,Goliwe,574.0,Germany,Male,41.0,8.0,110802.03,1.0,1.0,1.0,138770.93,0.0
+1478,15700813.0,Achebe,439.0,Germany,Female,34.0,9.0,102772.67,2.0,1.0,1.0,119216.46,0.0
+1479,15646563.0,Soto,675.0,France,Male,37.0,6.0,0.0,1.0,1.0,0.0,58814.41,1.0
+1480,15604576.0,P'an,559.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,75884.65,0.0
+1481,15655952.0,Ndubueze,662.0,France,Female,46.0,6.0,0.0,2.0,1.0,0.0,141860.62,0.0
+1482,15698932.0,Uchenna,571.0,France,Female,44.0,2.0,141349.43,1.0,1.0,1.0,182354.46,0.0
+1483,15682210.0,Folliero,687.0,France,Male,41.0,9.0,0.0,1.0,0.0,0.0,115509.59,0.0
+1484,15569666.0,Hs?,775.0,France,Male,34.0,6.0,0.0,2.0,0.0,1.0,171069.38,0.0
+1485,15744398.0,Kosisochukwu,584.0,France,Female,43.0,3.0,0.0,2.0,1.0,0.0,136902.01,0.0
+1486,15686670.0,Houghton,646.0,France,Male,34.0,3.0,0.0,2.0,0.0,0.0,130639.01,0.0
+1487,15783859.0,Pirozzi,577.0,France,Female,40.0,9.0,0.0,1.0,1.0,0.0,51637.67,1.0
+1488,15699468.0,Chiang,726.0,Spain,Female,35.0,6.0,95858.37,1.0,0.0,1.0,137476.71,0.0
+1489,15741416.0,Chidiebere,642.0,France,Male,46.0,4.0,0.0,1.0,1.0,0.0,164018.98,1.0
+1490,15657829.0,Smith,697.0,Germany,Female,44.0,8.0,123401.43,2.0,1.0,0.0,131644.31,1.0
+1491,15729377.0,Chukwuebuka,682.0,Germany,Female,35.0,8.0,92398.08,1.0,0.0,0.0,104982.39,0.0
+1492,15786042.0,Trevisan,777.0,France,Male,47.0,4.0,0.0,2.0,1.0,0.0,143683.75,0.0
+1493,15658716.0,Folliero,579.0,Germany,Female,56.0,6.0,141952.07,1.0,1.0,1.0,9984.4,1.0
+1494,15746333.0,Chukwubuikem,705.0,France,Male,36.0,5.0,132932.13,1.0,1.0,1.0,88698.83,0.0
+1495,15574072.0,Toscani,730.0,Germany,Female,35.0,2.0,107714.74,1.0,0.0,0.0,102083.78,1.0
+1496,15632826.0,Chiazagomekpere,709.0,Germany,Female,38.0,9.0,120847.11,2.0,1.0,0.0,171668.2,0.0
+1497,15648067.0,Hsieh,629.0,France,Female,40.0,4.0,0.0,2.0,0.0,0.0,117632.63,0.0
+1498,15663421.0,Onyekachi,561.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,141023.01,0.0
+1499,15658614.0,Hs?,588.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,47322.05,0.0
+1500,15776939.0,Lucciano,592.0,Spain,Female,45.0,7.0,0.0,1.0,0.0,0.0,58974.0,1.0
+1501,15811314.0,Shih,718.0,Spain,Female,37.0,0.0,0.0,2.0,1.0,0.0,179032.68,0.0
+1502,15757628.0,Toscani,610.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,84421.34,0.0
+1503,15739248.0,Tsao,699.0,France,Female,31.0,1.0,0.0,2.0,1.0,0.0,125518.32,0.0
+1504,15625698.0,Lucciano,682.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,163655.01,0.0
+1505,15573798.0,Chiang,614.0,France,Female,32.0,5.0,0.0,1.0,1.0,0.0,136846.75,0.0
+1506,15814465.0,Ch'ang,652.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,83084.33,0.0
+1507,15683553.0,Hsiung,662.0,France,Female,30.0,4.0,0.0,2.0,0.0,1.0,131778.58,0.0
+1508,15796074.0,Hsiung,681.0,Germany,Female,55.0,6.0,133476.04,1.0,1.0,1.0,178833.0,0.0
+1509,15582841.0,Grosse,575.0,Germany,Female,45.0,3.0,100374.58,1.0,1.0,0.0,158036.72,1.0
+1510,15797329.0,Mazzanti,671.0,Germany,Female,44.0,6.0,109032.23,1.0,1.0,0.0,175072.47,1.0
+1511,15721793.0,Nwankwo,754.0,France,Male,23.0,6.0,0.0,1.0,1.0,0.0,148814.54,1.0
+1512,15591257.0,Lucchesi,666.0,France,Female,40.0,3.0,98955.87,2.0,1.0,1.0,143960.72,0.0
+1513,15713774.0,Milne,593.0,Germany,Male,48.0,6.0,143542.36,1.0,0.0,0.0,126469.09,1.0
+1514,15763029.0,Chukwukadibia,592.0,France,Male,54.0,8.0,0.0,1.0,1.0,1.0,114722.42,1.0
+1515,15808023.0,Onyekachi,702.0,France,Male,41.0,0.0,111288.18,1.0,1.0,1.0,62868.33,0.0
+1516,15623649.0,Chizuoke,678.0,France,Male,30.0,9.0,139825.57,1.0,1.0,1.0,151317.23,0.0
+1517,15572022.0,Nnanna,627.0,Germany,Female,44.0,0.0,137455.99,1.0,1.0,0.0,136903.09,1.0
+1518,15574324.0,Hughes,701.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,149457.81,0.0
+1519,15730076.0,Genovesi,739.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,43821.56,0.0
+1520,15622454.0,Hs?,628.0,France,Male,39.0,9.0,0.0,1.0,1.0,0.0,142956.48,0.0
+1521,15624592.0,Yuryeva,602.0,France,Female,33.0,4.0,0.0,2.0,1.0,1.0,121061.89,0.0
+1522,15659931.0,Ozerova,613.0,France,Female,60.0,5.0,0.0,1.0,1.0,0.0,72873.33,1.0
+1523,15668669.0,Mancini,633.0,France,Male,54.0,7.0,0.0,2.0,1.0,0.0,114035.77,1.0
+1524,15742632.0,Nwachinemelu,833.0,France,Male,39.0,5.0,0.0,2.0,0.0,1.0,124749.08,0.0
+1525,15667871.0,Ch'ien,674.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,178067.33,0.0
+1526,15780779.0,Hsing,616.0,Spain,Female,49.0,2.0,0.0,1.0,1.0,0.0,131662.47,1.0
+1527,15751175.0,T'ien,665.0,France,Female,42.0,9.0,0.0,2.0,1.0,0.0,51565.98,0.0
+1528,15626243.0,Yermakov,578.0,France,Male,37.0,4.0,120150.39,1.0,0.0,1.0,152305.47,0.0
+1529,15779059.0,Tung,685.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,143635.33,0.0
+1530,15692570.0,Kao,641.0,France,Female,40.0,8.0,119624.6,1.0,1.0,1.0,141872.05,0.0
+1531,15643392.0,Hsu,698.0,Germany,Male,42.0,1.0,119499.42,1.0,1.0,0.0,101455.07,0.0
+1532,15804950.0,Chukwudi,841.0,Spain,Male,34.0,2.0,106831.69,1.0,1.0,0.0,114522.68,0.0
+1533,15743828.0,Palerma,634.0,Spain,Male,35.0,9.0,131284.53,1.0,0.0,1.0,157097.09,0.0
+1534,15707674.0,Chiazagomekpere,561.0,Spain,Male,37.0,2.0,102050.47,1.0,1.0,1.0,179128.69,0.0
+1535,15646942.0,Onyemauchechukwu,714.0,France,Male,36.0,6.0,0.0,2.0,0.0,1.0,19131.71,0.0
+1536,15807563.0,Uspensky,573.0,France,Male,30.0,6.0,0.0,1.0,1.0,0.0,60905.51,0.0
+1537,15786035.0,Kaur,721.0,France,Female,28.0,4.0,0.0,2.0,0.0,0.0,175072.47,0.0
+1538,15717290.0,Cattaneo,645.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,58905.08,1.0
+1539,15579781.0,Nnanna,687.0,Germany,Male,43.0,5.0,82373.94,1.0,1.0,1.0,127095.14,0.0
+1540,15571415.0,K?,710.0,Germany,Female,57.0,4.0,116954.26,1.0,1.0,0.0,57962.41,1.0
+1541,15689322.0,Hsiung,627.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,98810.78,0.0
+1542,15593959.0,Aitken,659.0,France,Male,25.0,6.0,0.0,2.0,1.0,0.0,163798.41,0.0
+1543,15595713.0,Rizzo,691.0,France,Female,31.0,9.0,0.0,1.0,1.0,1.0,184842.24,0.0
+1544,15644856.0,Hsueh,635.0,France,Female,52.0,7.0,171069.39,1.0,1.0,0.0,126761.2,1.0
+1545,15600575.0,Pirozzi,633.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,131736.23,0.0
+1546,15587584.0,McIntosh,667.0,Germany,Female,46.0,1.0,131647.01,2.0,1.0,0.0,131662.42,1.0
+1547,15607598.0,Baresi,717.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,149613.77,1.0
+1548,15765257.0,Ch'ang,567.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,1.0,141872.05,0.0
+1549,15658929.0,Chukwualuka,597.0,France,Female,37.0,3.0,120145.96,1.0,1.0,1.0,187900.12,0.0
+1550,15754577.0,Hsu,588.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,177099.71,0.0
+1551,15676521.0,White,669.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,1.0,62449.07,0.0
+1552,15732438.0,Nworie,667.0,Spain,Male,36.0,2.0,131834.8,1.0,1.0,1.0,76544.9,0.0
+1553,15700714.0,Shao,641.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,0.0,170518.55,0.0
+1554,15641183.0,Chiu,696.0,Spain,Male,34.0,8.0,0.0,2.0,0.0,1.0,47468.67,0.0
+1555,15780561.0,Mazzi,663.0,Germany,Male,36.0,9.0,117468.67,2.0,1.0,1.0,139735.54,0.0
+1556,15654562.0,Chigbogu,543.0,France,Male,36.0,4.0,0.0,1.0,1.0,1.0,148515.61,0.0
+1557,15707078.0,Chinedum,773.0,France,Male,35.0,3.0,105599.67,1.0,0.0,1.0,138344.7,0.0
+1558,15679429.0,Hsiung,641.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,116885.59,0.0
+1559,15723484.0,Mazzanti,633.0,France,Female,58.0,8.0,0.0,2.0,1.0,1.0,51711.57,0.0
+1560,15777616.0,Burns,706.0,Germany,Male,33.0,6.0,141501.33,1.0,0.0,0.0,181177.9,1.0
+1561,15692929.0,Chukwualuka,565.0,France,Male,45.0,1.0,0.0,2.0,1.0,1.0,112973.66,0.0
+1562,15772363.0,Chukwueloka,653.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,162599.51,0.0
+1563,15791045.0,Obiora,616.0,Spain,Female,35.0,3.0,92398.08,1.0,0.0,0.0,101866.39,0.0
+1564,15762218.0,Onyekachi,748.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,25292.53,0.0
+1565,15769312.0,Hs?eh,732.0,Spain,Female,32.0,4.0,153572.63,1.0,1.0,1.0,160979.66,0.0
+1566,15777599.0,Y?an,648.0,France,Female,44.0,7.0,138053.79,1.0,0.0,0.0,6175.53,1.0
+1567,15679024.0,Yudin,556.0,Spain,Female,46.0,3.0,0.0,1.0,0.0,0.0,79888.78,1.0
+1568,15737605.0,Fanucci,587.0,France,Male,38.0,6.0,71240.2,1.0,1.0,0.0,116471.43,0.0
+1569,15773098.0,Tsou,653.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,1.0,91574.92,0.0
+1570,15778418.0,Ijendu,667.0,France,Male,38.0,4.0,0.0,2.0,0.0,1.0,116971.09,0.0
+1571,15793723.0,Hsia,751.0,France,Female,30.0,2.0,0.0,2.0,1.0,0.0,51620.87,0.0
+1572,15654562.0,Shih,564.0,France,Male,33.0,8.0,0.0,1.0,1.0,0.0,176770.28,0.0
+1573,15775662.0,Hsueh,603.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,52831.13,0.0
+1574,15773934.0,White,651.0,Spain,Female,35.0,1.0,0.0,1.0,1.0,0.0,171345.02,0.0
+1575,15809772.0,Hsia,850.0,Germany,Male,63.0,9.0,139753.35,2.0,1.0,0.0,85746.52,1.0
+1576,15726234.0,P'eng,684.0,France,Male,44.0,8.0,0.0,1.0,1.0,0.0,143783.46,0.0
+1577,15605918.0,Ts'ai,826.0,France,Male,29.0,7.0,0.0,2.0,0.0,0.0,126565.84,0.0
+1578,15603764.0,Chukwumaobim,715.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,162599.51,0.0
+1579,15772573.0,H?,653.0,France,Female,33.0,9.0,161533.0,1.0,1.0,0.0,24156.03,0.0
+1580,15726557.0,Ch'ien,709.0,France,Male,29.0,3.0,148055.74,1.0,1.0,1.0,79919.13,0.0
+1581,15656613.0,Genovesi,809.0,France,Female,41.0,7.0,153895.35,1.0,0.0,0.0,152956.33,0.0
+1582,15737385.0,Chin,484.0,Spain,Female,57.0,2.0,0.0,1.0,0.0,0.0,86022.09,1.0
+1583,15732640.0,Yefimov,583.0,France,Male,55.0,2.0,0.0,2.0,1.0,1.0,97633.93,0.0
+1584,15739194.0,Pirozzi,535.0,Spain,Female,22.0,5.0,0.0,2.0,1.0,0.0,79919.13,0.0
+1585,15594577.0,Trevisani,589.0,Germany,Male,51.0,0.0,139973.65,1.0,1.0,1.0,129964.94,1.0
+1586,15645463.0,Chukwualuka,571.0,Spain,Male,28.0,4.0,0.0,2.0,1.0,0.0,160129.99,0.0
+1587,15656062.0,Yefremova,622.0,Germany,Female,44.0,3.0,79562.98,2.0,1.0,0.0,155700.15,1.0
+1588,15623210.0,O'Brien,624.0,France,Female,41.0,9.0,147903.45,1.0,1.0,1.0,86935.08,0.0
+1589,15788022.0,T'ao,599.0,France,Male,62.0,5.0,0.0,1.0,1.0,0.0,70370.24,1.0
+1590,15737071.0,Watson,758.0,Germany,Male,45.0,8.0,125903.22,1.0,1.0,1.0,120115.5,1.0
+1591,15584766.0,Nwankwo,468.0,Spain,Female,53.0,0.0,0.0,1.0,1.0,0.0,85523.24,1.0
+1592,15588566.0,Chidiebere,718.0,France,Female,41.0,8.0,81070.34,2.0,1.0,1.0,125079.65,0.0
+1593,15605293.0,Ch'ien,710.0,France,Male,40.0,9.0,0.0,1.0,0.0,1.0,142331.85,0.0
+1594,15679991.0,Okeke,539.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,196241.94,0.0
+1595,15751175.0,Yashina,729.0,France,Female,38.0,2.0,0.0,2.0,0.0,1.0,71133.12,0.0
+1596,15582404.0,O'Brien,667.0,Spain,Female,35.0,5.0,96974.97,1.0,0.0,1.0,86587.37,0.0
+1597,15651301.0,Toscani,687.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,80619.17,0.0
+1598,15754072.0,Rounsevell,643.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,143301.49,0.0
+1599,15662291.0,Kao,751.0,Germany,Male,30.0,0.0,134562.9,2.0,1.0,0.0,86414.41,0.0
+1600,15628007.0,Ugochukwutubelum,627.0,France,Female,43.0,8.0,0.0,2.0,0.0,1.0,170195.4,0.0
+1601,15706071.0,Akobundu,648.0,France,Female,40.0,4.0,137326.55,1.0,1.0,1.0,108841.83,0.0
+1602,15605059.0,Chukwualuka,745.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,1.0,74890.58,0.0
+1603,15629985.0,Chukwudi,687.0,Spain,Male,38.0,4.0,0.0,2.0,0.0,1.0,120321.09,0.0
+1604,15768219.0,Hsiung,604.0,France,Male,26.0,5.0,0.0,2.0,1.0,0.0,44251.33,0.0
+1605,15639278.0,Chinedum,752.0,Germany,Female,36.0,8.0,131834.8,1.0,0.0,1.0,132034.8,0.0
+1606,15756850.0,Zetticci,678.0,France,Female,31.0,4.0,0.0,2.0,0.0,0.0,171378.77,1.0
+1607,15643826.0,Tung,725.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,161653.5,0.0
+1608,15572390.0,Nnachetam,699.0,Spain,Male,38.0,8.0,0.0,1.0,1.0,0.0,72500.68,1.0
+1609,15742534.0,Mazzi,733.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,126812.63,0.0
+1610,15589435.0,Hsiao,704.0,Spain,Male,24.0,8.0,123365.85,1.0,1.0,1.0,158531.01,0.0
+1611,15574324.0,Scott,607.0,France,Male,23.0,7.0,0.0,2.0,1.0,0.0,102515.72,0.0
+1612,15602500.0,Onyemauchechi,760.0,France,Male,42.0,5.0,0.0,2.0,0.0,1.0,48778.46,0.0
+1613,15687558.0,Iadanza,685.0,Spain,Female,39.0,5.0,0.0,1.0,1.0,1.0,176843.53,0.0
+1614,15581586.0,Cattaneo,606.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,179908.71,0.0
+1615,15652934.0,Chiganu,685.0,Spain,Male,26.0,3.0,0.0,1.0,1.0,1.0,131772.51,0.0
+1616,15782939.0,Chukwuemeka,621.0,Spain,Female,36.0,9.0,0.0,2.0,0.0,1.0,61602.42,0.0
+1617,15762729.0,Ch'iu,584.0,Germany,Female,45.0,1.0,83074.0,1.0,1.0,0.0,121789.3,1.0
+1618,15746338.0,Chin,710.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,68400.14,0.0
+1619,15637523.0,Chialuka,583.0,France,Male,32.0,1.0,0.0,2.0,0.0,0.0,61326.43,0.0
+1620,15662494.0,Yudina,687.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,131097.9,0.0
+1621,15768471.0,Tsao,711.0,France,Female,39.0,7.0,0.0,1.0,1.0,0.0,174311.3,0.0
+1622,15672955.0,Macleod,789.0,Germany,Male,41.0,8.0,127059.05,1.0,0.0,1.0,161064.29,0.0
+1623,15696834.0,Kao,606.0,Spain,Female,34.0,3.0,0.0,1.0,1.0,0.0,156909.95,0.0
+1624,15721719.0,Calabresi,730.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,80190.36,0.0
+1625,15707962.0,Baresi,679.0,France,Female,31.0,9.0,89182.09,1.0,1.0,1.0,132973.17,0.0
+1626,15772618.0,Ch'ang,569.0,Germany,Female,56.0,4.0,116936.81,2.0,1.0,0.0,89048.46,1.0
+1627,15693764.0,Yevseyev,660.0,France,Male,48.0,3.0,72542.0,2.0,1.0,0.0,187915.9,1.0
+1628,15696098.0,Ch'ien,659.0,Germany,Female,38.0,5.0,176913.52,2.0,0.0,1.0,180716.1,0.0
+1629,15732778.0,Y?,732.0,France,Male,32.0,8.0,0.0,1.0,1.0,0.0,128528.83,0.0
+1630,15791030.0,K'ung,715.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,105784.85,0.0
+1631,15589869.0,Bergamaschi,775.0,Germany,Male,35.0,5.0,130830.22,1.0,0.0,0.0,86043.27,1.0
+1632,15722072.0,Hsiung,575.0,Spain,Male,30.0,1.0,0.0,2.0,0.0,1.0,53420.53,0.0
+1633,15761763.0,Genovesi,675.0,France,Male,42.0,6.0,0.0,2.0,1.0,1.0,179435.21,1.0
+1634,15660390.0,Lucciano,591.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,167087.34,0.0
+1635,15652674.0,K'ung,585.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,106024.02,0.0
+1636,15745324.0,Hsueh,640.0,Germany,Male,35.0,8.0,140004.96,2.0,1.0,0.0,98626.78,0.0
+1637,15771543.0,T'ao,633.0,France,Female,24.0,3.0,0.0,1.0,1.0,1.0,53710.23,0.0
+1638,15776433.0,Hsiung,850.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,91092.84,0.0
+1639,15689201.0,Yobachi,735.0,Spain,Male,58.0,6.0,0.0,1.0,0.0,0.0,92357.21,1.0
+1640,15782364.0,Y?an,669.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,139048.0,0.0
+1641,15642530.0,T'ao,758.0,France,Male,32.0,4.0,79475.3,1.0,0.0,1.0,116537.96,0.0
+1642,15765520.0,Ch'eng,603.0,Germany,Male,44.0,6.0,143397.61,2.0,1.0,0.0,154908.81,0.0
+1643,15672598.0,Brown,649.0,France,Female,47.0,6.0,0.0,2.0,1.0,1.0,86935.08,0.0
+1644,15653253.0,Hargreaves,556.0,France,Male,36.0,3.0,167664.83,1.0,1.0,0.0,142400.27,0.0
+1645,15611338.0,Chien,627.0,Spain,Female,42.0,6.0,0.0,2.0,1.0,0.0,62678.53,0.0
+1646,15754068.0,Iweobiegbunam,829.0,France,Male,31.0,7.0,0.0,2.0,0.0,1.0,155870.82,0.0
+1647,15583764.0,Manna,489.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,0.0,167795.83,0.0
+1648,15779915.0,Nwankwo,578.0,France,Male,47.0,7.0,0.0,1.0,0.0,0.0,78854.57,0.0
+1649,15650026.0,Folliero,696.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,14073.11,0.0
+1650,15645269.0,Ch'ien,773.0,Spain,Female,71.0,3.0,0.0,2.0,1.0,1.0,86413.11,0.0
+1651,15779947.0,Chigozie,575.0,France,Male,40.0,0.0,0.0,2.0,1.0,1.0,122268.75,0.0
+1652,15803078.0,Chu,567.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,112554.68,0.0
+1653,15729956.0,Chidumaga,687.0,Germany,Female,48.0,4.0,116968.91,1.0,1.0,0.0,163560.7,1.0
+1654,15660301.0,Ma,599.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,72402.0,0.0
+1655,15594786.0,Okechukwu,554.0,France,Female,48.0,3.0,143964.36,1.0,0.0,0.0,105965.25,1.0
+1656,15770498.0,Onwughara,732.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,63889.1,0.0
+1657,15776743.0,Obiajulu,669.0,Germany,Male,38.0,6.0,92783.42,2.0,1.0,1.0,184980.23,0.0
+1658,15651713.0,Pisani,637.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,132649.85,0.0
+1659,15591428.0,T'ien,655.0,Spain,Male,28.0,2.0,171325.98,1.0,1.0,1.0,79476.63,0.0
+1660,15706186.0,O'Donnell,607.0,France,Male,33.0,3.0,0.0,1.0,1.0,1.0,101694.67,0.0
+1661,15584766.0,Chiazagomekpere,738.0,France,Female,44.0,1.0,99824.45,1.0,0.0,0.0,119882.86,1.0
+1662,15642570.0,Chien,738.0,France,Male,31.0,5.0,97612.12,2.0,1.0,1.0,83052.03,0.0
+1663,15807768.0,T'ang,644.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,1.0,175853.64,0.0
+1664,15587584.0,Chuang,658.0,Spain,Male,39.0,1.0,0.0,1.0,0.0,0.0,96791.15,1.0
+1665,15594720.0,Hsiao,602.0,France,Female,40.0,5.0,0.0,1.0,0.0,0.0,168035.6,1.0
+1666,15738166.0,Hsieh,523.0,France,Male,45.0,5.0,128760.32,1.0,1.0,0.0,126837.72,1.0
+1667,15721715.0,Houghton,677.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,166516.84,0.0
+1668,15587712.0,Tsao,671.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,104721.5,0.0
+1669,15754849.0,Chiang,712.0,Spain,Male,34.0,7.0,0.0,2.0,0.0,0.0,79503.05,0.0
+1670,15635240.0,Chiu,684.0,France,Male,27.0,7.0,0.0,1.0,1.0,0.0,170142.09,0.0
+1671,15634262.0,Tretiakova,703.0,Germany,Female,32.0,6.0,128927.93,2.0,0.0,1.0,105291.26,0.0
+1672,15574324.0,White,628.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,1.0,171681.86,0.0
+1673,15764444.0,Onyemauchechukwu,773.0,France,Male,24.0,5.0,0.0,2.0,1.0,0.0,91092.84,0.0
+1674,15610755.0,Walker,639.0,Germany,Male,29.0,2.0,120105.43,2.0,1.0,1.0,180598.28,0.0
+1675,15734886.0,Kao,598.0,France,Female,31.0,4.0,81844.69,2.0,1.0,1.0,81617.4,0.0
+1676,15744273.0,Zetticci,642.0,France,Male,44.0,8.0,0.0,2.0,0.0,0.0,129964.94,0.0
+1677,15792453.0,Ndukaku,707.0,France,Female,36.0,0.0,0.0,2.0,0.0,1.0,66795.78,0.0
+1678,15777076.0,Onwuamaeze,565.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,132424.69,1.0
+1679,15679991.0,Okwuadigbo,676.0,Germany,Male,36.0,4.0,130140.24,2.0,1.0,0.0,89891.6,0.0
+1680,15585106.0,Chuang,717.0,Spain,Male,37.0,7.0,0.0,2.0,1.0,1.0,74835.65,0.0
+1681,15775886.0,Teng,643.0,France,Female,41.0,7.0,0.0,1.0,1.0,0.0,141896.74,1.0
+1682,15601892.0,Hsia,681.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,142400.27,0.0
+1683,15729083.0,Ch'en,703.0,Spain,Female,39.0,2.0,152582.5,1.0,0.0,0.0,79915.28,0.0
+1684,15791053.0,Maclean,605.0,Germany,Male,57.0,7.0,148853.29,1.0,1.0,0.0,163830.64,1.0
+1685,15720354.0,Eames,684.0,Germany,Male,29.0,1.0,88826.07,2.0,1.0,0.0,109742.2,0.0
+1686,15587562.0,Okwudilichukwu,622.0,Germany,Male,43.0,3.0,113781.5,1.0,0.0,0.0,138877.95,1.0
+1687,15811588.0,Ch'ien,710.0,France,Male,36.0,4.0,112419.92,1.0,1.0,0.0,86460.28,0.0
+1688,15775294.0,Ts'ao,735.0,Germany,Male,38.0,5.0,153545.43,1.0,1.0,0.0,72876.84,0.0
+1689,15602844.0,Chidimma,595.0,France,Female,47.0,3.0,110829.52,1.0,1.0,0.0,63737.19,0.0
+1690,15594720.0,Kosisochukwu,596.0,Spain,Female,56.0,4.0,130622.34,1.0,0.0,1.0,7861.68,1.0
+1691,15781347.0,Li Fonti,604.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,132973.17,0.0
+1692,15734246.0,Ch'ien,732.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,0.0,173495.77,0.0
+1693,15605067.0,Tretiakova,706.0,Spain,Male,47.0,8.0,0.0,2.0,1.0,0.0,90106.94,0.0
+1694,15684801.0,Toscani,748.0,Spain,Female,36.0,1.0,161591.11,1.0,1.0,0.0,61480.91,0.0
+1695,15689322.0,Hsu,669.0,France,Male,38.0,3.0,0.0,2.0,0.0,0.0,73562.05,0.0
+1696,15643361.0,Li Fonti,812.0,Spain,Female,45.0,4.0,0.0,1.0,1.0,1.0,62700.08,1.0
+1697,15796790.0,Lombardo,633.0,Spain,Female,47.0,5.0,0.0,2.0,0.0,1.0,179614.8,0.0
+1698,15719579.0,Walker,758.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,182372.35,0.0
+1699,15632050.0,Chinagorom,656.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,78650.95,0.0
+1700,15658475.0,Onyemauchechukwu,695.0,France,Female,35.0,9.0,0.0,2.0,0.0,1.0,38190.78,0.0
+1701,15576824.0,O'Donnell,735.0,France,Female,39.0,9.0,0.0,2.0,1.0,1.0,136983.77,0.0
+1702,15741875.0,Wright,547.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,6078.46,0.0
+1703,15658532.0,Chiekwugo,682.0,France,Female,30.0,3.0,0.0,2.0,0.0,1.0,88890.05,0.0
+1704,15641183.0,Lettiere,749.0,France,Male,40.0,8.0,141267.67,1.0,1.0,0.0,115068.99,0.0
+1705,15598884.0,Chinwenma,570.0,Germany,Male,37.0,2.0,119492.88,2.0,1.0,1.0,136902.01,0.0
+1706,15741728.0,Ch'eng,647.0,France,Female,49.0,9.0,0.0,2.0,1.0,1.0,4050.57,0.0
+1707,15765643.0,Teng,516.0,Spain,Male,32.0,3.0,0.0,2.0,0.0,1.0,179634.69,0.0
+1708,15793856.0,Lo Duca,729.0,Spain,Male,41.0,9.0,132934.89,1.0,1.0,1.0,63736.17,0.0
+1709,15684573.0,Maclean,733.0,Spain,Female,23.0,5.0,0.0,2.0,1.0,1.0,138543.83,0.0
+1710,15745196.0,Frolov,616.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,1.0,189428.75,0.0
+1711,15782759.0,Chibugo,614.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,132476.91,1.0
+1712,15674840.0,Tuan,648.0,France,Male,26.0,7.0,0.0,1.0,0.0,0.0,144880.81,0.0
+1713,15781409.0,Kambinachi,591.0,Spain,Female,30.0,6.0,0.0,2.0,1.0,1.0,121592.5,0.0
+1714,15772892.0,Chukwumaobim,613.0,Spain,Female,37.0,8.0,0.0,3.0,0.0,0.0,168822.01,1.0
+1715,15644878.0,Onyinyechukwuka,651.0,Spain,Male,36.0,7.0,61211.18,1.0,1.0,1.0,167749.54,1.0
+1716,15662662.0,Ch'iu,745.0,France,Female,34.0,7.0,0.0,1.0,1.0,1.0,128841.51,0.0
+1717,15583580.0,Nkemakolam,572.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,182787.18,0.0
+1718,15595136.0,Shubin,643.0,Germany,Male,51.0,7.0,118879.35,1.0,1.0,0.0,85421.48,1.0
+1719,15693893.0,Mai,644.0,Germany,Male,38.0,3.0,120596.93,1.0,1.0,1.0,136902.01,0.0
+1720,15791322.0,Chimaobim,714.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,86039.53,0.0
+1721,15646594.0,Zhirov,593.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,58879.31,0.0
+1722,15621685.0,Fiorentini,637.0,Spain,Male,43.0,4.0,0.0,4.0,1.0,0.0,8814.69,1.0
+1723,15791958.0,Chinweuba,624.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,26260.98,0.0
+1724,15671514.0,Mironova,615.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,160835.3,0.0
+1725,15753871.0,Chidiegwu,705.0,France,Female,44.0,1.0,0.0,3.0,1.0,0.0,150944.31,1.0
+1726,15765402.0,Omeokachie,679.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,25554.4,0.0
+1727,15647572.0,Pisani,691.0,Spain,Male,39.0,7.0,0.0,2.0,1.0,1.0,56793.02,0.0
+1728,15642098.0,Onyemauchechukwu,779.0,France,Female,41.0,6.0,124779.46,1.0,1.0,1.0,111020.24,0.0
+1729,15802869.0,Nkemakonam,687.0,France,Female,53.0,8.0,0.0,1.0,1.0,0.0,1713.1,1.0
+1730,15571586.0,Hsueh,736.0,Germany,Female,36.0,8.0,108309.0,1.0,1.0,0.0,188809.23,1.0
+1731,15624755.0,Palerma,662.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,116969.73,0.0
+1732,15693737.0,Estep,745.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,0.0,94739.3,0.0
+1733,15665014.0,Lucchese,685.0,France,Male,37.0,7.0,76989.97,1.0,1.0,0.0,126812.85,0.0
+1734,15617401.0,Ch'iu,516.0,France,Female,42.0,3.0,0.0,2.0,1.0,0.0,65618.01,0.0
+1735,15622171.0,Ch'en,704.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,174916.85,0.0
+1736,15654562.0,Chukwubuikem,663.0,France,Female,40.0,3.0,0.0,3.0,1.0,0.0,136054.45,1.0
+1737,15672439.0,Otoole,685.0,Germany,Male,19.0,3.0,121898.58,2.0,1.0,0.0,9241.52,0.0
+1738,15758056.0,Hsueh,576.0,Spain,Female,34.0,8.0,0.0,2.0,0.0,1.0,129333.69,0.0
+1739,15733661.0,Yuryeva,733.0,France,Female,36.0,3.0,177799.83,1.0,0.0,0.0,136540.09,0.0
+1740,15581552.0,McLean,667.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,0.0,173683.0,0.0
+1741,15803764.0,Y?,611.0,Germany,Male,34.0,6.0,145166.09,1.0,1.0,0.0,88890.05,0.0
+1742,15667417.0,Wright,672.0,France,Female,26.0,3.0,0.0,2.0,1.0,0.0,67840.81,0.0
+1743,15677921.0,Bage,623.0,Germany,Male,40.0,1.0,115335.32,2.0,1.0,0.0,107811.28,0.0
+1744,15654060.0,Chidiebere,565.0,France,Female,33.0,1.0,0.0,1.0,1.0,0.0,147797.54,0.0
+1745,15620505.0,Molineux,715.0,Spain,Male,37.0,2.0,0.0,1.0,1.0,0.0,64872.33,0.0
+1746,15726589.0,Milne,614.0,Spain,Male,39.0,6.0,162039.78,1.0,0.0,0.0,134415.58,0.0
+1747,15642934.0,Ch'iu,603.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,85516.37,0.0
+1748,15764033.0,Nwachukwu,695.0,France,Female,40.0,9.0,81844.81,1.0,1.0,1.0,91573.19,0.0
+1749,15741403.0,Ulyanova,671.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,28257.89,0.0
+1750,15572022.0,Shao,619.0,France,Male,28.0,2.0,0.0,2.0,0.0,1.0,83022.84,0.0
+1751,15747808.0,Kao,637.0,Spain,Male,30.0,1.0,144591.96,1.0,1.0,0.0,53832.8,0.0
+1752,15751561.0,Pinto,733.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,139946.68,0.0
+1753,15678720.0,T'ang,516.0,Spain,Male,29.0,0.0,0.0,2.0,0.0,1.0,81778.72,0.0
+1754,15714586.0,Purdy,725.0,France,Male,44.0,7.0,0.0,1.0,0.0,0.0,126471.14,0.0
+1755,15661629.0,Pinto,705.0,Spain,Male,37.0,2.0,0.0,1.0,1.0,1.0,164925.11,0.0
+1756,15692404.0,Ulyanova,681.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,16301.91,0.0
+1757,15813432.0,Onwumelu,648.0,Germany,Male,38.0,4.0,148042.97,2.0,1.0,0.0,44390.38,0.0
+1758,15707025.0,Iadanza,686.0,Spain,Male,32.0,3.0,0.0,2.0,0.0,1.0,91291.2,0.0
+1759,15796790.0,Chukwumaobim,773.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,153983.99,0.0
+1760,15597949.0,Hsia,721.0,Germany,Male,36.0,3.0,122029.15,2.0,1.0,0.0,141069.88,1.0
+1761,15578980.0,Shih,714.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,38691.34,0.0
+1762,15571144.0,Oluchi,806.0,Spain,Female,35.0,8.0,0.0,2.0,1.0,1.0,145562.4,0.0
+1763,15683053.0,Ch'eng,617.0,France,Male,26.0,8.0,0.0,2.0,0.0,1.0,66732.63,0.0
+1764,15632448.0,Yobachukwu,646.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,166588.76,0.0
+1765,15647842.0,Shao,559.0,Germany,Male,45.0,6.0,133889.35,1.0,1.0,0.0,141872.05,1.0
+1766,15806955.0,Ukaegbunam,709.0,France,Male,33.0,2.0,125919.35,1.0,1.0,1.0,143635.55,0.0
+1767,15595884.0,Ndubuisi,711.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,147955.91,0.0
+1768,15777046.0,Onwumelu,526.0,Germany,Female,54.0,9.0,106871.81,1.0,1.0,0.0,88858.49,1.0
+1769,15719561.0,Lu,724.0,Spain,Female,38.0,2.0,0.0,2.0,0.0,1.0,172576.43,0.0
+1770,15690670.0,Tardent,656.0,Spain,Female,31.0,5.0,0.0,2.0,0.0,1.0,88972.66,0.0
+1771,15594862.0,H?,692.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,91574.92,0.0
+1772,15724334.0,Hargreaves,599.0,France,Male,35.0,5.0,0.0,2.0,0.0,0.0,142903.44,0.0
+1773,15661656.0,Tung,645.0,Germany,Female,25.0,5.0,90536.81,2.0,0.0,1.0,179291.89,0.0
+1774,15663817.0,White,794.0,France,Female,34.0,1.0,142910.18,1.0,1.0,1.0,166468.21,0.0
+1775,15690733.0,Macleod,659.0,France,Male,35.0,9.0,0.0,1.0,1.0,0.0,59650.42,0.0
+1776,15592645.0,Chukwuemeka,481.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,112491.96,0.0
+1777,15738181.0,Chidozie,742.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,111241.37,1.0
+1778,15680587.0,Efimov,724.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,64327.26,0.0
+1779,15695301.0,Ugochukwu,665.0,France,Male,36.0,2.0,136525.99,1.0,1.0,1.0,119313.74,0.0
+1780,15667424.0,Ts'ui,584.0,Germany,Male,34.0,1.0,105206.7,1.0,1.0,0.0,180598.86,0.0
+1781,15691543.0,Smith,714.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,95969.96,0.0
+1782,15647890.0,Oluchi,619.0,Germany,Male,42.0,4.0,125921.37,2.0,0.0,0.0,153917.41,0.0
+1783,15789339.0,Iqbal,671.0,Germany,Male,48.0,9.0,123737.04,1.0,1.0,0.0,40313.47,1.0
+1784,15702432.0,Chienezie,741.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,70954.41,0.0
+1785,15676966.0,Hsu,663.0,Germany,Male,38.0,1.0,145056.59,2.0,1.0,1.0,85746.52,0.0
+1786,15781388.0,Zikoranachidimma,571.0,Germany,Male,40.0,5.0,136422.28,2.0,1.0,1.0,82391.15,0.0
+1787,15709476.0,Y?,675.0,France,Female,46.0,7.0,0.0,2.0,1.0,0.0,26261.42,0.0
+1788,15684516.0,Hsing,610.0,Spain,Male,49.0,6.0,121890.06,1.0,1.0,1.0,166495.2,0.0
+1789,15723053.0,Nwankwo,633.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,46675.51,0.0
+1790,15778515.0,Ch'ien,683.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,86920.97,0.0
+1791,15664808.0,Shao,659.0,Germany,Female,45.0,4.0,124495.98,1.0,1.0,0.0,74896.33,1.0
+1792,15679983.0,Niu,641.0,Germany,Female,43.0,6.0,107713.57,1.0,1.0,1.0,152210.81,1.0
+1793,15793049.0,Hao,850.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,66580.13,0.0
+1794,15626795.0,Yevseyev,572.0,Spain,Male,40.0,9.0,0.0,2.0,0.0,0.0,126561.09,0.0
+1795,15649853.0,Zaitsev,745.0,France,Male,23.0,7.0,0.0,2.0,1.0,1.0,152643.48,0.0
+1796,15693347.0,Chukwumaobim,648.0,France,Male,33.0,3.0,0.0,2.0,0.0,1.0,56583.88,0.0
+1797,15767793.0,Lettiere,716.0,Spain,Male,31.0,7.0,89799.13,1.0,1.0,0.0,152491.16,0.0
+1798,15706762.0,Nwabugwu,662.0,Spain,Female,46.0,6.0,0.0,2.0,1.0,0.0,91957.39,0.0
+1799,15656330.0,Onuora,744.0,France,Female,23.0,4.0,168964.77,1.0,1.0,1.0,182336.45,0.0
+1800,15652667.0,Chukwueloka,712.0,Germany,Male,51.0,8.0,99564.22,2.0,1.0,0.0,161241.65,1.0
+1801,15622454.0,Obiora,681.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,80410.68,0.0
+1802,15706899.0,Shao,649.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,1.0,91573.19,0.0
+1803,15742228.0,Chimaoke,562.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,49373.97,0.0
+1804,15587066.0,Macintyre,684.0,Spain,Male,39.0,7.0,0.0,2.0,0.0,0.0,158918.02,0.0
+1805,15673590.0,De Mestre,572.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,60420.07,0.0
+1806,15700476.0,Onyinyechukwuka,653.0,Spain,Female,41.0,2.0,0.0,1.0,0.0,1.0,142903.44,1.0
+1807,15798084.0,Chiemenam,835.0,France,Male,41.0,2.0,0.0,2.0,0.0,0.0,130446.65,0.0
+1808,15611786.0,Yermolayeva,754.0,France,Male,46.0,9.0,0.0,1.0,1.0,1.0,149911.33,0.0
+1809,15681180.0,Chimaobim,585.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,140134.43,0.0
+1810,15784761.0,Chidubem,815.0,Spain,Male,28.0,4.0,0.0,2.0,1.0,1.0,184312.88,0.0
+1811,15591150.0,Yevseyev,597.0,France,Male,31.0,5.0,119278.01,1.0,0.0,0.0,115227.02,0.0
+1812,15706537.0,O'Brien,653.0,Spain,Female,31.0,8.0,0.0,2.0,0.0,1.0,48475.12,0.0
+1813,15675380.0,Onwuamaeze,726.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,0.0,84419.78,0.0
+1814,15590820.0,Chiang,669.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,106977.53,0.0
+1815,15579183.0,Li Fonti,594.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,89938.08,0.0
+1816,15730038.0,Okwuadigbo,674.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,124625.08,0.0
+1817,15616168.0,Ojiofor,619.0,France,Male,63.0,1.0,98495.72,2.0,0.0,1.0,162922.65,0.0
+1818,15725028.0,Lettiere,686.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,81442.6,0.0
+1819,15634373.0,Hsueh,554.0,Germany,Male,47.0,8.0,71083.98,2.0,1.0,0.0,199290.68,0.0
+1820,15806922.0,Sagese,607.0,France,Female,65.0,9.0,0.0,3.0,1.0,0.0,171706.66,1.0
+1821,15584090.0,Tsai,710.0,France,Female,32.0,6.0,138032.15,1.0,1.0,1.0,116466.19,0.0
+1822,15702912.0,Uspenskaya,613.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,66526.01,0.0
+1823,15596976.0,Chiang,691.0,France,Male,25.0,7.0,168883.08,1.0,0.0,0.0,96892.8,0.0
+1824,15645896.0,Y?an,545.0,France,Male,30.0,1.0,142713.44,2.0,1.0,1.0,155047.11,0.0
+1825,15581766.0,Palerma,593.0,France,Male,28.0,1.0,91522.04,1.0,0.0,1.0,156768.45,0.0
+1826,15592540.0,Ch'eng,772.0,France,Female,32.0,9.0,0.0,1.0,1.0,1.0,177747.02,0.0
+1827,15778418.0,Chiganu,622.0,France,Female,51.0,9.0,0.0,1.0,1.0,0.0,174435.48,1.0
+1828,15680201.0,Ch'ien,668.0,France,Female,25.0,5.0,0.0,1.0,1.0,0.0,129563.86,0.0
+1829,15690164.0,Chukwumaobim,594.0,Spain,Male,36.0,8.0,0.0,1.0,1.0,1.0,94499.06,0.0
+1830,15777054.0,Chukwueloka,675.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,156424.4,0.0
+1831,15788442.0,Kuykendall,667.0,Spain,Female,43.0,5.0,0.0,1.0,1.0,1.0,81492.6,1.0
+1832,15642997.0,Hsueh,684.0,Germany,Female,37.0,8.0,141789.15,1.0,1.0,1.0,166423.66,0.0
+1833,15687629.0,Fanucci,587.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,70899.27,0.0
+1834,15621433.0,Ch'en,687.0,France,Male,24.0,7.0,0.0,1.0,1.0,1.0,60905.51,0.0
+1835,15771580.0,Ugochukwutubelum,564.0,France,Female,32.0,5.0,0.0,2.0,0.0,1.0,52404.47,0.0
+1836,15752409.0,Nwabugwu,651.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,1.0,186612.47,0.0
+1837,15732332.0,Fang,704.0,France,Female,66.0,5.0,0.0,2.0,0.0,1.0,161333.13,0.0
+1838,15620341.0,Ndubuagha,559.0,Germany,Male,36.0,5.0,126816.73,1.0,1.0,1.0,116885.59,0.0
+1839,15764033.0,Genovese,645.0,France,Male,29.0,2.0,71269.89,1.0,0.0,1.0,47832.82,0.0
+1840,15779947.0,Palerma,635.0,Germany,Female,47.0,9.0,124495.98,1.0,1.0,0.0,157921.25,1.0
+1841,15604073.0,Bage,469.0,Spain,Male,23.0,4.0,0.0,1.0,0.0,0.0,78858.07,0.0
+1842,15622117.0,Chukwujekwu,682.0,Germany,Male,33.0,4.0,134542.73,1.0,0.0,1.0,180344.69,0.0
+1843,15579826.0,Toscani,676.0,France,Female,39.0,2.0,130557.24,1.0,1.0,0.0,42012.55,1.0
+1844,15683483.0,Chiang,667.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,136537.97,0.0
+1845,15664897.0,Ch'ang,676.0,France,Female,50.0,2.0,0.0,1.0,1.0,0.0,112928.74,1.0
+1846,15749019.0,Ch'eng,717.0,Spain,Male,46.0,9.0,0.0,2.0,1.0,1.0,54949.73,0.0
+1847,15769246.0,Creswell,585.0,Spain,Male,57.0,7.0,0.0,2.0,1.0,1.0,72873.33,0.0
+1848,15805909.0,Lucchese,582.0,Germany,Male,43.0,4.0,122453.97,2.0,1.0,0.0,164815.59,0.0
+1849,15684921.0,Pugh,677.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,131807.14,0.0
+1850,15632439.0,Chiu,641.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,153537.55,0.0
+1851,15777033.0,Scott,589.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,1.0,125918.17,0.0
+1852,15642530.0,H?,546.0,Germany,Female,43.0,4.0,140197.18,1.0,1.0,1.0,142763.51,1.0
+1853,15769902.0,Oluchukwu,627.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,141400.51,0.0
+1854,15596962.0,Lai,663.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,162878.96,0.0
+1855,15751177.0,Bitter,543.0,France,Male,29.0,9.0,119688.81,1.0,0.0,0.0,55815.62,0.0
+1856,15613140.0,Ejimofor,679.0,France,Female,22.0,4.0,0.0,2.0,1.0,1.0,162599.54,0.0
+1857,15625023.0,Lombardi,528.0,Germany,Female,47.0,2.0,135203.81,1.0,1.0,1.0,103379.31,1.0
+1858,15613787.0,Chiang,658.0,Spain,Female,44.0,6.0,0.0,2.0,1.0,0.0,153895.31,0.0
+1859,15582404.0,Yefremova,628.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,171733.43,0.0
+1860,15656592.0,Ch'in,599.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,93844.81,0.0
+1861,15681843.0,Hsing,670.0,France,Female,54.0,7.0,0.0,1.0,1.0,1.0,162599.51,1.0
+1862,15592386.0,Cavenagh,599.0,France,Male,40.0,7.0,173459.45,1.0,1.0,1.0,66893.28,0.0
+1863,15572767.0,Ch'iu,629.0,France,Female,56.0,9.0,0.0,2.0,1.0,1.0,50679.9,0.0
+1864,15750545.0,Lucchese,713.0,Germany,Female,47.0,2.0,127289.28,1.0,1.0,0.0,57553.02,1.0
+1865,15650351.0,Ch'eng,643.0,Germany,Female,59.0,4.0,114203.34,1.0,1.0,0.0,15650.89,1.0
+1866,15678916.0,Ch'iu,667.0,France,Female,38.0,4.0,98362.35,1.0,1.0,0.0,83343.73,0.0
+1867,15763747.0,Mancini,719.0,France,Female,37.0,7.0,80613.93,1.0,1.0,1.0,152499.8,0.0
+1868,15674932.0,Tsao,616.0,France,Male,34.0,0.0,103564.83,1.0,1.0,0.0,57589.29,0.0
+1869,15759021.0,Oluchukwu,648.0,Spain,Female,59.0,7.0,0.0,1.0,1.0,1.0,123882.73,1.0
+1870,15796074.0,T'ien,602.0,France,Male,55.0,8.0,0.0,2.0,1.0,1.0,155823.2,0.0
+1871,15588080.0,Teng,651.0,France,Male,37.0,2.0,0.0,1.0,1.0,0.0,151925.25,0.0
+1872,15590094.0,Trevisani,632.0,Spain,Male,32.0,4.0,171671.9,1.0,1.0,1.0,147530.06,0.0
+1873,15754068.0,Chukwubuikem,684.0,France,Female,31.0,10.0,0.0,2.0,1.0,1.0,133573.29,0.0
+1874,15793854.0,Li Fonti,708.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,171718.56,0.0
+1875,15654562.0,Ch'ang,629.0,France,Male,45.0,2.0,0.0,1.0,1.0,0.0,92916.54,1.0
+1876,15683553.0,Erskine,732.0,Spain,Male,40.0,9.0,125510.82,1.0,0.0,0.0,94862.93,0.0
+1877,15740147.0,Chibuzo,781.0,France,Female,33.0,2.0,126473.99,1.0,1.0,0.0,122932.5,0.0
+1878,15737408.0,Folliero,592.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,149892.79,0.0
+1879,15589320.0,Y?,587.0,France,Male,28.0,5.0,0.0,2.0,0.0,0.0,179565.57,0.0
+1880,15753337.0,Hsiung,744.0,France,Female,41.0,9.0,0.0,1.0,1.0,0.0,140941.47,1.0
+1881,15569247.0,Onyemauchechukwu,652.0,France,Female,34.0,7.0,126066.13,1.0,1.0,1.0,133535.29,0.0
+1882,15677908.0,DeRose,605.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,129975.62,0.0
+1883,15815615.0,Nwankwo,718.0,Spain,Male,40.0,6.0,0.0,2.0,0.0,0.0,52795.56,0.0
+1884,15572467.0,Moore,710.0,France,Male,50.0,1.0,0.0,1.0,0.0,1.0,177747.03,0.0
+1885,15588854.0,Chukwueloka,678.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,0.0,85412.54,0.0
+1886,15688868.0,Lucchesi,703.0,France,Male,31.0,4.0,0.0,2.0,0.0,0.0,48242.38,0.0
+1887,15751203.0,Uspenskaya,602.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,152491.22,0.0
+1888,15696812.0,Chigozie,769.0,France,Female,49.0,1.0,0.0,1.0,1.0,1.0,48426.98,0.0
+1889,15588928.0,Aksyonov,577.0,Germany,Female,35.0,9.0,136291.71,1.0,1.0,0.0,138527.56,1.0
+1890,15785547.0,Hsia,724.0,Germany,Male,42.0,0.0,137714.4,1.0,1.0,1.0,103342.48,0.0
+1891,15645896.0,Shih,645.0,Germany,Female,51.0,7.0,79340.95,3.0,1.0,0.0,161574.19,1.0
+1892,15765345.0,Ch'ang,567.0,France,Male,28.0,10.0,0.0,1.0,0.0,0.0,54949.73,0.0
+1893,15736271.0,Ch'en,626.0,France,Female,49.0,0.0,0.0,1.0,1.0,0.0,143086.09,1.0
+1894,15699492.0,Kincaid,706.0,France,Male,28.0,1.0,0.0,2.0,1.0,1.0,172749.4,0.0
+1895,15746461.0,Manna,684.0,France,Female,35.0,9.0,0.0,1.0,0.0,0.0,78744.39,0.0
+1896,15761158.0,Iloerika,748.0,France,Male,42.0,2.0,0.0,2.0,1.0,1.0,129912.74,0.0
+1897,15809263.0,Cremonesi,526.0,France,Female,30.0,1.0,0.0,1.0,1.0,0.0,72993.65,0.0
+1898,15587266.0,Chigbogu,705.0,Germany,Female,33.0,7.0,123935.35,1.0,1.0,1.0,100433.83,1.0
+1899,15661745.0,Trevisani,774.0,France,Male,51.0,2.0,0.0,2.0,0.0,1.0,180890.4,0.0
+1900,15771473.0,Romani,605.0,Germany,Female,43.0,0.0,113657.83,2.0,1.0,0.0,8059.44,1.0
+1901,15667944.0,H?,616.0,France,Male,32.0,7.0,133658.24,1.0,1.0,1.0,143357.03,0.0
+1902,15625592.0,Dickson,632.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,26752.56,0.0
+1903,15750497.0,Lu,644.0,Spain,Male,34.0,5.0,71083.98,1.0,1.0,1.0,102908.91,0.0
+1904,15621267.0,Chiazagomekpere,627.0,Germany,Female,32.0,9.0,122528.18,1.0,1.0,1.0,115068.99,0.0
+1905,15662337.0,De Salis,719.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,0.0,92816.52,0.0
+1906,15577598.0,Nwabugwu,713.0,Germany,Female,40.0,7.0,109698.41,2.0,1.0,1.0,100791.36,0.0
+1907,15766659.0,Genovese,628.0,France,Female,31.0,9.0,0.0,1.0,1.0,0.0,171515.84,0.0
+1908,15795747.0,Nwankwo,850.0,France,Female,25.0,5.0,0.0,2.0,1.0,1.0,171657.57,0.0
+1909,15586870.0,Bischof,627.0,Germany,Female,31.0,4.0,132412.38,1.0,1.0,0.0,107874.39,0.0
+1910,15671915.0,Li Fonti,573.0,Germany,Male,25.0,8.0,134571.12,1.0,1.0,0.0,88842.8,0.0
+1911,15634218.0,Ndubueze,592.0,Germany,Female,56.0,2.0,146587.3,1.0,1.0,0.0,166380.93,1.0
+1912,15611287.0,Maclean,657.0,Germany,Female,43.0,2.0,123575.51,1.0,1.0,0.0,144553.4,1.0
+1913,15574858.0,Lucchese,721.0,Germany,Female,34.0,3.0,114519.57,2.0,1.0,1.0,170514.21,0.0
+1914,15711572.0,Ch'iu,691.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,88890.05,0.0
+1915,15748068.0,Ritchie,740.0,France,Male,50.0,8.0,0.0,3.0,1.0,0.0,167032.83,1.0
+1916,15593491.0,Nkemakonam,614.0,France,Male,23.0,1.0,0.0,1.0,1.0,0.0,151146.67,0.0
+1917,15784097.0,Hsiao,648.0,France,Female,37.0,2.0,120415.54,1.0,0.0,0.0,113483.96,1.0
+1918,15724307.0,Okwudilichukwu,587.0,Germany,Male,48.0,2.0,90547.48,1.0,1.0,0.0,107876.91,1.0
+1919,15586976.0,T'ao,611.0,France,Male,32.0,7.0,0.0,1.0,0.0,1.0,125222.36,0.0
+1920,15809772.0,Yobachi,682.0,Spain,Female,36.0,8.0,0.0,2.0,0.0,1.0,173365.89,0.0
+1921,15657868.0,Chinedum,663.0,France,Female,37.0,2.0,0.0,1.0,1.0,0.0,27359.19,0.0
+1922,15662658.0,Cremonesi,705.0,Germany,Female,39.0,0.0,157094.78,1.0,1.0,1.0,91560.63,0.0
+1923,15655875.0,Tien,644.0,Spain,Female,29.0,8.0,0.0,2.0,1.0,0.0,44220.4,0.0
+1924,15672053.0,Chukwujekwu,670.0,Spain,Male,39.0,7.0,0.0,2.0,1.0,0.0,676.06,0.0
+1925,15777896.0,Okwudilichukwu,605.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,175043.69,0.0
+1926,15644933.0,Hsiung,661.0,France,Female,37.0,9.0,121152.05,2.0,0.0,1.0,70774.97,0.0
+1927,15667933.0,Chukwudi,556.0,France,Male,35.0,8.0,132876.23,1.0,1.0,1.0,61823.31,0.0
+1928,15593782.0,Y?,576.0,Spain,Male,33.0,6.0,129717.3,1.0,0.0,0.0,103557.85,0.0
+1929,15631059.0,Dickson,591.0,France,Male,62.0,4.0,0.0,2.0,0.0,1.0,164039.51,0.0
+1930,15685653.0,Tien,656.0,France,Male,38.0,0.0,141078.57,1.0,1.0,0.0,137028.51,0.0
+1931,15668830.0,Hsiao,707.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,65413.41,0.0
+1932,15592761.0,Chimaobim,754.0,France,Female,51.0,5.0,0.0,1.0,1.0,0.0,144593.4,1.0
+1933,15750055.0,Martin,742.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,133897.32,0.0
+1934,15801488.0,Folliero,591.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,120852.85,0.0
+1935,15664523.0,Yeh,709.0,France,Male,57.0,2.0,0.0,1.0,0.0,1.0,151822.66,1.0
+1936,15606836.0,Ijendu,569.0,France,Female,38.0,4.0,0.0,1.0,0.0,0.0,72993.67,1.0
+1937,15757981.0,Walker,663.0,Spain,Male,34.0,6.0,0.0,2.0,0.0,0.0,136983.77,0.0
+1938,15735246.0,Uspensky,762.0,Spain,Female,66.0,4.0,0.0,1.0,1.0,1.0,38960.59,0.0
+1939,15642619.0,Trevisani,690.0,France,Female,35.0,2.0,0.0,1.0,0.0,1.0,99800.87,0.0
+1940,15764174.0,Kao,686.0,France,Male,48.0,9.0,0.0,1.0,0.0,0.0,51387.1,1.0
+1941,15792077.0,Chidozie,634.0,France,Male,40.0,9.0,116084.32,1.0,0.0,1.0,73656.22,0.0
+1942,15666096.0,Galkin,719.0,France,Female,54.0,8.0,0.0,1.0,1.0,0.0,109145.2,1.0
+1943,15628145.0,Nwachinemelu,758.0,France,Female,38.0,1.0,0.0,1.0,1.0,0.0,180893.85,0.0
+1944,15794868.0,Hsueh,651.0,Germany,Female,43.0,4.0,136955.97,1.0,1.0,1.0,72950.14,0.0
+1945,15672356.0,Hsiao,667.0,France,Male,46.0,3.0,0.0,2.0,0.0,0.0,143952.24,0.0
+1946,15797710.0,Martin,563.0,France,Male,48.0,6.0,0.0,1.0,1.0,0.0,105570.44,1.0
+1947,15671987.0,Ch'ien,531.0,France,Male,36.0,6.0,0.0,2.0,0.0,1.0,57553.02,0.0
+1948,15615247.0,P'eng,696.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,121440.8,0.0
+1949,15702300.0,Chukwudi,632.0,France,Female,32.0,0.0,61671.19,1.0,0.0,0.0,645.61,0.0
+1950,15673584.0,Chinwendu,627.0,France,Male,30.0,9.0,141782.57,1.0,1.0,0.0,115638.29,0.0
+1951,15765806.0,Nkemakonam,687.0,France,Male,51.0,0.0,0.0,2.0,1.0,0.0,93881.75,1.0
+1952,15588517.0,Obidimkpa,678.0,France,Female,46.0,8.0,0.0,1.0,1.0,0.0,137949.45,1.0
+1953,15809302.0,Sun,628.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,158049.9,0.0
+1954,15809817.0,Ch'iu,551.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,82438.13,0.0
+1955,15581229.0,Tseng,590.0,France,Female,51.0,3.0,0.0,1.0,1.0,1.0,177528.92,1.0
+1956,15682570.0,Fisk,607.0,France,Male,48.0,0.0,0.0,3.0,1.0,1.0,123969.64,1.0
+1957,15658032.0,Milne,708.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,176760.59,0.0
+1958,15593501.0,Mistry,574.0,France,Female,39.0,3.0,0.0,2.0,0.0,1.0,38772.65,0.0
+1959,15603683.0,Pinto,717.0,Spain,Male,29.0,5.0,0.0,2.0,0.0,1.0,97734.3,0.0
+1960,15693737.0,Manna,694.0,Germany,Male,48.0,1.0,124978.19,2.0,1.0,0.0,54474.48,1.0
+1961,15665159.0,Ch'iu,605.0,France,Female,27.0,0.0,0.0,1.0,1.0,0.0,86410.58,0.0
+1962,15720463.0,Ch'ien,597.0,Spain,Female,43.0,6.0,0.0,2.0,1.0,0.0,133501.94,0.0
+1963,15592025.0,Yermakov,763.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,103726.71,0.0
+1964,15683375.0,Ogochukwu,554.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,0.0,38315.04,0.0
+1965,15703793.0,Onyemauchechi,685.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,26342.33,0.0
+1966,15785815.0,Iadanza,628.0,France,Female,42.0,1.0,0.0,1.0,1.0,1.0,131807.14,1.0
+1967,15627385.0,Chiang,494.0,Germany,Female,58.0,7.0,110148.49,1.0,1.0,0.0,52795.15,1.0
+1968,15628272.0,Ulyanova,602.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,103921.43,0.0
+1969,15658577.0,Mazzi,629.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,104930.46,0.0
+1970,15748123.0,Chukwubuikem,607.0,France,Male,38.0,2.0,125540.05,1.0,1.0,0.0,178038.17,0.0
+1971,15603851.0,Chukwuebuka,535.0,France,Male,43.0,9.0,0.0,2.0,0.0,0.0,138526.96,0.0
+1972,15631739.0,De Salis,732.0,France,Female,43.0,3.0,0.0,1.0,1.0,0.0,96985.01,1.0
+1973,15781702.0,Chu,628.0,Spain,Female,46.0,3.0,98765.84,1.0,1.0,1.0,71830.1,0.0
+1974,15749557.0,Hsia,679.0,Germany,Female,32.0,2.0,130666.2,1.0,1.0,1.0,123499.27,0.0
+1975,15636024.0,Teng,586.0,France,Male,38.0,5.0,0.0,1.0,1.0,0.0,170195.4,1.0
+1976,15593973.0,Chidubem,718.0,Germany,Male,31.0,6.0,141535.52,1.0,1.0,0.0,74878.12,0.0
+1977,15641183.0,Hs?,543.0,Spain,Female,26.0,7.0,0.0,2.0,0.0,1.0,26318.72,0.0
+1978,15702801.0,Udegbulam,679.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,167984.61,0.0
+1979,15591036.0,Hsueh,449.0,Spain,Male,43.0,3.0,123845.36,1.0,1.0,0.0,141558.04,0.0
+1980,15776939.0,Scott,535.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,158071.69,0.0
+1981,15642530.0,Hsia,757.0,Germany,Female,44.0,7.0,108716.59,1.0,1.0,0.0,73329.08,0.0
+1982,15774328.0,Ch'iu,633.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,121559.6,0.0
+1983,15771483.0,Ch'en,627.0,France,Male,32.0,0.0,134542.73,1.0,1.0,1.0,166520.96,0.0
+1984,15615091.0,Okwuadigbo,676.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,120594.85,0.0
+1985,15776947.0,Ch'iu,718.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,83052.03,0.0
+1986,15641480.0,Lo,648.0,Germany,Male,35.0,4.0,151215.34,2.0,1.0,1.0,78650.95,0.0
+1987,15685314.0,Shao,644.0,Germany,Female,36.0,1.0,124043.8,1.0,0.0,0.0,135034.43,0.0
+1988,15714586.0,Chiemenam,693.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,186948.35,0.0
+1989,15771059.0,Pisani,565.0,France,Male,37.0,6.0,0.0,1.0,1.0,1.0,123475.88,0.0
+1990,15727281.0,Chidi,586.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,1.0,151871.84,0.0
+1991,15804586.0,Macdonald,742.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,0.0,111573.3,0.0
+1992,15713144.0,Chin,646.0,France,Male,29.0,3.0,0.0,2.0,0.0,0.0,140941.47,0.0
+1993,15697510.0,Chukwumaobim,583.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,82290.81,0.0
+1994,15786539.0,Baresi,525.0,France,Female,36.0,0.0,0.0,1.0,1.0,1.0,68730.91,0.0
+1995,15746065.0,Lai,685.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,142797.5,0.0
+1996,15664754.0,Tien,652.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,93524.19,0.0
+1997,15621611.0,Cremonesi,681.0,Spain,Female,37.0,1.0,0.0,2.0,0.0,1.0,77965.44,0.0
+1998,15777907.0,Kao,681.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,92840.67,0.0
+1999,15584928.0,Chikere,696.0,Germany,Male,33.0,8.0,133844.26,1.0,1.0,0.0,86791.9,0.0
+2000,15686957.0,Trevisani,706.0,France,Male,42.0,4.0,0.0,1.0,1.0,0.0,135634.41,1.0
+2001,15750772.0,Cremonesi,779.0,France,Female,35.0,4.0,0.0,1.0,1.0,0.0,43889.67,0.0
+2002,15667588.0,Pirozzi,641.0,Spain,Male,33.0,1.0,98635.77,1.0,1.0,1.0,53817.23,0.0
+2003,15744695.0,Y?an,663.0,France,Female,49.0,8.0,0.0,2.0,1.0,1.0,118570.53,0.0
+2004,15793722.0,Olisanugo,708.0,Germany,Male,35.0,8.0,109196.67,1.0,1.0,0.0,55775.72,1.0
+2005,15632503.0,Chibuzo,635.0,Spain,Male,22.0,7.0,0.0,2.0,1.0,0.0,117622.8,0.0
+2006,15622454.0,Chiu,722.0,Spain,Male,28.0,3.0,117020.08,1.0,1.0,0.0,158736.07,0.0
+2007,15661249.0,H?,790.0,France,Male,31.0,10.0,0.0,2.0,0.0,0.0,131473.31,0.0
+2008,15644400.0,Cremonesi,577.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,163714.92,0.0
+2009,15793883.0,Chukwumaobim,597.0,Germany,Male,43.0,9.0,121286.27,1.0,1.0,0.0,97939.3,1.0
+2010,15585867.0,Shih,589.0,Spain,Female,35.0,9.0,0.0,1.0,1.0,0.0,90857.95,1.0
+2011,15725028.0,Chukwumaobim,612.0,Spain,Male,36.0,5.0,0.0,1.0,1.0,0.0,166682.57,0.0
+2012,15686974.0,Chiang,681.0,France,Male,28.0,4.0,0.0,1.0,1.0,0.0,127622.46,1.0
+2013,15679743.0,Smith,794.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,129870.93,0.0
+2014,15769246.0,Ts'ai,562.0,Spain,Male,42.0,1.0,105736.32,1.0,1.0,0.0,179435.21,0.0
+2015,15683841.0,Maclean,807.0,Spain,Female,48.0,9.0,0.0,2.0,1.0,0.0,182531.74,1.0
+2016,15672754.0,Tsai,709.0,France,Male,67.0,2.0,0.0,2.0,1.0,1.0,163568.51,0.0
+2017,15625047.0,Ch'in,592.0,France,Male,31.0,1.0,156773.78,2.0,1.0,1.0,142238.54,0.0
+2018,15770474.0,Okwudiliolisa,727.0,Germany,Male,35.0,5.0,153325.7,2.0,1.0,0.0,140650.19,0.0
+2019,15614215.0,Onyeoruru,599.0,France,Female,41.0,4.0,0.0,2.0,0.0,1.0,153372.29,0.0
+2020,15653952.0,Hsu,628.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,49506.82,0.0
+2021,15803378.0,Chikezie,653.0,France,Male,44.0,7.0,0.0,1.0,1.0,1.0,59349.83,0.0
+2022,15642394.0,Scott,586.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,162697.93,0.0
+2023,15677871.0,Chia,597.0,France,Male,50.0,1.0,0.0,2.0,0.0,0.0,56793.07,0.0
+2024,15615456.0,Hs?,716.0,Spain,Female,40.0,3.0,148045.45,4.0,1.0,0.0,174800.31,1.0
+2025,15689268.0,Toscani,579.0,Germany,Female,34.0,4.0,132835.56,2.0,1.0,0.0,161519.77,0.0
+2026,15621685.0,Maclean,565.0,France,Female,51.0,8.0,117412.19,4.0,1.0,0.0,167636.15,1.0
+2027,15698028.0,Chibugo,648.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,139431.0,0.0
+2028,15661275.0,Fanucci,633.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,1.0,153012.13,0.0
+2029,15591512.0,Hsia,638.0,France,Male,34.0,3.0,76510.52,1.0,1.0,1.0,67830.2,0.0
+2030,15814816.0,Dyer,682.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,199392.14,0.0
+2031,15610156.0,Milano,705.0,France,Male,39.0,4.0,0.0,1.0,0.0,1.0,135067.03,0.0
+2032,15702631.0,Nnachetam,578.0,France,Male,33.0,4.0,135213.71,1.0,1.0,0.0,100717.54,0.0
+2033,15663883.0,Rolon,602.0,Germany,Female,40.0,8.0,136980.23,2.0,1.0,0.0,78310.04,0.0
+2034,15791326.0,Lassetter,633.0,Spain,Female,43.0,7.0,0.0,2.0,0.0,0.0,142763.51,0.0
+2035,15641158.0,Y?,662.0,Germany,Male,41.0,4.0,178713.98,2.0,0.0,1.0,177747.03,0.0
+2036,15786680.0,Tsou,679.0,Spain,Female,39.0,7.0,0.0,2.0,0.0,0.0,184936.77,0.0
+2037,15778290.0,T'ien,712.0,France,Male,26.0,4.0,0.0,2.0,0.0,1.0,163585.9,0.0
+2038,15573857.0,Chimezie,591.0,Germany,Male,34.0,7.0,149961.99,2.0,0.0,1.0,172618.52,0.0
+2039,15651593.0,Chia,587.0,Spain,Male,31.0,9.0,0.0,1.0,0.0,0.0,167973.63,1.0
+2040,15793723.0,Lucchesi,730.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,142400.27,0.0
+2041,15777599.0,Lucchese,594.0,Germany,Male,30.0,3.0,155736.42,1.0,1.0,1.0,73329.08,0.0
+2042,15719348.0,T'ang,619.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,80619.09,0.0
+2043,15747591.0,Trevisani,748.0,Germany,Male,36.0,4.0,105206.7,1.0,1.0,0.0,26767.85,0.0
+2044,15793723.0,Ts'ai,590.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,175894.24,0.0
+2045,15795519.0,Ch'ang,718.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,151815.76,0.0
+2046,15725997.0,Hs?,744.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,114775.53,0.0
+2047,15701392.0,Hs,572.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,162858.29,0.0
+2048,15636572.0,P'eng,521.0,France,Male,45.0,4.0,0.0,2.0,1.0,1.0,179291.09,1.0
+2049,15571059.0,Onwudiwe,718.0,France,Female,41.0,1.0,107848.57,1.0,1.0,0.0,136497.14,1.0
+2050,15595766.0,Pirozzi,645.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,108008.65,0.0
+2051,15778936.0,Iadanza,718.0,France,Female,36.0,7.0,0.0,2.0,0.0,1.0,146041.45,0.0
+2052,15757981.0,Scott,758.0,Spain,Male,69.0,3.0,0.0,2.0,0.0,1.0,86618.33,0.0
+2053,15677796.0,Chiawuotu,751.0,Spain,Male,51.0,7.0,95858.37,1.0,1.0,1.0,82647.61,1.0
+2054,15687828.0,Hsia,659.0,France,Female,25.0,3.0,0.0,2.0,0.0,0.0,168816.22,0.0
+2055,15723373.0,Hingston,547.0,Spain,Female,28.0,4.0,129848.19,1.0,1.0,1.0,53584.03,0.0
+2056,15623053.0,Cremonesi,665.0,France,Female,46.0,6.0,0.0,2.0,1.0,0.0,173580.45,1.0
+2057,15669957.0,Chu,727.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,93959.96,0.0
+2058,15699450.0,Okwudilichukwu,564.0,France,Female,36.0,3.0,0.0,2.0,0.0,0.0,128882.66,0.0
+2059,15607746.0,Lucchesi,659.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,94762.16,0.0
+2060,15805523.0,Yost,697.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,50294.09,0.0
+2061,15586680.0,Brown,706.0,France,Male,42.0,1.0,0.0,1.0,1.0,1.0,1568.91,0.0
+2062,15649060.0,Ijendu,794.0,France,Male,34.0,2.0,0.0,1.0,1.0,1.0,182944.47,0.0
+2063,15752508.0,Chukwueloka,627.0,Germany,Male,51.0,8.0,114754.08,3.0,0.0,0.0,163666.85,1.0
+2064,15672053.0,Bibi,748.0,Spain,Male,50.0,2.0,0.0,1.0,0.0,0.0,29586.73,1.0
+2065,15735782.0,Hsiao,725.0,France,Male,28.0,5.0,0.0,2.0,0.0,1.0,161579.85,0.0
+2066,15583857.0,Onyekaozulu,592.0,Germany,Female,52.0,5.0,105722.69,1.0,1.0,1.0,84972.9,0.0
+2067,15602909.0,Hsiao,633.0,Germany,Female,46.0,1.0,165350.61,1.0,1.0,1.0,173844.69,0.0
+2068,15652693.0,Hs?,762.0,France,Male,38.0,3.0,123835.05,3.0,1.0,0.0,142797.5,1.0
+2069,15780256.0,Mazzanti,678.0,France,Female,36.0,8.0,0.0,3.0,0.0,1.0,170819.01,1.0
+2070,15640034.0,Ch'ang,653.0,France,Male,25.0,5.0,0.0,2.0,1.0,0.0,145562.4,0.0
+2071,15632669.0,Lo Duca,698.0,Spain,Female,39.0,3.0,133676.65,1.0,1.0,0.0,93332.64,0.0
+2072,15739502.0,Ch'en,641.0,France,Male,32.0,8.0,0.0,2.0,0.0,0.0,184564.67,0.0
+2073,15715003.0,H?,644.0,Spain,Male,50.0,2.0,171646.76,1.0,1.0,0.0,178028.96,1.0
+2074,15699507.0,Dennys,535.0,France,Male,35.0,6.0,109949.05,1.0,1.0,1.0,48382.4,0.0
+2075,15766649.0,Y?an,743.0,France,Male,30.0,2.0,127763.24,1.0,1.0,1.0,131660.31,0.0
+2076,15573319.0,Chukwumaobim,628.0,France,Male,42.0,10.0,0.0,2.0,0.0,0.0,121151.46,0.0
+2077,15648069.0,Ch'ien,632.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,187945.01,0.0
+2078,15616454.0,Ndubueze,636.0,France,Male,60.0,5.0,0.0,2.0,0.0,0.0,170886.58,0.0
+2079,15672930.0,Chiawuotu,724.0,France,Male,34.0,2.0,0.0,1.0,1.0,0.0,81431.99,0.0
+2080,15653773.0,Ugochukwu,637.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,44965.44,0.0
+2081,15591286.0,Chiang,682.0,Spain,Male,32.0,0.0,0.0,2.0,1.0,0.0,155047.11,0.0
+2082,15734085.0,Ulyanova,704.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,93524.22,0.0
+2083,15661409.0,Chinedum,652.0,France,Female,28.0,8.0,137960.55,1.0,1.0,1.0,64442.47,0.0
+2084,15781710.0,Tung,581.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,1.0,108419.41,0.0
+2085,15582585.0,Ts'ao,681.0,France,Male,37.0,3.0,0.0,2.0,0.0,0.0,182059.28,0.0
+2086,15757867.0,Yermakov,554.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,147600.54,1.0
+2087,15802084.0,Chinwendu,602.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,165082.25,0.0
+2088,15637954.0,Genovesi,624.0,Spain,Male,43.0,4.0,106414.57,1.0,1.0,0.0,92811.4,0.0
+2089,15665181.0,Lombardi,719.0,Germany,Female,51.0,8.0,113873.22,1.0,0.0,1.0,109545.56,1.0
+2090,15594556.0,Pisani,687.0,France,Male,23.0,7.0,83347.25,1.0,0.0,0.0,146680.25,0.0
+2091,15802625.0,White,646.0,Spain,Male,38.0,2.0,113882.31,1.0,1.0,1.0,175743.92,0.0
+2092,15723006.0,Onwuka,642.0,France,Male,29.0,8.0,0.0,1.0,1.0,0.0,164804.02,0.0
+2093,15683625.0,Lo,507.0,France,Female,41.0,7.0,0.0,1.0,0.0,1.0,87412.24,0.0
+2094,15768295.0,Hsueh,661.0,Spain,Male,41.0,5.0,0.0,1.0,1.0,0.0,51362.82,0.0
+2095,15712264.0,Chiekwugo,678.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,161333.13,0.0
+2096,15642093.0,Milani,641.0,France,Female,40.0,9.0,101060.25,1.0,1.0,1.0,118368.2,1.0
+2097,15782765.0,Nnamutaezinwa,730.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,0.0,131710.59,0.0
+2098,15779947.0,Ritchie,659.0,France,Male,51.0,1.0,151922.94,1.0,1.0,0.0,103914.4,1.0
+2099,15779744.0,Ibezimako,590.0,Germany,Male,32.0,4.0,128152.82,2.0,1.0,1.0,145560.38,0.0
+2100,15809286.0,H?,613.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,1.0,72318.77,0.0
+2101,15677020.0,T'ang,589.0,France,Female,46.0,8.0,0.0,2.0,0.0,0.0,112202.64,0.0
+2102,15623062.0,Tuan,684.0,France,Male,52.0,7.0,0.0,2.0,1.0,0.0,76611.33,0.0
+2103,15636454.0,Ts'ao,646.0,France,Male,45.0,5.0,0.0,1.0,1.0,0.0,136620.64,1.0
+2104,15607874.0,Hsueh,675.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,172501.38,0.0
+2105,15585067.0,Chukwueloka,676.0,France,Male,43.0,8.0,0.0,2.0,0.0,1.0,126551.65,0.0
+2106,15809265.0,Yobanna,726.0,Germany,Female,57.0,4.0,111063.73,2.0,1.0,1.0,51116.06,0.0
+2107,15704466.0,Fennell,594.0,France,Male,42.0,2.0,106024.51,1.0,1.0,1.0,93548.46,0.0
+2108,15761047.0,Ch'ien,485.0,France,Female,47.0,1.0,133598.4,1.0,1.0,1.0,167984.72,0.0
+2109,15725048.0,Genovese,516.0,France,Female,32.0,7.0,0.0,1.0,1.0,0.0,170514.21,1.0
+2110,15578783.0,Mazzanti,786.0,France,Male,26.0,4.0,0.0,2.0,1.0,0.0,81482.2,0.0
+2111,15672977.0,Chizoba,592.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,109562.74,1.0
+2112,15604515.0,Akhtar,633.0,Germany,Male,31.0,7.0,133853.27,2.0,1.0,1.0,43496.36,0.0
+2113,15734491.0,Tung,803.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,98820.09,0.0
+2114,15655298.0,Mazzanti,769.0,Spain,Male,29.0,8.0,144855.34,2.0,1.0,0.0,180591.32,0.0
+2115,15690744.0,Ch'ang,619.0,Germany,Female,49.0,4.0,129255.2,1.0,1.0,0.0,163558.36,0.0
+2116,15764033.0,Bellucci,577.0,Germany,Female,29.0,1.0,99824.45,1.0,1.0,1.0,167522.19,0.0
+2117,15792222.0,Chukwuebuka,528.0,Germany,Female,24.0,7.0,113090.4,1.0,1.0,1.0,156878.46,1.0
+2118,15783097.0,Liao,603.0,France,Male,51.0,10.0,0.0,2.0,1.0,1.0,78896.59,1.0
+2119,15603594.0,Uspenskaya,605.0,France,Male,36.0,0.0,114091.38,1.0,0.0,1.0,74425.41,0.0
+2120,15597945.0,Chiawuotu,584.0,Spain,Female,33.0,2.0,0.0,1.0,1.0,1.0,131043.1,0.0
+2121,15793046.0,H?,597.0,France,Male,31.0,8.0,162261.01,1.0,1.0,0.0,138399.67,0.0
+2122,15806822.0,Zuyeva,594.0,Spain,Male,42.0,9.0,0.0,1.0,1.0,0.0,129392.75,1.0
+2123,15653065.0,Eluemuno,687.0,Germany,Female,48.0,7.0,102560.57,1.0,1.0,0.0,116097.26,1.0
+2124,15782418.0,Hsia,718.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,179436.6,0.0
+2125,15794895.0,Olisaemeka,594.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,1.0,159823.16,0.0
+2126,15792986.0,Tardent,711.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,73334.43,0.0
+2127,15766017.0,T'ien,746.0,Germany,Male,33.0,9.0,124425.99,1.0,1.0,0.0,187925.75,0.0
+2128,15641319.0,Iqbal,488.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,53817.23,0.0
+2129,15690958.0,McEncroe,738.0,France,Female,33.0,4.0,106022.73,2.0,1.0,1.0,148087.62,0.0
+2130,15644491.0,Nnachetam,578.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,177099.71,0.0
+2131,15654463.0,Onyemachukwu,579.0,Spain,Male,42.0,6.0,0.0,2.0,1.0,0.0,142514.66,1.0
+2132,15583592.0,Hsueh,539.0,France,Female,36.0,2.0,0.0,1.0,1.0,0.0,94738.54,0.0
+2133,15804919.0,Scott,670.0,Germany,Male,49.0,5.0,150844.94,2.0,1.0,0.0,115733.16,0.0
+2134,15733602.0,Martin,742.0,Germany,Female,42.0,4.0,161608.81,2.0,1.0,1.0,131300.68,0.0
+2135,15775572.0,Chidiebere,705.0,France,Male,34.0,0.0,81589.04,1.0,1.0,1.0,140166.95,0.0
+2136,15716236.0,Luffman,724.0,France,Male,56.0,3.0,107879.04,3.0,1.0,0.0,60908.46,1.0
+2137,15792473.0,Hsiung,596.0,France,Male,47.0,7.0,0.0,2.0,1.0,1.0,149772.61,0.0
+2138,15661380.0,Chiu,584.0,Germany,Male,33.0,7.0,128920.75,1.0,0.0,1.0,131807.44,0.0
+2139,15762588.0,Sagese,571.0,Germany,Male,44.0,7.0,124580.69,1.0,1.0,1.0,171463.83,0.0
+2140,15742534.0,Fanucci,619.0,France,Male,33.0,6.0,122717.4,1.0,1.0,1.0,71420.16,0.0
+2141,15629985.0,Tung,587.0,France,Male,53.0,7.0,0.0,1.0,1.0,0.0,148727.42,1.0
+2142,15576824.0,T'ien,611.0,France,Male,45.0,2.0,0.0,1.0,1.0,1.0,131660.31,1.0
+2143,15650864.0,Ijendu,651.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,184843.77,0.0
+2144,15742272.0,Pinto,759.0,France,Male,46.0,5.0,113599.74,1.0,1.0,1.0,60721.4,1.0
+2145,15682463.0,Nnachetam,605.0,Germany,Male,33.0,3.0,123040.38,1.0,1.0,0.0,124592.83,1.0
+2146,15795540.0,Chiabuotu,599.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,167032.49,0.0
+2147,15785831.0,Clements,739.0,Spain,Female,44.0,7.0,0.0,2.0,1.0,0.0,175072.47,0.0
+2148,15572390.0,Chukwubuikem,677.0,Spain,Female,37.0,6.0,0.0,1.0,0.0,1.0,166677.35,0.0
+2149,15578908.0,Ch'eng,584.0,Spain,Female,46.0,8.0,0.0,2.0,0.0,1.0,63687.06,1.0
+2150,15784594.0,Ts'ao,589.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,1.0,162858.28,0.0
+2151,15615254.0,Hsiung,635.0,Germany,Male,57.0,4.0,91611.12,1.0,0.0,0.0,58879.31,1.0
+2152,15791323.0,Atherton,816.0,Spain,Female,35.0,4.0,82729.49,1.0,1.0,1.0,122518.5,0.0
+2153,15568595.0,Whitehead,667.0,France,Female,39.0,7.0,0.0,1.0,1.0,1.0,179843.33,0.0
+2154,15664753.0,Pinto,565.0,France,Female,51.0,7.0,0.0,1.0,1.0,0.0,65986.48,1.0
+2155,15628583.0,Mazzi,632.0,France,Female,45.0,9.0,0.0,3.0,1.0,1.0,158750.43,1.0
+2156,15782089.0,Ch'ang,538.0,Spain,Female,40.0,1.0,106074.81,1.0,1.0,0.0,102628.98,0.0
+2157,15704340.0,Ch'iu,850.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,26389.92,0.0
+2158,15754579.0,Liao,603.0,Spain,Male,34.0,5.0,172463.45,1.0,0.0,1.0,132418.59,0.0
+2159,15762392.0,Chienezie,683.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,92797.23,0.0
+2160,15591607.0,Hsiao,543.0,France,Male,51.0,6.0,0.0,1.0,1.0,0.0,110593.62,1.0
+2161,15758362.0,P'an,754.0,Spain,Male,40.0,6.0,0.0,1.0,1.0,0.0,199805.63,1.0
+2162,15704910.0,Hsia,577.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,71862.79,0.0
+2163,15656592.0,Lucchesi,599.0,France,Female,21.0,2.0,152303.8,2.0,1.0,1.0,138490.03,0.0
+2164,15582841.0,Hsia,702.0,France,Male,45.0,4.0,0.0,1.0,1.0,0.0,53353.37,0.0
+2165,15725028.0,Tien,579.0,France,Female,34.0,3.0,0.0,2.0,0.0,0.0,123627.69,0.0
+2166,15807989.0,Ch'iu,641.0,Germany,Female,35.0,10.0,111209.64,3.0,1.0,0.0,132498.39,1.0
+2167,15805764.0,Chieloka,649.0,France,Male,41.0,9.0,0.0,2.0,0.0,0.0,150319.49,0.0
+2168,15746258.0,Lucchesi,611.0,France,Male,30.0,5.0,0.0,1.0,0.0,1.0,179521.24,0.0
+2169,15612103.0,Chiazagomekpere,730.0,Spain,Female,32.0,4.0,0.0,1.0,1.0,1.0,92865.41,1.0
+2170,15775203.0,Onyemere,748.0,Spain,Male,46.0,9.0,0.0,2.0,1.0,0.0,105784.85,0.0
+2171,15785078.0,Maclean,554.0,France,Male,57.0,10.0,0.0,1.0,1.0,1.0,88659.07,1.0
+2172,15702561.0,K?,592.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,85547.33,0.0
+2173,15675185.0,Shih,521.0,France,Female,38.0,6.0,0.0,1.0,1.0,0.0,115217.59,0.0
+2174,15670702.0,Lai,598.0,France,Female,54.0,4.0,0.0,2.0,0.0,1.0,84972.9,1.0
+2175,15756894.0,Chuang,591.0,France,Female,40.0,4.0,0.0,2.0,0.0,1.0,92826.07,0.0
+2176,15713559.0,Goliwe,708.0,France,Male,38.0,7.0,122950.19,1.0,1.0,1.0,125870.5,0.0
+2177,15723008.0,Maclean,609.0,Spain,Male,29.0,3.0,152296.83,1.0,1.0,1.0,119817.19,1.0
+2178,15628292.0,Hsiao,766.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,119346.88,0.0
+2179,15583595.0,Manna,558.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,175893.53,0.0
+2180,15582914.0,Ponomaryov,663.0,France,Male,23.0,6.0,0.0,2.0,1.0,0.0,124626.57,0.0
+2181,15767793.0,Zikoranachidimma,683.0,France,Female,36.0,6.0,0.0,1.0,1.0,1.0,90928.04,1.0
+2182,15609562.0,Kolesnikov,627.0,Germany,Female,37.0,3.0,139979.66,2.0,0.0,0.0,25017.28,1.0
+2183,15772363.0,Hsieh,637.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,126675.62,0.0
+2184,15603032.0,Udinese,544.0,France,Male,60.0,3.0,95829.13,2.0,1.0,0.0,115068.99,1.0
+2185,15727391.0,Hsia,772.0,France,Male,40.0,10.0,95507.12,1.0,1.0,1.0,47880.71,0.0
+2186,15734202.0,Kambinachi,679.0,Germany,Female,39.0,6.0,85915.52,2.0,1.0,0.0,162599.51,0.0
+2187,15741403.0,Pisano,659.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,87071.18,0.0
+2188,15647301.0,Cremonesi,714.0,Germany,Female,40.0,9.0,113348.5,1.0,1.0,1.0,79005.95,0.0
+2189,15570051.0,Hsueh,535.0,Spain,Female,31.0,1.0,120777.88,1.0,0.0,1.0,5408.74,0.0
+2190,15603741.0,Ch'in,805.0,Germany,Male,54.0,4.0,116220.5,1.0,1.0,0.0,120540.83,1.0
+2191,15726358.0,Hs?eh,582.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,52795.15,0.0
+2192,15675963.0,O'Kane,687.0,France,Male,29.0,7.0,0.0,1.0,1.0,1.0,183566.85,0.0
+2193,15657439.0,Hsieh,538.0,France,Female,29.0,9.0,0.0,2.0,1.0,1.0,177065.24,0.0
+2194,15665077.0,Pye,607.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,53445.17,0.0
+2195,15710408.0,Nwachukwu,666.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,1.0,135083.72,0.0
+2196,15693947.0,Chibuzo,679.0,France,Female,38.0,8.0,144326.09,1.0,1.0,1.0,69227.74,0.0
+2197,15595874.0,Ting,577.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,134417.31,0.0
+2198,15660390.0,Lai,602.0,Spain,Male,27.0,10.0,0.0,2.0,1.0,0.0,47722.79,0.0
+2199,15773789.0,Ch'ien,605.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,108675.01,0.0
+2200,15807560.0,Trevisani,583.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,125256.53,0.0
+2201,15807961.0,Rizzo,613.0,Germany,Female,42.0,8.0,130933.74,3.0,0.0,0.0,79355.87,1.0
+2202,15793671.0,Chukwudi,549.0,Spain,Female,31.0,4.0,79954.61,1.0,0.0,1.0,142911.99,0.0
+2203,15662578.0,Chiebuka,623.0,France,Male,38.0,0.0,0.0,1.0,1.0,1.0,51587.04,0.0
+2204,15637646.0,Yudina,667.0,Spain,Male,39.0,8.0,91291.7,1.0,1.0,0.0,156593.09,0.0
+2205,15680191.0,Iweobiegbunam,633.0,Spain,Male,36.0,10.0,0.0,1.0,1.0,1.0,123882.73,0.0
+2206,15783477.0,Ch'iu,727.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,115950.96,0.0
+2207,15585925.0,Smith,652.0,Germany,Female,43.0,6.0,115465.28,1.0,1.0,1.0,179670.31,1.0
+2208,15609913.0,Chiu,486.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,110916.15,0.0
+2209,15617790.0,McWilliams,601.0,France,Male,35.0,2.0,0.0,2.0,0.0,1.0,83087.13,0.0
+2210,15656592.0,Chiazagomekpele,712.0,France,Female,27.0,2.0,0.0,2.0,1.0,1.0,132468.76,0.0
+2211,15624424.0,Lettiere,644.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,94865.04,0.0
+2212,15702061.0,Okagbue,676.0,France,Male,48.0,1.0,142862.21,1.0,1.0,1.0,59568.24,1.0
+2213,15790829.0,Lo,619.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,49553.38,0.0
+2214,15779620.0,Nnanna,671.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,49865.04,0.0
+2215,15604588.0,Ch'ang,591.0,France,Male,39.0,2.0,0.0,1.0,0.0,1.0,85424.34,0.0
+2216,15624892.0,Mazzi,596.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,155799.86,0.0
+2217,15583595.0,Ch'iu,778.0,Spain,Male,71.0,0.0,88814.4,1.0,1.0,1.0,160526.36,0.0
+2218,15651389.0,Kaeppel,757.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,54949.73,0.0
+2219,15596674.0,Chinweike,573.0,France,Male,32.0,7.0,0.0,1.0,1.0,1.0,123969.64,0.0
+2220,15686835.0,Chikelu,573.0,Germany,Male,34.0,9.0,165969.26,2.0,1.0,1.0,83679.46,0.0
+2221,15628144.0,Yobachi,729.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,188007.99,0.0
+2222,15799435.0,Kistler,628.0,Spain,Female,36.0,5.0,115341.55,1.0,1.0,0.0,193502.93,0.0
+2223,15626383.0,Yobachukwu,716.0,France,Male,43.0,6.0,0.0,1.0,1.0,0.0,61792.76,0.0
+2224,15641310.0,Ting,602.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,120853.83,0.0
+2225,15622443.0,T'ang,729.0,Spain,Female,29.0,3.0,0.0,2.0,1.0,0.0,131043.1,0.0
+2226,15605827.0,L?,749.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,40357.39,0.0
+2227,15610594.0,Ch'ien,792.0,France,Male,33.0,3.0,0.0,1.0,1.0,0.0,171070.58,0.0
+2228,15806956.0,Clements,739.0,Spain,Female,27.0,9.0,0.0,2.0,1.0,1.0,145936.29,0.0
+2229,15721426.0,Chiu,815.0,France,Male,25.0,2.0,127738.75,1.0,1.0,1.0,55100.09,0.0
+2230,15609977.0,Tien,718.0,France,Male,22.0,2.0,0.0,2.0,1.0,1.0,57769.2,0.0
+2231,15636548.0,Chiemenam,572.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,86410.29,0.0
+2232,15693322.0,Piazza,653.0,France,Male,33.0,4.0,0.0,2.0,0.0,1.0,102298.59,0.0
+2233,15737647.0,Lai,535.0,France,Female,25.0,8.0,0.0,2.0,1.0,1.0,91560.63,0.0
+2234,15688880.0,Lucchese,675.0,France,Female,43.0,3.0,0.0,1.0,1.0,0.0,105986.01,1.0
+2235,15603085.0,K?,711.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,96885.19,0.0
+2236,15648876.0,Chiawuotu,635.0,Germany,Female,40.0,8.0,82344.84,1.0,1.0,0.0,126836.56,1.0
+2237,15806983.0,Mackenzie,635.0,Germany,Male,42.0,7.0,157960.49,2.0,1.0,0.0,10963.04,1.0
+2238,15765300.0,Nwagugheuzo,663.0,France,Male,28.0,8.0,0.0,2.0,0.0,1.0,80416.16,0.0
+2239,15784761.0,Yefimova,801.0,Spain,Female,29.0,6.0,122822.0,1.0,1.0,0.0,65868.28,0.0
+2240,15795895.0,Ch'iu,662.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,173498.45,0.0
+2241,15586069.0,Frolov,703.0,France,Female,37.0,1.0,0.0,2.0,1.0,0.0,183872.88,0.0
+2242,15680597.0,Lettiere,596.0,Germany,Male,50.0,9.0,134960.98,1.0,0.0,0.0,129964.94,1.0
+2243,15676521.0,Hsia,637.0,France,Female,42.0,8.0,154763.21,1.0,1.0,1.0,177065.24,0.0
+2244,15584364.0,Nnaife,778.0,France,Male,46.0,8.0,0.0,1.0,1.0,0.0,71154.1,0.0
+2245,15790489.0,Macdonald,713.0,France,Male,32.0,7.0,0.0,1.0,0.0,0.0,93548.16,0.0
+2246,15653037.0,Pisano,745.0,Spain,Male,44.0,5.0,139070.51,1.0,1.0,1.0,86658.26,0.0
+2247,15780213.0,Pisani,659.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,131647.31,0.0
+2248,15759991.0,Smith,597.0,Germany,Male,31.0,1.0,72594.0,1.0,1.0,1.0,86490.09,0.0
+2249,15752588.0,Ozoemena,588.0,Spain,Female,29.0,6.0,88594.14,1.0,1.0,0.0,116748.14,0.0
+2250,15681180.0,Fanucci,612.0,France,Female,35.0,5.0,136623.19,1.0,0.0,0.0,176913.42,0.0
+2251,15697567.0,Chukwujekwu,799.0,France,Female,47.0,2.0,0.0,1.0,1.0,1.0,134946.02,1.0
+2252,15776939.0,Yudina,671.0,France,Male,52.0,2.0,0.0,2.0,1.0,1.0,127558.26,0.0
+2253,15590097.0,T'ang,805.0,Spain,Male,42.0,7.0,127237.28,1.0,0.0,1.0,91055.74,0.0
+2254,15641835.0,Chukwukere,729.0,Germany,Male,44.0,9.0,118636.92,2.0,1.0,0.0,134878.19,1.0
+2255,15776051.0,Hsiung,850.0,France,Male,31.0,7.0,115897.12,1.0,1.0,1.0,163714.92,0.0
+2256,15790594.0,Li Fonti,556.0,Spain,Male,38.0,9.0,160980.03,1.0,1.0,0.0,147090.9,0.0
+2257,15697095.0,Kao,812.0,France,Male,40.0,6.0,122021.92,1.0,1.0,0.0,168085.97,0.0
+2258,15586039.0,K'ung,663.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,1.0,479.54,0.0
+2259,15742404.0,Onyekachi,588.0,France,Male,42.0,3.0,0.0,1.0,0.0,0.0,75825.57,0.0
+2260,15757981.0,Teng,587.0,France,Male,34.0,4.0,102530.7,1.0,1.0,1.0,116046.22,0.0
+2261,15583595.0,Chukwufumnanya,625.0,Germany,Female,31.0,8.0,95478.17,1.0,1.0,1.0,165008.97,0.0
+2262,15756926.0,Onyekachi,592.0,France,Female,31.0,1.0,0.0,2.0,1.0,0.0,114448.77,0.0
+2263,15658969.0,Maclean,677.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,121846.81,0.0
+2264,15741032.0,K?,682.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,161388.22,0.0
+2265,15654067.0,Pinto,613.0,Spain,Female,49.0,1.0,156864.2,1.0,1.0,1.0,67611.36,1.0
+2266,15581229.0,Yobachukwu,711.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,162858.28,0.0
+2267,15654495.0,Tsou,605.0,France,Female,45.0,1.0,0.0,3.0,1.0,1.0,86786.41,1.0
+2268,15697460.0,Lucchesi,682.0,Germany,Male,45.0,5.0,127818.52,1.0,1.0,1.0,58814.41,0.0
+2269,15733111.0,Chinedum,539.0,France,Male,30.0,2.0,0.0,2.0,0.0,1.0,83470.4,0.0
+2270,15682540.0,Palermo,744.0,France,Male,24.0,4.0,0.0,1.0,1.0,1.0,56584.11,0.0
+2271,15783816.0,White,681.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,102908.91,0.0
+2272,15597949.0,Chukwukadibia,592.0,France,Female,53.0,10.0,0.0,1.0,1.0,1.0,7063.6,1.0
+2273,15604588.0,Chia,745.0,Spain,Female,38.0,6.0,0.0,2.0,0.0,0.0,130846.79,0.0
+2274,15766869.0,Hsing,651.0,Germany,Male,33.0,8.0,145081.73,2.0,1.0,0.0,141023.01,0.0
+2275,15708505.0,Toscani,577.0,Germany,Male,41.0,8.0,173560.62,1.0,0.0,1.0,69227.74,0.0
+2276,15696812.0,Lombardi,691.0,France,Male,30.0,4.0,121702.73,1.0,1.0,0.0,116442.42,0.0
+2277,15791842.0,Chibugo,666.0,Germany,Male,34.0,1.0,127706.33,2.0,1.0,1.0,180595.02,0.0
+2278,15635277.0,Chiazagomekpele,678.0,France,Male,56.0,6.0,0.0,1.0,1.0,1.0,75578.67,1.0
+2279,15573741.0,Ch'en,739.0,France,Male,41.0,8.0,0.0,3.0,1.0,1.0,181449.51,1.0
+2280,15659800.0,Chiang,716.0,Spain,Female,42.0,7.0,0.0,2.0,1.0,0.0,145700.22,1.0
+2281,15789548.0,McGregor,638.0,France,Male,35.0,2.0,0.0,1.0,0.0,1.0,73669.94,1.0
+2282,15668818.0,Lettiere,543.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,147963.07,0.0
+2283,15777873.0,Tretiakova,608.0,Germany,Male,30.0,2.0,125552.96,1.0,1.0,0.0,121053.19,0.0
+2284,15668767.0,Baryshnikov,735.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,70532.53,0.0
+2285,15623062.0,Ch'eng,680.0,France,Male,44.0,6.0,0.0,2.0,0.0,0.0,138526.96,0.0
+2286,15602454.0,Yuriev,850.0,Germany,Male,35.0,2.0,104091.38,1.0,0.0,0.0,172001.44,0.0
+2287,15635404.0,Oguejiofor,597.0,France,Female,30.0,4.0,0.0,2.0,0.0,1.0,166692.04,0.0
+2288,15595874.0,Caffyn,694.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,9087.81,0.0
+2289,15592044.0,Chukwujamuike,745.0,Germany,Male,38.0,4.0,123041.56,2.0,0.0,1.0,99061.75,1.0
+2290,15587978.0,McIntyre,678.0,Spain,Female,49.0,1.0,0.0,2.0,1.0,1.0,79734.23,1.0
+2291,15778830.0,Mazzanti,627.0,France,Female,33.0,10.0,0.0,2.0,1.0,1.0,143938.28,0.0
+2292,15668893.0,Ch'ien,699.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,51380.9,0.0
+2293,15605968.0,Fanucci,755.0,France,Male,55.0,3.0,0.0,1.0,1.0,1.0,73563.05,1.0
+2294,15749945.0,Hs?,527.0,Germany,Female,46.0,4.0,124447.18,2.0,1.0,1.0,168034.9,0.0
+2295,15571843.0,Ozioma,643.0,France,Female,40.0,9.0,0.0,2.0,1.0,0.0,149814.68,0.0
+2296,15623346.0,Ritchie,584.0,France,Male,39.0,1.0,84719.98,1.0,1.0,0.0,91291.2,0.0
+2297,15717893.0,Chukwualuka,687.0,Spain,Female,29.0,5.0,113674.2,1.0,1.0,0.0,157072.91,0.0
+2298,15761497.0,Yobanna,721.0,France,Female,41.0,7.0,0.0,1.0,1.0,0.0,126494.82,1.0
+2299,15654562.0,De Luca,645.0,Spain,Male,37.0,1.0,0.0,1.0,1.0,1.0,169963.2,0.0
+2300,15639946.0,Ch'eng,616.0,France,Female,31.0,10.0,122334.26,1.0,1.0,0.0,79649.62,1.0
+2301,15679884.0,Tsou,691.0,France,Female,39.0,6.0,123894.43,1.0,1.0,1.0,91633.53,0.0
+2302,15665062.0,Genovesi,850.0,Germany,Female,27.0,7.0,137059.05,2.0,1.0,1.0,71159.84,0.0
+2303,15680167.0,Iadanza,579.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,1.0,164741.5,0.0
+2304,15807524.0,Onwuamaeze,691.0,France,Female,35.0,6.0,90450.0,1.0,1.0,1.0,94488.86,0.0
+2305,15665460.0,Chidumaga,769.0,Spain,Male,43.0,2.0,95196.26,1.0,0.0,1.0,153343.48,0.0
+2306,15641170.0,Hsia,607.0,France,Male,41.0,8.0,0.0,1.0,1.0,0.0,145527.25,0.0
+2307,15704763.0,Nebechi,710.0,Spain,Male,42.0,2.0,0.0,3.0,0.0,1.0,138380.11,1.0
+2308,15709324.0,Ch'ang,469.0,France,Male,45.0,8.0,120867.18,1.0,1.0,0.0,137037.97,1.0
+2309,15697480.0,Trevisano,641.0,Germany,Male,46.0,5.0,122486.47,2.0,1.0,0.0,61936.1,0.0
+2310,15652667.0,Trevisani,636.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,131768.45,1.0
+2311,15804054.0,Kao,637.0,Germany,Male,67.0,3.0,152328.88,1.0,1.0,1.0,88391.97,0.0
+2312,15645956.0,Onyemere,531.0,France,Male,35.0,3.0,0.0,1.0,0.0,0.0,67981.37,0.0
+2313,15802381.0,Teng,613.0,Germany,Female,39.0,4.0,127543.11,1.0,1.0,0.0,151411.2,0.0
+2314,15707637.0,Chiang,638.0,France,Male,35.0,7.0,96951.21,1.0,1.0,0.0,91036.74,1.0
+2315,15585853.0,Tsou,850.0,France,Male,67.0,6.0,81025.6,1.0,0.0,1.0,142911.99,0.0
+2316,15791674.0,Kao,506.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,87412.24,0.0
+2317,15773723.0,Liao,646.0,Spain,Female,28.0,9.0,0.0,2.0,1.0,0.0,81256.29,0.0
+2318,15687558.0,McLean,743.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,123882.28,0.0
+2319,15611669.0,Onwughara,653.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,118740.38,0.0
+2320,15715199.0,Trevisano,686.0,Germany,Female,35.0,4.0,70784.27,1.0,1.0,1.0,47921.22,0.0
+2321,15758856.0,Yuryeva,708.0,Spain,Male,39.0,1.0,0.0,2.0,1.0,0.0,124596.63,0.0
+2322,15648858.0,Chidozie,717.0,Spain,Male,28.0,5.0,0.0,2.0,0.0,1.0,105599.65,0.0
+2323,15694859.0,Efimov,678.0,France,Male,36.0,8.0,138333.03,1.0,1.0,0.0,175820.91,0.0
+2324,15579892.0,Trevisano,769.0,France,Male,31.0,6.0,0.0,1.0,1.0,0.0,35786.76,0.0
+2325,15640648.0,Kao,668.0,France,Female,21.0,5.0,122570.87,2.0,0.0,1.0,92378.54,0.0
+2326,15622507.0,Lucchese,585.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,168261.4,0.0
+2327,15573850.0,Teng,603.0,France,Female,63.0,6.0,160599.06,1.0,0.0,1.0,133535.29,0.0
+2328,15586069.0,Pinto,632.0,Spain,Female,43.0,7.0,0.0,1.0,1.0,1.0,146875.86,0.0
+2329,15657747.0,Chinwendu,678.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,93882.75,1.0
+2330,15809347.0,Chukwuemeka,831.0,Spain,Male,48.0,0.0,0.0,2.0,1.0,0.0,88838.14,0.0
+2331,15687634.0,McIntyre,537.0,Germany,Female,49.0,9.0,101191.83,1.0,1.0,0.0,93568.77,1.0
+2332,15752488.0,K'ung,742.0,France,Female,39.0,3.0,0.0,2.0,0.0,1.0,75832.53,1.0
+2333,15795571.0,Chiang,461.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,145986.35,0.0
+2334,15641175.0,T'ien,580.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,53710.23,0.0
+2335,15780144.0,Bancks,681.0,France,Male,38.0,6.0,0.0,1.0,1.0,1.0,158742.81,0.0
+2336,15783026.0,Onuora,604.0,France,Female,34.0,8.0,0.0,2.0,0.0,1.0,67311.12,0.0
+2337,15579914.0,Goliwe,646.0,Spain,Female,41.0,1.0,0.0,1.0,1.0,0.0,91943.94,1.0
+2338,15651022.0,Nwokezuike,663.0,Spain,Female,42.0,1.0,0.0,2.0,1.0,1.0,53511.12,0.0
+2339,15642570.0,Yobanna,649.0,France,Female,30.0,2.0,0.0,1.0,1.0,0.0,176925.69,1.0
+2340,15689322.0,Maclean,735.0,Spain,Female,43.0,7.0,0.0,1.0,1.0,1.0,88791.83,1.0
+2341,15736274.0,Ngozichukwuka,667.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,0.0,127587.28,0.0
+2342,15777045.0,Ch'iu,710.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,170638.98,0.0
+2343,15714046.0,Mironova,613.0,Germany,Female,57.0,1.0,124577.33,1.0,1.0,0.0,156774.75,1.0
+2344,15638267.0,Dickson,652.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,144870.28,0.0
+2345,15578788.0,Onyemauchechukwu,663.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,1.0,77878.62,0.0
+2346,15730447.0,Milano,673.0,France,Male,37.0,7.0,121260.19,1.0,1.0,1.0,894.75,0.0
+2347,15711750.0,Chukwuraenye,669.0,Spain,Male,51.0,9.0,0.0,1.0,1.0,0.0,141476.56,1.0
+2348,15757811.0,Chiemenam,585.0,Spain,Female,41.0,0.0,0.0,2.0,1.0,1.0,184524.74,0.0
+2349,15676933.0,Folliero,656.0,France,Male,41.0,4.0,122570.87,1.0,1.0,0.0,90189.72,0.0
+2350,15623989.0,Hao,726.0,Germany,Male,36.0,9.0,116576.45,1.0,0.0,1.0,83911.6,0.0
+2351,15678427.0,Okeke,605.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,141896.74,0.0
+2352,15709639.0,Tobeolisa,745.0,France,Male,66.0,2.0,0.0,1.0,1.0,1.0,91340.02,0.0
+2353,15610753.0,Chukwujamuike,683.0,France,Female,33.0,4.0,139196.0,1.0,1.0,1.0,162812.16,0.0
+2354,15795895.0,Chikezie,468.0,Germany,Female,46.0,5.0,117413.95,1.0,0.0,1.0,61994.2,1.0
+2355,15662093.0,Onwuamaeze,726.0,France,Female,42.0,7.0,89025.88,1.0,1.0,0.0,125918.14,0.0
+2356,15700749.0,Tsao,755.0,Spain,Male,38.0,10.0,119654.44,1.0,1.0,0.0,54474.48,0.0
+2357,15722090.0,Chia,681.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,1.0,85869.89,0.0
+2358,15654096.0,Onyemauchechukwu,755.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,147955.91,0.0
+2359,15587581.0,Ch'iu,721.0,Germany,Female,25.0,1.0,134740.24,1.0,1.0,0.0,71167.17,0.0
+2360,15606085.0,Tuan,613.0,France,Female,30.0,5.0,0.0,2.0,1.0,1.0,95611.47,0.0
+2361,15747385.0,Kaur,613.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,1.0,164741.5,0.0
+2362,15732903.0,Grieve,725.0,France,Female,48.0,4.0,0.0,1.0,1.0,0.0,171768.76,1.0
+2363,15797900.0,Ch'eng,619.0,Spain,Male,33.0,2.0,0.0,1.0,1.0,0.0,131736.23,0.0
+2364,15772995.0,Manna,605.0,Spain,Female,55.0,1.0,0.0,1.0,1.0,0.0,51117.06,1.0
+2365,15740404.0,Pape,687.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,145536.28,0.0
+2366,15791054.0,Chuang,615.0,France,Female,52.0,7.0,0.0,1.0,1.0,0.0,55829.25,1.0
+2367,15690182.0,Onyemauchechukwu,592.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,60584.33,0.0
+2368,15644878.0,Molineux,554.0,France,Male,45.0,8.0,0.0,2.0,1.0,0.0,72643.95,0.0
+2369,15746338.0,Chukwukadibia,703.0,Spain,Male,39.0,2.0,0.0,1.0,0.0,0.0,102397.22,0.0
+2370,15664759.0,Y?,727.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,51117.06,0.0
+2371,15645778.0,Okechukwu,629.0,France,Female,46.0,1.0,0.0,1.0,1.0,1.0,91510.71,1.0
+2372,15665088.0,Nucci,647.0,Spain,Male,45.0,4.0,0.0,2.0,1.0,0.0,108003.38,0.0
+2373,15764033.0,Chinedum,743.0,France,Female,42.0,7.0,0.0,2.0,1.0,1.0,150856.38,0.0
+2374,15597909.0,Pirozzi,605.0,France,Female,39.0,9.0,0.0,2.0,0.0,1.0,51703.09,0.0
+2375,15793311.0,Chiekwugo,516.0,France,Male,42.0,8.0,0.0,1.0,0.0,1.0,180551.76,0.0
+2376,15734312.0,Chinedum,653.0,Germany,Male,31.0,3.0,114023.64,1.0,1.0,1.0,18260.98,0.0
+2377,15614092.0,Ndukaku,634.0,Spain,Male,31.0,1.0,0.0,1.0,1.0,0.0,51555.17,0.0
+2378,15768474.0,Udinesi,718.0,Germany,Male,45.0,7.0,114959.12,2.0,1.0,1.0,99347.8,0.0
+2379,15798960.0,Milanesi,719.0,France,Female,38.0,3.0,0.0,1.0,1.0,1.0,182865.09,0.0
+2380,15762277.0,Kao,525.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,129892.93,0.0
+2381,15622897.0,Chiedozie,651.0,France,Female,36.0,1.0,0.0,2.0,0.0,0.0,165082.2,0.0
+2382,15754526.0,Pinto,705.0,France,Male,40.0,2.0,88977.39,2.0,1.0,1.0,131301.74,0.0
+2383,15664668.0,Chien,607.0,Spain,Male,40.0,3.0,0.0,1.0,1.0,0.0,16250.61,1.0
+2384,15803749.0,P'eng,579.0,France,Male,41.0,10.0,0.0,2.0,1.0,1.0,172576.43,0.0
+2385,15731555.0,Hao,597.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,155711.64,0.0
+2386,15585379.0,P'eng,572.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,142331.85,0.0
+2387,15777830.0,Brown,709.0,Spain,Female,40.0,9.0,0.0,2.0,1.0,1.0,110776.01,0.0
+2388,15778936.0,Calabresi,651.0,France,Female,61.0,1.0,0.0,2.0,1.0,1.0,108876.75,0.0
+2389,15691792.0,Y?an,583.0,France,Male,38.0,9.0,0.0,2.0,0.0,1.0,161889.95,0.0
+2390,15585853.0,Nwora,585.0,Spain,Male,56.0,8.0,0.0,2.0,1.0,0.0,93718.16,1.0
+2391,15600813.0,Yobachi,786.0,France,Male,66.0,7.0,0.0,2.0,0.0,1.0,82820.85,0.0
+2392,15712198.0,Palerma,752.0,France,Female,37.0,3.0,132527.9,1.0,1.0,0.0,118749.15,0.0
+2393,15702416.0,L?,662.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,179978.68,0.0
+2394,15752578.0,Nwachinemelu,730.0,France,Male,29.0,1.0,0.0,1.0,1.0,1.0,73679.31,0.0
+2395,15624374.0,Baresi,707.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,103570.8,0.0
+2396,15777892.0,Hsiung,553.0,Germany,Male,44.0,1.0,117438.92,2.0,1.0,1.0,120853.83,1.0
+2397,15772412.0,Toscani,682.0,France,Male,40.0,2.0,153870.23,1.0,0.0,0.0,137835.75,0.0
+2398,15634281.0,Wilson,592.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,96444.88,0.0
+2399,15790750.0,Chinedum,593.0,France,Male,42.0,2.0,0.0,2.0,1.0,1.0,95936.84,0.0
+2400,15762855.0,K?,652.0,Germany,Male,33.0,1.0,122793.96,1.0,1.0,0.0,102930.46,0.0
+2401,15662063.0,Niu,801.0,Spain,Male,35.0,1.0,0.0,1.0,0.0,1.0,181928.25,1.0
+2402,15642579.0,Kenechi,850.0,France,Male,32.0,2.0,123575.51,1.0,1.0,0.0,55725.04,0.0
+2403,15589725.0,Ni,632.0,France,Female,35.0,9.0,0.0,2.0,0.0,1.0,140763.17,0.0
+2404,15603683.0,Chinedum,677.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,1.0,82774.07,0.0
+2405,15588904.0,Ch'eng,687.0,Germany,Male,39.0,3.0,155907.68,1.0,1.0,1.0,137985.69,0.0
+2406,15650068.0,Ch'eng,713.0,Germany,Female,48.0,5.0,122314.5,1.0,0.0,1.0,51548.14,1.0
+2407,15795747.0,Hsieh,644.0,Spain,Female,40.0,7.0,96951.21,1.0,1.0,1.0,85412.54,0.0
+2408,15783398.0,Chukwukadibia,729.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,79866.73,0.0
+2409,15591035.0,Chiazagomekpere,651.0,France,Male,31.0,7.0,106968.96,1.0,1.0,1.0,52796.31,0.0
+2410,15713578.0,Lucchese,684.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,147530.06,0.0
+2411,15782210.0,Milano,682.0,Spain,Female,40.0,2.0,0.0,2.0,0.0,0.0,144517.19,0.0
+2412,15789563.0,Cattaneo,692.0,France,Female,38.0,8.0,0.0,1.0,1.0,0.0,24255.21,0.0
+2413,15693904.0,Chinweuba,685.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,178045.97,0.0
+2414,15806049.0,Maclean,613.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,152698.24,0.0
+2415,15662337.0,Ebelegbulam,628.0,France,Female,32.0,0.0,99786.07,1.0,1.0,1.0,169964.12,0.0
+2416,15599195.0,Obiajulu,748.0,France,Female,32.0,7.0,0.0,2.0,0.0,1.0,184839.7,0.0
+2417,15608873.0,Milani,695.0,France,Female,54.0,1.0,0.0,2.0,1.0,0.0,143952.24,1.0
+2418,15751747.0,Zito,584.0,France,Male,32.0,0.0,0.0,1.0,1.0,1.0,103042.2,0.0
+2419,15575605.0,Chidubem,565.0,France,Male,31.0,4.0,0.0,2.0,0.0,0.0,29333.01,0.0
+2420,15583394.0,P'eng,705.0,France,Female,32.0,1.0,138171.1,1.0,1.0,0.0,187905.12,0.0
+2421,15763063.0,Yudina,678.0,France,Female,48.0,3.0,0.0,2.0,1.0,1.0,86977.53,0.0
+2422,15700964.0,Ch'ang,710.0,Spain,Male,50.0,1.0,0.0,2.0,1.0,1.0,171045.35,0.0
+2423,15579566.0,Hs?,684.0,France,Female,35.0,1.0,0.0,1.0,1.0,1.0,86657.48,0.0
+2424,15798824.0,Tung,727.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,57961.41,0.0
+2425,15591932.0,Lucchese,683.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,38186.84,0.0
+2426,15799384.0,Ts'ui,587.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,86699.98,0.0
+2427,15671610.0,Ch'iu,588.0,Spain,Female,29.0,7.0,0.0,2.0,1.0,1.0,67840.81,0.0
+2428,15581282.0,Riddle,755.0,Germany,Male,56.0,3.0,97720.35,1.0,1.0,1.0,152290.58,1.0
+2429,15654733.0,Chukwualuka,678.0,Germany,Male,62.0,1.0,96486.57,1.0,1.0,1.0,185577.34,0.0
+2430,15644896.0,Ozerova,572.0,Spain,Female,42.0,9.0,0.0,2.0,1.0,1.0,115468.81,0.0
+2431,15636423.0,K'ung,665.0,France,Female,33.0,3.0,0.0,2.0,0.0,0.0,128882.66,0.0
+2432,15800517.0,Rolon,676.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,115509.59,0.0
+2433,15691119.0,Scott,683.0,Germany,Male,41.0,5.0,142724.48,1.0,1.0,1.0,128521.17,0.0
+2434,15718245.0,Ts'ui,629.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,103926.73,0.0
+2435,15616380.0,Milne,649.0,France,Female,42.0,2.0,0.0,1.0,1.0,0.0,140938.95,1.0
+2436,15692291.0,Chinweike,714.0,Germany,Male,36.0,6.0,142717.84,1.0,1.0,1.0,6776.92,0.0
+2437,15682773.0,Chidozie,592.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,86951.2,0.0
+2438,15730059.0,Folliero,576.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,77550.18,0.0
+2439,15679024.0,Lucciano,678.0,Spain,Female,32.0,7.0,116431.45,1.0,0.0,0.0,59297.34,0.0
+2440,15673367.0,Nnamutaezinwa,556.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,121537.91,0.0
+2441,15761684.0,Yobachi,684.0,Spain,Female,42.0,7.0,123911.61,1.0,1.0,0.0,74838.64,1.0
+2442,15692664.0,Chukwueloka,632.0,Spain,Male,38.0,1.0,0.0,1.0,1.0,1.0,114675.75,0.0
+2443,15639277.0,Lucchese,597.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,48378.4,1.0
+2444,15592300.0,Aitken,587.0,Germany,Female,25.0,1.0,152368.21,1.0,0.0,1.0,139971.01,0.0
+2445,15605827.0,McEwan,771.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,71672.56,0.0
+2446,15727605.0,Chukwualuka,706.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,162599.51,0.0
+2447,15662403.0,Hsieh,668.0,Germany,Male,30.0,3.0,127801.88,2.0,1.0,1.0,15650.89,0.0
+2448,15690796.0,Nnachetam,562.0,Spain,Female,29.0,5.0,0.0,2.0,1.0,0.0,151978.36,0.0
+2449,15690164.0,Ch'in,710.0,France,Male,20.0,7.0,0.0,2.0,1.0,1.0,99017.34,0.0
+2450,15583076.0,Chinedum,718.0,France,Female,47.0,2.0,0.0,1.0,1.0,0.0,82298.59,1.0
+2451,15567993.0,Baresi,553.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,188484.52,0.0
+2452,15782269.0,Ch'ien,695.0,France,Male,38.0,5.0,126817.65,1.0,0.0,1.0,179978.68,0.0
+2453,15682868.0,Chidiebere,674.0,Spain,Male,37.0,10.0,0.0,2.0,1.0,1.0,187900.12,0.0
+2454,15664227.0,Chukwukere,696.0,France,Female,27.0,5.0,0.0,2.0,0.0,0.0,186655.11,0.0
+2455,15807003.0,Onyeorulu,752.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,61290.99,1.0
+2456,15696826.0,Ch'iu,637.0,France,Male,41.0,3.0,0.0,1.0,1.0,0.0,66799.71,0.0
+2457,15702434.0,Udinese,838.0,Germany,Male,34.0,7.0,143390.51,1.0,1.0,0.0,165675.01,0.0
+2458,15677764.0,Ts'ai,730.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,86945.0,0.0
+2459,15673877.0,Y?an,706.0,France,Female,48.0,9.0,119499.42,1.0,1.0,0.0,48228.1,0.0
+2460,15673837.0,Scott,684.0,Spain,Male,30.0,7.0,0.0,2.0,0.0,1.0,102298.79,0.0
+2461,15742798.0,Ting,573.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,174729.3,0.0
+2462,15663438.0,T'ien,676.0,Germany,Female,49.0,5.0,123081.86,1.0,1.0,0.0,117622.8,0.0
+2463,15761348.0,Chien,704.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,175070.94,0.0
+2464,15629750.0,Calabrese,554.0,Germany,Male,37.0,4.0,139835.34,2.0,1.0,1.0,143739.35,0.0
+2465,15581229.0,Chiang,738.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,166370.81,0.0
+2466,15714689.0,Ugochukwutubelum,733.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,139971.01,0.0
+2467,15658946.0,Ma,595.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,133535.22,0.0
+2468,15682778.0,Chuang,586.0,Spain,Male,23.0,2.0,0.0,2.0,1.0,1.0,122925.44,0.0
+2469,15783530.0,P'eng,671.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,125979.36,0.0
+2470,15694321.0,Ting,538.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,147055.87,0.0
+2471,15695792.0,Ch'in,687.0,France,Male,42.0,2.0,0.0,1.0,1.0,1.0,58883.91,0.0
+2472,15654959.0,Dyer,729.0,France,Male,48.0,5.0,0.0,2.0,0.0,1.0,143859.66,0.0
+2473,15736397.0,T'ao,697.0,Germany,Male,32.0,2.0,128100.28,2.0,1.0,1.0,101102.39,0.0
+2474,15735302.0,Lai,699.0,France,Male,45.0,7.0,0.0,2.0,1.0,1.0,181718.76,0.0
+2475,15643640.0,Ch'ang,684.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,146663.63,0.0
+2476,15630167.0,Baryshnikov,746.0,France,Male,42.0,5.0,0.0,1.0,1.0,0.0,160589.18,0.0
+2477,15675141.0,Onyemauchechukwu,710.0,Germany,Female,53.0,2.0,151772.63,1.0,1.0,1.0,143783.46,1.0
+2478,15799468.0,Y?an,729.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,129975.94,0.0
+2479,15685314.0,De Luca,703.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,18082.06,0.0
+2480,15618581.0,Chiu,474.0,France,Female,39.0,9.0,131326.44,1.0,0.0,1.0,133814.28,0.0
+2481,15749328.0,Genovese,659.0,France,Male,49.0,0.0,0.0,1.0,1.0,0.0,150720.39,1.0
+2482,15585362.0,Clements,694.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,0.0,104889.3,0.0
+2483,15732784.0,Lo Duca,636.0,Spain,Female,55.0,4.0,0.0,3.0,0.0,0.0,26389.92,1.0
+2484,15577985.0,Hsing,684.0,Germany,Male,38.0,2.0,132623.6,2.0,1.0,0.0,176580.63,0.0
+2485,15750778.0,Hsia,651.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,48988.28,0.0
+2486,15795954.0,Teng,758.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,164825.09,0.0
+2487,15711571.0,Trevisano,559.0,France,Male,39.0,3.0,0.0,2.0,0.0,0.0,109805.17,0.0
+2488,15806913.0,Ozoemena,648.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,74865.61,0.0
+2489,15611371.0,Chiemenam,711.0,France,Male,36.0,2.0,131160.85,1.0,0.0,0.0,104533.85,0.0
+2490,15666032.0,Chidiebere,645.0,France,Male,38.0,4.0,0.0,1.0,0.0,1.0,133020.9,0.0
+2491,15708917.0,Chidiebele,679.0,Spain,Female,31.0,1.0,0.0,2.0,1.0,1.0,178833.4,0.0
+2492,15662483.0,Tuan,633.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,93336.3,0.0
+2493,15593365.0,Manna,730.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,1.0,176750.81,0.0
+2494,15576065.0,Yefremova,711.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,116692.8,0.0
+2495,15691287.0,Hsing,645.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,142331.85,0.0
+2496,15619494.0,Chukwumaobim,656.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,54954.51,0.0
+2497,15677908.0,O'Sullivan,581.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,91429.74,0.0
+2498,15662291.0,Chukwuemeka,706.0,France,Male,44.0,8.0,120777.88,1.0,1.0,1.0,131778.58,0.0
+2499,15629338.0,Olisaemeka,677.0,France,Male,35.0,1.0,64430.06,1.0,1.0,1.0,75937.38,0.0
+2500,15733169.0,Hsia,711.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,38153.19,0.0
+2501,15592765.0,Onwumelu,648.0,France,Female,42.0,2.0,0.0,2.0,1.0,0.0,93820.49,0.0
+2502,15642530.0,Nnachetam,763.0,France,Male,38.0,9.0,0.0,1.0,0.0,1.0,172450.04,0.0
+2503,15604497.0,Hsueh,617.0,France,Male,38.0,1.0,109135.11,1.0,1.0,1.0,72909.75,0.0
+2504,15682773.0,Rizzo,578.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,183490.99,0.0
+2505,15768449.0,T'ao,683.0,Germany,Male,42.0,10.0,112940.07,3.0,1.0,0.0,84475.13,1.0
+2506,15649024.0,Ugonnatubelum,565.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,49374.82,0.0
+2507,15754574.0,Chidiebere,681.0,France,Male,47.0,4.0,0.0,2.0,1.0,1.0,25885.72,0.0
+2508,15568120.0,Nkemakolam,716.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,101515.76,0.0
+2509,15707551.0,Cattaneo,587.0,Germany,Male,28.0,2.0,90696.78,1.0,1.0,1.0,96444.88,0.0
+2510,15607988.0,Ugochukwu,641.0,France,Male,43.0,8.0,146053.66,1.0,1.0,1.0,124550.88,0.0
+2511,15614962.0,Pisano,547.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,131284.93,0.0
+2512,15752578.0,Chukwualuka,596.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,123663.63,0.0
+2513,15740406.0,Lucchese,850.0,Germany,Female,35.0,5.0,104832.49,2.0,1.0,0.0,187915.7,0.0
+2514,15634602.0,Lu,553.0,France,Male,41.0,2.0,0.0,3.0,1.0,1.0,151903.6,1.0
+2515,15640648.0,Ch'eng,763.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,54495.82,0.0
+2516,15706269.0,Ch'ien,597.0,France,Female,49.0,0.0,0.0,2.0,0.0,0.0,184544.59,1.0
+2517,15621440.0,Johnson,644.0,Spain,Female,39.0,2.0,97764.41,2.0,1.0,0.0,140941.56,0.0
+2518,15718893.0,Toscani,725.0,France,Male,41.0,6.0,131101.04,1.0,0.0,1.0,181893.31,0.0
+2519,15697691.0,Lettiere,682.0,France,Male,42.0,1.0,129895.19,1.0,1.0,1.0,87422.15,0.0
+2520,15803689.0,T'ien,705.0,France,Male,38.0,5.0,0.0,1.0,1.0,0.0,96961.0,1.0
+2521,15762268.0,Lucciano,656.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,128521.17,0.0
+2522,15754849.0,Chinwendu,757.0,Spain,Female,40.0,3.0,0.0,1.0,1.0,0.0,121408.51,1.0
+2523,15636061.0,Calabrese,724.0,France,Male,33.0,4.0,138053.79,1.0,1.0,0.0,65720.92,0.0
+2524,15782758.0,H??,581.0,Germany,Female,29.0,7.0,119565.92,2.0,1.0,0.0,137537.22,0.0
+2525,15661409.0,Zikoranachidimma,635.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,121084.27,0.0
+2526,15795747.0,Kambinachi,571.0,France,Male,33.0,8.0,0.0,2.0,0.0,1.0,82422.97,0.0
+2527,15794468.0,P'eng,708.0,Spain,Male,37.0,6.0,0.0,1.0,0.0,0.0,150694.42,0.0
+2528,15734987.0,Hao,516.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,57898.58,0.0
+2529,15792453.0,Tien,688.0,France,Male,53.0,4.0,0.0,2.0,1.0,1.0,122960.98,0.0
+2530,15784844.0,Ts'ui,634.0,France,Male,43.0,6.0,0.0,2.0,1.0,0.0,177624.01,0.0
+2531,15599956.0,Kao,773.0,France,Male,38.0,5.0,126670.37,1.0,1.0,1.0,74900.9,0.0
+2532,15778515.0,Maughan,595.0,Spain,Female,61.0,5.0,0.0,2.0,1.0,1.0,70532.53,0.0
+2533,15684958.0,Fiorentini,695.0,France,Male,26.0,9.0,0.0,2.0,1.0,1.0,121555.51,0.0
+2534,15581295.0,Hao,652.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,53889.41,0.0
+2535,15639946.0,P'eng,710.0,Spain,Female,36.0,0.0,0.0,2.0,1.0,0.0,96833.0,0.0
+2536,15632143.0,Grosse,683.0,France,Female,55.0,1.0,0.0,1.0,1.0,1.0,152992.04,1.0
+2537,15742238.0,Ch'eng,850.0,France,Male,32.0,6.0,0.0,1.0,1.0,0.0,187800.63,1.0
+2538,15762216.0,Ch'iu,673.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,177747.03,0.0
+2539,15694518.0,Walker,632.0,Germany,Female,48.0,0.0,154737.88,1.0,1.0,0.0,62634.94,1.0
+2540,15644295.0,Hsing,645.0,Germany,Male,31.0,6.0,119276.31,2.0,1.0,1.0,150135.38,0.0
+2541,15751774.0,Chukwuemeka,651.0,Germany,Male,27.0,9.0,132403.56,1.0,1.0,0.0,823.36,0.0
+2542,15638774.0,Yefimov,686.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,178705.45,0.0
+2543,15757895.0,Pirozzi,671.0,Spain,Female,42.0,7.0,0.0,2.0,0.0,1.0,153831.6,0.0
+2544,15597954.0,Pisani,778.0,France,Female,37.0,2.0,109137.12,2.0,0.0,1.0,48378.4,0.0
+2545,15587443.0,Estrada,588.0,Germany,Female,40.0,3.0,148897.02,1.0,0.0,0.0,189362.59,1.0
+2546,15748473.0,Tuan,839.0,France,Female,25.0,8.0,0.0,2.0,1.0,0.0,175893.45,0.0
+2547,15713890.0,Milani,706.0,France,Female,34.0,6.0,0.0,1.0,1.0,1.0,7905.06,0.0
+2548,15683544.0,Buchi,590.0,France,Male,30.0,6.0,0.0,2.0,0.0,1.0,159899.97,0.0
+2549,15652674.0,Ch'ang,637.0,Germany,Female,44.0,2.0,103560.57,1.0,1.0,1.0,83058.14,0.0
+2550,15766374.0,Genovese,640.0,France,Female,44.0,6.0,138521.72,1.0,1.0,1.0,136610.02,1.0
+2551,15671390.0,Tuan,604.0,Spain,Male,24.0,2.0,0.0,2.0,0.0,1.0,103920.05,0.0
+2552,15625942.0,Walker,619.0,France,Female,30.0,3.0,0.0,2.0,0.0,1.0,96444.88,0.0
+2553,15729201.0,Ugochukwu,565.0,France,Female,39.0,10.0,0.0,2.0,1.0,0.0,116977.89,0.0
+2554,15675650.0,Ch'ang,652.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,135875.51,0.0
+2555,15793331.0,Ozioma,601.0,France,Male,43.0,1.0,106070.33,1.0,1.0,0.0,74940.5,0.0
+2556,15598070.0,Hsiao,790.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,142400.27,0.0
+2557,15652084.0,Fanucci,524.0,Germany,Male,31.0,9.0,105271.56,1.0,1.0,1.0,134732.99,0.0
+2558,15663888.0,Chigozie,681.0,France,Female,59.0,1.0,92930.92,1.0,1.0,0.0,125082.29,0.0
+2559,15571843.0,Tsao,681.0,Spain,Female,50.0,8.0,0.0,1.0,1.0,0.0,172665.21,1.0
+2560,15641158.0,Tuan,579.0,France,Male,51.0,2.0,0.0,1.0,1.0,1.0,28226.15,1.0
+2561,15796790.0,Ch'ang,741.0,France,Male,25.0,4.0,0.0,2.0,1.0,1.0,26263.44,0.0
+2562,15603914.0,Chinweuba,629.0,France,Female,46.0,7.0,0.0,1.0,0.0,0.0,149705.25,1.0
+2563,15662884.0,Yefremov,606.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,75825.57,0.0
+2564,15592751.0,Onyemere,659.0,France,Male,45.0,3.0,0.0,2.0,1.0,1.0,76621.49,0.0
+2565,15779913.0,Hsueh,586.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,146723.72,0.0
+2566,15736126.0,Doyne,686.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,78622.38,0.0
+2567,15658890.0,Obinna,694.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,52301.15,0.0
+2568,15613463.0,Chinedum,537.0,Germany,Female,47.0,10.0,91536.97,1.0,1.0,0.0,101617.64,1.0
+2569,15702561.0,Tien,678.0,France,Female,30.0,2.0,0.0,1.0,1.0,0.0,77965.67,0.0
+2570,15602030.0,Chiemenam,744.0,Spain,Female,40.0,2.0,165610.41,2.0,0.0,1.0,132436.71,0.0
+2571,15656086.0,Palerma,592.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,181495.77,0.0
+2572,15657779.0,Linger,553.0,France,Male,22.0,6.0,83724.35,1.0,1.0,1.0,106024.02,0.0
+2573,15602909.0,Ch'ang,629.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,107876.2,0.0
+2574,15794849.0,Tisdall,683.0,Spain,Male,39.0,2.0,100880.76,1.0,1.0,1.0,28461.29,0.0
+2575,15783030.0,P'eng,744.0,France,Female,34.0,7.0,0.0,1.0,1.0,1.0,54726.43,0.0
+2576,15767432.0,Hsueh,584.0,France,Male,29.0,0.0,141796.43,1.0,1.0,1.0,156774.75,0.0
+2577,15807555.0,Clements,850.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,112124.98,0.0
+2578,15602909.0,Nwabugwu,479.0,France,Female,31.0,4.0,0.0,2.0,1.0,0.0,150139.73,0.0
+2579,15583840.0,Scott,710.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,112366.98,0.0
+2580,15792726.0,Ts'ui,543.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,90977.48,0.0
+2581,15651740.0,Ch'eng,751.0,France,Female,60.0,3.0,0.0,1.0,1.0,0.0,15068.18,1.0
+2582,15793346.0,Zikoranachidimma,742.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,175380.77,0.0
+2583,15776051.0,Gidney,756.0,Germany,Female,43.0,8.0,84474.62,1.0,0.0,1.0,51695.41,0.0
+2584,15656840.0,Tung,679.0,France,Female,38.0,5.0,121164.11,1.0,1.0,0.0,167984.72,0.0
+2585,15738063.0,Chukwukere,641.0,France,Male,39.0,10.0,0.0,1.0,1.0,1.0,158610.89,0.0
+2586,15752588.0,Yobanna,562.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,1.0,105525.16,0.0
+2587,15602844.0,Onio,659.0,Spain,Male,38.0,2.0,135842.89,1.0,1.0,0.0,37296.13,0.0
+2588,15642710.0,Chuang,647.0,Germany,Female,40.0,7.0,104944.1,1.0,1.0,0.0,182867.84,1.0
+2589,15662955.0,Ponce,711.0,Spain,Male,40.0,8.0,176729.52,1.0,1.0,0.0,94670.77,0.0
+2590,15694879.0,Hs?,524.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,167032.49,0.0
+2591,15738662.0,Ting,811.0,Spain,Male,30.0,7.0,0.0,2.0,1.0,0.0,118545.49,0.0
+2592,15624695.0,Chizuoke,554.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,162719.69,0.0
+2593,15674541.0,Molineux,619.0,Germany,Male,35.0,4.0,132371.3,2.0,1.0,0.0,182692.8,0.0
+2594,15785042.0,Ponomarev,712.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,14081.64,0.0
+2595,15598493.0,She,707.0,Germany,Female,38.0,3.0,122570.69,1.0,1.0,1.0,179521.24,0.0
+2596,15780954.0,Ch'ang,539.0,France,Female,40.0,10.0,111096.91,1.0,0.0,1.0,155846.69,0.0
+2597,15662238.0,Ugochukwu,599.0,Germany,Male,46.0,4.0,109346.13,1.0,1.0,0.0,158027.45,1.0
+2598,15603954.0,Calabrese,682.0,Germany,Male,28.0,2.0,125534.51,2.0,1.0,0.0,175072.47,0.0
+2599,15798532.0,T'ang,746.0,Germany,Male,45.0,8.0,124580.69,2.0,1.0,0.0,140929.98,0.0
+2600,15615896.0,Chiu,732.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,141210.5,1.0
+2601,15686539.0,Toscani,733.0,France,Male,57.0,3.0,145219.3,1.0,0.0,1.0,155876.06,1.0
+2602,15783526.0,Ebelegbulam,805.0,Spain,Male,37.0,3.0,118560.0,1.0,1.0,0.0,180426.4,0.0
+2603,15758813.0,Young,673.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,182867.04,0.0
+2604,15606861.0,Ting,686.0,Germany,Male,40.0,7.0,143680.47,2.0,1.0,0.0,187811.71,0.0
+2605,15632522.0,Walker,593.0,France,Female,38.0,0.0,0.0,2.0,1.0,0.0,80190.36,0.0
+2606,15703923.0,Chu,748.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,105570.39,0.0
+2607,15749119.0,T'an,645.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,77837.63,0.0
+2608,15627352.0,Hsia,850.0,France,Female,38.0,6.0,0.0,1.0,1.0,0.0,91098.76,1.0
+2609,15699294.0,K'ung,526.0,Spain,Female,61.0,2.0,124577.33,1.0,1.0,0.0,166476.95,1.0
+2610,15674541.0,Calabresi,667.0,France,Female,45.0,1.0,0.0,2.0,1.0,0.0,183566.78,0.0
+2611,15662291.0,Nnonso,703.0,Spain,Female,35.0,9.0,118899.45,1.0,1.0,1.0,134720.51,0.0
+2612,15775295.0,Ch'ien,718.0,France,Male,48.0,1.0,82407.51,1.0,0.0,1.0,164806.89,0.0
+2613,15588614.0,Hsieh,691.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,40889.14,0.0
+2614,15652789.0,Teng,583.0,France,Male,31.0,0.0,108396.67,1.0,1.0,1.0,81861.1,0.0
+2615,15579969.0,Ndukaku,682.0,France,Male,47.0,9.0,129118.5,1.0,1.0,0.0,149991.32,1.0
+2616,15583857.0,Mazzanti,850.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,100618.17,0.0
+2617,15809616.0,Onyemauchechukwu,656.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,182081.45,0.0
+2618,15783875.0,O'Loughlin,604.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,0.0,162443.05,0.0
+2619,15591279.0,T'ien,739.0,France,Female,39.0,7.0,0.0,2.0,0.0,1.0,143317.57,0.0
+2620,15652693.0,Ritchie,697.0,France,Male,50.0,1.0,0.0,2.0,0.0,1.0,85421.48,0.0
+2621,15777586.0,Uspensky,678.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,177772.03,0.0
+2622,15684868.0,Chukwuemeka,749.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,129964.94,0.0
+2623,15591766.0,Rizzo,738.0,France,Male,29.0,0.0,129743.75,1.0,1.0,1.0,180134.88,0.0
+2624,15665590.0,Onyemere,726.0,Spain,Female,23.0,2.0,0.0,2.0,1.0,0.0,75832.53,0.0
+2625,15639552.0,Brown,597.0,Germany,Male,62.0,8.0,137933.07,1.0,1.0,0.0,180844.81,1.0
+2626,15610403.0,T'ao,678.0,Spain,Female,32.0,2.0,0.0,1.0,1.0,1.0,121098.65,0.0
+2627,15772423.0,Genovesi,678.0,Spain,Female,30.0,4.0,0.0,2.0,1.0,0.0,143681.85,0.0
+2628,15638231.0,Ch'ien,713.0,France,Female,42.0,7.0,151855.33,1.0,1.0,0.0,82698.09,1.0
+2629,15746451.0,Oguejiofor,758.0,France,Male,28.0,9.0,0.0,1.0,1.0,0.0,158043.11,0.0
+2630,15641170.0,Cremonesi,653.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,179908.71,0.0
+2631,15756056.0,Teng,683.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,102799.14,0.0
+2632,15737267.0,Tsao,769.0,France,Female,27.0,8.0,0.0,2.0,0.0,0.0,95804.33,0.0
+2633,15727350.0,Chinomso,638.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,1.0,133591.11,0.0
+2634,15641110.0,Chinedum,697.0,Germany,Male,33.0,6.0,79954.61,1.0,1.0,0.0,147822.44,0.0
+2635,15704583.0,Sun,682.0,France,Female,35.0,2.0,0.0,2.0,0.0,0.0,182336.45,0.0
+2636,15722533.0,Zito,583.0,France,Female,46.0,5.0,0.0,3.0,0.0,0.0,180890.4,1.0
+2637,15800514.0,Chukwueloka,730.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,162889.1,0.0
+2638,15795519.0,Chiemela,754.0,France,Male,32.0,6.0,0.0,2.0,0.0,1.0,126263.97,0.0
+2639,15585280.0,Walker,549.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,0.0,171463.83,0.0
+2640,15777855.0,Kornilova,553.0,Spain,Female,43.0,5.0,95727.05,1.0,0.0,0.0,151902.78,0.0
+2641,15644297.0,Chinweuba,703.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,134878.19,0.0
+2642,15725166.0,Tsao,723.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,135875.51,0.0
+2643,15682728.0,Galkin,705.0,France,Female,35.0,3.0,0.0,1.0,0.0,0.0,127939.19,1.0
+2644,15691844.0,Ch'eng,733.0,Germany,Male,45.0,8.0,108269.2,1.0,0.0,1.0,143859.66,1.0
+2645,15655410.0,Yermakov,594.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,162719.69,0.0
+2646,15717290.0,Chien,605.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,186489.95,0.0
+2647,15749669.0,Onyeorulu,669.0,France,Female,31.0,5.0,0.0,1.0,1.0,1.0,177524.19,0.0
+2648,15677387.0,Lettiere,706.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,184524.74,0.0
+2649,15774854.0,Nkemjika,627.0,Germany,Female,34.0,6.0,100260.88,2.0,1.0,1.0,62825.03,0.0
+2650,15617507.0,Shih,793.0,France,Female,35.0,10.0,125739.75,2.0,1.0,1.0,180439.75,0.0
+2651,15790442.0,T'ien,697.0,France,Male,40.0,7.0,0.0,1.0,1.0,1.0,114442.66,0.0
+2652,15704053.0,Palermo,637.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,109257.59,0.0
+2653,15729279.0,O'Brien,775.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,91624.11,0.0
+2654,15573869.0,Ch'iu,686.0,Spain,Male,29.0,9.0,0.0,2.0,0.0,0.0,106918.67,0.0
+2655,15668057.0,Nwachukwu,743.0,France,Male,30.0,5.0,157777.58,2.0,1.0,1.0,172097.4,0.0
+2656,15631693.0,Chin,638.0,France,Male,47.0,4.0,0.0,1.0,0.0,0.0,113645.4,1.0
+2657,15781298.0,Chikezie,689.0,Germany,Male,47.0,2.0,126873.52,1.0,1.0,1.0,49373.94,1.0
+2658,15785024.0,Nkemdirim,662.0,France,Male,29.0,8.0,0.0,1.0,1.0,1.0,120892.96,0.0
+2659,15690540.0,Nnanna,716.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,113931.57,1.0
+2660,15592761.0,Yobanna,547.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,155047.11,0.0
+2661,15675141.0,Chiekwugo,695.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,107625.36,0.0
+2662,15605665.0,Zito,658.0,France,Female,49.0,9.0,113829.82,1.0,1.0,0.0,28367.21,1.0
+2663,15768219.0,Hsiao,607.0,France,Female,39.0,3.0,0.0,2.0,0.0,1.0,133086.45,0.0
+2664,15757811.0,Ch'ang,850.0,Germany,Male,61.0,2.0,119839.69,2.0,1.0,1.0,62634.94,0.0
+2665,15713673.0,Hsu,774.0,France,Female,40.0,3.0,124525.52,1.0,0.0,0.0,12537.42,0.0
+2666,15738174.0,Chukwualuka,585.0,Germany,Male,59.0,1.0,122034.4,1.0,1.0,0.0,113950.51,1.0
+2667,15683958.0,Udegbunam,749.0,France,Male,41.0,9.0,90322.99,1.0,1.0,0.0,73881.68,0.0
+2668,15584975.0,Phelan,706.0,France,Male,51.0,5.0,0.0,2.0,1.0,0.0,116748.14,0.0
+2669,15708916.0,Kao,676.0,France,Male,52.0,2.0,141356.47,3.0,1.0,1.0,118971.74,1.0
+2670,15620577.0,Trevisano,553.0,Spain,Female,35.0,1.0,105209.54,1.0,0.0,0.0,179780.74,0.0
+2671,15790564.0,Yobachukwu,711.0,France,Male,53.0,9.0,118307.57,1.0,1.0,0.0,147784.68,0.0
+2672,15618363.0,Chukwujekwu,694.0,France,Female,28.0,6.0,0.0,2.0,0.0,0.0,79633.38,0.0
+2673,15638882.0,White,587.0,France,Female,29.0,5.0,0.0,2.0,0.0,0.0,138542.53,0.0
+2674,15592999.0,Shih,579.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,0.0,164038.07,0.0
+2675,15592765.0,Onyemauchechi,643.0,France,Male,34.0,2.0,0.0,1.0,1.0,0.0,53477.76,0.0
+2676,15792934.0,Hargreaves,850.0,Spain,Female,33.0,4.0,0.0,2.0,0.0,0.0,173516.71,0.0
+2677,15796313.0,Lu,720.0,France,Male,55.0,1.0,0.0,2.0,1.0,0.0,64872.33,1.0
+2678,15576774.0,Manna,706.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,58844.84,0.0
+2679,15611029.0,Fomin,615.0,Germany,Male,63.0,4.0,51522.75,1.0,0.0,1.0,86911.49,1.0
+2680,15690695.0,Dilibe,634.0,Germany,Male,39.0,7.0,129433.34,1.0,1.0,0.0,166677.48,0.0
+2681,15721935.0,Yermolayev,598.0,France,Female,55.0,2.0,0.0,2.0,1.0,1.0,134790.61,0.0
+2682,15578515.0,Onyinyechukwuka,729.0,France,Female,38.0,0.0,0.0,2.0,1.0,0.0,143538.52,0.0
+2683,15583349.0,Fomin,635.0,Germany,Female,40.0,4.0,103465.04,2.0,1.0,0.0,824.0,0.0
+2684,15604345.0,Ch'iu,647.0,Germany,Female,39.0,6.0,125919.35,2.0,1.0,0.0,127095.14,0.0
+2685,15715079.0,Pirozzi,849.0,Germany,Female,53.0,2.0,131692.43,1.0,0.0,0.0,171773.43,1.0
+2686,15684548.0,Chia,723.0,France,Male,28.0,8.0,0.0,2.0,0.0,0.0,129826.96,0.0
+2687,15728674.0,Chiang,831.0,France,Male,39.0,2.0,136444.25,1.0,1.0,0.0,161241.65,0.0
+2688,15769216.0,Ch'in,654.0,France,Male,37.0,9.0,94773.11,1.0,1.0,0.0,152883.95,0.0
+2689,15718773.0,P'eng,648.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,138380.12,0.0
+2690,15782639.0,Frolova,756.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,51721.52,0.0
+2691,15607634.0,Istomin,682.0,France,Female,31.0,9.0,0.0,2.0,0.0,0.0,103898.59,0.0
+2692,15653595.0,Mazzanti,651.0,France,Male,47.0,2.0,0.0,2.0,1.0,0.0,141230.07,1.0
+2693,15738166.0,Chidiebere,652.0,Germany,Male,22.0,2.0,102952.61,1.0,1.0,0.0,126145.08,0.0
+2694,15696744.0,Ch'ang,592.0,France,Male,32.0,5.0,0.0,2.0,0.0,1.0,50783.1,0.0
+2695,15582960.0,Chiazagomekpere,594.0,Germany,Male,31.0,7.0,125850.53,1.0,1.0,0.0,198490.33,0.0
+2696,15679991.0,Chikelu,765.0,France,Female,37.0,1.0,121490.05,1.0,0.0,1.0,62972.13,0.0
+2697,15678034.0,Mao,652.0,France,Female,33.0,6.0,122528.18,1.0,1.0,1.0,77965.44,0.0
+2698,15781875.0,Fisk,762.0,France,Female,34.0,2.0,0.0,1.0,1.0,0.0,136869.91,0.0
+2699,15806438.0,T'ien,669.0,Spain,Male,31.0,9.0,0.0,1.0,1.0,1.0,93081.97,0.0
+2700,15763065.0,Tung,638.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,75585.48,0.0
+2701,15729279.0,Smith,605.0,France,Male,43.0,4.0,81589.04,1.0,1.0,0.0,107849.65,0.0
+2702,15761487.0,Hovell,647.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,35450.53,0.0
+2703,15648032.0,Ch'iu,661.0,France,Male,33.0,8.0,0.0,2.0,0.0,0.0,103421.91,0.0
+2704,15705343.0,Trevisani,576.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,124592.83,0.0
+2705,15671390.0,Yermolayeva,751.0,Germany,Male,29.0,5.0,94722.88,1.0,1.0,1.0,91573.19,1.0
+2706,15694450.0,Martin,636.0,Germany,Female,51.0,3.0,133702.89,1.0,1.0,0.0,94499.06,1.0
+2707,15795878.0,P'eng,682.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,63736.87,0.0
+2708,15642570.0,Chibueze,678.0,France,Male,54.0,7.0,0.0,1.0,0.0,0.0,123870.83,0.0
+2709,15640865.0,T'ien,748.0,France,Female,51.0,1.0,0.0,1.0,0.0,1.0,57572.38,1.0
+2710,15578825.0,Cattaneo,678.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,62449.07,0.0
+2711,15737310.0,Ch'ang,573.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,83629.6,0.0
+2712,15617454.0,Mancini,605.0,France,Male,32.0,3.0,0.0,2.0,0.0,0.0,136816.37,0.0
+2713,15783522.0,Chukwuebuka,565.0,France,Male,26.0,3.0,142872.36,1.0,1.0,0.0,69586.27,0.0
+2714,15771483.0,Trevisan,757.0,Spain,Female,43.0,8.0,0.0,2.0,0.0,0.0,154598.58,0.0
+2715,15644572.0,Nnamdi,683.0,Germany,Male,45.0,4.0,148116.48,1.0,1.0,0.0,179655.87,1.0
+2716,15711352.0,Ritchie,651.0,France,Male,40.0,9.0,0.0,1.0,1.0,1.0,135875.51,1.0
+2717,15694444.0,Ch'en,726.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,125010.24,0.0
+2718,15807909.0,H?,641.0,France,Male,43.0,0.0,0.0,2.0,1.0,1.0,163587.9,0.0
+2719,15600813.0,T'ien,582.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,118319.5,0.0
+2720,15728474.0,K?,721.0,Spain,Male,30.0,5.0,153313.77,1.0,1.0,1.0,128882.66,0.0
+2721,15794479.0,Tuan,629.0,France,Male,52.0,7.0,0.0,1.0,1.0,1.0,102950.79,0.0
+2722,15595766.0,Onwumelu,615.0,France,Male,39.0,6.0,136623.19,1.0,1.0,1.0,183494.87,0.0
+2723,15581295.0,Chia,850.0,France,Female,45.0,2.0,0.0,2.0,0.0,0.0,172526.9,0.0
+2724,15581286.0,Genovesi,609.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,161574.19,0.0
+2725,15598574.0,Ngozichukwuka,682.0,Spain,Male,40.0,6.0,0.0,2.0,0.0,0.0,18364.19,0.0
+2726,15623369.0,Maclean,700.0,France,Male,27.0,4.0,109196.66,1.0,1.0,0.0,57572.38,0.0
+2727,15723169.0,Chin,646.0,France,Female,29.0,2.0,79450.09,1.0,1.0,0.0,125920.54,0.0
+2728,15594864.0,T'ien,601.0,Germany,Female,43.0,3.0,139562.05,1.0,1.0,0.0,123499.98,1.0
+2729,15591740.0,Hsiung,661.0,France,Female,56.0,7.0,0.0,1.0,0.0,0.0,181429.87,1.0
+2730,15768137.0,Chiu,579.0,France,Male,33.0,3.0,0.0,2.0,0.0,0.0,112442.63,0.0
+2731,15599081.0,Ch'ang,711.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,142223.94,0.0
+2732,15751185.0,Pagnotto,705.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,1.0,74898.8,0.0
+2733,15792986.0,Chien,644.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,56908.41,0.0
+2734,15755872.0,Efremov,567.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,115676.61,0.0
+2735,15806922.0,Sun,706.0,Spain,Female,36.0,4.0,0.0,2.0,1.0,0.0,183572.39,0.0
+2736,15574743.0,Hsia,790.0,Germany,Male,51.0,8.0,112701.04,1.0,1.0,0.0,171430.16,1.0
+2737,15658195.0,Lo Duca,605.0,France,Female,54.0,4.0,0.0,1.0,0.0,0.0,104719.66,1.0
+2738,15740258.0,Walker,709.0,France,Female,39.0,2.0,142910.15,1.0,1.0,0.0,183494.87,0.0
+2739,15576368.0,Hargreaves,769.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,81409.0,0.0
+2740,15600626.0,Tsou,695.0,Germany,Male,35.0,9.0,130627.66,1.0,1.0,1.0,103379.31,0.0
+2741,15589329.0,Pinto,724.0,France,Female,33.0,3.0,0.0,1.0,1.0,0.0,79005.95,0.0
+2742,15683955.0,Ch'ien,556.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,152665.85,0.0
+2743,15793343.0,Mazzi,758.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,158437.73,0.0
+2744,15803947.0,Ijendu,799.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,149913.84,0.0
+2745,15598884.0,Chibugo,701.0,France,Male,30.0,7.0,0.0,3.0,1.0,0.0,134889.95,1.0
+2746,15734659.0,Okwukwe,727.0,Germany,Female,22.0,9.0,125190.86,2.0,1.0,1.0,47795.43,0.0
+2747,15807993.0,Pirozzi,611.0,Germany,Female,50.0,8.0,138053.79,1.0,1.0,0.0,178038.17,1.0
+2748,15792650.0,Chialuka,545.0,France,Female,41.0,1.0,0.0,2.0,1.0,0.0,88689.4,0.0
+2749,15665327.0,Onochie,676.0,France,Female,36.0,1.0,128226.37,1.0,1.0,0.0,131043.0,0.0
+2750,15808971.0,Calabresi,678.0,France,Female,42.0,3.0,0.0,2.0,0.0,0.0,103372.79,0.0
+2751,15796096.0,O'Donnell,651.0,Spain,Male,35.0,9.0,0.0,2.0,0.0,1.0,132467.54,0.0
+2752,15672930.0,Ejimofor,635.0,France,Male,31.0,6.0,0.0,2.0,0.0,0.0,86099.23,0.0
+2753,15781710.0,Galkin,571.0,Spain,Male,25.0,3.0,0.0,1.0,1.0,1.0,145937.99,0.0
+2754,15631800.0,Brownless,573.0,Spain,Male,35.0,0.0,157989.35,1.0,0.0,1.0,153786.46,0.0
+2755,15806318.0,T'ien,729.0,France,Male,24.0,6.0,0.0,2.0,0.0,0.0,174882.06,0.0
+2756,15663939.0,Hsia,581.0,France,Male,35.0,3.0,102025.08,1.0,1.0,0.0,103931.49,0.0
+2757,15703793.0,Nnachetam,675.0,France,Male,30.0,8.0,0.0,1.0,1.0,0.0,103570.8,0.0
+2758,15662063.0,Calabrese,729.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,121239.65,0.0
+2759,15776433.0,Babbage,685.0,France,Male,34.0,2.0,0.0,2.0,0.0,0.0,125961.48,0.0
+2760,15640407.0,Maclean,746.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,115116.35,0.0
+2761,15643908.0,Trevisano,705.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,152992.04,0.0
+2762,15661629.0,Chukwufumnanya,667.0,Spain,Female,48.0,7.0,0.0,1.0,1.0,1.0,28266.9,0.0
+2763,15619116.0,Biryukov,578.0,France,Male,40.0,4.0,0.0,2.0,0.0,0.0,102397.22,0.0
+2764,15649033.0,Okwuadigbo,627.0,Spain,Female,50.0,7.0,0.0,2.0,1.0,0.0,51345.41,1.0
+2765,15799790.0,Tuan,653.0,Germany,Male,48.0,4.0,114515.26,1.0,1.0,0.0,77326.78,0.0
+2766,15811316.0,Trevisano,573.0,France,Male,35.0,7.0,103362.14,1.0,1.0,1.0,149602.54,0.0
+2767,15807120.0,Genovesi,688.0,Spain,Male,54.0,10.0,0.0,1.0,1.0,0.0,167873.49,1.0
+2768,15597930.0,Ch'eng,667.0,Germany,Male,33.0,9.0,113566.3,2.0,1.0,1.0,92811.2,0.0
+2769,15588933.0,Tsao,644.0,Germany,Female,33.0,8.0,139422.29,1.0,0.0,0.0,28149.06,0.0
+2770,15751208.0,T'an,771.0,Spain,Male,39.0,9.0,0.0,2.0,0.0,0.0,61961.57,0.0
+2771,15583736.0,Ch'in,715.0,Germany,Male,30.0,4.0,131778.66,2.0,1.0,1.0,93718.16,0.0
+2772,15724863.0,Ting,705.0,France,Male,36.0,0.0,127545.94,1.0,1.0,1.0,158545.94,0.0
+2773,15603319.0,Cattaneo,646.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,126561.07,0.0
+2774,15732644.0,O'Sullivan,679.0,France,Female,28.0,3.0,0.0,1.0,1.0,0.0,158071.69,0.0
+2775,15602479.0,L?,648.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,102908.63,0.0
+2776,15716085.0,Palerma,774.0,France,Male,63.0,9.0,0.0,1.0,1.0,0.0,86022.09,1.0
+2777,15676835.0,White,535.0,Germany,Male,36.0,2.0,97588.6,2.0,0.0,1.0,92220.12,0.0
+2778,15691874.0,K?,604.0,Germany,Male,45.0,4.0,125845.66,1.0,0.0,0.0,130541.1,1.0
+2779,15739803.0,Chidubem,705.0,France,Male,26.0,9.0,0.0,1.0,1.0,0.0,100940.91,0.0
+2780,15614168.0,Chien,777.0,Germany,Male,36.0,9.0,137964.51,1.0,1.0,1.0,143913.35,0.0
+2781,15585961.0,Cremonesi,693.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,59354.24,0.0
+2782,15596671.0,Ch'in,639.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,0.0,97938.59,0.0
+2783,15588933.0,Li Fonti,583.0,Spain,Female,40.0,9.0,128206.79,1.0,1.0,0.0,122580.48,0.0
+2784,15757577.0,Palerma,577.0,Germany,Male,54.0,4.0,129933.5,3.0,1.0,0.0,16402.11,1.0
+2785,15725166.0,Fiorentino,754.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,183515.13,0.0
+2786,15803526.0,T'ao,710.0,France,Male,24.0,9.0,0.0,2.0,1.0,1.0,131806.44,0.0
+2787,15655192.0,Chiang,708.0,Germany,Female,42.0,2.0,135559.9,1.0,0.0,0.0,199203.21,1.0
+2788,15581229.0,Onyekaozulu,714.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,136301.1,0.0
+2789,15623180.0,Ch'ien,579.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,175853.64,0.0
+2790,15725679.0,Udinese,616.0,France,Female,37.0,9.0,0.0,2.0,0.0,1.0,141890.32,0.0
+2791,15766427.0,Chin,730.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,88965.46,0.0
+2792,15737385.0,Yeh,567.0,France,Female,29.0,7.0,0.0,1.0,1.0,1.0,116978.35,0.0
+2793,15686963.0,Ch'iu,742.0,Germany,Male,44.0,8.0,122570.87,2.0,1.0,1.0,166957.82,1.0
+2794,15803689.0,Y?an,707.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,109056.26,0.0
+2795,15600708.0,Toscani,628.0,Spain,Male,47.0,9.0,104077.19,1.0,1.0,0.0,105568.5,0.0
+2796,15706021.0,Okwuadigbo,659.0,Germany,Male,44.0,8.0,121898.58,1.0,1.0,0.0,14081.64,1.0
+2797,15566563.0,Onyekachukwu,682.0,France,Female,29.0,0.0,127552.85,1.0,1.0,0.0,88842.8,0.0
+2798,15711889.0,Chia,687.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,56650.47,0.0
+2799,15641595.0,Calabrese,710.0,Spain,Male,44.0,6.0,0.0,1.0,1.0,1.0,51987.99,0.0
+2800,15598892.0,Liao,598.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,162683.06,0.0
+2801,15790744.0,Kao,561.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,82393.08,0.0
+2802,15744607.0,Maclean,613.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,62733.05,0.0
+2803,15751167.0,Ting,642.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,0.0,57548.38,1.0
+2804,15622083.0,Chukwujamuike,712.0,Spain,Male,32.0,10.0,0.0,2.0,1.0,1.0,143635.33,0.0
+2805,15642996.0,Onwumelu,666.0,France,Female,22.0,9.0,102717.15,1.0,1.0,1.0,180318.6,0.0
+2806,15658853.0,Ch'ien,682.0,France,Male,27.0,8.0,0.0,2.0,0.0,1.0,173516.71,0.0
+2807,15797329.0,Calabrese,632.0,Germany,Female,46.0,1.0,103396.08,1.0,1.0,0.0,141798.98,1.0
+2808,15598485.0,Pinto,747.0,France,Female,26.0,6.0,96473.59,2.0,1.0,0.0,137905.25,0.0
+2809,15778418.0,Fiorentino,642.0,France,Male,32.0,9.0,88902.21,1.0,1.0,1.0,38550.4,0.0
+2810,15803801.0,Pagnotto,592.0,Spain,Male,35.0,10.0,0.0,2.0,0.0,1.0,94977.3,0.0
+2811,15672692.0,T'ao,592.0,France,Female,36.0,6.0,127861.93,1.0,0.0,0.0,99842.45,0.0
+2812,15812706.0,P'eng,687.0,France,Male,29.0,8.0,120787.18,1.0,0.0,0.0,131852.01,0.0
+2813,15589323.0,Lai,758.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,175893.53,0.0
+2814,15718443.0,Uwaezuoke,649.0,France,Female,33.0,3.0,0.0,2.0,0.0,0.0,77414.19,0.0
+2815,15661412.0,Mazzanti,739.0,Germany,Female,43.0,4.0,171787.84,1.0,1.0,1.0,85890.75,1.0
+2816,15651022.0,Lo,477.0,France,Male,21.0,3.0,0.0,2.0,1.0,0.0,125518.32,0.0
+2817,15605666.0,Trevisano,641.0,France,Male,59.0,9.0,0.0,2.0,1.0,1.0,36122.96,0.0
+2818,15598883.0,Udegbunam,711.0,Spain,Female,38.0,3.0,140856.16,1.0,1.0,1.0,150507.84,0.0
+2819,15747174.0,Chiazagomekpere,717.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,199992.48,0.0
+2820,15783550.0,Genovesi,683.0,France,Female,37.0,6.0,121405.04,1.0,1.0,1.0,82868.34,0.0
+2821,15631927.0,Shih,537.0,Germany,Male,61.0,2.0,63492.94,3.0,1.0,0.0,131953.23,1.0
+2822,15786463.0,Chinedum,850.0,Germany,Female,34.0,5.0,113882.31,2.0,1.0,0.0,71139.8,0.0
+2823,15770121.0,Onyeorulu,591.0,Spain,Female,44.0,8.0,0.0,2.0,1.0,0.0,41967.03,0.0
+2824,15598884.0,Ozerova,671.0,France,Female,32.0,6.0,61688.61,1.0,1.0,1.0,79928.41,1.0
+2825,15585867.0,Mazzanti,681.0,France,Female,60.0,7.0,0.0,3.0,1.0,1.0,160548.12,1.0
+2826,15697020.0,Akabueze,591.0,France,Female,39.0,1.0,0.0,1.0,0.0,0.0,16081.62,0.0
+2827,15773322.0,Lo Duca,705.0,Spain,Male,40.0,9.0,0.0,2.0,1.0,0.0,88566.9,0.0
+2828,15761149.0,Yefremova,605.0,Spain,Male,36.0,4.0,0.0,2.0,0.0,0.0,122964.18,0.0
+2829,15588517.0,Tseng,676.0,France,Male,38.0,1.0,0.0,1.0,1.0,1.0,83629.6,0.0
+2830,15656300.0,Kao,708.0,Germany,Female,49.0,5.0,99219.47,1.0,1.0,0.0,26261.42,1.0
+2831,15764335.0,Young,679.0,Spain,Male,47.0,4.0,0.0,2.0,0.0,1.0,72993.65,0.0
+2832,15791321.0,Nnachetam,572.0,France,Male,33.0,7.0,0.0,1.0,1.0,1.0,180934.67,0.0
+2833,15616152.0,Ts'ai,624.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,111421.77,0.0
+2834,15653849.0,Palerma,597.0,Spain,Male,52.0,3.0,141359.37,1.0,1.0,1.0,48421.4,0.0
+2835,15632848.0,Chukwudi,686.0,France,Female,32.0,3.0,0.0,1.0,0.0,1.0,172967.87,1.0
+2836,15731553.0,Y?an,639.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,143960.72,0.0
+2837,15591036.0,Hsiung,763.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,128815.23,0.0
+2838,15766166.0,Ch'en,605.0,France,Female,46.0,9.0,0.0,2.0,1.0,0.0,124596.63,1.0
+2839,15609510.0,Uspenskaya,582.0,France,Male,33.0,7.0,137715.84,1.0,1.0,1.0,4395.45,0.0
+2840,15659800.0,Mazzanti,685.0,France,Male,29.0,6.0,162039.59,1.0,0.0,0.0,77951.84,0.0
+2841,15571756.0,Palermo,730.0,Germany,Male,39.0,8.0,138053.79,1.0,1.0,1.0,86022.09,0.0
+2842,15761685.0,Ikedinachukwu,714.0,Spain,Female,29.0,1.0,127845.86,1.0,1.0,0.0,107964.35,0.0
+2843,15684958.0,Onyemauchechukwu,598.0,France,Female,32.0,3.0,146566.01,1.0,0.0,1.0,189657.26,1.0
+2844,15691483.0,Li Fonti,709.0,France,Male,46.0,7.0,104568.41,1.0,1.0,1.0,175072.47,0.0
+2845,15702300.0,T'ao,613.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,143628.31,0.0
+2846,15763063.0,Trevisano,678.0,Germany,Male,38.0,3.0,144801.97,1.0,0.0,0.0,40318.33,0.0
+2847,15801466.0,Toscani,561.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,120853.83,0.0
+2848,15750929.0,Chukwueloka,559.0,France,Male,50.0,3.0,0.0,1.0,1.0,1.0,199304.74,0.0
+2849,15795519.0,Nnamutaezinwa,637.0,Spain,Male,36.0,9.0,143321.97,2.0,1.0,0.0,151648.66,0.0
+2850,15589948.0,Lo Duca,581.0,France,Male,35.0,6.0,0.0,2.0,0.0,1.0,582.59,0.0
+2851,15643523.0,P'eng,535.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,92612.88,0.0
+2852,15734438.0,Chukwudi,695.0,Germany,Male,39.0,4.0,118628.6,1.0,1.0,0.0,184570.04,0.0
+2853,15747222.0,Kao,774.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,1.0,152843.53,0.0
+2854,15589544.0,McLean,576.0,France,Female,19.0,0.0,0.0,2.0,1.0,0.0,109355.73,0.0
+2855,15808849.0,Mazzanti,774.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,176883.42,0.0
+2856,15750014.0,Ch'in,627.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,80134.65,0.0
+2857,15592025.0,Ch'iu,730.0,France,Male,34.0,1.0,0.0,2.0,1.0,1.0,136071.05,0.0
+2858,15799720.0,Hsiung,675.0,France,Male,42.0,3.0,0.0,1.0,1.0,1.0,179227.52,0.0
+2859,15789955.0,Nwankwo,619.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,86043.27,0.0
+2860,15684516.0,Chukwuemeka,628.0,Germany,Female,41.0,3.0,103391.38,2.0,1.0,1.0,147578.26,0.0
+2861,15674840.0,K?,592.0,Spain,Male,39.0,4.0,114514.71,1.0,1.0,1.0,179066.58,0.0
+2862,15643908.0,Lai,729.0,Germany,Male,45.0,2.0,131778.66,1.0,1.0,0.0,140745.91,1.0
+2863,15656921.0,H?,641.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,9241.78,0.0
+2864,15571244.0,Chukwufumnanya,809.0,Spain,Female,30.0,1.0,0.0,2.0,1.0,0.0,121505.61,0.0
+2865,15602487.0,Clements,687.0,France,Male,27.0,8.0,0.0,2.0,1.0,1.0,138360.29,0.0
+2866,15679611.0,Lai,479.0,Spain,Male,49.0,5.0,124580.69,1.0,1.0,0.0,138849.06,0.0
+2867,15665088.0,T'ien,763.0,France,Male,21.0,6.0,0.0,2.0,0.0,0.0,143682.01,0.0
+2868,15777873.0,Iweobiegbunam,591.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,1.0,155700.15,0.0
+2869,15731478.0,Manna,585.0,France,Male,32.0,7.0,95128.86,1.0,1.0,1.0,152417.97,0.0
+2870,15788659.0,Onyemauchechukwu,516.0,France,Male,45.0,5.0,0.0,1.0,0.0,0.0,138889.15,1.0
+2871,15794916.0,Fan,637.0,Germany,Female,47.0,4.0,102954.68,1.0,1.0,0.0,102950.79,1.0
+2872,15686983.0,Buchi,529.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,157567.37,0.0
+2873,15572408.0,Ts'ao,678.0,Germany,Male,30.0,6.0,108269.2,2.0,1.0,1.0,1843.24,0.0
+2874,15715888.0,Chukwuhaenye,634.0,France,Female,28.0,8.0,157955.83,1.0,1.0,0.0,62989.82,0.0
+2875,15568326.0,Ch'ang,667.0,Germany,Male,45.0,10.0,79962.55,2.0,1.0,0.0,173837.54,0.0
+2876,15774857.0,Cattaneo,603.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,0.0,171657.57,0.0
+2877,15581626.0,Tien,629.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,149471.13,0.0
+2878,15674929.0,Chin,705.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,156424.4,0.0
+2879,15644295.0,Hargreaves,721.0,Germany,Female,55.0,2.0,134105.01,1.0,1.0,0.0,74425.41,1.0
+2880,15751208.0,McIntyre,627.0,France,Male,35.0,5.0,123925.18,1.0,1.0,1.0,158887.09,0.0
+2881,15591035.0,Nnonso,616.0,Germany,Male,46.0,3.0,123873.19,1.0,1.0,0.0,142377.69,1.0
+2882,15798895.0,Yobanna,711.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,94912.78,0.0
+2883,15693637.0,Shih,649.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,141069.88,0.0
+2884,15694321.0,Chinweike,759.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,5791.85,0.0
+2885,15676966.0,Mackay,669.0,France,Female,23.0,5.0,0.0,2.0,0.0,0.0,175094.62,0.0
+2886,15806922.0,Logue,692.0,Germany,Male,40.0,4.0,104449.8,2.0,1.0,0.0,68730.91,0.0
+2887,15746905.0,White,651.0,Spain,Female,40.0,8.0,0.0,2.0,1.0,0.0,143628.31,0.0
+2888,15792329.0,Ku,622.0,France,Male,30.0,1.0,140576.11,1.0,1.0,1.0,172814.99,0.0
+2889,15673367.0,Palermo,531.0,France,Male,42.0,3.0,0.0,1.0,1.0,0.0,181430.91,0.0
+2890,15761733.0,Chinwendu,602.0,Germany,Female,51.0,1.0,126817.65,1.0,0.0,1.0,27886.06,1.0
+2891,15615177.0,Chukwuemeka,659.0,Germany,Male,27.0,8.0,132730.17,2.0,1.0,1.0,155782.89,0.0
+2892,15692761.0,Dreyer,645.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,180598.66,0.0
+2893,15671342.0,T'ang,850.0,Spain,Female,32.0,2.0,0.0,2.0,1.0,0.0,86678.48,0.0
+2894,15573741.0,Ch'iu,710.0,Spain,Female,45.0,3.0,0.0,2.0,1.0,0.0,121551.58,0.0
+2895,15629299.0,Chigozie,663.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,132498.39,0.0
+2896,15781389.0,Cawood,739.0,France,Female,51.0,7.0,156091.97,3.0,0.0,1.0,72008.61,1.0
+2897,15677512.0,Niu,638.0,Spain,Female,35.0,7.0,0.0,1.0,1.0,0.0,160529.71,1.0
+2898,15747358.0,Greco,568.0,Germany,Male,38.0,2.0,113978.97,2.0,1.0,0.0,47729.33,0.0
+2899,15667093.0,Onuchukwu,644.0,Germany,Female,31.0,3.0,153537.13,1.0,0.0,1.0,56636.28,0.0
+2900,15713505.0,Pape,598.0,France,Female,41.0,0.0,0.0,1.0,0.0,0.0,97629.31,0.0
+2901,15715643.0,Haddon,711.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,52301.15,0.0
+2902,15672966.0,Uwaezuoke,510.0,France,Male,34.0,10.0,0.0,1.0,1.0,1.0,182527.25,0.0
+2903,15605327.0,Lo,762.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,152992.04,0.0
+2904,15636407.0,Okwudilichukwu,726.0,Germany,Female,42.0,4.0,141349.43,2.0,1.0,1.0,131300.53,0.0
+2905,15683977.0,Chu,643.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,75937.35,0.0
+2906,15571571.0,Ponomarev,667.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,1.0,157891.86,0.0
+2907,15602851.0,Ch'ien,669.0,France,Male,36.0,2.0,128760.32,2.0,1.0,1.0,134878.22,0.0
+2908,15794875.0,Shao,589.0,France,Male,37.0,1.0,51522.75,1.0,1.0,0.0,99818.41,0.0
+2909,15585595.0,Chiawuotu,685.0,France,Male,44.0,9.0,0.0,2.0,1.0,1.0,186489.14,0.0
+2910,15706537.0,Chin,681.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,125870.5,0.0
+2911,15582762.0,Chien,516.0,France,Female,29.0,10.0,0.0,1.0,1.0,1.0,165038.41,0.0
+2912,15589017.0,Chukwubuikem,602.0,France,Male,24.0,7.0,0.0,2.0,0.0,1.0,94722.54,0.0
+2913,15758035.0,Zaitsev,718.0,Spain,Female,37.0,9.0,97720.35,1.0,1.0,0.0,79082.62,0.0
+2914,15683923.0,Chiu,681.0,France,Female,36.0,9.0,142643.54,1.0,1.0,0.0,183844.88,0.0
+2915,15803633.0,Y?,691.0,France,Male,41.0,2.0,0.0,1.0,0.0,1.0,154882.79,0.0
+2916,15581252.0,Clements,679.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,117031.2,0.0
+2917,15657829.0,Chukwumaobim,724.0,France,Male,38.0,6.0,120108.56,1.0,1.0,0.0,127059.07,0.0
+2918,15783522.0,Mazzanti,492.0,France,Male,28.0,8.0,0.0,2.0,0.0,0.0,64716.08,0.0
+2919,15626012.0,Chia,716.0,France,Female,46.0,2.0,0.0,2.0,1.0,1.0,93943.68,0.0
+2920,15763063.0,Chiazagomekpere,729.0,France,Male,64.0,6.0,0.0,1.0,1.0,1.0,113781.99,0.0
+2921,15665340.0,Yobanna,744.0,Spain,Female,39.0,8.0,0.0,1.0,0.0,0.0,136521.82,1.0
+2922,15682600.0,Scott,762.0,France,Male,23.0,5.0,143350.56,1.0,1.0,1.0,81452.29,0.0
+2923,15610343.0,Ives,722.0,France,Female,54.0,10.0,0.0,2.0,1.0,1.0,140800.66,0.0
+2924,15589296.0,L?,699.0,France,Female,56.0,0.0,0.0,2.0,0.0,0.0,88120.78,1.0
+2925,15682939.0,DeRose,695.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,142995.32,0.0
+2926,15800229.0,Ozioma,850.0,France,Male,40.0,10.0,0.0,2.0,1.0,0.0,71139.31,0.0
+2927,15697606.0,Gidney,760.0,Spain,Female,44.0,6.0,0.0,2.0,1.0,1.0,72402.0,0.0
+2928,15801917.0,Hsia,663.0,France,Female,46.0,2.0,121628.42,1.0,0.0,0.0,153605.44,1.0
+2929,15579777.0,Ndubuagha,683.0,France,Male,32.0,2.0,0.0,1.0,1.0,1.0,94875.03,0.0
+2930,15686936.0,Shih,729.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,109545.56,0.0
+2931,15685500.0,Lucchese,634.0,France,Male,26.0,7.0,0.0,2.0,1.0,1.0,167553.06,0.0
+2932,15601008.0,Ch'en,623.0,Germany,Male,28.0,7.0,142765.24,1.0,1.0,0.0,103921.43,0.0
+2933,15575060.0,Echezonachukwu,716.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,98896.06,0.0
+2934,15725068.0,Hsieh,559.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,86587.37,0.0
+2935,15758031.0,Milano,731.0,France,Female,30.0,3.0,0.0,2.0,0.0,0.0,180712.28,0.0
+2936,15603481.0,Ch'ien,773.0,France,Male,36.0,2.0,0.0,1.0,1.0,0.0,66893.28,0.0
+2937,15659736.0,Shih,687.0,Germany,Female,43.0,7.0,129893.56,1.0,1.0,0.0,172031.22,1.0
+2938,15683955.0,Maclean,710.0,Spain,Male,28.0,2.0,99760.84,2.0,1.0,0.0,90839.61,0.0
+2939,15756070.0,Shaver,635.0,France,Female,37.0,7.0,129431.36,1.0,1.0,0.0,72549.27,1.0
+2940,15607988.0,Chukwudi,756.0,France,Male,33.0,2.0,0.0,1.0,0.0,1.0,143960.72,0.0
+2941,15707025.0,Nwabugwu,674.0,Spain,Male,18.0,6.0,0.0,2.0,0.0,0.0,77326.78,0.0
+2942,15680449.0,Groom,659.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,157908.19,0.0
+2943,15705777.0,Li Fonti,717.0,Germany,Male,55.0,4.0,116121.64,1.0,1.0,0.0,171045.35,1.0
+2944,15728352.0,Palfreyman,678.0,Germany,Female,27.0,8.0,116870.12,1.0,1.0,0.0,158545.24,0.0
+2945,15746865.0,Nnamutaezinwa,706.0,France,Female,35.0,1.0,0.0,2.0,1.0,1.0,147797.54,0.0
+2946,15795874.0,Mazzanti,691.0,France,Female,44.0,10.0,0.0,1.0,1.0,1.0,180716.1,0.0
+2947,15757981.0,Pirozzi,678.0,Spain,Male,43.0,6.0,148897.02,1.0,1.0,1.0,100507.81,0.0
+2948,15691817.0,Chiazagomekpele,627.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,51972.92,0.0
+2949,15571529.0,Ch'eng,683.0,Germany,Male,55.0,6.0,138296.94,1.0,1.0,1.0,176913.42,0.0
+2950,15744273.0,Monds,785.0,Spain,Male,48.0,8.0,0.0,1.0,1.0,1.0,135089.32,0.0
+2951,15626156.0,Tsao,757.0,France,Male,31.0,8.0,0.0,2.0,0.0,0.0,162599.53,0.0
+2952,15789432.0,Fanucci,663.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,0.0,123982.02,0.0
+2953,15794560.0,Ponomarev,727.0,France,Female,37.0,9.0,0.0,1.0,0.0,0.0,100941.57,1.0
+2954,15603764.0,DeRose,699.0,Germany,Female,41.0,9.0,121513.31,1.0,1.0,1.0,68106.29,1.0
+2955,15662662.0,Hsiao,687.0,Spain,Male,37.0,8.0,161546.68,1.0,0.0,1.0,131177.22,0.0
+2956,15738751.0,Shao,709.0,France,Male,41.0,7.0,147530.06,2.0,1.0,0.0,138542.87,0.0
+2957,15658360.0,Pisani,718.0,Germany,Female,52.0,4.0,117700.86,1.0,1.0,0.0,91044.47,1.0
+2958,15642530.0,White,729.0,France,Male,45.0,7.0,139840.36,1.0,1.0,1.0,153566.97,0.0
+2959,15667632.0,Aksenova,738.0,Spain,Male,36.0,8.0,0.0,1.0,0.0,0.0,65868.28,0.0
+2960,15667424.0,Folliero,652.0,France,Female,44.0,1.0,0.0,2.0,1.0,0.0,152698.24,0.0
+2961,15775131.0,Chiemezie,712.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,115789.25,0.0
+2962,15802483.0,Milani,744.0,France,Male,24.0,6.0,0.0,1.0,1.0,0.0,95941.55,0.0
+2963,15775809.0,Walker,538.0,France,Female,42.0,8.0,105922.81,2.0,1.0,0.0,120512.08,0.0
+2964,15800515.0,Nwankwo,609.0,France,Male,36.0,5.0,104015.54,1.0,1.0,1.0,105568.6,0.0
+2965,15591257.0,Onyekachukwu,668.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,173843.21,0.0
+2966,15585855.0,Ting,699.0,France,Female,43.0,6.0,133871.5,1.0,1.0,0.0,171707.93,0.0
+2967,15686963.0,Ch'in,635.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,1375.46,0.0
+2968,15572361.0,Manna,651.0,France,Female,40.0,1.0,116537.6,1.0,0.0,0.0,51871.95,0.0
+2969,15676869.0,Nwabugwu,763.0,France,Male,35.0,5.0,116318.23,1.0,1.0,0.0,122218.61,0.0
+2970,15687913.0,Hao,707.0,Germany,Male,50.0,9.0,117864.85,2.0,1.0,0.0,90905.26,1.0
+2971,15680234.0,Lo,841.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,138526.96,0.0
+2972,15589380.0,Y?,684.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,125962.02,0.0
+2973,15686964.0,Chukwubuikem,648.0,Germany,Female,36.0,9.0,121263.62,1.0,1.0,1.0,115767.93,0.0
+2974,15685826.0,Trevisano,653.0,Germany,Male,52.0,2.0,105961.68,1.0,1.0,0.0,121239.65,1.0
+2975,15782530.0,Shen,581.0,Germany,Female,47.0,5.0,103543.11,1.0,1.0,0.0,167023.95,1.0
+2976,15622993.0,Tuan,637.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,141558.04,0.0
+2977,15763859.0,Chiu,516.0,Spain,Male,34.0,9.0,141827.33,1.0,1.0,0.0,161807.14,0.0
+2978,15633233.0,Manna,655.0,France,Female,31.0,6.0,0.0,1.0,1.0,1.0,168816.22,1.0
+2979,15681075.0,Folliero,633.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,163563.07,0.0
+2980,15574558.0,T'ien,731.0,France,Female,38.0,5.0,0.0,1.0,1.0,1.0,75664.56,1.0
+2981,15640034.0,Kao,641.0,Spain,Female,28.0,6.0,0.0,1.0,1.0,0.0,141453.62,0.0
+2982,15588560.0,Ch'in,705.0,France,Female,36.0,4.0,179960.2,1.0,1.0,0.0,149960.2,0.0
+2983,15715709.0,Cremonesi,717.0,Germany,Female,41.0,8.0,137781.65,2.0,0.0,0.0,86619.77,0.0
+2984,15762729.0,P'eng,673.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,168515.61,0.0
+2985,15706899.0,Chikwado,645.0,Spain,Female,29.0,7.0,0.0,2.0,0.0,1.0,120892.96,0.0
+2986,15598884.0,D'Albertis,712.0,Spain,Male,43.0,5.0,0.0,2.0,0.0,0.0,73979.86,0.0
+2987,15624595.0,Rizzo,709.0,Spain,Female,41.0,9.0,0.0,1.0,1.0,0.0,132798.95,1.0
+2988,15721730.0,H?,749.0,Germany,Male,41.0,2.0,126663.51,2.0,1.0,1.0,183566.87,0.0
+2989,15709994.0,Hargreaves,675.0,France,Male,49.0,2.0,117899.95,1.0,1.0,0.0,126475.79,1.0
+2990,15758056.0,Fomin,584.0,Spain,Male,50.0,8.0,0.0,1.0,1.0,0.0,147955.91,1.0
+2991,15696819.0,Genovese,731.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,102747.02,0.0
+2992,15781589.0,Onyekaozulu,672.0,Germany,Male,52.0,3.0,103457.76,2.0,0.0,1.0,137720.56,1.0
+2993,15699309.0,Lo Duca,683.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,53477.76,0.0
+2994,15690958.0,P'eng,609.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,82526.92,0.0
+2995,15609823.0,Okonkwo,664.0,France,Female,35.0,8.0,121704.32,1.0,1.0,1.0,152956.33,0.0
+2996,15597939.0,Clements,671.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,76510.46,0.0
+2997,15731519.0,Shao,709.0,Spain,Female,46.0,7.0,0.0,2.0,1.0,1.0,170138.68,1.0
+2998,15679813.0,Yobanna,515.0,France,Female,35.0,2.0,90538.51,1.0,1.0,0.0,51536.99,1.0
+2999,15771535.0,Chiemela,667.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,60778.11,0.0
+3000,15657778.0,Iadanza,635.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,107890.69,0.0
+3001,15671139.0,McIntosh,652.0,France,Female,31.0,6.0,0.0,1.0,1.0,0.0,145558.24,1.0
+3002,15624180.0,Onyemauchechukwu,709.0,France,Female,40.0,5.0,155726.85,1.0,1.0,1.0,161229.84,1.0
+3003,15671779.0,Uvarova,645.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,0.0,121231.39,0.0
+3004,15769246.0,Chiawuotu,641.0,France,Female,32.0,7.0,0.0,2.0,0.0,0.0,140168.36,0.0
+3005,15738018.0,Lo,677.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,140889.12,0.0
+3006,15686463.0,Manna,509.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,186984.34,0.0
+3007,15690188.0,Lucciano,712.0,Spain,Female,32.0,7.0,151675.65,1.0,1.0,1.0,123982.14,0.0
+3008,15640074.0,Ch'in,544.0,France,Male,41.0,4.0,0.0,1.0,1.0,1.0,96420.58,1.0
+3009,15718893.0,T'ien,677.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,125302.64,0.0
+3010,15775124.0,Okwuoma,613.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,90821.66,0.0
+3011,15807989.0,Genovesi,744.0,France,Female,40.0,10.0,0.0,2.0,0.0,1.0,94767.77,0.0
+3012,15771059.0,Uvarova,641.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,156774.94,0.0
+3013,15651955.0,Onochie,705.0,France,Male,48.0,3.0,0.0,2.0,1.0,0.0,119814.25,0.0
+3014,15654574.0,Kao,584.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,88920.68,0.0
+3015,15774192.0,Martin,653.0,France,Female,55.0,6.0,112494.99,1.0,0.0,0.0,170968.99,1.0
+3016,15633059.0,Chukwujamuike,670.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,164815.59,0.0
+3017,15727556.0,Chidubem,709.0,France,Male,52.0,10.0,0.0,1.0,1.0,1.0,80587.27,1.0
+3018,15779743.0,Nwankwo,642.0,France,Female,41.0,2.0,0.0,2.0,0.0,0.0,28415.36,0.0
+3019,15641833.0,Onwudiwe,624.0,Germany,Male,31.0,1.0,140511.85,1.0,1.0,1.0,115611.97,0.0
+3020,15686963.0,Pinto,741.0,France,Male,51.0,1.0,0.0,2.0,0.0,1.0,121402.72,0.0
+3021,15573077.0,Onwuamaegbu,586.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,62940.99,0.0
+3022,15578980.0,Chukwudi,586.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,1.0,166513.49,0.0
+3023,15588517.0,Tung,593.0,France,Female,41.0,4.0,106290.64,1.0,0.0,0.0,136877.31,0.0
+3024,15751147.0,Zito,684.0,France,Male,41.0,5.0,129731.64,1.0,1.0,1.0,98293.14,0.0
+3025,15719276.0,Hsia,627.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,60972.84,0.0
+3026,15636562.0,Ch'en,651.0,France,Male,40.0,2.0,0.0,2.0,0.0,0.0,74870.99,0.0
+3027,15657747.0,Hsia,705.0,France,Male,46.0,3.0,0.0,2.0,1.0,0.0,131473.31,0.0
+3028,15694558.0,Hsueh,718.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,0.0,86935.08,0.0
+3029,15687558.0,Hsueh,774.0,Spain,Male,47.0,5.0,0.0,2.0,1.0,1.0,53581.14,0.0
+3030,15793741.0,Ch'eng,757.0,France,Male,36.0,8.0,0.0,2.0,0.0,0.0,138871.12,0.0
+3031,15762543.0,Chinweike,633.0,Spain,Male,57.0,4.0,0.0,1.0,1.0,1.0,137029.97,1.0
+3032,15765952.0,Chimaobim,646.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,193018.51,0.0
+3033,15613144.0,Kao,589.0,France,Male,44.0,8.0,0.0,2.0,1.0,1.0,60778.11,0.0
+3034,15578980.0,Ch'ang,637.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,0.0,134778.01,0.0
+3035,15741554.0,Chiu,593.0,France,Female,43.0,8.0,0.0,2.0,0.0,0.0,100835.19,0.0
+3036,15670066.0,Okwudilichukwu,622.0,Germany,Female,36.0,3.0,139855.64,1.0,1.0,1.0,167873.49,1.0
+3037,15635972.0,Onyemere,638.0,France,Female,52.0,9.0,0.0,1.0,1.0,0.0,136377.21,1.0
+3038,15618258.0,Chibugo,587.0,France,Male,38.0,3.0,76914.21,1.0,1.0,0.0,148087.88,0.0
+3039,15690175.0,Ts'ui,762.0,France,Male,33.0,2.0,121405.04,2.0,1.0,0.0,123437.05,0.0
+3040,15644878.0,Uspenskaya,703.0,France,Female,40.0,0.0,0.0,1.0,1.0,0.0,150548.5,1.0
+3041,15661473.0,Chin,561.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,110899.3,0.0
+3042,15644788.0,Pirozzi,671.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,94450.97,0.0
+3043,15726354.0,Y?,726.0,France,Male,44.0,10.0,145754.91,1.0,1.0,1.0,66795.78,0.0
+3044,15668853.0,Baresi,850.0,Spain,Female,48.0,9.0,0.0,1.0,1.0,0.0,139435.17,1.0
+3045,15773869.0,Ch'ien,779.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,86733.61,0.0
+3046,15644896.0,Ch'en,679.0,Germany,Female,30.0,7.0,112291.34,2.0,1.0,0.0,164741.5,1.0
+3047,15696900.0,Obialo,850.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,0.0,48201.64,0.0
+3048,15729246.0,Lucchese,627.0,Spain,Male,27.0,0.0,0.0,2.0,1.0,1.0,124626.57,0.0
+3049,15679611.0,Oster,659.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,146041.45,0.0
+3050,15770101.0,Scott,733.0,Germany,Female,25.0,5.0,131644.88,2.0,1.0,0.0,133871.83,0.0
+3051,15679743.0,Azubuike,521.0,Germany,Male,56.0,0.0,132688.58,2.0,1.0,1.0,26920.43,1.0
+3052,15641660.0,Chikezie,716.0,Spain,Male,36.0,1.0,82293.82,1.0,1.0,1.0,182728.8,0.0
+3053,15730590.0,Greco,652.0,Germany,Female,37.0,4.0,123034.43,1.0,1.0,1.0,106036.22,0.0
+3054,15694125.0,McLean,559.0,France,Female,41.0,3.0,0.0,1.0,1.0,0.0,175743.92,1.0
+3055,15574886.0,Hsueh,634.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,75888.65,0.0
+3056,15809800.0,Okechukwu,571.0,France,Male,52.0,1.0,0.0,1.0,1.0,0.0,102799.14,1.0
+3057,15591512.0,Onyemachukwu,675.0,France,Male,28.0,9.0,0.0,2.0,0.0,1.0,138542.94,0.0
+3058,15780254.0,Chidozie,668.0,France,Male,30.0,7.0,0.0,2.0,0.0,1.0,167778.62,0.0
+3059,15772412.0,Gresswell,676.0,Germany,Female,31.0,7.0,134571.5,1.0,1.0,0.0,194808.51,1.0
+3060,15746594.0,T'ang,719.0,France,Female,28.0,4.0,0.0,2.0,1.0,1.0,56905.38,0.0
+3061,15703394.0,Shao,628.0,France,Female,41.0,3.0,0.0,1.0,0.0,1.0,136330.32,1.0
+3062,15741658.0,Onyemere,543.0,Germany,Male,35.0,5.0,127772.1,2.0,1.0,0.0,189703.65,0.0
+3063,15666884.0,H?,651.0,France,Female,64.0,4.0,0.0,2.0,0.0,1.0,57850.1,0.0
+3064,15659428.0,Chiang,661.0,Spain,Male,38.0,5.0,0.0,1.0,1.0,1.0,56780.97,0.0
+3065,15651664.0,Calabrese,545.0,France,Female,34.0,9.0,161371.5,1.0,1.0,0.0,174822.74,0.0
+3066,15745088.0,Chidiebere,652.0,Spain,Female,26.0,0.0,0.0,2.0,0.0,0.0,73977.23,0.0
+3067,15631823.0,Ch'iu,803.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,121763.72,0.0
+3068,15706637.0,Monds,718.0,Germany,Male,37.0,1.0,106425.75,2.0,1.0,1.0,131807.14,0.0
+3069,15615352.0,Yefimova,624.0,France,Female,38.0,1.0,0.0,2.0,0.0,0.0,128841.31,0.0
+3070,15623989.0,Milano,593.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,122663.63,0.0
+3071,15603764.0,White,682.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,161388.22,0.0
+3072,15579647.0,Hs?eh,608.0,Spain,Female,47.0,5.0,0.0,2.0,1.0,1.0,61290.99,0.0
+3073,15786063.0,Trevisano,705.0,Germany,Male,43.0,5.0,141223.68,1.0,1.0,1.0,183572.39,0.0
+3074,15696098.0,Palerma,587.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,91573.19,0.0
+3075,15662533.0,Chibueze,746.0,France,Male,34.0,9.0,0.0,1.0,1.0,0.0,137959.0,0.0
+3076,15647838.0,Hsueh,684.0,Germany,Male,43.0,4.0,162039.59,1.0,0.0,0.0,118188.15,1.0
+3077,15593976.0,Chinwemma,635.0,France,Male,47.0,2.0,0.0,2.0,1.0,1.0,83090.35,0.0
+3078,15740175.0,Chien,828.0,France,Female,43.0,9.0,0.0,2.0,1.0,0.0,126812.63,0.0
+3079,15690829.0,Hsu,545.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,125870.79,0.0
+3080,15705953.0,Ch'in,714.0,France,Female,29.0,5.0,162923.85,1.0,0.0,0.0,125041.52,0.0
+3081,15662063.0,Chiekwugo,661.0,France,Female,33.0,3.0,141786.78,1.0,1.0,1.0,5791.85,0.0
+3082,15629750.0,Scott,636.0,Germany,Male,44.0,1.0,135096.77,1.0,1.0,1.0,149536.15,0.0
+3083,15701376.0,Tsou,554.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,1.0,119377.36,0.0
+3084,15725794.0,Chin,596.0,France,Male,35.0,10.0,0.0,1.0,1.0,1.0,114775.53,0.0
+3085,15632143.0,Onuoha,667.0,Germany,Male,56.0,2.0,120171.58,1.0,1.0,0.0,97623.42,1.0
+3086,15776936.0,Genovese,567.0,Spain,Female,45.0,2.0,0.0,1.0,1.0,0.0,138889.15,1.0
+3087,15585360.0,Chukwuemeka,774.0,Spain,Male,33.0,6.0,0.0,2.0,0.0,1.0,80582.36,0.0
+3088,15702464.0,Chizoba,565.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,153535.35,0.0
+3089,15752506.0,Calabresi,596.0,France,Male,34.0,7.0,0.0,1.0,1.0,1.0,73066.73,0.0
+3090,15793671.0,Fan,690.0,Spain,Male,35.0,6.0,90964.57,2.0,1.0,1.0,180551.76,0.0
+3091,15770185.0,Hs?,775.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,86981.45,0.0
+3092,15784491.0,Ts'ao,645.0,France,Male,42.0,3.0,0.0,1.0,0.0,0.0,67840.81,1.0
+3093,15689031.0,Ugochukwutubelum,843.0,France,Female,43.0,8.0,104784.23,1.0,1.0,1.0,151871.83,0.0
+3094,15602929.0,Chien,708.0,Spain,Female,56.0,9.0,0.0,1.0,1.0,0.0,160110.65,1.0
+3095,15682533.0,McElhone,584.0,France,Male,61.0,4.0,160903.2,1.0,1.0,0.0,89892.66,1.0
+3096,15770174.0,Oguejiofor,683.0,Spain,Male,46.0,5.0,94416.78,1.0,1.0,0.0,89801.9,0.0
+3097,15692664.0,Lo Duca,588.0,Spain,Male,28.0,3.0,0.0,2.0,1.0,0.0,140881.2,0.0
+3098,15583718.0,Chibueze,754.0,Germany,Female,38.0,9.0,100900.5,2.0,1.0,0.0,173576.71,1.0
+3099,15644501.0,Chu,646.0,France,Female,38.0,7.0,103391.38,2.0,0.0,1.0,71154.1,0.0
+3100,15790829.0,Udobata,590.0,France,Female,41.0,1.0,0.0,1.0,1.0,1.0,26257.01,1.0
+3101,15652693.0,Chin,557.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,0.0,88827.67,1.0
+3102,15808023.0,Ch'in,717.0,Spain,Female,29.0,6.0,0.0,2.0,1.0,0.0,170195.4,0.0
+3103,15780561.0,Kibby,595.0,Germany,Female,41.0,10.0,123356.63,1.0,0.0,1.0,113046.81,1.0
+3104,15794566.0,Pisano,642.0,Germany,Female,46.0,2.0,116933.11,2.0,1.0,0.0,176943.59,1.0
+3105,15626383.0,Ni,850.0,France,Male,45.0,5.0,98906.22,1.0,1.0,0.0,107964.36,0.0
+3106,15612207.0,Pisani,578.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,71450.92,0.0
+3107,15679307.0,Nwora,696.0,Spain,Male,53.0,6.0,0.0,2.0,1.0,1.0,143635.52,0.0
+3108,15658969.0,Ch'ien,635.0,Spain,Female,44.0,1.0,0.0,1.0,1.0,1.0,156774.94,1.0
+3109,15798844.0,Yobachukwu,543.0,Germany,Female,45.0,7.0,125827.43,1.0,1.0,0.0,109257.59,1.0
+3110,15726313.0,T'ien,581.0,Germany,Male,39.0,2.0,125837.86,2.0,0.0,0.0,90928.02,0.0
+3111,15721917.0,Pardey,599.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,80619.09,0.0
+3112,15644896.0,Ijendu,624.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,43982.41,0.0
+3113,15592387.0,Ndubuisi,667.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,158891.79,0.0
+3114,15743016.0,Chin,584.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,100834.22,0.0
+3115,15721785.0,Kao,695.0,Germany,Female,35.0,8.0,79035.95,1.0,0.0,1.0,186945.45,0.0
+3116,15754569.0,Piazza,727.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,166929.43,0.0
+3117,15659399.0,Kudryashova,581.0,France,Male,31.0,10.0,0.0,2.0,0.0,1.0,36308.93,0.0
+3118,15701602.0,Hsiao,696.0,Germany,Male,37.0,8.0,124580.69,2.0,1.0,1.0,89801.9,0.0
+3119,15606436.0,Onwumelu,775.0,Spain,Male,47.0,3.0,0.0,3.0,0.0,1.0,104769.94,1.0
+3120,15674929.0,Hsiung,807.0,France,Female,45.0,8.0,0.0,2.0,0.0,0.0,143683.75,1.0
+3121,15642655.0,Watson,667.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,1.0,162599.51,0.0
+3122,15700540.0,Hargreaves,686.0,Germany,Male,46.0,6.0,97435.85,2.0,0.0,1.0,58844.84,0.0
+3123,15682453.0,Yeh,714.0,Spain,Female,48.0,9.0,0.0,1.0,1.0,1.0,107876.2,1.0
+3124,15633194.0,Mazzanti,679.0,France,Male,57.0,9.0,0.0,2.0,1.0,1.0,76510.46,0.0
+3125,15784320.0,Chiu,739.0,Germany,Male,35.0,6.0,104533.14,2.0,0.0,1.0,79919.13,0.0
+3126,15671345.0,Yevseyev,562.0,France,Male,36.0,4.0,0.0,1.0,1.0,0.0,146875.86,0.0
+3127,15693906.0,Genovese,706.0,France,Male,33.0,1.0,104469.58,1.0,0.0,0.0,122066.5,0.0
+3128,15643908.0,Hsiung,584.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,151146.67,0.0
+3129,15725698.0,Shih,755.0,Germany,Female,44.0,4.0,107712.57,1.0,1.0,0.0,180800.42,1.0
+3130,15803840.0,Kao,602.0,France,Female,28.0,9.0,0.0,2.0,1.0,1.0,184936.77,0.0
+3131,15741032.0,Young,583.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,152273.57,0.0
+3132,15741643.0,Genovese,539.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,173870.39,0.0
+3133,15686913.0,Chiawuotu,595.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,158313.87,0.0
+3134,15768474.0,Ch'ien,568.0,Germany,Male,42.0,6.0,113876.24,1.0,1.0,1.0,175823.71,0.0
+3135,15617320.0,Liao,682.0,France,Male,32.0,8.0,0.0,2.0,0.0,0.0,175743.92,0.0
+3136,15582033.0,Genovese,641.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,92357.21,0.0
+3137,15591724.0,Tien,729.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,101274.17,0.0
+3138,15652401.0,Nwachukwu,716.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,160524.36,0.0
+3139,15602811.0,Nwoye,646.0,Germany,Female,42.0,8.0,135962.4,1.0,1.0,0.0,138022.51,1.0
+3140,15750396.0,Y?an,569.0,France,Male,50.0,7.0,0.0,2.0,1.0,1.0,60920.03,0.0
+3141,15763898.0,Chibueze,627.0,France,Male,31.0,9.0,92618.62,1.0,1.0,1.0,168822.01,0.0
+3142,15770984.0,Chiang,559.0,Germany,Female,41.0,2.0,77846.9,2.0,1.0,0.0,125553.52,0.0
+3143,15781298.0,Smith,464.0,Germany,Female,33.0,5.0,128366.44,2.0,1.0,1.0,149853.89,0.0
+3144,15656822.0,Ch'in,588.0,France,Male,27.0,3.0,0.0,2.0,1.0,1.0,18094.96,0.0
+3145,15672437.0,Nnamutaezinwa,629.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,172497.73,0.0
+3146,15731989.0,Okwuadigbo,587.0,Spain,Female,39.0,6.0,0.0,2.0,0.0,0.0,113926.71,0.0
+3147,15586479.0,Chiu,463.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,106977.8,0.0
+3148,15605970.0,L?,681.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,131301.74,0.0
+3149,15585853.0,Babbage,588.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,172490.78,0.0
+3150,15600462.0,Chiazagomekpere,581.0,Spain,Female,40.0,2.0,0.0,1.0,1.0,0.0,136071.09,1.0
+3151,15772383.0,Hsiao,739.0,Spain,Male,51.0,2.0,0.0,1.0,0.0,1.0,162515.17,1.0
+3152,15641655.0,Nkemakonam,691.0,Spain,Male,36.0,10.0,0.0,2.0,1.0,0.0,62282.99,0.0
+3153,15645496.0,Ch'in,754.0,France,Male,54.0,8.0,0.0,1.0,1.0,1.0,111622.76,0.0
+3154,15720943.0,Chinwendu,645.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,173386.95,0.0
+3155,15806945.0,Chin,642.0,Spain,Male,42.0,2.0,0.0,1.0,1.0,1.0,100556.98,0.0
+3156,15632576.0,Shen,587.0,Germany,Male,30.0,6.0,109974.22,1.0,1.0,0.0,133693.88,0.0
+3157,15737506.0,Chinweuba,526.0,France,Female,28.0,4.0,98630.31,1.0,0.0,0.0,59816.41,0.0
+3158,15589320.0,Fomin,535.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,96670.2,1.0
+3159,15601012.0,Nwagugheuzo,633.0,Germany,Female,39.0,5.0,114655.58,2.0,1.0,0.0,48778.46,1.0
+3160,15739152.0,Shih,639.0,France,Female,36.0,5.0,0.0,1.0,1.0,1.0,169915.02,0.0
+3161,15766017.0,Kornilova,623.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,176868.89,0.0
+3162,15569572.0,Ch'ien,726.0,Germany,Female,34.0,8.0,134542.73,2.0,1.0,1.0,173784.04,0.0
+3163,15674727.0,Eluemuno,543.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,1.0,78970.58,0.0
+3164,15794560.0,Hsiao,849.0,France,Male,43.0,5.0,0.0,2.0,0.0,1.0,55775.72,0.0
+3165,15576676.0,Ch'en,850.0,Germany,Female,49.0,7.0,135814.7,1.0,0.0,0.0,49815.62,1.0
+3166,15653547.0,Trevisani,709.0,France,Female,37.0,9.0,0.0,1.0,0.0,0.0,126381.6,1.0
+3167,15684318.0,Chibueze,744.0,France,Male,40.0,5.0,81480.27,1.0,1.0,0.0,93332.64,1.0
+3168,15651740.0,Hs?,712.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,148516.79,0.0
+3169,15689616.0,Nkemdilim,726.0,Spain,Male,40.0,1.0,117640.38,1.0,1.0,0.0,159830.58,0.0
+3170,15691863.0,Chigozie,646.0,Germany,Male,52.0,5.0,131394.56,1.0,1.0,1.0,143668.28,1.0
+3171,15590820.0,Uvarova,573.0,France,Female,38.0,9.0,0.0,2.0,0.0,1.0,94440.45,0.0
+3172,15678882.0,Hsiung,662.0,Spain,Female,27.0,9.0,0.0,2.0,1.0,1.0,47886.44,0.0
+3173,15628298.0,Ch'iu,745.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,92357.21,0.0
+3174,15646569.0,O'Brien,554.0,France,Female,30.0,9.0,0.0,1.0,1.0,0.0,175894.24,0.0
+3175,15603481.0,Nkemakonam,752.0,France,Female,51.0,1.0,136596.85,1.0,1.0,0.0,79355.87,1.0
+3176,15764897.0,Tung,683.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,1.0,168515.61,0.0
+3177,15687634.0,Ozioma,651.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,140134.88,0.0
+3178,15696826.0,Tsao,640.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,1.0,78650.95,0.0
+3179,15661821.0,Chiu,798.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,194998.34,0.0
+3180,15644572.0,T'ang,645.0,Germany,Male,47.0,6.0,120591.19,1.0,0.0,0.0,146891.07,1.0
+3181,15684461.0,Zito,565.0,Spain,Male,52.0,4.0,82674.15,1.0,1.0,0.0,136620.64,1.0
+3182,15574324.0,Onyeorulu,635.0,France,Male,45.0,2.0,0.0,1.0,0.0,0.0,25881.0,1.0
+3183,15589969.0,Ch'ang,763.0,France,Female,26.0,0.0,0.0,2.0,0.0,0.0,111324.41,0.0
+3184,15720508.0,Shao,553.0,France,Female,38.0,8.0,0.0,1.0,1.0,0.0,161574.19,1.0
+3185,15587978.0,Chikelu,706.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,129526.57,0.0
+3186,15686913.0,Okwudilichukwu,642.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,157097.09,0.0
+3187,15691483.0,Fanucci,814.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,53437.1,0.0
+3188,15754574.0,Hsia,724.0,France,Male,30.0,9.0,0.0,1.0,1.0,0.0,166957.82,0.0
+3189,15687431.0,Nkemdilim,646.0,Spain,Female,44.0,5.0,0.0,3.0,1.0,0.0,1528.4,1.0
+3190,15658067.0,Hsiao,665.0,Spain,Male,36.0,5.0,84477.33,1.0,1.0,1.0,167873.55,0.0
+3191,15743617.0,Chiang,731.0,Germany,Female,49.0,4.0,121172.97,1.0,1.0,0.0,173586.63,1.0
+3192,15642515.0,O'Meara,554.0,France,Male,34.0,1.0,148061.89,1.0,0.0,0.0,166423.66,0.0
+3193,15713890.0,Chien,708.0,France,Male,25.0,7.0,0.0,2.0,1.0,1.0,81778.72,0.0
+3194,15595766.0,Chibueze,484.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,199992.48,0.0
+3195,15570379.0,Y?an,739.0,France,Female,36.0,1.0,0.0,2.0,1.0,1.0,152844.64,0.0
+3196,15741403.0,Nwabugwu,607.0,Spain,Female,58.0,10.0,0.0,2.0,1.0,1.0,135034.57,0.0
+3197,15731553.0,Chijindum,711.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,158750.53,0.0
+3198,15772421.0,O'Brien,717.0,France,Female,33.0,2.0,152462.79,1.0,1.0,1.0,89048.46,0.0
+3199,15582841.0,Nnamdi,582.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,12761.16,0.0
+3200,15686461.0,Onyemauchechukwu,732.0,Germany,Female,29.0,3.0,130682.11,2.0,0.0,0.0,56794.73,0.0
+3201,15755084.0,Ts'ui,545.0,Spain,Female,48.0,7.0,0.0,1.0,1.0,1.0,129870.93,1.0
+3202,15693732.0,Iqbal,578.0,Spain,Male,68.0,6.0,90124.64,1.0,1.0,0.0,63747.73,1.0
+3203,15636454.0,Zetticci,725.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,140168.36,0.0
+3204,15672894.0,Ulyanova,607.0,Spain,Female,32.0,7.0,145537.21,1.0,1.0,0.0,174098.28,0.0
+3205,15754950.0,Loyau,662.0,France,Male,37.0,2.0,83568.55,1.0,1.0,0.0,107876.2,1.0
+3206,15775797.0,Hargreaves,651.0,Germany,Male,38.0,2.0,161591.11,2.0,0.0,1.0,151871.84,0.0
+3207,15657779.0,Olisaemeka,738.0,Spain,Male,41.0,2.0,133964.3,1.0,0.0,1.0,177844.06,0.0
+3208,15745307.0,Houghton,738.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,94845.54,0.0
+3209,15675888.0,Trevisani,743.0,France,Male,51.0,7.0,108377.75,1.0,1.0,1.0,126728.27,1.0
+3210,15698932.0,Nnamutaezinwa,586.0,Spain,Male,30.0,10.0,0.0,2.0,1.0,1.0,80619.09,0.0
+3211,15651336.0,Onio,730.0,Germany,Female,41.0,1.0,121527.4,1.0,1.0,0.0,126794.56,1.0
+3212,15804586.0,Pirozzi,663.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,86935.08,0.0
+3213,15707597.0,Chinweike,592.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,70954.41,1.0
+3214,15732643.0,Kao,709.0,France,Female,50.0,3.0,0.0,1.0,1.0,0.0,162889.1,1.0
+3215,15724307.0,Lucchese,619.0,France,Female,32.0,8.0,0.0,1.0,1.0,1.0,113086.08,0.0
+3216,15572467.0,Ch'in,717.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,187616.16,0.0
+3217,15692819.0,Calabresi,567.0,France,Male,66.0,4.0,0.0,2.0,1.0,1.0,183587.12,0.0
+3218,15589435.0,Nwachukwu,554.0,France,Male,26.0,6.0,0.0,2.0,0.0,1.0,172450.04,0.0
+3219,15692540.0,Lu,751.0,Spain,Male,48.0,6.0,0.0,2.0,1.0,0.0,141872.05,0.0
+3220,15793331.0,Brown,705.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,126796.69,0.0
+3221,15585580.0,Chukwuemeka,716.0,Spain,Male,33.0,4.0,0.0,2.0,0.0,0.0,138880.24,0.0
+3222,15616172.0,Ts'ao,762.0,France,Male,41.0,1.0,0.0,2.0,0.0,0.0,10963.04,0.0
+3223,15764284.0,Hs?,634.0,France,Male,44.0,1.0,0.0,2.0,0.0,0.0,72350.17,0.0
+3224,15772996.0,Yevseyev,850.0,Germany,Female,41.0,8.0,111388.18,2.0,1.0,1.0,147720.27,0.0
+3225,15592461.0,Chizoba,544.0,Spain,Male,28.0,9.0,0.0,2.0,1.0,0.0,164925.32,0.0
+3226,15656597.0,Smith,586.0,France,Male,27.0,5.0,100013.51,1.0,1.0,0.0,57876.05,0.0
+3227,15781702.0,Hsiung,744.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,109843.16,0.0
+3228,15806918.0,Onwudiwe,696.0,Germany,Female,21.0,5.0,141074.59,2.0,1.0,1.0,67979.35,0.0
+3229,15675650.0,Houghton,710.0,France,Female,45.0,8.0,0.0,2.0,1.0,1.0,142797.5,0.0
+3230,15768449.0,H?,776.0,France,Female,18.0,7.0,132946.89,1.0,1.0,0.0,74415.2,0.0
+3231,15789560.0,Onwubiko,582.0,France,Male,48.0,7.0,0.0,1.0,1.0,0.0,175314.87,1.0
+3232,15605293.0,Wright,756.0,France,Female,40.0,4.0,0.0,2.0,0.0,0.0,103970.58,1.0
+3233,15751131.0,Iadanza,709.0,France,Male,27.0,8.0,0.0,1.0,1.0,0.0,133692.88,0.0
+3234,15604536.0,Onyeoruru,676.0,Germany,Male,36.0,5.0,93522.3,1.0,1.0,0.0,198269.08,0.0
+3235,15587592.0,Hsia,657.0,Spain,Male,55.0,7.0,0.0,1.0,1.0,1.0,182531.74,1.0
+3236,15693893.0,Trevisani,589.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,447.73,0.0
+3237,15744398.0,Lucciano,775.0,France,Female,53.0,2.0,0.0,1.0,1.0,1.0,166692.03,1.0
+3238,15691883.0,Ch'eng,573.0,France,Female,52.0,7.0,0.0,1.0,1.0,1.0,169935.46,1.0
+3239,15689517.0,Chukwumaobim,602.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,185547.71,0.0
+3240,15722583.0,Fanucci,662.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,161479.19,0.0
+3241,15615457.0,Y?an,678.0,Germany,Male,25.0,8.0,121405.04,2.0,1.0,0.0,138889.97,0.0
+3242,15793893.0,Tobeolisa,647.0,France,Male,40.0,8.0,0.0,1.0,1.0,1.0,64839.23,1.0
+3243,15798964.0,Udinese,746.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,1.0,70954.41,0.0
+3244,15682890.0,Okwuadigbo,678.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,176712.59,0.0
+3245,15593636.0,Kao,749.0,Spain,Male,33.0,4.0,0.0,2.0,0.0,0.0,117034.32,0.0
+3246,15707637.0,Chidiebere,602.0,France,Male,37.0,7.0,164951.56,1.0,1.0,1.0,125762.95,0.0
+3247,15576022.0,Hsiung,726.0,France,Female,30.0,8.0,0.0,2.0,1.0,1.0,158053.4,0.0
+3248,15585595.0,Onyemachukwu,545.0,Spain,Male,44.0,5.0,115465.98,1.0,1.0,0.0,185210.63,1.0
+3249,15715297.0,Rowntree,703.0,Germany,Male,35.0,5.0,160599.06,2.0,1.0,1.0,155840.33,0.0
+3250,15698664.0,Tung,531.0,France,Male,42.0,2.0,0.0,2.0,0.0,0.0,94876.65,0.0
+3251,15713374.0,McIntosh,766.0,Germany,Female,55.0,5.0,141806.0,1.0,1.0,1.0,73679.59,1.0
+3252,15729689.0,Chibueze,679.0,France,Male,45.0,5.0,0.0,2.0,0.0,0.0,124592.84,0.0
+3253,15592707.0,Uspensky,667.0,France,Female,29.0,3.0,0.0,1.0,1.0,0.0,134874.45,0.0
+3254,15578045.0,Chineze,753.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,87566.97,0.0
+3255,15687765.0,Chiazagomekpere,648.0,Germany,Female,44.0,1.0,102989.83,1.0,1.0,0.0,136886.86,1.0
+3256,15689517.0,Onuora,706.0,France,Male,29.0,1.0,0.0,2.0,0.0,0.0,71862.79,0.0
+3257,15593088.0,Pagnotto,557.0,Germany,Male,41.0,9.0,99842.5,2.0,1.0,1.0,186476.91,0.0
+3258,15738487.0,McLean,567.0,Spain,Male,40.0,9.0,0.0,1.0,1.0,0.0,110783.28,1.0
+3259,15736112.0,Tien,554.0,France,Female,51.0,2.0,0.0,1.0,1.0,0.0,89520.75,1.0
+3260,15743478.0,Ch'iu,733.0,Germany,Male,35.0,4.0,108760.27,1.0,1.0,1.0,93844.89,1.0
+3261,15676966.0,Eluemuno,523.0,Germany,Female,39.0,8.0,108924.33,1.0,1.0,0.0,145937.99,1.0
+3262,15578825.0,Ndubuisi,656.0,Spain,Female,57.0,7.0,0.0,2.0,1.0,1.0,164975.82,0.0
+3263,15810454.0,Chidubem,596.0,Spain,Female,43.0,4.0,0.0,2.0,0.0,1.0,140168.36,0.0
+3264,15667633.0,Mazzanti,685.0,France,Male,25.0,2.0,137464.04,1.0,0.0,0.0,148087.62,0.0
+3265,15589435.0,Chukwuemeka,739.0,Germany,Male,42.0,6.0,111432.13,2.0,1.0,0.0,132719.8,0.0
+3266,15690772.0,Gadsdon,666.0,France,Female,35.0,0.0,0.0,3.0,1.0,0.0,159418.1,1.0
+3267,15690526.0,Iweobiegbunam,661.0,Spain,Male,33.0,10.0,0.0,1.0,1.0,1.0,152417.97,0.0
+3268,15661827.0,Ch'iu,683.0,Germany,Male,54.0,10.0,110783.28,1.0,0.0,0.0,45422.13,1.0
+3269,15656912.0,Johnson,775.0,France,Female,41.0,7.0,156569.21,1.0,0.0,0.0,172096.08,0.0
+3270,15632575.0,White,622.0,France,Male,34.0,4.0,168094.01,2.0,1.0,1.0,174531.27,0.0
+3271,15693877.0,Pinto,545.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,0.0,113481.02,0.0
+3272,15718789.0,Fennell,686.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,105969.05,0.0
+3273,15685370.0,Chukwukadibia,674.0,France,Male,35.0,3.0,0.0,2.0,0.0,1.0,98894.64,0.0
+3274,15772423.0,Ch'ien,653.0,France,Female,30.0,2.0,0.0,2.0,1.0,0.0,184564.67,0.0
+3275,15732266.0,Kable,586.0,France,Female,35.0,6.0,0.0,2.0,0.0,1.0,172524.51,0.0
+3276,15757837.0,Chien,695.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,90877.76,0.0
+3277,15628600.0,Mazzanti,755.0,France,Male,36.0,7.0,145981.87,2.0,1.0,1.0,54495.82,0.0
+3278,15730137.0,Folliero,763.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,183494.87,0.0
+3279,15631070.0,Onio,678.0,Germany,Male,37.0,6.0,151955.59,2.0,1.0,1.0,121453.83,0.0
+3280,15777046.0,O'Sullivan,627.0,Germany,Male,32.0,4.0,177728.55,2.0,1.0,1.0,64421.02,0.0
+3281,15806901.0,Ch'ien,733.0,Germany,Male,45.0,9.0,109844.81,1.0,1.0,1.0,52876.41,0.0
+3282,15642870.0,Hsieh,619.0,France,Female,45.0,9.0,123916.67,1.0,1.0,1.0,163573.06,1.0
+3283,15711219.0,Okeke,738.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,121840.2,0.0
+3284,15788659.0,Chukwueloka,746.0,France,Female,45.0,6.0,0.0,2.0,1.0,1.0,155879.57,0.0
+3285,15593973.0,Nwachinemelu,682.0,France,Female,51.0,1.0,0.0,1.0,1.0,1.0,132973.18,1.0
+3286,15729763.0,Hsing,659.0,Germany,Female,42.0,4.0,151914.93,1.0,1.0,0.0,183980.56,1.0
+3287,15687540.0,Ch'iu,592.0,Germany,Male,41.0,7.0,152812.58,1.0,1.0,1.0,135096.78,0.0
+3288,15779743.0,Ts'ui,628.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,0.0,110916.15,0.0
+3289,15759717.0,Biryukova,749.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,12913.92,0.0
+3290,15684669.0,Ts'ai,611.0,France,Female,46.0,6.0,0.0,1.0,1.0,0.0,146652.01,1.0
+3291,15708046.0,Maclean,735.0,Spain,Male,42.0,8.0,0.0,1.0,1.0,0.0,152698.24,1.0
+3292,15587913.0,Bock,621.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,142838.64,0.0
+3293,15792360.0,Chizuoke,681.0,Spain,Male,44.0,6.0,0.0,1.0,0.0,1.0,74430.16,0.0
+3294,15637169.0,Sagese,721.0,Spain,Female,42.0,5.0,0.0,1.0,1.0,0.0,122925.44,1.0
+3295,15594878.0,T'ang,739.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,1.0,111981.19,0.0
+3296,15766776.0,White,634.0,France,Female,43.0,5.0,164902.43,1.0,0.0,0.0,59601.15,0.0
+3297,15659736.0,Ch'iu,559.0,Spain,Female,31.0,0.0,0.0,2.0,1.0,0.0,88866.92,0.0
+3298,15610383.0,Manna,682.0,France,Male,25.0,9.0,77910.23,1.0,1.0,0.0,81718.37,0.0
+3299,15596808.0,Chien,676.0,France,Male,31.0,6.0,161613.94,1.0,1.0,1.0,45424.13,0.0
+3300,15667215.0,Cremonesi,705.0,France,Male,34.0,3.0,130404.92,1.0,1.0,1.0,26767.85,0.0
+3301,15644878.0,Lucchese,554.0,Germany,Male,46.0,10.0,109908.06,1.0,1.0,0.0,138526.96,1.0
+3302,15579914.0,Tsao,742.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,104533.24,0.0
+3303,15762715.0,Martin,679.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,94878.1,1.0
+3304,15732299.0,Ch'iu,724.0,Spain,Male,30.0,2.0,151844.64,2.0,0.0,1.0,174738.2,0.0
+3305,15737133.0,Liao,583.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,83343.73,0.0
+3306,15586821.0,H?,588.0,Spain,Female,40.0,3.0,0.0,2.0,0.0,0.0,113557.9,1.0
+3307,15726720.0,Pirozzi,662.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,61820.41,0.0
+3308,15570002.0,Lo,695.0,Germany,Female,33.0,4.0,145578.28,2.0,1.0,0.0,1710.89,0.0
+3309,15599152.0,K?,590.0,Germany,Male,41.0,4.0,104784.23,2.0,1.0,0.0,62944.75,0.0
+3310,15800068.0,Yefremova,653.0,France,Female,23.0,1.0,0.0,2.0,0.0,1.0,152843.53,0.0
+3311,15704770.0,P'eng,706.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,122925.4,0.0
+3312,15677804.0,Hsing,686.0,Germany,Female,56.0,1.0,118628.8,1.0,1.0,0.0,186869.58,1.0
+3313,15706463.0,Folliero,576.0,France,Female,29.0,6.0,0.0,2.0,0.0,0.0,79616.37,0.0
+3314,15627212.0,Larkin,678.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,142797.5,0.0
+3315,15724127.0,Kao,786.0,Germany,Female,45.0,10.0,82407.51,2.0,1.0,0.0,72993.65,0.0
+3316,15687540.0,Fiorentini,718.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,0.0,58440.43,0.0
+3317,15623369.0,Ch'eng,772.0,France,Male,32.0,1.0,0.0,2.0,0.0,1.0,136902.01,0.0
+3318,15670266.0,Chiu,569.0,France,Female,35.0,7.0,0.0,1.0,1.0,0.0,160546.58,0.0
+3319,15726354.0,Clements,849.0,France,Male,57.0,2.0,0.0,3.0,1.0,1.0,191111.02,1.0
+3320,15695465.0,Rizzo,656.0,France,Female,37.0,0.0,102025.32,1.0,1.0,0.0,94951.27,1.0
+3321,15583748.0,Kornilova,683.0,Germany,Female,36.0,5.0,142388.61,2.0,1.0,1.0,96961.0,0.0
+3322,15773789.0,Onyekachi,633.0,France,Female,27.0,6.0,0.0,2.0,1.0,1.0,83473.82,0.0
+3323,15607874.0,Trevisani,673.0,France,Female,35.0,7.0,0.0,2.0,0.0,0.0,134427.32,0.0
+3324,15574858.0,T'ien,497.0,Germany,Male,37.0,4.0,133970.22,1.0,1.0,0.0,72990.62,1.0
+3325,15653779.0,Shao,543.0,Germany,Female,40.0,8.0,54901.01,2.0,1.0,1.0,125979.36,0.0
+3326,15771086.0,Ugoji,659.0,France,Male,32.0,5.0,0.0,2.0,0.0,1.0,171070.55,0.0
+3327,15754599.0,Nwora,694.0,Spain,Female,38.0,2.0,0.0,2.0,0.0,0.0,87822.14,0.0
+3328,15763612.0,Chibuzo,634.0,Germany,Male,36.0,4.0,134760.21,1.0,1.0,1.0,171518.52,0.0
+3329,15783026.0,Chimaobim,719.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,135875.51,0.0
+3330,15671917.0,Chikezie,758.0,Spain,Female,44.0,7.0,0.0,2.0,1.0,1.0,163560.11,0.0
+3331,15692761.0,Chieloka,642.0,France,Male,41.0,8.0,0.0,1.0,1.0,1.0,51467.92,0.0
+3332,15767781.0,Chibueze,669.0,Spain,Male,46.0,7.0,94222.54,1.0,1.0,1.0,79577.3,0.0
+3333,15790744.0,Lai,705.0,Spain,Female,51.0,1.0,102073.67,1.0,1.0,0.0,138880.24,1.0
+3334,15777599.0,Nwabugwu,553.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,0.0,68815.05,0.0
+3335,15794479.0,Chidubem,696.0,France,Male,38.0,8.0,107592.89,1.0,1.0,1.0,63687.06,0.0
+3336,15791925.0,Pirozzi,577.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,0.0,113048.79,0.0
+3337,15667554.0,Ch'ang,712.0,Germany,Female,50.0,9.0,162390.61,1.0,1.0,0.0,163511.6,1.0
+3338,15807008.0,De Luca,619.0,France,Female,37.0,7.0,0.0,1.0,1.0,0.0,155799.86,0.0
+3339,15672594.0,Johnson,593.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,133851.25,0.0
+3340,15659327.0,Chukwudi,703.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,155743.48,0.0
+3341,15657535.0,Ts'ui,633.0,France,Female,28.0,8.0,0.0,2.0,1.0,1.0,91346.03,0.0
+3342,15721592.0,Uchenna,705.0,France,Female,48.0,2.0,0.0,2.0,1.0,0.0,176902.83,0.0
+3343,15724223.0,K'ung,635.0,Germany,Male,44.0,4.0,146547.76,1.0,1.0,0.0,62980.96,1.0
+3344,15640648.0,Tuan,578.0,France,Female,38.0,2.0,0.0,1.0,0.0,1.0,85412.54,0.0
+3345,15809736.0,O'Loghlen,627.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,180942.33,0.0
+3346,15572472.0,Nnaife,616.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,0.0,144875.73,0.0
+3347,15768151.0,Ts'ai,596.0,Germany,Female,39.0,2.0,120599.38,2.0,1.0,0.0,82463.69,0.0
+3348,15701605.0,Ts'ui,710.0,Spain,Male,39.0,9.0,135559.9,1.0,1.0,0.0,117632.63,0.0
+3349,15723851.0,Hsiung,585.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,57862.88,0.0
+3350,15746028.0,P'eng,588.0,France,Female,47.0,5.0,0.0,1.0,0.0,0.0,78744.39,1.0
+3351,15679429.0,Kao,659.0,Spain,Female,39.0,8.0,121231.39,1.0,1.0,1.0,132468.76,0.0
+3352,15694765.0,T'ien,529.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,166677.35,0.0
+3353,15738174.0,Yermakova,586.0,France,Female,33.0,8.0,0.0,2.0,0.0,1.0,61326.43,0.0
+3354,15572291.0,T'ien,656.0,France,Female,37.0,7.0,112373.49,1.0,1.0,0.0,1713.1,0.0
+3355,15710390.0,Shih,773.0,France,Female,39.0,4.0,0.0,2.0,1.0,1.0,81718.37,0.0
+3356,15761706.0,Walker,616.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,1.0,131736.22,0.0
+3357,15722061.0,Nwankwo,683.0,France,Male,24.0,8.0,0.0,2.0,1.0,0.0,143463.28,0.0
+3358,15660155.0,Tsao,732.0,France,Female,33.0,0.0,0.0,2.0,1.0,1.0,100556.98,0.0
+3359,15576723.0,Ku,721.0,Spain,Male,52.0,4.0,0.0,1.0,1.0,1.0,126723.22,1.0
+3360,15659949.0,Aksenov,707.0,France,Female,31.0,4.0,105969.42,2.0,1.0,0.0,130113.78,0.0
+3361,15740264.0,Udinese,729.0,France,Male,55.0,1.0,0.0,1.0,1.0,0.0,48260.19,1.0
+3362,15697574.0,Nkemakonam,745.0,France,Male,34.0,7.0,0.0,1.0,1.0,0.0,184564.67,0.0
+3363,15662266.0,Brownells,696.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,0.0,91433.46,0.0
+3364,15675256.0,Moore,678.0,France,Male,33.0,3.0,0.0,1.0,1.0,0.0,132973.21,0.0
+3365,15780572.0,K?,669.0,Spain,Male,58.0,0.0,0.0,2.0,0.0,1.0,51565.98,0.0
+3366,15593900.0,Ch'ien,573.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,139947.17,0.0
+3367,15597914.0,Yermolayev,789.0,France,Male,37.0,8.0,170840.08,1.0,1.0,0.0,645.61,0.0
+3368,15634281.0,Frolova,834.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,110916.15,0.0
+3369,15781327.0,Hs?,714.0,Germany,Female,56.0,3.0,117020.08,1.0,1.0,0.0,25407.59,1.0
+3370,15662098.0,Iloerika,661.0,Germany,Female,32.0,5.0,84719.13,2.0,1.0,0.0,80552.36,0.0
+3371,15793890.0,K?,564.0,Spain,Male,37.0,9.0,0.0,1.0,1.0,0.0,136675.51,0.0
+3372,15785367.0,Pisani,617.0,France,Male,51.0,6.0,0.0,2.0,1.0,1.0,48502.88,0.0
+3373,15654760.0,T'an,661.0,France,Female,40.0,4.0,0.0,1.0,1.0,0.0,103557.85,1.0
+3374,15670345.0,Chukwukadibia,710.0,Germany,Female,36.0,8.0,147726.98,1.0,0.0,1.0,166677.35,0.0
+3375,15791674.0,Manna,577.0,France,Female,49.0,6.0,0.0,1.0,1.0,1.0,171364.18,0.0
+3376,15592999.0,Ozioma,576.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,126535.58,0.0
+3377,15788659.0,Hsueh,686.0,France,Female,33.0,7.0,127655.42,2.0,0.0,1.0,181208.47,0.0
+3378,15660200.0,Maclean,621.0,France,Female,29.0,5.0,0.0,1.0,1.0,1.0,89801.9,0.0
+3379,15681115.0,Onuchukwu,652.0,France,Male,50.0,7.0,0.0,1.0,1.0,0.0,27380.99,1.0
+3380,15709511.0,Okechukwu,668.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,90877.76,0.0
+3381,15728333.0,Chukwumaobim,646.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,95936.84,0.0
+3382,15809736.0,Baresi,762.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,71405.17,0.0
+3383,15731148.0,Ogochukwu,679.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,1.0,59325.21,0.0
+3384,15734765.0,Ch'ien,535.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,125440.11,1.0
+3385,15706799.0,Nwachukwu,652.0,France,Male,35.0,7.0,0.0,1.0,1.0,0.0,92865.46,1.0
+3386,15660144.0,Trevisani,705.0,Germany,Male,36.0,1.0,96353.8,2.0,1.0,1.0,61326.43,0.0
+3387,15768340.0,Ebelegbulam,751.0,Spain,Male,43.0,6.0,85311.7,1.0,0.0,1.0,121053.19,0.0
+3388,15631070.0,Yobachi,771.0,Germany,Female,51.0,9.0,104848.68,1.0,1.0,1.0,61602.42,1.0
+3389,15674541.0,Ugorji,782.0,Germany,Female,46.0,3.0,124099.13,2.0,0.0,1.0,177740.57,0.0
+3390,15768181.0,Hsieh,663.0,France,Male,43.0,8.0,113709.28,1.0,1.0,0.0,162699.5,0.0
+3391,15620372.0,Ives,682.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,172033.57,0.0
+3392,15587562.0,Iweobiegbulam,793.0,Germany,Male,32.0,1.0,142929.16,2.0,0.0,1.0,145764.4,0.0
+3393,15733447.0,Chukwuebuka,584.0,France,Male,37.0,9.0,0.0,2.0,0.0,1.0,105898.79,0.0
+3394,15659327.0,Shubin,726.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,1.0,181543.67,0.0
+3395,15658449.0,O'Loughlin,577.0,Germany,Female,31.0,8.0,132057.92,1.0,1.0,1.0,53511.94,0.0
+3396,15658589.0,Niu,839.0,France,Female,50.0,5.0,147832.15,2.0,1.0,0.0,54495.82,1.0
+3397,15680167.0,Kline,579.0,Germany,Female,40.0,2.0,125190.86,2.0,1.0,1.0,142903.44,0.0
+3398,15611157.0,Nwachukwu,624.0,Germany,Male,40.0,8.0,155973.88,1.0,0.0,0.0,51393.1,1.0
+3399,15732268.0,Rizzo,599.0,France,Female,31.0,1.0,0.0,1.0,1.0,1.0,125937.98,0.0
+3400,15725772.0,Olisanugo,693.0,France,Female,37.0,4.0,0.0,2.0,0.0,0.0,170518.33,0.0
+3401,15785325.0,Ting,477.0,France,Male,27.0,5.0,0.0,2.0,1.0,0.0,138344.7,0.0
+3402,15609824.0,Chizuoke,745.0,Spain,Male,38.0,7.0,96658.26,1.0,1.0,0.0,150319.49,0.0
+3403,15722350.0,T'ien,662.0,France,Male,32.0,7.0,0.0,1.0,0.0,1.0,111342.66,0.0
+3404,15752097.0,H?,651.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,140765.57,0.0
+3405,15590177.0,Chiebuka,641.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,172576.56,0.0
+3406,15756920.0,Y?an,585.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,188426.12,0.0
+3407,15641480.0,Trevisan,671.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,86927.53,0.0
+3408,15672640.0,Dennys,559.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,11296.98,0.0
+3409,15626795.0,Chimaobim,628.0,France,Male,34.0,4.0,0.0,1.0,0.0,1.0,120531.31,0.0
+3410,15749119.0,Fetherstonhaugh,712.0,Germany,Female,62.0,1.0,120108.56,2.0,1.0,1.0,67611.36,0.0
+3411,15626776.0,Onyemere,652.0,France,Female,22.0,1.0,0.0,2.0,1.0,0.0,182692.8,0.0
+3412,15593345.0,Chiang,773.0,France,Female,25.0,9.0,62212.29,1.0,1.0,1.0,94949.84,0.0
+3413,15566891.0,Chidubem,635.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,66893.28,0.0
+3414,15656937.0,Hsia,578.0,Germany,Male,44.0,8.0,110597.76,3.0,1.0,0.0,180626.68,1.0
+3415,15807593.0,Shao,704.0,Spain,Male,41.0,5.0,0.0,1.0,1.0,1.0,103570.8,0.0
+3416,15743478.0,P'eng,678.0,France,Male,33.0,8.0,182888.08,1.0,1.0,1.0,45956.05,0.0
+3417,15701354.0,Kaleski,597.0,Germany,Female,42.0,2.0,102989.83,1.0,0.0,0.0,43435.43,1.0
+3418,15679966.0,Ch'ien,714.0,France,Male,36.0,10.0,0.0,1.0,1.0,1.0,151460.86,0.0
+3419,15635197.0,Chukwualuka,613.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,121073.23,0.0
+3420,15713853.0,Chibugo,544.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,118863.38,0.0
+3421,15700353.0,Smith,597.0,Spain,Female,39.0,8.0,0.0,1.0,0.0,1.0,153064.87,0.0
+3422,15781598.0,Rizzo,725.0,France,Male,44.0,6.0,0.0,2.0,1.0,1.0,95625.03,0.0
+3423,15741181.0,Mazzanti,639.0,Germany,Male,35.0,1.0,121404.64,1.0,1.0,1.0,181850.74,0.0
+3424,15730673.0,Mazzi,779.0,France,Female,41.0,5.0,166031.45,1.0,1.0,1.0,64746.07,0.0
+3425,15638463.0,Chinwendu,724.0,France,Female,36.0,8.0,109196.66,2.0,0.0,1.0,66963.15,0.0
+3426,15625942.0,Lo Duca,583.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,0.0,136985.58,0.0
+3427,15790067.0,Nieves,729.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,86410.29,0.0
+3428,15642394.0,Onyekaozulu,560.0,France,Male,32.0,7.0,118897.1,1.0,1.0,1.0,111558.7,0.0
+3429,15615473.0,Chukwufumnanya,789.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,87400.53,0.0
+3430,15617304.0,Hsueh,624.0,France,Male,44.0,4.0,0.0,2.0,1.0,0.0,58854.88,0.0
+3431,15744529.0,Estrada,675.0,France,Male,33.0,7.0,0.0,4.0,1.0,0.0,136028.97,1.0
+3432,15630106.0,Shao,526.0,Spain,Male,47.0,4.0,0.0,1.0,0.0,0.0,56875.76,1.0
+3433,15588944.0,Iweobiegbunam,577.0,France,Female,27.0,8.0,0.0,2.0,1.0,1.0,38252.25,0.0
+3434,15714883.0,Cattaneo,627.0,France,Male,28.0,2.0,0.0,2.0,1.0,1.0,102950.78,0.0
+3435,15605447.0,Cattaneo,669.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,76510.46,0.0
+3436,15593769.0,Lu,544.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,1710.89,1.0
+3437,15607347.0,Nwora,649.0,France,Male,53.0,7.0,0.0,1.0,1.0,0.0,158937.55,1.0
+3438,15621440.0,T'ien,656.0,Germany,Male,35.0,3.0,107192.38,2.0,1.0,1.0,90876.95,0.0
+3439,15792232.0,Mazzanti,597.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,157957.82,0.0
+3440,15631693.0,Trevisani,710.0,France,Male,33.0,0.0,121823.4,1.0,0.0,1.0,151978.36,0.0
+3441,15634968.0,Chukwualuka,537.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,180693.61,0.0
+3442,15757541.0,Chizoba,756.0,France,Male,39.0,2.0,105770.7,2.0,0.0,1.0,82559.77,0.0
+3443,15718507.0,Chukwujekwu,619.0,France,Male,28.0,8.0,0.0,1.0,0.0,1.0,176730.02,0.0
+3444,15641688.0,Ch'ien,696.0,France,Male,33.0,8.0,0.0,1.0,1.0,0.0,140134.43,0.0
+3445,15770154.0,Onyeoruru,612.0,Germany,Male,28.0,6.0,141541.25,1.0,0.0,1.0,75848.44,0.0
+3446,15696744.0,Lo,641.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,180197.74,0.0
+3447,15668894.0,Manna,646.0,Spain,Female,35.0,5.0,88011.4,1.0,0.0,1.0,115800.1,0.0
+3448,15714689.0,Shih,681.0,France,Female,38.0,8.0,0.0,2.0,0.0,1.0,90106.94,0.0
+3449,15664543.0,Nwachukwu,651.0,France,Female,36.0,7.0,0.0,1.0,1.0,0.0,165035.17,0.0
+3450,15803538.0,Teng,529.0,France,Female,29.0,0.0,0.0,2.0,1.0,0.0,109041.53,0.0
+3451,15660144.0,Chidubem,608.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,86622.48,0.0
+3452,15775409.0,Ts'ui,503.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,122449.18,0.0
+3453,15745375.0,Ch'eng,753.0,Germany,Female,48.0,5.0,137555.44,2.0,0.0,1.0,65816.53,0.0
+3454,15803378.0,Kuo,667.0,France,Male,37.0,7.0,0.0,1.0,1.0,1.0,186489.95,0.0
+3455,15671789.0,Nkemakolam,718.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,120790.02,0.0
+3456,15583736.0,Fiorentino,615.0,Spain,Male,38.0,6.0,169824.46,1.0,1.0,1.0,159737.56,0.0
+3457,15707120.0,Ts'ui,591.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,102057.86,0.0
+3458,15596800.0,Calabresi,651.0,France,Male,60.0,5.0,102994.04,1.0,0.0,1.0,9983.88,1.0
+3459,15772383.0,Chukwuemeka,632.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,70977.33,0.0
+3460,15681671.0,Ts'ao,595.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,1.0,79580.39,0.0
+3461,15698932.0,Nnamutaezinwa,686.0,Germany,Female,36.0,2.0,113882.31,1.0,0.0,1.0,52795.15,0.0
+3462,15651022.0,Bufkin,741.0,Spain,Male,27.0,1.0,0.0,2.0,0.0,1.0,111340.36,0.0
+3463,15671591.0,Milano,735.0,Germany,Male,44.0,1.0,104944.1,1.0,0.0,0.0,173586.63,1.0
+3464,15586680.0,Nkemjika,714.0,Germany,Female,33.0,1.0,142705.95,2.0,1.0,1.0,52876.41,0.0
+3465,15653547.0,Lai,645.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,1.0,184312.88,0.0
+3466,15806922.0,Y?,683.0,Spain,Male,35.0,1.0,0.0,1.0,1.0,0.0,74838.64,0.0
+3467,15581626.0,Yobachi,667.0,Spain,Male,34.0,5.0,0.0,1.0,1.0,0.0,143783.28,0.0
+3468,15662533.0,Kao,607.0,Spain,Female,51.0,10.0,0.0,1.0,0.0,1.0,199357.24,1.0
+3469,15773098.0,Nnachetam,627.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,88279.6,0.0
+3470,15702789.0,Ugochukwu,692.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,114093.73,0.0
+3471,15686964.0,Milanesi,645.0,France,Female,48.0,6.0,0.0,2.0,1.0,1.0,172499.7,0.0
+3472,15651389.0,Ch'in,725.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,53394.12,0.0
+3473,15583480.0,Chukwueloka,597.0,Spain,Female,29.0,2.0,0.0,2.0,0.0,0.0,88728.47,0.0
+3474,15627002.0,Lucciano,602.0,Spain,Female,47.0,5.0,113328.93,1.0,1.0,0.0,48161.18,0.0
+3475,15684861.0,Ts'ai,754.0,France,Male,41.0,1.0,0.0,2.0,0.0,1.0,548.52,0.0
+3476,15751774.0,Folliero,526.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,107640.25,0.0
+3477,15785042.0,Smith,610.0,France,Female,53.0,2.0,94762.16,3.0,1.0,0.0,99347.8,1.0
+3478,15771086.0,Tsou,586.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,86678.48,0.0
+3479,15788676.0,T'ien,684.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,127059.05,0.0
+3480,15569394.0,Shih,592.0,Germany,Male,29.0,9.0,103914.17,1.0,1.0,1.0,120892.65,0.0
+3481,15802758.0,Ugorji,556.0,Germany,Male,56.0,1.0,105799.32,1.0,1.0,1.0,104786.59,1.0
+3482,15764072.0,Onyemauchechukwu,593.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,91044.47,0.0
+3483,15663625.0,Tung,682.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,167032.49,0.0
+3484,15753566.0,Chukwukadibia,696.0,Germany,Male,31.0,5.0,141252.18,2.0,1.0,1.0,60536.96,0.0
+3485,15641170.0,Lucchese,564.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,172031.22,0.0
+3486,15787035.0,Onyemachukwu,732.0,France,Male,52.0,3.0,0.0,3.0,1.0,0.0,184524.74,1.0
+3487,15807269.0,Okwudilichukwu,695.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,162478.11,0.0
+3488,15756610.0,Nwankwo,696.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,113469.65,0.0
+3489,15808849.0,Mazzanti,716.0,France,Female,23.0,8.0,132387.91,1.0,1.0,0.0,91817.46,0.0
+3490,15640855.0,Onio,659.0,Spain,Female,41.0,2.0,0.0,1.0,0.0,0.0,114474.98,1.0
+3491,15632848.0,Kornilova,557.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,129892.93,0.0
+3492,15624471.0,Maclean,633.0,France,Female,31.0,5.0,114514.71,1.0,1.0,1.0,122021.33,0.0
+3493,15778936.0,Onwumelu,662.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,70563.9,0.0
+3494,15662955.0,McGregor,628.0,France,Female,43.0,9.0,0.0,1.0,1.0,0.0,149853.89,1.0
+3495,15593954.0,Chikezie,704.0,Spain,Female,30.0,9.0,0.0,2.0,0.0,0.0,86935.08,0.0
+3496,15768455.0,Chiu,724.0,Germany,Male,44.0,3.0,84907.33,1.0,1.0,0.0,71738.56,0.0
+3497,15732610.0,H?,593.0,Germany,Male,41.0,4.0,137851.31,1.0,1.0,0.0,163477.22,0.0
+3498,15576156.0,Ch'eng,628.0,France,Male,28.0,2.0,137964.51,2.0,0.0,1.0,109545.56,0.0
+3499,15738884.0,T'an,711.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,88890.75,0.0
+3500,15576550.0,Pisani,581.0,Spain,Female,30.0,9.0,0.0,2.0,1.0,0.0,94442.6,0.0
+3501,15773098.0,Mazzi,731.0,France,Female,41.0,1.0,0.0,1.0,0.0,0.0,38691.34,1.0
+3502,15702298.0,Ts'ai,656.0,Spain,Female,32.0,3.0,0.0,2.0,1.0,1.0,94912.78,0.0
+3503,15738166.0,Lucchesi,619.0,France,Female,52.0,4.0,122220.19,2.0,1.0,0.0,170563.59,1.0
+3504,15784491.0,Cremonesi,638.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,1.0,143980.29,0.0
+3505,15698031.0,Nkemdilim,750.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,167066.95,0.0
+3506,15666095.0,Shih,551.0,France,Male,54.0,6.0,105918.1,1.0,1.0,1.0,180934.75,1.0
+3507,15586039.0,Uvarov,738.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,185821.41,0.0
+3508,15783865.0,Onio,669.0,France,Male,37.0,6.0,152303.8,1.0,1.0,0.0,64716.08,0.0
+3509,15735388.0,Uwaezuoke,537.0,Spain,Male,36.0,2.0,0.0,2.0,0.0,1.0,64831.36,0.0
+3510,15585961.0,Y?,684.0,France,Female,41.0,8.0,0.0,1.0,1.0,0.0,166677.35,1.0
+3511,15652400.0,Onyemauchechukwu,656.0,Spain,Male,44.0,8.0,186796.37,1.0,1.0,1.0,166495.2,0.0
+3512,15758813.0,H?,597.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,0.0,131300.68,0.0
+3513,15665963.0,Milano,707.0,Germany,Male,57.0,3.0,133845.36,1.0,1.0,0.0,77837.63,1.0
+3514,15782210.0,Hsu,714.0,Germany,Female,42.0,8.0,104386.53,2.0,1.0,1.0,94775.94,0.0
+3515,15656096.0,Yobanna,661.0,Germany,Male,51.0,1.0,103560.57,1.0,0.0,1.0,47795.43,1.0
+3516,15679743.0,Chukwumaobim,807.0,France,Male,41.0,9.0,0.0,2.0,0.0,0.0,181224.56,0.0
+3517,15761487.0,Hsia,648.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,115668.55,0.0
+3518,15782360.0,T'ien,846.0,Spain,Female,31.0,0.0,0.0,2.0,1.0,0.0,170518.52,0.0
+3519,15700749.0,Chukwumaobim,695.0,Germany,Female,30.0,5.0,116965.21,2.0,1.0,1.0,120512.08,0.0
+3520,15655339.0,Pagnotto,577.0,France,Male,28.0,2.0,0.0,2.0,0.0,0.0,142331.85,0.0
+3521,15728005.0,Chukwuemeka,648.0,Germany,Male,36.0,1.0,102645.48,2.0,1.0,0.0,72927.68,0.0
+3522,15582944.0,Chidiegwu,663.0,Germany,Male,38.0,10.0,108711.57,1.0,0.0,0.0,143481.79,1.0
+3523,15729019.0,Gentry,595.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,67323.36,0.0
+3524,15709354.0,Brown,615.0,Germany,Male,34.0,4.0,81732.88,2.0,0.0,1.0,126644.98,0.0
+3525,15703778.0,Lombardo,656.0,France,Female,63.0,3.0,0.0,2.0,0.0,1.0,77906.87,0.0
+3526,15675888.0,Kao,709.0,France,Male,36.0,4.0,123544.0,2.0,1.0,0.0,80552.36,0.0
+3527,15629010.0,Okwudilichukwu,596.0,Germany,Male,38.0,9.0,182275.23,1.0,1.0,1.0,123860.28,0.0
+3528,15753362.0,Pagnotto,602.0,Spain,Male,45.0,1.0,0.0,2.0,0.0,1.0,70810.6,0.0
+3529,15776936.0,Udinese,516.0,Germany,Male,44.0,0.0,113865.23,1.0,1.0,1.0,51548.14,0.0
+3530,15785078.0,Tsao,732.0,Spain,Female,28.0,4.0,0.0,2.0,0.0,1.0,133871.83,0.0
+3531,15662291.0,Farrar,581.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,1.0,176713.47,0.0
+3532,15656300.0,Kao,714.0,France,Male,40.0,6.0,148074.79,1.0,0.0,1.0,145562.4,1.0
+3533,15686913.0,Trevisan,603.0,Spain,Male,71.0,0.0,0.0,2.0,1.0,1.0,131300.68,0.0
+3534,15773731.0,Lucciano,778.0,France,Female,25.0,4.0,140549.4,1.0,0.0,0.0,44008.91,0.0
+3535,15756535.0,Ugochukwutubelum,501.0,Germany,Female,53.0,2.0,96553.52,1.0,1.0,0.0,139919.38,1.0
+3536,15752507.0,Lombardo,720.0,Germany,Female,32.0,1.0,123870.07,1.0,0.0,0.0,146891.07,1.0
+3537,15591047.0,Pinto,683.0,France,Female,43.0,7.0,0.0,2.0,1.0,0.0,185830.72,0.0
+3538,15724443.0,Chien,713.0,France,Male,38.0,4.0,122314.5,1.0,1.0,0.0,82696.84,0.0
+3539,15592999.0,Efremov,684.0,France,Male,29.0,9.0,121898.58,1.0,1.0,0.0,166038.13,0.0
+3540,15593636.0,Cattaneo,796.0,France,Male,40.0,9.0,109908.06,1.0,0.0,0.0,93439.75,0.0
+3541,15804053.0,K?,622.0,Germany,Female,40.0,9.0,136880.12,2.0,1.0,0.0,124489.88,0.0
+3542,15752984.0,Hs?eh,706.0,France,Female,30.0,1.0,111096.91,1.0,1.0,0.0,86937.2,0.0
+3543,15585580.0,Zikoranachidimma,593.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,0.0,129912.74,0.0
+3544,15641114.0,Tien,652.0,France,Female,35.0,1.0,167878.5,1.0,0.0,1.0,5983.8,0.0
+3545,15669946.0,Shao,587.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,0.0,77951.84,0.0
+3546,15706706.0,Walker,532.0,Germany,Male,72.0,6.0,125495.64,1.0,1.0,1.0,64679.3,0.0
+3547,15583857.0,Chidumaga,587.0,Spain,Male,25.0,5.0,0.0,2.0,1.0,1.0,147055.87,0.0
+3548,15603851.0,Ch'iu,576.0,Spain,Female,39.0,7.0,0.0,1.0,0.0,0.0,145558.42,1.0
+3549,15751455.0,Liao,686.0,France,Female,28.0,7.0,0.0,2.0,0.0,0.0,100101.06,0.0
+3550,15745012.0,Smith,731.0,France,Female,32.0,3.0,121399.98,1.0,1.0,1.0,84421.34,0.0
+3551,15619608.0,Chukwubuikem,662.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,179655.87,0.0
+3552,15626135.0,Ch'ang,579.0,France,Male,32.0,4.0,0.0,1.0,1.0,0.0,86410.29,0.0
+3553,15792862.0,P'eng,684.0,France,Female,30.0,6.0,0.0,2.0,0.0,0.0,129964.94,0.0
+3554,15687959.0,Palerma,569.0,France,Female,32.0,1.0,0.0,1.0,1.0,0.0,82762.02,1.0
+3555,15774586.0,K'ung,756.0,Germany,Female,32.0,3.0,124995.98,1.0,0.0,0.0,137537.19,0.0
+3556,15761348.0,Palermo,745.0,France,Male,38.0,2.0,82441.75,2.0,1.0,1.0,118368.2,0.0
+3557,15684440.0,Onyemauchechukwu,619.0,France,Female,35.0,1.0,0.0,1.0,1.0,0.0,157097.09,1.0
+3558,15624072.0,Tung,635.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,82814.32,0.0
+3559,15774847.0,Aitken,628.0,Germany,Male,36.0,2.0,77967.5,2.0,1.0,0.0,62768.8,0.0
+3560,15744327.0,Genovese,670.0,France,Male,45.0,3.0,0.0,3.0,1.0,0.0,183515.13,1.0
+3561,15741643.0,Walker,496.0,France,Female,41.0,8.0,123944.88,1.0,1.0,0.0,90877.76,0.0
+3562,15579996.0,Yobachi,709.0,Germany,Male,44.0,2.0,139432.46,1.0,0.0,1.0,122925.44,1.0
+3563,15572398.0,Yobanna,542.0,France,Male,35.0,7.0,118969.76,1.0,1.0,1.0,141872.05,0.0
+3564,15809837.0,Hs?,651.0,France,Male,41.0,2.0,0.0,2.0,0.0,0.0,77866.91,0.0
+3565,15742369.0,Trevisani,638.0,France,Male,43.0,2.0,0.0,1.0,1.0,1.0,181449.51,0.0
+3566,15684461.0,Ch'ien,721.0,France,Male,33.0,7.0,153611.83,2.0,1.0,1.0,64477.25,0.0
+3567,15746461.0,Chizuoke,724.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,1.0,174434.06,0.0
+3568,15800793.0,Nwachinemelu,683.0,France,Female,33.0,10.0,0.0,2.0,1.0,0.0,131097.9,0.0
+3569,15672798.0,Ch'ang,670.0,Spain,Male,37.0,9.0,101609.01,1.0,1.0,1.0,148527.56,0.0
+3570,15583076.0,Onwumelu,611.0,Germany,Female,46.0,7.0,142475.87,1.0,1.0,0.0,131389.39,1.0
+3571,15671209.0,Uchechukwu,655.0,Spain,Male,32.0,3.0,0.0,1.0,0.0,1.0,104786.59,1.0
+3572,15569050.0,Trevisan,676.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,172066.71,0.0
+3573,15690153.0,Calabresi,587.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,55775.84,0.0
+3574,15727041.0,Lucchese,603.0,France,Male,39.0,10.0,96877.21,1.0,1.0,0.0,59325.21,0.0
+3575,15791342.0,Chukwuemeka,651.0,France,Female,37.0,7.0,0.0,2.0,0.0,1.0,160968.44,1.0
+3576,15594556.0,Milanesi,745.0,France,Male,31.0,8.0,121394.42,1.0,1.0,0.0,177783.04,0.0
+3577,15814237.0,Ch'in,602.0,France,Male,46.0,6.0,162701.65,1.0,1.0,0.0,109179.89,0.0
+3578,15584580.0,Smith,706.0,France,Female,31.0,6.0,166518.63,1.0,1.0,0.0,143981.27,0.0
+3579,15771483.0,Chien,757.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,184843.77,0.0
+3580,15734886.0,Ch'ien,656.0,Germany,Female,45.0,4.0,92474.05,1.0,0.0,0.0,109626.09,1.0
+3581,15641835.0,Chukwujamuike,729.0,France,Female,41.0,8.0,80964.57,1.0,1.0,0.0,109638.78,0.0
+3582,15699238.0,Yuryeva,667.0,France,Male,22.0,5.0,0.0,2.0,1.0,1.0,72969.9,0.0
+3583,15686461.0,Hsu,647.0,France,Female,28.0,10.0,0.0,1.0,1.0,1.0,175749.36,0.0
+3584,15654409.0,Fokine,581.0,Spain,Female,42.0,7.0,143330.56,1.0,1.0,1.0,136983.78,0.0
+3585,15627385.0,Lucchese,635.0,France,Male,36.0,1.0,0.0,2.0,0.0,1.0,53817.23,0.0
+3586,15694188.0,Chialuka,578.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,0.0,10666.05,0.0
+3587,15652218.0,Ch'ien,663.0,France,Male,52.0,1.0,0.0,2.0,0.0,1.0,182055.36,0.0
+3588,15618774.0,Shih,669.0,France,Female,40.0,6.0,0.0,1.0,1.0,1.0,125445.8,1.0
+3589,15726487.0,Udinesi,663.0,France,Male,34.0,2.0,0.0,1.0,1.0,1.0,148087.62,0.0
+3590,15743543.0,Tuan,602.0,France,Male,35.0,3.0,0.0,2.0,0.0,1.0,183872.88,0.0
+3591,15773512.0,Nkemdilim,756.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,173870.39,1.0
+3592,15806945.0,Liao,595.0,Germany,Male,26.0,5.0,144837.79,1.0,1.0,1.0,127623.36,0.0
+3593,15696852.0,Ch'iu,651.0,France,Male,42.0,1.0,138653.51,1.0,1.0,0.0,178458.86,0.0
+3594,15641389.0,Hsieh,637.0,Germany,Male,34.0,10.0,119787.76,2.0,0.0,0.0,80701.72,0.0
+3595,15603594.0,Chu,583.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,1.0,148087.88,0.0
+3596,15726589.0,Nworie,493.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,0.0,158527.56,0.0
+3597,15795895.0,Manna,801.0,Spain,Female,34.0,4.0,0.0,2.0,1.0,0.0,131734.87,0.0
+3598,15645323.0,Nwora,670.0,France,Female,55.0,5.0,0.0,1.0,1.0,0.0,143397.14,1.0
+3599,15610156.0,Shih,561.0,Spain,Male,20.0,8.0,0.0,2.0,1.0,0.0,122433.09,0.0
+3600,15796835.0,Chibugo,774.0,Germany,Male,40.0,3.0,102016.38,1.0,0.0,0.0,120594.85,1.0
+3601,15702293.0,Ts'ao,627.0,Spain,Male,20.0,9.0,0.0,2.0,1.0,1.0,94570.52,0.0
+3602,15741180.0,Ch'en,642.0,France,Male,33.0,1.0,151855.33,1.0,1.0,0.0,74850.84,0.0
+3603,15644724.0,Maclean,747.0,France,Male,66.0,8.0,149940.04,1.0,1.0,1.0,126513.11,0.0
+3604,15662291.0,Y?an,674.0,Spain,Female,39.0,2.0,0.0,2.0,0.0,0.0,100556.98,0.0
+3605,15702300.0,K'ung,722.0,Spain,Male,26.0,2.0,0.0,2.0,0.0,0.0,123949.65,0.0
+3606,15762583.0,Chidozie,709.0,France,Female,39.0,5.0,0.0,1.0,1.0,0.0,152577.62,0.0
+3607,15777616.0,Chiemenam,628.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,101693.73,0.0
+3608,15672397.0,Nnamdi,603.0,Spain,Female,44.0,0.0,108432.88,1.0,0.0,0.0,180622.02,1.0
+3609,15780254.0,Estep,581.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,168034.9,0.0
+3610,15569410.0,Hsing,567.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,180344.69,0.0
+3611,15689425.0,Cremonesi,605.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,4035.46,0.0
+3612,15606096.0,Ch'ang,622.0,France,Female,40.0,3.0,0.0,2.0,0.0,1.0,111471.55,0.0
+3613,15731463.0,Macleod,656.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,14073.11,0.0
+3614,15650098.0,Ch'in,781.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,90558.24,0.0
+3615,15603096.0,Calabrese,569.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,96832.82,0.0
+3616,15648075.0,Maclean,592.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,64375.4,0.0
+3617,15585853.0,Chinwemma,738.0,France,Male,41.0,0.0,0.0,1.0,1.0,1.0,71709.12,0.0
+3618,15583480.0,Ch'en,584.0,Germany,Male,59.0,9.0,121898.82,1.0,1.0,0.0,19834.32,1.0
+3619,15795537.0,Chidozie,815.0,Germany,Male,27.0,1.0,123870.07,2.0,0.0,1.0,6078.46,0.0
+3620,15752626.0,Ch'ien,511.0,France,Female,45.0,4.0,98438.11,1.0,1.0,1.0,172437.12,0.0
+3621,15761950.0,Chukwuemeka,731.0,France,Male,30.0,2.0,113674.2,2.0,1.0,1.0,168895.06,0.0
+3622,15643908.0,Nnaife,678.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,1.0,35687.43,0.0
+3623,15775809.0,Ma,624.0,France,Male,39.0,9.0,151027.04,1.0,1.0,0.0,27330.59,0.0
+3624,15578098.0,Chin,730.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,92568.07,0.0
+3625,15674581.0,Hs?,683.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,6554.97,0.0
+3626,15781884.0,P'eng,569.0,France,Female,44.0,5.0,145988.65,1.0,1.0,0.0,188516.45,1.0
+3627,15577999.0,Toscani,754.0,France,Male,38.0,8.0,118640.24,1.0,1.0,0.0,90928.02,0.0
+3628,15776939.0,P'eng,590.0,Spain,Male,35.0,2.0,118022.06,1.0,1.0,1.0,143954.99,0.0
+3629,15604792.0,Pirozzi,607.0,Germany,Male,35.0,1.0,61297.05,1.0,1.0,0.0,107876.2,0.0
+3630,15628282.0,Tseng,709.0,France,Male,43.0,8.0,0.0,1.0,1.0,1.0,126535.92,0.0
+3631,15621822.0,Yeh,641.0,Germany,Female,41.0,4.0,157099.47,1.0,1.0,0.0,136910.19,0.0
+3632,15678460.0,Tsai,624.0,France,Female,28.0,6.0,0.0,2.0,1.0,0.0,49684.09,0.0
+3633,15572467.0,Pinto,667.0,France,Male,38.0,4.0,93694.22,1.0,0.0,0.0,94875.62,0.0
+3634,15662698.0,Ch'iu,597.0,France,Male,41.0,9.0,135096.05,1.0,1.0,1.0,83470.4,0.0
+3635,15741728.0,Nkemdilim,597.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,166516.84,0.0
+3636,15642725.0,P'an,712.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,57541.06,0.0
+3637,15765806.0,Ch'iu,598.0,Spain,Male,44.0,8.0,0.0,2.0,1.0,0.0,141364.62,0.0
+3638,15672908.0,Brown,648.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,172576.43,0.0
+3639,15678720.0,K?,766.0,France,Male,39.0,1.0,0.0,2.0,0.0,0.0,51380.9,0.0
+3640,15778936.0,Mazzanti,741.0,Germany,Male,23.0,4.0,84719.97,2.0,1.0,1.0,131662.47,0.0
+3641,15616276.0,Gentry,589.0,Germany,Female,56.0,3.0,122522.47,1.0,1.0,0.0,130404.53,1.0
+3642,15776467.0,Ch'in,678.0,France,Female,35.0,2.0,0.0,2.0,1.0,1.0,645.61,0.0
+3643,15692631.0,Chidiebere,712.0,France,Female,45.0,8.0,138089.97,3.0,1.0,0.0,143301.49,1.0
+3644,15577064.0,P'eng,712.0,France,Male,45.0,10.0,0.0,2.0,1.0,1.0,72876.43,0.0
+3645,15652674.0,Moore,565.0,Spain,Female,31.0,7.0,125169.26,1.0,0.0,0.0,77830.46,0.0
+3646,15617348.0,Ritchie,593.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,38196.24,0.0
+3647,15629150.0,Chiu,545.0,France,Male,49.0,8.0,0.0,2.0,1.0,0.0,143614.01,0.0
+3648,15791321.0,Genovese,689.0,Germany,Male,36.0,6.0,109646.83,2.0,0.0,1.0,55775.72,0.0
+3649,15749381.0,Chia,667.0,France,Male,46.0,3.0,0.0,2.0,1.0,1.0,164394.65,0.0
+3650,15698031.0,Chukwujamuike,679.0,Spain,Female,29.0,6.0,0.0,2.0,1.0,1.0,120770.75,0.0
+3651,15679760.0,Chiazagomekpere,767.0,France,Male,50.0,1.0,0.0,1.0,1.0,1.0,64716.08,0.0
+3652,15641359.0,Trevisano,655.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,172668.39,0.0
+3653,15752026.0,Nworie,677.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,91573.19,0.0
+3654,15626985.0,Lei,732.0,Germany,Female,39.0,4.0,135559.9,1.0,1.0,1.0,154460.18,1.0
+3655,15672930.0,Hsiao,622.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,133617.43,0.0
+3656,15644501.0,Hsieh,598.0,Germany,Male,36.0,5.0,168560.46,1.0,1.0,0.0,146041.45,0.0
+3657,15697574.0,L?,642.0,Germany,Male,30.0,6.0,133800.98,2.0,1.0,1.0,156858.2,0.0
+3658,15744789.0,Uspenskaya,586.0,France,Male,41.0,3.0,0.0,1.0,1.0,1.0,51362.82,0.0
+3659,15639552.0,Ch'eng,466.0,France,Male,35.0,3.0,0.0,1.0,1.0,0.0,109538.35,0.0
+3660,15594576.0,Chidubem,621.0,Spain,Female,40.0,4.0,0.0,2.0,1.0,1.0,61936.1,0.0
+3661,15807595.0,Chukwuhaenye,629.0,France,Male,36.0,7.0,0.0,1.0,1.0,1.0,61995.57,0.0
+3662,15754578.0,Teng,509.0,France,Male,55.0,1.0,0.0,2.0,1.0,1.0,15068.21,0.0
+3663,15708513.0,Maclean,675.0,Spain,Female,33.0,6.0,137227.04,1.0,1.0,1.0,126796.69,0.0
+3664,15592089.0,Ts'ui,551.0,France,Female,29.0,3.0,165969.26,1.0,1.0,0.0,71433.88,0.0
+3665,15735687.0,Lucciano,558.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,116547.31,0.0
+3666,15778861.0,Omeokachie,658.0,Germany,Female,58.0,5.0,152296.81,1.0,1.0,0.0,110371.84,1.0
+3667,15595039.0,Manna,704.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,102515.72,0.0
+3668,15602811.0,Chiemenam,712.0,Spain,Male,57.0,1.0,135120.72,1.0,1.0,1.0,153771.8,0.0
+3669,15640442.0,Chidi,769.0,France,Male,26.0,5.0,0.0,1.0,0.0,0.0,84760.32,0.0
+3670,15736431.0,Pirozzi,699.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,82970.69,0.0
+3671,15717984.0,Teng,850.0,France,Female,44.0,4.0,0.0,2.0,1.0,1.0,79559.02,0.0
+3672,15639669.0,Brown,718.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,84760.32,0.0
+3673,15734202.0,Dickson,738.0,France,Female,28.0,9.0,0.0,1.0,1.0,1.0,106977.53,0.0
+3674,15665963.0,Onyemere,589.0,France,Male,44.0,0.0,163943.89,2.0,1.0,1.0,110440.5,0.0
+3675,15802482.0,Mackey,548.0,Germany,Male,43.0,10.0,162036.92,1.0,1.0,0.0,158072.45,1.0
+3676,15784320.0,Genovese,602.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,130827.97,0.0
+3677,15586522.0,Chukwuemeka,811.0,Spain,Female,27.0,6.0,130114.39,1.0,1.0,1.0,149814.68,0.0
+3678,15697567.0,Folliero,633.0,France,Male,45.0,2.0,0.0,2.0,1.0,0.0,133086.32,0.0
+3679,15749123.0,Trevisani,588.0,Spain,Female,47.0,8.0,0.0,2.0,0.0,0.0,17095.49,1.0
+3680,15653741.0,Biryukov,677.0,France,Male,46.0,9.0,169824.46,1.0,1.0,1.0,151924.9,1.0
+3681,15784099.0,Scott,733.0,Spain,Male,44.0,9.0,0.0,2.0,1.0,0.0,161671.15,0.0
+3682,15611191.0,Fenton,699.0,France,Female,23.0,2.0,0.0,2.0,1.0,1.0,79919.13,0.0
+3683,15643359.0,Rizzo,584.0,France,Female,36.0,6.0,0.0,3.0,1.0,1.0,77866.91,0.0
+3684,15772896.0,Palerma,656.0,Spain,Male,59.0,6.0,118989.92,1.0,1.0,0.0,78436.36,1.0
+3685,15802823.0,Hs?,662.0,France,Male,31.0,7.0,0.0,1.0,1.0,1.0,179655.87,0.0
+3686,15641175.0,Yefremova,603.0,France,Male,33.0,6.0,0.0,1.0,0.0,1.0,160976.85,0.0
+3687,15592751.0,Ch'en,835.0,France,Male,34.0,8.0,0.0,1.0,1.0,1.0,125877.22,0.0
+3688,15658064.0,T'ao,559.0,Germany,Male,61.0,9.0,115617.24,1.0,0.0,0.0,71830.1,1.0
+3689,15702298.0,Nkemdirim,641.0,France,Female,32.0,1.0,0.0,2.0,0.0,0.0,152987.42,0.0
+3690,15658475.0,Trevisani,597.0,Germany,Female,41.0,9.0,113872.3,1.0,1.0,0.0,65986.48,0.0
+3691,15726720.0,Tseng,667.0,Germany,Male,33.0,1.0,125724.95,1.0,0.0,1.0,149614.81,1.0
+3692,15737080.0,Ozioma,569.0,Spain,Male,42.0,7.0,0.0,2.0,1.0,0.0,60905.51,0.0
+3693,15674868.0,Brown,595.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,91346.03,0.0
+3694,15750299.0,Akobundu,683.0,France,Female,44.0,4.0,0.0,2.0,1.0,0.0,94767.13,0.0
+3695,15810563.0,Kao,758.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,60901.72,0.0
+3696,15753092.0,Onyemere,752.0,France,Male,31.0,2.0,131355.6,1.0,1.0,1.0,52462.25,0.0
+3697,15806945.0,De Garis,686.0,France,Female,43.0,5.0,161766.87,1.0,1.0,1.0,129526.57,1.0
+3698,15581893.0,Ugochukwu,727.0,France,Female,32.0,9.0,74989.58,1.0,1.0,0.0,131660.31,0.0
+3699,15603851.0,Ch'ang,704.0,France,Male,49.0,6.0,0.0,1.0,1.0,0.0,102743.69,1.0
+3700,15620756.0,Ozioma,704.0,France,Male,42.0,4.0,154962.99,1.0,1.0,0.0,170819.01,1.0
+3701,15696834.0,Y?an,535.0,France,Male,41.0,2.0,0.0,1.0,1.0,1.0,60770.73,0.0
+3702,15653478.0,Milne,651.0,Germany,Female,40.0,7.0,130575.26,1.0,1.0,1.0,73334.43,0.0
+3703,15679550.0,Chidubem,653.0,France,Male,31.0,10.0,0.0,2.0,1.0,0.0,90537.97,0.0
+3704,15628583.0,H?,721.0,Spain,Female,37.0,3.0,0.0,2.0,0.0,1.0,161241.65,0.0
+3705,15663438.0,T'ang,559.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,51919.04,0.0
+3706,15590820.0,Y?an,723.0,France,Male,43.0,7.0,0.0,1.0,1.0,0.0,124045.65,0.0
+3707,15571571.0,Calabresi,724.0,France,Male,39.0,10.0,0.0,2.0,0.0,1.0,138527.56,0.0
+3708,15595440.0,Chia,605.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,1.0,141404.07,1.0
+3709,15662571.0,Chijindum,618.0,Germany,Female,34.0,1.0,131569.63,2.0,1.0,1.0,179436.6,0.0
+3710,15772573.0,Ritchie,554.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,1.0,156476.36,0.0
+3711,15618562.0,Fanucci,667.0,France,Female,64.0,2.0,122453.97,1.0,0.0,1.0,110468.73,0.0
+3712,15759436.0,H?,679.0,France,Male,38.0,7.0,97588.6,1.0,1.0,1.0,160523.33,0.0
+3713,15609977.0,Genovese,577.0,France,Male,58.0,8.0,0.0,2.0,1.0,1.0,85424.41,0.0
+3714,15602844.0,Chinagorom,762.0,Spain,Female,35.0,3.0,0.0,2.0,0.0,1.0,134589.58,0.0
+3715,15653547.0,Monds,703.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,0.0,89566.64,0.0
+3716,15772381.0,L?,706.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,101314.76,0.0
+3717,15722061.0,Fanucci,635.0,France,Male,37.0,6.0,124547.13,1.0,1.0,0.0,156424.4,0.0
+3718,15674811.0,Onwuamaeze,603.0,France,Female,53.0,9.0,0.0,2.0,0.0,1.0,158742.81,0.0
+3719,15646936.0,Hsiung,554.0,Spain,Female,39.0,5.0,123845.36,1.0,1.0,0.0,120540.83,1.0
+3720,15782736.0,P'eng,667.0,Spain,Female,31.0,1.0,107556.06,1.0,1.0,0.0,37688.31,0.0
+3721,15573318.0,Bogle,593.0,Germany,Female,34.0,4.0,150635.3,2.0,1.0,0.0,57558.95,0.0
+3722,15800688.0,Tuan,604.0,Germany,Female,39.0,2.0,98585.82,2.0,1.0,1.0,199357.24,0.0
+3723,15676966.0,Yegorov,633.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,80410.68,0.0
+3724,15735782.0,Cattaneo,572.0,Germany,Male,43.0,6.0,156848.13,2.0,0.0,0.0,52861.89,0.0
+3725,15578866.0,Chiazagomekpere,590.0,France,Male,19.0,4.0,0.0,2.0,1.0,1.0,143614.01,0.0
+3726,15583076.0,Ts'ai,653.0,France,Male,37.0,7.0,0.0,1.0,1.0,1.0,129525.58,0.0
+3727,15637753.0,Ikedinachukwu,565.0,Germany,Male,57.0,2.0,161889.79,1.0,1.0,0.0,104889.2,1.0
+3728,15763327.0,Hs?,599.0,Germany,Female,41.0,3.0,123823.3,2.0,0.0,1.0,51565.98,0.0
+3729,15806420.0,Dyer,766.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,0.0,107964.36,0.0
+3730,15600974.0,Ch'in,763.0,France,Male,42.0,7.0,117034.32,1.0,1.0,1.0,16929.41,0.0
+3731,15591344.0,K'ung,558.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,0.0,92888.52,0.0
+3732,15795586.0,Chiang,568.0,France,Female,58.0,8.0,0.0,2.0,1.0,1.0,123866.28,1.0
+3733,15586880.0,Chukwudi,716.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,113597.64,0.0
+3734,15696834.0,Ch'ang,629.0,France,Female,26.0,2.0,0.0,1.0,1.0,1.0,79577.4,0.0
+3735,15777045.0,Chidubem,711.0,France,Male,54.0,7.0,0.0,1.0,0.0,1.0,121592.5,1.0
+3736,15692671.0,Chiang,684.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,58854.88,0.0
+3737,15757912.0,Kaeppel,762.0,Spain,Male,27.0,2.0,0.0,2.0,0.0,1.0,182692.8,0.0
+3738,15732774.0,Chiedozie,661.0,Germany,Male,39.0,4.0,123863.64,2.0,1.0,0.0,1710.89,0.0
+3739,15677933.0,Chuang,755.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,83343.73,0.0
+3740,15572792.0,T'ien,683.0,Germany,Female,29.0,1.0,113599.55,2.0,0.0,0.0,104222.8,0.0
+3741,15665327.0,Chukwujekwu,689.0,Spain,Female,27.0,7.0,0.0,2.0,1.0,1.0,86413.11,0.0
+3742,15588928.0,Chidiegwu,635.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,59827.7,0.0
+3743,15743076.0,Li Fonti,559.0,France,Male,40.0,6.0,0.0,1.0,1.0,0.0,174435.48,0.0
+3744,15749019.0,Chukwujamuike,627.0,France,Male,32.0,8.0,0.0,1.0,1.0,1.0,134889.95,0.0
+3745,15590327.0,Dickson,703.0,Germany,Male,34.0,7.0,127209.0,1.0,1.0,1.0,164921.43,0.0
+3746,15645095.0,Chukwufumnanya,596.0,Germany,Female,39.0,4.0,128264.03,1.0,1.0,1.0,80552.11,1.0
+3747,15674573.0,Ritchie,743.0,France,Female,34.0,2.0,0.0,2.0,0.0,1.0,183682.09,0.0
+3748,15694566.0,Trevisan,641.0,Germany,Female,57.0,5.0,102724.38,2.0,1.0,0.0,123427.98,1.0
+3749,15750937.0,H?,629.0,Germany,Female,38.0,8.0,133141.34,1.0,0.0,0.0,171037.63,1.0
+3750,15682355.0,Onwubiko,556.0,Spain,Male,44.0,10.0,105770.7,1.0,1.0,0.0,179436.6,0.0
+3751,15680449.0,Chidumaga,671.0,Germany,Female,37.0,7.0,152303.8,1.0,1.0,0.0,96660.76,0.0
+3752,15774336.0,Onyemauchechukwu,589.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,152733.89,0.0
+3753,15734005.0,Chukwuemeka,655.0,France,Female,27.0,10.0,176914.8,1.0,1.0,0.0,102297.15,0.0
+3754,15651713.0,Obiajulu,709.0,France,Male,28.0,2.0,0.0,1.0,1.0,0.0,80190.36,0.0
+3755,15793424.0,Pinto,649.0,Germany,Female,39.0,9.0,118098.62,1.0,1.0,1.0,125877.22,1.0
+3756,15807433.0,Chinomso,673.0,France,Male,43.0,6.0,116471.43,1.0,1.0,0.0,124073.67,0.0
+3757,15751137.0,Chien,724.0,France,Female,45.0,5.0,0.0,2.0,0.0,1.0,176730.02,0.0
+3758,15655875.0,Frolov,571.0,France,Male,39.0,4.0,134950.19,1.0,1.0,0.0,163746.09,0.0
+3759,15749669.0,Fiorentino,718.0,France,Female,47.0,1.0,0.0,1.0,1.0,1.0,134491.09,1.0
+3760,15797964.0,Hsieh,598.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,98820.09,0.0
+3761,15723032.0,Milanesi,577.0,Spain,Male,33.0,5.0,0.0,1.0,0.0,1.0,66526.01,0.0
+3762,15631739.0,Nkemdilim,652.0,Germany,Male,28.0,3.0,136674.55,1.0,1.0,0.0,9984.4,0.0
+3763,15703731.0,Chukwujekwu,637.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,107867.38,0.0
+3764,15589475.0,Tuan,582.0,France,Male,32.0,6.0,0.0,2.0,0.0,1.0,27472.07,0.0
+3765,15689514.0,Onwumelu,596.0,Germany,Female,31.0,2.0,132763.49,2.0,1.0,0.0,126826.57,1.0
+3766,15748327.0,Ch'ien,632.0,France,Male,36.0,9.0,99513.87,1.0,1.0,1.0,79734.23,0.0
+3767,15784761.0,Ch'in,681.0,France,Female,62.0,7.0,0.0,1.0,1.0,0.0,136491.72,1.0
+3768,15690744.0,Chidiebere,679.0,Germany,Female,43.0,2.0,118889.66,2.0,1.0,0.0,141872.05,1.0
+3769,15593883.0,Onwuamaeze,619.0,France,Female,26.0,8.0,0.0,2.0,1.0,0.0,180329.83,0.0
+3770,15578722.0,Chukwufumnanya,573.0,France,Male,39.0,4.0,0.0,2.0,0.0,1.0,171774.5,0.0
+3771,15602034.0,Shao,651.0,France,Female,32.0,5.0,0.0,2.0,0.0,1.0,121232.58,0.0
+3772,15607537.0,Chukwukere,643.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,1.0,43821.86,0.0
+3773,15676526.0,Liao,682.0,France,Female,46.0,2.0,0.0,2.0,1.0,1.0,115818.51,0.0
+3774,15676966.0,Onyemachukwu,588.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,92220.09,0.0
+3775,15642545.0,Tsao,774.0,Spain,Male,36.0,6.0,137946.39,2.0,1.0,1.0,175094.62,0.0
+3776,15642997.0,Zikoranachidimma,537.0,France,Male,32.0,10.0,105791.06,2.0,1.0,1.0,140951.97,0.0
+3777,15770039.0,Lucchesi,651.0,Germany,Male,36.0,3.0,139848.17,1.0,1.0,0.0,106636.89,1.0
+3778,15649024.0,Iqbal,616.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,94862.93,0.0
+3779,15604544.0,H?,785.0,France,Male,41.0,8.0,123042.0,1.0,1.0,1.0,92914.67,0.0
+3780,15703413.0,Chibueze,647.0,Germany,Male,45.0,1.0,139825.34,1.0,1.0,1.0,102908.91,1.0
+3781,15782454.0,Toscani,592.0,Germany,Male,35.0,1.0,90124.64,2.0,1.0,0.0,136497.27,0.0
+3782,15649450.0,Nwankwo,607.0,France,Female,47.0,9.0,0.0,2.0,1.0,1.0,61977.82,0.0
+3783,15683544.0,Echezonachukwu,713.0,France,Male,34.0,2.0,96877.21,2.0,1.0,1.0,179258.68,0.0
+3784,15584037.0,Trevisani,687.0,France,Female,40.0,2.0,0.0,1.0,1.0,0.0,126013.58,1.0
+3785,15761763.0,Ch'ang,647.0,France,Female,48.0,8.0,0.0,1.0,1.0,0.0,184317.74,1.0
+3786,15586069.0,Brown,630.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,110440.5,0.0
+3787,15651581.0,Okwudiliolisa,757.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,143598.7,0.0
+3788,15663917.0,Nkemakonam,661.0,France,Female,32.0,1.0,100249.76,1.0,1.0,1.0,196261.64,0.0
+3789,15590344.0,Ch'ien,691.0,Spain,Female,42.0,4.0,0.0,2.0,1.0,1.0,18094.96,0.0
+3790,15596871.0,Chinedum,633.0,Germany,Female,38.0,2.0,104930.35,1.0,1.0,0.0,174777.98,0.0
+3791,15692540.0,Onwumelu,787.0,Germany,Male,42.0,6.0,101744.84,2.0,1.0,1.0,82463.69,0.0
+3792,15656484.0,Onwubiko,712.0,France,Male,38.0,2.0,0.0,1.0,1.0,0.0,129984.2,0.0
+3793,15783882.0,Tung,632.0,France,Female,29.0,2.0,0.0,2.0,0.0,1.0,35140.49,0.0
+3794,15792986.0,Pirozzi,662.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,72927.68,0.0
+3795,15684173.0,Teng,724.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,93901.61,0.0
+3796,15723488.0,Hs?,619.0,France,Female,43.0,3.0,0.0,1.0,0.0,1.0,66526.01,1.0
+3797,15806467.0,Chukwueloka,633.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,139946.68,0.0
+3798,15728474.0,Ch'en,651.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,1.0,143614.01,0.0
+3799,15672598.0,Shubin,587.0,Spain,Female,29.0,10.0,0.0,2.0,1.0,0.0,43821.56,0.0
+3800,15724296.0,Chukwuemeka,527.0,Germany,Female,39.0,5.0,140950.92,2.0,1.0,1.0,53328.7,0.0
+3801,15749177.0,Hsieh,619.0,Germany,Female,32.0,2.0,108914.17,1.0,0.0,0.0,172450.04,1.0
+3802,15669782.0,Echezonachukwu,716.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,95625.03,0.0
+3803,15568885.0,Tsao,773.0,Spain,Male,42.0,5.0,0.0,2.0,1.0,1.0,145936.21,0.0
+3804,15648067.0,Hsueh,641.0,France,Female,35.0,8.0,0.0,2.0,1.0,0.0,133598.8,0.0
+3805,15588933.0,Hsieh,739.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,1.0,86575.46,0.0
+3806,15604044.0,Yefremova,705.0,France,Female,34.0,2.0,0.0,2.0,0.0,0.0,109742.55,0.0
+3807,15733261.0,Onyekaozulu,685.0,France,Male,25.0,0.0,0.0,2.0,1.0,1.0,172668.39,0.0
+3808,15806901.0,Ch'iu,651.0,Germany,Female,39.0,1.0,120822.86,1.0,1.0,0.0,46811.77,1.0
+3809,15628999.0,Pinto,724.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,139297.71,0.0
+3810,15777901.0,Trevisani,612.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,138871.12,1.0
+3811,15599289.0,Hsieh,711.0,Spain,Male,37.0,7.0,0.0,2.0,0.0,0.0,180551.76,0.0
+3812,15779247.0,Lettiere,634.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,0.0,121525.48,0.0
+3813,15626457.0,Sun,619.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,123954.15,0.0
+3814,15746065.0,Chu,696.0,France,Male,43.0,1.0,0.0,2.0,1.0,0.0,53437.1,0.0
+3815,15806967.0,Scott,659.0,Spain,Female,32.0,5.0,0.0,1.0,0.0,0.0,84920.49,0.0
+3816,15688880.0,Gannon,639.0,France,Male,41.0,5.0,91536.97,1.0,1.0,1.0,93777.81,0.0
+3817,15589428.0,Chukwubuikem,629.0,Spain,Female,29.0,7.0,0.0,1.0,1.0,1.0,149384.43,0.0
+3818,15655341.0,Dickson,550.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,122960.98,0.0
+3819,15644977.0,Nkemdirim,685.0,France,Female,32.0,3.0,137482.19,1.0,1.0,1.0,58854.88,0.0
+3820,15771543.0,T'ang,584.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,57850.1,0.0
+3821,15756070.0,Chinweike,696.0,Germany,Female,36.0,1.0,131778.66,2.0,1.0,1.0,48242.11,0.0
+3822,15589948.0,Ochoa,757.0,France,Female,42.0,4.0,145079.72,2.0,1.0,0.0,113428.77,1.0
+3823,15694879.0,Onwuatuegwu,627.0,France,Male,43.0,1.0,0.0,2.0,1.0,1.0,134960.98,0.0
+3824,15725166.0,Rubeo,558.0,Germany,Female,43.0,3.0,78833.15,1.0,1.0,0.0,175893.53,1.0
+3825,15641655.0,Genovesi,712.0,Germany,Female,43.0,5.0,132623.6,1.0,1.0,0.0,122925.5,1.0
+3826,15749300.0,Hsueh,526.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,127569.8,0.0
+3827,15781665.0,Ritchie,753.0,France,Male,37.0,0.0,0.0,2.0,1.0,0.0,97734.3,0.0
+3828,15697042.0,Hsing,746.0,Germany,Male,36.0,7.0,94348.53,2.0,1.0,1.0,134901.56,0.0
+3829,15728006.0,Maclean,753.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,185541.2,0.0
+3830,15800326.0,Ch'in,629.0,France,Male,42.0,9.0,0.0,1.0,0.0,1.0,83681.2,0.0
+3831,15627665.0,Onyekachi,646.0,France,Female,28.0,8.0,0.0,1.0,1.0,0.0,82463.69,0.0
+3832,15676835.0,Ni,592.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,91943.94,0.0
+3833,15699195.0,Teng,673.0,Germany,Male,36.0,4.0,156848.23,2.0,1.0,0.0,60906.12,0.0
+3834,15618661.0,Chukwufumnanya,619.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,114448.77,0.0
+3835,15705313.0,Mironova,697.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,126726.33,0.0
+3836,15679429.0,Tsou,577.0,Spain,Male,41.0,7.0,0.0,2.0,1.0,1.0,58632.37,0.0
+3837,15698528.0,Shih,579.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,149772.61,0.0
+3838,15700696.0,Onyemachukwu,646.0,Spain,Female,45.0,9.0,0.0,1.0,1.0,0.0,179608.96,1.0
+3839,15744044.0,Ch'in,683.0,Germany,Male,37.0,2.0,116090.06,1.0,1.0,0.0,109614.57,0.0
+3840,15638355.0,T'ao,696.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,99298.8,0.0
+3841,15732265.0,Palerma,775.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,70956.75,0.0
+3842,15775235.0,O'Loghlin,516.0,France,Female,28.0,4.0,0.0,2.0,1.0,0.0,161407.48,0.0
+3843,15650098.0,Chukwueloka,613.0,France,Female,35.0,8.0,0.0,1.0,0.0,1.0,105570.39,0.0
+3844,15594812.0,Trevisani,775.0,France,Female,32.0,9.0,142862.21,1.0,1.0,1.0,137916.49,0.0
+3845,15689661.0,Y?,597.0,France,Male,28.0,7.0,0.0,1.0,0.0,0.0,127433.47,0.0
+3846,15699550.0,T'an,579.0,France,Male,37.0,7.0,113311.11,1.0,1.0,0.0,110230.4,0.0
+3847,15787550.0,T'ien,613.0,France,Male,43.0,10.0,0.0,1.0,1.0,1.0,101828.39,0.0
+3848,15712264.0,Chien,652.0,France,Male,40.0,5.0,0.0,4.0,1.0,0.0,72927.96,1.0
+3849,15705735.0,Toscani,683.0,Spain,Female,38.0,2.0,0.0,1.0,1.0,1.0,46775.29,0.0
+3850,15749119.0,K?,656.0,France,Female,44.0,9.0,91611.12,1.0,1.0,1.0,103832.58,0.0
+3851,15757837.0,Chibuzo,598.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,85422.14,0.0
+3852,15576706.0,Chukwuemeka,637.0,France,Female,31.0,4.0,135650.88,1.0,1.0,0.0,125440.11,0.0
+3853,15699225.0,Aksenov,642.0,France,Male,24.0,7.0,0.0,1.0,1.0,0.0,94977.3,0.0
+3854,15778915.0,P'an,669.0,France,Male,49.0,2.0,0.0,2.0,0.0,1.0,74580.8,0.0
+3855,15748552.0,Ch'ien,627.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,180862.26,0.0
+3856,15573876.0,Ch'iu,578.0,France,Female,36.0,3.0,119035.35,1.0,1.0,0.0,141446.01,0.0
+3857,15809057.0,Hsing,567.0,France,Male,41.0,8.0,0.0,2.0,0.0,0.0,158043.11,0.0
+3858,15734987.0,Chiemenam,688.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,1.0,113436.08,0.0
+3859,15709142.0,Chimezie,733.0,Spain,Male,31.0,3.0,0.0,2.0,1.0,1.0,166696.72,0.0
+3860,15591942.0,Smith,599.0,France,Male,44.0,6.0,0.0,2.0,1.0,0.0,161579.85,0.0
+3861,15597951.0,Zox,725.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,96680.16,0.0
+3862,15693737.0,T'ang,757.0,France,Male,25.0,8.0,0.0,2.0,1.0,0.0,126812.63,0.0
+3863,15732265.0,Ch'ien,590.0,Spain,Male,35.0,8.0,133636.04,1.0,1.0,0.0,151902.78,0.0
+3864,15589475.0,Kuo,727.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,175893.53,0.0
+3865,15799468.0,Ch'iu,735.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,161209.66,0.0
+3866,15747358.0,Palerma,588.0,France,Male,33.0,5.0,0.0,1.0,1.0,1.0,1795.93,0.0
+3867,15756975.0,Nnamdi,739.0,France,Female,41.0,5.0,0.0,2.0,0.0,0.0,131644.41,0.0
+3868,15673747.0,Ch'eng,684.0,France,Female,25.0,2.0,0.0,1.0,0.0,0.0,74965.61,1.0
+3869,15685920.0,Fanucci,678.0,France,Male,35.0,5.0,117641.43,1.0,1.0,1.0,154844.22,0.0
+3870,15572767.0,Tung,569.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,1.0,158750.14,0.0
+3871,15707189.0,Niu,602.0,France,Female,38.0,9.0,0.0,1.0,1.0,1.0,130189.72,0.0
+3872,15771323.0,H?,682.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,0.0,78854.57,0.0
+3873,15592021.0,Kao,683.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,74858.3,0.0
+3874,15665327.0,Ch'en,594.0,France,Male,25.0,3.0,0.0,2.0,1.0,0.0,123882.73,0.0
+3875,15672432.0,Tuan,593.0,France,Female,32.0,3.0,99546.25,1.0,1.0,1.0,164741.5,0.0
+3876,15758813.0,Ch'ang,850.0,France,Female,34.0,5.0,137851.31,1.0,0.0,0.0,74849.46,1.0
+3877,15621435.0,Chikelu,711.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,125848.88,0.0
+3878,15741643.0,Chiekwugo,785.0,France,Male,45.0,7.0,0.0,1.0,1.0,0.0,125424.42,1.0
+3879,15772423.0,Chukwudi,850.0,France,Male,38.0,3.0,102773.2,1.0,1.0,1.0,57929.9,0.0
+3880,15656835.0,Milano,559.0,Germany,Female,40.0,1.0,151912.49,2.0,1.0,0.0,120531.31,0.0
+3881,15721463.0,Gant,642.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,6076.62,0.0
+3882,15799358.0,Ch'ien,622.0,Spain,Female,39.0,3.0,144427.96,1.0,0.0,0.0,72861.84,0.0
+3883,15589230.0,Chukwumaobim,548.0,France,Male,31.0,6.0,117438.92,1.0,1.0,1.0,155915.24,0.0
+3884,15629885.0,Yefremov,543.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,166468.21,1.0
+3885,15597945.0,Nwankwo,684.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,107955.67,0.0
+3886,15570141.0,O'Donnell,446.0,France,Female,28.0,3.0,0.0,2.0,0.0,1.0,149913.94,0.0
+3887,15618243.0,Chin,668.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,0.0,152884.85,0.0
+3888,15752588.0,Chukwufumnanya,584.0,France,Male,30.0,6.0,0.0,2.0,1.0,0.0,107876.2,0.0
+3889,15690925.0,Okwuadigbo,589.0,Spain,Female,41.0,5.0,0.0,1.0,1.0,0.0,93536.38,1.0
+3890,15756560.0,Macleod,592.0,France,Male,35.0,10.0,114540.38,1.0,1.0,1.0,28707.69,0.0
+3891,15690731.0,Y?,812.0,Germany,Female,47.0,7.0,136724.69,1.0,1.0,0.0,126517.97,1.0
+3892,15782875.0,Hovell,635.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,136812.02,0.0
+3893,15761158.0,Dickson,607.0,France,Female,42.0,8.0,0.0,2.0,1.0,0.0,35450.53,0.0
+3894,15635277.0,Dickson,743.0,Spain,Male,37.0,7.0,0.0,2.0,0.0,1.0,86935.08,0.0
+3895,15794849.0,L?,743.0,France,Male,38.0,6.0,117300.02,1.0,1.0,1.0,59207.41,0.0
+3896,15711398.0,Li,733.0,France,Male,36.0,4.0,141952.07,1.0,1.0,0.0,173837.54,0.0
+3897,15665385.0,Liao,596.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,1.0,73873.65,0.0
+3898,15581388.0,Chidiebere,709.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,58440.43,0.0
+3899,15734850.0,Nkemdilim,679.0,Germany,Male,38.0,5.0,92895.56,2.0,1.0,0.0,158054.49,1.0
+3900,15733581.0,Chin,679.0,France,Female,46.0,8.0,0.0,1.0,1.0,1.0,18094.96,1.0
+3901,15729201.0,Chia,652.0,France,Female,30.0,8.0,0.0,2.0,1.0,1.0,131736.23,0.0
+3902,15726490.0,Chukwujekwu,736.0,Germany,Male,45.0,4.0,122486.11,1.0,1.0,0.0,121151.1,1.0
+3903,15672756.0,Nnaife,824.0,France,Female,48.0,8.0,0.0,3.0,1.0,1.0,10553.31,1.0
+3904,15773099.0,Ch'ien,602.0,France,Female,58.0,9.0,171646.76,1.0,1.0,0.0,136079.64,1.0
+3905,15800856.0,Chiang,723.0,Spain,Male,32.0,7.0,0.0,1.0,1.0,1.0,66463.62,0.0
+3906,15778089.0,White,578.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,126743.33,0.0
+3907,15577515.0,Nwachinemelu,667.0,France,Female,41.0,6.0,0.0,2.0,1.0,1.0,173870.39,0.0
+3908,15810563.0,Chukwukere,590.0,France,Male,36.0,6.0,97612.12,1.0,1.0,0.0,180439.75,0.0
+3909,15762588.0,Manna,565.0,France,Male,40.0,4.0,0.0,1.0,1.0,1.0,180551.76,1.0
+3910,15605672.0,Chukwualuka,673.0,France,Male,43.0,3.0,129120.64,1.0,0.0,0.0,2497.69,0.0
+3911,15691543.0,Chiang,651.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,1.0,97623.42,0.0
+3912,15605299.0,Lu,645.0,France,Male,43.0,1.0,0.0,1.0,0.0,0.0,61759.33,1.0
+3913,15762882.0,Mazzanti,629.0,France,Male,53.0,4.0,0.0,1.0,1.0,0.0,173464.43,1.0
+3914,15593854.0,Chiganu,735.0,Germany,Male,34.0,8.0,121898.58,1.0,0.0,1.0,84972.9,0.0
+3915,15694890.0,Maclean,758.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,90977.48,0.0
+3916,15582762.0,Lucchesi,576.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,0.0,109742.55,0.0
+3917,15772412.0,Piazza,646.0,Spain,Female,57.0,6.0,0.0,1.0,1.0,0.0,27862.58,1.0
+3918,15606841.0,Teng,726.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,46774.94,0.0
+3919,15698117.0,Macleod,705.0,Spain,Male,47.0,5.0,0.0,1.0,0.0,1.0,138881.02,1.0
+3920,15606841.0,Chiu,627.0,France,Male,45.0,9.0,0.0,1.0,1.0,0.0,93901.61,0.0
+3921,15594862.0,Nkemjika,709.0,Spain,Male,40.0,0.0,89696.84,1.0,1.0,1.0,143668.28,0.0
+3922,15680141.0,Tseng,584.0,Germany,Male,31.0,2.0,135927.03,1.0,0.0,1.0,108732.96,0.0
+3923,15764033.0,Scott,705.0,France,Female,27.0,2.0,0.0,2.0,1.0,0.0,132798.58,0.0
+3924,15745455.0,Hsu,758.0,France,Male,35.0,7.0,80728.15,1.0,1.0,0.0,80963.92,0.0
+3925,15589975.0,Ritchie,706.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,186885.01,0.0
+3926,15757897.0,Toscani,696.0,Germany,Female,64.0,7.0,124513.66,1.0,1.0,1.0,70563.9,0.0
+3927,15576680.0,Nwokike,842.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,62700.08,0.0
+3928,15758813.0,Bevan,730.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,139913.94,0.0
+3929,15602479.0,Ch'en,639.0,Germany,Male,32.0,3.0,73348.56,2.0,1.0,0.0,175820.91,0.0
+3930,15803526.0,Hs?,716.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,164741.5,0.0
+3931,15782530.0,Sun,616.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,1.0,148516.79,0.0
+3932,15782758.0,Genovesi,598.0,Germany,Female,43.0,10.0,79340.95,1.0,1.0,0.0,146511.2,1.0
+3933,15808784.0,Chinwenma,682.0,France,Male,51.0,5.0,0.0,2.0,0.0,0.0,141210.18,0.0
+3934,15609899.0,Ts'ui,678.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,170903.4,0.0
+3935,15641794.0,Chibueze,705.0,Spain,Female,33.0,10.0,0.0,2.0,0.0,1.0,181495.86,0.0
+3936,15613141.0,Toscani,607.0,France,Male,35.0,2.0,120875.23,1.0,1.0,0.0,171056.65,0.0
+3937,15672930.0,Manna,581.0,Germany,Male,40.0,2.0,128206.79,1.0,1.0,1.0,103792.53,1.0
+3938,15794056.0,Ts'ui,804.0,France,Male,38.0,0.0,0.0,2.0,1.0,0.0,120115.5,0.0
+3939,15627262.0,Ch'en,554.0,France,Male,40.0,9.0,0.0,1.0,1.0,0.0,81442.6,1.0
+3940,15706593.0,Shih,642.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,62368.33,0.0
+3941,15602034.0,P'eng,642.0,Spain,Male,24.0,2.0,0.0,1.0,1.0,0.0,91775.51,0.0
+3942,15617206.0,Nnamdi,660.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,1.0,114951.42,0.0
+3943,15633666.0,Chiu,653.0,Spain,Male,59.0,5.0,0.0,2.0,0.0,1.0,126688.83,0.0
+3944,15792489.0,Nucci,551.0,Germany,Female,40.0,6.0,131955.07,1.0,0.0,0.0,66463.62,1.0
+3945,15576680.0,P'eng,651.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,0.0,121541.56,0.0
+3946,15790442.0,Calabrese,596.0,France,Female,36.0,0.0,0.0,2.0,1.0,1.0,65612.12,0.0
+3947,15686983.0,Lo,629.0,France,Male,48.0,6.0,0.0,1.0,0.0,0.0,158887.09,1.0
+3948,15674727.0,Chiganu,564.0,France,Female,42.0,6.0,0.0,2.0,1.0,0.0,90905.28,0.0
+3949,15681068.0,Sun,708.0,France,Male,37.0,1.0,0.0,2.0,0.0,1.0,141821.32,0.0
+3950,15677906.0,Aitken,603.0,Germany,Female,29.0,5.0,96143.47,2.0,1.0,1.0,138350.79,0.0
+3951,15793723.0,Mazzanti,544.0,France,Female,35.0,1.0,0.0,1.0,1.0,1.0,189607.71,0.0
+3952,15794056.0,Ch'eng,636.0,Germany,Male,36.0,5.0,117206.3,4.0,1.0,0.0,52876.41,1.0
+3953,15777045.0,Tsao,607.0,Germany,Female,33.0,1.0,120124.35,2.0,1.0,0.0,18210.36,0.0
+3954,15755223.0,Shih,568.0,France,Male,36.0,8.0,0.0,1.0,0.0,1.0,121451.21,1.0
+3955,15594577.0,Efremov,642.0,France,Female,59.0,0.0,0.0,1.0,1.0,0.0,172749.65,1.0
+3956,15665811.0,Smith,697.0,Spain,Male,34.0,10.0,0.0,2.0,0.0,1.0,174825.57,0.0
+3957,15652999.0,T'ang,587.0,France,Male,46.0,5.0,0.0,1.0,0.0,0.0,179913.98,1.0
+3958,15765093.0,Chuang,632.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,129892.93,0.0
+3959,15702430.0,Yefimova,648.0,France,Female,27.0,2.0,0.0,1.0,1.0,1.0,74890.58,0.0
+3960,15589329.0,Chukwufumnanya,548.0,Germany,Female,43.0,3.0,99836.47,1.0,1.0,0.0,121440.8,0.0
+3961,15757577.0,Ch'ien,850.0,France,Male,35.0,5.0,0.0,2.0,0.0,0.0,120853.83,0.0
+3962,15592999.0,Ch'eng,709.0,France,Male,41.0,2.0,0.0,1.0,1.0,0.0,122221.33,0.0
+3963,15566633.0,Chidumaga,577.0,Germany,Male,51.0,5.0,143054.85,1.0,1.0,0.0,102908.91,1.0
+3964,15719649.0,Nkemakonam,775.0,France,Female,38.0,7.0,0.0,1.0,0.0,1.0,168918.47,0.0
+3965,15593737.0,Udinesi,746.0,France,Male,37.0,4.0,113743.37,1.0,1.0,1.0,72993.65,0.0
+3966,15612109.0,Hsia,681.0,Spain,Female,37.0,3.0,0.0,2.0,1.0,1.0,170968.99,0.0
+3967,15643679.0,Iqbal,646.0,Spain,Male,39.0,2.0,0.0,2.0,0.0,0.0,170984.97,0.0
+3968,15783029.0,Kao,553.0,Spain,Female,27.0,7.0,0.0,2.0,1.0,1.0,86950.72,0.0
+3969,15605827.0,Ozioma,665.0,France,Female,42.0,5.0,0.0,3.0,1.0,0.0,136054.45,1.0
+3970,15641640.0,Pinto,719.0,France,Female,40.0,2.0,0.0,1.0,1.0,1.0,79476.63,0.0
+3971,15663917.0,Tuan,707.0,Germany,Male,30.0,3.0,140733.74,1.0,1.0,1.0,177844.06,0.0
+3972,15645097.0,Ch'ang,597.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,175664.25,1.0
+3973,15724251.0,K?,586.0,Spain,Female,33.0,8.0,0.0,1.0,1.0,0.0,184524.65,0.0
+3974,15640074.0,Ch'ien,711.0,France,Male,29.0,10.0,0.0,2.0,1.0,0.0,156953.98,0.0
+3975,15797686.0,Trevisani,734.0,Germany,Male,37.0,7.0,115024.5,2.0,1.0,0.0,175893.53,0.0
+3976,15665416.0,Bellucci,753.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,111698.0,0.0
+3977,15738751.0,L?,654.0,France,Female,42.0,5.0,0.0,3.0,1.0,1.0,47981.54,1.0
+3978,15749381.0,Lombardo,542.0,Spain,Male,42.0,9.0,0.0,1.0,1.0,1.0,143683.75,0.0
+3979,15570141.0,T'ien,686.0,France,Male,58.0,7.0,0.0,2.0,1.0,1.0,147963.07,0.0
+3980,15775750.0,Chidubem,651.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,105986.01,0.0
+3981,15603465.0,Udinese,583.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,131662.47,0.0
+3982,15699450.0,Brown,708.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,173632.92,0.0
+3983,15593773.0,Calabresi,652.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,172363.41,0.0
+3984,15579112.0,Okwuadigbo,667.0,Spain,Male,35.0,0.0,116121.67,1.0,1.0,0.0,151401.33,0.0
+3985,15699095.0,Chukwubuikem,749.0,France,Female,30.0,6.0,0.0,1.0,1.0,1.0,176913.52,0.0
+3986,15684861.0,Maclean,638.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,79259.77,0.0
+3987,15691792.0,Galkin,790.0,France,Female,21.0,1.0,133467.77,1.0,1.0,1.0,141802.93,0.0
+3988,15803749.0,Hsia,584.0,France,Male,45.0,7.0,0.0,1.0,0.0,0.0,102021.39,1.0
+3989,15772381.0,McIntyre,645.0,France,Male,42.0,6.0,0.0,1.0,1.0,0.0,136491.72,1.0
+3990,15637550.0,Chiemela,695.0,Spain,Female,52.0,4.0,0.0,2.0,1.0,1.0,79005.95,0.0
+3991,15628308.0,Yobanna,684.0,France,Female,35.0,10.0,121874.89,1.0,0.0,1.0,189431.86,0.0
+3992,15637315.0,Hsiao,705.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,167033.83,0.0
+3993,15611669.0,Ugochukwu,743.0,France,Female,33.0,6.0,135984.15,1.0,1.0,1.0,183868.01,0.0
+3994,15671342.0,Tung,644.0,Germany,Male,44.0,5.0,130142.79,1.0,1.0,0.0,146511.6,1.0
+3995,15788686.0,Yefimova,520.0,France,Male,28.0,5.0,0.0,1.0,1.0,0.0,63218.85,0.0
+3996,15637593.0,Goliwe,707.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,95957.09,0.0
+3997,15757628.0,Chidiegwu,664.0,Germany,Male,24.0,5.0,121818.87,1.0,1.0,1.0,12093.61,0.0
+3998,15690796.0,Pinto,748.0,Spain,Female,29.0,3.0,0.0,2.0,1.0,0.0,87412.24,0.0
+3999,15738785.0,Yeh,743.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,158035.48,0.0
+4000,15690898.0,Chukwukere,733.0,France,Male,31.0,4.0,0.0,2.0,0.0,1.0,179833.45,0.0
+4001,15674812.0,Lai,591.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,0.0,94845.45,0.0
+4002,15771417.0,Ch'ang,774.0,Germany,Male,36.0,9.0,142880.28,1.0,1.0,1.0,99504.03,0.0
+4003,15682540.0,Pinto,753.0,France,Male,34.0,3.0,108711.57,1.0,0.0,1.0,80190.36,0.0
+4004,15796071.0,Yermakova,742.0,Spain,Female,26.0,1.0,149879.66,1.0,0.0,0.0,16081.62,0.0
+4005,15633840.0,Chiang,714.0,Germany,Male,39.0,10.0,120150.39,2.0,1.0,1.0,139410.08,0.0
+4006,15612455.0,Brown,659.0,France,Male,24.0,9.0,0.0,2.0,1.0,0.0,198193.75,0.0
+4007,15711378.0,Chiagoziem,679.0,France,Male,29.0,4.0,124576.65,1.0,1.0,1.0,140937.91,0.0
+4008,15722532.0,Ikedinachukwu,648.0,France,Male,47.0,2.0,0.0,2.0,1.0,1.0,125562.82,0.0
+4009,15620443.0,T'ao,684.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,6342.84,0.0
+4010,15599090.0,Maclean,496.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,142513.5,0.0
+4011,15793311.0,Hsing,720.0,Germany,Male,43.0,10.0,107233.85,2.0,1.0,1.0,173365.9,0.0
+4012,15623478.0,Pagnotto,658.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,172638.13,0.0
+4013,15589489.0,K'ung,724.0,Germany,Female,52.0,8.0,110112.55,2.0,1.0,0.0,151325.24,1.0
+4014,15705343.0,Chizuoke,592.0,France,Female,28.0,7.0,0.0,2.0,1.0,1.0,137079.86,0.0
+4015,15792525.0,Whitehead,679.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,171706.66,0.0
+4016,15622443.0,Chukwukadibia,651.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,181419.29,0.0
+4017,15763704.0,Cribb,644.0,France,Male,46.0,4.0,0.0,1.0,1.0,0.0,137961.41,0.0
+4018,15665896.0,Nkemdilim,757.0,Germany,Male,32.0,4.0,102994.04,2.0,0.0,1.0,115700.2,0.0
+4019,15701291.0,Ch'iu,636.0,France,Male,37.0,1.0,0.0,1.0,0.0,0.0,57320.92,0.0
+4020,15694829.0,Li Fonti,459.0,Spain,Male,43.0,0.0,0.0,1.0,1.0,0.0,121457.88,1.0
+4021,15604093.0,Nwankwo,719.0,France,Male,26.0,3.0,88758.93,1.0,1.0,1.0,60728.89,0.0
+4022,15722122.0,Eluemuno,725.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,0.0,158973.77,0.0
+4023,15795290.0,Ch'ang,706.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,132468.76,0.0
+4024,15798850.0,Shao,739.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,140941.47,0.0
+4025,15592461.0,Liao,597.0,Spain,Female,47.0,8.0,171770.55,1.0,1.0,0.0,137651.6,1.0
+4026,15673591.0,Bischof,624.0,France,Female,46.0,1.0,0.0,2.0,1.0,0.0,84440.5,0.0
+4027,15655274.0,Hsing,733.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,1.0,125962.02,0.0
+4028,15584091.0,Tsao,553.0,Spain,Male,41.0,4.0,0.0,2.0,1.0,0.0,136915.15,0.0
+4029,15646539.0,Hsueh,556.0,France,Female,44.0,3.0,0.0,1.0,0.0,0.0,160526.36,1.0
+4030,15783859.0,Obiajulu,538.0,France,Female,60.0,6.0,69413.44,3.0,1.0,1.0,157891.86,1.0
+4031,15658969.0,Milano,703.0,France,Male,48.0,8.0,0.0,2.0,1.0,1.0,86490.09,0.0
+4032,15746664.0,Clements,596.0,Spain,Female,32.0,6.0,0.0,2.0,1.0,0.0,162452.65,0.0
+4033,15624293.0,Genovesi,627.0,France,Male,46.0,5.0,0.0,2.0,1.0,0.0,173498.45,0.0
+4034,15734659.0,Chukwuebuka,639.0,France,Male,30.0,9.0,0.0,2.0,0.0,1.0,159835.94,0.0
+4035,15807595.0,Folliero,797.0,France,Female,39.0,3.0,0.0,2.0,1.0,0.0,38970.14,0.0
+4036,15691863.0,Lai,614.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,25310.82,0.0
+4037,15803965.0,Teng,755.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,81778.72,0.0
+4038,15625035.0,Calabresi,817.0,France,Female,42.0,8.0,0.0,2.0,0.0,1.0,91560.63,0.0
+4039,15626243.0,Chukwudi,716.0,France,Female,29.0,4.0,149379.66,1.0,1.0,0.0,75578.67,0.0
+4040,15660679.0,Chigozie,781.0,France,Male,43.0,4.0,162691.16,1.0,1.0,0.0,878.87,1.0
+4041,15790829.0,T'ang,679.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,118863.38,0.0
+4042,15589257.0,Ch'iu,501.0,Spain,Male,62.0,7.0,0.0,2.0,1.0,1.0,180427.24,0.0
+4043,15684440.0,Ozerova,732.0,Germany,Male,34.0,4.0,106414.57,2.0,1.0,1.0,185510.34,0.0
+4044,15709447.0,Tsai,705.0,Germany,Female,38.0,3.0,150635.3,2.0,1.0,0.0,124415.5,0.0
+4045,15628999.0,Nwachinemelu,704.0,France,Male,24.0,1.0,0.0,1.0,1.0,1.0,61312.15,0.0
+4046,15727299.0,Nwankwo,554.0,France,Male,27.0,8.0,0.0,2.0,1.0,1.0,148087.89,0.0
+4047,15752912.0,Ch'ang,652.0,Spain,Female,40.0,10.0,0.0,2.0,0.0,1.0,167969.15,0.0
+4048,15712176.0,Young,617.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,137510.41,0.0
+4049,15641640.0,Chukwudi,591.0,France,Female,36.0,4.0,123378.2,1.0,1.0,1.0,94738.54,0.0
+4050,15707968.0,T'ang,594.0,Germany,Male,32.0,3.0,119035.35,2.0,0.0,1.0,164741.5,0.0
+4051,15613197.0,Lucciano,688.0,Spain,Male,27.0,5.0,0.0,2.0,1.0,0.0,64421.02,0.0
+4052,15782468.0,Omeokachie,744.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,126761.1,0.0
+4053,15624995.0,Chiemela,756.0,Germany,Female,44.0,1.0,134571.5,2.0,1.0,1.0,127059.04,0.0
+4054,15741092.0,T'ao,544.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,90591.15,0.0
+4055,15736431.0,Onwughara,561.0,Germany,Female,33.0,1.0,125850.53,1.0,0.0,1.0,172749.4,0.0
+4056,15576928.0,Milne,712.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,121568.37,0.0
+4057,15724876.0,Chukwuemeka,590.0,France,Female,28.0,6.0,0.0,1.0,1.0,0.0,172524.51,0.0
+4058,15787830.0,Fomina,590.0,France,Male,25.0,7.0,0.0,2.0,1.0,1.0,132770.11,0.0
+4059,15578788.0,Hsu,718.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,1.0,164768.82,0.0
+4060,15793881.0,Ch'in,673.0,France,Female,37.0,2.0,0.0,2.0,1.0,1.0,10772.13,0.0
+4061,15771543.0,Mazzi,553.0,Germany,Female,46.0,5.0,130274.26,1.0,1.0,0.0,105720.09,1.0
+4062,15593943.0,Onyekachi,712.0,France,Female,34.0,4.0,0.0,2.0,0.0,0.0,136034.93,0.0
+4063,15592386.0,Mazzi,711.0,France,Male,35.0,3.0,0.0,1.0,1.0,0.0,139947.17,0.0
+4064,15691808.0,Y?an,571.0,France,Male,35.0,3.0,132088.59,1.0,1.0,1.0,193018.71,0.0
+4065,15746802.0,Mazzanti,675.0,France,Male,34.0,7.0,79340.95,1.0,1.0,0.0,87071.18,0.0
+4066,15674950.0,Frolov,607.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,145936.81,1.0
+4067,15702293.0,Eluemuno,663.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,179540.73,0.0
+4068,15724307.0,Fanucci,622.0,France,Male,38.0,8.0,0.0,2.0,0.0,0.0,121084.27,0.0
+4069,15596984.0,Lai,637.0,France,Female,46.0,5.0,0.0,2.0,1.0,1.0,121085.72,0.0
+4070,15764491.0,Scott,712.0,France,Male,33.0,1.0,69329.47,1.0,1.0,1.0,78698.04,0.0
+4071,15706799.0,Hsueh,553.0,France,Female,52.0,2.0,0.0,3.0,0.0,0.0,108764.69,1.0
+4072,15680430.0,Ts'ao,596.0,France,Male,42.0,9.0,61482.47,1.0,1.0,1.0,28238.25,0.0
+4073,15611287.0,Chukwukadibia,727.0,Spain,Female,32.0,7.0,113657.83,1.0,1.0,1.0,105817.63,1.0
+4074,15622443.0,T'ang,730.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,138780.93,0.0
+4075,15722533.0,Fiorentino,775.0,France,Male,45.0,8.0,0.0,2.0,1.0,1.0,86937.2,0.0
+4076,15612444.0,Folliero,466.0,France,Male,37.0,5.0,0.0,2.0,0.0,1.0,93739.71,0.0
+4077,15589924.0,Chiang,593.0,France,Female,24.0,2.0,0.0,2.0,1.0,1.0,84440.5,0.0
+4078,15643359.0,Ts'ao,603.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,1.0,80552.11,0.0
+4079,15685329.0,Hsia,525.0,Spain,Female,47.0,3.0,0.0,2.0,1.0,0.0,87822.14,1.0
+4080,15642545.0,Ritchie,707.0,France,Male,40.0,6.0,0.0,1.0,1.0,1.0,94951.27,0.0
+4081,15772781.0,Onwumelu,632.0,Spain,Female,39.0,9.0,0.0,2.0,1.0,1.0,82820.85,0.0
+4082,15605284.0,Yao,635.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,0.0,134589.58,0.0
+4083,15646764.0,Eluemuno,671.0,France,Male,25.0,5.0,0.0,2.0,1.0,0.0,137916.49,0.0
+4084,15762793.0,Chukwujamuike,542.0,France,Male,41.0,3.0,125495.64,1.0,1.0,0.0,1354.25,0.0
+4085,15707025.0,Zetticci,714.0,France,Female,38.0,9.0,0.0,2.0,1.0,0.0,141426.78,0.0
+4086,15581736.0,Ts'ui,730.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,141815.76,0.0
+4087,15665634.0,Mazzi,683.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,142223.94,0.0
+4088,15664539.0,Genovese,562.0,France,Male,23.0,5.0,0.0,2.0,1.0,1.0,151325.24,0.0
+4089,15620204.0,Palerma,646.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,146411.6,0.0
+4090,15642709.0,Hsueh,681.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,161519.79,0.0
+4091,15583576.0,Hsueh,624.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,48475.12,0.0
+4092,15578980.0,Lucciano,748.0,Spain,Male,49.0,8.0,0.0,1.0,1.0,0.0,52462.25,1.0
+4093,15791767.0,Chimezie,705.0,France,Male,31.0,3.0,81947.76,1.0,1.0,1.0,130789.6,0.0
+4094,15791535.0,Hsiao,683.0,France,Female,41.0,4.0,0.0,1.0,0.0,0.0,168895.06,0.0
+4095,15772073.0,Dyer,594.0,Germany,Female,34.0,5.0,143964.36,2.0,0.0,1.0,35279.74,0.0
+4096,15764033.0,Ts'ui,558.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,84133.48,0.0
+4097,15680752.0,Uvarov,636.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,113428.77,0.0
+4098,15670172.0,Ch'ang,642.0,France,Female,37.0,7.0,0.0,2.0,0.0,0.0,82709.8,0.0
+4099,15592230.0,Hs?,714.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,100423.88,0.0
+4100,15694829.0,Chibugo,637.0,Germany,Male,33.0,3.0,132412.38,2.0,1.0,0.0,155870.82,0.0
+4101,15750803.0,Obiajulu,706.0,France,Male,32.0,2.0,163760.82,1.0,1.0,0.0,145524.36,0.0
+4102,15598892.0,Okwudiliolisa,649.0,Spain,Male,50.0,8.0,0.0,1.0,1.0,1.0,136910.18,1.0
+4103,15610972.0,Rizzo,720.0,France,Female,25.0,7.0,148045.45,1.0,1.0,0.0,64945.57,0.0
+4104,15771857.0,Chukwuemeka,599.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,51454.06,0.0
+4105,15736420.0,Chikezie,627.0,France,Female,33.0,1.0,0.0,1.0,1.0,1.0,172668.39,0.0
+4106,15692991.0,Onyekachi,569.0,Spain,Male,30.0,3.0,0.0,2.0,0.0,0.0,140745.91,0.0
+4107,15641110.0,Ch'iu,540.0,France,Male,38.0,4.0,0.0,2.0,0.0,1.0,85421.48,0.0
+4108,15761141.0,DeRose,537.0,Germany,Female,35.0,9.0,153722.47,2.0,1.0,1.0,78700.02,0.0
+4109,15658577.0,Tuan,696.0,Germany,Female,37.0,9.0,121537.71,1.0,0.0,1.0,55775.84,1.0
+4110,15660144.0,Hsia,687.0,Spain,Female,37.0,2.0,109733.2,1.0,1.0,0.0,177718.17,0.0
+4111,15782688.0,Chiu,705.0,Germany,Male,31.0,2.0,141581.71,2.0,1.0,1.0,141404.07,0.0
+4112,15704081.0,Tsao,654.0,France,Male,34.0,7.0,0.0,1.0,1.0,1.0,131712.56,0.0
+4113,15790810.0,Hs?,634.0,France,Female,44.0,3.0,0.0,2.0,1.0,1.0,125154.57,0.0
+4114,15732540.0,T'ang,613.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,72350.47,0.0
+4115,15811200.0,Ts'ui,649.0,Spain,Male,32.0,2.0,0.0,2.0,0.0,0.0,133653.38,0.0
+4116,15706223.0,Nkemjika,766.0,Germany,Male,46.0,6.0,136623.19,1.0,1.0,0.0,165082.25,1.0
+4117,15714240.0,Onwumelu,537.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,133653.38,0.0
+4118,15589949.0,Onyekachukwu,714.0,Germany,Female,25.0,7.0,69712.84,2.0,1.0,1.0,123587.85,0.0
+4119,15793723.0,Ts'ai,682.0,Spain,Male,31.0,4.0,0.0,2.0,0.0,1.0,131886.46,0.0
+4120,15742210.0,White,612.0,Spain,Male,35.0,8.0,0.0,1.0,1.0,0.0,132798.26,0.0
+4121,15804919.0,Hsieh,762.0,France,Female,32.0,4.0,0.0,2.0,0.0,0.0,93524.19,0.0
+4122,15728352.0,Shih,638.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,0.0,38015.34,0.0
+4123,15783541.0,Hsiao,485.0,Spain,Female,54.0,2.0,145980.23,1.0,0.0,0.0,4272.0,1.0
+4124,15728773.0,Hsia,579.0,France,Male,41.0,7.0,0.0,2.0,0.0,0.0,132770.11,0.0
+4125,15602551.0,Shih,687.0,France,Female,31.0,3.0,0.0,1.0,1.0,0.0,146738.83,0.0
+4126,15788218.0,Kambinachi,539.0,France,Female,43.0,8.0,0.0,2.0,1.0,1.0,74835.65,0.0
+4127,15745250.0,Dyson,695.0,France,Male,42.0,4.0,131659.59,2.0,1.0,1.0,126794.56,0.0
+4128,15603764.0,Hsueh,521.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,186778.89,0.0
+4129,15752294.0,Pirozzi,710.0,France,Male,38.0,9.0,120599.38,1.0,1.0,0.0,162260.93,0.0
+4130,15780141.0,Ritchie,788.0,France,Male,34.0,1.0,151953.19,1.0,1.0,0.0,78854.58,0.0
+4131,15672598.0,Teng,599.0,Spain,Female,54.0,2.0,0.0,1.0,1.0,0.0,74854.97,1.0
+4132,15567339.0,Pisano,676.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,114442.66,0.0
+4133,15598894.0,Ubanwa,707.0,France,Female,35.0,5.0,0.0,3.0,1.0,0.0,143783.46,0.0
+4134,15764351.0,Scott,652.0,Spain,Female,23.0,5.0,0.0,2.0,1.0,0.0,75574.48,0.0
+4135,15738166.0,Lu,696.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,91086.74,0.0
+4136,15789924.0,Yefremova,559.0,France,Male,39.0,3.0,0.0,1.0,1.0,0.0,179833.45,1.0
+4137,15752588.0,Nkemdirim,683.0,Germany,Male,28.0,3.0,118064.85,2.0,1.0,0.0,131283.6,0.0
+4138,15798850.0,Chiu,683.0,France,Female,39.0,0.0,0.0,1.0,0.0,0.0,159629.64,1.0
+4139,15705620.0,Lo,655.0,France,Male,25.0,3.0,110738.89,1.0,1.0,0.0,138350.74,0.0
+4140,15719579.0,Hsia,575.0,France,Female,45.0,6.0,0.0,1.0,0.0,0.0,120880.72,1.0
+4141,15732704.0,Lu,683.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,136837.97,0.0
+4142,15713608.0,Ts'ui,700.0,France,Male,37.0,5.0,0.0,1.0,0.0,0.0,91359.79,0.0
+4143,15702293.0,Smith,850.0,Spain,Female,47.0,5.0,167949.48,1.0,1.0,1.0,141501.45,0.0
+4144,15655952.0,Martin,768.0,Spain,Female,44.0,7.0,107553.33,2.0,1.0,0.0,155711.64,0.0
+4145,15750248.0,McIntyre,705.0,France,Male,40.0,5.0,0.0,1.0,0.0,0.0,61251.25,1.0
+4146,15663410.0,T'ang,738.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,140931.57,0.0
+4147,15781451.0,Chukwudi,638.0,Germany,Female,29.0,5.0,152400.51,1.0,1.0,0.0,124964.82,0.0
+4148,15675328.0,Onyinyechukwuka,699.0,Spain,Male,33.0,7.0,0.0,1.0,1.0,1.0,49594.02,0.0
+4149,15608528.0,Ma,624.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,127734.16,0.0
+4150,15679294.0,Ozerova,743.0,France,Female,39.0,5.0,0.0,1.0,1.0,0.0,117323.31,1.0
+4151,15638231.0,Nkemdilim,776.0,France,Male,36.0,6.0,116393.59,1.0,1.0,0.0,93769.89,0.0
+4152,15620398.0,Chiang,772.0,France,Male,27.0,6.0,97612.12,1.0,0.0,0.0,60721.4,0.0
+4153,15701160.0,Ch'iu,468.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,105433.53,0.0
+4154,15727605.0,Palermo,540.0,Spain,Male,27.0,1.0,0.0,2.0,1.0,0.0,131772.51,0.0
+4155,15800515.0,Ndubuagha,574.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,61290.99,0.0
+4156,15693817.0,Chiang,705.0,Germany,Male,37.0,7.0,111837.78,2.0,1.0,1.0,91359.79,0.0
+4157,15658449.0,Iadanza,815.0,Spain,Female,46.0,7.0,0.0,2.0,0.0,1.0,82969.61,0.0
+4158,15752588.0,Scott,794.0,France,Female,26.0,4.0,98627.11,1.0,1.0,1.0,98015.85,0.0
+4159,15783619.0,Milanesi,711.0,Germany,Male,46.0,8.0,127974.07,1.0,1.0,1.0,119232.33,0.0
+4160,15749401.0,Lai,850.0,Germany,Female,45.0,5.0,122501.28,1.0,1.0,1.0,142977.05,1.0
+4161,15637461.0,Chigolum,596.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,1.0,60954.45,0.0
+4162,15610446.0,Cattaneo,641.0,France,Male,38.0,2.0,122522.32,2.0,1.0,1.0,149922.38,0.0
+4163,15797475.0,H?,645.0,France,Female,37.0,5.0,0.0,2.0,1.0,1.0,94767.77,0.0
+4164,15722692.0,Ts'ao,850.0,France,Male,33.0,6.0,133628.09,1.0,0.0,0.0,107876.2,0.0
+4165,15585379.0,Fiorentino,833.0,France,Male,29.0,3.0,122545.65,1.0,1.0,0.0,52709.55,0.0
+4166,15726557.0,Lo,679.0,France,Female,32.0,1.0,145512.5,1.0,0.0,0.0,121681.19,0.0
+4167,15647898.0,Wei,613.0,Germany,Female,38.0,2.0,94901.03,1.0,0.0,0.0,123587.85,0.0
+4168,15778025.0,Mazzanti,758.0,France,Male,36.0,2.0,0.0,1.0,1.0,1.0,120115.5,1.0
+4169,15651533.0,Ponomaryov,627.0,France,Male,35.0,4.0,121681.91,1.0,1.0,1.0,154971.64,0.0
+4170,15777922.0,Chidubem,850.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,35701.06,0.0
+4171,15661409.0,Ts'ai,581.0,France,Female,40.0,10.0,0.0,2.0,1.0,0.0,151871.84,0.0
+4172,15698664.0,Onwumelu,561.0,Spain,Female,41.0,2.0,160903.2,1.0,1.0,1.0,80619.17,0.0
+4173,15585738.0,Onyeorulu,581.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,167924.95,0.0
+4174,15672447.0,Tien,718.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,169435.3,0.0
+4175,15578835.0,Genovese,730.0,Germany,Female,36.0,6.0,132667.18,1.0,1.0,0.0,162599.51,1.0
+4176,15681671.0,Onyekachi,675.0,France,Female,23.0,8.0,0.0,1.0,1.0,0.0,168034.9,0.0
+4177,15737571.0,Onyekachukwu,668.0,France,Male,38.0,5.0,0.0,1.0,1.0,0.0,108419.41,0.0
+4178,15775809.0,Chiekwugo,646.0,France,Female,27.0,8.0,0.0,2.0,0.0,1.0,91943.94,0.0
+4179,15637315.0,Macleod,639.0,Germany,Male,32.0,3.0,125921.37,1.0,0.0,1.0,189683.7,1.0
+4180,15735339.0,Ugochukwu,581.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,123882.73,0.0
+4181,15584515.0,Yeh,589.0,Germany,Female,46.0,4.0,131365.37,1.0,0.0,0.0,164741.5,1.0
+4182,15761348.0,Calabrese,790.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,180712.28,0.0
+4183,15571666.0,Ting,494.0,Germany,Female,37.0,3.0,119496.25,1.0,0.0,0.0,166677.54,0.0
+4184,15782778.0,Mironova,686.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,143859.66,0.0
+4185,15807553.0,Folliero,732.0,France,Female,39.0,4.0,0.0,2.0,0.0,1.0,126565.9,0.0
+4186,15580623.0,Chukwuebuka,739.0,France,Male,60.0,4.0,0.0,1.0,1.0,0.0,164741.5,1.0
+4187,15721592.0,Y?,682.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,168683.57,0.0
+4188,15676904.0,Chidumaga,751.0,France,Male,47.0,5.0,0.0,1.0,0.0,0.0,158973.82,1.0
+4189,15708710.0,Chiekwugo,679.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,120853.83,0.0
+4190,15662426.0,Chiang,790.0,France,Male,32.0,9.0,81677.22,1.0,1.0,1.0,77906.87,0.0
+4191,15813422.0,Ts'ui,633.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,80134.65,0.0
+4192,15795990.0,Illingworth,641.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,61761.77,0.0
+4193,15659092.0,Ch'ien,633.0,Germany,Female,47.0,3.0,113501.72,1.0,1.0,1.0,99061.75,1.0
+4194,15784890.0,Chimezie,584.0,France,Female,38.0,3.0,151510.17,1.0,1.0,0.0,176760.9,0.0
+4195,15579826.0,Ch'iu,683.0,France,Male,47.0,9.0,151675.65,1.0,0.0,0.0,100717.39,1.0
+4196,15632365.0,Ugorji,686.0,France,Male,26.0,6.0,0.0,2.0,1.0,0.0,187905.12,0.0
+4197,15592029.0,Milanesi,705.0,France,Male,28.0,7.0,151510.17,2.0,0.0,1.0,93883.53,0.0
+4198,15670029.0,Onwudiwe,459.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,151325.24,0.0
+4199,15634602.0,Hsiao,594.0,Spain,Female,32.0,5.0,0.0,1.0,1.0,0.0,166957.82,0.0
+4200,15690175.0,Ch'in,586.0,Spain,Female,27.0,3.0,0.0,1.0,1.0,1.0,18761.92,0.0
+4201,15681332.0,Chukwufumnanya,653.0,Germany,Male,26.0,6.0,117412.19,1.0,1.0,1.0,120225.62,0.0
+4202,15798888.0,Hao,710.0,Spain,Female,37.0,5.0,98635.77,1.0,0.0,1.0,92664.09,0.0
+4203,15626156.0,Onwuamaeze,645.0,Germany,Female,61.0,8.0,121404.64,2.0,1.0,1.0,161241.65,0.0
+4204,15763922.0,Chiang,538.0,Spain,Female,39.0,4.0,0.0,2.0,1.0,0.0,116471.11,0.0
+4205,15797710.0,L?,554.0,Germany,Male,43.0,8.0,76671.06,1.0,1.0,0.0,116471.43,1.0
+4206,15802823.0,Baresi,632.0,Spain,Female,34.0,8.0,0.0,2.0,1.0,0.0,53478.02,0.0
+4207,15624347.0,Onyemaechi,602.0,France,Male,41.0,3.0,109922.61,1.0,1.0,1.0,168053.4,0.0
+4208,15571586.0,Chukwuebuka,556.0,France,Male,35.0,3.0,97378.54,1.0,0.0,0.0,132973.17,0.0
+4209,15763662.0,Tsao,663.0,Spain,Female,45.0,2.0,0.0,1.0,0.0,0.0,179673.11,1.0
+4210,15771702.0,Toscani,847.0,Germany,Male,55.0,9.0,92786.66,2.0,0.0,0.0,823.96,1.0
+4211,15603914.0,Kambinachi,704.0,France,Male,33.0,0.0,0.0,2.0,1.0,0.0,69055.45,0.0
+4212,15572390.0,Pinto,699.0,France,Male,26.0,0.0,0.0,2.0,0.0,0.0,169781.45,0.0
+4213,15594878.0,Onyemauchechukwu,652.0,France,Female,35.0,5.0,0.0,2.0,0.0,1.0,82222.86,0.0
+4214,15783875.0,Okwudiliolisa,719.0,Germany,Male,41.0,0.0,129895.19,1.0,0.0,1.0,138051.19,1.0
+4215,15692406.0,Onyekachukwu,682.0,France,Female,40.0,10.0,142946.13,2.0,1.0,0.0,122021.33,0.0
+4216,15649738.0,Calabresi,714.0,Spain,Female,42.0,2.0,0.0,2.0,1.0,1.0,121402.53,0.0
+4217,15797389.0,Fyodorova,579.0,France,Male,30.0,4.0,0.0,1.0,0.0,1.0,137791.18,0.0
+4218,15572038.0,T'an,850.0,France,Male,22.0,10.0,0.0,2.0,1.0,0.0,79577.4,0.0
+4219,15644849.0,Ts'ai,619.0,Germany,Male,52.0,9.0,102562.23,1.0,1.0,0.0,167873.49,1.0
+4220,15750447.0,Chidi,746.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,163666.85,0.0
+4221,15589949.0,Lettiere,628.0,Spain,Female,39.0,9.0,0.0,1.0,1.0,0.0,151401.33,1.0
+4222,15592229.0,Y?,508.0,Spain,Female,44.0,1.0,116442.39,2.0,1.0,1.0,46630.47,1.0
+4223,15784844.0,Hsieh,711.0,France,Female,37.0,8.0,113923.69,1.0,1.0,0.0,86701.4,0.0
+4224,15656597.0,Folliero,743.0,Germany,Female,33.0,8.0,128881.95,2.0,1.0,0.0,136812.02,0.0
+4225,15637678.0,Hsiung,667.0,France,Female,37.0,2.0,0.0,2.0,1.0,1.0,96864.46,0.0
+4226,15783883.0,Toscani,612.0,France,Male,54.0,5.0,0.0,1.0,0.0,0.0,1276.87,1.0
+4227,15791854.0,Ritchie,623.0,France,Male,31.0,3.0,152303.66,1.0,1.0,1.0,186948.35,0.0
+4228,15795540.0,Li Fonti,591.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,148811.49,0.0
+4229,15682955.0,Chiemela,588.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,112572.02,0.0
+4230,15723818.0,Yost,667.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,91098.76,0.0
+4231,15779947.0,Yuryeva,633.0,Spain,Female,29.0,4.0,0.0,1.0,0.0,0.0,161889.79,0.0
+4232,15596074.0,Mazzanti,625.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,138021.36,0.0
+4233,15675888.0,Chimaobim,708.0,Spain,Female,56.0,2.0,157832.15,2.0,0.0,1.0,156910.46,0.0
+4234,15671490.0,Shih,637.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,1.0,51999.5,0.0
+4235,15781347.0,Ch'in,707.0,France,Male,40.0,9.0,162560.32,1.0,1.0,1.0,113796.15,0.0
+4236,15743570.0,Illingworth,725.0,Spain,Male,30.0,4.0,126725.25,1.0,0.0,1.0,126726.33,0.0
+4237,15604544.0,Chiedozie,595.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,1.0,173899.42,0.0
+4238,15767231.0,Hsu,588.0,Germany,Female,33.0,1.0,91285.13,2.0,1.0,1.0,145558.42,0.0
+4239,15804787.0,Cattaneo,677.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,157097.09,0.0
+4240,15652693.0,Clements,579.0,Spain,Female,33.0,8.0,0.0,2.0,1.0,0.0,166526.26,0.0
+4241,15661903.0,Onyinyechukwuka,750.0,Germany,Male,51.0,5.0,83348.89,1.0,1.0,0.0,70323.68,0.0
+4242,15800482.0,Ogochukwu,679.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,169921.11,0.0
+4243,15671987.0,Hsia,682.0,Spain,Male,53.0,1.0,127864.4,1.0,0.0,1.0,187899.97,0.0
+4244,15747503.0,Lombardi,711.0,France,Male,30.0,8.0,0.0,1.0,1.0,1.0,131736.23,1.0
+4245,15775307.0,Okwuadigbo,706.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,179032.68,0.0
+4246,15733797.0,Hsing,591.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,1.0,156774.94,0.0
+4247,15713890.0,Brown,717.0,Spain,Male,18.0,8.0,0.0,2.0,1.0,0.0,146949.74,0.0
+4248,15699195.0,Nwabugwu,790.0,France,Female,31.0,3.0,121461.58,1.0,1.0,0.0,172601.62,0.0
+4249,15703923.0,Folliero,644.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,106826.92,0.0
+4250,15603035.0,Yeh,652.0,Spain,Male,56.0,8.0,125801.03,1.0,1.0,0.0,138399.67,1.0
+4251,15706534.0,O'Donnell,753.0,Germany,Male,28.0,9.0,93578.68,1.0,1.0,1.0,173486.39,0.0
+4252,15772573.0,Y?,710.0,Spain,Male,24.0,6.0,141749.74,1.0,0.0,1.0,172674.01,0.0
+4253,15797733.0,Nwachukwu,653.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,173899.42,0.0
+4254,15691817.0,Hsia,710.0,Germany,Female,51.0,3.0,108913.84,1.0,1.0,1.0,172601.62,1.0
+4255,15684594.0,Ch'in,684.0,France,Male,38.0,7.0,63663.93,1.0,1.0,0.0,72318.77,0.0
+4256,15700965.0,Tung,721.0,Spain,Male,59.0,5.0,0.0,2.0,0.0,1.0,92816.86,0.0
+4257,15702715.0,Chukwualuka,613.0,Germany,Male,36.0,10.0,134591.33,1.0,1.0,0.0,120790.02,0.0
+4258,15686913.0,Onwubiko,788.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,93901.61,0.0
+4259,15626900.0,Chien,593.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,149922.38,0.0
+4260,15772363.0,Ch'eng,683.0,France,Female,35.0,3.0,0.0,1.0,1.0,0.0,109545.56,0.0
+4261,15689955.0,Chuang,638.0,Germany,Female,61.0,2.0,101609.01,1.0,0.0,1.0,3232.82,1.0
+4262,15650066.0,K?,659.0,France,Male,43.0,5.0,127559.95,1.0,1.0,0.0,74858.3,1.0
+4263,15606554.0,Frolova,687.0,Germany,Female,38.0,8.0,125725.02,2.0,0.0,1.0,60954.45,0.0
+4264,15768451.0,Chiemenam,748.0,Spain,Male,25.0,9.0,0.0,2.0,0.0,0.0,131736.23,0.0
+4265,15771299.0,Pagnotto,628.0,France,Male,46.0,9.0,96166.88,2.0,1.0,0.0,91903.9,0.0
+4266,15593736.0,Ch'iu,648.0,Spain,Female,40.0,2.0,153639.11,1.0,0.0,1.0,86981.45,1.0
+4267,15807768.0,Ch'iu,850.0,Germany,Male,42.0,9.0,91760.46,2.0,0.0,1.0,147040.25,0.0
+4268,15698902.0,DeRose,628.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,130784.2,0.0
+4269,15702434.0,Hsieh,749.0,Spain,Female,26.0,10.0,81258.13,1.0,1.0,1.0,133535.75,0.0
+4270,15718773.0,Baresi,720.0,France,Female,37.0,1.0,120847.11,1.0,1.0,0.0,75632.78,0.0
+4271,15746410.0,Liao,569.0,France,Female,31.0,0.0,0.0,2.0,1.0,0.0,76549.64,0.0
+4272,15641786.0,Palerma,668.0,Germany,Male,38.0,0.0,134954.52,2.0,1.0,0.0,16081.62,0.0
+4273,15806964.0,Onyemauchechukwu,685.0,France,Female,40.0,6.0,0.0,1.0,1.0,0.0,177614.17,1.0
+4274,15704795.0,Tuan,538.0,Spain,Female,42.0,5.0,0.0,1.0,1.0,0.0,158545.25,1.0
+4275,15591279.0,Milani,695.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,121074.87,0.0
+4276,15737888.0,Chia,732.0,Germany,Female,32.0,3.0,128743.8,2.0,1.0,1.0,182738.0,0.0
+4277,15748552.0,T'ien,703.0,France,Female,32.0,8.0,0.0,1.0,1.0,1.0,160129.99,0.0
+4278,15585067.0,McIntyre,559.0,France,Male,43.0,1.0,0.0,2.0,1.0,1.0,38794.57,0.0
+4279,15569682.0,Ngozichukwuka,686.0,Spain,Female,45.0,4.0,142663.46,1.0,1.0,0.0,86786.41,0.0
+4280,15702300.0,Nwankwo,733.0,Germany,Female,35.0,9.0,125023.1,1.0,1.0,0.0,133535.29,1.0
+4281,15751177.0,Binder,667.0,Germany,Female,43.0,0.0,108308.0,2.0,1.0,0.0,141872.05,0.0
+4282,15783030.0,Onyinyechukwuka,685.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,138351.68,0.0
+4283,15700937.0,Ch'ang,573.0,Germany,Male,38.0,8.0,131754.11,1.0,1.0,1.0,82647.65,0.0
+4284,15662698.0,Mazzi,714.0,Germany,Male,40.0,1.0,102974.64,1.0,0.0,1.0,121681.19,0.0
+4285,15809772.0,Chukwufumnanya,699.0,Spain,Female,34.0,0.0,0.0,2.0,1.0,0.0,163667.56,0.0
+4286,15793881.0,Hsieh,477.0,France,Male,39.0,7.0,0.0,1.0,1.0,1.0,57517.71,0.0
+4287,15781804.0,Chidiegwu,713.0,Germany,Female,40.0,7.0,101070.69,2.0,1.0,1.0,108675.01,0.0
+4288,15683560.0,Onyekachi,544.0,France,Male,41.0,4.0,98675.74,1.0,0.0,0.0,171552.02,0.0
+4289,15702741.0,Shao,748.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,91943.94,0.0
+4290,15605916.0,Fanucci,713.0,Spain,Male,20.0,1.0,0.0,2.0,1.0,0.0,80178.54,0.0
+4291,15603574.0,Nwankwo,633.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,179908.71,0.0
+4292,15604792.0,Ijendu,615.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,124486.28,0.0
+4293,15645323.0,Chidubem,646.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,123697.95,0.0
+4294,15709513.0,Ejimofor,681.0,Spain,Male,41.0,3.0,0.0,1.0,0.0,0.0,136301.1,0.0
+4295,15596863.0,Mott,584.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,0.0,172097.4,0.0
+4296,15776733.0,Tien,659.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,166476.95,0.0
+4297,15740175.0,Uspenskaya,741.0,France,Male,53.0,9.0,114084.6,1.0,1.0,0.0,167066.95,1.0
+4298,15680918.0,Kao,569.0,France,Male,52.0,8.0,0.0,1.0,1.0,0.0,162511.55,1.0
+4299,15701280.0,Shao,712.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,93336.3,0.0
+4300,15642785.0,Hsing,652.0,France,Male,42.0,3.0,0.0,2.0,0.0,1.0,73679.59,0.0
+4301,15778290.0,Fan,691.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,137985.69,0.0
+4302,15583736.0,Shao,584.0,Spain,Female,52.0,9.0,0.0,1.0,1.0,0.0,141558.04,1.0
+4303,15574743.0,Chin,687.0,France,Female,39.0,1.0,0.0,1.0,1.0,0.0,132975.36,0.0
+4304,15569249.0,Tien,731.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,1.0,48553.77,0.0
+4305,15597930.0,Onwuamaeze,657.0,France,Male,39.0,8.0,134766.85,1.0,1.0,1.0,83093.25,0.0
+4306,15686947.0,Chiu,683.0,France,Male,36.0,10.0,118569.03,2.0,0.0,0.0,89566.74,0.0
+4307,15713673.0,Fennell,579.0,Spain,Female,32.0,4.0,0.0,1.0,1.0,0.0,156858.2,0.0
+4308,15648876.0,Chukwueloka,496.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,176770.76,0.0
+4309,15792329.0,Ozerova,679.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,1.0,142400.27,0.0
+4310,15709639.0,Pethard,543.0,Spain,Male,42.0,5.0,0.0,1.0,1.0,0.0,53412.08,1.0
+4311,15641411.0,Chinwendu,715.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,177740.58,0.0
+4312,15659962.0,Kenechukwu,598.0,France,Male,29.0,3.0,141581.71,1.0,0.0,1.0,187718.26,0.0
+4313,15811588.0,Chiazagomekpere,645.0,Spain,Female,22.0,9.0,0.0,1.0,1.0,1.0,57876.05,0.0
+4314,15578722.0,Trevisani,595.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,109659.12,0.0
+4315,15586039.0,K'ung,653.0,Spain,Male,50.0,2.0,0.0,2.0,0.0,1.0,182624.93,0.0
+4316,15791323.0,Fanucci,735.0,Germany,Male,33.0,9.0,123699.28,1.0,1.0,0.0,175072.58,0.0
+4317,15766017.0,Iadanza,714.0,Germany,Female,25.0,2.0,129740.54,3.0,0.0,1.0,134878.19,0.0
+4318,15709741.0,Hsia,675.0,France,Male,40.0,2.0,0.0,1.0,1.0,1.0,162523.66,1.0
+4319,15663885.0,Maclean,749.0,Germany,Female,35.0,3.0,149777.0,1.0,1.0,0.0,110932.24,0.0
+4320,15582960.0,Ponomaryov,622.0,Germany,Female,45.0,1.0,138340.06,1.0,0.0,1.0,102930.46,1.0
+4321,15689517.0,Mancini,779.0,Germany,Female,46.0,0.0,163700.78,1.0,1.0,1.0,140938.95,1.0
+4322,15744529.0,Liao,678.0,Germany,Male,21.0,3.0,144550.62,1.0,1.0,1.0,116511.12,0.0
+4323,15602811.0,Shao,702.0,Germany,Female,27.0,4.0,137852.96,1.0,1.0,0.0,116696.77,1.0
+4324,15690925.0,Hsing,689.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,35177.31,0.0
+4325,15761168.0,Ch'ien,564.0,France,Male,49.0,4.0,0.0,2.0,1.0,0.0,134589.58,0.0
+4326,15632051.0,Biryukov,692.0,Germany,Female,42.0,6.0,57929.81,2.0,1.0,1.0,89566.64,0.0
+4327,15654700.0,Chukwukadibia,832.0,Spain,Female,71.0,9.0,0.0,2.0,1.0,1.0,87400.53,0.0
+4328,15622370.0,Chikezie,749.0,France,Male,41.0,5.0,110942.9,1.0,1.0,1.0,131736.23,0.0
+4329,15781127.0,Yobanna,595.0,France,Male,27.0,6.0,0.0,2.0,1.0,0.0,124596.63,0.0
+4330,15803764.0,Ts'ui,753.0,France,Male,33.0,4.0,0.0,2.0,0.0,0.0,152992.04,0.0
+4331,15658067.0,Ch'iu,684.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,0.0,122488.59,0.0
+4332,15787699.0,Dore,674.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,182697.24,0.0
+4333,15779947.0,Okonkwo,678.0,France,Female,66.0,3.0,0.0,1.0,1.0,1.0,88751.45,1.0
+4334,15598948.0,Oluchukwu,696.0,Germany,Male,30.0,8.0,147187.84,1.0,1.0,1.0,26318.73,0.0
+4335,15638806.0,Chukwudi,598.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,1.0,180769.55,0.0
+4336,15769955.0,Y?an,656.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,136983.77,0.0
+4337,15594864.0,Folliero,646.0,Spain,Female,32.0,2.0,0.0,2.0,0.0,1.0,126812.63,0.0
+4338,15809909.0,Olisaemeka,636.0,France,Male,36.0,1.0,84309.57,1.0,1.0,0.0,180769.55,0.0
+4339,15691170.0,Hsueh,649.0,France,Female,55.0,3.0,0.0,1.0,1.0,0.0,172490.78,1.0
+4340,15616380.0,Hs?,712.0,France,Female,22.0,8.0,0.0,2.0,1.0,1.0,144849.1,0.0
+4341,15628893.0,Ozerova,686.0,Germany,Male,33.0,6.0,164557.95,1.0,0.0,1.0,101455.07,0.0
+4342,15713346.0,Hsia,614.0,France,Male,29.0,2.0,144326.09,1.0,0.0,0.0,73881.68,0.0
+4343,15624695.0,T'ien,634.0,France,Male,37.0,1.0,123783.25,1.0,1.0,0.0,58467.08,0.0
+4344,15587066.0,Bancks,685.0,France,Male,37.0,6.0,111769.14,1.0,1.0,0.0,169920.92,0.0
+4345,15598070.0,Chienezie,669.0,France,Female,33.0,2.0,0.0,1.0,1.0,1.0,199761.29,0.0
+4346,15575581.0,Ubanwa,731.0,France,Male,29.0,3.0,85891.55,1.0,1.0,1.0,52820.13,0.0
+4347,15734044.0,Chidiegwu,646.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,70810.6,0.0
+4348,15656829.0,Macleod,733.0,France,Female,23.0,2.0,0.0,2.0,1.0,1.0,60728.89,0.0
+4349,15789611.0,Ejimofor,686.0,France,Female,46.0,8.0,0.0,2.0,1.0,1.0,10472.31,0.0
+4350,15749300.0,Pinto,635.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,93739.71,0.0
+4351,15803337.0,White,607.0,Spain,Male,49.0,7.0,0.0,2.0,1.0,0.0,138536.08,0.0
+4352,15603088.0,Nwabugwu,579.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,125870.2,0.0
+4353,15803908.0,P'eng,636.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,170819.01,0.0
+4354,15667971.0,Yobachukwu,644.0,France,Male,32.0,3.0,132951.92,1.0,1.0,1.0,75969.19,0.0
+4355,15631481.0,Chu,641.0,Spain,Male,24.0,5.0,0.0,2.0,1.0,0.0,170819.01,0.0
+4356,15623644.0,Nwankwo,676.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,116548.02,0.0
+4357,15607690.0,Milani,602.0,France,Male,21.0,9.0,0.0,2.0,1.0,0.0,181224.56,0.0
+4358,15641298.0,Yevseyev,554.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,172097.95,0.0
+4359,15700476.0,McGregor,636.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,105985.01,1.0
+4360,15797760.0,Nnachetam,642.0,France,Male,39.0,5.0,125476.31,1.0,1.0,1.0,172033.57,0.0
+4361,15783097.0,Chimezie,697.0,France,Male,25.0,2.0,99540.63,1.0,0.0,1.0,170634.49,0.0
+4362,15654851.0,Ch'in,671.0,France,Male,34.0,4.0,0.0,2.0,0.0,1.0,178076.47,0.0
+4363,15704954.0,Chibugo,538.0,Spain,Male,33.0,10.0,0.0,2.0,1.0,1.0,136983.77,0.0
+4364,15773876.0,Mazzanti,581.0,Germany,Female,37.0,6.0,104760.82,1.0,1.0,0.0,85412.54,1.0
+4365,15691446.0,Hsing,705.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,143598.7,0.0
+4366,15704053.0,Uchechukwu,687.0,France,Female,26.0,7.0,0.0,2.0,0.0,1.0,133535.52,0.0
+4367,15697460.0,Chiu,684.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,1.0,144880.81,0.0
+4368,15694192.0,H?,730.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,96420.58,0.0
+4369,15628292.0,Toscani,611.0,Spain,Female,40.0,8.0,0.0,1.0,0.0,0.0,140934.57,1.0
+4370,15662397.0,Yobachi,656.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,10792.42,0.0
+4371,15585100.0,Toscani,711.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,138399.67,0.0
+4372,15772892.0,Palermo,811.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,139947.17,0.0
+4373,15642997.0,Maclean,638.0,France,Male,39.0,8.0,0.0,1.0,1.0,0.0,127543.81,1.0
+4374,15784958.0,Okonkwo,704.0,France,Male,24.0,4.0,0.0,2.0,1.0,1.0,154491.79,0.0
+4375,15680683.0,Tung,684.0,France,Female,26.0,2.0,162923.85,1.0,0.0,1.0,156774.94,0.0
+4376,15668058.0,Chukwumaobim,677.0,France,Female,46.0,9.0,0.0,2.0,1.0,0.0,80700.86,0.0
+4377,15618661.0,Smith,632.0,Spain,Female,30.0,10.0,0.0,2.0,1.0,0.0,48131.92,0.0
+4378,15679029.0,Ts'ui,581.0,Spain,Male,35.0,2.0,0.0,1.0,1.0,0.0,136497.28,0.0
+4379,15673747.0,Chiu,850.0,France,Female,59.0,7.0,0.0,2.0,1.0,1.0,199493.38,0.0
+4380,15646578.0,Ch'eng,705.0,Germany,Male,33.0,3.0,150802.03,2.0,1.0,1.0,51695.41,0.0
+4381,15775238.0,McFarland,598.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,134421.32,0.0
+4382,15689953.0,Onyinyechukwuka,584.0,Germany,Male,41.0,1.0,140510.89,2.0,1.0,0.0,56577.0,0.0
+4383,15708003.0,Tsao,583.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,122964.18,0.0
+4384,15762048.0,Hsia,557.0,France,Male,56.0,2.0,0.0,1.0,1.0,0.0,53353.37,1.0
+4385,15654955.0,Cattaneo,730.0,France,Male,36.0,6.0,123815.86,1.0,0.0,0.0,176750.81,0.0
+4386,15812888.0,Chidiegwu,597.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,1.0,120512.08,0.0
+4387,15683560.0,Yefremova,716.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,115700.1,0.0
+4388,15602811.0,Obialo,611.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,73562.05,0.0
+4389,15644849.0,Nwachinemelu,699.0,France,Female,25.0,3.0,0.0,2.0,1.0,1.0,152790.92,0.0
+4390,15792726.0,P'eng,659.0,France,Male,21.0,3.0,0.0,2.0,1.0,1.0,143980.28,0.0
+4391,15603741.0,Hsing,597.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,166682.57,0.0
+4392,15593976.0,Shih,538.0,France,Female,32.0,1.0,0.0,1.0,1.0,0.0,49594.02,0.0
+4393,15636069.0,Chukwudi,671.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,104469.06,0.0
+4394,15733032.0,Chiang,663.0,Germany,Female,34.0,0.0,146466.22,1.0,1.0,1.0,72469.38,0.0
+4395,15662578.0,Hsieh,636.0,Germany,Female,39.0,6.0,92786.66,1.0,1.0,0.0,146723.72,1.0
+4396,15789548.0,Mairinger,773.0,France,Male,39.0,9.0,0.0,2.0,0.0,0.0,72577.33,0.0
+4397,15592751.0,Chiedozie,686.0,Germany,Male,44.0,1.0,142289.28,2.0,1.0,0.0,71638.0,0.0
+4398,15705313.0,Iloerika,489.0,Germany,Female,42.0,2.0,127772.1,2.0,1.0,1.0,186655.11,0.0
+4399,15798885.0,Chikezie,622.0,Germany,Female,44.0,9.0,128927.13,1.0,1.0,0.0,76637.38,1.0
+4400,15697574.0,Ts'ui,588.0,France,Male,36.0,0.0,0.0,2.0,1.0,1.0,143086.09,0.0
+4401,15659236.0,Ch'eng,588.0,France,Female,47.0,5.0,0.0,1.0,1.0,0.0,140941.47,1.0
+4402,15605037.0,Yobanna,599.0,France,Male,50.0,5.0,153537.57,1.0,1.0,0.0,38308.34,0.0
+4403,15682268.0,Okagbue,466.0,Spain,Female,35.0,9.0,95984.21,1.0,1.0,1.0,121408.55,0.0
+4404,15596982.0,Onyemauchechukwu,706.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,153482.82,0.0
+4405,15632665.0,Lettiere,705.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,166656.18,0.0
+4406,15570533.0,Trevisano,685.0,France,Female,29.0,4.0,0.0,2.0,1.0,1.0,145562.4,0.0
+4407,15583067.0,Chuang,571.0,France,Male,34.0,7.0,0.0,2.0,0.0,1.0,182055.36,0.0
+4408,15595537.0,K'ung,663.0,Spain,Male,33.0,4.0,133723.21,1.0,1.0,1.0,141827.83,0.0
+4409,15792329.0,Tien,753.0,France,Female,37.0,7.0,94598.48,2.0,1.0,1.0,136497.28,0.0
+4410,15792668.0,P'eng,735.0,France,Female,39.0,7.0,0.0,1.0,1.0,1.0,47722.79,1.0
+4411,15574886.0,Hsia,655.0,France,Female,22.0,9.0,0.0,2.0,1.0,1.0,66810.48,0.0
+4412,15804053.0,Oguejiofor,588.0,Germany,Female,45.0,1.0,127675.39,2.0,1.0,0.0,97767.63,0.0
+4413,15646091.0,Pirozzi,850.0,Spain,Male,34.0,9.0,0.0,2.0,0.0,1.0,125877.22,0.0
+4414,15635078.0,Brownless,623.0,France,Male,22.0,1.0,0.0,2.0,0.0,0.0,110932.24,0.0
+4415,15642570.0,Ritchie,695.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,177028.0,0.0
+4416,15649744.0,Chukwufumnanya,719.0,France,Male,36.0,5.0,0.0,1.0,1.0,0.0,93982.41,0.0
+4417,15802501.0,Palerma,758.0,Germany,Female,42.0,9.0,114552.6,1.0,0.0,1.0,176943.59,1.0
+4418,15593346.0,Kenechukwu,684.0,France,Male,35.0,2.0,164701.68,1.0,1.0,0.0,48984.95,0.0
+4419,15612444.0,Chukwuemeka,586.0,France,Male,36.0,4.0,81742.94,1.0,1.0,0.0,64421.02,0.0
+4420,15786800.0,Ch'iu,576.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,0.0,70849.47,0.0
+4421,15769005.0,Brown,632.0,France,Female,24.0,1.0,121206.96,1.0,0.0,1.0,38186.85,0.0
+4422,15732704.0,Ch'ien,622.0,France,Male,56.0,2.0,0.0,2.0,1.0,1.0,127059.04,0.0
+4423,15800412.0,Nnaife,707.0,Germany,Female,25.0,7.0,139022.06,1.0,1.0,1.0,156917.19,0.0
+4424,15590730.0,Ch'ang,662.0,Germany,Female,47.0,1.0,100116.67,1.0,1.0,1.0,125816.2,1.0
+4425,15614215.0,Fanucci,612.0,Spain,Female,45.0,1.0,0.0,2.0,1.0,0.0,705.18,0.0
+4426,15593468.0,Walker,707.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,0.0,68600.36,0.0
+4427,15704053.0,Nwachinemelu,577.0,Spain,Male,34.0,2.0,162485.8,2.0,1.0,1.0,122996.76,0.0
+4428,15792934.0,Bage,696.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,86634.3,0.0
+4429,15656901.0,McDonald,670.0,Spain,Female,53.0,4.0,121162.74,1.0,0.0,0.0,92914.67,1.0
+4430,15705512.0,Hales,644.0,Spain,Male,30.0,3.0,0.0,2.0,1.0,0.0,26327.42,0.0
+4431,15724663.0,Hsu,648.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,74588.41,0.0
+4432,15758726.0,Hsueh,740.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,171378.77,0.0
+4433,15807560.0,Sagese,705.0,France,Male,35.0,1.0,0.0,1.0,1.0,1.0,141896.74,1.0
+4434,15717191.0,McLean,559.0,France,Male,40.0,7.0,133628.09,1.0,0.0,1.0,184581.68,0.0
+4435,15769596.0,Liao,693.0,France,Female,39.0,8.0,0.0,2.0,0.0,0.0,185555.63,0.0
+4436,15757537.0,Lo Duca,671.0,France,Male,31.0,0.0,0.0,2.0,1.0,1.0,181208.47,0.0
+4437,15812338.0,T'ien,706.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,155047.11,0.0
+4438,15773868.0,Zetticci,742.0,Germany,Female,37.0,8.0,90218.9,2.0,1.0,1.0,152883.95,0.0
+4439,15787035.0,Onio,696.0,Spain,Female,28.0,4.0,0.0,2.0,0.0,1.0,141896.74,0.0
+4440,15781409.0,Nwankwo,662.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,177613.19,0.0
+4441,15676571.0,Tuan,586.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,92220.12,0.0
+4442,15648721.0,Nwankwo,686.0,France,Female,42.0,5.0,0.0,2.0,0.0,1.0,122964.88,0.0
+4443,15687431.0,H?,619.0,France,Female,42.0,2.0,0.0,2.0,1.0,1.0,71497.23,0.0
+4444,15694544.0,Ulyanova,651.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,138700.75,0.0
+4445,15576615.0,Walker,710.0,France,Female,21.0,3.0,0.0,2.0,1.0,0.0,85746.42,0.0
+4446,15576077.0,Yefremova,681.0,France,Male,40.0,8.0,0.0,2.0,0.0,0.0,170984.97,0.0
+4447,15737795.0,Chigozie,682.0,France,Female,33.0,3.0,99811.44,1.0,1.0,0.0,81297.75,0.0
+4448,15631512.0,Kao,713.0,France,Female,36.0,7.0,0.0,2.0,0.0,0.0,112465.19,0.0
+4449,15790489.0,Rishel,681.0,Spain,Female,53.0,6.0,0.0,2.0,1.0,1.0,151402.82,0.0
+4450,15689526.0,Yefremov,597.0,France,Male,57.0,4.0,0.0,1.0,1.0,0.0,123880.19,1.0
+4451,15642821.0,McGregor,693.0,Spain,Male,43.0,7.0,115465.98,1.0,0.0,0.0,174918.46,1.0
+4452,15637366.0,Smith,646.0,Germany,Male,41.0,2.0,137592.04,2.0,1.0,1.0,136507.69,0.0
+4453,15743893.0,Olisaemeka,748.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,172939.3,0.0
+4454,15592707.0,Pirozzi,748.0,Germany,Male,43.0,8.0,139874.81,1.0,1.0,0.0,91936.1,1.0
+4455,15793046.0,Dickson,591.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,1375.41,0.0
+4456,15663478.0,Milanesi,662.0,Spain,Female,41.0,5.0,0.0,2.0,0.0,1.0,72375.03,0.0
+4457,15665621.0,H?,559.0,Germany,Female,32.0,1.0,107980.37,2.0,1.0,1.0,171070.55,0.0
+4458,15798960.0,P'eng,523.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,186418.85,0.0
+4459,15661349.0,Okwudiliolisa,676.0,Germany,Female,51.0,4.0,114512.59,1.0,0.0,0.0,147097.14,1.0
+4460,15683524.0,Tuan,526.0,Germany,Male,41.0,0.0,107363.28,1.0,1.0,0.0,103898.59,0.0
+4461,15772601.0,Manners,693.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,167773.55,0.0
+4462,15732643.0,Genovesi,630.0,France,Male,40.0,6.0,0.0,2.0,0.0,0.0,82709.8,1.0
+4463,15795690.0,O'Sullivan,637.0,Germany,Female,33.0,7.0,123544.0,2.0,1.0,0.0,176913.42,0.0
+4464,15702631.0,Hsing,669.0,Germany,Female,54.0,2.0,111837.78,1.0,1.0,0.0,104930.46,1.0
+4465,15603481.0,P'an,605.0,France,Male,46.0,10.0,94486.47,1.0,1.0,1.0,100557.03,0.0
+4466,15721377.0,Hao,754.0,France,Male,40.0,1.0,0.0,1.0,0.0,0.0,162523.66,1.0
+4467,15696733.0,Chukwumaobim,652.0,France,Female,38.0,1.0,101430.3,1.0,1.0,0.0,91347.76,0.0
+4468,15705544.0,Teng,569.0,Germany,Female,32.0,8.0,130170.82,2.0,1.0,1.0,54530.51,0.0
+4469,15722072.0,Chukwufumnanya,707.0,Germany,Male,49.0,1.0,127847.86,1.0,1.0,0.0,90896.06,1.0
+4470,15651336.0,Hsia,591.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,125518.32,0.0
+4471,15776850.0,Lucciano,714.0,France,Female,46.0,1.0,0.0,1.0,0.0,0.0,149402.54,1.0
+4472,15595588.0,Ch'iu,656.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,0.0,52861.89,0.0
+4473,15604576.0,Okwuoma,704.0,Spain,Female,44.0,7.0,0.0,1.0,1.0,0.0,167636.15,1.0
+4474,15633378.0,Ch'ang,594.0,France,Male,38.0,8.0,0.0,2.0,0.0,0.0,180595.02,0.0
+4475,15656613.0,Chibueze,769.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,171413.66,0.0
+4476,15581548.0,Pagnotto,573.0,France,Male,33.0,8.0,0.0,2.0,0.0,0.0,88837.17,0.0
+4477,15743582.0,Chidubem,642.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,152790.92,0.0
+4478,15655774.0,Ebelegbulam,776.0,Spain,Female,33.0,4.0,0.0,2.0,1.0,1.0,63687.06,0.0
+4479,15680430.0,Hsia,751.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,104722.47,0.0
+4480,15603743.0,Lo,642.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,172490.78,0.0
+4481,15734005.0,Onwuamaeze,554.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,184544.59,0.0
+4482,15777955.0,Nwachukwu,735.0,France,Male,37.0,1.0,140694.78,1.0,1.0,0.0,141872.05,0.0
+4483,15724889.0,Ch'iu,715.0,France,Male,37.0,1.0,0.0,1.0,1.0,0.0,160548.12,0.0
+4484,15769902.0,Ch'en,731.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,85412.64,0.0
+4485,15610390.0,Pisano,705.0,Germany,Male,36.0,5.0,118466.85,2.0,0.0,0.0,71519.4,0.0
+4486,15718369.0,Ugochukwu,598.0,France,Male,36.0,1.0,122314.5,1.0,0.0,1.0,136859.55,0.0
+4487,15581198.0,T'ien,684.0,France,Female,43.0,8.0,0.0,2.0,0.0,1.0,46824.08,0.0
+4488,15642821.0,Eluemuno,656.0,France,Female,50.0,4.0,0.0,1.0,1.0,0.0,143357.05,1.0
+4489,15767804.0,Fan,639.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,164039.51,0.0
+4490,15659937.0,Dickson,581.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,113048.79,0.0
+4491,15657535.0,Onyeorulu,776.0,France,Female,29.0,7.0,0.0,2.0,0.0,1.0,112554.68,0.0
+4492,15741385.0,Galkin,652.0,France,Male,51.0,7.0,0.0,1.0,1.0,0.0,38652.96,1.0
+4493,15766288.0,Chien,724.0,France,Female,29.0,6.0,0.0,2.0,1.0,1.0,122996.76,0.0
+4494,15719377.0,Ts'ao,644.0,Spain,Female,37.0,4.0,0.0,2.0,1.0,1.0,146651.99,0.0
+4495,15592389.0,Hsu,753.0,Spain,Male,45.0,1.0,0.0,2.0,1.0,0.0,124499.98,0.0
+4496,15756975.0,Shih,749.0,France,Male,36.0,5.0,0.0,1.0,1.0,1.0,101314.65,0.0
+4497,15787322.0,Nnaife,753.0,Spain,Female,34.0,3.0,0.0,2.0,0.0,0.0,152883.95,0.0
+4498,15703394.0,Chuang,713.0,France,Female,61.0,5.0,0.0,1.0,1.0,1.0,126336.72,1.0
+4499,15675888.0,Ndukaku,692.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,165562.84,0.0
+4500,15622532.0,Chukwueloka,726.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,77832.41,0.0
+4501,15714391.0,Udegbulam,644.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,51636.7,0.0
+4502,15679770.0,T'ao,637.0,Germany,Female,40.0,9.0,104305.77,2.0,1.0,0.0,131736.72,0.0
+4503,15604551.0,Hsing,629.0,Germany,Male,47.0,5.0,131778.08,1.0,1.0,0.0,44374.44,1.0
+4504,15796787.0,Genovese,599.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,1.0,173629.78,0.0
+4505,15747174.0,Teng,431.0,France,Male,34.0,3.0,0.0,2.0,0.0,0.0,97413.0,0.0
+4506,15570002.0,Smith,735.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,113931.57,0.0
+4507,15652266.0,Fanucci,683.0,Germany,Male,23.0,1.0,152303.8,1.0,1.0,1.0,138527.56,0.0
+4508,15573893.0,Ch'eng,686.0,Germany,Male,46.0,9.0,132312.08,1.0,1.0,1.0,167773.55,0.0
+4509,15808228.0,Ch'en,629.0,Spain,Female,57.0,7.0,0.0,2.0,1.0,1.0,153917.41,0.0
+4510,15679048.0,Nnamdi,487.0,France,Male,27.0,10.0,0.0,2.0,0.0,1.0,64345.61,0.0
+4511,15623369.0,Lai,751.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,143317.42,0.0
+4512,15571059.0,Chukwukere,654.0,Germany,Female,47.0,2.0,131160.85,1.0,1.0,1.0,120594.85,1.0
+4513,15571550.0,Ts'ui,628.0,France,Male,30.0,7.0,134177.06,1.0,1.0,1.0,64345.61,0.0
+4514,15660403.0,Tsao,762.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,161802.82,0.0
+4515,15647782.0,T'ang,587.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,0.0,160941.78,0.0
+4516,15733777.0,Ejimofor,671.0,France,Male,43.0,7.0,0.0,2.0,0.0,1.0,141364.62,0.0
+4517,15719561.0,Yevseyev,538.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,173498.45,0.0
+4518,15635116.0,Kornilova,516.0,France,Male,39.0,9.0,0.0,2.0,0.0,0.0,72549.11,0.0
+4519,15589572.0,Chia,731.0,Germany,Female,37.0,3.0,137964.46,2.0,1.0,0.0,116471.43,0.0
+4520,15783029.0,Hs?eh,488.0,Spain,Male,24.0,2.0,0.0,2.0,0.0,0.0,82709.8,0.0
+4521,15692631.0,Onyekachi,850.0,Spain,Female,47.0,1.0,0.0,1.0,1.0,0.0,142516.35,1.0
+4522,15571586.0,Kao,535.0,France,Female,37.0,1.0,0.0,2.0,0.0,0.0,61290.99,0.0
+4523,15677955.0,Folliero,679.0,Spain,Female,43.0,7.0,0.0,1.0,1.0,0.0,57844.26,1.0
+4524,15607263.0,Pisano,779.0,France,Male,58.0,1.0,0.0,2.0,1.0,1.0,95611.47,0.0
+4525,15732570.0,Folliero,684.0,France,Female,38.0,2.0,109052.04,1.0,0.0,1.0,121056.29,0.0
+4526,15675896.0,D'Albertis,756.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,51380.9,0.0
+4527,15583090.0,Onochie,743.0,Spain,Male,34.0,10.0,0.0,2.0,0.0,1.0,156476.36,0.0
+4528,15704581.0,Chien,674.0,Germany,Male,39.0,7.0,104584.11,1.0,1.0,1.0,83022.61,0.0
+4529,15670898.0,Ch'eng,675.0,Germany,Male,33.0,9.0,103907.28,2.0,0.0,1.0,131736.23,0.0
+4530,15751774.0,Mistry,821.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,81884.8,0.0
+4531,15806945.0,Chin,620.0,France,Female,37.0,4.0,0.0,3.0,1.0,0.0,146723.72,1.0
+4532,15614687.0,L?,554.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,157072.91,0.0
+4533,15636999.0,Onyemachukwu,613.0,France,Female,40.0,4.0,108309.0,1.0,1.0,1.0,139919.38,0.0
+4534,15695585.0,Ch'en,653.0,Germany,Female,36.0,8.0,128718.93,2.0,0.0,1.0,136865.63,0.0
+4535,15593601.0,Mancini,636.0,Germany,Male,43.0,5.0,109949.05,1.0,0.0,0.0,143618.32,0.0
+4536,15672056.0,Nwoye,687.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,122964.18,0.0
+4537,15643865.0,Chiganu,651.0,France,Male,31.0,2.0,131513.26,1.0,0.0,1.0,168834.38,0.0
+4538,15781598.0,Nnamutaezinwa,678.0,France,Male,45.0,6.0,129445.4,1.0,1.0,0.0,73334.43,1.0
+4539,15652914.0,Ting,558.0,Spain,Male,45.0,3.0,0.0,2.0,1.0,0.0,187961.87,0.0
+4540,15572547.0,Estrada,696.0,France,Female,55.0,1.0,0.0,1.0,1.0,0.0,174738.2,1.0
+4541,15653937.0,P'eng,624.0,France,Male,39.0,3.0,129818.61,1.0,1.0,0.0,140889.32,0.0
+4542,15788183.0,Chu,673.0,Germany,Male,35.0,6.0,125013.72,2.0,1.0,1.0,50783.1,0.0
+4543,15658052.0,Lo,627.0,Germany,Male,33.0,6.0,137986.47,2.0,1.0,1.0,9984.4,0.0
+4544,15644572.0,De Luca,798.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,86099.23,0.0
+4545,15598894.0,Hsieh,711.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,187985.85,0.0
+4546,15664001.0,Chigozie,713.0,Spain,Female,45.0,2.0,141359.37,1.0,1.0,0.0,62700.08,0.0
+4547,15806981.0,Y?an,832.0,Spain,Male,34.0,2.0,135939.99,1.0,0.0,0.0,181610.6,0.0
+4548,15594556.0,Nwachukwu,686.0,France,Male,33.0,1.0,145751.03,1.0,1.0,1.0,122073.48,0.0
+4549,15576760.0,Ts'ui,587.0,Germany,Male,36.0,9.0,91285.13,2.0,1.0,1.0,117036.06,0.0
+4550,15692991.0,Ch'ien,627.0,France,Female,44.0,1.0,0.0,2.0,1.0,0.0,143783.46,1.0
+4551,15627377.0,Eluemuno,774.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,176580.89,0.0
+4552,15792453.0,Hsiung,790.0,France,Male,37.0,0.0,0.0,2.0,1.0,0.0,99818.49,0.0
+4553,15694321.0,Okechukwu,706.0,Germany,Male,35.0,4.0,116077.48,2.0,1.0,1.0,123435.88,0.0
+4554,15760450.0,Ch'in,614.0,Germany,Male,35.0,3.0,126619.27,2.0,1.0,0.0,94790.34,0.0
+4555,15660505.0,Chien,749.0,France,Male,27.0,3.0,151027.05,2.0,1.0,1.0,116766.79,0.0
+4556,15721207.0,Chukwudi,567.0,Spain,Male,39.0,7.0,95120.59,1.0,1.0,0.0,162501.83,0.0
+4557,15751771.0,Shih,691.0,France,Male,35.0,10.0,172713.45,1.0,0.0,1.0,141137.52,0.0
+4558,15742291.0,Teng,678.0,Germany,Female,48.0,9.0,139756.12,3.0,0.0,0.0,173464.43,1.0
+4559,15779985.0,Mazzanti,615.0,France,Male,50.0,9.0,0.0,2.0,0.0,1.0,7900.46,0.0
+4560,15702801.0,Echezonachukwu,729.0,France,Male,25.0,1.0,0.0,2.0,1.0,1.0,27341.63,0.0
+4561,15667896.0,Lu,596.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,158545.25,0.0
+4562,15791550.0,De Bernales,535.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,166586.99,0.0
+4563,15784918.0,Ts'ui,732.0,Spain,Male,41.0,2.0,0.0,2.0,1.0,1.0,168521.72,0.0
+4564,15719561.0,Milne,849.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,176903.38,0.0
+4565,15756475.0,Ritchie,561.0,Spain,Male,45.0,9.0,0.0,1.0,1.0,0.0,113580.79,1.0
+4566,15734044.0,Onyemere,738.0,Spain,Female,46.0,9.0,160982.89,1.0,0.0,0.0,136079.64,1.0
+4567,15574523.0,Ch'in,571.0,France,Female,29.0,5.0,0.0,1.0,1.0,0.0,166692.03,0.0
+4568,15607065.0,Ch'iu,652.0,Germany,Female,52.0,4.0,126817.65,1.0,1.0,0.0,99294.22,1.0
+4569,15673747.0,Yermakova,728.0,Spain,Male,29.0,6.0,0.0,1.0,1.0,1.0,183893.31,0.0
+4570,15757911.0,Chien,830.0,France,Female,45.0,9.0,0.0,1.0,1.0,1.0,182650.15,1.0
+4571,15654577.0,Gorman,594.0,France,Male,50.0,8.0,132005.77,1.0,1.0,0.0,122218.27,1.0
+4572,15641158.0,Nnachetam,568.0,Germany,Female,40.0,4.0,123378.2,1.0,1.0,0.0,143882.25,1.0
+4573,15569098.0,Calabresi,603.0,France,Male,27.0,8.0,0.0,1.0,1.0,0.0,98820.09,0.0
+4574,15751524.0,Simmons,614.0,Spain,Female,30.0,3.0,0.0,2.0,1.0,0.0,131736.23,0.0
+4575,15643331.0,Ts'ai,763.0,Germany,Female,45.0,8.0,123345.85,2.0,1.0,1.0,173480.77,0.0
+4576,15729763.0,Hsing,745.0,Germany,Female,36.0,1.0,139914.7,2.0,1.0,0.0,129964.94,0.0
+4577,15771565.0,Manna,661.0,France,Female,31.0,6.0,0.0,2.0,0.0,1.0,170563.73,0.0
+4578,15645200.0,Cattaneo,564.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,98894.64,0.0
+4579,15645059.0,Niu,667.0,Spain,Male,46.0,7.0,117438.92,1.0,0.0,1.0,77866.92,1.0
+4580,15793856.0,Biryukova,584.0,France,Male,37.0,4.0,148962.76,2.0,0.0,1.0,143954.99,0.0
+4581,15792989.0,Iadanza,815.0,France,Male,32.0,6.0,139825.34,1.0,1.0,0.0,51117.06,0.0
+4582,15776824.0,Ch'en,652.0,France,Male,39.0,8.0,77959.39,1.0,1.0,1.0,91070.54,0.0
+4583,15673837.0,Chidumaga,743.0,France,Male,30.0,5.0,0.0,2.0,0.0,0.0,94498.81,0.0
+4584,15579548.0,Onwuamaeze,584.0,France,Female,38.0,0.0,0.0,2.0,1.0,0.0,122398.84,0.0
+4585,15790596.0,Milne,586.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,171575.54,0.0
+4586,15572073.0,Hsing,758.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,89543.25,0.0
+4587,15635905.0,Burt,651.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,45445.78,0.0
+4588,15709928.0,Ch'iu,665.0,Germany,Male,62.0,3.0,120787.18,2.0,1.0,1.0,138021.36,0.0
+4589,15771580.0,Ch'ien,712.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,166677.54,0.0
+4590,15694879.0,Manna,694.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,179833.43,1.0
+4591,15595588.0,Ting,619.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,176713.47,0.0
+4592,15775809.0,Onwuamaeze,716.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,81776.84,0.0
+4593,15636589.0,Ch'en,687.0,Germany,Female,34.0,7.0,81576.75,2.0,0.0,1.0,173882.54,0.0
+4594,15803685.0,Onyemere,622.0,France,Female,28.0,7.0,0.0,2.0,1.0,0.0,121061.89,0.0
+4595,15794560.0,Ts'ui,586.0,France,Female,72.0,4.0,153663.27,1.0,1.0,1.0,173792.42,0.0
+4596,15681476.0,Maclean,717.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,88724.69,0.0
+4597,15582591.0,T'ien,631.0,France,Female,36.0,1.0,128173.9,1.0,1.0,0.0,72950.14,0.0
+4598,15603674.0,Hs?eh,556.0,Spain,Female,32.0,0.0,0.0,1.0,1.0,1.0,129964.94,1.0
+4599,15745307.0,Hsueh,716.0,France,Male,61.0,9.0,0.0,2.0,1.0,0.0,85801.77,0.0
+4600,15690134.0,Hsing,545.0,Germany,Female,37.0,9.0,120145.96,1.0,0.0,0.0,120586.32,1.0
+4601,15697035.0,Chuang,752.0,France,Male,31.0,3.0,156847.29,1.0,0.0,1.0,88566.9,0.0
+4602,15747772.0,Y?,614.0,France,Male,30.0,6.0,91241.85,1.0,0.0,1.0,64945.57,0.0
+4603,15663625.0,Riddle,819.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,91903.9,0.0
+4604,15598884.0,Ch'in,686.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,184977.2,0.0
+4605,15662955.0,Chiang,597.0,France,Male,54.0,6.0,98765.84,1.0,1.0,1.0,153605.75,1.0
+4606,15641411.0,Hsia,687.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,121098.65,0.0
+4607,15783659.0,Genovesi,652.0,France,Female,46.0,2.0,0.0,1.0,1.0,0.0,73686.75,1.0
+4608,15643916.0,Yobachukwu,684.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,182336.45,0.0
+4609,15661532.0,Ts'ui,803.0,Germany,Female,47.0,7.0,106045.92,1.0,0.0,1.0,180736.24,1.0
+4610,15777033.0,Nnaife,577.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,164128.57,0.0
+4611,15610781.0,Li Fonti,644.0,France,Male,37.0,0.0,135139.33,1.0,1.0,1.0,134822.01,0.0
+4612,15643496.0,Kao,648.0,France,Female,33.0,3.0,141581.08,1.0,1.0,1.0,176450.19,0.0
+4613,15732265.0,Tsao,583.0,Germany,Female,30.0,5.0,126473.99,2.0,1.0,0.0,87071.18,0.0
+4614,15777893.0,Y?,687.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,128881.87,0.0
+4615,15746035.0,Mamelu,682.0,Germany,Male,37.0,8.0,132796.09,1.0,1.0,0.0,106564.88,0.0
+4616,15674442.0,Ritchie,681.0,Spain,Male,30.0,5.0,0.0,1.0,1.0,1.0,160122.66,0.0
+4617,15640034.0,Nwoye,562.0,Spain,Male,31.0,6.0,0.0,2.0,0.0,1.0,112465.19,0.0
+4618,15792721.0,Tuan,557.0,Germany,Female,54.0,7.0,146422.22,2.0,0.0,0.0,146723.72,1.0
+4619,15695097.0,H?,663.0,Spain,Male,27.0,7.0,0.0,2.0,1.0,1.0,25101.17,0.0
+4620,15752312.0,Yermakova,605.0,France,Female,40.0,1.0,0.0,2.0,1.0,1.0,126434.58,0.0
+4621,15763704.0,Milne,656.0,France,Male,33.0,9.0,89968.69,1.0,1.0,0.0,104220.5,0.0
+4622,15672610.0,Gentry,850.0,France,Female,22.0,6.0,0.0,2.0,1.0,0.0,94978.1,0.0
+4623,15770121.0,Hsia,699.0,Germany,Male,33.0,8.0,134760.21,1.0,1.0,0.0,187905.12,0.0
+4624,15591747.0,K?,650.0,Spain,Female,46.0,2.0,0.0,1.0,1.0,0.0,158043.11,1.0
+4625,15581252.0,Nkemjika,727.0,France,Female,25.0,2.0,0.0,2.0,1.0,0.0,75927.35,0.0
+4626,15694453.0,Milani,678.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,167723.25,0.0
+4627,15589431.0,Folliero,559.0,Spain,Female,49.0,7.0,141136.62,1.0,1.0,1.0,98273.01,0.0
+4628,15628303.0,Maclean,678.0,Spain,Male,44.0,9.0,0.0,1.0,1.0,0.0,142763.51,1.0
+4629,15750476.0,Lucchesi,631.0,France,Female,49.0,4.0,0.0,2.0,1.0,1.0,131736.23,0.0
+4630,15653952.0,Palerma,604.0,France,Male,33.0,0.0,78892.23,1.0,1.0,0.0,147720.27,1.0
+4631,15665811.0,T'ien,550.0,Spain,Male,37.0,3.0,136422.41,1.0,1.0,0.0,14083.12,0.0
+4632,15581323.0,Koger,709.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,168816.03,0.0
+4633,15592583.0,Cremonesi,684.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,118029.08,0.0
+4634,15747345.0,Chidiebere,636.0,Spain,Female,34.0,5.0,0.0,2.0,0.0,0.0,182354.46,0.0
+4635,15769254.0,Ch'in,729.0,France,Female,33.0,3.0,0.0,1.0,1.0,0.0,82867.55,0.0
+4636,15658057.0,Fokine,660.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,1.0,156917.11,0.0
+4637,15806913.0,Chibugo,569.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,1.0,70810.6,0.0
+4638,15799211.0,Smith,594.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,121525.48,0.0
+4639,15808566.0,Hsia,648.0,France,Female,47.0,9.0,0.0,2.0,1.0,1.0,142513.5,0.0
+4640,15728885.0,Chineze,684.0,France,Male,35.0,7.0,0.0,2.0,0.0,1.0,143086.09,0.0
+4641,15658935.0,Smith,664.0,France,Male,41.0,2.0,0.0,1.0,1.0,0.0,15607.27,0.0
+4642,15723339.0,Nworie,645.0,Spain,Female,35.0,6.0,184552.12,1.0,1.0,1.0,102999.33,1.0
+4643,15656901.0,Hsia,613.0,Germany,Male,49.0,3.0,119961.48,3.0,1.0,0.0,116663.46,1.0
+4644,15757837.0,Trevisano,690.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,145764.4,0.0
+4645,15738267.0,T'an,696.0,Spain,Male,36.0,5.0,142658.07,1.0,1.0,1.0,72467.99,0.0
+4646,15799480.0,Chu,659.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,75860.01,0.0
+4647,15763029.0,Shih,555.0,Germany,Male,35.0,1.0,106036.52,1.0,1.0,1.0,172646.88,0.0
+4648,15706464.0,Chiang,671.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,158344.63,0.0
+4649,15761102.0,Okeke,573.0,Germany,Female,26.0,0.0,143262.04,2.0,0.0,0.0,171587.9,0.0
+4650,15656522.0,Fanucci,537.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,179540.73,0.0
+4651,15752426.0,Obialo,648.0,Germany,Female,34.0,7.0,92398.08,1.0,1.0,0.0,74564.41,0.0
+4652,15577985.0,Olisaemeka,712.0,France,Female,31.0,4.0,0.0,1.0,1.0,0.0,55868.97,1.0
+4653,15632439.0,Tsao,679.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,0.0,86099.23,0.0
+4654,15750121.0,Nkemakolam,584.0,Germany,Female,46.0,5.0,131647.01,2.0,0.0,0.0,167984.72,1.0
+4655,15778830.0,Pinto,545.0,Germany,Female,32.0,8.0,133537.1,1.0,1.0,0.0,29346.1,0.0
+4656,15652955.0,Rischbieth,572.0,France,Male,48.0,5.0,0.0,1.0,1.0,0.0,64448.36,1.0
+4657,15693526.0,Walker,648.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,0.0,60721.4,0.0
+4658,15732967.0,Chia,709.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,167873.55,0.0
+4659,15620756.0,P'eng,634.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,127569.8,0.0
+4660,15596011.0,Efremova,706.0,France,Male,40.0,5.0,129490.43,1.0,1.0,0.0,106977.53,0.0
+4661,15791501.0,Chukwujekwu,790.0,France,Male,37.0,8.0,0.0,1.0,1.0,1.0,105570.9,0.0
+4662,15795738.0,Yudina,588.0,Spain,Male,51.0,4.0,0.0,1.0,1.0,1.0,95007.8,0.0
+4663,15622443.0,Ubanwa,656.0,France,Male,37.0,6.0,0.0,2.0,0.0,1.0,100324.01,0.0
+4664,15723032.0,T'ien,707.0,France,Male,29.0,5.0,0.0,1.0,1.0,0.0,160976.85,0.0
+4665,15733169.0,Ch'ang,687.0,France,Male,25.0,5.0,0.0,2.0,1.0,1.0,93982.02,0.0
+4666,15742110.0,Hsieh,667.0,Germany,Female,42.0,5.0,121669.93,2.0,1.0,1.0,176750.81,0.0
+4667,15706185.0,Chidozie,605.0,Spain,Female,33.0,3.0,0.0,2.0,1.0,1.0,121061.89,0.0
+4668,15679531.0,Chidubem,757.0,Germany,Female,31.0,9.0,122028.65,1.0,1.0,1.0,133672.61,0.0
+4669,15598259.0,Chukwukere,792.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,0.0,95719.73,0.0
+4670,15677828.0,Hsia,850.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,103726.71,0.0
+4671,15606861.0,Lucciano,733.0,Spain,Male,56.0,7.0,0.0,1.0,1.0,0.0,135077.01,1.0
+4672,15685019.0,Kao,646.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,126722.57,0.0
+4673,15656427.0,Iadanza,731.0,Spain,Male,33.0,1.0,0.0,2.0,1.0,1.0,178028.96,0.0
+4674,15763665.0,Lai,639.0,Germany,Female,46.0,3.0,92366.04,1.0,0.0,1.0,115818.51,1.0
+4675,15782418.0,Genovese,704.0,Spain,Male,38.0,1.0,0.0,1.0,1.0,1.0,149913.84,0.0
+4676,15757912.0,Ting,590.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,0.0,72876.43,0.0
+4677,15690772.0,Genovesi,664.0,Spain,Male,36.0,2.0,175575.95,1.0,1.0,0.0,160976.83,1.0
+4678,15762797.0,Tsou,778.0,France,Male,32.0,5.0,0.0,2.0,0.0,1.0,66893.28,0.0
+4679,15715199.0,Yobachi,694.0,France,Male,35.0,9.0,0.0,1.0,1.0,1.0,170621.43,0.0
+4680,15690796.0,Okwudiliolisa,670.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,88890.75,0.0
+4681,15624592.0,Hsing,663.0,France,Female,36.0,4.0,0.0,1.0,1.0,0.0,90860.97,0.0
+4682,15672091.0,Nwabugwu,677.0,Germany,Female,40.0,6.0,102773.2,2.0,1.0,0.0,12092.04,1.0
+4683,15790659.0,McGregor,706.0,France,Female,45.0,6.0,0.0,1.0,0.0,1.0,184580.24,1.0
+4684,15686588.0,Lo,637.0,Germany,Male,23.0,4.0,109483.54,1.0,1.0,1.0,85937.48,0.0
+4685,15605968.0,Hsiung,652.0,Germany,Male,38.0,6.0,100252.18,1.0,1.0,1.0,171060.01,0.0
+4686,15769915.0,Calabresi,590.0,France,Female,45.0,2.0,132271.2,1.0,1.0,1.0,126726.33,0.0
+4687,15803032.0,Chiu,707.0,France,Female,51.0,8.0,0.0,1.0,1.0,0.0,162022.47,1.0
+4688,15768072.0,Calabresi,596.0,Germany,Male,35.0,9.0,146780.52,2.0,1.0,1.0,120540.83,0.0
+4689,15705001.0,Shih,662.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,136079.64,0.0
+4690,15606915.0,Hs?,726.0,Spain,Male,52.0,0.0,0.0,2.0,0.0,1.0,93832.33,0.0
+4691,15666032.0,Onyekachukwu,667.0,France,Male,37.0,2.0,125951.81,1.0,0.0,1.0,19998.31,0.0
+4692,15714397.0,Clements,738.0,Spain,Female,43.0,4.0,0.0,2.0,1.0,1.0,50485.4,0.0
+4693,15598097.0,K'ung,775.0,France,Female,41.0,5.0,0.0,2.0,0.0,1.0,133617.43,0.0
+4694,15608916.0,Tien,753.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,181429.87,0.0
+4695,15589431.0,Chibugo,709.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,169278.51,0.0
+4696,15574324.0,McFarland,668.0,Germany,Male,30.0,4.0,91955.61,2.0,1.0,0.0,144584.29,0.0
+4697,15586974.0,Shao,743.0,France,Female,32.0,2.0,0.0,1.0,1.0,1.0,143598.7,1.0
+4698,15736420.0,Cremonesi,695.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,67979.35,0.0
+4699,15654025.0,She,553.0,France,Male,45.0,5.0,0.0,1.0,0.0,1.0,150856.38,0.0
+4700,15627365.0,Hsia,652.0,France,Female,20.0,2.0,0.0,2.0,1.0,1.0,83056.22,0.0
+4701,15767793.0,Ts'ui,543.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,132498.39,0.0
+4702,15682574.0,Lu,706.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,88827.67,0.0
+4703,15709621.0,Onyemachukwu,757.0,Spain,Female,42.0,1.0,0.0,2.0,1.0,1.0,183840.51,0.0
+4704,15693737.0,Chigozie,679.0,Spain,Male,33.0,10.0,0.0,2.0,1.0,1.0,138526.96,0.0
+4705,15759537.0,Cairns,478.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,106781.39,0.0
+4706,15597951.0,Hsiung,565.0,Germany,Male,52.0,6.0,91025.57,1.0,1.0,1.0,113505.93,1.0
+4707,15815560.0,Chidumaga,740.0,Spain,Female,43.0,8.0,0.0,2.0,1.0,1.0,126726.33,0.0
+4708,15752409.0,Johnstone,479.0,France,Male,39.0,1.0,155047.02,1.0,1.0,0.0,132547.02,1.0
+4709,15735270.0,Uwaezuoke,641.0,France,Male,48.0,6.0,141786.78,1.0,1.0,0.0,118913.53,1.0
+4710,15689492.0,Nkemakonam,589.0,France,Male,32.0,4.0,0.0,2.0,0.0,1.0,141872.05,0.0
+4711,15792989.0,Tipton,591.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,71179.31,0.0
+4712,15626233.0,Macleod,697.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,159483.76,0.0
+4713,15690733.0,Chiawuotu,729.0,France,Male,33.0,1.0,0.0,1.0,1.0,1.0,115441.66,0.0
+4714,15591747.0,Chikwado,718.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,60920.03,0.0
+4715,15651360.0,Chiazagomekpere,645.0,Germany,Female,37.0,7.0,116968.91,3.0,1.0,0.0,67979.35,1.0
+4716,15640268.0,Ch'ang,573.0,France,Male,25.0,3.0,0.0,2.0,1.0,0.0,140881.2,0.0
+4717,15652084.0,Ting,637.0,Spain,Male,34.0,9.0,139914.7,1.0,1.0,0.0,160968.44,0.0
+4718,15711750.0,Hs?,602.0,Spain,Male,29.0,1.0,125877.65,1.0,0.0,1.0,185855.21,0.0
+4719,15698902.0,Chibuzo,564.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,45959.86,0.0
+4720,15713483.0,McLean,714.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,57896.27,0.0
+4721,15595036.0,Kao,658.0,Germany,Female,58.0,2.0,123544.0,1.0,1.0,0.0,176943.59,1.0
+4722,15603764.0,Ch'iu,705.0,France,Male,39.0,0.0,0.0,2.0,0.0,1.0,71154.1,0.0
+4723,15711602.0,Manna,686.0,France,Female,28.0,9.0,0.0,2.0,1.0,1.0,183572.39,0.0
+4724,15617454.0,Hsia,714.0,France,Male,38.0,7.0,0.0,2.0,0.0,1.0,123587.93,0.0
+4725,15568429.0,Zito,806.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,0.0,27862.58,0.0
+4726,15757537.0,Chia,642.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,180598.66,0.0
+4727,15622985.0,Toscani,695.0,Germany,Male,35.0,2.0,102992.82,2.0,0.0,0.0,49418.87,0.0
+4728,15659710.0,Uspenskaya,602.0,France,Female,29.0,8.0,0.0,1.0,1.0,0.0,183598.77,1.0
+4729,15684469.0,Efimov,712.0,Germany,Female,61.0,6.0,113958.55,1.0,0.0,1.0,147055.87,1.0
+4730,15576676.0,Ts'ui,584.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,160532.88,0.0
+4731,15809906.0,Ch'iu,659.0,Spain,Male,39.0,1.0,0.0,1.0,1.0,0.0,133831.37,0.0
+4732,15604818.0,Chuang,603.0,Spain,Female,26.0,5.0,0.0,2.0,1.0,1.0,107499.58,0.0
+4733,15643439.0,Ts'ui,715.0,France,Female,33.0,9.0,0.0,1.0,1.0,0.0,160529.71,1.0
+4734,15737310.0,Ch'ang,628.0,France,Female,27.0,1.0,0.0,2.0,1.0,1.0,50488.91,0.0
+4735,15794566.0,Dickson,577.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,149139.13,0.0
+4736,15794849.0,Tuan,650.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,1.0,96528.0,0.0
+4737,15647570.0,Y?an,579.0,Germany,Female,32.0,6.0,70416.75,2.0,1.0,1.0,138360.29,0.0
+4738,15583548.0,Ch'ien,638.0,Germany,Female,40.0,7.0,135984.15,1.0,1.0,0.0,186984.34,1.0
+4739,15719508.0,Tsao,676.0,France,Male,37.0,0.0,0.0,1.0,1.0,1.0,179978.68,0.0
+4740,15774931.0,Nnonso,699.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,79919.13,0.0
+4741,15712776.0,Ozioma,538.0,Spain,Female,41.0,4.0,0.0,1.0,1.0,0.0,176913.42,0.0
+4742,15611579.0,Ma,581.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,123587.86,0.0
+4743,15796834.0,Hsia,572.0,France,Female,42.0,1.0,0.0,2.0,1.0,1.0,95009.6,0.0
+4744,15670172.0,Ozoemena,602.0,France,Female,29.0,2.0,0.0,1.0,1.0,0.0,44390.38,1.0
+4745,15653547.0,Hsieh,697.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,164768.82,0.0
+4746,15771749.0,Genovesi,665.0,France,Male,36.0,10.0,139825.26,1.0,1.0,1.0,166677.35,0.0
+4747,15773868.0,Toscani,638.0,Spain,Female,44.0,4.0,0.0,2.0,1.0,0.0,151757.81,0.0
+4748,15615352.0,O'Donnell,645.0,Germany,Male,30.0,5.0,144849.29,1.0,1.0,0.0,180622.03,0.0
+4749,15655341.0,Efremov,604.0,Spain,Male,37.0,9.0,0.0,1.0,1.0,1.0,107964.36,0.0
+4750,15774696.0,Chidozie,757.0,Germany,Male,31.0,8.0,172646.82,1.0,1.0,0.0,72924.56,0.0
+4751,15676526.0,Onwudiwe,739.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,102628.98,0.0
+4752,15777797.0,Maclean,645.0,France,Male,44.0,1.0,0.0,1.0,1.0,0.0,103931.49,1.0
+4753,15592765.0,Mazzi,590.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,150694.58,0.0
+4754,15675176.0,Yefremova,732.0,Spain,Male,40.0,7.0,209767.31,1.0,1.0,0.0,93783.0,1.0
+4755,15776850.0,Lattimore,607.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,179521.43,0.0
+4756,15711287.0,Yobachukwu,745.0,Spain,Female,33.0,3.0,117302.62,2.0,1.0,0.0,198802.9,0.0
+4757,15751203.0,Iweobiegbunam,682.0,France,Male,35.0,1.0,0.0,2.0,0.0,0.0,168043.6,0.0
+4758,15774262.0,Bazarova,537.0,Spain,Male,45.0,4.0,0.0,2.0,1.0,1.0,149991.32,0.0
+4759,15725679.0,Yeh,704.0,France,Male,31.0,5.0,0.0,1.0,1.0,1.0,111687.57,0.0
+4760,15681068.0,Chidiebere,758.0,Spain,Female,46.0,3.0,0.0,2.0,1.0,1.0,121866.87,0.0
+4761,15684461.0,Teng,634.0,Germany,Female,34.0,7.0,117866.92,1.0,1.0,0.0,83669.89,0.0
+4762,15775131.0,Ku,473.0,France,Male,52.0,1.0,0.0,1.0,0.0,1.0,183893.31,1.0
+4763,15700752.0,Chukwuhaenye,721.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,72496.05,0.0
+4764,15686941.0,Genovese,652.0,France,Female,49.0,9.0,0.0,2.0,1.0,0.0,165035.17,0.0
+4765,15662736.0,Yudin,599.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,126796.69,0.0
+4766,15712287.0,Hsueh,671.0,France,Male,32.0,4.0,122034.4,1.0,0.0,0.0,60420.07,0.0
+4767,15757837.0,Lo Duca,525.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,129562.74,0.0
+4768,15749688.0,Liao,569.0,France,Male,41.0,2.0,0.0,3.0,1.0,0.0,187926.6,1.0
+4769,15652658.0,Palerma,576.0,Germany,Female,41.0,8.0,132558.26,1.0,0.0,0.0,146738.82,0.0
+4770,15618647.0,Milano,733.0,France,Male,42.0,4.0,0.0,2.0,1.0,0.0,114566.47,0.0
+4771,15772329.0,Ch'ien,645.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,132770.11,0.0
+4772,15750056.0,Cattaneo,543.0,Germany,Female,52.0,1.0,106937.05,1.0,1.0,1.0,88391.97,1.0
+4773,15593900.0,Pagnotto,662.0,Spain,Male,49.0,2.0,0.0,2.0,1.0,0.0,74532.02,0.0
+4774,15708713.0,Chu,696.0,France,Female,23.0,2.0,0.0,2.0,1.0,0.0,140937.91,0.0
+4775,15633260.0,Nwokezuike,549.0,France,Male,56.0,8.0,0.0,1.0,1.0,1.0,107876.2,1.0
+4776,15646942.0,Tsou,749.0,Spain,Male,44.0,3.0,0.0,1.0,0.0,1.0,138526.96,0.0
+4777,15683658.0,Mazzi,675.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,147040.25,0.0
+4778,15720893.0,McLean,572.0,France,Male,31.0,6.0,145965.33,2.0,1.0,1.0,177844.06,0.0
+4779,15683980.0,Chinweuba,601.0,Germany,Male,40.0,4.0,96462.25,1.0,1.0,0.0,145796.49,0.0
+4780,15768151.0,Ikedinachukwu,581.0,Germany,Male,28.0,9.0,102290.56,2.0,1.0,0.0,116766.79,0.0
+4781,15584580.0,Tsai,578.0,France,Male,22.0,7.0,144962.74,1.0,0.0,1.0,121840.2,0.0
+4782,15735263.0,Lombardi,627.0,Germany,Female,47.0,1.0,137729.03,1.0,1.0,0.0,143681.83,1.0
+4783,15700813.0,Hsueh,605.0,France,Male,60.0,3.0,0.0,2.0,1.0,1.0,73334.43,0.0
+4784,15692631.0,Chiu,592.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,125877.22,0.0
+4785,15627212.0,Hs?,667.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,86937.2,0.0
+4786,15743067.0,Ndukaku,824.0,France,Male,31.0,9.0,0.0,1.0,1.0,1.0,115767.93,0.0
+4787,15765283.0,Dike,665.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,187987.01,0.0
+4788,15769945.0,Kolesnikova,576.0,Spain,Female,33.0,6.0,122088.67,1.0,1.0,0.0,149550.94,0.0
+4789,15589975.0,T'ien,676.0,Germany,Female,29.0,9.0,161274.05,1.0,1.0,1.0,121846.81,0.0
+4790,15679963.0,Yobachi,613.0,Germany,Male,47.0,2.0,113657.41,1.0,1.0,0.0,175296.76,1.0
+4791,15800811.0,K?,573.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,80701.72,0.0
+4792,15743076.0,K?,683.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,181757.19,0.0
+4793,15700511.0,Mazzanti,591.0,France,Female,43.0,4.0,0.0,1.0,1.0,1.0,161559.12,1.0
+4794,15731522.0,T'ien,632.0,Spain,Female,24.0,0.0,137781.65,1.0,1.0,0.0,102076.03,0.0
+4795,15754919.0,Maclean,737.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,181461.46,0.0
+4796,15763055.0,Chiu,646.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,173844.89,0.0
+4797,15583849.0,Hsia,607.0,Spain,Male,39.0,9.0,0.0,2.0,0.0,1.0,149474.69,0.0
+4798,15655339.0,Cattaneo,628.0,France,Male,41.0,2.0,0.0,1.0,1.0,1.0,114566.47,0.0
+4799,15592029.0,Ts'ao,558.0,France,Male,56.0,4.0,0.0,2.0,1.0,1.0,112777.38,0.0
+4800,15629551.0,Chikezie,690.0,France,Male,26.0,3.0,0.0,2.0,1.0,0.0,127630.88,0.0
+4801,15687069.0,Onyekaozulu,587.0,Germany,Female,36.0,9.0,86893.28,2.0,1.0,1.0,145796.49,0.0
+4802,15656300.0,T'ien,579.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,1.0,105898.79,0.0
+4803,15682472.0,Chiu,774.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,1801.77,0.0
+4804,15772573.0,Nwankwo,606.0,France,Male,47.0,8.0,0.0,2.0,1.0,1.0,175893.53,0.0
+4805,15694518.0,Hao,591.0,Spain,Female,25.0,1.0,0.0,2.0,1.0,1.0,88791.83,0.0
+4806,15578515.0,Tien,653.0,Germany,Male,52.0,6.0,111537.23,1.0,0.0,0.0,170950.48,1.0
+4807,15775293.0,Onwuamaeze,685.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,177772.03,0.0
+4808,15708534.0,Onyekachi,589.0,Germany,Female,30.0,7.0,106036.52,1.0,1.0,1.0,94544.88,0.0
+4809,15684516.0,Ritchie,581.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,121053.19,0.0
+4810,15590888.0,Chinwendu,584.0,France,Female,37.0,9.0,0.0,2.0,0.0,0.0,71830.1,0.0
+4811,15665077.0,Ojiofor,667.0,Germany,Male,31.0,1.0,99152.73,2.0,1.0,0.0,134732.99,0.0
+4812,15602914.0,Lombardi,683.0,France,Female,25.0,10.0,127763.24,1.0,1.0,0.0,126471.14,0.0
+4813,15632859.0,Pagnotto,705.0,France,Female,39.0,5.0,0.0,1.0,0.0,0.0,163427.18,1.0
+4814,15782236.0,Chin,754.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,121751.04,0.0
+4815,15664227.0,Hsia,662.0,France,Female,29.0,4.0,0.0,2.0,0.0,1.0,152996.89,0.0
+4816,15590730.0,Chiganu,649.0,Germany,Female,42.0,9.0,123971.51,1.0,0.0,1.0,59595.66,0.0
+4817,15772996.0,Zetticci,656.0,France,Female,33.0,6.0,0.0,2.0,0.0,0.0,199203.21,1.0
+4818,15761168.0,Brown,678.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,172668.39,0.0
+4819,15583595.0,Shih,557.0,France,Female,47.0,6.0,109730.22,1.0,1.0,1.0,140168.36,1.0
+4820,15744044.0,K?,589.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,173365.9,0.0
+4821,15572467.0,Chuang,756.0,France,Male,44.0,1.0,0.0,2.0,0.0,0.0,177738.98,0.0
+4822,15749482.0,Iadanza,704.0,France,Female,32.0,9.0,0.0,2.0,0.0,0.0,182728.8,0.0
+4823,15745455.0,Chiemezie,745.0,France,Female,38.0,7.0,105204.01,1.0,1.0,1.0,151924.9,0.0
+4824,15704586.0,Nwankwo,639.0,France,Male,37.0,5.0,121681.91,2.0,1.0,0.0,74564.41,0.0
+4825,15806967.0,Teng,755.0,France,Male,41.0,7.0,0.0,2.0,0.0,1.0,183569.25,0.0
+4826,15801293.0,Babbage,585.0,Spain,Female,30.0,1.0,160680.41,1.0,1.0,1.0,49848.86,0.0
+4827,15749472.0,Chukwuemeka,613.0,France,Female,62.0,5.0,0.0,1.0,1.0,1.0,145937.99,1.0
+4828,15680112.0,Chigozie,758.0,France,Female,44.0,7.0,69974.66,1.0,1.0,0.0,108543.21,0.0
+4829,15690164.0,Zetticci,554.0,France,Female,36.0,1.0,0.0,1.0,1.0,0.0,57762.0,1.0
+4830,15641114.0,Hsueh,609.0,Spain,Male,32.0,9.0,161814.64,1.0,1.0,1.0,182784.11,0.0
+4831,15688865.0,Shih,721.0,France,Female,35.0,2.0,107879.04,1.0,1.0,1.0,152992.03,0.0
+4832,15762268.0,Nwachinemelu,712.0,Germany,Female,44.0,9.0,161592.76,2.0,1.0,1.0,182692.8,1.0
+4833,15581526.0,Onuchukwu,719.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,145558.42,0.0
+4834,15682355.0,Chukwueloka,697.0,Spain,Female,49.0,4.0,0.0,2.0,1.0,0.0,14073.11,1.0
+4835,15763747.0,Hsueh,651.0,France,Male,46.0,1.0,0.0,1.0,1.0,0.0,183566.78,1.0
+4836,15679668.0,McLean,598.0,France,Male,38.0,4.0,0.0,1.0,0.0,1.0,62634.94,1.0
+4837,15765297.0,Hs?eh,652.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,125961.48,0.0
+4838,15656300.0,Chiazagomekpele,645.0,Spain,Male,40.0,2.0,0.0,1.0,1.0,0.0,101954.78,1.0
+4839,15755863.0,Hs?,679.0,Germany,Male,41.0,5.0,127095.14,1.0,1.0,0.0,123977.86,1.0
+4840,15779523.0,Nkemdilim,745.0,Germany,Female,34.0,3.0,129745.1,1.0,1.0,0.0,170152.02,1.0
+4841,15702464.0,Tung,594.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,28797.32,0.0
+4842,15791553.0,Nnanna,735.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,187905.12,0.0
+4843,15646969.0,Nkemakolam,653.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,145562.4,0.0
+4844,15757140.0,Mancini,642.0,Germany,Male,38.0,9.0,117271.8,2.0,1.0,1.0,183617.43,1.0
+4845,15788776.0,Fanucci,616.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,82996.47,0.0
+4846,15781298.0,Biryukov,471.0,Germany,Male,30.0,8.0,113674.2,1.0,1.0,1.0,78858.07,1.0
+4847,15632899.0,Chiemenam,726.0,Spain,Male,36.0,5.0,0.0,2.0,0.0,1.0,73371.65,0.0
+4848,15669611.0,Frolov,758.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,156424.4,0.0
+4849,15761043.0,Toscani,634.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,176902.83,0.0
+4850,15692452.0,Folliero,678.0,Germany,Male,31.0,5.0,98899.76,2.0,1.0,0.0,177026.59,0.0
+4851,15681476.0,Soto,579.0,Germany,Female,34.0,10.0,139781.06,1.0,0.0,1.0,28690.9,1.0
+4852,15644937.0,L?,651.0,France,Male,38.0,6.0,0.0,2.0,0.0,1.0,179032.68,0.0
+4853,15605918.0,Lo Duca,587.0,Spain,Female,34.0,1.0,0.0,2.0,0.0,1.0,152417.79,0.0
+4854,15752694.0,Ts'ui,571.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,0.0,140889.32,0.0
+4855,15594577.0,Young,577.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,146821.42,0.0
+4856,15584776.0,Ulyanova,645.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,73881.68,1.0
+4857,15773901.0,Fiorentino,617.0,Spain,Female,33.0,5.0,0.0,2.0,0.0,0.0,143315.57,0.0
+4858,15739576.0,Kao,682.0,France,Female,41.0,0.0,0.0,2.0,1.0,0.0,156774.94,0.0
+4859,15767681.0,Yefimova,624.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,51345.39,0.0
+4860,15807989.0,Chinedum,602.0,Spain,Male,37.0,1.0,0.0,1.0,1.0,0.0,92712.97,1.0
+4861,15723373.0,Aksyonova,663.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,113860.79,0.0
+4862,15713347.0,Chukwudi,577.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,88890.05,0.0
+4863,15804950.0,Ch'en,669.0,France,Female,55.0,1.0,0.0,1.0,1.0,1.0,125698.97,1.0
+4864,15775116.0,Smith,699.0,France,Male,28.0,9.0,141827.33,1.0,1.0,0.0,123876.22,0.0
+4865,15791114.0,Calabrese,637.0,Germany,Male,35.0,5.0,126550.7,1.0,1.0,1.0,109960.4,0.0
+4866,15809509.0,Chukwuemeka,602.0,Spain,Female,58.0,8.0,0.0,2.0,1.0,1.0,64831.36,0.0
+4867,15583716.0,Hsueh,587.0,France,Male,40.0,2.0,0.0,1.0,1.0,0.0,120593.85,1.0
+4868,15803764.0,Milanesi,709.0,France,Female,25.0,9.0,123042.0,1.0,1.0,0.0,169802.73,0.0
+4869,15691874.0,Chiemezie,516.0,Spain,Male,41.0,7.0,0.0,1.0,1.0,0.0,180962.32,0.0
+4870,15588566.0,Kao,683.0,France,Female,28.0,7.0,0.0,1.0,1.0,1.0,129188.18,0.0
+4871,15733888.0,Chiu,562.0,France,Female,26.0,0.0,0.0,2.0,0.0,1.0,127951.81,0.0
+4872,15733705.0,Shih,635.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,87731.02,0.0
+4873,15766044.0,Hsing,646.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,151903.6,0.0
+4874,15677052.0,Ozerova,661.0,France,Male,21.0,3.0,0.0,2.0,1.0,0.0,52831.13,0.0
+4875,15734987.0,Ugochukwu,710.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,103970.58,0.0
+4876,15762423.0,Chukwualuka,696.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,60920.03,0.0
+4877,15702571.0,Walker,554.0,Germany,Male,30.0,1.0,131569.63,2.0,0.0,1.0,147554.26,0.0
+4878,15665956.0,P'eng,590.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,138526.96,0.0
+4879,15583764.0,Okwudilichukwu,609.0,France,Male,42.0,2.0,143340.44,1.0,0.0,0.0,170821.43,0.0
+4880,15610446.0,Yobachi,679.0,France,Female,46.0,9.0,0.0,1.0,1.0,0.0,91711.66,1.0
+4881,15585893.0,Ch'in,673.0,France,Male,37.0,3.0,121073.23,1.0,1.0,1.0,44406.17,0.0
+4882,15763515.0,Bovee,598.0,Spain,Female,28.0,8.0,0.0,2.0,1.0,0.0,91574.92,0.0
+4883,15673584.0,Walker,697.0,Spain,Male,47.0,1.0,0.0,2.0,1.0,0.0,102763.69,0.0
+4884,15664754.0,Horsley,559.0,Spain,Female,40.0,0.0,0.0,2.0,1.0,1.0,96658.26,0.0
+4885,15736254.0,H?,599.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,185599.34,0.0
+4886,15639590.0,Udegbunam,638.0,Spain,Female,28.0,1.0,0.0,2.0,1.0,0.0,59650.42,0.0
+4887,15728505.0,Nnamdi,687.0,Germany,Male,49.0,5.0,110123.59,2.0,1.0,1.0,152845.99,0.0
+4888,15644296.0,Hsiung,781.0,France,Female,34.0,7.0,117038.96,1.0,1.0,1.0,180969.55,0.0
+4889,15791958.0,Manna,586.0,France,Female,37.0,8.0,0.0,1.0,1.0,1.0,60905.51,0.0
+4890,15721658.0,Shih,596.0,France,Female,22.0,3.0,0.0,1.0,1.0,1.0,9241.78,0.0
+4891,15641366.0,Tseng,776.0,France,Female,33.0,8.0,0.0,2.0,0.0,1.0,60728.89,0.0
+4892,15666884.0,T'ien,754.0,France,Male,27.0,7.0,0.0,2.0,1.0,0.0,54487.43,0.0
+4893,15593468.0,Ch'iu,673.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,173599.38,0.0
+4894,15570002.0,Shih,545.0,France,Female,33.0,5.0,166501.17,1.0,1.0,1.0,49463.44,0.0
+4895,15694829.0,Bibi,578.0,France,Female,38.0,3.0,0.0,3.0,1.0,0.0,107961.11,0.0
+4896,15679987.0,Chinomso,677.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,102799.14,0.0
+4897,15691952.0,Burtch,678.0,France,Male,43.0,8.0,0.0,2.0,0.0,1.0,159891.03,0.0
+4898,15661827.0,Hsieh,707.0,France,Female,31.0,4.0,94416.78,1.0,1.0,1.0,147530.06,0.0
+4899,15613886.0,Ch'iu,773.0,France,Female,56.0,9.0,0.0,1.0,0.0,0.0,156910.46,1.0
+4900,15630551.0,Chinwenma,708.0,France,Male,43.0,2.0,0.0,1.0,0.0,1.0,131043.2,1.0
+4901,15657778.0,Rowntree,730.0,France,Female,35.0,7.0,0.0,2.0,1.0,1.0,126794.56,0.0
+4902,15728005.0,Tung,602.0,France,Female,56.0,4.0,0.0,2.0,1.0,0.0,120801.65,1.0
+4903,15778089.0,Cremonesi,850.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,54448.22,0.0
+4904,15645029.0,Walker,719.0,France,Female,32.0,8.0,0.0,2.0,0.0,0.0,83419.15,0.0
+4905,15724223.0,Ndukaku,590.0,Spain,Male,46.0,10.0,0.0,1.0,1.0,0.0,141926.43,1.0
+4906,15740557.0,Palerma,850.0,France,Male,45.0,3.0,0.0,1.0,0.0,0.0,153566.97,0.0
+4907,15642885.0,Chibueze,729.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,91711.66,0.0
+4908,15661526.0,Hsiao,548.0,France,Male,40.0,1.0,0.0,1.0,0.0,0.0,110371.84,1.0
+4909,15681671.0,Cattaneo,646.0,Spain,Female,24.0,9.0,134562.8,2.0,1.0,1.0,128875.86,0.0
+4910,15605968.0,Y?,667.0,Germany,Female,29.0,8.0,121405.04,1.0,1.0,0.0,121232.51,0.0
+4911,15592222.0,Smith,678.0,France,Male,23.0,8.0,0.0,2.0,1.0,0.0,92985.78,1.0
+4912,15585823.0,Chuang,531.0,Spain,Female,44.0,9.0,0.0,1.0,0.0,1.0,138889.15,1.0
+4913,15640078.0,Ku,545.0,Spain,Female,60.0,8.0,0.0,1.0,1.0,1.0,119969.99,1.0
+4914,15730705.0,Shih,516.0,France,Male,44.0,2.0,0.0,2.0,1.0,1.0,123977.86,0.0
+4915,15751445.0,Y?an,644.0,France,Male,39.0,1.0,0.0,1.0,0.0,0.0,114093.73,0.0
+4916,15689671.0,Udinese,745.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,149913.94,0.0
+4917,15732672.0,Genovese,653.0,Germany,Female,37.0,6.0,81210.72,1.0,1.0,0.0,121747.98,1.0
+4918,15590730.0,Ch'ang,677.0,France,Female,41.0,0.0,0.0,2.0,0.0,0.0,132436.71,0.0
+4919,15754926.0,Milanesi,599.0,Spain,Male,44.0,2.0,0.0,2.0,1.0,0.0,153955.38,0.0
+4920,15747174.0,Macdonald,585.0,France,Male,32.0,8.0,0.0,1.0,1.0,0.0,177783.04,0.0
+4921,15577423.0,Macdonald,703.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,132770.11,0.0
+4922,15682355.0,Shih,684.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,121747.96,0.0
+4923,15681506.0,Hsieh,595.0,France,Male,27.0,8.0,155062.8,1.0,1.0,0.0,188203.58,0.0
+4924,15773792.0,Chin,638.0,France,Female,38.0,6.0,0.0,2.0,1.0,0.0,58854.88,0.0
+4925,15623062.0,Hs?,596.0,France,Male,62.0,3.0,0.0,1.0,1.0,1.0,88659.07,0.0
+4926,15642996.0,Chukwuhaenye,623.0,France,Female,33.0,7.0,0.0,2.0,0.0,0.0,138700.89,0.0
+4927,15633194.0,Hsu,726.0,Spain,Female,44.0,2.0,100549.43,1.0,1.0,0.0,131283.6,0.0
+4928,15671769.0,Bellucci,729.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,108411.66,0.0
+4929,15807609.0,Chiekwugo,718.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,47799.15,0.0
+4930,15600739.0,Chidubem,724.0,Germany,Female,34.0,6.0,146622.36,1.0,1.0,1.0,94898.1,0.0
+4931,15717290.0,Yermolayeva,637.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,0.0,55725.03,0.0
+4932,15764253.0,Genovese,850.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,96444.88,0.0
+4933,15658929.0,Chiang,583.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,93783.0,0.0
+4934,15662955.0,T'ien,678.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,167536.15,0.0
+4935,15771222.0,T'ien,696.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,28280.8,0.0
+4936,15806922.0,Wood,592.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,102260.98,0.0
+4937,15700813.0,Onyemauchechukwu,673.0,France,Female,36.0,10.0,110827.49,2.0,1.0,1.0,134790.61,0.0
+4938,15756975.0,Chibugo,631.0,Spain,Female,47.0,9.0,0.0,1.0,1.0,1.0,150856.38,1.0
+4939,15685564.0,Pisano,714.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,101311.95,0.0
+4940,15721469.0,Yermakova,711.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,179227.16,1.0
+4941,15798824.0,Hsu,619.0,Germany,Female,31.0,8.0,169824.46,2.0,0.0,0.0,142514.66,0.0
+4942,15587562.0,Chinwenma,633.0,Germany,Male,24.0,2.0,119799.27,1.0,1.0,0.0,147565.75,1.0
+4943,15776827.0,Chikezie,648.0,Spain,Male,46.0,9.0,0.0,1.0,1.0,1.0,182609.97,0.0
+4944,15572428.0,Udegbunam,705.0,France,Male,24.0,3.0,141786.78,1.0,1.0,0.0,180712.28,0.0
+4945,15578878.0,Cattaneo,711.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,146585.49,0.0
+4946,15677052.0,Hsueh,756.0,Germany,Male,33.0,9.0,129895.43,1.0,1.0,0.0,168822.01,0.0
+4947,15653572.0,Fennescey,707.0,France,Male,46.0,2.0,0.0,2.0,0.0,1.0,38276.84,0.0
+4948,15677550.0,Pinto,757.0,France,Male,29.0,6.0,0.0,1.0,1.0,0.0,135083.73,0.0
+4949,15578979.0,Ni,714.0,France,Male,42.0,10.0,0.0,2.0,0.0,0.0,167538.66,0.0
+4950,15694518.0,Ch'iu,684.0,France,Female,43.0,8.0,101438.0,1.0,1.0,0.0,172749.4,1.0
+4951,15781129.0,Okwudilichukwu,632.0,Germany,Male,28.0,5.0,61592.14,1.0,1.0,1.0,177655.69,0.0
+4952,15771059.0,Teng,738.0,France,Male,54.0,1.0,142876.88,1.0,1.0,1.0,172810.51,0.0
+4953,15588928.0,Ch'ien,542.0,France,Female,39.0,9.0,0.0,2.0,0.0,0.0,7905.06,0.0
+4954,15627262.0,De Luca,581.0,Spain,Female,34.0,1.0,0.0,2.0,1.0,1.0,122925.4,0.0
+4955,15632439.0,Chukwujamuike,727.0,Spain,Female,36.0,0.0,0.0,2.0,0.0,1.0,115525.13,0.0
+4956,15686885.0,Chuang,669.0,Germany,Male,37.0,3.0,108736.52,1.0,1.0,0.0,140136.1,0.0
+4957,15721921.0,Ndubueze,717.0,France,Male,28.0,9.0,126663.51,1.0,1.0,0.0,54416.18,0.0
+4958,15643916.0,Li Fonti,836.0,France,Male,29.0,2.0,124577.33,2.0,1.0,0.0,99800.87,0.0
+4959,15590876.0,Chibugo,782.0,Germany,Female,47.0,2.0,113657.83,1.0,1.0,1.0,182784.11,1.0
+4960,15633194.0,Lucchese,659.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,27231.26,0.0
+4961,15752912.0,L?,709.0,France,Female,22.0,1.0,130142.79,1.0,1.0,0.0,169935.46,0.0
+4962,15696812.0,Ts'ai,793.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,161051.75,0.0
+4963,15655007.0,Chuang,677.0,France,Male,38.0,3.0,122570.87,1.0,1.0,0.0,79577.38,0.0
+4964,15651581.0,Ejimofor,679.0,Germany,Male,49.0,5.0,116597.79,1.0,1.0,0.0,153372.39,1.0
+4965,15792360.0,Haddon,683.0,Spain,Female,33.0,8.0,0.0,2.0,1.0,0.0,145796.49,0.0
+4966,15661290.0,Nwachukwu,624.0,Germany,Female,54.0,5.0,152576.25,1.0,0.0,0.0,62370.67,1.0
+4967,15601263.0,O'Donnell,613.0,France,Male,33.0,9.0,103516.87,1.0,1.0,0.0,158978.79,0.0
+4968,15658889.0,Lo,516.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,123974.86,0.0
+4969,15694450.0,Ts'ui,653.0,France,Female,67.0,0.0,0.0,2.0,1.0,1.0,35140.49,0.0
+4970,15571816.0,Maclean,677.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,123866.28,0.0
+4971,15662494.0,Hs?,663.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,0.0,91624.11,0.0
+4972,15702434.0,Hao,638.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,1.0,125696.26,0.0
+4973,15771580.0,Fanucci,762.0,Spain,Male,34.0,9.0,139432.37,1.0,1.0,1.0,79342.31,0.0
+4974,15689952.0,Okechukwu,628.0,France,Female,22.0,8.0,0.0,2.0,1.0,1.0,95583.45,0.0
+4975,15686941.0,Onwumelu,659.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,89040.61,0.0
+4976,15644934.0,Linger,665.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,65864.4,0.0
+4977,15594556.0,Mazzanti,850.0,Germany,Male,41.0,7.0,115462.02,2.0,0.0,0.0,645.61,0.0
+4978,15803378.0,Ibezimako,577.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,49063.42,0.0
+4979,15670172.0,Ch'eng,645.0,France,Male,31.0,3.0,176913.52,2.0,1.0,1.0,58440.43,0.0
+4980,15791842.0,Nnanna,653.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,823.96,0.0
+4981,15567422.0,Huggins,553.0,Spain,Male,35.0,3.0,0.0,2.0,0.0,0.0,38308.34,0.0
+4982,15683523.0,Chukwufumnanya,556.0,Germany,Female,45.0,7.0,91092.52,2.0,1.0,0.0,51672.08,1.0
+4983,15573730.0,Kao,671.0,Germany,Female,35.0,2.0,143656.55,2.0,1.0,1.0,93524.19,0.0
+4984,15605000.0,Ch'ang,569.0,France,Male,31.0,5.0,117300.02,1.0,1.0,0.0,65612.01,1.0
+4985,15593795.0,Tretiakova,568.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,121747.98,0.0
+4986,15750808.0,T'ien,713.0,France,Female,27.0,4.0,0.0,2.0,1.0,0.0,58907.08,0.0
+4987,15615753.0,Chidimma,641.0,France,Male,44.0,3.0,0.0,2.0,1.0,1.0,139953.94,0.0
+4988,15646091.0,Genovesi,725.0,France,Male,25.0,3.0,0.0,2.0,1.0,1.0,26019.59,0.0
+4989,15753562.0,Echezonachukwu,652.0,France,Female,52.0,3.0,0.0,1.0,1.0,0.0,91565.25,1.0
+4990,15713347.0,Frolov,593.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,82709.8,0.0
+4991,15805637.0,Cremonesi,837.0,France,Male,38.0,1.0,147037.31,1.0,1.0,1.0,130483.19,0.0
+4992,15783025.0,Nwokeocha,594.0,Germany,Female,50.0,3.0,123311.39,1.0,0.0,0.0,174822.74,1.0
+4993,15734886.0,Mistry,741.0,France,Female,56.0,2.0,0.0,2.0,0.0,1.0,86767.48,0.0
+4994,15751943.0,Kincaid,850.0,France,Male,44.0,8.0,0.0,2.0,1.0,0.0,114957.22,0.0
+4995,15665943.0,Yashina,569.0,France,Female,50.0,7.0,0.0,4.0,1.0,0.0,166656.18,1.0
+4996,15679760.0,Chinweuba,635.0,Germany,Female,33.0,2.0,138825.19,1.0,0.0,1.0,138526.96,0.0
+4997,15806922.0,Hsing,706.0,France,Male,62.0,3.0,0.0,1.0,1.0,0.0,160176.47,1.0
+4998,15594902.0,Ts'ao,554.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,0.0,120830.73,0.0
+4999,15762729.0,Tuan,754.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,69080.46,0.0
+5000,15677522.0,Walker,588.0,France,Male,40.0,7.0,120867.18,1.0,0.0,1.0,141896.74,0.0
+5001,15743817.0,Padovano,553.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,127095.14,0.0
+5002,15607278.0,Chukwuemeka,602.0,France,Male,39.0,3.0,98443.15,1.0,1.0,1.0,174064.4,0.0
+5003,15584776.0,Zhirov,568.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,106040.97,0.0
+5004,15661034.0,Hsueh,594.0,Germany,Male,35.0,1.0,133800.98,1.0,1.0,1.0,166677.35,0.0
+5005,15754919.0,Bancks,681.0,France,Female,33.0,3.0,0.0,2.0,0.0,1.0,159742.33,0.0
+5006,15595557.0,Chukwuebuka,538.0,Spain,Male,39.0,1.0,0.0,1.0,1.0,0.0,62700.08,0.0
+5007,15690175.0,Chukwukere,661.0,Germany,Male,33.0,9.0,145747.67,1.0,1.0,1.0,103901.35,1.0
+5008,15661290.0,Kornilova,653.0,France,Male,36.0,2.0,0.0,2.0,0.0,1.0,14073.11,0.0
+5009,15582404.0,Cattaneo,850.0,Spain,Male,41.0,8.0,133467.77,1.0,1.0,0.0,87978.45,0.0
+5010,15725166.0,Eipper,642.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,142377.69,0.0
+5011,15697034.0,Bischof,537.0,Germany,Male,30.0,1.0,167545.32,1.0,0.0,1.0,69384.71,0.0
+5012,15682600.0,Onyekachi,656.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,151733.43,0.0
+5013,15641794.0,Trevisano,683.0,France,Female,35.0,9.0,121365.39,2.0,1.0,1.0,162719.69,0.0
+5014,15672945.0,Zikoranachidimma,713.0,Spain,Female,42.0,2.0,0.0,1.0,1.0,1.0,116885.59,0.0
+5015,15751336.0,Hsia,654.0,Germany,Female,51.0,6.0,90920.75,2.0,1.0,0.0,168034.83,1.0
+5016,15714046.0,Scott,594.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,129964.94,0.0
+5017,15704769.0,Chidiebele,558.0,France,Male,28.0,9.0,118897.1,1.0,1.0,1.0,82463.69,0.0
+5018,15597945.0,Fyodorov,687.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,101371.72,0.0
+5019,15722599.0,Okonkwo,662.0,Spain,Male,33.0,1.0,0.0,2.0,1.0,0.0,129918.43,0.0
+5020,15663478.0,Y?,687.0,France,Female,47.0,2.0,0.0,1.0,1.0,1.0,176678.72,1.0
+5021,15803681.0,Nwebube,577.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,111558.7,0.0
+5022,15669204.0,Gentry,653.0,France,Female,46.0,6.0,148046.39,1.0,1.0,0.0,103570.8,1.0
+5023,15795895.0,Nkemdilim,712.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,175072.47,0.0
+5024,15641674.0,Chinweuba,652.0,France,Female,38.0,4.0,0.0,2.0,0.0,0.0,4777.23,0.0
+5025,15765283.0,Chibugo,684.0,Spain,Male,30.0,2.0,96418.09,2.0,1.0,0.0,182784.11,0.0
+5026,15585901.0,O'Brien,553.0,Germany,Male,53.0,10.0,153610.61,1.0,1.0,1.0,73562.05,1.0
+5027,15797900.0,Manna,619.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,179834.45,0.0
+5028,15626806.0,Okonkwo,632.0,France,Female,35.0,4.0,0.0,1.0,1.0,0.0,180712.28,1.0
+5029,15789413.0,Maclean,716.0,Germany,Male,38.0,2.0,73348.56,2.0,1.0,0.0,149913.94,0.0
+5030,15798084.0,Hsiao,753.0,France,Male,37.0,7.0,0.0,2.0,0.0,0.0,176692.87,0.0
+5031,15685300.0,Onyemauchechukwu,769.0,Germany,Male,31.0,2.0,116324.53,1.0,0.0,0.0,25289.23,0.0
+5032,15585748.0,Chuang,663.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,118319.6,0.0
+5033,15744044.0,Pagnotto,687.0,Spain,Female,30.0,9.0,0.0,2.0,0.0,0.0,154700.61,0.0
+5034,15648032.0,Ch'en,592.0,Spain,Male,41.0,3.0,0.0,2.0,1.0,1.0,90177.3,0.0
+5035,15594812.0,Lombardi,585.0,Spain,Female,26.0,8.0,116933.11,1.0,0.0,1.0,118974.77,0.0
+5036,15707968.0,L?,733.0,France,Female,40.0,7.0,104982.57,1.0,1.0,1.0,28806.32,0.0
+5037,15709511.0,Tien,645.0,Germany,Male,30.0,1.0,132759.8,1.0,1.0,0.0,157891.86,0.0
+5038,15583736.0,P'eng,561.0,France,Female,43.0,5.0,0.0,2.0,1.0,0.0,140139.28,0.0
+5039,15790750.0,Ozoemena,794.0,France,Female,44.0,9.0,0.0,2.0,1.0,0.0,84569.13,0.0
+5040,15794916.0,Chukwujamuike,584.0,France,Female,33.0,3.0,0.0,2.0,0.0,1.0,168816.35,0.0
+5041,15771543.0,Pinto,845.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,137784.25,0.0
+5042,15690546.0,Iadanza,725.0,Germany,Male,39.0,9.0,115725.24,1.0,1.0,1.0,182025.95,0.0
+5043,15596962.0,Chiang,656.0,France,Female,29.0,7.0,0.0,1.0,1.0,1.0,181204.5,0.0
+5044,15692664.0,White,638.0,France,Male,36.0,10.0,0.0,2.0,0.0,1.0,98618.79,0.0
+5045,15794413.0,H?,696.0,France,Male,37.0,4.0,0.0,2.0,0.0,0.0,36315.03,0.0
+5046,15706593.0,Ijendu,516.0,France,Female,37.0,2.0,122295.17,1.0,0.0,0.0,115733.16,0.0
+5047,15579891.0,Ch'ien,648.0,Germany,Female,38.0,7.0,139914.67,2.0,1.0,1.0,162267.87,1.0
+5048,15581286.0,Ch'ien,586.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,163667.56,0.0
+5049,15690164.0,Folliero,696.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,0.0,149912.28,0.0
+5050,15739592.0,Onyeorulu,613.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,105784.85,0.0
+5051,15774536.0,Iheanacho,751.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,182081.45,0.0
+5052,15793049.0,Manna,577.0,France,Female,36.0,2.0,121404.64,1.0,1.0,1.0,16607.15,0.0
+5053,15775562.0,Lo Duca,668.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,0.0,175894.24,0.0
+5054,15680405.0,Chiu,587.0,Spain,Female,44.0,1.0,0.0,1.0,0.0,0.0,129892.93,1.0
+5055,15806938.0,Hsiung,683.0,France,Male,37.0,3.0,0.0,2.0,0.0,0.0,182784.11,0.0
+5056,15693902.0,T'ang,489.0,Spain,Female,47.0,4.0,91622.47,1.0,1.0,0.0,116848.79,0.0
+5057,15806967.0,Yuryeva,576.0,France,Male,35.0,4.0,175837.68,1.0,1.0,0.0,99800.87,0.0
+5058,15623210.0,Kambinachi,569.0,France,Male,27.0,10.0,139875.19,1.0,1.0,1.0,86414.41,0.0
+5059,15603883.0,Y?an,496.0,France,Female,68.0,4.0,156660.72,3.0,1.0,0.0,176091.31,1.0
+5060,15623536.0,Chiu,543.0,Germany,Female,33.0,2.0,131033.76,1.0,1.0,0.0,184842.08,0.0
+5061,15778936.0,Onyekachi,754.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,77951.84,0.0
+5062,15681695.0,Hsia,596.0,France,Female,43.0,7.0,0.0,1.0,1.0,0.0,113553.43,1.0
+5063,15634295.0,Uspensky,667.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,151315.76,0.0
+5064,15706071.0,Tsou,694.0,Germany,Male,39.0,3.0,114094.38,1.0,1.0,0.0,102908.91,0.0
+5065,15622289.0,Nwagugheuzo,678.0,France,Male,47.0,4.0,0.0,2.0,1.0,0.0,103799.71,0.0
+5066,15694890.0,Iloerika,710.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,130853.03,1.0
+5067,15754578.0,Kable,774.0,Germany,Male,41.0,5.0,108888.59,2.0,1.0,1.0,167034.83,0.0
+5068,15738672.0,Mazzi,629.0,Germany,Female,41.0,4.0,118827.35,1.0,0.0,1.0,70778.63,1.0
+5069,15682454.0,Toscani,687.0,Germany,Male,69.0,4.0,108862.76,1.0,1.0,1.0,73882.49,0.0
+5070,15607986.0,Mazzanti,681.0,France,Male,35.0,4.0,126815.87,1.0,1.0,1.0,161479.19,0.0
+5071,15640409.0,Pinto,733.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,131281.28,0.0
+5072,15684594.0,Fomin,535.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,127095.14,0.0
+5073,15781598.0,Hsueh,683.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,199409.19,0.0
+5074,15728669.0,Chien,681.0,France,Male,30.0,7.0,0.0,2.0,0.0,1.0,165038.27,0.0
+5075,15699225.0,Y?an,633.0,France,Female,48.0,5.0,139859.54,1.0,0.0,0.0,125553.52,0.0
+5076,15793949.0,Ch'iu,576.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,54949.73,0.0
+5077,15622454.0,Ijendu,637.0,Germany,Male,37.0,9.0,103549.74,1.0,1.0,0.0,121151.1,1.0
+5078,15804787.0,Ch'ien,731.0,France,Male,49.0,4.0,0.0,1.0,1.0,0.0,108411.66,1.0
+5079,15655192.0,Iloerika,567.0,France,Male,45.0,2.0,0.0,4.0,1.0,1.0,38960.59,1.0
+5080,15691823.0,Pisano,575.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,176570.28,0.0
+5081,15774105.0,Pagnotto,705.0,Spain,Female,41.0,9.0,0.0,2.0,1.0,0.0,74835.65,0.0
+5082,15802351.0,Hsiao,652.0,France,Male,34.0,2.0,96947.76,1.0,0.0,0.0,173837.54,0.0
+5083,15802362.0,Bibi,611.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,161064.29,0.0
+5084,15763980.0,Nebechukwu,704.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,1.0,133031.96,0.0
+5085,15625759.0,Chineze,635.0,France,Female,48.0,3.0,0.0,1.0,1.0,0.0,160696.72,1.0
+5086,15595842.0,Ch'ien,708.0,France,Female,29.0,7.0,131694.97,1.0,0.0,1.0,82222.86,0.0
+5087,15696733.0,White,838.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,113796.15,0.0
+5088,15780684.0,Hsieh,652.0,France,Female,28.0,4.0,0.0,2.0,1.0,0.0,177614.17,0.0
+5089,15634491.0,Tuan,815.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,92664.09,0.0
+5090,15751561.0,Chikelu,711.0,France,Male,36.0,5.0,0.0,2.0,0.0,0.0,64443.75,0.0
+5091,15746430.0,Nkemdilim,684.0,France,Male,46.0,7.0,0.0,1.0,0.0,1.0,35367.19,0.0
+5092,15635244.0,Milanesi,581.0,France,Female,56.0,3.0,0.0,3.0,1.0,0.0,73977.23,1.0
+5093,15679048.0,Shih,676.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,148516.79,0.0
+5094,15581626.0,Milano,683.0,Spain,Male,29.0,6.0,0.0,1.0,1.0,1.0,123663.63,0.0
+5095,15695097.0,Mironova,492.0,Germany,Male,40.0,7.0,134760.21,2.0,1.0,1.0,149853.89,0.0
+5096,15765532.0,Kao,681.0,France,Male,45.0,4.0,0.0,1.0,0.0,1.0,189366.03,1.0
+5097,15632859.0,Iloerika,607.0,France,Male,40.0,8.0,120905.03,1.0,0.0,1.0,147055.87,0.0
+5098,15725036.0,Pugh,656.0,France,Female,46.0,9.0,0.0,2.0,1.0,0.0,123981.36,0.0
+5099,15583595.0,Calabrese,604.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,121485.1,0.0
+5100,15737509.0,Palermo,682.0,France,Male,43.0,10.0,0.0,2.0,0.0,1.0,132468.76,0.0
+5101,15731543.0,Ulyanova,677.0,France,Female,37.0,9.0,0.0,2.0,0.0,1.0,143881.85,0.0
+5102,15629603.0,Chien,686.0,Germany,Male,40.0,2.0,147814.04,2.0,1.0,1.0,166038.27,0.0
+5103,15791326.0,Ritchie,726.0,France,Female,51.0,4.0,0.0,2.0,1.0,0.0,128841.31,1.0
+5104,15576065.0,Uchechukwu,582.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,171707.93,0.0
+5105,15736420.0,Folliero,569.0,Spain,Male,51.0,6.0,0.0,1.0,1.0,0.0,161519.77,1.0
+5106,15797787.0,Chiekwugo,667.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,185547.72,0.0
+5107,15772337.0,Fyodorova,633.0,France,Female,31.0,3.0,0.0,2.0,0.0,0.0,142331.85,0.0
+5108,15591386.0,Chiebuka,644.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,135925.72,0.0
+5109,15695097.0,Hsiao,653.0,France,Female,35.0,8.0,0.0,3.0,0.0,0.0,138051.19,1.0
+5110,15711687.0,Hsieh,717.0,Spain,Female,35.0,8.0,178074.33,1.0,1.0,0.0,67552.71,0.0
+5111,15679810.0,Ngozichukwuka,619.0,France,Male,36.0,4.0,0.0,2.0,0.0,1.0,167984.72,0.0
+5112,15583848.0,Y?,637.0,Spain,Male,35.0,5.0,0.0,1.0,1.0,1.0,179631.85,0.0
+5113,15631893.0,Smith,712.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,53584.03,0.0
+5114,15666096.0,Chiemenam,776.0,France,Male,35.0,4.0,0.0,2.0,0.0,1.0,85736.46,0.0
+5115,15658548.0,Ogg,810.0,France,Male,28.0,1.0,0.0,2.0,0.0,0.0,71862.79,0.0
+5116,15664547.0,Chinedum,589.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,103421.91,0.0
+5117,15790717.0,Chibugo,727.0,Germany,Female,39.0,7.0,145974.79,2.0,1.0,0.0,163471.01,0.0
+5118,15691627.0,Iloerika,724.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,97794.71,0.0
+5119,15625916.0,Nucci,559.0,France,Female,25.0,5.0,0.0,2.0,1.0,0.0,113428.77,0.0
+5120,15769216.0,Ch'in,640.0,France,Male,40.0,3.0,0.0,2.0,0.0,1.0,68621.98,0.0
+5121,15702656.0,Watson,622.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,92917.54,0.0
+5122,15582914.0,DeRose,684.0,France,Female,34.0,9.0,0.0,1.0,0.0,0.0,176984.34,0.0
+5123,15670587.0,Ts'ai,749.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,1.0,50560.15,0.0
+5124,15591747.0,Yermakova,675.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,127068.92,0.0
+5125,15810010.0,Onyemauchechukwu,685.0,France,Female,30.0,9.0,0.0,2.0,1.0,1.0,73334.41,0.0
+5126,15736271.0,Lu,689.0,France,Female,35.0,10.0,0.0,1.0,1.0,0.0,54474.48,0.0
+5127,15806945.0,Lucchese,722.0,Germany,Female,40.0,6.0,129810.6,2.0,1.0,1.0,169278.51,0.0
+5128,15629985.0,Kornilova,738.0,France,Male,43.0,1.0,182781.74,1.0,1.0,1.0,59595.66,0.0
+5129,15597905.0,Y?,597.0,France,Male,30.0,1.0,0.0,2.0,0.0,1.0,103713.93,0.0
+5130,15733387.0,Milani,804.0,France,Male,45.0,7.0,0.0,1.0,1.0,0.0,177738.98,1.0
+5131,15583394.0,Nwachinemelu,649.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,176743.51,0.0
+5132,15672930.0,Milne,577.0,Spain,Female,32.0,1.0,0.0,2.0,1.0,1.0,162697.93,0.0
+5133,15764444.0,Milani,616.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,118862.08,0.0
+5134,15709137.0,Chifley,813.0,France,Male,36.0,8.0,0.0,1.0,0.0,1.0,152577.62,0.0
+5135,15777639.0,Hs?,727.0,France,Female,29.0,7.0,171283.91,1.0,1.0,1.0,184842.21,0.0
+5136,15709780.0,Iloerika,749.0,France,Male,29.0,5.0,0.0,2.0,0.0,1.0,47952.45,0.0
+5137,15623063.0,Ponomaryova,687.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,108645.85,0.0
+5138,15651052.0,Li Fonti,604.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,143663.63,0.0
+5139,15790313.0,Smith,698.0,Germany,Female,57.0,2.0,144900.58,1.0,1.0,0.0,131389.39,1.0
+5140,15659325.0,Kao,708.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,1.0,93832.33,0.0
+5141,15642570.0,Y?,590.0,France,Male,39.0,9.0,109949.05,1.0,1.0,0.0,88890.05,0.0
+5142,15761158.0,Chibueze,684.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,54869.55,0.0
+5143,15756918.0,Smith,600.0,Germany,Male,60.0,3.0,106770.82,2.0,1.0,0.0,101828.39,1.0
+5144,15775886.0,Kao,699.0,Germany,Female,31.0,9.0,125437.86,2.0,1.0,0.0,164074.89,0.0
+5145,15794056.0,Smith,581.0,Spain,Male,41.0,5.0,0.0,2.0,0.0,0.0,93536.38,0.0
+5146,15782735.0,Ch'iu,596.0,France,Female,35.0,4.0,0.0,3.0,1.0,1.0,182372.35,1.0
+5147,15595883.0,Chien,579.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,102999.33,0.0
+5148,15726589.0,Teng,569.0,Spain,Female,52.0,10.0,0.0,1.0,1.0,1.0,52319.26,1.0
+5149,15668943.0,Pirozzi,627.0,France,Male,43.0,4.0,0.0,2.0,0.0,1.0,177528.92,0.0
+5150,15777045.0,Ch'ien,586.0,France,Male,38.0,7.0,0.0,1.0,1.0,1.0,116466.19,0.0
+5151,15617648.0,Oguejiofor,599.0,France,Female,36.0,4.0,0.0,2.0,0.0,0.0,162697.93,0.0
+5152,15598070.0,Ch'ien,686.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,122782.5,0.0
+5153,15659568.0,Ch'ang,735.0,France,Male,44.0,9.0,0.0,2.0,1.0,0.0,108419.41,1.0
+5154,15762745.0,Chidumaga,718.0,Germany,Female,43.0,8.0,118094.34,3.0,1.0,0.0,163650.53,1.0
+5155,15702434.0,Hsia,690.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,102515.57,0.0
+5156,15794413.0,T'ien,683.0,Spain,Male,39.0,5.0,148061.89,2.0,1.0,1.0,140937.91,0.0
+5157,15752885.0,Chiang,684.0,France,Male,36.0,8.0,0.0,2.0,0.0,1.0,187905.12,0.0
+5158,15742678.0,Chuang,809.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,67622.46,0.0
+5159,15652999.0,Chien,721.0,France,Male,27.0,2.0,0.0,2.0,0.0,1.0,131301.74,0.0
+5160,15737571.0,Ponomaryov,697.0,France,Male,27.0,8.0,0.0,2.0,1.0,0.0,163568.51,0.0
+5161,15691703.0,Macartney,717.0,France,Male,42.0,9.0,0.0,2.0,0.0,0.0,171706.66,0.0
+5162,15699247.0,Chukwuemeka,554.0,France,Male,45.0,4.0,0.0,2.0,1.0,0.0,62634.94,0.0
+5163,15784301.0,Nwokezuike,562.0,Spain,Female,33.0,9.0,144852.06,1.0,1.0,0.0,28805.09,0.0
+5164,15799358.0,Onyekachukwu,613.0,Spain,Male,39.0,6.0,0.0,1.0,1.0,1.0,116696.77,0.0
+5165,15705034.0,Chigolum,753.0,France,Female,46.0,9.0,128960.21,1.0,1.0,0.0,119357.93,1.0
+5166,15576723.0,Ch'iu,638.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,131886.46,0.0
+5167,15702788.0,T'ang,586.0,Germany,Female,36.0,9.0,90537.16,2.0,1.0,1.0,177069.24,0.0
+5168,15726589.0,Chukwudi,703.0,France,Male,25.0,4.0,0.0,2.0,0.0,0.0,140937.97,0.0
+5169,15660429.0,Zito,633.0,Germany,Male,29.0,5.0,63723.78,1.0,1.0,0.0,167058.75,1.0
+5170,15588928.0,Palermo,693.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,64907.48,0.0
+5171,15577078.0,Walker,643.0,France,Male,35.0,8.0,0.0,2.0,0.0,1.0,101314.76,0.0
+5172,15707962.0,Rowntree,714.0,France,Male,42.0,4.0,0.0,1.0,1.0,0.0,136859.13,1.0
+5173,15804919.0,Henty,594.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,0.0,71926.28,0.0
+5174,15641733.0,Chukwualuka,646.0,Spain,Male,26.0,7.0,0.0,2.0,1.0,0.0,134441.6,0.0
+5175,15643826.0,Hao,634.0,France,Male,45.0,8.0,0.0,1.0,1.0,0.0,115227.86,1.0
+5176,15743411.0,Chibugo,526.0,Spain,Male,50.0,1.0,0.0,2.0,1.0,1.0,114093.73,1.0
+5177,15679307.0,Tuan,814.0,France,Male,43.0,8.0,0.0,1.0,1.0,1.0,50213.81,0.0
+5178,15658675.0,Liao,706.0,France,Female,33.0,10.0,137948.51,1.0,1.0,1.0,142400.27,0.0
+5179,15692406.0,Nnamdi,663.0,Spain,Male,34.0,0.0,0.0,2.0,1.0,1.0,184399.76,0.0
+5180,15595047.0,Chinwendu,651.0,Spain,Female,40.0,8.0,0.0,2.0,1.0,1.0,83419.29,0.0
+5181,15697480.0,Chukwualuka,520.0,France,Female,34.0,4.0,93547.23,1.0,1.0,1.0,180890.4,0.0
+5182,15741032.0,Lu,539.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,110718.02,0.0
+5183,15680201.0,Hsia,639.0,France,Female,40.0,8.0,160736.63,1.0,0.0,0.0,60920.03,0.0
+5184,15762762.0,Brown,587.0,France,Male,36.0,4.0,115024.5,1.0,1.0,1.0,84920.49,0.0
+5185,15568326.0,Chukwujamuike,670.0,France,Male,53.0,8.0,0.0,2.0,1.0,1.0,89893.6,1.0
+5186,15660101.0,Hsia,732.0,Germany,Male,46.0,2.0,132602.88,1.0,1.0,0.0,148087.88,0.0
+5187,15725762.0,Macdonald,658.0,France,Female,47.0,8.0,0.0,2.0,0.0,1.0,181224.56,0.0
+5188,15713094.0,Yefimova,554.0,France,Female,34.0,9.0,83074.0,2.0,1.0,0.0,122783.04,0.0
+5189,15745012.0,Nucci,642.0,France,Female,38.0,3.0,73059.38,1.0,1.0,1.0,152417.97,0.0
+5190,15714883.0,Ch'in,609.0,Spain,Female,28.0,1.0,0.0,2.0,1.0,0.0,160548.12,0.0
+5191,15742210.0,Smith,589.0,Spain,Male,29.0,8.0,151607.56,1.0,1.0,0.0,90888.86,0.0
+5192,15652883.0,Udinese,636.0,Germany,Male,41.0,5.0,115465.98,1.0,1.0,0.0,52876.41,1.0
+5193,15664759.0,Chiu,687.0,France,Male,39.0,1.0,129860.37,1.0,1.0,0.0,183494.87,0.0
+5194,15574761.0,Tien,527.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,167830.26,0.0
+5195,15612505.0,Mamelu,745.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,96881.32,0.0
+5196,15589948.0,Fan,676.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,76544.9,0.0
+5197,15680998.0,Ch'ien,733.0,France,Male,41.0,1.0,0.0,2.0,0.0,0.0,88838.14,0.0
+5198,15723654.0,Chu,559.0,France,Male,33.0,2.0,0.0,2.0,0.0,1.0,80619.09,0.0
+5199,15661528.0,Ch'eng,749.0,France,Female,26.0,9.0,95710.11,1.0,1.0,0.0,94875.04,0.0
+5200,15806840.0,Y?,637.0,France,Female,41.0,1.0,0.0,3.0,1.0,0.0,102930.46,1.0
+5201,15791045.0,Chiawuotu,663.0,France,Male,54.0,8.0,0.0,1.0,0.0,1.0,182436.81,1.0
+5202,15745012.0,Genovese,741.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,61936.1,0.0
+5203,15741181.0,Lu,695.0,Germany,Male,39.0,7.0,109724.38,2.0,1.0,0.0,80792.83,0.0
+5204,15682048.0,Ts'ai,583.0,Germany,Male,44.0,4.0,133950.37,1.0,1.0,1.0,74836.64,1.0
+5205,15788442.0,Chia,525.0,France,Female,44.0,3.0,90547.48,1.0,1.0,1.0,66732.63,1.0
+5206,15761053.0,Hingston,687.0,France,Female,28.0,2.0,94486.47,2.0,1.0,1.0,124872.46,0.0
+5207,15592028.0,Lucchesi,682.0,France,Female,42.0,6.0,0.0,2.0,1.0,0.0,182528.44,0.0
+5208,15750728.0,K?,756.0,France,Male,34.0,5.0,0.0,1.0,1.0,0.0,84760.32,0.0
+5209,15682914.0,Ch'ang,614.0,Germany,Female,34.0,1.0,123544.0,2.0,1.0,0.0,74415.2,0.0
+5210,15612071.0,Nkemjika,611.0,France,Female,39.0,0.0,0.0,2.0,1.0,1.0,131778.58,0.0
+5211,15585067.0,Tien,581.0,France,Male,31.0,3.0,77967.5,1.0,1.0,1.0,53437.1,0.0
+5212,15747288.0,Efremova,769.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,176729.71,0.0
+5213,15589420.0,Chidozie,602.0,Germany,Female,42.0,5.0,109166.46,1.0,1.0,1.0,161668.15,1.0
+5214,15798895.0,Lu,632.0,Spain,Female,42.0,6.0,0.0,1.0,1.0,0.0,15068.18,1.0
+5215,15738220.0,Maclean,642.0,France,Male,45.0,8.0,136857.0,1.0,1.0,0.0,50485.7,1.0
+5216,15803479.0,Chukwufumnanya,684.0,France,Female,47.0,2.0,0.0,2.0,1.0,1.0,73066.71,0.0
+5217,15607988.0,Hsia,705.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,82870.75,0.0
+5218,15708534.0,Hsia,554.0,Spain,Female,39.0,9.0,0.0,1.0,1.0,0.0,158735.3,1.0
+5219,15676576.0,Ch'en,709.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,118862.08,0.0
+5220,15671353.0,Hsiung,726.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,183569.25,0.0
+5221,15624388.0,Nwankwo,725.0,Germany,Male,46.0,2.0,138054.59,1.0,1.0,1.0,171762.87,1.0
+5222,15571244.0,Toscani,595.0,Germany,Male,32.0,6.0,140681.68,2.0,1.0,1.0,124615.59,0.0
+5223,15569050.0,Onuoha,576.0,France,Male,49.0,0.0,135070.58,1.0,1.0,1.0,127095.14,1.0
+5224,15791373.0,Palerma,741.0,France,Female,34.0,4.0,0.0,2.0,0.0,1.0,163471.01,0.0
+5225,15761733.0,Piazza,652.0,France,Male,33.0,4.0,0.0,2.0,0.0,1.0,120586.32,0.0
+5226,15768282.0,Kao,645.0,Germany,Female,39.0,8.0,127343.4,1.0,1.0,1.0,138879.36,1.0
+5227,15679428.0,Kesteven,641.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,146895.86,0.0
+5228,15673539.0,Ch'eng,845.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,161479.19,0.0
+5229,15641185.0,Biryukov,567.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,151871.84,1.0
+5230,15626491.0,Fanucci,659.0,France,Female,40.0,4.0,80780.16,1.0,1.0,0.0,55701.62,1.0
+5231,15747757.0,Nnaife,684.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,102076.03,0.0
+5232,15784320.0,Chukwukere,592.0,Germany,Female,53.0,9.0,120530.32,1.0,1.0,0.0,62402.38,0.0
+5233,15668774.0,DeRose,634.0,Germany,Female,50.0,8.0,146566.01,1.0,1.0,0.0,143315.57,1.0
+5234,15783859.0,Tretiakova,695.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,146723.72,0.0
+5235,15778025.0,T'ang,748.0,France,Female,39.0,0.0,0.0,2.0,1.0,1.0,93769.89,0.0
+5236,15693381.0,Ngozichukwuka,687.0,France,Female,37.0,2.0,0.0,1.0,0.0,1.0,151481.86,1.0
+5237,15679550.0,Ch'ien,622.0,France,Male,53.0,7.0,0.0,1.0,1.0,0.0,171378.77,1.0
+5238,15753847.0,Hao,710.0,Spain,Male,22.0,1.0,0.0,2.0,1.0,1.0,126469.09,0.0
+5239,15623525.0,Yeh,706.0,Spain,Male,43.0,6.0,0.0,2.0,1.0,0.0,166495.2,0.0
+5240,15719309.0,Chien,649.0,France,Male,40.0,3.0,0.0,2.0,1.0,1.0,73334.43,0.0
+5241,15671630.0,Onyemauchechukwu,539.0,Spain,Male,30.0,1.0,0.0,2.0,1.0,1.0,142223.94,0.0
+5242,15603883.0,Ugonnatubelum,743.0,France,Male,61.0,3.0,0.0,2.0,1.0,1.0,143629.72,0.0
+5243,15627385.0,Kaur,679.0,France,Female,27.0,6.0,0.0,1.0,0.0,0.0,171706.66,0.0
+5244,15609928.0,Hsieh,775.0,France,Male,50.0,8.0,0.0,1.0,0.0,1.0,114066.77,0.0
+5245,15686907.0,Trevisano,575.0,France,Female,41.0,6.0,129731.64,1.0,0.0,0.0,185210.63,0.0
+5246,15674573.0,Tuan,572.0,France,Female,33.0,2.0,87612.64,2.0,1.0,0.0,171457.06,0.0
+5247,15661526.0,Hao,705.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,93718.35,0.0
+5248,15641158.0,Genovese,676.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,199505.53,0.0
+5249,15792736.0,K'ung,757.0,France,Female,39.0,3.0,0.0,2.0,0.0,0.0,182867.84,0.0
+5250,15761763.0,Ma,671.0,France,Female,46.0,2.0,0.0,2.0,0.0,0.0,120892.96,0.0
+5251,15667944.0,Nwebube,684.0,France,Female,36.0,0.0,0.0,2.0,1.0,0.0,55859.27,0.0
+5252,15641114.0,Kao,715.0,France,Female,31.0,7.0,0.0,1.0,1.0,0.0,149550.92,0.0
+5253,15747097.0,Ch'ang,739.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,74838.64,0.0
+5254,15694879.0,Ts'ui,648.0,Germany,Female,38.0,5.0,125437.86,2.0,1.0,1.0,99645.04,0.0
+5255,15687634.0,Li Fonti,738.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,122810.53,0.0
+5256,15656829.0,Babbage,710.0,Germany,Female,38.0,7.0,133964.4,2.0,0.0,1.0,167033.83,0.0
+5257,15761149.0,Hsueh,628.0,France,Female,35.0,7.0,122029.15,2.0,0.0,0.0,75837.58,0.0
+5258,15615444.0,Gboliwe,809.0,France,Male,28.0,1.0,121280.19,1.0,1.0,0.0,127587.22,0.0
+5259,15665802.0,P'eng,684.0,France,Female,52.0,3.0,0.0,2.0,1.0,0.0,114093.73,0.0
+5260,15638487.0,Trevisano,750.0,France,Female,26.0,8.0,0.0,2.0,0.0,1.0,66979.06,0.0
+5261,15581229.0,Chieloka,826.0,France,Female,48.0,8.0,0.0,3.0,1.0,0.0,179833.45,1.0
+5262,15785547.0,Tuan,644.0,France,Male,30.0,2.0,0.0,1.0,1.0,0.0,14073.11,0.0
+5263,15710390.0,Hs?,716.0,France,Female,39.0,6.0,0.0,2.0,1.0,0.0,62944.75,0.0
+5264,15689201.0,Chukwudi,652.0,Spain,Female,29.0,1.0,0.0,2.0,1.0,1.0,121840.2,0.0
+5265,15693771.0,Iadanza,762.0,Spain,Male,38.0,3.0,0.0,2.0,0.0,0.0,67976.57,0.0
+5266,15662091.0,Ngozichukwuka,683.0,Germany,Male,38.0,5.0,132405.52,2.0,1.0,1.0,16281.94,0.0
+5267,15711015.0,Chidiebere,743.0,France,Male,32.0,5.0,0.0,1.0,1.0,0.0,158977.6,0.0
+5268,15754929.0,Frolova,753.0,Germany,Female,33.0,5.0,103700.69,1.0,1.0,0.0,137980.58,0.0
+5269,15592222.0,Tuan,605.0,France,Female,39.0,3.0,0.0,2.0,1.0,1.0,83342.73,0.0
+5270,15588923.0,Chukwuemeka,684.0,France,Male,45.0,8.0,125512.98,1.0,1.0,1.0,74854.97,0.0
+5271,15662291.0,Hsu,571.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,132562.23,0.0
+5272,15690695.0,Mazzanti,595.0,France,Male,52.0,4.0,0.0,1.0,1.0,1.0,117060.3,1.0
+5273,15618363.0,Lucchese,579.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,115611.97,0.0
+5274,15671249.0,Piazza,598.0,France,Female,52.0,4.0,0.0,1.0,1.0,1.0,131919.48,1.0
+5275,15643361.0,Nwebube,565.0,France,Female,34.0,10.0,0.0,2.0,1.0,1.0,159742.33,0.0
+5276,15707488.0,Ch'in,584.0,Germany,Female,48.0,2.0,102989.83,1.0,1.0,0.0,176750.81,1.0
+5277,15681115.0,Y?,677.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,1.0,182038.6,0.0
+5278,15689328.0,Chiemenam,599.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,170819.01,0.0
+5279,15691483.0,Onuchukwu,696.0,Germany,Male,49.0,1.0,142765.24,1.0,1.0,0.0,160589.18,1.0
+5280,15737778.0,Hsiao,743.0,Spain,Male,35.0,9.0,0.0,1.0,0.0,1.0,172363.41,0.0
+5281,15629187.0,Ch'ang,737.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,48242.38,0.0
+5282,15656062.0,Nkemdirim,604.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,86935.08,0.0
+5283,15579914.0,Fiorentino,850.0,Germany,Female,55.0,0.0,92616.64,1.0,1.0,1.0,127875.1,0.0
+5284,15609823.0,Niu,742.0,France,Male,33.0,5.0,92674.2,1.0,0.0,1.0,172749.4,0.0
+5285,15787830.0,Udinese,665.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,180598.28,0.0
+5286,15777855.0,Chuang,713.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,60905.51,0.0
+5287,15793881.0,Okwudilichukwu,772.0,Germany,Female,51.0,3.0,123873.23,1.0,1.0,0.0,14917.09,1.0
+5288,15696722.0,Onyekaozulu,632.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,156774.75,0.0
+5289,15661528.0,Lucciano,806.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,1.0,118965.03,0.0
+5290,15575679.0,Chiekwugo,668.0,France,Female,61.0,5.0,120549.29,2.0,0.0,1.0,91957.39,0.0
+5291,15591512.0,Tuan,628.0,France,Male,31.0,4.0,0.0,1.0,1.0,1.0,51116.06,0.0
+5292,15671139.0,Linton,538.0,Spain,Female,39.0,5.0,0.0,1.0,1.0,1.0,116977.89,0.0
+5293,15568326.0,Pisani,637.0,France,Male,37.0,6.0,0.0,1.0,0.0,0.0,132973.17,0.0
+5294,15605950.0,Obiajulu,689.0,Spain,Female,29.0,5.0,0.0,2.0,1.0,1.0,121428.4,0.0
+5295,15636016.0,Tsao,569.0,France,Male,41.0,1.0,102703.62,1.0,0.0,0.0,19998.31,0.0
+5296,15730673.0,Cremonesi,733.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,126812.63,0.0
+5297,15732760.0,Onyemere,584.0,Germany,Female,39.0,3.0,113959.35,1.0,1.0,1.0,132038.65,1.0
+5298,15748327.0,Ch'ang,631.0,Spain,Female,30.0,1.0,0.0,2.0,0.0,1.0,25684.93,0.0
+5299,15754577.0,Lucchese,595.0,Germany,Male,39.0,3.0,124099.13,1.0,1.0,1.0,38196.24,0.0
+5300,15627395.0,Oluchi,682.0,Germany,Female,39.0,2.0,122446.81,2.0,1.0,0.0,46677.9,0.0
+5301,15781589.0,Cattaneo,667.0,France,Male,31.0,3.0,0.0,1.0,1.0,0.0,129891.66,0.0
+5302,15654901.0,Maclean,774.0,Germany,Male,25.0,6.0,122028.65,1.0,0.0,1.0,173586.63,0.0
+5303,15728474.0,P'eng,637.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,86634.3,0.0
+5304,15715888.0,Shih,718.0,France,Female,40.0,7.0,0.0,2.0,0.0,1.0,159800.16,0.0
+5305,15728352.0,Ting,667.0,Spain,Male,37.0,8.0,133658.24,1.0,1.0,0.0,47922.54,0.0
+5306,15787907.0,Hs?eh,731.0,France,Female,38.0,2.0,0.0,2.0,0.0,1.0,116971.05,0.0
+5307,15702432.0,Onyekachukwu,673.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,114532.68,0.0
+5308,15724453.0,Chizuoke,683.0,France,Female,38.0,1.0,0.0,1.0,1.0,0.0,128521.02,0.0
+5309,15619608.0,Hs?,796.0,Germany,Male,32.0,1.0,129974.79,2.0,1.0,1.0,141896.74,0.0
+5310,15790750.0,Chukwuebuka,526.0,Spain,Male,37.0,7.0,145980.23,1.0,1.0,1.0,164886.64,0.0
+5311,15749669.0,Hsiao,793.0,Spain,Female,30.0,8.0,0.0,2.0,0.0,1.0,83629.6,0.0
+5312,15778589.0,Chukwueloka,726.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,43932.54,0.0
+5313,15796910.0,Chin,602.0,France,Female,32.0,6.0,0.0,1.0,0.0,1.0,131660.31,0.0
+5314,15592914.0,Okwuoma,599.0,France,Male,31.0,8.0,151391.68,1.0,1.0,0.0,106095.82,0.0
+5315,15704597.0,Genovese,558.0,France,Female,48.0,8.0,0.0,1.0,0.0,1.0,136985.08,1.0
+5316,15701160.0,Ch'in,850.0,France,Male,32.0,2.0,130131.08,1.0,1.0,1.0,122381.02,0.0
+5317,15626012.0,Tsao,708.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,0.0,149614.81,0.0
+5318,15616152.0,Ch'in,604.0,France,Male,33.0,7.0,0.0,2.0,0.0,0.0,102628.56,0.0
+5319,15640034.0,Nwabugwu,634.0,France,Male,36.0,1.0,93522.3,1.0,1.0,1.0,94815.04,0.0
+5320,15677052.0,Chia,632.0,Spain,Male,43.0,5.0,0.0,1.0,0.0,1.0,123866.43,0.0
+5321,15727703.0,Pisani,633.0,France,Male,21.0,7.0,0.0,2.0,1.0,0.0,151146.67,0.0
+5322,15783398.0,Hsiung,714.0,France,Female,41.0,8.0,0.0,1.0,1.0,0.0,113481.02,0.0
+5323,15659710.0,Hao,663.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,96734.46,0.0
+5324,15733111.0,Rizzo,641.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,145558.42,0.0
+5325,15721207.0,Shih,742.0,France,Male,32.0,9.0,70417.79,1.0,1.0,1.0,38409.79,0.0
+5326,15641848.0,Frolova,545.0,France,Female,40.0,1.0,0.0,2.0,1.0,1.0,107876.2,0.0
+5327,15657540.0,Hargreaves,710.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,0.0,141501.45,0.0
+5328,15796813.0,Kao,631.0,Germany,Female,39.0,7.0,124043.8,2.0,1.0,1.0,126743.33,0.0
+5329,15802483.0,Hao,721.0,France,Male,36.0,1.0,0.0,2.0,0.0,1.0,54865.52,0.0
+5330,15773071.0,Niu,629.0,France,Female,27.0,6.0,0.0,2.0,1.0,1.0,187905.12,0.0
+5331,15791535.0,Chukwueloka,652.0,France,Male,24.0,2.0,0.0,2.0,1.0,0.0,150159.95,0.0
+5332,15722004.0,Chukwukere,516.0,France,Female,35.0,6.0,152685.4,1.0,1.0,0.0,172096.08,0.0
+5333,15618239.0,Chukwuemeka,595.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,141035.65,0.0
+5334,15707968.0,Ives,671.0,France,Male,31.0,5.0,138089.15,2.0,1.0,1.0,143086.49,0.0
+5335,15566292.0,Ch'ien,678.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,1.0,82463.69,0.0
+5336,15642725.0,Udinese,516.0,Spain,Female,26.0,1.0,128843.67,1.0,1.0,1.0,176692.65,0.0
+5337,15605037.0,Ch'eng,662.0,Germany,Male,35.0,8.0,111369.79,1.0,0.0,0.0,177719.88,1.0
+5338,15717893.0,Ch'eng,592.0,France,Female,52.0,5.0,0.0,1.0,1.0,0.0,180829.99,1.0
+5339,15633108.0,Rizzo,634.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,163331.65,0.0
+5340,15621075.0,Tsao,651.0,Germany,Male,39.0,3.0,116536.43,1.0,0.0,0.0,52301.15,1.0
+5341,15632143.0,T'ien,599.0,France,Male,38.0,10.0,123971.51,1.0,0.0,1.0,166678.28,0.0
+5342,15652955.0,Chijindum,678.0,France,Male,38.0,10.0,128743.55,1.0,1.0,1.0,142995.32,0.0
+5343,15697043.0,Okechukwu,732.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,117832.88,0.0
+5344,15572428.0,Kao,686.0,France,Female,38.0,4.0,0.0,1.0,1.0,1.0,110782.88,1.0
+5345,15593470.0,Trevisani,726.0,France,Female,29.0,6.0,137811.75,1.0,1.0,1.0,163317.5,0.0
+5346,15662578.0,Cawthorne,677.0,Spain,Male,29.0,8.0,0.0,2.0,0.0,0.0,65816.53,0.0
+5347,15724444.0,H?,736.0,Spain,Male,44.0,7.0,0.0,1.0,1.0,1.0,60917.24,1.0
+5348,15762091.0,Onwughara,553.0,France,Female,38.0,3.0,141786.57,1.0,1.0,0.0,49833.5,0.0
+5349,15652069.0,Fanucci,678.0,Spain,Male,36.0,3.0,118560.0,1.0,1.0,1.0,48545.1,0.0
+5350,15808846.0,Shih,729.0,France,Female,32.0,3.0,136416.82,1.0,1.0,0.0,187985.77,0.0
+5351,15791674.0,Hsia,675.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,61531.86,0.0
+5352,15582797.0,Hsiung,678.0,France,Female,45.0,9.0,0.0,1.0,1.0,1.0,175072.47,1.0
+5353,15625944.0,P'an,628.0,Spain,Female,36.0,3.0,0.0,2.0,1.0,0.0,12761.16,0.0
+5354,15770576.0,Tien,576.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,92381.01,0.0
+5355,15714087.0,Lo Duca,642.0,France,Male,31.0,0.0,143965.52,1.0,1.0,1.0,54865.52,0.0
+5356,15651369.0,Yermakova,659.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,51554.58,0.0
+5357,15765173.0,Dike,639.0,France,Male,41.0,7.0,135095.41,1.0,0.0,1.0,162599.51,0.0
+5358,15576124.0,Yermakova,656.0,France,Male,21.0,8.0,0.0,2.0,1.0,1.0,60721.4,0.0
+5359,15661249.0,Hs?,634.0,Germany,Female,52.0,5.0,139872.53,1.0,1.0,1.0,135875.51,1.0
+5360,15648898.0,Pisani,724.0,Spain,Female,33.0,8.0,0.0,2.0,1.0,0.0,138526.96,0.0
+5361,15709861.0,Ozoemena,756.0,Spain,Female,26.0,6.0,0.0,1.0,1.0,1.0,46857.52,0.0
+5362,15587562.0,Manna,678.0,France,Male,37.0,7.0,0.0,1.0,0.0,1.0,45422.13,0.0
+5363,15771299.0,Mazzanti,718.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,153727.32,0.0
+5364,15777893.0,Iweobiegbulam,679.0,Germany,Female,40.0,1.0,132512.26,1.0,1.0,1.0,122964.18,0.0
+5365,15768006.0,Ch'en,483.0,Spain,Male,26.0,3.0,0.0,2.0,1.0,0.0,168034.9,0.0
+5366,15779886.0,Maclean,710.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,123587.83,0.0
+5367,15743673.0,Ts'ao,627.0,France,Female,47.0,3.0,0.0,2.0,0.0,0.0,115733.16,0.0
+5368,15702571.0,Ts'ui,744.0,France,Female,45.0,1.0,154495.74,1.0,1.0,0.0,119259.54,1.0
+5369,15780550.0,Chieloka,656.0,France,Female,34.0,5.0,0.0,1.0,1.0,1.0,155870.82,0.0
+5370,15590820.0,Nkemakolam,725.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,122976.73,0.0
+5371,15766137.0,Lucchesi,581.0,France,Female,27.0,5.0,0.0,1.0,1.0,0.0,58907.08,0.0
+5372,15709737.0,Chiu,651.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,146723.72,0.0
+5373,15571221.0,Kambinachi,596.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,1.0,177384.45,0.0
+5374,15773421.0,Nnanna,615.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,80552.11,0.0
+5375,15700383.0,Chibugo,683.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,180850.4,0.0
+5376,15788676.0,K'ung,597.0,France,Female,37.0,7.0,0.0,2.0,0.0,1.0,62634.94,0.0
+5377,15607568.0,L?,652.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,140889.32,0.0
+5378,15648881.0,Chukwujekwu,589.0,Germany,Female,31.0,7.0,136967.18,1.0,1.0,1.0,176780.69,0.0
+5379,15810944.0,P'an,575.0,France,Female,44.0,6.0,0.0,1.0,1.0,0.0,143598.7,1.0
+5380,15766044.0,Uwaezuoke,681.0,Spain,Male,27.0,5.0,107564.03,1.0,0.0,0.0,125302.64,0.0
+5381,15722072.0,Li Fonti,583.0,Spain,Male,48.0,6.0,174937.64,3.0,1.0,0.0,26287.21,1.0
+5382,15642934.0,P'eng,584.0,France,Male,40.0,5.0,0.0,2.0,0.0,1.0,151925.25,0.0
+5383,15572294.0,Ch'en,554.0,Spain,Female,51.0,7.0,0.0,1.0,0.0,1.0,170142.09,0.0
+5384,15596552.0,Nwachukwu,587.0,France,Female,29.0,1.0,174894.53,2.0,0.0,0.0,69697.74,0.0
+5385,15667871.0,Chuang,642.0,Germany,Female,38.0,4.0,126495.57,1.0,1.0,0.0,52327.57,1.0
+5386,15691840.0,Ritchie,586.0,France,Female,39.0,2.0,160818.08,1.0,1.0,1.0,79618.47,0.0
+5387,15736254.0,Genovese,745.0,France,Female,55.0,3.0,123599.49,1.0,0.0,1.0,126796.69,0.0
+5388,15641782.0,Nnachetam,492.0,France,Female,23.0,6.0,110939.3,2.0,1.0,0.0,77951.84,0.0
+5389,15650098.0,Onyeoruru,559.0,France,Female,30.0,2.0,102989.83,1.0,1.0,1.0,140937.91,0.0
+5390,15762392.0,Ch'iu,703.0,Germany,Female,33.0,1.0,116947.76,2.0,1.0,0.0,134600.94,0.0
+5391,15670039.0,Nnamutaezinwa,717.0,Spain,Male,36.0,5.0,91560.58,1.0,1.0,0.0,94544.88,0.0
+5392,15702061.0,Ch'ang,637.0,Germany,Male,34.0,8.0,108862.76,1.0,1.0,1.0,96463.25,0.0
+5393,15730397.0,Hsia,593.0,France,Male,34.0,6.0,142721.52,1.0,1.0,1.0,173052.12,0.0
+5394,15718588.0,Tseng,595.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,162734.31,0.0
+5395,15785865.0,Bazarova,769.0,Spain,Male,46.0,6.0,0.0,2.0,1.0,1.0,141533.19,0.0
+5396,15682369.0,Genovesi,711.0,France,Male,28.0,2.0,157731.6,1.0,1.0,0.0,137519.92,0.0
+5397,15722993.0,Milani,612.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,113079.19,0.0
+5398,15656471.0,Ch'iu,648.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,52894.01,0.0
+5399,15745012.0,T'ien,691.0,Spain,Male,28.0,8.0,113781.5,1.0,1.0,0.0,141230.07,0.0
+5400,15678910.0,Oguejiofor,778.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,70778.63,0.0
+5401,15685500.0,Maclean,633.0,France,Female,48.0,3.0,0.0,2.0,1.0,1.0,75937.14,1.0
+5402,15635972.0,Fan,686.0,Germany,Male,34.0,2.0,106648.98,1.0,1.0,1.0,170514.21,1.0
+5403,15751336.0,Gannon,635.0,France,Male,32.0,9.0,0.0,2.0,0.0,0.0,27330.59,0.0
+5404,15672237.0,Chinomso,678.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,120834.48,0.0
+5405,15607877.0,Echezonachukwu,628.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,80963.92,0.0
+5406,15667438.0,DeRose,758.0,Spain,Male,32.0,8.0,110939.3,1.0,0.0,1.0,183572.39,0.0
+5407,15652914.0,K'ung,709.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,108764.69,0.0
+5408,15603743.0,Dettmann,670.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,189016.24,0.0
+5409,15624793.0,Udobata,726.0,Germany,Female,36.0,9.0,109799.55,1.0,1.0,1.0,185827.74,1.0
+5410,15581748.0,Genovese,496.0,Germany,Male,32.0,7.0,143396.54,2.0,1.0,1.0,57553.02,0.0
+5411,15771856.0,Toscani,695.0,Germany,Female,43.0,5.0,107908.03,2.0,1.0,1.0,158054.49,0.0
+5412,15596863.0,Mazzi,538.0,Germany,Female,26.0,4.0,146780.52,2.0,1.0,0.0,133617.43,0.0
+5413,15617401.0,Uspenskaya,545.0,France,Female,63.0,2.0,113715.36,1.0,1.0,1.0,53767.1,0.0
+5414,15710456.0,Maclean,621.0,France,Male,31.0,7.0,0.0,1.0,1.0,1.0,150242.44,0.0
+5415,15769903.0,Hsueh,613.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,199478.05,0.0
+5416,15643865.0,Martin,648.0,Spain,Male,29.0,1.0,0.0,1.0,0.0,1.0,28797.32,0.0
+5417,15742668.0,Efremov,629.0,Germany,Female,59.0,6.0,90350.77,1.0,1.0,0.0,155823.2,1.0
+5418,15793883.0,Bell,801.0,Spain,Male,35.0,6.0,0.0,2.0,0.0,1.0,122373.22,0.0
+5419,15652700.0,Ibeabuchi,585.0,France,Male,31.0,1.0,88814.4,1.0,1.0,0.0,41970.72,0.0
+5420,15676835.0,Onyemauchechukwu,683.0,France,Female,38.0,6.0,0.0,2.0,1.0,0.0,61324.87,0.0
+5421,15682573.0,Chinweike,583.0,Germany,Male,28.0,3.0,128015.83,2.0,0.0,1.0,97640.2,0.0
+5422,15661629.0,Hsiung,790.0,France,Male,40.0,2.0,0.0,2.0,0.0,0.0,86791.9,0.0
+5423,15582573.0,Manna,553.0,France,Male,42.0,6.0,0.0,1.0,1.0,0.0,151411.2,0.0
+5424,15676521.0,Onyekachukwu,687.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,0.0,35408.77,0.0
+5425,15737051.0,Obiajulu,635.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,133535.29,0.0
+5426,15648032.0,Scott,710.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,54869.54,0.0
+5427,15784700.0,T'ien,619.0,Spain,Male,43.0,2.0,0.0,2.0,0.0,0.0,70533.93,0.0
+5428,15775192.0,Chizoba,678.0,Spain,Male,46.0,7.0,0.0,2.0,0.0,1.0,55470.78,0.0
+5429,15719579.0,Cremonesi,686.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,26257.01,0.0
+5430,15709639.0,Pisani,801.0,Germany,Female,43.0,5.0,131081.66,1.0,1.0,1.0,35356.46,0.0
+5431,15655339.0,Tien,592.0,France,Male,48.0,10.0,0.0,2.0,1.0,1.0,61326.43,0.0
+5432,15757632.0,White,521.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,0.0,93524.19,0.0
+5433,15782404.0,Hsia,636.0,Spain,Female,24.0,2.0,0.0,2.0,1.0,1.0,52795.15,0.0
+5434,15661349.0,Hsia,769.0,Germany,Female,46.0,8.0,144705.87,1.0,0.0,0.0,88842.8,1.0
+5435,15690540.0,Uspensky,669.0,France,Female,42.0,1.0,107868.14,3.0,1.0,1.0,176913.42,1.0
+5436,15620398.0,Genovese,679.0,Spain,Female,26.0,8.0,0.0,2.0,1.0,1.0,142377.69,0.0
+5437,15762392.0,Udinesi,712.0,France,Male,37.0,6.0,0.0,1.0,1.0,1.0,146663.43,0.0
+5438,15641312.0,Ozioma,787.0,Germany,Female,32.0,5.0,134796.05,2.0,1.0,1.0,152992.04,0.0
+5439,15772777.0,Nkemakonam,671.0,France,Male,46.0,6.0,0.0,1.0,1.0,0.0,176883.42,1.0
+5440,15580149.0,Onwumelu,576.0,Spain,Male,37.0,6.0,0.0,2.0,0.0,0.0,93983.04,0.0
+5441,15694576.0,Nnamutaezinwa,743.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,181204.5,0.0
+5442,15696054.0,Liao,706.0,France,Male,38.0,1.0,0.0,1.0,0.0,0.0,129975.54,0.0
+5443,15681342.0,Oluchukwu,678.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,173498.45,0.0
+5444,15636396.0,De Luca,695.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,0.0,100423.88,0.0
+5445,15696588.0,Chukwukere,597.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,0.0,91098.76,0.0
+5446,15763431.0,Chukwukadibia,693.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,134875.37,0.0
+5447,15762573.0,Trevisano,712.0,France,Male,35.0,4.0,137222.77,1.0,1.0,1.0,117034.32,0.0
+5448,15642394.0,Frolova,626.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,72927.68,0.0
+5449,15765518.0,Ch'ang,583.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,134589.58,0.0
+5450,15785819.0,Nnamdi,775.0,Spain,Female,35.0,8.0,0.0,2.0,1.0,0.0,46864.0,0.0
+5451,15681873.0,T'ang,681.0,France,Male,51.0,8.0,0.0,2.0,0.0,1.0,163830.64,0.0
+5452,15584545.0,Ponomarev,751.0,France,Male,39.0,3.0,124125.26,1.0,1.0,0.0,184936.77,0.0
+5453,15775709.0,Hs?,583.0,France,Female,50.0,9.0,0.0,2.0,1.0,1.0,10962.04,1.0
+5454,15771543.0,Liao,637.0,Germany,Female,23.0,4.0,135020.21,1.0,0.0,1.0,143598.7,1.0
+5455,15633043.0,Chukwuhaenye,694.0,France,Male,32.0,1.0,0.0,2.0,0.0,0.0,126726.33,0.0
+5456,15596074.0,Lucchesi,762.0,France,Female,35.0,7.0,0.0,1.0,0.0,0.0,173365.9,0.0
+5457,15614893.0,Onyemachukwu,653.0,France,Male,42.0,3.0,0.0,1.0,0.0,1.0,147964.99,1.0
+5458,15732644.0,Ch'eng,622.0,Spain,Female,39.0,8.0,0.0,1.0,0.0,0.0,57898.58,1.0
+5459,15661903.0,Onyemauchechukwu,683.0,France,Male,44.0,1.0,84445.68,2.0,0.0,0.0,93777.61,0.0
+5460,15576723.0,Nucci,763.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,26327.42,0.0
+5461,15780034.0,Ch'ien,704.0,Spain,Male,42.0,1.0,0.0,2.0,1.0,1.0,56794.73,0.0
+5462,15603914.0,Krichauff,624.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,133086.45,0.0
+5463,15644572.0,Chiang,779.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,0.0,142995.32,0.0
+5464,15708048.0,Ts'ai,668.0,France,Male,26.0,2.0,0.0,2.0,0.0,0.0,157908.19,0.0
+5465,15772423.0,Ulyanov,662.0,France,Female,29.0,6.0,181768.32,1.0,1.0,1.0,10928.3,0.0
+5466,15684958.0,Hao,775.0,Germany,Male,28.0,9.0,148051.52,1.0,1.0,0.0,93536.38,0.0
+5467,15772423.0,Yobanna,577.0,France,Male,42.0,8.0,139541.58,1.0,1.0,0.0,142977.05,0.0
+5468,15769902.0,Pagnotto,558.0,Spain,Male,41.0,10.0,0.0,2.0,1.0,0.0,187699.76,0.0
+5469,15658057.0,Chin,684.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,0.0,147930.46,0.0
+5470,15627840.0,Ch'ang,493.0,Germany,Female,40.0,1.0,128135.99,3.0,1.0,1.0,9983.88,1.0
+5471,15724181.0,Martin,604.0,France,Male,45.0,1.0,0.0,2.0,1.0,1.0,85746.52,0.0
+5472,15625941.0,Kao,705.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,150720.39,0.0
+5473,15792489.0,T'ien,748.0,France,Male,38.0,2.0,84006.88,1.0,1.0,1.0,176763.27,0.0
+5474,15592448.0,Milani,775.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,124592.84,0.0
+5475,15645316.0,Chiang,614.0,France,Female,44.0,0.0,0.0,2.0,0.0,0.0,89937.65,0.0
+5476,15600792.0,Lo,679.0,Germany,Male,37.0,9.0,126550.7,2.0,1.0,0.0,79082.62,0.0
+5477,15617206.0,K?,762.0,Spain,Female,22.0,9.0,0.0,2.0,0.0,1.0,171413.66,0.0
+5478,15633260.0,Goliwe,679.0,Germany,Male,44.0,1.0,81564.1,1.0,1.0,0.0,121763.73,1.0
+5479,15723488.0,Yegorov,727.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,71682.54,0.0
+5480,15790067.0,Gannon,850.0,France,Male,31.0,2.0,0.0,2.0,0.0,1.0,59816.41,0.0
+5481,15787196.0,T'an,707.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,120880.72,0.0
+5482,15783338.0,Trevisani,613.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,1.0,154844.29,0.0
+5483,15668747.0,Chien,775.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,83679.66,0.0
+5484,15696588.0,Padovesi,730.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,179657.97,0.0
+5485,15597951.0,Okechukwu,592.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,163714.92,0.0
+5486,15796370.0,Onyemauchechukwu,822.0,France,Female,55.0,3.0,0.0,1.0,1.0,0.0,51112.8,1.0
+5487,15588933.0,Maughan,551.0,Spain,Female,45.0,5.0,0.0,1.0,0.0,0.0,125877.22,1.0
+5488,15795132.0,Ross,581.0,Spain,Female,32.0,7.0,0.0,2.0,0.0,1.0,72350.47,0.0
+5489,15770041.0,Iweobiegbunam,594.0,Germany,Female,54.0,0.0,120106.43,1.0,1.0,0.0,103921.41,1.0
+5490,15696733.0,Nwachinemelu,619.0,France,Male,47.0,3.0,0.0,2.0,1.0,0.0,134491.32,1.0
+5491,15643361.0,Ritchie,632.0,France,Female,37.0,9.0,0.0,1.0,1.0,1.0,101102.39,0.0
+5492,15794895.0,Ritchie,730.0,Spain,Male,39.0,4.0,0.0,1.0,1.0,0.0,67599.69,1.0
+5493,15791030.0,Yobanna,646.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,147910.64,0.0
+5494,15739662.0,K?,682.0,France,Female,29.0,8.0,0.0,2.0,1.0,1.0,136985.08,0.0
+5495,15750055.0,Zito,751.0,France,Female,31.0,6.0,0.0,1.0,1.0,0.0,135088.58,0.0
+5496,15641319.0,Ekechukwu,594.0,France,Male,36.0,9.0,123894.43,1.0,1.0,0.0,182566.76,0.0
+5497,15718281.0,Ch'ien,641.0,France,Male,47.0,6.0,0.0,1.0,1.0,1.0,82709.8,1.0
+5498,15631681.0,Chiazagomekpere,594.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,59249.83,0.0
+5499,15699753.0,Chinedum,694.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,10792.42,0.0
+5500,15791323.0,Chuang,646.0,France,Female,71.0,8.0,0.0,1.0,1.0,1.0,1276.87,1.0
+5501,15584897.0,Scott,655.0,France,Female,39.0,6.0,0.0,1.0,1.0,0.0,70774.97,0.0
+5502,15598435.0,Ugochukwu,657.0,Germany,Male,36.0,9.0,121558.42,1.0,0.0,0.0,101835.77,0.0
+5503,15797767.0,Chuang,556.0,Spain,Female,38.0,1.0,118633.08,1.0,1.0,0.0,151324.87,0.0
+5504,15603764.0,Maclean,661.0,France,Female,63.0,3.0,0.0,1.0,1.0,1.0,138536.09,1.0
+5505,15682576.0,H?,744.0,France,Female,39.0,2.0,0.0,1.0,1.0,0.0,155061.97,1.0
+5506,15763898.0,Chin,681.0,France,Female,42.0,9.0,0.0,3.0,1.0,0.0,175072.47,1.0
+5507,15782210.0,Chiang,638.0,Germany,Male,37.0,3.0,110827.49,1.0,1.0,1.0,183983.82,1.0
+5508,15682268.0,Fomin,594.0,Germany,Female,40.0,4.0,106625.07,1.0,0.0,0.0,85901.09,1.0
+5509,15797595.0,Onyemauchechukwu,549.0,France,Female,34.0,5.0,0.0,2.0,0.0,1.0,159823.16,0.0
+5510,15673929.0,Piazza,669.0,Germany,Female,44.0,9.0,134542.73,1.0,1.0,0.0,172524.51,1.0
+5511,15808689.0,Ch'iu,652.0,Germany,Female,31.0,9.0,102741.15,1.0,1.0,0.0,120853.85,1.0
+5512,15703413.0,Laidley,727.0,France,Male,28.0,8.0,0.0,2.0,0.0,0.0,90896.04,0.0
+5513,15808905.0,Ts'ui,581.0,France,Female,53.0,7.0,138881.04,1.0,0.0,1.0,182867.84,0.0
+5514,15632280.0,Onuora,567.0,France,Male,42.0,1.0,0.0,1.0,0.0,0.0,173784.04,1.0
+5515,15793896.0,Ugochukwutubelum,588.0,Spain,Female,43.0,1.0,0.0,1.0,1.0,0.0,103421.91,0.0
+5516,15592229.0,Ch'in,769.0,France,Male,48.0,9.0,0.0,1.0,1.0,0.0,9241.78,1.0
+5517,15794873.0,Ch'in,696.0,France,Male,29.0,4.0,109528.28,1.0,1.0,1.0,117572.59,0.0
+5518,15655648.0,Chinomso,651.0,France,Female,26.0,1.0,0.0,2.0,0.0,1.0,199304.74,0.0
+5519,15709396.0,Tuan,582.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,149604.18,0.0
+5520,15607537.0,T'ao,525.0,Spain,Male,37.0,9.0,0.0,2.0,0.0,1.0,151491.37,0.0
+5521,15662955.0,Zito,683.0,France,Male,23.0,7.0,0.0,2.0,1.0,0.0,73562.05,0.0
+5522,15733247.0,Onyemachukwu,695.0,Spain,Female,32.0,2.0,0.0,2.0,0.0,0.0,173683.0,0.0
+5523,15717164.0,Mironova,676.0,Spain,Male,24.0,7.0,0.0,2.0,1.0,1.0,170122.22,0.0
+5524,15591258.0,Niehaus,714.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,102750.8,0.0
+5525,15756475.0,Mironov,757.0,France,Female,46.0,6.0,129743.75,1.0,1.0,0.0,94499.06,0.0
+5526,15570192.0,Hao,739.0,Germany,Male,36.0,7.0,124620.01,1.0,0.0,1.0,136301.1,0.0
+5527,15577806.0,Genovese,634.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,73669.94,0.0
+5528,15706899.0,Ch'ang,654.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,1.0,138051.19,0.0
+5529,15669301.0,Nkemjika,592.0,France,Female,55.0,3.0,0.0,1.0,1.0,1.0,132673.23,1.0
+5530,15573599.0,Cattaneo,627.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,146891.07,0.0
+5531,15566289.0,Hsueh,627.0,Germany,Female,42.0,4.0,71996.09,1.0,0.0,1.0,9663.28,0.0
+5532,15672091.0,Calabresi,632.0,France,Female,39.0,8.0,0.0,1.0,1.0,0.0,103921.49,1.0
+5533,15683544.0,Okwuadigbo,587.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,151401.33,0.0
+5534,15754574.0,Ebelegbulam,709.0,Germany,Female,32.0,7.0,125193.96,1.0,1.0,0.0,140889.64,0.0
+5535,15643487.0,Lai,579.0,France,Male,40.0,9.0,0.0,1.0,0.0,1.0,129964.94,0.0
+5536,15592999.0,Lai,719.0,France,Female,38.0,6.0,0.0,1.0,1.0,0.0,173783.38,0.0
+5537,15733229.0,Onwuatuegwu,739.0,France,Female,53.0,4.0,0.0,2.0,0.0,1.0,141501.45,0.0
+5538,15700627.0,Macartney,651.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,145558.42,0.0
+5539,15601503.0,Chiu,642.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,120540.83,0.0
+5540,15671249.0,H?,727.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,175893.53,0.0
+5541,15765520.0,Young,639.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,164018.98,0.0
+5542,15675380.0,Chiang,757.0,France,Female,27.0,9.0,0.0,2.0,1.0,0.0,130590.35,0.0
+5543,15632446.0,Baresi,619.0,France,Female,51.0,9.0,0.0,1.0,1.0,0.0,40920.59,1.0
+5544,15692430.0,Chidubem,639.0,France,Female,47.0,3.0,0.0,2.0,1.0,1.0,152298.28,0.0
+5545,15802823.0,Mazzi,697.0,France,Male,50.0,1.0,0.0,2.0,1.0,1.0,138051.19,0.0
+5546,15729763.0,Fetherstonhaugh,575.0,France,Male,46.0,5.0,0.0,2.0,0.0,1.0,84475.13,0.0
+5547,15639133.0,Bazarova,679.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,95611.74,0.0
+5548,15742872.0,Onyemauchechi,635.0,France,Male,46.0,6.0,0.0,2.0,1.0,0.0,143960.72,0.0
+5549,15684929.0,Tuan,683.0,France,Male,48.0,4.0,0.0,1.0,1.0,0.0,161241.65,0.0
+5550,15644962.0,Nnachetam,627.0,Germany,Male,52.0,5.0,113034.72,3.0,1.0,0.0,102076.02,1.0
+5551,15733661.0,Nwachukwu,623.0,France,Female,25.0,8.0,0.0,2.0,1.0,0.0,116471.43,0.0
+5552,15593694.0,Yobanna,629.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,138871.12,0.0
+5553,15735106.0,Olisaemeka,794.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,161653.5,0.0
+5554,15727059.0,Chiemezie,573.0,France,Male,29.0,7.0,120599.38,1.0,0.0,0.0,72993.65,0.0
+5555,15642785.0,McLean,850.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,18092.75,1.0
+5556,15623102.0,Genovese,589.0,Germany,Female,45.0,6.0,126589.12,1.0,1.0,0.0,172363.41,1.0
+5557,15812720.0,Tikhonov,705.0,France,Male,35.0,4.0,0.0,1.0,1.0,0.0,160948.87,0.0
+5558,15758939.0,Onyemachukwu,697.0,Germany,Female,42.0,6.0,128362.59,1.0,1.0,0.0,102515.42,0.0
+5559,15589449.0,Ch'ang,670.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,120138.27,1.0
+5560,15628308.0,Calabresi,681.0,France,Female,41.0,0.0,110829.52,1.0,1.0,0.0,126475.79,1.0
+5561,15673693.0,Iadanza,746.0,France,Female,32.0,6.0,91592.06,1.0,1.0,1.0,64872.33,0.0
+5562,15810167.0,Zito,850.0,France,Female,28.0,7.0,0.0,2.0,0.0,0.0,84760.32,0.0
+5563,15654760.0,Hsia,589.0,France,Male,44.0,3.0,0.0,1.0,1.0,0.0,88890.05,0.0
+5564,15572547.0,Y?,619.0,Spain,Male,36.0,4.0,137780.74,1.0,1.0,1.0,121751.04,0.0
+5565,15781307.0,Nwankwo,551.0,France,Male,31.0,9.0,0.0,1.0,1.0,0.0,147794.63,1.0
+5566,15673372.0,McDavid,644.0,Spain,Female,48.0,3.0,0.0,2.0,1.0,1.0,186655.11,0.0
+5567,15690169.0,Milne,699.0,France,Female,43.0,6.0,0.0,2.0,1.0,1.0,158958.7,0.0
+5568,15670066.0,Ojiofor,713.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,181600.72,0.0
+5569,15711254.0,Neumann,639.0,Spain,Female,52.0,6.0,0.0,1.0,0.0,0.0,177719.88,1.0
+5570,15571261.0,Lu,703.0,France,Female,35.0,6.0,116536.43,1.0,0.0,0.0,126728.28,0.0
+5571,15598884.0,Hsia,652.0,France,Male,22.0,2.0,0.0,2.0,1.0,0.0,130553.47,0.0
+5572,15684925.0,Mazzanti,731.0,France,Male,44.0,7.0,0.0,2.0,0.0,0.0,142977.05,0.0
+5573,15586522.0,White,718.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,136534.15,0.0
+5574,15747647.0,Chukwufumnanya,735.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,166370.81,0.0
+5575,15592232.0,Maclean,597.0,Germany,Male,36.0,8.0,121874.88,1.0,1.0,1.0,144849.1,0.0
+5576,15732266.0,Ch'ang,714.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,102908.95,0.0
+5577,15792064.0,Manna,743.0,Germany,Male,59.0,9.0,94762.16,1.0,0.0,1.0,181224.56,1.0
+5578,15790689.0,Onyinyechukwuka,757.0,France,Male,38.0,3.0,137657.25,1.0,0.0,0.0,103349.74,1.0
+5579,15796957.0,T'ang,773.0,Spain,Male,35.0,9.0,0.0,2.0,0.0,0.0,83473.82,0.0
+5580,15781702.0,Chien,629.0,France,Male,43.0,9.0,148283.94,1.0,1.0,0.0,121568.37,1.0
+5581,15692664.0,Chukwufumnanya,616.0,France,Female,35.0,8.0,0.0,2.0,0.0,0.0,168034.9,0.0
+5582,15692226.0,Hsing,753.0,France,Male,22.0,4.0,0.0,2.0,1.0,1.0,72330.46,0.0
+5583,15695301.0,Pisano,695.0,Germany,Female,42.0,3.0,123863.64,1.0,1.0,0.0,100566.81,1.0
+5584,15691817.0,Tien,774.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,101515.76,0.0
+5585,15748068.0,Brierly,668.0,Germany,Male,30.0,3.0,126732.85,2.0,1.0,0.0,128841.31,0.0
+5586,15597949.0,Ngozichukwuka,807.0,Spain,Female,30.0,7.0,102773.2,1.0,1.0,0.0,60917.24,0.0
+5587,15687558.0,Maclean,652.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,96442.86,0.0
+5588,15626474.0,Chukwueloka,678.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,54949.73,0.0
+5589,15716085.0,Smith,599.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,25554.4,0.0
+5590,15686835.0,De Luca,581.0,France,Female,54.0,8.0,0.0,1.0,1.0,1.0,91359.79,1.0
+5591,15742798.0,Ts'ao,639.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,184524.74,0.0
+5592,15689322.0,Hsing,648.0,France,Female,56.0,4.0,103891.47,1.0,1.0,0.0,151871.84,1.0
+5593,15572408.0,Ch'ang,588.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,155743.48,0.0
+5594,15709511.0,Trevisani,745.0,Germany,Female,30.0,2.0,132767.17,1.0,0.0,0.0,49848.86,0.0
+5595,15574072.0,Chiawuotu,815.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,170514.21,0.0
+5596,15692599.0,Martin,691.0,Germany,Female,28.0,10.0,146679.77,1.0,1.0,1.0,78854.57,1.0
+5597,15696047.0,Hsing,681.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,1.0,175894.24,0.0
+5598,15703394.0,Chukwualuka,539.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,166850.57,0.0
+5599,15762277.0,Piazza,738.0,France,Male,24.0,4.0,0.0,1.0,0.0,0.0,60778.11,0.0
+5600,15642725.0,Pardey,581.0,France,Male,47.0,1.0,0.0,1.0,1.0,1.0,94738.54,0.0
+5601,15757911.0,Folliero,634.0,France,Male,21.0,6.0,0.0,2.0,1.0,0.0,162858.29,0.0
+5602,15679770.0,Ibezimako,521.0,France,Female,58.0,2.0,0.0,2.0,1.0,1.0,121747.96,0.0
+5603,15593331.0,Cattaneo,649.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,91429.74,0.0
+5604,15806486.0,Scott,636.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,0.0,152944.39,0.0
+5605,15721469.0,Biryukov,595.0,Spain,Male,37.0,1.0,152462.79,1.0,1.0,0.0,119087.25,0.0
+5606,15746461.0,Folliero,629.0,France,Female,43.0,7.0,118907.6,1.0,0.0,1.0,136863.93,0.0
+5607,15791958.0,Wilson,608.0,Germany,Male,40.0,2.0,118890.81,1.0,1.0,0.0,113410.82,1.0
+5608,15584091.0,Tuan,655.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,130789.6,0.0
+5609,15742210.0,Obiajulu,535.0,France,Male,37.0,4.0,0.0,1.0,1.0,0.0,119127.06,1.0
+5610,15623644.0,Mazzanti,711.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,0.0,129984.2,0.0
+5611,15802351.0,Ebelechukwu,671.0,France,Male,41.0,6.0,0.0,2.0,1.0,0.0,183682.09,0.0
+5612,15726234.0,Piazza,678.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,107846.87,0.0
+5613,15593973.0,Clements,586.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,88695.19,0.0
+5614,15592761.0,Kaeppel,816.0,France,Male,32.0,3.0,0.0,2.0,0.0,1.0,74849.46,0.0
+5615,15589348.0,Chinwendu,639.0,Germany,Female,44.0,3.0,131038.16,1.0,1.0,1.0,59249.83,0.0
+5616,15746012.0,Chiebuka,645.0,France,Male,32.0,4.0,117094.02,1.0,1.0,0.0,153662.08,1.0
+5617,15741403.0,T'ang,716.0,France,Male,34.0,9.0,0.0,2.0,0.0,1.0,122488.59,0.0
+5618,15674932.0,Macleod,684.0,France,Male,40.0,7.0,95377.13,1.0,1.0,0.0,199644.2,1.0
+5619,15664311.0,Ch'in,619.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,0.0,6078.46,0.0
+5620,15587033.0,Trevisano,742.0,Germany,Female,44.0,1.0,117438.92,2.0,1.0,1.0,74048.1,0.0
+5621,15715199.0,Ch'ang,553.0,France,Male,43.0,4.0,0.0,2.0,0.0,0.0,83090.35,0.0
+5622,15636407.0,Chidimma,633.0,Spain,Female,27.0,9.0,0.0,2.0,1.0,1.0,84760.32,0.0
+5623,15795519.0,Yevseyev,627.0,Germany,Female,27.0,7.0,131935.51,1.0,1.0,0.0,97623.42,0.0
+5624,15665861.0,Chu,690.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,79577.38,0.0
+5625,15686957.0,Lu,738.0,France,Male,38.0,7.0,0.0,2.0,0.0,1.0,176984.34,0.0
+5626,15645136.0,P'eng,577.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,136497.28,0.0
+5627,15782530.0,Nucci,793.0,France,Female,49.0,5.0,0.0,2.0,1.0,0.0,72927.68,0.0
+5628,15756850.0,Ndubueze,753.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,63889.1,0.0
+5629,15686588.0,Uspenskaya,709.0,France,Female,39.0,6.0,0.0,2.0,0.0,1.0,102299.81,0.0
+5630,15641640.0,Ch'ien,661.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,172618.52,0.0
+5631,15602841.0,Hsia,639.0,France,Male,31.0,5.0,141803.77,1.0,1.0,0.0,176091.31,0.0
+5632,15777822.0,Uspensky,668.0,Germany,Male,27.0,2.0,82373.94,2.0,1.0,1.0,146411.6,0.0
+5633,15657535.0,Manna,590.0,France,Female,41.0,6.0,0.0,2.0,1.0,1.0,49599.27,0.0
+5634,15750970.0,O'Brien,718.0,Spain,Male,38.0,7.0,130131.08,1.0,1.0,1.0,65464.66,0.0
+5635,15579914.0,Nwokeocha,595.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,120819.58,0.0
+5636,15583863.0,Tsai,739.0,France,Female,33.0,4.0,0.0,2.0,1.0,1.0,152665.85,0.0
+5637,15701160.0,Kao,644.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,181850.74,0.0
+5638,15798084.0,Chijindum,712.0,France,Male,21.0,6.0,0.0,2.0,1.0,1.0,73977.23,0.0
+5639,15713774.0,Hsia,645.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,59645.25,0.0
+5640,15768104.0,Chukwudi,565.0,France,Male,26.0,8.0,0.0,1.0,0.0,1.0,114774.35,0.0
+5641,15616530.0,Kao,556.0,Germany,Male,35.0,7.0,111379.55,1.0,1.0,0.0,177784.68,1.0
+5642,15705001.0,Clements,588.0,Germany,Female,38.0,7.0,120867.18,1.0,1.0,0.0,126253.31,1.0
+5643,15798053.0,Chia,749.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,1.0,186942.49,0.0
+5644,15775295.0,Folliero,667.0,Spain,Male,31.0,3.0,0.0,2.0,1.0,1.0,105570.39,0.0
+5645,15653715.0,Liao,642.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,199138.84,0.0
+5646,15690169.0,Chiu,748.0,France,Female,40.0,1.0,0.0,2.0,0.0,1.0,79577.38,0.0
+5647,15634262.0,Okwudilichukwu,707.0,Spain,Female,25.0,2.0,0.0,2.0,1.0,0.0,53954.24,0.0
+5648,15654025.0,Onio,706.0,Spain,Female,37.0,10.0,136980.23,1.0,1.0,1.0,64716.08,0.0
+5649,15572291.0,Buchi,602.0,France,Male,58.0,9.0,142084.04,1.0,1.0,1.0,169137.24,0.0
+5650,15685843.0,Hsia,672.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,112269.67,0.0
+5651,15581551.0,Uchechukwu,751.0,France,Male,21.0,6.0,112609.91,1.0,1.0,0.0,163550.58,0.0
+5652,15691823.0,Tsao,556.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,164741.81,0.0
+5653,15595047.0,Chukwufumnanya,696.0,France,Female,23.0,9.0,0.0,2.0,1.0,0.0,162022.87,0.0
+5654,15772892.0,Fiorentini,693.0,France,Female,34.0,1.0,152039.6,1.0,0.0,0.0,182624.56,0.0
+5655,15640667.0,Kable,434.0,Spain,Male,22.0,7.0,0.0,2.0,1.0,1.0,118545.49,0.0
+5656,15668818.0,Mazzanti,675.0,France,Female,35.0,2.0,98906.36,1.0,1.0,0.0,83470.4,0.0
+5657,15614215.0,Ch'iu,652.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,132298.28,1.0
+5658,15602845.0,Kolesnikov,727.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,54290.62,0.0
+5659,15654495.0,Mazzanti,687.0,Spain,Male,64.0,8.0,0.0,2.0,1.0,1.0,157891.86,1.0
+5660,15663826.0,Piazza,575.0,France,Female,37.0,6.0,0.0,1.0,1.0,1.0,158610.89,0.0
+5661,15636684.0,Onwumelu,683.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,139093.73,0.0
+5662,15767231.0,Walker,728.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,25885.72,0.0
+5663,15692919.0,Pagnotto,663.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,93953.84,0.0
+5664,15666032.0,Ndubueze,635.0,France,Female,62.0,4.0,102536.57,1.0,1.0,1.0,126336.72,0.0
+5665,15800061.0,Smith,705.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,138526.96,0.0
+5666,15774727.0,Ch'ang,592.0,France,Female,26.0,9.0,76914.21,2.0,1.0,0.0,62733.05,0.0
+5667,15664543.0,Chiang,713.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,86424.57,0.0
+5668,15651093.0,Nkemdirim,558.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,143913.32,0.0
+5669,15673877.0,Nwabugwu,684.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,158071.69,0.0
+5670,15589204.0,Lai,729.0,Spain,Female,30.0,1.0,0.0,2.0,1.0,1.0,109291.39,0.0
+5671,15610557.0,Ch'en,645.0,France,Male,35.0,3.0,0.0,1.0,1.0,0.0,130639.01,0.0
+5672,15687539.0,Macleod,579.0,France,Male,46.0,9.0,0.0,2.0,1.0,1.0,123850.58,0.0
+5673,15809847.0,T'ien,680.0,France,Male,29.0,7.0,0.0,2.0,0.0,1.0,127847.25,0.0
+5674,15804937.0,Ts'ui,756.0,France,Male,34.0,9.0,0.0,2.0,0.0,0.0,81298.09,0.0
+5675,15589431.0,White,684.0,Spain,Female,31.0,3.0,84521.48,1.0,0.0,1.0,48673.18,0.0
+5676,15605327.0,Sun,646.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,1.0,162599.51,0.0
+5677,15694879.0,Okwuadigbo,632.0,Germany,Female,31.0,4.0,125512.98,1.0,0.0,1.0,181224.56,0.0
+5678,15600738.0,Folliero,567.0,France,Male,38.0,3.0,0.0,1.0,0.0,0.0,168035.62,1.0
+5679,15760865.0,Liao,718.0,Spain,Male,39.0,5.0,0.0,1.0,1.0,1.0,52319.26,0.0
+5680,15577402.0,Young,466.0,France,Male,56.0,5.0,0.0,2.0,1.0,1.0,138399.67,0.0
+5681,15581811.0,Ch'in,641.0,France,Female,23.0,3.0,0.0,1.0,1.0,1.0,145562.4,0.0
+5682,15793331.0,Miah,600.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,182718.25,0.0
+5683,15665062.0,Hsieh,588.0,Germany,Female,42.0,1.0,169831.46,1.0,1.0,0.0,126728.27,1.0
+5684,15699492.0,Manna,643.0,France,Male,39.0,8.0,146466.46,1.0,1.0,1.0,180890.4,0.0
+5685,15618018.0,Liao,678.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,174431.01,0.0
+5686,15714993.0,Chukwufumnanya,632.0,Germany,Female,43.0,2.0,92887.13,1.0,1.0,1.0,101168.9,1.0
+5687,15746035.0,Chu,739.0,Spain,Male,23.0,9.0,0.0,1.0,0.0,0.0,96498.14,0.0
+5688,15777892.0,Lu,630.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,142903.44,0.0
+5689,15746662.0,Nnaife,653.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,54132.93,0.0
+5690,15698474.0,Hsieh,698.0,France,Male,53.0,3.0,0.0,1.0,1.0,0.0,105784.85,1.0
+5691,15699550.0,P'eng,559.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,141533.19,1.0
+5692,15746035.0,Chibueze,653.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,49361.84,0.0
+5693,15592765.0,H?,695.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,1.0,131778.67,0.0
+5694,15752508.0,Ebelechukwu,713.0,Germany,Female,29.0,9.0,151691.44,1.0,1.0,0.0,167553.06,0.0
+5695,15617454.0,Iweobiegbunam,632.0,France,Female,37.0,2.0,0.0,1.0,1.0,0.0,149892.79,0.0
+5696,15669611.0,Iloerika,564.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,1.0,113079.19,0.0
+5697,15690695.0,Onwumelu,646.0,France,Female,32.0,1.0,0.0,2.0,0.0,0.0,83084.33,0.0
+5698,15659325.0,Nwachukwu,526.0,France,Male,66.0,10.0,0.0,1.0,1.0,1.0,126812.63,0.0
+5699,15683522.0,Iweobiegbunam,632.0,France,Male,28.0,7.0,0.0,1.0,0.0,1.0,146875.76,0.0
+5700,15682533.0,Hsia,639.0,France,Male,36.0,5.0,123841.49,1.0,1.0,1.0,62905.29,0.0
+5701,15683947.0,Hs?,638.0,Germany,Female,46.0,4.0,141078.37,1.0,1.0,1.0,86043.27,1.0
+5702,15804052.0,Milano,753.0,France,Female,29.0,1.0,0.0,1.0,1.0,1.0,143683.75,0.0
+5703,15644962.0,Davison,577.0,Germany,Male,48.0,2.0,126732.85,1.0,1.0,0.0,53934.24,1.0
+5704,15595067.0,Rizzo,696.0,France,Male,44.0,0.0,96877.21,2.0,1.0,0.0,104393.78,1.0
+5705,15773098.0,Efremov,744.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,155870.82,0.0
+5706,15656901.0,Nkemdilim,613.0,Germany,Male,33.0,8.0,138653.51,1.0,1.0,1.0,177718.35,0.0
+5707,15692226.0,Ch'in,587.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,88592.8,0.0
+5708,15782530.0,Palerma,597.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,0.0,79691.4,0.0
+5709,15633586.0,Iadanza,684.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,98896.06,0.0
+5710,15611786.0,Ijendu,678.0,France,Female,35.0,4.0,0.0,2.0,1.0,0.0,141175.18,0.0
+5711,15740175.0,Chiang,651.0,France,Female,30.0,3.0,0.0,2.0,0.0,1.0,158937.55,0.0
+5712,15757911.0,Manna,794.0,France,Female,40.0,8.0,174400.36,1.0,0.0,0.0,122506.78,0.0
+5713,15657771.0,Hsueh,682.0,Germany,Female,29.0,8.0,137781.65,1.0,1.0,0.0,48778.46,0.0
+5714,15641490.0,Y?,649.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,171364.18,0.0
+5715,15651280.0,Uspensky,753.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,180622.02,0.0
+5716,15675380.0,Folliero,706.0,France,Female,26.0,0.0,0.0,2.0,1.0,0.0,118514.64,0.0
+5717,15770121.0,Aksenov,548.0,France,Female,27.0,4.0,0.0,2.0,1.0,0.0,121866.87,0.0
+5718,15746203.0,H?,696.0,France,Female,39.0,0.0,0.0,2.0,1.0,0.0,151316.37,0.0
+5719,15593976.0,Chiu,590.0,France,Male,35.0,5.0,0.0,1.0,1.0,1.0,52796.31,0.0
+5720,15686963.0,Lombardo,764.0,France,Female,57.0,9.0,0.0,1.0,1.0,0.0,66561.6,1.0
+5721,15632665.0,Chukwufumnanya,599.0,Germany,Male,26.0,5.0,125445.04,1.0,1.0,1.0,125435.81,0.0
+5722,15585255.0,Chien,693.0,France,Male,31.0,2.0,131752.71,1.0,1.0,1.0,87834.24,0.0
+5723,15778947.0,Manna,641.0,Spain,Female,35.0,2.0,0.0,1.0,1.0,0.0,9087.81,1.0
+5724,15594878.0,Yegorova,636.0,Germany,Female,37.0,7.0,129307.77,1.0,1.0,0.0,74048.1,0.0
+5725,15601550.0,Ts'ui,581.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,84460.18,0.0
+5726,15602841.0,K?,599.0,France,Female,30.0,10.0,0.0,2.0,1.0,1.0,101986.16,0.0
+5727,15699247.0,Hs?,562.0,Germany,Male,33.0,9.0,137964.55,2.0,0.0,0.0,123866.28,0.0
+5728,15793049.0,Smith,706.0,Spain,Female,42.0,1.0,0.0,3.0,0.0,0.0,160129.99,1.0
+5729,15592461.0,H?,769.0,France,Female,37.0,6.0,0.0,1.0,0.0,0.0,141449.86,0.0
+5730,15651585.0,Chukwudi,732.0,France,Male,36.0,10.0,0.0,1.0,1.0,1.0,58632.37,0.0
+5731,15605020.0,Tsao,651.0,France,Female,38.0,0.0,144427.96,1.0,1.0,1.0,92914.67,0.0
+5732,15724444.0,Uwaezuoke,667.0,France,Male,46.0,8.0,0.0,2.0,1.0,1.0,79919.13,0.0
+5733,15700366.0,Hsueh,730.0,Germany,Male,34.0,2.0,109052.04,1.0,1.0,1.0,115147.33,0.0
+5734,15585985.0,P'eng,635.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,179655.87,0.0
+5735,15802478.0,Tien,586.0,Germany,Male,38.0,2.0,95826.49,2.0,0.0,0.0,112979.6,0.0
+5736,15732917.0,Chin,471.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,177383.68,0.0
+5737,15745355.0,K'ung,637.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,176750.81,0.0
+5738,15632551.0,Fisk,637.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,135077.01,0.0
+5739,15646566.0,Uvarov,657.0,France,Male,36.0,0.0,0.0,2.0,1.0,1.0,167984.72,0.0
+5740,15737110.0,Ozerova,507.0,Germany,Male,27.0,3.0,105229.53,1.0,1.0,0.0,140941.47,1.0
+5741,15773949.0,Ts'ao,705.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,167066.95,0.0
+5742,15786418.0,Genovese,584.0,France,Female,32.0,2.0,117302.62,1.0,1.0,1.0,131609.48,0.0
+5743,15774277.0,Maclean,725.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,1.0,182855.21,0.0
+5744,15796496.0,Chien,614.0,Germany,Female,38.0,9.0,122530.18,1.0,0.0,0.0,101300.94,1.0
+5745,15766787.0,Iloerika,616.0,Germany,Male,41.0,3.0,101440.36,2.0,1.0,1.0,136491.72,0.0
+5746,15623566.0,T'ien,555.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,99449.86,0.0
+5747,15641675.0,Trevisano,633.0,France,Male,47.0,5.0,0.0,1.0,1.0,1.0,50899.91,1.0
+5748,15702571.0,Chin,629.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,65413.43,0.0
+5749,15597901.0,Bess,605.0,France,Male,41.0,2.0,0.0,1.0,1.0,1.0,130132.41,0.0
+5750,15682467.0,Chikwendu,728.0,Spain,Female,34.0,5.0,77526.66,2.0,1.0,1.0,159418.1,0.0
+5751,15766659.0,Iqbal,704.0,France,Female,38.0,1.0,133707.09,1.0,1.0,0.0,52331.15,0.0
+5752,15797733.0,Chukwuemeka,653.0,Spain,Male,38.0,9.0,0.0,1.0,1.0,1.0,100995.11,0.0
+5753,15704987.0,McIntyre,667.0,Germany,Female,54.0,4.0,113811.85,1.0,0.0,0.0,124218.53,1.0
+5754,15694518.0,Nwora,681.0,France,Male,23.0,5.0,0.0,2.0,0.0,1.0,167066.95,0.0
+5755,15771674.0,De Luca,729.0,France,Male,36.0,3.0,79932.41,1.0,1.0,1.0,126033.9,0.0
+5756,15641490.0,Hao,682.0,France,Male,61.0,9.0,0.0,1.0,1.0,0.0,189703.65,1.0
+5757,15792565.0,Ts'ui,640.0,France,Male,37.0,5.0,0.0,2.0,0.0,1.0,27239.28,0.0
+5758,15808905.0,Maclean,705.0,France,Female,22.0,3.0,0.0,2.0,0.0,1.0,151481.86,0.0
+5759,15602551.0,Ku,651.0,Germany,Male,71.0,1.0,118560.0,2.0,0.0,0.0,74858.3,1.0
+5760,15747222.0,Chiu,652.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,82868.34,0.0
+5761,15754569.0,Ritchie,748.0,Spain,Male,40.0,8.0,119755.0,2.0,1.0,0.0,86490.09,0.0
+5762,15654727.0,Ejimofor,595.0,Spain,Male,44.0,6.0,0.0,2.0,1.0,1.0,141872.05,0.0
+5763,15789936.0,Ewers,595.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,124592.83,0.0
+5764,15616454.0,Udegbulam,719.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,131283.6,1.0
+5765,15791501.0,Chinwendu,696.0,France,Male,37.0,9.0,130929.02,1.0,1.0,1.0,9551.49,0.0
+5766,15581736.0,Chidubem,628.0,Germany,Male,38.0,4.0,122265.24,2.0,1.0,1.0,86937.2,0.0
+5767,15786680.0,Ch'iu,581.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,75832.53,0.0
+5768,15791769.0,Onyemauchechi,580.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,0.0,93629.66,1.0
+5769,15622658.0,Chu,592.0,France,Female,41.0,6.0,0.0,2.0,1.0,0.0,99828.45,0.0
+5770,15590820.0,Lucchese,605.0,France,Male,28.0,9.0,0.0,2.0,1.0,1.0,150835.21,0.0
+5771,15592762.0,K'ung,709.0,France,Male,38.0,9.0,107959.39,1.0,0.0,0.0,137784.25,0.0
+5772,15628963.0,Haugh,574.0,Germany,Female,43.0,3.0,109341.87,1.0,1.0,0.0,115916.55,0.0
+5773,15692631.0,Lucchese,678.0,Germany,Female,31.0,8.0,116416.94,1.0,1.0,0.0,16657.68,1.0
+5774,15572428.0,Cawthorne,730.0,France,Male,29.0,6.0,0.0,2.0,0.0,0.0,12096.61,0.0
+5775,15598086.0,Ch'eng,712.0,Spain,Male,35.0,6.0,91592.06,1.0,1.0,1.0,175314.87,0.0
+5776,15622985.0,Okwudilichukwu,707.0,Spain,Female,43.0,9.0,0.0,1.0,1.0,0.0,184524.74,1.0
+5777,15674583.0,Y?an,699.0,Spain,Female,23.0,4.0,0.0,2.0,1.0,0.0,61811.1,0.0
+5778,15710671.0,Obijiaku,757.0,Spain,Female,47.0,2.0,0.0,2.0,1.0,1.0,142223.94,0.0
+5779,15595537.0,Y?an,769.0,Germany,Male,31.0,6.0,124447.68,2.0,0.0,0.0,180800.34,0.0
+5780,15757541.0,Chidimma,712.0,France,Female,41.0,8.0,0.0,2.0,1.0,0.0,115779.48,0.0
+5781,15807900.0,Ch'ang,776.0,France,Female,48.0,6.0,127864.4,1.0,1.0,1.0,96985.58,1.0
+5782,15691316.0,Fyodorov,661.0,France,Female,38.0,2.0,0.0,2.0,0.0,1.0,54724.03,0.0
+5783,15681081.0,Ponomaryova,620.0,Spain,Female,36.0,4.0,0.0,2.0,1.0,1.0,55868.97,0.0
+5784,15772423.0,Nieves,571.0,Germany,Male,39.0,5.0,133776.96,2.0,1.0,1.0,105780.18,0.0
+5785,15782531.0,Toscani,479.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,1.0,79616.37,0.0
+5786,15762615.0,Cisneros,675.0,Spain,Female,37.0,7.0,103711.56,1.0,0.0,0.0,141872.02,0.0
+5787,15631709.0,Chifley,638.0,Germany,Female,38.0,5.0,97445.49,2.0,1.0,1.0,109098.39,0.0
+5788,15763431.0,Tsao,676.0,France,Male,29.0,9.0,180520.29,1.0,1.0,0.0,15404.64,1.0
+5789,15719561.0,Fan,708.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,95583.45,0.0
+5790,15670866.0,Y?an,710.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,174311.3,0.0
+5791,15814022.0,Ch'ang,721.0,Germany,Male,36.0,9.0,138521.25,1.0,0.0,1.0,81718.37,0.0
+5792,15613673.0,Ku,682.0,Germany,Female,29.0,7.0,96486.57,1.0,1.0,0.0,127558.26,1.0
+5793,15788151.0,Ch'eng,577.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,25488.43,0.0
+5794,15744279.0,Eluemuno,659.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,0.0,129980.93,0.0
+5795,15692599.0,Trevisani,637.0,Germany,Female,60.0,8.0,117070.82,1.0,1.0,1.0,162719.69,1.0
+5796,15641480.0,Ch'iu,638.0,France,Female,54.0,9.0,0.0,1.0,1.0,1.0,155915.24,1.0
+5797,15631838.0,Sagese,639.0,France,Male,51.0,4.0,0.0,1.0,1.0,0.0,171378.77,1.0
+5798,15641662.0,Ugochukwutubelum,679.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,131718.12,0.0
+5799,15675787.0,Chidimma,653.0,Spain,Female,36.0,0.0,81576.75,1.0,0.0,1.0,172646.88,0.0
+5800,15665943.0,Fan,655.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,177784.68,0.0
+5801,15766776.0,P'eng,577.0,France,Female,44.0,1.0,87894.26,1.0,1.0,0.0,173498.45,0.0
+5802,15773921.0,De Luca,628.0,Spain,Female,33.0,4.0,0.0,2.0,1.0,1.0,115668.53,0.0
+5803,15644400.0,Chiang,752.0,Germany,Female,52.0,8.0,121577.33,1.0,1.0,0.0,184856.89,1.0
+5804,15683980.0,Nwebube,537.0,France,Female,27.0,2.0,153548.12,1.0,1.0,0.0,131718.12,0.0
+5805,15726358.0,Whitehead,724.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,112366.98,0.0
+5806,15576723.0,Ts'ui,641.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,1.0,96847.91,0.0
+5807,15687431.0,Brown,641.0,Germany,Male,31.0,8.0,128743.8,2.0,1.0,1.0,120138.27,0.0
+5808,15605275.0,Nkemdilim,826.0,Germany,Male,38.0,4.0,102025.32,2.0,1.0,1.0,153895.32,0.0
+5809,15674720.0,Miller,712.0,Germany,Female,48.0,2.0,107960.46,2.0,1.0,0.0,90876.95,1.0
+5810,15668767.0,Uwaezuoke,578.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,170968.99,0.0
+5811,15709639.0,Udinesi,704.0,France,Male,24.0,7.0,0.0,2.0,1.0,0.0,103832.58,0.0
+5812,15659501.0,Whitehouse,727.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,79577.4,0.0
+5813,15763612.0,Ch'iu,598.0,France,Male,38.0,4.0,0.0,1.0,0.0,1.0,138051.19,0.0
+5814,15641934.0,Pinto,612.0,France,Male,44.0,0.0,0.0,1.0,1.0,0.0,91406.62,1.0
+5815,15579345.0,Pinto,588.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,145796.49,0.0
+5816,15757916.0,Macleod,679.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,49722.34,0.0
+5817,15621227.0,Chukwudi,691.0,Spain,Female,42.0,3.0,170172.9,1.0,1.0,1.0,83052.03,0.0
+5818,15737071.0,Shih,639.0,France,Female,47.0,9.0,0.0,1.0,1.0,0.0,63736.17,0.0
+5819,15582910.0,Chidiebere,807.0,France,Male,28.0,7.0,0.0,2.0,0.0,1.0,170514.21,0.0
+5820,15581548.0,Ting,483.0,Spain,Male,32.0,7.0,0.0,2.0,1.0,0.0,142400.29,0.0
+5821,15627665.0,Fang,711.0,France,Female,36.0,3.0,0.0,1.0,1.0,0.0,72350.47,0.0
+5822,15594577.0,Lai,582.0,Spain,Male,54.0,8.0,0.0,2.0,0.0,1.0,130789.6,0.0
+5823,15703394.0,Ozerova,619.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,141896.74,0.0
+5824,15605968.0,DeRose,627.0,France,Female,41.0,6.0,0.0,1.0,1.0,0.0,67468.67,1.0
+5825,15668958.0,Lassetter,668.0,France,Male,49.0,3.0,123863.64,1.0,0.0,0.0,175893.53,1.0
+5826,15677404.0,Dickson,587.0,France,Female,29.0,9.0,0.0,2.0,0.0,0.0,161571.79,0.0
+5827,15814750.0,Ugochukwu,694.0,France,Male,35.0,7.0,0.0,1.0,1.0,1.0,103737.82,0.0
+5828,15743818.0,Onyemachukwu,614.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,127875.1,0.0
+5829,15761653.0,Ku,539.0,France,Male,38.0,1.0,129855.32,1.0,1.0,0.0,120858.18,0.0
+5830,15731553.0,Lombardo,753.0,Germany,Male,35.0,2.0,122295.17,1.0,1.0,1.0,138340.75,0.0
+5831,15657778.0,Iweobiegbulam,745.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,6936.27,0.0
+5832,15734438.0,Ch'ang,689.0,France,Male,38.0,6.0,0.0,2.0,0.0,0.0,151316.37,0.0
+5833,15581548.0,Ting,485.0,France,Male,50.0,1.0,0.0,2.0,1.0,1.0,70774.97,0.0
+5834,15726313.0,Onyemere,521.0,Germany,Male,40.0,2.0,99811.44,1.0,1.0,1.0,11525.72,1.0
+5835,15778947.0,Oluchi,749.0,Spain,Female,42.0,5.0,0.0,1.0,1.0,1.0,181794.86,1.0
+5836,15582841.0,Nwokezuike,591.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,131736.43,0.0
+5837,15768244.0,Lo Duca,584.0,France,Female,26.0,3.0,0.0,1.0,1.0,0.0,55829.25,1.0
+5838,15589715.0,Mazzanti,687.0,France,Male,44.0,5.0,127864.4,2.0,1.0,1.0,72924.29,1.0
+5839,15793901.0,Nnaife,712.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,102515.42,0.0
+5840,15651740.0,K'ung,663.0,France,Female,24.0,0.0,0.0,2.0,1.0,1.0,182728.8,0.0
+5841,15588928.0,Cattaneo,726.0,Spain,Female,57.0,4.0,0.0,2.0,1.0,1.0,121151.1,0.0
+5842,15611105.0,Shao,651.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,105868.96,0.0
+5843,15618363.0,Hsueh,589.0,France,Male,45.0,3.0,0.0,2.0,1.0,1.0,177747.03,0.0
+5844,15793424.0,Mazzanti,729.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,55725.04,0.0
+5845,15579892.0,Trevisan,699.0,France,Male,32.0,9.0,0.0,1.0,1.0,1.0,74564.41,0.0
+5846,15787907.0,P'eng,712.0,France,Female,51.0,6.0,136623.19,1.0,1.0,0.0,178267.07,1.0
+5847,15644765.0,Iloerika,719.0,Spain,Female,34.0,5.0,83888.54,1.0,0.0,0.0,134878.34,0.0
+5848,15792679.0,Bibi,489.0,France,Male,49.0,8.0,0.0,2.0,0.0,0.0,178705.45,0.0
+5849,15676521.0,Chukwueloka,697.0,France,Female,41.0,6.0,0.0,1.0,1.0,0.0,40357.39,1.0
+5850,15702669.0,Nwachinemelu,731.0,France,Male,31.0,2.0,0.0,1.0,1.0,1.0,143981.27,0.0
+5851,15659501.0,T'ang,684.0,France,Male,21.0,7.0,0.0,1.0,1.0,0.0,131647.31,1.0
+5852,15702095.0,Chu,705.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,51548.14,0.0
+5853,15772573.0,Martin,726.0,Spain,Female,62.0,8.0,133616.38,1.0,1.0,0.0,182270.88,1.0
+5854,15757628.0,Tsao,743.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,0.0,117523.41,0.0
+5855,15672640.0,Nnamdi,586.0,Spain,Male,25.0,5.0,151510.17,1.0,1.0,0.0,132730.07,0.0
+5856,15603741.0,Nwankwo,743.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,175071.29,0.0
+5857,15645045.0,H?,717.0,Spain,Male,33.0,9.0,132876.23,1.0,0.0,1.0,47795.43,0.0
+5858,15796969.0,Chukwuebuka,696.0,Spain,Male,26.0,5.0,0.0,2.0,1.0,0.0,48201.64,0.0
+5859,15692631.0,Baresi,670.0,France,Female,39.0,8.0,0.0,2.0,0.0,1.0,169894.4,0.0
+5860,15731865.0,Yermakova,662.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,131736.43,0.0
+5861,15754084.0,K'ung,699.0,France,Male,29.0,5.0,0.0,1.0,1.0,1.0,168413.18,0.0
+5862,15711889.0,Chiu,769.0,France,Female,27.0,6.0,0.0,2.0,0.0,0.0,167023.95,0.0
+5863,15633950.0,Hsing,703.0,Germany,Female,29.0,2.0,120596.93,2.0,1.0,1.0,87071.18,0.0
+5864,15573926.0,Dike,708.0,France,Female,37.0,1.0,0.0,2.0,0.0,0.0,77837.63,0.0
+5865,15635972.0,T'ien,565.0,Spain,Male,27.0,7.0,0.0,2.0,1.0,1.0,75837.57,0.0
+5866,15587562.0,Tien,602.0,Spain,Male,40.0,9.0,145578.28,2.0,1.0,1.0,123850.58,0.0
+5867,15697042.0,Ives,590.0,France,Female,39.0,3.0,0.0,2.0,1.0,1.0,103931.49,1.0
+5868,15809057.0,Ngozichukwuka,699.0,France,Male,27.0,9.0,0.0,2.0,1.0,0.0,86490.09,0.0
+5869,15673372.0,Chiganu,749.0,Germany,Female,41.0,4.0,98630.31,2.0,0.0,1.0,163798.41,1.0
+5870,15764444.0,Fomin,714.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,121866.87,0.0
+5871,15795540.0,Ugochukwu,696.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,168816.03,0.0
+5872,15751595.0,T'ao,645.0,France,Male,63.0,5.0,0.0,1.0,0.0,1.0,121151.1,1.0
+5873,15683955.0,Tien,684.0,Spain,Male,39.0,4.0,125725.02,1.0,1.0,1.0,142797.5,0.0
+5874,15774936.0,Onyemauchechukwu,681.0,France,Female,32.0,7.0,127539.3,1.0,1.0,1.0,123982.14,0.0
+5875,15799785.0,Chukwuemeka,663.0,Spain,Male,43.0,2.0,0.0,1.0,1.0,0.0,138051.19,1.0
+5876,15800517.0,Bancks,679.0,Germany,Male,43.0,2.0,135903.33,2.0,1.0,1.0,67734.79,0.0
+5877,15656249.0,Greco,757.0,Germany,Female,32.0,5.0,155878.76,2.0,0.0,1.0,125061.97,0.0
+5878,15812594.0,Chukwumaobim,679.0,France,Male,29.0,10.0,0.0,2.0,1.0,1.0,147797.54,0.0
+5879,15658614.0,Ch'ang,658.0,Germany,Male,35.0,8.0,92855.02,2.0,1.0,1.0,131953.23,0.0
+5880,15582741.0,Mazzanti,588.0,France,Male,40.0,1.0,0.0,2.0,0.0,0.0,122996.76,0.0
+5881,15756825.0,Chinwendu,751.0,Spain,Female,40.0,6.0,0.0,2.0,1.0,1.0,166476.95,0.0
+5882,15775318.0,Trevisano,629.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,158750.58,0.0
+5883,15737385.0,Ch'en,711.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,0.0,141180.66,0.0
+5884,15799358.0,Ts'ai,586.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,94711.43,0.0
+5885,15652981.0,Ts'ui,714.0,Germany,Female,35.0,3.0,111213.64,3.0,0.0,0.0,51548.14,1.0
+5886,15704769.0,Ch'ien,583.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,98894.64,0.0
+5887,15682472.0,Tsao,725.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,177747.72,0.0
+5888,15752294.0,Nkemdirim,584.0,France,Female,26.0,5.0,107569.96,1.0,1.0,0.0,71154.1,0.0
+5889,15624451.0,Hsiung,764.0,Germany,Female,23.0,4.0,103772.45,2.0,1.0,0.0,141148.21,0.0
+5890,15797710.0,Chukwukadibia,693.0,France,Male,37.0,7.0,119496.75,1.0,1.0,0.0,91347.76,0.0
+5891,15585047.0,Trevisani,687.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,1.0,75927.35,0.0
+5892,15738148.0,T'ang,687.0,France,Female,43.0,4.0,0.0,1.0,1.0,0.0,158049.9,1.0
+5893,15671249.0,Lu,730.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,92647.94,0.0
+5894,15572467.0,Trevisano,669.0,France,Male,28.0,9.0,0.0,1.0,1.0,0.0,56905.38,0.0
+5895,15672610.0,Kuo,581.0,Germany,Female,44.0,3.0,116022.73,1.0,1.0,0.0,27341.63,1.0
+5896,15705765.0,Biryukov,752.0,France,Male,35.0,8.0,0.0,2.0,0.0,1.0,52319.26,0.0
+5897,15791131.0,Chukwualuka,597.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,64345.61,0.0
+5898,15782758.0,Yevseyev,728.0,France,Male,34.0,9.0,0.0,2.0,0.0,0.0,64872.33,0.0
+5899,15791174.0,Zaytseva,704.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,152992.04,0.0
+5900,15773512.0,Rowntree,850.0,Spain,Male,26.0,2.0,0.0,2.0,1.0,0.0,133051.64,0.0
+5901,15657529.0,Mancini,774.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,121311.12,0.0
+5902,15809823.0,Tuan,603.0,Spain,Male,33.0,7.0,0.0,1.0,1.0,1.0,168816.22,0.0
+5903,15642824.0,Chiedozie,623.0,Germany,Male,50.0,8.0,129177.01,1.0,1.0,0.0,154598.58,1.0
+5904,15679961.0,Tuan,745.0,Spain,Male,42.0,6.0,0.0,1.0,1.0,1.0,142838.64,0.0
+5905,15595160.0,Manna,642.0,Spain,Male,42.0,2.0,0.0,1.0,1.0,0.0,136079.64,1.0
+5906,15757916.0,Chin,714.0,France,Female,30.0,7.0,0.0,1.0,1.0,1.0,63687.06,0.0
+5907,15670354.0,Hsu,599.0,Germany,Male,46.0,2.0,131287.36,1.0,1.0,0.0,53817.23,1.0
+5908,15583364.0,Nwoye,543.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,141501.45,0.0
+5909,15612095.0,Omeokachie,636.0,Spain,Female,49.0,2.0,0.0,1.0,0.0,0.0,166476.95,1.0
+5910,15756538.0,K?,665.0,Germany,Male,36.0,7.0,126817.65,1.0,0.0,0.0,162858.29,0.0
+5911,15595324.0,Hsiao,639.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,109153.04,0.0
+5912,15592765.0,Tung,556.0,France,Female,55.0,8.0,0.0,1.0,1.0,0.0,144870.28,1.0
+5913,15721740.0,Aitken,678.0,France,Male,37.0,2.0,0.0,1.0,1.0,0.0,38019.74,0.0
+5914,15592773.0,Lettiere,547.0,Spain,Male,42.0,9.0,0.0,2.0,0.0,1.0,176692.87,0.0
+5915,15752588.0,Ch'in,697.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,161579.85,0.0
+5916,15605684.0,Chidumaga,756.0,France,Male,32.0,7.0,0.0,1.0,0.0,1.0,75578.67,0.0
+5917,15789946.0,Nnamutaezinwa,682.0,France,Female,33.0,5.0,91611.12,1.0,1.0,0.0,77511.45,0.0
+5918,15741032.0,Zaytseva,598.0,France,Male,39.0,5.0,0.0,1.0,1.0,1.0,138542.89,0.0
+5919,15635752.0,Yobanna,742.0,Germany,Female,33.0,5.0,122923.94,2.0,1.0,0.0,70399.71,0.0
+5920,15719479.0,Tsao,645.0,France,Male,41.0,5.0,0.0,2.0,0.0,0.0,128875.86,0.0
+5921,15610653.0,Chukwumaobim,706.0,France,Male,40.0,0.0,0.0,2.0,0.0,0.0,7077.67,0.0
+5922,15619130.0,Hsiung,663.0,France,Male,42.0,10.0,0.0,2.0,1.0,0.0,94780.9,0.0
+5923,15777045.0,Iadanza,717.0,Germany,Male,37.0,8.0,121805.05,1.0,1.0,1.0,122925.4,0.0
+5924,15623668.0,Martin,604.0,France,Female,45.0,3.0,0.0,1.0,1.0,0.0,179543.73,1.0
+5925,15736963.0,T'ien,721.0,France,Female,38.0,7.0,0.0,2.0,0.0,1.0,132741.13,0.0
+5926,15753861.0,Zikoranachidimma,807.0,Spain,Female,31.0,8.0,132311.71,1.0,1.0,1.0,103934.12,0.0
+5927,15745355.0,Macleod,684.0,France,Female,41.0,6.0,0.0,1.0,1.0,1.0,143913.32,0.0
+5928,15762762.0,Oluchukwu,628.0,France,Male,43.0,6.0,0.0,1.0,1.0,1.0,141860.62,0.0
+5929,15771674.0,Mistry,684.0,France,Female,38.0,9.0,114446.74,1.0,1.0,1.0,69586.27,0.0
+5930,15694381.0,Hiebner,586.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,77500.81,0.0
+5931,15796958.0,De Luca,635.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,126233.18,0.0
+5932,15771543.0,Trevisan,729.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,178268.36,0.0
+5933,15786463.0,Hs?,644.0,France,Female,34.0,1.0,0.0,1.0,1.0,1.0,180962.32,0.0
+5934,15717156.0,Chibuzo,663.0,Germany,Female,44.0,2.0,122552.48,3.0,0.0,0.0,51721.52,1.0
+5935,15726774.0,Mazzanti,725.0,Spain,Male,29.0,0.0,0.0,2.0,1.0,1.0,83093.25,0.0
+5936,15682773.0,Mazzanti,592.0,Germany,Female,40.0,0.0,147816.16,1.0,1.0,0.0,171837.06,1.0
+5937,15683955.0,Milani,603.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,1.0,48606.92,0.0
+5938,15641789.0,P'an,565.0,Germany,Female,41.0,9.0,105922.81,1.0,1.0,0.0,38761.61,1.0
+5939,15796764.0,Ozioma,704.0,France,Female,43.0,8.0,95947.76,1.0,1.0,0.0,162858.29,0.0
+5940,15716041.0,T'ien,738.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,163666.85,0.0
+5941,15757537.0,T'ang,669.0,Spain,Male,28.0,10.0,0.0,1.0,1.0,0.0,176692.87,0.0
+5942,15607988.0,Padovano,554.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,120853.83,0.0
+5943,15746802.0,Walker,667.0,France,Female,39.0,6.0,0.0,2.0,1.0,0.0,132905.43,0.0
+5944,15687558.0,Ting,605.0,Spain,Female,30.0,9.0,0.0,1.0,1.0,1.0,132702.64,0.0
+5945,15620204.0,Ogochukwu,583.0,France,Male,66.0,0.0,0.0,2.0,0.0,0.0,60905.51,1.0
+5946,15691119.0,Kao,512.0,Germany,Female,24.0,3.0,90544.0,2.0,1.0,1.0,170347.1,0.0
+5947,15614778.0,Tien,588.0,France,Female,39.0,7.0,149155.53,1.0,1.0,0.0,81402.02,0.0
+5948,15704788.0,Ch'iu,575.0,Germany,Male,36.0,3.0,114435.48,2.0,1.0,1.0,151325.24,0.0
+5949,15602845.0,Li Fonti,646.0,France,Female,34.0,1.0,108286.5,1.0,0.0,0.0,77878.62,0.0
+5950,15803801.0,H?,562.0,Spain,Male,48.0,2.0,0.0,1.0,1.0,1.0,103921.43,1.0
+5951,15773934.0,Chimezie,636.0,Germany,Male,46.0,6.0,121702.73,1.0,1.0,0.0,117832.88,1.0
+5952,15579996.0,Chinedum,642.0,France,Male,35.0,10.0,0.0,1.0,1.0,1.0,155743.48,0.0
+5953,15584090.0,K'ung,714.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,121085.72,0.0
+5954,15772995.0,Milne,653.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,159800.16,0.0
+5955,15792650.0,Ndubueze,684.0,France,Male,51.0,4.0,0.0,2.0,1.0,1.0,82547.01,0.0
+5956,15768151.0,Onwumelu,653.0,France,Female,61.0,7.0,0.0,2.0,1.0,1.0,73979.85,0.0
+5957,15617790.0,Hsu,744.0,France,Male,34.0,8.0,139986.66,1.0,0.0,1.0,106761.47,0.0
+5958,15731744.0,Foveaux,545.0,Spain,Male,22.0,9.0,155423.21,1.0,1.0,1.0,9241.78,0.0
+5959,15583392.0,Ch'ang,556.0,France,Female,37.0,5.0,0.0,1.0,0.0,1.0,104706.29,1.0
+5960,15694717.0,Ch'ang,582.0,France,Male,33.0,10.0,0.0,2.0,1.0,1.0,181461.46,0.0
+5961,15586479.0,Ritchie,605.0,France,Male,42.0,9.0,0.0,1.0,1.0,0.0,130404.53,0.0
+5962,15691808.0,Palerma,578.0,Germany,Female,45.0,1.0,137367.94,2.0,1.0,1.0,165574.54,0.0
+5963,15631054.0,Ch'in,705.0,Spain,Female,39.0,9.0,0.0,1.0,0.0,0.0,147557.71,1.0
+5964,15691808.0,Chiedozie,607.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,127825.25,0.0
+5965,15596971.0,Fanucci,744.0,France,Male,44.0,9.0,121827.43,1.0,1.0,0.0,123969.64,0.0
+5966,15583394.0,Shih,686.0,Germany,Male,36.0,3.0,133658.24,1.0,1.0,0.0,89566.74,0.0
+5967,15718893.0,Chiang,471.0,Germany,Female,36.0,3.0,139930.18,1.0,1.0,1.0,76946.3,0.0
+5968,15790829.0,Ts'ui,752.0,France,Female,47.0,7.0,0.0,2.0,1.0,0.0,149892.79,0.0
+5969,15622442.0,Chiawuotu,677.0,Germany,Male,25.0,2.0,121240.76,2.0,1.0,0.0,175743.92,0.0
+5970,15647975.0,Chien,683.0,Spain,Female,35.0,4.0,162442.35,1.0,1.0,1.0,96498.14,0.0
+5971,15578211.0,Gentry,667.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,1.0,168515.61,0.0
+5972,15792916.0,Lo Duca,753.0,France,Male,61.0,9.0,0.0,2.0,1.0,1.0,163736.09,0.0
+5973,15592768.0,Ch'ang,678.0,France,Male,41.0,2.0,0.0,2.0,0.0,1.0,45956.05,0.0
+5974,15587584.0,Onwughara,648.0,Germany,Female,41.0,9.0,167557.12,1.0,1.0,0.0,141756.32,1.0
+5975,15690540.0,Ch'ang,712.0,Spain,Female,43.0,10.0,0.0,1.0,0.0,0.0,167087.34,1.0
+5976,15591036.0,Toscani,599.0,Germany,Female,52.0,9.0,135096.77,2.0,1.0,1.0,51695.41,1.0
+5977,15667971.0,Yobanna,633.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,0.0,64345.61,0.0
+5978,15594862.0,Mazzi,547.0,Germany,Male,46.0,1.0,136576.02,1.0,1.0,1.0,179614.8,1.0
+5979,15651372.0,H?,703.0,Spain,Female,34.0,8.0,0.0,2.0,0.0,0.0,141533.19,0.0
+5980,15719302.0,Chukwuhaenye,779.0,Germany,Female,49.0,5.0,105795.9,1.0,1.0,0.0,83058.14,1.0
+5981,15791842.0,Shao,656.0,Spain,Male,37.0,2.0,134956.02,1.0,1.0,0.0,178028.96,0.0
+5982,15798975.0,Onyekachi,583.0,Spain,Male,46.0,8.0,0.0,2.0,1.0,1.0,182258.17,0.0
+5983,15654562.0,Fink,681.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,83419.49,0.0
+5984,15694253.0,Chiu,673.0,France,Female,47.0,5.0,0.0,1.0,1.0,0.0,158034.9,1.0
+5985,15764661.0,Aitken,707.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,66548.58,0.0
+5986,15709861.0,Nnaife,716.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,164864.67,0.0
+5987,15772216.0,Ch'en,733.0,France,Male,52.0,6.0,0.0,2.0,1.0,0.0,94774.12,1.0
+5988,15765322.0,Lo,850.0,Spain,Female,55.0,9.0,0.0,1.0,1.0,0.0,53512.16,1.0
+5989,15648047.0,Hsing,549.0,Spain,Female,38.0,10.0,98548.62,1.0,0.0,1.0,113679.75,0.0
+5990,15811032.0,O'Meara,598.0,France,Female,41.0,8.0,0.0,2.0,1.0,0.0,146700.22,0.0
+5991,15693771.0,Chuang,757.0,Germany,Female,58.0,5.0,126473.99,1.0,1.0,0.0,93769.89,1.0
+5992,15782758.0,Hsueh,717.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,131177.22,0.0
+5993,15594560.0,Ijendu,850.0,France,Female,50.0,2.0,0.0,1.0,1.0,1.0,176730.02,1.0
+5994,15681115.0,Rizzo,758.0,Spain,Male,24.0,9.0,118520.41,1.0,1.0,1.0,120225.62,0.0
+5995,15742285.0,P'eng,714.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,166650.32,0.0
+5996,15567993.0,T'ang,581.0,France,Male,29.0,2.0,114940.67,1.0,1.0,1.0,96660.76,0.0
+5997,15600626.0,Chiazagomekpere,678.0,France,Female,36.0,0.0,0.0,2.0,1.0,0.0,85901.09,0.0
+5998,15721383.0,Tien,627.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,148087.88,0.0
+5999,15623063.0,Maclean,673.0,Spain,Female,44.0,6.0,0.0,2.0,1.0,0.0,108675.01,0.0
+6000,15779743.0,Zikoranachukwudimma,728.0,France,Male,26.0,7.0,95829.13,1.0,1.0,0.0,96429.29,0.0
+6001,15727198.0,Ch'in,628.0,Spain,Male,50.0,7.0,0.0,2.0,1.0,1.0,161668.15,0.0
+6002,15778410.0,Onyemauchechukwu,718.0,France,Male,34.0,8.0,0.0,1.0,1.0,1.0,26215.85,0.0
+6003,15594039.0,Y?,558.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,140889.13,0.0
+6004,15771211.0,Lettiere,675.0,France,Male,25.0,4.0,0.0,2.0,1.0,1.0,120853.83,0.0
+6005,15659236.0,Onyemauchechukwu,660.0,France,Female,34.0,0.0,0.0,1.0,1.0,1.0,40652.33,0.0
+6006,15571529.0,McGregor,837.0,France,Female,60.0,4.0,0.0,1.0,1.0,0.0,170195.4,1.0
+6007,15693817.0,Galkin,722.0,France,Female,42.0,6.0,172463.45,1.0,0.0,0.0,167023.95,1.0
+6008,15690188.0,Trevisani,683.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,141872.05,0.0
+6009,15810563.0,Baresi,693.0,Spain,Female,31.0,7.0,107884.81,2.0,1.0,0.0,90977.48,0.0
+6010,15669987.0,Lucciano,605.0,Spain,Male,44.0,4.0,0.0,2.0,1.0,0.0,85890.75,0.0
+6011,15778975.0,Lombardi,664.0,France,Male,52.0,2.0,0.0,1.0,0.0,1.0,151481.86,1.0
+6012,15579548.0,Zikoranachidimma,648.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,89578.32,1.0
+6013,15633108.0,Ts'ui,713.0,France,Male,27.0,10.0,0.0,2.0,1.0,0.0,137519.92,0.0
+6014,15768777.0,Ch'ien,637.0,France,Female,28.0,4.0,68892.77,1.0,0.0,1.0,147521.35,0.0
+6015,15797910.0,Dike,682.0,France,Female,39.0,1.0,0.0,1.0,1.0,1.0,90964.54,0.0
+6016,15631693.0,Fyodorova,577.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,124592.84,0.0
+6017,15770214.0,Obialo,738.0,Germany,Male,44.0,0.0,127559.97,2.0,0.0,0.0,113580.79,1.0
+6018,15798053.0,Ch'ien,681.0,Germany,Male,37.0,2.0,125850.53,2.0,1.0,0.0,182059.28,0.0
+6019,15721921.0,T'ien,684.0,Spain,Female,33.0,9.0,142954.64,1.0,1.0,0.0,182532.23,0.0
+6020,15606944.0,Chinwendu,653.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,74858.3,0.0
+6021,15744109.0,Hsiao,733.0,France,Male,41.0,1.0,0.0,2.0,1.0,0.0,181718.73,0.0
+6022,15757588.0,Chibugo,627.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,181893.31,0.0
+6023,15783629.0,Okwudiliolisa,619.0,France,Female,32.0,4.0,152302.82,1.0,0.0,1.0,19150.14,0.0
+6024,15712264.0,Cremonesi,651.0,France,Male,25.0,7.0,121513.31,2.0,1.0,0.0,62762.12,0.0
+6025,15780498.0,Mazzanti,642.0,Germany,Male,48.0,7.0,141749.74,1.0,1.0,0.0,176570.55,0.0
+6026,15775430.0,Walker,516.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,92621.9,0.0
+6027,15777923.0,Teng,631.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,1.0,102057.86,0.0
+6028,15765322.0,Chiemenam,573.0,Spain,Male,34.0,8.0,0.0,1.0,1.0,1.0,104469.06,0.0
+6029,15659501.0,Tuan,745.0,France,Female,41.0,1.0,0.0,2.0,1.0,1.0,73881.68,0.0
+6030,15641733.0,Fomin,648.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,0.0,93739.71,0.0
+6031,15809616.0,Yobanna,653.0,France,Male,47.0,9.0,0.0,2.0,0.0,1.0,151871.84,0.0
+6032,15587553.0,Lueck,642.0,France,Female,37.0,2.0,117413.95,1.0,1.0,0.0,166953.62,0.0
+6033,15794122.0,Mancini,749.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,93901.61,0.0
+6034,15585928.0,Nkemjika,683.0,Germany,Female,40.0,1.0,122228.65,1.0,1.0,0.0,177695.67,0.0
+6035,15588928.0,Nwachukwu,666.0,France,Female,32.0,10.0,0.0,2.0,1.0,1.0,152723.23,0.0
+6036,15682070.0,Nnonso,758.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,95864.5,0.0
+6037,15767246.0,Tien,576.0,Spain,Female,47.0,4.0,0.0,2.0,1.0,0.0,74580.8,1.0
+6038,15572374.0,Sal,471.0,Germany,Female,43.0,6.0,123872.0,1.0,1.0,0.0,169386.81,1.0
+6039,15681402.0,Lo Duca,701.0,France,Female,33.0,6.0,0.0,1.0,1.0,0.0,59865.04,0.0
+6040,15792222.0,Chin,648.0,France,Male,58.0,3.0,0.0,1.0,1.0,0.0,91624.11,1.0
+6041,15782390.0,Tsou,646.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,70763.61,0.0
+6042,15763097.0,Ts'ui,730.0,Germany,Male,43.0,5.0,132730.17,1.0,1.0,0.0,158054.49,1.0
+6043,15774551.0,Ts'ai,646.0,Germany,Female,51.0,7.0,118560.0,1.0,1.0,0.0,163257.44,1.0
+6044,15718245.0,Hsueh,516.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,160589.12,0.0
+6045,15749300.0,Scott,741.0,Germany,Female,47.0,9.0,124976.19,2.0,1.0,1.0,66795.78,0.0
+6046,15806925.0,Young,711.0,France,Male,27.0,3.0,0.0,2.0,1.0,0.0,51380.9,0.0
+6047,15737267.0,Chidimma,602.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,151978.36,0.0
+6048,15623063.0,Milani,633.0,France,Male,38.0,3.0,0.0,2.0,0.0,1.0,161241.65,0.0
+6049,15672598.0,Tien,725.0,Spain,Female,31.0,3.0,123049.21,1.0,1.0,1.0,103921.43,0.0
+6050,15798737.0,P'eng,663.0,France,Female,40.0,8.0,0.0,1.0,1.0,1.0,62002.04,0.0
+6051,15607560.0,Tien,494.0,Germany,Female,41.0,5.0,134249.7,1.0,1.0,1.0,180862.26,1.0
+6052,15594404.0,Fyodorov,593.0,France,Female,28.0,5.0,70729.17,1.0,1.0,0.0,51548.14,0.0
+6053,15732674.0,Hooper,729.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,0.0,129892.93,0.0
+6054,15667932.0,Manna,506.0,France,Male,32.0,6.0,0.0,1.0,1.0,1.0,112366.98,0.0
+6055,15785559.0,Lucciano,683.0,France,Female,40.0,6.0,0.0,1.0,1.0,0.0,49063.42,1.0
+6056,15702337.0,Hsu,685.0,France,Female,49.0,1.0,79035.95,1.0,1.0,0.0,141501.45,1.0
+6057,15777892.0,Brown,593.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,95611.67,0.0
+6058,15584532.0,Ku,602.0,France,Female,44.0,5.0,0.0,1.0,1.0,1.0,124052.97,1.0
+6059,15775307.0,Ch'ien,556.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,70849.47,0.0
+6060,15805764.0,Nwankwo,597.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,166984.44,0.0
+6061,15602844.0,Baryshnikov,571.0,France,Female,26.0,7.0,0.0,2.0,1.0,1.0,142797.5,0.0
+6062,15673599.0,Lira,662.0,France,Female,40.0,7.0,141078.37,1.0,1.0,1.0,92708.1,0.0
+6063,15699450.0,Ewing,703.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,143783.46,0.0
+6064,15588928.0,Chibueze,697.0,Germany,Male,34.0,2.0,101487.18,2.0,1.0,0.0,130529.13,0.0
+6065,15689168.0,Chinweuba,546.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,1.0,104769.96,0.0
+6066,15786670.0,Tseng,661.0,Germany,Male,26.0,7.0,102088.68,2.0,1.0,1.0,74836.64,0.0
+6067,15674851.0,Onyemauchechukwu,806.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,60536.56,0.0
+6068,15645896.0,Nwankwo,681.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,1.0,94774.12,0.0
+6069,15693637.0,Lo,685.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,40807.27,0.0
+6070,15806923.0,Chiebuka,769.0,Spain,Male,44.0,1.0,0.0,1.0,1.0,1.0,86619.77,0.0
+6071,15661528.0,Fan,745.0,France,Male,43.0,3.0,0.0,2.0,0.0,1.0,74580.8,0.0
+6072,15680141.0,Hs?,598.0,France,Female,26.0,8.0,0.0,2.0,0.0,1.0,50578.4,0.0
+6073,15628308.0,Hsia,678.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,136826.32,0.0
+6074,15645248.0,Macdonald,719.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,132730.07,0.0
+6075,15807923.0,Kao,708.0,France,Male,40.0,9.0,119370.11,1.0,1.0,1.0,9241.52,0.0
+6076,15763515.0,Ch'eng,678.0,Germany,Male,59.0,8.0,121532.17,1.0,1.0,0.0,61230.83,1.0
+6077,15717010.0,Ch'iu,712.0,France,Female,55.0,6.0,116084.32,1.0,1.0,0.0,140763.18,0.0
+6078,15764033.0,Hsia,638.0,Germany,Female,62.0,10.0,152685.3,1.0,1.0,1.0,77500.81,1.0
+6079,15806901.0,Ch'ien,673.0,France,Male,23.0,4.0,0.0,2.0,0.0,0.0,152790.92,0.0
+6080,15653849.0,Pisano,667.0,France,Male,39.0,9.0,137963.07,1.0,1.0,1.0,52319.26,0.0
+6081,15652914.0,Chiang,850.0,Spain,Male,42.0,8.0,0.0,2.0,1.0,0.0,178038.17,0.0
+6082,15656683.0,Nwankwo,712.0,Germany,Female,43.0,6.0,116968.91,2.0,0.0,0.0,162858.29,1.0
+6083,15596962.0,Cisneros,521.0,France,Male,37.0,8.0,142072.02,1.0,1.0,0.0,141069.88,0.0
+6084,15709621.0,Clements,537.0,France,Male,47.0,6.0,0.0,2.0,0.0,1.0,120108.66,0.0
+6085,15724145.0,Teng,605.0,Spain,Female,37.0,2.0,0.0,1.0,0.0,0.0,199484.96,0.0
+6086,15705953.0,Lai,700.0,Germany,Male,46.0,5.0,132057.92,1.0,1.0,1.0,14073.11,1.0
+6087,15694444.0,Hsu,542.0,Germany,Male,46.0,7.0,103522.75,2.0,1.0,1.0,74836.64,0.0
+6088,15595350.0,Milanesi,613.0,France,Male,43.0,4.0,0.0,2.0,1.0,0.0,176984.34,0.0
+6089,15677049.0,Hsieh,644.0,Spain,Female,28.0,10.0,0.0,2.0,1.0,1.0,35279.74,0.0
+6090,15751185.0,Yashina,624.0,Spain,Male,67.0,6.0,0.0,2.0,1.0,1.0,166677.35,0.0
+6091,15806486.0,Calabresi,651.0,Germany,Male,32.0,8.0,122028.65,1.0,1.0,1.0,138399.67,0.0
+6092,15673578.0,T'ien,600.0,Spain,Male,33.0,1.0,0.0,2.0,0.0,1.0,171922.72,0.0
+6093,15778830.0,Udegbulam,652.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,183569.25,0.0
+6094,15772337.0,Hsueh,681.0,Spain,Male,29.0,7.0,0.0,2.0,1.0,0.0,158071.69,0.0
+6095,15737993.0,Chukwudi,636.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,102799.14,0.0
+6096,15691792.0,T'ien,697.0,Spain,Female,40.0,4.0,0.0,1.0,1.0,0.0,65868.28,0.0
+6097,15631222.0,Chukwumaobim,648.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,127847.25,0.0
+6098,15759741.0,Onwumelu,645.0,France,Male,24.0,7.0,0.0,2.0,1.0,0.0,151822.66,0.0
+6099,15634792.0,Nwankwo,679.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,91094.46,0.0
+6100,15694566.0,Y?,598.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,71865.31,0.0
+6101,15765322.0,Manna,679.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,161889.79,0.0
+6102,15683928.0,Ch'ien,807.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,53534.8,0.0
+6103,15571476.0,Smith,635.0,France,Female,55.0,9.0,0.0,1.0,1.0,0.0,115068.99,1.0
+6104,15799031.0,Y?,645.0,France,Male,50.0,7.0,0.0,2.0,0.0,1.0,160128.98,0.0
+6105,15632263.0,T'ien,708.0,Spain,Female,43.0,7.0,0.0,2.0,1.0,1.0,142400.27,0.0
+6106,15603674.0,Hsiao,652.0,France,Male,25.0,3.0,0.0,2.0,1.0,1.0,187976.88,0.0
+6107,15746674.0,Chia,731.0,Spain,Female,36.0,7.0,122570.87,1.0,1.0,1.0,10063.75,0.0
+6108,15775206.0,Yeh,629.0,Germany,Female,44.0,1.0,131834.8,1.0,1.0,0.0,113677.53,1.0
+6109,15694039.0,Ch'ien,701.0,France,Male,50.0,6.0,0.0,1.0,1.0,1.0,132702.64,1.0
+6110,15693637.0,Mairinger,687.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,143783.46,0.0
+6111,15641366.0,Dickson,674.0,Spain,Male,43.0,2.0,0.0,2.0,0.0,1.0,141827.83,0.0
+6112,15742809.0,Hsu,599.0,Germany,Male,40.0,1.0,112957.26,1.0,0.0,1.0,122949.71,0.0
+6113,15589428.0,Ts'ai,615.0,Germany,Male,37.0,4.0,174701.05,2.0,1.0,1.0,55313.44,0.0
+6114,15692408.0,Ch'iu,687.0,Spain,Female,36.0,1.0,0.0,2.0,1.0,0.0,164825.03,0.0
+6115,15582519.0,Fanucci,637.0,France,Female,57.0,1.0,0.0,2.0,1.0,1.0,184564.67,0.0
+6116,15628298.0,Genovese,678.0,France,Male,37.0,2.0,0.0,1.0,1.0,1.0,105780.18,0.0
+6117,15672692.0,Rowntree,545.0,Germany,Male,46.0,7.0,109978.83,1.0,0.0,0.0,136837.97,1.0
+6118,15727605.0,Lucciano,756.0,Spain,Male,46.0,6.0,0.0,2.0,1.0,1.0,136610.02,1.0
+6119,15743532.0,Trevisano,850.0,France,Female,30.0,7.0,0.0,2.0,1.0,1.0,71692.8,0.0
+6120,15802570.0,Tien,731.0,Germany,Female,53.0,4.0,133379.41,1.0,1.0,0.0,49373.97,1.0
+6121,15656901.0,L?,776.0,Spain,Male,44.0,4.0,0.0,2.0,1.0,0.0,147720.01,0.0
+6122,15709991.0,Y?,756.0,Spain,Male,22.0,2.0,0.0,2.0,1.0,0.0,86792.81,0.0
+6123,15662662.0,Walker,644.0,Germany,Female,41.0,1.0,137781.65,1.0,0.0,1.0,95007.8,0.0
+6124,15745452.0,T'ang,619.0,France,Male,34.0,8.0,139246.22,1.0,1.0,0.0,64869.32,1.0
+6125,15640865.0,Cremonesi,641.0,Germany,Female,32.0,8.0,119315.75,1.0,1.0,1.0,86935.06,0.0
+6126,15658889.0,H?,639.0,France,Female,34.0,1.0,132005.77,1.0,0.0,0.0,142903.44,0.0
+6127,15750466.0,T'ien,667.0,France,Male,24.0,1.0,0.0,2.0,0.0,0.0,105568.4,0.0
+6128,15793331.0,K'ung,646.0,France,Female,26.0,3.0,136623.19,1.0,0.0,1.0,141210.5,0.0
+6129,15583848.0,Ulyanova,786.0,France,Male,43.0,4.0,0.0,2.0,1.0,0.0,50783.1,0.0
+6130,15781598.0,Nwachinemelu,554.0,France,Female,34.0,8.0,0.0,1.0,0.0,1.0,27380.99,0.0
+6131,15582944.0,H?,659.0,France,Male,43.0,10.0,0.0,2.0,1.0,0.0,145986.35,1.0
+6132,15733032.0,Hsia,695.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,93073.06,0.0
+6133,15699550.0,Maclean,653.0,France,Male,33.0,5.0,0.0,1.0,1.0,0.0,137651.6,1.0
+6134,15752694.0,Nkemdirim,711.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,69428.79,0.0
+6135,15623062.0,Chizuoke,619.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,182855.42,0.0
+6136,15746802.0,Chiazagomekpere,695.0,France,Male,42.0,9.0,0.0,1.0,1.0,1.0,61312.15,0.0
+6137,15750839.0,Chukwuma,597.0,Germany,Male,53.0,5.0,118537.47,1.0,1.0,1.0,122218.57,1.0
+6138,15621870.0,Chikelu,637.0,France,Female,50.0,2.0,0.0,1.0,1.0,1.0,143598.7,1.0
+6139,15781495.0,McLean,543.0,France,Female,40.0,9.0,0.0,2.0,0.0,1.0,186489.95,0.0
+6140,15761047.0,Lo Duca,641.0,Germany,Male,36.0,3.0,125537.52,1.0,0.0,1.0,164864.67,0.0
+6141,15685314.0,Kao,703.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,163550.58,0.0
+6142,15763063.0,H?,729.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,0.0,142515.35,0.0
+6143,15636478.0,Pisano,627.0,France,Male,43.0,4.0,0.0,2.0,0.0,1.0,176680.16,0.0
+6144,15677310.0,Hsueh,733.0,Germany,Male,31.0,3.0,130854.59,2.0,1.0,0.0,172363.41,0.0
+6145,15789548.0,T'ien,652.0,Germany,Male,40.0,6.0,105770.7,1.0,1.0,1.0,103727.91,0.0
+6146,15698028.0,Chukwufumnanya,651.0,Germany,Female,55.0,4.0,139265.63,1.0,0.0,0.0,166694.93,1.0
+6147,15777616.0,Onyeorulu,711.0,Germany,Male,34.0,3.0,99167.54,2.0,1.0,0.0,161217.18,0.0
+6148,15733768.0,Chukwubuikem,591.0,Spain,Male,54.0,6.0,0.0,2.0,1.0,1.0,138399.67,0.0
+6149,15603914.0,Ruse,806.0,France,Male,28.0,5.0,0.0,1.0,1.0,0.0,146700.22,0.0
+6150,15630167.0,Chu,584.0,France,Female,57.0,8.0,93816.7,1.0,1.0,1.0,156768.45,0.0
+6151,15697042.0,Echezonachukwu,556.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,0.0,101694.67,0.0
+6152,15624781.0,Chukwualuka,605.0,France,Male,50.0,7.0,110812.91,1.0,1.0,1.0,82815.85,0.0
+6153,15700656.0,Chikelu,613.0,Germany,Female,51.0,8.0,105879.27,1.0,1.0,0.0,159823.16,1.0
+6154,15686588.0,O'Sullivan,598.0,France,Male,29.0,5.0,0.0,2.0,0.0,1.0,177747.03,0.0
+6155,15673820.0,Davison,622.0,France,Male,38.0,6.0,0.0,1.0,1.0,0.0,126726.33,1.0
+6156,15806913.0,K?,653.0,France,Male,31.0,3.0,120911.75,1.0,1.0,0.0,96670.2,0.0
+6157,15726289.0,Nwankwo,646.0,France,Female,30.0,4.0,0.0,1.0,1.0,0.0,159800.16,0.0
+6158,15676576.0,Hsueh,708.0,Spain,Male,30.0,10.0,0.0,1.0,1.0,1.0,96542.42,0.0
+6159,15642098.0,Nnanna,774.0,France,Male,57.0,1.0,0.0,2.0,1.0,1.0,141210.18,0.0
+6160,15722072.0,Ozerova,623.0,France,Male,30.0,0.0,102773.15,1.0,1.0,1.0,143980.29,0.0
+6161,15581733.0,Ugochukwu,703.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,1.0,149536.15,0.0
+6162,15670103.0,Obialo,659.0,France,Female,49.0,6.0,0.0,1.0,1.0,1.0,78744.19,1.0
+6163,15681316.0,Ch'en,616.0,Germany,Male,49.0,5.0,98710.89,1.0,1.0,1.0,94544.88,1.0
+6164,15654851.0,Shillito,577.0,Germany,Male,40.0,5.0,109730.22,2.0,1.0,0.0,188193.25,1.0
+6165,15665590.0,Tung,589.0,Spain,Female,38.0,8.0,0.0,1.0,0.0,0.0,119348.28,0.0
+6166,15737104.0,Ch'ang,678.0,France,Male,26.0,4.0,0.0,2.0,0.0,1.0,166038.27,0.0
+6167,15742442.0,K?,762.0,Germany,Male,56.0,2.0,119624.6,1.0,1.0,0.0,177099.71,1.0
+6168,15584776.0,Lo Duca,753.0,Spain,Male,43.0,10.0,0.0,2.0,1.0,1.0,174777.98,0.0
+6169,15595039.0,Ch'ang,684.0,France,Female,35.0,3.0,0.0,2.0,0.0,1.0,100727.68,0.0
+6170,15632280.0,Ch'iu,516.0,France,Female,40.0,7.0,0.0,2.0,1.0,0.0,56865.62,0.0
+6171,15594927.0,T'ao,723.0,France,Female,31.0,0.0,0.0,2.0,0.0,0.0,66463.62,0.0
+6172,15662291.0,Ndubueze,663.0,France,Female,50.0,1.0,0.0,2.0,1.0,0.0,120415.61,0.0
+6173,15654025.0,Uspenskaya,709.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,104902.68,0.0
+6174,15737194.0,Chukwudi,693.0,France,Female,31.0,7.0,96858.35,1.0,1.0,1.0,37264.11,0.0
+6175,15654489.0,Chienezie,582.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,182624.93,0.0
+6176,15747323.0,Udegbunam,635.0,France,Male,23.0,4.0,102097.62,1.0,1.0,1.0,51587.04,0.0
+6177,15654025.0,Ts'ai,695.0,Germany,Male,36.0,1.0,114440.24,1.0,1.0,1.0,113316.77,0.0
+6178,15666856.0,Aksenov,520.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,91561.91,0.0
+6179,15661290.0,T'ang,620.0,France,Male,38.0,10.0,0.0,1.0,1.0,0.0,185543.67,0.0
+6180,15643024.0,Mollison,696.0,Spain,Female,43.0,7.0,0.0,2.0,1.0,0.0,116957.6,0.0
+6181,15594902.0,Onwumelu,611.0,France,Female,46.0,2.0,0.0,2.0,1.0,0.0,173837.54,1.0
+6182,15672640.0,Tung,752.0,France,Male,33.0,2.0,124540.38,1.0,1.0,0.0,138340.12,0.0
+6183,15761487.0,Onyeoruru,681.0,Spain,Female,30.0,9.0,0.0,2.0,1.0,1.0,169920.92,0.0
+6184,15625080.0,O'Donnell,704.0,France,Male,34.0,6.0,117318.23,1.0,1.0,1.0,166850.57,0.0
+6185,15772423.0,Onyemauchechukwu,703.0,Germany,Female,31.0,4.0,135984.15,2.0,1.0,1.0,161559.12,0.0
+6186,15756950.0,Tsao,633.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,133076.44,0.0
+6187,15607526.0,Chidiegwu,709.0,Germany,Female,34.0,6.0,136082.0,1.0,1.0,1.0,94760.32,0.0
+6188,15639217.0,Galkin,476.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,141533.19,0.0
+6189,15572390.0,Ch'iu,571.0,Spain,Male,34.0,9.0,0.0,1.0,1.0,1.0,177092.16,0.0
+6190,15743375.0,Baresi,700.0,France,Male,61.0,3.0,0.0,2.0,1.0,1.0,113581.17,0.0
+6191,15649112.0,Tien,752.0,France,Male,37.0,3.0,0.0,1.0,1.0,1.0,174437.77,0.0
+6192,15588854.0,T'ao,628.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,54871.02,0.0
+6193,15573318.0,Cremonesi,706.0,France,Female,25.0,1.0,0.0,2.0,0.0,0.0,186826.22,0.0
+6194,15648800.0,Ch'ien,662.0,France,Male,23.0,1.0,125879.27,1.0,1.0,1.0,158036.72,0.0
+6195,15807345.0,Hsia,751.0,Germany,Male,30.0,2.0,85845.43,1.0,1.0,1.0,126565.94,0.0
+6196,15656822.0,Maclean,678.0,France,Male,19.0,5.0,0.0,2.0,0.0,1.0,91757.66,0.0
+6197,15738166.0,Genovese,763.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,177844.06,0.0
+6198,15752578.0,Ukaegbunam,691.0,Spain,Male,22.0,3.0,105303.73,2.0,1.0,0.0,55868.91,0.0
+6199,15697045.0,Nnaife,739.0,France,Female,36.0,7.0,0.0,1.0,0.0,0.0,97908.01,0.0
+6200,15737571.0,Onwumelu,725.0,Germany,Female,24.0,5.0,107959.39,1.0,0.0,1.0,113597.64,0.0
+6201,15642785.0,Clements,584.0,France,Female,33.0,4.0,125724.95,1.0,1.0,1.0,143980.29,0.0
+6202,15691543.0,Ch'ien,678.0,France,Female,30.0,8.0,0.0,2.0,0.0,0.0,48606.71,0.0
+6203,15635177.0,Calabrese,535.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,878.87,0.0
+6204,15672955.0,Li,652.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,145789.71,0.0
+6205,15722532.0,Chia,718.0,Germany,Male,41.0,5.0,153548.11,2.0,0.0,1.0,126551.65,0.0
+6206,15693637.0,Onyemere,639.0,Spain,Male,35.0,10.0,0.0,2.0,1.0,0.0,138526.96,0.0
+6207,15682048.0,Tuan,655.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,155700.22,0.0
+6208,15731989.0,Chiu,733.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,59195.05,0.0
+6209,15711687.0,Iadanza,605.0,Spain,Male,55.0,3.0,179960.2,1.0,1.0,1.0,176913.42,1.0
+6210,15649418.0,Onwuamaeze,663.0,Germany,Female,27.0,7.0,61687.33,1.0,1.0,1.0,72402.0,0.0
+6211,15778049.0,Aksenov,594.0,Germany,Male,33.0,2.0,132658.64,1.0,1.0,1.0,97939.4,0.0
+6212,15672754.0,Jamieson,742.0,France,Female,51.0,3.0,0.0,2.0,1.0,1.0,182531.74,0.0
+6213,15572792.0,Lucchesi,592.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,66728.81,0.0
+6214,15591915.0,Echezonachukwu,581.0,France,Female,46.0,2.0,0.0,1.0,1.0,1.0,131660.31,0.0
+6215,15575024.0,Galkin,652.0,France,Female,57.0,3.0,142319.03,3.0,1.0,0.0,115818.51,1.0
+6216,15729362.0,Sun,733.0,France,Female,26.0,7.0,0.0,2.0,0.0,1.0,106040.97,0.0
+6217,15593794.0,P'an,547.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,140947.05,0.0
+6218,15680430.0,Kao,633.0,Spain,Male,48.0,6.0,0.0,2.0,1.0,1.0,61977.82,0.0
+6219,15786463.0,Nnaife,633.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,177065.24,0.0
+6220,15599074.0,Nnanna,695.0,Spain,Male,45.0,6.0,0.0,2.0,1.0,1.0,182355.21,0.0
+6221,15708713.0,Yobanna,753.0,Germany,Male,59.0,7.0,127738.75,2.0,1.0,0.0,140941.47,1.0
+6222,15809302.0,Hs?,751.0,France,Female,35.0,7.0,0.0,2.0,0.0,1.0,131886.46,0.0
+6223,15659949.0,Yobachi,683.0,France,Female,47.0,2.0,0.0,1.0,1.0,0.0,151902.78,1.0
+6224,15576680.0,Ch'ang,629.0,France,Male,42.0,2.0,0.0,2.0,0.0,1.0,181605.85,1.0
+6225,15689953.0,Hsiung,629.0,France,Male,31.0,3.0,131698.57,1.0,0.0,1.0,91510.73,0.0
+6226,15594878.0,Chukwujamuike,712.0,Germany,Male,46.0,4.0,137998.2,1.0,1.0,0.0,109184.24,1.0
+6227,15594576.0,Hsueh,709.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,35608.88,0.0
+6228,15766163.0,Hsia,755.0,France,Female,23.0,7.0,80542.81,1.0,1.0,0.0,133051.64,0.0
+6229,15793894.0,Li,583.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,109742.55,0.0
+6230,15702601.0,Nnaife,638.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,187915.7,0.0
+6231,15584090.0,Nnachetam,794.0,Spain,Male,31.0,10.0,0.0,2.0,1.0,0.0,185830.72,0.0
+6232,15613786.0,Uspensky,644.0,Germany,Male,35.0,9.0,147531.82,1.0,1.0,0.0,78700.03,0.0
+6233,15624781.0,Chiang,727.0,France,Female,40.0,2.0,0.0,2.0,0.0,1.0,96660.76,0.0
+6234,15739168.0,Ponomaryov,686.0,France,Male,40.0,3.0,0.0,1.0,0.0,0.0,160941.78,0.0
+6235,15640865.0,Ch'iu,696.0,Germany,Male,38.0,3.0,161889.79,2.0,1.0,1.0,138350.75,0.0
+6236,15778975.0,Palerma,850.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,170819.01,0.0
+6237,15567834.0,Ts'ui,479.0,Spain,Male,42.0,3.0,0.0,2.0,1.0,1.0,113639.64,0.0
+6238,15773949.0,Ch'eng,579.0,France,Female,52.0,9.0,0.0,2.0,1.0,0.0,123587.83,1.0
+6239,15664523.0,Chia,619.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,1.0,108577.97,0.0
+6240,15793049.0,Chu,637.0,France,Female,32.0,2.0,0.0,2.0,0.0,1.0,121542.33,0.0
+6241,15793046.0,Tretiakova,704.0,France,Male,34.0,3.0,142872.36,1.0,0.0,1.0,129891.66,0.0
+6242,15616152.0,Fiorentino,494.0,Germany,Male,23.0,2.0,94722.88,2.0,1.0,1.0,140941.47,0.0
+6243,15672447.0,Dore,696.0,Germany,Male,26.0,7.0,129735.3,1.0,1.0,0.0,126336.72,0.0
+6244,15668958.0,Walker,492.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,59893.8,0.0
+6245,15660871.0,Chiu,725.0,Germany,Female,40.0,5.0,115074.02,1.0,1.0,0.0,138328.24,1.0
+6246,15745307.0,Ozioma,708.0,France,Male,37.0,0.0,0.0,2.0,1.0,0.0,180622.02,0.0
+6247,15788126.0,Maclean,679.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,1.0,96444.88,0.0
+6248,15593501.0,McGregor,773.0,Germany,Female,52.0,2.0,153590.31,1.0,1.0,0.0,70783.61,1.0
+6249,15787258.0,Ngozichukwuka,549.0,France,Male,44.0,7.0,0.0,1.0,1.0,0.0,103557.85,1.0
+6250,15703394.0,Toscani,745.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,937.15,0.0
+6251,15651581.0,Udinese,629.0,France,Female,49.0,2.0,0.0,1.0,1.0,0.0,153786.46,1.0
+6252,15644720.0,Groom,576.0,Spain,Male,38.0,6.0,0.0,2.0,0.0,1.0,11514.39,0.0
+6253,15687557.0,Mazzi,615.0,France,Female,37.0,9.0,100433.8,1.0,1.0,0.0,134441.6,1.0
+6254,15599090.0,Chukwumaobim,573.0,France,Female,52.0,1.0,163549.63,1.0,1.0,1.0,86622.47,0.0
+6255,15651075.0,Chukwuemeka,619.0,France,Male,45.0,4.0,0.0,2.0,0.0,1.0,186612.47,0.0
+6256,15604792.0,Shao,753.0,France,Male,29.0,5.0,0.0,2.0,0.0,0.0,89566.64,0.0
+6257,15626144.0,Yeh,637.0,France,Female,47.0,8.0,128876.71,1.0,1.0,1.0,94440.45,0.0
+6258,15597909.0,Ch'en,648.0,France,Female,32.0,5.0,0.0,1.0,1.0,0.0,169505.83,0.0
+6259,15596979.0,Pisano,516.0,Germany,Female,29.0,4.0,123599.49,1.0,1.0,0.0,58467.08,0.0
+6260,15789924.0,Chukwudi,695.0,Germany,Male,23.0,2.0,116318.23,1.0,0.0,1.0,78670.52,0.0
+6261,15806901.0,Nwachinemelu,669.0,France,Female,42.0,8.0,87070.23,1.0,1.0,0.0,176720.64,0.0
+6262,15572626.0,Chin,711.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,162697.93,0.0
+6263,15771855.0,Chukwujamuike,619.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,106024.02,0.0
+6264,15574071.0,Sun,647.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,88958.49,0.0
+6265,15580462.0,Ch'ang,754.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,142517.31,0.0
+6266,15723886.0,Ch'ang,774.0,France,Female,46.0,4.0,0.0,1.0,1.0,0.0,66469.2,1.0
+6267,15608988.0,Nnanna,651.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,44904.26,0.0
+6268,15677243.0,Cattaneo,716.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,79919.13,0.0
+6269,15576680.0,Chiang,696.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,113098.58,0.0
+6270,15772383.0,Ugorji,807.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,130866.95,0.0
+6271,15570194.0,Macleod,637.0,France,Female,41.0,2.0,0.0,2.0,0.0,0.0,131807.14,0.0
+6272,15782467.0,Toth,681.0,France,Female,34.0,6.0,0.0,2.0,1.0,0.0,157937.35,0.0
+6273,15688876.0,Onyemauchechukwu,727.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,187776.49,0.0
+6274,15777772.0,Cremonesi,607.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,100127.71,0.0
+6275,15771742.0,Y?,707.0,Spain,Female,47.0,1.0,0.0,1.0,1.0,1.0,130189.53,1.0
+6276,15687431.0,Ch'ang,745.0,Spain,Female,34.0,7.0,132623.6,1.0,1.0,0.0,167522.19,0.0
+6277,15809906.0,Palerma,711.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,116581.1,0.0
+6278,15573741.0,Liao,613.0,Germany,Female,46.0,8.0,81710.46,1.0,1.0,0.0,66732.63,1.0
+6279,15683367.0,Chigbogu,544.0,Spain,Female,44.0,4.0,0.0,2.0,1.0,0.0,147090.9,0.0
+6280,15757537.0,Uvarova,682.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,98896.06,0.0
+6281,15701074.0,Shih,472.0,France,Male,56.0,8.0,0.0,1.0,1.0,0.0,51362.82,1.0
+6282,15583392.0,Kuo,753.0,Germany,Female,45.0,10.0,69974.66,2.0,1.0,1.0,176843.53,1.0
+6283,15644878.0,Ch'iu,714.0,France,Female,39.0,6.0,0.0,2.0,0.0,1.0,69414.13,0.0
+6284,15592078.0,Hs?,778.0,Spain,Female,47.0,6.0,104449.7,1.0,1.0,1.0,173486.39,1.0
+6285,15614103.0,Toscani,616.0,Spain,Female,24.0,3.0,0.0,1.0,0.0,1.0,145593.85,0.0
+6286,15624595.0,Fanucci,701.0,France,Male,38.0,7.0,122216.9,1.0,0.0,0.0,123663.63,0.0
+6287,15677087.0,Onwuamaeze,684.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,40807.27,0.0
+6288,15778515.0,Hsiao,627.0,France,Male,37.0,3.0,141434.04,1.0,0.0,0.0,171344.09,0.0
+6289,15671917.0,Goliwe,670.0,France,Male,32.0,2.0,125860.2,1.0,0.0,1.0,163558.16,0.0
+6290,15680430.0,Iloerika,543.0,France,Male,30.0,9.0,125552.96,1.0,1.0,0.0,57862.88,0.0
+6291,15762928.0,Y?,649.0,Spain,Male,43.0,6.0,0.0,1.0,1.0,1.0,26342.33,1.0
+6292,15658052.0,Brown,756.0,Spain,Female,28.0,8.0,145553.07,1.0,1.0,0.0,189683.7,0.0
+6293,15708904.0,Nwachukwu,677.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,115441.66,0.0
+6294,15782758.0,Mazzanti,695.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,96660.76,0.0
+6295,15793311.0,Ku,651.0,France,Female,50.0,7.0,0.0,2.0,1.0,1.0,36342.79,0.0
+6296,15772995.0,Nnamdi,726.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,157577.18,0.0
+6297,15587712.0,Cremonesi,683.0,Spain,Male,30.0,5.0,0.0,2.0,1.0,0.0,119647.6,0.0
+6298,15690647.0,Trevisano,706.0,Germany,Female,39.0,3.0,103499.08,2.0,1.0,0.0,60906.12,0.0
+6299,15712807.0,Chialuka,553.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,0.0,133692.88,0.0
+6300,15644765.0,Udinese,766.0,Spain,Male,41.0,0.0,0.0,2.0,1.0,0.0,126997.53,1.0
+6301,15685805.0,Baryshnikov,613.0,France,Female,28.0,4.0,0.0,2.0,1.0,0.0,141069.88,0.0
+6302,15569571.0,Chikelu,556.0,France,Male,41.0,3.0,133434.57,1.0,0.0,0.0,113079.19,0.0
+6303,15642816.0,Obidimkpa,709.0,France,Female,35.0,7.0,0.0,1.0,0.0,0.0,111839.71,1.0
+6304,15734005.0,Fennescey,685.0,France,Male,36.0,0.0,0.0,2.0,0.0,0.0,101617.64,0.0
+6305,15679611.0,Mazzanti,706.0,Germany,Female,39.0,1.0,176913.52,1.0,0.0,0.0,180934.48,0.0
+6306,15752488.0,Tuan,687.0,France,Female,43.0,4.0,0.0,2.0,0.0,1.0,88852.47,0.0
+6307,15578908.0,Yudin,706.0,Spain,Female,36.0,7.0,0.0,2.0,0.0,1.0,66463.62,0.0
+6308,15805627.0,Chiang,615.0,France,Male,41.0,4.0,0.0,2.0,1.0,1.0,132801.57,0.0
+6309,15776936.0,Chukwuemeka,646.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,152882.95,0.0
+6310,15657566.0,Ts'ui,695.0,France,Male,27.0,8.0,0.0,2.0,1.0,0.0,167984.72,0.0
+6311,15758813.0,Fomin,730.0,Spain,Male,38.0,8.0,112045.67,2.0,1.0,1.0,83093.25,0.0
+6312,15588517.0,Lu,639.0,France,Male,30.0,9.0,0.0,1.0,1.0,1.0,123437.05,0.0
+6313,15777553.0,Young,663.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,158043.11,0.0
+6314,15627830.0,Chidozie,624.0,Spain,Male,54.0,0.0,138577.34,1.0,1.0,0.0,166038.13,1.0
+6315,15781802.0,Tsao,722.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,174729.3,0.0
+6316,15792388.0,Tuan,694.0,France,Female,28.0,4.0,0.0,2.0,1.0,0.0,79377.45,0.0
+6317,15799479.0,Chukwumaobim,674.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,0.0,124874.23,0.0
+6318,15699550.0,T'ang,599.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,94670.77,0.0
+6319,15628145.0,Nnachetam,597.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,158071.69,0.0
+6320,15613141.0,K?,714.0,Spain,Male,36.0,4.0,0.0,1.0,1.0,1.0,160976.85,0.0
+6321,15764314.0,T'ien,589.0,France,Female,32.0,4.0,0.0,2.0,1.0,1.0,141039.51,0.0
+6322,15797710.0,Trevisano,821.0,France,Male,38.0,4.0,0.0,2.0,0.0,0.0,79577.38,0.0
+6323,15784099.0,Ts'ai,753.0,France,Male,21.0,1.0,141441.75,1.0,1.0,0.0,137537.32,0.0
+6324,15586699.0,Dickson,590.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,149550.95,0.0
+6325,15785975.0,Tien,676.0,Spain,Male,35.0,6.0,0.0,1.0,1.0,0.0,126494.82,0.0
+6326,15671778.0,Palermo,674.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,63795.8,0.0
+6327,15792250.0,Chiang,652.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,1.0,131173.9,0.0
+6328,15652218.0,Kosisochukwu,558.0,Spain,Female,47.0,8.0,0.0,2.0,0.0,1.0,91086.73,0.0
+6329,15682757.0,Uspensky,669.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,136869.31,0.0
+6330,15592412.0,Li Fonti,684.0,Germany,Female,38.0,4.0,102874.24,1.0,1.0,1.0,149614.81,0.0
+6331,15643048.0,Kao,744.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,83700.66,0.0
+6332,15750769.0,Onyekachukwu,734.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,0.0,28257.89,0.0
+6333,15662488.0,Ts'ui,586.0,Spain,Female,37.0,6.0,135120.72,1.0,1.0,1.0,111243.09,0.0
+6334,15765861.0,Nucci,687.0,Germany,Male,46.0,2.0,92340.61,1.0,0.0,0.0,131772.51,1.0
+6335,15676526.0,Ch'iu,597.0,France,Female,41.0,9.0,0.0,2.0,0.0,0.0,143439.38,1.0
+6336,15646521.0,Onyemauchechukwu,589.0,Germany,Female,45.0,7.0,142511.26,1.0,1.0,0.0,67789.99,1.0
+6337,15590094.0,Hsieh,663.0,France,Female,44.0,4.0,0.0,1.0,1.0,1.0,141872.05,1.0
+6338,15695499.0,Ives,634.0,France,Female,42.0,5.0,0.0,2.0,1.0,1.0,174918.46,1.0
+6339,15702912.0,Ndubueze,649.0,Germany,Female,30.0,2.0,131629.17,1.0,1.0,0.0,58883.91,0.0
+6340,15616555.0,Ni,683.0,France,Female,48.0,5.0,0.0,1.0,1.0,0.0,87412.24,1.0
+6341,15719276.0,P'eng,684.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,126761.2,0.0
+6342,15791323.0,Hsieh,571.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,181605.85,0.0
+6343,15595737.0,Chiu,596.0,France,Male,35.0,7.0,0.0,2.0,0.0,0.0,60427.3,0.0
+6344,15737509.0,Lucchesi,595.0,Spain,Female,29.0,5.0,0.0,2.0,1.0,1.0,124596.63,0.0
+6345,15690163.0,Chinonyelum,729.0,France,Female,44.0,5.0,0.0,1.0,1.0,0.0,143960.72,1.0
+6346,15676937.0,Walker,716.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,143773.07,0.0
+6347,15616152.0,Hao,628.0,France,Male,40.0,2.0,0.0,2.0,0.0,0.0,180427.24,0.0
+6348,15655952.0,Hs?,652.0,Germany,Female,36.0,4.0,103688.19,1.0,0.0,1.0,169921.11,1.0
+6349,15638487.0,Ugoji,613.0,France,Male,39.0,8.0,103060.63,1.0,1.0,1.0,100433.83,0.0
+6350,15690540.0,Shih,628.0,France,Female,42.0,10.0,0.0,1.0,1.0,0.0,145562.4,1.0
+6351,15613787.0,Olisaemeka,659.0,Spain,Female,26.0,9.0,0.0,1.0,0.0,1.0,56793.08,1.0
+6352,15692761.0,Shih,577.0,France,Female,42.0,6.0,0.0,1.0,1.0,0.0,120830.73,1.0
+6353,15725028.0,K'ung,641.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,168219.75,0.0
+6354,15778515.0,Onwughara,637.0,France,Male,42.0,3.0,0.0,2.0,0.0,0.0,69227.42,0.0
+6355,15683363.0,Mazzanti,544.0,France,Female,56.0,2.0,135962.9,1.0,1.0,0.0,124749.09,1.0
+6356,15754072.0,Tuan,567.0,Spain,Female,37.0,5.0,0.0,2.0,1.0,0.0,127068.91,0.0
+6357,15698927.0,Hsiao,598.0,France,Female,43.0,5.0,0.0,1.0,1.0,1.0,56585.2,1.0
+6358,15769246.0,Efremova,538.0,Germany,Female,52.0,2.0,141787.31,1.0,0.0,1.0,177357.17,1.0
+6359,15631512.0,Dickson,699.0,Spain,Female,41.0,8.0,0.0,1.0,1.0,1.0,80130.54,0.0
+6360,15653893.0,Nwokike,686.0,Germany,Male,34.0,1.0,111093.01,1.0,0.0,1.0,120250.58,0.0
+6361,15685500.0,Y?,713.0,France,Male,28.0,8.0,125561.97,1.0,1.0,1.0,110454.46,0.0
+6362,15671630.0,Chigolum,685.0,Spain,Female,41.0,2.0,0.0,1.0,1.0,0.0,177519.39,0.0
+6363,15699507.0,Chiu,619.0,Spain,Female,30.0,7.0,113680.53,1.0,1.0,1.0,135077.01,0.0
+6364,15632263.0,Calabrese,669.0,Spain,Female,42.0,6.0,130878.75,1.0,1.0,1.0,180622.05,0.0
+6365,15648881.0,Chuang,583.0,France,Male,25.0,4.0,0.0,2.0,1.0,0.0,116747.51,0.0
+6366,15686963.0,Naylor,663.0,France,Female,22.0,1.0,0.0,2.0,1.0,1.0,159823.16,0.0
+6367,15795872.0,Hao,683.0,Germany,Male,38.0,4.0,101979.16,2.0,1.0,0.0,106981.03,0.0
+6368,15663964.0,Ritchie,685.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,179421.84,0.0
+6369,15596972.0,Chukwumaobim,652.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,122964.18,0.0
+6370,15740175.0,Pirozzi,793.0,France,Male,41.0,10.0,0.0,1.0,1.0,0.0,82796.29,0.0
+6371,15615012.0,T'an,651.0,Spain,Female,29.0,6.0,110941.93,1.0,1.0,1.0,62746.55,0.0
+6372,15597930.0,De Bernales,709.0,Spain,Male,37.0,2.0,89175.06,1.0,1.0,1.0,5962.3,0.0
+6373,15661821.0,Chukwukadibia,708.0,France,Female,39.0,1.0,0.0,3.0,1.0,1.0,166370.81,1.0
+6374,15795511.0,Chikwado,564.0,Spain,Male,27.0,8.0,115285.85,1.0,1.0,0.0,179540.73,0.0
+6375,15742668.0,Chinweuba,716.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,65316.5,0.0
+6376,15742272.0,Mbanefo,543.0,France,Female,35.0,5.0,0.0,2.0,0.0,0.0,111471.55,0.0
+6377,15720706.0,Hao,678.0,France,Male,49.0,8.0,0.0,2.0,1.0,1.0,118711.75,0.0
+6378,15616507.0,Milanesi,539.0,Germany,Female,43.0,9.0,110022.74,1.0,1.0,0.0,161642.02,1.0
+6379,15703588.0,Ch'ien,705.0,France,Male,29.0,6.0,0.0,1.0,0.0,1.0,123954.15,0.0
+6380,15753955.0,Lockington,615.0,Germany,Male,42.0,2.0,105858.68,1.0,0.0,1.0,127939.18,1.0
+6381,15757310.0,Mays,611.0,Germany,Male,39.0,0.0,106901.91,2.0,1.0,1.0,71159.84,0.0
+6382,15592751.0,Nebechukwu,674.0,France,Female,40.0,8.0,0.0,1.0,1.0,0.0,51672.08,1.0
+6383,15757535.0,Chukwuemeka,703.0,Spain,Female,31.0,4.0,0.0,2.0,1.0,0.0,175324.74,0.0
+6384,15652320.0,Dyer,571.0,France,Male,21.0,5.0,0.0,2.0,1.0,1.0,25571.37,0.0
+6385,15646091.0,Laidley,569.0,France,Female,41.0,5.0,0.0,2.0,0.0,1.0,69320.97,0.0
+6386,15792453.0,Toscani,545.0,Germany,Male,39.0,3.0,129900.28,1.0,0.0,1.0,163641.53,0.0
+6387,15773973.0,Chidimma,603.0,Germany,Female,33.0,6.0,99774.5,2.0,1.0,1.0,63795.8,0.0
+6388,15804009.0,H?an,753.0,France,Male,30.0,6.0,0.0,2.0,1.0,0.0,185827.74,0.0
+6389,15695872.0,Hsing,709.0,France,Male,40.0,6.0,0.0,2.0,1.0,1.0,189657.18,0.0
+6390,15631406.0,Maclean,616.0,Spain,Male,27.0,4.0,101070.69,1.0,0.0,0.0,173486.39,0.0
+6391,15681446.0,Chidiebere,478.0,France,Male,34.0,0.0,0.0,1.0,1.0,1.0,86460.28,1.0
+6392,15701121.0,P'an,686.0,Spain,Male,36.0,3.0,0.0,2.0,0.0,1.0,163667.56,0.0
+6393,15768295.0,Ts'ui,687.0,Germany,Male,44.0,3.0,104533.15,1.0,1.0,0.0,131807.14,1.0
+6394,15741643.0,Fanucci,624.0,Germany,Male,37.0,10.0,137523.02,1.0,1.0,0.0,100447.53,0.0
+6395,15674929.0,Chinwenma,605.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,116471.43,0.0
+6396,15783026.0,P'eng,684.0,France,Male,39.0,6.0,0.0,2.0,0.0,0.0,74858.3,0.0
+6397,15770039.0,Ch'ien,749.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,93360.35,0.0
+6398,15774382.0,Chiemezie,633.0,Germany,Male,42.0,1.0,92436.86,2.0,1.0,1.0,88592.8,0.0
+6399,15807563.0,Hsiung,753.0,France,Male,49.0,7.0,0.0,1.0,1.0,0.0,77867.23,1.0
+6400,15702014.0,Lo,776.0,France,Female,22.0,4.0,90560.3,1.0,1.0,1.0,54477.51,0.0
+6401,15713374.0,Chiu,595.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,125244.18,0.0
+6402,15635364.0,Lu,607.0,Spain,Female,46.0,1.0,128306.7,1.0,1.0,1.0,60972.84,1.0
+6403,15682533.0,Lai,667.0,France,Female,25.0,2.0,0.0,2.0,1.0,1.0,107959.01,0.0
+6404,15720371.0,Fanucci,592.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,112239.03,0.0
+6405,15753362.0,Pagnotto,732.0,France,Male,33.0,10.0,0.0,2.0,1.0,0.0,187987.01,0.0
+6406,15702541.0,Ts'ai,667.0,France,Male,36.0,8.0,167557.12,1.0,1.0,1.0,146636.44,0.0
+6407,15664150.0,Lo,583.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,170338.35,0.0
+6408,15662294.0,Trevisano,585.0,Germany,Female,46.0,10.0,126606.63,2.0,1.0,1.0,130504.49,0.0
+6409,15768474.0,Ugochukwu,583.0,France,Male,28.0,2.0,0.0,2.0,1.0,1.0,157921.25,0.0
+6410,15615177.0,Mazzanti,589.0,France,Male,39.0,5.0,0.0,2.0,0.0,1.0,55400.17,0.0
+6411,15762882.0,Tuan,700.0,Germany,Female,47.0,2.0,87070.23,2.0,1.0,0.0,121866.87,0.0
+6412,15736397.0,Zito,582.0,France,Male,40.0,7.0,0.0,1.0,1.0,0.0,85547.33,1.0
+6413,15802758.0,Brient,569.0,Germany,Female,32.0,1.0,128262.72,1.0,1.0,0.0,137959.0,0.0
+6414,15772995.0,Fomin,687.0,France,Male,28.0,9.0,0.0,1.0,1.0,1.0,184399.77,1.0
+6415,15804586.0,Ch'ien,714.0,France,Male,52.0,4.0,0.0,2.0,1.0,1.0,72873.33,0.0
+6416,15696564.0,Burt,711.0,France,Male,44.0,1.0,0.0,1.0,1.0,1.0,64872.55,1.0
+6417,15786680.0,Okwuoma,685.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,57549.65,0.0
+6418,15777784.0,Hightower,748.0,France,Male,30.0,5.0,84445.68,2.0,0.0,1.0,180862.26,0.0
+6419,15639217.0,Y?an,516.0,France,Male,25.0,8.0,0.0,2.0,1.0,0.0,166682.57,0.0
+6420,15727281.0,Ugochukwutubelum,678.0,Spain,Male,34.0,7.0,0.0,1.0,0.0,1.0,132798.78,0.0
+6421,15757306.0,Okeke,681.0,France,Female,34.0,1.0,127864.4,1.0,1.0,1.0,147545.65,0.0
+6422,15648067.0,Shih,850.0,France,Male,26.0,2.0,0.0,2.0,1.0,0.0,170950.58,0.0
+6423,15682533.0,Farrar,682.0,France,Female,25.0,7.0,0.0,2.0,1.0,1.0,77326.78,0.0
+6424,15585255.0,Yermolayev,613.0,France,Male,48.0,8.0,0.0,1.0,1.0,1.0,157959.03,1.0
+6425,15711687.0,T'ien,635.0,Spain,Female,25.0,7.0,0.0,2.0,0.0,1.0,115676.61,0.0
+6426,15690695.0,Nnonso,632.0,France,Female,37.0,5.0,135907.65,1.0,1.0,1.0,73069.94,0.0
+6427,15780961.0,Maclean,683.0,France,Female,31.0,2.0,0.0,2.0,0.0,1.0,65765.22,0.0
+6428,15749482.0,P'eng,586.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,90162.35,0.0
+6429,15665062.0,Tien,694.0,France,Female,44.0,8.0,0.0,2.0,1.0,1.0,79633.38,0.0
+6430,15623357.0,Y?an,637.0,France,Male,29.0,1.0,121629.22,1.0,1.0,0.0,134732.99,0.0
+6431,15732265.0,Macleod,633.0,France,Female,65.0,1.0,131283.11,3.0,1.0,0.0,175072.47,1.0
+6432,15611331.0,Chiang,596.0,Spain,Female,36.0,6.0,126728.16,1.0,1.0,1.0,140179.68,0.0
+6433,15615091.0,Ritchie,665.0,Spain,Female,31.0,6.0,0.0,2.0,0.0,0.0,164769.13,0.0
+6434,15737047.0,Nwachinemelu,709.0,France,Male,48.0,3.0,0.0,2.0,1.0,0.0,163830.64,0.0
+6435,15741554.0,T'ang,637.0,France,Male,35.0,10.0,0.0,2.0,1.0,1.0,46824.08,0.0
+6436,15598883.0,Chidiebere,636.0,Germany,Male,47.0,2.0,116360.07,2.0,1.0,0.0,57541.08,0.0
+6437,15704586.0,Lo,681.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,72350.17,0.0
+6438,15626684.0,Chukwumaobim,624.0,Germany,Female,38.0,8.0,141349.43,2.0,1.0,0.0,184954.11,0.0
+6439,15737354.0,Chiganu,705.0,France,Male,27.0,1.0,0.0,1.0,1.0,0.0,96444.88,1.0
+6440,15705034.0,Zetticci,671.0,Germany,Female,28.0,7.0,97495.8,1.0,0.0,1.0,136902.01,0.0
+6441,15674928.0,Pirozzi,676.0,France,Female,37.0,9.0,0.0,2.0,1.0,1.0,143049.72,0.0
+6442,15700511.0,Onwughara,678.0,France,Male,37.0,6.0,127862.93,1.0,1.0,0.0,70783.61,0.0
+6443,15717185.0,Milano,678.0,France,Male,35.0,5.0,96888.39,1.0,1.0,0.0,166520.96,0.0
+6444,15803942.0,Ch'ang,653.0,France,Male,38.0,0.0,0.0,2.0,1.0,1.0,163666.85,0.0
+6445,15766017.0,Onyekaozulu,695.0,France,Male,47.0,1.0,0.0,2.0,1.0,1.0,172576.43,0.0
+6446,15654562.0,Ch'eng,554.0,Germany,Male,48.0,1.0,151922.94,1.0,0.0,0.0,66731.98,1.0
+6447,15751774.0,Hsia,707.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,116581.1,0.0
+6448,15769902.0,Lo Duca,679.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,51919.04,0.0
+6449,15731519.0,Tien,579.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,121840.2,0.0
+6450,15673591.0,P'eng,678.0,France,Male,56.0,1.0,0.0,1.0,1.0,0.0,7063.6,1.0
+6451,15773869.0,Obidimkpa,583.0,Spain,Female,28.0,9.0,0.0,2.0,1.0,0.0,139065.94,0.0
+6452,15725036.0,Ts'ui,704.0,France,Female,38.0,5.0,0.0,2.0,0.0,0.0,66463.62,0.0
+6453,15773973.0,Young,485.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,142566.75,0.0
+6454,15576723.0,Macfarlan,706.0,Germany,Female,45.0,3.0,123544.0,1.0,1.0,1.0,165562.84,1.0
+6455,15743817.0,Smith,667.0,France,Male,44.0,7.0,0.0,2.0,1.0,0.0,171519.06,1.0
+6456,15794868.0,Scott,665.0,France,Male,37.0,3.0,86616.35,1.0,0.0,1.0,81252.85,0.0
+6457,15783883.0,Ch'ien,687.0,France,Male,39.0,2.0,0.0,2.0,0.0,0.0,28690.9,0.0
+6458,15656627.0,Trevisano,446.0,Germany,Female,47.0,6.0,98775.23,1.0,1.0,1.0,176692.87,0.0
+6459,15732621.0,Smith,709.0,France,Female,40.0,7.0,0.0,2.0,0.0,0.0,131363.57,0.0
+6460,15795511.0,Kao,716.0,Germany,Male,23.0,5.0,138333.03,2.0,1.0,0.0,57553.02,0.0
+6461,15641411.0,Teng,682.0,Germany,Female,48.0,0.0,79731.91,1.0,1.0,0.0,143681.2,1.0
+6462,15602487.0,Pagnotto,743.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,1.0,447.73,0.0
+6463,15706021.0,Miah,665.0,France,Female,32.0,5.0,0.0,2.0,0.0,0.0,50906.65,0.0
+6464,15778368.0,Ch'en,742.0,Spain,Female,25.0,9.0,0.0,2.0,1.0,0.0,170563.73,0.0
+6465,15767954.0,Lai,614.0,France,Female,39.0,9.0,135923.39,1.0,1.0,1.0,158750.16,0.0
+6466,15750458.0,Tseng,591.0,Germany,Male,30.0,9.0,97544.29,1.0,1.0,1.0,123613.81,0.0
+6467,15595831.0,Yeh,637.0,France,Female,50.0,4.0,140595.45,1.0,1.0,0.0,70927.11,1.0
+6468,15674541.0,Chiemenam,625.0,France,Male,33.0,4.0,132934.89,1.0,1.0,0.0,162267.91,0.0
+6469,15685500.0,Chukwuebuka,660.0,France,Male,53.0,6.0,94443.77,1.0,1.0,1.0,26589.92,1.0
+6470,15660211.0,Ch'ien,639.0,France,Male,47.0,7.0,125397.21,1.0,1.0,1.0,87949.67,0.0
+6471,15801934.0,Chiu,730.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,88698.83,0.0
+6472,15720671.0,Onyekachi,581.0,France,Male,43.0,0.0,0.0,2.0,1.0,0.0,123882.73,1.0
+6473,15751208.0,Gentry,646.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,1.0,130461.09,0.0
+6474,15714087.0,O'Donnell,596.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,0.0,139971.01,0.0
+6475,1581102.0,Chidumaga,602.0,France,Male,63.0,5.0,92466.21,1.0,1.0,0.0,176913.42,1.0
+6476,15616380.0,Y?an,678.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,0.0,105599.65,0.0
+6477,15602572.0,Hsing,630.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,1.0,108008.05,0.0
+6478,15810878.0,Uspenskaya,721.0,France,Male,25.0,2.0,127763.24,1.0,1.0,0.0,184800.27,0.0
+6479,15571917.0,Baryshnikov,650.0,Germany,Female,32.0,2.0,155413.08,2.0,1.0,1.0,94442.77,0.0
+6480,15692931.0,L?,597.0,France,Female,36.0,9.0,185734.75,1.0,1.0,0.0,165562.84,1.0
+6481,15654096.0,Maughan,683.0,France,Male,29.0,5.0,0.0,1.0,0.0,1.0,149558.83,0.0
+6482,15702416.0,Nwankwo,648.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,74870.99,0.0
+6483,15797710.0,Onyekachukwu,545.0,France,Male,31.0,8.0,128743.8,1.0,1.0,0.0,139048.0,0.0
+6484,15581548.0,Onuoha,593.0,France,Male,40.0,0.0,113226.47,1.0,1.0,1.0,53477.76,0.0
+6485,15690925.0,Wang,723.0,Spain,Female,42.0,4.0,88915.37,1.0,1.0,0.0,132439.31,0.0
+6486,15774133.0,Lettiere,564.0,Spain,Male,23.0,1.0,152367.21,1.0,1.0,1.0,86950.72,0.0
+6487,15735078.0,Uwaezuoke,602.0,Germany,Female,41.0,2.0,91592.06,2.0,1.0,1.0,94951.27,0.0
+6488,15802741.0,Yobachukwu,597.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,162699.7,0.0
+6489,15780128.0,Ch'in,765.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,1.0,182692.8,0.0
+6490,15794345.0,Pirozzi,714.0,Germany,Male,46.0,1.0,115764.32,4.0,1.0,1.0,72945.32,1.0
+6491,15656937.0,Kao,615.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,111241.37,0.0
+6492,15613132.0,T'ien,774.0,France,Male,36.0,5.0,92961.61,1.0,0.0,0.0,78770.84,0.0
+6493,15702571.0,Chiedozie,709.0,Spain,Male,34.0,8.0,0.0,2.0,0.0,1.0,91045.46,0.0
+6494,15673529.0,Scott,592.0,Germany,Male,32.0,2.0,171325.98,1.0,1.0,0.0,65769.3,0.0
+6495,15775294.0,L?,696.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,0.0,188007.99,0.0
+6496,15785899.0,Onyemauchechukwu,645.0,Spain,Female,26.0,2.0,98640.77,1.0,1.0,1.0,125870.5,0.0
+6497,15582940.0,Ejimofor,850.0,France,Male,35.0,7.0,0.0,1.0,1.0,0.0,161574.19,0.0
+6498,15661249.0,Lo Duca,682.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,118862.08,0.0
+6499,15697028.0,Macleod,711.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,125518.32,0.0
+6500,15702464.0,Nwankwo,587.0,Spain,Female,34.0,5.0,137852.96,1.0,1.0,1.0,109843.16,1.0
+6501,15685320.0,Mazzanti,717.0,France,Male,45.0,2.0,0.0,2.0,1.0,1.0,93777.61,0.0
+6502,15602497.0,Chuter,688.0,Germany,Male,37.0,8.0,83600.24,2.0,1.0,0.0,166677.54,0.0
+6503,15611058.0,Pinto,756.0,France,Female,43.0,6.0,110765.68,1.0,0.0,0.0,77864.41,1.0
+6504,15729018.0,Tien,707.0,Germany,Female,27.0,3.0,118079.47,2.0,1.0,1.0,159418.1,0.0
+6505,15617301.0,Ch'eng,731.0,Spain,Female,37.0,7.0,0.0,1.0,0.0,1.0,97629.31,0.0
+6506,15642098.0,Martin,590.0,Germany,Female,44.0,2.0,92887.06,1.0,1.0,1.0,68600.66,1.0
+6507,15774551.0,Bancks,683.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,157891.86,0.0
+6508,15703382.0,Scott,653.0,Spain,Male,29.0,5.0,0.0,1.0,0.0,1.0,171463.83,0.0
+6509,15721779.0,Kao,655.0,France,Male,64.0,5.0,0.0,2.0,1.0,1.0,62903.44,0.0
+6510,15684461.0,Tsou,623.0,France,Male,36.0,1.0,0.0,1.0,1.0,1.0,109208.23,0.0
+6511,15775231.0,Ch'ien,439.0,France,Female,26.0,5.0,0.0,2.0,1.0,1.0,44965.44,0.0
+6512,15745399.0,Ch'iu,697.0,France,Female,34.0,7.0,0.0,1.0,1.0,0.0,91036.74,0.0
+6513,15736413.0,Ch'ang,663.0,France,Female,53.0,1.0,0.0,1.0,0.0,1.0,185667.72,0.0
+6514,15661528.0,Dyson,638.0,Spain,Male,38.0,7.0,0.0,2.0,0.0,1.0,38152.01,0.0
+6515,15628112.0,T'ang,626.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,0.0,55313.51,0.0
+6516,15742986.0,Erskine,690.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,138835.78,0.0
+6517,15627829.0,White,569.0,France,Female,32.0,3.0,139528.23,1.0,1.0,1.0,151146.67,0.0
+6518,15588933.0,Ch'iu,582.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,56784.11,0.0
+6519,15649129.0,Lai,850.0,France,Male,29.0,3.0,0.0,2.0,1.0,0.0,126761.2,0.0
+6520,15705085.0,Nnonso,634.0,France,Female,24.0,3.0,0.0,2.0,0.0,1.0,56767.67,0.0
+6521,15796218.0,Tien,551.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,155700.15,0.0
+6522,15587573.0,Toscani,817.0,France,Male,56.0,8.0,96888.39,1.0,1.0,0.0,156618.41,1.0
+6523,15661827.0,Tien,641.0,Spain,Male,34.0,2.0,91057.37,1.0,0.0,0.0,53954.24,0.0
+6524,15768367.0,Lai,602.0,France,Female,53.0,7.0,0.0,2.0,1.0,0.0,90878.13,1.0
+6525,15612043.0,Genovese,712.0,Spain,Male,43.0,10.0,81571.05,1.0,0.0,1.0,79602.86,0.0
+6526,15733888.0,Shih,709.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,5983.8,0.0
+6527,15766172.0,Nnamutaezinwa,576.0,Germany,Male,38.0,3.0,84587.9,1.0,0.0,0.0,143598.7,1.0
+6528,15781665.0,Palerma,638.0,France,Female,35.0,4.0,0.0,1.0,0.0,0.0,56650.47,0.0
+6529,15777873.0,Trevisani,712.0,Spain,Male,45.0,7.0,0.0,1.0,1.0,1.0,118716.63,1.0
+6530,15694209.0,Nnamdi,592.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,111981.19,0.0
+6531,15644878.0,Tien,724.0,France,Male,41.0,3.0,69712.74,1.0,1.0,1.0,137763.93,0.0
+6532,15798895.0,Ch'en,622.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,87834.24,0.0
+6533,15586974.0,Chibueze,729.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,166850.57,0.0
+6534,15687913.0,Kao,595.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,51387.1,1.0
+6535,15603594.0,Hsia,601.0,Germany,Female,40.0,6.0,85935.82,1.0,1.0,0.0,89935.71,0.0
+6536,15603481.0,Nnonso,635.0,France,Male,44.0,0.0,0.0,1.0,1.0,1.0,83022.92,1.0
+6537,15637138.0,T'ien,706.0,France,Male,33.0,7.0,92566.54,1.0,1.0,1.0,142913.98,0.0
+6538,15792429.0,Aksenov,527.0,France,Female,32.0,4.0,0.0,2.0,1.0,1.0,79919.13,0.0
+6539,15763895.0,Chizuoke,730.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,157932.75,0.0
+6540,15649853.0,Ch'ien,669.0,Spain,Male,41.0,8.0,0.0,2.0,0.0,1.0,64375.4,0.0
+6541,15635116.0,Lai,628.0,France,Male,40.0,6.0,0.0,2.0,1.0,1.0,135925.72,0.0
+6542,15581229.0,Tsao,633.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,88659.07,0.0
+6543,15635277.0,P'eng,635.0,France,Male,41.0,7.0,0.0,1.0,1.0,1.0,176570.28,0.0
+6544,15754599.0,Y?,757.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,1.0,50610.62,0.0
+6545,15665943.0,Yobachi,730.0,France,Male,30.0,6.0,0.0,2.0,1.0,0.0,148516.79,0.0
+6546,15799128.0,Manna,592.0,France,Female,41.0,1.0,0.0,2.0,1.0,0.0,93524.19,0.0
+6547,15595440.0,Lombardo,669.0,Spain,Female,52.0,6.0,0.0,1.0,1.0,0.0,98820.09,1.0
+6548,15793881.0,Ozoemena,711.0,Spain,Male,47.0,4.0,0.0,1.0,1.0,1.0,176779.46,0.0
+6549,15754747.0,Tuan,613.0,France,Female,31.0,1.0,0.0,2.0,0.0,0.0,126812.63,0.0
+6550,15595883.0,Frolova,611.0,Spain,Male,39.0,3.0,0.0,2.0,0.0,0.0,179445.28,0.0
+6551,15777892.0,Pirozzi,706.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,140941.47,0.0
+6552,15628145.0,Hsieh,629.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,103099.22,0.0
+6553,15796957.0,Lucciano,782.0,Spain,Female,34.0,3.0,0.0,1.0,1.0,1.0,152883.95,0.0
+6554,15724161.0,Chien,794.0,France,Female,28.0,8.0,0.0,2.0,1.0,1.0,166476.95,0.0
+6555,15581700.0,Toscani,532.0,Germany,Male,40.0,2.0,121453.63,2.0,0.0,1.0,56787.8,0.0
+6556,15671358.0,Okwudiliolisa,521.0,France,Female,32.0,6.0,86549.74,1.0,1.0,1.0,131093.99,0.0
+6557,15700964.0,Hsu,850.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,134778.02,0.0
+6558,15698619.0,Chukwuhaenye,646.0,Germany,Male,36.0,10.0,134796.05,1.0,1.0,1.0,102297.15,1.0
+6559,15771543.0,Aikenhead,774.0,Spain,Male,42.0,9.0,133525.1,2.0,1.0,0.0,119747.08,0.0
+6560,15765694.0,Onyinyechukwuka,616.0,France,Male,31.0,4.0,0.0,1.0,0.0,0.0,74580.8,0.0
+6561,15594796.0,Iadanza,708.0,France,Male,32.0,8.0,0.0,2.0,0.0,1.0,186489.95,0.0
+6562,15685920.0,Hsing,711.0,Spain,Female,52.0,5.0,0.0,1.0,1.0,0.0,94440.45,1.0
+6563,15776939.0,Cremonesi,745.0,France,Female,55.0,5.0,0.0,2.0,1.0,0.0,58974.0,1.0
+6564,15808846.0,Cawood,713.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,154908.52,0.0
+6565,15596962.0,Milne,719.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,145558.22,0.0
+6566,15752881.0,Ch'ang,678.0,Germany,Male,38.0,9.0,110624.99,3.0,0.0,0.0,185510.34,1.0
+6567,15783882.0,Tsou,656.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,90537.47,0.0
+6568,15679048.0,Ts'ui,730.0,France,Male,29.0,6.0,73023.26,1.0,1.0,0.0,9241.78,0.0
+6569,15730137.0,Onyemauchechukwu,676.0,France,Female,41.0,0.0,0.0,1.0,1.0,0.0,87834.89,1.0
+6570,15567446.0,Nwachukwu,829.0,France,Female,25.0,1.0,0.0,1.0,1.0,1.0,182612.38,0.0
+6571,15750658.0,Ts'ui,805.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,181208.47,0.0
+6572,15731760.0,T'ien,754.0,Germany,Male,22.0,3.0,104784.23,1.0,1.0,1.0,57972.45,1.0
+6573,15643487.0,Nnonso,670.0,Germany,Male,43.0,5.0,141787.31,2.0,1.0,1.0,169291.7,0.0
+6574,15593470.0,Lucchese,601.0,France,Female,52.0,6.0,0.0,1.0,1.0,1.0,173680.54,1.0
+6575,15659710.0,Chimezie,754.0,France,Female,43.0,5.0,155973.88,1.0,0.0,0.0,117632.63,1.0
+6576,15640078.0,Walker,605.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,1.0,70778.63,0.0
+6577,15684516.0,Ogochukwu,611.0,France,Male,30.0,7.0,169832.57,1.0,1.0,0.0,126812.63,0.0
+6578,15585066.0,P'an,584.0,Spain,Female,28.0,3.0,0.0,2.0,1.0,1.0,91346.03,0.0
+6579,15639357.0,Toscani,543.0,France,Male,32.0,7.0,0.0,2.0,0.0,0.0,125877.22,0.0
+6580,15641136.0,Frolov,578.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,64746.07,0.0
+6581,15589019.0,Hsia,633.0,Germany,Female,51.0,3.0,139914.13,1.0,1.0,1.0,70532.53,0.0
+6582,15623196.0,Kao,545.0,France,Female,37.0,1.0,90322.99,1.0,1.0,1.0,151325.24,1.0
+6583,15588859.0,T'ien,717.0,Germany,Female,46.0,6.0,117696.67,1.0,1.0,0.0,165675.01,1.0
+6584,15784736.0,Chukwualuka,627.0,Germany,Female,25.0,7.0,98640.95,1.0,1.0,0.0,44363.42,0.0
+6585,15770576.0,Chigolum,651.0,France,Male,39.0,8.0,141829.67,1.0,1.0,1.0,129975.54,0.0
+6586,15704795.0,Shih,850.0,Germany,Male,29.0,8.0,122724.45,2.0,1.0,1.0,71173.64,0.0
+6587,15744327.0,Iloerika,604.0,Spain,Male,32.0,5.0,0.0,1.0,1.0,0.0,101168.3,1.0
+6588,15777576.0,Chiawuotu,850.0,Spain,Male,35.0,2.0,0.0,2.0,0.0,0.0,51112.8,0.0
+6589,15738785.0,Ts'ui,669.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,132973.17,0.0
+6590,15696985.0,Chukwualuka,645.0,Germany,Female,60.0,9.0,164523.5,1.0,1.0,0.0,167773.55,1.0
+6591,15671172.0,Ch'ien,598.0,France,Female,52.0,6.0,0.0,1.0,1.0,0.0,5708.19,1.0
+6592,15581237.0,Iloerika,850.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,146723.72,0.0
+6593,15711759.0,Shih,595.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,184796.84,0.0
+6594,15766066.0,Martin,557.0,Spain,Female,31.0,9.0,0.0,1.0,0.0,1.0,61936.1,1.0
+6595,15672598.0,Ch'ang,718.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,73329.08,1.0
+6596,15668818.0,Chineze,611.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,152417.79,0.0
+6597,15582335.0,Frolov,653.0,Spain,Female,37.0,3.0,0.0,1.0,0.0,0.0,114522.68,1.0
+6598,15684951.0,Palermo,659.0,Spain,Male,39.0,3.0,0.0,2.0,0.0,1.0,141872.05,1.0
+6599,15655389.0,P'eng,667.0,Spain,Male,42.0,3.0,0.0,2.0,1.0,0.0,196257.67,0.0
+6600,15655961.0,Chiu,602.0,France,Male,40.0,5.0,0.0,2.0,0.0,1.0,92612.88,0.0
+6601,15590740.0,Fomin,725.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,1.0,166957.82,0.0
+6602,15808905.0,Genovese,730.0,France,Male,43.0,9.0,134378.96,1.0,1.0,1.0,119259.34,0.0
+6603,15795581.0,Nwabugwu,683.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,168816.23,0.0
+6604,15683553.0,De Luca,671.0,Spain,Female,47.0,0.0,0.0,2.0,0.0,1.0,147055.87,0.0
+6605,15652948.0,Hsiung,656.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,63736.14,0.0
+6606,15743456.0,K'ung,685.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,0.0,144870.28,0.0
+6607,15671450.0,Nkemdirim,740.0,Spain,Male,44.0,3.0,0.0,2.0,1.0,0.0,176750.81,0.0
+6608,15626383.0,Chien,634.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,134878.19,0.0
+6609,15638269.0,Dyer,648.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,104469.08,0.0
+6610,15793424.0,Chukwuemeka,725.0,France,Male,46.0,0.0,0.0,1.0,1.0,0.0,122965.14,1.0
+6611,15667632.0,De Luca,717.0,Spain,Female,44.0,3.0,123345.65,2.0,0.0,1.0,61531.86,0.0
+6612,15773487.0,P'eng,684.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,121592.5,0.0
+6613,15737506.0,Onyekaozulu,592.0,France,Male,40.0,0.0,0.0,2.0,1.0,0.0,91763.67,0.0
+6614,15710390.0,Koo,783.0,Germany,Male,37.0,7.0,144591.96,2.0,1.0,1.0,82393.06,0.0
+6615,15802220.0,Ejimofor,683.0,France,Male,40.0,1.0,0.0,2.0,0.0,0.0,173783.38,0.0
+6616,15806511.0,Chidubem,642.0,France,Male,29.0,8.0,0.0,2.0,0.0,0.0,171770.79,0.0
+6617,15582335.0,Yegorova,613.0,France,Female,36.0,7.0,0.0,1.0,1.0,1.0,172618.52,0.0
+6618,15586479.0,Chukwufumnanya,509.0,Spain,Male,59.0,10.0,0.0,2.0,1.0,1.0,180551.76,0.0
+6619,15775124.0,Pinto,649.0,France,Male,32.0,10.0,0.0,2.0,1.0,0.0,24207.86,0.0
+6620,15643908.0,Maclean,684.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,116977.89,0.0
+6621,15668058.0,Nnachetam,710.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,0.0,116781.71,0.0
+6622,15694349.0,Chibueze,627.0,Germany,Male,40.0,2.0,104349.45,1.0,1.0,1.0,165614.4,0.0
+6623,15581323.0,Iqbal,774.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,51553.43,0.0
+6624,15718779.0,Rizzo,657.0,France,Female,43.0,9.0,0.0,2.0,1.0,0.0,129890.94,0.0
+6625,15753343.0,Iqbal,567.0,France,Male,42.0,9.0,113858.95,1.0,1.0,1.0,162515.17,1.0
+6626,15591711.0,Chinweike,579.0,France,Male,29.0,9.0,77910.23,1.0,0.0,1.0,103934.12,0.0
+6627,15594556.0,Udinesi,599.0,Germany,Female,48.0,7.0,117154.9,2.0,1.0,1.0,179451.42,1.0
+6628,15663625.0,Chiawuotu,596.0,France,Female,59.0,5.0,0.0,1.0,1.0,1.0,199108.88,0.0
+6629,15775663.0,Trejo,703.0,France,Male,46.0,1.0,0.0,2.0,0.0,0.0,168816.22,0.0
+6630,15609562.0,Obialo,850.0,France,Male,44.0,6.0,111379.55,1.0,1.0,1.0,167723.25,0.0
+6631,15690169.0,Pinto,721.0,France,Male,38.0,3.0,158719.57,1.0,1.0,0.0,123866.37,0.0
+6632,15702715.0,Tung,692.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,64872.55,0.0
+6633,15760662.0,Hsueh,710.0,France,Male,34.0,1.0,0.0,1.0,1.0,0.0,105599.79,0.0
+6634,15658435.0,Ch'ien,705.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,157862.82,0.0
+6635,15607434.0,Chin,678.0,France,Female,35.0,6.0,106328.08,1.0,1.0,1.0,116973.48,0.0
+6636,15687079.0,Lettiere,704.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,172576.43,0.0
+6637,15592570.0,Chidiebele,520.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,185821.41,0.0
+6638,15732963.0,Yermakova,586.0,Germany,Female,33.0,2.0,171178.25,2.0,0.0,1.0,138192.98,0.0
+6639,15662901.0,Trevisani,531.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,93959.96,0.0
+6640,15578738.0,Udegbulam,572.0,Germany,Male,39.0,6.0,122552.34,1.0,1.0,0.0,84760.32,0.0
+6641,15769006.0,Chia,704.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,162889.1,0.0
+6642,15801414.0,Yeh,645.0,France,Female,62.0,1.0,0.0,2.0,1.0,0.0,48963.59,1.0
+6643,15641611.0,Phelan,461.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,55775.72,0.0
+6644,15699430.0,Chin,622.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,152733.89,0.0
+6645,15761854.0,T'ien,636.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,58814.41,0.0
+6646,15776467.0,Fanucci,718.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,138700.89,0.0
+6647,15770474.0,Eluemuno,615.0,France,Female,39.0,4.0,124577.33,1.0,0.0,1.0,823.96,0.0
+6648,15604314.0,Chinwenma,685.0,France,Female,48.0,9.0,0.0,1.0,0.0,1.0,136071.05,1.0
+6649,15788131.0,Lucciano,492.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,1.0,122823.84,0.0
+6650,15701160.0,Y?an,628.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,28789.94,0.0
+6651,15802809.0,Onyemauchechukwu,596.0,France,Female,45.0,1.0,0.0,1.0,0.0,0.0,65801.44,1.0
+6652,15684928.0,K?,674.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,147720.27,0.0
+6653,15777784.0,Aksenov,778.0,France,Male,36.0,1.0,92716.96,2.0,0.0,0.0,143774.06,0.0
+6654,15706005.0,Chinwendu,706.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,124589.58,0.0
+6655,15629448.0,Chiang,592.0,France,Male,33.0,6.0,0.0,1.0,0.0,0.0,138380.12,0.0
+6656,15631159.0,Zetticci,749.0,France,Male,28.0,9.0,95994.98,2.0,1.0,0.0,175324.74,0.0
+6657,15591344.0,Yefremov,714.0,France,Male,32.0,10.0,146033.62,1.0,1.0,0.0,53534.6,0.0
+6658,15605741.0,Lucciano,773.0,France,Male,40.0,7.0,0.0,1.0,1.0,1.0,98949.85,0.0
+6659,15636562.0,Yobanna,593.0,France,Male,41.0,3.0,0.0,2.0,1.0,0.0,170661.02,0.0
+6660,15572294.0,Martin,666.0,Spain,Female,33.0,5.0,89047.14,1.0,1.0,0.0,168895.06,0.0
+6661,15649430.0,Ch'iu,718.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,83093.25,0.0
+6662,15776939.0,T'ang,826.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,199357.24,0.0
+6663,15766659.0,Ch'eng,589.0,France,Male,38.0,8.0,123049.21,1.0,1.0,0.0,159800.16,0.0
+6664,15794849.0,Pinto,682.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,149996.74,0.0
+6665,15646091.0,Ch'ang,726.0,Germany,Male,31.0,4.0,125789.69,2.0,1.0,1.0,26260.98,0.0
+6666,15589449.0,Tien,637.0,France,Female,44.0,4.0,0.0,1.0,1.0,0.0,67622.42,1.0
+6667,15689168.0,Pirozzi,696.0,Germany,Female,39.0,5.0,135095.58,1.0,0.0,0.0,78670.52,1.0
+6668,15793723.0,K'ung,669.0,France,Female,34.0,6.0,0.0,2.0,0.0,0.0,102515.43,0.0
+6669,15707861.0,Lo Duca,625.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,182867.84,0.0
+6670,15656683.0,P'eng,576.0,France,Male,58.0,3.0,0.0,2.0,0.0,0.0,86920.97,1.0
+6671,15613102.0,Nwankwo,621.0,France,Female,34.0,10.0,147910.3,1.0,1.0,1.0,153036.97,0.0
+6672,15760294.0,Ch'ang,754.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,115186.97,0.0
+6673,15778290.0,Tien,537.0,Spain,Male,35.0,9.0,0.0,3.0,0.0,0.0,119899.52,1.0
+6674,15763798.0,Zito,628.0,Germany,Male,50.0,4.0,92566.53,1.0,1.0,0.0,40333.94,1.0
+6675,15648069.0,Chibugo,744.0,Germany,Female,41.0,9.0,135937.94,1.0,1.0,1.0,134901.57,0.0
+6676,15652022.0,Nworie,724.0,France,Female,21.0,7.0,0.0,2.0,1.0,0.0,62830.97,0.0
+6677,15772423.0,Chukwukere,607.0,France,Male,39.0,7.0,115518.07,1.0,1.0,1.0,120853.87,0.0
+6678,15609637.0,Hayslett,584.0,France,Female,27.0,4.0,0.0,2.0,0.0,1.0,173784.04,0.0
+6679,15809847.0,Galkin,641.0,France,Male,26.0,2.0,130206.28,1.0,1.0,0.0,179631.85,0.0
+6680,15589401.0,Bair,602.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,122925.4,0.0
+6681,15597957.0,Hs?,659.0,France,Female,24.0,7.0,122311.59,1.0,1.0,0.0,181203.6,0.0
+6682,15736990.0,Hsiung,605.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,160929.52,0.0
+6683,15607986.0,Ts'ui,581.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,157567.37,0.0
+6684,15716284.0,Genovesi,724.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,60721.4,0.0
+6685,15754068.0,Pritchard,653.0,France,Female,45.0,3.0,0.0,2.0,0.0,0.0,126012.57,0.0
+6686,15809872.0,Chia,741.0,Germany,Male,43.0,0.0,133550.77,1.0,1.0,0.0,133086.45,1.0
+6687,15762762.0,Ch'iu,580.0,France,Male,42.0,7.0,161801.47,1.0,0.0,0.0,97627.25,0.0
+6688,15777573.0,Tuan,557.0,Germany,Female,42.0,6.0,105599.67,1.0,1.0,1.0,163799.52,1.0
+6689,15657565.0,Iadanza,577.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,136537.45,0.0
+6690,15796074.0,Chibugo,558.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,111184.67,0.0
+6691,15602851.0,Hsing,634.0,France,Female,33.0,1.0,0.0,1.0,1.0,0.0,175262.49,1.0
+6692,15761018.0,Hsieh,681.0,France,Female,34.0,6.0,0.0,2.0,0.0,1.0,93524.22,0.0
+6693,15789097.0,Ndubueze,729.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,18921.82,0.0
+6694,15806327.0,Hsueh,721.0,Spain,Female,39.0,0.0,133501.33,2.0,1.0,0.0,120115.5,0.0
+6695,15770101.0,Cattaneo,755.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,181534.04,0.0
+6696,15636407.0,Chidiebere,656.0,Germany,Male,36.0,3.0,113311.11,1.0,1.0,1.0,81259.25,0.0
+6697,15691875.0,White,597.0,Spain,Female,28.0,4.0,0.0,1.0,0.0,1.0,122221.71,0.0
+6698,15799452.0,Ch'ang,850.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,69080.46,0.0
+6699,15642619.0,Yermakov,753.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,82782.08,0.0
+6700,15641411.0,Scott,586.0,France,Male,31.0,10.0,84421.62,1.0,1.0,0.0,132973.21,0.0
+6701,15792360.0,Chukwujekwu,592.0,France,Female,36.0,1.0,0.0,2.0,1.0,1.0,63531.19,0.0
+6702,15798532.0,Chiemenam,682.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,126434.61,0.0
+6703,15788676.0,L?,710.0,Germany,Male,40.0,7.0,85845.43,2.0,0.0,1.0,136859.55,0.0
+6704,15594762.0,Ch'iu,684.0,France,Male,48.0,0.0,0.0,1.0,1.0,0.0,138564.46,1.0
+6705,15700460.0,Ts'ui,704.0,France,Female,46.0,2.0,0.0,2.0,0.0,0.0,180598.32,0.0
+6706,15799720.0,Hs?eh,648.0,Spain,Male,25.0,7.0,0.0,2.0,1.0,0.0,82547.01,0.0
+6707,15702030.0,Tien,599.0,Spain,Female,35.0,8.0,0.0,2.0,1.0,1.0,86935.73,0.0
+6708,15586029.0,Hsia,546.0,Germany,Male,39.0,9.0,131769.04,1.0,1.0,1.0,131301.74,0.0
+6709,15573741.0,Okwudiliolisa,648.0,Germany,Male,42.0,6.0,109728.08,1.0,1.0,1.0,177613.19,1.0
+6710,15671534.0,Clements,605.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,167984.72,0.0
+6711,15705765.0,Chidiegwu,553.0,Spain,Male,57.0,9.0,0.0,1.0,1.0,0.0,51705.01,0.0
+6712,15759067.0,Fitts,694.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,1.0,28367.19,0.0
+6713,15672397.0,Lettiere,678.0,Germany,Female,55.0,1.0,101960.74,1.0,1.0,0.0,153356.8,1.0
+6714,15676908.0,L?,671.0,France,Male,30.0,2.0,0.0,2.0,0.0,1.0,142911.99,0.0
+6715,15761720.0,Chibueze,528.0,France,Male,33.0,8.0,0.0,1.0,1.0,1.0,49778.79,0.0
+6716,15699598.0,Manna,579.0,Germany,Male,38.0,5.0,93577.3,2.0,1.0,0.0,142906.75,0.0
+6717,15603328.0,Kao,676.0,Germany,Male,41.0,5.0,132406.86,1.0,1.0,0.0,25571.37,0.0
+6718,15743545.0,Ch'iu,687.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,143585.29,0.0
+6719,15584776.0,Hsiung,679.0,Germany,Male,51.0,2.0,137781.65,1.0,1.0,1.0,10908.33,1.0
+6720,15780984.0,Fetherstonhaugh,664.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,116978.19,0.0
+6721,15656426.0,Chukwueloka,691.0,France,Male,35.0,4.0,0.0,1.0,0.0,1.0,187826.11,1.0
+6722,15658852.0,Nnachetam,710.0,France,Female,48.0,4.0,0.0,2.0,1.0,0.0,140176.47,1.0
+6723,15712596.0,Rizzo,584.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,54290.62,0.0
+6724,15640056.0,Hs?,682.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,126494.82,0.0
+6725,15762803.0,Yeh,850.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,0.0,116973.48,0.0
+6726,15635244.0,Oguejiofor,554.0,Germany,Female,47.0,4.0,137794.18,1.0,1.0,0.0,88391.97,1.0
+6727,15810839.0,Lo,667.0,Spain,Female,42.0,7.0,0.0,2.0,1.0,0.0,170621.43,0.0
+6728,15805764.0,Onio,569.0,France,Female,28.0,2.0,0.0,1.0,0.0,0.0,146723.72,0.0
+6729,15588854.0,Cremonesi,719.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,147521.35,0.0
+6730,15589230.0,Mironov,697.0,France,Male,34.0,4.0,0.0,1.0,1.0,1.0,52319.26,0.0
+6731,15694946.0,Kao,756.0,Germany,Female,34.0,10.0,111098.85,1.0,1.0,0.0,60905.51,0.0
+6732,15746664.0,Chukwuraenye,850.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,85746.52,1.0
+6733,15684908.0,Nnanna,611.0,Germany,Male,33.0,4.0,88915.85,2.0,1.0,0.0,168895.06,0.0
+6734,15642099.0,H?,663.0,France,Male,23.0,4.0,0.0,2.0,1.0,1.0,176570.28,0.0
+6735,15609623.0,Nwankwo,629.0,Spain,Female,35.0,4.0,0.0,1.0,1.0,1.0,1485.27,1.0
+6736,15764293.0,Chiu,786.0,France,Female,40.0,8.0,0.0,2.0,0.0,1.0,7921.36,0.0
+6737,15805955.0,Martin,667.0,Germany,Male,50.0,4.0,96471.3,1.0,1.0,0.0,51143.43,1.0
+6738,15598884.0,Onyemauchechukwu,597.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,121402.53,1.0
+6739,15689633.0,Yefimov,648.0,France,Male,39.0,3.0,122029.15,1.0,0.0,1.0,141998.89,0.0
+6740,15585823.0,Chuang,592.0,France,Male,23.0,4.0,155059.1,1.0,0.0,1.0,123866.47,0.0
+6741,15677184.0,T'ang,659.0,France,Female,38.0,5.0,0.0,1.0,1.0,0.0,79919.13,0.0
+6742,15643523.0,Uchenna,850.0,France,Male,40.0,9.0,154007.21,1.0,0.0,1.0,94739.3,0.0
+6743,15643826.0,Ch'ien,674.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,138526.96,0.0
+6744,15770225.0,Onyekachukwu,710.0,France,Male,34.0,0.0,0.0,1.0,1.0,1.0,92727.43,0.0
+6745,15618363.0,K?,850.0,Spain,Male,61.0,7.0,0.0,2.0,1.0,1.0,86786.42,0.0
+6746,15777826.0,Teng,661.0,France,Male,25.0,1.0,0.0,2.0,1.0,1.0,94949.84,0.0
+6747,15800961.0,Chukwudi,826.0,Germany,Female,36.0,5.0,83739.32,1.0,1.0,0.0,163550.58,0.0
+6748,15756071.0,Ahern,624.0,Germany,Male,40.0,3.0,116220.5,1.0,1.0,0.0,152884.85,0.0
+6749,15809847.0,Chia,671.0,France,Female,48.0,4.0,0.0,2.0,1.0,0.0,62944.75,0.0
+6750,15740164.0,Pirozzi,590.0,France,Female,36.0,7.0,154737.88,1.0,1.0,0.0,152733.89,0.0
+6751,15773973.0,Crawford,697.0,Germany,Female,47.0,4.0,145081.73,1.0,1.0,1.0,116411.6,1.0
+6752,15696744.0,Chidubem,732.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,108732.96,0.0
+6753,15610900.0,Pinto,567.0,France,Female,45.0,5.0,0.0,4.0,1.0,0.0,101371.05,1.0
+6754,15573318.0,Okechukwu,597.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,131301.74,0.0
+6755,15772573.0,Fanucci,850.0,France,Male,63.0,3.0,91547.84,2.0,1.0,0.0,176750.81,1.0
+6756,15614893.0,Efremov,682.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,75832.53,0.0
+6757,15643824.0,T'ang,591.0,France,Female,47.0,1.0,146780.52,1.0,1.0,1.0,122925.5,0.0
+6758,15682868.0,Okoli,671.0,Spain,Female,36.0,8.0,0.0,2.0,1.0,1.0,150659.42,0.0
+6759,15788151.0,Mazzi,764.0,Germany,Female,40.0,7.0,80764.03,1.0,0.0,1.0,89891.4,1.0
+6760,15760456.0,Chikwendu,743.0,France,Female,49.0,6.0,0.0,2.0,1.0,0.0,184843.77,1.0
+6761,15717138.0,Ch'iu,850.0,France,Female,56.0,4.0,136622.55,1.0,1.0,0.0,93524.22,1.0
+6762,15772383.0,Hsia,498.0,Spain,Male,43.0,8.0,115265.21,1.0,1.0,1.0,162889.1,0.0
+6763,15766166.0,Pisano,686.0,Germany,Male,45.0,3.0,113938.27,1.0,1.0,0.0,51345.41,1.0
+6764,15735246.0,Chiang,682.0,France,Male,60.0,1.0,0.0,3.0,0.0,1.0,162889.1,1.0
+6765,15743628.0,T'an,628.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,159475.08,0.0
+6766,15675450.0,Chukwufumnanya,773.0,France,Female,50.0,3.0,0.0,1.0,1.0,1.0,86937.2,0.0
+6767,15726490.0,Yegorova,718.0,France,Female,35.0,10.0,0.0,1.0,1.0,0.0,171515.84,1.0
+6768,15757577.0,Baresi,739.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,0.0,121482.2,0.0
+6769,15809143.0,Chukwudi,684.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,0.0,88651.67,0.0
+6770,15635143.0,Ch'iu,683.0,France,Male,33.0,10.0,0.0,1.0,0.0,1.0,112366.98,0.0
+6771,15687558.0,Ts'ai,658.0,Germany,Male,36.0,4.0,157960.38,2.0,1.0,1.0,174098.28,0.0
+6772,15611579.0,Ch'ang,752.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,161849.15,0.0
+6773,15808187.0,Ritchie,679.0,Spain,Male,29.0,7.0,0.0,2.0,1.0,0.0,183868.21,0.0
+6774,15676937.0,T'ien,638.0,France,Female,33.0,5.0,0.0,1.0,1.0,0.0,169963.2,1.0
+6775,15702298.0,Hs?,648.0,France,Male,43.0,6.0,145081.73,1.0,0.0,0.0,159891.03,0.0
+6776,15683522.0,T'ang,706.0,France,Male,32.0,9.0,0.0,1.0,0.0,0.0,94815.04,1.0
+6777,15593365.0,Genovese,682.0,Spain,Female,30.0,3.0,0.0,2.0,0.0,0.0,1187.88,0.0
+6778,15790810.0,Eluemuno,657.0,Spain,Female,45.0,4.0,0.0,2.0,0.0,0.0,180598.86,0.0
+6779,15638774.0,Folliero,557.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,158344.63,0.0
+6780,15667046.0,Hightower,651.0,Spain,Male,43.0,9.0,146498.76,1.0,0.0,0.0,66526.01,0.0
+6781,15771543.0,Hsia,622.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,88721.84,0.0
+6782,15572626.0,Ewing,850.0,France,Male,35.0,10.0,0.0,2.0,1.0,0.0,152843.53,0.0
+6783,15783522.0,Dickson,682.0,Germany,Male,36.0,4.0,110112.54,2.0,0.0,1.0,117034.32,0.0
+6784,15595874.0,Kistler,711.0,Spain,Female,32.0,9.0,129378.43,1.0,1.0,0.0,172524.51,0.0
+6785,15615140.0,Hao,623.0,France,Female,49.0,5.0,0.0,1.0,1.0,1.0,121522.73,1.0
+6786,15689341.0,Tsao,706.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,163830.64,0.0
+6787,15768219.0,Ndubueze,535.0,Germany,Female,44.0,4.0,123428.69,1.0,1.0,0.0,131301.74,1.0
+6788,15641585.0,K'ung,554.0,Germany,Male,23.0,2.0,96341.0,2.0,0.0,1.0,122268.75,0.0
+6789,15727299.0,Ugoji,583.0,France,Male,26.0,5.0,0.0,2.0,1.0,0.0,49826.68,0.0
+6790,15790846.0,Chien,653.0,France,Female,45.0,5.0,0.0,1.0,1.0,1.0,24506.95,1.0
+6791,15737647.0,Ugoji,741.0,Germany,Male,30.0,4.0,131365.37,1.0,1.0,1.0,121894.31,0.0
+6792,15751137.0,Cremonesi,719.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,152479.19,0.0
+6793,15597913.0,Chin,724.0,Germany,Female,35.0,2.0,82498.14,2.0,1.0,0.0,176868.89,0.0
+6794,15575676.0,Ndukaku,724.0,Germany,Female,43.0,1.0,104592.89,2.0,1.0,1.0,82774.06,1.0
+6795,15746451.0,Nwabugwu,732.0,France,Female,26.0,9.0,0.0,2.0,0.0,1.0,96985.58,0.0
+6796,15709928.0,T'ien,714.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,123977.86,0.0
+6797,15702430.0,Nworie,695.0,France,Male,20.0,5.0,0.0,2.0,1.0,0.0,137565.57,0.0
+6798,15603914.0,Onuoha,790.0,France,Female,36.0,9.0,131240.24,1.0,1.0,1.0,89099.18,0.0
+6799,15593501.0,Teng,627.0,France,Male,39.0,10.0,0.0,2.0,1.0,1.0,185580.89,0.0
+6800,15734886.0,Chuang,652.0,Spain,Female,30.0,6.0,0.0,1.0,1.0,0.0,1710.89,1.0
+6801,15721383.0,Kudryashova,684.0,France,Male,29.0,9.0,97560.16,2.0,0.0,1.0,122268.75,0.0
+6802,15648032.0,Tuan,707.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,94820.85,0.0
+6803,15769346.0,Dickson,652.0,France,Male,43.0,9.0,161254.78,1.0,1.0,0.0,137537.22,0.0
+6804,15789924.0,Okwudilichukwu,733.0,France,Male,26.0,4.0,0.0,2.0,1.0,1.0,111590.35,0.0
+6805,15644857.0,Chimezie,657.0,France,Male,52.0,3.0,0.0,3.0,1.0,1.0,103560.98,1.0
+6806,15783541.0,Tien,767.0,France,Female,30.0,6.0,0.0,1.0,1.0,0.0,162734.31,0.0
+6807,15649414.0,Chukwukere,652.0,Germany,Male,33.0,2.0,93821.95,2.0,1.0,0.0,102298.79,0.0
+6808,15757911.0,Genovesi,661.0,France,Male,43.0,5.0,0.0,2.0,0.0,0.0,141364.62,0.0
+6809,15744240.0,Folliero,516.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,55893.37,0.0
+6810,15732903.0,Yobachi,710.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,108699.76,0.0
+6811,15665963.0,Ch'en,682.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,168886.39,0.0
+6812,15692226.0,Manna,693.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,122937.57,0.0
+6813,15701265.0,Tsou,656.0,France,Male,54.0,6.0,104857.19,1.0,1.0,1.0,51387.1,1.0
+6814,15605020.0,Y?,663.0,France,Male,29.0,8.0,0.0,1.0,1.0,1.0,183318.79,0.0
+6815,15758023.0,Cremonesi,684.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,130789.6,0.0
+6816,15628292.0,Tseng,685.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,90878.13,1.0
+6817,15809800.0,Li Fonti,710.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,141896.74,0.0
+6818,15743817.0,Ch'ien,668.0,France,Female,33.0,4.0,125973.17,2.0,1.0,0.0,50974.57,0.0
+6819,15597914.0,Ijendu,710.0,France,Male,28.0,5.0,0.0,2.0,1.0,0.0,123882.73,0.0
+6820,15724076.0,Chukwukadibia,669.0,Germany,Female,34.0,1.0,130024.87,1.0,1.0,0.0,123427.98,1.0
+6821,15792668.0,Milne,694.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,131167.98,0.0
+6822,15792649.0,Dike,679.0,France,Male,30.0,8.0,0.0,2.0,0.0,0.0,105720.09,0.0
+6823,15694319.0,Hsing,477.0,France,Female,37.0,4.0,0.0,1.0,1.0,1.0,184936.77,0.0
+6824,15792250.0,Tsou,738.0,France,Female,48.0,3.0,0.0,2.0,1.0,1.0,173948.4,0.0
+6825,15576723.0,Ritchie,712.0,France,Female,32.0,10.0,0.0,2.0,1.0,1.0,119882.7,0.0
+6826,15773071.0,O'Donnell,773.0,Germany,Male,24.0,2.0,131142.57,1.0,1.0,1.0,156791.37,0.0
+6827,15780256.0,Erskine,649.0,Germany,Male,35.0,9.0,125584.17,1.0,1.0,1.0,89935.73,0.0
+6828,15586959.0,Chiu,645.0,France,Male,36.0,7.0,0.0,1.0,1.0,0.0,68730.91,0.0
+6829,15580148.0,Udinesi,744.0,Germany,Male,30.0,6.0,135847.07,1.0,1.0,0.0,54439.53,1.0
+6830,15641604.0,Chukwujekwu,565.0,Spain,Male,61.0,7.0,0.0,2.0,1.0,1.0,66721.98,0.0
+6831,15735402.0,She,643.0,Germany,Female,42.0,6.0,125842.6,1.0,1.0,0.0,102517.83,0.0
+6832,15785831.0,Ch'in,732.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,51364.56,0.0
+6833,15650288.0,Chukwufumnanya,678.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,116747.63,0.0
+6834,15633866.0,Ugoji,706.0,France,Male,34.0,1.0,0.0,2.0,0.0,0.0,82796.29,0.0
+6835,15694628.0,Chijioke,714.0,France,Female,25.0,3.0,0.0,1.0,1.0,1.0,180150.49,0.0
+6836,15682463.0,White,732.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,0.0,72990.62,0.0
+6837,15780056.0,Paling,754.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,126743.33,0.0
+6838,15644962.0,Babbage,781.0,Germany,Male,32.0,6.0,96956.1,2.0,1.0,1.0,91957.39,0.0
+6839,15785547.0,Ch'iu,577.0,France,Male,31.0,8.0,0.0,1.0,1.0,0.0,93777.91,0.0
+6840,15796074.0,Lai,627.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,0.0,158835.49,0.0
+6841,15806049.0,Ch'iu,738.0,Germany,Female,48.0,9.0,129934.64,1.0,1.0,0.0,92624.62,1.0
+6842,15714241.0,Ch'ien,749.0,Spain,Female,58.0,7.0,88109.81,1.0,1.0,0.0,60905.51,1.0
+6843,15672754.0,Maclean,607.0,Germany,Female,33.0,6.0,118889.66,1.0,1.0,0.0,64345.61,0.0
+6844,15663410.0,Pisano,624.0,France,Male,42.0,9.0,0.0,1.0,1.0,1.0,141558.04,0.0
+6845,15667871.0,Hsia,687.0,Spain,Male,22.0,1.0,0.0,1.0,1.0,1.0,94888.6,0.0
+6846,15777907.0,Scott,651.0,France,Male,41.0,5.0,0.0,2.0,0.0,0.0,163365.4,0.0
+6847,15588923.0,Tsao,683.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,94977.3,0.0
+6848,15691823.0,Smith,739.0,France,Female,29.0,3.0,172463.45,1.0,1.0,1.0,73977.23,0.0
+6849,15679297.0,Ch'ien,567.0,Germany,Male,32.0,2.0,141349.43,1.0,0.0,0.0,71557.12,1.0
+6850,15672091.0,Chukwujamuike,649.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,160532.88,0.0
+6851,15686983.0,Mazzanti,590.0,Spain,Female,23.0,4.0,0.0,2.0,1.0,0.0,166587.9,0.0
+6852,15572038.0,Nebechukwu,597.0,Spain,Male,38.0,7.0,0.0,2.0,0.0,1.0,59115.77,0.0
+6853,15592925.0,Hsieh,677.0,Spain,Female,31.0,1.0,0.0,2.0,1.0,0.0,129892.93,0.0
+6854,15675185.0,Nwachinemelu,544.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,176680.16,1.0
+6855,15691813.0,Chukwufumnanya,649.0,France,Male,42.0,4.0,95858.37,1.0,1.0,1.0,149772.61,0.0
+6856,15735263.0,Yobachukwu,681.0,France,Female,37.0,6.0,0.0,2.0,0.0,1.0,73882.68,0.0
+6857,15789563.0,Chukwudi,636.0,France,Female,34.0,3.0,117052.82,1.0,0.0,0.0,114448.77,0.0
+6858,15713559.0,Tuan,685.0,France,Female,38.0,4.0,0.0,2.0,0.0,0.0,116046.22,0.0
+6859,15695872.0,Milani,712.0,France,Female,29.0,8.0,0.0,2.0,1.0,1.0,78744.39,0.0
+6860,15801152.0,Walker,678.0,France,Male,40.0,2.0,0.0,2.0,0.0,0.0,50400.32,0.0
+6861,15799294.0,Sagese,583.0,Germany,Male,44.0,1.0,72535.45,1.0,1.0,1.0,55893.37,0.0
+6862,15644962.0,Clements,683.0,France,Male,39.0,3.0,0.0,1.0,1.0,0.0,188533.11,0.0
+6863,15691823.0,Chiang,587.0,France,Male,29.0,3.0,0.0,1.0,1.0,0.0,152491.22,0.0
+6864,15702464.0,Iweobiegbunam,543.0,France,Female,34.0,9.0,0.0,1.0,1.0,1.0,158973.59,0.0
+6865,15749807.0,Ewers,709.0,Spain,Male,34.0,3.0,122325.56,1.0,1.0,0.0,86566.79,0.0
+6866,15588923.0,Tretyakova,614.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,55470.78,0.0
+6867,15642833.0,Cattaneo,850.0,France,Female,44.0,5.0,0.0,2.0,1.0,0.0,93777.15,0.0
+6868,15740147.0,Chukwumaobim,732.0,Spain,Female,37.0,3.0,123356.63,1.0,1.0,1.0,176720.28,0.0
+6869,15574743.0,Ch'en,731.0,France,Male,33.0,2.0,109339.17,1.0,0.0,1.0,51637.57,0.0
+6870,15566139.0,Chikere,659.0,France,Male,59.0,1.0,123943.18,1.0,1.0,1.0,135034.43,0.0
+6871,15634373.0,Fanucci,594.0,Spain,Female,28.0,9.0,0.0,2.0,0.0,1.0,86927.53,0.0
+6872,15631310.0,Pirozzi,714.0,Spain,Male,40.0,1.0,0.0,1.0,1.0,0.0,166310.82,0.0
+6873,15609510.0,Nwachukwu,683.0,France,Male,22.0,6.0,0.0,2.0,1.0,1.0,70760.69,0.0
+6874,15723884.0,Fanucci,605.0,Spain,Male,41.0,2.0,0.0,2.0,1.0,0.0,66893.28,0.0
+6875,15790314.0,Hs?eh,603.0,Germany,Male,31.0,2.0,130142.79,2.0,0.0,1.0,134589.34,0.0
+6876,15614365.0,Chu,711.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,119333.95,0.0
+6877,15682421.0,Udegbulam,673.0,Spain,Male,33.0,1.0,118046.88,2.0,0.0,0.0,158043.11,0.0
+6878,15705521.0,Fanucci,644.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,141756.32,0.0
+6879,15603743.0,Chinwemma,774.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,77837.63,0.0
+6880,15689955.0,Chidozie,569.0,France,Male,29.0,4.0,109799.55,2.0,1.0,1.0,127059.02,0.0
+6881,15768219.0,Chiazagomekpere,691.0,France,Female,41.0,10.0,0.0,1.0,0.0,0.0,120880.72,1.0
+6882,15593365.0,Chia,712.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,152843.53,0.0
+6883,15790750.0,Hsueh,749.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,0.0,156954.48,0.0
+6884,15659475.0,Rowntree,749.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,176576.71,0.0
+6885,15656683.0,Ch'iu,754.0,Spain,Female,32.0,10.0,0.0,2.0,1.0,0.0,53817.23,0.0
+6886,15593601.0,Nwankwo,683.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,138399.67,0.0
+6887,15635244.0,Fan,439.0,Germany,Female,28.0,7.0,119074.28,2.0,0.0,1.0,165576.55,0.0
+6888,15806880.0,T'ao,709.0,Germany,Male,50.0,8.0,104495.79,2.0,1.0,1.0,90212.38,0.0
+6889,15656426.0,Y?,549.0,France,Female,52.0,5.0,89364.91,1.0,0.0,1.0,153662.88,1.0
+6890,15627840.0,Yashina,615.0,France,Male,33.0,2.0,131762.94,1.0,1.0,0.0,67734.94,0.0
+6891,15604576.0,O'Donnell,732.0,France,Male,34.0,2.0,0.0,2.0,0.0,0.0,119647.6,0.0
+6892,15765520.0,Hs?,717.0,France,Male,39.0,10.0,0.0,2.0,0.0,0.0,180622.02,0.0
+6893,15783030.0,Chidubem,730.0,France,Male,39.0,7.0,91057.37,1.0,1.0,1.0,114793.45,0.0
+6894,15571940.0,Trevisani,707.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,56793.02,0.0
+6895,15651802.0,Trevisano,613.0,France,Male,34.0,5.0,180520.29,1.0,0.0,1.0,124954.56,0.0
+6896,15671779.0,Loyau,531.0,France,Male,54.0,1.0,0.0,2.0,1.0,0.0,130234.63,1.0
+6897,15685920.0,Hsieh,596.0,France,Female,32.0,9.0,0.0,2.0,0.0,0.0,62370.67,0.0
+6898,15672091.0,Hsia,809.0,France,Male,34.0,2.0,0.0,1.0,1.0,1.0,116973.48,0.0
+6899,15572467.0,T'ao,668.0,Germany,Female,38.0,5.0,106977.04,2.0,1.0,0.0,177670.57,0.0
+6900,15656592.0,Shih,692.0,France,Female,34.0,9.0,0.0,2.0,0.0,1.0,103799.76,0.0
+6901,15751445.0,Ch'eng,667.0,Germany,Male,37.0,9.0,129845.43,2.0,1.0,0.0,52319.26,0.0
+6902,15764661.0,Mazzanti,698.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,87067.73,0.0
+6903,15802478.0,Kornilova,516.0,France,Male,33.0,3.0,0.0,1.0,1.0,1.0,57589.29,0.0
+6904,15730353.0,Clements,598.0,France,Female,33.0,2.0,65848.36,1.0,1.0,0.0,38019.74,0.0
+6905,15751591.0,Dyer,687.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,93783.0,0.0
+6906,15659820.0,Macleod,646.0,Germany,Female,45.0,7.0,128306.7,1.0,0.0,0.0,171045.46,1.0
+6907,15704583.0,Shao,598.0,France,Female,33.0,9.0,0.0,2.0,1.0,1.0,86920.97,0.0
+6908,15755863.0,Smith,649.0,Germany,Female,40.0,8.0,127637.92,1.0,1.0,0.0,92794.61,1.0
+6909,15741032.0,Hsueh,622.0,Spain,Female,46.0,4.0,0.0,2.0,0.0,0.0,110525.6,0.0
+6910,15644878.0,She,725.0,France,Female,40.0,2.0,0.0,1.0,1.0,0.0,131736.43,0.0
+6911,15656840.0,Lombardo,598.0,Germany,Female,37.0,6.0,114446.84,1.0,1.0,0.0,8059.44,1.0
+6912,15589924.0,Ritchie,582.0,France,Female,25.0,4.0,0.0,2.0,1.0,0.0,70563.9,0.0
+6913,15791045.0,Kao,535.0,France,Female,38.0,4.0,0.0,1.0,1.0,0.0,164083.72,1.0
+6914,15754574.0,Ch'ien,773.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,97465.44,0.0
+6915,15569807.0,Brown,691.0,Germany,Male,34.0,8.0,127157.04,2.0,1.0,0.0,105715.01,0.0
+6916,15763544.0,Fanucci,632.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,91711.66,0.0
+6917,15672357.0,Onyemauchechukwu,779.0,Germany,Male,30.0,3.0,108862.76,1.0,1.0,1.0,102628.98,0.0
+6918,15669922.0,Maclean,764.0,Germany,Female,39.0,8.0,131283.11,1.0,1.0,1.0,57862.88,0.0
+6919,15794874.0,Tretiakova,583.0,France,Female,48.0,7.0,0.0,2.0,1.0,1.0,141706.43,0.0
+6920,15789924.0,Onyemachukwu,634.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,149892.79,0.0
+6921,15633194.0,Aksenov,653.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,106781.59,0.0
+6922,15785815.0,Greco,705.0,Germany,Female,38.0,7.0,110837.73,1.0,1.0,0.0,135851.3,0.0
+6923,15614491.0,Genovese,634.0,Spain,Female,27.0,5.0,149950.19,1.0,1.0,0.0,57553.98,1.0
+6924,15642937.0,Ch'in,685.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,87844.89,0.0
+6925,15636069.0,Ngozichukwuka,594.0,France,Female,41.0,6.0,0.0,2.0,1.0,1.0,179436.6,0.0
+6926,15681928.0,Cattaneo,697.0,France,Female,35.0,9.0,177624.59,1.0,1.0,0.0,173498.45,1.0
+6927,15791867.0,Shih,778.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,172667.39,0.0
+6928,15793042.0,Nkemjika,687.0,France,Male,47.0,10.0,0.0,2.0,1.0,1.0,186826.22,0.0
+6929,15682355.0,Chuang,642.0,Spain,Female,28.0,3.0,0.0,2.0,1.0,1.0,176743.51,0.0
+6930,15775888.0,H?,685.0,Germany,Female,30.0,8.0,145127.85,2.0,1.0,0.0,187985.85,0.0
+6931,15692593.0,Hsieh,700.0,France,Female,22.0,8.0,0.0,3.0,1.0,1.0,171021.44,1.0
+6932,15729579.0,Ugochukwutubelum,765.0,France,Female,63.0,5.0,139542.41,1.0,1.0,1.0,35671.45,0.0
+6933,15649523.0,Tien,686.0,France,Male,34.0,6.0,0.0,2.0,0.0,0.0,139065.94,0.0
+6934,15772632.0,Ugochukwu,655.0,France,Male,34.0,5.0,0.0,2.0,0.0,0.0,142223.94,0.0
+6935,15786539.0,T'ao,538.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,185561.89,0.0
+6936,15665088.0,Macleod,577.0,Germany,Male,41.0,2.0,98894.39,2.0,1.0,0.0,83022.92,0.0
+6937,15671249.0,Ch'ang,677.0,Germany,Male,50.0,9.0,128940.24,1.0,1.0,0.0,52876.41,1.0
+6938,15776096.0,Chiang,639.0,Spain,Male,32.0,9.0,98439.37,1.0,1.0,0.0,162599.51,0.0
+6939,15782899.0,Chukwuemeka,725.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,173576.71,0.0
+6940,15776433.0,Nickson,549.0,France,Male,28.0,9.0,120962.74,1.0,1.0,0.0,100862.36,0.0
+6941,15726796.0,Ahern,735.0,France,Female,37.0,6.0,131778.08,1.0,1.0,1.0,100722.72,0.0
+6942,15646726.0,Chibueze,681.0,France,Female,31.0,6.0,0.0,2.0,1.0,1.0,167984.72,0.0
+6943,15678882.0,P'eng,769.0,France,Male,38.0,1.0,0.0,1.0,1.0,0.0,83912.24,0.0
+6944,15680683.0,Chinweuba,687.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,1.0,94875.03,0.0
+6945,15597978.0,Kao,578.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,58815.25,0.0
+6946,15655952.0,Hao,636.0,Germany,Male,19.0,9.0,97786.08,2.0,1.0,0.0,142223.94,0.0
+6947,15800811.0,Gadsden,753.0,Spain,Female,45.0,3.0,0.0,1.0,1.0,1.0,162719.69,1.0
+6948,15723989.0,Ts'ui,656.0,France,Male,59.0,4.0,0.0,2.0,1.0,1.0,199357.24,0.0
+6949,15731582.0,Shubin,628.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,180439.75,0.0
+6950,15703793.0,Hsiao,556.0,Germany,Female,46.0,4.0,115285.85,2.0,1.0,0.0,171657.57,0.0
+6951,15763431.0,Lombardo,498.0,Germany,Female,48.0,5.0,161254.78,1.0,1.0,0.0,71178.09,1.0
+6952,15631162.0,Ts'ui,622.0,France,Female,27.0,4.0,0.0,2.0,1.0,0.0,126728.27,0.0
+6953,15673747.0,Nnachetam,644.0,Germany,Female,51.0,8.0,133903.12,1.0,1.0,0.0,177747.03,1.0
+6954,15737051.0,Hsieh,663.0,Germany,Male,40.0,10.0,123783.25,2.0,1.0,0.0,169921.11,0.0
+6955,15790829.0,Biryukov,743.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,151902.78,0.0
+6956,15596060.0,Ch'iu,811.0,Germany,Female,34.0,4.0,131365.37,2.0,1.0,1.0,131521.72,0.0
+6957,15674929.0,Ting,627.0,France,Male,28.0,7.0,0.0,2.0,0.0,0.0,133692.88,0.0
+6958,15761047.0,K?,649.0,France,Female,38.0,8.0,86047.74,1.0,1.0,1.0,138360.29,0.0
+6959,15589660.0,Ritchie,588.0,France,Female,29.0,1.0,0.0,2.0,1.0,1.0,113428.77,0.0
+6960,15583716.0,Shih,691.0,Spain,Female,32.0,6.0,0.0,2.0,0.0,1.0,98820.09,0.0
+6961,15791342.0,Shao,538.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,140937.91,1.0
+6962,15571529.0,Ch'ien,683.0,France,Male,29.0,3.0,0.0,1.0,1.0,0.0,155711.64,0.0
+6963,15582519.0,Toscani,634.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,162889.1,0.0
+6964,15731751.0,Bibi,667.0,Spain,Male,47.0,2.0,111432.77,1.0,1.0,0.0,132959.33,0.0
+6965,15744044.0,Ch'eng,741.0,Spain,Male,30.0,10.0,0.0,2.0,1.0,0.0,178074.04,0.0
+6966,15651022.0,Brown,811.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,170142.09,0.0
+6967,15752508.0,Hs?eh,807.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,180598.66,0.0
+6968,15651369.0,Y?,695.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,1.0,44904.26,0.0
+6969,15695792.0,Ch'en,710.0,France,Male,31.0,3.0,132405.52,1.0,1.0,1.0,86490.09,0.0
+6970,15653857.0,Cremonesi,773.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,91574.92,0.0
+6971,15601012.0,Ch'ien,691.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,82938.99,0.0
+6972,15711889.0,Ch'ang,684.0,France,Male,38.0,5.0,0.0,1.0,1.0,0.0,87938.85,0.0
+6973,15592846.0,Ch'en,752.0,France,Male,31.0,7.0,0.0,1.0,1.0,1.0,188516.45,0.0
+6974,15654891.0,Chin,605.0,Germany,Male,31.0,7.0,125552.96,1.0,1.0,0.0,151481.86,0.0
+6975,15611171.0,Tsao,639.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,97640.2,0.0
+6976,15691808.0,Fanucci,526.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,67840.81,0.0
+6977,15793693.0,Echezonachukwu,730.0,France,Male,31.0,4.0,96173.17,1.0,1.0,0.0,129525.5,0.0
+6978,15575694.0,Nkemakolam,710.0,Germany,Female,41.0,1.0,92887.06,2.0,1.0,1.0,172576.47,0.0
+6979,15802381.0,Sun,570.0,France,Male,27.0,8.0,0.0,2.0,1.0,1.0,48545.1,0.0
+6980,15656300.0,Ch'iu,726.0,Spain,Female,24.0,9.0,128743.55,1.0,1.0,0.0,167873.49,0.0
+6981,15727299.0,Chien,691.0,Germany,Male,38.0,1.0,71097.23,1.0,0.0,0.0,97624.02,0.0
+6982,15772798.0,Chijindum,682.0,France,Female,52.0,7.0,0.0,1.0,1.0,1.0,82820.85,1.0
+6983,15739046.0,Tien,572.0,France,Male,28.0,7.0,160980.04,1.0,1.0,0.0,82298.61,0.0
+6984,15739476.0,L?,656.0,France,Male,44.0,1.0,0.0,2.0,1.0,1.0,127068.92,0.0
+6985,15769412.0,Lucchese,726.0,France,Male,25.0,5.0,121260.19,1.0,1.0,0.0,168034.9,0.0
+6986,15651627.0,Pagnotto,839.0,France,Male,37.0,3.0,0.0,1.0,0.0,1.0,86920.97,0.0
+6987,15656062.0,Chigolum,642.0,Spain,Male,52.0,4.0,0.0,2.0,1.0,1.0,157567.37,0.0
+6988,15733229.0,Folliero,809.0,France,Male,20.0,8.0,0.0,1.0,1.0,1.0,100557.03,0.0
+6989,15659194.0,Scott,694.0,France,Male,23.0,9.0,0.0,2.0,1.0,1.0,125823.79,0.0
+6990,15579526.0,Folliero,705.0,France,Female,39.0,5.0,0.0,3.0,0.0,1.0,89801.9,1.0
+6991,15572467.0,Chiang,681.0,Germany,Male,43.0,6.0,118336.95,1.0,0.0,0.0,162858.24,1.0
+6992,15680224.0,Scott,708.0,France,Female,24.0,8.0,0.0,2.0,1.0,1.0,177519.39,0.0
+6993,15736578.0,Scott,595.0,France,Male,34.0,8.0,0.0,1.0,0.0,0.0,79329.7,0.0
+6994,15716608.0,Tsai,562.0,Germany,Male,38.0,1.0,116880.53,1.0,0.0,1.0,71047.51,0.0
+6995,15592222.0,Tien,773.0,Germany,Female,39.0,9.0,106603.74,1.0,0.0,1.0,122449.18,0.0
+6996,15656627.0,Maclean,678.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,143783.28,0.0
+6997,15588535.0,Ch'in,637.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,143614.01,0.0
+6998,15771742.0,Tseng,741.0,Spain,Female,41.0,10.0,0.0,1.0,1.0,0.0,152567.75,0.0
+6999,15770185.0,Frolov,674.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,0.0,86981.45,0.0
+7000,15646539.0,L?,749.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,46857.52,0.0
+7001,15646566.0,Pagnotto,599.0,France,Male,23.0,7.0,0.0,2.0,1.0,1.0,121840.2,0.0
+7002,15759499.0,Okwudiliolisa,554.0,Germany,Male,34.0,6.0,116936.81,1.0,1.0,0.0,72846.58,1.0
+7003,15594786.0,Liao,646.0,Germany,Female,40.0,4.0,108914.17,1.0,1.0,0.0,142782.25,1.0
+7004,15776743.0,Hs?,732.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,106655.88,0.0
+7005,15656613.0,Toscani,616.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,57929.8,0.0
+7006,15771543.0,Ch'ang,653.0,France,Female,34.0,4.0,0.0,2.0,0.0,0.0,153727.32,0.0
+7007,15598044.0,Oluchukwu,739.0,France,Male,31.0,3.0,0.0,1.0,1.0,1.0,100137.7,0.0
+7008,15700460.0,Trevisani,686.0,Spain,Female,54.0,5.0,0.0,1.0,1.0,0.0,172490.78,1.0
+7009,15790744.0,Hsieh,775.0,Germany,Female,37.0,10.0,99462.8,2.0,1.0,0.0,74580.8,0.0
+7010,15749167.0,Smith,741.0,Germany,Female,46.0,10.0,71842.91,3.0,1.0,0.0,61538.43,1.0
+7011,15771535.0,Lo,699.0,Spain,Male,39.0,2.0,118680.57,1.0,1.0,1.0,153535.38,0.0
+7012,15790689.0,Folliero,645.0,Germany,Male,35.0,8.0,153313.77,2.0,1.0,0.0,164741.5,0.0
+7013,15670080.0,Chien,751.0,France,Male,25.0,7.0,0.0,2.0,1.0,1.0,60906.12,0.0
+7014,15701054.0,Nieves,740.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,68600.36,0.0
+7015,15659366.0,O'Donnell,653.0,France,Male,23.0,8.0,0.0,2.0,1.0,0.0,74048.1,0.0
+7016,15774551.0,Scott,713.0,France,Male,24.0,6.0,183572.84,1.0,1.0,0.0,161217.18,0.0
+7017,15773773.0,Smith,678.0,Spain,Female,33.0,0.0,0.0,2.0,1.0,1.0,151623.91,0.0
+7018,15592078.0,McKay,663.0,France,Female,30.0,9.0,0.0,2.0,0.0,1.0,180551.76,0.0
+7019,15584897.0,Chikwado,732.0,Germany,Female,30.0,2.0,115309.75,2.0,1.0,1.0,26327.42,0.0
+7020,15806922.0,Mazzanti,640.0,France,Female,63.0,7.0,0.0,1.0,1.0,0.0,125962.02,1.0
+7021,15775042.0,Nkemakolam,763.0,Spain,Male,34.0,7.0,0.0,2.0,0.0,0.0,134732.99,0.0
+7022,15596074.0,H?,743.0,France,Female,39.0,3.0,0.0,2.0,1.0,1.0,58467.08,0.0
+7023,15682914.0,Chigolum,827.0,Spain,Male,56.0,6.0,0.0,1.0,1.0,1.0,143022.43,1.0
+7024,15810418.0,Chiazagomekpere,706.0,Spain,Male,62.0,9.0,97445.49,1.0,1.0,1.0,58879.8,0.0
+7025,15633378.0,Chu,634.0,France,Male,34.0,1.0,129728.5,1.0,1.0,1.0,47799.15,0.0
+7026,15662093.0,H?,673.0,France,Male,38.0,1.0,0.0,1.0,0.0,0.0,109895.16,0.0
+7027,15687310.0,Nnaife,653.0,Spain,Female,49.0,9.0,131354.53,1.0,1.0,0.0,123840.68,1.0
+7028,15628600.0,Yip,587.0,Spain,Male,67.0,4.0,0.0,2.0,0.0,1.0,166677.54,0.0
+7029,15714883.0,Kao,639.0,France,Female,38.0,1.0,0.0,1.0,1.0,0.0,86937.2,0.0
+7030,15667932.0,Lombardi,628.0,France,Female,60.0,4.0,0.0,1.0,1.0,1.0,125979.36,1.0
+7031,15591512.0,Nnachetam,573.0,France,Male,30.0,1.0,136526.26,1.0,1.0,0.0,96422.44,0.0
+7032,15723685.0,Udegbulam,595.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,62222.81,0.0
+7033,15783882.0,Obielumani,832.0,France,Male,36.0,8.0,92219.29,1.0,1.0,0.0,6078.46,0.0
+7034,15597901.0,P'eng,685.0,France,Male,33.0,3.0,141581.07,1.0,0.0,1.0,143783.98,0.0
+7035,15681081.0,Toscani,703.0,Germany,Female,37.0,1.0,106920.98,1.0,1.0,1.0,97640.2,0.0
+7036,15702434.0,Kao,593.0,France,Female,37.0,2.0,0.0,2.0,0.0,0.0,64907.48,0.0
+7037,15566543.0,Milani,686.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,138021.36,0.0
+7038,15602551.0,Hsiao,653.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,0.0,136071.05,0.0
+7039,15752956.0,Okeke,592.0,Germany,Female,35.0,9.0,127723.25,1.0,1.0,0.0,140130.22,1.0
+7040,15628420.0,Chukwuemeka,516.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,48673.18,0.0
+7041,15782659.0,Nwankwo,803.0,Germany,Female,42.0,1.0,116546.59,3.0,1.0,0.0,174438.54,1.0
+7042,15644400.0,Efremov,623.0,France,Female,33.0,9.0,0.0,2.0,1.0,1.0,55601.44,0.0
+7043,15664793.0,Tisdall,651.0,France,Female,56.0,8.0,0.0,1.0,0.0,0.0,134417.31,1.0
+7044,15698716.0,Echezonachukwu,707.0,France,Female,46.0,6.0,0.0,1.0,1.0,0.0,121840.2,1.0
+7045,15634373.0,Onyekaozulu,597.0,France,Female,33.0,8.0,0.0,1.0,1.0,0.0,83090.35,1.0
+7046,15794345.0,Ch'in,712.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,124749.08,0.0
+7047,15602034.0,Tuan,614.0,France,Female,39.0,1.0,158718.13,1.0,1.0,1.0,71154.1,0.0
+7048,15709741.0,Aitken,714.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,88958.49,0.0
+7049,15677322.0,Chukwuemeka,706.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,91510.71,0.0
+7050,15684921.0,Cremonesi,717.0,Spain,Female,32.0,7.0,151236.96,2.0,1.0,0.0,152992.04,0.0
+7051,15715394.0,Y?an,742.0,Germany,Male,30.0,3.0,141074.59,1.0,0.0,1.0,146785.44,0.0
+7052,15684296.0,Lu,749.0,Spain,Male,39.0,5.0,131078.47,1.0,0.0,1.0,155870.79,0.0
+7053,15751177.0,Sagese,637.0,France,Female,48.0,5.0,0.0,1.0,1.0,1.0,152298.28,1.0
+7054,15749401.0,Ch'ien,773.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,148098.95,0.0
+7055,15596979.0,Palerma,556.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,166495.2,0.0
+7056,15639272.0,Burdekin,697.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,151822.66,0.0
+7057,15675888.0,Chukwukere,719.0,France,Female,29.0,7.0,68432.45,1.0,0.0,0.0,89048.46,0.0
+7058,15773394.0,O'Loughlin,768.0,Germany,Female,38.0,2.0,125470.81,1.0,0.0,0.0,173870.39,1.0
+7059,15705739.0,Chukwukadibia,565.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,136330.32,0.0
+7060,15802466.0,K?,649.0,Spain,Male,49.0,8.0,0.0,2.0,1.0,1.0,182059.28,0.0
+7061,15786739.0,Sagese,653.0,France,Male,41.0,2.0,0.0,1.0,1.0,0.0,164975.82,1.0
+7062,15630402.0,Ch'iu,526.0,France,Female,51.0,10.0,0.0,1.0,0.0,1.0,171378.57,1.0
+7063,15750731.0,Chia,748.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,111340.36,0.0
+7064,15722404.0,Ts'ui,644.0,France,Female,39.0,7.0,0.0,2.0,0.0,0.0,180862.26,0.0
+7065,15768359.0,Ch'ien,758.0,France,Male,25.0,4.0,0.0,2.0,0.0,1.0,163989.66,0.0
+7066,15766017.0,Rizzo,684.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,66797.79,0.0
+7067,15691703.0,Palerma,602.0,France,Male,38.0,5.0,0.0,2.0,0.0,0.0,145561.9,1.0
+7068,15576094.0,H?,709.0,France,Male,32.0,6.0,113781.1,1.0,1.0,0.0,179908.71,0.0
+7069,15696744.0,Chin,554.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,159800.16,0.0
+7070,15691457.0,Mazzanti,731.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,17848.3,0.0
+7071,15800524.0,Wright,622.0,France,Female,48.0,4.0,0.0,2.0,1.0,0.0,135650.29,0.0
+7072,15783029.0,Shao,850.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,83052.03,0.0
+7073,15636407.0,Macdonald,583.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,132468.76,0.0
+7074,15743490.0,Ch'iu,669.0,France,Female,27.0,7.0,0.0,2.0,0.0,0.0,163570.25,0.0
+7075,15769451.0,Chizoba,712.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,159737.56,0.0
+7076,15646726.0,Lucciano,605.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,122925.4,0.0
+7077,15589428.0,Iweobiegbunam,584.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,166475.44,0.0
+7078,15750874.0,Trevisani,634.0,France,Male,53.0,7.0,137148.68,1.0,0.0,0.0,115751.03,1.0
+7079,15809909.0,T'ang,716.0,Spain,Male,37.0,2.0,153316.14,1.0,0.0,0.0,73882.49,0.0
+7080,15585823.0,Hsueh,758.0,France,Female,52.0,1.0,0.0,1.0,1.0,1.0,5907.11,1.0
+7081,15796413.0,Ndubuagha,731.0,France,Male,44.0,2.0,0.0,2.0,1.0,1.0,170835.32,0.0
+7082,15697480.0,Scott,669.0,Germany,Female,32.0,10.0,104405.54,1.0,1.0,0.0,138882.98,0.0
+7083,15641158.0,Lassetter,755.0,France,Female,51.0,4.0,169831.46,1.0,0.0,1.0,185599.34,1.0
+7084,15712772.0,Okwudiliolisa,597.0,Germany,Male,28.0,0.0,141952.07,1.0,0.0,0.0,54871.02,0.0
+7085,15650889.0,Lu,734.0,France,Male,59.0,6.0,0.0,2.0,1.0,1.0,170487.4,0.0
+7086,15682834.0,Ch'in,659.0,Spain,Female,43.0,5.0,146034.74,1.0,1.0,1.0,173948.4,0.0
+7087,15761720.0,De Garis,683.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,107499.8,0.0
+7088,15783875.0,Smith,668.0,France,Female,49.0,8.0,0.0,2.0,1.0,0.0,172437.12,0.0
+7089,15595883.0,Chien,603.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,167773.55,0.0
+7090,15782236.0,Lu,678.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,0.0,167984.72,0.0
+7091,15667944.0,Hsing,634.0,France,Female,46.0,9.0,0.0,3.0,1.0,1.0,147794.63,1.0
+7092,15685314.0,Mironova,479.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,167087.34,1.0
+7093,15777639.0,Zetticci,594.0,Germany,Female,37.0,9.0,141788.63,2.0,0.0,1.0,176770.39,0.0
+7094,15684908.0,Chiawuotu,573.0,Spain,Female,42.0,2.0,0.0,2.0,1.0,0.0,106826.92,0.0
+7095,15709511.0,Hsia,706.0,Spain,Male,35.0,5.0,0.0,2.0,0.0,0.0,126223.84,0.0
+7096,15757777.0,Huddart,718.0,Germany,Female,38.0,1.0,145127.85,1.0,1.0,1.0,38962.94,1.0
+7097,15643908.0,Onwuamaeze,614.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,92381.01,0.0
+7098,15742378.0,Pisano,762.0,Germany,Female,35.0,3.0,145605.44,1.0,1.0,0.0,142516.35,0.0
+7099,15650034.0,Onyekachukwu,705.0,Germany,Male,34.0,3.0,117731.79,1.0,0.0,1.0,59636.43,0.0
+7100,15662641.0,De Luca,775.0,Spain,Male,41.0,8.0,0.0,2.0,1.0,1.0,170819.01,0.0
+7101,15623062.0,Aitken,614.0,France,Female,36.0,2.0,136624.6,1.0,0.0,1.0,119927.81,0.0
+7102,15576077.0,Olisanugo,727.0,France,Female,67.0,1.0,0.0,2.0,0.0,1.0,102036.82,0.0
+7103,15777076.0,Genovese,828.0,Spain,Female,50.0,4.0,0.0,1.0,1.0,1.0,88890.75,1.0
+7104,15681316.0,Genovese,750.0,France,Male,48.0,7.0,0.0,1.0,1.0,1.0,146949.74,1.0
+7105,15750056.0,Chu,786.0,Germany,Female,40.0,6.0,126473.33,2.0,1.0,0.0,60736.39,0.0
+7106,15793881.0,Chukwujamuike,724.0,France,Male,40.0,9.0,129745.1,1.0,1.0,1.0,197885.72,0.0
+7107,15571752.0,Molineux,609.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,72318.77,0.0
+7108,15682453.0,Rizzo,662.0,Spain,Female,40.0,1.0,132837.7,2.0,0.0,0.0,164967.21,0.0
+7109,15764195.0,Yefimova,671.0,France,Male,31.0,2.0,106059.4,1.0,1.0,1.0,187826.11,0.0
+7110,15635244.0,Ndukaku,705.0,France,Male,37.0,0.0,0.0,2.0,1.0,0.0,79495.01,0.0
+7111,15752994.0,Ndubuagha,613.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,111097.39,0.0
+7112,15778916.0,Tung,651.0,Spain,Male,49.0,0.0,0.0,2.0,1.0,0.0,106920.57,1.0
+7113,15585748.0,Yermolayev,585.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,159835.48,0.0
+7114,15628999.0,Ogochukwu,717.0,Spain,Male,45.0,5.0,0.0,2.0,1.0,1.0,179964.55,0.0
+7115,15571958.0,P'eng,769.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,193014.26,0.0
+7116,15749671.0,Shih,576.0,France,Male,25.0,8.0,0.0,2.0,1.0,0.0,91044.47,0.0
+7117,15606552.0,T'ao,581.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,0.0,143096.49,0.0
+7118,15763515.0,Onyekaozulu,705.0,France,Male,29.0,3.0,0.0,1.0,1.0,0.0,183515.13,0.0
+7119,15693902.0,Kao,682.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,172668.39,0.0
+7120,15604093.0,Hsu,683.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,143859.66,0.0
+7121,15794468.0,Fiorentino,731.0,Germany,Female,43.0,9.0,127924.95,1.0,1.0,0.0,109496.45,1.0
+7122,15699294.0,Ch'ien,663.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,129933.5,0.0
+7123,15572390.0,Onwuamaeze,653.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,122964.18,0.0
+7124,15694641.0,Ting,584.0,France,Male,27.0,8.0,123599.49,1.0,1.0,0.0,86977.53,0.0
+7125,15752912.0,Ponomaryova,687.0,Germany,Female,30.0,9.0,90218.11,1.0,1.0,1.0,168053.4,0.0
+7126,15733705.0,Manna,657.0,Spain,Female,24.0,2.0,0.0,2.0,1.0,0.0,79734.23,0.0
+7127,15592229.0,Chukwukadibia,669.0,France,Male,32.0,1.0,0.0,1.0,0.0,0.0,145562.4,0.0
+7128,15723658.0,Uspensky,526.0,Spain,Male,49.0,2.0,80728.15,1.0,0.0,1.0,149584.61,1.0
+7129,15657829.0,Ch'iu,634.0,France,Female,43.0,3.0,0.0,1.0,0.0,1.0,63795.62,1.0
+7130,15654562.0,Nkemakonam,681.0,France,Male,36.0,7.0,140700.61,1.0,1.0,0.0,161579.85,0.0
+7131,15689886.0,Ch'in,703.0,France,Female,35.0,10.0,132944.35,1.0,1.0,0.0,107874.39,0.0
+7132,15757931.0,Palerma,603.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,102908.91,0.0
+7133,15807433.0,Shao,585.0,France,Male,24.0,2.0,0.0,2.0,1.0,0.0,84870.23,0.0
+7134,15581551.0,H?,597.0,Spain,Male,27.0,7.0,0.0,2.0,1.0,1.0,66580.13,0.0
+7135,15645593.0,Ch'iu,741.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,113868.4,0.0
+7136,15631877.0,Watson,669.0,France,Female,42.0,1.0,0.0,1.0,1.0,0.0,17095.49,1.0
+7137,15615753.0,Shubin,633.0,Germany,Male,28.0,1.0,133658.24,1.0,1.0,0.0,140197.42,0.0
+7138,15616122.0,Scott,667.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,109135.11,0.0
+7139,15585892.0,Liao,613.0,France,Male,31.0,9.0,0.0,2.0,0.0,0.0,149575.18,0.0
+7140,15782531.0,Soto,633.0,Germany,Female,31.0,5.0,121461.63,1.0,1.0,0.0,44351.77,0.0
+7141,15684318.0,Chidozie,747.0,Germany,Male,39.0,6.0,153946.14,2.0,1.0,0.0,94949.84,0.0
+7142,15589212.0,Fomin,648.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,174094.24,0.0
+7143,15759717.0,Longstaff,584.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,134901.56,0.0
+7144,15724269.0,Milani,635.0,France,Male,29.0,8.0,0.0,1.0,1.0,0.0,55868.97,0.0
+7145,15795878.0,Ch'in,710.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,110525.6,0.0
+7146,15606945.0,Nwankwo,694.0,Germany,Female,40.0,8.0,129934.64,1.0,1.0,0.0,75562.47,0.0
+7147,15680303.0,Ch'en,679.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,0.0,71154.1,0.0
+7148,15623989.0,Lockington,585.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,139607.71,0.0
+7149,15795511.0,O'Brien,638.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,157567.37,0.0
+7150,15783629.0,Nkemdilim,699.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,160129.99,0.0
+7151,15808831.0,Onuoha,590.0,Germany,Female,29.0,8.0,169831.46,2.0,1.0,0.0,35356.46,0.0
+7152,15642725.0,Hsueh,675.0,Spain,Female,41.0,7.0,0.0,2.0,0.0,0.0,151902.78,0.0
+7153,15605037.0,Onyekaozulu,628.0,France,Male,30.0,9.0,141827.33,1.0,1.0,1.0,124489.88,0.0
+7154,15713559.0,Tuan,648.0,France,Female,33.0,5.0,0.0,1.0,1.0,0.0,162022.47,1.0
+7155,15791533.0,Mamelu,711.0,France,Male,22.0,8.0,0.0,2.0,1.0,1.0,86022.09,0.0
+7156,15734659.0,Hsia,628.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,151335.24,0.0
+7157,15731555.0,Lucciano,724.0,Germany,Female,52.0,1.0,122398.08,1.0,1.0,1.0,131778.58,1.0
+7158,15696733.0,Yegorova,670.0,Spain,Female,39.0,7.0,0.0,2.0,0.0,1.0,144413.92,0.0
+7159,15777901.0,Nwora,656.0,France,Female,35.0,4.0,0.0,2.0,1.0,0.0,174027.3,0.0
+7160,15591135.0,Shih,642.0,France,Male,31.0,8.0,98795.61,1.0,1.0,0.0,113695.99,0.0
+7161,15744517.0,Toscani,651.0,Spain,Female,42.0,4.0,0.0,1.0,1.0,0.0,45445.78,1.0
+7162,15713578.0,Nucci,641.0,France,Female,56.0,8.0,139584.57,1.0,1.0,0.0,167984.72,1.0
+7163,15682211.0,Ch'ien,613.0,France,Male,36.0,2.0,128747.69,1.0,1.0,1.0,143783.28,0.0
+7164,15572374.0,Zikoranachidimma,659.0,France,Male,34.0,3.0,0.0,1.0,1.0,0.0,136910.18,0.0
+7165,15672970.0,Folliero,732.0,France,Male,44.0,3.0,0.0,3.0,0.0,0.0,105720.09,0.0
+7166,15633260.0,Chidubem,561.0,Spain,Female,27.0,8.0,0.0,2.0,1.0,1.0,107961.11,0.0
+7167,15796834.0,Nkemdilim,762.0,Spain,Female,41.0,8.0,0.0,1.0,1.0,0.0,140763.18,1.0
+7168,15698872.0,Macartney,645.0,France,Female,28.0,3.0,114084.6,2.0,1.0,0.0,74532.02,0.0
+7169,15764021.0,Nwebube,628.0,France,Male,52.0,1.0,0.0,1.0,1.0,0.0,133537.53,1.0
+7170,15705085.0,Rizzo,590.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,1.0,10789.3,0.0
+7171,15781210.0,Chiu,769.0,Spain,Female,34.0,1.0,0.0,2.0,1.0,1.0,82438.13,0.0
+7172,15708063.0,Fanucci,661.0,France,Male,26.0,3.0,0.0,2.0,1.0,1.0,101168.9,0.0
+7173,15695792.0,Fenton,716.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,95611.47,0.0
+7174,15640074.0,Dyer,667.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,56793.02,0.0
+7175,15624677.0,Nwokeocha,738.0,Spain,Female,35.0,8.0,0.0,2.0,1.0,1.0,122433.09,0.0
+7176,15658532.0,Chien,686.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,59601.15,0.0
+7177,15643826.0,T'ien,642.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,168080.53,0.0
+7178,15611644.0,Mazzanti,697.0,France,Male,39.0,0.0,83061.26,1.0,1.0,0.0,121894.31,0.0
+7179,15704954.0,Milanesi,714.0,Germany,Female,26.0,4.0,108432.88,1.0,1.0,1.0,91044.65,0.0
+7180,15652218.0,Mays,682.0,France,Male,30.0,1.0,131394.56,1.0,1.0,1.0,143952.24,0.0
+7181,15703394.0,Iadanza,638.0,France,Female,43.0,2.0,0.0,3.0,0.0,0.0,82820.85,1.0
+7182,15671630.0,Hs?,662.0,Spain,Female,71.0,6.0,0.0,2.0,1.0,1.0,61977.82,0.0
+7183,15769959.0,Chia,641.0,France,Male,40.0,8.0,127475.69,1.0,1.0,0.0,106061.47,0.0
+7184,15651958.0,Tuan,712.0,France,Female,53.0,0.0,0.0,2.0,1.0,1.0,56518.0,0.0
+7185,15619340.0,Chidubem,588.0,France,Male,39.0,0.0,127900.03,1.0,0.0,1.0,187811.71,0.0
+7186,15576824.0,Nkemdirim,544.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,118863.42,0.0
+7187,15662854.0,Tuan,716.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,101300.94,0.0
+7188,15593550.0,Uspenskaya,579.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,150475.14,0.0
+7189,15572390.0,Toscani,669.0,France,Female,72.0,4.0,0.0,1.0,1.0,1.0,142400.27,0.0
+7190,15737571.0,Chibugo,613.0,Spain,Female,25.0,9.0,0.0,2.0,0.0,1.0,68106.29,0.0
+7191,15784320.0,Ch'ang,605.0,Germany,Female,30.0,9.0,141952.07,1.0,1.0,1.0,164549.74,0.0
+7192,15579130.0,Onyemauchechi,710.0,France,Male,36.0,2.0,0.0,1.0,1.0,1.0,161479.19,0.0
+7193,15632256.0,Ts'ui,646.0,France,Male,36.0,1.0,0.0,1.0,1.0,1.0,173486.39,0.0
+7194,15733705.0,Hsueh,708.0,France,Male,47.0,4.0,0.0,2.0,1.0,1.0,155870.82,0.0
+7195,15672756.0,Ch'eng,706.0,France,Male,41.0,8.0,0.0,1.0,0.0,0.0,152490.79,1.0
+7196,15656300.0,Tsou,627.0,Spain,Male,34.0,2.0,0.0,1.0,1.0,0.0,137763.99,0.0
+7197,15780128.0,Bellucci,775.0,France,Male,35.0,5.0,0.0,2.0,0.0,1.0,162734.31,0.0
+7198,15651713.0,K?,648.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,142400.27,0.0
+7199,15648047.0,Chibuzo,624.0,Germany,Male,42.0,2.0,137714.25,1.0,0.0,0.0,78416.14,1.0
+7200,15737509.0,L?,564.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,152843.53,0.0
+7201,15578251.0,Pinto,656.0,Germany,Female,34.0,8.0,144974.55,1.0,1.0,1.0,58974.0,1.0
+7202,15664504.0,Onyekaozulu,774.0,Germany,Male,45.0,6.0,152582.2,1.0,0.0,0.0,46415.09,0.0
+7203,15631739.0,Omeokachie,773.0,France,Female,51.0,0.0,0.0,1.0,1.0,0.0,56583.88,1.0
+7204,15647800.0,Maclean,581.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,18082.06,0.0
+7205,15694530.0,Yobachukwu,773.0,France,Male,38.0,1.0,69423.52,1.0,1.0,0.0,162760.96,0.0
+7206,15787772.0,Genovesi,683.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,79691.4,0.0
+7207,15698932.0,Ritchie,572.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,75562.47,0.0
+7208,15571081.0,Macnamara,803.0,Germany,Female,46.0,4.0,135756.96,1.0,0.0,0.0,150664.97,1.0
+7209,15776047.0,Hsieh,558.0,France,Female,49.0,5.0,0.0,1.0,1.0,0.0,146636.44,1.0
+7210,15775131.0,Onyekaozulu,599.0,Spain,Male,29.0,6.0,0.0,2.0,0.0,0.0,121408.56,0.0
+7211,15724453.0,Baryshnikov,792.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,132029.95,0.0
+7212,15661745.0,McGregor,684.0,Spain,Female,33.0,3.0,145105.64,1.0,1.0,0.0,122964.18,0.0
+7213,15617454.0,Teng,683.0,France,Male,26.0,8.0,0.0,1.0,1.0,1.0,74835.65,0.0
+7214,15759034.0,Lai,637.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,183358.21,0.0
+7215,15634606.0,Macleod,535.0,France,Female,60.0,5.0,0.0,1.0,1.0,1.0,167553.06,1.0
+7216,15664523.0,Ritchie,773.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,48962.59,0.0
+7217,15710257.0,T'an,549.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,52449.61,0.0
+7218,15638247.0,Udegbunam,705.0,France,Male,36.0,5.0,83348.89,1.0,0.0,0.0,179843.33,0.0
+7219,15663888.0,Chin,698.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,57896.27,0.0
+7220,15731555.0,Fanucci,682.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,1.0,182257.61,0.0
+7221,15681174.0,Ch'eng,599.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,177092.16,0.0
+7222,15793369.0,Ch'in,719.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,74858.3,0.0
+7223,15762268.0,Chukwudi,710.0,France,Female,35.0,7.0,0.0,2.0,1.0,1.0,53420.53,0.0
+7224,15605039.0,Ugorji,614.0,Germany,Male,47.0,6.0,100549.42,1.0,1.0,0.0,152499.8,1.0
+7225,15781477.0,Ch'eng,757.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,146723.72,0.0
+7226,15713346.0,Niu,807.0,Germany,Female,43.0,7.0,127864.4,1.0,1.0,0.0,105732.9,1.0
+7227,15592877.0,T'ien,754.0,France,Female,45.0,8.0,0.0,2.0,1.0,1.0,143960.72,0.0
+7228,15742824.0,Uvarova,556.0,France,Female,25.0,6.0,0.0,2.0,1.0,0.0,164925.11,0.0
+7229,15596971.0,Walker,748.0,Spain,Male,36.0,10.0,0.0,2.0,1.0,0.0,109096.71,0.0
+7230,15621370.0,Hsiao,615.0,France,Female,47.0,3.0,0.0,4.0,1.0,0.0,171096.2,1.0
+7231,15585928.0,She,613.0,Germany,Female,44.0,9.0,152992.91,2.0,0.0,1.0,137674.55,0.0
+7232,15651460.0,Milanesi,718.0,Germany,Female,35.0,8.0,123953.31,1.0,1.0,0.0,11.58,1.0
+7233,15605037.0,Chigolum,554.0,Spain,Male,48.0,2.0,0.0,2.0,1.0,1.0,125979.36,0.0
+7234,15659710.0,Hsieh,604.0,France,Female,30.0,2.0,123311.39,1.0,1.0,0.0,73581.05,0.0
+7235,15780128.0,Pirozzi,487.0,France,Female,43.0,1.0,0.0,1.0,1.0,1.0,60427.3,0.0
+7236,15617648.0,Liao,606.0,France,Male,33.0,4.0,122793.96,1.0,0.0,0.0,106939.34,0.0
+7237,15687923.0,Y?,707.0,France,Male,23.0,5.0,0.0,1.0,1.0,0.0,155799.86,0.0
+7238,15774730.0,Yobachukwu,616.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,0.0,163477.22,0.0
+7239,15703399.0,Chiang,584.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,135875.51,0.0
+7240,15679997.0,Smith,731.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,58879.31,0.0
+7241,15683817.0,Ch'ang,850.0,Germany,Female,40.0,3.0,122021.92,2.0,1.0,1.0,126241.4,0.0
+7242,15642826.0,Ozioma,633.0,France,Male,33.0,6.0,123823.43,1.0,1.0,1.0,151146.67,0.0
+7243,15636061.0,Lo,684.0,Germany,Male,35.0,3.0,109733.2,2.0,1.0,0.0,173989.66,0.0
+7244,15782475.0,Tien,613.0,France,Female,33.0,1.0,121345.88,1.0,0.0,1.0,166475.79,0.0
+7245,15781298.0,Y?an,686.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,134901.58,0.0
+7246,15592028.0,Hsiao,676.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,102083.78,1.0
+7247,15708063.0,Chuang,599.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,45610.63,0.0
+7248,15590094.0,Smith,636.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,57531.14,0.0
+7249,15652693.0,Ch'eng,591.0,Spain,Female,41.0,6.0,0.0,1.0,1.0,0.0,179634.69,1.0
+7250,15691119.0,K?,584.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,0.0,184514.01,0.0
+7251,15576077.0,T'ang,594.0,France,Male,36.0,2.0,0.0,2.0,0.0,1.0,57862.88,0.0
+7252,15591552.0,Chukwumaobim,642.0,France,Male,36.0,6.0,0.0,2.0,0.0,0.0,129526.57,0.0
+7253,15571550.0,Smith,768.0,France,Male,22.0,5.0,138162.81,1.0,1.0,1.0,83715.66,0.0
+7254,15583748.0,Chukwukadibia,567.0,France,Female,36.0,5.0,0.0,1.0,1.0,0.0,181297.65,1.0
+7255,15641389.0,Chin,593.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,187921.5,0.0
+7256,15583857.0,Hsieh,730.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,149853.89,0.0
+7257,15802479.0,Fisk,579.0,France,Male,45.0,5.0,0.0,2.0,0.0,0.0,176913.42,0.0
+7258,15774164.0,Okwudilichukwu,732.0,Spain,Female,27.0,4.0,0.0,2.0,1.0,0.0,149697.78,0.0
+7259,15746515.0,K'ung,714.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,164768.82,0.0
+7260,15787046.0,Rizzo,613.0,France,Female,45.0,4.0,0.0,2.0,0.0,0.0,52887.06,0.0
+7261,15741092.0,Chien,638.0,France,Male,25.0,2.0,0.0,2.0,1.0,1.0,72993.65,0.0
+7262,15794273.0,Yevseyev,718.0,Germany,Female,46.0,5.0,133618.01,1.0,1.0,0.0,80593.49,1.0
+7263,15743293.0,Chinwendu,730.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,97939.3,0.0
+7264,15793042.0,Ch'en,636.0,France,Female,35.0,10.0,0.0,1.0,1.0,0.0,95611.47,0.0
+7265,15625706.0,Nkemjika,628.0,Germany,Male,41.0,8.0,102576.06,1.0,1.0,0.0,141152.28,1.0
+7266,15623502.0,Ch'ien,681.0,Germany,Male,47.0,2.0,164701.68,1.0,1.0,0.0,72318.77,0.0
+7267,15605020.0,Martin,597.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,104889.3,1.0
+7268,15644833.0,Cattaneo,498.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,79559.02,0.0
+7269,15580249.0,Chiemenam,519.0,France,Male,33.0,9.0,71996.09,1.0,1.0,1.0,171463.83,0.0
+7270,15658946.0,Y?,542.0,France,Female,41.0,5.0,0.0,1.0,1.0,0.0,169915.02,0.0
+7271,15658935.0,Iadanza,651.0,France,Male,32.0,0.0,0.0,2.0,0.0,1.0,181224.56,0.0
+7272,15592761.0,Ch'ien,727.0,France,Male,51.0,6.0,0.0,2.0,0.0,1.0,79082.76,0.0
+7273,15602851.0,Aitken,704.0,France,Male,39.0,6.0,0.0,2.0,0.0,0.0,153831.6,0.0
+7274,15594450.0,Nwebube,706.0,France,Female,44.0,1.0,141787.31,1.0,1.0,1.0,607.49,0.0
+7275,15686835.0,Lo Duca,613.0,Spain,Male,36.0,9.0,0.0,1.0,1.0,0.0,11199.04,0.0
+7276,15738219.0,Genovesi,599.0,Germany,Female,45.0,2.0,73218.89,3.0,0.0,1.0,149331.01,1.0
+7277,15640667.0,Hs?,762.0,France,Female,26.0,3.0,0.0,2.0,1.0,0.0,171681.86,0.0
+7278,15792937.0,Nwachukwu,538.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,157921.25,0.0
+7279,15659325.0,Macdonald,738.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,84484.82,0.0
+7280,15631603.0,Chukwuemeka,637.0,Germany,Female,43.0,1.0,140694.78,2.0,1.0,0.0,90928.48,1.0
+7281,15586880.0,Chukwubuikem,646.0,Germany,Female,51.0,10.0,99811.44,2.0,0.0,0.0,149599.62,1.0
+7282,15687094.0,H?,644.0,Spain,Male,40.0,2.0,138912.19,1.0,1.0,1.0,50368.63,0.0
+7283,15741403.0,Bell,676.0,France,Female,26.0,9.0,63669.42,2.0,1.0,0.0,177065.24,0.0
+7284,15704954.0,Hsiao,755.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,65765.22,0.0
+7285,15606472.0,Chineze,745.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,74564.41,0.0
+7286,15687079.0,Ozerova,656.0,France,Male,49.0,3.0,0.0,1.0,1.0,0.0,139712.72,1.0
+7287,15641782.0,Bogle,637.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,0.0,136521.82,0.0
+7288,15576990.0,Tseng,573.0,Germany,Female,46.0,7.0,127864.4,1.0,0.0,0.0,181461.46,1.0
+7289,15807961.0,Maclean,714.0,France,Female,34.0,6.0,0.0,1.0,1.0,0.0,183515.13,1.0
+7290,15667093.0,Onwumelu,846.0,France,Female,38.0,10.0,0.0,2.0,0.0,0.0,40889.14,0.0
+7291,15684319.0,Chigbogu,604.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,108528.74,0.0
+7292,15739123.0,T'ao,693.0,Germany,Male,32.0,0.0,134542.73,1.0,1.0,1.0,62703.38,0.0
+7293,15796790.0,T'ien,707.0,France,Female,26.0,6.0,0.0,2.0,1.0,1.0,143598.7,0.0
+7294,15643487.0,L?,673.0,Germany,Male,35.0,4.0,102723.35,2.0,0.0,0.0,122960.98,0.0
+7295,15729771.0,Chizoba,535.0,Spain,Female,29.0,2.0,0.0,1.0,1.0,0.0,176962.31,0.0
+7296,15732660.0,Chidiebere,684.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,142670.61,1.0
+7297,15652069.0,Cattaneo,721.0,Spain,Male,35.0,5.0,0.0,1.0,1.0,1.0,47388.41,0.0
+7298,15719170.0,Nnaemeka,679.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,65464.66,0.0
+7299,15747772.0,Trevisani,708.0,France,Male,23.0,4.0,0.0,2.0,1.0,0.0,80582.36,0.0
+7300,15618239.0,Chukwudi,637.0,France,Female,36.0,1.0,122793.96,1.0,1.0,1.0,160948.87,0.0
+7301,15659800.0,Chiang,613.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,77951.84,0.0
+7302,15681446.0,Ts'ao,707.0,Spain,Male,41.0,5.0,125561.97,1.0,1.0,1.0,142963.29,0.0
+7303,15648075.0,Chidiegwu,539.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,88965.46,0.0
+7304,15579548.0,T'ien,585.0,Spain,Male,30.0,4.0,149995.72,1.0,1.0,1.0,184842.21,0.0
+7305,15608726.0,Ch'ien,683.0,France,Female,43.0,7.0,0.0,1.0,0.0,1.0,84760.32,1.0
+7306,15730590.0,Padovano,628.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,67840.81,0.0
+7307,15722072.0,Hsieh,699.0,France,Male,48.0,10.0,127580.93,1.0,1.0,1.0,86920.97,0.0
+7308,15724663.0,Lo Duca,535.0,France,Female,40.0,3.0,0.0,1.0,0.0,0.0,140947.05,1.0
+7309,15750014.0,Ch'ien,678.0,Spain,Male,44.0,9.0,0.0,2.0,1.0,1.0,91098.76,0.0
+7310,15748649.0,Whitehead,549.0,Germany,Female,44.0,2.0,92783.68,2.0,1.0,0.0,63736.17,0.0
+7311,15672598.0,Lucchesi,682.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,40257.79,0.0
+7312,15736397.0,Clements,645.0,France,Female,51.0,3.0,110976.23,1.0,0.0,1.0,182528.43,1.0
+7313,15807525.0,Chukwufumnanya,551.0,France,Female,29.0,1.0,0.0,1.0,1.0,1.0,121798.52,1.0
+7314,15588944.0,K?,611.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,121207.66,0.0
+7315,15596863.0,Ts'ui,716.0,Germany,Male,32.0,7.0,108432.88,1.0,1.0,0.0,119903.2,0.0
+7316,15798885.0,Uwaezuoke,850.0,Spain,Male,23.0,5.0,126588.81,1.0,1.0,1.0,116971.05,0.0
+7317,15746028.0,Ch'in,679.0,Germany,Female,26.0,9.0,61811.23,2.0,1.0,1.0,158061.89,0.0
+7318,15769353.0,Maclean,527.0,France,Female,26.0,9.0,0.0,2.0,1.0,1.0,68730.91,0.0
+7319,15585388.0,K?,745.0,Spain,Female,25.0,5.0,135559.9,1.0,1.0,0.0,14083.12,0.0
+7320,15676571.0,Aiken,576.0,France,Female,31.0,10.0,0.0,2.0,1.0,1.0,81901.6,0.0
+7321,15781347.0,Kao,718.0,Spain,Male,55.0,7.0,0.0,1.0,1.0,1.0,18082.06,0.0
+7322,15722583.0,Ch'ang,619.0,France,Female,31.0,4.0,110219.14,1.0,1.0,0.0,90591.15,0.0
+7323,15752294.0,Cremonesi,632.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,121408.46,0.0
+7324,15727384.0,Ch'iu,549.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,171045.31,0.0
+7325,15654574.0,Hs?,673.0,Germany,Male,38.0,3.0,95727.05,2.0,0.0,0.0,82634.41,0.0
+7326,15693764.0,Nkemdilim,681.0,France,Male,30.0,6.0,142644.64,2.0,0.0,0.0,64732.82,0.0
+7327,15782688.0,Manna,632.0,France,Female,37.0,9.0,0.0,2.0,1.0,1.0,82526.92,0.0
+7328,15791525.0,L?,624.0,Germany,Female,33.0,7.0,88915.84,2.0,1.0,1.0,131736.23,0.0
+7329,15659475.0,Yevdokimova,682.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,132870.15,0.0
+7330,15692430.0,Chiu,758.0,France,Male,30.0,9.0,0.0,2.0,0.0,0.0,171922.72,0.0
+7331,15611157.0,Mazzanti,656.0,Germany,Male,30.0,5.0,112414.81,1.0,1.0,1.0,146700.22,0.0
+7332,15774127.0,Onyemauchechukwu,632.0,Germany,Male,42.0,0.0,113688.63,1.0,1.0,1.0,178822.32,1.0
+7333,15775809.0,Y?,662.0,France,Female,32.0,9.0,96399.31,2.0,1.0,0.0,131283.6,0.0
+7334,15630053.0,Lettiere,641.0,France,Female,42.0,8.0,0.0,2.0,1.0,0.0,92920.03,0.0
+7335,15611800.0,Echezonachukwu,582.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,122580.48,0.0
+7336,15694518.0,Lo,607.0,France,Male,46.0,6.0,0.0,2.0,1.0,0.0,183542.08,0.0
+7337,15704771.0,Chineze,738.0,Spain,Male,46.0,6.0,0.0,2.0,0.0,1.0,52795.15,0.0
+7338,15633141.0,Chukwukadibia,658.0,France,Male,33.0,2.0,94867.18,1.0,1.0,1.0,167181.77,0.0
+7339,15615176.0,Ch'iu,773.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,38190.78,0.0
+7340,15761693.0,Nwankwo,611.0,France,Male,41.0,4.0,102773.2,1.0,1.0,0.0,134878.19,0.0
+7341,15628893.0,Chibugo,628.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,48988.28,0.0
+7342,15700965.0,Scott,686.0,Spain,Female,43.0,6.0,0.0,2.0,1.0,1.0,82522.61,0.0
+7343,15593969.0,Ch'ang,613.0,Germany,Female,34.0,4.0,148061.49,1.0,1.0,1.0,174815.54,0.0
+7344,15593795.0,Shih,635.0,France,Female,40.0,10.0,0.0,2.0,1.0,1.0,125962.02,0.0
+7345,15754599.0,Tien,714.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,182270.88,0.0
+7346,15579914.0,Maclean,529.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,58879.31,0.0
+7347,15795878.0,Tretiakova,539.0,France,Male,38.0,9.0,88902.21,1.0,1.0,0.0,113481.02,0.0
+7348,15765192.0,Ch'ien,721.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,0.0,166476.95,0.0
+7349,15737521.0,Kolesnikov,684.0,France,Female,33.0,4.0,129855.63,2.0,1.0,1.0,47322.05,0.0
+7350,15632847.0,Tien,714.0,France,Male,29.0,6.0,0.0,1.0,1.0,0.0,179655.87,1.0
+7351,15625023.0,Fang,477.0,Germany,Female,32.0,6.0,125241.17,2.0,1.0,1.0,152698.24,0.0
+7352,15693690.0,Lettiere,684.0,France,Male,40.0,6.0,118829.03,1.0,1.0,0.0,166850.57,0.0
+7353,15711398.0,Chialuka,742.0,Germany,Female,29.0,7.0,112212.14,2.0,1.0,1.0,131718.12,0.0
+7354,15581748.0,Palermo,569.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,100101.06,0.0
+7355,15603883.0,Li Fonti,683.0,France,Female,52.0,9.0,0.0,1.0,1.0,0.0,63737.19,1.0
+7356,15671610.0,Ponomaryova,523.0,France,Male,29.0,4.0,134956.02,1.0,1.0,1.0,87835.43,0.0
+7357,15809663.0,Maclean,542.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,186884.04,0.0
+7358,15596671.0,Yobanna,722.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,119817.19,0.0
+7359,15687079.0,Lo Duca,682.0,France,Male,59.0,8.0,0.0,1.0,1.0,0.0,79623.28,1.0
+7360,15761717.0,Thompson,684.0,Germany,Female,36.0,6.0,95858.37,2.0,0.0,1.0,167564.82,0.0
+7361,15603764.0,Kao,670.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,85411.01,0.0
+7362,15609562.0,Hinton,705.0,France,Female,51.0,6.0,0.0,2.0,1.0,1.0,149705.25,0.0
+7363,15742210.0,Cremonesi,615.0,France,Female,41.0,8.0,0.0,2.0,1.0,1.0,61995.57,0.0
+7364,15650432.0,Palerma,706.0,Spain,Male,36.0,3.0,125822.21,2.0,1.0,1.0,168035.63,0.0
+7365,15650018.0,Cremonesi,716.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,51587.04,0.0
+7366,15659949.0,Ch'iu,716.0,Germany,Female,39.0,6.0,103399.87,1.0,1.0,1.0,86957.42,0.0
+7367,15768763.0,Chukwudi,589.0,Germany,Male,32.0,3.0,91523.84,1.0,1.0,0.0,28690.9,0.0
+7368,15681446.0,Genovesi,686.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,82566.46,0.0
+7369,15750141.0,Pugh,711.0,France,Male,42.0,5.0,132578.92,2.0,1.0,1.0,184954.11,1.0
+7370,15793956.0,K'ung,562.0,Germany,Female,31.0,5.0,91091.06,2.0,1.0,0.0,148516.72,0.0
+7371,15701354.0,Nwabugwu,707.0,France,Male,44.0,6.0,0.0,2.0,1.0,1.0,26796.73,1.0
+7372,15767794.0,Fyodorova,632.0,France,Female,31.0,1.0,0.0,2.0,1.0,1.0,131768.98,0.0
+7373,15590011.0,Pagnotto,718.0,Germany,Male,35.0,6.0,101851.8,2.0,1.0,0.0,158059.03,0.0
+7374,15592751.0,Mironova,582.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,0.0,48988.28,0.0
+7375,15784918.0,Tsai,686.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,182495.4,0.0
+7376,15643487.0,Fiorentini,598.0,Spain,Female,31.0,10.0,0.0,2.0,1.0,1.0,161479.19,0.0
+7377,15803633.0,Hsiao,633.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,113436.08,0.0
+7378,15586699.0,Johnson,684.0,France,Female,46.0,8.0,0.0,1.0,1.0,0.0,173779.4,1.0
+7379,15675337.0,Lai,646.0,Spain,Male,40.0,1.0,0.0,1.0,1.0,0.0,103381.47,0.0
+7380,15784323.0,Ejikemeifeuwa,646.0,Germany,Female,46.0,5.0,121365.39,1.0,1.0,0.0,51581.89,1.0
+7381,15594556.0,Tsao,703.0,Spain,Female,34.0,4.0,121702.73,1.0,1.0,1.0,81431.99,0.0
+7382,15589475.0,Dickson,565.0,France,Male,32.0,9.0,126629.58,1.0,1.0,1.0,162719.69,0.0
+7383,15758813.0,Kline,739.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,81442.6,0.0
+7384,15667554.0,Milani,828.0,Spain,Male,35.0,4.0,0.0,2.0,0.0,1.0,147720.27,0.0
+7385,15579996.0,Chizuoke,556.0,France,Male,43.0,6.0,0.0,3.0,1.0,0.0,187616.16,1.0
+7386,15784597.0,Ch'en,523.0,Germany,Female,33.0,8.0,102708.77,2.0,1.0,1.0,62722.44,0.0
+7387,15803352.0,Ch'ien,645.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,84928.4,0.0
+7388,15712264.0,Bancks,696.0,France,Male,51.0,4.0,132576.25,1.0,1.0,1.0,182738.0,0.0
+7389,15594761.0,T'an,662.0,Spain,Male,52.0,9.0,0.0,3.0,1.0,0.0,170980.86,1.0
+7390,15689344.0,Ebelechukwu,821.0,France,Male,51.0,0.0,131802.21,1.0,1.0,1.0,85801.77,1.0
+7391,15693526.0,Ekechukwu,706.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,28204.98,0.0
+7392,15761654.0,Hs?,657.0,France,Male,35.0,6.0,76595.08,2.0,1.0,1.0,179565.57,0.0
+7393,15797787.0,Fanucci,678.0,Germany,Male,46.0,7.0,160777.9,2.0,1.0,1.0,118971.74,0.0
+7394,15623082.0,Iadanza,679.0,France,Female,37.0,8.0,0.0,2.0,0.0,1.0,124592.84,0.0
+7395,15774854.0,Ibezimako,686.0,Germany,Female,27.0,4.0,117438.91,2.0,1.0,1.0,143481.85,0.0
+7396,15624793.0,Chiemenam,622.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,82709.8,0.0
+7397,15682773.0,White,663.0,France,Female,33.0,1.0,0.0,1.0,1.0,0.0,123850.58,0.0
+7398,15586870.0,Tsao,682.0,Spain,Male,37.0,8.0,123557.65,2.0,1.0,1.0,182627.25,0.0
+7399,15733444.0,Goliwe,648.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,99645.04,0.0
+7400,15629299.0,Pirozzi,608.0,France,Male,30.0,0.0,0.0,2.0,1.0,1.0,151902.78,0.0
+7401,15613886.0,Chinedum,594.0,Spain,Female,47.0,2.0,0.0,2.0,1.0,0.0,16897.19,0.0
+7402,15615245.0,Cattaneo,651.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,1.0,103603.65,0.0
+7403,15644765.0,Hsiung,581.0,Spain,Male,42.0,3.0,0.0,2.0,0.0,0.0,177784.68,0.0
+7404,15592386.0,Chinwenma,619.0,France,Male,22.0,6.0,0.0,2.0,1.0,0.0,180427.24,0.0
+7405,15642870.0,Iadanza,530.0,France,Male,45.0,1.0,0.0,2.0,1.0,1.0,90424.09,0.0
+7406,15777893.0,Ngozichukwuka,727.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,1.0,49594.02,0.0
+7407,15754926.0,Ch'ang,678.0,Germany,Male,38.0,5.0,139972.18,1.0,1.0,0.0,173784.04,0.0
+7408,15652658.0,T'ien,676.0,Germany,Female,33.0,7.0,108825.34,1.0,1.0,1.0,52469.06,0.0
+7409,15593694.0,McElhone,652.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,138775.69,1.0
+7410,15791867.0,Galkin,669.0,Germany,Female,29.0,6.0,125961.74,2.0,1.0,0.0,158887.09,0.0
+7411,15586870.0,Nkemdirim,571.0,Germany,Female,64.0,5.0,114084.6,2.0,1.0,0.0,119647.6,1.0
+7412,15629787.0,Y?,709.0,Germany,Male,46.0,10.0,108895.85,2.0,1.0,0.0,26387.72,0.0
+7413,15646764.0,Trevisani,731.0,France,Male,45.0,5.0,0.0,2.0,0.0,1.0,83053.97,0.0
+7414,15694340.0,Y?,637.0,France,Male,29.0,7.0,0.0,1.0,1.0,1.0,121365.45,0.0
+7415,15668889.0,Farrar,675.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,79633.38,0.0
+7416,15733444.0,Nwabugwu,716.0,France,Male,34.0,10.0,0.0,2.0,0.0,0.0,11525.72,0.0
+7417,15685826.0,K'ung,705.0,France,Female,41.0,2.0,0.0,1.0,0.0,0.0,117622.8,0.0
+7418,15639004.0,Sagese,713.0,France,Male,44.0,0.0,0.0,1.0,0.0,0.0,70548.96,1.0
+7419,15578077.0,Chukwukere,576.0,France,Male,42.0,1.0,68423.89,1.0,0.0,0.0,104469.06,1.0
+7420,15647842.0,Chukwujamuike,586.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,125877.22,0.0
+7421,15670343.0,Nebechukwu,678.0,Germany,Male,39.0,1.0,99842.5,2.0,1.0,1.0,177519.39,1.0
+7422,15668971.0,Ejimofor,613.0,Spain,Male,30.0,4.0,0.0,2.0,0.0,1.0,59981.62,0.0
+7423,15583692.0,Y?an,719.0,France,Male,47.0,0.0,0.0,2.0,1.0,1.0,140941.47,1.0
+7424,15612893.0,Ch'iu,757.0,Spain,Male,39.0,5.0,139825.34,1.0,0.0,1.0,163580.48,0.0
+7425,15773723.0,Shih,694.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,79919.13,0.0
+7426,15630113.0,Maclean,699.0,Spain,Male,39.0,8.0,0.0,1.0,1.0,0.0,121053.19,1.0
+7427,15614215.0,Lettiere,708.0,Spain,Female,33.0,5.0,0.0,1.0,1.0,0.0,133091.32,0.0
+7428,15768095.0,Echezonachukwu,598.0,Germany,Male,59.0,9.0,114739.92,1.0,1.0,1.0,72402.0,1.0
+7429,15716284.0,Chiemenam,577.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,93336.3,0.0
+7430,15753094.0,Liao,558.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,167538.66,0.0
+7431,15631310.0,H?,598.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,116097.26,0.0
+7432,15757537.0,Trevisani,721.0,France,Male,35.0,4.0,131834.76,1.0,1.0,0.0,115927.85,0.0
+7433,15715117.0,Ch'iu,565.0,France,Male,36.0,3.0,0.0,1.0,0.0,0.0,159823.16,0.0
+7434,15803415.0,Kaeppel,697.0,France,Female,36.0,3.0,0.0,2.0,0.0,1.0,183656.12,0.0
+7435,15667093.0,Fiorentino,682.0,Germany,Male,44.0,6.0,113383.07,1.0,1.0,0.0,147553.06,1.0
+7436,15604544.0,Chimezie,696.0,France,Female,40.0,7.0,133634.02,1.0,1.0,1.0,126471.14,0.0
+7437,15662089.0,Ch'iu,581.0,France,Female,37.0,9.0,0.0,1.0,1.0,1.0,114092.87,0.0
+7438,15775318.0,Chiazagomekpere,606.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,97938.59,0.0
+7439,15782418.0,Ch'ang,711.0,France,Male,38.0,10.0,0.0,2.0,1.0,0.0,157862.82,0.0
+7440,15756975.0,Chiazagomekpere,850.0,France,Male,42.0,2.0,0.0,2.0,0.0,1.0,89891.4,0.0
+7441,15717893.0,Ch'en,586.0,France,Male,47.0,9.0,0.0,2.0,1.0,1.0,102638.35,0.0
+7442,15662884.0,Chukwudi,591.0,France,Female,32.0,7.0,109327.65,1.0,1.0,1.0,69531.22,0.0
+7443,15633014.0,Chukwualuka,704.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,9241.83,0.0
+7444,15629150.0,Ch'ien,576.0,France,Female,28.0,2.0,0.0,2.0,0.0,0.0,152577.62,0.0
+7445,15711309.0,Genovesi,607.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,1.0,103560.98,0.0
+7446,15727059.0,Chidumaga,584.0,France,Male,48.0,3.0,0.0,1.0,1.0,1.0,56791.8,1.0
+7447,15642709.0,Eluemuno,706.0,Spain,Male,50.0,1.0,0.0,1.0,1.0,1.0,75927.05,1.0
+7448,15654611.0,Yegorova,712.0,Germany,Female,33.0,6.0,116546.29,1.0,1.0,0.0,121151.1,1.0
+7449,15804052.0,Onwumelu,678.0,Spain,Male,63.0,2.0,0.0,2.0,1.0,1.0,73881.68,0.0
+7450,15670172.0,Maclean,751.0,France,Female,35.0,5.0,123845.36,2.0,0.0,1.0,92568.05,0.0
+7451,15802593.0,Ratten,648.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,1.0,182354.46,0.0
+7452,15675141.0,Chidubem,642.0,France,Female,61.0,8.0,121276.32,1.0,1.0,1.0,61759.33,0.0
+7453,15579526.0,Rowntree,633.0,France,Male,31.0,7.0,133501.36,1.0,1.0,1.0,82970.69,0.0
+7454,15718789.0,Hsing,605.0,France,Male,38.0,1.0,0.0,1.0,1.0,1.0,103089.03,1.0
+7455,15605835.0,Young,724.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,162422.91,0.0
+7456,15683483.0,Ch'ien,850.0,Spain,Female,51.0,8.0,0.0,2.0,1.0,0.0,184936.77,1.0
+7457,15665062.0,Macleod,592.0,Germany,Female,33.0,2.0,127924.25,2.0,0.0,0.0,124626.57,0.0
+7458,15636592.0,Echezonachukwu,816.0,Germany,Male,33.0,4.0,90218.9,2.0,1.0,1.0,147090.9,0.0
+7459,15621815.0,T'ien,823.0,France,Female,44.0,10.0,0.0,1.0,0.0,0.0,142223.94,1.0
+7460,15725509.0,Ugochukwutubelum,667.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,142995.32,0.0
+7461,15673570.0,L?,673.0,Spain,Male,43.0,4.0,101648.5,1.0,1.0,0.0,103557.85,0.0
+7462,15754919.0,Chukwuemeka,573.0,France,Male,33.0,6.0,0.0,1.0,1.0,0.0,134526.87,0.0
+7463,15767359.0,Chukwudi,492.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,91957.39,0.0
+7464,15624072.0,Ch'iu,686.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,91830.75,0.0
+7465,15624293.0,Milanesi,715.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,145796.49,0.0
+7466,15792679.0,Pisani,712.0,France,Female,35.0,1.0,0.0,2.0,0.0,0.0,66977.68,0.0
+7467,15783526.0,Chukwuebuka,599.0,France,Female,39.0,1.0,0.0,1.0,1.0,1.0,175743.92,1.0
+7468,15718789.0,Obidimkpa,748.0,Spain,Female,26.0,5.0,0.0,2.0,0.0,1.0,60956.85,0.0
+7469,15632575.0,Pirozzi,694.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,64421.02,0.0
+7470,15790744.0,Nkemjika,659.0,France,Female,35.0,10.0,105245.34,1.0,1.0,0.0,163563.07,0.0
+7471,15583681.0,Brown,792.0,Germany,Female,33.0,6.0,137964.55,2.0,1.0,0.0,172495.83,0.0
+7472,15790067.0,Nnanna,597.0,France,Female,39.0,6.0,0.0,2.0,1.0,0.0,176843.53,0.0
+7473,15790599.0,K'ung,675.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,120511.01,0.0
+7474,15636428.0,Yeh,627.0,Germany,Female,43.0,4.0,118590.41,1.0,0.0,0.0,144880.81,1.0
+7475,15592230.0,Milanesi,698.0,France,Male,38.0,0.0,69712.74,1.0,1.0,1.0,170835.32,0.0
+7476,15574071.0,Ch'ien,683.0,Germany,Male,37.0,5.0,133970.21,2.0,0.0,1.0,147530.06,0.0
+7477,15583049.0,Ts'ao,671.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,86764.46,0.0
+7478,15654562.0,Chikwado,726.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,102297.15,0.0
+7479,15579914.0,Ozioma,582.0,France,Female,66.0,0.0,0.0,2.0,1.0,1.0,53353.36,0.0
+7480,15736118.0,Hargreaves,627.0,France,Male,28.0,7.0,79871.02,1.0,1.0,0.0,147964.99,0.0
+7481,15791925.0,Chiedozie,649.0,Germany,Female,56.0,2.0,106948.43,1.0,1.0,0.0,129964.94,1.0
+7482,15658067.0,Hsiung,593.0,France,Male,54.0,2.0,0.0,2.0,1.0,1.0,67976.57,0.0
+7483,15642821.0,Yobachukwu,632.0,France,Male,47.0,5.0,0.0,1.0,1.0,0.0,97640.2,1.0
+7484,15694641.0,Milano,685.0,Spain,Male,28.0,9.0,0.0,2.0,1.0,0.0,27149.27,0.0
+7485,15593636.0,Manna,628.0,France,Male,41.0,4.0,0.0,2.0,1.0,1.0,100153.75,0.0
+7486,15570134.0,T'ien,674.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,171045.35,0.0
+7487,15791842.0,Chidumaga,516.0,Germany,Female,32.0,7.0,152926.9,2.0,1.0,1.0,60212.51,0.0
+7488,15614782.0,Uspensky,542.0,France,Female,44.0,3.0,135870.01,1.0,1.0,1.0,172097.4,0.0
+7489,15803378.0,Chineze,710.0,France,Male,41.0,4.0,0.0,2.0,0.0,1.0,57844.69,0.0
+7490,15696733.0,Chidiegwu,716.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,60972.84,0.0
+7491,15787322.0,Okoli,676.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,86764.46,0.0
+7492,15618018.0,Aksenov,667.0,France,Male,25.0,6.0,0.0,2.0,0.0,1.0,172668.39,0.0
+7493,15603565.0,Yudina,705.0,Germany,Male,38.0,3.0,161807.18,2.0,0.0,1.0,92787.42,0.0
+7494,15641183.0,Teng,581.0,France,Male,36.0,7.0,0.0,2.0,0.0,1.0,152541.89,0.0
+7495,15804586.0,Pinto,583.0,Spain,Female,26.0,1.0,0.0,2.0,0.0,0.0,146033.52,0.0
+7496,15685829.0,Chiemenam,652.0,France,Male,39.0,3.0,0.0,1.0,0.0,0.0,145764.4,0.0
+7497,15673619.0,Nkemdilim,733.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,88890.05,0.0
+7498,15608701.0,K?,753.0,France,Female,48.0,5.0,0.0,2.0,1.0,0.0,164741.5,1.0
+7499,15672447.0,Riggs,756.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,94780.9,0.0
+7500,15669987.0,Ritchie,656.0,France,Male,46.0,8.0,0.0,2.0,1.0,0.0,65762.02,0.0
+7501,15572728.0,Nnachetam,679.0,Spain,Female,38.0,0.0,0.0,2.0,1.0,0.0,67944.09,0.0
+7502,15731519.0,Chukwudi,706.0,France,Female,30.0,3.0,0.0,2.0,0.0,1.0,175743.92,0.0
+7503,15572374.0,Chukwuemeka,676.0,Germany,Male,40.0,2.0,117633.85,1.0,1.0,0.0,158948.63,1.0
+7504,15746664.0,Lettiere,759.0,France,Male,45.0,2.0,0.0,2.0,1.0,1.0,60552.44,0.0
+7505,15776596.0,Soto,731.0,Germany,Male,43.0,4.0,113690.85,1.0,1.0,1.0,60721.4,1.0
+7506,15588560.0,Ritchie,678.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,182867.84,0.0
+7507,15665460.0,De Luca,710.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,124592.84,0.0
+7508,15800773.0,Nwankwo,679.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,114722.42,0.0
+7509,15737605.0,Johnstone,741.0,Germany,Male,46.0,4.0,133501.36,1.0,0.0,1.0,84760.32,1.0
+7510,15644551.0,Onwuamaeze,579.0,France,Male,31.0,9.0,117700.86,1.0,1.0,1.0,160968.44,0.0
+7511,15678412.0,Nwachukwu,683.0,France,Female,60.0,2.0,103907.28,1.0,1.0,0.0,186869.58,1.0
+7512,15793856.0,Chukwuemeka,652.0,Germany,Male,26.0,8.0,141827.33,2.0,0.0,1.0,63737.19,0.0
+7513,15603065.0,T'ien,516.0,Germany,Male,38.0,1.0,124043.8,1.0,1.0,1.0,180551.76,1.0
+7514,15621653.0,Tien,578.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,87071.18,0.0
+7515,15756481.0,Yefremova,713.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,182787.17,0.0
+7516,15790448.0,Liao,710.0,Spain,Female,46.0,7.0,0.0,2.0,1.0,0.0,65675.47,0.0
+7517,15624595.0,Toscani,659.0,France,Male,36.0,1.0,102773.2,1.0,0.0,0.0,55893.33,0.0
+7518,15588517.0,O'Toole,558.0,France,Male,36.0,0.0,101242.98,1.0,1.0,0.0,93779.66,0.0
+7519,15765532.0,Hayslett,705.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,62389.35,0.0
+7520,15753826.0,Brown,667.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,0.0,64443.75,0.0
+7521,15768072.0,Fan,710.0,France,Male,39.0,7.0,123599.49,1.0,1.0,0.0,187632.22,0.0
+7522,15760774.0,Koo,583.0,France,Male,40.0,3.0,0.0,1.0,0.0,1.0,72678.75,1.0
+7523,15736397.0,Estrada,792.0,Germany,Female,41.0,7.0,143595.87,2.0,1.0,1.0,164975.82,0.0
+7524,15766272.0,Chukwudi,595.0,Germany,Male,48.0,5.0,125724.95,1.0,0.0,0.0,107875.1,1.0
+7525,15652981.0,Ch'in,741.0,Spain,Male,51.0,3.0,0.0,1.0,1.0,0.0,49826.68,1.0
+7526,15705620.0,Yobanna,749.0,France,Male,44.0,5.0,121355.99,1.0,1.0,1.0,184317.74,0.0
+7527,15723028.0,Hovell,526.0,Germany,Female,36.0,3.0,121405.04,1.0,1.0,1.0,132450.09,1.0
+7528,15679810.0,Hao,709.0,Germany,Male,42.0,8.0,96340.81,2.0,1.0,0.0,30781.77,0.0
+7529,15757897.0,H?,602.0,Spain,Female,56.0,5.0,0.0,1.0,0.0,0.0,113034.81,1.0
+7530,15759499.0,Kolesnikov,771.0,France,Male,23.0,3.0,0.0,2.0,1.0,1.0,125445.8,0.0
+7531,15572038.0,Aksenov,587.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,120115.5,0.0
+7532,15655238.0,P'an,627.0,Germany,Female,43.0,6.0,152760.24,1.0,1.0,1.0,88872.33,1.0
+7533,15725997.0,McLean,588.0,France,Female,58.0,6.0,0.0,1.0,1.0,0.0,171707.93,1.0
+7534,15641582.0,K'ung,653.0,Germany,Female,32.0,9.0,108449.23,1.0,1.0,0.0,127939.18,1.0
+7535,15565706.0,Nwankwo,589.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,71972.95,0.0
+7536,15792668.0,Mironov,702.0,France,Female,27.0,6.0,101014.08,2.0,1.0,0.0,137985.69,0.0
+7537,15719352.0,Iloerika,633.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,164768.82,0.0
+7538,15632977.0,Lo Duca,644.0,Germany,Female,34.0,7.0,129969.68,1.0,0.0,0.0,175399.13,0.0
+7539,15765311.0,O'Brien,624.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,142331.85,0.0
+7540,15786717.0,Chidimma,758.0,Germany,Female,32.0,1.0,134919.35,2.0,0.0,0.0,146955.71,0.0
+7541,15776844.0,Yefremova,641.0,Germany,Female,40.0,8.0,131411.24,2.0,1.0,0.0,188997.08,0.0
+7542,15772329.0,Houghton,695.0,France,Female,32.0,8.0,111756.5,1.0,1.0,0.0,180622.44,0.0
+7543,15649724.0,Chiu,468.0,France,Female,45.0,5.0,0.0,1.0,1.0,0.0,177611.57,1.0
+7544,15592761.0,Dickson,727.0,Spain,Male,23.0,3.0,155413.08,1.0,1.0,1.0,141896.74,0.0
+7545,15661821.0,Nwokeocha,521.0,Spain,Female,29.0,7.0,0.0,2.0,1.0,1.0,25803.59,0.0
+7546,15579548.0,Brown,599.0,France,Male,33.0,6.0,123835.52,1.0,1.0,0.0,62634.94,0.0
+7547,15790594.0,Genovesi,729.0,Germany,Male,45.0,2.0,128226.37,1.0,1.0,0.0,99061.75,1.0
+7548,15591766.0,Yobanna,726.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,132498.39,0.0
+7549,15643048.0,Onio,678.0,Germany,Male,38.0,10.0,102570.37,2.0,1.0,1.0,141069.88,0.0
+7550,15599131.0,Trevisani,676.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,1.0,124489.88,0.0
+7551,15713378.0,Sun,737.0,France,Female,38.0,6.0,0.0,2.0,0.0,1.0,123882.73,0.0
+7552,15713845.0,Cattaneo,696.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,100950.48,0.0
+7553,15720508.0,Li Fonti,573.0,France,Male,44.0,3.0,0.0,2.0,1.0,1.0,171457.06,0.0
+7554,15742273.0,Cremonesi,678.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,1.0,74588.41,0.0
+7555,15682974.0,Nwoye,632.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,114869.55,0.0
+7556,15667938.0,Lucchesi,793.0,France,Male,33.0,2.0,148659.48,1.0,1.0,1.0,72989.82,0.0
+7557,15693764.0,T'ien,677.0,France,Female,40.0,1.0,0.0,2.0,0.0,0.0,141449.86,0.0
+7558,15571244.0,K?,637.0,France,Female,39.0,1.0,170600.78,1.0,1.0,1.0,676.06,0.0
+7559,15760090.0,Chidiebere,679.0,Germany,Male,43.0,9.0,131078.42,2.0,0.0,1.0,69320.97,0.0
+7560,15653315.0,Tuan,592.0,France,Male,38.0,2.0,129289.99,1.0,1.0,1.0,70523.92,0.0
+7561,15583548.0,Onwumelu,559.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,168034.9,0.0
+7562,15725028.0,Chin,659.0,France,Female,30.0,5.0,124923.35,1.0,1.0,1.0,85983.54,0.0
+7563,15723053.0,Ch'en,584.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,58363.94,0.0
+7564,15641582.0,Chukwueloka,663.0,Spain,Female,34.0,8.0,0.0,2.0,1.0,0.0,152567.75,0.0
+7565,15742886.0,Clements,712.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,1.0,153698.65,0.0
+7566,15661830.0,Ozerova,681.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,120880.72,0.0
+7567,15574795.0,Milne,696.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,62944.75,0.0
+7568,15593468.0,Mao,587.0,France,Female,52.0,1.0,134540.24,1.0,1.0,1.0,109572.58,1.0
+7569,15598088.0,Chiu,496.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,103934.22,0.0
+7570,15780359.0,Baresi,632.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,182855.24,0.0
+7571,15596969.0,H?,693.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,151843.54,0.0
+7572,15769783.0,Nwora,678.0,France,Male,24.0,8.0,0.0,2.0,0.0,0.0,180345.44,0.0
+7573,15605293.0,Ch'en,754.0,France,Male,31.0,9.0,0.0,1.0,1.0,0.0,62845.64,0.0
+7574,15636572.0,Yermolayev,595.0,France,Male,42.0,9.0,146777.44,2.0,1.0,0.0,172572.64,1.0
+7575,15642709.0,Y?,651.0,France,Male,50.0,7.0,0.0,2.0,1.0,0.0,120115.5,0.0
+7576,15637977.0,Tsao,743.0,France,Female,55.0,8.0,121874.89,1.0,1.0,0.0,181224.56,1.0
+7577,15589949.0,K'ung,635.0,France,Female,31.0,0.0,0.0,2.0,1.0,1.0,52795.15,0.0
+7578,15682928.0,Hsueh,598.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,121681.19,0.0
+7579,15774304.0,McIntyre,635.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,50743.83,1.0
+7580,15673584.0,Padovano,597.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,59816.41,0.0
+7581,15643582.0,Aitken,729.0,France,Female,31.0,6.0,0.0,2.0,1.0,1.0,160129.99,0.0
+7582,15656471.0,Mazzi,714.0,France,Female,28.0,5.0,0.0,2.0,1.0,1.0,27654.44,0.0
+7583,15705301.0,Teng,714.0,Germany,Male,37.0,1.0,86614.06,1.0,1.0,0.0,142287.65,0.0
+7584,15679024.0,Hinton,657.0,Germany,Female,47.0,5.0,122917.71,2.0,1.0,0.0,78650.95,1.0
+7585,15662085.0,Mazzanti,648.0,France,Male,46.0,5.0,137851.31,1.0,1.0,0.0,106841.12,0.0
+7586,15722993.0,Chukwufumnanya,666.0,Spain,Female,40.0,5.0,0.0,1.0,1.0,0.0,129891.66,1.0
+7587,15752266.0,Shih,704.0,France,Male,27.0,8.0,0.0,2.0,1.0,0.0,162223.66,0.0
+7588,15803378.0,Milanesi,549.0,Spain,Female,34.0,7.0,0.0,2.0,0.0,0.0,177738.98,0.0
+7589,15745012.0,Rios,622.0,France,Female,36.0,9.0,0.0,1.0,0.0,1.0,131609.48,1.0
+7590,15679075.0,Ch'in,605.0,France,Female,58.0,6.0,0.0,2.0,1.0,0.0,172495.81,1.0
+7591,15696859.0,T'ien,731.0,France,Male,32.0,3.0,0.0,2.0,0.0,0.0,143680.73,0.0
+7592,15673074.0,Chinedum,807.0,France,Female,37.0,4.0,0.0,1.0,1.0,0.0,151871.84,1.0
+7593,15752626.0,Smith,648.0,France,Female,26.0,9.0,0.0,2.0,1.0,0.0,38308.34,0.0
+7594,15792722.0,Bischof,559.0,France,Male,32.0,6.0,123863.64,1.0,1.0,0.0,93820.49,0.0
+7595,15733797.0,Miah,656.0,Spain,Male,59.0,9.0,0.0,1.0,0.0,1.0,66548.58,1.0
+7596,15756481.0,Onyemere,643.0,Germany,Male,36.0,6.0,110738.89,2.0,0.0,1.0,185821.41,0.0
+7597,15592386.0,Kaeppel,565.0,Spain,Female,28.0,6.0,0.0,1.0,0.0,1.0,129912.74,0.0
+7598,15780649.0,K?,667.0,Spain,Female,37.0,8.0,0.0,1.0,1.0,1.0,110142.41,0.0
+7599,15763460.0,Cattaneo,762.0,France,Male,37.0,1.0,151836.36,1.0,1.0,1.0,173870.43,0.0
+7600,15612140.0,Watson,601.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,55893.33,0.0
+7601,15750049.0,Chia,588.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,1.0,84790.34,0.0
+7602,15673837.0,DeRose,582.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,152992.04,0.0
+7603,15651868.0,Maclean,669.0,Spain,Female,37.0,7.0,0.0,1.0,1.0,0.0,79623.28,1.0
+7604,15796071.0,Chizuoke,706.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,125140.72,0.0
+7605,15797905.0,Ojiofor,593.0,Germany,Male,62.0,4.0,121542.57,1.0,1.0,1.0,82969.61,0.0
+7606,15744607.0,Tien,558.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,131768.98,0.0
+7607,15812191.0,Ts'ao,593.0,France,Female,30.0,8.0,0.0,1.0,1.0,1.0,114957.22,0.0
+7608,15732672.0,Tretiakov,622.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,84740.68,0.0
+7609,15653776.0,Ch'ien,738.0,Spain,Female,32.0,1.0,0.0,2.0,1.0,0.0,104786.58,0.0
+7610,15768474.0,Chikelu,569.0,France,Male,40.0,8.0,0.0,2.0,0.0,0.0,141822.5,0.0
+7611,15693326.0,Shih,588.0,France,Male,41.0,2.0,0.0,2.0,0.0,0.0,114522.68,0.0
+7612,15677903.0,Kerr,709.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,106977.8,1.0
+7613,15781495.0,Nkemdilim,644.0,France,Male,44.0,3.0,96947.19,1.0,1.0,1.0,152956.33,1.0
+7614,15713483.0,Trevisan,438.0,France,Female,30.0,6.0,0.0,2.0,0.0,1.0,82872.64,0.0
+7615,15674811.0,Chukwuma,719.0,France,Male,46.0,2.0,0.0,2.0,1.0,0.0,172459.39,0.0
+7616,15782467.0,Ugochukwutubelum,571.0,Germany,Male,38.0,8.0,119152.1,1.0,1.0,1.0,116912.45,1.0
+7617,15758513.0,Chibugo,597.0,France,Male,25.0,7.0,107764.75,1.0,1.0,1.0,28368.37,0.0
+7618,15717185.0,Maclean,705.0,France,Male,54.0,0.0,0.0,1.0,1.0,0.0,100995.11,1.0
+7619,15662337.0,Rizzo,667.0,France,Male,47.0,4.0,0.0,2.0,1.0,1.0,140941.47,0.0
+7620,15697034.0,Maclean,511.0,France,Female,39.0,1.0,106871.81,1.0,0.0,0.0,142913.67,0.0
+7621,15600583.0,Yefremova,683.0,France,Male,47.0,5.0,0.0,2.0,1.0,0.0,149912.28,1.0
+7622,15675787.0,Ch'iu,676.0,France,Male,40.0,3.0,0.0,1.0,1.0,0.0,116378.82,0.0
+7623,15655298.0,Tien,682.0,France,Female,38.0,8.0,169824.46,1.0,1.0,0.0,178229.04,0.0
+7624,15803415.0,Ulyanov,683.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,173952.5,0.0
+7625,15623471.0,Onuchukwu,684.0,France,Male,28.0,6.0,0.0,1.0,1.0,1.0,132973.17,1.0
+7626,15727499.0,Ch'in,611.0,France,Female,41.0,7.0,0.0,2.0,1.0,0.0,176730.02,0.0
+7627,15627840.0,Ebelegbulam,676.0,Spain,Male,33.0,10.0,0.0,2.0,1.0,0.0,53710.23,0.0
+7628,15737087.0,P'an,606.0,Germany,Female,40.0,4.0,113090.4,1.0,0.0,0.0,121409.06,1.0
+7629,15752467.0,Yeh,667.0,France,Female,37.0,1.0,127570.93,1.0,1.0,0.0,196257.67,0.0
+7630,15723720.0,Calabrese,683.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,152956.33,0.0
+7631,15730705.0,Chin,743.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,83056.22,0.0
+7632,15688251.0,Nnanna,618.0,France,Female,30.0,7.0,0.0,2.0,1.0,1.0,154775.7,0.0
+7633,15744044.0,Yeh,710.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,582.59,0.0
+7634,15723006.0,K'ung,579.0,France,Female,38.0,2.0,162560.32,2.0,1.0,0.0,71139.31,0.0
+7635,15659399.0,Nkemdilim,587.0,Spain,Male,40.0,7.0,144875.37,1.0,1.0,1.0,182355.21,0.0
+7636,15724848.0,Ch'iu,558.0,Germany,Male,35.0,2.0,111599.32,1.0,1.0,1.0,168460.12,1.0
+7637,15648032.0,Chukwualuka,803.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,162452.65,0.0
+7638,15802758.0,Chidi,721.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,151903.6,0.0
+7639,15736299.0,Chidimma,681.0,Germany,Female,35.0,6.0,139756.12,2.0,0.0,1.0,182495.7,0.0
+7640,15651533.0,Bancroft,710.0,France,Male,36.0,8.0,0.0,1.0,1.0,0.0,88890.05,0.0
+7641,15651315.0,Mazzi,646.0,France,Female,42.0,8.0,0.0,2.0,0.0,0.0,155721.15,0.0
+7642,15718106.0,Lai,850.0,France,Female,45.0,1.0,0.0,2.0,1.0,0.0,73977.23,0.0
+7643,15589969.0,Hsiao,708.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,154971.63,0.0
+7644,15665784.0,Hsieh,667.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,0.0,582.59,0.0
+7645,15728963.0,P'eng,682.0,Spain,Male,35.0,7.0,172646.82,1.0,1.0,0.0,182728.8,0.0
+7646,15594576.0,Chukwuebuka,579.0,Germany,Female,22.0,1.0,121231.39,1.0,1.0,1.0,161063.89,1.0
+7647,15796413.0,Chuang,776.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,0.0,10063.75,0.0
+7648,15678916.0,Liao,598.0,Spain,Male,37.0,6.0,124495.98,1.0,1.0,0.0,108872.45,0.0
+7649,15713890.0,Lai,640.0,France,Male,40.0,2.0,0.0,1.0,1.0,0.0,101828.39,0.0
+7650,15626452.0,Chiedozie,673.0,Germany,Male,40.0,6.0,122325.58,2.0,1.0,0.0,104889.3,0.0
+7651,15758035.0,Chuang,743.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,1.0,92888.52,0.0
+7652,15678725.0,Ts'ui,685.0,Germany,Female,33.0,6.0,121407.68,2.0,1.0,1.0,64516.8,0.0
+7653,15586479.0,Chukwukadibia,682.0,France,Male,31.0,7.0,122029.15,1.0,1.0,0.0,142911.99,0.0
+7654,15699325.0,Hs?,516.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,140800.66,0.0
+7655,15733444.0,Bell,583.0,France,Male,34.0,6.0,0.0,2.0,0.0,0.0,166650.9,0.0
+7656,15664752.0,Babbage,714.0,France,Female,31.0,8.0,142388.61,2.0,1.0,0.0,66463.62,1.0
+7657,15610755.0,Toscani,729.0,France,Male,55.0,1.0,0.0,1.0,1.0,0.0,77326.78,1.0
+7658,15680141.0,Kuo,634.0,France,Female,55.0,5.0,182364.53,1.0,1.0,0.0,167554.86,1.0
+7659,15714227.0,Ting,651.0,Spain,Female,30.0,3.0,135650.88,1.0,1.0,1.0,54726.43,0.0
+7660,15791216.0,Ewing,584.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,88703.04,0.0
+7661,15693637.0,P'an,634.0,France,Male,25.0,4.0,0.0,2.0,0.0,1.0,151411.4,0.0
+7662,15659931.0,Niu,611.0,France,Female,29.0,5.0,0.0,2.0,1.0,0.0,183358.21,0.0
+7663,15604576.0,Onwumelu,632.0,Germany,Female,43.0,2.0,100116.82,1.0,1.0,0.0,138399.67,1.0
+7664,15665385.0,Bibi,655.0,France,Female,26.0,9.0,0.0,1.0,0.0,1.0,86657.48,0.0
+7665,15594878.0,Hsia,733.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,88890.05,0.0
+7666,15634143.0,Chukwudi,590.0,Germany,Female,35.0,2.0,114007.5,3.0,1.0,0.0,121894.31,1.0
+7667,15732437.0,Lai,597.0,France,Male,34.0,4.0,0.0,2.0,0.0,0.0,48684.93,0.0
+7668,15675380.0,K'ung,793.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,148811.81,0.0
+7669,15762882.0,Cattaneo,711.0,Spain,Male,43.0,4.0,0.0,2.0,0.0,1.0,85890.75,0.0
+7670,15719579.0,Chibugo,721.0,Spain,Male,23.0,4.0,105000.23,1.0,1.0,1.0,94875.88,0.0
+7671,15605742.0,Ch'iu,668.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,71173.64,0.0
+7672,15717164.0,Chiang,562.0,France,Female,33.0,5.0,0.0,1.0,0.0,1.0,38276.84,1.0
+7673,15609510.0,Lo,583.0,France,Male,32.0,0.0,0.0,2.0,1.0,1.0,126794.56,0.0
+7674,15680683.0,P'an,588.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,142223.94,0.0
+7675,15639552.0,Hsiung,579.0,Germany,Female,35.0,1.0,134599.33,1.0,1.0,1.0,60906.12,0.0
+7676,15778830.0,De Luca,699.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,131283.6,0.0
+7677,15795881.0,Teng,584.0,Spain,Female,29.0,1.0,0.0,1.0,1.0,0.0,27886.06,0.0
+7678,15585067.0,Ch'in,586.0,Spain,Male,62.0,9.0,0.0,2.0,1.0,1.0,150850.29,0.0
+7679,15672798.0,Okonkwo,614.0,France,Female,36.0,9.0,136659.75,1.0,1.0,0.0,89935.73,0.0
+7680,15591279.0,K?,814.0,Germany,Female,35.0,6.0,161887.81,1.0,1.0,0.0,126565.57,0.0
+7681,15684592.0,Chukwuhaenye,663.0,France,Female,29.0,5.0,0.0,1.0,1.0,1.0,19720.08,0.0
+7682,15701176.0,Okechukwu,804.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,62390.59,0.0
+7683,15782454.0,Ting,705.0,Spain,Male,27.0,10.0,0.0,2.0,1.0,0.0,124532.9,0.0
+7684,15572792.0,Pinto,619.0,Spain,Male,50.0,2.0,0.0,2.0,0.0,1.0,48606.71,0.0
+7685,15635197.0,Hsia,616.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,151316.37,0.0
+7686,15652667.0,Biryukov,713.0,France,Female,41.0,9.0,160838.13,1.0,1.0,0.0,133871.83,0.0
+7687,15641411.0,McGregor,645.0,Spain,Male,33.0,3.0,87541.06,1.0,1.0,0.0,135077.01,1.0
+7688,15670552.0,Chieloka,573.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,105969.05,0.0
+7689,15802758.0,K?,587.0,France,Male,36.0,10.0,0.0,1.0,1.0,0.0,147720.27,0.0
+7690,15679394.0,Teng,597.0,France,Female,41.0,1.0,92350.49,1.0,1.0,0.0,121504.18,0.0
+7691,15702464.0,Ts'ui,748.0,Spain,Male,40.0,6.0,0.0,1.0,1.0,1.0,121751.04,0.0
+7692,15686973.0,Toscani,637.0,Germany,Female,40.0,8.0,123048.38,1.0,0.0,0.0,65219.85,0.0
+7693,15799480.0,O'Connor,742.0,Germany,Male,58.0,6.0,124954.94,1.0,1.0,1.0,79919.13,0.0
+7694,15602554.0,Chukwumaobim,614.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,171482.56,0.0
+7695,15778287.0,Clements,680.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,1.0,138882.98,0.0
+7696,15793723.0,Ch'en,712.0,Germany,Male,33.0,1.0,95858.37,2.0,1.0,1.0,80552.11,0.0
+7697,15775335.0,Hsia,556.0,Spain,Male,42.0,2.0,112088.04,1.0,1.0,1.0,62944.75,0.0
+7698,15680449.0,Chibueze,656.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,140947.05,0.0
+7699,15793042.0,Maclean,667.0,France,Male,28.0,4.0,0.0,2.0,0.0,0.0,131778.58,0.0
+7700,15694566.0,Mazzanti,762.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,127569.8,0.0
+7701,15626135.0,Cattaneo,683.0,France,Female,27.0,0.0,0.0,1.0,0.0,0.0,132436.71,0.0
+7702,15652901.0,Chiu,565.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,88852.47,0.0
+7703,15804721.0,McIntyre,529.0,France,Male,24.0,8.0,0.0,1.0,0.0,0.0,114083.73,1.0
+7704,15675501.0,Chiganu,645.0,France,Male,33.0,7.0,129738.61,1.0,1.0,1.0,151871.84,0.0
+7705,15587584.0,Trevisani,677.0,France,Male,58.0,1.0,0.0,1.0,1.0,1.0,148087.62,1.0
+7706,15589230.0,Ozerova,597.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,143463.28,0.0
+7707,15794356.0,Ritchie,695.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,1.0,145796.49,0.0
+7708,15684908.0,Chimezie,584.0,Spain,Female,34.0,5.0,0.0,1.0,1.0,0.0,71175.94,0.0
+7709,15587562.0,Toscani,705.0,France,Female,22.0,10.0,0.0,2.0,0.0,1.0,77326.78,0.0
+7710,15592089.0,Chizoba,652.0,Spain,Female,48.0,9.0,0.0,1.0,1.0,0.0,52449.62,0.0
+7711,15654891.0,T'ang,641.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,86657.48,0.0
+7712,15671390.0,Trevisan,651.0,France,Female,32.0,5.0,0.0,2.0,0.0,0.0,180969.55,0.0
+7713,15662067.0,L?,682.0,Spain,Male,30.0,1.0,114370.41,1.0,1.0,1.0,66825.33,0.0
+7714,15757981.0,Ch'ien,665.0,Germany,Male,25.0,8.0,95675.62,2.0,1.0,1.0,187839.9,0.0
+7715,15594515.0,K?,543.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,138871.12,0.0
+7716,15792473.0,Toscani,663.0,France,Male,56.0,8.0,0.0,1.0,1.0,1.0,55701.62,1.0
+7717,15684461.0,Cattaneo,649.0,France,Female,61.0,1.0,0.0,3.0,1.0,0.0,157549.6,1.0
+7718,15762045.0,Zetticci,806.0,Germany,Male,37.0,1.0,121093.28,1.0,0.0,0.0,167621.16,0.0
+7719,15665590.0,Ewing,752.0,Germany,Male,56.0,7.0,107944.39,1.0,0.0,0.0,153913.38,1.0
+7720,15659736.0,Ch'eng,818.0,Spain,Female,36.0,5.0,0.0,1.0,1.0,0.0,25161.61,1.0
+7721,15672237.0,Tien,709.0,France,Male,33.0,6.0,0.0,1.0,1.0,1.0,175025.28,0.0
+7722,15778975.0,Hs?,635.0,Germany,Female,70.0,9.0,131519.12,1.0,0.0,1.0,126812.63,1.0
+7723,15701602.0,Gell,739.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,182528.89,0.0
+7724,15690743.0,Ch'iu,578.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,130590.35,0.0
+7725,15777896.0,Rizzo,726.0,Germany,Female,49.0,1.0,127801.88,1.0,1.0,0.0,174027.3,1.0
+7726,15620276.0,Manna,592.0,France,Male,29.0,1.0,122378.88,2.0,1.0,1.0,114951.42,1.0
+7727,15654489.0,Chukwudi,553.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,123499.82,0.0
+7728,15686963.0,Pugh,850.0,France,Female,47.0,9.0,0.0,1.0,0.0,1.0,137028.51,1.0
+7729,15779886.0,Nucci,677.0,Spain,Female,36.0,3.0,0.0,2.0,1.0,1.0,175853.64,0.0
+7730,15773421.0,Lucciano,643.0,Germany,Male,36.0,3.0,119782.72,1.0,1.0,0.0,161574.01,0.0
+7731,15757911.0,Davison,749.0,France,Male,54.0,2.0,107720.64,1.0,1.0,0.0,103480.69,1.0
+7732,15800295.0,Chu,581.0,France,Male,25.0,4.0,0.0,1.0,1.0,0.0,130189.72,0.0
+7733,15692463.0,Tsai,545.0,France,Female,39.0,1.0,0.0,1.0,1.0,0.0,139431.0,0.0
+7734,15694821.0,Dyer,687.0,France,Male,25.0,5.0,0.0,2.0,1.0,0.0,132770.12,0.0
+7735,15603550.0,Tuan,577.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,145796.49,0.0
+7736,15752294.0,Chukwualuka,589.0,Germany,Female,43.0,2.0,134956.02,1.0,1.0,1.0,172618.52,1.0
+7737,15690925.0,Ritchie,684.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,66588.6,0.0
+7738,15681075.0,Ting,749.0,France,Male,52.0,9.0,0.0,1.0,0.0,1.0,121408.06,1.0
+7739,15781619.0,Hsia,753.0,France,Male,52.0,6.0,0.0,2.0,1.0,0.0,185599.12,0.0
+7740,15766064.0,Chia,733.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,0.0,36292.94,0.0
+7741,15661290.0,Nwankwo,569.0,France,Female,64.0,5.0,0.0,1.0,1.0,0.0,107964.36,1.0
+7742,15764021.0,Chukwualuka,656.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,173632.92,0.0
+7743,15592461.0,Nnonso,629.0,France,Male,33.0,6.0,0.0,2.0,0.0,1.0,12577.21,0.0
+7744,15758031.0,Ma,769.0,Spain,Male,21.0,3.0,125561.97,1.0,1.0,1.0,38170.04,0.0
+7745,15605067.0,Nnamutaezinwa,722.0,France,Female,27.0,2.0,0.0,2.0,1.0,0.0,167931.81,0.0
+7746,15592583.0,Yobanna,637.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,49775.58,0.0
+7747,15614643.0,Chiang,654.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,102057.86,0.0
+7748,15571917.0,Kuznetsova,629.0,France,Female,66.0,4.0,0.0,2.0,1.0,1.0,60906.12,0.0
+7749,15754849.0,Longo,652.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,151902.78,0.0
+7750,15743817.0,Folliero,629.0,Spain,Male,23.0,2.0,135903.33,1.0,1.0,0.0,52796.31,1.0
+7751,15574324.0,Tung,663.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,125962.02,0.0
+7752,15807065.0,Kao,850.0,France,Female,35.0,1.0,0.0,2.0,0.0,1.0,82298.59,0.0
+7753,15753842.0,Shih,585.0,France,Female,44.0,4.0,0.0,2.0,1.0,1.0,66462.79,0.0
+7754,15691792.0,Yobachi,850.0,Germany,Male,48.0,2.0,133950.67,1.0,0.0,1.0,97623.42,1.0
+7755,15650086.0,P'eng,646.0,Germany,Male,37.0,3.0,141792.61,1.0,0.0,1.0,46884.1,0.0
+7756,15738785.0,Ogochukwu,596.0,Spain,Female,22.0,4.0,0.0,2.0,1.0,1.0,79919.13,0.0
+7757,15728885.0,Chiu,581.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,119882.86,0.0
+7758,15690731.0,Tuan,683.0,France,Female,38.0,2.0,0.0,1.0,1.0,0.0,186489.95,1.0
+7759,15697360.0,Maclean,613.0,France,Female,44.0,5.0,0.0,2.0,1.0,0.0,180197.74,0.0
+7760,15779973.0,Manna,694.0,France,Male,38.0,1.0,0.0,1.0,1.0,1.0,1713.1,1.0
+7761,15765806.0,Scott,588.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,97938.59,0.0
+7762,15639552.0,Hs?,567.0,Germany,Male,28.0,0.0,122031.55,1.0,1.0,0.0,182650.15,0.0
+7763,15757981.0,Cremonesi,581.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,52876.41,0.0
+7764,15766355.0,Kaur,593.0,Germany,Male,36.0,7.0,109681.61,2.0,1.0,0.0,70563.9,0.0
+7765,15653937.0,Naylor,667.0,France,Female,37.0,3.0,145729.71,1.0,1.0,1.0,163570.25,0.0
+7766,15593694.0,Lai,705.0,Spain,Male,28.0,4.0,0.0,2.0,0.0,1.0,62454.01,0.0
+7767,15716186.0,Okeke,695.0,Germany,Male,34.0,2.0,149961.99,2.0,0.0,1.0,82709.8,0.0
+7768,15631838.0,Ch'ien,707.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,184524.65,0.0
+7769,15613017.0,Padovesi,696.0,France,Male,41.0,6.0,142721.52,2.0,1.0,1.0,44244.49,0.0
+7770,15662085.0,De Luca,711.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,0.0,86797.41,0.0
+7771,15702293.0,Chiu,676.0,France,Female,41.0,9.0,0.0,1.0,1.0,0.0,56594.36,0.0
+7772,15625126.0,Lombardo,636.0,Germany,Male,41.0,4.0,99106.73,2.0,1.0,1.0,157549.6,0.0
+7773,15771535.0,Li Fonti,633.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,54448.36,0.0
+7774,15756875.0,Chidimma,633.0,Germany,Female,60.0,0.0,143903.12,1.0,1.0,1.0,134901.58,0.0
+7775,15747542.0,De Luca,713.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,93524.19,0.0
+7776,15782530.0,Chidiebele,717.0,Spain,Male,41.0,2.0,125265.61,1.0,1.0,1.0,46824.08,0.0
+7777,15767414.0,Hsiao,646.0,France,Male,33.0,1.0,91621.69,1.0,1.0,0.0,138565.96,0.0
+7778,15775803.0,Chiazagomekpere,625.0,Spain,Male,30.0,7.0,0.0,2.0,1.0,0.0,179978.68,0.0
+7779,15699450.0,Macnamara,721.0,Spain,Male,37.0,1.0,61297.05,1.0,1.0,0.0,45956.05,1.0
+7780,15793890.0,Uwaezuoke,726.0,Spain,Female,36.0,3.0,129837.72,1.0,1.0,0.0,106534.15,0.0
+7781,15640078.0,Chu,673.0,France,Male,20.0,7.0,0.0,2.0,1.0,1.0,158580.05,0.0
+7782,15572461.0,Okwuoma,484.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,99645.04,0.0
+7783,15702656.0,Chin,637.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,0.0,168886.18,0.0
+7784,15780359.0,Brown,759.0,Germany,Male,48.0,3.0,111379.55,1.0,1.0,0.0,64716.08,1.0
+7785,15598070.0,Yevseyev,681.0,France,Male,32.0,3.0,135814.7,1.0,1.0,1.0,134851.12,0.0
+7786,15610686.0,Tseng,753.0,France,Male,27.0,5.0,0.0,2.0,1.0,1.0,72876.65,0.0
+7787,15586699.0,Pinto,543.0,Germany,Female,37.0,9.0,120193.42,1.0,1.0,0.0,48606.71,0.0
+7788,15623210.0,Young,655.0,France,Female,44.0,2.0,0.0,2.0,1.0,0.0,184843.77,0.0
+7789,15690182.0,Chienezie,571.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,153400.24,0.0
+7790,15683363.0,Watson,683.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,64771.61,0.0
+7791,15723488.0,Maclean,722.0,France,Female,43.0,1.0,0.0,1.0,1.0,0.0,150422.32,1.0
+7792,15618182.0,Genovesi,710.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,71830.1,0.0
+7793,15647842.0,Mao,742.0,Germany,Male,42.0,6.0,122084.63,1.0,1.0,0.0,111241.37,1.0
+7794,15663885.0,White,678.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,170829.87,0.0
+7795,15682528.0,Nebechukwu,645.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,120107.1,0.0
+7796,15697360.0,Hsing,738.0,France,Male,55.0,3.0,0.0,1.0,1.0,1.0,183584.14,1.0
+7797,15802466.0,Niu,556.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,141827.83,0.0
+7798,15774536.0,Kepley,547.0,France,Female,31.0,8.0,0.0,2.0,0.0,0.0,56562.57,0.0
+7799,15581548.0,Milne,683.0,France,Male,33.0,6.0,125552.4,1.0,1.0,1.0,91652.43,0.0
+7800,15663883.0,Ts'ao,627.0,Germany,Male,39.0,1.0,112615.86,1.0,1.0,1.0,121551.93,0.0
+7801,15745399.0,Pisano,667.0,Germany,Female,24.0,5.0,152296.84,1.0,1.0,0.0,177084.04,0.0
+7802,15754526.0,Trevisani,848.0,Germany,Male,34.0,5.0,106901.42,1.0,0.0,1.0,152571.61,0.0
+7803,15677020.0,Chidiegwu,656.0,Spain,Male,54.0,10.0,0.0,1.0,1.0,0.0,5983.8,1.0
+7804,15663438.0,T'ao,768.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,79919.13,0.0
+7805,15792726.0,Pisano,589.0,Germany,Female,45.0,6.0,132951.92,2.0,1.0,1.0,159800.16,0.0
+7806,15806570.0,Ch'in,679.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,166495.2,1.0
+7807,15574910.0,Obiajulu,632.0,Spain,Female,28.0,2.0,0.0,1.0,0.0,1.0,62634.94,1.0
+7808,15641389.0,Ch'eng,619.0,Germany,Male,26.0,4.0,152880.97,2.0,1.0,0.0,113483.96,0.0
+7809,15592089.0,Fiorentini,633.0,Germany,Female,67.0,8.0,119327.93,1.0,1.0,1.0,16281.94,0.0
+7810,15779915.0,Hs?,738.0,France,Male,43.0,5.0,0.0,2.0,0.0,0.0,51637.67,0.0
+7811,15698528.0,Hsia,635.0,France,Female,40.0,6.0,0.0,1.0,0.0,1.0,100101.06,0.0
+7812,15751177.0,Ch'iu,588.0,Germany,Female,55.0,8.0,144961.97,1.0,1.0,0.0,171344.09,1.0
+7813,15677906.0,Hsia,651.0,Germany,Female,50.0,3.0,108724.98,1.0,1.0,0.0,143301.49,1.0
+7814,15796413.0,Nwebube,537.0,France,Male,37.0,8.0,122570.87,1.0,1.0,0.0,131363.57,0.0
+7815,15658432.0,Hsiung,576.0,France,Male,58.0,1.0,131342.53,1.0,1.0,1.0,175544.02,0.0
+7816,15589437.0,Toscani,684.0,France,Female,32.0,8.0,0.0,1.0,1.0,0.0,170634.49,1.0
+7817,15733476.0,Genovesi,597.0,Spain,Female,38.0,8.0,0.0,1.0,0.0,0.0,164967.21,1.0
+7818,15638159.0,Tien,682.0,France,Male,41.0,10.0,0.0,1.0,0.0,1.0,123866.28,0.0
+7819,15624229.0,Nwabugwu,651.0,France,Male,46.0,3.0,80793.58,1.0,1.0,1.0,165008.97,0.0
+7820,15690546.0,Chikezie,554.0,Germany,Male,39.0,8.0,122451.46,2.0,1.0,0.0,140941.47,1.0
+7821,15592937.0,Chuang,592.0,Spain,Female,40.0,6.0,0.0,2.0,0.0,0.0,72945.96,0.0
+7822,15809263.0,Chukwubuikem,667.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,121210.09,0.0
+7823,15771580.0,Nwachinemelu,615.0,France,Male,26.0,2.0,0.0,1.0,1.0,1.0,53710.23,0.0
+7824,15747534.0,Hsiung,752.0,Germany,Male,57.0,3.0,121152.05,2.0,1.0,1.0,82820.85,0.0
+7825,15794468.0,Mazzanti,652.0,France,Male,38.0,6.0,123544.0,1.0,1.0,1.0,9241.52,0.0
+7826,15715888.0,Ch'ien,711.0,Spain,Male,26.0,2.0,0.0,2.0,1.0,0.0,158264.62,0.0
+7827,15596074.0,Udinesi,745.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,140937.97,0.0
+7828,15606841.0,Zuev,663.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,182738.0,0.0
+7829,15793893.0,Chin,667.0,France,Male,41.0,9.0,0.0,2.0,1.0,1.0,166588.76,0.0
+7830,15701175.0,Chidubem,686.0,France,Female,43.0,7.0,0.0,2.0,1.0,0.0,79577.38,0.0
+7831,15704844.0,Chibugo,682.0,Germany,Female,40.0,8.0,71667.97,2.0,1.0,1.0,159823.16,0.0
+7832,15592773.0,O'Donnell,687.0,France,Male,33.0,9.0,0.0,2.0,0.0,0.0,128881.87,0.0
+7833,15585300.0,Lucchesi,686.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,169117.74,1.0
+7834,15731478.0,Hsiao,768.0,France,Female,40.0,9.0,0.0,1.0,1.0,0.0,149913.84,1.0
+7835,15719302.0,Ting,762.0,Germany,Male,54.0,4.0,123675.69,1.0,1.0,0.0,119830.08,1.0
+7836,15661036.0,Nkemjika,659.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,143463.18,0.0
+7837,15798960.0,Lucchesi,681.0,France,Female,41.0,10.0,139994.66,1.0,1.0,0.0,94820.85,0.0
+7838,15806945.0,Chukwudi,617.0,Spain,Male,27.0,10.0,0.0,2.0,0.0,0.0,162452.65,0.0
+7839,15680370.0,Chien,542.0,France,Male,39.0,5.0,0.0,2.0,0.0,0.0,163944.18,1.0
+7840,15623647.0,Chukwubuikem,599.0,France,Female,37.0,6.0,0.0,1.0,1.0,0.0,189268.81,1.0
+7841,15612478.0,Tsao,541.0,France,Male,24.0,5.0,0.0,2.0,0.0,1.0,180829.99,0.0
+7842,15771857.0,T'ao,556.0,France,Female,26.0,7.0,0.0,1.0,1.0,1.0,95393.43,0.0
+7843,15665087.0,Y?,711.0,France,Female,37.0,6.0,118627.07,1.0,1.0,1.0,161579.85,0.0
+7844,15802274.0,Hsieh,709.0,France,Male,31.0,0.0,0.0,2.0,1.0,0.0,56767.67,0.0
+7845,15612187.0,Lo Duca,729.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,1.0,170819.01,0.0
+7846,15737795.0,Trevisani,539.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,126837.72,0.0
+7847,15689614.0,Hsing,538.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,159800.16,0.0
+7848,15685826.0,Ku,738.0,France,Male,39.0,7.0,0.0,1.0,1.0,0.0,74850.84,0.0
+7849,15710408.0,Chiu,544.0,France,Female,40.0,1.0,81231.85,2.0,1.0,1.0,132959.33,0.0
+7850,15781702.0,Hs?,632.0,Germany,Female,41.0,9.0,122949.14,1.0,1.0,0.0,111032.79,1.0
+7851,15626141.0,Pagnotto,687.0,Germany,Female,35.0,7.0,111852.84,1.0,1.0,0.0,103557.97,0.0
+7852,15641480.0,Pirozzi,596.0,France,Male,38.0,9.0,0.0,2.0,0.0,1.0,168515.61,0.0
+7853,15779176.0,Chidozie,678.0,France,Male,33.0,3.0,0.0,1.0,1.0,1.0,168460.12,0.0
+7854,15692406.0,Maclean,592.0,France,Female,46.0,3.0,0.0,2.0,1.0,1.0,182266.01,0.0
+7855,15662085.0,Y?,543.0,Germany,Male,27.0,4.0,116318.23,1.0,1.0,0.0,164061.6,0.0
+7856,15733797.0,Lucchese,638.0,France,Female,47.0,4.0,0.0,1.0,1.0,1.0,164741.5,1.0
+7857,15572390.0,Nnaemeka,712.0,Germany,Male,39.0,3.0,119741.77,1.0,1.0,1.0,147059.16,1.0
+7858,15785831.0,Ku,667.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,137961.41,0.0
+7859,15604130.0,Clements,538.0,France,Female,28.0,6.0,74963.5,1.0,1.0,0.0,65986.48,0.0
+7860,15632264.0,Onyekachi,806.0,France,Female,33.0,7.0,0.0,1.0,1.0,1.0,121073.23,0.0
+7861,15771856.0,Chia,633.0,France,Male,48.0,10.0,0.0,2.0,1.0,1.0,121311.12,0.0
+7862,15757577.0,T'ien,593.0,Germany,Female,42.0,3.0,120905.03,1.0,1.0,0.0,51959.82,1.0
+7863,15690169.0,Chukwuma,589.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,89983.02,0.0
+7864,15660301.0,Chigbogu,641.0,Spain,Male,41.0,3.0,0.0,2.0,0.0,0.0,104469.06,0.0
+7865,15741875.0,Pirozzi,710.0,France,Female,38.0,4.0,0.0,1.0,1.0,0.0,184474.72,0.0
+7866,15723006.0,Hsia,704.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,35701.06,0.0
+7867,15603665.0,Tung,637.0,Germany,Male,33.0,9.0,157955.83,2.0,1.0,1.0,41970.72,0.0
+7868,15730273.0,Pisano,686.0,France,Male,59.0,5.0,0.0,2.0,1.0,1.0,176730.02,0.0
+7869,15612109.0,Manna,717.0,France,Male,25.0,1.0,0.0,2.0,1.0,0.0,174027.3,0.0
+7870,15780128.0,Ch'ien,683.0,France,Male,35.0,7.0,0.0,1.0,1.0,0.0,120853.83,0.0
+7871,15764537.0,Ch'iu,699.0,Germany,Female,44.0,0.0,82291.1,1.0,1.0,0.0,131304.45,0.0
+7872,15576085.0,Macleod,661.0,France,Female,37.0,10.0,153366.13,1.0,1.0,0.0,124596.63,1.0
+7873,15674543.0,L?,669.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,67830.2,0.0
+7874,15797733.0,Chukwudi,737.0,Germany,Male,38.0,4.0,109978.83,2.0,1.0,0.0,5791.85,0.0
+7875,15751561.0,Chukwualuka,652.0,Spain,Female,44.0,7.0,0.0,1.0,1.0,1.0,137519.92,1.0
+7876,15683553.0,Palerma,673.0,France,Male,22.0,2.0,0.0,1.0,1.0,1.0,176692.87,0.0
+7877,15574858.0,Zikoranachidimma,613.0,Spain,Female,47.0,3.0,100628.0,2.0,0.0,0.0,123982.14,1.0
+7878,15793723.0,L?,645.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,184520.0,0.0
+7879,15796834.0,P'eng,613.0,Spain,Female,34.0,9.0,0.0,2.0,0.0,1.0,141872.05,0.0
+7880,15806570.0,Yefimova,681.0,Spain,Female,38.0,4.0,0.0,1.0,1.0,0.0,158891.81,1.0
+7881,15576078.0,T'ien,668.0,France,Male,34.0,7.0,0.0,1.0,1.0,0.0,178393.68,0.0
+7882,15652981.0,Chibueze,651.0,France,Female,32.0,9.0,0.0,1.0,1.0,0.0,79919.13,0.0
+7883,15598046.0,Nnachetam,652.0,Germany,Female,49.0,4.0,115371.94,2.0,1.0,1.0,138880.24,1.0
+7884,15600882.0,Mazzi,663.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,58883.91,0.0
+7885,15703437.0,Fanucci,778.0,Germany,Male,45.0,6.0,143391.07,1.0,0.0,1.0,181224.56,0.0
+7886,15612073.0,Onyemere,510.0,France,Female,44.0,1.0,0.0,2.0,0.0,1.0,178038.17,0.0
+7887,15587585.0,Uspenskaya,569.0,Spain,Female,41.0,1.0,0.0,2.0,1.0,0.0,79919.13,0.0
+7888,15642870.0,Mazzi,667.0,Spain,Male,46.0,9.0,0.0,2.0,1.0,1.0,85901.09,0.0
+7889,15685300.0,Mault,692.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,48963.59,0.0
+7890,15617320.0,Mazzanti,738.0,Germany,Female,48.0,6.0,145644.05,1.0,1.0,0.0,176712.59,1.0
+7891,15714642.0,Chin,749.0,France,Male,28.0,3.0,0.0,1.0,0.0,0.0,102547.56,0.0
+7892,15605835.0,Ts'ai,762.0,Germany,Male,41.0,2.0,125789.69,2.0,1.0,0.0,52861.89,0.0
+7893,15650575.0,Macleod,696.0,Spain,Female,63.0,1.0,0.0,2.0,1.0,1.0,79734.23,0.0
+7894,15802088.0,Folliero,632.0,France,Male,42.0,8.0,112708.2,1.0,1.0,1.0,74564.41,0.0
+7895,15807312.0,Okwudilichukwu,535.0,Germany,Female,58.0,3.0,121894.31,1.0,1.0,0.0,120892.72,1.0
+7896,15585892.0,H?,652.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,84760.32,0.0
+7897,15703544.0,O'Sullivan,649.0,Germany,Male,32.0,1.0,138053.69,1.0,1.0,0.0,129964.54,0.0
+7898,15642824.0,Baresi,619.0,France,Female,39.0,6.0,104944.1,1.0,1.0,1.0,71830.1,0.0
+7899,15624072.0,Chuang,581.0,Spain,Female,33.0,1.0,145537.21,1.0,1.0,0.0,158840.99,0.0
+7900,15732832.0,Yefimova,582.0,France,Female,42.0,9.0,0.0,1.0,0.0,0.0,148087.88,1.0
+7901,15652658.0,Tsao,714.0,Spain,Female,35.0,8.0,0.0,2.0,1.0,1.0,131660.31,0.0
+7902,15626243.0,Ozerova,590.0,Spain,Male,36.0,3.0,0.0,1.0,1.0,0.0,187905.12,0.0
+7903,15787830.0,Ch'iu,554.0,France,Male,25.0,5.0,133040.32,1.0,1.0,0.0,14917.09,0.0
+7904,15686909.0,Lo,704.0,Germany,Female,43.0,3.0,106328.08,1.0,1.0,0.0,171519.06,1.0
+7905,15733261.0,Kambinachi,707.0,Spain,Male,39.0,1.0,138053.79,1.0,1.0,0.0,142911.99,0.0
+7906,15588955.0,Nnaife,850.0,France,Male,39.0,2.0,0.0,1.0,1.0,0.0,116537.96,0.0
+7907,15800845.0,Chiawuotu,619.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,94875.09,0.0
+7908,15751175.0,Smith,653.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,70532.53,0.0
+7909,15633136.0,Ch'ien,598.0,France,Male,38.0,10.0,0.0,2.0,0.0,0.0,63889.1,1.0
+7910,15666032.0,Chiekwugo,632.0,France,Female,37.0,0.0,132730.17,1.0,1.0,0.0,159483.76,0.0
+7911,15683618.0,Uvarova,692.0,Spain,Female,39.0,4.0,0.0,2.0,1.0,1.0,87422.15,0.0
+7912,15676571.0,Tien,714.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,146895.86,0.0
+7913,15733261.0,Ch'iu,593.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,92920.04,0.0
+7914,15633586.0,Trevisani,714.0,France,Male,44.0,9.0,0.0,2.0,1.0,0.0,147913.56,0.0
+7915,15733777.0,Ch'iu,637.0,France,Male,21.0,5.0,71283.09,1.0,1.0,1.0,127847.25,0.0
+7916,15689612.0,Pisani,612.0,France,Male,30.0,1.0,121405.04,1.0,1.0,1.0,55829.25,0.0
+7917,15756538.0,Lettiere,535.0,Germany,Female,54.0,2.0,116969.28,1.0,0.0,0.0,199808.1,1.0
+7918,15802486.0,Ch'iu,630.0,Spain,Male,28.0,6.0,0.0,2.0,1.0,1.0,161653.5,0.0
+7919,15738267.0,Ts'ui,696.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,57549.65,0.0
+7920,15785547.0,Ts'ui,581.0,France,Male,47.0,1.0,0.0,1.0,0.0,0.0,158891.81,1.0
+7921,15706186.0,Okwuoma,679.0,Spain,Male,35.0,1.0,117524.72,1.0,1.0,1.0,148814.54,0.0
+7922,15583595.0,Trevisani,707.0,France,Male,42.0,9.0,121523.51,1.0,1.0,0.0,7953.62,0.0
+7923,15675256.0,Yefremova,685.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,96590.39,0.0
+7924,15573798.0,Okechukwu,691.0,Spain,Male,40.0,7.0,99169.67,1.0,1.0,1.0,152491.31,0.0
+7925,15656912.0,Martin,619.0,France,Female,49.0,0.0,0.0,1.0,1.0,0.0,145562.4,1.0
+7926,15686588.0,Nkemdirim,554.0,France,Male,39.0,5.0,0.0,2.0,0.0,1.0,126561.09,0.0
+7927,15645896.0,Y?,792.0,France,Female,44.0,2.0,0.0,2.0,0.0,1.0,182843.24,0.0
+7928,15577423.0,Hao,744.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,65986.48,0.0
+7929,15596074.0,Genovese,697.0,France,Male,35.0,4.0,0.0,2.0,0.0,1.0,198269.08,0.0
+7930,15811290.0,Trevisani,645.0,Germany,Female,38.0,7.0,144852.06,2.0,1.0,1.0,113557.7,0.0
+7931,15613282.0,Chidiebere,634.0,Germany,Male,44.0,6.0,124442.42,1.0,1.0,1.0,120830.73,1.0
+7932,15594928.0,Onyekachi,579.0,Germany,Male,42.0,7.0,111168.97,2.0,1.0,0.0,160526.36,0.0
+7933,15642530.0,Chidubem,653.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,131734.43,0.0
+7934,15683053.0,Cattaneo,697.0,France,Male,22.0,10.0,0.0,2.0,1.0,1.0,128841.31,0.0
+7935,15641690.0,Ch'eng,775.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,60721.4,0.0
+7936,15717290.0,Macleod,641.0,France,Female,31.0,0.0,0.0,2.0,1.0,0.0,112979.6,0.0
+7937,15776631.0,Grieve,575.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,0.0,72969.9,0.0
+7938,15671514.0,Scott,758.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,52318.66,0.0
+7939,15643908.0,Ch'ien,531.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,52710.7,0.0
+7940,15580148.0,Piazza,652.0,France,Female,39.0,4.0,123544.0,1.0,1.0,0.0,126469.09,0.0
+7941,15711889.0,Hsu,724.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,24991.92,0.0
+7942,15578073.0,Chiu,754.0,Germany,Female,42.0,2.0,118317.27,1.0,1.0,0.0,52795.56,1.0
+7943,15732967.0,Shao,762.0,France,Female,39.0,7.0,0.0,2.0,0.0,1.0,83679.46,0.0
+7944,15803750.0,Onwuamaeze,569.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,162858.29,0.0
+7945,15654574.0,Hsing,687.0,Germany,Female,32.0,2.0,113034.22,2.0,1.0,0.0,185547.71,0.0
+7946,15698732.0,Nwabugwu,592.0,Spain,Female,40.0,0.0,108477.49,1.0,1.0,1.0,59905.29,1.0
+7947,15693904.0,Ch'iu,561.0,France,Male,41.0,0.0,91760.11,1.0,0.0,1.0,144874.67,0.0
+7948,15707596.0,Cremonesi,673.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,159800.16,0.0
+7949,15690670.0,Pritchard,699.0,France,Male,48.0,5.0,0.0,2.0,1.0,1.0,145593.85,0.0
+7950,15771857.0,Biryukov,725.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,4395.45,0.0
+7951,15757952.0,T'ao,538.0,Spain,Male,22.0,2.0,0.0,2.0,0.0,1.0,127630.88,0.0
+7952,15685500.0,Manna,738.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,152835.3,0.0
+7953,15592991.0,Lucciano,721.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,185510.34,0.0
+7954,15662758.0,Ch'eng,641.0,France,Male,43.0,6.0,0.0,2.0,0.0,1.0,183569.25,0.0
+7955,15706029.0,Ch'eng,675.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,92915.84,0.0
+7956,15736413.0,Tuan,622.0,France,Male,41.0,0.0,0.0,2.0,1.0,1.0,73464.88,0.0
+7957,15658889.0,Lombardo,611.0,France,Male,37.0,1.0,137946.39,1.0,0.0,0.0,171657.57,0.0
+7958,15650266.0,Chukwualuka,644.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,131736.23,0.0
+7959,15686885.0,Onuora,763.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,79577.4,0.0
+7960,15580988.0,Kao,678.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,141872.05,0.0
+7961,15726153.0,Ch'in,684.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,131662.47,0.0
+7962,15690164.0,Maclean,711.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,111558.7,0.0
+7963,15735221.0,Obijiaku,619.0,France,Female,45.0,2.0,0.0,2.0,1.0,1.0,113931.57,1.0
+7964,15597901.0,Ritchie,714.0,France,Male,26.0,1.0,117294.12,1.0,0.0,1.0,51565.98,0.0
+7965,15610337.0,Chukwuemeka,705.0,France,Female,29.0,6.0,129855.72,1.0,1.0,1.0,155853.52,0.0
+7966,15665061.0,K?,722.0,France,Male,36.0,7.0,79954.61,1.0,1.0,0.0,134516.71,0.0
+7967,15783501.0,Ts'ai,649.0,Germany,Female,34.0,1.0,152462.79,2.0,0.0,1.0,167984.61,0.0
+7968,15729574.0,Ch'iu,739.0,France,Male,36.0,2.0,170184.99,2.0,0.0,0.0,155853.64,0.0
+7969,15750776.0,K'ung,644.0,France,Female,35.0,2.0,0.0,2.0,0.0,0.0,151581.79,0.0
+7970,15773421.0,Udegbunam,558.0,Germany,Female,52.0,5.0,129794.67,1.0,1.0,0.0,93073.09,1.0
+7971,15628999.0,Liao,679.0,Spain,Female,56.0,7.0,0.0,2.0,1.0,1.0,137674.55,0.0
+7972,15620557.0,Ugochukwu,605.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,174815.54,0.0
+7973,15571221.0,Shih,611.0,France,Female,44.0,7.0,0.0,2.0,1.0,0.0,138889.15,1.0
+7974,15729238.0,Calabresi,662.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,133591.36,0.0
+7975,15800229.0,Shih,683.0,Spain,Male,32.0,9.0,0.0,2.0,0.0,0.0,63795.62,0.0
+7976,15663933.0,Mazzanti,567.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,142538.64,0.0
+7977,15794360.0,Okechukwu,686.0,France,Male,34.0,7.0,141078.57,1.0,0.0,1.0,64907.48,0.0
+7978,15672447.0,Nebechukwu,850.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,91346.03,0.0
+7979,15766649.0,H?,613.0,Spain,Female,52.0,8.0,0.0,1.0,1.0,0.0,162858.29,1.0
+7980,15645371.0,Yermakov,834.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,161845.81,0.0
+7981,15811594.0,Lombardo,645.0,France,Female,33.0,2.0,125534.51,1.0,1.0,0.0,158028.96,0.0
+7982,15750574.0,Smith,622.0,France,Male,34.0,5.0,131038.38,1.0,1.0,1.0,135077.01,0.0
+7983,15627305.0,Palermo,656.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,1.0,94815.04,0.0
+7984,15600583.0,O'Donnell,743.0,France,Male,29.0,3.0,0.0,2.0,1.0,0.0,143598.7,0.0
+7985,15785819.0,Chiawuotu,579.0,Spain,Male,38.0,9.0,0.0,2.0,0.0,0.0,100722.72,0.0
+7986,15737354.0,Oguejiofor,652.0,France,Female,60.0,9.0,0.0,1.0,0.0,1.0,135875.51,1.0
+7987,15804075.0,Hsia,667.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,123969.64,0.0
+7988,15696822.0,Nnamdi,653.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,101057.95,0.0
+7989,15703793.0,Hsiao,667.0,France,Female,38.0,2.0,137452.09,1.0,1.0,1.0,51587.04,0.0
+7990,15716134.0,T'an,703.0,Germany,Female,46.0,2.0,102962.62,1.0,1.0,0.0,121207.66,1.0
+7991,15593973.0,Omeokachie,710.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,132734.87,0.0
+7992,15753831.0,Johnstone,730.0,Spain,Male,33.0,5.0,0.0,1.0,1.0,1.0,164768.82,1.0
+7993,15603846.0,Phelan,683.0,Germany,Female,47.0,4.0,122088.67,2.0,0.0,0.0,152883.95,1.0
+7994,15576256.0,Frolova,718.0,Spain,Female,26.0,6.0,0.0,2.0,0.0,0.0,149891.79,0.0
+7995,15741633.0,Chiu,682.0,Spain,Male,34.0,8.0,0.0,2.0,0.0,1.0,180595.02,0.0
+7996,15795519.0,Ch'iu,683.0,Germany,Female,44.0,9.0,120962.13,1.0,1.0,1.0,82900.25,1.0
+7997,15746451.0,Doyne,635.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,156774.75,0.0
+7998,15783097.0,Olisaemeka,781.0,Spain,Female,33.0,2.0,0.0,2.0,0.0,0.0,166588.76,0.0
+7999,15659355.0,Teng,697.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,144880.81,0.0
+8000,15654060.0,Ozerova,639.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,147963.07,0.0
+8001,15735246.0,Hsing,537.0,Germany,Female,35.0,7.0,151701.45,2.0,1.0,0.0,182531.74,1.0
+8002,15686436.0,Chineze,628.0,Germany,Female,48.0,3.0,90547.48,1.0,1.0,0.0,143049.72,1.0
+8003,15795895.0,Mazzi,559.0,France,Male,46.0,3.0,0.0,2.0,1.0,0.0,40463.03,0.0
+8004,15603065.0,Uvarova,684.0,Germany,Male,47.0,2.0,123959.97,1.0,1.0,0.0,136837.97,0.0
+8005,15763415.0,Ch'in,567.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,9085.0,0.0
+8006,15784700.0,Tien,725.0,Spain,Male,25.0,1.0,0.0,2.0,1.0,0.0,116696.77,0.0
+8007,15691446.0,Chukwukadibia,705.0,Germany,Female,53.0,1.0,160903.2,1.0,1.0,0.0,142903.38,1.0
+8008,15729238.0,Okwuadigbo,710.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,121537.91,0.0
+8009,15705512.0,Ch'iu,554.0,Spain,Female,34.0,3.0,136855.94,1.0,1.0,0.0,56770.76,0.0
+8010,15796071.0,Tretiakova,648.0,France,Male,42.0,3.0,118890.81,1.0,1.0,1.0,112181.08,0.0
+8011,15672597.0,Zetticci,678.0,France,Female,32.0,1.0,104474.97,1.0,0.0,1.0,144870.28,0.0
+8012,15566111.0,Onyemauchechukwu,663.0,France,Female,46.0,3.0,0.0,1.0,1.0,1.0,132973.18,0.0
+8013,15664547.0,Hsiung,644.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,38772.82,0.0
+8014,15567442.0,Hsia,683.0,Germany,Female,35.0,1.0,81828.22,2.0,1.0,1.0,26578.69,0.0
+8015,15683375.0,Pirozzi,758.0,Germany,Male,51.0,4.0,112119.48,3.0,1.0,0.0,115136.51,1.0
+8016,15682355.0,Nwachinemelu,587.0,France,Female,37.0,4.0,128718.78,1.0,0.0,0.0,147521.35,0.0
+8017,15627395.0,T'ao,659.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,140941.47,0.0
+8018,15586970.0,Uspensky,588.0,Germany,Female,39.0,5.0,154712.58,1.0,1.0,1.0,143774.06,1.0
+8019,15804586.0,Ogochukwu,571.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,64872.33,0.0
+8020,15692664.0,Ch'in,582.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,1.0,183494.87,0.0
+8021,15587584.0,Ting,753.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,180622.02,0.0
+8022,15633840.0,Chiazagomekpere,593.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,121365.45,0.0
+8023,15659937.0,Lueck,807.0,France,Male,38.0,3.0,0.0,1.0,0.0,0.0,142935.83,1.0
+8024,15708713.0,Lucchesi,806.0,Germany,Male,54.0,2.0,122227.79,1.0,1.0,0.0,151924.9,1.0
+8025,15773779.0,Nnonso,705.0,France,Female,65.0,0.0,0.0,1.0,0.0,1.0,171575.54,1.0
+8026,15658935.0,Chukwuhaenye,729.0,France,Male,29.0,4.0,0.0,1.0,1.0,1.0,79734.23,1.0
+8027,15668032.0,Smith,681.0,Germany,Female,30.0,9.0,98630.31,1.0,1.0,0.0,169964.12,0.0
+8028,15729019.0,Ch'ien,733.0,France,Male,34.0,3.0,137852.96,1.0,1.0,1.0,111243.09,0.0
+8029,15598884.0,Pagnotto,633.0,Germany,Male,39.0,6.0,127772.1,1.0,1.0,0.0,94816.45,1.0
+8030,15624892.0,Onochie,667.0,Spain,Female,31.0,6.0,0.0,2.0,0.0,0.0,187800.63,0.0
+8031,15658716.0,Palerma,709.0,France,Female,33.0,2.0,133936.99,1.0,1.0,1.0,167023.95,0.0
+8032,15778947.0,Onochie,646.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,86413.11,0.0
+8033,15656005.0,Milanesi,569.0,Spain,Male,45.0,8.0,0.0,2.0,1.0,0.0,71071.47,0.0
+8034,15648726.0,L?,706.0,France,Female,38.0,3.0,116084.32,2.0,1.0,1.0,107841.77,0.0
+8035,15654495.0,Milanesi,524.0,France,Male,43.0,0.0,0.0,2.0,1.0,0.0,48990.5,0.0
+8036,15579891.0,T'ien,649.0,France,Male,38.0,4.0,91621.69,1.0,1.0,1.0,168035.6,0.0
+8037,15687094.0,Ch'in,637.0,France,Female,43.0,5.0,181671.14,1.0,1.0,0.0,147913.56,0.0
+8038,15599433.0,Onyemauchechukwu,641.0,France,Male,33.0,7.0,131834.76,1.0,1.0,1.0,46675.51,0.0
+8039,15722072.0,Chia,574.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,0.0,180197.74,0.0
+8040,15720354.0,Chukwukadibia,494.0,Germany,Male,32.0,2.0,163715.84,2.0,1.0,0.0,153036.97,0.0
+8041,15723484.0,McLean,619.0,France,Male,45.0,9.0,95377.13,1.0,1.0,0.0,132424.69,0.0
+8042,15566689.0,Milanesi,521.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,51380.9,0.0
+8043,15748123.0,Shih,835.0,France,Female,39.0,8.0,0.0,2.0,0.0,1.0,105760.01,0.0
+8044,15572461.0,Obidimkpa,645.0,France,Male,45.0,8.0,0.0,1.0,1.0,0.0,179866.01,1.0
+8045,15806913.0,T'ang,705.0,Spain,Male,40.0,9.0,0.0,2.0,1.0,1.0,25684.93,0.0
+8046,15675252.0,Chukwujamuike,540.0,Spain,Male,44.0,1.0,0.0,2.0,1.0,1.0,101491.23,0.0
+8047,15760216.0,Chukwualuka,692.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,125877.22,0.0
+8048,15596939.0,Nkemdirim,562.0,France,Male,29.0,1.0,0.0,1.0,0.0,0.0,88838.14,0.0
+8049,15791040.0,Ch'eng,725.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,6936.27,0.0
+8050,15703588.0,Mazzanti,582.0,France,Female,40.0,2.0,0.0,1.0,1.0,0.0,114439.66,1.0
+8051,15738662.0,Trevisani,593.0,France,Female,37.0,5.0,132351.29,1.0,0.0,1.0,187616.16,0.0
+8052,15723169.0,Onyekaozulu,730.0,Germany,Female,34.0,6.0,139799.54,1.0,1.0,0.0,64421.02,0.0
+8053,15766686.0,Smith,582.0,Spain,Female,30.0,7.0,0.0,1.0,1.0,1.0,161479.19,0.0
+8054,15780144.0,Atherton,677.0,France,Female,28.0,3.0,130233.14,1.0,1.0,0.0,146668.56,0.0
+8055,15656096.0,Trevisani,663.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,172576.43,0.0
+8056,15793046.0,Lu,683.0,France,Male,34.0,4.0,132838.07,2.0,1.0,0.0,95009.6,0.0
+8057,15806945.0,Chidumaga,753.0,Spain,Female,33.0,3.0,0.0,2.0,1.0,1.0,116969.89,0.0
+8058,15657535.0,K'ung,637.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,1.0,143665.44,0.0
+8059,15795586.0,Onyekachi,754.0,France,Female,36.0,6.0,178056.54,1.0,1.0,1.0,136521.82,0.0
+8060,15716186.0,Ch'eng,645.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,158887.09,0.0
+8061,15683963.0,Yobachi,652.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,109201.53,0.0
+8062,15777922.0,Chia,590.0,France,Female,37.0,0.0,0.0,2.0,1.0,1.0,152956.33,0.0
+8063,15729771.0,Chukwubuikem,673.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,151822.66,0.0
+8064,15691808.0,Chinwendu,656.0,France,Male,42.0,0.0,0.0,2.0,0.0,0.0,86044.98,0.0
+8065,15811947.0,Onwuamaeze,724.0,Spain,Female,23.0,4.0,0.0,2.0,0.0,1.0,49185.57,0.0
+8066,15782530.0,Chukwumaobim,645.0,Spain,Male,51.0,6.0,107467.56,1.0,1.0,0.0,163477.22,1.0
+8067,15648075.0,Chidiegwu,562.0,France,Female,37.0,8.0,0.0,2.0,0.0,1.0,136675.22,0.0
+8068,15630167.0,Tuan,639.0,Germany,Male,41.0,2.0,123378.2,1.0,1.0,0.0,74849.46,0.0
+8069,15572792.0,Tuan,605.0,France,Female,43.0,7.0,0.0,2.0,0.0,0.0,74850.84,0.0
+8070,15674950.0,Obialo,584.0,France,Female,40.0,3.0,0.0,1.0,1.0,0.0,119259.34,1.0
+8071,15579526.0,Ritchie,712.0,Germany,Female,29.0,1.0,103033.83,1.0,1.0,1.0,142797.5,1.0
+8072,15642093.0,Piazza,592.0,Spain,Male,23.0,8.0,146093.47,1.0,1.0,1.0,134794.02,0.0
+8073,15653547.0,Chidiebere,581.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,1.0,156660.21,0.0
+8074,15641359.0,Chukwuemeka,526.0,France,Female,56.0,1.0,0.0,2.0,1.0,0.0,26767.85,1.0
+8075,15767594.0,Tsao,577.0,Spain,Female,52.0,10.0,139825.53,2.0,1.0,0.0,142223.94,1.0
+8076,15809178.0,Ting,775.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,63736.17,0.0
+8077,15697035.0,Lucciano,682.0,France,Male,42.0,6.0,0.0,2.0,0.0,1.0,160948.87,0.0
+8078,15794868.0,Chukwumaobim,763.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,84760.32,0.0
+8079,15673971.0,Wilson,599.0,Germany,Male,29.0,9.0,123894.35,1.0,1.0,0.0,180598.86,0.0
+8080,15632448.0,Kenechukwu,592.0,France,Female,49.0,3.0,0.0,2.0,1.0,1.0,168544.84,1.0
+8081,15572777.0,Mazzi,704.0,France,Male,38.0,6.0,0.0,2.0,0.0,0.0,159418.1,0.0
+8082,15693468.0,Toscani,695.0,France,Female,31.0,2.0,121457.88,1.0,1.0,1.0,123663.63,0.0
+8083,15592229.0,Ch'en,553.0,Germany,Male,51.0,9.0,142084.04,1.0,1.0,0.0,51454.08,1.0
+8084,15657778.0,Gadsdon,683.0,Germany,Male,36.0,6.0,145537.21,1.0,0.0,0.0,176570.55,1.0
+8085,15653776.0,Gilleland,635.0,France,Female,46.0,2.0,0.0,2.0,1.0,0.0,127587.22,0.0
+8086,15748625.0,Chiu,683.0,France,Female,29.0,3.0,0.0,2.0,0.0,1.0,132973.17,0.0
+8087,15673971.0,Ch'en,744.0,France,Female,46.0,3.0,0.0,1.0,0.0,0.0,111893.09,1.0
+8088,15717995.0,Chiemenam,719.0,France,Female,37.0,8.0,126645.13,1.0,1.0,0.0,79919.13,0.0
+8089,15661745.0,Chidubem,689.0,Germany,Female,45.0,1.0,125190.86,1.0,1.0,0.0,83983.54,1.0
+8090,15765457.0,Lucchese,746.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,164018.98,0.0
+8091,15737173.0,Ts'ui,850.0,France,Female,47.0,4.0,0.0,2.0,1.0,0.0,102999.33,0.0
+8092,15605918.0,Ch'ang,679.0,France,Male,43.0,6.0,0.0,2.0,1.0,0.0,156903.44,0.0
+8093,15567442.0,Hsing,692.0,France,Male,36.0,7.0,0.0,1.0,1.0,0.0,50368.63,0.0
+8094,15653253.0,Ts'ui,686.0,Germany,Male,56.0,9.0,102540.61,1.0,1.0,0.0,100127.71,1.0
+8095,15777784.0,Genovese,593.0,France,Female,40.0,6.0,0.0,1.0,1.0,0.0,121175.86,0.0
+8096,15678629.0,Whitehead,592.0,Spain,Male,40.0,1.0,0.0,2.0,0.0,0.0,146785.44,0.0
+8097,15654060.0,Yermakova,678.0,France,Male,22.0,1.0,121453.63,1.0,1.0,1.0,79927.28,0.0
+8098,15654074.0,Hsing,738.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,155915.24,0.0
+8099,15753969.0,Nwachukwu,586.0,France,Male,33.0,4.0,0.0,1.0,0.0,1.0,151335.24,0.0
+8100,15692583.0,Onyemere,709.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,94415.2,0.0
+8101,15686463.0,Pirozzi,663.0,Germany,Female,42.0,8.0,93320.8,3.0,1.0,0.0,182076.97,1.0
+8102,15678720.0,Fan,621.0,Germany,Female,46.0,2.0,103417.74,2.0,1.0,0.0,90106.94,0.0
+8103,15798895.0,Chin,602.0,France,Female,47.0,6.0,0.0,1.0,1.0,0.0,100101.06,1.0
+8104,15792453.0,Martin,706.0,Germany,Male,50.0,5.0,113220.2,1.0,1.0,0.0,126565.84,1.0
+8105,15580146.0,Hs?eh,684.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,79005.95,0.0
+8106,15671445.0,Ch'ien,683.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,180932.46,0.0
+8107,15732704.0,Chiazagomekpere,634.0,France,Male,24.0,5.0,0.0,2.0,1.0,1.0,153015.42,0.0
+8108,15578865.0,Hsia,661.0,France,Male,33.0,10.0,0.0,1.0,1.0,1.0,143357.71,0.0
+8109,15652693.0,Watson,841.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,175082.2,0.0
+8110,15772421.0,P'eng,583.0,Spain,Male,55.0,1.0,121206.96,1.0,0.0,1.0,83700.66,0.0
+8111,15686776.0,Trevisani,647.0,Germany,Male,61.0,4.0,138032.15,1.0,1.0,1.0,53710.23,0.0
+8112,15651335.0,Chuang,682.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,105986.01,0.0
+8113,15579777.0,Aksyonova,572.0,France,Male,39.0,1.0,80780.16,1.0,1.0,1.0,152876.13,0.0
+8114,15670266.0,Manna,786.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,0.0,73881.68,0.0
+8115,15714689.0,Ch'ang,712.0,Spain,Female,50.0,2.0,0.0,1.0,1.0,0.0,148811.14,1.0
+8116,15609998.0,Chien,662.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,81776.84,0.0
+8117,15595588.0,Udinese,813.0,Spain,Female,51.0,3.0,0.0,2.0,1.0,0.0,82709.8,1.0
+8118,15727041.0,Chukwumaobim,704.0,Germany,Male,46.0,7.0,131284.46,1.0,1.0,1.0,105525.36,1.0
+8119,15587597.0,Hs?,635.0,Spain,Female,33.0,4.0,0.0,2.0,1.0,1.0,187632.22,0.0
+8120,15632665.0,K'ung,694.0,Spain,Male,20.0,6.0,0.0,2.0,1.0,1.0,58854.88,0.0
+8121,15589660.0,Chu,683.0,France,Female,33.0,8.0,101266.51,1.0,0.0,1.0,89048.46,0.0
+8122,15700710.0,Lu,619.0,France,Female,23.0,6.0,0.0,2.0,1.0,1.0,78418.85,0.0
+8123,15646521.0,Eluemuno,709.0,France,Female,33.0,6.0,0.0,1.0,1.0,1.0,90876.95,0.0
+8124,15735346.0,H?,702.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,183572.84,0.0
+8125,15644501.0,McIntyre,803.0,France,Female,52.0,6.0,0.0,1.0,1.0,0.0,179866.01,1.0
+8126,15641733.0,Onyemauchechukwu,675.0,Spain,Female,63.0,3.0,0.0,1.0,1.0,0.0,58907.08,1.0
+8127,15691387.0,Niu,705.0,France,Male,31.0,10.0,113693.0,1.0,1.0,0.0,113079.19,0.0
+8128,15716164.0,Dickson,651.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,1.0,51387.1,0.0
+8129,15664772.0,Smith,637.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,10883.52,0.0
+8130,15720508.0,Genovese,627.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,48502.88,0.0
+8131,15592389.0,Chienezie,681.0,Germany,Female,26.0,9.0,120596.93,2.0,1.0,0.0,186612.47,0.0
+8132,15582910.0,Nwachinemelu,817.0,Spain,Male,36.0,2.0,0.0,1.0,1.0,1.0,83470.4,0.0
+8133,15600575.0,Mays,727.0,Spain,Female,33.0,3.0,0.0,2.0,0.0,1.0,80178.54,0.0
+8134,15672640.0,White,687.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,58814.41,0.0
+8135,15607988.0,Hsueh,537.0,Germany,Male,27.0,5.0,141581.71,2.0,0.0,1.0,164924.11,0.0
+8136,15783882.0,Iweobiegbunam,675.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,1.0,183572.39,0.0
+8137,15726791.0,Hao,562.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,158918.03,0.0
+8138,15676526.0,Genovese,651.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,1408.68,0.0
+8139,15790299.0,Milano,678.0,France,Female,56.0,5.0,0.0,1.0,1.0,0.0,173425.68,1.0
+8140,15761102.0,Nwankwo,636.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,151315.76,0.0
+8141,15768449.0,Chia,679.0,France,Female,35.0,4.0,130170.82,1.0,1.0,0.0,152992.04,0.0
+8142,15773876.0,Chukwuemeka,597.0,France,Female,42.0,7.0,121523.51,1.0,0.0,1.0,177065.24,0.0
+8143,15585192.0,Hsia,621.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,133535.29,0.0
+8144,15748696.0,Chukwufumnanya,609.0,Germany,Female,32.0,8.0,123939.36,1.0,1.0,0.0,54473.6,1.0
+8145,15732781.0,Uspensky,535.0,Spain,Female,42.0,5.0,0.0,2.0,1.0,0.0,107964.35,0.0
+8146,15683842.0,Ts'ai,640.0,Spain,Male,39.0,6.0,0.0,1.0,1.0,0.0,185543.34,1.0
+8147,15727467.0,Lucciano,649.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,82696.84,0.0
+8148,15782899.0,Ch'eng,653.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,175893.53,0.0
+8149,15626008.0,T'ien,741.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,90821.69,0.0
+8150,15696054.0,Yefimova,646.0,France,Female,28.0,7.0,0.0,2.0,1.0,0.0,176913.42,0.0
+8151,15795563.0,Ugochukwutubelum,632.0,Germany,Male,35.0,5.0,133800.98,2.0,1.0,1.0,159830.58,0.0
+8152,15797910.0,Chu,675.0,Spain,Female,28.0,2.0,93656.95,1.0,1.0,0.0,163471.01,0.0
+8153,15752026.0,Ch'en,696.0,Germany,Female,28.0,4.0,155973.88,2.0,1.0,0.0,142903.44,0.0
+8154,15635964.0,Ugochukwutubelum,678.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,154486.01,0.0
+8155,15732266.0,Ch'ien,727.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,46791.09,0.0
+8156,15762332.0,Hsueh,704.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,54949.73,0.0
+8157,15761674.0,Mazzi,659.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,162053.92,0.0
+8158,15619360.0,T'ien,850.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,140764.67,0.0
+8159,15583841.0,Chin,645.0,Spain,Female,46.0,9.0,150635.3,1.0,1.0,1.0,90928.48,1.0
+8160,15747795.0,Chidumaga,516.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,90925.72,0.0
+8161,15584545.0,Ch'eng,728.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,74838.64,0.0
+8162,15704771.0,Oluchukwu,643.0,France,Female,32.0,3.0,0.0,1.0,1.0,1.0,168895.06,1.0
+8163,15629010.0,Chukwubuikem,605.0,Germany,Male,32.0,2.0,124586.93,2.0,1.0,0.0,176984.34,0.0
+8164,15751788.0,Nnanna,593.0,Germany,Male,33.0,4.0,111565.91,1.0,1.0,0.0,133571.29,1.0
+8165,15707974.0,Tung,676.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,187800.63,0.0
+8166,15605425.0,Lo Duca,613.0,France,Male,32.0,2.0,129431.36,1.0,1.0,0.0,140134.43,0.0
+8167,15685829.0,Teng,697.0,France,Male,31.0,3.0,0.0,2.0,0.0,0.0,158071.69,0.0
+8168,15743153.0,Ch'in,529.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,1.0,170819.01,0.0
+8169,15794849.0,Nkemdilim,710.0,France,Male,33.0,3.0,0.0,2.0,0.0,1.0,103570.8,0.0
+8170,15625641.0,Ku,648.0,France,Female,46.0,5.0,0.0,1.0,0.0,0.0,121525.48,1.0
+8171,15692259.0,Maclean,723.0,France,Female,31.0,5.0,0.0,2.0,1.0,0.0,122580.48,0.0
+8172,15773322.0,Ch'iu,638.0,France,Female,41.0,4.0,0.0,1.0,0.0,0.0,175324.74,1.0
+8173,15739803.0,Ch'en,628.0,France,Female,71.0,4.0,0.0,1.0,1.0,1.0,113666.48,1.0
+8174,15631339.0,O'Loughlin,576.0,Germany,Female,40.0,2.0,80931.75,1.0,1.0,1.0,162719.69,0.0
+8175,15615245.0,Liao,754.0,Germany,Male,47.0,2.0,150500.64,2.0,0.0,1.0,180622.02,0.0
+8176,15627395.0,Tretiakova,705.0,Germany,Female,35.0,3.0,152899.24,3.0,0.0,0.0,141896.74,1.0
+8177,15783338.0,Teng,693.0,France,Female,36.0,5.0,59893.85,1.0,1.0,0.0,106977.8,0.0
+8178,15766166.0,Goliwe,624.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,119446.64,0.0
+8179,15641837.0,Ch'in,634.0,France,Female,49.0,3.0,76515.71,1.0,0.0,0.0,645.61,1.0
+8180,15689094.0,Tung,663.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,60427.3,0.0
+8181,15785547.0,Hsiung,735.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,94820.85,0.0
+8182,15641585.0,Genovese,642.0,Germany,Female,62.0,2.0,152264.81,1.0,1.0,0.0,74838.64,1.0
+8183,15795586.0,Chidimma,671.0,France,Male,39.0,7.0,0.0,1.0,1.0,0.0,176943.59,0.0
+8184,15705735.0,P'eng,713.0,France,Male,46.0,4.0,131354.53,1.0,1.0,1.0,143357.03,0.0
+8185,15793688.0,Atherton,632.0,Spain,Male,26.0,10.0,0.0,2.0,1.0,0.0,177737.03,0.0
+8186,15809248.0,Ewing,473.0,France,Male,40.0,5.0,0.0,1.0,0.0,1.0,141210.5,0.0
+8187,15768887.0,Chukwudi,744.0,France,Male,25.0,6.0,0.0,2.0,0.0,1.0,50759.8,0.0
+8188,15685564.0,Eluemuno,763.0,France,Male,38.0,5.0,106259.63,1.0,1.0,0.0,65632.33,0.0
+8189,15790846.0,Nwachinemelu,707.0,Germany,Female,21.0,2.0,81571.05,2.0,1.0,1.0,182726.22,0.0
+8190,15772896.0,Ch'eng,681.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,9087.81,0.0
+8191,15662658.0,Iloerika,576.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,152699.8,0.0
+8192,15582944.0,Bischof,645.0,Germany,Male,43.0,7.0,126725.25,1.0,0.0,1.0,69320.97,0.0
+8193,15693326.0,Aitken,775.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,48226.76,0.0
+8194,15679963.0,Nwokezuike,774.0,Germany,Male,38.0,1.0,106089.3,1.0,1.0,1.0,71175.94,0.0
+8195,15573520.0,Lo Duca,653.0,Spain,Female,33.0,7.0,0.0,2.0,0.0,1.0,172618.52,0.0
+8196,15589435.0,Hs?,598.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,179521.24,0.0
+8197,15742369.0,Onwumelu,549.0,Spain,Female,33.0,3.0,0.0,1.0,1.0,1.0,88705.14,0.0
+8198,15802088.0,Ch'ang,594.0,France,Male,25.0,5.0,82373.94,1.0,1.0,1.0,184524.65,0.0
+8199,15685920.0,Dyer,572.0,France,Male,46.0,7.0,0.0,2.0,0.0,1.0,133617.83,0.0
+8200,15795881.0,Nnamutaezinwa,675.0,France,Male,48.0,7.0,0.0,1.0,1.0,0.0,139435.12,1.0
+8201,15585595.0,Hsiao,756.0,Germany,Female,34.0,1.0,97541.24,2.0,1.0,0.0,135851.3,1.0
+8202,15616270.0,O'Brien,595.0,France,Male,41.0,2.0,0.0,2.0,0.0,0.0,176733.72,0.0
+8203,15792228.0,Cremonesi,696.0,France,Male,26.0,2.0,0.0,2.0,1.0,0.0,6078.46,0.0
+8204,15734762.0,Hsing,562.0,Germany,Female,31.0,2.0,115930.87,1.0,0.0,1.0,141180.66,0.0
+8205,15682355.0,Hsia,717.0,France,Female,31.0,3.0,0.0,1.0,0.0,1.0,77500.81,0.0
+8206,15732672.0,Hs?,619.0,Spain,Female,42.0,4.0,0.0,2.0,1.0,0.0,180829.99,0.0
+8207,15701200.0,Shih,668.0,Spain,Female,37.0,4.0,0.0,1.0,1.0,0.0,52367.29,0.0
+8208,15754919.0,Hsing,651.0,Germany,Male,26.0,6.0,145059.97,2.0,1.0,0.0,170821.43,0.0
+8209,15702462.0,Chukwudi,667.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,171070.55,0.0
+8210,15742272.0,Chiemenam,687.0,France,Male,40.0,5.0,107960.06,1.0,1.0,0.0,178393.68,0.0
+8211,15691808.0,Lira,588.0,Germany,Male,39.0,8.0,106521.48,1.0,1.0,1.0,170122.22,1.0
+8212,15605067.0,Smith,572.0,France,Male,49.0,8.0,178032.52,1.0,1.0,0.0,149853.96,0.0
+8213,15777772.0,Onwuamaeze,782.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,131807.14,0.0
+8214,15567855.0,McElhone,708.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,187597.98,0.0
+8215,15605827.0,Chukwufumnanya,758.0,Germany,Female,45.0,5.0,122453.97,2.0,1.0,0.0,16081.62,1.0
+8216,15792365.0,Chidiebere,651.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,174921.92,0.0
+8217,15793331.0,Palerma,709.0,Spain,Male,36.0,3.0,0.0,2.0,0.0,0.0,172490.78,0.0
+8218,15662901.0,Onyemachukwu,539.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,64516.8,0.0
+8219,15718588.0,Pisani,686.0,Spain,Male,36.0,5.0,104305.74,1.0,1.0,0.0,80181.12,0.0
+8220,15604497.0,Fanucci,644.0,Spain,Male,30.0,5.0,105701.91,1.0,0.0,1.0,152417.79,0.0
+8221,15772573.0,Sazonova,628.0,France,Male,54.0,3.0,0.0,3.0,1.0,0.0,71682.54,1.0
+8222,15684474.0,Okagbue,775.0,Germany,Male,51.0,5.0,122486.47,1.0,1.0,0.0,84760.32,1.0
+8223,15636562.0,Onio,796.0,Spain,Female,44.0,4.0,133636.04,2.0,0.0,1.0,71173.64,0.0
+8224,15618391.0,Chiu,712.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,161064.29,0.0
+8225,15586629.0,Chiemenam,754.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,97938.59,0.0
+8226,15643908.0,Obialo,706.0,Germany,Male,29.0,1.0,161766.87,1.0,1.0,1.0,182566.77,0.0
+8227,15705918.0,Clements,659.0,France,Male,57.0,2.0,92674.94,1.0,1.0,1.0,187616.16,0.0
+8228,15692744.0,Ch'en,539.0,France,Male,49.0,9.0,0.0,2.0,1.0,1.0,71709.12,0.0
+8229,15746341.0,Y?,545.0,France,Female,27.0,4.0,156067.05,1.0,0.0,0.0,162878.96,0.0
+8230,15793901.0,Eluemuno,673.0,Spain,Female,24.0,8.0,0.0,2.0,1.0,1.0,180829.99,0.0
+8231,15775625.0,Ts'ui,531.0,Germany,Male,32.0,8.0,119960.29,2.0,0.0,1.0,187985.77,0.0
+8232,15664752.0,Ndubueze,678.0,France,Female,39.0,3.0,0.0,2.0,0.0,1.0,86480.29,0.0
+8233,15586870.0,Lettiere,726.0,Germany,Male,32.0,4.0,125889.3,2.0,1.0,1.0,66463.62,0.0
+8234,15570051.0,Fiorentini,521.0,Germany,Female,46.0,1.0,113905.48,1.0,1.0,1.0,96833.0,1.0
+8235,15692406.0,Tuan,686.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,18094.96,0.0
+8236,15662426.0,Chinonyelum,577.0,France,Female,29.0,1.0,0.0,1.0,0.0,0.0,179866.01,0.0
+8237,15702300.0,Ch'eng,850.0,France,Female,40.0,6.0,0.0,3.0,1.0,0.0,103570.28,1.0
+8238,15667934.0,Mancini,593.0,France,Male,34.0,5.0,0.0,2.0,0.0,0.0,90212.38,0.0
+8239,15689268.0,Okwuadigbo,667.0,France,Male,23.0,2.0,0.0,2.0,1.0,0.0,125848.88,0.0
+8240,15809302.0,Onyekachi,584.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,46857.52,0.0
+8241,15735388.0,Chukwunonso,686.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,84908.2,0.0
+8242,15738884.0,O'Brien,735.0,Spain,Male,44.0,6.0,0.0,1.0,1.0,0.0,175853.64,1.0
+8243,15750776.0,Nwokezuike,794.0,France,Male,35.0,8.0,0.0,1.0,1.0,1.0,152884.85,1.0
+8244,15651554.0,Ch'ien,598.0,France,Female,47.0,1.0,0.0,1.0,0.0,1.0,129891.66,1.0
+8245,15604345.0,Hsieh,599.0,Germany,Female,41.0,1.0,109960.06,1.0,1.0,0.0,735.79,1.0
+8246,15636590.0,Ting,724.0,France,Male,45.0,1.0,0.0,2.0,1.0,0.0,89566.64,0.0
+8247,15609083.0,Onio,755.0,Germany,Male,30.0,4.0,139973.65,1.0,1.0,1.0,118959.64,0.0
+8248,15707637.0,Chiekwugo,708.0,Germany,Male,56.0,0.0,120867.18,1.0,1.0,0.0,44719.5,1.0
+8249,15644977.0,Mazzi,727.0,France,Male,57.0,7.0,154733.97,1.0,1.0,1.0,102799.22,1.0
+8250,15741745.0,Trevisano,627.0,France,Female,36.0,9.0,0.0,1.0,0.0,0.0,146723.72,1.0
+8251,15731553.0,Lu,632.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,95611.47,0.0
+8252,15791767.0,Pinto,850.0,France,Female,37.0,2.0,0.0,1.0,1.0,0.0,158527.56,0.0
+8253,15688865.0,Tsai,678.0,France,Male,47.0,7.0,0.0,2.0,1.0,0.0,90876.95,0.0
+8254,15649430.0,Lucchese,619.0,Spain,Female,32.0,5.0,90721.52,1.0,1.0,0.0,177740.58,1.0
+8255,15646594.0,Hs?,717.0,Spain,Male,40.0,4.0,0.0,2.0,0.0,1.0,130827.97,0.0
+8256,15756538.0,Trevisano,713.0,France,Female,30.0,6.0,147972.19,1.0,1.0,0.0,141860.62,0.0
+8257,15674671.0,White,633.0,Germany,Male,32.0,9.0,125540.05,1.0,1.0,1.0,173792.77,1.0
+8258,15807568.0,Folliero,583.0,France,Female,42.0,7.0,0.0,2.0,1.0,1.0,102986.15,0.0
+8259,15796370.0,Chiang,713.0,France,Male,34.0,6.0,0.0,1.0,1.0,1.0,84469.13,0.0
+8260,15781689.0,Maclean,635.0,Spain,Male,33.0,9.0,0.0,1.0,1.0,1.0,73882.65,0.0
+8261,15791533.0,K'ung,678.0,Spain,Male,41.0,8.0,0.0,2.0,1.0,0.0,179540.73,0.0
+8262,15575060.0,Hsiung,703.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,158845.49,0.0
+8263,15679299.0,Toscani,696.0,France,Male,32.0,5.0,130627.66,1.0,1.0,1.0,86786.41,0.0
+8264,15565714.0,Okeke,516.0,Germany,Female,51.0,8.0,91285.13,1.0,0.0,0.0,164806.89,1.0
+8265,15805637.0,L?,671.0,Germany,Female,36.0,4.0,107556.06,1.0,1.0,1.0,181850.74,0.0
+8266,15768471.0,Chukwuemeka,696.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,100423.88,0.0
+8267,15805671.0,Mazzanti,693.0,Germany,Male,38.0,8.0,151532.4,2.0,0.0,1.0,103726.71,0.0
+8268,15706232.0,Chukwuemeka,724.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,94739.2,1.0
+8269,15579914.0,Walker,667.0,France,Female,32.0,1.0,90971.85,1.0,1.0,1.0,104889.2,0.0
+8270,15669293.0,Mazzanti,705.0,France,Female,39.0,0.0,0.0,1.0,0.0,0.0,175373.46,1.0
+8271,15732437.0,Y?an,677.0,France,Female,48.0,7.0,0.0,2.0,1.0,1.0,103557.93,0.0
+8272,15681697.0,Nkemdilim,656.0,France,Female,40.0,5.0,133845.26,1.0,1.0,0.0,94782.12,0.0
+8273,15609824.0,DeRose,769.0,France,Male,44.0,4.0,0.0,2.0,0.0,0.0,148087.62,0.0
+8274,15758856.0,Lo Duca,778.0,France,Female,41.0,3.0,0.0,2.0,0.0,1.0,144532.85,0.0
+8275,15682778.0,Nnamdi,744.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,109145.2,0.0
+8276,15684958.0,Chiazagomekpere,649.0,Spain,Male,46.0,1.0,126875.62,1.0,1.0,0.0,74800.99,0.0
+8277,15744127.0,Kao,767.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,159475.08,0.0
+8278,15794472.0,Onyekaozulu,662.0,France,Male,26.0,7.0,0.0,2.0,1.0,0.0,182076.97,0.0
+8279,15676521.0,Chukwubuikem,658.0,Spain,Female,61.0,3.0,0.0,1.0,1.0,1.0,61995.57,1.0
+8280,15777035.0,Pisano,695.0,Germany,Male,29.0,3.0,161806.09,1.0,1.0,0.0,176780.69,0.0
+8281,15635143.0,Chiabuotu,629.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,88890.75,0.0
+8282,15795895.0,Ch'in,659.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,0.0,157097.09,0.0
+8283,15618703.0,Kerr,587.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,171867.08,0.0
+8284,15777615.0,Mazzi,658.0,Spain,Male,41.0,1.0,96654.72,1.0,0.0,0.0,161479.19,0.0
+8285,15731478.0,Lettiere,758.0,France,Male,36.0,5.0,93871.95,1.0,1.0,1.0,91291.2,0.0
+8286,15776921.0,White,648.0,France,Male,35.0,7.0,131778.08,1.0,1.0,1.0,167573.06,0.0
+8287,15602312.0,Hao,592.0,Spain,Male,42.0,2.0,0.0,2.0,1.0,1.0,126188.42,0.0
+8288,15783629.0,Biryukov,706.0,Germany,Female,29.0,0.0,145747.67,1.0,1.0,0.0,187987.01,0.0
+8289,15646969.0,Chibugo,646.0,Spain,Male,38.0,7.0,127837.52,1.0,1.0,0.0,168034.9,0.0
+8290,15796074.0,Aksenov,661.0,France,Female,33.0,1.0,165438.26,1.0,1.0,0.0,138344.7,0.0
+8291,15800784.0,Garmon,668.0,France,Female,56.0,1.0,0.0,2.0,1.0,0.0,177747.03,1.0
+8292,15791851.0,Hao,678.0,Germany,Female,31.0,6.0,137841.53,2.0,0.0,0.0,103787.36,0.0
+8293,15663921.0,Hsing,683.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,167066.95,0.0
+8294,15657535.0,P'eng,714.0,France,Male,26.0,3.0,0.0,2.0,1.0,1.0,122580.48,0.0
+8295,15733111.0,Hsieh,543.0,France,Male,31.0,2.0,89568.19,1.0,1.0,0.0,162452.65,0.0
+8296,15586069.0,Genovese,651.0,Germany,Male,24.0,9.0,145081.73,2.0,1.0,1.0,51581.07,0.0
+8297,15751167.0,Ch'ang,669.0,Germany,Female,46.0,5.0,81547.89,2.0,1.0,1.0,113553.43,0.0
+8298,15628674.0,Fadden,710.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,121408.55,0.0
+8299,15591036.0,Ch'iu,685.0,France,Male,29.0,6.0,0.0,2.0,0.0,1.0,64448.36,0.0
+8300,15578799.0,Smith,628.0,Spain,Male,45.0,5.0,0.0,1.0,1.0,0.0,141069.88,1.0
+8301,15686917.0,Nwebube,485.0,France,Male,43.0,2.0,136674.51,1.0,1.0,1.0,63795.8,0.0
+8302,15737571.0,Chukwukere,568.0,France,Female,42.0,8.0,0.0,1.0,1.0,1.0,177670.57,1.0
+8303,15590199.0,Teng,762.0,Germany,Male,53.0,6.0,132623.6,1.0,1.0,0.0,84932.4,1.0
+8304,15616380.0,Iweobiegbunam,526.0,Spain,Male,27.0,3.0,0.0,2.0,0.0,1.0,36315.03,0.0
+8305,15796910.0,Ch'ien,628.0,France,Female,49.0,6.0,0.0,1.0,1.0,0.0,93839.3,1.0
+8306,15664881.0,Walker,712.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,74858.3,0.0
+8307,15569410.0,Hs?,549.0,Germany,Female,51.0,0.0,134542.73,1.0,0.0,0.0,146511.2,1.0
+8308,15756976.0,Yudina,692.0,France,Male,30.0,3.0,0.0,1.0,1.0,0.0,107964.35,0.0
+8309,15648047.0,Toscani,706.0,Spain,Male,35.0,7.0,124379.14,1.0,1.0,1.0,169920.92,0.0
+8310,15702566.0,Chukwufumnanya,676.0,Spain,Male,34.0,5.0,0.0,1.0,1.0,1.0,167984.72,0.0
+8311,15593795.0,Calabrese,542.0,Spain,Male,29.0,4.0,0.0,2.0,0.0,1.0,141364.27,0.0
+8312,15682523.0,Trevisani,490.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,146875.86,0.0
+8313,15688637.0,Chu,575.0,Germany,Female,33.0,9.0,115378.94,1.0,1.0,1.0,71095.41,0.0
+8314,15769402.0,Baresi,581.0,France,Male,54.0,5.0,0.0,1.0,1.0,0.0,173498.45,1.0
+8315,15787151.0,Smith,637.0,Germany,Male,34.0,7.0,117183.74,1.0,1.0,0.0,78670.38,0.0
+8316,15614187.0,L?,662.0,Spain,Female,29.0,7.0,109949.05,2.0,1.0,1.0,154767.34,0.0
+8317,15664808.0,Napolitano,684.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,114093.73,0.0
+8318,15699415.0,Hsia,628.0,Spain,Male,26.0,1.0,0.0,2.0,0.0,0.0,143598.7,0.0
+8319,15646942.0,Palerma,599.0,Germany,Male,26.0,1.0,150685.79,1.0,1.0,0.0,49337.84,0.0
+8320,15788776.0,T'ien,584.0,Germany,Male,41.0,5.0,106328.08,1.0,1.0,1.0,133535.29,0.0
+8321,15734850.0,Yeh,637.0,Spain,Female,54.0,9.0,0.0,1.0,1.0,0.0,140889.12,1.0
+8322,15798532.0,Padovesi,651.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,86935.73,0.0
+8323,15632899.0,T'ao,824.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,161241.65,0.0
+8324,15586029.0,Y?,591.0,France,Female,33.0,4.0,0.0,1.0,0.0,1.0,88890.05,0.0
+8325,15743759.0,T'ang,606.0,Germany,Male,51.0,4.0,98630.31,1.0,1.0,0.0,161465.31,1.0
+8326,15590177.0,Tsai,682.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,0.0,142548.31,0.0
+8327,15773869.0,Shao,757.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,47777.15,0.0
+8328,15766378.0,Manna,744.0,Spain,Male,35.0,5.0,61592.14,1.0,1.0,0.0,155845.74,1.0
+8329,15689514.0,Manna,683.0,France,Male,46.0,2.0,121418.62,1.0,1.0,0.0,163989.66,1.0
+8330,15759717.0,Chidubem,709.0,Germany,Male,32.0,1.0,109387.9,2.0,1.0,1.0,188533.11,0.0
+8331,15603088.0,Uvarova,729.0,Spain,Female,29.0,4.0,0.0,1.0,1.0,1.0,183490.99,0.0
+8332,15671351.0,Ts'ai,657.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,166650.9,0.0
+8333,15714586.0,Ch'ien,582.0,France,Male,39.0,6.0,164557.95,1.0,1.0,1.0,162858.46,0.0
+8334,15602816.0,Aitken,603.0,France,Female,32.0,5.0,129855.72,1.0,1.0,0.0,180890.4,0.0
+8335,15610454.0,Cattaneo,587.0,France,Male,35.0,9.0,0.0,2.0,0.0,0.0,55829.25,0.0
+8336,15586479.0,Shao,798.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,193018.71,0.0
+8337,15692991.0,Chia,675.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,140934.57,0.0
+8338,15766066.0,Calabresi,667.0,Germany,Female,42.0,0.0,117565.74,1.0,1.0,0.0,120415.61,1.0
+8339,15752578.0,Chikelu,754.0,France,Male,30.0,8.0,133800.98,1.0,1.0,0.0,158750.53,0.0
+8340,15733229.0,Mazzanti,613.0,Spain,Male,34.0,3.0,0.0,2.0,0.0,1.0,51695.41,0.0
+8341,15644400.0,Chukwujamuike,537.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,180962.32,0.0
+8342,15775490.0,Okechukwu,707.0,Spain,Male,29.0,3.0,0.0,2.0,0.0,1.0,105969.05,0.0
+8343,15706463.0,H?,645.0,Spain,Male,38.0,7.0,143340.44,1.0,0.0,1.0,166586.99,0.0
+8344,15583692.0,Chibueze,648.0,Germany,Male,31.0,10.0,153610.61,1.0,0.0,0.0,162697.93,0.0
+8345,15576065.0,Milanesi,635.0,France,Female,41.0,1.0,0.0,2.0,1.0,0.0,181543.67,0.0
+8346,15671342.0,T'ien,778.0,Spain,Female,36.0,2.0,108914.17,2.0,1.0,0.0,121277.78,0.0
+8347,15673747.0,Walker,579.0,France,Female,27.0,10.0,0.0,2.0,1.0,0.0,44251.33,0.0
+8348,15622578.0,Nwankwo,704.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,92664.09,0.0
+8349,15697460.0,Kao,462.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,47880.71,0.0
+8350,15652667.0,Y?an,648.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,1.0,156598.44,0.0
+8351,15690153.0,Tseng,697.0,France,Male,39.0,4.0,0.0,1.0,1.0,1.0,94898.1,0.0
+8352,15649354.0,Hargreaves,642.0,France,Male,43.0,5.0,0.0,2.0,1.0,1.0,60972.84,0.0
+8353,15791326.0,Chien,624.0,France,Female,36.0,3.0,143542.36,1.0,1.0,0.0,150687.65,0.0
+8354,15662533.0,Tsou,498.0,France,Female,30.0,6.0,0.0,2.0,0.0,0.0,182784.11,0.0
+8355,15775108.0,H?,726.0,Germany,Female,34.0,6.0,85845.43,2.0,0.0,0.0,117038.96,0.0
+8356,15762216.0,Trevisani,711.0,France,Male,42.0,4.0,0.0,1.0,1.0,0.0,116471.14,0.0
+8357,15608886.0,Shao,622.0,France,Female,66.0,9.0,0.0,1.0,1.0,0.0,80619.09,1.0
+8358,15725794.0,Liao,732.0,France,Female,59.0,5.0,0.0,2.0,1.0,1.0,54816.57,0.0
+8359,15603851.0,Frolova,706.0,Germany,Male,33.0,8.0,116968.91,2.0,1.0,1.0,137537.86,0.0
+8360,15571752.0,Ugochukwu,607.0,Spain,Male,41.0,7.0,0.0,1.0,1.0,0.0,135077.01,0.0
+8361,15768270.0,Nebechi,646.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,49503.5,0.0
+8362,15661409.0,Yefimova,612.0,France,Female,32.0,3.0,0.0,1.0,1.0,0.0,168834.43,0.0
+8363,15790336.0,Hsia,850.0,France,Female,37.0,7.0,0.0,1.0,0.0,0.0,83343.73,1.0
+8364,15763063.0,Ch'ang,478.0,Spain,Male,43.0,4.0,0.0,1.0,1.0,0.0,131281.28,0.0
+8365,15670343.0,Chukwujamuike,753.0,Spain,Male,40.0,9.0,0.0,1.0,1.0,0.0,122999.14,0.0
+8366,15690772.0,Walker,748.0,France,Female,39.0,0.0,0.0,2.0,0.0,0.0,149913.94,0.0
+8367,15641688.0,Chukwualuka,686.0,Germany,Male,32.0,1.0,108905.29,2.0,1.0,1.0,120835.65,0.0
+8368,15696733.0,Shih,725.0,Spain,Female,34.0,6.0,151914.93,2.0,1.0,0.0,89822.48,0.0
+8369,15788022.0,Chiu,635.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,118862.08,0.0
+8370,15741719.0,Chidubem,699.0,France,Male,39.0,3.0,0.0,1.0,1.0,0.0,171021.44,0.0
+8371,15744044.0,Pagnotto,644.0,France,Female,40.0,6.0,0.0,1.0,1.0,1.0,166650.32,0.0
+8372,15787597.0,Ch'iu,590.0,France,Male,45.0,9.0,0.0,2.0,1.0,0.0,180553.48,0.0
+8373,15808960.0,Hsiung,609.0,France,Female,35.0,7.0,0.0,1.0,1.0,0.0,178373.43,1.0
+8374,15813844.0,Hao,637.0,Spain,Male,29.0,3.0,0.0,1.0,1.0,1.0,62733.05,0.0
+8375,15680901.0,Genovese,590.0,France,Female,28.0,1.0,0.0,2.0,1.0,0.0,172097.4,0.0
+8376,15718289.0,Hs?,745.0,France,Female,36.0,7.0,161637.75,1.0,1.0,0.0,118977.43,0.0
+8377,15674833.0,Uspensky,679.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,101667.0,0.0
+8378,15626135.0,Chinwendu,651.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,159800.16,0.0
+8379,15668575.0,Nucci,712.0,France,Female,40.0,7.0,0.0,1.0,0.0,1.0,141210.18,0.0
+8380,15744240.0,T'ang,651.0,Germany,Male,41.0,5.0,99836.47,1.0,1.0,0.0,152884.85,0.0
+8381,15794580.0,H?,551.0,Germany,Male,38.0,2.0,138857.59,1.0,0.0,1.0,172363.41,0.0
+8382,15589435.0,Chukwuemeka,691.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,77965.67,0.0
+8383,15727059.0,Tsao,663.0,Spain,Male,39.0,2.0,0.0,2.0,1.0,1.0,162643.15,0.0
+8384,15656330.0,Nnamdi,607.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,146700.22,0.0
+8385,15657829.0,Shih,709.0,France,Male,41.0,5.0,0.0,2.0,0.0,1.0,79919.13,0.0
+8386,15611551.0,Chidimma,747.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,44965.44,0.0
+8387,15806966.0,Chidimma,705.0,France,Male,39.0,10.0,0.0,1.0,0.0,0.0,139469.11,0.0
+8388,15757867.0,Hsia,711.0,Germany,Male,44.0,4.0,127785.16,1.0,1.0,1.0,134773.41,0.0
+8389,15658946.0,Kao,762.0,France,Male,38.0,4.0,0.0,2.0,0.0,0.0,113860.32,0.0
+8390,15800366.0,Chu,850.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,59103.13,0.0
+8391,15780561.0,Eiland,543.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,54871.02,0.0
+8392,15808760.0,Iweobiegbunam,710.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,151903.6,0.0
+8393,15665590.0,Obiajulu,642.0,Germany,Female,41.0,8.0,131938.92,1.0,0.0,0.0,66548.58,1.0
+8394,15662483.0,Chukwuhaenye,683.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,167636.15,0.0
+8395,15772996.0,Hsu,692.0,France,Male,31.0,6.0,0.0,1.0,0.0,0.0,70810.6,0.0
+8396,15649024.0,Tuan,668.0,Germany,Female,37.0,9.0,141136.89,1.0,1.0,0.0,93820.49,0.0
+8397,15736963.0,Chienezie,589.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,85470.45,0.0
+8398,15667588.0,Olisaemeka,561.0,France,Male,38.0,3.0,137060.88,1.0,0.0,1.0,103042.2,0.0
+8399,15743817.0,Ch'ien,549.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,83058.14,0.0
+8400,15575247.0,Rizzo,581.0,France,Female,33.0,1.0,0.0,1.0,1.0,1.0,84420.5,1.0
+8401,15653772.0,DeRose,711.0,France,Male,47.0,5.0,0.0,2.0,1.0,1.0,82709.8,0.0
+8402,15590828.0,Pirozzi,646.0,France,Male,37.0,3.0,139432.55,1.0,0.0,1.0,162763.01,0.0
+8403,15634564.0,Teng,656.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,118570.53,0.0
+8404,15603562.0,Hsiao,668.0,France,Male,35.0,6.0,0.0,1.0,1.0,1.0,151375.57,0.0
+8405,15780212.0,Chien,825.0,France,Male,30.0,1.0,0.0,2.0,0.0,1.0,138380.11,0.0
+8406,15773520.0,Chiu,721.0,Spain,Female,41.0,7.0,145553.07,1.0,1.0,1.0,88604.89,0.0
+8407,15731166.0,Yobachukwu,599.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,105732.9,0.0
+8408,15798960.0,Tseng,712.0,France,Male,26.0,7.0,0.0,2.0,1.0,1.0,176984.34,0.0
+8409,15612594.0,Mironova,850.0,Spain,Male,37.0,2.0,95675.62,1.0,1.0,1.0,121894.31,0.0
+8410,15675252.0,Tung,703.0,France,Female,34.0,5.0,0.0,1.0,1.0,1.0,93091.02,0.0
+8411,15763550.0,Ch'iu,588.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,182334.45,0.0
+8412,15786418.0,Eames,603.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,132029.95,0.0
+8413,15774133.0,Chin,535.0,France,Female,34.0,3.0,0.0,2.0,0.0,0.0,97734.4,0.0
+8414,15802220.0,Trevisani,741.0,Spain,Female,39.0,3.0,0.0,1.0,0.0,1.0,135875.51,0.0
+8415,15573045.0,Trevisan,762.0,France,Female,34.0,6.0,0.0,1.0,0.0,1.0,102763.69,0.0
+8416,15809847.0,Obidimkpa,721.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,0.0,141359.11,0.0
+8417,15782899.0,Mazzi,704.0,France,Male,44.0,9.0,0.0,2.0,0.0,1.0,140579.17,0.0
+8418,15652999.0,H?,537.0,France,Male,28.0,4.0,0.0,1.0,1.0,0.0,130404.53,1.0
+8419,15772383.0,Ozuoke,752.0,France,Male,50.0,3.0,108867.0,2.0,1.0,1.0,94440.45,0.0
+8420,15807900.0,Kao,559.0,Germany,Male,47.0,7.0,126442.56,2.0,1.0,1.0,131807.14,0.0
+8421,15600710.0,Chienezie,547.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,75884.65,0.0
+8422,15573852.0,Maclean,663.0,France,Male,34.0,5.0,0.0,1.0,0.0,1.0,73329.08,0.0
+8423,15600357.0,Liao,525.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,50368.63,0.0
+8424,15810723.0,Y?,641.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,84863.85,0.0
+8425,15737169.0,O'Loghlin,686.0,France,Female,37.0,5.0,142631.87,1.0,1.0,1.0,91429.74,0.0
+8426,15605737.0,Li,598.0,Spain,Female,31.0,6.0,0.0,2.0,1.0,1.0,118522.73,0.0
+8427,15721719.0,Lucchese,652.0,Germany,Male,27.0,0.0,140931.57,1.0,0.0,1.0,139971.01,0.0
+8428,15642530.0,Chinwendu,739.0,France,Male,40.0,3.0,141525.55,1.0,1.0,1.0,131283.6,0.0
+8429,15690589.0,Folliero,592.0,France,Male,24.0,8.0,81732.88,1.0,1.0,1.0,115403.31,0.0
+8430,15693817.0,Iadanza,649.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,140169.38,1.0
+8431,15809800.0,Yashina,644.0,Spain,Female,39.0,5.0,98765.84,1.0,1.0,0.0,125961.48,0.0
+8432,15623450.0,T'ang,725.0,France,Male,36.0,4.0,0.0,2.0,0.0,0.0,82820.85,0.0
+8433,15773723.0,Ugochukwu,619.0,France,Female,30.0,4.0,0.0,2.0,0.0,0.0,126792.69,0.0
+8434,15788215.0,Hao,473.0,France,Female,35.0,8.0,0.0,1.0,1.0,0.0,164741.81,1.0
+8435,15602034.0,Hsiung,635.0,Spain,Female,30.0,9.0,0.0,2.0,1.0,0.0,77830.63,0.0
+8436,15628583.0,Ch'iu,587.0,France,Female,38.0,8.0,119703.1,1.0,1.0,0.0,68193.92,0.0
+8437,15801296.0,Tsao,684.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,170903.4,0.0
+8438,15622003.0,Chinagorom,679.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,187980.21,0.0
+8439,15718588.0,Hsing,599.0,France,Male,56.0,8.0,0.0,2.0,1.0,1.0,119127.06,0.0
+8440,15751177.0,Olisaemeka,632.0,Spain,Female,43.0,9.0,0.0,2.0,1.0,1.0,25448.31,1.0
+8441,15742668.0,Ch'iu,537.0,Spain,Male,41.0,1.0,0.0,2.0,0.0,0.0,171457.06,0.0
+8442,15690079.0,Iloerika,727.0,France,Male,48.0,1.0,0.0,1.0,1.0,0.0,50610.62,1.0
+8443,15756743.0,Tsao,636.0,Spain,Female,27.0,1.0,0.0,1.0,1.0,1.0,170770.44,0.0
+8444,15723858.0,Ch'iu,588.0,Germany,Female,36.0,2.0,119413.62,1.0,1.0,0.0,60972.84,1.0
+8445,15713505.0,Dancy,636.0,France,Female,34.0,6.0,122453.37,1.0,1.0,0.0,142916.43,0.0
+8446,15695097.0,Zaytseva,622.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,117832.88,0.0
+8447,15804009.0,Aitken,679.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,166694.93,0.0
+8448,15730397.0,Liao,637.0,France,Female,39.0,5.0,0.0,2.0,0.0,0.0,167778.62,0.0
+8449,15803801.0,Iweobiegbunam,641.0,Spain,Male,26.0,2.0,0.0,2.0,0.0,1.0,91775.51,0.0
+8450,15628600.0,O'Sullivan,521.0,France,Female,40.0,10.0,0.0,2.0,0.0,0.0,102908.91,0.0
+8451,15628936.0,Chukwufumnanya,556.0,Spain,Male,39.0,4.0,0.0,1.0,1.0,1.0,140800.66,0.0
+8452,15806420.0,Nwankwo,729.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,0.0,60536.56,0.0
+8453,15608408.0,Ugochukwu,664.0,France,Male,46.0,9.0,0.0,1.0,1.0,0.0,137985.69,1.0
+8454,15773789.0,Trevisano,469.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,57320.92,0.0
+8455,15570051.0,Chukwujekwu,684.0,Germany,Female,40.0,5.0,134760.21,1.0,1.0,1.0,73551.44,1.0
+8456,15718369.0,Pape,663.0,Spain,Female,36.0,8.0,0.0,2.0,1.0,1.0,58855.85,0.0
+8457,15591257.0,H?,706.0,Spain,Female,41.0,6.0,121513.31,1.0,1.0,1.0,149991.32,0.0
+8458,15696743.0,Rolon,531.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,142331.85,0.0
+8459,15740175.0,Biryukov,684.0,Germany,Female,37.0,8.0,131691.33,3.0,1.0,0.0,48486.25,1.0
+8460,15572767.0,Chinwendu,549.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,8982.75,0.0
+8461,15637593.0,Onio,634.0,France,Male,30.0,8.0,113599.42,1.0,1.0,0.0,162734.31,0.0
+8462,15747043.0,Mazzi,584.0,France,Male,40.0,10.0,119654.44,1.0,1.0,1.0,1002.39,0.0
+8463,15778418.0,Tsao,673.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,47832.82,0.0
+8464,15683625.0,Nkemjika,684.0,France,Male,43.0,1.0,0.0,1.0,1.0,0.0,131283.6,1.0
+8465,15714642.0,Manna,562.0,Germany,Female,24.0,5.0,99529.7,2.0,1.0,0.0,28461.29,0.0
+8466,15674442.0,Ch'eng,634.0,Germany,Female,35.0,4.0,123544.0,2.0,1.0,1.0,158937.64,0.0
+8467,15806486.0,K'ung,679.0,France,Female,35.0,4.0,132876.55,1.0,0.0,1.0,142963.27,0.0
+8468,15691723.0,Chukwukadibia,602.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,152956.33,0.0
+8469,15589195.0,Manna,699.0,Spain,Female,44.0,8.0,0.0,1.0,1.0,1.0,139410.08,1.0
+8470,15576935.0,Chiang,683.0,France,Female,27.0,2.0,0.0,1.0,0.0,0.0,193209.11,0.0
+8471,15778025.0,Maclean,682.0,France,Female,37.0,4.0,103960.38,1.0,1.0,1.0,178067.34,0.0
+8472,15614168.0,T'ien,704.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,138536.09,0.0
+8473,15653595.0,Ojiofor,705.0,Spain,Female,26.0,5.0,0.0,2.0,1.0,1.0,166585.9,0.0
+8474,15641640.0,Chimezie,850.0,Germany,Female,29.0,10.0,123783.25,2.0,1.0,1.0,74870.99,0.0
+8475,15592461.0,Hsiao,588.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,152956.33,0.0
+8476,15620505.0,Ch'in,587.0,France,Female,41.0,3.0,0.0,2.0,1.0,0.0,87412.74,0.0
+8477,15706021.0,Chiazagomekpere,652.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,0.0,121789.3,0.0
+8478,15802071.0,DeRose,687.0,France,Female,42.0,9.0,0.0,1.0,1.0,0.0,48963.59,1.0
+8479,15728019.0,O'Brien,850.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,80619.09,0.0
+8480,15681755.0,Lucchese,751.0,France,Male,37.0,3.0,0.0,2.0,0.0,0.0,131834.87,0.0
+8481,15733111.0,Nucci,713.0,France,Male,22.0,5.0,0.0,2.0,1.0,0.0,145562.4,0.0
+8482,15731815.0,Hs?,590.0,France,Female,30.0,5.0,121558.42,1.0,1.0,1.0,138380.12,0.0
+8483,15795519.0,Y?an,651.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,76569.64,0.0
+8484,15762594.0,Ting,554.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,96670.2,0.0
+8485,15638159.0,McIntyre,732.0,Germany,Male,40.0,3.0,92786.66,1.0,1.0,0.0,122949.68,1.0
+8486,15697360.0,Kaur,747.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,153068.96,1.0
+8487,15734850.0,Yeh,655.0,France,Female,44.0,10.0,0.0,1.0,1.0,0.0,145558.42,1.0
+8488,15599301.0,McGregor,769.0,Germany,Female,33.0,5.0,54980.81,1.0,0.0,1.0,156598.23,1.0
+8489,15609913.0,Babbage,586.0,France,Male,32.0,8.0,0.0,1.0,1.0,0.0,143635.83,0.0
+8490,15585067.0,Trevisano,463.0,Spain,Male,35.0,3.0,151421.44,1.0,0.0,0.0,93713.47,0.0
+8491,15741643.0,White,622.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,0.0,68830.71,0.0
+8492,15785406.0,Chibuzo,773.0,Germany,Male,54.0,0.0,107747.57,1.0,1.0,0.0,82868.34,1.0
+8493,15609899.0,Uvarov,656.0,Germany,Male,46.0,3.0,81732.88,2.0,0.0,0.0,187800.63,0.0
+8494,15716134.0,Rizzo,607.0,France,Male,39.0,9.0,0.0,1.0,1.0,1.0,94816.35,1.0
+8495,15681007.0,Chukwufumnanya,753.0,France,Female,40.0,5.0,108204.52,1.0,1.0,0.0,187946.55,0.0
+8496,15609987.0,Pirozzi,637.0,Germany,Male,47.0,7.0,105770.7,2.0,1.0,0.0,140941.47,1.0
+8497,15581551.0,Nkemakonam,721.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,161241.65,0.0
+8498,15665861.0,Ts'ai,577.0,France,Male,40.0,8.0,81231.85,1.0,1.0,1.0,112256.57,0.0
+8499,15754574.0,H?,598.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,180593.45,0.0
+8500,15750049.0,Tien,581.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,171060.01,0.0
+8501,15703394.0,Manna,613.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,56865.62,0.0
+8502,15682355.0,Maclean,707.0,France,Female,59.0,9.0,0.0,2.0,1.0,1.0,141533.19,0.0
+8503,15665088.0,Chikezie,627.0,France,Female,33.0,1.0,114514.71,1.0,1.0,0.0,122625.68,0.0
+8504,15648047.0,Sacco,677.0,Germany,Female,51.0,2.0,163894.35,2.0,1.0,0.0,103902.11,1.0
+8505,15792986.0,Y?an,754.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,54453.72,0.0
+8506,15803801.0,Trevisani,616.0,France,Male,29.0,1.0,102016.38,1.0,1.0,0.0,122996.76,1.0
+8507,15675888.0,Genovesi,592.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,51919.04,0.0
+8508,15592761.0,Rizzo,781.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,140889.32,0.0
+8509,15606472.0,Lu,850.0,France,Female,47.0,2.0,148636.21,1.0,1.0,0.0,89566.74,0.0
+8510,15662067.0,Milano,597.0,Spain,Male,45.0,6.0,0.0,1.0,1.0,0.0,96670.2,1.0
+8511,15651581.0,Smith,656.0,France,Male,51.0,4.0,174937.64,1.0,1.0,0.0,176984.34,1.0
+8512,15754599.0,Yermakov,599.0,Spain,Female,41.0,8.0,129605.99,1.0,1.0,1.0,180622.05,1.0
+8513,15803749.0,Hargreaves,667.0,Germany,Male,44.0,1.0,132406.43,3.0,1.0,0.0,171070.52,1.0
+8514,15682355.0,Ch'in,595.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,90877.76,0.0
+8515,15661162.0,McElhone,615.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,54487.43,0.0
+8516,15659931.0,K'ung,607.0,France,Male,32.0,7.0,0.0,2.0,0.0,1.0,79019.74,0.0
+8517,15620372.0,Onio,643.0,Germany,Female,32.0,2.0,127450.14,1.0,1.0,1.0,109742.55,0.0
+8518,15646521.0,Palerma,678.0,France,Male,46.0,3.0,0.0,1.0,1.0,0.0,161579.85,1.0
+8519,15768777.0,Chukwuemeka,627.0,Germany,Female,31.0,5.0,102723.35,2.0,1.0,0.0,157539.29,0.0
+8520,15782404.0,Ch'in,710.0,France,Male,41.0,2.0,131341.46,1.0,0.0,0.0,173365.9,0.0
+8521,15686835.0,Milani,727.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,140941.47,0.0
+8522,15776939.0,Chukwujamuike,596.0,France,Male,35.0,4.0,124577.17,1.0,1.0,0.0,139946.68,0.0
+8523,15644296.0,Miah,543.0,Germany,Male,43.0,2.0,133501.36,2.0,1.0,1.0,149379.1,0.0
+8524,15581552.0,Ch'ang,631.0,Germany,Male,37.0,0.0,138053.67,2.0,1.0,0.0,173632.92,0.0
+8525,15581548.0,Lai,735.0,France,Male,36.0,5.0,137781.65,1.0,1.0,0.0,70563.9,0.0
+8526,15752048.0,T'ien,703.0,France,Male,34.0,10.0,152365.21,1.0,1.0,0.0,5983.7,0.0
+8527,15803685.0,Hsing,599.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,147720.52,0.0
+8528,15621083.0,Onuora,773.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,181419.29,0.0
+8529,15787222.0,T'ao,612.0,France,Female,43.0,2.0,81076.8,1.0,1.0,1.0,121840.2,1.0
+8530,15704788.0,Chiedozie,590.0,Germany,Male,49.0,2.0,125445.04,2.0,1.0,0.0,125445.8,0.0
+8531,15583692.0,Tien,528.0,France,Female,47.0,3.0,0.0,1.0,0.0,1.0,104469.06,1.0
+8532,15748068.0,Okwudilichukwu,819.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,1.0,78899.23,0.0
+8533,15777596.0,Ting,678.0,France,Female,63.0,7.0,0.0,1.0,1.0,0.0,166476.95,1.0
+8534,15586517.0,Udinese,712.0,France,Male,28.0,6.0,117060.08,1.0,1.0,1.0,75927.35,0.0
+8535,15571586.0,Maclean,678.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,0.0,77965.44,0.0
+8536,15757895.0,Cattaneo,749.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,115611.97,0.0
+8537,15743532.0,Bock,638.0,Spain,Male,42.0,5.0,0.0,1.0,1.0,0.0,74042.0,0.0
+8538,15633378.0,Chia,646.0,Spain,Female,34.0,7.0,106518.52,1.0,1.0,1.0,101371.03,0.0
+8539,15794560.0,Hs?,756.0,Spain,Male,29.0,4.0,185966.64,1.0,0.0,1.0,16402.11,0.0
+8540,15589493.0,Onyemauchechi,599.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,109057.56,0.0
+8541,15779744.0,Trevisani,706.0,France,Male,52.0,7.0,0.0,1.0,0.0,1.0,181429.87,1.0
+8542,15738662.0,Shubin,526.0,France,Male,31.0,7.0,0.0,2.0,0.0,0.0,199409.19,0.0
+8543,15650098.0,Hsing,735.0,Germany,Male,36.0,4.0,127095.14,2.0,0.0,1.0,149913.92,0.0
+8544,15701164.0,Mazzanti,694.0,France,Male,32.0,1.0,141078.37,1.0,1.0,1.0,142287.65,0.0
+8545,15746333.0,Milne,733.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,1.0,114092.87,0.0
+8546,15598044.0,Nwankwo,692.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,90189.72,0.0
+8547,15799600.0,Ch'ang,749.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,115789.25,0.0
+8548,15772073.0,Nnamutaezinwa,619.0,Spain,Female,38.0,1.0,0.0,1.0,1.0,1.0,160810.85,0.0
+8549,15775307.0,Chiang,697.0,Spain,Male,23.0,1.0,0.0,2.0,1.0,1.0,69381.05,0.0
+8550,15792525.0,Chukwukere,700.0,France,Male,62.0,9.0,0.0,2.0,1.0,1.0,55857.48,0.0
+8551,15642448.0,Nwokike,567.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,47468.56,0.0
+8552,15777076.0,Ch'eng,615.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,124626.57,0.0
+8553,15572728.0,Chikezie,723.0,France,Female,47.0,6.0,139872.41,1.0,1.0,1.0,116969.89,1.0
+8554,15636423.0,Hs?eh,633.0,Germany,Female,44.0,4.0,127545.14,1.0,1.0,0.0,43932.54,1.0
+8555,15658233.0,Maclean,587.0,France,Male,33.0,7.0,0.0,2.0,0.0,1.0,161083.73,0.0
+8556,15756539.0,Ndubueze,439.0,France,Female,44.0,7.0,118627.8,1.0,1.0,1.0,100615.14,1.0
+8557,15681316.0,Ritchie,686.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,160979.66,0.0
+8558,15682355.0,Nkemakonam,526.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,186948.35,0.0
+8559,15589496.0,Baresi,651.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,182262.66,0.0
+8560,15612139.0,Ch'ien,677.0,France,Male,27.0,6.0,0.0,2.0,1.0,0.0,100843.82,0.0
+8561,15671172.0,White,535.0,Spain,Female,31.0,1.0,0.0,2.0,1.0,1.0,159742.33,0.0
+8562,15699325.0,Hsu,665.0,Spain,Female,35.0,5.0,141535.52,1.0,1.0,1.0,70563.9,0.0
+8563,15663438.0,Trevisani,699.0,Germany,Male,21.0,4.0,113973.23,1.0,1.0,1.0,168816.73,0.0
+8564,15794580.0,Toscani,714.0,France,Male,24.0,4.0,0.0,2.0,0.0,1.0,59595.66,0.0
+8565,15809309.0,Nnamutaezinwa,592.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,181499.77,0.0
+8566,15593636.0,Lucchesi,637.0,Germany,Male,42.0,6.0,128369.75,1.0,1.0,0.0,144870.28,1.0
+8567,15797329.0,Ulyanov,596.0,France,Female,36.0,2.0,0.0,2.0,0.0,1.0,59645.24,0.0
+8568,15702464.0,Tikhonov,602.0,France,Female,47.0,5.0,0.0,1.0,1.0,0.0,14081.64,1.0
+8569,15672637.0,Kambinachi,622.0,France,Female,47.0,2.0,0.0,1.0,1.0,0.0,118959.64,1.0
+8570,15762392.0,Obiajulu,582.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,125953.83,0.0
+8571,15591130.0,Padovesi,743.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,129892.93,0.0
+8572,15652667.0,Chukwuemeka,697.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,135077.01,0.0
+8573,15795564.0,Ch'ien,663.0,France,Female,44.0,9.0,0.0,2.0,1.0,0.0,151912.49,1.0
+8574,15752694.0,Maclean,616.0,France,Female,43.0,7.0,105443.68,2.0,1.0,1.0,136028.97,0.0
+8575,15756497.0,Fyodorov,663.0,Germany,Female,41.0,7.0,160139.59,2.0,1.0,1.0,80425.65,0.0
+8576,15686964.0,Tuan,706.0,Germany,Male,37.0,7.0,80923.75,2.0,1.0,0.0,124210.53,0.0
+8577,15744529.0,Chikezie,647.0,France,Male,43.0,1.0,0.0,1.0,1.0,0.0,172096.08,1.0
+8578,15641690.0,Nkemakonam,705.0,France,Male,38.0,7.0,102773.2,2.0,1.0,0.0,80130.54,0.0
+8579,15757559.0,Chukwudi,662.0,France,Female,39.0,5.0,113922.64,1.0,1.0,0.0,124218.33,0.0
+8580,15726403.0,Frolov,611.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,92381.01,0.0
+8581,15667460.0,Chukwudi,653.0,France,Male,52.0,2.0,0.0,1.0,1.0,0.0,182624.38,1.0
+8582,15729771.0,Chukwujamuike,669.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,86490.09,0.0
+8583,15714750.0,Macleod,608.0,France,Female,27.0,7.0,145260.23,1.0,1.0,0.0,156774.75,0.0
+8584,15779915.0,De Luca,663.0,Germany,Female,31.0,4.0,110150.43,2.0,1.0,1.0,110265.24,0.0
+8585,15622478.0,Udegbulam,602.0,Germany,Male,30.0,9.0,128882.49,2.0,1.0,0.0,158086.64,0.0
+8586,15790772.0,Onwuka,678.0,France,Male,43.0,1.0,0.0,2.0,1.0,1.0,170819.01,0.0
+8587,15578908.0,Pirozzi,807.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,123882.73,0.0
+8588,15678058.0,Burt,699.0,France,Male,33.0,2.0,106819.65,2.0,1.0,1.0,94820.85,0.0
+8589,15627042.0,Milne,731.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,91711.59,0.0
+8590,15682048.0,Chiazagomekpere,594.0,Spain,Male,34.0,8.0,0.0,1.0,0.0,1.0,145562.4,0.0
+8591,15701265.0,Ch'eng,714.0,Germany,Male,34.0,10.0,119859.52,2.0,1.0,0.0,158043.11,0.0
+8592,15612109.0,Onyemauchechi,637.0,Germany,Female,67.0,6.0,147511.26,2.0,1.0,1.0,161440.6,0.0
+8593,15723884.0,Ts'ao,775.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,86634.4,0.0
+8594,15662884.0,K'ung,648.0,France,Female,29.0,10.0,0.0,2.0,1.0,1.0,114774.35,0.0
+8595,15715673.0,Nwankwo,699.0,Spain,Male,50.0,1.0,0.0,1.0,1.0,0.0,101778.9,1.0
+8596,15803378.0,Nwachinemelu,568.0,France,Male,41.0,6.0,0.0,2.0,0.0,1.0,87071.18,0.0
+8597,15694376.0,Macleod,538.0,Germany,Male,37.0,9.0,123936.97,2.0,0.0,0.0,166984.44,0.0
+8598,15681550.0,Chiu,630.0,Germany,Female,31.0,5.0,79715.36,2.0,0.0,0.0,85522.84,0.0
+8599,15752953.0,Ch'iu,713.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,0.0,76614.04,0.0
+8600,15793042.0,Ndubueze,704.0,France,Male,23.0,7.0,0.0,2.0,1.0,1.0,114566.47,0.0
+8601,15807457.0,Eluemuno,711.0,France,Male,33.0,1.0,162482.76,1.0,0.0,0.0,159123.82,0.0
+8602,15764654.0,Hsiung,656.0,France,Female,50.0,4.0,155973.88,1.0,1.0,1.0,173365.89,0.0
+8603,15689168.0,Tseng,598.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,162884.4,0.0
+8604,15759915.0,Walker,732.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,47428.73,0.0
+8605,15644572.0,Okonkwo,630.0,Spain,Male,46.0,7.0,0.0,1.0,1.0,1.0,100843.82,1.0
+8606,15745455.0,Hsia,616.0,Germany,Male,51.0,7.0,121513.31,2.0,1.0,1.0,180716.1,1.0
+8607,15662751.0,Ch'iu,707.0,Spain,Female,57.0,6.0,121172.97,1.0,1.0,1.0,175749.36,0.0
+8608,15623072.0,Chidiebere,622.0,France,Female,30.0,7.0,161801.47,1.0,1.0,0.0,120115.5,0.0
+8609,15728352.0,Tuan,486.0,France,Male,44.0,2.0,0.0,1.0,0.0,0.0,58877.61,1.0
+8610,15792388.0,Chukwukere,705.0,Spain,Female,46.0,4.0,0.0,2.0,1.0,1.0,9684.52,0.0
+8611,15772896.0,Clements,699.0,France,Male,41.0,3.0,141136.79,1.0,1.0,1.0,167033.83,0.0
+8612,15600813.0,Pirozzi,597.0,Germany,Male,56.0,1.0,121438.65,1.0,1.0,1.0,165675.01,1.0
+8613,15794414.0,O'Sullivan,619.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,199203.21,0.0
+8614,15623450.0,Chibugo,653.0,Germany,Male,43.0,0.0,83600.24,1.0,1.0,0.0,140947.05,1.0
+8615,15797733.0,Chiang,706.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,735.59,0.0
+8616,15686963.0,Hsieh,721.0,Spain,Male,29.0,2.0,119670.18,1.0,1.0,0.0,102628.98,0.0
+8617,15657540.0,Palerma,597.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,1.0,131093.99,0.0
+8618,15615352.0,O'Donnell,635.0,France,Female,44.0,5.0,0.0,2.0,0.0,0.0,140889.32,0.0
+8619,15671345.0,Chigozie,642.0,Germany,Male,29.0,5.0,121263.62,2.0,1.0,1.0,67976.57,0.0
+8620,15705512.0,Mancini,728.0,Germany,Male,30.0,1.0,108286.5,1.0,1.0,0.0,944.41,0.0
+8621,15690767.0,Onyemauchechukwu,579.0,Germany,Male,49.0,8.0,149950.19,1.0,0.0,0.0,177099.71,0.0
+8622,15577771.0,Toscani,571.0,Germany,Male,30.0,0.0,143391.07,2.0,1.0,0.0,103931.49,0.0
+8623,15696812.0,Ch'ien,679.0,Germany,Female,49.0,3.0,108128.96,1.0,1.0,1.0,173516.71,1.0
+8624,15628292.0,Curnow,646.0,France,Male,25.0,9.0,0.0,2.0,1.0,0.0,54869.54,0.0
+8625,15771573.0,Kao,850.0,France,Female,43.0,2.0,0.0,2.0,0.0,1.0,176570.55,0.0
+8626,15588517.0,Mazzanti,700.0,Spain,Male,38.0,9.0,95331.83,1.0,1.0,0.0,184839.7,0.0
+8627,15728608.0,Chinedum,669.0,France,Female,41.0,1.0,0.0,1.0,0.0,1.0,170984.97,0.0
+8628,15704770.0,Chia,687.0,France,Male,41.0,7.0,0.0,2.0,0.0,0.0,177519.39,0.0
+8629,15809846.0,Rizzo,697.0,France,Male,34.0,10.0,121898.58,1.0,1.0,0.0,9221.78,0.0
+8630,15643826.0,Hsueh,602.0,France,Male,41.0,6.0,0.0,2.0,1.0,0.0,35644.79,0.0
+8631,15694444.0,Yobanna,718.0,France,Male,36.0,5.0,82498.14,1.0,0.0,1.0,118716.63,0.0
+8632,15757837.0,Ch'ang,644.0,Germany,Female,53.0,4.0,127545.94,2.0,1.0,0.0,184836.01,1.0
+8633,15804316.0,Ts'ai,721.0,Germany,Female,32.0,4.0,85845.43,2.0,1.0,0.0,119374.74,0.0
+8634,15682472.0,Yuriev,711.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,100618.17,0.0
+8635,15638231.0,McGregor,642.0,France,Male,36.0,6.0,0.0,3.0,1.0,0.0,123410.23,1.0
+8636,15737194.0,Chigozie,663.0,France,Female,38.0,2.0,102800.65,1.0,1.0,1.0,117616.29,0.0
+8637,15762418.0,Ibeabuchi,739.0,Spain,Female,43.0,0.0,0.0,1.0,1.0,0.0,54448.51,1.0
+8638,15635141.0,Chidiebele,528.0,Germany,Male,60.0,1.0,93578.68,1.0,1.0,1.0,63581.19,1.0
+8639,15759436.0,Chinagorom,637.0,France,Female,27.0,6.0,0.0,2.0,1.0,0.0,75825.57,0.0
+8640,15679307.0,Chidiebere,579.0,France,Female,36.0,8.0,0.0,2.0,1.0,0.0,124596.63,0.0
+8641,15672051.0,Onyekaozulu,613.0,Germany,Female,30.0,7.0,130170.82,2.0,1.0,0.0,158580.05,0.0
+8642,15575409.0,Yevseyev,592.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,174027.3,0.0
+8643,15601787.0,Tuan,734.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,130514.78,0.0
+8644,15797900.0,Okwuoma,850.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,141148.21,0.0
+8645,15646563.0,Ibezimako,651.0,France,Female,23.0,5.0,0.0,2.0,0.0,0.0,199203.21,0.0
+8646,15598046.0,Ku,707.0,France,Female,44.0,5.0,0.0,4.0,1.0,1.0,127059.03,1.0
+8647,15806936.0,K?,687.0,France,Male,35.0,8.0,0.0,2.0,0.0,0.0,151757.81,0.0
+8648,15782418.0,Boulger,850.0,France,Male,23.0,1.0,82453.96,1.0,1.0,0.0,163798.41,0.0
+8649,15747808.0,Nwabugwu,726.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,161242.99,0.0
+8650,15594577.0,Ch'ang,684.0,France,Female,40.0,3.0,0.0,2.0,1.0,0.0,97908.01,0.0
+8651,15663943.0,Chidubem,597.0,France,Male,44.0,5.0,0.0,1.0,1.0,1.0,106969.98,1.0
+8652,15740557.0,Smith,613.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,1.0,146891.07,0.0
+8653,15609823.0,Nnonso,652.0,France,Male,35.0,9.0,127862.93,1.0,1.0,1.0,83470.4,0.0
+8654,15778368.0,T'ao,715.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,168512.64,0.0
+8655,15751412.0,McIntyre,587.0,France,Male,48.0,2.0,141528.88,1.0,1.0,0.0,65951.65,1.0
+8656,15777076.0,Liao,746.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,164741.5,0.0
+8657,15781589.0,Smith,681.0,France,Female,48.0,5.0,0.0,1.0,1.0,1.0,123693.97,0.0
+8658,15693906.0,Onnugo,733.0,Germany,Female,27.0,8.0,127660.46,2.0,1.0,1.0,141035.65,0.0
+8659,15620758.0,Chuang,548.0,France,Male,51.0,9.0,0.0,1.0,1.0,0.0,56787.8,1.0
+8660,15739189.0,Hs?,508.0,France,Female,42.0,4.0,0.0,1.0,1.0,1.0,126475.79,0.0
+8661,15774931.0,Hsueh,598.0,France,Male,21.0,1.0,0.0,1.0,1.0,1.0,166516.84,0.0
+8662,15692671.0,Lucchese,682.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,181224.56,0.0
+8663,15694581.0,P'an,710.0,France,Female,40.0,9.0,0.0,2.0,1.0,1.0,142996.89,0.0
+8664,15698872.0,Ch'ang,746.0,Spain,Female,39.0,8.0,0.0,1.0,1.0,1.0,167984.72,0.0
+8665,15753955.0,Hao,632.0,France,Male,37.0,6.0,0.0,1.0,1.0,1.0,51637.67,0.0
+8666,15784890.0,Haugh,669.0,France,Male,48.0,8.0,0.0,1.0,1.0,0.0,177670.57,1.0
+8667,15665629.0,Toscani,684.0,France,Female,25.0,3.0,108541.04,1.0,1.0,0.0,51387.1,0.0
+8668,15784323.0,Iweobiegbunam,653.0,France,Female,47.0,3.0,145984.02,1.0,1.0,0.0,79728.86,1.0
+8669,15690743.0,O'Donnell,543.0,France,Female,41.0,9.0,150461.07,1.0,0.0,1.0,9983.88,0.0
+8670,15793881.0,Hargreaves,697.0,France,Female,59.0,10.0,0.0,1.0,0.0,0.0,51364.56,1.0
+8671,15783671.0,Nwankwo,675.0,Germany,Male,41.0,6.0,130878.75,1.0,1.0,0.0,121537.91,0.0
+8672,15652626.0,Milano,605.0,Germany,Male,43.0,0.0,131082.89,1.0,1.0,0.0,60721.4,1.0
+8673,15689514.0,Ch'ang,570.0,France,Male,52.0,4.0,0.0,2.0,1.0,0.0,94450.97,0.0
+8674,15732299.0,Ngozichukwuka,521.0,Spain,Female,38.0,9.0,153861.1,2.0,0.0,0.0,100615.14,0.0
+8675,15621440.0,Hs?,559.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,1.0,142331.85,0.0
+8676,15772423.0,Toscani,616.0,Germany,Male,31.0,3.0,141252.18,2.0,1.0,1.0,99645.04,0.0
+8677,15750868.0,Fiorentino,590.0,France,Male,45.0,4.0,0.0,1.0,0.0,0.0,78622.46,1.0
+8678,15774872.0,Hsueh,682.0,France,Male,25.0,4.0,0.0,2.0,1.0,0.0,125083.29,0.0
+8679,15631189.0,Mironova,697.0,Spain,Male,43.0,3.0,180520.29,1.0,1.0,0.0,132798.65,1.0
+8680,15612667.0,K'ung,727.0,Germany,Female,48.0,8.0,124976.19,1.0,1.0,0.0,187976.88,1.0
+8681,15758513.0,Ch'iu,539.0,Germany,Female,32.0,8.0,106901.42,1.0,0.0,0.0,62722.44,0.0
+8682,15606436.0,Fanucci,676.0,France,Male,38.0,4.0,96486.57,1.0,1.0,0.0,142963.29,0.0
+8683,15655192.0,Chieloka,699.0,Spain,Male,37.0,3.0,0.0,1.0,1.0,0.0,175310.38,0.0
+8684,15661591.0,Obidimkpa,731.0,France,Male,32.0,7.0,163546.14,1.0,0.0,1.0,115403.31,0.0
+8685,15646091.0,Skelton,648.0,France,Female,50.0,9.0,0.0,3.0,1.0,0.0,164741.5,1.0
+8686,15614302.0,Ts'ui,684.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,0.0,175324.74,0.0
+8687,15753831.0,T'ang,704.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,93091.02,0.0
+8688,15713819.0,Chukwunonso,622.0,France,Male,24.0,8.0,0.0,2.0,0.0,1.0,141400.51,0.0
+8689,15602497.0,Okwukwe,715.0,Germany,Female,37.0,7.0,103033.83,2.0,1.0,1.0,9241.52,0.0
+8690,15683544.0,Okagbue,707.0,Germany,Female,38.0,4.0,127726.33,1.0,1.0,0.0,103660.38,1.0
+8691,15644796.0,Nkemakonam,613.0,Spain,Male,45.0,5.0,0.0,2.0,1.0,1.0,185561.89,0.0
+8692,15806918.0,Ch'in,678.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,37294.88,0.0
+8693,15750055.0,Gresswell,656.0,France,Female,52.0,8.0,0.0,1.0,1.0,0.0,115288.24,1.0
+8694,15758035.0,Ts'ai,742.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,105760.01,0.0
+8695,15748123.0,Chin,631.0,Spain,Female,44.0,4.0,0.0,2.0,1.0,0.0,79323.61,0.0
+8696,15728608.0,Onyekachi,591.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,64872.33,0.0
+8697,15704763.0,White,520.0,France,Male,37.0,4.0,0.0,1.0,1.0,0.0,85746.52,0.0
+8698,15584812.0,Shih,588.0,France,Female,29.0,8.0,0.0,2.0,1.0,1.0,93901.61,0.0
+8699,15663770.0,Toscani,714.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,161519.77,0.0
+8700,15592761.0,Yevseyev,648.0,France,Female,38.0,4.0,0.0,2.0,0.0,0.0,131710.44,0.0
+8701,15749119.0,DeRose,729.0,France,Male,40.0,3.0,0.0,2.0,1.0,1.0,86099.23,0.0
+8702,15661532.0,Onyemauchechukwu,757.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,84760.9,0.0
+8703,15602851.0,Hsueh,682.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,94820.85,0.0
+8704,15683375.0,O'Sullivan,629.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,125870.5,0.0
+8705,15746693.0,Hsieh,599.0,France,Female,33.0,1.0,0.0,2.0,0.0,1.0,167784.28,0.0
+8706,15614813.0,Milne,775.0,Spain,Male,28.0,6.0,0.0,2.0,1.0,1.0,95611.47,0.0
+8707,15751203.0,L?,742.0,France,Male,34.0,7.0,0.0,2.0,0.0,1.0,166317.71,0.0
+8708,15631457.0,Pagnotto,712.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,173878.87,0.0
+8709,15694518.0,Chukwualuka,792.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,155047.11,0.0
+8710,15777315.0,Hsueh,647.0,Germany,Male,33.0,2.0,71379.53,2.0,1.0,1.0,139919.38,0.0
+8711,15795511.0,Omeokachie,677.0,France,Male,34.0,4.0,86038.21,2.0,1.0,1.0,176843.53,0.0
+8712,15728885.0,Kao,741.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,135851.3,0.0
+8713,15776939.0,Udegbunam,661.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,1.0,54487.43,0.0
+8714,15631685.0,Tsai,754.0,France,Male,30.0,5.0,0.0,2.0,0.0,1.0,59890.29,0.0
+8715,15794468.0,Cattaneo,559.0,Spain,Male,41.0,0.0,0.0,2.0,0.0,0.0,171060.01,0.0
+8716,15594878.0,Fiorentino,850.0,France,Female,41.0,3.0,0.0,2.0,1.0,1.0,136983.77,0.0
+8717,15583462.0,Chiemela,678.0,Germany,Male,30.0,8.0,141784.02,2.0,1.0,1.0,166586.99,0.0
+8718,15758710.0,Naylor,730.0,France,Male,32.0,6.0,0.0,2.0,0.0,0.0,151457.82,0.0
+8719,15644878.0,Trevisani,607.0,France,Male,44.0,2.0,0.0,1.0,1.0,0.0,136812.08,0.0
+8720,15582914.0,Ch'eng,738.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,49653.05,0.0
+8721,15673372.0,Iweobiegbunam,559.0,France,Female,35.0,0.0,0.0,2.0,1.0,1.0,161465.31,0.0
+8722,15584928.0,Hsiao,684.0,France,Female,37.0,10.0,0.0,1.0,1.0,0.0,84927.41,1.0
+8723,15733777.0,Teng,631.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,61977.82,0.0
+8724,15801316.0,Omeokachie,719.0,France,Male,39.0,0.0,0.0,2.0,1.0,0.0,92916.54,0.0
+8725,15713854.0,Yobachi,695.0,France,Male,43.0,3.0,0.0,2.0,1.0,1.0,128521.16,0.0
+8726,15702095.0,T'ien,647.0,Spain,Female,23.0,10.0,0.0,2.0,1.0,1.0,176913.42,0.0
+8727,15754053.0,Laidley,652.0,Germany,Female,33.0,2.0,131394.47,1.0,1.0,0.0,74850.84,1.0
+8728,15579183.0,Hsiung,637.0,France,Male,31.0,0.0,0.0,2.0,1.0,1.0,199242.65,0.0
+8729,15574071.0,Hs?,685.0,Spain,Female,34.0,6.0,0.0,1.0,0.0,0.0,132861.9,0.0
+8730,15592736.0,Chukwuemeka,704.0,France,Male,58.0,4.0,0.0,2.0,1.0,1.0,94810.88,0.0
+8731,15610755.0,Chibueze,628.0,France,Male,37.0,7.0,0.0,2.0,0.0,1.0,104786.59,0.0
+8732,15636016.0,Onyemere,656.0,Spain,Male,22.0,2.0,0.0,2.0,1.0,0.0,131778.58,0.0
+8733,15648933.0,Hs?,573.0,Germany,Female,35.0,3.0,129646.91,2.0,1.0,0.0,102517.54,1.0
+8734,15785078.0,Ch'en,587.0,France,Female,29.0,4.0,133598.7,1.0,0.0,1.0,133535.52,0.0
+8735,15651740.0,Chinwemma,637.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,174094.24,0.0
+8736,15662758.0,Ting,506.0,France,Female,26.0,9.0,0.0,2.0,0.0,0.0,133672.61,0.0
+8737,15593897.0,Chiawuotu,604.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,1.0,132973.21,0.0
+8738,15578788.0,Maclean,584.0,Spain,Female,45.0,8.0,0.0,1.0,0.0,1.0,131710.59,1.0
+8739,15582519.0,Chinedum,561.0,France,Female,41.0,4.0,0.0,1.0,1.0,0.0,122518.5,0.0
+8740,15794048.0,Chukwukadibia,705.0,Germany,Female,39.0,9.0,121172.97,1.0,1.0,0.0,138399.67,1.0
+8741,15677739.0,Chukwueloka,572.0,Spain,Male,43.0,6.0,0.0,2.0,1.0,1.0,183542.08,0.0
+8742,15747232.0,Martin,790.0,Germany,Male,53.0,2.0,102278.85,2.0,1.0,1.0,120801.65,0.0
+8743,15790829.0,Yobachi,700.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,100929.19,0.0
+8744,15775339.0,T'ien,705.0,Germany,Female,31.0,3.0,151659.52,2.0,1.0,1.0,140745.91,0.0
+8745,15576550.0,Ch'ang,564.0,France,Female,39.0,9.0,0.0,1.0,1.0,1.0,177362.45,0.0
+8746,15615477.0,Ch'ien,651.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,158043.11,0.0
+8747,15796790.0,Chiazagomekpere,662.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,45610.63,0.0
+8748,15793881.0,Nkemakonam,637.0,Germany,Female,53.0,5.0,161274.05,1.0,1.0,1.0,27474.81,1.0
+8749,15727041.0,Nkemakonam,726.0,Spain,Male,24.0,2.0,0.0,2.0,1.0,1.0,150850.29,0.0
+8750,15741094.0,Ojiofor,718.0,Germany,Male,42.0,5.0,122529.15,1.0,1.0,0.0,43932.56,1.0
+8751,15643903.0,O'Donnell,716.0,France,Female,23.0,7.0,144848.74,1.0,1.0,1.0,171463.83,0.0
+8752,15767231.0,Chien,744.0,Germany,Male,38.0,6.0,108117.1,1.0,1.0,0.0,151871.84,1.0
+8753,15595766.0,Macleod,585.0,Spain,Female,51.0,3.0,0.0,3.0,1.0,0.0,58855.85,1.0
+8754,15784320.0,Zetticci,739.0,Spain,Female,33.0,10.0,0.0,2.0,0.0,0.0,162599.51,0.0
+8755,15644551.0,Okwuadigbo,634.0,Germany,Female,28.0,6.0,129893.56,1.0,1.0,0.0,188203.58,1.0
+8756,15688760.0,Ch'ien,718.0,France,Male,32.0,5.0,142319.03,1.0,1.0,1.0,126012.57,0.0
+8757,15715117.0,Chiu,721.0,Spain,Male,39.0,1.0,0.0,2.0,0.0,0.0,119882.86,0.0
+8758,15779267.0,Foveaux,705.0,France,Male,32.0,8.0,0.0,1.0,1.0,0.0,116471.42,0.0
+8759,15765173.0,Hs?,604.0,Spain,Female,22.0,6.0,0.0,2.0,1.0,1.0,129563.86,0.0
+8760,15770185.0,Hazon,652.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,134443.83,0.0
+8761,15686463.0,Chinwenma,742.0,France,Female,30.0,4.0,92636.96,1.0,0.0,1.0,151335.24,0.0
+8762,15575060.0,Tien,714.0,France,Male,44.0,8.0,0.0,2.0,0.0,1.0,106969.98,0.0
+8763,15673074.0,K'ung,724.0,France,Female,31.0,7.0,0.0,1.0,1.0,0.0,77951.84,0.0
+8764,15668058.0,Wilson,748.0,France,Female,57.0,1.0,0.0,1.0,1.0,0.0,120853.87,1.0
+8765,15756918.0,Nucci,644.0,France,Male,32.0,4.0,113858.95,2.0,1.0,0.0,173586.63,0.0
+8766,15711602.0,Chiebuka,656.0,France,Female,28.0,9.0,0.0,2.0,0.0,1.0,139475.1,0.0
+8767,15621611.0,Hsia,685.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,82967.97,0.0
+8768,15610903.0,Chinomso,705.0,France,Male,36.0,1.0,0.0,2.0,0.0,1.0,132702.64,0.0
+8769,15600792.0,Chukwualuka,578.0,Spain,Female,38.0,0.0,167738.82,2.0,0.0,1.0,94450.97,0.0
+8770,15641794.0,Li,719.0,Spain,Female,45.0,9.0,0.0,1.0,1.0,0.0,183598.77,1.0
+8771,15774536.0,H?,587.0,Spain,Male,23.0,8.0,131778.66,1.0,1.0,1.0,166677.35,0.0
+8772,15661723.0,Trevisano,632.0,Germany,Male,37.0,5.0,133060.63,2.0,0.0,1.0,60905.51,0.0
+8773,15815690.0,Macleod,624.0,Spain,Female,36.0,10.0,0.0,2.0,0.0,1.0,127825.25,0.0
+8774,15752953.0,Pagnotto,681.0,France,Male,26.0,1.0,0.0,2.0,1.0,1.0,148887.69,0.0
+8775,15677178.0,Yobanna,757.0,Germany,Male,30.0,7.0,132512.26,1.0,1.0,0.0,53394.12,0.0
+8776,15596962.0,Tsai,705.0,France,Male,30.0,8.0,0.0,1.0,1.0,0.0,79919.13,0.0
+8777,15792329.0,Lueck,682.0,Germany,Male,47.0,1.0,119545.48,1.0,1.0,1.0,125877.22,1.0
+8778,15605918.0,P'eng,588.0,France,Female,55.0,4.0,161450.04,1.0,0.0,1.0,106761.47,1.0
+8779,15726313.0,Chukwuhaenye,574.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,50783.1,0.0
+8780,15615473.0,Cameron,668.0,Germany,Female,43.0,2.0,107764.75,1.0,1.0,0.0,55857.48,1.0
+8781,15658577.0,Hsu,596.0,Spain,Female,40.0,8.0,0.0,2.0,1.0,1.0,183893.31,0.0
+8782,15675184.0,Hsiung,685.0,Spain,Male,46.0,3.0,0.0,1.0,0.0,0.0,177528.92,1.0
+8783,15709387.0,Chien,583.0,France,Male,37.0,2.0,0.0,1.0,1.0,0.0,138882.98,0.0
+8784,15725676.0,Chinonyelum,646.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,126451.14,0.0
+8785,15706762.0,Ch'ien,656.0,France,Female,53.0,5.0,0.0,1.0,1.0,1.0,125762.95,1.0
+8786,15702656.0,Palerma,538.0,France,Male,34.0,7.0,0.0,1.0,1.0,1.0,57541.06,0.0
+8787,15634373.0,Ch'ang,710.0,France,Female,37.0,9.0,149571.94,1.0,1.0,1.0,67611.36,0.0
+8788,15592705.0,Ponomaryov,610.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,51454.06,0.0
+8789,15764708.0,Fitts,484.0,Spain,Female,39.0,8.0,0.0,2.0,1.0,1.0,176984.34,0.0
+8790,15756743.0,Chu,729.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,84870.23,0.0
+8791,15569432.0,Macgroarty,582.0,Spain,Female,34.0,4.0,0.0,1.0,1.0,0.0,126477.41,1.0
+8792,15807989.0,Ch'ien,671.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,175310.23,0.0
+8793,15567778.0,Smith,758.0,France,Male,43.0,4.0,122314.5,1.0,1.0,0.0,187987.01,0.0
+8794,15806901.0,Kolesnikov,614.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,159418.1,0.0
+8795,15664808.0,Genovesi,564.0,France,Male,33.0,7.0,0.0,1.0,1.0,0.0,170950.58,0.0
+8796,15658982.0,Mazzi,624.0,Germany,Female,33.0,2.0,101633.04,1.0,1.0,0.0,121747.96,0.0
+8797,15604551.0,Hsueh,671.0,Spain,Male,33.0,9.0,0.0,2.0,0.0,1.0,158887.09,0.0
+8798,15786078.0,Smith,679.0,France,Male,33.0,7.0,123760.68,1.0,0.0,1.0,172490.78,0.0
+8799,15605067.0,Hs?,652.0,France,Female,30.0,7.0,104568.41,1.0,1.0,0.0,177719.88,1.0
+8800,15706587.0,Calabrese,576.0,France,Male,49.0,1.0,0.0,1.0,1.0,0.0,171928.04,1.0
+8801,15602846.0,T'ien,543.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,96961.0,0.0
+8802,15744127.0,Yobachi,627.0,Spain,Male,29.0,1.0,137963.07,1.0,1.0,0.0,86957.42,0.0
+8803,15652948.0,Soto,635.0,France,Male,35.0,9.0,0.0,3.0,1.0,1.0,93524.19,1.0
+8804,15609913.0,Obiajulu,775.0,Spain,Male,33.0,10.0,0.0,1.0,1.0,1.0,149550.97,0.0
+8805,15739160.0,Teng,467.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,53949.98,0.0
+8806,15670039.0,Ch'ien,692.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,91573.18,0.0
+8807,15585100.0,Calabrese,806.0,Spain,Female,35.0,1.0,0.0,2.0,1.0,1.0,122580.48,0.0
+8808,15791674.0,Ch'in,572.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,60920.03,0.0
+8809,15772781.0,K?,706.0,Germany,Female,46.0,7.0,117413.95,1.0,1.0,0.0,139861.53,1.0
+8810,15678725.0,Mazzanti,528.0,France,Male,58.0,8.0,0.0,2.0,1.0,1.0,91947.74,0.0
+8811,15682048.0,Toscani,634.0,Spain,Female,36.0,4.0,0.0,1.0,0.0,1.0,69093.39,0.0
+8812,15589329.0,Nnonso,662.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,150319.49,0.0
+8813,15748673.0,Tsao,792.0,France,Male,42.0,10.0,73493.17,1.0,1.0,0.0,171575.06,0.0
+8814,15763063.0,Aitken,659.0,France,Female,44.0,7.0,0.0,2.0,0.0,1.0,100618.17,0.0
+8815,15807900.0,Uchenna,656.0,Germany,Female,48.0,2.0,132767.66,1.0,1.0,0.0,14217.77,1.0
+8816,15775294.0,Hsing,684.0,France,Male,44.0,10.0,0.0,2.0,1.0,0.0,111893.09,0.0
+8817,15588928.0,Manna,687.0,France,Female,61.0,1.0,0.0,2.0,0.0,1.0,131281.09,0.0
+8818,15766047.0,Ch'iu,642.0,France,Female,42.0,10.0,0.0,1.0,1.0,1.0,91903.9,0.0
+8819,15814930.0,Ch'en,773.0,France,Male,26.0,2.0,107556.06,2.0,1.0,0.0,92568.05,0.0
+8820,15798521.0,Trevisani,707.0,France,Male,29.0,7.0,0.0,2.0,0.0,0.0,94898.1,0.0
+8821,15585901.0,Onyemachukwu,634.0,Spain,Female,51.0,0.0,153946.14,1.0,1.0,1.0,101348.88,1.0
+8822,15643679.0,Onyemachukwu,683.0,France,Female,37.0,10.0,0.0,2.0,0.0,0.0,134878.19,0.0
+8823,15678460.0,Ts'ui,652.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,122960.98,0.0
+8824,15702300.0,K?,739.0,France,Male,30.0,5.0,0.0,2.0,1.0,0.0,116977.89,0.0
+8825,15648047.0,Bair,781.0,France,Male,28.0,2.0,0.0,2.0,1.0,1.0,43982.41,0.0
+8826,15684319.0,Chuang,677.0,Spain,Female,33.0,6.0,111827.27,1.0,1.0,1.0,97906.75,0.0
+8827,15589924.0,Onuora,509.0,France,Male,36.0,9.0,0.0,2.0,0.0,1.0,146891.07,0.0
+8828,15813363.0,Chidumaga,569.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,183682.09,0.0
+8829,15812198.0,Hsing,717.0,Germany,Female,42.0,6.0,132725.1,2.0,1.0,1.0,149614.81,0.0
+8830,15633059.0,Kistler,713.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,171681.86,0.0
+8831,15702801.0,Chuang,726.0,France,Male,44.0,2.0,91611.12,1.0,0.0,1.0,26589.56,0.0
+8832,15743828.0,Zetticci,678.0,France,Male,40.0,6.0,123086.39,1.0,0.0,0.0,55829.25,0.0
+8833,15712807.0,Tien,589.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,878.87,0.0
+8834,15573741.0,Nnamutaezinwa,599.0,France,Female,51.0,6.0,0.0,1.0,0.0,0.0,105780.18,1.0
+8835,15613140.0,Calabrese,528.0,Spain,Female,38.0,5.0,83074.0,2.0,1.0,0.0,102950.79,0.0
+8836,15697567.0,Chukwualuka,576.0,France,Female,33.0,3.0,127726.33,1.0,1.0,0.0,114796.83,0.0
+8837,15693637.0,Lai,679.0,Spain,Female,47.0,5.0,0.0,2.0,1.0,1.0,58965.04,0.0
+8838,15571221.0,Chukwujamuike,445.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,176913.42,1.0
+8839,15733429.0,Pagnotto,838.0,France,Male,46.0,9.0,0.0,1.0,0.0,1.0,143955.72,1.0
+8840,15682442.0,Nnamutaezinwa,678.0,Spain,Male,33.0,2.0,0.0,2.0,0.0,0.0,145796.49,0.0
+8841,15573086.0,McIntyre,547.0,France,Female,34.0,7.0,0.0,2.0,0.0,1.0,69052.87,0.0
+8842,15702300.0,Chiu,727.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,0.0,139861.53,0.0
+8843,15762543.0,Genovese,705.0,Germany,Male,44.0,0.0,125920.98,1.0,1.0,1.0,59638.85,1.0
+8844,15573730.0,T'an,569.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,134901.56,1.0
+8845,15756538.0,Milne,676.0,Spain,Male,32.0,1.0,131769.04,1.0,1.0,0.0,129964.94,0.0
+8846,15784890.0,Ku,585.0,France,Female,26.0,0.0,121863.61,1.0,1.0,0.0,173498.45,1.0
+8847,15642570.0,Fanucci,642.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,127543.11,0.0
+8848,15582892.0,Zhirov,554.0,France,Male,41.0,5.0,0.0,2.0,0.0,1.0,175072.47,0.0
+8849,15723873.0,Hsu,611.0,France,Female,43.0,9.0,125837.86,1.0,1.0,0.0,121210.09,0.0
+8850,15623989.0,Maclean,682.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,104469.06,0.0
+8851,15714054.0,Chukwujamuike,677.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,149813.18,0.0
+8852,15702789.0,P'an,705.0,Germany,Male,29.0,1.0,162701.65,2.0,1.0,1.0,125877.25,0.0
+8853,15761364.0,Chukwudi,686.0,Germany,Male,41.0,0.0,129718.3,3.0,1.0,0.0,81622.52,1.0
+8854,15578788.0,Yudina,707.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,50783.1,0.0
+8855,15660271.0,Biryukov,739.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,97939.4,0.0
+8856,15583736.0,Chidubem,567.0,France,Female,52.0,7.0,109799.55,1.0,1.0,1.0,131304.45,0.0
+8857,15751561.0,Scott,619.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,82566.01,0.0
+8858,15692761.0,Chin,694.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,0.0,140889.12,0.0
+8859,15783030.0,Ts'ui,714.0,France,Male,41.0,5.0,0.0,1.0,1.0,1.0,183494.87,0.0
+8860,15757837.0,Teng,684.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,160968.44,0.0
+8861,15622470.0,Galkin,605.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,126517.97,0.0
+8862,15694456.0,K'ung,737.0,France,Male,36.0,7.0,111837.78,1.0,1.0,1.0,132468.76,0.0
+8863,15617166.0,Pisani,591.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,83022.92,0.0
+8864,15766166.0,Chiang,679.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,168369.37,0.0
+8865,15592761.0,Chukwuemeka,449.0,France,Male,39.0,4.0,95826.22,1.0,0.0,1.0,174098.24,0.0
+8866,15589715.0,Ting,753.0,France,Male,54.0,6.0,0.0,1.0,1.0,0.0,177065.24,1.0
+8867,15783025.0,Calabresi,678.0,France,Male,40.0,10.0,0.0,1.0,1.0,0.0,60988.21,0.0
+8868,15591287.0,Pirozzi,713.0,Spain,Female,28.0,3.0,0.0,2.0,1.0,0.0,121151.1,0.0
+8869,15789955.0,Milne,678.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,102280.29,0.0
+8870,15586039.0,Chiemenam,636.0,Germany,Male,40.0,4.0,129753.69,1.0,1.0,0.0,163471.01,1.0
+8871,15593973.0,Nnonso,745.0,France,Female,34.0,8.0,81132.21,1.0,1.0,0.0,134889.95,0.0
+8872,15684548.0,Chijindum,652.0,France,Male,42.0,1.0,104682.83,1.0,0.0,1.0,168035.63,0.0
+8873,15743076.0,Ozioma,647.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,183598.77,0.0
+8874,15736043.0,Yefremov,726.0,France,Female,43.0,9.0,151963.26,1.0,1.0,0.0,72993.65,0.0
+8875,15733661.0,Niu,584.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,162889.1,1.0
+8876,15682778.0,Ch'ien,695.0,Spain,Male,42.0,10.0,0.0,2.0,1.0,1.0,80582.34,0.0
+8877,15647974.0,Hs?eh,762.0,France,Female,34.0,6.0,0.0,2.0,1.0,0.0,53420.53,0.0
+8878,15576680.0,O'Loghlen,681.0,France,Male,38.0,5.0,0.0,2.0,0.0,1.0,82463.69,0.0
+8879,15702298.0,Sagese,678.0,France,Male,35.0,2.0,137842.96,1.0,1.0,0.0,117020.07,0.0
+8880,15743838.0,Chiang,682.0,Spain,Male,36.0,5.0,0.0,1.0,1.0,1.0,141031.06,0.0
+8881,15669281.0,Ch'iu,638.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,104786.59,0.0
+8882,15704770.0,Chin,844.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,116977.89,0.0
+8883,15692761.0,Rizzo,709.0,France,Female,41.0,9.0,0.0,1.0,1.0,0.0,64495.78,1.0
+8884,15685829.0,Onwumelu,837.0,France,Male,35.0,5.0,135645.29,1.0,1.0,0.0,136816.37,0.0
+8885,15808849.0,Okwuadigbo,699.0,France,Male,46.0,3.0,0.0,3.0,1.0,0.0,166677.35,1.0
+8886,15579566.0,Li Fonti,559.0,France,Female,47.0,8.0,0.0,1.0,1.0,0.0,101319.76,1.0
+8887,15584518.0,Chinwendu,656.0,France,Female,49.0,9.0,0.0,2.0,1.0,1.0,79547.39,0.0
+8888,15626040.0,Fomin,731.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,1.0,173948.3,0.0
+8889,15806964.0,Ch'en,637.0,France,Male,35.0,2.0,98854.34,2.0,1.0,0.0,62972.13,1.0
+8890,15704844.0,Ch'en,651.0,Spain,Female,50.0,1.0,0.0,2.0,1.0,0.0,72876.88,0.0
+8891,15735221.0,Hsueh,735.0,France,Male,34.0,1.0,0.0,1.0,1.0,1.0,170563.73,0.0
+8892,15593969.0,Mironov,695.0,France,Male,45.0,1.0,0.0,2.0,1.0,1.0,107787.42,0.0
+8893,15803347.0,Oluchi,526.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,72849.47,0.0
+8894,15780359.0,Yudina,577.0,France,Male,49.0,5.0,127290.61,1.0,1.0,0.0,184524.74,0.0
+8895,15597914.0,Nock,526.0,Germany,Female,47.0,5.0,143773.07,3.0,0.0,0.0,91573.19,1.0
+8896,15683980.0,Hales,591.0,France,Male,29.0,3.0,0.0,1.0,1.0,1.0,161241.65,0.0
+8897,15617206.0,Ch'ang,719.0,Germany,Female,29.0,4.0,96471.4,2.0,0.0,1.0,171518.1,0.0
+8898,15694321.0,Onio,710.0,Germany,Male,37.0,4.0,133899.23,1.0,1.0,1.0,120511.03,0.0
+8899,15747264.0,Chukwudi,775.0,France,Male,44.0,9.0,0.0,2.0,1.0,1.0,102763.69,0.0
+8900,15793890.0,Ch'ang,646.0,Spain,Female,51.0,9.0,0.0,1.0,1.0,0.0,137985.67,1.0
+8901,15621426.0,Hsueh,619.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,173851.11,0.0
+8902,15682600.0,Aksenov,626.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,77330.35,0.0
+8903,15592222.0,Tuan,583.0,Spain,Male,44.0,3.0,0.0,2.0,1.0,0.0,122268.75,0.0
+8904,15674551.0,Hs?eh,758.0,Germany,Male,36.0,8.0,107714.73,1.0,1.0,0.0,146955.71,0.0
+8905,15755868.0,Chukwufumnanya,705.0,France,Female,41.0,1.0,0.0,2.0,0.0,1.0,55868.97,0.0
+8906,15763063.0,Yeh,652.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,180553.48,0.0
+8907,15651802.0,Oluchukwu,605.0,France,Female,59.0,3.0,110153.27,1.0,1.0,1.0,142995.32,1.0
+8908,15794868.0,Chiedozie,574.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,75578.67,0.0
+8909,15646513.0,Chidumaga,692.0,Spain,Female,39.0,9.0,0.0,1.0,0.0,1.0,83084.33,1.0
+8910,15769191.0,Chukwuma,637.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,1.0,85547.33,0.0
+8911,15747168.0,Hsiung,711.0,France,Male,38.0,9.0,0.0,1.0,0.0,1.0,177737.06,0.0
+8912,15807563.0,Tuan,841.0,Spain,Male,51.0,1.0,161757.87,1.0,1.0,1.0,163667.56,0.0
+8913,15767064.0,Shih,691.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,1.0,121085.72,0.0
+8914,15738018.0,K'ung,730.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,62944.75,0.0
+8915,15636999.0,Olisaemeka,592.0,France,Female,35.0,3.0,0.0,1.0,1.0,0.0,151316.37,1.0
+8916,15772383.0,Nnachetam,669.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,182988.94,0.0
+8917,15774738.0,Kao,642.0,Germany,Male,24.0,9.0,122453.97,1.0,1.0,1.0,199249.29,0.0
+8918,15709142.0,Chinwenma,682.0,Spain,Male,36.0,3.0,0.0,2.0,0.0,1.0,184399.76,0.0
+8919,15570931.0,Hsia,652.0,France,Female,42.0,0.0,0.0,2.0,0.0,1.0,180769.55,0.0
+8920,15662901.0,Zetticci,542.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,55867.38,0.0
+8921,15648069.0,Hsia,610.0,France,Male,27.0,9.0,0.0,1.0,1.0,1.0,80178.54,0.0
+8922,15655341.0,Hsu,630.0,France,Male,37.0,0.0,0.0,2.0,1.0,0.0,100995.11,0.0
+8923,15723605.0,Nwabugwu,611.0,France,Male,47.0,3.0,0.0,2.0,1.0,1.0,158023.1,0.0
+8924,15801488.0,Aitken,772.0,Germany,Female,28.0,1.0,105435.76,2.0,0.0,1.0,161574.19,0.0
+8925,15593501.0,P'eng,545.0,Germany,Male,44.0,1.0,127545.75,1.0,1.0,0.0,18094.96,1.0
+8926,15651020.0,K?,633.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,1.0,93717.55,0.0
+8927,15626042.0,Chiawuotu,592.0,Spain,Female,44.0,3.0,0.0,1.0,1.0,1.0,80410.68,1.0
+8928,15746461.0,T'ien,584.0,Germany,Male,37.0,9.0,139825.34,1.0,1.0,0.0,61164.45,1.0
+8929,15593773.0,Macartney,538.0,Spain,Male,67.0,0.0,0.0,1.0,1.0,1.0,163365.85,1.0
+8930,15772329.0,Scott,581.0,France,Male,50.0,9.0,102282.61,1.0,1.0,0.0,82526.92,0.0
+8931,15682600.0,Okwuadigbo,804.0,France,Female,30.0,10.0,0.0,2.0,1.0,1.0,158054.49,0.0
+8932,15752846.0,Ch'eng,667.0,France,Male,28.0,9.0,123070.89,1.0,1.0,0.0,149913.84,0.0
+8933,15609558.0,Ch'iu,593.0,France,Male,49.0,6.0,0.0,1.0,1.0,0.0,67364.42,1.0
+8934,15674833.0,Fiorentini,693.0,France,Female,50.0,4.0,0.0,1.0,1.0,1.0,160546.58,0.0
+8935,15581736.0,Chukwuemeka,633.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,1.0,104889.3,0.0
+8936,15686963.0,Ch'en,749.0,France,Male,31.0,6.0,84475.67,1.0,1.0,1.0,104469.06,0.0
+8937,15624170.0,Iloerika,648.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,181850.74,0.0
+8938,15724153.0,H?,709.0,France,Female,19.0,7.0,0.0,2.0,1.0,1.0,160670.37,0.0
+8939,15568162.0,Scott,682.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,102325.19,0.0
+8940,15670755.0,Trevisani,685.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,162022.47,0.0
+8941,15777076.0,Tien,579.0,Spain,Female,32.0,5.0,74963.5,1.0,0.0,1.0,158036.72,0.0
+8942,15693888.0,Y?an,684.0,France,Male,41.0,4.0,161613.94,1.0,0.0,1.0,149913.84,0.0
+8943,15702416.0,Yobanna,697.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,63737.19,0.0
+8944,15672798.0,Onyemauchechukwu,562.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,50774.81,0.0
+8945,15631927.0,Hsueh,648.0,Germany,Male,68.0,3.0,116563.41,1.0,0.0,0.0,58854.88,1.0
+8946,15573854.0,Whitehead,632.0,France,Male,25.0,6.0,0.0,2.0,0.0,1.0,143619.52,0.0
+8947,15762268.0,Lo Duca,654.0,Spain,Female,47.0,5.0,0.0,1.0,1.0,1.0,136983.77,1.0
+8948,15591037.0,Hs?,715.0,Germany,Female,37.0,10.0,108822.5,2.0,1.0,1.0,121537.91,0.0
+8949,15662658.0,Macleod,588.0,France,Male,38.0,0.0,0.0,2.0,1.0,1.0,130853.03,0.0
+8950,15796958.0,Zito,716.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,95625.03,0.0
+8951,15731751.0,Dyer,743.0,France,Male,43.0,7.0,0.0,1.0,1.0,0.0,145796.49,0.0
+8952,15706071.0,Ts'ai,721.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,72318.77,0.0
+8953,15705313.0,Nwagugheuzo,735.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,88728.47,0.0
+8954,15587914.0,Hsia,699.0,Germany,Male,29.0,2.0,121513.34,2.0,1.0,0.0,113481.02,0.0
+8955,15622532.0,Okechukwu,714.0,Germany,Male,37.0,6.0,104584.11,2.0,0.0,1.0,175722.31,0.0
+8956,15682778.0,Hs?eh,754.0,France,Male,43.0,4.0,0.0,1.0,1.0,1.0,61843.73,0.0
+8957,15776047.0,Smith,730.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,60552.44,0.0
+8958,15750055.0,Mazzanti,658.0,Germany,Male,43.0,9.0,121898.58,1.0,1.0,0.0,163666.85,1.0
+8959,15662494.0,Genovesi,604.0,France,Female,30.0,6.0,0.0,1.0,1.0,0.0,177783.04,1.0
+8960,15648047.0,T'ao,628.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,71450.96,0.0
+8961,15776733.0,Ugoji,684.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,180932.46,0.0
+8962,15735080.0,Nkemakolam,622.0,Germany,Female,46.0,6.0,108204.52,2.0,1.0,0.0,97734.3,1.0
+8963,15800326.0,Hsiung,681.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,0.0,92888.52,0.0
+8964,15600992.0,T'ang,682.0,France,Female,35.0,2.0,0.0,2.0,0.0,0.0,48226.37,0.0
+8965,15807481.0,Yobanna,558.0,France,Male,33.0,4.0,89047.14,1.0,1.0,0.0,90537.47,0.0
+8966,15696054.0,T'ang,712.0,France,Male,44.0,5.0,106603.74,1.0,1.0,1.0,156036.19,0.0
+8967,15753092.0,Ch'eng,684.0,Germany,Female,32.0,4.0,126473.33,2.0,1.0,1.0,123775.15,0.0
+8968,15690134.0,Chidiegwu,691.0,Spain,Male,46.0,5.0,81811.71,1.0,1.0,1.0,164741.81,0.0
+8969,15704770.0,Lai,581.0,Germany,Male,35.0,1.0,118627.15,1.0,1.0,1.0,184514.01,0.0
+8970,15654574.0,Y?an,598.0,France,Female,29.0,6.0,86911.8,1.0,1.0,1.0,133535.29,0.0
+8971,15682640.0,Ch'ien,679.0,Spain,Male,36.0,7.0,157970.23,1.0,1.0,0.0,114323.66,0.0
+8972,15758048.0,Pisano,526.0,Spain,Male,42.0,1.0,0.0,2.0,1.0,1.0,157072.91,0.0
+8973,15571059.0,McIntosh,537.0,Spain,Male,41.0,6.0,0.0,2.0,1.0,0.0,59129.72,0.0
+8974,15617982.0,Galkin,679.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,81884.8,1.0
+8975,15579207.0,Chiu,671.0,France,Female,38.0,4.0,86837.95,1.0,1.0,0.0,141872.05,0.0
+8976,15578788.0,Nucci,526.0,France,Female,45.0,7.0,0.0,1.0,1.0,0.0,163427.18,1.0
+8977,15757933.0,White,646.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,0.0,167909.52,0.0
+8978,15788494.0,Onyemachukwu,645.0,Germany,Male,48.0,6.0,131834.76,1.0,0.0,1.0,143450.61,1.0
+8979,15757867.0,Fenton,731.0,Spain,Female,44.0,6.0,102962.62,1.0,0.0,1.0,142331.85,0.0
+8980,15642098.0,Kornilova,690.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,86460.28,0.0
+8981,15643496.0,Lucchese,579.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,178291.09,0.0
+8982,15754577.0,H?,598.0,France,Female,44.0,9.0,0.0,1.0,1.0,0.0,63687.06,1.0
+8983,15641733.0,Nwokeocha,648.0,Germany,Male,50.0,3.0,125851.93,1.0,0.0,1.0,177747.72,1.0
+8984,15729359.0,Brown,717.0,Germany,Male,31.0,5.0,111100.98,2.0,0.0,1.0,160670.37,0.0
+8985,15728474.0,Milanesi,712.0,Germany,Female,30.0,9.0,118100.59,1.0,1.0,1.0,143463.28,0.0
+8986,15678067.0,Hs?,652.0,Spain,Female,39.0,3.0,111063.73,1.0,0.0,0.0,176780.2,0.0
+8987,15636396.0,Ts'ai,652.0,France,Male,45.0,7.0,0.0,3.0,1.0,0.0,4861.72,1.0
+8988,15591747.0,Nwankwo,572.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,103560.98,0.0
+8989,15693637.0,Chiedozie,580.0,Germany,Male,35.0,8.0,132693.56,2.0,1.0,1.0,167810.26,0.0
+8990,15702464.0,Nwachinemelu,731.0,France,Female,27.0,8.0,0.0,2.0,1.0,1.0,125870.5,0.0
+8991,15662291.0,Chidiebere,665.0,France,Male,51.0,2.0,0.0,2.0,1.0,1.0,38190.78,0.0
+8992,15706762.0,Wright,721.0,France,Female,41.0,9.0,103711.56,3.0,1.0,1.0,178822.07,1.0
+8993,15633666.0,Ch'in,850.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,1.0,94798.13,0.0
+8994,15621550.0,Nwankwo,712.0,France,Female,27.0,0.0,0.0,2.0,1.0,0.0,83419.49,1.0
+8995,15709511.0,Yeh,727.0,France,Male,49.0,2.0,0.0,1.0,1.0,0.0,177092.16,1.0
+8996,15727696.0,Olague,573.0,Spain,Male,21.0,9.0,0.0,1.0,0.0,1.0,140937.91,0.0
+8997,15810390.0,Chiu,753.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,176750.81,0.0
+8998,15752912.0,Lucchese,619.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,89566.64,0.0
+8999,15796834.0,Okwudilichukwu,585.0,France,Female,37.0,9.0,0.0,2.0,1.0,1.0,136028.98,0.0
+9000,15751180.0,McGregor,539.0,Germany,Male,58.0,4.0,92578.14,1.0,1.0,1.0,131712.52,0.0
+9001,15615016.0,Chinomso,699.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,141364.67,0.0
+9002,15609735.0,Ts'ui,682.0,France,Male,32.0,1.0,0.0,2.0,0.0,1.0,181490.42,0.0
+9003,15647782.0,Uspensky,605.0,France,Female,35.0,10.0,0.0,2.0,1.0,1.0,129964.94,0.0
+9004,15591131.0,Hs?,592.0,Germany,Female,56.0,8.0,111756.5,1.0,1.0,0.0,173516.71,1.0
+9005,15604576.0,Ch'ang,704.0,Spain,Male,33.0,7.0,128398.38,2.0,1.0,1.0,155700.15,0.0
+9006,15588944.0,Ch'iu,602.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,198798.44,0.0
+9007,15605286.0,Chukwumaobim,726.0,France,Male,26.0,8.0,0.0,1.0,1.0,0.0,93777.61,0.0
+9008,15749050.0,Chukwueloka,724.0,France,Male,21.0,6.0,0.0,2.0,1.0,0.0,169952.82,0.0
+9009,15793901.0,Shih,521.0,France,Female,49.0,7.0,0.0,3.0,1.0,0.0,145986.35,1.0
+9010,15722532.0,Ch'ien,637.0,Germany,Female,52.0,3.0,122338.7,1.0,0.0,0.0,173837.54,1.0
+9011,15661629.0,Chiang,668.0,Spain,Male,22.0,9.0,0.0,2.0,1.0,1.0,48606.92,0.0
+9012,15677929.0,Ch'ien,610.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,89963.29,0.0
+9013,15804937.0,Hsiung,496.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,131718.12,0.0
+9014,15683658.0,Chinwemma,727.0,France,Male,22.0,7.0,0.0,2.0,0.0,1.0,116442.42,0.0
+9015,15676521.0,Chin,705.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,152855.96,0.0
+9016,15751771.0,Kenechukwu,651.0,France,Male,57.0,2.0,0.0,2.0,1.0,1.0,143739.21,0.0
+9017,15608968.0,Ijendu,714.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,153662.88,0.0
+9018,15762692.0,Tuan,714.0,Germany,Male,43.0,9.0,98415.37,2.0,0.0,0.0,118029.09,0.0
+9019,15640855.0,Ch'ien,471.0,France,Female,32.0,7.0,0.0,1.0,1.0,1.0,103787.28,1.0
+9020,15694188.0,Lupton,646.0,France,Female,46.0,9.0,0.0,1.0,1.0,1.0,93568.77,1.0
+9021,15579994.0,Chukwubuikem,627.0,Germany,Female,37.0,2.0,141787.31,1.0,1.0,0.0,59645.24,0.0
+9022,15690772.0,Chukwudi,614.0,Spain,Male,43.0,7.0,128264.03,1.0,1.0,0.0,114323.66,1.0
+9023,15640866.0,McLean,624.0,France,Male,62.0,4.0,0.0,2.0,1.0,0.0,62627.55,1.0
+9024,15641604.0,Fiorentini,699.0,Spain,Male,29.0,1.0,0.0,1.0,0.0,0.0,125518.32,0.0
+9025,15712596.0,Tuan,752.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,127543.81,0.0
+9026,15743522.0,Nickson,593.0,France,Female,51.0,8.0,125785.23,1.0,1.0,0.0,74838.64,0.0
+9027,15682860.0,Macleod,628.0,Spain,Female,42.0,9.0,0.0,1.0,1.0,0.0,124496.63,1.0
+9028,15757535.0,Chukwunonso,725.0,France,Female,36.0,4.0,0.0,1.0,1.0,0.0,72922.75,0.0
+9029,15685564.0,McIntyre,681.0,France,Male,47.0,2.0,0.0,4.0,1.0,1.0,105818.51,1.0
+9030,15705515.0,Onyemauchechukwu,543.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,155782.89,0.0
+9031,15746190.0,Pisano,637.0,France,Female,33.0,1.0,0.0,2.0,1.0,0.0,70523.92,0.0
+9032,15788483.0,Smith,651.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,97640.2,0.0
+9033,15665067.0,Smith,567.0,Germany,Female,49.0,8.0,100471.34,1.0,1.0,0.0,108645.85,1.0
+9034,15605968.0,Ndubinachi,579.0,Germany,Female,64.0,9.0,116537.6,1.0,1.0,0.0,176712.59,1.0
+9035,15674832.0,Chukwumaobim,730.0,Spain,Male,28.0,4.0,149950.19,1.0,0.0,0.0,114522.68,0.0
+9036,15673820.0,Smith,669.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,95611.47,0.0
+9037,15604551.0,Zito,553.0,France,Female,46.0,7.0,96781.39,1.0,1.0,1.0,110457.99,0.0
+9038,15587562.0,Manna,678.0,France,Female,38.0,7.0,0.0,1.0,1.0,0.0,167554.86,0.0
+9039,15794874.0,Dickson,710.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,176894.35,0.0
+9040,15643824.0,P'an,595.0,Germany,Male,38.0,5.0,143595.87,1.0,1.0,1.0,153392.28,0.0
+9041,15639552.0,Chigbogu,769.0,Germany,Female,37.0,8.0,104430.08,1.0,1.0,0.0,117832.88,1.0
+9042,15610643.0,Ch'iu,526.0,Spain,Male,33.0,8.0,108960.0,1.0,1.0,0.0,71552.04,0.0
+9043,15640409.0,T'ien,633.0,France,Male,28.0,3.0,151858.98,1.0,0.0,0.0,149913.92,0.0
+9044,15747649.0,Chukwudi,695.0,Spain,Female,38.0,3.0,149950.19,1.0,0.0,1.0,184800.31,0.0
+9045,15675794.0,Hsia,604.0,Spain,Male,46.0,5.0,0.0,1.0,1.0,1.0,62733.03,1.0
+9046,15702788.0,T'ao,614.0,Germany,Male,29.0,5.0,115046.74,1.0,1.0,0.0,25395.83,0.0
+9047,15716276.0,Smith,709.0,Spain,Female,37.0,1.0,0.0,1.0,1.0,1.0,116973.48,0.0
+9048,15680197.0,Chukwufumnanya,644.0,Germany,Female,46.0,1.0,96679.71,3.0,0.0,1.0,101515.76,1.0
+9049,15679284.0,Chukwueloka,558.0,France,Female,22.0,5.0,0.0,2.0,1.0,1.0,77961.41,0.0
+9050,15645316.0,Hsiao,628.0,Germany,Male,40.0,3.0,88815.56,1.0,0.0,0.0,166677.54,0.0
+9051,15661526.0,O'Loghlen,662.0,Germany,Female,36.0,3.0,111568.01,1.0,1.0,1.0,5791.85,0.0
+9052,15775294.0,Ts'ao,611.0,France,Female,34.0,6.0,0.0,2.0,0.0,1.0,122964.18,0.0
+9053,15659428.0,Milani,586.0,France,Male,47.0,6.0,0.0,1.0,1.0,0.0,162260.93,1.0
+9054,15595588.0,Nnanna,678.0,Spain,Female,43.0,0.0,155519.59,1.0,0.0,1.0,105570.69,0.0
+9055,15651369.0,Ch'ien,758.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,78854.57,0.0
+9056,15631681.0,Baresi,682.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,136497.28,0.0
+9057,15705657.0,Chidimma,556.0,Germany,Male,31.0,5.0,122314.5,1.0,1.0,1.0,133692.88,0.0
+9058,15607598.0,Hs?,727.0,France,Female,43.0,2.0,0.0,1.0,1.0,1.0,143954.99,0.0
+9059,15681873.0,Udegbulam,682.0,France,Male,42.0,4.0,0.0,3.0,1.0,0.0,52894.01,1.0
+9060,15734578.0,Tikhonov,596.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,187811.71,0.0
+9061,15792922.0,Hsia,731.0,France,Female,30.0,9.0,0.0,2.0,1.0,0.0,60917.24,0.0
+9062,15754537.0,Chiu,634.0,Germany,Female,57.0,7.0,139859.54,2.0,1.0,0.0,164038.07,1.0
+9063,15673591.0,Doyne,850.0,France,Male,38.0,2.0,0.0,1.0,1.0,1.0,62845.64,0.0
+9064,15573926.0,Ch'ang,679.0,Germany,Male,33.0,7.0,148074.79,2.0,1.0,0.0,48224.72,0.0
+9065,15703923.0,Mazzi,717.0,Germany,Male,38.0,8.0,138053.67,1.0,1.0,0.0,152841.45,0.0
+9066,15633461.0,Aitken,618.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,86622.56,0.0
+9067,15795936.0,Chukwunonso,748.0,Spain,Male,42.0,3.0,104449.8,1.0,1.0,0.0,132547.03,1.0
+9068,15643496.0,Zikoranachidimma,676.0,France,Female,29.0,5.0,0.0,1.0,1.0,0.0,131807.14,0.0
+9069,15677906.0,Ch'ien,587.0,France,Female,41.0,8.0,121207.12,2.0,1.0,0.0,79957.95,0.0
+9070,15701555.0,Obialo,615.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,145528.22,0.0
+9071,15662232.0,Chukwufumnanya,809.0,France,Female,24.0,7.0,182025.95,1.0,1.0,1.0,182055.42,0.0
+9072,15732437.0,Chibueze,679.0,Spain,Female,35.0,8.0,135927.06,1.0,1.0,1.0,121151.1,0.0
+9073,15647898.0,Nwankwo,757.0,France,Male,33.0,5.0,137714.5,1.0,0.0,1.0,26387.71,0.0
+9074,15728693.0,Ts'ao,692.0,France,Male,28.0,1.0,0.0,2.0,0.0,1.0,78744.39,0.0
+9075,15641675.0,Ch'iu,644.0,Germany,Male,38.0,6.0,119496.25,1.0,0.0,0.0,175877.7,0.0
+9076,15626452.0,Hs?,727.0,France,Male,31.0,10.0,0.0,2.0,1.0,0.0,138399.67,0.0
+9077,15575060.0,Nwankwo,755.0,Germany,Male,41.0,1.0,144962.74,1.0,1.0,0.0,53420.53,0.0
+9078,15651868.0,Ch'en,577.0,France,Female,35.0,4.0,137725.34,1.0,1.0,1.0,38196.24,0.0
+9079,15738219.0,P'eng,677.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,135077.01,0.0
+9080,15598948.0,Yudina,682.0,France,Female,30.0,5.0,0.0,1.0,1.0,1.0,51620.87,0.0
+9081,15709621.0,Chiemenam,681.0,France,Male,26.0,2.0,0.0,2.0,1.0,0.0,170518.83,0.0
+9082,15628272.0,Fomin,705.0,Germany,Female,48.0,5.0,137204.77,1.0,1.0,0.0,183868.01,1.0
+9083,15638003.0,Chidubem,850.0,France,Male,49.0,3.0,0.0,2.0,1.0,1.0,55893.37,0.0
+9084,15593794.0,Yermakov,683.0,France,Male,44.0,8.0,0.0,1.0,1.0,1.0,86937.2,0.0
+9085,15613292.0,Lo Duca,565.0,Spain,Female,32.0,5.0,0.0,1.0,1.0,1.0,137791.18,0.0
+9086,15731463.0,Mao,434.0,Spain,Male,49.0,3.0,131354.25,1.0,0.0,0.0,182336.45,1.0
+9087,15757559.0,Iloerika,746.0,Spain,Female,50.0,8.0,0.0,1.0,1.0,0.0,149913.84,1.0
+9088,15758769.0,Trevisani,696.0,France,Female,27.0,6.0,0.0,1.0,1.0,1.0,115994.52,0.0
+9089,15614103.0,Nwankwo,758.0,France,Female,37.0,5.0,131834.76,1.0,0.0,0.0,96985.58,0.0
+9090,15670345.0,Ting,565.0,Spain,Female,33.0,6.0,154870.28,1.0,1.0,1.0,103914.4,1.0
+9091,15658432.0,Onwumelu,673.0,France,Male,34.0,1.0,108825.75,2.0,1.0,1.0,151425.82,0.0
+9092,15592571.0,Obidimkpa,662.0,France,Female,45.0,3.0,0.0,1.0,0.0,0.0,135851.3,1.0
+9093,15658972.0,Chidiebere,710.0,France,Male,28.0,8.0,0.0,1.0,1.0,1.0,171706.66,0.0
+9094,15693905.0,Blesing,656.0,Germany,Female,43.0,9.0,110641.18,1.0,0.0,0.0,172049.84,1.0
+9095,15585350.0,Obiora,663.0,France,Male,52.0,2.0,0.0,2.0,1.0,0.0,66810.48,1.0
+9096,15725094.0,Linger,628.0,Spain,Male,36.0,9.0,0.0,1.0,1.0,0.0,185630.76,0.0
+9097,15607301.0,O'Brien,709.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,0.0,168515.61,0.0
+9098,15687540.0,Maclean,528.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,142963.29,0.0
+9099,15801942.0,Nwabugwu,588.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,136909.85,0.0
+9100,15800412.0,Nwankwo,556.0,France,Male,38.0,6.0,141806.0,1.0,0.0,0.0,113089.43,0.0
+9101,15642099.0,Hsiung,597.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,53511.74,1.0
+9102,15578878.0,O'Kane,635.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,147930.46,0.0
+9103,15573893.0,P'eng,667.0,Germany,Male,46.0,5.0,137847.41,1.0,0.0,0.0,164864.66,1.0
+9104,15609899.0,Nwachinemelu,636.0,Spain,Male,45.0,1.0,0.0,2.0,1.0,1.0,161889.15,0.0
+9105,15684419.0,Chiemenam,718.0,Germany,Male,40.0,3.0,134760.21,1.0,1.0,0.0,26318.73,1.0
+9106,15740458.0,Lo Duca,602.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,85901.09,0.0
+9107,15693382.0,Kaleski,646.0,France,Male,41.0,5.0,92899.27,1.0,1.0,0.0,179435.31,0.0
+9108,15707918.0,Tung,705.0,Germany,Female,47.0,2.0,92887.06,1.0,1.0,0.0,82938.99,1.0
+9109,15766064.0,White,590.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,0.0,74900.9,0.0
+9110,15707974.0,Chiang,850.0,Spain,Male,29.0,2.0,0.0,1.0,1.0,1.0,162022.47,0.0
+9111,15709861.0,Ritchie,645.0,France,Female,37.0,2.0,0.0,1.0,1.0,0.0,177762.57,0.0
+9112,15578098.0,Genovese,589.0,Spain,Male,33.0,6.0,155059.1,1.0,1.0,0.0,131736.43,0.0
+9113,15665460.0,P'eng,583.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,78310.04,0.0
+9114,15605665.0,Nwachukwu,562.0,France,Male,42.0,6.0,0.0,2.0,1.0,1.0,103480.29,0.0
+9115,15783883.0,Hsueh,589.0,Spain,Female,41.0,7.0,105663.47,1.0,1.0,1.0,180962.32,0.0
+9116,15651022.0,Shao,674.0,Germany,Female,56.0,5.0,140956.99,1.0,1.0,1.0,141896.74,1.0
+9117,15601263.0,Iadanza,687.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,103832.58,0.0
+9118,15730076.0,Udegbunam,562.0,Spain,Male,37.0,8.0,112290.22,1.0,1.0,1.0,89566.64,0.0
+9119,15589210.0,Hs?,697.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,187816.59,0.0
+9120,15769959.0,Chijindum,769.0,France,Male,43.0,1.0,106804.26,1.0,1.0,1.0,1375.46,0.0
+9121,15773876.0,Fanucci,584.0,France,Male,48.0,9.0,0.0,1.0,1.0,1.0,181270.84,0.0
+9122,15805671.0,Efremov,743.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,130806.93,0.0
+9123,15774336.0,Genovesi,494.0,France,Female,33.0,2.0,163870.07,2.0,1.0,0.0,140800.66,0.0
+9124,15773591.0,Obialo,651.0,France,Female,37.0,8.0,0.0,1.0,0.0,1.0,74945.11,0.0
+9125,15696588.0,Nnamutaezinwa,623.0,Germany,Female,24.0,8.0,137946.39,2.0,1.0,1.0,11407.58,0.0
+9126,15631693.0,Hsia,581.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,140763.18,0.0
+9127,15582944.0,Lo Duca,631.0,France,Female,41.0,9.0,121823.7,1.0,0.0,1.0,167984.61,0.0
+9128,15690169.0,P'eng,755.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,131647.31,0.0
+9129,15806922.0,Hs?,711.0,Germany,Female,67.0,9.0,111168.97,1.0,1.0,1.0,100727.68,0.0
+9130,15803801.0,Ukaegbulam,607.0,France,Male,45.0,4.0,0.0,1.0,1.0,0.0,183542.08,1.0
+9131,15784092.0,Chibugo,693.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,95583.45,0.0
+9132,15652999.0,Onyemauchechukwu,560.0,France,Male,29.0,0.0,120135.55,1.0,1.0,0.0,60536.56,0.0
+9133,15791958.0,Calabrese,562.0,Spain,Female,34.0,0.0,0.0,2.0,0.0,1.0,9041.71,0.0
+9134,15788483.0,Mai,659.0,Germany,Female,39.0,5.0,85982.07,2.0,1.0,0.0,82662.73,0.0
+9135,15673908.0,Onwuatuegwu,664.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,176924.21,0.0
+9136,15773779.0,Sacco,699.0,Germany,Male,23.0,7.0,129828.58,1.0,1.0,1.0,79577.38,0.0
+9137,15656592.0,Ritchie,707.0,France,Male,42.0,7.0,0.0,2.0,0.0,0.0,1843.24,0.0
+9138,15726186.0,Chidozie,691.0,France,Male,26.0,5.0,0.0,2.0,1.0,0.0,170980.86,0.0
+9139,15603851.0,Pagnotto,707.0,Spain,Female,26.0,7.0,0.0,1.0,1.0,1.0,105570.39,0.0
+9140,15691808.0,Onyemauchechukwu,707.0,Germany,Male,32.0,9.0,149620.88,2.0,0.0,0.0,151481.86,0.0
+9141,15682868.0,Ts'ui,683.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,143614.01,0.0
+9142,15760641.0,Chin,809.0,Spain,Male,44.0,8.0,0.0,2.0,0.0,1.0,155700.15,0.0
+9143,15693468.0,Hsiao,685.0,Germany,Female,34.0,2.0,97565.74,1.0,1.0,0.0,97913.67,0.0
+9144,15664897.0,P'eng,637.0,France,Male,33.0,3.0,0.0,2.0,0.0,0.0,91086.73,0.0
+9145,15683403.0,MacDevitt,637.0,France,Male,29.0,9.0,143330.56,1.0,0.0,1.0,146411.7,0.0
+9146,15748352.0,Chidubem,593.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,56787.8,0.0
+9147,15673908.0,Li,611.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,111241.37,0.0
+9148,15641480.0,Udinese,667.0,Germany,Male,32.0,4.0,136624.6,2.0,1.0,1.0,145792.01,0.0
+9149,15640266.0,Olisaemeka,559.0,France,Male,45.0,5.0,0.0,1.0,0.0,0.0,142963.29,1.0
+9150,15717164.0,Ch'iu,585.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,142238.54,0.0
+9151,15622443.0,Chukwuhaenye,672.0,France,Female,39.0,5.0,0.0,1.0,1.0,1.0,152992.04,0.0
+9152,15790717.0,Okwudilichukwu,683.0,Spain,Male,26.0,2.0,0.0,2.0,1.0,0.0,64375.4,0.0
+9153,15641640.0,Ch'in,635.0,France,Female,27.0,3.0,0.0,1.0,1.0,1.0,179093.26,0.0
+9154,15607884.0,Chiagoziem,538.0,Germany,Male,26.0,4.0,125734.2,2.0,0.0,0.0,115022.94,0.0
+9155,15575486.0,O'Sullivan,651.0,France,Female,30.0,3.0,0.0,1.0,1.0,0.0,9085.0,0.0
+9156,15806796.0,Nnaife,735.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,131660.31,0.0
+9157,15780144.0,Nwankwo,649.0,Germany,Female,27.0,8.0,108269.2,2.0,1.0,1.0,158086.33,0.0
+9158,15649033.0,Ugorji,844.0,Germany,Female,49.0,2.0,122453.97,2.0,1.0,1.0,126794.57,1.0
+9159,15690772.0,Loyau,651.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,137028.51,0.0
+9160,15814267.0,Onio,657.0,France,Male,34.0,10.0,0.0,2.0,0.0,0.0,105760.01,0.0
+9161,15683958.0,Teng,686.0,France,Female,36.0,7.0,0.0,2.0,0.0,1.0,170621.55,0.0
+9162,15702912.0,Eluemuno,498.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,82298.59,0.0
+9163,15752884.0,Chukwueloka,705.0,Spain,Male,39.0,7.0,0.0,2.0,0.0,1.0,128841.31,0.0
+9164,15635197.0,Tung,675.0,France,Male,35.0,3.0,0.0,1.0,0.0,0.0,150659.35,1.0
+9165,15631709.0,Yefremov,592.0,France,Female,25.0,2.0,0.0,2.0,0.0,0.0,80149.27,0.0
+9166,15640507.0,Tuan,732.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,180326.83,0.0
+9167,15685920.0,Aksyonova,790.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,121457.88,0.0
+9168,15603328.0,Yashina,663.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,27330.59,0.0
+9169,15589924.0,Walker,704.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,121151.1,0.0
+9170,15671140.0,Gartrell,638.0,Germany,Male,40.0,8.0,115961.58,1.0,1.0,0.0,184842.24,1.0
+9171,15791114.0,Ts'ao,682.0,Spain,Male,31.0,4.0,139875.7,1.0,1.0,0.0,158736.07,0.0
+9172,15598086.0,Mironova,558.0,Germany,Female,45.0,10.0,102702.69,1.0,1.0,0.0,80591.15,0.0
+9173,15690330.0,Hsieh,545.0,Germany,Male,49.0,2.0,97565.74,1.0,1.0,0.0,129372.94,1.0
+9174,15642063.0,Nnachetam,597.0,Spain,Female,27.0,4.0,0.0,1.0,1.0,1.0,152097.02,0.0
+9175,15777033.0,Folliero,709.0,Germany,Female,47.0,5.0,137715.66,1.0,1.0,0.0,176924.21,1.0
+9176,15638231.0,Hsueh,706.0,France,Female,39.0,5.0,0.0,2.0,1.0,0.0,160618.61,0.0
+9177,15576044.0,Kuo,651.0,Spain,Female,36.0,1.0,0.0,2.0,0.0,0.0,187905.12,0.0
+9178,15783025.0,Gaffney,657.0,France,Male,26.0,9.0,123981.31,1.0,1.0,0.0,157097.02,0.0
+9179,15750574.0,Ch'in,648.0,Spain,Female,41.0,1.0,0.0,2.0,0.0,1.0,175082.2,0.0
+9180,15705735.0,Chidiebele,582.0,Spain,Female,59.0,4.0,0.0,2.0,1.0,1.0,82900.25,0.0
+9181,15686696.0,Trevisano,635.0,France,Female,23.0,6.0,0.0,2.0,1.0,1.0,126837.72,0.0
+9182,15604093.0,T'ang,642.0,Spain,Female,61.0,3.0,0.0,2.0,1.0,1.0,113860.81,0.0
+9183,15588929.0,O'Meara,719.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,99654.03,0.0
+9184,15648047.0,Obialo,697.0,Germany,Female,50.0,2.0,133656.85,1.0,1.0,0.0,177065.24,1.0
+9185,15621201.0,Ch'in,850.0,France,Male,45.0,1.0,0.0,2.0,0.0,1.0,94440.45,0.0
+9186,15605826.0,Nucci,597.0,France,Female,39.0,4.0,134096.55,1.0,1.0,1.0,112946.67,0.0
+9187,15607748.0,Frolov,637.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,95007.8,0.0
+9188,15702412.0,Chiang,598.0,France,Male,36.0,2.0,113599.42,1.0,1.0,0.0,86927.53,0.0
+9189,15672447.0,Brown,571.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,1.0,145796.49,0.0
+9190,15593973.0,Tung,605.0,France,Male,31.0,8.0,0.0,1.0,1.0,1.0,38798.79,0.0
+9191,15773520.0,Ts'ai,659.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,0.0,145567.34,0.0
+9192,15752588.0,Martin,658.0,France,Female,47.0,3.0,145860.98,1.0,0.0,0.0,103970.58,1.0
+9193,15778947.0,Calabresi,751.0,France,Female,36.0,8.0,0.0,2.0,0.0,1.0,180800.42,0.0
+9194,15763431.0,Ch'eng,632.0,Germany,Female,40.0,1.0,131778.08,2.0,1.0,0.0,59385.24,0.0
+9195,15692819.0,Tsao,646.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,0.0,143357.03,0.0
+9196,15686696.0,Pacheco,597.0,Germany,Male,33.0,10.0,121729.42,1.0,1.0,0.0,119346.88,0.0
+9197,15668893.0,Uspenskaya,763.0,France,Male,30.0,3.0,137974.15,1.0,0.0,1.0,109538.35,0.0
+9198,15692570.0,Palermo,521.0,France,Female,58.0,5.0,141786.78,1.0,0.0,1.0,172792.43,1.0
+9199,15633064.0,Fink,786.0,Spain,Male,38.0,8.0,0.0,1.0,0.0,1.0,141364.62,0.0
+9200,15768137.0,Chiemeka,613.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,158750.58,0.0
+9201,15650258.0,Pinto,681.0,France,Female,29.0,3.0,130170.82,1.0,1.0,1.0,51380.9,0.0
+9202,15692599.0,Kenechukwu,641.0,France,Female,32.0,9.0,0.0,1.0,0.0,0.0,170903.4,0.0
+9203,15622171.0,Chukwudi,596.0,Spain,Male,32.0,10.0,0.0,2.0,1.0,1.0,129964.94,0.0
+9204,15588580.0,Onyeorulu,634.0,Spain,Male,23.0,5.0,0.0,2.0,0.0,0.0,80587.31,0.0
+9205,15752622.0,Tsao,642.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,74048.1,0.0
+9206,15772363.0,Illingworth,685.0,France,Female,31.0,6.0,0.0,1.0,0.0,1.0,151912.49,0.0
+9207,15745355.0,Li Fonti,584.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,134790.74,0.0
+9208,15579617.0,Obiajulu,729.0,Germany,Female,46.0,2.0,91938.71,1.0,0.0,1.0,107712.57,0.0
+9209,15801436.0,Ch'iu,616.0,France,Male,21.0,1.0,0.0,2.0,1.0,1.0,163562.25,0.0
+9210,15636016.0,Hsia,573.0,France,Female,42.0,3.0,0.0,2.0,1.0,1.0,141872.05,0.0
+9211,15770214.0,H?,674.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,138889.15,0.0
+9212,15643487.0,Macartney,658.0,Germany,Female,42.0,7.0,108269.2,1.0,1.0,0.0,111868.23,0.0
+9213,15785819.0,Chidozie,590.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,0.0,86799.49,0.0
+9214,15589475.0,Liao,733.0,Spain,Female,37.0,0.0,0.0,2.0,0.0,1.0,145558.42,0.0
+9215,15699340.0,Genovesi,739.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,72402.0,0.0
+9216,15723884.0,Shih,571.0,France,Male,38.0,2.0,0.0,1.0,0.0,0.0,73873.65,0.0
+9217,15696564.0,Nwachinemelu,598.0,France,Male,55.0,3.0,161608.81,1.0,0.0,0.0,141756.32,1.0
+9218,15652048.0,Nwoye,646.0,France,Male,32.0,2.0,0.0,2.0,0.0,0.0,86791.9,0.0
+9219,15745250.0,Chiang,581.0,France,Female,32.0,4.0,121205.15,1.0,1.0,0.0,164886.64,0.0
+9220,15792228.0,Hsieh,607.0,France,Female,21.0,8.0,0.0,2.0,1.0,0.0,71139.31,0.0
+9221,15627995.0,Tuan,592.0,France,Female,35.0,8.0,0.0,1.0,1.0,0.0,179634.69,1.0
+9222,15671410.0,Ch'in,590.0,France,Female,60.0,3.0,122524.61,1.0,0.0,1.0,147794.63,0.0
+9223,15645271.0,Y?,794.0,France,Female,42.0,8.0,95921.06,1.0,1.0,1.0,106761.47,0.0
+9224,15748797.0,Chimezie,732.0,France,Female,43.0,5.0,0.0,1.0,0.0,0.0,155799.86,0.0
+9225,15767488.0,O'Brien,584.0,France,Female,30.0,2.0,0.0,2.0,0.0,1.0,176943.59,0.0
+9226,15807457.0,Hsueh,806.0,France,Female,46.0,3.0,0.0,1.0,1.0,1.0,99128.13,1.0
+9227,15709639.0,Y?an,682.0,France,Male,29.0,9.0,0.0,2.0,0.0,1.0,187776.49,0.0
+9228,15648881.0,H?,663.0,France,Male,42.0,7.0,147069.78,1.0,1.0,0.0,170950.58,0.0
+9229,15593555.0,T'ang,711.0,Spain,Female,57.0,5.0,0.0,2.0,1.0,1.0,170886.01,0.0
+9230,15657779.0,Udinesi,681.0,Germany,Male,33.0,2.0,111543.27,2.0,1.0,1.0,113553.43,0.0
+9231,15590177.0,Y?,638.0,France,Male,26.0,7.0,0.0,2.0,1.0,0.0,111981.19,0.0
+9232,15718588.0,Hsieh,679.0,France,Male,29.0,7.0,0.0,2.0,0.0,0.0,93769.89,0.0
+9233,15579777.0,Tsao,712.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,145700.22,0.0
+9234,15585867.0,Mazzanti,614.0,France,Female,40.0,8.0,0.0,1.0,1.0,0.0,69157.93,1.0
+9235,15673984.0,Chidubem,635.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,55775.72,0.0
+9236,15608956.0,Mazzanti,612.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,156774.75,0.0
+9237,15588908.0,Chukwujamuike,619.0,France,Female,30.0,7.0,0.0,1.0,0.0,1.0,133897.32,0.0
+9238,15603597.0,Ch'in,705.0,Spain,Male,47.0,1.0,0.0,1.0,1.0,0.0,143859.66,1.0
+9239,15672930.0,Chukwualuka,749.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,48984.95,0.0
+9240,15606901.0,Chia,644.0,Germany,Female,40.0,9.0,113386.36,2.0,1.0,0.0,172586.89,1.0
+9241,15807525.0,Yefremova,695.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,10771.46,0.0
+9242,15581620.0,Fomin,545.0,France,Female,45.0,9.0,92889.91,1.0,0.0,0.0,163666.85,1.0
+9243,15684801.0,Chukwudi,567.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,155870.82,0.0
+9244,15579892.0,Tsai,613.0,France,Female,38.0,1.0,0.0,2.0,0.0,1.0,91433.38,0.0
+9245,15797710.0,Tien,582.0,France,Female,46.0,9.0,147521.35,1.0,0.0,0.0,114774.35,1.0
+9246,15715643.0,Tretiakova,627.0,Germany,Female,30.0,2.0,91625.15,2.0,0.0,1.0,46888.76,0.0
+9247,15585865.0,Yudina,724.0,Spain,Female,30.0,6.0,0.0,2.0,1.0,0.0,94780.9,0.0
+9248,15665760.0,Maclean,673.0,France,Male,58.0,2.0,104584.11,1.0,0.0,1.0,132798.78,0.0
+9249,15670822.0,L?,749.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,135851.3,0.0
+9250,15704788.0,Pisano,678.0,France,Male,62.0,8.0,0.0,2.0,0.0,1.0,182372.35,0.0
+9251,15583026.0,Nwachinemelu,748.0,France,Male,27.0,0.0,0.0,1.0,1.0,0.0,70916.0,1.0
+9252,15751203.0,Okeke,672.0,Spain,Male,48.0,4.0,0.0,1.0,1.0,0.0,82868.34,1.0
+9253,15752344.0,Hsueh,645.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,91903.9,0.0
+9254,15712607.0,L?,632.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,134875.37,0.0
+9255,15802857.0,Liao,652.0,Spain,Female,64.0,4.0,0.0,1.0,1.0,1.0,65636.92,0.0
+9256,15643330.0,Kibby,640.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,100601.73,0.0
+9257,15649112.0,Yeh,708.0,France,Male,35.0,7.0,93920.47,1.0,1.0,0.0,71178.09,0.0
+9258,15672930.0,Ting,773.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,72467.99,0.0
+9259,15806918.0,P'eng,631.0,France,Male,40.0,7.0,0.0,1.0,1.0,0.0,102934.74,0.0
+9260,15737687.0,Mancini,724.0,Germany,Male,31.0,4.0,122947.92,1.0,1.0,1.0,79240.9,1.0
+9261,15719090.0,Hsiung,583.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,189428.75,0.0
+9262,15583681.0,Zikoranachidimma,712.0,Spain,Male,33.0,2.0,0.0,2.0,0.0,0.0,157097.09,0.0
+9263,15758813.0,Eluemuno,642.0,Germany,Male,28.0,10.0,166902.82,2.0,1.0,1.0,151902.78,0.0
+9264,15686963.0,Chiang,652.0,Germany,Male,43.0,1.0,142663.46,1.0,1.0,0.0,93953.84,1.0
+9265,15576000.0,Hsueh,559.0,France,Female,30.0,2.0,0.0,2.0,0.0,0.0,176780.13,0.0
+9266,15585892.0,Chinomso,627.0,Germany,Female,33.0,8.0,98899.91,1.0,1.0,0.0,479.54,0.0
+9267,15789974.0,Chidimma,648.0,France,Male,30.0,3.0,120777.88,1.0,1.0,0.0,167023.95,0.0
+9268,15642824.0,Milano,608.0,France,Male,55.0,6.0,113672.18,1.0,1.0,1.0,129984.2,0.0
+9269,15643361.0,Maclean,703.0,France,Male,33.0,10.0,0.0,1.0,1.0,1.0,51620.94,0.0
+9270,15809302.0,Hs?,591.0,Spain,Female,49.0,9.0,0.0,3.0,1.0,1.0,60905.51,1.0
+9271,15768474.0,Shih,681.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,1.0,126475.79,0.0
+9272,15737542.0,Kincaid,686.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,0.0,162719.69,0.0
+9273,15788215.0,Chinwenma,497.0,France,Female,47.0,9.0,0.0,2.0,1.0,0.0,51454.06,0.0
+9274,15680804.0,Rizzo,678.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,103737.82,0.0
+9275,15632264.0,Yeh,681.0,Germany,Male,44.0,4.0,120718.28,1.0,0.0,1.0,1354.25,1.0
+9276,15702789.0,Chuang,795.0,Germany,Male,31.0,1.0,120717.28,2.0,1.0,1.0,79577.38,0.0
+9277,15639087.0,Chukwubuikem,647.0,France,Male,30.0,4.0,95922.9,1.0,0.0,1.0,64849.74,0.0
+9278,15733138.0,Soto,812.0,Spain,Female,53.0,9.0,0.0,1.0,1.0,0.0,86044.98,1.0
+9279,15684512.0,Manna,687.0,Spain,Female,42.0,7.0,0.0,2.0,1.0,0.0,154844.22,0.0
+9280,15804787.0,Ch'ang,774.0,France,Male,28.0,9.0,0.0,1.0,1.0,1.0,170984.87,0.0
+9281,15680804.0,Mamelu,615.0,France,Male,40.0,2.0,71553.83,1.0,1.0,0.0,163911.32,0.0
+9282,15804482.0,Chukwukadibia,613.0,France,Female,44.0,8.0,69413.44,2.0,1.0,0.0,129964.94,0.0
+9283,15769902.0,Hightower,705.0,France,Male,26.0,7.0,96651.03,1.0,1.0,0.0,158887.09,0.0
+9284,15583090.0,Lucchesi,651.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,133535.29,0.0
+9285,15814553.0,Lucchesi,568.0,France,Male,54.0,6.0,0.0,3.0,1.0,0.0,147055.87,1.0
+9286,15793723.0,Bales,673.0,Spain,Female,32.0,10.0,0.0,1.0,1.0,0.0,119071.56,0.0
+9287,15769312.0,Nkemdirim,687.0,Germany,Male,48.0,2.0,123575.51,2.0,1.0,1.0,9983.88,0.0
+9288,15638774.0,Sun,634.0,France,Female,33.0,10.0,0.0,2.0,1.0,0.0,173870.39,0.0
+9289,15648047.0,Ts'ui,499.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,9984.4,0.0
+9290,15647898.0,De Luca,696.0,France,Female,51.0,6.0,0.0,2.0,1.0,1.0,131919.48,0.0
+9291,15783030.0,Frolova,565.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,75578.67,0.0
+9292,15609510.0,Nnamdi,725.0,France,Female,35.0,2.0,0.0,2.0,0.0,0.0,24495.03,1.0
+9293,15650258.0,Mays,561.0,France,Male,46.0,8.0,0.0,2.0,1.0,1.0,182059.28,0.0
+9294,15661528.0,Chiekwugo,615.0,Germany,Male,44.0,3.0,106045.92,1.0,1.0,0.0,139861.53,1.0
+9295,15589296.0,Onwubiko,545.0,France,Female,36.0,2.0,90612.34,1.0,1.0,1.0,80613.54,0.0
+9296,15689061.0,Li,605.0,France,Male,52.0,5.0,107637.97,1.0,1.0,1.0,153723.28,0.0
+9297,15704053.0,Ch'ang,775.0,France,Male,64.0,9.0,0.0,1.0,1.0,0.0,105568.6,1.0
+9298,15572631.0,Hsia,718.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,164844.37,0.0
+9299,15752849.0,White,553.0,France,Female,35.0,2.0,146034.74,1.0,1.0,1.0,84475.13,0.0
+9300,15703059.0,Kao,594.0,Germany,Male,42.0,1.0,120962.59,1.0,1.0,0.0,56594.36,0.0
+9301,15689953.0,Nwachinemelu,622.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,142783.98,0.0
+9302,15783026.0,Zetticci,749.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,168035.6,0.0
+9303,15711015.0,Brown,659.0,Spain,Female,43.0,7.0,169312.13,2.0,1.0,1.0,170819.01,0.0
+9304,15790829.0,Yeh,683.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,4491.77,0.0
+9305,15783591.0,Macdonald,758.0,France,Male,38.0,6.0,0.0,2.0,0.0,1.0,56767.67,0.0
+9306,15757837.0,Chidozie,577.0,France,Female,37.0,2.0,155413.08,1.0,1.0,0.0,182784.11,0.0
+9307,15791958.0,Rounsevell,635.0,Germany,Female,39.0,8.0,105837.86,1.0,1.0,1.0,111175.09,1.0
+9308,15794345.0,Zhirov,634.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,35786.76,0.0
+9309,15699772.0,Chukwuhaenye,609.0,Spain,Male,48.0,1.0,0.0,2.0,1.0,0.0,150266.81,0.0
+9310,15746338.0,Oluchukwu,628.0,Spain,Female,41.0,9.0,0.0,2.0,1.0,0.0,106663.46,1.0
+9311,15579548.0,Kao,596.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,93524.19,1.0
+9312,15781586.0,Chizuoke,591.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,132908.52,0.0
+9313,15668893.0,T'ang,583.0,Germany,Female,34.0,6.0,95927.04,2.0,1.0,0.0,683.37,0.0
+9314,15631162.0,Chiedozie,714.0,Germany,Male,41.0,8.0,77910.23,2.0,1.0,1.0,160121.15,0.0
+9315,15643330.0,Shao,735.0,France,Male,59.0,1.0,0.0,1.0,1.0,1.0,135635.75,0.0
+9316,15797733.0,Chukwualuka,697.0,France,Male,45.0,6.0,0.0,1.0,0.0,0.0,142238.54,1.0
+9317,15769596.0,Ch'ang,839.0,Spain,Male,26.0,5.0,0.0,2.0,0.0,1.0,183542.08,0.0
+9318,15808849.0,Ch'en,619.0,Spain,Male,37.0,7.0,0.0,2.0,0.0,0.0,121098.65,0.0
+9319,15607312.0,Ojiofor,707.0,France,Male,25.0,9.0,0.0,1.0,1.0,0.0,100717.54,0.0
+9320,15665416.0,Cattaneo,745.0,France,Male,38.0,3.0,123042.0,1.0,1.0,1.0,12162.76,0.0
+9321,15572762.0,Hargreaves,639.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,97623.31,0.0
+9322,15792329.0,Yeh,588.0,Spain,Male,50.0,1.0,138089.97,1.0,1.0,0.0,110405.9,0.0
+9323,15723008.0,Dore,677.0,Germany,Female,40.0,7.0,123372.3,2.0,1.0,0.0,173783.38,0.0
+9324,15691150.0,P'eng,649.0,Germany,Female,33.0,2.0,157848.08,1.0,1.0,1.0,127939.19,0.0
+9325,15796313.0,Pinto,567.0,France,Female,27.0,2.0,0.0,2.0,1.0,1.0,134901.56,0.0
+9326,15671330.0,Ch'in,526.0,Germany,Female,51.0,3.0,156847.29,1.0,1.0,0.0,120531.31,1.0
+9327,15585066.0,Kesteven,627.0,Spain,Female,47.0,4.0,0.0,2.0,1.0,0.0,143665.44,0.0
+9328,15723654.0,Ch'iu,624.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,111346.22,0.0
+9329,15708904.0,Nwebube,595.0,Germany,Male,49.0,7.0,117561.49,1.0,1.0,0.0,86044.98,1.0
+9330,15801548.0,Chinweike,699.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,103315.74,0.0
+9331,15664808.0,Chuang,754.0,France,Female,37.0,9.0,0.0,2.0,1.0,1.0,88849.89,0.0
+9332,15668580.0,McIntosh,686.0,France,Male,50.0,6.0,124442.58,1.0,1.0,0.0,15650.73,1.0
+9333,15682918.0,Hs?,687.0,France,Female,39.0,3.0,0.0,3.0,0.0,0.0,176780.39,1.0
+9334,15714391.0,Toscani,624.0,France,Female,35.0,10.0,0.0,2.0,0.0,0.0,160941.78,0.0
+9335,15702912.0,T'ao,638.0,Spain,Female,34.0,1.0,0.0,2.0,0.0,0.0,171021.44,0.0
+9336,15743828.0,De Luca,637.0,Germany,Male,52.0,7.0,131629.17,1.0,1.0,0.0,131660.31,1.0
+9337,15739292.0,Okwudilichukwu,656.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,176450.19,0.0
+9338,15793883.0,Chikelu,678.0,France,Male,47.0,7.0,0.0,2.0,1.0,0.0,73977.23,0.0
+9339,15603548.0,Chukwukadibia,678.0,Germany,Female,31.0,6.0,122781.51,1.0,1.0,0.0,178997.89,0.0
+9340,15792679.0,Hs?,489.0,Germany,Male,31.0,3.0,142721.52,2.0,1.0,1.0,149170.92,0.0
+9341,15785899.0,H?,678.0,Germany,Female,32.0,1.0,104763.41,2.0,1.0,0.0,94820.85,0.0
+9342,15570194.0,Lucchesi,614.0,Spain,Female,39.0,6.0,0.0,1.0,1.0,1.0,122925.4,0.0
+9343,15640407.0,Ndukaku,781.0,France,Female,29.0,3.0,0.0,1.0,1.0,0.0,96864.36,1.0
+9344,15679029.0,Cavenagh,584.0,France,Male,37.0,5.0,0.0,2.0,0.0,1.0,121073.23,0.0
+9345,15733581.0,Ts'ao,627.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,62002.04,0.0
+9346,15693737.0,Chiawuotu,615.0,Spain,Female,50.0,9.0,148556.74,1.0,1.0,0.0,141533.57,1.0
+9347,15761023.0,Tuan,627.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,55775.72,1.0
+9348,15761292.0,Nwankwo,752.0,France,Male,41.0,3.0,0.0,1.0,1.0,0.0,131736.23,0.0
+9349,15681409.0,Chiawuotu,793.0,Spain,Male,30.0,4.0,127631.62,1.0,1.0,1.0,92888.52,0.0
+9350,15689425.0,Toscani,713.0,Spain,Male,35.0,10.0,0.0,2.0,1.0,1.0,39789.62,0.0
+9351,15589329.0,Uspensky,485.0,France,Male,37.0,3.0,0.0,1.0,1.0,1.0,62944.75,0.0
+9352,15573926.0,Ts'ai,684.0,France,Male,38.0,1.0,0.0,2.0,0.0,1.0,86950.72,0.0
+9353,15754929.0,Cawood,543.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,94780.9,0.0
+9354,15801125.0,Y?an,706.0,Germany,Female,39.0,9.0,99713.87,2.0,1.0,1.0,130404.53,0.0
+9355,15783526.0,Bock,607.0,Germany,Male,43.0,2.0,108726.57,2.0,0.0,1.0,84932.4,0.0
+9356,15635034.0,Chukwuemeka,748.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,0.0,88958.49,0.0
+9357,15729954.0,Chijioke,687.0,France,Female,33.0,9.0,0.0,1.0,0.0,0.0,25554.4,0.0
+9358,15732943.0,Hsiao,590.0,France,Female,40.0,4.0,132954.66,1.0,1.0,0.0,74854.97,1.0
+9359,15758713.0,Monds,636.0,Spain,Male,25.0,1.0,0.0,2.0,0.0,1.0,62703.38,0.0
+9360,15636413.0,Ponomaryova,545.0,France,Female,27.0,2.0,0.0,2.0,1.0,0.0,102233.73,0.0
+9361,15706463.0,Ting,725.0,France,Female,45.0,9.0,0.0,1.0,0.0,0.0,166520.96,1.0
+9362,15731463.0,Chiang,675.0,Spain,Female,39.0,3.0,0.0,1.0,0.0,0.0,185547.41,1.0
+9363,15628155.0,Lo Duca,588.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,153400.24,0.0
+9364,15621063.0,Onyemauchechukwu,732.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,172063.09,0.0
+9365,15758035.0,Nwankwo,619.0,Spain,Female,47.0,9.0,0.0,1.0,0.0,0.0,72873.33,1.0
+9366,15746664.0,H?,603.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,171039.56,0.0
+9367,15695792.0,Chikelu,667.0,Germany,Female,63.0,0.0,122723.35,2.0,1.0,0.0,85901.09,1.0
+9368,15762927.0,Chidiebere,678.0,France,Female,48.0,5.0,180520.21,1.0,1.0,0.0,176943.59,1.0
+9369,15578835.0,Chukwumaobim,477.0,Spain,Male,35.0,9.0,0.0,2.0,0.0,1.0,100941.57,0.0
+9370,15571571.0,Scott,724.0,France,Male,35.0,8.0,0.0,2.0,0.0,0.0,167984.72,0.0
+9371,15592386.0,T'ien,619.0,France,Female,41.0,7.0,0.0,1.0,0.0,0.0,100809.99,1.0
+9372,15692991.0,Milani,696.0,France,Female,38.0,9.0,0.0,3.0,1.0,0.0,142963.29,1.0
+9373,15788151.0,Ku,649.0,France,Female,35.0,8.0,0.0,2.0,1.0,0.0,94538.54,0.0
+9374,15799358.0,Genovese,743.0,France,Female,52.0,8.0,0.0,1.0,1.0,0.0,154775.7,1.0
+9375,15793896.0,Liao,651.0,France,Male,25.0,7.0,123544.0,1.0,1.0,0.0,71333.44,0.0
+9376,15571144.0,Ting,642.0,Germany,Male,37.0,1.0,123906.55,1.0,1.0,1.0,125062.02,0.0
+9377,15602831.0,Shao,735.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,0.0,166650.9,0.0
+9378,15753847.0,Calabresi,663.0,France,Male,36.0,1.0,96652.86,1.0,1.0,1.0,119368.11,0.0
+9379,15611171.0,Maclean,644.0,France,Male,40.0,5.0,0.0,1.0,1.0,0.0,62002.04,1.0
+9380,15644833.0,Nnachetam,749.0,France,Female,35.0,1.0,0.0,1.0,1.0,0.0,120165.83,0.0
+9381,15710689.0,Ch'eng,584.0,France,Male,43.0,8.0,0.0,1.0,0.0,0.0,142940.39,1.0
+9382,15745355.0,Sagese,696.0,France,Female,61.0,9.0,88963.31,1.0,1.0,1.0,106655.88,1.0
+9383,15690670.0,Yashina,694.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,121440.8,0.0
+9384,15592914.0,Chidimma,735.0,Spain,Female,37.0,0.0,0.0,1.0,0.0,0.0,177747.03,0.0
+9385,15692671.0,Chukwumaobim,850.0,Spain,Male,27.0,6.0,99095.73,1.0,1.0,0.0,93821.95,0.0
+9386,15775888.0,H?,686.0,Spain,Female,29.0,8.0,0.0,2.0,0.0,0.0,25662.27,0.0
+9387,15754068.0,Ebelechukwu,573.0,France,Female,43.0,1.0,0.0,2.0,1.0,0.0,179521.24,0.0
+9388,15605968.0,Shao,592.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,1.0,64872.33,0.0
+9389,15807239.0,Chukwueloka,589.0,France,Female,43.0,2.0,0.0,2.0,0.0,0.0,73069.9,0.0
+9390,15625023.0,Lo,711.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,133535.29,0.0
+9391,15806926.0,Macleod,594.0,France,Male,36.0,6.0,0.0,2.0,0.0,0.0,131736.46,0.0
+9392,15601589.0,Ch'iu,675.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,93336.3,0.0
+9393,15799031.0,Pisano,675.0,Spain,Male,35.0,8.0,0.0,2.0,0.0,0.0,43921.36,0.0
+9394,15596979.0,T'ang,674.0,Spain,Male,40.0,9.0,0.0,2.0,0.0,0.0,106841.12,0.0
+9395,15569624.0,Hsu,637.0,France,Female,46.0,0.0,0.0,1.0,1.0,1.0,124626.59,0.0
+9396,15778861.0,Chinedum,554.0,France,Female,25.0,9.0,0.0,2.0,1.0,0.0,162719.69,0.0
+9397,15680243.0,Chiekwugo,769.0,France,Male,34.0,1.0,104449.8,1.0,1.0,1.0,66580.13,0.0
+9398,15759966.0,Bales,713.0,Germany,Female,45.0,5.0,102989.84,1.0,1.0,0.0,171430.16,1.0
+9399,15806967.0,Macdonald,678.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,121408.55,0.0
+9400,15721251.0,Lucciano,742.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,162719.69,0.0
+9401,15655274.0,Ozerova,644.0,France,Male,36.0,4.0,97565.74,1.0,1.0,1.0,136497.28,0.0
+9402,15777784.0,Hsia,614.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,57817.87,0.0
+9403,15629273.0,Ponomaryov,479.0,Germany,Male,20.0,3.0,84903.33,2.0,1.0,1.0,84932.4,0.0
+9404,15682322.0,Onwuamaeze,706.0,Spain,Female,21.0,3.0,99557.94,1.0,1.0,1.0,25951.91,0.0
+9405,15737404.0,Bancroft,651.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,142838.57,0.0
+9406,15802088.0,Nwebube,635.0,Germany,Male,47.0,9.0,133598.3,2.0,1.0,0.0,125445.8,1.0
+9407,15690925.0,Pisani,713.0,Germany,Female,39.0,3.0,102088.29,1.0,1.0,0.0,160526.36,1.0
+9408,15788215.0,Chiazagomekpele,553.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,176576.62,0.0
+9409,15632859.0,Iweobiegbunam,684.0,France,Male,55.0,9.0,127480.53,1.0,0.0,1.0,1568.91,1.0
+9410,15702656.0,Chin,598.0,France,Male,33.0,6.0,0.0,2.0,0.0,0.0,103557.91,0.0
+9411,15602034.0,Hs?,744.0,Germany,Female,48.0,6.0,122334.26,3.0,1.0,0.0,86587.37,1.0
+9412,15662426.0,Tien,521.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,87822.14,0.0
+9413,15613463.0,Cremonesi,758.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,79704.12,0.0
+9414,15654901.0,Johnstone,684.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,106781.59,0.0
+9415,15804962.0,Chu,771.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,0.0,72673.33,0.0
+9416,15805627.0,Chiu,722.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,180712.52,0.0
+9417,15752300.0,Lettiere,583.0,Germany,Female,24.0,5.0,105355.76,1.0,1.0,0.0,46857.52,1.0
+9418,15616280.0,H?,659.0,France,Male,39.0,3.0,0.0,2.0,0.0,0.0,126598.23,1.0
+9419,15747542.0,Nnonso,682.0,France,Male,23.0,9.0,97382.53,1.0,0.0,1.0,152290.28,0.0
+9420,15569572.0,Maclean,553.0,France,Female,31.0,1.0,164948.98,1.0,1.0,1.0,121751.04,0.0
+9421,15772573.0,Pirozzi,699.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,100615.14,0.0
+9422,15583552.0,Ulyanova,599.0,Spain,Female,32.0,1.0,140931.55,1.0,1.0,1.0,111448.92,0.0
+9423,15705735.0,Farrar,539.0,France,Female,52.0,4.0,0.0,1.0,1.0,1.0,175853.64,1.0
+9424,15735402.0,Goliwe,730.0,Spain,Female,44.0,9.0,0.0,2.0,1.0,0.0,94949.84,0.0
+9425,15786035.0,T'ien,705.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,70523.92,0.0
+9426,15716164.0,Yobachukwu,753.0,France,Male,37.0,5.0,140596.95,2.0,1.0,0.0,64420.5,0.0
+9427,15704763.0,Chiu,850.0,Germany,Female,40.0,8.0,92760.55,2.0,1.0,1.0,105898.79,0.0
+9428,15569187.0,Fan,594.0,Spain,Male,37.0,5.0,0.0,1.0,0.0,1.0,144584.29,0.0
+9429,15646937.0,Hsueh,637.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,94498.82,0.0
+9430,15774931.0,Cattaneo,615.0,France,Female,36.0,1.0,0.0,1.0,1.0,0.0,164967.21,0.0
+9431,15671934.0,Yobanna,572.0,France,Male,32.0,4.0,0.0,1.0,1.0,1.0,44114.88,0.0
+9432,15715199.0,Manna,713.0,France,Female,35.0,6.0,0.0,1.0,1.0,1.0,86935.08,0.0
+9433,15682355.0,Toscani,744.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,102628.98,0.0
+9434,15592765.0,Okechukwu,759.0,Germany,Female,46.0,2.0,121345.88,1.0,1.0,0.0,94815.04,1.0
+9435,15586039.0,Echezonachukwu,719.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,85746.52,0.0
+9436,15725882.0,Ch'ien,721.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,138360.29,0.0
+9437,15778861.0,Y?an,648.0,France,Male,49.0,2.0,0.0,1.0,0.0,0.0,131906.44,1.0
+9438,15579808.0,Onyeorulu,850.0,Germany,Male,32.0,3.0,63218.85,1.0,1.0,0.0,109794.31,0.0
+9439,15631359.0,Ch'ang,584.0,Germany,Male,38.0,9.0,121863.61,1.0,1.0,0.0,126837.72,0.0
+9440,15647800.0,Ndubueze,683.0,Spain,Female,45.0,4.0,122028.65,1.0,1.0,1.0,86786.41,0.0
+9441,15648034.0,Trevisano,643.0,Germany,Male,44.0,1.0,131938.91,1.0,1.0,1.0,180626.68,1.0
+9442,15771580.0,Gadsden,731.0,Germany,Female,38.0,9.0,129855.31,1.0,1.0,0.0,151978.36,0.0
+9443,15700394.0,Tsai,671.0,France,Female,41.0,9.0,0.0,2.0,1.0,1.0,72350.47,0.0
+9444,15691452.0,Yudina,689.0,France,Male,32.0,10.0,0.0,2.0,1.0,0.0,117977.75,0.0
+9445,15576077.0,Ogochukwu,741.0,Germany,Female,37.0,1.0,156974.26,2.0,1.0,1.0,84475.13,0.0
+9446,15583595.0,Tsao,753.0,Germany,Female,40.0,2.0,167738.82,1.0,1.0,1.0,49833.5,0.0
+9447,15681327.0,Aksenov,632.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,92277.53,0.0
+9448,15711299.0,Nwachukwu,621.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,52404.47,0.0
+9449,15784092.0,Onyemauchechukwu,559.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,96424.57,0.0
+9450,15793883.0,Frolov,637.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,184564.67,0.0
+9451,15617454.0,Uspensky,676.0,France,Male,35.0,8.0,0.0,2.0,0.0,0.0,706.5,0.0
+9452,15692929.0,Nnachetam,683.0,Spain,Male,35.0,1.0,0.0,2.0,0.0,1.0,181490.42,0.0
+9453,15745354.0,Chiganu,553.0,France,Female,30.0,1.0,111095.58,1.0,0.0,1.0,83679.46,0.0
+9454,15794049.0,Chinwenma,705.0,France,Male,34.0,8.0,0.0,1.0,0.0,0.0,149814.68,0.0
+9455,15740487.0,Tsao,711.0,Spain,Female,33.0,3.0,0.0,1.0,1.0,1.0,167973.63,0.0
+9456,15664204.0,Udegbunam,705.0,France,Male,31.0,2.0,94486.47,1.0,1.0,1.0,158059.95,0.0
+9457,15649280.0,Okwuadigbo,684.0,Spain,Female,34.0,8.0,0.0,2.0,1.0,0.0,142210.94,1.0
+9458,15737489.0,Ebelegbulam,709.0,France,Male,38.0,2.0,91621.69,1.0,0.0,1.0,160941.78,0.0
+9459,15809800.0,Udinesi,662.0,France,Male,59.0,1.0,152667.32,1.0,1.0,0.0,110299.77,1.0
+9460,15794566.0,O'Brien,613.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,102747.02,0.0
+9461,15789563.0,Okeke,813.0,Germany,Female,41.0,4.0,116803.8,2.0,0.0,1.0,126475.79,0.0
+9462,15772941.0,Fanucci,684.0,Germany,Female,47.0,1.0,169824.46,1.0,0.0,0.0,139761.25,0.0
+9463,15585853.0,Hsing,632.0,Spain,Male,33.0,7.0,133903.12,1.0,1.0,1.0,60721.4,0.0
+9464,15631481.0,Chien,801.0,France,Female,35.0,6.0,0.0,1.0,0.0,1.0,177784.68,1.0
+9465,15590730.0,Ch'iu,731.0,France,Male,33.0,3.0,0.0,1.0,1.0,0.0,134638.97,0.0
+9466,15772423.0,Fanucci,593.0,France,Male,33.0,8.0,0.0,2.0,0.0,1.0,64493.99,0.0
+9467,15690175.0,Milanesi,706.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,158840.99,0.0
+9468,15676959.0,Lucchesi,537.0,France,Female,50.0,9.0,0.0,3.0,1.0,1.0,182270.88,1.0
+9469,15744983.0,Aksenov,699.0,France,Female,38.0,4.0,133636.04,1.0,1.0,0.0,122433.09,0.0
+9470,15631838.0,Nnaife,633.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,93716.38,0.0
+9471,15582892.0,Nnanna,809.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,187905.12,0.0
+9472,15609510.0,Ugochukwutubelum,602.0,France,Male,32.0,9.0,134590.18,1.0,1.0,1.0,85902.39,0.0
+9473,15616954.0,Toscani,628.0,France,Male,42.0,8.0,0.0,1.0,1.0,1.0,177719.88,1.0
+9474,15585823.0,Kibby,679.0,France,Male,34.0,1.0,171937.27,1.0,1.0,1.0,70849.47,0.0
+9475,15721181.0,Zhirov,743.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,142513.5,0.0
+9476,15572398.0,T'ao,651.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,145986.35,0.0
+9477,15771483.0,Chukwudi,543.0,France,Male,35.0,4.0,114512.59,1.0,0.0,1.0,27278.42,0.0
+9478,15649414.0,Ritchie,636.0,France,Female,39.0,2.0,164557.95,1.0,1.0,0.0,174960.66,1.0
+9479,15803941.0,Pisani,705.0,Spain,Male,27.0,7.0,0.0,2.0,1.0,1.0,166677.35,0.0
+9480,15585855.0,Chiu,775.0,France,Female,48.0,0.0,151843.35,1.0,1.0,1.0,173843.21,1.0
+9481,15768340.0,Chukwudi,641.0,Spain,Female,27.0,1.0,0.0,2.0,1.0,1.0,113581.85,0.0
+9482,15649744.0,T'ien,679.0,France,Female,50.0,2.0,0.0,2.0,1.0,1.0,183542.08,0.0
+9483,15663625.0,Chiawuotu,678.0,Germany,Female,46.0,1.0,133744.26,1.0,1.0,0.0,158845.49,1.0
+9484,15585853.0,Smith,703.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,51548.14,0.0
+9485,15676938.0,Tung,645.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,1.0,182266.01,0.0
+9486,15749671.0,Sagese,598.0,Germany,Male,39.0,3.0,125951.82,2.0,1.0,0.0,168359.98,0.0
+9487,15640865.0,Trevisani,762.0,Spain,Male,33.0,2.0,0.0,2.0,0.0,0.0,116969.89,0.0
+9488,15641850.0,Trevisano,657.0,France,Female,40.0,3.0,0.0,1.0,1.0,1.0,131662.47,1.0
+9489,15657535.0,Chia,647.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,145765.83,0.0
+9490,15593954.0,Trevisano,601.0,France,Male,40.0,0.0,0.0,1.0,0.0,1.0,85801.77,1.0
+9491,15767064.0,Chikezie,687.0,Germany,Male,31.0,10.0,111096.91,2.0,1.0,1.0,90928.02,0.0
+9492,15666032.0,Chidi,560.0,Spain,Male,23.0,9.0,106596.29,2.0,1.0,1.0,79476.63,0.0
+9493,15711299.0,Onyekachi,633.0,France,Female,33.0,4.0,0.0,2.0,0.0,0.0,123537.05,0.0
+9494,15683955.0,Ch'ang,731.0,Spain,Female,45.0,0.0,133658.24,1.0,1.0,0.0,166513.49,1.0
+9495,15705403.0,Nwachukwu,586.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,108699.59,0.0
+9496,15683555.0,Tien,671.0,Germany,Male,37.0,2.0,98899.76,2.0,1.0,0.0,96444.88,0.0
+9497,15590097.0,Chiazagomekpere,565.0,Spain,Female,34.0,1.0,113348.2,1.0,1.0,0.0,182738.0,0.0
+9498,15594812.0,Ch'eng,526.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,168816.09,1.0
+9499,15671514.0,Hsing,663.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,92914.67,0.0
+9500,15719561.0,Chibugo,751.0,Spain,Female,30.0,7.0,0.0,2.0,1.0,0.0,109742.55,0.0
+9501,15620507.0,Maclean,735.0,France,Male,47.0,8.0,0.0,2.0,0.0,0.0,91347.76,0.0
+9502,15645059.0,Chiu,729.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,28368.37,0.0
+9503,15616088.0,Chukwualuka,814.0,France,Male,42.0,10.0,0.0,2.0,1.0,1.0,68695.38,0.0
+9504,15764174.0,K'ung,756.0,Germany,Female,37.0,3.0,167735.69,2.0,0.0,1.0,18369.33,0.0
+9505,15651627.0,Chukwuemeka,632.0,France,Male,36.0,8.0,0.0,1.0,1.0,1.0,101954.78,0.0
+9506,15596624.0,McIntosh,577.0,Spain,Male,41.0,0.0,63723.78,1.0,1.0,0.0,91036.74,0.0
+9507,15726058.0,Okechukwu,729.0,France,Male,24.0,3.0,0.0,2.0,0.0,1.0,183515.13,0.0
+9508,15775104.0,Chukwuemeka,759.0,France,Male,29.0,8.0,133436.52,2.0,0.0,0.0,102628.98,0.0
+9509,15705899.0,Hsiung,603.0,Spain,Male,39.0,7.0,0.0,1.0,1.0,1.0,83700.66,0.0
+9510,15628298.0,Chidubem,707.0,France,Female,46.0,1.0,0.0,2.0,1.0,1.0,57541.07,0.0
+9511,15782360.0,Chimaobim,611.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,85522.84,0.0
+9512,15605742.0,Hs?,604.0,Germany,Female,45.0,8.0,114756.14,1.0,1.0,0.0,164040.94,1.0
+9513,15791942.0,Chinweike,549.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,148097.58,0.0
+9514,15643487.0,Chidiegwu,706.0,Spain,Male,23.0,6.0,88963.31,1.0,0.0,1.0,79957.95,0.0
+9515,15781451.0,Bibi,687.0,Germany,Male,45.0,6.0,154902.66,1.0,1.0,1.0,62944.75,0.0
+9516,15703923.0,Lueck,683.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,1.0,141364.27,0.0
+9517,15801915.0,Obiajulu,538.0,Germany,Female,43.0,9.0,157099.47,1.0,1.0,0.0,138051.19,1.0
+9518,15765093.0,Ndubueze,605.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,109523.88,0.0
+9519,15602851.0,Chiazagomekpele,633.0,Spain,Male,26.0,8.0,90560.3,1.0,1.0,0.0,158937.55,0.0
+9520,15642448.0,Kuo,603.0,France,Male,29.0,7.0,141581.08,1.0,1.0,1.0,125979.36,0.0
+9521,15656717.0,Hs?,612.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,142400.27,0.0
+9522,15673929.0,Chinedum,805.0,Spain,Female,38.0,3.0,87098.91,1.0,0.0,0.0,69039.43,0.0
+9523,15571244.0,Ch'ien,740.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,72985.61,0.0
+9524,15637354.0,Chukwuebuka,727.0,France,Male,28.0,1.0,124620.01,1.0,1.0,1.0,126794.56,0.0
+9525,15724307.0,Ma,648.0,Spain,Female,24.0,8.0,142872.36,1.0,1.0,0.0,133672.61,0.0
+9526,15741047.0,Onyinyechukwuka,596.0,Spain,Male,36.0,1.0,0.0,2.0,0.0,1.0,131924.38,0.0
+9527,15789924.0,Yegorova,728.0,Spain,Male,35.0,5.0,100116.67,1.0,0.0,1.0,168816.22,0.0
+9528,15605293.0,Hs?,531.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,1795.93,0.0
+9529,15684925.0,P'eng,644.0,France,Female,52.0,7.0,0.0,1.0,1.0,1.0,46786.92,1.0
+9530,15569678.0,Nwoye,677.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,1.0,49506.82,0.0
+9531,15799042.0,Hsing,682.0,Germany,Male,50.0,4.0,106011.59,1.0,1.0,0.0,103560.98,1.0
+9532,15638494.0,Ch'in,664.0,Germany,Male,55.0,8.0,161572.24,1.0,1.0,0.0,141558.04,1.0
+9533,15620505.0,Trevisano,677.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,86734.41,0.0
+9534,15588923.0,T'ien,729.0,Germany,Male,35.0,1.0,130830.22,1.0,1.0,1.0,160532.88,0.0
+9535,15686963.0,Walker,635.0,Germany,Male,26.0,2.0,121884.99,2.0,0.0,0.0,77519.62,0.0
+9536,15628112.0,Obidimkpa,571.0,France,Female,32.0,4.0,145984.92,1.0,1.0,0.0,141210.18,0.0
+9537,15746696.0,White,675.0,Germany,Male,29.0,4.0,98268.84,1.0,0.0,1.0,187900.12,0.0
+9538,15757310.0,Y?an,681.0,Spain,Female,46.0,6.0,0.0,1.0,1.0,1.0,164967.21,1.0
+9539,15642570.0,P'eng,646.0,France,Male,31.0,3.0,122552.51,1.0,1.0,0.0,121551.93,0.0
+9540,15772995.0,Lombardo,676.0,Spain,Female,38.0,8.0,110112.54,1.0,1.0,0.0,163477.22,0.0
+9541,15710671.0,O'Sullivan,564.0,France,Female,40.0,7.0,0.0,2.0,1.0,0.0,71167.17,0.0
+9542,15759034.0,Palerma,686.0,France,Male,45.0,5.0,0.0,2.0,1.0,1.0,96881.32,1.0
+9543,15722072.0,Burt,708.0,France,Female,41.0,7.0,0.0,2.0,0.0,0.0,134600.96,0.0
+9544,15804919.0,Piazza,581.0,France,Female,30.0,9.0,0.0,2.0,0.0,0.0,157862.82,0.0
+9545,15612667.0,Monds,614.0,Spain,Male,34.0,5.0,166833.86,1.0,1.0,0.0,126471.13,0.0
+9546,15762799.0,Chukwudi,537.0,Germany,Female,25.0,3.0,106687.76,2.0,1.0,1.0,138160.11,0.0
+9547,15806318.0,Hs?,721.0,Germany,Male,62.0,7.0,103210.27,1.0,0.0,1.0,47318.75,1.0
+9548,15803840.0,Lo Duca,619.0,Spain,Male,35.0,1.0,163760.82,1.0,0.0,1.0,122268.75,0.0
+9549,15773639.0,T'ao,564.0,France,Male,46.0,5.0,0.0,2.0,1.0,1.0,184524.74,0.0
+9550,15691627.0,Fyodorova,714.0,France,Male,47.0,7.0,0.0,1.0,0.0,0.0,74870.99,1.0
+9551,15593499.0,Y?an,565.0,Spain,Female,25.0,9.0,0.0,2.0,1.0,0.0,183494.87,0.0
+9552,15735784.0,Chinedum,758.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,173498.45,0.0
+9553,15726267.0,T'ien,554.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,124489.88,0.0
+9554,15754929.0,K?,729.0,Germany,Female,29.0,2.0,90727.79,1.0,1.0,0.0,139946.68,0.0
+9555,15626144.0,Yefimova,561.0,France,Female,33.0,6.0,0.0,1.0,1.0,0.0,180318.6,0.0
+9556,15724689.0,Y?an,644.0,France,Female,59.0,2.0,123372.3,1.0,0.0,0.0,129988.56,1.0
+9557,15733597.0,Hsing,555.0,France,Male,39.0,4.0,141782.57,1.0,1.0,1.0,47428.73,0.0
+9558,15752626.0,Chukwuebuka,587.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,80178.54,0.0
+9559,15636572.0,Shih,593.0,France,Male,45.0,9.0,0.0,1.0,0.0,0.0,126794.56,1.0
+9560,15772421.0,Nkemdirim,678.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,175893.53,0.0
+9561,15721592.0,Ch'en,620.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,106837.06,0.0
+9562,15763898.0,Toth,751.0,France,Male,35.0,9.0,0.0,2.0,0.0,1.0,153920.27,0.0
+9563,15673790.0,K?,596.0,Germany,Male,44.0,4.0,152797.9,2.0,1.0,0.0,93873.13,0.0
+9564,15588854.0,Uchenna,667.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,38962.94,0.0
+9565,15632550.0,Chiedozie,648.0,France,Male,44.0,6.0,0.0,2.0,1.0,1.0,151401.33,0.0
+9566,15737521.0,Ch'en,709.0,France,Female,47.0,7.0,0.0,1.0,1.0,0.0,129590.18,1.0
+9567,15694946.0,Ch'in,619.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,136837.03,0.0
+9568,15659327.0,Whitehead,523.0,France,Female,29.0,8.0,0.0,1.0,1.0,0.0,152956.33,0.0
+9569,15699550.0,Chukwujamuike,699.0,France,Female,23.0,6.0,0.0,1.0,0.0,1.0,120834.46,0.0
+9570,15808846.0,Nwokike,648.0,France,Male,33.0,0.0,0.0,1.0,0.0,0.0,166513.49,1.0
+9571,15793346.0,Chiekwugo,683.0,Germany,Male,29.0,3.0,97588.6,2.0,1.0,0.0,82870.75,0.0
+9572,15782591.0,Hsia,651.0,Spain,Female,33.0,1.0,0.0,2.0,1.0,1.0,69055.45,0.0
+9573,15704466.0,K'ung,681.0,France,Male,31.0,9.0,154495.79,1.0,0.0,1.0,173516.71,0.0
+9574,15686878.0,McGregor,568.0,Germany,Male,39.0,2.0,107556.06,1.0,0.0,0.0,174770.14,1.0
+9575,15655232.0,Onyemauchechi,596.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,142913.43,0.0
+9576,15773789.0,Scott,593.0,Spain,Male,32.0,9.0,68423.69,1.0,1.0,1.0,136902.01,0.0
+9577,15595037.0,Chukwudi,743.0,France,Male,35.0,6.0,0.0,1.0,0.0,1.0,184544.59,0.0
+9578,15663933.0,Chiang,699.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,166038.27,0.0
+9579,15589660.0,Ch'in,627.0,Germany,Female,32.0,5.0,80780.16,1.0,1.0,0.0,145528.22,0.0
+9580,15594720.0,Fiorentini,775.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,67468.67,0.0
+9581,15662976.0,Ugochukwutubelum,716.0,Germany,Female,43.0,10.0,100262.88,1.0,1.0,0.0,167973.63,1.0
+9582,15604314.0,H?,567.0,France,Female,41.0,8.0,0.0,1.0,0.0,0.0,123437.05,1.0
+9583,15770995.0,Gell,642.0,France,Female,47.0,0.0,0.0,1.0,1.0,0.0,115091.38,1.0
+9584,15726178.0,Chiawuotu,708.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,152883.95,0.0
+9585,15795519.0,Chu,709.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,86410.28,0.0
+9586,15776467.0,Teng,590.0,Germany,Male,46.0,1.0,128743.55,1.0,1.0,0.0,6761.84,1.0
+9587,15702912.0,Lo,726.0,Spain,Female,47.0,7.0,0.0,2.0,1.0,0.0,172618.52,1.0
+9588,15600934.0,Onwumelu,721.0,France,Male,37.0,4.0,147510.34,1.0,1.0,0.0,75937.47,0.0
+9589,15729083.0,Lu,588.0,France,Female,33.0,0.0,86055.17,1.0,0.0,1.0,143049.72,0.0
+9590,15662641.0,Manna,598.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,130809.95,0.0
+9591,15646720.0,O'Sullivan,760.0,Germany,Female,43.0,9.0,117034.32,1.0,1.0,1.0,92320.37,1.0
+9592,15726058.0,Chu,691.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,1.0,143980.29,0.0
+9593,15576084.0,Chiu,496.0,France,Female,55.0,2.0,0.0,3.0,1.0,1.0,79342.31,1.0
+9594,15672983.0,Hsiung,637.0,Germany,Female,46.0,5.0,123681.32,1.0,1.0,0.0,130830.22,1.0
+9595,15693637.0,Chikezie,592.0,France,Female,44.0,4.0,0.0,1.0,1.0,1.0,123587.85,0.0
+9596,15697558.0,Shubin,531.0,France,Male,30.0,1.0,151191.31,2.0,1.0,1.0,187616.16,0.0
+9597,15632576.0,Folliero,682.0,France,Male,33.0,8.0,0.0,1.0,1.0,0.0,143393.24,0.0
+9598,15736578.0,Chukwujamuike,695.0,France,Female,28.0,3.0,0.0,2.0,1.0,1.0,105986.01,0.0
+9599,15608916.0,Tuan,745.0,Germany,Female,33.0,3.0,113988.53,1.0,1.0,0.0,171707.93,0.0
+9600,15612043.0,Chukwudi,686.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,1.0,145593.85,0.0
+9601,15797149.0,Uspenskaya,695.0,France,Male,32.0,1.0,130404.92,1.0,1.0,0.0,129964.94,0.0
+9602,15633650.0,Chin,663.0,Spain,Female,38.0,10.0,0.0,2.0,1.0,0.0,123583.85,0.0
+9603,15571940.0,Y?,678.0,France,Male,49.0,5.0,148061.89,1.0,1.0,0.0,126961.1,0.0
+9604,15794939.0,Chin,744.0,France,Male,28.0,5.0,0.0,2.0,1.0,0.0,167973.63,0.0
+9605,15676958.0,Chinweike,554.0,Spain,Male,35.0,2.0,0.0,2.0,0.0,1.0,88890.75,0.0
+9606,15591135.0,Y?,677.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,172967.87,0.0
+9607,15752801.0,Whitehead,558.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,88920.29,0.0
+9608,15578098.0,Dyer,714.0,Germany,Female,33.0,10.0,143330.56,2.0,1.0,1.0,19700.24,0.0
+9609,15624072.0,Nwankwo,567.0,France,Female,36.0,10.0,0.0,2.0,0.0,1.0,143022.31,0.0
+9610,15756820.0,P'eng,710.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,178393.68,0.0
+9611,15769353.0,Chinedum,681.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,95583.45,0.0
+9612,15638751.0,Fiorentino,717.0,France,Male,25.0,5.0,0.0,2.0,0.0,1.0,120830.73,0.0
+9613,15694192.0,Dickson,744.0,France,Male,42.0,4.0,0.0,2.0,0.0,0.0,121440.8,0.0
+9614,15685828.0,T'ang,627.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,0.0,70613.52,0.0
+9615,15648800.0,Chukwubuikem,586.0,Spain,Male,48.0,1.0,0.0,1.0,0.0,1.0,149946.43,1.0
+9616,15695819.0,Fiorentino,543.0,Germany,Male,42.0,6.0,184926.61,1.0,0.0,0.0,64849.74,0.0
+9617,15743076.0,Ts'ui,609.0,Spain,Female,52.0,6.0,0.0,2.0,1.0,1.0,123587.96,0.0
+9618,15641110.0,Chuang,687.0,Germany,Male,33.0,7.0,101629.3,2.0,0.0,1.0,121551.93,0.0
+9619,15698872.0,Chienezie,663.0,France,Male,31.0,9.0,131244.65,1.0,1.0,0.0,167873.55,0.0
+9620,15586475.0,Hsu,829.0,Spain,Female,28.0,7.0,0.0,2.0,1.0,0.0,14081.64,0.0
+9621,15623695.0,Brown,545.0,France,Male,36.0,8.0,0.0,1.0,1.0,1.0,164484.78,0.0
+9622,15662426.0,Uvarova,651.0,France,Female,34.0,10.0,0.0,1.0,1.0,0.0,74849.46,1.0
+9623,15689322.0,Nnanna,559.0,France,Male,31.0,7.0,0.0,1.0,1.0,1.0,113860.81,0.0
+9624,15615177.0,Trevisano,651.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,158887.09,0.0
+9625,15684592.0,Nwabugwu,645.0,Spain,Male,41.0,3.0,0.0,2.0,1.0,1.0,106799.49,0.0
+9626,15761681.0,Ch'eng,677.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,119439.66,0.0
+9627,15766272.0,Chukwuebuka,696.0,Spain,Female,30.0,9.0,0.0,1.0,1.0,0.0,154767.34,1.0
+9628,15785865.0,Tsao,732.0,Germany,Male,43.0,7.0,121797.09,1.0,1.0,0.0,77330.35,0.0
+9629,15633213.0,Calabrese,695.0,France,Female,48.0,4.0,0.0,1.0,1.0,0.0,26215.85,1.0
+9630,15806837.0,Obialo,678.0,Germany,Male,34.0,5.0,98775.23,2.0,1.0,1.0,124596.63,0.0
+9631,15762443.0,Chiu,577.0,Germany,Male,32.0,5.0,104944.1,2.0,1.0,1.0,123499.98,0.0
+9632,15756610.0,Hsing,637.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,173495.77,0.0
+9633,15653547.0,Nkemdilim,651.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,0.0,81468.99,0.0
+9634,15794257.0,Burt,731.0,Spain,Female,33.0,4.0,0.0,1.0,0.0,0.0,134467.08,0.0
+9635,15687913.0,Ts'ui,706.0,Spain,Male,31.0,3.0,0.0,1.0,1.0,0.0,187915.4,0.0
+9636,15797246.0,Okwuadigbo,639.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,178361.48,0.0
+9637,15707479.0,Hs?,717.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,171364.18,0.0
+9638,15593943.0,Lo Duca,667.0,Germany,Male,32.0,9.0,133970.23,2.0,1.0,0.0,93336.3,0.0
+9639,15593776.0,Chin,705.0,Spain,Male,40.0,5.0,0.0,1.0,1.0,0.0,138052.51,0.0
+9640,15600462.0,Kibby,684.0,Germany,Male,39.0,10.0,153366.13,1.0,1.0,0.0,115676.61,1.0
+9641,15675896.0,DeRose,669.0,France,Female,41.0,3.0,0.0,3.0,0.0,1.0,62830.97,1.0
+9642,15709920.0,Ch'iu,727.0,Spain,Male,23.0,5.0,0.0,2.0,1.0,1.0,121440.8,0.0
+9643,15671640.0,Trevisani,682.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,140934.57,0.0
+9644,15723886.0,Fanucci,687.0,France,Male,23.0,2.0,0.0,2.0,1.0,1.0,62612.88,0.0
+9645,15707962.0,Chukwujamuike,738.0,France,Female,26.0,8.0,134318.21,1.0,1.0,0.0,57972.43,0.0
+9646,15693326.0,Ts'ai,704.0,France,Male,42.0,8.0,0.0,1.0,1.0,1.0,166520.96,1.0
+9647,15803842.0,Onyekachukwu,684.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,181794.86,0.0
+9648,15777076.0,Kao,850.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,53477.76,0.0
+9649,15791533.0,Ritchie,565.0,France,Female,54.0,6.0,0.0,1.0,0.0,1.0,105887.04,0.0
+9650,15801417.0,Chukwudi,691.0,Germany,Female,52.0,9.0,126679.69,1.0,0.0,0.0,106837.06,1.0
+9651,15782941.0,Oguejiofor,614.0,France,Male,35.0,9.0,134811.3,1.0,0.0,1.0,120892.93,1.0
+9652,15786433.0,Hsia,651.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,0.0,103315.74,0.0
+9653,15732270.0,Yermakova,537.0,Spain,Male,63.0,9.0,0.0,1.0,1.0,0.0,128882.66,1.0
+9654,15632275.0,Chinwendu,722.0,France,Female,33.0,5.0,0.0,2.0,0.0,1.0,108419.41,0.0
+9655,15600688.0,Nnachetam,705.0,Germany,Female,36.0,4.0,127864.4,1.0,1.0,1.0,172930.28,0.0
+9656,15699415.0,Martin,695.0,Germany,Female,42.0,5.0,131772.57,2.0,0.0,0.0,94488.82,1.0
+9657,15665590.0,Ch'ien,545.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,142587.32,0.0
+9658,15647800.0,Mazzanti,748.0,France,Male,39.0,6.0,0.0,2.0,0.0,0.0,132979.43,0.0
+9659,15699430.0,Chiang,602.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,50390.98,0.0
+9660,15755868.0,Ch'ang,686.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,189366.04,0.0
+9661,15746012.0,Y?,614.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,27330.59,0.0
+9662,15737354.0,Onyemere,568.0,France,Female,29.0,1.0,0.0,2.0,1.0,1.0,99800.87,0.0
+9663,15768455.0,Avent,705.0,France,Male,34.0,4.0,0.0,1.0,1.0,0.0,143783.28,0.0
+9664,15768455.0,Ch'iu,687.0,France,Male,37.0,8.0,110245.13,1.0,1.0,1.0,179267.94,0.0
+9665,15601503.0,Pinto,545.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,74320.75,0.0
+9666,15591257.0,Chuang,653.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,153392.28,0.0
+9667,15724307.0,Yobachukwu,634.0,France,Female,35.0,9.0,180530.51,1.0,1.0,0.0,116971.05,0.0
+9668,15593365.0,Hsu,569.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,116893.87,0.0
+9669,15752801.0,Milanesi,773.0,Spain,Male,39.0,2.0,0.0,1.0,1.0,1.0,16081.62,0.0
+9670,15685500.0,Fennescey,743.0,France,Female,27.0,5.0,0.0,2.0,1.0,1.0,145894.9,0.0
+9671,15765297.0,Tsou,703.0,France,Male,34.0,4.0,169025.83,1.0,1.0,1.0,166677.35,0.0
+9672,15652999.0,Shih,488.0,Spain,Male,50.0,9.0,0.0,1.0,1.0,1.0,123826.83,1.0
+9673,15798844.0,Lombardi,577.0,France,Female,51.0,5.0,83772.95,1.0,0.0,0.0,138873.15,1.0
+9674,15641733.0,Hsueh,757.0,France,Female,46.0,2.0,0.0,2.0,1.0,0.0,141533.19,1.0
+9675,15732672.0,Chibueze,684.0,Germany,Female,38.0,7.0,103564.83,1.0,1.0,0.0,151925.25,0.0
+9676,15615091.0,Kao,597.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,90537.47,0.0
+9677,15593470.0,Y?an,741.0,France,Female,36.0,7.0,0.0,1.0,1.0,0.0,82298.59,0.0
+9678,15773869.0,Palerma,628.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,91406.62,0.0
+9679,15592846.0,O'Brien,651.0,France,Female,40.0,0.0,0.0,1.0,1.0,0.0,116547.31,1.0
+9680,15793901.0,Smith,717.0,France,Female,38.0,6.0,0.0,3.0,1.0,0.0,158973.77,0.0
+9681,15611186.0,Zito,661.0,France,Male,46.0,10.0,150844.94,1.0,1.0,1.0,158887.09,0.0
+9682,15635177.0,Hsueh,686.0,France,Male,54.0,5.0,133844.22,1.0,1.0,1.0,95611.74,0.0
+9683,15612455.0,Lucchesi,705.0,France,Female,32.0,5.0,160982.89,1.0,1.0,0.0,172718.35,0.0
+9684,15682355.0,Nkemakonam,814.0,France,Male,40.0,9.0,0.0,1.0,1.0,1.0,92320.37,0.0
+9685,15628308.0,Hsueh,556.0,France,Male,22.0,2.0,0.0,2.0,1.0,1.0,145593.85,0.0
+9686,15602914.0,Lo,724.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,133537.53,0.0
+9687,15697510.0,Nwankwo,669.0,Spain,Male,37.0,10.0,0.0,1.0,1.0,1.0,1235.49,0.0
+9688,15796467.0,Tung,596.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,114774.35,0.0
+9689,15740406.0,Nnachetam,677.0,France,Male,54.0,1.0,180516.8,1.0,1.0,0.0,174431.01,1.0
+9690,15701687.0,Maclean,607.0,Germany,Female,41.0,3.0,110934.54,3.0,1.0,0.0,28698.17,1.0
+9691,15672894.0,Nnamdi,632.0,France,Male,33.0,8.0,129728.8,1.0,1.0,1.0,85901.09,0.0
+9692,15664547.0,Ch'iu,751.0,France,Male,46.0,8.0,0.0,2.0,0.0,0.0,125939.19,0.0
+9693,15757837.0,Chukwudi,724.0,France,Male,42.0,8.0,0.0,1.0,0.0,0.0,161388.22,0.0
+9694,15583840.0,Ritchie,587.0,France,Female,33.0,2.0,0.0,2.0,0.0,1.0,159344.43,0.0
+9695,15690829.0,Hao,705.0,France,Female,42.0,0.0,0.0,2.0,1.0,0.0,90905.94,0.0
+9696,15736778.0,Tsao,561.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,1.0,132424.69,0.0
+9697,15572408.0,Tien,671.0,France,Male,36.0,7.0,66101.89,1.0,0.0,0.0,176743.51,1.0
+9698,15692258.0,Piazza,667.0,Germany,Male,38.0,8.0,132876.23,1.0,1.0,1.0,18082.06,1.0
+9699,15695872.0,Obialo,593.0,Germany,Male,58.0,9.0,99095.73,1.0,1.0,0.0,167033.83,1.0
+9700,15736495.0,Ma,589.0,France,Female,63.0,9.0,161591.11,1.0,1.0,0.0,86927.53,1.0
+9701,15580956.0,Onyemauchechukwu,685.0,France,Female,47.0,9.0,102278.71,1.0,1.0,1.0,93536.38,0.0
+9702,15740147.0,Toscani,611.0,France,Male,49.0,7.0,0.0,1.0,1.0,0.0,161579.85,1.0
+9703,15747265.0,Hsu,613.0,Germany,Female,36.0,2.0,153058.64,1.0,0.0,0.0,49653.37,1.0
+9704,15643359.0,P'eng,719.0,France,Male,35.0,2.0,0.0,1.0,1.0,1.0,162865.25,0.0
+9705,15762573.0,Sun,652.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,94767.77,0.0
+9706,15733261.0,K?,583.0,France,Female,30.0,1.0,0.0,1.0,0.0,1.0,38652.96,0.0
+9707,15676909.0,Onyekaozulu,614.0,Spain,Male,21.0,7.0,123882.49,1.0,0.0,0.0,137037.97,0.0
+9708,15637324.0,Chukwueloka,785.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,152012.36,0.0
+9709,15652266.0,Ch'ang,535.0,France,Male,31.0,1.0,113909.69,1.0,1.0,1.0,174916.85,0.0
+9710,15705034.0,Chiu,785.0,Spain,Male,36.0,5.0,133801.35,1.0,1.0,1.0,153015.42,0.0
+9711,15666043.0,Nnachetam,605.0,France,Male,45.0,9.0,0.0,1.0,1.0,1.0,142513.5,1.0
+9712,15603594.0,Chiang,669.0,Spain,Female,43.0,3.0,0.0,1.0,1.0,0.0,78660.95,1.0
+9713,15756743.0,Uspensky,676.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,101704.19,0.0
+9714,15653547.0,Chikwado,746.0,France,Female,43.0,3.0,95508.1,1.0,1.0,0.0,175674.01,1.0
+9715,15795519.0,P'eng,711.0,France,Male,29.0,8.0,125845.66,1.0,0.0,0.0,158845.49,0.0
+9716,15664793.0,Chuang,607.0,Germany,Female,37.0,7.0,121537.71,2.0,1.0,0.0,125518.32,0.0
+9717,15635388.0,Cone,667.0,France,Female,45.0,9.0,141749.74,1.0,1.0,0.0,118738.96,0.0
+9718,15589329.0,McIntyre,665.0,Germany,Female,43.0,4.0,135927.04,1.0,0.0,0.0,113645.8,1.0
+9719,15708534.0,Hsiung,683.0,Spain,Female,47.0,9.0,0.0,1.0,0.0,1.0,5791.85,1.0
+9720,15641359.0,Lo,691.0,Spain,Female,24.0,2.0,0.0,2.0,1.0,1.0,95181.06,0.0
+9721,15610271.0,Ndukaku,622.0,France,Female,35.0,9.0,0.0,1.0,0.0,1.0,199505.53,0.0
+9722,15597945.0,Hsueh,705.0,France,Male,33.0,6.0,97530.25,1.0,1.0,0.0,144290.25,0.0
+9723,15653777.0,Yashina,678.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,61793.47,0.0
+9724,15669491.0,Manna,605.0,Germany,Male,40.0,1.0,137714.25,1.0,1.0,1.0,116748.14,1.0
+9725,15658032.0,Iadanza,535.0,France,Female,30.0,2.0,0.0,2.0,0.0,0.0,60416.76,0.0
+9726,15751208.0,Aksenov,613.0,France,Male,35.0,4.0,137817.52,1.0,0.0,0.0,174770.14,0.0
+9727,15707596.0,P'an,587.0,Spain,Male,37.0,7.0,135962.0,1.0,1.0,1.0,161574.19,0.0
+9728,15641675.0,Ts'ui,613.0,Spain,Male,23.0,5.0,0.0,2.0,1.0,1.0,149575.13,0.0
+9729,15789611.0,Chukwujekwu,803.0,Germany,Male,46.0,1.0,141789.15,1.0,0.0,1.0,130189.72,0.0
+9730,15571261.0,T'ang,591.0,France,Male,22.0,8.0,0.0,2.0,1.0,1.0,171575.54,0.0
+9731,15756901.0,Ch'iu,757.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,133831.37,0.0
+9732,15688637.0,Chiemela,584.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,117977.45,0.0
+9733,15726589.0,Tretiakova,645.0,Germany,Female,43.0,3.0,120107.56,2.0,0.0,0.0,84920.49,1.0
+9734,15804822.0,Smith,581.0,Germany,Male,60.0,8.0,137841.53,1.0,1.0,1.0,181224.56,0.0
+9735,15667934.0,Lucchese,668.0,Spain,Female,24.0,4.0,113625.93,1.0,1.0,0.0,94739.2,0.0
+9736,15716605.0,Chidimma,573.0,France,Female,44.0,6.0,0.0,1.0,1.0,0.0,26015.8,1.0
+9737,15590820.0,Tien,520.0,Germany,Female,45.0,7.0,109570.21,1.0,0.0,0.0,15068.18,1.0
+9738,15726103.0,Ting,622.0,France,Female,36.0,6.0,136658.61,1.0,1.0,1.0,65316.5,0.0
+9739,15636551.0,Ts'ui,602.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,89801.9,0.0
+9740,15806918.0,Genovese,568.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,57549.65,0.0
+9741,15586880.0,Atherton,673.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,121053.19,0.0
+9742,15754929.0,Johnson,556.0,Spain,Male,55.0,2.0,0.0,1.0,1.0,0.0,131177.22,1.0
+9743,15632272.0,Ch'ien,729.0,France,Male,36.0,4.0,131583.59,1.0,1.0,0.0,198251.52,0.0
+9744,15803378.0,Ibragimova,673.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,69643.31,0.0
+9745,15808930.0,Mazzi,684.0,Germany,Female,55.0,1.0,147026.61,1.0,0.0,0.0,103832.58,1.0
+9746,15806511.0,Ma,683.0,Spain,Female,42.0,7.0,0.0,1.0,1.0,0.0,153064.85,1.0
+9747,15712584.0,Tsao,613.0,Germany,Male,30.0,6.0,108004.64,2.0,1.0,0.0,59497.01,0.0
+9748,15729771.0,Ch'iu,687.0,Spain,Female,46.0,3.0,153366.13,1.0,1.0,0.0,172668.38,1.0
+9749,15682563.0,Uspenskaya,707.0,Spain,Female,37.0,4.0,0.0,2.0,1.0,0.0,52301.15,0.0
+9750,15767894.0,Lombardo,497.0,France,Male,36.0,9.0,169743.83,1.0,1.0,0.0,52327.57,0.0
+9751,15709396.0,Hsueh,764.0,Germany,Female,54.0,9.0,118287.01,1.0,1.0,0.0,182055.36,1.0
+9752,15658716.0,Ts'ui,590.0,France,Male,36.0,1.0,0.0,1.0,1.0,0.0,135875.51,0.0
+9753,15582951.0,Ch'eng,574.0,Germany,Female,29.0,1.0,150844.94,1.0,0.0,1.0,89891.4,0.0
+9754,15758738.0,Toscani,667.0,Germany,Female,51.0,9.0,120782.7,1.0,1.0,1.0,138871.12,1.0
+9755,15629787.0,Hsueh,613.0,France,Male,41.0,9.0,0.0,2.0,1.0,1.0,98894.64,0.0
+9756,15792986.0,Hales,682.0,Germany,Female,40.0,4.0,142862.21,2.0,1.0,1.0,131903.6,0.0
+9757,15696900.0,Chukwukadibia,644.0,France,Male,46.0,3.0,146053.66,1.0,1.0,1.0,158973.77,1.0
+9758,15593715.0,Chidiebere,714.0,Germany,Male,59.0,4.0,135814.7,1.0,1.0,0.0,152298.28,1.0
+9759,15634177.0,P'eng,755.0,France,Male,43.0,3.0,0.0,2.0,0.0,1.0,97938.59,0.0
+9760,15770174.0,Hirst,733.0,Germany,Male,52.0,7.0,123082.08,1.0,1.0,0.0,143774.06,1.0
+9761,15626452.0,Ch'ien,679.0,France,Female,44.0,7.0,0.0,2.0,0.0,1.0,157959.02,0.0
+9762,15782530.0,Fitts,695.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,93719.87,0.0
+9763,15617507.0,Fanucci,725.0,France,Female,40.0,6.0,0.0,2.0,0.0,1.0,171364.15,0.0
+9764,15696852.0,Onyemauchechukwu,628.0,Germany,Female,41.0,5.0,111432.13,2.0,1.0,0.0,146851.73,0.0
+9765,15692226.0,Yeh,598.0,Spain,Male,45.0,5.0,0.0,1.0,1.0,0.0,56594.36,1.0
+9766,15583668.0,Ch'ang,769.0,Germany,Male,45.0,1.0,93798.42,1.0,0.0,1.0,137049.24,1.0
+9767,15782659.0,Chia,699.0,France,Male,34.0,8.0,0.0,1.0,1.0,1.0,113796.15,0.0
+9768,15746553.0,Folliero,468.0,France,Male,43.0,7.0,84904.45,1.0,0.0,1.0,66799.28,0.0
+9769,15732644.0,Kao,508.0,Germany,Female,42.0,10.0,121729.42,1.0,1.0,0.0,158344.63,1.0
+9770,15654901.0,Martin,535.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,136915.15,0.0
+9771,15697100.0,Yobachukwu,530.0,Spain,Male,38.0,2.0,109538.75,1.0,0.0,1.0,143938.27,0.0
+9772,15799217.0,P'eng,611.0,France,Male,39.0,10.0,150905.03,1.0,1.0,0.0,28368.37,0.0
+9773,15792463.0,Tuan,711.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,170893.87,0.0
+9774,15757916.0,H?,695.0,France,Female,35.0,3.0,0.0,1.0,1.0,0.0,177528.92,1.0
+9775,15583768.0,Nwabugwu,710.0,France,Male,32.0,7.0,141040.01,1.0,1.0,1.0,1235.49,0.0
+9776,15802351.0,Nnachetam,714.0,France,Male,40.0,6.0,0.0,2.0,1.0,1.0,136071.03,0.0
+9777,15603665.0,Onyekachi,632.0,Germany,Male,32.0,2.0,124695.72,2.0,1.0,0.0,132480.75,0.0
+9778,15747288.0,Yermakov,585.0,France,Male,35.0,4.0,95984.69,1.0,1.0,1.0,90177.3,0.0
+9779,15776947.0,De Luca,715.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,166317.71,0.0
+9780,15758056.0,Fomin,659.0,France,Female,61.0,9.0,145359.99,1.0,1.0,1.0,16081.62,0.0
+9781,15572022.0,Ch'ien,727.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,0.0,180598.32,0.0
+9782,15636397.0,Chukwujamuike,690.0,France,Male,44.0,4.0,121151.05,1.0,1.0,0.0,177896.87,0.0
+9783,15630551.0,Chidiebere,648.0,Germany,Female,43.0,1.0,133658.24,1.0,1.0,0.0,86957.42,1.0
+9784,15655175.0,Tsou,773.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,143681.83,0.0
+9785,15644882.0,H?,850.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,153373.71,0.0
+9786,15793331.0,Hsiung,662.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,93844.67,0.0
+9787,15658813.0,Hsiao,588.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,55313.51,0.0
+9788,15671165.0,Chinwendu,741.0,Spain,Female,24.0,8.0,0.0,2.0,1.0,1.0,169920.92,0.0
+9789,15671591.0,Hsing,850.0,Spain,Male,35.0,1.0,116576.45,1.0,1.0,1.0,160579.15,0.0
+9790,15769955.0,Lettiere,706.0,Germany,Male,41.0,8.0,88980.32,2.0,1.0,0.0,128528.83,0.0
+9791,15651352.0,Ndukaku,604.0,Spain,Male,55.0,6.0,169824.46,1.0,1.0,1.0,132798.78,1.0
+9792,15642997.0,Okwuoma,733.0,France,Female,50.0,6.0,0.0,1.0,1.0,0.0,139947.17,1.0
+9793,15681928.0,T'ien,628.0,Spain,Female,46.0,5.0,136416.82,2.0,1.0,0.0,179421.84,1.0
+9794,15772632.0,Gadsden,679.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,80587.27,0.0
+9795,15630661.0,Yobanna,632.0,France,Male,25.0,4.0,0.0,2.0,1.0,0.0,114094.73,0.0
+9796,15700854.0,Nnaife,588.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,65636.92,0.0
+9797,15582591.0,Nnamutaezinwa,474.0,France,Female,25.0,5.0,0.0,2.0,1.0,0.0,180591.32,0.0
+9798,15661380.0,Li Fonti,644.0,France,Female,52.0,1.0,0.0,1.0,1.0,1.0,158750.16,1.0
+9799,15576676.0,Chukwuemeka,599.0,Spain,Female,33.0,1.0,0.0,2.0,0.0,1.0,180800.42,0.0
+9800,15785576.0,Hs?,713.0,France,Female,56.0,7.0,117301.87,1.0,1.0,1.0,153895.33,1.0
+9801,15692971.0,Lo Duca,651.0,Spain,Female,41.0,4.0,0.0,2.0,1.0,1.0,173516.71,0.0
+9802,15603743.0,Hsiao,706.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,100995.68,0.0
+9803,15756535.0,Iadanza,550.0,France,Female,51.0,0.0,0.0,3.0,1.0,1.0,136034.32,1.0
+9804,15691543.0,Lo,557.0,Germany,Female,47.0,3.0,133616.39,1.0,1.0,0.0,166476.95,1.0
+9805,15757811.0,Toscani,687.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,85424.34,0.0
+9806,15693349.0,Hs?,716.0,France,Male,36.0,3.0,141334.43,2.0,1.0,0.0,157959.02,0.0
+9807,15621550.0,Pagnotto,577.0,France,Female,27.0,7.0,0.0,2.0,1.0,1.0,120893.07,0.0
+9808,15631436.0,Ch'ang,704.0,Germany,Female,35.0,5.0,170840.08,2.0,1.0,1.0,176743.51,0.0
+9809,15748649.0,Yeh,633.0,France,Male,31.0,7.0,79340.95,1.0,1.0,1.0,161326.73,1.0
+9810,15704770.0,Hsia,718.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,84760.32,0.0
+9811,15723169.0,Ozerova,571.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,71179.31,0.0
+9812,15686957.0,Nkemjika,731.0,Spain,Female,35.0,2.0,0.0,1.0,0.0,0.0,98413.1,1.0
+9813,15749050.0,Onyekachi,754.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,49848.86,0.0
+9814,15697948.0,Teng,626.0,Spain,Male,34.0,6.0,131778.66,1.0,1.0,1.0,142587.32,0.0
+9815,15711789.0,Tien,577.0,Germany,Male,44.0,2.0,105000.85,2.0,1.0,1.0,124489.88,0.0
+9816,15741032.0,Ch'eng,729.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,145558.42,0.0
+9817,15702715.0,Chiang,555.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,82870.75,0.0
+9818,15777315.0,Iqbal,718.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,92568.53,0.0
+9819,15708504.0,Pisano,673.0,France,Male,39.0,6.0,0.0,1.0,1.0,1.0,130789.15,0.0
+9820,15634245.0,Ch'iu,731.0,France,Male,29.0,7.0,0.0,2.0,0.0,1.0,161241.65,0.0
+9821,15789863.0,Hsieh,637.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,82967.97,0.0
+9822,15587562.0,T'ao,627.0,Spain,Male,34.0,7.0,0.0,1.0,1.0,0.0,93901.61,0.0
+9823,15807525.0,P'eng,684.0,Spain,Male,27.0,4.0,0.0,2.0,0.0,1.0,174918.46,0.0
+9824,15787322.0,Aikenhead,741.0,Germany,Male,35.0,1.0,141356.47,2.0,1.0,1.0,57589.29,0.0
+9825,15751697.0,Milani,714.0,France,Male,31.0,5.0,0.0,2.0,0.0,0.0,10054.51,0.0
+9826,15673908.0,Udinesi,705.0,France,Female,43.0,10.0,0.0,2.0,1.0,0.0,131609.48,1.0
+9827,15657535.0,Ponomaryov,573.0,France,Female,44.0,9.0,73493.17,3.0,0.0,0.0,83093.25,1.0
+9828,15753847.0,Maclean,678.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,83056.22,0.0
+9829,15764174.0,Hsia,681.0,Germany,Female,47.0,5.0,127570.93,1.0,1.0,0.0,83629.6,1.0
+9830,15698732.0,Hsing,638.0,France,Female,24.0,1.0,0.0,2.0,1.0,0.0,88838.14,0.0
+9831,15748595.0,Maughan,588.0,Spain,Male,23.0,5.0,141787.31,1.0,1.0,1.0,122964.18,0.0
+9832,15783859.0,Y?an,599.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,141756.32,0.0
+9833,15784092.0,Hsueh,659.0,France,Male,41.0,3.0,0.0,1.0,1.0,0.0,132480.75,1.0
+9834,15797760.0,Hsueh,611.0,Germany,Male,34.0,6.0,142717.84,1.0,0.0,1.0,92982.61,0.0
+9835,15759034.0,Hsia,681.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,103737.82,0.0
+9836,15656521.0,Ugochukwutubelum,449.0,France,Male,32.0,2.0,161889.79,1.0,1.0,1.0,61251.25,0.0
+9837,15635125.0,Chukwudi,758.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,116973.48,0.0
+9838,15667633.0,Hsu,599.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,175518.31,0.0
+9839,15649086.0,Clements,659.0,Spain,Female,38.0,9.0,0.0,1.0,1.0,0.0,119882.7,1.0
+9840,15603764.0,Hsueh,595.0,France,Female,37.0,6.0,127739.35,1.0,1.0,0.0,67769.94,0.0
+9841,15796074.0,Ndukaku,686.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,83983.02,0.0
+9842,15595440.0,Mazzanti,716.0,France,Male,36.0,10.0,0.0,1.0,0.0,1.0,131807.14,0.0
+9843,15709639.0,Rowntree,706.0,Spain,Female,29.0,4.0,0.0,1.0,1.0,1.0,180862.26,0.0
+9844,15646726.0,Estrada,528.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,103511.96,0.0
+9845,15691840.0,Tsao,627.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,0.0,159418.1,0.0
+9846,15630195.0,Fanucci,721.0,Spain,Female,39.0,3.0,95252.72,1.0,1.0,1.0,61569.07,0.0
+9847,15680430.0,Mao,754.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,0.0,169763.89,1.0
+9848,15781298.0,Ch'in,649.0,France,Female,37.0,10.0,0.0,1.0,0.0,0.0,89519.47,1.0
+9849,15787655.0,Udinesi,584.0,France,Male,41.0,2.0,121702.73,1.0,1.0,1.0,120138.27,0.0
+9850,15799785.0,Nwachukwu,579.0,Germany,Male,38.0,7.0,112211.61,1.0,1.0,0.0,85431.37,0.0
+9851,15746749.0,Kao,754.0,France,Male,45.0,10.0,0.0,2.0,0.0,1.0,93883.53,0.0
+9852,15795936.0,Lo Duca,619.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,181177.9,0.0
+9853,15579891.0,Ch'iu,697.0,France,Male,60.0,9.0,0.0,1.0,1.0,0.0,89205.54,1.0
+9854,15684669.0,Hsueh,581.0,Spain,Male,32.0,7.0,0.0,2.0,0.0,0.0,151822.66,0.0
+9855,15613282.0,Fanucci,678.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,95611.47,0.0
+9856,15801699.0,Manna,842.0,Germany,Male,40.0,10.0,81742.84,2.0,1.0,1.0,163558.42,0.0
+9857,15572706.0,Tuan,569.0,France,Male,63.0,8.0,0.0,1.0,1.0,0.0,74048.1,1.0
+9858,15643819.0,Kao,535.0,Germany,Female,35.0,1.0,107712.57,2.0,1.0,0.0,151869.35,1.0
+9859,15681332.0,Nebeolisa,676.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,181419.29,0.0
+9860,15748696.0,Chimaobim,758.0,Spain,Male,42.0,1.0,0.0,1.0,1.0,0.0,147545.65,1.0
+9861,15608701.0,Tien,585.0,France,Male,27.0,3.0,0.0,2.0,1.0,1.0,96420.58,0.0
+9862,15785899.0,Hs?eh,730.0,France,Male,29.0,1.0,0.0,2.0,0.0,0.0,142548.33,0.0
+9863,15583462.0,Ugochukwu,695.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,0.0,78915.68,0.0
+9864,15723032.0,Lettiere,652.0,Germany,Female,32.0,8.0,132934.89,1.0,1.0,0.0,171706.66,0.0
+9865,15743617.0,K'ung,551.0,Germany,Male,42.0,5.0,87967.42,2.0,1.0,1.0,79957.95,0.0
+9866,15750545.0,Lu,733.0,Spain,Female,26.0,1.0,167738.82,2.0,1.0,0.0,64515.5,0.0
+9867,15737452.0,Kao,624.0,France,Male,31.0,6.0,0.0,2.0,0.0,0.0,180553.48,0.0
+9868,15679743.0,Udegbulam,697.0,France,Male,27.0,2.0,0.0,2.0,1.0,0.0,85937.47,0.0
+9869,15591130.0,Cremonesi,673.0,France,Male,36.0,4.0,0.0,2.0,0.0,1.0,124489.88,0.0
+9870,15752300.0,Ts'ai,653.0,France,Female,31.0,8.0,0.0,2.0,0.0,0.0,143783.46,0.0
+9871,15775625.0,Oluchukwu,669.0,Spain,Male,32.0,8.0,0.0,1.0,1.0,1.0,125870.5,0.0
+9872,15783097.0,Udinese,850.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,1187.88,0.0
+9873,15753969.0,Tretiakova,595.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,54865.92,0.0
+9874,15625175.0,Hs?,516.0,Germany,Male,33.0,6.0,98443.14,1.0,1.0,1.0,145551.6,0.0
+9875,15652981.0,Iloerika,649.0,France,Male,44.0,7.0,0.0,1.0,1.0,0.0,114793.45,1.0
+9876,15701280.0,Onyemauchechukwu,667.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,1.0,27359.19,0.0
+9877,15706810.0,Chu,730.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,90977.48,0.0
+9878,15792729.0,Tsao,662.0,France,Female,31.0,3.0,165798.1,1.0,1.0,1.0,134901.34,0.0
+9879,15682355.0,Tien,571.0,France,Male,30.0,2.0,0.0,2.0,0.0,1.0,93081.87,0.0
+9880,15574584.0,Chinedum,844.0,France,Male,27.0,9.0,0.0,2.0,1.0,0.0,131807.14,0.0
+9881,15744368.0,Folliero,704.0,France,Female,43.0,1.0,0.0,1.0,1.0,1.0,74564.41,1.0
+9882,15656249.0,Trevisani,650.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,129890.94,0.0
+9883,15661526.0,O'Neill,707.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,79270.02,0.0
+9884,15585192.0,Nwachinemelu,535.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,14081.64,1.0
+9885,15709487.0,Nwachinemelu,542.0,Spain,Male,54.0,9.0,0.0,1.0,0.0,0.0,97767.63,1.0
+9886,15643908.0,Chienezie,662.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,167636.15,0.0
+9887,15777599.0,Ibezimako,599.0,France,Male,38.0,7.0,0.0,1.0,1.0,1.0,26265.18,0.0
+9888,15673837.0,Lai,644.0,France,Female,26.0,8.0,0.0,2.0,0.0,1.0,35644.79,0.0
+9889,15753037.0,Hsia,704.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,51117.06,0.0
+9890,15672945.0,Ch'in,683.0,Spain,Male,35.0,8.0,127864.4,1.0,1.0,1.0,161083.53,0.0
+9891,15793331.0,Chukwuebuka,710.0,France,Male,37.0,9.0,0.0,2.0,0.0,0.0,126743.33,0.0
+9892,15682618.0,Hsia,613.0,France,Male,37.0,6.0,0.0,2.0,0.0,0.0,89048.46,0.0
+9893,15803749.0,Lu,667.0,Spain,Male,27.0,4.0,131852.81,1.0,1.0,1.0,131043.9,0.0
+9894,15649160.0,Walker,634.0,Germany,Male,37.0,9.0,121818.84,1.0,1.0,1.0,154631.35,0.0
+9895,15712176.0,T'ien,629.0,Germany,Male,41.0,1.0,139532.39,1.0,1.0,1.0,126812.63,0.0
+9896,15732269.0,Yuryeva,648.0,Spain,Male,43.0,4.0,0.0,2.0,0.0,0.0,168816.23,0.0
+9897,15773876.0,Hsueh,704.0,Germany,Female,58.0,7.0,122471.09,1.0,1.0,0.0,67311.12,1.0
+9898,15713774.0,Ch'ang,651.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,96498.14,0.0
+9899,15772479.0,Chiganu,679.0,France,Male,43.0,8.0,0.0,1.0,1.0,1.0,171463.83,0.0
+9900,15787772.0,Lettiere,669.0,France,Male,30.0,5.0,120961.5,1.0,1.0,0.0,56760.97,0.0
+9901,15648721.0,Genovesi,753.0,Germany,Male,31.0,3.0,137948.51,2.0,1.0,0.0,170886.77,0.0
+9902,15774727.0,Okeke,659.0,France,Male,19.0,9.0,0.0,2.0,1.0,0.0,170980.04,0.0
+9903,15699598.0,Lai,638.0,France,Male,33.0,7.0,126588.81,2.0,1.0,0.0,149535.97,0.0
+9904,15724444.0,Chukwuemeka,653.0,France,Male,35.0,3.0,0.0,1.0,0.0,0.0,121151.1,0.0
+9905,15585362.0,Ch'eng,735.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,199753.97,0.0
+9906,15612071.0,Milani,669.0,France,Female,39.0,8.0,0.0,2.0,0.0,1.0,138992.48,0.0
+9907,15705735.0,Ch'ien,589.0,Germany,Male,41.0,3.0,123599.49,1.0,1.0,0.0,183490.99,1.0
+9908,15677123.0,Chuang,754.0,France,Female,56.0,5.0,171069.39,1.0,0.0,1.0,124777.14,1.0
+9909,15730579.0,Chibugo,683.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,145796.49,0.0
+9910,15662488.0,Bogle,792.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,93832.33,0.0
+9911,15599251.0,White,723.0,Spain,Female,66.0,6.0,0.0,2.0,1.0,1.0,64515.5,0.0
+9912,15737047.0,Chukwueloka,697.0,Spain,Male,44.0,1.0,108465.65,1.0,1.0,1.0,173495.77,1.0
+9913,15681697.0,P'eng,725.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,125426.67,0.0
+9914,15761158.0,Chidumaga,585.0,Germany,Female,37.0,8.0,80542.81,1.0,1.0,1.0,83470.2,0.0
+9915,15566689.0,Yudina,627.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,62733.05,0.0
+9916,15710390.0,Y?,608.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,161064.29,0.0
+9917,15741032.0,Iweobiegbulam,718.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,118913.53,1.0
+9918,15603665.0,Ch'in,619.0,Spain,Male,30.0,9.0,0.0,2.0,0.0,0.0,64715.24,0.0
+9919,15621435.0,Onyemere,646.0,France,Male,39.0,9.0,0.0,2.0,0.0,0.0,85421.48,0.0
+9920,15583595.0,P'eng,678.0,Spain,Male,34.0,2.0,114330.95,1.0,0.0,1.0,153343.48,0.0
+9921,15696054.0,Tsai,496.0,France,Male,24.0,3.0,0.0,2.0,0.0,1.0,140881.2,0.0
+9922,15672826.0,Chien,678.0,Spain,Male,30.0,5.0,0.0,2.0,1.0,1.0,124964.82,1.0
+9923,15754577.0,Hsu,699.0,France,Male,32.0,3.0,111095.55,1.0,0.0,0.0,38190.78,0.0
+9924,15652693.0,Nnonso,730.0,Spain,Male,28.0,1.0,167026.61,1.0,1.0,0.0,177519.19,0.0
+9925,15596088.0,Hsiung,705.0,Spain,Male,47.0,9.0,0.0,1.0,1.0,0.0,71709.12,1.0
+9926,15643679.0,Fomin,697.0,Spain,Male,28.0,8.0,0.0,2.0,1.0,0.0,183572.39,0.0
+9927,15777576.0,Maclean,684.0,France,Female,43.0,10.0,0.0,2.0,0.0,1.0,9902.39,0.0
+9928,15718289.0,Dancy,707.0,Spain,Male,31.0,8.0,86977.96,2.0,1.0,0.0,84476.62,0.0
+9929,15646728.0,T'ien,641.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,1.0,56585.2,0.0
+9930,15768124.0,Loyau,678.0,Spain,Male,40.0,7.0,126689.71,1.0,1.0,1.0,182436.81,0.0
+9931,15784700.0,Zetticci,682.0,France,Male,27.0,3.0,0.0,2.0,1.0,0.0,148811.14,0.0
+9932,15662976.0,Zito,710.0,France,Female,62.0,3.0,0.0,2.0,0.0,1.0,65618.01,0.0
+9933,15796074.0,Trevisano,703.0,Spain,Female,32.0,6.0,122486.47,1.0,1.0,1.0,177065.24,0.0
+9934,15674583.0,Cawker,663.0,France,Female,30.0,5.0,0.0,2.0,0.0,0.0,94973.64,0.0
+9935,15755731.0,Nnaemeka,663.0,Spain,Male,35.0,10.0,0.0,3.0,1.0,0.0,38772.82,1.0
+9936,15658057.0,Kaeppel,572.0,Germany,Male,36.0,7.0,100480.53,1.0,1.0,1.0,100240.2,0.0
+9937,15672798.0,Liao,697.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,80552.36,0.0
+9938,15595036.0,Manna,724.0,Germany,Male,48.0,3.0,118889.66,1.0,1.0,0.0,57876.05,1.0
+9939,15773869.0,Iadanza,572.0,Spain,Female,37.0,8.0,0.0,3.0,1.0,1.0,142903.44,1.0
+9940,15641715.0,Chukwufumnanya,603.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,118959.64,0.0
+9941,15578045.0,Brown,699.0,France,Female,36.0,2.0,0.0,1.0,1.0,1.0,131712.52,0.0
+9942,15643826.0,L?,676.0,France,Female,41.0,2.0,0.0,2.0,0.0,1.0,102397.22,0.0
+9943,15753861.0,Shao,721.0,Spain,Male,67.0,1.0,0.0,2.0,1.0,1.0,143317.18,0.0
+9944,15792453.0,Tien,711.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,85522.84,0.0
+9945,15763515.0,Hickey,683.0,Spain,Female,28.0,0.0,0.0,2.0,1.0,0.0,184524.65,0.0
+9946,15651713.0,T'an,850.0,Germany,Male,40.0,7.0,123575.51,1.0,1.0,0.0,160976.85,1.0
+9947,15708904.0,Maclean,624.0,France,Male,48.0,10.0,0.0,2.0,0.0,0.0,89081.41,1.0
+9948,15603879.0,Lo Duca,628.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,161579.85,0.0
+9949,15766417.0,Chuang,634.0,France,Male,46.0,5.0,108760.32,1.0,0.0,0.0,69454.24,0.0
+9950,15715208.0,Ch'ang,622.0,Spain,Male,35.0,7.0,161592.76,1.0,1.0,0.0,27149.27,0.0
+9951,15757535.0,Chukwudi,705.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,138527.56,0.0
+9952,15707025.0,Nwankwo,753.0,France,Male,36.0,0.0,0.0,2.0,1.0,1.0,49833.5,0.0
+9953,15777922.0,Tien,498.0,Spain,Male,35.0,6.0,104443.82,1.0,1.0,1.0,149991.32,0.0
+9954,15777826.0,Hsieh,594.0,Germany,Female,49.0,5.0,126436.29,1.0,0.0,0.0,122949.31,1.0
+9955,15800366.0,Teng,704.0,France,Male,30.0,7.0,0.0,2.0,0.0,0.0,183682.09,0.0
+9956,15786418.0,Nkemdilim,757.0,Germany,Female,38.0,2.0,92877.33,2.0,1.0,1.0,163798.41,0.0
+9957,15775234.0,Chukwufumnanya,684.0,Spain,Male,31.0,2.0,0.0,2.0,0.0,0.0,150720.39,0.0
+9958,15696539.0,Mazzi,659.0,Spain,Male,33.0,1.0,0.0,2.0,1.0,0.0,176743.51,0.0
+9959,15603096.0,Chien,559.0,France,Male,46.0,4.0,0.0,1.0,1.0,0.0,151335.24,1.0
+9960,15732437.0,Hsieh,683.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,141210.5,0.0
+9961,15653572.0,Chukwueloka,585.0,Spain,Male,59.0,1.0,104832.49,1.0,1.0,1.0,129964.94,0.0
+9962,15605327.0,Zetticci,576.0,France,Female,35.0,10.0,128509.63,1.0,0.0,1.0,103970.58,0.0
+9963,15753895.0,Chiganu,582.0,France,Male,28.0,4.0,98668.18,1.0,1.0,0.0,57589.29,0.0
+9964,15657566.0,Hsueh,714.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,119817.19,0.0
+9965,15803526.0,Onyekachukwu,544.0,France,Female,51.0,1.0,0.0,2.0,1.0,1.0,155700.15,0.0
+9966,15591698.0,Olisaemeka,619.0,Spain,Female,71.0,2.0,0.0,2.0,1.0,1.0,152235.3,0.0
+9967,15610226.0,Trevisano,711.0,Germany,Male,42.0,9.0,119825.75,1.0,1.0,0.0,124954.56,1.0
+9968,15806511.0,Kao,709.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,60420.07,0.0
+9969,15792646.0,Yobachi,751.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,0.0,156774.75,0.0
+9970,15581229.0,Kao,794.0,France,Female,29.0,7.0,71283.09,2.0,1.0,0.0,49361.84,0.0
+9971,15600692.0,Buchi,697.0,France,Male,42.0,4.0,0.0,1.0,1.0,0.0,124071.71,1.0
+9972,15772030.0,Yefremova,651.0,Germany,Female,44.0,3.0,126442.59,1.0,1.0,0.0,155711.64,1.0
+9973,15610594.0,Li Fonti,644.0,France,Male,46.0,2.0,106511.85,1.0,1.0,1.0,143783.46,1.0
+9974,15815236.0,Hsia,543.0,Germany,Female,45.0,1.0,107714.74,1.0,1.0,0.0,152290.28,1.0
+9975,15604813.0,Macartney,724.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,171706.66,0.0
+9976,15672598.0,Calabrese,594.0,France,Male,37.0,6.0,0.0,1.0,1.0,1.0,125244.18,0.0
+9977,15798084.0,Hsia,712.0,France,Male,37.0,4.0,123835.52,1.0,1.0,0.0,75884.65,0.0
+9978,15691457.0,Ts'ui,623.0,France,Male,38.0,1.0,160971.87,1.0,1.0,1.0,87071.18,0.0
+9979,15772421.0,Genovese,587.0,France,Male,35.0,2.0,0.0,2.0,0.0,1.0,167032.99,0.0
+9980,15782089.0,Chidumaga,699.0,France,Male,43.0,4.0,0.0,2.0,1.0,0.0,116672.02,0.0
+9981,15773868.0,Onyekachi,655.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,143628.56,0.0
+9982,15668886.0,Pisano,726.0,Spain,Female,29.0,10.0,0.0,1.0,1.0,0.0,167066.95,0.0
+9983,15722583.0,Chin,715.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,0.0,171378.77,0.0
+9984,15603914.0,P'eng,653.0,France,Female,31.0,0.0,131344.52,1.0,1.0,0.0,51387.1,0.0
+9985,15677908.0,Nwachukwu,634.0,France,Male,29.0,1.0,97460.03,1.0,1.0,1.0,179797.79,0.0
+9986,15747772.0,Pisano,621.0,Germany,Female,27.0,7.0,116084.32,2.0,1.0,1.0,97623.42,0.0
+9987,15657535.0,Chiu,678.0,France,Male,28.0,2.0,0.0,2.0,0.0,0.0,126469.09,0.0
+9988,15658852.0,Chiang,705.0,Germany,Female,46.0,6.0,116537.6,1.0,1.0,0.0,102567.05,1.0
+9989,15736112.0,Kao,707.0,France,Male,30.0,8.0,0.0,1.0,1.0,0.0,118959.64,1.0
+9990,15734987.0,Chukwudi,687.0,France,Male,26.0,5.0,0.0,2.0,1.0,0.0,81753.92,0.0
+9991,15809907.0,Ch'ien,785.0,France,Male,39.0,0.0,0.0,2.0,0.0,1.0,173870.39,0.0
+9992,15634213.0,Young,645.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,128521.03,0.0
+9993,15782530.0,Hsia,684.0,France,Male,34.0,8.0,136067.82,1.0,1.0,0.0,114448.77,0.0
+9994,15585867.0,Mazzanti,545.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,90280.7,0.0
+9995,15704910.0,Lockington,746.0,France,Male,28.0,7.0,0.0,1.0,0.0,1.0,139049.24,0.0
+9996,15768342.0,Eluemuno,745.0,France,Male,33.0,7.0,0.0,2.0,0.0,0.0,120512.08,0.0
+9997,15644878.0,Shao,516.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,92357.21,0.0
+9998,15790744.0,Ts'ui,735.0,Spain,Female,48.0,9.0,0.0,2.0,1.0,0.0,182355.21,0.0
+9999,15779947.0,Scott,712.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,72402.0,0.0
+10000,15808023.0,H?,635.0,France,Female,41.0,1.0,0.0,1.0,1.0,0.0,178779.52,1.0
+10001,15730038.0,Maclean,639.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,166957.82,0.0
+10002,15641877.0,Chukwukadibia,679.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,1.0,85890.55,0.0
+10003,15656319.0,Chimaobim,679.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,50974.57,0.0
+10004,15782096.0,Genovese,628.0,Spain,Female,35.0,1.0,0.0,1.0,1.0,1.0,28407.4,0.0
+10005,15627212.0,K'ung,731.0,France,Female,34.0,4.0,0.0,1.0,1.0,1.0,122449.15,0.0
+10006,15568897.0,Udinese,486.0,Germany,Female,29.0,6.0,131778.66,1.0,1.0,0.0,151146.67,0.0
+10007,15733661.0,Ndubueze,598.0,France,Male,31.0,9.0,0.0,2.0,0.0,1.0,80582.36,0.0
+10008,15692664.0,Chu,672.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,51387.1,0.0
+10009,15776545.0,Chia,564.0,France,Male,47.0,1.0,0.0,3.0,1.0,0.0,171922.72,1.0
+10010,15806922.0,H?,573.0,Germany,Female,29.0,2.0,90432.92,2.0,1.0,0.0,71825.23,0.0
+10011,15774586.0,Fink,756.0,France,Female,43.0,1.0,112045.67,1.0,1.0,0.0,73977.23,1.0
+10012,15604345.0,Mollison,627.0,France,Male,61.0,2.0,0.0,1.0,1.0,1.0,48228.1,1.0
+10013,15662884.0,H?,692.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,60590.72,0.0
+10014,15797736.0,Teng,727.0,Spain,Male,35.0,5.0,143054.85,1.0,1.0,0.0,92708.1,0.0
+10015,15682600.0,Okeke,649.0,Germany,Male,32.0,3.0,132057.92,1.0,1.0,0.0,114514.64,0.0
+10016,15724892.0,Okwudilichukwu,659.0,Germany,Male,44.0,4.0,128960.21,1.0,1.0,1.0,82782.08,1.0
+10017,15700864.0,Padovesi,663.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,0.0,100816.29,0.0
+10018,15712121.0,Chukwualuka,592.0,Germany,Female,50.0,2.0,135096.05,2.0,1.0,0.0,152883.95,0.0
+10019,15674840.0,H?,762.0,Spain,Male,54.0,4.0,0.0,1.0,0.0,1.0,26327.42,1.0
+10020,15567383.0,Chukwujamuike,544.0,France,Female,43.0,5.0,0.0,2.0,1.0,1.0,78730.31,0.0
+10021,15790744.0,Chibueze,697.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,140941.47,0.0
+10022,15682540.0,Chikezie,447.0,France,Male,39.0,9.0,148061.49,1.0,0.0,0.0,169415.54,0.0
+10023,15655171.0,Chinweike,683.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,122925.44,0.0
+10024,15749472.0,Ugorji,588.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,151924.9,0.0
+10025,15772995.0,Moseley,597.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,171552.02,0.0
+10026,15675320.0,Ts'ui,613.0,France,Male,27.0,1.0,0.0,2.0,1.0,0.0,49503.5,0.0
+10027,15791268.0,Onwuatuegwu,729.0,Spain,Female,31.0,4.0,0.0,2.0,1.0,0.0,170621.55,0.0
+10028,15759913.0,Pinto,705.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,131736.23,0.0
+10029,15785815.0,P'eng,705.0,Spain,Female,46.0,1.0,0.0,1.0,0.0,0.0,133831.37,1.0
+10030,15593725.0,Manna,707.0,Germany,Female,26.0,4.0,127948.51,1.0,0.0,0.0,155700.15,1.0
+10031,15584518.0,Ting,850.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,162478.11,0.0
+10032,15690796.0,Chidumaga,539.0,Germany,Male,49.0,6.0,114233.18,1.0,0.0,1.0,170886.01,1.0
+10033,15774164.0,Ch'iu,583.0,France,Female,50.0,6.0,0.0,2.0,1.0,0.0,71179.31,0.0
+10034,15704583.0,Whitelegge,653.0,France,Female,31.0,3.0,0.0,2.0,0.0,0.0,180326.83,0.0
+10035,15637138.0,Chiemenam,516.0,Spain,Male,27.0,9.0,0.0,2.0,1.0,0.0,97767.38,0.0
+10036,15642787.0,Ritchie,719.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,1.0,93883.53,0.0
+10037,15668818.0,Ngozichukwuka,558.0,France,Male,32.0,4.0,101060.25,1.0,1.0,1.0,160574.09,0.0
+10038,15654851.0,Yermakova,656.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,158750.24,0.0
+10039,15709256.0,Hsia,712.0,Spain,Male,30.0,6.0,116465.53,1.0,1.0,1.0,110114.19,0.0
+10040,15578980.0,Kambinachi,662.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,86022.09,0.0
+10041,15683522.0,Trevisano,809.0,Germany,Male,35.0,4.0,93616.35,2.0,1.0,0.0,99097.67,0.0
+10042,15772479.0,Mao,675.0,France,Female,48.0,3.0,0.0,1.0,1.0,0.0,173599.38,1.0
+10043,15788776.0,Ch'ien,642.0,Spain,Female,43.0,5.0,152992.91,1.0,1.0,0.0,57553.02,0.0
+10044,15783030.0,Frolova,706.0,Spain,Male,48.0,2.0,0.0,2.0,0.0,0.0,121562.33,0.0
+10045,15594404.0,Tsao,648.0,Germany,Female,43.0,3.0,113909.67,2.0,1.0,1.0,186785.01,0.0
+10046,15570360.0,Onyemachukwu,686.0,France,Female,30.0,9.0,0.0,2.0,1.0,0.0,183585.01,0.0
+10047,15796790.0,Zaytseva,850.0,Spain,Male,24.0,7.0,0.0,2.0,1.0,1.0,122518.5,0.0
+10048,15807008.0,Hao,713.0,Germany,Female,40.0,4.0,127806.91,2.0,0.0,0.0,92865.46,0.0
+10049,15723013.0,Pardey,683.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,171096.2,0.0
+10050,15726403.0,Pagnotto,705.0,Spain,Female,31.0,6.0,96951.21,2.0,1.0,0.0,71492.28,0.0
+10051,15625905.0,Folliero,614.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,114566.47,0.0
+10052,15593470.0,Chukwueloka,605.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,48559.19,0.0
+10053,15665062.0,Horsley,669.0,France,Female,64.0,3.0,0.0,1.0,1.0,0.0,152577.62,1.0
+10054,15579062.0,Hsing,653.0,Germany,Male,37.0,7.0,119370.11,2.0,1.0,1.0,163365.85,0.0
+10055,15643048.0,Martin,678.0,Spain,Female,25.0,8.0,0.0,2.0,1.0,0.0,158037.76,0.0
+10056,15759991.0,Trentino,697.0,Germany,Male,43.0,4.0,151855.33,2.0,1.0,1.0,110593.62,1.0
+10057,15585595.0,Nwachukwu,647.0,Germany,Female,69.0,4.0,123210.55,1.0,1.0,1.0,175743.92,0.0
+10058,15583849.0,Nwankwo,614.0,Spain,Female,46.0,9.0,0.0,1.0,1.0,0.0,137793.35,1.0
+10059,15612043.0,Milanesi,836.0,Spain,Male,64.0,5.0,120599.21,1.0,1.0,1.0,56636.28,0.0
+10060,15636767.0,Fyans,652.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,180595.02,0.0
+10061,15744105.0,K?,678.0,France,Male,47.0,1.0,0.0,1.0,1.0,1.0,187597.98,1.0
+10062,15702789.0,L?,706.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,143598.7,1.0
+10063,15695078.0,Ch'in,613.0,France,Male,41.0,5.0,0.0,2.0,1.0,0.0,182728.8,0.0
+10064,15724307.0,Chinweuba,628.0,Germany,Male,32.0,1.0,113736.27,1.0,1.0,0.0,181598.25,0.0
+10065,15714240.0,Lu,805.0,France,Female,33.0,8.0,0.0,2.0,0.0,0.0,100598.98,0.0
+10066,15798895.0,Genovese,709.0,Germany,Female,37.0,4.0,129855.72,2.0,1.0,0.0,122506.78,1.0
+10067,15720353.0,Okeke,559.0,France,Female,39.0,9.0,0.0,2.0,0.0,0.0,74878.12,0.0
+10068,15691875.0,Yobachi,588.0,Spain,Female,25.0,8.0,0.0,2.0,1.0,0.0,167087.34,0.0
+10069,15667588.0,Martin,724.0,Germany,Female,47.0,3.0,114754.08,2.0,1.0,0.0,156878.42,0.0
+10070,15777873.0,Hsueh,753.0,France,Female,42.0,3.0,0.0,1.0,0.0,0.0,93336.3,1.0
+10071,15593900.0,Chiedozie,727.0,Germany,Male,38.0,10.0,139762.13,2.0,1.0,0.0,131736.23,0.0
+10072,15702919.0,Manna,579.0,France,Male,33.0,9.0,160980.02,1.0,0.0,1.0,134878.34,0.0
+10073,15803639.0,Milne,593.0,France,Female,54.0,0.0,0.0,1.0,1.0,0.0,177738.98,1.0
+10074,15624995.0,Calabrese,774.0,France,Male,20.0,2.0,0.0,2.0,1.0,0.0,174815.54,0.0
+10075,15586870.0,Teng,696.0,France,Male,40.0,2.0,0.0,1.0,1.0,1.0,61312.15,0.0
+10076,15635388.0,Milano,579.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,96670.2,0.0
+10077,15709941.0,Goliwe,627.0,France,Female,30.0,9.0,0.0,2.0,1.0,0.0,93883.87,0.0
+10078,15787222.0,Nucci,535.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,167984.72,0.0
+10079,15701176.0,H?,850.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,181605.85,0.0
+10080,15632210.0,Yermakov,646.0,France,Female,32.0,4.0,0.0,2.0,1.0,1.0,63736.36,0.0
+10081,15779176.0,Hsu,591.0,France,Male,42.0,1.0,0.0,2.0,1.0,1.0,96498.14,0.0
+10082,15769955.0,Pirozzi,644.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,129890.94,0.0
+10083,15683625.0,Toscani,582.0,Germany,Male,22.0,6.0,143571.52,2.0,0.0,1.0,118862.08,0.0
+10084,15635502.0,T'ao,682.0,Germany,Female,32.0,3.0,113383.05,1.0,1.0,0.0,141872.05,0.0
+10085,15798943.0,Nwabugwu,531.0,France,Female,33.0,1.0,141749.68,1.0,1.0,1.0,129740.11,0.0
+10086,15659677.0,Yobachi,613.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,149913.84,0.0
+10087,15794056.0,Onwuamaeze,645.0,France,Male,52.0,6.0,0.0,1.0,0.0,1.0,110317.39,0.0
+10088,15665088.0,Shih,651.0,Spain,Female,71.0,4.0,0.0,2.0,1.0,1.0,174431.01,0.0
+10089,15717116.0,Ebelegbulam,758.0,Spain,Female,29.0,9.0,110949.41,1.0,1.0,0.0,80416.16,0.0
+10090,15688071.0,Kibby,593.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,52876.41,0.0
+10091,15625082.0,Ejimofor,745.0,France,Female,31.0,5.0,83456.79,1.0,1.0,1.0,164925.58,0.0
+10092,15636016.0,Kao,683.0,Spain,Male,34.0,2.0,0.0,2.0,0.0,0.0,121866.87,0.0
+10093,15769783.0,Aitken,627.0,Germany,Male,45.0,2.0,116270.72,1.0,0.0,1.0,162643.07,0.0
+10094,15725028.0,Hsiung,819.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,9983.88,0.0
+10095,15583073.0,Ts'ao,710.0,Germany,Male,40.0,6.0,108881.73,1.0,0.0,0.0,156911.35,0.0
+10096,15791323.0,Yuriev,479.0,Germany,Male,51.0,4.0,132412.34,1.0,1.0,0.0,8982.75,1.0
+10097,15574072.0,Hsu,633.0,France,Female,39.0,4.0,0.0,2.0,1.0,1.0,187718.26,0.0
+10098,15603743.0,Ch'ien,653.0,France,Female,43.0,6.0,0.0,1.0,1.0,0.0,161519.76,1.0
+10099,15616122.0,H?,656.0,Spain,Male,28.0,3.0,107556.06,1.0,1.0,0.0,160546.58,0.0
+10100,15615444.0,Maclean,646.0,France,Male,32.0,5.0,135096.58,1.0,1.0,1.0,61936.1,0.0
+10101,15572631.0,Folliero,565.0,France,Male,41.0,8.0,131513.26,1.0,1.0,0.0,84531.79,0.0
+10102,15585241.0,Pirozzi,778.0,Spain,Female,31.0,4.0,0.0,1.0,1.0,0.0,162820.07,0.0
+10103,15590177.0,Lucciano,619.0,Spain,Female,33.0,8.0,123971.16,1.0,1.0,0.0,119759.24,0.0
+10104,15576077.0,Galkina,719.0,Germany,Female,49.0,3.0,79562.98,2.0,1.0,1.0,171060.01,1.0
+10105,15774882.0,Yobachi,560.0,Spain,Male,44.0,9.0,87926.24,2.0,1.0,0.0,67926.18,0.0
+10106,15624641.0,Zikoranachidimma,627.0,France,Female,35.0,0.0,0.0,2.0,1.0,0.0,133086.45,0.0
+10107,15603481.0,Ts'ui,757.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,126554.54,0.0
+10108,15762927.0,Hsia,676.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,123880.19,0.0
+10109,15674928.0,DeRose,693.0,France,Female,38.0,10.0,0.0,2.0,1.0,0.0,15039.41,0.0
+10110,15736413.0,Chien,628.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,125426.67,0.0
+10111,15576676.0,Ch'ang,594.0,Spain,Male,23.0,7.0,0.0,2.0,1.0,0.0,80587.31,0.0
+10112,15594720.0,Chikezie,627.0,France,Female,32.0,2.0,131647.01,1.0,1.0,0.0,152698.24,0.0
+10113,15652999.0,Iadanza,611.0,Germany,Male,38.0,7.0,98986.28,1.0,1.0,1.0,189428.75,0.0
+10114,15685826.0,Okoli,693.0,Germany,Female,50.0,1.0,152674.51,1.0,1.0,1.0,86701.4,1.0
+10115,15764343.0,Tsai,731.0,France,Female,39.0,8.0,0.0,1.0,1.0,0.0,64350.6,1.0
+10116,15662091.0,Onio,634.0,Germany,Male,40.0,3.0,122388.38,1.0,1.0,0.0,179866.01,1.0
+10117,15778947.0,Chiawuotu,703.0,Germany,Female,37.0,5.0,122570.69,2.0,1.0,0.0,144290.97,1.0
+10118,15595071.0,Hs?eh,644.0,Germany,Male,50.0,5.0,96147.55,1.0,1.0,0.0,122996.76,1.0
+10119,15682357.0,Chu,581.0,Germany,Male,38.0,9.0,145536.9,2.0,1.0,1.0,138380.11,0.0
+10120,15682463.0,Teng,624.0,France,Male,36.0,1.0,0.0,1.0,0.0,1.0,16081.62,0.0
+10121,15723169.0,Nwachinemelu,745.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,143668.58,0.0
+10122,15702656.0,P'an,535.0,Spain,Female,24.0,3.0,0.0,2.0,1.0,1.0,129964.94,0.0
+10123,15603851.0,Tien,721.0,France,Male,44.0,8.0,0.0,1.0,1.0,1.0,110782.88,1.0
+10124,15804052.0,Shih,590.0,Spain,Female,24.0,2.0,0.0,1.0,1.0,0.0,104786.59,1.0
+10125,15791174.0,T'ien,710.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,0.0,120108.72,0.0
+10126,15784923.0,Fyodorov,678.0,France,Female,28.0,9.0,0.0,1.0,1.0,1.0,60416.76,0.0
+10127,15581299.0,T'ao,613.0,Spain,Male,34.0,1.0,0.0,1.0,1.0,0.0,158043.11,0.0
+10128,15651802.0,Onyekachukwu,751.0,Germany,Female,53.0,4.0,102085.72,2.0,1.0,1.0,123982.14,0.0
+10129,15640258.0,Chidiebere,572.0,France,Female,26.0,2.0,0.0,2.0,1.0,1.0,168515.61,0.0
+10130,15684516.0,Hsueh,602.0,Germany,Male,41.0,2.0,127818.52,1.0,1.0,0.0,136620.64,1.0
+10131,15688865.0,Toscani,643.0,Germany,Male,40.0,9.0,71026.77,2.0,1.0,1.0,117023.08,0.0
+10132,15763093.0,Tuan,593.0,France,Female,32.0,2.0,102768.42,1.0,1.0,1.0,71865.31,0.0
+10133,15757981.0,Scott,562.0,Germany,Male,39.0,5.0,142289.28,1.0,1.0,1.0,92814.92,0.0
+10134,15604588.0,T'ien,775.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,65612.12,0.0
+10135,15747168.0,Fiorentino,675.0,Spain,Female,33.0,8.0,0.0,1.0,0.0,1.0,193009.21,0.0
+10136,15756894.0,Nwabugwu,615.0,France,Male,24.0,4.0,62833.86,1.0,1.0,1.0,153342.38,0.0
+10137,15609977.0,Crawford,732.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,65765.22,0.0
+10138,15737795.0,Hs?,652.0,Spain,Male,37.0,1.0,102787.42,1.0,1.0,0.0,149991.32,0.0
+10139,15623202.0,Nkemdirim,738.0,Germany,Male,37.0,1.0,108926.02,2.0,1.0,1.0,138536.09,0.0
+10140,15738954.0,Dyer,559.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,43943.09,0.0
+10141,15782688.0,T'ien,587.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,25183.01,0.0
+10142,15752731.0,Ch'ien,558.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,83910.4,0.0
+10143,15756871.0,Hsing,741.0,France,Male,40.0,6.0,0.0,2.0,0.0,0.0,167573.06,0.0
+10144,15782530.0,Chubb,731.0,Germany,Female,22.0,6.0,94493.03,2.0,1.0,1.0,47777.15,0.0
+10145,15651958.0,Palerma,603.0,France,Male,47.0,8.0,0.0,2.0,0.0,0.0,143022.31,0.0
+10146,15654506.0,Chiemenam,708.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,107964.35,0.0
+10147,15804862.0,Chukwumaobim,678.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,113557.7,0.0
+10148,15809837.0,Ts'ai,749.0,France,Male,35.0,3.0,91893.32,1.0,1.0,0.0,174777.98,0.0
+10149,15626233.0,Udegbunam,677.0,France,Male,27.0,5.0,0.0,2.0,1.0,1.0,151316.37,0.0
+10150,15566708.0,Palerma,636.0,Germany,Female,28.0,1.0,113034.63,2.0,1.0,0.0,100601.73,0.0
+10151,15763898.0,Chineze,711.0,France,Female,42.0,2.0,109949.05,1.0,1.0,1.0,26136.13,1.0
+10152,15760446.0,Chukwudi,657.0,France,Male,38.0,7.0,87739.08,1.0,0.0,0.0,152880.55,0.0
+10153,15791045.0,Uspenskaya,663.0,Spain,Female,40.0,3.0,0.0,1.0,1.0,0.0,166825.57,1.0
+10154,15611756.0,Ch'eng,689.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,160128.99,0.0
+10155,15739366.0,Kenechi,554.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,1.0,66810.48,0.0
+10156,15662494.0,Chukwumaobim,686.0,France,Female,35.0,4.0,80764.03,2.0,1.0,1.0,126477.41,0.0
+10157,15804822.0,Palerma,741.0,Spain,Female,33.0,5.0,104339.56,1.0,1.0,1.0,60416.76,1.0
+10158,15692291.0,Hsia,732.0,France,Male,48.0,9.0,0.0,1.0,1.0,0.0,187841.89,1.0
+10159,15594915.0,Ugochukwutubelum,696.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,81450.97,0.0
+10160,15612095.0,Smith,543.0,Spain,Male,40.0,9.0,0.0,2.0,1.0,0.0,176780.69,0.0
+10161,15671779.0,Chukwualuka,686.0,Germany,Male,50.0,5.0,163723.41,1.0,1.0,0.0,28257.89,1.0
+10162,15573077.0,Efimov,710.0,Germany,Female,29.0,4.0,116927.89,1.0,1.0,1.0,60905.51,1.0
+10163,15643826.0,Nkemdirim,706.0,Germany,Male,53.0,4.0,106913.43,2.0,1.0,0.0,82393.08,1.0
+10164,15734461.0,Mazzanti,850.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,72950.14,0.0
+10165,15598883.0,Milanesi,673.0,Spain,Male,41.0,7.0,0.0,2.0,1.0,1.0,143952.24,0.0
+10166,15614361.0,Chukwuemeka,697.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,180551.76,0.0
+10167,15644962.0,Shih,659.0,Germany,Female,48.0,5.0,148045.35,1.0,0.0,0.0,114514.64,1.0
+10168,15784491.0,Chukwuebuka,704.0,France,Female,27.0,6.0,0.0,2.0,1.0,0.0,182609.97,0.0
+10169,15673055.0,P'eng,624.0,Germany,Male,40.0,4.0,127948.51,1.0,1.0,0.0,159800.16,0.0
+10170,15639004.0,Hsiung,807.0,France,Female,24.0,4.0,0.0,1.0,1.0,1.0,60941.27,1.0
+10171,15686885.0,Manna,743.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,160976.85,0.0
+10172,15792228.0,Nkemdirim,730.0,France,Female,44.0,8.0,155413.07,1.0,1.0,1.0,126475.79,1.0
+10173,15591429.0,Ch'iu,705.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,71672.86,0.0
+10174,15698732.0,Chidimma,724.0,Spain,Male,47.0,3.0,0.0,1.0,1.0,1.0,102233.73,0.0
+10175,15766017.0,Chukwualuka,613.0,France,Female,36.0,2.0,72948.58,2.0,0.0,0.0,167573.06,0.0
+10176,15739692.0,Chiu,682.0,France,Male,43.0,7.0,0.0,2.0,0.0,1.0,84932.4,0.0
+10177,15646563.0,Blythe,727.0,Spain,Male,33.0,9.0,123601.56,1.0,1.0,0.0,131647.31,0.0
+10178,15776824.0,O'Sullivan,649.0,Germany,Male,42.0,7.0,109949.05,2.0,1.0,1.0,176580.93,0.0
+10179,15645496.0,Yegorova,564.0,Germany,Male,52.0,8.0,104336.43,1.0,1.0,0.0,121747.96,1.0
+10180,15600575.0,Tsao,724.0,France,Male,36.0,3.0,0.0,1.0,1.0,0.0,103832.58,0.0
+10181,15684861.0,Hsieh,792.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,1.0,48502.88,0.0
+10182,15798975.0,Nwankwo,613.0,France,Female,58.0,8.0,171787.84,1.0,1.0,0.0,79270.02,1.0
+10183,15624374.0,Ch'in,521.0,France,Female,42.0,9.0,0.0,2.0,1.0,0.0,136909.87,0.0
+10184,15665629.0,Smith,850.0,France,Male,40.0,3.0,95507.12,1.0,1.0,1.0,161848.03,1.0
+10185,15741629.0,Atherton,600.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,1.0,86957.42,0.0
+10186,15606076.0,Zetticci,704.0,Spain,Female,37.0,7.0,0.0,1.0,1.0,1.0,179908.71,0.0
+10187,15642570.0,Onwumelu,624.0,France,Male,32.0,3.0,0.0,1.0,1.0,1.0,131712.56,0.0
+10188,15661349.0,Chidiegwu,766.0,Spain,Male,46.0,6.0,0.0,2.0,1.0,1.0,64872.33,0.0
+10189,15649024.0,Ts'ai,807.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,171060.01,0.0
+10190,15697948.0,Chukwueloka,584.0,Germany,Female,36.0,8.0,110480.48,1.0,1.0,1.0,158891.02,1.0
+10191,15800440.0,Y?,636.0,Spain,Male,46.0,0.0,0.0,1.0,1.0,0.0,161243.09,1.0
+10192,15791535.0,K'ung,574.0,France,Female,34.0,2.0,57530.06,1.0,0.0,1.0,171020.09,0.0
+10193,15591607.0,Obiajulu,712.0,Spain,Male,66.0,2.0,0.0,2.0,1.0,1.0,51548.14,0.0
+10194,15692599.0,Nwoye,684.0,Germany,Male,30.0,8.0,112670.48,1.0,1.0,0.0,127569.8,0.0
+10195,15787884.0,Teng,516.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,163989.66,0.0
+10196,15625941.0,T'ien,714.0,France,Female,51.0,10.0,0.0,2.0,1.0,1.0,40318.33,0.0
+10197,15681081.0,Liao,652.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,163517.71,0.0
+10198,15605059.0,Ch'en,678.0,Spain,Female,52.0,3.0,0.0,1.0,1.0,1.0,176882.08,1.0
+10199,15731519.0,Iweobiegbunam,707.0,France,Male,36.0,10.0,143680.47,2.0,1.0,1.0,157871.55,1.0
+10200,15598510.0,Milne,635.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,166476.95,0.0
+10201,15585255.0,Shih,684.0,France,Female,38.0,6.0,0.0,1.0,0.0,1.0,97939.4,0.0
+10202,15790683.0,Fanucci,637.0,Germany,Female,39.0,5.0,137657.71,1.0,0.0,1.0,155722.66,1.0
+10203,15773394.0,Iheatu,711.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,38196.24,0.0
+10204,15592999.0,Li,718.0,Germany,Male,38.0,1.0,125437.88,1.0,1.0,1.0,86937.2,0.0
+10205,15788448.0,Chukwudi,678.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,105618.14,0.0
+10206,15762682.0,Genovese,850.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,64716.08,0.0
+10207,15646615.0,Chukwukere,588.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,71178.09,0.0
+10208,15733114.0,Chiu,828.0,France,Female,27.0,2.0,0.0,2.0,1.0,1.0,109638.78,0.0
+10209,15743893.0,Ch'in,671.0,Spain,Female,38.0,10.0,0.0,2.0,1.0,0.0,143433.03,0.0
+10210,15679983.0,Niu,850.0,Spain,Female,40.0,6.0,142929.16,3.0,0.0,1.0,103570.8,1.0
+10211,15672965.0,Macleod,599.0,Germany,Female,45.0,0.0,114512.59,1.0,1.0,1.0,178291.09,0.0
+10212,15784923.0,Tsao,624.0,Spain,Male,51.0,5.0,0.0,2.0,0.0,1.0,88890.07,0.0
+10213,15658852.0,Uwakwe,745.0,Germany,Male,30.0,2.0,121276.32,2.0,1.0,0.0,154486.01,0.0
+10214,15732235.0,Onyemauchechukwu,675.0,France,Male,41.0,8.0,0.0,2.0,0.0,1.0,123982.14,0.0
+10215,15812647.0,Trevisano,710.0,Germany,Male,34.0,2.0,88815.56,1.0,0.0,1.0,172646.88,0.0
+10216,15770039.0,Gant,644.0,France,Male,39.0,8.0,0.0,1.0,1.0,1.0,48233.75,0.0
+10217,15716609.0,Chukwualuka,719.0,France,Female,63.0,6.0,0.0,2.0,1.0,1.0,172495.81,0.0
+10218,15583595.0,Doyne,682.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,161407.48,0.0
+10219,15787597.0,Lai,605.0,France,Male,37.0,3.0,124577.33,1.0,1.0,0.0,177524.19,0.0
+10220,15678886.0,Liao,696.0,Spain,Male,36.0,5.0,92833.89,1.0,1.0,1.0,105983.09,0.0
+10221,15749489.0,Nucci,597.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,61326.43,0.0
+10222,15698749.0,T'ang,648.0,France,Female,57.0,3.0,0.0,1.0,0.0,1.0,93844.82,1.0
+10223,15671390.0,Tretiakova,732.0,France,Female,36.0,9.0,0.0,2.0,0.0,1.0,182566.76,0.0
+10224,15711571.0,Ch'ang,774.0,France,Female,35.0,3.0,116270.72,1.0,1.0,0.0,179436.6,0.0
+10225,15686983.0,Maclean,651.0,France,Female,45.0,7.0,0.0,1.0,1.0,1.0,54495.82,1.0
+10226,15804919.0,Cremonesi,705.0,France,Female,38.0,9.0,143350.41,1.0,0.0,0.0,152855.96,0.0
+10227,15685920.0,Toscani,706.0,Germany,Male,40.0,5.0,117700.86,1.0,1.0,0.0,140000.29,0.0
+10228,15794560.0,Goliwe,652.0,France,Female,32.0,3.0,0.0,1.0,0.0,1.0,183682.09,1.0
+10229,15707968.0,Zito,712.0,Germany,Male,34.0,7.0,128306.7,1.0,0.0,0.0,146637.45,1.0
+10230,15780650.0,Yermolayev,637.0,Spain,Female,27.0,8.0,0.0,2.0,1.0,1.0,181276.22,0.0
+10231,15802362.0,Chien,596.0,France,Male,54.0,9.0,115743.6,1.0,1.0,0.0,151146.67,1.0
+10232,15575417.0,Brown,571.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,1.0,139953.94,0.0
+10233,15705890.0,Udinesi,535.0,Germany,Male,43.0,9.0,129717.3,1.0,0.0,0.0,53477.76,1.0
+10234,15594862.0,Mazzanti,678.0,France,Male,39.0,0.0,0.0,2.0,1.0,0.0,101778.9,0.0
+10235,15585835.0,Hsu,549.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,163798.41,0.0
+10236,15749130.0,Zito,619.0,France,Female,48.0,1.0,0.0,1.0,1.0,1.0,106920.57,1.0
+10237,15780144.0,Nkemakolam,748.0,Germany,Female,49.0,8.0,119787.76,1.0,1.0,1.0,160532.88,1.0
+10238,15709511.0,Chukwuebuka,590.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,38570.13,0.0
+10239,15718789.0,Hs?,732.0,France,Female,37.0,9.0,145521.07,1.0,1.0,0.0,64448.36,1.0
+10240,15657439.0,Milani,656.0,Germany,Female,43.0,9.0,121543.26,1.0,1.0,0.0,100403.05,1.0
+10241,15571221.0,Chiganu,554.0,Spain,Male,44.0,4.0,115636.05,1.0,1.0,1.0,71682.54,0.0
+10242,15797767.0,Tuan,670.0,Spain,Female,38.0,2.0,122453.97,2.0,1.0,1.0,51919.04,0.0
+10243,15683677.0,Chidiebele,581.0,Germany,Female,39.0,9.0,107871.72,2.0,0.0,1.0,180142.42,0.0
+10244,15621210.0,Hs?,589.0,Germany,Female,40.0,1.0,142406.43,2.0,1.0,1.0,172096.08,0.0
+10245,15757425.0,Pinto,683.0,France,Male,41.0,7.0,105795.9,1.0,1.0,0.0,69079.85,0.0
+10246,15768574.0,Chukwuemeka,711.0,France,Male,33.0,8.0,0.0,2.0,0.0,0.0,131647.31,0.0
+10247,15802478.0,Hao,686.0,Germany,Female,52.0,1.0,130169.41,3.0,1.0,0.0,108699.59,1.0
+10248,15736397.0,Tuan,746.0,Spain,Male,58.0,8.0,81818.49,1.0,1.0,1.0,28184.7,0.0
+10249,15595537.0,Ch'iu,554.0,Spain,Female,46.0,2.0,0.0,2.0,1.0,0.0,100101.06,0.0
+10250,15625023.0,Udegbulam,713.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,0.0,16250.61,0.0
+10251,15578885.0,T'ien,810.0,France,Male,43.0,9.0,0.0,3.0,0.0,1.0,85547.33,1.0
+10252,15578515.0,Milne,675.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,163477.22,0.0
+10253,15752626.0,Yudin,597.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,143598.7,0.0
+10254,15762682.0,Chukwualuka,632.0,Germany,Male,41.0,2.0,130682.11,2.0,1.0,0.0,72318.77,0.0
+10255,15582335.0,Chiekwugo,684.0,France,Female,42.0,2.0,0.0,1.0,1.0,0.0,116969.89,1.0
+10256,15700174.0,Chukwuebuka,543.0,Spain,Male,38.0,2.0,98373.26,1.0,1.0,0.0,95727.31,0.0
+10257,15751203.0,Rizzo,590.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,83629.6,0.0
+10258,15614813.0,Lucchesi,850.0,France,Female,43.0,10.0,0.0,2.0,1.0,1.0,136526.12,0.0
+10259,15726167.0,Chia,792.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,81776.84,0.0
+10260,15596972.0,T'ao,753.0,Spain,Female,29.0,5.0,0.0,2.0,0.0,0.0,117038.96,0.0
+10261,15650391.0,Y?an,716.0,Spain,Male,43.0,3.0,129502.49,1.0,1.0,0.0,141404.08,0.0
+10262,15646616.0,Lo,652.0,Spain,Male,33.0,7.0,122038.34,1.0,1.0,0.0,141753.97,0.0
+10263,15622117.0,Oluchukwu,646.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,50695.97,0.0
+10264,15617476.0,Aitken,656.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,182784.11,0.0
+10265,15641640.0,Chizuoke,663.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,130789.15,0.0
+10266,15723320.0,Chidiegwu,579.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,162719.69,0.0
+10267,15598883.0,Chiu,753.0,France,Male,21.0,3.0,151963.26,1.0,1.0,1.0,164741.5,0.0
+10268,15794964.0,Lucchese,538.0,France,Male,48.0,5.0,0.0,1.0,1.0,0.0,91086.73,1.0
+10269,15690175.0,Nkemakonam,663.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,143450.61,0.0
+10270,15621205.0,Yermolayev,711.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,176945.96,0.0
+10271,15791302.0,Walker,565.0,Germany,Male,40.0,8.0,124576.65,1.0,0.0,0.0,176768.59,1.0
+10272,15771483.0,P'eng,636.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,7900.46,0.0
+10273,15702541.0,Chuang,592.0,France,Male,32.0,5.0,0.0,1.0,0.0,1.0,75825.57,0.0
+10274,15798429.0,Tung,744.0,France,Male,30.0,1.0,0.0,1.0,1.0,1.0,96832.82,0.0
+10275,15761497.0,Yefremov,674.0,France,Female,36.0,4.0,102962.65,1.0,1.0,1.0,106912.35,0.0
+10276,15584536.0,Kao,545.0,Germany,Female,45.0,2.0,126619.27,3.0,0.0,1.0,104775.7,1.0
+10277,15757837.0,Ch'ang,619.0,France,Male,39.0,9.0,152086.15,2.0,1.0,0.0,115719.08,0.0
+10278,15581871.0,Aksyonov,628.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,147040.25,0.0
+10279,15754952.0,Fanucci,627.0,Spain,Female,27.0,4.0,181671.16,1.0,1.0,0.0,88815.25,0.0
+10280,15761023.0,Ts'ui,553.0,France,Female,28.0,9.0,0.0,2.0,1.0,0.0,172097.95,0.0
+10281,15703614.0,Tung,667.0,France,Male,32.0,8.0,0.0,2.0,0.0,1.0,61636.22,0.0
+10282,15650670.0,Onyemere,743.0,France,Male,47.0,6.0,0.0,1.0,0.0,0.0,118862.08,1.0
+10283,15662658.0,Onwuamaeze,718.0,Germany,Male,41.0,6.0,139822.41,1.0,1.0,1.0,172459.39,0.0
+10284,15700627.0,Cremonesi,743.0,Germany,Female,36.0,8.0,71142.77,2.0,1.0,1.0,57975.8,0.0
+10285,15757837.0,Chukwuemeka,635.0,Spain,Male,36.0,6.0,0.0,1.0,1.0,0.0,134600.94,1.0
+10286,15592925.0,Chimaobim,564.0,Spain,Male,45.0,3.0,82697.28,1.0,1.0,0.0,134960.98,0.0
+10287,15791114.0,H?,627.0,France,Female,37.0,3.0,131033.76,1.0,0.0,0.0,151581.79,1.0
+10288,15794046.0,Yeh,575.0,France,Female,52.0,7.0,0.0,1.0,1.0,0.0,65636.92,1.0
+10289,15580249.0,Nnanna,725.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,115676.61,0.0
+10290,15594717.0,Chukwumaobim,637.0,France,Male,27.0,3.0,0.0,1.0,0.0,1.0,64448.36,0.0
+10291,15775124.0,Young,603.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,47832.82,0.0
+10292,15731327.0,Martin,850.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,72851.74,0.0
+10293,15605067.0,Chijindum,547.0,Germany,Female,39.0,0.0,111379.55,2.0,0.0,1.0,19150.14,0.0
+10294,15672930.0,Yobanna,645.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,1.0,81409.0,0.0
+10295,15736399.0,Chukwudi,693.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,109742.55,0.0
+10296,15664543.0,Genovese,731.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,55701.62,0.0
+10297,15768455.0,Shih,596.0,France,Male,38.0,3.0,0.0,2.0,0.0,0.0,129964.94,0.0
+10298,15782418.0,McWilliam,743.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,136329.96,0.0
+10299,15743478.0,Chukwuemeka,652.0,France,Male,25.0,2.0,0.0,2.0,1.0,1.0,98898.86,0.0
+10300,15792329.0,Yermolayev,660.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,71709.12,0.0
+10301,15576256.0,Nwachukwu,714.0,Germany,Female,46.0,4.0,139874.09,1.0,0.0,0.0,25669.1,1.0
+10302,15684960.0,Ting,576.0,Germany,Male,42.0,8.0,126473.99,1.0,1.0,0.0,37688.31,1.0
+10303,15661290.0,Mazzi,468.0,France,Male,23.0,3.0,116467.43,1.0,1.0,0.0,67311.12,0.0
+10304,15682533.0,Chukwudi,587.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,0.0,117946.3,0.0
+10305,15671243.0,Lai,694.0,France,Male,39.0,8.0,0.0,1.0,1.0,0.0,152567.75,0.0
+10306,15640407.0,Hsueh,538.0,France,Male,33.0,8.0,0.0,1.0,1.0,0.0,178709.98,0.0
+10307,15662434.0,Nwokike,633.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,100324.01,0.0
+10308,15807961.0,Kao,706.0,France,Male,45.0,4.0,0.0,2.0,1.0,0.0,142763.51,0.0
+10309,15594812.0,Mazzi,719.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,77864.41,0.0
+10310,15571081.0,Ch'en,609.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,137739.02,0.0
+10311,15614103.0,Chinwemma,553.0,Spain,Male,32.0,4.0,132944.55,1.0,1.0,1.0,126475.79,0.0
+10312,15758531.0,Chinwenma,619.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,142903.44,0.0
+10313,15643392.0,Tsao,627.0,France,Female,32.0,3.0,0.0,2.0,1.0,1.0,93769.89,0.0
+10314,15573741.0,Yashina,786.0,France,Female,56.0,7.0,0.0,1.0,1.0,1.0,77906.87,1.0
+10315,15766183.0,Mancini,633.0,France,Male,56.0,2.0,0.0,1.0,1.0,0.0,93959.96,1.0
+10316,15595047.0,Fiorentino,727.0,France,Female,26.0,4.0,120962.13,1.0,1.0,0.0,99490.01,0.0
+10317,15687913.0,Udinesi,684.0,France,Female,40.0,8.0,131659.59,1.0,0.0,0.0,45613.75,0.0
+10318,15571244.0,K'ung,695.0,France,Female,31.0,8.0,121647.54,1.0,1.0,1.0,147545.65,0.0
+10319,15780961.0,Chikezie,697.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,119127.06,0.0
+10320,15725039.0,Ch'eng,661.0,Spain,Male,40.0,3.0,0.0,2.0,1.0,1.0,131301.74,0.0
+10321,15770405.0,Onyekaozulu,543.0,Germany,Female,41.0,3.0,132576.75,1.0,1.0,0.0,79871.16,0.0
+10322,15770405.0,T'ang,656.0,France,Male,52.0,8.0,0.0,1.0,0.0,0.0,143357.07,1.0
+10323,15682533.0,Onyemachukwu,621.0,France,Female,30.0,3.0,99816.46,1.0,1.0,0.0,62389.35,0.0
+10324,15616088.0,Y?,587.0,Spain,Female,50.0,5.0,0.0,1.0,1.0,0.0,158750.21,1.0
+10325,15682357.0,Nnaife,651.0,Spain,Female,33.0,8.0,0.0,3.0,1.0,0.0,82298.59,1.0
+10326,15643359.0,P'eng,628.0,Germany,Female,38.0,1.0,102290.53,2.0,1.0,1.0,58601.61,0.0
+10327,15689412.0,Manna,602.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,181879.56,0.0
+10328,15729771.0,Y?an,535.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,84760.32,0.0
+10329,15595388.0,Onyekachi,771.0,Germany,Male,42.0,7.0,133800.98,2.0,1.0,1.0,89893.6,0.0
+10330,15752650.0,Chiu,594.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,180712.28,0.0
+10331,15659820.0,Hsieh,684.0,Germany,Male,54.0,0.0,122822.0,1.0,1.0,1.0,92840.67,1.0
+10332,15643865.0,H?,708.0,Germany,Male,30.0,1.0,145177.84,1.0,1.0,1.0,171099.22,0.0
+10333,15684958.0,Maclean,579.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,95562.81,0.0
+10334,15692761.0,K'ung,713.0,France,Female,31.0,9.0,0.0,1.0,0.0,1.0,88947.62,0.0
+10335,15651802.0,Ritchie,584.0,Germany,Male,43.0,5.0,121394.42,1.0,0.0,0.0,71139.31,1.0
+10336,15700325.0,Smith,573.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,1.0,45959.86,0.0
+10337,15682470.0,Ngozichukwuka,544.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,91346.03,0.0
+10338,15743456.0,Ijendu,662.0,France,Female,37.0,5.0,0.0,2.0,1.0,1.0,97985.2,0.0
+10339,15742285.0,Hsueh,775.0,Germany,Male,43.0,7.0,130786.92,2.0,1.0,1.0,162697.93,0.0
+10340,15652693.0,Kao,667.0,France,Female,25.0,9.0,0.0,2.0,1.0,0.0,152845.99,0.0
+10341,15793883.0,Lucchese,696.0,France,Male,23.0,9.0,0.0,2.0,0.0,0.0,126812.63,0.0
+10342,15750547.0,Ting,735.0,Germany,Male,40.0,0.0,121325.84,1.0,1.0,1.0,167522.19,0.0
+10343,15583724.0,Cremonesi,773.0,Spain,Female,37.0,3.0,0.0,2.0,0.0,0.0,162858.29,0.0
+10344,15785782.0,Nwabugwu,706.0,France,Female,31.0,1.0,0.0,1.0,1.0,0.0,73977.23,0.0
+10345,15702300.0,Baresi,471.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,122547.58,0.0
+10346,15802466.0,Fiorentini,687.0,France,Male,42.0,0.0,0.0,1.0,1.0,0.0,123428.98,0.0
+10347,15659710.0,Martin,672.0,Spain,Female,44.0,7.0,106070.89,1.0,1.0,0.0,91673.6,1.0
+10348,15725762.0,Hsiao,721.0,France,Male,46.0,2.0,0.0,2.0,0.0,0.0,140173.17,0.0
+10349,15597913.0,K?,575.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,171096.2,0.0
+10350,15684769.0,Chikwendu,646.0,Germany,Female,33.0,3.0,98848.19,2.0,0.0,0.0,102515.42,0.0
+10351,15711287.0,Hsing,711.0,Germany,Male,47.0,4.0,120777.88,1.0,1.0,0.0,180622.03,1.0
+10352,15581229.0,Chiu,615.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,171762.87,0.0
+10353,15776051.0,Olisanugo,682.0,France,Male,43.0,0.0,0.0,2.0,1.0,0.0,121409.06,0.0
+10354,15657540.0,Aksenov,755.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,0.0,172576.43,0.0
+10355,15641595.0,Chikelu,575.0,Germany,Male,29.0,10.0,145081.73,1.0,1.0,0.0,64907.48,1.0
+10356,15641640.0,Ozerova,635.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,64872.55,0.0
+10357,15635177.0,Houghton,544.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,45445.78,0.0
+10358,15573318.0,Rizzo,712.0,Spain,Male,45.0,8.0,0.0,1.0,1.0,0.0,102930.46,0.0
+10359,15735080.0,Ch'ang,595.0,France,Female,34.0,7.0,121669.23,1.0,1.0,1.0,18094.96,0.0
+10360,15690526.0,Chukwudi,592.0,Spain,Female,38.0,6.0,137986.55,1.0,1.0,1.0,166495.2,0.0
+10361,15775318.0,Lo Duca,562.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,131718.12,0.0
+10362,15788676.0,Liao,644.0,France,Female,34.0,3.0,0.0,2.0,0.0,1.0,166682.57,0.0
+10363,15642996.0,Cremonesi,643.0,France,Male,43.0,2.0,0.0,2.0,0.0,0.0,138344.7,0.0
+10364,15664498.0,Henty,628.0,France,Female,34.0,10.0,0.0,2.0,1.0,0.0,133086.45,0.0
+10365,15692761.0,Chinwendu,696.0,France,Male,35.0,2.0,146033.62,1.0,0.0,1.0,98810.78,0.0
+10366,15745307.0,K?,624.0,France,Female,61.0,3.0,0.0,1.0,1.0,1.0,89566.64,1.0
+10367,15809823.0,Dettmann,711.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,1.0,79577.4,0.0
+10368,15733581.0,O'Donnell,703.0,France,Male,28.0,5.0,0.0,1.0,0.0,0.0,172363.41,1.0
+10369,15672912.0,Nnamutaezinwa,572.0,France,Male,32.0,5.0,0.0,1.0,1.0,1.0,159629.64,0.0
+10370,15752953.0,Chiawuotu,696.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,85424.34,0.0
+10371,15592877.0,Yevseyev,715.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,51362.82,0.0
+10372,15758813.0,Binder,649.0,Germany,Male,29.0,9.0,107556.06,1.0,1.0,0.0,71139.31,0.0
+10373,15805627.0,Y?,667.0,France,Female,41.0,5.0,0.0,2.0,1.0,0.0,129912.74,0.0
+10374,15749693.0,Smith,673.0,France,Female,47.0,9.0,120787.18,2.0,1.0,1.0,171928.04,0.0
+10375,15591100.0,Chigozie,628.0,Germany,Male,56.0,4.0,108916.1,2.0,1.0,0.0,177355.8,1.0
+10376,15700965.0,Lucchese,671.0,Spain,Female,40.0,6.0,0.0,1.0,1.0,1.0,147074.67,0.0
+10377,15644765.0,Shubin,691.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,61961.57,0.0
+10378,15571917.0,Burns,831.0,Germany,Female,34.0,9.0,106344.73,1.0,1.0,1.0,120834.58,1.0
+10379,15580213.0,Aksakova,646.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,70783.61,0.0
+10380,15771742.0,Trevisano,599.0,France,Female,49.0,2.0,0.0,2.0,0.0,0.0,181208.47,0.0
+10381,15772777.0,T'ao,577.0,France,Male,34.0,3.0,137811.75,1.0,0.0,1.0,172097.95,0.0
+10382,15598088.0,Maclean,644.0,France,Female,44.0,3.0,0.0,2.0,1.0,0.0,149614.81,0.0
+10383,15681332.0,Onwumelu,694.0,France,Female,46.0,1.0,0.0,1.0,0.0,0.0,120790.02,1.0
+10384,15579548.0,Lettiere,841.0,France,Male,39.0,2.0,0.0,1.0,1.0,1.0,76640.29,0.0
+10385,15803964.0,P'eng,714.0,Spain,Male,52.0,9.0,98877.95,1.0,0.0,1.0,131300.68,0.0
+10386,15637774.0,Lo,684.0,Spain,Male,45.0,8.0,0.0,1.0,1.0,0.0,129562.74,1.0
+10387,15595037.0,Ts'ao,611.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,95611.47,0.0
+10388,15676521.0,Teng,736.0,Spain,Male,36.0,1.0,0.0,1.0,1.0,1.0,95611.47,0.0
+10389,15576560.0,Ch'iu,663.0,Germany,Male,24.0,4.0,121702.73,2.0,1.0,0.0,134441.6,0.0
+10390,15792726.0,Nucci,715.0,France,Male,42.0,0.0,0.0,1.0,1.0,1.0,132029.95,0.0
+10391,15692671.0,Palerma,614.0,France,Female,34.0,9.0,0.0,1.0,0.0,1.0,133814.28,0.0
+10392,15653547.0,Ch'ien,790.0,France,Female,52.0,3.0,0.0,1.0,0.0,0.0,166799.93,1.0
+10393,15592937.0,Ch'eng,684.0,France,Female,39.0,2.0,178058.06,1.0,1.0,0.0,145518.31,0.0
+10394,15668309.0,Hsiung,544.0,Spain,Male,38.0,8.0,129022.06,1.0,1.0,1.0,143913.32,0.0
+10395,15728683.0,Bibi,613.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,0.0,95007.8,0.0
+10396,15582276.0,Ch'ang,563.0,France,Female,47.0,7.0,0.0,2.0,0.0,0.0,144532.85,0.0
+10397,15772073.0,Yobachi,648.0,Germany,Female,49.0,4.0,131023.76,1.0,1.0,1.0,170518.85,0.0
+10398,15661412.0,Chukwufumnanya,758.0,Germany,Male,44.0,8.0,92560.57,1.0,1.0,0.0,123427.98,0.0
+10399,15802501.0,Nkemakolam,663.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,0.0,176614.86,0.0
+10400,15641850.0,Onyemauchechukwu,592.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,1.0,63687.06,0.0
+10401,15700554.0,Ch'ien,658.0,France,Female,27.0,3.0,0.0,1.0,1.0,1.0,91510.71,0.0
+10402,15612109.0,Shih,691.0,Germany,Female,35.0,8.0,96779.97,2.0,1.0,0.0,79577.38,0.0
+10403,15623649.0,Chukwujamuike,627.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,152956.33,0.0
+10404,15732967.0,Piazza,641.0,France,Female,36.0,10.0,0.0,2.0,1.0,1.0,121846.81,0.0
+10405,15702571.0,Obialo,673.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,159483.76,0.0
+10406,15652674.0,Chukwuemeka,705.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,154775.7,0.0
+10407,15583073.0,Yermakova,599.0,Germany,Female,46.0,6.0,116574.85,1.0,1.0,0.0,161074.85,1.0
+10408,15704769.0,Chiu,543.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,180843.57,0.0
+10409,15682472.0,Baresi,585.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,140889.16,0.0
+10410,15731327.0,Chidubem,586.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,65801.44,0.0
+10411,15662955.0,Pisani,724.0,Germany,Female,43.0,4.0,115463.16,1.0,1.0,1.0,135925.72,1.0
+10412,15677906.0,Toscani,579.0,France,Male,61.0,1.0,0.0,2.0,1.0,1.0,38596.34,0.0
+10413,15687558.0,Hsueh,670.0,France,Male,35.0,7.0,148061.89,1.0,1.0,1.0,124592.84,0.0
+10414,15809800.0,Ch'in,679.0,Germany,Male,31.0,3.0,127738.35,2.0,1.0,0.0,88890.05,0.0
+10415,15591279.0,Chukwufumnanya,586.0,France,Female,37.0,1.0,0.0,1.0,1.0,1.0,58844.84,1.0
+10416,15679760.0,Onyemauchechukwu,669.0,France,Male,39.0,4.0,0.0,2.0,0.0,0.0,125083.29,0.0
+10417,15704770.0,Yobanna,667.0,France,Male,31.0,1.0,0.0,1.0,1.0,1.0,116977.89,0.0
+10418,15596863.0,Nwokeocha,676.0,France,Female,34.0,1.0,90544.0,1.0,0.0,1.0,73329.08,0.0
+10419,15691721.0,Mbanefo,603.0,Spain,Female,28.0,4.0,117565.03,1.0,1.0,0.0,113695.99,0.0
+10420,15700864.0,Frolov,686.0,France,Male,26.0,9.0,0.0,2.0,0.0,1.0,169862.01,0.0
+10421,15772363.0,O'Donnell,545.0,Spain,Male,59.0,4.0,0.0,1.0,0.0,1.0,179705.13,1.0
+10422,15583480.0,Tien,660.0,Germany,Male,36.0,6.0,92510.9,2.0,1.0,1.0,46635.11,0.0
+10423,15742638.0,Ch'ien,717.0,Germany,Female,74.0,3.0,156570.86,2.0,1.0,1.0,180553.48,0.0
+10424,15637753.0,Ndubueze,687.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,138516.1,0.0
+10425,15769596.0,Hs?,805.0,France,Female,30.0,7.0,141136.62,2.0,1.0,0.0,164967.21,0.0
+10426,15589563.0,Milani,704.0,France,Female,37.0,8.0,0.0,1.0,0.0,0.0,56562.57,1.0
+10427,15808960.0,Aitken,718.0,France,Male,40.0,1.0,133261.48,1.0,0.0,0.0,71682.54,0.0
+10428,15677522.0,Pye,622.0,Germany,Male,27.0,4.0,127543.11,1.0,0.0,1.0,180712.28,1.0
+10429,15725166.0,Biryukov,725.0,France,Female,34.0,7.0,138032.15,1.0,1.0,0.0,131662.47,0.0
+10430,15788683.0,Chukwudi,647.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,89543.25,0.0
+10431,15646513.0,Koo,767.0,Spain,Male,33.0,2.0,98894.39,1.0,0.0,0.0,67311.12,0.0
+10432,15645896.0,Ch'eng,729.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,174027.3,0.0
+10433,15743538.0,Chiu,554.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,1.0,115818.51,0.0
+10434,15640078.0,Lucchesi,574.0,France,Male,40.0,9.0,0.0,2.0,0.0,1.0,176692.87,0.0
+10435,15801296.0,Ch'iu,611.0,France,Male,60.0,7.0,0.0,1.0,1.0,0.0,169669.33,1.0
+10436,15643908.0,Fries,577.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,142377.69,0.0
+10437,15753362.0,Chiemela,633.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,57553.02,0.0
+10438,15626040.0,Ndubuagha,696.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,55859.27,0.0
+10439,15614878.0,Chukwualuka,549.0,France,Male,46.0,8.0,0.0,1.0,1.0,0.0,87908.01,1.0
+10440,15735222.0,Ch'ang,659.0,Spain,Female,32.0,2.0,117866.92,1.0,1.0,0.0,94440.45,0.0
+10441,15694129.0,Chidimma,697.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,141872.05,0.0
+10442,15573790.0,Logue,613.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,180890.4,0.0
+10443,15737556.0,Niu,628.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,164741.5,1.0
+10444,15576517.0,T'ien,589.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,164975.82,0.0
+10445,15806945.0,Ch'eng,706.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,0.0,598.8,0.0
+10446,15658577.0,Palerma,656.0,France,Female,32.0,0.0,0.0,2.0,1.0,0.0,86410.28,0.0
+10447,15803790.0,Chinweike,619.0,Spain,Female,36.0,0.0,0.0,2.0,1.0,1.0,135650.18,0.0
+10448,15631406.0,Chukwudi,812.0,Spain,Female,41.0,6.0,123845.24,1.0,1.0,1.0,95857.18,0.0
+10449,15621520.0,Y?an,670.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,183572.39,0.0
+10450,15589380.0,K'ung,646.0,France,Male,35.0,3.0,0.0,2.0,0.0,0.0,120892.93,0.0
+10451,15702561.0,Hsiao,634.0,France,Male,35.0,1.0,0.0,1.0,1.0,0.0,6057.81,1.0
+10452,15631868.0,Ch'in,690.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,107959.01,0.0
+10453,15734987.0,Chin,588.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,132770.15,0.0
+10454,15574558.0,Udokamma,686.0,France,Female,46.0,9.0,0.0,1.0,1.0,0.0,58137.42,1.0
+10455,15802617.0,Hsia,564.0,France,Male,38.0,9.0,98263.46,1.0,0.0,0.0,133570.29,0.0
+10456,15624677.0,Chiekwugo,607.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,103726.71,0.0
+10457,15593470.0,Zikoranachidimma,837.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,156774.75,0.0
+10458,15693526.0,Hsing,718.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,147557.71,0.0
+10459,15620557.0,Mazzi,543.0,France,Female,22.0,6.0,0.0,2.0,1.0,1.0,188533.11,0.0
+10460,15603764.0,Chidiebere,699.0,France,Male,26.0,3.0,0.0,1.0,0.0,1.0,88890.05,0.0
+10461,15726167.0,Tuan,589.0,Spain,Male,40.0,3.0,111233.09,1.0,1.0,0.0,110916.15,0.0
+10462,15781495.0,Whitehouse,565.0,France,Female,34.0,2.0,0.0,2.0,0.0,1.0,158420.14,0.0
+10463,15692575.0,Chidiebele,735.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,62449.07,0.0
+10464,15642530.0,Chinedum,645.0,France,Female,42.0,1.0,0.0,1.0,1.0,1.0,127558.26,0.0
+10465,15602994.0,Lo,644.0,Spain,Male,39.0,0.0,0.0,1.0,1.0,0.0,97939.4,0.0
+10466,15809901.0,Smith,720.0,Germany,Female,40.0,3.0,127552.85,1.0,1.0,0.0,131647.31,1.0
+10467,15656330.0,K'ung,597.0,France,Female,39.0,10.0,0.0,2.0,1.0,1.0,76510.46,0.0
+10468,15748667.0,Lu,704.0,France,Female,49.0,9.0,152606.82,1.0,1.0,0.0,181757.19,0.0
+10469,15572390.0,K?,592.0,France,Male,40.0,1.0,148989.15,1.0,1.0,0.0,117791.06,1.0
+10470,15763381.0,Napolitani,642.0,France,Female,38.0,1.0,0.0,1.0,1.0,0.0,91346.03,1.0
+10471,15803526.0,Hsu,754.0,France,Female,52.0,4.0,0.0,2.0,1.0,1.0,47921.22,0.0
+10472,15668580.0,Rizzo,707.0,Spain,Female,57.0,3.0,0.0,1.0,1.0,0.0,126082.29,1.0
+10473,15636673.0,Nwebube,637.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,171733.43,0.0
+10474,15772933.0,Shao,738.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,161428.25,0.0
+10475,15644878.0,Fiorentino,679.0,Germany,Female,59.0,5.0,125850.7,1.0,1.0,0.0,166376.54,1.0
+10476,15709741.0,Lettiere,578.0,France,Male,40.0,2.0,0.0,2.0,0.0,1.0,183566.85,0.0
+10477,15681075.0,Pagnotto,687.0,France,Male,40.0,3.0,154969.96,1.0,1.0,0.0,178045.97,0.0
+10478,15662662.0,Ugochukwu,581.0,France,Male,42.0,1.0,0.0,2.0,0.0,1.0,73882.49,0.0
+10479,15694556.0,Chukwufumnanya,757.0,Germany,Female,42.0,5.0,122028.65,1.0,1.0,0.0,134720.51,1.0
+10480,15802758.0,White,585.0,France,Male,43.0,8.0,0.0,2.0,0.0,0.0,113950.51,0.0
+10481,15702300.0,Ebelechukwu,571.0,France,Male,42.0,6.0,0.0,2.0,1.0,1.0,140937.91,0.0
+10482,15576683.0,K'ung,648.0,France,Male,46.0,5.0,0.0,1.0,1.0,0.0,179453.66,1.0
+10483,15752588.0,Hsu,678.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,78854.57,0.0
+10484,15761783.0,Pirozzi,697.0,Spain,Female,44.0,3.0,0.0,2.0,0.0,0.0,81622.51,0.0
+10485,15588535.0,Ch'en,613.0,Germany,Male,27.0,8.0,131834.7,1.0,1.0,1.0,53710.23,0.0
+10486,15682928.0,Uspenskaya,582.0,France,Female,60.0,5.0,0.0,1.0,1.0,1.0,136827.92,1.0
+10487,15723484.0,Mazzanti,699.0,Germany,Male,39.0,1.0,115569.21,1.0,1.0,1.0,26767.85,0.0
+10488,15692991.0,De Luca,703.0,Spain,Female,32.0,3.0,0.0,2.0,1.0,1.0,83343.73,0.0
+10489,15581198.0,Nwankwo,516.0,France,Male,32.0,0.0,0.0,2.0,0.0,0.0,173599.38,0.0
+10490,15757628.0,Mazzanti,581.0,France,Male,31.0,5.0,91955.61,1.0,1.0,0.0,74838.64,0.0
+10491,15749401.0,Chukwueloka,652.0,Spain,Female,39.0,3.0,0.0,1.0,0.0,0.0,171706.66,1.0
+10492,15742110.0,Nwankwo,749.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,1.0,62413.01,0.0
+10493,15769955.0,Walker,594.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,180932.46,0.0
+10494,15809847.0,Liao,585.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,116978.19,0.0
+10495,15702298.0,Teng,635.0,Germany,Female,29.0,6.0,129837.72,1.0,1.0,1.0,86981.45,0.0
+10496,15637324.0,Hsieh,743.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,131804.86,0.0
+10497,15724251.0,Chukwujamuike,671.0,Spain,Female,33.0,7.0,97544.29,1.0,1.0,1.0,188203.58,0.0
+10498,15643907.0,Smith,707.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,0.0,162452.65,0.0
+10499,15710105.0,Ch'in,635.0,France,Male,41.0,3.0,0.0,1.0,1.0,0.0,18826.08,1.0
+10500,15722072.0,Dickson,697.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,47775.34,0.0
+10501,15802585.0,Bellucci,585.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,147964.99,0.0
+10502,15583692.0,H?,814.0,Germany,Male,36.0,2.0,129855.32,2.0,1.0,1.0,143960.72,0.0
+10503,15806420.0,Tung,667.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,62367.72,0.0
+10504,15641732.0,Ch'iu,681.0,France,Male,28.0,7.0,0.0,1.0,1.0,0.0,54487.43,0.0
+10505,15750121.0,Trevisani,735.0,France,Male,39.0,5.0,0.0,1.0,1.0,0.0,172576.43,0.0
+10506,15793949.0,P'eng,661.0,France,Female,40.0,8.0,0.0,2.0,0.0,0.0,160942.48,0.0
+10507,15688409.0,Yudin,704.0,France,Male,44.0,4.0,0.0,2.0,0.0,1.0,64448.36,0.0
+10508,15679048.0,Lo,731.0,Germany,Female,54.0,7.0,83765.35,1.0,0.0,0.0,81718.37,1.0
+10509,15792868.0,Chidiebere,738.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,1.0,158071.69,0.0
+10510,15642967.0,Chiang,645.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,90877.76,0.0
+10511,15683677.0,Hsing,581.0,France,Male,31.0,7.0,0.0,2.0,0.0,1.0,110805.79,0.0
+10512,15758035.0,Scott,682.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,185210.63,0.0
+10513,15690647.0,Lucchesi,586.0,France,Female,57.0,2.0,0.0,1.0,1.0,0.0,177065.24,1.0
+10514,15751748.0,Walker,641.0,France,Male,29.0,1.0,0.0,2.0,1.0,1.0,148087.88,0.0
+10515,15593636.0,Kaeppel,695.0,Germany,Female,44.0,8.0,108883.29,2.0,1.0,0.0,75574.48,1.0
+10516,15722993.0,H?,781.0,Germany,Female,52.0,7.0,102954.68,3.0,1.0,0.0,153662.88,1.0
+10517,15804787.0,Mazzi,762.0,France,Female,40.0,5.0,0.0,2.0,0.0,0.0,120108.56,0.0
+10518,15573741.0,Nnaife,686.0,France,Male,44.0,4.0,0.0,1.0,0.0,0.0,51535.19,0.0
+10519,15652320.0,Treacy,594.0,France,Female,22.0,2.0,0.0,2.0,1.0,0.0,48876.84,0.0
+10520,15694253.0,Kashiwagi,850.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,172450.04,0.0
+10521,15591130.0,Teng,605.0,France,Male,26.0,4.0,113637.62,1.0,1.0,1.0,104876.8,0.0
+10522,15734886.0,Zetticci,726.0,France,Male,32.0,8.0,0.0,1.0,1.0,1.0,161479.19,0.0
+10523,15796230.0,Kambinachi,730.0,France,Female,37.0,2.0,145975.78,2.0,1.0,1.0,91763.67,0.0
+10524,15782236.0,Okechukwu,605.0,Germany,Male,38.0,5.0,104631.67,2.0,1.0,1.0,171762.87,0.0
+10525,15765806.0,Lucchese,792.0,Germany,Male,54.0,9.0,92340.69,1.0,1.0,1.0,172096.08,0.0
+10526,15603065.0,Nucci,704.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,88566.9,0.0
+10527,15689612.0,Ch'iu,595.0,Spain,Female,41.0,6.0,129289.94,1.0,0.0,0.0,139947.17,0.0
+10528,15697028.0,Nkemdilim,604.0,Germany,Male,33.0,7.0,111201.41,1.0,0.0,1.0,178067.33,0.0
+10529,15746553.0,Ch'ien,739.0,France,Male,49.0,4.0,0.0,1.0,0.0,0.0,173783.38,1.0
+10530,15726354.0,Maclean,554.0,France,Male,51.0,8.0,0.0,1.0,0.0,1.0,91366.42,1.0
+10531,15694192.0,Chibugo,706.0,France,Male,27.0,6.0,0.0,2.0,1.0,0.0,60972.84,0.0
+10532,15705085.0,Naylor,663.0,France,Female,52.0,6.0,0.0,3.0,1.0,0.0,152883.95,1.0
+10533,15651713.0,Nebechukwu,637.0,France,Male,29.0,2.0,0.0,1.0,1.0,0.0,167067.34,1.0
+10534,15607877.0,Yobachi,692.0,Spain,Male,46.0,1.0,0.0,2.0,0.0,1.0,65868.28,0.0
+10535,15701376.0,Onyemere,682.0,France,Male,20.0,3.0,0.0,2.0,1.0,0.0,160810.85,0.0
+10536,15747168.0,Nwachinemelu,646.0,France,Male,44.0,7.0,0.0,2.0,0.0,0.0,122964.18,0.0
+10537,15686963.0,Smith,619.0,France,Male,40.0,3.0,0.0,2.0,0.0,0.0,102908.91,0.0
+10538,15617648.0,Chiawuotu,812.0,Germany,Female,43.0,6.0,113383.52,1.0,0.0,1.0,133672.61,0.0
+10539,15793683.0,Azikiwe,642.0,France,Male,35.0,7.0,109949.05,1.0,0.0,1.0,178252.73,0.0
+10540,15684924.0,Tuan,775.0,Germany,Female,33.0,5.0,130933.74,1.0,0.0,1.0,158028.36,1.0
+10541,15682868.0,Obialo,635.0,France,Male,46.0,3.0,0.0,2.0,1.0,0.0,187900.12,0.0
+10542,15776545.0,Ch'ang,801.0,France,Male,35.0,2.0,0.0,1.0,0.0,1.0,176925.69,0.0
+10543,15641411.0,Pagnotto,733.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,141872.05,0.0
+10544,15748691.0,K'ung,596.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,56594.36,0.0
+10545,15773804.0,Shih,731.0,France,Female,40.0,6.0,0.0,1.0,1.0,1.0,115800.1,1.0
+10546,15709354.0,Martin,683.0,France,Male,64.0,9.0,0.0,3.0,1.0,0.0,180345.44,1.0
+10547,15734365.0,Ch'eng,741.0,Germany,Female,40.0,8.0,128966.13,2.0,0.0,1.0,108764.69,0.0
+10548,15652674.0,Chineze,583.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,51980.25,0.0
+10549,15700476.0,Ch'ang,602.0,Germany,Male,33.0,3.0,120105.43,1.0,0.0,0.0,92320.37,0.0
+10550,15635388.0,Hsia,678.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,133535.29,0.0
+10551,15577274.0,Hsiung,564.0,France,Female,46.0,3.0,0.0,1.0,1.0,0.0,94878.14,1.0
+10552,15607990.0,Shih,633.0,France,Female,35.0,9.0,0.0,1.0,1.0,0.0,154844.22,0.0
+10553,15605276.0,Zetticci,625.0,Germany,Female,67.0,10.0,116197.65,1.0,0.0,1.0,140166.95,0.0
+10554,15591135.0,Bogle,616.0,France,Female,31.0,1.0,98205.77,1.0,1.0,1.0,79919.13,0.0
+10555,15759733.0,Ch'ien,567.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,72462.25,0.0
+10556,15673908.0,Y?an,599.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,523.0,0.0
+10557,15732967.0,Chia,753.0,France,Male,34.0,2.0,124577.33,1.0,0.0,1.0,121846.81,0.0
+10558,15806901.0,Ugochukwu,671.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,162699.7,0.0
+10559,15751561.0,Yudina,678.0,Germany,Male,48.0,3.0,121863.61,1.0,1.0,1.0,103747.51,0.0
+10560,15731519.0,Chukwudi,649.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,84760.32,0.0
+10561,15711901.0,Ch'ang,614.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,134594.99,0.0
+10562,15745080.0,Chien,811.0,France,Female,28.0,5.0,0.0,2.0,1.0,1.0,115818.51,0.0
+10563,15626507.0,Tuan,531.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,199409.19,0.0
+10564,15610972.0,Y?,695.0,France,Female,31.0,1.0,131834.76,1.0,1.0,1.0,177740.58,0.0
+10565,15680167.0,Eiland,740.0,France,Female,31.0,1.0,0.0,2.0,1.0,1.0,182059.28,0.0
+10566,15669922.0,Ch'en,535.0,France,Male,43.0,1.0,162485.8,1.0,1.0,1.0,90876.95,1.0
+10567,15808960.0,Y?,648.0,Spain,Female,41.0,2.0,0.0,1.0,1.0,0.0,145562.4,1.0
+10568,15681956.0,Chiedozie,596.0,France,Male,38.0,0.0,0.0,2.0,1.0,1.0,93832.33,0.0
+10569,15794468.0,Mancini,850.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,156048.64,0.0
+10570,15793049.0,Okwudilichukwu,516.0,Germany,Female,56.0,1.0,115029.4,1.0,1.0,0.0,123846.07,1.0
+10571,15723169.0,Chiedozie,632.0,Germany,Male,44.0,5.0,139432.37,1.0,1.0,1.0,151903.6,0.0
+10572,15761241.0,Hsueh,739.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,0.0,57320.92,0.0
+10573,15717995.0,Hs?,634.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,142287.65,0.0
+10574,15582892.0,Mamelu,685.0,France,Female,31.0,4.0,0.0,2.0,1.0,0.0,97939.4,0.0
+10575,15745201.0,T'ien,582.0,Germany,Female,29.0,0.0,129387.94,2.0,1.0,1.0,155850.9,0.0
+10576,15689953.0,Calabrese,606.0,France,Male,26.0,9.0,0.0,2.0,1.0,0.0,111558.7,0.0
+10577,15706537.0,Baresi,691.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,95611.47,0.0
+10578,15662758.0,Cattaneo,699.0,France,Male,26.0,9.0,0.0,2.0,1.0,0.0,111184.67,0.0
+10579,15604119.0,K'ung,701.0,France,Male,27.0,5.0,0.0,2.0,1.0,1.0,101300.94,0.0
+10580,15609083.0,Iweobiegbunam,693.0,France,Male,21.0,1.0,103818.41,1.0,1.0,0.0,171069.39,0.0
+10581,15755872.0,Achebe,662.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,4696.44,0.0
+10582,15605684.0,Kisch,849.0,Germany,Male,46.0,1.0,93716.72,2.0,1.0,0.0,164002.02,1.0
+10583,15586039.0,Chidubem,686.0,France,Male,43.0,2.0,0.0,3.0,0.0,0.0,143952.24,1.0
+10584,15624343.0,Fanucci,676.0,France,Male,23.0,8.0,127763.24,1.0,1.0,0.0,78854.57,0.0
+10585,15636089.0,Hsieh,584.0,Germany,Male,45.0,4.0,117307.44,1.0,0.0,1.0,113089.43,0.0
+10586,15640789.0,H?,690.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,143665.44,0.0
+10587,15586069.0,Chigolum,777.0,Germany,Male,34.0,10.0,141787.31,1.0,1.0,0.0,57553.02,0.0
+10588,15756560.0,Scott,615.0,France,Male,45.0,2.0,0.0,2.0,0.0,1.0,75969.19,0.0
+10589,15611551.0,Lo,549.0,Germany,Female,46.0,7.0,98955.84,1.0,1.0,0.0,182076.97,1.0
+10590,15773868.0,McGregor,803.0,Spain,Female,61.0,8.0,0.0,1.0,1.0,1.0,125920.54,1.0
+10591,15627999.0,Chukwueloka,662.0,Spain,Male,53.0,5.0,0.0,1.0,1.0,1.0,88827.67,0.0
+10592,15652955.0,Pinto,559.0,France,Female,24.0,4.0,0.0,2.0,1.0,1.0,120512.08,0.0
+10593,15631709.0,Fiorentini,586.0,Spain,Male,32.0,4.0,0.0,1.0,0.0,1.0,95936.84,0.0
+10594,15761348.0,Lo,567.0,Spain,Female,32.0,4.0,0.0,2.0,0.0,0.0,71405.17,0.0
+10595,15603088.0,Chin,667.0,France,Female,24.0,2.0,0.0,2.0,1.0,1.0,153662.88,0.0
+10596,15609913.0,Manna,738.0,France,Male,43.0,3.0,0.0,1.0,0.0,1.0,134417.31,1.0
+10597,15633666.0,Zikoranachidimma,659.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,156774.75,0.0
+10598,15635141.0,Chukwufumnanya,749.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,135482.26,0.0
+10599,15673074.0,Ku,679.0,France,Female,48.0,4.0,0.0,1.0,0.0,0.0,50610.62,1.0
+10600,15750099.0,T'ien,766.0,Germany,Female,20.0,9.0,104118.5,2.0,1.0,0.0,62811.03,0.0
+10601,15796370.0,Tuan,684.0,Germany,Male,31.0,7.0,111095.58,2.0,0.0,1.0,4798.12,0.0
+10602,15682467.0,Tung,557.0,Germany,Male,48.0,8.0,131659.52,1.0,1.0,0.0,155799.86,1.0
+10603,15771769.0,Biryukov,593.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,0.0,176576.71,0.0
+10604,15775803.0,Teng,594.0,France,Female,33.0,5.0,0.0,1.0,1.0,1.0,199505.53,1.0
+10605,15603914.0,Chiazagomekpere,616.0,France,Male,40.0,8.0,0.0,1.0,0.0,1.0,176743.51,1.0
+10606,15662434.0,Onuchukwu,673.0,France,Female,33.0,6.0,176913.52,1.0,1.0,1.0,114083.73,0.0
+10607,15703437.0,Ch'eng,539.0,Spain,Female,50.0,9.0,0.0,1.0,1.0,0.0,159334.93,1.0
+10608,15771855.0,Ts'ui,595.0,Spain,Female,53.0,7.0,0.0,2.0,0.0,1.0,165562.84,1.0
+10609,15682048.0,Lu,641.0,Germany,Male,47.0,8.0,113599.74,1.0,1.0,0.0,91830.75,1.0
+10610,15724145.0,Tretiakova,651.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,91366.41,0.0
+10611,15605835.0,Chukwukere,749.0,Spain,Female,41.0,7.0,0.0,1.0,1.0,1.0,72876.41,1.0
+10612,15700813.0,Palerma,603.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,1.0,149991.32,0.0
+10613,15627848.0,Hao,687.0,Germany,Female,40.0,1.0,76510.52,2.0,1.0,1.0,58974.0,0.0
+10614,15699467.0,Chiu,537.0,Spain,Male,35.0,3.0,0.0,1.0,1.0,1.0,53477.81,0.0
+10615,15746076.0,Iadanza,821.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,102547.56,0.0
+10616,15690164.0,Zetticci,696.0,Germany,Male,39.0,4.0,162261.01,2.0,1.0,0.0,111605.9,0.0
+10617,15663917.0,Young,611.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,172576.43,0.0
+10618,15723858.0,Scott,685.0,Spain,Male,29.0,3.0,126479.27,1.0,1.0,0.0,149853.89,0.0
+10619,15643496.0,Chidimma,562.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,89566.64,0.0
+10620,15777797.0,Tipton,627.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,100862.54,0.0
+10621,15702470.0,Chin,679.0,Germany,Female,48.0,4.0,113599.69,1.0,1.0,0.0,64448.36,1.0
+10622,15585580.0,Miah,754.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,115429.32,0.0
+10623,15594812.0,Lai,667.0,Germany,Male,62.0,3.0,127197.14,1.0,1.0,1.0,91573.19,0.0
+10624,15690829.0,Chukwumaobim,710.0,France,Female,55.0,9.0,0.0,1.0,1.0,0.0,177783.04,1.0
+10625,15593976.0,Fitts,683.0,Germany,Female,23.0,2.0,68345.86,2.0,1.0,1.0,176580.95,0.0
+10626,15671165.0,Ku,679.0,Germany,Female,36.0,7.0,102933.74,2.0,1.0,0.0,166929.43,1.0
+10627,15626233.0,Bergamaschi,675.0,Germany,Male,34.0,4.0,102085.32,1.0,1.0,1.0,158037.57,0.0
+10628,15575060.0,Chukwueloka,494.0,France,Female,32.0,10.0,91057.37,1.0,1.0,1.0,167554.86,0.0
+10629,15778089.0,Trevisano,527.0,France,Female,48.0,4.0,0.0,1.0,1.0,0.0,126726.33,1.0
+10630,15648858.0,Chidimma,641.0,Spain,Female,24.0,4.0,0.0,1.0,1.0,0.0,186869.58,0.0
+10631,15636562.0,Y?an,692.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,1.0,161407.48,0.0
+10632,15768104.0,Walker,676.0,Germany,Female,33.0,2.0,102773.2,1.0,1.0,0.0,173683.0,0.0
+10633,15673837.0,Chukwuebuka,646.0,Germany,Male,25.0,3.0,96019.5,2.0,1.0,0.0,162703.0,0.0
+10634,15677522.0,Uvarova,755.0,Germany,Female,29.0,1.0,160625.17,1.0,1.0,1.0,182627.25,0.0
+10635,15773934.0,Fomin,730.0,Germany,Female,46.0,5.0,126589.12,1.0,1.0,0.0,175043.69,1.0
+10636,15585284.0,H?,705.0,France,Male,40.0,4.0,0.0,1.0,1.0,1.0,142377.69,0.0
+10637,15727177.0,Y?an,645.0,France,Male,40.0,1.0,0.0,2.0,0.0,1.0,84760.32,0.0
+10638,15662658.0,Mironov,674.0,France,Male,44.0,6.0,0.0,2.0,0.0,1.0,150422.35,0.0
+10639,15740406.0,Udinesi,723.0,France,Male,30.0,1.0,117738.97,1.0,1.0,0.0,124095.69,0.0
+10640,15813168.0,Shih,633.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,161571.79,0.0
+10641,15737265.0,Brown,616.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,82709.8,0.0
+10642,15758845.0,Hsia,731.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,161668.15,0.0
+10643,15772030.0,Pirozzi,697.0,France,Male,40.0,6.0,127209.0,1.0,0.0,1.0,487.3,0.0
+10644,15612444.0,Ch'iu,712.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,2560.11,0.0
+10645,15696823.0,T'ien,755.0,France,Male,21.0,4.0,0.0,1.0,0.0,0.0,118716.63,0.0
+10646,15803801.0,Martin,744.0,France,Male,51.0,4.0,0.0,1.0,0.0,1.0,162443.05,1.0
+10647,15796764.0,Mancini,592.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,10054.53,0.0
+10648,15689031.0,Nkemdilim,646.0,France,Female,36.0,3.0,134599.33,1.0,1.0,0.0,90876.95,0.0
+10649,15730673.0,Chin,651.0,Germany,Male,46.0,5.0,127864.42,2.0,1.0,1.0,55324.49,0.0
+10650,15808905.0,Zuyev,498.0,Spain,Female,32.0,8.0,0.0,1.0,1.0,1.0,92915.84,0.0
+10651,15795579.0,Smith,732.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,145562.4,0.0
+10652,15774738.0,Lucciano,663.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,74898.8,0.0
+10653,15701885.0,Bellucci,686.0,France,Male,55.0,5.0,0.0,2.0,1.0,1.0,181534.04,0.0
+10654,15796958.0,Teng,539.0,France,Male,40.0,8.0,148962.76,1.0,1.0,1.0,61820.41,0.0
+10655,15757931.0,Iloerika,645.0,Spain,Female,43.0,7.0,127849.51,2.0,1.0,1.0,4035.46,0.0
+10656,15729954.0,Nnonso,636.0,Spain,Female,36.0,9.0,127771.35,1.0,0.0,1.0,129975.75,0.0
+10657,15782231.0,Hsiung,758.0,France,Female,31.0,5.0,0.0,2.0,0.0,0.0,68815.05,0.0
+10658,15589435.0,Cairns,652.0,France,Female,40.0,4.0,99835.17,1.0,0.0,0.0,95957.09,0.0
+10659,15711398.0,Ch'in,595.0,France,Female,38.0,3.0,0.0,1.0,0.0,1.0,66893.28,0.0
+10660,15795878.0,Chibueze,687.0,France,Male,60.0,6.0,0.0,2.0,0.0,1.0,148811.14,0.0
+10661,15694890.0,Balsillie,709.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,61820.41,0.0
+10662,15756539.0,Shubin,582.0,France,Female,72.0,4.0,121898.98,1.0,1.0,1.0,72993.67,0.0
+10663,15661380.0,Macleod,681.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,86424.57,0.0
+10664,15757617.0,Hsieh,579.0,France,Female,37.0,7.0,0.0,1.0,1.0,1.0,93835.64,0.0
+10665,15715888.0,Lu,590.0,Spain,Female,32.0,8.0,0.0,2.0,0.0,0.0,161811.23,0.0
+10666,15654075.0,T'ao,540.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,148814.54,0.0
+10667,15594577.0,Udokamma,624.0,Spain,Male,44.0,3.0,142872.36,1.0,1.0,0.0,113921.64,1.0
+10668,15629985.0,Padovesi,679.0,France,Female,37.0,0.0,0.0,2.0,1.0,0.0,180716.1,0.0
+10669,15600724.0,Shih,593.0,France,Female,45.0,8.0,0.0,2.0,1.0,0.0,94774.12,0.0
+10670,15765537.0,Tien,711.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,0.0,66463.62,0.0
+10671,15607986.0,Chiu,674.0,Spain,Male,41.0,8.0,89182.09,1.0,1.0,1.0,102638.35,0.0
+10672,15693732.0,P'eng,581.0,Spain,Male,39.0,7.0,0.0,2.0,1.0,0.0,96670.2,0.0
+10673,15724453.0,Lucciano,781.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,1.0,122960.98,0.0
+10674,15762682.0,Ritchie,588.0,France,Male,49.0,7.0,0.0,2.0,1.0,0.0,143425.56,0.0
+10675,15671934.0,Niu,679.0,France,Male,45.0,7.0,0.0,1.0,1.0,1.0,104769.96,1.0
+10676,15702631.0,Bellucci,575.0,France,Female,34.0,2.0,0.0,2.0,0.0,0.0,53584.03,0.0
+10677,15792463.0,Yeh,574.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,58907.08,0.0
+10678,15642099.0,Bess,651.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,1.0,51553.43,0.0
+10679,15651280.0,Fanucci,827.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,115950.96,0.0
+10680,15732778.0,Hsueh,667.0,Germany,Male,37.0,3.0,122394.0,2.0,1.0,1.0,35418.79,0.0
+10681,15692259.0,P'eng,641.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,64853.51,0.0
+10682,15588123.0,Chukwuemeka,642.0,Germany,Female,36.0,7.0,129855.32,1.0,1.0,0.0,110782.88,0.0
+10683,15702460.0,Ch'iu,591.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,152499.8,0.0
+10684,15599869.0,Ch'eng,718.0,France,Male,40.0,6.0,0.0,2.0,0.0,0.0,96823.32,0.0
+10685,15761487.0,Chukwudi,833.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,69303.15,0.0
+10686,15798885.0,Hsueh,635.0,Germany,Male,21.0,5.0,127661.69,2.0,1.0,1.0,141446.01,0.0
+10687,15579130.0,Calabresi,646.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,78670.52,0.0
+10688,15766686.0,Chibugo,558.0,Germany,Female,35.0,5.0,104930.38,1.0,1.0,0.0,103787.28,1.0
+10689,15769312.0,Pisano,668.0,France,Female,31.0,8.0,0.0,2.0,0.0,0.0,174777.98,0.0
+10690,15707974.0,Ch'en,625.0,France,Female,41.0,6.0,0.0,2.0,1.0,0.0,181177.9,0.0
+10691,15632447.0,Hsueh,754.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,91775.51,0.0
+10692,15778947.0,Hsueh,668.0,France,Male,29.0,2.0,131834.8,1.0,1.0,1.0,126812.63,0.0
+10693,15644868.0,Pisano,582.0,Germany,Male,38.0,0.0,165413.81,2.0,1.0,0.0,149402.54,0.0
+10694,15653857.0,Fitts,651.0,France,Female,29.0,6.0,148874.01,2.0,1.0,0.0,137961.41,0.0
+10695,15627995.0,Chigolum,648.0,Germany,Male,22.0,2.0,170935.94,2.0,1.0,0.0,103713.93,0.0
+10696,15666975.0,Uwaezuoke,652.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,162960.05,0.0
+10697,15681220.0,Ts'ao,706.0,Spain,Female,26.0,7.0,153366.13,1.0,1.0,1.0,71519.4,0.0
+10698,15698902.0,Hsia,573.0,France,Female,45.0,5.0,0.0,1.0,1.0,1.0,152698.24,1.0
+10699,15742879.0,Chukwudi,679.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,180318.6,0.0
+10700,15806822.0,Ugoji,623.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,1.0,108887.44,0.0
+10701,15769246.0,Mancini,608.0,France,Male,34.0,6.0,0.0,2.0,0.0,1.0,56984.58,0.0
+10702,15751137.0,Hsu,571.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,110405.9,0.0
+10703,15607986.0,Chidozie,602.0,France,Female,40.0,5.0,161371.5,1.0,1.0,0.0,101348.88,1.0
+10704,15763898.0,Chin,551.0,Spain,Female,48.0,2.0,0.0,2.0,0.0,1.0,50368.87,0.0
+10705,15665956.0,Nwankwo,666.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,108699.59,0.0
+10706,15773869.0,Genovese,733.0,France,Male,31.0,6.0,0.0,1.0,1.0,1.0,645.61,1.0
+10707,15763515.0,Chidozie,705.0,France,Male,40.0,3.0,137794.18,1.0,1.0,0.0,173495.77,1.0
+10708,15763515.0,Kuykendall,648.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,113410.41,0.0
+10709,15757981.0,Hs?,617.0,France,Female,44.0,9.0,0.0,2.0,0.0,0.0,142839.85,1.0
+10710,15746199.0,Fiorentino,545.0,Spain,Male,39.0,1.0,0.0,2.0,0.0,0.0,150679.65,0.0
+10711,15722409.0,Houghton,623.0,Spain,Male,38.0,9.0,0.0,1.0,0.0,0.0,90876.95,0.0
+10712,15674720.0,Onyekaozulu,648.0,Germany,Female,20.0,5.0,125445.04,1.0,1.0,1.0,107499.34,0.0
+10713,15805627.0,Ngozichukwuka,709.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,1.0,110916.15,0.0
+10714,15633233.0,Okwuadigbo,653.0,France,Male,47.0,7.0,0.0,1.0,0.0,1.0,136491.72,1.0
+10715,15778838.0,P'eng,527.0,Spain,Male,27.0,5.0,142400.77,1.0,1.0,0.0,126812.63,0.0
+10716,15791371.0,Hargraves,553.0,France,Female,35.0,8.0,0.0,2.0,0.0,0.0,123866.28,0.0
+10717,15677522.0,De Luca,724.0,France,Female,39.0,10.0,0.0,1.0,1.0,0.0,93551.36,0.0
+10718,15583074.0,Yuryeva,620.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,121866.87,0.0
+10719,15723105.0,Aitken,575.0,France,Male,23.0,7.0,0.0,1.0,1.0,0.0,151902.78,0.0
+10720,15686885.0,Chiang,611.0,Germany,Female,40.0,2.0,114318.78,1.0,0.0,1.0,121551.58,1.0
+10721,15693771.0,Nnonso,587.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,64872.33,0.0
+10722,15790355.0,Hsia,636.0,Spain,Male,54.0,8.0,0.0,1.0,1.0,0.0,172096.08,1.0
+10723,15799435.0,P'eng,721.0,France,Female,32.0,4.0,0.0,2.0,0.0,1.0,150390.57,0.0
+10724,15603065.0,Chiu,652.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,183566.78,0.0
+10725,15744553.0,Chiu,745.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,82872.64,0.0
+10726,15644856.0,Chikezie,719.0,Germany,Female,36.0,5.0,133628.09,1.0,1.0,0.0,55725.04,1.0
+10727,15792646.0,Chukwudi,653.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,1276.87,0.0
+10728,15774558.0,Omeokachie,704.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,151460.86,0.0
+10729,15663438.0,Hao,683.0,Germany,Male,32.0,8.0,108914.17,1.0,1.0,1.0,136610.02,0.0
+10730,15794941.0,Piazza,644.0,Germany,Male,36.0,9.0,91219.29,2.0,0.0,1.0,84487.62,0.0
+10731,15731246.0,Nwabugwu,652.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,116885.59,0.0
+10732,15642530.0,Ch'ang,624.0,France,Male,24.0,1.0,0.0,2.0,1.0,1.0,48559.19,0.0
+10733,15583681.0,Nnaife,599.0,Spain,Male,33.0,3.0,0.0,2.0,0.0,0.0,51454.06,0.0
+10734,15637240.0,Nkemjika,773.0,Germany,Male,47.0,8.0,125890.49,1.0,1.0,0.0,82391.06,1.0
+10735,15579287.0,Tsao,607.0,Spain,Male,68.0,9.0,0.0,1.0,1.0,0.0,113906.55,1.0
+10736,15693871.0,Shao,649.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,184570.04,0.0
+10737,15667871.0,K?,544.0,France,Male,40.0,3.0,0.0,2.0,0.0,1.0,72350.47,0.0
+10738,15781589.0,Chikezie,706.0,France,Female,35.0,4.0,0.0,1.0,1.0,1.0,183542.08,0.0
+10739,15798521.0,Baresi,605.0,France,Male,52.0,8.0,0.0,1.0,1.0,0.0,167973.63,1.0
+10740,15593973.0,Olisanugo,667.0,Germany,Female,36.0,5.0,99640.89,2.0,1.0,0.0,122996.76,0.0
+10741,15583692.0,Ch'ien,678.0,France,Male,50.0,2.0,0.0,2.0,1.0,1.0,125079.65,0.0
+10742,15632263.0,Ch'ien,648.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,54949.73,0.0
+10743,15577695.0,Hsiao,683.0,Germany,Female,47.0,1.0,140431.98,1.0,1.0,0.0,103379.46,1.0
+10744,15656901.0,Hsueh,713.0,Spain,Male,40.0,10.0,0.0,2.0,1.0,1.0,107640.25,0.0
+10745,15593683.0,Maughan,744.0,France,Male,41.0,3.0,0.0,1.0,1.0,0.0,93718.44,1.0
+10746,15662494.0,Hsia,663.0,Spain,Male,33.0,6.0,171671.9,1.0,0.0,1.0,158049.9,0.0
+10747,15615050.0,Pisano,604.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,0.0,94767.77,0.0
+10748,15782659.0,K'ung,743.0,Spain,Male,36.0,5.0,0.0,2.0,0.0,0.0,115700.1,0.0
+10749,15802731.0,Hingston,516.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,161671.15,0.0
+10750,15591257.0,K?,583.0,Spain,Male,33.0,9.0,0.0,2.0,0.0,0.0,93739.71,0.0
+10751,15776824.0,Fanucci,679.0,France,Male,20.0,2.0,0.0,1.0,1.0,1.0,121525.48,0.0
+10752,15601271.0,Iadanza,682.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,0.0,115136.51,0.0
+10753,15632264.0,Ch'en,724.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,106920.57,0.0
+10754,15569423.0,Chien,579.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,0.0,141137.52,0.0
+10755,15677955.0,Onyeorulu,640.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,122925.44,0.0
+10756,15683841.0,Chia,613.0,Germany,Male,32.0,3.0,123374.46,1.0,1.0,1.0,103954.24,0.0
+10757,15577598.0,Cremonesi,604.0,Spain,Female,36.0,1.0,0.0,1.0,1.0,0.0,16081.62,0.0
+10758,15573851.0,Onyemauchechukwu,726.0,France,Male,31.0,6.0,123040.63,1.0,1.0,1.0,92977.95,0.0
+10759,15757952.0,Maclean,635.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,103480.69,1.0
+10760,15736146.0,Yobanna,608.0,Spain,Male,31.0,9.0,118082.89,1.0,1.0,1.0,129893.93,0.0
+10761,15628893.0,Nwankwo,603.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,116466.19,0.0
+10762,15604020.0,Hs?,577.0,Germany,Female,31.0,8.0,121827.43,1.0,1.0,0.0,140937.97,1.0
+10763,15795738.0,Chinomso,669.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,136859.55,0.0
+10764,15814275.0,Onwuamaegbu,687.0,Germany,Female,47.0,1.0,127570.93,1.0,1.0,0.0,143961.27,1.0
+10765,15618774.0,Chinomso,721.0,France,Male,29.0,2.0,121286.32,1.0,1.0,1.0,113931.57,0.0
+10766,15651802.0,Tien,629.0,France,Male,31.0,2.0,0.0,2.0,0.0,0.0,161465.31,0.0
+10767,15794413.0,Chuang,712.0,Spain,Male,42.0,4.0,0.0,1.0,1.0,0.0,116971.05,1.0
+10768,15793854.0,Maclean,554.0,Germany,Female,43.0,6.0,85982.07,1.0,1.0,1.0,80178.54,0.0
+10769,15694859.0,Chiu,679.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,97734.3,0.0
+10770,15789425.0,Pisano,567.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,158977.8,0.0
+10771,15736287.0,Chukwufumnanya,619.0,France,Male,28.0,8.0,0.0,1.0,1.0,0.0,75578.67,0.0
+10772,15672930.0,Chin,714.0,Germany,Male,33.0,6.0,117020.08,1.0,0.0,1.0,28805.09,0.0
+10773,15732020.0,Lucchesi,724.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,126336.72,0.0
+10774,15806945.0,Chukwudi,674.0,Germany,Male,48.0,5.0,116520.73,1.0,1.0,1.0,128875.86,0.0
+10775,15607786.0,T'ien,716.0,Spain,Male,43.0,4.0,125167.02,1.0,1.0,1.0,7861.68,0.0
+10776,15815070.0,Zuyeva,596.0,France,Female,32.0,5.0,0.0,2.0,0.0,1.0,151401.33,0.0
+10777,15795511.0,Chukwuemeka,600.0,Spain,Male,35.0,5.0,0.0,1.0,1.0,0.0,14073.11,0.0
+10778,15676905.0,Nkemdirim,579.0,France,Female,36.0,3.0,0.0,1.0,0.0,1.0,122518.5,1.0
+10779,15693690.0,Tsao,622.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,180800.42,0.0
+10780,15581526.0,Fiorentini,599.0,Spain,Male,30.0,3.0,0.0,2.0,1.0,1.0,180800.42,0.0
+10781,15652981.0,Liao,567.0,Spain,Female,22.0,2.0,0.0,1.0,1.0,1.0,108003.38,0.0
+10782,15587584.0,Ting,752.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,46824.08,0.0
+10783,15662426.0,Baresi,653.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,98896.06,0.0
+10784,15655175.0,McIntosh,657.0,France,Female,56.0,3.0,0.0,1.0,1.0,0.0,73679.59,1.0
+10785,15771211.0,Wilson,535.0,France,Female,51.0,7.0,126550.0,1.0,1.0,0.0,173792.49,1.0
+10786,15638247.0,McLean,652.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,177852.57,0.0
+10787,15757537.0,Tuan,731.0,Germany,Male,45.0,3.0,118627.53,2.0,0.0,0.0,118029.07,1.0
+10788,15717286.0,Mazzanti,682.0,France,Female,55.0,9.0,0.0,1.0,1.0,0.0,168080.53,1.0
+10789,15737404.0,Chigbogu,604.0,France,Male,39.0,5.0,0.0,2.0,0.0,0.0,126728.27,0.0
+10790,15662291.0,T'ien,645.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,184570.04,0.0
+10791,15732266.0,Hao,621.0,Spain,Male,25.0,5.0,138881.04,1.0,1.0,1.0,143022.31,0.0
+10792,15740164.0,Y?,576.0,France,Female,36.0,7.0,122314.5,1.0,1.0,1.0,73977.23,0.0
+10793,15632050.0,Hsiao,559.0,France,Female,56.0,10.0,0.0,2.0,1.0,1.0,158071.69,1.0
+10794,15641850.0,H?,668.0,Spain,Male,32.0,4.0,0.0,2.0,0.0,1.0,84475.13,0.0
+10795,15682421.0,Yermolayeva,679.0,France,Female,41.0,8.0,0.0,2.0,1.0,1.0,136675.22,0.0
+10796,15805062.0,Ch'ien,592.0,France,Female,28.0,5.0,96888.39,1.0,1.0,1.0,91094.46,0.0
+10797,15673582.0,Nnamutaezinwa,602.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,12761.16,0.0
+10798,15646936.0,Tsao,821.0,France,Female,52.0,10.0,0.0,1.0,1.0,1.0,103608.06,1.0
+10799,15594862.0,Hsia,717.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,123882.73,0.0
+10800,15720671.0,Chigbogu,697.0,Spain,Female,42.0,1.0,81677.22,1.0,1.0,0.0,134873.41,0.0
+10801,15781689.0,Mancini,659.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,113581.87,0.0
+10802,15680752.0,Chiemenam,703.0,France,Male,39.0,7.0,141203.68,1.0,1.0,0.0,169964.12,0.0
+10803,15741865.0,Fan,714.0,France,Male,47.0,5.0,0.0,2.0,0.0,1.0,173365.89,0.0
+10804,15684861.0,Chukwudi,730.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,57553.98,0.0
+10805,15628059.0,Shao,743.0,France,Female,40.0,4.0,0.0,1.0,1.0,1.0,74042.0,1.0
+10806,15753831.0,K?,639.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,80190.36,0.0
+10807,15697042.0,Ch'iu,624.0,France,Male,34.0,4.0,171740.69,1.0,1.0,0.0,121451.21,0.0
+10808,15728706.0,Tien,726.0,Spain,Male,29.0,7.0,0.0,2.0,1.0,1.0,102655.31,0.0
+10809,15780128.0,Shih,655.0,France,Female,42.0,3.0,0.0,2.0,1.0,1.0,167033.83,0.0
+10810,15752312.0,Mazzanti,619.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,53943.21,0.0
+10811,15758025.0,Yermolayeva,699.0,Spain,Female,34.0,5.0,0.0,1.0,0.0,1.0,171922.72,0.0
+10812,15703394.0,Ch'in,624.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,0.0,117622.8,0.0
+10813,15712596.0,Genovese,662.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,127068.92,0.0
+10814,15666856.0,Pirozzi,579.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,178348.33,0.0
+10815,15695872.0,Goliwe,642.0,France,Female,45.0,3.0,0.0,2.0,1.0,1.0,24991.92,0.0
+10816,15695499.0,Ch'iu,806.0,France,Male,30.0,6.0,0.0,1.0,1.0,1.0,126726.33,0.0
+10817,15585047.0,Fiorentini,669.0,Spain,Female,35.0,4.0,0.0,1.0,0.0,0.0,56865.62,1.0
+10818,15642885.0,Nnonso,659.0,Germany,Female,29.0,3.0,121702.73,1.0,1.0,0.0,176580.89,0.0
+10819,15691446.0,Nkemakolam,521.0,France,Female,30.0,9.0,0.0,2.0,0.0,1.0,93717.55,0.0
+10820,15686963.0,Pagnotto,730.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,49133.45,0.0
+10821,15650098.0,Chidiegwu,611.0,Germany,Female,22.0,5.0,116695.27,1.0,1.0,1.0,137739.02,1.0
+10822,15642515.0,Zikoranachidimma,602.0,France,Female,41.0,5.0,0.0,2.0,0.0,0.0,47422.09,0.0
+10823,15576623.0,Chidiegwu,653.0,France,Female,37.0,2.0,0.0,2.0,0.0,1.0,155743.48,0.0
+10824,15728167.0,Ch'ien,754.0,France,Female,35.0,6.0,0.0,2.0,0.0,0.0,172049.84,0.0
+10825,15701175.0,Chiganu,684.0,Germany,Female,40.0,9.0,141827.33,1.0,1.0,1.0,52795.15,1.0
+10826,15585892.0,Chukwujekwu,544.0,France,Male,35.0,2.0,0.0,2.0,0.0,0.0,137029.97,0.0
+10827,15750447.0,Calabrese,678.0,France,Female,29.0,10.0,0.0,2.0,0.0,0.0,117946.3,0.0
+10828,15645593.0,Uwaezuoke,698.0,France,Female,46.0,1.0,0.0,1.0,0.0,0.0,145789.71,1.0
+10829,15654870.0,Ts'ai,543.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,121551.93,0.0
+10830,15764892.0,Mazzi,699.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,92797.23,0.0
+10831,15656937.0,Kao,613.0,France,Male,33.0,8.0,102723.35,1.0,1.0,0.0,88827.67,0.0
+10832,15742229.0,Chukwufumnanya,707.0,Germany,Male,52.0,6.0,152685.4,1.0,1.0,0.0,86950.72,1.0
+10833,15636396.0,Mazzi,742.0,France,Female,48.0,5.0,133537.1,1.0,1.0,0.0,137674.55,1.0
+10834,15585892.0,Ts'ao,619.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,177357.16,0.0
+10835,15796218.0,Hsueh,656.0,France,Male,40.0,3.0,0.0,2.0,0.0,0.0,134600.94,0.0
+10836,15603465.0,Chiemenam,628.0,Germany,Male,43.0,1.0,119963.45,1.0,1.0,0.0,55775.72,1.0
+10837,15710365.0,Chuang,636.0,France,Female,32.0,10.0,0.0,1.0,1.0,0.0,151902.78,0.0
+10838,15574785.0,Chukwualuka,789.0,France,Male,40.0,10.0,0.0,2.0,1.0,0.0,85516.37,0.0
+10839,15636684.0,Nnanna,689.0,Spain,Male,24.0,2.0,0.0,2.0,1.0,1.0,132870.15,0.0
+10840,15583736.0,T'ien,685.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,110114.38,0.0
+10841,15661275.0,T'ang,703.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,169862.01,0.0
+10842,15632210.0,Nwabugwu,639.0,Germany,Male,43.0,3.0,141136.65,2.0,1.0,0.0,153698.65,1.0
+10843,15633650.0,Nwachinemelu,714.0,Spain,Male,28.0,9.0,0.0,1.0,1.0,0.0,115083.73,1.0
+10844,15586931.0,Pacheco,850.0,France,Female,40.0,7.0,0.0,2.0,0.0,0.0,61793.47,0.0
+10845,15570087.0,Hsing,611.0,France,Female,40.0,9.0,0.0,1.0,0.0,1.0,126726.33,0.0
+10846,15655574.0,T'ao,597.0,France,Male,30.0,4.0,0.0,2.0,0.0,0.0,100127.71,0.0
+10847,15806771.0,Calabresi,735.0,Germany,Female,35.0,4.0,109949.05,1.0,0.0,1.0,142514.66,0.0
+10848,15756871.0,Zetticci,577.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,28391.52,1.0
+10849,15581736.0,Ch'ien,621.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,0.0,187597.98,0.0
+10850,15672216.0,Hsieh,663.0,France,Male,31.0,5.0,128742.57,1.0,1.0,0.0,11950.29,0.0
+10851,15646936.0,K?,697.0,France,Female,22.0,7.0,0.0,1.0,1.0,0.0,162599.51,0.0
+10852,15721582.0,Okechukwu,716.0,France,Male,27.0,8.0,0.0,2.0,1.0,1.0,118913.53,0.0
+10853,15672966.0,Tung,749.0,France,Female,56.0,8.0,0.0,2.0,1.0,1.0,57549.65,0.0
+10854,15672640.0,Nwachinemelu,537.0,France,Female,30.0,5.0,106152.68,2.0,1.0,1.0,92916.53,0.0
+10855,15594577.0,Sagese,677.0,Germany,Male,36.0,4.0,134497.27,1.0,1.0,1.0,114093.73,1.0
+10856,15571928.0,Y?an,746.0,France,Female,47.0,5.0,0.0,2.0,1.0,1.0,145524.36,0.0
+10857,15751032.0,Chukwuemeka,679.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,75959.1,0.0
+10858,15761348.0,Genovese,676.0,Spain,Female,39.0,3.0,156848.13,1.0,1.0,1.0,173870.39,0.0
+10859,15756056.0,Ritchie,539.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,85547.33,0.0
+10860,15702571.0,Onwumelu,644.0,France,Female,42.0,3.0,0.0,1.0,1.0,0.0,83419.15,1.0
+10861,15756471.0,Chukwualuka,757.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,84760.32,0.0
+10862,15615797.0,Rizzo,660.0,France,Female,36.0,5.0,164850.54,1.0,1.0,1.0,155711.64,0.0
+10863,15631739.0,Tien,661.0,Spain,Male,44.0,4.0,121355.19,1.0,0.0,0.0,74637.57,0.0
+10864,15696674.0,Hs?,573.0,Spain,Male,32.0,5.0,0.0,1.0,1.0,1.0,50911.41,0.0
+10865,15782591.0,Ch'en,775.0,Germany,Male,29.0,2.0,154733.94,2.0,0.0,1.0,90928.02,0.0
+10866,15581252.0,Iheanacho,605.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,79342.31,0.0
+10867,15601172.0,Ting,717.0,France,Male,35.0,3.0,0.0,1.0,0.0,1.0,74836.64,0.0
+10868,15692929.0,Genovesi,632.0,Spain,Male,42.0,1.0,125724.95,2.0,0.0,1.0,125909.85,0.0
+10869,15649160.0,Hao,678.0,France,Male,34.0,7.0,187530.66,1.0,1.0,0.0,126812.63,0.0
+10870,15718673.0,Davide,682.0,France,Female,33.0,2.0,0.0,1.0,0.0,0.0,73371.65,1.0
+10871,15800811.0,Chinwendu,653.0,Germany,Male,29.0,5.0,143595.87,2.0,1.0,0.0,182436.81,0.0
+10872,15603764.0,Golubev,553.0,Germany,Male,38.0,9.0,102088.68,1.0,1.0,1.0,108577.97,1.0
+10873,15633866.0,Zuyeva,678.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,43486.73,0.0
+10874,15694143.0,Lu,619.0,Germany,Male,43.0,3.0,89086.31,2.0,0.0,1.0,142566.75,0.0
+10875,15807923.0,Chukwueloka,727.0,Germany,Female,41.0,9.0,152827.99,1.0,1.0,1.0,9085.0,0.0
+10876,15622454.0,Hsia,714.0,France,Female,31.0,2.0,0.0,1.0,1.0,1.0,110593.62,0.0
+10877,15774393.0,T'ien,535.0,France,Male,28.0,7.0,0.0,2.0,0.0,1.0,93783.0,0.0
+10878,15809847.0,Ts'ao,551.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,56905.38,0.0
+10879,15625247.0,Hsueh,648.0,Germany,Male,32.0,1.0,99097.33,1.0,1.0,1.0,152097.02,0.0
+10880,15762715.0,Nwachukwu,708.0,Spain,Female,34.0,1.0,61688.61,2.0,1.0,1.0,177938.52,0.0
+10881,15581526.0,Smith,632.0,Spain,Male,36.0,2.0,0.0,2.0,0.0,0.0,26689.18,0.0
+10882,15703793.0,T'ang,539.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,57898.58,0.0
+10883,15586699.0,L?,703.0,Germany,Female,36.0,1.0,148507.24,2.0,1.0,0.0,91510.61,0.0
+10884,15587584.0,Tien,581.0,Germany,Male,36.0,10.0,140170.61,2.0,1.0,0.0,121904.14,0.0
+10885,15573888.0,Chu,675.0,Germany,Female,32.0,0.0,97266.1,2.0,1.0,0.0,61792.76,0.0
+10886,15674929.0,Chukwukadibia,677.0,France,Female,36.0,0.0,0.0,1.0,1.0,0.0,9983.88,0.0
+10887,15631739.0,Hsueh,706.0,Spain,Female,42.0,8.0,0.0,1.0,1.0,0.0,114020.06,1.0
+10888,15784597.0,Chinonyelum,809.0,Germany,Male,51.0,4.0,148071.69,1.0,1.0,1.0,161811.23,1.0
+10889,15633108.0,Onyekachukwu,661.0,France,Female,61.0,9.0,0.0,1.0,1.0,1.0,38550.4,1.0
+10890,15762708.0,Gartrell,785.0,France,Male,41.0,0.0,0.0,1.0,1.0,1.0,81409.0,1.0
+10891,15726490.0,Ch'ang,762.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,62634.94,0.0
+10892,15778025.0,Yefimova,585.0,France,Male,45.0,5.0,0.0,1.0,1.0,0.0,93360.37,1.0
+10893,15651823.0,Milano,643.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,48849.89,1.0
+10894,15578799.0,Tsou,818.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,102908.96,0.0
+10895,15585466.0,Chukwuebuka,662.0,Spain,Female,48.0,2.0,118627.06,3.0,0.0,0.0,46824.08,1.0
+10896,15576676.0,Obinna,648.0,Germany,Female,37.0,4.0,71497.79,1.0,0.0,0.0,19161.82,1.0
+10897,15757811.0,Ugochukwutubelum,754.0,Spain,Male,42.0,10.0,132319.26,1.0,1.0,1.0,74838.64,0.0
+10898,15769005.0,Linger,704.0,Germany,Male,41.0,6.0,106648.98,1.0,1.0,0.0,183569.25,0.0
+10899,15703068.0,Lettiere,592.0,France,Male,38.0,1.0,0.0,1.0,1.0,0.0,64350.8,0.0
+10900,15717995.0,Kao,549.0,Spain,Female,43.0,3.0,0.0,3.0,1.0,0.0,97734.3,1.0
+10901,15682533.0,Chukwuemeka,633.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,70550.0,0.0
+10902,15586069.0,Ting,613.0,Spain,Male,36.0,6.0,0.0,1.0,1.0,1.0,102665.95,0.0
+10903,15589488.0,McIntosh,621.0,France,Male,46.0,3.0,0.0,1.0,1.0,0.0,133692.88,1.0
+10904,15704509.0,Hsieh,594.0,France,Female,39.0,6.0,116967.68,1.0,1.0,1.0,115700.2,0.0
+10905,15701200.0,Ch'iu,633.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,27380.99,0.0
+10906,15766047.0,Yeh,749.0,Germany,Female,37.0,7.0,135096.77,2.0,1.0,0.0,176868.89,0.0
+10907,15595592.0,Uwaezuoke,599.0,Spain,Female,34.0,4.0,0.0,2.0,0.0,1.0,131644.31,0.0
+10908,15682467.0,Lucciano,612.0,France,Female,37.0,7.0,106922.93,1.0,1.0,1.0,169415.54,0.0
+10909,15634375.0,Ch'iu,771.0,Spain,Female,44.0,9.0,0.0,2.0,0.0,0.0,153605.75,0.0
+10910,15631681.0,Clements,677.0,France,Female,42.0,9.0,81733.74,1.0,1.0,0.0,132959.55,0.0
+10911,15783629.0,Lai,662.0,Germany,Female,48.0,6.0,132558.26,1.0,1.0,1.0,23305.01,1.0
+10912,15745354.0,Fanucci,648.0,France,Female,33.0,4.0,0.0,2.0,1.0,1.0,86622.77,0.0
+10913,15775490.0,Brown,653.0,Germany,Female,52.0,9.0,106134.46,3.0,1.0,1.0,179365.1,1.0
+10914,15694209.0,Nkemdilim,679.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,82782.08,0.0
+10915,15605837.0,Chien,762.0,Germany,Female,35.0,6.0,122314.5,2.0,1.0,0.0,104902.68,0.0
+10916,15569438.0,Chuang,667.0,France,Female,44.0,9.0,0.0,1.0,1.0,0.0,142400.27,1.0
+10917,15702789.0,Chukwueloka,643.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,167036.94,0.0
+10918,15798895.0,Hsia,769.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,75632.78,0.0
+10919,15583548.0,Nwankwo,653.0,France,Female,31.0,3.0,0.0,1.0,1.0,0.0,65801.44,0.0
+10920,15697034.0,Chukwumaobim,646.0,France,Female,44.0,6.0,0.0,1.0,1.0,0.0,92916.54,1.0
+10921,15732643.0,Kao,716.0,Spain,Male,43.0,10.0,0.0,1.0,1.0,0.0,117632.63,1.0
+10922,15651264.0,Chukwufumnanya,627.0,Spain,Male,44.0,2.0,0.0,2.0,1.0,0.0,74860.99,0.0
+10923,15610355.0,Chiang,597.0,France,Male,56.0,3.0,0.0,4.0,1.0,0.0,102936.71,1.0
+10924,15659100.0,Nwora,681.0,Spain,Female,48.0,6.0,0.0,1.0,1.0,0.0,121053.19,1.0
+10925,15692408.0,Okechukwu,597.0,Germany,Female,30.0,6.0,153344.72,1.0,1.0,1.0,51380.9,0.0
+10926,15806360.0,Ch'ang,596.0,France,Male,36.0,7.0,0.0,1.0,1.0,0.0,94488.82,0.0
+10927,15683553.0,Chiang,707.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,124571.09,0.0
+10928,15586522.0,Balsillie,729.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,1.0,80582.34,0.0
+10929,15668830.0,Y?,758.0,Germany,Male,38.0,1.0,127475.24,1.0,0.0,1.0,155721.15,0.0
+10930,15579387.0,Hs?,758.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,1.0,174431.01,0.0
+10931,15704763.0,T'ien,687.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,24208.84,0.0
+10932,15773639.0,Eluemuno,644.0,Germany,Female,40.0,6.0,115532.52,1.0,0.0,1.0,91903.9,1.0
+10933,15654495.0,Shao,709.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,121789.3,0.0
+10934,15786800.0,Hsueh,607.0,France,Female,37.0,8.0,0.0,1.0,1.0,1.0,92887.06,0.0
+10935,15739803.0,Chukwuemeka,850.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,132468.76,0.0
+10936,15710365.0,Chiang,646.0,Spain,Female,32.0,6.0,0.0,2.0,1.0,1.0,167066.95,0.0
+10937,15776939.0,Mao,744.0,France,Female,45.0,5.0,0.0,1.0,1.0,0.0,75825.57,1.0
+10938,15761706.0,Nwachukwu,627.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,165614.4,0.0
+10939,15732643.0,Yeh,586.0,Spain,Female,41.0,9.0,142319.02,1.0,1.0,1.0,140650.19,0.0
+10940,15597896.0,Ozerova,748.0,France,Male,40.0,8.0,0.0,1.0,1.0,1.0,128521.17,0.0
+10941,15762682.0,H?,648.0,Spain,Male,47.0,2.0,129745.1,1.0,1.0,0.0,97629.31,1.0
+10942,15699005.0,Chukwuemeka,714.0,Spain,Female,55.0,2.0,0.0,2.0,0.0,1.0,163798.41,0.0
+10943,15806967.0,Aitken,648.0,Germany,Male,41.0,8.0,118560.0,1.0,1.0,1.0,92913.94,0.0
+10944,15671293.0,Ch'iu,645.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,1.0,141528.56,0.0
+10945,15609356.0,Genovese,646.0,Spain,Female,42.0,2.0,139914.17,4.0,1.0,0.0,104889.3,1.0
+10946,15672945.0,Teng,602.0,France,Male,39.0,1.0,0.0,2.0,0.0,1.0,86460.28,0.0
+10947,15593973.0,T'ien,633.0,France,Male,37.0,4.0,133377.8,1.0,1.0,1.0,123775.15,0.0
+10948,15804072.0,Hsueh,678.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,79304.38,0.0
+10949,15576085.0,Hsu,638.0,Spain,Female,41.0,2.0,0.0,1.0,1.0,1.0,149535.75,1.0
+10950,15733883.0,Ch'eng,687.0,France,Male,24.0,9.0,143930.92,2.0,1.0,1.0,165035.17,0.0
+10951,15666916.0,Iloerika,572.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,126172.11,0.0
+10952,15771483.0,Czajkowski,678.0,Spain,Male,36.0,1.0,137794.18,1.0,1.0,1.0,193502.9,0.0
+10953,15643826.0,Chidiebere,709.0,Spain,Male,46.0,4.0,0.0,1.0,1.0,1.0,182963.31,1.0
+10954,15634310.0,Wang,516.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,137580.11,0.0
+10955,15583857.0,Onwuamaeze,850.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,1.0,89566.64,0.0
+10956,15802758.0,Mazzanti,663.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,161465.31,0.0
+10957,15806945.0,Ch'iu,692.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,69079.85,0.0
+10958,15768193.0,K'ung,671.0,France,Female,31.0,6.0,0.0,1.0,1.0,0.0,155711.64,0.0
+10959,15589715.0,Chiazagomekpere,651.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,162719.69,0.0
+10960,15666884.0,Nwankwo,705.0,Germany,Male,35.0,5.0,124401.6,2.0,1.0,0.0,90876.95,0.0
+10961,15607986.0,Nucci,774.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,141180.66,0.0
+10962,15617197.0,Ma,775.0,Spain,Female,43.0,8.0,0.0,2.0,0.0,0.0,132498.39,1.0
+10963,15765311.0,Lucchesi,575.0,France,Male,28.0,7.0,160515.37,1.0,1.0,1.0,82709.8,0.0
+10964,15778915.0,Onwuamaegbu,651.0,Spain,Male,67.0,8.0,0.0,2.0,1.0,0.0,130132.54,0.0
+10965,15732917.0,Onochie,637.0,Spain,Male,29.0,3.0,140133.08,1.0,1.0,1.0,180551.76,0.0
+10966,15689671.0,Ukaegbunam,593.0,Spain,Male,47.0,4.0,0.0,2.0,1.0,1.0,137793.35,0.0
+10967,15766355.0,Onyemauchechukwu,683.0,Germany,Female,32.0,2.0,127864.26,2.0,0.0,0.0,158750.53,0.0
+10968,15787597.0,Nnaife,635.0,Spain,Male,32.0,8.0,0.0,2.0,0.0,0.0,92568.05,0.0
+10969,15700650.0,Chukwudi,605.0,Spain,Male,39.0,7.0,0.0,2.0,0.0,0.0,90896.04,0.0
+10970,15571221.0,K'ung,526.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,64443.75,1.0
+10971,15766295.0,Mazzanti,663.0,France,Female,52.0,6.0,98635.77,1.0,1.0,1.0,184524.65,1.0
+10972,15777873.0,Ts'ui,584.0,France,Female,29.0,8.0,91078.76,1.0,1.0,0.0,66937.6,0.0
+10973,15598044.0,Pirozzi,662.0,France,Male,37.0,8.0,137817.52,1.0,0.0,0.0,122964.18,0.0
+10974,15783816.0,Ebelegbulam,554.0,France,Male,52.0,6.0,135645.29,1.0,1.0,0.0,141706.43,1.0
+10975,15656829.0,Genovese,637.0,France,Male,33.0,2.0,0.0,1.0,1.0,0.0,141860.62,0.0
+10976,15574830.0,Ch'in,793.0,France,Male,31.0,2.0,133676.65,1.0,1.0,1.0,101728.46,0.0
+10977,15656426.0,Chukwueloka,597.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,179436.6,0.0
+10978,15664521.0,Chijindum,516.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,49656.24,0.0
+10979,15798850.0,Uwaezuoke,609.0,Spain,Female,33.0,9.0,0.0,2.0,1.0,1.0,151368.37,0.0
+10980,15803947.0,Nwora,627.0,France,Female,33.0,8.0,120165.3,1.0,1.0,0.0,52411.99,0.0
+10981,15774936.0,Hsueh,710.0,France,Male,29.0,10.0,133501.36,1.0,0.0,0.0,125079.65,0.0
+10982,15808760.0,Shih,716.0,France,Male,26.0,3.0,0.0,2.0,1.0,0.0,72993.67,0.0
+10983,15791154.0,Yobachi,681.0,France,Male,36.0,2.0,155619.36,1.0,1.0,0.0,88890.05,0.0
+10984,15682355.0,H?,625.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,87071.18,0.0
+10985,15719352.0,Chiu,603.0,Spain,Female,27.0,9.0,0.0,1.0,1.0,0.0,85736.46,0.0
+10986,15758813.0,H?,549.0,France,Female,38.0,0.0,0.0,2.0,1.0,1.0,129912.77,0.0
+10987,15628893.0,Brown,659.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,74048.1,0.0
+10988,15749693.0,Hsu,456.0,France,Female,38.0,8.0,113882.31,1.0,1.0,1.0,89566.74,0.0
+10989,15677908.0,Chiekwugo,599.0,Germany,Female,55.0,9.0,114493.82,1.0,1.0,0.0,145894.9,1.0
+10990,15692761.0,Y?,663.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,85937.52,0.0
+10991,15770030.0,T'ang,565.0,France,Female,43.0,3.0,0.0,1.0,1.0,0.0,57553.02,0.0
+10992,15613140.0,Trevisani,586.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,0.0,134626.3,0.0
+10993,15769955.0,Tuan,745.0,France,Female,35.0,7.0,0.0,2.0,0.0,0.0,65464.66,0.0
+10994,15773017.0,Mazzi,687.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,93713.13,0.0
+10995,15682773.0,Nwokike,669.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,138275.01,0.0
+10996,15803037.0,Yobanna,577.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,184843.77,0.0
+10997,15798824.0,Genovesi,719.0,Spain,Female,31.0,4.0,0.0,2.0,1.0,1.0,90876.95,0.0
+10998,15721921.0,Maclean,836.0,Spain,Male,40.0,1.0,0.0,2.0,0.0,1.0,130846.98,0.0
+10999,15643865.0,Ts'ui,573.0,France,Female,24.0,6.0,82498.14,1.0,0.0,0.0,130132.41,0.0
+11000,15729582.0,Onwuamaeze,545.0,Germany,Male,46.0,9.0,102278.71,1.0,1.0,0.0,136902.01,1.0
+11001,15716041.0,Milanesi,686.0,France,Male,24.0,6.0,0.0,2.0,1.0,1.0,184524.74,0.0
+11002,15584812.0,K'ung,662.0,Spain,Male,45.0,7.0,0.0,2.0,1.0,0.0,143585.29,0.0
+11003,15708713.0,Lo Duca,571.0,Germany,Female,33.0,5.0,122340.54,2.0,1.0,1.0,99136.49,0.0
+11004,15781802.0,Kao,762.0,Spain,Female,34.0,5.0,0.0,1.0,1.0,1.0,4791.8,0.0
+11005,15805909.0,Biryukov,494.0,France,Female,34.0,3.0,0.0,2.0,0.0,0.0,176570.28,0.0
+11006,15750121.0,Nwabugwu,678.0,Germany,Female,46.0,8.0,108722.57,1.0,1.0,0.0,176580.63,1.0
+11007,15750808.0,Chibugo,579.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,169763.89,0.0
+11008,15623053.0,Onyekachi,624.0,Germany,Female,55.0,3.0,130652.52,1.0,1.0,0.0,182336.45,1.0
+11009,15736146.0,Onyekachukwu,753.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,68400.14,0.0
+11010,15796480.0,Ch'ien,707.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,156424.4,0.0
+11011,15781689.0,Zuev,604.0,Spain,Male,47.0,9.0,0.0,2.0,1.0,0.0,117572.59,0.0
+11012,15611186.0,Hs?eh,632.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,0.0,180591.31,0.0
+11013,15794048.0,Tuan,531.0,France,Male,43.0,9.0,0.0,1.0,1.0,1.0,133537.57,0.0
+11014,15610936.0,Genovese,613.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,55775.72,0.0
+11015,15764294.0,Ch'in,729.0,Spain,Male,38.0,4.0,0.0,1.0,0.0,1.0,79005.95,0.0
+11016,15776545.0,Chu,544.0,Spain,Female,29.0,1.0,0.0,2.0,1.0,0.0,149170.92,0.0
+11017,15592089.0,Bibi,719.0,Spain,Female,26.0,8.0,0.0,1.0,1.0,1.0,125423.42,0.0
+11018,15789560.0,McLean,634.0,France,Male,28.0,9.0,0.0,2.0,1.0,1.0,103713.93,0.0
+11019,15707025.0,Yudina,637.0,Spain,Male,47.0,2.0,0.0,2.0,1.0,0.0,36251.0,0.0
+11020,15682773.0,Chiganu,692.0,France,Male,32.0,9.0,0.0,2.0,0.0,0.0,172810.52,0.0
+11021,15781589.0,Shao,559.0,Spain,Male,38.0,4.0,93920.47,1.0,1.0,0.0,140780.8,0.0
+11022,15723847.0,Hsueh,659.0,Spain,Female,35.0,1.0,0.0,2.0,0.0,1.0,142917.54,1.0
+11023,15591130.0,Chiawuotu,478.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,158071.69,0.0
+11024,15571752.0,Kao,729.0,France,Male,52.0,9.0,122570.87,1.0,0.0,1.0,140136.1,0.0
+11025,15787035.0,Ch'in,745.0,France,Male,32.0,6.0,152576.25,1.0,1.0,0.0,120107.1,0.0
+11026,15804787.0,Ch'ien,578.0,France,Male,33.0,4.0,0.0,2.0,0.0,1.0,99645.04,0.0
+11027,15773520.0,Teng,599.0,Germany,Male,31.0,1.0,110654.02,2.0,1.0,1.0,53394.11,0.0
+11028,15651369.0,Iadanza,638.0,France,Female,46.0,1.0,0.0,2.0,1.0,1.0,57876.06,0.0
+11029,15675176.0,Ebelegbulam,605.0,France,Female,44.0,7.0,0.0,1.0,0.0,1.0,199505.53,0.0
+11030,15575391.0,Chia,721.0,Spain,Female,36.0,9.0,92716.95,1.0,1.0,0.0,81292.33,0.0
+11031,15642710.0,Chiang,584.0,Germany,Male,38.0,9.0,141806.46,1.0,1.0,1.0,163666.85,0.0
+11032,15681924.0,Pirozzi,794.0,Germany,Male,44.0,8.0,170184.99,1.0,1.0,0.0,104889.3,0.0
+11033,15783338.0,Chidubem,558.0,Spain,Female,44.0,0.0,93578.68,1.0,1.0,1.0,157822.54,1.0
+11034,15685300.0,Ugochukwu,611.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,27244.35,0.0
+11035,15713644.0,Oluchi,683.0,France,Male,24.0,4.0,0.0,2.0,1.0,1.0,98820.09,0.0
+11036,15690772.0,Ugochukwutubelum,656.0,France,Female,45.0,5.0,0.0,1.0,1.0,0.0,141180.66,1.0
+11037,15781598.0,Nkemdilim,539.0,France,Male,59.0,6.0,118897.1,1.0,1.0,0.0,67840.81,1.0
+11038,15656829.0,Milanesi,753.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,35300.08,0.0
+11039,15667971.0,Smith,582.0,France,Female,40.0,6.0,0.0,1.0,0.0,0.0,81251.24,1.0
+11040,15763683.0,Walker,527.0,Germany,Male,33.0,5.0,133501.33,2.0,1.0,0.0,159737.56,0.0
+11041,15684512.0,Obialo,678.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,101102.39,0.0
+11042,15798964.0,Ch'en,592.0,Germany,Male,33.0,7.0,109908.06,1.0,1.0,1.0,73881.68,0.0
+11043,15730161.0,Hsing,678.0,Spain,Male,34.0,3.0,123557.81,1.0,1.0,1.0,136079.64,0.0
+11044,15622171.0,Uspensky,586.0,Spain,Male,31.0,2.0,0.0,2.0,0.0,0.0,79957.95,0.0
+11045,15583090.0,H?,699.0,France,Male,35.0,0.0,0.0,2.0,0.0,0.0,136028.97,0.0
+11046,15570073.0,K?,683.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,177784.68,0.0
+11047,15745080.0,Chukwukere,722.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,1.0,166495.2,0.0
+11048,15569247.0,P'eng,686.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,158750.53,0.0
+11049,15636444.0,Yobanna,700.0,Spain,Female,60.0,6.0,0.0,3.0,1.0,0.0,60920.03,1.0
+11050,15748691.0,Udegbulam,757.0,France,Male,49.0,8.0,0.0,1.0,1.0,0.0,142935.83,1.0
+11051,15603088.0,Iweobiegbunam,712.0,France,Female,28.0,5.0,0.0,1.0,1.0,1.0,57844.26,0.0
+11052,15642725.0,Tien,726.0,France,Male,37.0,1.0,0.0,2.0,0.0,1.0,136497.28,0.0
+11053,15574324.0,Chizuoke,688.0,France,Female,37.0,3.0,65812.5,1.0,1.0,0.0,138564.48,1.0
+11054,15800517.0,Zuyev,723.0,Germany,Female,36.0,4.0,73481.05,1.0,1.0,1.0,150242.44,0.0
+11055,15616676.0,Burtch,602.0,Spain,Male,32.0,2.0,134766.89,1.0,1.0,1.0,182258.17,0.0
+11056,15584517.0,Martin,633.0,Germany,Male,29.0,9.0,71281.29,2.0,1.0,0.0,106851.6,0.0
+11057,15641091.0,Iadanza,774.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,1.0,154491.39,0.0
+11058,15634373.0,Yermolayeva,822.0,France,Male,32.0,1.0,139872.41,1.0,1.0,1.0,56780.97,0.0
+11059,15804064.0,McWilliams,717.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,81299.17,0.0
+11060,15775235.0,Wright,773.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,60584.33,0.0
+11061,15645095.0,Kaur,526.0,Germany,Male,53.0,3.0,104533.24,1.0,1.0,0.0,85902.83,1.0
+11062,15692671.0,Ch'iu,646.0,France,Male,37.0,9.0,0.0,1.0,1.0,1.0,71179.31,0.0
+11063,15606841.0,Nwora,627.0,Spain,Male,41.0,1.0,0.0,1.0,1.0,0.0,143981.27,1.0
+11064,15762091.0,Tuan,746.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,115668.53,0.0
+11065,15728474.0,T'ien,686.0,France,Female,60.0,9.0,0.0,1.0,1.0,0.0,123977.86,1.0
+11066,15778395.0,McLean,614.0,France,Female,41.0,8.0,0.0,2.0,0.0,0.0,126554.96,0.0
+11067,15716085.0,Nwachukwu,592.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,142838.64,0.0
+11068,15585100.0,Onyemachukwu,653.0,France,Female,48.0,10.0,0.0,1.0,1.0,0.0,127413.0,1.0
+11069,15738220.0,Fanucci,720.0,Spain,Male,43.0,9.0,130486.57,1.0,1.0,0.0,51565.96,1.0
+11070,15675320.0,Liao,656.0,France,Male,29.0,1.0,0.0,1.0,1.0,0.0,97938.59,0.0
+11071,15702561.0,Ch'iu,574.0,Germany,Female,63.0,1.0,155519.59,1.0,1.0,0.0,116766.79,1.0
+11072,15671779.0,Chidozie,652.0,France,Female,23.0,3.0,0.0,2.0,1.0,0.0,143345.65,0.0
+11073,15762480.0,Lu,792.0,Spain,Female,43.0,0.0,108586.86,1.0,1.0,0.0,107876.2,0.0
+11074,15661828.0,Tuan,584.0,Spain,Female,41.0,9.0,151858.98,1.0,1.0,0.0,93835.64,0.0
+11075,15699450.0,Ch'iu,681.0,France,Male,39.0,2.0,0.0,2.0,0.0,0.0,173870.39,0.0
+11076,15648726.0,Chien,602.0,France,Male,43.0,6.0,0.0,1.0,1.0,1.0,113089.43,0.0
+11077,15792934.0,McIntyre,762.0,Germany,Female,49.0,3.0,122447.76,1.0,0.0,1.0,118716.63,1.0
+11078,15767414.0,Tardent,516.0,France,Male,60.0,1.0,0.0,1.0,1.0,1.0,108419.41,1.0
+11079,15581244.0,Hsia,708.0,Germany,Female,40.0,2.0,150725.53,2.0,1.0,0.0,14081.64,0.0
+11080,15671244.0,Ugorji,559.0,Spain,Female,33.0,3.0,0.0,1.0,1.0,1.0,142377.69,1.0
+11081,15600174.0,Ting,669.0,France,Male,25.0,4.0,0.0,2.0,1.0,0.0,162858.29,0.0
+11082,15747757.0,Ch'en,735.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,64872.55,0.0
+11083,15746410.0,McElhone,635.0,France,Male,39.0,1.0,0.0,2.0,0.0,0.0,140579.17,0.0
+11084,15790846.0,Fan,687.0,Germany,Female,45.0,7.0,92674.94,3.0,1.0,1.0,91633.53,1.0
+11085,15794479.0,Ting,709.0,Germany,Male,36.0,1.0,170214.23,2.0,1.0,1.0,85746.52,0.0
+11086,15656627.0,Nwankwo,730.0,Germany,Male,34.0,5.0,81732.88,1.0,1.0,0.0,135635.25,0.0
+11087,15685300.0,Palerma,569.0,Germany,Male,22.0,4.0,103914.17,1.0,0.0,1.0,135088.58,0.0
+11088,15789563.0,Ch'en,686.0,Spain,Female,31.0,7.0,138333.03,1.0,1.0,0.0,60905.51,0.0
+11089,15780128.0,Ijendu,683.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,167773.55,0.0
+11090,15626212.0,Dike,687.0,Germany,Male,32.0,4.0,125724.95,2.0,0.0,1.0,100557.03,0.0
+11091,15658258.0,H?,684.0,France,Male,27.0,6.0,0.0,2.0,1.0,0.0,180598.66,0.0
+11092,15592022.0,Ugochukwu,705.0,France,Male,31.0,7.0,95377.13,1.0,0.0,0.0,88695.19,0.0
+11093,15661528.0,Liao,583.0,Spain,Female,39.0,8.0,0.0,2.0,1.0,0.0,89891.9,0.0
+11094,15661036.0,Ch'ien,726.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,156048.64,0.0
+11095,15795519.0,Frolov,764.0,Germany,Male,42.0,9.0,86911.7,1.0,0.0,0.0,88965.46,1.0
+11096,15665861.0,Wilson,537.0,Germany,Female,51.0,1.0,103818.47,1.0,0.0,1.0,89566.64,1.0
+11097,15726588.0,Chukwubuikem,614.0,Spain,Male,67.0,2.0,0.0,2.0,1.0,1.0,164741.15,0.0
+11098,15696859.0,Mazzanti,671.0,France,Male,34.0,2.0,116099.82,2.0,1.0,1.0,143086.09,0.0
+11099,15623450.0,Genovese,751.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,134901.56,0.0
+11100,15768342.0,K?,591.0,France,Female,40.0,7.0,0.0,2.0,1.0,0.0,6076.62,0.0
+11101,15733247.0,Fokine,708.0,France,Male,45.0,5.0,142872.36,1.0,1.0,0.0,142995.32,0.0
+11102,15646615.0,Teng,611.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,142915.84,0.0
+11103,15677317.0,Chukwukere,645.0,Spain,Female,39.0,7.0,0.0,1.0,1.0,0.0,199256.98,1.0
+11104,15607986.0,Hsing,735.0,France,Female,46.0,4.0,0.0,2.0,1.0,1.0,86935.08,0.0
+11105,15579969.0,Chin,644.0,Germany,Female,38.0,4.0,102994.04,1.0,1.0,0.0,120834.81,0.0
+11106,15633043.0,Tuan,631.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,79240.9,0.0
+11107,15783338.0,Chidiebele,605.0,France,Male,28.0,9.0,0.0,1.0,1.0,1.0,152843.53,0.0
+11108,15571940.0,Pisani,677.0,France,Male,33.0,3.0,0.0,2.0,0.0,1.0,92888.52,0.0
+11109,15753837.0,Tuan,591.0,France,Male,39.0,0.0,111098.85,1.0,0.0,1.0,74838.64,0.0
+11110,15799785.0,Mazzanti,525.0,France,Female,51.0,2.0,117035.89,1.0,1.0,0.0,49374.82,1.0
+11111,15597591.0,Lu,707.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,80963.92,0.0
+11112,15753747.0,Nwankwo,609.0,France,Female,33.0,9.0,0.0,1.0,0.0,1.0,140800.66,0.0
+11113,15593365.0,Ch'in,681.0,Spain,Female,42.0,6.0,0.0,2.0,1.0,0.0,118862.08,0.0
+11114,15671769.0,Nkemakonam,656.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,176763.27,0.0
+11115,15713374.0,Kable,773.0,France,Male,41.0,5.0,0.0,2.0,1.0,0.0,52331.15,0.0
+11116,15789413.0,Nkemakolam,685.0,France,Male,39.0,8.0,0.0,2.0,0.0,1.0,172495.81,0.0
+11117,15586870.0,Ch'ien,718.0,Germany,Female,54.0,2.0,101207.75,1.0,1.0,0.0,152499.8,1.0
+11118,15650086.0,Chuang,614.0,Spain,Male,35.0,10.0,61710.42,1.0,0.0,0.0,53394.24,0.0
+11119,15734461.0,Ting,692.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,16081.62,0.0
+11120,15616666.0,Y?an,467.0,Germany,Female,46.0,4.0,102955.14,1.0,1.0,0.0,85547.33,1.0
+11121,15690731.0,Chukwufumnanya,706.0,Germany,Male,45.0,2.0,105522.66,1.0,1.0,1.0,132770.15,0.0
+11122,15696812.0,Onwuamaeze,681.0,France,Male,28.0,4.0,0.0,2.0,1.0,1.0,162960.05,0.0
+11123,15641170.0,Sheppard,686.0,Germany,Female,38.0,6.0,130854.59,2.0,0.0,1.0,75562.47,0.0
+11124,15752312.0,Chu,730.0,France,Female,35.0,9.0,0.0,1.0,1.0,0.0,184524.65,0.0
+11125,15786463.0,Chikezie,624.0,France,Female,34.0,5.0,0.0,1.0,0.0,0.0,113931.57,0.0
+11126,15663923.0,Hsing,678.0,France,Female,41.0,8.0,0.0,2.0,1.0,1.0,48161.18,0.0
+11127,15635367.0,Lai,687.0,France,Female,27.0,3.0,129834.67,1.0,1.0,1.0,94780.9,0.0
+11128,15643496.0,Ch'ien,663.0,Spain,Male,43.0,9.0,0.0,2.0,1.0,0.0,47777.15,1.0
+11129,15713505.0,Shih,551.0,Spain,Male,43.0,9.0,0.0,2.0,1.0,0.0,150468.31,0.0
+11130,15622535.0,Nwankwo,595.0,France,Male,57.0,3.0,0.0,2.0,1.0,0.0,96463.25,1.0
+11131,15685829.0,Chizoba,825.0,Spain,Female,25.0,1.0,0.0,1.0,1.0,0.0,180591.32,0.0
+11132,15772554.0,Kao,583.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,100834.22,0.0
+11133,15742632.0,Rizzo,681.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,171430.16,0.0
+11134,15630698.0,Y?,724.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,97623.42,0.0
+11135,15782941.0,Chigozie,646.0,Spain,Male,26.0,5.0,0.0,2.0,0.0,1.0,142548.64,0.0
+11136,15719309.0,Hsiung,684.0,Germany,Male,41.0,1.0,115341.55,1.0,1.0,0.0,53511.85,0.0
+11137,15615032.0,Chu,804.0,France,Male,24.0,9.0,125830.74,1.0,1.0,0.0,155879.57,0.0
+11138,15803378.0,Ogochukwu,648.0,Germany,Female,53.0,3.0,119961.48,1.0,1.0,0.0,159718.16,1.0
+11139,15781347.0,Zuyeva,717.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,63981.37,0.0
+11140,15743582.0,Lai,656.0,France,Male,40.0,8.0,0.0,1.0,1.0,1.0,177655.69,0.0
+11141,15627801.0,Ch'in,733.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,172618.52,0.0
+11142,15696900.0,Pinto,587.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,159800.16,0.0
+11143,15756969.0,King,773.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,67979.35,0.0
+11144,15731781.0,Y?,743.0,France,Male,35.0,1.0,0.0,1.0,1.0,0.0,93714.29,0.0
+11145,15737506.0,Clements,684.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,180716.1,0.0
+11146,15659557.0,Zetticci,571.0,Spain,Female,38.0,5.0,136733.24,1.0,1.0,0.0,102299.14,0.0
+11147,15736295.0,O'Donnell,708.0,France,Male,28.0,4.0,0.0,2.0,0.0,0.0,146891.07,0.0
+11148,15621267.0,Chiu,729.0,Spain,Male,22.0,3.0,0.0,2.0,1.0,0.0,151733.43,0.0
+11149,15803689.0,L?,564.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,180890.4,0.0
+11150,15693906.0,Zetticci,572.0,France,Male,28.0,6.0,0.0,1.0,1.0,0.0,130846.82,0.0
+11151,15654878.0,Dike,612.0,Germany,Male,33.0,5.0,148897.03,1.0,1.0,0.0,136497.28,0.0
+11152,15727489.0,Nnachetam,741.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,158973.04,0.0
+11153,15722061.0,Hs?,687.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,1.0,176692.65,0.0
+11154,15688849.0,Hsu,707.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,160529.71,0.0
+11155,15632789.0,Chukwukere,607.0,France,Female,39.0,1.0,0.0,1.0,1.0,1.0,56650.47,0.0
+11156,15597945.0,Ts'ao,645.0,Spain,Female,32.0,2.0,68423.89,2.0,1.0,0.0,138889.15,1.0
+11157,15596979.0,T'an,644.0,Spain,Male,33.0,4.0,0.0,2.0,0.0,1.0,186841.71,0.0
+11158,15667460.0,Chukwualuka,677.0,France,Female,29.0,7.0,0.0,2.0,1.0,1.0,79866.73,0.0
+11159,15751180.0,Chukwufumnanya,594.0,Germany,Male,21.0,2.0,123971.51,2.0,1.0,1.0,61251.25,0.0
+11160,15626008.0,Chia,745.0,France,Male,28.0,6.0,0.0,2.0,0.0,0.0,128881.87,0.0
+11161,15666055.0,Chien,811.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,145789.71,0.0
+11162,15682600.0,Iloerika,735.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,123866.43,0.0
+11163,15705343.0,Uchechukwu,562.0,France,Male,38.0,8.0,108722.57,1.0,1.0,1.0,165008.97,0.0
+11164,15728885.0,Chukwualuka,577.0,Spain,Male,32.0,6.0,88915.85,1.0,0.0,1.0,72993.67,0.0
+11165,15632275.0,Chikelu,711.0,Spain,Male,35.0,5.0,88902.21,1.0,1.0,0.0,103931.49,0.0
+11166,15751180.0,Trevisano,755.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,86657.48,0.0
+11167,15585855.0,Lai,603.0,France,Male,33.0,7.0,77599.23,1.0,1.0,1.0,131625.14,0.0
+11168,15808846.0,Chinomso,632.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,94420.5,0.0
+11169,15708048.0,Macdonald,599.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,88878.15,0.0
+11170,15690958.0,Nwebube,615.0,France,Male,33.0,5.0,150139.59,2.0,1.0,1.0,79919.97,0.0
+11171,15633194.0,White,678.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,582.59,0.0
+11172,15637646.0,Folliero,705.0,France,Male,51.0,8.0,0.0,2.0,1.0,0.0,118959.64,1.0
+11173,15662494.0,Walker,613.0,France,Male,40.0,6.0,108797.63,1.0,1.0,1.0,159800.16,0.0
+11174,15686916.0,Toscani,648.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,122433.09,0.0
+11175,15791045.0,Chukwuebuka,790.0,Spain,Female,34.0,0.0,0.0,2.0,1.0,0.0,151842.31,0.0
+11176,15682467.0,Zito,663.0,Germany,Male,37.0,5.0,95560.04,1.0,0.0,1.0,146411.6,0.0
+11177,15675380.0,Ugoji,544.0,France,Female,49.0,5.0,0.0,1.0,0.0,0.0,131718.12,1.0
+11178,15802071.0,Nnaife,753.0,Germany,Female,31.0,1.0,108432.88,1.0,0.0,1.0,82298.59,0.0
+11179,15704844.0,Hsiao,726.0,Germany,Female,51.0,2.0,104305.77,2.0,1.0,0.0,123955.83,1.0
+11180,15807593.0,Douglas,633.0,Germany,Male,39.0,1.0,117468.67,1.0,0.0,1.0,173844.89,0.0
+11181,15790659.0,Tien,690.0,France,Female,39.0,6.0,91285.22,1.0,1.0,0.0,937.15,0.0
+11182,15652626.0,T'ao,585.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,186489.97,0.0
+11183,15726796.0,Young,686.0,France,Female,33.0,6.0,0.0,2.0,0.0,1.0,53437.1,0.0
+11184,15633870.0,Chidiegwu,602.0,France,Female,35.0,9.0,153663.13,1.0,0.0,0.0,93777.15,0.0
+11185,15681703.0,Hs?,727.0,France,Male,44.0,5.0,0.0,1.0,1.0,1.0,148527.56,0.0
+11186,15664808.0,Aikenhead,777.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,141776.62,0.0
+11187,15576623.0,P'an,477.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,108732.96,0.0
+11188,15751531.0,Lucciano,679.0,Germany,Male,32.0,9.0,111096.91,2.0,1.0,0.0,187597.98,0.0
+11189,15630106.0,Pirozzi,719.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,152883.95,0.0
+11190,15611759.0,Nnanna,613.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,131312.56,0.0
+11191,15777797.0,Tung,756.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,141533.19,0.0
+11192,15593501.0,Ch'ang,806.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,10928.3,0.0
+11193,15684960.0,Olisanugo,577.0,France,Male,41.0,9.0,120916.52,1.0,1.0,1.0,70783.61,0.0
+11194,15695792.0,Trevisano,586.0,France,Female,46.0,6.0,116880.53,1.0,0.0,0.0,161241.65,1.0
+11195,15626452.0,Chukwufumnanya,628.0,France,Female,39.0,9.0,0.0,2.0,0.0,1.0,64833.08,0.0
+11196,15642870.0,Onwumelu,699.0,France,Female,37.0,3.0,0.0,1.0,1.0,1.0,126812.63,0.0
+11197,15756680.0,Nnaife,754.0,France,Male,22.0,3.0,0.0,2.0,1.0,1.0,152883.95,0.0
+11198,15699238.0,Golibe,679.0,France,Male,46.0,6.0,0.0,2.0,0.0,1.0,108469.2,0.0
+11199,15732643.0,Chinedum,684.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,107640.25,0.0
+11200,15672610.0,Ogochukwu,585.0,Germany,Male,27.0,1.0,137761.55,1.0,1.0,1.0,171430.16,0.0
+11201,15656822.0,Nnonso,594.0,Spain,Male,24.0,2.0,0.0,2.0,0.0,1.0,143301.49,0.0
+11202,15757837.0,Yermolayeva,711.0,Spain,Male,43.0,8.0,0.0,1.0,0.0,1.0,51364.56,1.0
+11203,15781802.0,Milne,628.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,71830.1,0.0
+11204,15794890.0,Lombardo,581.0,France,Male,37.0,7.0,63723.78,1.0,1.0,1.0,25951.91,1.0
+11205,15645571.0,Ch'ang,824.0,France,Male,41.0,8.0,0.0,1.0,1.0,0.0,180329.83,0.0
+11206,15608968.0,P'eng,749.0,Germany,Female,38.0,5.0,108907.8,2.0,1.0,0.0,63889.1,0.0
+11207,15781702.0,Chukwualuka,743.0,Germany,Female,31.0,9.0,103391.38,1.0,1.0,1.0,73979.85,0.0
+11208,15781409.0,Shih,713.0,Germany,Female,48.0,7.0,107715.84,1.0,0.0,0.0,121406.62,1.0
+11209,15673372.0,T'ao,785.0,Spain,Female,42.0,10.0,0.0,2.0,1.0,0.0,114774.35,0.0
+11210,15770121.0,Kao,580.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,105887.04,0.0
+11211,15750121.0,Ukaegbunam,597.0,France,Male,39.0,2.0,140614.15,1.0,1.0,0.0,193018.71,0.0
+11212,15699340.0,Iloerika,577.0,France,Female,58.0,2.0,0.0,2.0,1.0,1.0,98640.78,0.0
+11213,15711457.0,Ch'ang,688.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,120906.83,0.0
+11214,15587565.0,Houghton,588.0,Germany,Female,42.0,3.0,121730.49,1.0,1.0,0.0,51565.89,1.0
+11215,15632118.0,Ch'eng,660.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,157091.34,0.0
+11216,15626044.0,Udinesi,711.0,France,Female,43.0,0.0,171671.9,1.0,1.0,0.0,171364.22,1.0
+11217,15691817.0,Nnonso,646.0,France,Male,35.0,1.0,0.0,1.0,1.0,0.0,187976.88,1.0
+11218,15641490.0,O'Loughlin,516.0,France,Female,26.0,1.0,0.0,2.0,1.0,1.0,81861.1,0.0
+11219,15758035.0,Fanucci,488.0,Spain,Male,33.0,2.0,87637.35,1.0,1.0,0.0,120830.73,0.0
+11220,15592461.0,Ts'ao,695.0,Spain,Male,44.0,10.0,0.0,2.0,1.0,0.0,60905.51,0.0
+11221,15773789.0,Zuyev,727.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,0.0,96832.82,0.0
+11222,15578908.0,McIntosh,833.0,France,Male,45.0,3.0,0.0,3.0,1.0,0.0,169152.76,1.0
+11223,15647725.0,Milanesi,705.0,Germany,Female,31.0,3.0,87772.52,1.0,0.0,1.0,130610.78,0.0
+11224,15777045.0,H?,739.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,55775.72,0.0
+11225,15698031.0,Kornilova,594.0,France,Male,53.0,1.0,0.0,2.0,1.0,0.0,187616.16,1.0
+11226,15787830.0,Goliwe,653.0,France,Female,34.0,10.0,0.0,2.0,0.0,0.0,170829.87,0.0
+11227,15648858.0,Ch'in,757.0,Germany,Female,34.0,4.0,106434.94,1.0,1.0,0.0,59755.14,0.0
+11228,15662232.0,Ni,487.0,Germany,Female,28.0,7.0,124577.33,1.0,1.0,0.0,86786.42,1.0
+11229,15767391.0,Nnonso,739.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,185599.34,0.0
+11230,15607877.0,Mazzanti,698.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,9087.81,0.0
+11231,15793883.0,Chukwufumnanya,684.0,Germany,Female,30.0,7.0,114944.0,1.0,0.0,0.0,181294.39,0.0
+11232,15648876.0,Lucchese,655.0,France,Male,59.0,7.0,0.0,1.0,1.0,0.0,175015.12,1.0
+11233,15581740.0,Chiang,651.0,Germany,Female,54.0,3.0,137523.02,1.0,1.0,1.0,137537.13,0.0
+11234,15765266.0,Fanucci,603.0,France,Female,33.0,2.0,0.0,1.0,1.0,0.0,129563.86,0.0
+11235,15778526.0,Teng,686.0,Germany,Female,56.0,6.0,132405.52,1.0,1.0,1.0,107876.2,1.0
+11236,15673367.0,Onyeorulu,676.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,122662.98,0.0
+11237,15616172.0,Hargreaves,746.0,Germany,Female,40.0,2.0,116499.43,2.0,1.0,1.0,182059.28,0.0
+11238,15723008.0,Nwachinemelu,596.0,France,Male,35.0,5.0,111827.27,1.0,1.0,1.0,38131.77,0.0
+11239,15793893.0,Chinedum,549.0,France,Male,51.0,8.0,0.0,1.0,1.0,1.0,132673.23,1.0
+11240,15652693.0,Shih,605.0,France,Female,46.0,4.0,0.0,1.0,1.0,0.0,121189.3,1.0
+11241,15583582.0,Lai,748.0,Spain,Male,30.0,4.0,0.0,2.0,0.0,0.0,131736.23,0.0
+11242,15758031.0,Ndubuagha,687.0,France,Male,25.0,2.0,0.0,2.0,1.0,0.0,172576.43,0.0
+11243,15705890.0,Chimaobim,564.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,126551.65,0.0
+11244,15693322.0,Hsiung,730.0,Spain,Male,33.0,1.0,0.0,3.0,1.0,1.0,152992.04,0.0
+11245,15589488.0,Galkin,624.0,Spain,Male,35.0,9.0,178058.06,2.0,0.0,1.0,25684.93,0.0
+11246,15752984.0,P'an,667.0,France,Male,39.0,3.0,0.0,2.0,0.0,0.0,174094.24,0.0
+11247,15796218.0,Ponomarev,662.0,Germany,Female,32.0,8.0,114193.24,1.0,1.0,1.0,51554.58,0.0
+11248,15619304.0,Kornilova,738.0,France,Male,50.0,2.0,143391.07,1.0,1.0,1.0,184514.01,1.0
+11249,15704910.0,Ch'in,642.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,177099.71,0.0
+11250,15733169.0,Maclean,678.0,France,Female,53.0,3.0,0.0,2.0,1.0,1.0,27145.73,1.0
+11251,15745012.0,Onio,651.0,Spain,Male,44.0,5.0,151607.56,2.0,0.0,1.0,76544.9,1.0
+11252,15749489.0,Kincaid,752.0,France,Female,31.0,2.0,0.0,1.0,1.0,1.0,171345.02,0.0
+11253,15763625.0,Chukwujamuike,596.0,France,Female,42.0,5.0,0.0,2.0,1.0,1.0,75732.25,0.0
+11254,15731511.0,Chien,592.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,47322.05,0.0
+11255,15631681.0,Calabresi,744.0,France,Male,38.0,7.0,86847.19,1.0,1.0,1.0,107876.2,0.0
+11256,15761706.0,Lettiere,738.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,0.0,134878.19,0.0
+11257,15688079.0,Mazzanti,582.0,France,Male,31.0,8.0,110349.82,1.0,0.0,1.0,125710.53,0.0
+11258,15597957.0,Ch'eng,757.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,54474.48,0.0
+11259,15793046.0,Ch'eng,721.0,France,Male,29.0,10.0,0.0,2.0,1.0,1.0,142839.81,0.0
+11260,15661349.0,Onyeorulu,704.0,Spain,Male,40.0,5.0,0.0,1.0,1.0,0.0,162515.17,1.0
+11261,15780140.0,Fanucci,826.0,Germany,Male,54.0,6.0,151139.65,3.0,1.0,0.0,124964.82,1.0
+11262,15692991.0,Lueck,593.0,Germany,Female,36.0,5.0,83624.55,2.0,1.0,0.0,143096.49,1.0
+11263,15767339.0,Toscani,678.0,Spain,Female,34.0,1.0,0.0,1.0,1.0,1.0,186489.95,0.0
+11264,15781465.0,Nkemdirim,643.0,Germany,Female,44.0,6.0,119496.85,1.0,1.0,0.0,166513.49,1.0
+11265,15662532.0,Ch'in,574.0,France,Female,42.0,6.0,0.0,1.0,1.0,1.0,150944.41,1.0
+11266,15615473.0,Macleod,724.0,France,Male,29.0,3.0,140733.74,1.0,0.0,1.0,96929.24,0.0
+11267,15682585.0,Chiu,696.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,0.0,152884.85,0.0
+11268,15649033.0,Onyemauchechi,681.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,1099.95,0.0
+11269,15800440.0,Folliero,704.0,Germany,Male,29.0,6.0,114430.16,1.0,1.0,0.0,72993.65,0.0
+11270,15688074.0,Nnachetam,637.0,France,Female,44.0,5.0,0.0,2.0,1.0,0.0,1285.48,0.0
+11271,15777047.0,Ch'iu,710.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,163516.16,0.0
+11272,15770995.0,Chiganu,619.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,56984.58,0.0
+11273,15789611.0,Mironova,556.0,Germany,Male,54.0,9.0,153060.63,1.0,0.0,1.0,134778.01,1.0
+11274,15602844.0,Lu,627.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,167931.81,0.0
+11275,15572472.0,Shih,643.0,France,Female,40.0,9.0,0.0,2.0,1.0,0.0,182988.94,0.0
+11276,15639133.0,Chien,685.0,France,Female,67.0,2.0,0.0,1.0,1.0,1.0,127875.1,0.0
+11277,15671534.0,Smith,665.0,Germany,Male,44.0,1.0,104488.19,1.0,1.0,1.0,57817.84,1.0
+11278,15723720.0,Onwuamaeze,577.0,France,Female,34.0,2.0,0.0,2.0,0.0,0.0,107985.4,0.0
+11279,15691792.0,T'an,751.0,France,Female,26.0,2.0,0.0,1.0,0.0,1.0,123872.0,0.0
+11280,15775335.0,Manna,584.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,0.0,113950.51,0.0
+11281,15593973.0,Brownless,678.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,1.0,151481.86,0.0
+11282,15596972.0,Ch'ien,688.0,France,Female,36.0,4.0,0.0,2.0,0.0,1.0,84902.4,0.0
+11283,15783865.0,Tung,749.0,Germany,Male,30.0,9.0,135889.3,1.0,1.0,0.0,171039.56,0.0
+11284,15625916.0,Oluchukwu,805.0,France,Male,39.0,10.0,0.0,2.0,1.0,0.0,131177.17,0.0
+11285,15573318.0,Hsia,627.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,645.61,0.0
+11286,15673599.0,Palerma,689.0,Germany,Female,40.0,3.0,103469.69,1.0,1.0,0.0,77500.81,1.0
+11287,15696900.0,Chiemenam,652.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,66580.13,0.0
+11288,15763613.0,Palerma,605.0,France,Male,48.0,7.0,0.0,2.0,1.0,1.0,177613.17,0.0
+11289,15683524.0,Fanucci,665.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,52327.57,0.0
+11290,15655952.0,Hsia,686.0,France,Female,42.0,4.0,134919.54,2.0,1.0,0.0,145551.6,1.0
+11291,15683522.0,Dufresne,758.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,93844.89,0.0
+11292,15694366.0,T'ao,755.0,France,Female,24.0,5.0,0.0,2.0,1.0,1.0,47777.15,0.0
+11293,15586699.0,Nwora,663.0,Germany,Male,52.0,1.0,108891.7,1.0,1.0,0.0,194808.51,1.0
+11294,15681115.0,Chiu,613.0,Germany,Female,29.0,5.0,128747.69,2.0,1.0,1.0,161465.31,1.0
+11295,15784700.0,Liao,577.0,France,Male,33.0,5.0,92612.88,1.0,1.0,0.0,61811.1,0.0
+11296,15703544.0,O'Donnell,599.0,France,Male,40.0,3.0,108128.96,1.0,1.0,1.0,102640.52,0.0
+11297,15679961.0,Piazza,850.0,Germany,Female,42.0,1.0,131394.56,1.0,1.0,1.0,79476.63,0.0
+11298,15631261.0,Hsueh,466.0,Spain,Female,43.0,1.0,0.0,2.0,1.0,1.0,142223.94,0.0
+11299,15706899.0,Olisanugo,678.0,Germany,Female,36.0,6.0,178718.19,2.0,0.0,1.0,170195.4,0.0
+11300,15755722.0,Padovesi,593.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,148087.62,0.0
+11301,15766066.0,Onyemauchechukwu,710.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,24218.65,0.0
+11302,15626212.0,Zito,535.0,France,Female,30.0,5.0,166082.18,2.0,1.0,1.0,144584.29,0.0
+11303,15642570.0,Ch'ang,614.0,France,Male,40.0,3.0,0.0,2.0,0.0,0.0,146680.28,0.0
+11304,15671172.0,Onyekachi,637.0,Germany,Male,42.0,5.0,115221.36,1.0,1.0,1.0,6761.84,0.0
+11305,15790829.0,McIntyre,678.0,Germany,Male,35.0,2.0,152805.05,1.0,0.0,1.0,90908.95,1.0
+11306,15751203.0,Nwoye,718.0,Spain,Male,36.0,8.0,95120.59,1.0,0.0,1.0,53437.1,0.0
+11307,15665524.0,Palerma,611.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,0.0,107876.2,0.0
+11308,15634143.0,Ch'iu,758.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,86678.48,0.0
+11309,15763093.0,Chukwuemeka,627.0,France,Female,26.0,1.0,0.0,1.0,1.0,1.0,93959.96,0.0
+11310,15791268.0,Macdonald,719.0,France,Male,32.0,1.0,0.0,1.0,0.0,1.0,123969.64,0.0
+11311,15583692.0,Mazzanti,605.0,Germany,Female,40.0,6.0,81564.1,2.0,1.0,1.0,126812.63,0.0
+11312,15613097.0,Tien,686.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,131712.52,0.0
+11313,15809800.0,Palermo,653.0,France,Female,31.0,5.0,0.0,2.0,0.0,1.0,90878.13,0.0
+11314,15781777.0,L?,699.0,Spain,Female,34.0,5.0,148069.69,1.0,0.0,0.0,79866.73,0.0
+11315,15734246.0,T'ien,807.0,France,Male,29.0,0.0,108632.48,2.0,1.0,0.0,131048.24,0.0
+11316,15690731.0,Chiang,646.0,Spain,Female,42.0,5.0,0.0,1.0,0.0,0.0,151903.6,1.0
+11317,15665327.0,Ugochukwutubelum,637.0,France,Male,30.0,9.0,145981.87,1.0,0.0,1.0,106040.97,0.0
+11318,15782360.0,O'Brien,709.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,145562.4,0.0
+11319,15663883.0,Yobanna,659.0,Spain,Male,41.0,8.0,121519.59,1.0,1.0,1.0,81299.17,0.0
+11320,15736413.0,Shen,714.0,France,Female,46.0,6.0,122314.5,1.0,1.0,1.0,64511.44,1.0
+11321,15662263.0,Tsou,678.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,121866.87,0.0
+11322,15705001.0,Udegbunam,749.0,Spain,Male,31.0,1.0,146622.35,1.0,0.0,0.0,152887.23,0.0
+11323,15682585.0,Hsiung,565.0,Germany,Male,46.0,2.0,122314.5,1.0,1.0,1.0,117832.88,1.0
+11324,15782530.0,Ibezimako,602.0,France,Male,40.0,1.0,137781.65,1.0,1.0,0.0,162501.83,0.0
+11325,15792986.0,Lucchesi,633.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,116973.48,0.0
+11326,15803378.0,Ts'ui,708.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,0.0,65765.22,0.0
+11327,15589019.0,Onwuamaeze,653.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,61977.82,0.0
+11328,15777907.0,Ch'in,684.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,1.0,136869.31,0.0
+11329,15594878.0,Palerma,678.0,Germany,Male,59.0,5.0,108437.89,1.0,0.0,1.0,66893.28,1.0
+11330,15593976.0,Kistler,835.0,Spain,Female,47.0,7.0,0.0,1.0,1.0,1.0,58854.88,1.0
+11331,15726494.0,Toscani,679.0,Germany,Female,34.0,3.0,126663.51,2.0,1.0,1.0,179861.79,0.0
+11332,15641582.0,Chukwuemeka,677.0,France,Female,36.0,5.0,0.0,2.0,0.0,1.0,79458.12,0.0
+11333,15716347.0,Onio,778.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,181490.42,0.0
+11334,15643824.0,Mancini,621.0,Germany,Male,36.0,9.0,79857.64,1.0,0.0,1.0,176984.34,0.0
+11335,15808960.0,Genovese,667.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,71433.88,0.0
+11336,15609510.0,Ch'ien,558.0,Germany,Female,42.0,2.0,92636.96,2.0,0.0,1.0,183963.34,0.0
+11337,15643826.0,Garmon,730.0,France,Female,35.0,1.0,0.0,1.0,0.0,0.0,106837.06,1.0
+11338,15668058.0,Chiang,692.0,France,Male,36.0,2.0,145974.79,1.0,1.0,0.0,183566.78,0.0
+11339,15802466.0,Trevisani,598.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,165082.25,0.0
+11340,15805769.0,Scott,727.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,58879.11,0.0
+11341,15782630.0,Lucciano,697.0,France,Female,56.0,1.0,0.0,1.0,1.0,0.0,154957.46,1.0
+11342,15684958.0,Chukwufumnanya,546.0,France,Male,43.0,8.0,140576.11,1.0,1.0,0.0,96794.95,0.0
+11343,15605076.0,Cremonesi,653.0,France,Male,25.0,2.0,0.0,2.0,1.0,0.0,72008.61,0.0
+11344,15737571.0,Chidubem,704.0,France,Female,37.0,4.0,0.0,1.0,1.0,1.0,161811.23,1.0
+11345,15662532.0,Milanesi,756.0,France,Female,38.0,7.0,157556.06,1.0,0.0,0.0,100940.91,0.0
+11346,15783541.0,Ogochukwu,669.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,83910.4,0.0
+11347,15697510.0,Nkemdilim,637.0,France,Male,59.0,8.0,0.0,2.0,1.0,1.0,85901.09,0.0
+11348,15693382.0,Hsu,710.0,France,Female,58.0,10.0,0.0,2.0,1.0,1.0,52327.57,0.0
+11349,15570932.0,Chijioke,684.0,France,Male,32.0,9.0,112712.17,1.0,1.0,0.0,103603.65,0.0
+11350,15631071.0,Ch'iu,535.0,France,Female,33.0,1.0,0.0,2.0,0.0,0.0,120810.63,0.0
+11351,15756850.0,Onwumelu,743.0,France,Male,30.0,10.0,0.0,2.0,1.0,1.0,84760.32,0.0
+11352,15643658.0,Ch'eng,535.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,173629.66,0.0
+11353,15732621.0,Ts'ui,721.0,France,Male,33.0,4.0,146710.76,1.0,0.0,0.0,102628.98,0.0
+11354,15707025.0,Hsing,516.0,France,Male,40.0,2.0,145536.9,1.0,1.0,1.0,185547.71,0.0
+11355,15677317.0,Fanucci,687.0,France,Male,51.0,8.0,121523.51,1.0,1.0,0.0,160574.06,0.0
+11356,15651369.0,Ch'eng,762.0,Germany,Male,45.0,8.0,132450.24,1.0,1.0,0.0,28789.94,0.0
+11357,15651883.0,Chiang,642.0,Germany,Male,35.0,10.0,90425.15,1.0,1.0,1.0,100834.22,0.0
+11358,15643976.0,Nkemakonam,735.0,France,Male,44.0,0.0,0.0,2.0,1.0,0.0,176984.34,1.0
+11359,15586039.0,Mazzanti,611.0,France,Female,34.0,3.0,0.0,1.0,1.0,1.0,186489.95,0.0
+11360,15683625.0,Kao,553.0,Spain,Female,55.0,2.0,0.0,2.0,1.0,1.0,143829.54,0.0
+11361,15732270.0,T'ien,607.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,74832.46,0.0
+11362,15724161.0,Biryukova,726.0,France,Female,20.0,3.0,0.0,1.0,1.0,1.0,77965.44,0.0
+11363,15743709.0,Ch'in,523.0,France,Male,55.0,10.0,0.0,1.0,1.0,1.0,153012.13,1.0
+11364,15726354.0,Rizzo,684.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,52449.62,0.0
+11365,15659937.0,Mazzanti,713.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,149458.61,0.0
+11366,15744273.0,Chukwuebuka,710.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,62347.71,0.0
+11367,15609998.0,Fyodorova,634.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,175894.24,0.0
+11368,15655410.0,Y?,664.0,Spain,Female,48.0,9.0,0.0,2.0,1.0,0.0,160129.99,0.0
+11369,15777784.0,Milanesi,616.0,France,Male,49.0,5.0,0.0,1.0,0.0,0.0,93524.19,1.0
+11370,15813067.0,Ch'iu,699.0,Spain,Male,40.0,9.0,0.0,2.0,0.0,0.0,142238.54,0.0
+11371,15808845.0,Scott,597.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,94774.12,0.0
+11372,15761076.0,Chuang,738.0,Spain,Female,32.0,2.0,0.0,2.0,1.0,1.0,67611.36,0.0
+11373,15659710.0,Ch'iu,592.0,France,Female,30.0,4.0,0.0,1.0,1.0,1.0,158853.98,1.0
+11374,15583079.0,Smith,633.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,100556.98,0.0
+11375,15743545.0,Chukwuebuka,682.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,178373.93,0.0
+11376,15576085.0,Lu,588.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,138526.96,0.0
+11377,15761497.0,Chukwuemeka,646.0,France,Female,27.0,1.0,0.0,2.0,1.0,1.0,145558.42,0.0
+11378,15722072.0,Folliero,581.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,129975.54,0.0
+11379,15794857.0,Hs?,611.0,France,Male,58.0,1.0,121355.99,1.0,1.0,0.0,74850.93,1.0
+11380,15618562.0,Chukwufumnanya,732.0,France,Male,45.0,1.0,0.0,1.0,1.0,1.0,115566.57,1.0
+11381,15642099.0,Hs?,703.0,France,Female,40.0,8.0,145988.65,1.0,1.0,0.0,131501.72,0.0
+11382,15609899.0,Goliwe,705.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,116748.14,0.0
+11383,15686957.0,Hsiao,659.0,France,Female,48.0,9.0,0.0,2.0,0.0,1.0,186869.86,0.0
+11384,15708505.0,Smith,679.0,Germany,Female,55.0,8.0,118890.75,1.0,1.0,0.0,138350.74,1.0
+11385,15658449.0,Dyer,721.0,France,Male,30.0,7.0,131287.36,1.0,1.0,1.0,59325.21,0.0
+11386,15658946.0,Hsing,619.0,Spain,Male,41.0,4.0,141786.78,1.0,1.0,0.0,88890.05,1.0
+11387,15593683.0,Ts'ui,711.0,Spain,Female,37.0,9.0,0.0,2.0,0.0,1.0,177613.19,0.0
+11388,15613463.0,Nwabugwu,651.0,Spain,Male,33.0,7.0,121263.62,2.0,1.0,0.0,62733.05,0.0
+11389,15746708.0,Hs?,556.0,Germany,Male,49.0,9.0,141099.43,1.0,1.0,1.0,170528.63,1.0
+11390,15585455.0,Nwabugwu,656.0,Spain,Female,53.0,7.0,0.0,2.0,1.0,1.0,49848.86,0.0
+11391,15703394.0,Ch'en,662.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,16911.66,0.0
+11392,15752622.0,Hsu,531.0,Germany,Male,34.0,4.0,126492.57,2.0,1.0,1.0,115468.84,0.0
+11393,15745804.0,Ch'ien,588.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,151369.31,1.0
+11394,15771674.0,P'an,639.0,France,Female,43.0,7.0,0.0,1.0,0.0,1.0,187632.23,1.0
+11395,15582404.0,P'eng,692.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,69409.37,0.0
+11396,15593976.0,Tien,582.0,France,Female,42.0,2.0,0.0,1.0,1.0,1.0,131753.94,1.0
+11397,15583857.0,Toscani,643.0,France,Male,32.0,7.0,97565.74,1.0,0.0,0.0,167036.94,0.0
+11398,15671148.0,Shih,581.0,France,Male,31.0,4.0,134919.34,1.0,1.0,0.0,95611.47,0.0
+11399,15792228.0,Niu,543.0,France,Female,35.0,3.0,128173.9,1.0,0.0,1.0,103799.77,0.0
+11400,15681180.0,Chiang,479.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,0.0,151349.35,0.0
+11401,15676835.0,Chidozie,817.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,136869.31,0.0
+11402,15642824.0,Nkemakolam,724.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,81523.06,0.0
+11403,15572390.0,Chiemenam,648.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,133020.9,0.0
+11404,15602034.0,Chukwudi,602.0,Spain,Female,47.0,4.0,0.0,4.0,1.0,0.0,126477.41,1.0
+11405,15641626.0,Ch'ien,632.0,France,Female,60.0,4.0,0.0,2.0,1.0,1.0,166588.61,0.0
+11406,15592999.0,Mancini,675.0,Germany,Female,26.0,3.0,131647.01,2.0,1.0,1.0,130504.49,0.0
+11407,15682668.0,Lo Duca,645.0,France,Male,58.0,7.0,0.0,1.0,1.0,0.0,82870.75,1.0
+11408,15694444.0,Chukwualuka,641.0,Germany,Female,66.0,2.0,141535.55,3.0,0.0,0.0,136620.64,1.0
+11409,15748595.0,Brown,730.0,Spain,Male,44.0,7.0,0.0,2.0,1.0,1.0,130789.6,0.0
+11410,15677522.0,Mazzanti,554.0,France,Male,43.0,6.0,0.0,2.0,1.0,0.0,80190.36,0.0
+11411,15753874.0,Nnaife,615.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,176576.62,0.0
+11412,15634141.0,Uchenna,678.0,Germany,Male,46.0,10.0,140197.18,1.0,1.0,1.0,61820.41,0.0
+11413,15632264.0,Chinwenma,651.0,Germany,Female,36.0,4.0,132557.26,2.0,1.0,0.0,141756.32,0.0
+11414,15600974.0,Chu,641.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,0.0,112239.03,0.0
+11415,15656417.0,Chu,613.0,Spain,Female,52.0,1.0,0.0,1.0,1.0,1.0,143450.61,1.0
+11416,15730579.0,Mazzi,571.0,France,Female,46.0,4.0,0.0,1.0,0.0,1.0,125961.48,0.0
+11417,15654074.0,Yobachi,623.0,France,Female,59.0,1.0,0.0,2.0,1.0,1.0,113048.79,0.0
+11418,15689152.0,Goliwe,705.0,France,Male,29.0,9.0,0.0,2.0,0.0,0.0,94620.0,0.0
+11419,15771856.0,Hsiung,645.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,53630.9,0.0
+11420,15654495.0,Yevdokimova,586.0,Spain,Male,51.0,4.0,91893.32,1.0,1.0,1.0,177783.04,0.0
+11421,15609824.0,T'ien,634.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,1.0,94845.45,0.0
+11422,15752694.0,Chukwuemeka,705.0,France,Male,50.0,0.0,0.0,2.0,1.0,1.0,158580.05,0.0
+11423,15673372.0,Sagese,619.0,France,Female,46.0,4.0,0.0,2.0,0.0,0.0,110953.33,1.0
+11424,15777893.0,Onyemaechi,693.0,France,Male,36.0,1.0,139432.37,1.0,1.0,1.0,183569.25,0.0
+11425,15771565.0,Yobachi,628.0,Germany,Male,33.0,9.0,134760.21,1.0,1.0,0.0,146700.22,0.0
+11426,15738889.0,Calabresi,695.0,France,Female,47.0,9.0,79259.99,1.0,1.0,0.0,130489.37,1.0
+11427,15578908.0,Calabrese,762.0,France,Female,25.0,2.0,0.0,2.0,1.0,1.0,138380.12,0.0
+11428,15791030.0,Ch'eng,717.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,87834.24,1.0
+11429,15751788.0,Ch'iu,725.0,Spain,Female,36.0,3.0,0.0,2.0,0.0,0.0,175894.24,0.0
+11430,15723658.0,Lombardo,520.0,France,Female,32.0,3.0,0.0,1.0,1.0,1.0,126722.57,0.0
+11431,15754574.0,Lo Duca,667.0,France,Female,46.0,1.0,0.0,1.0,0.0,1.0,142513.5,1.0
+11432,15802362.0,Zhirov,735.0,France,Female,24.0,5.0,0.0,2.0,1.0,0.0,80619.17,0.0
+11433,15781875.0,Toscani,593.0,Germany,Male,29.0,6.0,119714.25,1.0,1.0,1.0,160526.36,0.0
+11434,15772632.0,Smith,579.0,France,Female,37.0,5.0,0.0,1.0,0.0,1.0,72993.67,0.0
+11435,15647898.0,Ch'ien,711.0,France,Male,31.0,1.0,0.0,2.0,0.0,1.0,161249.24,0.0
+11436,15685564.0,Ting,678.0,Germany,Female,47.0,5.0,98141.57,2.0,1.0,0.0,124964.82,1.0
+11437,15603065.0,Onwuamaeze,588.0,France,Female,38.0,8.0,0.0,2.0,0.0,1.0,96985.58,0.0
+11438,15670866.0,Dickson,553.0,Spain,Male,38.0,9.0,76467.16,1.0,1.0,1.0,182627.25,0.0
+11439,15782390.0,Onwuamaeze,678.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,123882.73,0.0
+11440,15723299.0,Chinedum,716.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,125920.54,0.0
+11441,15649450.0,Hs?eh,559.0,Spain,Female,24.0,7.0,0.0,2.0,1.0,1.0,122925.44,0.0
+11442,15690526.0,Yevdokimova,685.0,France,Female,26.0,5.0,0.0,1.0,1.0,0.0,179655.87,1.0
+11443,15686885.0,Chiu,755.0,Germany,Male,36.0,1.0,126816.18,1.0,1.0,0.0,107292.91,0.0
+11444,15622518.0,Lucciano,699.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,139994.68,0.0
+11445,15763898.0,Nkemdilim,686.0,Germany,Male,41.0,6.0,131033.76,2.0,0.0,1.0,107955.67,0.0
+11446,15750929.0,Maughan,631.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,199304.74,0.0
+11447,15797900.0,Fanucci,653.0,Germany,Male,43.0,10.0,154640.41,2.0,0.0,1.0,4175.68,0.0
+11448,15663885.0,Nkemjika,689.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,84908.2,0.0
+11449,15673929.0,Chiu,651.0,Germany,Female,35.0,9.0,102723.35,2.0,1.0,1.0,122373.42,0.0
+11450,15781574.0,Ch'eng,663.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,26116.82,0.0
+11451,15642578.0,Fang,687.0,Spain,Male,46.0,2.0,0.0,2.0,1.0,1.0,131736.23,0.0
+11452,15767358.0,Zetticci,678.0,Germany,Male,37.0,9.0,133384.55,1.0,0.0,1.0,50610.62,0.0
+11453,15803801.0,P'eng,694.0,France,Male,38.0,6.0,0.0,1.0,0.0,0.0,83611.45,0.0
+11454,15631838.0,Hsueh,575.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,47848.56,0.0
+11455,15667042.0,Yobanna,713.0,France,Male,31.0,2.0,110791.97,1.0,1.0,0.0,143360.56,0.0
+11456,15635116.0,Pagnotto,730.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,104502.04,0.0
+11457,15775203.0,Baresi,584.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,122964.18,0.0
+11458,15810444.0,Tsao,449.0,France,Female,39.0,9.0,0.0,2.0,1.0,0.0,82968.14,0.0
+11459,15740264.0,Palerma,678.0,France,Female,28.0,7.0,0.0,1.0,1.0,0.0,131097.9,1.0
+11460,15733581.0,Milani,598.0,France,Male,35.0,9.0,143330.56,1.0,1.0,1.0,136886.86,0.0
+11461,15637522.0,Mazzanti,681.0,Spain,Female,38.0,4.0,141827.33,1.0,0.0,1.0,38276.84,0.0
+11462,15742504.0,Ch'ang,706.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,127413.0,0.0
+11463,15672437.0,Tsao,739.0,Spain,Male,37.0,10.0,0.0,2.0,1.0,0.0,72849.29,0.0
+11464,15683927.0,Iweobiegbunam,545.0,Germany,Female,46.0,10.0,135095.78,1.0,1.0,0.0,57843.75,1.0
+11465,15795878.0,Ch'ang,572.0,Spain,Male,28.0,8.0,114430.18,1.0,1.0,1.0,91775.51,0.0
+11466,15663478.0,Chia,478.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,188516.45,0.0
+11467,15681316.0,Uspenskaya,711.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,131177.22,0.0
+11468,15582892.0,Hsueh,657.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,176963.17,0.0
+11469,15770309.0,Pirozzi,593.0,France,Female,48.0,2.0,66582.81,1.0,1.0,0.0,160976.85,1.0
+11470,15641114.0,Chukwukere,719.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,1.0,173516.71,0.0
+11471,15727605.0,Folliero,705.0,France,Female,49.0,8.0,0.0,1.0,1.0,1.0,67552.76,1.0
+11472,15765650.0,Chiganu,658.0,France,Female,29.0,5.0,0.0,2.0,0.0,1.0,92888.52,0.0
+11473,15809817.0,P'eng,729.0,Germany,Female,40.0,9.0,128276.83,1.0,1.0,1.0,75585.48,1.0
+11474,15681115.0,Chibugo,762.0,Germany,Female,64.0,0.0,116322.28,1.0,1.0,1.0,97734.3,1.0
+11475,15623989.0,Hsueh,593.0,France,Female,28.0,2.0,0.0,1.0,1.0,1.0,60584.33,1.0
+11476,15751943.0,Sagese,685.0,France,Male,38.0,4.0,117060.03,1.0,1.0,0.0,74838.64,0.0
+11477,15604792.0,Nwokeocha,562.0,Germany,Female,42.0,3.0,162701.65,1.0,1.0,0.0,152992.89,1.0
+11478,15807525.0,Lu,584.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,91346.03,0.0
+11479,15794868.0,Walker,614.0,Spain,Male,28.0,8.0,109679.72,1.0,1.0,0.0,149991.32,0.0
+11480,15723250.0,Shaver,597.0,France,Male,24.0,3.0,0.0,2.0,1.0,0.0,120115.3,0.0
+11481,15631882.0,Walker,724.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,107837.55,0.0
+11482,15764293.0,Chimaobim,678.0,Germany,Female,48.0,8.0,129847.26,1.0,1.0,0.0,55324.49,1.0
+11483,15786078.0,Okagbue,596.0,Germany,Male,32.0,8.0,134796.89,1.0,1.0,0.0,898.51,1.0
+11484,15765461.0,Okonkwo,478.0,Spain,Male,31.0,2.0,0.0,1.0,1.0,1.0,145562.4,0.0
+11485,15773520.0,Ch'iu,583.0,France,Male,30.0,1.0,0.0,2.0,0.0,1.0,93835.64,0.0
+11486,15766787.0,Chia,583.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,162858.29,0.0
+11487,15675896.0,Ijendu,683.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,61994.2,0.0
+11488,15642093.0,Ch'iu,706.0,France,Male,24.0,4.0,101713.84,1.0,1.0,1.0,102515.34,0.0
+11489,15672821.0,Trevisani,682.0,France,Female,39.0,7.0,153697.53,1.0,1.0,0.0,149892.79,1.0
+11490,15623210.0,Baryshnikov,686.0,France,Male,27.0,1.0,0.0,2.0,0.0,0.0,162760.96,0.0
+11491,15672945.0,Mironova,705.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,1.0,86911.49,0.0
+11492,15790750.0,Chibueze,556.0,Spain,Female,55.0,5.0,0.0,1.0,1.0,1.0,164844.37,1.0
+11493,15796959.0,Udegbulam,689.0,France,Female,31.0,8.0,0.0,1.0,0.0,1.0,102021.39,0.0
+11494,15804075.0,Onyeorulu,498.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,1.0,120512.08,0.0
+11495,15702293.0,Tsao,582.0,Spain,Female,39.0,6.0,121863.61,1.0,1.0,1.0,179436.6,0.0
+11496,15782875.0,Y?,586.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,185599.12,0.0
+11497,15597591.0,Yost,585.0,Spain,Male,33.0,9.0,131307.11,1.0,1.0,1.0,121151.1,0.0
+11498,15598884.0,Ulyanov,569.0,France,Male,45.0,5.0,0.0,2.0,1.0,0.0,171706.66,1.0
+11499,15634602.0,Onyekaozulu,656.0,Germany,Male,36.0,2.0,81370.07,2.0,0.0,1.0,116469.19,0.0
+11500,15795872.0,Chukwudi,633.0,France,Female,42.0,6.0,0.0,4.0,1.0,0.0,131647.31,1.0
+11501,15709928.0,Chukwuemeka,622.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,93478.95,0.0
+11502,15631217.0,Mancini,670.0,Spain,Female,41.0,3.0,120599.21,1.0,1.0,1.0,91817.38,0.0
+11503,15668818.0,Oluchukwu,646.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,53630.9,0.0
+11504,15663917.0,Chizoba,585.0,France,Male,30.0,6.0,0.0,1.0,0.0,0.0,73329.08,0.0
+11505,15715199.0,Ch'eng,583.0,France,Male,28.0,5.0,128920.75,1.0,0.0,0.0,79577.4,0.0
+11506,15586870.0,Ts'ao,682.0,Germany,Female,39.0,10.0,122523.21,1.0,1.0,0.0,74850.84,1.0
+11507,15793331.0,Ch'ang,637.0,France,Female,35.0,7.0,99564.22,1.0,1.0,0.0,82500.84,0.0
+11508,15677317.0,Yashina,486.0,France,Female,37.0,5.0,0.0,1.0,1.0,0.0,61257.83,0.0
+11509,15639882.0,Trevisani,673.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,97629.31,0.0
+11510,15778049.0,Eames,568.0,Germany,Male,41.0,4.0,146587.3,2.0,0.0,1.0,4292.94,0.0
+11511,15783859.0,H?,559.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,88724.49,0.0
+11512,15694450.0,Onuora,590.0,Spain,Male,42.0,7.0,0.0,2.0,0.0,0.0,72643.95,0.0
+11513,15802351.0,Shih,590.0,France,Male,24.0,2.0,0.0,2.0,1.0,0.0,116977.89,0.0
+11514,15670103.0,Chien,571.0,France,Male,37.0,0.0,0.0,1.0,1.0,1.0,52876.41,0.0
+11515,15705403.0,Udegbulam,614.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,81602.02,0.0
+11516,15772996.0,T'ang,756.0,France,Female,31.0,1.0,0.0,2.0,1.0,0.0,172646.88,0.0
+11517,15571940.0,Chukwufumnanya,748.0,Germany,Male,51.0,7.0,133384.66,1.0,1.0,1.0,182787.17,1.0
+11518,15570508.0,Bogle,651.0,France,Female,26.0,9.0,0.0,1.0,1.0,1.0,62339.64,0.0
+11519,15567442.0,Trevisano,521.0,Germany,Male,36.0,5.0,116363.37,1.0,1.0,0.0,67734.79,0.0
+11520,15693947.0,Martin,716.0,Germany,Female,40.0,2.0,129974.79,2.0,1.0,1.0,138700.86,0.0
+11521,15595588.0,Hsia,636.0,Spain,Female,47.0,2.0,0.0,1.0,0.0,0.0,177783.04,1.0
+11522,15618661.0,Tien,632.0,Germany,Female,30.0,7.0,125537.16,1.0,0.0,0.0,141069.88,0.0
+11523,15783026.0,Ch'iu,695.0,Germany,Female,38.0,8.0,71481.79,2.0,1.0,1.0,121151.1,0.0
+11524,15718773.0,Pugh,571.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,88838.14,0.0
+11525,15743076.0,Hsueh,559.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,139972.01,0.0
+11526,15745012.0,Nkemakonam,621.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,48606.92,0.0
+11527,15794468.0,Ch'iu,584.0,France,Female,41.0,9.0,0.0,1.0,1.0,1.0,176692.65,1.0
+11528,15612444.0,H?,650.0,Spain,Male,43.0,4.0,134954.53,1.0,1.0,1.0,123850.58,0.0
+11529,15591150.0,Udegbulam,707.0,Germany,Female,54.0,9.0,150561.02,1.0,0.0,1.0,122398.84,0.0
+11530,15792489.0,Genovesi,721.0,France,Female,32.0,1.0,0.0,2.0,0.0,0.0,139048.0,1.0
+11531,15796969.0,Ni,748.0,France,Female,27.0,3.0,0.0,2.0,1.0,0.0,121681.19,0.0
+11532,15759733.0,Chinedum,577.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,173779.29,0.0
+11533,15665309.0,Fiorentino,561.0,Germany,Female,45.0,5.0,121730.49,1.0,1.0,1.0,48849.89,1.0
+11534,15571286.0,Ch'ang,592.0,Germany,Male,45.0,1.0,125437.86,1.0,1.0,0.0,78833.15,1.0
+11535,15790067.0,Ch'ien,685.0,France,Female,40.0,4.0,0.0,1.0,1.0,0.0,147794.63,0.0
+11536,15730488.0,Yobanna,586.0,France,Male,27.0,7.0,100565.94,1.0,1.0,1.0,71830.1,0.0
+11537,15713578.0,Calabrese,622.0,France,Female,33.0,1.0,61319.63,2.0,1.0,0.0,139971.01,0.0
+11538,15708714.0,Y?an,605.0,Germany,Male,33.0,10.0,163894.35,2.0,0.0,0.0,57904.22,0.0
+11539,15593973.0,Onyekachi,554.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,9984.4,0.0
+11540,15662067.0,McIntyre,676.0,France,Male,50.0,8.0,0.0,1.0,1.0,1.0,166698.18,1.0
+11541,15777901.0,Ch'ang,703.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,0.0,99818.41,0.0
+11542,15603743.0,Ch'iu,715.0,Spain,Male,41.0,6.0,0.0,1.0,1.0,0.0,158043.11,1.0
+11543,15772030.0,Chidubem,543.0,Germany,Female,42.0,2.0,107712.57,1.0,0.0,0.0,101433.76,1.0
+11544,15672246.0,Hsueh,762.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,116467.35,0.0
+11545,15735240.0,Hsia,609.0,Spain,Male,37.0,10.0,148021.12,1.0,1.0,0.0,92664.09,0.0
+11546,15632185.0,T'ao,603.0,France,Female,31.0,8.0,99844.71,1.0,1.0,1.0,197264.35,0.0
+11547,15770309.0,Shih,748.0,Spain,Male,38.0,9.0,0.0,2.0,0.0,0.0,131660.31,0.0
+11548,15751593.0,Clements,685.0,Germany,Male,30.0,4.0,102278.52,1.0,1.0,0.0,167032.49,0.0
+11549,15775335.0,Ngozichukwuka,762.0,Germany,Female,45.0,4.0,115290.27,1.0,1.0,0.0,176576.62,1.0
+11550,15794895.0,Lucchese,544.0,France,Male,34.0,2.0,0.0,2.0,0.0,1.0,121789.8,0.0
+11551,15700801.0,Ngozichukwuka,705.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,56636.28,1.0
+11552,15745232.0,Tien,655.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,15859.95,0.0
+11553,15806796.0,Hsueh,633.0,Germany,Female,52.0,8.0,113522.64,1.0,0.0,0.0,92712.97,1.0
+11554,15696744.0,Iloerika,732.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,60721.4,0.0
+11555,15686958.0,Chigozie,613.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,0.0,104077.51,0.0
+11556,15582951.0,Hsia,663.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,138540.09,0.0
+11557,15614103.0,Ch'ien,691.0,France,Female,31.0,1.0,141581.71,1.0,1.0,1.0,132973.17,0.0
+11558,15769974.0,Ch'iu,678.0,Spain,Male,39.0,9.0,94488.17,1.0,0.0,1.0,173586.63,0.0
+11559,15778897.0,Hs?,687.0,France,Male,28.0,1.0,120952.74,1.0,1.0,0.0,53353.37,0.0
+11560,15591512.0,Ch'ang,521.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,154882.79,0.0
+11561,15789863.0,Hsieh,535.0,France,Female,56.0,2.0,111756.5,1.0,0.0,0.0,102076.03,1.0
+11562,15617482.0,Chukwumaobim,589.0,Spain,Female,25.0,9.0,0.0,1.0,1.0,0.0,160529.71,0.0
+11563,15737051.0,Yobanna,487.0,France,Female,51.0,6.0,0.0,2.0,0.0,1.0,122662.98,1.0
+11564,15658935.0,Uwakwe,684.0,France,Male,42.0,2.0,153323.1,1.0,1.0,0.0,61811.1,0.0
+11565,15769216.0,O'Sullivan,678.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,104722.43,0.0
+11566,15798960.0,Smith,725.0,France,Male,46.0,4.0,0.0,2.0,1.0,0.0,173989.66,0.0
+11567,15694510.0,Chibugo,695.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,62678.53,0.0
+11568,15724443.0,Chikezie,588.0,France,Female,44.0,7.0,145974.79,1.0,1.0,0.0,105618.14,1.0
+11569,15655238.0,Ch'iu,706.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,179631.85,0.0
+11570,15696733.0,Hsia,669.0,Germany,Male,35.0,6.0,109387.9,2.0,1.0,0.0,131804.86,0.0
+11571,15679294.0,Chukwueloka,753.0,Germany,Female,36.0,2.0,130274.22,2.0,1.0,1.0,55775.72,0.0
+11572,15692406.0,Y?,520.0,Germany,Male,31.0,9.0,148074.79,2.0,1.0,0.0,148848.76,0.0
+11573,15658935.0,P'eng,711.0,Spain,Male,34.0,3.0,0.0,2.0,0.0,1.0,71047.51,0.0
+11574,15600575.0,Onwumelu,714.0,Spain,Male,42.0,3.0,0.0,1.0,1.0,1.0,130260.11,1.0
+11575,15756850.0,Milano,633.0,France,Female,38.0,3.0,91286.51,1.0,1.0,1.0,143317.19,0.0
+11576,15787229.0,K?,663.0,France,Female,41.0,9.0,0.0,1.0,1.0,0.0,183584.14,1.0
+11577,15638231.0,Shih,611.0,France,Male,42.0,1.0,0.0,1.0,1.0,0.0,64907.48,1.0
+11578,15624892.0,Ts'ai,659.0,France,Female,31.0,4.0,0.0,2.0,0.0,0.0,59938.81,0.0
+11579,15593973.0,Ch'ang,681.0,Germany,Female,54.0,7.0,129490.33,2.0,1.0,0.0,140881.2,1.0
+11580,15665088.0,Young,592.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,133617.73,0.0
+11581,15715184.0,Sagese,753.0,Germany,Female,39.0,1.0,141334.95,1.0,1.0,1.0,65765.22,1.0
+11582,15729582.0,Oluchukwu,682.0,France,Female,66.0,9.0,0.0,2.0,1.0,1.0,101617.43,0.0
+11583,15652835.0,Onyemauchechukwu,597.0,France,Female,42.0,8.0,0.0,3.0,1.0,0.0,147557.71,1.0
+11584,15751336.0,Lucciano,662.0,Spain,Male,39.0,9.0,154525.5,1.0,0.0,0.0,138555.46,0.0
+11585,15778884.0,Tung,551.0,France,Female,43.0,6.0,0.0,2.0,1.0,1.0,151843.54,0.0
+11586,15592999.0,Shubina,586.0,France,Female,60.0,1.0,0.0,2.0,1.0,1.0,108977.5,0.0
+11587,15754568.0,Ts'ai,587.0,France,Female,32.0,9.0,101543.51,2.0,1.0,0.0,110359.68,0.0
+11588,15709222.0,Chukwualuka,632.0,France,Female,24.0,6.0,0.0,2.0,1.0,0.0,161465.31,0.0
+11589,15690772.0,Tsai,706.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,67972.45,0.0
+11590,15706021.0,Ch'en,724.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,127569.8,0.0
+11591,15780804.0,Chu,762.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,143317.19,0.0
+11592,15777923.0,Chidiegwu,685.0,France,Female,42.0,10.0,0.0,2.0,1.0,1.0,86791.9,0.0
+11593,15803965.0,Chikezie,521.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,131736.23,0.0
+11594,15616380.0,P'an,644.0,France,Female,37.0,10.0,0.0,2.0,1.0,1.0,145551.6,0.0
+11595,15655410.0,Fan,685.0,Spain,Female,39.0,2.0,73844.26,1.0,1.0,0.0,133021.32,0.0
+11596,15689612.0,Chinedum,710.0,Germany,Female,40.0,3.0,130169.79,1.0,0.0,1.0,153662.88,0.0
+11597,15647975.0,Mazzi,545.0,Spain,Male,36.0,0.0,0.0,2.0,1.0,1.0,149772.61,0.0
+11598,15753550.0,Pinto,604.0,France,Male,23.0,1.0,0.0,2.0,0.0,0.0,167984.72,0.0
+11599,15742681.0,Rizzo,602.0,France,Male,37.0,2.0,0.0,1.0,1.0,1.0,177524.19,0.0
+11600,15642099.0,Nnaife,593.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,81898.81,0.0
+11601,15575060.0,McIntosh,599.0,Germany,Female,39.0,5.0,111098.85,1.0,1.0,1.0,9983.88,1.0
+11602,15645896.0,Ch'ien,667.0,France,Female,27.0,5.0,0.0,2.0,1.0,1.0,117060.2,0.0
+11603,15633233.0,Lucciano,741.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,152422.91,0.0
+11604,15674720.0,Obidimkpa,667.0,France,Female,29.0,4.0,0.0,2.0,1.0,1.0,131363.57,0.0
+11605,15701164.0,Chizoba,634.0,France,Male,38.0,7.0,144953.63,1.0,1.0,0.0,175458.21,1.0
+11606,15678173.0,Y?an,842.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,177065.24,0.0
+11607,15595883.0,K?,684.0,Germany,Female,42.0,5.0,122417.19,1.0,1.0,0.0,172459.39,0.0
+11608,15791700.0,Ch'ien,649.0,Spain,Male,35.0,2.0,129209.03,1.0,1.0,0.0,60920.03,0.0
+11609,15686463.0,Tien,581.0,France,Female,34.0,6.0,0.0,2.0,0.0,0.0,90876.95,0.0
+11610,15755262.0,Hsu,671.0,Germany,Female,37.0,5.0,116886.59,1.0,0.0,0.0,103396.08,0.0
+11611,15628298.0,Calabrese,637.0,Germany,Female,43.0,9.0,117103.26,1.0,1.0,0.0,182785.01,1.0
+11612,15603851.0,Hs?,644.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,0.0,121894.31,0.0
+11613,15617982.0,Chuang,667.0,France,Male,57.0,2.0,141581.07,2.0,1.0,1.0,62944.75,0.0
+11614,15682912.0,Chukwujamuike,521.0,Spain,Female,39.0,1.0,127864.4,1.0,1.0,0.0,163798.41,1.0
+11615,15592448.0,Houghton,671.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,131712.52,0.0
+11616,15772650.0,Tien,684.0,France,Male,62.0,3.0,0.0,2.0,1.0,1.0,89983.02,0.0
+11617,15605918.0,Onyeorulu,678.0,Germany,Male,39.0,6.0,137714.25,1.0,1.0,0.0,179435.21,0.0
+11618,15813504.0,Liao,633.0,Spain,Female,37.0,6.0,0.0,2.0,1.0,0.0,120512.08,0.0
+11619,15651582.0,Udobata,711.0,Spain,Female,25.0,3.0,0.0,2.0,1.0,1.0,167810.26,0.0
+11620,15722563.0,Ritchie,588.0,Spain,Female,40.0,7.0,0.0,1.0,1.0,1.0,158610.89,0.0
+11621,15772781.0,Udinese,743.0,Germany,Female,48.0,9.0,130878.75,1.0,0.0,0.0,145518.31,1.0
+11622,15595136.0,Maccallum,678.0,France,Female,51.0,3.0,135096.05,1.0,0.0,1.0,51554.58,1.0
+11623,15667438.0,Sagese,684.0,France,Male,47.0,5.0,153590.89,1.0,1.0,0.0,59890.29,1.0
+11624,15589320.0,Okwudiliolisa,506.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,51554.58,0.0
+11625,15626787.0,Ts'ai,607.0,Germany,Male,31.0,2.0,121525.52,1.0,1.0,0.0,117622.8,0.0
+11626,15722090.0,Chien,696.0,France,Female,39.0,2.0,125566.9,1.0,1.0,1.0,113410.89,0.0
+11627,15603665.0,Mazzanti,710.0,Spain,Female,38.0,0.0,114655.58,1.0,1.0,0.0,159418.1,0.0
+11628,15629244.0,Chukwuebuka,668.0,France,Male,25.0,8.0,133467.77,1.0,1.0,1.0,107964.36,0.0
+11629,15624995.0,Ch'iu,593.0,Spain,Male,46.0,7.0,78314.63,1.0,1.0,0.0,124592.84,0.0
+11630,15778142.0,Ch'ang,645.0,Germany,Female,41.0,1.0,108432.88,1.0,1.0,1.0,161579.85,1.0
+11631,15624592.0,Chin,734.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,35669.31,0.0
+11632,15774931.0,Ts'ui,676.0,France,Male,48.0,2.0,0.0,1.0,1.0,1.0,162699.7,0.0
+11633,15661528.0,Maclean,743.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,55724.24,0.0
+11634,15654577.0,O'Loughlin,705.0,Germany,Male,44.0,4.0,148045.45,2.0,1.0,0.0,94499.06,1.0
+11635,15696733.0,Hs?eh,684.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,86460.28,0.0
+11636,15761158.0,Trevisano,659.0,France,Male,41.0,7.0,151858.98,1.0,1.0,1.0,83629.4,0.0
+11637,15627195.0,De Luca,841.0,France,Female,49.0,1.0,0.0,2.0,1.0,1.0,143668.58,0.0
+11638,15628292.0,Foveaux,554.0,France,Male,48.0,1.0,151766.71,1.0,0.0,0.0,74564.41,1.0
+11639,15574372.0,Lucchesi,619.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,162515.17,1.0
+11640,15782454.0,Kao,594.0,France,Male,34.0,1.0,72788.03,1.0,1.0,1.0,171922.72,0.0
+11641,15717893.0,Chizoba,730.0,Spain,Male,24.0,8.0,102520.37,1.0,0.0,0.0,67769.94,0.0
+11642,15804002.0,Clements,588.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,38409.79,0.0
+11643,15765415.0,Onyeoruru,577.0,Germany,Male,32.0,4.0,125547.51,1.0,1.0,1.0,161671.15,0.0
+11644,15574865.0,Lo Duca,555.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,59981.62,0.0
+11645,15723299.0,Hs?eh,550.0,France,Male,55.0,9.0,0.0,2.0,0.0,1.0,168515.61,0.0
+11646,15757912.0,Onyemachukwu,850.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,168034.9,0.0
+11647,15596870.0,Yeh,605.0,Germany,Female,47.0,8.0,110635.32,1.0,0.0,1.0,51117.06,1.0
+11648,15618182.0,Yefimova,653.0,Spain,Female,41.0,3.0,0.0,2.0,0.0,1.0,181499.77,0.0
+11649,15650068.0,Cattaneo,669.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,163427.18,0.0
+11650,15793723.0,Lucchesi,679.0,France,Female,50.0,4.0,0.0,2.0,1.0,1.0,80410.68,0.0
+11651,15569247.0,Ch'ang,687.0,France,Female,41.0,3.0,0.0,1.0,0.0,0.0,180712.28,0.0
+11652,15651740.0,Chukwumaobim,648.0,France,Male,38.0,3.0,0.0,2.0,0.0,0.0,94738.54,0.0
+11653,15612092.0,K'ung,714.0,Spain,Female,59.0,6.0,0.0,1.0,0.0,1.0,162858.29,1.0
+11654,15808023.0,Olisaemeka,704.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,117572.59,0.0
+11655,15674542.0,Blesing,584.0,Spain,Male,28.0,7.0,0.0,2.0,0.0,1.0,153064.87,0.0
+11656,15692819.0,Cattaneo,588.0,Spain,Female,34.0,7.0,0.0,3.0,1.0,0.0,134626.3,1.0
+11657,15585823.0,Stevenson,693.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,113645.4,0.0
+11658,15678727.0,Nwachukwu,663.0,Germany,Male,44.0,9.0,168085.98,2.0,1.0,0.0,131807.16,0.0
+11659,15792232.0,Nnaife,738.0,France,Female,61.0,7.0,0.0,2.0,1.0,1.0,48161.18,0.0
+11660,15732430.0,T'ang,719.0,Germany,Female,24.0,2.0,122029.15,2.0,1.0,1.0,172437.12,0.0
+11661,15598892.0,Tuan,678.0,Germany,Male,38.0,5.0,130404.92,1.0,1.0,1.0,150580.88,0.0
+11662,15672966.0,Nwachukwu,684.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,172601.62,0.0
+11663,15605020.0,Pinto,595.0,France,Female,33.0,6.0,0.0,1.0,1.0,1.0,166586.99,0.0
+11664,15777892.0,Nwokeocha,628.0,Germany,Female,40.0,6.0,61319.63,1.0,1.0,0.0,189366.04,1.0
+11665,15591100.0,Chiang,682.0,Germany,Female,38.0,2.0,122947.92,1.0,1.0,1.0,147040.25,0.0
+11666,15704509.0,Chidubem,585.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,105746.94,0.0
+11667,15697567.0,Hs?,599.0,Germany,Male,41.0,9.0,142289.28,1.0,0.0,0.0,60778.11,1.0
+11668,15589401.0,Tseng,639.0,France,Male,39.0,3.0,137714.5,1.0,1.0,0.0,90820.04,0.0
+11669,15595440.0,Palerma,596.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,166513.49,0.0
+11670,15699225.0,Chiabuotu,588.0,France,Male,28.0,5.0,71816.74,1.0,1.0,1.0,53394.24,0.0
+11671,15762218.0,Scott,564.0,Germany,Male,36.0,3.0,145974.79,2.0,1.0,0.0,60552.44,0.0
+11672,15682890.0,Teng,727.0,France,Male,33.0,7.0,0.0,2.0,0.0,1.0,84870.23,0.0
+11673,15655574.0,Nwankwo,676.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,116973.48,0.0
+11674,15672798.0,T'ien,685.0,France,Female,55.0,7.0,0.0,1.0,1.0,0.0,119882.86,1.0
+11675,15713774.0,Onwuka,604.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,120112.5,0.0
+11676,15803048.0,Sagese,636.0,Germany,Male,28.0,1.0,117094.02,1.0,0.0,1.0,174431.01,0.0
+11677,15571917.0,Tsou,629.0,France,Male,48.0,3.0,0.0,1.0,0.0,0.0,149535.75,1.0
+11678,15614491.0,Zikoranachidimma,713.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,118862.08,0.0
+11679,15707132.0,Trevisani,595.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,1710.89,0.0
+11680,15682211.0,Zetticci,651.0,Spain,Male,38.0,7.0,109342.82,1.0,1.0,0.0,80552.11,0.0
+11681,15641640.0,Onwughara,678.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,86410.29,0.0
+11682,15665524.0,Yip,659.0,Spain,Male,35.0,1.0,0.0,2.0,0.0,1.0,150694.58,0.0
+11683,15686099.0,Fyodorova,554.0,Spain,Male,33.0,3.0,91718.2,1.0,0.0,0.0,116944.3,0.0
+11684,15602312.0,Manna,592.0,France,Female,30.0,1.0,0.0,2.0,1.0,0.0,86981.45,0.0
+11685,15576723.0,P'eng,666.0,Germany,Male,50.0,5.0,99824.45,1.0,1.0,0.0,123722.15,1.0
+11686,15627854.0,Kao,683.0,Spain,Female,51.0,10.0,108632.48,1.0,1.0,1.0,60778.12,1.0
+11687,15768346.0,Chidubem,542.0,Germany,Male,42.0,6.0,128131.53,3.0,1.0,0.0,114957.22,1.0
+11688,15669411.0,Muecke,667.0,France,Male,35.0,10.0,0.0,1.0,1.0,1.0,175894.24,0.0
+11689,15650034.0,T'ang,653.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,109895.16,0.0
+11690,15619340.0,Cremonesi,792.0,France,Male,40.0,5.0,65612.55,1.0,1.0,0.0,165038.41,0.0
+11691,15592091.0,T'ang,704.0,France,Female,48.0,3.0,0.0,2.0,1.0,0.0,152097.02,0.0
+11692,15662291.0,Chiawuotu,714.0,Spain,Female,37.0,7.0,0.0,2.0,0.0,0.0,168460.12,0.0
+11693,15673529.0,Shubina,611.0,France,Female,41.0,5.0,0.0,1.0,1.0,1.0,152883.95,1.0
+11694,15677783.0,Hs?,683.0,France,Male,30.0,6.0,102025.35,1.0,1.0,0.0,176317.71,0.0
+11695,15800515.0,Ponomarev,712.0,France,Male,38.0,9.0,137851.31,1.0,0.0,0.0,24765.53,1.0
+11696,15808838.0,Ginn,738.0,France,Female,44.0,9.0,0.0,2.0,1.0,0.0,124964.82,0.0
+11697,15677052.0,Iweobiegbulam,593.0,Germany,Male,43.0,6.0,96651.03,2.0,1.0,1.0,72852.74,0.0
+11698,15801488.0,Hs?,641.0,France,Female,38.0,6.0,0.0,2.0,0.0,1.0,170638.98,0.0
+11699,15718572.0,Ts'ao,598.0,Germany,Female,46.0,2.0,93616.35,2.0,1.0,0.0,157072.91,0.0
+11700,15730044.0,Tretiakova,542.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,92381.01,0.0
+11701,15636089.0,Onwuatuegwu,651.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,82709.8,0.0
+11702,15669411.0,Mazzanti,645.0,Spain,Female,28.0,7.0,0.0,2.0,0.0,0.0,86678.48,0.0
+11703,15677146.0,Ogochukwu,699.0,France,Male,66.0,10.0,0.0,2.0,1.0,1.0,83084.33,0.0
+11704,15792989.0,O'Brien,707.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,88827.67,0.0
+11705,15649024.0,Onyekachukwu,653.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,101617.64,0.0
+11706,15612108.0,Iloerika,586.0,France,Male,27.0,9.0,0.0,2.0,1.0,0.0,162760.96,0.0
+11707,15686936.0,Nwabugwu,641.0,France,Male,35.0,10.0,0.0,2.0,1.0,0.0,645.61,0.0
+11708,15696812.0,Chukwuhaenye,581.0,Spain,Male,38.0,3.0,0.0,1.0,1.0,1.0,74965.61,0.0
+11709,15770995.0,Chukwualuka,758.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,1.0,67734.89,0.0
+11710,15716609.0,Hs?,667.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,1.0,132498.39,0.0
+11711,15740458.0,Chukwudi,656.0,Germany,Male,30.0,8.0,131765.72,1.0,1.0,1.0,123614.01,0.0
+11712,15737778.0,Pinto,595.0,France,Male,30.0,7.0,146033.62,1.0,0.0,0.0,125062.02,0.0
+11713,15775307.0,Hsueh,730.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,123535.04,1.0
+11714,15727353.0,Onwuamaeze,559.0,Spain,Female,41.0,4.0,0.0,2.0,1.0,0.0,136983.77,0.0
+11715,15682618.0,Chien,535.0,Spain,Male,55.0,3.0,0.0,1.0,0.0,0.0,60721.4,1.0
+11716,15692406.0,T'ien,562.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,1.0,98912.69,0.0
+11717,15646513.0,Nwabugwu,652.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,94775.54,0.0
+11718,15800890.0,Ch'ien,628.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,144870.28,0.0
+11719,15783859.0,Chin,678.0,Spain,Female,38.0,5.0,161608.81,2.0,1.0,1.0,110114.39,0.0
+11720,15724858.0,Mao,677.0,Germany,Female,58.0,7.0,141952.07,1.0,1.0,0.0,114869.51,1.0
+11721,15773731.0,Milanesi,652.0,Spain,Female,57.0,3.0,152341.55,2.0,1.0,0.0,75732.25,1.0
+11722,15763665.0,Chinweuba,754.0,Spain,Female,34.0,2.0,145553.97,2.0,1.0,0.0,135067.02,0.0
+11723,15626608.0,Okechukwu,575.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,1.0,95655.16,0.0
+11724,15792668.0,Obiajulu,592.0,France,Female,38.0,9.0,0.0,1.0,1.0,0.0,59961.81,0.0
+11725,15734765.0,Zito,637.0,France,Female,32.0,6.0,0.0,2.0,0.0,0.0,169469.3,0.0
+11726,15689886.0,Baryshnikov,549.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,1.0,151325.24,0.0
+11727,15642063.0,Pagnotto,627.0,France,Female,34.0,0.0,0.0,2.0,1.0,1.0,85890.75,0.0
+11728,15615444.0,Okagbue,659.0,France,Male,45.0,5.0,0.0,1.0,1.0,0.0,142331.85,1.0
+11729,15800287.0,Ts'ao,587.0,France,Male,30.0,5.0,0.0,2.0,0.0,0.0,133871.83,0.0
+11730,15797767.0,Niu,491.0,Spain,Female,56.0,8.0,0.0,1.0,1.0,1.0,89891.4,1.0
+11731,15590320.0,Chukwuemeka,553.0,France,Female,30.0,2.0,166824.59,2.0,1.0,0.0,134491.32,1.0
+11732,15597951.0,Mazzi,612.0,France,Female,61.0,3.0,152462.79,1.0,1.0,1.0,61666.87,1.0
+11733,15700964.0,Hsueh,574.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,199761.29,0.0
+11734,15583076.0,Hazon,738.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,75946.4,0.0
+11735,15757827.0,Onwuamaeze,651.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,106655.88,0.0
+11736,15748696.0,Ts'ao,748.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,124596.63,0.0
+11737,15692761.0,T'ien,615.0,Germany,Male,52.0,6.0,123894.22,1.0,1.0,1.0,141446.01,1.0
+11738,15781533.0,Nwankwo,756.0,Germany,Male,42.0,4.0,135536.7,2.0,1.0,1.0,100496.84,0.0
+11739,15743075.0,Nkemdilim,670.0,France,Male,60.0,1.0,0.0,1.0,1.0,1.0,173870.39,0.0
+11740,15634896.0,Onwuatuegwu,724.0,Germany,Male,52.0,1.0,116891.72,1.0,1.0,0.0,142331.85,1.0
+11741,15583548.0,Hao,583.0,Germany,Male,43.0,9.0,119961.48,2.0,1.0,1.0,153810.41,0.0
+11742,15718247.0,Chiazagomekpele,663.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,182765.14,0.0
+11743,15774536.0,K?,565.0,France,Male,37.0,10.0,84945.58,1.0,1.0,1.0,106663.46,0.0
+11744,15622494.0,Ch'iu,656.0,Germany,Male,62.0,8.0,132953.96,1.0,1.0,1.0,118863.38,1.0
+11745,15682928.0,Kao,637.0,France,Female,37.0,10.0,0.0,1.0,1.0,1.0,182059.28,0.0
+11746,15654891.0,Chidozie,579.0,France,Male,39.0,5.0,0.0,1.0,1.0,1.0,125977.22,0.0
+11747,15612207.0,Scott,724.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,81452.29,0.0
+11748,15752626.0,Chia,757.0,Spain,Female,39.0,9.0,93920.47,1.0,1.0,0.0,172749.4,0.0
+11749,15756852.0,Nwebube,710.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,135067.02,0.0
+11750,15697028.0,Mazzanti,717.0,France,Male,34.0,0.0,0.0,2.0,0.0,0.0,174815.54,0.0
+11751,15797246.0,Lo,733.0,France,Female,55.0,5.0,0.0,1.0,1.0,1.0,161653.5,1.0
+11752,15592925.0,Sun,609.0,France,Male,41.0,10.0,61297.05,1.0,1.0,1.0,167060.49,0.0
+11753,15682084.0,Toscani,477.0,Germany,Male,52.0,2.0,123870.07,1.0,0.0,0.0,27448.33,1.0
+11754,15605960.0,Chukwukadibia,516.0,Spain,Female,48.0,6.0,0.0,2.0,1.0,1.0,53954.24,0.0
+11755,15635078.0,Hsiung,724.0,Germany,Male,38.0,3.0,92889.91,1.0,0.0,1.0,84321.03,1.0
+11756,15753895.0,Y?an,570.0,France,Male,35.0,5.0,0.0,1.0,1.0,0.0,171056.65,0.0
+11757,15728006.0,Ts'ui,619.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,6554.97,0.0
+11758,15663438.0,Kao,712.0,Germany,Male,38.0,3.0,99799.76,1.0,1.0,0.0,124210.53,0.0
+11759,15668818.0,Ch'ang,539.0,France,Female,55.0,1.0,0.0,2.0,1.0,0.0,78896.59,1.0
+11760,15591100.0,Yobachi,642.0,Spain,Male,37.0,7.0,0.0,1.0,0.0,0.0,78730.95,0.0
+11761,15566790.0,Lu,581.0,France,Male,39.0,6.0,92599.55,1.0,1.0,1.0,151623.91,0.0
+11762,15644833.0,Manna,567.0,Germany,Male,71.0,8.0,104449.4,1.0,1.0,1.0,79919.13,0.0
+11763,15644295.0,Fenton,763.0,France,Male,32.0,6.0,110132.55,1.0,1.0,1.0,183542.08,0.0
+11764,15751167.0,Cremonesi,683.0,Germany,Male,60.0,1.0,121730.49,1.0,0.0,0.0,63584.55,1.0
+11765,15623471.0,Baresi,613.0,France,Male,35.0,5.0,0.0,2.0,0.0,0.0,105715.01,0.0
+11766,15771749.0,Onyemauchechukwu,753.0,France,Male,46.0,1.0,0.0,1.0,1.0,0.0,130189.53,0.0
+11767,15806467.0,H?,597.0,France,Male,40.0,7.0,0.0,2.0,0.0,1.0,151677.31,0.0
+11768,15662337.0,Bock,542.0,France,Female,48.0,6.0,0.0,3.0,1.0,0.0,133042.33,1.0
+11769,15783025.0,Lo Duca,628.0,Germany,Male,32.0,1.0,71996.09,2.0,1.0,1.0,152417.97,0.0
+11770,15676513.0,Fanucci,710.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,151738.54,0.0
+11771,15690134.0,McFarland,729.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,170980.86,0.0
+11772,15604818.0,Nnonso,687.0,France,Male,41.0,7.0,122917.71,1.0,1.0,0.0,64771.61,0.0
+11773,15777033.0,Chibugo,781.0,France,Male,35.0,3.0,0.0,1.0,1.0,0.0,67979.35,0.0
+11774,15684461.0,Tsao,686.0,Germany,Female,46.0,9.0,168094.01,1.0,1.0,0.0,57320.92,1.0
+11775,15728362.0,Chidozie,554.0,Spain,Male,33.0,2.0,0.0,1.0,1.0,1.0,124749.03,0.0
+11776,15778936.0,Baresi,684.0,France,Female,42.0,3.0,0.0,2.0,1.0,0.0,149913.84,0.0
+11777,15781451.0,Hsieh,712.0,France,Male,44.0,8.0,0.0,2.0,1.0,0.0,121485.1,0.0
+11778,15752300.0,Tuan,616.0,Germany,Male,35.0,10.0,92347.96,2.0,1.0,0.0,60416.76,0.0
+11779,15690695.0,Chikezie,749.0,France,Male,36.0,3.0,0.0,2.0,0.0,1.0,48965.03,0.0
+11780,15757933.0,Hsieh,587.0,Germany,Male,33.0,3.0,71264.02,2.0,1.0,1.0,181270.84,0.0
+11781,15738751.0,Ting,739.0,Spain,Male,46.0,1.0,0.0,1.0,1.0,0.0,149535.91,1.0
+11782,15671137.0,Lo Duca,721.0,Germany,Male,41.0,1.0,124577.11,3.0,1.0,1.0,41967.03,1.0
+11783,15702298.0,H?,686.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,121053.19,0.0
+11784,15671148.0,H?,660.0,France,Female,57.0,1.0,0.0,1.0,1.0,1.0,177028.0,1.0
+11785,15693326.0,Chidozie,542.0,France,Male,37.0,8.0,0.0,1.0,1.0,0.0,173516.71,1.0
+11786,15578546.0,Udegbunam,674.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,0.0,18082.06,0.0
+11787,15644501.0,Toscani,762.0,Spain,Female,41.0,6.0,0.0,2.0,0.0,1.0,61240.83,0.0
+11788,15752588.0,Ch'in,644.0,France,Female,44.0,9.0,0.0,1.0,1.0,0.0,110371.84,1.0
+11789,15656901.0,Pirozzi,663.0,France,Male,31.0,7.0,128747.69,1.0,1.0,0.0,159418.1,0.0
+11790,15622578.0,Chu,543.0,France,Female,26.0,6.0,118945.08,1.0,1.0,1.0,157891.86,0.0
+11791,15652914.0,Chukwuemeka,850.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,183556.12,0.0
+11792,15803522.0,Chidiebere,537.0,France,Male,41.0,3.0,0.0,1.0,1.0,1.0,82222.86,0.0
+11793,15605872.0,Iadanza,663.0,France,Female,35.0,7.0,0.0,2.0,0.0,1.0,76776.36,0.0
+11794,15684930.0,Pisano,649.0,France,Female,42.0,6.0,0.0,2.0,1.0,1.0,99503.19,0.0
+11795,15593795.0,Ch'in,558.0,France,Female,29.0,1.0,118336.95,1.0,0.0,0.0,139048.0,0.0
+11796,15577683.0,Milanesi,520.0,Germany,Male,44.0,7.0,132876.23,2.0,1.0,0.0,131473.31,1.0
+11797,15649430.0,Chien,669.0,Germany,Male,44.0,6.0,137592.89,1.0,1.0,0.0,102889.84,0.0
+11798,15581323.0,Chialuka,713.0,Spain,Male,33.0,10.0,0.0,2.0,1.0,0.0,179435.21,0.0
+11799,15735024.0,Tsao,521.0,France,Male,29.0,1.0,0.0,2.0,0.0,1.0,148814.54,0.0
+11800,15616240.0,Ts'ui,579.0,France,Female,43.0,4.0,0.0,1.0,0.0,0.0,126192.54,0.0
+11801,15796958.0,T'ien,635.0,France,Male,28.0,2.0,0.0,1.0,1.0,1.0,86935.08,1.0
+11802,15606097.0,Maclean,683.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,82855.99,0.0
+11803,15571586.0,Uspenskaya,684.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,123499.98,0.0
+11804,15800524.0,Pagnotto,535.0,Germany,Male,41.0,9.0,148874.01,1.0,1.0,1.0,175750.21,0.0
+11805,15751175.0,Chiu,651.0,France,Male,31.0,6.0,88814.4,2.0,1.0,0.0,125422.66,0.0
+11806,15780561.0,Ritchie,681.0,France,Male,31.0,8.0,81576.75,1.0,1.0,1.0,112157.31,0.0
+11807,15706593.0,Ch'eng,636.0,Germany,Male,59.0,4.0,134796.05,4.0,1.0,0.0,147075.69,1.0
+11808,15605059.0,Mazzanti,721.0,France,Male,27.0,7.0,0.0,2.0,0.0,1.0,72350.47,0.0
+11809,15719508.0,Ch'iu,586.0,Germany,Male,30.0,4.0,150213.71,2.0,0.0,1.0,69056.26,0.0
+11810,15692744.0,Smith,615.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,148087.62,0.0
+11811,15651868.0,Zaytseva,687.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,77466.72,0.0
+11812,15772423.0,Tretiakova,748.0,France,Female,23.0,6.0,0.0,2.0,0.0,1.0,121311.12,0.0
+11813,15577587.0,Ch'ang,651.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,170984.97,0.0
+11814,15654964.0,Uspensky,686.0,Germany,Female,35.0,3.0,171325.98,1.0,0.0,0.0,109538.35,1.0
+11815,15676895.0,Kambinachi,635.0,France,Female,37.0,9.0,0.0,2.0,0.0,1.0,121537.91,0.0
+11816,15658852.0,Hs?,627.0,France,Male,35.0,5.0,0.0,2.0,0.0,0.0,105557.78,0.0
+11817,15716134.0,Chu,592.0,Spain,Female,28.0,4.0,0.0,2.0,1.0,0.0,52796.31,0.0
+11818,15714586.0,Hsing,646.0,Spain,Female,40.0,5.0,0.0,1.0,1.0,0.0,143882.25,1.0
+11819,15653952.0,Chidiebele,706.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,1.0,60988.21,0.0
+11820,15761487.0,Hsia,683.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,8996.79,0.0
+11821,15623196.0,Piazza,549.0,Germany,Male,46.0,1.0,116537.6,2.0,1.0,0.0,171045.49,1.0
+11822,15667085.0,Palermo,705.0,Spain,Male,28.0,1.0,80728.15,1.0,1.0,1.0,158948.63,0.0
+11823,15684512.0,T'ien,588.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,1.0,147937.91,0.0
+11824,15787597.0,Fiorentini,602.0,Spain,Male,31.0,4.0,0.0,1.0,1.0,1.0,152499.8,0.0
+11825,15749679.0,Lucchese,588.0,France,Female,35.0,1.0,0.0,2.0,1.0,0.0,83084.33,0.0
+11826,15783026.0,Hsueh,671.0,France,Male,34.0,6.0,0.0,2.0,0.0,0.0,173989.66,0.0
+11827,15589475.0,Chikelu,715.0,France,Female,50.0,3.0,0.0,2.0,1.0,1.0,166830.71,0.0
+11828,15717185.0,Hsiung,684.0,France,Female,42.0,5.0,0.0,2.0,1.0,1.0,161076.92,0.0
+11829,15603319.0,Milani,758.0,France,Female,26.0,10.0,0.0,2.0,1.0,0.0,111241.37,0.0
+11830,15706021.0,Toscani,592.0,Germany,Female,44.0,2.0,104982.57,1.0,1.0,1.0,126082.18,0.0
+11831,15572735.0,Ch'ang,705.0,France,Male,43.0,6.0,0.0,1.0,1.0,0.0,132418.59,1.0
+11832,15803801.0,Ch'ang,577.0,Germany,Male,34.0,1.0,131326.47,2.0,1.0,1.0,73656.38,0.0
+11833,15654891.0,Mazzanti,676.0,Germany,Male,44.0,5.0,140931.83,1.0,1.0,1.0,131304.45,0.0
+11834,15667554.0,Tuan,682.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,129563.86,0.0
+11835,15705657.0,Nwebube,754.0,Germany,Female,53.0,2.0,92398.08,1.0,1.0,0.0,147090.9,1.0
+11836,15736154.0,Chikezie,615.0,France,Male,49.0,7.0,0.0,2.0,1.0,0.0,86622.56,0.0
+11837,15672970.0,Pinto,596.0,Germany,Female,41.0,8.0,108914.17,1.0,1.0,0.0,134778.01,0.0
+11838,15649744.0,H?,632.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,72861.94,0.0
+11839,15590094.0,Eluemuno,715.0,France,Female,29.0,3.0,0.0,1.0,1.0,1.0,823.96,1.0
+11840,15751167.0,P'eng,739.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,131834.91,0.0
+11841,15757931.0,Yermolayeva,539.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,125962.02,0.0
+11842,15585823.0,Yobachi,712.0,Spain,Male,64.0,8.0,0.0,1.0,0.0,0.0,44937.02,1.0
+11843,15795511.0,Bancks,662.0,France,Female,30.0,1.0,0.0,2.0,1.0,0.0,118716.63,0.0
+11844,15793854.0,Onyeorulu,684.0,France,Female,34.0,8.0,81210.72,1.0,1.0,1.0,133535.92,0.0
+11845,15810845.0,Ritchie,725.0,France,Male,37.0,1.0,0.0,2.0,0.0,1.0,173365.9,0.0
+11846,15790594.0,Hsia,769.0,Spain,Female,38.0,8.0,0.0,1.0,1.0,0.0,62402.38,0.0
+11847,15573045.0,Hao,694.0,France,Female,36.0,8.0,0.0,2.0,1.0,0.0,141872.05,0.0
+11848,15714256.0,Ch'ien,459.0,Germany,Male,39.0,6.0,99141.86,1.0,1.0,0.0,72993.65,1.0
+11849,15702656.0,Mazzanti,678.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,137028.51,0.0
+11850,15627190.0,Hs?,850.0,Germany,Male,34.0,8.0,111091.01,1.0,1.0,0.0,179670.31,1.0
+11851,15773338.0,Okonkwo,594.0,Germany,Female,39.0,7.0,119545.48,1.0,1.0,1.0,160122.66,0.0
+11852,15582797.0,Aksenov,656.0,Germany,Female,24.0,2.0,123971.16,1.0,1.0,0.0,121798.52,0.0
+11853,15645248.0,Iloerika,591.0,Germany,Male,37.0,6.0,124580.69,2.0,1.0,1.0,131734.43,0.0
+11854,15659428.0,Onyekachi,592.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,35356.46,0.0
+11855,15742285.0,Okwudilichukwu,709.0,Germany,Male,37.0,9.0,140576.11,1.0,1.0,1.0,122218.57,1.0
+11856,15736274.0,Ch'iu,567.0,Spain,Female,27.0,3.0,0.0,2.0,1.0,0.0,117632.63,0.0
+11857,15776733.0,Genovesi,705.0,France,Female,31.0,1.0,0.0,2.0,0.0,1.0,119374.74,1.0
+11858,15697035.0,Fetherstonhaugh,602.0,Germany,Female,42.0,1.0,119782.72,1.0,1.0,0.0,180800.42,1.0
+11859,15757577.0,Nwebube,850.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,89519.47,0.0
+11860,15683441.0,Pinto,562.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,90547.94,0.0
+11861,15797710.0,Naylor,576.0,Germany,Female,35.0,5.0,161807.18,2.0,1.0,1.0,171706.66,0.0
+11862,15763662.0,Nwankwo,531.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,180553.48,0.0
+11863,15745355.0,Ts'ai,679.0,Germany,Female,37.0,2.0,161572.24,1.0,0.0,0.0,142223.94,0.0
+11864,15582090.0,Ts'ao,661.0,France,Female,44.0,10.0,0.0,2.0,1.0,1.0,96658.26,0.0
+11865,15746035.0,Padovano,741.0,France,Male,39.0,8.0,91216.0,1.0,1.0,0.0,97938.39,0.0
+11866,15583595.0,Yobachukwu,608.0,Spain,Male,30.0,7.0,147531.82,1.0,0.0,1.0,56585.2,0.0
+11867,15572361.0,P'an,559.0,Germany,Male,57.0,3.0,118788.57,1.0,1.0,0.0,139366.58,1.0
+11868,15676958.0,Onwuamaeze,528.0,France,Female,32.0,1.0,106901.42,1.0,0.0,1.0,158887.09,0.0
+11869,15764174.0,Milano,721.0,Spain,Male,34.0,0.0,0.0,1.0,0.0,0.0,134778.01,0.0
+11870,15794048.0,H?,716.0,Spain,Male,29.0,2.0,0.0,1.0,1.0,1.0,138849.06,0.0
+11871,15657085.0,Maclean,666.0,France,Male,35.0,5.0,124730.26,1.0,0.0,0.0,175082.2,0.0
+11872,15672594.0,Yuryeva,597.0,France,Female,35.0,3.0,0.0,1.0,0.0,1.0,71519.4,0.0
+11873,15634792.0,Kao,826.0,Germany,Female,35.0,8.0,94920.75,1.0,1.0,0.0,158036.72,1.0
+11874,15611186.0,Obidimkpa,604.0,Spain,Female,36.0,4.0,0.0,2.0,1.0,0.0,114448.77,0.0
+11875,15677307.0,Okwudiliolisa,677.0,Germany,Male,52.0,5.0,144848.74,1.0,0.0,0.0,131083.73,1.0
+11876,15795936.0,Nwagugheuzo,691.0,France,Male,33.0,1.0,0.0,1.0,1.0,0.0,128882.31,0.0
+11877,15731751.0,Zuyev,677.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,101694.67,0.0
+11878,15654851.0,Ts'ai,728.0,France,Male,24.0,3.0,121898.59,1.0,1.0,1.0,82796.29,0.0
+11879,15691794.0,Shao,651.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,137763.93,0.0
+11880,15678067.0,Fang,733.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,1.0,130664.79,0.0
+11881,15777033.0,Chigbogu,543.0,Spain,Female,35.0,5.0,0.0,2.0,0.0,0.0,81299.17,0.0
+11882,15757912.0,Chialuka,685.0,Spain,Female,44.0,8.0,121520.39,1.0,1.0,0.0,90591.15,0.0
+11883,15805764.0,Onyekaozulu,564.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,162599.51,0.0
+11884,15771674.0,Liao,577.0,France,Male,46.0,9.0,0.0,1.0,1.0,0.0,49089.36,0.0
+11885,15732563.0,White,732.0,France,Female,40.0,6.0,0.0,2.0,0.0,0.0,1408.68,0.0
+11886,15672970.0,Ch'iu,710.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,117832.88,0.0
+11887,15753955.0,Zetticci,587.0,Spain,Male,41.0,4.0,108691.95,1.0,0.0,1.0,163365.85,0.0
+11888,15603764.0,Tuan,599.0,France,Female,34.0,5.0,0.0,1.0,1.0,0.0,91903.9,0.0
+11889,15681342.0,Kibble,685.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,91574.92,0.0
+11890,15669611.0,Eluemuno,637.0,Spain,Male,40.0,7.0,0.0,1.0,1.0,1.0,121525.48,0.0
+11891,15640953.0,Ejimofor,819.0,Spain,Female,41.0,4.0,0.0,2.0,1.0,1.0,126794.56,0.0
+11892,15645323.0,Ikedinachukwu,632.0,Germany,Female,62.0,6.0,130334.37,1.0,1.0,0.0,151822.66,1.0
+11893,15661380.0,Kao,749.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,0.0,75825.57,0.0
+11894,15780220.0,Nnachetam,652.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,1.0,103764.55,0.0
+11895,15576094.0,Uvarova,596.0,France,Male,41.0,4.0,0.0,2.0,1.0,1.0,77822.13,0.0
+11896,15768282.0,White,733.0,France,Male,33.0,7.0,135095.47,1.0,1.0,0.0,113931.57,0.0
+11897,15727556.0,Manna,497.0,Spain,Male,43.0,7.0,0.0,2.0,1.0,0.0,120531.31,0.0
+11898,15782096.0,Chuang,707.0,Spain,Female,31.0,4.0,0.0,1.0,1.0,1.0,94888.6,0.0
+11899,15742848.0,Tsao,627.0,France,Female,24.0,3.0,71996.09,1.0,1.0,1.0,138825.19,0.0
+11900,15682463.0,Manna,681.0,Spain,Female,66.0,7.0,0.0,2.0,1.0,1.0,77830.36,0.0
+11901,15667554.0,Ch'in,637.0,France,Male,45.0,4.0,0.0,1.0,1.0,0.0,143463.28,1.0
+11902,15672574.0,Hsu,588.0,Germany,Female,28.0,6.0,126265.88,2.0,1.0,0.0,50249.16,0.0
+11903,15610355.0,Chidiebere,577.0,France,Female,36.0,8.0,106603.74,1.0,0.0,1.0,125083.29,0.0
+11904,15729771.0,Palerma,730.0,Germany,Male,32.0,7.0,117300.02,2.0,1.0,0.0,140000.29,0.0
+11905,15581229.0,Onwumelu,635.0,Spain,Female,33.0,4.0,0.0,1.0,1.0,0.0,69113.14,0.0
+11906,15661626.0,Nnonso,682.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,126675.62,0.0
+11907,15782360.0,Ch'ang,629.0,Spain,Female,28.0,3.0,71902.52,1.0,1.0,1.0,147530.06,0.0
+11908,15750569.0,Ts'ai,769.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,178067.33,0.0
+11909,15697597.0,H?,547.0,Germany,Female,41.0,2.0,139561.46,1.0,1.0,1.0,142223.94,1.0
+11910,15680278.0,Okwudilichukwu,753.0,France,Female,34.0,0.0,103499.09,1.0,0.0,0.0,130189.53,0.0
+11911,15770039.0,Milne,597.0,Spain,Female,29.0,5.0,0.0,2.0,0.0,1.0,100127.71,0.0
+11912,15656426.0,Udegbulam,697.0,France,Female,66.0,4.0,100194.44,1.0,0.0,0.0,127560.46,0.0
+11913,15585914.0,Tsou,596.0,Spain,Female,40.0,6.0,0.0,2.0,1.0,1.0,164741.8,0.0
+11914,15726234.0,T'ien,705.0,Germany,Female,37.0,4.0,113693.0,2.0,0.0,0.0,171021.44,0.0
+11915,15649150.0,Smith,713.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,143683.75,0.0
+11916,15757897.0,Toscani,713.0,France,Male,59.0,5.0,0.0,2.0,0.0,1.0,107839.03,0.0
+11917,15736431.0,Chiemela,447.0,Germany,Male,51.0,7.0,142663.46,1.0,1.0,0.0,113655.48,1.0
+11918,15790782.0,Yermakova,676.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,0.0,132498.39,0.0
+11919,15775797.0,Hsueh,709.0,France,Male,40.0,8.0,0.0,2.0,0.0,0.0,87711.61,0.0
+11920,15762238.0,Chukwuemeka,535.0,Germany,Female,35.0,1.0,108348.66,2.0,1.0,0.0,141926.43,0.0
+11921,15712608.0,P'eng,681.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,55835.3,1.0
+11922,15775318.0,Lettiere,526.0,France,Female,43.0,8.0,0.0,1.0,1.0,1.0,145538.35,1.0
+11923,15591822.0,Chienezie,712.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,182059.28,0.0
+11924,15640074.0,Tuan,635.0,France,Female,44.0,3.0,0.0,2.0,0.0,0.0,90928.02,0.0
+11925,15782875.0,Yermolayev,634.0,France,Female,37.0,9.0,0.0,1.0,1.0,0.0,150684.23,0.0
+11926,15781598.0,Ibezimako,597.0,Spain,Male,28.0,4.0,0.0,2.0,1.0,0.0,163562.08,0.0
+11927,15732061.0,Ch'eng,721.0,Spain,Male,40.0,3.0,0.0,2.0,1.0,1.0,86575.46,0.0
+11928,15641110.0,Fiorentini,745.0,Germany,Female,43.0,8.0,104982.57,1.0,1.0,0.0,167969.15,0.0
+11929,15754849.0,T'ang,763.0,France,Male,41.0,5.0,124043.8,1.0,1.0,1.0,134878.19,0.0
+11930,15806049.0,Okwudilichukwu,581.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,71139.31,0.0
+11931,15644724.0,Milani,669.0,Germany,Female,44.0,1.0,155903.82,1.0,1.0,0.0,109041.53,1.0
+11932,15607837.0,Pagnotto,562.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,131281.29,0.0
+11933,15679810.0,Chioke,585.0,France,Female,61.0,2.0,0.0,1.0,1.0,1.0,52327.57,0.0
+11934,15704819.0,Ch'in,713.0,Spain,Female,38.0,0.0,98400.61,1.0,0.0,1.0,93894.41,0.0
+11935,15648067.0,Chukwumaobim,595.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,1.0,80591.15,0.0
+11936,15805627.0,Yeh,624.0,Spain,Male,48.0,9.0,116967.68,1.0,1.0,1.0,91346.03,1.0
+11937,15572390.0,Mazzi,744.0,Germany,Female,30.0,9.0,135650.88,1.0,1.0,1.0,166692.03,0.0
+11938,15802486.0,Chiemenam,624.0,France,Female,52.0,9.0,0.0,1.0,1.0,1.0,51703.09,1.0
+11939,15679024.0,Kao,545.0,Germany,Female,45.0,1.0,122311.21,1.0,1.0,0.0,140765.44,1.0
+11940,15689594.0,Lai,569.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,173948.4,0.0
+11941,15806964.0,Tseng,569.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,0.0,138516.1,0.0
+11942,15682576.0,Genovese,506.0,Spain,Female,52.0,3.0,0.0,2.0,0.0,1.0,170819.01,1.0
+11943,15799925.0,T'ien,579.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,92381.01,0.0
+11944,15701160.0,Tuan,652.0,Spain,Male,30.0,2.0,0.0,2.0,0.0,1.0,160976.85,0.0
+11945,15773789.0,Nwachinemelu,633.0,Spain,Female,25.0,9.0,0.0,2.0,0.0,1.0,159845.68,0.0
+11946,15673599.0,Chibugo,646.0,France,Male,35.0,0.0,0.0,2.0,1.0,0.0,176768.91,0.0
+11947,15773804.0,Palermo,769.0,Spain,Male,40.0,3.0,82453.96,1.0,1.0,1.0,174032.0,0.0
+11948,15702465.0,Ch'ang,642.0,France,Female,42.0,3.0,0.0,1.0,0.0,0.0,92888.52,1.0
+11949,15785819.0,Ts'ui,723.0,Spain,Male,38.0,5.0,76495.04,1.0,1.0,1.0,152298.28,0.0
+11950,15576065.0,Yermakova,683.0,France,Male,42.0,5.0,0.0,1.0,0.0,1.0,126475.79,0.0
+11951,15718280.0,Trevisani,633.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,183494.87,0.0
+11952,15571752.0,Chukwufumnanya,561.0,Germany,Female,41.0,3.0,123356.63,2.0,0.0,1.0,111243.09,0.0
+11953,15585066.0,Ch'eng,579.0,Germany,Male,56.0,6.0,118547.74,1.0,1.0,0.0,183963.34,1.0
+11954,15773098.0,K'ung,697.0,Germany,Male,38.0,2.0,82697.28,2.0,1.0,0.0,50839.85,0.0
+11955,15759991.0,Mamelu,850.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,151902.78,0.0
+11956,15605872.0,Chien,724.0,France,Male,24.0,10.0,0.0,2.0,1.0,1.0,121529.59,0.0
+11957,15687828.0,Onyekachukwu,633.0,Germany,Male,61.0,7.0,119452.03,1.0,0.0,0.0,170829.87,1.0
+11958,15647975.0,Nwabugwu,748.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,68814.23,0.0
+11959,15650026.0,O'Sullivan,651.0,Germany,Female,42.0,2.0,148962.76,2.0,1.0,0.0,83084.33,0.0
+11960,15684319.0,Milanesi,605.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,142331.85,0.0
+11961,15766044.0,Nwankwo,544.0,France,Female,31.0,10.0,0.0,1.0,1.0,0.0,96670.2,0.0
+11962,15658475.0,Onyemere,573.0,Spain,Male,44.0,8.0,0.0,2.0,1.0,0.0,130504.49,0.0
+11963,15811486.0,Ting,752.0,France,Male,36.0,9.0,0.0,2.0,0.0,0.0,67789.99,0.0
+11964,15611365.0,Aksenov,707.0,France,Male,45.0,9.0,0.0,2.0,1.0,1.0,108301.45,0.0
+11965,15689953.0,Pirozzi,607.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,145792.01,0.0
+11966,15686696.0,Mazzanti,573.0,France,Male,23.0,1.0,0.0,1.0,1.0,1.0,138542.87,0.0
+11967,15682453.0,Yermolayev,628.0,France,Female,36.0,3.0,0.0,4.0,0.0,0.0,97623.42,1.0
+11968,15571284.0,Ulyanova,609.0,France,Male,47.0,3.0,125712.2,1.0,0.0,1.0,133672.61,1.0
+11969,15654074.0,Nwankwo,744.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,0.0,131097.9,0.0
+11970,15632826.0,Chigolum,738.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,0.0,105720.09,0.0
+11971,15641794.0,Eluemuno,678.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,97468.44,0.0
+11972,15761141.0,Chiemezie,743.0,France,Female,44.0,5.0,116803.8,1.0,1.0,1.0,62972.13,0.0
+11973,15741643.0,Hsueh,686.0,France,Female,28.0,5.0,0.0,2.0,1.0,1.0,103799.77,0.0
+11974,15691910.0,Hao,632.0,Germany,Male,30.0,4.0,121345.86,1.0,0.0,0.0,93917.68,0.0
+11975,15761497.0,Ponomarev,687.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,159815.68,0.0
+11976,15682563.0,Martin,545.0,France,Female,52.0,1.0,133384.66,1.0,1.0,0.0,145796.49,1.0
+11977,15642710.0,K?,652.0,Germany,Male,37.0,7.0,83347.25,1.0,1.0,1.0,173629.78,0.0
+11978,15647898.0,Pagnotto,604.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,142513.5,0.0
+11979,15804787.0,Fanucci,727.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,0.0,26170.08,0.0
+11980,15587735.0,Nnamutaezinwa,553.0,Spain,Male,28.0,3.0,179904.0,1.0,1.0,0.0,126469.09,0.0
+11981,15783714.0,Pirozzi,712.0,France,Male,51.0,5.0,152324.66,2.0,1.0,1.0,164064.44,1.0
+11982,15766017.0,Kuo,753.0,France,Female,38.0,1.0,82498.14,1.0,1.0,0.0,103099.22,1.0
+11983,15737506.0,Chiemenam,677.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,63736.19,0.0
+11984,15761149.0,Yirawala,749.0,France,Male,33.0,1.0,0.0,1.0,1.0,0.0,143317.42,0.0
+11985,15771873.0,Okwudilichukwu,537.0,Germany,Female,31.0,5.0,132412.13,1.0,1.0,1.0,80552.11,0.0
+11986,15705315.0,Bischof,635.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,132702.64,0.0
+11987,15774872.0,Chikwendu,850.0,France,Female,45.0,8.0,0.0,3.0,1.0,1.0,94912.78,1.0
+11988,15579183.0,Hsueh,644.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,51345.44,0.0
+11989,15698872.0,Onyemauchechukwu,754.0,France,Female,31.0,7.0,102994.03,1.0,0.0,0.0,147822.54,1.0
+11990,15646521.0,Chiemezie,696.0,France,Male,42.0,9.0,179960.2,1.0,1.0,1.0,122763.95,0.0
+11991,15662085.0,P'eng,769.0,France,Male,45.0,9.0,0.0,2.0,1.0,1.0,161845.81,0.0
+11992,15804009.0,Palermo,681.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,0.0,123587.83,0.0
+11993,15592025.0,Hsiao,579.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,170142.09,0.0
+11994,15592979.0,Shih,708.0,France,Female,55.0,8.0,0.0,3.0,1.0,0.0,88407.51,1.0
+11995,15593937.0,Ch'in,684.0,France,Male,49.0,7.0,0.0,4.0,1.0,1.0,142963.18,1.0
+11996,15777076.0,Chukwufumnanya,687.0,France,Female,51.0,8.0,0.0,1.0,1.0,0.0,115818.51,1.0
+11997,15736287.0,Y?an,629.0,France,Male,33.0,3.0,0.0,1.0,1.0,1.0,181449.51,0.0
+11998,15769346.0,Hsia,648.0,France,Male,31.0,7.0,0.0,2.0,0.0,0.0,141533.19,0.0
+11999,15662854.0,Chinedum,590.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,1.0,138380.11,0.0
+12000,15696061.0,Ch'ien,635.0,Germany,Female,43.0,3.0,122313.88,1.0,1.0,1.0,117632.63,1.0
+12001,15594560.0,Toscani,648.0,Germany,Female,51.0,0.0,123835.52,1.0,1.0,0.0,121551.58,1.0
+12002,15768006.0,Nwabugwu,685.0,France,Male,55.0,7.0,82231.51,1.0,1.0,0.0,95983.54,1.0
+12003,15786063.0,Hsing,713.0,Spain,Female,28.0,6.0,0.0,2.0,1.0,1.0,86699.27,0.0
+12004,15584928.0,Hsing,711.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,26342.33,0.0
+12005,15769703.0,Tien,714.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,180622.42,0.0
+12006,15747174.0,Hs?,602.0,France,Male,36.0,4.0,132623.6,1.0,1.0,1.0,181204.5,0.0
+12007,15589563.0,Tien,705.0,France,Female,43.0,5.0,0.0,2.0,1.0,1.0,125909.87,0.0
+12008,15650391.0,Nwabugwu,576.0,France,Male,24.0,4.0,0.0,2.0,1.0,0.0,121151.1,0.0
+12009,15652084.0,Pinto,638.0,France,Male,29.0,6.0,0.0,2.0,0.0,0.0,62703.38,0.0
+12010,15627377.0,Chiang,685.0,Spain,Female,33.0,2.0,0.0,1.0,1.0,0.0,91936.1,1.0
+12011,15815271.0,Yashina,608.0,France,Male,25.0,3.0,0.0,2.0,1.0,1.0,51721.52,0.0
+12012,15756975.0,Chidiegwu,553.0,Germany,Male,34.0,8.0,130147.96,2.0,1.0,1.0,172749.65,0.0
+12013,15592222.0,Chinedum,697.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,36557.55,0.0
+12014,15617507.0,Ch'ien,673.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,103660.31,1.0
+12015,15603088.0,Rieke,775.0,Germany,Female,43.0,9.0,108632.48,1.0,1.0,1.0,63795.8,1.0
+12016,15724756.0,Shao,602.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,0.0,76637.38,0.0
+12017,15697597.0,Ch'en,594.0,Germany,Male,33.0,7.0,111432.77,1.0,1.0,0.0,49374.82,0.0
+12018,15691808.0,Folliero,645.0,France,Female,35.0,5.0,98899.76,1.0,1.0,0.0,143628.56,0.0
+12019,15681755.0,Trevisano,667.0,France,Male,29.0,8.0,0.0,1.0,1.0,1.0,168515.61,0.0
+12020,15770309.0,Uvarova,569.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,86622.48,0.0
+12021,15663917.0,Ojiofor,717.0,Germany,Male,43.0,7.0,124043.8,2.0,1.0,0.0,80344.96,0.0
+12022,15645794.0,Chukwufumnanya,667.0,France,Female,49.0,6.0,0.0,1.0,1.0,1.0,182336.45,1.0
+12023,15693637.0,Chuang,775.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,87566.97,0.0
+12024,15802570.0,Henty,633.0,France,Female,25.0,5.0,0.0,2.0,0.0,1.0,122518.5,0.0
+12025,15756680.0,Onyenachiya,804.0,France,Female,26.0,10.0,0.0,2.0,1.0,1.0,91718.12,0.0
+12026,15581229.0,Hargreaves,543.0,Germany,Female,58.0,10.0,109137.12,1.0,1.0,1.0,171867.08,0.0
+12027,15672756.0,Ch'eng,431.0,France,Male,36.0,5.0,0.0,1.0,1.0,0.0,147055.87,1.0
+12028,15572735.0,Hsia,592.0,France,Female,29.0,9.0,0.0,2.0,0.0,0.0,70954.41,0.0
+12029,15727556.0,Iheanacho,555.0,France,Male,27.0,1.0,91897.8,1.0,0.0,0.0,164040.94,0.0
+12030,15579706.0,Nkemjika,637.0,France,Male,31.0,9.0,130142.79,1.0,1.0,0.0,176450.19,0.0
+12031,15671915.0,Cattaneo,706.0,France,Male,30.0,10.0,0.0,2.0,1.0,1.0,90876.95,0.0
+12032,15611905.0,Okechukwu,756.0,Spain,Male,35.0,3.0,0.0,1.0,0.0,0.0,80552.11,0.0
+12033,15674929.0,K'ung,624.0,Spain,Male,42.0,8.0,0.0,2.0,1.0,0.0,183542.08,0.0
+12034,15773520.0,Milne,594.0,France,Female,45.0,4.0,0.0,2.0,1.0,0.0,55857.48,0.0
+12035,15578738.0,Chiu,850.0,Germany,Male,34.0,5.0,81677.22,2.0,0.0,0.0,59981.62,0.0
+12036,15572022.0,Mazzi,705.0,Germany,Male,35.0,7.0,108913.84,1.0,1.0,0.0,172967.87,1.0
+12037,15591723.0,Yermolayeva,531.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,0.0,74849.46,0.0
+12038,15744691.0,Cremonesi,537.0,Spain,Male,41.0,10.0,0.0,2.0,0.0,1.0,163440.8,0.0
+12039,15672598.0,Kosisochukwu,681.0,France,Male,45.0,1.0,0.0,2.0,1.0,0.0,36754.13,0.0
+12040,15682868.0,Nkemdilim,604.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,173912.45,0.0
+12041,15737542.0,Ch'in,579.0,France,Male,36.0,10.0,0.0,2.0,0.0,1.0,178373.93,0.0
+12042,15742369.0,Chiang,651.0,France,Female,46.0,6.0,0.0,1.0,1.0,0.0,1187.88,1.0
+12043,15659557.0,Chibugo,678.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,97640.2,0.0
+12044,15686936.0,Mancini,653.0,France,Female,47.0,7.0,0.0,2.0,1.0,0.0,148087.62,0.0
+12045,15732672.0,Uspenskaya,665.0,Spain,Female,47.0,4.0,0.0,1.0,1.0,0.0,54447.71,1.0
+12046,15576065.0,Chukwukadibia,732.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,149772.61,0.0
+12047,15616709.0,Fanucci,716.0,Germany,Male,33.0,2.0,105858.71,2.0,1.0,1.0,157891.86,0.0
+12048,15641582.0,Gadsdon,712.0,France,Female,51.0,3.0,0.0,1.0,1.0,0.0,59864.56,1.0
+12049,15720338.0,Efremov,714.0,Spain,Female,31.0,8.0,83348.89,1.0,0.0,1.0,65464.66,0.0
+12050,15605872.0,P'an,605.0,Germany,Male,30.0,5.0,114991.45,2.0,1.0,0.0,73857.94,0.0
+12051,15808621.0,P'eng,641.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,51919.04,0.0
+12052,15750772.0,Shih,724.0,Spain,Female,41.0,9.0,116536.43,1.0,1.0,1.0,175006.78,0.0
+12053,15609623.0,L?,664.0,France,Female,53.0,5.0,0.0,1.0,1.0,0.0,181870.24,1.0
+12054,15637354.0,Ch'eng,748.0,France,Male,35.0,7.0,0.0,2.0,0.0,1.0,107876.2,0.0
+12055,15631512.0,Ugochukwu,648.0,France,Male,38.0,6.0,113657.83,1.0,0.0,1.0,143747.68,1.0
+12056,15777904.0,Ch'ang,725.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,36342.33,0.0
+12057,15742834.0,Fanucci,719.0,France,Female,35.0,9.0,123365.85,1.0,1.0,0.0,142963.39,0.0
+12058,15642736.0,Chin,691.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,153036.97,0.0
+12059,15665064.0,Fyodorov,718.0,France,Female,46.0,9.0,0.0,1.0,0.0,0.0,103799.77,1.0
+12060,15640454.0,Ebelechukwu,642.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,147554.26,0.0
+12061,15773904.0,Chidiegwu,678.0,Spain,Female,66.0,2.0,0.0,3.0,1.0,1.0,55725.04,1.0
+12062,15631220.0,Zikoranachidimma,648.0,France,Male,27.0,5.0,144773.29,1.0,0.0,0.0,71830.1,0.0
+12063,15768474.0,Oguejiofor,652.0,France,Male,25.0,6.0,0.0,2.0,1.0,1.0,122584.29,0.0
+12064,15768346.0,Baresi,699.0,France,Male,35.0,8.0,122486.11,1.0,1.0,0.0,77965.67,0.0
+12065,15709199.0,Udegbunam,755.0,France,Male,54.0,5.0,134105.01,1.0,1.0,0.0,159654.0,1.0
+12066,15614643.0,Chizuoke,721.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,136934.91,0.0
+12067,15731553.0,Shih,586.0,France,Male,36.0,8.0,0.0,1.0,1.0,0.0,171575.54,0.0
+12068,15641582.0,Chukwufumnanya,646.0,France,Male,32.0,9.0,0.0,2.0,0.0,0.0,79577.38,0.0
+12069,15592028.0,Nwokike,677.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,113645.4,0.0
+12070,15644446.0,Chinweike,687.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,144493.07,0.0
+12071,15662955.0,Ts'ui,705.0,Germany,Female,25.0,2.0,121056.63,2.0,1.0,0.0,75937.47,0.0
+12072,15696852.0,Onyemachukwu,637.0,France,Male,30.0,7.0,0.0,1.0,1.0,1.0,145796.49,0.0
+12073,15610105.0,Echezonachukwu,675.0,Germany,Male,43.0,6.0,153535.43,3.0,0.0,1.0,116957.6,1.0
+12074,15638247.0,Ch'ien,695.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,126812.85,0.0
+12075,15689980.0,Iqbal,584.0,France,Male,50.0,3.0,0.0,2.0,1.0,0.0,84760.32,0.0
+12076,15790744.0,Frolov,590.0,France,Male,40.0,6.0,132623.6,1.0,1.0,0.0,186489.95,1.0
+12077,15639252.0,Biryukova,583.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,0.0,163558.66,0.0
+12078,15757952.0,Milne,693.0,France,Male,36.0,1.0,116574.79,1.0,1.0,1.0,179066.58,0.0
+12079,15635277.0,Li,585.0,France,Female,32.0,8.0,0.0,2.0,0.0,1.0,74593.84,0.0
+12080,15624993.0,Hs?,688.0,Germany,Female,47.0,3.0,108449.23,1.0,1.0,0.0,113553.42,1.0
+12081,15601116.0,Onwuamaeze,559.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,187987.01,0.0
+12082,15776051.0,Pisani,705.0,Germany,Male,25.0,9.0,124619.33,1.0,1.0,0.0,162683.06,1.0
+12083,15751774.0,Wright,706.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,149550.93,0.0
+12084,15698031.0,Chukwuemeka,581.0,Spain,Female,48.0,6.0,0.0,1.0,1.0,1.0,99792.82,1.0
+12085,15798895.0,Chukwumaobim,724.0,France,Male,48.0,1.0,106425.75,1.0,1.0,1.0,100816.29,0.0
+12086,15584540.0,Trevisani,725.0,France,Male,38.0,9.0,113599.42,1.0,1.0,1.0,177065.24,0.0
+12087,15780128.0,Tretiakova,741.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,147794.63,0.0
+12088,15691793.0,Maclean,498.0,France,Male,26.0,7.0,0.0,2.0,1.0,0.0,88827.67,0.0
+12089,15682600.0,Ibezimako,616.0,France,Female,43.0,6.0,0.0,2.0,1.0,0.0,85522.84,1.0
+12090,15779915.0,Mazzi,544.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,121866.87,0.0
+12091,15810660.0,Chiemezie,721.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,145553.4,0.0
+12092,15803479.0,Yermolayev,516.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,64872.33,0.0
+12093,15798885.0,Yobanna,751.0,France,Female,25.0,9.0,0.0,2.0,1.0,0.0,94738.54,0.0
+12094,15742369.0,Tung,676.0,France,Female,27.0,9.0,152303.8,1.0,1.0,0.0,96658.26,0.0
+12095,15694859.0,Manna,537.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,130132.54,0.0
+12096,15771543.0,Ch'eng,640.0,France,Male,43.0,1.0,0.0,1.0,0.0,1.0,139024.31,0.0
+12097,15747772.0,Piazza,642.0,Germany,Male,47.0,2.0,129743.75,1.0,1.0,1.0,61995.57,0.0
+12098,15724444.0,Nwachinemelu,731.0,Germany,Female,31.0,10.0,119825.75,2.0,0.0,1.0,134901.57,0.0
+12099,15721779.0,Teng,594.0,Germany,Female,30.0,5.0,110646.54,2.0,1.0,0.0,70899.27,0.0
+12100,15652999.0,Ts'ui,683.0,France,Female,31.0,6.0,129745.1,1.0,1.0,0.0,72924.56,1.0
+12101,15679810.0,Tien,704.0,Germany,Female,53.0,3.0,161637.75,1.0,1.0,0.0,163798.41,1.0
+12102,15596984.0,Onyekachi,537.0,Germany,Female,41.0,1.0,146720.98,1.0,1.0,0.0,160526.36,0.0
+12103,15777784.0,Kuo,732.0,France,Male,50.0,3.0,0.0,1.0,1.0,0.0,27239.28,1.0
+12104,15743828.0,Smith,641.0,France,Female,45.0,6.0,0.0,2.0,1.0,0.0,96832.82,1.0
+12105,15572631.0,Kaleski,633.0,Spain,Female,40.0,4.0,0.0,2.0,1.0,0.0,70563.9,0.0
+12106,15783501.0,Ch'ien,682.0,Germany,Female,51.0,3.0,127837.54,1.0,1.0,1.0,38772.45,1.0
+12107,15661734.0,Lombardo,619.0,Germany,Male,54.0,1.0,122265.24,1.0,1.0,0.0,157564.75,1.0
+12108,15591512.0,Iadanza,712.0,Germany,Female,41.0,4.0,116932.53,1.0,1.0,1.0,140745.91,0.0
+12109,15711750.0,Lettiere,681.0,Spain,Male,42.0,5.0,0.0,2.0,1.0,0.0,81299.17,0.0
+12110,15643487.0,Ch'in,578.0,Spain,Female,29.0,2.0,0.0,1.0,1.0,1.0,174032.0,0.0
+12111,15778955.0,Li,607.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,18082.06,0.0
+12112,15737506.0,Clements,748.0,France,Male,31.0,7.0,0.0,1.0,1.0,0.0,100720.02,0.0
+12113,15671351.0,Hsiao,559.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,142839.82,0.0
+12114,15630053.0,Hs?,739.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,181419.29,0.0
+12115,15668958.0,Tien,547.0,France,Male,50.0,8.0,0.0,2.0,1.0,1.0,94875.03,0.0
+12116,15682454.0,Pirozzi,554.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,0.0,147538.26,0.0
+12117,15655252.0,Maclean,667.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,94544.88,0.0
+12118,15629582.0,Zhirov,742.0,France,Female,41.0,10.0,0.0,3.0,1.0,0.0,150218.11,1.0
+12119,15753831.0,Folliero,714.0,Germany,Male,46.0,6.0,121525.33,1.0,0.0,1.0,166792.71,1.0
+12120,15778023.0,Onyekaozulu,724.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,51536.99,0.0
+12121,15773792.0,Tsai,555.0,France,Female,33.0,8.0,0.0,2.0,0.0,1.0,52367.29,1.0
+12122,15743818.0,Brown,633.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,166850.57,0.0
+12123,15770185.0,Fanucci,667.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,89566.64,0.0
+12124,15697567.0,K'ung,662.0,Spain,Female,35.0,1.0,0.0,2.0,1.0,1.0,127095.14,0.0
+12125,15684469.0,Ch'ien,598.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,95625.03,0.0
+12126,15782404.0,Chu,593.0,Germany,Male,29.0,1.0,146053.66,2.0,0.0,1.0,93717.46,0.0
+12127,15671789.0,Manna,707.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,143617.43,0.0
+12128,15663438.0,Yefimova,712.0,Spain,Female,29.0,8.0,0.0,1.0,1.0,1.0,89566.64,0.0
+12129,15606861.0,Chiemenam,589.0,France,Male,46.0,6.0,132084.66,1.0,1.0,0.0,136079.64,0.0
+12130,15771742.0,Cone,708.0,France,Female,58.0,9.0,0.0,2.0,1.0,1.0,133030.96,0.0
+12131,15651581.0,Udegbunam,591.0,Spain,Male,36.0,6.0,128966.13,1.0,1.0,0.0,187921.3,0.0
+12132,15813095.0,Hs?,730.0,Spain,Female,38.0,1.0,0.0,1.0,0.0,0.0,164864.66,0.0
+12133,15718572.0,Ch'ien,850.0,France,Male,33.0,10.0,0.0,2.0,1.0,0.0,66463.62,0.0
+12134,15791342.0,Ch'iu,732.0,France,Male,40.0,7.0,0.0,1.0,1.0,0.0,121485.1,1.0
+12135,15773520.0,Hsiung,708.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,183490.99,0.0
+12136,15782360.0,Yobachi,710.0,France,Female,41.0,9.0,103544.0,1.0,1.0,1.0,175893.53,0.0
+12137,15635939.0,Chukwujamuike,615.0,France,Male,41.0,6.0,187841.99,1.0,1.0,1.0,164996.68,0.0
+12138,15587562.0,Nnamdi,677.0,Germany,Female,37.0,4.0,138089.97,2.0,1.0,1.0,123587.85,0.0
+12139,15763747.0,Chukwufumnanya,615.0,Spain,Male,32.0,7.0,133776.65,1.0,1.0,0.0,160546.58,0.0
+12140,15697034.0,Onyemauchechukwu,684.0,Germany,Female,38.0,1.0,113599.82,1.0,1.0,1.0,177624.01,0.0
+12141,15772601.0,Onwubiko,607.0,France,Male,38.0,6.0,0.0,2.0,0.0,0.0,95611.47,0.0
+12142,15753337.0,Nkemjika,828.0,Germany,Female,46.0,1.0,107534.79,1.0,1.0,0.0,74898.8,1.0
+12143,15647800.0,H?,613.0,Germany,Male,31.0,1.0,122451.46,1.0,1.0,1.0,176925.69,0.0
+12144,15781598.0,Onuchukwu,592.0,Germany,Female,33.0,1.0,153313.66,2.0,1.0,0.0,40257.79,0.0
+12145,15797381.0,Chiemenam,594.0,France,Male,30.0,3.0,129697.69,1.0,1.0,1.0,187910.6,0.0
+12146,15791747.0,Galkin,794.0,Spain,Female,60.0,3.0,88594.93,1.0,0.0,1.0,173365.89,0.0
+12147,15617982.0,Nwabugwu,681.0,Spain,Male,38.0,9.0,0.0,2.0,0.0,1.0,181605.85,0.0
+12148,15678058.0,Chu,699.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,0.0,177783.04,0.0
+12149,15658972.0,Lo,599.0,France,Female,49.0,7.0,0.0,1.0,1.0,0.0,162668.33,1.0
+12150,15753840.0,Ch'iu,726.0,France,Male,25.0,4.0,0.0,2.0,1.0,1.0,141364.62,0.0
+12151,15654878.0,Yobanna,810.0,France,Male,58.0,3.0,0.0,1.0,1.0,0.0,151822.66,1.0
+12152,15804721.0,Lu,611.0,France,Male,24.0,7.0,0.0,2.0,0.0,0.0,61581.2,0.0
+12153,15794468.0,Chien,571.0,Germany,Male,50.0,10.0,139879.41,2.0,0.0,0.0,57975.8,0.0
+12154,15744398.0,Ch'ang,577.0,Spain,Female,42.0,9.0,108541.04,1.0,1.0,0.0,147794.63,1.0
+12155,15636478.0,Sagese,730.0,Germany,Female,40.0,6.0,116497.31,1.0,1.0,1.0,173495.77,1.0
+12156,15568240.0,Hsia,652.0,Spain,Female,25.0,7.0,0.0,2.0,1.0,1.0,60954.45,0.0
+12157,15599329.0,Trevisani,704.0,France,Female,34.0,0.0,0.0,2.0,1.0,0.0,85547.33,0.0
+12158,15683471.0,Chiu,713.0,France,Female,54.0,7.0,103818.41,1.0,1.0,0.0,126702.41,1.0
+12159,15593365.0,T'ang,679.0,Germany,Male,37.0,1.0,128960.3,1.0,0.0,0.0,101371.72,0.0
+12160,15593555.0,Hsiao,582.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,1.0,51387.1,0.0
+12161,15600110.0,Onyinyechukwuka,578.0,Germany,Female,31.0,5.0,154983.98,2.0,0.0,1.0,97939.4,0.0
+12162,15615477.0,K'ung,593.0,Germany,Female,41.0,6.0,77526.66,2.0,1.0,1.0,102628.98,0.0
+12163,15589437.0,Nwokeocha,739.0,France,Female,28.0,4.0,0.0,2.0,1.0,1.0,126555.74,0.0
+12164,15594812.0,Hsueh,578.0,France,Female,46.0,2.0,128289.6,1.0,1.0,0.0,71167.17,0.0
+12165,15742404.0,Ojiofor,624.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,84871.99,0.0
+12166,15665088.0,Hsiao,456.0,France,Female,36.0,7.0,0.0,2.0,0.0,0.0,130664.79,0.0
+12167,15574072.0,Hsueh,646.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,96670.2,0.0
+12168,15592745.0,Piazza,591.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,88890.75,0.0
+12169,15582335.0,Liao,648.0,Spain,Female,41.0,9.0,0.0,2.0,1.0,0.0,71323.23,0.0
+12170,15674551.0,Tung,719.0,Germany,Male,32.0,2.0,163894.35,1.0,1.0,1.0,163714.82,0.0
+12171,15586029.0,Tseng,741.0,France,Male,48.0,5.0,0.0,1.0,0.0,0.0,127875.1,1.0
+12172,15569394.0,Chinweike,706.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,153698.65,0.0
+12173,15600575.0,Chuang,615.0,Spain,Female,51.0,6.0,76914.21,1.0,1.0,1.0,132649.85,0.0
+12174,15587597.0,Chinweuba,589.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,93901.61,0.0
+12175,15756026.0,T'ien,633.0,Spain,Female,50.0,8.0,0.0,3.0,0.0,0.0,9149.96,1.0
+12176,15585367.0,Chiang,570.0,Spain,Male,44.0,5.0,81733.74,1.0,1.0,0.0,27330.59,0.0
+12177,15737571.0,Hsueh,671.0,France,Female,31.0,2.0,0.0,2.0,0.0,1.0,167984.72,0.0
+12178,15645896.0,Dennys,706.0,Germany,Male,30.0,2.0,97473.61,2.0,0.0,1.0,156906.15,0.0
+12179,15619932.0,Chukwualuka,585.0,Germany,Male,36.0,8.0,77992.97,2.0,1.0,1.0,85746.52,0.0
+12180,15589475.0,Tung,678.0,France,Male,28.0,10.0,0.0,2.0,1.0,0.0,163550.58,0.0
+12181,15778861.0,Eluemuno,683.0,Germany,Female,35.0,7.0,132571.67,2.0,1.0,1.0,181224.56,0.0
+12182,15743490.0,Chukwualuka,464.0,Germany,Male,57.0,1.0,151958.19,1.0,1.0,0.0,86797.41,1.0
+12183,15779985.0,H?,616.0,Germany,Male,47.0,10.0,108867.0,1.0,1.0,0.0,78416.14,1.0
+12184,15631053.0,Ugochukwu,568.0,Germany,Male,29.0,8.0,93903.68,2.0,1.0,1.0,163774.01,0.0
+12185,15635502.0,Ritchie,634.0,France,Male,30.0,7.0,0.0,1.0,1.0,0.0,185561.89,0.0
+12186,15799494.0,Nkemdirim,505.0,Germany,Male,28.0,6.0,113988.35,2.0,1.0,1.0,124218.82,0.0
+12187,15600710.0,Iloerika,529.0,France,Female,43.0,9.0,129895.57,1.0,1.0,1.0,62390.59,1.0
+12188,15593694.0,Hsia,697.0,France,Female,39.0,5.0,176666.62,3.0,0.0,0.0,94670.77,1.0
+12189,15684596.0,Chidimma,656.0,Spain,Female,27.0,6.0,0.0,2.0,1.0,1.0,60972.84,0.0
+12190,15576774.0,Maclean,732.0,Spain,Male,40.0,2.0,0.0,2.0,0.0,0.0,163317.5,0.0
+12191,15809462.0,Ch'ien,584.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,72849.29,0.0
+12192,15783097.0,Ch'ang,628.0,France,Female,47.0,3.0,138901.61,1.0,1.0,0.0,111421.77,1.0
+12193,15652914.0,Lo Duca,624.0,France,Female,54.0,8.0,97585.32,2.0,0.0,1.0,142956.48,1.0
+12194,15814816.0,Lassetter,714.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,171364.18,0.0
+12195,15594720.0,Lucchese,699.0,Germany,Female,33.0,10.0,122384.22,2.0,1.0,0.0,159463.8,0.0
+12196,15635116.0,Kao,641.0,France,Female,61.0,6.0,0.0,1.0,0.0,1.0,91943.94,1.0
+12197,15746076.0,Pipes,583.0,France,Male,24.0,5.0,0.0,2.0,0.0,0.0,44506.09,0.0
+12198,15771942.0,Hao,705.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,83983.54,0.0
+12199,15605835.0,Udobata,659.0,Germany,Male,43.0,9.0,116360.07,1.0,1.0,1.0,76637.38,1.0
+12200,15766896.0,Hs?,564.0,France,Male,29.0,0.0,79450.09,1.0,1.0,0.0,164081.42,0.0
+12201,15806964.0,Hao,708.0,Spain,Male,47.0,6.0,0.0,1.0,1.0,0.0,176883.42,1.0
+12202,15772030.0,Mazzanti,703.0,France,Male,35.0,7.0,126473.33,1.0,1.0,1.0,47764.99,0.0
+12203,15790810.0,Nnachetam,771.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,160979.66,0.0
+12204,15729083.0,Chukwufumnanya,817.0,Germany,Male,48.0,7.0,150840.03,2.0,1.0,1.0,78616.92,0.0
+12205,15688072.0,Chidumaga,682.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,1.0,72924.56,0.0
+12206,15771086.0,Fanucci,636.0,Germany,Female,58.0,3.0,121161.57,2.0,0.0,1.0,90189.72,0.0
+12207,15682778.0,Doyne,639.0,France,Male,58.0,9.0,152309.41,1.0,0.0,1.0,183868.01,0.0
+12208,15656829.0,Ritchie,684.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,111180.3,0.0
+12209,15790455.0,Shih,771.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,1.0,94778.12,0.0
+12210,15772030.0,Lo,705.0,France,Male,52.0,7.0,126728.77,1.0,1.0,0.0,139947.17,0.0
+12211,15675176.0,Okechukwu,655.0,Spain,Male,32.0,3.0,170426.13,1.0,1.0,0.0,187987.01,0.0
+12212,15656829.0,Cattaneo,652.0,France,Male,34.0,3.0,132412.13,1.0,0.0,0.0,125977.25,0.0
+12213,15668893.0,Macleod,778.0,Spain,Male,51.0,10.0,115576.44,1.0,0.0,0.0,83419.29,1.0
+12214,15743760.0,Ch'iu,724.0,France,Male,47.0,1.0,0.0,2.0,0.0,1.0,136540.09,0.0
+12215,15784958.0,Hsu,599.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,91347.76,0.0
+12216,15652955.0,Kao,678.0,France,Male,35.0,6.0,0.0,1.0,1.0,1.0,152996.89,0.0
+12217,15592583.0,Ts'ao,681.0,Spain,Female,50.0,1.0,0.0,2.0,1.0,0.0,161378.02,1.0
+12218,15578098.0,Trevisani,754.0,Germany,Male,29.0,4.0,80790.74,2.0,1.0,1.0,112331.19,0.0
+12219,15652674.0,Chiu,607.0,Germany,Female,33.0,1.0,93616.45,1.0,1.0,0.0,187980.21,0.0
+12220,15795895.0,Fanucci,704.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,47799.15,0.0
+12221,15692406.0,Martin,644.0,Germany,Female,36.0,5.0,153344.72,2.0,1.0,0.0,56784.11,0.0
+12222,15734588.0,Tsou,653.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,94774.12,0.0
+12223,15805627.0,Pirozzi,627.0,Spain,Female,42.0,4.0,0.0,1.0,1.0,0.0,147963.07,0.0
+12224,15596074.0,Lo Duca,585.0,Germany,Male,40.0,3.0,115532.52,2.0,1.0,1.0,93901.61,1.0
+12225,15770174.0,Frolov,692.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,172531.23,0.0
+12226,15711446.0,Chukwualuka,718.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,106977.8,0.0
+12227,15682454.0,Maclean,654.0,France,Female,38.0,9.0,0.0,2.0,1.0,0.0,944.41,0.0
+12228,15763693.0,Chijindum,665.0,Germany,Female,41.0,0.0,111065.93,1.0,1.0,0.0,132450.19,1.0
+12229,15758813.0,Chukwudi,757.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,152845.99,0.0
+12230,15602844.0,Ejimofor,681.0,France,Male,71.0,8.0,0.0,2.0,1.0,1.0,75825.43,0.0
+12231,15687094.0,Iweobiegbunam,526.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,149853.89,0.0
+12232,15738291.0,Brown,678.0,Spain,Male,40.0,6.0,119370.11,1.0,1.0,0.0,70810.6,0.0
+12233,15721719.0,Calabresi,708.0,France,Male,37.0,9.0,68423.89,1.0,0.0,0.0,124592.84,0.0
+12234,15795738.0,Hsueh,590.0,France,Male,33.0,9.0,144974.55,1.0,0.0,0.0,120853.83,0.0
+12235,15706593.0,Akabueze,639.0,Spain,Female,39.0,8.0,0.0,2.0,1.0,1.0,156774.75,0.0
+12236,15642099.0,Trevisani,707.0,France,Male,21.0,3.0,0.0,2.0,1.0,1.0,148564.76,0.0
+12237,15572801.0,Hs?eh,724.0,Spain,Male,49.0,4.0,0.0,1.0,1.0,1.0,47318.75,1.0
+12238,15681554.0,Pirozzi,677.0,France,Female,30.0,4.0,0.0,1.0,1.0,1.0,136887.58,0.0
+12239,15702641.0,Ch'iu,743.0,France,Female,22.0,3.0,0.0,2.0,1.0,0.0,72678.75,0.0
+12240,15622454.0,Chukwuhaenye,659.0,France,Male,39.0,4.0,0.0,3.0,0.0,0.0,142377.69,1.0
+12241,15616172.0,P'eng,601.0,Spain,Female,43.0,6.0,114631.23,1.0,1.0,1.0,111687.57,0.0
+12242,15634310.0,Nwabugwu,616.0,France,Female,66.0,6.0,0.0,1.0,1.0,1.0,162699.7,0.0
+12243,15584580.0,Hao,711.0,Germany,Male,39.0,7.0,81153.82,2.0,0.0,1.0,64448.36,0.0
+12244,15737051.0,Lu,556.0,Germany,Male,25.0,9.0,123942.0,1.0,1.0,0.0,97985.4,0.0
+12245,15656322.0,Lo Duca,707.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,160176.47,0.0
+12246,15795934.0,Okwudilichukwu,597.0,Germany,Male,28.0,0.0,137843.8,1.0,1.0,0.0,131736.23,1.0
+12247,15748352.0,Yobanna,638.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,94767.77,0.0
+12248,15590820.0,Trevisani,538.0,France,Female,27.0,7.0,0.0,1.0,1.0,1.0,133086.45,0.0
+12249,15643359.0,Chidimma,531.0,Germany,Male,47.0,4.0,138857.59,1.0,1.0,0.0,96862.56,1.0
+12250,15682355.0,Chikezie,755.0,France,Male,30.0,2.0,0.0,1.0,1.0,0.0,86634.3,0.0
+12251,15797760.0,Ritchie,689.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,119174.88,0.0
+12252,15756850.0,Ch'ien,659.0,France,Female,33.0,9.0,0.0,1.0,0.0,1.0,149892.79,0.0
+12253,15643865.0,Fang,844.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,79734.23,0.0
+12254,15763612.0,Nnaife,611.0,France,Female,41.0,8.0,0.0,1.0,1.0,1.0,83084.33,0.0
+12255,15793046.0,Fyodorova,682.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,72876.43,0.0
+12256,15758031.0,Hsieh,587.0,Spain,Female,41.0,2.0,0.0,1.0,1.0,0.0,182855.42,0.0
+12257,15604818.0,L?,711.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,0.0,174094.24,0.0
+12258,15644878.0,Fan,602.0,France,Male,32.0,6.0,0.0,2.0,0.0,1.0,81622.49,0.0
+12259,15713774.0,Onyemauchechukwu,568.0,France,Male,44.0,2.0,0.0,1.0,1.0,0.0,142839.64,1.0
+12260,15598097.0,Burt,681.0,France,Female,40.0,4.0,0.0,1.0,1.0,1.0,147074.67,1.0
+12261,15723488.0,Tretiakova,651.0,France,Male,34.0,3.0,0.0,2.0,0.0,1.0,16250.61,0.0
+12262,15685839.0,Chukwubuikem,727.0,Spain,Male,30.0,5.0,0.0,2.0,1.0,1.0,153538.51,0.0
+12263,15768474.0,Y?,635.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,82867.55,0.0
+12264,15592229.0,Calabrese,585.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,149043.78,0.0
+12265,15646632.0,Onio,645.0,Germany,Male,46.0,6.0,113348.14,1.0,1.0,1.0,50299.14,1.0
+12266,15673599.0,Y?an,696.0,Germany,Male,37.0,9.0,152926.2,1.0,1.0,0.0,170819.01,0.0
+12267,15769980.0,Ts'ai,686.0,Spain,Female,33.0,2.0,0.0,2.0,0.0,0.0,136435.75,0.0
+12268,15607827.0,Udokamma,581.0,France,Female,35.0,2.0,0.0,2.0,1.0,1.0,116973.48,0.0
+12269,15756847.0,Chukwuebuka,684.0,France,Male,39.0,7.0,0.0,1.0,1.0,0.0,161435.02,0.0
+12270,15590820.0,T'ien,708.0,Germany,Male,28.0,0.0,126645.13,2.0,1.0,0.0,47416.44,0.0
+12271,15813659.0,Pirozzi,624.0,France,Female,39.0,9.0,133866.23,1.0,1.0,1.0,56780.97,0.0
+12272,15781598.0,Chiemela,730.0,France,Male,45.0,9.0,0.0,2.0,1.0,1.0,131807.15,0.0
+12273,15692761.0,Nwachinemelu,691.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,89983.02,0.0
+12274,15794468.0,Chiu,697.0,France,Male,46.0,3.0,0.0,2.0,1.0,1.0,143617.83,0.0
+12275,15645353.0,Chu,628.0,Germany,Male,22.0,5.0,139914.27,1.0,1.0,0.0,79927.99,0.0
+12276,15722533.0,Ts'ai,554.0,Spain,Male,33.0,5.0,152264.81,1.0,1.0,0.0,72814.31,0.0
+12277,15756926.0,McClinton,665.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,155711.64,0.0
+12278,15591279.0,O'Sullivan,644.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,103914.4,0.0
+12279,15703437.0,Onio,746.0,France,Male,32.0,3.0,0.0,1.0,1.0,0.0,122998.28,0.0
+12280,15584928.0,Walker,641.0,Germany,Female,33.0,2.0,116968.91,1.0,1.0,1.0,111249.62,0.0
+12281,15566563.0,Calabresi,479.0,Germany,Female,42.0,2.0,102721.49,3.0,1.0,0.0,122547.58,1.0
+12282,15806822.0,Chiemenam,681.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,0.0,85547.33,0.0
+12283,15661409.0,T'an,676.0,France,Male,36.0,1.0,0.0,1.0,1.0,1.0,108008.65,0.0
+12284,15707559.0,Pinto,559.0,France,Male,51.0,8.0,0.0,2.0,1.0,0.0,174770.14,1.0
+12285,15725698.0,Mazzanti,598.0,France,Female,29.0,7.0,0.0,1.0,1.0,0.0,153917.41,0.0
+12286,15691792.0,Chiu,642.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,168418.35,0.0
+12287,15664539.0,Lo Duca,648.0,Germany,Male,31.0,2.0,142263.45,2.0,1.0,1.0,123415.05,0.0
+12288,15683339.0,Chidiegwu,523.0,Spain,Female,42.0,1.0,0.0,1.0,1.0,1.0,146723.72,0.0
+12289,15631693.0,Okwudilichukwu,497.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,97767.63,0.0
+12290,15774586.0,Ibeamaka,461.0,France,Female,30.0,3.0,121172.97,1.0,1.0,1.0,137916.49,0.0
+12291,15573319.0,Shih,758.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,105732.9,0.0
+12292,15763415.0,Fanucci,697.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,64420.5,0.0
+12293,15618446.0,Trevisani,711.0,France,Male,37.0,2.0,112414.81,1.0,1.0,1.0,105525.16,0.0
+12294,15672432.0,Liao,585.0,France,Female,42.0,5.0,0.0,3.0,1.0,0.0,165580.93,1.0
+12295,15664555.0,Eluemuno,554.0,France,Male,36.0,4.0,0.0,2.0,0.0,1.0,131736.23,0.0
+12296,15732967.0,Onyemauchechukwu,604.0,Germany,Male,32.0,2.0,118627.7,2.0,1.0,1.0,163714.92,0.0
+12297,15672216.0,Ch'ang,479.0,Germany,Female,40.0,8.0,111363.1,1.0,1.0,0.0,83499.89,0.0
+12298,15659495.0,Maclean,591.0,France,Male,38.0,8.0,121730.49,1.0,1.0,1.0,87412.24,0.0
+12299,15615140.0,Tsao,724.0,France,Male,42.0,3.0,0.0,1.0,0.0,0.0,62634.94,0.0
+12300,15771580.0,Chukwuemeka,850.0,Germany,Female,41.0,8.0,72535.45,2.0,1.0,0.0,27341.63,0.0
+12301,15682600.0,Ting,684.0,France,Female,32.0,10.0,0.0,2.0,1.0,1.0,50404.72,0.0
+12302,15739850.0,Chienezie,590.0,Spain,Male,32.0,5.0,0.0,2.0,0.0,1.0,53584.03,0.0
+12303,15787699.0,O'Donnell,562.0,Germany,Male,30.0,2.0,103515.39,2.0,1.0,1.0,88698.83,0.0
+12304,15690175.0,Nwachinemelu,611.0,France,Female,57.0,8.0,113563.46,1.0,1.0,1.0,145558.42,0.0
+12305,15762169.0,Martin,590.0,France,Female,29.0,7.0,0.0,1.0,1.0,0.0,131312.56,1.0
+12306,15612109.0,Ch'ang,711.0,Germany,Male,32.0,7.0,102773.2,1.0,0.0,1.0,157072.91,0.0
+12307,15774738.0,Hsu,663.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,167036.94,0.0
+12308,15742272.0,Ch'iu,582.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,157959.02,0.0
+12309,15640268.0,Chidiegwu,585.0,Germany,Female,36.0,7.0,135842.89,1.0,1.0,1.0,82452.06,0.0
+12310,15786463.0,Ch'ang,619.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,132770.11,0.0
+12311,15730044.0,Onwubiko,679.0,Spain,Male,72.0,7.0,0.0,1.0,1.0,1.0,162428.05,0.0
+12312,15652069.0,K'ung,590.0,France,Female,40.0,8.0,143582.96,1.0,1.0,0.0,4175.68,0.0
+12313,15656683.0,H?,578.0,France,Female,37.0,1.0,0.0,2.0,0.0,0.0,164061.4,0.0
+12314,15726153.0,Onyemere,584.0,France,Male,33.0,3.0,0.0,2.0,0.0,0.0,38550.4,0.0
+12315,15704053.0,Ch'eng,592.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,49418.87,0.0
+12316,15624781.0,Chiu,573.0,France,Male,27.0,1.0,147069.78,1.0,0.0,1.0,161083.73,0.0
+12317,15647838.0,Nnaife,633.0,France,Male,29.0,2.0,0.0,2.0,0.0,1.0,153036.96,0.0
+12318,15668309.0,Uspenskaya,542.0,Germany,Female,47.0,2.0,111099.79,1.0,1.0,1.0,143913.16,1.0
+12319,15693637.0,Chukwukadibia,681.0,Spain,Female,40.0,1.0,0.0,1.0,0.0,0.0,102280.29,1.0
+12320,15793425.0,Walker,678.0,Germany,Male,33.0,6.0,76665.71,1.0,1.0,0.0,129892.93,0.0
+12321,15804052.0,Chukwuemeka,658.0,Germany,Male,39.0,3.0,141859.12,1.0,1.0,0.0,82351.06,1.0
+12322,15747647.0,Lai,757.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,86837.32,0.0
+12323,15732437.0,Chinwenma,699.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,129569.92,0.0
+12324,15705890.0,Y?an,549.0,Spain,Female,38.0,9.0,0.0,1.0,0.0,1.0,162599.51,0.0
+12325,15746203.0,Milanesi,554.0,France,Male,33.0,8.0,82228.67,1.0,0.0,1.0,161407.48,0.0
+12326,15627484.0,Onwumelu,732.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,183568.55,0.0
+12327,15641877.0,Ngozichukwuka,706.0,Germany,Male,37.0,1.0,80790.74,2.0,1.0,0.0,27012.0,0.0
+12328,15650442.0,Sazonova,564.0,Spain,Female,46.0,5.0,0.0,1.0,1.0,0.0,153917.54,1.0
+12329,15685920.0,Maclean,641.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,102999.13,0.0
+12330,15769596.0,Hs?,651.0,Germany,Female,35.0,4.0,99263.09,1.0,0.0,1.0,159069.37,1.0
+12331,15762762.0,Maclean,687.0,France,Female,45.0,7.0,0.0,1.0,1.0,1.0,159840.58,1.0
+12332,15736851.0,Hsia,679.0,France,Male,34.0,2.0,112708.2,1.0,1.0,0.0,68815.03,0.0
+12333,15656683.0,Ch'ang,747.0,France,Female,34.0,2.0,124923.35,1.0,1.0,1.0,176763.27,0.0
+12334,15680683.0,P'eng,583.0,France,Female,37.0,2.0,88963.31,1.0,1.0,0.0,115183.92,0.0
+12335,15791053.0,Chidiebere,590.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,74850.84,0.0
+12336,15615896.0,Nwoye,652.0,France,Male,35.0,1.0,0.0,2.0,0.0,1.0,51703.09,0.0
+12337,15652955.0,L?,850.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,171070.54,0.0
+12338,15632576.0,Onwuatuegwu,635.0,France,Female,39.0,3.0,0.0,1.0,1.0,1.0,77965.44,0.0
+12339,15669805.0,Chidiebele,730.0,Germany,Female,44.0,2.0,148962.46,2.0,1.0,0.0,173495.77,0.0
+12340,15661528.0,Onyemachukwu,581.0,France,Male,45.0,7.0,0.0,1.0,1.0,0.0,151169.85,1.0
+12341,15642991.0,Uspenskaya,612.0,Spain,Female,56.0,9.0,0.0,1.0,1.0,0.0,180551.76,1.0
+12342,15682600.0,Genovesi,634.0,Spain,Female,48.0,1.0,0.0,1.0,1.0,1.0,28228.35,1.0
+12343,15748481.0,Nnamutaezinwa,585.0,France,Male,33.0,8.0,146781.24,1.0,1.0,1.0,115818.51,0.0
+12344,15769451.0,Macleod,642.0,Germany,Male,37.0,1.0,141789.15,2.0,1.0,1.0,181429.87,0.0
+12345,15696812.0,Nucci,703.0,France,Female,51.0,9.0,0.0,2.0,1.0,1.0,80592.8,1.0
+12346,15624391.0,Hsia,609.0,France,Male,28.0,8.0,0.0,2.0,0.0,1.0,122949.71,0.0
+12347,15661412.0,Okonkwo,721.0,Germany,Female,38.0,3.0,183572.84,1.0,0.0,0.0,64477.25,1.0
+12348,15650442.0,Onyemauchechukwu,656.0,Germany,Male,41.0,4.0,104568.41,2.0,1.0,0.0,105599.65,0.0
+12349,15670374.0,Ugoji,494.0,France,Female,31.0,6.0,0.0,2.0,1.0,1.0,147093.84,0.0
+12350,15744327.0,Genovese,711.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,176984.34,0.0
+12351,15642725.0,Tsao,593.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,0.0,60552.44,0.0
+12352,15800773.0,Lombardi,775.0,France,Female,35.0,2.0,142550.52,1.0,1.0,0.0,165626.6,0.0
+12353,15732299.0,Ts'ui,640.0,Germany,Female,27.0,9.0,104678.62,1.0,1.0,1.0,137028.51,0.0
+12354,15798895.0,Ch'in,628.0,France,Male,36.0,4.0,160625.17,1.0,1.0,1.0,53420.43,0.0
+12355,15586029.0,Mancini,542.0,France,Male,35.0,1.0,0.0,2.0,0.0,0.0,122449.15,0.0
+12356,15595588.0,Chukwudi,738.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,161388.22,0.0
+12357,15702298.0,Onwuamaeze,758.0,France,Male,30.0,7.0,0.0,1.0,1.0,0.0,92277.47,0.0
+12358,15759035.0,Ibezimako,749.0,France,Female,49.0,6.0,0.0,2.0,1.0,0.0,65612.12,0.0
+12359,15780359.0,Nwebube,583.0,Spain,Male,57.0,9.0,0.0,1.0,1.0,0.0,158071.69,1.0
+12360,15728012.0,Chukwuemeka,561.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,84424.57,0.0
+12361,15785042.0,Liao,686.0,Germany,Male,38.0,9.0,123971.51,2.0,0.0,1.0,168816.73,0.0
+12362,15789974.0,Trevisani,779.0,Germany,Male,46.0,4.0,103421.91,1.0,0.0,0.0,86797.92,1.0
+12363,15678944.0,L?,615.0,France,Male,33.0,9.0,127070.73,1.0,0.0,0.0,78854.57,0.0
+12364,15632345.0,Walker,605.0,Germany,Male,22.0,4.0,143680.47,1.0,1.0,1.0,171706.66,1.0
+12365,15740147.0,Chukwujamuike,597.0,France,Male,34.0,3.0,0.0,1.0,1.0,1.0,135034.43,0.0
+12366,15808930.0,Ch'ien,699.0,France,Female,26.0,4.0,0.0,2.0,1.0,1.0,86950.72,0.0
+12367,15641675.0,Ndubueze,556.0,Germany,Female,35.0,7.0,98765.84,1.0,1.0,1.0,161653.5,0.0
+12368,15745201.0,Pagnotto,562.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,136329.96,0.0
+12369,15594917.0,Hsing,710.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,93959.96,0.0
+12370,15583076.0,Chimaobim,643.0,Spain,Male,49.0,4.0,125739.26,1.0,0.0,0.0,161241.65,0.0
+12371,15576680.0,Logue,775.0,France,Male,28.0,7.0,0.0,1.0,1.0,0.0,166650.32,0.0
+12372,15807889.0,Davison,579.0,France,Male,47.0,4.0,0.0,1.0,1.0,0.0,116971.05,1.0
+12373,15689031.0,Yermolayev,628.0,France,Female,40.0,9.0,0.0,2.0,1.0,1.0,151733.43,0.0
+12374,15586674.0,Ts'ai,582.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,0.0,126796.69,0.0
+12375,15673529.0,Lai,639.0,France,Male,34.0,1.0,0.0,2.0,1.0,1.0,131302.74,0.0
+12376,15784744.0,Nkemakolam,684.0,Germany,Male,31.0,3.0,108432.88,2.0,1.0,0.0,126082.18,0.0
+12377,15780572.0,P'eng,648.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,185561.89,0.0
+12378,15784597.0,Chu,526.0,France,Male,40.0,5.0,0.0,2.0,0.0,0.0,173632.92,0.0
+12379,15618581.0,Onyemere,601.0,France,Female,35.0,6.0,125860.2,1.0,1.0,1.0,152845.99,0.0
+12380,15721469.0,Ugochukwu,552.0,Germany,Female,52.0,1.0,100471.34,1.0,1.0,1.0,58854.88,1.0
+12381,15669011.0,Fang,765.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,76515.79,0.0
+12382,15784923.0,Chin,635.0,France,Male,40.0,7.0,81804.64,1.0,1.0,1.0,171096.2,0.0
+12383,15681327.0,Onyinyechukwuka,691.0,France,Male,35.0,0.0,138577.34,1.0,0.0,1.0,151677.31,0.0
+12384,15624453.0,De Salis,695.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,1.0,151903.6,0.0
+12385,15606901.0,Smith,684.0,Germany,Male,46.0,4.0,102773.15,4.0,1.0,0.0,183566.85,1.0
+12386,15579548.0,Lo Duca,676.0,France,Female,55.0,8.0,0.0,1.0,0.0,0.0,180693.61,1.0
+12387,15666032.0,Chia,635.0,Germany,Male,45.0,7.0,116498.22,1.0,1.0,0.0,140937.91,0.0
+12388,15626452.0,Maclean,652.0,France,Female,42.0,4.0,0.0,1.0,1.0,0.0,103901.28,1.0
+12389,15800811.0,Hao,673.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,153810.41,0.0
+12390,15665088.0,Hsia,572.0,France,Male,42.0,1.0,118899.45,1.0,1.0,0.0,70613.52,0.0
+12391,15788483.0,Watson,757.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,6761.84,0.0
+12392,15763612.0,Nwachinemelu,733.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,0.0,127528.79,0.0
+12393,15663885.0,Pisani,802.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,74042.0,0.0
+12394,15616367.0,Chinweike,710.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,174825.57,0.0
+12395,15641490.0,Macleod,572.0,France,Male,19.0,7.0,0.0,2.0,1.0,0.0,86767.48,0.0
+12396,15598493.0,Trevisano,850.0,Spain,Male,50.0,4.0,98800.0,1.0,1.0,1.0,152479.64,0.0
+12397,15583499.0,Onwuka,579.0,France,Male,39.0,6.0,0.0,2.0,0.0,0.0,72950.14,0.0
+12398,15809837.0,Cattaneo,756.0,Germany,Male,34.0,6.0,132670.53,2.0,0.0,1.0,155722.69,0.0
+12399,15592736.0,T'ien,732.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,27011.8,0.0
+12400,15778838.0,Chimezie,633.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,141796.39,0.0
+12401,15739292.0,Boulger,711.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,116951.1,0.0
+12402,15593365.0,Chidimma,669.0,France,Male,41.0,1.0,0.0,2.0,1.0,0.0,103713.93,0.0
+12403,15652400.0,Goliwe,544.0,Germany,Female,31.0,5.0,115518.07,1.0,1.0,0.0,171482.56,0.0
+12404,15780954.0,Okwudilichukwu,646.0,Spain,Female,35.0,9.0,0.0,1.0,1.0,0.0,173843.21,1.0
+12405,15695078.0,Trevisano,663.0,France,Male,33.0,0.0,143542.36,1.0,1.0,1.0,56518.0,0.0
+12406,15747043.0,Nwokezuike,628.0,Germany,Female,37.0,2.0,97612.12,2.0,1.0,1.0,86797.41,0.0
+12407,15729083.0,Hsia,590.0,France,Male,39.0,6.0,0.0,2.0,0.0,0.0,177524.19,0.0
+12408,15631359.0,Chien,733.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,178393.68,0.0
+12409,15783882.0,Onyeoruru,663.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,18094.96,0.0
+12410,15650751.0,Ch'ien,676.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,103921.43,0.0
+12411,15582910.0,Tien,614.0,France,Female,41.0,7.0,0.0,1.0,1.0,0.0,177028.0,0.0
+12412,15742442.0,Ko,571.0,France,Male,56.0,6.0,0.0,1.0,1.0,0.0,120892.96,1.0
+12413,15662397.0,Ugoji,652.0,Spain,Male,36.0,9.0,0.0,2.0,0.0,0.0,151481.86,0.0
+12414,15698522.0,Lucchese,589.0,Germany,Female,32.0,2.0,125537.52,2.0,0.0,0.0,122518.5,0.0
+12415,15584576.0,Chidubem,609.0,France,Female,51.0,4.0,0.0,1.0,1.0,0.0,187911.52,1.0
+12416,15644977.0,Molineux,597.0,Spain,Male,25.0,3.0,113811.75,1.0,1.0,1.0,1843.24,0.0
+12417,15745080.0,Lucciano,663.0,Germany,Female,34.0,6.0,105489.31,2.0,1.0,0.0,173498.45,0.0
+12418,15773520.0,Milani,610.0,France,Male,29.0,3.0,98400.61,1.0,1.0,1.0,186612.47,0.0
+12419,15576723.0,Ugoji,753.0,France,Male,36.0,3.0,148071.39,1.0,1.0,0.0,136050.44,0.0
+12420,15682928.0,Ni,479.0,Spain,Female,66.0,8.0,144470.77,1.0,1.0,1.0,166468.21,0.0
+12421,15670681.0,Nwachinemelu,679.0,France,Male,27.0,7.0,0.0,2.0,1.0,1.0,187905.12,0.0
+12422,15623210.0,Chiemenam,535.0,France,Female,42.0,7.0,0.0,1.0,1.0,0.0,172792.43,1.0
+12423,15781589.0,Chizuoke,682.0,France,Male,43.0,5.0,0.0,1.0,1.0,0.0,28266.9,1.0
+12424,15794868.0,T'ang,681.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,188426.12,0.0
+12425,15607986.0,Chu,496.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,165562.84,0.0
+12426,15608701.0,Teng,516.0,Spain,Male,19.0,5.0,0.0,2.0,1.0,0.0,96660.76,0.0
+12427,15712303.0,Hsing,613.0,Germany,Male,28.0,3.0,114543.27,2.0,0.0,0.0,156036.19,0.0
+12428,15766378.0,Liao,744.0,France,Male,41.0,9.0,110837.73,1.0,1.0,0.0,141210.5,0.0
+12429,15631639.0,Onyeoruru,686.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,11517.39,0.0
+12430,15617657.0,Ch'ien,755.0,France,Female,45.0,2.0,0.0,2.0,1.0,0.0,131644.31,1.0
+12431,15593499.0,Milanesi,561.0,France,Male,37.0,10.0,131834.76,1.0,1.0,1.0,143317.31,0.0
+12432,15666055.0,Chibueze,709.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,152273.59,0.0
+12433,15713346.0,Chibueze,651.0,France,Female,29.0,7.0,0.0,1.0,1.0,0.0,62830.97,0.0
+12434,15658980.0,Horsley,539.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,107876.2,0.0
+12435,15643048.0,Ts'ao,637.0,France,Male,45.0,6.0,0.0,1.0,1.0,0.0,96792.49,1.0
+12436,15762682.0,Ch'iu,599.0,France,Female,43.0,7.0,0.0,2.0,0.0,1.0,162960.05,0.0
+12437,15586931.0,Miller,850.0,Germany,Female,51.0,4.0,93560.55,1.0,1.0,0.0,120852.85,1.0
+12438,15719356.0,Chiemezie,678.0,France,Female,40.0,2.0,142987.46,1.0,1.0,1.0,77866.92,0.0
+12439,15725141.0,Nwora,678.0,France,Male,41.0,4.0,132578.92,1.0,1.0,0.0,141533.19,0.0
+12440,15776824.0,O'Loughlin,708.0,France,Female,38.0,3.0,0.0,2.0,0.0,0.0,140745.91,0.0
+12441,15690188.0,Aitken,724.0,Spain,Male,33.0,5.0,138162.41,1.0,1.0,0.0,105720.09,0.0
+12442,15698856.0,Manna,714.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,183868.01,0.0
+12443,15756680.0,Pagnotto,686.0,France,Male,32.0,0.0,0.0,2.0,1.0,0.0,121681.19,0.0
+12444,15736250.0,Hsueh,749.0,France,Male,51.0,8.0,0.0,2.0,1.0,0.0,160979.66,0.0
+12445,15812706.0,Shih,648.0,France,Female,43.0,1.0,0.0,2.0,1.0,1.0,52337.97,0.0
+12446,15748649.0,Ch'ien,663.0,Germany,Male,41.0,7.0,71460.67,1.0,1.0,0.0,162668.33,0.0
+12447,15703541.0,Manna,729.0,France,Female,35.0,9.0,0.0,1.0,1.0,0.0,48478.47,0.0
+12448,15655238.0,Ch'iu,591.0,France,Female,40.0,4.0,151702.72,1.0,1.0,1.0,126812.63,0.0
+12449,15794493.0,Tsao,653.0,France,Female,42.0,1.0,113781.95,1.0,1.0,0.0,93777.81,0.0
+12450,15576550.0,Olisaemeka,633.0,France,Female,34.0,10.0,0.0,1.0,1.0,1.0,78854.57,0.0
+12451,15602841.0,Ku,724.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,107977.53,1.0
+12452,15631838.0,Ch'in,683.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,0.0,94739.2,0.0
+12453,15709941.0,Ch'iu,642.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,56767.67,0.0
+12454,15667438.0,Smith,622.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,45613.75,0.0
+12455,15617226.0,T'ien,678.0,France,Male,36.0,3.0,0.0,1.0,1.0,0.0,93777.15,0.0
+12456,15650751.0,Chin,633.0,Spain,Female,34.0,4.0,0.0,2.0,1.0,0.0,147040.25,0.0
+12457,15651389.0,Young,598.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,149517.71,0.0
+12458,15691875.0,T'ao,571.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,0.0,149604.18,0.0
+12459,15696812.0,Chikezie,670.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,94570.58,0.0
+12460,15782736.0,Hsia,733.0,France,Male,35.0,9.0,0.0,1.0,1.0,1.0,59864.96,0.0
+12461,15593973.0,Ch'iu,766.0,France,Female,31.0,2.0,132351.29,1.0,1.0,1.0,67789.99,0.0
+12462,15804721.0,Yudina,687.0,Germany,Male,29.0,7.0,133658.24,2.0,0.0,1.0,52861.89,0.0
+12463,15609804.0,T'ao,633.0,France,Female,30.0,3.0,0.0,1.0,1.0,1.0,121138.77,0.0
+12464,15703614.0,Fiorentini,554.0,Spain,Male,26.0,9.0,141462.13,1.0,1.0,1.0,74878.12,0.0
+12465,15726403.0,Walker,576.0,France,Female,30.0,7.0,0.0,2.0,0.0,1.0,108977.5,0.0
+12466,15658670.0,Chia,575.0,Spain,Male,30.0,1.0,0.0,2.0,1.0,1.0,180318.6,0.0
+12467,15797246.0,Hsueh,616.0,France,Male,34.0,3.0,0.0,2.0,0.0,1.0,62347.71,0.0
+12468,15632439.0,Tuan,658.0,France,Female,35.0,1.0,0.0,2.0,1.0,0.0,48963.59,0.0
+12469,15788483.0,Tung,526.0,Germany,Female,42.0,3.0,108528.07,1.0,1.0,1.0,134417.37,0.0
+12470,15668575.0,Onio,669.0,Spain,Male,28.0,2.0,114948.76,2.0,1.0,0.0,181208.47,0.0
+12471,15597951.0,Chien,546.0,Spain,Male,38.0,6.0,139994.66,1.0,0.0,0.0,152883.95,1.0
+12472,15579826.0,Chien,684.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,0.0,184843.77,0.0
+12473,15724663.0,Iadanza,694.0,Germany,Female,41.0,8.0,137222.77,1.0,0.0,1.0,145562.4,0.0
+12474,15682890.0,Chidubem,738.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,124486.28,0.0
+12475,15684319.0,Tien,707.0,France,Male,37.0,10.0,123966.21,2.0,1.0,0.0,115452.51,0.0
+12476,15709511.0,Ch'iu,681.0,France,Male,31.0,5.0,0.0,1.0,1.0,1.0,112256.57,0.0
+12477,15626452.0,Chiu,590.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,88604.89,0.0
+12478,15611905.0,Ch'iu,613.0,France,Female,40.0,3.0,0.0,2.0,1.0,1.0,161064.29,0.0
+12479,15581626.0,Ku,715.0,France,Male,57.0,10.0,0.0,2.0,1.0,0.0,162719.69,1.0
+12480,15762048.0,Trevisani,561.0,France,Female,38.0,5.0,76989.97,1.0,0.0,1.0,66795.78,0.0
+12481,15723884.0,Lu,622.0,Germany,Male,45.0,4.0,137648.41,1.0,0.0,0.0,136031.01,1.0
+12482,15753074.0,Palerma,594.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,65765.22,0.0
+12483,15777896.0,H?,717.0,France,Male,29.0,9.0,0.0,1.0,1.0,0.0,75927.35,0.0
+12484,15796787.0,P'eng,637.0,France,Male,40.0,2.0,132436.58,1.0,1.0,1.0,121440.8,0.0
+12485,15646521.0,T'ang,785.0,Spain,Male,39.0,4.0,0.0,2.0,0.0,0.0,179908.71,0.0
+12486,15596074.0,Ijendu,608.0,Germany,Male,33.0,8.0,152303.8,1.0,1.0,1.0,121151.1,0.0
+12487,15766458.0,Chiu,668.0,France,Male,38.0,7.0,0.0,1.0,0.0,0.0,199689.65,0.0
+12488,15665763.0,Okwudiliolisa,605.0,Germany,Female,44.0,3.0,124099.13,1.0,1.0,0.0,92887.06,1.0
+12489,15593365.0,Hs?,706.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,163581.67,0.0
+12490,15594404.0,Onyemauchechukwu,722.0,France,Female,32.0,1.0,113922.21,1.0,1.0,0.0,86791.9,0.0
+12491,15691396.0,Nnaife,732.0,France,Male,42.0,4.0,0.0,2.0,1.0,0.0,166513.49,0.0
+12492,15583043.0,Galkin,681.0,France,Female,36.0,7.0,0.0,1.0,1.0,1.0,47729.33,0.0
+12493,15572051.0,Chigolum,615.0,France,Female,31.0,9.0,139432.37,1.0,1.0,1.0,142754.71,1.0
+12494,15751336.0,Pisani,683.0,Germany,Female,28.0,9.0,149961.99,1.0,0.0,0.0,67468.67,0.0
+12495,15669957.0,Mancini,558.0,France,Male,51.0,5.0,0.0,1.0,1.0,1.0,76611.33,1.0
+12496,15752300.0,Chukwujekwu,583.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,0.0,176984.34,0.0
+12497,15580149.0,Lucciano,707.0,France,Male,34.0,1.0,0.0,2.0,1.0,1.0,162878.96,0.0
+12498,15762583.0,Iloerika,594.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,187622.42,0.0
+12499,15809847.0,Trevisani,685.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,138889.15,0.0
+12500,15726695.0,Y?,629.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,158041.12,0.0
+12501,15617320.0,Chukwukadibia,683.0,Spain,Male,38.0,1.0,128743.55,1.0,0.0,1.0,64490.67,0.0
+12502,15662884.0,K'ung,646.0,Spain,Male,42.0,4.0,0.0,2.0,0.0,0.0,53843.71,0.0
+12503,15777892.0,Tsao,683.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,143393.24,0.0
+12504,15567114.0,Shao,596.0,Germany,Male,27.0,10.0,127076.68,1.0,0.0,0.0,40889.14,0.0
+12505,15763980.0,Ch'in,681.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,0.0,152298.28,0.0
+12506,15651581.0,Houghton,619.0,France,Male,38.0,8.0,124547.13,1.0,1.0,1.0,163427.18,0.0
+12507,15668943.0,Chin,777.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,0.0,126469.09,0.0
+12508,15752543.0,Niu,646.0,France,Male,37.0,3.0,178095.55,1.0,0.0,1.0,95611.47,0.0
+12509,15678747.0,Hsia,706.0,Spain,Female,60.0,1.0,106967.18,1.0,1.0,1.0,63736.17,0.0
+12510,15756472.0,H?,679.0,France,Male,45.0,7.0,0.0,3.0,1.0,0.0,179634.69,1.0
+12511,15636551.0,Chukwufumnanya,696.0,Spain,Female,32.0,2.0,0.0,2.0,1.0,1.0,132628.98,0.0
+12512,15594878.0,Kudryashova,586.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,123639.64,0.0
+12513,15614215.0,Nnamdi,643.0,Germany,Female,37.0,8.0,126815.97,1.0,1.0,0.0,119327.75,1.0
+12514,15589329.0,Pirozzi,651.0,Spain,Male,29.0,2.0,89688.94,2.0,1.0,1.0,131167.98,0.0
+12515,15622289.0,Nwankwo,687.0,France,Male,31.0,8.0,120599.38,1.0,1.0,1.0,153955.38,0.0
+12516,15658614.0,Maitland,682.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,98820.09,0.0
+12517,15655175.0,Hsia,551.0,France,Male,40.0,10.0,0.0,1.0,1.0,1.0,70954.41,1.0
+12518,15751491.0,Ponomarev,632.0,Germany,Male,51.0,1.0,107594.11,1.0,0.0,1.0,53379.52,1.0
+12519,15789611.0,Narelle,745.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,152054.33,0.0
+12520,15775295.0,Nkemdirim,749.0,Spain,Male,24.0,5.0,121669.23,1.0,1.0,0.0,47880.71,0.0
+12521,15677324.0,Chukwuemeka,651.0,Germany,Male,40.0,9.0,122356.39,1.0,1.0,0.0,122547.58,1.0
+12522,15742834.0,Cattaneo,732.0,Spain,Male,39.0,8.0,0.0,1.0,1.0,1.0,55857.48,0.0
+12523,15602551.0,Maclean,662.0,France,Male,46.0,4.0,127157.04,1.0,1.0,0.0,56787.8,1.0
+12524,15792668.0,Nwachinemelu,562.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,0.0,102683.0,0.0
+12525,15791342.0,Iheatu,599.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,1.0,88827.67,0.0
+12526,15593601.0,Ts'ui,730.0,France,Male,49.0,6.0,0.0,2.0,0.0,1.0,27330.27,0.0
+12527,15792934.0,T'an,704.0,Spain,Female,37.0,5.0,152310.55,1.0,0.0,1.0,156660.33,0.0
+12528,15761670.0,Ch'eng,678.0,France,Male,33.0,5.0,0.0,2.0,0.0,0.0,165562.84,0.0
+12529,15808044.0,Mazzanti,703.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,143628.56,0.0
+12530,15625092.0,Ch'ang,659.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,176750.72,0.0
+12531,15568562.0,Biryukova,584.0,Germany,Male,25.0,8.0,123378.2,2.0,1.0,0.0,84869.64,0.0
+12532,15683625.0,Chidiebere,744.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,144870.28,0.0
+12533,15783883.0,Ch'iu,593.0,France,Male,49.0,2.0,0.0,2.0,1.0,0.0,180622.01,0.0
+12534,15803633.0,Yudina,663.0,France,Female,42.0,0.0,94937.77,1.0,1.0,0.0,126469.8,0.0
+12535,15595071.0,Hs?,642.0,France,Male,32.0,5.0,106431.97,1.0,1.0,0.0,74425.41,1.0
+12536,15771087.0,Pagnotto,710.0,France,Male,29.0,9.0,110938.34,1.0,1.0,1.0,158071.69,0.0
+12537,15678058.0,K?,753.0,Spain,Male,33.0,6.0,0.0,1.0,1.0,0.0,62402.38,1.0
+12538,15572767.0,K'ung,589.0,France,Female,35.0,4.0,154495.74,1.0,1.0,0.0,126561.07,0.0
+12539,15732299.0,Hsueh,774.0,Germany,Male,56.0,10.0,102025.32,1.0,1.0,1.0,155743.48,1.0
+12540,15592025.0,Teng,675.0,Germany,Male,42.0,8.0,137774.55,1.0,0.0,0.0,142400.27,1.0
+12541,15794849.0,Olisaemeka,807.0,Spain,Female,40.0,2.0,108711.57,1.0,1.0,0.0,189558.45,1.0
+12542,15809847.0,Chioke,705.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,106977.8,0.0
+12543,15755978.0,Martin,569.0,France,Female,40.0,3.0,142872.36,1.0,0.0,0.0,175082.2,0.0
+12544,15629885.0,Ngozichukwuka,695.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,130567.02,0.0
+12545,15683560.0,Obijiaku,576.0,France,Male,31.0,10.0,0.0,2.0,1.0,1.0,158840.99,0.0
+12546,15764170.0,Ch'eng,637.0,France,Male,31.0,4.0,98382.72,1.0,1.0,0.0,71738.56,0.0
+12547,15782311.0,Walker,579.0,France,Male,41.0,10.0,0.0,2.0,1.0,0.0,60906.12,0.0
+12548,15779204.0,Ch'en,678.0,Spain,Male,25.0,10.0,0.0,1.0,1.0,0.0,62933.03,0.0
+12549,15631063.0,Ibeamaka,794.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,55816.2,0.0
+12550,15642996.0,Aksenov,554.0,Spain,Male,43.0,9.0,0.0,1.0,1.0,1.0,74564.41,0.0
+12551,15790455.0,Pinto,667.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,151843.54,0.0
+12552,15691817.0,Ch'ien,707.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,135088.58,0.0
+12553,15734886.0,T'ien,516.0,France,Female,46.0,5.0,0.0,1.0,1.0,1.0,82438.34,0.0
+12554,15785576.0,Iweobiegbunam,713.0,France,Female,26.0,7.0,111852.84,1.0,1.0,0.0,94670.77,0.0
+12555,15777907.0,Biryukov,706.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,172490.78,0.0
+12556,15682600.0,Smith,668.0,Germany,Female,29.0,5.0,106365.29,2.0,1.0,1.0,159938.82,0.0
+12557,15792222.0,Ch'ang,757.0,Spain,Female,30.0,9.0,0.0,2.0,1.0,1.0,103901.31,0.0
+12558,15694125.0,McEwan,763.0,France,Female,40.0,9.0,0.0,2.0,1.0,1.0,40980.87,0.0
+12559,15705890.0,Chiawuotu,710.0,France,Female,29.0,3.0,0.0,2.0,0.0,0.0,113553.43,1.0
+12560,15672894.0,Chien,579.0,France,Male,23.0,1.0,0.0,2.0,1.0,0.0,117946.3,0.0
+12561,15679963.0,Fanucci,678.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,55893.33,0.0
+12562,15654096.0,T'ao,562.0,Germany,Female,35.0,5.0,129748.54,1.0,0.0,0.0,86981.45,0.0
+12563,15775295.0,Tien,637.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,98640.15,0.0
+12564,15665327.0,Ts'ai,543.0,Spain,Male,25.0,9.0,90425.15,1.0,1.0,1.0,146895.87,0.0
+12565,15699294.0,Trevisan,679.0,Germany,Female,48.0,5.0,139859.23,1.0,1.0,0.0,173683.0,1.0
+12566,15799128.0,Tuan,699.0,France,Male,29.0,2.0,0.0,1.0,1.0,0.0,9241.78,0.0
+12567,15662263.0,Fetherstonhaugh,588.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,93881.75,0.0
+12568,15793723.0,Hsueh,618.0,France,Female,31.0,0.0,135645.43,1.0,1.0,1.0,73329.08,0.0
+12569,15661433.0,Hao,611.0,Germany,Female,40.0,5.0,143391.07,1.0,1.0,1.0,145792.01,1.0
+12570,15652674.0,K'ung,579.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,128528.83,0.0
+12571,15592765.0,Ch'en,641.0,France,Female,49.0,2.0,0.0,1.0,0.0,0.0,168890.33,1.0
+12572,15706005.0,Shih,644.0,Spain,Male,29.0,2.0,0.0,2.0,0.0,1.0,155700.22,0.0
+12573,15673055.0,H?,715.0,France,Male,40.0,8.0,0.0,1.0,1.0,0.0,73873.65,0.0
+12574,15580134.0,Onyekachukwu,584.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,1.0,122433.09,0.0
+12575,15769902.0,Fanucci,516.0,France,Female,35.0,9.0,0.0,1.0,1.0,1.0,151887.16,1.0
+12576,15579820.0,Hao,678.0,France,Female,21.0,7.0,0.0,2.0,0.0,0.0,102076.02,0.0
+12577,15602797.0,Nwabugwu,643.0,Spain,Male,31.0,9.0,0.0,2.0,0.0,1.0,120107.1,0.0
+12578,15782210.0,Chiazagomekpere,667.0,Spain,Female,32.0,1.0,0.0,2.0,1.0,0.0,25950.49,0.0
+12579,15656683.0,Chiemenam,734.0,France,Male,35.0,2.0,0.0,1.0,1.0,0.0,68860.3,0.0
+12580,15729804.0,Tien,652.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,150468.35,0.0
+12581,15760662.0,Hsia,588.0,France,Male,24.0,5.0,0.0,2.0,1.0,1.0,102517.35,0.0
+12582,15742292.0,Ch'iu,714.0,France,Male,37.0,2.0,125510.82,1.0,1.0,1.0,121053.19,0.0
+12583,15662501.0,Yobachi,624.0,France,Female,53.0,1.0,0.0,1.0,0.0,0.0,163550.58,1.0
+12584,15635125.0,Macleod,708.0,France,Male,26.0,3.0,0.0,2.0,0.0,0.0,97782.92,0.0
+12585,15747323.0,Manna,652.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,139469.11,0.0
+12586,15687540.0,Hao,774.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,149593.61,0.0
+12587,15752955.0,Nwankwo,765.0,Spain,Female,42.0,9.0,144953.31,1.0,1.0,0.0,137028.51,1.0
+12588,15792934.0,Chukwumaobim,809.0,France,Female,49.0,6.0,0.0,2.0,1.0,1.0,97623.31,0.0
+12589,15676966.0,Cattaneo,556.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,148564.76,0.0
+12590,15665590.0,Clements,682.0,Spain,Female,38.0,5.0,130788.71,1.0,1.0,1.0,131048.35,0.0
+12591,15736327.0,Ch'iu,850.0,Spain,Male,35.0,1.0,107749.03,1.0,1.0,0.0,182988.94,0.0
+12592,15699772.0,Ch'eng,539.0,France,Female,29.0,7.0,102286.9,1.0,1.0,1.0,95957.09,0.0
+12593,15772383.0,Yobachi,663.0,France,Male,29.0,9.0,0.0,2.0,0.0,0.0,140723.04,0.0
+12594,15642099.0,Ponomaryova,693.0,France,Female,47.0,10.0,0.0,1.0,1.0,0.0,178342.63,1.0
+12595,15738088.0,Nnonso,567.0,France,Male,39.0,7.0,122453.97,1.0,1.0,1.0,47728.0,0.0
+12596,15767894.0,Chinonyelum,662.0,France,Male,29.0,5.0,0.0,1.0,0.0,0.0,94949.84,0.0
+12597,15690620.0,Hsieh,558.0,Spain,Male,41.0,7.0,0.0,2.0,1.0,1.0,126812.63,0.0
+12598,15644856.0,Chien,684.0,Germany,Male,25.0,5.0,96147.55,1.0,1.0,0.0,95611.47,0.0
+12599,15706163.0,T'ao,521.0,Germany,Male,33.0,10.0,133467.77,2.0,1.0,1.0,65763.66,0.0
+12600,15673908.0,Ch'iu,850.0,France,Male,33.0,7.0,161450.03,1.0,1.0,0.0,110900.43,0.0
+12601,15803750.0,Brazenor,693.0,Germany,Female,32.0,4.0,152827.99,1.0,1.0,1.0,184842.21,0.0
+12602,15757425.0,Hsieh,688.0,Germany,Male,34.0,9.0,130170.82,2.0,1.0,0.0,133672.61,0.0
+12603,15778947.0,Zikoranachidimma,644.0,Spain,Male,40.0,7.0,0.0,2.0,0.0,1.0,163714.92,0.0
+12604,15579914.0,Ritchie,745.0,France,Female,38.0,4.0,0.0,1.0,1.0,0.0,164741.5,1.0
+12605,15779915.0,Trevisani,652.0,Germany,Female,38.0,1.0,131765.72,2.0,1.0,1.0,135635.75,0.0
+12606,15682773.0,Milano,745.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,1.0,68600.36,0.0
+12607,15713374.0,Nkemdirim,695.0,France,Male,44.0,7.0,0.0,2.0,1.0,0.0,79871.16,0.0
+12608,15738785.0,Binder,686.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,133086.45,0.0
+12609,15757577.0,Smith,719.0,France,Female,18.0,4.0,80793.58,2.0,1.0,1.0,86569.13,0.0
+12610,15786463.0,Frolova,624.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,129826.89,0.0
+12611,15577683.0,Ch'ang,588.0,France,Male,37.0,6.0,0.0,2.0,0.0,1.0,130567.02,0.0
+12612,15744295.0,Cattaneo,688.0,France,Female,41.0,3.0,0.0,2.0,1.0,0.0,58601.61,0.0
+12613,15811690.0,Hsiao,543.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,147040.25,0.0
+12614,15743490.0,Chiu,663.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,101677.46,0.0
+12615,15737521.0,Tsou,679.0,France,Female,39.0,9.0,0.0,2.0,0.0,0.0,62389.35,0.0
+12616,15789425.0,Iweobiegbulam,714.0,Spain,Male,64.0,7.0,0.0,2.0,0.0,1.0,123499.13,0.0
+12617,15722533.0,Pisani,735.0,Spain,Male,36.0,10.0,0.0,1.0,1.0,1.0,86764.46,0.0
+12618,15736287.0,Chikwendu,667.0,France,Male,26.0,5.0,0.0,2.0,1.0,0.0,183487.98,0.0
+12619,15662698.0,Ch'en,739.0,Spain,Female,35.0,2.0,0.0,2.0,0.0,1.0,82782.08,0.0
+12620,15789611.0,Macleod,683.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,141706.43,0.0
+12621,15679075.0,Y?,682.0,France,Male,35.0,5.0,0.0,1.0,1.0,0.0,77830.6,0.0
+12622,15688337.0,Erskine,758.0,France,Male,66.0,8.0,96473.57,1.0,1.0,1.0,63732.9,0.0
+12623,15792922.0,Ponce,681.0,France,Female,38.0,8.0,108632.46,1.0,0.0,1.0,63736.17,0.0
+12624,15597930.0,Hs?,683.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,126477.41,0.0
+12625,15768777.0,Ejikemeifeuwa,724.0,France,Female,52.0,1.0,126645.13,1.0,0.0,0.0,109742.8,1.0
+12626,15665416.0,L?,498.0,Germany,Female,24.0,2.0,123894.22,2.0,1.0,0.0,168544.84,0.0
+12627,15674442.0,Obidimkpa,569.0,Germany,Male,42.0,5.0,107701.85,2.0,1.0,1.0,177668.55,0.0
+12628,15660263.0,Lo,584.0,France,Male,39.0,8.0,147803.48,1.0,1.0,1.0,178331.36,1.0
+12629,15778089.0,Chiu,613.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,79342.31,0.0
+12630,15694566.0,Chuang,837.0,France,Female,43.0,8.0,0.0,1.0,1.0,0.0,71159.84,0.0
+12631,15673570.0,Chin,708.0,France,Male,38.0,7.0,128644.46,1.0,0.0,0.0,82970.69,0.0
+12632,15685826.0,Trevisani,479.0,Germany,Female,62.0,4.0,108632.48,1.0,1.0,0.0,104469.06,1.0
+12633,15741875.0,Onuoha,679.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,97939.4,0.0
+12634,15671209.0,Shih,596.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,0.0,38798.79,0.0
+12635,15617486.0,Chukwuebuka,629.0,France,Female,28.0,1.0,106851.81,1.0,1.0,1.0,130529.13,0.0
+12636,15756901.0,K?,675.0,France,Female,52.0,2.0,0.0,1.0,0.0,0.0,172665.21,1.0
+12637,15734987.0,O'Sullivan,581.0,Germany,Male,32.0,4.0,125890.72,2.0,1.0,1.0,132959.3,0.0
+12638,15783883.0,Lo Duca,678.0,France,Male,30.0,9.0,116622.73,1.0,0.0,0.0,164768.82,0.0
+12639,15730868.0,Pagnotto,679.0,France,Male,34.0,7.0,0.0,2.0,0.0,0.0,133020.9,0.0
+12640,15644296.0,T'ien,719.0,France,Female,46.0,4.0,0.0,2.0,1.0,0.0,121747.96,1.0
+12641,15692575.0,Clements,711.0,Spain,Female,47.0,5.0,96950.21,1.0,1.0,0.0,143618.47,1.0
+12642,15725028.0,Walker,721.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,65864.4,0.0
+12643,15626383.0,Ch'en,588.0,France,Female,49.0,6.0,0.0,1.0,1.0,0.0,182728.8,1.0
+12644,15671165.0,Ch'iu,633.0,France,Male,30.0,2.0,0.0,1.0,1.0,1.0,125806.3,0.0
+12645,15752300.0,Onwuamaeze,686.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,158021.09,0.0
+12646,15777033.0,Pagnotto,714.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,28750.67,0.0
+12647,15791501.0,Chukwukadibia,732.0,France,Female,46.0,10.0,0.0,2.0,1.0,0.0,147521.35,0.0
+12648,15650098.0,Ch'iu,567.0,Germany,Female,33.0,1.0,81564.1,2.0,1.0,1.0,124749.08,0.0
+12649,15583049.0,Lettiere,676.0,France,Female,30.0,9.0,0.0,1.0,0.0,0.0,143598.5,0.0
+12650,15591724.0,Onuchukwu,668.0,France,Female,47.0,5.0,84719.13,1.0,0.0,1.0,70774.97,0.0
+12651,15585901.0,Chiu,726.0,France,Female,35.0,2.0,116974.26,1.0,1.0,1.0,164864.67,0.0
+12652,15681956.0,Mazzi,715.0,France,Female,30.0,1.0,0.0,1.0,0.0,1.0,91711.66,0.0
+12653,15704987.0,Lo Duca,743.0,France,Female,44.0,4.0,0.0,2.0,1.0,0.0,105780.18,0.0
+12654,15721207.0,Outhwaite,667.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,138052.51,0.0
+12655,15726484.0,Aitken,633.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,48986.18,0.0
+12656,15782530.0,Yobachukwu,592.0,Germany,Male,45.0,7.0,141829.67,2.0,1.0,1.0,138504.28,0.0
+12657,15684461.0,Iweobiegbunam,684.0,Spain,Male,36.0,2.0,0.0,2.0,0.0,1.0,180716.1,0.0
+12658,15636016.0,Tseng,695.0,France,Female,43.0,4.0,0.0,2.0,0.0,0.0,105570.39,0.0
+12659,15579892.0,Nwankwo,673.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,162760.96,0.0
+12660,15644765.0,Hsieh,758.0,France,Male,40.0,6.0,116537.6,1.0,1.0,0.0,71830.1,0.0
+12661,15803886.0,Ch'in,520.0,France,Male,31.0,6.0,0.0,1.0,1.0,0.0,141706.43,0.0
+12662,15729454.0,Onyemauchechukwu,634.0,Germany,Male,48.0,7.0,135979.01,2.0,0.0,1.0,116977.89,0.0
+12663,15643679.0,Palermo,685.0,France,Male,33.0,2.0,0.0,2.0,0.0,1.0,161428.25,0.0
+12664,15674840.0,H?,726.0,France,Male,36.0,5.0,0.0,2.0,0.0,0.0,167564.82,0.0
+12665,15667938.0,T'ien,709.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,95957.09,0.0
+12666,15627665.0,Sagese,587.0,France,Male,47.0,3.0,65942.26,1.0,1.0,0.0,140765.57,1.0
+12667,15581620.0,Uspenskaya,683.0,France,Male,26.0,4.0,0.0,2.0,0.0,1.0,74425.41,0.0
+12668,15668971.0,Hsing,663.0,France,Male,43.0,2.0,0.0,1.0,1.0,0.0,169719.35,1.0
+12669,15758510.0,Ts'ai,749.0,Germany,Female,43.0,3.0,137580.69,1.0,1.0,0.0,171922.72,1.0
+12670,15738166.0,Echezonachukwu,668.0,Germany,Male,37.0,7.0,114731.76,1.0,1.0,0.0,135083.73,1.0
+12671,15573741.0,Ritchie,685.0,France,Female,23.0,9.0,116465.53,1.0,1.0,0.0,147553.06,0.0
+12672,15622527.0,Chukwukadibia,563.0,France,Male,37.0,5.0,155973.13,1.0,1.0,1.0,64448.36,0.0
+12673,15795519.0,Trevisani,689.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,130862.11,0.0
+12674,15616367.0,Fanucci,627.0,France,Female,37.0,1.0,0.0,1.0,0.0,1.0,121408.55,0.0
+12675,15751777.0,Okwuadigbo,604.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,167522.19,0.0
+12676,15649423.0,Maclean,632.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,180134.88,0.0
+12677,15732299.0,Ting,583.0,France,Male,28.0,3.0,0.0,1.0,1.0,1.0,61324.87,0.0
+12678,15692664.0,Zito,561.0,Spain,Male,35.0,2.0,0.0,1.0,1.0,1.0,141035.65,0.0
+12679,15773973.0,Toscani,632.0,Germany,Male,32.0,5.0,91285.22,2.0,1.0,1.0,168034.83,0.0
+12680,15787071.0,Mazzanti,717.0,Spain,Male,32.0,4.0,0.0,2.0,0.0,1.0,54726.43,0.0
+12681,15772995.0,Ch'ang,733.0,Spain,Female,39.0,8.0,0.0,2.0,1.0,1.0,73329.08,0.0
+12682,15699507.0,Palermo,593.0,France,Female,35.0,6.0,125889.3,1.0,1.0,0.0,1528.4,0.0
+12683,15662501.0,Ch'ien,637.0,Spain,Male,29.0,2.0,0.0,1.0,1.0,0.0,151239.65,0.0
+12684,15679810.0,Frolova,565.0,Germany,Female,31.0,3.0,121411.9,2.0,1.0,1.0,176903.35,0.0
+12685,15703394.0,Chukwujamuike,692.0,Spain,Female,38.0,6.0,0.0,1.0,0.0,1.0,131662.47,0.0
+12686,15756820.0,Maclean,587.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,152855.96,0.0
+12687,15633922.0,Chibugo,637.0,Spain,Male,33.0,2.0,0.0,2.0,0.0,0.0,172646.88,0.0
+12688,15591512.0,Chukwukadibia,743.0,Germany,Male,29.0,5.0,130483.95,1.0,1.0,0.0,106040.97,1.0
+12689,15727281.0,Tsai,751.0,Spain,Male,38.0,5.0,0.0,1.0,1.0,1.0,118716.63,0.0
+12690,15771543.0,Hsia,743.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,93881.75,0.0
+12691,15739357.0,T'ien,692.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,176750.81,0.0
+12692,15743538.0,Lo Duca,752.0,Spain,Male,48.0,4.0,109848.6,1.0,1.0,0.0,116052.92,0.0
+12693,15766179.0,Uspenskaya,714.0,Germany,Female,36.0,3.0,107569.93,1.0,0.0,0.0,119348.88,1.0
+12694,15603550.0,Oluchukwu,652.0,France,Male,37.0,8.0,139723.9,1.0,1.0,1.0,100940.91,0.0
+12695,15782995.0,Ch'ang,721.0,Germany,Female,31.0,7.0,133040.32,2.0,1.0,1.0,180934.67,0.0
+12696,15645496.0,Chidiebele,642.0,France,Female,47.0,7.0,112544.65,1.0,1.0,0.0,127543.83,1.0
+12697,15778947.0,Tuan,592.0,France,Male,28.0,1.0,0.0,1.0,1.0,0.0,95583.45,1.0
+12698,15778936.0,Chikere,769.0,Germany,Male,35.0,0.0,98675.74,1.0,0.0,0.0,121151.1,1.0
+12699,15782939.0,Hs?,648.0,France,Male,36.0,9.0,0.0,2.0,0.0,1.0,161076.92,0.0
+12700,15786078.0,Ngozichukwuka,694.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,108699.59,0.0
+12701,15684934.0,Chin,707.0,Germany,Male,42.0,4.0,134956.02,1.0,1.0,0.0,80619.09,1.0
+12702,15724451.0,Chukwualuka,673.0,Germany,Male,43.0,6.0,105239.1,1.0,0.0,0.0,158973.77,0.0
+12703,15776921.0,McWilliams,577.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,92220.12,0.0
+12704,15721719.0,Pinto,725.0,Germany,Female,37.0,1.0,133793.89,1.0,0.0,0.0,47722.79,1.0
+12705,15646563.0,Knepper,685.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,138882.98,0.0
+12706,15595036.0,Maclean,663.0,France,Female,35.0,3.0,0.0,3.0,1.0,0.0,161971.42,1.0
+12707,15671610.0,Calabrese,638.0,Germany,Male,31.0,9.0,144287.6,1.0,0.0,0.0,137565.54,0.0
+12708,15619360.0,Chien,667.0,France,Female,29.0,0.0,0.0,2.0,1.0,0.0,177747.72,0.0
+12709,15677522.0,Oluchi,656.0,France,Male,32.0,5.0,0.0,3.0,1.0,1.0,135875.51,1.0
+12710,15752826.0,Udinese,809.0,France,Female,32.0,0.0,144974.52,1.0,1.0,0.0,127413.0,0.0
+12711,15781574.0,Dickson,644.0,France,Male,31.0,6.0,0.0,2.0,0.0,0.0,93524.19,0.0
+12712,15802486.0,De Luca,594.0,Germany,Male,35.0,5.0,121365.39,1.0,0.0,0.0,135851.4,1.0
+12713,15694192.0,Lucciano,753.0,Spain,Female,33.0,4.0,0.0,2.0,1.0,0.0,84710.65,0.0
+12714,15654531.0,Yevseyev,703.0,France,Male,41.0,7.0,0.0,1.0,1.0,0.0,164967.22,1.0
+12715,15757577.0,Hs?,738.0,Spain,Female,54.0,3.0,142646.68,1.0,1.0,1.0,96598.16,1.0
+12716,15615444.0,T'ao,603.0,France,Male,32.0,6.0,131244.65,1.0,0.0,0.0,176768.59,0.0
+12717,15774510.0,Whitehouse,611.0,France,Female,47.0,4.0,127763.24,1.0,1.0,0.0,160576.55,1.0
+12718,15785899.0,Palermo,562.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,44203.55,0.0
+12719,15786035.0,Milanesi,577.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,84927.42,0.0
+12720,15802220.0,Ch'iu,687.0,France,Female,34.0,2.0,153572.61,1.0,1.0,1.0,143913.27,0.0
+12721,15641389.0,Ch'in,589.0,Spain,Female,63.0,1.0,0.0,1.0,1.0,1.0,161465.31,1.0
+12722,15763625.0,T'ao,716.0,Germany,Female,35.0,4.0,124616.23,2.0,1.0,0.0,102517.83,0.0
+12723,15571843.0,Okwukwe,730.0,France,Female,34.0,1.0,116576.45,1.0,1.0,1.0,143598.7,0.0
+12724,15592583.0,Hsu,572.0,France,Female,48.0,8.0,144875.71,1.0,1.0,1.0,94810.84,0.0
+12725,15803801.0,Brown,581.0,Spain,Male,46.0,4.0,0.0,2.0,1.0,1.0,71830.1,0.0
+12726,15792232.0,Ts'ui,678.0,Spain,Female,31.0,3.0,0.0,1.0,1.0,0.0,159823.16,0.0
+12727,15798905.0,Nwankwo,572.0,France,Female,23.0,8.0,0.0,2.0,1.0,0.0,107499.55,0.0
+12728,15651020.0,Hsing,682.0,Germany,Female,29.0,1.0,124616.23,3.0,1.0,0.0,131736.43,1.0
+12729,15593365.0,Lucchese,681.0,Germany,Male,46.0,4.0,120568.41,1.0,1.0,0.0,147530.06,1.0
+12730,15605059.0,Okonkwo,663.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,0.0,172576.43,0.0
+12731,15682914.0,Chimaobim,633.0,France,Female,55.0,8.0,0.0,1.0,1.0,0.0,157549.6,1.0
+12732,15678067.0,Lettiere,760.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,101839.77,0.0
+12733,15592222.0,Chinwemma,676.0,France,Female,28.0,6.0,0.0,2.0,0.0,1.0,141053.85,0.0
+12734,15780144.0,Hs?eh,663.0,Germany,Female,48.0,7.0,141356.46,1.0,1.0,0.0,135851.3,1.0
+12735,15800268.0,Chukwufumnanya,716.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,0.0,81492.6,0.0
+12736,15793723.0,Chiang,699.0,Spain,Male,29.0,5.0,118433.94,1.0,1.0,0.0,187866.21,0.0
+12737,15649616.0,Shao,713.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,94488.82,0.0
+12738,15773731.0,Folliero,639.0,Spain,Female,46.0,6.0,0.0,1.0,1.0,1.0,183490.99,1.0
+12739,15628008.0,Ch'in,675.0,Germany,Male,37.0,8.0,133845.34,2.0,1.0,0.0,153036.97,0.0
+12740,15591512.0,H?,716.0,France,Male,36.0,1.0,0.0,2.0,0.0,0.0,56984.58,0.0
+12741,15771728.0,Trevisani,697.0,France,Male,44.0,8.0,0.0,2.0,1.0,0.0,168823.33,0.0
+12742,15785865.0,Efimov,648.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,89099.61,0.0
+12743,15634606.0,Ch'ang,730.0,Spain,Male,32.0,7.0,0.0,1.0,1.0,0.0,117547.76,0.0
+12744,15709447.0,Ch'ien,679.0,France,Male,52.0,4.0,129735.3,1.0,1.0,0.0,16081.62,1.0
+12745,15659820.0,Martin,509.0,Spain,Female,37.0,7.0,113348.5,2.0,1.0,0.0,103726.71,1.0
+12746,15777599.0,Ndubueze,841.0,France,Female,37.0,3.0,0.0,3.0,1.0,1.0,170821.43,1.0
+12747,15646513.0,Folliero,593.0,France,Male,38.0,6.0,0.0,1.0,1.0,1.0,155876.06,0.0
+12748,15775886.0,Folliero,678.0,France,Male,28.0,4.0,0.0,1.0,1.0,1.0,176750.81,0.0
+12749,15585367.0,McLean,577.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,0.0,122518.5,1.0
+12750,15798960.0,Ch'in,687.0,France,Female,29.0,6.0,0.0,2.0,1.0,1.0,166650.9,0.0
+12751,15651533.0,Nkemdirim,744.0,France,Male,40.0,3.0,0.0,1.0,1.0,0.0,123499.98,1.0
+12752,15640266.0,Chidubem,668.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,0.0,70763.69,0.0
+12753,15682479.0,Chin,635.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,182692.8,0.0
+12754,15724076.0,Hargreaves,669.0,Spain,Female,33.0,2.0,131434.96,1.0,1.0,1.0,138880.24,0.0
+12755,15692744.0,Galkin,585.0,Spain,Male,45.0,4.0,0.0,2.0,1.0,0.0,175893.53,0.0
+12756,15636572.0,Ts'ao,599.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,143030.09,0.0
+12757,15754084.0,Yermakov,661.0,Germany,Female,51.0,0.0,141786.78,2.0,1.0,1.0,101839.77,0.0
+12758,15603883.0,Hsieh,850.0,Spain,Female,34.0,5.0,92618.62,1.0,1.0,1.0,164083.72,0.0
+12759,15759499.0,Tuan,838.0,France,Male,38.0,9.0,0.0,1.0,0.0,0.0,28750.67,0.0
+12760,15775590.0,Palermo,590.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,167033.83,0.0
+12761,15680167.0,Ch'ang,627.0,France,Female,57.0,3.0,0.0,2.0,0.0,1.0,95500.98,0.0
+12762,15655339.0,Olisaemeka,614.0,France,Female,33.0,8.0,0.0,1.0,1.0,1.0,110916.15,0.0
+12763,15781554.0,Hsiao,671.0,France,Male,34.0,1.0,0.0,1.0,0.0,0.0,141210.5,0.0
+12764,15706557.0,Chiu,612.0,France,Male,58.0,4.0,0.0,2.0,0.0,1.0,181605.85,0.0
+12765,15794479.0,Nnamutaezinwa,651.0,Spain,Male,32.0,6.0,121580.42,1.0,1.0,1.0,129892.93,0.0
+12766,15582762.0,Ibezimako,653.0,Germany,Female,52.0,8.0,122265.21,1.0,0.0,1.0,105720.09,1.0
+12767,15723484.0,Chidumaga,679.0,Spain,Female,33.0,3.0,145988.65,1.0,1.0,0.0,115676.61,0.0
+12768,15693637.0,Ts'ui,821.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,135034.57,0.0
+12769,15764033.0,Hsueh,746.0,France,Male,31.0,4.0,0.0,2.0,0.0,0.0,184350.78,0.0
+12770,15677136.0,Lo Duca,586.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,113050.92,0.0
+12771,15707473.0,Manna,786.0,Germany,Male,35.0,3.0,106074.89,2.0,0.0,1.0,91574.92,0.0
+12772,15711571.0,Chidiebere,739.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,89566.64,0.0
+12773,15595350.0,Hsia,749.0,France,Male,22.0,7.0,0.0,2.0,1.0,1.0,142935.83,0.0
+12774,15772479.0,Hsia,697.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,93524.19,0.0
+12775,15571823.0,Fiorentino,607.0,France,Female,57.0,7.0,0.0,1.0,0.0,0.0,187905.12,1.0
+12776,15612494.0,Nnamdi,521.0,France,Male,48.0,4.0,117265.21,1.0,0.0,0.0,172321.21,1.0
+12777,15803378.0,T'ien,581.0,Spain,Male,39.0,4.0,0.0,1.0,1.0,1.0,153723.73,0.0
+12778,15674720.0,Gentry,586.0,Spain,Female,54.0,5.0,0.0,2.0,0.0,1.0,114006.35,0.0
+12779,15590344.0,Shao,605.0,Germany,Female,41.0,8.0,125685.79,4.0,1.0,0.0,138399.67,1.0
+12780,15745375.0,Lai,678.0,France,Female,36.0,8.0,65848.36,1.0,1.0,0.0,61164.45,0.0
+12781,15672437.0,Nwabugwu,757.0,France,Male,36.0,4.0,0.0,1.0,0.0,0.0,159483.76,0.0
+12782,15780056.0,Chinedum,640.0,France,Male,37.0,7.0,152582.2,1.0,0.0,1.0,107839.03,0.0
+12783,15582903.0,McIntyre,562.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,182612.38,1.0
+12784,15723008.0,Chizoba,579.0,France,Female,32.0,5.0,102773.2,1.0,1.0,0.0,177783.04,0.0
+12785,15608701.0,Hsiao,731.0,France,Female,33.0,8.0,0.0,1.0,1.0,1.0,135630.32,0.0
+12786,15777045.0,Ch'ang,770.0,Spain,Male,26.0,2.0,0.0,2.0,1.0,1.0,96590.39,0.0
+12787,15682533.0,White,592.0,Spain,Female,47.0,7.0,0.0,2.0,1.0,0.0,177613.19,0.0
+12788,15642710.0,Chuang,718.0,Spain,Male,29.0,6.0,0.0,1.0,0.0,0.0,4497.55,0.0
+12789,15651802.0,Hargreaves,682.0,Germany,Female,42.0,1.0,122314.5,1.0,1.0,1.0,49722.36,1.0
+12790,15793726.0,Ch'iu,679.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,152698.24,0.0
+12791,15684921.0,Shih,749.0,Germany,Female,45.0,9.0,119278.44,1.0,1.0,0.0,48242.18,0.0
+12792,15642883.0,Lucchesi,641.0,Germany,Male,43.0,6.0,100900.5,2.0,0.0,1.0,131919.48,0.0
+12793,15701175.0,Obiajulu,639.0,Germany,Female,37.0,2.0,81877.38,1.0,1.0,0.0,162668.33,1.0
+12794,15642545.0,Pagnotto,718.0,France,Male,39.0,9.0,0.0,1.0,1.0,0.0,100862.36,1.0
+12795,15757577.0,Chidubem,582.0,Spain,Male,31.0,8.0,0.0,2.0,0.0,0.0,158545.25,0.0
+12796,15600879.0,Cremonesi,684.0,France,Female,52.0,4.0,0.0,1.0,0.0,1.0,140941.47,1.0
+12797,15571940.0,Tikhonov,652.0,France,Male,67.0,1.0,166969.28,1.0,1.0,1.0,149913.92,0.0
+12798,15800326.0,Manna,491.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,50783.1,0.0
+12799,15689294.0,Ch'in,709.0,France,Male,23.0,1.0,0.0,2.0,1.0,0.0,93777.61,0.0
+12800,15597909.0,Udegbunam,755.0,Germany,Male,45.0,7.0,76915.4,1.0,0.0,1.0,125553.51,1.0
+12801,15567013.0,Nkemakolam,553.0,France,Male,30.0,9.0,109844.81,1.0,1.0,1.0,157932.75,0.0
+12802,15634564.0,Tsao,751.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,147527.02,0.0
+12803,15795913.0,Ch'in,659.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,125961.48,0.0
+12804,15672882.0,Onyemauchechukwu,595.0,France,Male,32.0,9.0,0.0,2.0,0.0,0.0,138775.75,0.0
+12805,15807563.0,Ts'ui,713.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,57898.57,0.0
+12806,15750874.0,Oluchukwu,571.0,Germany,Female,38.0,8.0,118899.45,1.0,1.0,0.0,29346.1,0.0
+12807,15777923.0,Chidiebere,636.0,France,Female,37.0,4.0,115625.07,1.0,1.0,1.0,172668.39,0.0
+12808,15592740.0,Hsieh,733.0,France,Female,41.0,4.0,0.0,1.0,1.0,1.0,121210.09,0.0
+12809,15604963.0,Zito,754.0,Germany,Female,66.0,0.0,111097.87,3.0,0.0,1.0,26287.21,1.0
+12810,15789526.0,Erskine,645.0,Spain,Male,29.0,1.0,0.0,1.0,1.0,1.0,172097.4,0.0
+12811,15745375.0,Wang,697.0,France,Male,46.0,7.0,0.0,2.0,1.0,0.0,120511.03,0.0
+12812,15651022.0,Uvarova,580.0,France,Male,55.0,8.0,0.0,1.0,0.0,0.0,52861.89,1.0
+12813,15645029.0,T'ien,728.0,Germany,Female,44.0,4.0,116980.53,1.0,1.0,0.0,72876.43,1.0
+12814,15618363.0,Cremonesi,671.0,France,Male,22.0,7.0,0.0,2.0,1.0,0.0,49594.02,0.0
+12815,15652914.0,Chinedum,585.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,173837.54,0.0
+12816,15589017.0,Efremov,661.0,Spain,Male,37.0,5.0,0.0,2.0,0.0,1.0,61312.15,0.0
+12817,15678882.0,Hsueh,438.0,Germany,Male,40.0,1.0,145731.83,2.0,0.0,0.0,81898.81,0.0
+12818,15761706.0,Chibugo,717.0,France,Male,43.0,1.0,122453.97,1.0,0.0,1.0,142783.98,0.0
+12819,15641794.0,Chidumaga,704.0,Spain,Female,35.0,9.0,0.0,2.0,0.0,1.0,180712.52,0.0
+12820,15774738.0,Chukwudi,573.0,France,Male,38.0,10.0,103769.26,1.0,1.0,1.0,120790.02,0.0
+12821,15594917.0,Tien,627.0,Germany,Female,29.0,1.0,117412.18,2.0,1.0,0.0,177737.07,0.0
+12822,15627190.0,Hsu,651.0,Germany,Female,44.0,9.0,106756.01,2.0,0.0,1.0,199953.33,0.0
+12823,15578738.0,Chiu,695.0,Germany,Male,55.0,5.0,116537.6,1.0,1.0,1.0,80619.09,0.0
+12824,15780128.0,Ts'ai,633.0,France,Male,35.0,2.0,129855.32,1.0,1.0,0.0,162865.35,0.0
+12825,15602909.0,Nweke,713.0,Germany,Male,48.0,8.0,111432.77,1.0,1.0,1.0,163736.09,0.0
+12826,15750545.0,Rowntree,594.0,Germany,Female,38.0,4.0,102773.2,2.0,1.0,0.0,141230.07,1.0
+12827,15679770.0,Chuang,684.0,Germany,Male,33.0,3.0,123403.85,1.0,1.0,0.0,131083.56,0.0
+12828,15698522.0,McLean,521.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,140745.81,0.0
+12829,15682946.0,Ndubueze,738.0,France,Female,33.0,2.0,145843.82,1.0,0.0,0.0,62733.03,0.0
+12830,15762218.0,Ch'ang,592.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,121763.68,0.0
+12831,15668894.0,Ugochukwu,850.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,48502.88,0.0
+12832,15690898.0,Frolov,715.0,France,Female,30.0,1.0,0.0,2.0,1.0,1.0,85891.02,0.0
+12833,15730793.0,Chiekwugo,577.0,France,Female,42.0,2.0,0.0,2.0,1.0,1.0,182354.46,0.0
+12834,15705383.0,Brown,662.0,France,Male,25.0,6.0,0.0,2.0,0.0,1.0,98015.85,0.0
+12835,15655213.0,Ting,691.0,France,Male,39.0,7.0,0.0,1.0,1.0,0.0,125877.22,0.0
+12836,15665058.0,Nwachinemelu,633.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,179453.66,1.0
+12837,15593477.0,Trevisani,665.0,Germany,Male,43.0,2.0,116646.76,2.0,1.0,1.0,125553.52,0.0
+12838,15792986.0,Ch'ien,588.0,Spain,Male,38.0,9.0,141547.26,1.0,0.0,1.0,141152.28,0.0
+12839,15777062.0,Okwudilichukwu,722.0,Germany,Female,43.0,7.0,109978.83,1.0,0.0,1.0,199336.63,0.0
+12840,15743617.0,Trevisano,651.0,France,Male,29.0,2.0,0.0,1.0,1.0,1.0,66548.58,0.0
+12841,15649793.0,Walker,850.0,France,Female,30.0,4.0,0.0,1.0,1.0,1.0,103787.28,0.0
+12842,15705918.0,Chioke,559.0,France,Male,38.0,7.0,127132.96,1.0,1.0,0.0,139435.75,0.0
+12843,15649121.0,Palerma,749.0,Germany,Male,19.0,10.0,79468.96,2.0,1.0,0.0,171457.06,0.0
+12844,15707042.0,Cremonesi,850.0,France,Male,38.0,9.0,0.0,1.0,1.0,0.0,146875.86,0.0
+12845,15699550.0,Moore,646.0,France,Female,67.0,5.0,0.0,2.0,0.0,1.0,155870.79,0.0
+12846,15810864.0,Tsai,592.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,184524.65,0.0
+12847,15654074.0,Cremonesi,708.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,104889.3,0.0
+12848,15566689.0,T'ien,579.0,France,Male,36.0,7.0,143390.51,1.0,1.0,0.0,119817.19,0.0
+12849,15596671.0,Lucciano,589.0,Spain,Male,33.0,1.0,110153.33,1.0,1.0,0.0,125823.79,0.0
+12850,15678720.0,Erskine,646.0,Germany,Female,39.0,6.0,115462.44,2.0,0.0,0.0,174822.74,0.0
+12851,15798850.0,Hargreaves,683.0,Spain,Female,43.0,6.0,0.0,1.0,0.0,0.0,54776.64,1.0
+12852,15717290.0,Toscani,598.0,Germany,Female,46.0,8.0,91068.56,1.0,1.0,0.0,102681.16,1.0
+12853,15639238.0,Ts'ai,613.0,France,Male,37.0,4.0,0.0,2.0,0.0,1.0,70529.0,0.0
+12854,15576256.0,Chukwubuikem,496.0,France,Female,21.0,2.0,0.0,2.0,1.0,1.0,87822.14,0.0
+12855,15793042.0,Ch'eng,687.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,1.0,170142.09,0.0
+12856,15660211.0,Okeke,661.0,France,Male,36.0,2.0,122924.75,1.0,1.0,0.0,11.58,0.0
+12857,15682453.0,Yegorov,597.0,France,Female,41.0,3.0,0.0,1.0,1.0,0.0,64375.4,0.0
+12858,15654858.0,Yermakov,727.0,France,Female,66.0,9.0,0.0,2.0,1.0,1.0,143960.72,0.0
+12859,15609653.0,Ebelegbulam,542.0,France,Male,32.0,3.0,97408.03,1.0,1.0,1.0,113860.81,0.0
+12860,15659962.0,Hsu,589.0,Spain,Female,38.0,3.0,0.0,1.0,1.0,1.0,127413.0,0.0
+12861,15617197.0,Chiekwugo,778.0,Germany,Male,32.0,5.0,155973.88,2.0,1.0,0.0,122449.18,0.0
+12862,15786042.0,Ts'ai,438.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,108577.97,0.0
+12863,15731519.0,Pomeroy,531.0,France,Female,43.0,9.0,125903.22,1.0,0.0,1.0,142903.44,0.0
+12864,15606435.0,Milne,678.0,France,Male,39.0,7.0,0.0,1.0,1.0,1.0,103970.58,0.0
+12865,15615176.0,Hsueh,635.0,Spain,Male,40.0,7.0,0.0,1.0,1.0,0.0,65675.36,1.0
+12866,15682890.0,Calabrese,683.0,Spain,Male,35.0,4.0,0.0,1.0,1.0,1.0,86786.41,0.0
+12867,15743236.0,Cremonesi,531.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,168816.73,0.0
+12868,15790247.0,Manna,605.0,Spain,Male,31.0,9.0,106920.92,1.0,1.0,0.0,172810.45,0.0
+12869,15754068.0,Chin,632.0,Spain,Male,23.0,3.0,0.0,2.0,1.0,0.0,113048.79,0.0
+12870,15783338.0,Ch'ang,706.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,163666.85,0.0
+12871,15747571.0,Zikoranachidimma,600.0,France,Male,37.0,0.0,121399.98,1.0,1.0,0.0,74800.99,0.0
+12872,15598097.0,Genovesi,738.0,Germany,Male,55.0,7.0,91219.29,1.0,0.0,1.0,194998.34,1.0
+12873,15637366.0,T'ao,688.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,94499.06,0.0
+12874,15663410.0,Fomin,688.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,122449.21,0.0
+12875,15759733.0,Cremonesi,774.0,France,Male,28.0,10.0,0.0,2.0,1.0,1.0,162878.96,0.0
+12876,15758025.0,Chien,717.0,France,Female,33.0,4.0,123372.3,1.0,1.0,1.0,161465.31,0.0
+12877,15685826.0,Ch'iu,735.0,France,Female,36.0,2.0,0.0,2.0,0.0,0.0,182650.15,0.0
+12878,15757895.0,T'ien,613.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,86043.27,0.0
+12879,15700813.0,Nkemdilim,586.0,France,Male,39.0,7.0,133040.32,1.0,1.0,0.0,80552.11,0.0
+12880,15593954.0,Chizuoke,556.0,France,Female,57.0,7.0,0.0,1.0,1.0,1.0,167066.95,1.0
+12881,15737509.0,Sagese,676.0,France,Male,33.0,7.0,122314.5,1.0,1.0,0.0,156774.75,0.0
+12882,15661708.0,Ts'ui,565.0,France,Female,46.0,2.0,0.0,2.0,1.0,0.0,150580.88,0.0
+12883,15697460.0,Chukwuebuka,628.0,Germany,Female,31.0,6.0,153313.67,2.0,1.0,0.0,127847.25,0.0
+12884,15691457.0,Chidiebere,623.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,1.0,5983.8,0.0
+12885,15605286.0,Bibi,707.0,France,Male,63.0,9.0,0.0,2.0,1.0,1.0,165562.84,0.0
+12886,15704442.0,Oluchukwu,725.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,166677.54,0.0
+12887,15613471.0,McIntosh,579.0,France,Female,47.0,1.0,0.0,1.0,1.0,0.0,100130.95,1.0
+12888,15602354.0,Toscani,652.0,France,Female,31.0,1.0,0.0,1.0,1.0,0.0,27330.27,0.0
+12889,15775192.0,Chidubem,591.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,79919.13,0.0
+12890,15784761.0,Smith,706.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,152290.28,0.0
+12891,15758813.0,Kambinachi,659.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,0.0,83473.82,0.0
+12892,15772423.0,Young,707.0,France,Male,37.0,4.0,0.0,2.0,0.0,1.0,148527.56,0.0
+12893,15722543.0,Okechukwu,633.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,158947.58,0.0
+12894,15621868.0,Ting,556.0,Germany,Male,55.0,4.0,136678.52,1.0,1.0,0.0,161559.12,1.0
+12895,15731543.0,Hsieh,639.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,176580.89,0.0
+12896,15640442.0,Pugh,639.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,143585.29,0.0
+12897,15589230.0,Ch'ang,488.0,France,Male,42.0,2.0,106968.96,1.0,0.0,0.0,147759.84,0.0
+12898,15643657.0,Nnamutaezinwa,617.0,France,Male,40.0,7.0,0.0,1.0,0.0,0.0,158973.45,1.0
+12899,15605684.0,Hsia,724.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,171519.06,0.0
+12900,15592707.0,Nwankwo,703.0,France,Female,26.0,4.0,0.0,2.0,1.0,1.0,158973.78,0.0
+12901,15779414.0,Ugoji,678.0,Germany,Male,39.0,1.0,123675.69,2.0,1.0,0.0,107876.2,0.0
+12902,15623346.0,T'ang,674.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,141872.05,0.0
+12903,15791834.0,Ch'ien,714.0,Spain,Female,43.0,5.0,117805.21,1.0,0.0,0.0,151349.37,1.0
+12904,15648858.0,Cheng,471.0,France,Female,27.0,3.0,0.0,2.0,1.0,1.0,99818.45,0.0
+12905,15607988.0,Ch'en,762.0,Spain,Male,32.0,7.0,0.0,2.0,1.0,0.0,130789.6,0.0
+12906,15763415.0,Ngozichukwuka,597.0,Spain,Female,33.0,1.0,0.0,1.0,1.0,1.0,57929.9,0.0
+12907,15707962.0,Uwaezuoke,714.0,France,Female,33.0,6.0,0.0,2.0,0.0,0.0,167038.51,0.0
+12908,15690188.0,Hao,635.0,Germany,Male,47.0,5.0,106301.85,4.0,1.0,0.0,147794.63,1.0
+12909,15694928.0,Ts'ui,705.0,France,Female,52.0,1.0,99824.45,1.0,1.0,1.0,137565.87,0.0
+12910,15611058.0,Obidimkpa,693.0,Spain,Male,38.0,1.0,0.0,1.0,0.0,1.0,50368.63,0.0
+12911,15666916.0,Hsia,781.0,France,Male,34.0,7.0,83543.37,1.0,0.0,1.0,59981.62,0.0
+12912,15782879.0,Baryshnikov,793.0,France,Male,36.0,4.0,0.0,1.0,0.0,0.0,180345.44,0.0
+12913,15783097.0,Mazzi,521.0,France,Female,37.0,6.0,120787.18,1.0,1.0,0.0,171922.72,0.0
+12914,15770375.0,Olisanugo,678.0,Spain,Male,36.0,3.0,97565.74,1.0,0.0,1.0,103764.55,0.0
+12915,15732791.0,Oluchukwu,681.0,France,Male,39.0,10.0,0.0,1.0,1.0,0.0,131753.97,0.0
+12916,15691703.0,O'Meara,516.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,63337.19,0.0
+12917,15614168.0,De Salis,644.0,France,Male,56.0,4.0,0.0,2.0,1.0,1.0,74580.8,1.0
+12918,15636016.0,Pisani,681.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,121408.55,0.0
+12919,15585853.0,Nwachinemelu,809.0,Spain,Male,49.0,9.0,0.0,1.0,1.0,1.0,173629.78,0.0
+12920,15624072.0,Shih,589.0,France,Female,41.0,5.0,0.0,2.0,1.0,1.0,158021.43,0.0
+12921,15655389.0,Lai,619.0,France,Male,43.0,7.0,0.0,2.0,0.0,0.0,138700.75,0.0
+12922,15690744.0,Iweobiegbunam,727.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,73881.68,0.0
+12923,15806901.0,Ijendu,732.0,Germany,Male,48.0,4.0,164948.98,1.0,1.0,1.0,60920.03,1.0
+12924,15632789.0,Tien,671.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,51919.04,0.0
+12925,15671610.0,Chiu,645.0,Spain,Female,42.0,9.0,0.0,2.0,0.0,1.0,676.06,0.0
+12926,15664311.0,Nkemjika,663.0,Germany,Female,38.0,10.0,59532.18,2.0,1.0,1.0,146875.86,0.0
+12927,15639303.0,Yeh,715.0,France,Male,54.0,9.0,0.0,2.0,1.0,1.0,170835.32,0.0
+12928,15694890.0,Chien,661.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,88890.05,0.0
+12929,15672692.0,T'ao,792.0,France,Male,27.0,9.0,121827.43,1.0,1.0,1.0,161241.65,0.0
+12930,15803378.0,Kambinachi,614.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,137037.97,0.0
+12931,15775756.0,Ch'ien,695.0,Spain,Male,21.0,7.0,114493.79,1.0,0.0,1.0,146585.79,0.0
+12932,15691703.0,Gorman,586.0,Germany,Male,47.0,3.0,142663.46,1.0,1.0,1.0,50783.1,0.0
+12933,15682021.0,Mazzanti,531.0,Spain,Female,44.0,5.0,0.0,1.0,1.0,0.0,47160.01,1.0
+12934,15748649.0,Lai,640.0,Spain,Male,41.0,6.0,96798.79,1.0,1.0,0.0,113580.79,0.0
+12935,15627196.0,Yobanna,644.0,France,Male,50.0,4.0,0.0,2.0,1.0,1.0,152479.64,1.0
+12936,15602497.0,Chibuzo,599.0,Germany,Female,34.0,5.0,95808.19,2.0,0.0,1.0,81451.21,0.0
+12937,15651336.0,Chien,581.0,France,Male,46.0,3.0,131513.26,1.0,1.0,0.0,54949.73,1.0
+12938,15724076.0,Tretiakova,602.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,94820.85,0.0
+12939,15808960.0,K'ung,671.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,1.0,163798.41,0.0
+12940,15700255.0,Knepper,644.0,France,Female,39.0,3.0,113858.95,2.0,1.0,0.0,158918.03,0.0
+12941,15803378.0,Onwuamaeze,682.0,Germany,Female,43.0,2.0,96888.39,1.0,0.0,0.0,57841.89,1.0
+12942,15801395.0,Okwudilichukwu,706.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,161407.48,0.0
+12943,15772941.0,Hsing,634.0,Germany,Male,51.0,8.0,126588.81,3.0,1.0,0.0,170621.55,1.0
+12944,15743582.0,T'ien,495.0,France,Female,36.0,5.0,0.0,1.0,1.0,1.0,185543.68,0.0
+12945,15633112.0,Chukwuhaenye,691.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,84870.23,0.0
+12946,15671917.0,Mazzanti,753.0,Germany,Male,40.0,9.0,114435.47,1.0,1.0,0.0,55313.51,1.0
+12947,15682533.0,Ch'ang,579.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,140941.47,0.0
+12948,15806922.0,Ch'iu,523.0,France,Female,35.0,2.0,0.0,1.0,1.0,1.0,114515.28,0.0
+12949,15671124.0,Okwudilichukwu,732.0,Germany,Male,36.0,5.0,115171.71,1.0,0.0,1.0,25289.23,0.0
+12950,15796721.0,Milano,656.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,1.0,144532.85,0.0
+12951,15716041.0,Lucchese,644.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,171768.89,0.0
+12952,15702416.0,Chukwualuka,724.0,Spain,Male,47.0,7.0,0.0,1.0,0.0,0.0,59650.42,1.0
+12953,15795511.0,Nwabugwu,567.0,France,Male,37.0,7.0,0.0,2.0,0.0,0.0,142913.98,0.0
+12954,15585855.0,Kosisochukwu,687.0,France,Female,57.0,5.0,0.0,1.0,1.0,0.0,112222.64,1.0
+12955,15603594.0,Hs?,681.0,Germany,Male,44.0,9.0,141334.57,1.0,1.0,1.0,94862.93,1.0
+12956,15731553.0,Ch'ang,651.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,160529.71,0.0
+12957,15793881.0,Chien,712.0,France,Female,33.0,3.0,108465.65,1.0,0.0,1.0,51999.5,1.0
+12958,15662494.0,Tsai,707.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,53394.12,0.0
+12959,15737509.0,Ch'ien,727.0,France,Male,29.0,1.0,124663.54,1.0,1.0,0.0,118716.63,1.0
+12960,15598044.0,Ch'in,660.0,France,Female,42.0,5.0,0.0,2.0,0.0,1.0,152577.62,0.0
+12961,15808838.0,Chukwualuka,551.0,Spain,Female,40.0,2.0,0.0,1.0,1.0,1.0,166513.49,0.0
+12962,15784280.0,H?,604.0,Germany,Male,34.0,5.0,114023.64,1.0,1.0,1.0,171707.93,0.0
+12963,15700366.0,Ch'ien,671.0,France,Female,41.0,8.0,0.0,2.0,1.0,0.0,121409.06,0.0
+12964,15682453.0,Mazzanti,773.0,France,Female,48.0,8.0,0.0,1.0,1.0,0.0,129590.18,1.0
+12965,15689061.0,Chiang,633.0,Germany,Male,55.0,4.0,107963.23,1.0,0.0,0.0,151336.96,1.0
+12966,15661409.0,Chinonyelum,835.0,Spain,Female,44.0,8.0,0.0,1.0,1.0,0.0,185561.89,1.0
+12967,15645045.0,Lucchesi,607.0,France,Female,27.0,3.0,0.0,2.0,0.0,1.0,71167.17,0.0
+12968,15585595.0,Lucciano,682.0,Spain,Male,40.0,1.0,0.0,1.0,1.0,0.0,172033.57,1.0
+12969,15630106.0,Yevdokimova,677.0,France,Male,41.0,3.0,0.0,1.0,0.0,1.0,51871.95,0.0
+12970,15627042.0,Eluemuno,611.0,Germany,Male,28.0,7.0,111432.13,1.0,1.0,0.0,124720.68,0.0
+12971,15598892.0,Ch'iu,713.0,Spain,Male,38.0,6.0,0.0,2.0,0.0,1.0,126281.83,0.0
+12972,15810864.0,Chibueze,719.0,Germany,Female,34.0,6.0,111348.18,2.0,1.0,0.0,94949.84,0.0
+12973,15648858.0,Tu,553.0,France,Male,42.0,8.0,0.0,1.0,1.0,1.0,4993.94,0.0
+12974,15773487.0,Manna,556.0,Germany,Female,19.0,2.0,118680.57,1.0,1.0,1.0,130567.02,0.0
+12975,15576945.0,T'ien,578.0,France,Female,43.0,1.0,0.0,2.0,1.0,1.0,180862.31,0.0
+12976,15654574.0,Milne,705.0,Spain,Female,31.0,4.0,132077.48,1.0,1.0,0.0,106977.53,0.0
+12977,15589425.0,T'ao,640.0,France,Male,42.0,4.0,0.0,1.0,1.0,1.0,175544.02,0.0
+12978,15626012.0,T'ien,627.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,74898.6,0.0
+12979,15803750.0,Iweobiegbunam,526.0,Spain,Male,29.0,10.0,0.0,1.0,1.0,0.0,102747.02,0.0
+12980,15665088.0,Macvitie,651.0,Spain,Male,54.0,9.0,123863.64,1.0,0.0,1.0,138350.74,1.0
+12981,15706464.0,Fanucci,721.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,178028.96,0.0
+12982,15804787.0,Chiemenam,675.0,Spain,Female,44.0,3.0,0.0,2.0,1.0,1.0,171345.02,0.0
+12983,15781702.0,Nwachinemelu,651.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,66721.98,0.0
+12984,15565701.0,Trevisani,609.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,167553.06,0.0
+12985,15620758.0,Mironova,648.0,Germany,Male,43.0,2.0,119545.43,1.0,1.0,1.0,161653.5,1.0
+12986,15692744.0,Ch'ien,588.0,France,Male,35.0,2.0,0.0,2.0,0.0,1.0,85901.09,0.0
+12987,15794396.0,Nwokezuike,564.0,France,Female,37.0,5.0,0.0,2.0,1.0,1.0,44251.33,0.0
+12988,15774179.0,Chigolum,678.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,59249.83,0.0
+12989,15693893.0,Ch'eng,683.0,France,Female,40.0,7.0,0.0,2.0,1.0,1.0,186942.49,0.0
+12990,15706904.0,Folliero,593.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,132210.53,0.0
+12991,15797733.0,Brown,603.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,96847.91,0.0
+12992,15668943.0,Ch'in,704.0,France,Male,27.0,4.0,107569.96,1.0,1.0,1.0,64716.08,0.0
+12993,15649160.0,Fan,733.0,Spain,Male,45.0,3.0,121823.7,1.0,1.0,0.0,136869.98,1.0
+12994,15646082.0,Ogochukwu,705.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,179908.71,0.0
+12995,15703394.0,Fanucci,659.0,Spain,Female,32.0,1.0,0.0,1.0,1.0,1.0,10054.53,1.0
+12996,15782468.0,Hs?eh,554.0,Germany,Male,52.0,1.0,125445.04,1.0,1.0,1.0,109545.56,1.0
+12997,15780215.0,Lombardo,745.0,Spain,Male,33.0,5.0,94435.45,1.0,1.0,1.0,148087.62,0.0
+12998,15757531.0,L?,651.0,France,Female,45.0,8.0,0.0,2.0,0.0,0.0,55313.44,0.0
+12999,15671390.0,Tien,569.0,France,Male,30.0,10.0,0.0,1.0,1.0,1.0,61240.83,0.0
+13000,15775294.0,Ulyanova,648.0,France,Male,35.0,10.0,0.0,2.0,1.0,1.0,19116.97,0.0
+13001,15797910.0,Nwachinemelu,651.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,91086.73,0.0
+13002,15583692.0,Yuryeva,721.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,104181.78,0.0
+13003,15572762.0,Hsueh,698.0,France,Male,34.0,6.0,142644.64,1.0,1.0,0.0,59249.83,0.0
+13004,15641299.0,Maclean,667.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,128521.16,0.0
+13005,15678886.0,Chia,682.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,45985.52,0.0
+13006,15689886.0,Ch'ang,656.0,Germany,Male,32.0,8.0,117412.18,1.0,1.0,1.0,58814.41,0.0
+13007,15771668.0,Macvitie,565.0,France,Female,33.0,9.0,0.0,1.0,1.0,0.0,57962.41,1.0
+13008,15576676.0,Ch'en,579.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,120790.02,0.0
+13009,15709136.0,Chigolum,583.0,Germany,Female,30.0,1.0,74896.92,2.0,1.0,1.0,166677.54,0.0
+13010,15672499.0,Watson,726.0,Spain,Female,46.0,4.0,0.0,1.0,0.0,0.0,172668.39,1.0
+13011,15782941.0,Ch'en,584.0,France,Female,42.0,3.0,0.0,1.0,1.0,1.0,81602.02,0.0
+13012,15702801.0,Onio,724.0,Germany,Male,37.0,5.0,142905.51,1.0,1.0,1.0,153786.46,0.0
+13013,15768359.0,Hsueh,572.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,149613.77,0.0
+13014,15758035.0,Ch'ang,727.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,161076.92,0.0
+13015,15705707.0,Lu,573.0,France,Female,40.0,8.0,0.0,3.0,1.0,1.0,88695.19,1.0
+13016,15777784.0,Chukwuemeka,686.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,71095.41,0.0
+13017,15654901.0,Chukwualuka,705.0,France,Female,33.0,7.0,0.0,2.0,0.0,1.0,169894.4,0.0
+13018,15691808.0,Shih,635.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,139947.17,0.0
+13019,15782659.0,Teng,573.0,Spain,Male,41.0,5.0,96196.24,1.0,0.0,0.0,129964.94,0.0
+13020,15686917.0,Onyeoruru,646.0,Germany,Male,32.0,4.0,106938.11,2.0,1.0,1.0,124045.65,0.0
+13021,15594862.0,Maclean,643.0,France,Male,42.0,0.0,103772.73,1.0,1.0,0.0,187866.21,1.0
+13022,15604891.0,P'an,648.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,61513.87,0.0
+13023,15579781.0,Shand,708.0,Spain,Female,33.0,9.0,0.0,2.0,0.0,1.0,103379.31,0.0
+13024,15593496.0,Mazzanti,771.0,Germany,Male,39.0,7.0,141803.77,2.0,1.0,1.0,179521.24,0.0
+13025,15604020.0,Hsing,676.0,France,Male,48.0,5.0,0.0,1.0,0.0,1.0,152417.79,1.0
+13026,15667554.0,Hsing,615.0,Germany,Female,37.0,10.0,133658.24,1.0,1.0,0.0,66810.48,0.0
+13027,15631162.0,Ts'ui,757.0,Spain,Male,39.0,2.0,0.0,2.0,0.0,1.0,122488.59,0.0
+13028,15750264.0,Chukwumaobim,685.0,Spain,Male,32.0,2.0,0.0,1.0,1.0,1.0,136859.55,0.0
+13029,15667588.0,Fenton,686.0,France,Female,32.0,8.0,129974.79,1.0,1.0,1.0,129964.94,0.0
+13030,15695816.0,Miah,705.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,94450.97,0.0
+13031,15620756.0,Chin,544.0,Spain,Male,35.0,7.0,151191.31,2.0,1.0,1.0,122580.48,0.0
+13032,15713250.0,Chukwufumnanya,595.0,France,Female,41.0,1.0,0.0,3.0,0.0,1.0,48876.84,1.0
+13033,15629133.0,Tsao,717.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,162501.83,0.0
+13034,15686963.0,Hsia,648.0,France,Male,26.0,9.0,0.0,2.0,1.0,0.0,167723.25,0.0
+13035,15723008.0,Chizuoke,729.0,France,Male,30.0,6.0,0.0,2.0,0.0,1.0,124795.84,0.0
+13036,15636423.0,Mazzanti,719.0,France,Male,32.0,8.0,62717.84,1.0,1.0,1.0,138882.98,0.0
+13037,15791452.0,P'eng,775.0,France,Female,27.0,6.0,0.0,2.0,1.0,1.0,161845.81,0.0
+13038,15659949.0,Shih,609.0,Spain,Female,59.0,7.0,0.0,1.0,1.0,0.0,126812.85,1.0
+13039,15677371.0,Kao,725.0,Germany,Male,50.0,2.0,118287.01,1.0,0.0,0.0,138360.74,1.0
+13040,15635597.0,Chukwumaobim,647.0,Germany,Male,46.0,8.0,132944.53,1.0,0.0,0.0,185827.74,1.0
+13041,15697042.0,Maclean,598.0,France,Female,28.0,9.0,0.0,2.0,1.0,0.0,90876.95,0.0
+13042,15594917.0,Ugorji,637.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,103760.53,0.0
+13043,15730059.0,McIntyre,696.0,France,Female,46.0,9.0,105264.88,1.0,1.0,1.0,119924.8,1.0
+13044,15793881.0,Ch'eng,706.0,France,Female,42.0,6.0,0.0,2.0,0.0,0.0,12761.16,0.0
+13045,15780804.0,Rowntree,645.0,France,Male,32.0,1.0,0.0,1.0,1.0,0.0,169952.82,1.0
+13046,15735400.0,Hsueh,658.0,France,Female,50.0,6.0,0.0,1.0,1.0,0.0,60954.45,1.0
+13047,15662662.0,Nwachukwu,636.0,France,Female,48.0,9.0,0.0,1.0,0.0,1.0,51117.06,1.0
+13048,15627854.0,Ch'eng,583.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,158974.84,0.0
+13049,15724851.0,Hs?,683.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,103396.06,0.0
+13050,15803337.0,Chibueze,812.0,France,Female,31.0,10.0,0.0,1.0,1.0,1.0,176945.96,0.0
+13051,15618562.0,Ch'eng,706.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,44965.54,0.0
+13052,15658364.0,T'ien,698.0,Spain,Female,24.0,2.0,0.0,2.0,1.0,1.0,54954.51,0.0
+13053,15672598.0,Ch'ien,547.0,France,Female,37.0,4.0,113090.4,1.0,0.0,0.0,124777.14,0.0
+13054,15709604.0,Nkemjika,721.0,France,Female,36.0,4.0,0.0,2.0,0.0,0.0,16402.11,0.0
+13055,15756871.0,Nkemdirim,521.0,France,Female,41.0,1.0,0.0,2.0,1.0,1.0,52795.15,0.0
+13056,15696812.0,Shih,686.0,France,Male,22.0,7.0,0.0,2.0,1.0,1.0,163666.85,0.0
+13057,15811978.0,Hsu,694.0,Germany,Male,44.0,8.0,141604.76,1.0,1.0,1.0,62944.75,0.0
+13058,15581286.0,McGregor,638.0,Spain,Female,40.0,10.0,0.0,1.0,0.0,0.0,141756.32,1.0
+13059,15768451.0,Hsia,659.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,141860.62,0.0
+13060,15637235.0,Frolov,837.0,Spain,Female,48.0,6.0,0.0,1.0,1.0,0.0,112946.67,1.0
+13061,15566380.0,Lucciano,634.0,France,Male,31.0,8.0,0.0,1.0,0.0,1.0,141872.05,0.0
+13062,15785519.0,Lo,578.0,Spain,Female,43.0,1.0,0.0,1.0,1.0,0.0,163473.07,1.0
+13063,15621205.0,Lo,652.0,Germany,Male,47.0,8.0,123692.0,1.0,1.0,0.0,158043.11,1.0
+13064,15581733.0,Ni,568.0,Germany,Male,49.0,4.0,131468.44,1.0,1.0,0.0,186655.11,1.0
+13065,15798964.0,H?,663.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,129562.74,0.0
+13066,15743893.0,Gilleland,647.0,France,Male,29.0,5.0,90544.0,2.0,0.0,1.0,87412.24,0.0
+13067,15656029.0,Fanucci,607.0,Spain,Female,45.0,3.0,0.0,1.0,1.0,0.0,4993.94,1.0
+13068,15662494.0,Ch'eng,713.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,107839.03,0.0
+13069,15768006.0,Ts'ui,814.0,France,Male,35.0,2.0,0.0,1.0,1.0,1.0,74850.84,0.0
+13070,15771674.0,Mazzi,731.0,Spain,Male,33.0,5.0,119492.88,1.0,1.0,0.0,177719.88,0.0
+13071,15698028.0,Obielumani,628.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,174921.91,0.0
+13072,15764444.0,Nwankwo,707.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,119830.08,0.0
+13073,15697035.0,K'ung,758.0,France,Female,40.0,6.0,96763.89,1.0,1.0,1.0,162960.05,0.0
+13074,15768387.0,Tretiakova,671.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,122960.98,0.0
+13075,15635240.0,Sun,753.0,Germany,Male,29.0,2.0,137657.71,1.0,1.0,1.0,62463.01,0.0
+13076,15808389.0,Zaytseva,516.0,France,Female,23.0,9.0,0.0,2.0,1.0,1.0,160596.93,0.0
+13077,15682758.0,Chikezie,709.0,France,Female,26.0,2.0,0.0,2.0,1.0,0.0,106841.12,0.0
+13078,15684952.0,Nkemjika,754.0,France,Male,60.0,5.0,0.0,1.0,1.0,0.0,184524.65,1.0
+13079,15802368.0,Mazzanti,727.0,Spain,Male,49.0,1.0,0.0,1.0,0.0,0.0,151335.45,1.0
+13080,15806901.0,Obiajulu,713.0,France,Female,41.0,5.0,0.0,1.0,1.0,1.0,14081.64,0.0
+13081,15733111.0,Ch'ang,578.0,Germany,Female,29.0,6.0,141528.88,3.0,0.0,0.0,185561.89,1.0
+13082,15765287.0,Onyekachi,849.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,91429.74,0.0
+13083,15785547.0,K?,679.0,Spain,Male,30.0,4.0,0.0,2.0,0.0,1.0,127847.25,0.0
+13084,15770174.0,Nwabugwu,638.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,151335.96,0.0
+13085,15653773.0,Ojiofor,678.0,Germany,Male,35.0,8.0,134571.9,2.0,0.0,1.0,143598.7,0.0
+13086,15771483.0,Pipes,539.0,France,Male,45.0,7.0,0.0,2.0,1.0,0.0,106977.06,1.0
+13087,15776947.0,Onwuamaeze,565.0,France,Female,45.0,10.0,0.0,1.0,1.0,1.0,145558.42,1.0
+13088,15757981.0,Nnaife,638.0,France,Female,35.0,5.0,0.0,1.0,0.0,0.0,136910.18,1.0
+13089,15701765.0,Ugochukwutubelum,574.0,France,Female,34.0,6.0,0.0,2.0,0.0,0.0,142331.85,0.0
+13090,15756936.0,P'eng,589.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,109629.59,0.0
+13091,15716984.0,Chiang,526.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,138526.96,0.0
+13092,15661827.0,Pisano,850.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,185547.71,0.0
+13093,15632859.0,H?,554.0,Germany,Male,34.0,3.0,152086.15,2.0,1.0,1.0,48571.14,0.0
+13094,15746662.0,Chia,648.0,France,Female,38.0,4.0,150186.92,1.0,0.0,1.0,130827.97,0.0
+13095,15695097.0,Shih,678.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,67830.2,0.0
+13096,15674868.0,Hsiao,676.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,114732.62,0.0
+13097,15751185.0,Nucci,811.0,France,Male,34.0,4.0,0.0,2.0,0.0,1.0,68814.23,0.0
+13098,15671630.0,Ting,593.0,France,Male,27.0,9.0,0.0,2.0,1.0,0.0,95957.09,0.0
+13099,15766183.0,Lucchesi,706.0,France,Male,38.0,8.0,138881.04,1.0,1.0,0.0,172495.81,0.0
+13100,15789548.0,Ch'iu,692.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,123583.85,0.0
+13101,15723884.0,Hsiung,653.0,France,Male,41.0,1.0,138876.06,1.0,1.0,0.0,164816.6,0.0
+13102,15684592.0,Hsing,696.0,France,Male,38.0,0.0,0.0,2.0,1.0,0.0,71433.88,0.0
+13103,15635240.0,Mistry,773.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,158086.33,0.0
+13104,15592762.0,Tien,659.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,143668.88,0.0
+13105,15637461.0,Onwuamaeze,685.0,France,Male,41.0,9.0,0.0,2.0,0.0,0.0,129826.73,0.0
+13106,15700511.0,Cremonesi,581.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,121681.19,0.0
+13107,15803032.0,Chiang,708.0,Germany,Male,38.0,4.0,99564.22,1.0,1.0,1.0,151822.66,0.0
+13108,15741652.0,Udegbulam,824.0,France,Female,33.0,9.0,125338.8,1.0,1.0,1.0,171035.38,0.0
+13109,15668894.0,T'ao,607.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,1.0,139537.43,0.0
+13110,15632069.0,Nwankwo,586.0,France,Male,35.0,6.0,123545.14,1.0,0.0,0.0,88890.05,0.0
+13111,15587584.0,Hsieh,650.0,France,Male,31.0,9.0,133950.19,1.0,1.0,1.0,158870.28,0.0
+13112,15574886.0,Chiang,841.0,Germany,Female,38.0,9.0,61636.97,2.0,1.0,1.0,108732.96,0.0
+13113,15735078.0,Pisano,537.0,Germany,Male,62.0,1.0,85859.76,1.0,1.0,0.0,44965.54,1.0
+13114,15757926.0,Obiajulu,673.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,140745.91,0.0
+13115,15623489.0,Maclean,692.0,Germany,Female,39.0,4.0,103772.45,1.0,1.0,1.0,102908.91,0.0
+13116,15696852.0,Chibugo,581.0,Spain,Female,36.0,6.0,122453.97,1.0,1.0,1.0,176924.21,0.0
+13117,15634310.0,Chiang,766.0,Spain,Female,32.0,1.0,0.0,1.0,1.0,1.0,142516.35,1.0
+13118,15683553.0,Chikezie,721.0,Germany,Male,20.0,6.0,153663.23,1.0,1.0,0.0,51380.9,0.0
+13119,15659736.0,Chin,705.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,171457.06,0.0
+13120,15668580.0,Gartrell,569.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,182372.35,0.0
+13121,15714586.0,Yermakov,659.0,France,Female,42.0,9.0,0.0,1.0,1.0,0.0,160979.66,1.0
+13122,15689344.0,Kaeppel,594.0,France,Female,39.0,2.0,0.0,1.0,1.0,1.0,62762.03,1.0
+13123,15693764.0,Pugh,730.0,France,Female,60.0,0.0,118097.82,1.0,1.0,0.0,176580.86,1.0
+13124,15636590.0,Biryukova,664.0,France,Male,32.0,5.0,0.0,1.0,1.0,0.0,188426.13,0.0
+13125,15783030.0,Chiu,755.0,France,Male,55.0,7.0,0.0,1.0,1.0,1.0,49553.38,1.0
+13126,15592751.0,P'eng,696.0,Spain,Male,55.0,7.0,0.0,2.0,1.0,1.0,82820.85,0.0
+13127,15586535.0,Pinto,724.0,Spain,Female,40.0,4.0,135962.4,1.0,1.0,0.0,66721.98,1.0
+13128,15635776.0,Ukaegbunam,707.0,France,Male,32.0,5.0,118616.4,1.0,1.0,0.0,87067.73,0.0
+13129,15709256.0,Hs?,596.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,62825.03,0.0
+13130,15694860.0,Chukwubuikem,686.0,Germany,Female,45.0,7.0,129855.32,2.0,1.0,1.0,41970.72,0.0
+13131,15591047.0,Hsing,635.0,France,Male,21.0,8.0,0.0,2.0,0.0,0.0,185210.63,0.0
+13132,15734659.0,O'Brien,604.0,Germany,Female,46.0,4.0,145974.79,1.0,0.0,1.0,179833.45,0.0
+13133,15583480.0,Bazarova,695.0,Germany,Male,41.0,7.0,92526.15,1.0,1.0,1.0,65986.48,0.0
+13134,15658935.0,Hsiung,584.0,Spain,Female,47.0,3.0,0.0,2.0,1.0,0.0,160529.71,1.0
+13135,15786035.0,Cavenagh,641.0,France,Female,32.0,2.0,74323.2,1.0,1.0,0.0,161479.19,0.0
+13136,15757577.0,Ch'ien,659.0,France,Male,42.0,1.0,117325.56,1.0,1.0,1.0,146585.29,1.0
+13137,15690620.0,Smith,521.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,1.0,102284.2,0.0
+13138,15680901.0,Tien,621.0,France,Male,29.0,6.0,0.0,2.0,0.0,0.0,159835.53,0.0
+13139,15659399.0,Walker,643.0,Germany,Male,24.0,3.0,125889.3,1.0,1.0,1.0,147059.16,0.0
+13140,15675316.0,Ojiofor,686.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,121747.96,0.0
+13141,15665521.0,Chinweike,651.0,Germany,Male,36.0,5.0,132767.66,2.0,1.0,0.0,168080.53,0.0
+13142,15749583.0,Iadanza,758.0,Spain,Female,60.0,1.0,0.0,1.0,1.0,1.0,167032.99,1.0
+13143,15778385.0,McElhone,613.0,Germany,Male,42.0,5.0,137841.53,2.0,1.0,1.0,138536.09,0.0
+13144,15782659.0,Hsia,709.0,France,Female,59.0,4.0,0.0,1.0,1.0,0.0,55775.72,1.0
+13145,15728082.0,Chiemenam,691.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,121747.92,0.0
+13146,15694300.0,Smith,562.0,France,Male,41.0,4.0,0.0,2.0,1.0,1.0,10656.89,0.0
+13147,15647301.0,Hsiung,618.0,Spain,Female,37.0,2.0,104930.38,1.0,1.0,0.0,61820.41,0.0
+13148,15772383.0,Fiorentini,525.0,France,Female,26.0,1.0,0.0,2.0,0.0,0.0,136859.62,0.0
+13149,15694958.0,Onwuamaeze,557.0,Germany,Female,65.0,3.0,113882.73,1.0,1.0,1.0,100538.8,1.0
+13150,15696733.0,Ch'ien,807.0,France,Female,63.0,5.0,0.0,2.0,1.0,1.0,47764.99,0.0
+13151,15775293.0,Udinesi,581.0,France,Female,39.0,6.0,139542.86,1.0,1.0,1.0,70774.97,0.0
+13152,15592386.0,Ch'ang,689.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,122449.18,0.0
+13153,15733261.0,Ndukaku,751.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,171096.2,0.0
+13154,15704053.0,Zaytseva,632.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,170819.01,0.0
+13155,15602497.0,Palerma,692.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,65769.3,0.0
+13156,15798885.0,Onwuamaeze,588.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,96444.88,0.0
+13157,15644724.0,Hung,738.0,France,Male,46.0,7.0,0.0,1.0,1.0,0.0,93769.89,1.0
+13158,15675320.0,Cremonesi,577.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,95860.03,0.0
+13159,15691457.0,Fiorentini,639.0,France,Female,25.0,9.0,121927.92,1.0,1.0,1.0,110273.84,0.0
+13160,15588695.0,Milanesi,592.0,France,Male,31.0,4.0,0.0,1.0,1.0,0.0,151335.24,0.0
+13161,15683544.0,Hsu,629.0,Germany,Male,40.0,3.0,140694.78,2.0,1.0,1.0,157547.71,0.0
+13162,15672610.0,Hsueh,682.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,47775.34,0.0
+13163,15609562.0,Chienezie,599.0,Spain,Male,26.0,7.0,0.0,2.0,1.0,1.0,154844.22,0.0
+13164,15624641.0,Nnonso,772.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,84760.32,0.0
+13165,15797733.0,Ch'iu,587.0,Spain,Male,36.0,4.0,116473.65,1.0,1.0,1.0,136859.86,0.0
+13166,15679909.0,Ch'in,575.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,71548.96,0.0
+13167,15577126.0,Chiu,687.0,Germany,Male,33.0,6.0,125845.66,2.0,1.0,0.0,147963.07,0.0
+13168,15804937.0,Chiang,633.0,Germany,Female,32.0,7.0,118028.36,1.0,0.0,1.0,175015.12,1.0
+13169,15604797.0,Yobanna,705.0,Germany,Female,41.0,9.0,90727.79,2.0,1.0,1.0,735.79,0.0
+13170,15612103.0,Genovesi,549.0,France,Female,29.0,7.0,93723.28,1.0,1.0,1.0,108665.89,1.0
+13171,15620284.0,P'an,756.0,Spain,Male,27.0,7.0,0.0,2.0,0.0,1.0,93769.89,0.0
+13172,15770309.0,Chiang,616.0,Germany,Female,37.0,9.0,81564.1,1.0,1.0,1.0,51959.82,0.0
+13173,15782591.0,Iadanza,749.0,France,Male,28.0,9.0,0.0,2.0,0.0,0.0,79792.4,0.0
+13174,15686963.0,Chukwumaobim,679.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,131646.36,0.0
+13175,15578980.0,Nwachukwu,598.0,France,Male,45.0,5.0,0.0,2.0,1.0,0.0,119969.99,0.0
+13176,15692406.0,Okwukwe,706.0,Germany,Male,35.0,3.0,127924.25,1.0,1.0,1.0,82696.84,0.0
+13177,15627830.0,Fanucci,668.0,France,Female,28.0,4.0,0.0,2.0,1.0,0.0,49826.68,0.0
+13178,15661409.0,Cattaneo,850.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,26257.01,0.0
+13179,15622985.0,Ch'iu,730.0,Spain,Female,43.0,8.0,0.0,1.0,1.0,0.0,143315.57,0.0
+13180,15682547.0,Milanesi,687.0,France,Male,27.0,2.0,0.0,2.0,1.0,0.0,36108.5,0.0
+13181,15707128.0,Ch'ang,645.0,Germany,Male,36.0,6.0,151933.63,1.0,0.0,0.0,167033.83,0.0
+13182,15753337.0,Chukwudi,712.0,Germany,Female,32.0,4.0,105261.76,1.0,1.0,1.0,53717.02,0.0
+13183,15677955.0,Ch'ien,738.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,74838.64,0.0
+13184,15665088.0,Wright,667.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,0.0,135630.32,0.0
+13185,15792525.0,Ukaegbunam,551.0,France,Male,45.0,3.0,0.0,2.0,1.0,1.0,100183.05,0.0
+13186,15768072.0,Ch'ien,684.0,France,Male,41.0,10.0,0.0,2.0,1.0,1.0,58440.45,0.0
+13187,15623649.0,Chu,617.0,Germany,Female,65.0,9.0,120849.29,1.0,1.0,1.0,152298.28,1.0
+13188,15614302.0,Trevisani,718.0,Germany,Female,38.0,6.0,129949.49,2.0,1.0,0.0,92811.2,0.0
+13189,15760090.0,L?,694.0,France,Male,35.0,10.0,0.0,2.0,1.0,0.0,181224.56,0.0
+13190,15756472.0,Sagese,648.0,Germany,Male,28.0,8.0,108128.96,2.0,1.0,0.0,62810.91,0.0
+13191,15612092.0,Folliero,622.0,France,Female,41.0,10.0,0.0,1.0,1.0,0.0,63795.8,0.0
+13192,15671514.0,Palermo,535.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,131281.28,0.0
+13193,15682022.0,Ndubueze,684.0,France,Female,48.0,4.0,0.0,1.0,0.0,0.0,75969.19,1.0
+13194,15596713.0,Hsiung,567.0,Spain,Male,35.0,0.0,155973.88,1.0,1.0,1.0,100834.22,0.0
+13195,15672091.0,Palermo,731.0,France,Male,31.0,0.0,0.0,2.0,0.0,1.0,93814.41,0.0
+13196,15672826.0,Rowntree,584.0,France,Male,42.0,9.0,0.0,1.0,1.0,0.0,137898.57,1.0
+13197,15742210.0,Kao,813.0,France,Female,39.0,8.0,0.0,1.0,1.0,0.0,177092.16,1.0
+13198,15643496.0,Onyekaozulu,553.0,Germany,Male,37.0,6.0,146034.74,1.0,1.0,1.0,133814.28,0.0
+13199,15609998.0,Chiu,738.0,Germany,Female,51.0,5.0,134919.85,1.0,1.0,0.0,93739.71,1.0
+13200,15771087.0,Ritchie,652.0,France,Female,47.0,6.0,0.0,1.0,1.0,0.0,163666.85,1.0
+13201,15712403.0,P'an,703.0,France,Male,29.0,5.0,141803.77,1.0,1.0,0.0,171587.9,0.0
+13202,15597901.0,Genovese,753.0,Germany,Male,49.0,1.0,107949.31,1.0,1.0,1.0,645.61,0.0
+13203,15796074.0,Nkemdirim,705.0,Spain,Female,29.0,5.0,109542.29,1.0,1.0,0.0,125083.29,0.0
+13204,15761158.0,Mazzanti,695.0,Germany,Male,37.0,4.0,122021.92,2.0,1.0,1.0,94047.75,0.0
+13205,15807961.0,Martin,762.0,France,Female,51.0,10.0,0.0,1.0,1.0,0.0,136519.94,1.0
+13206,15593973.0,Hsia,713.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,142839.94,0.0
+13207,15649078.0,Bess,624.0,Spain,Male,34.0,0.0,0.0,1.0,1.0,1.0,175894.24,0.0
+13208,15718247.0,Yobanna,632.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,62733.05,0.0
+13209,15592999.0,Onyekachi,545.0,France,Male,45.0,3.0,0.0,2.0,1.0,1.0,126153.24,0.0
+13210,15682533.0,Okwudiliolisa,708.0,France,Female,48.0,4.0,0.0,2.0,1.0,1.0,167723.25,1.0
+13211,15662397.0,Hs?,642.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,57517.71,0.0
+13212,15722090.0,Onuora,645.0,Germany,Female,33.0,2.0,84560.04,1.0,0.0,1.0,142911.99,1.0
+13213,15632448.0,Ch'eng,613.0,France,Male,46.0,7.0,169831.46,1.0,1.0,0.0,152733.89,1.0
+13214,15762048.0,Ts'ao,607.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,83052.03,0.0
+13215,15634146.0,Tsou,714.0,France,Female,36.0,9.0,0.0,2.0,0.0,0.0,10963.04,0.0
+13216,15693322.0,Ewen,681.0,Germany,Female,48.0,0.0,150186.22,1.0,1.0,1.0,178822.31,0.0
+13217,15589329.0,Lai,635.0,France,Female,26.0,1.0,0.0,2.0,1.0,1.0,173321.65,0.0
+13218,15723786.0,Ch'iu,598.0,Germany,Female,45.0,6.0,111900.14,1.0,1.0,0.0,121084.27,1.0
+13219,15626383.0,T'ao,577.0,France,Female,41.0,7.0,0.0,2.0,0.0,0.0,127543.81,0.0
+13220,15596074.0,Udinesi,758.0,Germany,Female,55.0,9.0,116923.81,1.0,1.0,0.0,170360.11,1.0
+13221,15749365.0,DeRose,693.0,France,Male,23.0,8.0,0.0,2.0,1.0,0.0,175893.53,0.0
+13222,15699195.0,Kambinachi,605.0,Spain,Female,30.0,2.0,0.0,2.0,1.0,0.0,137136.29,0.0
+13223,15592999.0,Young,641.0,France,Male,44.0,6.0,0.0,2.0,0.0,0.0,77906.87,0.0
+13224,15662091.0,Nucci,537.0,Germany,Male,40.0,9.0,157780.93,1.0,1.0,0.0,5983.7,1.0
+13225,15665579.0,Chikelu,710.0,France,Female,31.0,7.0,0.0,1.0,1.0,1.0,158036.72,0.0
+13226,15744983.0,Mancini,707.0,France,Male,37.0,2.0,0.0,1.0,1.0,0.0,80134.65,0.0
+13227,15659710.0,Walker,637.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,51536.99,0.0
+13228,15757837.0,Chiekwugo,724.0,France,Female,58.0,3.0,0.0,1.0,1.0,0.0,126307.25,1.0
+13229,15739271.0,Ch'iu,641.0,France,Female,43.0,4.0,0.0,2.0,1.0,0.0,162599.51,0.0
+13230,15777896.0,Onwuamaeze,686.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,73365.41,0.0
+13231,15780128.0,Lo Duca,683.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,170903.4,0.0
+13232,15643658.0,Goliwe,668.0,France,Female,37.0,9.0,0.0,1.0,1.0,1.0,51959.82,0.0
+13233,15691875.0,Mazzi,646.0,France,Male,45.0,5.0,0.0,1.0,1.0,1.0,180932.46,1.0
+13234,15591135.0,Chin,677.0,France,Male,47.0,4.0,0.0,2.0,1.0,0.0,88827.67,1.0
+13235,15694852.0,Tuan,594.0,Germany,Female,39.0,5.0,109740.62,1.0,1.0,1.0,137794.63,1.0
+13236,15705514.0,Nwankwo,683.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,183566.78,0.0
+13237,15803749.0,Palerma,594.0,Spain,Male,22.0,5.0,0.0,2.0,0.0,1.0,100929.59,0.0
+13238,15723884.0,Chinedum,639.0,Germany,Male,33.0,2.0,123674.28,1.0,1.0,1.0,94875.04,0.0
+13239,15589435.0,Chikelu,729.0,France,Male,46.0,1.0,0.0,2.0,1.0,1.0,177065.24,0.0
+13240,15752912.0,Yobanna,614.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,0.0,161409.66,0.0
+13241,15759480.0,Macleod,644.0,France,Male,37.0,6.0,108862.76,1.0,1.0,0.0,61792.76,0.0
+13242,15804052.0,T'ien,678.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,161671.15,0.0
+13243,15692317.0,Teng,608.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,177092.16,0.0
+13244,15686461.0,Nucci,588.0,Spain,Male,31.0,1.0,100116.82,1.0,1.0,1.0,60552.44,0.0
+13245,15795878.0,Iloerika,614.0,Spain,Female,40.0,3.0,91622.42,1.0,1.0,1.0,126812.63,0.0
+13246,15588955.0,Kuo,697.0,Germany,Male,63.0,6.0,133889.35,1.0,1.0,1.0,119818.17,0.0
+13247,15589969.0,Nnaife,683.0,France,Female,37.0,8.0,139432.37,1.0,1.0,1.0,19244.58,0.0
+13248,15793049.0,Ch'en,582.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,133831.37,0.0
+13249,15793856.0,Nwabugwu,628.0,Germany,Male,43.0,4.0,121161.57,1.0,1.0,0.0,166468.21,1.0
+13250,15683544.0,Hs?eh,537.0,Germany,Male,37.0,9.0,148363.38,1.0,1.0,0.0,70899.27,0.0
+13251,15743871.0,Chia,596.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,160129.99,0.0
+13252,15799031.0,T'ang,710.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,124592.84,0.0
+13253,15763063.0,Ch'ien,648.0,Spain,Female,27.0,6.0,0.0,2.0,1.0,1.0,67734.79,0.0
+13254,15730137.0,Ch'eng,635.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,163587.9,0.0
+13255,15673929.0,Hsueh,651.0,France,Female,29.0,4.0,0.0,2.0,0.0,1.0,141035.65,0.0
+13256,15688963.0,Chukwukere,644.0,Germany,Female,46.0,3.0,123971.51,1.0,1.0,0.0,180594.34,1.0
+13257,15749489.0,Aitken,850.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,96658.26,0.0
+13258,15674541.0,Ting,704.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,100816.29,0.0
+13259,15597949.0,Tsai,649.0,France,Male,43.0,1.0,0.0,3.0,1.0,0.0,178458.86,1.0
+13260,15762708.0,Liao,549.0,France,Male,39.0,5.0,123935.35,1.0,1.0,0.0,199661.5,0.0
+13261,15652948.0,Chiu,706.0,Spain,Male,38.0,9.0,0.0,1.0,1.0,0.0,180553.48,0.0
+13262,15610594.0,Chin,598.0,France,Male,38.0,0.0,0.0,2.0,1.0,0.0,171070.43,0.0
+13263,15600379.0,Cattaneo,684.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,35179.18,0.0
+13264,15584766.0,Hsueh,690.0,Germany,Male,45.0,3.0,145731.83,1.0,1.0,1.0,135650.72,1.0
+13265,15739168.0,Tung,695.0,Germany,Male,42.0,4.0,79715.36,1.0,1.0,1.0,7698.6,0.0
+13266,15701166.0,Mazzanti,725.0,France,Male,35.0,2.0,89750.21,1.0,0.0,1.0,118324.75,0.0
+13267,15738219.0,Nkemakolam,596.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,175025.27,0.0
+13268,15690743.0,Teng,691.0,Spain,Female,48.0,5.0,0.0,2.0,1.0,0.0,71139.31,0.0
+13269,15673342.0,O'Loughlin,697.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,142963.26,0.0
+13270,15797960.0,Okwuoma,667.0,France,Male,40.0,7.0,178056.54,1.0,1.0,1.0,99298.8,0.0
+13271,15794566.0,Shubina,653.0,France,Female,28.0,4.0,0.0,1.0,1.0,1.0,122218.81,0.0
+13272,15694518.0,Genovese,710.0,France,Female,41.0,0.0,0.0,2.0,0.0,1.0,182738.0,0.0
+13273,15733883.0,T'ien,685.0,France,Female,47.0,4.0,0.0,2.0,1.0,1.0,78858.07,0.0
+13274,15776985.0,Ch'in,597.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,4696.44,0.0
+13275,15729019.0,Pirozzi,619.0,France,Male,41.0,0.0,0.0,1.0,0.0,0.0,65720.92,1.0
+13276,15628558.0,Ch'iu,710.0,Spain,Female,30.0,3.0,0.0,2.0,0.0,0.0,107839.03,0.0
+13277,15694518.0,Chinomso,681.0,Germany,Female,39.0,9.0,91955.61,1.0,1.0,0.0,183535.24,0.0
+13278,15813192.0,Scott,679.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,58854.88,0.0
+13279,15759717.0,Frolov,845.0,France,Male,36.0,2.0,0.0,1.0,1.0,0.0,118469.38,0.0
+13280,15723894.0,T'ien,592.0,France,Female,44.0,4.0,53772.73,1.0,1.0,1.0,71519.4,0.0
+13281,15790958.0,Onyeorulu,643.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,807.06,0.0
+13282,15803764.0,Fennell,695.0,Germany,Male,36.0,1.0,108262.84,2.0,1.0,0.0,183646.41,0.0
+13283,15662021.0,Padovesi,565.0,France,Female,31.0,8.0,0.0,1.0,0.0,1.0,4497.55,0.0
+13284,15626143.0,Nwabugwu,605.0,Spain,Male,23.0,8.0,0.0,2.0,1.0,1.0,184842.37,0.0
+13285,15716800.0,Y?an,678.0,France,Male,43.0,3.0,0.0,1.0,1.0,0.0,56908.41,0.0
+13286,15651664.0,Toscani,669.0,France,Male,21.0,0.0,152264.81,1.0,1.0,1.0,119817.19,0.0
+13287,15699195.0,Chuang,748.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,93835.64,0.0
+13288,15591248.0,Obidimkpa,633.0,France,Female,29.0,7.0,122373.52,1.0,1.0,1.0,172363.41,0.0
+13289,15777873.0,Y?,682.0,France,Male,47.0,1.0,0.0,1.0,0.0,1.0,104079.62,0.0
+13290,15756481.0,Ibezimako,713.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,178393.68,0.0
+13291,15593468.0,Ch'iu,693.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,162443.05,0.0
+13292,15572767.0,Chukwudi,775.0,France,Female,33.0,6.0,121355.99,1.0,1.0,0.0,141152.28,0.0
+13293,15632859.0,Chienezie,699.0,France,Female,33.0,8.0,125437.86,1.0,1.0,1.0,119374.74,0.0
+13294,15605742.0,T'ien,628.0,France,Female,49.0,4.0,0.0,2.0,1.0,0.0,126281.83,0.0
+13295,15710366.0,Macleod,705.0,France,Female,24.0,4.0,0.0,2.0,0.0,0.0,173989.67,0.0
+13296,15652693.0,Rizzo,682.0,Germany,Male,34.0,10.0,122570.87,2.0,1.0,0.0,130805.54,0.0
+13297,15633260.0,Onuchukwu,619.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,77511.45,0.0
+13298,15796834.0,Frolov,488.0,France,Male,49.0,7.0,0.0,1.0,1.0,0.0,134878.19,1.0
+13299,15576623.0,Udegbulam,707.0,France,Male,46.0,5.0,0.0,3.0,1.0,1.0,147964.99,1.0
+13300,15784594.0,Ch'ien,850.0,Spain,Male,31.0,3.0,0.0,2.0,1.0,0.0,140889.95,0.0
+13301,15738174.0,Oluchukwu,583.0,France,Male,30.0,5.0,0.0,1.0,1.0,1.0,122433.09,0.0
+13302,15684198.0,Toscani,498.0,France,Male,29.0,2.0,0.0,1.0,0.0,0.0,199484.96,1.0
+13303,15700212.0,Chiu,554.0,France,Male,39.0,3.0,0.0,1.0,1.0,0.0,76515.79,0.0
+13304,15756560.0,Teng,713.0,France,Male,72.0,2.0,0.0,1.0,1.0,1.0,66561.6,0.0
+13305,15795527.0,Ch'iu,565.0,France,Male,38.0,2.0,137936.94,1.0,1.0,1.0,113909.66,0.0
+13306,15790717.0,Toscani,586.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,0.0,172097.4,0.0
+13307,15662291.0,Palermo,644.0,France,Female,46.0,2.0,0.0,1.0,0.0,0.0,138526.96,1.0
+13308,15752081.0,Tien,743.0,France,Male,56.0,5.0,0.0,1.0,1.0,0.0,40333.94,1.0
+13309,15775238.0,Onochie,497.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,151925.25,0.0
+13310,15581548.0,Folliero,587.0,France,Female,37.0,0.0,0.0,2.0,0.0,0.0,93883.53,0.0
+13311,15737051.0,Tsou,613.0,Germany,Female,27.0,1.0,151607.56,2.0,1.0,0.0,103931.49,0.0
+13312,15728505.0,Okorie,654.0,France,Male,38.0,5.0,0.0,1.0,0.0,0.0,173365.89,0.0
+13313,15762351.0,Hsia,703.0,Spain,Male,28.0,5.0,0.0,1.0,0.0,0.0,180595.02,0.0
+13314,15806822.0,Folliero,698.0,Spain,Female,39.0,1.0,0.0,1.0,1.0,0.0,60916.56,0.0
+13315,15778089.0,Zetticci,642.0,France,Male,46.0,4.0,122471.09,1.0,1.0,1.0,137720.56,0.0
+13316,15572073.0,Ma,563.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,136079.64,1.0
+13317,15641110.0,O'Donnell,667.0,Spain,Male,31.0,8.0,0.0,1.0,0.0,0.0,105618.14,0.0
+13318,15752588.0,Nwachinemelu,753.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,55775.84,0.0
+13319,15777076.0,Teng,595.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,151924.9,0.0
+13320,15602851.0,Clements,731.0,France,Female,45.0,4.0,0.0,2.0,1.0,0.0,103726.71,1.0
+13321,15585067.0,Trevisani,576.0,Spain,Male,38.0,9.0,152683.54,1.0,0.0,1.0,63736.17,0.0
+13322,15702442.0,Yobanna,805.0,Spain,Female,35.0,7.0,0.0,1.0,1.0,0.0,177065.24,0.0
+13323,15761706.0,Chia,632.0,France,Female,44.0,4.0,0.0,1.0,1.0,0.0,112269.67,0.0
+13324,15781495.0,Babbage,743.0,France,Male,35.0,5.0,0.0,1.0,1.0,0.0,126675.62,0.0
+13325,15615473.0,Nnamdi,681.0,Germany,Male,35.0,2.0,135953.97,2.0,0.0,1.0,173486.39,0.0
+13326,15742009.0,Ch'iu,678.0,Germany,Female,43.0,2.0,110153.33,1.0,1.0,1.0,179206.92,1.0
+13327,15640161.0,Tuan,535.0,Spain,Male,26.0,5.0,0.0,2.0,1.0,0.0,125010.24,0.0
+13328,15791053.0,Ch'iu,663.0,Germany,Male,33.0,7.0,152212.88,2.0,1.0,0.0,132298.52,0.0
+13329,15794566.0,Hsieh,673.0,France,Male,47.0,3.0,0.0,2.0,1.0,1.0,124489.88,0.0
+13330,15592336.0,K?,632.0,France,Male,26.0,10.0,173590.61,1.0,0.0,1.0,168544.85,0.0
+13331,15662578.0,Shih,683.0,France,Female,57.0,4.0,0.0,2.0,1.0,0.0,167984.72,1.0
+13332,15743617.0,Chibugo,658.0,Spain,Male,23.0,8.0,0.0,2.0,1.0,0.0,73669.94,0.0
+13333,15620505.0,Young,630.0,France,Female,34.0,9.0,0.0,2.0,0.0,0.0,90876.95,0.0
+13334,15804009.0,Chiang,794.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,141180.66,0.0
+13335,15716085.0,Palerma,614.0,Spain,Female,40.0,2.0,0.0,1.0,1.0,1.0,143360.56,0.0
+13336,15589401.0,H?,738.0,France,Female,36.0,3.0,0.0,1.0,1.0,1.0,174738.2,0.0
+13337,15732644.0,Ch'ien,579.0,France,Male,40.0,9.0,0.0,1.0,1.0,1.0,93826.31,0.0
+13338,15628963.0,Frolova,556.0,Germany,Female,52.0,2.0,102800.65,1.0,1.0,0.0,176883.42,1.0
+13339,15771086.0,Maclean,735.0,France,Male,43.0,4.0,0.0,2.0,1.0,0.0,93835.64,0.0
+13340,15793331.0,Onyekachi,692.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,138889.15,0.0
+13341,15782879.0,Ts'ui,559.0,France,Male,39.0,7.0,145988.65,1.0,1.0,0.0,80416.16,0.0
+13342,15778915.0,Napolitano,679.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,97465.44,0.0
+13343,15662494.0,T'ien,642.0,France,Female,34.0,0.0,141359.37,1.0,1.0,1.0,126761.2,0.0
+13344,15731760.0,Smith,607.0,Spain,Female,53.0,3.0,0.0,1.0,0.0,1.0,82900.25,1.0
+13345,15597909.0,Yeh,721.0,Spain,Female,40.0,5.0,63723.53,1.0,1.0,0.0,142963.29,1.0
+13346,15624186.0,Rolon,706.0,Germany,Female,46.0,4.0,129433.38,3.0,0.0,0.0,175310.38,1.0
+13347,15794414.0,Ndubuagha,646.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,164816.6,0.0
+13348,15759499.0,Tretiakova,735.0,Germany,Male,59.0,3.0,121540.26,3.0,1.0,1.0,94739.2,1.0
+13349,15622083.0,P'eng,629.0,France,Female,60.0,1.0,0.0,2.0,1.0,0.0,160589.15,1.0
+13350,15696900.0,Lai,838.0,Germany,Male,28.0,8.0,102785.16,2.0,1.0,1.0,47951.12,0.0
+13351,15667971.0,Dickson,579.0,Germany,Male,31.0,2.0,141356.47,2.0,1.0,0.0,184856.89,0.0
+13352,15801256.0,Hsiung,586.0,France,Male,49.0,5.0,0.0,1.0,1.0,1.0,134626.2,1.0
+13353,15668289.0,Chizoba,730.0,France,Male,35.0,1.0,127286.04,1.0,1.0,0.0,127549.6,0.0
+13354,15793331.0,Macleod,562.0,Spain,Female,31.0,6.0,0.0,2.0,1.0,1.0,112781.5,0.0
+13355,15568104.0,Baresi,577.0,Germany,Male,44.0,6.0,121063.8,1.0,1.0,1.0,85411.01,0.0
+13356,15694556.0,T'ang,632.0,France,Male,38.0,5.0,0.0,2.0,0.0,0.0,148087.62,0.0
+13357,15769353.0,Ch'iu,628.0,Germany,Female,43.0,6.0,133537.7,1.0,0.0,1.0,185547.71,1.0
+13358,15792360.0,Nnanna,700.0,Spain,Female,29.0,7.0,0.0,2.0,1.0,1.0,176962.41,0.0
+13359,15746662.0,Shih,637.0,Germany,Male,42.0,9.0,133432.59,1.0,1.0,0.0,49653.01,0.0
+13360,15635942.0,Palerma,557.0,France,Male,56.0,5.0,0.0,2.0,1.0,0.0,80552.11,1.0
+13361,15671630.0,Palerma,636.0,Spain,Male,44.0,6.0,89416.99,1.0,1.0,1.0,18364.19,1.0
+13362,15754068.0,Chukwufumnanya,596.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,117038.96,0.0
+13363,15578788.0,Ritchie,579.0,Spain,Male,38.0,7.0,0.0,1.0,1.0,0.0,55763.66,0.0
+13364,15793883.0,Ts'ao,505.0,France,Male,20.0,6.0,0.0,2.0,1.0,1.0,59249.83,0.0
+13365,15603743.0,Kao,679.0,Germany,Male,35.0,7.0,99092.45,2.0,0.0,1.0,123587.85,0.0
+13366,15704910.0,Lo,571.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,152956.33,0.0
+13367,15654074.0,Fan,581.0,Germany,Female,31.0,5.0,121669.93,2.0,1.0,0.0,123977.86,0.0
+13368,15796467.0,Ch'eng,632.0,France,Male,45.0,1.0,98897.1,1.0,1.0,0.0,187985.85,1.0
+13369,15679963.0,O'Loghlen,683.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,57762.0,0.0
+13370,15772781.0,Ch'in,596.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,35786.76,0.0
+13371,15570194.0,Mazzanti,698.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,92320.37,0.0
+13372,15671137.0,Maclean,734.0,Germany,Male,32.0,3.0,169902.92,1.0,1.0,0.0,126535.92,0.0
+13373,15756875.0,Piazza,648.0,Spain,Female,35.0,7.0,0.0,2.0,1.0,0.0,167984.72,0.0
+13374,15578825.0,Lo Duca,683.0,France,Female,38.0,10.0,0.0,1.0,1.0,0.0,181718.73,1.0
+13375,15636756.0,Chukwujamuike,581.0,Spain,Male,47.0,9.0,0.0,1.0,1.0,0.0,125986.18,0.0
+13376,15771855.0,Uspenskaya,682.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,0.0,180969.55,0.0
+13377,15691808.0,Chin,597.0,France,Female,28.0,5.0,0.0,2.0,1.0,1.0,82796.29,0.0
+13378,15674868.0,Nnaife,748.0,Germany,Female,49.0,3.0,143258.85,1.0,0.0,0.0,158974.45,1.0
+13379,15795519.0,Li,648.0,France,Male,31.0,9.0,0.0,2.0,0.0,1.0,79240.9,0.0
+13380,15574068.0,Shubin,687.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,167873.49,0.0
+13381,15626114.0,Oluchukwu,724.0,France,Female,32.0,9.0,0.0,2.0,0.0,1.0,118313.36,0.0
+13382,15694444.0,Onwumelu,584.0,France,Male,28.0,6.0,0.0,1.0,1.0,0.0,171413.66,0.0
+13383,15779581.0,Chimaoke,595.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,176450.19,0.0
+13384,15756475.0,Lucchesi,646.0,France,Male,31.0,7.0,119963.45,1.0,1.0,1.0,157097.09,0.0
+13385,15810563.0,T'ao,637.0,France,Male,37.0,5.0,106171.68,1.0,1.0,1.0,54141.5,0.0
+13386,15668521.0,Hs?,652.0,France,Female,33.0,4.0,111098.79,1.0,0.0,1.0,120540.83,0.0
+13387,15800746.0,Chibuzo,634.0,France,Male,40.0,9.0,0.0,1.0,1.0,1.0,162697.93,0.0
+13388,15794048.0,Zetticci,709.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,172576.56,0.0
+13389,15715003.0,Milano,733.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,114514.64,0.0
+13390,15592078.0,Chu,683.0,Spain,Female,39.0,9.0,0.0,2.0,1.0,0.0,187915.7,0.0
+13391,15812197.0,Iloerika,767.0,France,Male,31.0,8.0,0.0,1.0,1.0,0.0,183566.85,0.0
+13392,15705458.0,Chinedum,646.0,France,Female,29.0,4.0,131354.37,1.0,1.0,0.0,182038.6,0.0
+13393,15586974.0,Lu,706.0,France,Female,52.0,9.0,0.0,1.0,1.0,0.0,52301.15,1.0
+13394,15632439.0,Iweobiegbunam,526.0,France,Male,38.0,9.0,128226.37,1.0,1.0,1.0,83629.6,0.0
+13395,15783522.0,Folliero,607.0,Germany,Male,39.0,8.0,106070.89,3.0,1.0,0.0,118059.19,1.0
+13396,15771674.0,Nnamutaezinwa,739.0,Germany,Male,39.0,7.0,131778.08,1.0,1.0,0.0,180736.24,0.0
+13397,15593468.0,Ch'iu,730.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,1.0,108008.65,0.0
+13398,15736250.0,Chiang,697.0,France,Male,36.0,4.0,0.0,1.0,1.0,1.0,121551.58,0.0
+13399,15646616.0,Yevseyev,646.0,Spain,Male,21.0,7.0,0.0,2.0,1.0,1.0,82565.01,0.0
+13400,15578878.0,K'ung,635.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,1.0,131473.31,0.0
+13401,15661409.0,Ch'eng,543.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,55725.04,0.0
+13402,15749669.0,Macdonald,714.0,France,Male,52.0,7.0,0.0,1.0,1.0,0.0,111590.35,1.0
+13403,15706232.0,Hsueh,724.0,France,Male,27.0,4.0,0.0,2.0,1.0,0.0,120880.72,0.0
+13404,15588948.0,Mancini,697.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,93881.75,0.0
+13405,15656426.0,Okeke,774.0,Spain,Female,36.0,4.0,0.0,2.0,1.0,0.0,127749.61,0.0
+13406,15702801.0,Cattaneo,597.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,132436.71,1.0
+13407,15793723.0,Chukwudi,714.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,153917.54,0.0
+13408,15704586.0,Calabrese,652.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,129991.66,0.0
+13409,15638247.0,Maclean,661.0,France,Male,38.0,0.0,0.0,2.0,1.0,1.0,152567.75,0.0
+13410,15636562.0,Rizzo,543.0,France,Male,35.0,7.0,0.0,1.0,1.0,0.0,10771.46,1.0
+13411,15663817.0,Hao,651.0,Germany,Female,34.0,5.0,92636.96,2.0,0.0,0.0,75860.01,0.0
+13412,15767794.0,Harker,582.0,Germany,Male,30.0,8.0,122453.97,1.0,1.0,0.0,167984.61,0.0
+13413,15757895.0,Kudryashova,681.0,France,Female,26.0,3.0,127638.34,1.0,0.0,1.0,162734.31,0.0
+13414,15743759.0,Yobanna,644.0,France,Male,41.0,1.0,0.0,2.0,1.0,0.0,152740.16,0.0
+13415,15711219.0,Ritchie,743.0,Germany,Female,64.0,4.0,131766.71,1.0,0.0,0.0,123982.14,1.0
+13416,15576935.0,Nnanna,657.0,France,Female,26.0,0.0,0.0,1.0,0.0,1.0,164741.5,1.0
+13417,15672397.0,Clements,584.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,0.0,71179.31,0.0
+13418,15667971.0,Walker,615.0,Germany,Male,31.0,5.0,145973.88,2.0,1.0,1.0,65612.12,0.0
+13419,15757537.0,Lu,616.0,France,Male,41.0,6.0,0.0,2.0,1.0,0.0,131301.74,0.0
+13420,15673584.0,Tuan,850.0,Germany,Female,39.0,6.0,103391.38,2.0,1.0,0.0,60905.51,0.0
+13421,15633474.0,Nwachukwu,628.0,Germany,Male,32.0,2.0,161525.96,1.0,1.0,1.0,100919.19,0.0
+13422,15595588.0,Koo,712.0,Germany,Female,42.0,2.0,111669.68,1.0,1.0,1.0,83478.12,0.0
+13423,15648800.0,Nkemdilim,748.0,Spain,Female,36.0,8.0,0.0,2.0,1.0,0.0,105965.25,0.0
+13424,15613363.0,Chukwujamuike,596.0,France,Male,43.0,8.0,0.0,1.0,0.0,1.0,152876.13,0.0
+13425,15622230.0,Ch'iu,603.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,143481.85,0.0
+13426,15732266.0,Henty,659.0,France,Female,55.0,6.0,0.0,1.0,1.0,0.0,10883.52,1.0
+13427,15579891.0,Teng,657.0,Spain,Female,26.0,4.0,0.0,2.0,1.0,1.0,171070.58,0.0
+13428,15763029.0,Y?,743.0,France,Male,35.0,6.0,0.0,1.0,1.0,1.0,66893.28,0.0
+13429,15664214.0,Tung,600.0,Germany,Female,34.0,3.0,102994.04,1.0,0.0,1.0,14073.11,0.0
+13430,15598838.0,Y?an,619.0,France,Male,66.0,5.0,0.0,2.0,1.0,1.0,106534.15,0.0
+13431,15677105.0,Hsiao,573.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,26116.82,0.0
+13432,15740104.0,Pinto,635.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,170705.53,0.0
+13433,15666032.0,Hargrave,756.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,44251.33,0.0
+13434,15785547.0,Nnanna,659.0,France,Male,39.0,8.0,0.0,2.0,0.0,1.0,51380.9,1.0
+13435,15684960.0,Nkemdilim,714.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,136540.09,0.0
+13436,15649858.0,Kao,633.0,Spain,Male,40.0,7.0,95377.13,2.0,1.0,0.0,163534.6,0.0
+13437,15647570.0,Zikoranachidimma,675.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,179908.71,0.0
+13438,15688868.0,Hsing,677.0,France,Female,37.0,3.0,0.0,2.0,0.0,0.0,92888.52,0.0
+13439,15658032.0,T'ien,790.0,France,Male,64.0,0.0,0.0,2.0,0.0,1.0,47799.15,0.0
+13440,15804950.0,Scott,624.0,France,Female,32.0,9.0,91955.61,1.0,1.0,1.0,132973.21,0.0
+13441,15652948.0,White,718.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,132973.17,0.0
+13442,15751167.0,P'an,714.0,Spain,Female,45.0,7.0,154533.51,1.0,1.0,0.0,142977.05,1.0
+13443,15665579.0,Manna,562.0,France,Male,43.0,10.0,0.0,2.0,1.0,0.0,115842.1,0.0
+13444,15808905.0,Chiazagomekpere,699.0,Germany,Male,35.0,2.0,103391.38,1.0,1.0,0.0,84487.62,0.0
+13445,15709447.0,Chidiebele,641.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,149705.25,0.0
+13446,15720509.0,Kao,639.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,0.0,123954.15,1.0
+13447,15636562.0,Genovese,679.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,102640.52,0.0
+13448,15610557.0,Kornilova,589.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,161579.85,0.0
+13449,15733777.0,Lo,751.0,France,Male,31.0,10.0,0.0,2.0,0.0,1.0,96847.91,0.0
+13450,15674526.0,Ponomaryov,778.0,Germany,Male,36.0,3.0,91547.84,1.0,0.0,0.0,94951.27,0.0
+13451,15740356.0,P'an,709.0,France,Male,29.0,3.0,0.0,2.0,0.0,1.0,97640.2,0.0
+13452,15607598.0,Nnachetam,653.0,France,Male,30.0,8.0,144772.69,1.0,0.0,0.0,180932.46,0.0
+13453,15658972.0,Ponomaryov,719.0,Germany,Female,47.0,1.0,129855.72,1.0,1.0,0.0,97921.25,1.0
+13454,15780254.0,Chukwujamuike,615.0,France,Male,34.0,10.0,92895.56,1.0,1.0,0.0,167553.06,0.0
+13455,15626457.0,Hsueh,717.0,Germany,Male,43.0,1.0,124449.85,2.0,1.0,1.0,179655.87,0.0
+13456,15658969.0,Chidiebele,545.0,France,Male,43.0,6.0,128718.78,1.0,1.0,0.0,79259.77,0.0
+13457,15671591.0,Walker,551.0,Germany,Male,42.0,1.0,130334.35,2.0,0.0,1.0,89893.6,0.0
+13458,15686696.0,Piazza,850.0,France,Female,56.0,5.0,0.0,1.0,1.0,0.0,175874.68,1.0
+13459,15614365.0,Chukwudi,648.0,Germany,Female,50.0,3.0,129855.32,2.0,1.0,0.0,171922.72,1.0
+13460,15780212.0,Hsia,752.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,82662.73,0.0
+13461,15669987.0,Ch'eng,543.0,Spain,Male,40.0,10.0,0.0,2.0,0.0,0.0,147090.9,0.0
+13462,15583090.0,Okeke,812.0,Germany,Female,55.0,1.0,110153.58,1.0,0.0,1.0,82351.06,1.0
+13463,15719276.0,Lu,602.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,141756.32,0.0
+13464,15662232.0,Ch'iu,644.0,Spain,Male,34.0,5.0,127552.85,1.0,0.0,1.0,62733.05,0.0
+13465,15669922.0,Hao,631.0,Spain,Female,46.0,2.0,148106.84,1.0,0.0,1.0,172699.54,1.0
+13466,15803681.0,Hsueh,567.0,France,Male,37.0,5.0,0.0,1.0,0.0,1.0,95655.16,1.0
+13467,15782591.0,Ch'eng,738.0,Spain,Female,27.0,3.0,0.0,2.0,0.0,0.0,121408.55,0.0
+13468,15652674.0,Chiu,689.0,Germany,Female,40.0,4.0,95710.11,1.0,1.0,0.0,78650.95,1.0
+13469,15697691.0,Maclean,757.0,France,Male,44.0,8.0,0.0,2.0,1.0,0.0,57844.84,0.0
+13470,15599403.0,Ch'iu,719.0,Spain,Female,32.0,0.0,0.0,2.0,1.0,0.0,88724.84,0.0
+13471,15573971.0,Onyemauchechukwu,726.0,France,Female,52.0,4.0,106301.85,2.0,0.0,0.0,151310.16,1.0
+13472,15780038.0,Palermo,754.0,France,Male,27.0,4.0,0.0,2.0,0.0,0.0,146723.72,0.0
+13473,15580149.0,Hao,554.0,France,Male,55.0,8.0,123894.22,1.0,0.0,1.0,79304.38,1.0
+13474,15625247.0,Chukwujamuike,650.0,Spain,Male,26.0,2.0,82453.96,2.0,1.0,0.0,92612.52,0.0
+13475,15706464.0,Hayslett,751.0,France,Female,31.0,1.0,119948.09,1.0,1.0,1.0,180598.86,0.0
+13476,15769412.0,Pisani,705.0,France,Female,56.0,8.0,0.0,1.0,1.0,1.0,3560.0,0.0
+13477,15783629.0,Onwumelu,731.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,171552.02,0.0
+13478,15758813.0,Chinwendu,567.0,Germany,Female,48.0,9.0,154776.42,1.0,1.0,0.0,107876.91,0.0
+13479,15693526.0,Davidson,571.0,Germany,Male,28.0,4.0,82531.11,2.0,0.0,0.0,132973.18,0.0
+13480,15621116.0,Lettiere,597.0,Spain,Male,39.0,7.0,0.0,1.0,1.0,0.0,157862.82,0.0
+13481,15736302.0,Teng,554.0,France,Male,41.0,5.0,84427.58,1.0,1.0,1.0,104079.53,0.0
+13482,15646720.0,Tseng,678.0,Germany,Male,42.0,9.0,161889.79,1.0,1.0,0.0,79323.61,0.0
+13483,15701265.0,Hsia,633.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,118738.81,0.0
+13484,15703728.0,Zito,634.0,Spain,Female,42.0,9.0,0.0,2.0,1.0,0.0,124749.08,0.0
+13485,15791867.0,Lo Duca,731.0,France,Male,36.0,3.0,0.0,2.0,0.0,0.0,72846.58,1.0
+13486,15576065.0,Hsiao,714.0,France,Male,52.0,8.0,0.0,1.0,1.0,0.0,24218.65,1.0
+13487,15782311.0,Whitehead,607.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,0.0,142903.44,0.0
+13488,15600357.0,Brown,663.0,France,Male,46.0,8.0,152265.43,2.0,1.0,0.0,119333.38,0.0
+13489,15609823.0,Ch'iu,619.0,France,Male,46.0,2.0,0.0,2.0,1.0,0.0,73464.88,0.0
+13490,15626608.0,Chidubem,611.0,France,Female,37.0,7.0,0.0,2.0,0.0,1.0,84760.32,0.0
+13491,15730793.0,Ritchie,729.0,Spain,Female,51.0,8.0,0.0,2.0,1.0,0.0,88890.05,1.0
+13492,15648725.0,Obiajulu,731.0,Spain,Male,39.0,6.0,140418.81,1.0,1.0,1.0,117946.3,1.0
+13493,15618582.0,H?,748.0,Germany,Female,32.0,6.0,151607.56,1.0,1.0,0.0,138882.98,0.0
+13494,15711455.0,Chiang,660.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,152987.42,0.0
+13495,15641733.0,Y?,543.0,Germany,Male,30.0,2.0,112808.18,1.0,1.0,1.0,53478.02,0.0
+13496,15573741.0,Genovesi,629.0,France,Female,48.0,2.0,0.0,1.0,1.0,1.0,73679.76,1.0
+13497,15682778.0,Manna,526.0,France,Male,37.0,2.0,153366.13,1.0,0.0,0.0,10908.33,0.0
+13498,15743040.0,Tuan,521.0,France,Male,41.0,2.0,101827.07,1.0,1.0,0.0,185599.86,0.0
+13499,15673372.0,Yobachukwu,637.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,134851.12,0.0
+13500,15688876.0,Ewers,711.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,0.0,91366.42,0.0
+13501,15690733.0,Lucchese,745.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,126551.65,0.0
+13502,15682355.0,Hales,559.0,Germany,Male,39.0,7.0,132558.26,1.0,1.0,1.0,127543.81,0.0
+13503,15661528.0,Chiang,598.0,France,Female,22.0,10.0,112572.39,1.0,1.0,1.0,149550.95,0.0
+13504,15636353.0,Ts'ui,663.0,France,Male,30.0,9.0,0.0,2.0,0.0,0.0,11783.1,0.0
+13505,15772559.0,Chukwueloka,689.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,183617.49,0.0
+13506,15761506.0,Nweke,706.0,France,Male,24.0,4.0,0.0,2.0,1.0,0.0,182855.42,0.0
+13507,15786539.0,Nwokeocha,782.0,France,Male,43.0,3.0,113978.97,1.0,1.0,1.0,176913.42,0.0
+13508,15711130.0,Hs?,461.0,Germany,Female,46.0,9.0,103417.73,1.0,1.0,0.0,157097.09,1.0
+13509,15664214.0,Trevisano,550.0,France,Female,32.0,4.0,80780.16,1.0,1.0,0.0,170835.32,0.0
+13510,15667460.0,Nwankwo,658.0,Germany,Male,27.0,4.0,102945.01,1.0,0.0,0.0,175399.14,0.0
+13511,15694556.0,Chiu,676.0,Germany,Female,47.0,9.0,153819.58,2.0,1.0,0.0,100995.11,1.0
+13512,15729362.0,Pinto,559.0,France,Female,27.0,3.0,0.0,1.0,0.0,1.0,172097.4,0.0
+13513,15624397.0,Nwankwo,769.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,133692.88,0.0
+13514,15742238.0,Y?an,595.0,France,Female,24.0,5.0,0.0,1.0,1.0,0.0,170195.4,0.0
+13515,15769110.0,Ugochukwutubelum,682.0,France,Male,28.0,10.0,0.0,2.0,1.0,0.0,82972.65,0.0
+13516,15588918.0,O'Donnell,591.0,Germany,Male,33.0,3.0,130554.51,2.0,1.0,1.0,81778.72,0.0
+13517,15776518.0,Baryshnikov,790.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,138700.75,1.0
+13518,15674929.0,Obidimkpa,663.0,Germany,Male,41.0,4.0,81576.75,2.0,1.0,0.0,100941.57,0.0
+13519,15672757.0,Bazarova,828.0,France,Male,40.0,3.0,118050.79,1.0,1.0,0.0,121151.1,0.0
+13520,15674932.0,Mazzi,807.0,France,Female,38.0,10.0,0.0,1.0,1.0,1.0,74945.11,1.0
+13521,15753639.0,Goliwe,705.0,Germany,Female,45.0,3.0,111235.05,1.0,1.0,0.0,175750.21,1.0
+13522,15571261.0,Lo,724.0,France,Female,28.0,6.0,0.0,1.0,0.0,1.0,187985.85,0.0
+13523,15707634.0,Chiawuotu,682.0,Spain,Female,28.0,2.0,0.0,2.0,1.0,1.0,173899.42,0.0
+13524,15631451.0,Dreyer,638.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,173516.71,0.0
+13525,15573741.0,Pagnotto,731.0,France,Male,43.0,4.0,128509.63,1.0,1.0,0.0,73464.88,1.0
+13526,15776936.0,Hsia,637.0,France,Male,59.0,5.0,135923.16,1.0,1.0,1.0,111190.53,0.0
+13527,15771222.0,Goliwe,535.0,Germany,Female,59.0,3.0,115532.52,1.0,1.0,0.0,97939.4,1.0
+13528,15792726.0,Uspensky,706.0,France,Male,37.0,6.0,129745.1,1.0,1.0,1.0,110510.28,0.0
+13529,15782939.0,Chuang,754.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,80619.09,0.0
+13530,15761655.0,Ch'iu,487.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,64516.8,0.0
+13531,15753952.0,Obiajulu,718.0,Germany,Female,37.0,7.0,121063.85,2.0,1.0,1.0,174882.5,0.0
+13532,15572408.0,Ponomaryov,671.0,Germany,Male,31.0,3.0,141078.37,1.0,1.0,0.0,161203.6,0.0
+13533,15757827.0,Ch'en,641.0,France,Female,36.0,7.0,0.0,1.0,1.0,1.0,93624.55,0.0
+13534,15798824.0,Y?an,675.0,France,Female,33.0,7.0,150635.3,1.0,1.0,0.0,133086.45,0.0
+13535,15757933.0,Mironova,721.0,France,Male,35.0,8.0,138523.2,1.0,1.0,0.0,86587.37,0.0
+13536,15773512.0,Rueda,729.0,Germany,Female,38.0,8.0,90432.92,1.0,1.0,1.0,167065.94,0.0
+13537,15791501.0,Ch'en,553.0,France,Male,47.0,8.0,162658.18,2.0,0.0,1.0,52331.15,0.0
+13538,15654574.0,Ewing,688.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,1.0,52462.25,0.0
+13539,15803880.0,Teng,850.0,Germany,Male,23.0,8.0,101100.22,2.0,1.0,1.0,58885.88,0.0
+13540,15696834.0,Hsiung,624.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,167984.72,0.0
+13541,15760873.0,Horsley,697.0,Germany,Male,30.0,9.0,115046.74,2.0,1.0,1.0,54474.48,0.0
+13542,15794566.0,Pinto,727.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,1.0,159475.08,0.0
+13543,15709324.0,Ukaegbunam,826.0,Germany,Female,54.0,5.0,103549.24,1.0,1.0,1.0,113597.64,1.0
+13544,15671137.0,Zito,590.0,Germany,Female,44.0,8.0,110559.47,2.0,0.0,1.0,88724.49,0.0
+13545,15734999.0,Pagnotto,663.0,France,Female,35.0,4.0,152992.91,1.0,0.0,0.0,85890.05,0.0
+13546,15602497.0,Ts'ai,529.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,134442.79,0.0
+13547,15805312.0,Shao,690.0,France,Male,33.0,5.0,121681.91,1.0,0.0,0.0,73329.08,0.0
+13548,15669411.0,Fenton,613.0,Spain,Male,43.0,4.0,0.0,2.0,1.0,1.0,61759.33,0.0
+13549,15774192.0,Chukwufumnanya,624.0,Spain,Male,35.0,2.0,108019.27,1.0,1.0,1.0,55829.25,0.0
+13550,15757551.0,Hsieh,616.0,Germany,Male,38.0,8.0,132351.29,1.0,1.0,0.0,54816.57,0.0
+13551,15780389.0,Shih,611.0,France,Female,27.0,9.0,110738.89,2.0,1.0,1.0,147545.65,0.0
+13552,15626452.0,Lo,662.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,41970.72,0.0
+13553,15736656.0,Hsia,706.0,France,Female,43.0,4.0,0.0,1.0,1.0,0.0,177519.39,1.0
+13554,15782941.0,Oluchukwu,554.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,182867.84,0.0
+13555,15596939.0,Tsao,633.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,123866.43,0.0
+13556,15656592.0,Ritchie,649.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,198490.33,0.0
+13557,15806945.0,Lettiere,590.0,Spain,Female,28.0,8.0,0.0,2.0,1.0,1.0,126307.25,0.0
+13558,15583368.0,Chuang,712.0,France,Male,36.0,8.0,0.0,1.0,1.0,1.0,145936.28,0.0
+13559,15663826.0,Mazzanti,774.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,147055.87,0.0
+13560,15583841.0,Smith,641.0,France,Female,52.0,4.0,97541.24,1.0,1.0,1.0,137964.45,0.0
+13561,15780038.0,Ewing,691.0,Germany,Female,30.0,3.0,102954.68,2.0,1.0,1.0,117832.88,0.0
+13562,15795936.0,Ulyanov,584.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,180622.43,0.0
+13563,15578980.0,Li Fonti,704.0,Germany,Female,35.0,6.0,137781.65,2.0,1.0,0.0,66893.28,0.0
+13564,15571917.0,Ugorji,731.0,Spain,Male,32.0,5.0,0.0,2.0,0.0,1.0,173365.89,0.0
+13565,15773852.0,T'an,585.0,France,Male,59.0,4.0,88141.1,2.0,1.0,0.0,5669.31,1.0
+13566,15682563.0,Chidubem,538.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,582.59,0.0
+13567,15751788.0,Bevan,713.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,48606.71,0.0
+13568,15723873.0,Yudin,673.0,France,Female,69.0,7.0,0.0,2.0,1.0,1.0,131736.23,0.0
+13569,15574072.0,White,758.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,104469.06,0.0
+13570,15741094.0,Hsueh,659.0,France,Female,26.0,2.0,118022.73,1.0,1.0,1.0,168034.9,0.0
+13571,15642570.0,Ch'in,735.0,Spain,Female,45.0,7.0,123571.77,4.0,1.0,1.0,187811.71,1.0
+13572,15793046.0,Tsou,521.0,France,Male,42.0,5.0,153344.72,1.0,1.0,1.0,139953.94,1.0
+13573,15737354.0,Kumm,792.0,France,Female,35.0,5.0,0.0,1.0,1.0,0.0,179655.87,0.0
+13574,15581840.0,Mai,624.0,Germany,Female,42.0,5.0,152325.21,2.0,1.0,0.0,89972.25,0.0
+13575,15641094.0,Ukaegbunam,682.0,France,Female,43.0,6.0,0.0,2.0,0.0,0.0,100153.43,0.0
+13576,15771573.0,O'Brien,707.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,60552.44,0.0
+13577,15786680.0,Ch'ien,679.0,Germany,Female,41.0,0.0,92786.66,1.0,0.0,0.0,48984.18,1.0
+13578,15641640.0,T'ien,545.0,Spain,Male,53.0,4.0,0.0,2.0,1.0,1.0,82526.92,0.0
+13579,15658935.0,Chien,597.0,Germany,Male,30.0,10.0,143129.41,1.0,0.0,0.0,141827.83,1.0
+13580,15752731.0,Kashiwagi,743.0,Spain,Male,40.0,8.0,162448.69,1.0,1.0,1.0,183575.01,0.0
+13581,15606436.0,T'ien,549.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,88729.22,0.0
+13582,15672640.0,Nnamdi,725.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,96833.0,0.0
+13583,15690796.0,Smith,680.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,115916.55,0.0
+13584,15733581.0,Y?,577.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,0.0,25292.53,0.0
+13585,15704581.0,Deleon,714.0,Germany,Male,41.0,8.0,134542.73,1.0,0.0,0.0,137041.24,1.0
+13586,15700209.0,Nwebube,662.0,France,Female,28.0,6.0,0.0,2.0,0.0,1.0,181461.46,0.0
+13587,15676576.0,Ch'en,676.0,France,Male,33.0,3.0,0.0,1.0,1.0,1.0,72402.0,0.0
+13588,15676094.0,Martin,636.0,Spain,Female,34.0,8.0,133868.21,1.0,0.0,0.0,167522.19,0.0
+13589,15622478.0,Nwokezuike,607.0,France,Male,48.0,5.0,0.0,2.0,1.0,1.0,132973.21,0.0
+13590,15676835.0,Tsao,469.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,1.0,109742.55,0.0
+13591,15751026.0,Nnonso,679.0,Germany,Female,58.0,8.0,107594.11,1.0,1.0,0.0,70400.86,1.0
+13592,15686963.0,Pagnotto,678.0,France,Female,69.0,8.0,123936.54,1.0,1.0,0.0,156787.34,1.0
+13593,15632859.0,Udinesi,629.0,France,Male,42.0,7.0,0.0,1.0,0.0,0.0,99843.95,1.0
+13594,15737404.0,Uwaezuoke,677.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,167033.83,0.0
+13595,15751561.0,Aksenov,743.0,Spain,Female,37.0,3.0,0.0,2.0,0.0,1.0,117038.96,0.0
+13596,15679693.0,Tsai,679.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,61164.15,0.0
+13597,15763662.0,Hsieh,648.0,France,Male,46.0,5.0,0.0,2.0,0.0,1.0,86490.09,0.0
+13598,15643361.0,Ch'eng,678.0,France,Female,50.0,6.0,0.0,1.0,1.0,0.0,175235.99,1.0
+13599,15615207.0,Hsing,751.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,60729.41,0.0
+13600,15585853.0,Ch'in,769.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,1.0,153400.24,0.0
+13601,15624641.0,Okwudiliolisa,850.0,France,Male,36.0,8.0,136496.29,1.0,1.0,0.0,90189.72,0.0
+13602,15579189.0,Ting,529.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,0.0,123587.97,0.0
+13603,15790442.0,Udegbulam,641.0,Spain,Male,25.0,9.0,0.0,2.0,1.0,0.0,149170.92,0.0
+13604,15676526.0,T'ien,687.0,France,Male,31.0,1.0,124447.65,1.0,1.0,1.0,184976.76,0.0
+13605,15664752.0,K'ung,677.0,Spain,Male,64.0,6.0,0.0,2.0,0.0,1.0,102925.72,0.0
+13606,15608406.0,Chiang,598.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,56583.88,0.0
+13607,15687634.0,Zikoranachidimma,709.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,73656.38,1.0
+13608,15656592.0,Ogochukwu,614.0,France,Female,40.0,4.0,0.0,1.0,1.0,1.0,71492.28,1.0
+13609,15757588.0,Chiu,633.0,France,Female,46.0,6.0,0.0,2.0,1.0,0.0,91573.19,1.0
+13610,15596962.0,Onwudiwe,663.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,62746.54,0.0
+13611,15769222.0,Nwebube,669.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,62757.33,0.0
+13612,15783816.0,Chigbogu,746.0,Germany,Male,35.0,3.0,152283.39,1.0,1.0,0.0,138360.29,0.0
+13613,15766355.0,Pagnotto,590.0,France,Male,42.0,2.0,0.0,1.0,1.0,0.0,172749.72,0.0
+13614,15808582.0,Chialuka,571.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,86935.08,0.0
+13615,15644896.0,Zikoranachidimma,584.0,Spain,Female,39.0,5.0,0.0,1.0,1.0,0.0,180326.83,1.0
+13616,15592229.0,Chinedum,651.0,Spain,Male,40.0,3.0,0.0,2.0,1.0,1.0,164825.03,0.0
+13617,15634359.0,Lombardi,675.0,France,Female,34.0,4.0,119278.49,1.0,0.0,0.0,148611.52,0.0
+13618,15596761.0,Hsia,595.0,Spain,Female,56.0,9.0,144550.84,1.0,1.0,0.0,106040.97,1.0
+13619,15780056.0,Chigozie,683.0,France,Male,38.0,7.0,179960.2,1.0,1.0,0.0,185837.08,0.0
+13620,15754574.0,Y?,681.0,France,Female,41.0,5.0,0.0,2.0,1.0,1.0,91573.19,0.0
+13621,15600879.0,Ting,656.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,1.0,140579.17,0.0
+13622,15643633.0,T'ien,553.0,France,Female,41.0,5.0,0.0,2.0,1.0,0.0,53353.36,0.0
+13623,15667971.0,Ch'iu,535.0,Germany,Male,40.0,3.0,127616.56,1.0,1.0,1.0,131660.31,1.0
+13624,15721715.0,Chukwualuka,835.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,0.0,81753.92,0.0
+13625,15585879.0,Trevisani,753.0,France,Female,48.0,7.0,139979.07,1.0,1.0,1.0,67979.35,0.0
+13626,15767598.0,Zetticci,598.0,Spain,Female,36.0,5.0,0.0,1.0,0.0,0.0,77906.85,0.0
+13627,15800366.0,Chien,587.0,Spain,Female,33.0,5.0,0.0,2.0,0.0,0.0,131662.47,0.0
+13628,15766163.0,Kao,705.0,France,Male,26.0,1.0,0.0,2.0,1.0,1.0,91829.17,0.0
+13629,15682472.0,Chien,605.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,165082.25,0.0
+13630,15791172.0,Chiang,713.0,France,Female,40.0,7.0,123971.16,1.0,1.0,1.0,131970.86,0.0
+13631,15608968.0,Tsao,678.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,67944.09,0.0
+13632,15591360.0,McIntyre,578.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,63736.17,1.0
+13633,15598522.0,Mazzanti,599.0,France,Female,52.0,7.0,0.0,3.0,0.0,0.0,141756.32,1.0
+13634,15697042.0,Manna,736.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,99645.04,0.0
+13635,15615797.0,Chukwumaobim,707.0,Germany,Male,46.0,1.0,107174.58,1.0,0.0,0.0,158035.58,0.0
+13636,15739046.0,Chiwetelu,584.0,Spain,Female,40.0,4.0,0.0,1.0,1.0,0.0,129563.86,1.0
+13637,15721815.0,Ndubuagha,602.0,France,Male,30.0,5.0,0.0,1.0,1.0,0.0,103099.22,0.0
+13638,15690546.0,Horsley,602.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,73977.23,0.0
+13639,15785406.0,T'ao,537.0,Spain,Male,34.0,1.0,114543.43,1.0,1.0,1.0,54869.54,0.0
+13640,15643487.0,Ch'ien,607.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,149853.89,0.0
+13641,15656613.0,Tuan,614.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,126517.97,0.0
+13642,15591036.0,Milanesi,583.0,France,Female,29.0,4.0,0.0,1.0,1.0,0.0,27448.33,0.0
+13643,15690772.0,T'ien,841.0,France,Female,34.0,9.0,0.0,1.0,1.0,0.0,100919.19,0.0
+13644,15595037.0,Ch'ien,663.0,Spain,Male,27.0,7.0,0.0,1.0,1.0,0.0,86786.41,0.0
+13645,15783883.0,Chuang,498.0,Spain,Male,39.0,10.0,0.0,2.0,1.0,1.0,98301.61,0.0
+13646,15584930.0,Chibugo,733.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,79329.7,0.0
+13647,15759021.0,Tuan,714.0,France,Female,28.0,6.0,0.0,2.0,0.0,0.0,143598.7,0.0
+13648,15737782.0,H?,728.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,91086.46,0.0
+13649,15766017.0,Chiu,637.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,162599.51,0.0
+13650,15592994.0,H?,582.0,Spain,Female,28.0,8.0,0.0,2.0,1.0,1.0,92888.52,0.0
+13651,15757912.0,Mazzanti,594.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,1.0,180329.83,0.0
+13652,15699598.0,Kao,699.0,Spain,Female,43.0,6.0,0.0,2.0,1.0,0.0,62222.81,0.0
+13653,15746139.0,Mazzanti,712.0,Spain,Male,29.0,7.0,0.0,2.0,1.0,0.0,91340.02,0.0
+13654,15793425.0,T'ao,769.0,France,Female,32.0,1.0,121461.87,1.0,1.0,0.0,167973.63,0.0
+13655,15789425.0,Shih,723.0,Spain,Female,34.0,1.0,136492.92,1.0,1.0,0.0,100557.03,0.0
+13656,15595592.0,Mancini,579.0,France,Male,33.0,6.0,125689.29,1.0,1.0,1.0,86701.4,0.0
+13657,15642710.0,Chukwuemeka,717.0,France,Male,27.0,9.0,0.0,2.0,1.0,0.0,138350.94,0.0
+13658,15566211.0,Iweobiegbunam,718.0,France,Female,30.0,2.0,0.0,2.0,0.0,1.0,121537.91,0.0
+13659,15593973.0,Pirozzi,656.0,Germany,Female,37.0,10.0,123974.96,1.0,1.0,1.0,178074.04,0.0
+13660,15678725.0,H?,656.0,France,Male,35.0,10.0,113574.61,1.0,0.0,0.0,179521.24,0.0
+13661,15733361.0,Yeh,611.0,Spain,Male,36.0,9.0,121778.71,1.0,1.0,1.0,73856.28,0.0
+13662,15781689.0,Uvarov,669.0,Germany,Male,42.0,1.0,71264.02,2.0,1.0,1.0,93336.3,0.0
+13663,15765283.0,Calabrese,608.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,171706.66,0.0
+13664,15761168.0,Ritchie,575.0,Germany,Male,45.0,9.0,116886.59,2.0,1.0,1.0,131736.23,0.0
+13665,15731519.0,Calabresi,543.0,France,Male,37.0,3.0,141827.33,1.0,0.0,0.0,43496.36,0.0
+13666,15578825.0,Okoli,635.0,France,Male,34.0,3.0,149542.25,1.0,1.0,1.0,140889.32,0.0
+13667,15648775.0,Chiemenam,613.0,France,Male,38.0,9.0,0.0,2.0,0.0,0.0,86927.53,0.0
+13668,15681206.0,Chukwuhaenye,678.0,France,Female,38.0,3.0,128760.39,1.0,1.0,0.0,140889.72,0.0
+13669,15714260.0,K?,545.0,France,Male,46.0,7.0,0.0,2.0,1.0,1.0,161579.19,0.0
+13670,15675141.0,Ts'ui,584.0,Germany,Female,52.0,1.0,123544.0,2.0,0.0,0.0,143463.28,1.0
+13671,15794870.0,Zikoranachidimma,636.0,Germany,Female,42.0,10.0,138171.1,2.0,1.0,1.0,117431.1,0.0
+13672,15709142.0,Chijindum,556.0,Germany,Male,42.0,8.0,151739.65,2.0,1.0,0.0,126812.63,0.0
+13673,15698872.0,Chuang,850.0,Spain,Male,29.0,9.0,0.0,2.0,0.0,1.0,169415.54,0.0
+13674,15703068.0,Ch'in,633.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,645.61,0.0
+13675,15708513.0,Shih,635.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,1.0,162452.65,0.0
+13676,15704910.0,Illingworth,627.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,0.0,54869.54,0.0
+13677,15621659.0,Cattaneo,726.0,Spain,Female,36.0,6.0,0.0,2.0,0.0,0.0,115818.51,0.0
+13678,15701265.0,Lucchesi,709.0,France,Female,41.0,3.0,151191.31,2.0,1.0,0.0,143317.18,0.0
+13679,15714485.0,De Luca,593.0,France,Male,31.0,1.0,118680.57,1.0,1.0,1.0,92898.17,0.0
+13680,15576044.0,Ngozichukwuka,708.0,France,Male,36.0,8.0,0.0,1.0,1.0,0.0,87411.49,0.0
+13681,15628274.0,Ndubueze,738.0,France,Female,20.0,5.0,0.0,2.0,1.0,0.0,74838.64,0.0
+13682,15679307.0,Pisani,614.0,France,Female,34.0,9.0,0.0,2.0,0.0,0.0,73679.64,0.0
+13683,15581286.0,Yobachukwu,703.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,1.0,133672.61,0.0
+13684,15659325.0,Scott,755.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,121894.31,0.0
+13685,15617206.0,Shih,687.0,Germany,Male,38.0,10.0,158718.13,2.0,1.0,1.0,98463.99,0.0
+13686,15678057.0,Milani,679.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,85547.33,0.0
+13687,15643072.0,Trevisani,773.0,France,Female,40.0,0.0,81732.88,1.0,1.0,1.0,74870.99,0.0
+13688,15768471.0,K'ung,567.0,France,Female,49.0,3.0,0.0,2.0,1.0,0.0,183717.94,1.0
+13689,15726046.0,Ma,758.0,France,Male,41.0,9.0,0.0,2.0,1.0,1.0,122073.48,0.0
+13690,15750639.0,Brown,752.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,175892.04,0.0
+13691,15713890.0,Kao,605.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,142995.32,0.0
+13692,15579526.0,Ch'iu,498.0,Spain,Male,42.0,7.0,0.0,2.0,1.0,1.0,160176.47,0.0
+13693,15701109.0,Smith,696.0,France,Female,34.0,6.0,121827.43,1.0,0.0,1.0,99818.15,0.0
+13694,15585198.0,De Luca,585.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,153920.22,0.0
+13695,15794878.0,Yobachukwu,710.0,Germany,Male,31.0,2.0,109564.1,2.0,1.0,1.0,173844.89,0.0
+13696,15591512.0,Ch'ien,732.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,179631.85,0.0
+13697,15774164.0,Obiajulu,715.0,Spain,Male,38.0,3.0,0.0,1.0,1.0,1.0,94912.78,0.0
+13698,15666032.0,Aksenov,586.0,France,Male,60.0,3.0,0.0,1.0,1.0,1.0,80619.17,0.0
+13699,15644882.0,Watson,711.0,France,Male,33.0,2.0,0.0,2.0,0.0,1.0,176963.17,0.0
+13700,15583076.0,Dike,756.0,Germany,Female,34.0,4.0,157829.12,2.0,1.0,0.0,51536.99,0.0
+13701,15652914.0,Okechukwu,646.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,105886.77,0.0
+13702,15802088.0,Mancini,633.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,140579.17,0.0
+13703,15650237.0,Chiang,545.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,130664.79,0.0
+13704,15782591.0,Ndubueze,726.0,France,Male,48.0,10.0,0.0,1.0,1.0,0.0,62222.81,0.0
+13705,15684592.0,Tseng,684.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,175043.69,0.0
+13706,15786680.0,Hao,703.0,France,Female,56.0,9.0,0.0,1.0,1.0,0.0,156906.15,1.0
+13707,15724153.0,Chukwufumnanya,755.0,Spain,Female,33.0,8.0,0.0,2.0,0.0,1.0,121797.14,0.0
+13708,15652266.0,Chiu,651.0,Germany,Female,67.0,0.0,124043.8,1.0,1.0,1.0,113048.79,1.0
+13709,15643696.0,Ch'ien,572.0,Spain,Female,29.0,1.0,0.0,1.0,0.0,0.0,51959.82,0.0
+13710,15685300.0,Ch'ang,521.0,France,Female,26.0,10.0,126817.65,1.0,1.0,1.0,149614.81,0.0
+13711,15571526.0,Nwankwo,717.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,1.0,122925.44,0.0
+13712,15631693.0,Yermakov,695.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,149599.62,0.0
+13713,15680666.0,Chukwueloka,745.0,France,Male,39.0,3.0,136528.94,1.0,1.0,1.0,86927.53,0.0
+13714,15706071.0,Trevisano,638.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,160976.85,0.0
+13715,15757541.0,Yermolayev,745.0,France,Female,36.0,5.0,0.0,1.0,0.0,0.0,186489.95,0.0
+13716,15702468.0,Ts'ui,593.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,123627.96,0.0
+13717,15591135.0,Pirozzi,607.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,152733.89,0.0
+13718,15672432.0,Bibi,535.0,France,Male,33.0,3.0,0.0,2.0,0.0,0.0,116672.02,0.0
+13719,15668057.0,Ives,668.0,Germany,Male,33.0,2.0,123770.68,1.0,1.0,0.0,125083.29,0.0
+13720,15738174.0,Milanesi,850.0,Spain,Male,40.0,0.0,0.0,2.0,1.0,0.0,89566.64,0.0
+13721,15695097.0,Hs?,756.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,94738.54,0.0
+13722,15732438.0,Chia,578.0,France,Male,24.0,5.0,0.0,2.0,1.0,1.0,172576.43,0.0
+13723,15796413.0,Lai,652.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,82298.59,0.0
+13724,15634359.0,Tien,604.0,Spain,Male,34.0,4.0,168078.83,1.0,1.0,0.0,167078.97,0.0
+13725,15682868.0,Brown,696.0,France,Female,27.0,6.0,0.0,1.0,1.0,1.0,153356.8,0.0
+13726,15751788.0,Lu,773.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,1.0,70550.0,0.0
+13727,15776824.0,Lai,675.0,France,Male,26.0,7.0,0.0,2.0,1.0,0.0,69237.21,0.0
+13728,15631310.0,McLean,554.0,France,Female,49.0,3.0,0.0,1.0,1.0,0.0,163666.85,1.0
+13729,15758856.0,Chukwuemeka,710.0,Germany,Female,25.0,6.0,107963.11,1.0,1.0,1.0,126469.09,1.0
+13730,15678460.0,Chigolum,562.0,Spain,Female,31.0,6.0,0.0,2.0,0.0,0.0,59697.14,0.0
+13731,15790067.0,P'eng,526.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,107839.03,0.0
+13732,15746341.0,T'ao,679.0,Germany,Female,41.0,8.0,111973.97,2.0,1.0,0.0,63736.17,1.0
+13733,15622507.0,Eluemuno,612.0,Spain,Female,47.0,3.0,0.0,1.0,1.0,0.0,162260.93,1.0
+13734,15797736.0,T'an,674.0,Germany,Male,44.0,8.0,116074.89,1.0,0.0,0.0,67468.67,1.0
+13735,15674541.0,Ch'eng,579.0,France,Male,39.0,3.0,178095.55,1.0,1.0,1.0,4459.5,0.0
+13736,15809509.0,Trevisani,706.0,Germany,Female,39.0,8.0,121513.31,1.0,0.0,0.0,175072.47,1.0
+13737,15789413.0,Lucchese,691.0,France,Male,20.0,7.0,0.0,2.0,1.0,1.0,151887.16,0.0
+13738,15699446.0,Ch'ien,641.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,152097.02,0.0
+13739,15570931.0,Lucciano,667.0,France,Female,28.0,9.0,89743.05,1.0,1.0,1.0,131304.45,0.0
+13740,15811947.0,Chukwudi,675.0,France,Male,66.0,9.0,0.0,2.0,1.0,1.0,63795.8,0.0
+13741,15720354.0,Ch'in,659.0,Germany,Female,40.0,4.0,134796.05,3.0,1.0,0.0,132798.78,1.0
+13742,15726695.0,Frolov,748.0,France,Female,46.0,8.0,0.0,2.0,1.0,1.0,86044.98,0.0
+13743,15713374.0,Pacheco,592.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,0.0,131609.48,0.0
+13744,15758531.0,Onuora,739.0,France,Female,41.0,10.0,0.0,2.0,1.0,1.0,72330.46,0.0
+13745,15637169.0,Tsao,537.0,Spain,Male,31.0,5.0,121270.17,1.0,1.0,1.0,96885.19,0.0
+13746,15774277.0,Chimezie,605.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,16081.62,0.0
+13747,15632575.0,Lu,754.0,France,Male,58.0,7.0,148045.45,1.0,1.0,1.0,72969.9,0.0
+13748,15661830.0,Lucchesi,653.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,171045.38,0.0
+13749,15744606.0,Hsiung,535.0,Germany,Male,48.0,2.0,141038.57,1.0,0.0,0.0,117632.63,1.0
+13750,15665521.0,Ponomarev,558.0,Spain,Female,40.0,10.0,0.0,2.0,1.0,1.0,121408.55,0.0
+13751,15661827.0,Manna,648.0,France,Male,44.0,9.0,0.0,2.0,1.0,0.0,125062.02,0.0
+13752,15742854.0,Chu,592.0,France,Male,35.0,0.0,0.0,2.0,0.0,0.0,182372.35,0.0
+13753,15583736.0,Lettiere,649.0,Germany,Male,37.0,6.0,139180.97,1.0,1.0,0.0,79866.73,0.0
+13754,15730793.0,Lucchese,748.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,91359.79,0.0
+13755,15690744.0,Mairinger,562.0,France,Female,23.0,5.0,0.0,2.0,1.0,0.0,125870.5,0.0
+13756,15617454.0,Kao,572.0,Spain,Female,39.0,8.0,72948.58,1.0,0.0,1.0,170819.01,0.0
+13757,15581554.0,Ch'eng,686.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,47775.34,0.0
+13758,15594917.0,Milne,748.0,Germany,Male,32.0,0.0,138847.9,2.0,0.0,1.0,178827.3,0.0
+13759,15588566.0,Trevisano,658.0,Spain,Female,45.0,2.0,123544.0,1.0,0.0,1.0,159618.17,0.0
+13760,15768233.0,Chiu,531.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,71830.1,0.0
+13761,15684556.0,Ch'iu,652.0,Germany,Female,37.0,5.0,121685.31,1.0,1.0,0.0,93839.3,1.0
+13762,15689633.0,Genovesi,757.0,Spain,Female,36.0,5.0,139825.04,1.0,1.0,0.0,182055.36,1.0
+13763,15595039.0,Ts'ui,587.0,France,Male,32.0,2.0,124435.54,1.0,1.0,0.0,161217.18,0.0
+13764,15803078.0,Chidiegwu,599.0,Germany,Female,23.0,4.0,123544.0,2.0,0.0,0.0,144849.1,0.0
+13765,15744044.0,Shih,745.0,France,Male,30.0,1.0,129717.3,1.0,0.0,1.0,86733.61,0.0
+13766,15595713.0,Chiemenam,543.0,Spain,Female,38.0,0.0,125860.2,1.0,1.0,0.0,143980.29,0.0
+13767,15617790.0,Kao,598.0,France,Male,37.0,5.0,154902.66,1.0,1.0,0.0,122925.4,0.0
+13768,15582914.0,Smith,763.0,France,Female,54.0,3.0,0.0,1.0,1.0,0.0,71323.23,0.0
+13769,15593776.0,Onio,516.0,Germany,Male,43.0,6.0,131762.94,1.0,1.0,0.0,79259.77,1.0
+13770,15588839.0,Ch'iu,577.0,France,Male,42.0,10.0,131698.57,1.0,1.0,0.0,136435.75,1.0
+13771,15701687.0,Chimezie,714.0,Spain,Female,46.0,10.0,0.0,1.0,0.0,0.0,125920.54,1.0
+13772,15605037.0,Ts'ui,628.0,Spain,Female,37.0,1.0,160139.59,1.0,1.0,1.0,48606.71,0.0
+13773,15678317.0,Chukwueloka,703.0,France,Female,26.0,9.0,0.0,2.0,1.0,1.0,64477.25,0.0
+13774,15593973.0,Ch'en,809.0,France,Male,35.0,2.0,91897.8,1.0,1.0,1.0,72873.33,0.0
+13775,15788291.0,Ch'in,472.0,France,Male,41.0,4.0,0.0,1.0,1.0,0.0,170893.85,0.0
+13776,15792934.0,Teng,711.0,Germany,Female,47.0,6.0,91908.36,1.0,1.0,0.0,102908.96,1.0
+13777,15762729.0,Hsiung,652.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,16897.19,1.0
+13778,15578878.0,Milanesi,822.0,Germany,Female,51.0,3.0,141541.25,1.0,0.0,1.0,172618.52,1.0
+13779,15808831.0,Ch'iu,679.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,182913.95,0.0
+13780,15752626.0,Biryukov,696.0,Spain,Female,40.0,4.0,0.0,2.0,1.0,1.0,112503.24,0.0
+13781,15571666.0,Nucci,587.0,France,Male,51.0,9.0,0.0,2.0,1.0,1.0,53534.8,0.0
+13782,15605918.0,Horsley,629.0,Spain,Male,25.0,2.0,0.0,2.0,1.0,1.0,149853.89,0.0
+13783,15672905.0,Ts'ui,706.0,Spain,Male,34.0,5.0,141806.0,1.0,1.0,1.0,548.52,0.0
+13784,15679983.0,Folliero,652.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,48478.47,0.0
+13785,15641114.0,Tretiakova,637.0,France,Male,23.0,5.0,0.0,2.0,1.0,0.0,157097.09,0.0
+13786,15702566.0,Chidalu,751.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,174531.27,0.0
+13787,15641478.0,Onyemere,705.0,France,Female,45.0,6.0,0.0,2.0,1.0,0.0,178045.97,0.0
+13788,15583076.0,Fyodorova,579.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,140800.66,0.0
+13789,15591550.0,Ugorji,619.0,Germany,Male,62.0,6.0,171922.72,1.0,1.0,1.0,143952.24,0.0
+13790,15756896.0,Mazzanti,597.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,57549.65,0.0
+13791,15591915.0,Clements,714.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,152872.39,0.0
+13792,15570947.0,Kao,663.0,Spain,Male,41.0,9.0,0.0,2.0,0.0,1.0,27654.44,0.0
+13793,15767231.0,Udinese,684.0,Germany,Male,30.0,1.0,111568.01,2.0,0.0,0.0,132702.64,0.0
+13794,15806822.0,Hsia,576.0,Spain,Male,52.0,8.0,0.0,1.0,1.0,0.0,124448.77,1.0
+13795,15726153.0,Chibuzo,646.0,Germany,Male,27.0,8.0,102954.68,2.0,1.0,0.0,179206.92,0.0
+13796,15803526.0,Martin,651.0,Germany,Female,40.0,5.0,121729.42,1.0,1.0,0.0,130132.41,1.0
+13797,15763612.0,Chigozie,698.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,79623.28,0.0
+13798,15734577.0,Nwachinemelu,748.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,82762.03,0.0
+13799,15607634.0,Scott,682.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,1.0,181270.84,0.0
+13800,15804722.0,Zikoranachidimma,637.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,85421.48,0.0
+13801,15722993.0,McIntyre,714.0,France,Female,48.0,8.0,0.0,2.0,0.0,1.0,68730.91,1.0
+13802,15607637.0,Lo,646.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,1.0,136079.64,0.0
+13803,15745354.0,Chukwuemeka,583.0,Spain,Female,41.0,5.0,0.0,1.0,1.0,0.0,79616.37,0.0
+13804,15743817.0,Ts'ao,556.0,Germany,Male,37.0,6.0,113865.23,1.0,0.0,1.0,173365.89,1.0
+13805,15684461.0,Hsiung,710.0,France,Male,28.0,3.0,0.0,2.0,1.0,1.0,38962.94,0.0
+13806,15673820.0,Hsueh,565.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,144880.81,0.0
+13807,15601184.0,Lucciano,705.0,France,Female,41.0,3.0,0.0,1.0,1.0,0.0,130189.72,0.0
+13808,15761497.0,Ch'ang,581.0,France,Male,31.0,3.0,0.0,2.0,0.0,1.0,143317.42,0.0
+13809,15595067.0,Brown,638.0,Germany,Female,41.0,2.0,139859.54,3.0,1.0,0.0,12761.92,1.0
+13810,15682442.0,T'ien,710.0,Germany,Female,40.0,0.0,122793.96,1.0,0.0,0.0,88791.83,1.0
+13811,15723884.0,Nkemakolam,628.0,Spain,Male,29.0,1.0,133379.41,1.0,1.0,1.0,71830.1,0.0
+13812,15602731.0,De Luca,667.0,France,Male,35.0,8.0,143390.51,1.0,0.0,0.0,143598.7,0.0
+13813,15596013.0,Nwachukwu,667.0,Germany,Male,43.0,0.0,118342.26,1.0,1.0,0.0,74564.41,1.0
+13814,15756070.0,Gartrell,684.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,161579.85,0.0
+13815,15769902.0,T'ang,588.0,Spain,Female,29.0,1.0,0.0,2.0,0.0,1.0,157891.11,0.0
+13816,15677764.0,Ugochukwu,663.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,103564.25,0.0
+13817,15768124.0,Tseng,751.0,Germany,Female,35.0,8.0,90547.48,1.0,1.0,0.0,88604.89,1.0
+13818,15708710.0,Chiawuotu,516.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,95500.98,0.0
+13819,15637650.0,Obiajulu,589.0,Spain,Female,32.0,9.0,0.0,1.0,0.0,1.0,46422.36,0.0
+13820,15743709.0,Chukwufumnanya,619.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,120115.5,0.0
+13821,15660263.0,Kao,757.0,France,Female,45.0,7.0,0.0,2.0,1.0,0.0,122925.4,0.0
+13822,15716155.0,Mazzanti,722.0,France,Female,32.0,3.0,0.0,2.0,1.0,1.0,164608.98,0.0
+13823,15604073.0,Burt,661.0,France,Female,53.0,3.0,146710.76,1.0,1.0,0.0,138510.01,0.0
+13824,15663927.0,Iweobiegbulam,763.0,France,Female,48.0,8.0,0.0,1.0,1.0,1.0,138021.36,0.0
+13825,15783026.0,Nucci,696.0,Germany,Female,40.0,7.0,124532.68,1.0,1.0,0.0,158059.04,1.0
+13826,15770474.0,Tien,814.0,France,Male,24.0,8.0,0.0,2.0,1.0,1.0,119439.66,0.0
+13827,15692406.0,Onwuamaeze,738.0,France,Male,26.0,9.0,88815.56,1.0,1.0,1.0,139919.38,0.0
+13828,15699430.0,Y?,678.0,France,Female,42.0,0.0,0.0,1.0,0.0,1.0,142839.82,0.0
+13829,15632210.0,Trevisani,613.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,58469.98,0.0
+13830,15572792.0,T'ien,603.0,France,Male,24.0,9.0,0.0,2.0,1.0,0.0,144880.81,0.0
+13831,15646091.0,Calabresi,616.0,Germany,Female,44.0,4.0,109528.44,2.0,1.0,1.0,94815.04,0.0
+13832,15615020.0,Ch'eng,632.0,France,Female,35.0,2.0,0.0,2.0,0.0,1.0,177528.92,0.0
+13833,15631310.0,Mazzanti,682.0,Germany,Male,38.0,8.0,165438.26,1.0,1.0,1.0,148516.71,0.0
+13834,15602354.0,Y?an,767.0,France,Female,38.0,4.0,0.0,1.0,1.0,1.0,14073.11,0.0
+13835,15682465.0,Mazzanti,586.0,Germany,Male,35.0,7.0,122314.5,1.0,1.0,0.0,53954.24,1.0
+13836,15770121.0,Chukwueloka,553.0,France,Female,21.0,9.0,0.0,2.0,1.0,0.0,100637.07,0.0
+13837,15651063.0,Nwachinemelu,686.0,Germany,Male,42.0,7.0,148061.89,2.0,0.0,0.0,143317.42,0.0
+13838,15644833.0,Iadanza,613.0,France,Female,25.0,7.0,0.0,2.0,1.0,0.0,54869.54,0.0
+13839,15732265.0,Chukwuemeka,850.0,France,Male,50.0,8.0,0.0,2.0,0.0,1.0,67840.81,0.0
+13840,15641110.0,Kao,739.0,France,Male,48.0,7.0,90537.16,1.0,1.0,1.0,176882.08,0.0
+13841,15584785.0,Chien,714.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,64849.74,0.0
+13842,15794868.0,Chukwujekwu,793.0,France,Female,35.0,6.0,0.0,2.0,0.0,0.0,140934.87,0.0
+13843,15573926.0,Okwuoma,614.0,France,Male,50.0,6.0,0.0,1.0,1.0,1.0,178642.73,1.0
+13844,15591179.0,Smith,739.0,France,Male,25.0,9.0,0.0,2.0,0.0,1.0,83983.39,0.0
+13845,15566269.0,Gboliwe,596.0,Germany,Male,57.0,4.0,121532.17,2.0,0.0,1.0,74854.97,1.0
+13846,15665062.0,Onyemauchechi,687.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,180593.45,0.0
+13847,15669411.0,Naylor,599.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,137028.51,0.0
+13848,15597957.0,Ijendu,567.0,France,Male,26.0,6.0,107852.81,1.0,0.0,0.0,155061.97,0.0
+13849,15741554.0,Ch'ang,645.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,149913.84,0.0
+13850,15715079.0,Chukwuemeka,641.0,France,Female,37.0,1.0,0.0,1.0,0.0,0.0,145558.36,0.0
+13851,15583724.0,Ch'en,651.0,France,Male,29.0,2.0,0.0,1.0,1.0,0.0,123885.88,0.0
+13852,15782531.0,Mancini,663.0,France,Male,38.0,3.0,0.0,1.0,1.0,0.0,121151.1,0.0
+13853,15655007.0,Hsiao,557.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,97628.08,0.0
+13854,15572547.0,T'ien,653.0,France,Male,40.0,3.0,0.0,2.0,0.0,1.0,155047.11,0.0
+13855,15657812.0,Shih,764.0,Spain,Female,32.0,6.0,89900.24,2.0,0.0,0.0,187905.12,0.0
+13856,15704788.0,Onyekachi,728.0,France,Female,32.0,4.0,120167.42,2.0,1.0,1.0,121681.19,0.0
+13857,15671590.0,Nwachukwu,628.0,Spain,Male,36.0,10.0,0.0,2.0,1.0,0.0,140934.95,0.0
+13858,15711398.0,Hsieh,554.0,Germany,Female,23.0,6.0,113625.93,2.0,1.0,1.0,182266.01,0.0
+13859,15642636.0,Chukwufumnanya,599.0,Spain,Male,35.0,5.0,84327.77,1.0,1.0,0.0,175894.24,0.0
+13860,15729954.0,Onyemere,584.0,France,Male,70.0,5.0,0.0,2.0,1.0,1.0,25662.27,1.0
+13861,15681562.0,Chinwendu,677.0,France,Male,47.0,2.0,0.0,2.0,0.0,1.0,86587.37,0.0
+13862,15794468.0,Nnamdi,661.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,140168.36,0.0
+13863,15709136.0,Kaeppel,598.0,France,Female,61.0,6.0,0.0,1.0,1.0,1.0,121428.4,0.0
+13864,15773520.0,Chiu,712.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,132467.54,0.0
+13865,15714355.0,Ch'ien,657.0,France,Male,47.0,7.0,0.0,2.0,1.0,0.0,102076.02,1.0
+13866,15649858.0,Chukwumaobim,675.0,France,Female,23.0,4.0,0.0,2.0,1.0,1.0,97623.42,0.0
+13867,15731751.0,Shih,645.0,France,Male,36.0,5.0,0.0,1.0,1.0,0.0,89937.71,0.0
+13868,15798960.0,Chikezie,710.0,Germany,Male,49.0,6.0,79035.95,1.0,1.0,0.0,136675.22,1.0
+13869,15592877.0,Shih,628.0,France,Male,33.0,5.0,0.0,2.0,0.0,0.0,86935.08,0.0
+13870,15718289.0,Ch'ang,551.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,178122.71,0.0
+13871,15572415.0,Ch'ang,590.0,France,Female,66.0,5.0,123372.3,1.0,0.0,1.0,167558.26,0.0
+13872,15603328.0,Chinagorom,746.0,France,Female,32.0,1.0,0.0,1.0,0.0,1.0,143463.28,0.0
+13873,15789563.0,Nweke,544.0,France,Male,24.0,1.0,0.0,2.0,0.0,1.0,109626.01,0.0
+13874,15799480.0,Manna,757.0,France,Female,43.0,8.0,0.0,2.0,1.0,1.0,187905.12,0.0
+13875,15627220.0,Shih,730.0,France,Female,24.0,2.0,0.0,2.0,1.0,1.0,108716.16,0.0
+13876,15676966.0,Chiang,637.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,0.0,36706.56,0.0
+13877,15745794.0,Lu,516.0,France,Female,46.0,2.0,0.0,1.0,1.0,1.0,51907.72,1.0
+13878,15582797.0,Padovesi,731.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,0.0,139435.73,0.0
+13879,15682528.0,Udegbunam,712.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,155879.57,0.0
+13880,15761047.0,O'Sullivan,715.0,France,Male,61.0,2.0,0.0,2.0,1.0,1.0,90212.38,0.0
+13881,15766552.0,Lujan,649.0,Spain,Male,58.0,3.0,110132.55,1.0,0.0,1.0,49816.46,0.0
+13882,15659820.0,Nwora,586.0,Germany,Female,45.0,6.0,144428.87,1.0,0.0,0.0,101039.53,1.0
+13883,15631481.0,Hs?eh,657.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,153605.68,0.0
+13884,15726087.0,Chukwufumnanya,697.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,157959.02,0.0
+13885,15775203.0,De Luca,609.0,France,Male,64.0,5.0,0.0,2.0,1.0,1.0,179056.23,0.0
+13886,15771299.0,Rizzo,668.0,France,Female,41.0,5.0,0.0,1.0,1.0,1.0,169921.11,0.0
+13887,15649033.0,Fyodorov,571.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,94889.2,0.0
+13888,15771222.0,Ritchie,705.0,Spain,Male,45.0,6.0,0.0,2.0,1.0,1.0,164741.5,0.0
+13889,15750772.0,Kenechukwu,677.0,France,Male,29.0,5.0,0.0,2.0,0.0,0.0,40893.61,0.0
+13890,15610147.0,Nucci,714.0,France,Male,60.0,2.0,0.0,2.0,0.0,1.0,26149.25,0.0
+13891,15762993.0,Eluemuno,704.0,France,Male,26.0,8.0,117300.02,1.0,1.0,1.0,180622.02,0.0
+13892,15697574.0,Chiemela,697.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,95957.09,0.0
+13893,15704053.0,Ch'ang,569.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,109089.04,0.0
+13894,15703923.0,Maclean,775.0,Germany,Male,31.0,7.0,124328.88,1.0,1.0,1.0,172363.41,0.0
+13895,15714586.0,Onwumelu,697.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,6409.55,0.0
+13896,15721189.0,Gilleland,757.0,Spain,Female,47.0,3.0,137811.75,1.0,1.0,0.0,152417.79,1.0
+13897,15661670.0,Y?an,678.0,France,Female,37.0,8.0,121398.42,1.0,1.0,1.0,123499.98,0.0
+13898,15618446.0,Mazzi,638.0,Spain,Male,55.0,1.0,0.0,2.0,1.0,1.0,186885.01,0.0
+13899,15613471.0,Tseng,815.0,Spain,Female,61.0,5.0,0.0,2.0,1.0,1.0,175894.24,0.0
+13900,15674833.0,Brown,641.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,107787.42,0.0
+13901,15576085.0,Ch'ang,678.0,France,Male,39.0,9.0,0.0,1.0,1.0,0.0,26136.13,0.0
+13902,15794413.0,Trevisano,682.0,France,Male,25.0,7.0,0.0,2.0,1.0,1.0,77981.85,0.0
+13903,15727299.0,Ts'ai,641.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,102628.98,0.0
+13904,15684319.0,Fanucci,632.0,France,Male,47.0,8.0,0.0,2.0,1.0,1.0,121866.87,0.0
+13905,15581323.0,Nwabugwu,602.0,Germany,Female,46.0,6.0,130274.22,3.0,1.0,0.0,125823.79,1.0
+13906,15598892.0,Chiang,724.0,Germany,Female,38.0,8.0,103907.28,1.0,0.0,0.0,177844.06,1.0
+13907,15794964.0,Nwachinemelu,686.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,186869.58,0.0
+13908,15779497.0,Ts'ai,615.0,Germany,Male,33.0,5.0,133476.05,1.0,1.0,1.0,119127.06,1.0
+13909,15732265.0,Ndukaku,632.0,France,Male,42.0,8.0,0.0,1.0,1.0,1.0,47468.56,0.0
+13910,15652626.0,T'ao,684.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,79577.4,0.0
+13911,15809909.0,Oluchukwu,714.0,Spain,Male,39.0,2.0,0.0,2.0,0.0,0.0,131097.9,0.0
+13912,15654577.0,Chigozie,529.0,France,Male,37.0,1.0,0.0,1.0,1.0,1.0,105720.09,0.0
+13913,15736990.0,Ch'en,678.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,56905.42,1.0
+13914,15681671.0,Ts'ai,817.0,Germany,Female,39.0,4.0,141787.31,1.0,1.0,1.0,149184.15,1.0
+13915,15773491.0,Nucci,645.0,Spain,Male,35.0,9.0,0.0,1.0,1.0,0.0,129936.53,0.0
+13916,15752300.0,O'Sullivan,714.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,160979.66,0.0
+13917,15751412.0,Ch'eng,714.0,France,Male,31.0,8.0,0.0,2.0,0.0,0.0,44390.38,0.0
+13918,15621818.0,Nwankwo,729.0,France,Female,31.0,0.0,121516.9,1.0,1.0,0.0,101699.77,0.0
+13919,15601012.0,Udinesi,598.0,Germany,Female,40.0,5.0,87772.52,1.0,1.0,0.0,108008.65,1.0
+13920,15595440.0,Ritchie,506.0,France,Male,71.0,8.0,0.0,2.0,1.0,1.0,114448.77,0.0
+13921,15595713.0,Hs?,724.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,184524.65,0.0
+13922,15614687.0,Genovese,595.0,France,Female,45.0,9.0,0.0,2.0,0.0,1.0,170528.63,0.0
+13923,15644833.0,Chukwufumnanya,719.0,Germany,Male,30.0,2.0,156652.13,2.0,1.0,0.0,55868.97,0.0
+13924,15687565.0,Nwachinemelu,587.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,68600.36,0.0
+13925,15588854.0,Nucci,724.0,France,Female,47.0,5.0,0.0,3.0,1.0,0.0,9677.0,1.0
+13926,15683471.0,Toscani,610.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,163989.66,0.0
+13927,15783865.0,Hsu,744.0,Spain,Male,28.0,5.0,0.0,2.0,0.0,0.0,157547.71,0.0
+13928,15775234.0,Foveaux,592.0,France,Female,36.0,2.0,0.0,1.0,1.0,1.0,73977.23,0.0
+13929,15734649.0,Hs?,622.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,149991.32,0.0
+13930,15750049.0,Trevisani,705.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,70563.9,0.0
+13931,15773869.0,Ogochukwu,595.0,France,Female,48.0,2.0,0.0,2.0,1.0,0.0,182055.36,0.0
+13932,15592765.0,Walker,712.0,France,Male,35.0,5.0,128504.76,1.0,1.0,0.0,160529.71,0.0
+13933,15617676.0,Pagnotto,748.0,France,Male,50.0,5.0,0.0,1.0,1.0,1.0,97629.31,0.0
+13934,15796418.0,Tien,564.0,France,Male,52.0,2.0,141784.02,1.0,1.0,1.0,62232.6,1.0
+13935,15681755.0,Walker,663.0,Germany,Male,42.0,9.0,137814.5,1.0,1.0,1.0,96961.0,0.0
+13936,15571940.0,Udegbunam,703.0,Germany,Female,29.0,6.0,114446.74,1.0,1.0,0.0,181598.25,1.0
+13937,15691952.0,Onyemere,599.0,Spain,Male,28.0,5.0,0.0,2.0,0.0,0.0,93739.71,0.0
+13938,15771543.0,Onio,651.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,151903.6,1.0
+13939,15736228.0,Smith,733.0,France,Female,54.0,9.0,0.0,3.0,1.0,0.0,92664.09,1.0
+13940,15794580.0,Pacheco,687.0,France,Female,22.0,4.0,0.0,2.0,1.0,0.0,91098.76,0.0
+13941,15590199.0,Sun,634.0,France,Male,27.0,1.0,0.0,2.0,1.0,0.0,93073.04,0.0
+13942,15643496.0,Ch'ang,594.0,Spain,Male,30.0,5.0,0.0,2.0,1.0,0.0,117031.2,0.0
+13943,15748649.0,Tikhonov,605.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,125553.83,0.0
+13944,15775886.0,Uwaezuoke,556.0,France,Female,47.0,5.0,0.0,2.0,0.0,1.0,70954.41,0.0
+13945,15725429.0,Onyemauchechukwu,585.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,161887.84,0.0
+13946,15781574.0,Chia,686.0,France,Male,41.0,0.0,0.0,2.0,1.0,1.0,166476.95,0.0
+13947,15765266.0,Ts'ao,634.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,1.0,72993.67,0.0
+13948,15572777.0,L?,691.0,Spain,Male,37.0,9.0,0.0,2.0,0.0,1.0,100995.68,0.0
+13949,15731553.0,Ejikemeifeuwa,687.0,France,Female,40.0,1.0,51116.26,1.0,1.0,0.0,85421.48,1.0
+13950,15784597.0,Hsueh,727.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,1.0,103920.51,0.0
+13951,15792525.0,Ch'en,584.0,Spain,Male,36.0,7.0,124023.18,1.0,1.0,1.0,137791.18,0.0
+13952,15608528.0,Pagnotto,598.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,65986.48,0.0
+13953,15629985.0,Clements,709.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,147913.56,0.0
+13954,15806967.0,Hsia,724.0,Germany,Female,23.0,10.0,152759.0,2.0,0.0,1.0,60590.72,0.0
+13955,15777054.0,Yobachukwu,762.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,118929.64,1.0
+13956,15792650.0,Chineze,686.0,Germany,Male,38.0,1.0,115730.71,2.0,1.0,0.0,163584.85,0.0
+13957,15761674.0,Ritchie,597.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,100566.81,0.0
+13958,15795527.0,Hsia,706.0,Spain,Female,50.0,4.0,131078.42,1.0,1.0,0.0,50908.48,1.0
+13959,15787602.0,T'ien,589.0,France,Female,46.0,0.0,0.0,1.0,1.0,0.0,172668.39,1.0
+13960,15771217.0,Hsia,706.0,France,Male,34.0,2.0,159364.66,1.0,1.0,1.0,126675.62,0.0
+13961,15606397.0,Chikwado,595.0,France,Male,27.0,7.0,0.0,2.0,1.0,0.0,89935.73,0.0
+13962,15590146.0,Cattaneo,595.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,173365.9,0.0
+13963,15761525.0,Pagnotto,774.0,France,Female,49.0,6.0,0.0,2.0,1.0,0.0,114514.64,0.0
+13964,15808544.0,Uspenskaya,718.0,Germany,Male,38.0,7.0,80958.36,2.0,0.0,1.0,187905.12,0.0
+13965,15731751.0,Mao,629.0,Germany,Female,56.0,4.0,131852.81,1.0,1.0,0.0,91573.19,1.0
+13966,15697360.0,Yefimov,741.0,Germany,Male,37.0,6.0,134497.27,2.0,1.0,1.0,118855.26,0.0
+13967,15774336.0,Rizzo,731.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,91947.74,0.0
+13968,15745266.0,Genovese,627.0,Spain,Female,44.0,8.0,0.0,1.0,1.0,0.0,161889.95,1.0
+13969,15690731.0,Onyemaechi,679.0,Spain,Male,35.0,7.0,121863.61,1.0,1.0,1.0,172668.39,0.0
+13970,15621882.0,Ch'eng,741.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,738.88,0.0
+13971,15654574.0,Lucciano,594.0,France,Male,34.0,3.0,93920.47,1.0,0.0,1.0,160529.71,0.0
+13972,15598088.0,Uchenna,624.0,Spain,Male,47.0,2.0,133964.3,1.0,1.0,1.0,135939.3,0.0
+13973,15669414.0,Ts'ai,594.0,France,Male,23.0,3.0,0.0,2.0,1.0,0.0,160976.85,0.0
+13974,15744517.0,Olisaemeka,669.0,France,Female,30.0,9.0,71026.77,1.0,1.0,0.0,144880.81,0.0
+13975,15666884.0,Chukwujekwu,628.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,26260.98,0.0
+13976,15743817.0,Zetticci,573.0,Spain,Male,33.0,4.0,80764.03,1.0,0.0,1.0,91830.75,0.0
+13977,15746737.0,Onochie,648.0,Germany,Female,45.0,6.0,154495.79,1.0,0.0,1.0,142513.5,0.0
+13978,15731117.0,Tikhonov,602.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,44363.43,0.0
+13979,15809862.0,Kao,754.0,Spain,Female,44.0,6.0,0.0,1.0,1.0,0.0,120511.08,0.0
+13980,15690958.0,Ibezimako,539.0,France,Male,41.0,4.0,80922.75,1.0,1.0,0.0,125979.19,0.0
+13981,15752312.0,Okwudiliolisa,697.0,Germany,Female,44.0,4.0,117412.19,1.0,0.0,0.0,92898.17,1.0
+13982,15641170.0,Toscani,648.0,Spain,Male,32.0,7.0,0.0,2.0,1.0,0.0,166678.28,0.0
+13983,15679550.0,Biryukova,753.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,116748.14,0.0
+13984,15693947.0,DeRose,746.0,Germany,Female,54.0,7.0,134571.5,1.0,1.0,0.0,163534.75,1.0
+13985,15795872.0,Lucciano,527.0,Spain,Female,46.0,4.0,111095.58,1.0,0.0,0.0,52795.56,1.0
+13986,15670481.0,Chiu,682.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,127569.8,0.0
+13987,15654075.0,Fanucci,737.0,France,Male,40.0,6.0,0.0,2.0,0.0,0.0,146875.86,0.0
+13988,15627360.0,Nnachetam,537.0,France,Female,33.0,9.0,0.0,2.0,0.0,1.0,171345.02,0.0
+13989,15758035.0,Chukwubuikem,594.0,France,Male,43.0,9.0,0.0,1.0,0.0,0.0,100478.6,0.0
+13990,15586757.0,Ponomarev,758.0,France,Male,39.0,6.0,110941.93,1.0,1.0,1.0,145593.85,0.0
+13991,15733032.0,Chukwuemeka,624.0,France,Male,43.0,2.0,125903.22,1.0,0.0,0.0,67788.49,0.0
+13992,15656912.0,Chukwudi,633.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,131281.29,0.0
+13993,15753837.0,Chinonyelum,640.0,Germany,Male,37.0,10.0,116927.89,1.0,1.0,0.0,182988.94,1.0
+13994,15789939.0,Chukwuemeka,667.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,124550.88,0.0
+13995,15668575.0,K?,747.0,France,Female,42.0,9.0,0.0,3.0,0.0,0.0,158043.11,1.0
+13996,15668893.0,Hsia,610.0,France,Female,45.0,6.0,0.0,2.0,1.0,0.0,172601.62,0.0
+13997,15779985.0,McNeil,671.0,France,Male,30.0,5.0,0.0,2.0,0.0,1.0,171345.02,0.0
+13998,15597930.0,Onyekaozulu,736.0,Germany,Male,46.0,3.0,106091.97,2.0,1.0,1.0,159823.16,0.0
+13999,15566251.0,Chukwualuka,744.0,France,Male,26.0,6.0,124547.13,1.0,1.0,1.0,168823.31,0.0
+14000,15775490.0,Fetherstonhaugh,694.0,France,Male,33.0,10.0,0.0,1.0,0.0,1.0,162452.65,0.0
+14001,15576676.0,Harker,790.0,Spain,Female,36.0,3.0,0.0,2.0,1.0,1.0,87412.24,0.0
+14002,15645605.0,Iadanza,632.0,Spain,Male,50.0,4.0,0.0,2.0,1.0,0.0,91981.85,0.0
+14003,15761783.0,Chiekwugo,633.0,Spain,Male,38.0,3.0,145330.43,1.0,1.0,0.0,168035.62,0.0
+14004,15702377.0,Macleod,629.0,France,Male,49.0,8.0,137592.61,1.0,1.0,1.0,57844.26,0.0
+14005,15627995.0,Hsieh,762.0,Germany,Male,23.0,8.0,150412.14,1.0,1.0,1.0,155846.69,0.0
+14006,15633108.0,Milanesi,749.0,France,Male,57.0,2.0,0.0,2.0,1.0,0.0,121525.48,0.0
+14007,15765192.0,Teng,593.0,Germany,Male,42.0,2.0,102286.2,1.0,1.0,1.0,146723.72,0.0
+14008,15802560.0,Yefimova,629.0,Spain,Male,29.0,6.0,0.0,4.0,1.0,0.0,180551.76,0.0
+14009,15695034.0,H?,730.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,24802.77,0.0
+14010,15789941.0,Hao,678.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,0.0,151411.2,0.0
+14011,15684198.0,Folliero,697.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,72929.84,0.0
+14012,15606901.0,McIntosh,644.0,France,Female,47.0,3.0,0.0,1.0,1.0,0.0,136491.72,1.0
+14013,15641342.0,Chigbogu,613.0,Germany,Female,60.0,9.0,114668.89,1.0,1.0,0.0,174032.0,1.0
+14014,15713578.0,De Bernales,707.0,Spain,Male,44.0,9.0,0.0,2.0,1.0,0.0,187905.12,0.0
+14015,15734886.0,Hsiao,586.0,Spain,Male,63.0,9.0,0.0,1.0,0.0,1.0,179436.6,1.0
+14016,15574858.0,Chuang,739.0,France,Male,41.0,2.0,0.0,1.0,1.0,1.0,156048.64,0.0
+14017,15639882.0,Maclean,663.0,France,Female,56.0,6.0,92930.96,1.0,1.0,0.0,65612.12,1.0
+14018,15716781.0,Udobata,579.0,Spain,Male,35.0,5.0,141136.62,1.0,1.0,0.0,50400.32,1.0
+14019,15604544.0,Nucci,651.0,France,Male,36.0,6.0,0.0,1.0,1.0,1.0,157921.25,1.0
+14020,15663938.0,Chidiegwu,709.0,France,Female,29.0,3.0,0.0,2.0,0.0,0.0,79919.13,0.0
+14021,15619304.0,De Luca,738.0,Germany,Female,47.0,1.0,83772.95,1.0,1.0,0.0,14073.11,1.0
+14022,15581198.0,Chidiegwu,549.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,86791.9,0.0
+14023,15703923.0,Kao,684.0,Spain,Female,25.0,3.0,0.0,2.0,1.0,0.0,50777.77,0.0
+14024,15583076.0,De Luca,553.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,131534.96,0.0
+14025,15776939.0,Ugorji,587.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,88852.11,0.0
+14026,15809736.0,Ch'iu,681.0,Germany,Male,40.0,3.0,137983.07,2.0,1.0,1.0,74838.64,0.0
+14027,15709639.0,Uspensky,594.0,Spain,Male,55.0,10.0,0.0,2.0,1.0,1.0,169719.35,0.0
+14028,15648005.0,Shillito,850.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,1.0,164975.82,0.0
+14029,15603482.0,Bibi,727.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,152290.28,0.0
+14030,15707025.0,McIntyre,587.0,France,Male,62.0,9.0,0.0,1.0,1.0,0.0,157931.39,1.0
+14031,15723873.0,White,703.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,126551.65,0.0
+14032,15646810.0,Nnaife,644.0,France,Female,30.0,9.0,0.0,2.0,1.0,1.0,138536.09,0.0
+14033,15808849.0,Omeokachie,707.0,Spain,Male,34.0,1.0,113829.45,2.0,1.0,1.0,162267.91,0.0
+14034,15733261.0,Hsia,651.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,88658.73,0.0
+14035,15577647.0,Nnamutaezinwa,597.0,France,Female,37.0,9.0,137827.33,1.0,1.0,1.0,60928.51,0.0
+14036,15631481.0,Chiang,663.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,179908.71,0.0
+14037,15783883.0,K'ung,622.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,142935.83,0.0
+14038,15742272.0,Kaur,619.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,160122.66,0.0
+14039,15807432.0,Ting,627.0,Spain,Male,30.0,3.0,113383.07,1.0,1.0,1.0,101491.23,0.0
+14040,15609987.0,Chibuzo,705.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,38691.34,0.0
+14041,15795895.0,Smith,683.0,France,Female,28.0,7.0,105957.88,1.0,1.0,1.0,132029.95,0.0
+14042,15658935.0,Chinedum,641.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,9984.4,0.0
+14043,15776733.0,Uvarov,850.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,161653.5,0.0
+14044,15646810.0,Hsueh,677.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,1.0,73564.44,0.0
+14045,15802060.0,T'ang,712.0,Spain,Female,52.0,2.0,115532.52,1.0,1.0,1.0,169347.56,0.0
+14046,15692406.0,Chinwemma,671.0,Germany,Female,37.0,8.0,116893.28,2.0,1.0,1.0,145551.6,1.0
+14047,15797964.0,Ts'ai,616.0,France,Female,38.0,3.0,126473.33,1.0,1.0,0.0,120512.08,0.0
+14048,15673790.0,Manna,575.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,65769.3,0.0
+14049,15804017.0,Ch'iu,652.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,105887.04,0.0
+14050,15656592.0,Onyekachukwu,556.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,101693.73,1.0
+14051,15665524.0,Uwakwe,596.0,France,Male,33.0,9.0,142987.46,1.0,0.0,0.0,148087.88,0.0
+14052,15807167.0,Chiazagomekpere,685.0,Spain,Female,63.0,8.0,0.0,2.0,1.0,1.0,180593.45,0.0
+14053,15632882.0,Liao,667.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,177847.03,0.0
+14054,15687094.0,Chiemenam,685.0,Germany,Male,41.0,2.0,127738.94,2.0,1.0,1.0,152883.95,0.0
+14055,15642394.0,P'eng,596.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,95857.18,0.0
+14056,15643487.0,Toth,704.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,119903.2,0.0
+14057,15627298.0,Chia,844.0,France,Male,24.0,6.0,0.0,2.0,1.0,0.0,115068.99,0.0
+14058,15587584.0,Nwabugwu,691.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,127528.84,0.0
+14059,15730793.0,Fang,562.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,35795.85,0.0
+14060,15568819.0,Ch'ien,611.0,France,Male,49.0,2.0,119817.33,1.0,1.0,1.0,44965.44,1.0
+14061,15701364.0,Hs?eh,748.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,83470.4,0.0
+14062,15712584.0,Chiemenam,806.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,143859.66,0.0
+14063,15706463.0,Ugochukwu,592.0,France,Male,28.0,5.0,0.0,2.0,1.0,0.0,93713.12,0.0
+14064,15628274.0,Mazzanti,582.0,Germany,Male,46.0,4.0,130306.34,2.0,0.0,0.0,64872.33,1.0
+14065,15752622.0,Milne,683.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,138882.98,0.0
+14066,15663883.0,Iloerika,670.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,0.0,151903.6,0.0
+14067,15744041.0,Shao,649.0,France,Male,43.0,5.0,0.0,2.0,0.0,0.0,109071.71,0.0
+14068,15737509.0,Chienezie,537.0,France,Male,31.0,5.0,0.0,2.0,0.0,1.0,120892.96,0.0
+14069,15640034.0,Ch'ang,850.0,France,Male,31.0,3.0,80957.88,1.0,1.0,0.0,126561.05,0.0
+14070,15602034.0,Ts'ui,744.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,165562.84,0.0
+14071,15649738.0,Chukwukere,730.0,Germany,Female,48.0,9.0,90544.0,1.0,1.0,1.0,164741.5,1.0
+14072,15716085.0,Palerma,642.0,Spain,Female,37.0,6.0,119787.76,1.0,0.0,0.0,122662.98,0.0
+14073,15696054.0,Chukwueloka,684.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,125608.74,0.0
+14074,15603764.0,Kosisochukwu,659.0,France,Male,44.0,3.0,0.0,2.0,1.0,1.0,172576.43,0.0
+14075,15571856.0,Onyemachukwu,538.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,172618.52,0.0
+14076,15609998.0,Chizoba,564.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,141756.32,0.0
+14077,15648032.0,Chin,658.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,198490.33,0.0
+14078,15738166.0,Shao,535.0,France,Male,37.0,6.0,0.0,2.0,0.0,1.0,64746.07,0.0
+14079,15676869.0,Lettiere,545.0,France,Female,64.0,8.0,164902.43,1.0,1.0,0.0,103573.96,1.0
+14080,15757911.0,T'ien,621.0,France,Male,44.0,7.0,125801.03,1.0,0.0,1.0,120311.07,0.0
+14081,15665088.0,Okeke,594.0,France,Female,31.0,5.0,0.0,2.0,1.0,0.0,138526.96,0.0
+14082,15773868.0,Yobanna,803.0,France,Male,30.0,5.0,95927.04,1.0,0.0,0.0,91565.63,0.0
+14083,15701765.0,Mazzanti,748.0,France,Female,31.0,4.0,0.0,1.0,1.0,0.0,88724.67,0.0
+14084,15766289.0,McLean,661.0,France,Male,21.0,5.0,0.0,1.0,0.0,0.0,125962.02,0.0
+14085,15781689.0,Onyekachi,645.0,France,Female,42.0,7.0,0.0,2.0,0.0,1.0,68343.08,0.0
+14086,15671342.0,Tien,576.0,Germany,Male,35.0,3.0,121260.19,1.0,0.0,0.0,15404.64,0.0
+14087,15607877.0,Nwabugwu,722.0,France,Male,49.0,0.0,181821.96,1.0,1.0,0.0,26589.56,1.0
+14088,15699486.0,Palfreyman,718.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,126469.09,0.0
+14089,15642996.0,Udegbulam,721.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,1.0,123882.73,0.0
+14090,15615091.0,Cattaneo,762.0,Spain,Male,24.0,4.0,0.0,2.0,1.0,1.0,122925.4,0.0
+14091,15805523.0,Fanucci,601.0,Spain,Male,30.0,4.0,0.0,1.0,1.0,1.0,90928.02,0.0
+14092,15633352.0,Okwuadigbo,684.0,Spain,Male,36.0,9.0,0.0,1.0,1.0,1.0,158516.48,0.0
+14093,15704770.0,L?,548.0,France,Male,45.0,5.0,0.0,1.0,1.0,0.0,58854.88,0.0
+14094,15616088.0,Hsiao,671.0,Germany,Male,39.0,2.0,181461.46,2.0,1.0,1.0,85901.09,0.0
+14095,15574733.0,Nwachukwu,656.0,France,Male,35.0,2.0,167924.95,1.0,1.0,0.0,180852.12,0.0
+14096,15654060.0,K'ung,556.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,147963.07,0.0
+14097,15789425.0,Chiang,602.0,Spain,Female,34.0,8.0,74896.92,1.0,1.0,1.0,138775.86,0.0
+14098,15725429.0,Lucchese,648.0,France,Male,31.0,5.0,127675.39,1.0,0.0,0.0,103421.91,0.0
+14099,15796764.0,Hs?,674.0,Germany,Female,46.0,7.0,123860.18,2.0,1.0,0.0,130529.13,1.0
+14100,15694125.0,Yermakova,659.0,Spain,Male,34.0,2.0,124788.18,2.0,1.0,1.0,57549.65,0.0
+14101,15570069.0,Frolov,621.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,68860.3,0.0
+14102,15627185.0,Chinwendu,711.0,France,Male,27.0,0.0,0.0,1.0,0.0,1.0,162699.7,0.0
+14103,15789865.0,Chinedum,613.0,Germany,Male,28.0,8.0,71097.23,2.0,0.0,0.0,53420.53,0.0
+14104,15574858.0,Yermakova,758.0,Germany,Male,33.0,1.0,113599.48,2.0,1.0,0.0,178187.28,0.0
+14105,15581548.0,Smith,516.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,0.0,65951.28,0.0
+14106,15662291.0,Chikezie,724.0,Germany,Male,46.0,3.0,73314.04,1.0,1.0,1.0,58214.79,0.0
+14107,15762902.0,Trevisano,752.0,Germany,Female,42.0,2.0,114435.47,2.0,1.0,1.0,180622.02,0.0
+14108,15566563.0,K'ung,659.0,France,Male,32.0,8.0,104488.16,1.0,0.0,1.0,136902.01,0.0
+14109,15764343.0,Hsieh,543.0,Germany,Male,47.0,9.0,104449.8,1.0,0.0,1.0,121504.18,1.0
+14110,15665521.0,Chia,479.0,Germany,Female,39.0,5.0,123873.19,2.0,1.0,1.0,183963.34,1.0
+14111,15752881.0,Ch'iu,584.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,118749.58,0.0
+14112,15776631.0,Okechukwu,707.0,France,Male,28.0,1.0,0.0,1.0,0.0,0.0,171060.01,0.0
+14113,15748649.0,Teng,627.0,France,Female,30.0,5.0,0.0,2.0,0.0,0.0,87400.53,0.0
+14114,15592999.0,Li Fonti,542.0,France,Female,30.0,5.0,0.0,2.0,0.0,1.0,178074.04,0.0
+14115,15667460.0,Nnachetam,651.0,France,Male,25.0,2.0,0.0,2.0,1.0,0.0,181204.5,0.0
+14116,15761606.0,Chiu,748.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,123978.86,0.0
+14117,15603328.0,P'eng,707.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,1.0,57553.98,0.0
+14118,15777772.0,Ch'in,547.0,France,Male,30.0,2.0,86977.96,2.0,0.0,1.0,115818.51,0.0
+14119,15654495.0,Manna,602.0,France,Male,57.0,6.0,0.0,1.0,1.0,0.0,171762.87,1.0
+14120,15641389.0,Shao,678.0,Germany,Female,40.0,3.0,57784.22,1.0,0.0,0.0,52796.31,1.0
+14121,15749177.0,Lucchesi,596.0,France,Male,34.0,9.0,155619.58,1.0,0.0,1.0,81778.72,0.0
+14122,15600882.0,Fanucci,652.0,France,Female,63.0,6.0,0.0,2.0,1.0,1.0,143665.44,0.0
+14123,15650029.0,Ch'ien,648.0,France,Female,43.0,10.0,0.0,2.0,1.0,0.0,140941.47,0.0
+14124,15574071.0,Lu,750.0,France,Female,39.0,5.0,163549.63,1.0,1.0,0.0,147530.06,0.0
+14125,15809823.0,Uwaezuoke,591.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,182372.35,0.0
+14126,15810829.0,Ozerova,641.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,168816.22,0.0
+14127,15662658.0,Frolov,677.0,Spain,Female,53.0,10.0,109044.6,1.0,1.0,1.0,118982.51,0.0
+14128,15796721.0,Chikelu,773.0,Spain,Female,38.0,1.0,0.0,1.0,1.0,1.0,89801.9,0.0
+14129,15668594.0,Udegbulam,559.0,France,Male,42.0,5.0,0.0,1.0,1.0,1.0,135875.51,1.0
+14130,15789432.0,Yermakov,636.0,France,Female,30.0,8.0,0.0,2.0,0.0,1.0,131301.74,0.0
+14131,15737267.0,Tseng,726.0,France,Female,29.0,1.0,0.0,2.0,1.0,1.0,38570.13,0.0
+14132,15719606.0,L?,631.0,Spain,Female,34.0,6.0,0.0,1.0,1.0,1.0,118029.09,0.0
+14133,15701265.0,Nwachinemelu,745.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,104469.05,0.0
+14134,15652693.0,Chukwueloka,709.0,Spain,Male,44.0,9.0,0.0,2.0,1.0,1.0,64849.74,0.0
+14135,15741921.0,Lettiere,578.0,Spain,Male,34.0,7.0,0.0,2.0,0.0,1.0,151648.66,0.0
+14136,15594815.0,Toscani,714.0,France,Male,31.0,5.0,152347.01,2.0,1.0,1.0,151401.33,0.0
+14137,15600912.0,Chidiebere,726.0,Germany,Male,30.0,10.0,121325.84,2.0,1.0,1.0,56905.38,0.0
+14138,15723169.0,Onyemachukwu,683.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,73329.08,0.0
+14139,15646082.0,Lo Duca,706.0,France,Female,40.0,9.0,0.0,2.0,1.0,1.0,184936.77,0.0
+14140,15803378.0,Palerma,687.0,France,Female,39.0,7.0,0.0,1.0,1.0,1.0,51454.06,0.0
+14141,15653776.0,Fiorentini,593.0,France,Female,31.0,3.0,0.0,1.0,1.0,1.0,136491.72,1.0
+14142,15784097.0,P'eng,682.0,Spain,Male,43.0,8.0,0.0,1.0,1.0,0.0,199255.32,0.0
+14143,15699446.0,Onwumelu,652.0,Spain,Male,66.0,6.0,0.0,1.0,1.0,1.0,121562.33,1.0
+14144,15749130.0,Fadden,584.0,France,Female,30.0,1.0,0.0,1.0,0.0,1.0,51364.56,0.0
+14145,15703763.0,K'ung,679.0,France,Male,51.0,6.0,0.0,1.0,0.0,1.0,24113.91,1.0
+14146,15740164.0,Nwabugwu,642.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,5791.85,0.0
+14147,15766355.0,Goliwe,686.0,Germany,Female,45.0,9.0,92887.13,1.0,0.0,0.0,163717.16,1.0
+14148,15681562.0,Tuan,653.0,France,Female,34.0,7.0,0.0,2.0,0.0,0.0,104722.47,0.0
+14149,15743192.0,Yermakova,676.0,Spain,Female,45.0,4.0,0.0,2.0,1.0,1.0,156774.75,0.0
+14150,15725024.0,T'ang,662.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,180594.85,0.0
+14151,15717995.0,Hsiao,583.0,France,Female,36.0,1.0,123385.72,1.0,0.0,0.0,108872.45,0.0
+14152,15728505.0,Ch'eng,611.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,144870.28,0.0
+14153,15810834.0,Ting,707.0,France,Female,61.0,6.0,0.0,3.0,1.0,1.0,84870.23,1.0
+14154,15670753.0,Gboliwe,569.0,Germany,Female,39.0,7.0,123959.97,2.0,1.0,0.0,89938.07,0.0
+14155,15691627.0,Chiawuotu,637.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,85901.09,0.0
+14156,15638159.0,Lo,692.0,France,Male,47.0,2.0,0.0,1.0,1.0,1.0,127825.25,0.0
+14157,15589572.0,Ku,685.0,Spain,Male,36.0,9.0,0.0,1.0,0.0,0.0,70954.41,1.0
+14158,15773731.0,Iloerika,741.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,121408.55,0.0
+14159,15598521.0,Onwumelu,581.0,Germany,Male,39.0,8.0,132951.92,1.0,1.0,1.0,74849.46,1.0
+14160,15595766.0,Genovesi,706.0,Spain,Female,47.0,1.0,0.0,1.0,1.0,0.0,89566.64,1.0
+14161,15683560.0,Gartrell,651.0,Germany,Female,49.0,6.0,108004.64,1.0,1.0,0.0,83700.66,1.0
+14162,15586518.0,Tseng,838.0,Germany,Female,44.0,1.0,106376.85,1.0,1.0,0.0,156439.56,1.0
+14163,15715750.0,Fanucci,705.0,France,Female,56.0,1.0,0.0,1.0,1.0,1.0,91510.71,1.0
+14164,15645463.0,Ch'en,850.0,Germany,Female,35.0,7.0,120171.58,2.0,1.0,1.0,113046.81,0.0
+14165,15735358.0,T'ien,561.0,France,Male,41.0,6.0,0.0,2.0,0.0,1.0,71830.1,0.0
+14166,15760456.0,Hsiung,714.0,France,Female,36.0,7.0,94108.54,1.0,1.0,1.0,109538.35,0.0
+14167,15599195.0,Pirozzi,656.0,Germany,Female,43.0,5.0,114435.43,1.0,1.0,0.0,153983.99,1.0
+14168,15632850.0,Onuchukwu,719.0,France,Female,36.0,5.0,124053.03,1.0,0.0,0.0,160532.88,0.0
+14169,15586880.0,Chidiebere,550.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,90174.83,0.0
+14170,15793834.0,Pirozzi,584.0,France,Male,38.0,10.0,0.0,2.0,1.0,0.0,88890.05,0.0
+14171,15581252.0,Thompson,699.0,Germany,Female,33.0,7.0,129499.42,2.0,0.0,0.0,86951.6,1.0
+14172,15718231.0,Hs?eh,697.0,France,Female,24.0,8.0,0.0,2.0,1.0,0.0,94739.2,0.0
+14173,15766458.0,Onyemauchechukwu,605.0,France,Male,35.0,4.0,0.0,1.0,0.0,1.0,96881.32,0.0
+14174,15769974.0,Ch'eng,669.0,France,Female,34.0,8.0,0.0,1.0,1.0,0.0,178373.43,0.0
+14175,15692463.0,Scott,535.0,France,Male,46.0,1.0,0.0,1.0,1.0,1.0,177065.24,0.0
+14176,15679963.0,K?,678.0,Spain,Male,57.0,2.0,0.0,1.0,1.0,1.0,172063.09,1.0
+14177,15614168.0,Ch'ien,667.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,58974.0,0.0
+14178,15600708.0,Genovesi,577.0,Spain,Female,41.0,8.0,0.0,1.0,1.0,1.0,156792.89,1.0
+14179,15774720.0,Chikezie,850.0,Germany,Female,46.0,8.0,92474.05,2.0,1.0,0.0,118024.1,1.0
+14180,15631739.0,Manna,535.0,Germany,Female,49.0,6.0,136674.51,1.0,1.0,0.0,26900.63,1.0
+14181,15771483.0,Ts'ui,633.0,France,Male,28.0,8.0,0.0,1.0,1.0,1.0,177028.0,0.0
+14182,15733361.0,K?,662.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,182266.01,0.0
+14183,15662091.0,Dike,841.0,France,Male,31.0,1.0,113599.74,1.0,1.0,1.0,59872.3,0.0
+14184,15729224.0,Nwachukwu,638.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,153343.48,0.0
+14185,15672756.0,Kudryashova,565.0,France,Female,33.0,4.0,0.0,2.0,1.0,1.0,187900.12,0.0
+14186,15607746.0,Lucciano,651.0,France,Male,38.0,1.0,0.0,1.0,0.0,0.0,105618.14,0.0
+14187,15591037.0,Chukwuemeka,687.0,France,Female,43.0,1.0,0.0,1.0,1.0,0.0,149073.88,1.0
+14188,15703544.0,Chien,757.0,France,Male,40.0,4.0,118879.35,1.0,1.0,1.0,185599.74,0.0
+14189,15684921.0,Onwumelu,724.0,France,Male,29.0,1.0,0.0,2.0,1.0,1.0,147040.25,0.0
+14190,15734765.0,Ting,611.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,99792.82,0.0
+14191,15786463.0,Zetticci,706.0,Spain,Male,42.0,8.0,96950.41,1.0,1.0,0.0,142963.36,0.0
+14192,15674929.0,Ojiofor,683.0,Germany,Male,37.0,4.0,137301.87,1.0,0.0,1.0,28257.89,0.0
+14193,15661528.0,T'ien,667.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,56794.73,1.0
+14194,15582616.0,Aksenov,703.0,Germany,Male,39.0,3.0,135871.5,2.0,0.0,0.0,38773.74,0.0
+14195,15770030.0,Nkemdirim,667.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,0.0,143301.49,0.0
+14196,15794468.0,Tien,701.0,Spain,Male,44.0,4.0,0.0,2.0,0.0,1.0,29132.43,0.0
+14197,15734609.0,Chinweike,635.0,France,Female,41.0,9.0,0.0,1.0,0.0,0.0,140929.98,1.0
+14198,15769312.0,Hs?eh,663.0,France,Female,44.0,10.0,0.0,1.0,1.0,1.0,142754.71,0.0
+14199,15804862.0,Kuo,632.0,Germany,Female,32.0,4.0,119825.75,1.0,1.0,0.0,177737.04,1.0
+14200,15753362.0,Ugoji,645.0,Spain,Male,46.0,0.0,0.0,1.0,0.0,0.0,141180.66,1.0
+14201,15609998.0,Chukwuebuka,749.0,France,Female,23.0,4.0,0.0,2.0,1.0,0.0,67468.67,0.0
+14202,15782798.0,Uspenskaya,753.0,Germany,Male,47.0,9.0,90450.0,1.0,0.0,0.0,152499.8,1.0
+14203,15744044.0,Lucchese,669.0,France,Male,23.0,9.0,0.0,2.0,1.0,1.0,140586.08,0.0
+14204,15672798.0,Chukwudi,651.0,Spain,Male,39.0,6.0,0.0,2.0,0.0,0.0,122925.4,0.0
+14205,15583595.0,Hsieh,607.0,France,Female,39.0,10.0,0.0,2.0,1.0,0.0,147963.07,0.0
+14206,15793049.0,L?,686.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,1.0,60954.45,0.0
+14207,15679909.0,Shih,588.0,Spain,Male,36.0,4.0,0.0,2.0,0.0,1.0,87067.73,0.0
+14208,15655335.0,Hargreaves,731.0,Spain,Male,33.0,4.0,0.0,2.0,0.0,0.0,103557.85,0.0
+14209,15743016.0,Ch'iu,678.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,90106.94,0.0
+14210,15698872.0,Smith,609.0,France,Female,43.0,4.0,0.0,1.0,1.0,1.0,160131.58,1.0
+14211,15720509.0,Uspenskaya,624.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,0.0,120853.83,0.0
+14212,15648881.0,Goliwe,580.0,Spain,Male,32.0,10.0,0.0,1.0,1.0,0.0,138051.19,0.0
+14213,15633574.0,Ndubuagha,721.0,France,Male,32.0,1.0,0.0,1.0,1.0,0.0,134901.34,0.0
+14214,15641575.0,Ndubuagha,616.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,85523.84,0.0
+14215,15609510.0,Shao,579.0,Germany,Female,23.0,7.0,127801.88,2.0,1.0,0.0,116957.6,0.0
+14216,15771674.0,White,651.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,177524.19,0.0
+14217,15578835.0,Chiemenam,681.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,107839.03,0.0
+14218,15610226.0,Onuora,607.0,Germany,Female,47.0,10.0,137104.47,1.0,1.0,1.0,103557.85,1.0
+14219,15653760.0,DeRose,621.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,1.0,69052.87,0.0
+14220,15631912.0,Hsieh,634.0,Germany,Female,42.0,0.0,90218.9,1.0,1.0,1.0,53954.24,0.0
+14221,15770121.0,Tien,679.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,51387.1,0.0
+14222,15703793.0,Clements,683.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,130726.96,0.0
+14223,15810834.0,Tsao,679.0,Germany,Female,57.0,6.0,109628.44,2.0,1.0,0.0,131284.93,1.0
+14224,15651581.0,Gidney,658.0,France,Male,34.0,2.0,0.0,1.0,1.0,1.0,93536.38,0.0
+14225,15686957.0,Fanucci,642.0,France,Male,28.0,6.0,0.0,2.0,0.0,1.0,186785.01,0.0
+14226,15592877.0,Hsia,639.0,Germany,Male,29.0,9.0,132730.17,2.0,1.0,1.0,165576.55,1.0
+14227,15773869.0,Tien,582.0,Germany,Female,42.0,1.0,119839.69,2.0,1.0,0.0,93713.47,0.0
+14228,15623102.0,Chidiebele,721.0,France,Female,31.0,8.0,0.0,2.0,0.0,1.0,137791.18,0.0
+14229,15642991.0,Chibuzo,697.0,Spain,Male,35.0,6.0,0.0,2.0,0.0,0.0,109496.69,0.0
+14230,15706647.0,Chukwujamuike,753.0,France,Male,31.0,2.0,123082.08,1.0,1.0,1.0,130504.49,0.0
+14231,15785078.0,Chu,701.0,Spain,Male,35.0,6.0,117593.48,1.0,1.0,1.0,146636.44,0.0
+14232,15626900.0,Chigozie,645.0,Germany,Male,43.0,10.0,108528.07,1.0,1.0,0.0,115779.48,1.0
+14233,15761554.0,Chimaoke,554.0,Germany,Female,54.0,6.0,104449.8,1.0,1.0,1.0,133091.32,1.0
+14234,15779985.0,Uspenskaya,537.0,France,Male,24.0,3.0,0.0,2.0,0.0,1.0,141756.32,0.0
+14235,15597909.0,O'Brien,709.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,0.0,167984.72,0.0
+14236,15800806.0,Chinedum,684.0,France,Male,34.0,1.0,0.0,1.0,1.0,1.0,152992.04,0.0
+14237,15589210.0,Ch'ang,718.0,Germany,Female,51.0,4.0,126537.52,1.0,0.0,1.0,46555.15,1.0
+14238,15698716.0,Hs?,562.0,Spain,Male,39.0,3.0,133800.98,1.0,1.0,1.0,187915.4,0.0
+14239,15716334.0,Ch'eng,718.0,Spain,Female,47.0,3.0,0.0,1.0,1.0,0.0,173365.89,1.0
+14240,15757467.0,Chinonyelum,569.0,Germany,Female,56.0,7.0,124573.33,1.0,1.0,0.0,151411.4,1.0
+14241,15803526.0,Milano,686.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,143913.48,0.0
+14242,15753847.0,Nwankwo,749.0,France,Female,47.0,7.0,0.0,1.0,1.0,0.0,73856.28,1.0
+14243,15700511.0,Calabresi,496.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,35786.76,1.0
+14244,15763612.0,Iadanza,567.0,Germany,Male,49.0,6.0,106301.85,2.0,1.0,0.0,123882.73,1.0
+14245,15584518.0,Nwankwo,710.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,0.0,124592.84,0.0
+14246,15754569.0,Calabresi,466.0,Germany,Female,22.0,4.0,74988.59,1.0,1.0,0.0,113557.7,0.0
+14247,15642710.0,Ch'ien,758.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,187987.01,0.0
+14248,15636388.0,Mazzi,670.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,179655.83,0.0
+14249,15751771.0,Rizzo,686.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,109614.57,0.0
+14250,15750778.0,Ts'ao,645.0,Spain,Female,22.0,9.0,0.0,2.0,1.0,0.0,51919.04,0.0
+14251,15625080.0,Mazzanti,850.0,France,Male,64.0,8.0,108477.49,1.0,1.0,0.0,120801.65,1.0
+14252,15733261.0,Onyemachukwu,628.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,127734.16,0.0
+14253,15641860.0,T'ien,686.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,176433.6,0.0
+14254,15679024.0,Gomes,730.0,France,Male,41.0,0.0,0.0,2.0,0.0,1.0,142963.29,0.0
+14255,15586674.0,Hixson,686.0,France,Male,36.0,0.0,0.0,1.0,0.0,1.0,72991.62,0.0
+14256,15640407.0,Chialuka,749.0,France,Male,40.0,5.0,152341.55,1.0,1.0,1.0,88463.63,0.0
+14257,15592571.0,Macleod,762.0,France,Male,34.0,0.0,97460.2,1.0,0.0,1.0,134600.94,0.0
+14258,15734886.0,Ch'ien,523.0,Germany,Male,49.0,3.0,88902.21,1.0,1.0,1.0,157097.09,0.0
+14259,15724663.0,Yobachukwu,704.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,1.0,185541.6,0.0
+14260,15656484.0,Yobachi,686.0,Spain,Male,50.0,9.0,125876.95,1.0,1.0,0.0,162599.51,1.0
+14261,15725166.0,Iadanza,750.0,France,Male,29.0,5.0,110791.97,1.0,1.0,1.0,134600.94,0.0
+14262,15735222.0,Ch'ang,717.0,Spain,Male,49.0,5.0,0.0,2.0,1.0,1.0,180622.03,0.0
+14263,15616172.0,Ejimofor,589.0,France,Female,57.0,7.0,0.0,2.0,1.0,1.0,141453.62,0.0
+14264,15649024.0,Ku,569.0,France,Female,51.0,7.0,0.0,1.0,1.0,0.0,103570.8,1.0
+14265,15769353.0,Chiu,619.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,0.0,161253.08,0.0
+14266,15632575.0,Chukwualuka,494.0,France,Male,29.0,9.0,137060.88,1.0,1.0,0.0,136886.86,1.0
+14267,15582841.0,Houghton,583.0,France,Female,34.0,5.0,0.0,2.0,0.0,1.0,70618.0,0.0
+14268,15599195.0,P'eng,775.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,180598.66,0.0
+14269,15641640.0,Chidiebele,661.0,France,Male,31.0,8.0,0.0,1.0,0.0,1.0,115462.27,0.0
+14270,15781665.0,Pugh,512.0,France,Male,42.0,3.0,0.0,1.0,1.0,0.0,90212.38,1.0
+14271,15786078.0,Lucchese,707.0,France,Female,31.0,9.0,125167.74,1.0,1.0,0.0,162697.93,0.0
+14272,15744109.0,Ch'iu,646.0,France,Female,50.0,4.0,0.0,1.0,1.0,0.0,172586.89,1.0
+14273,15576928.0,Onyemauchechi,686.0,Spain,Male,37.0,6.0,0.0,2.0,0.0,1.0,142538.31,0.0
+14274,15683544.0,Hsia,609.0,France,Female,39.0,3.0,92636.96,1.0,1.0,0.0,107825.08,0.0
+14275,15662494.0,Hs?eh,646.0,France,Male,33.0,7.0,0.0,1.0,1.0,1.0,153810.51,0.0
+14276,15620557.0,Y?an,523.0,Spain,Male,27.0,8.0,0.0,2.0,1.0,0.0,181610.6,0.0
+14277,15801466.0,Kao,611.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,105890.55,0.0
+14278,15680197.0,Hsia,633.0,Spain,Male,37.0,9.0,86911.8,1.0,1.0,0.0,62454.01,0.0
+14279,15605872.0,Ugochukwu,577.0,France,Male,39.0,2.0,93816.7,1.0,1.0,1.0,96832.82,0.0
+14280,15592925.0,Lucchesi,710.0,France,Female,37.0,7.0,167557.12,1.0,0.0,0.0,9984.4,0.0
+14281,15774931.0,Yobanna,719.0,Spain,Male,38.0,1.0,0.0,1.0,1.0,1.0,93716.38,0.0
+14282,15576022.0,Hsueh,708.0,France,Male,41.0,4.0,0.0,2.0,0.0,0.0,27802.0,0.0
+14283,15648898.0,Fanucci,535.0,Germany,Female,37.0,1.0,124445.58,1.0,1.0,1.0,129980.93,0.0
+14284,15570932.0,T'ao,587.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,79866.73,0.0
+14285,15585961.0,Lo,632.0,Germany,Female,45.0,8.0,137148.68,1.0,1.0,0.0,174825.57,1.0
+14286,15573854.0,Teng,762.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,75837.58,0.0
+14287,15743817.0,Chinedum,741.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,79082.62,0.0
+14288,15572051.0,Lai,685.0,France,Male,39.0,5.0,0.0,3.0,1.0,0.0,86935.08,1.0
+14289,15796790.0,Ch'eng,763.0,France,Male,48.0,7.0,91522.06,1.0,1.0,0.0,937.15,0.0
+14290,15774857.0,Cremonesi,573.0,Germany,Male,36.0,8.0,111359.55,1.0,1.0,0.0,181224.56,1.0
+14291,15677382.0,Yobachukwu,677.0,France,Male,48.0,2.0,132596.92,1.0,1.0,0.0,58974.0,1.0
+14292,15683338.0,Chin,582.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,73979.85,0.0
+14293,15583392.0,Trevisani,744.0,Germany,Female,46.0,7.0,118311.06,1.0,1.0,0.0,129890.94,1.0
+14294,15772262.0,Trentini,684.0,Germany,Male,30.0,1.0,95853.39,2.0,1.0,1.0,51620.94,0.0
+14295,15648898.0,Chinedum,634.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,0.0,91830.75,0.0
+14296,15741180.0,Pisano,684.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,141872.05,0.0
+14297,15725024.0,Scott,739.0,Germany,Male,32.0,2.0,116338.68,2.0,1.0,1.0,69531.22,0.0
+14298,15656937.0,Shih,543.0,Germany,Male,64.0,7.0,152341.55,1.0,1.0,1.0,132798.95,1.0
+14299,15585599.0,Gaffney,583.0,France,Female,56.0,7.0,0.0,1.0,1.0,1.0,159629.64,1.0
+14300,15681412.0,Obidimkpa,718.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,118711.75,0.0
+14301,15778696.0,Palermo,683.0,Spain,Male,40.0,8.0,96674.2,1.0,0.0,1.0,175070.94,0.0
+14302,15644724.0,Onwubiko,667.0,France,Female,59.0,6.0,0.0,1.0,1.0,0.0,167036.94,1.0
+14303,15759537.0,Brown,635.0,Spain,Male,30.0,7.0,0.0,2.0,1.0,1.0,142377.69,0.0
+14304,15644724.0,Chidubem,633.0,Spain,Female,21.0,4.0,0.0,2.0,1.0,0.0,182691.31,0.0
+14305,15676521.0,Pisano,721.0,Germany,Female,46.0,6.0,142721.52,1.0,1.0,0.0,126013.58,1.0
+14306,15591740.0,Yobachi,636.0,Spain,Male,30.0,5.0,0.0,1.0,0.0,0.0,183318.79,0.0
+14307,15618018.0,Maclean,691.0,France,Female,34.0,6.0,0.0,1.0,1.0,0.0,187816.59,0.0
+14308,15567486.0,Pirozzi,634.0,Spain,Female,36.0,9.0,104584.11,1.0,1.0,1.0,48108.72,0.0
+14309,15732774.0,DeRose,431.0,France,Male,37.0,1.0,0.0,1.0,0.0,1.0,180598.86,0.0
+14310,15623102.0,Ch'in,629.0,France,Male,31.0,2.0,153316.14,1.0,1.0,1.0,88920.68,0.0
+14311,15698028.0,Chiawuotu,642.0,France,Male,43.0,8.0,0.0,1.0,1.0,0.0,139919.38,0.0
+14312,15731267.0,Whitelegge,676.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,62368.33,0.0
+14313,15602844.0,Chinwenma,572.0,France,Male,31.0,2.0,0.0,1.0,1.0,0.0,121834.81,0.0
+14314,15766787.0,Ritchie,675.0,France,Female,36.0,1.0,0.0,2.0,1.0,1.0,56780.97,0.0
+14315,15809817.0,Liao,615.0,Germany,Female,36.0,6.0,131644.26,1.0,1.0,1.0,141478.55,1.0
+14316,15807553.0,Chidubem,591.0,France,Male,36.0,6.0,96858.35,1.0,1.0,1.0,100995.11,0.0
+14317,15583499.0,Nnaife,706.0,Spain,Male,27.0,7.0,116084.32,1.0,1.0,0.0,181419.29,0.0
+14318,15659428.0,O'Kane,732.0,France,Male,43.0,3.0,0.0,2.0,1.0,1.0,125518.32,0.0
+14319,15680797.0,Walker,733.0,France,Female,44.0,2.0,0.0,1.0,0.0,0.0,185599.34,1.0
+14320,15803966.0,Iadanza,691.0,Germany,Male,39.0,10.0,146780.52,2.0,1.0,0.0,167916.35,0.0
+14321,15651369.0,Hsiao,673.0,Germany,Male,43.0,2.0,161806.09,1.0,1.0,1.0,175853.64,0.0
+14322,15676576.0,Chikwendu,685.0,France,Female,51.0,6.0,0.0,2.0,1.0,1.0,131718.12,0.0
+14323,15730488.0,P'eng,708.0,Spain,Female,55.0,2.0,0.0,3.0,0.0,1.0,121537.91,1.0
+14324,15796910.0,Nnachetam,653.0,France,Female,54.0,6.0,0.0,1.0,1.0,0.0,158845.49,1.0
+14325,15697424.0,Onyekachukwu,676.0,France,Male,36.0,1.0,0.0,1.0,1.0,1.0,72873.33,0.0
+14326,15686885.0,Y?an,582.0,France,Female,43.0,9.0,0.0,1.0,1.0,1.0,25881.0,0.0
+14327,15679991.0,Lo Duca,824.0,France,Male,40.0,1.0,0.0,1.0,1.0,0.0,182059.28,0.0
+14328,15683544.0,Y?,732.0,France,Male,37.0,5.0,115532.52,1.0,1.0,0.0,137037.97,0.0
+14329,15655339.0,Nwachukwu,706.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,182355.21,1.0
+14330,15751175.0,Kline,684.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,118716.63,0.0
+14331,15685329.0,Onio,695.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,0.0,95957.09,0.0
+14332,15567147.0,Chukwumaobim,653.0,Germany,Male,43.0,4.0,104783.81,1.0,1.0,1.0,4777.23,0.0
+14333,15733261.0,Zikoranachidimma,619.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,62449.07,0.0
+14334,15666884.0,Chiang,605.0,Germany,Female,41.0,1.0,104822.45,1.0,1.0,0.0,179964.55,1.0
+14335,15749050.0,Nnonso,649.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,91906.0,0.0
+14336,15742272.0,Ch'ang,715.0,France,Female,55.0,5.0,174852.89,1.0,1.0,0.0,57898.58,1.0
+14337,15700710.0,Ch'in,712.0,Germany,Male,47.0,9.0,118235.7,1.0,1.0,0.0,184514.01,0.0
+14338,15753837.0,Nwabugwu,712.0,Germany,Female,54.0,0.0,121629.22,1.0,1.0,1.0,167522.19,1.0
+14339,15719302.0,Chidozie,559.0,Germany,Male,37.0,7.0,132876.88,1.0,0.0,0.0,180593.45,0.0
+14340,15696904.0,Chukwumaobim,678.0,France,Male,60.0,7.0,0.0,2.0,1.0,1.0,132973.18,0.0
+14341,15665524.0,Piazza,554.0,France,Male,39.0,0.0,0.0,2.0,1.0,1.0,148098.95,0.0
+14342,15687558.0,Ting,653.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,132973.17,0.0
+14343,15618258.0,Dike,704.0,France,Male,22.0,4.0,0.0,1.0,1.0,0.0,126106.4,0.0
+14344,15742369.0,Linger,661.0,France,Male,35.0,8.0,128743.8,1.0,1.0,1.0,66926.83,0.0
+14345,15766185.0,Iloerika,581.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,92357.21,0.0
+14346,15651664.0,Calabresi,571.0,Spain,Male,32.0,2.0,134562.2,1.0,1.0,1.0,62868.33,0.0
+14347,15668073.0,Palerma,700.0,Spain,Male,31.0,9.0,131593.85,1.0,1.0,1.0,67622.46,0.0
+14348,15611287.0,Chidiebere,605.0,France,Female,41.0,9.0,0.0,2.0,0.0,0.0,86786.41,0.0
+14349,15760456.0,Chiang,687.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,89935.73,0.0
+14350,15771087.0,Shih,733.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,170634.49,0.0
+14351,15643679.0,Ch'in,696.0,Germany,Male,25.0,5.0,152462.79,1.0,0.0,1.0,114935.21,0.0
+14352,15649418.0,Ts'ai,685.0,France,Female,36.0,3.0,0.0,1.0,1.0,1.0,62282.99,0.0
+14353,15668679.0,Chizuoke,850.0,Germany,Female,39.0,7.0,114997.42,1.0,0.0,0.0,175072.47,1.0
+14354,15589475.0,Zikoranachidimma,745.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,166650.32,0.0
+14355,15775490.0,Chia,673.0,France,Male,41.0,1.0,0.0,2.0,0.0,1.0,76611.45,0.0
+14356,15668893.0,Lucciano,684.0,Spain,Female,31.0,3.0,0.0,1.0,0.0,0.0,173498.45,0.0
+14357,15618401.0,Nwora,633.0,France,Male,44.0,9.0,0.0,2.0,1.0,0.0,121276.36,0.0
+14358,15690733.0,Narelle,644.0,France,Male,49.0,4.0,0.0,2.0,0.0,0.0,94790.34,1.0
+14359,15773869.0,O'Donnell,622.0,France,Male,26.0,9.0,0.0,2.0,1.0,0.0,74870.99,0.0
+14360,15583681.0,L?,756.0,France,Female,59.0,8.0,0.0,2.0,0.0,1.0,169862.01,1.0
+14361,15660646.0,Smith,637.0,France,Male,32.0,8.0,0.0,2.0,0.0,0.0,18094.96,0.0
+14362,15621093.0,Iweobiegbulam,801.0,Germany,Female,43.0,6.0,127864.4,1.0,1.0,0.0,113089.43,1.0
+14363,15614491.0,Zikoranachidimma,588.0,France,Male,42.0,10.0,0.0,2.0,1.0,1.0,63736.17,0.0
+14364,15798834.0,Chukwubuikem,552.0,France,Male,32.0,8.0,0.0,2.0,0.0,1.0,122488.59,0.0
+14365,15642725.0,Chidimma,611.0,France,Male,34.0,8.0,0.0,2.0,0.0,1.0,179883.04,0.0
+14366,15680303.0,Chidiebele,590.0,Spain,Male,32.0,5.0,125584.17,1.0,1.0,1.0,26265.18,0.0
+14367,15708003.0,H?,615.0,Spain,Male,29.0,5.0,0.0,2.0,0.0,1.0,28407.4,0.0
+14368,15637324.0,Chukwuemeka,590.0,Spain,Male,30.0,6.0,0.0,1.0,1.0,0.0,94790.34,0.0
+14369,15772933.0,Fiorentino,553.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,122810.53,0.0
+14370,15742738.0,T'ang,718.0,France,Male,28.0,10.0,0.0,2.0,1.0,1.0,103599.67,0.0
+14371,15579212.0,K?,596.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,182624.93,0.0
+14372,15691705.0,Fyans,634.0,Germany,Male,29.0,3.0,142646.68,1.0,1.0,0.0,180716.1,0.0
+14373,15791045.0,Manna,605.0,Spain,Male,30.0,5.0,0.0,1.0,1.0,1.0,166526.26,0.0
+14374,15628893.0,Pirozzi,547.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,177065.24,0.0
+14375,15735572.0,Chukwufumnanya,601.0,France,Male,24.0,3.0,132944.48,1.0,1.0,0.0,129964.94,0.0
+14376,15696054.0,Hsiao,605.0,France,Male,47.0,5.0,0.0,3.0,1.0,0.0,100995.68,1.0
+14377,15733476.0,Nwankwo,691.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,166677.54,0.0
+14378,15674750.0,Chiang,678.0,France,Male,59.0,5.0,106968.96,1.0,1.0,0.0,3825.37,1.0
+14379,15583736.0,Udinese,644.0,Germany,Male,37.0,2.0,123276.69,1.0,0.0,0.0,91633.53,1.0
+14380,15605666.0,H?,684.0,Spain,Female,32.0,6.0,0.0,2.0,1.0,0.0,162719.69,0.0
+14381,15802478.0,Nwankwo,693.0,France,Male,26.0,9.0,0.0,2.0,1.0,0.0,126688.83,0.0
+14382,15743007.0,Nwachukwu,659.0,France,Female,37.0,4.0,0.0,1.0,1.0,0.0,153955.38,1.0
+14383,15696900.0,T'ang,712.0,Germany,Male,59.0,6.0,167557.12,1.0,1.0,1.0,167558.26,1.0
+14384,15752984.0,Hsueh,711.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,155786.39,0.0
+14385,15630709.0,Pye,646.0,France,Male,47.0,3.0,0.0,1.0,1.0,0.0,131736.23,1.0
+14386,15664204.0,Hao,689.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,101192.08,0.0
+14387,15794048.0,Ch'ang,756.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,126192.54,0.0
+14388,15736495.0,Mazzanti,704.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,0.0,151924.9,0.0
+14389,15677955.0,Shih,614.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,120540.83,0.0
+14390,15592745.0,Chiemenam,774.0,France,Male,26.0,1.0,0.0,2.0,1.0,0.0,136837.97,0.0
+14391,15636330.0,Mancini,554.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,124592.84,0.0
+14392,15686461.0,Chiedozie,691.0,France,Female,32.0,3.0,0.0,1.0,1.0,0.0,166513.49,0.0
+14393,15702293.0,Onyemere,775.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,100442.22,0.0
+14394,15746035.0,Hovell,709.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,121151.1,0.0
+14395,15693902.0,L?,645.0,Spain,Female,28.0,8.0,100683.54,1.0,0.0,0.0,178032.93,0.0
+14396,15656613.0,Dyer,717.0,France,Female,46.0,7.0,0.0,2.0,1.0,0.0,176883.42,0.0
+14397,15682473.0,Ch'eng,579.0,France,Female,43.0,1.0,0.0,2.0,1.0,0.0,71830.1,0.0
+14398,15769412.0,Iadanza,635.0,France,Male,43.0,5.0,177069.24,2.0,1.0,0.0,150836.93,0.0
+14399,15593555.0,Gadsden,697.0,Germany,Male,51.0,8.0,103819.57,1.0,1.0,1.0,180716.1,1.0
+14400,15751392.0,T'an,753.0,France,Male,36.0,8.0,0.0,1.0,1.0,1.0,159835.16,1.0
+14401,15754858.0,Lo Duca,730.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,101348.88,0.0
+14402,15791836.0,Horsley,568.0,Spain,Female,48.0,4.0,74483.97,3.0,0.0,0.0,171457.06,1.0
+14403,15752388.0,Chukwumaobim,629.0,Spain,Male,32.0,2.0,96651.03,1.0,1.0,0.0,86791.9,0.0
+14404,15689412.0,Hsieh,629.0,Spain,Male,37.0,2.0,110802.03,1.0,1.0,1.0,147794.63,0.0
+14405,15593365.0,Hsia,599.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,60533.96,0.0
+14406,15617790.0,Ch'ang,691.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,72318.77,0.0
+14407,15614818.0,Nucci,565.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,120853.83,0.0
+14408,15591740.0,Pinto,632.0,France,Male,49.0,3.0,0.0,2.0,1.0,1.0,143859.01,0.0
+14409,15631451.0,K'ung,675.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,124874.23,0.0
+14410,15679693.0,Hsieh,579.0,Germany,Female,42.0,6.0,113588.69,1.0,1.0,1.0,90876.95,1.0
+14411,15772363.0,P'eng,601.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,78955.8,0.0
+14412,15644296.0,Iqbal,706.0,France,Female,38.0,4.0,155879.07,1.0,0.0,1.0,100557.03,0.0
+14413,15705521.0,Aitken,624.0,Germany,Female,27.0,7.0,127837.86,2.0,1.0,1.0,161812.84,0.0
+14414,15659710.0,Ch'in,697.0,France,Female,34.0,4.0,0.0,2.0,0.0,1.0,149550.95,0.0
+14415,15706116.0,Chukwualuka,697.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,105735.92,0.0
+14416,15746035.0,Okeke,617.0,Germany,Female,25.0,3.0,99353.42,2.0,0.0,1.0,174770.14,0.0
+14417,15797786.0,Hsu,710.0,Spain,Male,34.0,6.0,96863.13,1.0,1.0,1.0,91094.46,0.0
+14418,15770041.0,Ritchie,646.0,France,Male,54.0,6.0,0.0,2.0,0.0,1.0,56984.58,0.0
+14419,15728605.0,Brown,686.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,15068.18,0.0
+14420,15645571.0,Milanesi,745.0,France,Male,34.0,2.0,0.0,2.0,0.0,0.0,136902.01,0.0
+14421,15691627.0,Y?,732.0,Spain,Female,41.0,5.0,110479.48,1.0,0.0,0.0,143049.72,0.0
+14422,15739189.0,Hsia,718.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,171482.56,0.0
+14423,15681332.0,Ives,636.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,147554.26,0.0
+14424,15781674.0,Nwachinemelu,699.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,64833.28,0.0
+14425,15692408.0,Chukwufumnanya,701.0,Spain,Male,39.0,3.0,0.0,2.0,0.0,0.0,144290.97,0.0
+14426,15718507.0,Chinedum,550.0,France,Female,36.0,10.0,0.0,2.0,1.0,1.0,143783.77,0.0
+14427,15752534.0,Onyinyechukwuka,648.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,92816.86,0.0
+14428,15763029.0,Nnonso,573.0,France,Female,30.0,1.0,0.0,2.0,1.0,0.0,93777.15,0.0
+14429,15602841.0,Onyekaozulu,686.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,1.0,180598.86,0.0
+14430,15774586.0,H?,632.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,125302.64,0.0
+14431,15690372.0,Lucchesi,695.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,118959.64,0.0
+14432,15622478.0,Sun,685.0,France,Female,29.0,4.0,0.0,2.0,0.0,1.0,141871.84,0.0
+14433,15673907.0,Fenton,684.0,France,Female,45.0,6.0,0.0,1.0,1.0,0.0,143635.33,1.0
+14434,15650288.0,Onyemauchechukwu,730.0,France,Male,45.0,10.0,120105.43,1.0,1.0,0.0,171706.66,0.0
+14435,15680804.0,Chuang,685.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,132973.17,1.0
+14436,15587712.0,Chiang,709.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,166957.82,0.0
+14437,15655341.0,Pagnotto,659.0,Spain,Male,35.0,4.0,0.0,2.0,0.0,1.0,90877.76,0.0
+14438,15657771.0,Nebeolisa,605.0,France,Female,41.0,8.0,0.0,1.0,1.0,0.0,158750.53,1.0
+14439,15701765.0,Chinwemma,684.0,France,Male,45.0,4.0,0.0,2.0,1.0,1.0,79355.87,0.0
+14440,15741875.0,Hsieh,655.0,France,Female,62.0,7.0,0.0,2.0,1.0,1.0,123982.29,0.0
+14441,15635141.0,Eluemuno,598.0,France,Male,35.0,6.0,142387.31,1.0,1.0,0.0,184954.11,0.0
+14442,15771270.0,Yermakov,677.0,France,Male,37.0,5.0,81536.93,1.0,1.0,1.0,183584.14,0.0
+14443,15709920.0,Aksenov,726.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,64448.36,0.0
+14444,15704081.0,Laidley,850.0,Germany,Male,31.0,9.0,125023.1,2.0,1.0,1.0,136909.87,0.0
+14445,15723886.0,Hsiung,762.0,Germany,Male,31.0,5.0,128882.49,2.0,1.0,1.0,117034.32,0.0
+14446,15620750.0,Ch'ang,633.0,France,Male,51.0,7.0,0.0,1.0,1.0,0.0,120892.96,1.0
+14447,15593976.0,Chidiebere,707.0,France,Female,28.0,1.0,0.0,2.0,1.0,0.0,107874.39,0.0
+14448,15605037.0,Chiekwugo,639.0,Spain,Male,24.0,4.0,0.0,2.0,1.0,1.0,158545.25,0.0
+14449,15589210.0,Nucci,745.0,France,Female,30.0,8.0,0.0,1.0,1.0,0.0,180326.83,0.0
+14450,15671139.0,Mancini,614.0,France,Female,53.0,10.0,171913.87,1.0,1.0,0.0,115719.08,1.0
+14451,15730394.0,Tuan,516.0,Spain,Male,38.0,6.0,111395.53,1.0,1.0,0.0,105715.01,0.0
+14452,15703779.0,Curnow,561.0,France,Female,35.0,8.0,115462.44,1.0,1.0,0.0,142516.35,0.0
+14453,15760450.0,Chiang,434.0,Spain,Male,52.0,5.0,116536.43,1.0,1.0,1.0,94715.24,1.0
+14454,15677087.0,Lo,586.0,Germany,Female,41.0,8.0,115315.04,1.0,0.0,1.0,5791.85,1.0
+14455,15748691.0,Ukaegbunam,709.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,64420.5,0.0
+14456,15583394.0,Pethard,642.0,France,Female,26.0,3.0,0.0,2.0,1.0,0.0,52327.57,0.0
+14457,15689633.0,Li,733.0,France,Male,42.0,1.0,89087.89,1.0,1.0,0.0,154491.39,1.0
+14458,15583576.0,Nwankwo,667.0,France,Female,50.0,1.0,0.0,1.0,1.0,1.0,35367.19,1.0
+14459,15798888.0,Uwaezuoke,667.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,123969.64,0.0
+14460,15682485.0,Nwachinemelu,714.0,Spain,Male,60.0,8.0,128306.7,2.0,1.0,1.0,183569.25,0.0
+14461,15677907.0,Chidubem,598.0,Spain,Male,28.0,8.0,0.0,2.0,1.0,1.0,125079.65,0.0
+14462,15658577.0,Tung,684.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,53483.21,0.0
+14463,15792250.0,Chuter,749.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,167984.61,0.0
+14464,15608918.0,Ch'eng,559.0,France,Female,30.0,8.0,0.0,1.0,1.0,1.0,92647.95,0.0
+14465,15807473.0,Ts'ai,557.0,France,Male,30.0,1.0,0.0,2.0,0.0,1.0,85902.14,0.0
+14466,15765520.0,Y?,638.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,0.0,138770.96,0.0
+14467,15610337.0,Yashina,710.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,114083.73,0.0
+14468,15723373.0,K?,678.0,France,Female,42.0,7.0,0.0,2.0,1.0,0.0,108699.59,0.0
+14469,15724443.0,H?,692.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,175893.49,0.0
+14470,15581871.0,Onwuka,819.0,Germany,Female,28.0,8.0,102723.35,1.0,1.0,0.0,152097.02,0.0
+14471,15576000.0,Shih,563.0,France,Male,37.0,3.0,131390.75,1.0,1.0,1.0,96548.88,0.0
+14472,15607314.0,Hsueh,690.0,Spain,Female,36.0,0.0,127841.52,1.0,1.0,1.0,92916.53,0.0
+14473,15649410.0,Fetherstonhaugh,682.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,126565.11,0.0
+14474,15767794.0,Olisaemeka,594.0,Spain,Male,40.0,3.0,0.0,1.0,1.0,1.0,121681.19,1.0
+14475,15707551.0,Aitken,731.0,Spain,Male,34.0,2.0,0.0,2.0,0.0,0.0,128882.66,0.0
+14476,15679983.0,Pirozzi,678.0,Germany,Male,33.0,7.0,118469.38,1.0,1.0,1.0,105887.08,0.0
+14477,15640789.0,Milne,713.0,Germany,Female,36.0,2.0,132623.6,1.0,1.0,1.0,133961.19,1.0
+14478,15635244.0,Ts'ui,572.0,Spain,Male,26.0,5.0,0.0,2.0,0.0,0.0,140941.47,0.0
+14479,15752626.0,O'Donnell,730.0,France,Female,35.0,8.0,0.0,2.0,1.0,0.0,125870.5,0.0
+14480,15772383.0,Tung,659.0,Germany,Female,41.0,8.0,116536.43,1.0,1.0,0.0,125167.92,1.0
+14481,15635396.0,K'ung,637.0,Germany,Female,34.0,2.0,116114.93,1.0,1.0,0.0,102057.86,1.0
+14482,15656468.0,H?,663.0,Germany,Female,37.0,2.0,131753.41,1.0,1.0,0.0,120853.83,0.0
+14483,15626042.0,Ch'ien,781.0,France,Male,58.0,4.0,104430.08,1.0,1.0,1.0,126761.2,0.0
+14484,15605279.0,Okwudilichukwu,579.0,France,Female,37.0,5.0,0.0,1.0,1.0,0.0,151925.25,0.0
+14485,15573893.0,Onyekachukwu,756.0,Spain,Male,39.0,9.0,0.0,2.0,0.0,0.0,116692.8,0.0
+14486,15667934.0,Chukwudi,656.0,Germany,Male,28.0,9.0,111911.63,2.0,1.0,0.0,138344.3,0.0
+14487,15644856.0,Chidozie,696.0,Germany,Female,37.0,3.0,81070.34,1.0,0.0,1.0,184524.74,0.0
+14488,15593365.0,T'ien,803.0,France,Female,37.0,2.0,0.0,2.0,1.0,1.0,149503.51,0.0
+14489,15724181.0,Okwudilichukwu,726.0,Germany,Male,51.0,0.0,104911.35,1.0,1.0,1.0,85746.52,1.0
+14490,15711130.0,Cremonesi,703.0,Spain,Female,61.0,8.0,0.0,1.0,1.0,1.0,140941.47,1.0
+14491,15771529.0,T'ao,537.0,Germany,Male,31.0,1.0,149641.53,2.0,1.0,1.0,131300.68,0.0
+14492,15674620.0,Udinese,741.0,Germany,Male,27.0,5.0,121159.65,2.0,1.0,1.0,149740.22,0.0
+14493,15704770.0,Chidiegwu,669.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,136914.56,0.0
+14494,15704053.0,Frolov,574.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,78970.58,0.0
+14495,15681554.0,Chukwuhaenye,581.0,France,Male,52.0,3.0,0.0,1.0,0.0,0.0,61315.37,1.0
+14496,15724307.0,Pinto,474.0,France,Male,42.0,3.0,0.0,2.0,0.0,1.0,153662.88,0.0
+14497,15735080.0,Manna,607.0,Germany,Female,55.0,8.0,98675.74,1.0,1.0,0.0,119969.99,1.0
+14498,15583392.0,Ebelegbulam,628.0,France,Male,55.0,4.0,0.0,1.0,0.0,1.0,187811.71,1.0
+14499,15674678.0,Chiemenam,573.0,Germany,Female,33.0,7.0,121132.26,2.0,0.0,0.0,185827.74,0.0
+14500,15624013.0,Nwokezuike,651.0,France,Female,48.0,8.0,0.0,3.0,1.0,1.0,170528.63,1.0
+14501,15784923.0,Fanucci,611.0,France,Male,42.0,2.0,0.0,2.0,0.0,1.0,126761.2,0.0
+14502,15574072.0,Mazzi,663.0,Germany,Male,42.0,4.0,104796.54,1.0,0.0,0.0,165083.92,1.0
+14503,15769125.0,Onyekachukwu,593.0,France,Male,49.0,7.0,0.0,1.0,1.0,1.0,151844.81,1.0
+14504,15592597.0,T'ien,678.0,France,Female,40.0,6.0,0.0,1.0,1.0,0.0,123585.89,0.0
+14505,15737605.0,Lucchesi,712.0,Germany,Male,26.0,4.0,82291.1,2.0,0.0,1.0,91574.92,0.0
+14506,15693817.0,Chukwualuka,571.0,Spain,Female,39.0,9.0,0.0,2.0,1.0,0.0,64872.55,0.0
+14507,15592877.0,Ch'iu,605.0,France,Male,47.0,9.0,0.0,2.0,1.0,0.0,104889.2,0.0
+14508,15605264.0,Luffman,571.0,France,Male,32.0,8.0,0.0,1.0,1.0,0.0,121846.81,0.0
+14509,15641690.0,Okwudiliolisa,675.0,France,Female,35.0,3.0,0.0,1.0,0.0,0.0,63736.17,0.0
+14510,15799211.0,Maclean,627.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,147545.65,0.0
+14511,15723004.0,Ndubueze,561.0,France,Female,30.0,4.0,115007.08,1.0,1.0,1.0,184842.21,0.0
+14512,15625141.0,Chukwumaobim,684.0,Germany,Male,46.0,4.0,87637.28,1.0,1.0,0.0,184564.67,1.0
+14513,15701206.0,Teng,526.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,129891.66,0.0
+14514,15587735.0,Ts'ui,769.0,Germany,Female,34.0,7.0,141788.63,1.0,1.0,0.0,177852.57,0.0
+14515,15579207.0,Mazzi,587.0,Germany,Female,47.0,1.0,123681.32,1.0,1.0,0.0,56784.11,1.0
+14516,15777904.0,Ch'ang,719.0,Germany,Male,38.0,6.0,74896.92,1.0,0.0,0.0,110498.1,0.0
+14517,15682355.0,Pisani,667.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,61569.07,0.0
+14518,15791513.0,Young,707.0,Spain,Male,30.0,1.0,0.0,2.0,0.0,0.0,81442.6,0.0
+14519,15642291.0,Chibueze,628.0,Spain,Female,49.0,4.0,0.0,3.0,0.0,1.0,70550.0,1.0
+14520,15771211.0,Ts'ao,669.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,93336.3,0.0
+14521,15593976.0,T'ien,706.0,France,Female,59.0,3.0,0.0,1.0,1.0,0.0,181893.31,1.0
+14522,15609356.0,Chukwujamuike,682.0,France,Male,35.0,9.0,121152.05,1.0,1.0,0.0,186489.15,0.0
+14523,15695585.0,Shao,729.0,France,Female,41.0,3.0,0.0,2.0,1.0,0.0,147055.87,0.0
+14524,15726494.0,Pugh,663.0,France,Female,51.0,3.0,0.0,3.0,1.0,0.0,109783.29,1.0
+14525,15744368.0,Chiang,712.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,168023.6,0.0
+14526,15655238.0,Udegbulam,597.0,France,Female,41.0,4.0,109909.06,1.0,1.0,0.0,66721.98,1.0
+14527,15661473.0,Dreyer,706.0,Spain,Male,35.0,1.0,123971.16,1.0,1.0,1.0,35300.08,0.0
+14528,15763029.0,Tseng,659.0,Spain,Male,34.0,5.0,104760.82,2.0,1.0,0.0,84760.32,0.0
+14529,15662337.0,Shih,724.0,Spain,Male,42.0,3.0,126066.26,2.0,0.0,0.0,100720.02,0.0
+14530,15692977.0,Maclean,706.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,147931.81,0.0
+14531,15667554.0,Dyer,644.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,86799.28,0.0
+14532,15651336.0,Hsueh,605.0,France,Female,44.0,5.0,0.0,1.0,1.0,1.0,73564.44,0.0
+14533,15779492.0,Uspensky,656.0,France,Male,45.0,6.0,133776.86,1.0,1.0,1.0,116511.12,0.0
+14534,15780498.0,Milne,708.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,180829.99,0.0
+14535,15578835.0,Chiekwugo,660.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,79476.63,0.0
+14536,15809830.0,Tseng,694.0,France,Female,26.0,1.0,142501.97,1.0,1.0,0.0,43954.21,0.0
+14537,15784761.0,Mamelu,733.0,France,Female,40.0,2.0,141806.0,2.0,1.0,0.0,162599.51,1.0
+14538,15699294.0,Lucciano,635.0,Spain,Female,46.0,4.0,129934.64,1.0,0.0,1.0,163798.41,0.0
+14539,15604130.0,O'Donnell,627.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,1.0,59129.72,0.0
+14540,15815095.0,Ifeanyichukwu,624.0,France,Female,26.0,7.0,0.0,2.0,1.0,0.0,12510.53,0.0
+14541,15772781.0,Ch'ien,516.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,168515.61,0.0
+14542,15604497.0,Onyemauchechi,709.0,Germany,Male,25.0,6.0,161805.95,2.0,1.0,1.0,138375.31,0.0
+14543,15643359.0,Y?an,602.0,France,Female,32.0,5.0,0.0,1.0,0.0,0.0,132038.65,0.0
+14544,15588902.0,Onyemere,548.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,82647.73,0.0
+14545,15667083.0,Lung,611.0,France,Male,39.0,0.0,0.0,2.0,1.0,1.0,77866.91,0.0
+14546,15734044.0,Milano,677.0,France,Male,38.0,4.0,119912.52,1.0,1.0,1.0,172450.04,0.0
+14547,15591628.0,Hs?,656.0,Spain,Male,56.0,2.0,0.0,1.0,1.0,0.0,177624.01,1.0
+14548,15771856.0,Udegbunam,691.0,France,Female,39.0,8.0,0.0,1.0,0.0,0.0,134901.56,0.0
+14549,15795874.0,Kao,701.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,89972.25,0.0
+14550,15593365.0,Maclean,624.0,France,Female,39.0,3.0,0.0,2.0,1.0,0.0,159584.61,0.0
+14551,15795954.0,Clements,706.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,48226.76,0.0
+14552,15598883.0,McLean,645.0,France,Male,29.0,10.0,0.0,2.0,1.0,0.0,179540.73,0.0
+14553,15733229.0,Onyekachi,731.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,143463.28,0.0
+14554,15572728.0,White,508.0,Spain,Male,36.0,6.0,0.0,2.0,0.0,0.0,161574.19,0.0
+14555,15759991.0,Ugochukwutubelum,699.0,Germany,Female,41.0,6.0,105599.67,1.0,1.0,1.0,90878.13,1.0
+14556,15571529.0,Lo,675.0,France,Male,42.0,1.0,0.0,1.0,1.0,1.0,136983.77,1.0
+14557,15783865.0,Watson,439.0,Germany,Female,36.0,3.0,116969.35,1.0,0.0,0.0,59872.9,1.0
+14558,15748752.0,Ponomaryov,738.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,67734.79,0.0
+14559,15752912.0,Hsueh,625.0,France,Female,35.0,1.0,0.0,2.0,0.0,1.0,55775.72,0.0
+14560,15688865.0,Chukwuma,767.0,Germany,Female,34.0,7.0,142987.46,1.0,1.0,0.0,62232.6,0.0
+14561,15631756.0,K'ung,682.0,Spain,Male,33.0,10.0,119624.6,1.0,1.0,1.0,151401.33,0.0
+14562,15758081.0,Hsia,635.0,France,Male,30.0,5.0,0.0,1.0,1.0,1.0,163714.92,0.0
+14563,15693906.0,Okeke,694.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,47729.33,0.0
+14564,15623478.0,Hsu,571.0,France,Male,22.0,2.0,106036.52,1.0,1.0,0.0,71173.64,0.0
+14565,15664543.0,Chiang,696.0,Germany,Female,32.0,6.0,116968.91,1.0,1.0,1.0,120770.76,0.0
+14566,15776096.0,Hsieh,579.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,166650.9,0.0
+14567,15703068.0,Hs?,813.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,10883.52,0.0
+14568,15656627.0,Chidimma,637.0,France,Male,37.0,4.0,116662.96,1.0,1.0,0.0,121210.09,0.0
+14569,15676519.0,Milanesi,669.0,Germany,Female,41.0,2.0,134984.66,3.0,1.0,0.0,185900.88,1.0
+14570,15572801.0,Chia,559.0,Germany,Female,53.0,4.0,114512.59,1.0,0.0,1.0,87067.73,1.0
+14571,15772601.0,Tuan,608.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,95655.16,0.0
+14572,15757628.0,Udegbulam,773.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,86022.09,1.0
+14573,15729377.0,Ch'ang,659.0,Spain,Female,35.0,1.0,0.0,2.0,1.0,0.0,95864.5,0.0
+14574,15724648.0,Ting,576.0,France,Female,53.0,7.0,0.0,1.0,0.0,0.0,185821.41,1.0
+14575,15715951.0,Nucci,721.0,Germany,Male,37.0,9.0,82498.14,2.0,1.0,0.0,115337.74,0.0
+14576,15682355.0,Chiawuotu,815.0,Germany,Male,27.0,7.0,123870.07,1.0,1.0,0.0,91981.85,0.0
+14577,15698872.0,Mancini,692.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,82298.59,0.0
+14578,15672894.0,Clements,594.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,116969.98,0.0
+14579,15751561.0,Maclean,732.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,91359.79,0.0
+14580,15803689.0,Nucci,719.0,France,Male,37.0,1.0,0.0,2.0,0.0,1.0,71519.4,0.0
+14581,15686588.0,Chukwuebuka,677.0,France,Male,51.0,1.0,131694.97,1.0,1.0,0.0,183494.87,1.0
+14582,15683483.0,Lettiere,756.0,France,Male,24.0,2.0,0.0,2.0,1.0,0.0,27474.81,0.0
+14583,15741416.0,H?,850.0,France,Female,46.0,7.0,0.0,1.0,1.0,0.0,161241.65,1.0
+14584,15634891.0,Ch'iu,648.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,121408.55,0.0
+14585,15672960.0,Loyau,635.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,80698.51,0.0
+14586,15652999.0,Okeke,697.0,Germany,Female,41.0,0.0,86006.21,2.0,1.0,1.0,184524.74,0.0
+14587,15651369.0,Koger,602.0,Spain,Female,30.0,7.0,131778.08,1.0,0.0,1.0,88827.67,0.0
+14588,15794849.0,Calabrese,684.0,France,Female,45.0,3.0,0.0,2.0,1.0,0.0,182266.01,0.0
+14589,15702561.0,Ts'ao,628.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,10072.55,0.0
+14590,15631406.0,Chiang,616.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,79886.06,0.0
+14591,15783029.0,Nnonso,729.0,France,Male,39.0,5.0,0.0,1.0,0.0,1.0,173843.21,0.0
+14592,15590314.0,Onyeorulu,757.0,France,Male,27.0,10.0,0.0,2.0,0.0,1.0,136902.01,0.0
+14593,15635143.0,Linger,594.0,France,Male,31.0,8.0,143390.51,2.0,1.0,0.0,74870.99,0.0
+14594,15585853.0,Trevisani,756.0,France,Female,25.0,0.0,0.0,2.0,1.0,1.0,171021.44,0.0
+14595,15644788.0,Chukwujamuike,614.0,Spain,Male,22.0,9.0,0.0,1.0,1.0,1.0,70523.92,0.0
+14596,15723671.0,Nwankwo,684.0,France,Male,52.0,7.0,0.0,1.0,0.0,1.0,128521.17,0.0
+14597,15648735.0,Yuriev,614.0,France,Female,48.0,8.0,111233.09,1.0,0.0,1.0,56793.02,0.0
+14598,15682955.0,L?,679.0,France,Male,27.0,6.0,133800.98,1.0,1.0,1.0,183494.87,0.0
+14599,15774744.0,Fiorentino,724.0,France,Female,44.0,9.0,0.0,1.0,0.0,1.0,163581.85,1.0
+14600,15702801.0,McLean,648.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,181419.29,0.0
+14601,15661528.0,Iadanza,756.0,Spain,Female,33.0,8.0,0.0,2.0,1.0,1.0,66963.15,0.0
+14602,15803790.0,Chibuzo,683.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,58844.88,0.0
+14603,15737408.0,Tung,749.0,France,Female,55.0,9.0,0.0,2.0,1.0,1.0,180345.44,0.0
+14604,15681174.0,Udegbulam,573.0,Germany,Male,41.0,6.0,148863.31,2.0,0.0,1.0,104886.65,0.0
+14605,15798844.0,Nwankwo,841.0,Germany,Female,34.0,4.0,130170.82,3.0,0.0,0.0,134531.96,1.0
+14606,15803790.0,Tien,805.0,France,Male,39.0,3.0,85845.43,1.0,0.0,0.0,68730.91,0.0
+14607,15803378.0,Chin,682.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,170980.86,0.0
+14608,15721779.0,Onyeorulu,711.0,Germany,Male,48.0,9.0,121166.89,1.0,1.0,1.0,153955.38,1.0
+14609,15673591.0,Trevisani,613.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,173495.77,0.0
+14610,15813132.0,Yeh,571.0,Germany,Female,46.0,5.0,162532.14,1.0,0.0,1.0,79577.38,0.0
+14611,15707078.0,Mazzanti,686.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,92612.88,0.0
+14612,15743826.0,Hsueh,752.0,France,Male,34.0,6.0,0.0,1.0,1.0,0.0,132547.02,0.0
+14613,15621353.0,Chukwudi,741.0,Spain,Female,59.0,4.0,113737.07,1.0,1.0,0.0,182547.04,1.0
+14614,15653572.0,Chiang,599.0,France,Female,45.0,9.0,133800.98,1.0,1.0,1.0,107841.77,0.0
+14615,15675794.0,Chia,643.0,Germany,Male,38.0,4.0,116546.23,1.0,1.0,0.0,96847.91,1.0
+14616,15715673.0,Tuan,679.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,88890.05,0.0
+14617,15806901.0,Pisani,762.0,Germany,Female,40.0,3.0,106901.42,1.0,1.0,0.0,64477.25,0.0
+14618,15794413.0,Krichauff,666.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,145558.42,0.0
+14619,15581332.0,Fanucci,667.0,Spain,Female,45.0,3.0,0.0,2.0,0.0,0.0,141856.33,0.0
+14620,15690182.0,Nwabugwu,551.0,France,Male,47.0,6.0,0.0,2.0,1.0,0.0,94774.12,0.0
+14621,15802741.0,Cremonesi,699.0,France,Male,24.0,9.0,0.0,2.0,1.0,1.0,121299.09,0.0
+14622,15756610.0,Trevisano,762.0,Spain,Female,38.0,5.0,150302.84,1.0,1.0,0.0,58467.08,0.0
+14623,15782735.0,Clements,722.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,131712.52,0.0
+14624,15809227.0,T'ang,568.0,Germany,Male,40.0,4.0,99513.87,1.0,1.0,0.0,135629.28,0.0
+14625,15658852.0,Nebechi,850.0,Germany,Male,44.0,5.0,137714.5,1.0,0.0,0.0,92865.41,1.0
+14626,15690540.0,Pagnotto,694.0,France,Female,58.0,9.0,137555.05,1.0,0.0,1.0,123587.83,1.0
+14627,15672640.0,Chukwubuikem,662.0,Spain,Female,33.0,7.0,0.0,1.0,1.0,1.0,56585.2,0.0
+14628,15684769.0,Chukwuebuka,602.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,582.59,0.0
+14629,15720687.0,Kepley,633.0,Spain,Female,46.0,1.0,0.0,3.0,1.0,0.0,183617.32,1.0
+14630,15738889.0,P'an,521.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,0.0,176925.19,0.0
+14631,15696834.0,K?,598.0,Germany,Male,36.0,5.0,111213.64,2.0,0.0,0.0,7953.62,1.0
+14632,15777076.0,Nnabuife,705.0,France,Female,51.0,0.0,0.0,1.0,1.0,0.0,159840.58,1.0
+14633,15677796.0,Trevisano,704.0,Germany,Female,40.0,5.0,129974.79,1.0,1.0,0.0,131768.98,1.0
+14634,15663917.0,Hsiung,681.0,Germany,Male,44.0,8.0,118629.53,3.0,1.0,1.0,175071.29,1.0
+14635,15693764.0,Tsao,850.0,France,Female,33.0,10.0,0.0,2.0,1.0,1.0,82469.06,0.0
+14636,15585599.0,Trevisani,678.0,France,Male,33.0,3.0,0.0,1.0,1.0,0.0,133516.51,1.0
+14637,15771299.0,Fiorentino,661.0,France,Male,51.0,5.0,0.0,1.0,1.0,1.0,75937.47,1.0
+14638,15600781.0,Ch'ang,730.0,Spain,Female,26.0,5.0,0.0,1.0,0.0,1.0,73073.68,0.0
+14639,15781665.0,Hs?,591.0,France,Male,42.0,0.0,0.0,2.0,1.0,1.0,185541.2,0.0
+14640,15740160.0,Mazzanti,611.0,France,Male,67.0,7.0,0.0,2.0,0.0,1.0,155711.64,0.0
+14641,15743617.0,Maclean,652.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,183569.25,0.0
+14642,15661526.0,P'eng,624.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,110887.14,0.0
+14643,15656683.0,Onwumelu,709.0,France,Female,34.0,0.0,0.0,2.0,0.0,0.0,152577.62,0.0
+14644,15807107.0,Pisani,579.0,France,Female,71.0,3.0,0.0,2.0,1.0,0.0,181419.29,0.0
+14645,15810390.0,Onyemauchechukwu,703.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,163944.18,0.0
+14646,15790744.0,Hsia,624.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,0.0,131778.87,0.0
+14647,15777907.0,Chukwufumnanya,636.0,Spain,Male,36.0,7.0,0.0,1.0,1.0,1.0,131804.86,1.0
+14648,15662758.0,Folliero,646.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,47799.15,0.0
+14649,15702968.0,Hao,597.0,France,Female,42.0,7.0,0.0,2.0,1.0,1.0,91406.62,0.0
+14650,15592999.0,Nnanna,713.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,1.0,158887.09,0.0
+14651,15716276.0,Chukwudi,637.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,155870.82,0.0
+14652,15681924.0,K?,613.0,Germany,Female,37.0,8.0,127120.62,1.0,1.0,0.0,134901.58,1.0
+14653,15609824.0,Fyodorov,588.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,53931.05,1.0
+14654,15718245.0,Kaur,681.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,1.0,82996.47,0.0
+14655,15745452.0,Ch'iu,591.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,1.0,121504.18,0.0
+14656,15741745.0,Y?,748.0,Germany,Male,43.0,5.0,133537.1,1.0,0.0,1.0,84749.96,0.0
+14657,15773456.0,Fanucci,573.0,France,Female,29.0,1.0,126815.71,1.0,1.0,0.0,71405.17,0.0
+14658,15632263.0,Liao,597.0,France,Female,34.0,4.0,0.0,2.0,0.0,0.0,140929.98,0.0
+14659,15654296.0,Ritchie,678.0,France,Male,43.0,5.0,0.0,2.0,1.0,1.0,151903.6,0.0
+14660,15790829.0,Genovesi,538.0,Germany,Female,49.0,5.0,129855.32,1.0,1.0,0.0,35356.46,1.0
+14661,15710671.0,Chukwuemeka,652.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,154767.34,0.0
+14662,15679642.0,Obialo,701.0,France,Female,45.0,1.0,148580.12,1.0,1.0,1.0,99061.75,0.0
+14663,15688868.0,Ibeamaka,687.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,141069.88,0.0
+14664,15746012.0,O'Loughlin,773.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,51587.04,0.0
+14665,15642710.0,Nkemakolam,689.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,1.0,140723.09,0.0
+14666,15709941.0,Trevisano,676.0,France,Male,39.0,0.0,0.0,2.0,1.0,1.0,170980.86,0.0
+14667,15631838.0,Hsu,633.0,Spain,Female,42.0,9.0,0.0,2.0,1.0,0.0,86634.3,0.0
+14668,15706812.0,Uspenskaya,695.0,Spain,Male,51.0,1.0,0.0,2.0,1.0,1.0,84872.66,0.0
+14669,15762588.0,Ch'ien,850.0,Spain,Male,42.0,1.0,0.0,2.0,1.0,0.0,26318.73,0.0
+14670,15593954.0,Okechukwu,635.0,France,Male,31.0,8.0,0.0,2.0,0.0,0.0,146511.2,0.0
+14671,15802351.0,Nwebube,639.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,102908.91,0.0
+14672,15645103.0,Folliero,588.0,France,Male,38.0,7.0,101090.27,1.0,1.0,0.0,47848.56,0.0
+14673,15643359.0,Teng,573.0,France,Female,29.0,10.0,0.0,1.0,1.0,1.0,182436.81,0.0
+14674,15677785.0,Hsiao,753.0,France,Male,45.0,4.0,0.0,2.0,1.0,0.0,181605.85,0.0
+14675,15763093.0,Ts'ai,589.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,176773.59,0.0
+14676,15609735.0,Tien,659.0,Germany,Male,25.0,2.0,117456.72,1.0,1.0,0.0,163666.85,1.0
+14677,15754849.0,Pirozzi,693.0,France,Male,34.0,1.0,0.0,1.0,1.0,0.0,185821.41,0.0
+14678,15732674.0,P'eng,694.0,France,Male,34.0,6.0,0.0,2.0,0.0,1.0,163471.01,0.0
+14679,15772363.0,Nkemjika,632.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,140166.95,0.0
+14680,15655368.0,Hsueh,639.0,France,Male,33.0,2.0,153895.33,1.0,0.0,0.0,180345.44,0.0
+14681,15697318.0,Chukwuhaenye,731.0,Germany,Female,50.0,2.0,118079.47,1.0,1.0,0.0,119313.74,1.0
+14682,15581552.0,K'ung,642.0,Spain,Female,37.0,9.0,153340.89,1.0,0.0,1.0,173365.89,1.0
+14683,15578825.0,Onwuatuegwu,709.0,France,Male,29.0,8.0,0.0,2.0,0.0,0.0,129964.94,0.0
+14684,15688880.0,Ugochukwutubelum,658.0,Spain,Female,61.0,4.0,0.0,1.0,0.0,1.0,81718.37,1.0
+14685,15706131.0,T'ao,633.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,131501.72,0.0
+14686,15611794.0,Ku,699.0,France,Male,30.0,7.0,0.0,2.0,0.0,1.0,145986.35,0.0
+14687,15752312.0,H?,704.0,France,Male,45.0,1.0,0.0,2.0,1.0,0.0,180622.09,0.0
+14688,15747174.0,P'eng,516.0,Germany,Female,51.0,6.0,132057.92,2.0,1.0,0.0,142763.51,1.0
+14689,15588839.0,Ibeamaka,653.0,Germany,Male,31.0,4.0,117056.46,2.0,1.0,0.0,176692.87,0.0
+14690,15658732.0,Martin,686.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,116973.27,0.0
+14691,15588854.0,Mamelu,597.0,France,Male,30.0,4.0,123971.51,2.0,1.0,0.0,48228.1,0.0
+14692,15661626.0,Ewing,607.0,Spain,Male,44.0,9.0,0.0,1.0,1.0,1.0,53437.1,1.0
+14693,15593470.0,Chiang,706.0,France,Male,37.0,3.0,111096.91,1.0,1.0,0.0,112256.57,0.0
+14694,15679760.0,Bogle,624.0,France,Male,28.0,3.0,99745.95,1.0,1.0,1.0,47832.82,0.0
+14695,15633378.0,Shih,684.0,Spain,Female,38.0,10.0,0.0,2.0,1.0,1.0,141364.27,0.0
+14696,15776433.0,Kao,729.0,Spain,Female,33.0,9.0,0.0,2.0,1.0,0.0,139861.53,0.0
+14697,15743456.0,Ritchie,648.0,France,Male,38.0,6.0,138162.81,1.0,0.0,0.0,89519.47,0.0
+14698,15747168.0,Onochie,567.0,France,Male,35.0,7.0,0.0,2.0,0.0,0.0,123866.28,0.0
+14699,15792453.0,Ch'in,663.0,France,Male,31.0,5.0,153611.83,1.0,1.0,0.0,141763.26,0.0
+14700,15710365.0,Ijendu,681.0,Germany,Male,38.0,8.0,109869.32,2.0,1.0,0.0,172096.08,0.0
+14701,15669414.0,Goliwe,707.0,Germany,Female,60.0,7.0,152805.05,1.0,0.0,1.0,49594.02,1.0
+14702,15653762.0,Chien,725.0,France,Female,31.0,1.0,105799.32,1.0,1.0,1.0,177747.03,0.0
+14703,15775192.0,Hsia,719.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,55775.84,0.0
+14704,15590097.0,Nnamdi,850.0,France,Male,51.0,1.0,0.0,2.0,0.0,1.0,155823.2,0.0
+14705,15807961.0,Folliero,615.0,France,Male,29.0,3.0,155619.58,1.0,1.0,0.0,176712.59,0.0
+14706,15809837.0,Henty,526.0,France,Female,31.0,4.0,0.0,2.0,0.0,1.0,152843.53,0.0
+14707,15715643.0,T'ao,543.0,Germany,Male,32.0,6.0,123763.61,2.0,0.0,0.0,100187.43,0.0
+14708,15690526.0,H?,579.0,Spain,Female,37.0,4.0,103960.38,1.0,1.0,1.0,161653.5,0.0
+14709,15776850.0,Ch'ang,726.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,168544.84,0.0
+14710,15661703.0,Cisneros,674.0,Spain,Female,41.0,3.0,0.0,1.0,1.0,1.0,172495.81,0.0
+14711,15571871.0,Ch'en,691.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,93524.19,0.0
+14712,15722090.0,Mazzi,619.0,Germany,Female,50.0,7.0,134919.85,3.0,1.0,0.0,115116.35,1.0
+14713,15749952.0,T'ien,680.0,France,Female,44.0,9.0,0.0,2.0,1.0,0.0,123866.28,0.0
+14714,15623346.0,Horsfall,569.0,Germany,Male,33.0,8.0,98993.02,2.0,1.0,0.0,161574.19,0.0
+14715,15689671.0,Macleod,753.0,Spain,Female,49.0,8.0,0.0,1.0,1.0,0.0,48505.1,1.0
+14716,15731519.0,Liao,595.0,Spain,Female,56.0,2.0,0.0,1.0,1.0,0.0,180344.69,1.0
+14717,15784099.0,Chiazagomekpere,714.0,France,Female,36.0,1.0,0.0,2.0,0.0,0.0,143433.33,0.0
+14718,15600839.0,De Bernales,683.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,147545.65,0.0
+14719,15576680.0,Obijiaku,599.0,France,Male,44.0,4.0,120847.11,1.0,1.0,0.0,77951.84,1.0
+14720,15598044.0,Cattaneo,652.0,France,Male,46.0,5.0,0.0,2.0,1.0,1.0,134423.31,0.0
+14721,15649523.0,Chinonyelum,719.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,103379.31,0.0
+14722,15703794.0,Kao,689.0,France,Male,43.0,9.0,91711.59,1.0,0.0,1.0,113645.4,0.0
+14723,15674727.0,Hsia,749.0,Germany,Female,30.0,3.0,141786.78,1.0,0.0,1.0,173783.38,0.0
+14724,15625494.0,Tien,608.0,France,Male,35.0,3.0,0.0,1.0,1.0,0.0,179258.49,0.0
+14725,15794493.0,Chiekwugo,695.0,France,Male,56.0,6.0,0.0,1.0,1.0,0.0,81602.02,1.0
+14726,15700752.0,Udegbulam,704.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,71047.51,0.0
+14727,15672945.0,Shih,547.0,Germany,Male,48.0,5.0,141749.74,2.0,1.0,0.0,90905.94,1.0
+14728,15752912.0,Nnanna,628.0,France,Female,36.0,4.0,0.0,2.0,0.0,1.0,176778.52,0.0
+14729,15576550.0,Aksenov,656.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,60721.4,1.0
+14730,15671358.0,Hs?,639.0,France,Female,38.0,2.0,0.0,1.0,0.0,1.0,133814.37,1.0
+14731,15659820.0,Pacheco,850.0,Germany,Female,31.0,9.0,115463.16,1.0,1.0,0.0,143481.79,0.0
+14732,15642833.0,Lucciano,637.0,France,Male,44.0,0.0,0.0,2.0,1.0,0.0,102986.15,0.0
+14733,15737782.0,Chukwuemeka,545.0,Spain,Male,26.0,5.0,0.0,2.0,1.0,1.0,85547.33,0.0
+14734,15576676.0,Chiang,578.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,140763.07,0.0
+14735,15651052.0,Chiang,735.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,64516.8,0.0
+14736,15721730.0,O'Donnell,596.0,France,Male,37.0,1.0,136855.24,1.0,1.0,0.0,64872.55,0.0
+14737,15685300.0,Hsiung,705.0,Germany,Male,36.0,5.0,167795.6,2.0,1.0,0.0,120790.02,0.0
+14738,15614778.0,Chiu,558.0,Germany,Female,48.0,3.0,118879.35,1.0,1.0,0.0,136902.01,1.0
+14739,15648881.0,Milano,686.0,France,Male,40.0,5.0,125921.37,1.0,0.0,0.0,179631.85,0.0
+14740,15671209.0,Ebelegbulam,731.0,Germany,Female,47.0,9.0,106301.85,1.0,0.0,0.0,109137.3,1.0
+14741,15592583.0,Milani,590.0,France,Male,40.0,3.0,0.0,3.0,1.0,0.0,149457.81,1.0
+14742,15600991.0,Goliwe,543.0,Spain,Male,42.0,1.0,0.0,2.0,1.0,1.0,143481.85,0.0
+14743,15630661.0,Chin,624.0,France,Male,32.0,3.0,0.0,2.0,0.0,0.0,75837.58,0.0
+14744,15691808.0,Ugochukwu,612.0,Germany,Male,48.0,0.0,135422.19,1.0,1.0,1.0,184936.77,1.0
+14745,15711398.0,Piazza,562.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,53584.03,0.0
+14746,15752312.0,Calabrese,661.0,France,Female,26.0,6.0,0.0,2.0,1.0,0.0,35671.45,0.0
+14747,15749117.0,Onwudiwe,634.0,France,Female,47.0,4.0,0.0,2.0,0.0,0.0,126796.69,0.0
+14748,15672996.0,Shih,606.0,France,Female,37.0,1.0,0.0,1.0,1.0,1.0,159830.58,0.0
+14749,15694566.0,Frolov,651.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,80552.36,0.0
+14750,15809585.0,T'ien,567.0,Spain,Male,35.0,2.0,114947.67,1.0,0.0,1.0,131283.6,0.0
+14751,15615032.0,Nwokezuike,711.0,France,Female,41.0,7.0,0.0,2.0,1.0,0.0,131501.72,0.0
+14752,15635097.0,Ndubuisi,729.0,Spain,Female,44.0,5.0,0.0,2.0,0.0,0.0,60941.27,0.0
+14753,15576085.0,Ch'en,724.0,Germany,Female,42.0,9.0,150635.3,1.0,1.0,1.0,86786.41,1.0
+14754,15723685.0,Udegbunam,554.0,Spain,Female,37.0,5.0,0.0,2.0,0.0,0.0,151345.49,0.0
+14755,15595557.0,Oluchukwu,758.0,France,Male,40.0,9.0,0.0,2.0,0.0,0.0,121189.3,0.0
+14756,15791842.0,Nkemjika,738.0,Spain,Female,42.0,1.0,0.0,1.0,1.0,1.0,91947.74,1.0
+14757,15621267.0,Frolov,638.0,Germany,Male,32.0,8.0,131835.88,1.0,1.0,1.0,4395.45,0.0
+14758,15594812.0,Ch'ien,653.0,France,Male,23.0,3.0,89087.89,1.0,0.0,1.0,184843.77,0.0
+14759,15798943.0,Ch'eng,758.0,Spain,Female,57.0,9.0,0.0,2.0,0.0,0.0,162643.15,1.0
+14760,15760244.0,Frolov,704.0,France,Female,62.0,6.0,116498.22,3.0,1.0,0.0,119903.2,1.0
+14761,15756481.0,Onyekachi,556.0,Spain,Male,39.0,1.0,0.0,2.0,1.0,1.0,152417.79,0.0
+14762,15771636.0,Okwuadigbo,714.0,France,Female,56.0,8.0,148045.45,1.0,1.0,0.0,70974.13,1.0
+14763,15585350.0,Rowntree,850.0,Spain,Female,31.0,4.0,0.0,2.0,1.0,0.0,158937.55,0.0
+14764,15724451.0,Dyer,676.0,Spain,Male,29.0,3.0,112291.34,1.0,1.0,0.0,173683.0,0.0
+14765,15728362.0,T'ien,850.0,France,Female,55.0,8.0,0.0,1.0,1.0,0.0,44946.29,1.0
+14766,15708904.0,Lettiere,614.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,130452.88,0.0
+14767,15692991.0,Chukwuemeka,634.0,Spain,Female,35.0,9.0,0.0,1.0,1.0,1.0,84300.4,0.0
+14768,15686964.0,Chialuka,553.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,1.0,163830.64,0.0
+14769,15789611.0,Chukwualuka,597.0,Spain,Female,32.0,0.0,161591.11,1.0,0.0,0.0,75937.47,0.0
+14770,15698716.0,Zito,611.0,France,Female,45.0,10.0,0.0,2.0,1.0,0.0,73686.75,0.0
+14771,15696989.0,Mazzi,636.0,Germany,Female,36.0,5.0,155973.88,1.0,1.0,1.0,173952.5,0.0
+14772,15661936.0,Shih,687.0,Germany,Female,31.0,6.0,129472.33,3.0,1.0,0.0,96670.2,1.0
+14773,15647572.0,T'ien,597.0,Spain,Female,36.0,8.0,0.0,2.0,1.0,0.0,64321.02,0.0
+14774,15787848.0,Oluchukwu,660.0,Spain,Male,41.0,3.0,166070.18,1.0,1.0,1.0,176576.62,0.0
+14775,15657565.0,Pisano,738.0,Germany,Male,33.0,2.0,141528.88,1.0,1.0,0.0,136915.15,0.0
+14776,15761043.0,Chukwumaobim,584.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,163558.18,0.0
+14777,15704771.0,Okorie,628.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,65720.92,0.0
+14778,15605531.0,Chien,593.0,Spain,Male,44.0,6.0,148384.64,1.0,0.0,0.0,59659.53,0.0
+14779,15592461.0,Udinese,663.0,France,Female,25.0,2.0,0.0,2.0,1.0,1.0,176763.27,0.0
+14780,15790596.0,Nucci,756.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,173498.45,0.0
+14781,15772250.0,Walker,596.0,France,Female,40.0,0.0,0.0,2.0,0.0,1.0,161853.12,0.0
+14782,15641629.0,Eluemuno,638.0,France,Female,43.0,6.0,145974.79,1.0,1.0,0.0,77965.44,1.0
+14783,15713094.0,Kuo,738.0,Germany,Male,36.0,4.0,144976.5,1.0,0.0,1.0,106564.88,0.0
+14784,15708713.0,Milne,569.0,Spain,Male,37.0,1.0,0.0,1.0,1.0,0.0,163625.56,0.0
+14785,15638068.0,Nnachetam,777.0,France,Male,31.0,1.0,99824.46,1.0,0.0,1.0,51670.82,0.0
+14786,15682618.0,Yobachi,721.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,16081.62,0.0
+14787,15589793.0,Aksenova,696.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,103560.98,0.0
+14788,15796370.0,Cremonesi,735.0,France,Female,38.0,3.0,0.0,1.0,1.0,1.0,71139.31,0.0
+14789,15718507.0,Hsieh,684.0,Germany,Female,56.0,1.0,118520.26,1.0,1.0,0.0,120736.04,1.0
+14790,15585066.0,Uvarov,554.0,France,Female,48.0,10.0,0.0,2.0,0.0,1.0,174738.2,1.0
+14791,15643056.0,Trevisan,731.0,France,Male,26.0,1.0,0.0,2.0,1.0,0.0,79775.19,0.0
+14792,15595388.0,Kuo,659.0,France,Male,51.0,2.0,0.0,1.0,1.0,0.0,150836.98,1.0
+14793,15723006.0,Chuang,598.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,0.0,55829.25,0.0
+14794,15645600.0,Lombardi,681.0,Germany,Female,40.0,4.0,109342.82,2.0,1.0,1.0,12092.03,1.0
+14795,15716357.0,Chukwumaobim,748.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,61666.87,0.0
+14796,15582892.0,Hsiung,585.0,Spain,Male,35.0,10.0,0.0,2.0,1.0,0.0,46635.11,0.0
+14797,15635939.0,Trevisani,637.0,Spain,Male,41.0,3.0,0.0,1.0,1.0,0.0,119348.28,0.0
+14798,15568328.0,Walker,578.0,Germany,Female,42.0,5.0,120599.38,1.0,1.0,1.0,49848.86,0.0
+14799,15671345.0,Lucciano,770.0,Spain,Male,62.0,5.0,107720.64,3.0,0.0,0.0,154347.95,1.0
+14800,15696054.0,Lai,584.0,France,Male,38.0,5.0,128743.8,1.0,1.0,0.0,86460.28,0.0
+14801,15798024.0,Olisaemeka,578.0,France,Male,45.0,4.0,0.0,2.0,1.0,0.0,126517.97,1.0
+14802,15791326.0,Ch'ang,438.0,France,Male,32.0,6.0,0.0,1.0,1.0,1.0,121440.8,0.0
+14803,15811594.0,Milanesi,572.0,France,Female,41.0,4.0,0.0,1.0,1.0,0.0,111421.77,1.0
+14804,15643361.0,Kambinachi,587.0,France,Female,34.0,4.0,0.0,2.0,0.0,1.0,90876.95,0.0
+14805,15777901.0,Chukwuebuka,675.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,185543.69,0.0
+14806,15728333.0,Ts'ui,661.0,Spain,Female,58.0,3.0,0.0,2.0,1.0,1.0,124872.42,0.0
+14807,15795953.0,Pisano,660.0,France,Female,39.0,9.0,0.0,1.0,1.0,1.0,89566.74,0.0
+14808,15621611.0,Hao,603.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,94440.45,0.0
+14809,15769412.0,Smith,613.0,Germany,Male,29.0,5.0,116465.24,1.0,0.0,1.0,159268.67,1.0
+14810,15705726.0,Hsia,602.0,France,Male,40.0,9.0,0.0,1.0,0.0,0.0,75860.01,1.0
+14811,15689412.0,Chiu,823.0,Spain,Male,40.0,8.0,121461.87,1.0,1.0,0.0,67740.08,0.0
+14812,15669954.0,Chung,644.0,Germany,Female,42.0,8.0,11582.9,1.0,1.0,0.0,144824.45,1.0
+14813,15693908.0,Olisaemeka,593.0,France,Male,34.0,10.0,0.0,1.0,1.0,1.0,91092.84,0.0
+14814,15784597.0,Pirozzi,678.0,France,Female,39.0,8.0,0.0,1.0,1.0,0.0,131093.99,1.0
+14815,15593969.0,Hsia,850.0,France,Male,57.0,3.0,0.0,2.0,1.0,1.0,172531.23,0.0
+14816,15724444.0,Ts'ui,644.0,Spain,Female,42.0,4.0,0.0,2.0,1.0,1.0,61666.87,0.0
+14817,15690169.0,Fanucci,635.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,0.0,88890.05,0.0
+14818,15797905.0,Padovano,653.0,France,Female,42.0,4.0,0.0,2.0,0.0,0.0,145792.01,0.0
+14819,15756425.0,Folliero,753.0,France,Female,43.0,3.0,0.0,2.0,1.0,1.0,185210.63,0.0
+14820,15602844.0,Martin,668.0,Germany,Male,33.0,5.0,105858.71,2.0,1.0,0.0,109394.62,0.0
+14821,15777599.0,Ubanwa,633.0,France,Male,28.0,1.0,124580.69,1.0,1.0,0.0,120892.96,0.0
+14822,15662066.0,Brown,686.0,France,Female,29.0,5.0,0.0,1.0,1.0,1.0,177622.38,0.0
+14823,15757577.0,Teng,495.0,France,Female,33.0,9.0,123835.46,1.0,1.0,0.0,157577.71,0.0
+14824,15762392.0,Ch'in,562.0,Spain,Male,33.0,8.0,0.0,1.0,0.0,1.0,51380.9,0.0
+14825,15673929.0,Shih,621.0,France,Female,33.0,7.0,162390.52,1.0,1.0,0.0,147090.9,0.0
+14826,15636592.0,Ts'ui,704.0,France,Female,30.0,8.0,0.0,1.0,0.0,0.0,160176.47,0.0
+14827,15651264.0,Cattaneo,752.0,France,Female,38.0,7.0,0.0,1.0,1.0,1.0,151401.33,0.0
+14828,15609987.0,Tsao,648.0,France,Male,48.0,2.0,0.0,2.0,1.0,1.0,88890.75,0.0
+14829,15611157.0,Onyeorulu,707.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,27286.1,0.0
+14830,15681924.0,Hao,592.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,90558.24,0.0
+14831,15579915.0,Fan,741.0,France,Female,41.0,2.0,129870.61,1.0,1.0,1.0,74637.57,0.0
+14832,15734461.0,Baresi,684.0,France,Female,61.0,5.0,0.0,2.0,0.0,1.0,187985.85,1.0
+14833,15701200.0,K?,653.0,France,Female,42.0,2.0,0.0,2.0,1.0,1.0,121537.91,0.0
+14834,15661409.0,Macdonald,605.0,France,Female,42.0,4.0,0.0,1.0,1.0,0.0,94916.68,1.0
+14835,15584091.0,Galkin,705.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,88721.84,0.0
+14836,15803801.0,Chinedum,651.0,Germany,Female,44.0,6.0,155870.93,2.0,1.0,1.0,90177.3,0.0
+14837,15594594.0,Ts'ui,544.0,France,Female,34.0,10.0,113090.4,1.0,1.0,1.0,106534.15,0.0
+14838,15765806.0,Udegbunam,850.0,Spain,Female,34.0,3.0,163700.78,1.0,1.0,1.0,199493.38,0.0
+14839,15691808.0,Udegbunam,584.0,France,Male,45.0,2.0,0.0,1.0,0.0,1.0,152577.62,1.0
+14840,15596671.0,Chukwuebuka,727.0,Spain,Female,33.0,0.0,120962.59,1.0,1.0,1.0,89972.25,0.0
+14841,15725794.0,Piazza,641.0,Germany,Male,37.0,8.0,116800.25,1.0,1.0,0.0,171733.43,1.0
+14842,15802483.0,Ch'ien,743.0,France,Male,48.0,8.0,0.0,1.0,1.0,0.0,176576.71,0.0
+14843,15572408.0,Ch'iu,661.0,Spain,Male,46.0,1.0,0.0,1.0,0.0,1.0,57876.05,1.0
+14844,15776596.0,Lombardo,642.0,Germany,Female,49.0,7.0,129750.68,1.0,1.0,0.0,97938.59,1.0
+14845,15783591.0,Yermolayeva,595.0,France,Male,26.0,5.0,0.0,1.0,0.0,1.0,132798.78,0.0
+14846,15742798.0,Trevisan,485.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,80587.37,0.0
+14847,15787097.0,Outhwaite,667.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,132973.17,0.0
+14848,15693877.0,Chia,679.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,0.0,134790.74,0.0
+14849,15628303.0,Onwumelu,627.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,86791.9,0.0
+14850,15802381.0,Ting,790.0,Germany,Male,33.0,3.0,96776.39,2.0,1.0,0.0,126722.57,0.0
+14851,15571529.0,Hs?,663.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,173899.42,0.0
+14852,15682048.0,Nucci,582.0,France,Male,42.0,2.0,0.0,1.0,0.0,0.0,75837.58,1.0
+14853,15679810.0,Frolov,653.0,France,Male,42.0,7.0,0.0,2.0,0.0,0.0,138344.7,0.0
+14854,15791174.0,Cattaneo,742.0,France,Male,50.0,3.0,0.0,2.0,0.0,0.0,182944.14,1.0
+14855,15632050.0,Lo,712.0,Germany,Male,44.0,7.0,134497.27,2.0,0.0,0.0,166526.26,0.0
+14856,15776824.0,Chizoba,637.0,Spain,Female,34.0,3.0,0.0,2.0,0.0,1.0,106061.47,0.0
+14857,15688963.0,Nwebube,687.0,France,Male,45.0,7.0,0.0,2.0,1.0,1.0,73888.65,0.0
+14858,15671779.0,Lucchese,473.0,France,Female,46.0,5.0,0.0,2.0,1.0,1.0,126743.33,0.0
+14859,15718247.0,Kerr,619.0,Spain,Female,27.0,9.0,0.0,1.0,0.0,0.0,161854.98,0.0
+14860,15673367.0,Chiekwugo,725.0,France,Female,49.0,0.0,0.0,2.0,0.0,1.0,5791.85,0.0
+14861,15692581.0,Baresi,709.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,146723.72,0.0
+14862,15803526.0,Ts'ui,628.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,82696.84,0.0
+14863,15769254.0,Blesing,742.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,199805.63,0.0
+14864,15588123.0,Palerma,803.0,France,Female,55.0,4.0,0.0,3.0,1.0,0.0,89602.86,1.0
+14865,15671293.0,Ch'ang,716.0,Germany,Female,43.0,8.0,112972.57,1.0,1.0,1.0,71167.79,1.0
+14866,15714087.0,White,595.0,Spain,Female,58.0,1.0,0.0,2.0,1.0,1.0,47468.67,0.0
+14867,15703923.0,Yocum,497.0,France,Female,28.0,6.0,0.0,2.0,1.0,0.0,165082.25,0.0
+14868,15692566.0,Chiu,583.0,France,Male,45.0,2.0,0.0,2.0,1.0,1.0,75732.25,0.0
+14869,15776096.0,Onyemachukwu,676.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,0.0,160979.66,0.0
+14870,15632859.0,Nwokeocha,633.0,Germany,Male,35.0,2.0,125584.17,1.0,1.0,1.0,83084.33,0.0
+14871,15616954.0,Lucciano,850.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,157527.6,0.0
+14872,15640074.0,Baddeley,695.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,81425.61,0.0
+14873,15631681.0,Manna,710.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,131662.47,0.0
+14874,15732051.0,Yudina,696.0,France,Male,20.0,7.0,120549.29,1.0,1.0,0.0,38153.19,0.0
+14875,15581528.0,Ch'en,647.0,France,Male,42.0,6.0,0.0,1.0,1.0,0.0,171045.31,1.0
+14876,15591386.0,Fiorentini,635.0,France,Male,26.0,0.0,81828.22,1.0,0.0,1.0,75860.01,0.0
+14877,15679148.0,Ch'ien,679.0,France,Female,27.0,2.0,0.0,2.0,0.0,0.0,93835.64,0.0
+14878,15802088.0,Nkemjika,628.0,Spain,Female,35.0,4.0,0.0,2.0,0.0,1.0,125961.48,0.0
+14879,15793723.0,Chidiebere,641.0,Germany,Female,34.0,5.0,106937.05,1.0,1.0,1.0,41967.05,0.0
+14880,15700826.0,Pagnotto,663.0,France,Male,45.0,10.0,0.0,2.0,1.0,0.0,116969.89,0.0
+14881,15791501.0,Burtch,699.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,141830.42,0.0
+14882,15732270.0,Maitland,528.0,France,Female,55.0,7.0,0.0,1.0,1.0,1.0,136915.15,1.0
+14883,15797710.0,Hs?,567.0,France,Male,55.0,2.0,0.0,2.0,1.0,1.0,151581.79,0.0
+14884,15703979.0,Martin,599.0,France,Male,41.0,6.0,73070.18,1.0,1.0,0.0,142235.75,0.0
+14885,15740557.0,Chukwuemeka,619.0,France,Male,34.0,8.0,113599.71,1.0,0.0,0.0,136910.18,0.0
+14886,15773421.0,Yobachi,637.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,96590.39,0.0
+14887,15663987.0,Chibueze,679.0,France,Male,33.0,8.0,97259.25,1.0,1.0,0.0,187811.71,0.0
+14888,15755642.0,Nkemakolam,640.0,Germany,Female,50.0,7.0,135919.33,1.0,1.0,0.0,179797.79,1.0
+14889,15697460.0,Chinonyelum,695.0,Germany,Female,35.0,1.0,123971.51,2.0,0.0,1.0,171045.35,0.0
+14890,15768367.0,Johnstone,635.0,France,Female,41.0,4.0,0.0,1.0,0.0,1.0,166677.35,1.0
+14891,15716134.0,Nwankwo,629.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,176680.16,0.0
+14892,15626452.0,Milano,778.0,France,Male,37.0,5.0,0.0,1.0,1.0,1.0,74425.41,0.0
+14893,15675185.0,Chien,689.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,75860.01,0.0
+14894,15675562.0,Y?an,679.0,France,Male,38.0,8.0,0.0,2.0,0.0,0.0,82647.65,0.0
+14895,15612893.0,Hs?,678.0,Spain,Male,47.0,3.0,0.0,2.0,1.0,1.0,139919.38,0.0
+14896,15761693.0,Fanucci,648.0,France,Female,26.0,6.0,0.0,2.0,1.0,0.0,25669.1,0.0
+14897,15777893.0,Chukwubuikem,678.0,Spain,Male,31.0,3.0,0.0,2.0,0.0,0.0,189428.75,0.0
+14898,15792726.0,Ch'iu,806.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,94670.77,0.0
+14899,15694300.0,Hsu,661.0,Spain,Female,31.0,2.0,0.0,1.0,1.0,1.0,183584.04,0.0
+14900,15687540.0,Manna,705.0,Germany,Female,45.0,7.0,134954.35,2.0,1.0,1.0,175094.62,0.0
+14901,15803479.0,Kao,602.0,France,Male,23.0,2.0,0.0,2.0,1.0,1.0,53954.24,0.0
+14902,15583681.0,Chiang,573.0,France,Male,31.0,4.0,107712.57,1.0,1.0,1.0,158750.53,0.0
+14903,15593694.0,Ch'ien,681.0,France,Female,29.0,7.0,138033.55,1.0,0.0,1.0,121525.48,0.0
+14904,15690182.0,Shih,589.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,131097.9,0.0
+14905,15796370.0,Onwuatuegwu,642.0,Spain,Male,48.0,2.0,0.0,2.0,1.0,1.0,937.15,0.0
+14906,15644297.0,Galkin,575.0,France,Male,42.0,1.0,120787.18,1.0,1.0,1.0,58854.88,1.0
+14907,15699238.0,Lu,598.0,France,Female,36.0,2.0,0.0,2.0,0.0,0.0,84760.32,0.0
+14908,15748381.0,P'an,711.0,Germany,Male,29.0,2.0,88915.68,2.0,0.0,0.0,122022.89,0.0
+14909,15736069.0,Hsing,526.0,France,Female,49.0,4.0,137843.8,1.0,1.0,0.0,126812.63,1.0
+14910,15680399.0,Onyemauchechi,678.0,Germany,Male,44.0,2.0,98899.91,1.0,1.0,1.0,126160.24,0.0
+14911,15603594.0,Hsieh,707.0,France,Male,33.0,1.0,121355.99,1.0,1.0,1.0,177524.19,0.0
+14912,15665802.0,Folliero,587.0,France,Male,30.0,3.0,155973.88,2.0,0.0,0.0,167066.95,0.0
+14913,15685500.0,Manna,582.0,France,Female,49.0,6.0,0.0,3.0,1.0,0.0,184856.89,1.0
+14914,15604093.0,Chiu,745.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,66926.83,0.0
+14915,15711309.0,Martin,521.0,Germany,Male,41.0,1.0,122311.21,1.0,0.0,0.0,60590.72,1.0
+14916,15727177.0,Ts'ai,535.0,France,Male,48.0,3.0,92476.88,1.0,0.0,0.0,134421.32,1.0
+14917,15597983.0,Ritchie,543.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,0.0,151815.76,0.0
+14918,15697310.0,Maconochie,516.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,134878.19,0.0
+14919,15714642.0,Toscani,599.0,Germany,Male,39.0,4.0,81556.89,2.0,1.0,0.0,149602.54,0.0
+14920,15576824.0,Hsing,588.0,Germany,Female,26.0,0.0,130652.52,2.0,1.0,1.0,105898.79,0.0
+14921,15757777.0,Ts'ai,571.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,1.0,166850.57,0.0
+14922,15603851.0,Frolov,710.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,1.0,3560.0,0.0
+14923,15810457.0,Young,667.0,Spain,Male,49.0,4.0,0.0,2.0,0.0,0.0,71692.8,0.0
+14924,15583079.0,Fiorentini,598.0,Germany,Male,39.0,4.0,117879.32,1.0,1.0,1.0,53718.28,1.0
+14925,15637366.0,McLean,775.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,1.0,149139.13,0.0
+14926,15602312.0,Chin,604.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,46422.46,0.0
+14927,15646091.0,Maclean,604.0,France,Male,46.0,8.0,0.0,1.0,1.0,0.0,1710.89,1.0
+14928,15808760.0,Tuan,684.0,Spain,Male,46.0,1.0,0.0,2.0,1.0,1.0,138889.15,0.0
+14929,15644400.0,Chiazagomekpere,677.0,France,Male,31.0,3.0,0.0,2.0,0.0,0.0,86619.77,0.0
+14930,15674868.0,Cattaneo,612.0,Spain,Female,25.0,8.0,136855.94,1.0,1.0,0.0,92887.06,0.0
+14931,15687558.0,Hovell,755.0,Germany,Female,60.0,1.0,90560.3,1.0,1.0,0.0,145764.4,1.0
+14932,15656627.0,Milano,699.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,162878.96,0.0
+14933,15597957.0,Ugochukwu,599.0,Spain,Female,35.0,1.0,0.0,2.0,0.0,0.0,182867.84,0.0
+14934,15583724.0,Toscani,695.0,France,Male,31.0,4.0,108004.64,1.0,1.0,1.0,162960.05,0.0
+14935,15681476.0,Nnachetam,605.0,Germany,Female,34.0,6.0,148897.03,2.0,1.0,1.0,187976.88,0.0
+14936,15651581.0,Shih,699.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,1.0,131249.46,0.0
+14937,15663817.0,Smith,679.0,France,Male,37.0,1.0,119706.22,2.0,1.0,1.0,65219.85,0.0
+14938,15589948.0,Onwuamaeze,594.0,Germany,Male,33.0,5.0,102773.2,2.0,1.0,0.0,166694.93,0.0
+14939,15808930.0,Ch'eng,683.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,51565.98,0.0
+14940,15763381.0,Trevisan,646.0,France,Male,35.0,3.0,0.0,1.0,0.0,0.0,127543.81,0.0
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+14942,15577018.0,Lo,716.0,Germany,Female,27.0,5.0,135095.58,1.0,1.0,1.0,143668.58,0.0
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+14944,15795519.0,Ch'ang,554.0,France,Male,29.0,2.0,0.0,2.0,0.0,1.0,93524.19,0.0
+14945,15792228.0,Dike,605.0,France,Female,41.0,3.0,0.0,2.0,1.0,1.0,173498.45,0.0
+14946,15792360.0,Chukwuemeka,737.0,Spain,Male,43.0,10.0,0.0,2.0,0.0,1.0,126743.33,0.0
+14947,15794560.0,Chiu,572.0,Spain,Female,29.0,8.0,0.0,2.0,0.0,1.0,645.61,0.0
+14948,15654891.0,Chiu,516.0,Germany,Male,36.0,7.0,140931.57,2.0,0.0,0.0,177065.24,0.0
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+14950,15638884.0,Hsieh,579.0,France,Male,26.0,8.0,0.0,1.0,1.0,1.0,104722.45,0.0
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+14952,15631240.0,Chukwudi,639.0,Spain,Female,46.0,7.0,93901.61,1.0,0.0,1.0,142903.44,0.0
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+14955,15771299.0,White,553.0,France,Male,43.0,5.0,0.0,2.0,0.0,1.0,162223.67,0.0
+14956,15777892.0,Ebelechukwu,693.0,Spain,Female,41.0,2.0,0.0,2.0,1.0,0.0,135875.78,0.0
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+14964,15776947.0,Ts'ui,629.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,111241.37,0.0
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+14966,15771299.0,Chiebuka,521.0,Germany,Female,40.0,10.0,103741.28,2.0,1.0,0.0,181257.84,1.0
+14967,15684196.0,Mazzanti,636.0,Germany,Female,44.0,8.0,114519.57,1.0,1.0,0.0,171681.86,1.0
+14968,15571550.0,Mazzi,573.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,1.0,147534.81,0.0
+14969,15777896.0,Chukwualuka,773.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,81861.1,0.0
+14970,15669411.0,Chiedozie,689.0,France,Male,36.0,6.0,0.0,1.0,1.0,0.0,127947.19,0.0
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+14973,15665087.0,Folliero,646.0,France,Female,25.0,4.0,0.0,2.0,1.0,1.0,119782.89,0.0
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+14975,15585985.0,Deleon,644.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,167554.86,0.0
+14976,15633213.0,Tuan,605.0,France,Male,52.0,0.0,0.0,2.0,1.0,1.0,94739.2,0.0
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+14978,15651264.0,Cattaneo,663.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,131736.23,0.0
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+14980,15808849.0,Pisano,577.0,Spain,Female,37.0,0.0,0.0,1.0,1.0,0.0,103737.85,0.0
+14981,15745232.0,Mazzanti,651.0,France,Male,39.0,10.0,123345.31,1.0,1.0,1.0,161479.19,0.0
+14982,15682778.0,Lo Duca,605.0,Germany,Female,51.0,2.0,122552.48,1.0,1.0,0.0,58601.61,1.0
+14983,15678916.0,Liardet,605.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,0.0,53477.76,0.0
+14984,15581552.0,Harker,738.0,Germany,Female,50.0,8.0,119027.28,1.0,1.0,0.0,77951.84,1.0
+14985,15754084.0,Ch'iu,681.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,189392.66,0.0
+14986,15767358.0,Chukwufumnanya,614.0,Germany,Male,43.0,4.0,109724.88,1.0,1.0,0.0,187925.75,1.0
+14987,15724444.0,Chien,676.0,France,Male,44.0,3.0,0.0,1.0,1.0,1.0,168834.38,0.0
+14988,15780142.0,Chukwualuka,693.0,France,Male,45.0,1.0,0.0,2.0,1.0,1.0,143960.72,0.0
+14989,15624391.0,Hsieh,709.0,Spain,Female,57.0,7.0,0.0,1.0,1.0,0.0,100834.22,1.0
+14990,15708505.0,Chidiebere,644.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,110114.39,0.0
+14991,15603088.0,Yobachukwu,498.0,Spain,Female,46.0,3.0,0.0,1.0,1.0,0.0,29483.87,0.0
+14992,15676521.0,Rizzo,757.0,Spain,Male,61.0,10.0,139129.42,1.0,1.0,1.0,96645.23,0.0
+14993,15797065.0,Hsia,622.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,94488.82,0.0
+14994,15612043.0,Onyemachukwu,683.0,France,Female,38.0,4.0,0.0,1.0,1.0,0.0,112777.38,0.0
+14995,15807989.0,Ch'iu,585.0,France,Male,33.0,3.0,0.0,1.0,1.0,0.0,54191.38,0.0
+14996,15731781.0,Ch'ang,678.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,53437.1,0.0
+14997,15667093.0,Goliwe,678.0,France,Female,54.0,4.0,0.0,1.0,1.0,0.0,147720.29,1.0
+14998,15732644.0,Fanucci,705.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,0.0,149550.95,0.0
+14999,15713890.0,Milanesi,648.0,France,Female,42.0,3.0,0.0,3.0,1.0,0.0,112442.63,1.0
diff --git a/bank_customer_churn_prediction/catboost_info/catboost_training.json b/bank_customer_churn_prediction/catboost_info/catboost_training.json
new file mode 100644
index 0000000..e7d979b
--- /dev/null
+++ b/bank_customer_churn_prediction/catboost_info/catboost_training.json
@@ -0,0 +1,9004 @@
+{
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diff --git a/bank_customer_churn_prediction/catboost_info/learn_error.tsv b/bank_customer_churn_prediction/catboost_info/learn_error.tsv
new file mode 100644
index 0000000..8987181
--- /dev/null
+++ b/bank_customer_churn_prediction/catboost_info/learn_error.tsv
@@ -0,0 +1,9001 @@
+iter	Logloss
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diff --git a/bank_customer_churn_prediction/catboost_info/test/events.out.tfevents b/bank_customer_churn_prediction/catboost_info/test/events.out.tfevents
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diff --git a/bank_customer_churn_prediction/catboost_info/test_error.tsv b/bank_customer_churn_prediction/catboost_info/test_error.tsv
new file mode 100644
index 0000000..4dd1023
--- /dev/null
+++ b/bank_customer_churn_prediction/catboost_info/test_error.tsv
@@ -0,0 +1,9001 @@
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diff --git a/bank_customer_churn_prediction/catboost_info/time_left.tsv b/bank_customer_churn_prediction/catboost_info/time_left.tsv
new file mode 100644
index 0000000..48f6625
--- /dev/null
+++ b/bank_customer_churn_prediction/catboost_info/time_left.tsv
@@ -0,0 +1,9001 @@
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diff --git a/bank_customer_churn_prediction/kaggle_competition.ipynb b/bank_customer_churn_prediction/kaggle_competition.ipynb
new file mode 100644
index 0000000..c3de5b8
--- /dev/null
+++ b/bank_customer_churn_prediction/kaggle_competition.ipynb
@@ -0,0 +1,5361 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:19.855016Z",
+     "start_time": "2025-06-20T18:11:19.850020Z"
+    }
+   },
+   "source": [
+    "import seaborn as sns, matplotlib.pyplot as plt, pandas as pd, numpy as np\n",
+    "\n",
+    "from category_encoders import TargetEncoder\n",
+    "\n",
+    "from sklearn import set_config\n",
+    "from sklearn.pipeline import Pipeline, make_pipeline, FunctionTransformer\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+    "\n",
+    "from catboost import CatBoostClassifier\n",
+    "from lightgbm import LGBMClassifier\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix, ConfusionMatrixDisplay\n",
+    "import os\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
+   ],
+   "outputs": [],
+   "execution_count": 70
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:19.977463Z",
+     "start_time": "2025-06-20T18:11:19.960524Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df = pd.read_csv('../assets/BCCP/train.csv')",
+   "id": "db2bbff7858a1e68",
+   "outputs": [],
+   "execution_count": 71
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:20.087037Z",
+     "start_time": "2025-06-20T18:11:20.076948Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df.head()",
+   "id": "4abe6d0d1382ac3d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   id  CustomerId   Surname  CreditScore Geography  Gender   Age  Tenure  \\\n",
+       "0   0  15779985.0   Nwankwo        678.0    France    Male  29.0     4.0   \n",
+       "1   1  15650086.0     Ch'in        687.0    France  Female  34.0     1.0   \n",
+       "2   2  15733602.0  Thompson        682.0    France  Female  52.0     6.0   \n",
+       "3   3  15645794.0   Macleod        753.0   Germany    Male  44.0     6.0   \n",
+       "4   4  15633840.0      Hsia        544.0   Germany  Female  55.0     0.0   \n",
+       "\n",
+       "     Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
+       "0       0.00            3.0        1.0             0.0        180626.36   \n",
+       "1       0.00            2.0        0.0             1.0         63736.17   \n",
+       "2       0.00            3.0        0.0             0.0        179655.87   \n",
+       "3   83347.25            2.0        1.0             0.0        161407.48   \n",
+       "4  107747.57            1.0        1.0             0.0        176580.86   \n",
+       "\n",
+       "   Exited  \n",
+       "0     0.0  \n",
+       "1     0.0  \n",
+       "2     1.0  \n",
+       "3     0.0  \n",
+       "4     1.0  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>CustomerId</th>\n",
+       "      <th>Surname</th>\n",
+       "      <th>CreditScore</th>\n",
+       "      <th>Geography</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Tenure</th>\n",
+       "      <th>Balance</th>\n",
+       "      <th>NumOfProducts</th>\n",
+       "      <th>HasCrCard</th>\n",
+       "      <th>IsActiveMember</th>\n",
+       "      <th>EstimatedSalary</th>\n",
+       "      <th>Exited</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>15779985.0</td>\n",
+       "      <td>Nwankwo</td>\n",
+       "      <td>678.0</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>180626.36</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>15650086.0</td>\n",
+       "      <td>Ch'in</td>\n",
+       "      <td>687.0</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>63736.17</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>15733602.0</td>\n",
+       "      <td>Thompson</td>\n",
+       "      <td>682.0</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>179655.87</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>15645794.0</td>\n",
+       "      <td>Macleod</td>\n",
+       "      <td>753.0</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>83347.25</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>161407.48</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15633840.0</td>\n",
+       "      <td>Hsia</td>\n",
+       "      <td>544.0</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107747.57</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>176580.86</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 72
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:20.231642Z",
+     "start_time": "2025-06-20T18:11:20.226378Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df.isnull().sum()",
+   "id": "c855a5ce55d07fa5",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                 0\n",
+       "CustomerId         0\n",
+       "Surname            0\n",
+       "CreditScore        0\n",
+       "Geography          0\n",
+       "Gender             0\n",
+       "Age                0\n",
+       "Tenure             0\n",
+       "Balance            0\n",
+       "NumOfProducts      0\n",
+       "HasCrCard          0\n",
+       "IsActiveMember     0\n",
+       "EstimatedSalary    0\n",
+       "Exited             0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 73
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:20.386391Z",
+     "start_time": "2025-06-20T18:11:20.379915Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df.info()",
+   "id": "e71d485fcf065280",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 15000 entries, 0 to 14999\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column           Non-Null Count  Dtype  \n",
+      "---  ------           --------------  -----  \n",
+      " 0   id               15000 non-null  int64  \n",
+      " 1   CustomerId       15000 non-null  float64\n",
+      " 2   Surname          15000 non-null  object \n",
+      " 3   CreditScore      15000 non-null  float64\n",
+      " 4   Geography        15000 non-null  object \n",
+      " 5   Gender           15000 non-null  object \n",
+      " 6   Age              15000 non-null  float64\n",
+      " 7   Tenure           15000 non-null  float64\n",
+      " 8   Balance          15000 non-null  float64\n",
+      " 9   NumOfProducts    15000 non-null  float64\n",
+      " 10  HasCrCard        15000 non-null  float64\n",
+      " 11  IsActiveMember   15000 non-null  float64\n",
+      " 12  EstimatedSalary  15000 non-null  float64\n",
+      " 13  Exited           15000 non-null  float64\n",
+      "dtypes: float64(10), int64(1), object(3)\n",
+      "memory usage: 1.6+ MB\n"
+     ]
+    }
+   ],
+   "execution_count": 74
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:20.702354Z",
+     "start_time": "2025-06-20T18:11:20.698071Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df['IsActiveMember'].unique()",
+   "id": "5a603dc5a07f5e9c",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 1.])"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 75
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:20.888178Z",
+     "start_time": "2025-06-20T18:11:20.885718Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df['IsActiveMember'] = df['IsActiveMember'].astype(int)",
+   "id": "ad36fb614843627e",
+   "outputs": [],
+   "execution_count": 76
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:20.993200Z",
+     "start_time": "2025-06-20T18:11:20.984158Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df.duplicated().sum()",
+   "id": "6e86f5fa7d3d40be",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 77
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:21.096528Z",
+     "start_time": "2025-06-20T18:11:21.091617Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df.isna().sum()",
+   "id": "8ef31bebd4668e42",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                 0\n",
+       "CustomerId         0\n",
+       "Surname            0\n",
+       "CreditScore        0\n",
+       "Geography          0\n",
+       "Gender             0\n",
+       "Age                0\n",
+       "Tenure             0\n",
+       "Balance            0\n",
+       "NumOfProducts      0\n",
+       "HasCrCard          0\n",
+       "IsActiveMember     0\n",
+       "EstimatedSalary    0\n",
+       "Exited             0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 78
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:21.220429Z",
+     "start_time": "2025-06-20T18:11:21.216950Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df['CustomerId'].duplicated().sum()",
+   "id": "c98be1476f20b0be",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8652"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 79
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:21.340004Z",
+     "start_time": "2025-06-20T18:11:21.336982Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df = df.drop(columns=['CustomerId'])",
+   "id": "50a48bd3bf1a4a97",
+   "outputs": [],
+   "execution_count": 80
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:21.528463Z",
+     "start_time": "2025-06-20T18:11:21.522308Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df.info()",
+   "id": "4c30c1160c988b42",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 15000 entries, 0 to 14999\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column           Non-Null Count  Dtype  \n",
+      "---  ------           --------------  -----  \n",
+      " 0   id               15000 non-null  int64  \n",
+      " 1   Surname          15000 non-null  object \n",
+      " 2   CreditScore      15000 non-null  float64\n",
+      " 3   Geography        15000 non-null  object \n",
+      " 4   Gender           15000 non-null  object \n",
+      " 5   Age              15000 non-null  float64\n",
+      " 6   Tenure           15000 non-null  float64\n",
+      " 7   Balance          15000 non-null  float64\n",
+      " 8   NumOfProducts    15000 non-null  float64\n",
+      " 9   HasCrCard        15000 non-null  float64\n",
+      " 10  IsActiveMember   15000 non-null  int32  \n",
+      " 11  EstimatedSalary  15000 non-null  float64\n",
+      " 12  Exited           15000 non-null  float64\n",
+      "dtypes: float64(8), int32(1), int64(1), object(3)\n",
+      "memory usage: 1.4+ MB\n"
+     ]
+    }
+   ],
+   "execution_count": 81
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:21.635734Z",
+     "start_time": "2025-06-20T18:11:21.631721Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df['Exited'].value_counts()",
+   "id": "37e897ad6ec47232",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Exited\n",
+       "0.0    11948\n",
+       "1.0     3052\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 82
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:21.798409Z",
+     "start_time": "2025-06-20T18:11:21.755373Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "colors = ['#2ECC71', '#E74C3C']\n",
+    "\n",
+    "plt.pie(\n",
+    "    df['Exited'].value_counts(),\n",
+    "    labels=['Not Exited', 'Exited'],\n",
+    "    colors=colors,\n",
+    "    shadow=False,\n",
+    "    autopct='%1.1f%%'\n",
+    ")\n",
+    "plt.title('Exited Customers Percentage', fontsize=15)\n",
+    "plt.show()"
+   ],
+   "id": "c46360e78f225dce",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 83
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:21.916350Z",
+     "start_time": "2025-06-20T18:11:21.912054Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df['Surname'].value_counts()",
+   "id": "37ce1efa66b0f907",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Surname\n",
+       "Ch'iu      252\n",
+       "Ch'ien     241\n",
+       "Ch'ang     225\n",
+       "Hsia       204\n",
+       "T'ien      187\n",
+       "          ... \n",
+       "Trejo        1\n",
+       "Purdy        1\n",
+       "Otoole       1\n",
+       "Sarratt      1\n",
+       "Liardet      1\n",
+       "Name: count, Length: 764, dtype: int64"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 84
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:22.240676Z",
+     "start_time": "2025-06-20T18:11:22.128025Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "sns.histplot(df.groupby('Surname')['Exited'].mean().sort_values(ascending=False))\n",
+    "plt.title('Surname Exited Rates')\n",
+    "plt.show()"
+   ],
+   "id": "621b24d0f973170e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 85
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:22.483003Z",
+     "start_time": "2025-06-20T18:11:22.356055Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "sns.histplot(df['CreditScore'])\n",
+    "plt.title('Credit Score')\n",
+    "plt.show()"
+   ],
+   "id": "7e09e934a7ac8ccb",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 86
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:22.715863Z",
+     "start_time": "2025-06-20T18:11:22.710351Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df['Geography'].value_counts()",
+   "id": "de5ee395a3c03603",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Geography\n",
+       "France     9032\n",
+       "Spain      3303\n",
+       "Germany    2665\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 87
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:22.880283Z",
+     "start_time": "2025-06-20T18:11:22.835520Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "flag_colors = {\n",
+    "    'France': '#0055A4',\n",
+    "    'Germany': '#FFCC00',\n",
+    "    'Spain': '#AA151B'\n",
+    "}\n",
+    "\n",
+    "labels = df['Geography'].value_counts().index.tolist()\n",
+    "colors = [flag_colors[label] for label in labels]\n",
+    "\n",
+    "plt.pie(\n",
+    "    df['Geography'].value_counts(),\n",
+    "    labels=labels,\n",
+    "    colors=colors,\n",
+    "    shadow=False,\n",
+    "    autopct='%1.1f%%'\n",
+    ")\n",
+    "plt.title('Geographies Percentages', fontsize=15)\n",
+    "plt.show()"
+   ],
+   "id": "4583a872457bc3a5",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 88
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:23.096397Z",
+     "start_time": "2025-06-20T18:11:22.995875Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "custom_palette = {'Male': '#4D90A0', 'Female': '#D883A3'}\n",
+    "\n",
+    "sns.countplot(data=df, x='Gender', edgecolor='black', palette=custom_palette)\n",
+    "plt.title('Gender Distribution')\n",
+    "plt.show()"
+   ],
+   "id": "c2dde4dcf64007ed",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 89
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:23.446696Z",
+     "start_time": "2025-06-20T18:11:23.279822Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "sns.histplot(data=df, x='Age', bins=80)\n",
+    "plt.axvline(df['Age'].median(), color='r', label=f\"Median Age {df['Age'].median()}\")\n",
+    "plt.title('Age Distribution');\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ],
+   "id": "3be24dd008cdde9e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 90
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:23.694957Z",
+     "start_time": "2025-06-20T18:11:23.564797Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "sns.countplot(data=df, x='Tenure', color='#A6CEE3', edgecolor='black')\n",
+    "plt.title('Tenure');\n",
+    "plt.show()"
+   ],
+   "id": "a0d45f28a58c8294",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 91
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:24.181585Z",
+     "start_time": "2025-06-20T18:11:23.795977Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "sns.histplot(data=df, x='Balance', ax=ax[0], edgecolor='black')\n",
+    "sns.histplot(data=df[df['Balance'] != 0], x='Balance', ax=ax[1], edgecolor='black')\n",
+    "ax[0].axvline(df['Balance'].median(), color='r', label=f\"Median Balance {df['Balance'].median()}\")\n",
+    "ax[0].set_title('Whole Balance')\n",
+    "\n",
+    "kk = df[df['Balance'] != 0]['Balance'].median()\n",
+    "ax[1].axvline(kk, color='r', label=f\"Median Balance {kk}\")\n",
+    "ax[1].set_title('Balance Without Zeros')\n",
+    "fig.suptitle('Balance Distribution', fontsize=16);\n",
+    "ax[0].legend();\n",
+    "ax[1].legend();\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "c316af7b29adaee0",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 92
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:25.284668Z",
+     "start_time": "2025-06-20T18:11:25.279860Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df['Balance'].value_counts()",
+   "id": "d89d930a53135bad",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Balance\n",
+       "0.00         9742\n",
+       "122314.50      15\n",
+       "123544.00      14\n",
+       "102773.20      12\n",
+       "127864.40      11\n",
+       "             ... \n",
+       "131081.66       1\n",
+       "144705.87       1\n",
+       "107868.14       1\n",
+       "124125.26       1\n",
+       "139129.42       1\n",
+       "Name: count, Length: 3395, dtype: int64"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 93
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:25.517041Z",
+     "start_time": "2025-06-20T18:11:25.404630Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "g = sns.countplot(data=df, x='NumOfProducts', palette='tab20', edgecolor='black')\n",
+    "g.bar_label(g.containers[0])\n",
+    "plt.title('Number of Products')\n",
+    "plt.show()"
+   ],
+   "id": "ed4eb9754a56288d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 94
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:25.636697Z",
+     "start_time": "2025-06-20T18:11:25.633089Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df['IsActiveMember'].value_counts()",
+   "id": "b0f7adc81cad56e4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "IsActiveMember\n",
+       "0    7655\n",
+       "1    7345\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 95
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:25.968094Z",
+     "start_time": "2025-06-20T18:11:25.883097Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
+    "sns.set_palette('Blues', 3)\n",
+    "ax[0].pie(df['HasCrCard'].value_counts(), labels=['Yes', 'No'], colors=colors, shadow=False, autopct='%1.1f%%')\n",
+    "ax[0].set_title('with Credit Card')\n",
+    "ax[1].pie(df['IsActiveMember'].value_counts(), labels=['No', 'Yes'], colors=colors, shadow=False, autopct='%1.1f%%')\n",
+    "ax[1].set_title('Active Member')\n",
+    "\n",
+    "fig.suptitle('Customers Information', fontsize=15)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "ac23d9ce7dc4c6d8",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 96
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:26.505561Z",
+     "start_time": "2025-06-20T18:11:26.082885Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "sns.histplot(data=df, x='EstimatedSalary', bins=40, kde=True)\n",
+    "plt.axvline(df['EstimatedSalary'].median(), color='r',\n",
+    "            label=f\"Median Estimated Salary {df['EstimatedSalary'].median()}\")\n",
+    "\n",
+    "plt.title('Estimated Salary Distribution')\n",
+    "plt.tight_layout()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ],
+   "id": "af66de90d3d99b94",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 97
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:27.451175Z",
+     "start_time": "2025-06-20T18:11:27.446451Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df.groupby('Geography')['Exited'].mean()",
+   "id": "539a77482e217c6a",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Geography\n",
+       "France     0.159212\n",
+       "Germany    0.413508\n",
+       "Spain      0.155011\n",
+       "Name: Exited, dtype: float64"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 98
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:28.296219Z",
+     "start_time": "2025-06-20T18:11:27.572192Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.figure(figsize=(13, 8))\n",
+    "for i, c in enumerate(['Geography', 'Gender', 'NumOfProducts', 'IsActiveMember', 'HasCrCard']):\n",
+    "    plt.subplot(2, 3, i + 1)\n",
+    "    ax = sns.histplot(x=c, hue='Exited', data=df, palette='BrBG', edgecolor='black'\n",
+    "                      , multiple='fill', discrete=True, shrink=.9)\n",
+    "    ax.bar_label(ax.containers[0])\n",
+    "    ax.set_xlabel('')\n",
+    "    ax.set_title(c)\n",
+    "    if i % 3 == 0:\n",
+    "        ax.set_ylabel('Propotion')\n",
+    "    else:\n",
+    "        ax.set_ylabel('')\n",
+    "\n",
+    "plt.suptitle('Exited Rates among Categories', size=16)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "848d74e882e8713d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1300x800 with 5 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 99
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:29.545893Z",
+     "start_time": "2025-06-20T18:11:29.247046Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "sns.heatmap(df.corr(numeric_only=True), cmap='BrBG', center=0, annot=True, vmin=-1, fmt='.2f', linewidths=.1)\n",
+    "plt.title('Numerical Features Heatmap', size=15)\n",
+    "plt.show()"
+   ],
+   "id": "29f5c993dc7fb6e7",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ],
+      "image/png": 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KiIhQPtft2XWau561vd28Tp3n+vHHH1XK+Ntvv3HkyBEaNmyocst3IYQoKjL8TwjxxnJxcdH4QNHWrVtTtWpVwsPD6devH02aNCEmJoajR4/yv//975XurvayPDw8OHnyJHv37uX8+fO0aNECfX19Dh48yL///kvHjh3p2rXray27adOm9O/fn7Vr1+Lq6oqTkxOGhobs379f+VDkihUr4uTkRLly5di0aRM3btzA2tqaO3fucOjQIR49eoSFhQUPHz4kIyND5Y52hcHCwoJZs2YxatQo+vXrh7OzMzVr1iQ6OpqjR49iZmbGjBkz0NfXx8TEhE6dOrFnzx7c3NxwcnICsr/onzt3jooVK3Lnzh2VBmXuHfVWr17Nv//+y6effkrlypX56KOP+Omnn+jXrx+dO3cmIyOD/fv3Y2VlRWxs7EuX/1Xq09XVlU2bNrFlyxYuXrxIkyZNePz4MSEhIVy7do0hQ4ZQuXJlLa5dVd999x2ffPIJP/zwA/v376dx48bcu3ePffv2oVAomDx5MtWqVQPAzc2NM2fOMGzYMDp16kSZMmW4cOECYWFhvPXWW6SlpSmv4YOn6zkkJISZM2fi4uKilWsZX6fOcx0/fhw3NzeaN29OQkIChw4domzZsvzwww8FLpcQQrwO6akSQrzRcocBPs/Q0JA1a9bQpUsXrly5wtq1a0lKSmLJkiV06NChUMpiaGjIihUr+OKLLzAzM2Pbtm3s3LmTChUq8O233zJnzhwMDAxee/lTpkxh1qxZVK1alV27drFlyxaqVavGokWLlM9pqlSpEmvWrMHJyYm///5b+QDVVq1asX37djp27Mjjx485fvy4ttJ+odatW7NlyxY6duzImTNnWL16NZcuXaJbt25s3bpV5W6G3333HSNGjCArK4sNGzawd+9eLCws8PPzY/78+QAqQz07depEt27duHXrFuvWrVP2bIwZM4YJEyZgYWHBxo0bCQkJ4aOPPmLBggWvVPZXqU9TU1MCAwPx9PREoVCwceNGtm3bRvny5ZkxYwaff/55Adfki1WtWpXt27czaNAgbt26xdq1awkJCcHOzo7AwEAGDBigjHV3d+f777/n3XffZffu3WzZsoWkpCRGjRrF3r17MTMzIyQkRHkHwHfeeYeJEydiYmLCunXrCAsL01q5X7XOc61bt45KlSqxefNmzp49S/fu3dmyZQvW1tZaK5sQQrwKvayiGlwvhBBCCFEAzs7OJCQk8M8//xR6T6sQQrwK6akSQgghhBBCiAKQRpUQQgghhBBCFIA0qoQQQgghhBCiAKRRJYQQQog3wqFDh7h48aJcTyVECZOUlES7du0IDw/XGHP06FG6deuGjY0NnTp1yvNcyBUrVtCqVStsbGzo378/MTExWi2jNKqEEEIIIYQQxVJkZCR9+/Z94SMxrl69yujRoxk7diynTp1i9OjRjBs3jps3bwKwY8cO1q5dS0BAAOHh4TRo0IAxY8bkebh4QUijSgghhBBCCFHs7Nixg0mTJjF+/Ph845o0aULbtm0pVaoUnTt35sMPP2TTpk0AbN68mY8//pi6detibGzMxIkTuX79+gt7vl6VNKqEEEIIIYQQRSI9PZ3k5GSVV3p6utpYR0dHDhw4QOfOnV+4zMuXL1OvXj2VaVZWVly4cEHtfENDQ2rVqqWcrw0yKFkIIYQQQgihkV5L2/yDXtIC9yEsWrRIZdqoUaMYPXp0nti33377pZaZkpKCqampyjQTExMUCsVLzdcGaVSVENrcGd4EWaFnAFCkPNRxSYqWmXlpAB6mpOi4JEWrtLk5AClaPDi+CczNzICSW98ldf9WKEpWfZuZlez6luNayZB7XCsJRowYweDBg1WmGRkZFWiZpqampKamqkxLTU3FPGe95jdfG2T4nxBCCCGEEEIzfX2tvYyMjLCwsFB5FbRRVa9ePaKiolSmXb58mbp16wJQt25dlfmPHz/m6tWreYYMFoQ0qoQQQgghhBCa6elr71UIXF1diYiIICgoiIyMDIKCgoiIiKB79+4A9OrVi3Xr1nHhwgXS0tKYM2cOFStWpEmTJlorgzSqhBBCCCGEEG8UW1tbdu3aBYClpSWLFy9m2bJlfPjhhyxZsoSFCxdSu3ZtAHr37s2gQYP47LPPaNasGefOnWPZsmUYGhpqrTx6Wdq8QbsotuSaqpJBrqmSaw9KArmmqmTVt1xTJce1kqC4X1Ol5/w/rS0r61CE1pZVnMiNKoQQQgghhBCa6cvgtvzIGhJCCCGEEEKIApCeKiGEEEIIIYRmhXSDif8SaVQJIYQQQgghNJPhf/mSNSSEEEIIIYQQBSA9VUIIIYQQQgjNpKcqX9KoEkIIIYQQQmimp6frEhR70uwUQgghhBBCiAKQRlUxdP36dWxtbbl+/Xqeedu3b8fZ2VkHpVJVsdxbRG34hdY29hpjOjVz5M9Vm0kODuPc2m10cWipMt/744HEbdtHcnAYhxesoF71moVd7AILPXaMPu59ae7giJtbb0JCQjXGZmZmMm/efFzatqeFYyvGjZ/A7dt38sQl3b2Lq2sPTp06VZhFfy1JSUlMnDCBNq1a4eLszBw/PzIyMtTGHjt2jL7u7jg6ONDbzY3QkBC1cbP9/Pjm668Ls9gFlpSUxITx42nVsiXOTk74vSjv0FDc+/TBoXlz3NzcCHku71WrVtGxQwccmjdn2NChXL16tQgyeD0ltb5zlbT9O1do6DH69HGneXMHtdvws7LznoeLS1tatHBk3Ljx3L59Wzk/KSmJ8eMn0LJlK5ycnF+47+haSatvbR7XcvnNmsXXX31VmMUusJJ+XNMafX3tvf6j/ruZvcHeffddzpw5w7vvvqvroqjl0KgxJ5auwqpaDY0xVtVqsG26H1MDllC2U0u+DvyJzdNm8m7FtwEY0LEbY3r1o8PEkVTo2obIi+fZNn12UaXwWq7FxuLt7cNILy9CQ47g6TkCH19fbt26pTbe3z+AEydPsn7dGvbvC8LY2Jhvv/tOJebs2bMMGjiYuPj4okjhlX3h64uZqSn79u9n9Zo1hEdE8PP69XniYmNj8fH2xsvLiyMhIYzw9MT3uXVz7949pk6ezMYNG4oyhdfi6+ODqZkZ+4ODWbN2LRHh4axXl/e1a3h7e+M1ciQhoaF4enri6+OjzHv3rl1s3LCBxUuWcPjIEerXr4/3pElkZWUVdUovpaTWN5TM/Rvg2rVYvL29GTnSi9DQEDw9PfHxeVHe/pw4cZL169exf/++7Ly/fZq3j48vZmamBAfvZ+3aNYSHR6jdd3StJNa3to5rkL1/T548mQ1vwP5dko9rWqWnr73Xf9R/N7M3WHx8PNbW1sTHxxMdHU3//v2xtbWlW7dunDt3TqdlG9CxGz9/9SOTVyx+YdzAjl0J/eMMv4QeITMzky2HD3D07GmGu/YCYFi3nizZuZlzV2NIS0/H96cF1KhchTa2TYogi9ezZ/cebG1tcHJqQ6lSpWjfvh12dvZs27ZDbfyOnb8weNBAqlSpgoWFBZ97T+L48TDic064u3bv4Ysvp/DZZyOLLolXEBcbS+SpU4wZNw4TU1OqVavG0KFD2bxpU57YPbt3Y2NrSxsnJ0qVKkW79u2xt7Njx7ZtACgUCnq5uWFRujTOLi5FncoriY2N5dSpU4wbOxbT3LyHDWPTxo15Ynfv2YOtrS1OOXm3b98eOzs7tuXkvWPHDvq4u2NpaYmxsTGjx4zhxo0bxfJX7JJa37lK2v6da8+e3S/chp+3Y8dOBg8e9DTvz705fvw48fHxyn1n7Nhxyn1n2LChbNyYdxvStZJW39o8rikUCtx69qR06dK4FPP9u6Qf10TRkkZVMZaRkcGIESOoW7cuJ0+eZO7cuRw8eFCnZdofEYblR93YfCj4hXENalvyV8xllWnnrsbQ2Kre0/nRT+dnZGYQFR+rnF8cRcfEYGVlpTKtTp3aXIq6lCf24cNkbt68qRJfoUIFypQpQ1RUdt4OzZuxe9dOOnRoX7gFf03RMTGULVuWt99+WzmtTp063Lhxg4cPH6rExqhZN7Xr1OFSVBQARkZGbN6yBR9fX8zMzAq/8AUQEx2dnXelSsppGvOOjlazTdQh6lL2NhH93HxDQ0Oq16jBpUt5txldK6n1nauk7d+5oqPV5V2HS5ei8sQ+fPjwBXlHER2dvQ1VqpT/NqRrJa2+tXlcMzIyYsvWrfi+Aft3ST+uaZUM/8vXfzez/4Bbt27x77//8vnnn2NsbEzdunUZPHiwTst0MymRzMzMfONKm5mTkvpIZZoiNRUL0+wDUWlTsxfOL45SUhSYmpqqTDMxMUGheJQnVqFIAcDU1ERNvAKAihUrUqpU8b0BpyIlBRM1+QLKHJ6NVbduHuXElSpVigoVKhRiabUnRaG+niFv3ika8s6NU2hY1qPnllMclNT6zlXS9u9c+W3Dz8qdpileoVC/rGc/W1yUtPrW5nHtTdq/S/pxTaukUZWv4nsEEJw+fZq33npLeQAAqFFD83VMxUnKo0eYGauegMxMTHiYc3JKSX2EmYnm+cVBQEAgAYErle8bNWxIamqqSkxqairm5nkbgrkHZnXxZmriiyNTU1O15Qcwf+5XOhMNsWbm5oVbyELworyf/3VSU6x5Tt6a5hfH9VLS6ruk7t8BAQEEBAQq3zdqpCnvvHX5wrzNzMnKevLS+05RK6n1nUubx7U3SUk7rgnd+u82F/8DmjRpQlJSEikpTxsaN27c0GGJXt7fVy7ToLalyrT3a9Xh75jonPnRNKj1dH4pg1LUrVaDv69EF2k5X8TDYwhhx0OVr0aNGhIdHaMSExNzBUtLyzyfLVOmDJUqVVKJv3PnDvfv38fK0ipPfHFkaWXF/Xv3SExMVE6LiYmhcuXKWJQurRpraUlMtGrdXYmJUbtuijsrS0vuaci79PN5W1kR/VzeMc/k/fx6efz4MXGxscVyvZS0+i6p+7eHhwdhYceVr0aNGr1wG37W07yfxivztrLE0tLqpfedolZS6zuXNo9rb5KSdlwrTHp6elp7/VdJo6oYq1SpErVr12b69Ok8evSIa9euERgYmP8Hi4G1+3+lja09fZzaYWBgQB+ndrSxtWdt8B4AAn/9hdG9PuIDy3oYGxkxw3MMN5OSCDl7Wscl16xLly5ERkYSHHyAjIwMgoMPEBkZSdcundXGu7p2wz8ggISEBFJSUvCbPQd7ezuqV69WxCV/PTVq1MDGxoY5s2eTkpJCQkIC/v7+uHbvnic2d90cCA4mIyODA8HBREZG0rlLFx2UvGBq1KyJja0ts/38nua9YgXde/TIE/t0mwjO2Say8+7StSsA3bt3Z+PGjVy6eJG0tDQWLlhA+fLlsbOzK+Ks8ldS6ztXSdu/c2nahrt2VV+Xrq6u+Ps/k7ffbOzt7alevTo1a9bA1tYGP7+n29CKFf706JF3G9K1klbf2jyuvUlK+nFNq2T4X77+u5n9B+jr67N8+XJu3bqFg4MDQ4cOLdZ32nm4/zgft+sEwMXYq/T4cgJf9vfgbtBRvho0nF5TvImKiwUg8NedzNu8nh0/zOH27kPY1rWmi89oMjKL5/NMAGrXrsXcObMJCFxJ6zbOLF+xAj+/mdSsmf18raCgvTi0ePosruHDhuHo6MgQj2F07NSZ9PR0Zs2coaviv5aZfn5kZmbi2q0bgwYMyN4Ohw0DoGWLFuwNCgKgVu3azJ4zh5WBgTi3acOKFSuY6eenXDdvGr+cvLt17cqA/v1xcHBgWE7eLRwcCMrJu3bt2syZO5fAgADatG7NiuXL8Xsm7+49evDJJ58wceJEXJyduXDxIgsWLsTQ0FBnub1ISa1vKJn7N2Rvw3PnziEgIJDWrduwfPkKlW04KCgIB4cWyvjhw3PyHuJBx46dsvOeNVM5P3ff6dq1G/37D1DZd4qTkljf2jquvWlK8nFNFC29rOL6wBShVXotbXVdhCKVFXoGAEVK8brjVGEzM88ezvAwpfhcm1YUSueMeU8pZhfDF7bcawJKan2X1P1bUYyuPS0KZmYlu77luFYylC7m127pu2uvp/LJ5j1aW1ZxIjeqEEIIIYQQQmj2H35or7bIGhJCCCGEEEKIApCeKiGEEEIIIYRm/+EbTGiLNKqEEEIIIYQQmun/d2+Fri3S7BRCCCGEEEKIApCeKiGEEEIIIYRmcqOKfEmjSgghhBBCCKGZXFOVL1lDQgghhBBCCFEA0lMlhBBCCCGE0Ex6qvIljSohhBBCCCGEZnJNVb5kDQkhhBBCCCFEAUhPlRBCCCGEEEIzeU5VvqRRJYQQQgghhNBMrqnKl6whIYQQQgghhCgAvaysrCxdF0IIIYQQQghRPOkPH6C1ZT1ZvkZryypOZPifEEIIIYQQQiM9Gf6XL2lUlRCKlIe6LkKRMjMvDYBeS1sdl6RoZYWeAUCRcl/HJSlaZuZlAUhRKHRckqJlbmYGgEKRouOSFC0zM3MA7qck67gkRausuQVQcuu7pOZdYo9rD2/quCRFy6x0ZV0XQRSQNKqEEEIIIYQQGulLT1W+pFElhBBCCCGE0EgaVfmTNSSEEEIIIYQQBSA9VUIIIYQQQgiNdNVTlZiYyNSpU4mIiMDAwABXV1d8fHwoVUq1CTN06FAiIyNVpikUCvr27cu3337LkydPsLe3JysrCz29pw8yPn78OGY51/EVlDSqhBBCCCGEEBrpqlE1btw4KleuTGhoKHfu3MHLy4tVq1YxdOhQlTh/f3+V91u3bmXRokWMGjUKgMuXL/P48WNOnz6NkZFRoZRVhv8JIYQQQgghipVr164RERGBt7c3pqamVK9enZEjR7J+/foXfi4mJobvvvuO2bNnU6lSJQD++usvrK2tC61BBdJTJYQQQgghhHgBbfZUpaenk56erjLNyMgoT4MnKiqKcuXKUbny09vNW1pacv36dR48eECZMmXULn/atGn06NGDJk2aKKf99ddfpKWl0atXLxISErC0tGTixInY2dlpLS/pqRJCCCGEEEJopK+np7XXsmXLsLe3V3ktW7Ysz/9MSUnB1NRUZVrue4WG57edOnWKP/74QznsL5eJiQkffPABS5Ys4ciRIzg7O+Ph4UFcXJyW1pD0VAkhhBBCCCGKyIgRIxg8eLDKNHXD8szMzHj06JHKtNz35ubmape9adMmOnXqxNtvv60y3dfXV+W9h4cH27dv5+jRo3z66aevnIM60lMlhBBCCCGE0EhfX19rLyMjIywsLFRe6hpVdevW5d69e9y5c0c5LTo6mipVqlC6dOk88RkZGfz222+4urrmmTdv3jzOnTunMi09PR1jY2MtrJ1s0qjKx9WrV3VdBCGEEEIIIXRGm42ql1WrVi3s7e354YcfSE5OJi4ujiVLltC7d2+18RcvXiQtLU3tdVKXLl3i+++/5/bt26Snp7No0SKSk5Np167da6+T5xX7RtWVK1fw8fGhVatW2Nra0rZtW2bPnk1KSorW/oe1tTXh4eEAdOnShV27dgGwfv16pk6dqox78uQJK1asoHPnztja2vLhhx/i4eHBmTNntFaWN0XosWP0ce9LcwdH3Nx6ExISqjE2MzOTefPm49K2PS0cWzFu/ARu376TJy7p7l1cXXtw6tSpwix6gVQs9xZRG36htY29xphOzRz5c9VmkoPDOLd2G10cWqrM9/54IHHb9pEcHMbhBSuoV71mYRe7wEKPHaePez+aO7TCzc39Jep7AS5tO9LCsQ3jxk9Sqe+Lly7h6TWK1m3a0rZdR6ZM/Zq7d+8VQRYvLykpiQnjx9OqZUucnZzw8/MjIyNDbeyx0FDc+/TBoXlz3NzcCAkJURvnN2sWX3/1VWEWW2tCQ4/Rp487zZs7vDAnyK3vebi4tKVFC0fGjRvP7du3lfOTkpIYP34CLVu2wsnJ+YXrUleSkpKYNGECzq1a087Zmbl+szWW8fixY/Rzd6eVQwvc3XoR+sy6SUtLY67fbLp27IRTq1YMHjCAU7//XlRpvLaSVt+5Slre2jyurVq1io4dOuDQvDnDhg59I36ADj12gj59B9LcsT1uvT8lJDRMY2xmZibz5i/BpX13WrTqwLgJX3D7md6S/cG/0aSpEw4tOyhfU6ZOL4o0SpwFCxaQkZGBi4sL7u7utGzZkpEjRwJga2ur/M4OEBcXR9myZdX2Pv3444/UqFGD7t2707RpUyIiIli5ciXlypXTWlmLdaPq9OnT9OzZk6pVq7Jz507OnDnDihUr+OOPPxgyZAiZmZla/5+//vqrstswKSlJZd7y5cvZsWMHCxYs4PTp04SEhNCsWTMGDhzItWvXtF6W4upabCze3j6M9PIiNOQInp4j8PH15datW2rj/f0DOHHyJOvXrWH/viCMjY359rvvVGLOnj3LoIGDiYuPL4oUXotDo8acWLoKq2o1NMZYVavBtul+TA1YQtlOLfk68Cc2T5vJuxWzx/YO6NiNMb360WHiSCp0bUPkxfNsmz67qFJ4Ldn17ctIrxGEhhzC03MYPr5fvqC+A3PqezX79+3Jqe/vAUhNTWXUqHE0/uADDh7Yy9YtG7l//wHffPNtUaaUL18fH0zNzNgfHMyatWuJCA9XewvX2GvX8Pb2xmvkSEJCQ/H09MTXx0dl3dy7d4/JkyezYcOGokzhtV27Fou3tzcjR3oRGhqCp6cnPj4v2r/9OXHiJOvXr2P//n3Z9f3t0/3bx8cXMzNTgoP3s3btGsLDI/K9HW5Rm+zri5mpGUH797FqzRoiIiLYsP7nPHGxsbH4en/OCC8vDoUcZZjnCL585ti3eMFC/vjjDwJWreTg4cN079GDCWPHcePff4s6pZdWEusbSmbe2jqu7d61i40bNrB4yRIOHzlC/fr18Z40iaysrKJO6aVdi43D22cqI72GEnokCM8RQ/Dx/Zpbt26rjfcPWMOJk7+zfs1y9gdtzzmPzVLO/+fcBbp0bk9Y6H7la/p3U4oqHZ3QRU8VQMWKFVmwYAHh4eGcOHECHx8fDAwMADhz5ozKUL+OHTsSFqa+sVyuXDl+/PFHwsLCOHPmDGvWrOG99957/RWiRrFuVH311Vf06NGDMWPGUL58eQBq167NvHnzqFChAnFxcVhbWzN9+nSaNm2Kp6cnAGFhYfTu3ZsmTZqo9DwBPH78mB9//JGmTZvSrFmzPA8Lc3Z2Zvv27ezYsYNly5Zx6tQp5S0ZIyMjadKkCVZWVujp6WFqasqwYcNwd3dXjvfMyMhg/vz5tG7dGjs7Oz755BMuXLgAZH+hnDVrFq1bt+bDDz+kf//+/Pnnn8r//aq56Mqe3XuwtbXByakNpUqVon37dtjZ2bNt2w618Tt2/sLgQQOpUqUKFhYWfO49iePHw4jPaUDt2r2HL76cwmefjSy6JF7RgI7d+PmrH5m8YvEL4wZ27EroH2f4JfQImZmZbDl8gKNnTzPctRcAw7r1ZMnOzZy7GkNaejq+Py2gRuUqtLFt8sLl6tKe3b+qqW87tm3bqTY+u74HUKVK5Zz6npBT3wncuHGTevXqMny4B4aGhpQrV45evXpyuhj19sbGxnLq1CnGjR2Lqakp1apVY+iwYWzauDFP7O49e7C1tcXJySln3bTPWTfbgOy7E7n17Enp0qVxcXEp6lRey549u1+Y0/N27NjJ4MGDnu7fn3tz/Phx4uPjlety7NhxynU5bNhQNm7cVMRZaRYXG0fkqUhGjxuLiakpVatVw2PoULZsylvGX3fvwcbWhjY566Zd+/bY2dmzc9t2ILunaoSXJ5WrVMHAwIAebm4YGRlx/vz5ok7rpZW0+s5V0vLW5nFtx44d9HF3x9LSEmNjY0aPGcONGzeK9SiTPXv2YWvzAU5tWmbn1M4ZO3sbtu3YrTZ+xy97GDzw45zzmDmfTxrD8bBw4uOvA9mNqvfrWxdlCjqnq0bVm6TYZhYbG0tUVBRdu3bNM69ixYosWbKEWrVqKWOPHDnCrFmzuHDhAl5eXgwfPpzw8HC+++47fvjhB0JDs4cr5d5KcevWrRw6dIhLly6p/f89e/ZkxIgRNGnSRHmg6NKlC1u3bmXMmDFs2bKFixcv8uTJE6ZMmYK9ffZwsKVLl7Jnzx4CAgL4/fff+d///seIESPIzMzkm2++4dixY6xZs4bjx4/Ttm1bBg0axPXr11XyftlcdCU6JgYrKyuVaXXq1OZSVN51+fBhMjdv3lSJr1ChAmXKlCEq6jIADs2bsXvXTjp0aF+4BS+A/RFhWH7Ujc2Hgl8Y16C2JX/FXFaZdu5qDI2t6j2dH/10fkZmBlHxscr5xVF2fVuqTHtxfd9SU9+liYqKolatmixeNF/5KxPAwYO/UV/LvxYVREx0NGXLluXtnAcGAtSpU4cbN27w8OHDPLF594U6ROUcV4yMjNiydSu+vr6YmZkVfuG1IDpa3f5dh0uXovLEPnz48AX7dxTR0TGULVuWSpXeVlmWunWpKzEx0ZQpW1blTlG169RWX98x0Vg+t25qP7MvfDFlMg4tWijn/R4RQXJyMvWsi++Xr5JW37lKWt7aPK5FPzff0NCQ6jVqaPw+VRxEx1zFyqqOyrQ6tWty6dLlPLEPk5O5efO2SnyFCuWzz2OXo3ny5AkXLlwi9NgJOnXtQ4fOvfjuez8ePCgedS10p9g2qnKH3lWsWDHf2K5du2JqakqZMmXYuHEjLi4utG/fHgMDA+zs7HB3d1d2cf/yyy94eHhQvXp1zMzMmDJlCnp6ei9Vph49erBmzRpMTExYtGgRrq6uNG/enLlz5yrHJe/YsYOhQ4diZWWFgYEBXl5ezJ8/n9TUVPbs2cPEiROpWbMmRkZGDBw4kDp16rBnz57XykVXUlIUeZ4bYGJigkLxKE+sQpF97ZupqYma+OxnDFSsWJFSpYr33f1vJiW+1HDT0mbmpKSqrgdFaioWptlfqEubmr1wfnH0evWdf3xWVhaLFy8lJOQY3t4TtVzq15eiUJ8v5H0uhrpnaDy7bZcqVYoKFSoUYmm1L7+cnpU7TVO8QqF+Wc9+VtcUKQq1xyfIW0aFhn3hkZp94a8//+ILH1+GjRhO1apVtVxq7Slp9Z2rpOWtzeOaQsOyHhWTXNXRlL/ikZrzWEpOfZs8H2+MQvGIu3fvYW1dl7Yubdi+dS2rApcQGxvP5Knf5VnWf4n0VOWv2H6Tzf3V8Pbt28oeqWfduXNH2eCq9MwvLwkJCZw8eVLlKcqZmZnUqJF9HcytW7d45513lPPKlClD2bJlX7pcTZo0US775s2bHDlyBD8/P/T19Rk3bhy3b9/m3XffVcYbGRlhY2PDrVu3ePz4MdWqVVNZXrVq1ZTD4F41l6ISEBBIQOBK5ftGDRuSmpqqEpOamoq5ed6GQe5BTF28mZr4N13Ko0eYGat+QTMzMeFhTmMjJfURZiaa5xcHAQErCQhcpXzfqGEDrdd3cnIyX3/zHefPXyDAfxl166r+KqpLpqamassP5Olt0hSr6fkZxVFAQAABAYHK940aadq/8+b0wvo2Mycr68lLr0tdMTE10VhG85es7+ePZTt37GDe7DkM9/LkEy09/0RbSmp9l9S8c2nzuKZ5Pyg+x72AwLUErFynfN+oYX31OampH831nYaZmSkVKpQncMWip/FVTBg3xpP+gzxJSVGoPTf+F/yXG0PaUmzXUNWqValXrx5BQUF55iUmJuLk5KTs4Xm2p6lKlSr07NmTU6dOKV/79+9n+fLlyvnPPj1ZoVC8VPd8SkoKNjY2HD58WDmtcuXK9O3bl969eyvHzL/zzjv8+8xFyY8fP+aHH34gKysLY2PjPE9ujo2NVWlIvUouRcXDYwhhx0OVr0aNGhIdHaMSExNzBUtLyzyfLVOmDJUqVVKJv3PnDvfv38fKsvh8kdaWv69cpkFt1fXwfq06/B0TnTM/mga1ns4vZVCKutVq8PeV6CIt54t4eAwm7PhR5Us79f0Aq5z4uLh4Pu0/iJSUFNavW12sGlQAVpaW3Lt3j8TEROW0mJgYKleunOe5GJZWVkRHq9ZdTEyM2nVTXHl4eBAWdlz5atSo0Uvn9LS+n8Yr928rSywtrV56XeqKpZUV9+/dVynjlZgrVKpcGYvnyljH0pKY5/aFK8/sC5mZmfw4/XuWLFyE39w5xa5BBSW3vktq3rm0eVyztLQk5pn5jx8/Ji42tlgd9zyG9Fe5iUSjhg2IjrmqEhNz5RqWlrXzfLZMmdJUqvQ20TFXlNPu3EnMPo9Z1eFSVDTzF/6kcmOO9MeP0dfXx9Cw2PZViCJQbBtVAFOnTmXbtm0sWrSIu3fvkpWVxfnz5/H09KRBgwZ06NAhz2d69+7Nnj17OHbsGE+ePOHq1at8+umnBAZm/0LVp08f/P39iY6OJi0tjRkzZmgc1mVsbExycjJZWVmYm5vj4uLCrFmzCA8PR6FQkJ6eTmRkJPv376d9++zrgdzc3AgICODKlStkZGSwbNkyDh48SPny5enVqxdz587l2rVrpKens3r1ai5fvkyXLl3U/v/8ctGVLl26EBkZSXDwATIyMggOPkBkZCRdu3RWG+/q2g3/gAASEhJISUnBb/Yc7O3tqF69mtr4N9na/b/SxtaePk7tMDAwoI9TO9rY2rM2OPsHgMBff2F0r4/4wLIexkZGzPAcw82kJELOntZxyTXr0qUzkZGnn6vv03Tt0kltvKtrV/wDAp+p73nK+n7w4AHDR4yk8QcfsGTxAt56q1zRJvMSatSsiY2tLbP9/EhJSSEhIQH/FSvo3qNHntin+0JwzroJJjIyki5qrgV9U2jKqWtX9ccpV1dX/P2f2b/9ZmNvb0/16tWpWbMGtrY2+PnNVq7LFSv86dGjexFnpVmNGjVobGPDvNlzlGUM8PfHtXveMnbu0pnTkZEcyFk3B4KDOR0ZSaecY/i8OXMICzvO6nXr+F/TpkWdymspafWdq6Tlrc3jWvfu3dm4cSOXcp4JtHDBAsqXL6/22UDFRZcu7YmMPEPwgUPZOR04RGTkGbp2zvs9EsC1Wyf8A9aQkHCdlBQFfnMWYm9nQ/VqVSlbpjSbNu9g9ZoNZGRk8O+Nm8ybv5RuXTuqfYDtf4WBvr7WXv9VelnF+R6YwJ9//slPP/3E2bNnefToERUrVqRjx46MGDECCwsLrK2tWbNmDU2fOYEdOXKEBQsWcO3aNUxNTenatSsTJkzAyMiIJ0+esGDBAjZt2kRGRgbu7u5s3bqVBQsW0LRpU5ydnRk1ahRubm5ERUUxfPhw7t+/z5EjRzAxMcHf35+9e/cqh+zVqVOHAQMG0D3nBJyRkcFPP/3E9u3buX//Po0aNWLq1KlYWlry6NEjFi5cyN69e7l37x7W1tZMnDiRDz/8EOCVc3kVihTtXkAZFnaC+QsWEh8fzzvvVGHs2DG0dHQEIChoL9O//4Gw49k31Hj8OIMlS5cSFLQXhSKFJk2aMHXKZOUdHZ9la9eEFct/Uhny+DrMzLN/edNraVug5aiTFXqGNqOHcvRsJAAP9x9nxOzp/HxgLwDt/9ecmZ5jsaxajWs3/uXzpfPZe/KY8vMT+vbnMzd33i73Fr+f/wfPOd8TFRertbIBKFLua2V5ubLrexHx8Qk59T2alo7ZF+QHBe1j+vc/Enb8KJBb3z8RFLQvp77tmTrlS8qXL8/adeuZO3c+JiYmea5lzP386zAzzx7Cm6KlMf2JiYnMnDGDU6dOoaenR9euXRkzdiwGBga0cHBg8pQpdO6c/SNCWFgYC+bPz9kX3mHs2LE4tmyZZ5m5z6ia9q32bh+fO3RFoeXho2FhYcyfv0Alp5Ytc/fvIKZP/56wsONA9q/US5YsJSgoCIVCkb1/T52i3L8TExOZMWOmyrocO3aMys1KXpWZWfYwo/spyQXMFGUZ/WbOJPLUKfT19OnctQujxmSXsXULR76Y/CUdc+r7RFgYixYsJCE+nirvvMPosWNo4ejIvbt36diufc6v1YYqy3/28wVR1twCKLn1XVLzLm7HtaysLNatXcvmzZu5e/cu7zdowOTJk6lZUzvPXFQe1x7e1MrycoWdiGD+wp+yz2NVqjB2jCctHZsDELQ3mOk/zCEsdD8AjzMyWLLUn6C9B1CkKGjSxJapk70pX/4tAE5FnmXh4mVER1/ByMiIDu1dGDfGU+3zkV6WWenKBU+yENX6brLWlnV16vdaW1ZxUuwbVUI7tN2oKu4Ks1FVnBVWo6q403aj6k1RWI2q4k7bjao3RWE1qoq7wmpUFXfablS9KQqrUVXcSaPqzSeDP4UQQgghhBAayY0q8ieNKiGEEEIIIYRG0qjKn6whIYQQQgghhCgA6akSQgghhBBCaCQ9VfmTRpUQQgghhBBCI2lU5U/WkBBCCCGEEEIUgPRUCSGEEEIIITTSf+7ZkiIvaVQJIYQQQgghNJLhf/mTNSSEEEIIIYQQBSA9VUIIIYQQQgiNpKcqf9KoEkIIIYQQQmgkjar8yRoSQgghhBBCiAKQniohhBBCCCGERtJTlT9pVAkhhBBCCCE0kkZV/qRRVUKYmZfWdRF0Iiv0jK6LoBNm5mV1XQSdMDcz03URdMLMzFzXRdCJsuYWui6CTpTU+i6peZfY41rpyroughCvRBpVQgghhBBCCI2kpyp/0qgqIR6mpOi6CEWqtHn2L5qKlPs6LknRyu2h0mtpq+OSFK3cHklFykMdl6Ro5fZAJzy4p9uCFLGqZcoB8CjxT90WpIiZVvgAAIWiZB3Pc3uoFMl3dVySomVm8RYAKQqFjktStHJ75jwP/qLjkhStn9p213URXshAGlX5kjUkhBBCCCGEEAUgPVVCCCGEEEIIjaSnKn/SqBJCCCGEEEJoJI2q/MkaEkIIIYQQQogCkJ4qIYQQQgghhEbSU5U/aVQJIYQQQgghNDIwkEZVfmQNCSGEEEIIIUQBSE+VEEIIIYQQQiMZ/pc/aVQJIYQQQgghNJJGVf5kDQkhhBBCCCFEAUhPlRBCCCGEEEIj6anKnzSqhBBCCCGEEBrpS6MqX7KGisj69euxtrZm1apVui5KgSQlJTFxwgTatGqFi7Mzc/z8yMjIUBt77Ngx+rq74+jgQG83N0JDQtTGzfbz45uvvy7MYmtN6LHj9HHvR3OHVri5uRMSEqoxNjMzk3nzFuDStiMtHNswbvwkbt++o5x/8dIlPL1G0bpNW9q268iUqV9z9+69Isji1VUs9xZRG36htY29xphOzRz5c9VmkoPDOLd2G10cWqrM9/54IHHb9pEcHMbhBSuoV71mYRe7wEKPHaOPe1+aOzji5tb7Jep7Pi5t29PCsRXjxk9Qqe9cSXfv4urag1OnThVm0V/L3aQkpk7yppuTCz3atmfRnLlkati/Tx4/jsdHH9O5ZWsG9enLidBjynlpqanM+3EGvTp0wtW5LRO9PiM6Kqqo0nhtoWGn6f3pBJo5f0rPfuMIOR6Z72cyMzOZ8IUfS/03a5zv8dnXTJ2+SNvF1ZrQ0GP06eNO8+YOuLm5EaLhWA252/k8XFza0qKFI+PGjef27dvK+UlJSYwfP4GWLVvh5OSM3wvOEboWeiyMPu6f0LxFG9x69SUk5JjG2MzMTOb930Jc2nWiRUtnxk3wVtm/IyJO0X/AEBxbOdO2fWdmzJpNampqUaTx0pKSkpgwfjytWrbE2cnphXVzLDQU9z59cGjeXO02sWrVKjp26IBD8+YMGzqUq1evFkEGryf9YTJ/LVtN6KSvOPb5N0Rt3cWTzMwXfib5+g2OjpvM3UvRKtMTQk5w8uuZhIyfQsT3c7nz17nCLLp4w0ijqoisX7+efv36sWbNmmJ7gnkZX/j6YmZqyr79+1m9Zg3hERH8vH59nrjY2Fh8vL3x8vLiSEgIIzw98fX15datW8qYe/fuMXXyZDZu2FCUKby2a7GxeHv7MtJrBKEhh/D0HIaP75cqOT3L3z+QEydPsn7davbv24OxsTHffvc9AKmpqYwaNY7GH3zAwQN72bplI/fvP+Cbb74typReikOjxpxYugqrajU0xlhVq8G26X5MDVhC2U4t+TrwJzZPm8m7Fd8GYEDHbozp1Y8OE0dSoWsbIi+eZ9v02UWVwmvJrm8fRnp5ERpyBE/PEfg8tw0/y98/IKe+17B/X1BOfX+nEnP27FkGDRxMXHx8UaTwyr77cjKmpmZs2fsrS1at5HTE72xRs3/Gx8byjc8XDPYcwe7DvzFo+DC+/eJLbuesm9Ur/ImLjWXl5o1s278Xy3p1+crbp6jTeSXX4v5l0pezGTnsI44Fr8ZzqDufT5nLzduJGj/z743bjJr4I4eORmiMWRa4hTN/nC+MImvFtWuxeHt7M3KkF6GhIXh6euLj86Lt3J8TJ06yfv069u/fl72df/t0O/fx8cXMzJTg4P2sXbuG8PAI1qs5R+jatdhYvD//gpFewwk9ehDPEcPw8Z2sOe+AlZw4Gc76tavYv3d3zv79A5D9Q8mYcRPp09uNkCMH2bB+DZGnzrBy1dqiTClfvj4+mJqZsT84mDVr1xIRHq62bmKvXcPb2xuvkSMJCQ3F09MTXx8f5brZvWsXGzdsYPGSJRw+coT69evjPWkSWVlZRZ3SS/knYB0GxkY4/DAF+89Hc/dCFPGHXvADWXo651b+zJPHj1Wm/3vyFFeDDvD+4H60nPsdNTs48bf/WtLu3S/sFIoFA319rb1eRWJiIiNHjqRJkyY0bdqU77//XuP36KFDh9KoUSNsbW2Vr2d/EFixYgWtWrXCxsaG/v37ExMTU6B18jxpVBWBEydOkJiYiK+vL0+ePGH//v3KeXfv3mX8+PHY29vj4uLC2rVref/994nP+dIVGxuLp6cnTZs2xcnJiXnz5pGenq6TPOJiY4k8dYox48ZhYmpKtWrVGDp0KJs3bcoTu2f3bmxsbWnj5ESpUqVo17499nZ27Ni2DQCFQkEvNzcsSpfG2cWlqFN5LXt2/4qtrQ1OTm0oVaoU7du3w87Ojm3bdqqN37HzFwYPGkCVKpWxsLDgc+8JHD8eRnx8Ajdu3KRevboMH+6BoaEh5cqVo1evnpw+c6YoU8rXgI7d+PmrH5m8YvEL4wZ27EroH2f4JfQImZmZbDl8gKNnTzPctRcAw7r1ZMnOzZy7GkNaejq+Py2gRuUqtLFtUgRZvJ49u/eoqW97tm3boTY+u74HUqVKlZz6npRT39n78q7de/jiyyl89tnIokviFSTExXE28jTDx4zCxMSEd6tVpb/HEHZu3pondv+vQTSyaYxjm9YYlCpFm3ZtaWxnx687dgJw7cpVsp5kkZUFWVlZ6OvrY2JiUsQZvZrdQUewbVwf59b/o1QpAzq4OGBv+z7bfjmoNv5a7HX6DfahUcO6NG5krTYm4tRfHDwSjkubZoVZ9ALZs2c3tra2OOUcq9u3b59zXNumNn7Hjp0MHjzo6Xb+uTfHjx8nPj6e2NhYTp06xdix4zDNOUcMGzaUjRvzniN0bc+eIGxtGuPk1Don77bY2duybfsvauN37NzF4IH9c47n5nw+aTzHw04QH59A+bfe4rcDQbi6dkVPT4/79++Tlp7GW2+VK9qkXiC3bsaNHausm6HDhrFp48Y8sbv37HnhNrFjxw76uLtjaWmJsbExo8eM4caNG8Wy911x6w73omKw7NEZAyMjTCtWoGYnF+KPhmn8zKWNO6jYuEGe6XG/hVC7WwfK1KqBnp4elZvYYj/xMwyK+bFNW3TVqBo3bhxmZmaEhoaydetWTpw4oXHU199//01AQABnzpxRvlq1agVkb7dr164lICCA8PBwGjRowJgxY7T6Y4A0qorA2rVrcXd3x8TEhI8//pjAwEDlvEmTJvHw4UN+++03tmzZwuHDh8nM6ZZWKBQMGjSIunXrEhISws8//0xYWBgLFy7USR7RMTGULVuWt99+WzmtTp063Lhxg4cPH6rExsTEYGVlpTKtdp06XMoZAmRkZMTmLVvw8fXFzMys8AuvBdExMVhZWapMq1OnNpeiLuWJffgwmZs3b6msgwoVKlCmTGmioqKoVasmixfNx8DAQDn/4MHfqP/ee4WXwGvYHxGG5Ufd2Hwo+IVxDWpb8lfMZZVp567G0Niq3tP50U/nZ2RmEBUfq5xfHEWr2YZfXN831dR3GaKisvN2aN6M3bt20qFD+8It+Gu6GhNDmbJlqPjM/l2zTm1u3bhB8nP799WYGOpYqq6bmrVrK4f4uX/yMVeio+nZrj2dW7XhQNBevvrx+8JPogCir8RR11K1N7ZOrWpcirqmNr5ihbfYvWUhI4f2pVQpgzzzk5Lu882PS/nxm7GYmBgVSpm1ITpa3XZeh0uX8g7XfPjw4Qu28yiio7PPEZUq5X+O0LXo6Ct5j+e1a2vIO/d4/jT+6fE8e/82NzcHoGNnV/r0/YSKFSvS3bVrIWbwamKio7PP35UqKadpPH9HR6vdJqIuZR/7op+bb2hoSPUaNbh0Ke+xUddS/r1JKXMzjMuVVU4zr1KZtLv3eKx4lCf+Rngkj24nUrtzO5XpmenppPx7Ez09fU7PXUro598QOXsxmemPKWViXOh5lFTXrl0jIiICb29vTE1NqV69OiNHjlTbwxoXF8f9+/d5//331S5r8+bNfPzxx9StWxdjY2MmTpzI9evXCQ8P11p5pVFVyBISEggNDeWTTz4BwN3dncuXLxMREcHNmzc5duwYX375JeXKlaN8+fJ8+eWXys8eOXKE9PR0JkyYgLGxMe+88w5jx47V2VAKRUoKJqamKtNyf31WKBR5Yk3VxD7KiStVqhQVKlQoxNJqX0qKQm1OCjUHZoUiBeCl4rOysli8eCkhIcfw9p6o5VIXzM2kRGUj/0VKm5mTkqqalyI1FQvT7AZzaVOzF84vjl6vvk3UxGdv8xUrVqRUqeJ7byBFigITE9V8jXP270fP7d+PUhSYPJersYkxj3LWTWZmJq2cndj06252/XYAxzatmTrJm/S0tELMoGAUilRMTVW/HJmYGPPokfrrYszNTSltYa523pMnT/hy2gL6f9QN67q1tF1UrUrRcKx+/pgOT4/zmuIVCvXLevazxUWKhrIqHr1i3s/F/7JjC/v37cZAX59Jn3+h5VK/vhSF+uMZ5K2b/LYJhYZlPX+cKA4y09IwMFL9USP3feZzx6OUG7eI2bWP9wf3Q++53pQMxSPIyiL2t6NYf9STFj9MoXITG/5cHMCjxKTCTaKY0GZPVXp6OsnJySovdaOwoqKiKFeuHJUrV1ZOs7S05Pr16zx48EAl9q+//sLc3Jzx48fTrFkzunbtytatT0daXL58mXr1nv6Qa2hoSK1atbhw4YLW1lHxPcP/R/z8889kZGTQvXt35bSMjAwCAwPx9PQEoFq1asp51atXV/6dkJBAUlISH374oXJaVlYWjx8/JjExscgbJaampnkuvM19b/5cb5OJhlgzc/VfQoqjgICVBASuUr5v1LCB2pzMzfM2DHJPOOrXwdP45ORkvv7mO86fv0CA/zLq1lX9dfBNkfLoEWbGql+yzUxMeJjT2EhJfYSZieb5xUFAQCABgSuV7xs1bKj1+i7O1O2zaTnvTZ/bb01MTdTEpmFqbkZGRgbTvviSH/9vrvJX8dHek3B1cuFUeAQOrVRvYKIr/qu3E7Bmu/J9o/frkpqqelJPTU3DzOzVh/YErNmBsbEh/fp0KnA5tS0gIICAgKejJRo10rSd5z1Wv3A7NzMnK+uJxnOErkckBASuIiBwtfK9xuO5mbq8TZTzn483ey7exMQEExMTxo75jP4DPXjw4AFlypTRVhqv7UXn7+frRlNs7jahaX5xPL8bGBnx5Lkv65k575/tYcp8/Jh/Atdj1dsVk/Jv5VmOXs4PYtWdW2L+bhUAqrVpQULoSZL+uUDVVg6FlUKxYWCgp7VlLVu2jEWLVG/eM2rUKEaPHq0yTV0DP/e9QqFQ2bfS09OxsbFh/Pjx1K1bl/DwcEaPHo25uTmdOnV6pR+QXpc0qgpRWloaW7du5fvvv8fB4ekOd+nSJYYPH86IESOA7MZT7dq1lX/nqlKlCjVq1GDfvn3KacnJySQmJlK+fPkiyuIpSysr7t+7p9Kgi4mJoXLlyliULq0aa2nJxeda/1diYqivoVu2OPLwGIyHx2Dl+0WLlnD+wkWVmJiYK7z/fv08ny1TpgyVKlXKGVqTPWTkzp073L//ACvL7PdxcfGMHjOOKlWqsH7d6mI1/v5V/X3lMnb1VNfD+7XqcOrCuZz50TSoZcmvYdkXB5cyKEXdajX4+0p0nmXpiofHEDw8hijfL1q0+DXrO7thnF3f97GyfDMayrUt6/Dg/n2SEhMpn7N/X4u5wtuVKmFhYfFcrCVRz62ba1euUK/+ezxSKHj44AGP059e5K2vr4+evj6GhoaFn8hLGjrQjaED3ZTvF/70MxcuXVGJibkaz/vvWT7/0Xz9ui+E23fu4th+IJDdOAM4HPI7x4JXv+ijhc7DwwMPDw/l+0WLFnH+vOqxOiYmRu0QmqfbeXTe7dzKkidPsrin4RxR+rlzRFHzGDIIjyGDlO8XLV6ad/++coX362vav99WGQJ+505izvG8Dmf/+JNp075n86Z1ym08/fFjDA0N83yJ0xUrS8uXrhtLKysunFe9ucqz24SlpSUx0dHKa1UeP35MXGwslpavvq8UNvN3K/M4RUH6g4cYlcnOM+XGTYzLlaXUM3Xz8Fo8j27d5uL6LVxcv0U5/a+lK6nc1B7rj3piWNqCJxnPjdzIekIxvT9HsTZixAgGDx6sMs3IKO8waTMzMx49Uh0dkvv++R9+evToQY8ePZTvHR0d6dGjB3v37qVTp075/ligDTL8rxDt3r0bPT09unXrRpUqVZSvVq1aUa9ePbZv345Tzm1N79+/z/3795k1a5by805OTqSkpODv7096ejoPHjzAx8eH8ePHo6envV8MXlaNGjWwsbFhzuzZpKSkkJCQgL+/P67P9MLl6tKlC5GRkRwIDiYjI4MDwcFERkbSuUuXIi+3tnTp0pnIyNMEBx8gIyOD4OADREaepmsX9b9Gu7p2xT8gkISEBFJSUvCbPQ97ezuqV6/GgwcPGD5iJI0/+IAlixe80Q0qgLX7f6WNrT19nNphYGBAH6d2tLG1Z23wHgACf/2F0b0+4gPLehgbGTHDcww3k5IIOXtaxyXXLHcbVq3vSLp26aw23tW1G/4BAc/U9xxlfb8JqtWoQSObxiyeOw9FSgr/JlxnbUAgnbq75olt17kTZ0+f5siBg2RmZHDkwEHOnj5Nu86dKV2mDI1sGrN80SLuJiWRnpbG8oWLKFuuLA1tGusgs5fTtWNrTp3+h/2/hZGRkcn+38I4dfofunZs9crL2rlxPscPruFY8GqOBa+mU3tHOrV31HmDSp2n23lwznaefazu2lX9sdrV1RV//2e2c7/Z2NvbU716dWrWrIGtrQ1+fk/PEStW+NOjR95zhK516dyJyMgzBAcfzMn7IJGRZzQfz7t1xd9/FQkJ17PznjMPe3tbqlevRr26VqSmpjJ/4WIeP37M9X//Zd68BfTo3q3Y/JBQo2ZNbGxtme3n9/T8vWIF3Z/5EppL0zbRpWv2NWLdu3dn48aNXLp4kbS0NBYuWED58uWxs7Mr4qzyZ1bpbcpa1iJq6y4yUlN5dCeJa3t/4x2HD1XiylnVpvX//UDL2d8qXwCNvAZj/VFPAKo6NuPq3oM8jLvOk8xM4g8fI+3efd5Wc1OL/yJtDv8zMjLCwsJC5aWuUVW3bl3u3bvHnTtPH18QHR1NlSpV8vwYsHXrVvbu3asyLT09HWNjY+Wyop55tMfjx4+5evWqypDAgpJGVSH6+eef6dZN/UG1b9++/PLLL3z//ffo6enRpk0bevbsqfwlyNDQEAsLC1atWkV4eDitWrWibdu26Ovrs3Tp0qJORWmmnx+ZmZm4duvGoAEDcHBwYOiwYQC0bNGCvUFBANSqXZvZc+awMjAQ5zZtWLFiBTP9/KhZs/g/m0iT2rVrMXfOLAICV9G6TVuWrwjAz2+GMqegoH04tGitjB8+bCiOji0Y4jGCjp26kp6exqyZ2bfg/WXXbm7cuEHwgYM4tnTCoUVr5etN8XD/cT5ul/0F5GLsVXp8OYEv+3twN+goXw0aTq8p3kTFxQIQ+OtO5m1ez44f5nB79yFs61rTxWc0GZnF9/EC2fU9m4DAlbRu48zyFSvw85v5TH3vxaHF06Fsw4cNw9HRkSEew+jYqTPp6enMmjlDV8V/LV/P+JHMzEw+7t6TzwYP4cPmzeif03vXuVUbDu7N7jWvUasW3/nNYv3KVbi6tGONfwDfzPyR6jVrKJdTrUYNhvb7BPcuXbl25QozFywoNr/aq1O7VlXmzficgDXbadVxEMsDtzL7h0nUrPEuAL/uD6W5y6c6LqX21a5dm7lz5xAQEEjr1m1YvnwFfs8cq4OCgnBwaKGMHz48Zzsf4kHHjp2yt/NZM5Xz/XLOEV27dqN//+xzxLCcc0RxUrt2LebOnknAytW0dmrPcv9A/Gb9SM2cbTgoaB8Ojk7K+OHDPHB0dGDI0BF07ORKelo6s2Zk33zFzMyMRYv+j+jLMbi068zQYV40a/Y/Jk0cp4vUNMqtm25duzKgf3+Vumnh4EBQzvm7du3azJk7l8CAANq0bs2K5ctVtonuPXrwySefMHHiRFycnblw8SILFi4sNg3I5zUc2p+sJ084+dUMIv0WUv59a2p1agtAyPgp3Ih4uR/3anVuS422rfkncB3HJn3NjYjTfDDSQ+UmGP9lurj7X61atbC3t+eHH34gOTmZuLg4lixZQu/evfPEJicn891333Hu3DmePHnCkSNH2LNnD3379gWgV69erFu3jgsXLpCWlsacOXOoWLEiTZpo7y7EelnF9cECJcTx48ext7dXXjB68eJFevTowdmzZ5Wta214mFJ8rl0pCqVzunMVKSXj+RG5zMyzD+56LW11XJKilRWafSt6RUrxusNYYTMzz/6lLuHBPd0WpIhVLVMOgEeJf+q2IEXMtMIHwNMbo5QUudctKZLv6rgkRcvMIvvanpRieAOIwpR7jbbnQfW3t/+v+qlt8evRfdaQfeofs/A6Ajv2eunYO3fu8O233xIeHo6+vj49evRg0qRJGBgYYGtry7Rp03B1dSUrK4ulS5eydetWEhMTqV69OqNGjaJjx45A9j0JVq5cyfr160lKSqJRo0ZMmzZNefmNNsg1VTo2c+ZMnJycGD16NKmpqSxduhQHBwetNqiEEEIIIYR4Xa/6fCltqVixIgsWLFA778wzz/bU09Nj5MiRjByp/lmQenp6DBkyhCFDhqidrw0y/E/H5syZw9mzZ2nWrBnOzs4YGBioXFclhBBCCCGELunq4b9vEump0rG6deuyenXxu3hZCCGEEEII8XKkUSWEEEIIIYTQ6L/cw6Qt0qgSQgghhBBCaCSNqvzJGhJCCCGEEEKIApCeKiGEEEIIIYRG0lOVP2lUCSGEEEIIITSSRlX+ZA0JIYQQQgghRAFIT5UQQgghhBBCIwMD6YfJjzSqhBBCCCGEEBrJ8L/8yRoSQgghhBBCiAKQniohhBBCCCGERtJTlT9pVAkhhBBCCCE0kkZV/vSysrKydF0IIYQQQgghRPH0TeRR7S3LvrXWllWcSE+VEEIIIYQQQiN96anKlzSqSogUhULXRShS5mZmQMnNW5HyUMclKVpm5qUB0Gtpq+OSFK2s0DMAKJLv6rgkRcvM4i2g5O7f91OSdVySolXW3AIARcp9HZekaJmZlwVK7nZeUs9jxZWBnp6ui1DsSbNTCCGEEEIIIQpAeqqEEEIIIYQQGhnoST9MfqRRJYQQQgghhNBIhv/lT5qdQgghhBBCCFEA0lMlhBBCCCGE0Eh6qvInjSohhBBCCCGERvpyTVW+ZA0JIYQQQgghRAFIT5UQQgghhBBCIxn+lz9pVAkhhBBCCCE0MtCXwW35kTUkhBBCCCGEEAUgPVVCCCGEEEIIjWT4X/6kp0qNr776CltbW2xtbWnUqBHvvfee8r2trS2nTp3SdRF1JikpiQnjx9OqZUucnZzw8/MjIyNDbeyx0FDc+/TBoXlz3NzcCAkJUZm/atUqOnbogEPz5gwbOpSrV68WQQavR5t55/KbNYuvv/qqMIutNaHHjtHHvS/NHRxxc+tNSEioxtjMzEzmzZuPS9v2tHBsxbjxE7h9+06euKS7d3F17VGs96eK5d4iasMvtLax1xjTqZkjf67aTHJwGOfWbqOLQ0uV+d4fDyRu2z6Sg8M4vGAF9arXLOxiF1josTD6uH9C8xZtcOvVl5CQYxpjMzMzmfd/C3Fp14kWLZ0ZN8Fbpb4vXorCc+RoWju1p237zkz5ahp3794rgixeXkndv5OSkpg0YQLOrVrTztmZuX6zNeZ9/Ngx+rm708qhBe5uvQh9Ju+0tDTm+s2ma8dOOLVqxeABAzj1++9FlcZrCz12nD7u/Wju0Ao3N/eXOK4twKVtR1o4tmHc+EnPbeeX8PQaRes2bWnbriNTpn4t23kxU1LPY9qir6entdd/lTSq1Pj22285c+YMZ86cYdq0abz77rvK92fOnKFJkya6LqLO+Pr4YGpmxv7gYNasXUtEeDjr16/PExd77Rre3t54jRxJSGgonp6e+Pr4cOvWLQB279rFxg0bWLxkCYePHKF+/fp4T5pEVlZWUaf0UrSVN8C9e/eYPHkyGzZsKMoUXtu12Fi8vX0Y6eVFaMgRPD1H4OPrq5LTs/z9Azhx8iTr161h/74gjI2N+fa771Rizp49y6CBg4mLjy+KFF6LQ6PGnFi6CqtqNTTGWFWrwbbpfkwNWELZTi35OvAnNk+bybsV3wZgQMdujOnVjw4TR1KhaxsiL55n2/TZRZXCa7kWG4v3518w0ms4oUcP4jliGD6+kzXXd8BKTpwMZ/3aVezfuzunvn8AIDU1lVGjx9P4gw84GPwrWzdv4P69+3wz7Tu1y9KVkrp/T/b1xczUjKD9+1i1Zg0RERFsWP9znrjY2Fh8vT9nhJcXh0KOMsxzBF8+cwxYvGAhf/zxBwGrVnLw8GG69+jBhLHjuPHvv0Wd0kvLPq75MtJrBKEhh/D0HIaP75cvOK4F5hzXVrN/356c7fx7IGc7HzUuezs/sJetWzZy//4Dvvnm26JMKV8ldTuHknseE0VLGlWvKDY2Fk9PT5o2bYqTkxPz5s0jPT0dgO3bt9OvXz+mT59Os2bNaN68OZMnT+bx48cA+Pr64uvrq7I8a2trwsPDAXB2duarr76iRYsW9OjRgydPnvDPP//Qv39/PvzwQ9q3b8+qVat01vCIjY3l1KlTjBs7FlNTU6pVq8bQYcPYtHFjntjde/Zga2uLk5MTpUqVon379tjZ2bFt2zYAduzYQR93dywtLTE2Nmb0mDHcuHGjWP7ao828FQoFbj17Urp0aVxcXIo6ldeyZ/cebG1tcHJqk5NTO+zs7Nm2bYfa+B07f2HwoIFUqVIFCwsLPveexPHjYcTnnHh27d7DF19O4bPPRhZdEq9oQMdu/PzVj0xesfiFcQM7diX0jzP8EnqEzMxMthw+wNGzpxnu2guAYd16smTnZs5djSEtPR3fnxZQo3IV2tgW3x9m9uwJwtamMU5OrXPquy129rZs2/6L2vgdO3cxeGB/qlSpjIWFOZ9PGs/xsBPExydw48ZN6tWzYviwIRgaGlKuXFl69erB6dNnizapFyip+3dcbByRpyIZPW4sJqamVK1WDY+hQ9myaVOe2F9378HG1oY2OXm3a98eOzt7dm7bDmT3VI3w8qRylSoYGBjQw80NIyMjzp8/X9RpvbQ9u39Vc1yzY9u2nWrjs49rA3K2cws+956Qc1zL3c7rMny4R852Xo5evXpy+syZokzphUrqdp6rJJ7HtM1AT19rr/+q/25mhUChUDBo0CDq1q1LSEgIP//8M2FhYSxcuFAZc/r0aSpUqEBoaCjLli0jKCiI4ODgl/4ff/75J3v37mXNmjXcvn2bgQMH0rFjR8LCwliyZAk///wzm9Sc9IpCTHQ0ZcuW5e1KlZTT6tSpw40bN3j48GGeWCsrK5VpderUIerSJQCin5tvaGhI9Ro1uJQzvzjRZt5GRkZs2boVX19fzMzMCr/wWhAdE6Mmp9pcispbVw8fJnPz5k2V+AoVKlCmTBmioi4D4NC8Gbt37aRDh/aFW/AC2B8RhuVH3dh86MX7boPalvwVc1ll2rmrMTS2qvd0fvTT+RmZGUTFxyrnF0fR0VewsrJUmVandm0uXYrKE5td37dU4rPruzRRUZepVasmixf+HwYGBsr5B387TP367xVeAq+opO7fMTHRlClblrfffls5rXad2urzjonG8rm8az9zDPhiymQcWrRQzvs9IoLk5GTqWVsXYgYFk31ce247f+Fx7Zaa41ppoqKisrfzRfNVt/ODv1H/PdnOi4uSeB7TNgM9Pa29/qukUfUKjhw5Qnp6OhMmTMDY2Jh33nmHsWPHqnSfm5iY4OnpiaGhIR988AHW1tZcuXLlpf9Hhw4dKFOmDGXKlGHXrl1YWlryySefYGhoiJWVFR4eHmq764tCikKBqampyjQTExMgu8GpEpuSojY2N06hYVmPnltOcaDNvEuVKkWFChUKsbTal5KiPn+F4lGeWIUiBQBTUxM18dnroGLFipQqVbzvkXMzKZHMzMx840qbmZOSqroeFKmpWJhmf9EobWr2wvnFUYpCwzb8KO++mVunLxOflZXF4iU/ERJyDO9J47Vc6tdXUvdvRYpC7X4KefNWaDgGPFJzDPjrz7/4wseXYSOGU7VqVS2XWnte77iWf3xWVhaLFy/N3s69J2q51K+vpG7nuUrieUwUPdkiXkFCQgJJSUl8+OGHymlZWVk8fvyYxMREIPvXDL1nWuGGhoavNFyv0jO/IiUkJPDPP/+oXMP15MkTlV/DipKpqSmpqakq03LfP/9rlaZYc3PzF843y5lfnGgz7zdBQEAgAYErle8bNWyoIae8DYPck5b6ui2+DYnXlfLoEWbGqideMxMTHuaclFNSH2Fmonl+cRAQuIqAwNXK940aNlBf32Z5t+HcLx1q6/uZ+OTkFL6e9h3nz18gYMVS6tZV/cVYl0ra/p3LxNREY97mL5n38/v0zh07mDd7DsO9PPnk008LodSvLyBgJQGBq5TvNW7nBTiuJScn8/U3Odu5/zLZznVIzmPa918etqct0qh6BVWqVKFGjRrs27dPOS05OZnExETKly+f7+f19fVJS0tTvk9KSsoT82yDrEqVKjRt2pSAgADltLt375KSopsvZFaWlty7d4/ExETlr1QxMTFUrlyZ0qVLq8RaWllx4bnx9DExMbz//vvZ8y0tiYmOplWrVgA8fvyYuNhYLC1Vh2MUB9rM+03g4TEED48hyveLFi3m/IWLKjExMVd4//36eT5bpkwZKlWqRHT006EWd+7c4f79+1hZFp8vGNry95XL2NVTXQ/v16rDqQvncuZH06CWJb+GZd9lqpRBKepWq8HfV6KLvKyaeAwZhMeQQcr3ixYvzVvfV67wfn1N9f22ylCqO3cSuX//AVaWdQCIi4tn9NgJVKlSmfVrV/HWW+UKLZfXUdL271yWVlbcv3dfJe8rMVeoVLkyFs/lXcfSkosXLqhMuxJzhfo5x4DMzExm/TiDw4cO4Td3Dv9r2rRokngFHh6D8fAYrHy/aNGS1zyu5W7nd3K28+z3cXHxjB4zjipVqrB+3WrZznVMzmPa918etqct0ux8BU5OTqSkpODv7096ejoPHjzAx8eH8ePHqzSGNLG0tOTUqVPcvHmT1NRUFi9e/MLPdevWjbNnz7Jr1y4yMjK4desWnp6ezJgxQ5tpvbQaNWtiY2vLbD8/UlJSSEhIwH/FCrr36JEntkuXLkRGRhIcHExGRgbBwcFERkbSpWtXALp3787GjRu5dPEiaWlpLFywgPLly2NnZ1fEWeVPm3m/iZ7mdCAnpwNERkbStUtntfGurt3wDwggISGBlJQU/GbPwd7ejurVqxVxyQvf2v2/0sbWnj5O7TAwMKCPUzva2NqzNngPAIG//sLoXh/xgWU9jI2MmOE5hptJSYScPa3jkmvWpXMnIiPPEBx8MKe+DxIZeYauXTqpjXft1hV//1UkJFzPru8587C3t6V69Wo8ePCA4Z6jaPxBI5Ysml/svmhCyd2/a9SoQWMbG+bNnqPMO8DfH9fu3fPEdu7SmdORkRzIyftAcDCnIyPp1KULAPPmzCEs7Dir160rlg0qdbp06Uxk5OnnjmunNW/nrl3xDwh85rg2T3lce/DgAcNHjKTxBx+wZPEC2c6LITmPiaIgjapXYGFhwapVqwgPD6dVq1a0bdsWfX19li5d+lKf79u3L7a2tri6utKuXTveeecd3n33XY3xVatWxd/fn02bNuHg4ED37t2pU6eOzhpVAH5+fmRmZtKta1cG9O+Pg4MDw4YNA6CFgwNBQUEA1K5dmzlz5xIYEECb1q1ZsXw5fn5+1KyZ/Yye7j168MknnzBx4kRcnJ25cPEiCxYuxNDQUGe5vYi28n4T1a5di7lzZhMQuJLWbZxZvmIFfn4zlTkFBe3FocXTZzMNHzYMR0dHhngMo2OnzqSnpzNrpu62WW17uP84H7fL/uJ1MfYqPb6cwJf9PbgbdJSvBg2n1xRvouJiAQj8dSfzNq9nxw9zuL37ELZ1reniM5qMTPXPhikOateuxdzZMwlYuZrWTu1Z7h+I36wfqVkz+9byQUH7cHB0UsYPH+aBo6MDQ4aOoGMnV9LT0pk1I/tW07/s2sONGzcIPvAbjq1ccHB0Ur6Kk5K6f8/wm0VGZgY9unVjyICBNHdojsewoQC0buHIvpy8a9Wuzaw5s1kVuJK2bZwIWOHPDL9Z1KxZk3t377J18xYS7yTyUZ8+tG7hqHzlfr44yj6uzSIgcBWt27Rl+YoA/PxmPHNc24dDi9bK+OHDhuLo2IIhHiPo2Kkr6elpzJqZ/eiAX3btztnOD+LY0gmHFq2Vr+KkpG7nIOcxbZDnVOVPL6u4PhhIaFVKMbwBRGHKvSagpOatSHmYT+R/i5l59vAVvZa2Oi5J0coKzb5lsyL5ro5LUrTMLN4CSu7+fT8lWcclKVplzS0AUKTc13FJipaZeVmg5G7nJfU8VlxtvXoh/6CX1LtW8bkzpjZJT5UQQgghhBBCFIDcqEIIIYQQQgihka5uVJGYmMjUqVOJiIjAwMAAV1dXfHx81N7SfsOGDaxatYpbt25RqVIlBgwYwCeffAJk3z3b3t6erKwslfsZHD9+XGvPW5NGlRBCCCGEEEIjXd1Sfdy4cVSuXJnQ0FDu3LmDl5cXq1atYujQoSpxBw8eZO7cuaxYsYLGjRtz9uxZhg8fTsWKFenQoQOXL1/m8ePHnD59GiMjo0Ipqwz/E0IIIYQQQhQr165dIyIiAm9vb0xNTalevTojR45k/fr1eWJv3rzJsGHDsLGxQU9PD1tbW5o2bcrvv/8OwF9//YW1tXWhNahAeqqEEEIIIYQQL6DN4X/p6emkp6erTDMyMsrT4ImKiqJcuXJUrlxZOc3S0pLr16/z4MEDypQpo5yeO8wvV2JiIr///jtffPEFkN2oSktLo1evXiQkJGBpacnEiRO1+igf6akSQgghhBBCaKSvp6+117Jly7C3t1d5LVu2LM//TElJwdTUVGVa7nvFC+6Kefv2bYYNG0bDhg3pmvN8NRMTEz744AOWLFnCkSNHcHZ2xsPDg7i4OK2tI+mpEkIIIYQQQhSJESNGMHjwYJVp6oblmZmZ8ejRI5Vpue/Nzc3VLvvs2bOMHTuWJk2a8OOPPypvaOHr66sS5+Hhwfbt2zl69Ciffvrpa+fyLOmpEkIIIYQQQmhkoKentZeRkREWFhYqL3WNqrp163Lv3j3u3LmjnBYdHU2VKlUoXTrvc722bt3KoEGDGDhwIHPmzFFZ5rx58zh37pxKfHp6OsbGxlpbR9KoEkIIIYQQQmikzUbVy6pVqxb29vb88MMPJCcnExcXx5IlS+jdu3ee2P379/PNN9+wcOFChgwZkmf+pUuX+P7777l9+zbp6eksWrSI5ORk2rVrV6D18ixpVAkhhBBCCCGKnQULFpCRkYGLiwvu7u60bNmSkSNHAmBra8uuXbsAWLRoEZmZmYwZMwZbW1vl66uvvgLgxx9/pEaNGnTv3p2mTZsSERHBypUrKVeunNbKqpeVlZWltaWJYivlBRf0/ReZ5zzIraTmrUh5qOOSFC0z8+xhAHotbXVckqKVFXoGAEXyXR2XpGiZWbwFlNz9+35Kso5LUrTKmlsAoEi5r+OSFC0z87JAyd3OS+p5rLg6ciNWa8tqU6WG1pZVnMiNKoQQQgghhBAa6Wvxlur/VTL8TwghhBBCCCEKQHqqhBBCCCGEEBoZ6Ek/TH7kmiohhBBCCCGERidvX9faspq9/a7WllWcSLNTCCGEEEIIIQpAhv+VEA9TUnRdhCJVOudJ2wpFycrbzCw774QH93RbkCJWtUw5oOTeBa+k3vXw0r5xui1IEavX8f+AkntXtJKad0m9+99fSbd0XJKi1ah8JV0X4YX0kRtV5EcaVUIIIYQQQgiN9KVNlS8Z/ieEEEIIIYQQBSA9VUIIIYQQQgiN9GT4X76kUSWEEEIIIYTQSB7+mz8Z/ieEEEIIIYQQBSA9VUIIIYQQQgiNpBcmf9KoEkIIIYQQQmgkw//yJw1PIYQQQgghhCgA6akSQgghhBBCaCS9MPmTRpUQQgghhBBCI7mlev6k4SmEEEIIIYQQBSA9VUIIIYQQQgiN5EYV+SuxPVXW1tZ88MEH2NraYmNjw4cffoiXlxf//vvvS30+Pj4ea2tr4uPjC7mkxUtSUhITJ0ygTatWuDg7M8fPj4yMDLWxx44do6+7O44ODvR2cyM0JERt3Gw/P775+uvCLLbWhIYeo08fd5o3d8DNzY0QDTkBZGZmMm/ePFxc2tKihSPjxo3n9u3byvlJSUmMHz+Bli1b4eTkjN8L1qWu3E1KYuokb7o5udCjbXsWzZlLpoYynjx+HI+PPqZzy9YM6tOXE6HHlPPSUlOZ9+MMenXohKtzWyZ6fUZ0VFRRpfHaQo+F0cf9E5q3aINbr76EhBzTGJuZmcm8/1uIS7tOtGjpzLgJ3ty+fUc5/+KlKDxHjqa1U3vatu/MlK+mcffuvSLI4tVVLPcWURt+obWNvcaYTs0c+XPVZpKDwzi3dhtdHFqqzPf+eCBx2/aRHBzG4QUrqFe9ZmEXu8BO/XOLUTNC6e0djNcPIUT8feulPrf210t4TDuiMu2PS4lMnBuG++fB9J/yG8u2niMtPbMQSl1woceO0ce9L80dHHFz601ISKjG2Ozj2nxc2ranhWMrxo2foLKd50q6exdX1x6cOnWqMIteICUt76SkJCaMH0+rli1xdnJ64TnnWGgo7n364NC8udpz3apVq+jYoQMOzZszbOhQrl69WgQZvJ77SXeZ6fMFA9p1YnDHrqyct0DjeSzXycNHGNnLXWXakydP+NS5PZ84teNT5/bKV+qjR4VZ/GJDX4uv/6r/cm75WrFiBWfOnOHs2bMcPnyYrKwsvL29dV2sYu0LX1/MTE3Zt38/q9esITwigp/Xr88TFxsbi4+3N15eXhwJCWGEpye+vr7cuvX0S8q9e/eYOnkyGzdsKMoUXtu1a7F4e3szcqQXoaEheHp64uOjmtOz/P39OXHiJOvXr2P//n0YGxvz7bffKef7+PhiZmZKcPB+1q5dQ3h4BOvVrEtd+u7LyZiamrFl768sWbWS0xG/s0VNfcXHxvKNzxcM9hzB7sO/MWj4ML794ktu56yb1Sv8iYuNZeXmjWzbvxfLenX5ytunqNN5JddiY/H+/AtGeg0n9OhBPEcMw8d3sub6DljJiZPhrF+7iv17d2fX93c/AJCamsqo0eNp/MEHHAz+la2bN3D/3n2+mfad2mXpkkOjxpxYugqrajU0xlhVq8G26X5MDVhC2U4t+TrwJzZPm8m7Fd8GYEDHbozp1Y8OE0dSoWsbIi+eZ9v02UWVwmu5fiuFH1ee4ZPO9dg0oy0fd6rLzFVnSLyX+sLP/XHxDtt/i1GZdj85jW+Xn6JTixpsnNGO+d4t+OtyIlsPxmhYiu5ci43F29uHkV5ehIYcwdNzBD6+LzquBXDi5EnWr1vD/n1BOdu56nZ89uxZBg0cTFwx/tGxJObt6+ODqZkZ+4ODWbN2LRHh4WrPObHXruHt7Y3XyJGEhIbi6emJr4+Pct3s3rWLjRs2sHjJEg4fOUL9+vXxnjSJrKysok7ppcyd+jUmpqas2L2DGQHL+fPUKfZs3Kw2NiMjg53r1jNv6jdkPVHNJ/7KVTIyMlgVHMS6Q8HKl4mpaVGkId4AJbpR9SwLCwvc3d35+++/ldOio6MZMWIEbdq04YMPPqBz584cPnxY7edPnz7NgAEDcHR0pFGjRri5uXH27FkAwsPDcXZ2ZunSpbRs2ZL//e9/jB49muTkZOXnV69eTbt27bC1tcXNzY0TJ04AkJWVxZo1a+jQoQNNmjTh448/ViljUYqLjSXy1CnGjBuHiakp1apVY+jQoWzetClP7J7du7GxtaWNkxOlSpWiXfv22NvZsWPbNgAUCgW93NywKF0aZxeXok7ltezZsxtbW1uccnJq3749dnZ2bMvJ6Xk7duxk8OBBVKlSBQsLCz7/3Jvjx48THx9PbGwsp06dYuzYcZjmrMthw4aycWPedakrCXFxnI08zfAxozAxMeHdalXp7zGEnZu35ond/2sQjWwa49imNQalStGmXVsa29nx646dAFy7cpWsJ1lkZWVv0/r6+piYmBRxRq9mz54gbG0a4+TUOqe+22Jnb8u27b+ojd+xcxeDB/anSpXKWFiY8/mk8RwPO0F8fAI3btykXj0rhg8bgqGhIeXKlaVXrx6cPn22aJPKx4CO3fj5qx+ZvGLxC+MGduxK6B9n+CX0CJmZmWw5fICjZ08z3LUXAMO69WTJzs2cuxpDWno6vj8toEblKrSxbVIEWbye335P4P06b9H8g8oYGOjT0vYdGlqVZ19YnMbP3H2QxsJNf9OtdS2V6WUtjFk73YW2TauhpwcPUx6T/vgJZS2MCjmLV7dn9x5sbW1wcmqTs523w87Onm3bdqiN37HzFwYPGvj0uOY9iePHw5SjNnbt3sMXX07hs89GFl0Sr6Gk5Z17zhk3dqzynDN02DA2bdyYJ3b3nj0vPNft2LGDPu7uWFpaYmxszOgxY7hx40ax7J37Ny6ef06fof9nIzE2MaFy1XfpPXgge7dtVxv/3dgJ/B15hh79P80z7/L589S0ssTQ0LCwi10s6evpae31XyWNqhz379/n119/pX379sppo0ePpl69ehw4cIBTp07h6OjIN998k+ezqampeHl50aFDB0JCQggPD6dGjRrMmjVLGZOQkMDNmzc5cOAAW7Zs4cyZM/z8888AbN++nSVLljBr1iwiIyPp168fXl5e3Lt3j59//pmVK1cyf/58Tpw4gZubG4MHD+bOnbzDDgpbdEwMZcuW5e2331ZOq1OnDjdu3ODhw4cqsTExMVhZWalMq12nDpdyhnwZGRmxecsWfHx9MTMzK/zCa0F0dN6c6tSpw6VLeYexPXz4kJs3b6rEV6hQgTJlyhAVFUV0dPa6rFQp/3WpK1djYihTtgwVn6nvmnVqc+vGDZKfK+PVmBjqWKqum5q1ayuH+Ll/8jFXoqPp2a49nVu14UDQXr768fvCT6IAoqOvYGVlqTKtTu3aGuo7mZs3b6nEZ9d3aaKiLlOrVk0WL/w/DAwMlPMP/naY+vXfK7wEXsP+iDAsP+rG5kPBL4xrUNuSv2Iuq0w7dzWGxlb1ns6Pfjo/IzODqPhY5fziKPbfZGq9U1plWo3KFly5/kBt/JMnWcxZ+we9XOpQo4pFnvlmJtmXLA/++jCjZh6jfFlj2jatqv2CF1C0mmN1nTq1uRR1KU9s9nau6biWXd8OzZuxe9dOOnRon+fzxUlJyzsmOjr7/F2pknKaxvN3dLTac13Upex1E/3cfENDQ6rXqMGlS3nXna7FXbmCRZkylH+7onJatVq1uHPjJilqzrVjvp7ClHmzqVLt3Tzzos9fID0tDZ8hwxjSqStTvUZx4c+/CrX8xYk+elp7/VeV6EaVp6cnTZo0wc7Ojv/9738cPXqUvn37KucvW7aM0aNHk5WVRUJCAmXKlOHmzZt5lmNoaMimTZv4+OOPSU9PJyEhgXLlyuWJ/eyzzzAxMaFmzZo0bdqUK1euANm/+vTt2xdbW1v09fXp06cPgYGBmJiYsH79ekaMGMF7772HoaEhvXv3xtLSkl27dhXuylFDkZKSp5s7t7dBoVDkiTVVE/soJ65UqVJUqFChEEurfSkacno+d3i6PjTFKxTql/XsZ3VNkaLAxES1jMY5ZXz0XBkfpSgwMTV5LtaYR4rsseaZmZm0cnZi06+72fXbARzbtGbqJG/S09IKMYOCSdFQR4pHr1jfz8VnZWWxeMlPhIQcw3vSeC2XumBuJiWSmZn/dT+lzcxJSVW9jkCRmoqFafYPJKVNzV44vzh6lJaBsbGByjRjIwNS09Svj80HojEzKUWnFpqHSQIsm9KaVd86oa+nx48rz2itvNqSkqLQcJzKe52IQpECgOlz+/qzx8GKFStSqlTxvwdWScs7RaE+X8h7zsnvXKfQsKznzwvFQapC3bkp+726a6EqPNPofJ6RsTF133+fz2f8wNId22ji2ILp4ydy8/p17RZavLGK7xGgCPz00080bdoUyO5tWr9+PQMHDmTTpk00aNCACxcuMHLkSG7fvo2lpSXly5dXO2bYwMCA8PBwhg0bhkKhwMrKilKlSuWJfbaHx9DQUDn/9u3bvPuu6q8idnZ2QHYP18yZM5k9++n1CBkZGTRs2FA7K+EVmJqakpqqen1B7nvz53qbTDTEmpmbF24htSggIICAgEDl+0aNGqrNyVxNTrknHLXrwMycrKwnGtdlcem5U1eHaTnvTZ/L2cTURE1sGqbmZmRkZDDtiy/58f/mKn8lHe09CVcnF06FR+DQSvUGB7oSELiKgMDVyveNGjZQX99m6urbRDn/+XizZ+KTk1P4etp3nD9/gYAVS6lbV/XX4DdFyqNHmBmrflExMzHhYc6Xz5TUR5iZaJ5fHGwOjmbLgWjl+3o1y5GW/kQlJi09E1PjvKfJvy8ncTA8nv+b1CLf/2NsZICxkQGDXK2ZOPcEyYrHWJjpbvhQQEAgAYErle8bNdR0XMt7HHrhcU1NfHFSUvPO9aLz9/PnHE2xuec6TfOL4/nd2MSUtFTVH+9yz2Mmr3iuHThmlMr77p/04/CvQZw+foJOfXoVrKBvgP/wqD2tKdGNqmeZmJjg4eHB8uXLCQsLo2LFiowdO5ZFixbh7OwMwP79+wkOzjss5o8//uC7775j48aNysZOYGCgsicqP++8806euw7OmzcPV1dXqlSpwpgxY+jSpYtyXmxsLOXKlXvNTF+fpZUV9+/dIzExUdnLFBMTQ+XKlbEorTpsxtLSkosXLqhMuxITQ/333y+y8haUh4cHHh4eyveLFi3i/HnVnGJiYnhfTU5lypShUqVKKsMk7ty5w/3797GysuTJkyzuaViXpZ9bl7pS27IOD+7fJykxkfI5ZbwWc4W3K1XCwsLiuVhLoi5cVJl27coV6tV/j0cKBQ8fPOBx+mPlPH19ffT09YvV2HSPIYPwGDJI+X7R4qWcfy6nmCtXeL9+/Tyfza7vt3OGFGUPAbxzJ5H79x9gZVkHgLi4eEaPnUCVKpVZv3YVb71VrtByKWx/X7mMXT3V9fB+rTqcunAuZ340DWpZ8mtY9t3UShmUom61Gvx9JTrPsnTFvb0l7u2fDtdcs+cS0fH3VWJibyZTt3rZPJ89cuo69x+mM/TbIwA8znjC44wnfOR7gKnD7NHX12P+z3+x0McRw1L6yphSBnoYGxnkWV5R8vAYgofHEOX7RYsW593OY67w/vuatvNKKkOhlcc1y+L9A0FJzTuXlaXlS59zLK2suHD+vMq0Z891lpaWxERH06pVKwAeP35MXGwslpaqw6WLgxqWtXl4/z73kpIoV748APFXr1KhUiXMLfIO232Rn39aTjOnNtSxfjqMOSP9MUbGxtoscrH1Xx62py0levjfszIyMti2bRsPHjzA3t6elJQUMjMzlb9QXb58mcWLsy/eTk9PV/nsw4cPVS68P3v2LGvWrMkTp4mbmxubNm3izz//5MmTJ2zbto3169fz1ltv4e7uztKlS4mOzv4yEhoaSpcuXfj999+1lfpLq1GjBjY2NsyZPZuUlBQSEhLw9/fHtXv3PLFdunQhMjKSA8HBZGRkcCA4mMjISDo/0zh80+TmFJyTU3BOTl27qs/J1dUVf/8AEhISSElJwc9vNvb29lSvXp2aNWtga2uDn9/TdblihT89euRdl7pSrUYNGtk0ZvHceShSUvg34TprAwLp1N01T2y7zp04e/o0Rw4cJDMjgyMHDnL29Gnade5M6TJlaGTTmOWLFnE3KYn0tDSWL1xE2XJlaWjTWAeZvZwunTsRGXmG4OCDOfV9kMjIM3Tt0kltvGu3rvj7ryIh4Xp2fc+Zh729LdWrV+PBgwcM9xxF4w8asWTR/De6QQWwdv+vtLG1p49TOwwMDOjj1I42tvasDd4DQOCvvzC610d8YFkPYyMjZniO4WZSEiFnT+u45Jo5ffguf19OIvTMv2RmPiH0zL/8fTkJpw/zXgc16qOGbPFrz8YZ7dg4ox1efRrw9lumbJzRjgaW5an1bmnS0jNZvfsijzOecCvpEYG/XKBds+rKRlZx8fS4diBnOz+QfVzr0lltvKtrN/wDnjmuzZ6Dvb0d1atXK+KSF0xJy7tGzZrY2Noy28/v6fl7xQq69+iRJ1bTua5L164AdO/enY0bN3Lp4kXS0tJYuGAB5cuXV46wKU7eqV6d9xp/wMr/W8CjFAU3r19n68rVOHd79e8isTFXWPl/C7ibmMjj9HS2BKxEoUjhf61bFULJxZtIL6u43gOzkFlbW2NsbKy8cFxPT49atWoxbNgwOnXK/tIUEBDAypUrefToEVWqVMHd3R0/Pz82btxIuXLlcHFx4bfffqNq1arMnDmTHTt28OTJE6pVq0bXrl2ZM2cOISEhREdHM2DAAC5efPqrmK+vLwAzZswAYP369axZs4bbt29jZWXFF198ga2tLZmZmaxcuZItW7Zw69YtKleujIeHB3369HmlfB+maGfYTWJiIrNmzuTUqVPo6+nRpWtXRo8Zg4GBAS1btODLyZPp1Dn7pHQiLIyFCxYQHx9PlXfeYczYsTg6OuZZZu4zqr6ZNk0rZQQonTMMQaHl4UZhYWHMn5+d0zvvvMPYsWNp2TI7p6CgIKZP/56wsONA9q93S5YsJSgoCIVCQZMmTZg6dQrlc34tS0xMZMaM7HWpp6dH165dGTt2jMrNDF5V7lCzhAf3CpZojqTERBb4zebsqUj09fVp17kTw0ePwsDAgM6t2jDhC1/aduoIwO8nTrJ84SKuJyRQuUoVho8ZRbMWLZTLWbZgIadOhpOZmUH9hg0ZOX481Wu++HqUl1W1TDkAFMl3tbK8XGFhJ5m/cDHx8Qm8804Vxo4ZRUtHBwCCgvYx/YeZhB3LviPo48cZLFm6jKC9+1CkKGjSxJ6pU3wpX748a9f9zNx5CzAxMUHvuTEUuZ9/HWYWbwGg19L2tZehSVboGdqMHsrRs5EAPNx/nBGzp/Pzgb0AtP9fc2Z6jsWyajWu3fiXz5fOZ+/Jp8/xmtC3P5+5ufN2ubf4/fw/eM75nqi4WK2VDeDSvnFaWV6u0+dvs2r3RW7cUfD2W6YMdrWmSYPsIatHTiWweNM/bPHLeyOCg+HxbNh3mYCv2yinxd54yIrt54mKvY+5qSFtmrzLRx0sMSz1+vt3vY7/B4AiRbs3swkLO8H8BQtzjmtVGDt2DC0dc49re5n+/Q+EHc/udczezpcSFLQXhSIl+7g2ZbLyuPYsW7smrFj+E02aFOyuj2bm2T0pJTXvFC1dq5SYmMjMGTNUzjljxo7FwMCAFg4OTJ4yhc455++wsDAWzJ+vcq5zbJk9VDsrK4t1a9eyefNm7t69y/sNGjB58mRq1tTOs+hyLyf4K+nlnhOXn3tJSfjPnsc/p8+gp69H644d+fQzTwwMDPjUuT3DfSbR6rkbjBz+NYjN/itZumOLctrD+w9Ys3ARp8NOkpaaitX79Rk0bjS1rLTTW9movObruYqDuPvaO79WL/uW1pZVnJTYRlVJo61G1ZuisBpVxZ22G1VvisJqVBV3hdmoKs4Kq1FV3BVWo6q4K6xGVXGn7UbVm0Lbjao3RXFvVGnze0XuOfu/pniNQxBCCCGEEEKIN4zcqEIIIYQQQgihkdyoIn/SqBJCCCGEEEJoJLdUz58M/xNCCCGEEEKIApCeKiGEEEIIIYRGMvwvf9JTJYQQQgghhBAFID1VQgghhBBCCI305aKqfEmjSgghhBBCCKGRDG3Ln6wjIYQQQgghhCgA6akSQgghhBBCaCQ3qsif9FQJIYQQQgghNNLT097rVSQmJjJy5EiaNGlC06ZN+f7778nIyFAbe/ToUbp164aNjQ2dOnXi8OHDKvNXrFhBq1atsLGxoX///sTExLzu6lBLGlVCCCGEEEKIYmfcuHGYmZkRGhrK1q1bOXHiBKtWrcoTd/XqVUaPHs3YsWM5deoUo0ePZty4cdy8eROAHTt2sHbtWgICAggPD6dBgwaMGTOGrKwsrZVVGlVCCCGEEEIIjfTR09rrZV27do2IiAi8vb0xNTWlevXqjBw5kvXr1+eJ3bFjB02aNKFt27aUKlWKzp078+GHH7Jp0yYANm/ezMcff0zdunUxNjZm4sSJXL9+nfDwcC2uIyGEEEIIIYTQQF9PT2uvlxUVFUW5cuWoXLmycpqlpSXXr1/nwYMHKrGXL1+mXr16KtOsrKy4cOGC2vmGhobUqlVLOV8b5EYVJURpc3NdF0EnzMxKZt5Vy5TTdRF0wsziLV0XQSeyQs/ougg6Ua/j/+m6CDphZl5a10XQiZKat7mZma6LoBONylfSdRFEIUlPTyc9PV1lmpGREUZGRirTUlJSMDU1VZmW+16hUFCmTJkXxpqYmKBQKF5qvjZIT5UQQgghhBBCIz2ytPZatmwZ9vb2Kq9ly5bl+Z9mZmY8evRIZVrue/PnOgtMTU1JTU1VmZaamqqMy2++NkhPVQmhSHmo6yIUqdxfNO+nJOu4JEWrrLkFAI8S/9RxSYqWaYUPAEjR4i9Ob4LcX7Av7Run24IUsdweKr2WtrotSBHL7ZF8mJKi45IUrdyRFknJJes8Vt4i+zxWUs/fiRd/1nFJilYF6491XYQXy3qitUWNGDGCwYMHq0x7vpcKoG7duty7d487d+5QsWJFAKKjo6lSpQqlS6v2XNerV49//vlHZdrly5dp2LChcllRUVE4OTkB8PjxY65evZpnyGBBSE+VEEIIIYQQokgYGRlhYWGh8lLXqKpVqxb29vb88MMPJCcnExcXx5IlS+jdu3eeWFdXVyIiIggKCiIjI4OgoCAiIiLo3r07AL169WLdunVcuHCBtLQ05syZQ8WKFWnSpInW8pJGlRBCCCGEEOIFnmjx9fIWLFhARkYGLi4uuLu707JlS0aOHAmAra0tu3btArJvYLF48WKWLVvGhx9+yJIlS1i4cCG1a9cGoHfv3gwaNIjPPvuMZs2ace7cOZYtW4ahoWFBVooKGf4nhBBCCCGE0EyLw/9eRcWKFVmwYIHaeWfOqN6kqWXLlrRs2VJtrJ6eHkOGDGHIkCFaL2Mu6akSQgghhBBCiAKQniohhBBCCCHEC+imp+pNIo0qIYQQQgghhGY6Gv73JpHhf0IIIYQQQghRANJTJYQQQgghhHgB6anKjzSqhBBCCCGEEJrJ8L98yfA/IYQQQgghhCgAaVQVQ5mZmcTFxem6GEIIIYQQQqCrh/++SV6rUWVtbc3w4cPJyspSmb59+3acnZ21UjB1bt++zbfffouzszONGzfG0dGRiRMncunSJZW4oKAgmjdvjr29PYcPH8bZ2ZlGjRpha2uLra0tNjY2ODo6MnPmTJ480V7l9u/fn4ULFxZ4OePHj2fnzp0FL1AhCj12jD7ufWnu4IibW29CQkI1xmZmZjJv3nxc2ranhWMrxo2fwO3bd/LEJd29i6trD06dOlWYRX8tSUlJTJowAedWrWnn7Mxcv9lkZGSojT1+7Bj93N1p5dACd7dehIaEKOelpaUx1282XTt2wqlVKwYPGMCp338vqjReW2jYaXp/OoFmzp/Ss984Qo5H5vuZzMxMJnzhx1L/zRrne3z2NVOnL9J2cQssKSmJCePH06plS5ydnPDz89NY38dCQ3Hv0weH5s1xc3Mj5Jn6fpbfrFl8/dVXhVlsrTn1zy1GzQilt3cwXj+EEPH3rZf63NpfL+Ex7YjKtD8uJTJxbhjunwfTf8pvLNt6jrT0zEIodcFVLPcWURt+obWNvcaYTs0c+XPVZpKDwzi3dhtdHFQfNOn98UDitu0jOTiMwwtWUK96zcIu9mtLSkpi4oQJtGnVChdnZ+a8aDs/doy+7u44OjjQ281N5bj2rNl+fnzz9deFWewCS0pKwmfCRNq1bkNHZxfmzZ6jMe+wY8f4xL0vTi0c+ahXb45pONft2rGT5vZNCrPYWlPSzt+5wk5F8enopTj3+YF+Ixdz/PdLGmPvP1Aw/f920nXAbDr0m8noKWu4FHNDOf9q/B3Gfb2O9v1m0HPIPFZtDuHJkyyNy/tPyHqivdd/1Gv3VB09ehR/f39tluWF4uPj6dmzJ7dv32bZsmWcOXOGX375herVq+Pu7s7x48eVsVu2bKFLly5ERkbi5OQEwLRp0zhz5gxnzpzh7NmzBAQEsHPnThYtKn5f6O7evavrIrzQtdhYvL19GOnlRWjIETw9R+Dj68utW+q/ePn7B3Di5EnWr1vD/n1BGBsb8+1336nEnD17lkEDBxMXH18UKbyyyb6+mJmaEbR/H6vWrCEiIoIN63/OExcbG4uv9+eM8PLiUMhRhnmO4Mtn1s3iBQv5448/CFi1koOHD9O9Rw8mjB3HjX//LeqUXtq1uH+Z9OVsRg77iGPBq/Ec6s7nU+Zy83aixs/8e+M2oyb+yKGjERpjlgVu4cwf5wujyAXm6+ODqZkZ+4ODWbN2LRHh4axfvz5PXOy1a3h7e+M1ciQhoaF4enri6+Ojsi/cu3ePyZMns2HDhqJM4bVdv5XCjyvP8Ennemya0ZaPO9Vl5qozJN5LfeHn/rh4h+2/xahMu5+cxrfLT9GpRQ02zmjHfO8W/HU5ka0HYzQsRXccGjXmxNJVWFWroTHGqloNtk33Y2rAEsp2asnXgT+xedpM3q34NgADOnZjTK9+dJg4kgpd2xB58Tzbps8uqhRe2Re+vpiZmrJv/35Wr1lDeEQEP6vbzmNj8fH2xsvLiyMhIYzw9MT3/9m777AojjeA4196ESsq2Auo0WgEMRZsgF1KFNQk/mJFBY29YklMosaGGrtRUGOLsSvYsItd0CSaxAYogoIKFqrU3x/A6QmIBTiE9/M89+jtzi3v3Mzu3uzMzr52zH/69CnfTZnClo+gnn/nNgk9fX28Dh7Ec/1v+F28wJbNmY/n94KDmTRhIoOHDOHwyRMMdHFhahbnusCAABYtWJBf4X+Qonj+Brh3P4LJs7cy6H/W+GxxY2AvK6bO2cajiOdZpv95yV6ePo9l09KheK8fx2d1qzDmh43ExScQG5fA6GkbMSpXgj1rx7B8dn+O+v7D2j9O5nOuREHz3o2q3r17s2jRIi5fvpzl+pCQEOrUqUPIKzvZkiVL6N27N5DWq9WrVy/mzJlDkyZNaNasGRs2bGDr1q1YW1tjYWHB969c1Z01axa1atVi8eLF1KpVC3V1dQwNDRk1ahS9e/fGzc2NpKQkunfvzvnz59myZQvt2rXLNv46derw+eef8++//yry4+bmhrW1NVZWVkRHR3Pjxg0GDRpEkyZNaN26NT/88ANRUVGKbWzbto22bdtibm7OxIkTiYuLU6xzc3PDzc0t09+8cOECkN7zMW4cn3/+OU2bNmX06NE8e/aMKVOm4Ofnx6+//oqrq6vie2vTpg1NmjTBycmJo0ePvlUZ5RVvL2/Mzc2wtrZCU1OTDh3a06iRBTt27Moy/a7de+jfry/GxsYYGBgwYfw4zpw5q6gbe728mTR5Kt9+OzT/MvEO7gXfw9/Pn+GjRqKrp0elypVxHjiQbX/8kSntPi9vzMzNsLK2RlNTk/YdOtCokQW7d+wE0nqqXIa4YmRsjIaGBl0dHdHW1ua//wpm4wLAa/8JzBvWxaZNEzQ1NejY1hIL83rs2HMky/R3g+/zdf+JNKhfi4YN6mSZ5qLfVY6cuEBbq2Z5Gfp7CQ4Oxs/Pj1EjR6Knp0flypUZOGgQf2zZkimtl7c35ubmWKeXd4cOHWjUqBE7duwAIDY2Fsdu3ShevDht27bN76y8l6OXQqlXszTNPzNCQ0OdVuYVqG9ahoNnsx+S/OT5C5b8cQ37NtWVlpc00GHDjLa0a1oZNTWIikkkITGFkgbaeZyLd9Onkz2bv5/FlNXL3piubyc7fP+6wh7fEyQnJ7Pt+GFO/nmZwQ5OAAyy78by3Vv5904gLxIScFu5mKpGxliZF7wejHvBwfj7+TFi1Ch0M+r5wIFszeK45u3lhZm5udJxzaJRI3a9Us+dHB0xKF4cmwJez+/du8dlf3++HTkCXT1dKlWuTP+BA9nxR+Ye9f3e3piZmdHGOu1c165De8wtLNiz8+W5Lj4unu8mTabn11/lYy7eX1E7f2fYf+wvGtarSptmn6CpoU7blp9iXr86ew5lHnWRmpqKmpoag/5nTckS+mhpadCrmyWRT2MIDo3g73+DefIshnEutujpalOhfCn69mzFrgN+mUZwFS4y/C8n792oat++PV9++SVjxozh6dOn77UNf39/jIyMOH/+PCNGjGDWrFlcuHCB/fv3s27dOrZv386lS5dISEjg5MmTfPHFF6ipqWXaTs+ePXn48CFXrlxh+/btNG7cGBcXF44cyfpHX2JiIhcuXOD8+fO0aNFCsfzs2bNs2bKFvXv3kpiYSJ8+fTA1NeXUqVPs2LGDoKAgJkyYAMC5c+f46aefmDFjBpcuXaJhw4ZcvXr1rfM+cuRIoqOj8fHx4ejRozx//pwff/yRmTNnKuJfuXIl58+f548//mDbtm1cuHCBHj16MGXKFBITE9/x2849AYGBmJqaKi2rWbMGN29l7kqPioomPDxcKb2hoSElSpTg1q3bAFg2b4bX3t107NghbwN/T4GBAZQoWZJy5copltWoWYOwsDClRnZGWpPXvpsar3w3k6ZOwfKVOnfp4kWio6OpXSfrxkdBEBB0j1omylfva1avzM1bd7NMX9awNF7bljB04JdoampkWh8Z+YwfZq1g1g8j0dUtWD+uIe2qc8mSJSlXvrxiWc2aNbMu74CALPaFmtxKH5Ksra3Ntu3bcXNzQ19fP++DzwXBD6KpXqG40rKqRgYE3c/6im5KSirzN/yFU9uaVDU2yLReXzdtktn+044zbM5pypTUoV3TSrkf+Ac4dPEsJl/Zs/WYzxvTfVrDhKuBt5WW/XsnkIamtV+uD3i5Pik5iVshwYr1BUlAYGBaPX/luJZtPc/imF+jZk1u3roFpNXzrdu2MfEjqOdBAZmP59VrZLd/B2Y+nteowe1XbjlwnzOHFq1a8XmTJnkbeC4paufvDEHBjzCpVl5pWfUqZbkVFJ4prZqaGrMnf0kdkwqKZcfP/IuerhbVKpclOSUFLU0NNDVf/oRWV1Mj8mkMUTFv7tH/qMnwvxx90EQVEydOpEyZMri5ub1X61xfX5++ffuirq5Oy5Yt0+6xcHZGT0+PBg0aUL58eUJDQ3ny5AmJiYmULVs2y+2UT//xk133NaQN/2vcuDGNGzemefPmTJ8+nf79+/PNN98o0rRu3RojIyNKlCjB0aNH0dLSYty4cejq6lKuXDm+++47jh07xqNHj9i7dy8dOnSgefPmaGpq0qtXL+rVq/dW+Q4NDeXixYtMnDiR0qVLY2BgwOzZsxkyZEimtDo6Ojx79oytW7fy77//0qNHD86dO4eWltZb/a28EBMTi56entIyXV1dYmPjMqWNjY0BQE9PN4v0sQCULVsWTc2CO7t/bExslvEDijwop8383cRl8d1c/fsqkya6MchlMJUqFawfma+KjY1HT09HaZmurg5xcVmfPIoV06O4QbEs16WkpDD5x8X0/sqeOrWq53aouSImNusyhMzlHRMTk82+kJZOU1MTQ0PDPIw298W9SEJHR7kxrKOtQfyLrO+D2no4AH1dTTq3yH7YHMCvU9uw7idr1NXUmLX2Sq7FmxvCIyNITs75Pq/i+sWIiVfel2Pj4zHQS2tIFNfTf+P6giQ2Jgbdt6znsdnU87iPsJ7Hxsaip5v18Tzu9XzHxmb6jnR0dYlNH5VycP9+7gQFMXiIax5GnLuK2vk7Q2zcC/Reu4inq6NFXHxCjp/1vXCDBasOMM7VFl0dLT6rWxUdbU1WrD9K/ItEHjx8yqZdZwF48SLre/NE0fBBe4K2tja//PIL3bp1Y82aNZQuXfqdPl+qVClFz5O6elr7rkSJEor16urqpKSkULp0aTQ1Nbl//36W28nohn71ytPrpk2bhqOj4xvjKf/KlemIiAgqVqyIhsbLHxeVK1cG0hpF4eHhfPrpp0qfr1Klyhu3n+HRo0cASj+ky5Url2X85ubmLFmyhA0bNuDh4YGuri69e/dmyJAhiu8sr3l6rsFzzVrF+wb16xMfr/yDOj4+nmLFMv9wyDh4Z5VeP4v0BZGunm6W8QMUe+2qrJ6e3lvldfeuXSx0n8/gIa7875WGfUHg8dtOPNfvVLxvUK8W8a+deOLjX6Cvr/v6R3PkuX4XOjpafN2j8wfHmVeyK0Mg01X47NIWK5Z1o7Ig2uoTwLbDAYr3tauV4kWC8pXEFwnJ6OlkPl1cux3JkQsh/DKuRaZ1r9PR1kBHW4N+DnUYu+Ac0bGJGOir7uLQ+4iJi0NfR7ne6+vqEpX+4zMmPg593ezXFyRvquevH9d0sz2ufTz1PIOu7pv2b+X86GVx7H8RH4++vj5379xh+ZKlrPRYXaAbFUX1/P3bVl/Wb385AUe92pWJf6E8wif+RSL6r10wfFVqairrtvqyccdpJo9woF2r+gAUN9Bl/rT/sWSND10HLKRyhTJ0tm7If7fuU9zg3c+LH4/C28OUWz74SFC1alWmT5/OhAkTlBotGY2RV4epvT4BQ1ZD+bKira1N27Zt2blzJ927d8/UmNi2bRvlypXD3Nz8fbORKZ5KlSpx//59kpOTFXkJDg4G0hpAxsbGmaY9DwsLo1atWkBag/DFixeKdZGRkYr/V6iQ1qV8//59qlevDsDt27fx9vZm1KhRStu8f/8+hoaGeHp6kpCQwLlz5xg2bBiffvopVlZWH5Tft+XsPABn5wGK90uXLuO/6zeU0gQGBlGvXt1Mny1RogTly5cnIODlkIPHjx/z7NkzTE1MM6UviExMTXn29BkRERGKq7FBgUGUNzLCoLjyMKmaJibcuH5daVlQYBB107+b5ORk5s6azfFjx5i3YD5NmjbNn0y8g4F9HRnY9+W+vGTlZq7fDFJKE3gnhHqfmLzztvcdPMWjx09o2aEvkNY4Azh+6hKnfX77gKhzj6mJCU+fPlUq78DAQIyMjCj+WnmbmJpy/bX74QIDA9+617og6NnBhJ4dXpbleu+bBIQ8U0oTHB5NrSolM332hN99nkUlMPCnEwAkJqWQmJTCV26H+W6QBerqaizafJUlE1uilT5UJjEpBU0NNXS0Mw8NLeiuBd2mUW3l41y96jXxu/5v+voAPq1uwr6zaT/mNDU0qVW5KteCAjJtS9XSjmtZ1/PXj2smWR7XAqn7EdXzDCamJjx79ozIiAjKpOf7TlBg+vFcefhq2vFc+VwXFBTEJ3XrcvzoUaKeP6dvr/8BKHo627exYpybGx07d8qH3OSsqJ6/+/ZsRd+eL2fmXLnhKDcDwpTS3Ln3mE9MK7z+USCtwfX9vO0E3n3I8ln9lYYCJiYmk5ySwpIZfRS/G3fuv0SNKuXQ1fm4LhS9C7VCfb9Y7siVro4uXbrg5OTEH6/c4GpoaEjJkiXZt28fqamp/PPPPxw8ePC9/8aUKVO4f/8+I0aMICAggJSUFMLDw1mwYAGbN2/m559/ztUhcW3atAHA3d2d+Ph4Hj16xMyZM2nWrBmVKlXCycmJI0eOcPz4cZKSkti1axd//fWX4vMmJib4+fkRHh5OfHw8y5YtU+x8RkZGtGjRgrlz5/L8+XOio6OZN2+eopGmra2tGNt99epVBg4cyPXr19HW1lac/N61VzA3Zcys6ONzmKSkJHx8DuPv74+dbZcs0zs42OPh6UloaCgxMTHMc5+PhUUjqlSpnM+Rv5+qVavS0MyMhe7ziYmJITQ0FE8PDxy++CJT2i62Xbjs789hHx+SkpI47OPDZX9/OtvaArBw/nzOnj3Dbxs3FsgGVVbsOrXB7/I/HDp6lqSkZA4dPYvf5X+w69T6nbe1e8sizhxZz2mf3zjt8xudO7Skc4eWBaZBBVC1WjXMzM1xnzdPUd4eq1fzRdeumdK+3Bd80vcFH/z9/bG1s8v/wHOJ9ecVuXY7Et8rD0hOTsH3ygOu3Y7E+vPMQ1SHfVWfbfM6sGV2e7bMbs+QHp9SrrQeW2a351OTMlSvWJwXCcn85nWDxKQUHkbGsWbPddo3q6JoZH1MNhzah5W5BT2s26OhoUEP6/ZYmVuwwccbgDX79jDc6Ss+M6mNjrY2s11HEB4Zyak/s57QSZWqVq2KmZkZ893dX9bzbI5rGfX81eOav78/XdKPax+TKunH81/mpx3P74eGstbDA/svHDKl7dTFlsv+/hxJP9cd8TmcfjzvQj9nZ46fOc3hkyc4fPIE7r8sBODwyRMFpkGVlaJ2/s7Qybohl6/d4ejpf0hKTuHo6X+4fO0OnawbZpn++3nbCX/0nDULBis1qABSSWX09xvxPnyF1NRUrt++z2/bfOnp8HGc00XeybWz2uTJk6lb9+WVDm1tbaZPn86BAwdo1KgRs2fPpmfPnu+9fSMjI3bv3k2FChUYPHgwjRo1wsnJiZCQELZu3Urr1u/+A+9Nihcvztq1a7l58yZt2rTBzs6OSpUqsWjRIgAsLCyYO3cus2fPpnHjxhw6dEhp0osvv/wSc3NzHBwcaN++PRUqVKBixYqK9e7u7hgYGNC5c2fatm1LmTJl+PHHHwHo2rUrO3bsoFevXnTs2JEBAwYwZMgQzMzMGDlyJJMnT6Zhw6wPBPmhRo3qLJjvjueatbSxsmHV6tXMmzeHatXSnseyf/8BLFu8vEI0eNAgWrZsyQDnQXTq3IWEhATmzpmtqvDfy+x5c0lKTqKrvT0D+vSluWVznAcNBKBNi5Yc3L8fgOo1ajB3vjvr1qylnZU1nqs9mD1vLtWqVePpkyds37qNiMcRfNWjB21atFS8Mj5fENWoXomFsyfguX4nrTv1Y9Wa7bj/PI5qVdPq875DvjRvW7CGMH6oefPmkZycjL2dHX1698bS0pJBgwYB0MLSkv3p5VWjRg3mL1jAGk9PrNq0YfWqVcybN0+xL3yMqhgZMMW5EdsOB/D1pCNsOXibSf3NqVQ+bWjUCb9Qeox/84QOGfR0NPlxSGPuPoii99SjTFpyAbM6ZRnk+EleZiFXRR06Q6/2acNVbwTfoevkMUzu7cyT/Sf5vt9gnKaO59a9tFEMa/btZuHWTez6eT6PvI5hXqsOthOHk5RcMO+zmJNezx3s7enXpw+WlpYMTK/nrVq04MArxzX3+fNZu2YNNlZWrF69mjkfcT3/ee4ckpKScbJ3YGDffjRtbkn/gWnHc5uWrTi0/wAA1WtUZ467O+vXrqWjtQ1rPVYza+4cqn6k+Yaief4GqF65LLMnf8n6bb506jWHNVtO8rNbT6pWSrtQfejE37Tt+TMANwIecPriTe6GPKKb80La9vxZ8frzn7toa2kye8pX7DzgR/uvZjN17na+cWrBFx2zf75d4SCz/+VELbVwz/8o0sXGROWcqBDRL5Y2fOVZTLSKI8lfJYulDV+Ji/hbxZHkLz3Dz4C0SSaKkox7X24eHKXaQPJZ7U6/AKDW6sOGfH9sUn3TJviIiil492jlpeLp925FRhet81gZg7TzWFE9f0fcyPzssMLMsE4vVYfwRnGR13NO9Jb0ynw8F9bexcc3/kIIIYQQQgghCpCCO2WNEEIIIYQQogAovMP2cos0qoQQQgghhBDZK8QP7c0tMvxPCCGEEEIIIT6A9FQJIYQQQggh3kB6qnIijSohhBBCCCFE9mT4X45k+J8QQgghhBBCfADpqRJCCCGEEEK8gfRU5UQaVUIIIYQQQojsyfC/HMnwPyGEEEIIIYT4ANJTJYQQQgghhHgD6anKiTSqhBBCCCGEENmT4X85kuF/QgghhBBCCPEBpKdKCCGEEEII8QbSU5UTtdTU1FRVByGEEEIIIYQomOLCT+fatvSMWubatgoSGf4nhBBCCCGEEB9Ahv8VEbGxMaoOIV/p6xcDJN9FRUa+n8VEqziS/FWymAEAsTFRKo4kf+kXKw5AVEzRqufFi6XVc7VW5iqOJH+l+l4BIDbmmYojyV/6xUoCRfd4XlTLu6BKTU1WdQgFnjSqhBBCCCGEENlKTZF7qnIiw/+EEEIIIYQQ4gNIT5UQQgghhBAiWzL8L2fSqBJCCCGEEEJkKzWl4DWqYmNjmT59OseOHSMpKYm2bdsybdo0iqXff/q6Q4cOsXz5cu7du0epUqVwdHRk6NChqKunDdzr3Lkz9+/fV7wH2L59OyYmJm8VjzSqhBBCCCGEEB+V6dOn8+DBAw4dOkRycjKjRo3C3d2dadOmZUp77do1JkyYwC+//EKbNm0ICgpi0KBB6OvrM2DAAKKjowkKCuLo0aNUqlTpveKRe6qEEEIIIYQQ2UpNTc61V26Ii4vDy8uLESNGUKpUKQwNDRk3bhw7d+4kLi4uU/rQ0FC++uorrK2tUVdXx8TEhPbt23Pp0iUgrdFVqlSp925QgfRUCSGEEEIIId5EBbP/xcfHEx4enuW6uLg4EhMTqV27tmKZiYkJ8fHx3Llzh7p16yql79ixIx07dlTa9okTJ7C3twfg6tWr6Onp8c0333Dr1i0qVarE8OHDsba2fut4pVElhBBCCCGEyBcJCQkkJCQoLdPW1kZbW1tp2V9//UWfPn2y3MbIkSMB0NfXVyzT09MDICaHZxhGR0czcuRIdHV16devHwBqamo0aNCAMWPGULFiRQ4ePMjw4cPZuHEjZmZmb5UvaVQJIYQQQgghspWbs//9+uuvLF26VGnZsGHDGD58uNKypk2bcuPGjSy38e+//7Jo0SLi4uIUE1NkDPszMDDI9m8HBgYyYsQIDA0NWb9+vSLtwIEDldI5ODjg7e3NoUOHpFElhBBCCCGE+HC5Ofufi4sL/fv3V1r2ei9VTmrUqIGWlha3b9+mYcOGAAQEBKClpUX16tWz/MzJkycZM2YMPXv2ZOzYsWhqvmwGeXp6Uq9ePZo3b65YlpCQgI6OzlvHJBNVCCGEEEIIIfKFtrY2BgYGSq93bVTp6enRuXNn3N3diYyMJDIyEnd3d+zs7NDV1c2U/s8//+Tbb79l0qRJTJw4UalBBfDgwQN+/PFH7t27R1JSEtu3b+fKlSt069btrWOSniohhBBCCCFEtgriw3+nTZvGnDlzsLe3JzExkbZt2/Ldd98p1tva2mJvb4+rqysrV64kKSmJmTNnMnPmTEUaCwsLPDw8mDBhAurq6vTq1YuoqChMTU1ZtWoV1apVe+t4PqqeKhsbG3bu3Jlp+c6dO7GxscmVv7Fv3z569+5N06ZN+fzzz/nyyy85ePBgjp9LSEjg119/xd7eHgsLCywtLRkyZAj//PNPrsSVwc3NDTc3t1zd5vvw9T1Njx49ad7cEkdHR06dOpVt2uTkZBYuXEjbtu1o0aIlo0aN5tGjR4r1kZGRjB49hlatWmNtbcO8efNISkrKj2y8M8l30ch3ZGQk48aMwaZ1G9rb2LBgnnu2MZ45fZqve/aktWULejo64fvKd/PixQsWzHPHrlNnrFu3pn+fPvilT99akPmePk2Pnl/S3LIljo7dOXXKN9u0aeW9iLbtOtCiZWtGjR7Do0ePM6WLfPIEB4eu+Pn55WXo7yUyMpKxY8Zg1bo1bW1smP+GOnn69Gm+7NmTlpaWdHd0VCrvV7nPm8cPWTwrpSAqW6o0t37fQxszi2zTdG7Wkr/XbSXa5yz/btiBrWUrpfXje/Xl3o6DRPuc5fji1dSu8vY/RFTF9/QZevT8muaWrXF07PkW9Xwxbdt1okVLK0aNHqdUz2/cvInrkGG0sWpHu/admPrdNJ48eZoPuXh3uXk8zxAZ+QQHB4cCuX9nKKrlnVtSU1Jy7ZVbDAwMmD59OmfOnOHixYvMmjVLaeKKffv24erqCsDKlSu5fv06V65cUXp5eHgAab1nkydPxtfXlz///JPt27fTtGnTd4rno2pU5bUZM2Ywd+5cBg4ciK+vL+fOnWPQoEFMmTKFTZs2Zfu5Fy9e8M033+Dr68ucOXO4dOkShw8f5rPPPuObb77h77//zsdc5L27d4MZP348Q4cOwdf3FK6urkyc6MbDhw+zTO/h4cG5c+fZtGkjhw4dREdHh59+mq5YP3GiG/r6evj4HGLDhvVcuHDxjd+3qki+i06+p7i5oa+nz/5DB1m3fj0XL17k902bM6ULDg7GbfwEXIYM4dipkwxydWGy28vvZtniJfz11194rlvLkePH+aJrV8aMHEXYgwf5naW3djc4mPHjJzJ0yBB8T53A1dWFiW5vKm9Pzp0/z6aN6zl0cH9aeU+frpTmzz//pF/f/twLCcmPLLyzSW5u6OvpcfDQIX5bv54LFy+yOYs6GRwczMTx4xkyZAgnTp3CxdUVt9e+m6dPn/LdlCls+f33/MzCe7Ns0JBzK9ZhWrlqtmlMK1dlx4x5fOe5nJKdWzFtzUq2/jiHimXLAdCnkz0jnL6m49ihGNpZ4X/jP3bMcM+vLLyXtHruxtAhLvieOoar6yAmuk1+Qz1fk17Pf+PQQe/0ep52tTs+Pp5hw0bR8LPPOHL4ANu3beHZs+f88MNP+Zmlt5Lbx3NI37/79eXevYK5f0PRLW+Rvwpdo+rYsWN89dVXNG/enIYNG/LNN99w584dIG0KxdGjR9O0aVNatGiBs7MzAQEBAPz9999s2LCBxYsX06ZNG7S1tdHU1KRdu3Z899133L17F0jrFXN0dGTAgAE0btwYLy8vNmzYQEhICCtXrqRevXqoq6tTrFgxhgwZwldffcXNmzcVf3/q1Kl06NABMzMzWrVqxcqVKxWx29jY8P3339OiRQu6du1KSkoKR48exdbWFjMzM1xcXHjy5En+fqFZ8Pb2wtzcHGtrazQ1NenQoQONGjVix44dWabftWs3/fv3w9jYGAMDAyZMGM+ZM2cICQkhODgYPz8/Ro4chZ6eHpUrV2bQoIFs2fJHPucqZ5LvopHve8H38PfzZ/iokejq6VGpcmWcBw5k2x+ZY9zn5Y2ZuRlW6d9N+w4daNTIgt070nrUX7x4gcsQV4yMjdHQ0KCroyPa2tr8999/+Z2tt+bt5Y25uRnW1lbp5d2eRo0s2LFjV5bpd+3eQ/9+fV+W9/hxnDlzlpD0BtReL28mTZ7Kt98Ozb9MvIN7wcH4+/kxYtQodNPr5MCBA9maRXl7e3lhZm6uVN4WjRqxK31fiI2NxcnREYPixbFp2za/s/LO+nSyZ/P3s5iyetkb0/XtZIfvX1fY43uC5ORkth0/zMk/LzPYwQmAQfbdWL57K//eCeRFQgJuKxdT1cgYK/PG+ZCL9+PttS+Let6IHTt2Z5k+rZ73wdjYKL2ej0mv56GEhYVTu3YtBg92RktLi1KlSuHk1I3LV67kZ5beSm4ezwH27vVi0qTJfPvtt/mZjXdWVMs7NxW0h/8WRB9do+rHH3+kcePGSq8ff/wRgLCwMEaOHMngwYM5d+4cJ06cIDU1lWXL0k4Ya9asITo6mpMnT3L8+HHKlSuHu3va1bRjx45RpUoVxQwir+ratSuTJ09WvP/nn3+wt7fn7NmztG/fnmPHjmFlZZXlFI4TJ06ke/fuALi7uxMSEqK4+W3q1KksXLhQ0WCDtMbdgQMHWL9+PXfu3GHkyJG4uLjg5+dHjx498PXNvrs6vwQEBGJqaqq0rGbNmty8eStT2qioKMLDw5XSGxoaUqJECW7dukVAQCAlS5akfPlyStsKCwsjKioq7zLxHiTfLxXmfAcGBlCiZEnKlXsZY42aNbKMMTAwAJPXvpsaNWtw81bahZRJU6dg2aKFYt2lixeJjo6mdp06eZiDDxMQmFV5v8zTq6Kiot9Q3rcBsGzeDK+9u+nYsUPeBv6eAgLT6uSr5Z1dnQzM4rupUbMmN2+l7Qva2tps3baNiW5uSkNQCqpDF89i8pU9W4/5vDHdpzVMuBp4W2nZv3cCaWha++X6gJfrk5KTuBUSrFhfEKXVcxOlZW+u5w+zqOfFuXXrFtWrV2PZ0kVoaGgo1h85cpS6n3ySdxl4T7l5PAewtGyOl9depYeqFkRFtbxzU2pKcq69CquPbqKKadOm4ejoqLRs586dLF26lDJlyrBv3z6qVq1KdHQ0YWFhlC5dWvE0Zl1dXa5fv87u3btp0aIFP//8M+rqae3KyMhIypYt+1YxaGlp8cUXXyh99vPPP8/xc8OHD0dDQwMDAwPCwsIU0zQ+fPhQcSNcx44dKVGiBADr16+nfv36ODg4ANCuXbt3erJzXomJiVE8YC2Drq4usbGxmdJmLHtT+qzWZXy2ePHiuRb3h5J8v1SY8x0bE4uenvLMQdnFmJY2c37iYuMybffq31eZNNGNQS6DqVSpUh5EnjtisslTbBZ5io1Ne8BiVt9XRnm/7XFVVWJjYtB9yzoZm82+EJeeV01NTQwNDfM44twTHhnxVumK6xcjJl65/GPj4zHQS2s4FtfTf+P6guj96nnO6VNTU1m+fCWnTp3G0+PXXI76w+X28byg798Zimp5i/z10TWq3kRLSwtvb2+2bNmCmpoatWvXJjo6WjFt4qBBg9DW1mb79u389NNPVKlShbFjx9KhQwfKly/PmTNnstzuixcvSEhIUJxcy5Urp2hQZbzPblzus2fP0NPTQ1tbm4iICGbOnMm///5L5cqVqV+/PgApr9y0V758ecX/w8PDqVixotL2qlatmu9DAD09PfH0XKN436BBfeLj45XSxMfHKx6+9qqMg1JW6fX1i5GampLlOkDlV3ol32mKSr4z6OrpZhtjsddi1NPTyzqvxZTT7d61i4Xu8xk8xJX/ffNNHkT9/jw91+C5Zq3ifYP62ZV35vJ5Y3lnkb4gyq4MIXN562Zb3pn3hcIkJi4OfR3lhrO+ri5R6T8+Y+Lj0NfNfn1B4Om5Fs816xTvG9T/NNfreXR0NNN+mM5//13H0+NXatVS7hFShbw8nhdkRbW881JhHraXWz664X9vcuDAATZu3MiGDRs4efIkq1evpl69eor1N27cwMbGhu3bt3PhwgUcHR0ZPXo0UVFRWFlZERISkuWkEn/88Qc2NjaKJzWrqakprbexseHUqVNER0dn+uyUKVMYMmQIACNHjqR+/fqcO3eOXbt2MWbMmEzpX922sbEx9+7dU1ofFhb2Dt9I7nB2dubs2TOKV4MGDRT3omUIDAzExMQk02dLlChB+fLlldI/fvyYZ8+eYWpqgomJKU+fPiUiIkJpW0ZGRirvtZB8F618ZzAxNeXZ02dKMQYFBlHeyAiD12KsaWJCYECg0rKgwCDFd5OcnMysGTNZvmQp8xbML3ANKgBn5wGcPeOreDVoUJ+A1/IU+EqeXvWyvF+mV5S3ycfxAyOtvLOuk6+Xt4mJCYGv7QtB2ewLhcm1oNt8WkM5j/Wq1+RaYED6+gA+rf5yvaaGJrUqV+VakPJ3pUrOzv05e+ak4pU79fw5punp790L4Zve/YiJiWHTxt8KzA/svDyeF2RFtbzzUkGc/a+gKVSNqqioKNTV1dHV1SU1NZVTp06xe/duEhMTAdi2bRsTJkwgIiJC8bAxfX19tLW1qV+/Pl9++SUjR47k1KlTJCUl8eLFC/bs2cOCBQsYMWJEpq7gDL169aJs2bIMGTKE69evk5qaypMnT5g/fz5nzpxhxIgRivh0dXXR0NAgMjKSGTNmACjie52DgwM3b95k69atJCUlcfr0aQ4fPpwH39y7sbW1xd/fHx8fH5KSkvDx8cHf3x87O9ss0zs4OODh4UloaCgxMTHMm+eOhYUFVapUoVq1qpibmzFvnjsxMTGEhoayerUHXbt+kc+5ypnku2jku2rVqjQ0M2Oh+3xFjJ4eHjh8kTnGLrZduOzvz+H07+awjw+X/f3pbJv23SycP5+zZ8/w28aNNHnHqVlV5WV5H04v78Np5W3bJcv0Dg72eHi+Ut7u87GwaESVKpXzOfL3U7VqVczMzJjv/rJOemRT3hnfzavl7e/vTxfbrPeFwmLDoX1YmVvQw7o9Ghoa9LBuj5W5BRt8vAFYs28Pw52+4jOT2uhoazPbdQThkZGc+vOyiiPPnq1tF/z9L79Wzy9jZ9s5y/QODnZ4eK55pZ4vVNTz58+fM9hlKA0/+4zlyxZTunSp/M3MO8jN4/nHpKiWt8hfaqmpqamqDuJt2djYMGzYsGzvqTp48CBTp07l2LFjaGhoULNmTZo3b86mTZvw9fUlMTGRn376iZMnT/LixQtq1qyJm5ub4n6o1NRUNm/ezI4dO7h37x6pqamYmprSv39/xU2YGX/r2LFjSjFER0ezbNkyjh49yuPHj9HV1cXMzIxvv/2WTz/9FABfX19+/vlnwsLCKFmyJF26dOHcuXPY29szYMCALPN37tw5Zs+ezZ07d/j0008xMjJCR0eH2bNnv9N3F5vLwzDOnj3LokWLCQkJoUKFCowcOZJWrVoCsH//fmbMmMnZs2nDKRMTE1m+fAX79+8nNjaWxo0b8913UylTpgwAERERzJ49Bz8/P9TU1LCzs2PkyBFKN4G+q4yhCZLv3PGx5PtZTObe4vcRERHBvDlz8PfzQ11NnS52tgwbkRZjmxYtmTRlMp26pDUyzp09y9LFSwgNCcG4QgWGjxxBi5YtefrkCZ3ad0BdXR0tLS2l7b/6+Q9Rslja5DixMbk7ycfZs+dYtHhJenkbM3LkCFq1zCjvA8yY+TNnz6RNmpOYmMTyFSvYv/8AsbExaeU9dYqivF9l3qgxq1etpHHjD5sVTr9YWg9SVEzu1POIiAjmzkmrk+pqatja2TE8vbxbtWjB5ClT6PxKeS9ZnLYvGFeowIiRI2mZ/t28KuMZVT+kT6SUG4qnD9FSa2Wea9vMkOp7BavhAzn5pz8AUYfO4OI+g82HDwDQoUlz5riOxKRSZe6GPWDCikUcOH9a8fkxX/bmW8eelCtVmkv//YPr/Jncuheca7EBxMY8y5XtZUir50sJCQlNr+fDadUybWKZ/fsPMmPmLM6eOQlk1POV7N9/ML2eW/Dd1MmUKVOGDRs3sWDBInR1dTONZMn4/PvQL1YSKNjH81eZmzdi9epVH75/Z5zHimh5F1ThV1fn2raMGgzKtW0VJB9Vo0q8v9w+KBd0edW4KOiKer5zq1H1scirRlVBl9uNqo9FXjaqCrK8alQVdHnVqCro8qpRVdAV+EbVXytzTvSWjBq65tq2CpJCNfxPCCGEEEIIIfJboZr9TwghhBBCCJG7ZPa/nEmjSgghhBBCCJGtwjxrX26R4X9CCCGEEEII8QGkp0oIIYQQQgiRLRn+lzNpVAkhhBBCCCGylZoijaqcyPA/IYQQQgghhPgA0lMlhBBCCCGEyJYM/8uZNKqEEEIIIYQQ2ZLZ/3Imw/+EEEIIIYQQ4gNIT5UQQgghhBAiWzL8L2fSqBJCCCGEEEJkS2b/y5kM/xNCCCGEEEKIDyA9VUIIIYQQQohsyfC/nEmjqojQ1y+m6hBUQvJdtJQsZqDqEFRCv1hxVYegEsWLFc16nup7RdUhqIR+sZKqDkEliurxvKiWd0Elw/9yJsP/hBBCCCGEEOIDSE9VEREbE6XqEPJVxpX72NgYFUeSvzKuaMZGP1FxJPlL36A0ALExz1QcSf7KuJJbVPfvyOiile8yBunHtSJaz9Vamas4kvyV0SNZVPfvuIdnVRxJ/tIrb6nqEN4oNVl6qnIijSohhBBCCCFEtmT4X85k+J8QQgghhBBCfADpqRJCCCGEEEJkS4b/5UwaVUIIIYQQQohspcjwvxzJ8D8hhBBCCCGE+ADSUyWEEEIIIYTIlgz/y5k0qoQQQgghhBDZkkZVzmT4nxBCCCGEEEJ8AOmpEkIIIYQQQmQrNSVJ1SEUeNKoEkIIIYQQQmQrRYb/5ahAD/97+PAhsbGxqg5DCCGEEEIIIbL1Xo2q3r17s2TJkrdO3717dxo0aMCjR4/e+jOPHz+mY8eOREZGArBy5UoGDhz4zrG+bsmSJdSpU4cxY8ZkWpeQkECzZs2oU6fOB/+drNjY2LBz58482XZ+8z19mh49v6S5ZUscHbtz6pRvtmmTk5NZuHARbdt1oEXL1owaPYZHjx5nShf55AkODl3x8/PLy9A/iK/vaXr06Enz5pY4Ojpy6tSpbNOm5Xshbdu2o0WLlowaNVppH4iMjGT06DG0atUaa2sb5s2bR1JSwexe9z19lh49/0fzFlY4On3JqVOns02bnJzMwl+W0LZ9Z1q0smHUmPFK5X3xoh+9+wygZWsb2nXowuy57sTHx+dHNt6Z7+kz9Oj5Nc0tW+Po2PMt6vli2rbrRIuWVowaPU4p3zdu3sR1yDDaWLWjXftOTP1uGk+ePM2HXLy7orZ/R0ZGMnHMWNq3saKTTVsWus/Pdl88e/o0/+v5JdYtWvKVU3dOZ/Pd7N21m+YWjfMy7FxTVOt52VKlufX7HtqYWWSbpnOzlvy9bivRPmf5d8MObC1bKa0f36sv93YcJNrnLMcXr6Z2lWp5HfYHK2r7dwbfc3/Rve9UmrV3ods3kzl15s8cP5OcnMKYKUtYsWa30vKguw8YMsadlp2H0slpLKvXe5GSkpI3gRcQqSnJufYqrPK8p+qvv/4iLCyM1q1bs3Hjxrf+XHx8vFIvlaurKx4eHrkSU+nSpTly5AhRUVFKy48dO0ZiYmKu/I3C7G5wMOPHT2TokCH4njqBq6sLE93cePjwYZbpPTw8OXf+PJs2rufQwf3o6Ojw0/TpSmn+/PNP+vXtz72QkPzIwnu5ezeY8ePHM3ToEHx9T+Hq6srEiW/Ktwfnzp1n06aNHDp0MC3fP73M98SJbujr6+Hjc4gNG9Zz4cJFNm3alF/ZeWt3g4MZP2ESQ4cMxvfkEVxdBjHRbUr2+fZcy7nzF9i0YR2HDnill/fPQNqJd8SosfTo7sipE0f4fdN6/P2usHbdhvzM0ltJq+duDB3igu+pY7i6DmKi2+Q3lPea9Hr+G4cOeqfneyaQdjwbNmwUDT/7jCOHD7B92xaePXvODz/8lJ9ZeitFcf/+zm0Sevr6eB08iOf63/C7eIEtmzdnSncvOJhJEyYyeMgQDp88wUAXF6Zm8d0EBgSwaMGC/Ar/gxTVem7ZoCHnVqzDtHLVbNOYVq7Kjhnz+M5zOSU7t2LampVs/XEOFcuWA6BPJ3tGOH1Nx7FDMbSzwv/Gf+yY4Z5fWXgvRXH/Brh7L4xxU5cx1NmR0weW4zqgKxOmrSD80ZNsP/MgPIJh4xdw7NRlpeWxsfEMHTsfYyNDDu9cyJplk/A5epFVv+3N62yoVGpycq69CqsPalRFR0czevRomjZtSosWLXB2diYgIEApzcaNG+nUqRO9evViy5YtxMXFKa0/c+YM3bt3x9zcHBsbGzZu3EhycjJ2dnYA2NnZsX//fpYsWULv3r1JSUnBxsaGP/74Q7GN5ORkWrVqxYEDBwA4e/Ys3bt3p3Hjxtja2rJ3r3JFr1WrFjVq1GD//v1Ky3fs2IGtra3SsuDgYFxdXWnatCnW1tYsXLiQhIQEAHbu3EmvXr2YM2cOTZo0oVmzZmzYsIGtW7dibW2NhYUF33//vdL2/vnnHxwdHWnSpAnOzs7cuXPnrf+Wo6MjAwYMoHHjxnh5eb1VGeUFby9vzM3NsLa2QlNTkw4d2tOokQU7duzKMv2u3Xvo368vxsbGGBgYMGH8OM6cOUtI+gF4r5c3kyZP5dtvh+ZfJt6Dt7cX5ubmWFtbp+e7A40aNWLHjh1Zpt+1azf9+/d7me8J4zlz5gwhISEEBwfj5+fHyJGj0NPTo3LlygwaNJAtW/7Icluq5O29H3Ozhlhbt0nPdzsaWZizY+eeLNPv2r2X/n17Y2xshIFBMSaMG82Zs+cICQmlTOnSHD28HwcHO9TU1Hj27BkvEl5QunSp/M3UW/D22pdFPW/Ejh27s0yfVs/7pOfbgAnjx6TX81DCwsKpXbsWgwc7o6WlRalSpXBy6sblK1fyM0tvpajt3/fu3eOyvz/fjhyBrp4ulSpXpv/Agez4Y2umtPu9vTEzM6ONddp3065De8wtLNiz8+V3Ex8Xz3eTJtPz66/yMRfvryjW8z6d7Nn8/SymrF72xnR9O9nh+9cV9vieIDk5mW3HD3Pyz8sMdnACYJB9N5bv3sq/dwJ5kZCA28rFVDUyxsq84PZQFrX9O4PXwTOYN6yNTetGaGpq0NGmCRZmddix90SW6e8Gh/G18w80+NSEhvVNldZduXqLyCfPmTymN3p6OlQ0LsvAPnZs232c1NTUfMiNyBAbG8ukSZNo2rQpFhYWTJgwgZiYmGzTT5s2jfr162Nubq54vdqe2LVrF+3bt8fMzAxHR0euvOOx64MaVWvWrCE6OpqTJ09y/PhxypUrh7v7y6s0ERERHDp0iK+//hpLS0sMDQ2VfoAGBQXh6urKV199xaVLl1i8eDELFizg7NmzeHt7A+Dt7U2XLl1eBqyujpOTE7t2vTwAnD59moSEBNq2bcv169cZMmQIgwcP5sKFC0yfPp2ff/4ZX1/l7u1u3bopbSM8PJyrV6/Srl07xbLY2Fj69etHrVq1OHXqFJs3b+bs2bNKQx/9/f0xMjLi/PnzjBgxglmzZnHhwgX279/PunXr2L59O5cuXVKkP3LkCLNmzcLX15fKlSvj4uJCUlLSW/2tf/75B3t7e86ePUv79u3fq8xyQ0BgIKamygeZmjVrcPPWzUxpo6KiCQ8PV0pvaGhIiRIluHXrNgCWzZvhtXc3HTt2yNvAP1BAQFb5rsnNm7cypY2KinpDvm8REBBIyZIlKV++nNK2wsLCMvWgqlpAQBCmpiZKy2rWqJFNvqMJD3+olD4t38UV5V2sWDEAOnVxoMeX/6Ns2bJ84WCXhzl4P2n1/LV8v7GeP8yivItz69YtqlevxrKli9DQ0FCsP3LkKHU/+STvMvCeitr+HRQQQImSJSlX7uW+WL1G1vtiYEAgJq99NzVq1OD2zZffjfucObRo1YrPmzTJ28BzSVGs54cunsXkK3u2HvN5Y7pPa5hwNfC20rJ/7wTS0LT2y/UBL9cnJSdxKyRYsb4gKmr7d4aAoPvUqllZaVnN6hW5eftelunLli2J15Y5DHXuhqamhtK65OQUtLQ0lZarqasTEfmcqOjCOw9AQRz+N336dB48eMChQ4fw8fHhwYMHSu2Q1129epXp06dz5coVxevLL78EULQZZs+ezaVLl3BwcGDIkCGZOoPe5IMaVbq6uly/fp3du3cTHh7Ozz//zIoVKxTr//jjD5o0aYKJiQlqamr07t2bdevWKcad7tu3j08//ZTu3bujqalJ/fr12bx5M59++ukb/2737t35+++/CQ4OBtJall988QXa2tps2bKFtm3b0qFDBzQ0NGjUqBE9e/bMNKzKwcGBa9euERQUBKT1BHXp0gUdHR1FmhMnTpCQkMCYMWPQ0dGhQoUKjBw5Umlb+vr69O3bF3V1dVq2bElycjLOzs7o6enRoEEDypcvT2hoqCL9gAEDqFOnDjo6Ori5uRESEsLff//9Vn9LS0tLkU9dXd13La5cExMTi56entIyXV1dYmMzV7zY2LQrBnp6ulmkTzv4lC1bFk3Ngj8RZUxMTDb5znwQzViWXfrY2Ky39epnC4qYbGKNjXvHfL+Wfs+ubRw66IWGujrjJkzK5ag/3PvV85zTp6amsmzZCk6dOs348WNzOeoPV9T279jYWPR0M8cPEPfavhgbG4vua9+Njq4usekn3YP793MnKIjBQ1zzMOLcVRTreXhkBMlvMQSpuH4xYuKV8xUbH4+Bnn7aej39N64viIra/p0hNjYePV0dpWW6utrExb3IMn0xfT2KG2RdjmYNaqGjo8XiX7cTF/+C+2GP+e33tJFS8S8ScjfwAiQlOTnXXrkhLi4OLy8vRowYQalSpTA0NGTcuHHs3Lkzy4ZQQkICN2/epH79+llub9u2bdja2mJhYYGWlhb9+vWjdOnSmUa1vckH7QmDBg1CW1ub7du389NPP1GlShXGjh1Lhw4dSEpKYsuWLTx//pymTZsCkJKSwvPnzzl8+DAdO3bk4cOHVKxYUWmbn6Rf0XrTD0sjIyNatWrF7t276devH8eOHVP0gIWGhnL+/HkaN37Z/Z6cnEzVqsrjpsuUKYOVlRW7d+9m9OjR7Nq1i19++UXpymRoaCiRkZF8/vnnimWpqakkJiYSEREBQKlSpVBTUwPSetEASpQooUivrq6udPNi5covr5To6elRqlQpwsPD3+pvlStXTvE38pOn5xo816xVvG9Qv36miQXi4+MpVizzASjj4J1Vev0s0hcknp6eeHquUbxv0CC7fBfL9Nk35lu/GKmpKVmug7SGuip5rlmH55rfFO8b1P8063zrZ5VvXcX619Prv5ZeV1cXXV1dRo74lt59nXn+/LnSvpPfPD3X4rlmneJ9tvn+gHoeHR3NtB+m899/1/H0+JVatZSvGKtCUd2/M+jq6r1hX1Sus3p6upnSvoiPR19fn7t37rB8yVJWeqwu0D8yi2o9fx8xcXHo6yg3KPR1dYlKb2zExMehr5v9+oKgqO7fHuu98dzorXjfoG5N4l8oN6Di4xPQ13/3C9QliuuzbN4Y3JduoZPTWKpUMsK+oyX//BeUbUNMKEtISFDc3pJBW1sbbW1tpWXx8fGEh4dnuY24uDgSExOpXftlz7CJiQnx8fHcuXOHunXrKqW/fv06SUlJLF68GH9/f4oXL46TkxMDBw5EXV2d27dv4+TkpPQZU1NTrl+//tb5+qAj/40bN7CxsaFfv35ERUWxefNmRo8ezfnz5xVD8vbt26c0FGDRokWsXbuWjh07UqFCBU6ePKm0zR07dmBoaJipe/p1PXr0YO7cuZQvX55PPvmEWrVqAWBsbEy3bt346aeXN8Y+fPgwy3Gu3bp1Y/r06VhaWlKsWDHq1avHhQsXFOuNjY2pWrUqBw8eVCyLjo4mIiKCMmXKACgaVG/r1ZtBo6OjefLkCZUqVSIpKSnX/1ZucXYegLPzAMX7pUuX8d/1G0ppAgODqFev7usfpUSJEpQvX15p6Nzjx4959uwZpiYF+0Tr7OyMs7Oz4v3SpUv57z/lnSswMJB69epl+uzLfAdkzrepCSkpqTx9+pSIiAgMDQ0V2zIyMqJ48eJ5mKucOQ/oh/OAfor3S5etyFzeQUHUq5tdeZdTGlL0+HEEz549x9SkJn/+9Tc//jiTrX9sREtLC4CExES0tLQyXT3Nb87O/XF27q94v3Tp8ves5xn5fpye77T39+6FMHzEKIyNjdm08bcCcx9ZUd2/M5iYmvDs2TMiIyIok74v3gkKpLyREQbFDZTS1jQx4cZr301QUBCf1K3L8aNHiXr+nL69/geg6Alp38aKcW5udOzcKR9yk7OiWs/fx7Wg2zSqrfw91KteE7/r/6avD+DT6ibsO5t2e4Gmhia1KlflWlBApm2pSlHdvwf2sWNgn5fDypes2sH1m3eV0gTeuU+9T6q/87YTE5NISk5m9aIJit9lW3cdo2b1ipl6wwqT3By29+uvv7J06VKlZcOGDWP48OFKy/766y/69OmT5TZGjhwJKF+IzvgdkdV9VVFRUTRp0oTevXuzYMEC/vvvP7799lvU1dUZOHDgO41Gys4HdXts27aNCRMmEBERgYGBAQYGBujr66Otrc3GjRuxt7enUqVKGBsbK169e/fmypUrXL58GVtbW/799192795NcnIy165dY/bs2WhqaiqG4UVHR2f5t62srIiNjWXVqlX06NFDsbx79+54e3tz+vRpUlJSuHPnDt988w1r1qzJtI02bdqQmJjIjBkz6N69e6b11tbWxMTE4OHhQUJCAs+fP2fixImMHj36vRs4a9asITAwkLi4OGbOnEndunWpX79+nvytvGJra4u/vz8+PodJSkrCx+cw/v7+2Nl2yTK9g4M9Hp6ehIaGEhMTwzz3+VhYNKJKlcpZpi+oXubbJz3fPmn5trPNMr2DgwMeHq/ke547FhYWVKlShWrVqmJubsa8ee7ExMQQGhrK6tUedO36RT7nKme2XTrj738FH58j6fk+gr//FexsO2eZ3sHeDg+PdYSG3k/L9/yFWFiYU6VKZWrXMiU+Pp5FS5aRmJjI/QcPWLhwMV2/sFc0sgoKW9su+Ptffq2eX84+3w52eHiueaWeL1TU8+fPnzPYZSgNP/uM5csWF+gfmkVt/65StSoNzcz4Zf58YmJiuB8ayloPD+y/cMiUtlMXWy77+3Mk/bs54nOYy/7+dLbtQj9nZ46fOc3hkyc4fPIE7r8sBODwyRMFpkGVlaJaz9/GhkP7sDK3oId1ezQ0NOhh3R4rcws2+KT1gKzZt4fhTl/xmUltdLS1me06gvDISE79eTmHLatOUdu/M9h1tMTvynUOHbtIUlIyh45dxO/Kdew6WL7ztlJTUxkyZj679/mSmprKvzfu4LHBm//1KNj3lX2o3Jz9z8XFBX9/f6WXi4tLpr/ZtGlTbty4keXLysoKQGmoX8b/DQwMMm2rRYsWrF+/niZNmqClpcVnn31G3759FcP79PSyHrWQ1Wik7HxQo2rMmDFUq1YNW1tbGjVqxM6dO1m+fDlBQUH4+fnh6OiY6TP16tWjfv36rFmzhqpVq7Jq1So2bdpEkyZNGDNmDG5ubrRs2ZKyZcvSvn17vvzyS37//fdM29HU1MTR0ZEnT57QufPLg3/Dhg1ZsGABCxYs4PPPP+ebb77BxsaGsWMzj+nW1NTEwcGBu3fvKmYbfJWBgQHr1q3jwoULtG7dmnbt2qGurq5039i7ateuHa6urrRu3Zpnz56xfPly1NXV8+Rv5ZUaNaqzYL47nmvW0sbKhlWrVzNv3hyqVUt7Psf+/QewbPHyWR6DBw2iZcuWDHAeRKfOXUhISGDunNmqCv+91ahRgwUL5uPpuYY2baxYtWo18+bNeyXf+7G0bKFIP3hwer4HONOpU+e0fM+do1g/b9689Jku7enduw+WlpYMGjQo3/OVkxo1qrPAfQ6ea3+jjXUHVnmsYd7cWVSrljakdv/+g1i2tFakHzzImZYtLRkw0IVOnR1IeJHA3NlpUy7r6+uzdOkvBNwOpG37LgwcNIRmzZowbuwoVWTtjdLq+Vw816yjjVU7Vq32ZN682a+U90EsW7RRpB88aCAtW7ZggLMLnTrbkZDwgrlz0qaS37PXi7CwMHwOH6FlK2ssW7RRvAqaorh//zx3DklJyTjZOzCwbz+aNrekf/pzEW1atuLQ/rT7JarXqM4cd3fWr11LR2sb1nqsZtbcOVStVvCfTZSdolrPsxN16Ay92qf9prgRfIeuk8cwubczT/af5Pt+g3GaOp5b99Lu516zbzcLt25i18/zeeR1DPNadbCdOJyk5IL5vEEomvs3QI1qFVj483A8N3jTusswVq3bi/uMb6lW1RiAfT7naN7h7e6F1NbW4pdZI9i66xgtOg5lwvfL6d+rC04OH089VzVtbW1FZ0zG6/WhfzmpUaMGWlpa3L79crKYgIAAtLS0qF69eqb0R44cYcuWLUrLEhISFPfQ1qpVi1u3lCfgun37tmIk3NtQS5X5H4uE2JiCNaNcXtMvljaELrYAjW3PDxn3gMRGZ//sjcJI36A0ALExz1QcSf7SL1YSKLr7d2R00cp3GYP041oRredqrcxVHEn+SvVNm865qO7fcQ/PqjiS/KVX/t17zfKT/2r7XNuWxaDceSzQ+PHjCQsLY9GiRUDakMBKlSoxe3bmhv/hw4cZN24cK1eupFmzZvz5558MGTKESZMm8cUXX3Du3Dm+/fZbli9fjoWFBZs2bWL58uX4+PhQqlSpt4qn4N5NK4QQQgghhFC5lFy8pyq3TJs2jTlz5mBvb09iYiJt27blu+++U6y3tbXF3t4eV1dX2rdvz6RJk/jhhx8IDw+nbNmyDB8+nC++SLvtonnz5kybNk2x3tTUlNWrV791gwqkp6rIKKpXuqSnqmiQnqqiuX9LT1XRID1VRaueS09VwXTp16zvu3sfn7u8/TTlHxPpqRJCCCGEEEJkKzWXni9VmEmjSgghhBBCCJGt3JxSvbDK/yfJCiGEEEIIIUQhIj1VQgghhBBCiGzJ8L+cSaNKCCGEEEIIka2COPtfQSPD/4QQQgghhBDiA0hPlRBCCCGEECJbMvwvZ9KoEkIIIYQQQmQrNSVJ1SEUeDL8TwghhBBCCCE+gPRUCSGEEEIIIbIlw/9yJo0qIYQQQgghRLZk9r+cyfA/IYQQQgghhPgAaqmpqamqDkIIIYQQQghRMJ346bNc25bV93/n2rYKEhn+J4QQQgghhMhWSor0weREGlVFRExsrKpDyFfF9PUByXdRIfkumvmOjYlScST5S79YcQBiY2NUHEn+0tcvBhTd8lZrZa7iSPJXqu8VAJ5GR6s4kvxVysBA1SGIDySNKiGEEEIIIUS2UlJSVB1CgSeNKiGEEEIIIUS2ZPhfzmT2PyGEEEIIIYT4ANJTJYQQQgghhMiW9FTlTBpVQgghhBBCiGylpMo9VTmR4X9CCCGEEEII8QGkp0oIIYQQQgiRLRn+lzNpVAkhhBBCCCGyJVOq50yG/wkhhBBCCCHEB5CeKiGEEEIIIUS2ZPhfzqRRJYQQQgghhMiWNKpylm/D/5KTk7l3716+/K07d+7ky98RQgghhBBCiHdqVNnY2NCgQQPMzc0zvfz8/N742dGjR7N7924A7t+/j7m5Offv33/vwLOzadMmvvvuu/f+/JIlS+jdu7fi/a1bt/j2229p2rQp5ubmdOjQgYULF5KQkPBW29u5cyc2NjbvHU9BExkZyZjRo2ndqhU21tbMmzePpKSkLNOe9vWlZ48eWDZvjqOjI6dOncoy3by5c5n2/fd5GfYHy818r1u3jk4dO2LZvDmDBg4s0BcBJN9Sz4tCeWfwPX2aHj2/pLllSxwdu3PqlG+2aZOTk1m4cBFt23WgRcvWjBo9hkePHmdKF/nkCQ4OXXM8R6qSr+9pevToSfPmlm+sw5CR74W0bduOFi1aMmrUaB49epQpXWTkExwcHAp2votoeZctVZpbv++hjZlFtmk6N2vJ3+u2Eu1zln837MDWspXS+vG9+nJvx0Gifc5yfPFqalepltdhv7fIyEjGjxlD2zZt6GBjwwJ392yPa2dOn6ZXz560adGCL52cOP3KvvDixQsWuLtj17kzNq1bM6BPH/wuXcqvbKhcSkpKrr0Kq3fuqfrxxx+5cuVKplfjxo3f+LknT54o/l+xYkWuXLlCxYoV3z3iHERGRubatqKjo+nduzcNGzbkxIkTXL58mWXLlnHs2DF+/PHHXPs7HxO3iRPR09fnkI8P6zds4OKFC2zatClTuuC7dxk/fjxDhg7llK8vrq6uuE2cyMOHDxVpnj59ypQpU/j999/zMwvvJbfy7bV3L1t+/51ly5dz/MQJ6taty/hx40hNLZjd6pJvqedFobwB7gYHM378RIYOGYLvqRO4urow0c1NqSxf5eHhybnz59m0cT2HDu5HR0eHn6ZPV0rz559/0q9vf+6FhORHFt7L3bvBjB8/nqFDh+DrewpXV1cmTnxTvj04d+48mzZt5NChg2n5/imLfPfry717BTjfRbS8LRs05NyKdZhWrpptGtPKVdkxYx7feS6nZOdWTFuzkq0/zqFi2XIA9Olkzwinr+k4diiGdlb43/iPHTPc8ysL72yKmxv6+vrsO3iQtevXc+niRX7fvDlTuuDgYCZNmIDLkCEcPXmSQS4uTH6lTixbsoS///oLz7VrOXz8OA5duzJ21CjCHjzI7yypREpKaq69CqtcHf536NAhbG1tsbCwoHPnzixfvhyAKVOm4Ofnx6+//oqrqyshISHUqVOHkPQDT506dfjjjz/o2LEjDRs2xNXVlWvXrvHVV19hbm6Ok5MTd+/eBSAhIYE5c+bQuXNnzM3Nad68OdOnTyc1NZVdu3bx66+/4ufnp2jkRUdH89NPP9GmTRuaN2/O6NGjefz45dWly5cv4+TkhJmZGV999ZUiJoDAwECePHlC165d0dPTQ01NjVq1ajFlyhRKlCihSHfs2DG++uormjdvTsOGDfnmm2+yvSq7fft2HB0dFT1fLi4uiobgkiVLGDBgAE5OTjRp0oSlS5diYWHBixcvFJ8/ePAg1tbWKvlxEhwcjJ+fH6NGjkRPT4/KlSszcNAg/tiyJVNaL29vzM3Nsba2RlNTkw4dOtCoUSN27NgBQGxsLI7dulG8eHHatm2b31l5J7mZ7127dtGjZ09MTEzQ0dFh+IgRhIWFFcirmpJvqedFobwzeHt5Y25uhrW1VXqe2tOokQU7duzKMv2u3Xvo368vxsbGGBgYMGH8OM6cOas4h+z18mbS5Kl8++3Q/MvEe/D29npjWb5u167d9O/f72W+J4znzJkzL/O914tJkybz7bff5mc23llRLO8+nezZ/P0spqxe9sZ0fTvZ4fvXFfb4niA5OZltxw9z8s/LDHZwAmCQfTeW797Kv3cCeZGQgNvKxVQ1MsbK/M0X11Xh3r17XPb3Z9jIkejq6VGpcmUGDBzI9j/+yJR2v7c3Dc3MaJO+L7Tr0IFGFhbs3rkTSOupGuzqipGxMRoaGnR1dERbW5vr//2X39kSBVSuNari4+MZP34833//Pf7+/syfP5/Vq1fz999/M3PmTBo3boyLiwsrV67M8vNeXl788ccfHD58GH9/f4YOHcrMmTM5c+YM2trais/99ttv+Pr68ttvv3HlyhWWL1/Oli1bOH/+PN26dcPFxYXGjRsrTt6TJ0/m7t277Ny5kyNHjmBgYMCwYcNITU3lyZMnuLi40LFjRy5dusT48eM5cuSIIqZPPvkEExMTvv76axYuXMipU6d4+vQpzZo1Y+LEiQCEhYUxcuRIBg8ezLlz5zhx4gSpqaksW5b5oPX3338zY8YMfvjhBy5cuMCBAwe4c+cO69evV6Q5d+4c48aN4/jx4wwePBgNDQ2OHj2qWL979266deuGmprahxfaOwoMCKBkyZKUK19esaxmzZqEhYURFRWVKa2pqanSspo1a3Lr5k0AtLW12bZ9O27pV5AKstzMd8Br67W0tKhStSo309cXJJJvqedFobwzBAQGZpGnGty8lTnmqKhowsPDldIbGhpSokQJbt26DYBl82Z47d1Nx44d8jbwDxQQkFW+a3Lz5q1MaaOiot6Q77T0lpbN8fLaS8eOHfM28A9UFMv70MWzmHxlz9ZjPm9M92kNE64G3lZa9u+dQBqa1n65PuDl+qTkJG6FBCvWFySBAQGUKFmScuXKKZbVqFHjrY9rNWrUUBzXJk2ZgmWLFop1fhcvEh0dTe06dfIwBwWH9FTl7J1n//vxxx/5+eeflZZVqFCBbdu2oaury/bt20lJSaFRo0b4+/ujrv527bZvvvmGUqVKAVCrVi3q1auHiYkJAM2aNcPf3x+Anj170q1bNwwNDXn48CHx8fEUK1aM8PDwTNuMiIjg0KFDHDhwAENDQyCtkdW4cWP++ecfbt26hZ6eHoMGDUJNTQ0LCwucnJz4L/2qg7a2Nlu3bmXz5s0cO3YMT09PkpKSaNSoEW5ubnz22WeUKVOGffv2UbVqVaKjowkLC6N06dJZxlO7dm28vb2pXLkyz5494+HDh5QpU0YpbZUqVWjevLnivZ2dHXv27KFLly5ERERw+vRppkyZ8lbfaW6LiY1FT09PaZmuri6QdkW+ePHiL9PGxGSZNjY2FgBNTU1FmRR0uZnv2Gy2FZe+viCRfL8k9bzwlneGmJisY46NjcuUNjY2BgA9Pd0s0qflsWzZsnkUae7KqSxflbHsTek/nnwXvfIOj4x4q3TF9YsRE6/8PcTGx2Ogl3ZhqLie/hvXFySxsbHo6WYut4x1rx7XYmNj0X2tTujo6hIXl7lOXL16lUlubgwcPJiKlSrlQeQFT0pq4b0XKre8c6Nq2rRpODo6Zrnu999/Z/ny5YwdO5bo6Gg6duzI1KlTKVmyZI7bzWhQAWhoaCh9Rl1dXTHcLS4ujp9++olLly5hbGxMvXr1SE1NzfLGt9DQUCCtIfYqDQ0NQkJCCA8Pp0KFCkq9PlWrVlU0qgAMDAwYPHgwgwcPJiEhgWvXrrF69Wr69+/PsWPHKFGiBN7e3mzZsgU1NTVq165NdHQ0mpqZv1p1dXXWr1+Pl5cX+vr61KlTh+joaKWhfOVfuUoM4OjoyJdffklERAR79+6lUaNGVKlSJcfvMy/o6ekRHx+vtCzj/etX4bNLW6xYsbwNMg/kZr6zW69fAL8XyfdLUs8LX3l7eq7Bc81axfsG9etnk6fMPxQzfoxnnceC98PyVZ6ennh6rlG8b9Agu3xnLqs35lu/4JRtVopqeb+PmLg49HWUGyL6urpEpTcuY+Lj0NfNfn1Boqerm+1xrdhrxzXdLI5bL+LjMx3/9uzaxcL58xns6kqvb77Jg6jFxyrXhv9FR0fz8OFD5s+fz9mzZ/njjz+4du1atsP9Xve2w9mmTp2Knp4ep0+fxsvLi1mzZmU7k4iRkREABw4cwM/PT/HauXMn1tbWGBsbExoaqvT5sLAwxf8XLlxIjx49FO+1tbVp1KgR8+bNIzo6muDgYA4cOMDGjRvZsGEDJ0+eZPXq1dSrVy/LeNatW8eZM2fw8vLi6NGjLF++nEqvXeF4/XuoX78+pqamHDp0iH379uHk5PRW31NeMDUx4enTp0REvLzaFRgYiJGRkdLVHgATU1MCAgKUlgUGBip6Hz8muZlvExMTAl9Zn5iYyL3g4AL5vUi+pZ4X5vJ2dh7A2TO+ileDBvUJCAhUShMYGJRlzCVKlKB8+fJK6R8/fsyzZ88wNTHNlL4gcXZ25uzZM4pXgwYN3roOv8z3y/SKfJsWnLLNSlEt7/dxLeg2n9ZQ/h7qVa/JtcCA9PUBfFr95XpNDU1qVa7KtSDlelQQ1DQ15dmzZ0rHtaCgIMobGWHw+nHNxISgQOU6ERQURM30OpGcnMysmTNZvnQpc+fPL3INKhn+l7Nca1TFxMQwaNAgvLy8SE1NpXz58qirq1O6dGkgrUHy+vjV9xEdHY2Ojg7q6upER0czd+5coqOjSUxMBEBHR0fR+2NkZISVlRUzZ87kyZMnJCYmsmLFCrp3787z58+xsbEhNTWVJUuWKHqhtm3bpvhbnTt35saNG/zyyy+EhoaSmprK48ePWbZsGdWqVaNOnTpERUWhrq6Orq4uqampnDp1it27dyvieT12TU1NtLS0SEpKYs+ePfj6+maZ9lWOjo5s3bqVO3fu0KGD6sZrV61WDTNzc9znzSMmJobQ0FA8Vq/mi65dM6W1tbXF398fHx8fkpKS8PHxwd/fH1s7u/wP/APlZr6/+OILtmzZws0bN3jx4gVLFi+mTJkyNGrUKJ9zlTPJt9TzolDeGV7m6XB6ntLu77Wz7ZJlegcHezw8PQkNDSUmJoZ57vOxsGhElSqV8znyD5NdWdrZ2WaZ3sHBAQ+PV/I9zx0LCwuVjaB4X0W1vN/GhkP7sDK3oId1ezQ0NOhh3R4rcws2+HgDsGbfHoY7fcVnJrXR0dZmtusIwiMjOfXnZRVHnlnVqlVpaGbGwvnziYmJ4X5oKGs8PHD44otMaTt36cJlf3+OpO8LR3x8uOzvTxfbtH3hl/nzOXfmDOs2bqRJ06b5nRWVkynVc/bOjapp06Zl+ZyqvXv3snjxYlavXk2jRo2ws7OjWbNm9OvXD4CuXbuyY8cOevXq9UEBT506levXr9OkSRM6depEdHQ0rVq1UtwAbW1tzdOnT7GwsOD58+fMnTuXEiVK0LVrV5o1a8bJkyfx8PCgXLlylChRAk9PT86dO0eTJk2YMmWK0s21n3zyCRs3buTmzZt0796dhg0b0rVrV54+fcqGDRvQ1tamW7duWFpaYmtrS7NmzVixYgV9+/YlKCgo07OsBgwYQIUKFbC2tqZVq1bs3buXXr165Xjztr29Pbdv36ZLly6ZxoDnt3nz5pGcnIy9nR19evfG0tKSQYMGAdDC0pL9+/cDaTd3zl+wgDWenli1acPqVauYN28e1aoV3GdZvElu5fuLrl353//+x9ixY2lrY8P1GzdYvGQJWlpaKsvbm0i+pZ4XhfIGqFGjOgvmu+O5Zi1trGxYtXo18+bNUeRp//4DWLZ4+ayewYMG0bJlSwY4D6JT5y4kJCQwd85sVYX/3mrUqMGCBfPx9FxDmzZWrFq1Wqks9+/fj6Xly5vzBw9Oz/cAZzp16pyW77lzVBX+eyuq5Z2dqENn6NW+MwA3gu/QdfIYJvd25sn+k3zfbzBOU8dz614wAGv27Wbh1k3s+nk+j7yOYV6rDrYTh5OUnPWzn1Rt1ty5JCcl0c3engF9+9KseXMGDBwIgFXLlhxMP65Vr1GDue7urFu7lvbW1nh6eDBr7lyqVqvG0ydP2L5tGxEREXzdowdWLVsqXhmfF0IttSA/OEQAaV3OLVu2ZOXKlTRs2PC9thFTgG8QzwsZY6Ul30WD5Lto5js25sNHP3xM9IulDVeKLYD3ruSljPu1imp5q7UyV3Ek+SvV9woAT6OjVRxJ/iplYKDqEN5o3UDjXNtWP4+wnBN9hN55ogqRv27dusWBAwcwNjZ+7waVEEIIIYQQ76sw3wuVW6RRVcC5uLgAsHjxYhVHIoQQQgghRMEQGxvL9OnTOXbsGElJSbRt25Zp06ZlOXvp999/j5eXl9Ky+Ph4LC0t8fT0BNLmUrh//77S46C2b9/+1pMsSaOqgDt27JiqQxBCCCGEEEVYQZxgYvr06Tx48IBDhw6RnJzMqFGjcHd3Z9q0aZnS/vTTT/z000+K96dPn2bs2LG4ubkBaZPJBQUFcfTo0Uwzc7+tXJv9TwghhBBCCFH4FLQp1ePi4vDy8mLEiBGUKlUKQ0NDxo0bx86dO7N8YPOrIiMjGTduHFOmTKFWrVoAXLt2jVKlSr13gwqkp0oIIYQQQgiRTxISEjLNkK2trY22trbSsvj4eMLDw7PcRlxcHImJidSuXVuxzMTEhPj4eO7cuUPdunWz/fvu7u7Ur18fBwcHxbKrV6+ip6fHN998w61bt6hUqRLDhw/H2tr6rfMljSohhBBCCCFEtnJzoopff/2VpUuXKi0bNmwYw4cPV1r2119/0adPnyy3MXLkSAD002eDBRSPHYqJyX6G1Hv37rF3716l59ICqKmp0aBBA8aMGUPFihU5ePAgw4cPZ+PGjZiZmb1VvqRRJYQQQgghhMhWbt5T5eLiQv/+/ZWWvd5LBdC0aVNu3LiR5Tb+/fdfFi1aRFxcnGJiioxhfwZvmJ5+x44dmJubZ+rJGpj+7LIMDg4OeHt7c+jQobduVMk9VUIIIYQQQoh8oa2tjYGBgdIrq0bVm9SoUQMtLS1u376tWBYQEICWlhbVq1fP9nM+Pj588cUXmZZ7enpy7tw5pWUJCQno6Oi8dUzSqBJCCCGEEEJkKyU1NddeuUFPT4/OnTvj7u5OZGQkkZGRuLu7Y2dnh66ubpafefLkCQEBAXz++eeZ1j148IAff/yRe/fukZSUxPbt27ly5QrdunV765hk+J8QQgghhBAiWwVxSvVp06YxZ84c7O3tSUxMpG3btnz33XeK9ba2ttjb2+Pq6gpASEgIAEZGRpm2NWHCBNTV1enVqxdRUVGYmpqyatUqqlWr9tbxqKWm5lKTURRoMbGxqg4hXxVLv3FR8l00SL6LZr5jY6JUHEn+0i9WHIDY2Oxvwi6M9PXT7pcoquWt1spcxZHkr1TfKwA8jY5WcST5q9Qb7gMqCBZ/WSLXtjXij+e5tq2CRHqqhBBCCCGEENnKzdn/CitpVAkhhBBCCCGyJY2qnMlEFUIIIYQQQgjxAaSnqogo9srD0YoSyXfRIvkuWjLuOSlqMu4xKmqKanln3GNU1BT0e4yKGumpypk0qoQQQgghhBDZkjZVzqRRVURExRSt2aKKpz9dOzYqXMWR5C/94mnThLoe2aPiSPLXynZpD/IrqrODXY18qOJI8leDMuUBiLixWcWR5C/DOr0AiI15puJI8pd+sZIAxD08q+JI8pdeeUug6M6CV1RnPRQfL2lUCSGEEEIIIbIlPVU5k0aVEEIIIYQQIlvJ0qrKkcz+J4QQQgghhBAfQHqqhBBCCCGEENmSjqqcSaNKCCGEEEIIkS1pVOVMhv8JIYQQQgghxAeQniohhBBCCCFEtqSnKmfSqBJCCCGEEEJkKyVF1REUfDL8TwghhBBCCCE+gPRUCSGEEEIIIbKVnCrj/3IiPVUqcOfOHVWHIIQQQgghxFtJSc29V2ElPVVvycbGhkePHqGpmfkrW716NY0bN872s7a2tri4uODg4MCmTZs4ePAgGzZseK84lixZwsWLF9/78x8qMjKSmTNm4O/nh4amJl06d2bk6NFZfi+nT59myeLFhIaEYGxszMhRo2jVunWmdO7z5hEdHc0PP/6YH1n4IL6nz7F4yUpCQh9Qwbg8o0YOpXUryyzTJicns3jpr3jvO0R8fDyfN27ElMljKVe2LACHfI4y5bsZaGtrKz5jY9WKGdOn5kte3kZCVDQ3Nu/g6a0A1NTVMWrSCJNutqhraGT7mej7YfjPXcJnQwdQuraJYnnoqXPcO3qKhOdR6JYtQ02HTpRtUC8/svHefE+fZvHiJYSEhFLB2JhRo0bSunWrLNMmJyezePFSvPftSyvvzxszZfJkypUrq5Qu8skT+vXtz/ffT33jcUMVnkU+YeWcufxz+U80NDRo3bEDfYYPRSOL/TvD+eMnWL90Oct3bFUsS0lJoU+7TqSmpqKmpqZY7rFvD7p6enmahw9x1u8Wy387wv2wJxiVK8mw/u1p8XntLNM+ex7LkjU+nL98m8TEZGqbVGD4gA7UrmkMwJ2Qx/yy+iD/3gyhmJ4OX3SyoE/3Vqirq2W5PVXyPX2GxYuXvlLPh+dQz5fhvW//K/XcTVHPb9y8ycKFi/nvv+toaWnSrFlTxo4ZTenSpfIxR2/H99xfLFq5jZD7j6hgZMjoIT1p3cLsjZ9JTk5h/PfLqGVShSEDuiqWB919wNxFm7j6XyAG+no4fWGF8ze2qKsXnGvXkZGRzJoxg8v+/mhoaNCpSxdGjBqV5fn7zOnTLFu8mNDQUIyNjRk+ciQt08/fL168YNmSJRw7epTYmBiqV6/O0OHDafz55/mdpXdStlRpzq1Yx8A5P3HyT/8s03Ru1pI5riOoWbEyweEPGL/iF/ad9VWsH9+rLyOcvqZ08RJcuv4PLvNmcPPe3fzKgijgCs7e/hH48ccfuXLlSqZXTj+M9u3bh4ODA5B2UPuYTXJzQ19Pj4OHDvHb+vVcuHiRzZs2ZUoXHBzMxPHjGTJkCCdOncLF1RU3NzcePnyoSPP06VO+mzKFLb//np9ZeG93g+8xfuJ3DB0yEN8T+3F1GcBEt2k8fPgoy/Qenus5d/4Sm9av4tD+nejo6PDT9LmK9f/8ex3bLh0463tI8SpIDSqAfzw3oqGjjeXPU7GYMJwn128Rcsw32/TJCQn8u3YzKYmJSssfnPfjzv7D1Ov/Na0WTKdaR2uueWzgxdNneZ2F93Y3OJjx4ycydMgQfE+dwNXVhYmv1eFXeXh4cu78eTZtXM+hg/vTy3u6Upo///yTfn37cy8kJD+y8M4WfDcNXT09VnvtYrbnKv7288N7y9Ys0yYlJbF74yYWfvcDqa9degwJukNSUhLrfPaz8ZiP4lWQG1T37kcwefZWBv3PGp8tbgzsZcXUOdt4FPE8y/Q/L9nL0+exbFo6FO/14/isbhXG/LCRuPgEYuMSGD1tI0blSrBn7RiWz+7PUd9/WPvHyXzOVc7S6rkbQ4e44HvqGK6ug5joNvkN9XxNej3/jUMHvdPr+UwA4uPjGTZsFA0/+4wjhw+wfdsWnj17zg8//JSfWXord++FMW7qMoY6O3L6wHJcB3RlwrQVhD96ku1nHoRHMGz8Ao6duqy0PDY2nqFj52NsZMjhnQtZs2wSPkcvsuq3vXmdjXcyxc0NfX199h08yNr167l08SK/b96cKV1wcDCTJkzAZcgQjp48ySAXFya/cuxbtmQJf//1F55r13L4+HEcunZl7KhRhD14kN9ZemuWDRpybsU6TCtXzTaNaeWq7Jgxj+88l1OycyumrVnJ1h/nULFsOQD6dLJnhNPXdBw7FEM7K/xv/MeOGe75lQWVS0nJvVdhJY2qXHD37l3Mzc3ZlN64iI6Opn379syfPx9I6+XauXMnu3bt4tdff8XPz0/REIuOjuann36iTZs2NG/enNGjR/P48WPFti9fvoyTkxNmZmZ89dVXhKjwx9i94GD8/fwYMWoUunp6VK5cmYEDB7L1jz8ypfX28sLM3Bwra2s0NTVp36EDFo0asWvHDgBiY2NxcnTEoHhxbNq2ze+svBdv74OYm32GtVUrNDU16dDehkYWZuzY5ZVl+l17vOnftxfGxkYYGBRjwrgRnDl7gZCQ+0Bao6pe3Tr5mYV3EvvwMU9vBWLStQsa2trolTWkWue2hJw8m+1nbm7ZRdmGn2Zafu/oKWrYd6RE9aqoqalh1Ngci7HfoqGrm5dZ+CDeXt6Ym5thbW2VVt4d2tOokQU7duzKMv2u3Xvo368vxsbGGBgYMGH8OM6cOavYZ/d6eTNp8lS+/XZo/mXiHTy4F8I/l6/Q+9uh6OjqYlSpIt379+XAjp1Zpp8+cgzX/K/Qtfc3mdbd/u8/qpmaoKWllddh55r9x/6iYb2qtGn2CZoa6rRt+Snm9auz51DmK9oZPXCD/mdNyRL6aGlp0KubJZFPYwgOjeDvf4N58iyGcS626OlqU6F8Kfr2bMWuA36kFrD7Ery99mVRzxuxY8fuLNOn1fM+6cc1AyaMH5Nez0MJCwundu1aDB7sjJaWFqVKlcLJqRuXr1zJzyy9Fa+DZzBvWBub1o3Q1NSgo00TLMzqsGPviSzT3w0O42vnH2jwqQkN65sqrbty9RaRT54zeUxv9PR0qGhcloF97Ni2+3iBKe979+5x2d+fYSNHoqunR6XKlRkwcCDbszh/7/f2pqGZGW3Sz9/tOnSgkYUFu3emHQtevHjBYFdXjIyN0dDQoKujI9ra2lz/77/8ztZb6dPJns3fz2LK6mVvTNe3kx2+f11hj+8JkpOT2Xb8MCf/vMxgBycABtl3Y/nurfx7J5AXCQm4rVxMVSNjrMwL1oiDvCLD/3ImjapcUK1aNaZNm4a7uzv37t1j2rRplC9fnlGjRiml69atGy4uLjRu3Bg/Pz8AJk+ezN27d9m5cydHjhzBwMCAYcOGkZqaypMnT3BxcaFjx45cunSJ8ePHc+TIERXkME1AYCAlS5akXLlyimU1a9YkLCyMqKgopbSBgYGYmiqfeGrUrMnNW7cA0NbWZuu2bUxMv3L2MQgIvIOpaU2lZTVrVOPmzduZ0kZFRxMe/kgpvaFhGUqUKM6t2wGkpKRw/fpNfE+fo7NdDzp2cWL6zHk8fx6VaVuqEvMgHM1i+uiUKqlYVszYiBdPnpIYG5cpfdgFf+IeRVCjS3ul5ckJCcQ8CEdNTZ3LC1bgO+EH/N2XkZyQiKauTp7n430FZFGHa9aswc1bNzOljYqKJjw8XCm9oaEhJUqU4NattPph2bwZXnt307Fjh7wN/D3dCwrCoEQJyrwyXLFy9eo8DgsnJipzvRwxbSpTF7pjXLlipnUB/10n4cULJg4YxIDOdnw3ZBjX/76ap/F/qKDgR5hUK6+0rHqVstwKCs+UVk1NjdmTv6SOSQXFsuNn/kVPV4tqlcuSnJKClqYGmpovT7HqampEPo0hKiY+7zLxHtLquYnSsjfX84dZ1PPi3Lp1i+rVq7Fs6SI0XhkefOTIUep+8kneZeA9BQTdp1bNykrLalavyM3b97JMX7ZsSby2zGGoczc0NZWHPycnp6Clpam0XE1dnYjI50RFx+Z+8O8hMCCAEq+dv2vUqJH1+TsgIPP5u0YNbt1MqxOTpkzBskULxTq/ixeJjo6mdp2CeZHw0MWzmHxlz9ZjPm9M92kNE64GKp/P/70TSEPT2i/XB7xcn5ScxK2QYMV6IaRR9Q5+/PFHGjdurPSyt7cHoGvXrrRr146+ffty9uxZFixYoHRiyUpERASHDh1iypQpGBoaUqxYMSZPnszVq1f5559/OHHiBHp6egwaNAgtLS0sLCxwcnLKj6xmKTYmJtPwHd30nobY2NhMafWySBuXnk5TUxNDQ8M8jDb3xcTGZpmn2LjMDYzYmLR86um+nl6H2Ng4njx5Sp06tWjX1oqd2zewbs1ygoNDmPLd9EzbUpXkFy/QeOV+L0DxPvnFC6XlMWEPCdx7kHr9v0bttXsIkmLjIDWV4KMnqfNVN1r8PBWjxmb8vcyTuIiCOxw2Jiab8s6iQRkbGwOAnp5uFunT6kLZsmWzvHehoIiPjUX3tfh10vfv+CzquGH58pmWZdDW0aFWvXpMmP0zK3btoHHLFswYPZbw+/dzN+hcFBv3Aj1d5fquq6NFXHxCjp/1vXCDBasOMM7VFl0dLT6rWxUdbU1WrD9K/ItEHjx8yqZdaT28L14k5Un87+v96nnO6VNTU1m2bAWnTp1m/PixuRz1h4uNjUfvtYs6urraxMW9yDJ9MX09ihtkfQHQrEEtdHS0WPzrduLiX3A/7DG//X4AgPgXOdef/BAbG4uebubjU8a619O+fq7X0dUlLovjwNWrV5nk5sbAwYOpWKlSLkedO8IjI0hOTs4xXXH9YsTEK+cxNj4eA720ci+up//G9YWd9FTlrOCe4QugadOm4ejomO363r17s3fvXrp27YqRkVGO2wsNDQWgZ8+eSss1NDQICQkhPDycChUqKN3oXbVqVf5TURe7np4e8fHKV1kz3hd7rbdJN5u0+sWK5W2QuchzzQY8125UvG9Qv26WeXo97/DyR0fm9C/Q19fD0LAMa1YvfZneWJdRI1zp3c+VmJhYihVT/UFaQ1ublATlHwTJ6e9f7WFKTkzknzWbMO3ugG6Z0pm2o5bekKhi04piFdNu4q9s1YJQ3/NE/nOdSq2znugjv3l6rsFzzVrF+wb162dd3lmUTfblHY9+ASjLt6Gjq8eLeOUflC/S86P7jr3JfUcMU3r/xf++5vi+/Vw+c47OPVR3YehVv231Zf32l/cH1qtdmfgXyvcCxr9IRF8v+97U1NRU1m31ZeOO00we4UC7VvUBKG6gy/xp/2PJGh+6DlhI5Qpl6GzdkP9u3ae4gWqHvHp6rsVzzTrF+wb1P831eh4dHc20H6bz33/X8fT4lVq1lHs9VMFjvTeeG70V7xvUrUn8axeH4uMT0Nd/9/IpUVyfZfPG4L50C52cxlKlkhH2HS3557+gbBti+U1PV/eDzt8v4uMzjSrZs2sXC+fPZ7CrK72+yTwM+GMTExeHvo5y+evr6hKVfjEhJj4Ofd3s1xd2MqV6zqRRlUsSEhL4/vvvsbOz49ChQ3Tp0oU2bdq88TMZDa8DBw4odcnfvn2bKlWqcODAAUJDQ0lJSVHMIBQWFpZ3mciBiakpz54+JSIiQtHLFBgYiJGREQbFiyunNTHhxvXrSsuCAgOpW69gz/b2KucBvXEe0Fvxfumy1fx3Q3lITGDQ3SzviypRojjly5cjIDBIMQTw8eMInj17jqlpTW7eCuDAwcOMGOaiaDQnJCairq6OllbB2C2LVTQiMSaWhOdRaJdIK9+YsHB0SpVE85WrmFF3Q4h7+Igbm7ZxY9M2xfKrK9Zi1NSCOl91Q6u4ASlJr10pTE2hIB2jnZ0H4Ow8QPF+6dJl/Hf9hlKawMAg6tWrm+mzJUqUoHz58gQEvBwy+PjxY549e4apiep/UL6NqiY1iHr2jKeRkZQqUwaAkDt3MCxfnmIGBu+0rc0rV9HM2oqadV4Oi0lKSERbp+AM9+zbsxV9e76c4W7lhqPcDFA+vt6595hPTCu8/lEgrcH1/bztBN59yPJZ/ZWGAiYmJpOcksKSGX0U+/fO/ZeoUaUcujqqvc/M2bk/zs79Fe+XLl3+nvU8bchgWj1/jqlJ2vt790IYPmIUxsbGbNr4W4GZ9W9gHzsG9rFTvF+yagfXbyrP2hZ45z71Pqn+zttOTEwiKTmZ1YsmKMp7665j1KxeMVNvmKrUNDXl2bNnSufvoKAgyr/t+TsoiE/qptWJ5ORk5s6ezYljx5g7fz5NmjbNn0zksWtBt2lUW7ne16teE7/r/6avD+DT6iaK2QA1NTSpVbkq14IC8j1WUTDJ8L9c4u7uTnJyMrNmzWLMmDG4ubnx6FHmWeF0dHSIjo4mNTUVIyMjrKysmDlzJk+ePCExMZEVK1bQvXt3nj9/jo2NDampqSxZsoSEhASuXbvGtm3bsvjr+aNq1aqYmZkx392dmJgYQkND8fDwwOGLLzKltbW1xd/fn8M+PiQlJXHYxwd/f3+62NqqIPLcYWvbAX//K/gcPkZSUhI+h4/h738Fuy4ds0zvYN8ZD8/1hIbeJyYmlnnzl2DRyIwqlStRskRx/ti6i9/W/05SUhIPwsJZuGgF9nadlKZYVyX98uUoaVKdW9v3khQfT9zjSO4eOEoFS+Vpc0uZ1qDNLz/Tyv0nxQugwZD+1PmqGwCVWjbjzoEjRN27T0pyMiHHT/Pi6TPKZTGpRUGRUYd9fA6nlbfPYfz9/bGz7ZJlegcHezw8PQkNDSUmJoZ57vOxsGhElSqVs0xf0FSoUoVPGn7G2l8WExcTS/j9+2xf+xs29u++zwYHBrH2l8U8iYggMSGBbZ5riY2NoUmbzI9UKCg6WTfk8rU7HD39D0nJKRw9/Q+Xr92hk3XDLNN/P2874Y+es2bBYKUGFUAqqYz+fiPeh6+QmprK9dv3+W2bLz0dCt6PT1vbLvj7X36tnl/GzrZzlukdHOzw8FzzSj1fqKjnz58/Z7DLUBp+9hnLly0uMA2qrNh1tMTvynUOHbtIUlIyh45dxO/Kdew6vHvPeWpqKkPGzGf3Pl9SU1P598YdPDZ4878eBef+yapVq9LQzIyF8+cTExPD/dBQ1mRz/u7cpQuX/f05kn7+PuLjw+VXzt+/zJ/PuTNnWLdxY6FpUAFsOLQPK3MLeli3R0NDgx7W7bEyt2CDT1oP55p9exju9BWfmdRGR1ub2a4jCI+M5NSfl3PYcuEgs//lTC21oExNU8C96TlVzs7OrFy5kq1bt1KvXj1SU1Pp27cvWlpaeHh40LZtW4YNG4ajoyO3bt1i8ODBPHv2jBMnTpCamsr8+fM5efIk0dHR1KpVi3HjxilmB7x+/To//PAD169fp1q1ajRs2JCgoKB3fk5VVEzudE9HREQwd84c/Pz8UFdTw9bOjuEjRqChoUGrFi2YPGUKnbuk/eg8d/YsSxYvJiQkBOMKFRgxciQtW7bMtM0fpk1L+zcXn1NVPH2YYWxU5pvMP8TZcxdZtGSl4nkuI0e40qplcwD2H/Bhxs/zOet7CIDEpCSWr/Bg/4HDxMbE0rixOd9NGU+Z9CFyfv5/smTZrwQEBKGtrU3HDm0ZNcIVnQ+4mq9fPK330/XIng/MaZqE51Hc3LqbpzcDQE0N46YWmHTtgpq6OqdGT6X2144YN2mU6XPHv52A2UgXxXOqUlNSuHfMl/tnLpDw9Dn6xuUxdbKnlGmNXIlzZbu0HwaxMbk70cfZs+dYtHgJISEhVKhgzMiRI2iVXof37z/AjJk/c/ZM2lXLxMQklq9Ywf79B4iNjaFx48Z8N3UKZdJ7fV5l3qgxq1et/ODnVOkXS7vCfDUy6+mv39XTyEg83Bfyz+UrqKmr0aZTJ7751hUNDQ2+senA4InjaP3aRBvH9+1nq8daVux6ecEn6tlz1i9ZyuWz53kRH49pvbr0GzWc6qa502vXoEza/VwRNzJPB/0hzl++zYrfjhCa/pyqb/u1x7JxLQAOnfibucu9Obp1MjcCHtB/9Cq0tTTQ0FC+Njl/2v8w+7QaV67dZbHnIe7dj6BUyWJ86dCUHnYf9gPUsE4vAGJjcvdRBGn1PP05VRWMGTlyOK1apk1EsH//QWbMnMXZM2nTwafV85Xs338wvZ5b8N3UyZQpU4YNGzexYMEidHV1lYatA4rPvw/9YmmT5cQ9zH7m0fdx9sJVflm5jZDQR1QwNmTUkB60ap7WiN7nc44Z7r9xzmdlps85D59NY/NPlJ5T5f/nDdyX/M7de+GUKV2c//XowNfd231QfHrl0xp4T6OjP2g7GSIiInCfMwd/Pz/U1dXpbGvLsPTzt1XLlrhNnkyn9PP3+bNnWbpkSdpzJitUYNiIEbRo2ZKnT57QuUOH9FEVyr2ur37+Q5RK7xlXa2X+wdt6XarvFayGD1Q8pyrq0Blc3Gew+XDaPXAdmjRnjutITCpV5m7YAyasWMSB86cVnx/zZW++dexJuVKlufTfP7jOn8mte8G5FltB9m3r3Bu6vOxUwZqwJ7dIo6qIyK1G1ccirxpVBV1uN6o+FnnVqCrocrtR9bHIq0ZVQZdXjaqCLq8aVQVdbjeqPhZ52agqyKRR9fErGDdvCCGEEEIIIQqkwjxrX26RRpUQQgghhBAiW4X5XqjcIhNVCCGEEEIIIcQHkJ4qIYQQQgghRLbkOVU5k54qIYQQQgghhPgA0lMlhBBCCCGEyJZMVJEzaVQJIYQQQgghsiUTVeRMhv8JIYQQQgghxAeQniohhBBCCCFEtmT4X86kUSWEEEIIIYTIljSqcibD/4QQQgghhBDiA0ijSgghhBBCCJGt5NTUXHvltri4OL788kt27tz5xnR//fUXPXr0wNzcHBsbG7Zt26a0fteuXbRv3x4zMzMcHR25cuXKO8UhjSohhBBCCCFEtlJScu+Vm27dusX//vc//vzzzzeme/bsGYMHD6Zr165cunSJmTNnMmvWLP7++28ALly4wPTp05k9ezaXLl3CwcGBIUOGEBcX99axSKNKCCGEEEII8VE5d+4cffv2pVu3blSsWPGNaX18fChVqhT/+9//0NTUpHnz5tjb27Np0yYAtm3bhq2tLRYWFmhpadGvXz9Kly7N/v373zoemaiiiCherJiqQ1AJ/eJGqg5BJVa2+0LVIaiEfrHiqg5BJRqUKa/qEFTCsE4vVYegEvrFSqo6BJXQK2+p6hBUopSBgapDUIlU33cbeiXyVm5OVJGQkEBCQoLSMm1tbbS1tZWWxcfHEx4enuU2ypUrxyeffMLx48fR0dFh7dq1b/ybt27donbt2krLTE1N2b59OwC3b9/Gyckp0/rr16+/VZ5AGlVCCCGEEEKIN9h2JSHnRG9pyZIlLF26VGnZsGHDGD58uNKyv/76iz59+mS5jWXLltGuXbu3/psxMTHo6ekpLdPV1SU2Nvat1r8NaVQJIYQQQggh8oWLiwv9+/dXWvZ6LxVA06ZNuXHjRq78TT09PaKiopSWxcfHUyx9JJeenh7x8fGZ1pcuXfqt/4Y0qoQQQgghhBD5Iquhfnmtdu3anDlzRmnZ7du3qVWrFgC1atXi1q1bmda3bt36rf+GTFQhhBBCCCGEKLTat2/P48ePWbduHYmJiZw/fx4vLy/FfVTdu3fHy8uL8+fPk5iYyLp164iIiKB9+/Zv/TekUSWEEEIIIYQoVGxtbVm5ciUApUuXZs2aNRw8eJCmTZsydepUpk6dSrNmzQBo3rw506ZN44cffqBJkybs27eP1atXU6pUqbf+e2qpqXnwFC4hhBBCG6o83AAANM9JREFUCCGEKCKkp0oIIYQQQgghPoA0qoQQQgghhBDiA0ijSgghhBBCCCE+gDSqhBBCCCGEEOIDSKNKCCGEEEIIIT6ANKqEEB8sMjJS1SEIIYQQQqiMNKqEEO8lKSmJhQsXYmFhgY2NDffu3cPJyYmHDx+qOjSRhyIjI1m3bh0zZ84kOjqa48ePqzqkfJGcnKz4/8mTJ/n7779VGI3IS/v27SMhIUHVYQghPjLSqBK5wsbGhrZt277xVdidOXMGV1dXHB0defToEXPmzCEpKUnVYeWZJUuWcP78eRYtWoSWlhaGhoYYGxszc+ZMVYeW57Zu3Yq9vT1Nmzbl/v37jBgxgpiYGFWHlef++ecfOnXqxMGDB9m+fTtPnjxh5MiR7NixQ9Wh5aljx47RqlUrAJYvX87w4cPp3bs3W7duVXFkIi/8+OOPqKmpqTqMfGdjY8PSpUsJDQ1VdShCfJQ0VR2AKByGDx8OpP3oOnr0KP3796dq1ao8ePCAtWvXFvpGlZeXF7NmzaJHjx5cunQJSPshpqamxoQJE1QcXd7w8vLi999/x8jICDU1NfT19Zk1axbt27dXdWh5at26dfz+++84Ozszd+5cihUrxsOHD5k1axYzZsxQdXh5atasWbi5ueHo6Mjnn39OlSpVWLZsGbNmzcLJyUnV4eWZFStWMGrUKFJSUti4cSNLlizB0NCQ0aNH07NnT1WHl+smTZqUY5pZs2blQySq0aBBA/bv388XX3yh6lDy1YQJE9i9ezcrV66kcePGODk50aFDB3R0dFQdWp7o3bt3jo3n9evX51M0ojCQniqRK7p160a3bt24ePEiHh4e9OnTBysrK77++mtWr17NsWPHVB1inlq1ahXLly9n9OjRqKurU65cOX799Ve8vb1VHVqeiY2NpUyZMgCkpqYCoKuri7p64T6s/P777yxfvpyePXuirq5OyZIlWbx4cZEYBnfz5k3FD82MHyOtWrUiPDxclWHlueDgYHr27Mn169eJi4ujRYsW1K9fn8ePH6s6tDz15MkT9u7dS1RUFKVKleLFixd4e3sX+qFxT58+ZeLEiXz22WeZRmEUZp06dWLlypWcPHmSNm3asH79elq1asUPP/zA1atXVR1ermvatClNmjShYsWK/Pvvv9StW5eOHTvSsGFDbty4QY0aNVQdovjISE+VyFX37t2jatWqSsuMjIwK/X02YWFhNGzYEHj5Y7NatWrExsaqMqw8ZWZmxtKlSxk9erQizxs2bKBBgwYqjixvPXnyRHGyzWhMGhoaFuqhnhnKlClDYGAgtWrVUiwLDAykbNmyKowq7+np6REREcGxY8ewsLBAU1OT69evU7p0aVWHlicyeqFcXV1ZvHixUmPi9OnTrFy5UlWh5YtvvvlG1SGolKGhIV988QW6urps3bqVHTt24OPjoxjeXbduXVWHmCuGDRsGQK9evVi1ahWNGjVSrOvYsSPfffedqkITHylpVIlcVb9+febMmcOECRPQ1tYmLi6OGTNmYGFhoerQ8lT16tU5evQo7dq1Uyw7e/Ys1apVU2FUeWvKlCn07duXXbt2ERMTQ5cuXYiJiWHt2rWqDi1PffLJJ/zxxx98/fXXisbk/v37lRoahVWvXr1wcXHB1dWVpKQk9u/fz4oVK/jyyy9VHVqecnJyomvXrjx//pzFixdz7do1Bg4cyIABA1QdWp66cOECy5cvV1rWvHlzxXDvwqpbt26K/0dGRip65Au7hIQEjh49yp49ezh9+jS1atWiW7du2NvbU7JkSZYuXcqwYcM4evSoqkPNVf/995/iomiGOnXqcOfOHdUEJD5aaqkZl1qFyAWBgYG4uLjw4MEDSpcuTWRkJDVr1mTVqlVUqFBB1eHlmbNnzzJ06FDatm3LkSNH6NatG97e3syfP582bdqoOrw8ExcXx/Hjx7l//z7GxsZYWVlhYGCg6rDy1D///EO/fv0wMTHh2rVrNG/enD///BMPD49MJ+bCaNOmTWzevJnQ0FCMjY3p2bMn/fr1K/TDPi9cuICOjg5mZmY8ePCAq1ev0qFDB1WHlafs7OwYMmQItra2imXbtm1j06ZN7N69W3WB5bGkpCSWLFnCxo0bSU5OxsvLi1GjRrFy5UrKlSun6vDyTOPGjdHQ0MDOzg5HR0c+/fRTpfW3b99m8ODBhW44f/fu3encuTPOzs6KZUuWLOHs2bP8/vvvKoxMfGykUSVyRVhYGMbGxty/f5+kpCTCw8O5cuUKFhYWlC9fHg0NDSpWrKjqMPPU9evX+eOPPxQ/Nrt3785nn32m6rDyzP3797NcrqWlRcmSJdHW1s7niPLPw4cP2bt3r6Ks7e3tC339zvD8+XN0dHTQ0dEhICCAMmXKFNphcBmGDBnCihUrMi3/5ptv2Lhxowoiyh9Hjx5l5MiRfPbZZ1SoUIGQkBBu3rzJypUradq0qarDyzMLFy7k/PnzDB8+nNGjR3Py5EnGjx+PpqYmixYtUnV4eWbZsmUMGjSoUB+7s3L58mVcXV3R19dX/I5JSUnB09OTOnXqqDo88RGRRpXIFY0aNeLy5ct88skniiFRqampqKmpKf7977//VBxl3hkyZAjz5s0r9L00r/r0009JSUnJcp26ujqWlpbMmTOn0A2dcXR0ZP369UWqrDOcP3+eIUOGsHbtWszMzPjll1/YvHkzHh4ehe4CQkhIiKI35tdff8XFxUVpfXR0NDt27FDM9llYBQUFsW/fPh4+fKi4gFClShVVh5WnbGxsFDObNmnShIsXL/L8+XPat2/PhQsXVB1enmnatCknTpxAT09P1aHku6dPn3LixAnCw8MxNjbGxsaG4sWLqzos8ZGRe6pErti3bx9AoRtr/bauXLlS5K7uTZo0iePHjzN58mSqVKlCSEgIc+fOpX79+nTo0IEVK1Ywa9Ys5s2bp+pQc1Vhn3TlTebNm8fkyZMxMzMDYNSoUVSpUoWff/6ZLVu2qDa4XFaxYkVu3bpFZGQkycnJmX5M6+joMG3aNBVFlz8yLiBk3NBfVBTVmU2rVKnC1atXadKkiapDyXelSpWidu3a6OvrY2VlRVRUlKpDEh8h6akSIhfMmDGDkJAQ7O3tKVeunNKzLz7//HMVRpZ32rdvz7Zt2yhVqpRi2bNnz3BycuLIkSNER0fTtm3bQndld9q0aVy9epWOHTtSvnx5pbLu2rWr6gLLBxYWFvj7+ystS01N5fPPP8fPz09FUeW9qVOnFvpnkGWlZcuWHDx4sMj1yrq6ulKnTh1Gjx6t6Kny9PTkwoULrFq1StXh5RlnZ2fOnz9P5cqVMx3bCvPzmiIiIvj222+5du0aWlpabN++ne7du7NmzRrMzc1VHZ74iEhPlRC5IOO+ihMnTigtL8zDHp88eYKGhobSMjU1NSIiIoC0aaizGx74MfP19QXgjz/+UFqupqZW6BtVhoaG/P3330pD/a5du1bop1SfMGECY8eOZejQoZiYmLBo0SJCQkL44YcfKFasmKrDyzNt27alT58+Re4CQlGd2dTc3LxINiJ+/vlnateuzdq1a2ndujUmJiYMHjyYuXPnykQV4p1IT5UQ4r2MHj2amJgYpkyZQsWKFbl//z5z585FTU2NBQsWsGzZMv766y/WrVun6lBFLvntt98UU6hXqlSJ+/fvs3XrVoYNG0avXr1UHV6eGTt2LM+ePWPOnDkYGhoSEBDAvHnzMDQ0ZObMmaoOL8/Y2NhkuVxNTa3QD/WOi4vjxIkTisloisLMpkVVixYtOHLkCHp6eoqeycTERCwtLQv9PZMid0lPlRC5JCwsDC8vL0JDQylfvjx2dnaZHoRcmEybNo2xY8fSsWNHxRVsKysrZs6ciZ+fHydOnGDBggUqjjL3vekkW1iHembo27cvxYsXZ/fu3fj4+FChQgUmT56MnZ2dqkPLU2fOnOHo0aOKXikTExPc3d1p3769iiPLW4Vt6ux3oaOjQ4UKFVBXV6dSpUpFokH15MkTNmzYQHh4uGKUQWJiIjdv3mTv3r0qji7vaGlpER8fj56enuIeupiYmELdCy3yhjSqhMgFV69epV+/ftSsWZPKlStz9epVVq1ahaenZ6F98HGpUqXw9PQkPDycsLAwUlNT2blzJzY2Nvz555/s2bNH1SHmid69e2dapq6uToUKFQr91XtIm7zA0dFR1WHkq5SUFJKTk5WWpaamZhr+Whjdu3eP8PBwxY/NjB/Z/fr1U21geeju3bu4uLgQEhJCqVKlePLkCfXq1WPZsmWUL19e1eHlmUmTJnHnzh3KlClDdHQ0FStW5PTp0/zvf/9TdWh5ysbGhvHjxzN16lTFEPYZM2bQunVrVYcmPjIy/E+IXNCnTx/atWtHnz59FMt+++03Dh48WOjHZPv5+eHp6cnJkyepVasWPXv2LPQn4VdFRkaybNkyKlWqxIABA1QdTp6KiYlh8+bN3LlzJ9P9crNmzVJRVHlv3LhxxMTEMGnSJCpUqMCDBw+YO3cuBgYGzJ49W9Xh5Zlff/2VhQsXZnpMRt26ddm5c6eKo8s7AwcOpHLlyri5uaGrq0t0dDQzZ84kKiqKpUuXqjq8PGNhYcH+/fsJDw9n1apVLF26lD179uDt7c3q1atVHV6eydi3fXx8gLThrW3atGHu3LmUKFFCxdGJj4k0qoTIBU2bNuXMmTNoar7s/E1MTKRZs2aZZksrDFJSUjh48CBr167l1q1bJCUlsWLFClq1aqXq0FQiPj6ejh07cvLkSVWHkqdGjBjBlStXaNq0KVpaWkrrCnOjKjIykpEjR3Lp0iVFA8PS0hJ3d/dC/eDjNm3aMGXKFLS1tTl27Bhjxoxh+vTpVKhQgXHjxqk6vDzTtGlTfH19lR6TERcXh5WVVaGbzfRVTZs25cKFCzx//hwnJycOHz5MUlISbdq04cyZM6oOL89FREQo7qErX7480dHRRWLYp8g9MvxPiFygp6fHgwcPlB6K+eDBA0qWLKnCqPLGb7/9xvr160lJSeHrr79m9erVdOrUidq1a6s6NJV59uwZL168UHUYee7ChQts37690D/89XVlypRhw4YN3L9/n0ePHmFsbIyRkZGqw8pzz58/p0OHDoSFhbF48WJKlSrFlClT6N69e6FuVFWqVIng4GBMTU0Vy8LCwpQeH1EYVapUiWvXrlG/fn1iYmKIjIxEU1OT+Ph4VYeWpzImpzA0NMTQ0FCx3MrKqlA/KkLkPmlUCZELunTpwvDhwxk7diyVK1cmODiYhQsX0qVLF1WHlutmzZpFr169cHNzK3IPPIa0+w5elZiYiL+/P5aWliqKKP/o6OgUicbE616fnCQ4OJjg4GCgcE9OknG13sjIiJCQEFJTUylTpgzPnj1TdWh5Yvfu3QA0atSIQYMG4ezsTKVKlXj48CFr1qyhXbt2qg0wj/Xq1YvevXuzb98+7Ozs6Nu3L5qamoWyjt+9e5fvv//+/+3deVTU1f8/8Oewqrghq2jmFpFLqYikEioq5YLhRrlAFlYuiKLiipKCipooKormUgqKHzQwkVxRKkVU0BDXvlaGkGigxSDCAPP7o8P8HEGtmJkr73k+zukc3u87fzw90My83vfe14VSqYRcLldbug8AcrmcS//oX+PyPyINKCkpQXBwMA4ePAiFQgFTU1MMHz4cs2fPhqmpqeh4GhUTE4Ndu3ahoKAAXl5eGD16NDw9PZGQkKAXX7ifLKoMDAzQpk0bvPfee5LvFhUVFYW7d+/Cz88PTZo0ER1HZxwcHKrc04fmJEFBQcjNzcWaNWvg7++Pjh07wtTUFElJSUhKShIdT+Oe1kK+kj60ks/MzISDgwNkMhm2b9+OoqIifPTRR5JcdRETE4P79+8jKioKEyZMUBszMTGBm5ub2mwl0fOwqCLSEKVSieLiYhQVFaGiogKWlpaS7g6WmpqK6OhofP/99ygvL8eSJUvg4eEh6X+zvnNzc0Nubq7aIbCVpHrIdXX0pTmJXC7HqlWrMGXKFOTn58Pf3x9yuRxhYWHo2bOn6HhEGpGQkCDpw6xJd1hUEWnAtWvXMHHiREREROD111/HsmXLcOzYMXzxxRdo3bq16HhalZOTg127dmHfvn0wMDDAkCFDMGfOHNGxtKa8vByHDx+utgOen5+foFS6cfbs2aeOdevWTYdJxNOX5iT66vz588jJycGTX5Gk+OXbzc2t2gclj5PiDF1iYiIGDx6sWvZZHSn+vkl7WFQRaYC3tzecnJwwadIkGBkZoaysDFFRUcjIyMC2bdtEx9OJ0tJSfPPNN9i1a5ek2y0HBQXh4MGDcHBwUOv2KJPJsGPHDoHJxCkoKNCr5YAAkJeXh3fffRdnzpwRHUUrsrKycOPGDdWZZAqFAhMnTsTUqVPRsWNHwem0Kzg4GHv37oW1tbVasSHV5X/x8fHPfc3QoUN1kES3Bg8ejMTExKcu+5Tq75u0h0UVkQZ07dpVrd0y8PeMxptvvlllkzvVbj179kRUVJTkv1hWJzMzEytWrEBeXp5qlk6hUKCgoABZWVmC02nP05qTdO7cGeHh4YJSac+VK1cwZswYjB49GoGBgQD+7gQ4b948nD59Grt27ap2n5lUODk5Yfv27ejQoYPoKC+EsrIytQdIRFQ9A9EBiKSgfv36+OWXX9TuZWdns3uQBFVUVKBdu3aiYwixePFiWFlZwcXFBa1atcLYsWNhaGiIGTNmiI6mU6ampvD29kZISIjoKFoRGRmJTz/9VFVQAUDDhg2xfv16jBkzRtIH4AJAgwYN9PKIiN9++w2zZ8/GuHHj4OPjAx8fH4waNQouLi6io2nV6tWrq9zLz8/H+PHjBaSh2owzVUQaEBERgaSkJIwfPx52dnbIzc3F1q1b4eHhgcmTJ4uORxq0ZMkSWFlZ4ZNPPhEdRefeeOMNpKWl4fbt21iyZAm2b9+OixcvYvHixZJe8qlvXFxccPToUdStW7fK2IMHDzB48GD88MMPApLpRlxcHM6ePQtfX98qD8bs7OwEpdI+b29vKJVKmJubIz8/H+3atUNCQgLGjRsn6f2i7u7usLGxQXh4OKysrPDdd99hzpw5sLe3x5dffik6HtUinM8l0gA/Pz8YGBggKioK9+7dQ9OmTTFs2DA+6ZKgy5cvIyMjAxs3bqyyj0jq6+8bNmyIOnXq4KWXXsJPP/0EAOjUqRNycnIEJ9OOfzIjI8Uvm48ePaq2oAKAxo0bS/4w2JKSEiQlJSExMVF1T6lUQiaTSbrLZVZWFk6ePKlqox8UFARXV1ds2rRJkn/nlb7++msEBwfD09MTrq6uOHToEKZPnw5vb2/R0aiWYVFFpAGGhoaYMmUKpkyZIjoKadnIkSMxcuRI0TGEaN26NXbv3o1Ro0ahXr16uHr1KkxMTJ7bOay2SktLA/B3kXHp0iW0a9cOzZs3R15eHn788UfJthW3s7PDtWvXqt03df36dVhYWAhIpTsbNmxAUFAQXFxcYGCgP7sk6tati0aNGsHIyAg3btwAALi6umL27NmCk2lX/fr1MW3aNHzwwQeIj4+Hh4cH3n//fdGxqBZiUUVUQ9nZ2fjjjz/QuXNnVFRUYP369bhy5QreeecdtmOVoMe7YOlb17upU6di4sSJ6NmzJ3x9feHl5QVDQ0OMGjVKdDSt2LlzJwBgzpw5GDZsmNq/Mz4+HkeOHBEVTasGDRqEJUuWYPPmzWozVg8fPsTSpUvRr18/gem0r7y8XLJ/08/SokULpKSkoFevXqioqEB2djZMTExQVlYmOppW7d69GytXrkT//v0xZswYBAcHY/jw4VixYoWkG7KQ5nFPFVENXLx4ER988AHee+89zJs3D6tXr8auXbswYsQIHD58GNOnT8fgwYNFxyQNKisrw7p16xAdHY3y8nIcOHAA06ZNQ1RUFKysrETH07qSkhIYGxvDwMAAmZmZKCwslOyMTaUuXbrg/PnzarMW5eXl6Nq1Ky5cuCAwmXaUlpZi7NixyM3NRe/evWFpaYl79+4hJSUFVlZWiImJQb169UTH1Jrly5ejadOm8PHxER1Fp5KTkxEQEIDExEQcOHAAMTExMDQ0RI8ePRAWFiY6ntZ06dIFCxcuVD0EVSgUWLlyJWJjY5GZmSk2HNUqLKqIauDjjz+Gs7Mzxo8fD6VSiTfffBMzZ87EyJEjkZ6ejrCwMMTFxYmOSRq0evVqnDlzBlOmTEFAQABSUlIQGBgIIyMjREREiI6nFbm5uc99jZQ38Pfr1w9LliyBs7Oz6l5KSgqWLl2Kw4cPC0ymPaWlpdixYwdOnDiBgoICWFlZwc3NDaNHj4aJiYnoeFo1ZswYpKenw8zMDI0aNVJb3ir1fZN5eXlo0qQJjI2NkZSUBLlcDk9PT0n+ztPT0+Ho6IjffvsNLVq0qDIeGRnJRlP0r7CoIqoBZ2dnfPfddzA1NcWNGzfw7rvvIiUlBdbW1lAoFHB2dkZGRobomKRBbm5u2L17N2xsbNCtWzecPXsWf/31F/r376/agyM1Dg4Oqi+WlRv2K+nDBv64uDiEhITg7bffhp2dHbKzs3Hs2DEsX74cAwYMEB1Paw4ePIh+/frB1NRUdBSdetZhuFI8BLc6hYWF+O233/Dqq69K9oyqLl26qH0+v/vuu9i/f/9Tx4meR5r/pxDpSGlpqeoLx48//ghbW1tYW1sDgOTXoeurhw8fqvZRVT6TqlOnjqQ3tEv96fzzjBw5Es2bN8c333yDrKws2Nra4ssvv0SXLl1ER9OqRYsWwd3dXXQMndO3fZOlpaUICQlBw4YNERgYiEuXLuHDDz+EXC5Hy5YtsWPHDtXnmpQ8Oafw5Iw85xzo35LutwAiHbCzs8PPP/8MAPj+++/VlgddunQJzZo1ExWNNCw9PR3A3y3EK1ttV87Y7Ny5Ex07dhSWTduaNWv21P9sbGzw559/io6odd27d8fMmTMxdepU+Pn5Sb6gAoCOHTsiKSlJdAydKysrw+rVq+Ho6Ag3NzdkZ2dj+PDhuHfvnuhoWhEZGYmLFy+iV69eAICwsDA4OzsjPT0dffr0keyy5ie7lj7vmuh5OFNFVAOenp7w9/eHo6Mjjh8/jh07dgD4e7/FihUr4OHhITghacrHH3+MjIwMzJs3D+PGjUN8fDyKioowcOBAFBUVYfv27aIjat3JkyexaNEi5OXlqT3FNTIywqVLlwQm0y65XI7Zs2cjOTlZda979+5Ys2ZNlcNhpeTBgweYPXs2FixYAEtLS73ZW7Ru3TqcOXMGERERCAgIgIWFBWxtbREaGirJAuPQoUPYsGED2rRpgz///BMZGRnYvXs3zMzM4OvrqzdLHolqikUVUQ2MHz8e5eXlyMjIwJIlS+Do6AgAmDZtGvr27QtfX1/BCUlTKouIFi1a4ODBgzhx4gRyc3Nha2uL3r17o379+oITat/nn38Od3d3NGzYENevX8fgwYMRGRmJESNGiI6mVatWrUJRURESExPRvHlz3Lp1C0uXLsXKlSsREhIiOp7WjB07VnQEIQ4cOKDaNymTyVCvXj0sW7YM/fv3Fx1NK+7du4c2bdoAADIzM2FsbIz27dsDACwtLVFYWCgyHlGtwaKKqAZkMhkmTJhQ5f6pU6dQr149yOVyGBsbC0hGmvb4U/q6deti4MCBAtOIkZ2djcDAQNy+fRtnzpyBu7s7WrdujYCAAHh7e4uOpzUnTpzAvn37VIfe2tvbY+XKlRgyZIiki6onZyhKSkr0ommFvu2bNDY2Vv1u09PT0aFDB9XnVm5uLszMzAQn1I6ysjIkJCSorhUKhdp1eXm57kNRrcaiikgDKrvAVao8w6V37944f/68qFikQcXFxejbt+8zXyPlJVEA0KRJExgYGMDOzg43b94EALRt2xZ37twRnEy7iouL0aBBA7V7DRs2REVFhaBE2rd7927cunULc+bMAfD3Esi+ffvC398fY8aMEZxOuyr3TQYEBOjFvkknJyfs3LkTnp6eSExMxPvvv68a27t3Lzp16iQunBZZWlpi7dq1qmtzc3O168qHKET/FIsqov/o1q1bWLhwIZRKJeRyeZWDIuVyuaT3W+gbY2Nj+Pn5iY4h1KuvvoqIiAhMnjwZFhYWSElJQZ06dSQ/e/HGG28gIiICM2fOhEwmg1KpREREhGS/ZB8+fBirVq3CggULVPdMTEwwefJkfP7557CxsUG/fv0EJtSu+fPn44MPPtCbfZPTpk2Dt7c3Vq1ahTZt2mD06NEAAC8vL9y4cQPR0dGCE2rH43skiTSB51QR1UBMTAzu37+PqKioKssATUxM4ObmhrZt2wpKR5rEM0uAmzdvwt/fH5s3b8aVK1cwbdo0VFRUYNasWfjwww9Fx9Oa69evw8fHByYmJmjWrBlycnIgk8mwfft21V4UKXn//fcxfvz4agun/fv3IzY2Frt37xaQTHeKi4v1at+kXC7Hzz//DAcHB9VBv2vWrME777wDBwcHwemIagcWVUQakJCQAE9PT9ExSIs6d+6MCxcuiI7xQrl79y6KiorQqlUr0VG06uHDhygtLcXx48eRn5+PZs2aoVevXpL9ku3s7IzU1NRq9xCVlpbCxcVFbbkzERFx+R9RjSQmJmLw4MEAoLbB9XEstqRhyJAhoiO8UK5evYrTp0+ja9euoqNo3eDBg/HNN99g+PDhoqPoTEVFRbVFlaGhoYA0uvHaa6899zVXr17VQRLd8vb2fu6ZTJXHhRDR07GoIqqBqKgoDB48WG1z6+NkMhmLKolYtGiR6AjC3LlzB4GBgcjKysI777wDLy8veHt7w8zMDOHh4Vi9ejXc3d1Fx9Sq4uJiyc5MPcne3h6pqal46623qoydPn0azZs3F5BK+xo1aoSysjIMGjQI/fv3Vy2Dk7rKQ+tv376NY8eOYfjw4WjRogXu3LmD//3vf3jnnXcEJySqHbj8j4iInmnSpElQKpXw8vJCYmIivv/+e0yYMAEfffQR9u3bh927d2Pv3r2iY2rN3LlzkZqaCldXV1hbW6uNSbF5ycGDB/H5559j7dq1as04srKy4O/vj/Hjx6uaGUiJQqFAcnIy9u3bh6ysLAwcOBAjRozQmz1Fo0ePxsyZM9GlSxfVvaysLCxYsADx8fECkxHVDiyqiGrg3Llzz32Nk5OTDpIQaY+zszOSk5NhZmaGP//8E87OzsjMzISJiQnKy8vh7Ows6aMDnnYGl0wmk+yyqNDQUERHR6NFixawtLTEvXv3cPv2bXh5eenFrG1eXh7i4+MRHx8PMzMzjBgxAh4eHlVa60tJ586dcf78ebUlngqFAt26deN+UqJ/gEUVUQ1UPsF8fD16o0aNUFhYiIqKCjRu3Bipqami4hFpxJNNOrp27apWRDk6OiI9PV1ENNKizMxMnDhxAgUFBbCyskKfPn3Qvn170bF07uzZswgJCUF2djYuXrwoOo7WjBgxAgMGDICvr6/q3rp163D69GnJd3sk0gTuqSKqgWvXrgEAtm7dihs3biAoKAgNGjTAw4cPERYWhkaNGglOSFRzT25if7KBgZSfza1fvx6XL1+Gi4uL5A+9fdLrr7+O119/XXUtl8tRWlqqN3uNbt26hfj4eOzfvx8KhQKjRo0SHUmr5s2bhwkTJmDnzp2wtbVFbm4uKioqsHXrVtHRiGoFFlVEGrB161YkJyejTp06AIB69eph/vz5cHV1xYwZMwSnI6qZiooKnD9/XlU8lZWVqV1XVFSIjKc1K1asQEJCArp27Yq1a9eiqKgIn3zyiehYOnPz5k2Eh4cjMjISR48eRUBAAMzMzLBhwwY4OjqKjqcVcrkc3377Lfbt24fLly+jd+/eWLBgAXr16iXpzofA32fxHTlyBCdPnkReXh5sbW3h5uYm6SWPRJrEoopIAyoqKlTn11S6ffu25D+EST88evQIY8eOVbv3+PXz2jHXVomJifjqq6/wyiuvIC0tDaGhoXpVVC1duhTW1tZQKpUIDw+Hv78/zMzMEBYWhri4ONHxNG7GjBk4fvw4WrVqhaFDh2LDhg1o0qSJ6Fg61bhxY9jb26NevXro3bs3CgsLRUciqjW4p4pIA5YtW4aUlBSMHz8eTZs2RXZ2NrZs2YIhQ4bA399fdDwi+g8e30tWVlaGHj166NWhty4uLjhx4gTy8vLw9ttvIy0tDWZmZnB0dERGRoboeBrn4OAAc3NztG3b9qkPCqTamAQA8vPzMXnyZGRlZcHY2Bh79+7FiBEjsG3bNnTu3Fl0PKIXHmeqiDQgMDAQ9erVw8aNG5GXl4emTZvCy8sLH3/8sehoRPQfPb53zMhI/z4uy8rKoFQqcerUKbRv3x7169dHQUEBTE1NRUfTCim2x/83li5dCnt7e2zfvh2urq5o06YNPvnkE6xYsYKNKoj+Ac5UERERVaNLly5qMzLdunXTq5mq6dOno6ioCNeuXYOvry/69OmDWbNmoWXLlli2bJnoeKRhPXv2xLFjx1C3bl3V37pCoUCPHj3+0fEhRPpO/x69EWnJqVOnEB0djby8PGzatAnbtm3DjBkz9PIJN5EUlJWVISEhQXWtUCjUrgHA09NTp5l0KSQkBNu2bYOjoyN8fHxw7do1tG/fHtOnTxcdTev08f3c2NgYjx49Qt26dVVNaIqKimBmZiY4GVHtIN13ByIdOnDgAJYtW4aRI0eqnmQnJydDJpNh1qxZgtMR0X9haWmJtWvXqq7Nzc3VrmUymaSLKjMzM0yZMkV17eDggKCgIJSVlQlMpX36+n7u5uaGwMBABAUFQSaTIT8/H6GhoXB1dRUdjahW4PI/Ig3w8PBASEgIOnXqBCcnJ5w7dw6//vorfHx88N1334mOR0T/QUpKCnr16iU6hjC//fYbIiMjkZeXp2qbr1Ao8Msvv+DMmTOC02mPvr6fFxUVYe7cuThy5AiAvx8a9OrVCytWrEDDhg0FpyN68XGmikgD7ty5gzfeeAPA/28v/fLLL+Phw4ciYxFRDcycORPnzp2Du7u76oumPpk/fz6USiXMzc2Rn5+Pdu3aISEhAePGjRMdTav09f3czMwMa9euRX5+PnJycmBrawtra2vI5XLR0YhqBRZVRBrQsmVLHD9+HP369VPdO336NF5++WWBqYioJoyNjbFkyRLk5uZi/fr11b5Gyh3jsrKycPLkSeTm5mLNmjUICgqCq6srNm3aJOl/t76+n1c2p7CwsICFhYXqfu/evXH+/HmByYhqBxZVRBoQEBCASZMmoW/fvigpKcFnn32GxMRErFq1SnQ0IvqPFixYgLi4OCiVSqSlpVUZl+qhx5Xq1q2LRo0awcjICDdu3AAAuLq6Yvbs2YKTaZc+vZ/funULCxcuhFKphFwuh4+Pj9q4XC7n0j+if4hFFZEG9OjRA7GxsdizZw+cnZ1RUVGBbdu24fXXXxcdjYj+owEDBmDAgAEYOXIkdu7cKTqOzrVo0UK1r6yiogLZ2dkwMTGRfKMKfXo/f/nll+Hu7o779+8jIyMD3bp1Uxs3MTGBm5uboHREtQsbVRBpwMSJE7Fy5UrUr19fdBQi0rDy8nJcuHABd+/ehbW1NTp37gxDQ0PRsbQuOTkZ06ZNw8GDB3HgwAHExMTA0NAQ3bt3x/Lly0XH05rDhw+jb9++km6fXp2EhARJd7Mk0jYWVUQa8Oabb+K7776DiYmJ6ChEpEGpqamYM2cO8vLyVPesrKwQFhaGnj17CkymG3l5ebCwsICRkRGSkpJQVFQET09PGBsbi46mNa6urlAoFPD09MSIESPQpk0b0ZF05syZM8jLy1OdU6VQKHD9+nUEBQUJTkb04mNRRaQBoaGhuH37Njw8PGBlZaW218LJyUlgMiL6r3766Sd4eXnB29sbI0eOhI2NDbKzsxEXF4fY2FjExcXhlVdeER1T49zc3J67X+z48eM6SqN7FRUV+P7775GQkIDk5GS89tprGDFiBAYOHIh69eqJjqc1oaGhiI2NVR32W15ejqKiIrz11lvYvHmz4HRELz4WVUQa4ODgUO19mUyGq1ev6jgNEWlCYGAgmjdvjqlTp1YZCw8PR05OjiSbF8THxwMAlEolFi9ejODg4CqvGTp0qK5jCVFYWIikpCRs2rQJDx48QEZGhuhIWtOzZ09ERkaiuLgY33zzDZYuXYrly5fj4cOHWLx4seh4RC88/VowTKQFSqUShw8fVmu3e/DgQbz99tt6tyafSErS0tIwd+7casfGjh2L4cOH6ziRbjxeMIWFhelNAfWk7Oxs7N+/HwcOHIBCoYC3t7foSFpVXFyMTp064d69e7h8+TJkMhn8/PwwcOBA0dGIagV+4yOqgYcPH+Kjjz6CpaWl6hyb/Px8zJ07FzExMdiyZYukl4sQSVlhYSGaNGlS7ZiVlRUPRZWouLg4xMfHIzMzEy4uLggMDESfPn0k35zE1tYW+fn5sLKywp07d6BQKFCnTh3+nRP9QwaiAxDVZhs3boSxsTEWLVqkumdhYYETJ06grKwMmzZtEpiOiGrC3NwcN2/erHbs559/VjsglaQjKioKLi4uOH78OKKiotCvXz/JF1QA0KtXL4wbNw4FBQVwcnLCvHnz8Nlnn6Fly5aioxHVCiyqiGrg8OHDCA0NrfLlysLCAosWLcKhQ4cEJSOimurTpw8iIiKqHYuIiEDfvn11nIh04dixY5g0aRJsbGxER9Gp6dOn491334WxsTEWLlyI+/fv4//+7/8QEhIiOhpRrcBGFUQ14OjoiPT09KeOd+nSRdIbm4mk7I8//sDQoUNhb2+PIUOGwMrKCjk5Ofj666/x+++/IyEhAY0bNxYdU+MSEhJUPy9atKjaRhVSPM/ok08+webNm+Ht7f3U7oc7duzQcSoiqi24p4qoBurXr4/79+/D3Ny8ytiDBw9Qt25dAamISBMsLS0RGxuLpUuXIigoCAqFAkZGRnBzc8OaNWskWVABwNq1a1U/m5ubq10Df3c1lWJR5ejoCABwdnYWnESM7OxsREVFIScnBxUVFWpjLCaJno9FFVENdO/eHTExMfDz86sytmvXLnTq1En3oYhIY5o1a4bIyEiUlpbiwYMHMDc3l/TBtwCQnJwsOoIQn376KQCgTZs2GDBgQJXxPXv26DqSTk2fPh3GxsZ48803YWDA3SFE/xaX/xHVwC+//IJhw4Zh2LBhGDhwIKysrHD37l18++232LdvH6Kjo9GhQwfRMYmohs6fP4+cnBw8+ZEpxRkbfVRcXIz79+8DAAYNGoSkpCS133VhYSHef/99XLhwQVRErevcuTNSU1NRp04d0VGIaiXOVBHVQKtWrbB161YEBwcjJiYGMpkMSqUS9vb2+OKLL1hQEUlAcHAw9u7dC2tra7W9NlJdBqeP5HI5Bg0ahEePHgH4u0kJANV7OgD0799fWD5dcHBwwJ07d9jtj+g/4kwVkYZkZ2ejoKAAVlZWsLOzEx2HiDTEyckJ27dv50MSicvPz0dxcTE8PDyQmJioNmZqagpLS0tByXTj8uXLmDx5Mtzd3dGwYUO1seqWuBOROs5UEWnISy+9hJdeekl0DCLSsAYNGsDe3l50DNKyyqMx0tPTkZqainbt2sHc3BwpKSkwMTGRfFG1bt06PHz4EJcvX1bbU/W0TohEpI4zVURERM8QFxeHs2fPwtfXt8oTfM5KS09MTAxWr16NXbt2wd7eHl9//TXCwsIwb948SS/37Ny5M44ePSr54pFIWzhTRURE9AwlJSVISkpSWxKmVCohk8lw9epVgclIG7Zv346vvvpKNTs5bNgw2NvbY8aMGZIuqqytrWFqaio6BlGtxaKKiIjoGTZs2ICgoCC4uLiw1bQeyM/Px2uvvaZ2r127dsjPzxeUSDd8fX0xadIk+Pj4oFGjRmrL/pycnAQmI6odWFQRERE9Q3l5OUaNGiU6BulI27ZtsX//fgwdOlR178CBA2jdurXAVNq3cOFCAMC5c+fU7nNGluif4Z4qIiKiZ1i+fDmaNm0KHx8f0VFIB06dOoWJEyeiffv2sLOzw++//44rV65g8+bN6Natm+h4RPSCYlFFRET0DGPGjEF6ejrMzMyqLIs6fvy4wGSkLb/++isSExNx7949NG3aFIMGDZJsd9c7d+7A1tYWubm5T30NG7IQPR+LKiIiomeIj49/6tjjS8RIui5evIht27Zh7dq1oqNoXJcuXZCRkQEHBwe1w44rf+byP6J/hkUVERERUTWOHj2Kbdu24cKFC+jYsSPi4uJER9K433//HU2bNkVOTs5TX9OsWTMdJiKqnVhUERERPYO3t/dTD0DdsWOHjtOQtpWUlGDv3r348ssvcfv2bQwdOhTe3t5VOgJKzcSJE7Fx48Yq98eOHYvo6GgBiYhqF3b/IyIiegZnZ2e16/v37+PQoUN47733BCUibcjPz8eOHTsQGxsLKysr+Pr6Ijw8HFOnToWNjY3oeFpx+/ZtJCQkAAB++OEHrF+/Xm1cLpfj+vXrApIR1T4sqoiIiJ7Bz8+vyr1hw4ZhxYoVAtKQtri5ucHV1RWrV69Gjx49AABr1qwRG0rL7Ozs8NNPP6GgoADl5eVIS0tTGzc1NUVwcLCgdES1C4sqIiKif6l9+/bIysoSHYM06JVXXsGFCxdga2sLOzs7tGzZUnQkrTMwMEBERAQAICgoCKGhoYITEdVeLKqIiIie4clW0wqFAgcPHkTTpk0FJSJt2Lt3LzIzMxEdHQ1PT09069YNJSUl0Jet56GhocjKykKHDh1QWFiIqKgoNGnSBB988AGMjPh1keh52KiCiIjoGSpbTVdSKpVo1KgRQkND0b9/f4HJSFsKCgoQGxuLPXv2QKFQwMPDA8OGDcOrr74qOprWbNy4EVu2bEF6ejpmzZqFrKwsGBgYoHv37pg/f77oeEQvPBZVREREz/Bkq2lDQ0NYWFjA2NhYUCLSlfLychw5cgTR0dHIyMiQ9HlNgwYNwqpVq9C6dWs4OTlhz549sLKywpAhQ3Dq1CnR8YheeJzPJSIiegae0aO/DA0N8dZbb6Fv37745ZdfRMfRqrt378LBwQGpqalo0KABHBwcAADFxcWCkxHVDiyqiIiIquHm5vbU86kAQCaT4dixYzpMRLpw8+ZNhIeHIzIyEkePHkVAQADMzMywYcMG0dG0ysbGBufOnUNCQgK6d+8OAEhMTMRLL70kOBlR7cCiioiIqBpTpkyp9v7FixexZ88etGvXTseJSBeWLl0Ka2trKJVKhIeHw9/fH2ZmZggLC0NcXJzoeFozZcoUjB8/HnXq1MHu3buRmpqKuXPnYt26daKjEdUK3FNFRET0D23btg3h4eEYOXIk5s6dCxMTE9GRSMNcXFxw4sQJ5OXl4e2330ZaWhrMzMzg6OiIjIwM0fE0Lj09HY6OjgCAkpISAH+fTyWXy1FUVITExET4+vqKjEhUKxiIDkBERPSi++uvvzBx4kRs3LgRK1euRHBwMAsqiSorK4NSqcSpU6fQvn171K9fH/fv34epqanoaFrx8ccfq342NTWFl5cXAKB+/fqwsbFBZGSkqGhEtQqX/xERET3DxYsXERAQAHNzc3z99dfcYyJxPXr0wJQpU3Dt2jX4+voiOzsbs2bNQq9evURH04onFyw9eS4bFzQR/TOcqSIiInqKLVu2wNvbG3379kVsbCwLKj0QEhKCDh06YOzYsfDx8UFRURHat2+P4OBg0dG04slmLM+7JqLqcaaKiIioGhMmTEBKSgrGjh0Ld3d3/Pjjj1Ve4+TkJCAZacP69etVP8tkMpSUlKjuNW7cGFu3boWfn5+oeET0gmNRRUREVI2TJ08CAHbu3ImdO3dWGZfJZJI+DFbfpKWlPXOcMzZE9CwsqoiIiKpx7do10RFIh6ornPVBWVkZEhISVNcKhULtury8XPehiGohtlQnIiIi0lNubm7PfU1ycrIOkhDVbiyqiIiIiIiIaoDd/4iIiIiIiGqARRUREREREVENsKgiIiIiIiKqARZVRERERERENcCiioiIiIiIqAZYVBEREREREdUAiyoiIiIiIqIaYFFFRERERERUA/8PrHOGeE+kwA4AAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 100
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:30.916404Z",
+     "start_time": "2025-06-20T18:11:30.762926Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "sns.set_palette('tab10')\n",
+    "\n",
+    "fig = plt.figure(figsize=(8, 5))\n",
+    "g = sns.kdeplot(data=df, x='Age', hue='Exited', label='Exited', fill=True)\n",
+    "g.set_xlabel('')\n",
+    "g.set_ylabel('')\n",
+    "plt.yticks([])\n",
+    "\n",
+    "fig.suptitle(f\"Age vs Exited\", fontsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "8f75f16072984f71",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 101
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:31.046919Z",
+     "start_time": "2025-06-20T18:11:31.033617Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "t_encoder = TargetEncoder()\n",
+    "df['SurnameExitedRate'] = t_encoder.fit_transform(df['Surname'], df['Exited'])"
+   ],
+   "id": "8cbecb04cd348ce1",
+   "outputs": [],
+   "execution_count": 102
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:32.741397Z",
+     "start_time": "2025-06-20T18:11:31.144759Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.figure(figsize=(12, 12))\n",
+    "sns.relplot(data=df, x='EstimatedSalary', y='Balance', hue='Exited', col='IsActiveMember', row='HasCrCard',\n",
+    "            kind='scatter', palette=sns.color_palette('colorblind', 2))\n",
+    "plt.suptitle('Salary vs Balance', fontsize=17)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "3906587e1dd40aa8",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x1200 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1069.38x1000 with 4 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 103
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:32.848810Z",
+     "start_time": "2025-06-20T18:11:32.843440Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df.groupby(['HasCrCard', 'IsActiveMember'])['Exited'].mean()",
+   "id": "5f16457db23acb1d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HasCrCard  IsActiveMember\n",
+       "0.0        0                 0.290219\n",
+       "           1                 0.146840\n",
+       "1.0        0                 0.277168\n",
+       "           1                 0.117432\n",
+       "Name: Exited, dtype: float64"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 104
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:33.086391Z",
+     "start_time": "2025-06-20T18:11:32.969207Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "a = sns.barplot(x='HasCrCard', y='Exited', hue='IsActiveMember', data=df, ci=None, edgecolor='black')\n",
+    "\n",
+    "a.bar_label(a.containers[0])\n",
+    "\n",
+    "plt.title('Mean Exited by HasCrCard and IsActiveMember')\n",
+    "plt.xlabel('Has Credit Card')\n",
+    "plt.ylabel('Mean Exited')\n",
+    "plt.xticks([0, 1], ['No', 'Yes'])\n",
+    "plt.legend(title='Active Member', loc='upper right', labels=['No', 'Yes'])\n",
+    "\n",
+    "plt.show()"
+   ],
+   "id": "5ade3fe20b43282",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 105
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:33.207710Z",
+     "start_time": "2025-06-20T18:11:33.204198Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df['ActiveCrCard'] = df['HasCrCard'] + (df['IsActiveMember'])",
+   "id": "16a3fcb5f0e9014e",
+   "outputs": [],
+   "execution_count": 106
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:33.625479Z",
+     "start_time": "2025-06-20T18:11:33.387057Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "sns.histplot(x=df['Tenure'] / df['Age'], data=df, hue='Exited', palette='colorblind')\n",
+    "plt.title(\"Tenure-Age Ratio\")\n",
+    "plt.show()"
+   ],
+   "id": "3078650d768e406d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 107
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:34.567536Z",
+     "start_time": "2025-06-20T18:11:34.563516Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df['TenureAgeRatio'] = df['Tenure'] / df['Age']",
+   "id": "84c08a8cb08a8c98",
+   "outputs": [],
+   "execution_count": 108
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:34.668317Z",
+     "start_time": "2025-06-20T18:11:34.663910Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df.groupby('Tenure')['Exited'].mean()",
+   "id": "721d6e848ce8f22e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Tenure\n",
+       "0.0     0.294416\n",
+       "1.0     0.219562\n",
+       "2.0     0.172654\n",
+       "3.0     0.208200\n",
+       "4.0     0.214286\n",
+       "5.0     0.209459\n",
+       "6.0     0.201211\n",
+       "7.0     0.173041\n",
+       "8.0     0.195791\n",
+       "9.0     0.224887\n",
+       "10.0    0.185111\n",
+       "Name: Exited, dtype: float64"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 109
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:35.161745Z",
+     "start_time": "2025-06-20T18:11:34.870393Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.figure(figsize=(9, 6))\n",
+    "sns.heatmap(df.corr(numeric_only=True), mask=np.triu(df.select_dtypes(exclude=[object]).corr()), cmap='BrBG', center=0,\n",
+    "            annot=True, vmin=-1, fmt='.2f', linewidths=.1)\n",
+    "plt.title('Numerical Features Heatmap', size=15)\n",
+    "plt.show()"
+   ],
+   "id": "34d8261df0bc224e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 110
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:35.294321Z",
+     "start_time": "2025-06-20T18:11:35.281184Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "set_config(transform_output='pandas')\n",
+    "\n",
+    "X, y = df.drop(columns=['Exited']), df['Exited']\n",
+    "\n",
+    "X_train, xtemp, y_train, ytemp = train_test_split(X, y, test_size=.3, stratify=y, random_state=3)\n",
+    "X_valid, X_test, y_valid, y_test = train_test_split(xtemp, ytemp, test_size=.5, stratify=ytemp, random_state=322)"
+   ],
+   "id": "f0910a78d32f7c09",
+   "outputs": [],
+   "execution_count": 111
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:35.481366Z",
+     "start_time": "2025-06-20T18:11:35.477012Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "print('Training set Shape: ', X_train.shape, y_train.shape)\n",
+    "print('Validation set Shape: ', X_valid.shape, y_valid.shape)\n",
+    "print('Testing set Shape: ', X_test.shape, y_test.shape)\n",
+    "print('\\n\\n')\n",
+    "print('\\nTrain Target Dist: ', y_train.value_counts())\n",
+    "print('\\nValid Target Dist: ', y_valid.value_counts)\n",
+    "print('\\nTest Target Dist: ', y_test.value_counts())"
+   ],
+   "id": "823cf6fade0fd686",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set Shape:  (10500, 15) (10500,)\n",
+      "Validation set Shape:  (2250, 15) (2250,)\n",
+      "Testing set Shape:  (2250, 15) (2250,)\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "Train Target Dist:  Exited\n",
+      "0.0    8364\n",
+      "1.0    2136\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Valid Target Dist:  <bound method IndexOpsMixin.value_counts of 2790     0.0\n",
+      "6163     1.0\n",
+      "6264     0.0\n",
+      "7704     0.0\n",
+      "3150     1.0\n",
+      "        ... \n",
+      "7391     0.0\n",
+      "14171    1.0\n",
+      "11498    1.0\n",
+      "8677     1.0\n",
+      "7699     0.0\n",
+      "Name: Exited, Length: 2250, dtype: float64>\n",
+      "\n",
+      "Test Target Dist:  Exited\n",
+      "0.0    1792\n",
+      "1.0     458\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "execution_count": 112
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:35.604398Z",
+     "start_time": "2025-06-20T18:11:35.599225Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "cat_pipeline = Pipeline([\n",
+    "    ('impute', SimpleImputer(strategy='most_frequent')),\n",
+    "    ('encode', OneHotEncoder(handle_unknown='ignore', sparse_output=False, drop='first'))\n",
+    "])\n",
+    "num_pipeline = Pipeline([\n",
+    "    ('impute', SimpleImputer(strategy='median')),\n",
+    "])\n",
+    "target_enc_pipeline = Pipeline([\n",
+    "    ('impute', SimpleImputer(strategy='most_frequent')),\n",
+    "    ('encode', TargetEncoder())\n",
+    "])\n",
+    "\n",
+    "\n",
+    "def ActiveCrCardFunc(d):\n",
+    "    d['ActiveCrCard'] = d['HasCrCard'] + (4 * d['IsActiveMember']) + 1\n",
+    "    return d[['ActiveCrCard']]\n",
+    "\n",
+    "\n",
+    "def TenurebyAgeFunc(d):\n",
+    "    d['Age'] = np.log(d['Age'])\n",
+    "    d['TenureAgeRatio'] = d['Tenure'] / d['Age']\n",
+    "    return d[['TenureAgeRatio']]\n",
+    "\n",
+    "\n",
+    "ActiveCrCard_pipeline = make_pipeline(num_pipeline, FunctionTransformer(ActiveCrCardFunc, validate=False))\n",
+    "TenureAge_pipeline = make_pipeline(num_pipeline, FunctionTransformer(TenurebyAgeFunc, validate=False))\n",
+    "\n",
+    "preprocessing = ColumnTransformer([\n",
+    "    ('N', num_pipeline, ['CreditScore', 'Balance', 'EstimatedSalary', 'Tenure']),\n",
+    "    ('ActiveCrCard', ActiveCrCard_pipeline, ['IsActiveMember', 'HasCrCard']),\n",
+    "    #     ('TenureAgeR',TenureAge_pipeline,['Age','Tenure']),\n",
+    "    ('Age', make_pipeline(num_pipeline, FunctionTransformer(func=np.log)), ['Age']),\n",
+    "    ('Cat', cat_pipeline, ['Geography', 'Gender', 'NumOfProducts']),\n",
+    "    ('Tar', target_enc_pipeline, ['Surname',\n",
+    "                                  #                                 'NumOfProducts','Gender','Geography','Tenure'\n",
+    "                                  ]),\n",
+    "], remainder='drop', verbose_feature_names_out=False)\n",
+    "\n",
+    "preprocessing = Pipeline([\n",
+    "    ('Preprocessing for columns', preprocessing),\n",
+    "    ('Scaling', StandardScaler())\n",
+    "])\n",
+    "\n",
+    "# preprocessing = ColumnTransformer([\n",
+    "#     ('numerical',num_pipeline,['CreditScore','Balance','EstimatedSalary','Tenure']),\n",
+    "#     ('ActiveCrCard',ActiveCrCard_pipeline,['IsActiveMember','HasCrCard']),\n",
+    "#     ('Age',make_pipeline(num_pipeline,FunctionTransformer(func=np.log)),['Age']),\n",
+    "#     ('category',cat_pipeline,['Geography','Gender','NumOfProducts']),\n",
+    "#     ('surname',surname_pipeline,['Surname']),\n",
+    "# ],remainder='drop',verbose_feature_names_out=False)\n",
+    "\n",
+    "# preprocessing = Pipeline([\n",
+    "#     ('Preprocessing for columns',preprocessing),\n",
+    "#     ('Scaling',StandardScaler())\n",
+    "# ])"
+   ],
+   "id": "ddd5da4a7d2c42c0",
+   "outputs": [],
+   "execution_count": 113
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:35.778548Z",
+     "start_time": "2025-06-20T18:11:35.707980Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "train_prepared = preprocessing.fit_transform(X_train, y_train).replace(0, -1)\n",
+    "valid_prepared = preprocessing.transform(X_valid).replace(0, -1)\n",
+    "test_prepared = preprocessing.transform(X_test).replace(0, -1)\n",
+    "\n",
+    "train_prepared.sample(2)"
+   ],
+   "id": "afb605d92a52dc00",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       CreditScore   Balance  EstimatedSalary    Tenure  ActiveCrCard  \\\n",
+       "1438      0.287594 -0.710855         0.333994 -1.089897     -0.865896   \n",
+       "12579     1.051194 -0.710855        -1.071925 -1.089897     -0.865896   \n",
+       "\n",
+       "           Age  Geography_Germany  Geography_Spain  Gender_Male  \\\n",
+       "1438   0.49584           -0.45821        -0.531822     0.862662   \n",
+       "12579 -0.24638           -0.45821        -0.531822     0.862662   \n",
+       "\n",
+       "       NumOfProducts_2.0  NumOfProducts_3.0  NumOfProducts_4.0  Surname  \n",
+       "1438            0.892244          -0.121601          -0.047864  0.57261  \n",
+       "12579          -1.120769          -0.121601          -0.047864 -1.14718  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CreditScore</th>\n",
+       "      <th>Balance</th>\n",
+       "      <th>EstimatedSalary</th>\n",
+       "      <th>Tenure</th>\n",
+       "      <th>ActiveCrCard</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Geography_Germany</th>\n",
+       "      <th>Geography_Spain</th>\n",
+       "      <th>Gender_Male</th>\n",
+       "      <th>NumOfProducts_2.0</th>\n",
+       "      <th>NumOfProducts_3.0</th>\n",
+       "      <th>NumOfProducts_4.0</th>\n",
+       "      <th>Surname</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1438</th>\n",
+       "      <td>0.287594</td>\n",
+       "      <td>-0.710855</td>\n",
+       "      <td>0.333994</td>\n",
+       "      <td>-1.089897</td>\n",
+       "      <td>-0.865896</td>\n",
+       "      <td>0.49584</td>\n",
+       "      <td>-0.45821</td>\n",
+       "      <td>-0.531822</td>\n",
+       "      <td>0.862662</td>\n",
+       "      <td>0.892244</td>\n",
+       "      <td>-0.121601</td>\n",
+       "      <td>-0.047864</td>\n",
+       "      <td>0.57261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12579</th>\n",
+       "      <td>1.051194</td>\n",
+       "      <td>-0.710855</td>\n",
+       "      <td>-1.071925</td>\n",
+       "      <td>-1.089897</td>\n",
+       "      <td>-0.865896</td>\n",
+       "      <td>-0.24638</td>\n",
+       "      <td>-0.45821</td>\n",
+       "      <td>-0.531822</td>\n",
+       "      <td>0.862662</td>\n",
+       "      <td>-1.120769</td>\n",
+       "      <td>-0.121601</td>\n",
+       "      <td>-0.047864</td>\n",
+       "      <td>-1.14718</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 114
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:37.330446Z",
+     "start_time": "2025-06-20T18:11:35.805989Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "rfmodel = RandomForestClassifier(n_estimators=200, random_state=3, max_depth=10)\n",
+    "rfmodel.fit(train_prepared, y_train)"
+   ],
+   "id": "f669257d496a83de",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestClassifier(max_depth=10, n_estimators=200, random_state=3)"
+      ],
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, n_estimators=200, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+       "            <td class=\"value\">200</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">criterion&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_depth&nbsp;</td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_features&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">oob_score&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_samples&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 115
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:38.106393Z",
+     "start_time": "2025-06-20T18:11:37.818055Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def evaluate_model(model):\n",
+    "    tr_preds = model.predict(train_prepared)\n",
+    "    print('Train Accuracy', accuracy_score(y_train, tr_preds))\n",
+    "    ts_preds = model.predict(valid_prepared)\n",
+    "    print('Validation Accuracy', accuracy_score(y_valid, ts_preds))\n",
+    "    print('\\n')\n",
+    "    ts_preds = model.predict_proba(valid_prepared)[:, 1]\n",
+    "    tr_preds = model.predict_proba(train_prepared)[:, 1]\n",
+    "    print('Train AUC Score', roc_auc_score(y_train, tr_preds))\n",
+    "    print('Validation AUC Score', roc_auc_score(y_valid, ts_preds))\n",
+    "\n",
+    "\n",
+    "evaluate_model(rfmodel)"
+   ],
+   "id": "446edf7376191f52",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Accuracy 0.9417142857142857\n",
+      "Validation Accuracy 0.8982222222222223\n",
+      "\n",
+      "\n",
+      "Train AUC Score 0.9818998109429211\n",
+      "Validation AUC Score 0.9274699781659389\n"
+     ]
+    }
+   ],
+   "execution_count": 116
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:38.392348Z",
+     "start_time": "2025-06-20T18:11:38.204252Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def plot_feature_importance(model):\n",
+    "    feature_importances = model.feature_importances_\n",
+    "    model_name = type(model).__name__\n",
+    "    feature_importance_df = pd.DataFrame({'Feature': train_prepared.columns,\n",
+    "                                          'Importance': feature_importances}).sort_values(by='Importance',\n",
+    "                                                                                          ascending=False)\n",
+    "    plt.figure(figsize=(9, 5))\n",
+    "    a = sns.barplot(y=feature_importance_df['Feature'][:18], x=feature_importance_df['Importance'][:18] * 100,\n",
+    "                    palette='Reds_r')\n",
+    "    heights = [str(round(height, 1)) + '%' for height in a.containers[0].datavalues]\n",
+    "    a.bar_label(a.containers[0], labels=heights, label_type='center')\n",
+    "    plt.title(f'{model_name.capitalize()} Feature Importance')\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "plot_feature_importance(rfmodel)"
+   ],
+   "id": "176997daa9dc6320",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 117
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:38.531736Z",
+     "start_time": "2025-06-20T18:11:38.495049Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "tt_preds = rfmodel.predict_proba(test_prepared)[:, 1]\n",
+    "print('Test AUC Score', roc_auc_score(y_test, tt_preds))"
+   ],
+   "id": "5de018c18d8ee099",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test AUC Score 0.9280755322052402\n"
+     ]
+    }
+   ],
+   "execution_count": 118
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:38.760831Z",
+     "start_time": "2025-06-20T18:11:38.626345Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "test_data = pd.read_csv('../assets/BCCP/test.csv')\n",
+    "CustomerID = test_data['id']\n",
+    "test_data = preprocessing.transform(test_data)\n",
+    "preds = rfmodel.predict_proba(test_data)[:, 1]"
+   ],
+   "id": "7bfb79c5c9fe66d5",
+   "outputs": [],
+   "execution_count": 119
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:38.887034Z",
+     "start_time": "2025-06-20T18:11:38.853703Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "submission = pd.DataFrame({'id': CustomerID, 'Exited': preds})\n",
+    "submission.to_csv('submission_rf.csv', index=False)\n",
+    "submission"
+   ],
+   "id": "81c59f3776bdbbef",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "         id    Exited\n",
+       "0     15000  0.166806\n",
+       "1     15001  0.022604\n",
+       "2     15002  0.043870\n",
+       "3     15003  0.695240\n",
+       "4     15004  0.031620\n",
+       "...     ...       ...\n",
+       "9995  24995  0.016138\n",
+       "9996  24996  0.009818\n",
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+       "<p>10000 rows × 2 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 120
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:40.168441Z",
+     "start_time": "2025-06-20T18:11:39.008441Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "xgmodel = XGBClassifier(n_estimators=1500, verbose=0, learning_rate=.009, njobs=-1, random_state=3)\n",
+    "xgmodel.fit(train_prepared, y_train, eval_set=[(valid_prepared, y_valid)], eval_metric='auc', early_stopping_rounds=30)"
+   ],
+   "id": "1344df5295a992cd",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[21:11:39] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0fdc6d574b9c0d168-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
+      "Parameters: { \"njobs\", \"verbose\" } are not used.\n",
+      "\n",
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+      "[303]\tvalidation_0-auc:0.92476\n",
+      "[304]\tvalidation_0-auc:0.92468\n",
+      "[305]\tvalidation_0-auc:0.92469\n",
+      "[306]\tvalidation_0-auc:0.92468\n",
+      "[307]\tvalidation_0-auc:0.92466\n",
+      "[308]\tvalidation_0-auc:0.92468\n",
+      "[309]\tvalidation_0-auc:0.92461\n",
+      "[310]\tvalidation_0-auc:0.92464\n",
+      "[311]\tvalidation_0-auc:0.92462\n",
+      "[312]\tvalidation_0-auc:0.92464\n",
+      "[313]\tvalidation_0-auc:0.92468\n",
+      "[314]\tvalidation_0-auc:0.92468\n",
+      "[315]\tvalidation_0-auc:0.92468\n",
+      "[316]\tvalidation_0-auc:0.92469\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=None, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
+       "              interaction_constraints=None, learning_rate=0.009, max_bin=None,\n",
+       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "              n_estimators=1500, n_jobs=None, njobs=-1, num_parallel_tree=None,\n",
+       "              predictor=None, ...)"
+      ],
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=None, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
+       "              interaction_constraints=None, learning_rate=0.009, max_bin=None,\n",
+       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "              n_estimators=1500, n_jobs=None, njobs=-1, num_parallel_tree=None,\n",
+       "              predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('objective',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">objective&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;binary:logistic&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('use_label_encoder',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">use_label_encoder&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('base_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">base_score&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('booster',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">booster&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('callbacks',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">callbacks&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('colsample_bylevel',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">colsample_bylevel&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('colsample_bynode',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">colsample_bynode&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('early_stopping_rounds',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">early_stopping_rounds&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('enable_categorical',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">enable_categorical&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('eval_metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">eval_metric&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_types',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_types&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">gamma&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gpu_id',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">gpu_id&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('grow_policy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">grow_policy&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('importance_type',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">importance_type&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('interaction_constraints',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">interaction_constraints&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
+       "            <td class=\"value\">0.009</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_bin',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_bin&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_cat_threshold',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_cat_threshold&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_cat_to_onehot',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_cat_to_onehot&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_delta_step',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_delta_step&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_depth&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaves',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_leaves&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_child_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotone_constraints',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">monotone_constraints&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+       "            <td class=\"value\">1500</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('num_parallel_tree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">num_parallel_tree&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('predictor',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">predictor&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('reg_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('reg_lambda',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sampling_method',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">sampling_method&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scale_pos_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">scale_pos_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">subsample&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tree_method',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tree_method&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate_parameters',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate_parameters&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbosity',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbosity&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('njobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">njobs&nbsp;</td>\n",
+       "            <td class=\"value\">-1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 121
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:40.353587Z",
+     "start_time": "2025-06-20T18:11:40.316800Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "evaluate_model(xgmodel)",
+   "id": "c4271bf3754b20ca",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Accuracy 0.9218095238095239\n",
+      "Validation Accuracy 0.9013333333333333\n",
+      "\n",
+      "\n",
+      "Train AUC Score 0.9605690665094028\n",
+      "Validation AUC Score 0.9249003333593262\n"
+     ]
+    }
+   ],
+   "execution_count": 122
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:40.902069Z",
+     "start_time": "2025-06-20T18:11:40.616069Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plot_feature_importance(xgmodel)",
+   "id": "ed191776045b1f06",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 123
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:41.918377Z",
+     "start_time": "2025-06-20T18:11:41.872680Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "preds = xgmodel.predict_proba(test_data)[:, 1]\n",
+    "submission = pd.DataFrame({'id': CustomerID, 'Exited': preds})\n",
+    "submission.to_csv('submission_xgb.csv', index=False)\n",
+    "submission"
+   ],
+   "id": "f0255d9dc4675132",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "         id    Exited\n",
+       "0     15000  0.132331\n",
+       "1     15001  0.054556\n",
+       "2     15002  0.051230\n",
+       "3     15003  0.792428\n",
+       "4     15004  0.068198\n",
+       "...     ...       ...\n",
+       "9995  24995  0.042877\n",
+       "9996  24996  0.047073\n",
+       "9997  24997  0.042877\n",
+       "9998  24998  0.127413\n",
+       "9999  24999  0.196946\n",
+       "\n",
+       "[10000 rows x 2 columns]"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>Exited</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15000</td>\n",
+       "      <td>0.132331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15001</td>\n",
+       "      <td>0.054556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15002</td>\n",
+       "      <td>0.051230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15003</td>\n",
+       "      <td>0.792428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15004</td>\n",
+       "      <td>0.068198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>24995</td>\n",
+       "      <td>0.042877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>24996</td>\n",
+       "      <td>0.047073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>24997</td>\n",
+       "      <td>0.042877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>24998</td>\n",
+       "      <td>0.127413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>24999</td>\n",
+       "      <td>0.196946</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 2 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 124
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:47.509255Z",
+     "start_time": "2025-06-20T18:11:42.056845Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "lgmodel = LGBMClassifier(learning_rate=0.0088, max_depth=16, n_estimators=2000, num_leaves=16, random_state=3)\n",
+    "lgmodel.fit(train_prepared, y_train, eval_set=[(valid_prepared, y_valid)], eval_metric='auc')"
+   ],
+   "id": "deb66bd1e37862a4",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Info] Number of positive: 2136, number of negative: 8364\n",
+      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000240 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 1084\n",
+      "[LightGBM] [Info] Number of data points in the train set: 10500, number of used features: 13\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203429 -> initscore=-1.365002\n",
+      "[LightGBM] [Info] Start training from score -1.365002\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LGBMClassifier(learning_rate=0.0088, max_depth=16, n_estimators=2000,\n",
+       "               num_leaves=16, random_state=3)"
+      ],
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(learning_rate=0.0088, max_depth=16, n_estimators=2000,\n",
+       "               num_leaves=16, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('boosting_type',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">boosting_type&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;gbdt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('num_leaves',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">num_leaves&nbsp;</td>\n",
+       "            <td class=\"value\">16</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_depth&nbsp;</td>\n",
+       "            <td class=\"value\">16</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
+       "            <td class=\"value\">0.0088</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+       "            <td class=\"value\">2000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample_for_bin',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">subsample_for_bin&nbsp;</td>\n",
+       "            <td class=\"value\">200000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('objective',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">objective&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_split_gain',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_split_gain&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_child_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_child_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_child_samples&nbsp;</td>\n",
+       "            <td class=\"value\">20</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">subsample&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample_freq',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">subsample_freq&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('reg_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('reg_lambda',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('importance_type',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">importance_type&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;split&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 125
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:47.952452Z",
+     "start_time": "2025-06-20T18:11:47.650664Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "evaluate_model(lgmodel)",
+   "id": "8cc117164f08cdb0",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Accuracy 0.943047619047619\n",
+      "Validation Accuracy 0.8982222222222223\n",
+      "\n",
+      "\n",
+      "Train AUC Score 0.9813163401379552\n",
+      "Validation AUC Score 0.9260736704616345\n"
+     ]
+    }
+   ],
+   "execution_count": 126
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:48.380159Z",
+     "start_time": "2025-06-20T18:11:48.200631Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plot_feature_importance(lgmodel)",
+   "id": "aa992a0b910eeecd",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ],
+      "image/png": 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zzjt07tyZd999FycnJ5YuXcpPP/3El19++UzuxaNs2rSJfPny4ePjw/79+1m0aBE9e/Yka9aslChRgmbNmjFt2jTi4uKoXLkyv//+O9OmTaNy5crUrFnzH4+dM2dOfvvtN/bv30/ZsmVTfJ+fJE+ePLz33nssWLAAR0dH6+Q73377Lb169cLe3j5F7S4itkfBTUREHuv8+fOMHj062fJmzZpRo0YNGjVqxO3bt5k7dy4rVqygZMmSDBgwgAEDBlh7De4bOXIk48aN49y5c5QqVYrZs2dbey2elp2dHbNnz2bixIkEBwdz+/ZtXn31VebMmZNkQooH6w4PD2fFihUsXryYAgUK4OPjQ+vWrfniiy84deoUJUqUYM6cOUydOpUBAwZw584dSpYsSXBwMFWqVAHuTes/adIkpk6dSnR0NO7u7nz88ccpnu3xYVmzZmXRokVMnDiR8ePHc+vWLQoVKkTv3r358MMP/9U9+ic1atRg7ty5TJs2je7du+Pg4MDrr7/O119/bZ0Ao1+/fiQkJDBlyhTi4+MpXLgwXbt25dSpU/z888/WoZBdunRhxowZdOzYkQ0bNvDyyy8zduxYQkJC6NSpE8WLF2f48OEMHz78iXW9+uqrLFq0iMmTJ/P5559jsVgoVaoU06dPf+IrENJSjx492L9/P0uXLsXd3Z1Bgwbx7rvvWtePHDmSl156iRUrVjB37lzy589PUFAQH3300ROfu/zwww8ZNWoU7du35+uvv07RfU6pzz77jHz58vHdd98xb948ChcuTP/+/WndujWQsnYXEdtjsKTVk8UiIpLp/PDDD5QuXTrJszTbtm2jc+fOrFmzhldffTUdqxN5OuHh4fj7+zN69GgCAgLSuxwREUA9biIi8i+sXbuWyZMn8+mnn+Lu7s7Zs2f58ssv8fb2VmgTERFJQwpuIiLy1MaOHWsd3hcVFUW+fPlo0KAB3bt3T+/SREREXigaKikiIiIiImLj9DoAERERERERG6fgJiIiIiIiYuMU3ERERERERGycgpuIiIiIiIiNU3ATERERERGxcXodgGQoUVG3MJvTuwp51gwGyJs3B9eu3ULz3r741N6Zi9o7c1F7Zy5q79S7f89SQsFNMhSDwYidXXpXIc+L0ahBAZmJ2jtzUXtnLmrvzCUjtbfZbMFszhgpU+9xExERERGRTMmUaOL6jTvpFt4MBsiXTz1u8gJa2ftz/vr1eHqXISIiIiIZnGuJEgROn4rRaMgQvW4KbpKhXP3f/7h4TMFNRERERDKXjDMAVUREREREJJNScBMREREREbFxCm4iIiIiIiI2Ts+4iYiIpJEL9kb2O9sn+a1ooUQz3nGJnHIwcsrRjjsGA1ksFkrEmyiR8P8vptyc1YHbRkOS4/nfTsDeYmFzNsckyy2A2WDANzaevGYLcQY47GRPpL0RgwWKJpooe9dkreOa0cARZ3tuGg04WeC1+EReTnj8SzH/cLTjlIMd8QZwMVmoGJdIjr8nof7TwY7fHe0wAKXjEyn+93HMwM9ZHah85/+3FRGRtKPgJiIikkai7AwUTTRTKS4xyfKLdkaOO9lT63YCLmYLUUYD27I6kNOcQH6ThQTgltFAg9h4sj0i8zSLibd+bwZ2ZnEgm9lM3r9nQQt1diCLxULDmHjiDLAniwN/OoBHgol4YHdWB0rfTeSVBDNX7QzsyeJALlMCeR4xi9pZeyOnHOyocSeB7GYLvzrZsTeLPXVvJ5AIHHWyo87tBCzAlqwOvJQQjz33Ap1bolmhTUTkGdFQyQzixo0bDBkyBB8fH8qXL0+NGjXo27cvly5dSu/SRETkb9FGIy6m5D1ZBU1m3oqJx8VswQzcNRgwAA5/Z5xoOwOOFh4Z2h72u6Mddw3gefdeOIwxQKS9kTJ3E7EHslvgtXgTpx3tAIhwMOJosVAiwYwRyG+yUDTBbF3/sDMOdrySYCKX2YIdUOauidtGA5F292qGez1+D7ptgHMORl6LNz35AkRE5KkouGUQPXv2JDo6muXLl3PkyBFWr15NfHw8H3zwAYmJiU8+gIiIPFMW4LqdgUv2RjZkc2R9NkcOOdlzv6/MAbhlMLAquyO7szrwSoIJl797vKLtjNgD27I4sDabI1uyOnDRLvlf0TGGe8MYK8Ylcj923bS7F8yyPJCmcpot3DYaiAduGA3kMiWNWjnNZq4/NCzzvpt2Sbc3AtnNFm4Y79XoeTeR3Vkc2JPFgYpx98LiL072vHHXxKOjoIiIpAUFtwzi0KFD1K1bF1dXVwDy5ctH//79KVeuHDdv3sTPz4+VK1datw8NDcXDwwOA8PBwPDw8GDNmDJUqVWLo0KEEBwfTvXt3+vTpg5eXF7Vq1WLixInW/S9fvsynn36Kn58f5cqVw9/fn+XLl1vXe3h4sHTpUurXr0+5cuXo0qULv/76K++88w6enp40b96cc+fOWbdfv349jRo1omLFigQEBLBr165nfctERJ6ruwbIbbJQKMFMvdh4fG/HE2M0sD/L/z+VkM1ioVlMPH6x8Vywt+PE/V4vC7iYzFSMS+Tt2HhKxpvYl8Weaw+FqxOO9rgl/v8QSYAEwO6hLjC7v4crJhogEUOyQGUHJBoeHdwSAfuH+tTsLPeOBfBKgpmGsfG8FRtPsUQzl+wMWIB8JjN7ne35b1YHwpzsefwTdCIi8jQU3DKIhg0bMnjwYIYMGcKGDRuIiIjA1dWVMWPGkCdPnhQdIzY2lt27d9OzZ08AfvzxR2rUqEFoaCjDhw9n9uzZHDlyBICBAwfi4ODA+vXrOXz4MG3btmX48OHExsZaj7du3TqWLl3Kf//7Xw4dOkS3bt0YOXIku3fvxtHRkZkzZwKwfft2Bg8ezKBBg9i/fz+ffPIJn3zyCX/++Wfa3iQRkXTkbIHadxJ4OdGMPZDVAmXuJnLJzkjC39sY//7KY7ZQMsHEBft7fw17JJio+vcEIEagaKKZ/CYz4Q7//9d0InDBwUjJhKTDEe2Bhwcomv4OZQ6WeyEs2XrA4THPotlxL+wlPR7YP2JzE3DMyZ5ydxM54WSHowXq3E4gxmjgjIP+iSEikpb0p2oGMWLECAYNGsRff/3FoEGD8PPzo27duqxduzbFx2jatCmOjo7kzJkTgGLFitG0aVPs7Ozw8fHB1dWVs2fPWs83ePBgHBwcuHjxItmyZSMuLo4bN25Yj9e2bVty585N/vz5KVmyJPXq1aN48eJkzZqVKlWqEBERAcC3337Lu+++S6VKlbCzs8PX1xc/Pz+WLFmSdjdIRCSdXTcaOOZol6SvymwAA3Da0Y59zknnAzMBjn9v/IeDHZftkoYlM4YkPWl/2RtxskC+h4c9mizEGw3EPbD7TaOBLGYLDtwbNnnzoZ67m0YjOR8xMQlALrOFmw/UYgZijAZympP3oZ10tKNIoplsFrhhNOJiNmMAcpvM3DTqnxgiImlJs0pmEEajkSZNmtCkSRMsFgunT59mzZo1fP7559bhk0+SP3/+JJ8f3s/BwQHz338xX7hwgXHjxnH27FmKFSvGSy+9BGBdD5A7d27r93Z2duTKlStJvZa/f5sbERHB/v37+e6776zrTSYTVapUSVHdIiIZgaPFwilHOxyBkvEm4gxw1MmelxLMFEg081tWBy7YGymcaOaanYFTDnbWCUbuGOGsgz3V7ySQ1QLn7I1cszNQIe7/+8qu2RnIZzLz8ADHHBYLeRPN/OJkT4W4ROIN9yYwKfZ3z1yhBDPHnOz508GO4gkmrtoZOO9gpNqdBB6lWIKJ3/4ekpnj71klnS0WXB8KjLEGiLA34nf73nFymM1cszNSLMFMtJ0Rt0QNlhQRSUsKbhnAzp076d69O1u3biV37twYDAZKlChB79692b17N7/99htGo5GEhP//Szg6OjrZcQyPeZ7hYQkJCXTu3JlevXrRunVrDAYDv/76a7LevZQez83NjaZNm9KpUyfrsosXL+Ls7Jyi/UVEMoKsFqhxJ4FjTvb87miHnQWKJJoo8/ekHVXuJHLcyY5DzvZkNVsofzeRIn+HmzJ3TeAE27I6kmC410tW/U4C2R/ISjFGAzlNj+4lqxqXQJiTPRuzOWLg3nvcSv89w6MTUPN2Ar8423PcyQ4nC5SPSyT/38eKtDOwK4sD9WPjyWqBYglmEjCxN4sDd/9+j1v124nJhugccban7N3/X+4RbyLU2YG12R0pkGimeIJmmBQRSUsKbhlApUqVyJs3L//5z3/49NNPeeWVV7h79y4///wzZ8+epXbt2uzfv58tW7bQpEkTbt26xTfffPPU50tISCAuLg5nZ2cMBgMXL15k/Pjx1nWpFRgYyIgRI6hSpQply5bl2LFjdOzYkW7duvHee+89dZ0iIrbG1WSx9kA9rKDJTMHbj+6FsgPK3zVR/u7jw06NO4+fQdjZAlXjHr8+j9mC72PqcjVZkrwnzgCUSjBR6gnBq/pD9WT5+xk/ERF5NhTcMgBnZ2cWL17MtGnT6Nq1K9euXcPBwYHy5cvz9ddfU7x4cfr06cOQIUOoXr06+fPn5/333+fQoUNPdb6sWbMyatQopk6dyogRI8ibNy+BgYGcOnWKkydP8vLLL6fqeG+++Sa3b9+mf//+XLx4kdy5c9OuXTuCgoKeqj4RERERkczGYLE8ZlopERv0VbMWnAs9mN5liIiIiEgGV7DM63y0eT3R0bEkptNzuQYD5MuXI0XbasonERERERERG6fgJiIiIiIiYuMU3ERERERERGycgpuIiIiIiIiNU3ATERERERGxcXodgGQo+V55hYTbd9K7DBERERHJ4FxLlEjvElJFrwMQEREREZFMyZRo4vqNO5jN6ROJUvM6APW4SYYSHR2b3iXIc+Likk3tnYmovTMXtXfmovbOXDJae5vNlnQLbaml4CYZitlsxpw+70eU58hguPdfk8mMxgS8+NTemYvaO3NRe2cuau9nS5OTiIiIiIiI2DgFNxERERERERunoZKSoRiNRoz6dUOmYWenxs5M1N6Zi9o7c1F7Zy5p0d4Z6dmz50WzSoqIiIiIiE0xJ5qITsfZHp8XzSopL6xt/xnI1d9+T+8yREREROQZcSn+Cv6TxmM0Gl744JYaCm6SoVw/c5arx39L7zJERERERJ4rDTgWERERERGxcQpuIiIiIiIiNk7BTURERERExMYpuImIiIiIiNg4BbdUMplMXLhw4bmc6+zZs8/lPCIiIiIiYtsyZXDz8/OjTJkyeHp6Jvs6ePDgP+7bs2dPVq9eDcDFixfx9PTk4sWLaV7jokWL+OKLL556/+DgYIKCgqyf//zzTz766CMqV66Mp6cn9erVY/LkycTHx6foeCtXrsTPz++p6xERERERkaeXaV8HMHToUAICAlK9X3R0tPX7ggULEhYWlpZlWUVFRaXZsWJiYggKCuLDDz9kwoQJODs7c+rUKXr16sXVq1cZOXJkmp1LRERERETSXqbscXuSzZs307BhQypWrEiDBg2YMWMGAAMGDODgwYPMmjWLLl26EB4ejoeHB+Hh4QB4eHiwdOlS6tevT7ly5ejSpQu//vor77zzDp6enjRv3pxz584BEB8fz9ixY2nQoAGenp5UrVqV4cOHY7FYWLVqFbNmzeLgwYN4eXkB98LXsGHD8PHxoWrVqvTs2ZOrV69aaz58+DDNmzenfPnyvPPOO9aaAP73v/8RHR1N06ZNyZIlCwaDgZIlSzJgwABy5sxp3e7nn3/mnXfeoWrVqpQrV462bds+drjm8uXLCQgIsPbgde7c2Ro2g4OD+fDDD2nevDne3t5MmzaNihUrcvfuXev+mzZtwtfXF4tFL1UUEREREXkSBbeHxMXF8dlnnzFo0CAOHTrExIkTmT17NkePHmXkyJF4eXnRuXNnZs6c+cj9161bx9KlS/nvf//LoUOH6NatGyNHjmT37t04Ojpa91uwYAE7d+5kwYIFhIWFMWPGDJYsWcK+ffto1qwZnTt3xsvLyzp0s3///pw7d46VK1fy008/kT17dj7++GMsFgvR0dF07tyZ+vXrc+DAAT777DN++ukna02vvvoqxYsX591332Xy5Mns2LGD69evU6VKFfr27QvApUuX6NGjB506dWLv3r1s27YNi8XC9OnTk13j0aNHGTFiBEOGDCE0NJSNGzdy9uxZvvnmG+s2e/fupU+fPmzdupVOnTphZ2fHli1brOtXr15Ns2bNMBgM/77RRERERERecJl6qOSoUaOSLHN3d2fZsmU4OzuzfPlyzGYzFSpU4NChQxiNKcu4bdu2JXfu3ACULFmS0qVLU7x4cQCqVKnCoUOHAAgMDKRZs2bkzZuXK1euEBcXR7Zs2bh8+XKyY167do3NmzezceNG8ubNC9wLcl5eXhw/fpw///yTLFmy0LFjRwwGAxUrVqR58+b8/vvvADg6OvL999+zePFifv75Z+bOnUtiYiIVKlSgX79+lC1bljx58rB+/XqKFi1KTEwMly5dwsXF5ZH1lCpVih9++IHChQtz48YNrly5Qp48eZJsW6RIEapWrWr9/Pbbb7NmzRreeustrl27xq5duxgwYECK7qmIiIiISGaXaYPb4MGDH/uM23fffceMGTPo3bs3MTEx1K9fn4EDB5IrV64nHvd+aAOws7NLso/RaLQODbxz5w7Dhg3jwIEDuLm5Ubp0aSwWC2azOdkxIyIigHth70F2dnaEh4dz+fJl3N3dk/ReFS1a1BrcALJnz06nTp3o1KkT8fHx/Prrr8yePZsPPviAn3/+mZw5c/LDDz+wZMkSDAYDpUqVIiYmBnv75D8iRqORb775hnXr1pE1a1Y8PDyIiYlJMuwxf/78SfYJCAigVatWXLt2jbVr11KhQgWKFCnyxPspIiIiIiKZOLg9TkxMDFeuXGHixIkA/P777/Tq1YuZM2dahxX+k5QO/bsfBHft2oWTkxNms5lKlSo9ctsCBQoAsHHjRlxdXa3LT506RZEiRdi4cSMRERGYzWZrz+ClS5es202ePJk9e/awbNky4F4PXIUKFRg/fjwVK1bk/PnzXLhwgW+//ZbvvvuOl156CYDhw4dz8uTJZPXMnz+f3bt3s27dOvLlywdAly5d/vE+vPHGG5QoUYLNmzezfv36JDNeioiIiIjIP9Mzbg+JjY2lY8eOrFu3DovFQv78+TEajbi4uAD3Qs+tW7f+9XliYmJwcnLCaDQSExPDuHHjiImJISEhAQAnJydrL1aBAgWoXbs2I0eOJDo6moSEBEJCQmjRogU3b97Ez88Pi8VCcHCwtTftfkgDaNCgAX/88QdTpkwhIiICi8XC1atXmT59Oi+99BIeHh7cunULo9GIs7MzFouFHTt2sHr1ams9D9dub2+Pg4MDiYmJrFmzhp07dz5y2wcFBATw/fffc/bsWerVq/ev76GIiIiISGaRaYPb4MGDH/ket7Vr1/Lll18ye/ZsKlSowNtvv02VKlVo164dAE2bNmXFihW0bt36X51/4MCBnDhxAm9vb958801iYmKoWbOmtYfL19eX69evU7FiRW7evMm4cePImTMnTZs2pUqVKmzfvp05c+bg6upKzpw5mTt3Lnv37sXb25sBAwZQv35967leffVVvv32W06ePEmLFi0oV64cTZs25fr16yxcuBBHR0eaNWtGtWrVaNiwIVWqVCEkJIT333+fM2fOJHvX24cffoi7uzu+vr7UrFmTtWvX0rp160f2zj2oUaNGnDp1irfeeossWbL8q/snIiIiIpKZGCyaj12eE5PJRI0aNZg5cyblypV7qmOsfqctlw4eSuPKRERERMRW5Hu9NC3WrCA6OpbExOTzP7xIDAbIly9HirbVM27yXPz5559s3LgRNze3pw5tIiIiIiKZlYKbPBedO3cG4Msvv0znSkREREREMh4FN3kufv755/QuQUREREQkw8q0k5OIiIiIiIhkFApuIiIiIiIiNk5DJSVDyf1yMRLv3EnvMkRERETkGXEp/kp6l2CT9DoAERERERGxKeZEE9E37mA2v9hRRa8DkBdWdHRsepcgz4mLSza1dyai9s5c1N6Zi9o7c0mr9jabLS98aEstBTfJUMxmM+YX+z2Mwr3fPgGYTGY0JuDFp/bOXNTemYvaO3NRez9bmpxERERERETExim4iYiIiIiI2DgNlZQMxWg0YtSvGzINOzs1dmai9s5c1N6Zi9rbdulZsoxDs0qKiIiIiGRSZpOJ6OtpM3vj/RkSr169pWfcUkizSsoL6+CgIUT//nt6lyEiIiKS4eV85RUqjx2N0WhQr1sGoOAmGcrNs2e5/vuJ9C5DREREROS50oBjERERERERG6fgJiIiIiIiYuMU3ERERERERGycgpuIiIiIiIiNU3CTVFm0aBEeHh7Mnz8/vUsREREREck0FNwkVRYtWsS7777LN998Q2JiYnqXIyIiIiKSKSi4SYrt3buXa9eu0a9fP8xmM5s3b7aui46OpmfPnlSsWBF/f38WLlxI6dKlCQ8PB+D8+fN06dKFypUr4+vry+TJk4mPj0+vSxERERERyVAU3CTFFi5cSGBgIM7OzrRu3Zp58+ZZ1/Xp04dbt26xZcsWli1bxtatWzGZTADcvn2bdu3aUbJkSXbs2MHixYvZs2cPwcHB6XUpIiIiIiIZioKbpEhERAQ7d+6kTZs2AAQGBnLq1Cn279/P5cuX2bVrF/379yd37tzkyZOH/v37W/fdtm0b8fHx9OrVCycnJ9zd3enRoweLFi1Kr8sREREREclQ7NO7AMkYFi9eTGJiIk2aNLEuS0xMZN68eXTp0gWAwoULW9cVKVLE+n1ERARRUVFUqlTJusxisZCQkMC1a9fImzfvc7gCEREREZGMS8FNnuju3bssX76ckSNHUq1aNevykydP0qlTJzp37gzcC2gvv/yy9fv73NzcKFq0KJs2bbIui4mJ4dq1a+TJk+c5XYWIiIiISMaloZLyROvWrcNgMNCoUSPc3NysX7Vq1aJUqVKsXLkSX19fxo8fz40bN7hx4wbjxo2z7u/r60tsbCxz5swhPj6emzdv0rdvX3r27InBYEjHKxMRERERyRgU3OSJFi9eTKNGjXBwcEi2rlWrVqxZs4aRI0diMBioXbs2zZo1o3Tp0gA4ODiQPXt25s+fT2hoKLVq1aJOnToYjUZCQkKe96WIiIiIiGRIBovFYknvIiTj2717NxUrVsTZ2RmAP/74g6ZNm3LkyBGcnJzS7Dw/v9eOa4fD0ux4IiIiIplV7tdepe6ypURHx5KYaP7XxzMYIF++HFy9egsljJS5f89SQj1ukibGjh1LSEgIiYmJxMTEEBISQrVq1dI0tImIiIiIZFYKbpImJk6cyJEjR6hSpQp+fn7Y2dklec5NRERERESenmaVlDRRsmRJFixYkN5liIiIiIi8kNTjJiIiIiIiYuMU3ERERERERGychkpKhpKzWDFMd+6kdxkiIiIiGV7OV15J7xIkFfQ6ABERERGRTMpsMhF9/Q5m87+PBHodQOql5nUA6nGTDCU6Oja9S5DnxMUlm9o7E1F7Zy5q78xF7W3bzGZLmoQ2efYU3CRDMZvNmP/9+yHFxhkM9/5rMpn1G7tMQO2duai9Mxe1t0ja0eQkIiIiIiIiNk7BTURERERExMZpqKRkKEajEaN+3ZBp2NmpsTMTtXfmovbOXB5sbz1TJfJ0NKukiIiIiDw3aTmLodgWzSqZeppVUl5YJ0aNIuaPP9K7DBEREXkKWYsVo/TQoRiNBgU3kVRScJMM5fa5c8ScPJneZYiIiIiIPFcaYC4iIiIiImLjFNxERERERERsnIKbiIiIiIiIjVNwExERERERsXEKbpnE2bNn07sEERERERF5Sgpuz8mZM2fo27cvtWrVwtPTkzp16jBhwgRiY2PT7BweHh6EhoYC0LBhQ9auXQvAokWL+OKLL6zbmc1mZs+ezVtvvYWnpyeVKlWiffv2hIWFpVktIiIiIiKSdhTcnoPDhw/TrFkzChUqxOrVqwkLC2P27Nn88ssvfPjhh5hMpjQ/5/r162ncuDEAUVFRSdZ99dVXrFq1ii+//JLDhw+zY8cOqlSpwvvvv8+5c+fSvBYREREREfl3FNyeg0GDBtG0aVO6d+9Onjx5AHj55ZeZPHkyefPm5cKFC3h4eDBixAgqV65Mly5dANizZw8tWrTAy8srSQ8aQEJCAqNHj6Zy5cpUqVKFOXPmJDmnn58fK1euZNWqVcyaNYuDBw/i5eUFwKFDh/Dy8qJEiRIYDAayZMlCx44dCQwM5OrVqwAkJiYydepUfHx8qFChAm3atOHEiRMAxMXFMW7cOHx8fKhUqRJBQUEcPXrUeu7UXouIiIiIiPwzvYD7GTt//jx//vknQ4YMSbYuX758zJgxI8m227ZtIyEhgRMnTtC1a1fGjx+Pv78/v/zyC926dcPFxYWaNWsyY8YMtm3bxvLly8mbN+8jjw/QrFkzwsPD2b9/PwsXLgTuDaPs378/169fp2bNmpQtW5aSJUsycOBA634hISH88MMPzJ07l5dffplp06bRuXNnfv75Z4YMGcJvv/3GN998g7u7O9999x3t2rXjhx9+oGDBgqm+FhERERER+WfqcXvG7g9TzJcv3xO3ffvtt8mSJQs5c+ZkyZIl+Pv7U69ePezs7KhQoQKBgYEsWrQIgDVr1tC+fXuKFClC1qxZGThwIAaDIUU1NW3alG+++QZnZ2emTZtG48aNqVq1KpMmTSIxMRGAVatW0aFDB0qUKIGdnR1du3Zl6tSpxMXF8cMPP9C7d29eeuklHB0def/993nllVf44YcfnupaRERERETkn6nH7RlzdXUFIDIykmLFiiVbf/XqVWuoy58/v3V5REQE+/btsw5vBDCZTBQtWhSAK1eu4O7ubl2XM2dOcuXKleK6vLy8rMe+fPky27ZtY/z48RiNRj799FMiIyOtvWcAjo6OlC9fnitXrpCQkEDhwoWTHK9w4cKEh4dbP6fmWkRERERE5J8puD1jhQoVolSpUmzYsIFKlSolWXft2jV8fX0ZPXo0QJIeMzc3N5o1a8awYcOsy65cuYLFYrGuv3DhgnXd7du3uXXr1hPriY2NpXr16kyePBlfX18AChQoQKtWrThz5gy///47AO7u7vz111/W/RISEhg/fjzt27fHycmJCxcuULx4cev68+fP4+fnZ/2cmmsREREREZF/pqGSz8EXX3zBihUrmDZtGtHR0VgsFn7//Xe6dOnC66+/Tv369ZPt06JFC3744Qd27dqF2Wzm7NmztG3blnnz5gHQsmVL5syZw+nTp7l79y5jxox57OyUTk5OxMTEYLFYyJYtG/7+/owbN47Q0FBu375NfHw8hw4dYvPmzdSrVw+AgIAA5s6dy5kzZ0hMTGTWrFn89NNP5MmTh+bNmzNp0iTOnTtHfHw8CxYs4NSpUzRs2PCR53/StYiIiIiIyD9Tj9tz4O3tzbfffsvMmTNp2LAhd+7cIV++fLz55pt07twZBweHZPuUK1eOSZMmMWnSJHr06EGWLFl4++236dWrFwAdO3bkzp07tG3blsTERAIDA8mdO/cjz+/r68t3331HxYoV2bZtG6NHj2bOnDmMGDHCOrzxlVde4dNPP6VJkyYAdOjQgcTERNq3b8+NGzcoU6YMs2fPxsHBgc8//5zg4GDatWvH9evX8fDwsE5i8ihPuhYREREREflnBovGq0kGcrhzZ24+8OoBERERyTiylyqF14IFREfHkphoTu9yJI0ZDJAvXw6uXr2FEkbK3L9nKaGhkiIiIiIiIjZOwU1ERERERMTGKbiJiIiIiIjYOAU3ERERERERG6fgJiIiIiIiYuP0OgDJULK+9BLmuLj0LkNERESeQtZixdK7BJEMS68DEBEREZHnxmwyEX39Dmaz/gn6otHrAFIvNa8DUI+bZCjR0bHpXYI8Jy4u2dTemYjaO3NRe2cuD7e32WxRaBN5CgpukqGYzWbMel/nC89guPdfk8ms39hlAmrvzEXtnbmovUXSjiYnERERERERsXEKbiIiIiIiIjZOQyUlQzEajRj164ZMw85OjZ2ZqL0zF1tubz2DJSK2SMFNMhQXl2zpXYI8R2rvzEXtnbnYcntr1kMRsUUKbpKhhH85gTunT6V3GSIi8oJyLlKUIn36YzQaFNxExKYouEmGcjc8nLjTf6Z3GSIiIiIiz5XtDjAXERERERERQMFNRERERETE5im4iYiIiIiI2DgFNxERERERERunyUkyOA8PD5ycnLCzs8NiseDg4ICXlxeDBg3C3d39ifuHh4fj7+/Pli1bKFy48HOoWEREREREUks9bi+A2bNnExYWxpEjR9i6dSsWi4XPPvssvcsSEREREZE0ouD2gsmePTuBgYH8+uuv1mWnT5+mc+fO1K5dm7Jly/LWW2+xdevWR+5/+PBh3nvvPWrUqEGZMmUICAjgyJEjAISGhuLn50dISAg1a9bE29ubTz75hJiYGOv+CxYsoG7dunh6ehIQEMDevXsBsFgsfPPNN9SvXx8vLy9at26dpEYREREREXk8BbcXzI0bN1i/fj316tWzLvvkk08oVaoU//3vfzl48CA1atRgyJAhyfaNi4uja9eu1K9fnx07dhAaGkrRokUZN26cdZuIiAguX77Mf//7X5YtW0ZYWBiLFy8GYOXKlcyYMYNx48Zx6NAh3n33Xbp27cr169dZvHgxX3/9NVOnTmXv3r0EBATwwQcfcPXq1Wd+T0REREREMjo94/YC6NKlC3Z2dpjNZmJjY8mRIwezZs2yrp81axYFChTAYrEQERFBzpw5uXz5crLjODg4sHTpUl566SXu3r1LREQEuXPn5tixY0m2++ijj3B2duall16icuXKnDlzBoBVq1bRqlUrPD09AWjZsiXFixfH2dmZRYsW0blzZ1599VUAWrRowfLly1m7di0ffvjhs7o1IiIiIiIvBAW3F8DMmTOpXLkycK/XbNGiRbz//vssXbqU119/nRMnTtCtWzciIyMpXrw4efLkwWKxJDuOnZ0doaGhdOzYkdu3b1OiRAns7e2Tbevq6mr93sHBwbo+MjKSggULJtm2QoUKwL2eurFjxzJhwgTrusTERN544420uQkiIiIiIi8wBbcXjLOzM+3bt+err75iz5495MuXjx49ejBt2jT8/PwA2Lx5Mz/++GOyfX/55ReGDx/OkiVLrIFq3rx51h61J3F3d+evv/5Ksmzy5Mk0btwYNzc3unfvTsOGDa3rzp8/T+7cuZ/ySkVEREREMg894/aCSUxMZMWKFdy8eZOKFSsSGxuLyWQiS5YsAJw6dYrp06cDEB8fn2TfW7duYTQacXZ2BuDIkSN88803ybZ7nICAAJYuXcrRo0cxm82sWLGCRYsW4eLiQmBgICEhIZw+fRqAnTt30rBhQw4cOJBWly4iIiIi8sJSj9sLoGPHjtjZ2QFgMBgoVqwYkyZNsg5T/Pzzz/nss8+4c+cObm5uBAYGMn78eE6ePJmkx6t69eq0bt2aNm3aYDabKVy4MEFBQUycODFFk4g0atSImzdv8tlnnxEZGUmJEiWYPXs2efLkoV27dlgsFrp168aVK1coUKAAgwYNwt/f/5ncExERERGRF4nB8qiHnURs1OnPP+X2b8eevKGIiMhTcC5ekpJTZxIdHUtiojm9y8nwDAbIly8HV6/eQv/ifPGpvVPv/j1LCQ2VFBERERERsXEKbiIiIiIiIjZOwU1ERERERMTGKbiJiIiIiIjYOAU3ERERERERG6fXAUiG4lS4MOa7celdhoiIvKCcixRN7xJERB5JwU0ylMLd+6R3CSIi8oIzm0yYzZrLXERsi4KbZCjR0bHpXYI8Jy4u2dTemYjaO3Ox9fY2my0KbiJicxTcJEMxm82Y9T7UF57BcO+/JpNZL/DMBNTemYvaW0Tk6WhyEhERERERERun4CYiIiIiImLjNFRSMhSj0YhRv27INOzs1Ni2Rs/+iIiIpA8FN8lQXFyypXcJ8hypvW2P2WQi+vodhTcREZHnTMFNMpTIr6dz99z/0rsMkUzJsWBh8nfqidFoUHATERF5zhTcJEOJvxRB/HkFNxERERHJXPQAiYiIiIiIiI1TcBMREREREbFxCm4iIiIiIiI2TsFNRERERETExim4iYiIiIiI2DjNKvkCGzRoEOvWrQMgMTGRhIQEsmTJYl0/e/ZsvLy80qs8ERERERFJIQW3F9iwYcMYNmwYACtXrmTatGn8/PPP6VyViIiIiIikloZKZlLnz5+nS5cuVK5cGV9fXyZPnkx8fDxwL+S9++67jBgxgipVqlC1alUGDBhAQkICAP369aNfv35Jjufh4UFoaCgAfn5+DBo0iOrVq9O0aVPMZjPHjx8nKCiISpUqUa9ePebPn4/Fohf4ioiIiIikhIJbJnT79m3atWtHyZIl2bFjB4sXL2bPnj0EBwdbtzl8+DB58+Zl586dzJo1iw0bNvDjjz+m+BxHjx5l48aNfPPNN0RGRvL+++/z5ptvsmfPHmbMmMHixYtZunTps7g8EREREZEXjoJbJrRt2zbi4+Pp1asXTk5OuLu706NHDxYtWmTdxtnZmS5duuDg4EDZsmXx8PDgzJkzKT5H/fr1yZkzJzlz5mTt2rUUL16cNm3a4ODgQIkSJWjfvn2S84mIiIiIyOPpGbdMKCIigqioKCpVqmRdZrFYSEhI4Nq1awDkzZsXg8FgXe/g4JCqoY358+dPcr7jx48nmQjFbDZjZ2f3by5DRERERCTTUHDLhNzc3ChatCibNm2yLouJieHatWvkyZPnifsbjUbu3r1r/RwVFZVsmwdDn5ubG5UrV2bu3LnWZdHR0cTGxj7tJYiIiIiIZCoaKpkJ+fr6Ehsby5w5c4iPj+fmzZv07duXnj17Jglcj1O8eHEOHjzI5cuXiYuLY/r06f+4X6NGjThy5Ahr164lMTGRK1eu0KVLF8aMGZOWlyUiIiIi8sJScMuEsmfPzvz58wkNDaVWrVrUqVMHo9FISEhIivZv1aoVnp6eNG7cmLp16+Lu7k7BggUfu32hQoWYM2cOS5cupVq1ajRp0oRXXnlFwU1EREREJIUMFs3JLhlIxOj+3P3z9/QuQyRTciz6CoWHTCQ6OpbERHOaHddggHz5cnD16i30N9KLT+2duai9Mxe1d+rdv2cpoR43ERERERERG6fgJiIiIiIiYuMU3ERERERERGzcUwe3Gzdu8Ouvv2I2m4mPj0/LmkREREREROQBqQ5usbGx9O7dm8qVK9O2bVvOnj1L3bp1+d///vcs6hMREREREcn0Uv0C7nHjxnH79m02btxIYGAgRYoUwdfXl5EjRyZ5wbLIs+DoVgjLAy//FpHnx7Fg4fQuQUREJNNKdXDbunUr69atI1euXBgMBhwcHOjXrx+1atV6FvWJJOH6wUfpXYJIpmY2mTCbNceziIjI85bq4GY2m3F0dATg/ivgHlwm8ixFR8emdwnynLi4ZFN72yCz2aLgJiIikg5SHdyqVKnCsGHDGDRoEAaDAYApU6bg7e2d5sWJPMxsNmNOu/f+io36+48WTCazXuApIiIiwlNMTvKf//yH06dPU6lSJW7duoWnpycHDhygb9++z6I+ERERERGRTC/VPW63b99m6dKlHDt2jIiICNzc3Chbtix2dnbPoj4REREREZFML9XBrVWrVvz444+ULVuWsmXLPouaRB7LaDRi1GvjMw07u8zV2Hp+TERERB4n1cEtd+7cXL58mezZsz+LekT+kYtLtvQuQZ6jzNbeFpOJqOt3FN5EREQkmVQHt5IlSxIYGEj58uXJnz9/knWjR49Os8JEHiV6xdck/nUuvcsQSXP2rgVxadUJo9Gg4CYiIiLJpDq4Zc2alXr16j2LWkSeyHT1EgkXz6d3GSIiIiIiz1Wqg5t61URERERERJ6vVAe3adOmPXbdxx9//K+KERERERERkeRSHdxCQ0OTfL5+/TqnT5/mzTffTLOiRERERERE5P+lOrgtXLgw2bI1a9YkC3TyZCaTiYsXL1KkSJH0LkVERERERGxYmrwkqUmTJmzZsiUtDpVhLVq0CA8PD+bPn5/ifXr27Mnq1asBuHjxIp6enly8eDFN6jl69Cjdu3enWrVqVKhQgQYNGjBr1iwSExOfuO/69esJCgqicuXKVKpUiVatWrFp06Y0qeu+lStX4ufnl6bHFBERERF5UaVJcNu/fz9Zs2ZNi0NlWIsWLeLdd9/lm2++SVE4AoiOjrZ+X7BgQcLCwihYsOC/rmXTpk289957VKpUiR9//JFDhw4xceJE1q1bR+/evf9x3xEjRjBu3Dg6dOjAzp072bt3Lx07dmTAgAEsWrToX9cmIiIiIiKpl+qhkn5+fhgMBuvnhIQErl69SteuXdO0sIxk7969XLt2jX79+rFt2zY2b95Mw4YNAYiKimLUqFFs374do9FItWrVGDJkCOPGjePgwYOEhYVx/PhxBg4ciL+/P1u2bGHFihXs3buXJUuWWM8xfvx4/vzzT7766iuuXr3KmDFj2Lt3LwaDAT8/Pz7//HOyZ8/O3bt3GTx4MB999BFBQUHW/UuXLs2ECROYMWMG169fJyYmBn9/fz744ANWrFjB22+/TbNmzVi4cCHff/895cqVs+5bp04dvvjiC3777TcALBYLs2fPZt26dfz1118YDAZq1arFyJEjcXZ2pl+/fty+fZs///yT6Ohovv/+exISEhgyZAi//vorhQsXpnLlys+pdUREREREMr5UB7dPPvkkyWej0Ujx4sV544030qyojGbhwoUEBgbi7OxM69atmTdvnjW49ejRg2zZsvHjjz/i4OBAjx49GDp0KJMmTeL8+fN4e3vzySefEB4ebj1eixYtmDlzJmfPnqVYsWKYTCbWrl3LF198gdlsplu3bhQrVozNmzeTkJDAf/7zHwYNGsSkSZM4fPgw169f5+23305W56uvvsqXX34JQExMDACxsbHs3r2buLg45s2bR5EiRZKEtvuaNm1K06ZNAdi4cSPffPMN3377LcWKFeP06dO0bt2adevW0bJlSwB27tzJ0qVLcXNzI0uWLDRo0IBatWoxZ84czp8/T8eOHTEa06TDV0RERETkhZfqfzlHRUXRrFkz61eTJk144403mDJlyjMoz/ZFRESwc+dO2rRpA0BgYCCnTp1i//79REREsH//fvr27YuLiwvZs2dnzJgxT+ydLFSoENWqVbM+/7Zr1y5MJhO+vr78+uuvHD9+nMGDB5M9e3ZcXFzo27cv69evJzo6mqioKADy5cuXovqbNm2Ko6MjOXPmJCoqKkX71apVi+XLl1OsWDGioqKIjo4md+7cXL582bpN+fLlKVWqFDlz5iQsLIy//vqLzz//HCcnJ0qWLMkHH3yQovpERERERCSFPW5RUVGcPn0agODgYMqVK4fFYrGuv3XrFgsWLODTTz99JkXassWLF5OYmEiTJk2syxITE5k3bx5dunQB7gWx+1xdXXF1dX3icVu2bMm4cePo0aMHq1atokmTJjg4OBAeHo7JZMLHxyfJ9o6Ojly4cMF67MjIyEc+LxcZGZnk/Pnz50/y/e7dux9Zz927d4mPjydHjhxYLBYmT57M1q1byZMnD6+99hoJCQlJfiYePO7ly5dxcXHB2dnZuqxo0aJPvAciIiIiInJPioKbo6Mj3bt3t06m0bZt22TrW7VqlfbV2bi7d++yfPlyRo4cSbVq1azLT548SadOnejYsSNwb8bIYsWKAXDq1Cl++OGHJ4Zcf39/hg4dyo4dO/j5559ZtWoVAG5ubjg7OxMaGoqdnR0A8fHxXLhwgZdeegmz2Uzu3LnZsGEDHTp0SHLMEydO0KRJE5YsWWINbw8+r1i7dm2Cg4M5evQoZcuWTbLv0qVLCQ4OZseOHUyYMIGLFy/y888/kz17dgAaNWqUZPsHj+vu7k5UVBSxsbFky5YNgEuXLv3j9YuIiIiIyP9L0VDJ7Nmzs3fvXk6cOEGxYsU4ceJEkq+jR48yYMCAZ12rzVm3bh0Gg4FGjRrh5uZm/apVqxalSpVi9erVVK9enXHjxnHz5k1iYmIYP348Fy5cAO4F3lu3bj3y2A4ODjRt2pShQ4fy+uuvU7x4cQDKli3LSy+9xJgxY4iNjSUuLo5Ro0bRrl07TCYTjo6ODBw4kGnTprFo0SJiY2MxmUwcPHiQHj16UL9+fTw9PR95zjfeeINWrVrRo0cPduzYQWJiInfv3mXNmjVMmjSJ7t27kyVLFmJiYnBycsLOzo67d+8yb948Tp48SUJCwiOP6+npycsvv8yIESO4c+cO586dY968eWnQAiIiIiIimUOqn3F73Pu87j9blZksXryYRo0a4eDgkGxdq1atWLNmDePGjSN79uw0aNAAf39/8uTJw9ChQ4F7z5etWLGC1q1bP/L4LVu2JCIighYtWliX2dvbM2vWLK5evUq9evWoUaMG58+f5+uvv8bJyQm41/s1bdo0tmzZgr+/P15eXgwaNIjmzZszceLEf7ymoUOH0qFDB6ZMmULVqlWpXr063333HWPHjrXOUvnpp58SFxdHtWrV8PPz48iRIzRp0oSTJ08+8ph2dnZ89dVXXLlyhWrVqtGhQwf8/f2ffINFRERERAQAg+XBB5NS4OjRo4wbN47Lly9jNpuBe68EiIqK4tdff30mRYrcd3XWaOLP/ZneZYikOYeCRXH9eAjR0bEkJprTu5znymCAfPlycPXqLVL3N5JkRGrvzEXtnbmovVPv/j1LiVT3uA0bNgxXV1dq1KjByy+/TNu2bbGzs3vii51FRERERETk6aQ6uP3555+MHj2aNm3aYDKZ+OCDD5g8eTLr1q17FvWJiIiIiIhkeqkObjlz5sTZ2ZkiRYrw55/3hqyVL1+eiIiINC9OREREREREniK4vfLKK3z33Xc4OTmRNWtWfv/9d06fPp1k+ncRERERERFJOyl6j9uDevToQdeuXalevTrt27cnMDAQOzs73n333WdRn4iIiIiISKaX6uBWoUIFduzYgaOjI0WLFuW1117j1q1bVK9e/VnUJyIiIiIikumlOrgBGAwGfvrpJyIiImjVqhXnzp1L67pEHskunxsOCXfTuwyRNGfvWjC9SxAREREblurgdv78eT788EMSEhK4efMmPj4+NG/enGnTpuHr6/ssahSxcmn+QXqXIPLMWEwmzGa9+EZERESSS3VwGzlyJAEBAXTt2hVvb29efvllRowYwZdffqngJs9cdHRsepcgz4mLS7ZM195ms0XBTURERB4p1cHtyJEjBAcHYzAYrDNJNmnShJEjR6Z5cSIPM5vNmM3pXYU8a/cnqTWZzFiUY0RERERS/zqAHDlycPXq1STLIiMjyZUrV5oVJSIiIiIiIv8v1cGtUaNGfPzxx+zevRuz2czRo0fp06cPDRs2fBb1iYiIiIiIZHqpHirZrVs34uLi+Pjjj7lz5w7vvfceLVq04OOPP34W9YkkYTQaMab61w2SUdnZ2VZj6xk0ERERSS8GiyVlT5C0b9+euXPnWj/fuXOHO3fu4OLiYn3WTUTkRWYxm4mKvq3wlsYMBsiXLwdXr97SM42ZgNo7c1F7Zy5q79S7f89SIsU9bmFhYUk++/j4sH///tRVJvIv3fhxGYlXLqZ3GZIJ2efJT6633sVoNCi4iYiIyHP3VC/gBkhhR51ImjJFXyXxSkR6lyEiIiIi8lw99QMkGh4pIiIiIiLyfNjWk/8iIiIiIiKSTIqHSiYmJrJ69Wrr54SEhCSfAZo2bZpGZYmIiIiIiMh9KQ5u+fLl48svv7R+dnFxSfLZYDAouGUAJpOJixcvUqRIkfQuRUREREREUijFwe3nn39+lnVYeXh44OPjw6xZs5I8R7dy5UqmTZv2zOqIjIwkJCSEbdu2ce3aNXLkyEHlypXp3LkzpUqVsm63YcMGhg8fTnx8PBMmTGD48OFERkZib3/vVlosFrJnz06jRo347LPPMKbRS8eCgoLw9vbmk08++VfH6dmzJyVLlvzXxwH47rvvmD9/PleuXCF//vy89957tGnT5pHbmkwmJkyYwJo1a7hz5w5VqlRh6NCh5M+f/1/XISIiIiLyorPJZ9y2b9/OnDlzntv5wsPDadasGZGRkcyaNYuwsDDWrFlDkSJFCAwMZPfu3dZtly1bRsOGDTl06BC+vr4ADB06lLCwMMLCwjhy5Ahz585l9erVTJs27bldQ0pFR0enyXF++uknJk2axNixYzl8+DBjxoxhypQpbN68+ZHbh4SEsHv3blasWMHOnTtxdnZm4MCBaVKLiIiIiMiLziaDW1BQEFOnTuXw4cOPXB8eHo6Hhwfh4eHWZcHBwQQFBQH3eudat27N2LFj8fb2pkqVKixcuJDvv/8eX19fKlasyKBBg6z7jh49mpIlS/Lll19SsmRJjEYjefPm5dNPPyUoKIh+/fqRmJhIixYt2LdvH0uWLKFOnTqPrd/Dw4NKlSrx22+/Wa+nX79++Pr6Urt2bWJiYvjjjz/o2LEj3t7e1KpViyFDhnDr1i3rMZYtW4a/vz+enp707duXO3fuWNf169ePfv36JTtnaGgoAFFRUfTp04dKlSpRuXJlevbsyY0bNxgwYAAHDx5k1qxZdOnSxXrffHx88Pb2pnnz5mzZsiVFbXT58mU6duxI+fLlMRgMeHp6UrlyZQ4cOPDI7ZctW0bHjh1xd3cne/bsDBgwgB07dnDhwoUUnU9EREREJDOzyeBWt25dWrVqRa9evbh+/fpTHePQoUMUKFCAffv20b17d0aPHk1oaCgbNmxg/vz5LF++nAMHDhAfH8/27dtp0qTJI19xEBgYyJUrVwgLC2P58uV4eXnRuXNnfvrpp0eeNyEhgdDQUPbt20f16tWty/fs2cOSJUtYu3YtCQkJvPfee5QoUYIdO3awYsUKzpw5w+effw7A3r17GTZsGCNGjODAgQOUK1eOY8eOpfjae/ToQUxMDD/++CNbtmzh5s2bDB06lJEjR1rrnzlzJvv27WPp0qUsW7aM0NBQWrZsyYABA0hISHjiOdq0aUOnTp2sn69du8aBAwd44403km1769YtLl26lGTIab58+ciVKxd//PFHiq9LRERERCSzeuoXcD9rffv2JSwsjH79+hESEpLq/bNmzcr777+PwWCgRo0amEwm2rdvT5YsWShTpgz58+cnIiKCokWLkpCQQL58+R55nPvPYF25cuWx5xo6dCijRo2yfnZzc+ODDz6gbdu21mW1atWiQIECACxfvhwHBwf69OmDnZ0dzs7OfPHFFzRs2JDIyEjWrl1LvXr1qFq1KgCtW7dm2bJlKbruiIgI9u/fz6ZNm3BxcQFgzJgxjwzATk5O3Lhxw9oT2bJlS1q1apXqd/RFRkbSuXNn3njjDd5+++1k62NjY4F7bfIgZ2dn6zoREREREXk8mw1ujo6OTJkyhWbNmjFv3jxrCEmp3LlzWwPI/QlCcubMaV1vNBoxm824uLhgb2/PxYsXH3mc+8MxXV1dH3uuwYMHExAQ8I/1PDgJx7Vr1yhYsCB2dnbWZYULFwbuBa/Lly/z+uuvJ9k/pbNARkZGAlCoUCHrMldX10fW7+npSXBwMAsXLmTOnDk4OzsTFBRE165dUzypypEjR+jRowdeXl6MHj3aOknLg7JkyQKQZLgnQFxcHNmyZUvReUREREREMjObHCp5X9GiRRk+fDiTJ0/myJEj1uX3A8+DQ/oennQjpb1Gjo6O+Pv7s3LlSsxmc7L1y5Ytw9XVFU9Pz6e4gkfXU6hQIS5evIjJZLIuO3/+PHAvZLm5uSV79uvSpUvW741GY5Jrj4qKsn7v7u4OkCSInjp1iilTpiSr6eLFi+TNm5e5c+eyf/9+xo4dy8yZM9mxY0eKrmn58uW0a9eO999/n4kTJ+Lo6PjI7XLlykWBAgU4deqUdVlkZCTXr19PMnxSREREREQezaaDG8Bbb71F8+bNWbp0qXVZ3rx5yZUrF+vXr8disXD8+HE2bdr01OcYMGAAFy9epHv37pw+fRqz2czly5eZNGkSixcvZtSoUTg4OKTF5QDg4+MDwIQJE4iLiyMyMpKRI0dSpUoVChUqRPPmzfnpp5/YunUriYmJrFq1il9++cW6f/HixTl48CCXL18mLi6O6dOnW4NhgQIFqF69OuPGjePmzZvExMQwfvx4axB0dHS0ToJy7NgxOnTowIkTJ3B0dCRv3rwAKerd3Lx5M0OGDCE4OJgPP/zwidsHBAQQEhLChQsXiImJYdSoUXh7e1O0aNHU3TwRERERkUzI5oMbQP/+/Xnttdesnx0dHRk+fDgbN26kQoUKjBkzhsDAwKc+foECBVi9ejXu7u506tSJChUq0Lx5c8LDw/n++++pVatWWlyGVY4cOfj66685efIkPj4+vP322xQqVIipU6cCULFiRcaNG8eYMWPw8vJi8+bNSSY6adWqFZ6enjRu3Ji6devi7u5OwYIFresnTJhA9uzZadCgAf7+/uTJk4ehQ4cC0LRpU1asWEHr1q2pX78+H374IV27dqV8+fL06NGD/v37U65cuSdew7Rp0zCZTHTv3h1PT0/r1/3ZOteuXZukl/Kjjz7Cx8eHNm3a4OPjw927dx/ZCygiIiIiIskZLBaLJb2LEEmpqKUhJEScSe8yJBOyz1+IvG17EB0dS2Ji8mHV8vQMBsiXLwdXr95CfyO9+NTemYvaO3NRe6fe/XuWEhmix01ERERERCQzs9lZJSX9bN68OdkLvh9UsWJF5syZ8xwrEhERERHJ3BTcJJn69etTv3799C5DRERERET+pqGSIiIiIiIiNk7BTURERERExMZpqKRkKHYu+bAkxKd3GZIJ2efJn94liIiISCam4CYZSq56LdO7BMnELGYzZrPmNxYREZHnT8FNMpTo6Nj0LkGeExeXbDbX3mazRcFNRERE0oWCm2QoZrMZs959/MIzGO7912Qy6wWeIiIiImhyEhEREREREZun4CYiIiIiImLjNFRSMhSj0YhRv27IcPRsmIiIiMi/o+AmGYqLS7b0LkGegsVsJir6tsKbiIiIyFNScJMM5daeTZiiLqd3GZIKdrnykqPm2xiNBgU3ERERkaek4CYZiulmlIKbiIiIiGQ6elpIRERERETExim4iYiIiIiI2DgFNxERERERERun4CYiIiIiImLjFNxERERERERsXLrNKnnx4kVmzZrFzp07iYqKwtHRkTJlyvDhhx9SvXr19CrrqYSHh+Pv78+WLVsoXLjwMznH6tWrWb58OSdPniQ+Pp4CBQpQp04dunbtSvbs2Z/JOUVERERExDakS4/byZMnady4MfHx8cyePZtDhw7x448/0rhxYz766CO2b9+eHmXZrAEDBjB58mTatGnDjz/+yMGDB5k2bRonT56kQ4cOWCx6N5aIiIiIyIssXYLboEGDqF69OqNHj6Z48eLY2dmRO3dumjRpwuDBg0lISABgz549tGjRAi8vLxo2bMjatWutxzCbzXz11VfUqVOHihUr0qJFC3bu3GldHx0dTc+ePalYsSL+/v4sXLiQ0qVLEx4eTnh4OB4eHowZM4ZKlSoxdOhQ4uPjGTt2LA0aNMDT05OqVasyfPhwaygKCgpizJgxBAQEUL58eQICAjh48GCS61q3bh0NGjSgfPnytGvXjsuX771vrEGDBsycOTPJto0aNWL58uVPvFe7d+9m1apVfPXVVzRo0IDcuXNjb29PyZIlGTduHJ6enty6dQuAmJgYhg0bho+PD1WrVqVnz55cvXoV4JHXHBwcTI8ePejbty8VKlSgVq1abNy4kenTp1OtWjW8vb2ZMWOGtZbDhw/z3nvvUaNGDcqUKUNAQABHjhwBIDQ0FD8/P0JCQqhZsybe3t588sknxMTEcPfuXSpVqsS6deusx4qPj6dy5crs3bv3ifdARERERCSze+7B7dKlS4SFhfHOO+88cn2zZs2oU6cOJ06coGvXrnTq1InQ0FCGDx/OqFGjrOFs+vTpLFq0iKlTpxIaGsqHH35It27dOHr0KAB9+vTh1q1bbNmyhWXLlrF161ZMJlOSc8XGxrJ792569uzJggUL2LlzJwsWLCAsLIwZM2awZMkS9u3bZ91+6dKlfP755+zfv5+6devStWtXoqOjreuPHz/O999/z/bt27lx4wbTp08HICAggDVr1li3+/XXXwkPD6dBgwZPvF8bNmzA09MTDw+PZOtcXFzo27cvOXPmBKB///6cO3eOlStX8tNPP5E9e3Y+/vjjJD1yD14zwObNm/H19eXQoUM0btyY3r17ExMTw/bt2xk1ahRTp04lIiKCuLg4unbtSv369dmxYwehoaEULVqUcePGWY8dERHB5cuX+e9//8uyZcsICwtj8eLFODk50bBhwyT3YOvWrWTLlo0qVao88R6IiIiIiGR26RLcANzc3KzL9u7di5eXF15eXnh6elK/fn2WLFmCv78/9erVw87OjgoVKhAYGMiiRYsAWLFiBZ06deL111/H3t6et956Cz8/P5YvX87ly5fZtWsX/fv3J3fu3OTJk4f+/fsnq6Vp06Y4OjqSM2dOAgMDmT9/Pq6urly5coW4uDiyZctm7TUDaN68OVWqVMHR0ZEuXbqQJUsWtm7dal3fpUsXcuTIQa5cuahZsybnz5+3nuf8+fMcO3YMuPe82ptvvkm2bNlSdL8evFcAH3zwgfV+lS1bltWrV3Pt2jU2b97MgAEDyJs3L9myZaN///4cO3aM48ePP/KaAUqUKMGbb76JwWCgevXqmEwmunTpgoODA35+fsC95xEdHBxYunQprVu3Jj4+noiICHLnzp3k/gB89NFHODs789JLL1G5cmXOnDljvXd79uwhMjISgFWrVhEQEIDBYHjiPRARERERyeye++Qkrq6uAFy+fJmXX34ZgKpVq1qHHa5cuZJp06YRERHBvn378PLysu5rMpkoWrQoAFevXqVIkSJJjl24cGFOnDjBX3/9Zf1838PbAuTPn9/6/Z07dxg2bBgHDhzAzc2N0qVLY7FYMJvN1m2KFStm/d5gMODm5mYNIgC5c+e2fu/g4GDt4XN1daVmzZqsWbOGV199lR9++IHg4OAU3K17NV64cCHJsq+//tr6vZ+fH2azmYiICAACAwOTbGtnZ0d4eLi1tgev+eGajcZ7OT5XrlxJPpvNZuzs7AgNDaVjx47cvn2bEiVKYG9vn+z5uvvte/8e3F9fpkwZihcvzvr162nUqBG7du1i4MCBKboHIiIiIiKZ3XMPboUKFaJMmTIsW7bsH4fJubm50axZM4YNG2ZdduXKFWsQKFSoULJAc+HCBfLnz0/BggWBe0P37ofD+8HmQQ/29gwcOJBcuXKxa9cunJycMJvNVKpUKcn2D/Yumc1mLl68iLu7e4quu3nz5gwdOpTq1auTI0eOZMd+nPr169OtWzdOnz5N8eLFH7tdgQIFANi4cWOS8HTq1CmKFCliDZgP93CltMfrl19+Yfjw4SxZsoQ33ngDgHnz5ll71FKiefPmrF+/HgcHB7y8vJ7ZDJwiIiIiIi+adJmc5P6zal988QVnzpzBYrEQExPD6tWrCQ4OJn/+/LRo0YIffviBXbt2YTabOXv2LG3btmXevHkAtGzZkq+++orjx49jMpnYuHEjP//8M82aNSN//vz4+voyfvx4bty4wY0bN5I8i/UoMTExODk5YTQaiYmJYdy4ccTExFgnSgFYtmwZv/76K/Hx8UyfPh2LxYKvr2+Krrl27dqYTCa+/PJLAgICUnyvateuTUBAAO3bt2fTpk3cuXMHi8XCyZMn+c9//sOlS5fImzcvBQoUoHbt2owcOZLo6GgSEhIICQmhRYsW3Lx5M8Xne5xbt25hNBpxdnYG4MiRI3zzzTfEx8en+BiNGzfmxIkTLFu2LFX3QEREREQks0uX97iVKlWKH374gdmzZ9OlSxciIyMxGAx4eHjQoUMHWrZsiaOjI5MmTWLSpEn06NGDLFmy8Pbbb9OrVy/g3nNeZrOZnj17EhkZyUsvvcSkSZPw9vYGYOTIkQwaNIjatWvj4uJC06ZN2bp1Kw4ODknC2H0DBw5k0KBBeHt7ky1bNmrXrk3NmjU5efKkdRtvb2+GDRvGqVOnKF26NPPmzSNHjhzcuHHjidfs4OBA48aN+eabbwgJCUnV/RoxYgQbN27k+++/Z8iQIcTFxZEnTx6qVq3KypUrefXVVwEYN24cEydOpGnTpsTExFCyZEnmzJmDq6sr4eHhqTrnw6pXr07r1q1p06YNZrOZwoULExQUxMSJE60zVz5Jnjx58PHxYd++fdSrV+9f1SMiIiIikpkYLC/oS8B2795NxYoVrT1Ef/zxB02bNuXIkSM4OTml+nhBQUHWKe6f1jfffMOOHTuYM2fOUx8joxs9ejRxcXEMHTr0qfa/vmkxpiv/LoTK82WXpwC5336f6OhYEhPNT94BMBggX74cXL16ixfzTyh5kNo7c1F7Zy5q78xF7Z169+9ZSqTLUMnnYezYsYSEhJCYmEhMTAwhISFUq1btqULbvxUZGcnRo0dZsGAB77777nM/vy3466+/2Lt3L6tXr37sqyBEREREROTR0mWo5PMwceJERowYQZUqVTAajdSsWfOJz7k9K9u2bWPEiBE0adIEf39/6/Kvv/6aL7/88rH7NWrUKMnkLBnZ999/z/z58+nQoQOvvfZaepcjIiIiIpKhvLBDJeXFpKGSGY+GSsqTqL0zF7V35qL2zlzU3qmnoZIiIiIiIiIvEAU3ERERERERG/fCPuMmLya7nHkgMfnrHMR22eXKm94liIiIiGR4Cm6SoeSo9mZ6lyBPwWI2YzZrsLuIiIjI01JwkwwlOjo2vUuQp2A2WxTcRERERP4FBTfJUMxmM+aUTUwoIiIiIvLC0OQkIiIiIiIiNk7BTURERERExMZpqKRkKEajEaN+3ZCu9LyaiIiIyPOn4CYZiotLtvQuIdOzmM1ERd9WeBMRERF5jhTcJEOJPbob882o9C4j0zJmz0W28rUwGg0KbiIiIiLPkYKbZCjm2JuYFNxEREREJJPR00IiIiIiIiI2TsFNRERERETExim4iYiIiIiI2DgFNxERERERERun4CYiIiIiImLjFNz+pYsXLzJ48GD8/PwoX7483t7etG/fnt27dz+T84WGhuLh4fFMjg3g5+eHh4cHO3fuTLbuxx9/xMPDg379+qXoWEFBQQQHB6d1iSIiIiIimY6C279w8uRJGjduTHx8PLNnz+bQoUP8+OOPNG7cmI8++ojt27end4lPxcXFhVWrViVbvmLFCrJnz54OFYmIiIiIZG56j9u/MGjQIKpXr87o0aOty3Lnzk2TJk0wm80kJCQAsGfPHiZNmsTZs2cpUKAAnTt3pnHjxgD069cPR0dHrly5QmhoKHny5OH999/nvffeA+DKlSsMGjSI/fv34+LiQsOGDZPUcP78eUaNGkVYWBhZs2a1hkZHR0dWrlzJt99+S+7cuTl69CiDBw+mUaNGT7yuRo0asXTpUm7dukWOHDmsdRw5coSaNWtat4uPj2fy5Mls27aNS5cu4ezszFtvvcXAgQMxGAxJjmmxWFi4cCGLFi3i2rVrlCpViv79+/PGG288xZ0XEREREclc1OP2lC5dukRYWBjvvPPOI9c3a9aMOnXqcOLECbp27UqnTp0IDQ1l+PDhjBo1KslQxJUrVxIUFMSBAwfo2LEjY8aM4fLlywD07NkTe3t7duzYwbfffsuOHTus+92+fZt27dpRsmRJduzYweLFi9mzZ0+S4YnHjx+nUaNG7Nmzh7p166bo2l577TVefvllNmzYYF22evVqGjRogLOzs3XZggUL2LlzJwsWLCAsLIwZM2awZMkS9u3bl+yYixcv5uuvv2bq1Kns3buXgIAAPvjgA65evZqimkREREREMjMFt6d06dIlANzc3KzL9u7di5eXF15eXnh6elK/fn2WLFmCv78/9erVw87OjgoVKhAYGMiiRYus+1WuXJnq1atjb29P8+bNMZlMnD9/noiICA4ePEifPn3Inj077u7ufPzxx9b9tm3bRnx8PL169cLJyQl3d3d69OiR5NgODg40adIER0fHJKHrSQICApIMl1yxYgXNmzdPsk1gYCDz58/H1dWVK1euEBcXR7Zs2ayh80GLFi2ic+fOvPrqqzg4ONCiRQuKFy/O2rVrU1yTiIiIiEhmpaGST8nV1RWAy5cv8/LLLwNQtWpVDh48CNzrRZs2bRoRERHs27cPLy8v674mk4miRYsmOxbcC1oAZrPZGoAKFixoXf/gfhEREURFRVGpUiXrMovFQkJCAteuXbMe22hMfT5v1KgR48eP58yZM1y7dg0nJyfKlCmTZJs7d+4wbNgwDhw4gJubG6VLl8ZisWA2m5MdLyIigrFjxzJhwgTrssTERA2VFBERERFJAQW3p1SoUCHKlCnDsmXLqFKlymO3c3Nzo1mzZgwbNsy67MqVK1gsliee435v3oULFyhevDjw/z1999cXLVqUTZs2WZfFxMRw7do18uTJA5DsWbOUypMnD7Vr12b16tVcuXKFFi1aJNtm4MCB5MqVi127duHk5ITZbE4SIh++lu7duyd5Ru/8+fPkzp37qeoTEREREclMNFTyX7j/rNoXX3zBmTNnsFgsxMTEsHr1aoKDg8mfPz8tWrTghx9+YNeuXZjNZs6ePUvbtm2ZN2/eE49fsGBBatSowejRo7lx4waRkZFMmzbNut7X15fY2FjmzJlDfHw8N2/epG/fvvTs2fOpA9uDAgICWLduHVu3bn3kpCYxMTE4OTlhNBqJiYlh3LhxxMTEWCdleVBgYCAhISGcPn0agJ07d9KwYUMOHDjwr+sUEREREXnRqcftXyhVqhQ//PADs2fPpkuXLkRGRmIwGPDw8KBDhw60bNkSR0dHJk2axKRJk+jRowdZsmTh7bffplevXik6x8SJExk6dCi+vr5kz56dgIAAfvnlFwCyZ8/O/PnzGTNmDHPmzMFsNlO5cmVCQkLS5Ppq1arF3bt3qVKlCi4uLsnWDxw4kEGDBuHt7U22bNmoXbs2NWvW5OTJk8m2bdeuHRaLhW7dunHlyhUKFCjAoEGD8Pf3T5NaRUREREReZAZLSsbsidiIW3s3Yoq+kt5lZFp2OfOQo0YjoqNjSUxM/ixjWjEYIF++HFy9egv9CfXiU3tnLmrvzEXtnbmovVPv/j1LCQ2VFBERERERsXEaKpmJBAQEcObMmceunz17dpLZL0VERERExDYouGUiK1euTO8SRERERETkKWiopIiIiIiIiI1TcBMREREREbFxGiopGYoxW04wJaZ3GZmWMXuu9C5BREREJFNScJMMJVvZ6uldQqZnMZsxmzXHr4iIiMjzpOAmGUp0dGx6l5Dpmc0WBTcRERGR50zBTTIUs9mM+dm991lERERExCZpchIREREREREbpx43yVCMRiPGTPrrBg1RFBEREcm8FNwkQ3FxyZbeJaQbi9lMVPRthTcRERGRTEjBTTKUuP8dx3znVnqX8dwZnbPhXLwMRqNBwU1EREQkE1JwkwzFfPc25tuZL7iJiIiISOaWSZ8WEhERERERyTgU3ERERERERGycgpuIiIiIiIiNU3ATERERERGxcQpumYzJZOLChQvpXYaIiIiIiKSCzQU3Dw8POnXqhMWSdMrzlStX4ufn98zOGxkZybBhw/Dz86NcuXLUqFGD3r17c/LkySTbbdiwgapVq1KxYkW2bt2Kn58fZcqUwdPTE09PT8qXL0+NGjUYO3YsZrM5zeoLCgoiODj4Xx+nZ8+erF69+l8fx2w2ExwcjI+PD56enjRq1IgNGzY8dnuTycTYsWOpVq0anp6edO3alStXrvzrOkREREREMgObC24A27dvZ86cOc/tfOHh4TRr1ozIyEhmzZpFWFgYa9asoUiRIgQGBrJ7927rtsuWLaNhw4YcOnQIX19fAIYOHUpYWBhhYWEcOXKEuXPnsnr1aqZNm/bcriGloqOj0+Q4ixYtYvXq1SxcuJCwsDB69epF7969OX/+/CO3DwkJYffu3axYsYKdO3fi7OzMwIED06QWEREREZEXnU0Gt6CgIKZOncrhw4cfuT48PBwPDw/Cw8Oty4KDgwkKCgLu9c61bt2asWPH4u3tTZUqVVi4cCHff/89vr6+VKxYkUGDBln3HT16NCVLluTLL7+kZMmSGI1G8ubNy6effkpQUBD9+vUjMTGRFi1asG/fPpYsWUKdOnUeW7+HhweVKlXit99+s15Pv3798PX1pXbt2sTExPDHH3/QsWNHvL29qVWrFkOGDOHWrf9/P9myZcvw9/fH09OTvn37cufOHeu6fv360a9fv2TnDA0NBSAqKoo+ffpQqVIlKleuTM+ePblx4wYDBgzg4MGDzJo1iy5duljvm4+PD97e3jRv3pwtW7akqI3atGnDunXrKFq0KPHx8URFRZElSxacnZ0fuf2yZcvo2LEj7u7uZM+enQEDBrBjxw4N2xQRERERSQGbDG5169alVatW9OrVi+vXrz/VMQ4dOkSBAgXYt28f3bt3Z/To0YSGhrJhwwbmz5/P8uXLOXDgAPHx8Wzfvp0mTZpgMBiSHScwMJArV64QFhbG8uXL8fLyonPnzvz000+PPG9CQgKhoaHs27eP6tWrW5fv2bOHJUuWsHbtWhISEnjvvfcoUaIEO3bsYMWKFZw5c4bPP/8cgL179zJs2DBGjBjBgQMHKFeuHMeOHUvxtffo0YOYmBh+/PFHtmzZws2bNxk6dCgjR4601j9z5kz27dvH0qVLWbZsGaGhobRs2ZIBAwaQkJDwxHMYjUayZs3Krl27KFeuHAMGDKBHjx7kz58/2ba3bt3i0qVLlCpVyrosX7585MqViz/++CPF1yUiIiIiklnZp3cBj9O3b1/CwsLo168fISEhqd4/a9asvP/++xgMBmrUqIHJZKJ9+/ZkyZKFMmXKkD9/fiIiIihatCgJCQnky5fvkce5H0T+6XmsoUOHMmrUKOtnNzc3PvjgA9q2bWtdVqtWLQoUKADA8uXLcXBwoE+fPtjZ2eHs7MwXX3xBw4YNiYyMZO3atdSrV4+qVasC0Lp1a5YtW5ai646IiGD//v1s2rQJFxcXAMaMGfPIAOzk5MSNGzesPZEtW7akVatWjwywj+Pt7c2xY8c4cOAA3bp1w9XVlbfeeivJNrGxscC9NnmQs7OzdZ2IiIiIiDyezQY3R0dHpkyZQrNmzZg3b541hKRU7ty5rQHEaLzXsZgzZ07reqPRiNlsxsXFBXt7ey5evPjI49wfjunq6vrYcw0ePJiAgIB/rOfBnqhr165RsGBB7OzsrMsKFy4M3Ately9f5vXXX0+yf5EiRf7x+PdFRkYCUKhQIesyV1fXR9bv6elJcHAwCxcuZM6cOTg7OxMUFETXrl2t9+xJHB0dAahatSpNmjRh3bp1yYJblixZAJIM9wSIi4sjW7ZsKTqPiIiIiEhmZpNDJe8rWrQow4cPZ/LkyRw5csS6/H7geXBI38OTbqS018jR0RF/f39Wrlz5yFkgly1bhqurK56enk9xBY+up1ChQly8eBGTyWRddn9SD1dXV9zc3JI9+3Xp0iXr90ajMcm1R0VFWb93d3cHSBJET506xZQpU5LVdPHiRfLmzcvcuXPZv38/Y8eOZebMmezYseOJ1zNmzBjGjBmTZFl8fDy5c+dOtm2uXLkoUKAAp06dsi6LjIzk+vXrSYZPioiIiIjIo9l0cAN46623aN68OUuXLrUuy5s3L7ly5WL9+vVYLBaOHz/Opk2bnvocAwYM4OLFi3Tv3p3Tp09jNpu5fPkykyZNYvHixYwaNQoHB4e0uBwAfHx8AJgwYQJxcXFERkYycuRIqlSpQqFChWjevDk//fQTW7duJTExkVWrVvHLL79Y9y9evDgHDx7k8uXLxMXFMX36dGswLFCgANWrV2fcuHHcvHmTmJgYxo8fbw2Cjo6O1klQjh07RocOHThx4gSOjo7kzZsXIEW9m15eXixZsoQDBw5gNpv5+eef2bBhAy1btnzk9gEBAYSEhHDhwgViYmIYNWoU3t7eFC1a9OlvpIiIiIhIJmHzwQ2gf//+vPbaa9bPjo6ODB8+nI0bN1KhQgXGjBlDYGDgUx+/QIECrF69Gnd3dzp16kSFChVo3rw54eHhfP/999SqVSstLsMqR44cfP3115w8eRIfHx/efvttChUqxNSpUwGoWLEi48aNY8yYMXh5ebF58+YkE520atUKT09PGjduTN26dXF3d6dgwYLW9RMmTCB79uw0aNAAf39/8uTJw9ChQwFo2rQpK1asoHXr1tSvX58PP/yQrl27Ur58eXr06EH//v0pV67cE6+hTp06DBw4kIEDB1KpUiWmT59OcHAwFSpUAGDt2rVJeik/+ugjfHx8aNOmDT4+Pty9e/eRvYAiIiIiIpKcwfLwm65FbNjt3w9gjrme3mU8d8asOcj6ehWio2NJTEy7F7vbKoMB8uXLwdWrt9CfUC8+tXfmovbOXNTemYvaO/Xu37OUyBA9biIiIiIiIpmZzc4qKeln8+bNyV7w/aCKFSsyZ86c51iRiIiIiEjmpuAmydSvX5/69eundxkiIiIiIvI3DZUUERERERGxcQpuIiIiIiIiNk5DJSVDMTplBbPpyRu+YIzO2dK7BBERERFJRwpukqE4v/J6epeQbixmM2az5tYVERERyYwU3CRDiY6OTe8S0o3ZbFFwExEREcmkFNwkQzGbzZhf/PdPi4iIiIgkoclJREREREREbJyCm4iIiIiIiI3TUEnJUIxGI0Yb+XWDnjkTERERkedFwU0yFBcX25kW32K2EBUdq/AmIiIiIs+cgptkKPGR4Vji76R3GRgcnHHMXwSj0aDgJiIiIiLPnIKbZCiWhLtY4uPSuwwRERERkefKRp4WEhERERERkcdRcBMREREREbFxCm4iIiIiIiI2TsFNRERERETExim4ZTImk4kLFy6kdxkiIiIiIpIKNhfcPDw86NSpExZL0inWV65ciZ+f3zM7b2RkJMOGDcPPz49y5cpRo0YNevfuzcmTJ5Nst2HDBqpWrUrFihXZunUrfn5+lClTBk9PTzw9PSlfvjw1atRg7NixmM3mNKsvKCiI4ODgf32cnj17snr16n9f0ANOnjxJuXLlCA0Nfew2JpOJsWPHUq1aNTw9PenatStXrlxJ0zpERERERF5UNhfcALZv386cOXOe2/nCw8Np1qwZkZGRzJo1i7CwMNasWUORIkUIDAxk9+7d1m2XLVtGw4YNOXToEL6+vgAMHTqUsLAwwsLCOHLkCHPnzmX16tVMmzbtuV1DSkVHR6fp8e7cuUPv3r2Ji/vnKfpDQkLYvXs3K1asYOfOnTg7OzNw4MA0rUVERERE5EVlk8EtKCiIqVOncvjw4UeuDw8Px8PDg/DwcOuy4OBggoKCgHu9c61bt2bs2LF4e3tTpUoVFi5cyPfff4+vry8VK1Zk0KBB1n1Hjx5NyZIl+fLLLylZsiRGo5G8efPy6aefEhQURL9+/UhMTKRFixbs27ePJUuWUKdOncfW7+HhQaVKlfjtt9+s19OvXz98fX2pXbs2MTEx/PHHH3Ts2BFvb29q1arFkCFDuHXrlvUYy5Ytw9/fH09PT/r27cudO///0ul+/frRr1+/ZOe83+MVFRVFnz59qFSpEpUrV6Znz57cuHGDAQMGcPDgQWbNmkWXLl2s983Hxwdvb2+aN2/Oli1bUtRG9w0dOvQf78WD19OxY0fc3d3Jnj07AwYMYMeOHRq2KSIiIiKSAjYZ3OrWrUurVq3o1asX169ff6pjHDp0iAIFCrBv3z66d+/O6NGjCQ0NZcOGDcyfP5/ly5dz4MAB4uPj2b59O02aNMFgMCQ7TmBgIFeuXCEsLIzly5fj5eVF586d+emnnx553oSEBEJDQ9m3bx/Vq1e3Lt+zZw9Llixh7dq1JCQk8N5771GiRAl27NjBihUrOHPmDJ9//jkAe/fuZdiwYYwYMYIDBw5Qrlw5jh07luJr79GjBzExMfz4449s2bKFmzdvMnToUEaOHGmtf+bMmezbt4+lS5eybNkyQkNDadmyJQMGDCAhISFF51m9ejXnzp3j448//sftbt26xaVLlyhVqpR1Wb58+ciVKxd//PFHiq9LRERERCSzsk/vAh6nb9++hIWF0a9fP0JCQlK9f9asWXn//fcxGAzUqFEDk8lE+/btyZIlC2XKlCF//vxERERQtGhREhISyJcv3yOPkz9/foB/fB5r6NChjBo1yvrZzc2NDz74gLZt21qX1apViwIFCgCwfPlyHBwc6NOnD3Z2djg7O/PFF1/QsGFDIiMjWbt2LfXq1aNq1aoAtG7dmmXLlqXouiMiIti/fz+bNm3CxcUFgDFjxjwyADs5OXHjxg1rT2TLli1p1arVIwPsw06fPs3kyZP57rvvsLOz+8dtY2NjgXtt8iBnZ2frOhEREREReTybDW6Ojo5MmTKFZs2aMW/ePGsISancuXNbA4jReK9jMWfOnNb1RqMRs9mMi4sL9vb2XLx48ZHHuT8c09XV9bHnGjx4MAEBAf9Yz/0ACHDt2jUKFiyYJPAULlwYuBe8Ll++zOuvv55k/yJFivzj8e+LjIwEoFChQtZlrq6uj6zf09OT4OBgFi5cyJw5c3B2diYoKIiuXbta79mj3L17l549e9K/f38KFiz4xJqyZMkCkGS4J0BcXBzZsmVL0XWJiIiIiGRmNjlU8r6iRYsyfPhwJk+ezJEjR6zL7weeB4f0PTzpRkp6jeBeQPT392flypWPnAVy2bJluLq64unp+RRX8Oh6ChUqxMWLFzGZTNZl58+fB+6FLDc3t2TPfl26dMn6vdFoTHLtUVFR1u/d3d0BkgTRU6dOMWXKlGQ1Xbx4kbx58zJ37lz279/P2LFjmTlzJjt27PjHazl27Bhnz55lwIABeHl54eXlBUCXLl0YMmRIsu1z5cpFgQIFOHXqlHVZZGQk169fTzJ8UkREREREHs2mgxvAW2+9RfPmzVm6dKl1Wd68ecmVKxfr16/HYrFw/PhxNm3a9NTnGDBgABcvXqR79+6cPn0as9nM5cuXmTRpEosXL2bUqFE4ODikxeUA4OPjA8CECROIi4sjMjKSkSNHUqVKFQoVKkTz5s356aef2Lp1K4mJiaxatYpffvnFun/x4sU5ePAgly9fJi4ujunTp1uDYYECBahevTrjxo3j5s2bxMTEMH78eGsQdHR0tE6CcuzYMTp06MCJEydwdHQkb968AE/s3fTy8uLo0aMcPHjQ+gUwc+bMRwY3gICAAEJCQrhw4QIxMTGMGjUKb29vihYt+vQ3UkREREQkk7D54AbQv39/XnvtNetnR0dHhg8fzsaNG6lQoQJjxowhMDDwqY9foEABVq9ejbu7O506daJChQo0b96c8PBwvv/+e2rVqpUWl2GVI0cOvv76a06ePImPjw9vv/02hQoVYurUqQBUrFiRcePGMWbMGLy8vNi8eXOSiU5atWqFp6cnjRs3pm7duri7uycZsjhhwgSyZ89OgwYN8Pf3J0+ePAwdOhSApk2bsmLFClq3bk39+vX58MMP6dq1K+XLl6dHjx7079+fcuXK/etrXLt2bZJeyo8++ggfHx/atGmDj48Pd+/efWQvoIiIiIiIJGewPPymaxEbdvfiaSx3b6d3GRgcnXEqVJLo6FgSE9PuRetyj8EA+fLl4OrVW+hPqBef2jtzUXtnLmrvzEXtnXr371lKZIgeNxERERERkczMZmeVlPSzefPmZC/4flDFihWZM2fOc6xIRERERCRzU3CTZOrXr0/9+vXTuwwREREREfmbhkqKiIiIiIjYOAU3ERERERERG6ehkpKhGBycwJL+szgaHJzTuwQRERERyUQU3CRDcXQtnN4lWFnMFsxmzXUrIiIiIs+egptkKNHRseldgpVZwU1EREREnhMFN8lQzGYz5vQfKSkiIiIi8lxpchIREREREREbp+AmIiIiIiJi4zRUUjIUo9GI8Rn+ukHPrYmIiIiILVJwkwzFxSXbMz2+xWIhKipW4U1EREREbIqCm2QopltRWBITnsmxDfb22OXIi9FoUHATEREREZui4CYZiiUxEUzPJrgpqomIiIiIrdLkJCIiIiIiIjZOwU1ERERERMTGKbiJiIiIiIjYOAU3ERERERERG/fCBbeLFy8yePBg/Pz8KF++PN7e3rRv357du3end2mpFh4ejoeHB+Hh4c/k+Fu2bOGdd96hQoUKVKhQgYCAAFatWpVmx/f09OTgwYNpdjwRERERkczqhZpV8uTJk7Ru3Zq6desye/ZsihUrxq1bt9i+fTsfffQRU6dOxcfHJ73LtAkHDx6kT58+TJkyhRo1agCwa9cuevbsidFopEmTJv/6HGFhYf/6GCIiIiIi8oL1uA0aNIjq1aszevRoihcvjp2dHblz56ZJkyYMHjyYhIR708jv2bOHFi1a4OXlRcOGDVm7dq31GGazma+++oo6depQsWJFWrRowc6dO63ro6Oj6dmzJxUrVsTf35+FCxdSunRpwsPDrT1kY8aMoVKlSgwdOpT4+HjGjh1LgwYN8PT0pGrVqgwfPhyL5d7k80FBQYwZM4aAgADKly9PQEBAsl6qdevW0aBBA8qXL0+7du24fPkyAA0aNGDmzJlJtm3UqBHLly9/4r0KCwvDzc2NWrVqYWdnh52dHT4+PvTu3dt6n1auXElgYCCDBg2iQoUK1KhRgxkzZlhrv3z5Mp9++il+fn6UK1cOf3//JOf28PAgNDQUAD8/P2bNmkXTpk3x9PSkadOm7Nu3L2UNKyIiIiKSyb0wwe3SpUuEhYXxzjvvPHJ9s2bNqFOnDidOnKBr16506tSJ0NBQhg8fzqhRo6zhbPr06SxatIipU6cSGhrKhx9+SLdu3Th69CgAffr04datW2zZsoVly5axdetWTCZTknPFxsaye/duevbsyYIFC9i5cycLFiwgLCyMGTNmsGTJkiShZenSpXz++efs37+funXr0rVrV6Kjo63rjx8/zvfff8/27du5ceMG06dPByAgIIA1a9ZYt/v1118JDw+nQYMGT7xfvr6+XLp0iXfffZd58+Zx6NAh7t69S5s2bWjRooV1u19++YUsWbKwd+9eQkJCWLBggTWcDRw4EAcHB9avX8/hw4dp27Ytw4cPJzY29pHnXLFiBVOnTmXPnj28+uqrDBky5Il1ioiIiIjICxbcANzc3KzL9u7di5eXF15eXnh6elK/fn2WLFmCv78/9erVw87OjgoVKhAYGMiiRYuAe+GiU6dOvP7669jb2/PWW2/h5+fH8uXLuXz5Mrt27aJ///7kzp2bPHny0L9//2S1NG3aFEdHR3LmzElgYCDz58/H1dWVK1euEBcXR7Zs2ay9ZgDNmzenSpUqODo60qVLF7JkycLWrVut67t06UKOHDnIlSsXNWvW5Pz589bznD9/nmPHjgGwevVq3nzzTbJly/bE+1WiRAnWrl1L+fLlWblyJW3atMHLy4tevXr9X3v3HhRl9f8B/L0ri4tEYsnFW5gamEW5uq54oQBTHAUrBB1BKjJHrUTNSCujSS0FFS9YTOpAXkfDu2hjNdpYiZSZOjajiUkoeFluxqK4sPv5/dGX5+emtmoZj8v7NcPM7jnnOefs83lY/fic5wiz2ay08/b2xptvvonmzZsjODgYI0eOVO5Qzp49G++//z50Oh1KS0vh6emJ2tpaXLp06YZjxsbGIiAgAB4eHoiOjkZRUZHTeRIRERERkQs94+bj4wPgz+V7Dz/8MACgT58+yrLDzZs3Y+nSpSgpKcGBAwdgNBqVY202Gx566CEAQFlZGTp06ODQd/v27XH8+HGcO3dOed/gr20BwNfXV3l95coVzJw5Ez/++CP8/f3RrVs3iAjsdrvSpmPHjsprjUYDf3//65KnBjqdTrnD5+Pjg9DQUGzbtg1du3ZFXl4eMjMzb+Fs/f/cp0+fDgCorq7GDz/8gIULF2LSpElYt24dAKBdu3bQ6XTKMW3atMHu3bsBAGfOnEF6ejqKiorQsWNHBAQEAIDDZ7tW69atlddubm7KkksiIiIiIvp7LpO4tWvXDsHBwcjNzUVISMhN2/n7++P555/HzJkzlbKLFy8qSUS7du1w5swZh2POnDkDX19ftG3bFgBQUlKiJIclJSXXjaHRaJTXM2bMQMuWLfHdd9+hefPmsNvt6NWrl0P7a+++2e12lJaWok2bNrf0uYcPH44PPvgA/fr1g5eX13V930xCQgKeeOIJTJs2DQDg5eWFAQMGQKPRYOrUqUq7hnPT8JnOnj2Ltm3boq6uDuPGjcMbb7yB+Ph4aDQaHDt2zOF5QSIiIiIi+ne4zFJJAMqzau+99x5Onz4NEYHFYsHWrVuRmZkJX19fxMbGIi8vD9999x3sdjuKioowevRoZGdnAwDi4uKwbNky/PLLL7DZbPjiiy+wZ88ePP/88/D19UV4eDjmzZuHS5cu4dKlS0hPT//bOVksFjRv3hxarRYWiwXp6emwWCzKBiAAkJubi2PHjsFqteLjjz+GiCA8PPyWPnNYWBhsNhuWLFmCmJiYWz5Xw4YNw/r167Ft2zZUVFTAbrfj9OnTWL16NQYNGqS0M5vNWLZsGerq6nD06FHk5uYiLi4OdXV1qK2thV6vh0ajQWlpKebNmwcADp+NiIiIiIj+OZe54wYAgYGByMvLw/LlyzF+/HiYzWZoNBoEBQXhlVdeQVxcHNzd3ZGRkYGMjAxMmjQJHh4eiIqKwhtvvAEASEpKgt1ux5QpU2A2mxEQEICMjAyYTCYAwIcffojU1FSEhYWhVatWeO6557B3717odLobJiwzZsxAamoqTCYTPD09ERYWhtDQUPz6669KG5PJhJkzZ6KwsBDdunVDdnY2vLy8bvqs2LV0Oh2GDRuGVatWISsr65bP1ciRI3HfffdhzZo1mDlzJurr6+Hn54eoqCiMHz9eaefj44OzZ8+if//+8PT0xKRJkzBkyBAAfybKixcvxuzZs/Hggw9ixIgRKCwsxK+//qrckSQiIiIion9OI3zQ6LZ8//336NmzJ/R6PQDgxIkTeO6553D48GE0b978tvtLTEyEyWTCxIkT73hOq1atwr59+7BixYo77uNGGp4L3LNnz7/a7z9RX3kRsFnvTufNdHBr5YfKyhrU19/4OT36b2g0QOvWXigrqwa/oVwf4920MN5NC+PdtDDet6/hnN0Kl1oq+V9IS0tDVlYW6uvrYbFYkJWVhb59+95R0vZPmc1mHD16FCtXrsSoUaP+8/GJiIiIiOi/4VJLJf8LCxYswOzZsxESEgKtVovQ0FCnz7ndLd988w1mz56NZ599FgMGDFDKc3JysGTJkpseFx0d7bA5CxERERERqRuXStI9hUslmwYutWhaGO+mhfFuWhjvpoXxvn1cKklERERERORCmLgRERERERGpHJ9xo3uKxs0Ngrtz713jxl8HIiIiIlIn/k2V7inNvB64q/2LCOx2LsomIiIiInVh4kb3lMrKmrvav93OxI2IiIiI1IeJG91TROyw3+UNHzWau9s/OdcQA8aiaWC8mxbGu2lhvJsWxvv23c654n8HQEREREREpHLcVZKIiIiIiEjlmLgRERERERGpHBM3IiIiIiIilWPiRkREREREpHJM3IiIiIiIiFSOiRsREREREZHKMXEjIiIiIiJSOSZuREREREREKsfEjYiIiIiISOWYuJHqlZeX49VXX4XRaETv3r3x4Ycfor6+vrGnRX9RUVGBgQMHoqCgQCk7cuQI4uLiYDAYEBERgdzcXIdjtmzZgoEDB6J79+6IiYnBzz//rNTZbDakpaWhb9++MBgMmDBhAi5evKjUO7sunI1Nd+b48eNISkqCyWRCv3798NZbb6GiogIA4+2K8vPzERcXhx49eqBfv36YNWsWamtrATDersxmsyExMRHTp09Xyhhv17Nr1y5069YNBoNB+UlJSQHAeKuWEKnc6NGjZerUqXL58mUpLi6WoUOHyvLlyxt7WnSNgwcPyjPPPCOBgYFy4MABERGpqqoSk8kka9askbq6Otm/f78YDAY5cuSIiIgcOHBADAaDHDx4UKxWq+Tk5Ejv3r3l8uXLIiKSmZkp0dHRUlpaKtXV1TJ58mQZO3asMubfXRfOxqY7c+XKFenXr58sXrxYrl69KhUVFTJ27FgZN24c4+2CysvLJTg4WDZt2iQ2m00uXLggUVFRsnjxYsbbxS1atEi6du0q06ZNExF+n7uquXPnyvTp068rZ7zVi4kbqVpRUZEEBgbK+fPnlbKdO3dKWFhYI86KrrV582YJCwuTnTt3OiRun3/+uQwaNMihbWpqqrz11lsiIjJ16lSZMWOGQ/3gwYNl48aNIiLy1FNPyfbt25U6s9ksQUFBUlxc7PS6cDY23ZlTp07JmDFjpL6+Xin7+uuvpUePHoy3i6qurhYREbvdLidOnJCBAwfK6tWrGW8Xtn//fhkyZIgkJycriRvj7ZoSEhJkzZo115Uz3urFpZKkaidPnoS3tzf8/PyUss6dO6O0tBR//PFHI86MGvTv3x9fffUVhgwZ4lB+8uRJBAYGOpR16dIFx48fBwAUFhbetL66uhrnz593qG/dujVatmyJEydOOL0unI1Nd6ZTp05YsWIFmjVrppTt3r0bjz32GOPtou677z4AwNNPP43o6Gj4+PggJiaG8XZR5eXlePfdd7FgwQJ4eHgo5Yy367Hb7fjll1/wzTffIDw8HE899RTee+89XLp0ifFWMSZupGo1NTUOf3gAUN5fvny5MaZEf+Hj4wM3N7frym8UO71er8Tt7+pramoAAC1atLiuvqamxul14Wxs+udEBAsXLsTevXvx7rvvMt4u7ssvv8S+ffug1WqRnJzMeLsgu92OlJQUJCUloWvXrg51jLfrqaioQLdu3RAZGYldu3Zh/fr1KCoqQkpKCuOtYkzcSNVatGiBK1euOJQ1vPf09GyMKdEt8vDwUDYxaFBbW6vE7e/qG760/xr7hnpn14WzsemfsVgsSE5Oxo4dO7BmzRoEBQUx3i5Or9fDz88PKSkp+PbbbxlvF/Tpp5/C3d0diYmJ19Ux3q6ndevWWLt2LWJjY+Hh4YG2bdsiJSUF+/btg4gw3irFxI1U7ZFHHkFVVRXKysqUslOnTsHf3x9eXl6NODNyJjAwECdPnnQoKywsxCOPPALgz9jerL5ly5bw8/NDYWGhUmc2m1FVVYXAwECn14WzsenOFRcXY/jw4bBYLNi4cSOCgoIAMN6u6NChQxg8eDCsVqtSZrVaodPp0KVLF8bbxWzbtg0//PADjEYjjEYj8vLykJeXB6PRyN9vF3T8+HHMnz8fIqKUWa1WaLVaPPHEE4y3WjXyM3ZETo0aNUqmTJki1dXVyu5DS5Ysaexp0Q1cuzlJRUWFGI1GycnJEavVKvn5+WIwGCQ/P19ERNkpKj8/X9mVqlevXlJZWSkiIgsXLpSoqCgpLi5WdqUaPXq0MtbfXRfOxqY7U1VVJWFhYTJ9+nSx2WwOdYy367FYLPL000/LRx99JFevXpWzZ89KbGysvP/++4x3EzBt2jRlcxLG2/WcO3dOunfvLsuWLZO6ujopKSmRESNGyDvvvMN4qxgTN1I9s9ksEydOFJPJJCEhITJ37lyHXe1IPa5N3EREjh49KiNHjhSDwSADBgyQTZs2ObTfunWrREZGSvfu3SU2NlYOHz6s1FmtVpk3b56EhoZKjx49ZMKECVJWVqbUO7sunI1Nty87O1sCAwPlySeflO7duzv8iDDerujkyZOSlJQkRqNRwsPDJSMjQ65evSoijLeruzZxE2G8XVFBQYFyXkNCQmTWrFlSW1srIoy3WmlErrlHSkRERERERKrDZ9yIiIiIiIhUjokbERERERGRyjFxIyIiIiIiUjkmbkRERERERCrHxI2IiIiIiEjlmLgRERERERGpHBM3IiIiIiIilWPiRkRERDf0+++/N/YUiIjof5i4ERERqUhiYiIyMzMbexpIS0tDVlZWY0+DiIj+h4kbERERXaeysrKxp0BERNdg4kZERKRCmzdvRnx8PNLS0mAymRASEoLVq1fj888/R3h4OHr27InU1FSlfUREBJYuXYrIyEgYDAYkJCSgsLBQqT948CASEhJgNBoRERGBRYsWwWq1AgAyMzPx8ssvY/jw4TCZTFi6dCl27NiBHTt2YNiwYQCAQ4cO4YUXXkD//v0RHByMmJgYHD58GABQUFCAiIgIZGVlITQ0FCaTCRMnToTFYlHGX7lyJQYOHAiDwYCYmBjk5+cDAEQEq1atQmRkJIxGI+Lj43Hs2LG7fXqJiO45TNyIiIhU6qeffoKfnx8OHDiA5ORkzJkzBwUFBdi1axc+++wzbNy4ET/++KPSfsOGDVi0aBHy8/PRuXNnjB8/HnV1dfjtt9+QlJSEQYMGYf/+/cjJycGePXuQnp6uHJufn48333wTe/fuxfjx4xEdHY3o6Ghs374dtbW1mDBhAiIjI7Fv3z4UFBTgoYcecji+pKQEFy5cwFdffYXc3Fz8/PPPWLduHYA/k9BPPvkE6enp+OmnnzBq1ChMmDABVVVVWLduHXJycrB48WLk5+cjJiYGSUlJKCsr++9ONBHRPYCJGxERkUq1aNECL774IrRaLfr37w+bzYYxY8bAw8MDwcHB8PX1RUlJidJ+zJgxePTRR6HX6/H222/j3LlzOHToEHbs2IGgoCC8+OKLcHd3R0BAAKZOnYrc3FzY7XYAQIcOHdCnTx94enrCzc3NYR46nQ4bNmxAfHw8rFYrSkpK4O3tjQsXLji0e+2116DX6xEQEIDevXvj9OnTAIAtW7Zg5MiRMBgM0Gq1iIuLQ3Z2NvR6PdauXYtx48aha9eu0Ol0iI2NRefOnbF9+/a7fHaJiO4tbs6bEBERUWPw9vaGRqMBAGi1f/5b6/3336/Ua7VaJfECgICAAOW1h4cHvL29YTabUV5ejg4dOjj03b59e9TW1qK8vBwA4Ovre9N5NGvWDAUFBRg7diwuX76MLl26wM3NDSLi0M7Hx0d5rdPplHqz2Yy2bds6tO3RoweAP+/UpaWlYf78+UpdfX09Hn/88ZvOh4ioKWLiRkREpFINSdutuvYOWE1NDSorK9GmTRu0a9cOX375pUPb4uJiuLu7o2XLlk7HOnLkCGbNmoX169crCVV2drZyR82ZNm3a4Ny5cw5lCxcuxLBhw+Dv74/k5GQMHTrUYW7e3t631DcRUVPBpZJEREQuIicnB7///juuXLmCOXPmoFOnTjAYDBg6dChOnTqFlStXwmq1ori4GBkZGYiOjoa7u/sN+3J3d0d1dTUAoLq6GlqtFnq9HgBw+PBhrFq1StncxJmYmBhs2LABR48ehd1ux6ZNm7B27Vq0atUKI0aMQFZWFk6dOgUA+PbbbzF06FCHZ/eIiIh33IiIiFxGz5498dprr6G0tBS9evXCsmXLoNVq0b59e6xYsQIZGRnIzMyEXq9HVFQUJk+efNO+hgwZgilTpiAsLAx79+5FfHw8EhISYLfb0b59eyQmJmLBggW3tIlIdHQ0/vjjD6SkpMBsNqNLly5Yvnw5HnjgAbz00ksQEbz66qu4ePEi/Pz8kJqaigEDBvyLZ4aI6N6nkb8uUCciIqJ7TkREBF5//XXExMQ09lSIiOgu4FJJIiIiIiIilWPiRkREREREpHJcKklERERERKRyvONGRERERESkckzciIiIiIiIVI6JGxERERERkcoxcSMiIiIiIlI5Jm5EREREREQqx8SNiIiIiIhI5Zi4ERERERERqRwTNyIiIiIiIpVj4kZERERERKRy/wd5+LJhBbxYqgAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 127
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:48.508201Z",
+     "start_time": "2025-06-20T18:11:48.475680Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "tt_preds = lgmodel.predict_proba(test_prepared)[:,1]\n",
+    "print('Test AUC Score',roc_auc_score(y_test,tt_preds))"
+   ],
+   "id": "40f267953aebaa12",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test AUC Score 0.9329918512164692\n"
+     ]
+    }
+   ],
+   "execution_count": 128
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:11:48.780475Z",
+     "start_time": "2025-06-20T18:11:48.629476Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "preds = lgmodel.predict_proba(test_data)[:,1]\n",
+    "submission = pd.DataFrame({'id': CustomerID, 'Exited': preds})\n",
+    "submission.to_csv('submission_lgbm.csv', index=False)\n",
+    "submission"
+   ],
+   "id": "76848a327e743af9",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "         id    Exited\n",
+       "0     15000  0.079817\n",
+       "1     15001  0.027843\n",
+       "2     15002  0.019557\n",
+       "3     15003  0.827549\n",
+       "4     15004  0.020200\n",
+       "...     ...       ...\n",
+       "9995  24995  0.015970\n",
+       "9996  24996  0.009182\n",
+       "9997  24997  0.000322\n",
+       "9998  24998  0.055128\n",
+       "9999  24999  0.316030\n",
+       "\n",
+       "[10000 rows x 2 columns]"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>Exited</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15000</td>\n",
+       "      <td>0.079817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15001</td>\n",
+       "      <td>0.027843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15002</td>\n",
+       "      <td>0.019557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15003</td>\n",
+       "      <td>0.827549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15004</td>\n",
+       "      <td>0.020200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>24995</td>\n",
+       "      <td>0.015970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>24996</td>\n",
+       "      <td>0.009182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>24997</td>\n",
+       "      <td>0.000322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>24998</td>\n",
+       "      <td>0.055128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>24999</td>\n",
+       "      <td>0.316030</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 2 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 129
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:12:24.252510Z",
+     "start_time": "2025-06-20T18:11:48.938905Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "cat_model = CatBoostClassifier(\n",
+    "    eval_metric='AUC',\n",
+    "    learning_rate=0.0009,\n",
+    "    iterations=9000,\n",
+    "    random_seed=3\n",
+    ")\n",
+    "\n",
+    "cat_model.fit(train_prepared,y_train,eval_set=(valid_prepared,y_valid),verbose=1000)"
+   ],
+   "id": "3559eabf9c960157",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0:\ttest: 0.8822837\tbest: 0.8822837 (0)\ttotal: 4.93ms\tremaining: 44.4s\n",
+      "1000:\ttest: 0.9282924\tbest: 0.9282924 (1000)\ttotal: 3.62s\tremaining: 28.9s\n",
+      "2000:\ttest: 0.9304307\tbest: 0.9304368 (1995)\ttotal: 7.59s\tremaining: 26.5s\n",
+      "3000:\ttest: 0.9312081\tbest: 0.9312605 (2954)\ttotal: 12s\tremaining: 24s\n",
+      "4000:\ttest: 0.9313592\tbest: 0.9313787 (3659)\ttotal: 16.8s\tremaining: 20.9s\n",
+      "5000:\ttest: 0.9312337\tbest: 0.9313787 (3659)\ttotal: 20.4s\tremaining: 16.3s\n",
+      "6000:\ttest: 0.9311764\tbest: 0.9313787 (3659)\ttotal: 23.9s\tremaining: 11.9s\n",
+      "7000:\ttest: 0.9310984\tbest: 0.9313787 (3659)\ttotal: 27.3s\tremaining: 7.8s\n",
+      "8000:\ttest: 0.9310022\tbest: 0.9313787 (3659)\ttotal: 30.8s\tremaining: 3.84s\n",
+      "8999:\ttest: 0.9309266\tbest: 0.9313787 (3659)\ttotal: 34.2s\tremaining: 0us\n",
+      "\n",
+      "bestTest = 0.931378665\n",
+      "bestIteration = 3659\n",
+      "\n",
+      "Shrink model to first 3660 iterations.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<catboost.core.CatBoostClassifier at 0x157eb0ccad0>"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 130
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:12:24.397799Z",
+     "start_time": "2025-06-20T18:12:24.372049Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "evaluate_model(cat_model)",
+   "id": "34ad60dbbaa770b5",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Accuracy 0.9151428571428571\n",
+      "Validation Accuracy 0.9026666666666666\n",
+      "\n",
+      "\n",
+      "Train AUC Score 0.9516252102375617\n",
+      "Validation AUC Score 0.9313786650031193\n"
+     ]
+    }
+   ],
+   "execution_count": 131
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:12:24.803347Z",
+     "start_time": "2025-06-20T18:12:24.622872Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plot_feature_importance(cat_model)",
+   "id": "262a877ebdea8b0",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 132
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:12:24.904779Z",
+     "start_time": "2025-06-20T18:12:24.897949Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "tt_preds = cat_model.predict_proba(test_prepared)[:,1]\n",
+    "print('Test AUC Score',roc_auc_score(y_test,tt_preds))"
+   ],
+   "id": "a0c5a11e54d12b91",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test AUC Score 0.9309997368215847\n"
+     ]
+    }
+   ],
+   "execution_count": 133
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-20T18:12:25.052118Z",
+     "start_time": "2025-06-20T18:12:25.011027Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "preds = cat_model.predict_proba(test_data)[:,1]\n",
+    "submission = pd.DataFrame({'id': CustomerID, 'Exited': preds})\n",
+    "submission.to_csv('submission_cat.csv', index=False)\n",
+    "submission"
+   ],
+   "id": "7193aafa5f76efa4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "         id    Exited\n",
+       "0     15000  0.086600\n",
+       "1     15001  0.029949\n",
+       "2     15002  0.046532\n",
+       "3     15003  0.779406\n",
+       "4     15004  0.043621\n",
+       "...     ...       ...\n",
+       "9995  24995  0.008009\n",
+       "9996  24996  0.011243\n",
+       "9997  24997  0.005530\n",
+       "9998  24998  0.137327\n",
+       "9999  24999  0.184573\n",
+       "\n",
+       "[10000 rows x 2 columns]"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
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+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
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+   "execution_count": 134
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diff --git a/bank_customer_churn_prediction/submission_cat.csv b/bank_customer_churn_prediction/submission_cat.csv
new file mode 100644
index 0000000..428cee0
--- /dev/null
+++ b/bank_customer_churn_prediction/submission_cat.csv
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diff --git a/bank_customer_churn_prediction/submission_lgbm.csv b/bank_customer_churn_prediction/submission_lgbm.csv
new file mode 100644
index 0000000..34ecff1
--- /dev/null
+++ b/bank_customer_churn_prediction/submission_lgbm.csv
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diff --git a/bank_customer_churn_prediction/submission_rf.csv b/bank_customer_churn_prediction/submission_rf.csv
new file mode 100644
index 0000000..ce19733
--- /dev/null
+++ b/bank_customer_churn_prediction/submission_rf.csv
@@ -0,0 +1,10001 @@
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diff --git a/bank_customer_churn_prediction/submission_xgb.csv b/bank_customer_churn_prediction/submission_xgb.csv
new file mode 100644
index 0000000..68306a1
--- /dev/null
+++ b/bank_customer_churn_prediction/submission_xgb.csv
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From 2f4278fd4e48cd8cf64a5846a415789da5465d3e Mon Sep 17 00:00:00 2001
From: Dmytro Chechenets <demavend12345@gmail.com>
Date: Tue, 24 Jun 2025 21:39:47 +0300
Subject: [PATCH 2/4] Add some features

---
 assets/BCCP/sample_submission.csv             | 10001 ---------
 assets/BCCP/test.csv                          | 10001 ---------
 assets/BCCP/train.csv                         | 15001 -------------
 ...ubmission_cat.csv => 0_submission_cat.csv} |     0
 ...mission_lgbm.csv => 0_submission_lgbm.csv} |     0
 ...{submission_rf.csv => 0_submission_rf.csv} |     0
 ...ubmission_xgb.csv => 0_submission_xgb.csv} |     0
 .../1_submission_cat_1.csv                    | 10001 +++++++++
 .../1_submission_lgbm.csv                     | 10001 +++++++++
 .../1_submission_rf.csv                       | 10001 +++++++++
 .../1_submission_xgb.csv                      | 10001 +++++++++
 .../2_submission_cat.csv                      | 10001 +++++++++
 .../2_submission_lgbm.csv                     | 10001 +++++++++
 .../2_submission_rf.csv                       | 10001 +++++++++
 .../2_submission_xgb.csv                      | 10001 +++++++++
 .../catboost_info/catboost_training.json      | 18000 ++++++++--------
 .../catboost_info/learn/events.out.tfevents   |   Bin 494870 -> 494870 bytes
 .../catboost_info/learn_error.tsv             | 18000 ++++++++--------
 .../catboost_info/test/events.out.tfevents    |   Bin 917740 -> 917740 bytes
 .../catboost_info/test_error.tsv              | 18000 ++++++++--------
 .../catboost_info/time_left.tsv               | 18000 ++++++++--------
 .../kaggle_competition.ipynb                  |  1451 +-
 22 files changed, 116630 insertions(+), 71832 deletions(-)
 delete mode 100644 assets/BCCP/sample_submission.csv
 delete mode 100644 assets/BCCP/test.csv
 delete mode 100644 assets/BCCP/train.csv
 rename bank_customer_churn_prediction/{submission_cat.csv => 0_submission_cat.csv} (100%)
 rename bank_customer_churn_prediction/{submission_lgbm.csv => 0_submission_lgbm.csv} (100%)
 rename bank_customer_churn_prediction/{submission_rf.csv => 0_submission_rf.csv} (100%)
 rename bank_customer_churn_prediction/{submission_xgb.csv => 0_submission_xgb.csv} (100%)
 create mode 100644 bank_customer_churn_prediction/1_submission_cat_1.csv
 create mode 100644 bank_customer_churn_prediction/1_submission_lgbm.csv
 create mode 100644 bank_customer_churn_prediction/1_submission_rf.csv
 create mode 100644 bank_customer_churn_prediction/1_submission_xgb.csv
 create mode 100644 bank_customer_churn_prediction/2_submission_cat.csv
 create mode 100644 bank_customer_churn_prediction/2_submission_lgbm.csv
 create mode 100644 bank_customer_churn_prediction/2_submission_rf.csv
 create mode 100644 bank_customer_churn_prediction/2_submission_xgb.csv

diff --git a/assets/BCCP/sample_submission.csv b/assets/BCCP/sample_submission.csv
deleted file mode 100644
index 57ca445..0000000
--- a/assets/BCCP/sample_submission.csv
+++ /dev/null
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diff --git a/assets/BCCP/test.csv b/assets/BCCP/test.csv
deleted file mode 100644
index bff1926..0000000
--- a/assets/BCCP/test.csv
+++ /dev/null
@@ -1,10001 +0,0 @@
-id,CustomerId,Surname,CreditScore,Geography,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary
-15000,15594796.0,Chu,584.0,Germany,Male,30.0,2.0,146053.66,1.0,1.0,1.0,157891.86
-15001,15642821.0,Mazzi,551.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,67431.28
-15002,15716284.0,Onyekachi,706.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,156768.45
-15003,15785078.0,Martin,717.0,Spain,Male,45.0,3.0,0.0,1.0,1.0,1.0,166909.87
-15004,15662955.0,Kenechukwu,592.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,143681.97
-15005,15759066.0,Genovesi,611.0,France,Male,36.0,7.0,61811.23,1.0,1.0,0.0,145796.49
-15006,15754926.0,Haddon,684.0,France,Female,36.0,0.0,0.0,2.0,1.0,0.0,161671.15
-15007,15664802.0,Chiang,588.0,France,Male,39.0,4.0,0.0,2.0,0.0,0.0,57862.88
-15008,15757537.0,Iweobiegbulam,612.0,Germany,Male,27.0,3.0,155413.06,2.0,1.0,0.0,140166.95
-15009,15674928.0,Hoolan,736.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,75969.19
-15010,15609510.0,Ts'ao,584.0,France,Male,34.0,5.0,125860.2,1.0,0.0,0.0,143393.24
-15011,15686964.0,Yudina,724.0,Germany,Female,57.0,5.0,107960.46,1.0,1.0,0.0,53954.24
-15012,15793949.0,Mazzanti,675.0,France,Male,36.0,8.0,89086.31,1.0,1.0,0.0,122218.61
-15013,15747168.0,Chukwualuka,628.0,Germany,Male,38.0,6.0,143330.56,1.0,1.0,1.0,146723.72
-15014,15635277.0,T'ien,543.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,85890.75
-15015,15758856.0,Chukwuhaenye,703.0,Spain,Female,34.0,2.0,0.0,2.0,0.0,1.0,79071.89
-15016,15744044.0,Ch'ien,638.0,Germany,Male,28.0,6.0,125909.85,2.0,1.0,0.0,155701.91
-15017,15729201.0,Chukwualuka,695.0,France,Female,36.0,2.0,0.0,2.0,0.0,1.0,183566.78
-15018,15595324.0,Hao,656.0,Germany,Female,39.0,3.0,108235.02,1.0,1.0,1.0,134442.9
-15019,15594556.0,Chiabuotu,687.0,France,Female,37.0,4.0,0.0,2.0,0.0,1.0,15859.95
-15020,15791842.0,Nnaife,633.0,France,Female,29.0,2.0,0.0,2.0,0.0,1.0,70974.13
-15021,15782454.0,Dore,673.0,France,Male,34.0,2.0,165610.41,2.0,1.0,1.0,50368.63
-15022,15647782.0,Trevisani,560.0,Germany,Female,51.0,0.0,124485.98,1.0,0.0,0.0,175853.64
-15023,15572374.0,Cremonesi,693.0,France,Male,39.0,2.0,0.0,1.0,1.0,0.0,72993.65
-15024,15692761.0,Lai,687.0,Germany,Female,32.0,8.0,80780.16,2.0,0.0,1.0,137985.69
-15025,15595588.0,Chukwufumnanya,741.0,Germany,Male,53.0,7.0,81732.88,1.0,0.0,1.0,169921.12
-15026,15792668.0,Chin,584.0,Germany,Female,46.0,6.0,116711.06,1.0,1.0,1.0,79957.95
-15027,15709861.0,Fomin,755.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,164825.07
-15028,15770225.0,T'ien,652.0,Spain,Male,38.0,5.0,0.0,1.0,1.0,1.0,88958.49
-15029,15651272.0,Chukwujamuike,609.0,Germany,Male,43.0,7.0,124095.54,1.0,1.0,0.0,54474.48
-15030,15686776.0,Genovese,627.0,Spain,Female,43.0,8.0,0.0,1.0,1.0,1.0,141035.65
-15031,15603883.0,Genovesi,727.0,Germany,Female,37.0,6.0,121399.98,1.0,1.0,1.0,164038.07
-15032,15717010.0,Houghton,648.0,Germany,Female,37.0,10.0,142321.72,2.0,0.0,1.0,113079.19
-15033,15780579.0,Milne,542.0,Spain,Male,45.0,7.0,0.0,2.0,1.0,0.0,123982.14
-15034,15636572.0,McGregor,552.0,France,Female,41.0,6.0,0.0,1.0,1.0,0.0,129964.94
-15035,15787071.0,Nwankwo,529.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,161854.98
-15036,15567557.0,Kisch,605.0,France,Male,36.0,4.0,0.0,2.0,0.0,1.0,182650.15
-15037,15702566.0,McGregor,748.0,Germany,Male,55.0,3.0,155519.59,1.0,1.0,1.0,131710.49
-15038,15608968.0,O'Loughlin,678.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,114522.68
-15039,15648800.0,Ch'ien,567.0,Spain,Male,41.0,2.0,0.0,2.0,0.0,1.0,57762.0
-15040,15804822.0,Chidiebere,730.0,Germany,Male,43.0,8.0,132044.6,2.0,0.0,0.0,79997.14
-15041,15763515.0,Chiabuotu,724.0,Germany,Male,29.0,2.0,82909.14,2.0,1.0,1.0,170593.87
-15042,15683553.0,Hsia,678.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,143315.57
-15043,15769783.0,Chukwuemeka,589.0,France,Female,35.0,2.0,0.0,2.0,0.0,1.0,159742.33
-15044,15706587.0,L?,682.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,147955.91
-15045,15751057.0,Li Fonti,731.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,105570.44
-15046,15752626.0,Pagnotto,745.0,France,Female,35.0,8.0,0.0,1.0,1.0,0.0,88890.05
-15047,15730579.0,Kambinachi,602.0,Germany,Male,47.0,1.0,97544.29,1.0,1.0,0.0,173498.45
-15048,15663917.0,Nnamutaezinwa,599.0,Spain,Female,28.0,1.0,0.0,2.0,1.0,1.0,61793.47
-15049,15750121.0,H?,526.0,France,Female,38.0,9.0,0.0,1.0,1.0,0.0,121428.4
-15050,15606855.0,Nucci,569.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,161241.65
-15051,15759872.0,Ebelechukwu,575.0,Germany,Male,37.0,3.0,131101.04,2.0,1.0,1.0,96875.52
-15052,15647975.0,Clements,637.0,France,Male,32.0,4.0,121260.19,1.0,1.0,1.0,143783.28
-15053,15745355.0,Nwabugwu,749.0,Germany,Female,41.0,0.0,108736.52,1.0,1.0,1.0,53394.12
-15054,15644501.0,White,632.0,Germany,Male,35.0,6.0,149961.99,1.0,1.0,0.0,93478.96
-15055,15650288.0,Flemming,516.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,145528.22
-15056,15782530.0,Li Fonti,710.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,121451.21
-15057,15788025.0,T'ang,577.0,France,Female,54.0,1.0,0.0,1.0,1.0,0.0,131301.74
-15058,15686588.0,Chia,643.0,France,Male,47.0,5.0,0.0,1.0,0.0,0.0,96472.34
-15059,15596088.0,P'eng,629.0,France,Female,36.0,4.0,151242.99,1.0,1.0,0.0,91433.88
-15060,15662698.0,McElhone,591.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,91673.6
-15061,15679048.0,Clements,686.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,118862.08
-15062,15627697.0,Fiorentini,752.0,Spain,Female,58.0,1.0,0.0,1.0,1.0,1.0,143683.75
-15063,15803689.0,Toscani,652.0,France,Female,46.0,1.0,0.0,1.0,0.0,1.0,180598.28
-15064,15587562.0,Hs?eh,693.0,Spain,Male,31.0,8.0,117152.3,1.0,1.0,1.0,113645.4
-15065,15645778.0,Y?,713.0,Spain,Female,42.0,2.0,0.0,2.0,1.0,1.0,90964.54
-15066,15805637.0,Blesing,707.0,France,Male,36.0,4.0,99165.84,1.0,1.0,1.0,113553.43
-15067,15652693.0,Chiu,636.0,France,Female,51.0,6.0,0.0,1.0,0.0,0.0,127543.81
-15068,15772573.0,Chimezie,611.0,France,Female,46.0,4.0,0.0,1.0,1.0,0.0,166957.82
-15069,15794849.0,Manna,704.0,France,Male,38.0,9.0,0.0,2.0,0.0,0.0,143628.31
-15070,15773322.0,Lucchese,730.0,Germany,Female,55.0,2.0,113657.83,1.0,1.0,1.0,57553.02
-15071,15732268.0,Chiazagomekpele,598.0,France,Female,36.0,2.0,0.0,2.0,0.0,1.0,180712.52
-15072,15742824.0,Lombardi,765.0,Germany,Female,44.0,2.0,96471.3,1.0,1.0,1.0,140937.98
-15073,15750181.0,Ting,756.0,Germany,Male,48.0,1.0,108862.76,1.0,0.0,1.0,91652.43
-15074,15803378.0,Zaytseva,535.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,59668.81
-15075,15681705.0,Trevisani,598.0,Spain,Male,37.0,7.0,0.0,2.0,1.0,1.0,53817.23
-15076,15642099.0,Chukwumaobim,681.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,167984.72
-15077,15592387.0,Uspenskaya,850.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,75884.65
-15078,15624293.0,Chu,595.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,88967.48
-15079,15624993.0,Davide,619.0,Germany,Male,27.0,1.0,144450.58,1.0,1.0,1.0,108872.45
-15080,15748673.0,Tung,713.0,Spain,Female,45.0,1.0,0.0,1.0,1.0,0.0,122925.4
-15081,15676937.0,Lombardi,706.0,France,Female,36.0,4.0,111257.59,1.0,1.0,1.0,73882.49
-15082,15776596.0,Tsai,679.0,Germany,Male,37.0,6.0,109949.05,2.0,1.0,0.0,179128.69
-15083,15798960.0,Ch'ien,521.0,France,Male,45.0,8.0,0.0,2.0,1.0,1.0,180598.86
-15084,15583681.0,Mazzanti,717.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,118711.75
-15085,15670602.0,Tien,675.0,Spain,Male,28.0,9.0,121313.88,1.0,1.0,1.0,103560.98
-15086,15752312.0,Kerr,748.0,France,Female,42.0,8.0,0.0,1.0,1.0,0.0,88729.22
-15087,15783526.0,Zikoranachidimma,730.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,184954.11
-15088,15581871.0,Milne,563.0,France,Female,36.0,2.0,0.0,2.0,0.0,1.0,109057.56
-15089,15770995.0,Genovese,680.0,France,Male,28.0,2.0,145578.28,1.0,1.0,0.0,93769.89
-15090,15709447.0,Chukwuemeka,634.0,Spain,Female,35.0,9.0,0.0,2.0,1.0,1.0,123587.85
-15091,15780144.0,Chinweuba,634.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,0.0,130500.65
-15092,15615473.0,Nwachukwu,646.0,France,Male,30.0,10.0,0.0,2.0,1.0,1.0,165562.84
-15093,15693337.0,T'ien,586.0,France,Male,38.0,4.0,130622.34,1.0,1.0,0.0,141364.62
-15094,15761047.0,Brient,729.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,91817.32
-15095,15632538.0,Hsia,619.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,166650.4
-15096,15626806.0,Milne,708.0,France,Female,47.0,9.0,130274.22,1.0,1.0,1.0,91573.19
-15097,15613180.0,Tien,556.0,Spain,Male,27.0,5.0,0.0,2.0,1.0,1.0,116978.19
-15098,15792868.0,Udinese,769.0,France,Male,41.0,5.0,0.0,2.0,0.0,1.0,99595.67
-15099,15661380.0,Nucci,724.0,France,Female,36.0,5.0,0.0,1.0,1.0,1.0,27345.18
-15100,15778861.0,Hoolan,637.0,France,Female,21.0,1.0,71264.02,2.0,0.0,1.0,176868.89
-15101,15783029.0,Ch'ang,614.0,France,Female,43.0,5.0,0.0,1.0,1.0,0.0,126475.79
-15102,15602572.0,Ch'in,661.0,France,Male,43.0,10.0,0.0,1.0,1.0,0.0,139161.64
-15103,15592571.0,Lo Duca,586.0,Spain,Male,42.0,0.0,0.0,1.0,1.0,1.0,70899.27
-15104,15706128.0,Cattaneo,639.0,France,Male,43.0,9.0,122417.17,1.0,1.0,0.0,136028.97
-15105,15716164.0,Hoolan,709.0,Germany,Female,36.0,7.0,82729.47,2.0,1.0,0.0,62421.97
-15106,15782578.0,H?,669.0,Spain,Male,38.0,1.0,0.0,1.0,1.0,1.0,161241.65
-15107,15626141.0,Chiganu,633.0,Germany,Female,51.0,5.0,115924.89,1.0,1.0,0.0,57896.27
-15108,15777743.0,Chukwualuka,559.0,Spain,Female,29.0,8.0,0.0,2.0,0.0,0.0,101382.86
-15109,15746729.0,Biryukova,622.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,28667.56
-15110,15674840.0,Dettmann,661.0,France,Male,38.0,5.0,0.0,1.0,1.0,0.0,139048.0
-15111,15751175.0,Hsiao,589.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,146585.29
-15112,15609618.0,Nwora,619.0,France,Female,39.0,7.0,130170.82,1.0,1.0,0.0,51548.14
-15113,15749583.0,Ch'iu,682.0,France,Female,38.0,7.0,0.0,2.0,0.0,1.0,170886.43
-15114,15689344.0,Chinedum,628.0,France,Female,35.0,8.0,0.0,2.0,0.0,1.0,177065.24
-15115,15707596.0,Tsao,447.0,France,Female,41.0,3.0,0.0,2.0,1.0,1.0,177028.0
-15116,15593969.0,Onyekachi,695.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,92898.14
-15117,15609562.0,Tuan,711.0,Germany,Female,41.0,7.0,124577.33,3.0,0.0,0.0,46824.08
-15118,15715597.0,Martin,661.0,Germany,Male,34.0,9.0,102535.35,2.0,1.0,0.0,176692.87
-15119,15649858.0,Scott,572.0,France,Male,44.0,1.0,0.0,2.0,1.0,0.0,140579.17
-15120,15622083.0,Ch'ang,757.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,115525.13
-15121,15585901.0,Calabresi,592.0,Spain,Male,40.0,3.0,130806.99,1.0,1.0,0.0,140801.64
-15122,15693683.0,T'ao,685.0,France,Male,37.0,4.0,0.0,2.0,0.0,0.0,57558.95
-15123,15791279.0,Obiajulu,669.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,1.0,158845.49
-15124,15682355.0,Pisano,667.0,Germany,Male,38.0,3.0,122724.45,1.0,1.0,0.0,146955.71
-15125,15705512.0,Goliwe,681.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,121061.89
-15126,15762351.0,Ngozichukwuka,595.0,Spain,Male,49.0,2.0,0.0,1.0,0.0,0.0,134435.75
-15127,15663987.0,Chin,619.0,France,Male,26.0,7.0,0.0,2.0,0.0,1.0,164967.21
-15128,15736420.0,Ugochukwutubelum,724.0,France,Male,36.0,1.0,104784.23,1.0,1.0,0.0,164966.17
-15129,15666043.0,Kenechukwu,703.0,Germany,Male,29.0,4.0,142417.97,1.0,1.0,1.0,152417.97
-15130,15591286.0,Hsu,683.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,114020.08
-15131,15803378.0,Mazzi,581.0,Germany,Female,42.0,2.0,107784.65,1.0,1.0,0.0,138700.94
-15132,15676810.0,Li Fonti,667.0,France,Female,25.0,9.0,103516.87,1.0,0.0,1.0,878.0
-15133,15651020.0,P'eng,774.0,France,Female,45.0,2.0,0.0,2.0,1.0,1.0,158420.14
-15134,15757911.0,Maclean,718.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,176945.96
-15135,15794895.0,Martin,703.0,Spain,Female,45.0,8.0,121355.99,1.0,1.0,0.0,43931.51
-15136,15746726.0,Pagnotto,639.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,1528.4
-15137,15601351.0,Y?an,634.0,Germany,Male,40.0,2.0,104433.08,2.0,1.0,0.0,170518.83
-15138,15778025.0,Chukwuemeka,619.0,France,Male,32.0,1.0,62974.64,1.0,1.0,1.0,156774.75
-15139,15682454.0,Onwudiwe,742.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,80619.17
-15140,15703763.0,Ch'ien,706.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,174800.31
-15141,15656300.0,Uwakwe,667.0,France,Female,47.0,9.0,0.0,1.0,1.0,0.0,174027.3
-15142,15778025.0,H?,685.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,151335.24
-15143,15605826.0,Trevisano,528.0,Germany,Female,44.0,7.0,136423.42,1.0,1.0,0.0,151887.16
-15144,15719352.0,Pisano,722.0,Germany,Male,43.0,10.0,127738.25,2.0,1.0,0.0,176925.69
-15145,15592999.0,Kao,632.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,121061.89
-15146,15616172.0,Hsing,595.0,Germany,Male,31.0,0.0,151709.33,2.0,1.0,1.0,137980.58
-15147,15593900.0,Lai,707.0,France,Male,59.0,7.0,0.0,2.0,0.0,0.0,140765.25
-15148,15572735.0,Hs?eh,608.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,138887.86
-15149,15582591.0,Manna,727.0,Germany,Female,54.0,6.0,150844.94,1.0,1.0,0.0,113410.21
-15150,15572398.0,T'ien,597.0,Spain,Male,31.0,8.0,97408.03,1.0,1.0,0.0,143628.31
-15151,15645323.0,Chukwueloka,677.0,France,Female,41.0,10.0,99610.92,1.0,1.0,0.0,179908.71
-15152,15777901.0,T'ien,479.0,Germany,Female,41.0,7.0,151659.52,3.0,1.0,1.0,141364.62
-15153,15625023.0,Onuora,567.0,France,Male,50.0,5.0,0.0,2.0,1.0,1.0,93719.85
-15154,15711889.0,Nnonso,686.0,France,Male,57.0,10.0,152390.29,1.0,1.0,0.0,146785.44
-15155,15637644.0,Chukwukere,727.0,Germany,Female,43.0,2.0,130229.14,2.0,1.0,1.0,149991.32
-15156,15679770.0,Chukwujamuike,653.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,121789.3
-15157,15752588.0,Ch'en,659.0,Germany,Female,33.0,2.0,107712.57,2.0,0.0,0.0,130189.72
-15158,15679622.0,Aksenov,714.0,Germany,Male,41.0,2.0,129901.21,1.0,0.0,1.0,93739.71
-15159,15772337.0,Nnamutaezinwa,583.0,France,Male,45.0,9.0,0.0,1.0,0.0,1.0,139953.94
-15160,15732265.0,Chineze,790.0,Spain,Female,47.0,5.0,0.0,2.0,0.0,0.0,81442.6
-15161,15583480.0,Ndubueze,749.0,France,Male,24.0,6.0,0.0,2.0,0.0,1.0,143544.48
-15162,15744044.0,De Salis,708.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,129803.08
-15163,15645593.0,Mazzanti,742.0,France,Male,32.0,6.0,0.0,2.0,0.0,0.0,82222.86
-15164,15766017.0,Pirozzi,678.0,France,Male,29.0,2.0,0.0,2.0,0.0,1.0,71179.31
-15165,15640074.0,Yobachi,613.0,France,Male,36.0,1.0,126473.33,1.0,0.0,0.0,95611.47
-15166,15594408.0,Ch'ang,490.0,Germany,Female,62.0,6.0,102365.49,2.0,1.0,0.0,168816.15
-15167,15581286.0,Smith,665.0,France,Male,41.0,3.0,172325.67,1.0,0.0,0.0,27474.81
-15168,15747097.0,Okwudilichukwu,637.0,France,Male,37.0,4.0,112949.71,1.0,1.0,0.0,101319.76
-15169,15658969.0,Chiazagomekpere,629.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,59645.24
-15170,15724863.0,Maclean,651.0,France,Male,41.0,2.0,95957.88,1.0,1.0,0.0,156774.75
-15171,15665963.0,Ch'eng,567.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,88729.22
-15172,15806049.0,Hsieh,602.0,France,Male,35.0,3.0,123372.3,1.0,1.0,0.0,93959.96
-15173,15578722.0,Lucchese,676.0,France,Female,52.0,1.0,0.0,1.0,1.0,0.0,122268.75
-15174,15791114.0,Nwabugwu,564.0,Germany,Female,46.0,3.0,106731.58,1.0,1.0,1.0,92811.2
-15175,15692570.0,Ch'iu,775.0,Germany,Female,36.0,2.0,122314.39,2.0,1.0,0.0,153036.97
-15176,15762583.0,Ch'iu,757.0,Spain,Female,30.0,7.0,0.0,2.0,0.0,1.0,163550.58
-15177,15757535.0,Walker,613.0,Germany,Male,25.0,6.0,131394.38,1.0,0.0,0.0,54495.82
-15178,15609618.0,Baresi,585.0,Germany,Female,36.0,9.0,135984.65,2.0,1.0,1.0,151677.31
-15179,15635177.0,Milano,559.0,France,Female,35.0,7.0,0.0,2.0,1.0,1.0,156910.18
-15180,15632210.0,Naylor,749.0,Germany,Female,43.0,8.0,96798.79,2.0,0.0,0.0,149665.65
-15181,15781574.0,Maclean,549.0,Germany,Male,60.0,3.0,122449.38,1.0,1.0,0.0,165038.43
-15182,15694879.0,Hsiung,592.0,France,Female,33.0,9.0,91291.1,1.0,1.0,0.0,139048.0
-15183,15806957.0,Hsiung,506.0,France,Female,63.0,2.0,92930.92,1.0,1.0,1.0,53534.8
-15184,15791842.0,Nwebube,569.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,151325.24
-15185,15691396.0,Ts'ui,717.0,France,Male,51.0,4.0,0.0,1.0,1.0,1.0,65864.4
-15186,15782591.0,Uspenskaya,668.0,France,Male,34.0,7.0,91246.53,1.0,1.0,0.0,76515.79
-15187,15774696.0,Akhtar,663.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,52337.97
-15188,15605916.0,Ch'en,692.0,Germany,Female,28.0,6.0,101641.14,2.0,1.0,1.0,67830.2
-15189,15758031.0,Lo,718.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,96515.02
-15190,15689953.0,Uspenskaya,628.0,Germany,Male,30.0,1.0,148853.29,1.0,1.0,1.0,35367.19
-15191,15725024.0,Biryukov,616.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,0.0,168034.9
-15192,15689061.0,Chukwudi,678.0,Germany,Male,35.0,1.0,114737.26,1.0,1.0,0.0,179937.38
-15193,15736112.0,Teng,717.0,France,Female,36.0,8.0,0.0,1.0,0.0,1.0,38960.59
-15194,15624293.0,Trevisano,679.0,Germany,Female,45.0,1.0,118889.66,1.0,1.0,1.0,161479.19
-15195,15703978.0,Shih,739.0,France,Male,41.0,4.0,77910.23,1.0,1.0,0.0,152790.92
-15196,15661249.0,Okechukwu,679.0,France,Male,49.0,9.0,131834.76,1.0,1.0,1.0,158043.11
-15197,15605020.0,Maclean,671.0,France,Male,26.0,9.0,0.0,2.0,1.0,1.0,88958.49
-15198,15604792.0,Nwabugwu,586.0,France,Male,55.0,2.0,108004.64,1.0,0.0,1.0,118024.1
-15199,15651396.0,Onyemauchechukwu,692.0,Germany,Male,37.0,1.0,142072.02,2.0,0.0,0.0,159830.58
-15200,15621140.0,Davide,621.0,France,Female,38.0,8.0,144428.99,1.0,1.0,0.0,56787.8
-15201,15585835.0,Clements,788.0,France,Male,43.0,5.0,0.0,2.0,0.0,1.0,78854.57
-15202,15597919.0,Ts'ai,727.0,France,Male,26.0,8.0,0.0,2.0,0.0,1.0,64323.24
-15203,15802823.0,P'eng,699.0,France,Female,27.0,8.0,0.0,2.0,1.0,1.0,170968.99
-15204,15803689.0,Dickson,604.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,155876.06
-15205,15591747.0,Chukwueloka,850.0,France,Male,46.0,5.0,0.0,2.0,0.0,0.0,158336.36
-15206,15600720.0,Hsing,600.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,115115.32
-15207,15802501.0,Ponomaryova,616.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,166677.35
-15208,15691817.0,Hao,756.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,25887.89
-15209,15772412.0,Lu,788.0,Spain,Male,40.0,3.0,98899.91,1.0,1.0,0.0,166929.43
-15210,15773520.0,Ukaegbunam,681.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,38960.59
-15211,15626233.0,Trevisani,709.0,Germany,Male,35.0,8.0,99848.86,2.0,0.0,0.0,134732.99
-15212,15711901.0,Martin,757.0,France,Female,41.0,5.0,0.0,1.0,1.0,0.0,126848.01
-15213,15615176.0,Yuryeva,733.0,France,Female,28.0,9.0,0.0,2.0,0.0,0.0,153566.97
-15214,15775124.0,Chinomso,662.0,France,Male,29.0,6.0,125167.02,1.0,1.0,0.0,152982.79
-15215,15629793.0,Ch'iu,544.0,France,Female,39.0,8.0,0.0,2.0,0.0,1.0,138700.75
-15216,15665834.0,Udobata,576.0,France,Female,45.0,9.0,0.0,1.0,1.0,1.0,67830.2
-15217,15602731.0,Hsieh,579.0,France,Male,35.0,0.0,156972.91,1.0,1.0,1.0,141210.5
-15218,15768282.0,Mazzanti,678.0,France,Female,43.0,4.0,0.0,2.0,1.0,1.0,82393.08
-15219,15791342.0,Teng,624.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,172526.9
-15220,15686974.0,Hsiung,646.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,122925.44
-15221,15803965.0,Pugh,741.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,1.0,102547.56
-15222,15689614.0,Hsiao,681.0,France,Male,35.0,6.0,0.0,1.0,1.0,1.0,125961.48
-15223,15569249.0,Ives,649.0,Spain,Male,36.0,5.0,0.0,2.0,0.0,0.0,103792.53
-15224,15621815.0,Biryukova,637.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,80701.72
-15225,15795874.0,Chu,684.0,France,Female,40.0,4.0,0.0,1.0,0.0,0.0,136985.08
-15226,15585067.0,Nwokeocha,597.0,France,Female,48.0,2.0,126534.6,1.0,1.0,1.0,179866.01
-15227,15627848.0,K'ung,594.0,France,Male,29.0,8.0,123971.27,2.0,1.0,0.0,152499.8
-15228,15606841.0,Thompson,599.0,Germany,Male,45.0,0.0,104784.23,1.0,1.0,1.0,142944.09
-15229,15641389.0,Onwumelu,730.0,France,Male,46.0,0.0,0.0,2.0,1.0,1.0,143022.32
-15230,15585047.0,Ogochukwu,850.0,Germany,Female,46.0,6.0,121898.58,1.0,0.0,0.0,109794.31
-15231,15751175.0,P'eng,675.0,Germany,Female,68.0,4.0,108477.27,1.0,0.0,1.0,179521.84
-15232,15692991.0,Efremov,746.0,France,Male,37.0,9.0,114939.75,1.0,0.0,1.0,71420.16
-15233,15782531.0,Onyekachukwu,590.0,Spain,Female,46.0,7.0,0.0,2.0,1.0,1.0,35367.19
-15234,15631868.0,Milanesi,809.0,Germany,Female,54.0,5.0,145081.73,3.0,1.0,0.0,123951.86
-15235,15744606.0,Tien,706.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,138021.36
-15236,15701109.0,Hs?eh,743.0,Germany,Female,32.0,7.0,141581.08,1.0,1.0,1.0,47729.33
-15237,15778936.0,Teng,754.0,France,Male,58.0,7.0,121254.65,2.0,1.0,1.0,53478.02
-15238,15788367.0,Ndubuagha,571.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,82322.86
-15239,15791114.0,Iadanza,719.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,168816.18
-15240,15598884.0,Hsia,635.0,France,Male,38.0,0.0,0.0,1.0,1.0,0.0,55725.04
-15241,15808958.0,Nkemjika,664.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,1.0,111307.98
-15242,15733705.0,Macleod,678.0,Germany,Female,27.0,1.0,138341.51,1.0,0.0,0.0,171707.93
-15243,15649523.0,Lettiere,567.0,France,Male,54.0,3.0,0.0,1.0,0.0,1.0,115818.51
-15244,15643770.0,Chuang,676.0,France,Female,31.0,8.0,0.0,2.0,0.0,0.0,93844.86
-15245,15730602.0,Hsueh,690.0,Germany,Female,56.0,3.0,141078.57,1.0,0.0,1.0,113931.57
-15246,15667620.0,Nkemdirim,587.0,France,Male,35.0,2.0,0.0,1.0,1.0,1.0,166526.26
-15247,15742607.0,Hsia,619.0,Spain,Female,43.0,1.0,0.0,2.0,0.0,0.0,109742.55
-15248,15641411.0,Nwankwo,710.0,France,Female,39.0,7.0,185966.64,1.0,0.0,0.0,78560.64
-15249,15784923.0,Chukwumaobim,724.0,France,Male,30.0,1.0,136093.74,1.0,1.0,0.0,61290.99
-15250,15788151.0,Chukwuemeka,583.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,1.0,141404.07
-15251,15576723.0,Chizoba,516.0,Spain,Female,30.0,2.0,0.0,2.0,1.0,0.0,51362.82
-15252,15670172.0,Chiekwugo,655.0,Germany,Female,20.0,4.0,91522.04,1.0,1.0,0.0,147955.91
-15253,15683625.0,Ch'eng,775.0,Spain,Male,45.0,4.0,0.0,2.0,1.0,0.0,176883.42
-15254,15809826.0,Chukwuebuka,521.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,165576.55
-15255,15672912.0,Ch'eng,624.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,122433.09
-15256,15584785.0,Chukwualuka,714.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,113946.67
-15257,15667934.0,Yeh,718.0,Spain,Female,31.0,1.0,99842.5,1.0,1.0,1.0,131647.31
-15258,15797733.0,Toscani,642.0,Germany,Male,46.0,3.0,124445.58,1.0,1.0,0.0,823.96
-15259,15768095.0,Chinedum,662.0,France,Male,38.0,8.0,0.0,1.0,0.0,1.0,104469.06
-15260,15730161.0,Udobata,741.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,1.0,115509.59
-15261,15634792.0,Galkin,707.0,France,Female,48.0,8.0,0.0,4.0,1.0,0.0,133871.83
-15262,15679062.0,Yermakov,516.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,146715.07
-15263,15742778.0,Nkemakonam,622.0,Germany,Male,65.0,2.0,127290.61,2.0,0.0,0.0,163427.18
-15264,15593736.0,Toscani,613.0,Germany,Male,29.0,3.0,110783.28,1.0,1.0,0.0,147545.65
-15265,15795571.0,Maclean,596.0,Spain,Female,48.0,8.0,98955.03,2.0,1.0,1.0,134417.31
-15266,15686957.0,Hsueh,711.0,France,Male,24.0,4.0,0.0,1.0,1.0,1.0,166370.81
-15267,15578980.0,Ch'ang,624.0,Germany,Male,41.0,8.0,168078.83,2.0,0.0,0.0,57589.29
-15268,15807312.0,Zaytseva,716.0,Spain,Male,39.0,7.0,0.0,2.0,1.0,1.0,94562.88
-15269,15735221.0,Ts'ao,706.0,France,Female,30.0,5.0,0.0,2.0,0.0,0.0,78436.36
-15270,15789560.0,Kaleski,583.0,France,Female,31.0,8.0,109941.51,1.0,1.0,0.0,151902.78
-15271,15651533.0,Ts'ui,632.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,4654.28
-15272,15770039.0,Hs?eh,635.0,France,Male,36.0,5.0,116880.53,1.0,1.0,0.0,164768.82
-15273,15634501.0,Milano,754.0,France,Female,24.0,4.0,131852.81,2.0,1.0,0.0,144870.28
-15274,15641633.0,Ku,657.0,Germany,Female,45.0,3.0,109958.12,1.0,0.0,0.0,181224.56
-15275,15589660.0,Olisaemeka,535.0,France,Male,46.0,2.0,0.0,1.0,0.0,0.0,176450.19
-15276,15783859.0,Lombardo,850.0,France,Female,39.0,1.0,91291.2,1.0,0.0,1.0,166370.81
-15277,15583595.0,Hsiung,705.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,51565.98
-15278,15694566.0,Onuchukwu,712.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,158059.95
-15279,15697480.0,Ch'iu,753.0,Spain,Male,43.0,5.0,0.0,2.0,0.0,0.0,187811.71
-15280,15734588.0,Chiedozie,675.0,France,Female,54.0,8.0,0.0,1.0,1.0,0.0,164083.72
-15281,15684769.0,Mott,678.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,182867.84
-15282,15726589.0,K?,605.0,Germany,Female,36.0,4.0,116498.22,2.0,1.0,0.0,53511.74
-15283,15756680.0,Kolesnikova,581.0,France,Male,60.0,0.0,0.0,1.0,1.0,1.0,113931.57
-15284,15663888.0,Hsia,720.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,126475.79
-15285,15672894.0,Yefremov,595.0,France,Female,47.0,5.0,0.0,1.0,1.0,0.0,126469.09
-15286,15649060.0,Ts'ui,826.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,83342.73
-15287,15598044.0,Bibi,646.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,176433.7
-15288,15781665.0,K'ung,749.0,Spain,Female,32.0,2.0,0.0,2.0,1.0,0.0,48606.71
-15289,15685329.0,Miah,538.0,France,Male,35.0,3.0,133934.4,1.0,1.0,1.0,117038.96
-15290,15571059.0,Hs?eh,741.0,France,Male,25.0,4.0,0.0,2.0,1.0,1.0,143628.31
-15291,15800295.0,Padovesi,603.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,123982.14
-15292,15572428.0,Chukwukere,659.0,Spain,Male,34.0,9.0,0.0,1.0,1.0,1.0,82526.92
-15293,15584899.0,Obidimkpa,656.0,Germany,Female,59.0,9.0,143680.47,1.0,1.0,0.0,81624.09
-15294,15808566.0,Nkemdirim,593.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,61766.76
-15295,15627212.0,Burt,663.0,Spain,Male,35.0,8.0,0.0,1.0,1.0,1.0,91830.75
-15296,15644833.0,Nnamutaezinwa,753.0,France,Male,33.0,1.0,91328.71,1.0,1.0,0.0,97939.3
-15297,15772601.0,Nkemjika,706.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,90821.72
-15298,15685500.0,Hargreaves,683.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,1.0,120540.83
-15299,15602572.0,Milanesi,762.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,158444.61
-15300,15591747.0,Nkemdirim,632.0,France,Male,42.0,2.0,0.0,2.0,0.0,1.0,82422.91
-15301,15792360.0,Chukwualuka,649.0,Spain,Male,63.0,7.0,116886.59,1.0,1.0,1.0,81622.52
-15302,15684296.0,Hsueh,758.0,Germany,Male,35.0,1.0,120911.75,1.0,1.0,0.0,96444.88
-15303,15717185.0,Ts'ui,639.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,94790.34
-15304,15765311.0,Ch'eng,749.0,France,Female,34.0,7.0,92616.64,2.0,1.0,0.0,181224.56
-15305,15714586.0,Chinweike,756.0,France,Male,29.0,0.0,0.0,1.0,1.0,0.0,153955.38
-15306,15750638.0,Chinagorom,627.0,France,Male,32.0,10.0,154506.75,1.0,1.0,1.0,143538.51
-15307,15750121.0,Tuan,739.0,Germany,Female,50.0,8.0,142028.36,1.0,1.0,1.0,90896.04
-15308,15585985.0,Y?,725.0,France,Male,27.0,9.0,0.0,2.0,1.0,1.0,163714.92
-15309,15592077.0,DeRose,721.0,Germany,Male,43.0,10.0,118613.26,3.0,0.0,0.0,145551.6
-15310,15772262.0,Iweobiegbunam,479.0,Germany,Male,45.0,4.0,126473.33,1.0,1.0,1.0,57549.65
-15311,15807312.0,Chinwendu,754.0,Germany,Female,27.0,8.0,118899.45,1.0,1.0,0.0,54816.57
-15312,15623053.0,Chinomso,667.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,189392.66
-15313,15724076.0,Nwankwo,526.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,76637.38
-15314,15642996.0,Chiang,682.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,66469.2
-15315,15582591.0,Larkin,598.0,France,Male,30.0,6.0,0.0,2.0,1.0,0.0,110273.84
-15316,15767414.0,Macleod,727.0,France,Male,31.0,1.0,0.0,1.0,1.0,0.0,129964.94
-15317,15752601.0,Thompson,637.0,Germany,Female,34.0,5.0,97750.07,2.0,1.0,1.0,123951.19
-15318,15735078.0,Ni,593.0,Spain,Female,46.0,0.0,105907.42,1.0,1.0,1.0,96961.0
-15319,15631721.0,Uwaezuoke,682.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,129912.76
-15320,15783714.0,Uspenskaya,572.0,France,Male,30.0,4.0,127864.85,1.0,1.0,1.0,160976.83
-15321,15659656.0,Ts'ao,646.0,France,Female,37.0,2.0,0.0,2.0,1.0,1.0,129107.97
-15322,15691792.0,Ch'ang,554.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,139431.0
-15323,15809227.0,Mancini,743.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,183872.88
-15324,15592091.0,Fanucci,549.0,France,Female,51.0,6.0,141581.08,1.0,1.0,0.0,74510.65
-15325,15728480.0,Chukwujamuike,774.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,145796.49
-15326,15798885.0,P'eng,693.0,France,Female,42.0,2.0,0.0,2.0,0.0,1.0,119374.74
-15327,15679743.0,Ting,545.0,France,Male,44.0,10.0,0.0,1.0,0.0,1.0,59961.81
-15328,15800524.0,Ch'iu,651.0,France,Female,29.0,3.0,0.0,1.0,1.0,1.0,121277.78
-15329,15763065.0,Mazzi,594.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,175070.58
-15330,15739292.0,Hsia,562.0,Spain,Female,32.0,2.0,167795.6,2.0,1.0,1.0,175071.29
-15331,15709604.0,Olisanugo,710.0,Spain,Male,37.0,9.0,105229.73,1.0,1.0,1.0,158973.45
-15332,15776733.0,Hs?eh,652.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,94450.97
-15333,15615176.0,Chukwujekwu,724.0,France,Male,30.0,9.0,122314.5,1.0,0.0,1.0,115818.17
-15334,15764073.0,Kibby,595.0,Germany,Male,38.0,2.0,126534.6,1.0,1.0,1.0,177896.92
-15335,15652667.0,Mazzanti,582.0,France,Male,31.0,7.0,0.0,2.0,0.0,0.0,178779.52
-15336,15598044.0,Chikezie,581.0,Spain,Female,31.0,9.0,115062.81,1.0,0.0,0.0,71926.28
-15337,15645323.0,Ts'ui,639.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,0.0,129562.74
-15338,15756950.0,Hsueh,736.0,Germany,Female,37.0,4.0,131394.56,2.0,0.0,0.0,16928.4
-15339,15717290.0,Ebelegbulam,638.0,Spain,Male,30.0,5.0,83772.95,1.0,1.0,1.0,158976.75
-15340,15752294.0,Lo,561.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,103557.85
-15341,15669501.0,Milne,748.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,121592.58
-15342,15781298.0,Ch'ien,542.0,France,Male,53.0,6.0,0.0,1.0,1.0,0.0,125962.02
-15343,15710295.0,Ch'ien,613.0,France,Male,31.0,4.0,105405.97,1.0,1.0,0.0,117323.31
-15344,15779497.0,Bales,646.0,France,Male,36.0,1.0,103764.95,1.0,1.0,0.0,72318.77
-15345,15772896.0,Maclean,638.0,Spain,Male,54.0,5.0,0.0,2.0,1.0,1.0,140941.47
-15346,15764409.0,Wang,633.0,Germany,Female,44.0,7.0,102773.2,1.0,1.0,0.0,169963.2
-15347,15756920.0,Hargreaves,667.0,France,Female,34.0,7.0,83348.89,1.0,1.0,0.0,174032.0
-15348,15598892.0,Lucchese,571.0,France,Female,31.0,7.0,140614.15,1.0,1.0,1.0,53581.18
-15349,15803801.0,Hsieh,690.0,France,Female,40.0,9.0,0.0,2.0,0.0,1.0,79919.13
-15350,15664339.0,Hsing,659.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,126726.33
-15351,15661526.0,Chiazagomekpere,677.0,Spain,Female,29.0,8.0,0.0,2.0,1.0,0.0,133831.37
-15352,15782530.0,Ejimofor,708.0,France,Female,36.0,4.0,118547.72,1.0,1.0,1.0,172792.43
-15353,15652934.0,Okwudiliolisa,646.0,France,Male,39.0,10.0,0.0,1.0,1.0,0.0,114448.77
-15354,15750549.0,Hsing,637.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,163327.27
-15355,15616280.0,Lo,762.0,Germany,Male,40.0,1.0,133658.24,2.0,1.0,1.0,186942.49
-15356,15690164.0,Smith,605.0,France,Female,40.0,4.0,0.0,1.0,1.0,0.0,171364.18
-15357,15603741.0,Ch'iu,661.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,110783.28
-15358,15583764.0,Ts'ui,605.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,144584.29
-15359,15798964.0,Dike,684.0,France,Male,24.0,2.0,0.0,2.0,1.0,1.0,79577.38
-15360,15685314.0,Toscani,636.0,Germany,Male,37.0,6.0,73348.56,2.0,1.0,1.0,129818.59
-15361,15674922.0,Hill,691.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,79450.61
-15362,15642835.0,Hargreaves,735.0,France,Female,36.0,2.0,0.0,2.0,0.0,1.0,187926.6
-15363,15614187.0,Ozerova,476.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,112465.19
-15364,15786026.0,Ch'eng,667.0,Spain,Male,38.0,6.0,119496.87,1.0,1.0,0.0,155915.79
-15365,15772383.0,Chigozie,589.0,Germany,Male,45.0,3.0,127261.35,1.0,1.0,1.0,74564.41
-15366,15591552.0,Milani,593.0,France,Male,60.0,1.0,126817.65,1.0,1.0,1.0,82273.82
-15367,15623478.0,Nwachukwu,642.0,France,Female,50.0,1.0,121496.63,1.0,1.0,0.0,79146.36
-15368,15763613.0,Chukwufumnanya,753.0,France,Male,29.0,7.0,127570.93,1.0,1.0,1.0,109394.62
-15369,15774285.0,T'ien,642.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,10963.04
-15370,15641675.0,Hao,645.0,France,Male,63.0,10.0,0.0,1.0,1.0,1.0,67622.46
-15371,15578738.0,Fiorentino,675.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,35342.33
-15372,15597951.0,Mazzanti,743.0,France,Female,33.0,10.0,0.0,2.0,1.0,0.0,141035.65
-15373,15679770.0,Achebe,648.0,Spain,Female,35.0,7.0,0.0,1.0,0.0,1.0,138871.12
-15374,15750407.0,Nwankwo,683.0,France,Male,43.0,5.0,154863.15,1.0,1.0,0.0,150802.41
-15375,15750569.0,Ch'eng,733.0,France,Male,24.0,7.0,0.0,1.0,1.0,1.0,121505.65
-15376,15652955.0,Chiang,627.0,Germany,Female,25.0,6.0,109327.65,1.0,1.0,1.0,199493.38
-15377,15763625.0,Chukwuebuka,696.0,France,Female,48.0,6.0,0.0,1.0,1.0,1.0,88890.05
-15378,15615004.0,Liao,712.0,Germany,Female,37.0,8.0,93569.86,2.0,0.0,0.0,131919.48
-15379,15746970.0,Ch'ien,703.0,Germany,Male,47.0,7.0,144757.02,1.0,0.0,0.0,62979.93
-15380,15783338.0,Hsu,627.0,Germany,Female,54.0,1.0,108935.85,1.0,1.0,1.0,174570.55
-15381,15711571.0,Li Fonti,687.0,France,Male,27.0,2.0,121498.11,2.0,1.0,0.0,71830.1
-15382,15769955.0,Y?an,577.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,56908.41
-15383,15589949.0,Chien,467.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,121765.0
-15384,15804937.0,Maclean,671.0,France,Male,64.0,5.0,0.0,1.0,1.0,1.0,82867.55
-15385,15781495.0,Genovese,743.0,France,Male,36.0,7.0,0.0,1.0,1.0,0.0,64872.33
-15386,15591036.0,Pirozzi,643.0,France,Male,43.0,6.0,125724.95,1.0,1.0,1.0,189143.94
-15387,15762543.0,L?,565.0,Spain,Male,37.0,7.0,101827.07,1.0,1.0,0.0,186841.71
-15388,15595537.0,Chibueze,613.0,Spain,Male,37.0,10.0,0.0,2.0,1.0,0.0,134954.85
-15389,15771569.0,Maclean,593.0,Germany,Female,39.0,7.0,77949.69,2.0,1.0,1.0,158973.77
-15390,15752816.0,Goliwe,568.0,France,Male,46.0,8.0,0.0,2.0,1.0,0.0,88783.63
-15391,15738220.0,Milne,597.0,France,Male,36.0,2.0,0.0,1.0,1.0,0.0,94875.03
-15392,15689328.0,Brown,708.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,96885.19
-15393,15810444.0,Nwora,572.0,Spain,Female,28.0,5.0,0.0,2.0,1.0,1.0,95941.55
-15394,15598884.0,Chukwuebuka,749.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,121440.8
-15395,15587584.0,Ch'eng,593.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,121681.19
-15396,15721487.0,Okeke,684.0,France,Male,44.0,9.0,0.0,2.0,1.0,0.0,93844.87
-15397,15652981.0,Tsou,714.0,Germany,Male,33.0,2.0,93796.42,2.0,1.0,1.0,136910.18
-15398,15673929.0,Shih,682.0,France,Female,29.0,9.0,0.0,2.0,0.0,0.0,162719.69
-15399,15722599.0,Chiazagomekpere,571.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,72993.67
-15400,15699340.0,Eipper,775.0,France,Female,49.0,4.0,170557.91,2.0,1.0,0.0,113557.4
-15401,15721433.0,Dickson,619.0,Spain,Female,25.0,2.0,0.0,1.0,1.0,1.0,157959.02
-15402,15622182.0,Iadanza,711.0,Spain,Male,33.0,6.0,0.0,2.0,0.0,0.0,1485.27
-15403,15780254.0,Onyekachi,641.0,France,Male,31.0,7.0,0.0,1.0,1.0,1.0,83475.44
-15404,15756475.0,Ts'ui,719.0,France,Female,61.0,2.0,0.0,2.0,1.0,1.0,71672.86
-15405,15806941.0,Chiazagomekpele,712.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,80178.54
-15406,15593623.0,Ch'ang,614.0,Germany,Female,35.0,5.0,107106.8,1.0,1.0,1.0,53437.1
-15407,15637169.0,P'eng,644.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,0.0,163534.6
-15408,15793723.0,Ch'ang,587.0,Spain,Male,25.0,5.0,0.0,1.0,1.0,1.0,100137.5
-15409,15736302.0,Tien,683.0,Germany,Female,39.0,6.0,127523.75,1.0,1.0,0.0,181224.56
-15410,15790750.0,Chineze,741.0,Germany,Male,39.0,4.0,141787.31,1.0,1.0,0.0,73881.68
-15411,15794849.0,Yefremov,723.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,97468.44
-15412,15780359.0,Hargreaves,662.0,Spain,Male,34.0,4.0,118032.53,1.0,1.0,0.0,70774.97
-15413,15622774.0,Toscani,662.0,Spain,Female,47.0,4.0,0.0,1.0,0.0,1.0,162858.29
-15414,15573741.0,Chiu,643.0,Germany,Female,56.0,6.0,148074.79,1.0,1.0,0.0,179655.87
-15415,15582892.0,Hsueh,699.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,159576.75
-15416,15797960.0,Lai,749.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,179445.24
-15417,15732266.0,Maclean,615.0,France,Male,40.0,9.0,0.0,1.0,1.0,1.0,157916.49
-15418,15643942.0,Toscani,583.0,France,Male,32.0,8.0,116121.84,1.0,0.0,0.0,116495.22
-15419,15656086.0,L?,644.0,France,Female,38.0,7.0,0.0,1.0,1.0,0.0,67622.84
-15420,15750731.0,Hsing,656.0,France,Male,42.0,4.0,0.0,2.0,0.0,1.0,152855.96
-15421,15799357.0,Toscani,567.0,Spain,Male,39.0,2.0,148962.76,2.0,1.0,1.0,91346.03
-15422,15652694.0,Chiang,696.0,France,Female,38.0,2.0,95377.13,1.0,1.0,0.0,179634.69
-15423,15693637.0,Smith,807.0,Germany,Male,41.0,6.0,71083.98,1.0,1.0,1.0,96832.82
-15424,15804072.0,Sholes,571.0,Germany,Male,32.0,1.0,127132.96,2.0,1.0,1.0,102908.96
-15425,15790314.0,Cattaneo,611.0,Spain,Female,49.0,3.0,0.0,1.0,1.0,0.0,180427.24
-15426,15760093.0,McGregor,595.0,Germany,Male,39.0,4.0,125920.98,1.0,1.0,0.0,152956.33
-15427,15690772.0,Okwudiliolisa,691.0,Spain,Male,37.0,2.0,118889.35,1.0,1.0,1.0,149946.43
-15428,15802478.0,Kao,565.0,Spain,Male,44.0,6.0,0.0,2.0,1.0,0.0,56760.71
-15429,15677796.0,Hsia,579.0,France,Female,31.0,8.0,114835.43,1.0,1.0,0.0,91817.38
-15430,15719348.0,Chien,682.0,France,Male,23.0,7.0,0.0,2.0,1.0,1.0,152884.85
-15431,15803415.0,Brown,652.0,Germany,Male,38.0,7.0,80780.16,2.0,1.0,1.0,132736.32
-15432,15713578.0,Chikezie,684.0,France,Male,40.0,0.0,0.0,2.0,0.0,0.0,109783.29
-15433,15737404.0,Ts'ai,686.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,121299.14
-15434,15600110.0,De Luca,641.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,81425.61
-15435,15576022.0,Hsiao,651.0,France,Male,23.0,5.0,0.0,2.0,1.0,0.0,180344.69
-15436,15643361.0,Y?an,726.0,France,Male,46.0,7.0,0.0,2.0,1.0,1.0,102638.35
-15437,15756070.0,Nkemdirim,753.0,Germany,Male,42.0,9.0,77919.78,2.0,1.0,0.0,35300.08
-15438,15684597.0,Tsao,547.0,Spain,Male,39.0,4.0,0.0,2.0,1.0,0.0,177670.57
-15439,15652981.0,Hsiung,627.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,84460.18
-15440,15570192.0,Hs?eh,673.0,France,Male,34.0,7.0,85845.43,1.0,1.0,1.0,54530.51
-15441,15725302.0,Manna,629.0,France,Female,40.0,1.0,0.0,3.0,1.0,1.0,127022.87
-15442,15787035.0,Chiang,697.0,Germany,Male,51.0,9.0,117020.08,1.0,1.0,1.0,133871.83
-15443,15568573.0,Chidiegwu,564.0,Germany,Female,36.0,3.0,123835.52,1.0,1.0,0.0,131718.12
-15444,15691446.0,Lai,699.0,Germany,Male,29.0,8.0,99745.95,2.0,0.0,1.0,62390.59
-15445,15583857.0,Lucchese,611.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,166513.49
-15446,15665416.0,Shih,646.0,France,Male,38.0,5.0,109978.83,1.0,0.0,0.0,62390.67
-15447,15761047.0,Lai,710.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,84920.49
-15448,15800620.0,Lucchesi,684.0,France,Male,53.0,8.0,0.0,1.0,1.0,0.0,91652.43
-15449,15773469.0,Pirozzi,686.0,Germany,Male,39.0,5.0,121513.31,2.0,1.0,1.0,49594.02
-15450,15779915.0,Rowntree,596.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,47880.71
-15451,15744127.0,Onyeorulu,721.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,164975.82
-15452,15802478.0,Pagnotto,603.0,France,Female,44.0,5.0,0.0,1.0,1.0,1.0,174825.57
-15453,15783715.0,Hsueh,721.0,Spain,Male,48.0,1.0,0.0,2.0,1.0,1.0,171716.81
-15454,15779915.0,Ugochukwutubelum,632.0,Germany,Male,22.0,6.0,168920.75,1.0,0.0,0.0,166677.53
-15455,15643496.0,Ejimofor,745.0,France,Male,54.0,2.0,0.0,1.0,1.0,0.0,85746.52
-15456,15593470.0,Onyemere,637.0,Spain,Male,35.0,3.0,122314.5,1.0,1.0,0.0,97623.42
-15457,15750874.0,Aksenov,728.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,108543.21
-15458,15582033.0,T'ang,837.0,Spain,Female,38.0,8.0,113625.94,1.0,1.0,0.0,132418.59
-15459,15693381.0,Nkemdilim,706.0,Germany,Male,40.0,4.0,112372.96,1.0,0.0,0.0,93718.01
-15460,15605327.0,Pirozzi,578.0,France,Male,66.0,1.0,0.0,1.0,1.0,1.0,81862.81
-15461,15585599.0,Lucchesi,637.0,France,Female,35.0,1.0,174937.64,1.0,1.0,1.0,149807.01
-15462,15686963.0,Hsueh,685.0,France,Female,41.0,7.0,0.0,2.0,1.0,0.0,149170.92
-15463,15751788.0,Milne,710.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,107839.03
-15464,15775192.0,Ch'iu,652.0,France,Male,51.0,2.0,0.0,1.0,1.0,0.0,182055.36
-15465,15793688.0,Macleod,571.0,Germany,Male,37.0,7.0,141918.09,1.0,1.0,0.0,80619.09
-15466,15614215.0,Chinwenma,635.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,165675.01
-15467,15691828.0,Hsiung,738.0,Germany,Male,46.0,2.0,132558.26,1.0,1.0,0.0,82422.97
-15468,15663939.0,De Luca,677.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,0.0,127059.04
-15469,15677550.0,Milani,556.0,France,Male,28.0,2.0,129743.75,1.0,0.0,1.0,123587.97
-15470,15735263.0,Oster,629.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,73977.23
-15471,15792722.0,Hsueh,664.0,France,Female,33.0,3.0,161591.11,2.0,1.0,1.0,109201.53
-15472,15579781.0,Ch'ien,663.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,94888.6
-15473,15686835.0,Hsu,687.0,France,Female,37.0,9.0,112935.87,1.0,0.0,0.0,182867.84
-15474,15798895.0,Ting,589.0,France,Male,32.0,5.0,0.0,1.0,0.0,0.0,167984.72
-15475,15683053.0,Chukwujamuike,823.0,Germany,Female,39.0,5.0,115532.52,1.0,1.0,1.0,94499.06
-15476,15795934.0,Fanucci,774.0,Germany,Female,35.0,4.0,137761.55,1.0,1.0,0.0,110359.68
-15477,15728006.0,Ch'in,461.0,Spain,Female,38.0,5.0,0.0,2.0,0.0,0.0,142416.35
-15478,15806911.0,Genovese,599.0,France,Male,28.0,5.0,0.0,2.0,1.0,0.0,53511.78
-15479,15637644.0,Cremonesi,633.0,France,Male,34.0,4.0,131283.11,1.0,1.0,0.0,182725.6
-15480,15621433.0,Fokina,593.0,France,Female,56.0,9.0,0.0,2.0,1.0,0.0,121408.55
-15481,15603925.0,Manna,634.0,Germany,Female,54.0,1.0,144849.74,1.0,1.0,0.0,177655.68
-15482,15743293.0,Trevisani,663.0,Spain,Male,43.0,8.0,0.0,2.0,0.0,0.0,10096.25
-15483,15677173.0,Nnachetam,585.0,France,Female,54.0,1.0,0.0,1.0,1.0,0.0,138526.96
-15484,15592645.0,Fiorentino,739.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,83978.86
-15485,15584899.0,Chiang,586.0,Germany,Female,26.0,1.0,125534.51,1.0,1.0,0.0,131473.31
-15486,15781129.0,O'Callaghan,757.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,142377.69
-15487,15620505.0,Chiang,597.0,France,Female,48.0,3.0,0.0,1.0,0.0,0.0,106977.8
-15488,15742404.0,Tsao,684.0,France,Female,38.0,3.0,0.0,2.0,0.0,1.0,44756.25
-15489,15802478.0,Chukwukere,739.0,France,Female,34.0,0.0,127785.17,1.0,1.0,0.0,82796.29
-15490,15668894.0,Smith,681.0,France,Female,49.0,7.0,0.0,1.0,1.0,0.0,173837.54
-15491,15685500.0,Y?an,579.0,Germany,Female,43.0,10.0,106770.82,1.0,1.0,1.0,126131.9
-15492,15751084.0,McIntyre,746.0,France,Male,47.0,9.0,0.0,1.0,1.0,0.0,62944.75
-15493,15639131.0,Lucchese,681.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,136812.04
-15494,15626900.0,Yefremova,597.0,France,Male,31.0,7.0,121063.8,1.0,1.0,0.0,134531.96
-15495,15759913.0,Ch'ien,660.0,France,Female,47.0,6.0,91711.59,2.0,1.0,0.0,99061.75
-15496,15786463.0,Ch'ien,721.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,152491.22
-15497,15592379.0,Chiemenam,713.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,155879.57
-15498,15759499.0,Hsieh,849.0,Germany,Female,55.0,8.0,100240.2,1.0,1.0,0.0,78650.95
-15499,15772528.0,Soto,632.0,Germany,Male,37.0,2.0,105957.44,1.0,1.0,1.0,56780.97
-15500,15643359.0,Shih,556.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,94774.12
-15501,15786463.0,Shih,696.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,1.0,187866.21
-15502,15589544.0,Ugochukwu,628.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,93959.96
-15503,15642937.0,Nkemakonam,584.0,France,Female,58.0,8.0,0.0,1.0,1.0,0.0,47775.39
-15504,15640059.0,Buchi,537.0,France,Male,27.0,5.0,0.0,1.0,1.0,1.0,111312.96
-15505,15618562.0,Yermakova,596.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,80425.65
-15506,15595067.0,Goliwe,678.0,Germany,Female,40.0,9.0,133658.24,2.0,1.0,1.0,35600.25
-15507,15583076.0,Nwachukwu,711.0,Spain,Male,27.0,2.0,0.0,2.0,1.0,1.0,115136.51
-15508,15692226.0,Ukaegbunam,752.0,Germany,Male,41.0,9.0,113922.81,1.0,0.0,1.0,176712.59
-15509,15628885.0,Chien,714.0,France,Male,30.0,10.0,0.0,2.0,0.0,0.0,142516.35
-15510,15763874.0,Kao,738.0,France,Female,33.0,10.0,0.0,1.0,1.0,1.0,110899.3
-15511,15694237.0,Liao,651.0,France,Male,36.0,9.0,167864.4,2.0,1.0,1.0,50488.91
-15512,15682447.0,Onodugoadiegbemma,597.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,93901.61
-15513,15598097.0,Chiganu,628.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,93536.38
-15514,15583480.0,Frolov,683.0,France,Male,36.0,2.0,89900.24,1.0,1.0,1.0,142200.15
-15515,15658670.0,Clements,565.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,132973.17
-15516,15694376.0,T'ien,496.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,86622.56
-15517,15747345.0,Yuryeva,775.0,Germany,Female,37.0,2.0,132876.23,1.0,1.0,1.0,64448.36
-15518,15592761.0,Hsia,592.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,162699.7
-15519,15589488.0,Yuriev,593.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,0.0,183584.14
-15520,15777060.0,Ch'en,584.0,France,Female,38.0,4.0,0.0,1.0,1.0,1.0,164083.72
-15521,15675501.0,Fiorentino,752.0,Germany,Male,39.0,2.0,143930.92,2.0,1.0,0.0,8751.59
-15522,15673837.0,Ritchie,584.0,Spain,Male,31.0,0.0,0.0,1.0,1.0,1.0,170142.09
-15523,15660602.0,Hsu,487.0,Spain,Male,29.0,0.0,125860.2,1.0,1.0,1.0,74853.94
-15524,15602845.0,Mazzanti,485.0,Spain,Female,43.0,4.0,0.0,2.0,1.0,1.0,62002.04
-15525,15800229.0,Chukwueloka,691.0,France,Male,28.0,4.0,0.0,2.0,1.0,1.0,85901.09
-15526,15598097.0,Hsing,735.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,82565.01
-15527,15604536.0,Tung,593.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,149517.71
-15528,15654489.0,Palerma,662.0,France,Male,26.0,3.0,0.0,2.0,1.0,0.0,176518.35
-15529,15595036.0,Macfarlan,695.0,France,Female,41.0,7.0,0.0,2.0,1.0,0.0,62733.05
-15530,15622442.0,Kenechukwu,561.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,173776.5
-15531,15692570.0,Chiang,579.0,Germany,Female,34.0,7.0,129401.87,2.0,1.0,1.0,153747.76
-15532,15593601.0,Fanucci,644.0,Germany,Male,35.0,2.0,101160.99,2.0,1.0,1.0,182720.07
-15533,15590828.0,Nnachetam,516.0,France,Female,33.0,2.0,106871.81,1.0,1.0,0.0,166476.95
-15534,15620204.0,Ch'ang,673.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,131097.9
-15535,15794048.0,White,602.0,France,Female,22.0,3.0,0.0,1.0,1.0,0.0,58974.0
-15536,15614778.0,Tien,683.0,France,Male,43.0,2.0,139532.34,1.0,1.0,0.0,91291.2
-15537,15719606.0,Ch'in,689.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,49594.02
-15538,15695078.0,O'Donnell,628.0,Germany,Male,39.0,1.0,105387.89,2.0,1.0,1.0,136675.22
-15539,15762573.0,Hsia,585.0,Spain,Male,37.0,7.0,0.0,2.0,0.0,0.0,112664.24
-15540,15777923.0,Ponomaryov,616.0,France,Male,44.0,4.0,0.0,2.0,0.0,1.0,124964.82
-15541,15731463.0,Ts'ui,589.0,France,Female,37.0,3.0,0.0,2.0,0.0,0.0,166586.99
-15542,15703394.0,Chinomso,588.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,141756.32
-15543,15620398.0,Galkin,676.0,France,Female,32.0,3.0,0.0,2.0,1.0,1.0,179689.07
-15544,15661249.0,Kenechukwu,694.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,128815.25
-15545,15665861.0,Chukwueloka,646.0,France,Male,34.0,2.0,122951.92,1.0,1.0,0.0,81537.85
-15546,15746564.0,Tseng,596.0,France,Male,42.0,9.0,0.0,1.0,0.0,0.0,130404.53
-15547,15695499.0,Padovano,613.0,France,Male,54.0,2.0,0.0,2.0,1.0,1.0,141300.53
-15548,15570192.0,Pinto,589.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,49594.02
-15549,15723339.0,Nnamutaezinwa,781.0,France,Female,44.0,2.0,0.0,1.0,0.0,0.0,86937.2
-15550,15790442.0,L?,694.0,France,Female,31.0,2.0,0.0,2.0,0.0,1.0,98640.15
-15551,15757537.0,H?,732.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,43496.36
-15552,15613292.0,Hsiung,667.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,168465.85
-15553,15675896.0,Pirozzi,543.0,Spain,Male,31.0,7.0,76548.6,1.0,0.0,1.0,146511.2
-15554,15681316.0,Chiawuotu,627.0,Spain,Male,56.0,7.0,144976.5,2.0,1.0,1.0,177737.07
-15555,15780650.0,Oguejiofor,617.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,137136.29
-15556,15610140.0,Manna,662.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,49833.5
-15557,15775294.0,Tien,850.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,91510.71
-15558,15659962.0,Shih,554.0,France,Male,33.0,1.0,125712.2,1.0,1.0,0.0,57876.05
-15559,15644878.0,Onyemachukwu,718.0,France,Male,31.0,9.0,141665.63,1.0,1.0,0.0,38798.79
-15560,15758845.0,T'ao,709.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,157937.15
-15561,15636700.0,P'eng,585.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,0.0,103726.71
-15562,15688876.0,Chukwujamuike,707.0,Spain,Female,32.0,0.0,0.0,2.0,0.0,1.0,170518.83
-15563,15774847.0,Pagnotto,681.0,France,Female,41.0,3.0,0.0,2.0,1.0,1.0,145565.25
-15564,15749130.0,Hs?,558.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,118959.64
-15565,15707038.0,Yobanna,645.0,France,Female,42.0,9.0,0.0,2.0,1.0,0.0,72350.47
-15566,15635277.0,Teng,541.0,Spain,Male,32.0,7.0,131778.08,1.0,1.0,1.0,88566.9
-15567,15592326.0,Tseng,682.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,83056.22
-15568,15668830.0,Nnamdi,725.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,180553.48
-15569,15610972.0,Onyekachukwu,635.0,France,Female,29.0,8.0,0.0,2.0,0.0,1.0,49492.15
-15570,15723008.0,Wright,611.0,France,Male,38.0,10.0,0.0,1.0,1.0,0.0,38308.34
-15571,15598892.0,Nwabugwu,627.0,Spain,Male,54.0,1.0,0.0,2.0,1.0,0.0,80425.65
-15572,15659105.0,Kuo,706.0,Spain,Female,50.0,8.0,0.0,1.0,1.0,0.0,59103.13
-15573,15627232.0,Liao,694.0,Spain,Female,27.0,6.0,86055.17,1.0,1.0,0.0,95611.47
-15574,15798975.0,Chien,669.0,France,Male,32.0,6.0,0.0,2.0,0.0,0.0,158973.77
-15575,15771570.0,Hsiung,497.0,France,Male,37.0,9.0,0.0,2.0,0.0,1.0,120115.5
-15576,15768104.0,Yobanna,637.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,163427.18
-15577,15717290.0,Yermakova,756.0,France,Female,46.0,3.0,0.0,1.0,1.0,1.0,129890.94
-15578,15602736.0,Dickson,599.0,Germany,Female,55.0,8.0,124577.33,1.0,0.0,1.0,104722.42
-15579,15756481.0,Trevisano,663.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,65765.22
-15580,15769974.0,Rizzo,542.0,Spain,Male,31.0,7.0,0.0,2.0,0.0,0.0,176712.59
-15581,15592979.0,Kao,573.0,France,Male,30.0,6.0,0.0,1.0,0.0,1.0,100862.36
-15582,15646632.0,Baresi,773.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,102899.16
-15583,15642725.0,Calabresi,771.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,83478.12
-15584,15750299.0,Y?,710.0,France,Female,41.0,8.0,0.0,2.0,0.0,0.0,72643.95
-15585,15726058.0,Cremonesi,775.0,France,Female,43.0,1.0,0.0,3.0,1.0,1.0,142331.85
-15586,15592387.0,Olisaemeka,712.0,Spain,Male,27.0,2.0,0.0,2.0,1.0,1.0,105599.82
-15587,15806447.0,Miah,678.0,Germany,Male,32.0,10.0,76915.4,2.0,1.0,1.0,118024.1
-15588,15581276.0,Yermakova,729.0,Spain,Female,44.0,3.0,0.0,2.0,1.0,1.0,67622.46
-15589,15605447.0,Onyinyechukwuka,628.0,France,Male,30.0,0.0,0.0,2.0,1.0,0.0,1710.89
-15590,15662533.0,Calabresi,571.0,France,Female,33.0,10.0,0.0,2.0,1.0,0.0,115468.84
-15591,15762927.0,Chiang,724.0,Spain,Female,38.0,5.0,0.0,2.0,0.0,0.0,78566.64
-15592,15682533.0,Ts'ao,653.0,France,Female,31.0,9.0,151307.11,1.0,1.0,1.0,140941.47
-15593,15571261.0,Ch'iu,545.0,France,Female,63.0,6.0,0.0,1.0,1.0,0.0,56583.88
-15594,15706463.0,Fomin,651.0,France,Male,49.0,6.0,0.0,1.0,1.0,1.0,103832.58
-15595,15771750.0,Dickson,655.0,Spain,Male,33.0,5.0,132057.92,1.0,1.0,1.0,173837.54
-15596,15807167.0,Nkemakonam,684.0,Germany,Female,49.0,6.0,139875.2,1.0,0.0,1.0,105969.05
-15597,15673591.0,Shih,611.0,France,Male,54.0,4.0,0.0,1.0,1.0,1.0,184312.88
-15598,15621227.0,Okwudilichukwu,674.0,Germany,Male,39.0,5.0,117294.12,1.0,1.0,1.0,88391.97
-15599,15771580.0,Balsillie,635.0,Spain,Male,31.0,1.0,0.0,1.0,1.0,0.0,113926.31
-15600,15775131.0,Nkemjika,775.0,Spain,Female,41.0,1.0,0.0,1.0,1.0,0.0,56767.67
-15601,15799128.0,O'Brien,667.0,Spain,Male,45.0,9.0,123783.25,1.0,1.0,0.0,179705.13
-15602,15676909.0,Chukwueloka,659.0,France,Female,32.0,6.0,0.0,2.0,0.0,1.0,115462.27
-15603,15794048.0,Brown,627.0,Spain,Male,41.0,2.0,0.0,2.0,1.0,1.0,164886.64
-15604,15775235.0,Zetticci,520.0,Spain,Male,44.0,5.0,111760.4,1.0,0.0,1.0,125877.22
-15605,15719606.0,Milanesi,850.0,Germany,Male,31.0,8.0,165896.0,1.0,0.0,0.0,71139.31
-15606,15771543.0,Walker,646.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,1.0,24275.32
-15607,15790442.0,Walker,832.0,France,Male,41.0,4.0,131283.11,1.0,1.0,1.0,152843.53
-15608,15623053.0,Tuan,709.0,France,Male,38.0,1.0,0.0,2.0,0.0,0.0,51554.58
-15609,15606472.0,T'ang,573.0,Spain,Female,35.0,2.0,0.0,2.0,0.0,0.0,60905.51
-15610,15594878.0,Chibueze,598.0,Germany,Female,53.0,6.0,117438.92,1.0,0.0,1.0,182531.74
-15611,15694188.0,Calabresi,717.0,France,Male,25.0,6.0,0.0,2.0,1.0,1.0,180626.68
-15612,15692577.0,Genovese,674.0,Spain,Male,36.0,2.0,0.0,1.0,1.0,0.0,129890.94
-15613,15680998.0,Chidumaga,632.0,Germany,Female,29.0,3.0,102515.37,1.0,1.0,1.0,125877.22
-15614,15705301.0,Ukaegbunam,489.0,France,Male,36.0,10.0,164948.98,1.0,0.0,1.0,127827.18
-15615,15646091.0,Genovese,598.0,France,Male,37.0,1.0,62400.48,1.0,0.0,0.0,71175.94
-15616,15676513.0,Mazzi,643.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,133042.9
-15617,15756118.0,Hao,615.0,France,Female,49.0,6.0,0.0,1.0,1.0,0.0,148564.76
-15618,15603741.0,Onio,716.0,Germany,Female,25.0,7.0,102535.57,1.0,0.0,0.0,119377.36
-15619,15691483.0,Chinedum,624.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,166476.95
-15620,15593882.0,Calabresi,731.0,France,Female,38.0,7.0,121702.73,1.0,1.0,0.0,93953.84
-15621,15732778.0,Shih,719.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,149913.84
-15622,15757559.0,Ibragimova,583.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,103914.4
-15623,15692991.0,Lucciano,629.0,Spain,Female,40.0,4.0,0.0,2.0,0.0,0.0,109881.71
-15624,15764105.0,Nnonso,686.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,176714.46
-15625,15776433.0,McGregor,648.0,Germany,Female,46.0,8.0,116939.06,1.0,1.0,0.0,143859.66
-15626,15610755.0,Onyemauchechi,523.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,55725.04
-15627,15779743.0,Ndubuagha,586.0,Germany,Female,33.0,8.0,98173.02,1.0,1.0,0.0,157871.55
-15628,15793070.0,Brierly,530.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,131736.43
-15629,15772423.0,Onwuamaeze,807.0,France,Female,43.0,6.0,0.0,2.0,0.0,0.0,80792.83
-15630,15742442.0,Ejikemeifeuwa,669.0,France,Male,35.0,9.0,109564.1,2.0,1.0,1.0,37266.43
-15631,15651737.0,Ch'eng,605.0,France,Female,37.0,1.0,0.0,1.0,1.0,1.0,182738.0
-15632,15570141.0,Ch'in,677.0,France,Male,31.0,1.0,0.0,2.0,0.0,0.0,161407.48
-15633,15793901.0,Nnamutaezinwa,613.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,1276.87
-15634,15719606.0,Ukaegbunam,746.0,France,Male,36.0,6.0,0.0,1.0,1.0,1.0,119969.99
-15635,15667634.0,Nnachetam,652.0,Germany,Male,47.0,3.0,103421.49,2.0,1.0,1.0,7900.46
-15636,15732438.0,Milne,648.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,187980.44
-15637,15769582.0,She,573.0,Germany,Female,26.0,6.0,154733.97,1.0,1.0,1.0,139712.79
-15638,15743293.0,Chiu,583.0,France,Male,41.0,5.0,0.0,2.0,0.0,0.0,115286.68
-15639,15622532.0,Ch'in,653.0,Spain,Female,38.0,2.0,0.0,1.0,1.0,0.0,126518.0
-15640,15747795.0,Chuang,619.0,Spain,Male,44.0,9.0,89385.92,1.0,1.0,0.0,85937.52
-15641,15775892.0,Ch'eng,726.0,Germany,Male,67.0,1.0,124450.36,1.0,1.0,1.0,177622.38
-15642,15662662.0,Chiazagomekpere,682.0,Germany,Male,50.0,8.0,106192.1,2.0,1.0,1.0,167033.83
-15643,15701175.0,Lu,717.0,France,Male,39.0,1.0,0.0,2.0,0.0,0.0,122949.71
-15644,15649450.0,Chukwueloka,568.0,France,Female,34.0,6.0,0.0,1.0,0.0,1.0,131389.39
-15645,15790750.0,Nnaife,714.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,112687.57
-15646,15750921.0,Ch'en,585.0,France,Male,28.0,5.0,0.0,1.0,1.0,1.0,92568.07
-15647,15794414.0,Chien,749.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,179267.94
-15648,15779497.0,Nucci,537.0,France,Male,50.0,3.0,0.0,2.0,1.0,1.0,93835.64
-15649,15611961.0,Kline,752.0,Spain,Male,38.0,2.0,0.0,1.0,1.0,1.0,115818.55
-15650,15771540.0,Hsiung,850.0,France,Male,32.0,2.0,0.0,2.0,0.0,0.0,179843.33
-15651,15782530.0,Shih,717.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,84927.42
-15652,15733387.0,Hsing,662.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,159899.97
-15653,15793881.0,Toscani,682.0,France,Male,46.0,7.0,123082.08,1.0,0.0,0.0,125898.26
-15654,15683375.0,Kornilova,743.0,France,Female,32.0,7.0,0.0,1.0,1.0,1.0,128881.87
-15655,15655341.0,Li Fonti,619.0,France,Female,49.0,6.0,0.0,1.0,1.0,1.0,102930.46
-15656,15775490.0,Yefimova,633.0,Germany,Female,29.0,1.0,116933.27,1.0,1.0,0.0,188343.05
-15657,15806941.0,McIntosh,657.0,France,Male,45.0,7.0,0.0,1.0,1.0,0.0,70810.6
-15658,15802758.0,Onio,665.0,Germany,Male,23.0,8.0,167795.5,2.0,0.0,1.0,182354.46
-15659,15651022.0,Ts'ui,591.0,Spain,Female,39.0,6.0,0.0,2.0,0.0,0.0,49361.84
-15660,15752622.0,Onwumelu,616.0,France,Male,36.0,0.0,0.0,2.0,1.0,0.0,183572.39
-15661,15589380.0,Walker,579.0,France,Female,33.0,2.0,0.0,1.0,1.0,0.0,38772.65
-15662,15769293.0,Zito,644.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,14073.11
-15663,15576683.0,Cattaneo,719.0,France,Male,40.0,4.0,0.0,2.0,0.0,0.0,130132.41
-15664,15662085.0,Onwuamaeze,694.0,Germany,Female,39.0,9.0,125190.86,2.0,0.0,1.0,174027.3
-15665,15773709.0,Hsia,721.0,Germany,Male,37.0,6.0,153360.02,1.0,1.0,0.0,29358.57
-15666,15722583.0,Nwachinemelu,587.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,77837.63
-15667,15621685.0,Calabresi,535.0,France,Male,36.0,4.0,0.0,2.0,0.0,0.0,61230.83
-15668,15612043.0,Ch'ien,634.0,France,Male,20.0,8.0,0.0,2.0,1.0,0.0,118383.18
-15669,15637315.0,Ts'ui,675.0,France,Female,36.0,11.0,0.0,1.0,1.0,0.0,38190.78
-15670,15594450.0,Chiedozie,688.0,Germany,Female,50.0,1.0,121286.27,2.0,0.0,0.0,121440.8
-15671,15591169.0,Chu,803.0,France,Female,71.0,5.0,0.0,2.0,1.0,1.0,157862.82
-15672,15742378.0,T'ao,691.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,122449.18
-15673,15644296.0,Nnamdi,635.0,Germany,Female,40.0,2.0,139840.36,2.0,1.0,1.0,185211.28
-15674,15613787.0,Nwora,638.0,France,Female,40.0,7.0,151914.93,1.0,1.0,1.0,11302.7
-15675,15686913.0,Chukwuebuka,638.0,France,Female,41.0,1.0,0.0,1.0,1.0,0.0,144593.4
-15676,15691910.0,Niu,703.0,Spain,Male,25.0,7.0,0.0,2.0,0.0,0.0,57553.02
-15677,15633112.0,Baresi,699.0,Spain,Male,43.0,4.0,0.0,1.0,0.0,0.0,50485.7
-15678,15625126.0,Ku,671.0,France,Male,31.0,8.0,99786.07,1.0,1.0,1.0,134790.74
-15679,15763063.0,Tsou,774.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,138880.24
-15680,15691792.0,Lu,684.0,France,Female,32.0,9.0,63488.7,1.0,1.0,1.0,126734.96
-15681,15799350.0,Chin,755.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,94003.11
-15682,15694860.0,Ponomaryova,697.0,Spain,Male,37.0,9.0,0.0,2.0,0.0,1.0,118045.98
-15683,15641655.0,Toscani,679.0,Germany,Female,31.0,3.0,154495.79,2.0,1.0,1.0,177524.19
-15684,15642933.0,Kambinachi,535.0,Spain,Female,29.0,5.0,0.0,2.0,0.0,0.0,151325.24
-15685,15681671.0,Y?an,712.0,France,Male,31.0,1.0,110527.17,1.0,1.0,0.0,101168.3
-15686,15662337.0,Mazzanti,590.0,Germany,Male,47.0,2.0,125801.03,1.0,1.0,1.0,147093.84
-15687,15592763.0,Iweobiegbunam,679.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,103379.31
-15688,15731553.0,Chukwujekwu,565.0,Germany,Female,40.0,4.0,117864.85,1.0,1.0,1.0,173498.45
-15689,15798850.0,Nwankwo,446.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,82422.91
-15690,15593468.0,Lai,713.0,France,Female,39.0,10.0,111396.97,1.0,1.0,0.0,93901.61
-15691,15732774.0,Lucciano,679.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,143913.17
-15692,15666884.0,Hsueh,722.0,Germany,Male,33.0,5.0,88770.71,1.0,1.0,1.0,175792.87
-15693,15654851.0,Tung,645.0,Spain,Female,42.0,9.0,0.0,2.0,0.0,0.0,170893.85
-15694,15667085.0,Chinedum,554.0,France,Female,22.0,2.0,0.0,2.0,1.0,1.0,91514.62
-15695,15727281.0,Chidubem,646.0,France,Female,39.0,9.0,0.0,2.0,1.0,1.0,161396.95
-15696,15586494.0,Chizoba,602.0,France,Female,33.0,0.0,0.0,2.0,1.0,0.0,149665.65
-15697,15591388.0,Fanucci,695.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,105760.01
-15698,15620507.0,Teng,632.0,France,Female,32.0,7.0,0.0,1.0,1.0,0.0,88890.05
-15699,15643908.0,Nwachukwu,506.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,1.0,187900.12
-15700,15676526.0,Ibeabuchi,659.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,0.0,118974.77
-15701,15597915.0,Brient,731.0,Germany,Female,28.0,6.0,154970.54,1.0,1.0,0.0,73066.73
-15702,15644765.0,Macleod,616.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,189366.04
-15703,15753549.0,Ibezimako,613.0,France,Female,38.0,1.0,0.0,1.0,1.0,1.0,177092.16
-15704,15786800.0,Onyekachukwu,704.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,126495.57
-15705,15732672.0,Lo Duca,596.0,Germany,Male,52.0,5.0,114940.67,1.0,1.0,0.0,123475.88
-15706,15588986.0,Nnachetam,594.0,Spain,Female,34.0,9.0,135559.9,1.0,1.0,0.0,105568.4
-15707,15700511.0,Ch'ien,714.0,Spain,Male,31.0,3.0,87541.06,1.0,1.0,0.0,179360.76
-15708,15677243.0,Chiazagomekpele,706.0,Spain,Female,45.0,4.0,106891.71,1.0,1.0,0.0,131736.23
-15709,15738954.0,Ch'ang,651.0,France,Male,27.0,5.0,0.0,2.0,0.0,1.0,164804.02
-15710,15704844.0,Ibeamaka,775.0,Germany,Female,45.0,1.0,172646.82,1.0,1.0,0.0,83679.46
-15711,15629002.0,Y?an,792.0,Germany,Female,42.0,8.0,124577.33,2.0,1.0,0.0,102260.16
-15712,15671591.0,Bitter,659.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,55859.27
-15713,15673481.0,Onyeorulu,667.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,91346.03
-15714,15631927.0,T'ang,713.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,162697.93
-15715,15627220.0,Ch'en,635.0,France,Male,54.0,2.0,0.0,1.0,0.0,1.0,93820.49
-15716,15757811.0,Tien,742.0,France,Male,33.0,9.0,159863.9,1.0,1.0,0.0,19799.26
-15717,15586029.0,P'an,723.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,79623.28
-15718,15716491.0,Clements,773.0,Spain,Female,29.0,8.0,157781.24,1.0,1.0,1.0,181204.4
-15719,15769169.0,Chinomso,602.0,Spain,Male,50.0,7.0,0.0,3.0,1.0,1.0,153080.4
-15720,15642570.0,Palerma,573.0,Spain,Male,43.0,8.0,112994.65,1.0,0.0,0.0,184843.77
-15721,15672983.0,Chukwualuka,684.0,France,Male,31.0,1.0,0.0,1.0,1.0,1.0,139162.15
-15722,15659677.0,Chizuoke,706.0,Spain,Male,28.0,6.0,0.0,2.0,1.0,1.0,78854.58
-15723,15756475.0,H?,693.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,119924.84
-15724,15731519.0,Pirozzi,682.0,Spain,Male,37.0,8.0,0.0,1.0,1.0,1.0,126728.27
-15725,15754796.0,Chia,718.0,Germany,Male,33.0,2.0,98899.91,2.0,1.0,0.0,80552.36
-15726,15595872.0,Ugoji,635.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,166909.95
-15727,15726310.0,Hoolan,655.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,62454.01
-15728,15693732.0,Fanucci,683.0,Spain,Female,21.0,1.0,0.0,2.0,0.0,0.0,136329.96
-15729,15782899.0,Nkemdilim,595.0,France,Female,45.0,9.0,0.0,1.0,0.0,0.0,110784.42
-15730,15679075.0,Ch'ien,569.0,France,Female,35.0,7.0,141787.27,1.0,0.0,1.0,177844.06
-15731,15664214.0,Ugochukwu,648.0,Spain,Male,32.0,8.0,0.0,2.0,0.0,1.0,162719.69
-15732,15570967.0,Ch'eng,581.0,France,Female,52.0,9.0,0.0,1.0,0.0,0.0,118711.75
-15733,15594041.0,Chukwuemeka,644.0,Germany,Male,39.0,5.0,128243.03,1.0,1.0,0.0,80582.34
-15734,15668081.0,Ch'ang,736.0,Spain,Female,39.0,4.0,0.0,2.0,0.0,0.0,53358.73
-15735,15619608.0,Chukwualuka,744.0,France,Female,39.0,5.0,0.0,1.0,1.0,0.0,177616.17
-15736,15656683.0,Baresi,652.0,Spain,Male,36.0,6.0,0.0,2.0,0.0,0.0,124045.65
-15737,15592386.0,H?,636.0,France,Female,41.0,5.0,80926.02,1.0,1.0,0.0,97640.2
-15738,15765650.0,Nwachinemelu,614.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,86950.72
-15739,15749679.0,White,636.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,1.0,38252.25
-15740,15803378.0,T'ien,583.0,France,Female,39.0,5.0,0.0,2.0,0.0,0.0,109096.71
-15741,15800233.0,Ch'iu,634.0,France,Male,64.0,7.0,0.0,2.0,1.0,1.0,163570.25
-15742,15643487.0,Smith,595.0,Spain,Female,43.0,1.0,0.0,1.0,0.0,0.0,133699.82
-15743,15747168.0,Lettiere,719.0,Spain,Female,40.0,2.0,0.0,1.0,1.0,0.0,144584.29
-15744,15602851.0,Kao,682.0,France,Female,32.0,8.0,0.0,2.0,0.0,1.0,52711.55
-15745,15771543.0,L?,619.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,132801.57
-15746,15802823.0,Dettmann,554.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,0.0,102681.32
-15747,15703793.0,Chukwuemeka,632.0,France,Female,39.0,5.0,0.0,1.0,1.0,0.0,71830.1
-15748,15745295.0,Chukwudi,605.0,Spain,Male,42.0,0.0,137811.75,1.0,0.0,0.0,176471.79
-15749,15699446.0,Ch'eng,662.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,92914.67
-15750,15761149.0,Hao,591.0,Spain,Male,32.0,9.0,134177.06,1.0,1.0,0.0,160529.71
-15751,15792922.0,Fitts,651.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,108419.41
-15752,15598510.0,Hargreaves,613.0,Germany,Male,36.0,7.0,113657.41,1.0,1.0,0.0,91510.71
-15753,15583552.0,Chuang,525.0,France,Female,38.0,4.0,0.0,2.0,0.0,1.0,169862.01
-15754,15567422.0,Ch'ien,684.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,93717.55
-15755,15663888.0,Pisano,640.0,Germany,Male,35.0,2.0,95502.11,1.0,1.0,1.0,176570.55
-15756,15739592.0,Ukaegbunam,590.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,1.0,189428.75
-15757,15781702.0,Ndubuagha,697.0,France,Female,24.0,1.0,0.0,2.0,1.0,0.0,6795.61
-15758,15602934.0,Hs?,758.0,Spain,Female,23.0,9.0,0.0,1.0,1.0,1.0,136028.97
-15759,15758027.0,Hs?eh,724.0,France,Female,24.0,4.0,117554.26,1.0,1.0,0.0,100722.72
-15760,15664339.0,Udegbulam,582.0,France,Female,40.0,4.0,0.0,2.0,0.0,0.0,174431.01
-15761,15703544.0,Udegbulam,667.0,France,Male,34.0,5.0,0.0,1.0,1.0,0.0,58883.91
-15762,15770174.0,Chia,516.0,France,Female,41.0,10.0,0.0,1.0,0.0,0.0,107825.08
-15763,15644796.0,Okwudilichukwu,753.0,Germany,Female,35.0,5.0,109632.85,3.0,1.0,0.0,137793.35
-15764,15726354.0,Lu,634.0,France,Female,31.0,5.0,0.0,2.0,0.0,0.0,184514.01
-15765,15795963.0,Nwachinemelu,708.0,France,Female,41.0,1.0,0.0,2.0,1.0,0.0,97468.44
-15766,15573599.0,Nkemakonam,577.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,138882.98
-15767,15672798.0,Cremonesi,578.0,Spain,Female,27.0,8.0,0.0,2.0,1.0,1.0,38296.21
-15768,15671209.0,Onyemauchechukwu,846.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,72990.62
-15769,15751206.0,Hsueh,733.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,68849.65
-15770,15665784.0,H?,645.0,Germany,Female,27.0,3.0,120596.93,1.0,1.0,1.0,171056.65
-15771,15574072.0,Chinweike,578.0,France,Male,33.0,5.0,0.0,1.0,1.0,1.0,93833.38
-15772,15605918.0,Genovese,804.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,123954.15
-15773,15685314.0,Chukwuhaenye,619.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,149614.17
-15774,15772892.0,Tung,576.0,Spain,Female,36.0,0.0,132576.25,1.0,1.0,1.0,176576.62
-15775,15620204.0,Biryukov,635.0,Spain,Male,26.0,3.0,145974.79,2.0,0.0,0.0,174921.91
-15776,15648800.0,Yuryeva,679.0,France,Male,35.0,2.0,0.0,1.0,0.0,0.0,26257.01
-15777,15761693.0,Smith,637.0,Spain,Female,47.0,6.0,95877.74,1.0,1.0,0.0,62678.53
-15778,15596915.0,Tikhonov,732.0,France,Female,51.0,8.0,0.0,1.0,1.0,0.0,139431.0
-15779,15584518.0,Ts'ai,719.0,France,Male,39.0,7.0,0.0,1.0,1.0,1.0,167909.52
-15780,15656319.0,Ibeabuchi,713.0,Germany,Male,42.0,1.0,132835.56,1.0,1.0,0.0,105720.09
-15781,15790596.0,Tuan,653.0,France,Male,36.0,10.0,0.0,2.0,0.0,0.0,6057.81
-15782,15662908.0,Oguejiofor,755.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,63687.06
-15783,15690796.0,Chidiebere,678.0,France,Male,26.0,1.0,100524.69,1.0,0.0,1.0,60721.4
-15784,15762729.0,Hsu,558.0,France,Male,31.0,2.0,146580.69,1.0,0.0,0.0,48924.73
-15785,15702641.0,Genovese,607.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,173632.92
-15786,15683637.0,Genovese,619.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,143635.55
-15787,15615020.0,Ndubueze,721.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,131281.22
-15788,15639552.0,Ts'ui,526.0,France,Female,46.0,8.0,0.0,1.0,1.0,1.0,80552.11
-15789,15682048.0,Lombardi,775.0,France,Male,44.0,2.0,0.0,1.0,0.0,0.0,185821.41
-15790,15759913.0,Pirozzi,704.0,France,Male,33.0,7.0,89122.3,1.0,0.0,0.0,61931.21
-15791,15807993.0,Nnachetam,821.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,152992.04
-15792,15626900.0,Gorman,659.0,Germany,Female,46.0,3.0,117070.21,1.0,1.0,0.0,27330.27
-15793,15604588.0,Padovesi,604.0,France,Female,29.0,3.0,0.0,2.0,0.0,1.0,129964.94
-15794,15632010.0,Chiang,730.0,Spain,Female,26.0,6.0,0.0,2.0,1.0,0.0,106785.7
-15795,15641850.0,Chukwufumnanya,710.0,France,Male,40.0,3.0,136858.42,1.0,0.0,0.0,58883.91
-15796,15753895.0,H?,752.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,121751.04
-15797,15593365.0,Chiang,756.0,Germany,Male,36.0,9.0,101160.99,1.0,1.0,1.0,174770.14
-15798,15651460.0,Ch'iu,600.0,Germany,Female,53.0,9.0,139529.93,1.0,1.0,0.0,115916.55
-15799,15691387.0,Chiemenam,595.0,France,Female,37.0,9.0,0.0,2.0,0.0,1.0,103787.28
-15800,15705544.0,Ch'iu,722.0,France,Male,41.0,5.0,0.0,1.0,1.0,0.0,88729.22
-15801,15761692.0,Onyemauchechukwu,726.0,Spain,Male,46.0,7.0,0.0,1.0,0.0,0.0,53710.23
-15802,15724150.0,Monds,678.0,Germany,Female,44.0,8.0,128524.19,2.0,1.0,0.0,177783.04
-15803,15756472.0,Chibueze,732.0,Germany,Male,39.0,7.0,121325.84,1.0,1.0,1.0,147822.81
-15804,15664504.0,Aitken,579.0,Germany,Female,31.0,7.0,151739.65,2.0,1.0,1.0,146637.45
-15805,15726167.0,T'an,585.0,France,Male,33.0,5.0,95741.75,1.0,1.0,0.0,96444.88
-15806,15752300.0,H?,704.0,Germany,Male,25.0,2.0,132951.92,2.0,1.0,0.0,122433.07
-15807,15777893.0,Iloerika,573.0,France,Female,56.0,9.0,133950.19,1.0,1.0,1.0,62634.94
-15808,15667046.0,Onwumelu,621.0,Germany,Female,39.0,4.0,186445.31,2.0,0.0,0.0,116969.89
-15809,15762331.0,Hs?,675.0,France,Female,27.0,2.0,0.0,2.0,1.0,0.0,131389.39
-15810,15740900.0,Yudina,688.0,Spain,Female,50.0,9.0,0.0,1.0,0.0,0.0,93901.61
-15811,15746065.0,Ts'ui,705.0,Spain,Male,72.0,8.0,0.0,1.0,1.0,1.0,83052.03
-15812,15662403.0,Genovesi,554.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,109614.57
-15813,15763579.0,Ch'iu,767.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,1.0,181490.42
-15814,15685320.0,Martin,635.0,Germany,Male,26.0,1.0,96143.47,2.0,1.0,1.0,147955.91
-15815,15622171.0,Baresi,526.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,0.0,75888.65
-15816,15672467.0,Chien,641.0,France,Female,37.0,1.0,113907.58,1.0,1.0,0.0,159835.82
-15817,15668057.0,Uvarov,622.0,France,Male,40.0,2.0,0.0,2.0,0.0,0.0,139048.0
-15818,15634359.0,Grieve,569.0,France,Female,32.0,9.0,122471.09,1.0,1.0,1.0,38131.77
-15819,15785819.0,Pisani,833.0,Spain,Female,41.0,9.0,0.0,2.0,1.0,0.0,44265.28
-15820,15793896.0,Eipper,634.0,Spain,Male,42.0,2.0,0.0,2.0,0.0,1.0,645.61
-15821,15791030.0,Onuora,685.0,France,Female,42.0,7.0,0.0,1.0,1.0,0.0,151349.35
-15822,15581229.0,Ch'ang,488.0,France,Female,32.0,7.0,114370.41,1.0,1.0,0.0,104730.52
-15823,15679743.0,Onwumelu,611.0,France,Male,67.0,8.0,125913.93,2.0,1.0,1.0,124749.08
-15824,15638774.0,Manna,615.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,143086.09
-15825,15809722.0,De Luca,629.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,141896.74
-15826,15801417.0,Fomin,559.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,38252.25
-15827,15665956.0,L?,682.0,France,Male,34.0,9.0,0.0,1.0,1.0,1.0,151581.79
-15828,15768006.0,Onyemachukwu,686.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,94811.85
-15829,15645136.0,Chiedozie,583.0,France,Female,35.0,2.0,0.0,1.0,1.0,0.0,88890.05
-15830,15695499.0,H?,704.0,Germany,Female,45.0,4.0,133658.24,1.0,1.0,0.0,185510.63
-15831,15783338.0,Nwabugwu,790.0,Germany,Female,32.0,4.0,122486.47,2.0,0.0,0.0,143357.07
-15832,15627212.0,T'ien,516.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,164696.28
-15833,15796790.0,Chukwudi,614.0,France,Female,42.0,0.0,0.0,1.0,1.0,0.0,82522.61
-15834,15657778.0,Lucciano,850.0,France,Male,40.0,5.0,107764.75,2.0,1.0,0.0,199409.19
-15835,15659305.0,Pirozzi,559.0,Spain,Female,29.0,8.0,0.0,2.0,1.0,1.0,48131.92
-15836,15629885.0,Onuchukwu,705.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,121798.52
-15837,15684769.0,Chidumaga,673.0,France,Male,45.0,9.0,113873.23,1.0,1.0,1.0,135925.72
-15838,15667046.0,Folliero,632.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,1.0,90162.35
-15839,15689671.0,Ts'ui,559.0,Spain,Male,23.0,4.0,0.0,2.0,1.0,1.0,121061.89
-15840,15672967.0,Onyemauchechukwu,634.0,Spain,Male,34.0,10.0,0.0,2.0,0.0,1.0,96670.2
-15841,15684999.0,Nwachinemelu,721.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,172096.08
-15842,15721719.0,Hao,819.0,Germany,Female,38.0,7.0,108924.33,2.0,1.0,0.0,61761.77
-15843,15611644.0,Fiorentino,699.0,France,Male,48.0,8.0,0.0,2.0,1.0,1.0,147964.99
-15844,15692761.0,Chinonyelum,742.0,Spain,Female,38.0,7.0,80958.39,1.0,1.0,1.0,169964.12
-15845,15645136.0,Ch'en,621.0,France,Female,43.0,10.0,129440.3,1.0,1.0,0.0,7861.68
-15846,15803523.0,McIntosh,587.0,Spain,Female,43.0,2.0,0.0,1.0,1.0,1.0,93336.3
-15847,15752508.0,Shih,598.0,France,Male,37.0,9.0,0.0,2.0,0.0,0.0,162697.93
-15848,15793854.0,Tung,592.0,France,Male,33.0,9.0,116967.68,1.0,1.0,0.0,116503.92
-15849,15800703.0,Okwudilichukwu,606.0,France,Female,23.0,1.0,118879.35,1.0,1.0,0.0,43821.56
-15850,15790282.0,Chibueze,705.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,135071.12
-15851,15595592.0,T'ien,591.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,0.0,123850.58
-15852,15771674.0,Chukwujamuike,741.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,79866.73
-15853,15648067.0,Cattaneo,682.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,144290.25
-15854,15736578.0,Ts'ao,773.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,93826.63
-15855,15748360.0,P'eng,758.0,France,Female,36.0,0.0,0.0,1.0,1.0,1.0,133897.32
-15856,15690731.0,Hsing,749.0,Spain,Female,25.0,7.0,160599.06,1.0,1.0,0.0,141364.62
-15857,15594878.0,Okeke,809.0,Spain,Female,42.0,6.0,0.0,2.0,1.0,0.0,152740.16
-15858,15794868.0,Yermakova,594.0,Germany,Male,34.0,7.0,161671.16,2.0,0.0,1.0,171457.06
-15859,15731751.0,Chiang,581.0,Spain,Female,67.0,8.0,0.0,2.0,1.0,0.0,102908.63
-15860,15757829.0,T'ien,678.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,87412.24
-15861,15722212.0,Horsley,622.0,Spain,Female,37.0,6.0,0.0,1.0,1.0,0.0,150836.98
-15862,15593468.0,Ts'ao,601.0,Germany,Female,30.0,2.0,108806.96,2.0,1.0,1.0,58854.88
-15863,15671137.0,Fiorentino,709.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,26589.56
-15864,15790810.0,Martin,651.0,Spain,Female,62.0,2.0,0.0,3.0,1.0,0.0,101485.72
-15865,15684769.0,Houghton,589.0,Germany,Female,27.0,1.0,155060.88,1.0,1.0,0.0,101039.53
-15866,15769353.0,Chinedum,670.0,Spain,Female,32.0,7.0,109570.21,1.0,1.0,1.0,131768.98
-15867,15727198.0,Ojiofor,684.0,Spain,Male,34.0,1.0,0.0,1.0,0.0,0.0,177719.88
-15868,15612444.0,Kornilova,697.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,142223.94
-15869,15767414.0,Ugoji,491.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,121789.3
-15870,15691808.0,Hsueh,704.0,France,Male,33.0,4.0,0.0,2.0,0.0,0.0,87566.97
-15871,15780572.0,Pinto,639.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,131283.6
-15872,15570134.0,Ndubueze,659.0,France,Male,28.0,6.0,0.0,1.0,1.0,1.0,55763.66
-15873,15672027.0,Chibueze,652.0,France,Male,40.0,6.0,0.0,1.0,1.0,0.0,93629.66
-15874,15654060.0,Ozerova,738.0,France,Male,26.0,0.0,0.0,2.0,1.0,1.0,143317.42
-15875,15640133.0,Lu,593.0,Spain,Female,42.0,0.0,0.0,2.0,1.0,1.0,172749.4
-15876,15672242.0,Chiemenam,678.0,France,Male,37.0,3.0,0.0,2.0,0.0,1.0,179521.24
-15877,15777873.0,Ch'in,554.0,France,Female,26.0,5.0,0.0,2.0,0.0,1.0,164066.46
-15878,15730793.0,Baresi,627.0,France,Female,31.0,2.0,0.0,1.0,1.0,0.0,154364.66
-15879,15780128.0,Nnonso,633.0,France,Male,45.0,10.0,0.0,1.0,0.0,0.0,47318.75
-15880,15682600.0,O'Brien,583.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,146785.44
-15881,15731166.0,Kao,669.0,Spain,Male,35.0,7.0,124548.99,1.0,1.0,1.0,147530.06
-15882,15588955.0,Walker,667.0,France,Male,41.0,7.0,123345.65,1.0,1.0,1.0,83342.73
-15883,15792922.0,Mancini,683.0,France,Male,29.0,6.0,125977.81,1.0,0.0,1.0,159475.08
-15884,15646720.0,Aksenov,580.0,Germany,Female,32.0,9.0,119452.03,2.0,0.0,0.0,125986.18
-15885,15779985.0,Okagbue,721.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,146041.45
-15886,15763662.0,Ch'in,709.0,Spain,Male,45.0,5.0,143938.27,1.0,1.0,0.0,131804.86
-15887,15681887.0,Chijindum,753.0,Spain,Female,37.0,1.0,0.0,2.0,0.0,0.0,66463.62
-15888,15661629.0,Chukwufumnanya,775.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,131807.14
-15889,15703588.0,Aksenov,629.0,France,Male,44.0,2.0,0.0,2.0,1.0,1.0,112157.31
-15890,15651389.0,Okwudilichukwu,579.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,157564.75
-15891,15721582.0,Ch'iu,638.0,France,Female,51.0,10.0,0.0,1.0,0.0,0.0,103099.55
-15892,15581280.0,Hsing,603.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,102083.78
-15893,15584518.0,Hsueh,588.0,France,Female,30.0,4.0,0.0,1.0,1.0,0.0,705.18
-15894,15732643.0,Tsao,695.0,Spain,Female,28.0,8.0,0.0,2.0,1.0,0.0,76264.27
-15895,15589969.0,Chidozie,555.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,121138.77
-15896,15786463.0,Mazzanti,676.0,France,Male,37.0,4.0,0.0,1.0,1.0,1.0,171457.06
-15897,15591036.0,Niu,535.0,Spain,Male,45.0,9.0,0.0,1.0,1.0,1.0,114869.35
-15898,15792726.0,Mancini,697.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,0.0,59129.72
-15899,15759066.0,Uwakwe,627.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,92223.59
-15900,15610686.0,Rizzo,684.0,France,Male,42.0,1.0,152496.82,1.0,1.0,0.0,176843.53
-15901,15612043.0,H?,520.0,France,Male,42.0,1.0,127543.13,1.0,1.0,0.0,108258.22
-15902,15806467.0,Y?an,731.0,France,Female,33.0,9.0,0.0,2.0,0.0,0.0,170968.99
-15903,15620579.0,Hsueh,632.0,France,Male,47.0,0.0,0.0,1.0,1.0,1.0,140189.72
-15904,15777896.0,Wright,651.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,131097.9
-15905,15607874.0,Scott,682.0,Germany,Female,41.0,1.0,138089.97,2.0,1.0,1.0,94875.04
-15906,15787907.0,Wright,705.0,France,Female,43.0,9.0,0.0,2.0,1.0,1.0,62850.87
-15907,15620341.0,Manna,652.0,France,Male,37.0,8.0,0.0,1.0,1.0,1.0,170764.08
-15908,15773520.0,Onwuamaeze,633.0,Spain,Male,44.0,2.0,0.0,2.0,1.0,0.0,131281.29
-15909,15718773.0,K'ung,549.0,France,Male,30.0,8.0,0.0,1.0,0.0,0.0,164081.42
-15910,15621974.0,Lucciano,649.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,141926.43
-15911,15593773.0,Chiebuka,724.0,France,Male,45.0,3.0,77526.66,2.0,1.0,0.0,131660.31
-15912,15662263.0,Chia,637.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,51362.82
-15913,15648075.0,T'ao,741.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,57323.18
-15914,15643677.0,Tung,479.0,Germany,Female,37.0,0.0,109542.25,1.0,1.0,1.0,26249.86
-15915,15629010.0,Hsia,644.0,France,Female,35.0,8.0,160903.2,1.0,1.0,1.0,93081.87
-15916,15647725.0,Padovano,646.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,0.0,52319.26
-15917,15747757.0,Tuan,633.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,96833.0
-15918,15749265.0,Ting,558.0,France,Female,27.0,9.0,0.0,2.0,1.0,1.0,25684.93
-15919,15788025.0,Ndubueze,623.0,France,Female,37.0,6.0,0.0,1.0,1.0,1.0,69320.97
-15920,15799479.0,Hs?,607.0,Germany,Female,43.0,6.0,119618.42,2.0,1.0,1.0,102073.46
-15921,15695792.0,Ch'iu,477.0,Germany,Male,33.0,0.0,118829.03,1.0,0.0,0.0,171345.02
-15922,15733491.0,Chibuzo,543.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,47941.84
-15923,15775892.0,Fiorentini,668.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,86927.53
-15924,15793331.0,Niu,601.0,Germany,Female,39.0,6.0,124976.19,1.0,1.0,0.0,88827.33
-15925,15715199.0,Chien,745.0,France,Female,32.0,10.0,0.0,2.0,1.0,1.0,89822.48
-15926,15779903.0,T'ang,559.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,121537.91
-15927,15751131.0,Kao,705.0,Spain,Female,47.0,6.0,0.0,1.0,1.0,0.0,142935.83
-15928,15732903.0,Pirozzi,656.0,France,Female,39.0,7.0,146053.66,2.0,1.0,0.0,121840.2
-15929,15630053.0,Mancini,573.0,Spain,Female,45.0,2.0,0.0,1.0,1.0,1.0,159800.16
-15930,15807473.0,Ch'ien,703.0,Germany,Female,45.0,4.0,116520.78,1.0,0.0,0.0,81778.72
-15931,15668270.0,Baresi,547.0,France,Female,39.0,8.0,141136.62,1.0,1.0,0.0,156792.89
-15932,15671165.0,Hsieh,749.0,Spain,Male,67.0,8.0,0.0,2.0,0.0,1.0,128815.29
-15933,15616280.0,Chukwufumnanya,828.0,France,Female,46.0,6.0,0.0,1.0,1.0,1.0,170821.43
-15934,15639265.0,T'ien,733.0,Spain,Female,32.0,1.0,0.0,2.0,0.0,1.0,147913.56
-15935,15782530.0,Tung,554.0,Spain,Male,28.0,9.0,0.0,2.0,1.0,1.0,149912.28
-15936,15737173.0,Maclean,612.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,81617.5
-15937,15702669.0,Iloerika,675.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,1.0,84300.4
-15938,15579826.0,Hsu,656.0,Germany,Male,37.0,1.0,124401.9,1.0,1.0,1.0,121053.19
-15939,15793949.0,Chinonyelum,582.0,Spain,Male,41.0,2.0,0.0,2.0,1.0,1.0,93777.81
-15940,15655410.0,Rizzo,643.0,France,Male,33.0,9.0,0.0,1.0,1.0,1.0,63736.16
-15941,15607690.0,Nkemakolam,725.0,France,Male,37.0,10.0,0.0,1.0,1.0,1.0,178067.33
-15942,15780954.0,Onyemachukwu,656.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,62368.33
-15943,15623525.0,Lucciano,588.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,47856.82
-15944,15672826.0,Hargreaves,686.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,97623.42
-15945,15751057.0,Zuev,686.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,0.0,67789.99
-15946,15756481.0,Ch'in,627.0,France,Female,31.0,8.0,117899.95,1.0,1.0,0.0,152571.61
-15947,15778897.0,Ts'ui,807.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,98623.78
-15948,15627995.0,Kao,729.0,Spain,Male,49.0,5.0,95858.37,1.0,1.0,0.0,78955.8
-15949,15781574.0,Mistry,719.0,France,Male,51.0,9.0,0.0,1.0,0.0,0.0,114563.66
-15950,15635244.0,White,543.0,France,Male,36.0,2.0,0.0,2.0,0.0,1.0,120790.02
-15951,15794413.0,Lucchesi,675.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,162599.51
-15952,15585039.0,K'ung,534.0,Germany,Female,56.0,7.0,73054.37,1.0,0.0,0.0,135662.17
-15953,15593854.0,Miller,639.0,Germany,Male,37.0,3.0,122550.05,1.0,0.0,1.0,73857.94
-15954,15684868.0,Hsieh,687.0,Spain,Male,40.0,9.0,102541.1,2.0,1.0,1.0,152843.23
-15955,15595874.0,Chukwumaobim,659.0,France,Male,28.0,6.0,181656.54,1.0,1.0,0.0,183572.39
-15956,15652469.0,Chu,730.0,France,Male,36.0,6.0,151818.51,2.0,1.0,1.0,64595.25
-15957,15702416.0,Ting,619.0,France,Male,66.0,7.0,0.0,2.0,1.0,1.0,72796.29
-15958,15732266.0,Onwumelu,567.0,Germany,Female,52.0,9.0,120916.52,2.0,1.0,0.0,136837.97
-15959,15687828.0,Tsou,656.0,France,Male,36.0,3.0,0.0,1.0,0.0,0.0,152992.04
-15960,15653762.0,Ch'ien,685.0,France,Male,34.0,4.0,0.0,2.0,0.0,0.0,146636.44
-15961,15675896.0,Chinweike,579.0,Spain,Female,42.0,3.0,0.0,1.0,1.0,0.0,141558.04
-15962,15708505.0,Tung,651.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,0.0,135650.21
-15963,15734365.0,Bancroft,732.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,40889.14
-15964,15649744.0,Uspenskaya,692.0,Spain,Female,21.0,6.0,0.0,1.0,1.0,1.0,116848.79
-15965,15657771.0,Pinto,706.0,Germany,Male,34.0,4.0,123870.07,1.0,1.0,1.0,147797.54
-15966,15772363.0,Wilson,836.0,Germany,Female,53.0,1.0,143566.12,1.0,1.0,0.0,172668.39
-15967,15689322.0,T'ien,685.0,Germany,Male,35.0,9.0,126815.97,2.0,0.0,1.0,94440.45
-15968,15638247.0,Ch'en,705.0,Germany,Female,31.0,4.0,84557.82,1.0,0.0,0.0,143981.31
-15969,15791877.0,Yevdokimova,697.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,137916.49
-15970,15722533.0,Kao,711.0,France,Male,48.0,7.0,0.0,2.0,0.0,1.0,123954.15
-15971,15731144.0,Uchenna,646.0,France,Female,28.0,3.0,123856.53,1.0,0.0,0.0,170528.63
-15972,15600739.0,Mazzanti,589.0,France,Female,49.0,0.0,0.0,3.0,1.0,0.0,132527.6
-15973,15750769.0,Chiu,669.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,107866.22
-15974,15572291.0,Johnstone,642.0,France,Male,36.0,5.0,116322.27,1.0,1.0,0.0,142331.85
-15975,15752312.0,H?,585.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,121189.3
-15976,15731522.0,Chien,646.0,France,Male,22.0,5.0,0.0,2.0,1.0,1.0,85983.54
-15977,15614878.0,Kambinachi,439.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,103955.31
-15978,15597946.0,Hsia,545.0,Germany,Male,37.0,6.0,116800.25,1.0,1.0,1.0,166370.81
-15979,15597951.0,Chiekwugo,496.0,France,Male,27.0,3.0,0.0,2.0,1.0,0.0,142956.33
-15980,15761950.0,Folliero,593.0,France,Female,30.0,9.0,79951.9,2.0,0.0,1.0,121408.55
-15981,15572390.0,Frolova,612.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,172782.69
-15982,15724863.0,Ahern,677.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,141954.11
-15983,15661349.0,Hsiao,717.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,60552.44
-15984,15741719.0,Trevisano,738.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,143357.07
-15985,15721779.0,Chu,576.0,France,Female,37.0,7.0,0.0,1.0,1.0,1.0,645.61
-15986,15766552.0,Chukwufumnanya,621.0,Germany,Male,38.0,10.0,137781.65,1.0,1.0,1.0,184954.11
-15987,15728505.0,Toscani,678.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,19264.33
-15988,15700255.0,Ch'ang,567.0,France,Female,27.0,7.0,0.0,2.0,0.0,0.0,180318.6
-15989,15780359.0,Chukwukere,579.0,Spain,Male,33.0,2.0,88827.53,1.0,1.0,0.0,158032.5
-15990,15635141.0,Chia,627.0,Spain,Male,52.0,6.0,0.0,1.0,1.0,1.0,142838.64
-15991,15806922.0,Chuang,705.0,Spain,Male,32.0,7.0,0.0,2.0,1.0,1.0,180829.99
-15992,15719276.0,Chiawuotu,663.0,Germany,Male,55.0,1.0,104930.38,1.0,1.0,0.0,98820.09
-15993,15704081.0,Ch'eng,479.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,104902.68
-15994,15597951.0,P'eng,705.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,61315.37
-15995,15580146.0,Lo Duca,678.0,France,Female,35.0,6.0,0.0,2.0,0.0,1.0,63889.1
-15996,15690541.0,Chidimma,752.0,Germany,Female,46.0,3.0,93925.4,1.0,0.0,0.0,94951.27
-15997,15569867.0,Onwumelu,539.0,Spain,Male,32.0,9.0,0.0,1.0,1.0,0.0,131177.22
-15998,15777060.0,Hsu,527.0,Germany,Male,31.0,3.0,158623.14,2.0,0.0,1.0,94870.23
-15999,15651802.0,Teng,678.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,133699.82
-16000,15803908.0,Chiang,687.0,France,Female,39.0,0.0,0.0,1.0,1.0,0.0,94670.77
-16001,15671351.0,Obiajulu,714.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,52319.26
-16002,15590730.0,Yuriev,712.0,Spain,Female,30.0,8.0,0.0,2.0,0.0,0.0,160546.58
-16003,15669326.0,Young,755.0,France,Male,33.0,7.0,0.0,2.0,0.0,0.0,28806.32
-16004,15646513.0,Onyekachi,593.0,Germany,Female,34.0,7.0,121542.57,2.0,1.0,0.0,100722.72
-16005,15781530.0,Hsing,629.0,France,Male,40.0,10.0,0.0,1.0,1.0,1.0,95611.47
-16006,15669414.0,Yermakova,709.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,0.0,101319.76
-16007,15761047.0,Ts'ai,637.0,France,Male,41.0,2.0,165413.57,1.0,0.0,1.0,70810.8
-16008,15691817.0,Nwabugwu,634.0,France,Female,31.0,10.0,0.0,2.0,1.0,1.0,46857.52
-16009,15764174.0,Iadanza,699.0,Germany,Male,44.0,9.0,131390.75,1.0,1.0,0.0,137791.18
-16010,15579892.0,Walker,627.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,173844.89
-16011,15576623.0,Nwabugwu,717.0,France,Female,42.0,2.0,0.0,1.0,1.0,0.0,77965.44
-16012,15641675.0,Ndubueze,730.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,1.0,131609.48
-16013,15699772.0,Ts'ui,663.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,70550.0
-16014,15593365.0,Chukwujamuike,548.0,Spain,Male,38.0,8.0,0.0,2.0,0.0,1.0,54871.02
-16015,15662884.0,Chukwuemeka,707.0,Germany,Male,38.0,6.0,91893.32,1.0,1.0,0.0,131189.34
-16016,15593795.0,Oluchukwu,707.0,Germany,Female,40.0,2.0,141535.52,1.0,1.0,0.0,141400.51
-16017,15694237.0,Uvarov,807.0,France,Female,24.0,3.0,0.0,1.0,0.0,0.0,82870.75
-16018,15591130.0,Ejimofor,732.0,France,Female,51.0,1.0,0.0,2.0,1.0,1.0,72990.62
-16019,15789874.0,Nwachukwu,634.0,France,Male,29.0,3.0,0.0,1.0,1.0,1.0,137519.92
-16020,15643826.0,Nwabugwu,678.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,126106.4
-16021,15692991.0,Chibugo,705.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,98820.09
-16022,15798532.0,Nnaife,714.0,France,Female,27.0,5.0,0.0,2.0,1.0,1.0,143393.24
-16023,15720371.0,Fanucci,643.0,France,Male,38.0,4.0,124442.58,1.0,0.0,0.0,100598.98
-16024,15682928.0,Onyemauchechukwu,692.0,France,Female,43.0,6.0,0.0,2.0,1.0,1.0,123982.14
-16025,15787035.0,Hao,679.0,Germany,Male,31.0,8.0,135842.41,2.0,1.0,1.0,47125.11
-16026,15652700.0,Hao,592.0,Germany,Male,48.0,2.0,121237.65,1.0,1.0,0.0,91810.71
-16027,15572762.0,Biryukova,786.0,Spain,Male,38.0,3.0,0.0,2.0,0.0,1.0,67611.36
-16028,15792868.0,Chidiegwu,649.0,France,Male,38.0,1.0,87637.28,1.0,1.0,1.0,140134.43
-16029,15668767.0,K'ung,683.0,France,Female,36.0,10.0,0.0,2.0,1.0,0.0,158623.94
-16030,15771299.0,Hsieh,621.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,65936.28
-16031,15761606.0,Hs?,731.0,Spain,Female,71.0,9.0,0.0,2.0,1.0,1.0,178267.07
-16032,15773421.0,Iadanza,774.0,Spain,Male,44.0,0.0,142658.04,1.0,1.0,0.0,163566.85
-16033,15630661.0,Chukwualuka,596.0,France,Male,26.0,2.0,0.0,1.0,1.0,1.0,110953.33
-16034,15762218.0,Lo Duca,729.0,Spain,Female,40.0,7.0,0.0,1.0,1.0,0.0,109626.01
-16035,15752409.0,Goliwe,733.0,Spain,Male,42.0,8.0,0.0,2.0,1.0,1.0,133535.29
-16036,15785547.0,Ch'ien,722.0,France,Female,26.0,6.0,0.0,1.0,1.0,1.0,90876.95
-16037,15641688.0,Biryukova,732.0,France,Male,34.0,7.0,0.0,1.0,0.0,0.0,86935.08
-16038,15765048.0,Zikoranachidimma,693.0,France,Female,34.0,3.0,0.0,1.0,1.0,0.0,122021.33
-16039,15790810.0,T'ang,748.0,France,Female,32.0,4.0,137963.07,1.0,1.0,0.0,133831.37
-16040,15778410.0,Ts'ui,753.0,France,Female,34.0,2.0,0.0,1.0,1.0,1.0,115268.86
-16041,15606471.0,Fanucci,717.0,Spain,Female,56.0,9.0,0.0,3.0,0.0,0.0,159638.77
-16042,15778915.0,White,611.0,Germany,Female,35.0,10.0,132571.48,2.0,0.0,0.0,71179.31
-16043,15566378.0,Li Fonti,584.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,109291.22
-16044,15714485.0,Chiawuotu,755.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,158918.02
-16045,15674840.0,Nwachukwu,683.0,Germany,Female,52.0,2.0,101583.11,1.0,0.0,0.0,142516.35
-16046,15585067.0,Pagnotto,763.0,Germany,Female,43.0,3.0,131519.49,1.0,1.0,1.0,174032.0
-16047,15752300.0,White,611.0,France,Male,50.0,4.0,0.0,2.0,1.0,0.0,146875.86
-16048,15792660.0,Shih,581.0,Spain,Male,38.0,4.0,117468.67,1.0,0.0,0.0,110454.46
-16049,15632575.0,Mancini,627.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,133091.32
-16050,15652401.0,Iloerika,678.0,Spain,Male,38.0,8.0,0.0,2.0,0.0,0.0,141679.73
-16051,15589147.0,McVey,681.0,Spain,Female,29.0,8.0,0.0,2.0,1.0,0.0,180551.76
-16052,15697948.0,Rizzo,653.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,138871.12
-16053,15611338.0,Tien,714.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,167984.72
-16054,15676909.0,Hsu,674.0,France,Male,43.0,8.0,0.0,1.0,1.0,0.0,38614.69
-16055,15673737.0,Hsieh,754.0,France,Male,31.0,5.0,0.0,1.0,1.0,0.0,162523.66
-16056,15642787.0,Macleod,678.0,Germany,Female,33.0,6.0,79962.92,1.0,0.0,1.0,72008.61
-16057,15592461.0,Udinesi,546.0,Germany,Female,45.0,1.0,137482.19,1.0,1.0,1.0,161479.19
-16058,15726487.0,Ch'ang,744.0,Spain,Female,44.0,4.0,123544.0,1.0,0.0,0.0,71047.51
-16059,15697095.0,Chia,721.0,France,Male,29.0,1.0,0.0,1.0,1.0,0.0,81409.0
-16060,15792650.0,McGregor,597.0,Germany,Female,38.0,5.0,113657.83,1.0,0.0,0.0,95857.18
-16061,15709928.0,Onwuamaeze,531.0,France,Female,39.0,3.0,0.0,2.0,1.0,0.0,78310.04
-16062,15797960.0,Hsing,539.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,120107.1
-16063,15683560.0,Okonkwo,632.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,122488.59
-16064,15794414.0,Efremova,592.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,184474.72
-16065,15807593.0,Rowntree,643.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,123499.28
-16066,15756538.0,Chiu,571.0,France,Female,27.0,3.0,0.0,2.0,1.0,0.0,120830.73
-16067,15689955.0,Teng,706.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,175070.94
-16068,15756071.0,Cameron,687.0,Germany,Female,37.0,4.0,166824.59,1.0,1.0,1.0,58814.41
-16069,15796470.0,Chin,535.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,108008.65
-16070,15733138.0,Chiazagomekpele,593.0,Germany,Female,45.0,7.0,111798.06,1.0,0.0,1.0,142963.29
-16071,15779711.0,White,656.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,109563.28
-16072,15747222.0,Baresi,769.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,0.0,97465.88
-16073,15631603.0,Chuang,758.0,Spain,Male,42.0,6.0,0.0,1.0,1.0,0.0,181224.56
-16074,15687852.0,Fisk,575.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,113089.43
-16075,15585490.0,Tien,571.0,France,Male,71.0,8.0,172463.45,1.0,1.0,1.0,119882.86
-16076,15814209.0,Ting,807.0,Spain,Female,39.0,2.0,0.0,1.0,1.0,0.0,172576.43
-16077,15687913.0,Ch'ien,657.0,France,Male,52.0,1.0,120549.21,1.0,0.0,1.0,144875.18
-16078,15584785.0,Ch'in,715.0,Spain,Female,55.0,3.0,0.0,1.0,0.0,1.0,173365.89
-16079,15730673.0,Ch'in,707.0,Germany,Male,59.0,4.0,131082.89,1.0,1.0,1.0,161428.25
-16080,15664543.0,Chinweike,562.0,Germany,Male,33.0,6.0,123544.0,2.0,1.0,1.0,91573.19
-16081,15685829.0,Yobanna,638.0,Spain,Female,24.0,5.0,0.0,2.0,0.0,0.0,163830.64
-16082,15637646.0,Folliero,730.0,Germany,Male,33.0,0.0,122034.4,1.0,1.0,0.0,161653.5
-16083,15584477.0,Hsing,651.0,Germany,Male,40.0,7.0,85845.43,2.0,1.0,0.0,59601.15
-16084,15637366.0,Hsueh,579.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,97794.71
-16085,15602909.0,Chia,677.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,175310.38
-16086,15765258.0,Fanucci,556.0,Spain,Male,50.0,9.0,166933.89,1.0,1.0,0.0,64448.36
-16087,15620372.0,Frolova,544.0,Spain,Female,22.0,2.0,0.0,2.0,0.0,0.0,122518.5
-16088,15573077.0,Fokina,684.0,France,Male,30.0,9.0,0.0,2.0,0.0,0.0,78854.57
-16089,15668971.0,Ch'ang,705.0,France,Male,33.0,9.0,61825.5,2.0,1.0,1.0,83912.24
-16090,15609913.0,Maclean,674.0,France,Male,52.0,2.0,0.0,1.0,1.0,0.0,64716.08
-16091,15648725.0,Nwachinemelu,689.0,France,Male,40.0,8.0,136623.19,1.0,1.0,0.0,174825.57
-16092,15773520.0,Chiu,635.0,Spain,Male,38.0,8.0,171932.26,1.0,1.0,0.0,70778.63
-16093,15683563.0,Ch'eng,743.0,Germany,Male,29.0,9.0,119688.81,1.0,0.0,1.0,133535.29
-16094,15643658.0,Chidimma,752.0,Germany,Male,42.0,5.0,113858.95,1.0,1.0,0.0,131301.74
-16095,15629133.0,Chinedum,581.0,France,Male,63.0,4.0,152039.4,1.0,1.0,1.0,178038.17
-16096,15598883.0,Yevdokimova,648.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,151843.54
-16097,15645316.0,Maughan,682.0,Germany,Male,40.0,5.0,115221.36,1.0,1.0,1.0,96498.14
-16098,15690744.0,L?,652.0,Germany,Male,33.0,3.0,125919.35,1.0,1.0,0.0,152996.89
-16099,15651022.0,Manna,758.0,France,Female,44.0,9.0,99836.47,1.0,1.0,1.0,53478.02
-16100,15688963.0,De Garis,718.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,61531.86
-16101,15642570.0,Rounsevell,714.0,France,Male,31.0,5.0,103377.81,1.0,1.0,1.0,183427.14
-16102,15775082.0,Uchenna,605.0,France,Male,38.0,10.0,121629.22,1.0,1.0,0.0,47777.15
-16103,15705001.0,Zikoranachidimma,581.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,97416.34
-16104,15744240.0,Onyekachi,526.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,177092.16
-16105,15644724.0,Ogochukwu,813.0,Spain,Female,45.0,8.0,0.0,1.0,0.0,0.0,157862.82
-16106,15803764.0,Onyekaozulu,537.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,133020.9
-16107,15602312.0,K'ung,671.0,France,Male,37.0,7.0,101238.24,1.0,1.0,1.0,175071.29
-16108,15701354.0,McWilliams,838.0,France,Male,59.0,9.0,0.0,1.0,1.0,0.0,102397.22
-16109,15746824.0,Tsao,612.0,Spain,Male,43.0,5.0,0.0,1.0,1.0,1.0,163666.85
-16110,15672945.0,Chiabuotu,604.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,1.0,91947.71
-16111,15805212.0,Ejimofor,671.0,Germany,Male,39.0,10.0,111097.87,1.0,1.0,0.0,172646.92
-16112,15649450.0,Chiu,696.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,141946.92
-16113,15603840.0,Lucchesi,602.0,Germany,Male,33.0,5.0,73088.06,1.0,1.0,1.0,129938.38
-16114,15791925.0,Fanucci,807.0,Germany,Male,26.0,3.0,129120.64,2.0,1.0,1.0,178827.3
-16115,15795519.0,Hsing,613.0,Spain,Male,52.0,8.0,0.0,3.0,0.0,1.0,152499.8
-16116,15740283.0,Chinedum,655.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,137793.35
-16117,15602844.0,Tuan,685.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,92915.84
-16118,15643496.0,Eluemuno,752.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,115022.94
-16119,15771086.0,K'ung,683.0,Germany,Female,45.0,10.0,115182.84,2.0,1.0,0.0,74836.64
-16120,15628893.0,Flemming,602.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,158043.11
-16121,15642344.0,Chizoba,637.0,Spain,Male,25.0,9.0,0.0,2.0,1.0,0.0,125877.22
-16122,15632899.0,Okechukwu,653.0,France,Male,34.0,2.0,130306.49,1.0,1.0,1.0,142967.68
-16123,15651533.0,Ts'ai,684.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,106977.8
-16124,15661275.0,Palermo,850.0,France,Male,31.0,9.0,132670.53,1.0,1.0,1.0,62402.38
-16125,15680597.0,Ch'ien,743.0,France,Male,27.0,9.0,0.0,2.0,1.0,1.0,61602.42
-16126,15681206.0,Nwankwo,721.0,France,Male,40.0,2.0,153663.77,1.0,1.0,1.0,97930.29
-16127,15665629.0,Genovesi,593.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,99800.87
-16128,15625247.0,Onyekachi,697.0,France,Female,50.0,7.0,0.0,2.0,1.0,1.0,125877.22
-16129,15781457.0,Chin,642.0,Spain,Female,32.0,6.0,128551.36,1.0,0.0,0.0,177519.39
-16130,15685500.0,Genovesi,635.0,France,Female,46.0,4.0,172646.82,1.0,1.0,0.0,55313.51
-16131,15653715.0,Trevisano,756.0,Spain,Male,33.0,9.0,109387.9,1.0,0.0,1.0,140134.42
-16132,15676937.0,T'an,539.0,Germany,Female,38.0,6.0,138825.34,1.0,1.0,0.0,157891.11
-16133,15700263.0,Tung,632.0,Germany,Female,47.0,4.0,104449.8,3.0,0.0,0.0,122518.5
-16134,15721582.0,Cawker,704.0,France,Female,38.0,1.0,0.0,1.0,0.0,0.0,175743.92
-16135,15792934.0,Hs?,632.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,1.0,51117.09
-16136,15794566.0,Calabresi,739.0,Germany,Male,31.0,9.0,90778.76,2.0,1.0,0.0,52629.73
-16137,15707559.0,Clements,627.0,France,Male,26.0,6.0,135422.33,1.0,0.0,0.0,161241.65
-16138,15638806.0,Chukwubuikem,578.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,89048.46
-16139,15585768.0,Tu,735.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,40318.33
-16140,15768449.0,H?,683.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,183490.99
-16141,15784890.0,Chidiegwu,605.0,Germany,Male,34.0,9.0,123575.51,1.0,1.0,0.0,177524.19
-16142,15612667.0,Bledsoe,669.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,121408.56
-16143,15777873.0,Mazzanti,667.0,France,Male,36.0,4.0,0.0,2.0,0.0,0.0,143393.24
-16144,15772412.0,Pirozzi,679.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,83056.22
-16145,15785024.0,Iweobiegbunam,646.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,74564.41
-16146,15765283.0,Chukwudi,678.0,Spain,Male,33.0,10.0,0.0,1.0,0.0,0.0,156768.45
-16147,15681075.0,Naylor,741.0,Germany,Male,35.0,7.0,116324.53,1.0,0.0,1.0,121866.81
-16148,15583480.0,Ch'ang,582.0,France,Male,46.0,9.0,112291.34,2.0,0.0,0.0,145562.4
-16149,15705968.0,Galkin,556.0,Germany,Female,44.0,5.0,141559.5,1.0,1.0,0.0,156953.52
-16150,15607537.0,Chukwuemeka,591.0,France,Male,35.0,7.0,0.0,1.0,1.0,1.0,174311.3
-16151,15589017.0,Hsing,648.0,Spain,Male,27.0,6.0,76548.6,1.0,1.0,0.0,119808.64
-16152,15604482.0,Yuryeva,604.0,France,Female,51.0,8.0,0.0,2.0,1.0,0.0,77500.81
-16153,15654870.0,Tsao,686.0,Spain,Male,47.0,6.0,108632.48,1.0,0.0,0.0,63737.19
-16154,15577078.0,Kao,657.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,126451.14
-16155,15788556.0,Okechukwu,523.0,France,Female,45.0,7.0,0.0,2.0,1.0,0.0,91560.69
-16156,15663885.0,Hsia,715.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,64839.23
-16157,15589969.0,Nkemjika,748.0,France,Female,32.0,1.0,0.0,1.0,1.0,1.0,47322.05
-16158,15737705.0,Hsueh,704.0,Spain,Female,46.0,3.0,0.0,2.0,1.0,0.0,187740.04
-16159,15651868.0,Nwankwo,703.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,137510.41
-16160,15750055.0,Cremonesi,602.0,France,Female,43.0,6.0,128743.55,1.0,0.0,1.0,134851.12
-16161,15763898.0,Napolitani,605.0,France,Female,37.0,2.0,0.0,1.0,1.0,0.0,161344.13
-16162,15792726.0,Kao,602.0,France,Female,25.0,2.0,0.0,2.0,0.0,1.0,52337.97
-16163,15739271.0,Onyekachi,644.0,Spain,Female,29.0,7.0,0.0,2.0,1.0,0.0,155879.57
-16164,15657818.0,Chukwueloka,703.0,France,Female,45.0,9.0,0.0,1.0,0.0,1.0,163471.01
-16165,15593973.0,Martin,744.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,172063.09
-16166,15648032.0,Milanesi,683.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,184544.59
-16167,15672598.0,Martin,691.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,107876.2
-16168,15694349.0,Hsing,610.0,France,Male,38.0,7.0,0.0,2.0,0.0,0.0,97794.71
-16169,15791513.0,Nwachinemelu,679.0,France,Female,42.0,8.0,0.0,2.0,0.0,1.0,168816.22
-16170,15572051.0,Hs?eh,572.0,France,Female,37.0,6.0,0.0,2.0,0.0,1.0,40496.72
-16171,15689516.0,Shih,598.0,Germany,Female,29.0,5.0,88963.31,1.0,0.0,0.0,143619.52
-16172,15592451.0,Dennys,690.0,Spain,Male,42.0,7.0,0.0,2.0,1.0,0.0,71139.31
-16173,15602844.0,Trevisano,649.0,Germany,Male,43.0,5.0,111128.04,1.0,1.0,0.0,182784.11
-16174,15609510.0,Chiedozie,725.0,France,Female,50.0,6.0,0.0,2.0,0.0,1.0,63736.17
-16175,15648735.0,Ch'iu,686.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,0.0,52894.01
-16176,15592877.0,Chijindum,597.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,142410.94
-16177,15716276.0,Onwuamaeze,687.0,Spain,Male,55.0,2.0,0.0,1.0,1.0,1.0,94978.1
-16178,15573741.0,Kao,531.0,France,Female,33.0,10.0,0.0,2.0,1.0,1.0,134916.41
-16179,15809837.0,Chin,599.0,Spain,Male,45.0,6.0,0.0,2.0,1.0,0.0,172668.39
-16180,15795861.0,Ch'iu,705.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,1.0,131653.31
-16181,15804864.0,Cattaneo,679.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,159899.97
-16182,15574879.0,Ts'ui,773.0,France,Female,39.0,8.0,136858.42,1.0,0.0,1.0,90174.54
-16183,15706186.0,Chia,745.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,87711.61
-16184,15704746.0,Trevisan,579.0,Germany,Male,32.0,3.0,123870.07,2.0,1.0,1.0,145700.22
-16185,15736287.0,Omeokachie,564.0,France,Male,32.0,5.0,89750.21,1.0,1.0,0.0,184399.76
-16186,15639277.0,T'ang,652.0,Germany,Female,46.0,1.0,170557.91,1.0,1.0,0.0,150468.28
-16187,15699335.0,Chukwufumnanya,590.0,France,Male,32.0,7.0,181627.14,1.0,1.0,0.0,176680.16
-16188,15806511.0,Chin,631.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,172363.41
-16189,15728078.0,Tuan,592.0,Spain,Female,34.0,4.0,161613.94,1.0,1.0,1.0,146891.07
-16190,15795586.0,O'Meara,678.0,Spain,Female,28.0,0.0,0.0,2.0,1.0,1.0,180439.75
-16191,15605039.0,Chiedozie,656.0,France,Male,48.0,5.0,0.0,1.0,1.0,0.0,126561.07
-16192,15650026.0,Pisano,743.0,France,Female,56.0,10.0,0.0,1.0,1.0,0.0,161344.13
-16193,15781710.0,Ch'ang,705.0,France,Female,42.0,9.0,0.0,1.0,1.0,1.0,173870.39
-16194,15684269.0,Ch'ang,593.0,Spain,Female,46.0,6.0,0.0,3.0,1.0,1.0,102930.46
-16195,15634141.0,Sacco,712.0,Germany,Male,28.0,5.0,119496.25,1.0,1.0,0.0,61636.22
-16196,15663438.0,Pinto,785.0,France,Female,30.0,6.0,0.0,1.0,1.0,0.0,101986.16
-16197,15776733.0,Lipton,586.0,France,Female,31.0,4.0,125724.95,1.0,1.0,1.0,92827.49
-16198,15808851.0,Chukwuemeka,728.0,France,Male,36.0,7.0,130666.2,1.0,0.0,0.0,72876.65
-16199,15715184.0,Chukwualuka,576.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,0.0,179540.73
-16200,15703923.0,Genovesi,758.0,France,Male,33.0,10.0,0.0,2.0,0.0,1.0,176750.81
-16201,15750772.0,Onyekachi,590.0,Germany,Female,29.0,0.0,160132.75,1.0,1.0,1.0,180134.88
-16202,15592761.0,T'ien,703.0,France,Male,33.0,6.0,0.0,1.0,1.0,0.0,183318.79
-16203,15669411.0,Smith,731.0,Germany,Female,44.0,2.0,115130.34,1.0,1.0,1.0,171587.9
-16204,15740356.0,Onyemauchechukwu,703.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,182609.97
-16205,15622532.0,Nkemakonam,678.0,France,Female,42.0,0.0,130169.23,1.0,1.0,0.0,58854.88
-16206,15702631.0,Chiazagomekpere,681.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,65466.93
-16207,15795429.0,Tuan,785.0,Spain,Male,37.0,3.0,104015.54,1.0,1.0,0.0,145562.4
-16208,15674671.0,Nnamutaezinwa,572.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,0.0,165675.01
-16209,15651389.0,Wallace,624.0,Germany,Female,37.0,0.0,108393.35,2.0,1.0,0.0,71173.03
-16210,15682533.0,Milne,662.0,Spain,Female,34.0,4.0,0.0,1.0,1.0,0.0,91482.5
-16211,15750055.0,H?,648.0,Germany,Female,42.0,10.0,181166.44,2.0,1.0,0.0,89048.46
-16212,15585047.0,Chibugo,667.0,Germany,Female,26.0,10.0,140197.18,2.0,1.0,0.0,28257.63
-16213,15770498.0,Walker,671.0,Spain,Female,63.0,5.0,0.0,2.0,0.0,1.0,155915.24
-16214,15769312.0,Hs?,657.0,France,Male,38.0,7.0,0.0,1.0,1.0,0.0,135851.3
-16215,15673372.0,Whitehead,516.0,France,Female,38.0,0.0,0.0,2.0,1.0,0.0,125848.88
-16216,15628600.0,Pagnotto,652.0,France,Male,22.0,2.0,0.0,1.0,1.0,1.0,161671.15
-16217,15617507.0,Hsiung,697.0,Germany,Female,43.0,8.0,106941.08,1.0,1.0,1.0,175836.03
-16218,15682757.0,Lettiere,619.0,Spain,Male,67.0,2.0,0.0,2.0,1.0,1.0,129964.94
-16219,15768295.0,Hsia,594.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,127734.16
-16220,15676895.0,Fan,710.0,Germany,Male,33.0,3.0,152606.82,2.0,1.0,1.0,121525.48
-16221,15736578.0,Mironov,653.0,France,Male,26.0,10.0,0.0,1.0,1.0,0.0,173899.42
-16222,15746805.0,Lucciano,689.0,Germany,Female,49.0,4.0,163894.35,2.0,0.0,1.0,95985.64
-16223,15761775.0,Zikoranachidimma,633.0,Germany,Male,54.0,4.0,110483.86,1.0,1.0,0.0,106655.88
-16224,15582591.0,Cremonesi,685.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,163482.7
-16225,15763544.0,T'ang,744.0,Spain,Male,35.0,7.0,0.0,1.0,0.0,1.0,79450.61
-16226,15594766.0,Genovese,592.0,France,Male,31.0,8.0,137936.94,1.0,0.0,0.0,176750.81
-16227,15735782.0,Ts'ui,689.0,France,Female,28.0,7.0,0.0,2.0,1.0,1.0,60907.49
-16228,15672390.0,Hsia,850.0,France,Female,36.0,8.0,152582.3,1.0,1.0,1.0,164038.07
-16229,15652658.0,Lucchese,589.0,Germany,Female,39.0,9.0,132591.36,2.0,1.0,0.0,131173.9
-16230,15808760.0,Yefimova,619.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,0.0,138526.96
-16231,15672027.0,Manna,731.0,Germany,Female,41.0,9.0,84557.82,1.0,0.0,1.0,103934.12
-16232,15728006.0,Smith,654.0,Spain,Male,59.0,2.0,0.0,2.0,1.0,0.0,56784.11
-16233,15660146.0,Biryukova,648.0,Spain,Female,58.0,3.0,144552.05,1.0,0.0,1.0,102057.86
-16234,15574886.0,Chidubem,743.0,Germany,Female,47.0,7.0,128927.93,1.0,1.0,0.0,143859.66
-16235,15792669.0,Hs?eh,692.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,149629.13
-16236,15582797.0,Ts'ai,758.0,Germany,Male,37.0,7.0,171325.98,2.0,1.0,1.0,169287.99
-16237,15665811.0,Tseng,807.0,Spain,Female,71.0,2.0,0.0,2.0,1.0,0.0,98820.09
-16238,15794875.0,Tretiakova,774.0,Germany,Male,40.0,7.0,150685.79,1.0,1.0,1.0,148564.76
-16239,15584114.0,Milanesi,683.0,France,Male,31.0,5.0,129743.75,1.0,1.0,1.0,60905.51
-16240,15654025.0,Hs?,662.0,France,Male,27.0,9.0,0.0,1.0,1.0,0.0,143882.25
-16241,15591169.0,Udinesi,678.0,France,Male,34.0,9.0,146622.35,1.0,1.0,1.0,104889.2
-16242,15806941.0,Tien,545.0,France,Female,36.0,1.0,0.0,2.0,1.0,1.0,106847.51
-16243,15751203.0,T'ang,549.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,0.0,163714.92
-16244,15691817.0,Shao,834.0,France,Female,35.0,9.0,62212.29,1.0,1.0,0.0,113048.79
-16245,15664523.0,Okwuadigbo,661.0,Germany,Male,36.0,2.0,87541.06,1.0,0.0,0.0,122964.18
-16246,15588928.0,Onwuka,730.0,Germany,Male,27.0,4.0,144432.04,2.0,1.0,1.0,156574.79
-16247,15793947.0,T'ao,732.0,France,Male,26.0,6.0,0.0,2.0,0.0,0.0,180427.24
-16248,15744044.0,Okechukwu,534.0,Germany,Female,48.0,5.0,127734.92,1.0,1.0,0.0,132418.59
-16249,15586931.0,Ch'iu,545.0,Germany,Female,31.0,4.0,91216.0,1.0,1.0,1.0,180595.02
-16250,15618255.0,Chiang,603.0,France,Female,45.0,4.0,0.0,1.0,1.0,1.0,93739.71
-16251,15715078.0,Fisk,712.0,Germany,Female,42.0,9.0,117852.26,1.0,1.0,0.0,174916.85
-16252,15737705.0,Obiajulu,711.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,134878.19
-16253,15625905.0,Tsao,637.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,72970.93
-16254,15652981.0,Tsao,633.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,93713.15
-16255,15779414.0,Chukwubuikem,733.0,France,Male,59.0,1.0,0.0,2.0,1.0,1.0,82298.59
-16256,15662063.0,Rizzo,695.0,France,Male,27.0,6.0,109848.6,1.0,1.0,1.0,63218.85
-16257,15707634.0,Lo Duca,521.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,40257.79
-16258,15623479.0,Eremenko,682.0,Germany,Female,39.0,10.0,79954.61,2.0,1.0,1.0,126565.84
-16259,15690796.0,T'ien,721.0,Spain,Female,39.0,3.0,107592.89,1.0,0.0,1.0,155711.64
-16260,15799031.0,Pisano,721.0,France,Female,26.0,8.0,0.0,2.0,1.0,0.0,142548.64
-16261,15582892.0,Onwuamaeze,614.0,France,Male,31.0,10.0,0.0,1.0,1.0,0.0,87412.24
-16262,15769272.0,Ulyanova,653.0,France,Male,31.0,1.0,115532.52,1.0,1.0,1.0,90174.84
-16263,15802362.0,Brownless,815.0,Spain,Male,41.0,3.0,129669.32,1.0,1.0,1.0,79475.3
-16264,15674929.0,Chidubem,577.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,162022.67
-16265,15718289.0,Ch'ang,709.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,138340.75
-16266,15703364.0,Onyemere,616.0,France,Female,48.0,9.0,0.0,1.0,0.0,1.0,26606.28
-16267,15733797.0,Onochie,816.0,France,Female,29.0,10.0,0.0,2.0,0.0,0.0,85522.84
-16268,15646936.0,Ch'ang,587.0,Spain,Female,29.0,3.0,90217.07,1.0,0.0,1.0,174770.14
-16269,15670668.0,Chien,756.0,France,Female,29.0,2.0,0.0,2.0,0.0,0.0,151325.24
-16270,15802857.0,Nwachinemelu,752.0,Spain,Female,30.0,8.0,0.0,1.0,0.0,1.0,136054.45
-16271,15570087.0,Shih,582.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,60552.44
-16272,15757521.0,Ch'eng,586.0,France,Male,35.0,3.0,0.0,1.0,0.0,1.0,149614.61
-16273,15771668.0,Onyekachukwu,547.0,France,Male,29.0,1.0,83061.26,1.0,0.0,0.0,159800.16
-16274,15575679.0,Chiemenam,577.0,France,Female,38.0,1.0,0.0,1.0,0.0,1.0,149991.32
-16275,15757911.0,Chien,785.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,26116.82
-16276,15805312.0,Mazzanti,718.0,France,Male,28.0,5.0,0.0,1.0,1.0,0.0,82565.01
-16277,15802362.0,Hsueh,735.0,France,Female,52.0,3.0,0.0,1.0,1.0,0.0,157937.35
-16278,15674833.0,Ohearn,628.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,0.0,67830.2
-16279,15574782.0,Toscani,543.0,France,Female,29.0,9.0,0.0,1.0,1.0,0.0,172524.51
-16280,15597901.0,Yermolayev,834.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,142797.5
-16281,15679810.0,Pisano,682.0,Germany,Male,43.0,3.0,131955.07,1.0,1.0,0.0,94415.3
-16282,15754929.0,Chukwuemeka,738.0,France,Male,41.0,5.0,0.0,2.0,1.0,0.0,143357.07
-16283,15572626.0,Kosisochukwu,790.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,168816.22
-16284,15625944.0,Dore,809.0,Germany,Female,44.0,7.0,123845.36,2.0,1.0,1.0,199249.29
-16285,15641158.0,Palerma,614.0,France,Female,34.0,2.0,131765.72,2.0,1.0,0.0,71139.31
-16286,15791851.0,Calabresi,687.0,Spain,Male,37.0,0.0,0.0,2.0,0.0,0.0,152882.95
-16287,15692761.0,Onyekachukwu,589.0,France,Female,41.0,9.0,0.0,2.0,1.0,1.0,178331.36
-16288,15725762.0,Ginn,564.0,Germany,Female,28.0,1.0,122314.5,1.0,1.0,0.0,143913.32
-16289,15634245.0,Milano,793.0,France,Male,32.0,5.0,106044.72,1.0,1.0,1.0,96444.88
-16290,15689955.0,Chinweuba,614.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,104079.62
-16291,15725776.0,Olisanugo,696.0,France,Female,31.0,5.0,0.0,2.0,0.0,1.0,61531.86
-16292,15643658.0,O'Donnell,738.0,Spain,Female,51.0,3.0,0.0,1.0,1.0,1.0,35600.25
-16293,15603328.0,Shih,749.0,Germany,Female,48.0,5.0,107856.57,1.0,1.0,1.0,57764.65
-16294,15574584.0,Ch'ang,775.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,164018.98
-16295,15790093.0,Shih,581.0,Spain,Male,37.0,7.0,0.0,1.0,1.0,1.0,168002.31
-16296,15800814.0,Genovese,644.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,0.0,83629.6
-16297,15620505.0,Hsia,678.0,France,Male,48.0,3.0,0.0,1.0,1.0,0.0,132029.95
-16298,15649744.0,Palermo,648.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,121798.52
-16299,15770309.0,Pirozzi,632.0,France,Male,49.0,0.0,0.0,1.0,1.0,0.0,24267.28
-16300,15714981.0,Shih,730.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,92810.6
-16301,15785042.0,Yermakov,638.0,France,Female,39.0,6.0,99097.33,1.0,1.0,0.0,176843.53
-16302,15795527.0,Ch'ien,558.0,France,Female,42.0,0.0,118199.97,1.0,1.0,0.0,60905.51
-16303,15807335.0,Hs?eh,521.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,1.0,89566.64
-16304,15584812.0,Ch'iu,687.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,138051.19
-16305,15752912.0,Nkemdirim,591.0,Spain,Male,35.0,10.0,0.0,1.0,0.0,1.0,177068.24
-16306,15642933.0,Olisaemeka,678.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,167033.83
-16307,15752388.0,Toscani,634.0,France,Male,51.0,4.0,123374.46,1.0,1.0,0.0,171774.5
-16308,15716605.0,Uvarov,731.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,117505.07
-16309,15625021.0,Lombardi,588.0,France,Female,46.0,5.0,111962.99,3.0,1.0,0.0,8751.59
-16310,15593973.0,Hsing,676.0,Spain,Male,35.0,8.0,0.0,1.0,1.0,1.0,172049.84
-16311,15699507.0,Chin,571.0,Spain,Male,34.0,4.0,121063.41,1.0,1.0,0.0,159629.64
-16312,15602797.0,Rischbieth,677.0,Germany,Female,50.0,1.0,109346.13,1.0,1.0,1.0,129964.94
-16313,15761554.0,Chidozie,597.0,France,Male,48.0,9.0,0.0,3.0,1.0,0.0,90979.64
-16314,15729018.0,Chijioke,749.0,France,Male,24.0,5.0,0.0,2.0,1.0,1.0,57876.05
-16315,15745080.0,Mazzanti,462.0,Germany,Female,39.0,9.0,141784.02,1.0,0.0,0.0,5862.53
-16316,15641794.0,Chigozie,529.0,France,Female,45.0,2.0,0.0,1.0,1.0,0.0,168034.9
-16317,15663888.0,Onwuamaeze,756.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,198490.33
-16318,15761298.0,Trevisano,653.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,181698.34
-16319,15690731.0,Onio,658.0,France,Male,43.0,5.0,119658.49,1.0,1.0,0.0,65816.53
-16320,15703437.0,Chidozie,605.0,Germany,Male,35.0,9.0,111739.4,1.0,1.0,0.0,75832.53
-16321,15743976.0,P'eng,591.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,123954.11
-16322,15692581.0,Okoli,653.0,Spain,Female,56.0,6.0,0.0,2.0,1.0,1.0,130553.47
-16323,15806981.0,Okwudilichukwu,595.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,1.0,143938.28
-16324,15792064.0,Ch'in,689.0,France,Female,26.0,6.0,0.0,2.0,1.0,1.0,91574.92
-16325,15662532.0,Shih,622.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,181449.74
-16326,15621550.0,Manna,718.0,France,Male,36.0,6.0,130170.82,1.0,1.0,0.0,141533.19
-16327,15586517.0,Milanesi,752.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,104469.06
-16328,15799031.0,Shao,707.0,France,Male,51.0,5.0,124023.24,2.0,1.0,0.0,62280.91
-16329,15807335.0,Hixson,755.0,Spain,Male,32.0,1.0,0.0,1.0,1.0,1.0,175043.69
-16330,15790782.0,Chidiegwu,551.0,France,Female,46.0,2.0,0.0,2.0,1.0,1.0,181429.87
-16331,15694237.0,Chu,599.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,1.0,158973.45
-16332,15662091.0,Frolova,676.0,Spain,Female,31.0,2.0,136680.23,1.0,0.0,0.0,64321.02
-16333,15711759.0,Chidiebere,812.0,France,Male,33.0,9.0,0.0,2.0,0.0,0.0,171708.66
-16334,15592762.0,Hsueh,609.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,168895.06
-16335,15605327.0,Nnonso,551.0,France,Male,44.0,8.0,0.0,2.0,1.0,1.0,126475.79
-16336,15621435.0,Pagnotto,622.0,France,Female,33.0,10.0,0.0,1.0,1.0,0.0,70954.41
-16337,15652218.0,Milanesi,581.0,Spain,Male,25.0,6.0,0.0,2.0,1.0,0.0,151411.3
-16338,15761141.0,Hsueh,699.0,France,Male,24.0,8.0,116084.22,1.0,1.0,1.0,81898.81
-16339,15807989.0,Genovese,725.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,102799.14
-16340,15605965.0,Y?an,679.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,1.0,143783.46
-16341,15702566.0,Okwudilichukwu,711.0,Germany,Male,45.0,9.0,130142.79,1.0,1.0,1.0,149043.78
-16342,15680141.0,Teng,640.0,France,Female,23.0,5.0,0.0,2.0,1.0,0.0,138380.11
-16343,15702788.0,T'ien,702.0,France,Male,36.0,7.0,0.0,1.0,1.0,0.0,170638.98
-16344,15750547.0,Onyekachi,599.0,France,Female,31.0,1.0,102723.35,1.0,1.0,1.0,151335.24
-16345,15716284.0,Sun,732.0,France,Male,35.0,9.0,0.0,2.0,0.0,0.0,176694.87
-16346,15793046.0,Ku,565.0,France,Female,60.0,7.0,118400.91,4.0,0.0,1.0,187816.59
-16347,15713774.0,Chiazagomekpere,644.0,France,Female,34.0,7.0,0.0,2.0,0.0,0.0,182705.05
-16348,15655298.0,Chiu,703.0,France,Male,36.0,0.0,0.0,2.0,1.0,0.0,147720.27
-16349,15654067.0,Chukwukadibia,599.0,Germany,Male,38.0,8.0,133050.92,1.0,1.0,1.0,38652.23
-16350,15773972.0,Gadsden,665.0,Spain,Male,29.0,7.0,115897.12,1.0,1.0,1.0,70783.61
-16351,15602851.0,Mancini,681.0,France,Female,38.0,7.0,0.0,2.0,0.0,1.0,59650.44
-16352,15765311.0,Teng,709.0,Spain,Male,35.0,2.0,0.0,2.0,0.0,0.0,51387.1
-16353,15765311.0,Chiazagomekpele,689.0,Spain,Female,36.0,9.0,0.0,2.0,1.0,1.0,88965.46
-16354,15712584.0,Ch'ang,559.0,Spain,Male,33.0,3.0,0.0,1.0,1.0,1.0,110783.28
-16355,15585853.0,Trentini,667.0,Germany,Female,41.0,10.0,94442.11,1.0,1.0,0.0,131804.86
-16356,15812071.0,Ch'ang,712.0,Germany,Female,43.0,2.0,115095.88,3.0,1.0,0.0,57517.71
-16357,15656426.0,Lombardo,561.0,Spain,Male,51.0,7.0,0.0,1.0,1.0,0.0,86701.4
-16358,15600982.0,Kao,613.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,142516.35
-16359,15777599.0,Genovese,649.0,France,Male,39.0,2.0,133970.24,1.0,0.0,0.0,77965.44
-16360,15646592.0,Nwora,651.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,174729.3
-16361,15804962.0,McLean,649.0,France,Female,49.0,6.0,0.0,1.0,1.0,1.0,144874.67
-16362,15742229.0,Calabresi,695.0,France,Male,29.0,1.0,0.0,1.0,1.0,1.0,182055.36
-16363,15686963.0,Pinto,584.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,52795.15
-16364,15765257.0,Bellucci,574.0,France,Male,35.0,10.0,0.0,2.0,1.0,0.0,134878.19
-16365,15627002.0,Chien,641.0,Spain,Female,36.0,2.0,0.0,1.0,1.0,0.0,164815.59
-16366,15659428.0,Nnonso,632.0,Spain,Female,40.0,9.0,0.0,1.0,1.0,0.0,116893.87
-16367,15735782.0,Lucchese,804.0,France,Male,32.0,7.0,123675.69,1.0,1.0,1.0,65612.01
-16368,15677387.0,Hsieh,678.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,173870.39
-16369,15686885.0,Rizzo,727.0,France,Male,45.0,9.0,0.0,2.0,1.0,0.0,68860.3
-16370,15706463.0,Onyekachi,676.0,France,Female,43.0,5.0,113599.74,1.0,0.0,0.0,110899.3
-16371,15708504.0,Kao,586.0,Spain,Male,31.0,8.0,151510.17,1.0,1.0,1.0,162668.33
-16372,15709861.0,DeRose,665.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,152054.33
-16373,15742668.0,Lucchesi,632.0,Spain,Male,35.0,4.0,0.0,1.0,1.0,1.0,160593.48
-16374,15693376.0,Tsai,703.0,France,Female,26.0,7.0,115309.75,1.0,1.0,0.0,48249.88
-16375,15733111.0,Chukwuemeka,696.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,160810.85
-16376,15760662.0,T'ang,634.0,Germany,Male,38.0,1.0,109730.22,1.0,1.0,1.0,87767.55
-16377,15587735.0,Biryukov,622.0,France,Male,44.0,4.0,116695.27,1.0,1.0,1.0,143357.02
-16378,15761149.0,Tretiakov,645.0,France,Male,24.0,7.0,0.0,2.0,1.0,0.0,145796.49
-16379,15657540.0,Lombardi,539.0,Germany,Male,43.0,4.0,117640.42,1.0,0.0,1.0,88837.15
-16380,15608408.0,P'eng,615.0,France,Male,42.0,2.0,0.0,1.0,0.0,0.0,59864.96
-16381,15702541.0,Pisano,495.0,Germany,Male,33.0,6.0,86843.61,1.0,1.0,0.0,169921.11
-16382,15654577.0,Ibeabuchi,627.0,Germany,Male,33.0,1.0,132439.75,2.0,1.0,1.0,167878.5
-16383,15626684.0,Chukwumaobim,806.0,Spain,Male,28.0,9.0,0.0,2.0,0.0,0.0,136054.45
-16384,15672374.0,Toscani,603.0,France,Female,29.0,5.0,166501.17,1.0,1.0,1.0,113049.72
-16385,15603096.0,Ch'ien,709.0,France,Male,43.0,4.0,0.0,2.0,1.0,0.0,127939.19
-16386,15807900.0,Chukwujekwu,667.0,Germany,Female,50.0,2.0,102708.97,2.0,0.0,0.0,96885.19
-16387,15627859.0,Trevisan,635.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,81884.8
-16388,15792868.0,Tien,577.0,Spain,Female,29.0,8.0,0.0,2.0,0.0,0.0,140168.36
-16389,15772262.0,Frolova,569.0,France,Female,50.0,4.0,0.0,1.0,1.0,0.0,173912.29
-16390,15602551.0,Ch'ang,583.0,Germany,Male,38.0,4.0,122384.22,1.0,0.0,0.0,153015.42
-16391,15682468.0,Chukwunonso,749.0,France,Female,33.0,0.0,0.0,1.0,1.0,0.0,140166.95
-16392,15765457.0,Chukwudi,634.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,0.0,198134.9
-16393,15708003.0,Ndubuagha,776.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,112333.22
-16394,15589017.0,Tien,666.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,72643.95
-16395,15691792.0,Hsing,667.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,152790.92
-16396,15775572.0,Yobanna,607.0,France,Female,37.0,8.0,0.0,1.0,1.0,0.0,7861.68
-16397,15686963.0,DeRose,654.0,Spain,Female,43.0,2.0,0.0,2.0,0.0,1.0,159742.33
-16398,15600710.0,Ndubuagha,659.0,Spain,Male,56.0,3.0,0.0,1.0,0.0,1.0,175043.69
-16399,15673837.0,McFarland,687.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,112442.63
-16400,15736249.0,Hovell,724.0,France,Male,31.0,7.0,0.0,1.0,0.0,0.0,92888.52
-16401,15798532.0,Yobachi,743.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,113597.64
-16402,15732266.0,T'ao,537.0,Spain,Male,50.0,6.0,0.0,1.0,1.0,0.0,48478.47
-16403,15600746.0,Uwaezuoke,826.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,36249.76
-16404,15642063.0,Kao,758.0,France,Male,24.0,5.0,0.0,2.0,0.0,1.0,162960.05
-16405,15658032.0,K?,678.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,159800.16
-16406,15744695.0,Macleod,496.0,France,Female,44.0,2.0,0.0,1.0,1.0,1.0,84872.66
-16407,15658364.0,Mazzanti,682.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,133501.94
-16408,15794868.0,K?,636.0,Spain,Female,42.0,8.0,112281.6,1.0,0.0,0.0,155721.15
-16409,15768282.0,Genovesi,724.0,Spain,Male,45.0,4.0,98775.23,2.0,1.0,1.0,36308.93
-16410,15690134.0,Ch'in,613.0,Spain,Male,34.0,10.0,0.0,1.0,0.0,1.0,160162.42
-16411,15756884.0,Y?an,676.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,72993.65
-16412,15636454.0,Chikelu,705.0,France,Female,55.0,3.0,0.0,1.0,1.0,0.0,168515.61
-16413,15666096.0,McGregor,757.0,France,Male,53.0,3.0,0.0,1.0,1.0,0.0,94498.82
-16414,15641250.0,Ndubueze,646.0,France,Female,43.0,5.0,0.0,2.0,0.0,1.0,171364.18
-16415,15767391.0,Hsia,594.0,France,Male,29.0,3.0,0.0,2.0,1.0,0.0,133617.57
-16416,15682369.0,Akhtar,628.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,141180.66
-16417,15658852.0,Kosisochukwu,704.0,France,Female,43.0,5.0,0.0,2.0,1.0,1.0,125939.22
-16418,15575119.0,Chidiebere,646.0,France,Male,33.0,8.0,0.0,2.0,0.0,0.0,91957.39
-16419,15581626.0,Folliero,738.0,France,Female,40.0,9.0,0.0,2.0,1.0,0.0,101168.9
-16420,15765694.0,Chukwukadibia,730.0,Germany,Male,34.0,6.0,146445.54,1.0,1.0,1.0,178827.3
-16421,15689514.0,Chukwumaobim,667.0,Spain,Male,34.0,2.0,120867.18,1.0,0.0,0.0,129964.94
-16422,15569917.0,P'eng,678.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,186869.58
-16423,15658946.0,Clements,585.0,France,Female,46.0,8.0,123835.52,1.0,1.0,1.0,187740.04
-16424,15747808.0,Milanesi,639.0,France,Male,27.0,6.0,0.0,2.0,1.0,0.0,173516.71
-16425,15783029.0,Onyemachukwu,646.0,France,Female,40.0,7.0,0.0,2.0,0.0,0.0,125979.36
-16426,15646563.0,Rizzo,649.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,97939.3
-16427,15619343.0,Akeroyd,681.0,France,Male,25.0,7.0,0.0,2.0,1.0,1.0,163746.09
-16428,15780954.0,Chinonyelum,697.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,151871.84
-16429,15809800.0,Chiang,564.0,Germany,Male,48.0,8.0,135518.76,1.0,1.0,0.0,182566.76
-16430,15677522.0,Chia,725.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,136863.36
-16431,15636700.0,Chidozie,633.0,France,Male,33.0,2.0,106947.26,1.0,0.0,1.0,60906.12
-16432,15757541.0,Trentino,809.0,France,Female,28.0,6.0,0.0,1.0,0.0,0.0,138344.7
-16433,15677052.0,Onyemauchechukwu,694.0,Spain,Male,31.0,6.0,0.0,1.0,1.0,0.0,18082.06
-16434,15631070.0,Iadanza,589.0,France,Male,42.0,4.0,0.0,1.0,1.0,0.0,52884.02
-16435,15692463.0,Ritchie,665.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,81884.8
-16436,15645200.0,De Luca,679.0,France,Male,32.0,7.0,0.0,2.0,0.0,0.0,173870.39
-16437,15583724.0,Piazza,696.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,125518.32
-16438,15769005.0,Chukwuemeka,526.0,France,Female,34.0,3.0,130812.91,1.0,1.0,1.0,143684.2
-16439,15798964.0,Uspensky,834.0,France,Female,35.0,5.0,99811.44,1.0,1.0,1.0,125920.54
-16440,15710365.0,McElhone,640.0,France,Male,26.0,4.0,0.0,2.0,1.0,1.0,59905.29
-16441,15590828.0,Chukwujamuike,641.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,1.0,168816.22
-16442,15605918.0,Kao,471.0,France,Female,38.0,1.0,136855.94,1.0,1.0,1.0,27355.8
-16443,15644878.0,Yobanna,594.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,103611.66
-16444,15739592.0,Chukwuemeka,634.0,France,Female,45.0,9.0,0.0,2.0,1.0,0.0,142513.5
-16445,15667932.0,Onwumelu,612.0,France,Female,34.0,8.0,114959.12,1.0,1.0,0.0,131736.23
-16446,15586523.0,Hsieh,741.0,Germany,Female,37.0,5.0,138053.67,2.0,0.0,1.0,196257.67
-16447,15784700.0,Tsou,676.0,France,Male,50.0,7.0,0.0,2.0,0.0,1.0,176576.71
-16448,15702464.0,Kao,583.0,Spain,Male,35.0,9.0,99816.46,1.0,1.0,1.0,102999.33
-16449,15609637.0,Yefimova,773.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,160968.44
-16450,15752508.0,Hargreaves,697.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,25407.59
-16451,15697035.0,Onyekachukwu,699.0,France,Male,27.0,9.0,115409.6,1.0,1.0,1.0,129562.74
-16452,15721569.0,Chiu,663.0,France,Female,46.0,8.0,71264.02,1.0,1.0,0.0,127951.81
-16453,15583857.0,Hsia,727.0,France,Female,44.0,4.0,0.0,2.0,1.0,1.0,175314.87
-16454,15641110.0,Tien,645.0,France,Female,45.0,5.0,0.0,2.0,1.0,0.0,138882.98
-16455,15782467.0,Fanucci,561.0,France,Female,33.0,8.0,133607.75,1.0,0.0,0.0,122580.48
-16456,15777893.0,Yobanna,763.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,172363.41
-16457,15585255.0,Trevisano,694.0,France,Male,46.0,4.0,121342.24,1.0,0.0,1.0,116848.79
-16458,15757912.0,Ts'ao,652.0,France,Female,36.0,2.0,80780.16,2.0,1.0,0.0,91943.94
-16459,15644976.0,Lai,633.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,58879.11
-16460,15609998.0,Iweobiegbunam,769.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,152992.04
-16461,15628292.0,Ch'ien,622.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,100556.98
-16462,15570087.0,Manna,705.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,114775.53
-16463,15806941.0,Hs?eh,564.0,Germany,Male,46.0,5.0,104584.16,1.0,1.0,0.0,131660.31
-16464,15680643.0,Chiu,694.0,France,Female,31.0,6.0,0.0,2.0,1.0,1.0,123882.47
-16465,15592707.0,Chukwumaobim,605.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,145551.6
-16466,15691840.0,Ch'iu,648.0,Germany,Male,31.0,9.0,178058.06,1.0,1.0,0.0,180712.52
-16467,15775490.0,Calabresi,529.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,179521.24
-16468,15709920.0,Chijioke,549.0,France,Male,45.0,7.0,108862.76,1.0,1.0,1.0,63717.49
-16469,15619407.0,Macintyre,581.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,142400.27
-16470,15572356.0,H?,663.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,155721.15
-16471,15668893.0,Ch'eng,683.0,Germany,Female,44.0,1.0,121411.9,2.0,1.0,0.0,16281.94
-16472,15641110.0,L?,697.0,France,Male,36.0,4.0,126473.99,1.0,1.0,1.0,75574.48
-16473,15632143.0,Palermo,604.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,172810.51
-16474,15769246.0,Trevisano,582.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,182867.83
-16475,15605425.0,Udegbunam,572.0,France,Female,46.0,7.0,0.0,1.0,1.0,0.0,110252.53
-16476,15719276.0,Chidiegwu,705.0,France,Male,64.0,5.0,0.0,1.0,1.0,0.0,131807.14
-16477,15727177.0,Ikedinachukwu,571.0,France,Male,35.0,4.0,0.0,2.0,0.0,1.0,60906.12
-16478,15585067.0,P'an,586.0,France,Female,33.0,9.0,0.0,2.0,1.0,1.0,166677.35
-16479,15608916.0,Ch'ang,706.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,68701.73
-16480,15701280.0,Grieve,695.0,France,Male,36.0,0.0,0.0,2.0,1.0,0.0,80701.72
-16481,15660679.0,Scott,624.0,France,Female,31.0,4.0,93791.38,1.0,1.0,1.0,79633.38
-16482,15795874.0,Bischof,697.0,France,Male,23.0,8.0,0.0,2.0,1.0,1.0,49085.0
-16483,15756535.0,Palerma,653.0,France,Female,44.0,9.0,103033.83,1.0,1.0,0.0,142995.32
-16484,15789563.0,Maconochie,775.0,Spain,Male,36.0,0.0,0.0,1.0,1.0,1.0,162267.91
-16485,15590820.0,Chien,663.0,France,Male,27.0,9.0,0.0,2.0,0.0,1.0,85733.33
-16486,15664539.0,Nwankwo,613.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,0.0,72929.96
-16487,15642725.0,Ch'en,577.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,121408.55
-16488,15671351.0,Kornilova,564.0,France,Male,38.0,9.0,0.0,2.0,0.0,1.0,74878.23
-16489,15781589.0,Milne,652.0,Germany,Male,61.0,2.0,143421.78,1.0,1.0,0.0,185547.76
-16490,15679307.0,Hsia,571.0,France,Female,43.0,8.0,0.0,2.0,1.0,1.0,71512.8
-16491,15707488.0,Onwudiwe,634.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,1710.89
-16492,15634896.0,Ch'ien,673.0,France,Female,43.0,5.0,0.0,1.0,1.0,1.0,64477.25
-16493,15771742.0,O'Donnell,634.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,141406.43
-16494,15607746.0,Ch'en,592.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,171045.41
-16495,15731829.0,Genovesi,699.0,France,Female,22.0,6.0,0.0,2.0,1.0,1.0,103799.71
-16496,15714220.0,Y?an,622.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,129975.25
-16497,15737178.0,Y?,727.0,Spain,Male,35.0,4.0,0.0,1.0,0.0,1.0,62722.44
-16498,15803790.0,Gilleland,708.0,France,Male,40.0,6.0,0.0,1.0,1.0,1.0,128875.86
-16499,15708713.0,Maclean,711.0,France,Male,27.0,0.0,127415.85,1.0,1.0,1.0,170968.99
-16500,15649744.0,Ts'ui,603.0,Spain,Male,29.0,4.0,75893.11,1.0,1.0,0.0,87400.49
-16501,15629338.0,Ch'ang,645.0,France,Male,37.0,6.0,0.0,2.0,0.0,0.0,110114.19
-16502,15809909.0,McIntosh,632.0,Germany,Male,31.0,4.0,110864.38,1.0,1.0,1.0,176984.34
-16503,15592387.0,K'ung,744.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,123978.86
-16504,15781234.0,Harker,554.0,Germany,Male,26.0,3.0,123345.85,2.0,1.0,1.0,147055.87
-16505,15704746.0,Ojiofor,622.0,France,Male,37.0,7.0,0.0,2.0,0.0,1.0,44220.5
-16506,15592751.0,Chidi,611.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,109626.64
-16507,15765537.0,Chibueze,745.0,Spain,Male,32.0,7.0,125512.98,1.0,0.0,1.0,178034.9
-16508,15717489.0,Whitehead,616.0,France,Female,48.0,2.0,0.0,2.0,0.0,1.0,134901.58
-16509,15602731.0,Shih,589.0,Germany,Male,38.0,6.0,136596.15,2.0,1.0,1.0,84760.32
-16510,15746674.0,Lo Duca,711.0,Germany,Female,37.0,8.0,121556.53,3.0,1.0,0.0,181221.09
-16511,15723169.0,Chidubem,781.0,Spain,Female,28.0,8.0,0.0,1.0,1.0,1.0,54776.64
-16512,15611786.0,Ijendu,714.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,104079.62
-16513,15762392.0,Pagnotto,763.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,151316.37
-16514,15704819.0,Iadanza,516.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,55725.04
-16515,15593622.0,Ch'ang,590.0,Spain,Male,29.0,7.0,116114.93,1.0,1.0,1.0,145936.81
-16516,15576615.0,Ch'ang,684.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,179674.83
-16517,15703778.0,Chien,663.0,France,Male,33.0,6.0,0.0,1.0,0.0,0.0,122996.69
-16518,15756875.0,Chiu,573.0,France,Male,59.0,1.0,0.0,2.0,0.0,1.0,155870.82
-16519,15792726.0,Chiebuka,539.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,47775.34
-16520,15777045.0,Chimaobim,586.0,Germany,Male,31.0,8.0,129974.79,2.0,0.0,0.0,120830.63
-16521,15576629.0,Lucchese,774.0,France,Female,26.0,5.0,0.0,2.0,1.0,0.0,111868.23
-16522,15772798.0,Chia,545.0,Spain,Female,41.0,2.0,0.0,2.0,1.0,1.0,140159.95
-16523,15633274.0,Onuchukwu,587.0,Spain,Male,42.0,5.0,0.0,1.0,1.0,0.0,171681.86
-16524,15776725.0,P'eng,631.0,France,Female,43.0,3.0,0.0,2.0,0.0,1.0,61995.57
-16525,15572547.0,De Salis,587.0,France,Female,47.0,2.0,0.0,2.0,1.0,0.0,48990.5
-16526,15658889.0,Shih,769.0,Spain,Male,31.0,8.0,90723.36,1.0,1.0,0.0,131906.44
-16527,15762392.0,Ts'ui,713.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,107876.2
-16528,15643696.0,Liao,633.0,Germany,Male,49.0,10.0,110829.52,1.0,1.0,0.0,172618.52
-16529,15787550.0,Hsing,681.0,Germany,Female,39.0,2.0,112650.89,2.0,1.0,0.0,151349.35
-16530,15687913.0,Chiu,667.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,53353.37
-16531,15652469.0,Pinto,755.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,1.0,164825.04
-16532,15731519.0,Ngozichukwuka,619.0,Spain,Male,46.0,2.0,0.0,1.0,1.0,0.0,176433.6
-16533,15688865.0,Ch'iu,655.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,140947.05
-16534,15593499.0,Toscani,739.0,France,Female,36.0,6.0,139914.7,1.0,1.0,1.0,71154.1
-16535,15806796.0,Hs?,537.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,136487.86
-16536,15667588.0,Ch'eng,678.0,Germany,Female,29.0,8.0,87070.23,2.0,1.0,0.0,173783.38
-16537,15794493.0,Chukwuemeka,707.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,186418.85
-16538,15690175.0,O'Brien,676.0,Germany,Male,29.0,6.0,133598.4,2.0,1.0,0.0,147822.54
-16539,15746490.0,P'eng,597.0,Germany,Female,37.0,2.0,117307.53,1.0,1.0,0.0,166929.43
-16540,15750141.0,Pipes,635.0,Spain,Female,35.0,2.0,0.0,1.0,1.0,0.0,53412.54
-16541,15689410.0,Iweobiegbunam,629.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,1.0,141400.51
-16542,15652914.0,Mironova,741.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,48426.98
-16543,15672216.0,Hsiung,632.0,France,Male,46.0,1.0,0.0,2.0,1.0,0.0,68860.3
-16544,15732299.0,Chukwualuka,648.0,Germany,Male,43.0,2.0,141947.67,1.0,1.0,1.0,126726.33
-16545,15701175.0,Brient,622.0,France,Female,40.0,4.0,140197.18,1.0,1.0,1.0,92840.67
-16546,15597909.0,Ch'ien,559.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,1.0,65986.48
-16547,15628303.0,Toscani,669.0,Germany,Female,37.0,8.0,106920.91,1.0,1.0,1.0,158958.9
-16548,15704918.0,Chukwualuka,641.0,France,Male,51.0,6.0,0.0,1.0,1.0,1.0,134878.19
-16549,15581840.0,Yeates,715.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,170518.83
-16550,15777599.0,Hsueh,661.0,France,Female,30.0,4.0,0.0,1.0,1.0,1.0,142331.85
-16551,15705544.0,Mazzanti,595.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,140168.36
-16552,15722548.0,Manna,603.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,181204.5
-16553,15658693.0,McElhone,704.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,59961.81
-16554,15679963.0,Moore,667.0,Spain,Male,36.0,8.0,132468.24,1.0,1.0,1.0,166677.54
-16555,15585733.0,O'Brien,633.0,France,Male,42.0,6.0,0.0,2.0,0.0,1.0,159820.92
-16556,15798895.0,Martin,636.0,Spain,Female,45.0,1.0,0.0,2.0,1.0,0.0,94498.82
-16557,15627848.0,Obiajulu,668.0,Germany,Female,45.0,7.0,121286.27,1.0,1.0,0.0,59325.21
-16558,15723654.0,P'eng,589.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,1.0,16250.61
-16559,15662658.0,She,586.0,France,Female,31.0,1.0,81564.1,1.0,1.0,0.0,141652.2
-16560,15793901.0,DeRose,562.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,0.0,126761.2
-16561,15570194.0,Ritchie,644.0,France,Male,32.0,2.0,105229.71,1.0,0.0,0.0,129562.04
-16562,15647572.0,Lucciano,730.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,0.0,165082.25
-16563,15798964.0,Shao,662.0,France,Female,36.0,7.0,0.0,2.0,0.0,1.0,62232.6
-16564,15792525.0,Onyinyechukwuka,592.0,Germany,Male,29.0,8.0,119315.75,1.0,0.0,1.0,180427.24
-16565,15624293.0,Genovese,599.0,Germany,Female,51.0,7.0,125512.98,1.0,1.0,0.0,171482.56
-16566,15604813.0,T'ien,678.0,France,Male,44.0,8.0,0.0,1.0,1.0,1.0,155879.57
-16567,15785831.0,O'Loughlin,752.0,France,Female,41.0,7.0,0.0,1.0,1.0,1.0,142223.94
-16568,15710671.0,Hsueh,607.0,France,Female,42.0,1.0,151843.51,1.0,1.0,0.0,97623.42
-16569,15631310.0,Lucciano,632.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,69011.66
-16570,15603665.0,Mazzanti,669.0,France,Female,36.0,7.0,152347.01,1.0,1.0,0.0,898.51
-16571,15701392.0,Hsia,717.0,France,Male,39.0,3.0,0.0,1.0,1.0,1.0,28461.29
-16572,15800517.0,Pisano,803.0,France,Male,35.0,0.0,0.0,2.0,1.0,0.0,166650.32
-16573,15758775.0,Chukwudi,677.0,Spain,Male,28.0,1.0,0.0,2.0,0.0,1.0,164038.07
-16574,15805884.0,Ch'en,741.0,Germany,Male,43.0,2.0,147933.07,1.0,1.0,0.0,162858.29
-16575,15734649.0,Chukwufumnanya,762.0,France,Male,33.0,10.0,0.0,2.0,1.0,0.0,189428.75
-16576,15692744.0,Ch'ang,739.0,Germany,Male,39.0,6.0,137492.35,2.0,1.0,1.0,93728.47
-16577,15794566.0,Chimezie,633.0,France,Male,38.0,5.0,138858.07,1.0,0.0,1.0,159742.33
-16578,15757811.0,Brown,850.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,161479.19
-16579,15793331.0,Chinedum,774.0,France,Male,36.0,1.0,125193.96,1.0,1.0,0.0,124486.28
-16580,15607629.0,Ch'in,753.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,5983.7
-16581,15665064.0,Shen,705.0,Germany,Male,34.0,7.0,122031.55,2.0,1.0,1.0,49815.62
-16582,15614778.0,Okwudiliolisa,616.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,0.0,109626.13
-16583,15603565.0,Lai,668.0,France,Male,39.0,3.0,0.0,2.0,0.0,0.0,93718.86
-16584,15764033.0,Nucci,571.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,136506.86
-16585,15626233.0,Folliero,653.0,France,Male,37.0,2.0,0.0,2.0,0.0,1.0,180932.46
-16586,15777904.0,Chukwukere,756.0,France,Female,43.0,8.0,0.0,1.0,1.0,0.0,141827.33
-16587,15657937.0,Yefimova,794.0,France,Male,36.0,10.0,61319.63,1.0,0.0,0.0,164039.51
-16588,15607560.0,Hsiung,554.0,France,Male,48.0,2.0,0.0,1.0,1.0,0.0,73977.23
-16589,15758513.0,Milne,621.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,92357.21
-16590,15809838.0,Milano,495.0,Spain,Female,45.0,3.0,137841.53,1.0,1.0,0.0,146878.89
-16591,15769353.0,Nwachinemelu,803.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,106655.88
-16592,15702300.0,Lucciano,588.0,Spain,Female,52.0,8.0,0.0,1.0,0.0,0.0,164247.51
-16593,15752885.0,Onwuamaeze,712.0,Spain,Female,34.0,7.0,0.0,1.0,1.0,0.0,86410.28
-16594,15748068.0,Chukwufumnanya,711.0,France,Female,50.0,5.0,0.0,1.0,1.0,1.0,116977.89
-16595,15598892.0,Chukwuemeka,593.0,Spain,Female,39.0,10.0,0.0,2.0,1.0,1.0,123882.73
-16596,15610226.0,Zaytseva,704.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,141069.88
-16597,15600821.0,Ch'in,554.0,Germany,Male,43.0,10.0,115062.61,1.0,1.0,0.0,71133.12
-16598,15573851.0,Sievier,546.0,Germany,Male,47.0,1.0,144837.79,2.0,0.0,0.0,82438.13
-16599,15659949.0,Nwankwo,680.0,Spain,Male,32.0,6.0,92899.27,1.0,1.0,1.0,79691.4
-16600,15689886.0,Zikoranachidimma,794.0,Germany,Male,43.0,7.0,132558.26,1.0,0.0,0.0,109291.39
-16601,15628112.0,Brown,751.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,74048.1
-16602,15576728.0,Ch'ien,724.0,Germany,Female,37.0,7.0,166501.17,1.0,1.0,0.0,189085.07
-16603,15776921.0,Olisanugo,669.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,104786.59
-16604,15671610.0,Yudin,694.0,Germany,Male,37.0,5.0,141462.13,2.0,0.0,0.0,94790.34
-16605,15736126.0,Chidi,603.0,France,Male,41.0,5.0,0.0,2.0,0.0,0.0,78700.02
-16606,15592854.0,Fiorentino,729.0,France,Female,23.0,0.0,78535.25,1.0,1.0,0.0,141946.92
-16607,15635277.0,Yobanna,732.0,France,Female,35.0,3.0,145396.49,1.0,0.0,1.0,60778.11
-16608,15670266.0,Iloerika,562.0,Spain,Male,48.0,6.0,0.0,1.0,0.0,0.0,90860.97
-16609,15682533.0,McGregor,760.0,Germany,Male,44.0,7.0,105422.61,1.0,0.0,0.0,80552.11
-16610,15770101.0,Fiorentini,573.0,France,Female,35.0,5.0,0.0,1.0,0.0,0.0,116467.35
-16611,15591336.0,T'ien,628.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,147040.25
-16612,15641136.0,White,681.0,France,Female,27.0,3.0,0.0,2.0,0.0,1.0,54409.48
-16613,15772363.0,Ch'ang,582.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,53793.1
-16614,15720649.0,Calabrese,645.0,Germany,Female,40.0,2.0,136494.85,1.0,1.0,1.0,598.8
-16615,15608528.0,Chigolum,572.0,France,Female,31.0,6.0,0.0,2.0,0.0,0.0,144870.28
-16616,15661723.0,Chukwumaobim,624.0,France,Female,38.0,4.0,0.0,1.0,0.0,1.0,76773.53
-16617,15649450.0,Nwachukwu,721.0,France,Male,37.0,4.0,136492.92,1.0,1.0,1.0,24255.21
-16618,15742272.0,Ts'ao,751.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,63736.17
-16619,15791767.0,Eluemuno,589.0,France,Male,37.0,0.0,180716.1,1.0,1.0,0.0,82662.73
-16620,15623668.0,Linger,599.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,140136.1
-16621,15575676.0,Teng,524.0,France,Male,50.0,1.0,139979.07,1.0,0.0,0.0,100127.71
-16622,15684868.0,Nwankwo,717.0,France,Female,55.0,2.0,105204.01,1.0,0.0,1.0,41970.72
-16623,15738662.0,Knepper,679.0,Germany,Male,37.0,1.0,126473.99,2.0,0.0,0.0,122925.44
-16624,15758831.0,Ebelechukwu,689.0,France,Male,52.0,9.0,122552.34,1.0,1.0,1.0,131167.98
-16625,15713490.0,Chien,591.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,163666.85
-16626,15573741.0,Fanucci,790.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,167023.95
-16627,15565879.0,Lettiere,580.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,103560.98
-16628,15788448.0,Smith,621.0,Spain,Male,28.0,1.0,0.0,2.0,0.0,1.0,83056.22
-16629,15643359.0,Ch'ang,675.0,Spain,Male,32.0,1.0,152347.01,1.0,0.0,1.0,124486.28
-16630,15669611.0,Onuchukwu,682.0,Germany,Female,31.0,7.0,150956.71,2.0,1.0,0.0,16150.34
-16631,15758051.0,Shih,652.0,Germany,Female,47.0,5.0,108431.85,1.0,0.0,1.0,55313.51
-16632,15806963.0,Tien,673.0,Germany,Male,36.0,2.0,152390.21,2.0,1.0,0.0,137986.45
-16633,15589715.0,Chiang,531.0,Germany,Male,32.0,8.0,109876.82,1.0,1.0,0.0,161811.23
-16634,15615444.0,Y?,710.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,71592.8
-16635,15789014.0,Ch'ang,730.0,Germany,Female,44.0,5.0,115024.5,1.0,1.0,0.0,26249.86
-16636,15702656.0,Hsueh,610.0,France,Female,39.0,9.0,0.0,2.0,1.0,0.0,167026.71
-16637,15731463.0,Ch'ien,667.0,Spain,Male,39.0,1.0,0.0,2.0,1.0,1.0,103421.91
-16638,15694860.0,Iloerika,753.0,Germany,Female,43.0,6.0,93522.2,2.0,1.0,1.0,187985.77
-16639,15809847.0,Nwabugwu,667.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,133501.94
-16640,15754569.0,Ch'eng,637.0,France,Male,40.0,8.0,0.0,1.0,1.0,0.0,81439.4
-16641,15745324.0,Onyekachukwu,663.0,Spain,Male,39.0,3.0,86055.17,2.0,1.0,1.0,120225.62
-16642,15746035.0,Iadanza,627.0,France,Female,29.0,4.0,0.0,2.0,1.0,1.0,132979.43
-16643,15802758.0,Loewenthal,554.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,97932.68
-16644,15682369.0,Iloerika,638.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,172792.43
-16645,15682778.0,Ch'ien,626.0,Germany,Female,32.0,1.0,145860.98,2.0,1.0,0.0,110498.1
-16646,15711489.0,Hsing,592.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,173464.43
-16647,15793723.0,Chiang,660.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,18082.06
-16648,15753841.0,Scott,613.0,France,Male,33.0,8.0,171922.72,1.0,0.0,0.0,92612.88
-16649,15702561.0,O'Donnell,769.0,Germany,Male,41.0,8.0,117866.92,1.0,1.0,1.0,582.53
-16650,15790630.0,Chia,682.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,142903.44
-16651,15735078.0,L?,578.0,Spain,Male,19.0,3.0,0.0,2.0,1.0,0.0,161242.99
-16652,15714260.0,Chidiebere,586.0,France,Male,37.0,3.0,131184.67,1.0,1.0,0.0,129890.94
-16653,15651460.0,Iqbal,704.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,117939.17
-16654,15577313.0,Zuyev,558.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,105886.85
-16655,15658032.0,Pisano,681.0,France,Male,21.0,9.0,0.0,2.0,0.0,0.0,96875.52
-16656,15587066.0,Palerma,584.0,Spain,Male,62.0,1.0,0.0,1.0,1.0,1.0,151324.19
-16657,15807432.0,Horsley,711.0,Germany,Male,36.0,1.0,108914.17,1.0,1.0,0.0,2498.67
-16658,15703614.0,Y?,547.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,96847.91
-16659,15775307.0,Hsiao,817.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,70529.0
-16660,15705918.0,Chinomso,748.0,France,Female,45.0,8.0,0.0,1.0,0.0,0.0,138021.36
-16661,15789425.0,Ku,652.0,France,Female,26.0,1.0,101070.69,1.0,1.0,0.0,114932.62
-16662,15697034.0,Hsu,727.0,France,Female,35.0,2.0,156570.86,1.0,1.0,1.0,137028.51
-16663,15586674.0,Nwachukwu,683.0,France,Female,39.0,3.0,0.0,2.0,0.0,1.0,141558.04
-16664,15590091.0,White,850.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,123866.28
-16665,15715003.0,Ch'in,583.0,France,Male,45.0,7.0,0.0,2.0,1.0,1.0,159677.46
-16666,15665783.0,Onyeorulu,571.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,26920.43
-16667,15671934.0,Palermo,628.0,Germany,Male,32.0,7.0,128770.88,2.0,0.0,1.0,93713.92
-16668,15665460.0,Whitehead,731.0,France,Female,54.0,6.0,0.0,2.0,1.0,0.0,187905.12
-16669,15641490.0,Yudina,634.0,Spain,Female,30.0,4.0,0.0,2.0,1.0,1.0,176780.39
-16670,15809847.0,Yobanna,611.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,153064.82
-16671,15631800.0,Chiemenam,847.0,France,Female,27.0,9.0,0.0,2.0,1.0,1.0,167916.35
-16672,15634282.0,Chukwueloka,635.0,Germany,Male,40.0,1.0,107963.38,1.0,1.0,1.0,79775.19
-16673,15768887.0,Hs?,582.0,Germany,Female,46.0,1.0,119624.6,1.0,1.0,1.0,43889.41
-16674,15650018.0,Doyne,714.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,129912.74
-16675,15691792.0,Pinto,590.0,France,Male,40.0,4.0,124577.33,1.0,1.0,1.0,114448.77
-16676,15647842.0,Dancy,663.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,60775.71
-16677,15597951.0,Walker,535.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,88890.05
-16678,15777054.0,Macleod,774.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,175310.38
-16679,15749123.0,Chidiegwu,607.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,117622.8
-16680,15731781.0,Hargreaves,667.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,143980.29
-16681,15651389.0,Yefimova,711.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,88852.47
-16682,15781451.0,Ojiofor,611.0,Spain,Female,38.0,8.0,0.0,1.0,1.0,0.0,177524.19
-16683,15578251.0,Onyemauchechukwu,662.0,France,Male,45.0,5.0,0.0,2.0,1.0,1.0,56594.36
-16684,15571081.0,Ch'iu,679.0,Spain,Female,32.0,0.0,0.0,2.0,1.0,1.0,182775.08
-16685,15793871.0,Chuang,591.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,1.0,107825.08
-16686,15593468.0,Chiganu,775.0,France,Male,42.0,7.0,0.0,1.0,1.0,0.0,142513.5
-16687,15732832.0,Young,653.0,France,Male,37.0,1.0,0.0,2.0,0.0,0.0,86410.28
-16688,15696852.0,Hinton,613.0,Spain,Male,45.0,1.0,109127.54,1.0,0.0,1.0,137793.35
-16689,15612776.0,Obiajulu,706.0,Germany,Male,29.0,4.0,148962.46,1.0,1.0,1.0,88827.67
-16690,15763798.0,Pirozzi,681.0,France,Male,62.0,8.0,0.0,3.0,1.0,0.0,129891.66
-16691,15691785.0,Toscani,676.0,Germany,Female,32.0,7.0,139301.68,1.0,1.0,1.0,109626.03
-16692,15737240.0,O'Toole,712.0,France,Female,31.0,0.0,0.0,2.0,1.0,1.0,88724.92
-16693,15591286.0,Cameron,748.0,Germany,Female,28.0,9.0,147974.16,1.0,0.0,1.0,141896.74
-16694,15609899.0,Doyne,753.0,France,Female,28.0,0.0,0.0,2.0,1.0,0.0,123978.86
-16695,15770101.0,Chinwemma,662.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,158043.11
-16696,15761600.0,Young,593.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,67840.81
-16697,15702912.0,Hsia,627.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,0.0,86622.82
-16698,15737792.0,Brown,684.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,0.0,136985.08
-16699,15641250.0,Chukwudi,654.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,94767.77
-16700,15610343.0,Chukwujamuike,809.0,Spain,Female,43.0,1.0,0.0,2.0,1.0,1.0,115509.32
-16701,15795963.0,Ch'en,553.0,France,Female,46.0,6.0,0.0,1.0,1.0,0.0,93844.88
-16702,15645593.0,Lo,654.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,143049.72
-16703,15698932.0,Shih,741.0,France,Female,37.0,8.0,0.0,2.0,0.0,1.0,119712.78
-16704,15584518.0,L?,651.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,86575.46
-16705,15647570.0,Ch'iu,624.0,France,Male,26.0,6.0,0.0,1.0,0.0,0.0,86079.67
-16706,15672798.0,Hsieh,632.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,1.0,82276.62
-16707,15723250.0,Onyemauchechukwu,656.0,Germany,Male,42.0,9.0,132796.04,1.0,0.0,0.0,171552.02
-16708,15577514.0,Chizuoke,702.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,51721.52
-16709,15779744.0,Pisano,755.0,France,Female,28.0,5.0,0.0,2.0,1.0,0.0,81602.02
-16710,15691822.0,Onyekaozulu,721.0,Germany,Male,42.0,2.0,146329.57,2.0,1.0,1.0,148811.52
-16711,15672798.0,Macleod,509.0,Germany,Male,42.0,4.0,136525.99,1.0,1.0,0.0,176730.02
-16712,15773868.0,Hs?,801.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,0.0,140745.91
-16713,15651802.0,Ibeabuchi,607.0,Germany,Female,46.0,2.0,103421.49,1.0,1.0,1.0,86490.09
-16714,15698031.0,Chukwufumnanya,582.0,Germany,Male,28.0,3.0,121376.15,2.0,0.0,0.0,124874.23
-16715,15574554.0,Frolova,708.0,France,Female,45.0,2.0,99824.45,1.0,1.0,1.0,177747.72
-16716,15697042.0,T'ang,679.0,Spain,Male,42.0,7.0,0.0,2.0,1.0,0.0,127429.56
-16717,15756481.0,Nwebube,442.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,166511.94
-16718,15585580.0,L?,724.0,Spain,Male,45.0,0.0,0.0,1.0,1.0,0.0,171657.57
-16719,15693376.0,Chidubem,713.0,Spain,Female,49.0,4.0,125456.07,1.0,1.0,0.0,95611.47
-16720,15653715.0,Y?an,578.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,102747.02
-16721,15593795.0,Chinomso,779.0,France,Female,50.0,3.0,0.0,2.0,1.0,0.0,88729.22
-16722,15586976.0,Chukwudi,681.0,Spain,Male,38.0,5.0,0.0,1.0,1.0,0.0,134625.09
-16723,15778936.0,K?,732.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,60920.03
-16724,15654408.0,Lo,628.0,Germany,Female,34.0,3.0,160979.68,2.0,1.0,1.0,16081.62
-16725,15672481.0,Nwankwo,710.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,177613.19
-16726,15663410.0,Onyemachukwu,710.0,Spain,Male,33.0,7.0,0.0,2.0,0.0,1.0,65612.12
-16727,15716134.0,Calabresi,849.0,France,Male,50.0,2.0,70927.55,4.0,1.0,1.0,83090.35
-16728,15768777.0,Ch'ang,639.0,France,Female,43.0,4.0,88011.4,1.0,0.0,1.0,72993.65
-16729,15601184.0,Cremonesi,683.0,Germany,Female,37.0,7.0,141581.08,2.0,0.0,1.0,176843.53
-16730,15809302.0,Manna,607.0,France,Male,25.0,8.0,0.0,2.0,0.0,1.0,119087.25
-16731,15706764.0,Teng,694.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,93959.96
-16732,15586931.0,Ch'eng,687.0,Germany,Female,54.0,1.0,114212.6,1.0,0.0,1.0,19287.06
-16733,15706464.0,Chizuoke,674.0,Spain,Female,39.0,2.0,0.0,2.0,0.0,1.0,152499.8
-16734,15792228.0,Ch'in,714.0,France,Male,34.0,3.0,159732.02,1.0,0.0,0.0,149188.41
-16735,15586425.0,Dickson,547.0,Germany,Male,39.0,4.0,104430.07,2.0,0.0,0.0,178074.04
-16736,15746333.0,McGregor,655.0,Spain,Female,54.0,1.0,0.0,2.0,1.0,0.0,103379.31
-16737,15602797.0,Chukwuemeka,628.0,Germany,Male,35.0,9.0,117469.92,2.0,1.0,1.0,81482.7
-16738,15684319.0,Frolov,567.0,Spain,Female,30.0,2.0,0.0,2.0,1.0,1.0,161845.81
-16739,15751788.0,Lucchese,678.0,France,Female,40.0,5.0,0.0,3.0,0.0,1.0,126241.4
-16740,15648967.0,Ts'ao,676.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,1.0,74564.41
-16741,15731760.0,Nnaife,583.0,France,Male,35.0,1.0,0.0,2.0,0.0,0.0,119232.33
-16742,15588928.0,Onyeoruru,743.0,Spain,Female,45.0,8.0,0.0,1.0,1.0,1.0,160529.71
-16743,15680201.0,Maclean,526.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,136902.01
-16744,15697574.0,White,633.0,France,Male,30.0,8.0,0.0,1.0,1.0,0.0,90876.95
-16745,15762927.0,Chukwudi,806.0,France,Female,26.0,6.0,0.0,2.0,0.0,1.0,177747.72
-16746,15708534.0,Tsou,722.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,164886.64
-16747,15741988.0,Onyekaozulu,651.0,France,Male,33.0,7.0,0.0,2.0,0.0,1.0,175843.68
-16748,15785831.0,Lo,694.0,France,Male,45.0,9.0,0.0,2.0,1.0,0.0,129569.92
-16749,15641733.0,McWilliams,633.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,139469.11
-16750,15641655.0,Chukwujekwu,683.0,France,Male,29.0,1.0,0.0,2.0,1.0,1.0,60905.51
-16751,15765520.0,Maclean,531.0,Spain,Male,25.0,5.0,152582.3,2.0,1.0,0.0,88890.05
-16752,15668818.0,Chikelu,706.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,122964.18
-16753,15683375.0,Ch'iu,850.0,France,Male,31.0,0.0,0.0,2.0,1.0,0.0,1112.76
-16754,15583681.0,Hsiung,733.0,Germany,Male,35.0,10.0,141806.0,2.0,1.0,1.0,82222.86
-16755,15620507.0,Ch'ang,585.0,France,Female,27.0,7.0,0.0,2.0,1.0,0.0,126585.2
-16756,15667460.0,Ponomaryov,739.0,Spain,Male,33.0,7.0,180520.21,1.0,1.0,0.0,85522.84
-16757,15763665.0,Milne,632.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,172437.12
-16758,15652674.0,Chimaobim,774.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,19998.31
-16759,15718454.0,Ting,709.0,France,Female,44.0,5.0,0.0,2.0,1.0,1.0,145562.4
-16760,15780256.0,Hsueh,681.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,150720.59
-16761,15779059.0,Fanucci,657.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,171706.66
-16762,15796617.0,Manners,694.0,Germany,Male,31.0,8.0,108881.73,2.0,1.0,1.0,155905.79
-16763,15785290.0,Chien,682.0,France,Female,42.0,9.0,0.0,2.0,1.0,0.0,154598.58
-16764,15752534.0,Hs?,579.0,Germany,Male,49.0,2.0,137781.65,1.0,1.0,1.0,180800.66
-16765,15789339.0,Zetticci,732.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,109393.19
-16766,15662662.0,Efimov,613.0,Germany,Male,35.0,7.0,96652.86,2.0,1.0,0.0,105887.04
-16767,15573599.0,Chukwufumnanya,703.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,97938.59
-16768,15781542.0,Genovesi,544.0,France,Male,35.0,9.0,120124.35,1.0,1.0,0.0,9241.83
-16769,15799042.0,Palerma,639.0,Germany,Male,38.0,9.0,92889.91,2.0,1.0,0.0,53817.23
-16770,15641604.0,Tuan,656.0,Germany,Male,47.0,4.0,115024.5,1.0,1.0,1.0,182081.45
-16771,15768282.0,Mancini,733.0,France,Male,27.0,2.0,0.0,2.0,1.0,0.0,94450.97
-16772,15664772.0,Chiang,633.0,France,Female,31.0,5.0,0.0,1.0,1.0,0.0,164162.44
-16773,15679668.0,Chijindum,613.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,131919.48
-16774,15584545.0,Chijioke,680.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,121277.78
-16775,15674929.0,T'ao,733.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,0.0,124625.08
-16776,15571281.0,P'eng,709.0,France,Female,27.0,7.0,132512.26,1.0,0.0,1.0,86587.37
-16777,15645544.0,Maclean,645.0,France,Male,48.0,7.0,122314.5,2.0,1.0,1.0,6975.02
-16778,15575214.0,Onyekachi,669.0,France,Female,46.0,4.0,0.0,2.0,1.0,0.0,98820.09
-16779,15810444.0,Genovese,554.0,Spain,Male,34.0,1.0,0.0,2.0,0.0,1.0,102391.06
-16780,15585823.0,Shih,678.0,France,Male,35.0,5.0,0.0,1.0,0.0,0.0,111241.37
-16781,15763907.0,Lettiere,704.0,Germany,Female,33.0,6.0,111096.91,1.0,1.0,1.0,108469.2
-16782,15593900.0,Ch'in,683.0,Germany,Female,45.0,5.0,132932.14,1.0,0.0,1.0,163783.0
-16783,15671766.0,Hsieh,598.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,173599.38
-16784,15735339.0,Chiemenam,611.0,France,Male,50.0,1.0,0.0,2.0,1.0,1.0,167066.95
-16785,15784890.0,Ch'ien,754.0,France,Male,37.0,9.0,0.0,2.0,0.0,1.0,125961.48
-16786,15627665.0,T'ien,651.0,France,Male,31.0,3.0,122314.5,1.0,1.0,1.0,159840.51
-16787,15757425.0,Y?an,558.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,94790.34
-16788,15737542.0,Aparicio,755.0,Spain,Female,41.0,2.0,0.0,2.0,1.0,1.0,130610.78
-16789,15707567.0,Kisch,605.0,France,Female,42.0,4.0,0.0,1.0,1.0,0.0,136859.55
-16790,15797165.0,Hsiao,746.0,France,Male,34.0,6.0,0.0,1.0,1.0,1.0,91326.72
-16791,15684960.0,Ekechukwu,650.0,Spain,Female,25.0,2.0,0.0,2.0,0.0,1.0,182355.21
-16792,15681956.0,Chiu,751.0,Spain,Female,42.0,5.0,0.0,2.0,0.0,0.0,138524.31
-16793,15620398.0,Okwudilichukwu,712.0,France,Female,41.0,0.0,0.0,2.0,1.0,1.0,90162.35
-16794,15597951.0,Ch'in,663.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,123870.81
-16795,15575883.0,Ts'ao,653.0,France,Male,43.0,5.0,0.0,1.0,1.0,0.0,130853.03
-16796,15802462.0,Ewers,581.0,Germany,Male,29.0,7.0,131082.06,2.0,0.0,0.0,86044.98
-16797,15667438.0,Hs?,573.0,France,Male,38.0,4.0,0.0,2.0,0.0,0.0,124532.79
-16798,15743759.0,Chinweuba,567.0,Germany,Male,29.0,6.0,139979.06,1.0,0.0,1.0,172629.67
-16799,15736302.0,Ch'ang,635.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,87422.15
-16800,15591279.0,Ts'ai,447.0,France,Male,37.0,9.0,122084.63,1.0,1.0,0.0,155786.39
-16801,15699446.0,Whitehead,708.0,France,Female,33.0,4.0,116022.73,1.0,1.0,0.0,142400.27
-16802,15804787.0,Iadanza,582.0,France,Female,31.0,4.0,125879.29,1.0,1.0,1.0,161845.81
-16803,15761141.0,Ch'ang,752.0,France,Male,29.0,1.0,0.0,2.0,1.0,1.0,128880.23
-16804,15586069.0,Houghton,558.0,France,Male,53.0,4.0,0.0,2.0,0.0,1.0,102908.91
-16805,15730657.0,Zaytseva,572.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,173016.46
-16806,15580149.0,Tsao,554.0,France,Female,25.0,4.0,0.0,2.0,1.0,1.0,72462.25
-16807,15654025.0,Lai,549.0,France,Female,48.0,1.0,0.0,2.0,0.0,0.0,116957.6
-16808,15623478.0,Macdonald,850.0,France,Male,24.0,4.0,0.0,2.0,0.0,0.0,121681.19
-16809,15757981.0,Iadanza,705.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,175843.68
-16810,15762418.0,Ch'eng,705.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,0.0,81441.8
-16811,15689492.0,Y?,687.0,Spain,Female,24.0,1.0,0.0,2.0,1.0,1.0,114876.65
-16812,15732628.0,Chukwueloka,673.0,Spain,Female,24.0,8.0,0.0,1.0,1.0,1.0,129891.66
-16813,15600688.0,T'ang,644.0,Spain,Female,37.0,8.0,181166.44,1.0,1.0,0.0,162599.51
-16814,15680370.0,Nnonso,613.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,175314.87
-16815,15663438.0,Hargreaves,756.0,Germany,Male,34.0,7.0,90560.3,1.0,1.0,1.0,171045.3
-16816,15774586.0,Omeokachie,717.0,France,Male,32.0,2.0,132796.04,1.0,1.0,1.0,173365.89
-16817,15760244.0,Lettiere,727.0,France,Male,34.0,3.0,139729.3,1.0,1.0,0.0,162053.92
-16818,15682533.0,Obidimkpa,707.0,Germany,Female,43.0,8.0,123959.97,1.0,1.0,0.0,62700.08
-16819,15611058.0,Martin,627.0,Spain,Female,49.0,7.0,0.0,3.0,1.0,0.0,133059.15
-16820,15589431.0,Obidimkpa,684.0,France,Female,37.0,7.0,182705.05,1.0,1.0,0.0,79479.74
-16821,15771086.0,Nwora,582.0,France,Female,55.0,9.0,148884.47,1.0,1.0,0.0,147600.54
-16822,15581279.0,Ch'ang,594.0,Spain,Male,23.0,9.0,0.0,2.0,1.0,0.0,176921.0
-16823,15637644.0,Burtch,627.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,119327.75
-16824,15757811.0,Macleod,590.0,France,Female,46.0,8.0,0.0,1.0,1.0,0.0,178122.71
-16825,15634896.0,Greco,603.0,Germany,Male,30.0,10.0,161628.66,2.0,1.0,0.0,130446.65
-16826,15605791.0,Ch'ien,693.0,France,Female,35.0,4.0,0.0,2.0,0.0,1.0,177747.72
-16827,15682563.0,Linton,597.0,France,Male,28.0,0.0,0.0,2.0,1.0,1.0,6761.84
-16828,15625092.0,Bibi,634.0,France,Female,25.0,4.0,0.0,2.0,1.0,1.0,120853.83
-16829,15693737.0,Chu,657.0,Spain,Female,56.0,0.0,94598.48,1.0,1.0,0.0,164040.94
-16830,15705676.0,Onyemauchechi,605.0,France,Male,46.0,9.0,157761.56,1.0,1.0,1.0,111243.09
-16831,15739357.0,Hsiao,545.0,France,Male,52.0,2.0,0.0,1.0,0.0,0.0,105965.25
-16832,15751175.0,Chidumaga,671.0,Germany,Female,56.0,7.0,121898.82,1.0,1.0,1.0,63795.8
-16833,15656484.0,Niu,704.0,Germany,Male,44.0,1.0,123711.73,1.0,1.0,0.0,71460.68
-16834,15581548.0,Findlay,596.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,1.0,120830.73
-16835,15649733.0,Lucchese,753.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,72846.57
-16836,15589224.0,T'ien,535.0,France,Male,55.0,9.0,0.0,2.0,1.0,1.0,178341.33
-16837,15707968.0,Calabresi,674.0,France,Female,32.0,0.0,0.0,1.0,1.0,0.0,82614.89
-16838,15706587.0,Y?an,733.0,Germany,Female,35.0,5.0,121545.43,1.0,1.0,0.0,96832.82
-16839,15706232.0,P'eng,753.0,Germany,Male,69.0,0.0,95984.69,1.0,0.0,1.0,72927.71
-16840,15613141.0,Kerr,663.0,France,Female,57.0,1.0,0.0,1.0,1.0,0.0,170829.87
-16841,15728005.0,Ting,738.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,125877.22
-16842,15811389.0,Golovanov,707.0,Germany,Male,43.0,7.0,151839.26,1.0,0.0,1.0,96670.2
-16843,15782454.0,Ejimofor,635.0,Spain,Male,35.0,7.0,0.0,1.0,1.0,0.0,96524.11
-16844,15579714.0,Zikoranachidimma,669.0,Spain,Female,47.0,9.0,0.0,1.0,0.0,0.0,176883.42
-16845,15731555.0,Nwebube,716.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,103560.98
-16846,15773876.0,Oluchukwu,645.0,France,Female,48.0,4.0,143500.82,1.0,0.0,0.0,166677.35
-16847,15605072.0,Echezonachukwu,638.0,France,Male,71.0,6.0,0.0,1.0,1.0,1.0,62810.91
-16848,15641640.0,Chialuka,598.0,France,Female,46.0,9.0,0.0,1.0,1.0,1.0,81753.92
-16849,15607837.0,Ch'iu,683.0,Spain,Male,44.0,3.0,0.0,2.0,1.0,0.0,64375.4
-16850,15772383.0,Nnachetam,581.0,France,Female,71.0,8.0,0.0,1.0,0.0,0.0,141023.01
-16851,15777599.0,Chigolum,738.0,France,Female,37.0,8.0,0.0,2.0,0.0,0.0,183598.77
-16852,15777076.0,Scott,821.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,0.0,145562.4
-16853,15591036.0,McIntyre,721.0,Spain,Female,38.0,4.0,0.0,1.0,1.0,1.0,92357.21
-16854,15687558.0,Tsai,632.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,170634.49
-16855,15680141.0,Ibezimako,646.0,France,Female,35.0,1.0,0.0,2.0,1.0,1.0,26019.59
-16856,15633666.0,Ozerova,725.0,Spain,Female,49.0,8.0,0.0,2.0,1.0,0.0,159899.97
-16857,15649024.0,Aksakova,582.0,France,Male,43.0,1.0,0.0,1.0,1.0,0.0,58469.75
-16858,15651352.0,T'ao,730.0,Germany,Female,43.0,2.0,90721.52,1.0,1.0,0.0,164924.11
-16859,15762543.0,Chia,637.0,Spain,Male,39.0,2.0,0.0,2.0,1.0,1.0,72549.65
-16860,15658364.0,Chiu,677.0,France,Male,36.0,2.0,0.0,1.0,0.0,0.0,48228.1
-16861,15619360.0,Chidubem,594.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,1.0,174435.48
-16862,15607986.0,Chibuzo,592.0,France,Female,29.0,3.0,0.0,2.0,0.0,0.0,96792.15
-16863,15722404.0,Toscani,567.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,171733.22
-16864,15765643.0,Tseng,706.0,France,Male,36.0,7.0,0.0,1.0,1.0,1.0,68860.3
-16865,15582841.0,Milne,538.0,Spain,Male,33.0,1.0,113882.31,1.0,1.0,1.0,50759.4
-16866,15744044.0,Obijiaku,590.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,5791.85
-16867,15612507.0,Bledsoe,656.0,France,Female,33.0,5.0,0.0,2.0,0.0,0.0,55775.84
-16868,15779985.0,Ch'eng,521.0,Spain,Male,40.0,4.0,122314.39,1.0,0.0,0.0,168834.39
-16869,15667438.0,Hsieh,545.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,82868.34
-16870,15690670.0,Pagnotto,602.0,France,Male,41.0,0.0,0.0,1.0,1.0,1.0,128881.87
-16871,15713644.0,Onyemauchechukwu,632.0,Germany,Female,41.0,4.0,105837.86,1.0,0.0,1.0,96875.52
-16872,15803378.0,Zuyeva,593.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,57380.13
-16873,15804075.0,Nucci,691.0,Spain,Female,30.0,9.0,84906.45,2.0,1.0,0.0,59887.15
-16874,15615126.0,Nkemjika,684.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,182765.14
-16875,15585892.0,Chizuoke,686.0,France,Male,48.0,5.0,0.0,1.0,1.0,0.0,51548.14
-16876,15752650.0,Ibezimako,599.0,France,Male,35.0,10.0,0.0,2.0,1.0,1.0,96985.58
-16877,15665460.0,Yobachi,811.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,147600.54
-16878,15728706.0,Olisaemeka,775.0,France,Male,32.0,3.0,0.0,2.0,0.0,1.0,166031.08
-16879,15812184.0,H?,724.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,77550.18
-16880,15693737.0,Teng,659.0,France,Male,37.0,9.0,0.0,1.0,0.0,1.0,139919.38
-16881,15661409.0,Zikoranachidimma,543.0,France,Female,33.0,1.0,0.0,2.0,1.0,0.0,186942.49
-16882,15800515.0,Chidozie,564.0,France,Female,36.0,10.0,0.0,2.0,1.0,1.0,60420.07
-16883,15702715.0,Mazzanti,593.0,France,Male,37.0,8.0,0.0,1.0,1.0,1.0,157937.35
-16884,15761694.0,Mazzi,633.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,118913.53
-16885,15573741.0,Ijendu,624.0,Spain,Female,34.0,1.0,0.0,2.0,1.0,0.0,144290.97
-16886,15627212.0,Ponce,614.0,Spain,Male,31.0,3.0,118829.03,2.0,1.0,1.0,144880.81
-16887,15736413.0,Onyemere,438.0,France,Male,40.0,8.0,0.0,1.0,0.0,1.0,143783.46
-16888,15654700.0,Y?,645.0,France,Male,31.0,7.0,0.0,2.0,0.0,1.0,151336.96
-16889,15675360.0,Liao,683.0,Germany,Female,36.0,7.0,104498.79,2.0,1.0,0.0,75927.35
-16890,15711889.0,Yuriev,587.0,France,Female,43.0,6.0,181821.96,1.0,1.0,1.0,103480.69
-16891,15713346.0,T'ao,637.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,170518.83
-16892,15640491.0,Y?,651.0,France,Female,39.0,10.0,0.0,2.0,1.0,0.0,126153.24
-16893,15671172.0,Chukwuemeka,648.0,Spain,Female,43.0,0.0,0.0,2.0,1.0,0.0,61290.99
-16894,15740223.0,Okonkwo,583.0,Germany,Male,35.0,9.0,135919.33,2.0,1.0,1.0,16491.64
-16895,15722692.0,Hsing,696.0,France,Female,26.0,6.0,0.0,1.0,1.0,1.0,63795.8
-16896,15593795.0,Okwuadigbo,775.0,Spain,Male,41.0,5.0,0.0,1.0,1.0,1.0,132038.65
-16897,15654760.0,Calabresi,431.0,Spain,Female,37.0,2.0,102870.93,2.0,1.0,1.0,167984.61
-16898,15571917.0,Ch'eng,743.0,France,Male,28.0,10.0,61297.05,1.0,1.0,0.0,174800.31
-16899,15615016.0,Lucciano,675.0,France,Male,31.0,9.0,134740.19,1.0,1.0,0.0,175795.87
-16900,15768887.0,Cawood,587.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,0.0,106040.97
-16901,15617078.0,Toscani,850.0,Spain,Female,24.0,2.0,0.0,2.0,1.0,0.0,50771.16
-16902,15699467.0,Nebechi,679.0,France,Female,51.0,7.0,0.0,1.0,0.0,0.0,131625.14
-16903,15668893.0,Chukwufumnanya,607.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,182988.94
-16904,15725028.0,Chidumaga,590.0,France,Female,42.0,3.0,0.0,1.0,1.0,1.0,56984.58
-16905,15777892.0,Young,685.0,France,Male,26.0,2.0,0.0,2.0,1.0,0.0,152290.28
-16906,15689955.0,Chimezie,597.0,France,Male,40.0,5.0,143637.58,1.0,1.0,1.0,103557.85
-16907,15656300.0,Ch'in,659.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,126192.54
-16908,15642835.0,P'eng,686.0,France,Female,36.0,8.0,0.0,2.0,1.0,0.0,92547.01
-16909,15696733.0,Fanucci,686.0,France,Male,27.0,7.0,0.0,2.0,0.0,1.0,138350.74
-16910,15752578.0,Chukwuhaenye,678.0,France,Male,31.0,4.0,0.0,2.0,0.0,1.0,141798.98
-16911,15586522.0,Liao,595.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,124489.88
-16912,15682472.0,Lucchesi,630.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,131283.6
-16913,15735246.0,Oguejiofor,686.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,141031.01
-16914,15708650.0,Ndubueze,684.0,France,Male,33.0,9.0,0.0,2.0,0.0,0.0,59195.05
-16915,15711299.0,Ugoji,671.0,Spain,Female,26.0,5.0,0.0,1.0,1.0,0.0,116537.96
-16916,15789941.0,Ts'ui,581.0,France,Male,26.0,0.0,0.0,2.0,1.0,0.0,163471.01
-16917,15686588.0,Cremonesi,850.0,Spain,Male,28.0,7.0,0.0,1.0,0.0,0.0,48849.89
-16918,15671148.0,Ponomaryov,613.0,Germany,Male,43.0,10.0,123793.18,1.0,0.0,0.0,134625.09
-16919,15785869.0,Ch'ang,667.0,France,Female,36.0,10.0,0.0,2.0,1.0,1.0,140941.47
-16920,15815560.0,Chidozie,740.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,131259.6
-16921,15704910.0,Genovesi,632.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,598.8
-16922,15793425.0,Ch'ang,648.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,132450.18
-16923,15773042.0,P'an,754.0,France,Male,32.0,3.0,0.0,1.0,1.0,0.0,142903.44
-16924,15718281.0,Hs?eh,662.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,79919.97
-16925,15690796.0,Ni,729.0,France,Male,31.0,1.0,0.0,1.0,0.0,1.0,156906.15
-16926,15727291.0,Ts'ui,697.0,Spain,Male,35.0,10.0,0.0,1.0,1.0,1.0,38962.94
-16927,15803406.0,Okwudiliolisa,716.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,0.0,113428.77
-16928,15770214.0,Chin,705.0,Spain,Male,49.0,3.0,0.0,1.0,1.0,1.0,24113.91
-16929,15803801.0,Chuang,613.0,France,Female,44.0,5.0,91286.51,1.0,1.0,0.0,181270.84
-16930,15594576.0,T'ien,718.0,France,Male,34.0,1.0,0.0,2.0,1.0,1.0,133537.53
-16931,15726289.0,Ch'ang,565.0,France,Female,48.0,2.0,0.0,1.0,0.0,1.0,105986.01
-16932,15681554.0,Mazzanti,646.0,France,Female,38.0,9.0,0.0,1.0,1.0,1.0,118570.56
-16933,15772601.0,Calabrese,573.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,64593.04
-16934,15762048.0,Yefimova,589.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,61164.45
-16935,15808930.0,O'Donnell,662.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,78670.46
-16936,15699340.0,Hsueh,705.0,France,Male,55.0,3.0,129968.67,1.0,1.0,0.0,174918.46
-16937,15666884.0,Chinedum,572.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,101311.95
-16938,15773098.0,Ukaegbunam,679.0,France,Female,22.0,6.0,0.0,2.0,1.0,1.0,140780.8
-16939,15763874.0,Chibugo,687.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,162501.83
-16940,15664339.0,Ibezimako,516.0,Germany,Female,33.0,8.0,133040.32,1.0,1.0,0.0,52484.01
-16941,15589323.0,Yeh,637.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,9087.81
-16942,15689633.0,Tien,709.0,France,Female,38.0,8.0,0.0,1.0,1.0,1.0,83342.82
-16943,15756475.0,Yeh,719.0,Germany,Male,55.0,9.0,107812.57,1.0,1.0,0.0,142223.94
-16944,15773972.0,Genovese,656.0,Spain,Male,36.0,6.0,0.0,2.0,0.0,0.0,86587.37
-16945,15636023.0,Ts'ao,557.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,0.0,177624.01
-16946,15678058.0,Lu,749.0,Germany,Male,41.0,1.0,116662.96,2.0,0.0,1.0,93959.96
-16947,15777893.0,Dyer,602.0,France,Male,33.0,9.0,0.0,1.0,1.0,1.0,124954.58
-16948,15700540.0,Onyekachi,664.0,Spain,Male,34.0,5.0,0.0,1.0,1.0,1.0,49374.82
-16949,15642579.0,Ch'ang,585.0,France,Male,54.0,1.0,0.0,3.0,1.0,0.0,176779.46
-16950,15593683.0,Genovese,627.0,France,Female,29.0,7.0,113000.92,1.0,1.0,0.0,77977.69
-16951,15663625.0,Hughes,719.0,Germany,Female,51.0,0.0,132730.17,1.0,1.0,0.0,161889.84
-16952,15594409.0,Walker,757.0,Spain,Male,38.0,8.0,125822.21,1.0,1.0,1.0,143628.31
-16953,15751491.0,Yobanna,749.0,France,Female,38.0,9.0,0.0,2.0,0.0,0.0,48606.92
-16954,15740406.0,Chigozie,724.0,France,Female,67.0,6.0,0.0,2.0,1.0,1.0,131301.74
-16955,15768474.0,Ch'en,731.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,104889.2
-16956,15672238.0,Zikoranachidimma,744.0,France,Male,30.0,4.0,0.0,1.0,1.0,0.0,71450.96
-16957,15733581.0,Rizzo,744.0,France,Female,46.0,9.0,0.0,2.0,1.0,0.0,90537.97
-16958,15663883.0,Hsia,705.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,126192.54
-16959,15689351.0,Nwachinemelu,651.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,117834.92
-16960,15752622.0,Nwora,554.0,Spain,Male,72.0,1.0,0.0,2.0,1.0,1.0,128875.86
-16961,15775750.0,Ch'ien,741.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,117523.41
-16962,15793324.0,Fisk,738.0,Spain,Female,33.0,8.0,0.0,2.0,0.0,0.0,141426.78
-16963,15756941.0,Chiang,636.0,France,Female,34.0,7.0,0.0,2.0,0.0,0.0,109742.55
-16964,15758056.0,Cattaneo,575.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,0.0,27758.36
-16965,15792868.0,Chiu,579.0,France,Female,28.0,0.0,0.0,2.0,0.0,1.0,59864.96
-16966,15756875.0,Hargreaves,683.0,France,Female,54.0,8.0,0.0,1.0,1.0,0.0,96528.0
-16967,15661745.0,Genovesi,684.0,France,Male,62.0,4.0,0.0,2.0,0.0,1.0,131097.9
-16968,15737571.0,Hsiung,714.0,Germany,Female,46.0,8.0,97588.6,1.0,1.0,0.0,125979.36
-16969,15778418.0,Chidimma,679.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,45909.87
-16970,15642063.0,Ting,769.0,France,Male,29.0,8.0,0.0,2.0,0.0,0.0,44390.38
-16971,15768387.0,Ekechukwu,643.0,Germany,Male,46.0,1.0,108698.96,1.0,0.0,1.0,116471.43
-16972,15807593.0,Rubeo,544.0,France,Female,47.0,1.0,0.0,1.0,1.0,0.0,102655.31
-16973,15653773.0,Maclean,683.0,Spain,Female,64.0,9.0,70655.48,1.0,1.0,1.0,161579.85
-16974,15602929.0,Maclean,758.0,France,Female,40.0,1.0,0.0,2.0,0.0,0.0,143534.28
-16975,15629338.0,Chu,590.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,86699.98
-16976,15687431.0,Brown,468.0,Germany,Female,29.0,6.0,138089.97,2.0,1.0,0.0,85746.82
-16977,15585067.0,Hs?,591.0,France,Female,29.0,7.0,109346.13,2.0,0.0,0.0,148564.76
-16978,15747168.0,Hs?,628.0,France,Female,36.0,7.0,0.0,1.0,1.0,1.0,72873.33
-16979,15593973.0,White,611.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,130514.78
-16980,15598084.0,Mistry,792.0,Spain,Male,50.0,8.0,143964.36,1.0,1.0,0.0,97623.42
-16981,15672374.0,Kao,529.0,France,Male,47.0,7.0,0.0,1.0,0.0,0.0,142223.94
-16982,15802625.0,Smith,574.0,France,Male,56.0,4.0,152265.43,1.0,1.0,0.0,116696.77
-16983,15652658.0,Ch'ang,716.0,France,Female,55.0,5.0,0.0,1.0,1.0,0.0,171706.66
-16984,15684801.0,Yoo,691.0,France,Female,40.0,2.0,0.0,2.0,0.0,1.0,180002.42
-16985,15747808.0,Ozerova,663.0,Spain,Female,35.0,9.0,0.0,2.0,1.0,1.0,61977.82
-16986,15638231.0,Y?an,729.0,Spain,Male,31.0,10.0,0.0,2.0,1.0,1.0,65816.53
-16987,15610686.0,K'ung,678.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,48988.86
-16988,15777315.0,Ch'ang,633.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,112928.74
-16989,15640034.0,Chinwendu,564.0,France,Female,43.0,5.0,142405.52,1.0,1.0,0.0,18082.06
-16990,15697035.0,Pirozzi,590.0,Spain,Male,38.0,10.0,0.0,2.0,0.0,1.0,146652.01
-16991,15732266.0,Bogle,587.0,Germany,Male,32.0,2.0,121093.44,2.0,1.0,1.0,153776.6
-16992,15640865.0,Chidozie,728.0,France,Female,42.0,5.0,132603.36,1.0,1.0,1.0,72350.47
-16993,15646936.0,T'ien,741.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,183515.12
-16994,15664802.0,Chinweike,591.0,France,Male,44.0,8.0,0.0,2.0,1.0,0.0,90967.48
-16995,15768282.0,Kao,703.0,Spain,Male,36.0,6.0,0.0,2.0,0.0,0.0,122810.53
-16996,15572022.0,Chin,725.0,France,Male,36.0,6.0,0.0,2.0,0.0,1.0,132649.85
-16997,15804864.0,Gell,650.0,Germany,Male,32.0,4.0,105245.34,2.0,1.0,0.0,158917.83
-16998,15705953.0,Udinese,681.0,France,Male,56.0,6.0,0.0,1.0,0.0,1.0,93833.38
-16999,15806486.0,Ch'ang,659.0,Spain,Female,34.0,1.0,0.0,2.0,1.0,0.0,189175.71
-17000,15781674.0,Nwokezuike,553.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,194998.34
-17001,15793070.0,Nwora,695.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,1.0,166370.81
-17002,15585287.0,Chinedum,705.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,176925.69
-17003,15625641.0,Ch'eng,632.0,Germany,Male,46.0,2.0,93522.3,1.0,1.0,1.0,124571.09
-17004,15732963.0,Kuznetsova,596.0,France,Male,35.0,9.0,0.0,1.0,1.0,1.0,54117.03
-17005,15757821.0,Udinese,724.0,France,Female,38.0,7.0,152324.66,1.0,0.0,0.0,151623.71
-17006,15655436.0,Iloerika,643.0,Spain,Male,32.0,8.0,0.0,1.0,1.0,1.0,92888.52
-17007,15764405.0,Gboliwe,678.0,France,Female,36.0,8.0,0.0,1.0,1.0,0.0,155061.97
-17008,15608541.0,Hsueh,648.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,131389.39
-17009,15634373.0,Nkemakonam,669.0,Spain,Male,44.0,2.0,0.0,1.0,1.0,0.0,172586.89
-17010,15791767.0,Nnaife,651.0,Germany,Male,37.0,9.0,118082.89,2.0,1.0,0.0,140197.42
-17011,15775116.0,Yefremova,521.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,0.0,56908.41
-17012,15679963.0,Smith,642.0,Spain,Female,36.0,1.0,0.0,2.0,0.0,1.0,156792.89
-17013,15628992.0,Tseng,690.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,161559.12
-17014,15583748.0,Chikwado,579.0,Germany,Male,42.0,9.0,93680.43,1.0,1.0,0.0,49667.28
-17015,15636595.0,Mironova,738.0,Germany,Female,39.0,6.0,107949.35,1.0,1.0,0.0,148814.54
-17016,15777639.0,Clements,697.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,161671.15
-17017,15682070.0,Fanucci,659.0,France,Female,35.0,8.0,133736.04,1.0,1.0,0.0,178028.96
-17018,15795895.0,Fiorentini,542.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,141756.32
-17019,15604792.0,Aitken,662.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,161479.19
-17020,15694404.0,Chidimma,662.0,France,Female,35.0,5.0,160838.13,1.0,1.0,1.0,27509.52
-17021,15589210.0,H?,620.0,France,Male,33.0,1.0,111739.4,1.0,1.0,1.0,153064.87
-17022,15654877.0,Lo,597.0,Germany,Female,37.0,8.0,131853.13,2.0,1.0,1.0,88703.04
-17023,15638355.0,L?,562.0,Germany,Female,48.0,4.0,114421.55,1.0,1.0,0.0,100727.68
-17024,15670080.0,Onyemauchechukwu,716.0,France,Female,35.0,1.0,0.0,2.0,1.0,1.0,166650.8
-17025,15663820.0,Milne,554.0,France,Female,38.0,9.0,0.0,2.0,0.0,1.0,111243.09
-17026,15695078.0,Chinagorom,569.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,57553.02
-17027,15681075.0,Chidimma,821.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,81251.24
-17028,15772067.0,Milani,656.0,Germany,Male,55.0,6.0,132906.88,1.0,0.0,1.0,127413.0
-17029,15696744.0,Tuan,689.0,France,Female,28.0,8.0,0.0,2.0,0.0,0.0,131363.57
-17030,15613180.0,Chukwudi,607.0,Spain,Male,31.0,3.0,0.0,2.0,1.0,1.0,133653.38
-17031,15616471.0,Hs?,692.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,130260.11
-17032,15686936.0,Obiajulu,521.0,Germany,Male,45.0,1.0,116520.73,1.0,0.0,1.0,85470.45
-17033,15751158.0,H?,589.0,France,Male,45.0,4.0,0.0,2.0,1.0,1.0,80552.36
-17034,15800524.0,Onyeoruru,677.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,86043.27
-17035,15644878.0,Hsing,539.0,Spain,Female,36.0,8.0,0.0,2.0,1.0,1.0,129936.53
-17036,15626794.0,Aitken,751.0,France,Male,38.0,7.0,0.0,2.0,0.0,0.0,119419.37
-17037,15698928.0,Nnamutaezinwa,593.0,Spain,Male,23.0,9.0,0.0,2.0,1.0,0.0,133871.83
-17038,15792388.0,Nwora,711.0,France,Male,26.0,7.0,121729.42,1.0,1.0,1.0,69643.31
-17039,15775490.0,Shao,622.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,153605.75
-17040,15592854.0,Chidumaga,526.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,111096.97
-17041,15732640.0,Yefimova,703.0,Spain,Female,28.0,7.0,0.0,2.0,1.0,1.0,140168.36
-17042,15697035.0,Mazzanti,576.0,Germany,Male,54.0,7.0,137811.75,1.0,1.0,0.0,141501.45
-17043,15641877.0,Ch'iu,778.0,France,Female,28.0,9.0,0.0,1.0,1.0,1.0,168816.38
-17044,15754526.0,Ch'ang,569.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,88669.89
-17045,15605622.0,Chu,637.0,France,Female,31.0,8.0,0.0,1.0,1.0,0.0,69409.47
-17046,15592994.0,Yermakova,643.0,Germany,Male,47.0,8.0,115465.98,1.0,0.0,1.0,172749.65
-17047,15604576.0,Li Fonti,658.0,France,Male,29.0,10.0,0.0,2.0,1.0,0.0,174969.94
-17048,15688865.0,H?,679.0,France,Male,38.0,3.0,0.0,1.0,1.0,1.0,132034.13
-17049,15737605.0,Trevisani,645.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,143613.74
-17050,15792029.0,Aksenov,579.0,Germany,Male,39.0,10.0,133970.22,2.0,1.0,1.0,185543.67
-17051,15800784.0,Chidiebele,675.0,France,Female,47.0,10.0,0.0,1.0,1.0,0.0,146680.86
-17052,15744983.0,Tsai,697.0,France,Female,34.0,3.0,131691.33,1.0,0.0,0.0,82868.33
-17053,15687310.0,Nwachinemelu,696.0,Spain,Male,52.0,3.0,0.0,1.0,1.0,0.0,142763.51
-17054,15589948.0,Ch'iu,591.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,149458.81
-17055,15740283.0,Chiu,611.0,France,Male,39.0,0.0,0.0,2.0,1.0,0.0,28123.99
-17056,15646921.0,Hsu,684.0,France,Male,35.0,9.0,117866.92,1.0,1.0,0.0,66580.13
-17057,15651389.0,Kao,602.0,Spain,Female,27.0,7.0,0.0,2.0,1.0,0.0,95500.98
-17058,15648858.0,Macleod,590.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,143835.52
-17059,15579892.0,Hao,715.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,123954.22
-17060,15628303.0,T'ien,543.0,Spain,Female,47.0,7.0,0.0,2.0,1.0,0.0,131772.51
-17061,15697948.0,Milne,646.0,Germany,Male,56.0,0.0,123575.51,1.0,1.0,0.0,125334.16
-17062,15691627.0,De Luca,653.0,France,Female,31.0,6.0,0.0,2.0,1.0,1.0,54865.92
-17063,15575581.0,Frolova,479.0,Germany,Female,22.0,3.0,124619.33,2.0,1.0,0.0,131764.52
-17064,15682563.0,Yevdokimova,439.0,France,Female,42.0,3.0,167735.69,1.0,1.0,1.0,129050.67
-17065,15771383.0,Hsueh,636.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,0.0,161807.21
-17066,15591036.0,Udinese,549.0,France,Female,46.0,10.0,0.0,1.0,0.0,1.0,138880.24
-17067,15686963.0,Uspensky,676.0,France,Female,40.0,8.0,0.0,1.0,1.0,0.0,169920.92
-17068,15613282.0,Pirozzi,692.0,Spain,Female,53.0,8.0,93680.43,1.0,1.0,1.0,64771.61
-17069,15733387.0,Ch'en,717.0,Germany,Female,39.0,9.0,123825.56,2.0,0.0,0.0,158049.9
-17070,15779627.0,Ch'in,577.0,Germany,Female,35.0,6.0,117337.54,1.0,1.0,0.0,96833.0
-17071,15800845.0,DeRose,587.0,Spain,Male,48.0,3.0,0.0,1.0,0.0,0.0,171774.5
-17072,15776467.0,Bibi,635.0,France,Female,33.0,6.0,0.0,2.0,0.0,0.0,67830.2
-17073,15716357.0,Ritchie,637.0,France,Male,41.0,2.0,0.0,2.0,0.0,1.0,140937.91
-17074,15722404.0,Chizoba,682.0,Germany,Female,35.0,2.0,95698.79,1.0,1.0,1.0,123475.78
-17075,15686917.0,Chukwujamuike,649.0,Spain,Male,36.0,7.0,118962.58,1.0,1.0,0.0,58320.06
-17076,15690670.0,Kao,757.0,France,Female,30.0,0.0,0.0,2.0,1.0,0.0,185541.2
-17077,15637178.0,Chigbogu,628.0,Germany,Male,41.0,4.0,95826.49,1.0,1.0,0.0,126192.54
-17078,15589257.0,Chineze,611.0,Germany,Female,34.0,7.0,172646.82,1.0,0.0,0.0,121555.51
-17079,15630106.0,Harker,676.0,Spain,Female,34.0,4.0,0.0,1.0,0.0,0.0,181419.25
-17080,15585324.0,Chibugo,733.0,Germany,Female,31.0,2.0,137841.53,1.0,1.0,1.0,182784.11
-17081,15802390.0,Ozioma,681.0,France,Female,31.0,6.0,0.0,2.0,0.0,0.0,142547.58
-17082,15641411.0,Li Fonti,656.0,Spain,Male,39.0,10.0,0.0,2.0,0.0,0.0,158958.2
-17083,15571928.0,Okechukwu,828.0,France,Male,34.0,8.0,0.0,1.0,1.0,1.0,116972.91
-17084,15596962.0,Hsia,695.0,Spain,Male,39.0,9.0,0.0,1.0,1.0,0.0,145562.4
-17085,15692583.0,Chiganu,687.0,France,Female,47.0,8.0,0.0,1.0,1.0,0.0,188486.94
-17086,15776631.0,Manna,645.0,Spain,Male,44.0,7.0,0.0,2.0,0.0,1.0,131919.48
-17087,15711251.0,Ch'in,598.0,Spain,Female,33.0,6.0,0.0,1.0,1.0,1.0,88920.68
-17088,15574761.0,P'eng,679.0,France,Female,28.0,4.0,0.0,2.0,1.0,1.0,177740.58
-17089,15601184.0,Yermakova,613.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,57898.58
-17090,15615091.0,Lucchese,738.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,1.0,161579.85
-17091,15705352.0,Ch'eng,635.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,180932.46
-17092,15659937.0,Chidimma,725.0,Spain,Male,43.0,4.0,0.0,1.0,1.0,1.0,108469.2
-17093,15587581.0,Ch'iu,727.0,Spain,Male,31.0,6.0,123845.36,1.0,1.0,1.0,71672.86
-17094,15622494.0,Lo,705.0,France,Male,38.0,8.0,161592.76,1.0,0.0,1.0,197111.99
-17095,15794493.0,Y?an,662.0,France,Female,45.0,0.0,0.0,1.0,1.0,0.0,177065.24
-17096,15593694.0,Hsia,592.0,Germany,Male,39.0,9.0,114422.85,1.0,1.0,0.0,118024.1
-17097,15726153.0,Pinto,648.0,Germany,Female,46.0,4.0,117438.92,2.0,1.0,1.0,116086.22
-17098,15767391.0,Chukwuemeka,684.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,82662.73
-17099,15653773.0,Ch'ang,704.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,79450.61
-17100,15739292.0,Ulyanova,479.0,Germany,Female,42.0,2.0,132436.86,1.0,0.0,1.0,180932.46
-17101,15788367.0,Ch'in,687.0,Germany,Male,39.0,4.0,125845.66,2.0,1.0,0.0,182650.15
-17102,15640377.0,Fanucci,649.0,Germany,Male,45.0,5.0,142987.46,1.0,1.0,0.0,61513.87
-17103,15768451.0,Chiu,706.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,0.0,129964.94
-17104,15685320.0,Lucciano,683.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,170968.99
-17105,15623107.0,McIntosh,648.0,France,Male,25.0,5.0,0.0,1.0,1.0,0.0,133518.76
-17106,15576774.0,Lo Duca,694.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,79577.4
-17107,15591698.0,Ozerova,845.0,France,Male,44.0,7.0,108269.2,1.0,1.0,1.0,55816.4
-17108,15768455.0,Toscani,835.0,Germany,Male,40.0,7.0,132351.29,2.0,1.0,0.0,88592.8
-17109,15663421.0,Okwukwe,730.0,France,Female,40.0,7.0,0.0,2.0,1.0,1.0,147040.25
-17110,15737104.0,Ugorji,548.0,Spain,Male,31.0,10.0,0.0,2.0,1.0,1.0,121316.07
-17111,15665590.0,Ts'ai,591.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,100941.57
-17112,15583681.0,Fomin,648.0,France,Male,38.0,6.0,0.0,1.0,1.0,1.0,187943.6
-17113,15782530.0,Chukwudi,766.0,France,Female,32.0,10.0,108465.65,1.0,1.0,0.0,187905.12
-17114,15583552.0,H?,661.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,118959.64
-17115,15585066.0,Chukwujekwu,611.0,France,Male,45.0,2.0,101514.89,1.0,1.0,1.0,91514.62
-17116,15772073.0,Mazzanti,633.0,Spain,Female,31.0,2.0,0.0,1.0,1.0,0.0,151843.54
-17117,15793425.0,Chukwujamuike,712.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,167784.28
-17118,15787907.0,Hsiung,585.0,Germany,Female,31.0,6.0,111094.05,1.0,0.0,0.0,122580.48
-17119,15656319.0,Yobanna,773.0,France,Female,42.0,8.0,0.0,2.0,0.0,1.0,163584.29
-17120,15727703.0,Ch'in,584.0,France,Male,30.0,1.0,0.0,1.0,1.0,0.0,70810.6
-17121,15654700.0,Hsueh,627.0,Germany,Male,29.0,3.0,135923.11,2.0,1.0,1.0,127439.16
-17122,15772632.0,Goliwe,691.0,France,Male,43.0,7.0,0.0,1.0,0.0,0.0,19370.73
-17123,15626793.0,Ch'eng,722.0,Spain,Female,38.0,2.0,168066.87,1.0,0.0,0.0,141822.5
-17124,15682757.0,De Salis,708.0,France,Female,56.0,6.0,0.0,1.0,0.0,0.0,103931.49
-17125,15635367.0,Hs?,850.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,127095.14
-17126,15648800.0,Pagnotto,593.0,France,Male,34.0,3.0,0.0,1.0,1.0,0.0,119313.74
-17127,15802088.0,Yuryeva,806.0,France,Male,26.0,8.0,151223.59,1.0,0.0,0.0,163558.42
-17128,15761364.0,Manna,549.0,Germany,Male,49.0,2.0,141642.92,1.0,1.0,0.0,51565.99
-17129,15761717.0,Chia,691.0,France,Male,41.0,1.0,0.0,2.0,1.0,0.0,122996.76
-17130,15722692.0,Kaeppel,635.0,Germany,Female,28.0,3.0,142987.46,1.0,1.0,0.0,126464.94
-17131,15766427.0,Shih,583.0,France,Male,24.0,7.0,128940.24,1.0,1.0,0.0,119903.2
-17132,15776936.0,T'ien,724.0,France,Male,49.0,8.0,0.0,2.0,1.0,0.0,63889.1
-17133,15802758.0,Chiang,553.0,France,Female,32.0,9.0,142905.51,1.0,1.0,1.0,53817.23
-17134,15608968.0,Chukwudi,641.0,Spain,Male,34.0,0.0,0.0,1.0,1.0,0.0,82526.92
-17135,15680370.0,Chukwuhaenye,644.0,Spain,Female,37.0,6.0,121411.9,1.0,1.0,0.0,147075.69
-17136,15724444.0,Chukwumaobim,712.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,158527.55
-17137,15708063.0,P'eng,628.0,France,Male,32.0,8.0,140553.46,1.0,1.0,0.0,94483.06
-17138,15650098.0,Eluemuno,489.0,Germany,Female,60.0,10.0,121151.05,1.0,1.0,0.0,164117.69
-17139,15583681.0,Mackay,639.0,France,Male,41.0,1.0,80931.75,1.0,0.0,0.0,148814.54
-17140,15638463.0,Chukwuemeka,839.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,123954.15
-17141,15761047.0,H?,675.0,Germany,Female,31.0,4.0,123374.46,1.0,1.0,0.0,18826.06
-17142,15687558.0,Ukaegbunam,685.0,France,Female,43.0,5.0,0.0,2.0,1.0,0.0,132418.59
-17143,15691792.0,Ch'iu,678.0,Germany,Male,39.0,3.0,103421.49,1.0,0.0,0.0,162719.69
-17144,15605059.0,Walker,637.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,91830.75
-17145,15603683.0,Fanucci,709.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,1.0,151344.09
-17146,15671534.0,Lettiere,574.0,France,Male,43.0,3.0,0.0,1.0,1.0,1.0,79240.9
-17147,15723481.0,Fomina,595.0,France,Male,62.0,2.0,0.0,2.0,1.0,1.0,147055.87
-17148,15684930.0,K'ung,634.0,France,Female,35.0,5.0,127290.61,1.0,1.0,1.0,187988.01
-17149,15700826.0,Okwudilichukwu,699.0,France,Female,38.0,4.0,97560.16,1.0,1.0,1.0,128528.83
-17150,15696564.0,Tobeolisa,589.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,140947.05
-17151,15576680.0,Yocum,673.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,1.0,67599.69
-17152,15694300.0,Milanesi,695.0,France,Male,44.0,8.0,0.0,2.0,0.0,1.0,89524.83
-17153,15717489.0,Fisk,694.0,Spain,Female,30.0,4.0,0.0,2.0,1.0,0.0,149535.72
-17154,15802486.0,Zikoranachidimma,558.0,France,Male,40.0,7.0,0.0,2.0,0.0,0.0,74838.64
-17155,15726167.0,Manna,624.0,France,Male,35.0,3.0,0.0,2.0,0.0,1.0,187597.98
-17156,15727197.0,Nwachukwu,681.0,Germany,Male,35.0,6.0,131341.46,1.0,0.0,0.0,179258.68
-17157,15807961.0,Brown,721.0,Germany,Female,33.0,7.0,133658.24,2.0,1.0,0.0,100538.8
-17158,15734365.0,Chiu,632.0,Spain,Male,38.0,5.0,0.0,1.0,0.0,0.0,85411.01
-17159,15691910.0,Hsing,685.0,Germany,Male,33.0,7.0,151675.65,1.0,1.0,1.0,123954.15
-17160,15793854.0,Onyemauchechukwu,699.0,France,Female,39.0,5.0,0.0,2.0,1.0,0.0,147878.05
-17161,15794479.0,H?,850.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,153392.28
-17162,15692929.0,De Bernales,540.0,France,Female,24.0,10.0,0.0,2.0,1.0,0.0,90821.48
-17163,15772528.0,Pisano,696.0,Germany,Female,43.0,0.0,104488.19,1.0,1.0,0.0,81299.17
-17164,15704081.0,Chineze,775.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,1.0,132673.23
-17165,15718779.0,Hsia,759.0,Spain,Male,26.0,7.0,0.0,2.0,1.0,1.0,90162.35
-17166,15641353.0,Bogle,633.0,Germany,Male,27.0,4.0,132405.52,1.0,0.0,1.0,143913.33
-17167,15737310.0,Chiu,559.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,152956.33
-17168,15780128.0,Trevisani,751.0,Germany,Female,34.0,4.0,104449.8,2.0,0.0,1.0,46422.46
-17169,15656319.0,Cremonesi,807.0,France,Female,42.0,2.0,0.0,1.0,0.0,0.0,100717.54
-17170,15761854.0,Fetherstonhaugh,679.0,France,Female,24.0,6.0,0.0,2.0,1.0,0.0,110317.39
-17171,15771742.0,Lucchesi,632.0,France,Female,34.0,8.0,0.0,2.0,0.0,1.0,92888.52
-17172,15702416.0,Chinagorom,559.0,France,Male,34.0,8.0,0.0,1.0,0.0,1.0,91565.61
-17173,15809800.0,Ch'ien,551.0,Germany,Male,37.0,6.0,146098.43,2.0,1.0,0.0,181605.85
-17174,15684319.0,Cattaneo,595.0,Germany,Male,40.0,3.0,120962.13,2.0,1.0,1.0,132424.69
-17175,15703394.0,Nnamutaezinwa,668.0,France,Female,42.0,2.0,0.0,1.0,1.0,1.0,147955.91
-17176,15592645.0,Hsiung,661.0,France,Male,32.0,6.0,141243.65,1.0,1.0,0.0,188516.45
-17177,15575955.0,Trevisani,579.0,France,Female,40.0,5.0,123694.42,1.0,1.0,0.0,144532.85
-17178,15655961.0,T'ien,715.0,Spain,Male,45.0,4.0,0.0,2.0,0.0,1.0,179521.24
-17179,15608726.0,Yobanna,724.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,152298.84
-17180,15686914.0,Nwabugwu,694.0,France,Male,30.0,1.0,123681.32,1.0,0.0,1.0,72350.21
-17181,15649738.0,T'ien,559.0,Spain,Male,46.0,8.0,0.0,2.0,0.0,0.0,179267.94
-17182,15627876.0,Nwankwo,693.0,France,Female,30.0,7.0,145984.92,1.0,0.0,0.0,6090905.51
-17183,15675888.0,Cattaneo,624.0,Germany,Female,36.0,7.0,131835.52,2.0,1.0,0.0,162452.65
-17184,15756655.0,Uwaezuoke,583.0,Germany,Female,33.0,5.0,101438.0,1.0,0.0,1.0,93739.71
-17185,15566139.0,Teng,744.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,74858.3
-17186,15795963.0,Lucciano,635.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,150803.44
-17187,15748797.0,Kao,724.0,Germany,Male,31.0,7.0,100433.8,2.0,1.0,0.0,102999.23
-17188,15762799.0,Chidiebele,649.0,Germany,Male,59.0,3.0,102708.77,1.0,1.0,1.0,134417.31
-17189,15672027.0,Lu,731.0,France,Female,39.0,9.0,0.0,2.0,1.0,1.0,137791.18
-17190,15777922.0,Chukwuemeka,744.0,Spain,Male,67.0,7.0,120877.24,1.0,1.0,1.0,68400.14
-17191,15589428.0,Chukwuemeka,567.0,France,Female,27.0,2.0,76915.4,1.0,1.0,1.0,142940.39
-17192,15571201.0,Yevdokimova,565.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,161668.13
-17193,15768233.0,Chukwufumnanya,684.0,France,Male,33.0,0.0,0.0,2.0,1.0,1.0,125557.95
-17194,15725036.0,K?,841.0,Spain,Female,35.0,9.0,0.0,2.0,1.0,1.0,60954.45
-17195,15733489.0,L?,619.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,152992.04
-17196,15803976.0,Ugochukwu,672.0,Germany,Male,35.0,6.0,81571.05,2.0,1.0,1.0,123870.07
-17197,15673584.0,Kepley,678.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,94498.82
-17198,15755223.0,Sargent,593.0,Spain,Female,27.0,5.0,0.0,1.0,1.0,0.0,101617.64
-17199,15654975.0,Okonkwo,545.0,France,Female,52.0,1.0,0.0,1.0,1.0,0.0,65636.92
-17200,15742019.0,Tseng,593.0,France,Male,31.0,4.0,0.0,1.0,0.0,1.0,73977.23
-17201,15589589.0,Lu,739.0,France,Male,40.0,8.0,0.0,1.0,0.0,1.0,151733.43
-17202,15576774.0,Hsing,751.0,France,Female,26.0,1.0,0.0,1.0,1.0,1.0,176713.47
-17203,15617401.0,Maclean,729.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,122920.69
-17204,15788218.0,Chiazagomekpere,598.0,Spain,Male,49.0,6.0,0.0,3.0,1.0,1.0,172495.81
-17205,15774179.0,De Salis,627.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,123614.01
-17206,15757952.0,Mironova,586.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,158045.49
-17207,15641366.0,Martin,776.0,Germany,Male,24.0,3.0,129743.75,1.0,1.0,1.0,168053.4
-17208,15593470.0,Chukwualuka,732.0,Spain,Male,33.0,3.0,102016.22,2.0,1.0,0.0,76621.49
-17209,15665956.0,Fomin,653.0,France,Male,30.0,3.0,0.0,2.0,0.0,0.0,144584.29
-17210,15774931.0,Mironova,633.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,0.0,103603.65
-17211,15705735.0,Chidozie,787.0,France,Female,24.0,2.0,0.0,2.0,1.0,1.0,168085.97
-17212,15716994.0,Toscani,688.0,Germany,Male,43.0,2.0,86911.8,2.0,1.0,1.0,120512.08
-17213,15740494.0,Yobachi,712.0,France,Female,33.0,3.0,0.0,1.0,1.0,1.0,159823.16
-17214,15777046.0,T'ien,624.0,France,Female,44.0,2.0,0.0,2.0,1.0,0.0,128882.66
-17215,15685564.0,Chukwudi,727.0,Germany,Female,55.0,3.0,113599.74,1.0,1.0,0.0,189558.44
-17216,15616555.0,Chukwujamuike,682.0,Spain,Male,31.0,0.0,0.0,2.0,1.0,0.0,172450.04
-17217,15620268.0,McLean,649.0,Spain,Male,41.0,9.0,0.0,1.0,0.0,0.0,82438.13
-17218,15617252.0,Chukwudi,581.0,Spain,Female,34.0,9.0,0.0,1.0,1.0,0.0,115287.99
-17219,15578878.0,Palerma,749.0,Spain,Male,39.0,2.0,144432.04,2.0,1.0,1.0,78896.59
-17220,15585738.0,Nwabugwu,628.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,173870.39
-17221,15706815.0,Yobachi,613.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,61995.57
-17222,15665416.0,Hs?,667.0,France,Male,53.0,10.0,0.0,2.0,0.0,1.0,19233.41
-17223,15799350.0,T'ien,668.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,143960.72
-17224,15776596.0,Chuang,745.0,France,Male,36.0,5.0,0.0,1.0,1.0,1.0,167636.15
-17225,15667934.0,H?,719.0,France,Female,36.0,1.0,66483.32,3.0,0.0,0.0,1375.0
-17226,15627854.0,Groom,658.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,149184.15
-17227,15707042.0,Ozioma,714.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,142935.83
-17228,15622494.0,Mancini,668.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,168034.9
-17229,15689351.0,Chukwualuka,749.0,Germany,Female,39.0,4.0,121152.05,1.0,1.0,0.0,166957.82
-17230,15721582.0,Ts'ai,714.0,France,Male,34.0,3.0,157531.82,1.0,1.0,1.0,146752.67
-17231,15724443.0,Lettiere,667.0,Germany,Male,27.0,1.0,160777.9,2.0,0.0,1.0,177099.71
-17232,15745455.0,Maclean,645.0,Spain,Male,31.0,6.0,0.0,2.0,0.0,0.0,89893.6
-17233,15700325.0,Nwoye,721.0,France,Female,32.0,1.0,101238.24,1.0,1.0,1.0,168002.31
-17234,15672930.0,Brazenor,477.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,131043.2
-17235,15761141.0,Mazzanti,616.0,France,Male,36.0,8.0,141665.63,1.0,1.0,1.0,88878.15
-17236,15765461.0,Nucci,678.0,France,Male,24.0,0.0,0.0,2.0,0.0,0.0,70783.61
-17237,15761717.0,Ch'ien,677.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,130862.11
-17238,15674899.0,Chukwumaobim,699.0,France,Female,45.0,5.0,73494.21,1.0,1.0,1.0,91573.19
-17239,15803941.0,Hsiung,637.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,0.0,90189.72
-17240,15798024.0,Bischof,599.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,1.0,102280.29
-17241,15730590.0,Onwuamaeze,686.0,France,Female,33.0,2.0,0.0,2.0,0.0,1.0,80194.83
-17242,15616365.0,Clements,584.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,176750.81
-17243,15783338.0,Mazzanti,646.0,France,Female,31.0,5.0,121685.31,1.0,1.0,1.0,111285.85
-17244,15764491.0,Chidimma,755.0,Spain,Male,43.0,4.0,0.0,1.0,1.0,0.0,176713.47
-17245,15581893.0,McLean,651.0,France,Female,23.0,2.0,153040.62,2.0,1.0,1.0,38190.78
-17246,15697948.0,Ngozichukwuka,738.0,Spain,Female,26.0,8.0,0.0,2.0,1.0,0.0,142730.27
-17247,15766355.0,Ndubueze,653.0,France,Male,30.0,5.0,0.0,2.0,1.0,0.0,168184.62
-17248,15682778.0,Y?,706.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,109742.8
-17249,15658696.0,Henty,681.0,France,Male,41.0,7.0,0.0,2.0,0.0,0.0,58440.41
-17250,15631838.0,Chinedum,595.0,France,Male,45.0,6.0,0.0,1.0,0.0,0.0,152845.99
-17251,15608845.0,Hsia,715.0,Spain,Female,47.0,9.0,122552.34,1.0,0.0,1.0,148564.76
-17252,15649101.0,Onyekachi,663.0,France,Female,35.0,5.0,153855.52,1.0,1.0,0.0,162699.7
-17253,15754952.0,Okwuadigbo,672.0,Spain,Female,30.0,8.0,122917.71,1.0,1.0,0.0,117832.88
-17254,15581229.0,Kosisochukwu,627.0,Germany,Male,32.0,5.0,127847.86,1.0,0.0,1.0,74836.64
-17255,15600478.0,Tsou,709.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,123825.83
-17256,15591036.0,Okwudilichukwu,684.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,1.0,142963.28
-17257,15700460.0,Chien,710.0,Germany,Female,49.0,5.0,108815.56,1.0,1.0,0.0,152882.79
-17258,15576723.0,Hsia,636.0,Spain,Female,47.0,5.0,0.0,2.0,1.0,0.0,152992.04
-17259,15603883.0,Mazzanti,732.0,Spain,Female,46.0,8.0,0.0,1.0,1.0,0.0,184980.23
-17260,15653776.0,Lombardi,693.0,Germany,Male,39.0,10.0,131468.44,2.0,1.0,0.0,171657.57
-17261,15649060.0,Pai,468.0,France,Female,29.0,3.0,94486.47,1.0,0.0,1.0,63908.86
-17262,15624703.0,Onyemauchechukwu,592.0,France,Female,36.0,3.0,127552.85,1.0,1.0,0.0,125953.83
-17263,15671334.0,Chidiegwu,743.0,France,Male,39.0,3.0,0.0,2.0,0.0,1.0,60420.07
-17264,15758056.0,H?,539.0,France,Female,41.0,9.0,141540.63,1.0,0.0,0.0,137985.69
-17265,15809800.0,Toscani,622.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,106655.88
-17266,15701555.0,Chiazagomekpere,553.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,74846.67
-17267,15782404.0,Nwankwo,756.0,France,Male,31.0,9.0,175575.95,1.0,0.0,0.0,176779.46
-17268,15654067.0,Nnaife,473.0,France,Female,55.0,9.0,0.0,2.0,1.0,1.0,137028.51
-17269,15798960.0,Tseng,625.0,Spain,Female,32.0,1.0,0.0,1.0,1.0,0.0,139844.04
-17270,15573971.0,Obidimkpa,662.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,92320.37
-17271,15716134.0,Onwughara,667.0,France,Male,28.0,5.0,0.0,1.0,0.0,1.0,146680.25
-17272,15653547.0,Ch'eng,643.0,Spain,Male,42.0,6.0,0.0,1.0,0.0,1.0,132473.17
-17273,15627365.0,Lucciano,579.0,France,Female,48.0,9.0,0.0,1.0,1.0,0.0,86622.56
-17274,15738588.0,Nnonso,743.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,0.0,167984.61
-17275,15646566.0,Ch'in,773.0,France,Female,44.0,2.0,0.0,2.0,0.0,0.0,49826.68
-17276,15662098.0,Onyeoruru,716.0,Germany,Male,35.0,3.0,107716.35,1.0,1.0,1.0,105887.04
-17277,15734044.0,Ch'eng,705.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,122433.09
-17278,15780409.0,P'eng,667.0,France,Male,37.0,10.0,126534.9,2.0,1.0,0.0,145536.32
-17279,15640161.0,Tsou,652.0,France,Female,47.0,3.0,0.0,1.0,1.0,0.0,186775.25
-17280,15577359.0,Chia,569.0,France,Female,37.0,1.0,0.0,2.0,0.0,0.0,67740.08
-17281,15592761.0,Ts'ui,619.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,130189.53
-17282,15569590.0,T'ien,773.0,France,Male,36.0,4.0,134571.5,1.0,1.0,0.0,150548.5
-17283,15676521.0,Tuan,603.0,Spain,Female,43.0,2.0,0.0,1.0,0.0,0.0,154767.34
-17284,15720583.0,Chinwendu,732.0,France,Female,46.0,4.0,0.0,2.0,1.0,1.0,186976.6
-17285,15607746.0,Iweobiegbunam,719.0,Spain,Male,33.0,3.0,153861.1,1.0,1.0,1.0,26318.73
-17286,15656865.0,K'ung,543.0,France,Male,37.0,6.0,146623.36,1.0,1.0,1.0,158531.01
-17287,15743819.0,Smith,584.0,France,Female,45.0,9.0,0.0,1.0,1.0,1.0,181429.87
-17288,15631739.0,Ch'ang,679.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,75562.47
-17289,15594827.0,Yeh,749.0,Spain,Male,31.0,5.0,0.0,1.0,1.0,0.0,172063.09
-17290,15572657.0,Pipes,732.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,152855.96
-17291,15572767.0,Chijindum,769.0,France,Male,36.0,8.0,0.0,2.0,0.0,1.0,141533.19
-17292,15729110.0,Tung,665.0,France,Female,34.0,5.0,0.0,1.0,1.0,0.0,149814.68
-17293,15641389.0,Ma,516.0,France,Female,43.0,6.0,0.0,1.0,1.0,1.0,141756.32
-17294,15778025.0,Chijioke,716.0,Germany,Female,41.0,7.0,121542.26,1.0,0.0,0.0,114442.66
-17295,15770474.0,Chukwufumnanya,807.0,France,Female,27.0,2.0,137555.66,1.0,1.0,1.0,124487.28
-17296,15672966.0,K'ung,713.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,160968.44
-17297,15709920.0,Genovesi,721.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,0.0,173495.77
-17298,15745080.0,De Salis,681.0,Germany,Male,34.0,7.0,102800.65,2.0,0.0,0.0,92840.67
-17299,15595036.0,McElhone,703.0,Germany,Female,32.0,7.0,125712.2,1.0,1.0,1.0,157549.6
-17300,15731117.0,Ijendu,729.0,France,Male,42.0,4.0,0.0,2.0,0.0,1.0,121866.87
-17301,15658716.0,Nwokezuike,546.0,France,Female,57.0,9.0,0.0,1.0,1.0,1.0,185547.71
-17302,15569423.0,Cattaneo,598.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,161479.19
-17303,15779947.0,Udinesi,691.0,France,Male,41.0,1.0,132759.9,1.0,0.0,1.0,123969.64
-17304,15616172.0,Ch'in,744.0,France,Male,35.0,4.0,0.0,1.0,0.0,1.0,137794.63
-17305,15763515.0,Ch'in,733.0,France,Female,28.0,5.0,0.0,2.0,0.0,1.0,82438.13
-17306,15603035.0,Liao,553.0,Germany,Male,33.0,6.0,106570.37,1.0,1.0,0.0,173899.42
-17307,15650476.0,Fiorentino,667.0,France,Male,32.0,8.0,88980.32,1.0,1.0,0.0,74415.2
-17308,15588928.0,Ponomarev,673.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,1.0,140650.19
-17309,15711572.0,Lo Duca,663.0,Germany,Female,50.0,0.0,133866.22,1.0,1.0,0.0,26578.69
-17310,15779581.0,Efremov,682.0,Spain,Male,40.0,6.0,72012.76,1.0,1.0,0.0,161479.19
-17311,15681671.0,Ch'iu,663.0,France,Male,33.0,2.0,0.0,1.0,1.0,0.0,151822.66
-17312,15609913.0,Chiazagomekpere,686.0,Spain,Male,50.0,2.0,0.0,1.0,1.0,0.0,128521.17
-17313,15802088.0,Maclean,663.0,Germany,Female,62.0,4.0,92895.56,2.0,1.0,0.0,187616.16
-17314,15620981.0,Udinesi,805.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,122899.01
-17315,15679299.0,Ts'ao,627.0,Spain,Female,46.0,9.0,151963.26,2.0,1.0,0.0,77830.06
-17316,15714493.0,Genovese,571.0,France,Male,37.0,5.0,0.0,1.0,1.0,1.0,143913.32
-17317,15641822.0,Walker,653.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,82463.69
-17318,15662397.0,Uwaezuoke,703.0,Germany,Male,27.0,7.0,114590.43,2.0,1.0,0.0,175823.2
-17319,15588923.0,Brownless,754.0,France,Female,36.0,1.0,0.0,2.0,0.0,0.0,143681.83
-17320,15593365.0,Mazzanti,632.0,Germany,Female,45.0,9.0,118897.1,3.0,1.0,1.0,92748.16
-17321,15642824.0,Trevisani,687.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,153920.28
-17322,15791321.0,K'ung,578.0,France,Male,31.0,9.0,142872.36,1.0,1.0,0.0,184564.67
-17323,15669922.0,Onyekachi,636.0,France,Female,59.0,2.0,0.0,1.0,1.0,0.0,160968.44
-17324,15791045.0,Y?an,646.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,145562.4
-17325,15776985.0,Onyemauchechukwu,718.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,180344.69
-17326,15778025.0,Lucchesi,625.0,France,Female,29.0,10.0,150635.3,1.0,1.0,0.0,131768.98
-17327,15572291.0,Yuryeva,710.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,162812.16
-17328,15590810.0,Nwabugwu,638.0,Germany,Male,41.0,9.0,83727.68,1.0,0.0,0.0,103570.8
-17329,15753861.0,Walker,594.0,France,Male,31.0,9.0,0.0,2.0,0.0,0.0,134483.08
-17330,15769169.0,Lucchesi,644.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,161479.19
-17331,15645045.0,Tsou,591.0,Spain,Male,58.0,2.0,0.0,2.0,1.0,1.0,166677.35
-17332,15682912.0,Li,624.0,France,Female,30.0,3.0,0.0,1.0,0.0,1.0,148516.79
-17333,15779985.0,Dickson,652.0,Germany,Female,52.0,5.0,146547.78,2.0,1.0,1.0,8991.17
-17334,15572657.0,Cremonesi,719.0,France,Female,41.0,5.0,91328.71,1.0,1.0,0.0,53584.03
-17335,15703682.0,Chiu,697.0,Germany,Male,32.0,8.0,121241.57,1.0,0.0,1.0,177025.79
-17336,15603665.0,Chibugo,638.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,53477.76
-17337,15777893.0,Yermakov,646.0,France,Male,40.0,6.0,127120.62,1.0,0.0,0.0,121061.89
-17338,15721740.0,Yobanna,733.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,157871.55
-17339,15782418.0,Chidozie,638.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,0.0,149991.32
-17340,15769412.0,Pirozzi,677.0,France,Female,45.0,7.0,0.0,2.0,1.0,1.0,171463.83
-17341,15682070.0,Tuan,706.0,France,Female,29.0,3.0,0.0,1.0,1.0,1.0,55893.37
-17342,15581548.0,Tuan,609.0,Spain,Female,48.0,1.0,110148.56,1.0,1.0,1.0,81652.02
-17343,15698716.0,Efremova,597.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,161051.75
-17344,15704763.0,O'Brien,703.0,Germany,Male,52.0,9.0,111257.59,1.0,1.0,0.0,164040.94
-17345,15591628.0,Tsai,683.0,France,Female,33.0,9.0,0.0,1.0,0.0,1.0,129975.54
-17346,15665879.0,Maclean,535.0,France,Male,34.0,9.0,0.0,1.0,0.0,1.0,183656.12
-17347,15650538.0,Hsia,639.0,France,Male,49.0,0.0,0.0,1.0,0.0,0.0,161579.85
-17348,15799128.0,T'ien,718.0,France,Male,43.0,9.0,0.0,1.0,1.0,0.0,97916.49
-17349,15795586.0,Maconochie,690.0,Spain,Male,24.0,0.0,0.0,2.0,1.0,0.0,102260.16
-17350,15602851.0,Brient,591.0,France,Female,37.0,1.0,0.0,2.0,0.0,1.0,145936.28
-17351,15667932.0,De Luca,680.0,France,Female,35.0,7.0,136525.99,1.0,0.0,1.0,123583.85
-17352,15703793.0,Hsia,707.0,Spain,Female,34.0,3.0,122917.71,1.0,0.0,1.0,172097.4
-17353,15788151.0,Lai,758.0,Spain,Male,36.0,6.0,99773.85,2.0,1.0,1.0,161809.95
-17354,15570060.0,Chukwuemeka,607.0,France,Female,50.0,3.0,0.0,2.0,1.0,1.0,100101.06
-17355,15673820.0,Omeokachie,715.0,France,Female,38.0,4.0,0.0,1.0,0.0,1.0,142838.64
-17356,15735782.0,Ulyanova,691.0,Spain,Male,47.0,2.0,0.0,1.0,1.0,0.0,170886.01
-17357,15629985.0,Chiebuka,632.0,Spain,Female,38.0,8.0,0.0,2.0,0.0,0.0,153012.13
-17358,15680643.0,T'ien,678.0,France,Female,28.0,4.0,0.0,2.0,1.0,1.0,131301.74
-17359,15776733.0,Maclean,667.0,Spain,Male,41.0,8.0,128551.36,1.0,1.0,1.0,87412.24
-17360,15681075.0,Tien,592.0,Spain,Male,50.0,5.0,0.0,2.0,1.0,1.0,184842.08
-17361,15624592.0,Maclean,634.0,France,Female,41.0,2.0,0.0,2.0,0.0,0.0,175399.14
-17362,15624397.0,Fyodorov,648.0,France,Female,40.0,10.0,162702.35,1.0,1.0,1.0,138490.03
-17363,15615020.0,K'ung,656.0,France,Female,51.0,6.0,0.0,3.0,1.0,0.0,114448.77
-17364,15672437.0,Chiagoziem,651.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,141210.5
-17365,15750055.0,Niu,633.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,183569.25
-17366,15783378.0,Okechukwu,447.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,121365.45
-17367,15782939.0,Uspenskaya,516.0,France,Female,29.0,0.0,0.0,2.0,1.0,0.0,151336.96
-17368,15603481.0,Onyekachi,624.0,France,Female,38.0,5.0,0.0,2.0,0.0,0.0,161428.25
-17369,15662532.0,Folliero,663.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,75578.67
-17370,15788676.0,Liao,758.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,124862.57
-17371,15732438.0,McIntyre,607.0,France,Female,71.0,9.0,0.0,1.0,1.0,0.0,182738.0
-17372,15764654.0,Y?an,708.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,136521.82
-17373,15673837.0,Scott,598.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,68815.05
-17374,15791767.0,Pirozzi,718.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,1.0,141804.86
-17375,15674727.0,Brown,565.0,France,Male,31.0,6.0,121161.57,1.0,1.0,1.0,121904.14
-17376,15728605.0,Chidubem,774.0,Germany,Male,57.0,4.0,112079.58,1.0,0.0,1.0,82662.73
-17377,15581840.0,Mao,528.0,France,Female,40.0,9.0,0.0,1.0,0.0,0.0,77830.36
-17378,15599433.0,Onuoha,609.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,105986.01
-17379,15729224.0,Chukwujekwu,804.0,France,Female,55.0,2.0,0.0,1.0,0.0,0.0,49480.66
-17380,15651713.0,Burdekin,607.0,Spain,Female,28.0,6.0,0.0,2.0,1.0,0.0,102908.91
-17381,15643396.0,Tsai,687.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,63889.1
-17382,15757867.0,Brown,588.0,Spain,Male,37.0,2.0,0.0,2.0,0.0,0.0,82709.8
-17383,15591607.0,Nkemjika,578.0,France,Male,28.0,6.0,96926.12,1.0,1.0,1.0,89520.75
-17384,15707025.0,Ch'iu,651.0,France,Male,33.0,0.0,0.0,2.0,1.0,0.0,129892.93
-17385,15698932.0,O'Toole,721.0,Germany,Female,46.0,3.0,129855.32,2.0,0.0,0.0,71811.9
-17386,15772412.0,Palermo,571.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,127528.84
-17387,15693764.0,Ch'iu,556.0,France,Female,35.0,2.0,0.0,1.0,1.0,0.0,196257.67
-17388,15645355.0,Ch'ien,757.0,Germany,Female,38.0,6.0,84006.88,1.0,1.0,1.0,183584.14
-17389,15715079.0,Zaytseva,629.0,Spain,Male,32.0,8.0,0.0,2.0,0.0,0.0,167984.72
-17390,15591489.0,Chukwudi,685.0,Spain,Male,42.0,4.0,0.0,3.0,1.0,0.0,78650.95
-17391,15794323.0,Eluemuno,585.0,France,Male,46.0,9.0,0.0,1.0,1.0,0.0,82463.69
-17392,15571928.0,Trevisano,607.0,Spain,Male,34.0,8.0,0.0,1.0,1.0,0.0,169915.02
-17393,15566494.0,Hsia,622.0,Spain,Female,27.0,5.0,0.0,1.0,1.0,0.0,122762.95
-17394,15698819.0,Chiang,705.0,France,Male,45.0,5.0,0.0,1.0,1.0,0.0,184839.7
-17395,15773591.0,Onyemauchechi,547.0,France,Male,35.0,9.0,0.0,1.0,1.0,0.0,65816.53
-17396,15747772.0,Ch'ang,762.0,France,Female,53.0,9.0,0.0,2.0,1.0,1.0,176902.83
-17397,15803689.0,Chiu,661.0,France,Male,47.0,8.0,0.0,2.0,0.0,1.0,63795.7
-17398,15801942.0,P'eng,646.0,Germany,Male,52.0,7.0,130142.79,1.0,1.0,0.0,52341.15
-17399,15644962.0,Calabrese,572.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,103382.79
-17400,15748597.0,Hsu,602.0,France,Female,34.0,0.0,142644.64,1.0,1.0,0.0,126554.96
-17401,15642635.0,Ts'ui,619.0,France,Male,25.0,6.0,0.0,2.0,1.0,0.0,181208.47
-17402,15760294.0,Nwankwo,705.0,Spain,Female,40.0,8.0,0.0,2.0,1.0,1.0,85890.75
-17403,15676521.0,H?,549.0,Germany,Female,43.0,7.0,137655.24,1.0,0.0,1.0,53440.43
-17404,15579892.0,Maclean,755.0,France,Female,39.0,5.0,0.0,2.0,1.0,0.0,117060.2
-17405,15761023.0,H?,573.0,France,Female,43.0,2.0,0.0,1.0,0.0,1.0,178268.36
-17406,15762762.0,Ch'in,717.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,102397.22
-17407,15720353.0,Nwankwo,663.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,140763.07
-17408,15722409.0,Okwuadigbo,699.0,Spain,Female,33.0,7.0,0.0,1.0,1.0,0.0,138051.19
-17409,15810563.0,Chikwendu,594.0,Germany,Male,26.0,2.0,125921.37,2.0,1.0,0.0,25650.04
-17410,15716449.0,Pinto,547.0,France,Male,34.0,10.0,126773.43,1.0,1.0,1.0,121497.61
-17411,15762391.0,Maclean,582.0,Spain,Male,30.0,3.0,0.0,2.0,1.0,1.0,71323.23
-17412,15641091.0,Ch'eng,648.0,France,Female,38.0,0.0,81589.04,1.0,1.0,0.0,141946.92
-17413,15614220.0,Onyeorulu,632.0,France,Female,42.0,5.0,0.0,2.0,1.0,1.0,173843.21
-17414,15631927.0,Dickson,529.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,164038.07
-17415,15707289.0,Frolov,663.0,Spain,Female,35.0,7.0,94464.81,1.0,0.0,0.0,166031.07
-17416,15764170.0,Ch'ang,633.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,173870.39
-17417,15723320.0,Ndubueze,572.0,France,Male,32.0,7.0,104960.46,1.0,1.0,0.0,163711.01
-17418,15651460.0,Hsing,637.0,Germany,Male,46.0,1.0,125437.86,1.0,0.0,1.0,82970.69
-17419,15802570.0,Tien,605.0,Germany,Female,35.0,6.0,128927.93,1.0,1.0,1.0,131644.41
-17420,15707974.0,Onyemauchechukwu,668.0,France,Female,34.0,9.0,0.0,2.0,0.0,1.0,142223.94
-17421,15664720.0,Fiorentini,597.0,France,Female,50.0,8.0,106328.08,1.0,1.0,0.0,51959.82
-17422,15679963.0,Chukwueloka,627.0,France,Male,35.0,4.0,121250.04,1.0,1.0,1.0,138399.67
-17423,15628992.0,Tien,602.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,175082.2
-17424,15689953.0,Nkemdirim,562.0,France,Female,41.0,1.0,142550.28,1.0,1.0,0.0,80552.36
-17425,15761158.0,Loyau,586.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,135034.43
-17426,15748068.0,Pisani,648.0,France,Female,32.0,1.0,0.0,1.0,0.0,1.0,90189.72
-17427,15765806.0,Tretiakova,645.0,France,Male,31.0,2.0,0.0,2.0,0.0,0.0,171575.54
-17428,15593954.0,Chigolum,556.0,France,Male,36.0,9.0,122034.4,2.0,0.0,1.0,121537.19
-17429,15632050.0,Ugochukwu,636.0,France,Female,35.0,7.0,110942.9,1.0,0.0,0.0,143598.7
-17430,15774872.0,Ch'ien,713.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,151871.84
-17431,15762268.0,Chiu,687.0,France,Female,34.0,10.0,113829.45,1.0,1.0,0.0,55832.6
-17432,15726313.0,Ch'iu,542.0,France,Male,34.0,3.0,0.0,1.0,1.0,1.0,26687.21
-17433,15653547.0,Ch'eng,685.0,Spain,Female,44.0,3.0,0.0,3.0,1.0,0.0,120892.96
-17434,15654574.0,Tuan,670.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,7861.68
-17435,15794413.0,Hsueh,613.0,France,Male,30.0,6.0,114005.94,1.0,1.0,1.0,126469.09
-17436,15571940.0,Omeokachie,744.0,Spain,Female,45.0,4.0,0.0,1.0,1.0,0.0,121451.21
-17437,15683363.0,Nkemakolam,651.0,France,Female,44.0,7.0,0.0,1.0,1.0,0.0,79792.3
-17438,15793723.0,Ch'ien,611.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,1.0,135077.01
-17439,15809100.0,Yobachukwu,663.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,146680.28
-17440,15690743.0,Trevisano,611.0,Spain,Male,36.0,2.0,120911.75,1.0,1.0,0.0,119377.36
-17441,15662758.0,Brierly,716.0,Spain,Male,22.0,4.0,0.0,2.0,1.0,0.0,172450.02
-17442,15606097.0,Chienezie,749.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,1.0,129984.2
-17443,15676921.0,Ch'ang,644.0,Spain,Male,50.0,6.0,0.0,2.0,1.0,1.0,142223.94
-17444,15744517.0,Onio,628.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,81723.8
-17445,15668894.0,Nwankwo,769.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,184514.01
-17446,15656627.0,Fiorentino,543.0,Germany,Female,49.0,5.0,131519.49,1.0,1.0,0.0,179540.73
-17447,15672915.0,Mazzi,663.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,136497.28
-17448,15782210.0,Ibeamaka,651.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,1.0,102640.53
-17449,15690164.0,Ndukaku,656.0,France,Male,49.0,6.0,132739.8,1.0,1.0,0.0,186489.99
-17450,15806447.0,Yermakova,628.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,0.0,69531.22
-17451,15721935.0,Nwankwo,679.0,Spain,Male,36.0,10.0,0.0,2.0,1.0,0.0,172049.84
-17452,15661290.0,K?,578.0,France,Female,39.0,6.0,0.0,2.0,0.0,0.0,107415.02
-17453,15681075.0,De Neeve,667.0,Spain,Male,42.0,5.0,0.0,2.0,0.0,1.0,120540.83
-17454,15614687.0,Hsia,678.0,Spain,Female,45.0,5.0,0.0,1.0,1.0,1.0,182059.28
-17455,15704763.0,Hsia,769.0,France,Female,50.0,8.0,0.0,2.0,1.0,1.0,137961.41
-17456,15632472.0,Li Fonti,521.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,142400.27
-17457,15711489.0,Palerma,706.0,Spain,Female,36.0,4.0,0.0,2.0,0.0,1.0,142963.29
-17458,15592999.0,Chinweuba,672.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,120115.5
-17459,15775042.0,Ting,703.0,Spain,Female,37.0,3.0,107467.56,1.0,1.0,0.0,113086.08
-17460,15640131.0,Shih,613.0,Germany,Female,31.0,0.0,108915.85,2.0,0.0,1.0,136985.08
-17461,15640507.0,Tseng,718.0,Germany,Male,44.0,7.0,110949.41,2.0,1.0,1.0,38308.34
-17462,15803764.0,Tretiakov,681.0,Spain,Female,45.0,6.0,0.0,1.0,1.0,1.0,134875.31
-17463,15704954.0,Pinto,757.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,136983.77
-17464,15695872.0,Eipper,751.0,Spain,Male,48.0,10.0,0.0,1.0,1.0,0.0,75578.67
-17465,15797960.0,Folliero,696.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,166650.32
-17466,15787835.0,Liao,642.0,Germany,Male,33.0,6.0,126426.22,2.0,1.0,1.0,128521.16
-17467,15672330.0,Onio,745.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,64872.33
-17468,15794142.0,Maclean,641.0,France,Male,33.0,10.0,0.0,2.0,1.0,0.0,199358.58
-17469,15593778.0,Walker,754.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,0.0,95551.22
-17470,15632079.0,Lucchese,593.0,France,Male,45.0,2.0,0.0,2.0,1.0,0.0,181605.85
-17471,15808831.0,Genovese,655.0,Germany,Female,44.0,3.0,138053.79,1.0,0.0,1.0,161407.48
-17472,15782231.0,Ch'ien,605.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,64595.25
-17473,15802869.0,Nnamutaezinwa,633.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,123866.28
-17474,15775295.0,Y?,679.0,France,Male,31.0,6.0,112949.71,1.0,1.0,1.0,120853.83
-17475,15576517.0,Ts'ui,599.0,France,Female,41.0,1.0,0.0,1.0,0.0,0.0,171345.02
-17476,15797733.0,Hsiung,593.0,France,Male,24.0,1.0,0.0,2.0,1.0,0.0,112256.57
-17477,15579969.0,Onyekachi,506.0,France,Male,31.0,4.0,137781.65,1.0,1.0,1.0,121151.1
-17478,15614687.0,Hs?,709.0,France,Female,36.0,5.0,0.0,2.0,0.0,1.0,75888.65
-17479,15732299.0,Hsieh,691.0,France,Male,42.0,8.0,99472.76,1.0,1.0,1.0,26920.43
-17480,15690670.0,Ch'iu,666.0,France,Male,32.0,4.0,0.0,2.0,0.0,0.0,136909.87
-17481,15751131.0,Ch'iu,714.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,51345.39
-17482,15635078.0,Bazarova,614.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,147074.67
-17483,15777784.0,Macleod,624.0,France,Female,35.0,3.0,0.0,1.0,1.0,0.0,184312.88
-17484,15707120.0,Chigozie,665.0,France,Female,34.0,8.0,125890.72,1.0,1.0,0.0,94460.97
-17485,15581552.0,Uchenna,648.0,France,Male,31.0,0.0,92887.13,2.0,1.0,1.0,92621.9
-17486,15602312.0,T'ien,592.0,Spain,Male,41.0,8.0,119817.35,1.0,1.0,0.0,166944.89
-17487,15725429.0,Yuryeva,578.0,Spain,Male,32.0,6.0,0.0,2.0,0.0,0.0,175894.24
-17488,15757559.0,Pagnotto,786.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,1.0,138862.98
-17489,15684314.0,Hsiung,706.0,Germany,Female,55.0,3.0,118469.38,1.0,1.0,0.0,55775.84
-17490,15742834.0,Yobachi,696.0,France,Female,67.0,5.0,0.0,1.0,1.0,1.0,134901.57
-17491,15626507.0,Chidubem,712.0,France,Male,28.0,2.0,93960.35,1.0,1.0,0.0,105837.38
-17492,15705298.0,Y?an,589.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,146663.38
-17493,15606901.0,White,677.0,France,Female,31.0,9.0,0.0,2.0,0.0,1.0,82522.61
-17494,15608916.0,Hs?eh,738.0,France,Male,32.0,5.0,0.0,2.0,0.0,1.0,67788.49
-17495,15642710.0,P'an,677.0,France,Male,71.0,2.0,0.0,2.0,1.0,1.0,85547.33
-17496,15579223.0,Chidimma,802.0,France,Female,38.0,2.0,0.0,2.0,0.0,1.0,121151.46
-17497,15585853.0,McEwan,609.0,France,Male,41.0,1.0,151218.98,1.0,0.0,0.0,158071.69
-17498,15618182.0,T'ien,640.0,France,Female,31.0,10.0,0.0,2.0,1.0,0.0,172524.51
-17499,15662533.0,Tuan,604.0,Spain,Female,40.0,2.0,0.0,1.0,0.0,0.0,52629.73
-17500,15589323.0,Palerma,641.0,Spain,Male,23.0,9.0,0.0,2.0,0.0,1.0,98640.15
-17501,15632256.0,Macleod,593.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,64516.8
-17502,15624995.0,P'eng,683.0,France,Male,45.0,0.0,97473.87,1.0,0.0,0.0,131043.9
-17503,15589715.0,Ts'ai,648.0,Spain,Female,36.0,4.0,0.0,2.0,0.0,0.0,172498.79
-17504,15652914.0,Fyodorova,841.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,180551.76
-17505,15632256.0,Chiu,648.0,France,Female,48.0,4.0,0.0,3.0,1.0,0.0,160129.99
-17506,15584785.0,Shao,585.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,177099.71
-17507,15643024.0,Bibi,717.0,France,Male,43.0,8.0,0.0,1.0,0.0,0.0,55829.25
-17508,15620341.0,Ch'ien,579.0,Germany,Female,46.0,5.0,98141.57,1.0,1.0,1.0,155870.82
-17509,15614137.0,Folliero,634.0,France,Male,35.0,8.0,0.0,2.0,0.0,1.0,135077.01
-17510,15626795.0,Chiganu,627.0,France,Female,42.0,9.0,0.0,1.0,0.0,1.0,79704.12
-17511,15783875.0,Hsieh,725.0,France,Male,38.0,3.0,0.0,1.0,1.0,1.0,38652.96
-17512,15762268.0,Dyer,805.0,Spain,Male,44.0,2.0,133800.98,1.0,1.0,1.0,80320.3
-17513,15682355.0,Walker,745.0,Germany,Female,37.0,5.0,134796.85,2.0,1.0,1.0,135853.62
-17514,15665062.0,Ting,506.0,Germany,Male,43.0,5.0,106603.74,1.0,1.0,0.0,89938.08
-17515,15791321.0,Onwuamaeze,692.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,166957.82
-17516,15661412.0,Milanesi,716.0,Spain,Male,48.0,3.0,0.0,2.0,1.0,1.0,129980.93
-17517,15686957.0,Chinweike,657.0,Germany,Female,58.0,7.0,102994.04,1.0,1.0,1.0,177397.49
-17518,15692671.0,Genovese,685.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,9085.0
-17519,15644882.0,Chijioke,763.0,Germany,Female,39.0,1.0,128663.9,1.0,1.0,0.0,143357.08
-17520,15747014.0,Ch'iu,681.0,France,Male,48.0,6.0,0.0,2.0,1.0,0.0,166677.54
-17521,15641480.0,Udinesi,704.0,Germany,Female,41.0,4.0,145747.67,1.0,1.0,0.0,147964.99
-17522,15642824.0,Pirozzi,651.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,110887.14
-17523,15609824.0,Walker,609.0,France,Female,33.0,8.0,135515.76,1.0,1.0,0.0,78940.59
-17524,15702541.0,Ts'ao,673.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,83419.15
-17525,15772573.0,Maclean,653.0,Germany,Female,33.0,4.0,139825.51,2.0,1.0,1.0,168816.22
-17526,15585855.0,T'an,705.0,France,Male,26.0,7.0,0.0,2.0,0.0,1.0,103832.58
-17527,15753955.0,Nwachinemelu,659.0,Spain,Male,40.0,10.0,0.0,2.0,1.0,0.0,122937.75
-17528,15694518.0,Hsueh,684.0,France,Male,35.0,7.0,0.0,2.0,0.0,1.0,172495.81
-17529,15792360.0,O'Brien,584.0,France,Female,55.0,0.0,0.0,1.0,1.0,0.0,130789.6
-17530,15602551.0,L?,794.0,France,Female,48.0,8.0,0.0,2.0,1.0,1.0,176925.69
-17531,15765322.0,Pagnotto,660.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,171681.86
-17532,15691808.0,Kuo,764.0,Germany,Female,58.0,1.0,148249.54,1.0,1.0,0.0,154483.54
-17533,15676835.0,Rizzo,545.0,France,Female,36.0,2.0,129855.63,2.0,1.0,0.0,76640.29
-17534,15764153.0,Onyekachi,588.0,France,Male,39.0,7.0,0.0,1.0,1.0,1.0,103560.98
-17535,15636731.0,Ts'ai,805.0,France,Male,40.0,9.0,0.0,2.0,0.0,1.0,155876.06
-17536,15783883.0,Ch'ang,744.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,138528.96
-17537,15691808.0,Nkemakolam,662.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,92708.1
-17538,15750487.0,Kenechukwu,721.0,Spain,Male,30.0,9.0,0.0,2.0,0.0,1.0,69767.14
-17539,15692977.0,Hsing,608.0,Germany,Male,32.0,8.0,161613.94,2.0,1.0,0.0,119374.74
-17540,15672374.0,Nwankwo,711.0,France,Male,49.0,1.0,130385.82,1.0,1.0,0.0,157871.55
-17541,15789413.0,Ibezimako,679.0,France,Female,43.0,3.0,93680.43,1.0,1.0,0.0,97936.38
-17542,15641835.0,Palerma,695.0,France,Female,32.0,10.0,0.0,2.0,0.0,0.0,166678.28
-17543,15606861.0,Luffman,703.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,96985.58
-17544,15723032.0,Fanucci,661.0,France,Male,25.0,8.0,0.0,2.0,1.0,1.0,180622.02
-17545,15604588.0,Hodge,564.0,Germany,Female,47.0,3.0,118628.4,1.0,1.0,0.0,170829.87
-17546,15585192.0,Chidiebere,637.0,Germany,Male,51.0,3.0,122265.22,2.0,1.0,1.0,59571.5
-17547,15676937.0,Chukwukadibia,595.0,France,Male,43.0,7.0,0.0,1.0,1.0,1.0,104769.96
-17548,15732774.0,T'ien,648.0,France,Female,39.0,9.0,131390.75,1.0,1.0,0.0,171706.66
-17549,15717995.0,O'Donnell,721.0,France,Female,43.0,3.0,0.0,1.0,0.0,1.0,175295.89
-17550,15725772.0,Hsia,759.0,France,Female,46.0,1.0,0.0,3.0,1.0,0.0,148564.76
-17551,15673372.0,Ch'ang,754.0,Germany,Female,31.0,6.0,142663.46,1.0,1.0,1.0,171681.86
-17552,15702801.0,Chukwukere,753.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,123866.43
-17553,15643359.0,Chiazagomekpere,644.0,France,Male,36.0,1.0,0.0,1.0,1.0,0.0,176984.34
-17554,15658169.0,Soto,648.0,Germany,Male,50.0,9.0,133598.4,1.0,1.0,0.0,171345.02
-17555,15637993.0,Onuora,576.0,Germany,Male,36.0,1.0,127545.75,2.0,1.0,0.0,88838.14
-17556,15672930.0,Lassetter,849.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,55893.37
-17557,15757541.0,Hsueh,614.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,159830.58
-17558,15657535.0,Nucci,675.0,France,Male,23.0,6.0,123981.31,1.0,1.0,0.0,46864.0
-17559,15632069.0,Watson,634.0,Germany,Female,28.0,7.0,152264.81,2.0,1.0,0.0,177065.24
-17560,15675450.0,Hsia,646.0,Spain,Male,44.0,7.0,0.0,2.0,1.0,0.0,91340.09
-17561,15758769.0,Liao,813.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,176713.47
-17562,15746664.0,Yuriev,677.0,Germany,Male,43.0,7.0,151233.62,2.0,0.0,0.0,93843.21
-17563,15697035.0,T'an,697.0,Germany,Male,40.0,1.0,122314.39,1.0,1.0,0.0,179655.87
-17564,15581840.0,Ch'iu,731.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,152845.99
-17565,15754926.0,Chinomso,496.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,0.0,86981.45
-17566,15793883.0,P'an,619.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,0.0,145558.42
-17567,15572547.0,Chiang,599.0,Germany,Female,31.0,8.0,125827.43,1.0,1.0,1.0,178722.24
-17568,15800554.0,Chidumaga,581.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,47251.79
-17569,15660147.0,Y?an,434.0,France,Male,39.0,2.0,118560.0,1.0,1.0,0.0,131501.72
-17570,15765283.0,Kao,646.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,61792.76
-17571,15793311.0,Yudina,641.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,0.0,111590.35
-17572,15604891.0,Hsing,850.0,Spain,Female,22.0,1.0,0.0,2.0,1.0,1.0,97939.4
-17573,15568573.0,T'ien,678.0,Germany,Male,35.0,4.0,135795.31,2.0,1.0,1.0,155711.64
-17574,15596863.0,Mancini,662.0,Germany,Male,33.0,6.0,90218.9,1.0,1.0,1.0,132973.17
-17575,15642093.0,Piazza,581.0,France,Male,43.0,7.0,0.0,1.0,1.0,0.0,151902.78
-17576,15672798.0,Toscani,687.0,France,Female,42.0,9.0,0.0,2.0,0.0,1.0,17095.49
-17577,15691817.0,Ch'iu,573.0,Germany,Male,38.0,4.0,98899.91,1.0,1.0,0.0,59645.24
-17578,15742442.0,Milanesi,581.0,France,Female,50.0,1.0,0.0,1.0,0.0,1.0,141796.39
-17579,15786035.0,Horsfall,593.0,Spain,Male,28.0,6.0,0.0,2.0,0.0,0.0,170195.4
-17580,15684516.0,Onyemachukwu,581.0,France,Female,43.0,9.0,165526.71,1.0,1.0,0.0,172576.43
-17581,15773971.0,Ngozichukwuka,615.0,Spain,Female,35.0,0.0,0.0,1.0,1.0,0.0,166513.49
-17582,15739997.0,Hsiao,648.0,France,Female,31.0,1.0,0.0,2.0,1.0,1.0,171840.24
-17583,15571577.0,Chukwuemeka,678.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,141827.33
-17584,15668767.0,Chikwado,616.0,Germany,Male,31.0,7.0,102025.32,2.0,1.0,1.0,198134.9
-17585,15783883.0,K'ung,556.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,1.0,161326.73
-17586,15806327.0,Ma,685.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,0.0,56562.57
-17587,15694450.0,Ch'iu,596.0,Germany,Male,31.0,10.0,96652.86,2.0,1.0,0.0,184474.15
-17588,15811588.0,Mazzi,648.0,France,Male,33.0,6.0,0.0,2.0,0.0,0.0,170950.58
-17589,15638646.0,Fanucci,709.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,79919.13
-17590,15805523.0,P'eng,651.0,Germany,Female,44.0,6.0,144855.34,2.0,1.0,1.0,94767.77
-17591,15815552.0,Ts'ui,757.0,France,Female,55.0,7.0,149297.18,1.0,1.0,1.0,138375.54
-17592,15738181.0,Teng,599.0,Germany,Male,35.0,3.0,79932.41,2.0,1.0,1.0,116663.47
-17593,15792726.0,Lu,716.0,Spain,Male,50.0,7.0,104944.1,1.0,0.0,1.0,145548.64
-17594,15568562.0,Onio,673.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,1.0,182076.97
-17595,15788045.0,Galkin,647.0,Germany,Male,26.0,3.0,148045.58,1.0,1.0,1.0,143668.44
-17596,15617078.0,Burt,538.0,France,Female,35.0,2.0,0.0,2.0,1.0,1.0,73464.88
-17597,15769504.0,Chibugo,709.0,France,Female,25.0,2.0,139529.93,1.0,1.0,0.0,72846.58
-17598,15808544.0,Mancini,692.0,Germany,Male,50.0,7.0,130142.79,2.0,1.0,1.0,179908.71
-17599,15738785.0,Kornilova,602.0,France,Female,40.0,3.0,167974.35,1.0,0.0,1.0,176580.46
-17600,15639882.0,Ts'ui,684.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,86786.41
-17601,15771750.0,Cavenagh,663.0,France,Male,45.0,5.0,129748.54,1.0,0.0,1.0,132873.23
-17602,15672692.0,Genovese,537.0,Germany,Male,43.0,4.0,54901.01,1.0,0.0,1.0,140763.18
-17603,15572390.0,Chia,718.0,France,Female,43.0,7.0,0.0,2.0,0.0,0.0,89566.64
-17604,15698324.0,Fomin,652.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,176913.42
-17605,15768600.0,Calabresi,645.0,France,Male,36.0,3.0,0.0,2.0,0.0,0.0,86413.11
-17606,15766289.0,Tuan,539.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,86044.98
-17607,15707042.0,Ugochukwu,565.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,1.0,142331.85
-17608,15686913.0,Fiorentini,559.0,Germany,Male,44.0,4.0,143054.56,2.0,1.0,1.0,70954.41
-17609,15628893.0,Chiedozie,632.0,France,Female,53.0,3.0,0.0,1.0,1.0,0.0,84908.2
-17610,15713379.0,Olisanugo,591.0,France,Female,35.0,3.0,0.0,2.0,0.0,1.0,139356.3
-17611,15646936.0,Chukwudi,667.0,France,Female,38.0,3.0,0.0,1.0,1.0,0.0,75884.65
-17612,15671934.0,Ts'ao,635.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,142548.33
-17613,15734438.0,P'eng,636.0,Spain,Male,42.0,3.0,135096.77,1.0,0.0,0.0,123954.15
-17614,15646513.0,Chinweuba,676.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,61994.2
-17615,15642063.0,Chuang,648.0,Germany,Female,35.0,0.0,146053.66,1.0,1.0,1.0,140941.47
-17616,15572390.0,Hsia,682.0,Germany,Male,35.0,10.0,132759.8,1.0,1.0,1.0,116977.89
-17617,15644878.0,Mancini,634.0,France,Male,34.0,5.0,0.0,1.0,0.0,1.0,152992.04
-17618,15721426.0,Zuyeva,577.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,187900.12
-17619,15807433.0,Udinesi,705.0,Germany,Male,56.0,1.0,104822.45,1.0,1.0,0.0,120801.65
-17620,15777772.0,Nnaife,729.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,57549.65
-17621,15762728.0,Shih,712.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,142377.69
-17622,15701392.0,Hsueh,691.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,59961.81
-17623,15692406.0,T'ao,674.0,France,Male,24.0,8.0,0.0,2.0,0.0,1.0,85547.33
-17624,15592765.0,Chukwuemeka,647.0,Germany,Male,33.0,3.0,152827.99,2.0,1.0,1.0,149970.59
-17625,15589449.0,Hsueh,581.0,Spain,Male,51.0,2.0,0.0,1.0,1.0,0.0,123437.05
-17626,15727361.0,Chidubem,850.0,France,Male,32.0,7.0,0.0,1.0,1.0,0.0,162515.17
-17627,15701354.0,Chidiegwu,479.0,Germany,Male,51.0,6.0,111827.27,1.0,1.0,0.0,161579.85
-17628,15771299.0,Hao,667.0,Spain,Male,39.0,4.0,0.0,2.0,1.0,0.0,145792.01
-17629,15687049.0,Chiagoziem,682.0,Spain,Female,52.0,4.0,0.0,2.0,0.0,1.0,48673.18
-17630,15644849.0,Macleod,653.0,France,Male,42.0,9.0,0.0,1.0,0.0,0.0,141230.07
-17631,15703778.0,Chu,850.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,131177.22
-17632,15769216.0,Tsao,584.0,France,Female,49.0,9.0,0.0,1.0,1.0,0.0,138700.94
-17633,15662294.0,Nwachukwu,645.0,France,Female,24.0,5.0,0.0,2.0,0.0,1.0,160523.33
-17634,15682639.0,Pisano,635.0,Spain,Male,47.0,5.0,135559.8,1.0,1.0,0.0,74849.48
-17635,15583680.0,T'ao,756.0,France,Male,36.0,5.0,147910.3,1.0,1.0,1.0,135925.72
-17636,15776985.0,L?,615.0,France,Female,44.0,3.0,0.0,2.0,1.0,1.0,143357.07
-17637,15677785.0,P'eng,676.0,Germany,Female,56.0,6.0,105961.68,1.0,1.0,0.0,172930.28
-17638,15597950.0,Shih,545.0,France,Male,58.0,4.0,131659.59,1.0,1.0,1.0,179565.57
-17639,15799720.0,Galkin,696.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,122964.18
-17640,15640161.0,Nucci,694.0,Germany,Male,38.0,2.0,83347.25,1.0,1.0,1.0,43956.83
-17641,15795519.0,Frolov,656.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,134901.58
-17642,15767781.0,Smith,718.0,Germany,Female,37.0,7.0,129755.99,2.0,1.0,0.0,175072.57
-17643,15685564.0,Fanucci,841.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,181879.56
-17644,15718773.0,Yermakova,683.0,France,Female,39.0,0.0,0.0,2.0,1.0,0.0,72861.94
-17645,15659568.0,Chukwumaobim,547.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,102513.69
-17646,15588535.0,Chiawuotu,679.0,Germany,Male,29.0,7.0,132057.78,1.0,1.0,0.0,175749.36
-17647,15774882.0,Mamelu,644.0,France,Male,41.0,1.0,0.0,1.0,1.0,0.0,174815.54
-17648,15793954.0,Blesing,763.0,France,Female,57.0,3.0,0.0,1.0,1.0,1.0,84863.85
-17649,15683544.0,Hsieh,669.0,France,Female,32.0,5.0,0.0,2.0,0.0,0.0,193995.37
-17650,15590320.0,Ch'ien,678.0,Spain,Male,28.0,4.0,0.0,2.0,1.0,0.0,95957.09
-17651,15635367.0,Milani,697.0,Germany,Male,31.0,2.0,81677.22,1.0,1.0,0.0,108010.78
-17652,15709256.0,Nkemakolam,647.0,Germany,Male,50.0,7.0,121215.9,3.0,1.0,0.0,80120.19
-17653,15802570.0,Lettiere,641.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,82870.75
-17654,15720203.0,Ch'iu,796.0,Spain,Male,39.0,9.0,0.0,1.0,0.0,1.0,87908.01
-17655,15778463.0,Nwankwo,714.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,107890.69
-17656,15783398.0,Macvitie,753.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,1.0,132870.15
-17657,15729377.0,Rizzo,678.0,Spain,Female,44.0,4.0,0.0,2.0,1.0,1.0,91957.39
-17658,15771535.0,Ch'en,643.0,Germany,Female,31.0,9.0,101012.77,2.0,1.0,1.0,61937.6
-17659,15583748.0,T'ang,758.0,Germany,Female,46.0,4.0,127837.54,1.0,0.0,0.0,179964.55
-17660,15793331.0,Toscani,543.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,126702.41
-17661,15805413.0,Wood,781.0,France,Male,39.0,9.0,124976.19,1.0,1.0,0.0,96832.82
-17662,15735339.0,Zikoranachidimma,597.0,France,Female,30.0,3.0,98955.84,1.0,1.0,0.0,166682.57
-17663,15592300.0,Ndubuisi,684.0,France,Male,42.0,2.0,127772.1,1.0,0.0,1.0,74854.97
-17664,15593969.0,Chuang,697.0,France,Male,37.0,5.0,0.0,1.0,0.0,1.0,81313.51
-17665,15708505.0,Chukwuemeka,714.0,France,Male,22.0,7.0,125782.89,1.0,1.0,0.0,149535.79
-17666,15711398.0,Chiu,587.0,France,Female,32.0,1.0,151421.44,2.0,0.0,0.0,109895.16
-17667,15682048.0,Ch'in,651.0,France,Male,37.0,2.0,0.0,1.0,1.0,1.0,173498.45
-17668,15624703.0,Efimov,597.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,161671.15
-17669,15591512.0,Smith,706.0,France,Female,34.0,3.0,81684.97,1.0,1.0,1.0,60905.51
-17670,15631868.0,Nnaife,597.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,127939.26
-17671,15582762.0,Chuang,684.0,France,Male,22.0,9.0,0.0,2.0,1.0,1.0,121866.87
-17672,15749489.0,Yudina,543.0,Germany,Female,33.0,9.0,151855.33,2.0,1.0,1.0,28226.15
-17673,15682533.0,Chiang,712.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,183515.85
-17674,15571261.0,Chiemenam,706.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,171457.06
-17675,15706810.0,Chukwujekwu,599.0,Spain,Female,29.0,7.0,57568.94,1.0,1.0,0.0,151335.24
-17676,15583074.0,Martin,644.0,Germany,Female,50.0,8.0,141947.67,2.0,1.0,0.0,134638.97
-17677,15654060.0,Hsia,697.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,115818.51
-17678,15796463.0,Milano,724.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,97939.3
-17679,15749964.0,Dickson,705.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,113481.02
-17680,15793331.0,Nnachetam,590.0,France,Female,36.0,10.0,0.0,2.0,0.0,1.0,90876.95
-17681,15582714.0,Huddart,651.0,France,Male,71.0,5.0,145081.73,1.0,1.0,1.0,161229.84
-17682,15745324.0,Zikoranachidimma,626.0,France,Female,44.0,0.0,89079.41,1.0,1.0,0.0,173495.77
-17683,15693637.0,Manna,676.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,95857.18
-17684,15683375.0,Chidubem,605.0,Germany,Female,50.0,2.0,129901.21,1.0,1.0,0.0,40315.27
-17685,15633608.0,Hsia,704.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,52615.62
-17686,15750258.0,Chigozie,599.0,Spain,Male,47.0,3.0,0.0,2.0,1.0,1.0,85421.48
-17687,15793723.0,Hsieh,793.0,Germany,Male,41.0,5.0,137964.01,2.0,1.0,1.0,105965.25
-17688,15750466.0,Y?,748.0,France,Male,38.0,1.0,0.0,2.0,0.0,0.0,133091.32
-17689,15589563.0,Teng,571.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,1.0,159800.16
-17690,15749472.0,Onwughara,678.0,France,Male,34.0,8.0,0.0,2.0,0.0,1.0,88958.49
-17691,15722479.0,Ch'eng,589.0,Spain,Male,33.0,8.0,0.0,2.0,0.0,0.0,122449.15
-17692,15583090.0,Ukaegbunam,595.0,Spain,Male,38.0,4.0,127307.32,1.0,0.0,0.0,88827.67
-17693,15746035.0,Fanucci,636.0,Spain,Female,31.0,4.0,0.0,1.0,1.0,0.0,173486.39
-17694,15758831.0,Onwuatuegwu,757.0,Spain,Female,34.0,9.0,0.0,2.0,0.0,1.0,81776.84
-17695,15779743.0,Chukwumaobim,468.0,France,Female,46.0,8.0,0.0,1.0,1.0,0.0,182336.45
-17696,15695846.0,Genovesi,643.0,France,Male,44.0,4.0,0.0,2.0,0.0,1.0,86937.2
-17697,15727041.0,Ma,713.0,France,Male,31.0,8.0,0.0,2.0,0.0,0.0,170514.21
-17698,15593501.0,Chuang,579.0,Germany,Female,38.0,3.0,141642.92,1.0,1.0,1.0,52462.25
-17699,15794056.0,Pirozzi,669.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,175082.2
-17700,15810978.0,Ch'ien,605.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,82938.75
-17701,15731790.0,Zetticci,599.0,Germany,Female,33.0,5.0,107879.04,2.0,1.0,1.0,143538.52
-17702,15643359.0,Manna,582.0,Spain,Female,28.0,9.0,141749.68,1.0,1.0,1.0,64831.36
-17703,15727059.0,Pirozzi,627.0,Germany,Male,48.0,9.0,133906.22,1.0,0.0,1.0,157098.28
-17704,15804009.0,Chiemenam,707.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,187866.21
-17705,15722404.0,Aksenov,696.0,France,Female,36.0,0.0,0.0,2.0,1.0,1.0,138051.19
-17706,15740283.0,P'eng,703.0,France,Female,50.0,1.0,61581.97,3.0,0.0,0.0,179435.21
-17707,15759133.0,Chukwueloka,663.0,Spain,Female,33.0,3.0,0.0,2.0,0.0,1.0,94499.06
-17708,15615140.0,Maclean,730.0,France,Male,34.0,8.0,109730.22,2.0,1.0,1.0,121210.09
-17709,15637523.0,Debellis,706.0,Spain,Male,27.0,1.0,0.0,2.0,1.0,0.0,121765.0
-17710,15785899.0,Yobanna,720.0,France,Female,30.0,9.0,0.0,2.0,1.0,1.0,60920.03
-17711,15794868.0,Lombardi,668.0,France,Male,24.0,5.0,132403.56,1.0,1.0,1.0,122084.32
-17712,15672397.0,Pisani,609.0,Spain,Male,42.0,9.0,0.0,2.0,0.0,0.0,115136.51
-17713,15747772.0,Zikoranachidimma,724.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,158610.89
-17714,15667632.0,Nnamutaezinwa,710.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,127022.87
-17715,15753888.0,Chia,613.0,Germany,Male,32.0,6.0,129895.19,2.0,1.0,0.0,81778.72
-17716,15588537.0,T'ien,598.0,Spain,Male,28.0,8.0,0.0,2.0,1.0,1.0,82967.97
-17717,15753840.0,H?,554.0,France,Male,30.0,1.0,0.0,1.0,1.0,0.0,137037.97
-17718,15803976.0,Frolova,537.0,France,Female,28.0,8.0,0.0,1.0,0.0,1.0,79082.62
-17719,15800326.0,Trejo,758.0,Spain,Female,34.0,1.0,0.0,1.0,1.0,0.0,99183.09
-17720,15797905.0,Nnachetam,598.0,Germany,Female,41.0,6.0,121235.05,1.0,1.0,0.0,56908.41
-17721,15728683.0,Chibueze,597.0,Spain,Male,39.0,0.0,0.0,1.0,1.0,0.0,84421.34
-17722,15585047.0,Ch'ien,773.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,176576.62
-17723,15698732.0,Ch'in,696.0,France,Female,31.0,6.0,0.0,2.0,0.0,1.0,159742.55
-17724,15789560.0,Smith,667.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,26215.85
-17725,15605037.0,Hsia,669.0,Spain,Male,50.0,10.0,0.0,1.0,1.0,0.0,75664.56
-17726,15611947.0,Milne,749.0,Germany,Male,45.0,9.0,96936.81,1.0,1.0,0.0,141180.66
-17727,15604314.0,Nkemakolam,754.0,France,Female,40.0,6.0,0.0,1.0,1.0,1.0,166677.53
-17728,15809837.0,Tung,649.0,France,Male,34.0,3.0,0.0,1.0,1.0,1.0,151871.84
-17729,15664802.0,Nwachinemelu,679.0,France,Male,39.0,5.0,131394.42,1.0,0.0,1.0,125062.02
-17730,15694192.0,Ts'ui,593.0,Spain,Male,42.0,6.0,0.0,1.0,1.0,0.0,79886.06
-17731,15803682.0,Chuang,537.0,Germany,Male,46.0,6.0,84438.57,2.0,1.0,0.0,56518.0
-17732,15746674.0,Nkemdirim,719.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,110421.31
-17733,15677317.0,Chukwuemeka,757.0,France,Female,36.0,8.0,120825.7,1.0,1.0,1.0,91947.74
-17734,15777904.0,Ts'ui,706.0,France,Female,43.0,8.0,113543.33,1.0,1.0,1.0,162719.69
-17735,15791326.0,Tsou,752.0,France,Female,39.0,8.0,0.0,1.0,1.0,0.0,174027.3
-17736,15665460.0,K'ung,661.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,57876.05
-17737,15765311.0,Chien,641.0,Germany,Male,39.0,9.0,78939.15,2.0,0.0,1.0,122933.73
-17738,15581388.0,Lombardi,645.0,Germany,Female,44.0,7.0,105845.26,2.0,0.0,0.0,149503.54
-17739,15593365.0,D'Albertis,724.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,71139.8
-17740,15785654.0,Chukwuhaenye,634.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,140745.91
-17741,15757559.0,Kao,648.0,France,Male,38.0,1.0,108895.93,2.0,1.0,1.0,158816.03
-17742,15800703.0,Nebechukwu,705.0,Spain,Male,31.0,3.0,0.0,1.0,1.0,1.0,121241.37
-17743,15643865.0,Maclean,681.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,113469.65
-17744,15642063.0,Chukwueloka,679.0,Germany,Female,32.0,1.0,143680.47,2.0,1.0,0.0,124795.84
-17745,15743582.0,Y?,714.0,France,Male,29.0,3.0,0.0,2.0,0.0,0.0,187905.12
-17746,15570947.0,Dore,611.0,France,Male,41.0,2.0,71497.79,1.0,1.0,1.0,52795.27
-17747,15672598.0,Uspenskaya,600.0,France,Female,40.0,5.0,174937.64,2.0,1.0,0.0,187597.98
-17748,15732967.0,Hsiung,717.0,Spain,Male,28.0,1.0,170890.05,1.0,1.0,0.0,5791.85
-17749,15582616.0,Tien,707.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,51695.41
-17750,15716609.0,Hs?eh,662.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,82870.75
-17751,15677908.0,Toth,543.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,35600.25
-17752,15723216.0,Chinonyelum,686.0,France,Male,36.0,8.0,129893.56,1.0,0.0,0.0,126837.72
-17753,15691387.0,P'an,672.0,Spain,Female,42.0,5.0,0.0,1.0,1.0,1.0,54474.48
-17754,15581840.0,Ritchie,714.0,Germany,Male,25.0,10.0,97560.16,1.0,1.0,1.0,126396.01
-17755,15766683.0,Y?,562.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,140763.18
-17756,15650591.0,Hsueh,679.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,0.0,182718.25
-17757,15623072.0,Oluchukwu,772.0,Germany,Male,44.0,4.0,107556.07,1.0,1.0,1.0,125445.65
-17758,15782941.0,P'an,613.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,44653.5
-17759,15643496.0,Manna,596.0,Germany,Male,39.0,8.0,113657.83,2.0,1.0,0.0,158973.85
-17760,15735270.0,Chuang,584.0,Spain,Female,39.0,9.0,0.0,1.0,1.0,1.0,52318.26
-17761,15693337.0,Chukwujamuike,652.0,France,Male,35.0,2.0,119948.09,1.0,1.0,0.0,645.61
-17762,15796496.0,P'eng,682.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,51703.09
-17763,15766185.0,Chinwendu,659.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,164064.44
-17764,15683625.0,P'eng,685.0,France,Female,35.0,3.0,118004.26,1.0,1.0,0.0,74322.75
-17765,15795540.0,Obialo,652.0,France,Female,50.0,7.0,162532.17,1.0,0.0,0.0,172524.51
-17766,15773876.0,Kaleski,732.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,98894.64
-17767,15797389.0,McIntyre,663.0,Germany,Female,48.0,9.0,142658.04,1.0,1.0,0.0,129964.94
-17768,15803457.0,L?,673.0,Germany,Female,45.0,2.0,126066.13,1.0,1.0,1.0,166317.71
-17769,15783883.0,Yeh,850.0,Germany,Female,63.0,9.0,126588.81,1.0,1.0,0.0,49656.24
-17770,15781530.0,Mazzanti,731.0,France,Female,27.0,6.0,0.0,2.0,0.0,0.0,28461.29
-17771,15726403.0,Ch'iu,669.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,99398.36
-17772,15701352.0,Ndubueze,621.0,Spain,Male,60.0,5.0,0.0,2.0,1.0,1.0,40339.9
-17773,15617476.0,Akabueze,671.0,Spain,Male,30.0,1.0,0.0,2.0,1.0,0.0,152740.16
-17774,15604891.0,Ugochukwutubelum,678.0,Spain,Male,40.0,9.0,0.0,1.0,1.0,1.0,93478.96
-17775,15782390.0,Lucchese,640.0,France,Male,43.0,8.0,0.0,1.0,1.0,0.0,96875.52
-17776,15724863.0,Uspenskaya,673.0,Germany,Female,41.0,9.0,114779.35,2.0,1.0,0.0,83419.15
-17777,15618661.0,Chidubem,611.0,France,Female,27.0,0.0,0.0,2.0,1.0,1.0,151871.84
-17778,15690164.0,Nwagugheuzo,716.0,France,Male,34.0,2.0,0.0,2.0,0.0,1.0,177772.03
-17779,15737173.0,Chizuoke,678.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,120892.96
-17780,15793331.0,Chizoba,652.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,53412.24
-17781,15642726.0,Hsu,673.0,Germany,Female,41.0,2.0,104488.17,1.0,1.0,0.0,124749.08
-17782,15753955.0,Tsai,633.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,117547.76
-17783,15707291.0,T'ien,577.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,53784.22
-17784,15709916.0,Martin,679.0,France,Male,33.0,7.0,101609.01,1.0,1.0,0.0,143665.44
-17785,15747677.0,T'ien,678.0,France,Female,28.0,8.0,0.0,2.0,1.0,1.0,167035.07
-17786,15763747.0,Chinweuba,554.0,France,Male,34.0,7.0,171913.87,1.0,1.0,0.0,199808.1
-17787,15709734.0,Yashina,835.0,Spain,Female,49.0,5.0,0.0,1.0,1.0,0.0,49337.84
-17788,15674573.0,Zikoranachidimma,568.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,38186.85
-17789,15605826.0,Fiorentini,715.0,Germany,Male,37.0,5.0,102895.1,2.0,1.0,0.0,120858.13
-17790,15749669.0,Kibble,769.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,180962.32
-17791,15587712.0,Hsieh,794.0,Germany,Male,37.0,5.0,95900.02,2.0,1.0,0.0,34948.76
-17792,15806822.0,Chiemenam,579.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,54448.41
-17793,15635141.0,Milanesi,730.0,France,Female,50.0,5.0,101506.66,1.0,1.0,1.0,170819.01
-17794,15659820.0,K?,595.0,Germany,Male,27.0,7.0,90560.3,2.0,1.0,0.0,116548.02
-17795,15641675.0,Chia,573.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,67979.35
-17796,15702480.0,K'ung,561.0,Spain,Male,29.0,2.0,125921.36,1.0,0.0,1.0,71173.64
-17797,15658969.0,H?,705.0,Spain,Female,54.0,1.0,137285.26,1.0,1.0,0.0,183963.34
-17798,15741652.0,Groom,576.0,France,Female,46.0,1.0,0.0,1.0,1.0,1.0,103712.05
-17799,15792463.0,Okwudilichukwu,677.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,181605.85
-17800,15788151.0,H?,631.0,Germany,Female,38.0,7.0,116933.89,2.0,1.0,1.0,124071.71
-17801,15621436.0,Chizoba,551.0,Spain,Female,37.0,2.0,115076.06,1.0,1.0,0.0,38962.54
-17802,15598084.0,Lombardo,834.0,Germany,Male,31.0,4.0,132576.25,2.0,1.0,1.0,136620.64
-17803,15763612.0,Folliero,629.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,138113.71
-17804,15572361.0,Tuan,554.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,75762.0
-17805,15689425.0,Chukwuhaenye,573.0,Germany,Female,29.0,4.0,132044.6,2.0,1.0,1.0,77864.41
-17806,15588517.0,Uspensky,606.0,Germany,Female,42.0,1.0,97792.21,1.0,1.0,0.0,95625.03
-17807,15597901.0,Li,556.0,France,Male,38.0,3.0,0.0,2.0,0.0,0.0,88890.75
-17808,15664547.0,Okechukwu,573.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,55775.84
-17809,15614215.0,Ch'eng,850.0,France,Female,58.0,1.0,0.0,1.0,1.0,1.0,130113.78
-17810,15772412.0,Hsieh,699.0,France,Male,28.0,7.0,0.0,2.0,0.0,0.0,102021.39
-17811,15631222.0,Chukwuemeka,752.0,Germany,Female,57.0,9.0,113657.83,3.0,1.0,0.0,72909.75
-17812,15594577.0,Iweobiegbunam,586.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,0.0,141478.06
-17813,15576623.0,Maclean,699.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,141856.81
-17814,15623082.0,Kao,537.0,France,Male,35.0,0.0,0.0,2.0,1.0,0.0,154071.27
-17815,15765952.0,Ugoji,583.0,Spain,Male,60.0,8.0,116536.43,2.0,0.0,1.0,28368.37
-17816,15643819.0,Ch'iu,608.0,Germany,Female,32.0,1.0,113873.45,1.0,0.0,1.0,72849.29
-17817,15795586.0,T'ang,670.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,184524.74
-17818,15804950.0,Hickey,697.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,113581.85
-17819,15696812.0,Dickson,752.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,138051.19
-17820,15661162.0,Fenton,651.0,Spain,Female,34.0,9.0,148666.99,1.0,1.0,0.0,64833.08
-17821,15750929.0,K?,603.0,France,Male,46.0,3.0,101609.01,1.0,1.0,1.0,173629.78
-17822,15681554.0,Pisani,711.0,France,Male,22.0,10.0,0.0,2.0,1.0,1.0,125870.5
-17823,15581295.0,Trevisani,703.0,France,Female,53.0,4.0,122359.5,2.0,1.0,0.0,114092.87
-17824,15589431.0,Chinagorom,743.0,France,Male,27.0,8.0,0.0,2.0,1.0,0.0,101319.76
-17825,15806901.0,Palerma,769.0,France,Female,29.0,3.0,123723.24,1.0,1.0,0.0,129964.94
-17826,15588928.0,H?,553.0,France,Male,31.0,1.0,156815.71,1.0,1.0,1.0,62674.11
-17827,15587584.0,Hsia,636.0,Germany,Female,34.0,8.0,121558.42,2.0,1.0,1.0,141896.74
-17828,15658057.0,Ch'iu,577.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,151581.79
-17829,15790744.0,H?,775.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,124071.71
-17830,15698528.0,Hsia,581.0,Spain,Female,33.0,8.0,0.0,1.0,1.0,0.0,1002.39
-17831,15681671.0,Calabresi,652.0,Spain,Female,42.0,10.0,0.0,2.0,0.0,0.0,48249.88
-17832,15733032.0,Cawthorne,724.0,France,Male,26.0,6.0,0.0,2.0,1.0,0.0,180002.42
-17833,15649024.0,Eluemuno,679.0,Spain,Female,61.0,7.0,0.0,1.0,1.0,1.0,78670.52
-17834,15791341.0,T'an,712.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,151739.67
-17835,15638269.0,Ch'ang,644.0,Germany,Male,45.0,2.0,140931.82,1.0,1.0,0.0,82782.08
-17836,15792649.0,Tsao,622.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,36405.21
-17837,15595588.0,Toscani,667.0,France,Female,42.0,5.0,148843.59,1.0,1.0,1.0,130553.47
-17838,15587562.0,Ozerova,704.0,Spain,Female,45.0,3.0,0.0,1.0,1.0,0.0,85890.75
-17839,15695097.0,Chiagoziem,644.0,Germany,Male,30.0,10.0,143637.52,1.0,0.0,0.0,76510.46
-17840,15609823.0,T'ien,648.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,185599.34
-17841,15782454.0,Manna,715.0,Germany,Female,31.0,5.0,163549.63,1.0,1.0,0.0,175894.24
-17842,15657779.0,P'eng,645.0,Germany,Male,32.0,1.0,133681.98,2.0,1.0,0.0,113581.85
-17843,15647838.0,Shih,713.0,Germany,Male,42.0,9.0,132351.29,2.0,1.0,1.0,85547.33
-17844,15605037.0,Tuan,842.0,France,Female,29.0,6.0,0.0,1.0,0.0,1.0,176692.65
-17845,15713378.0,Udinese,621.0,France,Female,38.0,2.0,0.0,1.0,1.0,0.0,80178.54
-17846,15654959.0,Chia,708.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,171463.83
-17847,15679801.0,Toscani,469.0,Germany,Male,33.0,8.0,137059.05,2.0,0.0,1.0,111241.37
-17848,15583849.0,Whitehead,749.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,146680.25
-17849,15748231.0,Watson,633.0,Germany,Female,52.0,4.0,105701.91,1.0,0.0,0.0,154844.22
-17850,15782802.0,Nwachinemelu,651.0,Germany,Male,45.0,8.0,125829.13,2.0,1.0,0.0,25650.04
-17851,15636024.0,Okechukwu,686.0,France,Male,33.0,7.0,130170.82,1.0,1.0,0.0,55775.84
-17852,15578865.0,T'ien,707.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,177065.24
-17853,15703763.0,Chigolum,637.0,Germany,Male,32.0,2.0,99845.04,2.0,1.0,1.0,166586.99
-17854,15687413.0,King,684.0,France,Male,29.0,8.0,134956.02,1.0,1.0,0.0,121311.12
-17855,15620341.0,Ts'ao,704.0,France,Male,30.0,3.0,97202.32,1.0,1.0,1.0,90928.48
-17856,15602851.0,Chikelu,622.0,Germany,Male,46.0,2.0,122034.4,1.0,0.0,1.0,177622.38
-17857,15790489.0,Linton,709.0,France,Female,28.0,0.0,0.0,1.0,1.0,0.0,86460.28
-17858,15640953.0,Chiebuka,624.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,138780.89
-17859,15731513.0,K'ung,689.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,75937.38
-17860,15684868.0,Calabrese,631.0,Germany,Female,44.0,1.0,106901.94,1.0,1.0,1.0,155746.55
-17861,15803689.0,Hsueh,704.0,France,Male,52.0,2.0,0.0,2.0,1.0,1.0,14081.64
-17862,15686963.0,Chidiegwu,658.0,Germany,Female,45.0,10.0,113348.6,1.0,0.0,0.0,90839.61
-17863,15613102.0,T'ien,635.0,Spain,Male,32.0,10.0,149961.99,1.0,0.0,1.0,94888.6
-17864,15632324.0,Hsiao,686.0,Spain,Male,41.0,6.0,0.0,2.0,1.0,1.0,175743.92
-17865,15640866.0,McGregor,757.0,Germany,Female,45.0,1.0,157960.49,1.0,1.0,0.0,103570.8
-17866,15692408.0,Tuan,706.0,Spain,Female,36.0,5.0,134498.54,1.0,1.0,1.0,91673.6
-17867,15621075.0,Chukwukere,723.0,Spain,Male,45.0,10.0,0.0,1.0,0.0,0.0,89566.64
-17868,15598884.0,Ch'ien,586.0,France,Male,37.0,7.0,0.0,2.0,0.0,1.0,141023.01
-17869,15583736.0,Yobanna,653.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,129936.53
-17870,15649858.0,Ch'iu,577.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,186841.71
-17871,15594864.0,Chiu,632.0,France,Male,25.0,6.0,126442.59,1.0,1.0,1.0,160976.87
-17872,15798895.0,T'ang,850.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,143681.97
-17873,15807989.0,Maclean,669.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,172629.67
-17874,15605827.0,Ibeabuchi,598.0,Spain,Female,41.0,0.0,171325.98,1.0,1.0,1.0,135034.43
-17875,15613787.0,Nnamdi,675.0,Spain,Female,43.0,0.0,0.0,2.0,1.0,1.0,91347.76
-17876,15792250.0,Nwora,597.0,France,Female,32.0,2.0,122338.21,1.0,1.0,0.0,166677.35
-17877,15769959.0,Iadanza,673.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,88947.62
-17878,15671351.0,Oluchukwu,584.0,France,Female,32.0,10.0,0.0,2.0,1.0,0.0,81436.68
-17879,15641595.0,Chibugo,681.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,162668.33
-17880,15596713.0,Onyekachukwu,645.0,France,Male,26.0,2.0,0.0,2.0,1.0,1.0,131303.1
-17881,15600882.0,Nock,696.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,93073.04
-17882,15667046.0,Onwuamaeze,569.0,France,Male,37.0,3.0,151855.33,1.0,1.0,0.0,56905.38
-17883,15779915.0,Onwuamaeze,667.0,France,Male,33.0,7.0,0.0,1.0,1.0,0.0,76582.95
-17884,15791326.0,Ch'iu,677.0,France,Female,31.0,4.0,161546.88,1.0,1.0,0.0,131647.31
-17885,15605067.0,Lucciano,738.0,France,Female,40.0,2.0,139561.94,1.0,1.0,0.0,121866.87
-17886,15793331.0,Teng,562.0,Germany,Male,35.0,4.0,139825.37,2.0,0.0,0.0,168886.18
-17887,15659949.0,Lo Duca,753.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,180962.32
-17888,15651664.0,Nwebube,710.0,Germany,Female,49.0,2.0,123971.51,1.0,1.0,1.0,125381.02
-17889,15617790.0,Toscani,699.0,Germany,Female,58.0,7.0,101242.98,1.0,1.0,0.0,84871.99
-17890,15686917.0,K?,774.0,Germany,Female,32.0,5.0,71269.89,2.0,0.0,1.0,185543.74
-17891,15589329.0,Tien,663.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,69578.49
-17892,15583067.0,Palerma,652.0,Germany,Male,40.0,0.0,128605.32,1.0,0.0,0.0,170518.31
-17893,15587562.0,Lucchese,679.0,Germany,Female,52.0,6.0,89182.09,1.0,1.0,0.0,70563.9
-17894,15654964.0,Chidimma,649.0,Germany,Male,46.0,1.0,127544.84,2.0,1.0,0.0,57320.92
-17895,15753847.0,Lu,587.0,France,Male,48.0,2.0,0.0,2.0,1.0,0.0,96832.82
-17896,15757811.0,Hsieh,682.0,Germany,Female,31.0,0.0,98955.87,1.0,1.0,0.0,164018.97
-17897,15572022.0,Lo Duca,583.0,France,Female,48.0,4.0,0.0,2.0,1.0,1.0,72970.93
-17898,15748597.0,Chuang,607.0,France,Male,42.0,1.0,138515.02,1.0,1.0,0.0,87834.24
-17899,15700946.0,Cawood,811.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,1.0,123427.98
-17900,15621205.0,Onyekachukwu,539.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,88890.75
-17901,15650018.0,Echezonachukwu,521.0,France,Female,37.0,3.0,0.0,2.0,0.0,1.0,162960.05
-17902,15653572.0,Hsueh,667.0,Spain,Female,22.0,5.0,88141.1,1.0,1.0,1.0,64422.81
-17903,15709441.0,Pirozzi,724.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,85890.75
-17904,15656319.0,Chibugo,699.0,Germany,Male,25.0,1.0,107192.38,1.0,1.0,0.0,52332.85
-17905,15795586.0,Ch'eng,718.0,Germany,Female,49.0,6.0,88902.21,1.0,1.0,0.0,103901.31
-17906,15739931.0,Hargreaves,758.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,141827.83
-17907,15725141.0,Zetticci,641.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,79495.01
-17908,15609824.0,Ch'ang,711.0,Germany,Male,51.0,5.0,113905.48,1.0,1.0,0.0,74564.41
-17909,15661409.0,Bischof,697.0,France,Male,27.0,9.0,141604.76,1.0,1.0,1.0,26793.82
-17910,15693814.0,Ch'ien,656.0,France,Female,36.0,4.0,84421.62,1.0,1.0,1.0,80591.16
-17911,15654519.0,Folliero,565.0,Germany,Male,36.0,5.0,120197.42,1.0,1.0,0.0,181490.42
-17912,15720353.0,Chiang,583.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,47722.79
-17913,15815295.0,K'ung,592.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,172490.78
-17914,15795895.0,H?,669.0,Germany,Female,46.0,9.0,156848.13,1.0,0.0,1.0,17095.49
-17915,15771942.0,Ch'eng,565.0,Spain,Female,35.0,3.0,0.0,2.0,0.0,1.0,85982.48
-17916,15741658.0,Nnachetam,727.0,France,Male,37.0,6.0,0.0,2.0,0.0,0.0,127059.04
-17917,15631457.0,Ch'ang,751.0,Germany,Male,33.0,6.0,117153.0,2.0,1.0,0.0,172668.39
-17918,15683363.0,Palermo,651.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,141872.05
-17919,15681705.0,Calabrese,756.0,France,Male,32.0,10.0,0.0,2.0,0.0,0.0,133871.83
-17920,15579130.0,Onyekachi,652.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,170886.01
-17921,15654463.0,Lai,681.0,France,Female,44.0,7.0,0.0,2.0,1.0,0.0,167984.72
-17922,15693326.0,Hsiung,651.0,Germany,Male,38.0,2.0,128927.93,1.0,1.0,1.0,94499.06
-17923,15614220.0,Gboliwe,681.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,172576.43
-17924,15757559.0,Chigolum,650.0,Germany,Male,31.0,1.0,113858.95,1.0,1.0,0.0,88890.05
-17925,15787830.0,Chiu,642.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,184544.59
-17926,15736397.0,Clements,687.0,France,Female,32.0,8.0,0.0,2.0,0.0,1.0,171519.06
-17927,15649738.0,T'ien,705.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,165562.84
-17928,15783097.0,Ch'ang,579.0,France,Male,44.0,5.0,0.0,2.0,1.0,0.0,136071.05
-17929,15727299.0,De Luca,664.0,France,Female,37.0,4.0,107716.93,1.0,1.0,1.0,26019.59
-17930,15576352.0,Maclean,584.0,Spain,Male,51.0,10.0,0.0,1.0,1.0,1.0,102655.31
-17931,15605916.0,Kornilova,722.0,Spain,Female,42.0,4.0,0.0,1.0,1.0,1.0,79623.28
-17932,15782418.0,T'ien,699.0,Germany,Male,56.0,5.0,125193.96,1.0,0.0,1.0,59981.62
-17933,15579112.0,Shih,739.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,74872.46
-17934,15674851.0,Nwankwo,598.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,127825.25
-17935,15655659.0,Hsueh,622.0,France,Female,63.0,1.0,140576.11,1.0,1.0,1.0,116474.35
-17936,15780056.0,Iadanza,582.0,France,Male,24.0,5.0,0.0,2.0,0.0,1.0,109626.73
-17937,15679963.0,Nnonso,633.0,Spain,Male,46.0,6.0,0.0,1.0,0.0,1.0,176943.59
-17938,15585595.0,Pirozzi,757.0,France,Male,33.0,5.0,136487.86,1.0,1.0,1.0,96883.42
-17939,15615896.0,Iadanza,620.0,Spain,Male,28.0,9.0,0.0,1.0,1.0,1.0,114675.75
-17940,15693906.0,Mazzanti,612.0,Germany,Female,37.0,4.0,121461.87,2.0,1.0,1.0,72993.65
-17941,15632446.0,Ch'iu,571.0,France,Male,22.0,8.0,0.0,1.0,1.0,0.0,149535.97
-17942,15778959.0,Rizzo,762.0,France,Female,30.0,7.0,0.0,2.0,1.0,1.0,180712.52
-17943,15649024.0,Mbanefo,850.0,Spain,Male,32.0,6.0,0.0,1.0,1.0,0.0,4861.72
-17944,15664543.0,Onwumelu,769.0,Germany,Female,46.0,6.0,116037.52,1.0,1.0,0.0,133814.28
-17945,15705860.0,Ch'in,636.0,Spain,Male,42.0,5.0,0.0,1.0,1.0,0.0,54439.64
-17946,15790689.0,Fomin,633.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,152996.89
-17947,15581525.0,P'an,658.0,France,Male,54.0,0.0,0.0,2.0,1.0,1.0,51637.67
-17948,15732660.0,Hs?,556.0,France,Female,42.0,8.0,83772.95,1.0,1.0,1.0,181893.31
-17949,15643361.0,Ting,693.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,88890.75
-17950,15807989.0,Hsiao,572.0,Germany,Male,32.0,2.0,92474.05,2.0,1.0,0.0,61257.83
-17951,15792566.0,Nnachetam,707.0,France,Male,29.0,7.0,0.0,1.0,1.0,1.0,168816.73
-17952,15593979.0,T'ien,705.0,Spain,Male,28.0,8.0,0.0,2.0,1.0,1.0,178373.43
-17953,15603563.0,Lo,556.0,Germany,Male,43.0,2.0,121542.57,1.0,1.0,0.0,113481.02
-17954,15589435.0,Rowntree,645.0,France,Female,32.0,9.0,121405.04,1.0,0.0,1.0,141400.51
-17955,15714993.0,Macdonald,698.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,86767.48
-17956,15611786.0,Chinedum,494.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,1.0,135077.01
-17957,15577274.0,Lucchesi,762.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,50899.91
-17958,15793854.0,Mollison,748.0,France,Female,35.0,3.0,0.0,1.0,1.0,1.0,142670.61
-17959,15778975.0,Liao,742.0,France,Male,42.0,7.0,0.0,3.0,1.0,0.0,65675.48
-17960,15586517.0,H?,755.0,Spain,Female,51.0,1.0,0.0,3.0,1.0,0.0,75860.01
-17961,15690829.0,Mazzi,729.0,Spain,Male,32.0,5.0,0.0,2.0,0.0,1.0,149614.17
-17962,15768095.0,Chien,730.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,24402.87
-17963,15745012.0,Chigolum,634.0,France,Male,46.0,2.0,135078.41,1.0,1.0,0.0,158750.43
-17964,15604813.0,Chibugo,687.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,73322.0
-17965,15727281.0,Onyemauchechukwu,677.0,Spain,Female,26.0,9.0,0.0,2.0,0.0,1.0,109659.12
-17966,15632826.0,Onyekachi,557.0,France,Female,37.0,1.0,0.0,1.0,0.0,1.0,105716.05
-17967,15809846.0,K'ung,705.0,France,Male,58.0,5.0,0.0,2.0,1.0,1.0,95719.73
-17968,15687634.0,Hsing,667.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,1.0,98820.09
-17969,15633650.0,Chiazagomekpere,727.0,France,Female,37.0,2.0,0.0,2.0,0.0,1.0,152733.89
-17970,15621063.0,Lucciano,622.0,France,Female,30.0,4.0,0.0,1.0,0.0,1.0,166678.28
-17971,15572265.0,Fiorentino,586.0,France,Male,25.0,7.0,0.0,2.0,1.0,0.0,141946.92
-17972,15626806.0,Ch'ang,622.0,France,Male,45.0,5.0,0.0,2.0,1.0,0.0,179634.69
-17973,15790744.0,K?,597.0,Spain,Male,43.0,8.0,0.0,2.0,0.0,1.0,168544.85
-17974,15764491.0,Chiu,734.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,109523.88
-17975,15577078.0,Yoo,696.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,118913.53
-17976,15793311.0,Ch'ang,612.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,153917.41
-17977,15733705.0,Ugochukwu,607.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,101455.07
-17978,15672950.0,Nweke,553.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,122925.44
-17979,15591747.0,Chien,777.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,133086.32
-17980,15628283.0,Tuan,681.0,France,Female,33.0,4.0,0.0,2.0,0.0,1.0,104889.3
-17981,15737778.0,Hs?eh,762.0,France,Male,49.0,5.0,0.0,1.0,0.0,1.0,136810.18
-17982,15686963.0,Onwughara,726.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,106826.92
-17983,15593897.0,Ch'in,520.0,France,Female,31.0,1.0,0.0,2.0,0.0,1.0,140889.32
-17984,15781409.0,Biryukov,785.0,France,Female,40.0,3.0,0.0,2.0,1.0,1.0,175043.69
-17985,15585978.0,Li Fonti,741.0,France,Male,40.0,2.0,98894.39,1.0,1.0,0.0,127939.19
-17986,15605424.0,Kenechukwu,748.0,Spain,Female,30.0,1.0,0.0,2.0,1.0,0.0,82565.01
-17987,15721383.0,Calabresi,588.0,Spain,Male,42.0,6.0,0.0,2.0,1.0,1.0,120892.63
-17988,15798964.0,Okechukwu,687.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,70810.6
-17989,15607988.0,Genovesi,681.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,132973.17
-17990,15761976.0,Chienezie,687.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,140765.55
-17991,15762953.0,Uchenna,755.0,Spain,Male,28.0,6.0,0.0,2.0,1.0,1.0,60901.72
-17992,15762803.0,Yancy,648.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,93091.02
-17993,15630244.0,Hs?,648.0,Spain,Female,37.0,2.0,114435.47,1.0,1.0,0.0,167030.51
-17994,15709621.0,O'Donnell,697.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,109545.56
-17995,15623450.0,Yashina,648.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,151411.2
-17996,15600813.0,Ch'iu,675.0,Germany,Male,37.0,7.0,121093.39,1.0,1.0,1.0,76679.6
-17997,15593973.0,Iadanza,724.0,Germany,Male,29.0,1.0,126670.37,1.0,1.0,1.0,72628.41
-17998,15595537.0,Y?,721.0,France,Male,33.0,6.0,0.0,2.0,0.0,1.0,176471.79
-17999,15589969.0,Pirozzi,652.0,Germany,Female,44.0,6.0,129855.32,1.0,1.0,0.0,106630.51
-18000,15796074.0,Maclean,850.0,France,Male,27.0,5.0,0.0,2.0,1.0,1.0,94003.11
-18001,15775153.0,Nnachetam,553.0,Spain,Female,60.0,3.0,0.0,1.0,0.0,1.0,159654.0
-18002,15581280.0,Pirozzi,731.0,France,Female,41.0,8.0,117413.95,1.0,0.0,1.0,171050.43
-18003,15585580.0,Maclean,627.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,85523.24
-18004,15607884.0,Fennell,714.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,173870.39
-18005,15757577.0,Onuoha,807.0,Spain,Female,41.0,1.0,121681.91,1.0,1.0,1.0,150242.44
-18006,15665760.0,Fyodorova,817.0,France,Male,40.0,4.0,130812.91,1.0,1.0,0.0,81861.1
-18007,15674442.0,Chigolum,542.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,141798.98
-18008,15589488.0,Hsia,680.0,Germany,Male,29.0,1.0,115142.9,1.0,1.0,1.0,104982.39
-18009,15717489.0,Smith,678.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,171681.86
-18010,15673539.0,H?,606.0,France,Male,31.0,1.0,0.0,1.0,1.0,1.0,61937.6
-18011,15703437.0,H?,708.0,France,Male,35.0,5.0,0.0,2.0,0.0,0.0,126796.69
-18012,15727138.0,Padovesi,593.0,Spain,Female,37.0,0.0,0.0,2.0,1.0,0.0,70523.92
-18013,15730287.0,Chuang,664.0,Germany,Male,32.0,1.0,123675.69,1.0,1.0,1.0,70563.9
-18014,15595440.0,Smith,682.0,France,Male,33.0,4.0,115897.73,1.0,1.0,1.0,157564.75
-18015,15792222.0,Ch'ang,582.0,France,Female,31.0,8.0,112713.34,2.0,1.0,1.0,67979.35
-18016,15716236.0,Chiang,663.0,Spain,Male,42.0,7.0,0.0,2.0,1.0,1.0,93624.55
-18017,15787014.0,Ch'ien,676.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,104889.3
-18018,15775153.0,Tien,593.0,France,Female,46.0,7.0,0.0,2.0,1.0,1.0,87908.01
-18019,15696834.0,Chukwuebuka,632.0,France,Female,29.0,3.0,0.0,1.0,1.0,1.0,171657.57
-18020,15655774.0,Babbage,743.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,167036.94
-18021,15693732.0,Tseng,584.0,Spain,Female,52.0,6.0,79563.46,1.0,1.0,1.0,79577.4
-18022,15671345.0,Nwachukwu,850.0,France,Male,40.0,8.0,153639.11,1.0,1.0,0.0,64849.74
-18023,15622230.0,Chigozie,684.0,France,Female,29.0,4.0,0.0,1.0,1.0,1.0,79919.13
-18024,15783030.0,Nwachinemelu,645.0,Spain,Female,47.0,4.0,0.0,1.0,1.0,0.0,102908.91
-18025,15608845.0,Bischof,686.0,Germany,Male,28.0,4.0,134896.04,2.0,1.0,0.0,165562.84
-18026,15754072.0,Ch'eng,706.0,Spain,Female,39.0,6.0,155973.88,1.0,1.0,0.0,164064.44
-18027,15814772.0,Onuchukwu,697.0,France,Female,34.0,10.0,161546.68,1.0,1.0,1.0,176791.27
-18028,15603846.0,Lu,703.0,Germany,Female,24.0,7.0,106920.91,2.0,1.0,0.0,109496.45
-18029,15571689.0,Calabrese,553.0,Germany,Female,55.0,1.0,137510.34,2.0,1.0,0.0,156476.36
-18030,15581295.0,Chiu,677.0,Spain,Male,42.0,10.0,0.0,2.0,1.0,1.0,180551.76
-18031,15747265.0,Ch'ien,549.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,0.0,67830.2
-18032,15790448.0,Nnachetam,605.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,38190.78
-18033,15686743.0,Y?,678.0,Spain,Male,44.0,1.0,156091.97,1.0,1.0,1.0,137793.39
-18034,15682021.0,Okwuadigbo,732.0,Germany,Male,38.0,5.0,144848.74,2.0,0.0,1.0,127547.18
-18035,15768295.0,Uvarov,684.0,France,Female,34.0,9.0,0.0,1.0,1.0,1.0,70550.0
-18036,15762045.0,Shao,646.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,101835.24
-18037,15592994.0,Chukwuemeka,839.0,Spain,Male,43.0,1.0,0.0,2.0,1.0,1.0,142956.48
-18038,15650266.0,Ritchie,835.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,87822.14
-18039,15652981.0,Chukwufumnanya,569.0,France,Female,31.0,10.0,0.0,1.0,1.0,1.0,132450.19
-18040,15652218.0,Ch'ien,639.0,France,Female,44.0,7.0,0.0,2.0,1.0,0.0,149814.68
-18041,15605037.0,Toscani,706.0,Germany,Female,41.0,1.0,127545.94,2.0,0.0,1.0,161519.66
-18042,15592550.0,K'ung,568.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,157553.98
-18043,15673908.0,Milne,473.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,102297.15
-18044,15715707.0,Ives,735.0,France,Male,32.0,3.0,163630.76,1.0,1.0,1.0,158032.5
-18045,15719302.0,T'ien,652.0,France,Male,27.0,4.0,0.0,2.0,0.0,0.0,64443.75
-18046,15700845.0,L?,673.0,Spain,Male,32.0,8.0,146587.3,1.0,0.0,0.0,186612.47
-18047,15641366.0,Chiazagomekpele,675.0,Germany,Male,37.0,2.0,151836.36,2.0,1.0,1.0,118059.19
-18048,15583681.0,Kao,659.0,Spain,Male,26.0,7.0,0.0,2.0,1.0,0.0,136812.09
-18049,15645778.0,Brown,668.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,91810.71
-18050,15803526.0,Ch'ang,616.0,Germany,Female,38.0,3.0,121537.71,1.0,1.0,1.0,185599.12
-18051,15787699.0,Ch'ien,569.0,Germany,Female,41.0,4.0,95984.69,2.0,1.0,1.0,171096.2
-18052,15764343.0,Tung,645.0,Spain,Female,30.0,2.0,0.0,2.0,1.0,0.0,66893.28
-18053,15702801.0,Toscani,729.0,France,Female,54.0,9.0,105420.18,1.0,0.0,0.0,137028.51
-18054,15756625.0,L?,708.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,138526.96
-18055,15782778.0,Folliero,705.0,France,Male,44.0,7.0,0.0,1.0,1.0,1.0,105533.97
-18056,15742824.0,Chien,729.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,153810.41
-18057,15589428.0,Chukwualuka,758.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,135431.72
-18058,15656005.0,Obialo,714.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,145562.4
-18059,15693906.0,Maclean,706.0,Germany,Male,42.0,6.0,112367.34,2.0,1.0,1.0,131772.47
-18060,15672534.0,Rizzo,625.0,Spain,Male,39.0,0.0,0.0,1.0,1.0,0.0,121311.12
-18061,15764314.0,Larkin,733.0,Spain,Female,35.0,7.0,0.0,2.0,1.0,0.0,142548.33
-18062,15576022.0,Hsiao,628.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,172033.57
-18063,15583764.0,Fanucci,543.0,France,Male,37.0,5.0,113308.77,1.0,0.0,0.0,137508.42
-18064,15617705.0,Ch'iu,710.0,Spain,Male,32.0,1.0,135645.01,1.0,0.0,0.0,175544.02
-18065,15770498.0,Li Fonti,656.0,France,Female,51.0,2.0,0.0,1.0,1.0,1.0,91673.6
-18066,15653849.0,Yashina,753.0,Spain,Female,40.0,1.0,0.0,2.0,0.0,1.0,170528.63
-18067,15770159.0,Chibugo,596.0,Germany,Female,31.0,1.0,102994.04,1.0,1.0,0.0,61290.99
-18068,15810978.0,P'eng,752.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,73656.38
-18069,15677310.0,Lucciano,785.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,1.0,186841.71
-18070,15652667.0,Shao,634.0,Spain,Male,24.0,2.0,0.0,1.0,1.0,1.0,145937.99
-18071,15803037.0,Mamelu,652.0,France,Female,31.0,1.0,96891.46,2.0,1.0,0.0,97923.24
-18072,15796864.0,Hsieh,595.0,France,Female,45.0,10.0,0.0,1.0,1.0,0.0,57572.38
-18073,15578098.0,Hsia,569.0,France,Female,47.0,9.0,0.0,1.0,1.0,0.0,140724.64
-18074,15618661.0,Genovesi,754.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,1.0,121208.77
-18075,15797293.0,Mancini,559.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,129994.63
-18076,15772423.0,De Luca,706.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,60917.24
-18077,15690620.0,Chukwudi,554.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,0.0,141872.05
-18078,15722599.0,Ts'ui,577.0,Spain,Male,39.0,5.0,0.0,2.0,0.0,1.0,122996.76
-18079,15718779.0,Macleod,592.0,Spain,Female,29.0,6.0,0.0,2.0,1.0,0.0,145562.4
-18080,15804064.0,Scott,752.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,5791.85
-18081,15662501.0,Nucci,605.0,France,Female,42.0,8.0,0.0,2.0,1.0,0.0,145528.22
-18082,15649738.0,Chukwuemeka,561.0,France,Male,35.0,9.0,155872.5,1.0,1.0,1.0,163558.36
-18083,15659486.0,Chienezie,686.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,1.0,9201.53
-18084,15674551.0,Cattaneo,531.0,Spain,Male,45.0,5.0,112211.61,1.0,1.0,1.0,75888.65
-18085,15699467.0,Ts'ai,616.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,0.0,83058.14
-18086,15589431.0,Ni,667.0,France,Female,54.0,8.0,0.0,1.0,1.0,0.0,186489.95
-18087,15798964.0,Nnachetam,695.0,France,Female,41.0,1.0,141749.68,1.0,1.0,0.0,88930.2
-18088,15573077.0,Tuan,663.0,France,Female,32.0,2.0,0.0,3.0,1.0,1.0,141946.92
-18089,15763515.0,Nnamutaezinwa,632.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,122518.5
-18090,15599410.0,Ch'iu,577.0,France,Female,27.0,0.0,0.0,2.0,1.0,1.0,148811.52
-18091,15807597.0,Chinweuba,648.0,France,Female,56.0,7.0,79450.09,1.0,1.0,0.0,40385.61
-18092,15707473.0,Tseng,605.0,Germany,Female,37.0,3.0,122550.05,1.0,1.0,0.0,185599.12
-18093,15693543.0,Ejikemeifeuwa,589.0,Germany,Female,35.0,6.0,121863.61,2.0,1.0,1.0,136521.82
-18094,15590094.0,Trevisani,639.0,Germany,Male,61.0,3.0,124577.33,1.0,1.0,1.0,161579.85
-18095,15668747.0,Trevisani,604.0,France,Male,28.0,1.0,136496.12,1.0,1.0,1.0,107841.77
-18096,15617482.0,Li Fonti,716.0,France,Female,36.0,8.0,0.0,2.0,1.0,0.0,90896.04
-18097,15803976.0,Chidimma,677.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,106977.8
-18098,15730590.0,Chuang,735.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,160129.99
-18099,15646632.0,L?,579.0,France,Female,32.0,2.0,118336.95,1.0,1.0,1.0,77830.25
-18100,15670039.0,Genovese,622.0,Spain,Male,40.0,2.0,0.0,1.0,1.0,0.0,176750.81
-18101,15627365.0,Shao,565.0,France,Female,40.0,4.0,0.0,4.0,1.0,0.0,60765.79
-18102,15675328.0,Maccallum,634.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,0.0,52795.56
-18103,15762332.0,Pirozzi,678.0,France,Male,40.0,4.0,164948.98,1.0,0.0,1.0,27758.36
-18104,15795519.0,Calabresi,683.0,France,Female,32.0,5.0,0.0,2.0,0.0,0.0,199693.84
-18105,15679294.0,Ch'iu,678.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,108891.3
-18106,15694408.0,Eluemuno,581.0,Spain,Female,49.0,3.0,80542.81,1.0,1.0,1.0,177065.24
-18107,15592222.0,H?,597.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,112157.31
-18108,15697948.0,Chiang,592.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,161653.5
-18109,15773338.0,McLean,637.0,France,Female,45.0,4.0,0.0,2.0,1.0,0.0,121763.69
-18110,15642344.0,Y?,743.0,France,Female,33.0,7.0,99564.22,1.0,0.0,0.0,124720.68
-18111,15763033.0,De Luca,807.0,Germany,Male,37.0,8.0,92887.06,1.0,1.0,1.0,79580.13
-18112,15763898.0,Gorman,641.0,France,Female,63.0,6.0,0.0,2.0,0.0,1.0,133091.32
-18113,15690164.0,P'eng,691.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,123866.43
-18114,15589475.0,Echezonachukwu,635.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,1.0,92794.61
-18115,15803801.0,Brierly,646.0,France,Male,47.0,1.0,0.0,2.0,1.0,1.0,185543.67
-18116,15793331.0,Milne,663.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,157567.37
-18117,15691150.0,Macleod,684.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,124489.88
-18118,15652789.0,Liao,634.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,182076.97
-18119,15777045.0,Ch'iu,739.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,138350.74
-18120,15738785.0,Bischof,679.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,187597.98
-18121,15775886.0,Ch'eng,718.0,Spain,Male,34.0,0.0,0.0,2.0,1.0,0.0,51112.8
-18122,15780561.0,Chukwualuka,627.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,163666.85
-18123,15584580.0,Hsieh,658.0,France,Male,36.0,0.0,0.0,2.0,1.0,0.0,103570.8
-18124,15599591.0,Uchechukwu,590.0,Germany,Female,40.0,7.0,120718.28,2.0,1.0,1.0,131629.82
-18125,15720353.0,Chiedozie,653.0,France,Female,34.0,2.0,170184.99,1.0,1.0,1.0,43889.41
-18126,15640407.0,Yobanna,675.0,France,Male,39.0,8.0,141441.36,1.0,1.0,0.0,126812.63
-18127,15793424.0,Ch'eng,831.0,Germany,Male,40.0,4.0,141325.56,1.0,1.0,0.0,186778.89
-18128,15647895.0,Chiemela,556.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,71557.12
-18129,15672983.0,Clements,676.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,115668.82
-18130,15611786.0,Maclean,659.0,France,Male,36.0,6.0,0.0,1.0,0.0,0.0,86935.08
-18131,15687558.0,Scott,694.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,86733.61
-18132,15740356.0,De Luca,583.0,Spain,Male,39.0,3.0,0.0,2.0,0.0,1.0,51670.82
-18133,15610271.0,Nnonso,603.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,645.61
-18134,15797710.0,Onyemauchechukwu,703.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,156906.15
-18135,15798964.0,Ts'ai,663.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,103511.35
-18136,15656822.0,Chien,666.0,France,Male,50.0,3.0,0.0,1.0,1.0,0.0,180598.66
-18137,15783691.0,Teng,635.0,Germany,Female,46.0,1.0,143452.74,1.0,0.0,0.0,124874.23
-18138,15643658.0,Trevisani,703.0,France,Male,44.0,5.0,0.0,2.0,1.0,0.0,169303.65
-18139,15783063.0,Y?an,786.0,France,Female,41.0,0.0,0.0,2.0,1.0,0.0,166677.35
-18140,15742229.0,Pirozzi,675.0,Spain,Female,37.0,4.0,123971.16,1.0,1.0,0.0,143681.83
-18141,15722583.0,Fiorentini,539.0,Spain,Male,34.0,3.0,0.0,2.0,0.0,1.0,121551.56
-18142,15701175.0,Ch'en,698.0,Spain,Male,26.0,2.0,0.0,2.0,1.0,0.0,131177.22
-18143,15652952.0,Shubin,489.0,Germany,Female,61.0,3.0,124577.15,1.0,1.0,0.0,90879.13
-18144,15654531.0,Ch'eng,627.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,126144.96
-18145,15724691.0,Tretiakova,775.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,176883.42
-18146,15599131.0,Chiu,692.0,France,Female,38.0,3.0,0.0,1.0,1.0,0.0,96420.58
-18147,15641640.0,Chukwuemeka,695.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,175894.24
-18148,15641114.0,Hsiao,721.0,France,Female,39.0,2.0,0.0,1.0,1.0,0.0,95957.09
-18149,15658435.0,Ch'in,648.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,52876.41
-18150,15600462.0,Ch'ien,721.0,France,Male,31.0,7.0,0.0,2.0,0.0,0.0,141872.05
-18151,15692408.0,Chiemezie,748.0,France,Female,69.0,1.0,0.0,2.0,0.0,1.0,102908.91
-18152,15709991.0,T'ien,672.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,176768.59
-18153,15585067.0,Chidiebere,705.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,140800.66
-18154,15592994.0,Ugochukwutubelum,602.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,82647.65
-18155,15662091.0,Macleod,624.0,France,Female,40.0,2.0,0.0,1.0,1.0,0.0,180598.28
-18156,15696733.0,Okwudilichukwu,697.0,France,Male,20.0,9.0,0.0,2.0,0.0,1.0,179227.16
-18157,15739476.0,Ndubueze,604.0,France,Male,35.0,9.0,0.0,1.0,1.0,0.0,131712.53
-18158,15763415.0,Chiganu,682.0,Germany,Male,48.0,1.0,106731.58,1.0,1.0,0.0,109523.88
-18159,15654891.0,Ch'ang,588.0,Spain,Male,32.0,3.0,147813.05,1.0,1.0,1.0,166677.35
-18160,15778936.0,Oluchukwu,616.0,France,Female,44.0,9.0,0.0,1.0,0.0,1.0,177614.17
-18161,15694829.0,Manna,686.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,168816.35
-18162,15757981.0,Nwankwo,506.0,France,Female,39.0,6.0,0.0,2.0,0.0,0.0,99800.87
-18163,15694450.0,Gant,588.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,169161.46
-18164,15670354.0,Nnonso,586.0,Spain,Male,48.0,7.0,121206.96,1.0,0.0,1.0,161253.08
-18165,15674541.0,K?,590.0,France,Male,36.0,6.0,168066.87,1.0,1.0,0.0,115818.51
-18166,15611786.0,Chidiebele,583.0,France,Female,37.0,9.0,72494.69,1.0,1.0,1.0,47322.05
-18167,15775709.0,Hsing,652.0,France,Female,46.0,1.0,0.0,1.0,1.0,1.0,153059.62
-18168,15641733.0,Li Fonti,635.0,Spain,Female,48.0,7.0,0.0,2.0,1.0,0.0,139431.0
-18169,15695872.0,Pirozzi,576.0,France,Male,40.0,7.0,0.0,2.0,0.0,1.0,177614.17
-18170,15584091.0,Okechukwu,651.0,Germany,Female,46.0,1.0,108738.92,1.0,1.0,0.0,131710.59
-18171,15607746.0,P'eng,622.0,Germany,Female,55.0,1.0,119024.75,1.0,1.0,0.0,103760.53
-18172,15692599.0,Ts'ui,828.0,France,Male,37.0,7.0,0.0,1.0,0.0,1.0,55867.38
-18173,15604544.0,Nwankwo,597.0,France,Male,33.0,0.0,0.0,2.0,1.0,0.0,90967.77
-18174,15585192.0,Chiganu,579.0,Spain,Male,33.0,8.0,121063.8,1.0,1.0,0.0,180934.67
-18175,15691446.0,Aksenov,587.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,49848.86
-18176,15732299.0,Ch'ang,745.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,187929.43
-18177,15785782.0,Cavenagh,576.0,France,Male,48.0,2.0,0.0,1.0,0.0,1.0,162260.93
-18178,15781598.0,Mancini,605.0,Spain,Female,31.0,3.0,141806.0,1.0,1.0,0.0,171868.08
-18179,15761132.0,Rizzo,706.0,France,Male,35.0,1.0,0.0,1.0,1.0,1.0,71159.84
-18180,15792077.0,Genovese,697.0,France,Female,64.0,6.0,0.0,3.0,1.0,0.0,152790.92
-18181,15595036.0,Lo Duca,676.0,France,Male,39.0,6.0,0.0,1.0,1.0,0.0,142911.99
-18182,15591932.0,K?,699.0,France,Male,21.0,1.0,0.0,2.0,1.0,1.0,82222.86
-18183,15576517.0,Udegbunam,763.0,France,Female,61.0,2.0,0.0,2.0,0.0,1.0,133020.9
-18184,15645463.0,Yermakov,712.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,148814.54
-18185,15740160.0,Onyemauchechukwu,592.0,Germany,Male,38.0,3.0,91285.21,1.0,1.0,0.0,57876.05
-18186,15637138.0,Hsueh,689.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,114869.55
-18187,15804937.0,Cattaneo,592.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,48228.1
-18188,15585595.0,Chuang,657.0,Germany,Female,33.0,10.0,95845.6,3.0,1.0,0.0,93717.46
-18189,15774285.0,Ch'ang,516.0,Spain,Male,37.0,10.0,0.0,1.0,0.0,1.0,91514.61
-18190,15791114.0,Sagese,678.0,France,Female,47.0,1.0,0.0,1.0,0.0,0.0,70778.63
-18191,15774393.0,Chukwualuka,644.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,137537.22
-18192,15588535.0,McLean,644.0,France,Female,55.0,4.0,0.0,1.0,1.0,1.0,173599.38
-18193,15632977.0,Chikwado,571.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,180712.28
-18194,15625592.0,Folliero,741.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,101418.05
-18195,15590736.0,Okagbue,648.0,France,Male,56.0,8.0,0.0,1.0,1.0,0.0,56908.41
-18196,15696054.0,Chinwendu,751.0,Spain,Male,39.0,10.0,0.0,2.0,1.0,1.0,151903.6
-18197,15662291.0,Scott,603.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,112976.67
-18198,15758856.0,Thompson,545.0,France,Male,45.0,0.0,97588.6,1.0,1.0,0.0,162683.06
-18199,15621520.0,Burt,662.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,0.0,68701.73
-18200,15662662.0,Bancks,669.0,Spain,Female,35.0,7.0,0.0,1.0,1.0,0.0,162960.05
-18201,15703059.0,Ting,676.0,France,Female,43.0,10.0,0.0,1.0,0.0,1.0,143598.7
-18202,15592078.0,Liao,604.0,France,Male,44.0,9.0,0.0,1.0,1.0,1.0,184474.15
-18203,15603554.0,Ch'ang,739.0,Spain,Female,38.0,4.0,0.0,1.0,0.0,0.0,141400.51
-18204,15611186.0,Johnson,691.0,Spain,Female,31.0,1.0,162039.59,1.0,1.0,1.0,177719.88
-18205,15611189.0,Hsieh,581.0,Germany,Male,24.0,2.0,123969.92,2.0,0.0,1.0,131073.9
-18206,15698528.0,Chidubem,596.0,Spain,Female,23.0,5.0,0.0,1.0,1.0,1.0,79329.7
-18207,15709928.0,Trentino,684.0,Germany,Female,40.0,10.0,134740.24,2.0,1.0,1.0,57589.29
-18208,15586629.0,Nwabugwu,681.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,171056.65
-18209,15803801.0,Hs?,662.0,France,Male,36.0,3.0,0.0,2.0,0.0,1.0,92664.09
-18210,15702463.0,Lucchese,539.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,72993.65
-18211,15641640.0,Genovese,675.0,Spain,Male,51.0,3.0,0.0,2.0,0.0,1.0,80582.34
-18212,15744368.0,Nnachetam,758.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,89938.08
-18213,15747043.0,Smith,716.0,Spain,Male,47.0,7.0,0.0,2.0,0.0,0.0,163830.64
-18214,15763613.0,Mao,651.0,Germany,Male,48.0,5.0,123761.68,3.0,1.0,1.0,142916.43
-18215,15781589.0,Ch'in,642.0,France,Female,39.0,9.0,0.0,2.0,1.0,1.0,57762.0
-18216,15805909.0,Ch'iu,594.0,Spain,Female,35.0,2.0,0.0,1.0,1.0,0.0,189085.07
-18217,15658768.0,Pinto,604.0,Spain,Female,56.0,6.0,69974.66,1.0,1.0,1.0,109296.18
-18218,15815443.0,Chukwudi,684.0,France,Female,31.0,8.0,0.0,1.0,0.0,1.0,56583.88
-18219,15673995.0,Nieves,596.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,158973.77
-18220,15781330.0,Nucci,628.0,France,Female,56.0,2.0,98630.31,1.0,1.0,1.0,137028.51
-18221,15694188.0,Lombardi,691.0,France,Male,31.0,9.0,0.0,1.0,1.0,0.0,65801.44
-18222,15585595.0,Onwudiwe,617.0,France,Male,26.0,0.0,0.0,2.0,1.0,0.0,67311.12
-18223,15606861.0,Onwughara,766.0,France,Female,40.0,0.0,0.0,1.0,1.0,1.0,52367.29
-18224,15690164.0,Niu,584.0,Spain,Female,47.0,3.0,125845.66,1.0,1.0,0.0,113469.65
-18225,15674932.0,Hsia,574.0,Germany,Male,26.0,1.0,100548.67,2.0,1.0,1.0,119348.28
-18226,15604217.0,Trentino,544.0,Germany,Male,48.0,8.0,121537.71,1.0,1.0,1.0,58883.91
-18227,15676571.0,Wright,701.0,France,Male,37.0,10.0,0.0,2.0,0.0,0.0,151887.16
-18228,15806922.0,Oguejiofor,716.0,Spain,Male,27.0,2.0,0.0,2.0,1.0,0.0,151902.78
-18229,15782210.0,Walker,705.0,France,Female,44.0,6.0,113882.31,1.0,1.0,0.0,183566.78
-18230,15778515.0,Cremonesi,623.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,64716.08
-18231,15722404.0,Zetticci,741.0,Spain,Female,45.0,9.0,0.0,1.0,1.0,0.0,57876.05
-18232,15782659.0,Smith,570.0,Spain,Female,47.0,8.0,0.0,1.0,1.0,0.0,119348.28
-18233,15617982.0,Okechukwu,613.0,Spain,Female,29.0,5.0,0.0,1.0,1.0,0.0,25310.82
-18234,15591512.0,Frolov,745.0,France,Male,48.0,4.0,0.0,1.0,1.0,0.0,121866.87
-18235,15777907.0,Nkemdilim,656.0,Germany,Female,52.0,4.0,102238.01,1.0,1.0,1.0,133831.37
-18236,15791114.0,T'ien,683.0,France,Male,35.0,9.0,87944.39,2.0,1.0,0.0,173568.3
-18237,15783541.0,Hsiung,652.0,Germany,Male,42.0,8.0,148051.81,1.0,1.0,1.0,163550.58
-18238,15762238.0,Nnaife,482.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,124532.9
-18239,15592979.0,Tien,556.0,France,Male,35.0,0.0,154969.79,1.0,1.0,0.0,110900.43
-18240,15634281.0,Hsieh,559.0,France,Male,26.0,5.0,0.0,1.0,1.0,0.0,61666.87
-18241,15769955.0,Chukwufumnanya,719.0,France,Female,40.0,8.0,0.0,1.0,0.0,0.0,120834.13
-18242,15729771.0,Onyemauchechukwu,545.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,25684.89
-18243,15731327.0,Ch'eng,731.0,France,Male,26.0,1.0,0.0,1.0,1.0,1.0,72927.68
-18244,15603065.0,Chigozie,699.0,Germany,Female,48.0,5.0,123356.63,2.0,0.0,0.0,134732.99
-18245,15567802.0,Nwoye,741.0,France,Male,39.0,9.0,104077.19,1.0,1.0,0.0,146785.44
-18246,15632576.0,Ch'ien,562.0,France,Female,44.0,6.0,0.0,2.0,1.0,1.0,61961.57
-18247,15646168.0,H?,774.0,Spain,Male,31.0,5.0,0.0,1.0,0.0,0.0,122268.75
-18248,15645323.0,Teng,594.0,Spain,Male,43.0,1.0,176913.52,1.0,1.0,0.0,134417.31
-18249,15642570.0,Manna,624.0,France,Female,44.0,2.0,0.0,2.0,1.0,1.0,58813.41
-18250,15572398.0,Hs?,750.0,France,Male,31.0,1.0,0.0,2.0,0.0,0.0,143317.19
-18251,15602479.0,Okechukwu,715.0,Spain,Male,37.0,7.0,0.0,2.0,0.0,0.0,169278.51
-18252,15747558.0,Chu,648.0,Spain,Female,35.0,0.0,0.0,2.0,1.0,1.0,125961.48
-18253,15690743.0,O'Donnell,745.0,Germany,Female,42.0,2.0,146053.66,1.0,1.0,0.0,147055.87
-18254,15764294.0,Yuryeva,619.0,Germany,Female,36.0,3.0,116576.45,1.0,1.0,1.0,199403.37
-18255,15659325.0,Lu,461.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,179833.45
-18256,15631063.0,Ebelegbulam,714.0,France,Female,33.0,4.0,0.0,2.0,1.0,1.0,85522.84
-18257,15799785.0,Ch'iu,699.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,51548.14
-18258,15724153.0,Maclean,649.0,Germany,Male,30.0,1.0,126588.81,1.0,1.0,0.0,92647.94
-18259,15789413.0,Chidumaga,662.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,91652.43
-18260,15602934.0,Lajoie,696.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,136983.77
-18261,15643042.0,Ch'eng,645.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,123977.86
-18262,15588933.0,Ting,712.0,Spain,Male,32.0,4.0,151532.4,1.0,1.0,1.0,72830.46
-18263,15624388.0,Fanucci,693.0,France,Male,38.0,6.0,0.0,1.0,1.0,0.0,171482.56
-18264,15738662.0,Cawood,645.0,Spain,Female,32.0,1.0,0.0,2.0,0.0,1.0,176750.81
-18265,15773934.0,Chiu,613.0,France,Male,45.0,9.0,123544.0,1.0,1.0,0.0,93795.86
-18266,15692929.0,Nott,696.0,France,Female,41.0,8.0,0.0,1.0,1.0,1.0,149550.62
-18267,15776847.0,Ebelegbulam,558.0,Germany,Female,39.0,8.0,97460.1,1.0,1.0,0.0,46811.77
-18268,15583976.0,Ch'ang,667.0,France,Male,27.0,10.0,0.0,2.0,1.0,0.0,169964.12
-18269,15778861.0,Gboliwe,628.0,Germany,Male,48.0,8.0,121263.62,3.0,1.0,0.0,137763.83
-18270,15790744.0,Nnamutaezinwa,850.0,Spain,Female,35.0,4.0,139825.34,1.0,1.0,0.0,163570.25
-18271,15804482.0,Napolitano,685.0,France,Male,42.0,7.0,0.0,2.0,0.0,0.0,85901.09
-18272,15602844.0,Pinto,762.0,Germany,Female,38.0,1.0,122029.15,1.0,1.0,0.0,131283.6
-18273,15574324.0,Nnonso,759.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,103379.31
-18274,15757566.0,Chukwudi,646.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,97640.2
-18275,15798850.0,T'ang,556.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,126796.69
-18276,15793893.0,Ts'ao,681.0,Spain,Male,45.0,3.0,0.0,1.0,1.0,0.0,133031.96
-18277,15751561.0,Nwankwo,696.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,160948.87
-18278,15753362.0,Chidiebele,501.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,82298.89
-18279,15802483.0,Trevisani,569.0,France,Female,28.0,3.0,0.0,2.0,1.0,1.0,140650.19
-18280,15660608.0,Chukwudi,748.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,182336.45
-18281,15646764.0,Shih,727.0,France,Male,61.0,1.0,0.0,2.0,1.0,1.0,9984.4
-18282,15751748.0,Chiu,729.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,0.0,171716.81
-18283,15640034.0,Onyemauchechukwu,656.0,Spain,Female,31.0,1.0,0.0,1.0,1.0,1.0,168816.38
-18284,15752499.0,Ts'ai,591.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,143628.31
-18285,15752488.0,Marshall,686.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,177670.57
-18286,15658670.0,Smith,726.0,France,Male,33.0,7.0,0.0,1.0,1.0,1.0,169964.12
-18287,15796790.0,Manna,703.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,127528.84
-18288,15745455.0,Ekechukwu,754.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,62868.33
-18289,15582335.0,Ch'eng,683.0,France,Female,38.0,4.0,0.0,1.0,0.0,0.0,81778.72
-18290,15583049.0,Moseley,581.0,France,Female,26.0,5.0,0.0,2.0,0.0,1.0,131919.48
-18291,15757537.0,Ch'iu,644.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,104072.53
-18292,15771750.0,Kornilova,643.0,France,Female,31.0,3.0,135470.9,1.0,1.0,1.0,117038.96
-18293,15749489.0,Pirozzi,673.0,Spain,Female,34.0,2.0,122453.97,1.0,0.0,1.0,123548.25
-18294,15609637.0,Y?an,635.0,Germany,Male,30.0,7.0,126588.81,2.0,1.0,1.0,49848.86
-18295,15651092.0,Onwumelu,596.0,France,Male,57.0,6.0,0.0,2.0,0.0,1.0,163830.64
-18296,15796370.0,Nebeolisa,636.0,Germany,Female,44.0,4.0,125845.66,1.0,0.0,0.0,182336.43
-18297,15682442.0,Hs?,668.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,80178.54
-18298,15603674.0,Ch'ang,603.0,France,Female,35.0,7.0,0.0,2.0,0.0,0.0,95009.8
-18299,15798895.0,P'eng,714.0,France,Female,34.0,7.0,0.0,1.0,1.0,1.0,67622.46
-18300,15702434.0,Y?an,621.0,Germany,Female,39.0,6.0,93499.82,2.0,0.0,0.0,117024.38
-18301,15686885.0,Bales,704.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,155743.48
-18302,15776844.0,Onuoha,634.0,Germany,Female,40.0,2.0,143330.56,1.0,1.0,0.0,63687.06
-18303,15690175.0,Genovese,567.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,141152.28
-18304,15706767.0,H?,634.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,176750.81
-18305,15592222.0,Onyekachi,565.0,Spain,Female,41.0,9.0,0.0,1.0,0.0,0.0,105760.01
-18306,15795571.0,Hao,586.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,155700.15
-18307,15679622.0,Yudina,628.0,Spain,Male,35.0,9.0,109558.36,1.0,0.0,1.0,121440.8
-18308,15654495.0,Chiang,637.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,26136.89
-18309,15750141.0,K?,615.0,France,Female,23.0,8.0,0.0,2.0,1.0,0.0,92647.95
-18310,15605425.0,Ch'en,681.0,France,Female,40.0,9.0,0.0,2.0,1.0,0.0,136071.05
-18311,15813659.0,Ch'ien,699.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,0.0,97640.5
-18312,15773789.0,O'Sullivan,718.0,France,Male,36.0,6.0,0.0,2.0,0.0,1.0,44335.54
-18313,15643496.0,Maclean,559.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,77977.69
-18314,15624293.0,Scott,770.0,Germany,Male,39.0,8.0,127660.46,2.0,1.0,1.0,132467.54
-18315,15694444.0,Nnachetam,691.0,Germany,Male,36.0,0.0,124577.33,2.0,1.0,1.0,110932.24
-18316,15592761.0,Nkemjika,704.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,68872.33
-18317,15613463.0,Yegorova,638.0,France,Female,29.0,7.0,125977.81,1.0,0.0,1.0,141030.08
-18318,15653547.0,Ch'ien,732.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,9984.4
-18319,15693864.0,White,678.0,Spain,Female,27.0,9.0,0.0,2.0,0.0,1.0,62232.6
-18320,15684248.0,Tuan,559.0,Germany,Female,33.0,9.0,148846.39,2.0,1.0,0.0,171060.01
-18321,15594898.0,Chukwueloka,656.0,Spain,Female,32.0,7.0,0.0,1.0,1.0,0.0,124795.84
-18322,15790829.0,Hsieh,749.0,France,Female,42.0,5.0,0.0,1.0,1.0,0.0,79005.95
-18323,15736299.0,Onyemere,584.0,France,Female,39.0,9.0,0.0,2.0,0.0,1.0,131281.28
-18324,15605826.0,Onyemauchechukwu,597.0,Germany,Female,42.0,8.0,96748.89,1.0,1.0,0.0,126554.96
-18325,15792453.0,Chukwueloka,588.0,Spain,Male,46.0,1.0,0.0,3.0,1.0,0.0,122925.5
-18326,15664037.0,T'ao,652.0,Spain,Female,48.0,6.0,0.0,2.0,1.0,1.0,161241.65
-18327,15571947.0,Ch'iu,671.0,France,Male,38.0,3.0,145933.27,1.0,1.0,1.0,189392.66
-18328,15595842.0,Chiemela,718.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,180800.42
-18329,15761976.0,T'ang,752.0,Germany,Female,39.0,6.0,136623.19,2.0,1.0,0.0,90177.3
-18330,15777904.0,Hao,501.0,Spain,Female,30.0,9.0,0.0,2.0,0.0,0.0,106636.89
-18331,15739502.0,Ch'ien,847.0,Germany,Female,37.0,5.0,122925.5,1.0,1.0,1.0,74425.41
-18332,15733797.0,Nwachukwu,624.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,143859.66
-18333,15752508.0,Lucciano,646.0,Spain,Female,26.0,6.0,104088.59,1.0,1.0,1.0,57949.72
-18334,15617304.0,Nwankwo,769.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,1.0,176570.38
-18335,15658716.0,Ch'ien,668.0,France,Female,72.0,7.0,0.0,2.0,1.0,1.0,57844.26
-18336,15686957.0,Ch'in,641.0,France,Male,32.0,6.0,76665.71,1.0,0.0,0.0,135875.51
-18337,15744517.0,Onyemauchechukwu,596.0,France,Male,46.0,3.0,0.0,1.0,1.0,0.0,114675.75
-18338,15692235.0,Onwuatuegwu,577.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,145089.64
-18339,15794566.0,Mazzanti,706.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,98894.64
-18340,15791045.0,Chibuzo,751.0,Spain,Male,40.0,7.0,106059.4,1.0,1.0,1.0,90928.02
-18341,15778838.0,Ch'ang,652.0,France,Male,38.0,6.0,112465.06,2.0,1.0,1.0,177655.68
-18342,15768887.0,Maclean,684.0,Spain,Female,36.0,2.0,107720.64,1.0,1.0,1.0,162697.93
-18343,15661528.0,Maclean,674.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,93901.61
-18344,15784890.0,Ch'eng,724.0,France,Male,51.0,4.0,0.0,3.0,1.0,0.0,105982.79
-18345,15658969.0,Onwuamaeze,693.0,France,Male,47.0,0.0,0.0,1.0,1.0,0.0,182336.45
-18346,15763327.0,Aksenov,741.0,France,Female,39.0,5.0,0.0,2.0,1.0,0.0,97792.02
-18347,15773520.0,O'Donnell,705.0,France,Male,38.0,10.0,0.0,2.0,0.0,0.0,157891.86
-18348,15646539.0,Smith,590.0,France,Male,27.0,7.0,0.0,2.0,1.0,0.0,125877.22
-18349,15665634.0,Ijendu,577.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,105570.39
-18350,15729773.0,Ch'en,732.0,France,Male,48.0,9.0,0.0,1.0,0.0,1.0,183487.98
-18351,15799217.0,Eluemuno,678.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,1.0,120830.73
-18352,15603683.0,Dickson,686.0,Spain,Male,54.0,5.0,0.0,2.0,1.0,1.0,126728.27
-18353,15637593.0,Palerma,719.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,120893.85
-18354,15656937.0,Chin,703.0,Spain,Female,32.0,1.0,0.0,2.0,1.0,0.0,167773.55
-18355,15701206.0,Tuan,791.0,France,Male,33.0,1.0,148848.54,2.0,1.0,1.0,79259.77
-18356,15620750.0,Ch'ang,661.0,France,Female,25.0,2.0,123857.51,1.0,1.0,1.0,46774.94
-18357,15586870.0,Nkemakonam,742.0,Spain,Female,57.0,7.0,0.0,1.0,1.0,1.0,1528.4
-18358,15590810.0,Pinto,699.0,France,Male,34.0,7.0,0.0,1.0,0.0,0.0,187800.63
-18359,15645463.0,Ch'ang,591.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,179436.6
-18360,15622578.0,Lai,733.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,136983.77
-18361,15688880.0,Zikoranachidimma,748.0,Germany,Male,48.0,3.0,123372.3,1.0,0.0,0.0,18094.96
-18362,15797065.0,Chidozie,599.0,France,Female,35.0,8.0,0.0,2.0,0.0,1.0,167984.72
-18363,15678725.0,Hsia,644.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,74593.84
-18364,15778830.0,Lombardo,516.0,France,Female,43.0,6.0,0.0,1.0,1.0,1.0,146585.77
-18365,15740383.0,Laidley,738.0,Spain,Male,50.0,7.0,0.0,1.0,1.0,0.0,164017.89
-18366,15571571.0,Uchenna,694.0,Spain,Male,44.0,9.0,0.0,1.0,1.0,0.0,182436.81
-18367,15807568.0,Chukwualuka,567.0,Spain,Female,28.0,1.0,0.0,2.0,1.0,1.0,97640.2
-18368,15615456.0,Zetticci,763.0,Spain,Female,28.0,3.0,0.0,2.0,0.0,1.0,125518.31
-18369,15690935.0,Ch'en,583.0,Germany,Female,32.0,1.0,51522.75,2.0,1.0,0.0,123956.83
-18370,15765311.0,Tien,590.0,Spain,Male,39.0,4.0,0.0,1.0,1.0,1.0,73679.59
-18371,15803337.0,Ch'en,656.0,Germany,Male,45.0,9.0,138341.51,1.0,1.0,0.0,116766.79
-18372,15780779.0,Yefimova,765.0,France,Male,35.0,3.0,0.0,2.0,0.0,0.0,160579.15
-18373,15658969.0,Tuan,716.0,France,Male,36.0,8.0,141078.37,1.0,0.0,0.0,14081.64
-18374,15754569.0,Milanesi,807.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,0.0,181461.46
-18375,15602354.0,Chinweuba,675.0,Spain,Female,31.0,2.0,0.0,1.0,0.0,0.0,91817.31
-18376,15576623.0,Maclean,682.0,Spain,Male,39.0,9.0,149762.08,1.0,1.0,0.0,121365.45
-18377,15617482.0,Nwachinemelu,745.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,82463.69
-18378,15682600.0,Tseng,565.0,France,Male,32.0,6.0,0.0,1.0,1.0,0.0,79082.62
-18379,15641359.0,Nnamdi,663.0,France,Female,31.0,6.0,0.0,2.0,0.0,1.0,88852.47
-18380,15646513.0,Chukwueloka,682.0,Germany,Male,45.0,1.0,126772.57,1.0,1.0,1.0,70954.41
-18381,15662291.0,Hsing,659.0,Germany,Female,52.0,1.0,148061.89,1.0,1.0,0.0,97640.2
-18382,15786800.0,Nnanna,600.0,Germany,Male,52.0,6.0,97445.49,2.0,1.0,1.0,182728.8
-18383,15571869.0,Ch'iu,464.0,France,Male,41.0,7.0,106913.43,1.0,1.0,0.0,64869.32
-18384,15661409.0,Chibugo,648.0,France,Female,28.0,7.0,0.0,2.0,1.0,1.0,184474.72
-18385,15741032.0,Ndubueze,707.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,1.0,140889.13
-18386,15631481.0,Chiu,645.0,Germany,Male,51.0,6.0,137794.18,1.0,1.0,0.0,166370.81
-18387,15782454.0,Echezonachukwu,586.0,Spain,Female,53.0,3.0,0.0,1.0,1.0,1.0,95936.84
-18388,15651144.0,Chukwualuka,686.0,Spain,Female,39.0,4.0,80780.16,1.0,1.0,0.0,182612.38
-18389,15785078.0,Nkemdilim,587.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,115733.16
-18390,15686461.0,Chimezie,479.0,Germany,Male,41.0,8.0,81570.06,1.0,1.0,1.0,103898.78
-18391,15711572.0,Tien,611.0,France,Male,46.0,5.0,112808.18,1.0,0.0,1.0,1375.46
-18392,15572038.0,Ts'ai,718.0,Spain,Male,36.0,5.0,0.0,1.0,1.0,1.0,181757.19
-18393,15577514.0,Ibezimako,645.0,Germany,Female,29.0,1.0,106521.49,1.0,1.0,0.0,147055.87
-18394,15707596.0,Cattaneo,711.0,Spain,Male,43.0,10.0,0.0,2.0,1.0,0.0,98931.5
-18395,15682355.0,Pisano,637.0,Germany,Male,40.0,4.0,118788.57,1.0,0.0,1.0,93769.89
-18396,15572398.0,Chimezie,595.0,France,Male,50.0,10.0,0.0,1.0,0.0,1.0,181419.29
-18397,15783541.0,Kao,714.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,120893.07
-18398,15742091.0,Iweobiegbunam,605.0,France,Male,56.0,3.0,0.0,1.0,1.0,0.0,126551.65
-18399,15681075.0,Trevisani,657.0,France,Male,37.0,2.0,0.0,1.0,1.0,0.0,111241.37
-18400,15607560.0,Kaeppel,717.0,France,Male,36.0,7.0,0.0,1.0,1.0,1.0,141806.44
-18401,15702631.0,Tung,683.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,121277.78
-18402,15807107.0,Chukwuemeka,656.0,France,Female,64.0,3.0,108377.75,1.0,1.0,1.0,103921.41
-18403,15777923.0,Biryukov,678.0,Germany,Male,45.0,1.0,133970.21,2.0,1.0,0.0,176750.81
-18404,15676521.0,Young,689.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,1.0,106826.92
-18405,15813095.0,Fiorentino,655.0,France,Female,30.0,7.0,137794.18,1.0,1.0,0.0,184800.27
-18406,15610105.0,Zaytseva,535.0,Spain,Male,61.0,8.0,0.0,2.0,0.0,0.0,169415.61
-18407,15803681.0,Onyemachukwu,556.0,France,Female,38.0,9.0,0.0,2.0,1.0,0.0,172049.84
-18408,15753361.0,Hsia,646.0,Spain,Female,52.0,8.0,0.0,1.0,1.0,1.0,143357.07
-18409,15581551.0,Ewing,721.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,1.0,141946.92
-18410,15604345.0,Folliero,526.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,10963.04
-18411,15617206.0,Ponomaryov,648.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,0.0,150422.35
-18412,15771417.0,Ch'iu,687.0,Spain,Female,32.0,3.0,0.0,2.0,1.0,0.0,52795.15
-18413,15672640.0,Okechukwu,587.0,France,Male,42.0,1.0,0.0,3.0,1.0,0.0,43982.41
-18414,15730705.0,Hovell,669.0,Germany,Male,33.0,9.0,162482.76,1.0,1.0,1.0,186884.04
-18415,15615126.0,Hsueh,675.0,France,Female,35.0,0.0,0.0,2.0,1.0,1.0,172096.08
-18416,15605826.0,Napolitano,549.0,Spain,Female,33.0,4.0,86614.06,1.0,1.0,0.0,133961.19
-18417,15632503.0,Martin,681.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,67926.18
-18418,15802741.0,T'ang,567.0,France,Female,56.0,8.0,0.0,3.0,1.0,1.0,136910.18
-18419,15745196.0,T'ao,684.0,France,Male,40.0,4.0,0.0,1.0,0.0,0.0,86786.41
-18420,15743817.0,Young,624.0,France,Male,33.0,6.0,0.0,2.0,0.0,1.0,143635.36
-18421,15747591.0,Brierly,718.0,France,Male,50.0,10.0,0.0,1.0,0.0,0.0,171097.39
-18422,15656029.0,Ch'iu,727.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,163471.01
-18423,15809837.0,Iadanza,601.0,France,Male,48.0,7.0,0.0,2.0,1.0,1.0,177670.57
-18424,15734246.0,Tung,663.0,France,Female,40.0,2.0,0.0,1.0,0.0,1.0,162858.29
-18425,15790763.0,Naylor,704.0,Germany,Male,48.0,2.0,151958.19,3.0,1.0,0.0,52876.41
-18426,15656812.0,Tuan,653.0,Germany,Female,37.0,6.0,87926.24,2.0,0.0,0.0,116969.73
-18427,15786418.0,Ndubueze,607.0,Spain,Male,50.0,2.0,113736.26,1.0,1.0,0.0,189392.66
-18428,15668894.0,Ch'iu,706.0,France,Male,29.0,2.0,0.0,2.0,0.0,1.0,130500.65
-18429,15740383.0,Hao,568.0,France,Female,43.0,4.0,0.0,2.0,1.0,0.0,143783.29
-18430,15617166.0,Okwudilichukwu,729.0,Germany,Male,48.0,5.0,115074.02,1.0,1.0,0.0,180622.02
-18431,15781534.0,Smith,712.0,France,Female,24.0,7.0,0.0,2.0,0.0,0.0,110114.19
-18432,15669611.0,Chukwufumnanya,616.0,France,Male,30.0,10.0,0.0,2.0,1.0,1.0,84760.32
-18433,15658982.0,Lai,627.0,France,Male,30.0,9.0,123593.22,1.0,1.0,1.0,134946.43
-18434,15578744.0,Ts'ai,545.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,56908.41
-18435,15633064.0,Ch'iu,706.0,Spain,Female,31.0,3.0,0.0,1.0,1.0,0.0,100365.51
-18436,15585738.0,Ngozichukwuka,707.0,Spain,Male,40.0,4.0,0.0,1.0,0.0,1.0,136983.77
-18437,15736146.0,Kao,601.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,61324.87
-18438,15783530.0,Fyans,715.0,France,Female,32.0,8.0,126473.99,1.0,1.0,0.0,92381.01
-18439,15704583.0,Lucchesi,754.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,63737.19
-18440,15668309.0,Iadanza,645.0,Germany,Male,31.0,1.0,106070.89,1.0,1.0,1.0,102930.46
-18441,15676937.0,T'ien,535.0,France,Male,31.0,4.0,0.0,1.0,0.0,0.0,182867.84
-18442,15583392.0,Ch'ang,676.0,Spain,Female,38.0,6.0,115409.6,1.0,1.0,1.0,71405.17
-18443,15751561.0,Ch'iu,588.0,France,Male,33.0,8.0,122325.53,1.0,1.0,1.0,63795.8
-18444,15651958.0,Chuang,554.0,France,Female,43.0,6.0,0.0,2.0,1.0,0.0,171457.06
-18445,15772363.0,Tsao,632.0,Germany,Female,44.0,9.0,121276.32,1.0,1.0,0.0,80698.54
-18446,15784323.0,Mancini,667.0,France,Female,44.0,5.0,0.0,1.0,0.0,0.0,174969.91
-18447,15701354.0,Hsu,594.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,95957.09
-18448,15576085.0,Hsia,731.0,Germany,Female,42.0,9.0,74430.1,1.0,1.0,0.0,184524.74
-18449,15691808.0,Toscani,616.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,126812.63
-18450,15783029.0,Ch'ien,736.0,Spain,Female,62.0,8.0,0.0,2.0,1.0,1.0,136909.87
-18451,15776947.0,Hsiung,582.0,Spain,Male,23.0,7.0,0.0,2.0,1.0,0.0,74850.84
-18452,15733705.0,Ch'iu,622.0,France,Female,45.0,7.0,0.0,1.0,1.0,0.0,153917.38
-18453,15630195.0,Sun,695.0,France,Female,36.0,4.0,0.0,2.0,0.0,0.0,180318.6
-18454,15742408.0,Hsiao,683.0,France,Female,47.0,9.0,0.0,1.0,1.0,0.0,23146.62
-18455,15589147.0,Nnamdi,683.0,Germany,Female,50.0,10.0,148045.45,2.0,1.0,1.0,149994.68
-18456,15592999.0,Mazzanti,771.0,France,Male,26.0,8.0,94416.78,1.0,1.0,0.0,82336.73
-18457,15598044.0,Dickson,624.0,Spain,Male,35.0,9.0,76665.71,1.0,1.0,1.0,122518.5
-18458,15706464.0,Toscani,611.0,France,Male,50.0,9.0,0.0,1.0,0.0,0.0,92381.01
-18459,15693376.0,K?,642.0,Germany,Female,44.0,4.0,132405.52,1.0,1.0,1.0,88890.05
-18460,15651389.0,Ch'eng,634.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,0.0,94767.17
-18461,15717185.0,Smith,594.0,France,Female,51.0,0.0,0.0,2.0,0.0,1.0,150835.21
-18462,15610379.0,Hs?,705.0,France,Male,39.0,2.0,115897.73,1.0,1.0,1.0,57553.02
-18463,15617166.0,Maclean,771.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,76614.06
-18464,15593939.0,Tseng,685.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,55867.38
-18465,15607301.0,Lucchese,682.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,106663.46
-18466,15785559.0,Yuryeva,710.0,France,Male,31.0,3.0,137761.55,1.0,0.0,1.0,159800.16
-18467,15754926.0,Clements,745.0,Spain,Female,42.0,8.0,0.0,2.0,1.0,0.0,57962.41
-18468,15712409.0,Onwuamaeze,573.0,Spain,Female,38.0,9.0,0.0,1.0,1.0,0.0,84920.49
-18469,15585853.0,Toscani,627.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,142425.07
-18470,15777047.0,Maclean,619.0,France,Female,40.0,0.0,93616.35,1.0,1.0,0.0,121277.38
-18471,15714227.0,Nkemdirim,488.0,Germany,Female,45.0,1.0,131834.76,1.0,1.0,0.0,126143.96
-18472,15759874.0,Ch'in,678.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,142516.35
-18473,15655389.0,Padovano,632.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,143315.57
-18474,15631460.0,Ch'eng,706.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,84760.32
-18475,15727299.0,Ch'iu,751.0,France,Female,39.0,9.0,0.0,2.0,1.0,0.0,160979.66
-18476,15675787.0,P'eng,741.0,France,Female,32.0,6.0,0.0,1.0,1.0,1.0,124749.08
-18477,15673020.0,Ts'ao,683.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,103760.53
-18478,15680399.0,Chidalu,685.0,France,Female,56.0,7.0,0.0,1.0,1.0,0.0,133624.56
-18479,15594902.0,Uspensky,621.0,France,Male,40.0,0.0,0.0,2.0,0.0,1.0,151315.76
-18480,15729110.0,Shih,561.0,France,Female,49.0,9.0,0.0,1.0,1.0,0.0,138380.12
-18481,15568487.0,McElhone,646.0,Germany,Female,26.0,7.0,87098.91,2.0,1.0,1.0,62972.13
-18482,15568106.0,Genovese,674.0,France,Female,47.0,8.0,166594.78,1.0,1.0,0.0,74854.97
-18483,15722532.0,T'ien,632.0,France,Male,27.0,8.0,124729.96,2.0,1.0,0.0,114775.53
-18484,15579891.0,Hs?eh,746.0,France,Female,64.0,4.0,0.0,2.0,1.0,1.0,74836.64
-18485,15572073.0,Milanesi,663.0,France,Male,63.0,9.0,0.0,1.0,0.0,0.0,157557.71
-18486,15576683.0,Onyemauchechi,673.0,Spain,Male,38.0,10.0,122471.09,1.0,0.0,0.0,47886.44
-18487,15807900.0,Yermolayev,717.0,France,Female,27.0,7.0,0.0,2.0,0.0,0.0,178373.43
-18488,15808846.0,Maclean,713.0,France,Female,47.0,9.0,0.0,1.0,1.0,0.0,92712.97
-18489,15801351.0,Ch'iu,705.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,1676.92
-18490,15786035.0,Cattaneo,711.0,Germany,Female,23.0,10.0,71497.79,2.0,1.0,1.0,84436.67
-18491,15642725.0,Ts'ui,709.0,Spain,Male,30.0,4.0,0.0,2.0,0.0,0.0,95957.09
-18492,15771750.0,Mazzanti,683.0,France,Female,44.0,6.0,0.0,1.0,1.0,0.0,91775.51
-18493,15600974.0,Hs?,568.0,Germany,Male,27.0,3.0,140197.18,1.0,0.0,0.0,173386.95
-18494,15795519.0,Cattaneo,637.0,France,Male,56.0,9.0,137688.71,1.0,1.0,1.0,132540.33
-18495,15682355.0,P'eng,607.0,Germany,Female,45.0,6.0,134006.77,1.0,1.0,0.0,175072.47
-18496,15627162.0,Yobachi,628.0,Germany,Male,42.0,4.0,126645.13,2.0,1.0,1.0,140938.95
-18497,15735572.0,Chukwukadibia,567.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,0.0,67552.71
-18498,15623102.0,Kao,637.0,Germany,Female,41.0,3.0,102749.72,1.0,1.0,0.0,163570.25
-18499,15618245.0,Hovell,683.0,France,Male,23.0,8.0,0.0,2.0,1.0,1.0,163484.8
-18500,15674727.0,Manna,716.0,France,Female,64.0,5.0,126473.33,1.0,1.0,1.0,44001.11
-18501,15635143.0,Hs?,579.0,France,Female,41.0,6.0,0.0,1.0,1.0,0.0,71926.28
-18502,15600792.0,Lucciano,773.0,France,Female,30.0,9.0,106414.57,1.0,1.0,1.0,124592.84
-18503,15792729.0,Nwagugheuzo,642.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,1.0,189307.7
-18504,15783883.0,Onio,738.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,0.0,152566.75
-18505,15592707.0,Onyemachukwu,699.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,0.0,81602.02
-18506,15592645.0,P'eng,526.0,Germany,Female,33.0,2.0,75801.74,2.0,0.0,0.0,27472.07
-18507,15584518.0,Ts'ao,781.0,France,Male,38.0,7.0,126473.33,1.0,1.0,0.0,180829.99
-18508,15671591.0,Scott,653.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,124071.71
-18509,15748473.0,Nnamutaezinwa,716.0,France,Male,31.0,6.0,0.0,2.0,0.0,0.0,143598.7
-18510,15618255.0,Yermakov,587.0,Germany,Female,34.0,2.0,129855.64,1.0,1.0,1.0,122639.73
-18511,15756539.0,Ebelegbulam,809.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,177719.88
-18512,15618018.0,Chuang,687.0,Spain,Male,37.0,4.0,167545.32,1.0,1.0,1.0,157862.82
-18513,15738785.0,Chiu,792.0,Germany,Male,49.0,9.0,125903.22,1.0,1.0,0.0,134822.04
-18514,15761148.0,Yermolayeva,679.0,France,Male,28.0,2.0,0.0,2.0,1.0,1.0,145562.4
-18515,15593968.0,Nwabugwu,658.0,Germany,Female,28.0,9.0,118560.0,2.0,1.0,0.0,62810.91
-18516,15674811.0,Teng,619.0,Spain,Male,38.0,10.0,99812.88,1.0,1.0,0.0,119313.74
-18517,15729201.0,Chibueze,792.0,France,Male,26.0,3.0,0.0,2.0,1.0,1.0,143783.46
-18518,15781678.0,White,679.0,Spain,Female,42.0,5.0,0.0,2.0,1.0,1.0,54113.38
-18519,15657778.0,Ts'ai,639.0,France,Male,39.0,10.0,0.0,2.0,0.0,0.0,162884.85
-18520,15778936.0,Ch'ien,753.0,France,Male,28.0,10.0,0.0,2.0,1.0,0.0,126796.69
-18521,15596972.0,Ch'in,585.0,Spain,Female,37.0,2.0,0.0,2.0,0.0,0.0,138526.96
-18522,15591766.0,P'eng,679.0,Spain,Female,29.0,2.0,0.0,2.0,0.0,0.0,96670.2
-18523,15645849.0,Brierly,573.0,Spain,Male,26.0,0.0,0.0,2.0,1.0,0.0,61315.37
-18524,15756743.0,Chukwufumnanya,741.0,France,Female,37.0,7.0,0.0,2.0,0.0,0.0,163587.9
-18525,15758726.0,Ch'iu,590.0,Germany,Male,29.0,1.0,107879.03,2.0,1.0,1.0,141180.66
-18526,15671779.0,L?,727.0,Spain,Male,38.0,3.0,0.0,1.0,1.0,0.0,102297.15
-18527,15707974.0,Chinomso,653.0,Germany,Female,56.0,6.0,135296.33,1.0,1.0,0.0,160574.09
-18528,15600813.0,Mistry,644.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,143022.31
-18529,15731790.0,Cattaneo,731.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,136079.64
-18530,15754068.0,Ritchie,699.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,89519.47
-18531,15616168.0,Chinomso,540.0,France,Male,38.0,4.0,0.0,2.0,0.0,1.0,61811.1
-18532,15633194.0,Hsieh,663.0,France,Female,43.0,9.0,0.0,2.0,1.0,0.0,118862.08
-18533,15767681.0,Chinwenma,774.0,France,Female,27.0,9.0,0.0,2.0,1.0,1.0,50679.81
-18534,15750778.0,Mazzanti,516.0,France,Female,34.0,0.0,0.0,1.0,1.0,0.0,136985.08
-18535,15691808.0,Iqbal,684.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,151317.27
-18536,15740383.0,Milano,686.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,143357.02
-18537,15757559.0,Shih,697.0,Spain,Male,35.0,6.0,0.0,3.0,1.0,1.0,38196.24
-18538,15572626.0,Chiu,640.0,Spain,Female,37.0,5.0,0.0,1.0,1.0,0.0,85746.52
-18539,15678058.0,Chin,598.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,71862.79
-18540,15722072.0,Ch'iu,571.0,France,Female,39.0,8.0,122388.34,1.0,0.0,0.0,176692.65
-18541,15594827.0,Yermakova,725.0,France,Female,28.0,4.0,0.0,2.0,0.0,0.0,176780.69
-18542,15640507.0,Onyemere,641.0,Spain,Male,38.0,7.0,0.0,1.0,1.0,0.0,161671.15
-18543,15717010.0,Tung,744.0,Spain,Male,39.0,2.0,0.0,2.0,1.0,0.0,94790.34
-18544,15581871.0,Calabrese,652.0,Germany,Female,35.0,6.0,103391.38,2.0,1.0,0.0,160532.88
-18545,15689886.0,Chiu,706.0,France,Female,35.0,8.0,0.0,2.0,0.0,0.0,103832.58
-18546,15642824.0,Nnonso,662.0,France,Male,34.0,4.0,0.0,1.0,0.0,1.0,51358.66
-18547,15681887.0,Okwudilichukwu,773.0,France,Male,30.0,0.0,0.0,1.0,1.0,0.0,102260.16
-18548,15667896.0,Chien,686.0,France,Female,44.0,9.0,0.0,1.0,0.0,0.0,143665.44
-18549,15762682.0,Olisanugo,653.0,France,Male,60.0,10.0,0.0,1.0,1.0,0.0,187800.63
-18550,15684801.0,Hs?,602.0,France,Female,48.0,4.0,124053.03,3.0,1.0,0.0,148948.17
-18551,15593897.0,Hs?,641.0,France,Male,30.0,7.0,128663.9,1.0,1.0,0.0,134600.94
-18552,15752084.0,Olisaemeka,646.0,France,Male,51.0,8.0,0.0,1.0,1.0,1.0,40920.59
-18553,15772638.0,Chiazagomekpele,645.0,Germany,Male,40.0,2.0,124043.8,1.0,1.0,0.0,184464.7
-18554,15661827.0,Tuan,731.0,France,Male,31.0,8.0,0.0,1.0,1.0,0.0,74532.04
-18555,15730796.0,T'an,753.0,Spain,Female,24.0,7.0,139825.3,1.0,1.0,0.0,118959.64
-18556,15598044.0,Cattaneo,779.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,91347.76
-18557,15748797.0,Chiang,593.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,170819.01
-18558,15777873.0,Ndubueze,664.0,Germany,Female,27.0,2.0,98765.84,2.0,1.0,1.0,24482.16
-18559,15742798.0,Horsley,603.0,France,Male,45.0,3.0,0.0,1.0,1.0,0.0,158840.99
-18560,15708046.0,Ch'ang,590.0,Spain,Male,47.0,10.0,0.0,2.0,1.0,0.0,90928.48
-18561,15727041.0,Martin,676.0,Germany,Male,30.0,9.0,102962.62,2.0,1.0,0.0,115387.4
-18562,15758035.0,Ch'eng,645.0,France,Female,42.0,9.0,0.0,1.0,1.0,1.0,180593.45
-18563,15780256.0,Ch'eng,599.0,Germany,Male,34.0,2.0,178058.06,2.0,0.0,1.0,177528.92
-18564,15633840.0,Li Fonti,584.0,France,Female,50.0,7.0,128262.72,1.0,1.0,0.0,75825.57
-18565,15732299.0,Dickson,671.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,1.0,179128.69
-18566,15572390.0,Pugh,507.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,1.0,170968.99
-18567,15813140.0,Chia,597.0,France,Male,43.0,3.0,0.0,2.0,0.0,1.0,120880.72
-18568,15587394.0,Nwokezuike,693.0,France,Male,25.0,7.0,117231.63,2.0,1.0,1.0,151401.33
-18569,15593959.0,Pirozzi,730.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,153537.57
-18570,15681843.0,Yefimova,653.0,France,Female,40.0,3.0,101952.96,1.0,0.0,0.0,135655.29
-18571,15624293.0,Toscani,699.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,114448.77
-18572,15692991.0,H?,726.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,67979.35
-18573,15803479.0,Pirozzi,724.0,France,Male,41.0,4.0,0.0,2.0,0.0,1.0,181694.44
-18574,15572792.0,Chidozie,758.0,Germany,Female,40.0,10.0,123374.46,2.0,1.0,1.0,177279.41
-18575,15646936.0,Lo Duca,644.0,France,Male,39.0,4.0,143340.44,2.0,0.0,0.0,147040.44
-18576,15668886.0,Hill,665.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,0.0,64716.08
-18577,15762392.0,Mironova,712.0,France,Male,42.0,5.0,0.0,2.0,0.0,0.0,91673.6
-18578,15700749.0,Ch'iu,645.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,1.0,105886.77
-18579,15748691.0,Zetticci,543.0,Spain,Male,57.0,9.0,0.0,2.0,1.0,1.0,92797.23
-18580,15775104.0,Onwuamaeze,706.0,France,Male,31.0,8.0,0.0,1.0,1.0,1.0,138380.11
-18581,15631512.0,Ch'ang,706.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,151903.6
-18582,15809837.0,Hsiung,704.0,Germany,Male,28.0,9.0,130142.79,1.0,1.0,1.0,93901.61
-18583,15655232.0,Chiang,749.0,Germany,Female,29.0,2.0,150944.94,2.0,1.0,1.0,161519.77
-18584,15773869.0,Nwebube,628.0,Germany,Female,30.0,6.0,88084.13,1.0,1.0,0.0,92220.12
-18585,15775892.0,Chidiebere,549.0,France,Male,32.0,7.0,88594.14,1.0,0.0,1.0,133692.88
-18586,15623695.0,Chia,756.0,Germany,Female,35.0,5.0,161889.79,2.0,1.0,0.0,99017.34
-18587,15762268.0,Hsia,794.0,France,Female,38.0,4.0,0.0,2.0,0.0,1.0,86043.27
-18588,15759066.0,Kao,666.0,Germany,Female,38.0,5.0,115924.8,1.0,1.0,0.0,167553.06
-18589,15681755.0,Buchi,562.0,France,Female,37.0,9.0,0.0,2.0,0.0,0.0,174434.06
-18590,15671915.0,Scott,731.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,168816.22
-18591,15597957.0,Lu,742.0,Spain,Female,46.0,4.0,143542.36,1.0,1.0,1.0,64345.61
-18592,15711219.0,Ku,733.0,France,Female,36.0,6.0,0.0,1.0,0.0,0.0,158735.1
-18593,15743759.0,Ch'ien,706.0,France,Female,28.0,7.0,132598.34,1.0,1.0,0.0,129964.94
-18594,15671172.0,Sun,574.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,121747.96
-18595,15804853.0,Nnamutaezinwa,668.0,France,Female,51.0,3.0,0.0,1.0,0.0,0.0,93717.55
-18596,15694566.0,Chikelu,675.0,France,Female,36.0,5.0,0.0,1.0,1.0,1.0,66810.48
-18597,15648067.0,Yeh,615.0,Germany,Male,25.0,10.0,129837.72,1.0,1.0,1.0,121537.91
-18598,15708046.0,Ts'ui,627.0,Spain,Male,50.0,9.0,0.0,1.0,0.0,1.0,47799.15
-18599,15711901.0,Hsu,753.0,France,Male,62.0,5.0,0.0,1.0,1.0,1.0,178342.63
-18600,15728010.0,P'an,637.0,France,Female,40.0,9.0,0.0,1.0,1.0,0.0,122518.5
-18601,15732967.0,Onyemere,642.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,1.0,108313.34
-18602,15658946.0,P'eng,699.0,Spain,Male,23.0,9.0,0.0,2.0,1.0,0.0,93874.87
-18603,15769346.0,Nucci,571.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,73686.75
-18604,15760873.0,Okwudiliolisa,719.0,Germany,Male,43.0,2.0,136576.02,1.0,1.0,0.0,75825.57
-18605,15646513.0,Hs?,640.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,53817.23
-18606,15774882.0,Hs?,624.0,France,Male,47.0,8.0,0.0,2.0,1.0,1.0,82696.84
-18607,15584518.0,Liao,542.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,158071.69
-18608,15774857.0,Onyekachi,669.0,France,Female,43.0,2.0,0.0,1.0,0.0,0.0,108863.38
-18609,15796313.0,Chidubem,682.0,Germany,Female,37.0,2.0,123959.97,1.0,0.0,1.0,134878.19
-18610,15741921.0,Teng,696.0,Spain,Female,39.0,8.0,0.0,1.0,1.0,1.0,86044.98
-18611,15670343.0,Kennedy,543.0,Spain,Male,31.0,3.0,0.0,2.0,0.0,1.0,170634.49
-18612,15681075.0,Lai,636.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,132973.39
-18613,15573319.0,Chukwuemeka,569.0,Germany,Female,50.0,7.0,114737.43,1.0,1.0,0.0,172939.3
-18614,15701176.0,Onwuamaeze,614.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,55857.48
-18615,15730038.0,Chukwukadibia,746.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,167984.61
-18616,15619494.0,Macleod,634.0,Spain,Male,32.0,10.0,0.0,2.0,0.0,1.0,111241.37
-18617,15633944.0,Frolov,850.0,France,Female,49.0,3.0,0.0,1.0,0.0,0.0,28707.69
-18618,15691867.0,Ch'ien,687.0,Germany,Female,29.0,2.0,119278.01,2.0,0.0,0.0,131097.9
-18619,15694376.0,K'ung,513.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,164864.67
-18620,15699415.0,Hsieh,684.0,France,Female,34.0,7.0,151027.56,2.0,0.0,1.0,189430.86
-18621,15787827.0,P'eng,691.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,54474.48
-18622,15652693.0,Tien,473.0,France,Male,26.0,8.0,82441.75,1.0,1.0,1.0,143952.24
-18623,15661936.0,Hsia,595.0,France,Female,41.0,2.0,0.0,2.0,0.0,0.0,96734.46
-18624,15809722.0,Udobata,602.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,141872.05
-18625,15765402.0,Kenechukwu,693.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,141069.88
-18626,15776596.0,Ugoji,679.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,167984.72
-18627,15662494.0,H?,675.0,Spain,Female,28.0,7.0,0.0,1.0,1.0,0.0,173486.39
-18628,15743016.0,Teng,535.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,129912.74
-18629,15605968.0,Shao,568.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,93769.89
-18630,15588537.0,Ch'iu,597.0,Spain,Female,64.0,1.0,0.0,2.0,1.0,1.0,140889.02
-18631,15592089.0,Milne,658.0,Germany,Male,42.0,0.0,93816.2,1.0,1.0,0.0,145558.42
-18632,15773798.0,Chigolum,694.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,1.0,123611.94
-18633,15656592.0,P'eng,696.0,France,Male,29.0,1.0,148846.39,1.0,1.0,1.0,155879.57
-18634,15665129.0,Milani,686.0,France,Male,44.0,9.0,51949.95,1.0,1.0,0.0,141756.32
-18635,15806822.0,Maclean,604.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,1.0,141069.88
-18636,15702293.0,Smith,551.0,France,Male,36.0,6.0,0.0,1.0,1.0,0.0,123882.73
-18637,15656937.0,Ch'ien,667.0,France,Male,53.0,4.0,124577.33,1.0,1.0,1.0,126570.79
-18638,15571244.0,Chuang,664.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,63737.19
-18639,15593468.0,Ukaegbunam,755.0,France,Male,55.0,9.0,0.0,2.0,0.0,1.0,173784.04
-18640,15750476.0,Chiazagomekpele,705.0,Germany,Female,37.0,2.0,76495.04,1.0,1.0,1.0,172668.38
-18641,15697311.0,Manna,538.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,141210.5
-18642,15698932.0,Hao,711.0,Spain,Male,63.0,6.0,0.0,1.0,1.0,1.0,185510.34
-18643,15569222.0,Onyinyechukwuka,639.0,Spain,Female,33.0,1.0,0.0,2.0,1.0,0.0,151738.54
-18644,15591723.0,Chiawuotu,763.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,87566.97
-18645,15742329.0,Hs?,724.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,142838.64
-18646,15686611.0,T'ang,732.0,France,Female,41.0,9.0,95823.3,1.0,1.0,0.0,82565.01
-18647,15662238.0,Ibezimako,595.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,103603.65
-18648,15657540.0,Chin,456.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,143397.28
-18649,15597591.0,Nkemdirim,753.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,123968.14
-18650,15580146.0,Onyemere,581.0,France,Male,33.0,9.0,0.0,1.0,1.0,1.0,185561.89
-18651,15665047.0,Chiang,755.0,France,Male,67.0,1.0,0.0,2.0,1.0,1.0,170528.63
-18652,15674840.0,Udegbulam,641.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,0.0,181224.56
-18653,15708713.0,P'eng,599.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,72862.94
-18654,15804787.0,Scott,682.0,France,Female,46.0,8.0,0.0,2.0,1.0,0.0,88834.41
-18655,15668575.0,Aitken,627.0,Spain,Female,37.0,8.0,0.0,2.0,0.0,0.0,180716.1
-18656,15795511.0,Chiawuotu,437.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,0.0,199242.65
-18657,15769504.0,Yefimova,722.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,131772.51
-18658,15657779.0,Tsou,678.0,France,Male,35.0,4.0,152347.01,1.0,0.0,0.0,59645.43
-18659,15791323.0,Calabresi,661.0,France,Male,24.0,8.0,0.0,1.0,1.0,0.0,77965.67
-18660,15776433.0,Ritchie,644.0,Spain,Male,27.0,3.0,0.0,2.0,1.0,0.0,60728.89
-18661,15661219.0,Tung,537.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,64726.65
-18662,15588537.0,Ch'ien,699.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,123663.63
-18663,15738219.0,Chibueze,730.0,France,Female,22.0,1.0,0.0,1.0,0.0,1.0,80134.65
-18664,15790594.0,Chidumaga,669.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,1.0,28257.89
-18665,15775203.0,T'ien,657.0,France,Female,28.0,4.0,115462.02,1.0,0.0,0.0,120893.07
-18666,15687329.0,Olisanugo,469.0,France,Male,29.0,3.0,0.0,1.0,1.0,0.0,93874.87
-18667,15680405.0,Zetticci,651.0,France,Male,39.0,0.0,0.0,2.0,1.0,0.0,166677.35
-18668,15785782.0,O'Sullivan,550.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,0.0,82537.01
-18669,15761713.0,Liao,717.0,France,Male,38.0,6.0,0.0,2.0,0.0,0.0,59249.83
-18670,15805523.0,Palerma,704.0,France,Female,29.0,1.0,0.0,1.0,1.0,1.0,174570.55
-18671,15583692.0,Nwankwo,793.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,152422.91
-18672,15635388.0,Ebelechukwu,653.0,Germany,Male,51.0,3.0,109733.2,1.0,1.0,0.0,57589.29
-18673,15688880.0,Ch'in,657.0,Germany,Female,38.0,1.0,118994.84,1.0,1.0,0.0,133600.89
-18674,15732430.0,Uspensky,749.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,140780.8
-18675,15675190.0,Trevisano,679.0,Germany,Female,46.0,4.0,92716.96,1.0,1.0,1.0,74800.99
-18676,15690744.0,P'an,595.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,113581.85
-18677,15761158.0,T'ao,667.0,France,Female,29.0,3.0,0.0,2.0,0.0,1.0,53478.02
-18678,15603594.0,Nwabugwu,710.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,91347.76
-18679,15637354.0,Genovese,597.0,Spain,Male,33.0,6.0,108862.76,1.0,1.0,1.0,162889.1
-18680,15616088.0,Kosisochukwu,553.0,France,Female,59.0,2.0,127862.93,1.0,1.0,1.0,100862.54
-18681,15666982.0,Ozerova,602.0,France,Male,51.0,5.0,79871.02,2.0,1.0,1.0,44406.17
-18682,15608688.0,Toscani,652.0,France,Female,39.0,3.0,123042.0,1.0,1.0,1.0,153373.71
-18683,15752915.0,Mistry,584.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,0.0,56582.95
-18684,15765537.0,Hsu,537.0,Germany,Male,56.0,3.0,111568.01,1.0,1.0,0.0,57553.98
-18685,15585312.0,Yegorova,727.0,France,Male,29.0,8.0,0.0,2.0,0.0,1.0,174094.24
-18686,15723400.0,Palerma,639.0,France,Female,36.0,2.0,115280.49,1.0,1.0,0.0,98820.09
-18687,15642530.0,Chukwuemeka,586.0,France,Female,46.0,0.0,0.0,1.0,1.0,0.0,179445.24
-18688,15730076.0,Tuan,599.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,156768.45
-18689,15729246.0,Nkemdirim,647.0,Spain,Female,39.0,6.0,123760.68,1.0,1.0,1.0,187900.12
-18690,15669414.0,Chukwuemeka,735.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,108841.83
-18691,15576517.0,Toscani,553.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,141035.65
-18692,15600724.0,Zhirov,682.0,Spain,Female,40.0,9.0,0.0,2.0,0.0,0.0,112104.55
-18693,15661290.0,Zaytseva,538.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,152491.03
-18694,15794048.0,Cattaneo,628.0,France,Male,29.0,1.0,0.0,1.0,0.0,1.0,94739.2
-18695,15701164.0,Chukwufumnanya,683.0,Germany,Female,58.0,4.0,120599.38,4.0,1.0,0.0,60721.4
-18696,15696812.0,Uwaezuoke,687.0,Germany,Male,43.0,5.0,150777.72,1.0,0.0,1.0,172363.41
-18697,15778915.0,L?,543.0,Spain,Female,42.0,8.0,156847.29,1.0,1.0,0.0,169963.2
-18698,15662494.0,Pagnotto,714.0,Germany,Male,50.0,9.0,85438.21,1.0,1.0,0.0,58854.88
-18699,15726058.0,Hsia,663.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,83058.14
-18700,15773447.0,Ebelechukwu,732.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,155846.69
-18701,15691792.0,Folliero,537.0,Germany,Male,36.0,2.0,141252.18,2.0,1.0,1.0,88842.8
-18702,15753874.0,Smith,571.0,France,Female,35.0,7.0,0.0,1.0,0.0,1.0,167973.63
-18703,15710390.0,Aitken,667.0,Spain,Male,25.0,3.0,0.0,2.0,1.0,0.0,75969.19
-18704,15587913.0,Ch'ang,577.0,Spain,Female,44.0,5.0,128760.32,1.0,1.0,0.0,172810.51
-18705,15804787.0,Nnachetam,716.0,France,Male,44.0,6.0,0.0,2.0,1.0,0.0,141533.19
-18706,15738751.0,T'ien,573.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,134778.01
-18707,15700801.0,Obidimkpa,751.0,France,Female,34.0,1.0,0.0,1.0,0.0,1.0,35600.25
-18708,15737998.0,T'ien,641.0,France,Female,31.0,6.0,0.0,2.0,0.0,0.0,141230.07
-18709,15702631.0,Smith,592.0,France,Female,42.0,4.0,0.0,1.0,1.0,0.0,139946.68
-18710,15679812.0,Macleod,604.0,France,Male,40.0,7.0,0.0,2.0,0.0,0.0,171681.86
-18711,15645103.0,Tung,575.0,France,Male,34.0,2.0,138843.7,1.0,1.0,1.0,139953.94
-18712,15613786.0,Okwudiliolisa,691.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,0.0,4989.33
-18713,15791334.0,Uchechukwu,635.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,99645.04
-18714,15682467.0,Mazzanti,703.0,France,Female,32.0,5.0,0.0,1.0,1.0,1.0,144874.67
-18715,15788659.0,Chukwueloka,667.0,Spain,Male,33.0,3.0,0.0,2.0,0.0,1.0,52885.23
-18716,15806081.0,Ch'in,790.0,Germany,Male,41.0,3.0,121461.63,1.0,0.0,0.0,180712.28
-18717,15714485.0,Kolesnikova,751.0,Spain,Male,33.0,4.0,142296.13,1.0,1.0,0.0,145551.6
-18718,15799031.0,Chiabuotu,581.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,0.0,102021.39
-18719,15655961.0,Young,645.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,158545.25
-18720,15673372.0,Ubanwa,567.0,France,Female,31.0,9.0,166501.16,1.0,1.0,0.0,128841.31
-18721,15578878.0,Kincaid,682.0,France,Male,24.0,4.0,0.0,1.0,0.0,1.0,146652.11
-18722,15762605.0,L?,685.0,France,Male,29.0,10.0,0.0,2.0,1.0,0.0,156792.89
-18723,15784761.0,Brown,773.0,Germany,Female,49.0,8.0,116439.65,1.0,1.0,1.0,38171.62
-18724,15655238.0,Udinese,603.0,Germany,Female,43.0,4.0,132287.92,1.0,0.0,1.0,82565.01
-18725,15766289.0,Lucchese,587.0,Spain,Female,46.0,4.0,0.0,1.0,1.0,0.0,156908.45
-18726,15664753.0,Tsai,667.0,Spain,Male,39.0,1.0,64345.21,1.0,1.0,0.0,82709.8
-18727,15591711.0,Hsia,709.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,180551.76
-18728,15772412.0,Chien,579.0,Germany,Male,36.0,1.0,98899.91,1.0,1.0,0.0,132973.21
-18729,15737265.0,Cawthorne,535.0,Germany,Male,40.0,4.0,119556.1,1.0,0.0,0.0,89972.25
-18730,15659428.0,Chiemenam,619.0,France,Male,33.0,3.0,109978.83,2.0,1.0,1.0,166476.95
-18731,15712551.0,Zetticci,656.0,Germany,Male,52.0,1.0,151855.33,1.0,1.0,0.0,16287.34
-18732,15695301.0,Chiazagomekpere,850.0,France,Female,25.0,8.0,0.0,2.0,1.0,0.0,98894.64
-18733,15656592.0,Ndubuagha,605.0,France,Female,52.0,1.0,0.0,3.0,1.0,1.0,126644.98
-18734,15654783.0,Macleod,584.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,4395.45
-18735,15698031.0,Yobachi,648.0,France,Male,46.0,6.0,0.0,2.0,1.0,0.0,74965.61
-18736,15699450.0,Chienezie,601.0,France,Male,44.0,7.0,0.0,2.0,1.0,0.0,180598.66
-18737,15602572.0,Iloerika,642.0,Germany,Female,32.0,2.0,77967.5,1.0,1.0,1.0,55313.51
-18738,15636595.0,Onochie,595.0,France,Female,42.0,5.0,0.0,2.0,0.0,0.0,108419.41
-18739,15792329.0,Pirozzi,726.0,France,Female,30.0,8.0,0.0,2.0,1.0,1.0,86911.49
-18740,15603088.0,Chineze,596.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,79623.28
-18741,15644878.0,Nkemdilim,593.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,160162.42
-18742,15692406.0,K?,850.0,Spain,Female,41.0,5.0,0.0,1.0,1.0,1.0,175877.7
-18743,15751158.0,Chukwuemeka,588.0,Germany,Male,22.0,5.0,107957.28,1.0,1.0,1.0,165035.17
-18744,15675963.0,Tsao,669.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,91763.67
-18745,15665067.0,Onuora,667.0,Spain,Female,27.0,2.0,0.0,2.0,1.0,0.0,115719.08
-18746,15690164.0,Calabrese,622.0,Spain,Male,32.0,3.0,0.0,1.0,1.0,0.0,144880.81
-18747,15582033.0,Hsueh,528.0,Spain,Female,27.0,4.0,80615.42,4.0,1.0,1.0,117622.8
-18748,15650026.0,Pinto,656.0,France,Female,51.0,3.0,0.0,1.0,1.0,0.0,175893.53
-18749,15607598.0,Milani,582.0,Germany,Female,67.0,7.0,89994.71,2.0,0.0,1.0,142903.44
-18750,15575486.0,Cremonesi,705.0,Spain,Male,42.0,1.0,0.0,2.0,1.0,1.0,97734.3
-18751,15661349.0,Chien,745.0,France,Male,52.0,7.0,0.0,2.0,1.0,1.0,160132.65
-18752,15670103.0,Ugorji,794.0,France,Male,24.0,3.0,0.0,2.0,1.0,1.0,196261.64
-18753,15660505.0,Onyemauchechukwu,703.0,Spain,Female,39.0,1.0,0.0,1.0,0.0,1.0,95625.03
-18754,15772381.0,Chia,635.0,France,Female,42.0,2.0,0.0,2.0,0.0,1.0,149471.13
-18755,15756475.0,Bibi,662.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,1.0,137519.92
-18756,15589431.0,Onwuemelie,742.0,France,Male,30.0,0.0,0.0,1.0,0.0,0.0,176576.71
-18757,15659324.0,Chinomso,699.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,126829.72
-18758,15624595.0,Ch'iu,678.0,France,Male,31.0,1.0,0.0,1.0,1.0,0.0,110916.15
-18759,15770420.0,Maclean,624.0,France,Male,59.0,5.0,62400.48,1.0,1.0,1.0,123536.52
-18760,15588535.0,Cremonesi,662.0,France,Female,29.0,10.0,0.0,2.0,1.0,0.0,73329.08
-18761,15690589.0,Mancini,598.0,France,Female,28.0,7.0,0.0,2.0,1.0,0.0,170634.49
-18762,15809901.0,Houghton,619.0,France,Male,35.0,5.0,0.0,1.0,0.0,0.0,153566.97
-18763,15582519.0,Okwudiliolisa,479.0,Germany,Female,41.0,8.0,129387.94,1.0,0.0,1.0,60538.28
-18764,15769412.0,Chienezie,629.0,Germany,Male,27.0,10.0,157085.98,2.0,1.0,1.0,103901.27
-18765,15582940.0,Iloerika,746.0,France,Female,32.0,8.0,149525.55,1.0,1.0,1.0,142223.94
-18766,15694946.0,Scott,678.0,France,Female,38.0,6.0,139810.34,1.0,1.0,0.0,4791.4
-18767,15595350.0,Mazzanti,590.0,France,Female,37.0,2.0,99844.68,1.0,1.0,1.0,78700.03
-18768,15611058.0,Hsiung,751.0,France,Male,35.0,9.0,171913.87,1.0,1.0,1.0,175310.41
-18769,15581550.0,Hsieh,604.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,51113.14
-18770,15772996.0,Walker,691.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,115779.48
-18771,15746422.0,Chukwujamuike,678.0,Spain,Female,34.0,4.0,0.0,2.0,1.0,1.0,142963.29
-18772,15693764.0,Ch'ang,697.0,Germany,Male,39.0,0.0,126670.37,2.0,1.0,1.0,94499.06
-18773,15611105.0,Chukwumaobim,754.0,Spain,Male,30.0,7.0,0.0,1.0,1.0,1.0,57553.02
-18774,15790298.0,Chibueze,678.0,Germany,Male,31.0,4.0,154483.98,1.0,0.0,0.0,167522.19
-18775,15754068.0,Chia,651.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,1.0,166957.82
-18776,15680597.0,Fanucci,547.0,France,Male,26.0,6.0,0.0,2.0,1.0,1.0,107841.77
-18777,15641794.0,Lai,682.0,Spain,Male,33.0,6.0,141223.68,1.0,0.0,1.0,75860.01
-18778,15809901.0,Lo,598.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,56908.41
-18779,15762573.0,Nnachetam,773.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,1.0,134423.66
-18780,15744368.0,Hsia,639.0,France,Male,33.0,1.0,147948.52,1.0,1.0,0.0,161653.5
-18781,15752488.0,Hsiao,659.0,Spain,Male,39.0,3.0,0.0,2.0,0.0,1.0,158086.33
-18782,15751185.0,Milani,719.0,France,Male,37.0,10.0,0.0,2.0,0.0,1.0,171060.01
-18783,15654625.0,Ozoemena,576.0,Spain,Female,43.0,8.0,146566.01,1.0,0.0,0.0,146821.42
-18784,15646720.0,Toscani,686.0,Germany,Male,48.0,4.0,143542.36,1.0,1.0,0.0,51548.14
-18785,15671390.0,Onwuamaeze,681.0,Germany,Female,25.0,4.0,107784.65,2.0,1.0,0.0,109538.39
-18786,15722599.0,Chukwuemeka,590.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,151336.96
-18787,15702464.0,Chimaobim,663.0,France,Male,29.0,3.0,133616.39,1.0,1.0,1.0,149517.72
-18788,15638231.0,Mironova,647.0,Germany,Female,33.0,3.0,73023.17,1.0,1.0,0.0,183868.01
-18789,15684925.0,Lajoie,708.0,France,Female,44.0,4.0,135655.66,1.0,1.0,1.0,138536.09
-18790,15793881.0,Udinesi,638.0,Germany,Male,43.0,9.0,141078.57,1.0,1.0,0.0,80587.41
-18791,15744327.0,Hs?,624.0,Spain,Male,27.0,2.0,0.0,2.0,1.0,1.0,99792.82
-18792,15672798.0,T'ang,644.0,Germany,Male,32.0,2.0,101744.84,2.0,1.0,0.0,171045.31
-18793,15665956.0,Omeokachie,674.0,Germany,Male,33.0,9.0,99027.61,2.0,0.0,1.0,120830.73
-18794,15614782.0,Mazzanti,714.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,177065.24
-18795,15574830.0,Chidubem,724.0,France,Female,34.0,2.0,0.0,1.0,1.0,0.0,176984.34
-18796,15703394.0,Hsieh,679.0,France,Female,32.0,9.0,112230.38,1.0,1.0,1.0,120892.96
-18797,15581282.0,Hickey,684.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,1.0,178229.04
-18798,15614365.0,Hsia,660.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,139297.71
-18799,15783398.0,Padovano,713.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,158853.98
-18800,15740356.0,Chukwueloka,651.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,1.0,125870.5
-18801,15774192.0,Ch'en,561.0,Spain,Male,37.0,1.0,0.0,2.0,0.0,1.0,65864.4
-18802,15684461.0,Ni,663.0,France,Female,59.0,8.0,0.0,1.0,1.0,1.0,166586.99
-18803,15791114.0,De Salis,619.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,107876.2
-18804,15636407.0,Onwuamaeze,807.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,139435.36
-18805,15678886.0,Nnachetam,466.0,France,Female,46.0,6.0,0.0,2.0,1.0,1.0,138526.96
-18806,15631848.0,Nnaife,539.0,Germany,Male,37.0,9.0,116195.58,2.0,1.0,1.0,123587.83
-18807,15592029.0,Chukwualuka,575.0,France,Female,44.0,3.0,0.0,2.0,0.0,1.0,141926.43
-18808,15787597.0,Hsia,648.0,Germany,Male,52.0,3.0,103291.25,2.0,0.0,1.0,51358.66
-18809,15711455.0,Chia,696.0,France,Male,42.0,10.0,0.0,2.0,1.0,1.0,162876.92
-18810,15806912.0,Lombardo,678.0,Germany,Male,37.0,6.0,124525.52,1.0,1.0,1.0,164741.5
-18811,15748327.0,Iloerika,634.0,France,Female,37.0,3.0,0.0,2.0,0.0,1.0,49732.9
-18812,15728006.0,Babbage,691.0,France,Female,34.0,2.0,0.0,2.0,0.0,1.0,36320.87
-18813,15618258.0,Cattaneo,769.0,France,Male,34.0,0.0,129847.75,1.0,1.0,1.0,86937.2
-18814,15688880.0,Chizoba,605.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,120540.83
-18815,15757521.0,Chidubem,632.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,65413.41
-18816,15583736.0,Lucchese,635.0,France,Female,31.0,4.0,0.0,2.0,1.0,0.0,167553.06
-18817,15570379.0,Dike,696.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,117031.2
-18818,15756026.0,Chifley,684.0,France,Male,25.0,9.0,0.0,1.0,0.0,1.0,131177.23
-18819,15648967.0,Ch'ien,725.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,180712.28
-18820,15573851.0,Milanesi,850.0,Germany,Female,29.0,8.0,126598.99,1.0,1.0,0.0,132424.69
-18821,15595588.0,Iweobiegbunam,563.0,France,Male,36.0,9.0,0.0,3.0,1.0,1.0,73581.05
-18822,15750410.0,D'Albertis,635.0,France,Male,38.0,2.0,155423.61,1.0,1.0,1.0,80190.36
-18823,15605531.0,Hsiung,751.0,France,Male,31.0,2.0,0.0,1.0,1.0,1.0,53534.8
-18824,15691823.0,Echezonachukwu,652.0,France,Female,48.0,4.0,0.0,1.0,1.0,0.0,115676.61
-18825,15654574.0,Chukwudi,587.0,Germany,Female,35.0,3.0,141796.63,2.0,1.0,1.0,61666.87
-18826,15652674.0,T'ien,638.0,Germany,Female,42.0,5.0,109162.82,1.0,1.0,1.0,59981.62
-18827,15781589.0,Shih,479.0,France,Male,44.0,8.0,0.0,1.0,1.0,0.0,152883.95
-18828,15576723.0,Nnamutaezinwa,554.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,166317.71
-18829,15628992.0,Chien,539.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,81409.0
-18830,15641114.0,O'Brien,716.0,Spain,Male,27.0,0.0,0.0,1.0,1.0,0.0,199409.19
-18831,15585599.0,Onwumelu,681.0,France,Female,56.0,1.0,81042.42,1.0,1.0,0.0,140931.57
-18832,15624188.0,Echezonachukwu,570.0,Germany,Male,46.0,1.0,111096.91,1.0,1.0,0.0,43932.54
-18833,15774743.0,Y?,650.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,52327.57
-18834,15571059.0,Yefremova,537.0,France,Male,42.0,10.0,0.0,1.0,1.0,0.0,88815.25
-18835,15804064.0,Milanesi,728.0,France,Female,38.0,1.0,0.0,1.0,0.0,1.0,67431.28
-18836,15803526.0,Chukwukere,735.0,Germany,Male,48.0,3.0,110822.9,1.0,1.0,1.0,86950.72
-18837,15733032.0,Mazzanti,727.0,Germany,Male,37.0,7.0,87772.52,1.0,1.0,0.0,103660.31
-18838,15602811.0,Chukwuemeka,703.0,France,Male,32.0,5.0,0.0,1.0,1.0,0.0,87822.14
-18839,15743192.0,Shao,697.0,Spain,Female,44.0,1.0,0.0,2.0,1.0,0.0,149665.65
-18840,15741417.0,O'Loughlin,589.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,164815.59
-18841,15582944.0,Hsia,731.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,0.0,177613.19
-18842,15676958.0,P'an,554.0,France,Male,40.0,6.0,0.0,2.0,1.0,1.0,142763.51
-18843,15693690.0,Obialo,590.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,123969.64
-18844,15761141.0,Ts'ao,717.0,France,Male,46.0,4.0,0.0,2.0,0.0,0.0,146651.99
-18845,15723488.0,Trevisani,696.0,Spain,Male,45.0,2.0,0.0,2.0,1.0,0.0,160976.83
-18846,15694829.0,Chiu,667.0,France,Male,31.0,5.0,144880.34,1.0,0.0,1.0,116973.42
-18847,15643679.0,Biryukov,633.0,France,Male,43.0,4.0,0.0,1.0,1.0,0.0,85411.01
-18848,15769346.0,De Salis,709.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,82970.14
-18849,15631739.0,Hsing,591.0,France,Male,29.0,0.0,0.0,2.0,1.0,0.0,172576.43
-18850,15591360.0,Chinomso,682.0,Spain,Male,48.0,8.0,0.0,1.0,1.0,0.0,153918.17
-18851,15659475.0,Rubeo,679.0,Germany,Male,26.0,6.0,123372.3,2.0,1.0,0.0,125963.14
-18852,15684865.0,Biryukov,597.0,France,Female,28.0,7.0,0.0,1.0,1.0,1.0,165574.54
-18853,15751022.0,Iloerika,586.0,Spain,Female,52.0,9.0,0.0,3.0,1.0,0.0,93336.3
-18854,15586699.0,K'ung,705.0,Spain,Male,32.0,7.0,0.0,1.0,1.0,0.0,162734.31
-18855,15800961.0,Ch'iu,613.0,Spain,Male,60.0,6.0,0.0,2.0,1.0,1.0,127558.26
-18856,15802560.0,Efremova,773.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,0.0,70529.0
-18857,15723669.0,Ugochukwu,732.0,France,Male,33.0,3.0,0.0,1.0,1.0,0.0,173844.89
-18858,15661827.0,Ojiofor,635.0,Spain,Male,29.0,1.0,0.0,2.0,0.0,1.0,167030.51
-18859,15733476.0,Chiedozie,651.0,Spain,Female,28.0,7.0,0.0,2.0,0.0,0.0,122433.09
-18860,15672940.0,Hsia,641.0,France,Male,51.0,8.0,0.0,1.0,0.0,1.0,54469.54
-18861,15704315.0,Toscani,643.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,107615.43
-18862,15628219.0,Calabrese,565.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,1.0,105122.85
-18863,15683524.0,Hsia,850.0,France,Female,37.0,5.0,116805.02,1.0,0.0,0.0,138540.08
-18864,15700353.0,Mironova,678.0,France,Male,32.0,4.0,0.0,1.0,1.0,0.0,131647.31
-18865,15784700.0,Binder,622.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,27244.35
-18866,15681316.0,Tretiakov,645.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,174971.64
-18867,15643658.0,Nucci,589.0,Spain,Female,26.0,8.0,0.0,2.0,0.0,1.0,60420.07
-18868,15793901.0,Hsing,557.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,0.0,86937.2
-18869,15756668.0,Lai,707.0,Spain,Female,29.0,1.0,121276.32,1.0,1.0,1.0,98820.09
-18870,15754578.0,Fanucci,754.0,France,Female,33.0,4.0,103471.52,1.0,1.0,0.0,131804.86
-18871,15792986.0,Scott,751.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,14081.64
-18872,15570681.0,Yefremova,706.0,France,Male,49.0,4.0,0.0,1.0,1.0,0.0,109891.6
-18873,15800062.0,Onuoha,682.0,Germany,Male,41.0,5.0,90218.9,1.0,1.0,0.0,125962.02
-18874,15809515.0,Chigbogu,584.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,122518.5
-18875,15651264.0,Walker,719.0,France,Male,29.0,7.0,161889.79,1.0,0.0,1.0,86767.48
-18876,15714586.0,Hsiung,767.0,Germany,Female,53.0,4.0,113674.2,1.0,1.0,0.0,169583.18
-18877,15605067.0,Ibeamaka,682.0,France,Male,35.0,2.0,0.0,2.0,0.0,1.0,58853.08
-18878,15745001.0,Hsu,600.0,Spain,Male,48.0,9.0,0.0,1.0,1.0,1.0,61315.37
-18879,15798860.0,K?,651.0,France,Male,32.0,3.0,0.0,1.0,1.0,1.0,138051.19
-18880,15678057.0,Chukwukadibia,775.0,France,Female,46.0,2.0,132932.13,1.0,1.0,0.0,179453.66
-18881,15752488.0,T'ien,729.0,Spain,Female,47.0,5.0,0.0,2.0,1.0,1.0,35786.76
-18882,15658364.0,Ch'eng,669.0,France,Female,39.0,1.0,0.0,1.0,1.0,1.0,167035.07
-18883,15588904.0,Ting,693.0,Germany,Male,37.0,6.0,121406.96,1.0,1.0,0.0,137079.86
-18884,15671333.0,Sagese,543.0,Germany,Male,50.0,5.0,125534.51,1.0,1.0,0.0,156598.23
-18885,15685920.0,Lucchesi,581.0,France,Male,43.0,1.0,0.0,4.0,1.0,0.0,146585.78
-18886,15694852.0,Chinedum,636.0,Germany,Female,62.0,9.0,91241.85,1.0,0.0,0.0,124777.14
-18887,15800620.0,Okwudilichukwu,516.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,122950.53
-18888,15583840.0,Lu,581.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,146785.44
-18889,15658935.0,Chidiebele,782.0,France,Female,38.0,9.0,0.0,2.0,1.0,0.0,52795.15
-18890,15676909.0,Shoebridge,634.0,France,Male,42.0,4.0,136914.6,1.0,1.0,0.0,158976.85
-18891,15598751.0,T'ao,629.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,175893.53
-18892,15743532.0,Ts'ui,698.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,140889.32
-18893,15707918.0,Laidley,684.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,135067.02
-18894,15782231.0,Ch'iu,673.0,France,Male,46.0,8.0,0.0,2.0,0.0,1.0,175544.02
-18895,15635277.0,Hs?,711.0,France,Male,42.0,0.0,186788.96,1.0,1.0,0.0,56583.88
-18896,15762218.0,Hs?,596.0,Germany,Female,44.0,5.0,128743.55,2.0,0.0,0.0,103557.95
-18897,15731569.0,Aksenov,714.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,86981.45
-18898,15686696.0,Iweobiegbunam,605.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,108764.69
-18899,15742671.0,Lucchese,516.0,France,Male,27.0,7.0,160599.06,1.0,0.0,0.0,124532.9
-18900,15807709.0,Hsia,724.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,168544.86
-18901,15676519.0,Hargreaves,678.0,Germany,Male,33.0,4.0,168735.74,1.0,1.0,0.0,4175.68
-18902,15628886.0,Chukwumaobim,667.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,75860.01
-18903,15726487.0,Goliwe,693.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,123587.83
-18904,15691817.0,Ch'ien,669.0,France,Male,42.0,7.0,0.0,2.0,0.0,1.0,94498.82
-18905,15652069.0,Onyemachukwu,774.0,France,Female,28.0,1.0,0.0,2.0,1.0,0.0,162428.05
-18906,15734886.0,Chukwudi,468.0,France,Female,45.0,6.0,0.0,2.0,1.0,1.0,85547.33
-18907,15803479.0,Uspenskaya,543.0,France,Female,23.0,8.0,0.0,2.0,0.0,1.0,168034.83
-18908,15651144.0,Ndubuagha,593.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,60375.18
-18909,15686917.0,Hsing,627.0,France,Male,50.0,6.0,156773.78,1.0,0.0,0.0,86490.09
-18910,15747323.0,Tien,613.0,Spain,Female,37.0,5.0,0.0,1.0,1.0,1.0,88550.28
-18911,15586870.0,Tuan,710.0,France,Male,40.0,4.0,0.0,1.0,1.0,1.0,106977.8
-18912,15769254.0,Lo Duca,733.0,Germany,Female,39.0,5.0,124576.65,1.0,1.0,0.0,169963.2
-18913,15730137.0,Fenton,659.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,107499.63
-18914,15643426.0,Ch'ang,624.0,France,Female,29.0,9.0,0.0,1.0,0.0,0.0,131283.6
-18915,15670266.0,Chuang,656.0,Germany,Male,40.0,1.0,133800.98,2.0,1.0,1.0,170968.99
-18916,15705309.0,McGregor,516.0,Germany,Female,45.0,5.0,122552.51,1.0,0.0,1.0,166513.49
-18917,15763515.0,Chidiebere,850.0,France,Female,29.0,1.0,0.0,2.0,1.0,1.0,91406.62
-18918,15765093.0,Lo Duca,633.0,Germany,Female,51.0,4.0,102800.72,1.0,1.0,0.0,157791.18
-18919,15754574.0,Ebelechukwu,577.0,Germany,Female,34.0,1.0,121166.89,1.0,1.0,0.0,163558.36
-18920,15806926.0,Okechukwu,602.0,Spain,Female,34.0,9.0,0.0,1.0,1.0,1.0,131302.74
-18921,15758031.0,Grant,624.0,Spain,Male,46.0,4.0,0.0,2.0,1.0,0.0,171096.2
-18922,15742282.0,Chukwualuka,796.0,Germany,Female,46.0,2.0,161592.76,2.0,1.0,0.0,126106.4
-18923,15769902.0,Nnaife,537.0,Spain,Male,53.0,9.0,0.0,1.0,1.0,0.0,61538.43
-18924,15771742.0,Okeke,619.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,83473.82
-18925,15747168.0,Eremenko,656.0,Germany,Female,37.0,4.0,113913.57,1.0,1.0,0.0,149535.97
-18926,15701200.0,Chukwumaobim,661.0,France,Male,38.0,5.0,135984.66,2.0,1.0,0.0,103396.08
-18927,15643679.0,Chien,613.0,Germany,Female,19.0,6.0,133723.43,2.0,1.0,1.0,107499.8
-18928,15667944.0,Chinonyelum,637.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,118863.15
-18929,15697318.0,Ch'in,706.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,142331.39
-18930,15762707.0,Mamelu,578.0,Spain,Male,66.0,4.0,124577.33,2.0,0.0,1.0,103430.46
-18931,15777060.0,Chinwemma,642.0,Spain,Female,58.0,5.0,170833.46,1.0,0.0,0.0,124749.08
-18932,15620443.0,Chinomso,603.0,France,Male,46.0,8.0,0.0,2.0,1.0,1.0,582.59
-18933,15690492.0,Gresswell,714.0,Spain,Male,54.0,6.0,102773.15,1.0,0.0,0.0,106969.98
-18934,15798888.0,Fomin,703.0,France,Male,26.0,4.0,122717.56,1.0,1.0,0.0,170980.86
-18935,15772995.0,Cawood,556.0,France,Female,47.0,8.0,0.0,2.0,1.0,0.0,75732.25
-18936,15773868.0,Mancini,585.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,0.0,92787.16
-18937,15614302.0,Pisano,537.0,France,Male,27.0,8.0,0.0,2.0,0.0,0.0,151903.6
-18938,15652981.0,Tsao,631.0,Spain,Female,31.0,3.0,0.0,1.0,0.0,0.0,134732.99
-18939,15744553.0,Hsiao,641.0,Spain,Female,58.0,4.0,0.0,2.0,0.0,0.0,181204.4
-18940,15756625.0,Fiorentino,707.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,180712.28
-18941,15659475.0,Ugoji,708.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,0.0,175893.53
-18942,15653595.0,Macleod,577.0,France,Female,38.0,6.0,0.0,2.0,0.0,1.0,131534.98
-18943,15783338.0,T'ao,675.0,France,Male,22.0,6.0,0.0,2.0,1.0,1.0,79871.16
-18944,15756475.0,Tsao,556.0,Spain,Female,47.0,7.0,0.0,1.0,0.0,0.0,93826.43
-18945,15762745.0,Genovese,598.0,France,Female,39.0,4.0,135645.76,1.0,1.0,1.0,120531.31
-18946,15752884.0,L?,714.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,131389.39
-18947,15627190.0,Chukwufumnanya,706.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,170908.95
-18948,15574873.0,Tsou,669.0,France,Female,26.0,9.0,0.0,2.0,1.0,0.0,57558.95
-18949,15745375.0,Chidubem,695.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,145796.49
-18950,15784323.0,Watson,564.0,France,Male,39.0,5.0,0.0,1.0,0.0,0.0,54530.51
-18951,15747534.0,Ch'iu,628.0,France,Female,64.0,3.0,132311.71,2.0,0.0,0.0,169161.46
-18952,15730657.0,Lucciano,766.0,Spain,Male,52.0,6.0,139972.18,1.0,1.0,1.0,84872.66
-18953,15648858.0,Padovano,774.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,64869.32
-18954,15654562.0,K'ung,686.0,France,Male,21.0,9.0,0.0,2.0,1.0,1.0,165035.17
-18955,15658675.0,Chukwujamuike,508.0,France,Male,42.0,7.0,119013.86,1.0,1.0,1.0,74858.3
-18956,15782231.0,Hsia,615.0,France,Male,53.0,1.0,0.0,1.0,1.0,0.0,26116.82
-18957,15581548.0,Chizuoke,595.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,134626.3
-18958,15771636.0,Ch'ien,544.0,France,Female,38.0,1.0,0.0,2.0,0.0,1.0,111590.35
-18959,15666095.0,Lucciano,748.0,France,Male,31.0,7.0,0.0,2.0,0.0,1.0,146775.75
-18960,15626679.0,P'eng,721.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,102908.91
-18961,15682472.0,Pirozzi,627.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,67976.05
-18962,15691703.0,Chukwukere,675.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,1.0,66893.28
-18963,15751788.0,Chukwuhaenye,646.0,France,Male,30.0,7.0,0.0,1.0,1.0,0.0,52831.13
-18964,15589660.0,H?,583.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,48673.18
-18965,15683055.0,Chukwuemeka,614.0,Spain,Male,39.0,6.0,0.0,3.0,1.0,1.0,91810.71
-18966,15672930.0,K?,645.0,Spain,Male,48.0,8.0,0.0,2.0,1.0,1.0,74945.11
-18967,15683993.0,Hsieh,719.0,Germany,Female,43.0,9.0,145536.7,1.0,1.0,0.0,19116.97
-18968,15738785.0,Hsiao,717.0,Germany,Female,47.0,7.0,114493.79,1.0,0.0,0.0,145536.28
-18969,15642636.0,Blesing,587.0,Germany,Male,33.0,2.0,132759.8,1.0,0.0,1.0,90591.15
-18970,15631392.0,Ts'ui,609.0,France,Female,52.0,3.0,0.0,1.0,1.0,1.0,92647.94
-18971,15603323.0,Maclean,756.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,92914.67
-18972,15769596.0,Ponomaryova,664.0,France,Female,29.0,5.0,140916.81,1.0,1.0,1.0,51117.06
-18973,15571081.0,P'eng,727.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,1.0,87834.24
-18974,15690589.0,Olisaemeka,741.0,France,Female,22.0,1.0,126495.57,1.0,0.0,1.0,59604.45
-18975,15708710.0,Ch'ang,773.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,172063.09
-18976,15783030.0,Chinweuba,774.0,Spain,Male,41.0,4.0,0.0,2.0,1.0,1.0,52301.15
-18977,15607537.0,Walker,558.0,France,Female,46.0,2.0,0.0,2.0,1.0,1.0,160523.33
-18978,15705512.0,Palerma,512.0,Germany,Female,38.0,8.0,114940.67,2.0,1.0,0.0,143628.31
-18979,15685826.0,Ibezimako,715.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,76510.49
-18980,15693737.0,K'ung,652.0,France,Male,33.0,8.0,0.0,3.0,1.0,1.0,94875.05
-18981,15572022.0,Kosisochukwu,683.0,Spain,Male,45.0,2.0,0.0,2.0,1.0,0.0,125918.17
-18982,15631063.0,Yermakova,731.0,Spain,Female,46.0,7.0,160566.11,1.0,1.0,1.0,176768.59
-18983,15642824.0,Kornilova,642.0,France,Male,32.0,2.0,112652.08,1.0,1.0,0.0,60552.44
-18984,15646615.0,Y?,605.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,1.0,130755.34
-18985,15757559.0,Genovesi,621.0,France,Male,26.0,9.0,0.0,2.0,0.0,1.0,112687.57
-18986,15679966.0,Chinedum,703.0,France,Female,56.0,9.0,0.0,1.0,1.0,0.0,93713.93
-18987,15748752.0,Y?,551.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,5791.85
-18988,15578908.0,Mazzanti,669.0,France,Male,24.0,1.0,0.0,2.0,0.0,1.0,112366.98
-18989,15604551.0,Onyemauchechukwu,547.0,France,Male,40.0,3.0,72594.0,1.0,1.0,1.0,131649.46
-18990,15620243.0,T'ien,721.0,Germany,Female,36.0,7.0,130461.02,1.0,0.0,1.0,82547.01
-18991,15785831.0,Maclean,679.0,France,Female,34.0,10.0,0.0,2.0,1.0,1.0,158887.09
-18992,15602551.0,Gough,654.0,Spain,Female,47.0,8.0,0.0,1.0,1.0,0.0,158937.55
-18993,15574558.0,Mazzanti,593.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,83056.22
-18994,15792229.0,Chiemenam,731.0,France,Male,33.0,2.0,0.0,1.0,0.0,0.0,82222.86
-18995,15654574.0,Houghton,659.0,France,Female,30.0,4.0,0.0,1.0,1.0,1.0,90878.13
-18996,15593976.0,Nweke,746.0,Germany,Female,51.0,3.0,80728.15,1.0,1.0,1.0,142963.17
-18997,15792986.0,Kambinachi,591.0,Germany,Female,40.0,1.0,146720.98,1.0,1.0,0.0,98686.4
-18998,15732265.0,Hsiung,581.0,France,Male,33.0,5.0,0.0,1.0,1.0,1.0,60917.24
-18999,15690925.0,Chiu,714.0,France,Male,28.0,5.0,56214.85,1.0,1.0,0.0,91406.62
-19000,15598046.0,Teng,682.0,Spain,Male,35.0,1.0,0.0,1.0,0.0,1.0,94433.08
-19001,15807609.0,Ch'iu,607.0,Spain,Male,44.0,7.0,0.0,2.0,1.0,1.0,40807.27
-19002,15581229.0,Chien,584.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,0.0,82276.62
-19003,15589715.0,Ts'ui,806.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,49374.82
-19004,15592386.0,Eames,775.0,Germany,Female,46.0,4.0,105419.73,1.0,0.0,0.0,161479.19
-19005,15806438.0,Scott,753.0,Germany,Female,29.0,5.0,125903.22,1.0,1.0,0.0,161069.73
-19006,15807900.0,Okwudilichukwu,643.0,France,Male,41.0,2.0,162448.69,1.0,0.0,0.0,130404.53
-19007,15583090.0,Iweobiegbunam,571.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,134526.87
-19008,15659843.0,Hsiao,594.0,Germany,Female,46.0,1.0,103267.8,1.0,1.0,0.0,80553.87
-19009,15707120.0,Chuang,678.0,Spain,Female,60.0,7.0,0.0,2.0,0.0,1.0,142730.27
-19010,15618363.0,Clements,652.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,172576.43
-19011,15728082.0,Chidozie,743.0,Germany,Male,34.0,4.0,124495.98,2.0,1.0,0.0,166586.99
-19012,15650437.0,Chien,545.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,1.0,72926.31
-19013,15693947.0,Ritchie,815.0,Spain,Male,37.0,0.0,0.0,2.0,1.0,1.0,94888.5
-19014,15662578.0,P'eng,705.0,France,Female,50.0,8.0,0.0,1.0,0.0,1.0,173495.77
-19015,15696900.0,Maclean,576.0,France,Male,39.0,2.0,0.0,2.0,0.0,0.0,121311.12
-19016,15781802.0,Folliero,565.0,France,Female,31.0,6.0,0.0,1.0,1.0,1.0,139919.38
-19017,15646091.0,Onwughara,637.0,Spain,Male,30.0,3.0,151963.26,1.0,1.0,0.0,185543.34
-19018,15659694.0,T'ien,815.0,France,Male,29.0,4.0,142862.21,2.0,1.0,1.0,87951.45
-19019,15789753.0,Tien,694.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,104469.06
-19020,15677739.0,Kao,717.0,Spain,Female,29.0,9.0,81564.1,1.0,1.0,1.0,112256.98
-19021,15656592.0,Chidimma,704.0,France,Female,60.0,3.0,0.0,2.0,1.0,1.0,67972.45
-19022,15773876.0,Ts'ai,653.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,156476.05
-19023,15750839.0,Koo,712.0,France,Male,42.0,6.0,0.0,2.0,0.0,1.0,26136.13
-19024,15737408.0,H?,693.0,France,Female,40.0,3.0,0.0,1.0,1.0,0.0,151903.6
-19025,15703682.0,Kao,850.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,143463.28
-19026,15750099.0,Trevisano,703.0,France,Female,19.0,2.0,0.0,1.0,1.0,0.0,95611.47
-19027,15798895.0,Chukwudi,724.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,166650.32
-19028,15693864.0,Ch'ien,583.0,Germany,Female,34.0,6.0,122570.87,1.0,0.0,0.0,167023.95
-19029,15636572.0,T'ien,835.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,86634.3
-19030,15792388.0,Manna,543.0,Spain,Female,37.0,5.0,129668.43,1.0,0.0,0.0,35179.46
-19031,15651943.0,Ndubueze,759.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,62449.07
-19032,15702741.0,Folliero,593.0,France,Female,32.0,4.0,0.0,2.0,0.0,0.0,149188.41
-19033,15682356.0,Ch'iu,695.0,Spain,Female,33.0,9.0,124495.98,1.0,1.0,1.0,180712.28
-19034,15670066.0,Lattimore,645.0,Germany,Male,25.0,2.0,152685.4,2.0,0.0,0.0,166038.13
-19035,15768777.0,Rizzo,719.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,181430.91
-19036,15804009.0,Kao,604.0,Spain,Male,24.0,5.0,118628.6,1.0,1.0,1.0,143783.46
-19037,15734987.0,Onyemauchechukwu,579.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,166650.9
-19038,15652914.0,Milanesi,684.0,France,Male,37.0,8.0,116251.24,2.0,1.0,1.0,59816.41
-19039,15635367.0,Ch'ang,710.0,Spain,Male,33.0,1.0,0.0,2.0,1.0,0.0,123475.78
-19040,15710203.0,Hsia,682.0,Germany,Female,45.0,7.0,127818.52,2.0,0.0,1.0,53478.02
-19041,15708917.0,Chiazagomekpele,629.0,France,Male,32.0,6.0,89772.3,1.0,1.0,0.0,142963.29
-19042,15758051.0,Teng,678.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,106036.94
-19043,15682442.0,Manna,738.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,94739.2
-19044,15665590.0,Chidozie,581.0,France,Male,36.0,7.0,121411.9,1.0,1.0,1.0,26318.73
-19045,15626474.0,Sagese,557.0,France,Female,70.0,4.0,105264.88,1.0,1.0,0.0,113481.02
-19046,15585362.0,Ch'ang,712.0,France,Female,27.0,7.0,0.0,2.0,1.0,1.0,134901.58
-19047,15726178.0,Chukwuebuka,505.0,Spain,Female,41.0,1.0,0.0,1.0,1.0,0.0,117431.1
-19048,15757541.0,Onochie,521.0,France,Male,33.0,8.0,92787.93,2.0,1.0,0.0,173486.39
-19049,15582903.0,Maclean,758.0,Germany,Female,31.0,5.0,114370.41,1.0,1.0,1.0,199293.01
-19050,15627697.0,Onyekaozulu,614.0,France,Male,34.0,4.0,133800.98,1.0,1.0,1.0,111180.4
-19051,15803685.0,T'ien,681.0,Spain,Male,27.0,6.0,0.0,1.0,0.0,0.0,62449.07
-19052,15756901.0,Oluchi,587.0,France,Female,48.0,7.0,0.0,3.0,1.0,1.0,135925.72
-19053,15692406.0,Li,599.0,Spain,Male,35.0,3.0,0.0,2.0,0.0,1.0,171039.56
-19054,15800620.0,Hsia,583.0,Germany,Female,39.0,7.0,74483.97,2.0,1.0,1.0,170621.43
-19055,15583681.0,Hsieh,683.0,Germany,Male,44.0,5.0,125889.3,2.0,1.0,0.0,113482.2
-19056,15700650.0,Nwachukwu,765.0,Germany,Male,32.0,5.0,122314.5,1.0,1.0,0.0,76510.46
-19057,15651336.0,Lu,525.0,France,Male,29.0,5.0,143390.51,2.0,1.0,0.0,180595.02
-19058,15798824.0,Chinweuba,632.0,France,Male,40.0,8.0,0.0,1.0,1.0,0.0,100717.54
-19059,15597901.0,Y?an,667.0,France,Female,42.0,3.0,0.0,2.0,0.0,0.0,153792.87
-19060,15642824.0,Kosisochukwu,653.0,France,Male,29.0,3.0,115290.32,1.0,0.0,0.0,157072.91
-19061,15639576.0,O'Sullivan,633.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,152956.33
-19062,15667938.0,Y?an,697.0,France,Female,35.0,9.0,0.0,2.0,0.0,1.0,180553.48
-19063,15800736.0,Chukwubuikem,646.0,France,Male,27.0,7.0,0.0,2.0,1.0,1.0,131284.93
-19064,15713853.0,Ting,613.0,France,Male,52.0,4.0,0.0,2.0,1.0,1.0,55829.25
-19065,15772632.0,Fanucci,576.0,France,Male,24.0,4.0,0.0,1.0,1.0,1.0,180800.42
-19066,15803886.0,Biryukov,629.0,Spain,Male,56.0,9.0,0.0,1.0,1.0,1.0,93777.61
-19067,15807593.0,Onyemauchechukwu,751.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,144584.29
-19068,15572038.0,Hs?,729.0,Germany,Male,32.0,1.0,105701.91,2.0,1.0,1.0,58320.06
-19069,15617320.0,Ch'en,738.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,128528.83
-19070,15631339.0,Teng,479.0,France,Male,36.0,7.0,0.0,1.0,1.0,1.0,165035.17
-19071,15783082.0,Ch'ang,699.0,Spain,Female,50.0,5.0,0.0,1.0,1.0,1.0,71095.41
-19072,15808760.0,Hsiung,597.0,Germany,Male,38.0,8.0,129745.3,1.0,1.0,1.0,139439.31
-19073,15572461.0,L?,729.0,France,Male,51.0,4.0,0.0,3.0,1.0,1.0,118024.1
-19074,15702298.0,Hsu,665.0,France,Male,25.0,6.0,0.0,2.0,1.0,1.0,171056.65
-19075,15738220.0,Pirozzi,571.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,127528.79
-19076,15742285.0,T'an,557.0,France,Male,34.0,4.0,0.0,1.0,1.0,1.0,133535.29
-19077,15681446.0,Nkemdilim,842.0,France,Female,46.0,9.0,136622.55,2.0,0.0,1.0,171840.24
-19078,15731519.0,Ugochukwu,635.0,France,Male,35.0,0.0,0.0,2.0,1.0,0.0,176902.83
-19079,15762228.0,Ch'ien,751.0,France,Male,44.0,9.0,0.0,1.0,1.0,1.0,114732.62
-19080,15790935.0,Chukwumaobim,584.0,France,Male,23.0,1.0,0.0,2.0,1.0,0.0,80190.36
-19081,15639277.0,Ch'ien,782.0,France,Female,31.0,4.0,114732.23,1.0,0.0,1.0,80181.12
-19082,15581229.0,Lo Duca,699.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,1.0,24255.21
-19083,15588944.0,Efremov,661.0,France,Female,47.0,5.0,118287.01,1.0,0.0,0.0,146636.44
-19084,15668830.0,Palerma,807.0,France,Male,32.0,2.0,121063.9,2.0,1.0,1.0,51380.9
-19085,15754926.0,Uvarova,731.0,Spain,Male,32.0,8.0,116644.29,1.0,1.0,1.0,108152.75
-19086,15570134.0,Chukwufumnanya,667.0,France,Female,45.0,1.0,0.0,1.0,0.0,0.0,112465.19
-19087,15648735.0,Chin,584.0,France,Female,39.0,3.0,0.0,1.0,0.0,0.0,136610.02
-19088,15696564.0,Chidozie,743.0,France,Male,36.0,3.0,0.0,1.0,1.0,1.0,118959.64
-19089,15597905.0,Ch'in,622.0,France,Male,42.0,8.0,0.0,2.0,0.0,0.0,91943.94
-19090,15709387.0,Chukwueloka,774.0,Germany,Male,35.0,4.0,98263.14,1.0,0.0,0.0,126013.58
-19091,15673747.0,Nnamutaezinwa,619.0,France,Female,38.0,2.0,0.0,1.0,1.0,0.0,166586.99
-19092,15753081.0,Fiorentino,763.0,France,Female,59.0,8.0,95994.98,1.0,1.0,0.0,62746.03
-19093,15772422.0,H?,652.0,France,Male,46.0,3.0,0.0,2.0,1.0,1.0,177734.49
-19094,15600399.0,Lockington,602.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,0.0,58879.31
-19095,15692452.0,Cawker,633.0,Spain,Female,37.0,4.0,142643.54,1.0,1.0,0.0,115789.25
-19096,15702912.0,Cattaneo,583.0,Spain,Female,32.0,1.0,0.0,2.0,0.0,0.0,183387.12
-19097,15767129.0,Hs?,684.0,Germany,Male,34.0,4.0,170935.94,2.0,1.0,0.0,177740.58
-19098,15777784.0,Sorenson,706.0,Germany,Male,49.0,6.0,136689.66,1.0,1.0,0.0,84420.5
-19099,15626474.0,Ch'iu,732.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,60988.21
-19100,15765283.0,T'ang,636.0,Germany,Male,46.0,8.0,126589.12,1.0,1.0,0.0,146785.44
-19101,15632264.0,Nwokeocha,641.0,Spain,Male,43.0,1.0,0.0,1.0,1.0,1.0,73881.68
-19102,15690743.0,Ch'ien,586.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,140941.47
-19103,15652693.0,De Luca,703.0,France,Male,31.0,6.0,128966.13,1.0,1.0,1.0,161076.92
-19104,15775124.0,Chiazagomekpere,516.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,49848.86
-19105,15701200.0,Yashina,793.0,France,Male,41.0,8.0,0.0,2.0,0.0,1.0,82290.81
-19106,15809837.0,Onuchukwu,627.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,177852.57
-19107,15605375.0,Omeokachie,627.0,France,Male,40.0,6.0,167735.83,1.0,0.0,0.0,159773.14
-19108,15806983.0,Chien,607.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,129912.74
-19109,15692226.0,Ch'in,697.0,France,Male,50.0,8.0,0.0,2.0,1.0,1.0,135079.62
-19110,15651144.0,Mazzi,739.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,182270.88
-19111,15771211.0,Onuchukwu,687.0,France,Female,28.0,1.0,61691.19,1.0,1.0,0.0,138504.28
-19112,15704771.0,Ritchie,550.0,Spain,Male,32.0,7.0,0.0,2.0,1.0,1.0,60905.51
-19113,15612455.0,Ts'ai,717.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,66515.87
-19114,15658813.0,Chuang,842.0,Germany,Male,37.0,6.0,125877.65,2.0,1.0,1.0,105563.41
-19115,15770174.0,O'Loghlin,669.0,Germany,Male,32.0,2.0,91622.42,1.0,1.0,1.0,71865.7
-19116,15585324.0,Mello,559.0,France,Male,36.0,5.0,140576.11,1.0,1.0,0.0,79577.38
-19117,15617301.0,Hsiung,679.0,Spain,Female,48.0,5.0,0.0,2.0,1.0,1.0,99843.98
-19118,15756538.0,Ch'iu,558.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,149912.28
-19119,15653050.0,Y?an,718.0,Germany,Male,39.0,1.0,117294.12,1.0,1.0,1.0,176763.27
-19120,15592379.0,Ch'ien,583.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,145700.22
-19121,15631392.0,P'eng,703.0,France,Female,29.0,1.0,116318.23,1.0,0.0,1.0,129964.94
-19122,15782454.0,Ch'en,696.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,57572.38
-19123,15681755.0,Yermakova,819.0,Spain,Female,39.0,9.0,0.0,2.0,0.0,1.0,170968.99
-19124,15737647.0,Dreyer,724.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,146585.53
-19125,15589544.0,Pinto,597.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,0.0,164815.59
-19126,15792934.0,Eluemuno,569.0,France,Male,25.0,3.0,0.0,1.0,1.0,1.0,151317.48
-19127,15807305.0,Walker,574.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,0.0,95007.8
-19128,15658364.0,Tuan,721.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,160696.72
-19129,15635143.0,Chibueze,767.0,Germany,Male,30.0,9.0,128728.96,2.0,1.0,1.0,1187.88
-19130,15589449.0,Shih,745.0,Germany,Female,48.0,8.0,139542.82,1.0,1.0,0.0,83617.53
-19131,15782899.0,Scott,771.0,Germany,Male,35.0,8.0,115897.12,1.0,0.0,1.0,153662.88
-19132,15723008.0,Yuryeva,705.0,Germany,Female,40.0,2.0,110939.3,1.0,1.0,1.0,172669.39
-19133,15641833.0,Okorie,757.0,France,Female,31.0,10.0,0.0,2.0,1.0,0.0,79082.76
-19134,15675184.0,Ts'ai,850.0,France,Male,25.0,5.0,0.0,2.0,0.0,1.0,121311.12
-19135,15742809.0,Zito,613.0,Spain,Male,46.0,9.0,0.0,1.0,1.0,0.0,82565.01
-19136,15732644.0,Ch'eng,651.0,France,Female,35.0,8.0,0.0,1.0,0.0,0.0,57876.05
-19137,15732672.0,Baresi,714.0,Spain,Female,31.0,6.0,0.0,2.0,0.0,1.0,114092.52
-19138,15677049.0,Uspenskaya,613.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,1.0,74850.84
-19139,15797065.0,Kaur,694.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,141946.92
-19140,15789413.0,Creswell,562.0,Germany,Male,38.0,8.0,119963.45,2.0,1.0,1.0,105570.39
-19141,15606386.0,Pinto,727.0,France,Female,37.0,4.0,0.0,1.0,1.0,1.0,82709.8
-19142,15752953.0,Ibezimako,669.0,Germany,Male,45.0,1.0,121161.57,1.0,1.0,0.0,70532.53
-19143,15689955.0,Fanucci,516.0,Spain,Male,38.0,8.0,0.0,1.0,1.0,0.0,164768.82
-19144,15642996.0,Yewen,516.0,France,Female,44.0,4.0,0.0,2.0,1.0,0.0,141558.04
-19145,15792360.0,T'ao,572.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,126728.27
-19146,15624239.0,Chidiebere,610.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,83090.35
-19147,15724334.0,Ch'in,619.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,100101.06
-19148,15734461.0,Folliero,524.0,Germany,Female,32.0,8.0,108914.17,1.0,0.0,1.0,134878.19
-19149,15778936.0,Ijendu,679.0,France,Female,32.0,2.0,153639.11,1.0,1.0,1.0,155743.48
-19150,15632789.0,Ku,729.0,France,Male,33.0,8.0,0.0,2.0,0.0,0.0,145558.42
-19151,15724443.0,Chu,539.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,182627.25
-19152,15677906.0,Tretiakova,675.0,France,Male,47.0,4.0,0.0,1.0,0.0,0.0,129563.86
-19153,15622003.0,Ku,607.0,France,Female,51.0,6.0,0.0,1.0,1.0,0.0,93825.83
-19154,15734886.0,Ch'en,677.0,Spain,Female,45.0,3.0,0.0,2.0,0.0,1.0,138537.03
-19155,15583692.0,Nwankwo,579.0,Germany,Male,38.0,2.0,103377.81,2.0,1.0,0.0,94774.12
-19156,15789425.0,Hsieh,643.0,France,Male,45.0,8.0,123356.63,1.0,1.0,1.0,96823.32
-19157,15591344.0,Okwuadigbo,579.0,France,Female,38.0,6.0,83693.34,1.0,0.0,1.0,72993.65
-19158,15618750.0,Chukwuebuka,600.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,161229.84
-19159,15643825.0,Ives,735.0,France,Female,28.0,8.0,0.0,1.0,1.0,1.0,133535.29
-19160,15707132.0,McGregor,653.0,Spain,Female,44.0,4.0,0.0,1.0,1.0,0.0,142670.61
-19161,15635942.0,Obiora,678.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,170819.01
-19162,15754713.0,Shih,727.0,France,Male,44.0,9.0,0.0,2.0,1.0,1.0,88947.56
-19163,15680197.0,Chiu,710.0,Germany,Female,32.0,3.0,121205.15,2.0,1.0,1.0,77965.67
-19164,15609998.0,Hsiung,683.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,0.0,112256.57
-19165,15742404.0,Udegbulam,662.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,91903.9
-19166,15772383.0,Chikwado,697.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,94862.93
-19167,15758035.0,Pinto,730.0,France,Male,35.0,1.0,143390.51,1.0,1.0,0.0,182262.66
-19168,15572631.0,Ts'ao,587.0,Germany,Female,50.0,4.0,123692.0,1.0,1.0,1.0,82967.93
-19169,15682533.0,Chukwudi,667.0,Spain,Male,37.0,8.0,0.0,1.0,1.0,0.0,81251.24
-19170,15772529.0,Chukwubuikem,744.0,Spain,Female,42.0,6.0,0.0,1.0,0.0,0.0,179861.79
-19171,15788659.0,Loyau,779.0,Spain,Male,44.0,1.0,0.0,2.0,1.0,1.0,139994.68
-19172,15764661.0,Hsia,589.0,France,Male,34.0,5.0,0.0,1.0,0.0,0.0,162820.5
-19173,15665963.0,Genovese,746.0,Spain,Male,32.0,8.0,125919.35,1.0,1.0,0.0,122021.33
-19174,15646521.0,Ndubueze,579.0,Spain,Female,33.0,0.0,0.0,2.0,1.0,1.0,155876.06
-19175,15765952.0,Yobanna,619.0,France,Male,37.0,1.0,0.0,2.0,0.0,0.0,122949.71
-19176,15697480.0,Chia,547.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,151317.27
-19177,15641389.0,Nnamutaezinwa,705.0,Germany,Female,21.0,5.0,131033.76,2.0,1.0,1.0,168035.62
-19178,15737476.0,Ts'ui,678.0,France,Male,30.0,3.0,117866.92,1.0,1.0,1.0,144880.81
-19179,15652401.0,Hargreaves,616.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,62733.05
-19180,15659098.0,Hs?,708.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,101728.46
-19181,15686983.0,Nwachukwu,639.0,France,Female,34.0,3.0,151218.98,1.0,1.0,0.0,93835.64
-19182,15641490.0,Hs?,581.0,Spain,Female,37.0,9.0,0.0,1.0,1.0,0.0,103931.49
-19183,15603594.0,Ch'eng,680.0,France,Male,42.0,4.0,0.0,2.0,1.0,0.0,86044.98
-19184,15667085.0,Nucci,754.0,Germany,Male,37.0,6.0,118879.35,2.0,1.0,0.0,122073.23
-19185,15804064.0,Genovese,695.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,61937.6
-19186,15797900.0,Chiganu,677.0,France,Female,29.0,8.0,0.0,2.0,0.0,0.0,181419.41
-19187,15651958.0,Mazzanti,678.0,Germany,Female,37.0,9.0,165798.1,1.0,0.0,1.0,54448.14
-19188,15648069.0,Hs?,686.0,France,Female,23.0,2.0,0.0,1.0,1.0,0.0,170518.83
-19189,15654870.0,Liao,602.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,88721.84
-19190,15585047.0,Kao,635.0,France,Male,35.0,3.0,123971.16,1.0,1.0,1.0,167030.51
-19191,15644765.0,Hsia,719.0,Spain,Female,39.0,1.0,137482.19,1.0,1.0,0.0,138021.36
-19192,15606945.0,Calabrese,572.0,Germany,Female,49.0,2.0,93522.2,2.0,1.0,0.0,151822.66
-19193,15614778.0,Okwuoma,850.0,Spain,Female,36.0,4.0,0.0,2.0,0.0,0.0,77965.67
-19194,15579892.0,Oluchi,698.0,France,Female,28.0,2.0,0.0,1.0,1.0,1.0,165576.55
-19195,15642570.0,Ts'ui,586.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,40385.61
-19196,15692819.0,Chiawuotu,678.0,Spain,Female,45.0,3.0,0.0,4.0,0.0,1.0,180693.61
-19197,15652069.0,Hsueh,679.0,Spain,Female,43.0,1.0,0.0,2.0,1.0,1.0,28750.67
-19198,15665327.0,Chimezie,710.0,France,Male,36.0,3.0,0.0,1.0,1.0,1.0,168816.22
-19199,15691906.0,Wilson,636.0,France,Male,59.0,8.0,0.0,1.0,0.0,0.0,153776.6
-19200,15622578.0,Ch'iu,594.0,Germany,Female,44.0,2.0,116070.58,1.0,1.0,0.0,72970.93
-19201,15591036.0,Shih,542.0,France,Female,43.0,9.0,0.0,2.0,1.0,0.0,173464.43
-19202,15749679.0,Hsia,676.0,France,Female,52.0,6.0,0.0,1.0,1.0,1.0,50783.1
-19203,15783025.0,P'eng,632.0,Germany,Male,23.0,8.0,140720.93,2.0,1.0,0.0,52462.25
-19204,15781989.0,Onyemauchechukwu,757.0,Germany,Male,28.0,5.0,121325.84,2.0,1.0,1.0,96498.14
-19205,15597945.0,Onwudiwe,572.0,France,Female,32.0,8.0,69434.4,1.0,1.0,1.0,163471.01
-19206,15656592.0,Fiorentini,711.0,Spain,Male,35.0,3.0,0.0,2.0,0.0,0.0,121232.51
-19207,15613197.0,Yashina,628.0,France,Female,47.0,3.0,0.0,1.0,0.0,0.0,176903.35
-19208,15698031.0,Ngozichukwuka,757.0,Spain,Male,30.0,3.0,0.0,2.0,1.0,0.0,161388.22
-19209,15661349.0,Hsueh,741.0,France,Male,50.0,8.0,0.0,1.0,1.0,0.0,132939.64
-19210,15605918.0,Baresi,610.0,France,Female,45.0,5.0,0.0,1.0,0.0,0.0,104079.62
-19211,15752601.0,McGregor,652.0,France,Male,35.0,1.0,0.0,1.0,0.0,0.0,83978.86
-19212,15578515.0,Martin,754.0,Germany,Female,59.0,5.0,122471.09,1.0,1.0,0.0,166694.93
-19213,15731751.0,Aiken,573.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,141364.27
-19214,15809772.0,Lucciano,707.0,Spain,Male,36.0,7.0,144961.97,1.0,1.0,0.0,182627.25
-19215,15596800.0,Pendergrass,564.0,France,Female,44.0,0.0,127616.56,1.0,0.0,1.0,49656.24
-19216,15639277.0,Pinto,667.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,131736.23
-19217,15667932.0,Ch'ang,682.0,Germany,Male,33.0,7.0,107648.94,2.0,0.0,1.0,45610.63
-19218,15762799.0,Chiu,683.0,Spain,Male,44.0,8.0,0.0,1.0,1.0,1.0,60429.43
-19219,15752953.0,Chinomso,730.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,67622.36
-19220,15653715.0,T'ien,683.0,Germany,Female,35.0,8.0,140745.33,1.0,1.0,0.0,179861.79
-19221,15744695.0,Chiazagomekpele,561.0,Germany,Female,32.0,7.0,119023.28,2.0,1.0,1.0,164806.89
-19222,15778395.0,Trevisan,538.0,Germany,Female,42.0,5.0,157624.85,2.0,1.0,1.0,16081.62
-19223,15686696.0,Li,561.0,France,Male,28.0,4.0,0.0,2.0,0.0,1.0,157891.86
-19224,15688071.0,H?,718.0,Spain,Male,39.0,6.0,0.0,1.0,0.0,0.0,179861.79
-19225,15621423.0,Pisani,639.0,Germany,Female,42.0,4.0,142765.24,1.0,1.0,1.0,136846.75
-19226,15768270.0,Nkemakolam,709.0,France,Female,43.0,7.0,0.0,1.0,1.0,1.0,50783.1
-19227,15809309.0,Chidubem,644.0,Spain,Female,37.0,2.0,0.0,1.0,1.0,1.0,141364.62
-19228,15624141.0,Kao,715.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,154844.22
-19229,15662021.0,Ch'ang,719.0,France,Male,42.0,1.0,0.0,2.0,1.0,1.0,116503.92
-19230,15627305.0,Chidumaga,671.0,France,Male,38.0,2.0,0.0,1.0,0.0,1.0,60552.44
-19231,15697546.0,Ch'iu,645.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,136535.75
-19232,15666032.0,Scott,611.0,Spain,Female,33.0,3.0,125537.38,1.0,0.0,1.0,100862.36
-19233,15708917.0,Y?,579.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,136985.08
-19234,15573769.0,Kao,671.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,37662.49
-19235,15679966.0,Chia,747.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,121681.19
-19236,15808960.0,Onwumelu,656.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,0.0,167984.72
-19237,15746199.0,Tuan,678.0,Spain,Female,32.0,6.0,0.0,1.0,1.0,0.0,38019.74
-19238,15797900.0,Genovese,613.0,France,Female,33.0,2.0,0.0,2.0,0.0,1.0,145986.35
-19239,15628893.0,Ogochukwu,603.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,182784.11
-19240,15613787.0,Chiazagomekpere,695.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,1.0,146652.11
-19241,15803689.0,Hsueh,683.0,France,Female,40.0,5.0,0.0,2.0,0.0,1.0,83084.33
-19242,15802220.0,Chibugo,684.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,100598.98
-19243,15791925.0,Smith,653.0,France,Male,54.0,5.0,0.0,1.0,1.0,0.0,90896.04
-19244,15631359.0,Tuan,714.0,France,Male,32.0,6.0,123863.64,1.0,1.0,1.0,169583.18
-19245,15644796.0,Chiazagomekpere,596.0,Germany,Female,28.0,1.0,143421.78,1.0,1.0,1.0,107876.2
-19246,15612095.0,Chukwufumnanya,751.0,France,Male,21.0,8.0,0.0,2.0,0.0,1.0,151310.16
-19247,15602312.0,Zetticci,632.0,France,Female,43.0,8.0,0.0,1.0,1.0,0.0,155722.73
-19248,15610147.0,Chizoba,682.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,66526.01
-19249,15703588.0,Chiu,754.0,Spain,Male,32.0,3.0,0.0,2.0,0.0,1.0,92826.49
-19250,15705512.0,Zetticci,687.0,France,Female,22.0,6.0,0.0,1.0,1.0,0.0,82647.65
-19251,15776526.0,Chukwujamuike,749.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,1.0,172576.43
-19252,15757981.0,Nnonso,694.0,Spain,Female,29.0,3.0,0.0,2.0,1.0,0.0,131644.41
-19253,15764284.0,Uspensky,712.0,Spain,Male,25.0,3.0,0.0,2.0,1.0,1.0,130132.54
-19254,15632899.0,Chukwufumnanya,714.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,110114.19
-19255,15778049.0,Bancks,753.0,Spain,Female,36.0,1.0,109805.4,1.0,1.0,1.0,152843.53
-19256,15583576.0,Ch'ien,694.0,Spain,Male,40.0,8.0,0.0,2.0,0.0,0.0,134778.01
-19257,15791045.0,Genovese,718.0,Germany,Male,32.0,10.0,141782.57,2.0,1.0,0.0,177028.0
-19258,15712608.0,Okorie,646.0,France,Female,48.0,4.0,0.0,1.0,0.0,0.0,185561.89
-19259,15702464.0,Hs?,710.0,France,Male,52.0,4.0,0.0,1.0,1.0,0.0,116081.97
-19260,15803609.0,Johnstone,752.0,France,Female,43.0,6.0,0.0,1.0,1.0,0.0,175722.71
-19261,15658972.0,Genovese,590.0,France,Female,33.0,1.0,0.0,2.0,1.0,0.0,167984.72
-19262,15669491.0,Mazzi,583.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,128881.87
-19263,15670266.0,Chuang,644.0,France,Male,23.0,9.0,0.0,2.0,1.0,1.0,10350.74
-19264,15662494.0,Iloerika,628.0,France,Female,40.0,3.0,129674.89,1.0,1.0,0.0,126812.63
-19265,15643359.0,Chibueze,649.0,Germany,Female,37.0,8.0,96471.2,2.0,0.0,1.0,64345.61
-19266,15678058.0,Hsing,705.0,France,Male,30.0,8.0,90218.9,1.0,1.0,1.0,71175.94
-19267,15589323.0,Onwuamaeze,695.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,76611.33
-19268,15629133.0,Chukwuebuka,738.0,Germany,Female,36.0,5.0,134542.73,1.0,1.0,0.0,133692.88
-19269,15699225.0,Baresi,639.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,161407.48
-19270,15785547.0,Onwuamaeze,652.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,132973.63
-19271,15631838.0,H?,697.0,France,Male,39.0,5.0,110822.9,1.0,1.0,0.0,127095.14
-19272,15624892.0,Hsiung,554.0,France,Male,38.0,9.0,0.0,1.0,1.0,0.0,151815.76
-19273,15794266.0,Clements,773.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,85801.77
-19274,15694518.0,Y?an,775.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,1.0,96794.46
-19275,15723488.0,T'ien,614.0,France,Male,41.0,1.0,0.0,1.0,1.0,1.0,132730.07
-19276,15602034.0,Hsiung,650.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,183492.9
-19277,15774854.0,Chikwado,642.0,France,Female,24.0,3.0,0.0,2.0,1.0,1.0,132973.18
-19278,15587913.0,Milne,757.0,France,Male,60.0,6.0,0.0,1.0,1.0,1.0,180593.45
-19279,15657535.0,Chiang,643.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,63795.62
-19280,15769312.0,Fyodorova,727.0,France,Female,39.0,9.0,0.0,2.0,1.0,0.0,36754.13
-19281,15697567.0,Liao,679.0,France,Female,28.0,5.0,0.0,2.0,0.0,0.0,142331.85
-19282,15688069.0,Scott,684.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,121210.09
-19283,15740476.0,Lucchese,671.0,Spain,Male,33.0,6.0,0.0,2.0,0.0,0.0,151316.37
-19284,15667417.0,Yermolayeva,586.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,182720.07
-19285,15704581.0,Nucci,697.0,France,Female,56.0,5.0,99513.87,1.0,0.0,1.0,64831.36
-19286,15663939.0,DeRose,573.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,147055.87
-19287,15694879.0,Hsia,636.0,France,Male,31.0,4.0,154969.79,1.0,0.0,1.0,134794.02
-19288,15566894.0,Rizzo,571.0,France,Male,40.0,9.0,0.0,2.0,0.0,0.0,52894.01
-19289,15597951.0,Hsiung,762.0,France,Male,24.0,10.0,123347.5,1.0,1.0,1.0,152499.8
-19290,15668894.0,Pisano,715.0,France,Male,48.0,4.0,0.0,2.0,0.0,0.0,86678.48
-19291,15737173.0,Folliero,677.0,France,Female,41.0,7.0,0.0,2.0,0.0,0.0,86699.98
-19292,15586429.0,Chukwudi,633.0,Germany,Male,34.0,1.0,148897.02,3.0,1.0,0.0,112649.22
-19293,15591036.0,Walker,599.0,Germany,Female,46.0,8.0,150856.79,1.0,1.0,0.0,140941.47
-19294,15735270.0,Clements,590.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,64595.25
-19295,15591428.0,Calabrese,709.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,52484.01
-19296,15765650.0,Hs?,501.0,Spain,Female,25.0,1.0,0.0,2.0,1.0,0.0,82634.41
-19297,15585599.0,Ebelechukwu,595.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,1.0,177624.01
-19298,15720509.0,Eluemuno,678.0,Spain,Male,60.0,4.0,0.0,1.0,1.0,0.0,106977.53
-19299,15635367.0,Chidiebele,667.0,Spain,Male,34.0,2.0,156091.97,1.0,1.0,1.0,143598.7
-19300,15759717.0,Teng,695.0,Germany,Male,37.0,1.0,125997.22,2.0,0.0,1.0,46635.11
-19301,15658935.0,Hsieh,554.0,Germany,Female,40.0,2.0,123936.54,2.0,0.0,1.0,125898.39
-19302,15584766.0,Sagese,608.0,Germany,Female,38.0,8.0,92676.3,2.0,1.0,0.0,80593.49
-19303,15743590.0,Lappin,638.0,France,Male,44.0,1.0,0.0,2.0,1.0,0.0,103901.58
-19304,15697042.0,Nnamdi,682.0,France,Male,46.0,3.0,0.0,1.0,1.0,0.0,83052.03
-19305,15679961.0,Ch'ien,579.0,Spain,Female,31.0,4.0,0.0,1.0,1.0,0.0,193014.26
-19306,15668818.0,Nnamutaezinwa,528.0,France,Male,30.0,3.0,91219.29,1.0,1.0,1.0,68759.57
-19307,15643574.0,Hsia,642.0,France,Female,40.0,1.0,0.0,2.0,1.0,1.0,90908.95
-19308,15694566.0,Tien,646.0,France,Female,31.0,1.0,0.0,1.0,1.0,0.0,57862.88
-19309,15642655.0,Ch'ang,652.0,France,Male,39.0,3.0,0.0,1.0,0.0,0.0,160594.85
-19310,15746333.0,Kambinachi,713.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,187826.11
-19311,15806945.0,Tung,714.0,Spain,Female,37.0,2.0,0.0,1.0,0.0,1.0,125877.22
-19312,15680752.0,Iloerika,717.0,Spain,Male,36.0,10.0,0.0,1.0,1.0,0.0,134851.12
-19313,15783029.0,Onyemauchechukwu,549.0,Germany,Male,38.0,8.0,105249.71,2.0,1.0,0.0,47880.71
-19314,15595728.0,Chukwubuikem,679.0,Spain,Female,40.0,6.0,0.0,1.0,1.0,0.0,189175.71
-19315,15579914.0,Ch'iu,683.0,Spain,Male,34.0,7.0,128927.93,1.0,0.0,0.0,125079.65
-19316,15596074.0,Mazzi,635.0,Spain,Female,40.0,7.0,150186.55,1.0,1.0,1.0,148516.79
-19317,15788775.0,Chuang,757.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,181208.47
-19318,15732774.0,Macdonald,535.0,Germany,Female,31.0,3.0,118588.83,1.0,0.0,1.0,166677.54
-19319,15740476.0,Maclean,613.0,Spain,Male,47.0,1.0,0.0,2.0,0.0,0.0,125962.02
-19320,15792489.0,Cattaneo,640.0,Spain,Male,27.0,2.0,0.0,2.0,0.0,0.0,131718.12
-19321,15756535.0,Li Fonti,637.0,France,Male,35.0,7.0,0.0,1.0,1.0,1.0,182436.81
-19322,15598892.0,Yobachukwu,681.0,France,Male,41.0,9.0,0.0,1.0,1.0,1.0,131283.6
-19323,15727198.0,Hsiung,710.0,Germany,Female,31.0,6.0,121513.88,1.0,1.0,0.0,114448.77
-19324,15719265.0,Lu,757.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,186489.95
-19325,15779176.0,Lo,724.0,Germany,Female,41.0,4.0,95128.86,2.0,1.0,0.0,94499.06
-19326,15632256.0,Chukwubuikem,687.0,Spain,Male,43.0,1.0,0.0,1.0,1.0,0.0,112157.31
-19327,15640407.0,Iloerika,592.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,101728.46
-19328,15656865.0,Chizuoke,675.0,France,Male,34.0,4.0,0.0,2.0,0.0,0.0,140937.97
-19329,15635572.0,H?,611.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,171575.54
-19330,15714057.0,Zikoranachidimma,641.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,107712.42
-19331,15770030.0,Ugochukwu,602.0,France,Female,57.0,9.0,0.0,1.0,1.0,0.0,57962.41
-19332,15794345.0,Lettiere,683.0,Spain,Male,44.0,6.0,0.0,2.0,1.0,1.0,105818.51
-19333,15659305.0,Chiazagomekpele,496.0,Germany,Male,26.0,4.0,100315.1,2.0,1.0,0.0,171457.06
-19334,15582914.0,Trevisani,553.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,138851.03
-19335,15794472.0,Pisano,716.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,17095.49
-19336,15702631.0,Chukwumaobim,678.0,Germany,Female,32.0,8.0,134442.81,1.0,1.0,1.0,146652.01
-19337,15754577.0,P'eng,667.0,France,Female,52.0,2.0,0.0,1.0,1.0,0.0,103940.68
-19338,15659962.0,Ting,535.0,France,Female,38.0,6.0,0.0,1.0,1.0,0.0,55324.49
-19339,15691792.0,Chiedozie,603.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,121151.1
-19340,15750778.0,Ogochukwu,595.0,France,Male,38.0,7.0,0.0,1.0,1.0,1.0,131710.59
-19341,15692571.0,Okwudiliolisa,538.0,France,Female,37.0,6.0,113744.72,1.0,1.0,1.0,52337.97
-19342,15757916.0,Okwudiliolisa,731.0,France,Male,30.0,3.0,0.0,2.0,0.0,1.0,175399.14
-19343,15684469.0,Kambinachi,588.0,France,Male,30.0,4.0,115488.52,1.0,1.0,0.0,116748.14
-19344,15752884.0,Ch'iu,586.0,France,Male,45.0,3.0,0.0,2.0,1.0,1.0,157527.6
-19345,15634359.0,Ejimofor,709.0,France,Male,40.0,1.0,127638.35,1.0,0.0,1.0,109742.8
-19346,15696733.0,Ch'iu,636.0,France,Male,21.0,5.0,0.0,2.0,1.0,1.0,77979.35
-19347,15593636.0,Ibeamaka,613.0,Germany,Male,35.0,10.0,130334.35,2.0,1.0,1.0,103921.43
-19348,15655229.0,Hsia,751.0,France,Male,35.0,5.0,141136.62,1.0,1.0,1.0,9221.78
-19349,15744044.0,Gadsdon,665.0,Germany,Male,46.0,1.0,105858.68,1.0,1.0,1.0,126535.57
-19350,15693737.0,Chiang,708.0,Germany,Male,34.0,5.0,68881.59,2.0,0.0,0.0,92938.74
-19351,15646720.0,Onyekaozulu,635.0,France,Male,44.0,7.0,0.0,2.0,0.0,1.0,185561.89
-19352,15575060.0,Hsieh,537.0,France,Female,67.0,5.0,0.0,1.0,1.0,1.0,125870.5
-19353,15771299.0,H?,733.0,France,Male,38.0,4.0,0.0,1.0,0.0,1.0,131239.65
-19354,15725882.0,Chukwuemeka,577.0,France,Female,33.0,7.0,125724.95,1.0,1.0,0.0,122964.18
-19355,15760456.0,Ts'ai,642.0,France,Female,32.0,2.0,141786.78,1.0,0.0,1.0,28367.21
-19356,15644977.0,Whitehead,741.0,Spain,Female,34.0,5.0,151857.81,1.0,1.0,1.0,134889.95
-19357,15681550.0,Ozerova,829.0,Spain,Female,40.0,2.0,0.0,2.0,0.0,1.0,57517.71
-19358,15739271.0,Hsing,684.0,Germany,Male,38.0,9.0,126147.84,2.0,1.0,0.0,146041.45
-19359,15761706.0,Hsiung,579.0,France,Female,51.0,1.0,0.0,2.0,1.0,1.0,111340.36
-19360,15781495.0,Chien,565.0,France,Female,35.0,3.0,0.0,1.0,1.0,1.0,187638.14
-19361,15616152.0,Y?an,741.0,Spain,Male,27.0,5.0,158713.08,1.0,1.0,1.0,105969.05
-19362,15778885.0,Nnachetam,539.0,France,Female,38.0,3.0,0.0,1.0,1.0,0.0,64420.5
-19363,15580988.0,Chukwudi,751.0,France,Male,37.0,1.0,0.0,1.0,1.0,1.0,142797.5
-19364,15673529.0,Kambinachi,545.0,France,Female,44.0,8.0,126597.89,1.0,1.0,1.0,111312.96
-19365,15673591.0,Nkemdirim,553.0,France,Female,47.0,9.0,0.0,2.0,0.0,0.0,93777.15
-19366,15804822.0,Hsueh,678.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,1.0,149614.17
-19367,15752507.0,Mazzanti,538.0,France,Female,41.0,5.0,0.0,1.0,1.0,0.0,141364.62
-19368,15672930.0,Martin,638.0,Germany,Male,43.0,6.0,116244.14,2.0,1.0,0.0,55893.33
-19369,15763431.0,Chiu,663.0,France,Female,40.0,2.0,0.0,2.0,0.0,0.0,85411.01
-19370,15628272.0,Cawood,586.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,48131.92
-19371,15584928.0,K?,718.0,France,Female,48.0,9.0,118448.15,1.0,1.0,0.0,35356.38
-19372,15783541.0,Ngozichukwuka,682.0,Spain,Female,32.0,1.0,0.0,2.0,0.0,1.0,47775.34
-19373,15741745.0,K?,632.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,134878.19
-19374,15621644.0,Lettiere,683.0,France,Male,34.0,9.0,116498.22,1.0,1.0,1.0,180712.52
-19375,15739248.0,Ch'ien,571.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,92381.01
-19376,15771855.0,Onwuamaeze,606.0,France,Male,27.0,5.0,0.0,2.0,1.0,0.0,109538.75
-19377,15654574.0,Udegbunam,711.0,Germany,Female,46.0,9.0,145541.56,1.0,1.0,0.0,46415.09
-19378,15754929.0,Okwuoma,637.0,France,Female,36.0,3.0,155739.76,1.0,1.0,0.0,87834.23
-19379,15644896.0,Onyemauchechukwu,634.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,1.0,137784.25
-19380,15771580.0,Cremonesi,648.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,146056.29
-19381,15748625.0,Onio,684.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,162699.7
-19382,15581539.0,Lo Duca,481.0,Spain,Male,31.0,9.0,148853.29,1.0,1.0,1.0,91092.84
-19383,15761706.0,Liao,607.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,66795.78
-19384,15605835.0,Kistler,706.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,143393.24
-19385,15691871.0,Scott,599.0,France,Female,42.0,2.0,0.0,2.0,0.0,1.0,141023.01
-19386,15743478.0,Chukwudi,637.0,Spain,Female,29.0,10.0,154878.79,1.0,1.0,1.0,185827.74
-19387,15651359.0,Ch'ien,535.0,France,Male,29.0,2.0,0.0,2.0,0.0,1.0,132719.8
-19388,15746708.0,Ejimofor,619.0,France,Male,45.0,0.0,141573.55,2.0,0.0,0.0,82815.93
-19389,15572022.0,Ch'eng,661.0,France,Male,49.0,1.0,0.0,3.0,1.0,0.0,132467.54
-19390,15568360.0,Onwubiko,605.0,France,Male,42.0,7.0,0.0,1.0,1.0,0.0,171657.57
-19391,15809847.0,Yobanna,595.0,France,Female,49.0,6.0,110610.41,1.0,0.0,0.0,106936.09
-19392,15589210.0,Y?,790.0,France,Female,40.0,1.0,0.0,2.0,1.0,1.0,152499.8
-19393,15604544.0,Ch'eng,619.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,178667.7
-19394,15783030.0,Clements,729.0,Germany,Male,34.0,8.0,153972.89,1.0,1.0,0.0,158036.72
-19395,15613292.0,Chidimma,471.0,Spain,Male,33.0,3.0,0.0,1.0,1.0,1.0,177627.33
-19396,15757916.0,Nwabugwu,586.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,148527.56
-19397,15575679.0,Hs?,755.0,France,Male,39.0,5.0,0.0,3.0,1.0,1.0,137959.0
-19398,15803907.0,Hs?,710.0,Germany,Male,32.0,7.0,91547.84,1.0,1.0,0.0,83022.75
-19399,15709368.0,Chigolum,611.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,82709.8
-19400,15633516.0,Chidozie,549.0,Germany,Female,37.0,2.0,106022.61,1.0,1.0,0.0,115566.57
-19401,15579189.0,Hs?,756.0,Spain,Female,47.0,4.0,0.0,2.0,1.0,1.0,70778.6
-19402,15754577.0,Palerma,535.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,183318.79
-19403,15792388.0,Lu,687.0,France,Male,48.0,10.0,0.0,2.0,1.0,0.0,128918.42
-19404,15707637.0,Trevisano,731.0,France,Male,38.0,4.0,96473.59,2.0,0.0,1.0,72970.67
-19405,15774931.0,Trentini,707.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,176962.31
-19406,15809901.0,Zetticci,682.0,Germany,Female,38.0,1.0,92510.9,1.0,1.0,0.0,7063.6
-19407,15631603.0,De Salis,738.0,France,Female,32.0,2.0,0.0,1.0,0.0,0.0,162812.16
-19408,15750731.0,Ch'en,634.0,France,Female,35.0,1.0,0.0,2.0,1.0,0.0,28157.34
-19409,15731519.0,Hao,648.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,61164.45
-19410,15624013.0,Pirozzi,644.0,France,Male,31.0,7.0,0.0,2.0,0.0,1.0,136985.08
-19411,15806922.0,Wood,745.0,France,Male,25.0,8.0,0.0,2.0,1.0,1.0,96847.91
-19412,15756960.0,Onwumelu,717.0,Spain,Female,34.0,6.0,0.0,2.0,0.0,1.0,87400.53
-19413,15806438.0,Iweobiegbunam,619.0,France,Male,29.0,6.0,0.0,2.0,0.0,1.0,97734.3
-19414,15641411.0,Hsu,706.0,Germany,Female,46.0,8.0,98592.88,1.0,0.0,1.0,120531.31
-19415,15688880.0,Nkemakolam,751.0,France,Female,26.0,8.0,0.0,2.0,1.0,0.0,132468.76
-19416,15727299.0,Martin,594.0,Germany,Female,41.0,9.0,110148.58,2.0,1.0,1.0,155061.97
-19417,15770185.0,Onyemachukwu,727.0,France,Male,54.0,3.0,0.0,3.0,1.0,0.0,96420.58
-19418,15808044.0,Chiang,646.0,France,Female,31.0,1.0,0.0,2.0,1.0,0.0,102975.72
-19419,15696852.0,Chukwudi,738.0,Germany,Female,38.0,7.0,115930.87,2.0,1.0,0.0,131886.46
-19420,15592229.0,Pape,637.0,Spain,Male,40.0,9.0,160139.59,1.0,1.0,1.0,187905.12
-19421,15623369.0,Smith,645.0,France,Female,62.0,5.0,0.0,2.0,1.0,0.0,141359.11
-19422,15606901.0,Genovese,725.0,Germany,Male,38.0,8.0,96542.16,2.0,1.0,0.0,156024.01
-19423,15696852.0,Tuan,547.0,France,Female,40.0,1.0,117466.03,1.0,1.0,1.0,126495.39
-19424,15754569.0,Rizzo,684.0,Germany,Female,41.0,3.0,118879.35,1.0,1.0,0.0,183542.08
-19425,15768295.0,Dickson,651.0,France,Female,28.0,10.0,0.0,2.0,1.0,1.0,182436.81
-19426,15706799.0,Lai,581.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,131736.44
-19427,15641298.0,Palerma,596.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,167984.72
-19428,15757867.0,Palerma,479.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,1.0,151325.24
-19429,15622750.0,Hsiao,613.0,France,Female,43.0,9.0,0.0,2.0,1.0,1.0,152499.8
-19430,15596671.0,Kenechi,661.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,176924.21
-19431,15649523.0,Johnson,714.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,158958.2
-19432,15576928.0,Ts'ui,756.0,France,Female,25.0,4.0,0.0,2.0,0.0,0.0,132467.54
-19433,15757911.0,Folliero,651.0,France,Female,41.0,5.0,0.0,2.0,1.0,1.0,103934.12
-19434,15761158.0,Mazzanti,710.0,France,Female,41.0,10.0,0.0,1.0,1.0,0.0,89524.83
-19435,15650432.0,Fiorentino,646.0,France,Male,36.0,7.0,115062.61,1.0,1.0,1.0,166520.96
-19436,15733597.0,Chidubem,754.0,France,Male,28.0,5.0,0.0,2.0,0.0,1.0,122580.48
-19437,15595036.0,Ts'ui,682.0,Germany,Female,57.0,2.0,122522.34,1.0,1.0,0.0,63795.8
-19438,15658969.0,Shao,850.0,Spain,Male,24.0,9.0,0.0,2.0,0.0,0.0,166957.82
-19439,15772381.0,Nieves,792.0,Spain,Female,52.0,0.0,0.0,2.0,0.0,0.0,179655.87
-19440,15654760.0,Hsing,645.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,172810.51
-19441,15575060.0,Chukwumaobim,595.0,Spain,Male,32.0,9.0,83807.86,1.0,0.0,0.0,145524.36
-19442,15668830.0,Nucci,648.0,Germany,Female,41.0,5.0,80923.56,2.0,1.0,0.0,131300.68
-19443,15631222.0,Yobanna,714.0,France,Female,35.0,2.0,0.0,2.0,0.0,1.0,35786.76
-19444,15798975.0,Hsu,583.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,162719.69
-19445,15648075.0,K'ung,579.0,France,Female,38.0,1.0,99745.95,1.0,1.0,1.0,56794.73
-19446,15682472.0,Ndukaku,612.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,158973.45
-19447,15751175.0,Frolova,758.0,Germany,Male,34.0,5.0,108432.88,2.0,1.0,1.0,186418.85
-19448,15578251.0,Trevisani,763.0,Germany,Female,29.0,1.0,93680.43,1.0,0.0,1.0,121798.52
-19449,15694890.0,T'ao,679.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,176925.69
-19450,15691823.0,Iheanacho,508.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,0.0,159899.97
-19451,15668893.0,Trevisan,523.0,France,Female,42.0,1.0,0.0,1.0,1.0,1.0,79178.31
-19452,15585367.0,Chiekwugo,607.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,100433.83
-19453,15776725.0,Obiajulu,613.0,Spain,Male,37.0,5.0,0.0,2.0,0.0,0.0,187811.71
-19454,15780215.0,T'ien,537.0,France,Female,48.0,5.0,0.0,1.0,1.0,1.0,47880.71
-19455,15796834.0,Chiu,592.0,Germany,Male,38.0,3.0,161884.99,1.0,1.0,0.0,141137.53
-19456,15733597.0,Chidiebele,554.0,Germany,Female,39.0,4.0,129745.1,1.0,1.0,0.0,181850.74
-19457,15669922.0,Olisaemeka,771.0,France,Male,44.0,1.0,0.0,2.0,1.0,0.0,91036.74
-19458,15751180.0,H?,579.0,France,Male,40.0,8.0,92887.13,1.0,1.0,1.0,71581.79
-19459,15576022.0,Ukaegbunam,663.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,183584.17
-19460,15684951.0,Chiazagomekpere,565.0,Germany,Female,60.0,2.0,146580.69,1.0,1.0,0.0,162599.51
-19461,15774854.0,Chiekwugo,677.0,France,Male,37.0,4.0,0.0,2.0,0.0,0.0,52404.47
-19462,15621063.0,D'Albertis,678.0,France,Female,25.0,3.0,0.0,2.0,1.0,0.0,150685.52
-19463,15621353.0,Pinto,587.0,Germany,Male,25.0,6.0,109922.61,2.0,1.0,0.0,60721.4
-19464,15586931.0,Iredale,713.0,France,Male,33.0,9.0,0.0,1.0,1.0,0.0,122284.38
-19465,15702789.0,Yobachukwu,745.0,Germany,Male,33.0,9.0,114435.47,1.0,1.0,0.0,141021.22
-19466,15711759.0,Fane,610.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,55313.44
-19467,15787597.0,Iweobiegbunam,709.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,134732.99
-19468,15747014.0,Ma,686.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,157862.82
-19469,15634218.0,Milne,623.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,109638.78
-19470,15772383.0,Chiemela,693.0,Spain,Male,60.0,1.0,0.0,2.0,1.0,1.0,91094.46
-19471,15797905.0,Lo,619.0,Germany,Female,39.0,10.0,111018.98,1.0,1.0,1.0,73562.05
-19472,15587573.0,Niu,757.0,Spain,Male,57.0,3.0,0.0,1.0,1.0,1.0,130862.11
-19473,15593694.0,Okonkwo,593.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,180329.83
-19474,15666856.0,Moore,569.0,France,Female,32.0,4.0,146466.13,1.0,1.0,1.0,92712.97
-19475,15586824.0,Genovese,775.0,France,Male,39.0,1.0,106234.02,1.0,1.0,0.0,121964.46
-19476,15772996.0,Teng,678.0,France,Male,29.0,7.0,0.0,2.0,0.0,0.0,10883.52
-19477,15693906.0,Hsueh,609.0,Germany,Female,45.0,3.0,132876.23,1.0,1.0,0.0,143544.48
-19478,15735246.0,Chukwufumnanya,619.0,France,Male,45.0,10.0,0.0,2.0,1.0,0.0,142238.54
-19479,15663917.0,Lai,663.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,56767.67
-19480,15677243.0,Nwokezuike,637.0,France,Male,30.0,8.0,0.0,1.0,1.0,1.0,51112.8
-19481,15766776.0,Dyer,638.0,Spain,Male,43.0,3.0,0.0,1.0,1.0,0.0,140941.47
-19482,15591915.0,Chiang,559.0,France,Male,24.0,0.0,0.0,1.0,1.0,0.0,121789.3
-19483,15627365.0,Ahern,613.0,France,Male,33.0,3.0,0.0,2.0,0.0,1.0,193309.58
-19484,15715750.0,Hsing,850.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,86039.27
-19485,15773796.0,De Luca,681.0,Spain,Male,32.0,7.0,0.0,2.0,0.0,1.0,79078.91
-19486,15788215.0,Fokina,596.0,France,Female,59.0,6.0,162390.61,1.0,0.0,0.0,146715.07
-19487,15781802.0,Ch'ang,684.0,France,Female,27.0,3.0,0.0,1.0,1.0,0.0,53584.03
-19488,15745355.0,Iweobiegbulam,651.0,France,Male,53.0,7.0,0.0,2.0,1.0,1.0,184312.88
-19489,15783097.0,Chidumaga,565.0,Spain,Female,36.0,9.0,0.0,1.0,1.0,1.0,106977.53
-19490,15692730.0,Ibezimako,703.0,France,Male,32.0,10.0,0.0,2.0,0.0,1.0,126475.79
-19491,15592387.0,Toscani,674.0,France,Male,38.0,1.0,0.0,1.0,1.0,1.0,151375.46
-19492,15773869.0,Ch'ang,606.0,Germany,Male,42.0,6.0,150126.44,2.0,1.0,1.0,86786.41
-19493,15671165.0,Okwudiliolisa,697.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,161845.81
-19494,15763063.0,Uwaezuoke,497.0,Germany,Female,56.0,9.0,96479.81,2.0,1.0,1.0,88779.06
-19495,15604345.0,Milano,662.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,53477.76
-19496,15797960.0,Liao,727.0,Germany,Male,59.0,5.0,121405.04,1.0,1.0,1.0,147720.27
-19497,15678916.0,Shao,753.0,France,Male,36.0,7.0,154953.94,1.0,1.0,1.0,76510.46
-19498,15602497.0,Dike,700.0,France,Female,30.0,9.0,0.0,1.0,1.0,1.0,64872.55
-19499,15652935.0,Ejikemeifeuwa,661.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,141148.21
-19500,15604782.0,Manna,659.0,France,Female,47.0,5.0,127475.29,1.0,1.0,0.0,142797.5
-19501,15744200.0,Moseley,699.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,131886.46
-19502,15792232.0,Nnaife,685.0,France,Female,56.0,6.0,0.0,1.0,1.0,0.0,89520.25
-19503,15721935.0,Folliero,632.0,Spain,Female,51.0,4.0,0.0,2.0,0.0,0.0,167984.72
-19504,15644724.0,Eluemuno,706.0,Spain,Female,53.0,7.0,0.0,1.0,1.0,0.0,143981.27
-19505,15643967.0,Chidiebere,652.0,Spain,Male,59.0,1.0,0.0,2.0,0.0,1.0,164741.7
-19506,15592028.0,Ritchie,527.0,France,Female,30.0,7.0,0.0,1.0,0.0,1.0,72876.43
-19507,15621893.0,Manna,804.0,Germany,Female,48.0,3.0,131078.42,1.0,1.0,1.0,54926.51
-19508,15809800.0,Pipes,627.0,Spain,Female,24.0,3.0,0.0,1.0,1.0,1.0,103832.58
-19509,15601387.0,Onyemere,742.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,78650.95
-19510,15772777.0,Milani,535.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,62989.82
-19511,15586523.0,Chiu,741.0,Germany,Male,33.0,9.0,137948.51,1.0,1.0,1.0,181224.56
-19512,15772997.0,Ts'ui,681.0,Spain,Male,38.0,4.0,0.0,1.0,1.0,0.0,52820.13
-19513,15680405.0,Lettiere,662.0,France,Male,39.0,10.0,0.0,2.0,1.0,0.0,142223.94
-19514,15674840.0,Ch'en,616.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,169781.45
-19515,15636407.0,Onyekachi,584.0,France,Female,49.0,5.0,0.0,1.0,1.0,1.0,67979.35
-19516,15706365.0,Trevisano,714.0,Germany,Female,44.0,5.0,104783.81,1.0,1.0,1.0,4395.45
-19517,15776824.0,Macleod,587.0,Germany,Male,42.0,0.0,123873.27,2.0,1.0,1.0,143981.27
-19518,15789560.0,Hsiung,629.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,88890.05
-19519,15632365.0,Ts'ui,850.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,120852.85
-19520,15593973.0,Iheanacho,774.0,France,Male,30.0,10.0,0.0,2.0,0.0,0.0,67734.79
-19521,15744691.0,Nkemdirim,639.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,94845.54
-19522,15796790.0,Hsiao,538.0,Germany,Male,43.0,8.0,106045.92,1.0,1.0,0.0,181224.56
-19523,15598086.0,Ni,661.0,Germany,Female,40.0,8.0,94762.89,2.0,1.0,0.0,1247772.61
-19524,15649414.0,Hsing,646.0,France,Female,36.0,7.0,121498.11,1.0,0.0,0.0,4861.72
-19525,15794580.0,Ch'ang,715.0,France,Male,34.0,2.0,129401.87,1.0,0.0,1.0,136079.64
-19526,15603088.0,Ijendu,812.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,157939.21
-19527,15711602.0,Ch'ien,774.0,France,Female,43.0,4.0,105770.14,1.0,0.0,1.0,120801.65
-19528,15675318.0,DeRose,641.0,Spain,Male,43.0,9.0,0.0,1.0,1.0,0.0,62868.33
-19529,15646513.0,Binder,718.0,France,Male,33.0,6.0,137755.76,1.0,1.0,1.0,6925.36
-19530,15689953.0,Ch'iu,652.0,Germany,Female,56.0,7.0,110827.49,1.0,0.0,1.0,102299.81
-19531,15782659.0,Yefremova,632.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,117616.29
-19532,15717706.0,Tung,731.0,France,Female,41.0,1.0,131647.01,1.0,1.0,0.0,126797.28
-19533,15787693.0,Ts'ai,722.0,France,Male,43.0,2.0,0.0,2.0,0.0,0.0,140723.05
-19534,15777892.0,Mazzanti,727.0,Spain,Female,59.0,4.0,0.0,2.0,1.0,1.0,151401.33
-19535,15785183.0,Chien,719.0,France,Male,30.0,7.0,136659.74,1.0,1.0,0.0,132719.8
-19536,15757837.0,Chukwuemeka,579.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,0.0,153535.56
-19537,15673529.0,Chidiegwu,850.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,101839.77
-19538,15692575.0,Chidiebere,661.0,France,Male,46.0,1.0,0.0,2.0,1.0,0.0,106937.06
-19539,15682884.0,Ch'en,553.0,Germany,Female,49.0,10.0,130854.59,2.0,1.0,0.0,89892.75
-19540,15690744.0,Hsing,730.0,Germany,Male,40.0,8.0,116560.57,1.0,1.0,0.0,139048.0
-19541,15761670.0,Iadanza,721.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,1.0,126477.41
-19542,15807003.0,Ch'ang,708.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,52484.01
-19543,15699598.0,Ch'ien,602.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,119333.95
-19544,15790782.0,Onyemauchechukwu,619.0,Germany,Female,52.0,2.0,139914.7,1.0,1.0,1.0,16402.11
-19545,15781884.0,Nkemjika,684.0,Germany,Male,56.0,9.0,103522.75,1.0,1.0,1.0,148564.76
-19546,15647842.0,Frolova,672.0,Spain,Female,54.0,9.0,0.0,1.0,1.0,1.0,36124.98
-19547,15775116.0,Chia,696.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,72990.62
-19548,15696964.0,Kao,676.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,108887.44
-19549,15691808.0,Liao,624.0,Spain,Male,33.0,6.0,102025.88,2.0,1.0,1.0,124596.63
-19550,15637178.0,Yobachi,628.0,Spain,Female,38.0,8.0,0.0,1.0,1.0,1.0,127543.81
-19551,15573456.0,Onyinyechukwuka,663.0,Germany,Female,37.0,3.0,112930.28,2.0,1.0,1.0,166896.01
-19552,15782530.0,Hsieh,719.0,France,Female,34.0,4.0,0.0,2.0,0.0,0.0,54448.41
-19553,15572038.0,Mazzi,706.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,158043.11
-19554,15632576.0,Ch'ien,559.0,France,Female,39.0,7.0,0.0,2.0,0.0,0.0,106920.57
-19555,15802088.0,Okwukwe,645.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,115509.59
-19556,15619340.0,Ch'in,449.0,France,Female,30.0,9.0,0.0,2.0,1.0,1.0,139919.38
-19557,15678067.0,Hsiao,629.0,Germany,Female,59.0,6.0,159316.64,1.0,1.0,0.0,153662.88
-19558,15694566.0,Hao,542.0,France,Male,26.0,8.0,0.0,2.0,0.0,1.0,86173.92
-19559,15585280.0,Chinweuba,681.0,France,Male,33.0,9.0,130806.99,1.0,1.0,0.0,167554.86
-19560,15572073.0,P'eng,726.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,134600.94
-19561,15681562.0,P'eng,616.0,Germany,Female,51.0,6.0,127122.79,1.0,0.0,0.0,55313.51
-19562,15746410.0,Chiganu,682.0,Spain,Male,45.0,7.0,0.0,2.0,0.0,1.0,146652.11
-19563,15584812.0,Ugoji,683.0,France,Female,43.0,9.0,0.0,2.0,1.0,0.0,54949.73
-19564,15769504.0,Lucchesi,725.0,France,Female,39.0,4.0,0.0,2.0,1.0,1.0,88729.08
-19565,15652400.0,Shih,722.0,France,Male,44.0,3.0,0.0,2.0,0.0,0.0,96420.58
-19566,15641582.0,Chukwukere,676.0,Germany,Female,32.0,2.0,114023.64,1.0,1.0,0.0,105424.02
-19567,15654611.0,Nwankwo,521.0,France,Female,46.0,8.0,0.0,1.0,1.0,0.0,162719.69
-19568,15693526.0,Kudryashova,640.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,199493.38
-19569,15765457.0,Pagnotto,754.0,France,Female,32.0,8.0,0.0,1.0,1.0,0.0,120892.83
-19570,15671766.0,Ch'ien,645.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,0.0,142331.82
-19571,15686983.0,Chuang,738.0,Spain,Female,23.0,3.0,0.0,2.0,1.0,0.0,167553.06
-19572,15717286.0,Kline,602.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,78896.59
-19573,15691627.0,Ch'in,685.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,97985.4
-19574,15792736.0,Zetticci,742.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,124796.84
-19575,15616240.0,Ojiofor,748.0,France,Female,33.0,4.0,0.0,1.0,1.0,0.0,122580.48
-19576,15588517.0,Manna,649.0,Germany,Female,62.0,1.0,113874.15,1.0,0.0,1.0,149593.61
-19577,15757981.0,Lu,642.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,153747.71
-19578,15731522.0,Y?an,569.0,France,Female,28.0,3.0,0.0,2.0,1.0,1.0,130486.57
-19579,15667932.0,Ts'ui,765.0,Germany,Female,34.0,1.0,142703.4,1.0,1.0,0.0,57862.88
-19580,15757811.0,Yermakov,583.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,102299.81
-19581,15792232.0,Milne,599.0,Germany,Female,41.0,0.0,134692.4,1.0,1.0,1.0,131312.56
-19582,15788367.0,Yermakov,634.0,Spain,Female,33.0,2.0,152367.21,1.0,0.0,0.0,131283.6
-19583,15691823.0,White,751.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,1.0,82868.34
-19584,15814405.0,Okwudilichukwu,582.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,145936.28
-19585,15737571.0,Lucchese,651.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,139475.0
-19586,15637354.0,Lucchese,633.0,France,Male,41.0,4.0,118068.93,1.0,1.0,0.0,182725.9
-19587,15602551.0,Yashina,634.0,France,Female,42.0,2.0,0.0,2.0,1.0,0.0,51380.9
-19588,15655952.0,Nwankwo,682.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,93081.87
-19589,15809826.0,Lucchesi,612.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,97629.31
-19590,15741180.0,Macartney,584.0,Spain,Male,40.0,1.0,154776.42,1.0,1.0,0.0,134778.01
-19591,15618647.0,Olisaemeka,678.0,Germany,Male,35.0,8.0,121581.56,1.0,1.0,1.0,62222.81
-19592,15651581.0,Ch'eng,681.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,50899.91
-19593,15729582.0,Nnamutaezinwa,535.0,France,Female,52.0,7.0,0.0,1.0,1.0,0.0,131281.28
-19594,15792360.0,Nkemdilim,656.0,Germany,Male,39.0,6.0,77354.37,2.0,1.0,0.0,64872.55
-19595,15650322.0,Ts'ui,557.0,Spain,Female,34.0,5.0,0.0,1.0,1.0,1.0,164975.82
-19596,15731989.0,Chijindum,531.0,France,Female,37.0,3.0,77526.66,1.0,1.0,0.0,161854.98
-19597,15665963.0,Yobachi,596.0,Spain,Male,66.0,2.0,0.0,2.0,1.0,1.0,136301.1
-19598,15615016.0,Onio,730.0,France,Male,26.0,2.0,0.0,2.0,1.0,0.0,173495.77
-19599,15642834.0,Biryukov,627.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,96444.88
-19600,15666884.0,Nwachukwu,544.0,Spain,Female,38.0,9.0,0.0,2.0,0.0,1.0,82969.61
-19601,15764170.0,Ch'ien,578.0,Germany,Female,44.0,8.0,106901.42,1.0,1.0,0.0,136497.28
-19602,15797748.0,Chiu,588.0,Germany,Female,46.0,8.0,123802.98,1.0,0.0,0.0,47880.71
-19603,15783028.0,Chigbogu,709.0,France,Male,52.0,4.0,0.0,1.0,0.0,0.0,181419.29
-19604,15782591.0,Cattaneo,687.0,France,Female,41.0,5.0,0.0,2.0,0.0,1.0,182787.17
-19605,15668958.0,Chidubem,718.0,Spain,Male,54.0,8.0,83568.55,1.0,1.0,1.0,142670.61
-19606,15694404.0,Hsueh,842.0,Germany,Male,31.0,4.0,122486.11,1.0,0.0,0.0,134732.99
-19607,15652218.0,Ch'eng,741.0,France,Male,43.0,3.0,0.0,2.0,1.0,1.0,171707.93
-19608,15608406.0,Onyemauchechukwu,693.0,France,Male,38.0,3.0,97541.24,1.0,1.0,1.0,163530.22
-19609,15813444.0,Aitken,592.0,Germany,Male,36.0,7.0,135984.15,2.0,1.0,1.0,123439.65
-19610,15742668.0,Nkemdilim,607.0,Germany,Female,48.0,9.0,106397.75,2.0,1.0,1.0,169469.3
-19611,15641640.0,Fiorentino,724.0,France,Female,39.0,6.0,0.0,1.0,1.0,0.0,122478.19
-19612,15677906.0,Ejimofor,604.0,France,Male,37.0,6.0,0.0,1.0,0.0,0.0,82662.73
-19613,15735782.0,Hsiung,627.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,93881.75
-19614,15641411.0,Yashina,602.0,France,Female,32.0,6.0,88915.37,1.0,0.0,1.0,121151.1
-19615,15643362.0,Ch'en,794.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,96542.46
-19616,15697424.0,Ukaegbulam,710.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,148087.62
-19617,15783863.0,P'eng,614.0,France,Male,27.0,9.0,0.0,2.0,0.0,0.0,181208.47
-19618,15578211.0,Zetticci,705.0,Spain,Male,56.0,5.0,0.0,2.0,1.0,0.0,120790.02
-19619,15590094.0,Chukwufumnanya,758.0,Germany,Male,37.0,2.0,113657.83,2.0,1.0,0.0,115468.91
-19620,15655436.0,Cremonesi,722.0,France,Male,47.0,7.0,0.0,2.0,1.0,1.0,162597.15
-19621,15750014.0,Chukwudi,714.0,France,Male,69.0,1.0,0.0,2.0,1.0,1.0,140197.42
-19622,15682533.0,P'eng,721.0,France,Male,46.0,8.0,0.0,3.0,1.0,0.0,166471.79
-19623,15773520.0,Nnonso,727.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,82696.84
-19624,15613197.0,Sagese,738.0,France,Male,37.0,4.0,0.0,1.0,1.0,1.0,181598.25
-19625,15782530.0,T'ien,754.0,Germany,Female,30.0,4.0,129974.79,2.0,1.0,1.0,105279.74
-19626,15585490.0,Okonkwo,707.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,82559.77
-19627,15729689.0,Tuan,714.0,France,Male,31.0,7.0,107749.03,1.0,0.0,1.0,141152.28
-19628,15586039.0,Chuang,598.0,Spain,Male,43.0,9.0,0.0,1.0,1.0,1.0,119071.56
-19629,15752588.0,Hsu,745.0,Germany,Male,32.0,6.0,107926.02,2.0,1.0,1.0,131772.48
-19630,15692761.0,Kisch,714.0,France,Female,40.0,8.0,0.0,2.0,0.0,1.0,52795.56
-19631,15677317.0,Pagnotto,663.0,Germany,Male,48.0,3.0,121326.42,3.0,1.0,1.0,181429.87
-19632,15802479.0,Genovese,679.0,France,Female,31.0,9.0,0.0,1.0,1.0,0.0,113860.81
-19633,15746203.0,Chiemenam,556.0,France,Male,43.0,4.0,61636.97,2.0,1.0,0.0,141872.05
-19634,15619407.0,Chimezie,527.0,Spain,Male,33.0,5.0,139840.36,1.0,1.0,1.0,105983.09
-19635,15806913.0,Nwankwo,775.0,Spain,Male,43.0,6.0,0.0,2.0,0.0,0.0,147055.87
-19636,15749489.0,Cattaneo,651.0,Germany,Female,39.0,4.0,131659.58,2.0,1.0,0.0,83679.46
-19637,15734491.0,Nkemakolam,652.0,Germany,Female,48.0,1.0,111098.85,1.0,0.0,1.0,113483.96
-19638,15683540.0,Y?,584.0,France,Female,39.0,4.0,0.0,2.0,1.0,1.0,125445.8
-19639,15695499.0,Chuang,749.0,Spain,Female,25.0,7.0,0.0,2.0,1.0,1.0,134878.19
-19640,15658675.0,Okonkwo,822.0,Spain,Male,29.0,9.0,97473.87,1.0,1.0,0.0,157874.79
-19641,15806917.0,Eiland,708.0,France,Female,26.0,7.0,0.0,2.0,0.0,1.0,59981.62
-19642,15569590.0,Nnachetam,664.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,0.0,143981.27
-19643,15672432.0,De Luca,661.0,France,Female,23.0,7.0,0.0,2.0,1.0,1.0,176743.51
-19644,15686936.0,Tien,697.0,Spain,Male,38.0,4.0,0.0,2.0,0.0,1.0,134531.96
-19645,15672756.0,Chuang,587.0,Spain,Female,71.0,3.0,0.0,2.0,0.0,1.0,91514.62
-19646,15793311.0,Macleod,593.0,Spain,Female,29.0,7.0,147069.69,1.0,1.0,0.0,143774.06
-19647,15578073.0,Chiemezie,596.0,France,Female,37.0,6.0,0.0,2.0,0.0,1.0,165082.25
-19648,15637366.0,Hao,745.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,172363.41
-19649,15720354.0,T'ao,586.0,Germany,Female,41.0,10.0,134760.21,2.0,1.0,0.0,94767.77
-19650,15769974.0,DeRose,613.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,127875.1
-19651,15685300.0,Iheanacho,645.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,108790.95
-19652,15645956.0,Frolova,648.0,Spain,Female,36.0,8.0,146186.28,1.0,0.0,0.0,133672.46
-19653,15794895.0,Ch'eng,619.0,France,Female,22.0,9.0,0.0,2.0,1.0,0.0,61792.76
-19654,15794468.0,Ch'ien,648.0,Germany,Female,46.0,2.0,129753.69,1.0,1.0,0.0,124571.09
-19655,15695034.0,Lai,678.0,Germany,Female,28.0,7.0,149372.55,2.0,0.0,1.0,123663.63
-19656,15782096.0,Onyemauchechukwu,521.0,France,Female,36.0,4.0,124495.98,1.0,0.0,1.0,162812.16
-19657,15792493.0,Onio,686.0,Spain,Male,42.0,2.0,0.0,2.0,1.0,1.0,152740.16
-19658,15642870.0,Udegbulam,747.0,France,Male,61.0,1.0,0.0,1.0,0.0,0.0,99818.41
-19659,15683553.0,K?,535.0,Spain,Male,25.0,7.0,0.0,2.0,1.0,1.0,199392.14
-19660,15696744.0,Onwuamaeze,501.0,France,Male,39.0,10.0,0.0,1.0,1.0,0.0,85411.01
-19661,15794566.0,Yobachukwu,739.0,France,Male,45.0,8.0,109949.05,1.0,1.0,1.0,148811.48
-19662,15768219.0,Palerma,543.0,Germany,Male,28.0,1.0,109724.38,1.0,0.0,1.0,188997.05
-19663,15725028.0,Zhirov,667.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,161479.19
-19664,15632185.0,McLean,628.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,119882.86
-19665,15643679.0,Y?an,730.0,France,Female,35.0,8.0,0.0,2.0,0.0,0.0,122518.5
-19666,15687913.0,Hs?,722.0,France,Female,37.0,1.0,0.0,2.0,1.0,0.0,59650.42
-19667,15757559.0,H?an,692.0,France,Female,42.0,8.0,0.0,2.0,1.0,0.0,187776.49
-19668,15672966.0,Yudin,651.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,179436.6
-19669,15692599.0,P'eng,643.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,170893.85
-19670,15748068.0,Nnaife,633.0,Germany,Female,31.0,6.0,133628.09,1.0,1.0,0.0,106977.53
-19671,15705890.0,Hsia,673.0,France,Male,39.0,2.0,0.0,1.0,1.0,0.0,95611.47
-19672,15652400.0,Iheatu,775.0,France,Female,37.0,1.0,131465.37,1.0,1.0,1.0,173516.71
-19673,15699005.0,Kao,535.0,France,Female,34.0,8.0,0.0,2.0,0.0,0.0,151903.6
-19674,15587562.0,Nnachetam,624.0,Spain,Male,42.0,1.0,116980.23,1.0,1.0,1.0,183868.01
-19675,15736146.0,Ukaegbunam,624.0,Spain,Male,45.0,7.0,0.0,1.0,1.0,1.0,81884.8
-19676,15641782.0,Folliero,647.0,Germany,Male,36.0,8.0,120952.74,2.0,0.0,1.0,11158.18
-19677,15734573.0,Hsieh,683.0,France,Male,22.0,1.0,0.0,2.0,1.0,1.0,88890.05
-19678,15731744.0,Chukwudi,683.0,France,Female,20.0,3.0,0.0,2.0,1.0,1.0,131736.23
-19679,15758021.0,Ni,697.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,143433.33
-19680,15767358.0,Ch'in,727.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,82336.73
-19681,15640106.0,Okwuoma,678.0,Germany,Female,52.0,2.0,141749.68,1.0,0.0,1.0,180150.49
-19682,15792360.0,Yeh,645.0,Germany,Female,56.0,6.0,101985.81,1.0,0.0,0.0,133535.29
-19683,15731951.0,Babbage,605.0,France,Male,48.0,8.0,0.0,2.0,1.0,1.0,166677.53
-19684,15763515.0,Ndukaku,521.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,85983.54
-19685,15626114.0,Lo Duca,753.0,Germany,Male,48.0,10.0,106486.66,1.0,1.0,1.0,91936.1
-19686,15642063.0,Chidiebere,673.0,Spain,Female,33.0,4.0,0.0,2.0,1.0,1.0,84760.32
-19687,15662488.0,Piazza,628.0,Spain,Female,26.0,7.0,0.0,1.0,0.0,1.0,118429.02
-19688,15665760.0,Whitehead,634.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,54439.73
-19689,15690733.0,Yobachi,641.0,France,Female,31.0,10.0,111589.33,1.0,1.0,0.0,93524.19
-19690,15791321.0,Gilleland,710.0,France,Male,47.0,7.0,106234.02,1.0,1.0,0.0,181419.35
-19691,15605742.0,Chiemenam,845.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,1.0,170829.87
-19692,15597913.0,K'ung,584.0,Germany,Male,51.0,3.0,107467.56,1.0,1.0,0.0,166513.49
-19693,15646936.0,Chiang,697.0,Spain,Male,33.0,0.0,0.0,2.0,0.0,0.0,126832.63
-19694,15700255.0,Hao,685.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,164074.89
-19695,15617052.0,Brown,705.0,France,Male,60.0,6.0,0.0,2.0,0.0,1.0,95421.0
-19696,15684958.0,Y?,714.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,173498.45
-19697,15771856.0,Okechukwu,619.0,France,Male,61.0,5.0,65812.2,1.0,1.0,1.0,46824.08
-19698,15682454.0,Ts'ui,603.0,France,Female,43.0,1.0,0.0,2.0,1.0,1.0,54474.48
-19699,15706904.0,Ulyanov,652.0,Germany,Male,39.0,9.0,140720.93,1.0,1.0,0.0,103570.52
-19700,15598046.0,Omeokachie,850.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,169915.02
-19701,15665963.0,Blackwood,706.0,France,Male,22.0,0.0,0.0,2.0,1.0,1.0,119818.17
-19702,15782735.0,Hsiung,684.0,Germany,Female,46.0,5.0,131765.72,1.0,0.0,1.0,152698.24
-19703,15693906.0,Chibugo,632.0,Spain,Female,24.0,8.0,0.0,2.0,0.0,1.0,175072.47
-19704,15651740.0,Y?an,659.0,France,Male,35.0,10.0,0.0,1.0,1.0,1.0,136079.64
-19705,15775490.0,Chu,651.0,Spain,Male,38.0,9.0,0.0,1.0,1.0,0.0,127569.8
-19706,15804787.0,Olisanugo,703.0,France,Female,44.0,5.0,0.0,1.0,1.0,1.0,84421.34
-19707,15780362.0,Ts'ui,716.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,1.0,161479.19
-19708,15798895.0,Ts'ui,697.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,158610.89
-19709,15748986.0,Ch'iu,689.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,55763.66
-19710,15703394.0,Ch'eng,590.0,Spain,Male,26.0,4.0,0.0,2.0,1.0,0.0,124872.42
-19711,15665460.0,Nwokezuike,691.0,Germany,Male,55.0,1.0,116880.53,1.0,0.0,0.0,176450.19
-19712,15786035.0,Chidiegwu,682.0,France,Female,40.0,4.0,89364.91,2.0,1.0,0.0,93829.17
-19713,15652674.0,T'ien,644.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,167636.15
-19714,15667458.0,Kao,633.0,France,Female,37.0,5.0,0.0,1.0,1.0,0.0,117034.32
-19715,15794868.0,Okwuadigbo,811.0,France,Male,58.0,7.0,0.0,2.0,1.0,1.0,107961.11
-19716,15618446.0,Folliero,717.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,146636.86
-19717,15652084.0,Hsieh,689.0,France,Male,33.0,4.0,0.0,1.0,1.0,0.0,131772.51
-19718,15801559.0,Manna,611.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,1.0,67364.42
-19719,15802220.0,Chuang,594.0,France,Female,24.0,4.0,0.0,2.0,1.0,1.0,56767.67
-19720,15672894.0,Bess,747.0,Germany,Female,37.0,5.0,94464.81,1.0,1.0,1.0,160968.44
-19721,15793049.0,Gebhart,642.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,129563.86
-19722,15678067.0,Tsao,610.0,France,Male,46.0,4.0,0.0,1.0,0.0,0.0,87985.2
-19723,15733234.0,Milani,656.0,Germany,Female,47.0,1.0,91592.06,1.0,1.0,0.0,23305.01
-19724,15642530.0,Tuan,588.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,91561.57
-19725,15602851.0,Hsieh,592.0,Germany,Male,35.0,6.0,98903.06,1.0,1.0,0.0,126153.24
-19726,15733032.0,Chiang,731.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,1.0,180829.99
-19727,15592078.0,Cremonesi,641.0,France,Male,48.0,3.0,0.0,2.0,1.0,1.0,113645.4
-19728,15707918.0,Nwabugwu,545.0,Spain,Female,48.0,7.0,0.0,2.0,1.0,1.0,53426.02
-19729,15798024.0,Nnachetam,583.0,France,Female,37.0,0.0,0.0,2.0,0.0,0.0,94977.3
-19730,15792934.0,Hsiao,685.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,185561.89
-19731,15800620.0,Ibezimako,635.0,Germany,Male,37.0,6.0,168085.38,1.0,1.0,0.0,26260.98
-19732,15691396.0,Chiang,559.0,France,Female,28.0,6.0,0.0,1.0,1.0,1.0,117622.8
-19733,15665943.0,Ts'ui,762.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,0.0,166830.71
-19734,15579566.0,Ritchie,593.0,France,Male,45.0,6.0,0.0,2.0,1.0,0.0,70783.61
-19735,15762799.0,Yermakova,676.0,France,Female,36.0,6.0,0.0,2.0,0.0,0.0,125658.28
-19736,15684440.0,Y?an,646.0,France,Female,44.0,3.0,0.0,2.0,1.0,1.0,177655.68
-19737,15771580.0,Mazzi,722.0,Germany,Female,65.0,2.0,90791.69,1.0,0.0,0.0,171060.01
-19738,15751455.0,Mancini,542.0,Germany,Female,43.0,5.0,95052.29,1.0,1.0,0.0,163798.41
-19739,15672056.0,Ch'eng,674.0,Spain,Male,52.0,3.0,122570.69,1.0,1.0,1.0,95985.64
-19740,15637644.0,Haddon,644.0,France,Male,34.0,6.0,0.0,2.0,0.0,0.0,149853.89
-19741,15667424.0,Oguejiofor,663.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,121085.72
-19742,15641416.0,Li,590.0,France,Female,43.0,5.0,0.0,2.0,0.0,1.0,120853.83
-19743,15607330.0,Maclean,538.0,France,Male,40.0,4.0,0.0,1.0,1.0,0.0,77981.54
-19744,15786800.0,P'an,711.0,France,Male,23.0,5.0,0.0,2.0,0.0,1.0,176750.81
-19745,15573452.0,Chinedum,679.0,France,Female,32.0,4.0,156811.74,1.0,1.0,1.0,100615.14
-19746,15637240.0,T'ien,707.0,France,Male,37.0,8.0,152296.84,1.0,0.0,1.0,62449.07
-19747,15766458.0,Chukwudi,616.0,Spain,Female,24.0,4.0,162390.52,1.0,1.0,0.0,168753.34
-19748,15739123.0,Fanucci,627.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,19515.48
-19749,15716164.0,Y?an,586.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,112554.68
-19750,15594577.0,Chigozie,678.0,Spain,Male,31.0,3.0,99842.6,1.0,0.0,1.0,156878.51
-19751,15609521.0,Yobanna,627.0,Germany,Female,36.0,5.0,135096.05,1.0,1.0,0.0,143981.67
-19752,15676521.0,Yefimova,635.0,France,Male,23.0,2.0,104356.54,1.0,1.0,0.0,156495.57
-19753,15718443.0,Chukwuemeka,640.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,88695.19
-19754,15769254.0,Kao,659.0,Spain,Male,46.0,7.0,85891.55,2.0,1.0,1.0,141533.19
-19755,15750545.0,Ch'ang,686.0,Spain,Female,26.0,9.0,0.0,2.0,1.0,1.0,48260.19
-19756,15720110.0,Nwebube,758.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,28805.09
-19757,15694321.0,Chuang,556.0,France,Male,33.0,8.0,94126.67,1.0,1.0,1.0,181490.42
-19758,15698927.0,Onwughara,641.0,France,Female,46.0,0.0,0.0,1.0,1.0,1.0,144849.1
-19759,15742886.0,Maclean,705.0,Germany,Female,30.0,5.0,124577.33,1.0,1.0,0.0,127569.8
-19760,15617482.0,Lettiere,686.0,Spain,Male,36.0,3.0,113348.5,1.0,1.0,0.0,178106.34
-19761,15702337.0,Fanucci,662.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,60728.89
-19762,15589924.0,Chidiebele,653.0,France,Male,33.0,5.0,0.0,2.0,0.0,1.0,170829.87
-19763,15572777.0,Hsieh,602.0,France,Female,22.0,0.0,0.0,2.0,1.0,1.0,141533.19
-19764,15583073.0,Chuang,582.0,France,Female,43.0,4.0,0.0,1.0,1.0,0.0,159638.77
-19765,15590177.0,Ch'en,585.0,Spain,Female,31.0,9.0,0.0,2.0,1.0,0.0,86099.23
-19766,15596575.0,Liao,567.0,Spain,Male,55.0,5.0,122917.71,1.0,1.0,0.0,115700.2
-19767,15662533.0,Ozioma,673.0,France,Male,42.0,7.0,0.0,1.0,1.0,1.0,147720.27
-19768,15697035.0,Nebechukwu,669.0,Germany,Male,54.0,7.0,102955.14,3.0,1.0,0.0,23135.65
-19769,15689671.0,Chidubem,477.0,France,Female,30.0,5.0,0.0,2.0,0.0,0.0,132436.71
-19770,15798960.0,Chukwudi,516.0,France,Male,34.0,4.0,106511.85,1.0,1.0,1.0,122477.38
-19771,15589969.0,Chuang,653.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,99818.31
-19772,15726494.0,H?,593.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,126551.65
-19773,15762682.0,Fomin,705.0,Spain,Male,41.0,7.0,143054.85,1.0,0.0,1.0,49418.87
-19774,15592765.0,Genovese,651.0,France,Female,52.0,6.0,0.0,1.0,1.0,0.0,172572.64
-19775,15632841.0,Chiang,705.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,160979.66
-19776,15739850.0,Ch'iu,769.0,France,Male,26.0,6.0,0.0,2.0,1.0,1.0,59595.66
-19777,15802857.0,Nnaife,634.0,Germany,Male,42.0,8.0,107362.52,1.0,1.0,1.0,121428.4
-19778,15729763.0,Yegorova,738.0,Germany,Male,41.0,4.0,99811.48,2.0,1.0,0.0,90877.76
-19779,15605916.0,Baresi,687.0,France,Female,30.0,2.0,141462.13,1.0,1.0,0.0,71672.86
-19780,15684956.0,Lucchese,676.0,France,Female,37.0,8.0,0.0,2.0,0.0,1.0,116748.14
-19781,15652999.0,Walker,637.0,Spain,Female,49.0,4.0,0.0,2.0,1.0,0.0,73322.0
-19782,15763662.0,Fadden,774.0,Spain,Male,31.0,2.0,121907.97,1.0,1.0,0.0,67552.98
-19783,15805884.0,T'an,652.0,France,Female,25.0,2.0,0.0,2.0,0.0,1.0,122580.48
-19784,15693817.0,Manna,781.0,Germany,Male,22.0,9.0,142391.08,1.0,0.0,1.0,154882.79
-19785,15674868.0,Kuo,678.0,Spain,Female,31.0,9.0,0.0,2.0,1.0,1.0,141180.66
-19786,15806926.0,Onwuatuegwu,581.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,61624.19
-19787,15790763.0,Chibueze,696.0,France,Female,31.0,2.0,0.0,2.0,0.0,1.0,124411.08
-19788,15686933.0,Ch'iu,492.0,France,Female,28.0,3.0,109196.67,1.0,1.0,1.0,53394.12
-19789,15617331.0,K'ung,637.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,1.0,93716.38
-19790,15801767.0,Ch'iu,657.0,Spain,Female,47.0,9.0,0.0,1.0,1.0,0.0,52615.62
-19791,15644572.0,Chukwueloka,605.0,Germany,Female,37.0,2.0,117300.02,1.0,1.0,0.0,176680.16
-19792,15627484.0,Uwaezuoke,652.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,165562.84
-19793,15674541.0,Ku,611.0,France,Male,41.0,6.0,106648.98,1.0,0.0,1.0,71672.86
-19794,15672894.0,Ikedinachukwu,727.0,Spain,Female,50.0,6.0,148167.84,1.0,1.0,0.0,113483.96
-19795,15662532.0,Y?,695.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,56767.67
-19796,15760861.0,Chioke,516.0,France,Male,38.0,8.0,129499.42,1.0,1.0,0.0,106977.8
-19797,15795954.0,Okwudilichukwu,762.0,Germany,Male,33.0,5.0,134032.05,1.0,0.0,1.0,171457.06
-19798,15736154.0,Nwabugwu,633.0,France,Male,27.0,5.0,0.0,2.0,1.0,0.0,102908.91
-19799,15701354.0,Trevisani,628.0,Germany,Female,57.0,6.0,116085.2,1.0,1.0,0.0,79734.23
-19800,15780492.0,Y?an,752.0,Spain,Male,36.0,7.0,0.0,2.0,0.0,0.0,158043.11
-19801,15609928.0,Hsia,617.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,139410.08
-19802,15671209.0,Pinto,632.0,France,Female,38.0,0.0,0.0,2.0,1.0,1.0,91574.92
-19803,15567114.0,Trevisano,478.0,Germany,Female,31.0,2.0,135067.02,1.0,1.0,0.0,184524.74
-19804,15595883.0,Hsia,577.0,Germany,Female,36.0,3.0,140553.46,4.0,0.0,0.0,121866.87
-19805,15692583.0,Chidi,528.0,France,Female,39.0,7.0,93844.69,1.0,1.0,1.0,4493.12
-19806,15648067.0,Smith,705.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,152577.62
-19807,15758510.0,Eluemuno,675.0,France,Female,30.0,3.0,107596.22,1.0,1.0,0.0,184514.01
-19808,15761497.0,Chin,632.0,Spain,Male,42.0,0.0,0.0,2.0,0.0,0.0,138527.56
-19809,15779103.0,Ozerova,671.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,172576.43
-19810,15578866.0,L?,585.0,Germany,Female,35.0,1.0,147974.19,2.0,1.0,1.0,176932.59
-19811,15653764.0,Lucchese,616.0,France,Male,24.0,8.0,0.0,2.0,1.0,0.0,79495.01
-19812,15592716.0,Nnamutaezinwa,662.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,88721.84
-19813,15593365.0,Ch'iu,669.0,France,Female,23.0,6.0,0.0,2.0,1.0,0.0,151401.33
-19814,15752953.0,Oguejiofor,543.0,France,Male,31.0,7.0,131834.8,1.0,0.0,0.0,162597.15
-19815,15652469.0,Teng,699.0,Spain,Female,44.0,9.0,0.0,2.0,0.0,1.0,143681.83
-19816,15667871.0,H?,733.0,Spain,Male,45.0,7.0,0.0,2.0,0.0,1.0,90876.95
-19817,15673929.0,Hsiao,576.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,1.0,133537.53
-19818,15623791.0,Maclean,626.0,France,Male,41.0,6.0,0.0,2.0,1.0,0.0,179291.89
-19819,15761606.0,Chiang,538.0,France,Male,26.0,4.0,0.0,2.0,1.0,1.0,77951.84
-19820,15780954.0,Chiemezie,681.0,Spain,Male,43.0,0.0,0.0,2.0,1.0,0.0,63736.17
-19821,15628203.0,Uwakwe,594.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,105291.22
-19822,15761168.0,Hsia,714.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,134483.85
-19823,15682355.0,Kao,713.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,156425.57
-19824,15751549.0,Ch'iu,619.0,Spain,Male,44.0,10.0,0.0,2.0,1.0,1.0,79414.0
-19825,15677906.0,Okechukwu,694.0,Spain,Male,37.0,7.0,0.0,1.0,0.0,1.0,185823.41
-19826,15794468.0,Hsieh,581.0,Germany,Male,31.0,3.0,130142.79,1.0,1.0,1.0,54947.51
-19827,15662578.0,Hsia,561.0,Germany,Male,31.0,7.0,123943.18,1.0,1.0,0.0,167023.95
-19828,15775318.0,Martin,671.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,94739.2
-19829,15654519.0,Ojiofor,463.0,Germany,Male,50.0,3.0,171922.72,1.0,1.0,1.0,136497.28
-19830,15595728.0,L?,653.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,0.0,158853.98
-19831,15663410.0,Frolov,706.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,1.0,44265.28
-19832,15637315.0,Lo,730.0,France,Female,46.0,2.0,0.0,2.0,1.0,1.0,139911.58
-19833,15753955.0,Omeokachie,711.0,Spain,Female,42.0,2.0,0.0,1.0,0.0,1.0,163587.9
-19834,15637650.0,Ch'ang,667.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,94775.54
-19835,15574830.0,Ch'en,652.0,Germany,Female,37.0,1.0,92659.64,1.0,1.0,0.0,81778.72
-19836,15653547.0,Tisdall,578.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,5791.85
-19837,15716276.0,Macdonald,628.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,38196.24
-19838,15691906.0,Onwumelu,678.0,Spain,Male,41.0,8.0,0.0,2.0,1.0,1.0,81753.92
-19839,15771409.0,Onyinyechukwuka,516.0,Spain,Male,59.0,4.0,0.0,2.0,1.0,1.0,83087.13
-19840,15589969.0,Bufkin,712.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,92381.01
-19841,15670889.0,Chinomso,827.0,Germany,Female,37.0,8.0,137648.41,1.0,0.0,0.0,142839.64
-19842,15608968.0,Obiajulu,699.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,93777.61
-19843,15690540.0,Hsing,724.0,Spain,Male,31.0,7.0,0.0,1.0,1.0,0.0,55775.84
-19844,15768455.0,McLean,714.0,France,Male,29.0,6.0,139859.54,1.0,1.0,1.0,187816.59
-19845,15572735.0,Iheanacho,590.0,Germany,Female,37.0,7.0,137648.41,1.0,1.0,0.0,64849.74
-19846,15600399.0,Nnachetam,668.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,189366.04
-19847,15641715.0,Manna,573.0,Spain,Female,32.0,7.0,0.0,2.0,0.0,0.0,171515.84
-19848,15769783.0,Nkemakolam,598.0,France,Male,28.0,4.0,0.0,1.0,1.0,1.0,146821.42
-19849,15676521.0,Chieloka,735.0,France,Male,31.0,9.0,121669.93,1.0,0.0,0.0,81673.5
-19850,15686743.0,Long,627.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,126307.25
-19851,15582033.0,Fanucci,683.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,180862.26
-19852,15705313.0,Ponomarev,743.0,Germany,Female,36.0,3.0,137761.55,2.0,1.0,0.0,171515.84
-19853,15754068.0,T'ang,711.0,France,Male,27.0,7.0,0.0,1.0,0.0,1.0,162683.06
-19854,15788025.0,Ts'ui,742.0,Germany,Female,56.0,2.0,120825.14,1.0,1.0,0.0,56905.38
-19855,15669994.0,Uspenskaya,521.0,France,Male,37.0,9.0,146766.72,1.0,1.0,1.0,139712.72
-19856,15752578.0,Hsia,524.0,Spain,Male,39.0,3.0,81677.22,1.0,1.0,0.0,105986.01
-19857,15659937.0,Trevisano,727.0,Germany,Female,55.0,7.0,81733.74,1.0,1.0,1.0,149550.95
-19858,15581840.0,Ch'ang,658.0,France,Female,38.0,1.0,0.0,1.0,1.0,1.0,163558.42
-19859,15572398.0,Mazzanti,718.0,Germany,Female,48.0,4.0,127758.09,2.0,1.0,0.0,86490.29
-19860,15751549.0,T'ien,434.0,France,Male,47.0,10.0,113569.43,1.0,1.0,0.0,155721.15
-19861,15762045.0,Hs?,562.0,Germany,Female,52.0,9.0,120867.18,3.0,1.0,0.0,49637.65
-19862,15702561.0,Gentry,622.0,Germany,Female,37.0,3.0,141819.02,2.0,1.0,1.0,94951.27
-19863,15757895.0,T'ien,567.0,France,Female,42.0,4.0,75915.75,1.0,1.0,1.0,182867.84
-19864,15750867.0,Mironova,632.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,127543.83
-19865,15748673.0,Chizuoke,537.0,France,Male,43.0,9.0,0.0,2.0,0.0,1.0,95937.53
-19866,15796834.0,Mazzi,771.0,Germany,Male,38.0,4.0,122570.87,2.0,1.0,0.0,140765.57
-19867,15585855.0,Zuyeva,714.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,122998.26
-19868,15690796.0,Chukwudi,575.0,Spain,Male,40.0,0.0,0.0,2.0,0.0,0.0,140941.47
-19869,15633274.0,Onyekachi,629.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,146785.44
-19870,15796218.0,Chinedum,642.0,France,Female,41.0,9.0,113882.31,2.0,1.0,0.0,176883.42
-19871,15776631.0,Ritchie,542.0,Germany,Male,32.0,4.0,161371.5,1.0,1.0,0.0,175082.2
-19872,15761047.0,Ts'ui,711.0,Germany,Male,35.0,7.0,115532.52,2.0,1.0,0.0,166694.93
-19873,15643630.0,Tsai,652.0,Spain,Female,40.0,2.0,109740.62,1.0,1.0,0.0,88890.05
-19874,15804962.0,Onyemauchechukwu,724.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,1.0,149558.83
-19875,15638355.0,Hsieh,681.0,Germany,Female,29.0,8.0,140694.78,1.0,1.0,1.0,43486.73
-19876,15737795.0,Chukwumaobim,746.0,France,Male,24.0,4.0,0.0,2.0,1.0,0.0,131283.6
-19877,15776936.0,T'ien,835.0,Spain,Female,45.0,2.0,0.0,2.0,1.0,1.0,85901.09
-19878,15672826.0,Nnaife,661.0,France,Male,28.0,3.0,0.0,2.0,0.0,1.0,1408.68
-19879,15672640.0,Trevisano,718.0,Spain,Female,32.0,6.0,0.0,2.0,1.0,0.0,126728.27
-19880,15658339.0,Lucciano,498.0,Spain,Female,42.0,4.0,0.0,2.0,1.0,1.0,143628.31
-19881,15715117.0,Mazzanti,721.0,France,Female,27.0,8.0,142360.62,1.0,0.0,1.0,172749.72
-19882,15758072.0,H?,696.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,74898.8
-19883,15602456.0,Gell,590.0,France,Male,35.0,8.0,116220.5,1.0,1.0,1.0,131764.57
-19884,15753092.0,Obialo,675.0,France,Female,55.0,7.0,0.0,2.0,1.0,1.0,100809.99
-19885,15581036.0,Frolov,599.0,Germany,Male,57.0,4.0,139848.17,1.0,0.0,0.0,124749.08
-19886,15664506.0,Hsiung,526.0,France,Male,33.0,1.0,113829.45,1.0,1.0,1.0,162443.05
-19887,15702277.0,Chukwubuikem,529.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,167023.95
-19888,15700854.0,Chukwumaobim,646.0,Germany,Female,43.0,5.0,146720.98,3.0,1.0,0.0,152821.15
-19889,15738785.0,Obidimkpa,687.0,France,Female,34.0,6.0,0.0,1.0,0.0,0.0,176943.59
-19890,15803479.0,Chukwujekwu,652.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,184524.74
-19891,15682355.0,Chia,632.0,Germany,Male,38.0,3.0,135096.77,1.0,1.0,0.0,142516.35
-19892,15677906.0,Pirozzi,659.0,Spain,Male,41.0,8.0,130142.79,1.0,1.0,1.0,145562.4
-19893,15714391.0,Hargreaves,696.0,France,Male,26.0,5.0,92472.1,1.0,1.0,0.0,61251.25
-19894,15644878.0,Chiu,604.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,70613.52
-19895,15726494.0,P'an,850.0,Spain,Male,48.0,2.0,0.0,1.0,1.0,1.0,117832.88
-19896,15777893.0,Chukwueloka,613.0,Spain,Male,27.0,9.0,0.0,2.0,1.0,0.0,196134.44
-19897,15793883.0,Chidozie,718.0,France,Male,54.0,5.0,0.0,1.0,1.0,1.0,122218.81
-19898,15797960.0,Mazzanti,588.0,France,Female,41.0,9.0,0.0,2.0,0.0,1.0,156424.4
-19899,15641870.0,Yobachukwu,528.0,France,Male,33.0,3.0,138521.47,1.0,1.0,0.0,57898.58
-19900,15684173.0,Pisani,756.0,Germany,Male,44.0,9.0,132570.59,2.0,0.0,1.0,157959.02
-19901,15775991.0,Mazzi,599.0,France,Female,32.0,4.0,0.0,1.0,0.0,1.0,120138.27
-19902,15676937.0,Thompson,642.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,145562.4
-19903,15773904.0,Hs?eh,556.0,France,Male,61.0,5.0,0.0,2.0,1.0,1.0,116520.07
-19904,15675256.0,Ch'iu,561.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,162960.05
-19905,15795881.0,Ch'ien,579.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,168034.9
-19906,15606077.0,Nkemakonam,683.0,France,Female,38.0,7.0,141441.75,1.0,1.0,0.0,75732.25
-19907,15795878.0,Chiawuotu,619.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,9209.21
-19908,15585748.0,Onyemachukwu,622.0,France,Male,28.0,4.0,0.0,2.0,1.0,1.0,185599.24
-19909,15582910.0,Chien,709.0,France,Male,30.0,2.0,0.0,1.0,1.0,1.0,135961.9
-19910,15646720.0,T'ien,579.0,Spain,Female,43.0,3.0,0.0,1.0,0.0,0.0,134790.74
-19911,15771442.0,Pinto,699.0,France,Male,40.0,1.0,0.0,1.0,0.0,0.0,116696.77
-19912,15793331.0,Chiazagomekpere,659.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,38798.79
-19913,15756538.0,Frolov,627.0,France,Male,34.0,6.0,0.0,2.0,0.0,0.0,19500.1
-19914,15732442.0,Rizzo,589.0,France,Male,37.0,2.0,104857.19,2.0,1.0,1.0,162858.28
-19915,15675380.0,Ebelechukwu,695.0,Germany,Male,43.0,2.0,121898.98,1.0,1.0,1.0,10488.44
-19916,15682540.0,Chibugo,708.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,95625.03
-19917,15675320.0,Cattaneo,629.0,France,Female,37.0,1.0,0.0,2.0,1.0,0.0,140198.18
-19918,15790810.0,Chia,583.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,1.0,78730.23
-19919,15593778.0,Kao,595.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,109783.29
-19920,15672955.0,Ts'ui,620.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,67622.34
-19921,15745804.0,Eluemuno,535.0,France,Male,31.0,9.0,127776.42,1.0,1.0,0.0,132034.13
-19922,15742585.0,Hsing,645.0,France,Female,41.0,4.0,107949.39,1.0,1.0,0.0,91036.74
-19923,15761728.0,Chukwudi,733.0,France,Female,34.0,7.0,108760.27,1.0,1.0,0.0,37490.84
-19924,15650751.0,Chuang,601.0,Germany,Male,41.0,4.0,84445.68,1.0,1.0,1.0,167564.82
-19925,15587914.0,Chiu,711.0,France,Male,46.0,6.0,0.0,2.0,1.0,0.0,113436.05
-19926,15605067.0,Chuang,721.0,Germany,Male,40.0,2.0,133384.66,1.0,1.0,1.0,25292.53
-19927,15792868.0,McFarland,751.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,0.0,131177.17
-19928,15688872.0,Genovese,656.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,111241.37
-19929,15744327.0,Piazza,684.0,Germany,Male,37.0,3.0,121729.42,1.0,0.0,1.0,70783.61
-19930,15612505.0,Cattaneo,591.0,France,Male,32.0,4.0,123475.04,1.0,1.0,0.0,142513.5
-19931,15747808.0,Hsiung,696.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,49684.09
-19932,15642530.0,Mazzanti,711.0,Spain,Male,57.0,8.0,0.0,2.0,0.0,1.0,173486.39
-19933,15711789.0,Lai,646.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,131281.29
-19934,15775791.0,Ch'iu,599.0,Germany,Female,32.0,3.0,108736.52,2.0,1.0,1.0,142903.44
-19935,15806049.0,Tien,706.0,Germany,Male,37.0,4.0,142926.64,2.0,1.0,1.0,88827.67
-19936,15616954.0,Yobachi,713.0,Spain,Male,29.0,10.0,0.0,2.0,1.0,1.0,178268.36
-19937,15656840.0,Ibezimako,633.0,France,Male,38.0,2.0,0.0,1.0,0.0,1.0,94780.8
-19938,15745375.0,Chiazagomekpere,551.0,France,Female,29.0,6.0,0.0,2.0,0.0,0.0,114775.76
-19939,15607312.0,Yermakov,565.0,Spain,Female,38.0,7.0,0.0,2.0,0.0,1.0,64539.64
-19940,15710206.0,Chinweuba,641.0,France,Male,35.0,10.0,0.0,2.0,1.0,1.0,168886.39
-19941,15707596.0,Chiekwugo,741.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,170638.98
-19942,15780650.0,Chiedozie,634.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,93835.64
-19943,15805769.0,Y?an,794.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,132973.15
-19944,15569247.0,Onyemauchechukwu,775.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,181224.56
-19945,15592387.0,Ozioma,511.0,Spain,Male,38.0,7.0,144326.09,1.0,1.0,1.0,115818.51
-19946,15572428.0,Shih,712.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,101728.46
-19947,15806901.0,Chien,667.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,100601.73
-19948,15727041.0,Smith,540.0,France,Male,57.0,4.0,0.0,2.0,0.0,1.0,88890.05
-19949,15783026.0,Palermo,679.0,Spain,Female,30.0,7.0,0.0,2.0,1.0,0.0,179421.84
-19950,15573851.0,Cremonesi,794.0,France,Female,35.0,2.0,0.0,1.0,1.0,1.0,61312.15
-19951,15793331.0,Hsu,694.0,France,Male,53.0,2.0,168085.98,1.0,0.0,1.0,159584.81
-19952,15658980.0,Manna,705.0,France,Female,36.0,9.0,0.0,2.0,0.0,0.0,141069.88
-19953,15622442.0,Li Fonti,581.0,Spain,Female,47.0,1.0,0.0,2.0,1.0,0.0,172437.12
-19954,15664343.0,Ting,675.0,France,Male,42.0,2.0,0.0,2.0,1.0,1.0,183492.9
-19955,15793042.0,Chukwujamuike,678.0,France,Male,27.0,7.0,74989.58,1.0,1.0,1.0,123982.14
-19956,15771668.0,Nwokike,710.0,France,Female,35.0,10.0,0.0,1.0,0.0,0.0,179634.49
-19957,15573599.0,Fan,634.0,France,Male,36.0,7.0,0.0,2.0,0.0,1.0,161479.19
-19958,15659736.0,Yobachi,633.0,France,Male,35.0,10.0,0.0,2.0,0.0,1.0,60552.44
-19959,15618171.0,O'Brien,594.0,France,Female,43.0,3.0,96951.21,2.0,1.0,1.0,92797.23
-19960,15677785.0,Pisano,714.0,France,Female,37.0,2.0,132876.23,1.0,1.0,0.0,86410.29
-19961,15654296.0,Chibugo,745.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,176902.83
-19962,15687079.0,Chu,714.0,Germany,Male,40.0,1.0,131184.67,2.0,1.0,1.0,54726.43
-19963,15581893.0,Chia,679.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,131834.75
-19964,15696852.0,Hsia,675.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,91346.03
-19965,15593601.0,Tsou,685.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,149583.67
-19966,15792649.0,T'ien,516.0,Germany,Female,38.0,7.0,114931.35,1.0,1.0,1.0,83700.66
-19967,15631244.0,Obiajulu,718.0,Germany,Female,31.0,7.0,127864.4,1.0,1.0,1.0,146821.42
-19968,15723169.0,Hargreaves,574.0,Germany,Female,52.0,4.0,152663.77,1.0,1.0,1.0,118913.53
-19969,15750476.0,Ch'ang,651.0,France,Female,72.0,2.0,0.0,1.0,1.0,1.0,153342.38
-19970,15749557.0,Chidubem,543.0,Germany,Male,38.0,3.0,132725.1,1.0,1.0,0.0,176768.59
-19971,15588535.0,Hsieh,564.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,120834.48
-19972,15665766.0,Uspenskaya,699.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,163504.33
-19973,15602741.0,Chukwueloka,644.0,France,Female,53.0,5.0,76318.64,1.0,1.0,0.0,139923.38
-19974,15800736.0,Tseng,632.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,102566.76
-19975,15641655.0,Pinto,707.0,Germany,Female,55.0,2.0,131647.01,1.0,1.0,0.0,54871.02
-19976,15584899.0,Toscani,586.0,France,Female,37.0,8.0,167866.87,1.0,1.0,1.0,67599.71
-19977,15796721.0,T'ien,718.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,84932.4
-19978,15682453.0,Onyemauchechukwu,521.0,Spain,Male,38.0,8.0,0.0,2.0,0.0,1.0,89017.38
-19979,15585041.0,Lucchese,575.0,France,Female,42.0,1.0,0.0,1.0,1.0,0.0,149575.59
-19980,15734588.0,Nwachukwu,729.0,France,Female,34.0,4.0,0.0,1.0,1.0,0.0,9241.83
-19981,15714087.0,Chiang,708.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,102908.91
-19982,15592461.0,Ch'ien,786.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,54726.43
-19983,15744691.0,Ch'ang,850.0,Spain,Male,44.0,7.0,0.0,1.0,1.0,0.0,16250.61
-19984,15676966.0,Chinwemma,611.0,France,Female,41.0,6.0,0.0,1.0,1.0,0.0,179341.83
-19985,15576065.0,T'ien,579.0,Germany,Male,32.0,0.0,139542.58,1.0,1.0,0.0,175380.77
-19986,15793883.0,K?,743.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,130795.52
-19987,15777076.0,McGregor,638.0,Germany,Male,43.0,1.0,125845.66,3.0,0.0,0.0,54865.92
-19988,15720559.0,Liao,590.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,160790.02
-19989,15631815.0,Chibuzo,677.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,1.0,172097.95
-19990,15758775.0,O'Brien,579.0,France,Female,44.0,1.0,0.0,2.0,0.0,0.0,107874.39
-19991,15783875.0,Chikezie,521.0,Spain,Female,29.0,3.0,0.0,2.0,1.0,1.0,83700.66
-19992,15790730.0,Rizzo,578.0,Germany,Male,52.0,7.0,119839.69,1.0,1.0,0.0,47775.29
-19993,15783691.0,Skelton,589.0,Spain,Male,36.0,2.0,132767.66,1.0,0.0,0.0,106841.12
-19994,15604829.0,P'eng,654.0,Germany,Female,50.0,2.0,124620.01,2.0,1.0,1.0,125518.32
-19995,15734161.0,Pirozzi,631.0,Spain,Male,33.0,3.0,0.0,1.0,1.0,1.0,113860.81
-19996,15809847.0,Hsia,741.0,Germany,Male,37.0,7.0,141428.99,2.0,1.0,0.0,73873.65
-19997,15585867.0,Ch'in,559.0,Spain,Female,26.0,9.0,0.0,2.0,1.0,1.0,71740.59
-19998,15794549.0,Chiang,593.0,France,Male,27.0,3.0,0.0,2.0,0.0,1.0,91096.76
-19999,15574886.0,Ch'ang,633.0,Spain,Male,25.0,3.0,0.0,2.0,0.0,0.0,64436.16
-20000,15703763.0,Ibragimova,634.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,0.0,57844.79
-20001,15694453.0,Folliero,588.0,Spain,Female,25.0,7.0,0.0,2.0,1.0,1.0,91433.92
-20002,15800583.0,Eipper,652.0,France,Male,27.0,10.0,0.0,1.0,1.0,1.0,115068.99
-20003,15623595.0,P'eng,590.0,France,Female,47.0,3.0,0.0,2.0,0.0,1.0,110142.34
-20004,15642823.0,Lucciano,547.0,France,Male,66.0,2.0,131142.53,2.0,1.0,1.0,91291.2
-20005,15739592.0,Nwankwo,580.0,Germany,Male,42.0,6.0,102773.2,1.0,1.0,1.0,61290.99
-20006,15705890.0,Calabrese,473.0,Germany,Female,54.0,8.0,107949.45,1.0,1.0,0.0,53353.51
-20007,15798895.0,Okeke,699.0,France,Female,43.0,8.0,121166.89,1.0,1.0,1.0,87060.77
-20008,15694188.0,Lucciano,645.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,107499.59
-20009,15709441.0,Hsieh,633.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,180134.88
-20010,15618255.0,Chukwuemeka,607.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,116097.26
-20011,15814675.0,Okwukwe,717.0,Germany,Female,34.0,8.0,106328.08,2.0,1.0,0.0,101694.67
-20012,15769980.0,Chukwuemeka,574.0,France,Female,29.0,0.0,0.0,2.0,0.0,1.0,111698.0
-20013,15681316.0,Shao,633.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,0.0,128527.83
-20014,15780572.0,Nwachukwu,667.0,France,Male,28.0,9.0,0.0,1.0,0.0,0.0,79071.89
-20015,15607098.0,Ch'ien,642.0,France,Female,38.0,7.0,0.0,2.0,0.0,0.0,84760.32
-20016,15746664.0,Chukwumaobim,721.0,Germany,Female,31.0,1.0,124640.41,2.0,1.0,1.0,120801.65
-20017,15756026.0,Shih,594.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,0.0,65612.12
-20018,15765518.0,Tien,727.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,64350.8
-20019,15742204.0,Onyemauchechukwu,756.0,France,Female,30.0,4.0,185714.28,2.0,1.0,1.0,113436.08
-20020,15594917.0,Nnachetam,786.0,France,Female,33.0,5.0,0.0,2.0,0.0,0.0,93717.55
-20021,15690540.0,Chibi,678.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,160129.99
-20022,15762762.0,Mazzi,638.0,France,Female,49.0,1.0,0.0,2.0,1.0,1.0,128882.66
-20023,15779985.0,Onwubiko,657.0,France,Male,37.0,4.0,165878.76,1.0,1.0,0.0,44384.57
-20024,15690664.0,Hsiao,675.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,86767.48
-20025,15752502.0,Chidimma,684.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,90876.95
-20026,15795540.0,Mazzanti,771.0,France,Male,40.0,6.0,0.0,2.0,0.0,0.0,24267.28
-20027,15684548.0,Toscani,634.0,France,Female,53.0,5.0,0.0,1.0,1.0,0.0,120852.85
-20028,15693322.0,Ch'ien,584.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,143317.42
-20029,15691792.0,O'Donnell,726.0,France,Female,49.0,8.0,0.0,1.0,1.0,1.0,60920.03
-20030,15634218.0,Chukwuebuka,576.0,Spain,Male,43.0,6.0,0.0,2.0,0.0,1.0,60920.03
-20031,15705512.0,Chiu,687.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,0.0,78622.46
-20032,15778861.0,Ch'en,623.0,Germany,Male,28.0,8.0,124483.53,1.0,1.0,1.0,91830.75
-20033,15686539.0,Toscani,652.0,France,Male,42.0,5.0,119349.69,1.0,1.0,0.0,182262.66
-20034,15631681.0,Chidubem,706.0,Germany,Male,31.0,7.0,137390.11,2.0,1.0,1.0,166677.35
-20035,15642099.0,Chinedum,744.0,France,Female,38.0,1.0,0.0,1.0,1.0,0.0,97416.34
-20036,15626008.0,Chiu,585.0,France,Female,29.0,6.0,0.0,2.0,0.0,1.0,180551.76
-20037,15775116.0,Yeh,639.0,Spain,Male,30.0,4.0,0.0,1.0,1.0,1.0,58854.88
-20038,15592765.0,Chiemenam,683.0,Germany,Female,57.0,2.0,128284.45,3.0,1.0,0.0,139844.04
-20039,15794916.0,Hsia,675.0,France,Male,40.0,5.0,0.0,1.0,1.0,0.0,93524.19
-20040,15615177.0,Tsai,592.0,Spain,Female,26.0,8.0,0.0,2.0,0.0,1.0,178373.93
-20041,15694530.0,H?,683.0,France,Male,30.0,4.0,0.0,2.0,0.0,0.0,144290.97
-20042,15653857.0,T'ien,633.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,121525.48
-20043,15776054.0,Omeokachie,614.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,1.0,149613.14
-20044,15806967.0,Ts'ui,632.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,76637.38
-20045,15731751.0,Ch'ien,760.0,Spain,Male,48.0,5.0,127290.61,1.0,1.0,1.0,152698.24
-20046,15779581.0,McIntyre,589.0,Spain,Female,57.0,8.0,0.0,1.0,1.0,1.0,178291.09
-20047,15809558.0,Chiang,651.0,France,Female,60.0,5.0,0.0,3.0,0.0,0.0,52301.15
-20048,15704954.0,Ts'ui,748.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,0.0,132770.11
-20049,15777599.0,Mazzi,637.0,Germany,Male,55.0,1.0,121365.39,1.0,0.0,1.0,72993.65
-20050,15642736.0,Yermakov,639.0,France,Male,69.0,9.0,119859.52,1.0,1.0,1.0,103557.93
-20051,15641490.0,Hsiung,599.0,France,Female,36.0,5.0,0.0,2.0,0.0,1.0,90905.26
-20052,15643394.0,Onyekaozulu,697.0,France,Female,41.0,4.0,0.0,2.0,0.0,1.0,61994.2
-20053,15661528.0,O'Donnell,636.0,France,Female,49.0,4.0,0.0,3.0,1.0,0.0,113581.86
-20054,15805769.0,P'eng,699.0,Spain,Male,35.0,10.0,0.0,2.0,0.0,0.0,131300.68
-20055,15774872.0,Ch'ien,571.0,France,Male,35.0,2.0,0.0,1.0,1.0,0.0,95625.03
-20056,15648032.0,Chiekwugo,662.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,75837.58
-20057,15680141.0,Lucciano,696.0,Spain,Female,34.0,4.0,160541.0,1.0,0.0,1.0,82565.01
-20058,15809558.0,Folliero,667.0,France,Male,41.0,7.0,0.0,1.0,0.0,1.0,171037.63
-20059,15752588.0,O'Brien,628.0,France,Female,33.0,7.0,0.0,1.0,1.0,1.0,93901.61
-20060,15739502.0,Ozioma,624.0,Spain,Female,40.0,4.0,0.0,2.0,1.0,0.0,142910.18
-20061,15621893.0,Young,579.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,163714.92
-20062,15626882.0,Ch'eng,554.0,France,Female,29.0,8.0,91536.93,1.0,1.0,1.0,135969.19
-20063,15784890.0,Chiemenam,694.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,92381.01
-20064,15724307.0,Mazzanti,739.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,140937.91
-20065,15589329.0,Ndubueze,613.0,Germany,Male,39.0,4.0,114722.05,1.0,1.0,0.0,150465.93
-20066,15598070.0,Chinomso,774.0,Germany,Female,36.0,4.0,135096.05,1.0,0.0,0.0,143952.24
-20067,15640161.0,Chukwuhaenye,683.0,France,Female,32.0,5.0,103544.11,1.0,1.0,1.0,120512.08
-20068,15734649.0,Trevisano,663.0,Germany,Male,36.0,1.0,134760.21,1.0,1.0,0.0,104353.26
-20069,15621815.0,Ch'ang,642.0,Germany,Male,43.0,1.0,152576.25,1.0,1.0,0.0,179655.85
-20070,15806447.0,Tretiakova,759.0,Germany,Male,34.0,8.0,131647.01,2.0,1.0,1.0,143980.29
-20071,15692575.0,Trevisani,589.0,Germany,Male,33.0,3.0,119919.12,2.0,0.0,1.0,134875.37
-20072,15774857.0,Genovese,727.0,Spain,Male,37.0,6.0,0.0,1.0,1.0,0.0,158750.53
-20073,15711571.0,Ch'in,794.0,France,Male,27.0,5.0,0.0,1.0,1.0,0.0,58426.81
-20074,15783477.0,Onwudiwe,635.0,France,Female,29.0,4.0,0.0,2.0,1.0,1.0,109523.88
-20075,15595766.0,Martin,757.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,73489.15
-20076,15704763.0,Chibugo,589.0,France,Female,42.0,6.0,77326.6,1.0,1.0,1.0,101108.85
-20077,15579892.0,Chukwuemeka,695.0,France,Male,35.0,4.0,0.0,1.0,1.0,1.0,9984.4
-20078,15705521.0,Ch'iu,629.0,France,Female,34.0,2.0,157780.84,2.0,1.0,0.0,147055.87
-20079,15811594.0,Fyodorova,850.0,France,Female,30.0,2.0,0.0,2.0,1.0,0.0,171045.48
-20080,15615473.0,Kistler,588.0,Germany,Female,38.0,6.0,120599.38,1.0,1.0,1.0,163796.01
-20081,15642098.0,Manna,469.0,France,Male,54.0,4.0,0.0,1.0,1.0,1.0,90908.95
-20082,15729954.0,Genovese,697.0,France,Male,35.0,8.0,167539.97,1.0,0.0,0.0,145562.4
-20083,15692977.0,Chukwuemeka,706.0,France,Male,38.0,9.0,0.0,1.0,1.0,0.0,175750.31
-20084,15701765.0,Lucchese,507.0,France,Male,53.0,6.0,0.0,1.0,1.0,0.0,56937.43
-20085,15598838.0,Bock,673.0,France,Female,26.0,0.0,0.0,2.0,1.0,0.0,172363.41
-20086,15735270.0,Chukwukere,619.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,146738.83
-20087,15802088.0,Obidimkpa,598.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,142400.27
-20088,15775293.0,Hsing,579.0,Germany,Male,38.0,10.0,97445.49,2.0,1.0,0.0,188795.85
-20089,15804864.0,Pinto,749.0,France,Female,34.0,7.0,0.0,2.0,0.0,0.0,71450.92
-20090,15672598.0,Onuora,715.0,France,Male,47.0,7.0,0.0,2.0,1.0,0.0,187821.84
-20091,15755077.0,Ch'ien,613.0,France,Male,40.0,3.0,0.0,1.0,1.0,1.0,180345.44
-20092,15682322.0,Mazzanti,597.0,Spain,Female,32.0,8.0,0.0,2.0,0.0,1.0,82662.73
-20093,15754577.0,Onwumelu,684.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,55796.83
-20094,15681755.0,Chidiebele,692.0,France,Male,40.0,9.0,97585.32,1.0,1.0,0.0,19720.08
-20095,15815271.0,Chien,693.0,Germany,Female,39.0,2.0,123757.52,1.0,0.0,0.0,82526.92
-20096,15736431.0,Maclean,549.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,128892.66
-20097,15781409.0,Onyemauchechukwu,598.0,Spain,Male,48.0,1.0,97560.16,1.0,1.0,1.0,75884.65
-20098,15691808.0,Nnamutaezinwa,705.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,132973.21
-20099,15677243.0,Ts'ao,815.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,75837.58
-20100,15574071.0,Ugochukwu,738.0,Spain,Male,50.0,1.0,0.0,1.0,0.0,0.0,158313.87
-20101,15674765.0,Chukwuebuka,725.0,France,Female,39.0,10.0,145984.92,1.0,0.0,0.0,123982.42
-20102,15648800.0,Kao,592.0,Germany,Male,33.0,2.0,72647.64,1.0,1.0,1.0,187985.85
-20103,15709135.0,Nkemdirim,758.0,France,Female,34.0,4.0,82940.25,2.0,1.0,0.0,176713.47
-20104,15694821.0,Chukwujamuike,554.0,Spain,Female,56.0,9.0,0.0,1.0,1.0,1.0,114448.13
-20105,15690134.0,Ponce,652.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,175043.69
-20106,15768359.0,Genovesi,593.0,France,Female,40.0,7.0,0.0,2.0,1.0,1.0,107013.59
-20107,15583718.0,Tuan,678.0,Germany,Female,55.0,9.0,137783.65,1.0,1.0,1.0,52795.56
-20108,15598536.0,Macknight,778.0,Spain,Female,36.0,1.0,0.0,2.0,0.0,1.0,171707.93
-20109,15631681.0,Tsou,753.0,Spain,Female,48.0,4.0,0.0,3.0,1.0,0.0,89891.4
-20110,15584897.0,Ts'ui,547.0,Germany,Female,40.0,7.0,121260.32,1.0,0.0,0.0,84760.32
-20111,15689517.0,Y?,678.0,France,Male,26.0,1.0,99812.88,1.0,1.0,0.0,172618.52
-20112,15583680.0,Hao,683.0,France,Female,40.0,2.0,0.0,2.0,0.0,1.0,111573.3
-20113,15751180.0,T'ang,637.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,55313.44
-20114,15682834.0,Li Fonti,543.0,Germany,Male,48.0,9.0,126570.86,1.0,0.0,0.0,152290.28
-20115,15598510.0,Hao,593.0,Germany,Female,47.0,6.0,113537.73,1.0,0.0,0.0,175373.46
-20116,15686538.0,Lucchese,605.0,France,Male,40.0,9.0,0.0,1.0,0.0,1.0,121894.31
-20117,15769216.0,Ch'ien,781.0,Spain,Male,31.0,3.0,0.0,2.0,1.0,1.0,48559.19
-20118,15659194.0,T'ang,721.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,180622.02
-20119,15694556.0,Mamelu,718.0,France,Female,39.0,4.0,123601.56,1.0,1.0,1.0,177065.24
-20120,15682355.0,Chidi,609.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,91514.62
-20121,15691808.0,Nnaife,678.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,83679.46
-20122,15594577.0,Chigolum,727.0,Germany,Female,28.0,7.0,134498.54,1.0,1.0,0.0,141533.19
-20123,15697574.0,Hsing,549.0,France,Male,38.0,5.0,85982.04,1.0,1.0,1.0,49848.86
-20124,15683522.0,Onyemauchechukwu,679.0,France,Male,36.0,1.0,0.0,1.0,1.0,0.0,98624.04
-20125,15777892.0,Chiang,731.0,Spain,Male,43.0,3.0,0.0,2.0,1.0,0.0,121455.81
-20126,15573851.0,Shih,605.0,France,Female,43.0,9.0,0.0,2.0,1.0,1.0,56865.62
-20127,15795936.0,Onyekachukwu,572.0,France,Male,45.0,7.0,0.0,2.0,1.0,1.0,51980.25
-20128,15576085.0,Martin,686.0,France,Male,56.0,4.0,0.0,1.0,0.0,0.0,178361.48
-20129,15756840.0,Nwachinemelu,656.0,France,Female,38.0,9.0,0.0,1.0,1.0,1.0,181461.46
-20130,15762573.0,H?,585.0,France,Female,38.0,6.0,0.0,1.0,1.0,0.0,176680.16
-20131,15777076.0,Smith,637.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,126419.9
-20132,15651352.0,Pinto,612.0,France,Female,35.0,4.0,119024.75,1.0,1.0,1.0,73881.68
-20133,15687421.0,Pisano,584.0,Spain,Male,24.0,8.0,0.0,2.0,1.0,1.0,75969.19
-20134,15627262.0,Ts'ai,675.0,Germany,Male,28.0,1.0,90696.78,1.0,1.0,1.0,180595.02
-20135,15803749.0,Uspenskaya,774.0,Spain,Female,55.0,6.0,0.0,2.0,1.0,0.0,187811.71
-20136,15680430.0,Chiekwugo,652.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,184843.77
-20137,15679770.0,Ch'ien,635.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,1276.87
-20138,15664555.0,Milanesi,511.0,Germany,Female,58.0,0.0,102520.37,1.0,1.0,0.0,136869.31
-20139,15798885.0,Ch'ien,704.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,0.0,161888.63
-20140,15795571.0,Lai,665.0,France,Female,45.0,4.0,134752.08,1.0,1.0,1.0,28367.19
-20141,15803764.0,Nkemdilim,682.0,Spain,Male,21.0,7.0,0.0,2.0,1.0,1.0,71862.79
-20142,15647842.0,Hsiung,537.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,121239.65
-20143,15769005.0,Chiang,757.0,Germany,Male,30.0,4.0,149961.99,2.0,1.0,0.0,48778.46
-20144,15589948.0,Pisani,665.0,Spain,Male,37.0,5.0,85957.88,1.0,1.0,0.0,166468.21
-20145,15656901.0,Hsieh,537.0,France,Female,24.0,7.0,0.0,2.0,1.0,1.0,55313.51
-20146,15697424.0,Nwabugwu,576.0,France,Male,26.0,9.0,161887.81,1.0,1.0,1.0,93959.17
-20147,15600974.0,Chukwufumnanya,539.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,143859.66
-20148,15791533.0,Uspensky,605.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,145593.85
-20149,15609510.0,Cattaneo,633.0,Germany,Male,46.0,9.0,117879.35,1.0,1.0,1.0,10488.44
-20150,15672640.0,Pinto,628.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,152843.53
-20151,15657535.0,Zito,544.0,Germany,Female,54.0,0.0,127785.16,1.0,1.0,0.0,170968.99
-20152,15791045.0,Tuan,774.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,167984.81
-20153,15759718.0,Hsiung,720.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,145936.28
-20154,15686957.0,Yobanna,667.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,0.0,159618.17
-20155,15602034.0,Ch'ang,695.0,France,Male,22.0,7.0,182781.74,1.0,1.0,1.0,168035.62
-20156,15581197.0,Zikoranachidimma,769.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,86767.48
-20157,15788025.0,Onwudiwe,703.0,Germany,Female,39.0,6.0,132944.53,2.0,1.0,0.0,152054.33
-20158,15772363.0,Aksyonova,758.0,Spain,Male,39.0,2.0,0.0,2.0,0.0,0.0,55775.72
-20159,15765952.0,K?,629.0,France,Female,38.0,4.0,95441.27,1.0,1.0,0.0,142400.27
-20160,15573520.0,Chibugo,590.0,France,Female,55.0,5.0,0.0,1.0,1.0,0.0,121567.91
-20161,15787825.0,Genovese,781.0,France,Male,23.0,3.0,124577.33,2.0,1.0,0.0,97629.31
-20162,15791342.0,Chidubem,702.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,136902.01
-20163,15703628.0,T'ien,642.0,France,Female,41.0,9.0,0.0,2.0,0.0,1.0,143980.28
-20164,15652069.0,Tung,637.0,Spain,Male,39.0,6.0,0.0,1.0,0.0,0.0,53483.21
-20165,15641312.0,Chukwujamuike,559.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,95936.84
-20166,15654067.0,Chukwumaobim,678.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,79919.13
-20167,15738448.0,Chidubem,663.0,Germany,Female,44.0,3.0,109528.16,1.0,1.0,1.0,171463.83
-20168,15808228.0,Lai,743.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,49667.28
-20169,15796468.0,Chijioke,790.0,France,Male,36.0,2.0,0.0,2.0,0.0,1.0,57929.9
-20170,15716994.0,Chibuzo,590.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,169952.82
-20171,15686588.0,Hsieh,639.0,France,Female,48.0,9.0,156660.72,1.0,1.0,0.0,173870.39
-20172,15738497.0,Hsia,543.0,France,Female,67.0,5.0,0.0,1.0,1.0,1.0,176883.42
-20173,15743075.0,Chidiebele,644.0,Germany,Female,61.0,8.0,120962.13,1.0,0.0,0.0,97734.3
-20174,15761453.0,Chukwuemeka,613.0,France,Female,33.0,2.0,118626.05,1.0,0.0,1.0,81251.24
-20175,15625592.0,Niu,677.0,Spain,Male,40.0,2.0,0.0,1.0,1.0,0.0,162501.83
-20176,15608387.0,Hsueh,724.0,Germany,Female,23.0,4.0,116471.43,2.0,1.0,0.0,180598.86
-20177,15632143.0,Onuchukwu,584.0,Spain,Male,28.0,6.0,0.0,2.0,1.0,0.0,183492.9
-20178,15705953.0,Tuan,651.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,0.0,10771.46
-20179,15650014.0,Chiu,678.0,Spain,Female,27.0,1.0,138476.41,1.0,1.0,1.0,62678.53
-20180,15739046.0,Cremonesi,685.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,60721.4
-20181,15636395.0,Chuang,598.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,63736.17
-20182,15653776.0,Y?an,706.0,France,Male,46.0,2.0,134969.13,2.0,0.0,1.0,159800.16
-20183,15604891.0,Biryukova,694.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,122949.71
-20184,15642719.0,Mancini,619.0,Spain,Female,49.0,7.0,0.0,1.0,0.0,0.0,93820.49
-20185,15778089.0,Manna,567.0,France,Female,33.0,7.0,0.0,1.0,1.0,0.0,60533.96
-20186,15668323.0,Ts'ui,583.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,162760.96
-20187,15762882.0,Ch'ien,721.0,Spain,Male,25.0,9.0,0.0,2.0,0.0,1.0,40315.27
-20188,15656822.0,Baresi,528.0,Spain,Female,40.0,1.0,101609.01,1.0,1.0,1.0,160546.58
-20189,15594409.0,Ugorji,730.0,France,Male,28.0,4.0,0.0,1.0,1.0,0.0,140134.43
-20190,15612092.0,Chiu,613.0,Germany,Female,51.0,1.0,120787.18,1.0,0.0,1.0,74425.41
-20191,15765537.0,Yermolayeva,577.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,161653.5
-20192,15768163.0,Pagnotto,696.0,Germany,Female,46.0,4.0,124976.19,1.0,1.0,0.0,172749.4
-20193,15736302.0,Chidiebere,769.0,France,Male,34.0,6.0,0.0,1.0,1.0,1.0,127543.81
-20194,15648800.0,Ch'iu,619.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,160828.98
-20195,15629002.0,Nwebube,709.0,Spain,Female,48.0,7.0,133964.3,1.0,1.0,0.0,180197.74
-20196,15700772.0,Liao,613.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,65769.3
-20197,15781298.0,Kornilova,678.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,0.0,94715.24
-20198,15686936.0,Nkemakonam,662.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,62746.54
-20199,15646921.0,Ch'iu,651.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,136910.21
-20200,15806926.0,Ch'iu,687.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,177099.71
-20201,15694409.0,Chibueze,597.0,France,Male,43.0,0.0,0.0,1.0,1.0,1.0,182266.01
-20202,15673747.0,Ting,597.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,0.0,96961.0
-20203,15728693.0,Yefremova,682.0,France,Male,22.0,10.0,0.0,1.0,0.0,0.0,150944.31
-20204,15779620.0,Nkemdilim,561.0,France,Female,28.0,7.0,84440.1,1.0,1.0,1.0,177020.0
-20205,15574558.0,Y?,679.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,134778.02
-20206,15596088.0,Ch'ang,835.0,Spain,Male,34.0,2.0,134177.06,1.0,1.0,0.0,173599.38
-20207,15682618.0,Oluchukwu,516.0,Spain,Male,57.0,5.0,0.0,3.0,1.0,1.0,161854.98
-20208,15719348.0,Yefremova,837.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,1.0,180934.67
-20209,15584091.0,Chiang,754.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,151648.66
-20210,15777893.0,Fanucci,705.0,Spain,Male,36.0,7.0,0.0,2.0,0.0,0.0,89543.25
-20211,15793723.0,Chiu,633.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,197226.32
-20212,15703394.0,Ch'ang,721.0,Spain,Female,55.0,1.0,116936.81,1.0,1.0,0.0,171364.18
-20213,15725429.0,Yefremova,521.0,Spain,Female,30.0,7.0,0.0,2.0,1.0,1.0,182354.46
-20214,15600337.0,Mironova,523.0,Spain,Male,46.0,9.0,0.0,2.0,1.0,0.0,160829.98
-20215,15795959.0,Martin,678.0,France,Female,41.0,9.0,121325.84,1.0,0.0,1.0,171668.2
-20216,15792934.0,Teng,599.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,158049.9
-20217,15682778.0,Dyer,651.0,Germany,Male,37.0,4.0,90432.92,2.0,0.0,1.0,133692.88
-20218,15789924.0,Ch'en,806.0,France,Male,54.0,7.0,0.0,2.0,1.0,1.0,131283.6
-20219,15782390.0,Chiu,551.0,Germany,Female,52.0,6.0,140931.57,1.0,0.0,0.0,1713.1
-20220,15567367.0,Palermo,633.0,France,Male,31.0,6.0,0.0,1.0,1.0,0.0,146738.83
-20221,15671172.0,Hsia,684.0,Germany,Female,41.0,5.0,127552.85,1.0,1.0,0.0,101693.73
-20222,15643908.0,Chia,569.0,France,Female,32.0,7.0,0.0,2.0,0.0,1.0,63795.62
-20223,15609637.0,Brown,613.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,140166.95
-20224,15653547.0,Nwankwo,849.0,France,Female,24.0,2.0,120599.21,1.0,1.0,0.0,166586.99
-20225,15797381.0,T'ien,738.0,France,Male,34.0,4.0,0.0,1.0,1.0,1.0,131807.14
-20226,15668971.0,King,730.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,9984.4
-20227,15791169.0,Nnamdi,616.0,Germany,Male,50.0,10.0,133970.21,2.0,1.0,0.0,52876.41
-20228,15765322.0,Hales,624.0,France,Female,52.0,2.0,151844.64,1.0,0.0,0.0,124592.84
-20229,15638247.0,Okwudilichukwu,496.0,Germany,Male,31.0,3.0,120822.86,1.0,1.0,1.0,176883.42
-20230,15592751.0,Kosisochukwu,698.0,France,Male,33.0,5.0,0.0,2.0,0.0,0.0,65769.3
-20231,15756975.0,Ponce,660.0,Germany,Female,48.0,8.0,108797.63,1.0,1.0,1.0,161671.15
-20232,15752344.0,T'ien,681.0,France,Male,49.0,4.0,117685.31,1.0,1.0,1.0,115441.66
-20233,15574859.0,Chiu,697.0,Germany,Male,26.0,6.0,131325.56,2.0,1.0,0.0,184954.11
-20234,15709846.0,Chukwumaobim,727.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,108764.69
-20235,15771668.0,Trevisano,648.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,1.0,92647.94
-20236,15657535.0,Chien,686.0,Spain,Female,50.0,5.0,145965.33,2.0,1.0,0.0,93524.19
-20237,15592412.0,Kepley,651.0,France,Female,49.0,4.0,71786.9,1.0,0.0,1.0,145562.4
-20238,15686885.0,Chia,686.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,147552.06
-20239,15591257.0,Scott,641.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,83087.13
-20240,15589754.0,Chukwualuka,697.0,France,Male,25.0,10.0,0.0,2.0,1.0,1.0,189392.66
-20241,15762799.0,T'an,591.0,France,Female,22.0,8.0,0.0,2.0,1.0,1.0,147090.9
-20242,15654060.0,Chukwuhaenye,679.0,France,Female,21.0,6.0,0.0,2.0,1.0,1.0,126996.67
-20243,15592089.0,Chinedum,644.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,171056.65
-20244,15671800.0,Chiazagomekpele,721.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,104786.59
-20245,15809823.0,Chien,734.0,Germany,Male,68.0,7.0,132405.51,1.0,1.0,1.0,60905.51
-20246,15581388.0,Ndubueze,583.0,France,Male,51.0,4.0,0.0,1.0,1.0,0.0,172501.38
-20247,15604813.0,Chia,557.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,0.0,132467.54
-20248,15772337.0,Tsou,619.0,France,Female,34.0,0.0,0.0,2.0,1.0,0.0,163798.41
-20249,15737795.0,Lu,679.0,France,Female,41.0,3.0,0.0,1.0,1.0,0.0,183617.33
-20250,15783585.0,Hsieh,632.0,France,Male,38.0,2.0,128929.88,1.0,1.0,1.0,140745.91
-20251,15573851.0,Ch'eng,572.0,France,Female,35.0,7.0,0.0,2.0,0.0,1.0,176580.63
-20252,15704769.0,Ch'ien,811.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,104469.06
-20253,15664521.0,Chidiebere,687.0,France,Male,26.0,3.0,0.0,2.0,1.0,0.0,155823.2
-20254,15665064.0,T'ao,732.0,Germany,Male,27.0,1.0,142767.39,1.0,1.0,1.0,55829.25
-20255,15798895.0,Yobachi,632.0,France,Female,39.0,2.0,154970.54,1.0,1.0,1.0,158853.98
-20256,15789865.0,Martin,567.0,Germany,Male,43.0,2.0,106871.81,1.0,1.0,0.0,113943.17
-20257,15609356.0,Chiang,695.0,Germany,Female,46.0,8.0,125510.82,1.0,0.0,0.0,183540.22
-20258,15648067.0,O'Donnell,558.0,France,Female,35.0,3.0,0.0,1.0,1.0,0.0,155846.69
-20259,15726684.0,Chien,589.0,Germany,Male,44.0,2.0,123971.51,2.0,1.0,0.0,173870.69
-20260,15654067.0,Bage,624.0,France,Male,29.0,4.0,0.0,2.0,0.0,0.0,66926.83
-20261,15731751.0,Kao,609.0,France,Female,37.0,10.0,0.0,1.0,0.0,0.0,162697.93
-20262,15626457.0,Iadanza,559.0,France,Male,38.0,5.0,101514.89,1.0,1.0,1.0,137784.25
-20263,15668943.0,Hao,715.0,France,Female,45.0,4.0,102395.79,3.0,1.0,1.0,140780.8
-20264,15593694.0,Shih,628.0,France,Female,29.0,6.0,0.0,1.0,1.0,1.0,81491.7
-20265,15583841.0,Mazzi,646.0,Germany,Male,32.0,6.0,131078.42,1.0,1.0,0.0,74846.67
-20266,15776743.0,Tuan,738.0,Germany,Female,48.0,5.0,121647.54,1.0,0.0,0.0,175310.38
-20267,15653773.0,Hsiung,769.0,Germany,Male,40.0,9.0,170840.08,1.0,1.0,0.0,147055.87
-20268,15647453.0,Chizuoke,547.0,France,Female,28.0,7.0,126873.52,2.0,1.0,0.0,79134.78
-20269,15672955.0,Ozerova,705.0,France,Male,35.0,2.0,0.0,1.0,1.0,1.0,100950.48
-20270,15752409.0,Onyekaozulu,663.0,France,Male,31.0,5.0,145536.7,1.0,1.0,0.0,152992.03
-20271,15776518.0,Liao,624.0,Spain,Female,37.0,7.0,0.0,1.0,0.0,1.0,181429.87
-20272,15769254.0,Iadanza,679.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,151925.25
-20273,15652527.0,P'eng,591.0,France,Female,37.0,9.0,127616.56,1.0,1.0,1.0,177834.79
-20274,15765650.0,Y?,850.0,Germany,Female,37.0,2.0,153656.85,2.0,0.0,0.0,119333.95
-20275,15779627.0,Houghton,678.0,France,Male,27.0,0.0,0.0,2.0,1.0,1.0,38962.94
-20276,15761854.0,Hao,726.0,Germany,Male,41.0,3.0,121532.17,1.0,1.0,0.0,102076.03
-20277,15586039.0,Lo,678.0,Germany,Female,42.0,8.0,123544.0,1.0,1.0,0.0,129891.66
-20278,15780359.0,Hao,577.0,France,Male,33.0,4.0,138053.67,1.0,1.0,0.0,122964.18
-20279,15761158.0,Lo Duca,581.0,Germany,Female,39.0,4.0,122570.87,1.0,1.0,1.0,50743.83
-20280,15664523.0,T'ien,757.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,105915.01
-20281,15581871.0,Ozerova,646.0,Spain,Male,69.0,8.0,0.0,2.0,1.0,1.0,40889.14
-20282,15727281.0,Chiang,684.0,Germany,Male,38.0,2.0,94416.78,2.0,0.0,1.0,73977.23
-20283,15587535.0,Efremov,577.0,Spain,Male,51.0,7.0,0.0,1.0,1.0,1.0,187921.5
-20284,15800173.0,Tsao,547.0,Spain,Female,37.0,3.0,0.0,2.0,1.0,1.0,171515.84
-20285,15679048.0,Cremonesi,762.0,France,Female,45.0,8.0,125537.38,1.0,0.0,0.0,172782.69
-20286,15807595.0,H?,733.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,27330.59
-20287,15578825.0,Eluemuno,669.0,France,Female,26.0,8.0,0.0,2.0,1.0,1.0,113428.77
-20288,15576774.0,Nkemdirim,741.0,France,Female,26.0,8.0,0.0,2.0,1.0,0.0,131804.86
-20289,15725355.0,Ma,648.0,France,Female,46.0,10.0,0.0,1.0,1.0,0.0,151335.24
-20290,15583692.0,Onyekachi,730.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,84760.32
-20291,15601594.0,Scott,559.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,97938.59
-20292,15636407.0,Okwudiliolisa,439.0,Germany,Male,45.0,5.0,141581.71,1.0,1.0,0.0,179521.24
-20293,15709142.0,Chuang,635.0,Spain,Male,46.0,5.0,0.0,1.0,1.0,0.0,176984.34
-20294,15777045.0,Pirozzi,636.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,164768.82
-20295,15580493.0,Aksyonova,569.0,France,Female,36.0,9.0,132954.64,2.0,1.0,0.0,161479.19
-20296,15581252.0,Ch'ien,652.0,Germany,Male,33.0,4.0,102238.01,1.0,1.0,0.0,199505.52
-20297,15587066.0,Uchenna,699.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,100423.88
-20298,15737571.0,Ch'ien,725.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,181461.46
-20299,15658364.0,P'eng,674.0,France,Female,47.0,4.0,0.0,1.0,0.0,0.0,187866.21
-20300,15772381.0,Milano,786.0,Germany,Female,31.0,9.0,121874.89,1.0,0.0,1.0,57589.29
-20301,15569452.0,Mazzanti,662.0,France,Male,27.0,8.0,0.0,2.0,1.0,0.0,92816.86
-20302,15746662.0,Pisano,675.0,Germany,Female,55.0,4.0,129731.64,1.0,1.0,0.0,43982.41
-20303,15681327.0,Toscani,652.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,158054.49
-20304,15698792.0,Mazzanti,678.0,France,Male,44.0,3.0,0.0,2.0,0.0,0.0,101319.76
-20305,15572735.0,Lipton,732.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,72945.29
-20306,15792767.0,Ts'ao,758.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,16897.19
-20307,15588854.0,Ch'in,712.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,1.0,176984.34
-20308,15798975.0,Ch'ang,850.0,France,Male,36.0,9.0,0.0,1.0,1.0,0.0,45422.13
-20309,15605424.0,Ch'ien,648.0,France,Male,41.0,1.0,129728.54,1.0,0.0,1.0,127847.25
-20310,15592854.0,Ogochukwu,683.0,France,Female,39.0,3.0,0.0,2.0,1.0,0.0,62980.96
-20311,15665238.0,Mazzanti,589.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,1.0,120107.1
-20312,15796569.0,Liao,619.0,France,Male,40.0,1.0,0.0,2.0,0.0,1.0,79019.65
-20313,15788483.0,De Salis,651.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,0.0,172459.39
-20314,15761600.0,Milne,543.0,Germany,Female,48.0,3.0,109941.56,1.0,0.0,1.0,140941.47
-20315,15721585.0,Lucchesi,567.0,France,Female,51.0,1.0,0.0,1.0,1.0,0.0,150156.82
-20316,15644295.0,Chukwuemeka,656.0,Spain,Male,33.0,4.0,162560.32,1.0,1.0,0.0,86767.48
-20317,15762927.0,Ch'iu,704.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,122433.09
-20318,15758750.0,Chukwubuikem,577.0,Spain,Female,28.0,1.0,145965.24,1.0,0.0,0.0,167772.06
-20319,15763415.0,Gomes,583.0,Spain,Female,47.0,2.0,0.0,1.0,1.0,0.0,115994.52
-20320,15589428.0,Chukwujekwu,667.0,Germany,Female,49.0,3.0,127637.92,1.0,0.0,0.0,61324.87
-20321,15717155.0,Chuang,659.0,Spain,Male,36.0,0.0,143734.01,1.0,0.0,1.0,164967.21
-20322,15728013.0,Eluemuno,622.0,France,Male,29.0,7.0,0.0,1.0,1.0,0.0,176570.55
-20323,15642093.0,Ugochukwu,676.0,France,Female,23.0,7.0,0.0,2.0,1.0,1.0,133692.88
-20324,15641783.0,Yeh,628.0,Spain,Female,48.0,7.0,147904.31,1.0,0.0,0.0,141501.45
-20325,15585599.0,O'Brien,706.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,143666.48
-20326,15792323.0,Chidubem,649.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,164966.18
-20327,15661670.0,Lueck,581.0,France,Male,33.0,3.0,123881.31,1.0,1.0,1.0,116471.43
-20328,15784958.0,Genovese,652.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,109805.17
-20329,15691543.0,Chukwukadibia,611.0,Germany,Male,29.0,1.0,139496.35,1.0,1.0,1.0,113931.57
-20330,15792228.0,Mancini,711.0,Germany,Female,40.0,9.0,112419.92,1.0,1.0,0.0,163558.36
-20331,15673971.0,Tuan,648.0,Spain,Male,36.0,9.0,0.0,4.0,1.0,0.0,110782.88
-20332,15701376.0,Lucchese,730.0,France,Male,45.0,5.0,123385.74,1.0,1.0,0.0,91097.46
-20333,15771087.0,Ch'ang,706.0,Germany,Female,38.0,9.0,120165.3,1.0,1.0,1.0,174825.57
-20334,15786063.0,Ch'ien,558.0,Spain,Male,51.0,8.0,0.0,1.0,0.0,0.0,94912.78
-20335,15631267.0,Hsing,645.0,Spain,Male,44.0,7.0,0.0,1.0,1.0,0.0,102628.98
-20336,15813067.0,Chienezie,850.0,Spain,Male,36.0,9.0,134740.24,1.0,1.0,0.0,120880.72
-20337,15573741.0,Tsao,652.0,Spain,Male,31.0,0.0,0.0,2.0,1.0,0.0,168816.07
-20338,15734461.0,Ni,632.0,Germany,Male,24.0,4.0,124976.19,1.0,1.0,0.0,120830.73
-20339,15689612.0,Tseng,657.0,Germany,Female,49.0,1.0,119501.88,1.0,1.0,1.0,167984.72
-20340,15672798.0,Teng,597.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,105433.53
-20341,15662494.0,Ch'iu,586.0,France,Female,31.0,3.0,0.0,1.0,1.0,1.0,126475.79
-20342,15600280.0,Oguejiofor,733.0,Spain,Male,39.0,0.0,0.0,2.0,1.0,1.0,177613.19
-20343,15710111.0,Zikoranaudodimma,539.0,France,Male,60.0,3.0,0.0,2.0,1.0,0.0,139712.16
-20344,15702601.0,Walker,812.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,143665.44
-20345,15686885.0,Fanucci,706.0,Germany,Male,62.0,7.0,132725.1,2.0,1.0,0.0,113481.02
-20346,15615352.0,Nwankwo,559.0,France,Female,28.0,6.0,0.0,1.0,1.0,1.0,97794.71
-20347,15727177.0,Onyemachukwu,632.0,Spain,Female,28.0,3.0,0.0,2.0,0.0,0.0,177738.98
-20348,15751561.0,P'eng,646.0,Germany,Female,40.0,5.0,105599.67,1.0,1.0,0.0,105784.85
-20349,15800773.0,Scott,594.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,102260.28
-20350,15725028.0,Ts'ao,651.0,Germany,Male,23.0,3.0,132454.45,2.0,0.0,1.0,184842.21
-20351,15804822.0,Folliero,592.0,France,Male,28.0,7.0,0.0,1.0,1.0,0.0,91510.71
-20352,15652981.0,Ts'ui,629.0,Germany,Female,37.0,9.0,121389.06,2.0,1.0,0.0,72994.62
-20353,15741416.0,T'ao,581.0,France,Female,34.0,7.0,0.0,2.0,0.0,1.0,140745.91
-20354,15783025.0,Trevisani,619.0,Spain,Female,32.0,5.0,0.0,1.0,1.0,0.0,91903.9
-20355,15793896.0,Ch'iu,567.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,73977.23
-20356,15622985.0,Trevisani,699.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,180622.02
-20357,15598338.0,Hsia,595.0,Germany,Male,64.0,7.0,95231.91,1.0,1.0,1.0,159823.16
-20358,15732621.0,Smith,713.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,172576.43
-20359,15598840.0,Nwebube,790.0,France,Male,38.0,5.0,118089.97,1.0,1.0,0.0,139710.24
-20360,15798084.0,Pacheco,603.0,Germany,Male,36.0,2.0,84474.62,1.0,1.0,0.0,41800.18
-20361,15730579.0,Chuang,724.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,83022.92
-20362,15786042.0,T'ien,663.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,88729.22
-20363,15592300.0,H?,732.0,France,Male,33.0,10.0,0.0,2.0,1.0,0.0,121798.53
-20364,15582762.0,H?,632.0,Spain,Male,51.0,5.0,0.0,1.0,1.0,0.0,90876.97
-20365,15704771.0,Hsu,632.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,121151.1
-20366,15670552.0,Nnachetam,567.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,25684.93
-20367,15756475.0,Onwuamaeze,622.0,France,Male,33.0,9.0,169312.13,1.0,0.0,1.0,80619.17
-20368,15761763.0,P'eng,695.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,101515.76
-20369,15691823.0,O'Donnell,706.0,Germany,Male,33.0,7.0,122214.0,2.0,1.0,1.0,177747.03
-20370,15809265.0,Burt,591.0,Spain,Male,37.0,10.0,164902.66,1.0,1.0,0.0,157552.08
-20371,15704586.0,Hsueh,766.0,Spain,Male,48.0,10.0,0.0,2.0,1.0,1.0,65731.53
-20372,15684960.0,Lu,606.0,Germany,Male,29.0,9.0,134980.74,2.0,1.0,1.0,178587.36
-20373,15764343.0,Ts'ai,679.0,France,Male,56.0,2.0,0.0,2.0,0.0,1.0,121537.85
-20374,15787655.0,Kao,705.0,Spain,Male,31.0,7.0,0.0,2.0,0.0,1.0,177740.58
-20375,15785042.0,Chidiebere,597.0,France,Female,36.0,4.0,0.0,2.0,0.0,0.0,111190.53
-20376,15707681.0,Nnamdi,814.0,Germany,Female,34.0,3.0,141782.57,2.0,1.0,1.0,166468.21
-20377,15803685.0,Onyekachi,679.0,France,Female,25.0,3.0,0.0,4.0,1.0,0.0,82438.64
-20378,15591577.0,Fyodorova,743.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,79577.4
-20379,15572758.0,Chidiebere,587.0,Germany,Male,34.0,5.0,128718.84,1.0,0.0,0.0,103713.93
-20380,15694409.0,Pinto,703.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,937.15
-20381,15627385.0,Ch'in,681.0,Germany,Male,47.0,6.0,154970.54,1.0,1.0,0.0,198446.91
-20382,15585985.0,O'Loghlin,718.0,Spain,Female,33.0,10.0,0.0,2.0,1.0,0.0,181893.31
-20383,15682541.0,K?,605.0,France,Female,50.0,8.0,0.0,1.0,1.0,0.0,183566.78
-20384,15748625.0,Shao,684.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,90162.35
-20385,15785367.0,Walker,651.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,109613.14
-20386,15743759.0,Pomeroy,712.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,169921.11
-20387,15646082.0,Hsieh,724.0,Germany,Female,28.0,2.0,121172.97,2.0,1.0,0.0,116520.07
-20388,15645571.0,Chukwukadibia,520.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,161388.22
-20389,15674811.0,Hargreaves,642.0,France,Female,37.0,2.0,130147.98,2.0,1.0,0.0,49778.79
-20390,15751131.0,Tung,663.0,Germany,Male,47.0,8.0,125396.24,1.0,0.0,1.0,185561.89
-20391,15683483.0,Hsiung,611.0,France,Male,47.0,0.0,0.0,2.0,0.0,0.0,145894.9
-20392,15751137.0,Burtch,659.0,France,Male,59.0,9.0,0.0,2.0,0.0,1.0,120862.46
-20393,15802088.0,Zito,671.0,France,Male,44.0,6.0,0.0,2.0,1.0,1.0,179913.98
-20394,15723210.0,Lo,579.0,Spain,Female,40.0,9.0,0.0,3.0,1.0,0.0,97613.96
-20395,15663888.0,Fang,676.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,75860.01
-20396,15769703.0,Ozerova,684.0,France,Male,37.0,6.0,0.0,1.0,1.0,1.0,96670.2
-20397,15592461.0,Tien,704.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,92320.37
-20398,15752884.0,Onyekachi,660.0,France,Female,55.0,4.0,0.0,1.0,1.0,0.0,101704.19
-20399,15725028.0,Macleod,585.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,126554.96
-20400,15772650.0,Ts'ui,745.0,France,Female,37.0,8.0,0.0,1.0,1.0,1.0,79547.39
-20401,15652289.0,T'ao,727.0,Spain,Male,50.0,7.0,120916.52,3.0,0.0,1.0,149913.94
-20402,15765952.0,Gant,678.0,France,Male,50.0,10.0,0.0,1.0,1.0,0.0,176763.27
-20403,15605059.0,Walker,653.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,61569.07
-20404,15596710.0,P'eng,753.0,France,Male,34.0,7.0,0.0,2.0,0.0,1.0,122925.4
-20405,15655232.0,Longo,671.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,102260.16
-20406,15686936.0,Calabresi,806.0,Spain,Male,29.0,7.0,118402.25,1.0,1.0,1.0,44904.26
-20407,15578866.0,T'ien,538.0,Spain,Male,39.0,8.0,0.0,1.0,1.0,0.0,124489.88
-20408,15734461.0,Baresi,598.0,France,Female,51.0,1.0,123086.39,1.0,1.0,1.0,185830.72
-20409,15790717.0,Efremov,586.0,France,Female,43.0,9.0,0.0,1.0,0.0,1.0,103560.98
-20410,15652266.0,Udinese,678.0,France,Male,49.0,9.0,0.0,1.0,0.0,1.0,79874.72
-20411,15715117.0,Chineze,706.0,France,Female,29.0,5.0,142862.21,1.0,1.0,0.0,167773.55
-20412,15713774.0,Ch'ien,577.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,88333.74
-20413,15682955.0,Okechukwu,628.0,France,Female,28.0,5.0,141334.95,1.0,1.0,0.0,162858.29
-20414,15777784.0,Uchenna,564.0,Germany,Female,59.0,7.0,172646.74,1.0,1.0,0.0,99490.01
-20415,15587562.0,Ch'ang,809.0,France,Female,47.0,4.0,0.0,1.0,0.0,0.0,131753.97
-20416,15628112.0,Chidimma,598.0,Spain,Female,33.0,6.0,0.0,2.0,0.0,0.0,166984.44
-20417,15783955.0,Ch'iu,673.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,161653.5
-20418,15593365.0,Ch'in,723.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,52615.62
-20419,15587581.0,Calabresi,614.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,1.0,104722.47
-20420,15767954.0,Y?an,656.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,101828.39
-20421,15735025.0,McLean,710.0,France,Male,46.0,10.0,0.0,2.0,1.0,0.0,142956.48
-20422,15642787.0,Yermolayev,686.0,Spain,Male,26.0,9.0,0.0,2.0,1.0,0.0,187734.49
-20423,15746802.0,Yegorova,663.0,France,Female,30.0,10.0,137494.28,1.0,1.0,1.0,108841.83
-20424,15770420.0,Chidumaga,680.0,Germany,Female,35.0,9.0,114268.85,2.0,1.0,0.0,54474.48
-20425,15764073.0,DeRose,583.0,France,Male,27.0,4.0,0.0,2.0,0.0,1.0,66825.33
-20426,15610686.0,Mazzi,662.0,France,Male,47.0,1.0,0.0,1.0,1.0,0.0,199398.68
-20427,15606841.0,Chukwubuikem,622.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,177747.72
-20428,15804822.0,Lo Duca,729.0,France,Female,52.0,1.0,0.0,1.0,1.0,0.0,183682.09
-20429,15799785.0,Ibeamaka,648.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,164084.82
-20430,15692594.0,Chidimma,559.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,167984.72
-20431,15576723.0,Tuan,684.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,135083.28
-20432,15762331.0,Bair,678.0,Spain,Male,35.0,7.0,0.0,1.0,1.0,1.0,94912.78
-20433,15659234.0,Ts'ai,685.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,113086.08
-20434,15594815.0,Chiang,699.0,Spain,Male,34.0,4.0,145071.24,1.0,1.0,0.0,133871.83
-20435,15571689.0,Mazzi,544.0,Spain,Male,27.0,1.0,0.0,2.0,1.0,1.0,183358.21
-20436,15677307.0,Yobanna,551.0,France,Male,28.0,1.0,0.0,2.0,1.0,1.0,135875.51
-20437,15790322.0,Chien,707.0,Germany,Female,47.0,8.0,97925.11,1.0,0.0,0.0,169952.82
-20438,15791321.0,O'Sullivan,696.0,Germany,Female,49.0,9.0,135470.9,1.0,0.0,0.0,66893.28
-20439,15795895.0,Chieloka,567.0,France,Male,31.0,6.0,142767.39,1.0,1.0,0.0,66810.48
-20440,15624781.0,Baresi,586.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,152290.29
-20441,15794875.0,T'ang,583.0,Spain,Female,36.0,1.0,84944.58,1.0,1.0,1.0,127939.19
-20442,15795519.0,Hsiung,669.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,1.0,181221.09
-20443,15617206.0,Lucciano,646.0,France,Female,45.0,6.0,0.0,2.0,1.0,0.0,61931.21
-20444,15794941.0,Trevisani,704.0,France,Female,45.0,9.0,0.0,2.0,1.0,0.0,151871.84
-20445,15651020.0,Iweobiegbunam,653.0,France,Female,34.0,3.0,0.0,1.0,1.0,1.0,166310.82
-20446,15682868.0,Ts'ai,652.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,93524.22
-20447,15703923.0,Ch'iu,648.0,Germany,Male,43.0,1.0,119794.67,1.0,0.0,0.0,73585.18
-20448,15651022.0,Ch'eng,595.0,France,Female,55.0,5.0,0.0,2.0,1.0,1.0,94415.2
-20449,15759717.0,Lo Duca,516.0,Spain,Male,41.0,7.0,0.0,4.0,1.0,0.0,87987.06
-20450,15777830.0,Piazza,684.0,Germany,Male,45.0,7.0,135070.78,1.0,1.0,0.0,131662.47
-20451,15684319.0,Ch'en,736.0,France,Female,55.0,2.0,0.0,1.0,0.0,0.0,43921.36
-20452,15638028.0,Calabrese,731.0,Spain,Male,48.0,7.0,0.0,2.0,1.0,1.0,183487.98
-20453,15602373.0,Ch'ang,574.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,90591.15
-20454,15611579.0,Chikwendu,711.0,France,Female,31.0,7.0,136416.82,1.0,0.0,0.0,158610.89
-20455,15696989.0,Chiang,696.0,Spain,Female,27.0,5.0,0.0,2.0,1.0,0.0,121084.27
-20456,15643396.0,Teng,716.0,Spain,Male,34.0,5.0,0.0,2.0,0.0,1.0,199336.63
-20457,15777053.0,Pinto,666.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,16081.62
-20458,15763544.0,Yobanna,712.0,France,Female,39.0,1.0,151553.19,1.0,1.0,0.0,76637.38
-20459,15736271.0,Chukwuemeka,638.0,Germany,Female,43.0,5.0,139706.31,1.0,0.0,0.0,53394.28
-20460,15642530.0,Manna,588.0,France,Male,63.0,8.0,88977.39,1.0,1.0,1.0,101178.85
-20461,15657321.0,P'eng,639.0,France,Male,41.0,8.0,0.0,2.0,0.0,1.0,129933.5
-20462,15785519.0,K'ung,627.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,0.0,88728.47
-20463,15693018.0,Walker,673.0,France,Female,39.0,8.0,121250.04,1.0,0.0,0.0,59129.72
-20464,15622003.0,Trevisani,716.0,France,Male,24.0,7.0,0.0,2.0,0.0,1.0,110525.6
-20465,15796370.0,Chidubem,559.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,88783.59
-20466,15794849.0,Yobanna,556.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,175894.24
-20467,15693771.0,Lombardi,644.0,Germany,Female,45.0,6.0,144386.32,1.0,1.0,0.0,167564.82
-20468,15637461.0,Chia,744.0,France,Male,34.0,8.0,85906.65,1.0,1.0,1.0,159800.16
-20469,15762216.0,Maughan,581.0,Germany,Female,39.0,2.0,178820.28,1.0,1.0,1.0,172063.09
-20470,15815271.0,Ch'eng,684.0,Spain,Male,34.0,1.0,0.0,2.0,0.0,1.0,179705.13
-20471,15730447.0,Ritchie,597.0,Germany,Female,38.0,4.0,121498.58,1.0,1.0,1.0,172096.08
-20472,15591720.0,McIntosh,792.0,Germany,Female,31.0,9.0,122950.94,2.0,1.0,1.0,164248.33
-20473,15652789.0,Iloerika,663.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,35113.08
-20474,15772423.0,Pagnotto,739.0,Spain,Female,31.0,4.0,0.0,2.0,1.0,1.0,70563.9
-20475,15576774.0,T'an,678.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,54487.43
-20476,15571928.0,Palerma,667.0,Spain,Male,21.0,6.0,0.0,1.0,1.0,1.0,77469.38
-20477,15732963.0,Chidumaga,662.0,Germany,Female,55.0,3.0,113625.83,2.0,1.0,0.0,170195.4
-20478,15693906.0,Onio,581.0,France,Female,41.0,6.0,0.0,2.0,1.0,0.0,1276.87
-20479,15651883.0,Onyekaozulu,810.0,Germany,Male,44.0,6.0,102870.93,1.0,1.0,1.0,92887.06
-20480,15614893.0,Ch'ang,641.0,France,Female,34.0,4.0,0.0,1.0,1.0,1.0,138527.56
-20481,15679622.0,O'Sullivan,727.0,France,Male,39.0,4.0,0.0,1.0,1.0,1.0,44965.44
-20482,15624641.0,Nwabugwu,682.0,France,Male,42.0,6.0,0.0,1.0,1.0,1.0,148727.42
-20483,15792489.0,Bellucci,671.0,France,Male,41.0,1.0,0.0,2.0,0.0,1.0,158411.65
-20484,15652883.0,Chukwubuikem,687.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,69011.66
-20485,15769246.0,Pugh,596.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,104774.67
-20486,15762268.0,Pisani,706.0,France,Female,43.0,5.0,0.0,2.0,1.0,1.0,176713.47
-20487,15758750.0,Folliero,586.0,Germany,Male,40.0,3.0,109730.22,1.0,1.0,0.0,61290.99
-20488,15682533.0,Kao,584.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,91482.5
-20489,15617301.0,Toscani,669.0,Germany,Male,28.0,9.0,89800.14,3.0,0.0,1.0,172572.64
-20490,15752409.0,Yeh,557.0,France,Female,47.0,5.0,0.0,3.0,1.0,0.0,52327.57
-20491,15644765.0,Mazzanti,623.0,France,Male,33.0,7.0,0.0,1.0,1.0,1.0,52319.26
-20492,15681075.0,K'ung,605.0,France,Male,38.0,5.0,114835.04,1.0,0.0,1.0,145568.28
-20493,15676571.0,Cattaneo,576.0,France,Male,42.0,1.0,0.0,2.0,1.0,1.0,114448.77
-20494,15656592.0,Calabresi,775.0,France,Male,51.0,7.0,0.0,2.0,1.0,1.0,103372.79
-20495,15753874.0,Genovese,725.0,France,Male,30.0,6.0,0.0,2.0,1.0,0.0,134878.19
-20496,15672960.0,Nwebube,694.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,133051.64
-20497,15671342.0,Hs?eh,673.0,Spain,Male,24.0,9.0,0.0,2.0,1.0,0.0,88724.49
-20498,15570141.0,Pinto,724.0,Germany,Male,34.0,2.0,100549.43,1.0,0.0,1.0,155711.64
-20499,15570134.0,Cremonesi,653.0,Germany,Male,44.0,9.0,121161.57,1.0,0.0,0.0,47911.03
-20500,15592994.0,DeRose,683.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,121681.19
-20501,15635097.0,Riggs,720.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,1.0,160941.78
-20502,15595881.0,Mazzi,696.0,Germany,Female,35.0,3.0,139825.34,1.0,1.0,0.0,172321.21
-20503,15672754.0,Hovell,593.0,Germany,Male,36.0,8.0,72535.45,1.0,1.0,0.0,106920.57
-20504,15782875.0,Young,527.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,56784.11
-20505,15622993.0,Chiu,718.0,Germany,Female,48.0,1.0,141665.63,1.0,1.0,0.0,115719.08
-20506,15697546.0,Gorman,562.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,166516.84
-20507,15637593.0,Maclean,708.0,France,Male,48.0,3.0,0.0,2.0,1.0,1.0,96832.82
-20508,15602844.0,Hsing,712.0,Germany,Female,39.0,7.0,107864.85,1.0,1.0,1.0,93336.3
-20509,15572728.0,Maclean,716.0,France,Female,45.0,6.0,0.0,1.0,1.0,0.0,166586.99
-20510,15782899.0,Yefremova,721.0,France,Female,33.0,8.0,137936.94,1.0,1.0,0.0,73881.68
-20511,15768887.0,Maclean,576.0,France,Male,23.0,5.0,0.0,2.0,1.0,1.0,138880.24
-20512,15641411.0,Ni,730.0,Germany,Male,54.0,1.0,123086.39,1.0,1.0,0.0,74580.8
-20513,15798903.0,Yobachukwu,565.0,Germany,Female,43.0,6.0,145965.24,1.0,0.0,1.0,161653.5
-20514,15602354.0,Iadanza,815.0,France,Male,39.0,0.0,0.0,2.0,1.0,0.0,136095.94
-20515,15772030.0,Chigolum,775.0,Germany,Female,41.0,3.0,103700.69,1.0,1.0,0.0,166370.81
-20516,15573851.0,Nwokike,574.0,Spain,Female,31.0,1.0,0.0,2.0,1.0,0.0,158840.99
-20517,15763662.0,Mazzi,523.0,Spain,Female,36.0,3.0,0.0,2.0,0.0,0.0,111342.66
-20518,15801125.0,Ts'ui,556.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,9041.71
-20519,15626383.0,Chien,744.0,France,Male,32.0,5.0,122314.5,1.0,1.0,1.0,64855.58
-20520,15790782.0,Ch'ien,749.0,France,Female,32.0,10.0,0.0,2.0,1.0,0.0,82696.84
-20521,15641585.0,Chimezie,730.0,Germany,Female,50.0,6.0,138476.41,1.0,0.0,1.0,88187.81
-20522,15662238.0,Chukwualuka,710.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,1.0,94978.1
-20523,15614103.0,Brown,604.0,Germany,Male,39.0,3.0,110114.42,2.0,0.0,0.0,82870.75
-20524,15654870.0,Teng,605.0,France,Male,67.0,7.0,0.0,2.0,1.0,1.0,161579.85
-20525,15589019.0,Hsia,588.0,Germany,Male,37.0,2.0,131406.56,2.0,1.0,1.0,46415.09
-20526,15731751.0,Hs?,735.0,Spain,Female,21.0,9.0,0.0,2.0,1.0,0.0,121408.55
-20527,15589924.0,Palerma,576.0,France,Female,30.0,5.0,142871.27,1.0,1.0,1.0,181224.56
-20528,15690164.0,Chukwujamuike,757.0,Spain,Male,29.0,3.0,98800.0,1.0,1.0,1.0,86490.09
-20529,15808971.0,Uchechukwu,567.0,Germany,Male,33.0,9.0,136444.25,1.0,1.0,1.0,126192.54
-20530,15791342.0,Hs?,581.0,Germany,Male,30.0,3.0,145984.92,2.0,0.0,0.0,166476.95
-20531,15740383.0,Nnachetam,582.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,141946.92
-20532,15796834.0,Kornilova,679.0,France,Female,39.0,3.0,90594.55,1.0,1.0,0.0,134889.95
-20533,15739592.0,Frolov,811.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,45438.38
-20534,15782089.0,Chiemela,556.0,Spain,Male,33.0,1.0,0.0,2.0,1.0,1.0,105986.01
-20535,15762228.0,Onyekaozulu,786.0,France,Male,40.0,6.0,133628.89,1.0,1.0,1.0,142995.32
-20536,15786035.0,Ts'ai,592.0,France,Male,31.0,9.0,0.0,2.0,0.0,1.0,182855.21
-20537,15641366.0,Lombardi,743.0,France,Female,46.0,1.0,110132.55,1.0,1.0,0.0,65951.65
-20538,15659475.0,Milano,636.0,Germany,Female,33.0,6.0,142400.77,2.0,1.0,0.0,96881.32
-20539,15761067.0,T'ao,667.0,Germany,Female,29.0,6.0,131694.97,2.0,1.0,1.0,54487.43
-20540,15661626.0,Zuyeva,729.0,France,Female,42.0,2.0,125837.76,1.0,1.0,0.0,140941.47
-20541,15721592.0,Chiu,647.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,103557.85
-20542,15809872.0,Trevisani,616.0,Germany,Female,28.0,6.0,152264.81,2.0,0.0,0.0,110953.33
-20543,15576680.0,Martin,678.0,Germany,Male,41.0,5.0,116474.35,2.0,1.0,0.0,26149.25
-20544,15738174.0,Hsiao,595.0,Spain,Female,62.0,4.0,0.0,1.0,1.0,1.0,96792.15
-20545,15766163.0,Ugochukwutubelum,693.0,France,Male,62.0,9.0,0.0,2.0,0.0,1.0,26265.18
-20546,15777616.0,Chukwukere,619.0,Spain,Male,26.0,10.0,0.0,2.0,1.0,0.0,125877.22
-20547,15780256.0,O'Sullivan,679.0,Spain,Male,45.0,9.0,0.0,2.0,0.0,0.0,102930.46
-20548,15775339.0,Chukwueloka,777.0,France,Male,49.0,9.0,0.0,1.0,1.0,0.0,118319.5
-20549,15754929.0,Chiang,724.0,France,Male,41.0,4.0,128876.71,1.0,1.0,1.0,61666.87
-20550,15790846.0,Chukwujamuike,774.0,France,Female,43.0,1.0,0.0,2.0,1.0,0.0,160131.58
-20551,15766887.0,Biryukova,553.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,115387.4
-20552,15671358.0,Yermakova,697.0,France,Female,38.0,2.0,57530.06,1.0,0.0,0.0,151902.78
-20553,15784918.0,Chukwuemeka,596.0,France,Male,33.0,4.0,0.0,2.0,0.0,0.0,180622.43
-20554,15687079.0,Zetticci,593.0,France,Female,43.0,8.0,0.0,2.0,1.0,1.0,133535.29
-20555,15780492.0,Hsueh,683.0,France,Female,38.0,3.0,0.0,2.0,0.0,0.0,124095.69
-20556,15691808.0,Nnaife,567.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,112331.19
-20557,15702293.0,Onuchukwu,578.0,France,Male,46.0,8.0,0.0,2.0,1.0,0.0,123663.63
-20558,15649160.0,Chukwualuka,709.0,France,Male,28.0,7.0,0.0,2.0,0.0,1.0,4791.8
-20559,15589948.0,Ritchie,559.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,141501.45
-20560,15668818.0,Eluemuno,711.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,158590.25
-20561,15614847.0,Yermakov,724.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,91574.92
-20562,15750335.0,Chiawuotu,639.0,Spain,Female,18.0,3.0,0.0,2.0,0.0,1.0,144874.67
-20563,15644295.0,Ch'ien,667.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,82531.91
-20564,15604072.0,Y?an,718.0,France,Male,32.0,7.0,0.0,1.0,0.0,1.0,131283.6
-20565,15587562.0,Yobanna,678.0,France,Male,59.0,8.0,0.0,2.0,1.0,1.0,134790.74
-20566,15606841.0,Chidiebere,669.0,France,Female,37.0,0.0,0.0,1.0,1.0,1.0,38570.13
-20567,15651336.0,Maughan,600.0,France,Female,22.0,7.0,84719.13,1.0,1.0,1.0,54517.25
-20568,15598838.0,Clements,678.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,144650.18
-20569,15655229.0,Ch'ang,779.0,Spain,Male,31.0,5.0,129717.3,1.0,1.0,0.0,124872.58
-20570,15673747.0,Y?,556.0,Spain,Male,43.0,9.0,0.0,2.0,1.0,0.0,151325.24
-20571,15707132.0,Chukwuma,679.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,106981.05
-20572,15641359.0,Brown,619.0,France,Male,29.0,2.0,0.0,1.0,1.0,1.0,40313.47
-20573,15793331.0,Chijindum,597.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,181543.67
-20574,15655875.0,P'eng,615.0,Spain,Female,41.0,1.0,102983.93,1.0,1.0,1.0,170984.97
-20575,15702631.0,Nwankwo,667.0,France,Male,40.0,4.0,71996.09,2.0,1.0,1.0,121277.78
-20576,15768106.0,Hsia,717.0,France,Male,38.0,2.0,0.0,1.0,1.0,0.0,177747.03
-20577,15616555.0,Nnachetam,716.0,France,Female,36.0,5.0,0.0,1.0,1.0,0.0,105784.85
-20578,15762238.0,Trevisani,564.0,France,Female,47.0,6.0,127415.85,1.0,1.0,0.0,144880.81
-20579,15618363.0,Trevisano,691.0,Spain,Female,39.0,4.0,0.0,2.0,1.0,1.0,56875.76
-20580,15668958.0,Ch'iu,769.0,France,Female,28.0,8.0,0.0,2.0,1.0,1.0,81298.09
-20581,15600724.0,Tsao,682.0,Spain,Female,38.0,5.0,79039.84,1.0,1.0,0.0,114323.66
-20582,15693376.0,K?,587.0,France,Male,48.0,8.0,0.0,2.0,0.0,1.0,171575.54
-20583,15632521.0,Kuo,645.0,Germany,Male,40.0,9.0,92716.96,1.0,1.0,0.0,118749.58
-20584,15792722.0,Walker,635.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,92915.84
-20585,15772262.0,Uvarova,715.0,France,Male,39.0,9.0,0.0,2.0,0.0,1.0,10350.74
-20586,15696849.0,Tuan,602.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,112928.74
-20587,15690670.0,Onyemauchechukwu,774.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,115789.25
-20588,15793346.0,Okechukwu,594.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,131283.6
-20589,15717185.0,Ijendu,602.0,France,Female,39.0,3.0,106116.5,3.0,1.0,1.0,84475.13
-20590,15681446.0,Tobeolisa,745.0,France,Male,47.0,3.0,122028.65,1.0,1.0,1.0,176728.28
-20591,15633141.0,Onwuamaeze,678.0,Germany,Male,35.0,9.0,122447.86,2.0,1.0,1.0,13018.96
-20592,15773039.0,Maclean,641.0,Germany,Female,63.0,3.0,118560.0,1.0,1.0,1.0,86022.09
-20593,15693637.0,Uwaezuoke,628.0,Germany,Female,39.0,5.0,92961.61,2.0,0.0,0.0,74850.84
-20594,15808876.0,Obialo,597.0,Germany,Male,35.0,8.0,93547.23,2.0,1.0,0.0,131736.23
-20595,15757777.0,Chiu,579.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,68621.98
-20596,15669301.0,Fennell,635.0,Germany,Male,40.0,9.0,117468.67,2.0,1.0,0.0,172646.88
-20597,15783029.0,Nwora,655.0,France,Female,27.0,4.0,117412.19,1.0,1.0,1.0,146411.6
-20598,15693332.0,Lombardo,610.0,Spain,Female,55.0,9.0,125789.69,1.0,1.0,1.0,106040.97
-20599,15773779.0,Hsueh,579.0,France,Female,61.0,3.0,0.0,2.0,1.0,1.0,4460.2
-20600,15768120.0,Chia,636.0,Germany,Female,47.0,6.0,126473.33,2.0,1.0,0.0,6076.62
-20601,15768201.0,Binder,631.0,Spain,Female,23.0,7.0,0.0,2.0,0.0,1.0,198208.0
-20602,15722585.0,Chukwujamuike,611.0,Germany,Male,40.0,2.0,91286.51,1.0,1.0,0.0,167522.19
-20603,15587584.0,Smith,597.0,France,Male,39.0,9.0,127289.28,2.0,0.0,0.0,143357.08
-20604,15763612.0,Chukwuemeka,628.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,79734.23
-20605,15755084.0,Yermakova,693.0,France,Male,35.0,2.0,0.0,2.0,0.0,0.0,88827.67
-20606,15754926.0,Fanucci,586.0,France,Female,35.0,8.0,0.0,1.0,1.0,0.0,64815.05
-20607,15645103.0,Lucchese,645.0,France,Female,28.0,1.0,0.0,2.0,1.0,0.0,161971.42
-20608,15729557.0,Ritchie,628.0,Germany,Female,61.0,3.0,118087.68,1.0,1.0,0.0,161253.08
-20609,15667438.0,Naylor,495.0,Spain,Male,49.0,8.0,0.0,1.0,1.0,0.0,88724.49
-20610,15652955.0,Chimaobim,646.0,France,Male,71.0,2.0,0.0,1.0,1.0,1.0,102299.82
-20611,15761477.0,Chiang,633.0,France,Female,44.0,4.0,0.0,2.0,1.0,1.0,152417.79
-20612,15652914.0,Maclean,597.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,151902.78
-20613,15658169.0,Clements,641.0,France,Female,37.0,6.0,0.0,1.0,1.0,1.0,137028.51
-20614,15752622.0,Cattaneo,538.0,Germany,Male,44.0,1.0,136659.66,3.0,0.0,0.0,164886.64
-20615,15659325.0,Ch'ang,687.0,France,Male,49.0,2.0,0.0,1.0,0.0,1.0,173870.39
-20616,15663917.0,Hsia,642.0,Germany,Male,35.0,10.0,142871.27,1.0,1.0,0.0,120135.58
-20617,15611331.0,Ch'en,556.0,France,Female,30.0,9.0,0.0,2.0,0.0,1.0,138021.41
-20618,15779915.0,Hsia,686.0,France,Male,29.0,3.0,106415.57,1.0,1.0,0.0,72402.0
-20619,15659736.0,Ch'iu,611.0,France,Male,27.0,9.0,0.0,2.0,0.0,1.0,88852.11
-20620,15728012.0,Davide,646.0,France,Female,37.0,3.0,0.0,1.0,1.0,1.0,86079.67
-20621,15690079.0,Ch'ien,641.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,170634.49
-20622,15769412.0,Nwachukwu,588.0,Spain,Male,32.0,0.0,0.0,2.0,1.0,1.0,122268.75
-20623,15771270.0,Ch'ang,671.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,47880.71
-20624,15756056.0,Pisano,713.0,Germany,Male,41.0,6.0,92316.31,1.0,0.0,0.0,111448.92
-20625,15706537.0,Chu,652.0,Germany,Male,37.0,1.0,96863.13,1.0,1.0,1.0,64831.36
-20626,15680430.0,Chinweuba,769.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,176580.63
-20627,15742238.0,H?,727.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,163911.32
-20628,15731781.0,Chiganu,653.0,France,Male,39.0,5.0,0.0,1.0,1.0,1.0,86414.41
-20629,15605918.0,Chukwukere,629.0,Germany,Female,53.0,6.0,113811.78,1.0,1.0,1.0,74532.02
-20630,15691323.0,Yermakova,661.0,Spain,Female,35.0,7.0,124423.65,1.0,1.0,1.0,77866.92
-20631,15775153.0,Chukwufumnanya,671.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,86678.48
-20632,15662085.0,P'eng,619.0,France,Female,42.0,0.0,0.0,2.0,1.0,1.0,88890.05
-20633,15593736.0,Ch'iu,663.0,France,Male,24.0,1.0,0.0,2.0,1.0,0.0,179403.04
-20634,15728885.0,Yashina,629.0,France,Female,49.0,0.0,0.0,1.0,1.0,1.0,160979.66
-20635,15596914.0,Olisaemeka,642.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,170360.11
-20636,15706637.0,Ugorji,651.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,166310.82
-20637,15757911.0,Trevisano,586.0,Spain,Female,33.0,4.0,0.0,1.0,0.0,0.0,151481.86
-20638,15679991.0,Onyekachukwu,516.0,Germany,Female,33.0,3.0,148363.38,1.0,1.0,0.0,134794.02
-20639,15572792.0,Hsia,641.0,France,Male,29.0,2.0,92636.96,1.0,1.0,0.0,83084.33
-20640,15668818.0,Lucchesi,581.0,France,Male,46.0,2.0,151958.19,1.0,1.0,1.0,72350.17
-20641,15752388.0,Tung,645.0,France,Male,27.0,5.0,0.0,1.0,1.0,1.0,78965.61
-20642,15667944.0,Pagnotto,773.0,France,Female,35.0,3.0,87098.91,2.0,1.0,1.0,121798.93
-20643,15775294.0,Ch'ang,676.0,France,Female,38.0,1.0,120599.38,1.0,1.0,1.0,176768.59
-20644,15754929.0,T'ao,535.0,Germany,Male,41.0,5.0,133467.77,1.0,1.0,0.0,94767.77
-20645,15690164.0,Hs?,754.0,Germany,Male,37.0,0.0,137262.71,1.0,1.0,1.0,71167.17
-20646,15752300.0,Pinto,699.0,France,Male,38.0,4.0,0.0,2.0,0.0,1.0,121451.21
-20647,15634262.0,Nkemakonam,732.0,France,Male,36.0,8.0,132835.56,1.0,1.0,0.0,14081.64
-20648,15677871.0,Nwachinemelu,710.0,France,Male,36.0,9.0,66220.17,1.0,1.0,1.0,111241.37
-20649,15681220.0,Calabresi,516.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,1.0,126585.9
-20650,15792989.0,Kao,725.0,Germany,Female,43.0,6.0,165901.59,2.0,1.0,1.0,60788.61
-20651,15690695.0,Ch'ang,599.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,93881.75
-20652,15791942.0,Pisano,584.0,Germany,Male,32.0,9.0,91522.04,1.0,0.0,0.0,132870.15
-20653,15787597.0,Ch'iu,707.0,France,Male,44.0,4.0,154575.76,1.0,1.0,1.0,163989.66
-20654,15765297.0,Kao,717.0,Spain,Female,52.0,4.0,132623.6,1.0,1.0,1.0,174094.24
-20655,15621451.0,Tung,602.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,54448.41
-20656,15685500.0,Chinomso,718.0,Spain,Female,50.0,7.0,0.0,1.0,1.0,1.0,136859.55
-20657,15663489.0,Chia,554.0,Spain,Male,46.0,8.0,0.0,2.0,1.0,0.0,55853.38
-20658,15729954.0,Hsieh,644.0,Germany,Male,36.0,7.0,93716.72,2.0,1.0,0.0,80120.19
-20659,15781298.0,Hsu,705.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,142937.97
-20660,15682454.0,Macintyre,620.0,Spain,Male,45.0,1.0,0.0,2.0,1.0,0.0,91844.18
-20661,15669927.0,Chienezie,676.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,82662.73
-20662,15798885.0,Yobanna,662.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,175092.4
-20663,15727198.0,Bogle,632.0,France,Male,30.0,3.0,0.0,1.0,1.0,1.0,43889.03
-20664,15652401.0,Liao,645.0,France,Female,23.0,7.0,0.0,2.0,1.0,0.0,160110.65
-20665,15570141.0,Chinwendu,638.0,France,Female,38.0,7.0,0.0,2.0,0.0,1.0,55868.97
-20666,15650591.0,Walker,646.0,France,Male,46.0,1.0,0.0,2.0,1.0,1.0,28257.89
-20667,15641280.0,Fanucci,638.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,94738.54
-20668,15737173.0,Ch'ien,653.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,118740.37
-20669,15662955.0,Shao,682.0,France,Female,48.0,5.0,0.0,2.0,1.0,1.0,121151.1
-20670,15692406.0,Eluemuno,554.0,Germany,Female,49.0,6.0,123845.36,3.0,0.0,1.0,70954.41
-20671,15793046.0,Tien,753.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,166513.49
-20672,15572735.0,Maclean,663.0,France,Female,47.0,7.0,0.0,1.0,0.0,0.0,45414.12
-20673,15641480.0,Nwankwo,656.0,Germany,Male,27.0,8.0,132351.29,1.0,0.0,1.0,111285.85
-20674,15783541.0,Ts'ao,510.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,85982.47
-20675,15673929.0,Chinwemma,707.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,182738.0
-20676,15776947.0,Chiang,710.0,Germany,Female,33.0,5.0,113569.23,2.0,1.0,0.0,95611.47
-20677,15602497.0,Tseng,683.0,France,Female,32.0,3.0,121580.41,1.0,0.0,1.0,107874.39
-20678,15602282.0,Ch'iu,583.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,1.0,160529.71
-20679,15585066.0,Hsiao,638.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,123583.85
-20680,15646726.0,Cremonesi,715.0,France,Male,60.0,7.0,0.0,1.0,1.0,1.0,152992.04
-20681,15675650.0,Lucchesi,596.0,France,Male,28.0,0.0,0.0,2.0,1.0,1.0,90174.84
-20682,15780144.0,Nwankwo,721.0,Spain,Male,52.0,6.0,122351.29,1.0,0.0,1.0,116471.43
-20683,15799790.0,Chukwualuka,624.0,France,Female,29.0,5.0,0.0,3.0,1.0,1.0,27472.07
-20684,15595037.0,Nkemakolam,774.0,France,Male,40.0,5.0,0.0,2.0,0.0,1.0,54871.02
-20685,15690164.0,Milani,576.0,France,Female,25.0,9.0,165896.0,1.0,1.0,0.0,84566.96
-20686,15586880.0,Iadanza,619.0,Spain,Male,39.0,4.0,0.0,2.0,1.0,0.0,160976.85
-20687,15806822.0,K'ung,648.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,176902.83
-20688,15757981.0,Hsing,551.0,Germany,Female,51.0,8.0,104784.23,1.0,1.0,1.0,118545.49
-20689,15721740.0,Kao,605.0,France,Male,54.0,7.0,0.0,2.0,1.0,0.0,5566.6
-20690,15632848.0,Onyeorulu,738.0,France,Female,35.0,2.0,0.0,2.0,1.0,1.0,159863.28
-20691,15808023.0,Yuryeva,753.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,162960.05
-20692,15585823.0,Hs?,538.0,Spain,Female,33.0,0.0,0.0,1.0,1.0,0.0,131283.6
-20693,15727059.0,Nwabugwu,588.0,Spain,Male,33.0,1.0,0.0,1.0,1.0,1.0,94790.34
-20694,15768574.0,Ch'ien,632.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,70790.8
-20695,15738148.0,Chu,682.0,Germany,Male,32.0,8.0,81828.22,2.0,0.0,0.0,119096.63
-20696,15741336.0,Pirozzi,667.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,59645.24
-20697,15646566.0,Rizzo,579.0,France,Female,56.0,6.0,0.0,1.0,1.0,0.0,164741.5
-20698,15783526.0,Tsao,636.0,Germany,Male,40.0,6.0,79460.98,2.0,1.0,1.0,187925.75
-20699,15737354.0,Chuang,653.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,167032.49
-20700,15686963.0,Udegbulam,632.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,182784.11
-20701,15592531.0,Folliero,649.0,France,Male,42.0,7.0,0.0,1.0,1.0,0.0,131718.48
-20702,15662066.0,T'ao,743.0,France,Male,29.0,3.0,95441.27,1.0,1.0,1.0,52894.01
-20703,15652981.0,Yuriev,708.0,France,Female,51.0,8.0,0.0,1.0,0.0,1.0,70778.62
-20704,15758056.0,Kao,486.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,146949.74
-20705,15682868.0,Pagnotto,634.0,France,Male,40.0,8.0,0.0,1.0,0.0,1.0,167984.72
-20706,15674928.0,P'an,775.0,France,Female,37.0,9.0,0.0,2.0,0.0,1.0,121210.09
-20707,15642394.0,Chukwuemeka,749.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,180197.74
-20708,15583090.0,Onwuamaeze,652.0,France,Male,36.0,7.0,0.0,1.0,0.0,1.0,152210.81
-20709,15574068.0,Y?,850.0,Spain,Female,31.0,5.0,123949.86,1.0,1.0,1.0,78854.57
-20710,15675316.0,Ts'ui,753.0,France,Male,41.0,2.0,83714.41,1.0,1.0,0.0,177519.39
-20711,15686909.0,Chukwudi,704.0,France,Male,25.0,2.0,0.0,1.0,1.0,1.0,110114.19
-20712,15715796.0,P'eng,588.0,France,Male,48.0,10.0,0.0,2.0,1.0,0.0,143913.62
-20713,15676937.0,Ting,539.0,Spain,Male,47.0,2.0,0.0,1.0,1.0,0.0,175696.16
-20714,15667460.0,H?,729.0,Spain,Female,51.0,1.0,0.0,1.0,1.0,1.0,193502.9
-20715,15781530.0,Chidumaga,646.0,France,Male,27.0,8.0,0.0,2.0,0.0,0.0,143980.29
-20716,15659234.0,Brown,645.0,France,Female,37.0,8.0,152532.3,1.0,1.0,0.0,97976.75
-20717,15803368.0,Chukwuraenye,635.0,France,Male,33.0,5.0,0.0,1.0,1.0,0.0,70954.41
-20718,15685829.0,Shih,516.0,France,Female,38.0,0.0,0.0,1.0,0.0,1.0,131043.9
-20719,15645371.0,Chikere,696.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,169963.2
-20720,15662532.0,Gorman,619.0,France,Female,28.0,1.0,0.0,1.0,1.0,1.0,127825.25
-20721,15576263.0,Onyemauchechukwu,743.0,Germany,Female,36.0,1.0,141359.27,2.0,1.0,0.0,110253.1
-20722,15693902.0,Toscani,686.0,Germany,Male,33.0,10.0,136576.02,1.0,1.0,0.0,114899.13
-20723,15595537.0,Nwankwo,678.0,France,Male,41.0,2.0,0.0,2.0,0.0,1.0,153387.28
-20724,15583576.0,Yashina,665.0,Spain,Male,30.0,5.0,139852.71,1.0,1.0,1.0,44965.44
-20725,15589420.0,Ibeamaka,641.0,Spain,Male,28.0,4.0,0.0,2.0,1.0,0.0,175324.74
-20726,15676966.0,Ch'ang,718.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,135088.58
-20727,15691792.0,Onyemere,721.0,Spain,Male,51.0,2.0,0.0,1.0,0.0,0.0,83052.03
-20728,15754588.0,Ch'iu,817.0,Spain,Male,40.0,6.0,84445.68,1.0,0.0,1.0,115767.93
-20729,15734005.0,Chidubem,562.0,France,Male,39.0,10.0,0.0,2.0,1.0,1.0,64849.74
-20730,15702499.0,Hsiao,579.0,France,Male,38.0,5.0,0.0,2.0,0.0,0.0,88890.05
-20731,15752816.0,Naylor,591.0,Germany,Male,39.0,7.0,113034.31,2.0,1.0,0.0,171021.44
-20732,15792229.0,Ch'iu,516.0,France,Female,31.0,4.0,98759.89,1.0,1.0,1.0,106660.46
-20733,15706899.0,Nkemdilim,648.0,France,Female,53.0,5.0,107592.23,1.0,1.0,0.0,155876.06
-20734,15687431.0,Yewen,581.0,France,Female,32.0,8.0,98548.62,1.0,0.0,0.0,82709.8
-20735,15657535.0,Onyemauchechukwu,652.0,France,Male,41.0,0.0,0.0,1.0,0.0,0.0,96881.32
-20736,15783875.0,Chizuoke,540.0,France,Male,35.0,1.0,0.0,2.0,0.0,1.0,166677.54
-20737,15720893.0,Chimaobim,549.0,France,Female,46.0,6.0,149961.11,2.0,1.0,0.0,159737.56
-20738,15804771.0,T'ien,655.0,Germany,Male,51.0,6.0,117899.95,1.0,0.0,1.0,140889.31
-20739,15700864.0,Ugochukwutubelum,705.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,0.0,81753.92
-20740,15732758.0,Baresi,624.0,France,Male,40.0,3.0,0.0,2.0,1.0,1.0,156495.56
-20741,15783029.0,Martin,685.0,Germany,Male,47.0,4.0,123783.25,2.0,1.0,0.0,92812.88
-20742,15688880.0,Tsai,775.0,Spain,Female,26.0,4.0,0.0,2.0,1.0,0.0,83681.2
-20743,15709511.0,Udegbulam,526.0,France,Female,40.0,9.0,0.0,2.0,0.0,1.0,143317.37
-20744,15651575.0,Maclean,818.0,France,Female,46.0,5.0,116251.24,1.0,1.0,1.0,156738.82
-20745,15763414.0,Chimezie,706.0,France,Male,49.0,3.0,0.0,1.0,1.0,0.0,62402.38
-20746,15762708.0,Onyekachukwu,556.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,172097.4
-20747,15791877.0,Scott,794.0,Germany,Male,39.0,7.0,93547.23,2.0,0.0,0.0,148887.69
-20748,15668057.0,Tung,712.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,153955.38
-20749,15771728.0,Udegbunam,730.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,166650.9
-20750,15643359.0,Nkemdilim,687.0,France,Female,24.0,10.0,0.0,1.0,1.0,0.0,79888.78
-20751,15592736.0,Chia,637.0,Germany,Female,54.0,4.0,119492.88,1.0,1.0,0.0,170980.86
-20752,15648362.0,Ting,648.0,France,Female,33.0,0.0,0.0,2.0,1.0,1.0,98810.78
-20753,15606097.0,Uchenna,704.0,Spain,Female,38.0,5.0,0.0,1.0,1.0,0.0,60552.44
-20754,15590199.0,Ch'ien,649.0,France,Male,34.0,1.0,0.0,1.0,1.0,1.0,147055.87
-20755,15769955.0,Chinomso,632.0,Germany,Female,38.0,6.0,144855.34,2.0,1.0,1.0,183494.87
-20756,15748589.0,Dyer,734.0,Germany,Male,31.0,9.0,96674.56,1.0,1.0,1.0,64849.74
-20757,15806975.0,Martin,730.0,Germany,Female,32.0,3.0,126679.69,2.0,1.0,1.0,82647.61
-20758,15675450.0,Ponomarev,757.0,France,Male,37.0,4.0,131834.76,1.0,1.0,0.0,65720.92
-20759,15703394.0,Onyemachukwu,592.0,Germany,Male,30.0,9.0,132944.34,1.0,1.0,0.0,117028.6
-20760,15647975.0,Chia,599.0,France,Male,37.0,1.0,0.0,2.0,0.0,0.0,79919.13
-20761,15677020.0,Kolesnikov,637.0,Spain,Male,44.0,1.0,0.0,2.0,0.0,1.0,143598.7
-20762,15643658.0,Chiawuotu,745.0,Germany,Male,36.0,7.0,76510.52,1.0,1.0,0.0,127528.84
-20763,15576526.0,Ibezimako,648.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,46786.92
-20764,15707007.0,Chikelu,579.0,Spain,Female,45.0,0.0,102945.01,1.0,1.0,0.0,182336.45
-20765,15800515.0,Ch'eng,619.0,France,Female,32.0,8.0,144428.99,1.0,1.0,0.0,35687.43
-20766,15781298.0,Hargreaves,776.0,Germany,Male,41.0,4.0,139872.81,2.0,1.0,1.0,164074.89
-20767,15793764.0,Ting,678.0,Germany,Male,49.0,1.0,132088.59,2.0,1.0,0.0,166677.35
-20768,15722448.0,Hsiao,695.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,185827.74
-20769,15717307.0,Fennell,646.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,1.0,91573.19
-20770,15789563.0,Chukwualuka,645.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,152643.48
-20771,15803750.0,K'ung,790.0,France,Male,37.0,2.0,0.0,1.0,1.0,1.0,125870.5
-20772,15762984.0,Cremonesi,682.0,France,Female,39.0,4.0,0.0,2.0,1.0,1.0,162719.69
-20773,15686983.0,Okechukwu,726.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,158071.69
-20774,15664793.0,Ch'ang,622.0,France,Male,32.0,9.0,106116.5,1.0,1.0,1.0,60906.12
-20775,15809100.0,Chibuzo,661.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,59249.83
-20776,15775803.0,Groom,748.0,France,Female,48.0,8.0,0.0,2.0,1.0,1.0,53814.71
-20777,15681406.0,Nwachinemelu,605.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,119782.89
-20778,15757913.0,Hsia,609.0,France,Male,36.0,6.0,141547.26,1.0,1.0,1.0,132673.23
-20779,15793322.0,P'eng,584.0,France,Female,55.0,4.0,0.0,1.0,1.0,1.0,46786.92
-20780,15602551.0,Hsiung,699.0,France,Female,58.0,1.0,143966.3,1.0,1.0,0.0,152210.81
-20781,15775293.0,Pisano,667.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,121440.8
-20782,15623595.0,Lucciano,598.0,France,Male,37.0,6.0,121520.37,2.0,1.0,0.0,102930.46
-20783,15642098.0,Hargreaves,714.0,Germany,Male,48.0,3.0,129667.77,1.0,1.0,1.0,138350.74
-20784,15622182.0,Ting,592.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,1.0,60552.44
-20785,15756975.0,Nnaife,691.0,France,Male,31.0,9.0,0.0,2.0,0.0,1.0,19500.1
-20786,15726234.0,Ch'eng,530.0,France,Female,56.0,3.0,0.0,1.0,1.0,0.0,107948.72
-20787,15706810.0,McElhone,567.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,36308.93
-20788,15632446.0,Li Fonti,685.0,France,Female,42.0,6.0,0.0,1.0,1.0,1.0,140166.95
-20789,15710257.0,H?,719.0,Germany,Male,50.0,9.0,148962.76,1.0,1.0,1.0,158887.09
-20790,15771054.0,Ponomaryov,708.0,Spain,Male,28.0,1.0,171671.9,1.0,1.0,0.0,41967.03
-20791,15642636.0,Martin,675.0,France,Female,44.0,8.0,0.0,1.0,1.0,1.0,75927.35
-20792,15758031.0,Toscani,596.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,87060.77
-20793,15680643.0,Okagbue,598.0,Spain,Male,30.0,1.0,0.0,2.0,1.0,1.0,9984.4
-20794,15777045.0,Zuev,710.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,180427.24
-20795,15589296.0,Whitehead,567.0,France,Male,25.0,9.0,0.0,1.0,1.0,1.0,57876.05
-20796,15634141.0,Tien,678.0,Germany,Male,43.0,7.0,120688.61,1.0,0.0,0.0,125240.8
-20797,15751549.0,Hs?,590.0,Spain,Male,50.0,2.0,0.0,2.0,1.0,0.0,120511.03
-20798,15598782.0,T'ien,625.0,France,Male,29.0,10.0,0.0,2.0,0.0,0.0,85890.75
-20799,15682600.0,Ch'eng,619.0,France,Female,67.0,1.0,0.0,2.0,1.0,1.0,120108.72
-20800,15597951.0,Nnonso,549.0,Germany,Female,49.0,3.0,117325.53,2.0,1.0,0.0,131712.52
-20801,15667093.0,Chimaoke,645.0,France,Female,55.0,9.0,141234.98,1.0,1.0,1.0,187597.98
-20802,15618661.0,Tuan,613.0,France,Male,35.0,4.0,0.0,1.0,0.0,0.0,61623.19
-20803,15723216.0,Soto,681.0,Germany,Female,51.0,2.0,109712.85,1.0,1.0,1.0,50610.79
-20804,15682472.0,Palermo,695.0,Spain,Female,22.0,3.0,0.0,1.0,0.0,1.0,145894.9
-20805,15617486.0,Iadanza,679.0,France,Female,26.0,10.0,113567.94,1.0,1.0,0.0,115916.55
-20806,15753874.0,Fiorentino,605.0,France,Male,44.0,4.0,125719.69,1.0,1.0,0.0,57844.69
-20807,15745012.0,Mazzanti,682.0,France,Female,33.0,10.0,0.0,2.0,0.0,0.0,132673.23
-20808,15764537.0,Trevisani,712.0,Germany,Male,34.0,4.0,170890.05,2.0,1.0,0.0,134822.04
-20809,15617507.0,Chukwuemeka,537.0,France,Female,32.0,3.0,0.0,1.0,1.0,0.0,178268.36
-20810,15791279.0,Tien,644.0,Germany,Male,33.0,2.0,78892.23,1.0,1.0,1.0,121402.53
-20811,15651022.0,Onyemauchechukwu,605.0,France,Female,28.0,1.0,127343.4,1.0,1.0,0.0,106761.47
-20812,15628205.0,Ch'eng,704.0,Spain,Male,40.0,2.0,152400.51,1.0,1.0,1.0,99843.98
-20813,15694849.0,Chia,651.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,184524.65
-20814,15760090.0,Trevisano,648.0,France,Female,30.0,5.0,141786.78,1.0,1.0,0.0,171060.01
-20815,15772798.0,Brient,684.0,Spain,Female,41.0,6.0,0.0,1.0,0.0,0.0,184954.11
-20816,15742886.0,Fanucci,667.0,France,Male,34.0,8.0,0.0,1.0,0.0,0.0,75664.56
-20817,15695097.0,Eiland,775.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,125962.02
-20818,15806918.0,Chukwuebuka,685.0,Spain,Male,19.0,8.0,0.0,2.0,1.0,1.0,115767.93
-20819,15613787.0,T'ien,611.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,103713.93
-20820,15694829.0,Ch'eng,683.0,Germany,Female,44.0,1.0,155413.07,2.0,1.0,0.0,136028.99
-20821,15589420.0,Y?an,632.0,France,Female,62.0,7.0,0.0,1.0,1.0,1.0,133086.48
-20822,15806945.0,K?,668.0,Germany,Female,28.0,9.0,148045.45,1.0,1.0,1.0,179908.71
-20823,15783097.0,Hsu,695.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,166650.9
-20824,15603743.0,Frolov,696.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,94822.47
-20825,15684865.0,Nwankwo,684.0,Germany,Female,38.0,0.0,98373.26,1.0,1.0,1.0,132770.15
-20826,15663883.0,Shubin,567.0,France,Male,39.0,8.0,0.0,2.0,0.0,0.0,150661.42
-20827,15775235.0,Hsia,575.0,Germany,Male,38.0,7.0,92340.69,1.0,1.0,1.0,60728.89
-20828,15605918.0,McGregor,674.0,France,Female,41.0,5.0,0.0,2.0,0.0,0.0,147059.16
-20829,15802372.0,Ch'ien,709.0,France,Female,29.0,6.0,0.0,1.0,1.0,0.0,156910.46
-20830,15657535.0,Chikelu,678.0,Germany,Female,41.0,9.0,71264.02,1.0,1.0,0.0,164075.51
-20831,15659931.0,Milani,583.0,Spain,Male,39.0,2.0,0.0,1.0,1.0,1.0,106761.47
-20832,15606841.0,Lucchesi,705.0,France,Female,36.0,8.0,0.0,2.0,0.0,0.0,86044.98
-20833,15805413.0,Lucciano,645.0,Spain,Female,44.0,8.0,0.0,1.0,1.0,0.0,143960.72
-20834,15797919.0,Yudina,767.0,Germany,Male,44.0,8.0,91285.22,1.0,1.0,0.0,64907.48
-20835,15731835.0,Chidiebere,474.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,102577.92
-20836,15636592.0,Chijioke,590.0,Germany,Male,47.0,6.0,107508.93,1.0,0.0,0.0,120801.65
-20837,15793861.0,Lai,749.0,France,Male,34.0,1.0,126817.65,2.0,1.0,1.0,126812.63
-20838,15687852.0,Fanucci,573.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,182528.44
-20839,15585599.0,Palermo,715.0,France,Male,46.0,3.0,0.0,2.0,1.0,1.0,126812.63
-20840,15785576.0,T'ien,701.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,125877.22
-20841,15622774.0,Ch'iu,683.0,France,Male,32.0,2.0,0.0,1.0,1.0,0.0,49594.02
-20842,15717290.0,Onyekachi,681.0,France,Male,58.0,9.0,123971.51,1.0,1.0,1.0,181449.74
-20843,15640106.0,Tuan,679.0,Spain,Male,30.0,9.0,131287.36,1.0,1.0,1.0,61513.67
-20844,15692599.0,Pisani,687.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,125435.81
-20845,15619116.0,Y?an,697.0,France,Female,47.0,9.0,90560.3,1.0,1.0,0.0,139266.48
-20846,15595842.0,Toscani,624.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,172967.87
-20847,15802220.0,Onyekaozulu,730.0,France,Male,37.0,6.0,125724.95,1.0,1.0,0.0,161453.62
-20848,15609510.0,P'eng,647.0,Spain,Female,22.0,8.0,0.0,2.0,0.0,1.0,61761.77
-20849,15703763.0,Chien,695.0,France,Female,32.0,7.0,0.0,2.0,0.0,1.0,61538.43
-20850,15582841.0,Trevisani,687.0,France,Male,38.0,8.0,141616.55,1.0,1.0,1.0,137028.51
-20851,15605918.0,Nwabugwu,732.0,Germany,Female,37.0,2.0,127864.4,2.0,0.0,1.0,144880.81
-20852,15775663.0,Maclean,679.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,131919.48
-20853,15583724.0,Nnamutaezinwa,653.0,Spain,Male,39.0,3.0,125909.85,1.0,1.0,1.0,149740.22
-20854,15603088.0,Ni,714.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,159418.1
-20855,15692226.0,Udinesi,730.0,France,Male,39.0,1.0,73070.18,1.0,0.0,0.0,106922.42
-20856,15806937.0,Milne,561.0,Germany,Male,39.0,9.0,154737.88,1.0,0.0,1.0,185537.76
-20857,15773731.0,Hsia,589.0,France,Male,44.0,5.0,0.0,1.0,1.0,0.0,138519.71
-20858,15789563.0,Kumm,686.0,France,Female,56.0,7.0,0.0,1.0,1.0,0.0,43982.41
-20859,15791268.0,Ugorji,678.0,France,Male,42.0,6.0,0.0,2.0,0.0,1.0,112605.9
-20860,15688085.0,Ndubuagha,711.0,Germany,Male,33.0,9.0,138523.2,1.0,0.0,1.0,133653.62
-20861,15639303.0,Yobachi,717.0,France,Female,35.0,4.0,0.0,2.0,1.0,0.0,142223.94
-20862,15784099.0,Yudin,627.0,Germany,Female,26.0,6.0,122314.5,1.0,1.0,1.0,64849.74
-20863,15794345.0,Ch'ien,614.0,Spain,Male,39.0,5.0,0.0,1.0,1.0,0.0,161465.31
-20864,15682600.0,Pisano,576.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,1.0,4272.0
-20865,15761950.0,Mancini,633.0,France,Female,30.0,1.0,132876.55,1.0,0.0,0.0,152698.24
-20866,15693904.0,T'ao,584.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,0.0,96660.76
-20867,15683483.0,Chuang,611.0,France,Male,30.0,3.0,0.0,2.0,0.0,0.0,149807.01
-20868,15731751.0,Nnaife,684.0,France,Female,48.0,7.0,0.0,1.0,1.0,0.0,171378.77
-20869,15761158.0,K?,476.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,71047.51
-20870,15791533.0,Chidubem,634.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,91574.92
-20871,15766427.0,Onwuamaeze,850.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,176925.69
-20872,15687526.0,Mazzanti,714.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,1.0,62845.32
-20873,15796969.0,Trevisano,731.0,France,Male,33.0,8.0,128940.24,1.0,1.0,1.0,138191.81
-20874,15665096.0,Chiu,605.0,Germany,Male,52.0,8.0,111096.91,2.0,1.0,0.0,137898.57
-20875,15576723.0,Hsieh,661.0,Spain,Female,43.0,3.0,116993.43,1.0,1.0,1.0,145537.32
-20876,15792388.0,Gadsden,694.0,Spain,Male,42.0,8.0,95984.21,1.0,1.0,1.0,111868.23
-20877,15592761.0,Tuan,746.0,Germany,Female,34.0,2.0,118626.55,1.0,1.0,1.0,173776.5
-20878,15592999.0,Zito,591.0,France,Male,33.0,2.0,143835.52,1.0,1.0,1.0,187810.74
-20879,15619016.0,Cattaneo,584.0,Spain,Male,23.0,1.0,0.0,2.0,0.0,0.0,172668.39
-20880,15722533.0,Nkemdilim,609.0,France,Female,29.0,6.0,0.0,2.0,0.0,0.0,110520.48
-20881,15670163.0,Chizuoke,745.0,Spain,Female,38.0,6.0,133707.18,1.0,1.0,1.0,113481.02
-20882,15581295.0,Mactier,635.0,Germany,Male,46.0,4.0,113873.22,1.0,1.0,1.0,148515.61
-20883,15676521.0,Ch'iu,615.0,France,Male,34.0,3.0,121669.23,1.0,1.0,1.0,122925.44
-20884,15776824.0,Macleod,683.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,164061.6
-20885,15629390.0,Ibezimako,655.0,France,Male,36.0,3.0,115341.19,1.0,1.0,1.0,9684.52
-20886,15646539.0,Olisaemeka,688.0,France,Male,41.0,4.0,88977.39,1.0,1.0,0.0,71179.31
-20887,15721793.0,Milne,684.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,61936.1
-20888,15668323.0,Chidiebere,649.0,France,Male,42.0,3.0,117307.58,1.0,1.0,1.0,120107.1
-20889,15583770.0,Onwuka,635.0,France,Male,33.0,3.0,165438.26,1.0,1.0,1.0,199392.14
-20890,15590097.0,Bogle,627.0,France,Male,23.0,4.0,0.0,1.0,1.0,0.0,170829.99
-20891,15674573.0,Pinto,678.0,France,Male,33.0,1.0,139973.65,1.0,1.0,0.0,166476.95
-20892,15705918.0,Kao,754.0,France,Male,24.0,8.0,0.0,1.0,1.0,0.0,94544.88
-20893,15592999.0,Yermakova,743.0,Germany,Male,40.0,2.0,153972.89,1.0,1.0,0.0,110776.01
-20894,15703394.0,Genovesi,678.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,66721.98
-20895,15690958.0,Genovesi,730.0,France,Female,37.0,1.0,111061.01,1.0,1.0,1.0,51117.06
-20896,15688071.0,Maclean,679.0,France,Female,50.0,4.0,0.0,1.0,1.0,1.0,150659.35
-20897,15598044.0,Ch'ien,850.0,Spain,Male,22.0,1.0,0.0,2.0,0.0,1.0,57851.8
-20898,15638231.0,Tisdall,850.0,France,Female,39.0,8.0,0.0,1.0,1.0,1.0,168034.9
-20899,15672594.0,Aksenov,644.0,France,Female,44.0,7.0,0.0,2.0,0.0,0.0,163317.5
-20900,15641413.0,Iweobiegbunam,807.0,France,Male,32.0,4.0,116320.68,1.0,1.0,0.0,167984.72
-20901,15796790.0,Kao,642.0,Germany,Male,35.0,4.0,91070.34,2.0,1.0,1.0,193502.93
-20902,15797919.0,Onwuamaeze,817.0,Germany,Female,34.0,6.0,81523.38,1.0,1.0,0.0,59981.62
-20903,15758031.0,K?,587.0,Germany,Female,48.0,7.0,123969.96,1.0,1.0,1.0,94872.45
-20904,15589428.0,Hazon,652.0,Spain,Female,30.0,2.0,0.0,2.0,1.0,0.0,151336.96
-20905,15589660.0,Onyemauchechukwu,628.0,France,Female,38.0,7.0,0.0,2.0,0.0,0.0,110371.84
-20906,15572461.0,Ts'ui,735.0,France,Male,36.0,4.0,0.0,1.0,0.0,1.0,89891.4
-20907,15576644.0,Fancher,652.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,152422.91
-20908,15644896.0,Kao,594.0,France,Male,28.0,9.0,0.0,2.0,1.0,1.0,137739.02
-20909,15587914.0,Ch'ang,609.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,80425.65
-20910,15690130.0,Aksenov,662.0,France,Female,46.0,10.0,0.0,2.0,1.0,0.0,80552.36
-20911,15759913.0,Lucciano,686.0,France,Female,43.0,5.0,0.0,1.0,1.0,1.0,94845.45
-20912,15733444.0,Kao,648.0,Spain,Male,29.0,7.0,0.0,2.0,1.0,1.0,140889.39
-20913,15590820.0,Tuan,641.0,France,Male,52.0,8.0,107907.34,1.0,1.0,0.0,177670.57
-20914,15591711.0,Brown,755.0,France,Male,42.0,3.0,125529.55,1.0,1.0,0.0,174032.0
-20915,15686963.0,Aksenov,614.0,Germany,Male,38.0,8.0,181671.14,2.0,0.0,1.0,106894.34
-20916,15795874.0,L?,743.0,France,Male,37.0,1.0,0.0,2.0,0.0,1.0,179655.87
-20917,15720644.0,Palerma,589.0,Spain,Male,28.0,10.0,122265.18,1.0,1.0,0.0,60536.56
-20918,15793331.0,Nnonso,678.0,France,Female,29.0,6.0,108541.04,1.0,1.0,1.0,128892.66
-20919,15753837.0,Ch'iu,667.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,148527.56
-20920,15758775.0,Chinweuba,762.0,Spain,Female,25.0,9.0,125692.03,1.0,1.0,0.0,147090.9
-20921,15767594.0,Liao,652.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,1.0,104930.46
-20922,15682778.0,Lombardo,741.0,Germany,Male,46.0,5.0,109632.85,1.0,1.0,0.0,136079.64
-20923,15649523.0,Genovese,719.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,48869.67
-20924,15572706.0,Chukwudi,603.0,Spain,Male,53.0,7.0,0.0,1.0,0.0,0.0,115116.35
-20925,15754929.0,Hsiung,564.0,Spain,Male,35.0,9.0,107569.96,1.0,1.0,0.0,53931.05
-20926,15655274.0,Lo Duca,668.0,Germany,Female,50.0,5.0,112727.06,3.0,1.0,0.0,159463.8
-20927,15693637.0,Lucchesi,743.0,Germany,Female,22.0,4.0,121207.66,2.0,0.0,1.0,142903.44
-20928,15687913.0,Calabresi,790.0,Germany,Male,31.0,5.0,127480.57,1.0,0.0,0.0,174729.3
-20929,15596713.0,Pinto,639.0,France,Male,43.0,6.0,0.0,1.0,1.0,1.0,185827.74
-20930,15773876.0,Chidi,646.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,183494.87
-20931,15571221.0,Manna,706.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,0.0,135875.51
-20932,15796074.0,Nnachetam,679.0,France,Female,29.0,7.0,0.0,2.0,1.0,1.0,176779.46
-20933,15597901.0,Fries,543.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,180553.48
-20934,15593969.0,Frolov,714.0,Spain,Male,44.0,3.0,0.0,2.0,1.0,1.0,120847.13
-20935,15724339.0,Chidimma,707.0,France,Male,37.0,4.0,99169.67,1.0,1.0,0.0,60920.03
-20936,15665416.0,Obiajulu,714.0,Germany,Female,47.0,4.0,115988.86,1.0,1.0,0.0,72402.0
-20937,15645571.0,Chukwuemeka,713.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,38252.25
-20938,15694566.0,Hsiao,645.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,53477.76
-20939,15589401.0,Nnaife,712.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,1.0,100538.8
-20940,15652048.0,Ch'iu,704.0,France,Female,38.0,5.0,0.0,1.0,1.0,0.0,140880.75
-20941,15651955.0,Hargreaves,632.0,Spain,Male,39.0,2.0,0.0,2.0,1.0,1.0,117832.88
-20942,15592091.0,Yeh,581.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,63736.17
-20943,15667216.0,P'an,602.0,France,Female,51.0,9.0,0.0,2.0,1.0,1.0,139946.68
-20944,15576723.0,Chukwujekwu,635.0,Germany,Male,52.0,1.0,137482.19,1.0,1.0,1.0,27308.58
-20945,15751167.0,Ting,711.0,France,Male,42.0,2.0,0.0,2.0,0.0,1.0,74510.65
-20946,15684960.0,Chiang,730.0,Spain,Female,51.0,4.0,167983.17,1.0,1.0,0.0,163570.25
-20947,15670345.0,Onuora,586.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,79577.4
-20948,15586300.0,Aksenova,559.0,France,Female,30.0,3.0,133800.98,1.0,1.0,1.0,88958.49
-20949,15750476.0,Tseng,597.0,France,Male,32.0,4.0,0.0,1.0,0.0,0.0,99843.98
-20950,15611189.0,Trevisani,521.0,Germany,Male,32.0,4.0,128206.79,2.0,1.0,1.0,93718.16
-20951,15792646.0,Toscani,739.0,France,Male,30.0,6.0,148481.09,1.0,1.0,0.0,131097.9
-20952,15693322.0,Chialuka,689.0,Spain,Female,51.0,6.0,0.0,1.0,1.0,1.0,80963.92
-20953,15664720.0,Y?,584.0,France,Male,40.0,7.0,82547.94,1.0,1.0,1.0,92647.94
-20954,15695585.0,Illingworth,651.0,France,Male,28.0,2.0,133658.24,1.0,1.0,0.0,176712.51
-20955,15709531.0,Onio,674.0,Spain,Male,60.0,1.0,0.0,2.0,1.0,1.0,108528.72
-20956,15764708.0,O'Donnell,613.0,France,Female,27.0,10.0,0.0,2.0,1.0,0.0,96823.32
-20957,15770291.0,Hao,573.0,France,Male,39.0,10.0,125193.96,1.0,1.0,1.0,46811.77
-20958,15641877.0,Chiedozie,683.0,Germany,Male,35.0,2.0,122486.47,2.0,1.0,0.0,99017.34
-20959,15652400.0,Yeh,753.0,Spain,Female,45.0,4.0,0.0,1.0,0.0,1.0,107499.66
-20960,15583724.0,Ngozichukwuka,597.0,France,Male,31.0,5.0,0.0,1.0,0.0,1.0,156048.64
-20961,15791780.0,P'an,564.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,174435.48
-20962,15793813.0,White,677.0,France,Female,33.0,5.0,91680.1,1.0,1.0,0.0,131662.47
-20963,15690620.0,Chukwualuka,792.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,91903.9
-20964,15794479.0,Bazarova,730.0,Germany,Male,30.0,4.0,151191.31,1.0,1.0,1.0,12128.69
-20965,15706904.0,Walker,629.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,71830.1
-20966,15640034.0,Chinomso,763.0,Germany,Male,39.0,10.0,51122.64,1.0,1.0,0.0,57871.34
-20967,15690772.0,Chu,591.0,Spain,Male,37.0,1.0,0.0,1.0,1.0,0.0,85421.48
-20968,15802560.0,K?,649.0,France,Male,35.0,7.0,88915.85,1.0,0.0,0.0,121592.5
-20969,15813144.0,Ojiofor,773.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,9984.4
-20970,15784597.0,Tsao,669.0,Spain,Female,27.0,9.0,0.0,2.0,1.0,1.0,64539.64
-20971,15784536.0,Folliero,678.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,181224.56
-20972,15606887.0,Ritchie,621.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,1.0,53630.9
-20973,15723006.0,Eluemuno,641.0,France,Female,29.0,9.0,0.0,2.0,0.0,0.0,161519.77
-20974,15802585.0,Li,744.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,27380.99
-20975,15749489.0,Hsueh,739.0,France,Male,38.0,4.0,0.0,1.0,0.0,0.0,89891.4
-20976,15656912.0,Tuan,733.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,64448.36
-20977,15803790.0,Nwankwo,721.0,Germany,Male,39.0,5.0,141252.18,1.0,1.0,1.0,131736.23
-20978,15671591.0,Chigolum,677.0,France,Male,34.0,8.0,0.0,1.0,0.0,0.0,69052.87
-20979,15659002.0,Pirozzi,633.0,France,Male,31.0,7.0,106301.85,1.0,1.0,1.0,120853.83
-20980,15593897.0,H?,687.0,France,Male,43.0,7.0,122088.67,1.0,1.0,0.0,174311.3
-20981,15633260.0,Nwora,707.0,France,Female,32.0,6.0,105858.71,1.0,1.0,1.0,143317.18
-20982,15809654.0,Chizuoke,648.0,France,Male,38.0,0.0,0.0,2.0,1.0,1.0,129964.94
-20983,15651389.0,H?,750.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,133571.23
-20984,15586870.0,Nucci,644.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,180962.32
-20985,15795586.0,Ritchie,731.0,Spain,Female,36.0,10.0,0.0,2.0,0.0,1.0,64323.24
-20986,15674583.0,Chiang,609.0,Spain,Female,42.0,4.0,0.0,2.0,0.0,0.0,140941.47
-20987,15679394.0,Onwuamaeze,585.0,France,Female,55.0,7.0,0.0,1.0,1.0,0.0,143393.24
-20988,15571843.0,Ch'iu,739.0,Spain,Male,49.0,9.0,0.0,1.0,1.0,0.0,73881.68
-20989,15681843.0,Lo,718.0,France,Female,29.0,6.0,0.0,1.0,1.0,1.0,35199.21
-20990,15659399.0,Hsieh,706.0,Germany,Male,44.0,6.0,123374.46,1.0,1.0,0.0,85733.33
-20991,15646513.0,Baresi,661.0,Spain,Female,42.0,7.0,0.0,3.0,1.0,1.0,171519.06
-20992,15762048.0,Hs?,850.0,France,Female,32.0,3.0,0.0,1.0,0.0,1.0,172557.77
-20993,15609623.0,T'ao,739.0,France,Male,32.0,10.0,0.0,2.0,1.0,0.0,129912.74
-20994,15727408.0,Ch'en,641.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,142548.33
-20995,15572626.0,Kao,584.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,175082.2
-20996,15588928.0,Yobachukwu,611.0,Spain,Male,36.0,7.0,121558.46,1.0,1.0,1.0,199392.14
-20997,15777045.0,O'Brien,594.0,France,Male,37.0,5.0,0.0,1.0,1.0,1.0,184796.84
-20998,15739194.0,Pisani,756.0,Germany,Male,30.0,3.0,125877.65,1.0,0.0,0.0,175894.24
-20999,15806918.0,Mazzanti,781.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,181893.31
-21000,15685920.0,Onwubiko,725.0,Germany,Female,60.0,2.0,145580.61,1.0,0.0,0.0,14379.01
-21001,15695034.0,Clements,593.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,88878.15
-21002,15594878.0,Ponomaryova,682.0,France,Female,49.0,9.0,185891.54,1.0,1.0,0.0,1173.23
-21003,15785078.0,Nwachukwu,720.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,111190.53
-21004,15773591.0,Chia,619.0,Germany,Female,38.0,9.0,109052.59,1.0,1.0,0.0,136435.75
-21005,15762928.0,Chukwukadibia,669.0,Germany,Male,35.0,9.0,97473.87,2.0,1.0,1.0,168840.33
-21006,15790299.0,Nnaife,669.0,Germany,Female,46.0,5.0,104449.8,1.0,1.0,0.0,166516.84
-21007,15690741.0,Ch'eng,749.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,163558.36
-21008,15652674.0,Lo Duca,648.0,France,Female,39.0,3.0,0.0,2.0,1.0,1.0,182038.6
-21009,15693326.0,Dore,628.0,Germany,Female,29.0,3.0,141819.02,2.0,0.0,0.0,74846.67
-21010,15799128.0,Hsieh,535.0,France,Female,46.0,10.0,0.0,1.0,1.0,0.0,146891.07
-21011,15729489.0,Lai,641.0,Germany,Male,40.0,5.0,82931.85,2.0,1.0,0.0,139093.88
-21012,15591552.0,Fanucci,667.0,Spain,Male,35.0,7.0,133964.3,1.0,0.0,1.0,189678.7
-21013,15721460.0,Chiemenam,575.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,58879.31
-21014,15705050.0,H?,663.0,France,Male,33.0,4.0,0.0,1.0,1.0,0.0,168816.22
-21015,15720354.0,Nwora,678.0,France,Male,35.0,9.0,0.0,1.0,1.0,0.0,38587.7
-21016,15674929.0,Bitter,705.0,France,Male,37.0,9.0,54901.01,1.0,1.0,1.0,124571.09
-21017,15804822.0,Ch'ang,809.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,151401.33
-21018,15801247.0,Mazzanti,732.0,France,Male,27.0,4.0,0.0,2.0,1.0,0.0,152298.28
-21019,15673971.0,L?,544.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,147075.69
-21020,15737647.0,Chiazagomekpele,549.0,France,Male,37.0,3.0,157559.7,1.0,1.0,1.0,149614.17
-21021,15679991.0,Teng,632.0,France,Male,29.0,5.0,123845.36,1.0,0.0,0.0,55893.33
-21022,15671241.0,T'an,658.0,Germany,Male,44.0,1.0,118907.6,1.0,0.0,0.0,147822.44
-21023,15730759.0,Chiganu,592.0,France,Male,42.0,1.0,0.0,2.0,1.0,1.0,89543.25
-21024,15590011.0,Hs?eh,684.0,Germany,Female,41.0,1.0,105864.6,1.0,1.0,0.0,119333.95
-21025,15662091.0,Hs?,681.0,Germany,Male,25.0,7.0,91537.88,1.0,1.0,0.0,116973.48
-21026,15766417.0,Scott,582.0,France,Female,35.0,8.0,0.0,1.0,1.0,1.0,182784.11
-21027,15623566.0,Lucchesi,655.0,France,Male,52.0,2.0,0.0,1.0,0.0,0.0,132467.54
-21028,15718789.0,Hsia,683.0,France,Female,47.0,1.0,0.0,1.0,1.0,1.0,184312.88
-21029,15612103.0,Ch'in,685.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,161579.85
-21030,15752507.0,Rolon,593.0,France,Female,24.0,9.0,0.0,1.0,0.0,1.0,48233.75
-21031,15636016.0,Okwuadigbo,569.0,Germany,Male,49.0,7.0,133616.39,1.0,0.0,1.0,177670.57
-21032,15670163.0,Shih,597.0,Spain,Female,40.0,6.0,0.0,2.0,1.0,0.0,121210.09
-21033,15701200.0,Kibble,583.0,France,Female,44.0,10.0,0.0,1.0,1.0,0.0,161559.12
-21034,15607986.0,Ts'ui,733.0,France,Female,40.0,0.0,0.0,3.0,1.0,0.0,153383.54
-21035,15758031.0,Y?,635.0,Spain,Female,35.0,9.0,0.0,2.0,1.0,0.0,176743.51
-21036,15783338.0,McIntyre,656.0,France,Female,47.0,7.0,0.0,1.0,1.0,0.0,94872.45
-21037,15578515.0,Hsing,593.0,France,Female,35.0,4.0,0.0,1.0,1.0,0.0,55867.38
-21038,15684960.0,Ch'ang,678.0,Spain,Female,28.0,2.0,0.0,2.0,1.0,0.0,142331.85
-21039,15658889.0,Chiu,521.0,Germany,Female,49.0,5.0,116968.91,1.0,1.0,0.0,102073.96
-21040,15588944.0,Ch'eng,603.0,Spain,Female,36.0,3.0,0.0,2.0,1.0,1.0,175722.31
-21041,15705001.0,Riggs,554.0,France,Male,35.0,2.0,0.0,1.0,1.0,1.0,189414.74
-21042,15727177.0,Chukwudi,696.0,France,Male,45.0,5.0,0.0,2.0,1.0,0.0,166588.76
-21043,15583736.0,Chinedum,758.0,Spain,Male,41.0,6.0,0.0,2.0,0.0,1.0,136079.64
-21044,15713578.0,Hsiao,619.0,France,Female,61.0,0.0,0.0,1.0,1.0,1.0,85891.02
-21045,15579820.0,Hsia,557.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,128521.16
-21046,15781702.0,Chinomso,595.0,Germany,Male,38.0,8.0,131394.56,1.0,1.0,0.0,85522.84
-21047,15783541.0,Lettiere,609.0,France,Female,48.0,8.0,0.0,1.0,0.0,0.0,151401.33
-21048,15740264.0,Mistry,718.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,84760.32
-21049,15701291.0,Chidumaga,717.0,Germany,Male,58.0,1.0,115341.19,1.0,1.0,0.0,94790.34
-21050,15741032.0,Hsueh,653.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,84584.67
-21051,15815490.0,Chukwukadibia,713.0,France,Female,41.0,3.0,131753.11,1.0,0.0,0.0,121551.93
-21052,15737782.0,Ch'eng,652.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,98896.06
-21053,15771535.0,Calabresi,707.0,Spain,Female,61.0,2.0,0.0,2.0,0.0,1.0,60533.96
-21054,15781598.0,Efremov,725.0,France,Female,51.0,5.0,0.0,2.0,1.0,0.0,1055.66
-21055,15701605.0,Y?an,758.0,France,Female,40.0,3.0,0.0,2.0,1.0,1.0,106785.4
-21056,15662865.0,Frolova,749.0,France,Female,30.0,4.0,141541.25,1.0,1.0,0.0,167538.66
-21057,15595588.0,Hsing,591.0,France,Female,58.0,4.0,0.0,1.0,0.0,0.0,140889.12
-21058,15664793.0,Lettiere,729.0,France,Female,37.0,10.0,0.0,2.0,1.0,1.0,106663.46
-21059,15595588.0,Fiorentini,645.0,France,Female,54.0,3.0,0.0,1.0,1.0,1.0,138350.74
-21060,15606435.0,Hsing,699.0,Germany,Female,44.0,2.0,115625.07,1.0,1.0,0.0,70523.92
-21061,15684722.0,Onyekaozulu,730.0,Germany,Male,29.0,4.0,91857.66,1.0,0.0,0.0,42012.55
-21062,15602551.0,Chinomso,583.0,France,Female,27.0,9.0,0.0,2.0,1.0,1.0,99654.13
-21063,15771750.0,Pirozzi,729.0,Spain,Male,38.0,2.0,90547.48,1.0,1.0,1.0,134526.87
-21064,15773421.0,Okechukwu,695.0,Germany,Male,46.0,1.0,123694.42,1.0,0.0,1.0,80618.47
-21065,15672640.0,Shih,663.0,Spain,Female,28.0,8.0,0.0,1.0,0.0,0.0,94878.1
-21066,15594084.0,Kao,731.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,60533.96
-21067,15631406.0,Genovese,663.0,Spain,Male,31.0,4.0,0.0,2.0,0.0,1.0,174311.3
-21068,15656484.0,Nnonso,699.0,France,Female,37.0,3.0,171490.57,1.0,1.0,0.0,121592.5
-21069,15753831.0,Hsueh,535.0,France,Male,33.0,2.0,101579.71,1.0,0.0,0.0,182855.21
-21070,15813504.0,Olisaemeka,744.0,France,Female,35.0,1.0,0.0,2.0,1.0,0.0,143859.66
-21071,15601383.0,Yermolayev,644.0,France,Female,42.0,1.0,0.0,1.0,1.0,1.0,152054.33
-21072,15796961.0,Clements,667.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,75969.19
-21073,15780561.0,Yudin,587.0,France,Male,49.0,5.0,0.0,2.0,1.0,0.0,62403.44
-21074,15789379.0,Mazzanti,554.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,174729.3
-21075,15735388.0,Chinomso,738.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,0.0,44965.54
-21076,15686588.0,Pagnotto,714.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,93874.87
-21077,15696744.0,Onyemauchechukwu,738.0,Germany,Male,32.0,8.0,123860.18,1.0,1.0,0.0,140934.87
-21078,15663410.0,Grieve,593.0,France,Female,32.0,5.0,0.0,1.0,1.0,0.0,145558.42
-21079,15667417.0,Yeates,598.0,Germany,Male,48.0,9.0,104474.62,1.0,1.0,0.0,153747.72
-21080,15781465.0,Tsao,593.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,121789.3
-21081,15588928.0,Mazzanti,644.0,France,Male,48.0,9.0,0.0,3.0,0.0,0.0,129569.92
-21082,15591035.0,Chukwudi,585.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,181850.74
-21083,15792922.0,Tung,581.0,France,Male,34.0,8.0,172325.67,1.0,1.0,1.0,153786.47
-21084,15765258.0,Okechukwu,554.0,Germany,Male,43.0,8.0,117726.33,1.0,1.0,1.0,52404.47
-21085,15691823.0,Ch'iu,545.0,France,Female,26.0,7.0,0.0,2.0,1.0,1.0,142223.94
-21086,15813503.0,L?,592.0,France,Female,47.0,5.0,0.0,2.0,1.0,1.0,62484.01
-21087,15742404.0,Ts'ui,602.0,France,Male,41.0,1.0,83714.41,1.0,1.0,0.0,181429.87
-21088,15707962.0,Shih,743.0,France,Male,37.0,7.0,0.0,1.0,1.0,0.0,174536.17
-21089,15611247.0,Hsu,628.0,Germany,Male,33.0,1.0,127637.92,1.0,1.0,0.0,97640.2
-21090,15590011.0,Genovesi,701.0,France,Male,39.0,5.0,133801.01,2.0,0.0,1.0,63795.62
-21091,15765806.0,Pinto,653.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,152883.95
-21092,15798895.0,Kuo,607.0,Spain,Female,28.0,1.0,0.0,1.0,1.0,0.0,131043.9
-21093,15727384.0,Tung,706.0,France,Male,27.0,5.0,0.0,2.0,1.0,0.0,132673.15
-21094,15656829.0,Mazzi,695.0,Spain,Male,40.0,8.0,0.0,2.0,0.0,1.0,130664.79
-21095,15684319.0,Dyer,694.0,Spain,Male,39.0,5.0,121827.43,1.0,1.0,1.0,67976.57
-21096,15781702.0,Onyemachukwu,694.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,161807.81
-21097,15626900.0,Chien,627.0,France,Male,28.0,4.0,128653.59,1.0,0.0,1.0,117948.0
-21098,15595036.0,Trevisani,743.0,Germany,Male,33.0,5.0,124067.32,2.0,0.0,0.0,115767.93
-21099,15636407.0,Kao,676.0,France,Female,34.0,2.0,122029.15,1.0,1.0,0.0,86424.57
-21100,15792679.0,Nnaife,695.0,France,Male,38.0,6.0,92316.31,1.0,1.0,0.0,94450.97
-21101,15598089.0,Scott,731.0,France,Male,38.0,4.0,0.0,2.0,0.0,1.0,172718.35
-21102,15642833.0,Onyekaozulu,622.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,62722.44
-21103,15646969.0,Hsiao,682.0,Germany,Male,32.0,8.0,162560.32,1.0,1.0,0.0,117622.8
-21104,15656822.0,Piazza,653.0,France,Female,40.0,4.0,103033.83,1.0,1.0,1.0,176487.05
-21105,15638803.0,Pirozzi,638.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,86575.46
-21106,15715079.0,Hsiao,681.0,Germany,Female,55.0,5.0,161532.14,1.0,1.0,1.0,109078.35
-21107,15775335.0,Kaleski,746.0,France,Female,39.0,4.0,0.0,2.0,0.0,0.0,9984.4
-21108,15785559.0,T'ao,718.0,France,Female,33.0,3.0,0.0,1.0,1.0,0.0,149550.95
-21109,15662494.0,Ozioma,678.0,Germany,Male,51.0,9.0,125870.5,1.0,1.0,0.0,198490.33
-21110,15595153.0,Ch'en,647.0,Germany,Female,43.0,0.0,121286.47,1.0,0.0,1.0,119056.76
-21111,15679760.0,Lo,592.0,Germany,Female,59.0,5.0,104944.1,4.0,0.0,0.0,121151.1
-21112,15707042.0,T'ien,850.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,140131.08
-21113,15585777.0,Onwughara,678.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,142548.33
-21114,15694879.0,Chu,724.0,Germany,Female,52.0,7.0,151532.4,1.0,1.0,0.0,135662.17
-21115,15654074.0,Onio,640.0,Germany,Male,32.0,8.0,140720.93,1.0,0.0,0.0,144593.3
-21116,15790689.0,Shubin,708.0,France,Female,51.0,6.0,0.0,1.0,1.0,0.0,168822.01
-21117,15685314.0,Brown,704.0,France,Male,32.0,10.0,0.0,1.0,0.0,0.0,138531.01
-21118,15788189.0,Eluemuno,683.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,126477.41
-21119,15721935.0,Macgroarty,516.0,Spain,Female,49.0,3.0,0.0,1.0,0.0,0.0,121681.19
-21120,15648881.0,Ts'ui,620.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,28257.89
-21121,15671609.0,Titheradge,651.0,Germany,Male,48.0,3.0,113905.48,2.0,1.0,1.0,103557.91
-21122,15709136.0,Lucchese,776.0,Germany,Female,27.0,9.0,102585.88,2.0,1.0,0.0,157559.06
-21123,15783589.0,Trevisano,718.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,98613.34
-21124,15649379.0,Chinomso,565.0,France,Female,40.0,7.0,0.0,2.0,1.0,1.0,92888.52
-21125,15697020.0,Genovese,581.0,Spain,Female,37.0,1.0,142724.48,1.0,1.0,1.0,163714.92
-21126,15706537.0,Chibueze,539.0,France,Male,24.0,1.0,0.0,2.0,1.0,0.0,80587.39
-21127,15574554.0,Frolova,709.0,France,Female,32.0,2.0,0.0,2.0,0.0,0.0,38962.94
-21128,15732644.0,De Luca,757.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,168924.64
-21129,15777639.0,Uspensky,730.0,France,Male,36.0,0.0,115409.6,1.0,1.0,1.0,130404.53
-21130,15775238.0,Obiajulu,664.0,France,Female,45.0,10.0,0.0,1.0,1.0,0.0,70563.9
-21131,15793331.0,Chidiegwu,850.0,Germany,Female,43.0,5.0,155062.8,1.0,1.0,1.0,159888.1
-21132,15651581.0,Chiemenam,632.0,France,Male,52.0,6.0,0.0,2.0,1.0,1.0,51380.9
-21133,15754084.0,Bischof,603.0,France,Male,33.0,2.0,148564.66,1.0,1.0,1.0,175843.68
-21134,15601503.0,Y?an,679.0,France,Male,39.0,9.0,0.0,2.0,0.0,1.0,116977.89
-21135,15737310.0,Liao,663.0,France,Female,49.0,5.0,0.0,1.0,1.0,0.0,57553.02
-21136,15694160.0,Ts'ui,652.0,Germany,Female,20.0,6.0,135470.9,2.0,1.0,1.0,171922.72
-21137,15782879.0,H?,635.0,Spain,Male,34.0,2.0,0.0,2.0,0.0,1.0,100919.19
-21138,15580462.0,Chiemenam,703.0,France,Female,22.0,9.0,0.0,2.0,1.0,0.0,175070.94
-21139,15711378.0,Shao,625.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,100447.53
-21140,15709861.0,K'ung,667.0,France,Female,47.0,3.0,0.0,1.0,1.0,1.0,103726.71
-21141,15775886.0,Tien,656.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,119377.36
-21142,15573045.0,Ts'ui,665.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,48963.59
-21143,15720353.0,Chidimma,850.0,Germany,Male,33.0,6.0,86911.7,1.0,0.0,1.0,138881.02
-21144,15737792.0,Ch'in,621.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,67611.36
-21145,15739248.0,Cawker,593.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,0.0,151481.86
-21146,15642710.0,L?,661.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,67769.4
-21147,15807961.0,Ts'ai,733.0,Germany,Female,35.0,6.0,127818.52,1.0,1.0,0.0,168816.22
-21148,15703541.0,Chiedozie,596.0,Spain,Female,33.0,4.0,0.0,1.0,1.0,0.0,164741.81
-21149,15641877.0,Uspensky,574.0,France,Male,39.0,6.0,0.0,2.0,0.0,1.0,84760.32
-21150,15783526.0,Chien,673.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,65769.2
-21151,15589017.0,L?,687.0,Germany,Female,44.0,1.0,132406.86,1.0,0.0,0.0,72876.43
-21152,15737571.0,Onwumelu,718.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,116748.14
-21153,15807525.0,Nwachukwu,635.0,Germany,Female,35.0,5.0,133634.02,1.0,1.0,0.0,120830.73
-21154,15726776.0,Yermakova,652.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,103914.4
-21155,15727177.0,Yobanna,567.0,France,Female,36.0,10.0,0.0,2.0,1.0,0.0,82276.62
-21156,15641410.0,Calabrese,653.0,Germany,Female,43.0,4.0,130142.79,1.0,1.0,1.0,121551.93
-21157,15643575.0,Pinto,687.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,53817.23
-21158,15681406.0,Tsou,631.0,France,Male,41.0,3.0,0.0,1.0,1.0,1.0,14073.11
-21159,15661526.0,Eluemuno,653.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,1.0,142903.44
-21160,15587913.0,Tuan,687.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,173365.89
-21161,15665861.0,T'ien,752.0,Spain,Male,31.0,4.0,0.0,2.0,0.0,1.0,1375.46
-21162,15589425.0,Chiang,598.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,139410.04
-21163,15792360.0,O'Donnell,684.0,Spain,Female,51.0,10.0,0.0,1.0,1.0,1.0,51454.06
-21164,15627195.0,Pino,715.0,France,Male,36.0,6.0,0.0,2.0,0.0,0.0,125986.18
-21165,15569503.0,Fanucci,535.0,Spain,Female,35.0,2.0,0.0,1.0,0.0,1.0,67979.35
-21166,15709741.0,Ch'ien,652.0,Germany,Male,52.0,8.0,126683.8,1.0,1.0,0.0,108764.69
-21167,15693817.0,Ch'iu,651.0,Spain,Male,60.0,4.0,0.0,2.0,1.0,1.0,38798.79
-21168,15783029.0,Chuang,712.0,Spain,Female,32.0,6.0,0.0,2.0,0.0,1.0,122662.98
-21169,15576517.0,Ts'ai,705.0,France,Male,47.0,9.0,108348.66,1.0,1.0,0.0,100557.03
-21170,15603839.0,T'ao,738.0,Spain,Female,39.0,8.0,0.0,1.0,1.0,0.0,118913.53
-21171,15791216.0,Ch'iu,636.0,France,Female,34.0,6.0,0.0,1.0,1.0,0.0,68860.3
-21172,15752801.0,Mazzanti,575.0,France,Male,37.0,8.0,0.0,1.0,0.0,1.0,138328.24
-21173,15612095.0,Hs?,622.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,180693.61
-21174,15791836.0,Nnamutaezinwa,554.0,Spain,Female,43.0,3.0,0.0,3.0,1.0,0.0,139065.54
-21175,15572412.0,Ibezimako,712.0,France,Female,32.0,6.0,0.0,1.0,1.0,0.0,82836.73
-21176,15672594.0,Ts'ai,695.0,France,Male,36.0,4.0,0.0,2.0,0.0,1.0,62674.38
-21177,15663410.0,Hs?,753.0,Spain,Female,45.0,1.0,0.0,1.0,1.0,1.0,176763.27
-21178,15769703.0,T'ang,582.0,Germany,Female,40.0,8.0,91241.85,1.0,1.0,0.0,98301.61
-21179,15665760.0,Chukwubuikem,676.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,54865.92
-21180,15665956.0,Ozioma,613.0,Germany,Female,52.0,2.0,133658.24,2.0,1.0,0.0,117622.8
-21181,15676937.0,Chukwuebuka,531.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,113557.7
-21182,15677052.0,Ch'in,729.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,36557.55
-21183,15787204.0,Chukwudi,654.0,Germany,Female,36.0,1.0,150348.37,2.0,1.0,1.0,176768.59
-21184,15693326.0,Shih,718.0,Germany,Male,53.0,0.0,163943.89,1.0,1.0,1.0,173464.43
-21185,15626787.0,T'ang,850.0,Spain,Female,36.0,1.0,0.0,1.0,1.0,1.0,46772.36
-21186,15653952.0,Lu,637.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,1.0,78955.8
-21187,15724444.0,Nwabugwu,634.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,159823.16
-21188,15631162.0,Chizuoke,583.0,France,Female,26.0,6.0,0.0,2.0,1.0,1.0,53412.08
-21189,15803790.0,Nnaife,633.0,Spain,Male,31.0,1.0,0.0,2.0,0.0,1.0,50783.1
-21190,15793901.0,Chukwuemeka,598.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,0.0,93783.0
-21191,15665062.0,Ch'ang,645.0,France,Female,42.0,0.0,0.0,1.0,1.0,0.0,152883.95
-21192,15589698.0,Chuang,543.0,Spain,Female,46.0,1.0,0.0,2.0,1.0,1.0,183584.14
-21193,15594409.0,Onyekachi,568.0,France,Female,46.0,2.0,0.0,2.0,1.0,0.0,171039.56
-21194,15701206.0,Tien,659.0,France,Female,43.0,4.0,0.0,3.0,0.0,0.0,129870.92
-21195,15807525.0,Trevisano,707.0,France,Male,38.0,2.0,116498.22,1.0,1.0,1.0,24677.54
-21196,15806945.0,Chialuka,825.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,51362.82
-21197,15811127.0,O'Donnell,649.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,146723.72
-21198,15604130.0,O'Donnell,738.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,0.0,187826.11
-21199,15750335.0,Yevseyev,565.0,Germany,Male,41.0,4.0,98877.95,1.0,1.0,0.0,140133.43
-21200,15665956.0,T'ien,579.0,France,Female,34.0,3.0,0.0,2.0,0.0,1.0,80593.31
-21201,15732963.0,Pagnotto,850.0,France,Female,31.0,5.0,109730.2,1.0,1.0,1.0,149641.58
-21202,15634295.0,Y?,703.0,France,Female,31.0,6.0,0.0,2.0,0.0,1.0,131083.72
-21203,15806922.0,Chibugo,593.0,France,Male,39.0,7.0,115587.49,1.0,1.0,0.0,173365.9
-21204,15631927.0,Ch'eng,593.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,167984.72
-21205,15689614.0,Cawthorne,682.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,104719.66
-21206,15792729.0,T'ien,666.0,Germany,Male,54.0,4.0,151922.94,1.0,1.0,0.0,96528.0
-21207,15639946.0,Yoo,691.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,158021.39
-21208,15701160.0,Yermakov,641.0,France,Male,23.0,3.0,127187.86,1.0,0.0,0.0,66799.69
-21209,15761706.0,Onyekachukwu,633.0,Germany,Female,47.0,4.0,102703.62,1.0,1.0,0.0,106851.6
-21210,15643966.0,Macleod,645.0,France,Male,42.0,9.0,118899.45,2.0,0.0,0.0,163550.58
-21211,15690540.0,Chibueze,686.0,Germany,Female,50.0,4.0,110949.41,1.0,1.0,0.0,97640.2
-21212,15752388.0,Chiang,850.0,France,Female,35.0,2.0,0.0,2.0,1.0,1.0,131473.31
-21213,15602841.0,Lettiere,634.0,Germany,Female,44.0,9.0,94598.48,1.0,1.0,0.0,88592.8
-21214,15632256.0,Tung,739.0,France,Male,27.0,5.0,0.0,1.0,0.0,0.0,86678.48
-21215,15752841.0,Hsu,594.0,France,Male,48.0,1.0,0.0,2.0,0.0,1.0,123982.42
-21216,15645496.0,Iadanza,602.0,France,Female,63.0,6.0,144757.02,1.0,1.0,1.0,17992.94
-21217,15578722.0,Nnaife,577.0,Spain,Female,38.0,8.0,0.0,1.0,0.0,1.0,83090.35
-21218,15698872.0,Udegbulam,706.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,88463.63
-21219,15673837.0,Lucciano,645.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,152843.53
-21220,15668854.0,Chukwukere,651.0,Germany,Male,33.0,6.0,133903.12,1.0,1.0,0.0,86791.9
-21221,15574741.0,Chineze,677.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,182336.45
-21222,15585595.0,Ts'ai,739.0,Spain,Female,34.0,7.0,0.0,2.0,0.0,1.0,8991.17
-21223,15786042.0,Pinto,678.0,Germany,Female,37.0,6.0,131938.92,2.0,1.0,1.0,94875.03
-21224,15687431.0,Shipton,703.0,Spain,Female,42.0,1.0,95293.86,1.0,1.0,0.0,160523.33
-21225,15757911.0,Walker,821.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,1.0,60427.3
-21226,15793424.0,Gboliwe,636.0,France,Female,39.0,9.0,83568.26,1.0,1.0,0.0,177670.57
-21227,15806901.0,Milanesi,715.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,120107.1
-21228,15690563.0,Udinesi,703.0,Germany,Female,45.0,5.0,97762.16,1.0,1.0,0.0,175893.53
-21229,15680597.0,Thompson,637.0,Germany,Female,47.0,5.0,121152.05,1.0,1.0,0.0,122021.33
-21230,15588449.0,Chiazagomekpele,712.0,Germany,Female,37.0,8.0,152806.74,1.0,1.0,1.0,13946.66
-21231,15722090.0,Kao,806.0,France,Male,47.0,7.0,0.0,1.0,1.0,0.0,187915.7
-21232,15672970.0,Chukwualuka,658.0,France,Male,41.0,7.0,79857.64,1.0,1.0,1.0,129892.93
-21233,15778830.0,Onwudiwe,686.0,France,Female,39.0,7.0,0.0,1.0,1.0,1.0,86699.98
-21234,15763980.0,Milanesi,605.0,Spain,Male,31.0,9.0,125924.35,1.0,0.0,1.0,127059.05
-21235,15578865.0,Nucci,730.0,France,Male,41.0,0.0,0.0,1.0,1.0,0.0,47880.71
-21236,15792668.0,Chidiegwu,560.0,France,Female,42.0,6.0,0.0,1.0,1.0,0.0,90591.15
-21237,15705343.0,Chukwuemeka,826.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,60427.3
-21238,15766064.0,Ch'in,641.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,64849.74
-21239,15743076.0,Lucchese,635.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,133086.45
-21240,15776051.0,Li Fonti,635.0,France,Female,24.0,1.0,0.0,2.0,0.0,1.0,85424.34
-21241,15697567.0,Iweobiegbunam,729.0,France,Female,40.0,1.0,0.0,1.0,0.0,1.0,167036.94
-21242,15677785.0,Uvarov,638.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,163331.65
-21243,15585041.0,Bischof,728.0,France,Male,39.0,7.0,0.0,1.0,0.0,0.0,96833.0
-21244,15793317.0,Toscani,678.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,152698.24
-21245,15654975.0,Udinesi,679.0,France,Female,51.0,9.0,0.0,1.0,1.0,0.0,86587.37
-21246,15747043.0,Onyemauchechukwu,731.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,69607.1
-21247,15605826.0,P'eng,749.0,France,Female,30.0,5.0,0.0,2.0,0.0,0.0,56984.53
-21248,15803840.0,Palermo,676.0,France,Male,41.0,9.0,91219.29,1.0,1.0,1.0,169952.82
-21249,15750649.0,Chiedozie,599.0,Germany,Female,51.0,8.0,122949.14,1.0,1.0,0.0,169920.92
-21250,15688963.0,Mazzanti,645.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,1.0,47428.73
-21251,15673539.0,Macleod,659.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,0.0,143635.36
-21252,15634295.0,Chiang,691.0,Spain,Female,27.0,6.0,136953.49,1.0,1.0,0.0,88827.67
-21253,15735080.0,Ch'en,582.0,France,Female,30.0,2.0,0.0,2.0,0.0,1.0,138527.56
-21254,15601383.0,Ch'en,691.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,174434.04
-21255,15589224.0,Hao,670.0,Spain,Male,41.0,2.0,112803.92,1.0,1.0,1.0,135088.58
-21256,15806938.0,Onyekachukwu,685.0,France,Male,37.0,10.0,142406.43,1.0,0.0,0.0,83056.22
-21257,15677369.0,Li Fonti,569.0,Spain,Male,54.0,7.0,0.0,1.0,1.0,1.0,120115.5
-21258,15611669.0,Mazzanti,663.0,Spain,Male,32.0,3.0,0.0,1.0,1.0,0.0,141827.83
-21259,15762331.0,Palermo,716.0,France,Male,34.0,5.0,121355.99,1.0,1.0,0.0,187841.71
-21260,15685500.0,Chin,607.0,Spain,Male,25.0,2.0,0.0,1.0,0.0,0.0,60533.96
-21261,15762855.0,Yuryeva,605.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,0.0,142210.94
-21262,15587735.0,Maclean,637.0,Spain,Male,41.0,8.0,0.0,2.0,1.0,1.0,182531.74
-21263,15593555.0,Tung,739.0,Germany,Male,34.0,4.0,131038.57,1.0,1.0,1.0,63831.36
-21264,15706593.0,Sun,716.0,France,Male,31.0,10.0,0.0,2.0,1.0,0.0,52709.55
-21265,15727499.0,Y?an,640.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,136507.69
-21266,15742834.0,Tseng,712.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,91346.03
-21267,15790322.0,Onyekachi,687.0,Spain,Male,38.0,3.0,148080.43,2.0,0.0,1.0,47414.15
-21268,15685327.0,Ch'en,633.0,France,Male,58.0,4.0,0.0,1.0,1.0,0.0,160110.65
-21269,15652979.0,Monds,586.0,Spain,Female,41.0,7.0,119336.29,1.0,0.0,0.0,145936.28
-21270,15667085.0,Skelton,731.0,France,Female,25.0,3.0,0.0,1.0,1.0,0.0,67944.09
-21271,15783097.0,Fiorentino,592.0,France,Male,41.0,4.0,129055.13,1.0,0.0,0.0,156868.21
-21272,15772995.0,P'eng,574.0,Spain,Female,50.0,10.0,0.0,2.0,1.0,1.0,82970.69
-21273,15656522.0,Okwudiliolisa,526.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,107876.2
-21274,15806913.0,Hargraves,627.0,Spain,Female,31.0,0.0,0.0,2.0,1.0,1.0,84584.69
-21275,15679813.0,Tseng,660.0,Germany,Female,40.0,5.0,107716.57,2.0,0.0,1.0,152298.28
-21276,15650864.0,Ch'ang,679.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,131644.41
-21277,15674840.0,Ritchie,699.0,France,Male,41.0,1.0,0.0,2.0,1.0,0.0,133535.29
-21278,15591135.0,Kerr,591.0,Germany,Male,49.0,6.0,122351.29,1.0,1.0,1.0,94898.1
-21279,15663932.0,Bellucci,684.0,France,Male,49.0,9.0,0.0,2.0,0.0,0.0,88992.05
-21280,15679024.0,Chukwuebuka,681.0,France,Male,33.0,6.0,0.0,1.0,0.0,1.0,175743.92
-21281,15700511.0,Nkemjika,589.0,Spain,Female,22.0,4.0,0.0,2.0,0.0,1.0,180800.42
-21282,15667588.0,Chidiebele,656.0,France,Female,33.0,5.0,0.0,1.0,1.0,0.0,102665.92
-21283,15683955.0,Chukwumaobim,619.0,Germany,Male,34.0,2.0,99838.71,1.0,1.0,0.0,91624.11
-21284,15738148.0,Nkemdilim,586.0,Spain,Male,55.0,6.0,0.0,1.0,0.0,0.0,86784.42
-21285,15671148.0,Hsing,644.0,France,Female,41.0,1.0,0.0,2.0,1.0,1.0,102280.29
-21286,15571873.0,Nnanna,588.0,France,Male,42.0,5.0,0.0,1.0,1.0,1.0,145593.85
-21287,15712176.0,Y?an,705.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,115818.51
-21288,15749807.0,Hsu,629.0,Spain,Male,36.0,9.0,0.0,2.0,0.0,0.0,134878.19
-21289,15622478.0,Cattaneo,762.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,135925.72
-21290,15654489.0,Ch'en,810.0,Germany,Male,40.0,9.0,124447.58,1.0,1.0,0.0,116046.22
-21291,15682048.0,Kashiwagi,813.0,Spain,Male,27.0,3.0,0.0,2.0,1.0,0.0,174098.28
-21292,15641640.0,Ts'ui,731.0,France,Male,52.0,5.0,0.0,3.0,1.0,1.0,93439.75
-21293,15593345.0,Chidubem,636.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,142783.98
-21294,15652400.0,Onyekachukwu,620.0,France,Male,38.0,3.0,0.0,1.0,1.0,1.0,143913.32
-21295,15743759.0,Chukwukere,718.0,Germany,Male,46.0,9.0,106901.42,2.0,1.0,0.0,82469.06
-21296,15664754.0,Iadanza,544.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,102799.22
-21297,15677052.0,Chukwuemeka,697.0,France,Female,45.0,4.0,0.0,3.0,1.0,0.0,49326.04
-21298,15749300.0,Hsueh,516.0,France,Male,39.0,8.0,0.0,1.0,1.0,1.0,143783.46
-21299,15736495.0,Ku,798.0,Germany,Male,50.0,5.0,177619.71,1.0,1.0,1.0,62634.87
-21300,15805909.0,Chia,645.0,Germany,Male,40.0,8.0,96781.39,2.0,1.0,0.0,172490.78
-21301,15678057.0,McIntosh,639.0,France,Female,48.0,4.0,0.0,1.0,0.0,0.0,96660.76
-21302,15752312.0,Tuan,744.0,France,Female,61.0,1.0,0.0,1.0,0.0,0.0,134767.13
-21303,15798850.0,Onuora,850.0,France,Male,34.0,4.0,0.0,2.0,0.0,1.0,165008.97
-21304,15800295.0,Pisano,687.0,France,Male,25.0,5.0,0.0,2.0,1.0,0.0,176713.47
-21305,15700772.0,Hsueh,686.0,Germany,Male,29.0,7.0,131038.14,2.0,1.0,0.0,175312.59
-21306,15677739.0,Maclean,708.0,France,Female,30.0,7.0,0.0,2.0,0.0,0.0,86410.29
-21307,15619360.0,Cawker,646.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,91560.63
-21308,15786539.0,Ts'ai,619.0,Spain,Male,41.0,7.0,111798.06,1.0,1.0,0.0,119882.7
-21309,15759913.0,Oluchukwu,551.0,France,Female,46.0,0.0,0.0,2.0,1.0,1.0,107815.31
-21310,15658852.0,Trevisani,646.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,146051.72
-21311,15642530.0,Pisano,613.0,France,Male,44.0,5.0,0.0,2.0,1.0,0.0,143345.69
-21312,15637993.0,Onwudiwe,584.0,France,Male,50.0,1.0,154483.98,1.0,1.0,1.0,179521.24
-21313,15605050.0,Ponomarev,531.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,105732.9
-21314,15791674.0,Chukwubuikem,679.0,France,Female,35.0,2.0,0.0,1.0,1.0,1.0,70956.75
-21315,15750778.0,Kenechukwu,662.0,France,Male,37.0,7.0,0.0,1.0,1.0,1.0,51620.94
-21316,15696989.0,Chinomso,644.0,France,Female,38.0,7.0,0.0,1.0,1.0,0.0,168080.53
-21317,15752914.0,Chukwufumnanya,726.0,Germany,Female,39.0,8.0,181671.14,1.0,0.0,1.0,5983.7
-21318,15734999.0,Milano,710.0,Spain,Male,51.0,7.0,133384.66,1.0,1.0,0.0,66548.58
-21319,15789953.0,Ugorji,634.0,Spain,Male,36.0,0.0,0.0,2.0,1.0,0.0,168834.41
-21320,15665088.0,Mironova,614.0,Spain,Male,39.0,4.0,0.0,2.0,0.0,1.0,143980.29
-21321,15634968.0,Chukwukadibia,699.0,France,Female,27.0,7.0,0.0,2.0,1.0,1.0,159899.97
-21322,15698902.0,Chukwualuka,633.0,Spain,Female,38.0,9.0,0.0,2.0,0.0,0.0,152959.02
-21323,15777033.0,Ts'ai,675.0,Germany,Female,52.0,1.0,107764.75,1.0,1.0,0.0,26387.71
-21324,15613787.0,Fyodorov,681.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,182059.28
-21325,15618243.0,Ts'ao,489.0,Spain,Female,28.0,2.0,0.0,2.0,0.0,0.0,148516.79
-21326,15786717.0,Milani,591.0,Germany,Female,39.0,5.0,90560.3,1.0,0.0,0.0,199293.01
-21327,15650488.0,Biryukova,850.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,175894.24
-21328,15700864.0,Mazzi,687.0,Germany,Female,34.0,5.0,86055.17,2.0,1.0,1.0,140941.47
-21329,15702061.0,P'an,627.0,Germany,Male,32.0,7.0,134543.7,2.0,1.0,0.0,96498.14
-21330,15690744.0,T'ao,805.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,93881.75
-21331,15630698.0,Ts'ui,696.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,49594.02
-21332,15651022.0,Maclean,639.0,France,Male,51.0,9.0,0.0,2.0,0.0,1.0,56562.57
-21333,15785831.0,Liao,695.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,1.0,139048.0
-21334,15665221.0,Tsao,661.0,France,Female,45.0,2.0,0.0,1.0,1.0,0.0,25662.27
-21335,15736299.0,Chukwuemeka,684.0,Spain,Male,40.0,5.0,0.0,1.0,1.0,0.0,155876.06
-21336,15762762.0,Onyemere,633.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,9984.4
-21337,15626882.0,Mancini,599.0,France,Male,39.0,8.0,0.0,1.0,1.0,0.0,151903.6
-21338,15782899.0,Nwachukwu,649.0,France,Female,42.0,4.0,0.0,1.0,0.0,0.0,166513.47
-21339,15603436.0,Chiang,829.0,Spain,Female,31.0,2.0,0.0,1.0,1.0,1.0,93817.34
-21340,15783526.0,Y?an,627.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,0.0,131473.31
-21341,15571940.0,Chukwumaobim,656.0,Spain,Male,37.0,7.0,0.0,2.0,1.0,1.0,142516.35
-21342,15691952.0,Yeh,588.0,France,Male,42.0,6.0,127864.4,2.0,1.0,1.0,74800.99
-21343,15677310.0,Cremonesi,635.0,Spain,Male,40.0,9.0,0.0,2.0,1.0,0.0,121408.46
-21344,15737489.0,Cavenagh,757.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,72993.65
-21345,15636590.0,Niu,708.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,59631.7
-21346,15808023.0,Manna,516.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,105720.09
-21347,15709511.0,Nnachetam,682.0,Spain,Male,43.0,1.0,0.0,2.0,1.0,1.0,114083.73
-21348,15773869.0,Nnaife,710.0,Germany,Male,57.0,1.0,121206.96,3.0,1.0,0.0,161800.77
-21349,15626383.0,Tien,742.0,France,Male,25.0,7.0,0.0,2.0,0.0,1.0,120591.19
-21350,15643630.0,Ndukaku,763.0,Spain,Male,35.0,6.0,148863.31,1.0,1.0,0.0,172363.41
-21351,15724281.0,Ch'eng,633.0,France,Male,46.0,0.0,0.0,1.0,1.0,0.0,88866.92
-21352,15641091.0,Gidney,624.0,France,Male,33.0,7.0,0.0,2.0,0.0,1.0,102681.32
-21353,15677908.0,Molineux,554.0,Spain,Female,48.0,9.0,0.0,1.0,0.0,0.0,95611.47
-21354,15582841.0,Ts'ai,712.0,France,Male,20.0,4.0,0.0,2.0,1.0,0.0,94898.1
-21355,15581548.0,Zikoranachidimma,629.0,France,Female,41.0,6.0,0.0,2.0,0.0,0.0,180427.24
-21356,15772412.0,Chukwumaobim,705.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,57600.66
-21357,15749489.0,Ch'ien,777.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,84421.34
-21358,15598070.0,Olague,628.0,Spain,Male,58.0,7.0,0.0,1.0,1.0,1.0,74900.9
-21359,15715951.0,Chu,607.0,France,Male,47.0,0.0,135536.4,1.0,1.0,0.0,135096.78
-21360,15636423.0,Milanesi,644.0,France,Female,46.0,1.0,0.0,2.0,1.0,0.0,93783.0
-21361,15594409.0,McIntyre,686.0,Spain,Male,56.0,9.0,0.0,3.0,1.0,0.0,90558.24
-21362,15639303.0,P'eng,628.0,Spain,Female,39.0,8.0,0.0,2.0,0.0,1.0,107876.2
-21363,15705034.0,Pisano,678.0,France,Male,35.0,7.0,0.0,1.0,0.0,1.0,119259.34
-21364,15640491.0,Chidubem,613.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,447.73
-21365,15785831.0,Dyer,719.0,France,Male,29.0,10.0,86911.8,1.0,0.0,1.0,111241.37
-21366,15696852.0,Chukwueloka,593.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,0.0,83700.66
-21367,15798850.0,Smith,674.0,Spain,Male,52.0,6.0,0.0,2.0,1.0,0.0,145562.4
-21368,15794874.0,Nwachinemelu,545.0,Germany,Male,63.0,4.0,128135.99,1.0,0.0,0.0,169920.92
-21369,15748473.0,Chukwudi,635.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,169952.82
-21370,15737354.0,Onyemachukwu,732.0,France,Female,47.0,8.0,155973.88,1.0,1.0,0.0,184536.01
-21371,15646513.0,Chu,669.0,Germany,Male,51.0,0.0,109528.16,1.0,1.0,0.0,169161.46
-21372,15595588.0,Y?an,577.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,180553.48
-21373,15768282.0,Hargreaves,652.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,1.0,170893.85
-21374,15778975.0,Ch'iu,599.0,Germany,Male,32.0,3.0,122874.6,2.0,1.0,1.0,182787.16
-21375,15643865.0,Trevisan,651.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,0.0,181490.42
-21376,15807663.0,Ch'in,577.0,Germany,Male,45.0,7.0,139432.35,1.0,0.0,1.0,91884.5
-21377,15757912.0,Ch'ien,777.0,Spain,Female,52.0,6.0,0.0,2.0,1.0,1.0,187826.11
-21378,15776827.0,Nnonso,707.0,France,Male,54.0,10.0,0.0,1.0,1.0,0.0,116097.26
-21379,15622585.0,Houghton,686.0,Germany,Male,48.0,2.0,98186.7,2.0,0.0,1.0,72993.67
-21380,15691627.0,Mazzanti,687.0,Spain,Female,40.0,5.0,0.0,1.0,1.0,0.0,54495.86
-21381,15726773.0,Chikelu,682.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,142995.32
-21382,15631685.0,Ting,682.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,119830.08
-21383,15652674.0,T'ien,731.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,73365.04
-21384,15741078.0,Uvarov,811.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,161854.98
-21385,15762793.0,Scott,753.0,France,Female,39.0,7.0,143391.07,1.0,0.0,0.0,170905.94
-21386,15571958.0,Hsu,675.0,France,Female,59.0,0.0,0.0,1.0,1.0,0.0,52820.13
-21387,15746012.0,Grieve,729.0,France,Male,40.0,7.0,123544.0,1.0,1.0,0.0,58974.0
-21388,15569764.0,Okwudilichukwu,644.0,France,Female,34.0,3.0,0.0,1.0,0.0,0.0,130132.49
-21389,15782758.0,Liao,681.0,Germany,Male,40.0,8.0,84438.57,2.0,1.0,0.0,136859.55
-21390,15732644.0,Maclean,724.0,France,Female,28.0,9.0,0.0,2.0,1.0,1.0,35786.76
-21391,15583857.0,Nkemakolam,581.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,65632.33
-21392,15714240.0,Rowntree,741.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,68293.93
-21393,15688880.0,Lo,587.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,182728.8
-21394,15588517.0,Chukwujamuike,662.0,France,Female,32.0,0.0,91246.53,1.0,1.0,0.0,153605.75
-21395,15642467.0,Iadanza,675.0,France,Female,60.0,5.0,0.0,1.0,1.0,1.0,113639.67
-21396,15751058.0,Iadanza,607.0,France,Male,34.0,1.0,0.0,1.0,1.0,0.0,9241.78
-21397,15682541.0,Ch'en,838.0,France,Female,48.0,3.0,0.0,1.0,0.0,0.0,175071.33
-21398,15699507.0,Galkin,745.0,France,Female,25.0,2.0,0.0,2.0,1.0,1.0,188007.99
-21399,15659931.0,Iloerika,683.0,France,Male,37.0,5.0,0.0,1.0,1.0,1.0,123882.73
-21400,15783859.0,Yuriev,805.0,Germany,Female,44.0,8.0,144591.96,1.0,1.0,0.0,125445.8
-21401,15806438.0,Bibi,669.0,Spain,Male,33.0,6.0,0.0,2.0,1.0,1.0,103557.91
-21402,15776051.0,H?,748.0,Spain,Female,39.0,8.0,0.0,2.0,1.0,1.0,129912.74
-21403,15723032.0,Manna,582.0,Germany,Female,43.0,2.0,99152.73,2.0,0.0,1.0,132424.69
-21404,15624471.0,Fanucci,648.0,Germany,Female,33.0,8.0,102540.61,2.0,1.0,1.0,130483.56
-21405,15572467.0,Liao,543.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,127939.18
-21406,15776985.0,White,705.0,France,Male,28.0,4.0,0.0,2.0,0.0,1.0,171706.66
-21407,15717185.0,Pinto,595.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,131712.52
-21408,15789924.0,Ch'eng,557.0,France,Male,41.0,5.0,0.0,2.0,0.0,1.0,133692.88
-21409,15681402.0,Uspenskaya,663.0,France,Female,38.0,1.0,0.0,2.0,0.0,0.0,168834.38
-21410,15773394.0,Maclean,620.0,France,Male,34.0,3.0,0.0,1.0,1.0,1.0,173486.39
-21411,15777907.0,Fiorentino,616.0,France,Female,32.0,2.0,0.0,1.0,1.0,0.0,94738.54
-21412,15741275.0,Chu,850.0,Germany,Female,32.0,3.0,143054.86,2.0,1.0,0.0,84469.13
-21413,15748481.0,Teng,605.0,Spain,Male,30.0,5.0,0.0,2.0,0.0,0.0,164741.5
-21414,15802071.0,Piazza,675.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,52795.15
-21415,15751531.0,Pirozzi,627.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,93769.89
-21416,15581575.0,Lai,677.0,Spain,Male,33.0,8.0,0.0,1.0,1.0,1.0,88838.14
-21417,15640648.0,Okechukwu,633.0,France,Male,36.0,3.0,138053.67,1.0,1.0,0.0,185557.66
-21418,15757837.0,Yobachukwu,699.0,Spain,Female,30.0,2.0,0.0,2.0,0.0,0.0,110253.2
-21419,15709604.0,Fenton,711.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,131285.92
-21420,15572093.0,Ch'en,586.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,82868.34
-21421,15778915.0,Hao,632.0,France,Male,36.0,6.0,0.0,1.0,1.0,0.0,199409.19
-21422,15790782.0,Yudina,685.0,Spain,Female,62.0,4.0,0.0,2.0,0.0,1.0,80177.8
-21423,15669342.0,Lucciano,591.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,92816.86
-21424,15662232.0,Hs?,545.0,Spain,Female,31.0,7.0,0.0,1.0,1.0,1.0,127847.25
-21425,15761348.0,Chukwuemeka,632.0,Germany,Male,41.0,8.0,140950.92,1.0,1.0,0.0,172938.29
-21426,15771087.0,Ch'in,665.0,France,Female,31.0,6.0,0.0,1.0,1.0,0.0,186948.35
-21427,15668775.0,Akabueze,653.0,France,Female,41.0,0.0,0.0,2.0,0.0,0.0,46811.77
-21428,15644551.0,Niu,678.0,Spain,Female,31.0,3.0,129745.1,1.0,0.0,1.0,88890.75
-21429,15591723.0,Fyodorov,806.0,Spain,Female,46.0,0.0,130114.17,1.0,0.0,1.0,79623.28
-21430,15748473.0,P'eng,744.0,France,Female,28.0,1.0,0.0,2.0,1.0,1.0,129891.66
-21431,15692991.0,Mazzi,651.0,France,Male,33.0,5.0,115532.52,1.0,0.0,1.0,172501.38
-21432,15706061.0,L?,756.0,Germany,Male,47.0,7.0,182888.08,2.0,1.0,0.0,107712.42
-21433,15774738.0,Pirozzi,640.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,74048.1
-21434,15578515.0,Baddeley,592.0,France,Male,33.0,7.0,0.0,2.0,0.0,0.0,38761.61
-21435,15622494.0,Nwokezuike,585.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,55857.48
-21436,15686917.0,Hao,623.0,Spain,Female,37.0,8.0,136658.61,1.0,0.0,1.0,63687.06
-21437,15793881.0,Uwaezuoke,686.0,Germany,Male,26.0,0.0,120549.29,1.0,1.0,0.0,118570.56
-21438,15766017.0,Chia,603.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,179521.24
-21439,15615020.0,Hsu,684.0,Germany,Female,35.0,6.0,98723.67,1.0,1.0,0.0,101617.64
-21440,15705050.0,Uwakwe,742.0,France,Male,38.0,5.0,0.0,2.0,0.0,0.0,126012.57
-21441,15622578.0,Ejimofor,850.0,France,Male,34.0,2.0,148863.31,1.0,1.0,0.0,181204.5
-21442,15793901.0,Uvarova,831.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,126728.27
-21443,15603665.0,Ratten,671.0,Spain,Male,37.0,2.0,166594.78,1.0,0.0,1.0,161465.31
-21444,15725355.0,Pirozzi,738.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,125870.5
-21445,15795519.0,Kuo,598.0,Spain,Male,29.0,1.0,0.0,1.0,1.0,1.0,152054.33
-21446,15762268.0,Udegbunam,572.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,175743.92
-21447,15681276.0,Ngozichukwuka,698.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,163789.49
-21448,15745399.0,Y?,625.0,Spain,Male,36.0,2.0,0.0,1.0,1.0,1.0,76515.79
-21449,15793491.0,Iweobiegbunam,686.0,France,Male,31.0,3.0,91612.91,1.0,0.0,0.0,49463.44
-21450,15728352.0,Calabrese,774.0,Germany,Female,28.0,2.0,161592.76,1.0,1.0,1.0,161854.98
-21451,15602312.0,Ch'in,594.0,Germany,Female,40.0,3.0,148046.39,1.0,0.0,0.0,125062.02
-21452,15757897.0,Ponomaryov,568.0,France,Female,41.0,8.0,0.0,1.0,1.0,0.0,181224.56
-21453,15742369.0,Dickson,556.0,France,Male,28.0,2.0,0.0,1.0,1.0,1.0,115441.66
-21454,15593468.0,Ch'iu,714.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,141359.11
-21455,15745404.0,Chidiegwu,711.0,Germany,Male,42.0,4.0,106012.2,1.0,1.0,0.0,134878.34
-21456,15727197.0,Zikoranachidimma,756.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,58632.37
-21457,15783030.0,P'eng,584.0,France,Male,33.0,5.0,0.0,2.0,0.0,0.0,112928.74
-21458,15773013.0,Hsueh,638.0,France,Female,49.0,9.0,0.0,2.0,0.0,0.0,173586.63
-21459,15647898.0,Chien,590.0,France,Female,36.0,5.0,143390.51,1.0,1.0,0.0,60584.33
-21460,15572467.0,Chiemezie,651.0,Spain,Male,34.0,3.0,95675.62,2.0,0.0,1.0,181694.44
-21461,15687828.0,L?,684.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,0.0,67364.41
-21462,15764033.0,T'ao,692.0,France,Female,48.0,8.0,0.0,1.0,0.0,0.0,186489.95
-21463,15727811.0,Calabrese,632.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,73066.72
-21464,15789548.0,Onwuamaeze,661.0,France,Male,38.0,3.0,146587.3,1.0,1.0,0.0,9854.72
-21465,15746199.0,Chukwuemeka,739.0,Germany,Male,41.0,3.0,106836.67,1.0,1.0,1.0,154360.97
-21466,15810845.0,Manna,703.0,Germany,Male,35.0,7.0,107418.28,2.0,1.0,0.0,174094.24
-21467,15582892.0,Chukwuemeka,746.0,France,Female,43.0,5.0,0.0,1.0,0.0,0.0,132979.69
-21468,15773098.0,Scott,594.0,Spain,Female,23.0,6.0,0.0,2.0,1.0,0.0,106977.5
-21469,15742297.0,Bogle,710.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,163550.58
-21470,15652999.0,DeRose,713.0,France,Female,45.0,9.0,0.0,2.0,1.0,0.0,96833.0
-21471,15718779.0,T'ien,756.0,France,Female,25.0,5.0,108269.2,2.0,1.0,0.0,60920.03
-21472,15594577.0,Hsueh,677.0,France,Male,46.0,4.0,161588.89,1.0,1.0,0.0,134794.02
-21473,15777825.0,McLean,633.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,148087.88
-21474,15606901.0,Milne,719.0,Germany,Male,50.0,0.0,128981.07,2.0,1.0,0.0,126434.61
-21475,15657565.0,Dickson,648.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,102260.16
-21476,15673367.0,Fadden,568.0,France,Male,23.0,2.0,0.0,2.0,1.0,1.0,93568.77
-21477,15631838.0,P'eng,629.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,123663.63
-21478,15742798.0,Gant,663.0,Germany,Male,29.0,9.0,97544.84,2.0,1.0,1.0,187826.11
-21479,15809847.0,Ch'iu,495.0,Spain,Female,46.0,8.0,0.0,1.0,1.0,0.0,133537.02
-21480,15792360.0,Ejikemeifeuwa,667.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,1.0,184520.0
-21481,15593959.0,Ch'ang,671.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,51454.06
-21482,15593601.0,Lo,641.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,1.0,1276.87
-21483,15680706.0,Lu,542.0,Germany,Male,46.0,5.0,130575.26,1.0,1.0,1.0,103932.58
-21484,15583849.0,Shao,682.0,France,Female,37.0,9.0,0.0,2.0,0.0,0.0,175310.78
-21485,15642824.0,Uwakwe,678.0,Germany,Male,38.0,3.0,127585.98,2.0,1.0,1.0,113469.65
-21486,15784320.0,H?,681.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,138878.54
-21487,15797329.0,McLean,684.0,Spain,Male,50.0,3.0,0.0,2.0,1.0,1.0,61761.66
-21488,15658158.0,Fomina,530.0,France,Female,47.0,9.0,110939.3,2.0,1.0,0.0,113428.77
-21489,15679743.0,Hovell,684.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,142331.39
-21490,15672499.0,Olisanugo,584.0,France,Male,33.0,6.0,0.0,2.0,0.0,1.0,136491.72
-21491,15672912.0,Chidumaga,562.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,10553.31
-21492,15579914.0,Milano,642.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,0.0,158054.49
-21493,15656322.0,Ch'ien,697.0,France,Female,47.0,9.0,0.0,2.0,1.0,0.0,131718.48
-21494,15692430.0,Shao,595.0,France,Male,30.0,8.0,138341.51,1.0,0.0,0.0,154775.7
-21495,15691808.0,Ugorji,596.0,France,Male,38.0,5.0,0.0,1.0,1.0,1.0,81298.09
-21496,15695078.0,Chukwuebuka,683.0,Spain,Female,37.0,7.0,0.0,2.0,0.0,1.0,52827.51
-21497,15589019.0,Chinagorom,695.0,France,Female,29.0,1.0,0.0,2.0,1.0,1.0,163327.27
-21498,15765311.0,Whitelegge,555.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,55859.27
-21499,15668830.0,Boni,656.0,France,Male,62.0,2.0,82321.88,1.0,1.0,1.0,142223.84
-21500,15702656.0,Maclean,671.0,France,Female,31.0,9.0,0.0,1.0,1.0,0.0,48876.84
-21501,15589312.0,Ugochukwutubelum,638.0,Germany,Female,35.0,8.0,87946.39,2.0,1.0,1.0,194808.51
-21502,15775797.0,Ch'in,561.0,France,Male,26.0,4.0,0.0,1.0,1.0,1.0,91574.94
-21503,15571666.0,Chukwukere,686.0,Germany,Female,35.0,7.0,131955.07,2.0,0.0,0.0,171021.44
-21504,15691483.0,Tobeolisa,577.0,Germany,Female,35.0,5.0,163700.78,2.0,1.0,0.0,114566.47
-21505,15585855.0,Onyemauchechukwu,806.0,Spain,Female,40.0,5.0,0.0,1.0,1.0,0.0,90860.97
-21506,15673747.0,Chu,699.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,128815.33
-21507,15571666.0,Chijindum,731.0,France,Male,37.0,1.0,0.0,2.0,0.0,0.0,93332.64
-21508,15631693.0,Chimaobim,695.0,France,Female,42.0,0.0,170833.46,1.0,1.0,0.0,184544.59
-21509,15607988.0,K'ung,543.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,74077.91
-21510,15665834.0,Chidumaga,636.0,France,Male,47.0,6.0,0.0,1.0,1.0,1.0,138526.96
-21511,15776596.0,Onyekaozulu,743.0,Germany,Female,39.0,2.0,134760.21,1.0,1.0,0.0,163534.75
-21512,15748381.0,Y?,591.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,0.0,147720.27
-21513,15754068.0,Lai,667.0,France,Male,44.0,7.0,0.0,2.0,1.0,0.0,194808.51
-21514,15615207.0,Lo Duca,707.0,Spain,Male,31.0,4.0,100628.0,2.0,1.0,0.0,133831.27
-21515,15630661.0,Liao,699.0,Spain,Female,42.0,9.0,0.0,2.0,1.0,0.0,74850.84
-21516,15587562.0,Ku,578.0,Germany,Male,37.0,9.0,125537.38,1.0,1.0,0.0,180595.02
-21517,15777784.0,Trevisano,745.0,France,Male,38.0,5.0,0.0,1.0,0.0,1.0,69381.05
-21518,15804822.0,H?,724.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,137916.49
-21519,15643818.0,Chien,648.0,France,Female,21.0,4.0,0.0,2.0,1.0,0.0,60905.51
-21520,15791373.0,Maclean,587.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,1.0,95625.03
-21521,15593694.0,Cattaneo,666.0,France,Female,39.0,5.0,0.0,2.0,1.0,0.0,181893.31
-21522,15584515.0,Dike,637.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,138510.01
-21523,15592999.0,Ch'ang,632.0,France,Male,26.0,7.0,77967.5,1.0,1.0,0.0,105817.63
-21524,15677382.0,Manna,751.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,1.0,71159.84
-21525,15781882.0,DeRose,703.0,France,Female,31.0,2.0,103471.49,1.0,1.0,0.0,93881.75
-21526,15757865.0,Chigolum,663.0,Germany,Male,52.0,4.0,107980.37,1.0,0.0,1.0,169920.92
-21527,15593601.0,Hsia,713.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,126517.43
-21528,15687310.0,Fang,559.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,105983.09
-21529,15728010.0,Chukwufumnanya,619.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,84760.32
-21530,15747591.0,Rizzo,676.0,Germany,Male,43.0,1.0,131468.44,2.0,1.0,0.0,4993.94
-21531,15617301.0,Tien,569.0,Germany,Male,33.0,0.0,106687.76,1.0,1.0,0.0,94499.06
-21532,15757952.0,Nwachinemelu,559.0,Germany,Female,60.0,6.0,130854.59,1.0,1.0,1.0,124411.08
-21533,15744327.0,Ch'ang,741.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,96444.89
-21534,15692761.0,Mancini,697.0,France,Female,34.0,8.0,97403.18,1.0,0.0,0.0,130590.35
-21535,15583576.0,Hsiung,648.0,Germany,Female,44.0,6.0,115062.02,2.0,0.0,1.0,140891.07
-21536,15735270.0,Shih,588.0,France,Male,47.0,8.0,0.0,1.0,1.0,0.0,161465.21
-21537,15709861.0,Hsueh,678.0,Germany,Female,36.0,5.0,113882.23,1.0,1.0,1.0,28204.98
-21538,15758939.0,Lettiere,745.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,73669.94
-21539,15644849.0,H?,704.0,France,Female,57.0,2.0,144962.74,1.0,0.0,1.0,118749.12
-21540,15683339.0,Ndubuagha,695.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,91810.71
-21541,15698619.0,Tsou,729.0,France,Female,48.0,7.0,0.0,2.0,1.0,1.0,164081.42
-21542,15686958.0,Lai,644.0,France,Female,37.0,7.0,0.0,1.0,1.0,1.0,121405.02
-21543,15782311.0,Eiland,748.0,France,Male,60.0,7.0,0.0,1.0,1.0,1.0,88890.05
-21544,15605067.0,Calabresi,739.0,Spain,Female,37.0,2.0,131691.75,1.0,1.0,1.0,84574.89
-21545,15804075.0,Onyekachi,849.0,Germany,Male,57.0,4.0,116220.5,1.0,1.0,0.0,163558.36
-21546,15741417.0,Rizzo,679.0,Spain,Female,33.0,3.0,0.0,1.0,1.0,0.0,87067.73
-21547,15684469.0,Lucciano,679.0,Spain,Male,26.0,10.0,0.0,2.0,1.0,1.0,199336.63
-21548,15658577.0,Fanucci,679.0,France,Female,39.0,7.0,0.0,2.0,0.0,1.0,72993.65
-21549,15621063.0,Liao,705.0,Germany,Female,29.0,8.0,162482.76,1.0,1.0,1.0,77965.67
-21550,15786167.0,Teng,676.0,France,Male,51.0,5.0,0.0,1.0,0.0,0.0,107874.39
-21551,15667633.0,Onwuatuegwu,653.0,Spain,Male,36.0,9.0,0.0,1.0,0.0,1.0,74849.46
-21552,15582325.0,Tien,613.0,France,Female,41.0,3.0,0.0,2.0,0.0,0.0,68730.91
-21553,15762861.0,Tsou,697.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,158887.09
-21554,15621140.0,Chukwufumnanya,735.0,France,Male,35.0,7.0,0.0,2.0,0.0,0.0,85424.34
-21555,15721426.0,Hsia,577.0,France,Male,25.0,0.0,0.0,1.0,1.0,0.0,180712.28
-21556,15726679.0,Nucci,748.0,Spain,Male,35.0,8.0,0.0,1.0,1.0,1.0,87422.15
-21557,15739972.0,Shih,677.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,124592.84
-21558,15771749.0,Hsueh,722.0,France,Male,29.0,8.0,0.0,1.0,1.0,0.0,158610.89
-21559,15646725.0,Hs?,578.0,France,Male,54.0,5.0,0.0,1.0,1.0,1.0,24472.23
-21560,15740283.0,Smith,751.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,48962.58
-21561,15752588.0,Chiemezie,679.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,1.0,129964.56
-21562,15669301.0,Toscani,699.0,Germany,Female,34.0,8.0,150561.03,2.0,1.0,1.0,122949.71
-21563,15650288.0,Ch'ang,589.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,147055.87
-21564,15716000.0,Li Fonti,714.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,86956.2
-21565,15721781.0,T'ao,554.0,France,Male,35.0,10.0,0.0,2.0,1.0,1.0,164768.82
-21566,15773520.0,Genovese,639.0,Germany,Male,28.0,1.0,121874.89,1.0,1.0,1.0,54439.53
-21567,15773098.0,Okonkwo,563.0,France,Female,23.0,8.0,0.0,2.0,1.0,1.0,89566.64
-21568,15750921.0,Dyson,667.0,France,Female,39.0,4.0,124693.39,1.0,1.0,0.0,165082.25
-21569,15806915.0,Tien,545.0,France,Female,34.0,9.0,0.0,2.0,0.0,0.0,133086.45
-21570,15681021.0,Calabresi,651.0,France,Male,46.0,5.0,137986.01,1.0,1.0,0.0,88701.74
-21571,15739850.0,Onyemauchechukwu,684.0,France,Male,32.0,2.0,105921.52,1.0,1.0,1.0,152992.04
-21572,15794276.0,Okeke,762.0,Germany,Female,50.0,2.0,154970.54,2.0,1.0,0.0,90964.54
-21573,15775492.0,Nwankwo,696.0,France,Male,37.0,2.0,85915.52,1.0,0.0,0.0,101866.39
-21574,15572294.0,Pirozzi,686.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,0.0,82463.69
-21575,15756625.0,Kincaid,445.0,Germany,Female,49.0,4.0,148045.68,1.0,1.0,0.0,132547.02
-21576,15665879.0,Udegbunam,758.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,138700.75
-21577,15581229.0,Li Fonti,598.0,France,Male,46.0,8.0,0.0,2.0,1.0,0.0,111868.23
-21578,15663438.0,Nwachukwu,743.0,Spain,Male,30.0,1.0,77919.78,1.0,1.0,0.0,64539.64
-21579,15651627.0,Ts'ui,753.0,France,Male,34.0,10.0,126473.99,1.0,1.0,0.0,146723.72
-21580,15787907.0,Ndubueze,605.0,France,Male,37.0,7.0,0.0,2.0,0.0,1.0,175314.87
-21581,15612103.0,Hsieh,622.0,France,Male,39.0,1.0,0.0,1.0,1.0,0.0,116851.6
-21582,15665879.0,Hsiung,667.0,France,Female,36.0,9.0,123544.0,1.0,0.0,1.0,44384.57
-21583,15634373.0,Folliero,527.0,Spain,Male,45.0,5.0,0.0,2.0,1.0,0.0,101918.96
-21584,15698872.0,Yermakova,691.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,0.0,98820.09
-21585,15573045.0,Hsing,545.0,France,Male,32.0,8.0,0.0,1.0,0.0,1.0,168080.53
-21586,15652289.0,Hs?,506.0,France,Female,43.0,6.0,0.0,1.0,1.0,0.0,67552.71
-21587,15628950.0,Ch'ien,569.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,44406.17
-21588,15640059.0,Nwebube,663.0,France,Female,42.0,0.0,112712.17,1.0,1.0,0.0,160532.88
-21589,15604482.0,Nnachetam,590.0,France,Male,26.0,7.0,152462.79,1.0,0.0,1.0,61761.77
-21590,15663883.0,Onyemauchechukwu,545.0,Spain,Female,35.0,7.0,148610.11,1.0,1.0,0.0,62634.94
-21591,15682585.0,Calabresi,649.0,France,Female,34.0,6.0,102540.61,1.0,1.0,0.0,103421.91
-21592,15711457.0,Rizzo,636.0,Germany,Male,34.0,5.0,140733.74,2.0,1.0,1.0,91830.75
-21593,15750772.0,Shih,727.0,Germany,Female,48.0,4.0,115341.55,1.0,1.0,0.0,44244.49
-21594,15641877.0,Lu,583.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,0.0,78616.92
-21595,15803378.0,Uspenskaya,641.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,126761.2
-21596,15586963.0,Hsia,706.0,France,Male,22.0,7.0,0.0,1.0,1.0,1.0,164038.07
-21597,15752912.0,Ch'eng,616.0,Spain,Female,37.0,3.0,122453.97,2.0,1.0,0.0,168085.97
-21598,15744273.0,Pagnotto,661.0,Germany,Male,48.0,5.0,121729.42,1.0,1.0,0.0,110265.24
-21599,15702801.0,Luffman,815.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,167040.94
-21600,15757617.0,Onio,607.0,France,Female,39.0,3.0,0.0,1.0,1.0,0.0,128881.87
-21601,15571917.0,Fanucci,648.0,Germany,Male,39.0,4.0,123544.0,2.0,1.0,1.0,177524.19
-21602,15700511.0,Onyemauchechukwu,687.0,Spain,Female,42.0,1.0,123699.48,2.0,0.0,0.0,140197.42
-21603,15589924.0,Chiazagomekpere,658.0,Spain,Female,54.0,8.0,0.0,1.0,0.0,1.0,142838.64
-21604,15605037.0,Dyson,641.0,France,Female,40.0,9.0,0.0,2.0,1.0,1.0,125870.5
-21605,15600781.0,Fiorentini,564.0,France,Female,62.0,7.0,0.0,2.0,1.0,1.0,116520.09
-21606,15810864.0,Chigolum,697.0,Spain,Male,37.0,10.0,0.0,2.0,1.0,1.0,121239.65
-21607,15733974.0,Nnanna,602.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,162858.29
-21608,15682600.0,Lucchese,553.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,89801.9
-21609,15692718.0,Chiazagomekpere,632.0,Spain,Female,38.0,5.0,0.0,1.0,1.0,0.0,143301.53
-21610,15572467.0,T'ao,705.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,56562.57
-21611,15671383.0,Onyemauchechukwu,679.0,Spain,Female,38.0,7.0,0.0,1.0,1.0,1.0,82698.84
-21612,15646513.0,Pinto,717.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,130489.37
-21613,15728362.0,Milne,682.0,Germany,Male,33.0,7.0,142905.51,2.0,1.0,0.0,136983.77
-21614,15785782.0,Ch'in,607.0,France,Female,38.0,0.0,0.0,1.0,1.0,0.0,52869.71
-21615,15583392.0,Nwachukwu,706.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,120108.56
-21616,15763898.0,Uchenna,584.0,Germany,Male,29.0,5.0,131468.44,1.0,1.0,0.0,102085.35
-21617,15658935.0,K?,646.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,162960.03
-21618,15727556.0,Onyekachi,651.0,France,Male,41.0,4.0,0.0,2.0,1.0,1.0,87412.24
-21619,15777797.0,Ndubueze,662.0,France,Male,42.0,7.0,0.0,1.0,1.0,0.0,146875.86
-21620,15780779.0,Yobachi,633.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,135962.42
-21621,15743192.0,Y?,730.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,172501.38
-21622,15689514.0,Okeke,682.0,Germany,Female,41.0,1.0,138052.81,1.0,1.0,0.0,7063.6
-21623,15599403.0,White,709.0,France,Male,41.0,3.0,110340.68,1.0,1.0,1.0,180626.36
-21624,15574554.0,Zikoranachidimma,696.0,Spain,Female,49.0,4.0,0.0,2.0,1.0,0.0,151954.39
-21625,15689168.0,Yobachukwu,635.0,France,Female,48.0,2.0,0.0,2.0,0.0,0.0,152491.31
-21626,15627262.0,Ch'in,850.0,Germany,Female,32.0,5.0,136658.61,1.0,0.0,1.0,92887.06
-21627,15684440.0,Lai,523.0,France,Female,46.0,1.0,0.0,2.0,1.0,0.0,143681.83
-21628,15758845.0,Dickson,537.0,Spain,Male,38.0,10.0,132088.59,1.0,1.0,0.0,161435.02
-21629,15750765.0,Lucchese,617.0,France,Male,26.0,1.0,131287.36,1.0,0.0,0.0,64516.8
-21630,15649450.0,Palerma,637.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,1.0,87951.83
-21631,15744515.0,Nnaife,531.0,France,Female,26.0,7.0,0.0,2.0,1.0,0.0,166677.54
-21632,15665956.0,Trevisano,478.0,Germany,Female,56.0,10.0,127864.4,1.0,1.0,1.0,162889.1
-21633,15673372.0,T'ang,741.0,France,Male,44.0,6.0,0.0,2.0,1.0,1.0,143096.49
-21634,15604813.0,Chiawuotu,498.0,Germany,Male,36.0,1.0,76510.52,1.0,1.0,1.0,168469.65
-21635,15633648.0,Chijindum,699.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,129964.94
-21636,15646936.0,Ch'eng,656.0,France,Male,26.0,9.0,0.0,1.0,1.0,1.0,166317.71
-21637,15780409.0,Mackenzie,667.0,Spain,Male,25.0,2.0,141806.0,1.0,1.0,1.0,184564.67
-21638,15705009.0,Chikezie,576.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,25261.27
-21639,15721592.0,Ts'ui,561.0,Germany,Female,46.0,6.0,149961.99,3.0,1.0,0.0,187925.75
-21640,15579781.0,Goliwe,638.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,87071.18
-21641,15664897.0,Hao,627.0,Germany,Female,34.0,2.0,154872.08,1.0,1.0,1.0,137959.0
-21642,15790067.0,Ch'iu,585.0,Germany,Female,29.0,9.0,100194.44,2.0,1.0,1.0,52331.15
-21643,15665088.0,Haugh,614.0,France,Male,37.0,6.0,137811.75,1.0,1.0,1.0,171457.06
-21644,15672945.0,Okwudilichukwu,682.0,France,Male,33.0,3.0,121823.4,1.0,1.0,1.0,130789.6
-21645,15575581.0,Pinto,645.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,129690.5
-21646,15612103.0,Chukwuebuka,565.0,France,Male,31.0,6.0,0.0,1.0,1.0,0.0,117948.0
-21647,15656596.0,Ch'in,652.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,44390.38
-21648,15766378.0,Onyemere,562.0,Germany,Female,60.0,9.0,130428.78,4.0,1.0,0.0,103511.26
-21649,15606229.0,Lo Duca,756.0,Spain,Male,34.0,10.0,0.0,1.0,1.0,1.0,136079.64
-21650,15756820.0,H?,627.0,France,Male,37.0,7.0,87739.08,1.0,0.0,0.0,122580.48
-21651,15796958.0,Nwankwo,708.0,France,Female,39.0,8.0,0.0,1.0,1.0,1.0,151316.37
-21652,15687540.0,Nwokezuike,697.0,Spain,Female,34.0,8.0,0.0,2.0,0.0,1.0,153662.28
-21653,15643523.0,Wei,678.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,1.0,97767.38
-21654,15623695.0,White,745.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,97938.54
-21655,15786469.0,Fiorentino,612.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,0.0,898.51
-21656,15793671.0,Kolesnikov,763.0,France,Female,37.0,1.0,134498.54,1.0,1.0,1.0,109742.8
-21657,15611800.0,Niu,539.0,France,Male,43.0,1.0,0.0,2.0,1.0,1.0,132468.76
-21658,15765537.0,Hsiung,699.0,France,Female,36.0,10.0,0.0,2.0,1.0,1.0,171762.87
-21659,15690079.0,Chung,781.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,63737.19
-21660,15572735.0,Nwokike,682.0,Spain,Male,47.0,2.0,0.0,2.0,1.0,0.0,105986.01
-21661,15785831.0,Shubin,559.0,France,Male,38.0,2.0,133618.01,1.0,1.0,1.0,26019.59
-21662,15731553.0,Y?,581.0,Germany,Female,36.0,8.0,137911.57,1.0,1.0,0.0,96463.25
-21663,15765257.0,Macleod,633.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,164018.98
-21664,15681316.0,Ch'ang,648.0,Germany,Female,31.0,5.0,96408.98,1.0,1.0,0.0,131718.12
-21665,15687913.0,Gadsden,611.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,105448.74
-21666,15776847.0,He,704.0,Germany,Female,29.0,6.0,125227.84,1.0,0.0,1.0,171037.63
-21667,15569807.0,Tsai,724.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,100556.98
-21668,15733447.0,Ch'ien,699.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,9984.4
-21669,15667046.0,Hs?,597.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,17974.08
-21670,15790564.0,H?,724.0,France,Male,48.0,6.0,0.0,1.0,1.0,1.0,141756.32
-21671,15602497.0,Scott,651.0,France,Female,29.0,4.0,0.0,2.0,0.0,0.0,80963.92
-21672,15567839.0,Chu,659.0,Germany,Male,32.0,10.0,84460.71,1.0,1.0,0.0,143668.58
-21673,15641675.0,Rizzo,708.0,Spain,Female,49.0,2.0,0.0,2.0,1.0,0.0,166984.44
-21674,15709441.0,Udegbunam,707.0,Spain,Male,38.0,5.0,125860.2,1.0,1.0,1.0,80552.36
-21675,15752342.0,P'an,651.0,Germany,Male,43.0,6.0,117036.52,1.0,1.0,0.0,160976.85
-21676,15795878.0,Chiang,594.0,Spain,Female,33.0,0.0,133800.98,1.0,1.0,1.0,58854.88
-21677,15658693.0,Chigozie,561.0,France,Male,37.0,9.0,0.0,2.0,0.0,0.0,94774.12
-21678,15683908.0,Chiemenam,521.0,France,Female,54.0,2.0,0.0,2.0,0.0,1.0,155786.39
-21679,15796434.0,Chidiebele,758.0,France,Male,30.0,5.0,0.0,2.0,1.0,0.0,133535.29
-21680,15632079.0,Kenechukwu,477.0,France,Female,30.0,1.0,0.0,1.0,1.0,0.0,116471.43
-21681,15638487.0,Mironova,753.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,1.0,125427.42
-21682,15694765.0,Calabrese,633.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,142513.5
-21683,15773013.0,Uwaezuoke,775.0,France,Female,54.0,2.0,112544.45,2.0,1.0,1.0,117704.65
-21684,15592300.0,Lucciano,686.0,France,Male,42.0,7.0,0.0,1.0,0.0,0.0,112946.67
-21685,15735330.0,Chiedozie,593.0,Germany,Male,44.0,1.0,123906.22,1.0,1.0,1.0,122937.57
-21686,15662488.0,Brown,696.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,162697.93
-21687,15782778.0,Ugonnatubelum,750.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,167557.71
-21688,15676715.0,Shih,521.0,France,Female,30.0,0.0,0.0,2.0,0.0,1.0,131363.57
-21689,15772073.0,Chukwuemeka,561.0,France,Female,41.0,2.0,0.0,1.0,1.0,0.0,180622.43
-21690,15709643.0,Nwebube,630.0,France,Female,69.0,3.0,0.0,1.0,1.0,1.0,24472.23
-21691,15578211.0,Zetticci,669.0,France,Male,26.0,4.0,121669.23,1.0,1.0,0.0,181258.76
-21692,15800233.0,Okwukwe,653.0,Germany,Male,39.0,8.0,131834.8,1.0,1.0,0.0,145547.71
-21693,15646726.0,H?,638.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,121440.8
-21694,15621021.0,Ch'en,689.0,France,Female,43.0,6.0,137933.18,1.0,1.0,1.0,106977.8
-21695,15689514.0,Tsao,717.0,Germany,Female,28.0,1.0,102562.23,2.0,1.0,1.0,38170.04
-21696,15660144.0,Koehler,635.0,France,Female,32.0,10.0,0.0,2.0,1.0,1.0,106920.57
-21697,15702499.0,T'ao,592.0,Spain,Male,31.0,3.0,85216.61,1.0,1.0,1.0,59351.68
-21698,15733661.0,Li,707.0,Spain,Female,34.0,8.0,0.0,2.0,1.0,0.0,8908.0
-21699,15763097.0,Sagese,537.0,Spain,Male,29.0,2.0,119912.46,2.0,1.0,1.0,72993.65
-21700,15631392.0,Soto,662.0,Germany,Female,40.0,9.0,110646.54,1.0,0.0,0.0,143301.49
-21701,15771086.0,Wright,685.0,Germany,Male,31.0,8.0,137851.31,1.0,1.0,1.0,26261.42
-21702,15603665.0,Yefimova,659.0,Spain,Male,36.0,2.0,0.0,1.0,1.0,1.0,125696.26
-21703,15758513.0,Hsu,584.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,1.0,94844.87
-21704,15658946.0,Trevisani,677.0,Germany,Female,37.0,9.0,114510.85,2.0,1.0,0.0,26342.33
-21705,15685768.0,Ts'ui,660.0,France,Male,38.0,9.0,0.0,1.0,1.0,1.0,134878.19
-21706,15712264.0,Chiang,645.0,France,Female,31.0,8.0,0.0,1.0,1.0,0.0,131283.6
-21707,15586976.0,Uwaezuoke,585.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,162683.06
-21708,15728505.0,Onyemere,605.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,143635.36
-21709,15787026.0,Yermolayev,567.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,81409.0
-21710,15774936.0,Hsia,738.0,Spain,Female,37.0,3.0,0.0,2.0,1.0,0.0,162452.79
-21711,15583090.0,H?,659.0,France,Female,61.0,9.0,0.0,2.0,1.0,1.0,168034.9
-21712,15628863.0,Olisaemeka,682.0,France,Male,38.0,2.0,111677.88,1.0,1.0,1.0,107876.2
-21713,15635502.0,Nweke,600.0,France,Male,54.0,4.0,0.0,2.0,0.0,0.0,126472.39
-21714,15689425.0,Ts'ao,641.0,Spain,Female,42.0,1.0,102723.35,1.0,1.0,1.0,121763.61
-21715,15742824.0,Chinwendu,592.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,174738.2
-21716,15762803.0,Hao,599.0,Spain,Female,49.0,9.0,0.0,1.0,1.0,0.0,170886.01
-21717,15572390.0,Iloerika,686.0,France,Female,31.0,1.0,0.0,2.0,1.0,1.0,92816.86
-21718,15663987.0,Hsiao,646.0,France,Female,30.0,5.0,0.0,1.0,1.0,1.0,598.8
-21719,15733234.0,Hsing,703.0,Germany,Male,51.0,5.0,125860.2,1.0,1.0,0.0,100835.19
-21720,15632793.0,Nkemakolam,678.0,Spain,Male,40.0,8.0,130786.92,1.0,0.0,0.0,189678.7
-21721,15653760.0,Ch'iu,592.0,France,Male,32.0,6.0,121823.7,1.0,1.0,1.0,154767.34
-21722,15631512.0,Ch'iu,652.0,France,Male,31.0,2.0,96769.04,1.0,1.0,1.0,1852.58
-21723,15771742.0,Chuang,635.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,84536.72
-21724,15682424.0,Chukwuemeka,757.0,France,Male,54.0,7.0,0.0,2.0,1.0,0.0,149614.17
-21725,15761733.0,Tuan,819.0,Spain,Male,33.0,6.0,131286.46,1.0,1.0,1.0,117626.9
-21726,15655213.0,Sells,638.0,Germany,Male,44.0,5.0,90218.9,1.0,1.0,1.0,47729.33
-21727,15771580.0,Maclean,684.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,1.0,180736.24
-21728,15667932.0,Iloerika,656.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,98810.78
-21729,15579826.0,Ch'iu,685.0,France,Male,21.0,7.0,67972.19,1.0,1.0,1.0,121551.93
-21730,15750014.0,Yermakov,486.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,114859.19
-21731,15598046.0,Sousa,633.0,France,Male,43.0,4.0,0.0,1.0,1.0,0.0,188343.05
-21732,15789863.0,Onio,645.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,24677.54
-21733,15654700.0,Hsing,646.0,France,Male,40.0,10.0,0.0,2.0,0.0,1.0,152491.22
-21734,15689955.0,Chinwenma,613.0,Germany,Male,44.0,8.0,99325.03,2.0,0.0,0.0,147964.99
-21735,15714993.0,Chiazagomekpere,736.0,France,Male,39.0,3.0,111095.85,1.0,1.0,0.0,135851.3
-21736,15679760.0,Scott,743.0,France,Female,45.0,4.0,123086.39,1.0,1.0,0.0,73857.94
-21737,15589437.0,Ch'ang,571.0,France,Female,28.0,7.0,0.0,2.0,1.0,1.0,166929.43
-21738,15697028.0,Manna,707.0,Germany,Female,31.0,5.0,116805.82,2.0,1.0,0.0,77475.61
-21739,15741094.0,Chukwubuikem,722.0,Spain,Female,42.0,0.0,0.0,2.0,1.0,0.0,166677.35
-21740,15689955.0,Chin,682.0,Spain,Female,48.0,1.0,0.0,1.0,1.0,0.0,66810.48
-21741,15602845.0,Ts'ui,696.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,116885.59
-21742,15581893.0,Chien,712.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,170819.01
-21743,15777529.0,T'ien,528.0,Spain,Female,46.0,7.0,0.0,1.0,1.0,1.0,65675.01
-21744,15644551.0,Nkemjika,689.0,France,Female,47.0,1.0,0.0,2.0,0.0,0.0,187943.6
-21745,15673619.0,Cattaneo,621.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,134878.24
-21746,15678727.0,Hsiao,675.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,88958.49
-21747,15657535.0,Yefremova,583.0,Spain,Male,40.0,3.0,0.0,1.0,1.0,0.0,182337.98
-21748,15771569.0,Chinwendu,645.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,96670.2
-21749,15585067.0,Walker,722.0,France,Male,28.0,3.0,0.0,2.0,1.0,1.0,28367.21
-21750,15756475.0,Kambinachi,638.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,140937.97
-21751,15602844.0,Ts'ao,671.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,9984.4
-21752,15605037.0,Ch'eng,676.0,Germany,Female,29.0,9.0,84909.12,1.0,1.0,0.0,114442.66
-21753,15662865.0,Onwuamaeze,545.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,141872.05
-21754,15594041.0,Onyekaozulu,703.0,Germany,Male,52.0,2.0,135936.1,1.0,0.0,0.0,159742.33
-21755,15784280.0,Onyemachukwu,669.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,151677.31
-21756,15593784.0,Nwabugwu,624.0,Germany,Male,38.0,4.0,75225.84,1.0,1.0,0.0,81491.7
-21757,15727408.0,Ch'eng,713.0,France,Male,29.0,8.0,0.0,2.0,0.0,0.0,161241.65
-21758,15658946.0,Chigozie,716.0,Germany,Male,45.0,10.0,99004.24,1.0,1.0,0.0,88773.04
-21759,15646764.0,Chidimma,635.0,France,Female,41.0,9.0,0.0,2.0,0.0,1.0,58854.88
-21760,15624013.0,Y?,771.0,Germany,Female,32.0,9.0,153360.02,1.0,1.0,1.0,60721.4
-21761,15806945.0,McIntyre,695.0,Spain,Female,49.0,10.0,0.0,1.0,1.0,0.0,120880.72
-21762,15747795.0,Ugochukwu,668.0,France,Female,43.0,1.0,0.0,2.0,1.0,1.0,58879.31
-21763,15776733.0,Folliero,694.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,163558.26
-21764,15772073.0,Calabresi,653.0,France,Female,52.0,1.0,0.0,1.0,0.0,0.0,83056.22
-21765,15617507.0,Udinese,696.0,France,Male,26.0,1.0,106328.08,1.0,1.0,1.0,121525.48
-21766,15598894.0,Hsueh,644.0,Spain,Male,55.0,7.0,119496.87,1.0,1.0,0.0,90928.02
-21767,15739576.0,Chidiebere,724.0,France,Female,36.0,7.0,90544.0,1.0,1.0,0.0,140745.96
-21768,15584090.0,Manna,585.0,Germany,Female,56.0,1.0,163760.82,1.0,1.0,0.0,71672.86
-21769,15691808.0,Chuang,576.0,Germany,Male,36.0,9.0,140956.99,1.0,1.0,1.0,60728.89
-21770,15782531.0,Hsueh,729.0,France,Male,36.0,8.0,80764.03,1.0,1.0,0.0,152490.02
-21771,15792650.0,Tung,587.0,Germany,Male,41.0,6.0,120586.56,2.0,0.0,1.0,80552.36
-21772,15794868.0,Pisano,573.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,0.0,151871.84
-21773,15754569.0,Chidumaga,542.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,1.0,121846.81
-21774,15782574.0,Ch'iu,516.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,122925.4
-21775,15607598.0,Martin,756.0,Germany,Female,54.0,1.0,123906.22,2.0,0.0,1.0,79146.36
-21776,15792360.0,Zuyeva,748.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,142377.69
-21777,15755084.0,Chiabuotu,694.0,France,Male,37.0,7.0,139825.53,1.0,1.0,1.0,63889.1
-21778,15732299.0,Ts'ui,678.0,France,Male,39.0,1.0,140170.61,2.0,0.0,1.0,135067.02
-21779,15809248.0,Y?an,590.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,147913.56
-21780,15693906.0,Chibueze,673.0,Germany,Male,44.0,1.0,126732.85,1.0,1.0,0.0,62282.99
-21781,15637354.0,Uwaezuoke,646.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,148098.95
-21782,15762216.0,K'ung,583.0,France,Female,55.0,8.0,0.0,1.0,0.0,1.0,141709.67
-21783,15709621.0,Ch'iu,684.0,France,Female,41.0,10.0,0.0,2.0,1.0,1.0,120586.32
-21784,15776433.0,Onwughara,807.0,France,Male,41.0,10.0,0.0,2.0,1.0,0.0,80178.54
-21785,15706812.0,Cremonesi,595.0,France,Male,42.0,0.0,0.0,2.0,0.0,1.0,167554.86
-21786,15679963.0,Nwankwo,556.0,France,Female,31.0,5.0,0.0,1.0,1.0,0.0,150475.14
-21787,15592994.0,Chukwualuka,537.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,50679.48
-21788,15592761.0,Onwuamaeze,710.0,France,Male,25.0,3.0,0.0,2.0,1.0,0.0,161069.73
-21789,15572428.0,Hsia,742.0,France,Male,39.0,10.0,151607.56,1.0,1.0,1.0,45985.52
-21790,15790448.0,Chidubem,683.0,Spain,Female,29.0,3.0,0.0,2.0,1.0,1.0,105715.01
-21791,15673908.0,Tuan,667.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,58420.21
-21792,15617454.0,Udegbunam,579.0,Germany,Male,41.0,1.0,155973.88,1.0,0.0,0.0,131807.14
-21793,15701364.0,Teng,565.0,Germany,Female,51.0,5.0,141334.98,1.0,1.0,1.0,185561.89
-21794,15724200.0,McIntyre,743.0,Spain,Female,37.0,1.0,0.0,1.0,0.0,0.0,158887.09
-21795,15661629.0,Lucchesi,684.0,France,Female,25.0,7.0,0.0,2.0,1.0,1.0,2423.9
-21796,15701264.0,Chukwudi,538.0,Spain,Male,41.0,1.0,0.0,1.0,1.0,0.0,85411.01
-21797,15633666.0,Chiu,722.0,Germany,Male,55.0,4.0,142987.46,1.0,1.0,0.0,118862.08
-21798,15636700.0,Tung,747.0,France,Male,44.0,7.0,0.0,2.0,1.0,0.0,126726.33
-21799,15788189.0,Calabresi,686.0,Germany,Female,43.0,9.0,127728.25,4.0,1.0,0.0,141827.83
-21800,15662294.0,Hsiao,624.0,Germany,Female,36.0,5.0,108705.52,1.0,1.0,0.0,167564.82
-21801,15799652.0,Ch'ang,756.0,France,Male,22.0,5.0,153656.85,1.0,1.0,0.0,167553.06
-21802,15686963.0,Lai,611.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,102970.69
-21803,15567802.0,Tuan,707.0,France,Female,43.0,6.0,0.0,2.0,1.0,0.0,26578.69
-21804,15750141.0,H?,751.0,Germany,Male,50.0,8.0,118812.5,2.0,0.0,1.0,114092.74
-21805,15641043.0,Tuan,704.0,France,Female,46.0,4.0,0.0,1.0,1.0,0.0,151733.43
-21806,15622443.0,Kline,757.0,France,Male,67.0,3.0,0.0,1.0,1.0,1.0,146738.83
-21807,15729763.0,Tsou,652.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,94774.12
-21808,15649853.0,Ch'ien,751.0,Germany,Male,52.0,5.0,136855.94,3.0,1.0,1.0,142911.99
-21809,15775761.0,Onyekachi,595.0,Germany,Male,53.0,7.0,104784.23,1.0,1.0,1.0,10070.4
-21810,15603594.0,Onyemere,543.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,126469.09
-21811,15642099.0,Bischof,605.0,France,Male,51.0,1.0,110976.23,1.0,1.0,0.0,73564.44
-21812,15654060.0,K'ung,775.0,Spain,Male,25.0,7.0,0.0,2.0,1.0,0.0,103931.49
-21813,15661526.0,Lettiere,722.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,86767.48
-21814,15771580.0,Chukwudi,520.0,Spain,Female,48.0,3.0,0.0,1.0,1.0,0.0,134878.19
-21815,15773779.0,Ts'ui,529.0,Spain,Female,46.0,5.0,0.0,1.0,1.0,1.0,181258.76
-21816,15683524.0,Hsia,748.0,Germany,Female,51.0,3.0,82559.42,4.0,1.0,1.0,72678.93
-21817,15673929.0,Nucci,605.0,Spain,Male,39.0,5.0,0.0,1.0,1.0,0.0,101168.9
-21818,15794560.0,Yermakov,792.0,Spain,Male,44.0,10.0,0.0,1.0,0.0,0.0,160979.66
-21819,15770174.0,Ts'ai,676.0,France,Male,30.0,7.0,0.0,2.0,0.0,1.0,81402.2
-21820,15724889.0,Ting,711.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,96517.97
-21821,15639669.0,Smith,686.0,France,Female,30.0,9.0,0.0,1.0,1.0,1.0,73329.08
-21822,15570002.0,Chidi,672.0,Spain,Male,38.0,6.0,138053.79,1.0,1.0,1.0,113860.79
-21823,15582892.0,Chiabuotu,703.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,0.0,24983.39
-21824,15671097.0,Y?an,679.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,132973.18
-21825,15649744.0,Ngozichukwuka,609.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,143983.99
-21826,15762218.0,Ch'eng,579.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,71047.51
-21827,15593555.0,T'ang,669.0,Spain,Female,44.0,7.0,0.0,1.0,1.0,0.0,79084.1
-21828,15572021.0,Chu,718.0,Germany,Male,58.0,3.0,122227.87,4.0,0.0,0.0,53394.12
-21829,15798084.0,Biryukov,710.0,France,Male,27.0,3.0,0.0,2.0,1.0,1.0,107841.77
-21830,15682778.0,Ch'iu,581.0,Germany,Male,31.0,3.0,119782.72,2.0,1.0,1.0,28274.6
-21831,15803358.0,Fanucci,619.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,51636.7
-21832,15646936.0,P'eng,694.0,Spain,Male,37.0,3.0,128298.74,1.0,1.0,1.0,71178.09
-21833,15727291.0,Milanesi,588.0,France,Female,31.0,6.0,0.0,1.0,0.0,0.0,100556.98
-21834,15747808.0,Shao,717.0,Spain,Male,49.0,3.0,106059.4,1.0,1.0,0.0,1187.88
-21835,15596962.0,Chiazagomekpere,578.0,France,Female,29.0,3.0,0.0,1.0,1.0,1.0,132973.17
-21836,15794278.0,Hsieh,637.0,France,Female,43.0,4.0,0.0,1.0,1.0,0.0,99377.27
-21837,15775082.0,Ch'iu,578.0,Spain,Female,30.0,6.0,84497.47,1.0,1.0,0.0,164825.07
-21838,15746065.0,Onwughara,669.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,82970.69
-21839,15602551.0,Lettiere,611.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,143357.02
-21840,15736656.0,Nucci,587.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,180712.28
-21841,15727696.0,Uspensky,706.0,Germany,Female,38.0,6.0,157848.57,1.0,1.0,1.0,173899.42
-21842,15668575.0,Hsieh,667.0,France,Female,40.0,10.0,0.0,1.0,1.0,0.0,176763.27
-21843,15654870.0,Uspenskaya,621.0,Germany,Female,45.0,4.0,77647.6,2.0,1.0,1.0,120852.85
-21844,15720644.0,Chiang,716.0,Germany,Female,36.0,9.0,116083.07,1.0,1.0,0.0,142331.85
-21845,15621093.0,Yermakova,850.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,65618.01
-21846,15593555.0,Eluemuno,819.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,175544.02
-21847,15709780.0,Goliwe,538.0,France,Female,41.0,3.0,0.0,2.0,1.0,1.0,85439.73
-21848,15749905.0,Genovese,674.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,172490.78
-21849,15573741.0,Milne,675.0,Spain,Male,51.0,7.0,0.0,3.0,0.0,1.0,142033.07
-21850,15807340.0,Lo,530.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,79082.76
-21851,15778410.0,Ch'in,521.0,Spain,Female,38.0,1.0,0.0,1.0,1.0,0.0,79082.62
-21852,15693526.0,Swayne,498.0,Germany,Male,50.0,5.0,117738.97,1.0,1.0,0.0,163141.18
-21853,15671244.0,Chia,637.0,Germany,Male,31.0,4.0,157848.57,1.0,1.0,1.0,181605.85
-21854,15602312.0,Hsia,582.0,Spain,Female,40.0,9.0,0.0,2.0,1.0,0.0,62282.99
-21855,15662494.0,Biryukov,850.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,1.0,166830.71
-21856,15644788.0,Okechukwu,648.0,France,Female,41.0,6.0,0.0,2.0,1.0,1.0,153727.32
-21857,15713594.0,Mazzanti,678.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,187816.59
-21858,15718216.0,Chukwuemeka,697.0,Spain,Female,43.0,2.0,0.0,2.0,1.0,1.0,91346.03
-21859,15778838.0,Ch'iu,468.0,Germany,Male,32.0,9.0,102741.15,2.0,1.0,1.0,67949.27
-21860,15574072.0,Tien,682.0,Germany,Female,34.0,8.0,120811.89,1.0,1.0,0.0,1055.66
-21861,15761348.0,Chukwuemeka,696.0,Spain,Male,34.0,8.0,0.0,2.0,0.0,0.0,45985.52
-21862,15646566.0,Hao,554.0,Spain,Male,25.0,8.0,0.0,2.0,1.0,1.0,139946.68
-21863,15698716.0,Chidiebere,529.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,81430.61
-21864,15704581.0,Hs?eh,741.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,128881.87
-21865,15730397.0,Chidiebere,477.0,Spain,Male,34.0,1.0,0.0,2.0,0.0,0.0,46786.92
-21866,15642826.0,Y?an,652.0,France,Male,46.0,0.0,0.0,1.0,1.0,0.0,86622.86
-21867,15789544.0,Ko,792.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,122964.18
-21868,15699507.0,Nnaife,599.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,51703.09
-21869,15782236.0,Calabresi,516.0,France,Male,31.0,6.0,0.0,1.0,1.0,0.0,165574.54
-21870,15642991.0,Fan,614.0,France,Male,19.0,1.0,0.0,2.0,1.0,1.0,94767.77
-21871,15658852.0,T'ang,434.0,Spain,Male,33.0,0.0,112333.63,1.0,1.0,0.0,136983.77
-21872,15655252.0,Padovesi,581.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,172601.62
-21873,15701265.0,Milani,598.0,Spain,Male,67.0,8.0,145965.33,1.0,0.0,1.0,180693.61
-21874,15792868.0,Y?,684.0,France,Male,33.0,5.0,128298.74,1.0,0.0,1.0,74898.8
-21875,15737169.0,Hsiao,652.0,France,Male,35.0,1.0,0.0,2.0,0.0,1.0,173365.89
-21876,15719579.0,Chiu,645.0,Spain,Female,50.0,5.0,0.0,1.0,1.0,1.0,80618.47
-21877,15778838.0,Tuan,619.0,Germany,Male,43.0,1.0,141078.37,2.0,1.0,1.0,176760.79
-21878,15671148.0,Hsiung,588.0,Germany,Male,38.0,3.0,135095.47,1.0,1.0,0.0,106977.8
-21879,15799932.0,Chukwueloka,592.0,France,Female,34.0,2.0,0.0,3.0,1.0,1.0,186976.6
-21880,15592025.0,Chiekwugo,662.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,84870.23
-21881,15783883.0,Chukwualuka,651.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,180598.86
-21882,15796787.0,Onyekachi,739.0,France,Male,20.0,7.0,152368.21,1.0,1.0,1.0,131772.51
-21883,15693690.0,Udobata,712.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,157871.55
-21884,15662494.0,Udinese,713.0,Germany,Female,49.0,9.0,121376.15,1.0,1.0,0.0,161069.73
-21885,15603328.0,Lucchese,632.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,136028.97
-21886,15752626.0,Yefremova,641.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,71154.1
-21887,15688876.0,Chia,633.0,France,Male,44.0,5.0,0.0,1.0,0.0,0.0,101300.94
-21888,15803947.0,Hsing,607.0,Germany,Female,30.0,0.0,161613.94,1.0,1.0,0.0,110359.68
-21889,15700657.0,Chia,683.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,0.0,35279.74
-21890,15607993.0,Ch'eng,611.0,France,Male,47.0,5.0,0.0,1.0,1.0,0.0,151415.81
-21891,15650889.0,Lu,579.0,Germany,Male,32.0,3.0,121542.57,2.0,1.0,1.0,166526.26
-21892,15604813.0,Ch'iu,637.0,Germany,Female,44.0,4.0,121669.93,1.0,0.0,1.0,182728.8
-21893,15781298.0,Ch'eng,682.0,Spain,Male,44.0,8.0,0.0,2.0,1.0,1.0,112269.67
-21894,15667460.0,H?,697.0,Germany,Male,62.0,3.0,113657.83,1.0,1.0,0.0,104079.53
-21895,15589420.0,Sun,661.0,Spain,Male,33.0,5.0,136067.82,1.0,1.0,1.0,102908.91
-21896,15690526.0,Shih,742.0,Germany,Male,30.0,7.0,141541.25,2.0,1.0,0.0,177844.06
-21897,15678916.0,Tuan,540.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,175893.53
-21898,15665129.0,Omeokachie,766.0,Spain,Female,40.0,1.0,0.0,1.0,1.0,0.0,121846.81
-21899,15642937.0,Hsiung,703.0,Germany,Female,49.0,5.0,113858.95,2.0,1.0,0.0,172501.38
-21900,15740557.0,Chibuzo,555.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,27239.28
-21901,15698816.0,T'ien,667.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,140745.96
-21902,15678916.0,Hsiao,645.0,Spain,Female,39.0,8.0,0.0,2.0,0.0,0.0,146785.44
-21903,15611786.0,Mazzi,673.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,180622.02
-21904,15775888.0,Chiu,586.0,Germany,Male,30.0,3.0,81733.74,1.0,0.0,1.0,120853.83
-21905,15631604.0,Hsia,584.0,Spain,Male,26.0,7.0,0.0,2.0,0.0,1.0,52321.22
-21906,15573741.0,Ch'iu,727.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,167984.72
-21907,15680197.0,Nwankwo,663.0,Germany,Male,47.0,1.0,113525.53,2.0,1.0,1.0,72549.27
-21908,15768471.0,Folliero,594.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,170638.98
-21909,15751082.0,Ndubuagha,607.0,France,Male,47.0,6.0,0.0,1.0,1.0,0.0,175722.31
-21910,15666163.0,Hs?eh,689.0,Germany,Male,36.0,1.0,120145.96,2.0,1.0,0.0,61315.37
-21911,15661734.0,Hsia,605.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,142937.57
-21912,15633059.0,Ch'iu,562.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,80428.48
-21913,15777904.0,Mazzanti,715.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,107429.72
-21914,15794566.0,Pinto,653.0,Spain,Male,36.0,9.0,0.0,2.0,0.0,0.0,130113.78
-21915,15602841.0,Chukwualuka,769.0,Spain,Female,41.0,3.0,0.0,2.0,1.0,0.0,143357.07
-21916,15751057.0,Onyekaozulu,633.0,Germany,Male,38.0,5.0,135962.4,1.0,1.0,1.0,123427.98
-21917,15730360.0,Ikedinachukwu,683.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,123883.52
-21918,15772383.0,Maclean,554.0,France,Male,33.0,0.0,118207.69,1.0,1.0,0.0,78854.57
-21919,15729763.0,Chiemenam,807.0,France,Male,27.0,4.0,0.0,1.0,1.0,0.0,109057.59
-21920,15740557.0,Hsieh,616.0,France,Male,42.0,1.0,0.0,1.0,1.0,1.0,184842.21
-21921,15784594.0,Gant,774.0,France,Female,38.0,1.0,0.0,1.0,1.0,1.0,137973.45
-21922,15762423.0,T'ang,662.0,Germany,Male,37.0,3.0,157842.37,1.0,1.0,1.0,51587.04
-21923,15632264.0,Ts'ui,707.0,Spain,Male,42.0,7.0,0.0,1.0,1.0,1.0,128815.23
-21924,15785865.0,Trevisano,652.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,162599.51
-21925,15591915.0,McIntyre,696.0,Spain,Male,36.0,7.0,0.0,1.0,1.0,1.0,62810.91
-21926,15579892.0,Chien,462.0,France,Female,25.0,9.0,0.0,2.0,0.0,1.0,92982.61
-21927,15574324.0,Fanucci,585.0,Spain,Male,32.0,3.0,160541.0,1.0,1.0,1.0,142223.94
-21928,15610147.0,Hs?eh,569.0,Germany,Female,35.0,8.0,121498.58,2.0,1.0,1.0,64448.36
-21929,15730273.0,Estrada,583.0,France,Male,50.0,4.0,0.0,2.0,0.0,1.0,93777.61
-21930,15752626.0,Aitken,516.0,France,Male,35.0,0.0,0.0,2.0,1.0,0.0,47322.05
-21931,15581229.0,Zetticci,756.0,Spain,Male,37.0,9.0,93716.72,1.0,0.0,0.0,121789.3
-21932,15802220.0,Milne,586.0,Germany,Female,48.0,8.0,122724.35,1.0,1.0,0.0,159132.83
-21933,15777892.0,Kenechi,706.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,36706.56
-21934,15579969.0,Hsieh,717.0,Spain,Male,42.0,2.0,0.0,2.0,1.0,1.0,125762.95
-21935,15768120.0,Bogle,689.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,103560.98
-21936,15732672.0,Nnachetam,755.0,France,Male,54.0,3.0,71281.29,1.0,1.0,0.0,124052.97
-21937,15594556.0,Liao,663.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,1.0,145792.01
-21938,15698528.0,Bales,590.0,Spain,Male,27.0,5.0,0.0,2.0,1.0,1.0,140745.91
-21939,15658486.0,Mazzi,573.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,171378.77
-21940,15753092.0,Hs?,516.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,1.0,36242.19
-21941,15642063.0,Folliero,757.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,91347.76
-21942,15589793.0,Ndubueze,531.0,Germany,Female,31.0,3.0,113743.37,1.0,1.0,0.0,80552.11
-21943,15641782.0,T'ao,716.0,France,Female,25.0,7.0,0.0,2.0,1.0,1.0,56780.97
-21944,15806926.0,Yuryeva,735.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,45424.13
-21945,15734659.0,Chia,584.0,Spain,Male,31.0,3.0,0.0,2.0,0.0,0.0,35367.19
-21946,15686957.0,Yobanna,592.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,160810.85
-21947,15608845.0,Trevisani,764.0,France,Male,36.0,3.0,0.0,2.0,0.0,0.0,66580.13
-21948,15663410.0,Ts'ui,605.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,169952.82
-21949,15571666.0,Hsieh,633.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,116581.1
-21950,15763065.0,Hsueh,628.0,France,Female,31.0,1.0,166518.63,1.0,1.0,0.0,62347.71
-21951,15648876.0,L?,743.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,119969.99
-21952,15687071.0,Chidubem,703.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,0.0,145765.83
-21953,15663917.0,White,607.0,France,Female,52.0,8.0,0.0,1.0,1.0,1.0,132798.65
-21954,15785519.0,Okwudiliolisa,748.0,Germany,Male,40.0,10.0,130142.79,1.0,1.0,1.0,142963.31
-21955,15812198.0,Pirozzi,686.0,France,Female,40.0,3.0,0.0,1.0,1.0,0.0,168816.22
-21956,15588580.0,Shih,711.0,Germany,Male,41.0,3.0,94773.11,1.0,1.0,0.0,121210.09
-21957,15652693.0,Iweobiegbunam,710.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,85746.52
-21958,15713347.0,Fitch,727.0,Spain,Female,35.0,5.0,0.0,2.0,0.0,0.0,10054.05
-21959,15777896.0,Sagese,622.0,Germany,Female,48.0,1.0,139265.31,1.0,1.0,0.0,62449.07
-21960,15771501.0,Ch'ang,604.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,159508.52
-21961,15690744.0,Iheatu,612.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,79919.13
-21962,15572762.0,Chidimma,724.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,182627.25
-21963,15795519.0,Chia,656.0,France,Female,40.0,5.0,120911.75,1.0,1.0,1.0,123969.64
-21964,15703793.0,Onyemauchechukwu,585.0,France,Male,33.0,6.0,0.0,1.0,1.0,1.0,66893.28
-21965,15783883.0,Chiu,596.0,France,Male,36.0,1.0,0.0,2.0,0.0,1.0,183492.9
-21966,15726289.0,Chikere,687.0,France,Male,40.0,5.0,0.0,1.0,1.0,1.0,150390.57
-21967,15581871.0,Hsiung,677.0,France,Female,27.0,4.0,90124.64,2.0,1.0,0.0,96875.52
-21968,15782778.0,Martin,706.0,Germany,Male,35.0,9.0,108876.91,1.0,0.0,0.0,155823.2
-21969,15761793.0,Ts'ui,579.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,170142.09
-21970,15751788.0,Pinto,683.0,France,Female,50.0,4.0,107949.45,3.0,1.0,0.0,119996.95
-21971,15627162.0,Palermo,577.0,France,Male,44.0,1.0,0.0,1.0,1.0,0.0,18082.06
-21972,15752846.0,Ch'en,635.0,Spain,Male,36.0,5.0,0.0,1.0,1.0,0.0,67611.36
-21973,15731865.0,P'eng,716.0,France,Male,27.0,4.0,0.0,1.0,0.0,0.0,142223.94
-21974,15692991.0,Kao,685.0,France,Male,42.0,1.0,0.0,1.0,0.0,0.0,166929.43
-21975,15784526.0,Nkemjika,627.0,France,Male,26.0,1.0,82547.11,1.0,1.0,1.0,172576.43
-21976,15671769.0,Kambinachi,721.0,Spain,Male,23.0,4.0,0.0,2.0,1.0,0.0,89804.83
-21977,15809901.0,Chidiegwu,573.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,0.0,60901.72
-21978,15731760.0,Chiu,679.0,Germany,Male,30.0,9.0,115251.64,1.0,1.0,1.0,182059.28
-21979,15583553.0,Onwuamaeze,739.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,81482.6
-21980,15572626.0,Calabrese,637.0,France,Female,28.0,10.0,0.0,2.0,0.0,0.0,143980.28
-21981,15612193.0,T'ang,677.0,France,Female,23.0,6.0,0.0,1.0,1.0,1.0,73322.0
-21982,15722425.0,Chukwubuikem,633.0,Germany,Female,43.0,5.0,79858.13,1.0,0.0,1.0,83556.65
-21983,15775490.0,Chidubem,607.0,Spain,Male,32.0,4.0,121404.64,1.0,1.0,1.0,60721.4
-21984,15591915.0,Ekechukwu,577.0,France,Female,39.0,7.0,162657.64,1.0,1.0,0.0,121765.0
-21985,15585853.0,Tien,794.0,Germany,Female,43.0,5.0,131254.81,3.0,1.0,0.0,107839.03
-21986,15771573.0,Ebelechukwu,763.0,Germany,Female,44.0,5.0,140733.74,3.0,1.0,0.0,163989.66
-21987,15682576.0,Chiemela,772.0,Germany,Female,61.0,1.0,116084.32,1.0,1.0,0.0,90977.48
-21988,15640491.0,Nwebube,714.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,113428.77
-21989,15595588.0,Liao,648.0,France,Male,44.0,9.0,0.0,2.0,0.0,1.0,136915.15
-21990,15655961.0,Zikoranachidimma,574.0,France,Female,39.0,0.0,123760.68,1.0,1.0,1.0,160553.87
-21991,15573628.0,T'ang,549.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,167033.83
-21992,15769216.0,Shih,611.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,1.0,118324.75
-21993,15582841.0,Ch'iu,696.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,105817.53
-21994,15679048.0,Lo Duca,632.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,63687.06
-21995,15767129.0,Nkemakolam,744.0,Spain,Female,25.0,8.0,85891.55,1.0,1.0,0.0,161571.79
-21996,15804962.0,Chiu,757.0,France,Male,29.0,7.0,0.0,1.0,1.0,0.0,105760.01
-21997,15579548.0,Chukwuemeka,645.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,160529.71
-21998,15767894.0,Palerma,706.0,France,Female,42.0,3.0,117052.82,1.0,0.0,1.0,161465.31
-21999,15757837.0,Yefremova,592.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,10963.04
-22000,15717191.0,Nnaife,709.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,145796.49
-22001,15698932.0,Ch'iu,598.0,Germany,Male,63.0,7.0,133636.04,1.0,1.0,1.0,170968.99
-22002,15591179.0,Shao,738.0,France,Male,45.0,2.0,126473.33,1.0,0.0,0.0,79577.38
-22003,15694860.0,White,693.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,125253.46
-22004,15648067.0,Yashina,738.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,168835.11
-22005,15584090.0,Liao,739.0,Spain,Female,32.0,9.0,0.0,1.0,1.0,0.0,149970.59
-22006,15619238.0,Nkemakolam,610.0,Spain,Male,54.0,8.0,0.0,1.0,1.0,0.0,53477.76
-22007,15661219.0,Chukwudi,662.0,Germany,Female,36.0,7.0,116939.35,1.0,1.0,0.0,51345.39
-22008,15778089.0,Chigolum,712.0,Germany,Female,38.0,3.0,137452.09,1.0,1.0,1.0,94898.1
-22009,15624592.0,Yermakova,584.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,159585.61
-22010,15666884.0,Lu,730.0,France,Female,40.0,3.0,167034.96,1.0,1.0,1.0,151623.91
-22011,15673747.0,Okwuadigbo,528.0,Spain,Male,49.0,0.0,0.0,1.0,1.0,0.0,184843.77
-22012,15607988.0,Hsiung,652.0,France,Male,29.0,5.0,0.0,2.0,0.0,0.0,177739.4
-22013,15684769.0,Hs?,645.0,Germany,Male,34.0,6.0,107784.65,1.0,1.0,0.0,151317.27
-22014,15729574.0,Rowntree,598.0,Spain,Male,31.0,6.0,0.0,2.0,0.0,0.0,161076.92
-22015,15625592.0,Hao,645.0,Germany,Male,36.0,2.0,104763.41,2.0,1.0,1.0,81298.09
-22016,15698664.0,Kosisochukwu,583.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,5791.85
-22017,15691170.0,Onyemauchechukwu,653.0,France,Female,32.0,4.0,125785.23,1.0,1.0,0.0,71738.56
-22018,15685920.0,O'Sullivan,592.0,France,Female,37.0,3.0,102936.72,1.0,1.0,1.0,158816.03
-22019,15636673.0,T'ao,687.0,France,Female,47.0,4.0,0.0,2.0,1.0,1.0,93718.16
-22020,15806467.0,Hs?,681.0,France,Male,45.0,2.0,0.0,2.0,0.0,0.0,98820.09
-22021,15761076.0,Shih,596.0,France,Female,38.0,6.0,164951.56,1.0,1.0,1.0,170968.99
-22022,15632050.0,Ch'ang,564.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,141168.22
-22023,15783816.0,Calabresi,725.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,83087.13
-22024,15685329.0,Lo Duca,631.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,92381.01
-22025,15748068.0,Onyekaozulu,695.0,France,Female,35.0,4.0,0.0,2.0,1.0,0.0,170634.49
-22026,15754569.0,T'ao,692.0,France,Female,36.0,6.0,0.0,3.0,1.0,0.0,176768.59
-22027,15595047.0,Burt,694.0,France,Male,47.0,3.0,0.0,2.0,1.0,0.0,128882.66
-22028,15673908.0,Ts'ui,683.0,France,Male,43.0,1.0,139432.37,1.0,1.0,1.0,35644.79
-22029,15757777.0,Ch'ang,719.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,157577.71
-22030,15589325.0,Lombardi,762.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,134794.02
-22031,15764033.0,Maclean,739.0,France,Female,24.0,10.0,0.0,2.0,1.0,1.0,67976.57
-22032,15785519.0,Chiang,592.0,France,Female,44.0,9.0,0.0,2.0,0.0,0.0,73686.75
-22033,15695792.0,Pisano,719.0,France,Female,32.0,6.0,74989.58,1.0,1.0,0.0,118913.53
-22034,15603846.0,Ponomarev,538.0,Germany,Male,41.0,9.0,92350.47,1.0,1.0,0.0,127875.1
-22035,15709639.0,Lu,740.0,France,Male,38.0,1.0,0.0,1.0,1.0,0.0,93439.75
-22036,15731781.0,Mazzi,714.0,France,Female,35.0,0.0,0.0,1.0,1.0,1.0,100153.43
-22037,15581748.0,K?,811.0,France,Female,28.0,4.0,0.0,1.0,1.0,0.0,167984.72
-22038,15794396.0,Genovesi,634.0,France,Male,40.0,4.0,0.0,2.0,0.0,0.0,38794.71
-22039,15605968.0,Onyemauchechi,493.0,France,Female,37.0,10.0,126663.51,1.0,1.0,1.0,184514.01
-22040,15736154.0,Cremonesi,734.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,100686.11
-22041,15777896.0,K'ung,705.0,Spain,Female,32.0,5.0,0.0,1.0,1.0,0.0,141827.33
-22042,15577274.0,Ch'eng,721.0,Spain,Male,32.0,3.0,0.0,2.0,0.0,1.0,182612.38
-22043,15796218.0,Chukwualuka,556.0,Spain,Female,39.0,4.0,0.0,2.0,0.0,0.0,60775.76
-22044,15737521.0,Pagnotto,599.0,Spain,Female,38.0,9.0,143988.0,2.0,1.0,1.0,107815.31
-22045,15798824.0,Chinwemma,714.0,Spain,Male,42.0,9.0,183579.54,2.0,1.0,1.0,175067.08
-22046,15788068.0,Pirozzi,521.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,0.0,60427.3
-22047,15590888.0,Nkemdilim,578.0,Germany,Female,35.0,7.0,133476.04,1.0,1.0,1.0,105760.01
-22048,15661626.0,Chuang,696.0,Spain,Female,28.0,2.0,161209.66,1.0,0.0,1.0,104076.51
-22049,15692406.0,O'Toole,751.0,Germany,Female,32.0,5.0,117833.3,2.0,1.0,1.0,82469.06
-22050,15659327.0,Obielumani,567.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,141501.45
-22051,15745324.0,Zetticci,607.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,0.0,129912.74
-22052,15736078.0,Ojiofor,704.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,151146.67
-22053,15652289.0,Nwachinemelu,556.0,Spain,Female,29.0,8.0,0.0,2.0,0.0,1.0,121568.37
-22054,15656086.0,Chiu,663.0,Germany,Female,37.0,3.0,98567.1,2.0,1.0,0.0,103945.58
-22055,15585748.0,Nkemakonam,628.0,France,Male,35.0,5.0,0.0,1.0,1.0,1.0,172718.35
-22056,15684958.0,Ting,599.0,Germany,Female,40.0,7.0,132211.85,1.0,1.0,1.0,79577.39
-22057,15742285.0,H?,730.0,France,Male,40.0,3.0,0.0,1.0,1.0,0.0,162683.06
-22058,15643496.0,Tung,669.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,0.0,49424.6
-22059,15756472.0,P'eng,709.0,France,Male,54.0,9.0,0.0,1.0,0.0,1.0,124598.59
-22060,15804822.0,Teng,595.0,France,Female,27.0,8.0,0.0,2.0,1.0,1.0,79414.0
-22061,15640034.0,Rizzo,669.0,France,Female,28.0,5.0,0.0,1.0,1.0,0.0,138526.96
-22062,15699335.0,Udinese,759.0,France,Female,29.0,8.0,104760.82,1.0,1.0,1.0,139953.94
-22063,15744105.0,Trevisano,634.0,Spain,Female,38.0,3.0,0.0,1.0,1.0,0.0,122925.4
-22064,15627859.0,Calabrese,682.0,France,Male,35.0,5.0,142082.07,2.0,1.0,0.0,143954.99
-22065,15571059.0,Ts'ai,707.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,89822.48
-22066,15683522.0,Zetticci,850.0,Spain,Female,40.0,9.0,0.0,1.0,1.0,1.0,181598.25
-22067,15783883.0,Ch'in,641.0,France,Female,42.0,9.0,0.0,2.0,0.0,1.0,62674.05
-22068,15731553.0,Lo Duca,627.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,142513.5
-22069,15724307.0,Hsia,717.0,France,Male,26.0,3.0,0.0,2.0,0.0,0.0,180622.03
-22070,15707681.0,Chinomso,675.0,France,Female,28.0,1.0,0.0,1.0,1.0,0.0,85516.37
-22071,15691624.0,Chialuka,684.0,France,Male,40.0,4.0,0.0,2.0,0.0,1.0,103396.08
-22072,15634792.0,Chukwueloka,662.0,France,Male,37.0,4.0,112666.67,1.0,1.0,0.0,26767.85
-22073,15697948.0,Tsai,749.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,121485.1
-22074,15717231.0,Chikelu,613.0,Germany,Female,40.0,1.0,95858.37,1.0,0.0,0.0,51112.8
-22075,15615254.0,Eames,596.0,France,Male,52.0,9.0,0.0,3.0,1.0,0.0,183572.39
-22076,15684930.0,Ch'ien,735.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,0.0,184524.74
-22077,15782418.0,Tsou,570.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,140763.18
-22078,15692631.0,Dore,685.0,France,Male,47.0,4.0,0.0,2.0,1.0,1.0,126794.56
-22079,15761497.0,Ch'iu,731.0,France,Male,28.0,4.0,0.0,2.0,1.0,1.0,122964.14
-22080,15654574.0,Hsia,722.0,Germany,Male,31.0,1.0,121206.96,1.0,0.0,1.0,91652.43
-22081,15783541.0,Fanucci,706.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,1.0,141872.05
-22082,15690670.0,Fetherstonhaugh,496.0,Germany,Female,45.0,5.0,117791.06,3.0,1.0,1.0,51620.94
-22083,15698028.0,Ch'ien,752.0,Germany,Male,45.0,6.0,131834.8,1.0,1.0,0.0,184954.11
-22084,15803886.0,Doyne,686.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,92917.54
-22085,15654495.0,Yobachukwu,644.0,Spain,Female,34.0,5.0,0.0,3.0,1.0,1.0,118570.05
-22086,15802758.0,Lai,639.0,France,Female,29.0,5.0,0.0,2.0,0.0,1.0,97623.28
-22087,15802570.0,Chukwuebuka,710.0,Spain,Male,29.0,4.0,0.0,2.0,0.0,1.0,134790.61
-22088,15571958.0,Bage,646.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,132418.59
-22089,15690175.0,Chukwualuka,738.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,173486.39
-22090,15782868.0,Iqbal,593.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,123866.43
-22091,15631451.0,Chiu,633.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,93439.75
-22092,15690169.0,Horsley,705.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,122398.54
-22093,15604576.0,Chin,516.0,France,Male,44.0,4.0,0.0,2.0,1.0,0.0,103557.85
-22094,15581552.0,Teng,659.0,Germany,Male,44.0,2.0,97565.74,2.0,1.0,1.0,178373.93
-22095,15799790.0,Ts'ao,649.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,1.0,143681.85
-22096,15639357.0,Tung,684.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,1.0,79734.23
-22097,15777784.0,Hsia,771.0,Germany,Male,49.0,4.0,115409.6,1.0,0.0,1.0,70849.47
-22098,15708505.0,Obialo,592.0,France,Male,47.0,3.0,142688.57,1.0,1.0,1.0,175820.91
-22099,15729201.0,Onyekaozulu,597.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,91514.62
-22100,15732113.0,Shih,651.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,93835.73
-22101,15726289.0,Nkemdilim,653.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,77965.44
-22102,15746805.0,Eluemuno,645.0,Germany,Male,34.0,9.0,120825.7,2.0,1.0,1.0,44946.29
-22103,15749693.0,Ives,821.0,France,Male,64.0,3.0,0.0,1.0,1.0,1.0,149614.61
-22104,15659327.0,Chia,678.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,49374.82
-22105,15614491.0,Lu,637.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,178067.33
-22106,15586974.0,Chuang,717.0,France,Male,33.0,0.0,0.0,2.0,0.0,0.0,152429.67
-22107,15693376.0,Martin,547.0,France,Female,31.0,7.0,132876.23,1.0,0.0,0.0,79871.16
-22108,15745196.0,Cattaneo,558.0,France,Female,45.0,6.0,138577.34,1.0,1.0,0.0,100015.79
-22109,15761686.0,Okwudiliolisa,584.0,Germany,Female,26.0,4.0,152212.88,1.0,0.0,1.0,120512.08
-22110,15632551.0,Chukwuemeka,777.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,148527.56
-22111,15739168.0,Ch'in,731.0,Spain,Female,46.0,2.0,0.0,2.0,1.0,0.0,172363.41
-22112,15747174.0,Zito,523.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,45985.52
-22113,15698028.0,Yobachi,649.0,Spain,Male,54.0,1.0,127864.4,1.0,1.0,0.0,141533.19
-22114,15762927.0,Chifley,657.0,Spain,Female,42.0,4.0,0.0,2.0,0.0,1.0,106781.59
-22115,15687828.0,Dike,619.0,Germany,Female,40.0,7.0,118098.62,1.0,0.0,1.0,67789.99
-22116,15794204.0,Onyemauchechukwu,556.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,84927.42
-22117,15604813.0,P'an,850.0,Spain,Male,35.0,9.0,0.0,1.0,1.0,0.0,136491.72
-22118,15646784.0,Lai,577.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,0.0,35770.84
-22119,15728773.0,Outhwaite,537.0,France,Female,23.0,7.0,0.0,2.0,1.0,1.0,106977.53
-22120,15634267.0,Chukwuebuka,597.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,171657.57
-22121,15781598.0,Yobachi,510.0,Spain,Male,39.0,4.0,0.0,1.0,1.0,1.0,122925.4
-22122,15621083.0,Manna,705.0,France,Male,34.0,9.0,0.0,1.0,1.0,0.0,121151.1
-22123,15686973.0,Ku,590.0,France,Male,49.0,1.0,112465.06,1.0,1.0,0.0,47911.03
-22124,15784320.0,Skelton,528.0,Germany,Male,36.0,2.0,147989.79,2.0,1.0,0.0,173989.66
-22125,15667554.0,Bibi,608.0,France,Male,34.0,6.0,141525.55,1.0,1.0,0.0,131521.72
-22126,15582090.0,Ch'en,659.0,France,Male,55.0,3.0,0.0,1.0,1.0,1.0,103832.58
-22127,15807524.0,O'Donnell,619.0,Germany,Female,44.0,8.0,116471.43,1.0,1.0,0.0,167873.49
-22128,15635125.0,Ch'ien,711.0,France,Male,42.0,6.0,0.0,2.0,1.0,1.0,28238.25
-22129,15674929.0,Kolesnikov,744.0,France,Male,39.0,6.0,129968.67,2.0,1.0,0.0,166038.27
-22130,15658435.0,Ts'ai,640.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,180551.76
-22131,15695475.0,Lo,778.0,Germany,Male,29.0,1.0,135470.8,1.0,1.0,1.0,62810.91
-22132,15777772.0,P'an,660.0,Germany,Female,25.0,8.0,126773.43,2.0,1.0,0.0,51695.41
-22133,15716085.0,Manna,655.0,France,Female,38.0,6.0,0.0,2.0,1.0,0.0,88481.32
-22134,15806922.0,Harker,673.0,Germany,Female,37.0,3.0,123737.04,2.0,1.0,0.0,162501.83
-22135,15592578.0,Hs?,582.0,France,Male,35.0,1.0,0.0,1.0,1.0,0.0,38190.78
-22136,15757577.0,Tien,678.0,France,Male,23.0,2.0,0.0,2.0,1.0,1.0,177099.71
-22137,15570194.0,Watson,724.0,Spain,Female,47.0,9.0,0.0,1.0,1.0,0.0,180622.02
-22138,15586870.0,Chiazagomekpele,645.0,Germany,Male,42.0,2.0,92877.33,2.0,1.0,0.0,179521.24
-22139,15763625.0,Lu,653.0,Germany,Male,40.0,1.0,112070.86,1.0,1.0,0.0,73464.88
-22140,15752912.0,Palermo,705.0,France,Male,40.0,8.0,121729.42,1.0,1.0,0.0,145551.6
-22141,15718773.0,T'ao,538.0,France,Female,41.0,8.0,0.0,2.0,1.0,1.0,152883.95
-22142,15691513.0,Iloerika,744.0,France,Male,36.0,8.0,0.0,1.0,1.0,0.0,180969.55
-22143,15767358.0,P'eng,644.0,France,Male,32.0,6.0,0.0,1.0,1.0,1.0,130789.15
-22144,15796958.0,Hsiung,686.0,Spain,Female,37.0,7.0,151933.63,1.0,1.0,0.0,72945.95
-22145,15677243.0,Hs?,610.0,France,Male,58.0,4.0,122446.61,1.0,1.0,0.0,53379.52
-22146,15790717.0,T'ao,588.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,52367.29
-22147,15632210.0,Iweobiegbunam,691.0,Spain,Female,34.0,9.0,128736.39,1.0,1.0,0.0,92664.09
-22148,15723299.0,Ch'ien,638.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,114448.77
-22149,15795963.0,Chinweike,576.0,France,Male,27.0,9.0,0.0,2.0,0.0,0.0,161243.09
-22150,15662091.0,Lai,721.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,138780.86
-22151,15606755.0,Hsieh,703.0,France,Male,43.0,2.0,0.0,1.0,0.0,1.0,91881.29
-22152,15794580.0,Onwumelu,678.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,181461.46
-22153,15753874.0,Ts'ao,558.0,Spain,Female,37.0,6.0,0.0,2.0,1.0,0.0,158973.77
-22154,15602851.0,Ch'iu,642.0,France,Male,36.0,8.0,126725.25,1.0,1.0,1.0,149614.81
-22155,15612109.0,T'an,686.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,160548.12
-22156,15763093.0,Maclean,578.0,Spain,Male,41.0,4.0,99844.68,2.0,1.0,0.0,71175.94
-22157,15620981.0,Hsueh,659.0,Germany,Male,31.0,9.0,96659.64,2.0,1.0,1.0,40321.87
-22158,15606901.0,Obiajulu,567.0,France,Female,46.0,5.0,0.0,2.0,0.0,0.0,64345.61
-22159,15696859.0,Okoli,516.0,France,Male,55.0,3.0,0.0,1.0,1.0,1.0,93478.96
-22160,15652626.0,Lai,617.0,France,Male,37.0,3.0,0.0,2.0,0.0,0.0,60917.24
-22161,15742285.0,Trevisano,543.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,82298.59
-22162,15687959.0,Toscani,850.0,France,Female,42.0,10.0,0.0,2.0,1.0,1.0,91354.41
-22163,15572792.0,T'ien,646.0,France,Female,40.0,1.0,0.0,2.0,0.0,1.0,91574.92
-22164,15641411.0,Iadanza,520.0,France,Male,31.0,0.0,106521.31,1.0,0.0,1.0,128881.87
-22165,15573452.0,H?,581.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,77951.84
-22166,15772892.0,Ch'iu,635.0,France,Female,38.0,3.0,97435.85,1.0,1.0,0.0,142331.85
-22167,15646563.0,Echezonachukwu,648.0,Germany,Male,41.0,3.0,141749.68,1.0,1.0,1.0,88890.75
-22168,15717995.0,Hsueh,609.0,France,Female,47.0,5.0,0.0,2.0,1.0,1.0,161388.22
-22169,15612108.0,Y?an,589.0,France,Male,30.0,0.0,0.0,1.0,1.0,0.0,183872.88
-22170,15680167.0,Chigozie,662.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,180360.34
-22171,15684319.0,Ch'iu,731.0,France,Female,40.0,3.0,0.0,1.0,1.0,0.0,119259.44
-22172,15750014.0,Scott,613.0,France,Male,38.0,4.0,113317.1,1.0,1.0,0.0,55313.51
-22173,15682533.0,Tung,713.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,134778.02
-22174,15692599.0,Hsia,520.0,France,Female,46.0,3.0,0.0,1.0,0.0,0.0,25310.82
-22175,15655274.0,Ugochukwu,703.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,77414.45
-22176,15671769.0,Hsueh,731.0,France,Male,27.0,1.0,0.0,2.0,1.0,0.0,26796.73
-22177,15646859.0,Smith,733.0,Germany,Male,47.0,9.0,138032.15,1.0,1.0,1.0,75825.57
-22178,15752502.0,Onyemauchechukwu,672.0,Germany,Male,28.0,9.0,120782.7,2.0,1.0,1.0,126761.2
-22179,15813681.0,Hsieh,850.0,France,Female,35.0,6.0,0.0,2.0,0.0,1.0,77830.78
-22180,15695792.0,Onyemachukwu,613.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,71047.51
-22181,15736146.0,De Mestre,746.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,115676.61
-22182,15798850.0,Hs?,590.0,Spain,Female,39.0,0.0,0.0,2.0,1.0,0.0,52629.73
-22183,15673570.0,Onio,549.0,Spain,Female,47.0,8.0,113858.95,1.0,1.0,0.0,82298.59
-22184,15635752.0,Scott,743.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,129936.53
-22185,15709511.0,Scott,757.0,France,Female,27.0,8.0,96415.57,1.0,1.0,1.0,26920.43
-22186,15754919.0,Teng,637.0,France,Male,56.0,8.0,0.0,1.0,0.0,0.0,104730.52
-22187,15578098.0,Chinwendu,679.0,Germany,Male,49.0,7.0,148074.79,1.0,1.0,0.0,114469.55
-22188,15600379.0,Milanesi,709.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,0.0,170195.4
-22189,15662232.0,Hs?,632.0,France,Male,46.0,8.0,137780.74,1.0,1.0,1.0,181757.19
-22190,15640409.0,Lai,570.0,France,Male,25.0,1.0,0.0,2.0,1.0,1.0,113677.53
-22191,15727499.0,Chukwualuka,539.0,Germany,Male,42.0,9.0,120911.75,1.0,1.0,0.0,85890.75
-22192,15677336.0,Chibugo,569.0,Germany,Male,29.0,1.0,139840.36,1.0,1.0,0.0,26389.92
-22193,15666055.0,Ugoji,594.0,Germany,Male,41.0,5.0,116037.76,1.0,0.0,0.0,131301.74
-22194,15798885.0,Galkin,614.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,152992.04
-22195,15615473.0,Nkemdirim,704.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,172782.69
-22196,15772381.0,Whitehead,648.0,France,Male,32.0,6.0,146584.19,1.0,1.0,1.0,176984.34
-22197,15668943.0,Martin,641.0,Spain,Female,50.0,4.0,0.0,2.0,1.0,1.0,120892.96
-22198,15772995.0,Ch'ang,738.0,France,Female,44.0,1.0,0.0,2.0,1.0,0.0,147963.07
-22199,15636023.0,Pinto,608.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,0.0,50610.62
-22200,15782899.0,Trevisano,627.0,Spain,Male,48.0,8.0,0.0,1.0,0.0,0.0,61290.99
-22201,15783875.0,Pirozzi,753.0,Germany,Female,28.0,7.0,118098.62,1.0,1.0,1.0,158049.9
-22202,15679048.0,Ts'ui,710.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,65765.22
-22203,15572461.0,Chukwukere,699.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,180329.83
-22204,15686963.0,Nwankwo,669.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,0.0,158420.14
-22205,15794560.0,Kao,582.0,France,Female,47.0,1.0,0.0,2.0,1.0,1.0,184842.21
-22206,15675650.0,O'Donnell,655.0,France,Male,41.0,9.0,125845.65,2.0,0.0,0.0,91429.74
-22207,15573077.0,Hsiao,850.0,Germany,Female,48.0,3.0,133656.93,1.0,1.0,1.0,64831.36
-22208,15635285.0,Lo,633.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,136610.01
-22209,15792726.0,DeRose,705.0,France,Male,49.0,3.0,0.0,2.0,1.0,0.0,72849.29
-22210,15690898.0,Calabresi,590.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,86414.24
-22211,15791131.0,Hsueh,609.0,Spain,Female,44.0,6.0,136526.26,1.0,1.0,0.0,168886.73
-22212,15803479.0,Hsia,581.0,Germany,Female,36.0,2.0,73348.56,2.0,1.0,1.0,152992.04
-22213,15656937.0,Palermo,676.0,France,Male,36.0,8.0,83116.68,1.0,1.0,1.0,103381.47
-22214,15807003.0,Ting,526.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,74890.58
-22215,15791767.0,Onuchukwu,516.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,5983.7
-22216,15617348.0,Milne,825.0,Germany,Female,51.0,3.0,128306.7,1.0,1.0,1.0,172601.62
-22217,15687959.0,Iweobiegbulam,763.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,40313.47
-22218,15686463.0,Udinesi,703.0,France,Female,27.0,3.0,148045.45,2.0,1.0,0.0,87816.86
-22219,15732963.0,Tsao,742.0,France,Female,29.0,8.0,0.0,2.0,0.0,0.0,131919.48
-22220,15750937.0,Hsing,638.0,Germany,Male,44.0,9.0,125537.38,1.0,1.0,0.0,172501.38
-22221,15808930.0,Palermo,618.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,44406.17
-22222,15738751.0,Rizzo,597.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,167564.82
-22223,15772383.0,Hs?,597.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,158071.69
-22224,15594812.0,Frolova,690.0,France,Female,33.0,4.0,123894.35,1.0,0.0,1.0,103914.4
-22225,15574743.0,Chukwukere,649.0,Germany,Female,33.0,1.0,97473.87,2.0,0.0,1.0,187597.98
-22226,15664504.0,McWilliams,585.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,82222.86
-22227,15658850.0,Hs?eh,592.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,169915.02
-22228,15593601.0,Hsia,619.0,Germany,Female,42.0,5.0,142387.31,1.0,1.0,0.0,80120.19
-22229,15668580.0,Tuan,805.0,France,Male,72.0,4.0,0.0,2.0,0.0,1.0,176924.21
-22230,15779985.0,Aksenov,554.0,Spain,Female,43.0,5.0,0.0,1.0,1.0,0.0,158043.11
-22231,15674880.0,Milani,641.0,Germany,Female,39.0,3.0,147726.98,1.0,1.0,0.0,171587.9
-22232,15669957.0,Yobanna,745.0,France,Male,46.0,5.0,0.0,2.0,1.0,0.0,149170.92
-22233,15777892.0,Chukwufumnanya,624.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,94498.82
-22234,15779904.0,L?,615.0,France,Female,33.0,7.0,142882.49,1.0,1.0,0.0,60906.12
-22235,15785042.0,Tikhonov,663.0,France,Female,27.0,5.0,0.0,2.0,0.0,1.0,89972.25
-22236,15763665.0,Tung,849.0,France,Male,31.0,2.0,0.0,1.0,1.0,0.0,138889.15
-22237,15629200.0,Ting,688.0,France,Male,40.0,7.0,0.0,2.0,0.0,1.0,163550.58
-22238,15593969.0,Hsiung,771.0,Germany,Female,42.0,9.0,92787.93,1.0,1.0,0.0,153419.62
-22239,15661629.0,Lo Duca,717.0,Germany,Male,37.0,6.0,93680.43,2.0,1.0,0.0,103945.58
-22240,15591932.0,Yefimov,590.0,France,Male,27.0,5.0,0.0,2.0,1.0,0.0,93551.36
-22241,15791501.0,Nnachetam,569.0,France,Female,39.0,6.0,0.0,2.0,1.0,0.0,94872.55
-22242,15664754.0,Aksenov,743.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,0.0,19370.73
-22243,15790683.0,Chidiebere,586.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,72350.47
-22244,15668575.0,Nucci,695.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,0.0,183656.12
-22245,15585928.0,Maclean,670.0,France,Male,35.0,0.0,151242.54,2.0,1.0,1.0,136497.28
-22246,15596972.0,Y?an,649.0,Germany,Male,40.0,8.0,121405.04,1.0,1.0,1.0,161217.18
-22247,15648064.0,Pinto,647.0,France,Female,38.0,7.0,0.0,2.0,0.0,1.0,103349.74
-22248,15732674.0,Ch'ien,586.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,62449.07
-22249,15682355.0,Ts'ui,731.0,France,Male,26.0,0.0,126729.41,1.0,1.0,0.0,172490.78
-22250,15776939.0,Tsou,607.0,Spain,Female,42.0,10.0,0.0,2.0,1.0,1.0,61312.15
-22251,15775625.0,Chia,681.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,61977.82
-22252,15772798.0,Chiemenam,644.0,Germany,Female,46.0,3.0,117899.95,1.0,1.0,0.0,186942.49
-22253,15697460.0,Uchenna,725.0,France,Male,40.0,7.0,141134.67,2.0,1.0,1.0,46811.77
-22254,15806420.0,Nwebube,651.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,161845.81
-22255,15588854.0,Maclean,627.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,92888.52
-22256,15702361.0,Nworie,559.0,France,Male,32.0,4.0,163733.82,1.0,0.0,0.0,126434.61
-22257,15583076.0,Folliero,627.0,Spain,Male,32.0,4.0,0.0,1.0,1.0,1.0,142331.85
-22258,15597909.0,T'ien,731.0,Spain,Male,38.0,9.0,114655.58,1.0,1.0,0.0,91098.76
-22259,15774727.0,Ugochukwu,724.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,38962.94
-22260,15807593.0,Tseng,634.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,110457.99
-22261,15793337.0,Ch'ang,630.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,93777.61
-22262,15706799.0,Ives,531.0,France,Female,35.0,9.0,0.0,1.0,1.0,0.0,132798.78
-22263,15588019.0,Ozoemena,746.0,France,Female,56.0,1.0,0.0,1.0,1.0,0.0,184843.77
-22264,15624781.0,White,724.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,116046.22
-22265,15679968.0,Manna,707.0,Spain,Male,21.0,1.0,0.0,2.0,1.0,1.0,66463.62
-22266,15707011.0,Chinwenma,685.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,145562.4
-22267,15697574.0,Nnamutaezinwa,593.0,France,Female,31.0,8.0,0.0,2.0,0.0,0.0,134901.56
-22268,15762928.0,Chukwuemeka,703.0,Spain,Male,42.0,5.0,116967.68,1.0,0.0,0.0,166471.79
-22269,15732628.0,Ch'ang,516.0,France,Male,28.0,5.0,0.0,2.0,0.0,1.0,68815.05
-22270,15665629.0,Ch'ang,757.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,44965.44
-22271,15632275.0,P'eng,711.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,59864.88
-22272,15782311.0,Ejimofor,705.0,France,Female,32.0,8.0,166833.86,1.0,0.0,0.0,124592.84
-22273,15583732.0,Tung,553.0,Germany,Male,46.0,1.0,141581.08,2.0,1.0,0.0,179631.85
-22274,15643438.0,Chia,581.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,91981.85
-22275,15619238.0,Chinonyelum,754.0,France,Female,48.0,8.0,0.0,2.0,1.0,0.0,121592.5
-22276,15760063.0,Hsiung,598.0,France,Male,44.0,1.0,130147.98,1.0,0.0,1.0,167636.15
-22277,15724296.0,Chiu,642.0,Germany,Female,49.0,6.0,143321.97,1.0,1.0,0.0,143317.42
-22278,15686964.0,Ibezimako,689.0,France,Female,67.0,1.0,104267.7,1.0,1.0,1.0,183490.99
-22279,15626008.0,DeRose,674.0,France,Male,31.0,1.0,82729.49,2.0,1.0,0.0,82662.73
-22280,15721251.0,Chukwuebuka,520.0,Germany,Male,45.0,6.0,123906.55,1.0,0.0,0.0,179655.85
-22281,15589924.0,Chigolum,521.0,Spain,Female,37.0,6.0,0.0,1.0,1.0,0.0,149517.71
-22282,15578251.0,Kosisochukwu,592.0,France,Male,48.0,0.0,110928.51,1.0,1.0,1.0,71420.16
-22283,15604576.0,Mazzanti,697.0,France,Female,47.0,7.0,121451.29,1.0,0.0,0.0,79866.73
-22284,15777922.0,Nnaife,637.0,Germany,Male,36.0,7.0,148061.89,1.0,0.0,1.0,141946.92
-22285,15681115.0,Smith,697.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,166476.95
-22286,15642715.0,DeRose,687.0,Spain,Female,36.0,9.0,0.0,2.0,1.0,1.0,97938.59
-22287,15702277.0,Fanucci,686.0,Germany,Male,33.0,1.0,121159.65,1.0,1.0,1.0,102260.16
-22288,15651740.0,Hsueh,774.0,France,Female,28.0,6.0,0.0,1.0,1.0,0.0,138871.12
-22289,15677052.0,Calabresi,551.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,143357.07
-22290,15622370.0,Onyinyechukwuka,554.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,143357.07
-22291,15576022.0,T'ang,779.0,Germany,Female,34.0,7.0,144895.04,2.0,1.0,1.0,148087.62
-22292,15572022.0,Chien,689.0,France,Male,42.0,8.0,0.0,2.0,0.0,0.0,136915.15
-22293,15614847.0,Chukwukere,692.0,Germany,Female,48.0,10.0,149318.55,4.0,1.0,0.0,113677.53
-22294,15780654.0,Yobachukwu,726.0,France,Male,32.0,5.0,117704.73,1.0,0.0,1.0,155711.64
-22295,15570134.0,Tuan,567.0,France,Male,37.0,2.0,0.0,1.0,1.0,1.0,114722.42
-22296,15605827.0,Dickson,649.0,Germany,Female,46.0,3.0,109052.04,1.0,1.0,0.0,51393.1
-22297,15632264.0,Hao,645.0,Spain,Female,45.0,7.0,0.0,1.0,1.0,0.0,177524.19
-22298,15663438.0,Chibuzo,547.0,France,Male,52.0,10.0,0.0,1.0,1.0,0.0,140763.18
-22299,15600510.0,T'ien,581.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,101240.08
-22300,15791326.0,Walker,709.0,Germany,Male,35.0,3.0,119266.69,2.0,1.0,1.0,179833.45
-22301,15800517.0,Tsao,543.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,121525.48
-22302,15775124.0,Trevisan,645.0,France,Female,32.0,3.0,101985.81,1.0,1.0,1.0,176925.69
-22303,15653776.0,Milne,769.0,France,Male,49.0,5.0,0.0,1.0,1.0,0.0,28698.17
-22304,15793424.0,Olague,709.0,France,Male,31.0,1.0,121365.38,1.0,1.0,1.0,143317.31
-22305,15571244.0,Sazonova,710.0,Germany,Female,30.0,1.0,125095.94,2.0,1.0,0.0,143481.85
-22306,15777060.0,Omeokachie,614.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,105720.09
-22307,15774931.0,Scott,614.0,France,Male,26.0,7.0,0.0,2.0,1.0,1.0,121751.04
-22308,15721462.0,Hs?eh,622.0,Germany,Female,49.0,1.0,152212.44,2.0,1.0,0.0,114066.77
-22309,15643916.0,Pagnotto,732.0,France,Female,35.0,10.0,0.0,1.0,1.0,1.0,9984.4
-22310,15710689.0,Shih,651.0,Germany,Female,47.0,0.0,102773.2,1.0,1.0,0.0,63736.68
-22311,15705009.0,Onyekachukwu,775.0,Spain,Male,36.0,8.0,127760.07,1.0,1.0,1.0,161069.73
-22312,15736302.0,Teng,754.0,Germany,Male,40.0,4.0,113386.36,3.0,1.0,0.0,58467.08
-22313,15765322.0,Rizzo,850.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,124749.04
-22314,15772363.0,T'ao,711.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,0.0,100556.98
-22315,15804937.0,Calabrese,628.0,Spain,Female,40.0,6.0,0.0,2.0,1.0,1.0,91981.85
-22316,15718057.0,Ch'ien,685.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,105720.09
-22317,15764284.0,Y?,703.0,France,Male,39.0,5.0,0.0,1.0,0.0,0.0,136050.44
-22318,15570134.0,Nworie,716.0,Germany,Female,51.0,3.0,92474.05,1.0,1.0,0.0,121525.48
-22319,15770041.0,Ch'ang,615.0,Germany,Female,42.0,1.0,99897.59,2.0,1.0,0.0,167036.94
-22320,15723989.0,McIntosh,715.0,France,Female,29.0,2.0,0.0,1.0,1.0,0.0,163736.09
-22321,15798084.0,Maclean,633.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,60905.51
-22322,15576526.0,Aksyonova,682.0,France,Male,69.0,8.0,0.0,2.0,1.0,1.0,48378.4
-22323,15701352.0,Ndubueze,660.0,Spain,Male,30.0,5.0,0.0,2.0,1.0,1.0,142223.94
-22324,15605968.0,Skelton,699.0,Germany,Male,27.0,10.0,127823.93,2.0,1.0,1.0,161344.13
-22325,15594812.0,Ts'ui,663.0,Germany,Female,39.0,4.0,131764.96,2.0,0.0,1.0,108469.2
-22326,15801277.0,Nweke,619.0,France,Female,48.0,9.0,0.0,1.0,1.0,1.0,108577.97
-22327,15581282.0,Okwuoma,729.0,France,Male,47.0,4.0,0.0,2.0,1.0,0.0,80582.34
-22328,15693543.0,Aitken,619.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,148769.08
-22329,15785819.0,Tuan,727.0,Spain,Male,39.0,1.0,0.0,1.0,0.0,0.0,173365.9
-22330,15676521.0,Chidiegwu,751.0,Germany,Female,46.0,8.0,121411.9,2.0,1.0,0.0,126812.63
-22331,15769216.0,T'ang,635.0,France,Male,49.0,8.0,0.0,2.0,1.0,0.0,166317.71
-22332,15592707.0,Iadanza,746.0,France,Male,28.0,9.0,102290.57,1.0,1.0,0.0,67789.99
-22333,15726153.0,Ch'iu,587.0,France,Male,37.0,4.0,110340.68,1.0,1.0,0.0,143585.29
-22334,15658813.0,Cremonesi,595.0,France,Female,34.0,9.0,0.0,1.0,0.0,1.0,141756.32
-22335,15623989.0,Oguejiofor,642.0,Spain,Male,44.0,6.0,0.0,2.0,0.0,0.0,145490.85
-22336,15672798.0,Ch'in,718.0,France,Male,41.0,9.0,0.0,1.0,0.0,1.0,60728.89
-22337,15651883.0,Krichauff,556.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,0.0,60905.51
-22338,15774738.0,Shih,718.0,Spain,Female,31.0,1.0,121581.56,1.0,1.0,0.0,126794.56
-22339,15703399.0,Calabrese,553.0,Germany,Male,40.0,9.0,122453.27,2.0,1.0,0.0,118429.02
-22340,15588517.0,Onyekachi,545.0,France,Male,34.0,6.0,0.0,1.0,1.0,1.0,76773.53
-22341,15631451.0,H?,589.0,France,Female,20.0,6.0,0.0,2.0,1.0,0.0,63736.17
-22342,15593094.0,Ts'ui,685.0,France,Male,32.0,1.0,83568.26,1.0,1.0,1.0,116766.79
-22343,15746664.0,Pirozzi,697.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,170984.87
-22344,15612043.0,Cawood,815.0,France,Male,44.0,5.0,0.0,2.0,1.0,0.0,145551.6
-22345,15766064.0,Folliero,707.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,115217.59
-22346,15591036.0,T'ao,565.0,France,Female,39.0,1.0,0.0,1.0,1.0,0.0,132038.65
-22347,15756848.0,McElhone,681.0,France,Female,43.0,7.0,0.0,1.0,1.0,0.0,70954.41
-22348,15700937.0,Tien,699.0,Spain,Female,48.0,7.0,0.0,1.0,1.0,0.0,59957.83
-22349,15592461.0,Ch'ang,684.0,France,Male,53.0,0.0,0.0,1.0,1.0,0.0,141803.77
-22350,15656300.0,Chidimma,695.0,France,Male,44.0,4.0,0.0,2.0,1.0,0.0,109057.56
-22351,15607786.0,Ugochukwu,583.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,133091.32
-22352,15775108.0,O'Donnell,678.0,Germany,Male,31.0,7.0,124098.54,1.0,1.0,1.0,181605.85
-22353,15749103.0,H?,590.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,66561.4
-22354,15663927.0,Chiang,717.0,France,Female,29.0,6.0,96152.68,1.0,1.0,1.0,169621.69
-22355,15667938.0,Onwubiko,642.0,Germany,Male,25.0,4.0,103033.83,1.0,1.0,0.0,115068.99
-22356,15602851.0,Ukaegbunam,521.0,France,Male,55.0,5.0,122550.05,1.0,1.0,1.0,177747.03
-22357,15631217.0,Hsiao,663.0,Germany,Male,29.0,4.0,90217.07,2.0,1.0,1.0,69244.24
-22358,15642824.0,Hs?eh,627.0,Germany,Male,28.0,2.0,112727.06,2.0,1.0,0.0,38596.34
-22359,15694456.0,T'ien,676.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,102628.98
-22360,15641723.0,Houghton,712.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,125062.02
-22361,15656613.0,Chukwukere,675.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,116973.19
-22362,15736302.0,Ch'ien,590.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,104889.3
-22363,15615254.0,Tuan,547.0,France,Female,51.0,1.0,114655.58,1.0,1.0,1.0,9209.21
-22364,15605020.0,Oluchukwu,850.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,0.0,88965.46
-22365,15770185.0,Frolova,596.0,France,Male,59.0,6.0,0.0,2.0,1.0,1.0,133086.45
-22366,15669326.0,Yancy,471.0,France,Male,37.0,4.0,122227.71,1.0,1.0,1.0,75832.53
-22367,15634896.0,Onyekachukwu,648.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,147090.9
-22368,15802585.0,Chiekwugo,706.0,France,Female,64.0,4.0,0.0,2.0,0.0,1.0,100557.03
-22369,15754569.0,Fiorentino,593.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,79329.7
-22370,15742272.0,Hsia,652.0,Spain,Female,27.0,6.0,0.0,2.0,1.0,0.0,134463.24
-22371,15626143.0,Maclean,463.0,Germany,Male,58.0,2.0,131563.88,1.0,1.0,0.0,131301.74
-22372,15589435.0,McLean,648.0,Spain,Female,53.0,7.0,0.0,1.0,1.0,1.0,143635.01
-22373,15795927.0,Niu,691.0,France,Female,37.0,9.0,93578.68,1.0,1.0,0.0,160546.58
-22374,15756901.0,Ch'ien,651.0,France,Female,40.0,6.0,0.0,4.0,1.0,0.0,120852.85
-22375,15724334.0,K'ung,724.0,France,Female,41.0,6.0,122329.26,1.0,1.0,0.0,125877.22
-22376,15730397.0,Okechukwu,775.0,France,Male,50.0,4.0,0.0,1.0,0.0,1.0,131807.14
-22377,15631681.0,Shao,703.0,France,Female,34.0,8.0,0.0,2.0,0.0,0.0,78834.41
-22378,15759499.0,H?,642.0,Germany,Female,43.0,9.0,147167.25,2.0,1.0,0.0,100941.57
-22379,15594815.0,Mazzanti,526.0,France,Female,44.0,2.0,0.0,1.0,1.0,0.0,96546.55
-22380,15778936.0,Ch'in,651.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,63747.51
-22381,15706458.0,Yobachukwu,706.0,France,Female,55.0,8.0,127269.75,1.0,1.0,0.0,75762.0
-22382,15654891.0,T'ang,850.0,Germany,Male,38.0,9.0,134740.19,2.0,1.0,0.0,86414.41
-22383,15798975.0,Onwumelu,681.0,France,Female,61.0,2.0,0.0,1.0,1.0,0.0,72852.74
-22384,15792922.0,P'eng,608.0,Spain,Male,38.0,5.0,123906.55,1.0,1.0,0.0,143829.54
-22385,15644296.0,Hsiao,583.0,Germany,Female,39.0,4.0,144875.34,1.0,0.0,0.0,103315.72
-22386,15769125.0,Scott,656.0,France,Male,51.0,7.0,0.0,2.0,1.0,1.0,93769.89
-22387,15707556.0,Okwudiliolisa,675.0,France,Female,53.0,9.0,0.0,1.0,1.0,0.0,168034.9
-22388,15743075.0,Hsia,607.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,161894.4
-22389,15757829.0,Onyemauchechukwu,677.0,Spain,Female,38.0,4.0,0.0,1.0,1.0,0.0,115818.17
-22390,15603880.0,Fennell,671.0,France,Female,29.0,6.0,0.0,2.0,1.0,1.0,76637.38
-22391,15734565.0,McLean,704.0,France,Male,25.0,3.0,0.0,2.0,1.0,0.0,122381.02
-22392,15681075.0,Hao,592.0,France,Female,42.0,6.0,0.0,2.0,1.0,1.0,88890.05
-22393,15777772.0,Tuan,598.0,Spain,Male,43.0,7.0,99106.73,1.0,1.0,0.0,17949.41
-22394,15687094.0,Mao,535.0,France,Female,53.0,9.0,0.0,1.0,1.0,0.0,136985.08
-22395,15595036.0,Hsieh,629.0,France,Male,29.0,2.0,137523.02,1.0,1.0,1.0,4050.57
-22396,15585068.0,Milani,709.0,France,Male,37.0,7.0,133537.1,1.0,1.0,0.0,146715.07
-22397,15583499.0,Ts'ai,731.0,France,Male,47.0,7.0,0.0,1.0,1.0,0.0,40313.47
-22398,15736287.0,T'ao,528.0,Spain,Female,41.0,1.0,0.0,2.0,1.0,1.0,107837.43
-22399,15631815.0,Hs?eh,667.0,France,Male,49.0,8.0,152303.8,1.0,0.0,0.0,149772.61
-22400,15730137.0,Ts'ai,724.0,France,Female,42.0,6.0,106301.85,1.0,1.0,0.0,4798.12
-22401,15709920.0,Echezonachukwu,635.0,France,Female,40.0,9.0,125845.66,1.0,0.0,1.0,60988.21
-22402,15651022.0,Ch'ang,744.0,France,Male,27.0,2.0,134562.9,1.0,0.0,0.0,117972.45
-22403,15643574.0,Chikezie,636.0,Germany,Male,31.0,7.0,146033.62,1.0,0.0,0.0,114448.77
-22404,15657566.0,Bitter,665.0,France,Male,38.0,3.0,0.0,2.0,0.0,1.0,59893.8
-22405,15641091.0,Chiang,627.0,France,Male,40.0,3.0,0.0,1.0,1.0,1.0,167773.55
-22406,15611171.0,Hsiao,773.0,Spain,Male,41.0,2.0,0.0,2.0,1.0,0.0,87537.32
-22407,15594878.0,Fang,755.0,France,Female,34.0,8.0,0.0,2.0,0.0,0.0,173365.89
-22408,15766272.0,Chiang,576.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,85982.47
-22409,15696733.0,Ch'in,710.0,France,Male,41.0,9.0,91536.93,1.0,1.0,1.0,118045.98
-22410,15786680.0,Lassetter,719.0,Germany,Female,36.0,9.0,153360.02,1.0,1.0,0.0,55313.51
-22411,15806447.0,Ponomaryov,682.0,France,Male,47.0,9.0,0.0,1.0,0.0,1.0,123954.15
-22412,15637354.0,Milani,646.0,Spain,Female,40.0,8.0,0.0,1.0,0.0,0.0,109742.8
-22413,15697318.0,Nickson,663.0,France,Female,38.0,8.0,0.0,2.0,0.0,1.0,122823.24
-22414,15765014.0,Nnaife,763.0,France,Female,30.0,4.0,143054.85,1.0,1.0,0.0,53710.23
-22415,15687765.0,Onyemauchechi,668.0,Spain,Male,39.0,9.0,0.0,1.0,0.0,1.0,170818.28
-22416,15703037.0,O'Donnell,587.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,139947.17
-22417,15583595.0,O'Toole,735.0,France,Female,33.0,2.0,0.0,1.0,0.0,1.0,143954.99
-22418,15714789.0,Chidubem,684.0,Germany,Male,49.0,4.0,116622.73,2.0,1.0,0.0,161671.15
-22419,15625941.0,Ch'en,636.0,Spain,Male,33.0,3.0,152265.43,1.0,1.0,1.0,143598.7
-22420,15784923.0,Iqbal,746.0,Germany,Female,41.0,7.0,105473.74,1.0,1.0,0.0,144290.97
-22421,15776631.0,Martin,632.0,Germany,Female,46.0,2.0,111756.5,1.0,1.0,0.0,172576.43
-22422,15643487.0,Shih,684.0,Germany,Male,43.0,5.0,145260.23,2.0,1.0,0.0,187905.12
-22423,15573077.0,Yobachi,711.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,49108.23
-22424,15811486.0,Chinwendu,693.0,France,Female,31.0,4.0,0.0,2.0,1.0,0.0,54871.02
-22425,15641298.0,Chigbogu,635.0,France,Female,46.0,7.0,0.0,1.0,1.0,0.0,60954.45
-22426,15674720.0,Liao,564.0,France,Female,37.0,5.0,0.0,1.0,1.0,0.0,122381.02
-22427,15809302.0,O'Meara,554.0,Spain,Female,31.0,7.0,0.0,2.0,0.0,0.0,93719.87
-22428,15771299.0,Hao,850.0,Spain,Female,38.0,3.0,0.0,1.0,0.0,0.0,169915.02
-22429,15782530.0,Trevisani,743.0,France,Female,27.0,8.0,121854.45,1.0,1.0,0.0,81256.29
-22430,15689633.0,Ch'ang,550.0,Germany,Male,51.0,1.0,104598.19,3.0,1.0,0.0,125777.28
-22431,15777772.0,Genovese,598.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,187616.16
-22432,15602845.0,Nwachinemelu,590.0,France,Female,45.0,2.0,0.0,1.0,1.0,1.0,125658.28
-22433,15791533.0,Manna,648.0,France,Male,32.0,1.0,137933.07,1.0,1.0,0.0,149550.97
-22434,15792650.0,Tien,549.0,Germany,Male,34.0,4.0,132598.38,2.0,1.0,1.0,51554.58
-22435,15798868.0,Chukwudi,599.0,Germany,Male,35.0,1.0,111095.85,1.0,1.0,1.0,122899.01
-22436,15677310.0,Ritchie,835.0,France,Female,45.0,6.0,108883.29,1.0,1.0,1.0,172063.09
-22437,15734762.0,Bibi,588.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,181918.26
-22438,15658048.0,Fang,582.0,France,Female,29.0,9.0,0.0,1.0,1.0,1.0,152733.89
-22439,15757912.0,Yobachukwu,604.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,1.0,64516.8
-22440,15788367.0,Y?,593.0,Spain,Female,32.0,2.0,0.0,2.0,1.0,1.0,45419.08
-22441,15773779.0,Olisanugo,615.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,25339.29
-22442,15643908.0,Teng,771.0,Spain,Female,35.0,8.0,0.0,1.0,0.0,1.0,70774.97
-22443,15669922.0,Chidimma,710.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,1710.89
-22444,15632578.0,Tsou,763.0,Spain,Female,28.0,1.0,0.0,2.0,1.0,1.0,73564.44
-22445,15585855.0,K?,781.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,121073.23
-22446,15596713.0,Maclean,697.0,Germany,Male,35.0,5.0,131729.17,2.0,1.0,0.0,123954.45
-22447,15636407.0,Hs?,730.0,Spain,Female,23.0,2.0,0.0,2.0,0.0,1.0,109296.18
-22448,15589475.0,Yeh,656.0,France,Male,45.0,1.0,0.0,1.0,0.0,1.0,144413.92
-22449,15718281.0,H?,682.0,Germany,Male,35.0,7.0,84309.57,2.0,1.0,0.0,79323.61
-22450,15756071.0,Onyekachi,850.0,France,Male,41.0,10.0,0.0,1.0,1.0,0.0,141533.19
-22451,15636595.0,Aitken,678.0,France,Female,34.0,7.0,0.0,1.0,1.0,1.0,97633.93
-22452,15700854.0,Maclean,659.0,Spain,Female,42.0,2.0,79450.09,1.0,0.0,1.0,175025.27
-22453,15682618.0,Teng,592.0,Spain,Male,37.0,6.0,0.0,2.0,0.0,0.0,162511.55
-22454,15656414.0,Nnachetam,699.0,France,Male,26.0,2.0,121404.64,1.0,1.0,1.0,161845.81
-22455,15800295.0,Yermakov,597.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,171045.31
-22456,15763617.0,P'an,714.0,Spain,Female,54.0,1.0,0.0,1.0,0.0,0.0,53817.23
-22457,15798839.0,P'an,479.0,France,Female,33.0,4.0,0.0,1.0,0.0,0.0,117036.38
-22458,15721740.0,Chukwuemeka,571.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,82647.65
-22459,15776743.0,Chien,595.0,France,Male,31.0,6.0,123960.97,1.0,0.0,1.0,175843.68
-22460,15752578.0,Aitken,561.0,France,Male,35.0,8.0,0.0,2.0,0.0,0.0,168527.56
-22461,15729377.0,O'Brien,537.0,Spain,Male,19.0,2.0,0.0,2.0,1.0,0.0,144423.17
-22462,15793726.0,Ewing,727.0,Germany,Female,35.0,3.0,139592.98,2.0,1.0,0.0,121407.48
-22463,15591512.0,Hsia,586.0,France,Male,35.0,2.0,74989.58,2.0,1.0,0.0,79623.28
-22464,15635244.0,Zito,778.0,Spain,Male,52.0,0.0,117565.03,1.0,1.0,1.0,167032.67
-22465,15795872.0,Ts'ai,615.0,Spain,Female,55.0,6.0,0.0,1.0,1.0,1.0,162599.51
-22466,15609704.0,Kerr,685.0,France,Female,41.0,1.0,137853.0,2.0,0.0,0.0,179436.6
-22467,15752388.0,Chiu,703.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,96660.76
-22468,15711352.0,Nwagugheuzo,738.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,149807.01
-22469,15571059.0,Bovee,850.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,0.0,102076.03
-22470,15585041.0,Nwachinemelu,745.0,France,Female,40.0,8.0,0.0,1.0,1.0,0.0,83681.2
-22471,15728352.0,Genovese,703.0,Spain,Female,60.0,6.0,178827.3,1.0,1.0,1.0,159576.75
-22472,15754849.0,Baresi,624.0,France,Female,44.0,0.0,0.0,1.0,1.0,0.0,146891.07
-22473,15791216.0,Trevisani,709.0,Spain,Female,33.0,2.0,0.0,2.0,0.0,1.0,84635.64
-22474,15770091.0,Hsiung,719.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,106564.88
-22475,15658548.0,Hsing,707.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,82951.12
-22476,15795540.0,Istomin,714.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,87071.18
-22477,15801559.0,Chienezie,602.0,France,Male,34.0,6.0,0.0,2.0,0.0,1.0,96833.0
-22478,15624703.0,Chidiebele,651.0,Spain,Female,46.0,5.0,114519.57,2.0,0.0,0.0,147040.25
-22479,15761497.0,Tsou,577.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,179780.74
-22480,15607988.0,Dickson,648.0,Spain,Male,33.0,7.0,0.0,1.0,1.0,0.0,180345.44
-22481,15746190.0,Yobanna,681.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,141533.19
-22482,15718507.0,Chukwuhaenye,571.0,Germany,Male,38.0,3.0,140745.33,2.0,1.0,0.0,173486.39
-22483,15793890.0,Ts'ao,682.0,France,Female,29.0,6.0,0.0,2.0,0.0,0.0,179421.84
-22484,15651022.0,Ch'ien,622.0,Germany,Female,34.0,2.0,132865.56,1.0,1.0,0.0,104722.47
-22485,15794870.0,Shih,571.0,France,Female,24.0,3.0,0.0,2.0,1.0,1.0,123880.19
-22486,15596979.0,Smith,678.0,France,Female,54.0,4.0,0.0,1.0,1.0,1.0,132770.11
-22487,15782390.0,Hsia,637.0,Germany,Female,45.0,8.0,141819.02,1.0,1.0,0.0,135088.58
-22488,15716134.0,Ni,667.0,Spain,Male,53.0,3.0,0.0,1.0,1.0,0.0,178291.09
-22489,15706268.0,Yuriev,651.0,France,Female,50.0,7.0,0.0,3.0,1.0,0.0,131644.41
-22490,15783882.0,Ubanwa,706.0,France,Male,47.0,4.0,0.0,2.0,0.0,1.0,92898.17
-22491,15674671.0,Chiang,746.0,Spain,Male,26.0,4.0,0.0,2.0,1.0,1.0,161069.73
-22492,15685920.0,Pirozzi,705.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,176858.5
-22493,15756920.0,Yermolayeva,850.0,France,Female,34.0,7.0,120718.28,1.0,1.0,1.0,143357.07
-22494,15782364.0,Ugochukwu,707.0,France,Male,46.0,7.0,132351.29,2.0,1.0,1.0,54865.92
-22495,15768293.0,Kolesnikov,588.0,Germany,Male,47.0,4.0,161608.81,2.0,1.0,0.0,126518.0
-22496,15579914.0,Chinwendu,705.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,140941.47
-22497,15656471.0,Kambinachi,683.0,France,Male,38.0,2.0,0.0,2.0,0.0,1.0,113796.15
-22498,15682536.0,Chukwualuka,633.0,Spain,Female,45.0,10.0,0.0,2.0,1.0,0.0,75632.78
-22499,15749167.0,Chin,667.0,France,Female,27.0,5.0,0.0,2.0,0.0,1.0,103921.43
-22500,15803976.0,Yobanna,604.0,Germany,Male,32.0,4.0,90723.36,2.0,1.0,1.0,166476.95
-22501,15591035.0,Tretiakova,704.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,160579.19
-22502,15662291.0,Kosisochukwu,667.0,France,Male,26.0,7.0,121172.97,1.0,1.0,1.0,124749.08
-22503,15713463.0,Chukwukadibia,586.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,134767.34
-22504,15686984.0,Chidumaga,719.0,Spain,Male,46.0,3.0,0.0,2.0,0.0,1.0,101694.67
-22505,15692991.0,Tretiakova,656.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,94774.12
-22506,15779915.0,Zikoranachidimma,652.0,France,Female,51.0,3.0,0.0,1.0,1.0,0.0,181543.67
-22507,15576131.0,Nkemdilim,691.0,France,Female,62.0,2.0,107541.06,1.0,1.0,1.0,126551.65
-22508,15807008.0,Ch'ien,579.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,138527.56
-22509,15772798.0,Iloerika,628.0,Germany,Female,46.0,6.0,138664.57,1.0,1.0,0.0,149822.04
-22510,15660155.0,Chigolum,710.0,Germany,Female,27.0,6.0,162702.35,2.0,1.0,1.0,121553.19
-22511,15604217.0,Shao,693.0,Spain,Female,57.0,9.0,0.0,1.0,1.0,1.0,160696.72
-22512,15779267.0,Yobanna,616.0,Spain,Male,37.0,4.0,118022.06,1.0,1.0,1.0,166694.93
-22513,15737408.0,Ch'iu,481.0,Spain,Male,34.0,2.0,0.0,2.0,0.0,0.0,125848.88
-22514,15642099.0,Onyekaozulu,576.0,France,Female,31.0,3.0,110689.07,1.0,1.0,0.0,172810.52
-22515,15656937.0,Pirozzi,685.0,France,Female,41.0,7.0,0.0,1.0,1.0,0.0,160548.12
-22516,15667632.0,Kudryashova,627.0,Spain,Female,27.0,5.0,0.0,2.0,0.0,1.0,55857.48
-22517,15673372.0,Dike,682.0,France,Female,43.0,5.0,0.0,1.0,1.0,0.0,60375.18
-22518,15699238.0,Tsao,749.0,Germany,Male,35.0,10.0,120158.74,2.0,1.0,0.0,77977.69
-22519,15809462.0,Hs?,573.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,131629.71
-22520,15714883.0,Eluemuno,659.0,France,Female,62.0,6.0,0.0,1.0,1.0,0.0,139469.11
-22521,15668894.0,McIntyre,684.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,133086.45
-22522,15570970.0,Hsiao,669.0,Germany,Male,27.0,7.0,147043.89,2.0,1.0,0.0,137028.51
-22523,15668575.0,Onyeorulu,581.0,France,Female,31.0,4.0,0.0,1.0,1.0,0.0,89566.74
-22524,15794849.0,Chibueze,645.0,Spain,Male,44.0,9.0,0.0,2.0,1.0,0.0,121239.65
-22525,15641091.0,Chinedum,613.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,28276.83
-22526,15576094.0,Uvarova,679.0,France,Female,42.0,2.0,0.0,2.0,1.0,1.0,176913.42
-22527,15735782.0,Nkemjika,656.0,Germany,Female,54.0,10.0,124580.69,1.0,0.0,0.0,178045.97
-22528,15785865.0,Onwuamaeze,773.0,Spain,Male,26.0,5.0,79562.98,1.0,0.0,1.0,99490.01
-22529,15775750.0,Chiazagomekpere,713.0,Germany,Female,52.0,5.0,150148.51,1.0,1.0,0.0,92888.52
-22530,15705663.0,Yefremova,635.0,France,Male,39.0,1.0,87842.98,1.0,0.0,1.0,168840.22
-22531,15581229.0,Ugochukwu,593.0,Spain,Female,34.0,4.0,0.0,1.0,1.0,1.0,51536.99
-22532,15665629.0,Chien,567.0,France,Male,41.0,3.0,131326.47,1.0,0.0,1.0,173321.65
-22533,15806447.0,Smith,706.0,Spain,Male,46.0,7.0,0.0,2.0,1.0,1.0,48249.88
-22534,15770309.0,Ch'iu,591.0,France,Female,29.0,8.0,0.0,1.0,1.0,1.0,178045.97
-22535,15594404.0,Lettiere,538.0,Germany,Female,41.0,3.0,135962.2,1.0,0.0,1.0,12796.43
-22536,15761053.0,Liao,678.0,France,Male,54.0,2.0,0.0,2.0,1.0,0.0,128247.03
-22537,15784761.0,Tsai,559.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,120790.02
-22538,15598884.0,Hsing,773.0,France,Female,40.0,2.0,0.0,2.0,0.0,1.0,97769.04
-22539,15740406.0,P'eng,555.0,Germany,Male,37.0,4.0,91893.32,1.0,1.0,0.0,71173.64
-22540,15651052.0,Chien,741.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,125870.5
-22541,15782360.0,Hsiao,731.0,France,Male,34.0,5.0,114715.71,2.0,1.0,1.0,78915.68
-22542,15787035.0,Ch'ang,708.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,60901.72
-22543,15681180.0,Ch'iu,753.0,France,Male,25.0,2.0,152314.66,1.0,1.0,1.0,114442.66
-22544,15789425.0,Lu,618.0,Spain,Male,32.0,4.0,0.0,1.0,1.0,1.0,85983.54
-22545,15762615.0,Hsueh,710.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,112554.68
-22546,15665438.0,Nwachukwu,612.0,Germany,Female,50.0,2.0,126152.84,2.0,1.0,0.0,96660.76
-22547,15750731.0,Chukwumaobim,554.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,182784.11
-22548,15648069.0,Chung,558.0,Germany,Female,44.0,8.0,132559.4,1.0,1.0,1.0,59938.81
-22549,15671209.0,H?,678.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,97939.4
-22550,15696534.0,Hsieh,669.0,France,Male,59.0,9.0,129855.32,1.0,1.0,1.0,54869.52
-22551,15684801.0,Chidozie,675.0,Germany,Female,40.0,10.0,102823.34,1.0,0.0,0.0,91936.1
-22552,15735688.0,Ts'ui,718.0,France,Female,39.0,5.0,181671.16,1.0,1.0,1.0,70550.0
-22553,15660390.0,Young,621.0,Spain,Male,38.0,6.0,0.0,2.0,0.0,0.0,132468.76
-22554,15796370.0,Iloerika,628.0,France,Female,50.0,3.0,116623.31,2.0,0.0,0.0,172586.89
-22555,15631310.0,Nwachukwu,573.0,Germany,Female,35.0,5.0,123815.86,1.0,0.0,0.0,52332.85
-22556,15603082.0,Ch'iu,614.0,France,Female,27.0,9.0,0.0,2.0,0.0,1.0,86786.41
-22557,15725698.0,Chia,698.0,Spain,Female,37.0,8.0,131389.06,1.0,0.0,1.0,57549.65
-22558,15605673.0,Milani,595.0,France,Male,38.0,2.0,0.0,1.0,1.0,0.0,87816.86
-22559,15692408.0,Dominguez,558.0,Germany,Male,24.0,2.0,153058.64,1.0,1.0,1.0,49480.66
-22560,15743067.0,Ch'eng,671.0,France,Male,46.0,3.0,0.0,2.0,1.0,0.0,54290.62
-22561,15585533.0,T'ien,705.0,France,Female,43.0,9.0,0.0,2.0,0.0,1.0,81773.82
-22562,15707962.0,Lai,679.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,123882.73
-22563,15741181.0,Lucchese,704.0,Spain,Female,27.0,5.0,116969.28,1.0,1.0,0.0,80701.72
-22564,15793049.0,Hsu,663.0,Germany,Female,57.0,6.0,132796.04,1.0,1.0,0.0,125518.32
-22565,15793046.0,Martin,714.0,Germany,Male,35.0,9.0,118788.57,1.0,1.0,0.0,52332.85
-22566,15790782.0,Hs?,686.0,Spain,Male,42.0,9.0,0.0,1.0,1.0,1.0,153955.38
-22567,15794941.0,P'eng,543.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,187616.16
-22568,15621546.0,Hsueh,715.0,France,Female,44.0,8.0,0.0,2.0,1.0,0.0,120225.62
-22569,15578006.0,Trevisani,643.0,Germany,Female,34.0,3.0,124788.18,2.0,1.0,1.0,118059.19
-22570,15643824.0,Ibeamaka,679.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,0.0,109538.35
-22571,15807595.0,Lucchesi,744.0,France,Male,31.0,9.0,68213.99,1.0,1.0,0.0,79355.87
-22572,15655238.0,Chikwendu,539.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,88890.05
-22573,15643359.0,Ponomaryova,652.0,Spain,Male,33.0,4.0,0.0,1.0,1.0,1.0,82951.12
-22574,15661670.0,Ch'in,748.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,107875.1
-22575,15604818.0,Kable,678.0,Spain,Female,52.0,9.0,151709.33,2.0,1.0,1.0,52327.57
-22576,15689955.0,Hsing,659.0,Germany,Male,42.0,7.0,102520.37,1.0,1.0,0.0,116748.14
-22577,15686957.0,Teng,592.0,France,Female,46.0,3.0,0.0,2.0,1.0,0.0,93959.96
-22578,15749083.0,Chibuzo,714.0,Spain,Male,45.0,2.0,137811.75,1.0,1.0,1.0,164815.59
-22579,15758031.0,King,850.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,172501.38
-22580,15751032.0,Shen,710.0,Germany,Male,44.0,8.0,151855.33,1.0,1.0,1.0,94875.03
-22581,15593776.0,Tsao,597.0,Spain,Male,30.0,1.0,0.0,2.0,0.0,1.0,90863.73
-22582,15583748.0,Iweobiegbunam,629.0,France,Female,42.0,4.0,0.0,1.0,1.0,1.0,60552.44
-22583,15667417.0,Nkemdilim,833.0,Germany,Female,37.0,3.0,144961.97,1.0,1.0,0.0,108469.2
-22584,15631639.0,Trevisani,588.0,Germany,Female,54.0,3.0,128486.11,1.0,1.0,0.0,120852.85
-22585,15671390.0,Lai,756.0,Spain,Female,35.0,5.0,0.0,2.0,0.0,0.0,102233.73
-22586,15794868.0,Chinweuba,656.0,Spain,Female,32.0,1.0,0.0,2.0,1.0,1.0,187632.22
-22587,15782210.0,Okonkwo,581.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,95583.45
-22588,15658577.0,Lo,749.0,Germany,Female,38.0,9.0,134796.87,2.0,1.0,1.0,93844.88
-22589,15647474.0,Lucchesi,648.0,Spain,Male,46.0,5.0,0.0,2.0,0.0,1.0,40320.3
-22590,15567335.0,Frolova,652.0,France,Female,61.0,4.0,0.0,1.0,1.0,0.0,141872.05
-22591,15635388.0,Romani,706.0,Germany,Male,28.0,3.0,105792.49,2.0,1.0,0.0,152290.28
-22592,15660809.0,Folliero,611.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,170886.88
-22593,15637428.0,Uwakwe,576.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,187811.71
-22594,15785547.0,H?,645.0,Germany,Male,37.0,3.0,81210.61,1.0,1.0,1.0,120893.85
-22595,15657085.0,Walker,545.0,Germany,Female,44.0,5.0,126835.32,1.0,1.0,1.0,82900.25
-22596,15636595.0,Onyemauchechukwu,638.0,Spain,Female,47.0,9.0,0.0,2.0,1.0,1.0,118749.58
-22597,15612455.0,Ch'iu,588.0,France,Female,37.0,7.0,0.0,1.0,1.0,0.0,137908.05
-22598,15680201.0,Mancini,721.0,France,Male,44.0,9.0,126442.59,1.0,1.0,0.0,79323.61
-22599,15793813.0,Onyekachukwu,529.0,Spain,Male,35.0,5.0,154475.74,1.0,1.0,1.0,146875.86
-22600,15572762.0,Chukwuebuka,554.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,138841.59
-22601,15671610.0,Teng,587.0,Spain,Male,26.0,8.0,0.0,1.0,1.0,0.0,131827.15
-22602,15737025.0,Hs?,714.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,84863.85
-22603,15670080.0,Obijiaku,627.0,France,Female,55.0,5.0,0.0,1.0,1.0,0.0,93873.13
-22604,15783659.0,Chiu,697.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,84863.85
-22605,15691543.0,Trevisani,735.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,55893.37
-22606,15586699.0,Maclean,718.0,France,Female,33.0,1.0,117989.73,1.0,0.0,1.0,113581.83
-22607,15760090.0,Pagnotto,596.0,France,Female,37.0,1.0,135936.1,1.0,0.0,1.0,199290.68
-22608,15572762.0,Hs?,727.0,France,Male,46.0,2.0,0.0,1.0,1.0,0.0,170893.85
-22609,15772383.0,Onyeorulu,671.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,64853.51
-22610,15684958.0,Chukwufumnanya,621.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,93844.82
-22611,15700937.0,Zhirov,706.0,France,Female,33.0,9.0,0.0,2.0,0.0,1.0,93777.63
-22612,15802625.0,Chidi,725.0,France,Female,48.0,8.0,0.0,1.0,1.0,0.0,135875.51
-22613,15795954.0,Teng,707.0,France,Female,36.0,8.0,126872.6,1.0,1.0,1.0,48682.1
-22614,15722758.0,Ch'iu,545.0,Germany,Female,46.0,7.0,116674.72,1.0,0.0,0.0,176712.59
-22615,15676571.0,O'Sullivan,652.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,72970.93
-22616,15607040.0,T'ang,773.0,Spain,Male,42.0,9.0,132558.26,1.0,1.0,0.0,129980.93
-22617,15761047.0,Chia,635.0,France,Female,40.0,3.0,0.0,2.0,1.0,1.0,184524.65
-22618,15691396.0,Chukwubuikem,554.0,France,Female,27.0,3.0,0.0,2.0,1.0,0.0,62762.12
-22619,15765258.0,Ch'en,563.0,France,Female,68.0,3.0,0.0,1.0,1.0,0.0,124489.88
-22620,15779947.0,Chidumaga,651.0,Germany,Male,52.0,7.0,153340.83,1.0,1.0,0.0,160532.88
-22621,15652883.0,Chin,637.0,France,Female,28.0,9.0,0.0,2.0,1.0,0.0,109523.88
-22622,15615296.0,Nwachukwu,582.0,Germany,Male,26.0,8.0,91301.72,2.0,1.0,0.0,71862.79
-22623,15754569.0,Nnonso,684.0,France,Female,42.0,7.0,0.0,2.0,1.0,1.0,79495.01
-22624,15747762.0,Toscani,665.0,France,Male,38.0,1.0,0.0,1.0,1.0,0.0,130500.65
-22625,15603035.0,Ch'iu,745.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,47911.04
-22626,15692406.0,Nwankwo,474.0,Spain,Male,32.0,5.0,169445.4,1.0,0.0,1.0,161465.31
-22627,15723644.0,Pirozzi,619.0,Germany,Male,50.0,1.0,125163.62,1.0,0.0,0.0,160593.32
-22628,15761487.0,Ts'ai,795.0,France,Female,24.0,7.0,0.0,1.0,1.0,0.0,78744.39
-22629,15738018.0,P'eng,758.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,158958.9
-22630,15730044.0,Ndubueze,577.0,France,Male,49.0,1.0,0.0,1.0,1.0,0.0,101694.67
-22631,15687634.0,Wright,646.0,France,Female,32.0,2.0,0.0,2.0,0.0,1.0,71179.31
-22632,15681705.0,Fetherstonhaugh,624.0,Spain,Female,49.0,1.0,0.0,1.0,1.0,0.0,170514.21
-22633,15588924.0,Martin,523.0,France,Male,43.0,5.0,0.0,2.0,1.0,1.0,171716.81
-22634,15768282.0,Calabrese,595.0,Germany,Male,42.0,6.0,82931.85,1.0,0.0,0.0,49826.68
-22635,15806913.0,Tretiakov,621.0,France,Female,46.0,2.0,0.0,2.0,0.0,1.0,97926.24
-22636,15609637.0,Mazzanti,615.0,Germany,Female,35.0,0.0,121389.78,1.0,1.0,1.0,74042.0
-22637,15783027.0,Buchi,732.0,Germany,Male,49.0,6.0,131762.94,1.0,1.0,0.0,177362.45
-22638,15609913.0,Onwuamaeze,681.0,Spain,Male,33.0,7.0,0.0,1.0,1.0,0.0,174098.28
-22639,15792868.0,Zito,678.0,France,Male,30.0,0.0,129938.07,1.0,1.0,0.0,144880.81
-22640,15614778.0,Nielson,663.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,0.0,99645.04
-22641,15618661.0,Chukwubuikem,652.0,France,Female,30.0,0.0,0.0,1.0,1.0,1.0,139048.0
-22642,15803947.0,Lucchesi,708.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,180626.68
-22643,15697028.0,Maclean,683.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,1276.87
-22644,15691792.0,Hs?eh,638.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,65323.11
-22645,15748673.0,T'ien,769.0,Germany,Male,35.0,7.0,121890.05,2.0,1.0,1.0,49865.04
-22646,15778861.0,Binder,832.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,148087.88
-22647,15641158.0,Chukwualuka,652.0,France,Male,41.0,8.0,116561.05,1.0,1.0,0.0,65765.55
-22648,15686963.0,Trevisano,754.0,Spain,Male,33.0,4.0,144976.5,1.0,1.0,1.0,88890.75
-22649,15724889.0,Kosisochukwu,667.0,Germany,Male,51.0,9.0,150540.43,1.0,1.0,0.0,160853.06
-22650,15568487.0,Chiu,553.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,102908.96
-22651,15716609.0,Chiu,661.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,116951.1
-22652,15732987.0,Okwudiliolisa,696.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,141180.66
-22653,15783882.0,Chibugo,568.0,Spain,Male,53.0,8.0,0.0,2.0,1.0,1.0,180427.24
-22654,15605534.0,Soto,622.0,Germany,Male,41.0,1.0,103522.64,1.0,1.0,0.0,77866.91
-22655,15736112.0,Okwukwe,587.0,Germany,Female,34.0,10.0,99538.3,2.0,1.0,1.0,147794.63
-22656,15806049.0,Kambinachi,708.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,85901.09
-22657,15593973.0,T'ien,585.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,180769.55
-22658,15751561.0,DeRose,662.0,France,Female,26.0,7.0,0.0,2.0,1.0,0.0,147551.09
-22659,15605804.0,Onyemauchechukwu,474.0,Germany,Female,46.0,9.0,131834.8,1.0,1.0,1.0,151902.78
-22660,15806967.0,Frolova,559.0,France,Female,35.0,1.0,0.0,2.0,1.0,1.0,171657.57
-22661,15589428.0,Genovese,632.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,54926.51
-22662,15761158.0,Y?an,667.0,Germany,Female,46.0,4.0,142072.02,1.0,0.0,0.0,104786.59
-22663,15743075.0,Nwachukwu,805.0,France,Male,34.0,2.0,0.0,1.0,0.0,0.0,140941.47
-22664,15792726.0,Onyinyechukwuka,579.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,123866.43
-22665,15669414.0,Walker,850.0,France,Male,32.0,7.0,107712.57,1.0,0.0,1.0,62282.99
-22666,15686917.0,Trevisano,738.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,144870.28
-22667,15651740.0,T'ang,643.0,France,Female,44.0,9.0,0.0,2.0,1.0,1.0,149575.59
-22668,15648766.0,Chibueze,528.0,Germany,Male,37.0,7.0,121231.39,2.0,0.0,1.0,158974.79
-22669,15593795.0,Chiu,581.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,114732.62
-22670,15677522.0,Li Fonti,524.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,129892.93
-22671,15631053.0,T'ao,607.0,France,Female,34.0,6.0,0.0,2.0,1.0,0.0,120801.65
-22672,15652667.0,K'ung,494.0,Spain,Male,38.0,7.0,143966.3,1.0,1.0,0.0,89566.64
-22673,15791513.0,Chukwujamuike,581.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,1.0,143683.75
-22674,15788025.0,Kaeppel,682.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,93901.61
-22675,15683585.0,Onwuamaegbu,642.0,France,Male,40.0,5.0,99713.76,1.0,0.0,1.0,54487.43
-22676,15585888.0,Kao,727.0,France,Male,38.0,3.0,0.0,2.0,0.0,1.0,101418.05
-22677,15679760.0,Nwagugheuzo,578.0,France,Male,39.0,3.0,0.0,3.0,1.0,0.0,161845.81
-22678,15613141.0,Aiken,663.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,166650.32
-22679,15809800.0,K?,543.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,193008.52
-22680,15741295.0,Ch'eng,730.0,Germany,Male,32.0,4.0,140614.15,1.0,1.0,1.0,16281.94
-22681,15656592.0,P'eng,572.0,France,Male,24.0,6.0,0.0,2.0,1.0,0.0,28391.52
-22682,15749557.0,Chidiebere,632.0,Germany,Female,38.0,7.0,95853.39,2.0,1.0,0.0,173837.54
-22683,15682868.0,Yermakova,696.0,France,Male,33.0,3.0,109632.85,2.0,1.0,1.0,147554.26
-22684,15798850.0,Fyodorova,644.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,171060.01
-22685,15757577.0,Hs?,526.0,France,Male,45.0,0.0,0.0,2.0,0.0,0.0,133692.88
-22686,15610105.0,Yobanna,634.0,France,Male,37.0,5.0,0.0,3.0,1.0,1.0,177524.19
-22687,15722350.0,Chidubem,481.0,France,Female,37.0,3.0,108711.57,1.0,1.0,1.0,116467.35
-22688,15607546.0,Brown,756.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,121299.14
-22689,15593773.0,Chukwudi,633.0,Germany,Male,34.0,2.0,116324.53,1.0,1.0,1.0,122925.4
-22690,15786418.0,Maclean,634.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,0.0,85421.47
-22691,15641158.0,Fang,850.0,Spain,Female,26.0,6.0,178713.98,1.0,0.0,1.0,28789.94
-22692,15616454.0,Chidubem,549.0,Germany,Male,59.0,9.0,114422.85,1.0,1.0,0.0,155061.97
-22693,15809462.0,Tuan,598.0,France,Male,40.0,8.0,0.0,1.0,1.0,0.0,126012.57
-22694,15573456.0,Shih,715.0,France,Female,45.0,7.0,0.0,1.0,1.0,0.0,85516.37
-22695,15717498.0,Chin,714.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,187905.12
-22696,15567367.0,Onio,669.0,Spain,Female,37.0,8.0,0.0,1.0,0.0,0.0,114522.68
-22697,15754919.0,Udinesi,623.0,France,Male,36.0,2.0,137847.41,1.0,0.0,1.0,163331.65
-22698,15700937.0,Chiazagomekpere,714.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,1.0,167554.86
-22699,15745804.0,Aksenov,771.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,142331.85
-22700,15803976.0,Ch'in,684.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,0.0,104889.3
-22701,15693764.0,Hao,593.0,Spain,Male,42.0,8.0,101641.14,1.0,1.0,0.0,74858.3
-22702,15690164.0,Ch'ien,724.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,176984.34
-22703,15648067.0,Yudina,667.0,Spain,Female,48.0,3.0,0.0,2.0,1.0,1.0,118313.38
-22704,15793897.0,Chiang,724.0,Spain,Female,28.0,3.0,0.0,2.0,1.0,1.0,118528.16
-22705,15594812.0,Fanucci,598.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,45956.05
-22706,15697046.0,Yobanna,707.0,Spain,Male,45.0,9.0,0.0,2.0,0.0,0.0,160523.33
-22707,15752507.0,H?,609.0,Germany,Female,35.0,7.0,142028.88,2.0,0.0,1.0,131284.62
-22708,15583764.0,Chia,765.0,France,Female,37.0,9.0,0.0,2.0,0.0,0.0,91673.6
-22709,15646810.0,Fanucci,648.0,France,Male,39.0,9.0,0.0,2.0,0.0,1.0,62634.94
-22710,15699467.0,Ugochukwu,596.0,France,Female,42.0,8.0,0.0,1.0,1.0,0.0,131736.23
-22711,15716781.0,Echezonachukwu,670.0,France,Male,45.0,2.0,0.0,2.0,1.0,0.0,26606.28
-22712,15688868.0,Pirozzi,699.0,France,Male,25.0,7.0,0.0,2.0,1.0,0.0,107961.11
-22713,15684999.0,Chiazagomekpere,667.0,Spain,Male,46.0,8.0,174588.8,1.0,1.0,0.0,178291.09
-22714,15642821.0,Lu,645.0,France,Male,33.0,7.0,144773.29,1.0,1.0,0.0,65816.53
-22715,15609824.0,Tseng,678.0,Spain,Male,45.0,7.0,0.0,2.0,1.0,0.0,187839.9
-22716,15716491.0,Chikwado,583.0,France,Male,35.0,0.0,0.0,2.0,1.0,1.0,91830.75
-22717,15791533.0,Ritchie,668.0,France,Male,31.0,5.0,0.0,1.0,1.0,0.0,108887.44
-22718,15752841.0,Omeokachie,753.0,Spain,Female,39.0,9.0,0.0,2.0,1.0,0.0,176762.91
-22719,15637644.0,Shao,607.0,Germany,Female,36.0,1.0,126732.85,2.0,1.0,1.0,107815.31
-22720,15697028.0,Hao,628.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,138834.94
-22721,15704795.0,Tien,639.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,166038.27
-22722,15639278.0,Chinweike,682.0,France,Male,42.0,7.0,111537.23,1.0,0.0,1.0,91514.62
-22723,15621768.0,Genovese,810.0,France,Female,41.0,9.0,0.0,2.0,0.0,1.0,73564.44
-22724,15749583.0,Oguejiofor,569.0,Germany,Male,37.0,9.0,129974.79,1.0,0.0,0.0,164804.02
-22725,15644796.0,O'Loghlen,694.0,France,Male,34.0,4.0,148962.76,1.0,1.0,1.0,78899.15
-22726,15622578.0,Ts'ui,559.0,Germany,Female,45.0,6.0,135120.56,2.0,1.0,1.0,137791.18
-22727,15626474.0,Tretiakova,633.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,0.0,143022.32
-22728,15651713.0,Frolov,641.0,Spain,Male,33.0,5.0,113220.3,1.0,1.0,1.0,126761.2
-22729,15662488.0,Ritchie,751.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,143668.44
-22730,15808978.0,Lucchese,684.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,26170.04
-22731,15779915.0,Chigozie,573.0,Germany,Female,39.0,4.0,120599.21,2.0,1.0,0.0,93881.75
-22732,15792649.0,Li Fonti,599.0,France,Male,37.0,9.0,106520.73,1.0,1.0,1.0,132970.57
-22733,15689425.0,Uspensky,577.0,France,Male,51.0,0.0,0.0,1.0,1.0,1.0,171096.2
-22734,15662291.0,McIver,671.0,France,Female,31.0,5.0,0.0,2.0,0.0,0.0,99490.01
-22735,15719606.0,Pagnotto,687.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,76515.79
-22736,15680430.0,Chukwukadibia,604.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,1.0,151336.96
-22737,15597591.0,Walker,588.0,Spain,Male,34.0,4.0,136492.92,1.0,0.0,1.0,136492.18
-22738,15635972.0,Chuang,679.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,71167.17
-22739,15797960.0,Yermolayeva,686.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,181461.46
-22740,15783026.0,Onyekachi,704.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,63736.16
-22741,15702909.0,Ch'ang,605.0,Spain,Female,23.0,6.0,0.0,2.0,0.0,0.0,65816.53
-22742,15595391.0,Manna,686.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,168515.61
-22743,15656613.0,Kao,588.0,France,Male,29.0,1.0,0.0,2.0,1.0,1.0,94912.78
-22744,15667460.0,Hsiao,498.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,106663.46
-22745,15677550.0,Lettiere,622.0,Germany,Female,22.0,3.0,102278.81,2.0,1.0,0.0,53534.8
-22746,15792489.0,Chijindum,679.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,92814.31
-22747,15690526.0,Nwachukwu,521.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,120853.83
-22748,15661380.0,Trevisani,628.0,France,Female,49.0,1.0,89588.35,2.0,1.0,0.0,169137.24
-22749,15798824.0,Obijiaku,648.0,Germany,Female,35.0,8.0,122793.96,1.0,1.0,0.0,59457.0
-22750,15782454.0,Tsou,590.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,171707.93
-22751,15680278.0,Tseng,678.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,146680.28
-22752,15671450.0,Ting,637.0,France,Female,29.0,7.0,0.0,2.0,1.0,1.0,168035.62
-22753,15777742.0,Hsueh,591.0,France,Female,41.0,6.0,82697.13,1.0,1.0,0.0,79704.12
-22754,15694946.0,Pagnotto,715.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,152698.24
-22755,15656901.0,Cattaneo,615.0,Spain,Male,43.0,5.0,144801.97,1.0,0.0,1.0,100101.06
-22756,15716347.0,Tuan,850.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,72994.71
-22757,15769451.0,Palermo,671.0,Spain,Male,41.0,7.0,0.0,2.0,1.0,1.0,87071.18
-22758,15680449.0,Ch'eng,729.0,France,Female,56.0,2.0,0.0,1.0,1.0,1.0,149614.81
-22759,15576774.0,DeRose,684.0,France,Female,41.0,4.0,0.0,2.0,0.0,0.0,118788.59
-22760,15724848.0,Genovese,556.0,France,Male,27.0,10.0,0.0,2.0,1.0,1.0,129936.56
-22761,15775590.0,Onwuamaeze,641.0,France,Male,35.0,1.0,122531.86,1.0,1.0,1.0,184839.7
-22762,15695819.0,O'Sullivan,710.0,Germany,Female,34.0,7.0,88815.56,2.0,0.0,0.0,113436.08
-22763,15715208.0,Ch'in,699.0,France,Female,45.0,5.0,137687.72,1.0,1.0,0.0,79414.0
-22764,15643679.0,Tsao,583.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,57549.65
-22765,15672930.0,Macleod,676.0,Germany,Male,51.0,9.0,144453.75,1.0,0.0,0.0,127630.88
-22766,15617401.0,Palermo,769.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,177025.79
-22767,15756875.0,Y?,705.0,France,Male,57.0,1.0,0.0,2.0,1.0,1.0,180716.1
-22768,15758813.0,Chidiebere,731.0,France,Male,43.0,6.0,0.0,2.0,1.0,0.0,83419.15
-22769,15761477.0,Onyekachi,724.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,157862.82
-22770,15791053.0,Chijioke,596.0,France,Male,40.0,3.0,0.0,1.0,1.0,0.0,116081.93
-22771,15724200.0,Ebelechukwu,607.0,Germany,Male,28.0,9.0,101827.07,1.0,1.0,1.0,55868.97
-22772,15802781.0,Hs?,850.0,Germany,Male,29.0,2.0,109974.01,2.0,0.0,1.0,108841.83
-22773,15656592.0,Hsing,751.0,France,Female,43.0,2.0,129299.28,1.0,1.0,0.0,66526.01
-22774,15644878.0,Mazzanti,712.0,Spain,Male,41.0,1.0,0.0,1.0,0.0,0.0,140134.43
-22775,15798824.0,Hs?,640.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,110593.62
-22776,15809309.0,Nwokezuike,589.0,Germany,Male,58.0,6.0,93722.73,1.0,1.0,1.0,68814.23
-22777,15653776.0,L?,639.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,186612.45
-22778,15605968.0,Mott,850.0,Germany,Female,31.0,8.0,108632.48,1.0,1.0,1.0,66721.98
-22779,15754926.0,Tien,681.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,168035.6
-22780,15761717.0,Hs?eh,588.0,France,Male,31.0,3.0,0.0,1.0,1.0,1.0,60425.03
-22781,15752835.0,Shoebridge,652.0,Germany,Male,39.0,0.0,130682.11,1.0,0.0,0.0,52404.47
-22782,15615032.0,Chiu,695.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,18082.06
-22783,15632669.0,Ch'in,645.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,135088.58
-22784,15646594.0,Chukwufumnanya,659.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,116951.1
-22785,15630195.0,Pinto,769.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,105570.44
-22786,15700212.0,Scott,643.0,Germany,Male,31.0,6.0,96643.32,1.0,1.0,1.0,133692.88
-22787,15759067.0,Hsing,695.0,France,Female,50.0,5.0,0.0,2.0,1.0,0.0,161241.65
-22788,15727489.0,K?,624.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,89566.64
-22789,15712403.0,Ts'ui,632.0,Germany,Female,52.0,2.0,125445.04,1.0,1.0,1.0,185561.89
-22790,15793331.0,Galkin,706.0,France,Male,26.0,7.0,0.0,1.0,1.0,0.0,48606.92
-22791,15744517.0,Zaytseva,607.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,166468.21
-22792,15589320.0,L?,663.0,Germany,Female,32.0,8.0,150329.15,2.0,1.0,0.0,199293.01
-22793,15786078.0,Brown,538.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,162858.29
-22794,15758048.0,Udinese,706.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,71173.64
-22795,15702541.0,Golubev,596.0,Germany,Female,37.0,7.0,121537.71,1.0,1.0,1.0,151811.01
-22796,15776743.0,Ndukaku,639.0,Germany,Male,41.0,9.0,102721.49,1.0,1.0,0.0,10472.31
-22797,15691627.0,Chuang,678.0,Spain,Male,27.0,5.0,0.0,2.0,1.0,0.0,163736.09
-22798,15786469.0,Genovese,596.0,Spain,Male,37.0,7.0,0.0,2.0,1.0,1.0,156903.44
-22799,15583764.0,Clements,724.0,Spain,Male,32.0,1.0,0.0,1.0,1.0,0.0,71692.8
-22800,15587587.0,Chinedum,624.0,Spain,Female,31.0,3.0,139810.34,1.0,1.0,1.0,183682.09
-22801,15792228.0,Shih,792.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,78854.57
-22802,15726588.0,Nwabugwu,710.0,France,Female,69.0,1.0,0.0,2.0,1.0,1.0,93524.19
-22803,15791958.0,Whitehead,631.0,France,Female,40.0,7.0,0.0,2.0,1.0,0.0,61977.82
-22804,15736124.0,Chidozie,758.0,France,Female,32.0,10.0,84719.13,1.0,1.0,1.0,71178.09
-22805,15709639.0,Ch'ang,633.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,91347.76
-22806,15765461.0,Hsu,582.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,1.0,143954.99
-22807,15597951.0,Hs?,495.0,France,Female,32.0,4.0,0.0,2.0,0.0,1.0,162699.7
-22808,15731166.0,Obiajulu,565.0,France,Male,38.0,6.0,88758.84,1.0,1.0,1.0,177097.24
-22809,15632436.0,Chimaobim,727.0,France,Male,41.0,3.0,125487.89,1.0,1.0,1.0,86983.53
-22810,15791767.0,Ugochukwutubelum,656.0,France,Female,31.0,4.0,160515.37,1.0,0.0,0.0,145562.4
-22811,15803683.0,Burt,679.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,105433.53
-22812,15704053.0,Chukwumaobim,637.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,157057.82
-22813,15671766.0,Hsueh,649.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,79734.23
-22814,15748231.0,Ch'ang,645.0,Germany,Male,47.0,3.0,127738.75,1.0,1.0,0.0,183572.39
-22815,15612071.0,Lucchese,648.0,France,Female,41.0,9.0,0.0,1.0,1.0,0.0,74425.41
-22816,15622442.0,Lettiere,552.0,Spain,Female,30.0,4.0,103862.31,1.0,1.0,1.0,142963.28
-22817,15758510.0,Chidumaga,712.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,96864.36
-22818,15691871.0,H?,553.0,France,Male,37.0,1.0,147069.78,1.0,1.0,0.0,83911.33
-22819,15639665.0,Martin,704.0,France,Male,32.0,5.0,142581.08,1.0,0.0,1.0,162511.55
-22820,15750487.0,Chukwukadibia,594.0,Germany,Male,37.0,2.0,117280.23,2.0,1.0,1.0,103563.23
-22821,15642570.0,T'ao,636.0,France,Female,30.0,6.0,0.0,2.0,0.0,0.0,183358.21
-22822,15679642.0,Hargreaves,667.0,France,Male,41.0,5.0,0.0,1.0,1.0,0.0,180593.45
-22823,15746190.0,Chukwuebuka,686.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,57552.02
-22824,15690744.0,Hsing,589.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,51705.01
-22825,15702300.0,Dyer,588.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,115724.78
-22826,15623062.0,Ts'ai,639.0,Spain,Female,46.0,3.0,84442.6,2.0,0.0,0.0,83052.06
-22827,15717282.0,Y?,628.0,Spain,Male,48.0,4.0,0.0,2.0,0.0,1.0,66799.28
-22828,15724145.0,Ch'iu,726.0,France,Male,32.0,8.0,142721.52,1.0,1.0,0.0,99449.86
-22829,15746662.0,Chiang,581.0,France,Female,39.0,9.0,0.0,2.0,1.0,0.0,85421.48
-22830,15808389.0,Ch'ien,753.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,176770.28
-22831,15734461.0,Chien,576.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,109041.53
-22832,15588515.0,Pinto,553.0,Germany,Male,28.0,7.0,123372.3,2.0,1.0,1.0,59354.24
-22833,15603749.0,Ts'ui,641.0,France,Female,39.0,9.0,0.0,1.0,1.0,1.0,147055.87
-22834,15585865.0,Nwabugwu,539.0,France,Female,46.0,5.0,0.0,1.0,0.0,0.0,104469.06
-22835,15622578.0,Ch'ang,677.0,Germany,Male,53.0,3.0,120549.29,1.0,1.0,0.0,82463.69
-22836,15673820.0,T'ien,678.0,France,Female,44.0,9.0,0.0,2.0,1.0,1.0,63736.36
-22837,15749947.0,P'eng,545.0,Spain,Female,35.0,10.0,147832.15,1.0,1.0,1.0,143022.31
-22838,15594577.0,Ch'eng,632.0,Germany,Male,37.0,2.0,91538.51,1.0,1.0,0.0,187985.85
-22839,15778955.0,Olisanugo,613.0,France,Male,46.0,0.0,141792.61,1.0,1.0,0.0,131281.28
-22840,15693381.0,Obielumani,663.0,France,Male,60.0,2.0,123681.32,2.0,0.0,0.0,99444.02
-22841,15789425.0,Onwuamaeze,729.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,0.0,88890.05
-22842,15580149.0,Young,656.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,177065.24
-22843,15575492.0,Chien,676.0,Spain,Female,35.0,9.0,0.0,2.0,1.0,1.0,123954.15
-22844,15583681.0,Aikenhead,576.0,France,Female,30.0,5.0,161588.89,1.0,0.0,1.0,141148.21
-22845,15571529.0,Rowntree,584.0,France,Female,44.0,9.0,0.0,1.0,1.0,1.0,132034.13
-22846,15752588.0,P'an,612.0,France,Female,43.0,5.0,0.0,1.0,1.0,0.0,146516.86
-22847,15662085.0,Onwuamaeze,699.0,France,Male,28.0,0.0,0.0,2.0,0.0,1.0,94440.45
-22848,15752488.0,Genovese,731.0,Spain,Male,44.0,2.0,0.0,1.0,1.0,0.0,73564.44
-22849,15633574.0,Chiemela,628.0,France,Female,29.0,6.0,0.0,2.0,0.0,0.0,158887.09
-22850,15720662.0,P'an,721.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,1.0,175082.2
-22851,15772896.0,Ibezimako,652.0,France,Female,39.0,6.0,144889.18,1.0,1.0,1.0,74532.02
-22852,15660541.0,Nwachinemelu,756.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,99503.19
-22853,15681446.0,McIntyre,751.0,France,Female,40.0,1.0,0.0,1.0,0.0,0.0,107787.28
-22854,15667588.0,Eluemuno,766.0,Spain,Female,37.0,6.0,69712.74,1.0,1.0,1.0,149814.68
-22855,15665629.0,Hsiao,695.0,France,Female,27.0,10.0,122453.37,1.0,1.0,0.0,79005.95
-22856,15626141.0,Chinedum,599.0,France,Male,24.0,6.0,137761.55,1.0,0.0,1.0,118711.75
-22857,15657812.0,Nwachukwu,657.0,France,Female,43.0,9.0,104784.23,1.0,1.0,0.0,165574.54
-22858,15676571.0,Ritchie,624.0,France,Female,58.0,10.0,162657.64,1.0,1.0,0.0,61602.42
-22859,15780779.0,Nwabugwu,707.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,110253.2
-22860,15585853.0,Tseng,635.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,168816.22
-22861,15770291.0,Onyemauchechukwu,732.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,109394.62
-22862,15802009.0,Babbage,627.0,France,Female,49.0,3.0,0.0,3.0,1.0,0.0,75562.47
-22863,15632476.0,Mactier,597.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,105780.18
-22864,15648069.0,Zikoranaudodimma,762.0,Germany,Female,30.0,7.0,113690.81,1.0,0.0,1.0,68730.91
-22865,15614215.0,Onyeoruru,592.0,Germany,Female,32.0,9.0,122446.61,1.0,1.0,1.0,180934.67
-22866,15631392.0,Manna,573.0,France,Male,26.0,7.0,0.0,1.0,1.0,1.0,90876.95
-22867,15759066.0,Okeke,597.0,Spain,Male,24.0,8.0,0.0,2.0,1.0,0.0,95625.03
-22868,15770345.0,Tretiakova,684.0,France,Female,29.0,8.0,134498.54,1.0,1.0,1.0,140947.05
-22869,15582848.0,Manners,597.0,Germany,Female,44.0,7.0,113858.95,1.0,1.0,0.0,166586.99
-22870,15751549.0,Ch'ien,724.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,171378.77
-22871,15574795.0,Manna,529.0,Germany,Male,32.0,2.0,100912.19,2.0,0.0,1.0,58879.31
-22872,15690772.0,Hsueh,694.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,106761.47
-22873,15641180.0,Folliero,627.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,51871.95
-22874,15598884.0,Ts'ui,729.0,France,Female,54.0,6.0,0.0,2.0,1.0,1.0,103379.31
-22875,15696744.0,Martin,758.0,Germany,Female,34.0,6.0,124580.69,1.0,1.0,0.0,97640.2
-22876,15737506.0,Chidubem,711.0,France,Female,52.0,2.0,0.0,2.0,1.0,0.0,108732.96
-22877,15571917.0,Okwudiliolisa,678.0,Spain,Male,59.0,1.0,0.0,1.0,1.0,0.0,169415.3
-22878,15691817.0,T'ang,628.0,France,Male,39.0,7.0,0.0,2.0,0.0,1.0,131647.31
-22879,15644878.0,P'eng,592.0,France,Male,49.0,6.0,0.0,2.0,1.0,1.0,116951.1
-22880,15578096.0,Ts'ui,679.0,Spain,Female,61.0,3.0,0.0,1.0,1.0,0.0,94949.84
-22881,15769980.0,O'Brien,684.0,Spain,Male,36.0,8.0,0.0,1.0,1.0,1.0,73679.59
-22882,15662091.0,Ch'in,545.0,France,Male,40.0,3.0,0.0,1.0,1.0,1.0,86937.2
-22883,15671769.0,Genovese,694.0,France,Female,35.0,2.0,92474.05,1.0,1.0,1.0,107875.69
-22884,15585287.0,Okwuadigbo,850.0,France,Female,31.0,10.0,115350.63,1.0,1.0,0.0,101314.98
-22885,15775294.0,Ch'ien,648.0,France,Female,43.0,3.0,0.0,1.0,1.0,0.0,86634.3
-22886,15583352.0,Brown,659.0,Germany,Male,29.0,9.0,96951.21,1.0,1.0,0.0,72852.74
-22887,15732628.0,Hs?,633.0,Spain,Female,51.0,7.0,0.0,2.0,1.0,1.0,141364.62
-22888,15692226.0,Iadanza,714.0,Spain,Male,36.0,5.0,162448.69,1.0,1.0,0.0,142911.99
-22889,15676526.0,Monds,591.0,France,Male,26.0,9.0,0.0,2.0,1.0,1.0,91094.46
-22890,15736154.0,Chibugo,596.0,France,Male,35.0,7.0,111565.28,1.0,0.0,0.0,130404.53
-22891,15758750.0,Nnanna,850.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,181224.56
-22892,15603840.0,Iqbal,592.0,Spain,Female,36.0,8.0,166070.23,1.0,1.0,0.0,100335.55
-22893,15717489.0,Nwora,663.0,Germany,Female,38.0,8.0,115315.04,1.0,1.0,0.0,125445.8
-22894,15790782.0,Tien,593.0,Spain,Female,40.0,1.0,72535.45,1.0,0.0,1.0,157097.09
-22895,15673529.0,P'eng,636.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,120531.31
-22896,15771477.0,Mazzanti,581.0,Spain,Male,48.0,2.0,154485.74,1.0,1.0,1.0,66732.63
-22897,15649858.0,Walker,695.0,Spain,Male,29.0,0.0,102821.45,2.0,1.0,0.0,79547.39
-22898,15632967.0,Hsiung,681.0,France,Male,38.0,9.0,0.0,1.0,1.0,1.0,58961.49
-22899,15573926.0,Goliwe,715.0,Spain,Female,45.0,4.0,0.0,1.0,1.0,0.0,174777.98
-22900,15772412.0,Ch'ien,621.0,Germany,Female,32.0,2.0,88775.23,2.0,0.0,1.0,76510.46
-22901,15664539.0,Tien,439.0,Germany,Female,34.0,9.0,130334.35,2.0,1.0,0.0,180427.24
-22902,15643574.0,Onyekachi,575.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,126726.33
-22903,15729238.0,Udinese,719.0,Germany,Male,48.0,5.0,129312.84,1.0,1.0,0.0,60552.44
-22904,15601184.0,Scott,633.0,Germany,Male,44.0,8.0,82909.14,2.0,1.0,0.0,81409.0
-22905,15654772.0,T'ien,727.0,Spain,Male,36.0,6.0,0.0,1.0,1.0,1.0,56767.67
-22906,15777045.0,Hsiung,601.0,Spain,Female,33.0,5.0,123372.3,1.0,1.0,1.0,149991.32
-22907,15609928.0,Onwuamaeze,616.0,France,Female,35.0,4.0,151233.62,1.0,1.0,0.0,61538.43
-22908,15780643.0,Brownless,609.0,France,Female,34.0,1.0,0.0,2.0,0.0,1.0,59249.83
-22909,15815534.0,Chinedum,619.0,France,Female,67.0,3.0,0.0,2.0,1.0,1.0,93820.49
-22910,15689514.0,K?,565.0,Germany,Male,38.0,6.0,142296.13,1.0,1.0,0.0,136902.01
-22911,15599152.0,Onwumelu,572.0,Spain,Female,31.0,4.0,160736.63,1.0,1.0,1.0,142377.69
-22912,15585595.0,Hsiung,815.0,France,Female,32.0,3.0,106521.49,1.0,1.0,1.0,176945.96
-22913,15569682.0,Okonkwo,653.0,Spain,Male,30.0,7.0,0.0,2.0,1.0,0.0,52827.51
-22914,15644765.0,Maclean,547.0,Spain,Female,30.0,4.0,0.0,2.0,1.0,1.0,3173.31
-22915,15661290.0,T'ien,705.0,Spain,Male,35.0,7.0,121172.97,1.0,1.0,0.0,109608.81
-22916,15672798.0,Pirozzi,593.0,Spain,Female,40.0,6.0,0.0,2.0,0.0,1.0,131281.29
-22917,15712176.0,Chiang,526.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,109805.17
-22918,15646563.0,Ch'iu,686.0,France,Female,30.0,9.0,0.0,2.0,1.0,0.0,120853.83
-22919,15609913.0,Knepper,774.0,Germany,Male,51.0,6.0,108862.76,1.0,0.0,0.0,86935.73
-22920,15683522.0,Hs?eh,622.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,154598.56
-22921,15793723.0,Chidiebere,646.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,63736.17
-22922,15581840.0,Ch'in,639.0,France,Male,46.0,5.0,0.0,1.0,1.0,1.0,171457.06
-22923,15585067.0,Chukwudi,629.0,Spain,Male,44.0,2.0,0.0,2.0,0.0,0.0,89048.46
-22924,15676521.0,T'ang,685.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,151325.24
-22925,15592999.0,Pisano,704.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,55725.04
-22926,15603319.0,Ch'in,665.0,France,Female,35.0,6.0,0.0,2.0,0.0,1.0,122284.38
-22927,15641640.0,Chinonyelum,678.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,63889.1
-22928,15723858.0,Nnonso,687.0,France,Male,39.0,1.0,0.0,2.0,0.0,0.0,48357.9
-22929,15661528.0,Ch'ang,589.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,143357.02
-22930,15768777.0,Onyemere,619.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,53630.9
-22931,15591169.0,Genovese,678.0,France,Male,35.0,7.0,0.0,2.0,0.0,0.0,82290.81
-22932,15669611.0,Ch'eng,731.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,143598.7
-22933,15570087.0,Iadanza,735.0,France,Female,48.0,8.0,0.0,1.0,1.0,0.0,131919.48
-22934,15691543.0,Chimezie,587.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,125870.5
-22935,15763817.0,Dore,705.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,182059.28
-22936,15763612.0,Obidimkpa,598.0,France,Female,60.0,2.0,0.0,1.0,1.0,1.0,71175.94
-22937,15679024.0,Nkemakolam,682.0,Germany,Male,38.0,8.0,141525.52,2.0,1.0,1.0,121451.21
-22938,15787318.0,Chukwuemeka,696.0,France,Female,30.0,8.0,118897.1,1.0,1.0,0.0,180712.28
-22939,15692406.0,Chidiegwu,793.0,Germany,Female,26.0,0.0,141099.43,1.0,1.0,0.0,142963.28
-22940,15694852.0,Chukwueloka,584.0,Germany,Male,38.0,7.0,84421.62,2.0,0.0,1.0,72990.62
-22941,15584897.0,H?,633.0,Germany,Male,60.0,5.0,108239.11,1.0,1.0,0.0,126469.09
-22942,15737275.0,Uchenna,738.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,92357.21
-22943,15586931.0,Nnamutaezinwa,701.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,114474.98
-22944,15798895.0,Pape,659.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,27448.33
-22945,15723216.0,Ts'ui,646.0,France,Male,38.0,6.0,129968.67,1.0,1.0,1.0,175025.27
-22946,15795564.0,Manna,635.0,Spain,Male,29.0,8.0,0.0,2.0,0.0,0.0,109895.16
-22947,15772632.0,P'eng,637.0,Germany,Male,36.0,6.0,135843.66,1.0,1.0,1.0,94760.32
-22948,15583692.0,Folliero,705.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,1.0,173365.89
-22949,15586880.0,Hsia,757.0,France,Male,45.0,2.0,0.0,2.0,1.0,0.0,115116.35
-22950,15681075.0,Yashina,594.0,France,Female,40.0,10.0,0.0,2.0,1.0,1.0,158580.05
-22951,15776939.0,Uwaezuoke,637.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,135655.29
-22952,15780124.0,Pirozzi,682.0,Spain,Female,42.0,9.0,0.0,2.0,1.0,0.0,142903.44
-22953,15636428.0,Hsueh,708.0,France,Female,46.0,0.0,0.0,1.0,1.0,0.0,149369.41
-22954,15662534.0,Nkemakolam,660.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,153080.4
-22955,15576219.0,Ch'ang,673.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,163989.66
-22956,15643908.0,Chukwumaobim,669.0,France,Male,43.0,1.0,113873.53,1.0,0.0,1.0,137510.41
-22957,15750638.0,Young,710.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,40807.27
-22958,15699461.0,Onyekaozulu,551.0,Spain,Female,35.0,0.0,99816.46,2.0,1.0,0.0,129964.94
-22959,15668893.0,Chigbogu,558.0,Spain,Male,31.0,6.0,0.0,2.0,0.0,0.0,29761.29
-22960,15810203.0,Chikezie,732.0,Germany,Male,38.0,10.0,114430.18,1.0,1.0,0.0,104272.04
-22961,15576723.0,Chiawuotu,638.0,Spain,Male,30.0,7.0,0.0,2.0,1.0,1.0,160129.99
-22962,15755239.0,Okwudilichukwu,699.0,France,Male,43.0,6.0,0.0,1.0,1.0,1.0,103099.22
-22963,15605534.0,Chizoba,705.0,Germany,Female,31.0,9.0,137910.45,1.0,1.0,1.0,149381.32
-22964,15622519.0,Zetticci,652.0,Spain,Female,30.0,7.0,0.0,2.0,1.0,1.0,95611.47
-22965,15805565.0,Maclean,582.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,1.0,28690.9
-22966,15683526.0,Ch'ien,529.0,France,Male,49.0,6.0,0.0,2.0,1.0,1.0,125870.5
-22967,15659149.0,Nnaemeka,679.0,France,Male,32.0,8.0,72783.42,2.0,1.0,1.0,39488.04
-22968,15585892.0,H?,598.0,France,Female,47.0,5.0,0.0,1.0,1.0,0.0,177092.16
-22969,15672598.0,P'eng,576.0,Spain,Male,33.0,6.0,0.0,2.0,1.0,0.0,19233.41
-22970,15802466.0,Hsing,618.0,Germany,Male,31.0,5.0,131694.97,1.0,1.0,1.0,91936.1
-22971,15763029.0,Scott,692.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,0.0,166476.95
-22972,15706021.0,Yobachi,632.0,Spain,Female,28.0,8.0,0.0,2.0,1.0,0.0,50783.1
-22973,15593250.0,Baresi,691.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,180598.28
-22974,15792646.0,P'eng,748.0,France,Female,48.0,9.0,0.0,2.0,1.0,0.0,140941.47
-22975,15583692.0,Hsiung,598.0,Germany,Male,39.0,2.0,121455.65,2.0,1.0,1.0,198103.32
-22976,15588449.0,Folliero,592.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,119357.93
-22977,15719606.0,H?,721.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,143049.72
-22978,15699340.0,Tsao,611.0,France,Female,33.0,1.0,0.0,2.0,1.0,0.0,182865.09
-22979,15787597.0,Nwabugwu,524.0,France,Male,42.0,3.0,0.0,1.0,1.0,0.0,69414.13
-22980,15785576.0,Toscani,648.0,France,Male,41.0,2.0,90727.79,1.0,1.0,0.0,131924.45
-22981,15596870.0,Frolova,670.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,118959.64
-22982,15676869.0,Chienezie,711.0,Spain,Male,44.0,3.0,0.0,1.0,1.0,1.0,131473.31
-22983,15674558.0,Chukwufumnanya,602.0,France,Male,25.0,8.0,115062.02,1.0,0.0,1.0,163666.85
-22984,15581388.0,Chibuzo,659.0,France,Male,40.0,1.0,144895.04,2.0,1.0,0.0,169286.74
-22985,15672598.0,Y?,607.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,99800.87
-22986,15578045.0,Young,559.0,France,Female,31.0,0.0,0.0,2.0,1.0,1.0,147097.26
-22987,15621818.0,Young,614.0,Spain,Male,35.0,8.0,0.0,2.0,0.0,1.0,125553.51
-22988,15769959.0,Chukwuemeka,628.0,Spain,Female,32.0,9.0,0.0,1.0,1.0,0.0,141449.86
-22989,15626042.0,Li,694.0,France,Female,70.0,5.0,0.0,1.0,1.0,1.0,152577.62
-22990,15626578.0,Tien,721.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,159638.77
-22991,15605284.0,Hsia,693.0,France,Male,32.0,6.0,140931.57,1.0,1.0,1.0,75562.47
-22992,15642997.0,Yermakov,663.0,France,Male,31.0,0.0,168001.34,1.0,1.0,0.0,131300.68
-22993,15648032.0,Cattaneo,710.0,France,Male,40.0,3.0,0.0,2.0,1.0,1.0,141872.05
-22994,15806771.0,Kao,611.0,France,Female,29.0,9.0,0.0,2.0,1.0,1.0,71333.44
-22995,15678727.0,Aksenov,669.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,106841.12
-22996,15735025.0,Maclean,652.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,1.0,185821.41
-22997,15656426.0,Ozioma,619.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,80701.72
-22998,15709487.0,Iadanza,581.0,Spain,Female,34.0,7.0,0.0,2.0,0.0,1.0,115068.99
-22999,15623369.0,T'ien,675.0,Germany,Female,29.0,9.0,97764.41,2.0,1.0,0.0,26170.08
-23000,15645600.0,Chinomso,591.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,61761.77
-23001,15773876.0,Chukwukere,721.0,France,Male,41.0,5.0,0.0,1.0,0.0,1.0,159618.17
-23002,15717962.0,Kao,677.0,France,Female,51.0,4.0,0.0,2.0,1.0,0.0,71865.31
-23003,15594576.0,Trevisani,739.0,France,Female,52.0,4.0,0.0,2.0,1.0,1.0,142903.44
-23004,15769216.0,Fanucci,554.0,Germany,Male,40.0,3.0,97720.35,1.0,1.0,1.0,156598.23
-23005,15751549.0,Cremonesi,639.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,61994.2
-23006,15768449.0,Chukwukere,684.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,161479.19
-23007,15776824.0,Hsueh,679.0,France,Male,45.0,8.0,0.0,2.0,1.0,0.0,61513.87
-23008,15661745.0,H?,683.0,Germany,Male,28.0,1.0,107712.57,1.0,1.0,1.0,90876.95
-23009,15748696.0,Hsiung,516.0,France,Female,65.0,4.0,0.0,1.0,1.0,0.0,26116.82
-23010,15649738.0,Chia,757.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,149474.69
-23011,15752694.0,Chukwujamuike,692.0,France,Female,35.0,7.0,0.0,2.0,1.0,1.0,122964.18
-23012,15651336.0,Chiawuotu,749.0,France,Female,37.0,2.0,0.0,1.0,0.0,1.0,48961.49
-23013,15627848.0,Kaeppel,487.0,Germany,Male,52.0,4.0,102800.65,1.0,1.0,0.0,166957.82
-23014,15792668.0,Ch'ang,727.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,171070.55
-23015,15801395.0,Lo,579.0,France,Male,41.0,6.0,0.0,1.0,1.0,0.0,141796.73
-23016,15702789.0,O'Donnell,776.0,Germany,Male,38.0,4.0,124098.54,1.0,1.0,0.0,62972.13
-23017,15712121.0,Fyodorova,753.0,Spain,Female,43.0,2.0,0.0,3.0,1.0,0.0,142670.61
-23018,15603319.0,Toscani,569.0,France,Male,35.0,6.0,0.0,1.0,1.0,1.0,72628.56
-23019,15717498.0,Chiagoziem,743.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,93777.15
-23020,15807167.0,Shih,727.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,164162.44
-23021,15688963.0,Hs?,621.0,Germany,Male,55.0,9.0,120170.07,1.0,1.0,0.0,131281.28
-23022,15773973.0,Hsieh,565.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,0.0,143315.07
-23023,15595588.0,P'an,763.0,Spain,Male,33.0,7.0,0.0,1.0,1.0,0.0,143960.72
-23024,15752588.0,Chukwuraenye,598.0,France,Female,46.0,7.0,0.0,1.0,0.0,0.0,170518.83
-23025,15693904.0,Zetticci,568.0,France,Female,26.0,6.0,0.0,1.0,1.0,1.0,64442.35
-23026,15609998.0,Yobachi,735.0,Germany,Female,37.0,2.0,104430.08,1.0,1.0,0.0,141926.43
-23027,15714986.0,Hs?,697.0,France,Female,50.0,0.0,0.0,2.0,1.0,1.0,141860.62
-23028,15572467.0,Hsia,551.0,Spain,Male,37.0,3.0,141573.55,1.0,1.0,1.0,131284.93
-23029,15597901.0,Biryukov,751.0,France,Female,46.0,8.0,0.0,2.0,0.0,0.0,70849.47
-23030,15730447.0,Molineux,705.0,France,Male,29.0,4.0,0.0,1.0,1.0,0.0,132798.78
-23031,15746995.0,Ts'ui,530.0,France,Male,45.0,4.0,0.0,2.0,0.0,0.0,167036.94
-23032,15710397.0,Nwebube,629.0,France,Female,35.0,2.0,0.0,1.0,1.0,1.0,86022.09
-23033,15704315.0,Toscani,699.0,Germany,Male,39.0,9.0,168883.08,2.0,1.0,1.0,120853.83
-23034,15734850.0,Okechukwu,679.0,France,Female,29.0,3.0,0.0,2.0,0.0,1.0,109895.16
-23035,15647800.0,Uspensky,575.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,83700.66
-23036,15782530.0,Fiorentino,663.0,Germany,Male,29.0,10.0,108891.7,1.0,0.0,0.0,154844.36
-23037,15810845.0,Ubanwa,752.0,France,Male,39.0,7.0,178713.98,1.0,1.0,0.0,95957.09
-23038,15747591.0,Ugorji,718.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,9085.0
-23039,15750141.0,Martin,567.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,84872.66
-23040,15641609.0,Chukwukadibia,709.0,Germany,Female,57.0,5.0,133432.59,1.0,1.0,0.0,173898.58
-23041,15646817.0,Ch'eng,552.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,598.8
-23042,15753861.0,Ting,565.0,Germany,Male,40.0,0.0,106942.08,2.0,1.0,0.0,152742.0
-23043,15768342.0,Horsley,841.0,France,Female,36.0,3.0,165896.2,1.0,0.0,0.0,131834.87
-23044,15633840.0,Kudryashova,705.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,60721.4
-23045,15697691.0,Harker,680.0,Germany,Female,47.0,8.0,126803.34,3.0,1.0,1.0,177613.19
-23046,15788151.0,Hsia,751.0,France,Male,31.0,1.0,150186.22,1.0,1.0,1.0,170835.32
-23047,15692671.0,T'ien,605.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,1.0,62733.05
-23048,15758057.0,Chidiebere,659.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,186948.35
-23049,15576526.0,Ch'in,678.0,France,Male,35.0,1.0,146093.39,2.0,1.0,0.0,115733.16
-23050,15782210.0,Nwachinemelu,605.0,Germany,Male,46.0,8.0,98899.91,3.0,1.0,0.0,106977.53
-23051,15695301.0,Nkemjika,595.0,France,Male,51.0,4.0,0.0,2.0,1.0,1.0,131312.56
-23052,15796787.0,Chukwufumnanya,775.0,Spain,Female,36.0,5.0,120911.75,1.0,1.0,1.0,177832.93
-23053,15663883.0,Echezonachukwu,627.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,152821.12
-23054,15588907.0,Ulyanov,678.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,103480.69
-23055,15617304.0,Hsiung,712.0,Spain,Male,50.0,6.0,148897.02,1.0,1.0,0.0,90905.26
-23056,15588928.0,Frolova,686.0,Spain,Female,38.0,8.0,0.0,2.0,0.0,0.0,83053.08
-23057,15759566.0,Chukwudi,638.0,France,Male,64.0,6.0,0.0,1.0,1.0,1.0,108887.44
-23058,15797065.0,Yeh,728.0,France,Male,60.0,2.0,0.0,2.0,1.0,1.0,97468.44
-23059,15626452.0,Tien,561.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,124052.97
-23060,15593365.0,Nnaife,466.0,Spain,Female,36.0,9.0,0.0,2.0,1.0,0.0,159823.16
-23061,15657540.0,Aksenov,709.0,France,Male,26.0,3.0,113574.61,1.0,1.0,0.0,162599.51
-23062,15694879.0,Chialuka,551.0,France,Male,46.0,1.0,0.0,2.0,1.0,1.0,150468.35
-23063,15775235.0,Chien,653.0,France,Male,30.0,9.0,0.0,1.0,1.0,0.0,92612.88
-23064,15582944.0,P'eng,815.0,Spain,Female,47.0,4.0,0.0,1.0,1.0,0.0,146895.58
-23065,15795571.0,Hsieh,669.0,France,Female,25.0,9.0,131284.37,1.0,1.0,0.0,163655.01
-23066,15690169.0,Gorman,662.0,France,Male,61.0,1.0,132571.36,1.0,0.0,1.0,171867.08
-23067,15794964.0,Chukwuemeka,622.0,France,Male,41.0,1.0,0.0,1.0,1.0,1.0,135851.3
-23068,15781530.0,Chuang,587.0,Germany,Female,51.0,9.0,117212.18,1.0,0.0,1.0,172269.21
-23069,15690164.0,Ritchie,669.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,1.0,129890.94
-23070,15749103.0,Chidubem,638.0,Spain,Male,26.0,3.0,0.0,2.0,0.0,0.0,82463.69
-23071,15671934.0,Tung,749.0,Germany,Female,46.0,6.0,157955.83,1.0,0.0,0.0,160532.88
-23072,15611058.0,Gadsdon,564.0,Spain,Male,21.0,8.0,0.0,1.0,1.0,1.0,183358.21
-23073,15671930.0,De Salis,681.0,France,Female,43.0,8.0,0.0,1.0,1.0,1.0,57764.65
-23074,15749300.0,Chukwufumnanya,652.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,93718.45
-23075,15585888.0,Kline,758.0,Germany,Male,33.0,8.0,131778.66,2.0,1.0,1.0,129562.74
-23076,15642530.0,Ch'en,713.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,157567.38
-23077,15790093.0,Liao,571.0,Spain,Male,33.0,3.0,125801.03,1.0,1.0,1.0,15650.73
-23078,15624592.0,Chukwudi,590.0,France,Male,41.0,3.0,0.0,2.0,1.0,0.0,70400.86
-23079,15722409.0,Clements,673.0,France,Female,29.0,2.0,152436.56,1.0,0.0,0.0,162452.65
-23080,15717286.0,Chu,703.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,172601.62
-23081,15750181.0,Teng,727.0,France,Female,37.0,4.0,120599.21,1.0,0.0,0.0,179834.45
-23082,15576935.0,Zetticci,594.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,147963.07
-23083,15687421.0,Lu,641.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,133535.29
-23084,15573854.0,Echezonachukwu,739.0,Germany,Female,45.0,8.0,118899.45,2.0,1.0,0.0,198355.28
-23085,15583049.0,Trevisano,591.0,Germany,Male,34.0,6.0,148843.56,2.0,0.0,1.0,28391.52
-23086,15762543.0,Ch'ang,817.0,France,Male,34.0,7.0,0.0,2.0,0.0,1.0,96517.97
-23087,15586039.0,Hsiung,710.0,France,Male,42.0,1.0,0.0,1.0,1.0,0.0,92888.52
-23088,15616471.0,Hs?,762.0,France,Female,29.0,5.0,172231.93,1.0,0.0,1.0,138380.12
-23089,15580579.0,Ugochukwu,645.0,France,Female,32.0,10.0,0.0,1.0,1.0,1.0,187597.98
-23090,15694381.0,McEwan,719.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,73069.3
-23091,15792679.0,Ch'ien,565.0,Spain,Male,38.0,8.0,86824.09,1.0,1.0,0.0,149772.61
-23092,15693690.0,Ch'ang,614.0,France,Male,25.0,2.0,0.0,2.0,1.0,1.0,141926.43
-23093,15781804.0,Yermolayev,679.0,Spain,Female,31.0,5.0,0.0,1.0,1.0,0.0,121537.91
-23094,15591386.0,Chukwujekwu,684.0,Germany,Female,39.0,3.0,93616.35,2.0,0.0,0.0,104222.8
-23095,15602845.0,Scott,684.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,123969.64
-23096,15803965.0,L?,599.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,1.0,67788.49
-23097,15659937.0,Hargraves,710.0,France,Female,44.0,4.0,0.0,2.0,1.0,1.0,165035.17
-23098,15750602.0,Chukwuebuka,646.0,France,Female,32.0,6.0,0.0,1.0,1.0,0.0,119808.64
-23099,15653784.0,Fyodorov,683.0,France,Male,22.0,0.0,125877.65,2.0,1.0,0.0,75585.48
-23100,15803689.0,Genovese,618.0,France,Male,38.0,6.0,0.0,2.0,0.0,1.0,51637.67
-23101,15677052.0,T'ao,723.0,Spain,Female,49.0,3.0,0.0,1.0,0.0,0.0,141023.01
-23102,15593900.0,Nebechukwu,646.0,Germany,Male,31.0,9.0,86006.21,2.0,1.0,0.0,77500.81
-23103,15774318.0,Aitken,588.0,Germany,Male,27.0,5.0,115141.18,2.0,1.0,0.0,146949.72
-23104,15688865.0,Onyemauchechukwu,611.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,90980.04
-23105,15783875.0,H?,576.0,France,Female,37.0,5.0,0.0,1.0,1.0,0.0,164083.72
-23106,15782688.0,Udegbunam,526.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,143450.61
-23107,15596761.0,Milano,645.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,3510.18
-23108,15664339.0,Chukwuemeka,714.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,145551.6
-23109,15792358.0,T'ien,638.0,France,Female,48.0,7.0,0.0,1.0,1.0,1.0,180932.46
-23110,15644962.0,Boulger,850.0,Germany,Female,40.0,1.0,106086.42,2.0,0.0,0.0,115818.51
-23111,15600882.0,Nkemjika,681.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,161519.77
-23112,15736420.0,Brown,753.0,Germany,Male,36.0,6.0,131778.08,1.0,1.0,1.0,181794.86
-23113,15632439.0,Hsia,591.0,France,Female,28.0,2.0,123978.96,1.0,1.0,1.0,81778.72
-23114,15654489.0,Yewen,704.0,France,Male,32.0,0.0,0.0,2.0,1.0,1.0,52861.89
-23115,15625126.0,Hsiung,711.0,Germany,Male,42.0,3.0,96353.8,1.0,1.0,1.0,141148.21
-23116,15643824.0,Chiawuotu,713.0,Spain,Male,34.0,4.0,99564.22,1.0,1.0,0.0,102021.39
-23117,15574830.0,De Luca,718.0,Germany,Female,46.0,1.0,113958.55,1.0,1.0,0.0,177611.57
-23118,15660524.0,Shih,620.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,150694.42
-23119,15809847.0,Chiu,581.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,61240.83
-23120,15802501.0,Nnamutaezinwa,556.0,France,Female,25.0,1.0,0.0,2.0,0.0,1.0,133571.14
-23121,15686870.0,Ts'ai,550.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,1.0,139946.68
-23122,15795895.0,Chidiebere,743.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,17978.68
-23123,15806926.0,Hsiung,627.0,France,Female,33.0,9.0,115897.12,1.0,0.0,0.0,47675.29
-23124,15736399.0,Zito,637.0,Germany,Female,54.0,4.0,140950.97,1.0,1.0,0.0,170968.99
-23125,15582944.0,McIntyre,635.0,Germany,Female,33.0,7.0,141952.07,2.0,1.0,0.0,180932.46
-23126,15756820.0,Trevisano,587.0,France,Male,41.0,0.0,142644.64,1.0,1.0,1.0,49618.37
-23127,15777639.0,Yudin,587.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,1.0,57975.8
-23128,15701291.0,Chukwujamuike,653.0,France,Male,33.0,3.0,133701.07,1.0,0.0,0.0,111324.41
-23129,15656029.0,Nwankwo,638.0,Germany,Male,50.0,6.0,118235.93,1.0,1.0,0.0,159418.1
-23130,15622171.0,White,692.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,125961.48
-23131,15676091.0,Okechukwu,543.0,France,Male,35.0,7.0,124577.33,1.0,1.0,1.0,106636.89
-23132,15783629.0,Toscani,662.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,1.0,36754.13
-23133,15673372.0,Nnamutaezinwa,531.0,Germany,Female,38.0,4.0,137781.65,1.0,1.0,1.0,134901.34
-23134,15716041.0,Manna,641.0,Germany,Male,47.0,7.0,116098.82,1.0,1.0,1.0,51380.9
-23135,15774586.0,Zetticci,674.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,91406.62
-23136,15687558.0,Pinto,705.0,Spain,Female,34.0,7.0,87099.85,1.0,1.0,0.0,141798.98
-23137,15692991.0,Onyemachukwu,774.0,France,Female,40.0,1.0,107868.14,1.0,1.0,1.0,171519.06
-23138,15773421.0,Iqbal,655.0,Spain,Female,33.0,9.0,0.0,1.0,1.0,0.0,90928.02
-23139,15738785.0,Tsou,794.0,France,Male,33.0,10.0,0.0,2.0,1.0,1.0,161671.15
-23140,15764226.0,Okwudiliolisa,637.0,France,Female,39.0,5.0,0.0,1.0,0.0,0.0,182650.15
-23141,15627995.0,Chu,596.0,France,Female,32.0,4.0,120107.71,1.0,1.0,1.0,24402.87
-23142,15641664.0,Chuang,727.0,France,Female,42.0,6.0,115569.21,1.0,1.0,0.0,158973.77
-23143,15792064.0,Biryukov,622.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,1.0,92914.67
-23144,15582335.0,Tien,678.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,92664.09
-23145,15758856.0,Fomin,602.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,101057.95
-23146,15651369.0,Tretiakova,629.0,France,Female,55.0,2.0,0.0,1.0,1.0,1.0,55313.44
-23147,15769902.0,Brazenor,679.0,Germany,Male,39.0,5.0,122524.61,2.0,1.0,0.0,130752.01
-23148,15711571.0,Chukwudi,696.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,83084.33
-23149,15642824.0,Fanucci,749.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,85522.84
-23150,15677522.0,P'eng,573.0,Spain,Male,45.0,2.0,0.0,2.0,1.0,1.0,98896.06
-23151,15701176.0,Chidiebere,694.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,140881.2
-23152,15689344.0,Diggs,713.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,91574.92
-23153,15804862.0,Hs?,589.0,France,Male,23.0,6.0,0.0,1.0,0.0,1.0,58854.88
-23154,15760090.0,Onio,707.0,France,Male,42.0,2.0,0.0,1.0,1.0,0.0,95421.0
-23155,15768574.0,Ch'en,838.0,Spain,Female,32.0,6.0,157577.28,1.0,1.0,0.0,100408.31
-23156,15735703.0,Ch'ien,687.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,142516.35
-23157,15774872.0,Hsia,529.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,74835.65
-23158,15682928.0,Chidiebele,695.0,France,Male,39.0,9.0,125191.69,1.0,1.0,0.0,124489.88
-23159,15680167.0,Cattaneo,696.0,Spain,Male,37.0,2.0,88977.39,1.0,1.0,0.0,180622.02
-23160,15622578.0,Nwachukwu,554.0,France,Male,40.0,3.0,95297.11,1.0,1.0,1.0,62674.38
-23161,15772601.0,Goliwe,710.0,Germany,Male,36.0,7.0,124099.13,2.0,1.0,0.0,62402.38
-23162,15724892.0,Chijioke,567.0,France,Female,57.0,2.0,0.0,1.0,1.0,0.0,151902.78
-23163,15592781.0,Hsueh,596.0,France,Male,46.0,6.0,0.0,2.0,1.0,0.0,85736.26
-23164,15797919.0,Mazzanti,757.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,152290.28
-23165,15570192.0,Nkemdilim,667.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,152298.28
-23166,15764284.0,Tien,616.0,Spain,Female,46.0,1.0,137781.65,3.0,1.0,0.0,187987.01
-23167,15737605.0,Hargreaves,577.0,France,Male,67.0,3.0,0.0,2.0,1.0,1.0,126419.8
-23168,15711378.0,Toscani,651.0,France,Male,44.0,8.0,88977.39,1.0,0.0,0.0,102982.79
-23169,15744044.0,Fiorentini,607.0,France,Female,39.0,3.0,109724.38,1.0,1.0,1.0,60728.89
-23170,15595557.0,Onwuamaegbu,529.0,France,Male,39.0,1.0,0.0,2.0,0.0,1.0,163798.41
-23171,15697042.0,Iadanza,692.0,France,Female,44.0,9.0,154983.98,1.0,1.0,0.0,104240.77
-23172,15591091.0,Ch'eng,656.0,Germany,Female,37.0,10.0,104744.89,1.0,0.0,1.0,136876.47
-23173,15755874.0,Shih,741.0,France,Male,41.0,6.0,0.0,2.0,1.0,0.0,170528.63
-23174,15705379.0,Chukwukere,645.0,France,Male,34.0,2.0,131244.65,2.0,1.0,1.0,166650.32
-23175,15704910.0,Atherton,681.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,131083.56
-23176,15594786.0,Galkin,776.0,France,Male,40.0,2.0,0.0,1.0,1.0,1.0,163736.09
-23177,15762762.0,Yobanna,678.0,France,Male,42.0,4.0,0.0,2.0,1.0,0.0,125979.36
-23178,15698932.0,Chidimma,651.0,Spain,Male,57.0,2.0,0.0,1.0,0.0,0.0,86480.29
-23179,15681446.0,Teng,584.0,France,Male,29.0,10.0,0.0,2.0,0.0,1.0,130789.6
-23180,15763460.0,Walker,683.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,90875.94
-23181,15572051.0,Yobanna,756.0,Germany,Male,47.0,4.0,154779.35,2.0,0.0,0.0,145789.71
-23182,15697318.0,Clements,597.0,France,Male,38.0,7.0,0.0,1.0,1.0,0.0,134626.3
-23183,15648032.0,Hsia,727.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,126535.97
-23184,15790717.0,Ch'iu,586.0,Germany,Male,33.0,6.0,134571.5,1.0,1.0,1.0,147955.91
-23185,15806967.0,Lai,613.0,France,Male,44.0,9.0,0.0,2.0,1.0,1.0,99398.36
-23186,15803685.0,P'eng,721.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,51711.57
-23187,15734892.0,P'eng,726.0,Spain,Female,51.0,0.0,0.0,1.0,0.0,0.0,52301.15
-23188,15582910.0,Obidimkpa,599.0,France,Male,36.0,1.0,0.0,2.0,0.0,1.0,75860.01
-23189,15574910.0,Nwankwo,771.0,France,Female,44.0,4.0,0.0,2.0,0.0,1.0,52471.36
-23190,15766649.0,Lettiere,758.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,69055.45
-23191,15677522.0,Onyemauchechukwu,530.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,76582.95
-23192,15806822.0,Ch'eng,622.0,Germany,Female,32.0,6.0,177619.71,2.0,1.0,0.0,177844.06
-23193,15692991.0,Hsieh,678.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,1.0,121232.51
-23194,15685920.0,Kambinachi,583.0,France,Male,29.0,10.0,0.0,2.0,1.0,1.0,44008.91
-23195,15648721.0,Nucci,731.0,France,Male,34.0,5.0,0.0,1.0,0.0,1.0,76510.46
-23196,15799785.0,Chia,757.0,France,Male,41.0,3.0,0.0,2.0,1.0,0.0,132973.21
-23197,15745804.0,Lucchesi,727.0,France,Female,48.0,5.0,0.0,2.0,1.0,0.0,143913.32
-23198,15810839.0,Ewing,724.0,France,Female,41.0,7.0,0.0,2.0,1.0,0.0,123663.63
-23199,15682467.0,Tseng,652.0,Germany,Male,36.0,1.0,127560.46,1.0,1.0,1.0,179227.52
-23200,15654574.0,Kuo,498.0,Germany,Male,47.0,5.0,127763.24,1.0,0.0,0.0,63736.17
-23201,15722593.0,Onyekachi,675.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,182728.8
-23202,15748327.0,Ch'iu,605.0,France,Male,51.0,8.0,0.0,1.0,1.0,1.0,139919.38
-23203,15608916.0,Chinwenma,545.0,France,Female,36.0,8.0,0.0,1.0,1.0,1.0,187632.22
-23204,15783883.0,Chukwuma,646.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,0.0,100538.8
-23205,15809823.0,Kaeppel,644.0,Germany,Male,30.0,2.0,121898.82,2.0,1.0,1.0,180598.28
-23206,15615477.0,Onyeorulu,555.0,Germany,Female,48.0,1.0,129895.19,1.0,0.0,0.0,152944.39
-23207,15737408.0,Chiazagomekpere,663.0,France,Male,57.0,3.0,0.0,2.0,0.0,1.0,67944.09
-23208,15751137.0,Chia,684.0,Spain,Female,35.0,8.0,0.0,3.0,1.0,1.0,168544.85
-23209,15657535.0,Hodge,648.0,France,Male,22.0,6.0,0.0,2.0,1.0,1.0,74048.1
-23210,15691808.0,Mott,667.0,France,Male,51.0,2.0,0.0,2.0,1.0,0.0,161388.22
-23211,15705737.0,H?,730.0,France,Male,32.0,4.0,0.0,1.0,1.0,1.0,126469.09
-23212,15794142.0,Walker,756.0,Germany,Female,32.0,4.0,124415.54,1.0,0.0,0.0,57844.69
-23213,15681697.0,Brown,762.0,France,Female,44.0,3.0,0.0,2.0,0.0,0.0,51554.58
-23214,15690695.0,H?,692.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,0.0,164975.82
-23215,15786617.0,Uspensky,675.0,France,Female,29.0,5.0,0.0,2.0,1.0,0.0,184514.01
-23216,15591035.0,Chiang,644.0,France,Female,36.0,3.0,121685.31,1.0,0.0,1.0,182867.84
-23217,15803801.0,Mamelu,687.0,France,Male,44.0,9.0,108449.23,1.0,1.0,1.0,53478.02
-23218,15749123.0,Ulyanov,762.0,Germany,Female,40.0,5.0,140576.11,1.0,1.0,0.0,91291.2
-23219,15745307.0,Yobachukwu,741.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,162699.7
-23220,15721658.0,Chibuzo,695.0,Spain,Female,42.0,7.0,0.0,2.0,1.0,1.0,120892.96
-23221,15753861.0,Chia,663.0,France,Female,42.0,3.0,0.0,2.0,1.0,1.0,86786.41
-23222,15769412.0,Chidiebere,667.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,171707.93
-23223,15578045.0,Folliero,691.0,France,Male,38.0,2.0,99824.45,1.0,1.0,0.0,158313.87
-23224,15601184.0,Calabrese,705.0,France,Male,42.0,3.0,0.0,1.0,0.0,0.0,158887.09
-23225,15671582.0,Chin,559.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,126797.9
-23226,15591036.0,Chidiegwu,663.0,France,Female,29.0,6.0,113348.5,1.0,1.0,1.0,147090.9
-23227,15682639.0,Ts'ao,619.0,Spain,Male,29.0,3.0,0.0,2.0,0.0,1.0,158840.99
-23228,15573077.0,Trevisano,651.0,Spain,Male,35.0,9.0,0.0,2.0,0.0,0.0,71865.31
-23229,15702442.0,Tsao,668.0,France,Male,38.0,5.0,141803.77,1.0,1.0,1.0,170528.63
-23230,15603065.0,Trevisan,699.0,France,Male,44.0,6.0,0.0,2.0,1.0,0.0,86937.2
-23231,15764409.0,Tuan,706.0,France,Male,40.0,6.0,125921.37,1.0,1.0,1.0,92916.53
-23232,15589435.0,Tien,627.0,France,Female,28.0,5.0,0.0,2.0,0.0,1.0,166370.83
-23233,15621205.0,Ijendu,714.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,0.0,60721.4
-23234,15804064.0,Ch'ang,652.0,France,Female,29.0,8.0,106511.85,2.0,1.0,0.0,129964.94
-23235,15794895.0,Hsiao,592.0,Spain,Male,64.0,9.0,112281.7,1.0,1.0,1.0,141069.88
-23236,15746871.0,Nwachinemelu,717.0,Spain,Female,34.0,6.0,0.0,1.0,1.0,1.0,62703.38
-23237,15615016.0,Fanucci,573.0,France,Male,41.0,6.0,0.0,2.0,0.0,0.0,185580.89
-23238,15775192.0,Hao,726.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,104786.59
-23239,15673599.0,Chiang,599.0,France,Female,41.0,3.0,0.0,2.0,1.0,0.0,158918.03
-23240,15711299.0,Hsiao,758.0,France,Female,46.0,4.0,69328.47,1.0,0.0,0.0,131473.31
-23241,15740442.0,Onyekachukwu,757.0,Spain,Male,33.0,7.0,0.0,2.0,0.0,0.0,79366.98
-23242,15775108.0,Nkemdirim,757.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,113868.2
-23243,15657868.0,Hsia,549.0,Spain,Male,69.0,4.0,121092.6,1.0,1.0,1.0,80593.31
-23244,15673747.0,Hsia,646.0,France,Male,48.0,6.0,0.0,2.0,1.0,1.0,151733.43
-23245,15572408.0,Kudryashova,704.0,France,Female,50.0,5.0,152323.66,1.0,1.0,0.0,164018.98
-23246,15800116.0,Chukwubuikem,648.0,France,Female,45.0,7.0,129893.56,1.0,1.0,0.0,26687.22
-23247,15736431.0,Chia,628.0,France,Male,38.0,7.0,0.0,1.0,1.0,0.0,181543.67
-23248,15675190.0,Ch'en,705.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,135083.73
-23249,15752694.0,Farrar,724.0,Germany,Female,52.0,1.0,118889.66,2.0,0.0,1.0,57541.06
-23250,15792668.0,Lai,622.0,France,Female,44.0,3.0,148061.46,1.0,1.0,0.0,141860.62
-23251,15658375.0,Kaur,659.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,94774.12
-23252,15591552.0,Lucchesi,699.0,Spain,Male,29.0,9.0,95196.26,1.0,0.0,1.0,150836.22
-23253,15714485.0,Tsao,842.0,Germany,Male,30.0,7.0,132623.12,1.0,1.0,0.0,60942.0
-23254,15751177.0,Nwabugwu,652.0,France,Female,40.0,4.0,0.0,2.0,0.0,0.0,161887.16
-23255,15791842.0,Ch'in,613.0,France,Male,36.0,4.0,0.0,2.0,0.0,0.0,85411.01
-23256,15765014.0,Shih,555.0,France,Female,32.0,7.0,0.0,2.0,0.0,0.0,67622.34
-23257,15673372.0,Lo,633.0,Spain,Female,33.0,8.0,0.0,2.0,1.0,1.0,131644.37
-23258,15768282.0,Maclean,671.0,France,Male,31.0,8.0,0.0,1.0,1.0,1.0,63736.17
-23259,15647574.0,Bess,751.0,Germany,Female,37.0,9.0,98710.89,1.0,1.0,1.0,161064.29
-23260,15658577.0,Davison,763.0,France,Female,52.0,7.0,0.0,1.0,1.0,0.0,64511.44
-23261,15639265.0,Ch'ang,679.0,Germany,Female,45.0,1.0,125879.27,1.0,1.0,0.0,158043.11
-23262,15807525.0,Brown,656.0,France,Male,32.0,3.0,161807.18,1.0,1.0,1.0,94876.65
-23263,15643865.0,Yefremova,691.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,114879.64
-23264,15778936.0,White,685.0,France,Male,24.0,5.0,125265.61,1.0,1.0,1.0,81313.51
-23265,15708063.0,Liao,685.0,France,Female,32.0,8.0,0.0,2.0,0.0,0.0,83470.4
-23266,15591286.0,Folliero,545.0,Spain,Male,50.0,1.0,141675.65,2.0,0.0,1.0,141175.18
-23267,15720671.0,Palerma,648.0,Germany,Male,33.0,3.0,91897.8,1.0,1.0,1.0,118313.87
-23268,15679760.0,Soto,473.0,Germany,Male,43.0,8.0,101257.46,1.0,1.0,0.0,131097.9
-23269,15758769.0,H?,697.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,103931.49
-23270,15596972.0,Chibuzo,699.0,Spain,Male,44.0,9.0,0.0,1.0,1.0,0.0,163714.92
-23271,15596649.0,Hsia,637.0,Germany,Male,35.0,2.0,157780.84,1.0,1.0,0.0,48673.18
-23272,15664214.0,Chimezie,714.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,66977.68
-23273,15626900.0,Ch'ang,593.0,France,Male,41.0,3.0,118537.47,1.0,0.0,1.0,132034.13
-23274,15810444.0,Chia,632.0,Germany,Male,28.0,2.0,147904.31,1.0,1.0,0.0,153604.44
-23275,15603065.0,Onuchukwu,545.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,181893.31
-23276,15680197.0,Ch'eng,577.0,France,Male,48.0,4.0,0.0,3.0,0.0,0.0,161811.23
-23277,15642710.0,Onyekachi,561.0,France,Male,28.0,7.0,0.0,1.0,1.0,0.0,121542.33
-23278,15573077.0,Ch'en,641.0,France,Female,31.0,2.0,113709.28,1.0,1.0,0.0,51711.57
-23279,15788775.0,Chia,663.0,France,Female,46.0,4.0,0.0,1.0,1.0,1.0,25136.95
-23280,15690079.0,Eluemuno,599.0,Germany,Female,45.0,5.0,123939.36,1.0,1.0,1.0,55868.97
-23281,15626144.0,Chukwueloka,526.0,Germany,Female,33.0,1.0,141814.64,1.0,1.0,0.0,72929.96
-23282,15758845.0,Calabresi,721.0,Spain,Female,52.0,5.0,0.0,3.0,1.0,0.0,153662.88
-23283,15651740.0,Fiorentini,812.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,88866.92
-23284,15781679.0,L?,676.0,Germany,Female,55.0,6.0,92676.3,1.0,1.0,1.0,172967.87
-23285,15762216.0,Genovese,636.0,France,Female,22.0,7.0,0.0,2.0,1.0,1.0,135851.3
-23286,15706464.0,DeRose,656.0,France,Male,31.0,0.0,0.0,2.0,1.0,1.0,184520.0
-23287,15663987.0,Tuan,646.0,Spain,Male,21.0,2.0,0.0,1.0,0.0,1.0,91943.94
-23288,15693526.0,T'ien,694.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,0.0,145562.4
-23289,15738219.0,Okwuadigbo,699.0,France,Female,31.0,2.0,119370.11,1.0,1.0,1.0,72924.56
-23290,15697283.0,Palerma,631.0,Germany,Male,20.0,1.0,126679.69,2.0,1.0,0.0,119996.95
-23291,15687558.0,Ts'ui,705.0,Germany,Male,29.0,6.0,127728.24,1.0,1.0,0.0,176913.42
-23292,15581548.0,Chinwenma,545.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,74854.97
-23293,15718247.0,Tsou,656.0,France,Male,33.0,7.0,0.0,2.0,0.0,1.0,98640.78
-23294,15678916.0,Miah,759.0,France,Male,41.0,5.0,136658.61,1.0,1.0,1.0,172501.38
-23295,15628992.0,Chukwukadibia,613.0,France,Female,31.0,7.0,0.0,2.0,0.0,0.0,125451.01
-23296,15572073.0,McLean,756.0,Germany,Male,41.0,1.0,118626.55,1.0,1.0,1.0,183494.87
-23297,15611682.0,Onwughara,692.0,France,Female,32.0,8.0,0.0,1.0,1.0,0.0,80190.36
-23298,15681562.0,Maclean,645.0,France,Female,32.0,2.0,0.0,1.0,1.0,1.0,121175.86
-23299,15680683.0,Ts'ui,588.0,Spain,Female,34.0,10.0,0.0,2.0,0.0,1.0,64872.55
-23300,15667938.0,Mazzanti,544.0,France,Female,28.0,7.0,0.0,2.0,0.0,1.0,91943.94
-23301,15718247.0,Shih,733.0,Germany,Female,40.0,3.0,97792.16,1.0,1.0,0.0,158313.87
-23302,15648032.0,Zito,748.0,France,Female,45.0,1.0,127305.5,1.0,1.0,0.0,107876.85
-23303,15661629.0,L?,675.0,France,Male,54.0,4.0,0.0,1.0,1.0,1.0,56323.21
-23304,15727391.0,Smith,663.0,Germany,Female,44.0,3.0,74988.59,1.0,1.0,0.0,94498.82
-23305,15752956.0,Lo Duca,669.0,France,Female,39.0,8.0,0.0,1.0,1.0,1.0,151336.96
-23306,15789563.0,Mazzanti,699.0,France,Female,42.0,5.0,0.0,1.0,1.0,1.0,75578.67
-23307,15768104.0,Lai,821.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,170968.99
-23308,15792250.0,Hsing,602.0,France,Female,62.0,2.0,0.0,1.0,1.0,1.0,138021.36
-23309,15757628.0,Lettiere,651.0,Spain,Male,43.0,9.0,169824.46,1.0,1.0,1.0,172501.38
-23310,15650098.0,Chiemenam,809.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,114957.22
-23311,15684769.0,De Salis,773.0,France,Female,42.0,5.0,91857.66,1.0,1.0,0.0,108402.54
-23312,15668580.0,Trevisani,682.0,France,Female,32.0,7.0,0.0,1.0,0.0,1.0,152571.33
-23313,15773421.0,Nwokeocha,731.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,135077.01
-23314,15641312.0,Cremonesi,699.0,Germany,Male,37.0,8.0,131734.96,1.0,0.0,0.0,163798.41
-23315,15721921.0,Hsiao,572.0,France,Male,35.0,9.0,0.0,2.0,0.0,1.0,121894.31
-23316,15574795.0,Onwuamaeze,596.0,Spain,Female,43.0,1.0,0.0,1.0,0.0,1.0,167984.61
-23317,15733114.0,Hsing,587.0,France,Male,39.0,6.0,0.0,1.0,1.0,0.0,176692.65
-23318,15601184.0,Mironova,581.0,Germany,Female,32.0,1.0,87927.45,2.0,1.0,1.0,162347.05
-23319,15714642.0,Hs?,717.0,France,Female,36.0,8.0,0.0,1.0,0.0,0.0,141796.79
-23320,15767238.0,Chukwukere,569.0,Germany,Female,40.0,1.0,121206.96,1.0,1.0,0.0,18924.92
-23321,15769346.0,Manna,652.0,France,Female,26.0,10.0,0.0,1.0,1.0,1.0,19370.73
-23322,15776824.0,Hsieh,762.0,France,Male,55.0,3.0,0.0,1.0,1.0,1.0,76621.99
-23323,15695301.0,Onwumelu,778.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,187811.71
-23324,15615473.0,T'ien,705.0,France,Male,44.0,9.0,0.0,2.0,0.0,0.0,145593.85
-23325,15605665.0,Shand,652.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,155700.15
-23326,15590146.0,Lucciano,709.0,Spain,Male,39.0,7.0,0.0,2.0,1.0,1.0,171378.77
-23327,15687828.0,Nwankwo,642.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,121883.87
-23328,15790782.0,Chibueze,753.0,France,Male,49.0,7.0,99792.82,3.0,0.0,0.0,107712.42
-23329,15788126.0,Calabresi,723.0,Spain,Female,44.0,5.0,0.0,2.0,0.0,0.0,88920.68
-23330,15777060.0,Fiorentini,751.0,Spain,Female,36.0,9.0,0.0,2.0,1.0,0.0,152843.53
-23331,15692291.0,Milani,675.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,54926.51
-23332,15808182.0,O'Brien,694.0,France,Male,38.0,2.0,0.0,1.0,1.0,1.0,139048.0
-23333,15628274.0,Hsueh,550.0,Germany,Male,29.0,4.0,106453.53,1.0,1.0,0.0,173617.09
-23334,15595036.0,Okonkwo,711.0,Germany,Female,32.0,2.0,126773.43,2.0,1.0,1.0,89874.82
-23335,15632050.0,Chiazagomekpele,695.0,France,Female,42.0,9.0,0.0,2.0,0.0,0.0,93524.19
-23336,15755722.0,Nnaife,634.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,187905.12
-23337,15661591.0,Chidiebele,599.0,France,Female,33.0,3.0,113328.65,1.0,0.0,0.0,129526.57
-23338,15718454.0,Ch'ang,714.0,France,Male,45.0,7.0,0.0,1.0,0.0,0.0,48244.64
-23339,15726167.0,T'ien,602.0,France,Female,59.0,5.0,0.0,1.0,1.0,1.0,58855.85
-23340,15654085.0,Nwebube,811.0,France,Female,37.0,1.0,0.0,1.0,1.0,0.0,114899.13
-23341,15811927.0,Hsing,709.0,Spain,Female,46.0,7.0,0.0,2.0,0.0,1.0,131534.96
-23342,15692599.0,Chinwendu,687.0,France,Male,25.0,0.0,115095.88,1.0,1.0,1.0,112585.19
-23343,15636658.0,Teng,712.0,Spain,Male,38.0,2.0,77846.9,1.0,1.0,1.0,178709.98
-23344,15682600.0,Milano,692.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,0.0,88566.9
-23345,15589572.0,Biryukov,681.0,France,Female,33.0,8.0,0.0,1.0,0.0,0.0,156910.38
-23346,15665760.0,Ts'ui,703.0,France,Female,67.0,2.0,129901.21,1.0,1.0,1.0,146585.57
-23347,15783501.0,Ma,569.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,143598.7
-23348,15769168.0,Hsiao,687.0,France,Female,35.0,1.0,0.0,2.0,1.0,0.0,187632.22
-23349,15605037.0,Hsieh,661.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,82867.55
-23350,15663421.0,Chukwudi,525.0,France,Male,39.0,4.0,0.0,1.0,1.0,1.0,93524.19
-23351,15770041.0,Hales,496.0,Germany,Male,51.0,5.0,96942.06,1.0,1.0,0.0,151903.6
-23352,15762929.0,Ch'en,593.0,Spain,Male,36.0,2.0,0.0,2.0,0.0,1.0,62368.33
-23353,15742216.0,Ikedinachukwu,715.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,93959.96
-23354,15583736.0,Nwabugwu,850.0,France,Female,44.0,9.0,90217.07,1.0,0.0,1.0,163989.66
-23355,15804482.0,Ch'ang,709.0,France,Female,40.0,8.0,0.0,1.0,0.0,0.0,180800.42
-23356,15773469.0,Shipton,695.0,Spain,Female,37.0,3.0,0.0,2.0,1.0,0.0,138399.67
-23357,15705735.0,Hsiao,581.0,Germany,Male,34.0,7.0,147127.81,1.0,1.0,1.0,64716.08
-23358,15666096.0,Palermo,591.0,Germany,Female,48.0,8.0,97023.21,1.0,1.0,0.0,172372.63
-23359,15766887.0,Hsiao,622.0,Spain,Female,41.0,9.0,0.0,2.0,1.0,1.0,141023.01
-23360,15751167.0,Sun,650.0,Germany,Female,28.0,5.0,165350.61,2.0,0.0,1.0,142903.44
-23361,15681014.0,Chukwufumnanya,622.0,Germany,Male,27.0,7.0,104944.1,2.0,1.0,0.0,121151.1
-23362,15607988.0,Hs?,681.0,Germany,Male,39.0,5.0,146098.43,2.0,1.0,1.0,152290.28
-23363,15698028.0,Chiang,753.0,France,Male,45.0,8.0,0.0,2.0,1.0,0.0,94978.1
-23364,15593976.0,Maclean,537.0,Germany,Female,35.0,4.0,140720.93,1.0,1.0,1.0,62830.97
-23365,15793890.0,Macleod,605.0,France,Female,43.0,8.0,114327.66,1.0,1.0,0.0,92982.61
-23366,15761706.0,Fanucci,667.0,Spain,Male,39.0,2.0,0.0,2.0,1.0,1.0,91830.75
-23367,15666884.0,Pagnotto,646.0,France,Male,27.0,1.0,132951.92,2.0,1.0,0.0,142377.69
-23368,15743192.0,Ibezimako,719.0,France,Female,35.0,4.0,124023.19,1.0,1.0,0.0,143668.44
-23369,15627665.0,Chukwufumnanya,703.0,France,Male,44.0,1.0,0.0,2.0,1.0,0.0,147964.99
-23370,15575581.0,P'eng,716.0,France,Male,33.0,6.0,0.0,1.0,1.0,1.0,139946.68
-23371,15567367.0,Chiang,696.0,Germany,Female,31.0,1.0,136855.94,1.0,1.0,1.0,100727.68
-23372,15597909.0,Ch'eng,651.0,Germany,Male,24.0,3.0,113873.27,1.0,1.0,0.0,131718.12
-23373,15650098.0,Shao,689.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,1.0,27011.8
-23374,15757829.0,Maccallum,638.0,France,Male,28.0,3.0,124547.13,1.0,1.0,0.0,170638.98
-23375,15589435.0,Hao,633.0,France,Male,36.0,2.0,127924.25,2.0,1.0,1.0,14081.64
-23376,15583090.0,Hsiung,578.0,Spain,Male,46.0,2.0,0.0,3.0,1.0,0.0,152577.62
-23377,15686538.0,Ch'in,649.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,131924.38
-23378,15761720.0,Chukwudi,724.0,Germany,Female,54.0,6.0,162657.64,4.0,0.0,0.0,90928.48
-23379,15778861.0,Chukwuemeka,637.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,46811.77
-23380,15605827.0,Folliero,711.0,Spain,Male,33.0,6.0,0.0,2.0,1.0,1.0,69011.66
-23381,15711457.0,Brown,687.0,Germany,Female,34.0,2.0,130306.49,1.0,1.0,1.0,87901.83
-23382,15759913.0,Chidozie,678.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,131283.6
-23383,15674583.0,T'ang,697.0,Germany,Female,47.0,8.0,109341.87,2.0,0.0,0.0,160941.78
-23384,15579996.0,Chia,678.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,114968.99
-23385,15719479.0,Pirozzi,678.0,France,Male,33.0,6.0,0.0,1.0,0.0,1.0,137961.41
-23386,15797733.0,Y?an,695.0,France,Female,37.0,8.0,144974.55,1.0,1.0,1.0,78858.07
-23387,15697318.0,Genovese,605.0,France,Male,51.0,1.0,121152.05,1.0,0.0,1.0,162668.33
-23388,15657105.0,Lu,727.0,France,Male,44.0,5.0,0.0,1.0,1.0,0.0,176743.51
-23389,15656912.0,Ugochukwu,554.0,France,Female,36.0,8.0,0.0,1.0,0.0,0.0,182867.84
-23390,15603594.0,Onyemauchechukwu,684.0,Germany,Female,56.0,3.0,102540.61,1.0,1.0,0.0,95857.18
-23391,15740404.0,Macleod,721.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,162260.93
-23392,15781298.0,T'ien,573.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,81492.8
-23393,15648735.0,Pirozzi,615.0,Germany,Female,33.0,6.0,109786.82,2.0,1.0,0.0,110230.4
-23394,15581748.0,Kuo,652.0,France,Male,29.0,0.0,0.0,1.0,1.0,1.0,149593.61
-23395,15777772.0,Hsieh,781.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,1.0,72909.75
-23396,15809826.0,Chienezie,478.0,France,Female,44.0,3.0,0.0,2.0,0.0,0.0,92621.9
-23397,15665083.0,Chukwufumnanya,718.0,France,Female,40.0,8.0,0.0,1.0,0.0,0.0,146785.44
-23398,15642099.0,Ch'in,631.0,Spain,Male,42.0,3.0,0.0,2.0,1.0,1.0,72350.47
-23399,15763898.0,Palerma,614.0,France,Female,51.0,10.0,0.0,2.0,1.0,1.0,19500.1
-23400,15750728.0,Macleod,637.0,France,Female,42.0,0.0,0.0,2.0,1.0,0.0,185466.85
-23401,15698872.0,Uchenna,667.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,823.96
-23402,15583748.0,Hsing,597.0,Germany,Male,37.0,3.0,112808.18,2.0,0.0,1.0,89801.9
-23403,15684868.0,Zito,749.0,Germany,Male,41.0,4.0,126619.27,1.0,1.0,1.0,135077.01
-23404,15700383.0,Cawthorne,678.0,France,Female,29.0,4.0,160838.13,1.0,1.0,0.0,43480.43
-23405,15585853.0,Iadanza,675.0,Spain,Male,49.0,8.0,0.0,1.0,1.0,1.0,136902.01
-23406,15776921.0,Martin,553.0,Germany,Female,51.0,8.0,121345.88,1.0,1.0,0.0,78854.57
-23407,15742404.0,Yashina,727.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,119882.7
-23408,15715796.0,Ch'ang,731.0,Germany,Female,34.0,5.0,146547.78,2.0,0.0,0.0,75585.48
-23409,15737705.0,Hsiung,643.0,Germany,Male,40.0,1.0,133968.96,2.0,1.0,0.0,172363.41
-23410,15663489.0,Hsieh,724.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,139537.43
-23411,15641670.0,Sells,714.0,Germany,Male,34.0,8.0,148897.02,1.0,1.0,1.0,143357.03
-23412,15684319.0,Ts'ui,720.0,France,Male,31.0,3.0,0.0,1.0,1.0,0.0,123663.63
-23413,15731830.0,Teng,588.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,125962.02
-23414,15690175.0,Chia,619.0,Germany,Male,29.0,9.0,165978.01,2.0,1.0,0.0,162858.29
-23415,15795895.0,Yobanna,599.0,France,Male,29.0,1.0,0.0,2.0,0.0,0.0,82526.92
-23416,15700801.0,Smith,555.0,France,Male,33.0,3.0,140197.18,2.0,0.0,1.0,166475.44
-23417,15642996.0,Hsueh,554.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,71830.1
-23418,15615060.0,O'Donnell,714.0,Germany,Female,33.0,3.0,92474.05,2.0,1.0,1.0,131312.56
-23419,15689671.0,Teng,718.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,168886.66
-23420,15742229.0,Chijindum,729.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,115611.97
-23421,15776939.0,Ebelechukwu,704.0,Germany,Male,46.0,6.0,90963.49,1.0,0.0,1.0,180593.45
-23422,15766166.0,Hsieh,670.0,Spain,Male,39.0,5.0,0.0,1.0,1.0,0.0,113581.85
-23423,15775339.0,Tsao,627.0,France,Female,34.0,9.0,0.0,2.0,0.0,0.0,132468.76
-23424,15635178.0,Milani,836.0,Germany,Male,29.0,8.0,144961.97,2.0,1.0,1.0,60721.4
-23425,15582591.0,Ch'eng,584.0,Spain,Male,43.0,7.0,97588.6,1.0,1.0,1.0,187632.22
-23426,15791700.0,Chin,632.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,158973.77
-23427,15697574.0,Akabueze,648.0,Spain,Male,35.0,7.0,0.0,2.0,0.0,1.0,103931.49
-23428,15790743.0,Ch'ien,635.0,France,Male,41.0,3.0,0.0,2.0,1.0,0.0,94875.03
-23429,15777596.0,Kornilova,812.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,0.0,198798.44
-23430,15790594.0,Ch'in,633.0,France,Female,36.0,1.0,113657.83,1.0,1.0,1.0,154598.58
-23431,15683337.0,Houghton,712.0,Germany,Female,40.0,3.0,128743.8,1.0,0.0,0.0,104902.68
-23432,15680141.0,Shao,644.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,84711.03
-23433,15592645.0,De Salis,528.0,Germany,Female,41.0,8.0,122351.29,1.0,1.0,1.0,110253.2
-23434,15771580.0,Pirozzi,691.0,France,Female,27.0,6.0,118742.71,1.0,1.0,0.0,64872.55
-23435,15578908.0,Walker,683.0,France,Male,38.0,2.0,121260.64,2.0,1.0,1.0,126726.33
-23436,15681476.0,Chiang,705.0,Spain,Female,40.0,3.0,0.0,1.0,1.0,0.0,161076.92
-23437,15733247.0,Fan,635.0,France,Female,72.0,8.0,0.0,2.0,1.0,1.0,133535.29
-23438,15652218.0,Onyekachi,691.0,Spain,Female,35.0,8.0,0.0,1.0,1.0,1.0,165574.54
-23439,15703763.0,Calabrese,662.0,France,Female,45.0,5.0,0.0,2.0,1.0,0.0,184524.65
-23440,15662294.0,Macintyre,724.0,Germany,Female,35.0,9.0,82696.09,1.0,1.0,1.0,176743.51
-23441,15768151.0,Uspensky,731.0,France,Male,34.0,0.0,0.0,2.0,0.0,0.0,94790.34
-23442,15640133.0,Tsai,678.0,Spain,Male,53.0,1.0,171913.87,1.0,0.0,1.0,144875.31
-23443,15572706.0,Okechukwu,572.0,France,Female,36.0,9.0,0.0,2.0,0.0,0.0,81861.1
-23444,15643794.0,Mazzanti,628.0,Germany,Male,45.0,3.0,100249.41,2.0,1.0,1.0,143629.72
-23445,15768777.0,Chiemenam,714.0,France,Female,35.0,4.0,0.0,2.0,0.0,0.0,63736.16
-23446,15572415.0,Lucchese,648.0,Spain,Male,38.0,6.0,141349.68,1.0,0.0,1.0,161249.07
-23447,15625021.0,Clements,731.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,136915.15
-23448,15642515.0,Lo,695.0,France,Male,42.0,4.0,121161.57,1.0,1.0,0.0,52794.71
-23449,15745307.0,Tseng,595.0,Germany,Male,36.0,5.0,87541.06,1.0,1.0,0.0,88827.67
-23450,15573851.0,Hsu,648.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,142331.85
-23451,15651140.0,Folliero,495.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,59981.62
-23452,15592762.0,Ndubuagha,662.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,182765.14
-23453,15695846.0,Trevisani,631.0,Germany,Female,62.0,5.0,73059.38,1.0,0.0,1.0,134878.19
-23454,15702571.0,Mazzanti,710.0,Spain,Female,56.0,1.0,0.0,1.0,0.0,0.0,95562.81
-23455,15772995.0,Chiu,590.0,France,Male,35.0,2.0,0.0,2.0,0.0,1.0,121061.89
-23456,15767339.0,Hsieh,652.0,France,Male,32.0,5.0,0.0,2.0,0.0,0.0,161435.02
-23457,15592765.0,Lucciano,662.0,France,Male,62.0,7.0,0.0,1.0,0.0,0.0,136071.05
-23458,15583076.0,Pinto,633.0,Spain,Male,36.0,2.0,0.0,1.0,1.0,0.0,173576.71
-23459,15803790.0,Mazzi,695.0,France,Female,38.0,0.0,102575.05,1.0,1.0,1.0,166677.54
-23460,15746564.0,Pagnotto,498.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,138882.98
-23461,15803790.0,Chukwudi,705.0,France,Male,33.0,5.0,0.0,1.0,0.0,1.0,51703.09
-23462,15720649.0,Lo Duca,706.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,140979.68
-23463,15570086.0,Ts'ai,646.0,Spain,Female,40.0,4.0,165798.1,1.0,1.0,0.0,155700.15
-23464,15758856.0,Chidiebere,634.0,France,Female,30.0,0.0,0.0,2.0,1.0,0.0,52876.41
-23465,15792868.0,Onuora,652.0,France,Female,40.0,8.0,125877.65,1.0,1.0,0.0,164924.11
-23466,15726676.0,Chuang,528.0,Germany,Male,41.0,6.0,163549.61,2.0,1.0,0.0,99251.24
-23467,15658364.0,Chimezie,850.0,Spain,Female,34.0,1.0,0.0,2.0,0.0,1.0,93783.0
-23468,15632439.0,Monds,748.0,France,Male,38.0,4.0,152063.59,1.0,0.0,0.0,161064.29
-23469,15682638.0,Ch'in,671.0,Spain,Female,39.0,7.0,0.0,2.0,0.0,0.0,146660.21
-23470,15758073.0,O'Donnell,730.0,France,Male,35.0,1.0,115888.04,1.0,1.0,0.0,77330.35
-23471,15645819.0,Aikenhead,591.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,90896.04
-23472,15568487.0,Smith,637.0,France,Female,43.0,10.0,153313.66,1.0,1.0,0.0,131363.57
-23473,15790379.0,Ugorji,712.0,France,Female,43.0,5.0,0.0,1.0,1.0,0.0,162697.93
-23474,15722692.0,Ch'ang,713.0,France,Female,44.0,6.0,0.0,2.0,1.0,1.0,142626.49
-23475,15644551.0,Padovano,667.0,Spain,Male,27.0,6.0,0.0,2.0,1.0,1.0,160529.71
-23476,15612893.0,Y?an,604.0,France,Female,47.0,4.0,103907.28,1.0,1.0,0.0,133501.94
-23477,15706232.0,Shih,684.0,France,Male,47.0,2.0,142360.61,1.0,1.0,1.0,166370.81
-23478,15654975.0,Baresi,589.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,102908.91
-23479,15653857.0,Nnaife,756.0,Germany,Male,37.0,3.0,109346.13,1.0,1.0,1.0,143711.02
-23480,15724453.0,Ozoemena,597.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,71926.28
-23481,15592461.0,Kao,637.0,Germany,Male,31.0,8.0,138577.34,1.0,1.0,1.0,100200.4
-23482,15721469.0,Chiedozie,725.0,France,Male,43.0,6.0,0.0,2.0,0.0,1.0,74580.8
-23483,15637646.0,Ts'ui,648.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,180622.02
-23484,15589754.0,Hs?,724.0,France,Female,54.0,4.0,138664.24,1.0,0.0,0.0,143960.72
-23485,15773469.0,Walker,619.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,180693.45
-23486,15752300.0,L?,591.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,120892.96
-23487,15581811.0,Ozioma,695.0,France,Female,25.0,5.0,0.0,2.0,1.0,1.0,152210.81
-23488,15582797.0,Ts'ui,695.0,France,Male,29.0,5.0,0.0,2.0,0.0,1.0,62634.94
-23489,15757310.0,Ch'iu,652.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,171378.77
-23490,15743817.0,Iheatu,763.0,Germany,Female,32.0,8.0,96486.57,1.0,0.0,1.0,107841.77
-23491,15663489.0,T'ao,649.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,123954.15
-23492,15576022.0,T'ao,746.0,Spain,Female,46.0,2.0,0.0,1.0,1.0,1.0,108747.12
-23493,15593601.0,Calabrese,663.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,105886.77
-23494,15702561.0,Fiorentino,678.0,Spain,Male,43.0,4.0,124723.92,2.0,0.0,1.0,87071.18
-23495,15693322.0,Nwabugwu,850.0,Spain,Female,49.0,1.0,0.0,2.0,0.0,1.0,71709.12
-23496,15596397.0,Fanucci,668.0,France,Female,41.0,6.0,0.0,2.0,0.0,1.0,164741.5
-23497,15622443.0,Nwachinemelu,633.0,France,Female,45.0,10.0,80764.03,1.0,1.0,0.0,72927.67
-23498,15592736.0,Mancini,667.0,Germany,Female,31.0,1.0,143595.87,2.0,1.0,0.0,153698.65
-23499,15591279.0,Hsing,708.0,France,Male,37.0,9.0,0.0,2.0,0.0,0.0,72929.96
-23500,15773973.0,Chien,627.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,91829.17
-23501,15573926.0,Ch'eng,712.0,France,Female,36.0,8.0,0.0,2.0,0.0,1.0,169921.11
-23502,15702300.0,Iadanza,697.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,135635.32
-23503,15663917.0,Milanesi,678.0,France,Female,31.0,8.0,125608.72,1.0,1.0,1.0,152577.62
-23504,15777576.0,Gentry,544.0,Germany,Male,43.0,10.0,185714.28,1.0,1.0,0.0,142566.75
-23505,15579706.0,Chien,692.0,Spain,Female,33.0,3.0,0.0,2.0,1.0,0.0,125986.18
-23506,15654495.0,Tung,748.0,France,Female,37.0,3.0,103516.87,2.0,1.0,1.0,1140.02
-23507,15569438.0,Babbage,573.0,Germany,Female,34.0,4.0,109346.13,2.0,1.0,1.0,64716.08
-23508,15643908.0,Calabresi,683.0,Germany,Male,37.0,2.0,131834.4,2.0,1.0,0.0,86920.57
-23509,15751203.0,Gboliwe,646.0,France,Female,29.0,4.0,115074.02,1.0,1.0,0.0,120540.83
-23510,15597914.0,Chiabuotu,592.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,167035.07
-23511,15704954.0,Udinesi,723.0,Germany,Male,41.0,9.0,119741.77,1.0,1.0,0.0,130113.78
-23512,15596797.0,Chiazagomekpele,710.0,France,Male,47.0,6.0,0.0,1.0,1.0,0.0,46811.77
-23513,15682442.0,Y?,833.0,France,Male,40.0,7.0,0.0,2.0,0.0,1.0,96834.15
-23514,15762169.0,Sagese,616.0,Germany,Male,43.0,9.0,113672.91,3.0,1.0,1.0,166694.93
-23515,15663932.0,Miah,655.0,Spain,Male,37.0,3.0,0.0,1.0,1.0,0.0,71492.28
-23516,15762803.0,Toscani,615.0,Spain,Male,33.0,0.0,0.0,2.0,1.0,0.0,40313.47
-23517,15666055.0,P'eng,586.0,France,Female,33.0,8.0,0.0,2.0,0.0,1.0,155047.11
-23518,15605968.0,Ch'iu,573.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,151924.9
-23519,15716347.0,Ma,535.0,France,Female,32.0,4.0,0.0,2.0,0.0,1.0,90928.02
-23520,15645778.0,Ch'iu,732.0,France,Male,41.0,2.0,0.0,1.0,1.0,1.0,139947.17
-23521,15626383.0,Kaur,550.0,Germany,Male,45.0,1.0,105303.74,1.0,0.0,0.0,187925.75
-23522,15702293.0,Dickson,757.0,Spain,Female,31.0,9.0,166501.17,1.0,1.0,1.0,106061.47
-23523,15745250.0,Liao,637.0,Spain,Male,35.0,0.0,135923.17,1.0,0.0,0.0,86937.2
-23524,15769308.0,Ch'ien,714.0,Spain,Male,30.0,8.0,0.0,2.0,0.0,0.0,67789.99
-23525,15724223.0,Shih,711.0,Spain,Male,43.0,6.0,0.0,1.0,0.0,0.0,113597.64
-23526,15766776.0,Mazzi,667.0,Germany,Male,45.0,5.0,122552.34,1.0,1.0,0.0,167984.72
-23527,15572398.0,Maclean,686.0,France,Female,23.0,10.0,0.0,1.0,1.0,1.0,170528.63
-23528,15747222.0,Hsu,707.0,Germany,Female,43.0,2.0,123372.3,2.0,1.0,1.0,123850.58
-23529,15727177.0,Okwukwe,705.0,France,Female,31.0,2.0,135020.21,1.0,1.0,0.0,185543.33
-23530,15662238.0,Hsia,709.0,Germany,Female,55.0,6.0,89750.21,1.0,1.0,1.0,134964.85
-23531,15773394.0,Tuan,505.0,France,Male,46.0,1.0,123938.6,1.0,0.0,1.0,40980.87
-23532,15585047.0,Fanucci,794.0,Germany,Male,41.0,8.0,96834.78,2.0,1.0,0.0,933.38
-23533,15809800.0,Yefremova,706.0,France,Male,38.0,2.0,103516.87,1.0,1.0,0.0,89872.33
-23534,15800773.0,Fang,588.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,55829.25
-23535,15671322.0,Bischof,597.0,France,Male,34.0,6.0,106089.3,1.0,1.0,0.0,93536.38
-23536,15783875.0,Chukwubuikem,704.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,91830.75
-23537,15627377.0,Chinomso,588.0,Spain,Male,33.0,6.0,0.0,2.0,1.0,1.0,54474.48
-23538,15729279.0,Onyekachi,792.0,Germany,Male,48.0,9.0,116498.22,1.0,0.0,1.0,131729.52
-23539,15722350.0,Macleod,741.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,78591.15
-23540,15664808.0,H?,594.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,131167.98
-23541,15772423.0,Lai,763.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,183575.01
-23542,15756901.0,Nwabugwu,676.0,France,Male,20.0,3.0,0.0,1.0,1.0,1.0,129892.93
-23543,15739502.0,Tuan,703.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,112946.67
-23544,15650437.0,Chiang,707.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,91830.75
-23545,15672970.0,Ch'in,543.0,Spain,Female,50.0,7.0,0.0,2.0,1.0,1.0,141364.56
-23546,15607301.0,Chukwumaobim,766.0,Spain,Male,37.0,1.0,0.0,3.0,1.0,1.0,82868.34
-23547,15786539.0,Ch'eng,849.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,1.0,90908.95
-23548,15725028.0,Hsu,659.0,Germany,Female,38.0,0.0,115625.07,2.0,1.0,1.0,163714.92
-23549,15674880.0,Chiazagomekpere,681.0,Germany,Male,37.0,7.0,97720.35,2.0,0.0,1.0,159483.76
-23550,15677324.0,Chu,683.0,France,Male,50.0,9.0,183768.47,1.0,1.0,1.0,142680.64
-23551,15624451.0,Chukwudi,602.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,181208.47
-23552,15806546.0,O'Loghlen,645.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,132547.02
-23553,15751208.0,Brownless,710.0,Germany,Female,58.0,7.0,125861.74,1.0,1.0,1.0,158918.03
-23554,15664759.0,Chukwudi,682.0,France,Female,31.0,1.0,0.0,2.0,0.0,0.0,77965.67
-23555,15803886.0,Zuev,663.0,France,Male,55.0,3.0,0.0,3.0,1.0,0.0,51907.72
-23556,15642997.0,Ko,656.0,Germany,Male,32.0,2.0,100375.39,2.0,1.0,0.0,147822.56
-23557,15771535.0,Bischof,535.0,France,Male,38.0,5.0,117294.12,1.0,0.0,0.0,101319.76
-23558,15659327.0,Okwudiliolisa,774.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,64539.64
-23559,15628950.0,Smith,691.0,France,Male,35.0,8.0,99883.16,1.0,1.0,1.0,141148.21
-23560,15774510.0,T'ao,673.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,105817.63
-23561,15791836.0,Tsou,735.0,Spain,Female,52.0,1.0,0.0,1.0,0.0,0.0,167773.55
-23562,15626144.0,Zetticci,653.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,86937.2
-23563,15664897.0,Achebe,543.0,Germany,Male,37.0,5.0,102708.77,1.0,1.0,1.0,172668.31
-23564,15688865.0,Smith,469.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,81602.02
-23565,15748123.0,Chukwukere,554.0,France,Female,38.0,1.0,0.0,1.0,1.0,1.0,57320.92
-23566,15757897.0,White,653.0,Spain,Female,33.0,3.0,81231.85,1.0,1.0,1.0,131662.47
-23567,15615016.0,Tuan,607.0,Spain,Female,40.0,6.0,0.0,1.0,0.0,1.0,130726.96
-23568,15768120.0,Chidubem,579.0,Germany,Male,43.0,3.0,93960.35,2.0,1.0,1.0,145551.6
-23569,15628674.0,H?,678.0,France,Female,45.0,9.0,0.0,1.0,0.0,1.0,51454.06
-23570,15723720.0,Iloerika,577.0,Germany,Female,49.0,1.0,108308.0,1.0,1.0,0.0,152740.16
-23571,15585360.0,Nkemdilim,568.0,Spain,Male,49.0,2.0,0.0,1.0,0.0,0.0,47722.79
-23572,15569209.0,Zhirov,745.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,1.0,152992.04
-23573,15728505.0,Nwachinemelu,705.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,158750.53
-23574,15701605.0,Ch'iu,671.0,France,Male,33.0,5.0,0.0,2.0,0.0,0.0,120531.31
-23575,15602934.0,Ch'iu,620.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,171021.44
-23576,15738672.0,Hsia,686.0,Spain,Female,34.0,4.0,0.0,2.0,0.0,0.0,52367.29
-23577,15670481.0,P'eng,710.0,Spain,Female,28.0,9.0,0.0,2.0,0.0,0.0,177670.57
-23578,15780492.0,Chukwudi,758.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,140130.22
-23579,15572728.0,Onwumelu,439.0,France,Male,51.0,7.0,0.0,1.0,1.0,1.0,62762.12
-23580,15632816.0,McIntyre,634.0,Germany,Male,39.0,7.0,141927.1,1.0,1.0,0.0,103349.74
-23581,15709135.0,Ch'in,637.0,Spain,Male,32.0,7.0,122486.11,1.0,1.0,0.0,99341.87
-23582,15596710.0,Maughan,634.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,138780.89
-23583,15644849.0,Trevisano,699.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,170819.01
-23584,15591766.0,De Luca,592.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,90591.15
-23585,15583480.0,Chukwukere,683.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,28269.86
-23586,15773394.0,Chidalu,556.0,France,Male,35.0,2.0,97445.49,1.0,1.0,1.0,115468.81
-23587,15586029.0,K'ung,594.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,176750.81
-23588,15732265.0,Chukwufumnanya,739.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,137037.97
-23589,15702912.0,P'eng,564.0,France,Female,32.0,0.0,0.0,1.0,0.0,0.0,184524.65
-23590,15662736.0,Yobanna,638.0,France,Male,29.0,10.0,0.0,2.0,1.0,1.0,91514.62
-23591,15788448.0,Tsao,588.0,France,Male,33.0,8.0,134936.94,1.0,1.0,0.0,172495.81
-23592,15793883.0,O'Donnell,760.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,152843.53
-23593,15775572.0,Manna,660.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,52301.15
-23594,15757981.0,Chiazagomekpere,746.0,Spain,Female,32.0,3.0,108914.17,1.0,0.0,1.0,182059.28
-23595,15775335.0,Milanesi,584.0,Spain,Female,47.0,6.0,0.0,1.0,0.0,0.0,116747.76
-23596,15680167.0,Chukwukere,729.0,France,Male,40.0,2.0,94464.81,1.0,1.0,0.0,28806.32
-23597,15658935.0,Milani,726.0,France,Male,52.0,0.0,138023.79,3.0,1.0,1.0,87412.24
-23598,15711386.0,Nwachukwu,695.0,France,Male,36.0,5.0,0.0,1.0,1.0,0.0,166677.35
-23599,15744942.0,Chikezie,794.0,Germany,Male,30.0,9.0,87099.85,2.0,1.0,1.0,91514.69
-23600,15766185.0,Aiken,663.0,France,Male,36.0,1.0,129901.91,1.0,0.0,0.0,146511.2
-23601,15744517.0,Tung,607.0,Spain,Female,46.0,8.0,0.0,2.0,1.0,1.0,141446.01
-23602,15783026.0,Ch'eng,850.0,France,Male,28.0,1.0,0.0,1.0,1.0,1.0,82222.86
-23603,15641640.0,Biryukov,653.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,135655.29
-23604,15659475.0,Onyekaozulu,743.0,France,Male,35.0,3.0,116318.23,1.0,1.0,0.0,35600.25
-23605,15772798.0,Ch'iu,581.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,185555.63
-23606,15730673.0,Ch'eng,725.0,Germany,Male,51.0,7.0,105103.33,2.0,0.0,0.0,143475.01
-23607,15770562.0,McGregor,682.0,Germany,Male,59.0,4.0,141533.55,2.0,1.0,0.0,120892.85
-23608,15777892.0,White,595.0,Spain,Male,39.0,4.0,0.0,2.0,0.0,0.0,89935.46
-23609,15667934.0,Chien,681.0,France,Male,42.0,6.0,0.0,2.0,0.0,0.0,105969.05
-23610,15667042.0,Nwankwo,554.0,France,Female,29.0,1.0,102918.38,1.0,1.0,0.0,147040.25
-23611,15761707.0,Uwaezuoke,581.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,161242.99
-23612,15771543.0,Ch'ien,836.0,France,Female,57.0,2.0,0.0,2.0,1.0,1.0,71179.31
-23613,15798885.0,P'eng,758.0,Spain,Female,38.0,2.0,0.0,1.0,0.0,1.0,152843.53
-23614,15640074.0,Scott,674.0,Spain,Male,26.0,3.0,0.0,2.0,1.0,0.0,161887.16
-23615,15591552.0,Ting,529.0,France,Female,61.0,7.0,0.0,1.0,0.0,1.0,100566.81
-23616,15634267.0,Onyekachi,714.0,Spain,Female,33.0,7.0,0.0,1.0,1.0,1.0,131710.59
-23617,15684395.0,Hsieh,667.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,1.0,121537.91
-23618,15772559.0,Yobachukwu,714.0,France,Male,52.0,4.0,0.0,1.0,1.0,0.0,71154.1
-23619,15702461.0,Hsia,516.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,141756.32
-23620,15763029.0,Chimaobim,641.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,160548.12
-23621,15665760.0,Nnamutaezinwa,606.0,France,Female,32.0,8.0,0.0,1.0,0.0,1.0,61820.41
-23622,15783875.0,Ch'en,817.0,France,Male,67.0,9.0,0.0,2.0,1.0,1.0,152290.28
-23623,15640491.0,Yudin,637.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,101102.39
-23624,15751043.0,Genovese,562.0,France,Male,41.0,3.0,102088.67,1.0,1.0,1.0,138380.12
-23625,15735263.0,Hs?eh,619.0,Spain,Female,63.0,6.0,0.0,1.0,1.0,0.0,179908.71
-23626,15623647.0,Chidubem,669.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,100995.68
-23627,15803522.0,Chu,659.0,France,Male,27.0,6.0,161606.56,2.0,0.0,0.0,54019.93
-23628,15752588.0,Onwumelu,651.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,137961.41
-23629,15807595.0,K?,652.0,Spain,Female,32.0,0.0,0.0,2.0,1.0,1.0,70778.63
-23630,15582761.0,Palermo,579.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,27330.59
-23631,15758813.0,T'ang,732.0,France,Female,48.0,7.0,0.0,3.0,1.0,0.0,141035.65
-23632,15671137.0,Mironov,646.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,16081.62
-23633,15697034.0,Chien,582.0,France,Male,51.0,8.0,162657.64,1.0,1.0,0.0,176750.81
-23634,15732643.0,Chiang,628.0,France,Female,36.0,3.0,0.0,2.0,0.0,1.0,166677.35
-23635,15605037.0,Ch'ien,599.0,France,Male,34.0,0.0,81811.71,1.0,0.0,1.0,129983.5
-23636,15714493.0,Pisano,691.0,France,Male,42.0,6.0,0.0,2.0,0.0,0.0,67611.36
-23637,15570051.0,Chiu,652.0,Spain,Male,26.0,3.0,0.0,2.0,1.0,1.0,129562.74
-23638,15709780.0,Chigozie,695.0,France,Female,39.0,7.0,178058.06,1.0,0.0,1.0,49089.46
-23639,15585599.0,Shih,707.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,152740.16
-23640,15649487.0,Clements,695.0,France,Male,22.0,5.0,0.0,2.0,1.0,1.0,161256.18
-23641,15690733.0,White,628.0,Spain,Female,24.0,6.0,0.0,2.0,0.0,0.0,180626.68
-23642,15573730.0,Wang,669.0,Germany,Female,37.0,4.0,132796.04,2.0,1.0,1.0,99490.01
-23643,15589493.0,Shih,598.0,France,Male,29.0,5.0,0.0,1.0,1.0,1.0,79555.08
-23644,15652266.0,Chinwendu,646.0,Germany,Female,23.0,9.0,167557.12,1.0,1.0,0.0,126743.33
-23645,15667460.0,Teng,729.0,Spain,Female,26.0,8.0,0.0,2.0,1.0,1.0,50610.78
-23646,15782659.0,Milani,650.0,France,Male,41.0,5.0,0.0,1.0,1.0,0.0,162812.16
-23647,15723105.0,Milne,677.0,France,Male,23.0,7.0,0.0,2.0,1.0,0.0,80701.72
-23648,15783883.0,T'ang,633.0,France,Female,33.0,1.0,0.0,1.0,1.0,0.0,28750.67
-23649,15764072.0,H?,718.0,France,Male,32.0,9.0,0.0,1.0,1.0,0.0,113926.31
-23650,15592025.0,Hs?,539.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,90876.95
-23651,15716347.0,Onuchukwu,714.0,France,Female,37.0,9.0,0.0,2.0,0.0,0.0,147059.16
-23652,15692463.0,Fiorentino,633.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,72549.27
-23653,15777896.0,Martin,733.0,Germany,Male,52.0,6.0,117799.28,1.0,1.0,0.0,143315.57
-23654,15715888.0,Chien,682.0,France,Female,36.0,8.0,0.0,2.0,0.0,1.0,27019.59
-23655,15722409.0,Fan,739.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,124625.08
-23656,15578098.0,Ts'ao,721.0,France,Male,37.0,3.0,134497.27,1.0,0.0,0.0,176902.83
-23657,15791767.0,Folliero,625.0,France,Male,45.0,1.0,0.0,2.0,1.0,0.0,182566.76
-23658,15586674.0,Toscani,667.0,Germany,Female,40.0,10.0,113599.42,1.0,1.0,0.0,92647.94
-23659,15602979.0,Ch'en,652.0,Germany,Male,35.0,8.0,93796.42,2.0,1.0,0.0,143357.02
-23660,15660263.0,Nkemakonam,561.0,Spain,Female,33.0,2.0,117879.32,1.0,1.0,1.0,138191.81
-23661,15718779.0,Krawczyk,662.0,France,Male,37.0,6.0,142862.21,1.0,1.0,0.0,82662.73
-23662,15702462.0,Ozoemena,588.0,Germany,Male,34.0,6.0,116537.6,1.0,0.0,1.0,183566.78
-23663,15740442.0,H?,663.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,1.0,27286.1
-23664,15759066.0,Ch'iu,579.0,France,Female,50.0,0.0,0.0,1.0,1.0,0.0,107948.72
-23665,15700813.0,Hargreaves,590.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,139024.31
-23666,15680918.0,Mazzanti,669.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,124874.23
-23667,15591139.0,Ch'iu,624.0,Germany,Male,30.0,8.0,113923.57,1.0,1.0,0.0,116442.42
-23668,15642991.0,Zikoranachidimma,714.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,115700.2
-23669,15632789.0,Hovell,749.0,Spain,Female,46.0,8.0,0.0,1.0,1.0,0.0,67431.28
-23670,15661528.0,Pisano,847.0,Germany,Male,34.0,3.0,174588.8,2.0,1.0,0.0,131177.22
-23671,15585892.0,Hsia,628.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,127587.22
-23672,15630113.0,Chibueze,684.0,France,Male,43.0,5.0,0.0,2.0,1.0,1.0,142516.35
-23673,15721582.0,Onyekachi,756.0,France,Female,37.0,2.0,0.0,2.0,1.0,1.0,183572.39
-23674,15662238.0,P'eng,584.0,Germany,Female,56.0,9.0,90963.49,2.0,1.0,0.0,93821.95
-23675,15793881.0,Pisano,648.0,Spain,Male,29.0,7.0,0.0,1.0,1.0,0.0,169505.83
-23676,15651802.0,Hs?eh,809.0,Spain,Male,36.0,0.0,0.0,2.0,1.0,1.0,143960.72
-23677,15579787.0,Ch'ang,779.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,151871.84
-23678,15706764.0,Hs?,678.0,Germany,Male,47.0,3.0,82228.67,1.0,1.0,0.0,199674.83
-23679,15613292.0,Ts'ui,593.0,Germany,Male,45.0,6.0,90560.3,2.0,0.0,1.0,75578.67
-23680,15767891.0,Yeh,715.0,Spain,Female,40.0,10.0,0.0,2.0,1.0,0.0,166677.53
-23681,15631848.0,Ndubuagha,632.0,France,Female,34.0,4.0,0.0,1.0,1.0,0.0,51620.94
-23682,15809838.0,Chinwendu,754.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,76569.64
-23683,15767358.0,Biryukov,659.0,Spain,Female,41.0,9.0,144237.91,1.0,1.0,1.0,176713.47
-23684,15725076.0,Fanucci,663.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,183566.78
-23685,15652658.0,Tien,681.0,Germany,Male,44.0,10.0,108691.95,1.0,1.0,1.0,70810.6
-23686,15675983.0,Tuan,629.0,France,Female,30.0,5.0,161889.79,1.0,1.0,1.0,183872.88
-23687,15758531.0,Genovese,567.0,France,Female,47.0,8.0,0.0,1.0,1.0,0.0,89801.9
-23688,15706815.0,P'eng,632.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,176570.28
-23689,15667588.0,Cattaneo,559.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,51907.72
-23690,15574523.0,Chukwukadibia,663.0,France,Male,41.0,0.0,103564.83,1.0,1.0,1.0,78670.52
-23691,15707634.0,Genovesi,599.0,Spain,Male,42.0,5.0,0.0,1.0,1.0,1.0,46635.47
-23692,15732061.0,K'ung,709.0,France,Male,24.0,10.0,0.0,2.0,0.0,1.0,126419.8
-23693,15658935.0,Uspenskaya,587.0,Germany,Female,42.0,1.0,119624.6,3.0,1.0,1.0,5983.8
-23694,15662494.0,Okwudilichukwu,685.0,France,Male,56.0,0.0,0.0,1.0,1.0,0.0,167778.62
-23695,15641733.0,Y?,659.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,25684.93
-23696,15734203.0,Watson,712.0,Germany,Male,40.0,4.0,117641.43,1.0,1.0,1.0,179445.28
-23697,15747649.0,Chikezie,744.0,France,Female,30.0,3.0,128743.55,2.0,1.0,0.0,71481.79
-23698,15739046.0,Udinesi,619.0,Germany,Male,40.0,7.0,102773.2,1.0,1.0,0.0,131283.6
-23699,15812279.0,Aitken,718.0,Germany,Male,39.0,5.0,131569.73,2.0,1.0,1.0,54949.73
-23700,15681410.0,Hsu,684.0,Spain,Male,39.0,4.0,142296.13,1.0,1.0,1.0,113553.43
-23701,15635598.0,Yefimova,667.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,108764.69
-23702,15642530.0,Mazzanti,559.0,Spain,Female,43.0,3.0,122316.31,1.0,1.0,0.0,115789.25
-23703,15807555.0,K'ung,681.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,158527.56
-23704,15796910.0,Ponomarev,763.0,France,Male,39.0,8.0,0.0,1.0,0.0,0.0,91943.94
-23705,15596797.0,Folliero,592.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,107815.31
-23706,15587978.0,Lucchesi,573.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,149535.97
-23707,15682271.0,Chigolum,732.0,France,Male,42.0,2.0,0.0,2.0,1.0,1.0,65731.53
-23708,15624781.0,Ts'ai,662.0,Germany,Male,48.0,9.0,105736.32,2.0,0.0,1.0,171021.44
-23709,15690829.0,Hsueh,724.0,France,Male,38.0,6.0,141462.13,1.0,0.0,0.0,74850.84
-23710,15746490.0,Folliero,850.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,81491.7
-23711,15693893.0,Smith,565.0,France,Male,44.0,8.0,0.0,2.0,1.0,1.0,161076.92
-23712,15603082.0,Okonkwo,697.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,141426.78
-23713,15656912.0,Maclean,685.0,France,Male,39.0,2.0,126817.65,1.0,1.0,0.0,77951.84
-23714,15756056.0,Ebelegbulam,850.0,Germany,Female,51.0,6.0,119027.28,2.0,0.0,0.0,51721.52
-23715,15689322.0,Chin,603.0,France,Female,61.0,2.0,0.0,1.0,1.0,0.0,103934.12
-23716,15570681.0,Ch'ien,537.0,France,Female,41.0,5.0,0.0,2.0,1.0,0.0,162858.29
-23717,15682070.0,Lo,703.0,France,Female,44.0,3.0,0.0,2.0,1.0,0.0,147822.81
-23718,15791045.0,Chizoba,648.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,100615.14
-23719,15694129.0,Ngozichukwuka,754.0,Spain,Female,33.0,7.0,139022.74,2.0,1.0,0.0,62370.67
-23720,15799653.0,Chukwuemeka,678.0,France,Female,40.0,3.0,71264.02,1.0,1.0,1.0,161344.09
-23721,15807923.0,Ch'eng,706.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,121789.3
-23722,15676909.0,Chinwemma,741.0,France,Male,38.0,5.0,0.0,2.0,0.0,1.0,149705.25
-23723,15705403.0,Onwumelu,712.0,France,Female,54.0,10.0,0.0,3.0,1.0,0.0,166692.02
-23724,15716236.0,Smith,621.0,Germany,Female,34.0,2.0,123544.0,2.0,1.0,1.0,175893.53
-23725,15740404.0,Chidimma,567.0,France,Female,25.0,8.0,0.0,2.0,1.0,0.0,122580.48
-23726,15641640.0,P'an,711.0,France,Male,35.0,9.0,0.0,2.0,0.0,0.0,52337.97
-23727,15586931.0,K?,682.0,Spain,Male,26.0,1.0,0.0,1.0,1.0,0.0,171587.9
-23728,15778915.0,Tuan,719.0,Germany,Female,41.0,1.0,111432.13,1.0,1.0,1.0,82696.84
-23729,15729377.0,Hsia,709.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,123880.19
-23730,15651737.0,Chiemela,537.0,Germany,Female,48.0,5.0,84719.98,1.0,1.0,0.0,166677.35
-23731,15789955.0,Walker,695.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,126471.13
-23732,15656086.0,T'ao,731.0,Spain,Female,34.0,1.0,68799.72,2.0,0.0,0.0,61931.21
-23733,15779915.0,Johnstone,710.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,184350.78
-23734,15603794.0,Houghton,683.0,Germany,Female,48.0,8.0,109799.55,1.0,0.0,0.0,167984.72
-23735,15714789.0,Tsou,659.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,132418.59
-23736,15677249.0,Yuriev,693.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,105983.09
-23737,15621550.0,Zikoranachidimma,687.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,121846.81
-23738,15745354.0,Tien,753.0,France,Male,39.0,3.0,152663.77,1.0,0.0,1.0,67789.99
-23739,15745080.0,Ndubuagha,678.0,Germany,Female,36.0,7.0,124495.98,2.0,1.0,0.0,141175.18
-23740,15756481.0,Maclean,641.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,152790.56
-23741,15658969.0,Fanucci,735.0,France,Female,45.0,6.0,0.0,1.0,1.0,1.0,96929.24
-23742,15597901.0,Udokamma,588.0,Germany,Female,42.0,8.0,146033.62,2.0,1.0,1.0,94774.12
-23743,15576269.0,Nwankwo,669.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,151903.6
-23744,15626012.0,Lo Duca,569.0,Germany,Male,36.0,10.0,82940.25,2.0,1.0,0.0,132298.59
-23745,15699247.0,Hs?,705.0,Germany,Male,56.0,5.0,103515.39,1.0,1.0,0.0,76611.33
-23746,15626141.0,Trevisani,718.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,82298.59
-23747,15812706.0,Chimaobim,661.0,France,Male,35.0,7.0,143773.07,1.0,1.0,1.0,57896.27
-23748,15794566.0,Fan,594.0,France,Male,51.0,2.0,0.0,1.0,1.0,0.0,141946.92
-23749,15814035.0,Chigozie,637.0,Germany,Male,40.0,2.0,131908.35,1.0,1.0,1.0,115037.94
-23750,15794560.0,Niu,705.0,France,Male,40.0,5.0,0.0,1.0,1.0,1.0,90928.02
-23751,15642530.0,Mazzi,762.0,Spain,Male,42.0,1.0,0.0,2.0,0.0,1.0,94888.6
-23752,15782659.0,Chiganu,755.0,Germany,Male,58.0,2.0,103033.83,1.0,1.0,0.0,176680.32
-23753,15749365.0,Iadanza,685.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,176730.02
-23754,15580149.0,Maclean,719.0,France,Male,35.0,8.0,0.0,2.0,0.0,0.0,120853.83
-23755,15788480.0,Ting,682.0,Spain,Male,36.0,3.0,0.0,1.0,1.0,1.0,168834.85
-23756,15662494.0,Chikwendu,663.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,140763.17
-23757,15629299.0,Milanesi,681.0,Germany,Female,35.0,5.0,103688.19,1.0,1.0,1.0,102260.16
-23758,15633194.0,Shao,585.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,140937.91
-23759,15803842.0,Fiorentini,632.0,France,Female,37.0,4.0,0.0,1.0,1.0,0.0,50695.93
-23760,15772896.0,Smith,591.0,Spain,Female,33.0,0.0,0.0,2.0,1.0,0.0,109614.57
-23761,15775235.0,Zhirov,823.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,56780.97
-23762,15697391.0,Ngozichukwuka,726.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,1.0,58440.43
-23763,15704053.0,Hsueh,611.0,France,Male,49.0,2.0,0.0,1.0,0.0,0.0,160529.71
-23764,15742229.0,Dike,669.0,Spain,Male,31.0,8.0,0.0,2.0,0.0,0.0,109843.16
-23765,15593776.0,Mazzanti,738.0,Germany,Male,33.0,0.0,133871.05,2.0,0.0,1.0,171706.66
-23766,15674583.0,Bibi,611.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,54776.55
-23767,15579777.0,Yermakova,636.0,France,Female,40.0,7.0,0.0,2.0,0.0,1.0,109794.31
-23768,15776733.0,Fanucci,648.0,Germany,Female,45.0,4.0,132436.39,1.0,1.0,0.0,93336.3
-23769,15711396.0,Chubb,675.0,Germany,Male,28.0,1.0,96471.2,1.0,1.0,1.0,177065.24
-23770,15740160.0,Hsing,619.0,France,Male,28.0,9.0,0.0,1.0,0.0,0.0,100720.02
-23771,15791700.0,Nwabugwu,677.0,Spain,Male,41.0,4.0,0.0,2.0,1.0,1.0,61553.51
-23772,15650034.0,Onyekachukwu,638.0,Germany,Male,36.0,1.0,123070.89,1.0,1.0,1.0,133041.15
-23773,15792668.0,Onwudiwe,645.0,France,Female,29.0,8.0,82456.95,2.0,0.0,1.0,147090.9
-23774,15781598.0,Hsiung,659.0,Spain,Male,42.0,2.0,137781.65,1.0,1.0,1.0,141558.04
-23775,15638231.0,Nwabugwu,602.0,France,Female,53.0,2.0,111096.91,1.0,0.0,1.0,67789.99
-23776,15716994.0,Chubb,806.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,79602.86
-23777,15791780.0,Udinese,713.0,France,Male,31.0,1.0,135296.33,1.0,1.0,0.0,121428.2
-23778,15766066.0,T'ien,647.0,France,Male,52.0,5.0,0.0,1.0,1.0,0.0,180598.66
-23779,15585241.0,Chigozie,727.0,Germany,Male,33.0,0.0,141547.26,1.0,0.0,0.0,59325.21
-23780,15690958.0,Lai,758.0,France,Female,50.0,2.0,0.0,1.0,1.0,0.0,78436.36
-23781,15706021.0,T'ao,681.0,Germany,Male,34.0,6.0,125927.51,2.0,1.0,1.0,168816.23
-23782,15603088.0,Pisani,703.0,France,Male,53.0,5.0,0.0,2.0,1.0,1.0,149587.92
-23783,15640686.0,Macleod,697.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,141152.28
-23784,15652674.0,Pisano,574.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,85470.45
-23785,15754919.0,Young,629.0,France,Male,21.0,9.0,0.0,2.0,1.0,0.0,70530.1
-23786,15624592.0,Yermolayev,664.0,France,Female,44.0,10.0,0.0,1.0,1.0,1.0,151146.67
-23787,15694829.0,Palerma,659.0,France,Male,37.0,4.0,0.0,1.0,1.0,0.0,141446.01
-23788,15654574.0,L?,687.0,France,Male,24.0,2.0,0.0,1.0,1.0,1.0,166677.35
-23789,15762729.0,Manna,757.0,Germany,Female,33.0,4.0,79715.36,1.0,1.0,0.0,143681.83
-23790,15809872.0,Nwankwo,580.0,Germany,Male,53.0,9.0,140694.78,1.0,1.0,1.0,156768.45
-23791,15792372.0,Yevdokimova,598.0,France,Female,24.0,6.0,0.0,2.0,1.0,1.0,145796.49
-23792,15619116.0,Lucchese,684.0,France,Female,33.0,1.0,0.0,2.0,0.0,1.0,82298.59
-23793,15768219.0,Calabrese,550.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,115611.97
-23794,15752601.0,Chidimma,620.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,101617.64
-23795,15803689.0,Milanesi,531.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,0.0,100423.88
-23796,15726695.0,Milne,682.0,France,Female,28.0,3.0,0.0,2.0,0.0,1.0,127543.83
-23797,15607748.0,Onwuamaeze,732.0,Germany,Female,45.0,9.0,108862.76,1.0,1.0,0.0,64872.55
-23798,15804009.0,Hsiung,781.0,France,Female,27.0,8.0,0.0,2.0,1.0,1.0,51907.72
-23799,15776824.0,T'ien,689.0,France,Female,50.0,2.0,116967.68,1.0,0.0,1.0,141400.51
-23800,15785863.0,Nwachinemelu,576.0,Germany,Male,36.0,4.0,132057.92,1.0,1.0,1.0,88838.14
-23801,15589435.0,Olisaemeka,569.0,France,Male,29.0,6.0,0.0,1.0,1.0,1.0,162858.29
-23802,15783338.0,T'ao,722.0,Germany,Male,35.0,1.0,141816.25,1.0,1.0,0.0,27149.27
-23803,15572735.0,Palermo,717.0,France,Male,46.0,8.0,116322.27,1.0,1.0,0.0,49833.5
-23804,15677105.0,T'ang,597.0,Germany,Male,35.0,9.0,154737.88,2.0,0.0,1.0,47764.99
-23805,15809823.0,Rizzo,639.0,France,Male,46.0,8.0,0.0,1.0,1.0,0.0,93768.94
-23806,15809837.0,Zetticci,569.0,Spain,Male,52.0,4.0,0.0,1.0,1.0,1.0,59129.72
-23807,15656829.0,P'eng,735.0,Spain,Female,37.0,3.0,0.0,1.0,1.0,1.0,89935.73
-23808,15643359.0,Chinomso,681.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,86764.46
-23809,15588854.0,Hs?,611.0,France,Male,51.0,1.0,0.0,1.0,1.0,1.0,43982.41
-23810,15671514.0,H?,596.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,149912.28
-23811,15777892.0,Lo,591.0,France,Female,41.0,6.0,165350.69,1.0,1.0,0.0,143635.33
-23812,15593501.0,Milani,671.0,France,Female,48.0,3.0,0.0,1.0,1.0,0.0,149043.78
-23813,15689201.0,Chidubem,669.0,Spain,Male,31.0,6.0,0.0,1.0,1.0,1.0,73977.23
-23814,15803801.0,Hsieh,712.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,116781.71
-23815,15776939.0,Lucciano,642.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,90927.48
-23816,15613886.0,Chiedozie,725.0,France,Male,32.0,2.0,110949.41,1.0,1.0,1.0,141476.56
-23817,15642815.0,H?,732.0,France,Male,47.0,8.0,0.0,1.0,0.0,0.0,171463.83
-23818,15641822.0,Iqbal,766.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,67772.52
-23819,15743760.0,Onyekaozulu,542.0,Germany,Female,36.0,8.0,108891.5,1.0,1.0,0.0,133042.33
-23820,15793896.0,Padovano,712.0,Spain,Female,30.0,6.0,0.0,2.0,1.0,1.0,61789.12
-23821,15592091.0,Y?,559.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,1276.87
-23822,15681708.0,Burt,699.0,France,Male,38.0,0.0,0.0,2.0,1.0,0.0,160968.44
-23823,15674929.0,Maclean,683.0,France,Female,31.0,4.0,151858.98,1.0,0.0,1.0,126812.63
-23824,15751203.0,Nnanna,645.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,52876.41
-23825,15673372.0,Yobachi,646.0,France,Male,33.0,4.0,99046.31,1.0,1.0,1.0,105732.9
-23826,15580203.0,T'ang,635.0,France,Female,41.0,3.0,0.0,1.0,0.0,1.0,131647.31
-23827,15631339.0,Lucciano,645.0,Spain,Female,37.0,4.0,0.0,1.0,1.0,1.0,170634.49
-23828,15651369.0,Lu,730.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,1.0,146891.08
-23829,15598162.0,Obiajulu,695.0,Germany,Female,34.0,2.0,149592.14,2.0,0.0,0.0,115186.97
-23830,15716078.0,Milano,739.0,Spain,Female,25.0,6.0,145580.61,1.0,1.0,0.0,99792.82
-23831,15741032.0,Chioke,590.0,Germany,Male,40.0,2.0,161807.18,2.0,1.0,0.0,179032.68
-23832,15591257.0,Walker,850.0,Spain,Male,52.0,8.0,117696.67,1.0,1.0,0.0,157937.35
-23833,15586848.0,Lu,696.0,France,Female,33.0,1.0,0.0,2.0,0.0,1.0,138889.15
-23834,15665802.0,Lucchese,756.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,125083.29
-23835,15682421.0,T'ien,642.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,52347.71
-23836,15757867.0,Ch'ien,642.0,France,Female,27.0,2.0,127450.54,2.0,1.0,0.0,196142.39
-23837,15576044.0,Onyemachukwu,486.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,120852.85
-23838,15737051.0,Liao,577.0,France,Male,25.0,4.0,97565.74,1.0,0.0,1.0,120893.07
-23839,15605918.0,K?,673.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,85890.75
-23840,15605926.0,Hsiao,736.0,Germany,Female,34.0,5.0,110717.84,1.0,1.0,0.0,94718.13
-23841,15745804.0,Chukwudi,586.0,Germany,Female,44.0,9.0,126619.27,1.0,1.0,0.0,75832.53
-23842,15583736.0,Trevisano,586.0,Germany,Male,41.0,2.0,127120.07,1.0,1.0,1.0,164038.07
-23843,15623668.0,Chiawuotu,559.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,175071.29
-23844,15729574.0,Zaitsev,657.0,Spain,Male,39.0,6.0,0.0,1.0,1.0,1.0,131301.74
-23845,15809057.0,Kambinachi,616.0,France,Female,39.0,9.0,178095.55,1.0,1.0,0.0,64421.02
-23846,15733661.0,Hsiao,686.0,France,Female,27.0,9.0,0.0,2.0,1.0,1.0,92647.94
-23847,15596800.0,Nwachukwu,757.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,146680.25
-23848,15715184.0,Y?an,682.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,0.0,91565.57
-23849,15585823.0,T'ien,602.0,France,Female,47.0,8.0,0.0,1.0,0.0,0.0,114083.73
-23850,15795878.0,Nnonso,677.0,Spain,Male,38.0,4.0,0.0,2.0,0.0,1.0,59748.19
-23851,15774166.0,Ch'ien,707.0,Germany,Male,41.0,7.0,99713.87,1.0,1.0,0.0,16081.62
-23852,15719309.0,Chukwudi,659.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,156774.75
-23853,15703793.0,Chiu,721.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,179565.57
-23854,15643826.0,Ch'ang,695.0,France,Male,48.0,7.0,0.0,1.0,0.0,1.0,95562.92
-23855,15806360.0,Onio,691.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,93332.64
-23856,15803801.0,Lucchesi,743.0,Germany,Female,40.0,9.0,132057.92,2.0,1.0,0.0,51565.96
-23857,15631512.0,Nwabugwu,813.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,81723.8
-23858,15606836.0,Folliero,633.0,France,Female,32.0,5.0,0.0,1.0,0.0,0.0,162861.89
-23859,15652835.0,Smith,485.0,France,Male,29.0,10.0,0.0,2.0,1.0,1.0,162683.06
-23860,15779913.0,T'ang,535.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,142911.99
-23861,15623220.0,Shao,637.0,France,Female,36.0,7.0,116968.91,2.0,0.0,1.0,178291.89
-23862,15650322.0,Maclean,753.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,86764.46
-23863,15732660.0,Efimov,749.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,105599.65
-23864,15677906.0,H?,678.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,132673.23
-23865,15649423.0,Hazon,703.0,France,Female,52.0,3.0,0.0,2.0,1.0,1.0,145528.22
-23866,15778025.0,Onwuamaeze,711.0,Germany,Male,38.0,3.0,163700.78,2.0,1.0,1.0,123954.32
-23867,15812351.0,Tuan,721.0,Germany,Male,54.0,1.0,118886.55,1.0,1.0,0.0,132973.21
-23868,15633194.0,Ijendu,667.0,Germany,Female,48.0,3.0,126428.38,2.0,0.0,1.0,105155.66
-23869,15750929.0,Chuang,678.0,France,Female,37.0,7.0,0.0,2.0,0.0,0.0,168034.9
-23870,15635078.0,Yevseyev,738.0,France,Male,37.0,3.0,81732.88,2.0,0.0,0.0,141023.01
-23871,15783383.0,Folliero,657.0,France,Female,48.0,10.0,108528.07,1.0,0.0,1.0,157097.09
-23872,15654562.0,Nwachukwu,659.0,France,Female,42.0,7.0,0.0,2.0,0.0,0.0,180969.55
-23873,15750181.0,T'ien,634.0,Spain,Female,36.0,9.0,91547.84,2.0,1.0,0.0,94450.97
-23874,15629750.0,Zaytseva,498.0,France,Female,43.0,4.0,0.0,1.0,1.0,0.0,81492.8
-23875,15683375.0,Chukwudi,811.0,Spain,Male,32.0,2.0,0.0,2.0,0.0,0.0,198490.33
-23876,15601659.0,Shih,637.0,France,Male,42.0,3.0,0.0,2.0,0.0,0.0,119924.8
-23877,15776947.0,Lo Duca,613.0,Germany,Female,31.0,10.0,129743.75,2.0,1.0,0.0,54495.82
-23878,15805764.0,Chukwudi,696.0,Germany,Female,54.0,9.0,123763.49,1.0,1.0,0.0,115068.99
-23879,15576824.0,Kambinachi,763.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,89891.4
-23880,15571529.0,Chinomso,755.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,1.0,54448.15
-23881,15641585.0,Shih,778.0,Spain,Female,49.0,10.0,0.0,1.0,0.0,0.0,87071.18
-23882,15784099.0,Lo,762.0,France,Female,45.0,10.0,0.0,1.0,1.0,0.0,62810.91
-23883,15786063.0,White,706.0,France,Female,28.0,1.0,0.0,1.0,0.0,1.0,59571.5
-23884,15753847.0,Boniwell,652.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,0.0,149536.62
-23885,15763327.0,Okechukwu,752.0,Germany,Female,59.0,8.0,119539.3,1.0,1.0,0.0,176773.59
-23886,15769254.0,Lettiere,717.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,103736.15
-23887,15588854.0,Shaver,682.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,1.0,6554.97
-23888,15763895.0,K?,741.0,France,Male,35.0,9.0,0.0,1.0,1.0,0.0,81491.7
-23889,15737506.0,Ch'iu,683.0,Germany,Female,44.0,9.0,109844.81,2.0,1.0,0.0,145536.28
-23890,15697460.0,Ijendu,653.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,119644.59
-23891,15792668.0,Hovell,545.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,146517.71
-23892,15672374.0,Mazzi,679.0,France,Female,33.0,4.0,0.0,1.0,1.0,0.0,124411.08
-23893,15737506.0,Chiganu,701.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,151375.57
-23894,15579566.0,P'an,550.0,France,Male,28.0,9.0,0.0,1.0,0.0,1.0,27330.59
-23895,15632264.0,Chimaobim,696.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,77878.62
-23896,15811947.0,Ch'en,669.0,France,Female,41.0,2.0,128264.03,1.0,1.0,1.0,153392.28
-23897,15693018.0,Smith,703.0,Germany,Female,33.0,3.0,106022.73,1.0,1.0,1.0,175837.06
-23898,15622305.0,Chinwendu,667.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,95686.4
-23899,15764237.0,Ch'iu,611.0,France,Male,33.0,0.0,133550.67,1.0,1.0,0.0,69531.22
-23900,15641582.0,Lucchese,637.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,163427.18
-23901,15595440.0,Onyemauchechukwu,642.0,Spain,Male,41.0,3.0,98765.84,2.0,1.0,0.0,82820.85
-23902,15721730.0,Yermakov,686.0,France,Male,38.0,1.0,0.0,2.0,0.0,0.0,184524.74
-23903,15654964.0,Onio,627.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,138350.74
-23904,15682533.0,Ugoji,593.0,France,Male,43.0,4.0,98202.76,2.0,0.0,0.0,44965.44
-23905,15736327.0,Ch'iu,686.0,France,Male,28.0,4.0,121854.45,1.0,1.0,1.0,141175.18
-23906,15743582.0,Macleod,686.0,France,Male,29.0,5.0,121250.04,2.0,1.0,0.0,171757.76
-23907,15687913.0,Yashina,756.0,France,Female,39.0,7.0,0.0,2.0,0.0,0.0,104889.3
-23908,15718454.0,Chinweike,696.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,184514.01
-23909,15570073.0,Chidubem,678.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,151822.68
-23910,15656627.0,Hsia,679.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,69056.26
-23911,15600974.0,Ch'ang,677.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,0.0,100615.14
-23912,15771580.0,Shipton,668.0,France,Female,67.0,4.0,0.0,2.0,0.0,1.0,60907.49
-23913,15759480.0,K'ung,679.0,Spain,Female,34.0,8.0,0.0,2.0,1.0,0.0,84487.62
-23914,15780359.0,Ch'iu,652.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,30772.86
-23915,15721460.0,Milne,706.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,116046.22
-23916,15593897.0,Ting,611.0,Germany,Female,39.0,1.0,106901.94,1.0,0.0,1.0,180712.28
-23917,15598838.0,Onyekachi,717.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,152843.53
-23918,15582847.0,Onwumelu,646.0,Germany,Female,48.0,6.0,119558.35,1.0,1.0,1.0,114675.75
-23919,15809872.0,Yefimova,573.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,84972.9
-23920,15629885.0,Ch'en,706.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,115217.59
-23921,15643378.0,Ch'ien,769.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,0.0,72876.41
-23922,15627395.0,Onyemauchechukwu,572.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,91055.46
-23923,15693737.0,Chiang,600.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,1710.89
-23924,15671353.0,Chukwudi,727.0,Spain,Female,33.0,9.0,0.0,2.0,1.0,0.0,132770.11
-23925,15809736.0,Hsiung,592.0,France,Male,47.0,5.0,0.0,3.0,1.0,0.0,88669.89
-23926,15585067.0,Bibi,646.0,Spain,Male,27.0,5.0,0.0,2.0,1.0,1.0,87906.83
-23927,15756513.0,Onyemauchechukwu,516.0,Germany,Female,39.0,2.0,82729.49,1.0,1.0,1.0,167984.72
-23928,15727299.0,Chiebuka,605.0,Germany,Female,33.0,9.0,107556.06,2.0,1.0,0.0,142995.32
-23929,15583090.0,Ch'in,589.0,Germany,Male,48.0,4.0,95039.12,2.0,0.0,0.0,176945.96
-23930,15651533.0,Udinese,671.0,France,Male,33.0,4.0,126817.65,2.0,1.0,1.0,157891.11
-23931,15748752.0,Kao,555.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,172501.38
-23932,15620750.0,Hsia,659.0,Germany,Female,39.0,4.0,138490.37,2.0,1.0,0.0,69244.24
-23933,15682454.0,Hsia,622.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,0.0,82438.13
-23934,15662483.0,Chukwujekwu,729.0,Spain,Female,38.0,7.0,102278.65,1.0,1.0,1.0,146895.86
-23935,15705639.0,Okechukwu,758.0,Spain,Male,31.0,5.0,116536.43,1.0,1.0,1.0,111249.62
-23936,15754072.0,H?,668.0,Spain,Female,39.0,10.0,0.0,2.0,0.0,0.0,48486.25
-23937,15775678.0,Nucci,639.0,Spain,Female,26.0,6.0,89750.21,1.0,0.0,1.0,158043.11
-23938,15606901.0,Ch'eng,516.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,64516.5
-23939,15774551.0,Ts'ai,743.0,France,Female,43.0,6.0,139825.53,1.0,0.0,1.0,156868.21
-23940,15775750.0,Zuyeva,581.0,Spain,Female,35.0,6.0,107960.46,1.0,1.0,0.0,179964.55
-23941,15812191.0,Nwabugwu,639.0,France,Female,48.0,4.0,0.0,1.0,1.0,0.0,80963.92
-23942,15578980.0,Pugh,632.0,Germany,Female,30.0,1.0,123816.5,2.0,1.0,0.0,77832.82
-23943,15779247.0,Nkemdilim,755.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,149814.68
-23944,15804787.0,Chidubem,526.0,Spain,Female,28.0,8.0,0.0,1.0,0.0,0.0,56984.58
-23945,15742504.0,Ibezimako,593.0,France,Male,66.0,6.0,0.0,2.0,1.0,1.0,92914.67
-23946,15656300.0,Ch'ang,667.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,94820.85
-23947,15656522.0,Maclean,635.0,Spain,Male,33.0,7.0,124099.13,1.0,0.0,1.0,65951.65
-23948,15717498.0,Kambinachi,615.0,France,Female,48.0,5.0,173340.83,1.0,1.0,1.0,123885.88
-23949,15734005.0,Nkemjika,684.0,France,Male,38.0,8.0,0.0,2.0,0.0,0.0,92621.9
-23950,15596962.0,Okoli,584.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,10063.75
-23951,15781702.0,H?,671.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,102515.42
-23952,15575679.0,Lo,708.0,France,Male,38.0,7.0,121551.58,1.0,1.0,1.0,71139.8
-23953,15627203.0,Palerma,686.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,109626.03
-23954,15720644.0,Tien,767.0,France,Male,54.0,6.0,0.0,2.0,1.0,1.0,168053.4
-23955,15586699.0,Nwabugwu,663.0,Germany,Male,33.0,5.0,133550.67,2.0,1.0,0.0,173629.78
-23956,15734008.0,Li,652.0,Spain,Male,50.0,7.0,0.0,2.0,1.0,1.0,133501.94
-23957,15633922.0,Yobachukwu,592.0,France,Male,32.0,7.0,0.0,2.0,0.0,0.0,132436.71
-23958,15585595.0,Ch'ien,711.0,Germany,Male,43.0,3.0,112419.92,1.0,1.0,0.0,149912.28
-23959,15572022.0,Lo,646.0,France,Male,29.0,3.0,0.0,2.0,1.0,0.0,24255.21
-23960,15712596.0,Aksenov,585.0,Spain,Female,58.0,5.0,0.0,2.0,0.0,1.0,143614.01
-23961,15783724.0,Nielson,658.0,Germany,Male,37.0,3.0,92350.85,1.0,1.0,1.0,155853.52
-23962,15772573.0,Chukwudi,775.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,176570.28
-23963,15651336.0,O'Loghlen,705.0,Germany,Female,39.0,4.0,122031.55,1.0,1.0,1.0,598.8
-23964,15699335.0,Babbage,710.0,France,Female,63.0,7.0,0.0,2.0,1.0,1.0,149912.28
-23965,15662641.0,Chukwudi,683.0,France,Female,31.0,2.0,130565.02,2.0,1.0,1.0,113846.59
-23966,15589496.0,Ch'in,613.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,95583.45
-23967,15656029.0,DeRose,583.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,48228.1
-23968,15693817.0,Hs?eh,663.0,France,Female,40.0,8.0,0.0,2.0,0.0,1.0,170950.58
-23969,15773869.0,Wright,634.0,France,Female,41.0,5.0,0.0,2.0,0.0,0.0,10656.89
-23970,15588933.0,Ting,568.0,Germany,Female,43.0,7.0,144590.92,1.0,0.0,0.0,119381.41
-23971,15662955.0,Lettiere,732.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,124532.9
-23972,15794187.0,Chukwudi,699.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,1.0,132467.54
-23973,15801488.0,Fomin,602.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,59893.84
-23974,15772896.0,Chibugo,726.0,Spain,Male,45.0,1.0,98899.91,1.0,1.0,1.0,126131.9
-23975,15762762.0,Ch'iu,683.0,Germany,Female,51.0,2.0,153325.1,1.0,1.0,0.0,53445.17
-23976,15725429.0,Okonkwo,777.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,171768.76
-23977,15571958.0,Shih,809.0,France,Female,53.0,7.0,0.0,1.0,0.0,0.0,183584.14
-23978,15662901.0,Yefremova,547.0,France,Female,24.0,3.0,0.0,2.0,1.0,1.0,86737.16
-23979,15649853.0,Jamieson,563.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,64421.02
-23980,15595883.0,Tretiakova,584.0,France,Female,39.0,7.0,0.0,1.0,0.0,0.0,123825.83
-23981,15796074.0,Mazzi,678.0,France,Male,34.0,6.0,0.0,2.0,0.0,1.0,159123.82
-23982,15586522.0,Nworie,663.0,France,Male,45.0,5.0,0.0,2.0,0.0,1.0,87941.85
-23983,15752984.0,Bibi,582.0,Germany,Female,57.0,9.0,118022.06,1.0,1.0,0.0,182728.8
-23984,15793896.0,Onwumelu,773.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,115818.51
-23985,15803965.0,Onwuamaeze,562.0,France,Female,34.0,2.0,0.0,2.0,0.0,1.0,72927.68
-23986,15741921.0,T'ien,607.0,Spain,Male,39.0,3.0,154483.98,1.0,0.0,1.0,199409.19
-23987,15679801.0,Toscani,652.0,France,Female,34.0,1.0,0.0,1.0,0.0,0.0,83629.6
-23988,15807524.0,Bazarova,683.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,100919.19
-23989,15769191.0,Ts'ao,603.0,Germany,Male,36.0,4.0,166824.59,1.0,0.0,1.0,145765.83
-23990,15803840.0,Chigozie,694.0,Germany,Male,33.0,1.0,166501.17,1.0,1.0,1.0,172490.78
-23991,15760063.0,Hsiung,632.0,Spain,Female,43.0,6.0,156811.74,1.0,1.0,0.0,90863.81
-23992,15635078.0,Ozioma,535.0,Germany,Female,24.0,9.0,119782.76,1.0,1.0,0.0,91573.19
-23993,15799494.0,Rischbieth,630.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,83983.54
-23994,15769402.0,Onyeorulu,603.0,Spain,Female,40.0,1.0,0.0,1.0,1.0,0.0,116471.43
-23995,15760090.0,T'ien,703.0,France,Male,26.0,7.0,0.0,1.0,1.0,1.0,52876.41
-23996,15646936.0,Chukwumaobim,724.0,France,Male,39.0,10.0,0.0,1.0,1.0,1.0,54871.02
-23997,15782741.0,Cawthorne,704.0,Spain,Female,33.0,1.0,147523.03,1.0,1.0,1.0,113048.79
-23998,15572051.0,Chidimma,727.0,France,Female,56.0,3.0,0.0,1.0,1.0,0.0,52327.47
-23999,15757588.0,Nebechi,506.0,Germany,Female,25.0,5.0,123971.51,1.0,0.0,0.0,127059.03
-24000,15658716.0,H?,669.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,145731.83
-24001,15596070.0,Chien,686.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,141180.66
-24002,15622578.0,Ibezimako,771.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,93835.64
-24003,15578747.0,Ch'ang,762.0,France,Female,43.0,1.0,0.0,2.0,1.0,1.0,126494.82
-24004,15796790.0,Cremonesi,586.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,65675.47
-24005,15654296.0,Hargreaves,607.0,Germany,Female,39.0,9.0,123378.2,1.0,1.0,0.0,134901.58
-24006,15783629.0,Hsiung,551.0,Spain,Male,36.0,10.0,0.0,1.0,1.0,0.0,121408.55
-24007,15711299.0,Iweobiegbunam,583.0,France,Male,24.0,9.0,0.0,2.0,1.0,1.0,62678.53
-24008,15762799.0,Chukwufumnanya,682.0,France,Female,36.0,8.0,0.0,2.0,1.0,0.0,184524.74
-24009,15778304.0,Hsia,757.0,France,Female,30.0,9.0,0.0,2.0,1.0,1.0,153771.8
-24010,15752956.0,Onwuamaegbu,713.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,105817.18
-24011,15684516.0,Macleod,745.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,181419.29
-24012,15727251.0,Mazzi,715.0,Germany,Male,29.0,3.0,92578.14,2.0,1.0,1.0,158610.89
-24013,15593973.0,T'ien,713.0,France,Male,66.0,2.0,98894.39,1.0,1.0,1.0,130132.54
-24014,15679622.0,Nwankwo,686.0,France,Male,39.0,8.0,0.0,3.0,1.0,0.0,167058.75
-24015,15686588.0,Mazzanti,489.0,Germany,Male,47.0,0.0,129974.79,2.0,1.0,0.0,1118.31
-24016,15671249.0,Ch'ien,607.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,181419.29
-24017,15662641.0,Ponomaryov,565.0,Germany,Female,39.0,3.0,132446.08,1.0,1.0,0.0,171718.52
-24018,15773876.0,Chiemezie,627.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,125307.82
-24019,15752300.0,Nwankwo,714.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,116816.53
-24020,15655666.0,Liao,697.0,France,Female,40.0,2.0,0.0,2.0,0.0,1.0,181694.44
-24021,15788189.0,Nwachukwu,539.0,France,Male,34.0,4.0,138033.55,1.0,1.0,1.0,26327.42
-24022,15572356.0,Nkemjika,693.0,Germany,Male,32.0,4.0,115680.81,2.0,0.0,0.0,85421.48
-24023,15766163.0,Ku,554.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,175072.47
-24024,15663917.0,Ku,656.0,France,Female,40.0,8.0,0.0,1.0,1.0,0.0,168580.73
-24025,15660301.0,Ogochukwu,609.0,France,Female,35.0,2.0,0.0,2.0,0.0,0.0,180329.83
-24026,15646810.0,Chukwueloka,732.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,113089.43
-24027,15693382.0,Pugh,751.0,France,Female,36.0,10.0,0.0,2.0,1.0,0.0,92612.88
-24028,15680430.0,Romano,648.0,Germany,Male,32.0,7.0,93522.2,2.0,1.0,1.0,89935.73
-24029,15679743.0,Ojiofor,751.0,France,Female,40.0,8.0,160599.08,1.0,1.0,0.0,126475.79
-24030,15733581.0,Young,619.0,France,Female,22.0,10.0,0.0,2.0,0.0,0.0,131736.23
-24031,15711759.0,Tsou,582.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,162889.1
-24032,15596710.0,Chinwenma,695.0,France,Female,37.0,5.0,94464.81,1.0,1.0,0.0,72929.84
-24033,15782210.0,Chuang,841.0,France,Female,36.0,1.0,0.0,2.0,0.0,1.0,161407.48
-24034,15769312.0,Chia,850.0,France,Male,58.0,7.0,0.0,2.0,1.0,1.0,64872.55
-24035,15780805.0,Onyemauchechukwu,708.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,82900.25
-24036,15651460.0,Hs?,850.0,Spain,Female,37.0,2.0,137003.68,1.0,1.0,0.0,54726.43
-24037,15571928.0,Ts'ao,644.0,Spain,Female,33.0,4.0,0.0,2.0,0.0,0.0,182624.51
-24038,15793331.0,Onyinyechukwuka,678.0,France,Male,39.0,8.0,0.0,1.0,1.0,1.0,143783.28
-24039,15795586.0,Wright,679.0,Germany,Female,45.0,2.0,116822.8,1.0,1.0,0.0,111573.3
-24040,15696900.0,Clements,753.0,France,Male,29.0,9.0,0.0,2.0,0.0,1.0,148811.14
-24041,15579223.0,Nnachetam,577.0,France,Female,49.0,5.0,0.0,1.0,1.0,0.0,151822.66
-24042,15661723.0,Sagese,850.0,Germany,Female,45.0,1.0,139799.63,2.0,1.0,0.0,157072.91
-24043,15751185.0,Chinwemma,544.0,France,Female,61.0,0.0,0.0,2.0,1.0,1.0,176778.76
-24044,15589643.0,Okwudilichukwu,629.0,Spain,Female,42.0,0.0,0.0,1.0,1.0,1.0,146875.86
-24045,15571059.0,Chukwuemeka,719.0,Spain,Male,49.0,5.0,0.0,3.0,1.0,0.0,187616.16
-24046,15749103.0,Ch'ang,579.0,France,Male,42.0,6.0,0.0,1.0,0.0,1.0,129964.94
-24047,15689061.0,Hsiung,634.0,France,Female,31.0,5.0,0.0,2.0,0.0,0.0,125240.8
-24048,15567114.0,Iloerika,484.0,France,Male,29.0,2.0,0.0,1.0,1.0,0.0,171045.46
-24049,15678720.0,Onyemere,646.0,France,Male,28.0,6.0,0.0,1.0,0.0,1.0,183569.25
-24050,15604792.0,Ch'ien,585.0,France,Male,66.0,2.0,0.0,2.0,1.0,1.0,93777.81
-24051,15722409.0,Ch'iu,472.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,181461.46
-24052,15754569.0,Maclean,685.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,121316.07
-24053,15663421.0,P'an,592.0,France,Male,44.0,1.0,0.0,2.0,1.0,0.0,56908.41
-24054,15680666.0,Ch'in,547.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,0.0,96670.2
-24055,15761043.0,Kuykendall,634.0,Spain,Male,42.0,8.0,141581.08,1.0,1.0,0.0,167036.92
-24056,15774857.0,Liao,590.0,Spain,Female,37.0,9.0,0.0,2.0,1.0,0.0,134600.94
-24057,15791927.0,Scott,581.0,France,Female,45.0,4.0,0.0,2.0,1.0,1.0,152298.28
-24058,15798024.0,K?,551.0,Germany,Male,35.0,8.0,87946.39,1.0,1.0,1.0,116977.89
-24059,15763898.0,T'ao,616.0,Germany,Female,47.0,8.0,147910.3,1.0,1.0,0.0,141896.74
-24060,15646535.0,Chinagorom,718.0,France,Male,48.0,0.0,0.0,1.0,1.0,0.0,102528.46
-24061,15677958.0,Ch'iu,659.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,95562.81
-24062,15782530.0,Onyemere,554.0,Germany,Male,31.0,2.0,130565.02,2.0,1.0,1.0,161243.09
-24063,15783030.0,Chia,567.0,France,Female,42.0,9.0,0.0,2.0,1.0,0.0,155870.82
-24064,15658577.0,Nnonso,616.0,Germany,Female,37.0,8.0,90194.34,2.0,1.0,1.0,137037.97
-24065,15598521.0,Chiu,656.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,138880.24
-24066,15778884.0,Ch'ien,772.0,Germany,Female,31.0,6.0,109949.05,2.0,0.0,0.0,72993.67
-24067,15750049.0,Baresi,679.0,France,Male,24.0,4.0,0.0,2.0,1.0,0.0,181850.74
-24068,15589475.0,Onyemachukwu,743.0,France,Male,47.0,1.0,0.0,2.0,1.0,1.0,147533.09
-24069,15583718.0,T'ao,599.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,111190.53
-24070,15720774.0,Niu,642.0,Spain,Female,52.0,2.0,0.0,2.0,1.0,1.0,102908.91
-24071,15809227.0,Ch'iu,543.0,Spain,Female,69.0,9.0,0.0,2.0,1.0,1.0,180197.74
-24072,15665524.0,Hsueh,590.0,France,Male,30.0,9.0,0.0,1.0,1.0,0.0,144593.3
-24073,15583718.0,Nwokezuike,717.0,Spain,Female,46.0,4.0,145984.92,1.0,1.0,0.0,56787.8
-24074,15766422.0,Chukwufumnanya,729.0,Germany,Female,34.0,7.0,127095.14,2.0,1.0,1.0,151902.78
-24075,15762799.0,Iloerika,563.0,France,Male,53.0,4.0,0.0,1.0,1.0,0.0,184581.68
-24076,15746139.0,Nucci,758.0,France,Male,32.0,9.0,118100.59,1.0,0.0,1.0,97938.59
-24077,15806945.0,Ch'ang,516.0,France,Female,39.0,10.0,118715.86,1.0,1.0,1.0,141807.51
-24078,15671351.0,Onwumelu,579.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,142839.71
-24079,15570086.0,Chibueze,636.0,Germany,Male,45.0,9.0,97041.16,1.0,1.0,0.0,182718.25
-24080,15768124.0,Nkemjika,732.0,France,Male,55.0,2.0,0.0,2.0,1.0,1.0,38019.74
-24081,15809663.0,Chu,794.0,France,Male,27.0,1.0,0.0,2.0,1.0,0.0,91943.94
-24082,15671610.0,Chukwuemeka,619.0,France,Female,42.0,3.0,90957.81,1.0,1.0,0.0,76611.26
-24083,15796956.0,Hsing,559.0,Germany,Female,43.0,9.0,93547.23,1.0,1.0,0.0,142514.71
-24084,15808526.0,Yashina,662.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,80552.36
-24085,15692599.0,Chibugo,592.0,Germany,Male,42.0,8.0,94762.16,1.0,1.0,1.0,185599.34
-24086,15590098.0,K'ung,633.0,France,Male,34.0,2.0,0.0,2.0,0.0,1.0,86935.08
-24087,15652948.0,Nwachukwu,691.0,France,Female,31.0,2.0,150500.64,1.0,1.0,1.0,50664.07
-24088,15587978.0,Miller,581.0,Germany,Female,40.0,3.0,135756.96,2.0,1.0,1.0,136909.87
-24089,15621818.0,Manna,612.0,Germany,Female,43.0,5.0,131411.24,1.0,1.0,1.0,163830.63
-24090,15605050.0,Yevseyev,757.0,Spain,Male,39.0,10.0,152663.77,1.0,1.0,0.0,125079.65
-24091,15731522.0,Chinedum,754.0,Germany,Female,43.0,8.0,137851.31,2.0,1.0,1.0,91937.21
-24092,15662093.0,Scott,581.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,111442.71
-24093,15705735.0,Calabresi,512.0,Spain,Male,36.0,8.0,127974.06,1.0,1.0,1.0,43486.73
-24094,15681446.0,Chiu,678.0,France,Male,31.0,2.0,0.0,1.0,1.0,0.0,61761.77
-24095,15772995.0,Dickson,648.0,Spain,Male,27.0,1.0,0.0,2.0,1.0,1.0,91086.73
-24096,15605916.0,Fanucci,686.0,France,Female,36.0,9.0,0.0,1.0,1.0,0.0,71179.31
-24097,15654074.0,Chiu,682.0,France,Male,35.0,10.0,0.0,1.0,1.0,0.0,145796.49
-24098,15609913.0,Hsiao,746.0,France,Female,38.0,10.0,0.0,1.0,1.0,0.0,86767.48
-24099,15716000.0,Pisano,543.0,France,Male,39.0,2.0,0.0,1.0,0.0,1.0,113796.15
-24100,15658716.0,Chiang,719.0,Spain,Female,31.0,4.0,0.0,2.0,0.0,0.0,38276.84
-24101,15589948.0,Lucchesi,660.0,Germany,Female,36.0,5.0,125175.56,2.0,1.0,1.0,175893.35
-24102,15682533.0,Hao,711.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,169921.11
-24103,15738672.0,Macleod,652.0,Germany,Male,58.0,2.0,102785.16,3.0,0.0,1.0,45978.39
-24104,15625247.0,Chibugo,726.0,France,Male,31.0,3.0,92833.89,1.0,0.0,0.0,137674.01
-24105,15597951.0,Uwaezuoke,711.0,France,Female,59.0,2.0,0.0,2.0,0.0,1.0,177613.19
-24106,15672798.0,Ritchie,696.0,France,Male,36.0,4.0,100524.69,2.0,1.0,1.0,95583.45
-24107,15622017.0,Padovano,779.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,147955.91
-24108,15757541.0,Chukwuemeka,704.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,1.0,158580.05
-24109,15777826.0,Onwumelu,653.0,France,Male,49.0,5.0,0.0,1.0,0.0,0.0,159463.8
-24110,15775306.0,Piazza,709.0,Germany,Female,49.0,5.0,148074.79,1.0,0.0,0.0,100538.8
-24111,15662403.0,Chien,609.0,France,Female,37.0,2.0,178056.54,3.0,1.0,0.0,122379.54
-24112,15681316.0,Cattaneo,633.0,France,Female,37.0,1.0,0.0,1.0,1.0,0.0,184954.11
-24113,15768104.0,Chukwufumnanya,596.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,122580.48
-24114,15736274.0,Hs?,762.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,1.0,131283.6
-24115,15652069.0,Kornilova,774.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,62347.71
-24116,15610912.0,H?,663.0,Spain,Female,56.0,7.0,0.0,1.0,1.0,0.0,158887.09
-24117,15698932.0,Smith,613.0,France,Male,37.0,7.0,0.0,1.0,1.0,1.0,106920.57
-24118,15665340.0,Chinedum,578.0,France,Female,39.0,3.0,0.0,2.0,1.0,0.0,92888.52
-24119,15717290.0,Ch'iu,584.0,France,Male,35.0,1.0,0.0,1.0,1.0,1.0,101274.17
-24120,15573926.0,Onyemere,661.0,France,Male,34.0,1.0,120107.71,1.0,1.0,0.0,126518.9
-24121,15578722.0,Chiang,756.0,France,Female,32.0,6.0,0.0,1.0,1.0,1.0,84908.2
-24122,15690164.0,Lucchese,584.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,0.0,52337.97
-24123,15777614.0,Yefimova,685.0,Spain,Male,38.0,6.0,0.0,2.0,0.0,1.0,90876.95
-24124,15792232.0,Chiemenam,534.0,Germany,Female,32.0,3.0,123034.43,1.0,1.0,0.0,140889.64
-24125,15693877.0,Folliero,707.0,France,Female,27.0,3.0,0.0,2.0,1.0,0.0,127939.19
-24126,15639357.0,Hsia,725.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,51553.43
-24127,15770091.0,McElhone,691.0,France,Female,27.0,0.0,0.0,2.0,0.0,1.0,115719.08
-24128,15762799.0,Ritchie,733.0,Spain,Female,33.0,9.0,0.0,2.0,1.0,1.0,73329.08
-24129,15758031.0,Nnamutaezinwa,669.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,131662.47
-24130,15700174.0,Kao,583.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,95857.18
-24131,15660646.0,Ch'ang,745.0,France,Female,31.0,1.0,92676.2,1.0,1.0,0.0,174918.46
-24132,15579223.0,Tsai,629.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,44220.4
-24133,15583049.0,Onyinyechukwuka,705.0,France,Female,22.0,9.0,99712.74,1.0,1.0,0.0,138527.56
-24134,15587712.0,Chukwudi,762.0,Germany,Female,53.0,5.0,101060.25,1.0,1.0,0.0,140746.13
-24135,15583857.0,Walker,528.0,Germany,Female,40.0,1.0,124439.18,1.0,1.0,0.0,177025.79
-24136,15658668.0,Aksenov,721.0,France,Female,61.0,5.0,0.0,2.0,1.0,1.0,18082.06
-24137,15643659.0,DeRose,648.0,France,Female,32.0,4.0,0.0,2.0,0.0,1.0,83052.03
-24138,15794468.0,Tuan,516.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,16081.62
-24139,15800515.0,Ch'iu,790.0,France,Male,32.0,4.0,0.0,2.0,0.0,0.0,131886.46
-24140,15605067.0,Smith,714.0,France,Female,28.0,4.0,0.0,2.0,1.0,1.0,158612.08
-24141,15646764.0,Sneddon,763.0,Spain,Male,52.0,6.0,0.0,1.0,1.0,0.0,163554.85
-24142,15773469.0,H?,718.0,France,Male,64.0,6.0,0.0,2.0,0.0,1.0,145558.42
-24143,15636999.0,Chia,544.0,France,Male,48.0,2.0,0.0,3.0,0.0,1.0,823.96
-24144,15721202.0,Onyemauchechukwu,651.0,Spain,Male,50.0,1.0,0.0,1.0,1.0,1.0,48963.59
-24145,15612071.0,Chukwukadibia,644.0,France,Female,37.0,2.0,0.0,2.0,0.0,1.0,69052.87
-24146,15702669.0,Milani,605.0,France,Female,61.0,7.0,0.0,2.0,1.0,1.0,160110.65
-24147,15754574.0,Hsieh,614.0,Germany,Male,26.0,2.0,142905.51,1.0,1.0,0.0,115022.94
-24148,15752312.0,Ch'ang,589.0,France,Male,38.0,7.0,120916.52,1.0,1.0,1.0,184856.89
-24149,15643826.0,Chiganu,590.0,Spain,Male,39.0,4.0,0.0,2.0,1.0,0.0,90877.76
-24150,15769959.0,Nwankwo,681.0,France,Female,35.0,4.0,114327.86,1.0,1.0,0.0,78854.57
-24151,15787051.0,Lucciano,628.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,106977.53
-24152,15752731.0,Martin,551.0,Spain,Female,48.0,0.0,0.0,1.0,0.0,1.0,163331.65
-24153,15625942.0,Hs?,717.0,Germany,Male,35.0,8.0,107363.31,2.0,1.0,0.0,116581.1
-24154,15721383.0,Ch'en,516.0,Germany,Female,48.0,2.0,133702.89,2.0,0.0,1.0,80552.11
-24155,15577598.0,Onwuatuegwu,581.0,France,Male,40.0,1.0,141136.62,1.0,1.0,1.0,81402.68
-24156,15726354.0,Hs?,757.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,91055.46
-24157,15596303.0,Mazzanti,561.0,France,Female,37.0,7.0,0.0,2.0,0.0,0.0,38190.78
-24158,15796370.0,Ebelechukwu,731.0,Germany,Female,57.0,4.0,100107.99,2.0,1.0,1.0,146598.28
-24159,15803526.0,Hao,605.0,Germany,Female,32.0,6.0,151027.56,2.0,1.0,1.0,179655.87
-24160,15573318.0,Chinedum,581.0,Germany,Male,28.0,3.0,153611.83,1.0,0.0,1.0,168034.9
-24161,15637979.0,Zikoranachidimma,575.0,Germany,Female,54.0,2.0,143903.11,1.0,1.0,0.0,85902.39
-24162,15591169.0,Johnson,713.0,France,Male,21.0,3.0,103818.47,2.0,1.0,1.0,163427.18
-24163,15687913.0,Aksenov,599.0,Germany,Female,38.0,7.0,112291.34,2.0,0.0,1.0,140579.17
-24164,15726776.0,Yobachi,599.0,France,Male,35.0,6.0,95826.49,1.0,0.0,1.0,104719.66
-24165,15726588.0,Hsiao,662.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,180932.46
-24166,15626012.0,Ch'ien,554.0,France,Male,35.0,10.0,148843.56,1.0,1.0,1.0,152992.04
-24167,15684920.0,Chiu,679.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,71433.88
-24168,15742329.0,Chukwukere,683.0,Germany,Male,34.0,9.0,107435.83,2.0,1.0,0.0,70532.53
-24169,15753840.0,Chukwuemeka,711.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,116893.87
-24170,15595043.0,Chukwumaobim,639.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,143859.66
-24171,15696054.0,Lo Duca,679.0,Germany,Male,21.0,1.0,118079.47,1.0,1.0,0.0,49506.82
-24172,15794870.0,Teng,633.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,120511.03
-24173,15678916.0,L?,715.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,84920.49
-24174,15813144.0,Hsieh,635.0,France,Female,46.0,8.0,0.0,1.0,0.0,0.0,19287.06
-24175,15802070.0,Ch'iu,709.0,Germany,Male,29.0,8.0,139603.22,1.0,1.0,1.0,51364.56
-24176,15699238.0,Kao,711.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,88842.8
-24177,15718779.0,Okechukwu,676.0,France,Male,34.0,7.0,0.0,1.0,0.0,1.0,53584.03
-24178,15656592.0,Ch'in,619.0,Germany,Male,54.0,1.0,116932.58,1.0,1.0,0.0,53581.17
-24179,15677796.0,Hsia,661.0,France,Male,38.0,6.0,160903.2,1.0,1.0,1.0,27802.0
-24180,15757837.0,Shao,599.0,Spain,Male,37.0,6.0,0.0,1.0,1.0,1.0,141364.62
-24181,15634310.0,Sagese,632.0,Germany,Male,38.0,7.0,118400.91,1.0,1.0,1.0,137510.41
-24182,15648933.0,Ogochukwu,598.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,129563.86
-24183,15617507.0,Nwachukwu,639.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,135089.57
-24184,15616471.0,Fan,663.0,France,Female,41.0,3.0,132088.59,1.0,1.0,0.0,12796.43
-24185,15569526.0,Linton,556.0,Germany,Female,44.0,1.0,140720.93,1.0,1.0,0.0,124571.09
-24186,15629448.0,Chukwufumnanya,683.0,Germany,Female,30.0,5.0,82228.67,2.0,1.0,1.0,175314.87
-24187,15644788.0,O'Donnell,613.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,167023.95
-24188,15663917.0,Lu,708.0,Spain,Male,37.0,0.0,0.0,2.0,1.0,0.0,124486.28
-24189,15738448.0,Biryukov,699.0,France,Male,37.0,5.0,113625.93,1.0,1.0,0.0,170893.85
-24190,15659495.0,Ch'eng,762.0,France,Male,35.0,1.0,0.0,1.0,1.0,0.0,84790.59
-24191,15588560.0,Hao,724.0,France,Male,35.0,10.0,0.0,2.0,1.0,0.0,113860.81
-24192,15705001.0,Cremonesi,721.0,Spain,Female,55.0,4.0,0.0,1.0,0.0,0.0,57929.8
-24193,15754926.0,Li Fonti,708.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,182960.46
-24194,15586931.0,Ch'eng,850.0,France,Male,37.0,5.0,0.0,2.0,0.0,1.0,143980.27
-24195,15603328.0,Kao,694.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,169183.66
-24196,15583576.0,Hs?,709.0,France,Female,40.0,5.0,0.0,2.0,0.0,0.0,142223.94
-24197,15628274.0,Martin,481.0,Germany,Male,40.0,5.0,122334.26,1.0,1.0,1.0,178994.13
-24198,15611947.0,Chukwudi,686.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,180932.46
-24199,15643361.0,Olisanugo,642.0,France,Male,46.0,2.0,0.0,1.0,1.0,0.0,85421.48
-24200,15705113.0,Chukwufumnanya,605.0,France,Female,35.0,4.0,109876.82,1.0,1.0,1.0,164925.11
-24201,15639277.0,Kao,584.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,10054.51
-24202,15716082.0,Lombardi,653.0,France,Female,31.0,3.0,130935.56,1.0,1.0,0.0,79734.23
-24203,15604020.0,Bitter,597.0,Germany,Female,43.0,5.0,121161.57,2.0,1.0,0.0,139435.11
-24204,15744327.0,Yefimova,632.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,0.0,82951.12
-24205,15589715.0,Chidozie,581.0,France,Male,29.0,3.0,96858.35,1.0,1.0,0.0,71139.31
-24206,15791742.0,Nwokike,771.0,France,Male,36.0,3.0,0.0,1.0,0.0,1.0,52795.56
-24207,15705544.0,Chibugo,645.0,France,Male,44.0,2.0,0.0,2.0,0.0,0.0,101866.39
-24208,15708513.0,Ebelegbulam,645.0,Spain,Female,36.0,1.0,0.0,2.0,0.0,0.0,58879.31
-24209,15686964.0,Chiang,671.0,France,Male,38.0,10.0,133616.39,1.0,1.0,0.0,70523.92
-24210,15791040.0,Hsueh,638.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,85746.52
-24211,15586674.0,Eluemuno,699.0,France,Female,37.0,1.0,138341.51,1.0,1.0,1.0,141926.43
-24212,15656829.0,Onwuamaeze,624.0,Spain,Male,27.0,9.0,0.0,2.0,0.0,0.0,177848.02
-24213,15692819.0,Hsueh,597.0,Spain,Female,32.0,9.0,0.0,1.0,1.0,1.0,66463.62
-24214,15674750.0,Nnachetam,668.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,75732.25
-24215,15752984.0,Wallace,496.0,Germany,Female,36.0,1.0,135927.06,1.0,1.0,0.0,135077.01
-24216,15614893.0,Shih,850.0,Germany,Female,29.0,5.0,120711.73,1.0,1.0,1.0,62390.59
-24217,15603065.0,Uspenskaya,709.0,Spain,Male,41.0,7.0,0.0,2.0,1.0,1.0,161465.31
-24218,15741921.0,Chukwujekwu,753.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,93736.69
-24219,15658956.0,Cremonesi,593.0,Spain,Male,40.0,5.0,0.0,1.0,1.0,0.0,88659.07
-24220,15726484.0,K'ung,528.0,France,Female,34.0,0.0,0.0,2.0,1.0,1.0,46774.94
-24221,15584462.0,Ch'in,743.0,France,Female,40.0,6.0,121537.73,1.0,0.0,0.0,146738.83
-24222,15705001.0,Aitken,850.0,Germany,Male,25.0,8.0,118433.94,1.0,1.0,1.0,136050.44
-24223,15681671.0,Dickson,712.0,Germany,Male,22.0,1.0,175575.95,1.0,0.0,1.0,56865.62
-24224,15742428.0,Hsueh,586.0,Germany,Male,35.0,1.0,108269.2,1.0,0.0,0.0,52327.57
-24225,15753362.0,Lucciano,693.0,France,Female,31.0,1.0,0.0,1.0,1.0,0.0,82647.65
-24226,15572467.0,Yudina,759.0,Spain,Male,38.0,8.0,0.0,1.0,1.0,1.0,177092.16
-24227,15799479.0,Kaeppel,484.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,0.0,103349.72
-24228,15649430.0,Hsia,657.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,82662.73
-24229,15806837.0,Hsia,704.0,France,Male,50.0,8.0,0.0,2.0,0.0,1.0,191198.92
-24230,15637324.0,Ch'in,616.0,France,Female,40.0,3.0,0.0,1.0,1.0,0.0,85901.09
-24231,15809909.0,Tien,725.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,153662.88
-24232,15579617.0,Zikoranachidimma,716.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,28269.86
-24233,15604020.0,Ts'ui,603.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,172618.52
-24234,15586974.0,Nwachukwu,639.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,111698.0
-24235,15793331.0,Uvarova,681.0,Spain,Female,33.0,5.0,0.0,1.0,1.0,1.0,131389.39
-24236,15597951.0,Hsueh,607.0,France,Female,57.0,0.0,0.0,1.0,1.0,1.0,93832.33
-24237,15658195.0,Olisaemeka,694.0,Spain,Male,31.0,3.0,0.0,1.0,1.0,0.0,156660.31
-24238,15758510.0,Tretiakova,671.0,France,Male,36.0,1.0,114514.73,1.0,0.0,1.0,80410.68
-24239,15572735.0,Ch'en,679.0,France,Male,39.0,6.0,0.0,1.0,1.0,0.0,128882.66
-24240,15775886.0,P'an,667.0,France,Female,41.0,2.0,0.0,2.0,0.0,1.0,180553.48
-24241,15731519.0,Fanucci,635.0,Germany,Female,56.0,9.0,122359.5,2.0,1.0,1.0,63736.17
-24242,15654562.0,Nnaife,646.0,France,Male,24.0,1.0,0.0,2.0,1.0,1.0,185599.34
-24243,15784097.0,Chu,614.0,Spain,Female,38.0,4.0,0.0,1.0,1.0,1.0,163714.92
-24244,15771054.0,Onwughara,491.0,Germany,Male,32.0,7.0,121866.89,2.0,1.0,0.0,120314.75
-24245,15603594.0,Lucciano,777.0,Spain,Female,35.0,2.0,0.0,2.0,0.0,0.0,83470.9
-24246,15593943.0,Udokamma,619.0,Spain,Male,38.0,7.0,123971.51,1.0,1.0,1.0,90174.83
-24247,15757912.0,Chibueze,755.0,France,Female,31.0,8.0,80728.15,1.0,1.0,1.0,140159.95
-24248,15643908.0,De Luca,619.0,France,Male,29.0,1.0,119266.69,2.0,0.0,0.0,48930.8
-24249,15681332.0,T'ang,741.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,177065.24
-24250,15743490.0,Onyekachukwu,621.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,160976.87
-24251,15733299.0,Tien,588.0,France,Female,21.0,10.0,0.0,2.0,1.0,0.0,109685.61
-24252,15736254.0,Ts'ui,748.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,168515.61
-24253,15692408.0,T'ien,683.0,France,Female,34.0,9.0,176511.08,1.0,1.0,0.0,150401.53
-24254,15783629.0,Gilleland,653.0,Germany,Male,46.0,2.0,142296.13,1.0,1.0,0.0,53394.24
-24255,15611338.0,Houghton,752.0,France,Male,38.0,8.0,0.0,2.0,0.0,0.0,81482.4
-24256,15775124.0,Folliero,717.0,France,Female,33.0,0.0,0.0,2.0,0.0,1.0,132673.23
-24257,15709920.0,Ch'eng,751.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,143952.24
-24258,15690743.0,Ch'en,640.0,Spain,Male,33.0,1.0,0.0,1.0,0.0,0.0,151903.6
-24259,15777906.0,Chinedum,634.0,Germany,Female,38.0,5.0,130142.79,2.0,0.0,0.0,136875.31
-24260,15803790.0,T'ang,614.0,Spain,Female,25.0,9.0,117412.19,1.0,1.0,0.0,116081.97
-24261,15658577.0,Hao,720.0,France,Male,49.0,6.0,0.0,2.0,0.0,1.0,153920.22
-24262,15702561.0,Tuan,646.0,France,Male,28.0,6.0,121161.57,1.0,1.0,1.0,82662.73
-24263,15726773.0,Hsieh,619.0,France,Male,42.0,6.0,75893.11,1.0,0.0,1.0,142977.05
-24264,15709441.0,Aksenov,727.0,France,Male,35.0,8.0,0.0,2.0,0.0,1.0,164083.72
-24265,15604792.0,Hsueh,717.0,Spain,Female,42.0,5.0,0.0,2.0,1.0,0.0,145796.49
-24266,15770255.0,Ting,709.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,168515.61
-24267,15722583.0,Mazzi,699.0,Germany,Male,41.0,9.0,129299.79,2.0,1.0,1.0,99490.01
-24268,15609623.0,T'ang,823.0,Spain,Female,37.0,4.0,0.0,2.0,1.0,0.0,160162.42
-24269,15704844.0,Chidimma,642.0,Spain,Male,35.0,2.0,112262.84,1.0,0.0,0.0,155853.62
-24270,15685829.0,Maclean,691.0,Germany,Female,44.0,3.0,107949.39,1.0,1.0,0.0,162452.65
-24271,15593894.0,Hs?eh,751.0,Spain,Male,34.0,9.0,0.0,2.0,0.0,0.0,62722.44
-24272,15662294.0,Mazzanti,686.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,0.0,61315.37
-24273,15665783.0,Wright,748.0,France,Male,35.0,3.0,0.0,2.0,0.0,0.0,185510.34
-24274,15791172.0,Hs?,678.0,France,Male,38.0,1.0,126670.37,1.0,1.0,1.0,106564.88
-24275,15753362.0,Chien,598.0,Germany,Female,43.0,0.0,107747.57,1.0,1.0,0.0,44147.95
-24276,15789055.0,Chin,711.0,France,Male,58.0,1.0,0.0,2.0,1.0,1.0,142513.5
-24277,15769353.0,Iweobiegbunam,573.0,Spain,Female,29.0,5.0,0.0,1.0,1.0,0.0,48988.28
-24278,15792679.0,Smith,598.0,Germany,Male,38.0,3.0,104930.38,2.0,1.0,1.0,177866.87
-24279,15713559.0,Kincaid,591.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,108764.69
-24280,15777616.0,Ts'ao,633.0,Spain,Female,63.0,1.0,0.0,2.0,1.0,1.0,186489.95
-24281,15603550.0,Uspenskaya,646.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,80630.32
-24282,15794895.0,Maclean,616.0,Germany,Female,36.0,7.0,118082.89,1.0,1.0,0.0,139024.31
-24283,15607877.0,T'ang,849.0,France,Male,36.0,2.0,118287.01,1.0,1.0,1.0,70956.75
-24284,15581229.0,Chukwudi,613.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,170529.63
-24285,15646936.0,Nkemakolam,738.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,82870.75
-24286,15570073.0,Chigolum,684.0,France,Male,41.0,9.0,129895.57,1.0,0.0,0.0,84487.62
-24287,15578211.0,Onwuamaeze,623.0,Spain,Female,46.0,1.0,91216.0,1.0,1.0,1.0,174825.57
-24288,15646764.0,Fomin,833.0,France,Male,40.0,2.0,0.0,1.0,0.0,0.0,193009.21
-24289,15795586.0,Ch'ang,644.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,67944.09
-24290,15684936.0,Kaeppel,678.0,France,Female,38.0,7.0,135096.05,1.0,1.0,0.0,79458.12
-24291,15601787.0,Hsu,543.0,France,Male,40.0,2.0,82344.84,2.0,0.0,0.0,77867.23
-24292,15693637.0,Chiang,553.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,0.0,73551.44
-24293,15590327.0,Cattaneo,613.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,1.0,129964.94
-24294,15771086.0,K'ung,637.0,France,Male,34.0,8.0,0.0,1.0,0.0,0.0,153727.32
-24295,15761606.0,P'eng,647.0,Germany,Male,34.0,2.0,102962.62,2.0,1.0,1.0,116051.44
-24296,15626474.0,Ch'iu,726.0,France,Female,34.0,3.0,0.0,2.0,0.0,0.0,131284.92
-24297,15635388.0,Trevisano,738.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,142730.27
-24298,15758023.0,Hsieh,651.0,Spain,Female,41.0,9.0,0.0,1.0,1.0,1.0,180598.66
-24299,15705544.0,Ch'ang,642.0,Germany,Male,37.0,5.0,125445.04,1.0,0.0,1.0,52332.85
-24300,15756056.0,Onwuamaeze,709.0,Spain,Male,39.0,10.0,0.0,2.0,1.0,0.0,143980.29
-24301,15781689.0,Milano,755.0,France,Male,40.0,8.0,120171.58,1.0,0.0,1.0,142331.85
-24302,15723488.0,Lucchese,579.0,France,Male,36.0,8.0,0.0,2.0,0.0,1.0,161822.66
-24303,15795878.0,K?,777.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,160968.48
-24304,15582325.0,T'ien,545.0,France,Male,26.0,4.0,0.0,2.0,1.0,1.0,94977.3
-24305,15682467.0,Hsueh,686.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,187905.12
-24306,15621093.0,Brown,714.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,140800.62
-24307,15724181.0,Mancini,703.0,Germany,Female,45.0,9.0,101168.3,1.0,1.0,0.0,101168.3
-24308,15653572.0,Goliwe,663.0,Spain,Female,47.0,3.0,0.0,1.0,1.0,1.0,140763.18
-24309,15701300.0,Y?,633.0,France,Male,32.0,1.0,132387.91,1.0,1.0,1.0,164864.66
-24310,15712403.0,Yobachi,756.0,France,Female,40.0,8.0,0.0,2.0,0.0,1.0,61602.42
-24311,15756668.0,Chukwukere,614.0,France,Male,33.0,1.0,94771.61,1.0,1.0,0.0,131301.74
-24312,15740383.0,Tien,751.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,78670.52
-24313,15796790.0,Liao,632.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,70533.96
-24314,15736413.0,Chia,485.0,Spain,Female,29.0,3.0,0.0,2.0,0.0,0.0,176962.31
-24315,15790750.0,Onyemauchechi,712.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,135077.01
-24316,15650266.0,Knepper,649.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,95583.45
-24317,15702464.0,Ch'ien,551.0,France,Female,41.0,3.0,139180.2,1.0,1.0,1.0,90878.13
-24318,15742269.0,Maclean,568.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,93336.3
-24319,15682355.0,Nkemjika,657.0,Germany,Female,53.0,4.0,91286.51,1.0,1.0,1.0,116848.79
-24320,15798975.0,Pisani,543.0,France,Male,23.0,1.0,0.0,2.0,0.0,0.0,142730.27
-24321,15802274.0,Tsou,669.0,France,Male,33.0,1.0,121411.9,1.0,1.0,1.0,64375.4
-24322,15696733.0,Y?an,535.0,Spain,Male,37.0,5.0,143566.12,1.0,1.0,0.0,108872.45
-24323,15579996.0,Okonkwo,535.0,Spain,Male,33.0,7.0,109646.83,1.0,1.0,1.0,90977.48
-24324,15672357.0,Hsia,582.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,176943.59
-24325,15612095.0,Chigozie,642.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,15650.89
-24326,15731463.0,Manna,567.0,Spain,Female,49.0,5.0,0.0,3.0,1.0,0.0,183490.99
-24327,15683522.0,Chukwuebuka,779.0,France,Male,33.0,5.0,119496.25,1.0,0.0,1.0,120853.83
-24328,15589312.0,Chidiebere,556.0,France,Male,27.0,7.0,0.0,2.0,1.0,0.0,123882.73
-24329,15616666.0,Eluemuno,596.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,0.0,141501.45
-24330,15632275.0,Obinna,595.0,Germany,Male,43.0,6.0,152603.45,1.0,0.0,1.0,71830.1
-24331,15587914.0,Zito,598.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,59961.81
-24332,15699257.0,Chien,588.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,64448.36
-24333,15667633.0,Cattaneo,709.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,127825.25
-24334,15693906.0,Chukwuemeka,710.0,France,Female,38.0,0.0,0.0,1.0,1.0,1.0,152843.53
-24335,15670172.0,Hsiao,850.0,Germany,Female,30.0,7.0,123601.56,1.0,1.0,0.0,197305.91
-24336,15593468.0,Gadsdon,637.0,France,Female,49.0,10.0,0.0,3.0,0.0,0.0,174434.06
-24337,15645544.0,Mironova,710.0,France,Male,36.0,5.0,0.0,1.0,1.0,0.0,93718.86
-24338,15721433.0,Dyer,605.0,France,Male,43.0,5.0,121898.82,1.0,1.0,1.0,143635.33
-24339,15592736.0,Ch'en,573.0,Spain,Female,40.0,1.0,0.0,1.0,1.0,0.0,48924.73
-24340,15721460.0,Manna,627.0,Germany,Male,39.0,7.0,97796.04,2.0,1.0,1.0,122964.14
-24341,15690744.0,Chukwuebuka,702.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,1.0,94670.77
-24342,15794345.0,Piazza,708.0,Germany,Female,42.0,6.0,142072.02,2.0,1.0,1.0,29333.01
-24343,15762331.0,Ch'ang,687.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,162511.55
-24344,15624695.0,Yuryeva,696.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,169498.61
-24345,15635367.0,Kao,634.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,74898.8
-24346,15652084.0,Uvarova,622.0,France,Female,41.0,5.0,0.0,2.0,1.0,1.0,147074.67
-24347,15638882.0,Mazzi,712.0,Spain,Female,35.0,5.0,61687.33,1.0,1.0,1.0,104769.89
-24348,15593778.0,Nebeolisa,577.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,182059.28
-24349,15807167.0,Onio,829.0,France,Female,46.0,5.0,0.0,1.0,0.0,0.0,50783.1
-24350,15754569.0,Chineze,706.0,Germany,Male,37.0,8.0,118894.22,2.0,0.0,0.0,89566.64
-24351,15588928.0,Ogochukwu,633.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,120531.31
-24352,15677288.0,Chiawuotu,615.0,France,Female,46.0,8.0,0.0,2.0,1.0,0.0,111324.41
-24353,15607098.0,Cremonesi,807.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,27286.1
-24354,15803526.0,Mbanefo,675.0,Spain,Male,26.0,3.0,137714.66,1.0,1.0,0.0,73066.71
-24355,15695879.0,Nnachetam,844.0,France,Female,27.0,5.0,132446.08,1.0,1.0,1.0,71405.17
-24356,15777892.0,Nwankwo,748.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,159840.94
-24357,15761487.0,Maclean,614.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,173365.9
-24358,15670822.0,Hsia,675.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,143614.01
-24359,15808223.0,Chukwujamuike,697.0,France,Male,30.0,3.0,113957.01,1.0,1.0,1.0,183399.12
-24360,15661936.0,Chukwuemeka,526.0,Spain,Female,27.0,8.0,0.0,2.0,0.0,0.0,108008.65
-24361,15592854.0,Chiu,663.0,Spain,Male,38.0,3.0,0.0,1.0,1.0,0.0,120893.07
-24362,15664547.0,Chiang,598.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,185561.89
-24363,15581323.0,Hsieh,560.0,France,Female,49.0,1.0,0.0,1.0,0.0,0.0,121210.09
-24364,15672374.0,Onyemauchechi,646.0,France,Male,33.0,2.0,0.0,1.0,0.0,1.0,183598.77
-24365,15694188.0,Chibugo,629.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,79919.13
-24366,15697028.0,Lai,781.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,92914.67
-24367,15628292.0,Lueck,554.0,France,Female,34.0,4.0,98545.4,1.0,0.0,0.0,168034.9
-24368,15749177.0,Li,605.0,Spain,Male,40.0,4.0,121513.31,1.0,1.0,1.0,164825.07
-24369,15701376.0,Ukaegbunam,667.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,80178.54
-24370,15673971.0,Aksenov,584.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,130755.34
-24371,15765283.0,Ch'ien,520.0,France,Male,36.0,7.0,153861.1,1.0,1.0,0.0,184856.89
-24372,15603065.0,Chidi,537.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,125877.22
-24373,15786063.0,K?,695.0,Germany,Male,51.0,1.0,127864.4,1.0,1.0,0.0,59816.41
-24374,15758513.0,Ts'ui,663.0,France,Female,33.0,7.0,0.0,1.0,0.0,1.0,179631.85
-24375,15761047.0,P'eng,729.0,Spain,Female,47.0,6.0,0.0,1.0,1.0,0.0,60420.07
-24376,15672912.0,Tretiakova,629.0,Germany,Female,29.0,5.0,150679.71,1.0,1.0,1.0,168886.31
-24377,15717995.0,Hsiao,714.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,149369.41
-24378,15602647.0,Nwebube,678.0,France,Male,20.0,10.0,0.0,2.0,1.0,0.0,121747.96
-24379,15626452.0,Milano,628.0,Germany,Female,43.0,2.0,125927.51,1.0,1.0,1.0,142903.44
-24380,15809558.0,Pirozzi,481.0,Germany,Female,46.0,3.0,118087.89,1.0,1.0,0.0,79178.37
-24381,15657535.0,Houghton,591.0,Germany,Male,35.0,9.0,117833.3,3.0,1.0,1.0,182691.31
-24382,15702656.0,O'Brien,703.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,142903.44
-24383,15682868.0,Ch'ien,603.0,Germany,Female,27.0,9.0,72494.69,1.0,1.0,0.0,175072.47
-24384,15572767.0,Aitken,589.0,Spain,Female,44.0,7.0,0.0,1.0,1.0,1.0,138841.81
-24385,15714689.0,Onyeoruru,758.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,151317.48
-24386,15718572.0,Nwankwo,598.0,Germany,Male,32.0,4.0,115764.32,1.0,1.0,0.0,48382.6
-24387,15798521.0,Lai,743.0,France,Female,61.0,1.0,0.0,1.0,1.0,1.0,104721.5
-24388,15769955.0,Walker,707.0,France,Female,26.0,3.0,0.0,2.0,0.0,0.0,171045.35
-24389,15777892.0,Ts'ui,545.0,France,Female,39.0,3.0,0.0,2.0,1.0,1.0,126419.8
-24390,15586674.0,Onyekachukwu,580.0,France,Female,35.0,7.0,100116.82,1.0,1.0,1.0,121798.52
-24391,15745452.0,Nkemakolam,725.0,France,Female,43.0,5.0,0.0,1.0,0.0,1.0,77522.61
-24392,15613471.0,Onwumelu,689.0,France,Female,27.0,1.0,0.0,1.0,1.0,1.0,84475.13
-24393,15785024.0,Ts'ao,657.0,France,Male,38.0,4.0,142412.14,1.0,0.0,1.0,181879.56
-24394,15708534.0,O'Sullivan,567.0,Spain,Male,33.0,4.0,102989.83,1.0,0.0,0.0,66463.62
-24395,15592716.0,Mancini,634.0,France,Female,38.0,5.0,0.0,2.0,0.0,0.0,131283.6
-24396,15704771.0,O'Brien,681.0,France,Female,48.0,1.0,0.0,1.0,1.0,0.0,116692.8
-24397,15658946.0,Chiedozie,721.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,93524.19
-24398,15781534.0,Chinomso,587.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,125880.53
-24399,15732644.0,Hao,773.0,France,Male,32.0,7.0,0.0,2.0,0.0,1.0,80698.54
-24400,15628292.0,Onyeorulu,608.0,Germany,Male,29.0,6.0,107915.94,2.0,1.0,1.0,180622.43
-24401,15581323.0,Ch'iu,695.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,151822.66
-24402,15770101.0,Y?,636.0,Spain,Female,37.0,10.0,0.0,2.0,1.0,1.0,176750.81
-24403,15581388.0,H?,605.0,France,Male,62.0,2.0,0.0,3.0,1.0,1.0,148811.71
-24404,15789563.0,Calabresi,576.0,Spain,Female,25.0,4.0,0.0,2.0,1.0,0.0,143315.57
-24405,15680706.0,Manna,709.0,Germany,Female,39.0,7.0,104960.46,2.0,0.0,1.0,177844.06
-24406,15649858.0,Nnamdi,787.0,Germany,Male,25.0,7.0,82909.14,2.0,0.0,0.0,84917.4
-24407,15653773.0,Chiagoziem,485.0,Spain,Male,54.0,7.0,119380.37,1.0,0.0,1.0,138380.12
-24408,15675176.0,Oguejiofor,584.0,France,Female,42.0,7.0,0.0,1.0,1.0,0.0,51907.72
-24409,15665963.0,Chiang,677.0,France,Male,34.0,9.0,113907.58,2.0,1.0,1.0,82709.8
-24410,15771668.0,Rolon,542.0,France,Male,50.0,4.0,0.0,1.0,0.0,1.0,66561.6
-24411,15720371.0,T'ien,615.0,France,Female,27.0,7.0,0.0,2.0,0.0,0.0,54495.82
-24412,15785831.0,Genovesi,729.0,France,Male,35.0,7.0,144875.74,1.0,1.0,0.0,124872.66
-24413,15744553.0,Chinonyelum,717.0,France,Female,31.0,3.0,0.0,2.0,0.0,0.0,74898.8
-24414,15571144.0,Naylor,605.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,172668.39
-24415,15806420.0,P'eng,604.0,France,Male,28.0,9.0,0.0,2.0,1.0,1.0,51548.14
-24416,15586870.0,Ukaegbunam,543.0,France,Female,23.0,1.0,0.0,2.0,1.0,1.0,124720.68
-24417,15723339.0,Tretyakova,593.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,136497.28
-24418,15698522.0,Fiorentino,714.0,Germany,Male,45.0,4.0,122486.11,1.0,1.0,0.0,62768.8
-24419,15591550.0,Hs?eh,689.0,France,Male,32.0,6.0,116244.14,2.0,1.0,0.0,103901.35
-24420,15636016.0,Tung,645.0,France,Female,31.0,2.0,0.0,2.0,0.0,0.0,110468.51
-24421,15736250.0,Hsiao,593.0,Spain,Female,26.0,7.0,0.0,2.0,1.0,1.0,164864.67
-24422,15739271.0,Mazzi,704.0,Spain,Female,41.0,3.0,0.0,1.0,1.0,0.0,134901.34
-24423,15732266.0,Yermakov,593.0,France,Male,26.0,8.0,0.0,2.0,0.0,0.0,100862.36
-24424,15788131.0,Hsueh,535.0,Spain,Female,32.0,10.0,0.0,2.0,1.0,1.0,105898.79
-24425,15588928.0,Kao,850.0,France,Female,47.0,9.0,0.0,2.0,1.0,1.0,131083.73
-24426,15744200.0,Trevisani,754.0,France,Male,45.0,7.0,115029.4,1.0,0.0,1.0,10034.77
-24427,15733883.0,Zuyev,682.0,France,Female,32.0,8.0,0.0,2.0,0.0,0.0,102628.98
-24428,15701404.0,Ugochukwu,717.0,France,Male,34.0,2.0,0.0,1.0,1.0,0.0,132673.23
-24429,15687946.0,Nwabugwu,706.0,Germany,Female,40.0,3.0,127864.4,1.0,1.0,0.0,180551.76
-24430,15653937.0,Fiorentino,841.0,France,Female,45.0,1.0,0.0,2.0,1.0,1.0,86787.34
-24431,15595592.0,Yuriev,635.0,Germany,Male,32.0,9.0,102773.2,2.0,1.0,0.0,179833.45
-24432,15690526.0,Onwubiko,711.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,118545.49
-24433,15708714.0,Onyemauchechi,613.0,Spain,Female,39.0,4.0,0.0,2.0,1.0,0.0,110782.88
-24434,15676521.0,White,651.0,Spain,Female,27.0,7.0,0.0,2.0,1.0,0.0,130113.58
-24435,15635078.0,Onyemachukwu,582.0,France,Male,38.0,2.0,0.0,1.0,1.0,1.0,66926.83
-24436,15775430.0,Yermakova,719.0,Spain,Female,37.0,0.0,85304.29,1.0,1.0,0.0,59259.44
-24437,15670584.0,Hsu,545.0,Spain,Male,43.0,9.0,0.0,1.0,1.0,0.0,104533.88
-24438,15621974.0,Onyinyechukwuka,773.0,France,Male,24.0,3.0,0.0,2.0,1.0,1.0,147600.54
-24439,15772892.0,Fomin,730.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,85470.45
-24440,15689661.0,Ch'eng,678.0,Spain,Male,35.0,9.0,0.0,2.0,0.0,1.0,61623.19
-24441,15752508.0,Ch'ien,535.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,151623.71
-24442,15652693.0,Hsiao,707.0,France,Male,30.0,4.0,133656.95,1.0,1.0,1.0,51565.99
-24443,15785519.0,Hsieh,712.0,Germany,Male,48.0,7.0,113715.36,1.0,1.0,1.0,121568.37
-24444,15643908.0,Fanucci,835.0,France,Male,38.0,1.0,0.0,1.0,1.0,0.0,138526.96
-24445,15593365.0,Onwughara,703.0,Germany,Female,48.0,1.0,117419.35,1.0,1.0,0.0,51565.98
-24446,15685829.0,Kaleski,695.0,Spain,Male,32.0,8.0,0.0,1.0,1.0,0.0,85891.02
-24447,15686696.0,Nwachinemelu,646.0,France,Female,42.0,6.0,118897.1,1.0,1.0,0.0,86696.77
-24448,15583480.0,Ch'ang,790.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,177670.57
-24449,15682757.0,T'ien,636.0,France,Female,34.0,0.0,0.0,1.0,1.0,0.0,158750.58
-24450,15763613.0,Genovese,648.0,Spain,Male,27.0,2.0,0.0,2.0,0.0,1.0,69079.85
-24451,15811032.0,Nnachetam,535.0,France,Male,45.0,2.0,0.0,2.0,1.0,1.0,182738.0
-24452,15686964.0,Kumm,558.0,France,Male,49.0,7.0,93569.86,1.0,1.0,0.0,71830.1
-24453,15586029.0,Ritchie,593.0,Germany,Male,35.0,1.0,112013.81,1.0,0.0,0.0,134878.19
-24454,15643908.0,Shih,594.0,France,Female,38.0,10.0,0.0,2.0,1.0,0.0,160968.44
-24455,15642996.0,Yancy,571.0,France,Female,42.0,6.0,0.0,2.0,1.0,1.0,118029.09
-24456,15631681.0,Chukwukere,753.0,France,Female,37.0,10.0,128940.24,1.0,1.0,1.0,121451.21
-24457,15595228.0,Rizzo,704.0,France,Male,38.0,3.0,119658.49,1.0,1.0,0.0,108162.13
-24458,15588854.0,Ch'in,671.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,85901.09
-24459,15701301.0,Maclean,582.0,Spain,Female,37.0,5.0,145081.73,1.0,1.0,1.0,59905.29
-24460,15568562.0,Folliero,586.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,79555.08
-24461,15665790.0,Johnson,731.0,France,Female,28.0,7.0,0.0,2.0,1.0,0.0,124045.65
-24462,15807595.0,Shih,703.0,Germany,Female,51.0,3.0,123345.49,1.0,1.0,1.0,88890.75
-24463,15800856.0,Walker,632.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,133007.34
-24464,15776936.0,Uspenskaya,751.0,France,Male,41.0,1.0,141726.88,1.0,1.0,0.0,60917.24
-24465,15588904.0,Obiora,678.0,Spain,Female,40.0,1.0,0.0,1.0,0.0,0.0,151401.33
-24466,15768340.0,O'Donnell,731.0,Germany,Female,44.0,1.0,152628.98,1.0,1.0,0.0,176925.69
-24467,15741094.0,Udinese,624.0,France,Female,34.0,0.0,151766.71,1.0,1.0,1.0,149550.94
-24468,15802570.0,Nwankwo,834.0,France,Male,28.0,5.0,0.0,2.0,1.0,0.0,67789.99
-24469,15626985.0,Ch'ang,707.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,172459.39
-24470,15662488.0,Teng,620.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,0.0,179883.04
-24471,15584515.0,Eluemuno,686.0,Germany,Female,40.0,9.0,122570.69,3.0,1.0,0.0,122925.4
-24472,15671917.0,Cattaneo,755.0,Spain,Male,45.0,6.0,0.0,2.0,1.0,0.0,99800.87
-24473,15603594.0,Curnow,545.0,France,Female,51.0,1.0,0.0,1.0,0.0,1.0,101491.23
-24474,15593736.0,Hsiung,730.0,France,Male,29.0,8.0,76665.71,1.0,1.0,0.0,136079.64
-24475,15591747.0,Chukwualuka,709.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,179521.24
-24476,15711287.0,Calabresi,710.0,France,Male,39.0,5.0,0.0,2.0,0.0,1.0,85901.09
-24477,15782311.0,Hao,682.0,Spain,Female,44.0,8.0,0.0,2.0,1.0,1.0,106977.8
-24478,15694450.0,Chiawuotu,717.0,France,Female,36.0,4.0,0.0,1.0,1.0,1.0,102908.91
-24479,15711478.0,Nkemakolam,715.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,1.0,178106.34
-24480,15728505.0,Zito,645.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,162889.1
-24481,15571529.0,K'ung,591.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,180736.24
-24482,15740104.0,Tien,634.0,Germany,Male,48.0,9.0,115301.31,1.0,0.0,1.0,120106.5
-24483,15707968.0,Chukwuemeka,619.0,Germany,Female,40.0,0.0,141796.43,2.0,1.0,1.0,93829.17
-24484,15748625.0,Bibi,666.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,111179.84
-24485,15731478.0,K'ung,790.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,132870.15
-24486,15589493.0,Liao,661.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,128815.23
-24487,15768359.0,Chialuka,634.0,Germany,Male,35.0,5.0,125977.81,1.0,0.0,1.0,46824.08
-24488,15624180.0,Kao,709.0,Spain,Male,39.0,9.0,0.0,1.0,1.0,0.0,71926.36
-24489,15796370.0,Hsu,651.0,France,Female,38.0,2.0,138705.02,2.0,1.0,1.0,116973.48
-24490,15782688.0,Onwuamaeze,545.0,France,Female,39.0,5.0,89039.9,2.0,1.0,1.0,155799.86
-24491,15787459.0,Ch'ang,825.0,Germany,Male,42.0,1.0,142765.24,1.0,0.0,0.0,28157.34
-24492,15616700.0,Hsia,665.0,Germany,Male,33.0,6.0,118962.58,1.0,0.0,1.0,86950.72
-24493,15714391.0,H?,672.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,66795.78
-24494,15699430.0,Nnonso,497.0,Spain,Female,34.0,2.0,133616.39,1.0,1.0,0.0,88827.67
-24495,15624293.0,Chiemezie,589.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,105898.79
-24496,15709295.0,Chukwuhaenye,769.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,145544.48
-24497,15588928.0,Shen,697.0,Germany,Male,35.0,5.0,133105.47,2.0,1.0,0.0,35337.31
-24498,15683544.0,Chien,807.0,France,Female,33.0,3.0,0.0,1.0,1.0,1.0,108804.28
-24499,15635277.0,Onwumelu,683.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,85746.52
-24500,15704250.0,Rizzo,678.0,Spain,Male,23.0,5.0,0.0,2.0,0.0,1.0,80178.54
-24501,15682455.0,Ku,646.0,France,Male,38.0,2.0,0.0,1.0,1.0,1.0,141148.21
-24502,15789955.0,Walker,743.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,80425.65
-24503,15693864.0,Iheatu,665.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,0.0,102750.6
-24504,15771299.0,Mazzanti,794.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,121151.46
-24505,15718893.0,Kao,695.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,169921.11
-24506,15581531.0,K?,704.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,151843.54
-24507,15677117.0,Chiu,684.0,France,Male,26.0,10.0,0.0,2.0,0.0,0.0,27472.07
-24508,15753362.0,T'ao,637.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,144880.18
-24509,15676966.0,Nnamutaezinwa,717.0,France,Male,44.0,4.0,0.0,2.0,1.0,0.0,53581.18
-24510,15684461.0,Shao,692.0,France,Male,40.0,10.0,117325.02,1.0,1.0,1.0,93524.22
-24511,15742210.0,Yobanna,537.0,Germany,Male,40.0,6.0,100549.42,1.0,1.0,1.0,160979.66
-24512,15601263.0,McEncroe,591.0,Spain,Male,41.0,9.0,141616.55,1.0,0.0,1.0,155062.02
-24513,15581548.0,Kao,706.0,France,Female,29.0,8.0,0.0,2.0,1.0,1.0,147600.54
-24514,15719479.0,Ch'ien,636.0,France,Female,53.0,9.0,0.0,1.0,1.0,1.0,53784.22
-24515,15802857.0,Uvarov,577.0,Germany,Male,24.0,6.0,130788.71,1.0,1.0,0.0,99503.19
-24516,15667871.0,Ch'ang,611.0,Germany,Male,32.0,8.0,129335.65,2.0,1.0,1.0,76611.33
-24517,15799932.0,Ch'ien,731.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,183399.12
-24518,15795881.0,Ozerova,642.0,Germany,Female,51.0,9.0,116968.91,1.0,1.0,0.0,177065.24
-24519,15803750.0,K?,517.0,Germany,Female,55.0,4.0,148143.84,1.0,1.0,0.0,52795.56
-24520,15775886.0,Yobanna,663.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,40339.9
-24521,15752894.0,Fiorentino,709.0,France,Male,38.0,1.0,112595.85,1.0,1.0,0.0,182855.42
-24522,15623478.0,Ts'ai,756.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,145936.28
-24523,15662901.0,Mao,628.0,France,Female,56.0,1.0,61688.61,1.0,1.0,1.0,127569.2
-24524,15778915.0,Ch'eng,733.0,France,Male,42.0,2.0,0.0,2.0,1.0,1.0,53477.76
-24525,15594576.0,Ozerova,543.0,Germany,Female,34.0,4.0,152462.78,1.0,1.0,1.0,61513.87
-24526,15662641.0,Ts'ui,696.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,91406.62
-24527,15711352.0,Ts'ai,624.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,125451.01
-24528,15777772.0,Sagese,699.0,France,Female,32.0,5.0,107467.35,1.0,1.0,1.0,124964.82
-24529,15754053.0,Whitelegge,645.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,166031.08
-24530,15606861.0,Okwukwe,684.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,26136.13
-24531,15768495.0,Tien,707.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,141069.88
-24532,15769596.0,Hightower,696.0,Germany,Female,54.0,3.0,133656.91,1.0,0.0,0.0,166440.32
-24533,15604020.0,Ts'ai,834.0,Germany,Male,42.0,9.0,114446.84,1.0,0.0,0.0,141756.32
-24534,15792668.0,Bellucci,594.0,France,Female,40.0,5.0,155413.08,1.0,1.0,1.0,179436.6
-24535,15658195.0,Ch'ien,724.0,France,Female,35.0,5.0,0.0,1.0,1.0,1.0,184564.67
-24536,15622017.0,Yevdokimova,705.0,France,Male,42.0,2.0,160680.41,1.0,0.0,0.0,74945.11
-24537,15793881.0,Macleod,670.0,Germany,Male,34.0,5.0,151769.23,1.0,0.0,1.0,9983.88
-24538,15757571.0,Chialuka,727.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,0.0,129562.74
-24539,15691387.0,H?,588.0,France,Male,47.0,5.0,0.0,2.0,1.0,0.0,171482.56
-24540,15725772.0,Y?an,605.0,Spain,Female,45.0,1.0,0.0,1.0,1.0,0.0,133871.83
-24541,15762543.0,Ch'ang,656.0,France,Male,31.0,9.0,128842.56,2.0,1.0,0.0,182266.01
-24542,15794468.0,Teng,683.0,Germany,Male,35.0,10.0,153610.61,1.0,0.0,0.0,92727.41
-24543,15734886.0,Palermo,535.0,France,Female,43.0,10.0,0.0,2.0,1.0,1.0,113931.57
-24544,15614491.0,Folliero,602.0,France,Male,46.0,5.0,0.0,2.0,0.0,0.0,182913.95
-24545,15574859.0,Chukwudi,756.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,126812.63
-24546,15776947.0,Maclean,850.0,Germany,Female,36.0,4.0,145219.3,1.0,0.0,1.0,182055.36
-24547,15718572.0,Ch'iu,675.0,Germany,Male,39.0,6.0,126431.54,2.0,0.0,0.0,100950.48
-24548,15576774.0,Chidumaga,769.0,France,Female,56.0,3.0,0.0,1.0,1.0,0.0,49373.97
-24549,15685576.0,Lai,529.0,France,Male,35.0,1.0,138296.94,1.0,0.0,0.0,117038.96
-24550,15690021.0,Trevisano,579.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,146700.22
-24551,15722409.0,T'ang,678.0,France,Female,29.0,7.0,0.0,2.0,1.0,1.0,177834.79
-24552,15751185.0,Nwabugwu,651.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,0.0,120853.83
-24553,15730657.0,Ch'iu,716.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,111590.35
-24554,15687828.0,Achebe,726.0,France,Female,42.0,6.0,0.0,2.0,1.0,1.0,69079.85
-24555,15665460.0,Yobachi,632.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,167032.49
-24556,15604792.0,Dickson,694.0,Spain,Male,25.0,6.0,0.0,2.0,1.0,0.0,131281.29
-24557,15629885.0,Watson,697.0,France,Female,36.0,6.0,135096.05,2.0,1.0,0.0,141822.5
-24558,15711889.0,Lo Duca,479.0,Germany,Male,60.0,0.0,114948.76,1.0,1.0,0.0,184954.11
-24559,15805671.0,Onochie,681.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,26019.59
-24560,15651737.0,Ch'en,656.0,Spain,Female,46.0,2.0,0.0,1.0,0.0,0.0,103557.93
-24561,15672894.0,Tuan,763.0,France,Female,39.0,7.0,0.0,1.0,1.0,0.0,182822.5
-24562,15583736.0,Okwuoma,584.0,Spain,Male,53.0,2.0,0.0,1.0,1.0,0.0,158973.77
-24563,15757867.0,Chukwudi,633.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,0.0,153036.97
-24564,15749365.0,Pinto,507.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,176984.34
-24565,15642996.0,Smith,556.0,Germany,Female,40.0,10.0,109196.66,2.0,1.0,1.0,45978.39
-24566,15702631.0,K?,644.0,France,Female,38.0,7.0,119496.88,2.0,1.0,1.0,152290.28
-24567,15644501.0,Nnamutaezinwa,710.0,Spain,Female,36.0,4.0,0.0,2.0,1.0,0.0,183487.98
-24568,15761763.0,Genovesi,573.0,Spain,Male,50.0,4.0,95927.04,1.0,1.0,1.0,149648.41
-24569,15640268.0,Echezonachukwu,631.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,153015.42
-24570,15663438.0,Nwokike,712.0,France,Female,46.0,3.0,0.0,2.0,1.0,0.0,167032.94
-24571,15808023.0,Pagnotto,688.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,143960.72
-24572,15781598.0,Lettiere,595.0,Germany,Male,31.0,3.0,152063.59,1.0,1.0,0.0,103557.85
-24573,15757911.0,Chiu,774.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,62944.75
-24574,15616709.0,Ch'ang,659.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,173952.5
-24575,15754929.0,T'ien,686.0,Germany,Male,33.0,6.0,122446.61,2.0,1.0,1.0,78854.57
-24576,15684516.0,Okwuoma,711.0,Germany,Female,51.0,9.0,130933.74,1.0,1.0,0.0,142377.69
-24577,15605447.0,Chukwudi,593.0,Spain,Female,27.0,7.0,0.0,2.0,1.0,0.0,143598.7
-24578,15660390.0,Y?an,678.0,France,Female,23.0,0.0,0.0,2.0,1.0,1.0,119647.6
-24579,15570192.0,Ugochukwu,571.0,France,Female,46.0,6.0,0.0,1.0,1.0,0.0,134417.31
-24580,15684512.0,Hsia,605.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,156618.35
-24581,15802351.0,Calabresi,605.0,Spain,Female,54.0,8.0,0.0,1.0,1.0,0.0,175310.78
-24582,15634262.0,Iweobiegbulam,685.0,Spain,Female,42.0,7.0,82344.84,1.0,0.0,0.0,68701.23
-24583,15694556.0,Goliwe,746.0,Germany,Female,47.0,2.0,121534.43,1.0,1.0,0.0,119087.25
-24584,15599131.0,Chuter,627.0,Germany,Female,49.0,5.0,135937.86,1.0,1.0,0.0,181208.47
-24585,15796787.0,Shih,718.0,France,Female,30.0,8.0,0.0,2.0,1.0,1.0,140937.91
-24586,15653572.0,Yobachi,705.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,106785.5
-24587,15709639.0,Nnamutaezinwa,624.0,France,Male,42.0,2.0,102962.62,1.0,1.0,1.0,51907.72
-24588,15596870.0,Onio,712.0,France,Male,27.0,3.0,99462.8,2.0,0.0,0.0,143953.96
-24589,15724453.0,Chibugo,742.0,France,Male,27.0,2.0,92674.97,1.0,0.0,1.0,56650.47
-24590,15569410.0,Chin,699.0,Germany,Female,32.0,5.0,121215.9,1.0,1.0,0.0,129964.94
-24591,15756056.0,O'Donnell,665.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,61961.57
-24592,15627485.0,Mancini,683.0,France,Male,41.0,8.0,0.0,1.0,1.0,1.0,121151.1
-24593,15691840.0,Obidimkpa,727.0,France,Male,45.0,7.0,0.0,2.0,0.0,1.0,136054.45
-24594,15679991.0,Nnamutaezinwa,721.0,Spain,Female,46.0,2.0,144591.96,1.0,1.0,1.0,176883.42
-24595,15631063.0,Ch'in,678.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,4459.5
-24596,15773876.0,Houghton,730.0,Spain,Female,53.0,9.0,0.0,1.0,1.0,0.0,120770.75
-24597,15583392.0,Nwora,581.0,France,Male,40.0,8.0,0.0,2.0,0.0,0.0,143683.75
-24598,15655590.0,Li,641.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,43821.56
-24599,15664772.0,Ritchie,746.0,Spain,Female,30.0,10.0,0.0,2.0,1.0,0.0,138849.08
-24600,15661380.0,Chukwumaobim,850.0,France,Male,47.0,9.0,0.0,2.0,1.0,0.0,154767.34
-24601,15682778.0,Hsiao,682.0,Germany,Female,41.0,4.0,161592.76,2.0,0.0,0.0,127528.86
-24602,15634281.0,Genovese,705.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,58855.85
-24603,15809777.0,Aksenov,685.0,France,Male,29.0,0.0,0.0,2.0,0.0,0.0,157871.55
-24604,15693732.0,Ponomaryova,684.0,Spain,Male,29.0,8.0,0.0,2.0,0.0,1.0,60552.44
-24605,15795895.0,Young,646.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,57905.05
-24606,15642725.0,Kenechukwu,656.0,France,Female,45.0,4.0,0.0,1.0,0.0,0.0,63795.8
-24607,15747097.0,L?,696.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,160594.85
-24608,15782551.0,K?,718.0,Germany,Male,37.0,7.0,123826.07,2.0,0.0,1.0,109523.88
-24609,15800845.0,Ni,707.0,Germany,Female,24.0,9.0,100116.67,1.0,1.0,0.0,129964.94
-24610,15704795.0,Yobanna,745.0,Spain,Male,30.0,1.0,0.0,2.0,1.0,1.0,60552.44
-24611,15798960.0,Kao,682.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,1.0,98894.64
-24612,15796071.0,Lucchesi,716.0,France,Female,46.0,4.0,0.0,1.0,1.0,1.0,88958.49
-24613,15674724.0,Yegorov,586.0,France,Male,32.0,10.0,108924.33,1.0,1.0,1.0,142400.27
-24614,15650864.0,Onyemachukwu,684.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,160529.71
-24615,15598884.0,H?,589.0,France,Male,32.0,6.0,0.0,1.0,1.0,1.0,127429.56
-24616,15704081.0,Manna,632.0,Germany,Female,46.0,1.0,124577.33,1.0,1.0,0.0,104889.2
-24617,15766417.0,De Luca,481.0,France,Male,33.0,5.0,82228.67,1.0,1.0,0.0,88592.8
-24618,15767064.0,Hsueh,738.0,Germany,Female,46.0,3.0,110527.17,1.0,1.0,0.0,92887.06
-24619,15746333.0,Y?,773.0,France,Male,24.0,1.0,0.0,1.0,0.0,1.0,78955.8
-24620,15638487.0,Ewing,755.0,Spain,Female,44.0,7.0,0.0,2.0,1.0,0.0,189428.75
-24621,15676835.0,Chukwumaobim,646.0,France,Male,46.0,7.0,0.0,1.0,1.0,1.0,156858.2
-24622,15673372.0,Nwankwo,589.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,169291.7
-24623,15781347.0,Nwankwo,693.0,Spain,Male,35.0,5.0,0.0,1.0,1.0,0.0,158071.69
-24624,15608541.0,Bellucci,778.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,177747.72
-24625,15705309.0,Kosisochukwu,658.0,France,Male,37.0,9.0,0.0,3.0,0.0,0.0,95551.36
-24626,15759717.0,Ch'ang,678.0,Germany,Female,39.0,9.0,122949.14,1.0,1.0,1.0,43486.73
-24627,15636562.0,Chukwujamuike,573.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,1.0,122268.33
-24628,15736126.0,Cremonesi,544.0,France,Male,35.0,10.0,0.0,1.0,1.0,0.0,88890.05
-24629,15672822.0,De Luca,667.0,Spain,Female,29.0,5.0,146766.72,1.0,1.0,1.0,143450.61
-24630,15688612.0,Ch'iu,684.0,Germany,Female,43.0,4.0,120599.21,2.0,1.0,0.0,5985.36
-24631,15578219.0,Tien,686.0,France,Female,41.0,9.0,0.0,2.0,1.0,1.0,66518.0
-24632,15681554.0,Hsiao,709.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,135841.35
-24633,15727408.0,Chu,682.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,162820.6
-24634,15656901.0,Ts'ai,729.0,France,Male,42.0,7.0,0.0,1.0,0.0,1.0,143665.44
-24635,15732448.0,Bovee,669.0,Germany,Male,29.0,7.0,94486.47,1.0,1.0,1.0,74870.99
-24636,15687094.0,Trevisano,681.0,Germany,Female,29.0,9.0,113657.83,1.0,0.0,1.0,60906.12
-24637,15643679.0,Okwudiliolisa,727.0,France,Male,45.0,9.0,0.0,2.0,1.0,0.0,100809.99
-24638,15583729.0,Chiu,691.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,123775.15
-24639,15805025.0,Yeh,633.0,France,Male,31.0,7.0,111284.53,1.0,1.0,1.0,57549.65
-24640,15606914.0,Walker,748.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,78899.22
-24641,15758449.0,Y?,730.0,Spain,Male,49.0,3.0,123826.07,1.0,1.0,0.0,151859.98
-24642,15643903.0,Genovese,637.0,France,Male,33.0,8.0,0.0,3.0,1.0,1.0,100127.71
-24643,15752650.0,H?,597.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,82565.01
-24644,15609735.0,Chinedum,669.0,Germany,Male,36.0,1.0,88902.21,1.0,1.0,1.0,123866.43
-24645,15751774.0,Palerma,646.0,France,Female,40.0,9.0,145974.79,1.0,0.0,1.0,84760.32
-24646,15642816.0,Nwabugwu,743.0,France,Female,40.0,5.0,0.0,1.0,0.0,0.0,56585.2
-24647,15704014.0,Chizuoke,639.0,Germany,Male,37.0,5.0,120530.13,1.0,1.0,1.0,119400.08
-24648,15690134.0,Folliero,521.0,France,Male,59.0,4.0,118680.57,1.0,1.0,1.0,162022.48
-24649,15756425.0,Nnaife,592.0,France,Female,28.0,6.0,0.0,2.0,1.0,0.0,142223.94
-24650,15727177.0,Chinwenma,633.0,Germany,Male,37.0,3.0,95984.69,2.0,0.0,1.0,161559.12
-24651,15791877.0,Ch'ien,676.0,Spain,Male,35.0,9.0,153711.57,1.0,0.0,1.0,183494.87
-24652,15656592.0,Okechukwu,554.0,France,Male,45.0,7.0,0.0,2.0,0.0,0.0,176903.35
-24653,15660608.0,Lo Duca,731.0,Germany,Male,35.0,10.0,118022.06,1.0,1.0,0.0,167538.66
-24654,15636105.0,Chien,569.0,Germany,Male,56.0,5.0,140418.81,1.0,0.0,0.0,124592.84
-24655,15778947.0,Onyemachukwu,619.0,Germany,Male,32.0,6.0,114027.7,2.0,1.0,1.0,91346.03
-24656,15792668.0,Chiedozie,667.0,France,Male,34.0,1.0,0.0,2.0,0.0,0.0,96615.23
-24657,15735784.0,Onyekachi,568.0,France,Male,49.0,4.0,0.0,1.0,0.0,0.0,53353.37
-24658,15698522.0,Lucciano,593.0,France,Female,42.0,2.0,71996.09,1.0,1.0,0.0,129333.69
-24659,15686983.0,Piazza,628.0,France,Male,29.0,3.0,0.0,2.0,0.0,1.0,158054.49
-24660,15616380.0,Horsley,679.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,141756.32
-24661,15808971.0,Fanucci,493.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,142513.5
-24662,15629117.0,Chukwuebuka,598.0,France,Female,54.0,10.0,0.0,1.0,0.0,1.0,48606.92
-24663,15782688.0,Yobanna,751.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,154844.22
-24664,15777892.0,Rizzo,668.0,France,Male,33.0,8.0,71264.02,1.0,0.0,0.0,88846.92
-24665,15657535.0,Ch'ang,600.0,France,Male,34.0,8.0,101486.96,2.0,1.0,1.0,62390.59
-24666,15717191.0,DeRose,729.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,126143.26
-24667,15695034.0,Chukwufumnanya,774.0,France,Female,32.0,5.0,132730.17,1.0,0.0,0.0,8991.17
-24668,15783859.0,Onwuamaeze,738.0,France,Female,55.0,4.0,0.0,1.0,1.0,1.0,81898.81
-24669,15570141.0,Yevdokimova,584.0,France,Male,41.0,7.0,0.0,1.0,1.0,0.0,140947.05
-24670,15651001.0,Nnonso,809.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,141364.27
-24671,15579112.0,Milano,635.0,France,Female,54.0,5.0,0.0,1.0,1.0,0.0,171045.65
-24672,15761158.0,Chinweuba,590.0,France,Male,30.0,8.0,0.0,1.0,0.0,1.0,92712.97
-24673,15714493.0,Nwachukwu,731.0,Spain,Male,27.0,9.0,0.0,2.0,1.0,0.0,112554.68
-24674,15592379.0,Onyekaozulu,581.0,Germany,Male,48.0,1.0,155973.88,2.0,1.0,1.0,109523.88
-24675,15711889.0,Mazzanti,697.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,161076.92
-24676,15788659.0,Chukwumaobim,678.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,0.0,146636.44
-24677,15585888.0,Nucci,610.0,Germany,Female,45.0,3.0,163894.35,1.0,0.0,0.0,27330.27
-24678,15746422.0,Trevisani,600.0,Germany,Male,60.0,7.0,101633.03,1.0,1.0,0.0,27468.72
-24679,15793723.0,Ebelegbulam,713.0,France,Male,52.0,3.0,0.0,2.0,1.0,1.0,51907.72
-24680,15652289.0,H?,739.0,Germany,Male,49.0,8.0,149940.04,1.0,1.0,0.0,115115.32
-24681,15777901.0,Mazzanti,638.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,53793.1
-24682,15705034.0,Eluemuno,663.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,127587.22
-24683,15663885.0,Ts'ai,678.0,France,Female,43.0,4.0,0.0,2.0,1.0,0.0,99398.36
-24684,15617348.0,Ch'ang,590.0,France,Female,51.0,6.0,0.0,1.0,1.0,0.0,129149.06
-24685,15646764.0,Cremonesi,718.0,France,Female,43.0,7.0,0.0,2.0,1.0,0.0,63736.17
-24686,15671610.0,Li Fonti,605.0,France,Male,48.0,3.0,0.0,2.0,0.0,1.0,116547.31
-24687,15804844.0,Fiorentini,748.0,Spain,Male,32.0,7.0,0.0,1.0,1.0,1.0,88838.14
-24688,15651581.0,Iadanza,479.0,France,Male,46.0,1.0,0.0,1.0,1.0,1.0,75825.57
-24689,15726791.0,Ch'in,579.0,France,Male,46.0,6.0,0.0,1.0,1.0,1.0,103934.22
-24690,15756901.0,Watson,709.0,France,Male,31.0,9.0,0.0,2.0,0.0,0.0,79723.78
-24691,15642098.0,Chiang,576.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,163550.58
-24692,15815660.0,Chiu,757.0,Spain,Female,50.0,3.0,171982.95,1.0,0.0,0.0,167973.63
-24693,15731553.0,Tuan,662.0,France,Male,42.0,8.0,121365.39,1.0,1.0,1.0,148087.88
-24694,15615184.0,Mazzanti,705.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,93360.35
-24695,15750141.0,Nwachukwu,758.0,France,Female,29.0,7.0,123870.07,1.0,1.0,1.0,84760.32
-24696,15603594.0,Ulyanov,663.0,Germany,Male,42.0,10.0,145984.92,1.0,1.0,1.0,174729.3
-24697,15773868.0,K?,748.0,Spain,Male,22.0,5.0,0.0,2.0,1.0,1.0,66586.56
-24698,15643523.0,T'ang,628.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,57553.02
-24699,15662483.0,Ebelechukwu,585.0,France,Female,44.0,7.0,136576.02,1.0,1.0,0.0,146056.29
-24700,15648766.0,Pethard,757.0,France,Female,31.0,2.0,178713.98,1.0,1.0,1.0,94440.45
-24701,15782899.0,Scott,695.0,France,Female,33.0,9.0,0.0,2.0,1.0,1.0,126812.63
-24702,15735295.0,Onwuamaeze,646.0,France,Male,35.0,7.0,0.0,1.0,1.0,1.0,95794.98
-24703,15727391.0,Ts'ui,622.0,France,Female,38.0,3.0,0.0,2.0,0.0,1.0,18082.06
-24704,15601594.0,Lucciano,615.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,174431.01
-24705,15696812.0,Onyekachukwu,711.0,Germany,Female,31.0,9.0,127763.25,1.0,1.0,0.0,172363.41
-24706,15658946.0,Ts'ui,651.0,Spain,Female,39.0,6.0,0.0,1.0,1.0,1.0,141031.08
-24707,15792993.0,Onuora,639.0,Germany,Male,63.0,8.0,114370.91,1.0,1.0,1.0,174738.2
-24708,15671534.0,Chibugo,498.0,Spain,Male,31.0,6.0,0.0,2.0,0.0,0.0,168544.85
-24709,15690939.0,Chiemenam,774.0,Germany,Male,29.0,4.0,122552.51,1.0,0.0,0.0,64443.75
-24710,15737104.0,Li,616.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,178667.7
-24711,15592761.0,Lo,748.0,Spain,Male,42.0,2.0,0.0,2.0,0.0,1.0,145796.49
-24712,15671779.0,Mancini,617.0,France,Female,46.0,3.0,174400.36,2.0,1.0,0.0,73334.43
-24713,15648967.0,Lucchesi,778.0,France,Male,37.0,5.0,0.0,1.0,1.0,1.0,72678.75
-24714,15623357.0,Lucciano,581.0,Spain,Male,38.0,2.0,0.0,1.0,0.0,1.0,159800.16
-24715,15626491.0,Bancroft,714.0,France,Male,42.0,2.0,0.0,2.0,0.0,0.0,143913.32
-24716,15661036.0,Hao,760.0,France,Female,62.0,8.0,165947.99,1.0,1.0,1.0,108876.75
-24717,15626485.0,Tsai,682.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,141533.19
-24718,15714240.0,Ch'eng,588.0,Spain,Male,26.0,7.0,0.0,2.0,0.0,1.0,139410.08
-24719,15716706.0,Smith,636.0,France,Female,37.0,9.0,0.0,1.0,1.0,1.0,145789.71
-24720,15772798.0,Clements,559.0,Germany,Female,34.0,8.0,124525.52,1.0,1.0,1.0,152843.53
-24721,15812356.0,Teng,705.0,Germany,Female,35.0,9.0,133467.77,1.0,0.0,0.0,145089.64
-24722,15726234.0,Nwachukwu,733.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,55893.37
-24723,15625080.0,Chukwudi,686.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,198193.75
-24724,15733476.0,Yermakova,565.0,France,Male,33.0,5.0,116537.6,1.0,0.0,0.0,142911.99
-24725,15585867.0,Ugoji,683.0,France,Female,44.0,9.0,0.0,1.0,1.0,1.0,130664.79
-24726,15624343.0,Chiu,706.0,France,Female,33.0,7.0,0.0,1.0,1.0,0.0,142963.29
-24727,15655274.0,Biryukov,714.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,147668.64
-24728,15571244.0,McIntyre,648.0,Germany,Female,48.0,3.0,133903.12,1.0,1.0,1.0,177852.57
-24729,15631162.0,Hs?,597.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,62449.07
-24730,15607560.0,T'ien,679.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,133051.64
-24731,15593949.0,Bancks,602.0,Spain,Male,35.0,4.0,96173.17,1.0,1.0,0.0,62002.04
-24732,15717995.0,Yobachi,545.0,Spain,Female,29.0,0.0,119919.12,1.0,1.0,1.0,110454.46
-24733,15758775.0,Johnstone,562.0,Spain,Male,23.0,1.0,156864.2,1.0,1.0,0.0,134593.23
-24734,15746664.0,Groom,582.0,France,Female,45.0,6.0,0.0,2.0,0.0,0.0,107875.5
-24735,15783025.0,Macleod,676.0,France,Female,24.0,7.0,0.0,2.0,1.0,1.0,119927.81
-24736,15694946.0,Nnaife,598.0,France,Female,32.0,8.0,0.0,1.0,0.0,0.0,120512.09
-24737,15806945.0,Onio,741.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,1.0,36108.5
-24738,15585580.0,Iweobiegbunam,594.0,France,Male,36.0,1.0,0.0,1.0,1.0,0.0,184524.74
-24739,15759387.0,White,712.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,95009.6
-24740,15745375.0,Ebelegbulam,724.0,France,Female,31.0,8.0,0.0,1.0,1.0,0.0,183872.88
-24741,15651943.0,Y?,652.0,Germany,Female,38.0,3.0,144895.05,2.0,0.0,1.0,128875.86
-24742,15747174.0,Chukwumaobim,757.0,France,Male,47.0,3.0,0.0,1.0,0.0,1.0,126477.41
-24743,15756875.0,Onwuamaeze,648.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,67769.94
-24744,15602841.0,Manna,748.0,Germany,Male,32.0,1.0,102800.65,1.0,1.0,1.0,131283.6
-24745,15696900.0,McNeill,639.0,France,Male,31.0,5.0,0.0,1.0,1.0,0.0,118959.64
-24746,15658890.0,L?,718.0,Germany,Female,24.0,7.0,123861.5,2.0,1.0,1.0,92865.92
-24747,15730038.0,Ch'eng,774.0,France,Male,55.0,9.0,0.0,2.0,1.0,1.0,115800.1
-24748,15702968.0,Lo Duca,651.0,Germany,Female,42.0,6.0,125724.95,1.0,1.0,1.0,125909.95
-24749,15639368.0,Mancini,628.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,88728.47
-24750,15589401.0,Ch'in,713.0,France,Male,39.0,7.0,96659.64,1.0,1.0,0.0,129870.93
-24751,15591036.0,Chukwuemeka,641.0,Spain,Male,25.0,6.0,90218.9,1.0,1.0,0.0,92984.78
-24752,15795586.0,Ch'iu,663.0,France,Female,34.0,8.0,0.0,2.0,0.0,1.0,67840.81
-24753,15769312.0,Pisani,615.0,France,Female,43.0,3.0,0.0,2.0,1.0,0.0,141364.62
-24754,15616152.0,Chukwudi,748.0,Germany,Female,42.0,4.0,97960.03,2.0,0.0,0.0,132979.43
-24755,15677288.0,Nkemdirim,577.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,49831.7
-24756,15782360.0,Olisaemeka,682.0,France,Male,23.0,7.0,0.0,2.0,1.0,1.0,106977.53
-24757,15702380.0,Genovese,663.0,France,Male,50.0,8.0,0.0,2.0,1.0,0.0,181177.9
-24758,15752312.0,Folliero,676.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,158580.05
-24759,15610972.0,Smith,714.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,1.0,10963.04
-24760,15795519.0,Hsiao,545.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,1.0,171070.44
-24761,15693817.0,Y?an,682.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,185561.89
-24762,15593973.0,Oluchi,697.0,Spain,Male,39.0,5.0,0.0,1.0,1.0,0.0,172668.39
-24763,15798532.0,Chukwuemeka,573.0,France,Female,45.0,2.0,0.0,2.0,1.0,1.0,88890.05
-24764,15587913.0,Ojiofor,595.0,France,Male,37.0,2.0,0.0,2.0,0.0,1.0,92357.21
-24765,15570912.0,Onyekachukwu,662.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,101728.46
-24766,15674840.0,Ch'ang,569.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,115950.96
-24767,15787196.0,Genovese,691.0,France,Male,34.0,3.0,97338.19,1.0,0.0,0.0,107825.08
-24768,15757911.0,Onio,694.0,Germany,Male,22.0,2.0,101579.28,2.0,1.0,0.0,76640.29
-24769,15652626.0,Yefremova,583.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,102681.32
-24770,15654489.0,Trevisani,707.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,71420.16
-24771,15653715.0,Hazon,571.0,France,Male,34.0,2.0,0.0,2.0,0.0,1.0,169920.92
-24772,15777045.0,Kolesnikov,645.0,France,Female,27.0,7.0,0.0,1.0,1.0,1.0,151822.61
-24773,15701166.0,Iloerika,705.0,France,Male,29.0,5.0,127649.64,1.0,1.0,0.0,47675.36
-24774,15598883.0,Rizzo,684.0,Germany,Female,27.0,3.0,135553.5,1.0,1.0,1.0,151902.78
-24775,15664503.0,Hs?,579.0,France,Male,31.0,3.0,0.0,2.0,0.0,1.0,134790.74
-24776,15760865.0,Iadanza,589.0,Germany,Male,33.0,9.0,107467.56,1.0,1.0,0.0,52319.26
-24777,15598046.0,T'ang,705.0,Germany,Female,33.0,2.0,121541.56,2.0,1.0,1.0,162668.33
-24778,15775891.0,Hsing,558.0,Germany,Female,40.0,5.0,131834.8,1.0,1.0,0.0,163746.09
-24779,15785541.0,Ch'in,752.0,Spain,Female,35.0,1.0,0.0,2.0,1.0,0.0,153080.4
-24780,15705512.0,Tsao,681.0,France,Female,34.0,9.0,0.0,2.0,0.0,0.0,82870.75
-24781,15576928.0,Ts'ui,679.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,93716.38
-24782,15730059.0,H?,439.0,Germany,Female,44.0,3.0,135071.12,1.0,0.0,0.0,177480.54
-24783,15625759.0,Teng,562.0,France,Male,39.0,7.0,137262.71,1.0,0.0,0.0,94949.84
-24784,15759140.0,Onyemachukwu,709.0,Germany,Female,37.0,9.0,116711.06,1.0,0.0,1.0,131710.59
-24785,15652266.0,Trevisani,731.0,France,Female,48.0,3.0,0.0,2.0,1.0,0.0,66462.79
-24786,15739248.0,Yermolayev,681.0,Germany,Male,29.0,5.0,119782.72,1.0,0.0,0.0,97628.08
-24787,15801277.0,Y?an,631.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,90928.02
-24788,15597945.0,Obidimkpa,652.0,Spain,Female,46.0,8.0,0.0,1.0,1.0,1.0,122221.71
-24789,15687946.0,Tsai,632.0,Germany,Female,32.0,6.0,160566.11,2.0,1.0,0.0,77469.38
-24790,15658057.0,Pirozzi,710.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,1.0,65816.53
-24791,15609083.0,Pinto,596.0,Spain,Female,28.0,6.0,0.0,1.0,1.0,0.0,12905.89
-24792,15703778.0,Ting,585.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,98309.27
-24793,15662483.0,Hsueh,559.0,Germany,Male,48.0,7.0,131765.72,2.0,1.0,0.0,125986.18
-24794,15796414.0,Onyeorulu,590.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,79278.22
-24795,15756472.0,Hsiao,661.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,96961.0
-24796,15593501.0,Ch'ien,730.0,Germany,Female,58.0,9.0,111096.91,1.0,0.0,0.0,43486.73
-24797,15706464.0,Onyemauchechukwu,605.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,1.0,73066.72
-24798,15697546.0,Hsueh,674.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,186612.47
-24799,15806901.0,Lo,593.0,Spain,Female,42.0,1.0,0.0,2.0,1.0,1.0,138526.96
-24800,15598883.0,Hsieh,710.0,Germany,Male,40.0,8.0,127772.1,2.0,1.0,1.0,103557.85
-24801,15746076.0,Chukwudi,651.0,Spain,Female,27.0,2.0,0.0,2.0,1.0,1.0,109626.18
-24802,15782598.0,Palerma,590.0,Spain,Female,41.0,8.0,0.0,1.0,1.0,0.0,158918.02
-24803,15781702.0,Ch'in,699.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,1.0,166495.2
-24804,15761348.0,Chigozie,748.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,26752.56
-24805,15691387.0,Aitken,773.0,France,Male,29.0,9.0,0.0,1.0,1.0,1.0,57896.27
-24806,15604551.0,Ch'en,748.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,122949.71
-24807,15747222.0,Babbage,676.0,Germany,Female,27.0,9.0,140576.11,2.0,0.0,0.0,90591.15
-24808,15697480.0,Chia,635.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,173576.71
-24809,15589475.0,Ts'ui,544.0,Germany,Female,31.0,3.0,101470.29,1.0,1.0,0.0,43496.36
-24810,15727281.0,Wang,493.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,164064.44
-24811,15642098.0,Lombardo,559.0,France,Female,28.0,0.0,0.0,2.0,0.0,0.0,84927.42
-24812,15677184.0,Fan,706.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,107839.03
-24813,15734886.0,Iqbal,521.0,Spain,Female,37.0,7.0,132005.77,1.0,1.0,0.0,120830.73
-24814,15631756.0,Nwokeocha,571.0,France,Male,33.0,5.0,0.0,1.0,1.0,1.0,73881.68
-24815,15763515.0,Y?an,661.0,Spain,Male,36.0,3.0,0.0,2.0,0.0,1.0,79495.01
-24816,15577771.0,Trevisano,598.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,52301.15
-24817,15759034.0,Mazzanti,757.0,France,Male,42.0,4.0,0.0,2.0,0.0,0.0,62851.6
-24818,15678412.0,Tuan,652.0,Germany,Male,33.0,8.0,122384.22,2.0,1.0,1.0,133059.15
-24819,15803790.0,Ni,593.0,France,Female,44.0,8.0,57784.22,1.0,1.0,1.0,137028.51
-24820,15666055.0,McIntyre,586.0,Spain,Male,48.0,3.0,117153.0,1.0,1.0,0.0,112973.23
-24821,15809847.0,Efremov,531.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,95611.47
-24822,15716609.0,Scott,715.0,France,Male,67.0,3.0,0.0,2.0,1.0,1.0,95583.45
-24823,15584533.0,K?,773.0,France,Male,34.0,5.0,0.0,1.0,1.0,1.0,147090.9
-24824,15759480.0,Chikelu,587.0,Germany,Male,33.0,7.0,141078.57,1.0,1.0,1.0,112979.6
-24825,15676895.0,Kline,581.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,96429.29
-24826,15750447.0,Nwebube,588.0,France,Female,40.0,9.0,0.0,1.0,1.0,0.0,180134.88
-24827,15737173.0,Chu,678.0,France,Male,33.0,6.0,0.0,2.0,0.0,0.0,149599.71
-24828,15625759.0,Obialo,628.0,Germany,Male,38.0,2.0,150777.2,2.0,1.0,1.0,52709.55
-24829,15609562.0,Chialuka,596.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,107499.8
-24830,15592761.0,Chukwuebuka,535.0,France,Female,34.0,9.0,0.0,1.0,1.0,0.0,171706.66
-24831,15752694.0,Onio,614.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,1.0,183487.98
-24832,15713374.0,Chukwuemeka,709.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,109626.13
-24833,15602841.0,K'ung,743.0,France,Male,34.0,1.0,0.0,1.0,0.0,1.0,124626.83
-24834,15658935.0,Onwumelu,579.0,France,Male,36.0,8.0,131406.56,1.0,1.0,1.0,158071.69
-24835,15574783.0,Ugochukwu,718.0,France,Male,47.0,2.0,0.0,2.0,1.0,1.0,141453.62
-24836,15628674.0,Trevisani,594.0,France,Female,36.0,7.0,127543.11,1.0,1.0,1.0,94719.66
-24837,15665629.0,Pirozzi,469.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,72350.17
-24838,15629078.0,Y?,597.0,Spain,Male,46.0,4.0,0.0,1.0,1.0,1.0,153400.24
-24839,15682868.0,Lucciano,568.0,Germany,Male,38.0,5.0,113899.92,1.0,0.0,1.0,199505.53
-24840,15588535.0,Ch'en,706.0,France,Female,55.0,5.0,136525.98,1.0,0.0,0.0,93777.53
-24841,15787884.0,Ch'ang,781.0,Germany,Male,58.0,4.0,84474.62,1.0,1.0,0.0,108675.01
-24842,15779059.0,Hsieh,586.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,121866.87
-24843,15689786.0,Chukwubuikem,753.0,Germany,Male,28.0,9.0,142388.61,1.0,1.0,0.0,38596.34
-24844,15793070.0,Smith,727.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,171345.02
-24845,15574886.0,Ch'iu,512.0,France,Male,26.0,5.0,0.0,2.0,0.0,1.0,107815.31
-24846,15666095.0,L?,564.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,0.0,154486.01
-24847,15648725.0,Hsieh,589.0,France,Male,52.0,6.0,0.0,1.0,1.0,1.0,121365.45
-24848,15617603.0,Ch'ien,581.0,Spain,Female,34.0,3.0,0.0,2.0,0.0,1.0,97734.3
-24849,15717489.0,Hs?eh,551.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,170518.17
-24850,15596962.0,Genovese,712.0,France,Female,30.0,2.0,0.0,2.0,0.0,0.0,93478.96
-24851,15793491.0,Onyemauchechi,718.0,Germany,Female,40.0,5.0,105736.32,1.0,1.0,1.0,180622.44
-24852,15589428.0,McIntosh,667.0,Germany,Male,37.0,10.0,139859.54,1.0,1.0,1.0,118959.64
-24853,15690829.0,Nnamdi,678.0,Spain,Male,42.0,2.0,0.0,2.0,1.0,1.0,139911.58
-24854,15604782.0,Chiu,619.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,1.0,121789.3
-24855,15711889.0,Chiazagomekpere,634.0,France,Female,54.0,4.0,0.0,1.0,1.0,0.0,1375.46
-24856,15750547.0,Yobachukwu,654.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,95655.16
-24857,15643330.0,Chijindum,678.0,Spain,Male,40.0,9.0,0.0,2.0,1.0,1.0,141454.79
-24858,15676835.0,Y?an,521.0,Spain,Male,28.0,5.0,0.0,1.0,1.0,1.0,77550.18
-24859,15813067.0,T'ang,717.0,France,Female,33.0,2.0,122314.5,1.0,1.0,0.0,143301.49
-24860,15697360.0,Walker,646.0,Germany,Male,38.0,3.0,141078.37,1.0,1.0,1.0,143973.38
-24861,15795519.0,Udobata,589.0,Spain,Male,41.0,5.0,131394.39,1.0,1.0,0.0,169920.92
-24862,15593601.0,Ch'iu,703.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,1.0,171707.93
-24863,15605698.0,Hsiung,667.0,Germany,Female,41.0,5.0,139874.79,1.0,1.0,1.0,54290.62
-24864,15800366.0,Milne,595.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,119374.74
-24865,15658889.0,McNeil,615.0,France,Male,38.0,9.0,0.0,1.0,1.0,0.0,126517.46
-24866,15569248.0,Genovese,611.0,Spain,Male,55.0,3.0,0.0,3.0,1.0,0.0,155700.15
-24867,15793881.0,Ch'in,605.0,Germany,Male,51.0,2.0,91285.17,1.0,1.0,1.0,104353.26
-24868,15703588.0,Ch'ien,684.0,France,Female,31.0,2.0,185513.67,1.0,1.0,1.0,153662.88
-24869,15679048.0,Chukwumaobim,584.0,France,Male,46.0,5.0,0.0,2.0,0.0,1.0,60920.03
-24870,15673367.0,Chidi,671.0,Germany,Male,52.0,6.0,161598.98,1.0,1.0,1.0,44756.25
-24871,15709474.0,Nwachinemelu,682.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,55775.84
-24872,15793434.0,Yobanna,706.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,96444.88
-24873,15705082.0,Lu,762.0,Spain,Male,38.0,0.0,0.0,2.0,0.0,0.0,140763.17
-24874,15588580.0,Chukwualuka,744.0,France,Male,27.0,2.0,0.0,1.0,1.0,0.0,160532.88
-24875,15670562.0,Ch'ien,711.0,France,Male,48.0,9.0,0.0,2.0,1.0,1.0,96823.32
-24876,15673971.0,Nnachetam,589.0,Spain,Male,37.0,6.0,178058.06,1.0,0.0,0.0,171770.58
-24877,15641114.0,De Salis,749.0,Spain,Male,47.0,0.0,0.0,2.0,0.0,0.0,92621.9
-24878,15749905.0,Obialo,669.0,Germany,Male,28.0,2.0,74596.15,2.0,0.0,0.0,3560.0
-24879,15592773.0,Hsiao,589.0,Germany,Male,25.0,6.0,123871.3,1.0,0.0,0.0,169920.92
-24880,15693376.0,Yeh,546.0,Spain,Male,58.0,1.0,0.0,2.0,1.0,1.0,111605.9
-24881,15802060.0,Hsia,710.0,Germany,Female,42.0,5.0,153611.83,2.0,1.0,1.0,163587.9
-24882,15787830.0,Cattaneo,806.0,Germany,Female,36.0,1.0,107569.93,2.0,1.0,1.0,93629.66
-24883,15698028.0,Lai,583.0,Germany,Female,41.0,2.0,120721.52,1.0,1.0,0.0,176450.19
-24884,15655007.0,Chien,537.0,France,Male,34.0,6.0,0.0,1.0,1.0,0.0,79734.23
-24885,15582910.0,Trentino,616.0,Germany,Male,37.0,7.0,128966.13,1.0,1.0,1.0,70810.8
-24886,15669987.0,Li Fonti,763.0,France,Female,27.0,4.0,0.0,1.0,0.0,1.0,86044.98
-24887,15680804.0,Palerma,649.0,France,Female,26.0,7.0,0.0,2.0,1.0,0.0,103570.8
-24888,15571586.0,Rios,645.0,France,Female,32.0,1.0,0.0,2.0,0.0,0.0,179634.69
-24889,15796721.0,Lo,594.0,France,Female,49.0,8.0,0.0,1.0,1.0,1.0,175843.68
-24890,15626452.0,Hsing,663.0,France,Female,40.0,3.0,0.0,2.0,0.0,1.0,121592.5
-24891,15752312.0,Chidiebere,639.0,Germany,Male,39.0,0.0,102290.81,2.0,1.0,1.0,177614.17
-24892,15626983.0,Udegbulam,632.0,France,Female,35.0,5.0,0.0,2.0,0.0,1.0,155711.64
-24893,15681476.0,Efremov,710.0,France,Female,39.0,7.0,155047.56,1.0,1.0,0.0,166588.76
-24894,15594796.0,Fancher,678.0,Spain,Male,35.0,9.0,123070.89,1.0,0.0,1.0,141872.05
-24895,15596671.0,Tien,683.0,Germany,Female,57.0,3.0,136948.09,1.0,1.0,0.0,73679.29
-24896,15585867.0,Chibueze,648.0,Germany,Female,58.0,4.0,122570.69,1.0,1.0,0.0,83093.25
-24897,15633574.0,Maclean,526.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,82565.01
-24898,15691543.0,Shih,667.0,Germany,Female,51.0,9.0,138341.51,1.0,0.0,0.0,100433.83
-24899,15750765.0,Nkemdilim,633.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,108008.65
-24900,15803790.0,Chimezie,694.0,France,Female,42.0,2.0,0.0,2.0,1.0,0.0,117832.88
-24901,15815552.0,Iadanza,624.0,France,Male,33.0,7.0,141819.02,1.0,1.0,1.0,60721.4
-24902,15720644.0,Ch'ang,628.0,France,Male,44.0,3.0,0.0,1.0,0.0,1.0,135077.01
-24903,15643496.0,Y?,645.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,16081.62
-24904,15569430.0,Brient,752.0,France,Male,31.0,2.0,140187.1,1.0,1.0,1.0,94888.6
-24905,15579915.0,Chidimma,599.0,Germany,Male,51.0,3.0,105473.74,1.0,1.0,1.0,72496.05
-24906,15716609.0,Chiu,540.0,France,Male,38.0,6.0,123866.22,1.0,1.0,0.0,88721.84
-24907,15684364.0,T'an,603.0,Germany,Male,45.0,9.0,125879.73,1.0,1.0,1.0,134600.94
-24908,15809654.0,Chukwuemeka,561.0,Spain,Male,47.0,1.0,104508.1,1.0,1.0,1.0,88721.84
-24909,15793883.0,Chiu,587.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,55313.51
-24910,15579969.0,Lucchese,633.0,France,Male,31.0,2.0,0.0,2.0,0.0,0.0,181224.24
-24911,15791040.0,Nwankwo,756.0,France,Female,30.0,7.0,0.0,2.0,1.0,1.0,73679.59
-24912,15738487.0,Manna,583.0,France,Male,39.0,5.0,120911.75,1.0,0.0,1.0,176450.19
-24913,15637354.0,Tikhonov,751.0,Spain,Male,42.0,2.0,0.0,2.0,1.0,1.0,172363.52
-24914,15790594.0,Ni,562.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,132673.23
-24915,15650437.0,Pinto,656.0,France,Male,74.0,5.0,0.0,2.0,1.0,1.0,187985.85
-24916,15734461.0,Onio,524.0,Germany,Female,55.0,9.0,140197.71,1.0,1.0,1.0,118522.73
-24917,15659710.0,Smith,526.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,53581.19
-24918,15585853.0,O'Donnell,632.0,Spain,Male,54.0,9.0,0.0,1.0,1.0,1.0,131173.9
-24919,15575060.0,Y?,638.0,France,Female,30.0,5.0,0.0,2.0,1.0,1.0,171706.66
-24920,15742971.0,Palerma,694.0,Germany,Male,34.0,7.0,123974.96,1.0,0.0,1.0,176713.47
-24921,15590177.0,Hsia,580.0,France,Female,45.0,5.0,0.0,1.0,1.0,1.0,9087.81
-24922,15676937.0,Nwachukwu,596.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,71926.28
-24923,15601383.0,Ch'iu,607.0,France,Male,28.0,8.0,0.0,1.0,1.0,1.0,111241.37
-24924,15697997.0,Frolov,712.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,64907.48
-24925,15688865.0,Yegorova,602.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,38315.04
-24926,15705313.0,Ch'en,679.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,89048.46
-24927,15771535.0,Macfarlan,733.0,Spain,Male,40.0,5.0,115378.94,1.0,0.0,1.0,87537.32
-24928,15574886.0,Okwudilichukwu,535.0,Spain,Male,46.0,8.0,0.0,2.0,1.0,1.0,105898.79
-24929,15702300.0,Chidozie,607.0,France,Female,26.0,1.0,120867.18,1.0,0.0,0.0,177737.03
-24930,15592078.0,Onyekaozulu,627.0,Germany,Female,40.0,3.0,174852.89,1.0,1.0,1.0,52796.31
-24931,15682355.0,Hixson,813.0,Spain,Female,50.0,7.0,0.0,1.0,1.0,0.0,148087.62
-24932,15700696.0,Pirozzi,595.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,127875.1
-24933,15746995.0,Pagnotto,743.0,France,Female,43.0,0.0,0.0,2.0,1.0,1.0,164038.07
-24934,15767757.0,Chuang,567.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,122588.33
-24935,15644765.0,Zetticci,642.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,137932.99
-24936,15611961.0,Udinese,710.0,France,Male,39.0,5.0,98431.37,1.0,0.0,0.0,60533.96
-24937,15751549.0,Chukwufumnanya,704.0,Germany,Female,45.0,4.0,144889.18,1.0,0.0,0.0,66463.62
-24938,15709621.0,Lo,749.0,Germany,Female,57.0,8.0,101514.89,1.0,1.0,0.0,49872.33
-24939,15696054.0,Ts'ui,678.0,France,Male,36.0,3.0,132287.92,1.0,1.0,0.0,88837.11
-24940,15794870.0,Chukwufumnanya,506.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,105965.05
-24941,15581036.0,Whitehead,706.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,78854.58
-24942,15798916.0,Nwachinemelu,730.0,France,Male,30.0,5.0,0.0,2.0,1.0,0.0,167636.15
-24943,15628205.0,Ch'ang,738.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,87067.73
-24944,15593973.0,White,727.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,71830.1
-24945,15573326.0,Chiemenam,721.0,France,Female,32.0,3.0,0.0,2.0,1.0,1.0,88992.05
-24946,15772363.0,Toscani,695.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,133535.29
-24947,15702801.0,Smith,544.0,France,Male,34.0,3.0,141806.0,1.0,0.0,1.0,164074.89
-24948,15806922.0,Lu,651.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,0.0,149946.43
-24949,15771750.0,Tien,632.0,France,Female,36.0,1.0,0.0,2.0,1.0,1.0,123587.93
-24950,15665416.0,Toscani,653.0,France,Male,63.0,10.0,0.0,2.0,1.0,1.0,126082.29
-24951,15612771.0,Mollison,757.0,France,Male,43.0,2.0,0.0,2.0,0.0,0.0,62347.71
-24952,15628007.0,T'ien,569.0,Spain,Male,28.0,3.0,153058.64,1.0,1.0,0.0,119400.08
-24953,15571747.0,K?,645.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,173683.0
-24954,15677020.0,Ijendu,619.0,Spain,Male,36.0,9.0,115576.44,1.0,1.0,0.0,151902.78
-24955,15641640.0,Chibugo,731.0,France,Female,46.0,4.0,0.0,2.0,1.0,1.0,183572.39
-24956,15756070.0,Mironova,535.0,France,Female,30.0,0.0,135969.26,1.0,0.0,1.0,88890.05
-24957,15771086.0,K'ung,598.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,84569.13
-24958,15777586.0,Hsiao,707.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,1.0,139431.0
-24959,15622454.0,Chukwubuikem,749.0,Spain,Male,35.0,8.0,0.0,1.0,0.0,0.0,136812.02
-24960,15610403.0,Macnamara,521.0,Spain,Female,39.0,3.0,0.0,1.0,0.0,0.0,143681.85
-24961,15711446.0,Hsueh,718.0,France,Male,30.0,7.0,98640.01,1.0,0.0,1.0,116442.42
-24962,15694518.0,P'eng,579.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,113645.4
-24963,15661526.0,Ting,641.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,93718.16
-24964,15596972.0,Macdonald,687.0,France,Male,36.0,6.0,0.0,2.0,0.0,1.0,141400.51
-24965,15739576.0,Ch'en,559.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,123613.81
-24966,15782630.0,Ch'ien,548.0,France,Female,43.0,5.0,0.0,1.0,1.0,0.0,145593.85
-24967,15682048.0,O'Kane,707.0,Spain,Female,28.0,2.0,0.0,2.0,1.0,1.0,160523.33
-24968,15626383.0,P'eng,641.0,Spain,Female,32.0,6.0,107418.27,1.0,1.0,1.0,86981.45
-24969,15704770.0,Niu,598.0,France,Female,42.0,2.0,0.0,1.0,1.0,0.0,102925.76
-24970,15726250.0,Chukwudi,643.0,Germany,Female,43.0,9.0,117469.91,1.0,1.0,0.0,96473.5
-24971,15754926.0,Chibugo,558.0,Germany,Male,38.0,9.0,126473.99,2.0,1.0,1.0,84760.7
-24972,15659234.0,Hsia,587.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,128882.66
-24973,15579994.0,Chioke,588.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,186489.95
-24974,15682355.0,Tsai,582.0,France,Male,41.0,8.0,0.0,2.0,0.0,1.0,159418.1
-24975,15721207.0,Chidiebere,633.0,France,Male,40.0,10.0,0.0,1.0,0.0,1.0,151375.24
-24976,15600724.0,Aksenov,684.0,France,Female,39.0,3.0,0.0,1.0,1.0,1.0,130132.41
-24977,15648067.0,Chibueze,568.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,121894.31
-24978,15583576.0,Kao,753.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,9984.4
-24979,15634792.0,T'ang,708.0,Germany,Male,37.0,0.0,140720.93,1.0,1.0,0.0,117060.2
-24980,15648725.0,Chia,671.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,99800.87
-24981,15708710.0,Lo,599.0,France,Female,40.0,5.0,116922.92,1.0,1.0,0.0,46632.87
-24982,15762799.0,Baresi,683.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,71405.17
-24983,15747772.0,Chidubem,592.0,Spain,Male,37.0,10.0,0.0,2.0,1.0,0.0,79498.02
-24984,15589296.0,Liao,678.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,134420.75
-24985,15693526.0,Maclean,559.0,France,Male,44.0,9.0,103914.17,1.0,0.0,0.0,137028.51
-24986,15603764.0,Gboliwe,628.0,Germany,Female,46.0,8.0,131659.57,1.0,0.0,0.0,171770.58
-24987,15773098.0,Tsao,605.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,93336.3
-24988,15738785.0,Fanucci,775.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,177747.02
-24989,15781574.0,Hs?,683.0,Germany,Female,41.0,3.0,131354.25,1.0,1.0,0.0,153955.38
-24990,15583568.0,Kao,675.0,France,Female,51.0,3.0,114543.26,1.0,0.0,0.0,142903.44
-24991,15809100.0,Chiang,682.0,Germany,Male,37.0,10.0,76495.04,1.0,0.0,0.0,130638.53
-24992,15779985.0,Houghton,598.0,France,Male,34.0,1.0,0.0,2.0,1.0,1.0,168816.22
-24993,15759499.0,L?,689.0,France,Female,61.0,9.0,0.0,2.0,1.0,1.0,96429.29
-24994,15737542.0,Ts'ui,729.0,Germany,Female,47.0,7.0,90560.48,3.0,1.0,0.0,123663.63
-24995,15642997.0,Chukwumaobim,639.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,1.0,49637.65
-24996,15739271.0,Clements,678.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,1.0,142513.5
-24997,15756743.0,Chidiebere,774.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,4861.72
-24998,15680167.0,Yermakova,595.0,France,Male,38.0,6.0,144875.79,1.0,1.0,0.0,126469.09
-24999,15662901.0,Ting,660.0,France,Male,30.0,3.0,0.0,1.0,1.0,0.0,105784.85
diff --git a/assets/BCCP/train.csv b/assets/BCCP/train.csv
deleted file mode 100644
index b410559..0000000
--- a/assets/BCCP/train.csv
+++ /dev/null
@@ -1,15001 +0,0 @@
-id,CustomerId,Surname,CreditScore,Geography,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Exited
-0,15779985.0,Nwankwo,678.0,France,Male,29.0,4.0,0.0,3.0,1.0,0.0,180626.36,0.0
-1,15650086.0,Ch'in,687.0,France,Female,34.0,1.0,0.0,2.0,0.0,1.0,63736.17,0.0
-2,15733602.0,Thompson,682.0,France,Female,52.0,6.0,0.0,3.0,0.0,0.0,179655.87,1.0
-3,15645794.0,Macleod,753.0,Germany,Male,44.0,6.0,83347.25,2.0,1.0,0.0,161407.48,0.0
-4,15633840.0,Hsia,544.0,Germany,Female,55.0,0.0,107747.57,1.0,1.0,0.0,176580.86,1.0
-5,15781598.0,Hsieh,602.0,Spain,Male,33.0,1.0,120599.38,1.0,0.0,1.0,72950.14,0.0
-6,15763093.0,Kao,739.0,France,Female,36.0,1.0,0.0,1.0,1.0,1.0,134626.3,0.0
-7,15631715.0,Hargreaves,763.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,0.0,19283.14,0.0
-8,15632275.0,Hs?,620.0,Germany,Female,56.0,4.0,113743.37,1.0,1.0,0.0,121440.8,1.0
-9,15577598.0,Chukwuemeka,525.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,160670.58,0.0
-10,15809302.0,Hsueh,667.0,Germany,Male,41.0,5.0,106116.56,2.0,1.0,0.0,69676.55,0.0
-11,15639357.0,P'an,711.0,France,Female,71.0,8.0,0.0,1.0,1.0,0.0,176924.21,1.0
-12,15598088.0,Nwachukwu,649.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,173498.45,0.0
-13,15622083.0,Lucciano,644.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,166678.28,0.0
-14,15576676.0,Ugochukwutubelum,645.0,Spain,Female,22.0,5.0,0.0,2.0,1.0,1.0,35644.79,0.0
-15,15667460.0,Akobundu,731.0,Germany,Female,23.0,3.0,130788.81,2.0,1.0,1.0,105720.09,0.0
-16,15702095.0,Pagnotto,814.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,28707.69,0.0
-17,15806049.0,Ku,628.0,Germany,Female,41.0,2.0,115532.52,1.0,0.0,0.0,77965.67,1.0
-18,15594862.0,Hs?,685.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,0.0,171378.77,0.0
-19,15756901.0,Lombardi,635.0,France,Female,46.0,6.0,0.0,3.0,1.0,0.0,143683.75,1.0
-20,15584899.0,H?,712.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,136837.97,0.0
-21,15769499.0,Onochie,716.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,62944.75,0.0
-22,15803801.0,Kao,663.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,54372.37,0.0
-23,15654574.0,Tuan,659.0,France,Female,34.0,3.0,0.0,2.0,0.0,1.0,147553.06,0.0
-24,15787204.0,Chukwudi,668.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,114522.68,0.0
-25,15777922.0,Ch'ang,644.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,175749.36,0.0
-26,15715117.0,McLean,769.0,France,Male,55.0,6.0,76915.4,1.0,1.0,1.0,123906.19,1.0
-27,15802351.0,Teng,694.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,101491.23,0.0
-28,15777045.0,Nnachetam,667.0,Spain,Male,30.0,5.0,124576.65,1.0,1.0,0.0,120145.65,0.0
-29,15701116.0,P'an,745.0,France,Male,29.0,6.0,0.0,1.0,1.0,1.0,145792.01,0.0
-30,15681332.0,Aksakova,529.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,108764.69,0.0
-31,15733429.0,Zetticci,592.0,France,Female,48.0,4.0,0.0,1.0,0.0,1.0,100137.5,1.0
-32,15708193.0,Ndubuagha,593.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,103921.43,0.0
-33,15679963.0,Mazzanti,628.0,France,Female,50.0,2.0,0.0,3.0,1.0,0.0,106799.29,1.0
-34,15796370.0,Onyemachukwu,705.0,France,Female,35.0,1.0,134692.3,1.0,0.0,1.0,177783.04,0.0
-35,15684960.0,Genovese,642.0,France,Female,35.0,2.0,0.0,3.0,1.0,1.0,171413.66,1.0
-36,15695097.0,Ejimofor,633.0,France,Female,39.0,1.0,0.0,1.0,1.0,1.0,81299.17,0.0
-37,15778838.0,Mamelu,684.0,France,Male,28.0,7.0,141541.25,1.0,1.0,0.0,64831.36,0.0
-38,15651369.0,Ch'ien,549.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,175870.59,0.0
-39,15659962.0,Ts'ao,812.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,158437.73,0.0
-40,15652693.0,Chukwufumnanya,730.0,France,Female,25.0,2.0,0.0,2.0,0.0,1.0,95941.55,0.0
-41,15594577.0,Cattaneo,593.0,Spain,Male,28.0,9.0,0.0,2.0,1.0,0.0,5194.95,0.0
-42,15784594.0,Maclean,743.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,96444.88,0.0
-43,15744517.0,Ts'ao,671.0,France,Female,32.0,9.0,0.0,2.0,0.0,1.0,166520.96,0.0
-44,15698732.0,Echezonachukwu,668.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,97939.4,0.0
-45,15639277.0,Chiang,596.0,Germany,Male,27.0,6.0,135020.21,1.0,1.0,0.0,9087.81,1.0
-46,15589329.0,Macleod,786.0,Spain,Male,34.0,3.0,110765.68,2.0,1.0,0.0,187955.87,0.0
-47,15692406.0,Tsou,651.0,Germany,Male,25.0,5.0,80926.02,2.0,1.0,0.0,175235.99,0.0
-48,15743964.0,Ch'ien,686.0,Germany,Male,28.0,2.0,101238.24,2.0,0.0,0.0,141827.83,0.0
-49,15702669.0,Biryukov,593.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,0.0,141069.88,0.0
-50,15792453.0,Chidubem,684.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,108411.66,0.0
-51,15584899.0,Fisk,577.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,125877.22,0.0
-52,15636016.0,Chukwubuikem,807.0,France,Female,51.0,3.0,0.0,3.0,1.0,0.0,169781.45,1.0
-53,15631392.0,Hsiao,588.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,172459.39,0.0
-54,15743817.0,Ch'iu,735.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,142331.85,0.0
-55,15718779.0,Maughan,639.0,France,Male,25.0,2.0,0.0,2.0,1.0,1.0,90558.24,0.0
-56,15591747.0,Onyinyechukwuka,584.0,Germany,Female,37.0,5.0,74989.58,1.0,0.0,0.0,131807.14,1.0
-57,15600911.0,Hsiung,707.0,Germany,Female,43.0,3.0,101247.06,2.0,1.0,0.0,73329.08,0.0
-58,15690021.0,Maclean,662.0,France,Male,46.0,2.0,0.0,2.0,0.0,0.0,176903.35,1.0
-59,15653050.0,Macdonald,437.0,France,Male,39.0,4.0,0.0,1.0,0.0,0.0,125877.22,0.0
-60,15803764.0,Aksyonova,528.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,19720.08,0.0
-61,15641794.0,H?,682.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,95583.48,0.0
-62,15693381.0,Pirozzi,615.0,France,Male,32.0,9.0,134446.04,1.0,1.0,1.0,94978.1,0.0
-63,15771483.0,Fomin,743.0,France,Male,36.0,5.0,0.0,2.0,0.0,1.0,89893.6,0.0
-64,15792360.0,Chiekwugo,679.0,Spain,Male,38.0,4.0,137593.07,2.0,0.0,0.0,83700.66,1.0
-65,15565706.0,Ch'ang,607.0,Spain,Female,33.0,5.0,0.0,2.0,0.0,1.0,71405.17,0.0
-66,15767339.0,Hirst,724.0,Spain,Male,27.0,4.0,0.0,2.0,1.0,0.0,162743.53,0.0
-67,15632272.0,Hargreaves,589.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,1.0,71844.3,0.0
-68,15584897.0,Okechukwu,694.0,France,Male,54.0,1.0,0.0,1.0,0.0,1.0,181850.74,0.0
-69,15771573.0,T'ao,586.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,1.0,153066.97,0.0
-70,15756070.0,Maclean,756.0,France,Female,53.0,2.0,0.0,3.0,1.0,1.0,104721.5,1.0
-71,15654851.0,Ross,751.0,France,Female,26.0,2.0,0.0,2.0,1.0,0.0,51345.41,0.0
-72,15671781.0,Iweobiegbunam,633.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,88604.89,0.0
-73,15671244.0,Ch'ien,744.0,France,Male,29.0,7.0,0.0,2.0,0.0,0.0,160523.33,0.0
-74,15705918.0,P'eng,477.0,France,Male,66.0,7.0,92786.66,1.0,0.0,1.0,50906.65,0.0
-75,15744789.0,Shih,523.0,Spain,Male,27.0,7.0,0.0,2.0,0.0,0.0,122518.5,0.0
-76,15813363.0,Fenton,704.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,85891.02,0.0
-77,15640161.0,Ch'eng,582.0,France,Female,47.0,8.0,0.0,1.0,1.0,1.0,102057.86,0.0
-78,15730673.0,Lettiere,706.0,France,Female,41.0,1.0,121159.65,1.0,1.0,1.0,71862.79,0.0
-79,15711396.0,Hsiung,537.0,France,Female,45.0,5.0,0.0,1.0,1.0,0.0,16081.62,1.0
-80,15774586.0,Ch'in,705.0,Germany,Male,35.0,6.0,116320.68,2.0,1.0,0.0,174431.01,0.0
-81,15762902.0,T'ien,681.0,Spain,Female,22.0,6.0,0.0,2.0,0.0,0.0,65733.53,0.0
-82,15732563.0,Lettiere,613.0,Germany,Female,28.0,2.0,137980.23,1.0,1.0,1.0,158887.09,0.0
-83,15672357.0,Yermolayev,575.0,Germany,Female,33.0,9.0,143124.87,2.0,1.0,0.0,57898.58,0.0
-84,15720662.0,Kornilova,632.0,France,Female,23.0,6.0,0.0,2.0,1.0,0.0,95625.03,0.0
-85,15772996.0,Onyekachi,627.0,Spain,Male,45.0,7.0,0.0,2.0,1.0,1.0,167916.35,0.0
-86,15681755.0,Ts'ao,680.0,Germany,Female,29.0,5.0,119657.53,1.0,1.0,1.0,177655.68,0.0
-87,15764314.0,Uvarov,645.0,Spain,Male,33.0,4.0,141162.08,1.0,1.0,0.0,142238.54,0.0
-88,15793331.0,Okwukwe,774.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,57896.24,0.0
-89,15588928.0,Hsiung,583.0,Germany,Female,54.0,0.0,121558.46,1.0,0.0,0.0,140765.57,1.0
-90,15761043.0,Bogle,737.0,France,Female,41.0,6.0,0.0,2.0,1.0,0.0,67445.85,0.0
-91,15577515.0,Ch'eng,696.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,86657.52,0.0
-92,15572547.0,Palerma,679.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,0.0,96670.2,0.0
-93,15716781.0,Li,687.0,Spain,Female,34.0,7.0,0.0,1.0,0.0,1.0,96670.2,1.0
-94,15607874.0,Dike,611.0,France,Male,47.0,9.0,0.0,2.0,0.0,0.0,122518.5,0.0
-95,15690744.0,Onyekachi,741.0,France,Male,33.0,1.0,0.0,2.0,0.0,1.0,118862.08,0.0
-96,15601172.0,Milne,753.0,France,Female,30.0,0.0,138171.1,1.0,0.0,0.0,157891.86,0.0
-97,15576680.0,Trevisani,686.0,Germany,Male,42.0,8.0,111827.27,1.0,0.0,1.0,126469.09,0.0
-98,15738785.0,Mironova,583.0,Spain,Female,45.0,2.0,0.0,1.0,1.0,1.0,114876.82,1.0
-99,15606472.0,Bazarova,589.0,Spain,Female,35.0,2.0,135984.15,1.0,1.0,1.0,130862.11,0.0
-100,15658716.0,Onwumelu,711.0,Germany,Male,48.0,2.0,122726.56,1.0,0.0,0.0,51117.06,1.0
-101,15679307.0,Hsiao,535.0,France,Male,37.0,1.0,0.0,2.0,0.0,0.0,123663.63,0.0
-102,15605426.0,Chibueze,535.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,0.0,65906.07,0.0
-103,15785547.0,Uspensky,554.0,France,Female,40.0,10.0,0.0,2.0,1.0,0.0,67734.79,0.0
-104,15723484.0,Milanesi,632.0,Germany,Male,52.0,9.0,117300.02,1.0,1.0,0.0,154598.56,1.0
-105,15756475.0,Palermo,535.0,Germany,Female,29.0,8.0,115315.04,1.0,0.0,1.0,175094.63,0.0
-106,15703394.0,Hsieh,652.0,Germany,Male,31.0,3.0,124576.65,1.0,1.0,0.0,118570.55,0.0
-107,15644724.0,Ch'ien,707.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,71862.79,0.0
-108,15724127.0,Olisaemeka,682.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,199290.68,0.0
-109,15646539.0,Nnamdi,705.0,Spain,Female,31.0,9.0,92599.55,1.0,1.0,0.0,61820.41,0.0
-110,15718231.0,Nkemdilim,592.0,Spain,Male,34.0,10.0,0.0,2.0,1.0,0.0,168034.83,0.0
-111,15733602.0,Brown,679.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,136916.54,0.0
-112,15639265.0,Chiazagomekpere,681.0,Germany,Male,40.0,3.0,108881.73,1.0,0.0,0.0,17095.49,1.0
-113,15741385.0,Ch'in,706.0,France,Female,46.0,9.0,0.0,1.0,1.0,0.0,143086.09,1.0
-114,15761023.0,Chukwuemeka,663.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,140723.05,0.0
-115,15656426.0,Teng,578.0,France,Female,41.0,7.0,0.0,1.0,0.0,0.0,180197.74,1.0
-116,15596979.0,Ritchie,692.0,France,Male,35.0,9.0,0.0,1.0,1.0,1.0,142903.44,0.0
-117,15670172.0,Chin,678.0,France,Female,50.0,5.0,0.0,2.0,1.0,1.0,146504.35,0.0
-118,15684951.0,Toscani,583.0,Spain,Female,40.0,6.0,113328.93,1.0,1.0,0.0,64375.4,0.0
-119,15733303.0,Tien,679.0,France,Male,36.0,1.0,0.0,1.0,1.0,0.0,48778.46,0.0
-120,15750458.0,Chinomso,679.0,Germany,Female,47.0,0.0,118628.8,1.0,0.0,0.0,184842.21,1.0
-121,15583681.0,Smith,597.0,France,Male,57.0,2.0,0.0,1.0,1.0,1.0,84760.32,1.0
-122,15736431.0,Ruggiero,586.0,France,Female,27.0,2.0,0.0,2.0,1.0,1.0,86791.9,0.0
-123,15704763.0,Ponomarev,752.0,France,Female,34.0,7.0,132387.91,1.0,1.0,1.0,154486.01,0.0
-124,15803562.0,Ch'iu,850.0,Spain,Female,34.0,4.0,0.0,2.0,1.0,0.0,144870.28,0.0
-125,15728167.0,Onyekachi,682.0,France,Male,34.0,8.0,90425.15,1.0,0.0,0.0,122580.48,0.0
-126,15735246.0,Yermakova,724.0,Spain,Female,56.0,1.0,0.0,1.0,0.0,0.0,137519.92,1.0
-127,15801817.0,Kao,763.0,Spain,Male,29.0,6.0,0.0,1.0,1.0,1.0,151887.16,0.0
-128,15580149.0,Folliero,588.0,Spain,Male,61.0,10.0,0.0,2.0,1.0,1.0,90883.12,0.0
-129,15733261.0,Ts'ui,684.0,France,Male,35.0,1.0,91547.84,1.0,1.0,1.0,115733.16,0.0
-130,15618243.0,Whitehead,678.0,France,Male,24.0,2.0,0.0,2.0,0.0,1.0,127059.05,0.0
-131,15757559.0,Chukwumaobim,699.0,Germany,Female,36.0,9.0,110148.49,1.0,1.0,1.0,177065.24,0.0
-132,15768151.0,Tuan,619.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,143585.29,0.0
-133,15736112.0,Yobachi,712.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,95611.47,0.0
-134,15750772.0,Lo Duca,677.0,France,Male,36.0,5.0,0.0,1.0,1.0,1.0,120801.65,1.0
-135,15644692.0,Ch'in,752.0,Germany,Male,51.0,2.0,114435.52,3.0,1.0,0.0,173495.77,1.0
-136,15666095.0,Clements,646.0,France,Male,29.0,1.0,127763.24,1.0,1.0,1.0,95611.47,0.0
-137,15748995.0,P'eng,599.0,Germany,Female,42.0,8.0,118520.41,1.0,1.0,1.0,141175.21,1.0
-138,15692259.0,Chifley,547.0,Germany,Female,44.0,4.0,120107.56,2.0,1.0,0.0,172576.43,1.0
-139,15750181.0,Calabresi,619.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,108008.65,0.0
-140,15605275.0,Tsai,674.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,120512.08,0.0
-141,15795537.0,Ts'ui,564.0,Germany,Male,38.0,1.0,131563.88,1.0,1.0,0.0,133465.57,1.0
-142,15621818.0,Zikoranachidimma,646.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,120892.96,0.0
-143,15662232.0,Trevisano,678.0,France,Female,39.0,7.0,0.0,2.0,0.0,0.0,88890.05,0.0
-144,15694566.0,Hsu,665.0,France,Male,39.0,9.0,144801.97,1.0,1.0,0.0,132798.65,0.0
-145,15589969.0,Lai,597.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,169386.81,0.0
-146,15623369.0,Onwuka,709.0,Germany,Male,36.0,4.0,79962.92,2.0,1.0,1.0,18082.06,0.0
-147,15624667.0,Maclean,671.0,France,Female,39.0,2.0,0.0,2.0,0.0,1.0,162889.1,0.0
-148,15680234.0,T'ien,595.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,84972.9,0.0
-149,15651389.0,Lucchese,763.0,France,Male,47.0,3.0,123374.46,2.0,1.0,0.0,61569.07,0.0
-150,15794566.0,Ch'ang,725.0,Germany,Female,50.0,1.0,126444.25,1.0,1.0,0.0,158853.98,1.0
-151,15690620.0,Zikoranachidimma,755.0,Germany,Male,31.0,7.0,110997.76,1.0,1.0,1.0,118862.08,0.0
-152,15796721.0,T'ao,729.0,France,Male,32.0,1.0,0.0,2.0,0.0,1.0,189428.75,0.0
-153,15592761.0,Hsing,754.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,120596.83,0.0
-154,15602909.0,Ndubuagha,590.0,France,Female,32.0,5.0,0.0,2.0,0.0,1.0,61977.82,0.0
-155,15780961.0,Ch'iu,762.0,France,Male,38.0,2.0,0.0,1.0,1.0,0.0,77906.87,0.0
-156,15642725.0,Yashina,575.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,131301.74,0.0
-157,15636562.0,Clements,773.0,France,Male,38.0,8.0,0.0,2.0,0.0,1.0,81861.1,0.0
-158,15782390.0,Shih,850.0,Spain,Male,26.0,9.0,123971.16,1.0,1.0,0.0,176902.83,0.0
-159,15659420.0,Kao,564.0,France,Male,30.0,4.0,72541.48,1.0,1.0,0.0,90857.76,0.0
-160,15756570.0,Hsieh,581.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,93524.19,0.0
-161,15691817.0,Eluemuno,714.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,67740.08,0.0
-162,15741728.0,Iweobiegbunam,696.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,51820.87,0.0
-163,15583857.0,T'ien,651.0,France,Male,56.0,3.0,135067.02,3.0,1.0,0.0,147963.07,1.0
-164,15650391.0,Chukwualuka,678.0,France,Male,35.0,5.0,0.0,1.0,1.0,1.0,114442.66,0.0
-165,15773469.0,Larkin,704.0,Spain,Male,40.0,1.0,123694.0,1.0,1.0,1.0,134901.58,0.0
-166,15662294.0,Ch'in,639.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,107625.08,0.0
-167,15736154.0,Okwudiliolisa,648.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,161848.03,0.0
-168,15659949.0,Chukwumaobim,727.0,France,Male,43.0,1.0,0.0,2.0,1.0,1.0,81861.1,0.0
-169,15661526.0,Hsia,567.0,Germany,Female,32.0,9.0,120599.21,4.0,1.0,1.0,159418.1,1.0
-170,15638963.0,Ch'ang,597.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,167023.95,1.0
-171,15640078.0,Hsia,665.0,Germany,Female,43.0,5.0,106328.08,1.0,1.0,1.0,117622.8,1.0
-172,15691874.0,McElhone,714.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,0.0,102297.15,0.0
-173,15783883.0,Walker,678.0,Spain,Female,32.0,3.0,134714.3,1.0,1.0,1.0,48137.08,0.0
-174,15692575.0,Fancher,567.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,122925.4,0.0
-175,15772383.0,Mazzi,693.0,France,Male,26.0,4.0,0.0,1.0,1.0,0.0,90928.04,0.0
-176,15721251.0,Chinomso,641.0,France,Female,35.0,8.0,0.0,2.0,1.0,0.0,164741.5,0.0
-177,15743193.0,Azubuike,699.0,Spain,Male,33.0,1.0,155060.41,1.0,1.0,1.0,73977.23,0.0
-178,15663438.0,Yudina,763.0,Spain,Female,39.0,2.0,0.0,2.0,0.0,0.0,144870.28,0.0
-179,15689781.0,De Neeve,581.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,106781.59,1.0
-180,15775339.0,Mazzanti,730.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,167873.49,0.0
-181,15759733.0,Tung,537.0,Germany,Female,33.0,3.0,126592.47,1.0,1.0,0.0,48382.4,0.0
-182,15731569.0,Chukwuemeka,681.0,Germany,Female,56.0,8.0,123971.51,1.0,0.0,0.0,178291.09,1.0
-183,15643826.0,McWilliams,578.0,France,Female,38.0,9.0,0.0,2.0,0.0,1.0,161229.84,0.0
-184,15796834.0,Pugh,656.0,France,Female,34.0,5.0,0.0,1.0,1.0,1.0,176902.83,0.0
-185,15684461.0,Yobachi,521.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,121409.06,0.0
-186,15600792.0,Chiang,636.0,France,Female,32.0,5.0,0.0,1.0,1.0,1.0,178067.33,0.0
-187,15589431.0,Ugochukwu,694.0,France,Male,32.0,4.0,0.0,2.0,0.0,1.0,127825.25,0.0
-188,15808566.0,Milne,748.0,France,Male,46.0,3.0,0.0,2.0,1.0,0.0,82696.84,0.0
-189,15627854.0,O'Kane,523.0,France,Male,62.0,7.0,68892.77,1.0,1.0,1.0,189366.04,0.0
-190,15809838.0,Nnanna,757.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,161579.85,0.0
-191,15806837.0,Yobanna,733.0,Spain,Male,45.0,3.0,0.0,2.0,1.0,1.0,80410.68,0.0
-192,15573319.0,Aksenov,695.0,France,Male,27.0,3.0,0.0,2.0,1.0,0.0,121565.51,0.0
-193,15803801.0,Biryukov,706.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,113079.19,0.0
-194,15623595.0,Efremov,607.0,France,Female,38.0,5.0,0.0,1.0,1.0,0.0,57844.26,0.0
-195,15773322.0,Chidubem,682.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,83473.82,0.0
-196,15772601.0,Ndubuagha,696.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,102655.31,0.0
-197,15662085.0,Chinweike,667.0,Germany,Male,37.0,9.0,126670.37,2.0,1.0,0.0,131729.48,0.0
-198,15600992.0,Mazzi,573.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,150548.5,0.0
-199,15786539.0,Lu,594.0,France,Female,57.0,1.0,0.0,2.0,1.0,1.0,102655.31,0.0
-200,15677828.0,Chukwujamuike,726.0,Spain,Male,54.0,6.0,0.0,1.0,0.0,0.0,144880.81,1.0
-201,15723786.0,Chukwubuikem,712.0,France,Male,47.0,9.0,122359.5,1.0,1.0,0.0,186778.89,0.0
-202,15801994.0,L?,565.0,France,Female,54.0,3.0,0.0,1.0,1.0,0.0,122284.38,1.0
-203,15599792.0,Chibueze,619.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,146875.86,0.0
-204,15754574.0,Chinomso,635.0,France,Male,38.0,6.0,81231.85,1.0,1.0,1.0,79178.31,0.0
-205,15696733.0,Onyemauchechukwu,651.0,France,Female,37.0,5.0,0.0,2.0,1.0,1.0,149614.17,0.0
-206,15758081.0,Ts'ui,576.0,France,Female,38.0,3.0,116870.12,1.0,1.0,0.0,43982.41,0.0
-207,15699772.0,Sarratt,554.0,Germany,Female,33.0,9.0,152390.52,4.0,1.0,0.0,138552.74,1.0
-208,15591711.0,Fiorentino,539.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,168035.62,0.0
-209,15742809.0,Chiazagomekpere,635.0,France,Female,50.0,5.0,0.0,2.0,1.0,1.0,131301.74,1.0
-210,15672432.0,McFarland,599.0,Spain,Female,46.0,7.0,99208.46,1.0,0.0,0.0,47414.15,1.0
-211,15776922.0,Iloerika,596.0,Germany,Female,50.0,5.0,116993.43,1.0,1.0,1.0,60905.51,1.0
-212,15726087.0,Bales,713.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,143681.85,0.0
-213,15748797.0,Haugh,678.0,France,Female,43.0,9.0,0.0,1.0,0.0,0.0,177738.98,1.0
-214,15627697.0,Iloerika,714.0,Germany,Male,47.0,7.0,80780.16,1.0,0.0,1.0,177670.57,0.0
-215,15742238.0,Chukwukadibia,779.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,1.0,168834.38,0.0
-216,15686977.0,Okoli,663.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,0.0,51907.72,0.0
-217,15596060.0,Chien,727.0,France,Male,36.0,9.0,94741.74,2.0,0.0,1.0,141970.02,0.0
-218,15769980.0,Lu,732.0,Spain,Male,39.0,4.0,0.0,1.0,0.0,1.0,142377.69,0.0
-219,15634180.0,Chukwudi,602.0,France,Female,37.0,7.0,0.0,2.0,0.0,1.0,51548.14,0.0
-220,15749964.0,Ts'ui,850.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,1.0,180712.28,0.0
-221,15788151.0,Calabrese,569.0,France,Male,42.0,8.0,94486.47,1.0,1.0,0.0,15120.86,0.0
-222,15792388.0,Mott,597.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,147040.25,0.0
-223,15785373.0,Kao,694.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,1.0,111285.85,0.0
-224,15596713.0,Chinweuba,555.0,France,Male,28.0,3.0,0.0,2.0,1.0,1.0,88566.9,0.0
-225,15680201.0,Kao,682.0,France,Male,44.0,9.0,0.0,1.0,1.0,0.0,28188.96,0.0
-226,15662494.0,Ch'in,559.0,France,Female,30.0,4.0,0.0,1.0,1.0,0.0,131473.31,1.0
-227,15603594.0,Ugochukwu,731.0,France,Male,40.0,8.0,0.0,2.0,0.0,1.0,119446.64,0.0
-228,15673877.0,Ts'ai,810.0,France,Male,27.0,4.0,0.0,1.0,1.0,1.0,128841.31,0.0
-229,15606901.0,Ponomaryov,653.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,92811.2,0.0
-230,15771483.0,Fanucci,641.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,73881.68,0.0
-231,15763381.0,Chukwuemeka,571.0,Germany,Female,35.0,9.0,142862.21,1.0,1.0,1.0,5669.31,0.0
-232,15646513.0,Mazzanti,678.0,Germany,Female,48.0,9.0,95853.33,1.0,1.0,1.0,179689.07,1.0
-233,15797748.0,Lucciano,779.0,Spain,Male,27.0,2.0,0.0,2.0,1.0,0.0,141023.01,0.0
-234,15592914.0,K?,628.0,France,Female,37.0,1.0,0.0,2.0,1.0,0.0,97633.73,0.0
-235,15810944.0,Hsueh,705.0,France,Male,41.0,4.0,118627.1,1.0,1.0,1.0,60427.3,0.0
-236,15751208.0,Tsou,727.0,Germany,Male,34.0,8.0,127706.33,1.0,1.0,1.0,119333.95,0.0
-237,15667417.0,Tien,612.0,Germany,Male,54.0,6.0,116322.22,1.0,1.0,1.0,164815.59,1.0
-238,15756481.0,Li Fonti,642.0,France,Female,36.0,8.0,0.0,1.0,1.0,1.0,141180.66,1.0
-239,15658935.0,McLean,588.0,France,Male,39.0,0.0,127864.4,1.0,1.0,1.0,62370.67,0.0
-240,15664668.0,Trevisani,754.0,Spain,Male,36.0,5.0,0.0,2.0,0.0,1.0,57675.88,0.0
-241,15756820.0,Smith,652.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,1.0,92712.97,0.0
-242,15736069.0,Hsiao,609.0,Germany,Male,36.0,3.0,123870.07,2.0,1.0,1.0,167973.63,0.0
-243,15732963.0,Lombardi,572.0,France,Male,29.0,1.0,106939.08,1.0,0.0,0.0,170819.01,0.0
-244,15694946.0,Chialuka,632.0,Spain,Male,40.0,10.0,0.0,2.0,1.0,1.0,40368.65,0.0
-245,15797389.0,Fanucci,716.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,183358.21,0.0
-246,15674950.0,Clements,709.0,Spain,Male,34.0,4.0,103818.47,1.0,1.0,1.0,79314.1,0.0
-247,15781802.0,Chiang,602.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,162960.05,0.0
-248,15782418.0,P'eng,745.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,163558.43,0.0
-249,15651062.0,Nwachukwu,775.0,Spain,Female,36.0,2.0,0.0,1.0,0.0,0.0,91359.79,0.0
-250,15782758.0,Okwudilichukwu,632.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,177528.92,0.0
-251,15605665.0,Nnamutaezinwa,646.0,Spain,Female,26.0,9.0,113328.65,1.0,1.0,0.0,149991.32,1.0
-252,15804962.0,Fiorentino,668.0,Germany,Male,37.0,1.0,125877.65,1.0,1.0,0.0,69113.14,0.0
-253,15791216.0,Omeokachie,662.0,France,Male,42.0,0.0,0.0,1.0,1.0,0.0,199693.84,1.0
-254,15753969.0,Lucciano,678.0,France,Female,24.0,9.0,0.0,1.0,1.0,0.0,159800.16,0.0
-255,15573876.0,H?,689.0,Germany,Male,35.0,4.0,133681.98,1.0,0.0,1.0,176580.93,0.0
-256,15796957.0,Ch'eng,634.0,Germany,Male,45.0,7.0,123894.42,1.0,0.0,1.0,182650.15,1.0
-257,15585388.0,Tsai,676.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,86733.61,0.0
-258,15789432.0,L?,714.0,Spain,Female,41.0,2.0,0.0,2.0,1.0,0.0,141533.19,0.0
-259,15750055.0,Maclean,686.0,Germany,Female,38.0,7.0,141806.0,1.0,1.0,1.0,93551.36,0.0
-260,15635367.0,Black,584.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,129964.94,0.0
-261,15726186.0,Shubin,677.0,France,Female,28.0,5.0,0.0,2.0,0.0,1.0,149911.99,0.0
-262,15585901.0,Martin,682.0,France,Male,44.0,8.0,0.0,1.0,1.0,0.0,139024.31,1.0
-263,15733581.0,Onwumelu,599.0,France,Female,27.0,3.0,0.0,2.0,0.0,0.0,57844.26,0.0
-264,15681254.0,De Luca,627.0,France,Female,45.0,6.0,111018.98,1.0,1.0,1.0,107541.07,0.0
-265,15776629.0,Udegbulam,597.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,1.0,151739.67,0.0
-266,15613102.0,Macleod,624.0,France,Male,27.0,10.0,122726.56,1.0,1.0,1.0,77414.45,0.0
-267,15720509.0,Chiu,610.0,Spain,Male,35.0,3.0,0.0,2.0,0.0,1.0,81251.24,0.0
-268,15627232.0,Maclean,573.0,France,Male,34.0,1.0,72535.45,1.0,1.0,1.0,158937.55,0.0
-269,15696588.0,Chidimma,577.0,France,Male,30.0,6.0,108736.52,2.0,1.0,1.0,172618.52,0.0
-270,15815070.0,Chukwuemeka,709.0,France,Male,30.0,2.0,0.0,2.0,0.0,1.0,80619.09,0.0
-271,15644446.0,Tisdall,619.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,126761.2,0.0
-272,15642824.0,Folliero,703.0,France,Female,45.0,9.0,0.0,2.0,1.0,1.0,59354.24,0.0
-273,15646535.0,Ch'ang,678.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,176680.32,0.0
-274,15721426.0,Chiu,726.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,179908.71,0.0
-275,15752844.0,Mazzanti,646.0,France,Male,34.0,3.0,102562.23,1.0,1.0,1.0,114514.64,0.0
-276,15732963.0,Aitken,656.0,Germany,Female,43.0,5.0,122781.51,1.0,1.0,1.0,122488.59,0.0
-277,15780124.0,L?,597.0,Spain,Male,42.0,6.0,0.0,2.0,0.0,0.0,157097.09,0.0
-278,15724876.0,Hsia,763.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,0.0,165576.55,0.0
-279,15771086.0,Lo,706.0,France,Female,30.0,8.0,0.0,1.0,1.0,0.0,172450.04,1.0
-280,15632079.0,Chiang,718.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,185543.35,0.0
-281,15795895.0,Onuoha,719.0,France,Male,45.0,6.0,0.0,2.0,0.0,1.0,182728.8,0.0
-282,15759874.0,Nwokike,755.0,France,Male,35.0,2.0,0.0,2.0,0.0,0.0,167066.95,0.0
-283,15802585.0,Hsiung,668.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,96444.88,0.0
-284,15639946.0,Maclean,644.0,France,Female,39.0,6.0,0.0,2.0,1.0,0.0,161074.85,0.0
-285,15585599.0,Chiazagomekpere,663.0,France,Male,39.0,5.0,143340.44,1.0,0.0,0.0,71333.44,0.0
-286,15663438.0,Mao,630.0,France,Female,42.0,4.0,0.0,1.0,1.0,0.0,157543.11,1.0
-287,15742369.0,Hsia,731.0,Germany,Female,35.0,2.0,134564.41,1.0,1.0,0.0,106538.99,1.0
-288,15602373.0,McLean,596.0,France,Female,34.0,1.0,0.0,2.0,0.0,1.0,158973.65,0.0
-289,15598846.0,Ritchie,581.0,France,Male,38.0,3.0,0.0,2.0,0.0,1.0,126812.63,0.0
-290,15586674.0,Ch'ang,539.0,France,Female,46.0,8.0,0.0,3.0,1.0,0.0,121834.81,1.0
-291,15776824.0,Chiemenam,596.0,Spain,Female,37.0,5.0,0.0,2.0,1.0,1.0,99818.46,0.0
-292,15570947.0,Chinweike,730.0,France,Female,42.0,3.0,129440.3,1.0,1.0,0.0,91036.74,0.0
-293,15787035.0,Tien,704.0,France,Female,44.0,4.0,0.0,2.0,1.0,0.0,85890.75,0.0
-294,15794473.0,Lucchesi,602.0,France,Female,34.0,5.0,0.0,1.0,1.0,0.0,182632.64,0.0
-295,15568487.0,Ogochukwu,754.0,France,Male,72.0,8.0,98765.84,1.0,1.0,1.0,147635.33,0.0
-296,15679743.0,Ch'ang,668.0,Germany,Male,32.0,3.0,145605.44,1.0,1.0,1.0,179565.57,0.0
-297,15632669.0,Hsiao,604.0,Spain,Female,28.0,4.0,0.0,1.0,1.0,1.0,88658.73,1.0
-298,15782941.0,Uspensky,755.0,Spain,Male,39.0,2.0,0.0,2.0,0.0,0.0,144593.3,0.0
-299,15797919.0,Akobundu,602.0,Spain,Female,31.0,10.0,0.0,1.0,1.0,0.0,156868.21,1.0
-300,15763028.0,Nwankwo,757.0,Spain,Male,45.0,6.0,161613.94,1.0,0.0,0.0,164038.07,1.0
-301,15572051.0,Bell,581.0,France,Female,21.0,4.0,0.0,2.0,1.0,0.0,170634.49,0.0
-302,15777893.0,Hsieh,639.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,1.0,130910.78,0.0
-303,15702541.0,Ch'iu,668.0,France,Male,41.0,10.0,0.0,2.0,1.0,0.0,91086.73,0.0
-304,15788683.0,H?,586.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,61531.86,0.0
-305,15768244.0,Chiazagomekpere,572.0,France,Male,32.0,8.0,0.0,1.0,1.0,1.0,104991.28,0.0
-306,15607877.0,Udokamma,756.0,France,Male,49.0,10.0,0.0,1.0,1.0,0.0,177719.88,1.0
-307,15750181.0,Cremonesi,684.0,France,Male,25.0,9.0,0.0,1.0,1.0,1.0,159830.58,0.0
-308,15671769.0,Shih,576.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,154844.22,0.0
-309,15726186.0,Ting,712.0,France,Female,41.0,4.0,0.0,3.0,1.0,0.0,182495.7,1.0
-310,15768120.0,Fanucci,542.0,France,Male,45.0,8.0,0.0,2.0,1.0,0.0,145593.85,1.0
-311,15595883.0,Yeh,602.0,Spain,Female,44.0,4.0,0.0,1.0,1.0,0.0,152843.53,1.0
-312,15616709.0,Lombardi,627.0,Germany,Male,51.0,9.0,121355.99,1.0,1.0,0.0,76510.46,1.0
-313,15728474.0,Fanucci,769.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,1.0,168035.63,0.0
-314,15585047.0,Ch'iu,645.0,France,Female,35.0,8.0,0.0,1.0,1.0,0.0,142039.67,0.0
-315,15692406.0,Mamelu,670.0,France,Female,34.0,2.0,139859.55,1.0,1.0,1.0,35338.96,0.0
-316,15779915.0,Okwudiliolisa,667.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,107874.39,0.0
-317,15571586.0,Chiemenam,614.0,France,Male,35.0,6.0,124586.93,2.0,1.0,0.0,142400.27,0.0
-318,15775335.0,Mazzanti,581.0,Germany,Female,39.0,6.0,121207.65,2.0,1.0,1.0,114020.08,0.0
-319,15583462.0,T'ien,558.0,Germany,Male,39.0,2.0,122570.87,2.0,1.0,1.0,61538.43,0.0
-320,15684526.0,Tien,659.0,Germany,Female,43.0,9.0,121827.43,2.0,1.0,1.0,121074.33,0.0
-321,15633227.0,T'ao,637.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,0.0,125440.11,0.0
-322,15747168.0,Olisaemeka,730.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,106977.52,0.0
-323,15593882.0,Ukaegbunam,651.0,Spain,Male,34.0,7.0,0.0,2.0,0.0,1.0,158036.72,0.0
-324,15785042.0,P'an,738.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,148087.88,0.0
-325,15731865.0,Ch'en,728.0,Spain,Male,43.0,6.0,0.0,3.0,1.0,1.0,158750.58,1.0
-326,15641723.0,Mazzanti,646.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,152876.13,0.0
-327,15608760.0,H?,748.0,Germany,Female,47.0,10.0,102025.32,1.0,1.0,0.0,136079.64,1.0
-328,15682467.0,Milani,597.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,10963.04,0.0
-329,15794580.0,Trevisani,656.0,France,Male,41.0,9.0,0.0,2.0,1.0,1.0,175324.74,0.0
-330,15801488.0,Smith,731.0,France,Male,31.0,8.0,0.0,2.0,0.0,0.0,100862.54,0.0
-331,15632439.0,Lo Duca,569.0,Germany,Male,52.0,7.0,125889.3,1.0,0.0,0.0,60212.81,1.0
-332,15713578.0,Mazzanti,554.0,France,Male,43.0,5.0,121555.55,1.0,0.0,0.0,118913.53,0.0
-333,15583595.0,Hargreaves,653.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,1.0,82463.69,0.0
-334,15605276.0,Hsiung,633.0,France,Male,39.0,2.0,57871.18,1.0,0.0,1.0,63889.1,1.0
-335,15632324.0,Nkemjika,632.0,Spain,Male,44.0,8.0,0.0,1.0,1.0,1.0,151871.84,1.0
-336,15661219.0,Trevisano,686.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,179655.85,0.0
-337,15628697.0,Onochie,762.0,Germany,Female,26.0,9.0,142658.04,2.0,0.0,1.0,154775.7,0.0
-338,15807989.0,P'eng,579.0,Spain,Female,31.0,8.0,0.0,1.0,0.0,0.0,148814.54,0.0
-339,15766017.0,Iloerika,646.0,France,Male,35.0,5.0,0.0,2.0,0.0,1.0,80701.72,0.0
-340,15664204.0,Hsueh,542.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,142516.35,0.0
-341,15775294.0,Ts'ui,632.0,Germany,Male,28.0,7.0,103909.86,1.0,1.0,1.0,185547.71,0.0
-342,15779913.0,Hs?,686.0,France,Male,44.0,7.0,0.0,1.0,0.0,0.0,118914.4,0.0
-343,15651533.0,De Salis,516.0,France,Female,51.0,6.0,0.0,2.0,1.0,1.0,123587.83,0.0
-344,15732442.0,Nwachinemelu,634.0,France,Male,38.0,7.0,0.0,1.0,1.0,1.0,125646.82,0.0
-345,15658852.0,Lucchesi,596.0,France,Female,44.0,5.0,0.0,1.0,0.0,0.0,106977.8,1.0
-346,15592222.0,Ts'ai,706.0,France,Female,40.0,3.0,122314.5,1.0,1.0,0.0,87985.2,0.0
-347,15584364.0,Chukwueloka,651.0,France,Female,37.0,1.0,0.0,2.0,1.0,0.0,43932.54,0.0
-348,15695846.0,Onyekaozulu,678.0,Spain,Female,49.0,8.0,0.0,1.0,1.0,0.0,38773.74,1.0
-349,15728474.0,Maclean,658.0,France,Male,41.0,4.0,0.0,2.0,0.0,1.0,163570.25,0.0
-350,15729689.0,Milanesi,748.0,Spain,Male,42.0,2.0,0.0,1.0,1.0,0.0,169915.02,0.0
-351,15724153.0,Ewing,686.0,France,Female,32.0,9.0,0.0,1.0,1.0,0.0,97416.34,0.0
-352,15633114.0,Bazarova,709.0,Spain,Male,39.0,4.0,0.0,2.0,1.0,0.0,123870.28,0.0
-353,15701392.0,Onyemauchechi,581.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,71323.23,0.0
-354,15641250.0,Udobata,645.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,141364.62,0.0
-355,15592773.0,Manna,662.0,Germany,Male,41.0,9.0,139528.95,2.0,1.0,1.0,189607.71,0.0
-356,15775307.0,Chu,538.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,138510.01,0.0
-357,15800811.0,Hargreaves,778.0,France,Male,32.0,10.0,148061.89,2.0,0.0,1.0,165082.25,0.0
-358,15793949.0,Lucchesi,757.0,France,Male,49.0,5.0,0.0,1.0,1.0,0.0,142514.71,1.0
-359,15600103.0,Nkemjika,646.0,France,Male,26.0,7.0,0.0,1.0,1.0,1.0,149536.15,0.0
-360,15772383.0,Ugochukwu,681.0,France,Male,34.0,2.0,0.0,1.0,0.0,0.0,147059.14,0.0
-361,15702434.0,Chia,567.0,Germany,Male,26.0,6.0,127678.8,2.0,1.0,0.0,126012.57,0.0
-362,15763063.0,Ndubueze,769.0,France,Female,48.0,9.0,0.0,1.0,1.0,0.0,88827.67,1.0
-363,15662063.0,Ts'ui,696.0,Germany,Female,44.0,8.0,132351.29,1.0,1.0,1.0,141706.43,0.0
-364,15627298.0,Nwankwo,687.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,103557.91,0.0
-365,15662494.0,Hs?eh,683.0,Spain,Female,40.0,2.0,119624.6,1.0,1.0,1.0,157567.37,0.0
-366,15813363.0,Hsiao,709.0,France,Female,28.0,3.0,0.0,1.0,1.0,0.0,147545.65,0.0
-367,15631267.0,Nwankwo,781.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,169920.92,0.0
-368,15736778.0,Ch'iu,634.0,Spain,Female,33.0,4.0,105435.73,1.0,1.0,0.0,134600.94,0.0
-369,15648067.0,Baresi,704.0,France,Male,44.0,4.0,177092.39,2.0,0.0,1.0,53373.61,1.0
-370,15638751.0,Chiu,686.0,France,Female,51.0,2.0,151510.17,1.0,1.0,0.0,72402.0,1.0
-371,15702541.0,Sun,708.0,France,Female,35.0,9.0,89045.25,1.0,1.0,1.0,115767.93,0.0
-372,15640648.0,Chimaobim,696.0,Germany,Male,36.0,3.0,151766.71,1.0,1.0,1.0,111421.77,0.0
-373,15762927.0,Hsueh,676.0,France,Male,46.0,3.0,178820.91,1.0,1.0,0.0,118749.62,0.0
-374,15605275.0,Chiemezie,732.0,Germany,Male,48.0,6.0,109949.05,1.0,1.0,1.0,170360.11,1.0
-375,15750545.0,Hsiao,659.0,France,Female,49.0,9.0,0.0,1.0,1.0,0.0,105760.01,1.0
-376,15728669.0,Nnamutaezinwa,719.0,Spain,Female,42.0,3.0,123948.54,1.0,1.0,1.0,184856.89,0.0
-377,15642093.0,Kuo,535.0,Germany,Female,56.0,5.0,63492.94,1.0,1.0,0.0,161241.65,1.0
-378,15584899.0,Chijindum,609.0,France,Female,39.0,2.0,138171.1,1.0,1.0,1.0,121408.06,0.0
-379,15667932.0,Walker,606.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,126679.69,0.0
-380,15672594.0,P'eng,588.0,Germany,Female,39.0,7.0,102238.01,2.0,1.0,1.0,85987.35,0.0
-381,15693893.0,Okwudilichukwu,648.0,Spain,Male,24.0,7.0,0.0,2.0,0.0,1.0,128881.87,0.0
-382,15782939.0,Nwankwo,706.0,France,Male,61.0,9.0,0.0,2.0,1.0,1.0,158937.57,0.0
-383,15790829.0,Teng,675.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,0.0,49374.82,0.0
-384,15809999.0,Chukwukadibia,628.0,France,Male,28.0,0.0,0.0,2.0,1.0,0.0,57905.25,0.0
-385,15742269.0,O'Callaghan,724.0,Germany,Female,45.0,5.0,123969.96,1.0,1.0,0.0,1099.95,1.0
-386,15690546.0,Ch'ien,590.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,1.0,47428.73,0.0
-387,15798834.0,Kao,706.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,38410.74,0.0
-388,15692977.0,Napolitani,685.0,France,Female,41.0,5.0,0.0,2.0,0.0,1.0,85522.88,0.0
-389,15732265.0,Toscani,673.0,Germany,Female,57.0,6.0,113865.23,1.0,0.0,1.0,102908.91,1.0
-390,15676937.0,Ojiofor,516.0,France,Male,43.0,5.0,0.0,1.0,1.0,0.0,146652.11,1.0
-391,15705281.0,Chiang,637.0,Spain,Female,44.0,5.0,0.0,2.0,0.0,1.0,103760.53,0.0
-392,15762218.0,Chiu,711.0,France,Male,39.0,6.0,97734.79,1.0,1.0,1.0,51972.92,0.0
-393,15791302.0,Tseng,810.0,Spain,Female,32.0,6.0,0.0,1.0,1.0,0.0,134732.99,0.0
-394,15699195.0,Nwebube,682.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,1.0,106040.97,0.0
-395,15766659.0,Chiemezie,598.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,0.0,121138.77,0.0
-396,15678058.0,Trevisano,706.0,France,Male,45.0,2.0,122917.71,1.0,1.0,0.0,151871.84,1.0
-397,15585036.0,Kaur,596.0,Spain,Female,35.0,5.0,86891.84,1.0,1.0,0.0,175893.53,0.0
-398,15570601.0,Brazenor,769.0,France,Female,26.0,8.0,0.0,2.0,1.0,1.0,56793.02,0.0
-399,15588517.0,Ch'ang,724.0,France,Female,31.0,1.0,0.0,2.0,0.0,1.0,81602.02,0.0
-400,15752488.0,Chineze,584.0,France,Male,35.0,4.0,148021.12,1.0,0.0,0.0,93336.3,0.0
-401,15672516.0,Kerr,586.0,Spain,Female,33.0,9.0,0.0,1.0,0.0,0.0,162053.92,1.0
-402,15581548.0,Genovesi,633.0,France,Female,33.0,9.0,0.0,3.0,1.0,0.0,136610.02,1.0
-403,15756981.0,Kornilova,678.0,Germany,Male,39.0,5.0,125470.81,2.0,1.0,1.0,169781.45,0.0
-404,15705918.0,Lettiere,647.0,France,Female,43.0,4.0,0.0,2.0,1.0,1.0,51548.14,0.0
-405,15759872.0,Maclean,605.0,Spain,Female,44.0,5.0,0.0,1.0,0.0,0.0,172501.38,1.0
-406,15751022.0,Kornilova,656.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,1.0,116885.59,0.0
-407,15751336.0,Creswell,648.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,40318.33,0.0
-408,15604020.0,Ch'ien,663.0,France,Male,36.0,4.0,127475.39,1.0,1.0,1.0,159629.64,0.0
-409,15677946.0,Mazzanti,679.0,Spain,Male,41.0,9.0,0.0,1.0,1.0,1.0,92982.61,1.0
-410,15600478.0,Ejimofor,627.0,Spain,Male,54.0,5.0,143350.41,3.0,1.0,0.0,101314.65,1.0
-411,15763665.0,Hsiung,686.0,France,Male,35.0,3.0,0.0,2.0,0.0,0.0,159835.91,0.0
-412,15597930.0,Hsia,790.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,171706.66,1.0
-413,15661827.0,Lucciano,732.0,Germany,Male,32.0,8.0,135219.57,2.0,1.0,0.0,170819.01,0.0
-414,15576085.0,Okwudilichukwu,757.0,France,Female,45.0,4.0,0.0,2.0,1.0,0.0,62280.91,0.0
-415,15690695.0,Nnachetam,696.0,France,Male,31.0,10.0,0.0,2.0,1.0,0.0,51548.14,0.0
-416,15793725.0,Chidiebere,671.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,113943.17,0.0
-417,15704583.0,Ibragimova,668.0,Germany,Female,28.0,2.0,131834.8,1.0,1.0,1.0,6076.62,1.0
-418,15752578.0,Trevisan,597.0,France,Female,39.0,5.0,0.0,1.0,1.0,1.0,176882.08,0.0
-419,15610557.0,Genovese,650.0,France,Male,37.0,7.0,0.0,1.0,0.0,1.0,97734.3,0.0
-420,15596972.0,Akabueze,754.0,France,Male,40.0,1.0,0.0,1.0,1.0,0.0,64872.55,1.0
-421,15607746.0,Okwudiliolisa,456.0,Germany,Female,41.0,8.0,102773.2,1.0,1.0,0.0,124095.69,1.0
-422,15692406.0,Chuang,647.0,France,Female,36.0,5.0,121376.15,1.0,1.0,1.0,53437.1,0.0
-423,15802466.0,Hsia,706.0,Spain,Female,29.0,8.0,141784.02,1.0,0.0,0.0,70774.97,0.0
-424,15745307.0,Mancini,651.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,189428.75,0.0
-425,15802478.0,Chukwudi,573.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,79919.13,0.0
-426,15626679.0,Shen,667.0,Germany,Female,50.0,7.0,157989.13,2.0,1.0,0.0,9087.81,0.0
-427,15696989.0,Ch'in,625.0,France,Female,57.0,8.0,0.0,1.0,1.0,0.0,121747.96,1.0
-428,15695475.0,Li,711.0,France,Male,54.0,7.0,0.0,1.0,1.0,0.0,51721.52,0.0
-429,15727041.0,Yuryeva,704.0,France,Male,38.0,1.0,0.0,1.0,1.0,1.0,136497.28,0.0
-430,15575060.0,Chukwuemeka,683.0,Spain,Male,40.0,6.0,0.0,1.0,0.0,0.0,166677.53,1.0
-431,15798895.0,Chukwujamuike,576.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,1.0,158918.02,0.0
-432,15590097.0,Chiedozie,652.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,166475.44,0.0
-433,15766686.0,Hsiao,605.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,51572.08,0.0
-434,15717185.0,Onyemere,648.0,Spain,Male,33.0,5.0,0.0,2.0,0.0,1.0,143683.75,0.0
-435,15656683.0,Chidimma,648.0,France,Male,61.0,2.0,0.0,2.0,1.0,1.0,79577.4,0.0
-436,15772250.0,Ukaegbunam,738.0,Spain,Male,41.0,9.0,0.0,1.0,1.0,0.0,53534.8,0.0
-437,15786063.0,Pisani,589.0,Germany,Female,36.0,1.0,108736.52,2.0,1.0,1.0,71823.15,0.0
-438,15700601.0,Cattaneo,649.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,52314.71,0.0
-439,15732266.0,Ch'eng,726.0,France,Male,39.0,0.0,142598.47,1.0,1.0,1.0,143598.7,0.0
-440,15628203.0,Chiu,661.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,82969.61,0.0
-441,15770174.0,Hao,586.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,63747.51,0.0
-442,15772412.0,Uchenna,605.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,166652.02,0.0
-443,15806925.0,Palermo,687.0,Germany,Female,43.0,9.0,119027.28,2.0,1.0,1.0,51554.58,0.0
-444,15574879.0,Chidiebele,707.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,111241.37,0.0
-445,15640648.0,Chiang,682.0,France,Female,31.0,5.0,0.0,1.0,1.0,0.0,16081.62,0.0
-446,15571778.0,Ugochukwutubelum,597.0,France,Male,33.0,9.0,0.0,1.0,1.0,0.0,106977.8,0.0
-447,15767757.0,Lucciano,567.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,153787.42,0.0
-448,15737133.0,Chukwueloka,631.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,57962.41,0.0
-449,15809837.0,Nwoye,673.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,65316.5,0.0
-450,15772896.0,Nucci,694.0,France,Male,26.0,7.0,0.0,1.0,1.0,1.0,102950.79,0.0
-451,15690733.0,K'ung,663.0,Germany,Female,28.0,9.0,127763.24,2.0,1.0,1.0,92888.52,0.0
-452,15596074.0,Chukwufumnanya,602.0,Germany,Male,27.0,2.0,117524.72,2.0,1.0,0.0,25395.83,0.0
-453,15702298.0,Sagese,528.0,Spain,Female,43.0,9.0,0.0,2.0,1.0,0.0,15959.01,1.0
-454,15793881.0,T'ang,735.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,170142.09,0.0
-455,15701885.0,P'an,682.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,51548.14,0.0
-456,15752463.0,Y?,554.0,France,Male,47.0,4.0,0.0,2.0,1.0,0.0,4791.8,0.0
-457,15792232.0,Chieloka,582.0,France,Male,40.0,2.0,0.0,2.0,0.0,1.0,86657.48,0.0
-458,15753043.0,Chinwenma,645.0,Germany,Female,48.0,10.0,155519.59,2.0,1.0,1.0,35640.12,0.0
-459,15777896.0,Hsieh,753.0,Germany,Female,40.0,8.0,133686.52,2.0,0.0,1.0,171070.59,0.0
-460,15664808.0,Lo,584.0,Spain,Male,23.0,8.0,0.0,2.0,0.0,0.0,105570.44,0.0
-461,15656592.0,Teng,636.0,Spain,Male,46.0,1.0,0.0,2.0,1.0,1.0,109145.2,0.0
-462,15708003.0,Fanucci,598.0,Spain,Male,20.0,9.0,0.0,2.0,1.0,1.0,114020.06,0.0
-463,15703778.0,Ch'ien,682.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,143882.25,0.0
-464,15589715.0,Nwankwo,750.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,0.0,149593.61,0.0
-465,15679961.0,Tien,705.0,France,Male,61.0,3.0,0.0,2.0,1.0,1.0,170950.58,0.0
-466,15616555.0,Chin,721.0,Spain,Female,43.0,1.0,0.0,3.0,1.0,0.0,136521.82,1.0
-467,15656121.0,Hsueh,654.0,Spain,Female,61.0,5.0,0.0,1.0,0.0,0.0,129870.93,1.0
-468,15725028.0,Udegbulam,648.0,France,Male,45.0,4.0,0.0,1.0,1.0,1.0,91560.63,0.0
-469,15723488.0,Shih,596.0,Germany,Male,25.0,8.0,133598.4,1.0,1.0,0.0,171922.72,0.0
-470,15683544.0,Nwankwo,735.0,Spain,Male,57.0,6.0,0.0,2.0,1.0,1.0,183542.08,0.0
-471,15754577.0,Baresi,596.0,France,Female,38.0,9.0,0.0,2.0,1.0,0.0,103570.8,0.0
-472,15808621.0,Chineze,732.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,83343.73,0.0
-473,15797767.0,Eluemuno,516.0,Spain,Male,45.0,0.0,0.0,1.0,1.0,0.0,90966.79,1.0
-474,15743545.0,Chukwuemeka,643.0,France,Male,34.0,6.0,81732.88,1.0,1.0,0.0,199392.14,0.0
-475,15732329.0,T'ang,656.0,Spain,Female,44.0,2.0,0.0,2.0,0.0,1.0,66795.78,0.0
-476,15759034.0,Iweobiegbunam,653.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,134589.58,0.0
-477,15693902.0,Bazarova,568.0,Spain,Male,70.0,2.0,0.0,1.0,1.0,0.0,146785.44,1.0
-478,15757837.0,Folliero,636.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,181879.56,1.0
-479,15624993.0,Chiu,703.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,182822.5,0.0
-480,15674932.0,Milanesi,637.0,France,Male,36.0,0.0,0.0,1.0,1.0,0.0,167030.51,0.0
-481,15729574.0,Shao,564.0,France,Male,36.0,10.0,0.0,1.0,0.0,0.0,144593.5,0.0
-482,15663438.0,Trevisani,605.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,108699.59,0.0
-483,15780650.0,Onwuamaeze,582.0,France,Male,29.0,8.0,89900.24,2.0,1.0,1.0,58854.88,0.0
-484,15656592.0,Liao,584.0,Spain,Male,33.0,6.0,0.0,2.0,1.0,1.0,145524.36,0.0
-485,15742204.0,Ch'ang,656.0,Spain,Male,34.0,9.0,0.0,2.0,0.0,1.0,1187.88,0.0
-486,15656829.0,Ogochukwu,704.0,Germany,Male,50.0,1.0,106301.85,1.0,0.0,0.0,171378.77,1.0
-487,15719304.0,Onwumelu,539.0,France,Female,40.0,0.0,0.0,2.0,0.0,0.0,75937.48,0.0
-488,15618581.0,Iweobiegbunam,605.0,Germany,Female,37.0,7.0,72783.42,1.0,0.0,0.0,131259.6,0.0
-489,15645316.0,L?,709.0,France,Female,31.0,9.0,83008.31,1.0,1.0,0.0,54474.48,0.0
-490,15773792.0,Bischof,677.0,Germany,Female,34.0,8.0,106045.92,2.0,1.0,1.0,131043.1,0.0
-491,15722010.0,Tuan,582.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,167564.82,0.0
-492,15792649.0,T'ien,637.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,103560.98,0.0
-493,15605916.0,Hsueh,581.0,Germany,Male,28.0,5.0,111835.94,1.0,1.0,0.0,123207.17,0.0
-494,15681562.0,Olisaemeka,587.0,France,Male,40.0,1.0,0.0,1.0,1.0,0.0,82872.64,1.0
-495,15602979.0,Liao,661.0,Spain,Male,38.0,4.0,0.0,1.0,0.0,0.0,140937.97,0.0
-496,15760450.0,Ch'eng,743.0,France,Male,43.0,7.0,137592.96,1.0,1.0,1.0,155876.06,0.0
-497,15778936.0,Ch'in,633.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,173365.89,0.0
-498,15777060.0,Ekechukwu,632.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,148527.56,0.0
-499,15765322.0,Yegorov,523.0,France,Female,57.0,1.0,0.0,1.0,1.0,1.0,79919.13,0.0
-500,15758032.0,Chiu,579.0,France,Male,41.0,5.0,0.0,2.0,0.0,0.0,105898.69,0.0
-501,15723484.0,Sagese,576.0,Spain,Female,40.0,10.0,0.0,1.0,1.0,1.0,171706.66,1.0
-502,15798975.0,Ozerova,631.0,Spain,Male,28.0,3.0,0.0,2.0,1.0,1.0,127543.81,0.0
-503,15681956.0,Hsing,665.0,Germany,Female,54.0,3.0,171740.69,1.0,1.0,0.0,164866.87,1.0
-504,15669957.0,Milanesi,709.0,Germany,Female,31.0,4.0,141749.68,2.0,0.0,1.0,107841.77,0.0
-505,15616666.0,Piazza,712.0,France,Male,33.0,8.0,0.0,1.0,0.0,1.0,127543.81,0.0
-506,15793317.0,Fanucci,695.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,50368.63,0.0
-507,15651737.0,Ting,589.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,134878.19,0.0
-508,15616700.0,Milanesi,730.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,180800.42,0.0
-509,15641043.0,Ch'en,741.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,178458.86,0.0
-510,15634602.0,Calabrese,584.0,France,Female,42.0,8.0,152622.34,1.0,1.0,0.0,61820.41,1.0
-511,15691150.0,Ch'iu,680.0,Germany,Female,33.0,5.0,122726.56,1.0,1.0,0.0,175893.53,0.0
-512,15687959.0,Hsieh,691.0,France,Male,45.0,5.0,0.0,2.0,1.0,1.0,121592.5,0.0
-513,15625023.0,Teng,706.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,140586.08,0.0
-514,15761417.0,Chigozie,545.0,France,Male,45.0,5.0,0.0,2.0,1.0,1.0,183515.13,0.0
-515,15586914.0,Yudina,619.0,France,Female,51.0,9.0,152462.79,1.0,1.0,1.0,166472.81,1.0
-516,15668894.0,P'eng,542.0,Germany,Female,32.0,8.0,142644.64,1.0,1.0,0.0,162760.96,0.0
-517,15758031.0,Hsieh,678.0,France,Male,24.0,9.0,0.0,2.0,1.0,1.0,93839.3,0.0
-518,15681075.0,Chukwuemeka,558.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,56905.38,0.0
-519,15724444.0,Chukwuemeka,609.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,159718.16,0.0
-520,15593854.0,K'ung,683.0,Germany,Female,48.0,3.0,134446.04,2.0,1.0,1.0,179861.79,0.0
-521,15646513.0,Ch'eng,850.0,France,Female,28.0,10.0,0.0,2.0,1.0,0.0,171096.2,0.0
-522,15764708.0,Folliero,645.0,Spain,Male,24.0,9.0,141078.57,1.0,1.0,0.0,141501.45,0.0
-523,15793046.0,Ch'iu,645.0,Spain,Female,39.0,2.0,0.0,2.0,0.0,1.0,187638.34,0.0
-524,15576723.0,Ritchie,758.0,Spain,Male,40.0,5.0,145747.67,1.0,1.0,0.0,181258.76,0.0
-525,15670066.0,Lombardo,579.0,Spain,Male,48.0,1.0,0.0,1.0,0.0,1.0,125557.95,1.0
-526,15803456.0,Chu,528.0,Germany,Female,29.0,4.0,135096.05,1.0,1.0,1.0,164038.07,0.0
-527,15572767.0,Ugochukwu,757.0,France,Female,50.0,0.0,0.0,1.0,1.0,0.0,113868.4,1.0
-528,15641181.0,Ch'ang,639.0,France,Male,29.0,5.0,118862.76,1.0,1.0,0.0,82970.69,0.0
-529,15711287.0,Chinwemma,675.0,France,Male,43.0,2.0,0.0,2.0,0.0,1.0,120586.32,0.0
-530,15797767.0,Y?,848.0,France,Male,32.0,2.0,0.0,1.0,1.0,1.0,91098.76,0.0
-531,15621611.0,Trevisani,748.0,France,Male,31.0,3.0,0.0,1.0,1.0,1.0,95655.16,0.0
-532,15692235.0,Nock,665.0,Spain,Male,38.0,8.0,123544.0,1.0,1.0,1.0,189414.74,0.0
-533,15765173.0,Fitch,697.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,120790.02,0.0
-534,15726354.0,Rizzo,545.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,1.0,112491.96,0.0
-535,15774847.0,Lucchesi,716.0,France,Female,60.0,9.0,0.0,1.0,1.0,0.0,180716.1,1.0
-536,15750099.0,Ratten,713.0,France,Female,30.0,6.0,119961.48,1.0,1.0,1.0,147822.54,0.0
-537,15589435.0,Eluemuno,609.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,60920.03,0.0
-538,15726588.0,Chia,653.0,France,Male,33.0,7.0,133489.12,1.0,0.0,1.0,125961.48,0.0
-539,15632551.0,Ulyanova,684.0,France,Male,31.0,3.0,0.0,2.0,0.0,1.0,161465.31,0.0
-540,15773971.0,DeRose,756.0,Germany,Male,36.0,3.0,144961.97,1.0,0.0,0.0,185543.68,1.0
-541,15794868.0,Paling,721.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,159823.16,0.0
-542,15681075.0,Fisk,633.0,Germany,Female,38.0,1.0,113715.36,2.0,0.0,0.0,170980.86,0.0
-543,15804052.0,Chia,632.0,France,Male,37.0,1.0,93791.38,1.0,0.0,1.0,72993.65,0.0
-544,15643865.0,Nwachetam,721.0,Spain,Female,35.0,5.0,147493.89,3.0,1.0,0.0,129980.93,1.0
-545,15751137.0,Hs?,652.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,91560.63,0.0
-546,15804950.0,Chukwuebuka,792.0,Spain,Male,36.0,7.0,97565.74,1.0,1.0,1.0,133897.32,0.0
-547,15635893.0,Teng,743.0,Germany,Male,37.0,3.0,77846.9,1.0,1.0,1.0,84467.38,0.0
-548,15747358.0,Hsia,749.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,90883.96,0.0
-549,15659800.0,Chiganu,794.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,0.0,110916.15,0.0
-550,15569503.0,H?,651.0,France,Male,37.0,6.0,140614.15,1.0,1.0,0.0,160129.99,0.0
-551,15783526.0,Iloerika,591.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,97985.4,0.0
-552,15591512.0,Macleod,728.0,Germany,Female,30.0,7.0,111837.78,1.0,1.0,1.0,163736.09,0.0
-553,15678497.0,Chiu,445.0,France,Male,45.0,9.0,0.0,2.0,0.0,0.0,162501.83,1.0
-554,15663883.0,Liao,667.0,France,Male,31.0,6.0,106011.59,1.0,1.0,0.0,153396.05,0.0
-555,15614962.0,Chukwueloka,850.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,153727.32,0.0
-556,15576676.0,Chikwendu,699.0,Germany,Female,56.0,6.0,132088.59,1.0,1.0,1.0,92914.67,1.0
-557,15675337.0,Harker,731.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,114935.21,0.0
-558,15756026.0,Nkemakonam,709.0,Spain,Male,30.0,3.0,0.0,1.0,1.0,0.0,9241.78,0.0
-559,15585928.0,Toscani,613.0,France,Male,28.0,7.0,0.0,1.0,0.0,1.0,51387.1,0.0
-560,15764033.0,Biryukov,682.0,Spain,Male,42.0,9.0,0.0,1.0,1.0,1.0,87822.14,0.0
-561,15615352.0,Chiang,627.0,France,Male,31.0,7.0,152303.8,1.0,1.0,0.0,112503.24,0.0
-562,15772337.0,Chukwuebuka,686.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,143450.61,0.0
-563,15595440.0,Trevisano,818.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,176913.42,0.0
-564,15673790.0,Kao,661.0,Germany,Male,39.0,3.0,113590.73,1.0,1.0,0.0,62703.38,1.0
-565,15662063.0,Ch'iu,738.0,Spain,Male,50.0,8.0,0.0,2.0,1.0,1.0,82298.59,0.0
-566,15680405.0,Chukwualuka,721.0,Spain,Male,27.0,2.0,0.0,2.0,0.0,0.0,187900.12,0.0
-567,15583718.0,Lucciano,713.0,France,Female,43.0,3.0,0.0,2.0,1.0,1.0,152992.04,0.0
-568,15598510.0,Chikezie,571.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,82822.34,0.0
-569,15632050.0,K'ung,559.0,France,Male,23.0,3.0,0.0,2.0,1.0,1.0,70532.53,0.0
-570,15673529.0,Y?an,636.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,133535.29,0.0
-571,15690829.0,Nnanna,629.0,France,Male,29.0,1.0,73070.18,1.0,0.0,0.0,132798.65,0.0
-572,15771742.0,Liao,683.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,1.0,145792.01,0.0
-573,15653780.0,Pirozzi,682.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,49707.85,0.0
-574,15804052.0,P'eng,622.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,86935.08,0.0
-575,15568106.0,Macdonald,745.0,France,Female,39.0,5.0,111094.05,1.0,1.0,0.0,133051.64,0.0
-576,15646521.0,Chin,729.0,France,Female,41.0,1.0,0.0,1.0,1.0,0.0,103480.69,1.0
-577,15811588.0,Mazzi,641.0,Spain,Male,30.0,2.0,0.0,1.0,1.0,0.0,109145.2,0.0
-578,15647898.0,Ch'in,699.0,Spain,Male,30.0,8.0,0.0,1.0,0.0,0.0,91514.62,0.0
-579,15666884.0,Milanesi,749.0,France,Male,29.0,8.0,0.0,2.0,0.0,1.0,88890.05,0.0
-580,15656937.0,Linger,651.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,86099.23,0.0
-581,15724150.0,Ibeamaka,691.0,Spain,Male,33.0,3.0,102645.48,2.0,1.0,0.0,135925.72,0.0
-582,15691823.0,Chukwudi,627.0,France,Female,41.0,2.0,0.0,1.0,1.0,0.0,182784.11,0.0
-583,15771856.0,T'ien,595.0,France,Male,24.0,8.0,0.0,2.0,1.0,1.0,132038.65,0.0
-584,15591286.0,Iloerika,669.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,146891.07,0.0
-585,15783718.0,Shih,673.0,Germany,Female,44.0,1.0,124099.13,1.0,1.0,0.0,91573.19,1.0
-586,15784761.0,Ukaegbunam,582.0,Spain,Female,48.0,1.0,0.0,1.0,0.0,1.0,182738.0,1.0
-587,15654096.0,Pinto,607.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,1354.25,0.0
-588,15813140.0,Chiu,468.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,172049.84,0.0
-589,15600974.0,Tuan,526.0,Spain,Female,36.0,8.0,141573.55,1.0,1.0,0.0,75832.53,0.0
-590,15682533.0,Young,717.0,Germany,Female,31.0,5.0,157791.06,1.0,1.0,1.0,153662.88,0.0
-591,15741875.0,Ugochukwu,563.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,47832.82,0.0
-592,15720354.0,Ch'eng,696.0,France,Male,33.0,8.0,134446.04,1.0,1.0,1.0,126396.01,0.0
-593,15769254.0,Onuoha,758.0,Germany,Male,32.0,7.0,102703.62,2.0,0.0,1.0,188244.4,0.0
-594,15643487.0,Zuyev,608.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,187943.6,0.0
-595,15598044.0,Martin,646.0,France,Female,39.0,2.0,151242.99,1.0,1.0,1.0,62722.44,0.0
-596,15776629.0,Ts'ai,681.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,149665.65,0.0
-597,15672798.0,Onyemauchechukwu,592.0,France,Male,28.0,1.0,0.0,2.0,1.0,1.0,93835.64,0.0
-598,15681696.0,Fiorentini,521.0,Germany,Female,23.0,4.0,110822.9,1.0,1.0,1.0,160979.66,0.0
-599,15668747.0,Mazzanti,623.0,Germany,Male,30.0,3.0,106036.52,2.0,1.0,0.0,171733.22,0.0
-600,15802859.0,Maclean,790.0,France,Female,31.0,6.0,110864.38,1.0,1.0,1.0,61636.22,0.0
-601,15783029.0,Yeh,633.0,Germany,Male,31.0,8.0,141528.88,2.0,1.0,0.0,55859.27,0.0
-602,15711015.0,Nkemdilim,613.0,Germany,Male,37.0,1.0,116498.22,1.0,1.0,1.0,88866.92,0.0
-603,15640855.0,Fennell,569.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,73564.44,0.0
-604,15783026.0,T'ien,685.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,140881.2,0.0
-605,15656592.0,Ch'iu,661.0,Spain,Male,32.0,6.0,0.0,2.0,0.0,1.0,85411.01,0.0
-606,15589210.0,Hs?,738.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,163798.41,0.0
-607,15624592.0,K?,595.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,9241.83,0.0
-608,15773776.0,Ozioma,609.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,103944.2,0.0
-609,15796969.0,Macleod,687.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,1.0,79633.38,0.0
-610,15776939.0,Chu,667.0,Germany,Female,40.0,9.0,79340.95,1.0,1.0,1.0,133573.29,0.0
-611,15627395.0,Young,684.0,France,Female,27.0,8.0,0.0,2.0,0.0,0.0,64350.8,0.0
-612,15724296.0,P'eng,633.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,111698.0,0.0
-613,15743016.0,Hsia,635.0,Spain,Male,44.0,3.0,124532.4,1.0,1.0,0.0,120853.83,0.0
-614,15718773.0,Y?,645.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,83617.53,0.0
-615,15626485.0,Ch'ien,603.0,France,Male,24.0,1.0,125837.42,1.0,1.0,1.0,138540.24,0.0
-616,15646615.0,Yefremova,683.0,Spain,Male,61.0,8.0,0.0,1.0,1.0,1.0,180716.1,0.0
-617,15726695.0,Chidozie,850.0,Germany,Male,39.0,4.0,115282.3,2.0,1.0,1.0,94820.85,0.0
-618,15799653.0,Chukwubuikem,605.0,Germany,Male,29.0,6.0,120787.68,1.0,1.0,0.0,129890.93,0.0
-619,15687431.0,Maclean,662.0,France,Male,22.0,9.0,0.0,2.0,1.0,1.0,134612.41,0.0
-620,15631267.0,Okorie,603.0,France,Female,50.0,6.0,59532.18,1.0,1.0,0.0,87071.18,1.0
-621,15568748.0,Onuoha,716.0,France,Male,52.0,7.0,78653.05,1.0,0.0,0.0,164717.95,1.0
-622,15576526.0,Clements,538.0,Spain,Male,44.0,9.0,111723.08,1.0,0.0,0.0,143463.28,0.0
-623,15692226.0,Ch'ien,667.0,France,Female,41.0,6.0,0.0,2.0,1.0,0.0,70774.97,0.0
-624,15668032.0,Hsia,651.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,167909.52,0.0
-625,15601012.0,Ts'ai,726.0,Spain,Female,39.0,4.0,0.0,2.0,1.0,1.0,158973.77,0.0
-626,15756901.0,Burt,590.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,0.0,167553.06,0.0
-627,15652030.0,Nnachetam,556.0,France,Female,30.0,10.0,0.0,2.0,1.0,1.0,179445.28,0.0
-628,15679760.0,Nwabugwu,684.0,Germany,Male,29.0,9.0,150826.99,1.0,0.0,1.0,132038.65,0.0
-629,15703614.0,Shih,644.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,184748.23,1.0
-630,15686884.0,Ikedinachukwu,714.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,147964.99,0.0
-631,15787597.0,Kao,628.0,Germany,Male,46.0,1.0,140187.1,1.0,1.0,1.0,155876.06,0.0
-632,15801994.0,Onwuamaeze,663.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,141872.05,0.0
-633,15799652.0,Udegbunam,739.0,Spain,Male,35.0,3.0,0.0,2.0,0.0,0.0,121763.68,0.0
-634,15661821.0,L?,653.0,Spain,Male,32.0,9.0,141749.74,1.0,1.0,1.0,94875.03,0.0
-635,15692575.0,L?,683.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,141180.66,0.0
-636,15623210.0,Ritchie,644.0,France,Male,45.0,7.0,0.0,2.0,1.0,1.0,171483.82,0.0
-637,15579566.0,Mancini,659.0,France,Male,24.0,4.0,0.0,2.0,1.0,0.0,163331.65,0.0
-638,15754569.0,T'ien,641.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,86022.09,0.0
-639,15752731.0,L?,704.0,France,Female,60.0,7.0,0.0,1.0,0.0,0.0,95957.09,1.0
-640,15638806.0,Maclean,711.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,82438.13,0.0
-641,15594577.0,Ting,717.0,France,Male,36.0,0.0,126473.99,1.0,1.0,0.0,182867.84,0.0
-642,15686963.0,Chiang,749.0,Spain,Male,47.0,5.0,0.0,2.0,1.0,1.0,62845.64,0.0
-643,15625126.0,Y?,703.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,140745.91,0.0
-644,15793046.0,Li Fonti,675.0,France,Male,27.0,3.0,143566.12,1.0,1.0,1.0,164040.94,0.0
-645,15750728.0,Yermakova,707.0,France,Male,39.0,3.0,0.0,1.0,1.0,1.0,129561.87,0.0
-646,15775238.0,Mironova,641.0,France,Male,35.0,7.0,121537.71,1.0,1.0,0.0,25803.59,0.0
-647,15703588.0,Nwachinemelu,719.0,Germany,Female,50.0,6.0,116576.45,1.0,1.0,0.0,172629.67,1.0
-648,15753895.0,Ting,723.0,France,Male,30.0,0.0,0.0,2.0,1.0,0.0,121063.9,0.0
-649,15790235.0,Nwokeocha,544.0,France,Male,35.0,4.0,0.0,1.0,1.0,0.0,57844.26,1.0
-650,15812191.0,Chiang,676.0,Germany,Female,40.0,9.0,111065.93,2.0,1.0,0.0,81898.81,0.0
-651,15712121.0,Martin,648.0,France,Female,62.0,9.0,0.0,1.0,1.0,1.0,172668.39,1.0
-652,15803378.0,Onio,673.0,France,Female,43.0,4.0,155739.76,1.0,0.0,1.0,111622.76,0.0
-653,15682454.0,Nucci,727.0,France,Female,37.0,5.0,0.0,1.0,0.0,1.0,120106.1,0.0
-654,15585595.0,T'an,602.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,0.0,177614.17,0.0
-655,15782941.0,Onwumelu,586.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,60954.45,0.0
-656,15718289.0,Hsiao,669.0,Germany,Female,50.0,5.0,131834.76,1.0,0.0,0.0,70774.97,1.0
-657,15696900.0,Ch'ien,699.0,France,Male,37.0,7.0,98548.62,1.0,0.0,0.0,63736.17,0.0
-658,15572291.0,Pethard,604.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,52409.87,0.0
-659,15604497.0,Milne,597.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,123981.36,0.0
-660,15773789.0,Kao,656.0,France,Female,34.0,1.0,0.0,2.0,0.0,0.0,173498.45,0.0
-661,15653547.0,Ts'ui,742.0,France,Female,38.0,2.0,107784.65,1.0,1.0,0.0,174729.3,0.0
-662,15705521.0,Hsiung,850.0,Germany,Male,25.0,3.0,103656.82,2.0,1.0,1.0,91070.58,0.0
-663,15703763.0,Onyinyechukwuka,751.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,120511.04,0.0
-664,15649024.0,Genovesi,521.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,53478.02,0.0
-665,15771054.0,Hsia,686.0,France,Female,53.0,6.0,0.0,1.0,1.0,1.0,137028.51,0.0
-666,15597915.0,Ch'ien,684.0,Spain,Female,28.0,6.0,126817.65,1.0,1.0,1.0,148527.56,0.0
-667,15631813.0,P'eng,721.0,Germany,Female,42.0,10.0,124427.33,1.0,1.0,0.0,132973.15,1.0
-668,15702541.0,Pirozzi,646.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,176712.59,0.0
-669,15736112.0,Ukaegbunam,592.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,131300.68,0.0
-670,15703923.0,Palermo,641.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,180551.76,0.0
-671,15776051.0,Yefimova,721.0,France,Male,35.0,7.0,0.0,1.0,1.0,0.0,96660.76,1.0
-672,15780128.0,Nwabugwu,612.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,132649.85,0.0
-673,15792868.0,Onyemauchechukwu,554.0,France,Male,46.0,3.0,0.0,1.0,1.0,1.0,706.5,0.0
-674,15641640.0,Kuo,554.0,Spain,Male,29.0,10.0,0.0,2.0,1.0,1.0,109843.16,0.0
-675,15770039.0,Tien,581.0,France,Male,36.0,8.0,0.0,2.0,0.0,0.0,132424.69,0.0
-676,15621644.0,Maclean,573.0,France,Female,39.0,10.0,175314.72,1.0,1.0,1.0,92647.94,0.0
-677,15786717.0,De Luca,714.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,130883.9,0.0
-678,15651022.0,Fanucci,538.0,France,Male,19.0,1.0,0.0,2.0,0.0,0.0,125445.8,0.0
-679,15595427.0,Ch'in,773.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,94003.11,0.0
-680,15746461.0,Nwachukwu,725.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,0.0,100433.83,0.0
-681,15586680.0,Kosisochukwu,716.0,Germany,Male,32.0,9.0,122724.51,1.0,1.0,1.0,74800.99,0.0
-682,15762799.0,Onwubiko,682.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,186489.95,0.0
-683,15569249.0,Bibi,563.0,Germany,Male,36.0,3.0,119657.53,1.0,1.0,1.0,96444.88,0.0
-684,15697567.0,Chiemela,805.0,Germany,Female,45.0,4.0,108239.11,2.0,1.0,1.0,180598.28,0.0
-685,15577587.0,DeRose,586.0,France,Female,42.0,6.0,0.0,2.0,1.0,1.0,141763.27,0.0
-686,15758813.0,McIntyre,621.0,Germany,Male,47.0,9.0,123971.27,2.0,0.0,0.0,78650.95,1.0
-687,15780654.0,Chin,635.0,France,Male,35.0,8.0,0.0,2.0,0.0,1.0,64872.55,0.0
-688,15778959.0,Manna,646.0,France,Male,44.0,2.0,0.0,2.0,1.0,1.0,181419.29,0.0
-689,15775231.0,Ch'ien,775.0,Spain,Male,44.0,3.0,0.0,2.0,1.0,0.0,170634.49,0.0
-690,15761053.0,Onwubiko,706.0,France,Male,39.0,4.0,119336.29,1.0,0.0,0.0,55732.9,0.0
-691,15651955.0,O'Sullivan,676.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,1.0,145789.71,0.0
-692,15774551.0,Babbage,641.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,89286.4,0.0
-693,15777784.0,Lo Duca,741.0,France,Male,42.0,6.0,0.0,2.0,1.0,1.0,166694.93,0.0
-694,15716276.0,Lo,669.0,Spain,Male,43.0,4.0,0.0,2.0,0.0,1.0,176902.83,0.0
-695,15743538.0,Lucciano,633.0,Germany,Male,37.0,10.0,131935.56,1.0,0.0,0.0,146511.2,0.0
-696,15664555.0,Tretiakov,642.0,Germany,Male,39.0,9.0,131326.47,1.0,1.0,0.0,83058.14,0.0
-697,15691840.0,Ku,693.0,Germany,Female,39.0,1.0,118628.8,1.0,0.0,0.0,142963.29,0.0
-698,15705860.0,Chidimma,662.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,59325.21,0.0
-699,15769917.0,De Luca,637.0,France,Male,40.0,4.0,0.0,2.0,0.0,1.0,176843.53,0.0
-700,15605698.0,Ch'ien,585.0,Spain,Female,48.0,9.0,0.0,1.0,1.0,1.0,111285.85,1.0
-701,15603749.0,K'ung,676.0,Germany,Male,27.0,7.0,165445.04,2.0,0.0,1.0,73334.04,0.0
-702,15585961.0,Nwora,744.0,France,Male,31.0,10.0,94748.76,1.0,1.0,1.0,167023.95,0.0
-703,15768682.0,Chibugo,803.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,100998.5,0.0
-704,15702801.0,Zikoranachidimma,684.0,Spain,Male,42.0,6.0,0.0,2.0,1.0,0.0,81884.8,0.0
-705,15582914.0,Ts'ai,663.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,141501.45,0.0
-706,15680197.0,Ku,586.0,Spain,Female,36.0,9.0,0.0,1.0,0.0,1.0,56905.38,0.0
-707,15609637.0,Hsiung,685.0,Germany,Male,36.0,8.0,96679.69,2.0,1.0,0.0,52462.25,0.0
-708,15623989.0,Tsao,621.0,Spain,Female,37.0,3.0,0.0,1.0,1.0,0.0,54474.48,0.0
-709,15717225.0,Kepley,708.0,France,Male,37.0,6.0,113909.67,1.0,1.0,0.0,175749.36,0.0
-710,15662091.0,Goliwe,703.0,France,Male,41.0,4.0,0.0,1.0,1.0,0.0,112269.67,1.0
-711,15812191.0,Chibugo,681.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,90839.61,0.0
-712,15751561.0,Iadanza,717.0,Spain,Male,51.0,4.0,0.0,2.0,1.0,1.0,52487.98,0.0
-713,15663438.0,P'eng,691.0,Spain,Female,32.0,3.0,110182.54,1.0,1.0,0.0,134878.19,0.0
-714,15782736.0,Mazzanti,543.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,170821.43,0.0
-715,15720354.0,Hsieh,757.0,Germany,Male,53.0,5.0,137453.43,1.0,1.0,0.0,171708.06,1.0
-716,15746035.0,Liao,675.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,125986.18,0.0
-717,15751175.0,Onyekaozulu,707.0,France,Male,29.0,1.0,0.0,2.0,0.0,0.0,128892.66,0.0
-718,15667633.0,Chung,592.0,Spain,Male,43.0,4.0,0.0,1.0,1.0,0.0,63908.86,1.0
-719,15682355.0,Chibugo,737.0,Spain,Male,24.0,3.0,0.0,2.0,1.0,0.0,143783.46,0.0
-720,15598510.0,Lucchesi,686.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,84908.2,0.0
-721,15654463.0,H?,468.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,171378.77,0.0
-722,15693376.0,Chukwuemeka,599.0,Germany,Female,53.0,10.0,117561.49,1.0,1.0,1.0,127558.26,1.0
-723,15732293.0,Nkemakonam,664.0,France,Male,37.0,1.0,94464.81,1.0,1.0,0.0,54474.48,0.0
-724,15768282.0,Walker,584.0,Germany,Male,32.0,6.0,103819.43,1.0,1.0,1.0,62347.71,0.0
-725,15606097.0,Nnaemeka,567.0,Spain,Male,24.0,5.0,0.0,2.0,1.0,1.0,166476.95,0.0
-726,15771409.0,Onwumelu,725.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,55313.51,0.0
-727,15658969.0,Calabresi,644.0,France,Female,35.0,10.0,0.0,2.0,1.0,1.0,54487.43,0.0
-728,15641158.0,Ch'in,705.0,France,Male,51.0,9.0,0.0,1.0,1.0,0.0,109057.56,1.0
-729,15811490.0,Padovano,581.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,0.0,83022.09,0.0
-730,15716041.0,Ch'ien,839.0,France,Female,36.0,9.0,116886.59,1.0,1.0,0.0,60905.51,0.0
-731,15586699.0,Trevisani,752.0,Germany,Male,38.0,9.0,126573.33,2.0,1.0,1.0,136497.28,0.0
-732,15672397.0,Chiu,486.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,176868.89,0.0
-733,15739168.0,Atherton,582.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,95957.09,0.0
-734,15578908.0,Nnaife,681.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,162523.66,0.0
-735,15778975.0,Maclean,637.0,France,Female,31.0,4.0,140133.08,1.0,1.0,0.0,12483.56,0.0
-736,15578788.0,Uwaezuoke,586.0,Germany,Male,39.0,0.0,127616.56,1.0,0.0,0.0,149913.84,0.0
-737,15773776.0,Chukwualuka,762.0,France,Male,41.0,10.0,0.0,2.0,1.0,1.0,66469.2,0.0
-738,15572467.0,Chukwujamuike,568.0,France,Male,42.0,1.0,0.0,1.0,1.0,1.0,49826.68,1.0
-739,15649150.0,Shih,684.0,France,Female,60.0,1.0,137714.7,1.0,0.0,1.0,71179.31,1.0
-740,15713774.0,Chien,706.0,France,Female,44.0,8.0,0.0,2.0,1.0,0.0,176903.35,0.0
-741,15792631.0,Chia,691.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,52462.25,0.0
-742,15634143.0,Hsing,689.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,131662.47,0.0
-743,15650026.0,Rischbieth,569.0,Germany,Male,28.0,5.0,77002.2,2.0,0.0,0.0,35337.31,0.0
-744,15660385.0,Pugh,829.0,France,Male,53.0,5.0,0.0,1.0,1.0,0.0,123882.73,1.0
-745,15783659.0,Hsing,775.0,France,Female,38.0,5.0,121355.99,1.0,1.0,1.0,64477.25,0.0
-746,15773394.0,Tsou,669.0,France,Male,39.0,6.0,0.0,1.0,1.0,0.0,63889.1,0.0
-747,15724181.0,H?,651.0,Germany,Male,38.0,6.0,122417.17,2.0,1.0,1.0,146511.2,0.0
-748,15740356.0,Chijioke,755.0,Spain,Male,43.0,5.0,122449.18,1.0,0.0,0.0,142783.98,0.0
-749,15679760.0,Yobachukwu,678.0,France,Female,50.0,9.0,0.0,1.0,1.0,0.0,88827.67,1.0
-750,15729557.0,K'ung,682.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,115779.48,0.0
-751,15688865.0,Yobachi,586.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,26793.82,0.0
-752,15585853.0,Piazza,632.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,79919.13,0.0
-753,15806926.0,Genovese,706.0,Spain,Female,51.0,3.0,106687.76,1.0,1.0,0.0,64593.07,1.0
-754,15592994.0,Ch'ang,704.0,France,Female,23.0,8.0,107594.11,1.0,0.0,1.0,132467.76,0.0
-755,15798850.0,Hsing,666.0,Spain,Female,38.0,4.0,0.0,1.0,1.0,1.0,17095.49,0.0
-756,15778025.0,Y?an,587.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,1.0,157959.02,1.0
-757,15802593.0,Hsia,684.0,France,Male,41.0,1.0,153696.97,1.0,0.0,1.0,117572.59,0.0
-758,15716781.0,Chigozie,687.0,Germany,Female,44.0,6.0,102290.56,1.0,1.0,0.0,172576.43,1.0
-759,15570087.0,Zetticci,699.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,126812.63,0.0
-760,15624072.0,Skelton,619.0,Germany,Female,31.0,2.0,134006.77,2.0,1.0,1.0,96864.36,0.0
-761,15729238.0,Chiagoziem,543.0,Spain,Male,42.0,10.0,178032.53,1.0,1.0,1.0,88837.15,0.0
-762,15652693.0,Baresi,577.0,France,Male,25.0,5.0,0.0,2.0,1.0,1.0,60917.24,0.0
-763,15789863.0,Hsueh,637.0,Germany,Male,34.0,6.0,81564.1,1.0,1.0,0.0,158027.57,0.0
-764,15617166.0,Onyekaozulu,679.0,Germany,Male,49.0,4.0,109740.53,1.0,1.0,0.0,117832.88,1.0
-765,15705968.0,Pinto,769.0,France,Male,42.0,2.0,0.0,1.0,0.0,0.0,3185.67,0.0
-766,15743826.0,Nwokezuike,648.0,France,Male,54.0,6.0,139872.81,1.0,1.0,1.0,175072.47,1.0
-767,15750447.0,Hsieh,746.0,Germany,Male,29.0,10.0,144772.69,2.0,1.0,1.0,71830.1,0.0
-768,15739931.0,Palerma,692.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,0.0,83052.03,0.0
-769,15608528.0,Onyemere,678.0,France,Female,25.0,5.0,0.0,2.0,1.0,0.0,163534.6,0.0
-770,15584364.0,Chinwemma,640.0,France,Male,49.0,6.0,0.0,2.0,0.0,0.0,91573.19,0.0
-771,15718057.0,T'an,686.0,Germany,Male,33.0,8.0,122295.17,1.0,1.0,0.0,173632.92,0.0
-772,15718289.0,Nkemdirim,604.0,France,Male,31.0,4.0,120623.21,1.0,1.0,0.0,102515.42,0.0
-773,15668958.0,Trevisano,633.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,107964.36,0.0
-774,15791373.0,Tsao,632.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,180553.48,0.0
-775,15700654.0,Iadanza,587.0,France,Female,58.0,2.0,0.0,2.0,1.0,1.0,11783.1,0.0
-776,15806964.0,Smith,663.0,France,Female,38.0,9.0,0.0,1.0,1.0,1.0,170621.55,0.0
-777,15667460.0,Clements,726.0,France,Female,34.0,7.0,105473.74,2.0,1.0,0.0,49136.29,0.0
-778,15710397.0,Nwankwo,583.0,France,Male,34.0,2.0,0.0,1.0,1.0,0.0,141872.05,1.0
-779,15684861.0,Genovese,708.0,France,Male,32.0,3.0,0.0,1.0,0.0,1.0,150580.88,0.0
-780,15676835.0,Ch'ang,559.0,Spain,Male,32.0,3.0,136689.66,1.0,1.0,1.0,135083.73,1.0
-781,15772383.0,Teng,699.0,Germany,Female,55.0,2.0,118613.26,1.0,0.0,1.0,93795.86,1.0
-782,15721917.0,Oluchukwu,624.0,France,Female,36.0,2.0,141642.92,1.0,0.0,1.0,187811.71,1.0
-783,15703588.0,Nnaemeka,636.0,Germany,Male,36.0,3.0,113034.31,1.0,1.0,1.0,57558.95,1.0
-784,15593501.0,Ritchie,677.0,France,Female,45.0,8.0,0.0,2.0,0.0,0.0,141798.98,0.0
-785,15627000.0,Onochie,726.0,France,Female,30.0,9.0,0.0,2.0,1.0,0.0,126469.09,0.0
-786,15589488.0,Lettiere,659.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,88815.25,0.0
-787,15590094.0,Lo,614.0,Germany,Female,49.0,7.0,120530.34,1.0,1.0,0.0,148087.62,1.0
-788,15668057.0,Ch'ang,688.0,France,Female,34.0,3.0,0.0,1.0,0.0,1.0,69381.05,0.0
-789,15743076.0,McLean,468.0,France,Male,36.0,2.0,0.0,1.0,0.0,0.0,136886.86,1.0
-790,15613787.0,Milanesi,559.0,Spain,Female,33.0,1.0,0.0,2.0,1.0,0.0,72375.03,0.0
-791,15602354.0,Ritchie,632.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,166677.54,0.0
-792,15793883.0,Goliwe,634.0,Germany,Female,54.0,0.0,123331.36,1.0,1.0,0.0,96660.76,1.0
-793,15579915.0,Yeh,663.0,Germany,Male,37.0,6.0,105435.74,2.0,1.0,0.0,106977.53,0.0
-794,15715951.0,Iweobiegbunam,683.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,52896.31,0.0
-795,15593365.0,Chin,575.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,102517.83,0.0
-796,15706761.0,Genovese,669.0,Spain,Female,33.0,3.0,0.0,2.0,1.0,0.0,10488.44,0.0
-797,15596939.0,Nucci,729.0,France,Female,31.0,6.0,0.0,2.0,0.0,1.0,165576.55,0.0
-798,15588854.0,Ch'ien,612.0,France,Female,39.0,3.0,0.0,1.0,0.0,0.0,125518.32,1.0
-799,15671256.0,De Luca,516.0,France,Female,48.0,8.0,82696.09,1.0,1.0,0.0,103372.79,1.0
-800,15591512.0,Genovese,710.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,67789.99,0.0
-801,15609928.0,Hs?,721.0,France,Male,49.0,3.0,0.0,2.0,1.0,1.0,145551.6,1.0
-802,15782404.0,Calabresi,658.0,Spain,Female,31.0,6.0,123544.0,1.0,1.0,0.0,121451.21,1.0
-803,15769293.0,Davide,586.0,France,Female,41.0,1.0,0.0,1.0,1.0,1.0,51919.04,1.0
-804,15656086.0,Tsao,685.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,141533.19,0.0
-805,15786800.0,Ts'ai,719.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,155743.48,0.0
-806,15663410.0,Trevisani,627.0,France,Male,43.0,8.0,104867.18,1.0,1.0,0.0,145560.82,0.0
-807,15774727.0,Ch'ien,678.0,France,Female,28.0,1.0,0.0,1.0,1.0,1.0,85891.02,0.0
-808,15717290.0,Lettiere,699.0,France,Male,38.0,4.0,0.0,1.0,0.0,0.0,168034.9,1.0
-809,15575247.0,Ch'iu,634.0,France,Male,31.0,1.0,146126.75,1.0,1.0,1.0,90212.38,0.0
-810,15780498.0,Horsley,662.0,France,Male,34.0,1.0,0.0,1.0,1.0,1.0,56984.58,0.0
-811,15583748.0,Zetticci,559.0,France,Male,35.0,0.0,133681.98,1.0,0.0,1.0,178045.97,0.0
-812,15645593.0,Ch'iu,710.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,1.0,178458.86,0.0
-813,15809837.0,Chiu,605.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,172668.39,0.0
-814,15796787.0,Hsiao,664.0,France,Male,30.0,3.0,171770.84,1.0,1.0,0.0,52796.31,0.0
-815,15793346.0,Pagnotto,692.0,Germany,Male,51.0,7.0,158621.04,1.0,1.0,0.0,140941.47,0.0
-816,15693326.0,Onyekachi,479.0,Germany,Female,28.0,6.0,132835.56,1.0,0.0,1.0,175893.53,1.0
-817,15791323.0,T'ang,653.0,Spain,Male,48.0,5.0,0.0,2.0,1.0,0.0,100618.17,0.0
-818,15769246.0,Chiang,595.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,152992.04,0.0
-819,15679810.0,Ch'ien,607.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,141827.33,0.0
-820,15578747.0,Chukwuemeka,629.0,Germany,Male,39.0,7.0,79610.92,2.0,1.0,1.0,84760.32,0.0
-821,15735388.0,Hsu,711.0,Germany,Male,40.0,5.0,131563.88,3.0,1.0,0.0,59604.45,1.0
-822,15670822.0,Hsieh,607.0,Germany,Female,50.0,6.0,91955.61,1.0,0.0,1.0,187985.7,1.0
-823,15723216.0,Pirozzi,710.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,61324.87,0.0
-824,15611157.0,Chimezie,758.0,Spain,Male,38.0,7.0,0.0,2.0,0.0,0.0,143980.28,0.0
-825,15776931.0,Lucciano,593.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,170122.22,0.0
-826,15621075.0,Clements,746.0,Germany,Female,33.0,4.0,121730.49,2.0,1.0,1.0,73881.68,0.0
-827,15809006.0,Chia,579.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,147090.9,0.0
-828,15793883.0,Fan,632.0,Spain,Female,34.0,9.0,0.0,2.0,0.0,0.0,174431.01,0.0
-829,15583548.0,T'ien,637.0,France,Male,45.0,9.0,0.0,1.0,1.0,0.0,53353.36,1.0
-830,15606097.0,Yobachi,562.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,161203.6,0.0
-831,15757837.0,Chiemenam,676.0,France,Female,32.0,9.0,165947.99,1.0,1.0,1.0,112973.33,0.0
-832,15632002.0,Frolova,617.0,Spain,Male,29.0,5.0,97565.74,1.0,1.0,0.0,113557.9,0.0
-833,15631693.0,Lucchese,553.0,Germany,Male,60.0,9.0,118772.71,1.0,1.0,0.0,150850.29,1.0
-834,15786800.0,Zito,673.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,136521.82,0.0
-835,15662291.0,Nwabugwu,553.0,Spain,Female,40.0,6.0,0.0,1.0,0.0,1.0,91098.76,0.0
-836,15806945.0,Palerma,512.0,France,Female,52.0,0.0,0.0,1.0,1.0,1.0,51454.06,1.0
-837,15728005.0,Chigolum,682.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,96734.46,0.0
-838,15668894.0,White,738.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,66979.06,0.0
-839,15815236.0,Manna,850.0,France,Male,48.0,10.0,0.0,1.0,1.0,0.0,13898.31,1.0
-840,15721383.0,Chibugo,755.0,Germany,Female,34.0,8.0,101514.89,1.0,1.0,0.0,180551.76,0.0
-841,15681562.0,Lai,548.0,France,Female,39.0,5.0,117103.26,1.0,0.0,1.0,180800.42,0.0
-842,15699325.0,Okwudilichukwu,805.0,Germany,Male,25.0,8.0,115141.18,1.0,1.0,1.0,149614.81,0.0
-843,15604818.0,Hs?,644.0,Spain,Female,45.0,4.0,0.0,2.0,1.0,1.0,151316.37,0.0
-844,15692406.0,Chifley,724.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,172939.3,0.0
-845,15749381.0,Okwudilichukwu,539.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,141872.05,0.0
-846,15757867.0,O'Neill,637.0,France,Male,29.0,0.0,0.0,2.0,1.0,1.0,179521.84,0.0
-847,15682533.0,Ts'ai,699.0,France,Male,29.0,2.0,132450.24,1.0,0.0,1.0,100433.83,0.0
-848,15611409.0,Lupton,731.0,Germany,Female,42.0,10.0,137781.65,1.0,1.0,0.0,140929.98,0.0
-849,15651052.0,Nwebube,604.0,Spain,Female,30.0,10.0,0.0,1.0,0.0,0.0,125553.51,1.0
-850,15662662.0,Ritchie,588.0,France,Male,52.0,6.0,0.0,1.0,1.0,0.0,171575.54,1.0
-851,15581871.0,Tien,707.0,France,Female,39.0,7.0,0.0,1.0,1.0,0.0,73371.65,1.0
-852,15748709.0,Shih,551.0,Germany,Female,35.0,2.0,141078.37,2.0,1.0,0.0,104936.88,0.0
-853,15729689.0,Wang,661.0,France,Male,46.0,5.0,114631.23,1.0,1.0,1.0,169642.13,0.0
-854,15702300.0,Hsieh,705.0,Germany,Female,29.0,2.0,108546.16,1.0,1.0,1.0,76946.6,0.0
-855,15652069.0,Teng,708.0,Spain,Female,61.0,2.0,0.0,2.0,1.0,1.0,74850.84,0.0
-856,15738219.0,Tretiakova,628.0,Spain,Female,45.0,10.0,0.0,1.0,1.0,0.0,115136.51,1.0
-857,15654074.0,Biryukov,653.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,166495.2,0.0
-858,15783030.0,Pagnotto,607.0,France,Female,38.0,4.0,0.0,1.0,0.0,1.0,125918.17,0.0
-859,15759499.0,Ch'in,726.0,Germany,Male,47.0,3.0,114435.47,1.0,1.0,0.0,169287.99,1.0
-860,15684592.0,L?,597.0,France,Male,31.0,10.0,123870.07,1.0,1.0,1.0,161519.77,0.0
-861,15662501.0,Ch'in,572.0,Germany,Female,55.0,8.0,127772.1,1.0,1.0,0.0,179718.17,1.0
-862,15751177.0,Lucchese,580.0,France,Male,46.0,1.0,126576.62,1.0,0.0,0.0,172450.04,1.0
-863,15812071.0,Milne,682.0,Germany,Female,43.0,10.0,84327.77,2.0,0.0,1.0,186826.22,0.0
-864,15622033.0,Chidubem,537.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,177613.19,0.0
-865,15761986.0,H?,682.0,Germany,Male,48.0,5.0,92716.96,3.0,0.0,0.0,28797.32,1.0
-866,15727361.0,Yobanna,684.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,19233.41,0.0
-867,15663650.0,Trevisano,641.0,Germany,Male,39.0,9.0,143500.82,2.0,1.0,1.0,5907.11,0.0
-868,15736327.0,Onyenachiya,645.0,France,Female,32.0,4.0,116927.89,1.0,1.0,1.0,87412.24,0.0
-869,15641480.0,Palerma,628.0,France,Male,28.0,8.0,0.0,2.0,0.0,0.0,93839.3,0.0
-870,15716164.0,Hsieh,850.0,France,Female,35.0,5.0,122314.5,1.0,1.0,0.0,162697.93,0.0
-871,15725794.0,Yobanna,652.0,France,Female,45.0,5.0,0.0,2.0,1.0,1.0,60905.51,0.0
-872,15697311.0,Milanesi,614.0,France,Female,50.0,8.0,100476.46,1.0,1.0,0.0,121277.78,0.0
-873,15783865.0,Walker,644.0,Germany,Male,42.0,9.0,111837.78,2.0,1.0,0.0,120853.83,0.0
-874,15762793.0,Hao,560.0,France,Female,28.0,9.0,0.0,2.0,1.0,1.0,90876.95,0.0
-875,15636023.0,Sun,675.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,131772.51,0.0
-876,15779738.0,Udinese,790.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,0.0,163558.18,0.0
-877,15741180.0,Mazzanti,619.0,Germany,Male,27.0,5.0,152462.79,1.0,1.0,0.0,171519.06,0.0
-878,15757827.0,Young,607.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,106981.02,0.0
-879,15654074.0,McIntyre,645.0,Germany,Female,53.0,9.0,105575.06,1.0,0.0,0.0,178045.97,1.0
-880,15671165.0,T'ien,649.0,France,Female,48.0,7.0,0.0,2.0,1.0,0.0,123954.15,1.0
-881,15687913.0,Ponomarev,642.0,France,Male,28.0,7.0,0.0,1.0,1.0,1.0,60552.44,0.0
-882,15763798.0,Cremonesi,671.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,67830.2,0.0
-883,15654562.0,Lucchesi,678.0,Germany,Male,51.0,6.0,117879.32,3.0,0.0,0.0,163558.42,1.0
-884,15776947.0,Ebelegbulam,648.0,France,Male,31.0,8.0,113386.36,1.0,1.0,0.0,187985.85,0.0
-885,15782899.0,Chukwudi,751.0,Germany,Male,45.0,3.0,126473.99,1.0,0.0,1.0,15650.89,0.0
-886,15657778.0,Chien,656.0,France,Female,38.0,8.0,117852.26,1.0,0.0,1.0,96985.58,0.0
-887,15616270.0,Ch'ang,564.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,73656.38,0.0
-888,15662736.0,H?,596.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,114093.73,0.0
-889,15699446.0,Dike,652.0,France,Female,37.0,7.0,0.0,2.0,0.0,1.0,102297.15,0.0
-890,15658675.0,Tung,678.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,183980.56,0.0
-891,15600997.0,P'eng,700.0,France,Male,45.0,8.0,0.0,2.0,1.0,1.0,121098.65,0.0
-892,15659475.0,Lucciano,589.0,Spain,Male,40.0,6.0,0.0,1.0,0.0,1.0,158027.45,1.0
-893,15749401.0,Kosisochukwu,724.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,157891.86,0.0
-894,15761663.0,P'eng,641.0,France,Female,25.0,5.0,0.0,2.0,1.0,0.0,142453.07,0.0
-895,15782231.0,Goliwe,596.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,157891.86,0.0
-896,15595071.0,Goliwe,773.0,France,Female,39.0,2.0,0.0,1.0,1.0,0.0,140579.19,1.0
-897,15683657.0,Scott,677.0,France,Male,39.0,3.0,113657.41,1.0,1.0,1.0,176925.69,0.0
-898,15715078.0,Chiawuotu,637.0,France,Female,39.0,5.0,0.0,1.0,1.0,1.0,71173.83,0.0
-899,15703784.0,Chiemezie,634.0,France,Female,42.0,3.0,0.0,2.0,1.0,0.0,102076.03,0.0
-900,15595726.0,Ch'ang,603.0,France,Female,29.0,1.0,120811.89,1.0,1.0,1.0,176580.23,0.0
-901,15813917.0,Maclean,765.0,France,Female,33.0,6.0,141267.67,1.0,1.0,0.0,165038.41,0.0
-902,15761670.0,Pagnotto,731.0,France,Male,40.0,1.0,123081.42,1.0,1.0,0.0,60905.51,0.0
-903,15594720.0,Neumayer,657.0,Germany,Female,29.0,2.0,156864.2,1.0,1.0,0.0,122379.56,1.0
-904,15768072.0,Ch'ang,649.0,Germany,Male,60.0,2.0,118851.05,1.0,1.0,0.0,92612.88,1.0
-905,15711378.0,DeRose,636.0,Germany,Female,46.0,3.0,150186.82,1.0,0.0,0.0,59981.62,1.0
-906,15620579.0,Ikedinachukwu,694.0,France,Female,31.0,3.0,0.0,1.0,1.0,1.0,128882.79,0.0
-907,15592386.0,Lombardo,703.0,France,Female,43.0,5.0,0.0,3.0,1.0,1.0,174825.57,1.0
-908,15596074.0,Lo,625.0,France,Male,67.0,9.0,0.0,2.0,1.0,1.0,58814.41,0.0
-909,15804919.0,Chikelu,551.0,Spain,Male,66.0,2.0,0.0,2.0,1.0,1.0,83949.68,0.0
-910,15644765.0,Ku,730.0,Germany,Male,56.0,9.0,106036.52,1.0,1.0,1.0,149614.17,1.0
-911,15643359.0,Uwaezuoke,678.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,97939.3,0.0
-912,15592761.0,Ts'ai,743.0,France,Male,38.0,5.0,127585.98,1.0,1.0,1.0,145578.25,0.0
-913,15778861.0,Chukwukadibia,690.0,France,Female,30.0,9.0,0.0,2.0,0.0,1.0,70954.41,0.0
-914,15693326.0,Maclean,449.0,France,Female,39.0,4.0,0.0,2.0,0.0,0.0,153316.16,0.0
-915,15593365.0,Ritchie,719.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,159899.97,0.0
-916,15589924.0,Okwuoma,746.0,France,Female,39.0,10.0,0.0,1.0,1.0,0.0,26136.13,1.0
-917,15792679.0,Hs?,554.0,Germany,Male,25.0,10.0,90450.0,2.0,1.0,1.0,59195.05,0.0
-918,15750772.0,Genovese,757.0,Germany,Female,40.0,1.0,111855.03,1.0,1.0,0.0,156911.35,0.0
-919,15602312.0,Lucciano,675.0,France,Female,31.0,0.0,0.0,2.0,1.0,0.0,160589.18,0.0
-920,15759717.0,Pagnotto,743.0,Germany,Female,32.0,7.0,133658.24,1.0,1.0,0.0,151401.33,0.0
-921,15736990.0,Olisaemeka,685.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,0.0,152643.48,0.0
-922,15714485.0,Nwabugwu,705.0,Spain,Male,40.0,3.0,0.0,2.0,1.0,1.0,184977.2,0.0
-923,15594576.0,Fancher,639.0,France,Female,40.0,9.0,0.0,1.0,1.0,0.0,82462.25,1.0
-924,15796496.0,Lucchesi,681.0,France,Female,45.0,5.0,0.0,2.0,1.0,1.0,106977.8,0.0
-925,15740164.0,Ugochukwu,724.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,184839.7,0.0
-926,15676513.0,Chidiebele,644.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,126307.25,0.0
-927,15737571.0,Chiemenam,578.0,Spain,Male,23.0,4.0,0.0,2.0,1.0,1.0,48249.88,0.0
-928,15634591.0,Chiang,694.0,France,Female,45.0,5.0,0.0,2.0,0.0,0.0,159463.7,1.0
-929,15798824.0,Lucchese,850.0,France,Female,25.0,3.0,0.0,2.0,1.0,0.0,65816.53,0.0
-930,15722533.0,Brown,728.0,France,Female,31.0,9.0,113146.98,2.0,0.0,0.0,120511.03,0.0
-931,15773013.0,P'eng,712.0,France,Female,31.0,9.0,0.0,2.0,0.0,0.0,134901.41,0.0
-932,15681957.0,Brown,615.0,France,Male,34.0,2.0,123031.56,2.0,1.0,1.0,125518.29,0.0
-933,15649024.0,Nwachukwu,727.0,Spain,Male,57.0,7.0,115725.24,1.0,1.0,1.0,180829.99,0.0
-934,15732643.0,P'eng,828.0,France,Female,31.0,10.0,114318.94,1.0,0.0,0.0,92357.21,0.0
-935,15652883.0,Trevisani,676.0,Spain,Female,59.0,3.0,0.0,2.0,1.0,1.0,120511.01,0.0
-936,15656901.0,Mazzanti,598.0,France,Male,25.0,5.0,118311.76,1.0,1.0,0.0,145558.49,0.0
-937,15603741.0,Ts'ui,762.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,123946.13,0.0
-938,15685500.0,Scott,693.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,75888.65,0.0
-939,15713354.0,Lo Duca,658.0,Germany,Female,47.0,3.0,152264.81,2.0,1.0,1.0,119927.81,1.0
-940,15641655.0,T'ien,581.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,24983.39,0.0
-941,15690664.0,Yudina,632.0,France,Male,41.0,2.0,0.0,3.0,1.0,1.0,185821.41,0.0
-942,15747014.0,Chia,762.0,Spain,Female,37.0,1.0,0.0,1.0,1.0,1.0,82709.8,1.0
-943,15707362.0,Hsueh,659.0,France,Female,35.0,1.0,120811.89,1.0,1.0,0.0,158610.89,0.0
-944,15750921.0,Chia,648.0,Spain,Female,22.0,4.0,0.0,2.0,0.0,1.0,102908.91,0.0
-945,15663917.0,Yefremova,640.0,France,Male,35.0,9.0,0.0,1.0,1.0,1.0,97908.01,1.0
-946,15632051.0,Maclean,712.0,Spain,Female,39.0,9.0,0.0,2.0,0.0,0.0,126131.9,0.0
-947,15742523.0,Chimaoke,677.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,1.0,102747.02,0.0
-948,15753861.0,Onio,712.0,Germany,Female,32.0,8.0,108407.34,1.0,0.0,1.0,88669.89,0.0
-949,15723488.0,Hsia,697.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,170528.63,0.0
-950,15669922.0,Pinto,709.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,105746.94,0.0
-951,15727059.0,Tsou,637.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,170968.99,0.0
-952,15592736.0,Biryukova,584.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,177524.19,0.0
-953,15794479.0,Onyemachukwu,632.0,France,Female,46.0,9.0,0.0,2.0,1.0,1.0,124615.59,0.0
-954,15644878.0,Lo Duca,728.0,Germany,Male,43.0,8.0,110997.76,1.0,1.0,0.0,163798.41,1.0
-955,15640648.0,Chien,712.0,Spain,Male,54.0,4.0,0.0,1.0,0.0,0.0,170893.85,1.0
-956,15702277.0,Kepley,520.0,France,Male,28.0,0.0,0.0,2.0,1.0,0.0,121098.65,0.0
-957,15808689.0,Maclean,568.0,France,Male,38.0,4.0,87894.26,1.0,0.0,1.0,49833.5,0.0
-958,15692406.0,Ts'ui,535.0,France,Male,36.0,1.0,0.0,1.0,1.0,1.0,126761.2,0.0
-959,15694366.0,Chukwukere,714.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,75562.47,0.0
-960,15629133.0,Chikezie,670.0,France,Female,39.0,2.0,88980.32,1.0,1.0,0.0,136983.77,0.0
-961,15591747.0,Hsueh,712.0,Germany,Male,40.0,5.0,71667.74,1.0,1.0,1.0,152450.96,0.0
-962,15600882.0,Chien,589.0,France,Female,26.0,3.0,0.0,2.0,1.0,1.0,57553.02,0.0
-963,15746664.0,Yudina,659.0,France,Male,22.0,6.0,0.0,2.0,1.0,1.0,142331.85,0.0
-964,15705639.0,Chikwado,659.0,France,Female,30.0,8.0,0.0,2.0,1.0,1.0,57549.65,0.0
-965,15640409.0,Milanesi,661.0,France,Male,35.0,7.0,145081.73,1.0,1.0,1.0,176984.34,0.0
-966,15677123.0,Nkemakolam,675.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,153481.85,0.0
-967,15653773.0,Nwankwo,551.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,126565.92,0.0
-968,15757577.0,O'Donnell,754.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,109805.52,0.0
-969,15585985.0,Mazzi,508.0,Germany,Male,31.0,6.0,139432.37,1.0,1.0,1.0,133692.88,1.0
-970,15701376.0,McBurney,648.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,180862.26,0.0
-971,15779586.0,Hsu,549.0,Germany,Female,39.0,0.0,115074.02,1.0,1.0,0.0,122518.5,1.0
-972,15658946.0,Chiang,564.0,Germany,Male,49.0,6.0,113590.73,1.0,1.0,0.0,163550.58,1.0
-973,15623595.0,Ch'ang,537.0,France,Female,54.0,2.0,0.0,2.0,0.0,1.0,90905.26,0.0
-974,15681697.0,Ch'iu,614.0,France,Male,37.0,7.0,0.0,2.0,0.0,0.0,52471.36,0.0
-975,15624397.0,Fanucci,577.0,France,Female,42.0,3.0,0.0,1.0,1.0,0.0,173940.59,1.0
-976,15698872.0,Ijendu,696.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,142911.99,0.0
-977,15778830.0,Ch'ang,635.0,France,Male,31.0,3.0,0.0,2.0,0.0,1.0,56767.67,0.0
-978,15754849.0,McElhone,711.0,France,Male,33.0,6.0,0.0,2.0,0.0,0.0,82222.86,0.0
-979,15792729.0,Hsing,538.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,48778.46,0.0
-980,15668289.0,Trevisano,604.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,79792.3,0.0
-981,15784958.0,Milne,593.0,Germany,Male,39.0,9.0,127290.61,1.0,0.0,1.0,64375.3,0.0
-982,15573045.0,Martin,597.0,Spain,Female,29.0,6.0,0.0,2.0,1.0,0.0,125870.79,0.0
-983,15736250.0,Trevisan,704.0,France,Male,31.0,3.0,0.0,2.0,0.0,1.0,118959.64,0.0
-984,15732265.0,Toscani,693.0,Spain,Female,39.0,9.0,0.0,2.0,1.0,0.0,123954.16,0.0
-985,15600813.0,Ikedinachukwu,634.0,Germany,Female,59.0,4.0,136652.01,1.0,1.0,0.0,107964.36,1.0
-986,15815040.0,Y?an,683.0,Spain,Male,21.0,5.0,0.0,2.0,1.0,1.0,143882.25,0.0
-987,15579166.0,Pirozzi,645.0,Spain,Male,29.0,5.0,111827.27,1.0,1.0,0.0,71433.88,0.0
-988,15756539.0,T'ien,707.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,93873.8,0.0
-989,15691627.0,Mazzanti,474.0,Spain,Male,39.0,8.0,0.0,2.0,0.0,1.0,83093.25,0.0
-990,15751177.0,Ting,707.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,173851.11,0.0
-991,15591698.0,O'Connor,635.0,Germany,Male,51.0,0.0,114440.38,1.0,1.0,1.0,131553.82,0.0
-992,15669611.0,Biryukov,599.0,France,Female,31.0,5.0,0.0,2.0,1.0,0.0,53477.76,0.0
-993,15807989.0,Ch'ang,638.0,France,Male,36.0,8.0,0.0,1.0,1.0,0.0,132973.21,0.0
-994,15684440.0,Tung,744.0,Spain,Male,31.0,5.0,0.0,1.0,1.0,1.0,73329.08,0.0
-995,15740406.0,Chinomso,538.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,124595.95,0.0
-996,15766289.0,Hsieh,673.0,Spain,Female,46.0,9.0,0.0,2.0,1.0,1.0,88890.03,0.0
-997,15643770.0,Shih,850.0,Spain,Male,31.0,3.0,0.0,2.0,0.0,0.0,112554.68,0.0
-998,15646566.0,Genovese,718.0,France,Female,44.0,5.0,0.0,2.0,1.0,1.0,149599.62,0.0
-999,15751788.0,Nnamdi,602.0,Germany,Female,52.0,6.0,131834.76,1.0,1.0,0.0,84584.69,1.0
-1000,15751057.0,Cattaneo,641.0,France,Male,37.0,5.0,142987.46,1.0,1.0,0.0,156774.94,0.0
-1001,15723028.0,Ch'ien,774.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,0.0,79377.45,0.0
-1002,15626012.0,Onyemauchechukwu,672.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,127553.79,0.0
-1003,15641411.0,Kao,649.0,France,Female,36.0,1.0,0.0,2.0,0.0,0.0,52335.15,0.0
-1004,15600106.0,Chukwuemeka,806.0,Spain,Male,26.0,4.0,0.0,2.0,1.0,0.0,143711.02,0.0
-1005,15662698.0,Cattaneo,699.0,Spain,Male,28.0,10.0,0.0,2.0,1.0,0.0,48571.14,0.0
-1006,15681115.0,Pipes,703.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,180598.66,0.0
-1007,15642785.0,Hsing,746.0,Germany,Female,40.0,5.0,108895.93,1.0,1.0,1.0,55868.97,1.0
-1008,15593583.0,Ch'ang,667.0,France,Male,40.0,10.0,0.0,2.0,0.0,0.0,82815.14,0.0
-1009,15741643.0,T'ao,727.0,Spain,Male,43.0,4.0,99836.47,1.0,1.0,0.0,153535.17,0.0
-1010,15696835.0,Lu,581.0,Spain,Male,30.0,5.0,0.0,2.0,0.0,0.0,50368.63,0.0
-1011,15607632.0,Ejimofor,673.0,Spain,Male,46.0,6.0,0.0,1.0,1.0,0.0,152996.89,1.0
-1012,15700710.0,Ch'ang,595.0,Spain,Female,23.0,3.0,0.0,2.0,1.0,1.0,119996.95,0.0
-1013,15587562.0,Yao,713.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,103787.29,0.0
-1014,15581840.0,Chien,687.0,Germany,Male,32.0,3.0,143500.82,1.0,0.0,0.0,87400.53,0.0
-1015,15689517.0,McLean,696.0,France,Female,35.0,6.0,0.0,1.0,1.0,0.0,111180.3,0.0
-1016,15697480.0,Milani,671.0,Germany,Male,25.0,0.0,107747.57,2.0,0.0,0.0,170142.09,0.0
-1017,15758050.0,Nwora,605.0,Spain,Male,54.0,8.0,0.0,1.0,0.0,0.0,125451.01,1.0
-1018,15682468.0,Baresi,731.0,France,Male,31.0,9.0,0.0,2.0,0.0,0.0,69531.22,0.0
-1019,15654096.0,Hsia,763.0,Germany,Female,39.0,1.0,108586.86,1.0,1.0,0.0,134794.02,0.0
-1020,15690164.0,Ch'ang,699.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,1.0,85746.52,0.0
-1021,15700654.0,Obidimkpa,581.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,115186.97,0.0
-1022,15667424.0,Ch'ang,617.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,131609.48,0.0
-1023,15597949.0,Onwumelu,597.0,Spain,Male,41.0,8.0,0.0,2.0,1.0,0.0,158840.99,0.0
-1024,15598884.0,Teng,586.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,180134.88,0.0
-1025,15675141.0,Martin,661.0,Germany,Female,51.0,9.0,106415.57,1.0,1.0,1.0,126812.63,0.0
-1026,15589212.0,Neitenstein,686.0,Germany,Male,42.0,2.0,133467.77,1.0,1.0,0.0,28276.83,1.0
-1027,15726186.0,Folliero,651.0,Germany,Female,38.0,6.0,151858.98,2.0,1.0,1.0,96429.29,0.0
-1028,15782530.0,Pye,657.0,France,Female,44.0,9.0,0.0,2.0,1.0,0.0,152698.24,0.0
-1029,15571081.0,Ch'eng,636.0,Spain,Female,23.0,7.0,0.0,2.0,1.0,1.0,87985.2,0.0
-1030,15651144.0,De Salis,646.0,France,Male,31.0,3.0,103711.56,1.0,0.0,0.0,75969.19,0.0
-1031,15682533.0,Chinagorom,579.0,Spain,Male,43.0,7.0,0.0,2.0,1.0,0.0,10553.31,0.0
-1032,15736287.0,Milanesi,664.0,Germany,Male,40.0,1.0,76319.64,1.0,1.0,0.0,63736.17,1.0
-1033,15812198.0,Nwebube,564.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,142331.85,0.0
-1034,15724281.0,Chiazagomekpere,584.0,France,Male,38.0,1.0,125512.98,1.0,1.0,1.0,153662.88,0.0
-1035,15677522.0,Clements,582.0,France,Female,29.0,8.0,81844.81,1.0,1.0,1.0,107811.28,1.0
-1036,15642815.0,Chibugo,850.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,180345.44,0.0
-1037,15662501.0,Tien,710.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,150694.42,0.0
-1038,15681755.0,Cattaneo,603.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,110317.39,0.0
-1039,15768282.0,Tsao,584.0,France,Male,23.0,5.0,0.0,1.0,1.0,1.0,160548.12,0.0
-1040,15700212.0,Udegbunam,644.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,121894.31,0.0
-1041,15777825.0,Ch'in,705.0,France,Male,49.0,4.0,0.0,3.0,0.0,0.0,81481.9,1.0
-1042,15781598.0,Nwora,607.0,Spain,Female,28.0,7.0,0.0,2.0,1.0,0.0,172618.52,0.0
-1043,15623695.0,Yevseyev,596.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,139024.31,0.0
-1044,15684769.0,Ch'iu,645.0,France,Male,39.0,9.0,0.0,1.0,1.0,0.0,167036.94,1.0
-1045,15671766.0,Chinedum,706.0,France,Male,41.0,2.0,0.0,2.0,0.0,1.0,91036.74,0.0
-1046,15594812.0,Chibuzo,727.0,Germany,Female,42.0,1.0,121325.84,1.0,1.0,0.0,72993.65,1.0
-1047,15764174.0,Bazarova,634.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,0.0,123880.19,0.0
-1048,15675888.0,Palerma,586.0,France,Female,25.0,9.0,0.0,2.0,1.0,0.0,65906.07,0.0
-1049,15641662.0,Chien,648.0,France,Female,25.0,8.0,0.0,2.0,1.0,0.0,180326.83,0.0
-1050,15709451.0,Chukwumaobim,543.0,France,Male,44.0,3.0,0.0,2.0,0.0,0.0,97629.31,0.0
-1051,15664311.0,Shao,706.0,France,Male,45.0,6.0,0.0,2.0,1.0,0.0,60906.12,1.0
-1052,15686957.0,Chuang,644.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,158545.25,0.0
-1053,15746986.0,Palermo,679.0,France,Male,28.0,5.0,97268.1,1.0,0.0,1.0,46630.47,0.0
-1054,15774841.0,Calabresi,634.0,France,Female,53.0,1.0,0.0,3.0,1.0,0.0,160968.44,1.0
-1055,15628059.0,Ni,682.0,Spain,Female,51.0,1.0,0.0,1.0,0.0,0.0,173576.71,1.0
-1056,15803032.0,Y?an,634.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,94499.06,0.0
-1057,15810347.0,Maclean,637.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,153036.97,0.0
-1058,15688612.0,Hales,559.0,Spain,Female,32.0,8.0,0.0,1.0,1.0,1.0,70550.0,0.0
-1059,15585100.0,Efimov,684.0,France,Female,25.0,2.0,0.0,2.0,0.0,0.0,7861.68,0.0
-1060,15658977.0,Toscani,603.0,France,Female,39.0,9.0,97588.8,1.0,1.0,0.0,122379.73,1.0
-1061,15628697.0,Nkemakolam,619.0,Spain,Male,32.0,9.0,0.0,1.0,1.0,1.0,150694.42,1.0
-1062,15784099.0,Chukwuemeka,511.0,France,Female,41.0,2.0,0.0,1.0,1.0,1.0,51387.1,1.0
-1063,15661528.0,Trevisano,795.0,Germany,Male,39.0,8.0,133705.74,2.0,1.0,1.0,71905.77,0.0
-1064,15667944.0,Ritchie,661.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,129569.92,0.0
-1065,15635197.0,Hsieh,706.0,France,Male,38.0,3.0,0.0,2.0,0.0,1.0,177852.57,0.0
-1066,15691875.0,Ewing,578.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,93835.64,0.0
-1067,15793046.0,Chidiegwu,730.0,Spain,Female,43.0,8.0,0.0,2.0,1.0,0.0,83981.02,0.0
-1068,15781530.0,Aikenhead,726.0,Germany,Female,31.0,4.0,141571.5,2.0,1.0,0.0,180862.26,0.0
-1069,15795571.0,Ndukaku,553.0,France,Female,34.0,0.0,96654.72,1.0,0.0,1.0,167973.65,0.0
-1070,15583090.0,Bitter,710.0,France,Female,35.0,9.0,0.0,3.0,0.0,0.0,69414.13,1.0
-1071,15614827.0,Walker,733.0,Spain,Female,41.0,3.0,0.0,2.0,1.0,0.0,83629.4,0.0
-1072,15716134.0,Mazzi,635.0,Germany,Male,54.0,7.0,129585.19,1.0,1.0,0.0,124874.23,1.0
-1073,15604818.0,Tuan,657.0,Germany,Male,35.0,9.0,91286.51,2.0,1.0,0.0,153917.41,0.0
-1074,15605020.0,Chukwudi,543.0,France,Male,29.0,7.0,0.0,1.0,1.0,0.0,94925.62,0.0
-1075,15614832.0,Pagnotto,664.0,Spain,Female,22.0,9.0,126492.24,2.0,0.0,1.0,149614.61,0.0
-1076,15648067.0,Uvarova,719.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,162858.29,0.0
-1077,15726588.0,Lo,642.0,Spain,Male,28.0,1.0,0.0,2.0,0.0,0.0,153810.41,0.0
-1078,15676571.0,Tretiakova,774.0,Germany,Female,35.0,7.0,123711.73,2.0,1.0,0.0,137028.51,0.0
-1079,15641733.0,Tsai,683.0,France,Female,49.0,2.0,0.0,1.0,0.0,1.0,89972.25,1.0
-1080,15740161.0,Fan,661.0,Spain,Male,31.0,9.0,125445.04,1.0,1.0,1.0,184350.78,0.0
-1081,15649033.0,Nnachetam,684.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,182988.94,1.0
-1082,15629793.0,Y?,769.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,0.0,138527.56,0.0
-1083,15766378.0,Shih,667.0,France,Male,43.0,3.0,0.0,2.0,1.0,1.0,85891.09,0.0
-1084,15788676.0,Yobanna,619.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,1.0,131662.47,0.0
-1085,15682758.0,MacDonnell,645.0,France,Male,25.0,5.0,125510.82,1.0,1.0,0.0,147822.81,0.0
-1086,15632029.0,Y?an,709.0,France,Male,38.0,1.0,160858.13,1.0,1.0,1.0,37240.11,0.0
-1087,15806945.0,Shubin,498.0,Germany,Male,30.0,6.0,86911.7,2.0,1.0,1.0,151843.54,0.0
-1088,15733768.0,Bogle,534.0,France,Female,56.0,2.0,0.0,1.0,0.0,1.0,171045.31,1.0
-1089,15624391.0,Brown,850.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,161579.85,0.0
-1090,15632185.0,Trevisano,724.0,France,Male,35.0,0.0,0.0,1.0,0.0,1.0,137791.18,0.0
-1091,15701687.0,Lu,662.0,France,Male,55.0,7.0,99844.81,1.0,0.0,1.0,131736.23,0.0
-1092,15634218.0,Chukwukere,701.0,France,Female,32.0,9.0,90727.79,1.0,1.0,1.0,133516.51,0.0
-1093,15735346.0,McLean,729.0,Germany,Male,33.0,5.0,107963.38,1.0,1.0,0.0,108003.38,1.0
-1094,15782895.0,Iloerika,824.0,France,Female,48.0,3.0,0.0,1.0,1.0,0.0,107847.02,1.0
-1095,15760085.0,Brown,633.0,Spain,Male,29.0,2.0,0.0,1.0,0.0,1.0,82868.34,0.0
-1096,15777315.0,Onyemauchechukwu,652.0,France,Female,36.0,3.0,109493.62,2.0,1.0,1.0,86786.41,0.0
-1097,15715047.0,Liao,729.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,145789.71,0.0
-1098,15654025.0,T'ao,641.0,France,Female,69.0,5.0,0.0,2.0,1.0,1.0,52629.73,0.0
-1099,15798885.0,Yeh,576.0,Spain,Male,57.0,7.0,0.0,2.0,1.0,1.0,140941.47,1.0
-1100,15605375.0,P'an,641.0,Germany,Male,32.0,4.0,143396.54,2.0,1.0,0.0,123882.73,0.0
-1101,15748473.0,Hsiung,646.0,France,Male,40.0,6.0,0.0,2.0,1.0,1.0,180551.76,0.0
-1102,15643438.0,Nnamutaezinwa,753.0,France,Male,21.0,4.0,129804.17,1.0,1.0,1.0,65801.44,0.0
-1103,15589323.0,Chiemenam,595.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,122964.18,0.0
-1104,15712807.0,Udegbunam,762.0,France,Male,50.0,5.0,0.0,2.0,0.0,1.0,151316.37,0.0
-1105,15781598.0,Ch'ien,667.0,France,Female,32.0,2.0,0.0,2.0,1.0,1.0,142539.71,0.0
-1106,15783097.0,Yudin,656.0,France,Female,40.0,6.0,0.0,1.0,1.0,0.0,133871.83,0.0
-1107,15574558.0,Bitter,669.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,186612.47,1.0
-1108,15571666.0,Nwabugwu,758.0,Spain,Male,28.0,10.0,0.0,2.0,1.0,1.0,82709.8,0.0
-1109,15783589.0,Chidubem,656.0,France,Male,46.0,4.0,0.0,2.0,1.0,1.0,129912.74,0.0
-1110,15597951.0,O'Connor,850.0,France,Female,32.0,1.0,0.0,1.0,1.0,1.0,104079.62,1.0
-1111,15698031.0,Chidiegwu,591.0,France,Female,33.0,9.0,0.0,1.0,0.0,1.0,174434.06,0.0
-1112,15708710.0,Chiemela,590.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,84484.13,0.0
-1113,15643359.0,Lo,679.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,1.0,55725.04,0.0
-1114,15769499.0,Chukwubuikem,693.0,France,Female,24.0,8.0,0.0,2.0,1.0,1.0,60954.45,0.0
-1115,15587038.0,Chiedozie,663.0,France,Male,62.0,8.0,0.0,2.0,1.0,1.0,103970.58,0.0
-1116,15753092.0,Nnaife,631.0,Germany,Male,33.0,5.0,115557.0,1.0,1.0,0.0,76510.46,0.0
-1117,15592461.0,Smith,624.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,147785.83,0.0
-1118,15807524.0,Nwokezuike,732.0,Germany,Male,35.0,3.0,115725.24,1.0,1.0,1.0,110248.88,0.0
-1119,15782404.0,T'ang,699.0,France,Male,39.0,8.0,123761.68,1.0,1.0,0.0,145900.89,0.0
-1120,15757981.0,Ts'ui,590.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,143628.31,0.0
-1121,15694860.0,Ts'ai,554.0,France,Female,42.0,1.0,0.0,2.0,1.0,1.0,132547.02,0.0
-1122,15694556.0,Toscani,662.0,Germany,Male,35.0,9.0,115350.63,1.0,1.0,1.0,60905.51,0.0
-1123,15624793.0,Nkemakolam,754.0,Germany,Female,34.0,4.0,147832.15,2.0,1.0,1.0,181605.85,1.0
-1124,15769682.0,Chukwueloka,724.0,Germany,Female,39.0,2.0,107848.71,2.0,1.0,0.0,50488.91,0.0
-1125,15678915.0,Chialuka,708.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,152549.62,0.0
-1126,15652789.0,Lo Duca,819.0,France,Male,25.0,4.0,0.0,2.0,1.0,0.0,58601.61,0.0
-1127,15727197.0,Tseng,676.0,France,Female,28.0,7.0,0.0,2.0,1.0,0.0,98810.78,0.0
-1128,15759499.0,Y?an,748.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,176943.59,0.0
-1129,15746986.0,K?,837.0,France,Female,29.0,1.0,0.0,1.0,1.0,1.0,83983.39,0.0
-1130,15674750.0,K?,741.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,48242.19,0.0
-1131,15642655.0,Olague,611.0,France,Female,35.0,5.0,0.0,1.0,1.0,1.0,176943.59,0.0
-1132,15641835.0,H?,571.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,132770.11,0.0
-1133,15579287.0,Chukwumaobim,642.0,Spain,Male,44.0,9.0,0.0,2.0,0.0,0.0,47921.22,0.0
-1134,15676810.0,Milanesi,589.0,France,Female,44.0,2.0,0.0,1.0,1.0,1.0,124908.52,1.0
-1135,15629793.0,Onyeoruru,573.0,Spain,Female,31.0,1.0,0.0,2.0,1.0,1.0,175324.74,0.0
-1136,15736371.0,Okechukwu,755.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,93874.87,0.0
-1137,15674928.0,Yobanna,579.0,Spain,Female,40.0,9.0,0.0,1.0,1.0,0.0,162599.51,1.0
-1138,15684958.0,Tien,642.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,1.0,177065.24,0.0
-1139,15658670.0,Kambinachi,516.0,Germany,Female,25.0,7.0,116932.59,1.0,1.0,1.0,109291.39,0.0
-1140,15751412.0,Toscani,682.0,Spain,Female,32.0,3.0,144855.34,1.0,1.0,1.0,51536.99,0.0
-1141,15633059.0,Tseng,662.0,Germany,Male,33.0,8.0,138341.51,1.0,1.0,0.0,163440.8,0.0
-1142,15800062.0,Nnonso,696.0,Spain,Male,35.0,4.0,123593.22,1.0,0.0,0.0,142548.33,0.0
-1143,15797900.0,Chiang,562.0,Germany,Male,34.0,5.0,122227.71,2.0,0.0,0.0,91086.73,0.0
-1144,15652048.0,P'eng,559.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,176778.89,0.0
-1145,15694300.0,Ch'ang,576.0,Spain,Female,46.0,10.0,154495.74,1.0,1.0,1.0,136519.58,1.0
-1146,15768340.0,Calabresi,619.0,Germany,Male,41.0,4.0,141547.13,2.0,1.0,0.0,107876.2,0.0
-1147,15626141.0,Uvarova,641.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,60721.4,0.0
-1148,15581525.0,Teng,709.0,Germany,Male,30.0,2.0,99897.76,1.0,0.0,0.0,4389.4,0.0
-1149,15679760.0,K'ung,651.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,1.0,103379.31,0.0
-1150,15654760.0,Lombardo,850.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,120893.07,0.0
-1151,15780256.0,Onyekachukwu,708.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,142238.54,0.0
-1152,15754577.0,Nnaife,669.0,Germany,Male,36.0,6.0,99760.85,2.0,1.0,0.0,79734.23,1.0
-1153,15782778.0,Chukwudi,471.0,Germany,Female,52.0,0.0,133870.01,1.0,1.0,0.0,152996.89,1.0
-1154,15742204.0,Ch'iu,679.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,11663.09,0.0
-1155,15602354.0,P'eng,696.0,Spain,Male,32.0,1.0,133658.24,1.0,1.0,1.0,185830.72,0.0
-1156,15728352.0,Bair,718.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,121053.19,0.0
-1157,15612207.0,Nwachinemelu,668.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,141679.73,0.0
-1158,15651369.0,Chiawuotu,602.0,Spain,Male,39.0,4.0,0.0,2.0,0.0,1.0,94498.82,0.0
-1159,15665416.0,Onuchukwu,678.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,138871.12,0.0
-1160,15790846.0,Hs?,632.0,France,Female,37.0,8.0,134980.23,1.0,1.0,1.0,180002.42,0.0
-1161,15592025.0,Chukwuhaenye,602.0,France,Male,23.0,3.0,0.0,2.0,1.0,1.0,64448.36,0.0
-1162,15645572.0,Chukwufumnanya,604.0,France,Female,57.0,5.0,141806.0,1.0,1.0,0.0,182867.84,1.0
-1163,15658577.0,Ch'ang,558.0,France,Male,44.0,6.0,145536.28,1.0,0.0,0.0,101986.16,0.0
-1164,15681316.0,Goliwe,669.0,Spain,Male,42.0,7.0,109974.22,1.0,1.0,1.0,149853.89,0.0
-1165,15614893.0,Hs?eh,439.0,France,Female,36.0,4.0,96679.71,1.0,0.0,1.0,85516.37,0.0
-1166,15769312.0,Chuang,736.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,51919.04,0.0
-1167,15748589.0,Lu,683.0,Spain,Male,34.0,2.0,0.0,1.0,1.0,0.0,167784.28,0.0
-1168,15784597.0,Yermakov,778.0,Germany,Female,31.0,9.0,121874.89,2.0,1.0,1.0,189607.71,0.0
-1169,15806975.0,Yeh,769.0,France,Female,39.0,1.0,134598.47,1.0,1.0,1.0,163798.41,0.0
-1170,15595728.0,Chizoba,641.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,123547.28,0.0
-1171,15647800.0,Chinwendu,658.0,Spain,Male,37.0,10.0,131659.59,2.0,1.0,1.0,109718.44,0.0
-1172,15616700.0,Chiebuka,663.0,France,Male,46.0,8.0,0.0,1.0,1.0,0.0,141242.57,0.0
-1173,15778515.0,Kuykendall,486.0,Spain,Male,41.0,10.0,0.0,2.0,1.0,1.0,48226.76,0.0
-1174,15633574.0,Chukwukadibia,637.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,0.0,151336.96,0.0
-1175,15792486.0,Fan,707.0,France,Female,44.0,1.0,0.0,1.0,0.0,0.0,179908.71,1.0
-1176,15602312.0,Chidumaga,676.0,Spain,Male,32.0,5.0,134740.24,1.0,0.0,0.0,170122.22,0.0
-1177,15708713.0,Uspensky,665.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,145700.22,0.0
-1178,15581252.0,Hsia,521.0,Germany,Female,51.0,5.0,128829.25,2.0,1.0,0.0,171096.2,1.0
-1179,15765643.0,Chukwueloka,602.0,France,Female,49.0,3.0,0.0,1.0,0.0,0.0,171922.72,1.0
-1180,15589428.0,Milano,661.0,France,Male,41.0,4.0,0.0,1.0,1.0,0.0,146851.74,1.0
-1181,15773789.0,Chiwetelu,739.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,164967.98,0.0
-1182,15699467.0,Chien,598.0,France,Female,32.0,8.0,0.0,2.0,0.0,0.0,163581.85,0.0
-1183,15813645.0,Oster,850.0,France,Male,39.0,3.0,0.0,1.0,1.0,0.0,172646.88,0.0
-1184,15588854.0,Hsiung,637.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,27330.59,0.0
-1185,15746986.0,T'ang,569.0,Spain,Male,30.0,6.0,91070.18,1.0,1.0,1.0,145562.4,0.0
-1186,15684962.0,Yobanna,850.0,France,Female,20.0,4.0,0.0,2.0,1.0,1.0,114442.66,0.0
-1187,15586069.0,Iadanza,681.0,Spain,Female,49.0,9.0,0.0,2.0,0.0,1.0,172495.81,1.0
-1188,15674678.0,Shih,679.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,26687.22,0.0
-1189,15668767.0,Ch'in,581.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,90876.95,0.0
-1190,15698872.0,Mactier,699.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,172646.88,0.0
-1191,15750808.0,Chikezie,586.0,France,Female,29.0,5.0,164850.54,1.0,1.0,1.0,95421.0,0.0
-1192,15662337.0,Nkemjika,641.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,0.0,128882.66,0.0
-1193,15584897.0,Tretiakov,587.0,France,Male,51.0,4.0,0.0,1.0,1.0,0.0,80587.37,1.0
-1194,15734588.0,T'ang,648.0,Spain,Male,39.0,7.0,153548.13,1.0,1.0,1.0,137476.09,0.0
-1195,15644796.0,Chukwualuka,682.0,Spain,Female,43.0,4.0,0.0,2.0,0.0,1.0,126809.15,0.0
-1196,15686964.0,Nkemdirim,645.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,173899.42,0.0
-1197,15651460.0,Chukwualuka,706.0,Spain,Female,37.0,3.0,0.0,2.0,1.0,1.0,152987.42,0.0
-1198,15707505.0,Trevisano,667.0,Spain,Male,26.0,10.0,0.0,2.0,0.0,0.0,172674.36,0.0
-1199,15765257.0,McIntosh,633.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,179258.68,0.0
-1200,15693526.0,Cattaneo,648.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,105732.9,0.0
-1201,15708003.0,K'ung,502.0,Spain,Female,39.0,5.0,141038.57,1.0,1.0,1.0,130474.03,0.0
-1202,15636551.0,Iweobiegbunam,715.0,Germany,Male,31.0,8.0,96774.55,2.0,0.0,1.0,165035.17,0.0
-1203,15775886.0,Nkemakonam,633.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,151645.96,1.0
-1204,15656471.0,Rieke,633.0,France,Male,42.0,1.0,0.0,1.0,0.0,0.0,199390.45,1.0
-1205,15803750.0,Chidubem,691.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,94738.54,0.0
-1206,15803801.0,Chiedozie,553.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,1.0,49815.62,0.0
-1207,15677510.0,Chukwufumnanya,725.0,France,Male,48.0,5.0,0.0,1.0,0.0,1.0,166038.28,1.0
-1208,15643770.0,Onyekachi,678.0,France,Male,39.0,1.0,0.0,2.0,0.0,1.0,132719.8,0.0
-1209,15590011.0,Chiazagomekpele,596.0,Germany,Male,32.0,0.0,162532.14,2.0,1.0,0.0,143598.7,0.0
-1210,15701074.0,Whitehead,582.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,121084.27,0.0
-1211,15687913.0,Uspenskaya,773.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,86786.41,0.0
-1212,15725068.0,Onio,706.0,France,Male,36.0,2.0,117438.92,1.0,0.0,0.0,198402.37,0.0
-1213,15772423.0,Omeokachie,645.0,France,Male,29.0,7.0,0.0,1.0,1.0,1.0,115733.16,0.0
-1214,15572390.0,Nkemakolam,683.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,161479.19,0.0
-1215,15603319.0,Chieloka,564.0,Spain,Male,33.0,2.0,107966.39,2.0,1.0,1.0,162599.51,0.0
-1216,15585867.0,Hs?,727.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,90537.47,0.0
-1217,15694518.0,Shih,743.0,France,Male,45.0,2.0,123544.21,2.0,0.0,1.0,134889.95,0.0
-1218,15788775.0,Yobanna,748.0,France,Female,39.0,9.0,133866.22,1.0,0.0,0.0,104889.3,0.0
-1219,15742798.0,Ch'iu,735.0,France,Male,42.0,1.0,0.0,1.0,1.0,1.0,107822.39,0.0
-1220,15592089.0,Shih,544.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,83910.4,0.0
-1221,15573741.0,Chukwualuka,774.0,France,Female,28.0,9.0,0.0,1.0,1.0,0.0,7921.57,0.0
-1222,15706647.0,P'eng,473.0,Germany,Female,31.0,4.0,111432.77,2.0,1.0,1.0,147545.65,0.0
-1223,15745307.0,Chizuoke,756.0,Germany,Female,41.0,2.0,127545.75,2.0,0.0,0.0,95719.73,1.0
-1224,15783883.0,Yevseyev,611.0,France,Female,39.0,3.0,135470.9,1.0,1.0,1.0,158887.09,0.0
-1225,15687903.0,Maclean,693.0,France,Female,34.0,7.0,0.0,1.0,1.0,1.0,128880.69,0.0
-1226,15603088.0,Nwebube,586.0,Spain,Female,44.0,5.0,0.0,2.0,1.0,1.0,163769.89,0.0
-1227,15595228.0,Zikoranachidimma,651.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,159475.08,0.0
-1228,15705885.0,Mault,769.0,France,Female,67.0,6.0,0.0,2.0,1.0,1.0,168034.83,0.0
-1229,15745804.0,Ch'eng,775.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,65765.22,0.0
-1230,15630698.0,Rubeo,545.0,France,Male,41.0,7.0,0.0,2.0,0.0,0.0,151843.54,0.0
-1231,15799064.0,Chiazagomekpere,719.0,France,Male,22.0,5.0,120912.98,1.0,0.0,1.0,134720.51,0.0
-1232,15667620.0,Maclean,850.0,Spain,Male,33.0,10.0,0.0,2.0,1.0,0.0,70532.53,0.0
-1233,15570194.0,Chu,602.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,168034.9,0.0
-1234,15631481.0,Trevisan,774.0,Spain,Female,28.0,2.0,152309.21,1.0,1.0,1.0,114448.77,0.0
-1235,15750055.0,Yermolayeva,667.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,138526.96,0.0
-1236,15769308.0,Ukaegbunam,590.0,Spain,Female,28.0,3.0,123699.28,1.0,1.0,0.0,102391.06,0.0
-1237,15598883.0,Tien,705.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,149583.67,0.0
-1238,15583555.0,Clements,715.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,1.0,48170.57,0.0
-1239,15699309.0,Uspensky,678.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,67944.09,0.0
-1240,15796370.0,Kuykendall,594.0,Spain,Male,23.0,5.0,0.0,2.0,1.0,1.0,133086.45,0.0
-1241,15734312.0,Okwuadigbo,807.0,Spain,Male,40.0,6.0,138521.87,1.0,1.0,0.0,180551.76,0.0
-1242,15705512.0,Hsia,696.0,France,Female,46.0,9.0,0.0,1.0,1.0,0.0,61759.33,0.0
-1243,15652789.0,Hsieh,774.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,81298.09,0.0
-1244,15782941.0,Nkemakonam,614.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,80619.09,0.0
-1245,15803801.0,Teng,642.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,96875.52,0.0
-1246,15768137.0,Shih,674.0,France,Male,48.0,1.0,117866.92,1.0,1.0,1.0,145986.35,1.0
-1247,15673599.0,Ch'en,758.0,Spain,Male,31.0,8.0,116937.05,1.0,1.0,1.0,130567.02,0.0
-1248,15698528.0,Chien,633.0,France,Female,41.0,4.0,0.0,1.0,1.0,1.0,84749.89,0.0
-1249,15752300.0,Chiwetelu,753.0,France,Female,38.0,1.0,0.0,1.0,1.0,1.0,177519.39,0.0
-1250,15798521.0,Chinonwu,588.0,Germany,Male,42.0,2.0,79460.96,2.0,0.0,0.0,177747.03,0.0
-1251,15669957.0,Udinese,667.0,Spain,Female,27.0,9.0,0.0,1.0,1.0,1.0,73371.65,1.0
-1252,15771483.0,Naylor,653.0,France,Female,34.0,5.0,126670.37,1.0,1.0,1.0,158973.46,0.0
-1253,15749093.0,Nwachinemelu,542.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,44965.44,0.0
-1254,15715510.0,Kao,712.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,152790.92,0.0
-1255,15743067.0,Chiu,757.0,Spain,Male,45.0,5.0,148962.76,1.0,1.0,1.0,4791.8,0.0
-1256,15627995.0,Pinto,697.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,91366.42,0.0
-1257,15611682.0,Shao,686.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,131647.31,0.0
-1258,15742009.0,Liao,605.0,France,Female,43.0,2.0,0.0,2.0,0.0,0.0,77330.35,0.0
-1259,15809817.0,Echezonachukwu,613.0,France,Female,29.0,4.0,0.0,2.0,1.0,1.0,172501.38,0.0
-1260,15712264.0,Chigozie,597.0,Germany,Female,47.0,6.0,107784.65,1.0,1.0,0.0,132436.71,1.0
-1261,15595842.0,Onwuamaeze,602.0,France,Female,48.0,3.0,0.0,1.0,1.0,0.0,51705.01,1.0
-1262,15736371.0,Calabresi,673.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,1.0,125083.29,0.0
-1263,15771483.0,Nkemjika,689.0,Spain,Female,44.0,4.0,0.0,2.0,0.0,1.0,79408.17,0.0
-1264,15658969.0,Palermo,690.0,France,Male,30.0,4.0,0.0,2.0,0.0,1.0,71139.31,0.0
-1265,15605968.0,Chidiegwu,663.0,France,Female,31.0,3.0,165350.61,1.0,1.0,1.0,53534.8,0.0
-1266,15622182.0,P'eng,773.0,Spain,Female,66.0,2.0,0.0,2.0,1.0,1.0,99843.98,0.0
-1267,15592230.0,Ts'ai,578.0,Spain,Male,34.0,3.0,104760.82,1.0,0.0,0.0,130883.8,0.0
-1268,15643671.0,Lombardo,668.0,Germany,Female,52.0,9.0,146098.43,3.0,1.0,0.0,118570.53,1.0
-1269,15632848.0,Lo,561.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,182270.88,0.0
-1270,15705739.0,Hsiao,748.0,Spain,Female,40.0,5.0,166501.17,1.0,0.0,1.0,156424.4,0.0
-1271,15600813.0,Yermakova,508.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,49424.6,0.0
-1272,15629133.0,Tung,694.0,Spain,Male,58.0,7.0,166594.78,1.0,1.0,1.0,136859.47,0.0
-1273,15685536.0,Ting,598.0,Germany,Male,41.0,3.0,123863.64,1.0,1.0,0.0,176868.89,0.0
-1274,15590344.0,McLean,636.0,France,Female,30.0,5.0,0.0,2.0,0.0,1.0,175324.74,0.0
-1275,15592386.0,Nwokeocha,616.0,Germany,Female,38.0,3.0,122220.19,1.0,0.0,0.0,179445.24,1.0
-1276,15792650.0,Fanucci,678.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,94790.34,0.0
-1277,15596939.0,Chukwumaobim,573.0,Spain,Male,38.0,9.0,0.0,2.0,0.0,0.0,74077.91,0.0
-1278,15714087.0,Chu,663.0,France,Female,62.0,6.0,0.0,2.0,1.0,1.0,131281.29,0.0
-1279,15787026.0,Lucchesi,627.0,France,Male,40.0,5.0,133658.24,1.0,1.0,0.0,48963.73,0.0
-1280,15576723.0,Manna,586.0,Germany,Male,63.0,1.0,125547.43,1.0,1.0,1.0,8123.11,1.0
-1281,15585853.0,Chukwubuikem,679.0,Germany,Female,52.0,7.0,107141.27,1.0,1.0,0.0,129980.93,1.0
-1282,15694879.0,Kambinachi,637.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,55893.37,0.0
-1283,15585066.0,Y?,706.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,160129.99,0.0
-1284,15661380.0,Chiganu,719.0,France,Male,34.0,8.0,0.0,1.0,1.0,1.0,173576.71,1.0
-1285,15572631.0,Y?an,697.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,131644.41,0.0
-1286,15690164.0,Onyemere,682.0,France,Female,25.0,9.0,147832.13,1.0,1.0,1.0,96833.0,0.0
-1287,15691703.0,Nwokeocha,667.0,France,Male,30.0,1.0,88363.03,1.0,1.0,0.0,57553.98,0.0
-1288,15801934.0,T'ien,694.0,Spain,Male,37.0,9.0,71097.23,2.0,0.0,0.0,143783.28,0.0
-1289,15659656.0,Li Fonti,637.0,France,Female,40.0,0.0,0.0,2.0,1.0,1.0,88724.49,0.0
-1290,15703337.0,Biryukov,729.0,Germany,Female,42.0,5.0,108805.42,1.0,1.0,0.0,109742.8,1.0
-1291,15586970.0,Ch'en,538.0,Germany,Male,31.0,7.0,151802.29,1.0,1.0,0.0,127875.1,0.0
-1292,15709920.0,Chiang,749.0,France,Male,34.0,3.0,143321.97,1.0,1.0,0.0,1187.88,1.0
-1293,15658057.0,Okwuadigbo,733.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,645.61,0.0
-1294,15687079.0,Hsing,696.0,France,Male,29.0,7.0,152302.72,1.0,1.0,0.0,153955.38,0.0
-1295,15773709.0,Hs?eh,659.0,France,Female,42.0,3.0,0.0,2.0,1.0,1.0,126223.84,1.0
-1296,15639669.0,T'ang,738.0,Spain,Female,41.0,5.0,121669.23,1.0,0.0,1.0,147534.16,0.0
-1297,15696812.0,De Luca,596.0,Spain,Male,25.0,7.0,0.0,1.0,1.0,1.0,107876.2,0.0
-1298,15602797.0,Okwudiliolisa,497.0,Germany,Female,24.0,2.0,76920.86,1.0,1.0,0.0,116973.48,1.0
-1299,15645839.0,Hsieh,637.0,France,Female,34.0,10.0,0.0,2.0,1.0,0.0,72945.62,0.0
-1300,15714586.0,T'ao,712.0,France,Male,40.0,1.0,125547.53,1.0,0.0,1.0,78707.97,0.0
-1301,15795878.0,Obiajulu,567.0,Germany,Female,45.0,0.0,124493.03,2.0,1.0,0.0,52629.71,1.0
-1302,15672894.0,Ibezimako,645.0,France,Female,37.0,8.0,0.0,3.0,1.0,0.0,82498.09,1.0
-1303,15672598.0,De Luca,561.0,France,Male,37.0,3.0,0.0,2.0,0.0,1.0,73669.94,0.0
-1304,15736271.0,Chinonyelum,634.0,France,Male,33.0,8.0,120777.88,1.0,0.0,0.0,152298.28,0.0
-1305,15605827.0,Niu,633.0,Germany,Male,47.0,9.0,125608.72,2.0,1.0,0.0,153955.38,1.0
-1306,15657540.0,T'ao,537.0,Germany,Female,47.0,3.0,141541.25,1.0,1.0,0.0,85890.75,1.0
-1307,15690772.0,Hs?,578.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,0.0,82868.34,0.0
-1308,15771299.0,Chukwumaobim,590.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,96985.58,0.0
-1309,15771742.0,Ch'in,573.0,France,Female,46.0,8.0,0.0,1.0,1.0,1.0,171463.83,1.0
-1310,15762799.0,Cattaneo,584.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,78854.58,0.0
-1311,15662641.0,Chukwumaobim,653.0,Spain,Male,31.0,1.0,0.0,1.0,1.0,0.0,102640.52,0.0
-1312,15751628.0,H?,778.0,France,Female,29.0,5.0,125265.61,1.0,1.0,1.0,67468.67,0.0
-1313,15672798.0,K'ung,726.0,Spain,Female,26.0,1.0,0.0,2.0,1.0,1.0,79577.4,0.0
-1314,15774507.0,Hsing,645.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,184524.65,0.0
-1315,15594896.0,Chiang,692.0,France,Male,43.0,3.0,139972.18,1.0,1.0,1.0,129983.2,0.0
-1316,15671930.0,Wright,642.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,143007.49,0.0
-1317,15681671.0,Ch'in,611.0,Spain,Female,29.0,6.0,0.0,2.0,1.0,0.0,9085.0,0.0
-1318,15572762.0,Pisano,733.0,Germany,Male,30.0,10.0,91611.12,1.0,1.0,0.0,158845.49,0.0
-1319,15736271.0,Ts'ao,561.0,France,Female,50.0,4.0,0.0,1.0,1.0,0.0,181419.29,1.0
-1320,15813894.0,Shao,667.0,France,Female,37.0,9.0,0.0,1.0,1.0,1.0,180553.48,0.0
-1321,15707559.0,Obialo,644.0,Spain,Male,35.0,9.0,85982.05,1.0,0.0,0.0,104469.05,0.0
-1322,15739271.0,T'ien,682.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,50213.81,0.0
-1323,15710365.0,Hsu,545.0,Germany,Male,39.0,7.0,129490.33,2.0,1.0,1.0,168834.39,1.0
-1324,15809847.0,Chu,534.0,France,Male,45.0,7.0,0.0,2.0,1.0,0.0,67468.67,1.0
-1325,15752626.0,Chidiebele,611.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,121098.65,0.0
-1326,15638003.0,Shao,687.0,Spain,Male,58.0,2.0,0.0,2.0,1.0,1.0,171837.06,0.0
-1327,15719606.0,Teng,679.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,158420.14,0.0
-1328,15590199.0,Yip,705.0,Germany,Male,58.0,4.0,117989.76,1.0,0.0,1.0,172097.95,0.0
-1329,15656096.0,Okwudiliolisa,679.0,France,Female,47.0,7.0,79811.72,1.0,0.0,0.0,172749.65,1.0
-1330,15674940.0,Dickson,687.0,Spain,Female,46.0,9.0,0.0,1.0,1.0,0.0,116081.93,0.0
-1331,15788215.0,Tuan,718.0,France,Female,33.0,7.0,135020.21,1.0,0.0,0.0,26136.13,1.0
-1332,15679991.0,T'ien,585.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,103604.65,0.0
-1333,15641655.0,Milanesi,685.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,57524.71,0.0
-1334,15752320.0,Nickson,656.0,Germany,Male,35.0,9.0,144470.77,1.0,1.0,1.0,179631.85,0.0
-1335,15671784.0,Walker,677.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,163830.64,0.0
-1336,15656029.0,Udegbulam,762.0,Germany,Male,29.0,0.0,127258.79,1.0,1.0,0.0,158845.49,0.0
-1337,15716134.0,Moore,850.0,Spain,Male,36.0,6.0,144953.31,1.0,0.0,0.0,177747.71,0.0
-1338,15783875.0,Ijendu,721.0,France,Female,44.0,8.0,0.0,1.0,0.0,1.0,116467.35,1.0
-1339,15782974.0,Ch'ien,609.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,174094.24,0.0
-1340,15623346.0,P'eng,639.0,France,Male,40.0,7.0,0.0,2.0,0.0,0.0,104170.48,0.0
-1341,15752694.0,Nwachinemelu,662.0,France,Male,66.0,2.0,0.0,2.0,1.0,1.0,125658.28,0.0
-1342,15664881.0,Lucchesi,567.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,150390.57,0.0
-1343,15778934.0,Hsia,567.0,Germany,Male,42.0,9.0,93925.3,2.0,1.0,0.0,118045.97,0.0
-1344,15692575.0,Brown,850.0,Germany,Male,56.0,1.0,128173.9,1.0,1.0,1.0,94949.84,0.0
-1345,15650018.0,Chukwujamuike,706.0,France,Male,32.0,8.0,0.0,2.0,0.0,1.0,180598.28,0.0
-1346,15570073.0,Ch'ien,656.0,Spain,Male,32.0,3.0,0.0,2.0,0.0,1.0,180622.02,0.0
-1347,15658532.0,Chin,602.0,France,Male,35.0,1.0,93955.83,1.0,1.0,0.0,93717.55,0.0
-1348,15783378.0,T'ien,687.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,121840.2,0.0
-1349,15582910.0,Tien,748.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,96442.86,0.0
-1350,15782231.0,Hsing,684.0,France,Female,46.0,8.0,0.0,2.0,1.0,0.0,163477.22,0.0
-1351,15567630.0,Milne,622.0,France,Female,28.0,5.0,0.0,2.0,1.0,0.0,131283.6,0.0
-1352,15708714.0,Lu,699.0,Spain,Male,39.0,4.0,0.0,2.0,0.0,0.0,159823.16,0.0
-1353,15806918.0,Yobanna,543.0,France,Female,41.0,6.0,0.0,2.0,1.0,1.0,121277.78,0.0
-1354,15793723.0,Pinto,643.0,Spain,Male,22.0,6.0,165438.26,1.0,1.0,1.0,47848.86,0.0
-1355,15789863.0,Smith,663.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,51380.9,1.0
-1356,15698031.0,Chukwudi,684.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,122218.62,0.0
-1357,15742971.0,P'eng,579.0,Spain,Female,70.0,2.0,0.0,4.0,1.0,1.0,170819.01,1.0
-1358,15636023.0,Iweobiegbulam,582.0,Germany,Male,29.0,4.0,122661.39,1.0,1.0,0.0,172576.56,0.0
-1359,15782688.0,Iweobiegbunam,714.0,Germany,Male,34.0,5.0,122314.5,1.0,1.0,1.0,121592.5,0.0
-1360,15624595.0,Liao,706.0,Spain,Male,34.0,2.0,125860.2,2.0,1.0,0.0,104889.3,0.0
-1361,15801417.0,Hsia,561.0,Spain,Male,29.0,10.0,0.0,1.0,1.0,1.0,75927.35,0.0
-1362,15610337.0,Nwoye,621.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,171587.9,0.0
-1363,15653620.0,H?,705.0,Germany,Male,38.0,6.0,146798.81,2.0,1.0,1.0,130883.9,0.0
-1364,15726588.0,Zaytseva,684.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,88659.07,0.0
-1365,15690182.0,Clements,689.0,France,Female,33.0,6.0,98903.06,1.0,1.0,1.0,71709.12,0.0
-1366,15688865.0,Chia,684.0,Spain,Female,36.0,4.0,0.0,1.0,1.0,1.0,138880.24,0.0
-1367,15715394.0,Nwokezuike,695.0,France,Female,43.0,10.0,0.0,2.0,1.0,0.0,7900.46,0.0
-1368,15751180.0,Chidumaga,778.0,France,Male,21.0,1.0,0.0,2.0,1.0,1.0,181224.56,0.0
-1369,15793455.0,Onyekaozulu,685.0,France,Female,36.0,7.0,0.0,1.0,1.0,1.0,155782.89,0.0
-1370,15766047.0,Martin,705.0,Germany,Female,41.0,7.0,172448.77,2.0,1.0,1.0,60954.45,0.0
-1371,15773520.0,Shih,753.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,167636.15,0.0
-1372,15691483.0,Ch'in,599.0,France,Male,29.0,1.0,0.0,2.0,0.0,1.0,91086.73,0.0
-1373,15799435.0,Shih,707.0,France,Female,27.0,4.0,0.0,2.0,1.0,0.0,131043.2,0.0
-1374,15645355.0,Nwachukwu,628.0,Germany,Female,41.0,8.0,151855.33,4.0,1.0,0.0,51705.01,1.0
-1375,15750055.0,Ts'ai,716.0,Spain,Female,25.0,0.0,0.0,2.0,0.0,0.0,164768.82,0.0
-1376,15598884.0,Hsiao,535.0,France,Female,34.0,1.0,136526.26,1.0,1.0,1.0,175743.92,0.0
-1377,15703588.0,Pagnotto,578.0,France,Male,26.0,8.0,0.0,1.0,1.0,1.0,113481.02,0.0
-1378,15603378.0,Kerr,678.0,France,Female,49.0,10.0,0.0,1.0,1.0,0.0,140723.02,1.0
-1379,15663917.0,Iweobiegbulam,542.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,96444.88,0.0
-1380,15770406.0,Iadanza,683.0,Spain,Female,41.0,7.0,0.0,1.0,1.0,0.0,137041.15,0.0
-1381,15693381.0,Chibugo,625.0,Germany,Female,34.0,5.0,142862.21,1.0,1.0,0.0,93959.96,1.0
-1382,15702561.0,Maughan,726.0,Germany,Female,44.0,3.0,105795.9,4.0,1.0,0.0,141446.01,1.0
-1383,15586680.0,Chukwumaobim,646.0,Spain,Male,39.0,7.0,0.0,1.0,1.0,1.0,93883.53,0.0
-1384,15588854.0,Hsiao,589.0,Spain,Female,38.0,6.0,0.0,2.0,0.0,1.0,141364.27,1.0
-1385,15622442.0,Chizoba,683.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,117948.0,0.0
-1386,15780128.0,Pankhurst,794.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,153917.41,0.0
-1387,15680346.0,Pinto,549.0,France,Female,42.0,1.0,0.0,2.0,1.0,1.0,54726.43,0.0
-1388,15773098.0,Chikwado,722.0,France,Female,35.0,0.0,0.0,1.0,1.0,0.0,192505.62,0.0
-1389,15700710.0,Ngozichukwuka,593.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,53584.03,0.0
-1390,15643487.0,Mazzi,616.0,Germany,Female,27.0,10.0,107304.39,1.0,1.0,1.0,75927.35,0.0
-1391,15690925.0,Ugoji,648.0,France,Male,32.0,7.0,0.0,1.0,1.0,0.0,171575.54,0.0
-1392,15572408.0,Iloerika,693.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,173486.39,0.0
-1393,15583595.0,Ritchie,622.0,Spain,Female,56.0,7.0,0.0,1.0,0.0,0.0,126381.6,1.0
-1394,15598884.0,Y?,592.0,Spain,Male,42.0,3.0,84944.58,1.0,1.0,1.0,153662.88,0.0
-1395,15593976.0,Uchechukwu,715.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,71139.31,0.0
-1396,15682912.0,Yeh,671.0,Germany,Female,46.0,4.0,134497.27,1.0,1.0,1.0,120892.96,1.0
-1397,15602405.0,Ch'ang,641.0,France,Male,43.0,6.0,0.0,2.0,1.0,0.0,167784.28,0.0
-1398,15691840.0,Kao,649.0,Spain,Male,37.0,8.0,0.0,2.0,0.0,1.0,138886.08,0.0
-1399,15772216.0,Sagese,683.0,France,Male,58.0,7.0,0.0,1.0,1.0,1.0,53584.04,1.0
-1400,15797905.0,Ch'ien,707.0,France,Male,28.0,4.0,169824.46,2.0,1.0,1.0,158973.77,0.0
-1401,15572767.0,Baresi,684.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,47850.1,0.0
-1402,15626135.0,Ritchie,621.0,France,Male,30.0,5.0,119624.6,2.0,1.0,0.0,55893.33,0.0
-1403,15800366.0,Linger,651.0,Germany,Female,37.0,9.0,132948.51,2.0,1.0,0.0,161253.08,0.0
-1404,15731511.0,Chikelu,561.0,Germany,Female,24.0,2.0,86891.84,2.0,1.0,1.0,121537.91,0.0
-1405,15649418.0,Olisaemeka,663.0,France,Male,48.0,4.0,0.0,2.0,1.0,0.0,99800.87,0.0
-1406,15684460.0,Obiajulu,594.0,Germany,Male,33.0,9.0,106854.21,2.0,1.0,0.0,91366.42,0.0
-1407,15698031.0,Yevdokimova,728.0,France,Male,37.0,6.0,0.0,2.0,0.0,0.0,142783.98,0.0
-1408,15691483.0,Genovese,714.0,France,Male,41.0,2.0,0.0,1.0,1.0,0.0,67979.35,0.0
-1409,15798844.0,Chijindum,716.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,63737.19,0.0
-1410,15794895.0,Lo Duca,684.0,France,Male,27.0,3.0,145578.28,1.0,1.0,0.0,166516.84,0.0
-1411,15794236.0,Onwuamaeze,438.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,74593.84,0.0
-1412,15632848.0,Ts'ai,643.0,France,Male,38.0,3.0,0.0,2.0,0.0,0.0,146851.76,0.0
-1413,15570086.0,Tien,636.0,Germany,Male,46.0,1.0,102025.32,1.0,0.0,1.0,125870.5,1.0
-1414,15640852.0,Nwachinemelu,758.0,France,Male,38.0,1.0,99836.47,1.0,1.0,1.0,64477.25,0.0
-1415,15651264.0,De Luca,642.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,86950.72,0.0
-1416,15582519.0,Pagnotto,629.0,Germany,Male,29.0,8.0,142406.43,2.0,1.0,1.0,177747.72,0.0
-1417,15783029.0,Lucchese,693.0,France,Male,37.0,7.0,0.0,1.0,1.0,0.0,163550.58,1.0
-1418,15671345.0,Chibugo,637.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,131768.98,0.0
-1419,15758035.0,Tsao,496.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,141446.01,0.0
-1420,15754574.0,Nwora,850.0,Spain,Female,39.0,5.0,63763.49,1.0,0.0,0.0,115442.16,0.0
-1421,15756680.0,Chifley,705.0,France,Male,43.0,4.0,126679.73,1.0,0.0,0.0,77866.91,0.0
-1422,15716041.0,Yobanna,709.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,1.0,164844.37,0.0
-1423,15583692.0,Chukwudi,583.0,France,Male,33.0,10.0,0.0,2.0,1.0,1.0,105780.18,0.0
-1424,15713378.0,Chinweike,645.0,France,Female,27.0,5.0,91893.32,1.0,1.0,1.0,125079.65,0.0
-1425,15751167.0,Mironova,572.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,142331.85,0.0
-1426,15635078.0,Pirozzi,664.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,88815.25,0.0
-1427,15771059.0,Manna,656.0,Germany,Female,37.0,3.0,119948.09,2.0,1.0,1.0,102515.43,0.0
-1428,15809279.0,T'ien,685.0,Spain,Male,44.0,8.0,0.0,2.0,1.0,1.0,93874.87,0.0
-1429,15699450.0,Hsiao,622.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,1.0,122810.53,0.0
-1430,15806447.0,Chinagorom,627.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,86043.27,0.0
-1431,15734987.0,Gadsdon,708.0,France,Female,56.0,5.0,0.0,1.0,1.0,0.0,100423.88,1.0
-1432,15780124.0,Cremonesi,716.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,112349.51,0.0
-1433,15756986.0,Martin,617.0,Spain,Male,45.0,8.0,95772.13,1.0,1.0,0.0,157072.91,0.0
-1434,15697318.0,Kistler,659.0,France,Male,37.0,7.0,132932.15,1.0,1.0,1.0,59961.81,0.0
-1435,15707974.0,Genovese,556.0,France,Female,65.0,3.0,0.0,1.0,1.0,0.0,103349.74,1.0
-1436,15803801.0,Ebelegbulam,584.0,Spain,Female,27.0,1.0,0.0,2.0,0.0,1.0,93551.36,0.0
-1437,15706021.0,Clements,738.0,France,Male,32.0,2.0,125512.98,1.0,1.0,1.0,59601.15,0.0
-1438,15758531.0,Toscani,679.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,132973.34,0.0
-1439,15761286.0,Onio,595.0,Spain,Female,27.0,2.0,109052.04,1.0,1.0,1.0,85522.84,0.0
-1440,15746553.0,Obidimkpa,590.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,1.0,149614.61,0.0
-1441,15620557.0,Chidalu,573.0,Spain,Female,36.0,8.0,99842.6,1.0,1.0,1.0,73329.08,0.0
-1442,15670266.0,Trentino,749.0,Germany,Male,37.0,4.0,108888.56,1.0,1.0,1.0,176902.83,0.0
-1443,15682355.0,Genovese,687.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,84421.34,0.0
-1444,15810457.0,Pinto,667.0,France,Female,42.0,3.0,0.0,2.0,1.0,0.0,162452.65,0.0
-1445,15677317.0,Ch'iu,539.0,Germany,Male,29.0,4.0,121172.97,2.0,1.0,0.0,48131.92,0.0
-1446,15627002.0,Shao,679.0,Germany,Male,36.0,5.0,98630.31,1.0,1.0,0.0,73669.94,0.0
-1447,15573851.0,Onyemauchechi,642.0,Germany,Female,23.0,6.0,89799.47,3.0,1.0,1.0,141679.66,0.0
-1448,15803764.0,Okeke,556.0,Germany,Female,46.0,9.0,128940.24,3.0,1.0,1.0,61936.1,1.0
-1449,15605872.0,T'ien,758.0,Germany,Female,59.0,1.0,114959.12,3.0,0.0,0.0,83629.6,1.0
-1450,15652700.0,Yobanna,651.0,Germany,Male,33.0,5.0,152582.2,2.0,1.0,1.0,1112.76,0.0
-1451,15730038.0,Chibugo,516.0,Spain,Male,22.0,5.0,0.0,2.0,1.0,0.0,168035.6,1.0
-1452,15572022.0,Diggs,567.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,1.0,176750.81,0.0
-1453,15623791.0,Chiu,652.0,Spain,Female,33.0,6.0,85845.43,1.0,1.0,1.0,138759.15,0.0
-1454,15792650.0,Hsu,573.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,173870.39,0.0
-1455,15739248.0,Scott,682.0,France,Male,35.0,3.0,120623.21,1.0,0.0,0.0,149517.72,0.0
-1456,15798975.0,Ch'en,565.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,1.0,107839.03,0.0
-1457,15660316.0,Hsing,586.0,France,Male,45.0,4.0,130170.82,1.0,1.0,0.0,93839.4,0.0
-1458,15681446.0,Mancini,686.0,Spain,Female,48.0,5.0,0.0,1.0,1.0,1.0,96864.36,1.0
-1459,15605968.0,Chibueze,586.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,97623.42,0.0
-1460,15697035.0,Yeh,603.0,Spain,Female,36.0,5.0,0.0,1.0,1.0,0.0,84935.49,1.0
-1461,15690546.0,Chialuka,678.0,Spain,Female,28.0,9.0,0.0,2.0,1.0,0.0,168034.9,0.0
-1462,15589320.0,Tsai,676.0,France,Female,47.0,2.0,0.0,2.0,1.0,1.0,140941.47,0.0
-1463,15692761.0,Hs?,642.0,Germany,Female,27.0,6.0,108698.96,1.0,1.0,1.0,180598.32,1.0
-1464,15750049.0,Iweobiegbunam,619.0,Germany,Female,34.0,3.0,168066.87,2.0,1.0,0.0,110273.84,0.0
-1465,15662063.0,Chukwueloka,724.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,1.0,93717.55,0.0
-1466,15620751.0,Chidimma,649.0,Spain,Female,39.0,1.0,0.0,1.0,1.0,1.0,95857.18,0.0
-1467,15616270.0,Obialo,544.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,63737.19,0.0
-1468,15685329.0,Hs?,614.0,France,Male,30.0,7.0,0.0,1.0,1.0,0.0,121866.87,0.0
-1469,15778696.0,Chigozie,679.0,Spain,Female,29.0,2.0,0.0,2.0,0.0,1.0,187638.34,0.0
-1470,15586029.0,Chiganu,539.0,Spain,Female,27.0,7.0,0.0,2.0,0.0,0.0,90928.02,1.0
-1471,15780128.0,Onyemauchechukwu,714.0,France,Male,26.0,4.0,0.0,2.0,1.0,1.0,85890.75,0.0
-1472,15769902.0,Nickson,645.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,153064.87,0.0
-1473,15646037.0,Kashiwagi,489.0,Germany,Male,30.0,9.0,119496.25,2.0,1.0,1.0,157891.11,0.0
-1474,15605968.0,Martin,648.0,Germany,Female,39.0,4.0,120911.75,2.0,1.0,1.0,89048.46,1.0
-1475,15692461.0,Ejimofor,645.0,Spain,Female,55.0,7.0,0.0,1.0,1.0,1.0,177528.92,1.0
-1476,15741402.0,Mazzanti,679.0,France,Male,37.0,1.0,119278.01,1.0,1.0,0.0,86950.72,0.0
-1477,15585928.0,Goliwe,574.0,Germany,Male,41.0,8.0,110802.03,1.0,1.0,1.0,138770.93,0.0
-1478,15700813.0,Achebe,439.0,Germany,Female,34.0,9.0,102772.67,2.0,1.0,1.0,119216.46,0.0
-1479,15646563.0,Soto,675.0,France,Male,37.0,6.0,0.0,1.0,1.0,0.0,58814.41,1.0
-1480,15604576.0,P'an,559.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,75884.65,0.0
-1481,15655952.0,Ndubueze,662.0,France,Female,46.0,6.0,0.0,2.0,1.0,0.0,141860.62,0.0
-1482,15698932.0,Uchenna,571.0,France,Female,44.0,2.0,141349.43,1.0,1.0,1.0,182354.46,0.0
-1483,15682210.0,Folliero,687.0,France,Male,41.0,9.0,0.0,1.0,0.0,0.0,115509.59,0.0
-1484,15569666.0,Hs?,775.0,France,Male,34.0,6.0,0.0,2.0,0.0,1.0,171069.38,0.0
-1485,15744398.0,Kosisochukwu,584.0,France,Female,43.0,3.0,0.0,2.0,1.0,0.0,136902.01,0.0
-1486,15686670.0,Houghton,646.0,France,Male,34.0,3.0,0.0,2.0,0.0,0.0,130639.01,0.0
-1487,15783859.0,Pirozzi,577.0,France,Female,40.0,9.0,0.0,1.0,1.0,0.0,51637.67,1.0
-1488,15699468.0,Chiang,726.0,Spain,Female,35.0,6.0,95858.37,1.0,0.0,1.0,137476.71,0.0
-1489,15741416.0,Chidiebere,642.0,France,Male,46.0,4.0,0.0,1.0,1.0,0.0,164018.98,1.0
-1490,15657829.0,Smith,697.0,Germany,Female,44.0,8.0,123401.43,2.0,1.0,0.0,131644.31,1.0
-1491,15729377.0,Chukwuebuka,682.0,Germany,Female,35.0,8.0,92398.08,1.0,0.0,0.0,104982.39,0.0
-1492,15786042.0,Trevisan,777.0,France,Male,47.0,4.0,0.0,2.0,1.0,0.0,143683.75,0.0
-1493,15658716.0,Folliero,579.0,Germany,Female,56.0,6.0,141952.07,1.0,1.0,1.0,9984.4,1.0
-1494,15746333.0,Chukwubuikem,705.0,France,Male,36.0,5.0,132932.13,1.0,1.0,1.0,88698.83,0.0
-1495,15574072.0,Toscani,730.0,Germany,Female,35.0,2.0,107714.74,1.0,0.0,0.0,102083.78,1.0
-1496,15632826.0,Chiazagomekpere,709.0,Germany,Female,38.0,9.0,120847.11,2.0,1.0,0.0,171668.2,0.0
-1497,15648067.0,Hsieh,629.0,France,Female,40.0,4.0,0.0,2.0,0.0,0.0,117632.63,0.0
-1498,15663421.0,Onyekachi,561.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,141023.01,0.0
-1499,15658614.0,Hs?,588.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,47322.05,0.0
-1500,15776939.0,Lucciano,592.0,Spain,Female,45.0,7.0,0.0,1.0,0.0,0.0,58974.0,1.0
-1501,15811314.0,Shih,718.0,Spain,Female,37.0,0.0,0.0,2.0,1.0,0.0,179032.68,0.0
-1502,15757628.0,Toscani,610.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,84421.34,0.0
-1503,15739248.0,Tsao,699.0,France,Female,31.0,1.0,0.0,2.0,1.0,0.0,125518.32,0.0
-1504,15625698.0,Lucciano,682.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,163655.01,0.0
-1505,15573798.0,Chiang,614.0,France,Female,32.0,5.0,0.0,1.0,1.0,0.0,136846.75,0.0
-1506,15814465.0,Ch'ang,652.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,83084.33,0.0
-1507,15683553.0,Hsiung,662.0,France,Female,30.0,4.0,0.0,2.0,0.0,1.0,131778.58,0.0
-1508,15796074.0,Hsiung,681.0,Germany,Female,55.0,6.0,133476.04,1.0,1.0,1.0,178833.0,0.0
-1509,15582841.0,Grosse,575.0,Germany,Female,45.0,3.0,100374.58,1.0,1.0,0.0,158036.72,1.0
-1510,15797329.0,Mazzanti,671.0,Germany,Female,44.0,6.0,109032.23,1.0,1.0,0.0,175072.47,1.0
-1511,15721793.0,Nwankwo,754.0,France,Male,23.0,6.0,0.0,1.0,1.0,0.0,148814.54,1.0
-1512,15591257.0,Lucchesi,666.0,France,Female,40.0,3.0,98955.87,2.0,1.0,1.0,143960.72,0.0
-1513,15713774.0,Milne,593.0,Germany,Male,48.0,6.0,143542.36,1.0,0.0,0.0,126469.09,1.0
-1514,15763029.0,Chukwukadibia,592.0,France,Male,54.0,8.0,0.0,1.0,1.0,1.0,114722.42,1.0
-1515,15808023.0,Onyekachi,702.0,France,Male,41.0,0.0,111288.18,1.0,1.0,1.0,62868.33,0.0
-1516,15623649.0,Chizuoke,678.0,France,Male,30.0,9.0,139825.57,1.0,1.0,1.0,151317.23,0.0
-1517,15572022.0,Nnanna,627.0,Germany,Female,44.0,0.0,137455.99,1.0,1.0,0.0,136903.09,1.0
-1518,15574324.0,Hughes,701.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,149457.81,0.0
-1519,15730076.0,Genovesi,739.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,43821.56,0.0
-1520,15622454.0,Hs?,628.0,France,Male,39.0,9.0,0.0,1.0,1.0,0.0,142956.48,0.0
-1521,15624592.0,Yuryeva,602.0,France,Female,33.0,4.0,0.0,2.0,1.0,1.0,121061.89,0.0
-1522,15659931.0,Ozerova,613.0,France,Female,60.0,5.0,0.0,1.0,1.0,0.0,72873.33,1.0
-1523,15668669.0,Mancini,633.0,France,Male,54.0,7.0,0.0,2.0,1.0,0.0,114035.77,1.0
-1524,15742632.0,Nwachinemelu,833.0,France,Male,39.0,5.0,0.0,2.0,0.0,1.0,124749.08,0.0
-1525,15667871.0,Ch'ien,674.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,178067.33,0.0
-1526,15780779.0,Hsing,616.0,Spain,Female,49.0,2.0,0.0,1.0,1.0,0.0,131662.47,1.0
-1527,15751175.0,T'ien,665.0,France,Female,42.0,9.0,0.0,2.0,1.0,0.0,51565.98,0.0
-1528,15626243.0,Yermakov,578.0,France,Male,37.0,4.0,120150.39,1.0,0.0,1.0,152305.47,0.0
-1529,15779059.0,Tung,685.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,143635.33,0.0
-1530,15692570.0,Kao,641.0,France,Female,40.0,8.0,119624.6,1.0,1.0,1.0,141872.05,0.0
-1531,15643392.0,Hsu,698.0,Germany,Male,42.0,1.0,119499.42,1.0,1.0,0.0,101455.07,0.0
-1532,15804950.0,Chukwudi,841.0,Spain,Male,34.0,2.0,106831.69,1.0,1.0,0.0,114522.68,0.0
-1533,15743828.0,Palerma,634.0,Spain,Male,35.0,9.0,131284.53,1.0,0.0,1.0,157097.09,0.0
-1534,15707674.0,Chiazagomekpere,561.0,Spain,Male,37.0,2.0,102050.47,1.0,1.0,1.0,179128.69,0.0
-1535,15646942.0,Onyemauchechukwu,714.0,France,Male,36.0,6.0,0.0,2.0,0.0,1.0,19131.71,0.0
-1536,15807563.0,Uspensky,573.0,France,Male,30.0,6.0,0.0,1.0,1.0,0.0,60905.51,0.0
-1537,15786035.0,Kaur,721.0,France,Female,28.0,4.0,0.0,2.0,0.0,0.0,175072.47,0.0
-1538,15717290.0,Cattaneo,645.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,58905.08,1.0
-1539,15579781.0,Nnanna,687.0,Germany,Male,43.0,5.0,82373.94,1.0,1.0,1.0,127095.14,0.0
-1540,15571415.0,K?,710.0,Germany,Female,57.0,4.0,116954.26,1.0,1.0,0.0,57962.41,1.0
-1541,15689322.0,Hsiung,627.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,98810.78,0.0
-1542,15593959.0,Aitken,659.0,France,Male,25.0,6.0,0.0,2.0,1.0,0.0,163798.41,0.0
-1543,15595713.0,Rizzo,691.0,France,Female,31.0,9.0,0.0,1.0,1.0,1.0,184842.24,0.0
-1544,15644856.0,Hsueh,635.0,France,Female,52.0,7.0,171069.39,1.0,1.0,0.0,126761.2,1.0
-1545,15600575.0,Pirozzi,633.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,131736.23,0.0
-1546,15587584.0,McIntosh,667.0,Germany,Female,46.0,1.0,131647.01,2.0,1.0,0.0,131662.42,1.0
-1547,15607598.0,Baresi,717.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,149613.77,1.0
-1548,15765257.0,Ch'ang,567.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,1.0,141872.05,0.0
-1549,15658929.0,Chukwualuka,597.0,France,Female,37.0,3.0,120145.96,1.0,1.0,1.0,187900.12,0.0
-1550,15754577.0,Hsu,588.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,177099.71,0.0
-1551,15676521.0,White,669.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,1.0,62449.07,0.0
-1552,15732438.0,Nworie,667.0,Spain,Male,36.0,2.0,131834.8,1.0,1.0,1.0,76544.9,0.0
-1553,15700714.0,Shao,641.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,0.0,170518.55,0.0
-1554,15641183.0,Chiu,696.0,Spain,Male,34.0,8.0,0.0,2.0,0.0,1.0,47468.67,0.0
-1555,15780561.0,Mazzi,663.0,Germany,Male,36.0,9.0,117468.67,2.0,1.0,1.0,139735.54,0.0
-1556,15654562.0,Chigbogu,543.0,France,Male,36.0,4.0,0.0,1.0,1.0,1.0,148515.61,0.0
-1557,15707078.0,Chinedum,773.0,France,Male,35.0,3.0,105599.67,1.0,0.0,1.0,138344.7,0.0
-1558,15679429.0,Hsiung,641.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,116885.59,0.0
-1559,15723484.0,Mazzanti,633.0,France,Female,58.0,8.0,0.0,2.0,1.0,1.0,51711.57,0.0
-1560,15777616.0,Burns,706.0,Germany,Male,33.0,6.0,141501.33,1.0,0.0,0.0,181177.9,1.0
-1561,15692929.0,Chukwualuka,565.0,France,Male,45.0,1.0,0.0,2.0,1.0,1.0,112973.66,0.0
-1562,15772363.0,Chukwueloka,653.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,162599.51,0.0
-1563,15791045.0,Obiora,616.0,Spain,Female,35.0,3.0,92398.08,1.0,0.0,0.0,101866.39,0.0
-1564,15762218.0,Onyekachi,748.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,25292.53,0.0
-1565,15769312.0,Hs?eh,732.0,Spain,Female,32.0,4.0,153572.63,1.0,1.0,1.0,160979.66,0.0
-1566,15777599.0,Y?an,648.0,France,Female,44.0,7.0,138053.79,1.0,0.0,0.0,6175.53,1.0
-1567,15679024.0,Yudin,556.0,Spain,Female,46.0,3.0,0.0,1.0,0.0,0.0,79888.78,1.0
-1568,15737605.0,Fanucci,587.0,France,Male,38.0,6.0,71240.2,1.0,1.0,0.0,116471.43,0.0
-1569,15773098.0,Tsou,653.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,1.0,91574.92,0.0
-1570,15778418.0,Ijendu,667.0,France,Male,38.0,4.0,0.0,2.0,0.0,1.0,116971.09,0.0
-1571,15793723.0,Hsia,751.0,France,Female,30.0,2.0,0.0,2.0,1.0,0.0,51620.87,0.0
-1572,15654562.0,Shih,564.0,France,Male,33.0,8.0,0.0,1.0,1.0,0.0,176770.28,0.0
-1573,15775662.0,Hsueh,603.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,52831.13,0.0
-1574,15773934.0,White,651.0,Spain,Female,35.0,1.0,0.0,1.0,1.0,0.0,171345.02,0.0
-1575,15809772.0,Hsia,850.0,Germany,Male,63.0,9.0,139753.35,2.0,1.0,0.0,85746.52,1.0
-1576,15726234.0,P'eng,684.0,France,Male,44.0,8.0,0.0,1.0,1.0,0.0,143783.46,0.0
-1577,15605918.0,Ts'ai,826.0,France,Male,29.0,7.0,0.0,2.0,0.0,0.0,126565.84,0.0
-1578,15603764.0,Chukwumaobim,715.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,162599.51,0.0
-1579,15772573.0,H?,653.0,France,Female,33.0,9.0,161533.0,1.0,1.0,0.0,24156.03,0.0
-1580,15726557.0,Ch'ien,709.0,France,Male,29.0,3.0,148055.74,1.0,1.0,1.0,79919.13,0.0
-1581,15656613.0,Genovesi,809.0,France,Female,41.0,7.0,153895.35,1.0,0.0,0.0,152956.33,0.0
-1582,15737385.0,Chin,484.0,Spain,Female,57.0,2.0,0.0,1.0,0.0,0.0,86022.09,1.0
-1583,15732640.0,Yefimov,583.0,France,Male,55.0,2.0,0.0,2.0,1.0,1.0,97633.93,0.0
-1584,15739194.0,Pirozzi,535.0,Spain,Female,22.0,5.0,0.0,2.0,1.0,0.0,79919.13,0.0
-1585,15594577.0,Trevisani,589.0,Germany,Male,51.0,0.0,139973.65,1.0,1.0,1.0,129964.94,1.0
-1586,15645463.0,Chukwualuka,571.0,Spain,Male,28.0,4.0,0.0,2.0,1.0,0.0,160129.99,0.0
-1587,15656062.0,Yefremova,622.0,Germany,Female,44.0,3.0,79562.98,2.0,1.0,0.0,155700.15,1.0
-1588,15623210.0,O'Brien,624.0,France,Female,41.0,9.0,147903.45,1.0,1.0,1.0,86935.08,0.0
-1589,15788022.0,T'ao,599.0,France,Male,62.0,5.0,0.0,1.0,1.0,0.0,70370.24,1.0
-1590,15737071.0,Watson,758.0,Germany,Male,45.0,8.0,125903.22,1.0,1.0,1.0,120115.5,1.0
-1591,15584766.0,Nwankwo,468.0,Spain,Female,53.0,0.0,0.0,1.0,1.0,0.0,85523.24,1.0
-1592,15588566.0,Chidiebere,718.0,France,Female,41.0,8.0,81070.34,2.0,1.0,1.0,125079.65,0.0
-1593,15605293.0,Ch'ien,710.0,France,Male,40.0,9.0,0.0,1.0,0.0,1.0,142331.85,0.0
-1594,15679991.0,Okeke,539.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,196241.94,0.0
-1595,15751175.0,Yashina,729.0,France,Female,38.0,2.0,0.0,2.0,0.0,1.0,71133.12,0.0
-1596,15582404.0,O'Brien,667.0,Spain,Female,35.0,5.0,96974.97,1.0,0.0,1.0,86587.37,0.0
-1597,15651301.0,Toscani,687.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,80619.17,0.0
-1598,15754072.0,Rounsevell,643.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,143301.49,0.0
-1599,15662291.0,Kao,751.0,Germany,Male,30.0,0.0,134562.9,2.0,1.0,0.0,86414.41,0.0
-1600,15628007.0,Ugochukwutubelum,627.0,France,Female,43.0,8.0,0.0,2.0,0.0,1.0,170195.4,0.0
-1601,15706071.0,Akobundu,648.0,France,Female,40.0,4.0,137326.55,1.0,1.0,1.0,108841.83,0.0
-1602,15605059.0,Chukwualuka,745.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,1.0,74890.58,0.0
-1603,15629985.0,Chukwudi,687.0,Spain,Male,38.0,4.0,0.0,2.0,0.0,1.0,120321.09,0.0
-1604,15768219.0,Hsiung,604.0,France,Male,26.0,5.0,0.0,2.0,1.0,0.0,44251.33,0.0
-1605,15639278.0,Chinedum,752.0,Germany,Female,36.0,8.0,131834.8,1.0,0.0,1.0,132034.8,0.0
-1606,15756850.0,Zetticci,678.0,France,Female,31.0,4.0,0.0,2.0,0.0,0.0,171378.77,1.0
-1607,15643826.0,Tung,725.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,161653.5,0.0
-1608,15572390.0,Nnachetam,699.0,Spain,Male,38.0,8.0,0.0,1.0,1.0,0.0,72500.68,1.0
-1609,15742534.0,Mazzi,733.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,126812.63,0.0
-1610,15589435.0,Hsiao,704.0,Spain,Male,24.0,8.0,123365.85,1.0,1.0,1.0,158531.01,0.0
-1611,15574324.0,Scott,607.0,France,Male,23.0,7.0,0.0,2.0,1.0,0.0,102515.72,0.0
-1612,15602500.0,Onyemauchechi,760.0,France,Male,42.0,5.0,0.0,2.0,0.0,1.0,48778.46,0.0
-1613,15687558.0,Iadanza,685.0,Spain,Female,39.0,5.0,0.0,1.0,1.0,1.0,176843.53,0.0
-1614,15581586.0,Cattaneo,606.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,179908.71,0.0
-1615,15652934.0,Chiganu,685.0,Spain,Male,26.0,3.0,0.0,1.0,1.0,1.0,131772.51,0.0
-1616,15782939.0,Chukwuemeka,621.0,Spain,Female,36.0,9.0,0.0,2.0,0.0,1.0,61602.42,0.0
-1617,15762729.0,Ch'iu,584.0,Germany,Female,45.0,1.0,83074.0,1.0,1.0,0.0,121789.3,1.0
-1618,15746338.0,Chin,710.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,68400.14,0.0
-1619,15637523.0,Chialuka,583.0,France,Male,32.0,1.0,0.0,2.0,0.0,0.0,61326.43,0.0
-1620,15662494.0,Yudina,687.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,131097.9,0.0
-1621,15768471.0,Tsao,711.0,France,Female,39.0,7.0,0.0,1.0,1.0,0.0,174311.3,0.0
-1622,15672955.0,Macleod,789.0,Germany,Male,41.0,8.0,127059.05,1.0,0.0,1.0,161064.29,0.0
-1623,15696834.0,Kao,606.0,Spain,Female,34.0,3.0,0.0,1.0,1.0,0.0,156909.95,0.0
-1624,15721719.0,Calabresi,730.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,80190.36,0.0
-1625,15707962.0,Baresi,679.0,France,Female,31.0,9.0,89182.09,1.0,1.0,1.0,132973.17,0.0
-1626,15772618.0,Ch'ang,569.0,Germany,Female,56.0,4.0,116936.81,2.0,1.0,0.0,89048.46,1.0
-1627,15693764.0,Yevseyev,660.0,France,Male,48.0,3.0,72542.0,2.0,1.0,0.0,187915.9,1.0
-1628,15696098.0,Ch'ien,659.0,Germany,Female,38.0,5.0,176913.52,2.0,0.0,1.0,180716.1,0.0
-1629,15732778.0,Y?,732.0,France,Male,32.0,8.0,0.0,1.0,1.0,0.0,128528.83,0.0
-1630,15791030.0,K'ung,715.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,105784.85,0.0
-1631,15589869.0,Bergamaschi,775.0,Germany,Male,35.0,5.0,130830.22,1.0,0.0,0.0,86043.27,1.0
-1632,15722072.0,Hsiung,575.0,Spain,Male,30.0,1.0,0.0,2.0,0.0,1.0,53420.53,0.0
-1633,15761763.0,Genovesi,675.0,France,Male,42.0,6.0,0.0,2.0,1.0,1.0,179435.21,1.0
-1634,15660390.0,Lucciano,591.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,167087.34,0.0
-1635,15652674.0,K'ung,585.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,106024.02,0.0
-1636,15745324.0,Hsueh,640.0,Germany,Male,35.0,8.0,140004.96,2.0,1.0,0.0,98626.78,0.0
-1637,15771543.0,T'ao,633.0,France,Female,24.0,3.0,0.0,1.0,1.0,1.0,53710.23,0.0
-1638,15776433.0,Hsiung,850.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,91092.84,0.0
-1639,15689201.0,Yobachi,735.0,Spain,Male,58.0,6.0,0.0,1.0,0.0,0.0,92357.21,1.0
-1640,15782364.0,Y?an,669.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,139048.0,0.0
-1641,15642530.0,T'ao,758.0,France,Male,32.0,4.0,79475.3,1.0,0.0,1.0,116537.96,0.0
-1642,15765520.0,Ch'eng,603.0,Germany,Male,44.0,6.0,143397.61,2.0,1.0,0.0,154908.81,0.0
-1643,15672598.0,Brown,649.0,France,Female,47.0,6.0,0.0,2.0,1.0,1.0,86935.08,0.0
-1644,15653253.0,Hargreaves,556.0,France,Male,36.0,3.0,167664.83,1.0,1.0,0.0,142400.27,0.0
-1645,15611338.0,Chien,627.0,Spain,Female,42.0,6.0,0.0,2.0,1.0,0.0,62678.53,0.0
-1646,15754068.0,Iweobiegbunam,829.0,France,Male,31.0,7.0,0.0,2.0,0.0,1.0,155870.82,0.0
-1647,15583764.0,Manna,489.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,0.0,167795.83,0.0
-1648,15779915.0,Nwankwo,578.0,France,Male,47.0,7.0,0.0,1.0,0.0,0.0,78854.57,0.0
-1649,15650026.0,Folliero,696.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,14073.11,0.0
-1650,15645269.0,Ch'ien,773.0,Spain,Female,71.0,3.0,0.0,2.0,1.0,1.0,86413.11,0.0
-1651,15779947.0,Chigozie,575.0,France,Male,40.0,0.0,0.0,2.0,1.0,1.0,122268.75,0.0
-1652,15803078.0,Chu,567.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,112554.68,0.0
-1653,15729956.0,Chidumaga,687.0,Germany,Female,48.0,4.0,116968.91,1.0,1.0,0.0,163560.7,1.0
-1654,15660301.0,Ma,599.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,72402.0,0.0
-1655,15594786.0,Okechukwu,554.0,France,Female,48.0,3.0,143964.36,1.0,0.0,0.0,105965.25,1.0
-1656,15770498.0,Onwughara,732.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,63889.1,0.0
-1657,15776743.0,Obiajulu,669.0,Germany,Male,38.0,6.0,92783.42,2.0,1.0,1.0,184980.23,0.0
-1658,15651713.0,Pisani,637.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,132649.85,0.0
-1659,15591428.0,T'ien,655.0,Spain,Male,28.0,2.0,171325.98,1.0,1.0,1.0,79476.63,0.0
-1660,15706186.0,O'Donnell,607.0,France,Male,33.0,3.0,0.0,1.0,1.0,1.0,101694.67,0.0
-1661,15584766.0,Chiazagomekpere,738.0,France,Female,44.0,1.0,99824.45,1.0,0.0,0.0,119882.86,1.0
-1662,15642570.0,Chien,738.0,France,Male,31.0,5.0,97612.12,2.0,1.0,1.0,83052.03,0.0
-1663,15807768.0,T'ang,644.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,1.0,175853.64,0.0
-1664,15587584.0,Chuang,658.0,Spain,Male,39.0,1.0,0.0,1.0,0.0,0.0,96791.15,1.0
-1665,15594720.0,Hsiao,602.0,France,Female,40.0,5.0,0.0,1.0,0.0,0.0,168035.6,1.0
-1666,15738166.0,Hsieh,523.0,France,Male,45.0,5.0,128760.32,1.0,1.0,0.0,126837.72,1.0
-1667,15721715.0,Houghton,677.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,166516.84,0.0
-1668,15587712.0,Tsao,671.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,104721.5,0.0
-1669,15754849.0,Chiang,712.0,Spain,Male,34.0,7.0,0.0,2.0,0.0,0.0,79503.05,0.0
-1670,15635240.0,Chiu,684.0,France,Male,27.0,7.0,0.0,1.0,1.0,0.0,170142.09,0.0
-1671,15634262.0,Tretiakova,703.0,Germany,Female,32.0,6.0,128927.93,2.0,0.0,1.0,105291.26,0.0
-1672,15574324.0,White,628.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,1.0,171681.86,0.0
-1673,15764444.0,Onyemauchechukwu,773.0,France,Male,24.0,5.0,0.0,2.0,1.0,0.0,91092.84,0.0
-1674,15610755.0,Walker,639.0,Germany,Male,29.0,2.0,120105.43,2.0,1.0,1.0,180598.28,0.0
-1675,15734886.0,Kao,598.0,France,Female,31.0,4.0,81844.69,2.0,1.0,1.0,81617.4,0.0
-1676,15744273.0,Zetticci,642.0,France,Male,44.0,8.0,0.0,2.0,0.0,0.0,129964.94,0.0
-1677,15792453.0,Ndukaku,707.0,France,Female,36.0,0.0,0.0,2.0,0.0,1.0,66795.78,0.0
-1678,15777076.0,Onwuamaeze,565.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,132424.69,1.0
-1679,15679991.0,Okwuadigbo,676.0,Germany,Male,36.0,4.0,130140.24,2.0,1.0,0.0,89891.6,0.0
-1680,15585106.0,Chuang,717.0,Spain,Male,37.0,7.0,0.0,2.0,1.0,1.0,74835.65,0.0
-1681,15775886.0,Teng,643.0,France,Female,41.0,7.0,0.0,1.0,1.0,0.0,141896.74,1.0
-1682,15601892.0,Hsia,681.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,142400.27,0.0
-1683,15729083.0,Ch'en,703.0,Spain,Female,39.0,2.0,152582.5,1.0,0.0,0.0,79915.28,0.0
-1684,15791053.0,Maclean,605.0,Germany,Male,57.0,7.0,148853.29,1.0,1.0,0.0,163830.64,1.0
-1685,15720354.0,Eames,684.0,Germany,Male,29.0,1.0,88826.07,2.0,1.0,0.0,109742.2,0.0
-1686,15587562.0,Okwudilichukwu,622.0,Germany,Male,43.0,3.0,113781.5,1.0,0.0,0.0,138877.95,1.0
-1687,15811588.0,Ch'ien,710.0,France,Male,36.0,4.0,112419.92,1.0,1.0,0.0,86460.28,0.0
-1688,15775294.0,Ts'ao,735.0,Germany,Male,38.0,5.0,153545.43,1.0,1.0,0.0,72876.84,0.0
-1689,15602844.0,Chidimma,595.0,France,Female,47.0,3.0,110829.52,1.0,1.0,0.0,63737.19,0.0
-1690,15594720.0,Kosisochukwu,596.0,Spain,Female,56.0,4.0,130622.34,1.0,0.0,1.0,7861.68,1.0
-1691,15781347.0,Li Fonti,604.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,132973.17,0.0
-1692,15734246.0,Ch'ien,732.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,0.0,173495.77,0.0
-1693,15605067.0,Tretiakova,706.0,Spain,Male,47.0,8.0,0.0,2.0,1.0,0.0,90106.94,0.0
-1694,15684801.0,Toscani,748.0,Spain,Female,36.0,1.0,161591.11,1.0,1.0,0.0,61480.91,0.0
-1695,15689322.0,Hsu,669.0,France,Male,38.0,3.0,0.0,2.0,0.0,0.0,73562.05,0.0
-1696,15643361.0,Li Fonti,812.0,Spain,Female,45.0,4.0,0.0,1.0,1.0,1.0,62700.08,1.0
-1697,15796790.0,Lombardo,633.0,Spain,Female,47.0,5.0,0.0,2.0,0.0,1.0,179614.8,0.0
-1698,15719579.0,Walker,758.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,182372.35,0.0
-1699,15632050.0,Chinagorom,656.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,78650.95,0.0
-1700,15658475.0,Onyemauchechukwu,695.0,France,Female,35.0,9.0,0.0,2.0,0.0,1.0,38190.78,0.0
-1701,15576824.0,O'Donnell,735.0,France,Female,39.0,9.0,0.0,2.0,1.0,1.0,136983.77,0.0
-1702,15741875.0,Wright,547.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,6078.46,0.0
-1703,15658532.0,Chiekwugo,682.0,France,Female,30.0,3.0,0.0,2.0,0.0,1.0,88890.05,0.0
-1704,15641183.0,Lettiere,749.0,France,Male,40.0,8.0,141267.67,1.0,1.0,0.0,115068.99,0.0
-1705,15598884.0,Chinwenma,570.0,Germany,Male,37.0,2.0,119492.88,2.0,1.0,1.0,136902.01,0.0
-1706,15741728.0,Ch'eng,647.0,France,Female,49.0,9.0,0.0,2.0,1.0,1.0,4050.57,0.0
-1707,15765643.0,Teng,516.0,Spain,Male,32.0,3.0,0.0,2.0,0.0,1.0,179634.69,0.0
-1708,15793856.0,Lo Duca,729.0,Spain,Male,41.0,9.0,132934.89,1.0,1.0,1.0,63736.17,0.0
-1709,15684573.0,Maclean,733.0,Spain,Female,23.0,5.0,0.0,2.0,1.0,1.0,138543.83,0.0
-1710,15745196.0,Frolov,616.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,1.0,189428.75,0.0
-1711,15782759.0,Chibugo,614.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,132476.91,1.0
-1712,15674840.0,Tuan,648.0,France,Male,26.0,7.0,0.0,1.0,0.0,0.0,144880.81,0.0
-1713,15781409.0,Kambinachi,591.0,Spain,Female,30.0,6.0,0.0,2.0,1.0,1.0,121592.5,0.0
-1714,15772892.0,Chukwumaobim,613.0,Spain,Female,37.0,8.0,0.0,3.0,0.0,0.0,168822.01,1.0
-1715,15644878.0,Onyinyechukwuka,651.0,Spain,Male,36.0,7.0,61211.18,1.0,1.0,1.0,167749.54,1.0
-1716,15662662.0,Ch'iu,745.0,France,Female,34.0,7.0,0.0,1.0,1.0,1.0,128841.51,0.0
-1717,15583580.0,Nkemakolam,572.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,182787.18,0.0
-1718,15595136.0,Shubin,643.0,Germany,Male,51.0,7.0,118879.35,1.0,1.0,0.0,85421.48,1.0
-1719,15693893.0,Mai,644.0,Germany,Male,38.0,3.0,120596.93,1.0,1.0,1.0,136902.01,0.0
-1720,15791322.0,Chimaobim,714.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,86039.53,0.0
-1721,15646594.0,Zhirov,593.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,58879.31,0.0
-1722,15621685.0,Fiorentini,637.0,Spain,Male,43.0,4.0,0.0,4.0,1.0,0.0,8814.69,1.0
-1723,15791958.0,Chinweuba,624.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,26260.98,0.0
-1724,15671514.0,Mironova,615.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,160835.3,0.0
-1725,15753871.0,Chidiegwu,705.0,France,Female,44.0,1.0,0.0,3.0,1.0,0.0,150944.31,1.0
-1726,15765402.0,Omeokachie,679.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,25554.4,0.0
-1727,15647572.0,Pisani,691.0,Spain,Male,39.0,7.0,0.0,2.0,1.0,1.0,56793.02,0.0
-1728,15642098.0,Onyemauchechukwu,779.0,France,Female,41.0,6.0,124779.46,1.0,1.0,1.0,111020.24,0.0
-1729,15802869.0,Nkemakonam,687.0,France,Female,53.0,8.0,0.0,1.0,1.0,0.0,1713.1,1.0
-1730,15571586.0,Hsueh,736.0,Germany,Female,36.0,8.0,108309.0,1.0,1.0,0.0,188809.23,1.0
-1731,15624755.0,Palerma,662.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,116969.73,0.0
-1732,15693737.0,Estep,745.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,0.0,94739.3,0.0
-1733,15665014.0,Lucchese,685.0,France,Male,37.0,7.0,76989.97,1.0,1.0,0.0,126812.85,0.0
-1734,15617401.0,Ch'iu,516.0,France,Female,42.0,3.0,0.0,2.0,1.0,0.0,65618.01,0.0
-1735,15622171.0,Ch'en,704.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,174916.85,0.0
-1736,15654562.0,Chukwubuikem,663.0,France,Female,40.0,3.0,0.0,3.0,1.0,0.0,136054.45,1.0
-1737,15672439.0,Otoole,685.0,Germany,Male,19.0,3.0,121898.58,2.0,1.0,0.0,9241.52,0.0
-1738,15758056.0,Hsueh,576.0,Spain,Female,34.0,8.0,0.0,2.0,0.0,1.0,129333.69,0.0
-1739,15733661.0,Yuryeva,733.0,France,Female,36.0,3.0,177799.83,1.0,0.0,0.0,136540.09,0.0
-1740,15581552.0,McLean,667.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,0.0,173683.0,0.0
-1741,15803764.0,Y?,611.0,Germany,Male,34.0,6.0,145166.09,1.0,1.0,0.0,88890.05,0.0
-1742,15667417.0,Wright,672.0,France,Female,26.0,3.0,0.0,2.0,1.0,0.0,67840.81,0.0
-1743,15677921.0,Bage,623.0,Germany,Male,40.0,1.0,115335.32,2.0,1.0,0.0,107811.28,0.0
-1744,15654060.0,Chidiebere,565.0,France,Female,33.0,1.0,0.0,1.0,1.0,0.0,147797.54,0.0
-1745,15620505.0,Molineux,715.0,Spain,Male,37.0,2.0,0.0,1.0,1.0,0.0,64872.33,0.0
-1746,15726589.0,Milne,614.0,Spain,Male,39.0,6.0,162039.78,1.0,0.0,0.0,134415.58,0.0
-1747,15642934.0,Ch'iu,603.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,85516.37,0.0
-1748,15764033.0,Nwachukwu,695.0,France,Female,40.0,9.0,81844.81,1.0,1.0,1.0,91573.19,0.0
-1749,15741403.0,Ulyanova,671.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,28257.89,0.0
-1750,15572022.0,Shao,619.0,France,Male,28.0,2.0,0.0,2.0,0.0,1.0,83022.84,0.0
-1751,15747808.0,Kao,637.0,Spain,Male,30.0,1.0,144591.96,1.0,1.0,0.0,53832.8,0.0
-1752,15751561.0,Pinto,733.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,139946.68,0.0
-1753,15678720.0,T'ang,516.0,Spain,Male,29.0,0.0,0.0,2.0,0.0,1.0,81778.72,0.0
-1754,15714586.0,Purdy,725.0,France,Male,44.0,7.0,0.0,1.0,0.0,0.0,126471.14,0.0
-1755,15661629.0,Pinto,705.0,Spain,Male,37.0,2.0,0.0,1.0,1.0,1.0,164925.11,0.0
-1756,15692404.0,Ulyanova,681.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,16301.91,0.0
-1757,15813432.0,Onwumelu,648.0,Germany,Male,38.0,4.0,148042.97,2.0,1.0,0.0,44390.38,0.0
-1758,15707025.0,Iadanza,686.0,Spain,Male,32.0,3.0,0.0,2.0,0.0,1.0,91291.2,0.0
-1759,15796790.0,Chukwumaobim,773.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,153983.99,0.0
-1760,15597949.0,Hsia,721.0,Germany,Male,36.0,3.0,122029.15,2.0,1.0,0.0,141069.88,1.0
-1761,15578980.0,Shih,714.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,38691.34,0.0
-1762,15571144.0,Oluchi,806.0,Spain,Female,35.0,8.0,0.0,2.0,1.0,1.0,145562.4,0.0
-1763,15683053.0,Ch'eng,617.0,France,Male,26.0,8.0,0.0,2.0,0.0,1.0,66732.63,0.0
-1764,15632448.0,Yobachukwu,646.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,166588.76,0.0
-1765,15647842.0,Shao,559.0,Germany,Male,45.0,6.0,133889.35,1.0,1.0,0.0,141872.05,1.0
-1766,15806955.0,Ukaegbunam,709.0,France,Male,33.0,2.0,125919.35,1.0,1.0,1.0,143635.55,0.0
-1767,15595884.0,Ndubuisi,711.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,147955.91,0.0
-1768,15777046.0,Onwumelu,526.0,Germany,Female,54.0,9.0,106871.81,1.0,1.0,0.0,88858.49,1.0
-1769,15719561.0,Lu,724.0,Spain,Female,38.0,2.0,0.0,2.0,0.0,1.0,172576.43,0.0
-1770,15690670.0,Tardent,656.0,Spain,Female,31.0,5.0,0.0,2.0,0.0,1.0,88972.66,0.0
-1771,15594862.0,H?,692.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,91574.92,0.0
-1772,15724334.0,Hargreaves,599.0,France,Male,35.0,5.0,0.0,2.0,0.0,0.0,142903.44,0.0
-1773,15661656.0,Tung,645.0,Germany,Female,25.0,5.0,90536.81,2.0,0.0,1.0,179291.89,0.0
-1774,15663817.0,White,794.0,France,Female,34.0,1.0,142910.18,1.0,1.0,1.0,166468.21,0.0
-1775,15690733.0,Macleod,659.0,France,Male,35.0,9.0,0.0,1.0,1.0,0.0,59650.42,0.0
-1776,15592645.0,Chukwuemeka,481.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,112491.96,0.0
-1777,15738181.0,Chidozie,742.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,111241.37,1.0
-1778,15680587.0,Efimov,724.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,64327.26,0.0
-1779,15695301.0,Ugochukwu,665.0,France,Male,36.0,2.0,136525.99,1.0,1.0,1.0,119313.74,0.0
-1780,15667424.0,Ts'ui,584.0,Germany,Male,34.0,1.0,105206.7,1.0,1.0,0.0,180598.86,0.0
-1781,15691543.0,Smith,714.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,95969.96,0.0
-1782,15647890.0,Oluchi,619.0,Germany,Male,42.0,4.0,125921.37,2.0,0.0,0.0,153917.41,0.0
-1783,15789339.0,Iqbal,671.0,Germany,Male,48.0,9.0,123737.04,1.0,1.0,0.0,40313.47,1.0
-1784,15702432.0,Chienezie,741.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,70954.41,0.0
-1785,15676966.0,Hsu,663.0,Germany,Male,38.0,1.0,145056.59,2.0,1.0,1.0,85746.52,0.0
-1786,15781388.0,Zikoranachidimma,571.0,Germany,Male,40.0,5.0,136422.28,2.0,1.0,1.0,82391.15,0.0
-1787,15709476.0,Y?,675.0,France,Female,46.0,7.0,0.0,2.0,1.0,0.0,26261.42,0.0
-1788,15684516.0,Hsing,610.0,Spain,Male,49.0,6.0,121890.06,1.0,1.0,1.0,166495.2,0.0
-1789,15723053.0,Nwankwo,633.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,46675.51,0.0
-1790,15778515.0,Ch'ien,683.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,86920.97,0.0
-1791,15664808.0,Shao,659.0,Germany,Female,45.0,4.0,124495.98,1.0,1.0,0.0,74896.33,1.0
-1792,15679983.0,Niu,641.0,Germany,Female,43.0,6.0,107713.57,1.0,1.0,1.0,152210.81,1.0
-1793,15793049.0,Hao,850.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,66580.13,0.0
-1794,15626795.0,Yevseyev,572.0,Spain,Male,40.0,9.0,0.0,2.0,0.0,0.0,126561.09,0.0
-1795,15649853.0,Zaitsev,745.0,France,Male,23.0,7.0,0.0,2.0,1.0,1.0,152643.48,0.0
-1796,15693347.0,Chukwumaobim,648.0,France,Male,33.0,3.0,0.0,2.0,0.0,1.0,56583.88,0.0
-1797,15767793.0,Lettiere,716.0,Spain,Male,31.0,7.0,89799.13,1.0,1.0,0.0,152491.16,0.0
-1798,15706762.0,Nwabugwu,662.0,Spain,Female,46.0,6.0,0.0,2.0,1.0,0.0,91957.39,0.0
-1799,15656330.0,Onuora,744.0,France,Female,23.0,4.0,168964.77,1.0,1.0,1.0,182336.45,0.0
-1800,15652667.0,Chukwueloka,712.0,Germany,Male,51.0,8.0,99564.22,2.0,1.0,0.0,161241.65,1.0
-1801,15622454.0,Obiora,681.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,80410.68,0.0
-1802,15706899.0,Shao,649.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,1.0,91573.19,0.0
-1803,15742228.0,Chimaoke,562.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,49373.97,0.0
-1804,15587066.0,Macintyre,684.0,Spain,Male,39.0,7.0,0.0,2.0,0.0,0.0,158918.02,0.0
-1805,15673590.0,De Mestre,572.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,60420.07,0.0
-1806,15700476.0,Onyinyechukwuka,653.0,Spain,Female,41.0,2.0,0.0,1.0,0.0,1.0,142903.44,1.0
-1807,15798084.0,Chiemenam,835.0,France,Male,41.0,2.0,0.0,2.0,0.0,0.0,130446.65,0.0
-1808,15611786.0,Yermolayeva,754.0,France,Male,46.0,9.0,0.0,1.0,1.0,1.0,149911.33,0.0
-1809,15681180.0,Chimaobim,585.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,140134.43,0.0
-1810,15784761.0,Chidubem,815.0,Spain,Male,28.0,4.0,0.0,2.0,1.0,1.0,184312.88,0.0
-1811,15591150.0,Yevseyev,597.0,France,Male,31.0,5.0,119278.01,1.0,0.0,0.0,115227.02,0.0
-1812,15706537.0,O'Brien,653.0,Spain,Female,31.0,8.0,0.0,2.0,0.0,1.0,48475.12,0.0
-1813,15675380.0,Onwuamaeze,726.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,0.0,84419.78,0.0
-1814,15590820.0,Chiang,669.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,106977.53,0.0
-1815,15579183.0,Li Fonti,594.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,89938.08,0.0
-1816,15730038.0,Okwuadigbo,674.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,124625.08,0.0
-1817,15616168.0,Ojiofor,619.0,France,Male,63.0,1.0,98495.72,2.0,0.0,1.0,162922.65,0.0
-1818,15725028.0,Lettiere,686.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,81442.6,0.0
-1819,15634373.0,Hsueh,554.0,Germany,Male,47.0,8.0,71083.98,2.0,1.0,0.0,199290.68,0.0
-1820,15806922.0,Sagese,607.0,France,Female,65.0,9.0,0.0,3.0,1.0,0.0,171706.66,1.0
-1821,15584090.0,Tsai,710.0,France,Female,32.0,6.0,138032.15,1.0,1.0,1.0,116466.19,0.0
-1822,15702912.0,Uspenskaya,613.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,66526.01,0.0
-1823,15596976.0,Chiang,691.0,France,Male,25.0,7.0,168883.08,1.0,0.0,0.0,96892.8,0.0
-1824,15645896.0,Y?an,545.0,France,Male,30.0,1.0,142713.44,2.0,1.0,1.0,155047.11,0.0
-1825,15581766.0,Palerma,593.0,France,Male,28.0,1.0,91522.04,1.0,0.0,1.0,156768.45,0.0
-1826,15592540.0,Ch'eng,772.0,France,Female,32.0,9.0,0.0,1.0,1.0,1.0,177747.02,0.0
-1827,15778418.0,Chiganu,622.0,France,Female,51.0,9.0,0.0,1.0,1.0,0.0,174435.48,1.0
-1828,15680201.0,Ch'ien,668.0,France,Female,25.0,5.0,0.0,1.0,1.0,0.0,129563.86,0.0
-1829,15690164.0,Chukwumaobim,594.0,Spain,Male,36.0,8.0,0.0,1.0,1.0,1.0,94499.06,0.0
-1830,15777054.0,Chukwueloka,675.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,156424.4,0.0
-1831,15788442.0,Kuykendall,667.0,Spain,Female,43.0,5.0,0.0,1.0,1.0,1.0,81492.6,1.0
-1832,15642997.0,Hsueh,684.0,Germany,Female,37.0,8.0,141789.15,1.0,1.0,1.0,166423.66,0.0
-1833,15687629.0,Fanucci,587.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,70899.27,0.0
-1834,15621433.0,Ch'en,687.0,France,Male,24.0,7.0,0.0,1.0,1.0,1.0,60905.51,0.0
-1835,15771580.0,Ugochukwutubelum,564.0,France,Female,32.0,5.0,0.0,2.0,0.0,1.0,52404.47,0.0
-1836,15752409.0,Nwabugwu,651.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,1.0,186612.47,0.0
-1837,15732332.0,Fang,704.0,France,Female,66.0,5.0,0.0,2.0,0.0,1.0,161333.13,0.0
-1838,15620341.0,Ndubuagha,559.0,Germany,Male,36.0,5.0,126816.73,1.0,1.0,1.0,116885.59,0.0
-1839,15764033.0,Genovese,645.0,France,Male,29.0,2.0,71269.89,1.0,0.0,1.0,47832.82,0.0
-1840,15779947.0,Palerma,635.0,Germany,Female,47.0,9.0,124495.98,1.0,1.0,0.0,157921.25,1.0
-1841,15604073.0,Bage,469.0,Spain,Male,23.0,4.0,0.0,1.0,0.0,0.0,78858.07,0.0
-1842,15622117.0,Chukwujekwu,682.0,Germany,Male,33.0,4.0,134542.73,1.0,0.0,1.0,180344.69,0.0
-1843,15579826.0,Toscani,676.0,France,Female,39.0,2.0,130557.24,1.0,1.0,0.0,42012.55,1.0
-1844,15683483.0,Chiang,667.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,136537.97,0.0
-1845,15664897.0,Ch'ang,676.0,France,Female,50.0,2.0,0.0,1.0,1.0,0.0,112928.74,1.0
-1846,15749019.0,Ch'eng,717.0,Spain,Male,46.0,9.0,0.0,2.0,1.0,1.0,54949.73,0.0
-1847,15769246.0,Creswell,585.0,Spain,Male,57.0,7.0,0.0,2.0,1.0,1.0,72873.33,0.0
-1848,15805909.0,Lucchese,582.0,Germany,Male,43.0,4.0,122453.97,2.0,1.0,0.0,164815.59,0.0
-1849,15684921.0,Pugh,677.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,131807.14,0.0
-1850,15632439.0,Chiu,641.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,153537.55,0.0
-1851,15777033.0,Scott,589.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,1.0,125918.17,0.0
-1852,15642530.0,H?,546.0,Germany,Female,43.0,4.0,140197.18,1.0,1.0,1.0,142763.51,1.0
-1853,15769902.0,Oluchukwu,627.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,141400.51,0.0
-1854,15596962.0,Lai,663.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,162878.96,0.0
-1855,15751177.0,Bitter,543.0,France,Male,29.0,9.0,119688.81,1.0,0.0,0.0,55815.62,0.0
-1856,15613140.0,Ejimofor,679.0,France,Female,22.0,4.0,0.0,2.0,1.0,1.0,162599.54,0.0
-1857,15625023.0,Lombardi,528.0,Germany,Female,47.0,2.0,135203.81,1.0,1.0,1.0,103379.31,1.0
-1858,15613787.0,Chiang,658.0,Spain,Female,44.0,6.0,0.0,2.0,1.0,0.0,153895.31,0.0
-1859,15582404.0,Yefremova,628.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,171733.43,0.0
-1860,15656592.0,Ch'in,599.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,93844.81,0.0
-1861,15681843.0,Hsing,670.0,France,Female,54.0,7.0,0.0,1.0,1.0,1.0,162599.51,1.0
-1862,15592386.0,Cavenagh,599.0,France,Male,40.0,7.0,173459.45,1.0,1.0,1.0,66893.28,0.0
-1863,15572767.0,Ch'iu,629.0,France,Female,56.0,9.0,0.0,2.0,1.0,1.0,50679.9,0.0
-1864,15750545.0,Lucchese,713.0,Germany,Female,47.0,2.0,127289.28,1.0,1.0,0.0,57553.02,1.0
-1865,15650351.0,Ch'eng,643.0,Germany,Female,59.0,4.0,114203.34,1.0,1.0,0.0,15650.89,1.0
-1866,15678916.0,Ch'iu,667.0,France,Female,38.0,4.0,98362.35,1.0,1.0,0.0,83343.73,0.0
-1867,15763747.0,Mancini,719.0,France,Female,37.0,7.0,80613.93,1.0,1.0,1.0,152499.8,0.0
-1868,15674932.0,Tsao,616.0,France,Male,34.0,0.0,103564.83,1.0,1.0,0.0,57589.29,0.0
-1869,15759021.0,Oluchukwu,648.0,Spain,Female,59.0,7.0,0.0,1.0,1.0,1.0,123882.73,1.0
-1870,15796074.0,T'ien,602.0,France,Male,55.0,8.0,0.0,2.0,1.0,1.0,155823.2,0.0
-1871,15588080.0,Teng,651.0,France,Male,37.0,2.0,0.0,1.0,1.0,0.0,151925.25,0.0
-1872,15590094.0,Trevisani,632.0,Spain,Male,32.0,4.0,171671.9,1.0,1.0,1.0,147530.06,0.0
-1873,15754068.0,Chukwubuikem,684.0,France,Female,31.0,10.0,0.0,2.0,1.0,1.0,133573.29,0.0
-1874,15793854.0,Li Fonti,708.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,171718.56,0.0
-1875,15654562.0,Ch'ang,629.0,France,Male,45.0,2.0,0.0,1.0,1.0,0.0,92916.54,1.0
-1876,15683553.0,Erskine,732.0,Spain,Male,40.0,9.0,125510.82,1.0,0.0,0.0,94862.93,0.0
-1877,15740147.0,Chibuzo,781.0,France,Female,33.0,2.0,126473.99,1.0,1.0,0.0,122932.5,0.0
-1878,15737408.0,Folliero,592.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,149892.79,0.0
-1879,15589320.0,Y?,587.0,France,Male,28.0,5.0,0.0,2.0,0.0,0.0,179565.57,0.0
-1880,15753337.0,Hsiung,744.0,France,Female,41.0,9.0,0.0,1.0,1.0,0.0,140941.47,1.0
-1881,15569247.0,Onyemauchechukwu,652.0,France,Female,34.0,7.0,126066.13,1.0,1.0,1.0,133535.29,0.0
-1882,15677908.0,DeRose,605.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,129975.62,0.0
-1883,15815615.0,Nwankwo,718.0,Spain,Male,40.0,6.0,0.0,2.0,0.0,0.0,52795.56,0.0
-1884,15572467.0,Moore,710.0,France,Male,50.0,1.0,0.0,1.0,0.0,1.0,177747.03,0.0
-1885,15588854.0,Chukwueloka,678.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,0.0,85412.54,0.0
-1886,15688868.0,Lucchesi,703.0,France,Male,31.0,4.0,0.0,2.0,0.0,0.0,48242.38,0.0
-1887,15751203.0,Uspenskaya,602.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,152491.22,0.0
-1888,15696812.0,Chigozie,769.0,France,Female,49.0,1.0,0.0,1.0,1.0,1.0,48426.98,0.0
-1889,15588928.0,Aksyonov,577.0,Germany,Female,35.0,9.0,136291.71,1.0,1.0,0.0,138527.56,1.0
-1890,15785547.0,Hsia,724.0,Germany,Male,42.0,0.0,137714.4,1.0,1.0,1.0,103342.48,0.0
-1891,15645896.0,Shih,645.0,Germany,Female,51.0,7.0,79340.95,3.0,1.0,0.0,161574.19,1.0
-1892,15765345.0,Ch'ang,567.0,France,Male,28.0,10.0,0.0,1.0,0.0,0.0,54949.73,0.0
-1893,15736271.0,Ch'en,626.0,France,Female,49.0,0.0,0.0,1.0,1.0,0.0,143086.09,1.0
-1894,15699492.0,Kincaid,706.0,France,Male,28.0,1.0,0.0,2.0,1.0,1.0,172749.4,0.0
-1895,15746461.0,Manna,684.0,France,Female,35.0,9.0,0.0,1.0,0.0,0.0,78744.39,0.0
-1896,15761158.0,Iloerika,748.0,France,Male,42.0,2.0,0.0,2.0,1.0,1.0,129912.74,0.0
-1897,15809263.0,Cremonesi,526.0,France,Female,30.0,1.0,0.0,1.0,1.0,0.0,72993.65,0.0
-1898,15587266.0,Chigbogu,705.0,Germany,Female,33.0,7.0,123935.35,1.0,1.0,1.0,100433.83,1.0
-1899,15661745.0,Trevisani,774.0,France,Male,51.0,2.0,0.0,2.0,0.0,1.0,180890.4,0.0
-1900,15771473.0,Romani,605.0,Germany,Female,43.0,0.0,113657.83,2.0,1.0,0.0,8059.44,1.0
-1901,15667944.0,H?,616.0,France,Male,32.0,7.0,133658.24,1.0,1.0,1.0,143357.03,0.0
-1902,15625592.0,Dickson,632.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,26752.56,0.0
-1903,15750497.0,Lu,644.0,Spain,Male,34.0,5.0,71083.98,1.0,1.0,1.0,102908.91,0.0
-1904,15621267.0,Chiazagomekpere,627.0,Germany,Female,32.0,9.0,122528.18,1.0,1.0,1.0,115068.99,0.0
-1905,15662337.0,De Salis,719.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,0.0,92816.52,0.0
-1906,15577598.0,Nwabugwu,713.0,Germany,Female,40.0,7.0,109698.41,2.0,1.0,1.0,100791.36,0.0
-1907,15766659.0,Genovese,628.0,France,Female,31.0,9.0,0.0,1.0,1.0,0.0,171515.84,0.0
-1908,15795747.0,Nwankwo,850.0,France,Female,25.0,5.0,0.0,2.0,1.0,1.0,171657.57,0.0
-1909,15586870.0,Bischof,627.0,Germany,Female,31.0,4.0,132412.38,1.0,1.0,0.0,107874.39,0.0
-1910,15671915.0,Li Fonti,573.0,Germany,Male,25.0,8.0,134571.12,1.0,1.0,0.0,88842.8,0.0
-1911,15634218.0,Ndubueze,592.0,Germany,Female,56.0,2.0,146587.3,1.0,1.0,0.0,166380.93,1.0
-1912,15611287.0,Maclean,657.0,Germany,Female,43.0,2.0,123575.51,1.0,1.0,0.0,144553.4,1.0
-1913,15574858.0,Lucchese,721.0,Germany,Female,34.0,3.0,114519.57,2.0,1.0,1.0,170514.21,0.0
-1914,15711572.0,Ch'iu,691.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,88890.05,0.0
-1915,15748068.0,Ritchie,740.0,France,Male,50.0,8.0,0.0,3.0,1.0,0.0,167032.83,1.0
-1916,15593491.0,Nkemakonam,614.0,France,Male,23.0,1.0,0.0,1.0,1.0,0.0,151146.67,0.0
-1917,15784097.0,Hsiao,648.0,France,Female,37.0,2.0,120415.54,1.0,0.0,0.0,113483.96,1.0
-1918,15724307.0,Okwudilichukwu,587.0,Germany,Male,48.0,2.0,90547.48,1.0,1.0,0.0,107876.91,1.0
-1919,15586976.0,T'ao,611.0,France,Male,32.0,7.0,0.0,1.0,0.0,1.0,125222.36,0.0
-1920,15809772.0,Yobachi,682.0,Spain,Female,36.0,8.0,0.0,2.0,0.0,1.0,173365.89,0.0
-1921,15657868.0,Chinedum,663.0,France,Female,37.0,2.0,0.0,1.0,1.0,0.0,27359.19,0.0
-1922,15662658.0,Cremonesi,705.0,Germany,Female,39.0,0.0,157094.78,1.0,1.0,1.0,91560.63,0.0
-1923,15655875.0,Tien,644.0,Spain,Female,29.0,8.0,0.0,2.0,1.0,0.0,44220.4,0.0
-1924,15672053.0,Chukwujekwu,670.0,Spain,Male,39.0,7.0,0.0,2.0,1.0,0.0,676.06,0.0
-1925,15777896.0,Okwudilichukwu,605.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,175043.69,0.0
-1926,15644933.0,Hsiung,661.0,France,Female,37.0,9.0,121152.05,2.0,0.0,1.0,70774.97,0.0
-1927,15667933.0,Chukwudi,556.0,France,Male,35.0,8.0,132876.23,1.0,1.0,1.0,61823.31,0.0
-1928,15593782.0,Y?,576.0,Spain,Male,33.0,6.0,129717.3,1.0,0.0,0.0,103557.85,0.0
-1929,15631059.0,Dickson,591.0,France,Male,62.0,4.0,0.0,2.0,0.0,1.0,164039.51,0.0
-1930,15685653.0,Tien,656.0,France,Male,38.0,0.0,141078.57,1.0,1.0,0.0,137028.51,0.0
-1931,15668830.0,Hsiao,707.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,65413.41,0.0
-1932,15592761.0,Chimaobim,754.0,France,Female,51.0,5.0,0.0,1.0,1.0,0.0,144593.4,1.0
-1933,15750055.0,Martin,742.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,133897.32,0.0
-1934,15801488.0,Folliero,591.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,120852.85,0.0
-1935,15664523.0,Yeh,709.0,France,Male,57.0,2.0,0.0,1.0,0.0,1.0,151822.66,1.0
-1936,15606836.0,Ijendu,569.0,France,Female,38.0,4.0,0.0,1.0,0.0,0.0,72993.67,1.0
-1937,15757981.0,Walker,663.0,Spain,Male,34.0,6.0,0.0,2.0,0.0,0.0,136983.77,0.0
-1938,15735246.0,Uspensky,762.0,Spain,Female,66.0,4.0,0.0,1.0,1.0,1.0,38960.59,0.0
-1939,15642619.0,Trevisani,690.0,France,Female,35.0,2.0,0.0,1.0,0.0,1.0,99800.87,0.0
-1940,15764174.0,Kao,686.0,France,Male,48.0,9.0,0.0,1.0,0.0,0.0,51387.1,1.0
-1941,15792077.0,Chidozie,634.0,France,Male,40.0,9.0,116084.32,1.0,0.0,1.0,73656.22,0.0
-1942,15666096.0,Galkin,719.0,France,Female,54.0,8.0,0.0,1.0,1.0,0.0,109145.2,1.0
-1943,15628145.0,Nwachinemelu,758.0,France,Female,38.0,1.0,0.0,1.0,1.0,0.0,180893.85,0.0
-1944,15794868.0,Hsueh,651.0,Germany,Female,43.0,4.0,136955.97,1.0,1.0,1.0,72950.14,0.0
-1945,15672356.0,Hsiao,667.0,France,Male,46.0,3.0,0.0,2.0,0.0,0.0,143952.24,0.0
-1946,15797710.0,Martin,563.0,France,Male,48.0,6.0,0.0,1.0,1.0,0.0,105570.44,1.0
-1947,15671987.0,Ch'ien,531.0,France,Male,36.0,6.0,0.0,2.0,0.0,1.0,57553.02,0.0
-1948,15615247.0,P'eng,696.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,121440.8,0.0
-1949,15702300.0,Chukwudi,632.0,France,Female,32.0,0.0,61671.19,1.0,0.0,0.0,645.61,0.0
-1950,15673584.0,Chinwendu,627.0,France,Male,30.0,9.0,141782.57,1.0,1.0,0.0,115638.29,0.0
-1951,15765806.0,Nkemakonam,687.0,France,Male,51.0,0.0,0.0,2.0,1.0,0.0,93881.75,1.0
-1952,15588517.0,Obidimkpa,678.0,France,Female,46.0,8.0,0.0,1.0,1.0,0.0,137949.45,1.0
-1953,15809302.0,Sun,628.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,158049.9,0.0
-1954,15809817.0,Ch'iu,551.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,82438.13,0.0
-1955,15581229.0,Tseng,590.0,France,Female,51.0,3.0,0.0,1.0,1.0,1.0,177528.92,1.0
-1956,15682570.0,Fisk,607.0,France,Male,48.0,0.0,0.0,3.0,1.0,1.0,123969.64,1.0
-1957,15658032.0,Milne,708.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,176760.59,0.0
-1958,15593501.0,Mistry,574.0,France,Female,39.0,3.0,0.0,2.0,0.0,1.0,38772.65,0.0
-1959,15603683.0,Pinto,717.0,Spain,Male,29.0,5.0,0.0,2.0,0.0,1.0,97734.3,0.0
-1960,15693737.0,Manna,694.0,Germany,Male,48.0,1.0,124978.19,2.0,1.0,0.0,54474.48,1.0
-1961,15665159.0,Ch'iu,605.0,France,Female,27.0,0.0,0.0,1.0,1.0,0.0,86410.58,0.0
-1962,15720463.0,Ch'ien,597.0,Spain,Female,43.0,6.0,0.0,2.0,1.0,0.0,133501.94,0.0
-1963,15592025.0,Yermakov,763.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,103726.71,0.0
-1964,15683375.0,Ogochukwu,554.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,0.0,38315.04,0.0
-1965,15703793.0,Onyemauchechi,685.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,26342.33,0.0
-1966,15785815.0,Iadanza,628.0,France,Female,42.0,1.0,0.0,1.0,1.0,1.0,131807.14,1.0
-1967,15627385.0,Chiang,494.0,Germany,Female,58.0,7.0,110148.49,1.0,1.0,0.0,52795.15,1.0
-1968,15628272.0,Ulyanova,602.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,103921.43,0.0
-1969,15658577.0,Mazzi,629.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,104930.46,0.0
-1970,15748123.0,Chukwubuikem,607.0,France,Male,38.0,2.0,125540.05,1.0,1.0,0.0,178038.17,0.0
-1971,15603851.0,Chukwuebuka,535.0,France,Male,43.0,9.0,0.0,2.0,0.0,0.0,138526.96,0.0
-1972,15631739.0,De Salis,732.0,France,Female,43.0,3.0,0.0,1.0,1.0,0.0,96985.01,1.0
-1973,15781702.0,Chu,628.0,Spain,Female,46.0,3.0,98765.84,1.0,1.0,1.0,71830.1,0.0
-1974,15749557.0,Hsia,679.0,Germany,Female,32.0,2.0,130666.2,1.0,1.0,1.0,123499.27,0.0
-1975,15636024.0,Teng,586.0,France,Male,38.0,5.0,0.0,1.0,1.0,0.0,170195.4,1.0
-1976,15593973.0,Chidubem,718.0,Germany,Male,31.0,6.0,141535.52,1.0,1.0,0.0,74878.12,0.0
-1977,15641183.0,Hs?,543.0,Spain,Female,26.0,7.0,0.0,2.0,0.0,1.0,26318.72,0.0
-1978,15702801.0,Udegbulam,679.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,167984.61,0.0
-1979,15591036.0,Hsueh,449.0,Spain,Male,43.0,3.0,123845.36,1.0,1.0,0.0,141558.04,0.0
-1980,15776939.0,Scott,535.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,158071.69,0.0
-1981,15642530.0,Hsia,757.0,Germany,Female,44.0,7.0,108716.59,1.0,1.0,0.0,73329.08,0.0
-1982,15774328.0,Ch'iu,633.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,121559.6,0.0
-1983,15771483.0,Ch'en,627.0,France,Male,32.0,0.0,134542.73,1.0,1.0,1.0,166520.96,0.0
-1984,15615091.0,Okwuadigbo,676.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,120594.85,0.0
-1985,15776947.0,Ch'iu,718.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,83052.03,0.0
-1986,15641480.0,Lo,648.0,Germany,Male,35.0,4.0,151215.34,2.0,1.0,1.0,78650.95,0.0
-1987,15685314.0,Shao,644.0,Germany,Female,36.0,1.0,124043.8,1.0,0.0,0.0,135034.43,0.0
-1988,15714586.0,Chiemenam,693.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,186948.35,0.0
-1989,15771059.0,Pisani,565.0,France,Male,37.0,6.0,0.0,1.0,1.0,1.0,123475.88,0.0
-1990,15727281.0,Chidi,586.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,1.0,151871.84,0.0
-1991,15804586.0,Macdonald,742.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,0.0,111573.3,0.0
-1992,15713144.0,Chin,646.0,France,Male,29.0,3.0,0.0,2.0,0.0,0.0,140941.47,0.0
-1993,15697510.0,Chukwumaobim,583.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,82290.81,0.0
-1994,15786539.0,Baresi,525.0,France,Female,36.0,0.0,0.0,1.0,1.0,1.0,68730.91,0.0
-1995,15746065.0,Lai,685.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,142797.5,0.0
-1996,15664754.0,Tien,652.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,93524.19,0.0
-1997,15621611.0,Cremonesi,681.0,Spain,Female,37.0,1.0,0.0,2.0,0.0,1.0,77965.44,0.0
-1998,15777907.0,Kao,681.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,92840.67,0.0
-1999,15584928.0,Chikere,696.0,Germany,Male,33.0,8.0,133844.26,1.0,1.0,0.0,86791.9,0.0
-2000,15686957.0,Trevisani,706.0,France,Male,42.0,4.0,0.0,1.0,1.0,0.0,135634.41,1.0
-2001,15750772.0,Cremonesi,779.0,France,Female,35.0,4.0,0.0,1.0,1.0,0.0,43889.67,0.0
-2002,15667588.0,Pirozzi,641.0,Spain,Male,33.0,1.0,98635.77,1.0,1.0,1.0,53817.23,0.0
-2003,15744695.0,Y?an,663.0,France,Female,49.0,8.0,0.0,2.0,1.0,1.0,118570.53,0.0
-2004,15793722.0,Olisanugo,708.0,Germany,Male,35.0,8.0,109196.67,1.0,1.0,0.0,55775.72,1.0
-2005,15632503.0,Chibuzo,635.0,Spain,Male,22.0,7.0,0.0,2.0,1.0,0.0,117622.8,0.0
-2006,15622454.0,Chiu,722.0,Spain,Male,28.0,3.0,117020.08,1.0,1.0,0.0,158736.07,0.0
-2007,15661249.0,H?,790.0,France,Male,31.0,10.0,0.0,2.0,0.0,0.0,131473.31,0.0
-2008,15644400.0,Cremonesi,577.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,163714.92,0.0
-2009,15793883.0,Chukwumaobim,597.0,Germany,Male,43.0,9.0,121286.27,1.0,1.0,0.0,97939.3,1.0
-2010,15585867.0,Shih,589.0,Spain,Female,35.0,9.0,0.0,1.0,1.0,0.0,90857.95,1.0
-2011,15725028.0,Chukwumaobim,612.0,Spain,Male,36.0,5.0,0.0,1.0,1.0,0.0,166682.57,0.0
-2012,15686974.0,Chiang,681.0,France,Male,28.0,4.0,0.0,1.0,1.0,0.0,127622.46,1.0
-2013,15679743.0,Smith,794.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,129870.93,0.0
-2014,15769246.0,Ts'ai,562.0,Spain,Male,42.0,1.0,105736.32,1.0,1.0,0.0,179435.21,0.0
-2015,15683841.0,Maclean,807.0,Spain,Female,48.0,9.0,0.0,2.0,1.0,0.0,182531.74,1.0
-2016,15672754.0,Tsai,709.0,France,Male,67.0,2.0,0.0,2.0,1.0,1.0,163568.51,0.0
-2017,15625047.0,Ch'in,592.0,France,Male,31.0,1.0,156773.78,2.0,1.0,1.0,142238.54,0.0
-2018,15770474.0,Okwudiliolisa,727.0,Germany,Male,35.0,5.0,153325.7,2.0,1.0,0.0,140650.19,0.0
-2019,15614215.0,Onyeoruru,599.0,France,Female,41.0,4.0,0.0,2.0,0.0,1.0,153372.29,0.0
-2020,15653952.0,Hsu,628.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,49506.82,0.0
-2021,15803378.0,Chikezie,653.0,France,Male,44.0,7.0,0.0,1.0,1.0,1.0,59349.83,0.0
-2022,15642394.0,Scott,586.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,162697.93,0.0
-2023,15677871.0,Chia,597.0,France,Male,50.0,1.0,0.0,2.0,0.0,0.0,56793.07,0.0
-2024,15615456.0,Hs?,716.0,Spain,Female,40.0,3.0,148045.45,4.0,1.0,0.0,174800.31,1.0
-2025,15689268.0,Toscani,579.0,Germany,Female,34.0,4.0,132835.56,2.0,1.0,0.0,161519.77,0.0
-2026,15621685.0,Maclean,565.0,France,Female,51.0,8.0,117412.19,4.0,1.0,0.0,167636.15,1.0
-2027,15698028.0,Chibugo,648.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,139431.0,0.0
-2028,15661275.0,Fanucci,633.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,1.0,153012.13,0.0
-2029,15591512.0,Hsia,638.0,France,Male,34.0,3.0,76510.52,1.0,1.0,1.0,67830.2,0.0
-2030,15814816.0,Dyer,682.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,199392.14,0.0
-2031,15610156.0,Milano,705.0,France,Male,39.0,4.0,0.0,1.0,0.0,1.0,135067.03,0.0
-2032,15702631.0,Nnachetam,578.0,France,Male,33.0,4.0,135213.71,1.0,1.0,0.0,100717.54,0.0
-2033,15663883.0,Rolon,602.0,Germany,Female,40.0,8.0,136980.23,2.0,1.0,0.0,78310.04,0.0
-2034,15791326.0,Lassetter,633.0,Spain,Female,43.0,7.0,0.0,2.0,0.0,0.0,142763.51,0.0
-2035,15641158.0,Y?,662.0,Germany,Male,41.0,4.0,178713.98,2.0,0.0,1.0,177747.03,0.0
-2036,15786680.0,Tsou,679.0,Spain,Female,39.0,7.0,0.0,2.0,0.0,0.0,184936.77,0.0
-2037,15778290.0,T'ien,712.0,France,Male,26.0,4.0,0.0,2.0,0.0,1.0,163585.9,0.0
-2038,15573857.0,Chimezie,591.0,Germany,Male,34.0,7.0,149961.99,2.0,0.0,1.0,172618.52,0.0
-2039,15651593.0,Chia,587.0,Spain,Male,31.0,9.0,0.0,1.0,0.0,0.0,167973.63,1.0
-2040,15793723.0,Lucchesi,730.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,142400.27,0.0
-2041,15777599.0,Lucchese,594.0,Germany,Male,30.0,3.0,155736.42,1.0,1.0,1.0,73329.08,0.0
-2042,15719348.0,T'ang,619.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,80619.09,0.0
-2043,15747591.0,Trevisani,748.0,Germany,Male,36.0,4.0,105206.7,1.0,1.0,0.0,26767.85,0.0
-2044,15793723.0,Ts'ai,590.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,175894.24,0.0
-2045,15795519.0,Ch'ang,718.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,151815.76,0.0
-2046,15725997.0,Hs?,744.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,114775.53,0.0
-2047,15701392.0,Hs,572.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,162858.29,0.0
-2048,15636572.0,P'eng,521.0,France,Male,45.0,4.0,0.0,2.0,1.0,1.0,179291.09,1.0
-2049,15571059.0,Onwudiwe,718.0,France,Female,41.0,1.0,107848.57,1.0,1.0,0.0,136497.14,1.0
-2050,15595766.0,Pirozzi,645.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,108008.65,0.0
-2051,15778936.0,Iadanza,718.0,France,Female,36.0,7.0,0.0,2.0,0.0,1.0,146041.45,0.0
-2052,15757981.0,Scott,758.0,Spain,Male,69.0,3.0,0.0,2.0,0.0,1.0,86618.33,0.0
-2053,15677796.0,Chiawuotu,751.0,Spain,Male,51.0,7.0,95858.37,1.0,1.0,1.0,82647.61,1.0
-2054,15687828.0,Hsia,659.0,France,Female,25.0,3.0,0.0,2.0,0.0,0.0,168816.22,0.0
-2055,15723373.0,Hingston,547.0,Spain,Female,28.0,4.0,129848.19,1.0,1.0,1.0,53584.03,0.0
-2056,15623053.0,Cremonesi,665.0,France,Female,46.0,6.0,0.0,2.0,1.0,0.0,173580.45,1.0
-2057,15669957.0,Chu,727.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,93959.96,0.0
-2058,15699450.0,Okwudilichukwu,564.0,France,Female,36.0,3.0,0.0,2.0,0.0,0.0,128882.66,0.0
-2059,15607746.0,Lucchesi,659.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,94762.16,0.0
-2060,15805523.0,Yost,697.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,50294.09,0.0
-2061,15586680.0,Brown,706.0,France,Male,42.0,1.0,0.0,1.0,1.0,1.0,1568.91,0.0
-2062,15649060.0,Ijendu,794.0,France,Male,34.0,2.0,0.0,1.0,1.0,1.0,182944.47,0.0
-2063,15752508.0,Chukwueloka,627.0,Germany,Male,51.0,8.0,114754.08,3.0,0.0,0.0,163666.85,1.0
-2064,15672053.0,Bibi,748.0,Spain,Male,50.0,2.0,0.0,1.0,0.0,0.0,29586.73,1.0
-2065,15735782.0,Hsiao,725.0,France,Male,28.0,5.0,0.0,2.0,0.0,1.0,161579.85,0.0
-2066,15583857.0,Onyekaozulu,592.0,Germany,Female,52.0,5.0,105722.69,1.0,1.0,1.0,84972.9,0.0
-2067,15602909.0,Hsiao,633.0,Germany,Female,46.0,1.0,165350.61,1.0,1.0,1.0,173844.69,0.0
-2068,15652693.0,Hs?,762.0,France,Male,38.0,3.0,123835.05,3.0,1.0,0.0,142797.5,1.0
-2069,15780256.0,Mazzanti,678.0,France,Female,36.0,8.0,0.0,3.0,0.0,1.0,170819.01,1.0
-2070,15640034.0,Ch'ang,653.0,France,Male,25.0,5.0,0.0,2.0,1.0,0.0,145562.4,0.0
-2071,15632669.0,Lo Duca,698.0,Spain,Female,39.0,3.0,133676.65,1.0,1.0,0.0,93332.64,0.0
-2072,15739502.0,Ch'en,641.0,France,Male,32.0,8.0,0.0,2.0,0.0,0.0,184564.67,0.0
-2073,15715003.0,H?,644.0,Spain,Male,50.0,2.0,171646.76,1.0,1.0,0.0,178028.96,1.0
-2074,15699507.0,Dennys,535.0,France,Male,35.0,6.0,109949.05,1.0,1.0,1.0,48382.4,0.0
-2075,15766649.0,Y?an,743.0,France,Male,30.0,2.0,127763.24,1.0,1.0,1.0,131660.31,0.0
-2076,15573319.0,Chukwumaobim,628.0,France,Male,42.0,10.0,0.0,2.0,0.0,0.0,121151.46,0.0
-2077,15648069.0,Ch'ien,632.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,187945.01,0.0
-2078,15616454.0,Ndubueze,636.0,France,Male,60.0,5.0,0.0,2.0,0.0,0.0,170886.58,0.0
-2079,15672930.0,Chiawuotu,724.0,France,Male,34.0,2.0,0.0,1.0,1.0,0.0,81431.99,0.0
-2080,15653773.0,Ugochukwu,637.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,44965.44,0.0
-2081,15591286.0,Chiang,682.0,Spain,Male,32.0,0.0,0.0,2.0,1.0,0.0,155047.11,0.0
-2082,15734085.0,Ulyanova,704.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,93524.22,0.0
-2083,15661409.0,Chinedum,652.0,France,Female,28.0,8.0,137960.55,1.0,1.0,1.0,64442.47,0.0
-2084,15781710.0,Tung,581.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,1.0,108419.41,0.0
-2085,15582585.0,Ts'ao,681.0,France,Male,37.0,3.0,0.0,2.0,0.0,0.0,182059.28,0.0
-2086,15757867.0,Yermakov,554.0,Spain,Female,40.0,8.0,0.0,1.0,1.0,0.0,147600.54,1.0
-2087,15802084.0,Chinwendu,602.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,165082.25,0.0
-2088,15637954.0,Genovesi,624.0,Spain,Male,43.0,4.0,106414.57,1.0,1.0,0.0,92811.4,0.0
-2089,15665181.0,Lombardi,719.0,Germany,Female,51.0,8.0,113873.22,1.0,0.0,1.0,109545.56,1.0
-2090,15594556.0,Pisani,687.0,France,Male,23.0,7.0,83347.25,1.0,0.0,0.0,146680.25,0.0
-2091,15802625.0,White,646.0,Spain,Male,38.0,2.0,113882.31,1.0,1.0,1.0,175743.92,0.0
-2092,15723006.0,Onwuka,642.0,France,Male,29.0,8.0,0.0,1.0,1.0,0.0,164804.02,0.0
-2093,15683625.0,Lo,507.0,France,Female,41.0,7.0,0.0,1.0,0.0,1.0,87412.24,0.0
-2094,15768295.0,Hsueh,661.0,Spain,Male,41.0,5.0,0.0,1.0,1.0,0.0,51362.82,0.0
-2095,15712264.0,Chiekwugo,678.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,161333.13,0.0
-2096,15642093.0,Milani,641.0,France,Female,40.0,9.0,101060.25,1.0,1.0,1.0,118368.2,1.0
-2097,15782765.0,Nnamutaezinwa,730.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,0.0,131710.59,0.0
-2098,15779947.0,Ritchie,659.0,France,Male,51.0,1.0,151922.94,1.0,1.0,0.0,103914.4,1.0
-2099,15779744.0,Ibezimako,590.0,Germany,Male,32.0,4.0,128152.82,2.0,1.0,1.0,145560.38,0.0
-2100,15809286.0,H?,613.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,1.0,72318.77,0.0
-2101,15677020.0,T'ang,589.0,France,Female,46.0,8.0,0.0,2.0,0.0,0.0,112202.64,0.0
-2102,15623062.0,Tuan,684.0,France,Male,52.0,7.0,0.0,2.0,1.0,0.0,76611.33,0.0
-2103,15636454.0,Ts'ao,646.0,France,Male,45.0,5.0,0.0,1.0,1.0,0.0,136620.64,1.0
-2104,15607874.0,Hsueh,675.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,172501.38,0.0
-2105,15585067.0,Chukwueloka,676.0,France,Male,43.0,8.0,0.0,2.0,0.0,1.0,126551.65,0.0
-2106,15809265.0,Yobanna,726.0,Germany,Female,57.0,4.0,111063.73,2.0,1.0,1.0,51116.06,0.0
-2107,15704466.0,Fennell,594.0,France,Male,42.0,2.0,106024.51,1.0,1.0,1.0,93548.46,0.0
-2108,15761047.0,Ch'ien,485.0,France,Female,47.0,1.0,133598.4,1.0,1.0,1.0,167984.72,0.0
-2109,15725048.0,Genovese,516.0,France,Female,32.0,7.0,0.0,1.0,1.0,0.0,170514.21,1.0
-2110,15578783.0,Mazzanti,786.0,France,Male,26.0,4.0,0.0,2.0,1.0,0.0,81482.2,0.0
-2111,15672977.0,Chizoba,592.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,109562.74,1.0
-2112,15604515.0,Akhtar,633.0,Germany,Male,31.0,7.0,133853.27,2.0,1.0,1.0,43496.36,0.0
-2113,15734491.0,Tung,803.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,98820.09,0.0
-2114,15655298.0,Mazzanti,769.0,Spain,Male,29.0,8.0,144855.34,2.0,1.0,0.0,180591.32,0.0
-2115,15690744.0,Ch'ang,619.0,Germany,Female,49.0,4.0,129255.2,1.0,1.0,0.0,163558.36,0.0
-2116,15764033.0,Bellucci,577.0,Germany,Female,29.0,1.0,99824.45,1.0,1.0,1.0,167522.19,0.0
-2117,15792222.0,Chukwuebuka,528.0,Germany,Female,24.0,7.0,113090.4,1.0,1.0,1.0,156878.46,1.0
-2118,15783097.0,Liao,603.0,France,Male,51.0,10.0,0.0,2.0,1.0,1.0,78896.59,1.0
-2119,15603594.0,Uspenskaya,605.0,France,Male,36.0,0.0,114091.38,1.0,0.0,1.0,74425.41,0.0
-2120,15597945.0,Chiawuotu,584.0,Spain,Female,33.0,2.0,0.0,1.0,1.0,1.0,131043.1,0.0
-2121,15793046.0,H?,597.0,France,Male,31.0,8.0,162261.01,1.0,1.0,0.0,138399.67,0.0
-2122,15806822.0,Zuyeva,594.0,Spain,Male,42.0,9.0,0.0,1.0,1.0,0.0,129392.75,1.0
-2123,15653065.0,Eluemuno,687.0,Germany,Female,48.0,7.0,102560.57,1.0,1.0,0.0,116097.26,1.0
-2124,15782418.0,Hsia,718.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,179436.6,0.0
-2125,15794895.0,Olisaemeka,594.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,1.0,159823.16,0.0
-2126,15792986.0,Tardent,711.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,73334.43,0.0
-2127,15766017.0,T'ien,746.0,Germany,Male,33.0,9.0,124425.99,1.0,1.0,0.0,187925.75,0.0
-2128,15641319.0,Iqbal,488.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,53817.23,0.0
-2129,15690958.0,McEncroe,738.0,France,Female,33.0,4.0,106022.73,2.0,1.0,1.0,148087.62,0.0
-2130,15644491.0,Nnachetam,578.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,177099.71,0.0
-2131,15654463.0,Onyemachukwu,579.0,Spain,Male,42.0,6.0,0.0,2.0,1.0,0.0,142514.66,1.0
-2132,15583592.0,Hsueh,539.0,France,Female,36.0,2.0,0.0,1.0,1.0,0.0,94738.54,0.0
-2133,15804919.0,Scott,670.0,Germany,Male,49.0,5.0,150844.94,2.0,1.0,0.0,115733.16,0.0
-2134,15733602.0,Martin,742.0,Germany,Female,42.0,4.0,161608.81,2.0,1.0,1.0,131300.68,0.0
-2135,15775572.0,Chidiebere,705.0,France,Male,34.0,0.0,81589.04,1.0,1.0,1.0,140166.95,0.0
-2136,15716236.0,Luffman,724.0,France,Male,56.0,3.0,107879.04,3.0,1.0,0.0,60908.46,1.0
-2137,15792473.0,Hsiung,596.0,France,Male,47.0,7.0,0.0,2.0,1.0,1.0,149772.61,0.0
-2138,15661380.0,Chiu,584.0,Germany,Male,33.0,7.0,128920.75,1.0,0.0,1.0,131807.44,0.0
-2139,15762588.0,Sagese,571.0,Germany,Male,44.0,7.0,124580.69,1.0,1.0,1.0,171463.83,0.0
-2140,15742534.0,Fanucci,619.0,France,Male,33.0,6.0,122717.4,1.0,1.0,1.0,71420.16,0.0
-2141,15629985.0,Tung,587.0,France,Male,53.0,7.0,0.0,1.0,1.0,0.0,148727.42,1.0
-2142,15576824.0,T'ien,611.0,France,Male,45.0,2.0,0.0,1.0,1.0,1.0,131660.31,1.0
-2143,15650864.0,Ijendu,651.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,184843.77,0.0
-2144,15742272.0,Pinto,759.0,France,Male,46.0,5.0,113599.74,1.0,1.0,1.0,60721.4,1.0
-2145,15682463.0,Nnachetam,605.0,Germany,Male,33.0,3.0,123040.38,1.0,1.0,0.0,124592.83,1.0
-2146,15795540.0,Chiabuotu,599.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,167032.49,0.0
-2147,15785831.0,Clements,739.0,Spain,Female,44.0,7.0,0.0,2.0,1.0,0.0,175072.47,0.0
-2148,15572390.0,Chukwubuikem,677.0,Spain,Female,37.0,6.0,0.0,1.0,0.0,1.0,166677.35,0.0
-2149,15578908.0,Ch'eng,584.0,Spain,Female,46.0,8.0,0.0,2.0,0.0,1.0,63687.06,1.0
-2150,15784594.0,Ts'ao,589.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,1.0,162858.28,0.0
-2151,15615254.0,Hsiung,635.0,Germany,Male,57.0,4.0,91611.12,1.0,0.0,0.0,58879.31,1.0
-2152,15791323.0,Atherton,816.0,Spain,Female,35.0,4.0,82729.49,1.0,1.0,1.0,122518.5,0.0
-2153,15568595.0,Whitehead,667.0,France,Female,39.0,7.0,0.0,1.0,1.0,1.0,179843.33,0.0
-2154,15664753.0,Pinto,565.0,France,Female,51.0,7.0,0.0,1.0,1.0,0.0,65986.48,1.0
-2155,15628583.0,Mazzi,632.0,France,Female,45.0,9.0,0.0,3.0,1.0,1.0,158750.43,1.0
-2156,15782089.0,Ch'ang,538.0,Spain,Female,40.0,1.0,106074.81,1.0,1.0,0.0,102628.98,0.0
-2157,15704340.0,Ch'iu,850.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,26389.92,0.0
-2158,15754579.0,Liao,603.0,Spain,Male,34.0,5.0,172463.45,1.0,0.0,1.0,132418.59,0.0
-2159,15762392.0,Chienezie,683.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,92797.23,0.0
-2160,15591607.0,Hsiao,543.0,France,Male,51.0,6.0,0.0,1.0,1.0,0.0,110593.62,1.0
-2161,15758362.0,P'an,754.0,Spain,Male,40.0,6.0,0.0,1.0,1.0,0.0,199805.63,1.0
-2162,15704910.0,Hsia,577.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,71862.79,0.0
-2163,15656592.0,Lucchesi,599.0,France,Female,21.0,2.0,152303.8,2.0,1.0,1.0,138490.03,0.0
-2164,15582841.0,Hsia,702.0,France,Male,45.0,4.0,0.0,1.0,1.0,0.0,53353.37,0.0
-2165,15725028.0,Tien,579.0,France,Female,34.0,3.0,0.0,2.0,0.0,0.0,123627.69,0.0
-2166,15807989.0,Ch'iu,641.0,Germany,Female,35.0,10.0,111209.64,3.0,1.0,0.0,132498.39,1.0
-2167,15805764.0,Chieloka,649.0,France,Male,41.0,9.0,0.0,2.0,0.0,0.0,150319.49,0.0
-2168,15746258.0,Lucchesi,611.0,France,Male,30.0,5.0,0.0,1.0,0.0,1.0,179521.24,0.0
-2169,15612103.0,Chiazagomekpere,730.0,Spain,Female,32.0,4.0,0.0,1.0,1.0,1.0,92865.41,1.0
-2170,15775203.0,Onyemere,748.0,Spain,Male,46.0,9.0,0.0,2.0,1.0,0.0,105784.85,0.0
-2171,15785078.0,Maclean,554.0,France,Male,57.0,10.0,0.0,1.0,1.0,1.0,88659.07,1.0
-2172,15702561.0,K?,592.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,85547.33,0.0
-2173,15675185.0,Shih,521.0,France,Female,38.0,6.0,0.0,1.0,1.0,0.0,115217.59,0.0
-2174,15670702.0,Lai,598.0,France,Female,54.0,4.0,0.0,2.0,0.0,1.0,84972.9,1.0
-2175,15756894.0,Chuang,591.0,France,Female,40.0,4.0,0.0,2.0,0.0,1.0,92826.07,0.0
-2176,15713559.0,Goliwe,708.0,France,Male,38.0,7.0,122950.19,1.0,1.0,1.0,125870.5,0.0
-2177,15723008.0,Maclean,609.0,Spain,Male,29.0,3.0,152296.83,1.0,1.0,1.0,119817.19,1.0
-2178,15628292.0,Hsiao,766.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,119346.88,0.0
-2179,15583595.0,Manna,558.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,175893.53,0.0
-2180,15582914.0,Ponomaryov,663.0,France,Male,23.0,6.0,0.0,2.0,1.0,0.0,124626.57,0.0
-2181,15767793.0,Zikoranachidimma,683.0,France,Female,36.0,6.0,0.0,1.0,1.0,1.0,90928.04,1.0
-2182,15609562.0,Kolesnikov,627.0,Germany,Female,37.0,3.0,139979.66,2.0,0.0,0.0,25017.28,1.0
-2183,15772363.0,Hsieh,637.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,126675.62,0.0
-2184,15603032.0,Udinese,544.0,France,Male,60.0,3.0,95829.13,2.0,1.0,0.0,115068.99,1.0
-2185,15727391.0,Hsia,772.0,France,Male,40.0,10.0,95507.12,1.0,1.0,1.0,47880.71,0.0
-2186,15734202.0,Kambinachi,679.0,Germany,Female,39.0,6.0,85915.52,2.0,1.0,0.0,162599.51,0.0
-2187,15741403.0,Pisano,659.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,87071.18,0.0
-2188,15647301.0,Cremonesi,714.0,Germany,Female,40.0,9.0,113348.5,1.0,1.0,1.0,79005.95,0.0
-2189,15570051.0,Hsueh,535.0,Spain,Female,31.0,1.0,120777.88,1.0,0.0,1.0,5408.74,0.0
-2190,15603741.0,Ch'in,805.0,Germany,Male,54.0,4.0,116220.5,1.0,1.0,0.0,120540.83,1.0
-2191,15726358.0,Hs?eh,582.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,52795.15,0.0
-2192,15675963.0,O'Kane,687.0,France,Male,29.0,7.0,0.0,1.0,1.0,1.0,183566.85,0.0
-2193,15657439.0,Hsieh,538.0,France,Female,29.0,9.0,0.0,2.0,1.0,1.0,177065.24,0.0
-2194,15665077.0,Pye,607.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,53445.17,0.0
-2195,15710408.0,Nwachukwu,666.0,Spain,Female,41.0,5.0,0.0,2.0,1.0,1.0,135083.72,0.0
-2196,15693947.0,Chibuzo,679.0,France,Female,38.0,8.0,144326.09,1.0,1.0,1.0,69227.74,0.0
-2197,15595874.0,Ting,577.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,134417.31,0.0
-2198,15660390.0,Lai,602.0,Spain,Male,27.0,10.0,0.0,2.0,1.0,0.0,47722.79,0.0
-2199,15773789.0,Ch'ien,605.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,108675.01,0.0
-2200,15807560.0,Trevisani,583.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,125256.53,0.0
-2201,15807961.0,Rizzo,613.0,Germany,Female,42.0,8.0,130933.74,3.0,0.0,0.0,79355.87,1.0
-2202,15793671.0,Chukwudi,549.0,Spain,Female,31.0,4.0,79954.61,1.0,0.0,1.0,142911.99,0.0
-2203,15662578.0,Chiebuka,623.0,France,Male,38.0,0.0,0.0,1.0,1.0,1.0,51587.04,0.0
-2204,15637646.0,Yudina,667.0,Spain,Male,39.0,8.0,91291.7,1.0,1.0,0.0,156593.09,0.0
-2205,15680191.0,Iweobiegbunam,633.0,Spain,Male,36.0,10.0,0.0,1.0,1.0,1.0,123882.73,0.0
-2206,15783477.0,Ch'iu,727.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,115950.96,0.0
-2207,15585925.0,Smith,652.0,Germany,Female,43.0,6.0,115465.28,1.0,1.0,1.0,179670.31,1.0
-2208,15609913.0,Chiu,486.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,110916.15,0.0
-2209,15617790.0,McWilliams,601.0,France,Male,35.0,2.0,0.0,2.0,0.0,1.0,83087.13,0.0
-2210,15656592.0,Chiazagomekpele,712.0,France,Female,27.0,2.0,0.0,2.0,1.0,1.0,132468.76,0.0
-2211,15624424.0,Lettiere,644.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,94865.04,0.0
-2212,15702061.0,Okagbue,676.0,France,Male,48.0,1.0,142862.21,1.0,1.0,1.0,59568.24,1.0
-2213,15790829.0,Lo,619.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,49553.38,0.0
-2214,15779620.0,Nnanna,671.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,49865.04,0.0
-2215,15604588.0,Ch'ang,591.0,France,Male,39.0,2.0,0.0,1.0,0.0,1.0,85424.34,0.0
-2216,15624892.0,Mazzi,596.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,155799.86,0.0
-2217,15583595.0,Ch'iu,778.0,Spain,Male,71.0,0.0,88814.4,1.0,1.0,1.0,160526.36,0.0
-2218,15651389.0,Kaeppel,757.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,54949.73,0.0
-2219,15596674.0,Chinweike,573.0,France,Male,32.0,7.0,0.0,1.0,1.0,1.0,123969.64,0.0
-2220,15686835.0,Chikelu,573.0,Germany,Male,34.0,9.0,165969.26,2.0,1.0,1.0,83679.46,0.0
-2221,15628144.0,Yobachi,729.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,188007.99,0.0
-2222,15799435.0,Kistler,628.0,Spain,Female,36.0,5.0,115341.55,1.0,1.0,0.0,193502.93,0.0
-2223,15626383.0,Yobachukwu,716.0,France,Male,43.0,6.0,0.0,1.0,1.0,0.0,61792.76,0.0
-2224,15641310.0,Ting,602.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,120853.83,0.0
-2225,15622443.0,T'ang,729.0,Spain,Female,29.0,3.0,0.0,2.0,1.0,0.0,131043.1,0.0
-2226,15605827.0,L?,749.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,40357.39,0.0
-2227,15610594.0,Ch'ien,792.0,France,Male,33.0,3.0,0.0,1.0,1.0,0.0,171070.58,0.0
-2228,15806956.0,Clements,739.0,Spain,Female,27.0,9.0,0.0,2.0,1.0,1.0,145936.29,0.0
-2229,15721426.0,Chiu,815.0,France,Male,25.0,2.0,127738.75,1.0,1.0,1.0,55100.09,0.0
-2230,15609977.0,Tien,718.0,France,Male,22.0,2.0,0.0,2.0,1.0,1.0,57769.2,0.0
-2231,15636548.0,Chiemenam,572.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,86410.29,0.0
-2232,15693322.0,Piazza,653.0,France,Male,33.0,4.0,0.0,2.0,0.0,1.0,102298.59,0.0
-2233,15737647.0,Lai,535.0,France,Female,25.0,8.0,0.0,2.0,1.0,1.0,91560.63,0.0
-2234,15688880.0,Lucchese,675.0,France,Female,43.0,3.0,0.0,1.0,1.0,0.0,105986.01,1.0
-2235,15603085.0,K?,711.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,96885.19,0.0
-2236,15648876.0,Chiawuotu,635.0,Germany,Female,40.0,8.0,82344.84,1.0,1.0,0.0,126836.56,1.0
-2237,15806983.0,Mackenzie,635.0,Germany,Male,42.0,7.0,157960.49,2.0,1.0,0.0,10963.04,1.0
-2238,15765300.0,Nwagugheuzo,663.0,France,Male,28.0,8.0,0.0,2.0,0.0,1.0,80416.16,0.0
-2239,15784761.0,Yefimova,801.0,Spain,Female,29.0,6.0,122822.0,1.0,1.0,0.0,65868.28,0.0
-2240,15795895.0,Ch'iu,662.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,173498.45,0.0
-2241,15586069.0,Frolov,703.0,France,Female,37.0,1.0,0.0,2.0,1.0,0.0,183872.88,0.0
-2242,15680597.0,Lettiere,596.0,Germany,Male,50.0,9.0,134960.98,1.0,0.0,0.0,129964.94,1.0
-2243,15676521.0,Hsia,637.0,France,Female,42.0,8.0,154763.21,1.0,1.0,1.0,177065.24,0.0
-2244,15584364.0,Nnaife,778.0,France,Male,46.0,8.0,0.0,1.0,1.0,0.0,71154.1,0.0
-2245,15790489.0,Macdonald,713.0,France,Male,32.0,7.0,0.0,1.0,0.0,0.0,93548.16,0.0
-2246,15653037.0,Pisano,745.0,Spain,Male,44.0,5.0,139070.51,1.0,1.0,1.0,86658.26,0.0
-2247,15780213.0,Pisani,659.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,131647.31,0.0
-2248,15759991.0,Smith,597.0,Germany,Male,31.0,1.0,72594.0,1.0,1.0,1.0,86490.09,0.0
-2249,15752588.0,Ozoemena,588.0,Spain,Female,29.0,6.0,88594.14,1.0,1.0,0.0,116748.14,0.0
-2250,15681180.0,Fanucci,612.0,France,Female,35.0,5.0,136623.19,1.0,0.0,0.0,176913.42,0.0
-2251,15697567.0,Chukwujekwu,799.0,France,Female,47.0,2.0,0.0,1.0,1.0,1.0,134946.02,1.0
-2252,15776939.0,Yudina,671.0,France,Male,52.0,2.0,0.0,2.0,1.0,1.0,127558.26,0.0
-2253,15590097.0,T'ang,805.0,Spain,Male,42.0,7.0,127237.28,1.0,0.0,1.0,91055.74,0.0
-2254,15641835.0,Chukwukere,729.0,Germany,Male,44.0,9.0,118636.92,2.0,1.0,0.0,134878.19,1.0
-2255,15776051.0,Hsiung,850.0,France,Male,31.0,7.0,115897.12,1.0,1.0,1.0,163714.92,0.0
-2256,15790594.0,Li Fonti,556.0,Spain,Male,38.0,9.0,160980.03,1.0,1.0,0.0,147090.9,0.0
-2257,15697095.0,Kao,812.0,France,Male,40.0,6.0,122021.92,1.0,1.0,0.0,168085.97,0.0
-2258,15586039.0,K'ung,663.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,1.0,479.54,0.0
-2259,15742404.0,Onyekachi,588.0,France,Male,42.0,3.0,0.0,1.0,0.0,0.0,75825.57,0.0
-2260,15757981.0,Teng,587.0,France,Male,34.0,4.0,102530.7,1.0,1.0,1.0,116046.22,0.0
-2261,15583595.0,Chukwufumnanya,625.0,Germany,Female,31.0,8.0,95478.17,1.0,1.0,1.0,165008.97,0.0
-2262,15756926.0,Onyekachi,592.0,France,Female,31.0,1.0,0.0,2.0,1.0,0.0,114448.77,0.0
-2263,15658969.0,Maclean,677.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,121846.81,0.0
-2264,15741032.0,K?,682.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,161388.22,0.0
-2265,15654067.0,Pinto,613.0,Spain,Female,49.0,1.0,156864.2,1.0,1.0,1.0,67611.36,1.0
-2266,15581229.0,Yobachukwu,711.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,162858.28,0.0
-2267,15654495.0,Tsou,605.0,France,Female,45.0,1.0,0.0,3.0,1.0,1.0,86786.41,1.0
-2268,15697460.0,Lucchesi,682.0,Germany,Male,45.0,5.0,127818.52,1.0,1.0,1.0,58814.41,0.0
-2269,15733111.0,Chinedum,539.0,France,Male,30.0,2.0,0.0,2.0,0.0,1.0,83470.4,0.0
-2270,15682540.0,Palermo,744.0,France,Male,24.0,4.0,0.0,1.0,1.0,1.0,56584.11,0.0
-2271,15783816.0,White,681.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,102908.91,0.0
-2272,15597949.0,Chukwukadibia,592.0,France,Female,53.0,10.0,0.0,1.0,1.0,1.0,7063.6,1.0
-2273,15604588.0,Chia,745.0,Spain,Female,38.0,6.0,0.0,2.0,0.0,0.0,130846.79,0.0
-2274,15766869.0,Hsing,651.0,Germany,Male,33.0,8.0,145081.73,2.0,1.0,0.0,141023.01,0.0
-2275,15708505.0,Toscani,577.0,Germany,Male,41.0,8.0,173560.62,1.0,0.0,1.0,69227.74,0.0
-2276,15696812.0,Lombardi,691.0,France,Male,30.0,4.0,121702.73,1.0,1.0,0.0,116442.42,0.0
-2277,15791842.0,Chibugo,666.0,Germany,Male,34.0,1.0,127706.33,2.0,1.0,1.0,180595.02,0.0
-2278,15635277.0,Chiazagomekpele,678.0,France,Male,56.0,6.0,0.0,1.0,1.0,1.0,75578.67,1.0
-2279,15573741.0,Ch'en,739.0,France,Male,41.0,8.0,0.0,3.0,1.0,1.0,181449.51,1.0
-2280,15659800.0,Chiang,716.0,Spain,Female,42.0,7.0,0.0,2.0,1.0,0.0,145700.22,1.0
-2281,15789548.0,McGregor,638.0,France,Male,35.0,2.0,0.0,1.0,0.0,1.0,73669.94,1.0
-2282,15668818.0,Lettiere,543.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,147963.07,0.0
-2283,15777873.0,Tretiakova,608.0,Germany,Male,30.0,2.0,125552.96,1.0,1.0,0.0,121053.19,0.0
-2284,15668767.0,Baryshnikov,735.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,70532.53,0.0
-2285,15623062.0,Ch'eng,680.0,France,Male,44.0,6.0,0.0,2.0,0.0,0.0,138526.96,0.0
-2286,15602454.0,Yuriev,850.0,Germany,Male,35.0,2.0,104091.38,1.0,0.0,0.0,172001.44,0.0
-2287,15635404.0,Oguejiofor,597.0,France,Female,30.0,4.0,0.0,2.0,0.0,1.0,166692.04,0.0
-2288,15595874.0,Caffyn,694.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,9087.81,0.0
-2289,15592044.0,Chukwujamuike,745.0,Germany,Male,38.0,4.0,123041.56,2.0,0.0,1.0,99061.75,1.0
-2290,15587978.0,McIntyre,678.0,Spain,Female,49.0,1.0,0.0,2.0,1.0,1.0,79734.23,1.0
-2291,15778830.0,Mazzanti,627.0,France,Female,33.0,10.0,0.0,2.0,1.0,1.0,143938.28,0.0
-2292,15668893.0,Ch'ien,699.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,51380.9,0.0
-2293,15605968.0,Fanucci,755.0,France,Male,55.0,3.0,0.0,1.0,1.0,1.0,73563.05,1.0
-2294,15749945.0,Hs?,527.0,Germany,Female,46.0,4.0,124447.18,2.0,1.0,1.0,168034.9,0.0
-2295,15571843.0,Ozioma,643.0,France,Female,40.0,9.0,0.0,2.0,1.0,0.0,149814.68,0.0
-2296,15623346.0,Ritchie,584.0,France,Male,39.0,1.0,84719.98,1.0,1.0,0.0,91291.2,0.0
-2297,15717893.0,Chukwualuka,687.0,Spain,Female,29.0,5.0,113674.2,1.0,1.0,0.0,157072.91,0.0
-2298,15761497.0,Yobanna,721.0,France,Female,41.0,7.0,0.0,1.0,1.0,0.0,126494.82,1.0
-2299,15654562.0,De Luca,645.0,Spain,Male,37.0,1.0,0.0,1.0,1.0,1.0,169963.2,0.0
-2300,15639946.0,Ch'eng,616.0,France,Female,31.0,10.0,122334.26,1.0,1.0,0.0,79649.62,1.0
-2301,15679884.0,Tsou,691.0,France,Female,39.0,6.0,123894.43,1.0,1.0,1.0,91633.53,0.0
-2302,15665062.0,Genovesi,850.0,Germany,Female,27.0,7.0,137059.05,2.0,1.0,1.0,71159.84,0.0
-2303,15680167.0,Iadanza,579.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,1.0,164741.5,0.0
-2304,15807524.0,Onwuamaeze,691.0,France,Female,35.0,6.0,90450.0,1.0,1.0,1.0,94488.86,0.0
-2305,15665460.0,Chidumaga,769.0,Spain,Male,43.0,2.0,95196.26,1.0,0.0,1.0,153343.48,0.0
-2306,15641170.0,Hsia,607.0,France,Male,41.0,8.0,0.0,1.0,1.0,0.0,145527.25,0.0
-2307,15704763.0,Nebechi,710.0,Spain,Male,42.0,2.0,0.0,3.0,0.0,1.0,138380.11,1.0
-2308,15709324.0,Ch'ang,469.0,France,Male,45.0,8.0,120867.18,1.0,1.0,0.0,137037.97,1.0
-2309,15697480.0,Trevisano,641.0,Germany,Male,46.0,5.0,122486.47,2.0,1.0,0.0,61936.1,0.0
-2310,15652667.0,Trevisani,636.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,131768.45,1.0
-2311,15804054.0,Kao,637.0,Germany,Male,67.0,3.0,152328.88,1.0,1.0,1.0,88391.97,0.0
-2312,15645956.0,Onyemere,531.0,France,Male,35.0,3.0,0.0,1.0,0.0,0.0,67981.37,0.0
-2313,15802381.0,Teng,613.0,Germany,Female,39.0,4.0,127543.11,1.0,1.0,0.0,151411.2,0.0
-2314,15707637.0,Chiang,638.0,France,Male,35.0,7.0,96951.21,1.0,1.0,0.0,91036.74,1.0
-2315,15585853.0,Tsou,850.0,France,Male,67.0,6.0,81025.6,1.0,0.0,1.0,142911.99,0.0
-2316,15791674.0,Kao,506.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,87412.24,0.0
-2317,15773723.0,Liao,646.0,Spain,Female,28.0,9.0,0.0,2.0,1.0,0.0,81256.29,0.0
-2318,15687558.0,McLean,743.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,123882.28,0.0
-2319,15611669.0,Onwughara,653.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,118740.38,0.0
-2320,15715199.0,Trevisano,686.0,Germany,Female,35.0,4.0,70784.27,1.0,1.0,1.0,47921.22,0.0
-2321,15758856.0,Yuryeva,708.0,Spain,Male,39.0,1.0,0.0,2.0,1.0,0.0,124596.63,0.0
-2322,15648858.0,Chidozie,717.0,Spain,Male,28.0,5.0,0.0,2.0,0.0,1.0,105599.65,0.0
-2323,15694859.0,Efimov,678.0,France,Male,36.0,8.0,138333.03,1.0,1.0,0.0,175820.91,0.0
-2324,15579892.0,Trevisano,769.0,France,Male,31.0,6.0,0.0,1.0,1.0,0.0,35786.76,0.0
-2325,15640648.0,Kao,668.0,France,Female,21.0,5.0,122570.87,2.0,0.0,1.0,92378.54,0.0
-2326,15622507.0,Lucchese,585.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,168261.4,0.0
-2327,15573850.0,Teng,603.0,France,Female,63.0,6.0,160599.06,1.0,0.0,1.0,133535.29,0.0
-2328,15586069.0,Pinto,632.0,Spain,Female,43.0,7.0,0.0,1.0,1.0,1.0,146875.86,0.0
-2329,15657747.0,Chinwendu,678.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,93882.75,1.0
-2330,15809347.0,Chukwuemeka,831.0,Spain,Male,48.0,0.0,0.0,2.0,1.0,0.0,88838.14,0.0
-2331,15687634.0,McIntyre,537.0,Germany,Female,49.0,9.0,101191.83,1.0,1.0,0.0,93568.77,1.0
-2332,15752488.0,K'ung,742.0,France,Female,39.0,3.0,0.0,2.0,0.0,1.0,75832.53,1.0
-2333,15795571.0,Chiang,461.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,145986.35,0.0
-2334,15641175.0,T'ien,580.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,53710.23,0.0
-2335,15780144.0,Bancks,681.0,France,Male,38.0,6.0,0.0,1.0,1.0,1.0,158742.81,0.0
-2336,15783026.0,Onuora,604.0,France,Female,34.0,8.0,0.0,2.0,0.0,1.0,67311.12,0.0
-2337,15579914.0,Goliwe,646.0,Spain,Female,41.0,1.0,0.0,1.0,1.0,0.0,91943.94,1.0
-2338,15651022.0,Nwokezuike,663.0,Spain,Female,42.0,1.0,0.0,2.0,1.0,1.0,53511.12,0.0
-2339,15642570.0,Yobanna,649.0,France,Female,30.0,2.0,0.0,1.0,1.0,0.0,176925.69,1.0
-2340,15689322.0,Maclean,735.0,Spain,Female,43.0,7.0,0.0,1.0,1.0,1.0,88791.83,1.0
-2341,15736274.0,Ngozichukwuka,667.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,0.0,127587.28,0.0
-2342,15777045.0,Ch'iu,710.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,170638.98,0.0
-2343,15714046.0,Mironova,613.0,Germany,Female,57.0,1.0,124577.33,1.0,1.0,0.0,156774.75,1.0
-2344,15638267.0,Dickson,652.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,144870.28,0.0
-2345,15578788.0,Onyemauchechukwu,663.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,1.0,77878.62,0.0
-2346,15730447.0,Milano,673.0,France,Male,37.0,7.0,121260.19,1.0,1.0,1.0,894.75,0.0
-2347,15711750.0,Chukwuraenye,669.0,Spain,Male,51.0,9.0,0.0,1.0,1.0,0.0,141476.56,1.0
-2348,15757811.0,Chiemenam,585.0,Spain,Female,41.0,0.0,0.0,2.0,1.0,1.0,184524.74,0.0
-2349,15676933.0,Folliero,656.0,France,Male,41.0,4.0,122570.87,1.0,1.0,0.0,90189.72,0.0
-2350,15623989.0,Hao,726.0,Germany,Male,36.0,9.0,116576.45,1.0,0.0,1.0,83911.6,0.0
-2351,15678427.0,Okeke,605.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,141896.74,0.0
-2352,15709639.0,Tobeolisa,745.0,France,Male,66.0,2.0,0.0,1.0,1.0,1.0,91340.02,0.0
-2353,15610753.0,Chukwujamuike,683.0,France,Female,33.0,4.0,139196.0,1.0,1.0,1.0,162812.16,0.0
-2354,15795895.0,Chikezie,468.0,Germany,Female,46.0,5.0,117413.95,1.0,0.0,1.0,61994.2,1.0
-2355,15662093.0,Onwuamaeze,726.0,France,Female,42.0,7.0,89025.88,1.0,1.0,0.0,125918.14,0.0
-2356,15700749.0,Tsao,755.0,Spain,Male,38.0,10.0,119654.44,1.0,1.0,0.0,54474.48,0.0
-2357,15722090.0,Chia,681.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,1.0,85869.89,0.0
-2358,15654096.0,Onyemauchechukwu,755.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,147955.91,0.0
-2359,15587581.0,Ch'iu,721.0,Germany,Female,25.0,1.0,134740.24,1.0,1.0,0.0,71167.17,0.0
-2360,15606085.0,Tuan,613.0,France,Female,30.0,5.0,0.0,2.0,1.0,1.0,95611.47,0.0
-2361,15747385.0,Kaur,613.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,1.0,164741.5,0.0
-2362,15732903.0,Grieve,725.0,France,Female,48.0,4.0,0.0,1.0,1.0,0.0,171768.76,1.0
-2363,15797900.0,Ch'eng,619.0,Spain,Male,33.0,2.0,0.0,1.0,1.0,0.0,131736.23,0.0
-2364,15772995.0,Manna,605.0,Spain,Female,55.0,1.0,0.0,1.0,1.0,0.0,51117.06,1.0
-2365,15740404.0,Pape,687.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,145536.28,0.0
-2366,15791054.0,Chuang,615.0,France,Female,52.0,7.0,0.0,1.0,1.0,0.0,55829.25,1.0
-2367,15690182.0,Onyemauchechukwu,592.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,60584.33,0.0
-2368,15644878.0,Molineux,554.0,France,Male,45.0,8.0,0.0,2.0,1.0,0.0,72643.95,0.0
-2369,15746338.0,Chukwukadibia,703.0,Spain,Male,39.0,2.0,0.0,1.0,0.0,0.0,102397.22,0.0
-2370,15664759.0,Y?,727.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,51117.06,0.0
-2371,15645778.0,Okechukwu,629.0,France,Female,46.0,1.0,0.0,1.0,1.0,1.0,91510.71,1.0
-2372,15665088.0,Nucci,647.0,Spain,Male,45.0,4.0,0.0,2.0,1.0,0.0,108003.38,0.0
-2373,15764033.0,Chinedum,743.0,France,Female,42.0,7.0,0.0,2.0,1.0,1.0,150856.38,0.0
-2374,15597909.0,Pirozzi,605.0,France,Female,39.0,9.0,0.0,2.0,0.0,1.0,51703.09,0.0
-2375,15793311.0,Chiekwugo,516.0,France,Male,42.0,8.0,0.0,1.0,0.0,1.0,180551.76,0.0
-2376,15734312.0,Chinedum,653.0,Germany,Male,31.0,3.0,114023.64,1.0,1.0,1.0,18260.98,0.0
-2377,15614092.0,Ndukaku,634.0,Spain,Male,31.0,1.0,0.0,1.0,1.0,0.0,51555.17,0.0
-2378,15768474.0,Udinesi,718.0,Germany,Male,45.0,7.0,114959.12,2.0,1.0,1.0,99347.8,0.0
-2379,15798960.0,Milanesi,719.0,France,Female,38.0,3.0,0.0,1.0,1.0,1.0,182865.09,0.0
-2380,15762277.0,Kao,525.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,129892.93,0.0
-2381,15622897.0,Chiedozie,651.0,France,Female,36.0,1.0,0.0,2.0,0.0,0.0,165082.2,0.0
-2382,15754526.0,Pinto,705.0,France,Male,40.0,2.0,88977.39,2.0,1.0,1.0,131301.74,0.0
-2383,15664668.0,Chien,607.0,Spain,Male,40.0,3.0,0.0,1.0,1.0,0.0,16250.61,1.0
-2384,15803749.0,P'eng,579.0,France,Male,41.0,10.0,0.0,2.0,1.0,1.0,172576.43,0.0
-2385,15731555.0,Hao,597.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,155711.64,0.0
-2386,15585379.0,P'eng,572.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,142331.85,0.0
-2387,15777830.0,Brown,709.0,Spain,Female,40.0,9.0,0.0,2.0,1.0,1.0,110776.01,0.0
-2388,15778936.0,Calabresi,651.0,France,Female,61.0,1.0,0.0,2.0,1.0,1.0,108876.75,0.0
-2389,15691792.0,Y?an,583.0,France,Male,38.0,9.0,0.0,2.0,0.0,1.0,161889.95,0.0
-2390,15585853.0,Nwora,585.0,Spain,Male,56.0,8.0,0.0,2.0,1.0,0.0,93718.16,1.0
-2391,15600813.0,Yobachi,786.0,France,Male,66.0,7.0,0.0,2.0,0.0,1.0,82820.85,0.0
-2392,15712198.0,Palerma,752.0,France,Female,37.0,3.0,132527.9,1.0,1.0,0.0,118749.15,0.0
-2393,15702416.0,L?,662.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,179978.68,0.0
-2394,15752578.0,Nwachinemelu,730.0,France,Male,29.0,1.0,0.0,1.0,1.0,1.0,73679.31,0.0
-2395,15624374.0,Baresi,707.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,103570.8,0.0
-2396,15777892.0,Hsiung,553.0,Germany,Male,44.0,1.0,117438.92,2.0,1.0,1.0,120853.83,1.0
-2397,15772412.0,Toscani,682.0,France,Male,40.0,2.0,153870.23,1.0,0.0,0.0,137835.75,0.0
-2398,15634281.0,Wilson,592.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,96444.88,0.0
-2399,15790750.0,Chinedum,593.0,France,Male,42.0,2.0,0.0,2.0,1.0,1.0,95936.84,0.0
-2400,15762855.0,K?,652.0,Germany,Male,33.0,1.0,122793.96,1.0,1.0,0.0,102930.46,0.0
-2401,15662063.0,Niu,801.0,Spain,Male,35.0,1.0,0.0,1.0,0.0,1.0,181928.25,1.0
-2402,15642579.0,Kenechi,850.0,France,Male,32.0,2.0,123575.51,1.0,1.0,0.0,55725.04,0.0
-2403,15589725.0,Ni,632.0,France,Female,35.0,9.0,0.0,2.0,0.0,1.0,140763.17,0.0
-2404,15603683.0,Chinedum,677.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,1.0,82774.07,0.0
-2405,15588904.0,Ch'eng,687.0,Germany,Male,39.0,3.0,155907.68,1.0,1.0,1.0,137985.69,0.0
-2406,15650068.0,Ch'eng,713.0,Germany,Female,48.0,5.0,122314.5,1.0,0.0,1.0,51548.14,1.0
-2407,15795747.0,Hsieh,644.0,Spain,Female,40.0,7.0,96951.21,1.0,1.0,1.0,85412.54,0.0
-2408,15783398.0,Chukwukadibia,729.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,79866.73,0.0
-2409,15591035.0,Chiazagomekpere,651.0,France,Male,31.0,7.0,106968.96,1.0,1.0,1.0,52796.31,0.0
-2410,15713578.0,Lucchese,684.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,147530.06,0.0
-2411,15782210.0,Milano,682.0,Spain,Female,40.0,2.0,0.0,2.0,0.0,0.0,144517.19,0.0
-2412,15789563.0,Cattaneo,692.0,France,Female,38.0,8.0,0.0,1.0,1.0,0.0,24255.21,0.0
-2413,15693904.0,Chinweuba,685.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,178045.97,0.0
-2414,15806049.0,Maclean,613.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,152698.24,0.0
-2415,15662337.0,Ebelegbulam,628.0,France,Female,32.0,0.0,99786.07,1.0,1.0,1.0,169964.12,0.0
-2416,15599195.0,Obiajulu,748.0,France,Female,32.0,7.0,0.0,2.0,0.0,1.0,184839.7,0.0
-2417,15608873.0,Milani,695.0,France,Female,54.0,1.0,0.0,2.0,1.0,0.0,143952.24,1.0
-2418,15751747.0,Zito,584.0,France,Male,32.0,0.0,0.0,1.0,1.0,1.0,103042.2,0.0
-2419,15575605.0,Chidubem,565.0,France,Male,31.0,4.0,0.0,2.0,0.0,0.0,29333.01,0.0
-2420,15583394.0,P'eng,705.0,France,Female,32.0,1.0,138171.1,1.0,1.0,0.0,187905.12,0.0
-2421,15763063.0,Yudina,678.0,France,Female,48.0,3.0,0.0,2.0,1.0,1.0,86977.53,0.0
-2422,15700964.0,Ch'ang,710.0,Spain,Male,50.0,1.0,0.0,2.0,1.0,1.0,171045.35,0.0
-2423,15579566.0,Hs?,684.0,France,Female,35.0,1.0,0.0,1.0,1.0,1.0,86657.48,0.0
-2424,15798824.0,Tung,727.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,57961.41,0.0
-2425,15591932.0,Lucchese,683.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,38186.84,0.0
-2426,15799384.0,Ts'ui,587.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,86699.98,0.0
-2427,15671610.0,Ch'iu,588.0,Spain,Female,29.0,7.0,0.0,2.0,1.0,1.0,67840.81,0.0
-2428,15581282.0,Riddle,755.0,Germany,Male,56.0,3.0,97720.35,1.0,1.0,1.0,152290.58,1.0
-2429,15654733.0,Chukwualuka,678.0,Germany,Male,62.0,1.0,96486.57,1.0,1.0,1.0,185577.34,0.0
-2430,15644896.0,Ozerova,572.0,Spain,Female,42.0,9.0,0.0,2.0,1.0,1.0,115468.81,0.0
-2431,15636423.0,K'ung,665.0,France,Female,33.0,3.0,0.0,2.0,0.0,0.0,128882.66,0.0
-2432,15800517.0,Rolon,676.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,115509.59,0.0
-2433,15691119.0,Scott,683.0,Germany,Male,41.0,5.0,142724.48,1.0,1.0,1.0,128521.17,0.0
-2434,15718245.0,Ts'ui,629.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,103926.73,0.0
-2435,15616380.0,Milne,649.0,France,Female,42.0,2.0,0.0,1.0,1.0,0.0,140938.95,1.0
-2436,15692291.0,Chinweike,714.0,Germany,Male,36.0,6.0,142717.84,1.0,1.0,1.0,6776.92,0.0
-2437,15682773.0,Chidozie,592.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,86951.2,0.0
-2438,15730059.0,Folliero,576.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,77550.18,0.0
-2439,15679024.0,Lucciano,678.0,Spain,Female,32.0,7.0,116431.45,1.0,0.0,0.0,59297.34,0.0
-2440,15673367.0,Nnamutaezinwa,556.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,121537.91,0.0
-2441,15761684.0,Yobachi,684.0,Spain,Female,42.0,7.0,123911.61,1.0,1.0,0.0,74838.64,1.0
-2442,15692664.0,Chukwueloka,632.0,Spain,Male,38.0,1.0,0.0,1.0,1.0,1.0,114675.75,0.0
-2443,15639277.0,Lucchese,597.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,48378.4,1.0
-2444,15592300.0,Aitken,587.0,Germany,Female,25.0,1.0,152368.21,1.0,0.0,1.0,139971.01,0.0
-2445,15605827.0,McEwan,771.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,71672.56,0.0
-2446,15727605.0,Chukwualuka,706.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,162599.51,0.0
-2447,15662403.0,Hsieh,668.0,Germany,Male,30.0,3.0,127801.88,2.0,1.0,1.0,15650.89,0.0
-2448,15690796.0,Nnachetam,562.0,Spain,Female,29.0,5.0,0.0,2.0,1.0,0.0,151978.36,0.0
-2449,15690164.0,Ch'in,710.0,France,Male,20.0,7.0,0.0,2.0,1.0,1.0,99017.34,0.0
-2450,15583076.0,Chinedum,718.0,France,Female,47.0,2.0,0.0,1.0,1.0,0.0,82298.59,1.0
-2451,15567993.0,Baresi,553.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,188484.52,0.0
-2452,15782269.0,Ch'ien,695.0,France,Male,38.0,5.0,126817.65,1.0,0.0,1.0,179978.68,0.0
-2453,15682868.0,Chidiebere,674.0,Spain,Male,37.0,10.0,0.0,2.0,1.0,1.0,187900.12,0.0
-2454,15664227.0,Chukwukere,696.0,France,Female,27.0,5.0,0.0,2.0,0.0,0.0,186655.11,0.0
-2455,15807003.0,Onyeorulu,752.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,61290.99,1.0
-2456,15696826.0,Ch'iu,637.0,France,Male,41.0,3.0,0.0,1.0,1.0,0.0,66799.71,0.0
-2457,15702434.0,Udinese,838.0,Germany,Male,34.0,7.0,143390.51,1.0,1.0,0.0,165675.01,0.0
-2458,15677764.0,Ts'ai,730.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,86945.0,0.0
-2459,15673877.0,Y?an,706.0,France,Female,48.0,9.0,119499.42,1.0,1.0,0.0,48228.1,0.0
-2460,15673837.0,Scott,684.0,Spain,Male,30.0,7.0,0.0,2.0,0.0,1.0,102298.79,0.0
-2461,15742798.0,Ting,573.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,174729.3,0.0
-2462,15663438.0,T'ien,676.0,Germany,Female,49.0,5.0,123081.86,1.0,1.0,0.0,117622.8,0.0
-2463,15761348.0,Chien,704.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,175070.94,0.0
-2464,15629750.0,Calabrese,554.0,Germany,Male,37.0,4.0,139835.34,2.0,1.0,1.0,143739.35,0.0
-2465,15581229.0,Chiang,738.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,166370.81,0.0
-2466,15714689.0,Ugochukwutubelum,733.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,139971.01,0.0
-2467,15658946.0,Ma,595.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,133535.22,0.0
-2468,15682778.0,Chuang,586.0,Spain,Male,23.0,2.0,0.0,2.0,1.0,1.0,122925.44,0.0
-2469,15783530.0,P'eng,671.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,125979.36,0.0
-2470,15694321.0,Ting,538.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,147055.87,0.0
-2471,15695792.0,Ch'in,687.0,France,Male,42.0,2.0,0.0,1.0,1.0,1.0,58883.91,0.0
-2472,15654959.0,Dyer,729.0,France,Male,48.0,5.0,0.0,2.0,0.0,1.0,143859.66,0.0
-2473,15736397.0,T'ao,697.0,Germany,Male,32.0,2.0,128100.28,2.0,1.0,1.0,101102.39,0.0
-2474,15735302.0,Lai,699.0,France,Male,45.0,7.0,0.0,2.0,1.0,1.0,181718.76,0.0
-2475,15643640.0,Ch'ang,684.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,146663.63,0.0
-2476,15630167.0,Baryshnikov,746.0,France,Male,42.0,5.0,0.0,1.0,1.0,0.0,160589.18,0.0
-2477,15675141.0,Onyemauchechukwu,710.0,Germany,Female,53.0,2.0,151772.63,1.0,1.0,1.0,143783.46,1.0
-2478,15799468.0,Y?an,729.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,129975.94,0.0
-2479,15685314.0,De Luca,703.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,18082.06,0.0
-2480,15618581.0,Chiu,474.0,France,Female,39.0,9.0,131326.44,1.0,0.0,1.0,133814.28,0.0
-2481,15749328.0,Genovese,659.0,France,Male,49.0,0.0,0.0,1.0,1.0,0.0,150720.39,1.0
-2482,15585362.0,Clements,694.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,0.0,104889.3,0.0
-2483,15732784.0,Lo Duca,636.0,Spain,Female,55.0,4.0,0.0,3.0,0.0,0.0,26389.92,1.0
-2484,15577985.0,Hsing,684.0,Germany,Male,38.0,2.0,132623.6,2.0,1.0,0.0,176580.63,0.0
-2485,15750778.0,Hsia,651.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,48988.28,0.0
-2486,15795954.0,Teng,758.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,164825.09,0.0
-2487,15711571.0,Trevisano,559.0,France,Male,39.0,3.0,0.0,2.0,0.0,0.0,109805.17,0.0
-2488,15806913.0,Ozoemena,648.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,74865.61,0.0
-2489,15611371.0,Chiemenam,711.0,France,Male,36.0,2.0,131160.85,1.0,0.0,0.0,104533.85,0.0
-2490,15666032.0,Chidiebere,645.0,France,Male,38.0,4.0,0.0,1.0,0.0,1.0,133020.9,0.0
-2491,15708917.0,Chidiebele,679.0,Spain,Female,31.0,1.0,0.0,2.0,1.0,1.0,178833.4,0.0
-2492,15662483.0,Tuan,633.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,93336.3,0.0
-2493,15593365.0,Manna,730.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,1.0,176750.81,0.0
-2494,15576065.0,Yefremova,711.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,116692.8,0.0
-2495,15691287.0,Hsing,645.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,142331.85,0.0
-2496,15619494.0,Chukwumaobim,656.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,54954.51,0.0
-2497,15677908.0,O'Sullivan,581.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,91429.74,0.0
-2498,15662291.0,Chukwuemeka,706.0,France,Male,44.0,8.0,120777.88,1.0,1.0,1.0,131778.58,0.0
-2499,15629338.0,Olisaemeka,677.0,France,Male,35.0,1.0,64430.06,1.0,1.0,1.0,75937.38,0.0
-2500,15733169.0,Hsia,711.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,38153.19,0.0
-2501,15592765.0,Onwumelu,648.0,France,Female,42.0,2.0,0.0,2.0,1.0,0.0,93820.49,0.0
-2502,15642530.0,Nnachetam,763.0,France,Male,38.0,9.0,0.0,1.0,0.0,1.0,172450.04,0.0
-2503,15604497.0,Hsueh,617.0,France,Male,38.0,1.0,109135.11,1.0,1.0,1.0,72909.75,0.0
-2504,15682773.0,Rizzo,578.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,183490.99,0.0
-2505,15768449.0,T'ao,683.0,Germany,Male,42.0,10.0,112940.07,3.0,1.0,0.0,84475.13,1.0
-2506,15649024.0,Ugonnatubelum,565.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,49374.82,0.0
-2507,15754574.0,Chidiebere,681.0,France,Male,47.0,4.0,0.0,2.0,1.0,1.0,25885.72,0.0
-2508,15568120.0,Nkemakolam,716.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,101515.76,0.0
-2509,15707551.0,Cattaneo,587.0,Germany,Male,28.0,2.0,90696.78,1.0,1.0,1.0,96444.88,0.0
-2510,15607988.0,Ugochukwu,641.0,France,Male,43.0,8.0,146053.66,1.0,1.0,1.0,124550.88,0.0
-2511,15614962.0,Pisano,547.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,131284.93,0.0
-2512,15752578.0,Chukwualuka,596.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,123663.63,0.0
-2513,15740406.0,Lucchese,850.0,Germany,Female,35.0,5.0,104832.49,2.0,1.0,0.0,187915.7,0.0
-2514,15634602.0,Lu,553.0,France,Male,41.0,2.0,0.0,3.0,1.0,1.0,151903.6,1.0
-2515,15640648.0,Ch'eng,763.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,54495.82,0.0
-2516,15706269.0,Ch'ien,597.0,France,Female,49.0,0.0,0.0,2.0,0.0,0.0,184544.59,1.0
-2517,15621440.0,Johnson,644.0,Spain,Female,39.0,2.0,97764.41,2.0,1.0,0.0,140941.56,0.0
-2518,15718893.0,Toscani,725.0,France,Male,41.0,6.0,131101.04,1.0,0.0,1.0,181893.31,0.0
-2519,15697691.0,Lettiere,682.0,France,Male,42.0,1.0,129895.19,1.0,1.0,1.0,87422.15,0.0
-2520,15803689.0,T'ien,705.0,France,Male,38.0,5.0,0.0,1.0,1.0,0.0,96961.0,1.0
-2521,15762268.0,Lucciano,656.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,128521.17,0.0
-2522,15754849.0,Chinwendu,757.0,Spain,Female,40.0,3.0,0.0,1.0,1.0,0.0,121408.51,1.0
-2523,15636061.0,Calabrese,724.0,France,Male,33.0,4.0,138053.79,1.0,1.0,0.0,65720.92,0.0
-2524,15782758.0,H??,581.0,Germany,Female,29.0,7.0,119565.92,2.0,1.0,0.0,137537.22,0.0
-2525,15661409.0,Zikoranachidimma,635.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,121084.27,0.0
-2526,15795747.0,Kambinachi,571.0,France,Male,33.0,8.0,0.0,2.0,0.0,1.0,82422.97,0.0
-2527,15794468.0,P'eng,708.0,Spain,Male,37.0,6.0,0.0,1.0,0.0,0.0,150694.42,0.0
-2528,15734987.0,Hao,516.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,57898.58,0.0
-2529,15792453.0,Tien,688.0,France,Male,53.0,4.0,0.0,2.0,1.0,1.0,122960.98,0.0
-2530,15784844.0,Ts'ui,634.0,France,Male,43.0,6.0,0.0,2.0,1.0,0.0,177624.01,0.0
-2531,15599956.0,Kao,773.0,France,Male,38.0,5.0,126670.37,1.0,1.0,1.0,74900.9,0.0
-2532,15778515.0,Maughan,595.0,Spain,Female,61.0,5.0,0.0,2.0,1.0,1.0,70532.53,0.0
-2533,15684958.0,Fiorentini,695.0,France,Male,26.0,9.0,0.0,2.0,1.0,1.0,121555.51,0.0
-2534,15581295.0,Hao,652.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,53889.41,0.0
-2535,15639946.0,P'eng,710.0,Spain,Female,36.0,0.0,0.0,2.0,1.0,0.0,96833.0,0.0
-2536,15632143.0,Grosse,683.0,France,Female,55.0,1.0,0.0,1.0,1.0,1.0,152992.04,1.0
-2537,15742238.0,Ch'eng,850.0,France,Male,32.0,6.0,0.0,1.0,1.0,0.0,187800.63,1.0
-2538,15762216.0,Ch'iu,673.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,177747.03,0.0
-2539,15694518.0,Walker,632.0,Germany,Female,48.0,0.0,154737.88,1.0,1.0,0.0,62634.94,1.0
-2540,15644295.0,Hsing,645.0,Germany,Male,31.0,6.0,119276.31,2.0,1.0,1.0,150135.38,0.0
-2541,15751774.0,Chukwuemeka,651.0,Germany,Male,27.0,9.0,132403.56,1.0,1.0,0.0,823.36,0.0
-2542,15638774.0,Yefimov,686.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,178705.45,0.0
-2543,15757895.0,Pirozzi,671.0,Spain,Female,42.0,7.0,0.0,2.0,0.0,1.0,153831.6,0.0
-2544,15597954.0,Pisani,778.0,France,Female,37.0,2.0,109137.12,2.0,0.0,1.0,48378.4,0.0
-2545,15587443.0,Estrada,588.0,Germany,Female,40.0,3.0,148897.02,1.0,0.0,0.0,189362.59,1.0
-2546,15748473.0,Tuan,839.0,France,Female,25.0,8.0,0.0,2.0,1.0,0.0,175893.45,0.0
-2547,15713890.0,Milani,706.0,France,Female,34.0,6.0,0.0,1.0,1.0,1.0,7905.06,0.0
-2548,15683544.0,Buchi,590.0,France,Male,30.0,6.0,0.0,2.0,0.0,1.0,159899.97,0.0
-2549,15652674.0,Ch'ang,637.0,Germany,Female,44.0,2.0,103560.57,1.0,1.0,1.0,83058.14,0.0
-2550,15766374.0,Genovese,640.0,France,Female,44.0,6.0,138521.72,1.0,1.0,1.0,136610.02,1.0
-2551,15671390.0,Tuan,604.0,Spain,Male,24.0,2.0,0.0,2.0,0.0,1.0,103920.05,0.0
-2552,15625942.0,Walker,619.0,France,Female,30.0,3.0,0.0,2.0,0.0,1.0,96444.88,0.0
-2553,15729201.0,Ugochukwu,565.0,France,Female,39.0,10.0,0.0,2.0,1.0,0.0,116977.89,0.0
-2554,15675650.0,Ch'ang,652.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,135875.51,0.0
-2555,15793331.0,Ozioma,601.0,France,Male,43.0,1.0,106070.33,1.0,1.0,0.0,74940.5,0.0
-2556,15598070.0,Hsiao,790.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,142400.27,0.0
-2557,15652084.0,Fanucci,524.0,Germany,Male,31.0,9.0,105271.56,1.0,1.0,1.0,134732.99,0.0
-2558,15663888.0,Chigozie,681.0,France,Female,59.0,1.0,92930.92,1.0,1.0,0.0,125082.29,0.0
-2559,15571843.0,Tsao,681.0,Spain,Female,50.0,8.0,0.0,1.0,1.0,0.0,172665.21,1.0
-2560,15641158.0,Tuan,579.0,France,Male,51.0,2.0,0.0,1.0,1.0,1.0,28226.15,1.0
-2561,15796790.0,Ch'ang,741.0,France,Male,25.0,4.0,0.0,2.0,1.0,1.0,26263.44,0.0
-2562,15603914.0,Chinweuba,629.0,France,Female,46.0,7.0,0.0,1.0,0.0,0.0,149705.25,1.0
-2563,15662884.0,Yefremov,606.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,75825.57,0.0
-2564,15592751.0,Onyemere,659.0,France,Male,45.0,3.0,0.0,2.0,1.0,1.0,76621.49,0.0
-2565,15779913.0,Hsueh,586.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,146723.72,0.0
-2566,15736126.0,Doyne,686.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,78622.38,0.0
-2567,15658890.0,Obinna,694.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,52301.15,0.0
-2568,15613463.0,Chinedum,537.0,Germany,Female,47.0,10.0,91536.97,1.0,1.0,0.0,101617.64,1.0
-2569,15702561.0,Tien,678.0,France,Female,30.0,2.0,0.0,1.0,1.0,0.0,77965.67,0.0
-2570,15602030.0,Chiemenam,744.0,Spain,Female,40.0,2.0,165610.41,2.0,0.0,1.0,132436.71,0.0
-2571,15656086.0,Palerma,592.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,181495.77,0.0
-2572,15657779.0,Linger,553.0,France,Male,22.0,6.0,83724.35,1.0,1.0,1.0,106024.02,0.0
-2573,15602909.0,Ch'ang,629.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,107876.2,0.0
-2574,15794849.0,Tisdall,683.0,Spain,Male,39.0,2.0,100880.76,1.0,1.0,1.0,28461.29,0.0
-2575,15783030.0,P'eng,744.0,France,Female,34.0,7.0,0.0,1.0,1.0,1.0,54726.43,0.0
-2576,15767432.0,Hsueh,584.0,France,Male,29.0,0.0,141796.43,1.0,1.0,1.0,156774.75,0.0
-2577,15807555.0,Clements,850.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,112124.98,0.0
-2578,15602909.0,Nwabugwu,479.0,France,Female,31.0,4.0,0.0,2.0,1.0,0.0,150139.73,0.0
-2579,15583840.0,Scott,710.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,112366.98,0.0
-2580,15792726.0,Ts'ui,543.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,90977.48,0.0
-2581,15651740.0,Ch'eng,751.0,France,Female,60.0,3.0,0.0,1.0,1.0,0.0,15068.18,1.0
-2582,15793346.0,Zikoranachidimma,742.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,175380.77,0.0
-2583,15776051.0,Gidney,756.0,Germany,Female,43.0,8.0,84474.62,1.0,0.0,1.0,51695.41,0.0
-2584,15656840.0,Tung,679.0,France,Female,38.0,5.0,121164.11,1.0,1.0,0.0,167984.72,0.0
-2585,15738063.0,Chukwukere,641.0,France,Male,39.0,10.0,0.0,1.0,1.0,1.0,158610.89,0.0
-2586,15752588.0,Yobanna,562.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,1.0,105525.16,0.0
-2587,15602844.0,Onio,659.0,Spain,Male,38.0,2.0,135842.89,1.0,1.0,0.0,37296.13,0.0
-2588,15642710.0,Chuang,647.0,Germany,Female,40.0,7.0,104944.1,1.0,1.0,0.0,182867.84,1.0
-2589,15662955.0,Ponce,711.0,Spain,Male,40.0,8.0,176729.52,1.0,1.0,0.0,94670.77,0.0
-2590,15694879.0,Hs?,524.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,167032.49,0.0
-2591,15738662.0,Ting,811.0,Spain,Male,30.0,7.0,0.0,2.0,1.0,0.0,118545.49,0.0
-2592,15624695.0,Chizuoke,554.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,162719.69,0.0
-2593,15674541.0,Molineux,619.0,Germany,Male,35.0,4.0,132371.3,2.0,1.0,0.0,182692.8,0.0
-2594,15785042.0,Ponomarev,712.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,14081.64,0.0
-2595,15598493.0,She,707.0,Germany,Female,38.0,3.0,122570.69,1.0,1.0,1.0,179521.24,0.0
-2596,15780954.0,Ch'ang,539.0,France,Female,40.0,10.0,111096.91,1.0,0.0,1.0,155846.69,0.0
-2597,15662238.0,Ugochukwu,599.0,Germany,Male,46.0,4.0,109346.13,1.0,1.0,0.0,158027.45,1.0
-2598,15603954.0,Calabrese,682.0,Germany,Male,28.0,2.0,125534.51,2.0,1.0,0.0,175072.47,0.0
-2599,15798532.0,T'ang,746.0,Germany,Male,45.0,8.0,124580.69,2.0,1.0,0.0,140929.98,0.0
-2600,15615896.0,Chiu,732.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,141210.5,1.0
-2601,15686539.0,Toscani,733.0,France,Male,57.0,3.0,145219.3,1.0,0.0,1.0,155876.06,1.0
-2602,15783526.0,Ebelegbulam,805.0,Spain,Male,37.0,3.0,118560.0,1.0,1.0,0.0,180426.4,0.0
-2603,15758813.0,Young,673.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,182867.04,0.0
-2604,15606861.0,Ting,686.0,Germany,Male,40.0,7.0,143680.47,2.0,1.0,0.0,187811.71,0.0
-2605,15632522.0,Walker,593.0,France,Female,38.0,0.0,0.0,2.0,1.0,0.0,80190.36,0.0
-2606,15703923.0,Chu,748.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,105570.39,0.0
-2607,15749119.0,T'an,645.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,77837.63,0.0
-2608,15627352.0,Hsia,850.0,France,Female,38.0,6.0,0.0,1.0,1.0,0.0,91098.76,1.0
-2609,15699294.0,K'ung,526.0,Spain,Female,61.0,2.0,124577.33,1.0,1.0,0.0,166476.95,1.0
-2610,15674541.0,Calabresi,667.0,France,Female,45.0,1.0,0.0,2.0,1.0,0.0,183566.78,0.0
-2611,15662291.0,Nnonso,703.0,Spain,Female,35.0,9.0,118899.45,1.0,1.0,1.0,134720.51,0.0
-2612,15775295.0,Ch'ien,718.0,France,Male,48.0,1.0,82407.51,1.0,0.0,1.0,164806.89,0.0
-2613,15588614.0,Hsieh,691.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,40889.14,0.0
-2614,15652789.0,Teng,583.0,France,Male,31.0,0.0,108396.67,1.0,1.0,1.0,81861.1,0.0
-2615,15579969.0,Ndukaku,682.0,France,Male,47.0,9.0,129118.5,1.0,1.0,0.0,149991.32,1.0
-2616,15583857.0,Mazzanti,850.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,100618.17,0.0
-2617,15809616.0,Onyemauchechukwu,656.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,182081.45,0.0
-2618,15783875.0,O'Loughlin,604.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,0.0,162443.05,0.0
-2619,15591279.0,T'ien,739.0,France,Female,39.0,7.0,0.0,2.0,0.0,1.0,143317.57,0.0
-2620,15652693.0,Ritchie,697.0,France,Male,50.0,1.0,0.0,2.0,0.0,1.0,85421.48,0.0
-2621,15777586.0,Uspensky,678.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,177772.03,0.0
-2622,15684868.0,Chukwuemeka,749.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,129964.94,0.0
-2623,15591766.0,Rizzo,738.0,France,Male,29.0,0.0,129743.75,1.0,1.0,1.0,180134.88,0.0
-2624,15665590.0,Onyemere,726.0,Spain,Female,23.0,2.0,0.0,2.0,1.0,0.0,75832.53,0.0
-2625,15639552.0,Brown,597.0,Germany,Male,62.0,8.0,137933.07,1.0,1.0,0.0,180844.81,1.0
-2626,15610403.0,T'ao,678.0,Spain,Female,32.0,2.0,0.0,1.0,1.0,1.0,121098.65,0.0
-2627,15772423.0,Genovesi,678.0,Spain,Female,30.0,4.0,0.0,2.0,1.0,0.0,143681.85,0.0
-2628,15638231.0,Ch'ien,713.0,France,Female,42.0,7.0,151855.33,1.0,1.0,0.0,82698.09,1.0
-2629,15746451.0,Oguejiofor,758.0,France,Male,28.0,9.0,0.0,1.0,1.0,0.0,158043.11,0.0
-2630,15641170.0,Cremonesi,653.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,179908.71,0.0
-2631,15756056.0,Teng,683.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,102799.14,0.0
-2632,15737267.0,Tsao,769.0,France,Female,27.0,8.0,0.0,2.0,0.0,0.0,95804.33,0.0
-2633,15727350.0,Chinomso,638.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,1.0,133591.11,0.0
-2634,15641110.0,Chinedum,697.0,Germany,Male,33.0,6.0,79954.61,1.0,1.0,0.0,147822.44,0.0
-2635,15704583.0,Sun,682.0,France,Female,35.0,2.0,0.0,2.0,0.0,0.0,182336.45,0.0
-2636,15722533.0,Zito,583.0,France,Female,46.0,5.0,0.0,3.0,0.0,0.0,180890.4,1.0
-2637,15800514.0,Chukwueloka,730.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,162889.1,0.0
-2638,15795519.0,Chiemela,754.0,France,Male,32.0,6.0,0.0,2.0,0.0,1.0,126263.97,0.0
-2639,15585280.0,Walker,549.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,0.0,171463.83,0.0
-2640,15777855.0,Kornilova,553.0,Spain,Female,43.0,5.0,95727.05,1.0,0.0,0.0,151902.78,0.0
-2641,15644297.0,Chinweuba,703.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,134878.19,0.0
-2642,15725166.0,Tsao,723.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,135875.51,0.0
-2643,15682728.0,Galkin,705.0,France,Female,35.0,3.0,0.0,1.0,0.0,0.0,127939.19,1.0
-2644,15691844.0,Ch'eng,733.0,Germany,Male,45.0,8.0,108269.2,1.0,0.0,1.0,143859.66,1.0
-2645,15655410.0,Yermakov,594.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,162719.69,0.0
-2646,15717290.0,Chien,605.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,186489.95,0.0
-2647,15749669.0,Onyeorulu,669.0,France,Female,31.0,5.0,0.0,1.0,1.0,1.0,177524.19,0.0
-2648,15677387.0,Lettiere,706.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,184524.74,0.0
-2649,15774854.0,Nkemjika,627.0,Germany,Female,34.0,6.0,100260.88,2.0,1.0,1.0,62825.03,0.0
-2650,15617507.0,Shih,793.0,France,Female,35.0,10.0,125739.75,2.0,1.0,1.0,180439.75,0.0
-2651,15790442.0,T'ien,697.0,France,Male,40.0,7.0,0.0,1.0,1.0,1.0,114442.66,0.0
-2652,15704053.0,Palermo,637.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,109257.59,0.0
-2653,15729279.0,O'Brien,775.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,91624.11,0.0
-2654,15573869.0,Ch'iu,686.0,Spain,Male,29.0,9.0,0.0,2.0,0.0,0.0,106918.67,0.0
-2655,15668057.0,Nwachukwu,743.0,France,Male,30.0,5.0,157777.58,2.0,1.0,1.0,172097.4,0.0
-2656,15631693.0,Chin,638.0,France,Male,47.0,4.0,0.0,1.0,0.0,0.0,113645.4,1.0
-2657,15781298.0,Chikezie,689.0,Germany,Male,47.0,2.0,126873.52,1.0,1.0,1.0,49373.94,1.0
-2658,15785024.0,Nkemdirim,662.0,France,Male,29.0,8.0,0.0,1.0,1.0,1.0,120892.96,0.0
-2659,15690540.0,Nnanna,716.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,113931.57,1.0
-2660,15592761.0,Yobanna,547.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,155047.11,0.0
-2661,15675141.0,Chiekwugo,695.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,107625.36,0.0
-2662,15605665.0,Zito,658.0,France,Female,49.0,9.0,113829.82,1.0,1.0,0.0,28367.21,1.0
-2663,15768219.0,Hsiao,607.0,France,Female,39.0,3.0,0.0,2.0,0.0,1.0,133086.45,0.0
-2664,15757811.0,Ch'ang,850.0,Germany,Male,61.0,2.0,119839.69,2.0,1.0,1.0,62634.94,0.0
-2665,15713673.0,Hsu,774.0,France,Female,40.0,3.0,124525.52,1.0,0.0,0.0,12537.42,0.0
-2666,15738174.0,Chukwualuka,585.0,Germany,Male,59.0,1.0,122034.4,1.0,1.0,0.0,113950.51,1.0
-2667,15683958.0,Udegbunam,749.0,France,Male,41.0,9.0,90322.99,1.0,1.0,0.0,73881.68,0.0
-2668,15584975.0,Phelan,706.0,France,Male,51.0,5.0,0.0,2.0,1.0,0.0,116748.14,0.0
-2669,15708916.0,Kao,676.0,France,Male,52.0,2.0,141356.47,3.0,1.0,1.0,118971.74,1.0
-2670,15620577.0,Trevisano,553.0,Spain,Female,35.0,1.0,105209.54,1.0,0.0,0.0,179780.74,0.0
-2671,15790564.0,Yobachukwu,711.0,France,Male,53.0,9.0,118307.57,1.0,1.0,0.0,147784.68,0.0
-2672,15618363.0,Chukwujekwu,694.0,France,Female,28.0,6.0,0.0,2.0,0.0,0.0,79633.38,0.0
-2673,15638882.0,White,587.0,France,Female,29.0,5.0,0.0,2.0,0.0,0.0,138542.53,0.0
-2674,15592999.0,Shih,579.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,0.0,164038.07,0.0
-2675,15592765.0,Onyemauchechi,643.0,France,Male,34.0,2.0,0.0,1.0,1.0,0.0,53477.76,0.0
-2676,15792934.0,Hargreaves,850.0,Spain,Female,33.0,4.0,0.0,2.0,0.0,0.0,173516.71,0.0
-2677,15796313.0,Lu,720.0,France,Male,55.0,1.0,0.0,2.0,1.0,0.0,64872.33,1.0
-2678,15576774.0,Manna,706.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,58844.84,0.0
-2679,15611029.0,Fomin,615.0,Germany,Male,63.0,4.0,51522.75,1.0,0.0,1.0,86911.49,1.0
-2680,15690695.0,Dilibe,634.0,Germany,Male,39.0,7.0,129433.34,1.0,1.0,0.0,166677.48,0.0
-2681,15721935.0,Yermolayev,598.0,France,Female,55.0,2.0,0.0,2.0,1.0,1.0,134790.61,0.0
-2682,15578515.0,Onyinyechukwuka,729.0,France,Female,38.0,0.0,0.0,2.0,1.0,0.0,143538.52,0.0
-2683,15583349.0,Fomin,635.0,Germany,Female,40.0,4.0,103465.04,2.0,1.0,0.0,824.0,0.0
-2684,15604345.0,Ch'iu,647.0,Germany,Female,39.0,6.0,125919.35,2.0,1.0,0.0,127095.14,0.0
-2685,15715079.0,Pirozzi,849.0,Germany,Female,53.0,2.0,131692.43,1.0,0.0,0.0,171773.43,1.0
-2686,15684548.0,Chia,723.0,France,Male,28.0,8.0,0.0,2.0,0.0,0.0,129826.96,0.0
-2687,15728674.0,Chiang,831.0,France,Male,39.0,2.0,136444.25,1.0,1.0,0.0,161241.65,0.0
-2688,15769216.0,Ch'in,654.0,France,Male,37.0,9.0,94773.11,1.0,1.0,0.0,152883.95,0.0
-2689,15718773.0,P'eng,648.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,138380.12,0.0
-2690,15782639.0,Frolova,756.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,51721.52,0.0
-2691,15607634.0,Istomin,682.0,France,Female,31.0,9.0,0.0,2.0,0.0,0.0,103898.59,0.0
-2692,15653595.0,Mazzanti,651.0,France,Male,47.0,2.0,0.0,2.0,1.0,0.0,141230.07,1.0
-2693,15738166.0,Chidiebere,652.0,Germany,Male,22.0,2.0,102952.61,1.0,1.0,0.0,126145.08,0.0
-2694,15696744.0,Ch'ang,592.0,France,Male,32.0,5.0,0.0,2.0,0.0,1.0,50783.1,0.0
-2695,15582960.0,Chiazagomekpere,594.0,Germany,Male,31.0,7.0,125850.53,1.0,1.0,0.0,198490.33,0.0
-2696,15679991.0,Chikelu,765.0,France,Female,37.0,1.0,121490.05,1.0,0.0,1.0,62972.13,0.0
-2697,15678034.0,Mao,652.0,France,Female,33.0,6.0,122528.18,1.0,1.0,1.0,77965.44,0.0
-2698,15781875.0,Fisk,762.0,France,Female,34.0,2.0,0.0,1.0,1.0,0.0,136869.91,0.0
-2699,15806438.0,T'ien,669.0,Spain,Male,31.0,9.0,0.0,1.0,1.0,1.0,93081.97,0.0
-2700,15763065.0,Tung,638.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,75585.48,0.0
-2701,15729279.0,Smith,605.0,France,Male,43.0,4.0,81589.04,1.0,1.0,0.0,107849.65,0.0
-2702,15761487.0,Hovell,647.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,35450.53,0.0
-2703,15648032.0,Ch'iu,661.0,France,Male,33.0,8.0,0.0,2.0,0.0,0.0,103421.91,0.0
-2704,15705343.0,Trevisani,576.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,124592.83,0.0
-2705,15671390.0,Yermolayeva,751.0,Germany,Male,29.0,5.0,94722.88,1.0,1.0,1.0,91573.19,1.0
-2706,15694450.0,Martin,636.0,Germany,Female,51.0,3.0,133702.89,1.0,1.0,0.0,94499.06,1.0
-2707,15795878.0,P'eng,682.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,63736.87,0.0
-2708,15642570.0,Chibueze,678.0,France,Male,54.0,7.0,0.0,1.0,0.0,0.0,123870.83,0.0
-2709,15640865.0,T'ien,748.0,France,Female,51.0,1.0,0.0,1.0,0.0,1.0,57572.38,1.0
-2710,15578825.0,Cattaneo,678.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,62449.07,0.0
-2711,15737310.0,Ch'ang,573.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,83629.6,0.0
-2712,15617454.0,Mancini,605.0,France,Male,32.0,3.0,0.0,2.0,0.0,0.0,136816.37,0.0
-2713,15783522.0,Chukwuebuka,565.0,France,Male,26.0,3.0,142872.36,1.0,1.0,0.0,69586.27,0.0
-2714,15771483.0,Trevisan,757.0,Spain,Female,43.0,8.0,0.0,2.0,0.0,0.0,154598.58,0.0
-2715,15644572.0,Nnamdi,683.0,Germany,Male,45.0,4.0,148116.48,1.0,1.0,0.0,179655.87,1.0
-2716,15711352.0,Ritchie,651.0,France,Male,40.0,9.0,0.0,1.0,1.0,1.0,135875.51,1.0
-2717,15694444.0,Ch'en,726.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,125010.24,0.0
-2718,15807909.0,H?,641.0,France,Male,43.0,0.0,0.0,2.0,1.0,1.0,163587.9,0.0
-2719,15600813.0,T'ien,582.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,118319.5,0.0
-2720,15728474.0,K?,721.0,Spain,Male,30.0,5.0,153313.77,1.0,1.0,1.0,128882.66,0.0
-2721,15794479.0,Tuan,629.0,France,Male,52.0,7.0,0.0,1.0,1.0,1.0,102950.79,0.0
-2722,15595766.0,Onwumelu,615.0,France,Male,39.0,6.0,136623.19,1.0,1.0,1.0,183494.87,0.0
-2723,15581295.0,Chia,850.0,France,Female,45.0,2.0,0.0,2.0,0.0,0.0,172526.9,0.0
-2724,15581286.0,Genovesi,609.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,161574.19,0.0
-2725,15598574.0,Ngozichukwuka,682.0,Spain,Male,40.0,6.0,0.0,2.0,0.0,0.0,18364.19,0.0
-2726,15623369.0,Maclean,700.0,France,Male,27.0,4.0,109196.66,1.0,1.0,0.0,57572.38,0.0
-2727,15723169.0,Chin,646.0,France,Female,29.0,2.0,79450.09,1.0,1.0,0.0,125920.54,0.0
-2728,15594864.0,T'ien,601.0,Germany,Female,43.0,3.0,139562.05,1.0,1.0,0.0,123499.98,1.0
-2729,15591740.0,Hsiung,661.0,France,Female,56.0,7.0,0.0,1.0,0.0,0.0,181429.87,1.0
-2730,15768137.0,Chiu,579.0,France,Male,33.0,3.0,0.0,2.0,0.0,0.0,112442.63,0.0
-2731,15599081.0,Ch'ang,711.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,142223.94,0.0
-2732,15751185.0,Pagnotto,705.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,1.0,74898.8,0.0
-2733,15792986.0,Chien,644.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,56908.41,0.0
-2734,15755872.0,Efremov,567.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,115676.61,0.0
-2735,15806922.0,Sun,706.0,Spain,Female,36.0,4.0,0.0,2.0,1.0,0.0,183572.39,0.0
-2736,15574743.0,Hsia,790.0,Germany,Male,51.0,8.0,112701.04,1.0,1.0,0.0,171430.16,1.0
-2737,15658195.0,Lo Duca,605.0,France,Female,54.0,4.0,0.0,1.0,0.0,0.0,104719.66,1.0
-2738,15740258.0,Walker,709.0,France,Female,39.0,2.0,142910.15,1.0,1.0,0.0,183494.87,0.0
-2739,15576368.0,Hargreaves,769.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,81409.0,0.0
-2740,15600626.0,Tsou,695.0,Germany,Male,35.0,9.0,130627.66,1.0,1.0,1.0,103379.31,0.0
-2741,15589329.0,Pinto,724.0,France,Female,33.0,3.0,0.0,1.0,1.0,0.0,79005.95,0.0
-2742,15683955.0,Ch'ien,556.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,152665.85,0.0
-2743,15793343.0,Mazzi,758.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,158437.73,0.0
-2744,15803947.0,Ijendu,799.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,149913.84,0.0
-2745,15598884.0,Chibugo,701.0,France,Male,30.0,7.0,0.0,3.0,1.0,0.0,134889.95,1.0
-2746,15734659.0,Okwukwe,727.0,Germany,Female,22.0,9.0,125190.86,2.0,1.0,1.0,47795.43,0.0
-2747,15807993.0,Pirozzi,611.0,Germany,Female,50.0,8.0,138053.79,1.0,1.0,0.0,178038.17,1.0
-2748,15792650.0,Chialuka,545.0,France,Female,41.0,1.0,0.0,2.0,1.0,0.0,88689.4,0.0
-2749,15665327.0,Onochie,676.0,France,Female,36.0,1.0,128226.37,1.0,1.0,0.0,131043.0,0.0
-2750,15808971.0,Calabresi,678.0,France,Female,42.0,3.0,0.0,2.0,0.0,0.0,103372.79,0.0
-2751,15796096.0,O'Donnell,651.0,Spain,Male,35.0,9.0,0.0,2.0,0.0,1.0,132467.54,0.0
-2752,15672930.0,Ejimofor,635.0,France,Male,31.0,6.0,0.0,2.0,0.0,0.0,86099.23,0.0
-2753,15781710.0,Galkin,571.0,Spain,Male,25.0,3.0,0.0,1.0,1.0,1.0,145937.99,0.0
-2754,15631800.0,Brownless,573.0,Spain,Male,35.0,0.0,157989.35,1.0,0.0,1.0,153786.46,0.0
-2755,15806318.0,T'ien,729.0,France,Male,24.0,6.0,0.0,2.0,0.0,0.0,174882.06,0.0
-2756,15663939.0,Hsia,581.0,France,Male,35.0,3.0,102025.08,1.0,1.0,0.0,103931.49,0.0
-2757,15703793.0,Nnachetam,675.0,France,Male,30.0,8.0,0.0,1.0,1.0,0.0,103570.8,0.0
-2758,15662063.0,Calabrese,729.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,121239.65,0.0
-2759,15776433.0,Babbage,685.0,France,Male,34.0,2.0,0.0,2.0,0.0,0.0,125961.48,0.0
-2760,15640407.0,Maclean,746.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,115116.35,0.0
-2761,15643908.0,Trevisano,705.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,152992.04,0.0
-2762,15661629.0,Chukwufumnanya,667.0,Spain,Female,48.0,7.0,0.0,1.0,1.0,1.0,28266.9,0.0
-2763,15619116.0,Biryukov,578.0,France,Male,40.0,4.0,0.0,2.0,0.0,0.0,102397.22,0.0
-2764,15649033.0,Okwuadigbo,627.0,Spain,Female,50.0,7.0,0.0,2.0,1.0,0.0,51345.41,1.0
-2765,15799790.0,Tuan,653.0,Germany,Male,48.0,4.0,114515.26,1.0,1.0,0.0,77326.78,0.0
-2766,15811316.0,Trevisano,573.0,France,Male,35.0,7.0,103362.14,1.0,1.0,1.0,149602.54,0.0
-2767,15807120.0,Genovesi,688.0,Spain,Male,54.0,10.0,0.0,1.0,1.0,0.0,167873.49,1.0
-2768,15597930.0,Ch'eng,667.0,Germany,Male,33.0,9.0,113566.3,2.0,1.0,1.0,92811.2,0.0
-2769,15588933.0,Tsao,644.0,Germany,Female,33.0,8.0,139422.29,1.0,0.0,0.0,28149.06,0.0
-2770,15751208.0,T'an,771.0,Spain,Male,39.0,9.0,0.0,2.0,0.0,0.0,61961.57,0.0
-2771,15583736.0,Ch'in,715.0,Germany,Male,30.0,4.0,131778.66,2.0,1.0,1.0,93718.16,0.0
-2772,15724863.0,Ting,705.0,France,Male,36.0,0.0,127545.94,1.0,1.0,1.0,158545.94,0.0
-2773,15603319.0,Cattaneo,646.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,126561.07,0.0
-2774,15732644.0,O'Sullivan,679.0,France,Female,28.0,3.0,0.0,1.0,1.0,0.0,158071.69,0.0
-2775,15602479.0,L?,648.0,France,Male,30.0,8.0,0.0,2.0,1.0,1.0,102908.63,0.0
-2776,15716085.0,Palerma,774.0,France,Male,63.0,9.0,0.0,1.0,1.0,0.0,86022.09,1.0
-2777,15676835.0,White,535.0,Germany,Male,36.0,2.0,97588.6,2.0,0.0,1.0,92220.12,0.0
-2778,15691874.0,K?,604.0,Germany,Male,45.0,4.0,125845.66,1.0,0.0,0.0,130541.1,1.0
-2779,15739803.0,Chidubem,705.0,France,Male,26.0,9.0,0.0,1.0,1.0,0.0,100940.91,0.0
-2780,15614168.0,Chien,777.0,Germany,Male,36.0,9.0,137964.51,1.0,1.0,1.0,143913.35,0.0
-2781,15585961.0,Cremonesi,693.0,France,Female,42.0,9.0,0.0,2.0,1.0,1.0,59354.24,0.0
-2782,15596671.0,Ch'in,639.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,0.0,97938.59,0.0
-2783,15588933.0,Li Fonti,583.0,Spain,Female,40.0,9.0,128206.79,1.0,1.0,0.0,122580.48,0.0
-2784,15757577.0,Palerma,577.0,Germany,Male,54.0,4.0,129933.5,3.0,1.0,0.0,16402.11,1.0
-2785,15725166.0,Fiorentino,754.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,183515.13,0.0
-2786,15803526.0,T'ao,710.0,France,Male,24.0,9.0,0.0,2.0,1.0,1.0,131806.44,0.0
-2787,15655192.0,Chiang,708.0,Germany,Female,42.0,2.0,135559.9,1.0,0.0,0.0,199203.21,1.0
-2788,15581229.0,Onyekaozulu,714.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,136301.1,0.0
-2789,15623180.0,Ch'ien,579.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,175853.64,0.0
-2790,15725679.0,Udinese,616.0,France,Female,37.0,9.0,0.0,2.0,0.0,1.0,141890.32,0.0
-2791,15766427.0,Chin,730.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,88965.46,0.0
-2792,15737385.0,Yeh,567.0,France,Female,29.0,7.0,0.0,1.0,1.0,1.0,116978.35,0.0
-2793,15686963.0,Ch'iu,742.0,Germany,Male,44.0,8.0,122570.87,2.0,1.0,1.0,166957.82,1.0
-2794,15803689.0,Y?an,707.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,109056.26,0.0
-2795,15600708.0,Toscani,628.0,Spain,Male,47.0,9.0,104077.19,1.0,1.0,0.0,105568.5,0.0
-2796,15706021.0,Okwuadigbo,659.0,Germany,Male,44.0,8.0,121898.58,1.0,1.0,0.0,14081.64,1.0
-2797,15566563.0,Onyekachukwu,682.0,France,Female,29.0,0.0,127552.85,1.0,1.0,0.0,88842.8,0.0
-2798,15711889.0,Chia,687.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,56650.47,0.0
-2799,15641595.0,Calabrese,710.0,Spain,Male,44.0,6.0,0.0,1.0,1.0,1.0,51987.99,0.0
-2800,15598892.0,Liao,598.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,162683.06,0.0
-2801,15790744.0,Kao,561.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,82393.08,0.0
-2802,15744607.0,Maclean,613.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,62733.05,0.0
-2803,15751167.0,Ting,642.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,0.0,57548.38,1.0
-2804,15622083.0,Chukwujamuike,712.0,Spain,Male,32.0,10.0,0.0,2.0,1.0,1.0,143635.33,0.0
-2805,15642996.0,Onwumelu,666.0,France,Female,22.0,9.0,102717.15,1.0,1.0,1.0,180318.6,0.0
-2806,15658853.0,Ch'ien,682.0,France,Male,27.0,8.0,0.0,2.0,0.0,1.0,173516.71,0.0
-2807,15797329.0,Calabrese,632.0,Germany,Female,46.0,1.0,103396.08,1.0,1.0,0.0,141798.98,1.0
-2808,15598485.0,Pinto,747.0,France,Female,26.0,6.0,96473.59,2.0,1.0,0.0,137905.25,0.0
-2809,15778418.0,Fiorentino,642.0,France,Male,32.0,9.0,88902.21,1.0,1.0,1.0,38550.4,0.0
-2810,15803801.0,Pagnotto,592.0,Spain,Male,35.0,10.0,0.0,2.0,0.0,1.0,94977.3,0.0
-2811,15672692.0,T'ao,592.0,France,Female,36.0,6.0,127861.93,1.0,0.0,0.0,99842.45,0.0
-2812,15812706.0,P'eng,687.0,France,Male,29.0,8.0,120787.18,1.0,0.0,0.0,131852.01,0.0
-2813,15589323.0,Lai,758.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,175893.53,0.0
-2814,15718443.0,Uwaezuoke,649.0,France,Female,33.0,3.0,0.0,2.0,0.0,0.0,77414.19,0.0
-2815,15661412.0,Mazzanti,739.0,Germany,Female,43.0,4.0,171787.84,1.0,1.0,1.0,85890.75,1.0
-2816,15651022.0,Lo,477.0,France,Male,21.0,3.0,0.0,2.0,1.0,0.0,125518.32,0.0
-2817,15605666.0,Trevisano,641.0,France,Male,59.0,9.0,0.0,2.0,1.0,1.0,36122.96,0.0
-2818,15598883.0,Udegbunam,711.0,Spain,Female,38.0,3.0,140856.16,1.0,1.0,1.0,150507.84,0.0
-2819,15747174.0,Chiazagomekpere,717.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,199992.48,0.0
-2820,15783550.0,Genovesi,683.0,France,Female,37.0,6.0,121405.04,1.0,1.0,1.0,82868.34,0.0
-2821,15631927.0,Shih,537.0,Germany,Male,61.0,2.0,63492.94,3.0,1.0,0.0,131953.23,1.0
-2822,15786463.0,Chinedum,850.0,Germany,Female,34.0,5.0,113882.31,2.0,1.0,0.0,71139.8,0.0
-2823,15770121.0,Onyeorulu,591.0,Spain,Female,44.0,8.0,0.0,2.0,1.0,0.0,41967.03,0.0
-2824,15598884.0,Ozerova,671.0,France,Female,32.0,6.0,61688.61,1.0,1.0,1.0,79928.41,1.0
-2825,15585867.0,Mazzanti,681.0,France,Female,60.0,7.0,0.0,3.0,1.0,1.0,160548.12,1.0
-2826,15697020.0,Akabueze,591.0,France,Female,39.0,1.0,0.0,1.0,0.0,0.0,16081.62,0.0
-2827,15773322.0,Lo Duca,705.0,Spain,Male,40.0,9.0,0.0,2.0,1.0,0.0,88566.9,0.0
-2828,15761149.0,Yefremova,605.0,Spain,Male,36.0,4.0,0.0,2.0,0.0,0.0,122964.18,0.0
-2829,15588517.0,Tseng,676.0,France,Male,38.0,1.0,0.0,1.0,1.0,1.0,83629.6,0.0
-2830,15656300.0,Kao,708.0,Germany,Female,49.0,5.0,99219.47,1.0,1.0,0.0,26261.42,1.0
-2831,15764335.0,Young,679.0,Spain,Male,47.0,4.0,0.0,2.0,0.0,1.0,72993.65,0.0
-2832,15791321.0,Nnachetam,572.0,France,Male,33.0,7.0,0.0,1.0,1.0,1.0,180934.67,0.0
-2833,15616152.0,Ts'ai,624.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,111421.77,0.0
-2834,15653849.0,Palerma,597.0,Spain,Male,52.0,3.0,141359.37,1.0,1.0,1.0,48421.4,0.0
-2835,15632848.0,Chukwudi,686.0,France,Female,32.0,3.0,0.0,1.0,0.0,1.0,172967.87,1.0
-2836,15731553.0,Y?an,639.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,143960.72,0.0
-2837,15591036.0,Hsiung,763.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,128815.23,0.0
-2838,15766166.0,Ch'en,605.0,France,Female,46.0,9.0,0.0,2.0,1.0,0.0,124596.63,1.0
-2839,15609510.0,Uspenskaya,582.0,France,Male,33.0,7.0,137715.84,1.0,1.0,1.0,4395.45,0.0
-2840,15659800.0,Mazzanti,685.0,France,Male,29.0,6.0,162039.59,1.0,0.0,0.0,77951.84,0.0
-2841,15571756.0,Palermo,730.0,Germany,Male,39.0,8.0,138053.79,1.0,1.0,1.0,86022.09,0.0
-2842,15761685.0,Ikedinachukwu,714.0,Spain,Female,29.0,1.0,127845.86,1.0,1.0,0.0,107964.35,0.0
-2843,15684958.0,Onyemauchechukwu,598.0,France,Female,32.0,3.0,146566.01,1.0,0.0,1.0,189657.26,1.0
-2844,15691483.0,Li Fonti,709.0,France,Male,46.0,7.0,104568.41,1.0,1.0,1.0,175072.47,0.0
-2845,15702300.0,T'ao,613.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,143628.31,0.0
-2846,15763063.0,Trevisano,678.0,Germany,Male,38.0,3.0,144801.97,1.0,0.0,0.0,40318.33,0.0
-2847,15801466.0,Toscani,561.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,120853.83,0.0
-2848,15750929.0,Chukwueloka,559.0,France,Male,50.0,3.0,0.0,1.0,1.0,1.0,199304.74,0.0
-2849,15795519.0,Nnamutaezinwa,637.0,Spain,Male,36.0,9.0,143321.97,2.0,1.0,0.0,151648.66,0.0
-2850,15589948.0,Lo Duca,581.0,France,Male,35.0,6.0,0.0,2.0,0.0,1.0,582.59,0.0
-2851,15643523.0,P'eng,535.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,92612.88,0.0
-2852,15734438.0,Chukwudi,695.0,Germany,Male,39.0,4.0,118628.6,1.0,1.0,0.0,184570.04,0.0
-2853,15747222.0,Kao,774.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,1.0,152843.53,0.0
-2854,15589544.0,McLean,576.0,France,Female,19.0,0.0,0.0,2.0,1.0,0.0,109355.73,0.0
-2855,15808849.0,Mazzanti,774.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,176883.42,0.0
-2856,15750014.0,Ch'in,627.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,80134.65,0.0
-2857,15592025.0,Ch'iu,730.0,France,Male,34.0,1.0,0.0,2.0,1.0,1.0,136071.05,0.0
-2858,15799720.0,Hsiung,675.0,France,Male,42.0,3.0,0.0,1.0,1.0,1.0,179227.52,0.0
-2859,15789955.0,Nwankwo,619.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,86043.27,0.0
-2860,15684516.0,Chukwuemeka,628.0,Germany,Female,41.0,3.0,103391.38,2.0,1.0,1.0,147578.26,0.0
-2861,15674840.0,K?,592.0,Spain,Male,39.0,4.0,114514.71,1.0,1.0,1.0,179066.58,0.0
-2862,15643908.0,Lai,729.0,Germany,Male,45.0,2.0,131778.66,1.0,1.0,0.0,140745.91,1.0
-2863,15656921.0,H?,641.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,9241.78,0.0
-2864,15571244.0,Chukwufumnanya,809.0,Spain,Female,30.0,1.0,0.0,2.0,1.0,0.0,121505.61,0.0
-2865,15602487.0,Clements,687.0,France,Male,27.0,8.0,0.0,2.0,1.0,1.0,138360.29,0.0
-2866,15679611.0,Lai,479.0,Spain,Male,49.0,5.0,124580.69,1.0,1.0,0.0,138849.06,0.0
-2867,15665088.0,T'ien,763.0,France,Male,21.0,6.0,0.0,2.0,0.0,0.0,143682.01,0.0
-2868,15777873.0,Iweobiegbunam,591.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,1.0,155700.15,0.0
-2869,15731478.0,Manna,585.0,France,Male,32.0,7.0,95128.86,1.0,1.0,1.0,152417.97,0.0
-2870,15788659.0,Onyemauchechukwu,516.0,France,Male,45.0,5.0,0.0,1.0,0.0,0.0,138889.15,1.0
-2871,15794916.0,Fan,637.0,Germany,Female,47.0,4.0,102954.68,1.0,1.0,0.0,102950.79,1.0
-2872,15686983.0,Buchi,529.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,157567.37,0.0
-2873,15572408.0,Ts'ao,678.0,Germany,Male,30.0,6.0,108269.2,2.0,1.0,1.0,1843.24,0.0
-2874,15715888.0,Chukwuhaenye,634.0,France,Female,28.0,8.0,157955.83,1.0,1.0,0.0,62989.82,0.0
-2875,15568326.0,Ch'ang,667.0,Germany,Male,45.0,10.0,79962.55,2.0,1.0,0.0,173837.54,0.0
-2876,15774857.0,Cattaneo,603.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,0.0,171657.57,0.0
-2877,15581626.0,Tien,629.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,149471.13,0.0
-2878,15674929.0,Chin,705.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,156424.4,0.0
-2879,15644295.0,Hargreaves,721.0,Germany,Female,55.0,2.0,134105.01,1.0,1.0,0.0,74425.41,1.0
-2880,15751208.0,McIntyre,627.0,France,Male,35.0,5.0,123925.18,1.0,1.0,1.0,158887.09,0.0
-2881,15591035.0,Nnonso,616.0,Germany,Male,46.0,3.0,123873.19,1.0,1.0,0.0,142377.69,1.0
-2882,15798895.0,Yobanna,711.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,94912.78,0.0
-2883,15693637.0,Shih,649.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,141069.88,0.0
-2884,15694321.0,Chinweike,759.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,5791.85,0.0
-2885,15676966.0,Mackay,669.0,France,Female,23.0,5.0,0.0,2.0,0.0,0.0,175094.62,0.0
-2886,15806922.0,Logue,692.0,Germany,Male,40.0,4.0,104449.8,2.0,1.0,0.0,68730.91,0.0
-2887,15746905.0,White,651.0,Spain,Female,40.0,8.0,0.0,2.0,1.0,0.0,143628.31,0.0
-2888,15792329.0,Ku,622.0,France,Male,30.0,1.0,140576.11,1.0,1.0,1.0,172814.99,0.0
-2889,15673367.0,Palermo,531.0,France,Male,42.0,3.0,0.0,1.0,1.0,0.0,181430.91,0.0
-2890,15761733.0,Chinwendu,602.0,Germany,Female,51.0,1.0,126817.65,1.0,0.0,1.0,27886.06,1.0
-2891,15615177.0,Chukwuemeka,659.0,Germany,Male,27.0,8.0,132730.17,2.0,1.0,1.0,155782.89,0.0
-2892,15692761.0,Dreyer,645.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,180598.66,0.0
-2893,15671342.0,T'ang,850.0,Spain,Female,32.0,2.0,0.0,2.0,1.0,0.0,86678.48,0.0
-2894,15573741.0,Ch'iu,710.0,Spain,Female,45.0,3.0,0.0,2.0,1.0,0.0,121551.58,0.0
-2895,15629299.0,Chigozie,663.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,132498.39,0.0
-2896,15781389.0,Cawood,739.0,France,Female,51.0,7.0,156091.97,3.0,0.0,1.0,72008.61,1.0
-2897,15677512.0,Niu,638.0,Spain,Female,35.0,7.0,0.0,1.0,1.0,0.0,160529.71,1.0
-2898,15747358.0,Greco,568.0,Germany,Male,38.0,2.0,113978.97,2.0,1.0,0.0,47729.33,0.0
-2899,15667093.0,Onuchukwu,644.0,Germany,Female,31.0,3.0,153537.13,1.0,0.0,1.0,56636.28,0.0
-2900,15713505.0,Pape,598.0,France,Female,41.0,0.0,0.0,1.0,0.0,0.0,97629.31,0.0
-2901,15715643.0,Haddon,711.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,52301.15,0.0
-2902,15672966.0,Uwaezuoke,510.0,France,Male,34.0,10.0,0.0,1.0,1.0,1.0,182527.25,0.0
-2903,15605327.0,Lo,762.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,152992.04,0.0
-2904,15636407.0,Okwudilichukwu,726.0,Germany,Female,42.0,4.0,141349.43,2.0,1.0,1.0,131300.53,0.0
-2905,15683977.0,Chu,643.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,75937.35,0.0
-2906,15571571.0,Ponomarev,667.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,1.0,157891.86,0.0
-2907,15602851.0,Ch'ien,669.0,France,Male,36.0,2.0,128760.32,2.0,1.0,1.0,134878.22,0.0
-2908,15794875.0,Shao,589.0,France,Male,37.0,1.0,51522.75,1.0,1.0,0.0,99818.41,0.0
-2909,15585595.0,Chiawuotu,685.0,France,Male,44.0,9.0,0.0,2.0,1.0,1.0,186489.14,0.0
-2910,15706537.0,Chin,681.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,125870.5,0.0
-2911,15582762.0,Chien,516.0,France,Female,29.0,10.0,0.0,1.0,1.0,1.0,165038.41,0.0
-2912,15589017.0,Chukwubuikem,602.0,France,Male,24.0,7.0,0.0,2.0,0.0,1.0,94722.54,0.0
-2913,15758035.0,Zaitsev,718.0,Spain,Female,37.0,9.0,97720.35,1.0,1.0,0.0,79082.62,0.0
-2914,15683923.0,Chiu,681.0,France,Female,36.0,9.0,142643.54,1.0,1.0,0.0,183844.88,0.0
-2915,15803633.0,Y?,691.0,France,Male,41.0,2.0,0.0,1.0,0.0,1.0,154882.79,0.0
-2916,15581252.0,Clements,679.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,117031.2,0.0
-2917,15657829.0,Chukwumaobim,724.0,France,Male,38.0,6.0,120108.56,1.0,1.0,0.0,127059.07,0.0
-2918,15783522.0,Mazzanti,492.0,France,Male,28.0,8.0,0.0,2.0,0.0,0.0,64716.08,0.0
-2919,15626012.0,Chia,716.0,France,Female,46.0,2.0,0.0,2.0,1.0,1.0,93943.68,0.0
-2920,15763063.0,Chiazagomekpere,729.0,France,Male,64.0,6.0,0.0,1.0,1.0,1.0,113781.99,0.0
-2921,15665340.0,Yobanna,744.0,Spain,Female,39.0,8.0,0.0,1.0,0.0,0.0,136521.82,1.0
-2922,15682600.0,Scott,762.0,France,Male,23.0,5.0,143350.56,1.0,1.0,1.0,81452.29,0.0
-2923,15610343.0,Ives,722.0,France,Female,54.0,10.0,0.0,2.0,1.0,1.0,140800.66,0.0
-2924,15589296.0,L?,699.0,France,Female,56.0,0.0,0.0,2.0,0.0,0.0,88120.78,1.0
-2925,15682939.0,DeRose,695.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,142995.32,0.0
-2926,15800229.0,Ozioma,850.0,France,Male,40.0,10.0,0.0,2.0,1.0,0.0,71139.31,0.0
-2927,15697606.0,Gidney,760.0,Spain,Female,44.0,6.0,0.0,2.0,1.0,1.0,72402.0,0.0
-2928,15801917.0,Hsia,663.0,France,Female,46.0,2.0,121628.42,1.0,0.0,0.0,153605.44,1.0
-2929,15579777.0,Ndubuagha,683.0,France,Male,32.0,2.0,0.0,1.0,1.0,1.0,94875.03,0.0
-2930,15686936.0,Shih,729.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,109545.56,0.0
-2931,15685500.0,Lucchese,634.0,France,Male,26.0,7.0,0.0,2.0,1.0,1.0,167553.06,0.0
-2932,15601008.0,Ch'en,623.0,Germany,Male,28.0,7.0,142765.24,1.0,1.0,0.0,103921.43,0.0
-2933,15575060.0,Echezonachukwu,716.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,98896.06,0.0
-2934,15725068.0,Hsieh,559.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,86587.37,0.0
-2935,15758031.0,Milano,731.0,France,Female,30.0,3.0,0.0,2.0,0.0,0.0,180712.28,0.0
-2936,15603481.0,Ch'ien,773.0,France,Male,36.0,2.0,0.0,1.0,1.0,0.0,66893.28,0.0
-2937,15659736.0,Shih,687.0,Germany,Female,43.0,7.0,129893.56,1.0,1.0,0.0,172031.22,1.0
-2938,15683955.0,Maclean,710.0,Spain,Male,28.0,2.0,99760.84,2.0,1.0,0.0,90839.61,0.0
-2939,15756070.0,Shaver,635.0,France,Female,37.0,7.0,129431.36,1.0,1.0,0.0,72549.27,1.0
-2940,15607988.0,Chukwudi,756.0,France,Male,33.0,2.0,0.0,1.0,0.0,1.0,143960.72,0.0
-2941,15707025.0,Nwabugwu,674.0,Spain,Male,18.0,6.0,0.0,2.0,0.0,0.0,77326.78,0.0
-2942,15680449.0,Groom,659.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,157908.19,0.0
-2943,15705777.0,Li Fonti,717.0,Germany,Male,55.0,4.0,116121.64,1.0,1.0,0.0,171045.35,1.0
-2944,15728352.0,Palfreyman,678.0,Germany,Female,27.0,8.0,116870.12,1.0,1.0,0.0,158545.24,0.0
-2945,15746865.0,Nnamutaezinwa,706.0,France,Female,35.0,1.0,0.0,2.0,1.0,1.0,147797.54,0.0
-2946,15795874.0,Mazzanti,691.0,France,Female,44.0,10.0,0.0,1.0,1.0,1.0,180716.1,0.0
-2947,15757981.0,Pirozzi,678.0,Spain,Male,43.0,6.0,148897.02,1.0,1.0,1.0,100507.81,0.0
-2948,15691817.0,Chiazagomekpele,627.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,51972.92,0.0
-2949,15571529.0,Ch'eng,683.0,Germany,Male,55.0,6.0,138296.94,1.0,1.0,1.0,176913.42,0.0
-2950,15744273.0,Monds,785.0,Spain,Male,48.0,8.0,0.0,1.0,1.0,1.0,135089.32,0.0
-2951,15626156.0,Tsao,757.0,France,Male,31.0,8.0,0.0,2.0,0.0,0.0,162599.53,0.0
-2952,15789432.0,Fanucci,663.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,0.0,123982.02,0.0
-2953,15794560.0,Ponomarev,727.0,France,Female,37.0,9.0,0.0,1.0,0.0,0.0,100941.57,1.0
-2954,15603764.0,DeRose,699.0,Germany,Female,41.0,9.0,121513.31,1.0,1.0,1.0,68106.29,1.0
-2955,15662662.0,Hsiao,687.0,Spain,Male,37.0,8.0,161546.68,1.0,0.0,1.0,131177.22,0.0
-2956,15738751.0,Shao,709.0,France,Male,41.0,7.0,147530.06,2.0,1.0,0.0,138542.87,0.0
-2957,15658360.0,Pisani,718.0,Germany,Female,52.0,4.0,117700.86,1.0,1.0,0.0,91044.47,1.0
-2958,15642530.0,White,729.0,France,Male,45.0,7.0,139840.36,1.0,1.0,1.0,153566.97,0.0
-2959,15667632.0,Aksenova,738.0,Spain,Male,36.0,8.0,0.0,1.0,0.0,0.0,65868.28,0.0
-2960,15667424.0,Folliero,652.0,France,Female,44.0,1.0,0.0,2.0,1.0,0.0,152698.24,0.0
-2961,15775131.0,Chiemezie,712.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,115789.25,0.0
-2962,15802483.0,Milani,744.0,France,Male,24.0,6.0,0.0,1.0,1.0,0.0,95941.55,0.0
-2963,15775809.0,Walker,538.0,France,Female,42.0,8.0,105922.81,2.0,1.0,0.0,120512.08,0.0
-2964,15800515.0,Nwankwo,609.0,France,Male,36.0,5.0,104015.54,1.0,1.0,1.0,105568.6,0.0
-2965,15591257.0,Onyekachukwu,668.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,173843.21,0.0
-2966,15585855.0,Ting,699.0,France,Female,43.0,6.0,133871.5,1.0,1.0,0.0,171707.93,0.0
-2967,15686963.0,Ch'in,635.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,1375.46,0.0
-2968,15572361.0,Manna,651.0,France,Female,40.0,1.0,116537.6,1.0,0.0,0.0,51871.95,0.0
-2969,15676869.0,Nwabugwu,763.0,France,Male,35.0,5.0,116318.23,1.0,1.0,0.0,122218.61,0.0
-2970,15687913.0,Hao,707.0,Germany,Male,50.0,9.0,117864.85,2.0,1.0,0.0,90905.26,1.0
-2971,15680234.0,Lo,841.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,138526.96,0.0
-2972,15589380.0,Y?,684.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,125962.02,0.0
-2973,15686964.0,Chukwubuikem,648.0,Germany,Female,36.0,9.0,121263.62,1.0,1.0,1.0,115767.93,0.0
-2974,15685826.0,Trevisano,653.0,Germany,Male,52.0,2.0,105961.68,1.0,1.0,0.0,121239.65,1.0
-2975,15782530.0,Shen,581.0,Germany,Female,47.0,5.0,103543.11,1.0,1.0,0.0,167023.95,1.0
-2976,15622993.0,Tuan,637.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,141558.04,0.0
-2977,15763859.0,Chiu,516.0,Spain,Male,34.0,9.0,141827.33,1.0,1.0,0.0,161807.14,0.0
-2978,15633233.0,Manna,655.0,France,Female,31.0,6.0,0.0,1.0,1.0,1.0,168816.22,1.0
-2979,15681075.0,Folliero,633.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,163563.07,0.0
-2980,15574558.0,T'ien,731.0,France,Female,38.0,5.0,0.0,1.0,1.0,1.0,75664.56,1.0
-2981,15640034.0,Kao,641.0,Spain,Female,28.0,6.0,0.0,1.0,1.0,0.0,141453.62,0.0
-2982,15588560.0,Ch'in,705.0,France,Female,36.0,4.0,179960.2,1.0,1.0,0.0,149960.2,0.0
-2983,15715709.0,Cremonesi,717.0,Germany,Female,41.0,8.0,137781.65,2.0,0.0,0.0,86619.77,0.0
-2984,15762729.0,P'eng,673.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,168515.61,0.0
-2985,15706899.0,Chikwado,645.0,Spain,Female,29.0,7.0,0.0,2.0,0.0,1.0,120892.96,0.0
-2986,15598884.0,D'Albertis,712.0,Spain,Male,43.0,5.0,0.0,2.0,0.0,0.0,73979.86,0.0
-2987,15624595.0,Rizzo,709.0,Spain,Female,41.0,9.0,0.0,1.0,1.0,0.0,132798.95,1.0
-2988,15721730.0,H?,749.0,Germany,Male,41.0,2.0,126663.51,2.0,1.0,1.0,183566.87,0.0
-2989,15709994.0,Hargreaves,675.0,France,Male,49.0,2.0,117899.95,1.0,1.0,0.0,126475.79,1.0
-2990,15758056.0,Fomin,584.0,Spain,Male,50.0,8.0,0.0,1.0,1.0,0.0,147955.91,1.0
-2991,15696819.0,Genovese,731.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,102747.02,0.0
-2992,15781589.0,Onyekaozulu,672.0,Germany,Male,52.0,3.0,103457.76,2.0,0.0,1.0,137720.56,1.0
-2993,15699309.0,Lo Duca,683.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,53477.76,0.0
-2994,15690958.0,P'eng,609.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,82526.92,0.0
-2995,15609823.0,Okonkwo,664.0,France,Female,35.0,8.0,121704.32,1.0,1.0,1.0,152956.33,0.0
-2996,15597939.0,Clements,671.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,76510.46,0.0
-2997,15731519.0,Shao,709.0,Spain,Female,46.0,7.0,0.0,2.0,1.0,1.0,170138.68,1.0
-2998,15679813.0,Yobanna,515.0,France,Female,35.0,2.0,90538.51,1.0,1.0,0.0,51536.99,1.0
-2999,15771535.0,Chiemela,667.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,60778.11,0.0
-3000,15657778.0,Iadanza,635.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,107890.69,0.0
-3001,15671139.0,McIntosh,652.0,France,Female,31.0,6.0,0.0,1.0,1.0,0.0,145558.24,1.0
-3002,15624180.0,Onyemauchechukwu,709.0,France,Female,40.0,5.0,155726.85,1.0,1.0,1.0,161229.84,1.0
-3003,15671779.0,Uvarova,645.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,0.0,121231.39,0.0
-3004,15769246.0,Chiawuotu,641.0,France,Female,32.0,7.0,0.0,2.0,0.0,0.0,140168.36,0.0
-3005,15738018.0,Lo,677.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,140889.12,0.0
-3006,15686463.0,Manna,509.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,186984.34,0.0
-3007,15690188.0,Lucciano,712.0,Spain,Female,32.0,7.0,151675.65,1.0,1.0,1.0,123982.14,0.0
-3008,15640074.0,Ch'in,544.0,France,Male,41.0,4.0,0.0,1.0,1.0,1.0,96420.58,1.0
-3009,15718893.0,T'ien,677.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,125302.64,0.0
-3010,15775124.0,Okwuoma,613.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,90821.66,0.0
-3011,15807989.0,Genovesi,744.0,France,Female,40.0,10.0,0.0,2.0,0.0,1.0,94767.77,0.0
-3012,15771059.0,Uvarova,641.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,156774.94,0.0
-3013,15651955.0,Onochie,705.0,France,Male,48.0,3.0,0.0,2.0,1.0,0.0,119814.25,0.0
-3014,15654574.0,Kao,584.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,88920.68,0.0
-3015,15774192.0,Martin,653.0,France,Female,55.0,6.0,112494.99,1.0,0.0,0.0,170968.99,1.0
-3016,15633059.0,Chukwujamuike,670.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,164815.59,0.0
-3017,15727556.0,Chidubem,709.0,France,Male,52.0,10.0,0.0,1.0,1.0,1.0,80587.27,1.0
-3018,15779743.0,Nwankwo,642.0,France,Female,41.0,2.0,0.0,2.0,0.0,0.0,28415.36,0.0
-3019,15641833.0,Onwudiwe,624.0,Germany,Male,31.0,1.0,140511.85,1.0,1.0,1.0,115611.97,0.0
-3020,15686963.0,Pinto,741.0,France,Male,51.0,1.0,0.0,2.0,0.0,1.0,121402.72,0.0
-3021,15573077.0,Onwuamaegbu,586.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,62940.99,0.0
-3022,15578980.0,Chukwudi,586.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,1.0,166513.49,0.0
-3023,15588517.0,Tung,593.0,France,Female,41.0,4.0,106290.64,1.0,0.0,0.0,136877.31,0.0
-3024,15751147.0,Zito,684.0,France,Male,41.0,5.0,129731.64,1.0,1.0,1.0,98293.14,0.0
-3025,15719276.0,Hsia,627.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,60972.84,0.0
-3026,15636562.0,Ch'en,651.0,France,Male,40.0,2.0,0.0,2.0,0.0,0.0,74870.99,0.0
-3027,15657747.0,Hsia,705.0,France,Male,46.0,3.0,0.0,2.0,1.0,0.0,131473.31,0.0
-3028,15694558.0,Hsueh,718.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,0.0,86935.08,0.0
-3029,15687558.0,Hsueh,774.0,Spain,Male,47.0,5.0,0.0,2.0,1.0,1.0,53581.14,0.0
-3030,15793741.0,Ch'eng,757.0,France,Male,36.0,8.0,0.0,2.0,0.0,0.0,138871.12,0.0
-3031,15762543.0,Chinweike,633.0,Spain,Male,57.0,4.0,0.0,1.0,1.0,1.0,137029.97,1.0
-3032,15765952.0,Chimaobim,646.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,193018.51,0.0
-3033,15613144.0,Kao,589.0,France,Male,44.0,8.0,0.0,2.0,1.0,1.0,60778.11,0.0
-3034,15578980.0,Ch'ang,637.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,0.0,134778.01,0.0
-3035,15741554.0,Chiu,593.0,France,Female,43.0,8.0,0.0,2.0,0.0,0.0,100835.19,0.0
-3036,15670066.0,Okwudilichukwu,622.0,Germany,Female,36.0,3.0,139855.64,1.0,1.0,1.0,167873.49,1.0
-3037,15635972.0,Onyemere,638.0,France,Female,52.0,9.0,0.0,1.0,1.0,0.0,136377.21,1.0
-3038,15618258.0,Chibugo,587.0,France,Male,38.0,3.0,76914.21,1.0,1.0,0.0,148087.88,0.0
-3039,15690175.0,Ts'ui,762.0,France,Male,33.0,2.0,121405.04,2.0,1.0,0.0,123437.05,0.0
-3040,15644878.0,Uspenskaya,703.0,France,Female,40.0,0.0,0.0,1.0,1.0,0.0,150548.5,1.0
-3041,15661473.0,Chin,561.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,110899.3,0.0
-3042,15644788.0,Pirozzi,671.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,94450.97,0.0
-3043,15726354.0,Y?,726.0,France,Male,44.0,10.0,145754.91,1.0,1.0,1.0,66795.78,0.0
-3044,15668853.0,Baresi,850.0,Spain,Female,48.0,9.0,0.0,1.0,1.0,0.0,139435.17,1.0
-3045,15773869.0,Ch'ien,779.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,86733.61,0.0
-3046,15644896.0,Ch'en,679.0,Germany,Female,30.0,7.0,112291.34,2.0,1.0,0.0,164741.5,1.0
-3047,15696900.0,Obialo,850.0,Spain,Female,39.0,1.0,0.0,2.0,1.0,0.0,48201.64,0.0
-3048,15729246.0,Lucchese,627.0,Spain,Male,27.0,0.0,0.0,2.0,1.0,1.0,124626.57,0.0
-3049,15679611.0,Oster,659.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,146041.45,0.0
-3050,15770101.0,Scott,733.0,Germany,Female,25.0,5.0,131644.88,2.0,1.0,0.0,133871.83,0.0
-3051,15679743.0,Azubuike,521.0,Germany,Male,56.0,0.0,132688.58,2.0,1.0,1.0,26920.43,1.0
-3052,15641660.0,Chikezie,716.0,Spain,Male,36.0,1.0,82293.82,1.0,1.0,1.0,182728.8,0.0
-3053,15730590.0,Greco,652.0,Germany,Female,37.0,4.0,123034.43,1.0,1.0,1.0,106036.22,0.0
-3054,15694125.0,McLean,559.0,France,Female,41.0,3.0,0.0,1.0,1.0,0.0,175743.92,1.0
-3055,15574886.0,Hsueh,634.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,75888.65,0.0
-3056,15809800.0,Okechukwu,571.0,France,Male,52.0,1.0,0.0,1.0,1.0,0.0,102799.14,1.0
-3057,15591512.0,Onyemachukwu,675.0,France,Male,28.0,9.0,0.0,2.0,0.0,1.0,138542.94,0.0
-3058,15780254.0,Chidozie,668.0,France,Male,30.0,7.0,0.0,2.0,0.0,1.0,167778.62,0.0
-3059,15772412.0,Gresswell,676.0,Germany,Female,31.0,7.0,134571.5,1.0,1.0,0.0,194808.51,1.0
-3060,15746594.0,T'ang,719.0,France,Female,28.0,4.0,0.0,2.0,1.0,1.0,56905.38,0.0
-3061,15703394.0,Shao,628.0,France,Female,41.0,3.0,0.0,1.0,0.0,1.0,136330.32,1.0
-3062,15741658.0,Onyemere,543.0,Germany,Male,35.0,5.0,127772.1,2.0,1.0,0.0,189703.65,0.0
-3063,15666884.0,H?,651.0,France,Female,64.0,4.0,0.0,2.0,0.0,1.0,57850.1,0.0
-3064,15659428.0,Chiang,661.0,Spain,Male,38.0,5.0,0.0,1.0,1.0,1.0,56780.97,0.0
-3065,15651664.0,Calabrese,545.0,France,Female,34.0,9.0,161371.5,1.0,1.0,0.0,174822.74,0.0
-3066,15745088.0,Chidiebere,652.0,Spain,Female,26.0,0.0,0.0,2.0,0.0,0.0,73977.23,0.0
-3067,15631823.0,Ch'iu,803.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,121763.72,0.0
-3068,15706637.0,Monds,718.0,Germany,Male,37.0,1.0,106425.75,2.0,1.0,1.0,131807.14,0.0
-3069,15615352.0,Yefimova,624.0,France,Female,38.0,1.0,0.0,2.0,0.0,0.0,128841.31,0.0
-3070,15623989.0,Milano,593.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,122663.63,0.0
-3071,15603764.0,White,682.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,161388.22,0.0
-3072,15579647.0,Hs?eh,608.0,Spain,Female,47.0,5.0,0.0,2.0,1.0,1.0,61290.99,0.0
-3073,15786063.0,Trevisano,705.0,Germany,Male,43.0,5.0,141223.68,1.0,1.0,1.0,183572.39,0.0
-3074,15696098.0,Palerma,587.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,91573.19,0.0
-3075,15662533.0,Chibueze,746.0,France,Male,34.0,9.0,0.0,1.0,1.0,0.0,137959.0,0.0
-3076,15647838.0,Hsueh,684.0,Germany,Male,43.0,4.0,162039.59,1.0,0.0,0.0,118188.15,1.0
-3077,15593976.0,Chinwemma,635.0,France,Male,47.0,2.0,0.0,2.0,1.0,1.0,83090.35,0.0
-3078,15740175.0,Chien,828.0,France,Female,43.0,9.0,0.0,2.0,1.0,0.0,126812.63,0.0
-3079,15690829.0,Hsu,545.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,125870.79,0.0
-3080,15705953.0,Ch'in,714.0,France,Female,29.0,5.0,162923.85,1.0,0.0,0.0,125041.52,0.0
-3081,15662063.0,Chiekwugo,661.0,France,Female,33.0,3.0,141786.78,1.0,1.0,1.0,5791.85,0.0
-3082,15629750.0,Scott,636.0,Germany,Male,44.0,1.0,135096.77,1.0,1.0,1.0,149536.15,0.0
-3083,15701376.0,Tsou,554.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,1.0,119377.36,0.0
-3084,15725794.0,Chin,596.0,France,Male,35.0,10.0,0.0,1.0,1.0,1.0,114775.53,0.0
-3085,15632143.0,Onuoha,667.0,Germany,Male,56.0,2.0,120171.58,1.0,1.0,0.0,97623.42,1.0
-3086,15776936.0,Genovese,567.0,Spain,Female,45.0,2.0,0.0,1.0,1.0,0.0,138889.15,1.0
-3087,15585360.0,Chukwuemeka,774.0,Spain,Male,33.0,6.0,0.0,2.0,0.0,1.0,80582.36,0.0
-3088,15702464.0,Chizoba,565.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,153535.35,0.0
-3089,15752506.0,Calabresi,596.0,France,Male,34.0,7.0,0.0,1.0,1.0,1.0,73066.73,0.0
-3090,15793671.0,Fan,690.0,Spain,Male,35.0,6.0,90964.57,2.0,1.0,1.0,180551.76,0.0
-3091,15770185.0,Hs?,775.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,86981.45,0.0
-3092,15784491.0,Ts'ao,645.0,France,Male,42.0,3.0,0.0,1.0,0.0,0.0,67840.81,1.0
-3093,15689031.0,Ugochukwutubelum,843.0,France,Female,43.0,8.0,104784.23,1.0,1.0,1.0,151871.83,0.0
-3094,15602929.0,Chien,708.0,Spain,Female,56.0,9.0,0.0,1.0,1.0,0.0,160110.65,1.0
-3095,15682533.0,McElhone,584.0,France,Male,61.0,4.0,160903.2,1.0,1.0,0.0,89892.66,1.0
-3096,15770174.0,Oguejiofor,683.0,Spain,Male,46.0,5.0,94416.78,1.0,1.0,0.0,89801.9,0.0
-3097,15692664.0,Lo Duca,588.0,Spain,Male,28.0,3.0,0.0,2.0,1.0,0.0,140881.2,0.0
-3098,15583718.0,Chibueze,754.0,Germany,Female,38.0,9.0,100900.5,2.0,1.0,0.0,173576.71,1.0
-3099,15644501.0,Chu,646.0,France,Female,38.0,7.0,103391.38,2.0,0.0,1.0,71154.1,0.0
-3100,15790829.0,Udobata,590.0,France,Female,41.0,1.0,0.0,1.0,1.0,1.0,26257.01,1.0
-3101,15652693.0,Chin,557.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,0.0,88827.67,1.0
-3102,15808023.0,Ch'in,717.0,Spain,Female,29.0,6.0,0.0,2.0,1.0,0.0,170195.4,0.0
-3103,15780561.0,Kibby,595.0,Germany,Female,41.0,10.0,123356.63,1.0,0.0,1.0,113046.81,1.0
-3104,15794566.0,Pisano,642.0,Germany,Female,46.0,2.0,116933.11,2.0,1.0,0.0,176943.59,1.0
-3105,15626383.0,Ni,850.0,France,Male,45.0,5.0,98906.22,1.0,1.0,0.0,107964.36,0.0
-3106,15612207.0,Pisani,578.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,71450.92,0.0
-3107,15679307.0,Nwora,696.0,Spain,Male,53.0,6.0,0.0,2.0,1.0,1.0,143635.52,0.0
-3108,15658969.0,Ch'ien,635.0,Spain,Female,44.0,1.0,0.0,1.0,1.0,1.0,156774.94,1.0
-3109,15798844.0,Yobachukwu,543.0,Germany,Female,45.0,7.0,125827.43,1.0,1.0,0.0,109257.59,1.0
-3110,15726313.0,T'ien,581.0,Germany,Male,39.0,2.0,125837.86,2.0,0.0,0.0,90928.02,0.0
-3111,15721917.0,Pardey,599.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,80619.09,0.0
-3112,15644896.0,Ijendu,624.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,43982.41,0.0
-3113,15592387.0,Ndubuisi,667.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,158891.79,0.0
-3114,15743016.0,Chin,584.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,100834.22,0.0
-3115,15721785.0,Kao,695.0,Germany,Female,35.0,8.0,79035.95,1.0,0.0,1.0,186945.45,0.0
-3116,15754569.0,Piazza,727.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,166929.43,0.0
-3117,15659399.0,Kudryashova,581.0,France,Male,31.0,10.0,0.0,2.0,0.0,1.0,36308.93,0.0
-3118,15701602.0,Hsiao,696.0,Germany,Male,37.0,8.0,124580.69,2.0,1.0,1.0,89801.9,0.0
-3119,15606436.0,Onwumelu,775.0,Spain,Male,47.0,3.0,0.0,3.0,0.0,1.0,104769.94,1.0
-3120,15674929.0,Hsiung,807.0,France,Female,45.0,8.0,0.0,2.0,0.0,0.0,143683.75,1.0
-3121,15642655.0,Watson,667.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,1.0,162599.51,0.0
-3122,15700540.0,Hargreaves,686.0,Germany,Male,46.0,6.0,97435.85,2.0,0.0,1.0,58844.84,0.0
-3123,15682453.0,Yeh,714.0,Spain,Female,48.0,9.0,0.0,1.0,1.0,1.0,107876.2,1.0
-3124,15633194.0,Mazzanti,679.0,France,Male,57.0,9.0,0.0,2.0,1.0,1.0,76510.46,0.0
-3125,15784320.0,Chiu,739.0,Germany,Male,35.0,6.0,104533.14,2.0,0.0,1.0,79919.13,0.0
-3126,15671345.0,Yevseyev,562.0,France,Male,36.0,4.0,0.0,1.0,1.0,0.0,146875.86,0.0
-3127,15693906.0,Genovese,706.0,France,Male,33.0,1.0,104469.58,1.0,0.0,0.0,122066.5,0.0
-3128,15643908.0,Hsiung,584.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,151146.67,0.0
-3129,15725698.0,Shih,755.0,Germany,Female,44.0,4.0,107712.57,1.0,1.0,0.0,180800.42,1.0
-3130,15803840.0,Kao,602.0,France,Female,28.0,9.0,0.0,2.0,1.0,1.0,184936.77,0.0
-3131,15741032.0,Young,583.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,152273.57,0.0
-3132,15741643.0,Genovese,539.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,173870.39,0.0
-3133,15686913.0,Chiawuotu,595.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,158313.87,0.0
-3134,15768474.0,Ch'ien,568.0,Germany,Male,42.0,6.0,113876.24,1.0,1.0,1.0,175823.71,0.0
-3135,15617320.0,Liao,682.0,France,Male,32.0,8.0,0.0,2.0,0.0,0.0,175743.92,0.0
-3136,15582033.0,Genovese,641.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,92357.21,0.0
-3137,15591724.0,Tien,729.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,101274.17,0.0
-3138,15652401.0,Nwachukwu,716.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,160524.36,0.0
-3139,15602811.0,Nwoye,646.0,Germany,Female,42.0,8.0,135962.4,1.0,1.0,0.0,138022.51,1.0
-3140,15750396.0,Y?an,569.0,France,Male,50.0,7.0,0.0,2.0,1.0,1.0,60920.03,0.0
-3141,15763898.0,Chibueze,627.0,France,Male,31.0,9.0,92618.62,1.0,1.0,1.0,168822.01,0.0
-3142,15770984.0,Chiang,559.0,Germany,Female,41.0,2.0,77846.9,2.0,1.0,0.0,125553.52,0.0
-3143,15781298.0,Smith,464.0,Germany,Female,33.0,5.0,128366.44,2.0,1.0,1.0,149853.89,0.0
-3144,15656822.0,Ch'in,588.0,France,Male,27.0,3.0,0.0,2.0,1.0,1.0,18094.96,0.0
-3145,15672437.0,Nnamutaezinwa,629.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,172497.73,0.0
-3146,15731989.0,Okwuadigbo,587.0,Spain,Female,39.0,6.0,0.0,2.0,0.0,0.0,113926.71,0.0
-3147,15586479.0,Chiu,463.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,106977.8,0.0
-3148,15605970.0,L?,681.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,131301.74,0.0
-3149,15585853.0,Babbage,588.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,172490.78,0.0
-3150,15600462.0,Chiazagomekpere,581.0,Spain,Female,40.0,2.0,0.0,1.0,1.0,0.0,136071.09,1.0
-3151,15772383.0,Hsiao,739.0,Spain,Male,51.0,2.0,0.0,1.0,0.0,1.0,162515.17,1.0
-3152,15641655.0,Nkemakonam,691.0,Spain,Male,36.0,10.0,0.0,2.0,1.0,0.0,62282.99,0.0
-3153,15645496.0,Ch'in,754.0,France,Male,54.0,8.0,0.0,1.0,1.0,1.0,111622.76,0.0
-3154,15720943.0,Chinwendu,645.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,173386.95,0.0
-3155,15806945.0,Chin,642.0,Spain,Male,42.0,2.0,0.0,1.0,1.0,1.0,100556.98,0.0
-3156,15632576.0,Shen,587.0,Germany,Male,30.0,6.0,109974.22,1.0,1.0,0.0,133693.88,0.0
-3157,15737506.0,Chinweuba,526.0,France,Female,28.0,4.0,98630.31,1.0,0.0,0.0,59816.41,0.0
-3158,15589320.0,Fomin,535.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,96670.2,1.0
-3159,15601012.0,Nwagugheuzo,633.0,Germany,Female,39.0,5.0,114655.58,2.0,1.0,0.0,48778.46,1.0
-3160,15739152.0,Shih,639.0,France,Female,36.0,5.0,0.0,1.0,1.0,1.0,169915.02,0.0
-3161,15766017.0,Kornilova,623.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,176868.89,0.0
-3162,15569572.0,Ch'ien,726.0,Germany,Female,34.0,8.0,134542.73,2.0,1.0,1.0,173784.04,0.0
-3163,15674727.0,Eluemuno,543.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,1.0,78970.58,0.0
-3164,15794560.0,Hsiao,849.0,France,Male,43.0,5.0,0.0,2.0,0.0,1.0,55775.72,0.0
-3165,15576676.0,Ch'en,850.0,Germany,Female,49.0,7.0,135814.7,1.0,0.0,0.0,49815.62,1.0
-3166,15653547.0,Trevisani,709.0,France,Female,37.0,9.0,0.0,1.0,0.0,0.0,126381.6,1.0
-3167,15684318.0,Chibueze,744.0,France,Male,40.0,5.0,81480.27,1.0,1.0,0.0,93332.64,1.0
-3168,15651740.0,Hs?,712.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,148516.79,0.0
-3169,15689616.0,Nkemdilim,726.0,Spain,Male,40.0,1.0,117640.38,1.0,1.0,0.0,159830.58,0.0
-3170,15691863.0,Chigozie,646.0,Germany,Male,52.0,5.0,131394.56,1.0,1.0,1.0,143668.28,1.0
-3171,15590820.0,Uvarova,573.0,France,Female,38.0,9.0,0.0,2.0,0.0,1.0,94440.45,0.0
-3172,15678882.0,Hsiung,662.0,Spain,Female,27.0,9.0,0.0,2.0,1.0,1.0,47886.44,0.0
-3173,15628298.0,Ch'iu,745.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,92357.21,0.0
-3174,15646569.0,O'Brien,554.0,France,Female,30.0,9.0,0.0,1.0,1.0,0.0,175894.24,0.0
-3175,15603481.0,Nkemakonam,752.0,France,Female,51.0,1.0,136596.85,1.0,1.0,0.0,79355.87,1.0
-3176,15764897.0,Tung,683.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,1.0,168515.61,0.0
-3177,15687634.0,Ozioma,651.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,140134.88,0.0
-3178,15696826.0,Tsao,640.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,1.0,78650.95,0.0
-3179,15661821.0,Chiu,798.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,194998.34,0.0
-3180,15644572.0,T'ang,645.0,Germany,Male,47.0,6.0,120591.19,1.0,0.0,0.0,146891.07,1.0
-3181,15684461.0,Zito,565.0,Spain,Male,52.0,4.0,82674.15,1.0,1.0,0.0,136620.64,1.0
-3182,15574324.0,Onyeorulu,635.0,France,Male,45.0,2.0,0.0,1.0,0.0,0.0,25881.0,1.0
-3183,15589969.0,Ch'ang,763.0,France,Female,26.0,0.0,0.0,2.0,0.0,0.0,111324.41,0.0
-3184,15720508.0,Shao,553.0,France,Female,38.0,8.0,0.0,1.0,1.0,0.0,161574.19,1.0
-3185,15587978.0,Chikelu,706.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,129526.57,0.0
-3186,15686913.0,Okwudilichukwu,642.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,157097.09,0.0
-3187,15691483.0,Fanucci,814.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,53437.1,0.0
-3188,15754574.0,Hsia,724.0,France,Male,30.0,9.0,0.0,1.0,1.0,0.0,166957.82,0.0
-3189,15687431.0,Nkemdilim,646.0,Spain,Female,44.0,5.0,0.0,3.0,1.0,0.0,1528.4,1.0
-3190,15658067.0,Hsiao,665.0,Spain,Male,36.0,5.0,84477.33,1.0,1.0,1.0,167873.55,0.0
-3191,15743617.0,Chiang,731.0,Germany,Female,49.0,4.0,121172.97,1.0,1.0,0.0,173586.63,1.0
-3192,15642515.0,O'Meara,554.0,France,Male,34.0,1.0,148061.89,1.0,0.0,0.0,166423.66,0.0
-3193,15713890.0,Chien,708.0,France,Male,25.0,7.0,0.0,2.0,1.0,1.0,81778.72,0.0
-3194,15595766.0,Chibueze,484.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,199992.48,0.0
-3195,15570379.0,Y?an,739.0,France,Female,36.0,1.0,0.0,2.0,1.0,1.0,152844.64,0.0
-3196,15741403.0,Nwabugwu,607.0,Spain,Female,58.0,10.0,0.0,2.0,1.0,1.0,135034.57,0.0
-3197,15731553.0,Chijindum,711.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,158750.53,0.0
-3198,15772421.0,O'Brien,717.0,France,Female,33.0,2.0,152462.79,1.0,1.0,1.0,89048.46,0.0
-3199,15582841.0,Nnamdi,582.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,12761.16,0.0
-3200,15686461.0,Onyemauchechukwu,732.0,Germany,Female,29.0,3.0,130682.11,2.0,0.0,0.0,56794.73,0.0
-3201,15755084.0,Ts'ui,545.0,Spain,Female,48.0,7.0,0.0,1.0,1.0,1.0,129870.93,1.0
-3202,15693732.0,Iqbal,578.0,Spain,Male,68.0,6.0,90124.64,1.0,1.0,0.0,63747.73,1.0
-3203,15636454.0,Zetticci,725.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,140168.36,0.0
-3204,15672894.0,Ulyanova,607.0,Spain,Female,32.0,7.0,145537.21,1.0,1.0,0.0,174098.28,0.0
-3205,15754950.0,Loyau,662.0,France,Male,37.0,2.0,83568.55,1.0,1.0,0.0,107876.2,1.0
-3206,15775797.0,Hargreaves,651.0,Germany,Male,38.0,2.0,161591.11,2.0,0.0,1.0,151871.84,0.0
-3207,15657779.0,Olisaemeka,738.0,Spain,Male,41.0,2.0,133964.3,1.0,0.0,1.0,177844.06,0.0
-3208,15745307.0,Houghton,738.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,94845.54,0.0
-3209,15675888.0,Trevisani,743.0,France,Male,51.0,7.0,108377.75,1.0,1.0,1.0,126728.27,1.0
-3210,15698932.0,Nnamutaezinwa,586.0,Spain,Male,30.0,10.0,0.0,2.0,1.0,1.0,80619.09,0.0
-3211,15651336.0,Onio,730.0,Germany,Female,41.0,1.0,121527.4,1.0,1.0,0.0,126794.56,1.0
-3212,15804586.0,Pirozzi,663.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,86935.08,0.0
-3213,15707597.0,Chinweike,592.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,70954.41,1.0
-3214,15732643.0,Kao,709.0,France,Female,50.0,3.0,0.0,1.0,1.0,0.0,162889.1,1.0
-3215,15724307.0,Lucchese,619.0,France,Female,32.0,8.0,0.0,1.0,1.0,1.0,113086.08,0.0
-3216,15572467.0,Ch'in,717.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,187616.16,0.0
-3217,15692819.0,Calabresi,567.0,France,Male,66.0,4.0,0.0,2.0,1.0,1.0,183587.12,0.0
-3218,15589435.0,Nwachukwu,554.0,France,Male,26.0,6.0,0.0,2.0,0.0,1.0,172450.04,0.0
-3219,15692540.0,Lu,751.0,Spain,Male,48.0,6.0,0.0,2.0,1.0,0.0,141872.05,0.0
-3220,15793331.0,Brown,705.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,126796.69,0.0
-3221,15585580.0,Chukwuemeka,716.0,Spain,Male,33.0,4.0,0.0,2.0,0.0,0.0,138880.24,0.0
-3222,15616172.0,Ts'ao,762.0,France,Male,41.0,1.0,0.0,2.0,0.0,0.0,10963.04,0.0
-3223,15764284.0,Hs?,634.0,France,Male,44.0,1.0,0.0,2.0,0.0,0.0,72350.17,0.0
-3224,15772996.0,Yevseyev,850.0,Germany,Female,41.0,8.0,111388.18,2.0,1.0,1.0,147720.27,0.0
-3225,15592461.0,Chizoba,544.0,Spain,Male,28.0,9.0,0.0,2.0,1.0,0.0,164925.32,0.0
-3226,15656597.0,Smith,586.0,France,Male,27.0,5.0,100013.51,1.0,1.0,0.0,57876.05,0.0
-3227,15781702.0,Hsiung,744.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,109843.16,0.0
-3228,15806918.0,Onwudiwe,696.0,Germany,Female,21.0,5.0,141074.59,2.0,1.0,1.0,67979.35,0.0
-3229,15675650.0,Houghton,710.0,France,Female,45.0,8.0,0.0,2.0,1.0,1.0,142797.5,0.0
-3230,15768449.0,H?,776.0,France,Female,18.0,7.0,132946.89,1.0,1.0,0.0,74415.2,0.0
-3231,15789560.0,Onwubiko,582.0,France,Male,48.0,7.0,0.0,1.0,1.0,0.0,175314.87,1.0
-3232,15605293.0,Wright,756.0,France,Female,40.0,4.0,0.0,2.0,0.0,0.0,103970.58,1.0
-3233,15751131.0,Iadanza,709.0,France,Male,27.0,8.0,0.0,1.0,1.0,0.0,133692.88,0.0
-3234,15604536.0,Onyeoruru,676.0,Germany,Male,36.0,5.0,93522.3,1.0,1.0,0.0,198269.08,0.0
-3235,15587592.0,Hsia,657.0,Spain,Male,55.0,7.0,0.0,1.0,1.0,1.0,182531.74,1.0
-3236,15693893.0,Trevisani,589.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,447.73,0.0
-3237,15744398.0,Lucciano,775.0,France,Female,53.0,2.0,0.0,1.0,1.0,1.0,166692.03,1.0
-3238,15691883.0,Ch'eng,573.0,France,Female,52.0,7.0,0.0,1.0,1.0,1.0,169935.46,1.0
-3239,15689517.0,Chukwumaobim,602.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,185547.71,0.0
-3240,15722583.0,Fanucci,662.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,161479.19,0.0
-3241,15615457.0,Y?an,678.0,Germany,Male,25.0,8.0,121405.04,2.0,1.0,0.0,138889.97,0.0
-3242,15793893.0,Tobeolisa,647.0,France,Male,40.0,8.0,0.0,1.0,1.0,1.0,64839.23,1.0
-3243,15798964.0,Udinese,746.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,1.0,70954.41,0.0
-3244,15682890.0,Okwuadigbo,678.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,176712.59,0.0
-3245,15593636.0,Kao,749.0,Spain,Male,33.0,4.0,0.0,2.0,0.0,0.0,117034.32,0.0
-3246,15707637.0,Chidiebere,602.0,France,Male,37.0,7.0,164951.56,1.0,1.0,1.0,125762.95,0.0
-3247,15576022.0,Hsiung,726.0,France,Female,30.0,8.0,0.0,2.0,1.0,1.0,158053.4,0.0
-3248,15585595.0,Onyemachukwu,545.0,Spain,Male,44.0,5.0,115465.98,1.0,1.0,0.0,185210.63,1.0
-3249,15715297.0,Rowntree,703.0,Germany,Male,35.0,5.0,160599.06,2.0,1.0,1.0,155840.33,0.0
-3250,15698664.0,Tung,531.0,France,Male,42.0,2.0,0.0,2.0,0.0,0.0,94876.65,0.0
-3251,15713374.0,McIntosh,766.0,Germany,Female,55.0,5.0,141806.0,1.0,1.0,1.0,73679.59,1.0
-3252,15729689.0,Chibueze,679.0,France,Male,45.0,5.0,0.0,2.0,0.0,0.0,124592.84,0.0
-3253,15592707.0,Uspensky,667.0,France,Female,29.0,3.0,0.0,1.0,1.0,0.0,134874.45,0.0
-3254,15578045.0,Chineze,753.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,87566.97,0.0
-3255,15687765.0,Chiazagomekpere,648.0,Germany,Female,44.0,1.0,102989.83,1.0,1.0,0.0,136886.86,1.0
-3256,15689517.0,Onuora,706.0,France,Male,29.0,1.0,0.0,2.0,0.0,0.0,71862.79,0.0
-3257,15593088.0,Pagnotto,557.0,Germany,Male,41.0,9.0,99842.5,2.0,1.0,1.0,186476.91,0.0
-3258,15738487.0,McLean,567.0,Spain,Male,40.0,9.0,0.0,1.0,1.0,0.0,110783.28,1.0
-3259,15736112.0,Tien,554.0,France,Female,51.0,2.0,0.0,1.0,1.0,0.0,89520.75,1.0
-3260,15743478.0,Ch'iu,733.0,Germany,Male,35.0,4.0,108760.27,1.0,1.0,1.0,93844.89,1.0
-3261,15676966.0,Eluemuno,523.0,Germany,Female,39.0,8.0,108924.33,1.0,1.0,0.0,145937.99,1.0
-3262,15578825.0,Ndubuisi,656.0,Spain,Female,57.0,7.0,0.0,2.0,1.0,1.0,164975.82,0.0
-3263,15810454.0,Chidubem,596.0,Spain,Female,43.0,4.0,0.0,2.0,0.0,1.0,140168.36,0.0
-3264,15667633.0,Mazzanti,685.0,France,Male,25.0,2.0,137464.04,1.0,0.0,0.0,148087.62,0.0
-3265,15589435.0,Chukwuemeka,739.0,Germany,Male,42.0,6.0,111432.13,2.0,1.0,0.0,132719.8,0.0
-3266,15690772.0,Gadsdon,666.0,France,Female,35.0,0.0,0.0,3.0,1.0,0.0,159418.1,1.0
-3267,15690526.0,Iweobiegbunam,661.0,Spain,Male,33.0,10.0,0.0,1.0,1.0,1.0,152417.97,0.0
-3268,15661827.0,Ch'iu,683.0,Germany,Male,54.0,10.0,110783.28,1.0,0.0,0.0,45422.13,1.0
-3269,15656912.0,Johnson,775.0,France,Female,41.0,7.0,156569.21,1.0,0.0,0.0,172096.08,0.0
-3270,15632575.0,White,622.0,France,Male,34.0,4.0,168094.01,2.0,1.0,1.0,174531.27,0.0
-3271,15693877.0,Pinto,545.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,0.0,113481.02,0.0
-3272,15718789.0,Fennell,686.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,105969.05,0.0
-3273,15685370.0,Chukwukadibia,674.0,France,Male,35.0,3.0,0.0,2.0,0.0,1.0,98894.64,0.0
-3274,15772423.0,Ch'ien,653.0,France,Female,30.0,2.0,0.0,2.0,1.0,0.0,184564.67,0.0
-3275,15732266.0,Kable,586.0,France,Female,35.0,6.0,0.0,2.0,0.0,1.0,172524.51,0.0
-3276,15757837.0,Chien,695.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,90877.76,0.0
-3277,15628600.0,Mazzanti,755.0,France,Male,36.0,7.0,145981.87,2.0,1.0,1.0,54495.82,0.0
-3278,15730137.0,Folliero,763.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,183494.87,0.0
-3279,15631070.0,Onio,678.0,Germany,Male,37.0,6.0,151955.59,2.0,1.0,1.0,121453.83,0.0
-3280,15777046.0,O'Sullivan,627.0,Germany,Male,32.0,4.0,177728.55,2.0,1.0,1.0,64421.02,0.0
-3281,15806901.0,Ch'ien,733.0,Germany,Male,45.0,9.0,109844.81,1.0,1.0,1.0,52876.41,0.0
-3282,15642870.0,Hsieh,619.0,France,Female,45.0,9.0,123916.67,1.0,1.0,1.0,163573.06,1.0
-3283,15711219.0,Okeke,738.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,121840.2,0.0
-3284,15788659.0,Chukwueloka,746.0,France,Female,45.0,6.0,0.0,2.0,1.0,1.0,155879.57,0.0
-3285,15593973.0,Nwachinemelu,682.0,France,Female,51.0,1.0,0.0,1.0,1.0,1.0,132973.18,1.0
-3286,15729763.0,Hsing,659.0,Germany,Female,42.0,4.0,151914.93,1.0,1.0,0.0,183980.56,1.0
-3287,15687540.0,Ch'iu,592.0,Germany,Male,41.0,7.0,152812.58,1.0,1.0,1.0,135096.78,0.0
-3288,15779743.0,Ts'ui,628.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,0.0,110916.15,0.0
-3289,15759717.0,Biryukova,749.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,12913.92,0.0
-3290,15684669.0,Ts'ai,611.0,France,Female,46.0,6.0,0.0,1.0,1.0,0.0,146652.01,1.0
-3291,15708046.0,Maclean,735.0,Spain,Male,42.0,8.0,0.0,1.0,1.0,0.0,152698.24,1.0
-3292,15587913.0,Bock,621.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,142838.64,0.0
-3293,15792360.0,Chizuoke,681.0,Spain,Male,44.0,6.0,0.0,1.0,0.0,1.0,74430.16,0.0
-3294,15637169.0,Sagese,721.0,Spain,Female,42.0,5.0,0.0,1.0,1.0,0.0,122925.44,1.0
-3295,15594878.0,T'ang,739.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,1.0,111981.19,0.0
-3296,15766776.0,White,634.0,France,Female,43.0,5.0,164902.43,1.0,0.0,0.0,59601.15,0.0
-3297,15659736.0,Ch'iu,559.0,Spain,Female,31.0,0.0,0.0,2.0,1.0,0.0,88866.92,0.0
-3298,15610383.0,Manna,682.0,France,Male,25.0,9.0,77910.23,1.0,1.0,0.0,81718.37,0.0
-3299,15596808.0,Chien,676.0,France,Male,31.0,6.0,161613.94,1.0,1.0,1.0,45424.13,0.0
-3300,15667215.0,Cremonesi,705.0,France,Male,34.0,3.0,130404.92,1.0,1.0,1.0,26767.85,0.0
-3301,15644878.0,Lucchese,554.0,Germany,Male,46.0,10.0,109908.06,1.0,1.0,0.0,138526.96,1.0
-3302,15579914.0,Tsao,742.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,104533.24,0.0
-3303,15762715.0,Martin,679.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,94878.1,1.0
-3304,15732299.0,Ch'iu,724.0,Spain,Male,30.0,2.0,151844.64,2.0,0.0,1.0,174738.2,0.0
-3305,15737133.0,Liao,583.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,83343.73,0.0
-3306,15586821.0,H?,588.0,Spain,Female,40.0,3.0,0.0,2.0,0.0,0.0,113557.9,1.0
-3307,15726720.0,Pirozzi,662.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,61820.41,0.0
-3308,15570002.0,Lo,695.0,Germany,Female,33.0,4.0,145578.28,2.0,1.0,0.0,1710.89,0.0
-3309,15599152.0,K?,590.0,Germany,Male,41.0,4.0,104784.23,2.0,1.0,0.0,62944.75,0.0
-3310,15800068.0,Yefremova,653.0,France,Female,23.0,1.0,0.0,2.0,0.0,1.0,152843.53,0.0
-3311,15704770.0,P'eng,706.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,122925.4,0.0
-3312,15677804.0,Hsing,686.0,Germany,Female,56.0,1.0,118628.8,1.0,1.0,0.0,186869.58,1.0
-3313,15706463.0,Folliero,576.0,France,Female,29.0,6.0,0.0,2.0,0.0,0.0,79616.37,0.0
-3314,15627212.0,Larkin,678.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,142797.5,0.0
-3315,15724127.0,Kao,786.0,Germany,Female,45.0,10.0,82407.51,2.0,1.0,0.0,72993.65,0.0
-3316,15687540.0,Fiorentini,718.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,0.0,58440.43,0.0
-3317,15623369.0,Ch'eng,772.0,France,Male,32.0,1.0,0.0,2.0,0.0,1.0,136902.01,0.0
-3318,15670266.0,Chiu,569.0,France,Female,35.0,7.0,0.0,1.0,1.0,0.0,160546.58,0.0
-3319,15726354.0,Clements,849.0,France,Male,57.0,2.0,0.0,3.0,1.0,1.0,191111.02,1.0
-3320,15695465.0,Rizzo,656.0,France,Female,37.0,0.0,102025.32,1.0,1.0,0.0,94951.27,1.0
-3321,15583748.0,Kornilova,683.0,Germany,Female,36.0,5.0,142388.61,2.0,1.0,1.0,96961.0,0.0
-3322,15773789.0,Onyekachi,633.0,France,Female,27.0,6.0,0.0,2.0,1.0,1.0,83473.82,0.0
-3323,15607874.0,Trevisani,673.0,France,Female,35.0,7.0,0.0,2.0,0.0,0.0,134427.32,0.0
-3324,15574858.0,T'ien,497.0,Germany,Male,37.0,4.0,133970.22,1.0,1.0,0.0,72990.62,1.0
-3325,15653779.0,Shao,543.0,Germany,Female,40.0,8.0,54901.01,2.0,1.0,1.0,125979.36,0.0
-3326,15771086.0,Ugoji,659.0,France,Male,32.0,5.0,0.0,2.0,0.0,1.0,171070.55,0.0
-3327,15754599.0,Nwora,694.0,Spain,Female,38.0,2.0,0.0,2.0,0.0,0.0,87822.14,0.0
-3328,15763612.0,Chibuzo,634.0,Germany,Male,36.0,4.0,134760.21,1.0,1.0,1.0,171518.52,0.0
-3329,15783026.0,Chimaobim,719.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,135875.51,0.0
-3330,15671917.0,Chikezie,758.0,Spain,Female,44.0,7.0,0.0,2.0,1.0,1.0,163560.11,0.0
-3331,15692761.0,Chieloka,642.0,France,Male,41.0,8.0,0.0,1.0,1.0,1.0,51467.92,0.0
-3332,15767781.0,Chibueze,669.0,Spain,Male,46.0,7.0,94222.54,1.0,1.0,1.0,79577.3,0.0
-3333,15790744.0,Lai,705.0,Spain,Female,51.0,1.0,102073.67,1.0,1.0,0.0,138880.24,1.0
-3334,15777599.0,Nwabugwu,553.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,0.0,68815.05,0.0
-3335,15794479.0,Chidubem,696.0,France,Male,38.0,8.0,107592.89,1.0,1.0,1.0,63687.06,0.0
-3336,15791925.0,Pirozzi,577.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,0.0,113048.79,0.0
-3337,15667554.0,Ch'ang,712.0,Germany,Female,50.0,9.0,162390.61,1.0,1.0,0.0,163511.6,1.0
-3338,15807008.0,De Luca,619.0,France,Female,37.0,7.0,0.0,1.0,1.0,0.0,155799.86,0.0
-3339,15672594.0,Johnson,593.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,133851.25,0.0
-3340,15659327.0,Chukwudi,703.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,155743.48,0.0
-3341,15657535.0,Ts'ui,633.0,France,Female,28.0,8.0,0.0,2.0,1.0,1.0,91346.03,0.0
-3342,15721592.0,Uchenna,705.0,France,Female,48.0,2.0,0.0,2.0,1.0,0.0,176902.83,0.0
-3343,15724223.0,K'ung,635.0,Germany,Male,44.0,4.0,146547.76,1.0,1.0,0.0,62980.96,1.0
-3344,15640648.0,Tuan,578.0,France,Female,38.0,2.0,0.0,1.0,0.0,1.0,85412.54,0.0
-3345,15809736.0,O'Loghlen,627.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,180942.33,0.0
-3346,15572472.0,Nnaife,616.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,0.0,144875.73,0.0
-3347,15768151.0,Ts'ai,596.0,Germany,Female,39.0,2.0,120599.38,2.0,1.0,0.0,82463.69,0.0
-3348,15701605.0,Ts'ui,710.0,Spain,Male,39.0,9.0,135559.9,1.0,1.0,0.0,117632.63,0.0
-3349,15723851.0,Hsiung,585.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,57862.88,0.0
-3350,15746028.0,P'eng,588.0,France,Female,47.0,5.0,0.0,1.0,0.0,0.0,78744.39,1.0
-3351,15679429.0,Kao,659.0,Spain,Female,39.0,8.0,121231.39,1.0,1.0,1.0,132468.76,0.0
-3352,15694765.0,T'ien,529.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,166677.35,0.0
-3353,15738174.0,Yermakova,586.0,France,Female,33.0,8.0,0.0,2.0,0.0,1.0,61326.43,0.0
-3354,15572291.0,T'ien,656.0,France,Female,37.0,7.0,112373.49,1.0,1.0,0.0,1713.1,0.0
-3355,15710390.0,Shih,773.0,France,Female,39.0,4.0,0.0,2.0,1.0,1.0,81718.37,0.0
-3356,15761706.0,Walker,616.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,1.0,131736.22,0.0
-3357,15722061.0,Nwankwo,683.0,France,Male,24.0,8.0,0.0,2.0,1.0,0.0,143463.28,0.0
-3358,15660155.0,Tsao,732.0,France,Female,33.0,0.0,0.0,2.0,1.0,1.0,100556.98,0.0
-3359,15576723.0,Ku,721.0,Spain,Male,52.0,4.0,0.0,1.0,1.0,1.0,126723.22,1.0
-3360,15659949.0,Aksenov,707.0,France,Female,31.0,4.0,105969.42,2.0,1.0,0.0,130113.78,0.0
-3361,15740264.0,Udinese,729.0,France,Male,55.0,1.0,0.0,1.0,1.0,0.0,48260.19,1.0
-3362,15697574.0,Nkemakonam,745.0,France,Male,34.0,7.0,0.0,1.0,1.0,0.0,184564.67,0.0
-3363,15662266.0,Brownells,696.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,0.0,91433.46,0.0
-3364,15675256.0,Moore,678.0,France,Male,33.0,3.0,0.0,1.0,1.0,0.0,132973.21,0.0
-3365,15780572.0,K?,669.0,Spain,Male,58.0,0.0,0.0,2.0,0.0,1.0,51565.98,0.0
-3366,15593900.0,Ch'ien,573.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,139947.17,0.0
-3367,15597914.0,Yermolayev,789.0,France,Male,37.0,8.0,170840.08,1.0,1.0,0.0,645.61,0.0
-3368,15634281.0,Frolova,834.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,110916.15,0.0
-3369,15781327.0,Hs?,714.0,Germany,Female,56.0,3.0,117020.08,1.0,1.0,0.0,25407.59,1.0
-3370,15662098.0,Iloerika,661.0,Germany,Female,32.0,5.0,84719.13,2.0,1.0,0.0,80552.36,0.0
-3371,15793890.0,K?,564.0,Spain,Male,37.0,9.0,0.0,1.0,1.0,0.0,136675.51,0.0
-3372,15785367.0,Pisani,617.0,France,Male,51.0,6.0,0.0,2.0,1.0,1.0,48502.88,0.0
-3373,15654760.0,T'an,661.0,France,Female,40.0,4.0,0.0,1.0,1.0,0.0,103557.85,1.0
-3374,15670345.0,Chukwukadibia,710.0,Germany,Female,36.0,8.0,147726.98,1.0,0.0,1.0,166677.35,0.0
-3375,15791674.0,Manna,577.0,France,Female,49.0,6.0,0.0,1.0,1.0,1.0,171364.18,0.0
-3376,15592999.0,Ozioma,576.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,126535.58,0.0
-3377,15788659.0,Hsueh,686.0,France,Female,33.0,7.0,127655.42,2.0,0.0,1.0,181208.47,0.0
-3378,15660200.0,Maclean,621.0,France,Female,29.0,5.0,0.0,1.0,1.0,1.0,89801.9,0.0
-3379,15681115.0,Onuchukwu,652.0,France,Male,50.0,7.0,0.0,1.0,1.0,0.0,27380.99,1.0
-3380,15709511.0,Okechukwu,668.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,90877.76,0.0
-3381,15728333.0,Chukwumaobim,646.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,95936.84,0.0
-3382,15809736.0,Baresi,762.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,71405.17,0.0
-3383,15731148.0,Ogochukwu,679.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,1.0,59325.21,0.0
-3384,15734765.0,Ch'ien,535.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,125440.11,1.0
-3385,15706799.0,Nwachukwu,652.0,France,Male,35.0,7.0,0.0,1.0,1.0,0.0,92865.46,1.0
-3386,15660144.0,Trevisani,705.0,Germany,Male,36.0,1.0,96353.8,2.0,1.0,1.0,61326.43,0.0
-3387,15768340.0,Ebelegbulam,751.0,Spain,Male,43.0,6.0,85311.7,1.0,0.0,1.0,121053.19,0.0
-3388,15631070.0,Yobachi,771.0,Germany,Female,51.0,9.0,104848.68,1.0,1.0,1.0,61602.42,1.0
-3389,15674541.0,Ugorji,782.0,Germany,Female,46.0,3.0,124099.13,2.0,0.0,1.0,177740.57,0.0
-3390,15768181.0,Hsieh,663.0,France,Male,43.0,8.0,113709.28,1.0,1.0,0.0,162699.5,0.0
-3391,15620372.0,Ives,682.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,172033.57,0.0
-3392,15587562.0,Iweobiegbulam,793.0,Germany,Male,32.0,1.0,142929.16,2.0,0.0,1.0,145764.4,0.0
-3393,15733447.0,Chukwuebuka,584.0,France,Male,37.0,9.0,0.0,2.0,0.0,1.0,105898.79,0.0
-3394,15659327.0,Shubin,726.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,1.0,181543.67,0.0
-3395,15658449.0,O'Loughlin,577.0,Germany,Female,31.0,8.0,132057.92,1.0,1.0,1.0,53511.94,0.0
-3396,15658589.0,Niu,839.0,France,Female,50.0,5.0,147832.15,2.0,1.0,0.0,54495.82,1.0
-3397,15680167.0,Kline,579.0,Germany,Female,40.0,2.0,125190.86,2.0,1.0,1.0,142903.44,0.0
-3398,15611157.0,Nwachukwu,624.0,Germany,Male,40.0,8.0,155973.88,1.0,0.0,0.0,51393.1,1.0
-3399,15732268.0,Rizzo,599.0,France,Female,31.0,1.0,0.0,1.0,1.0,1.0,125937.98,0.0
-3400,15725772.0,Olisanugo,693.0,France,Female,37.0,4.0,0.0,2.0,0.0,0.0,170518.33,0.0
-3401,15785325.0,Ting,477.0,France,Male,27.0,5.0,0.0,2.0,1.0,0.0,138344.7,0.0
-3402,15609824.0,Chizuoke,745.0,Spain,Male,38.0,7.0,96658.26,1.0,1.0,0.0,150319.49,0.0
-3403,15722350.0,T'ien,662.0,France,Male,32.0,7.0,0.0,1.0,0.0,1.0,111342.66,0.0
-3404,15752097.0,H?,651.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,140765.57,0.0
-3405,15590177.0,Chiebuka,641.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,172576.56,0.0
-3406,15756920.0,Y?an,585.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,188426.12,0.0
-3407,15641480.0,Trevisan,671.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,86927.53,0.0
-3408,15672640.0,Dennys,559.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,11296.98,0.0
-3409,15626795.0,Chimaobim,628.0,France,Male,34.0,4.0,0.0,1.0,0.0,1.0,120531.31,0.0
-3410,15749119.0,Fetherstonhaugh,712.0,Germany,Female,62.0,1.0,120108.56,2.0,1.0,1.0,67611.36,0.0
-3411,15626776.0,Onyemere,652.0,France,Female,22.0,1.0,0.0,2.0,1.0,0.0,182692.8,0.0
-3412,15593345.0,Chiang,773.0,France,Female,25.0,9.0,62212.29,1.0,1.0,1.0,94949.84,0.0
-3413,15566891.0,Chidubem,635.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,66893.28,0.0
-3414,15656937.0,Hsia,578.0,Germany,Male,44.0,8.0,110597.76,3.0,1.0,0.0,180626.68,1.0
-3415,15807593.0,Shao,704.0,Spain,Male,41.0,5.0,0.0,1.0,1.0,1.0,103570.8,0.0
-3416,15743478.0,P'eng,678.0,France,Male,33.0,8.0,182888.08,1.0,1.0,1.0,45956.05,0.0
-3417,15701354.0,Kaleski,597.0,Germany,Female,42.0,2.0,102989.83,1.0,0.0,0.0,43435.43,1.0
-3418,15679966.0,Ch'ien,714.0,France,Male,36.0,10.0,0.0,1.0,1.0,1.0,151460.86,0.0
-3419,15635197.0,Chukwualuka,613.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,121073.23,0.0
-3420,15713853.0,Chibugo,544.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,118863.38,0.0
-3421,15700353.0,Smith,597.0,Spain,Female,39.0,8.0,0.0,1.0,0.0,1.0,153064.87,0.0
-3422,15781598.0,Rizzo,725.0,France,Male,44.0,6.0,0.0,2.0,1.0,1.0,95625.03,0.0
-3423,15741181.0,Mazzanti,639.0,Germany,Male,35.0,1.0,121404.64,1.0,1.0,1.0,181850.74,0.0
-3424,15730673.0,Mazzi,779.0,France,Female,41.0,5.0,166031.45,1.0,1.0,1.0,64746.07,0.0
-3425,15638463.0,Chinwendu,724.0,France,Female,36.0,8.0,109196.66,2.0,0.0,1.0,66963.15,0.0
-3426,15625942.0,Lo Duca,583.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,0.0,136985.58,0.0
-3427,15790067.0,Nieves,729.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,86410.29,0.0
-3428,15642394.0,Onyekaozulu,560.0,France,Male,32.0,7.0,118897.1,1.0,1.0,1.0,111558.7,0.0
-3429,15615473.0,Chukwufumnanya,789.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,87400.53,0.0
-3430,15617304.0,Hsueh,624.0,France,Male,44.0,4.0,0.0,2.0,1.0,0.0,58854.88,0.0
-3431,15744529.0,Estrada,675.0,France,Male,33.0,7.0,0.0,4.0,1.0,0.0,136028.97,1.0
-3432,15630106.0,Shao,526.0,Spain,Male,47.0,4.0,0.0,1.0,0.0,0.0,56875.76,1.0
-3433,15588944.0,Iweobiegbunam,577.0,France,Female,27.0,8.0,0.0,2.0,1.0,1.0,38252.25,0.0
-3434,15714883.0,Cattaneo,627.0,France,Male,28.0,2.0,0.0,2.0,1.0,1.0,102950.78,0.0
-3435,15605447.0,Cattaneo,669.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,76510.46,0.0
-3436,15593769.0,Lu,544.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,1710.89,1.0
-3437,15607347.0,Nwora,649.0,France,Male,53.0,7.0,0.0,1.0,1.0,0.0,158937.55,1.0
-3438,15621440.0,T'ien,656.0,Germany,Male,35.0,3.0,107192.38,2.0,1.0,1.0,90876.95,0.0
-3439,15792232.0,Mazzanti,597.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,157957.82,0.0
-3440,15631693.0,Trevisani,710.0,France,Male,33.0,0.0,121823.4,1.0,0.0,1.0,151978.36,0.0
-3441,15634968.0,Chukwualuka,537.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,180693.61,0.0
-3442,15757541.0,Chizoba,756.0,France,Male,39.0,2.0,105770.7,2.0,0.0,1.0,82559.77,0.0
-3443,15718507.0,Chukwujekwu,619.0,France,Male,28.0,8.0,0.0,1.0,0.0,1.0,176730.02,0.0
-3444,15641688.0,Ch'ien,696.0,France,Male,33.0,8.0,0.0,1.0,1.0,0.0,140134.43,0.0
-3445,15770154.0,Onyeoruru,612.0,Germany,Male,28.0,6.0,141541.25,1.0,0.0,1.0,75848.44,0.0
-3446,15696744.0,Lo,641.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,180197.74,0.0
-3447,15668894.0,Manna,646.0,Spain,Female,35.0,5.0,88011.4,1.0,0.0,1.0,115800.1,0.0
-3448,15714689.0,Shih,681.0,France,Female,38.0,8.0,0.0,2.0,0.0,1.0,90106.94,0.0
-3449,15664543.0,Nwachukwu,651.0,France,Female,36.0,7.0,0.0,1.0,1.0,0.0,165035.17,0.0
-3450,15803538.0,Teng,529.0,France,Female,29.0,0.0,0.0,2.0,1.0,0.0,109041.53,0.0
-3451,15660144.0,Chidubem,608.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,86622.48,0.0
-3452,15775409.0,Ts'ui,503.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,122449.18,0.0
-3453,15745375.0,Ch'eng,753.0,Germany,Female,48.0,5.0,137555.44,2.0,0.0,1.0,65816.53,0.0
-3454,15803378.0,Kuo,667.0,France,Male,37.0,7.0,0.0,1.0,1.0,1.0,186489.95,0.0
-3455,15671789.0,Nkemakolam,718.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,120790.02,0.0
-3456,15583736.0,Fiorentino,615.0,Spain,Male,38.0,6.0,169824.46,1.0,1.0,1.0,159737.56,0.0
-3457,15707120.0,Ts'ui,591.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,102057.86,0.0
-3458,15596800.0,Calabresi,651.0,France,Male,60.0,5.0,102994.04,1.0,0.0,1.0,9983.88,1.0
-3459,15772383.0,Chukwuemeka,632.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,70977.33,0.0
-3460,15681671.0,Ts'ao,595.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,1.0,79580.39,0.0
-3461,15698932.0,Nnamutaezinwa,686.0,Germany,Female,36.0,2.0,113882.31,1.0,0.0,1.0,52795.15,0.0
-3462,15651022.0,Bufkin,741.0,Spain,Male,27.0,1.0,0.0,2.0,0.0,1.0,111340.36,0.0
-3463,15671591.0,Milano,735.0,Germany,Male,44.0,1.0,104944.1,1.0,0.0,0.0,173586.63,1.0
-3464,15586680.0,Nkemjika,714.0,Germany,Female,33.0,1.0,142705.95,2.0,1.0,1.0,52876.41,0.0
-3465,15653547.0,Lai,645.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,1.0,184312.88,0.0
-3466,15806922.0,Y?,683.0,Spain,Male,35.0,1.0,0.0,1.0,1.0,0.0,74838.64,0.0
-3467,15581626.0,Yobachi,667.0,Spain,Male,34.0,5.0,0.0,1.0,1.0,0.0,143783.28,0.0
-3468,15662533.0,Kao,607.0,Spain,Female,51.0,10.0,0.0,1.0,0.0,1.0,199357.24,1.0
-3469,15773098.0,Nnachetam,627.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,88279.6,0.0
-3470,15702789.0,Ugochukwu,692.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,114093.73,0.0
-3471,15686964.0,Milanesi,645.0,France,Female,48.0,6.0,0.0,2.0,1.0,1.0,172499.7,0.0
-3472,15651389.0,Ch'in,725.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,53394.12,0.0
-3473,15583480.0,Chukwueloka,597.0,Spain,Female,29.0,2.0,0.0,2.0,0.0,0.0,88728.47,0.0
-3474,15627002.0,Lucciano,602.0,Spain,Female,47.0,5.0,113328.93,1.0,1.0,0.0,48161.18,0.0
-3475,15684861.0,Ts'ai,754.0,France,Male,41.0,1.0,0.0,2.0,0.0,1.0,548.52,0.0
-3476,15751774.0,Folliero,526.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,107640.25,0.0
-3477,15785042.0,Smith,610.0,France,Female,53.0,2.0,94762.16,3.0,1.0,0.0,99347.8,1.0
-3478,15771086.0,Tsou,586.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,86678.48,0.0
-3479,15788676.0,T'ien,684.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,127059.05,0.0
-3480,15569394.0,Shih,592.0,Germany,Male,29.0,9.0,103914.17,1.0,1.0,1.0,120892.65,0.0
-3481,15802758.0,Ugorji,556.0,Germany,Male,56.0,1.0,105799.32,1.0,1.0,1.0,104786.59,1.0
-3482,15764072.0,Onyemauchechukwu,593.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,91044.47,0.0
-3483,15663625.0,Tung,682.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,167032.49,0.0
-3484,15753566.0,Chukwukadibia,696.0,Germany,Male,31.0,5.0,141252.18,2.0,1.0,1.0,60536.96,0.0
-3485,15641170.0,Lucchese,564.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,172031.22,0.0
-3486,15787035.0,Onyemachukwu,732.0,France,Male,52.0,3.0,0.0,3.0,1.0,0.0,184524.74,1.0
-3487,15807269.0,Okwudilichukwu,695.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,162478.11,0.0
-3488,15756610.0,Nwankwo,696.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,113469.65,0.0
-3489,15808849.0,Mazzanti,716.0,France,Female,23.0,8.0,132387.91,1.0,1.0,0.0,91817.46,0.0
-3490,15640855.0,Onio,659.0,Spain,Female,41.0,2.0,0.0,1.0,0.0,0.0,114474.98,1.0
-3491,15632848.0,Kornilova,557.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,129892.93,0.0
-3492,15624471.0,Maclean,633.0,France,Female,31.0,5.0,114514.71,1.0,1.0,1.0,122021.33,0.0
-3493,15778936.0,Onwumelu,662.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,70563.9,0.0
-3494,15662955.0,McGregor,628.0,France,Female,43.0,9.0,0.0,1.0,1.0,0.0,149853.89,1.0
-3495,15593954.0,Chikezie,704.0,Spain,Female,30.0,9.0,0.0,2.0,0.0,0.0,86935.08,0.0
-3496,15768455.0,Chiu,724.0,Germany,Male,44.0,3.0,84907.33,1.0,1.0,0.0,71738.56,0.0
-3497,15732610.0,H?,593.0,Germany,Male,41.0,4.0,137851.31,1.0,1.0,0.0,163477.22,0.0
-3498,15576156.0,Ch'eng,628.0,France,Male,28.0,2.0,137964.51,2.0,0.0,1.0,109545.56,0.0
-3499,15738884.0,T'an,711.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,88890.75,0.0
-3500,15576550.0,Pisani,581.0,Spain,Female,30.0,9.0,0.0,2.0,1.0,0.0,94442.6,0.0
-3501,15773098.0,Mazzi,731.0,France,Female,41.0,1.0,0.0,1.0,0.0,0.0,38691.34,1.0
-3502,15702298.0,Ts'ai,656.0,Spain,Female,32.0,3.0,0.0,2.0,1.0,1.0,94912.78,0.0
-3503,15738166.0,Lucchesi,619.0,France,Female,52.0,4.0,122220.19,2.0,1.0,0.0,170563.59,1.0
-3504,15784491.0,Cremonesi,638.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,1.0,143980.29,0.0
-3505,15698031.0,Nkemdilim,750.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,167066.95,0.0
-3506,15666095.0,Shih,551.0,France,Male,54.0,6.0,105918.1,1.0,1.0,1.0,180934.75,1.0
-3507,15586039.0,Uvarov,738.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,185821.41,0.0
-3508,15783865.0,Onio,669.0,France,Male,37.0,6.0,152303.8,1.0,1.0,0.0,64716.08,0.0
-3509,15735388.0,Uwaezuoke,537.0,Spain,Male,36.0,2.0,0.0,2.0,0.0,1.0,64831.36,0.0
-3510,15585961.0,Y?,684.0,France,Female,41.0,8.0,0.0,1.0,1.0,0.0,166677.35,1.0
-3511,15652400.0,Onyemauchechukwu,656.0,Spain,Male,44.0,8.0,186796.37,1.0,1.0,1.0,166495.2,0.0
-3512,15758813.0,H?,597.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,0.0,131300.68,0.0
-3513,15665963.0,Milano,707.0,Germany,Male,57.0,3.0,133845.36,1.0,1.0,0.0,77837.63,1.0
-3514,15782210.0,Hsu,714.0,Germany,Female,42.0,8.0,104386.53,2.0,1.0,1.0,94775.94,0.0
-3515,15656096.0,Yobanna,661.0,Germany,Male,51.0,1.0,103560.57,1.0,0.0,1.0,47795.43,1.0
-3516,15679743.0,Chukwumaobim,807.0,France,Male,41.0,9.0,0.0,2.0,0.0,0.0,181224.56,0.0
-3517,15761487.0,Hsia,648.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,115668.55,0.0
-3518,15782360.0,T'ien,846.0,Spain,Female,31.0,0.0,0.0,2.0,1.0,0.0,170518.52,0.0
-3519,15700749.0,Chukwumaobim,695.0,Germany,Female,30.0,5.0,116965.21,2.0,1.0,1.0,120512.08,0.0
-3520,15655339.0,Pagnotto,577.0,France,Male,28.0,2.0,0.0,2.0,0.0,0.0,142331.85,0.0
-3521,15728005.0,Chukwuemeka,648.0,Germany,Male,36.0,1.0,102645.48,2.0,1.0,0.0,72927.68,0.0
-3522,15582944.0,Chidiegwu,663.0,Germany,Male,38.0,10.0,108711.57,1.0,0.0,0.0,143481.79,1.0
-3523,15729019.0,Gentry,595.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,67323.36,0.0
-3524,15709354.0,Brown,615.0,Germany,Male,34.0,4.0,81732.88,2.0,0.0,1.0,126644.98,0.0
-3525,15703778.0,Lombardo,656.0,France,Female,63.0,3.0,0.0,2.0,0.0,1.0,77906.87,0.0
-3526,15675888.0,Kao,709.0,France,Male,36.0,4.0,123544.0,2.0,1.0,0.0,80552.36,0.0
-3527,15629010.0,Okwudilichukwu,596.0,Germany,Male,38.0,9.0,182275.23,1.0,1.0,1.0,123860.28,0.0
-3528,15753362.0,Pagnotto,602.0,Spain,Male,45.0,1.0,0.0,2.0,0.0,1.0,70810.6,0.0
-3529,15776936.0,Udinese,516.0,Germany,Male,44.0,0.0,113865.23,1.0,1.0,1.0,51548.14,0.0
-3530,15785078.0,Tsao,732.0,Spain,Female,28.0,4.0,0.0,2.0,0.0,1.0,133871.83,0.0
-3531,15662291.0,Farrar,581.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,1.0,176713.47,0.0
-3532,15656300.0,Kao,714.0,France,Male,40.0,6.0,148074.79,1.0,0.0,1.0,145562.4,1.0
-3533,15686913.0,Trevisan,603.0,Spain,Male,71.0,0.0,0.0,2.0,1.0,1.0,131300.68,0.0
-3534,15773731.0,Lucciano,778.0,France,Female,25.0,4.0,140549.4,1.0,0.0,0.0,44008.91,0.0
-3535,15756535.0,Ugochukwutubelum,501.0,Germany,Female,53.0,2.0,96553.52,1.0,1.0,0.0,139919.38,1.0
-3536,15752507.0,Lombardo,720.0,Germany,Female,32.0,1.0,123870.07,1.0,0.0,0.0,146891.07,1.0
-3537,15591047.0,Pinto,683.0,France,Female,43.0,7.0,0.0,2.0,1.0,0.0,185830.72,0.0
-3538,15724443.0,Chien,713.0,France,Male,38.0,4.0,122314.5,1.0,1.0,0.0,82696.84,0.0
-3539,15592999.0,Efremov,684.0,France,Male,29.0,9.0,121898.58,1.0,1.0,0.0,166038.13,0.0
-3540,15593636.0,Cattaneo,796.0,France,Male,40.0,9.0,109908.06,1.0,0.0,0.0,93439.75,0.0
-3541,15804053.0,K?,622.0,Germany,Female,40.0,9.0,136880.12,2.0,1.0,0.0,124489.88,0.0
-3542,15752984.0,Hs?eh,706.0,France,Female,30.0,1.0,111096.91,1.0,1.0,0.0,86937.2,0.0
-3543,15585580.0,Zikoranachidimma,593.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,0.0,129912.74,0.0
-3544,15641114.0,Tien,652.0,France,Female,35.0,1.0,167878.5,1.0,0.0,1.0,5983.8,0.0
-3545,15669946.0,Shao,587.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,0.0,77951.84,0.0
-3546,15706706.0,Walker,532.0,Germany,Male,72.0,6.0,125495.64,1.0,1.0,1.0,64679.3,0.0
-3547,15583857.0,Chidumaga,587.0,Spain,Male,25.0,5.0,0.0,2.0,1.0,1.0,147055.87,0.0
-3548,15603851.0,Ch'iu,576.0,Spain,Female,39.0,7.0,0.0,1.0,0.0,0.0,145558.42,1.0
-3549,15751455.0,Liao,686.0,France,Female,28.0,7.0,0.0,2.0,0.0,0.0,100101.06,0.0
-3550,15745012.0,Smith,731.0,France,Female,32.0,3.0,121399.98,1.0,1.0,1.0,84421.34,0.0
-3551,15619608.0,Chukwubuikem,662.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,179655.87,0.0
-3552,15626135.0,Ch'ang,579.0,France,Male,32.0,4.0,0.0,1.0,1.0,0.0,86410.29,0.0
-3553,15792862.0,P'eng,684.0,France,Female,30.0,6.0,0.0,2.0,0.0,0.0,129964.94,0.0
-3554,15687959.0,Palerma,569.0,France,Female,32.0,1.0,0.0,1.0,1.0,0.0,82762.02,1.0
-3555,15774586.0,K'ung,756.0,Germany,Female,32.0,3.0,124995.98,1.0,0.0,0.0,137537.19,0.0
-3556,15761348.0,Palermo,745.0,France,Male,38.0,2.0,82441.75,2.0,1.0,1.0,118368.2,0.0
-3557,15684440.0,Onyemauchechukwu,619.0,France,Female,35.0,1.0,0.0,1.0,1.0,0.0,157097.09,1.0
-3558,15624072.0,Tung,635.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,82814.32,0.0
-3559,15774847.0,Aitken,628.0,Germany,Male,36.0,2.0,77967.5,2.0,1.0,0.0,62768.8,0.0
-3560,15744327.0,Genovese,670.0,France,Male,45.0,3.0,0.0,3.0,1.0,0.0,183515.13,1.0
-3561,15741643.0,Walker,496.0,France,Female,41.0,8.0,123944.88,1.0,1.0,0.0,90877.76,0.0
-3562,15579996.0,Yobachi,709.0,Germany,Male,44.0,2.0,139432.46,1.0,0.0,1.0,122925.44,1.0
-3563,15572398.0,Yobanna,542.0,France,Male,35.0,7.0,118969.76,1.0,1.0,1.0,141872.05,0.0
-3564,15809837.0,Hs?,651.0,France,Male,41.0,2.0,0.0,2.0,0.0,0.0,77866.91,0.0
-3565,15742369.0,Trevisani,638.0,France,Male,43.0,2.0,0.0,1.0,1.0,1.0,181449.51,0.0
-3566,15684461.0,Ch'ien,721.0,France,Male,33.0,7.0,153611.83,2.0,1.0,1.0,64477.25,0.0
-3567,15746461.0,Chizuoke,724.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,1.0,174434.06,0.0
-3568,15800793.0,Nwachinemelu,683.0,France,Female,33.0,10.0,0.0,2.0,1.0,0.0,131097.9,0.0
-3569,15672798.0,Ch'ang,670.0,Spain,Male,37.0,9.0,101609.01,1.0,1.0,1.0,148527.56,0.0
-3570,15583076.0,Onwumelu,611.0,Germany,Female,46.0,7.0,142475.87,1.0,1.0,0.0,131389.39,1.0
-3571,15671209.0,Uchechukwu,655.0,Spain,Male,32.0,3.0,0.0,1.0,0.0,1.0,104786.59,1.0
-3572,15569050.0,Trevisan,676.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,172066.71,0.0
-3573,15690153.0,Calabresi,587.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,55775.84,0.0
-3574,15727041.0,Lucchese,603.0,France,Male,39.0,10.0,96877.21,1.0,1.0,0.0,59325.21,0.0
-3575,15791342.0,Chukwuemeka,651.0,France,Female,37.0,7.0,0.0,2.0,0.0,1.0,160968.44,1.0
-3576,15594556.0,Milanesi,745.0,France,Male,31.0,8.0,121394.42,1.0,1.0,0.0,177783.04,0.0
-3577,15814237.0,Ch'in,602.0,France,Male,46.0,6.0,162701.65,1.0,1.0,0.0,109179.89,0.0
-3578,15584580.0,Smith,706.0,France,Female,31.0,6.0,166518.63,1.0,1.0,0.0,143981.27,0.0
-3579,15771483.0,Chien,757.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,184843.77,0.0
-3580,15734886.0,Ch'ien,656.0,Germany,Female,45.0,4.0,92474.05,1.0,0.0,0.0,109626.09,1.0
-3581,15641835.0,Chukwujamuike,729.0,France,Female,41.0,8.0,80964.57,1.0,1.0,0.0,109638.78,0.0
-3582,15699238.0,Yuryeva,667.0,France,Male,22.0,5.0,0.0,2.0,1.0,1.0,72969.9,0.0
-3583,15686461.0,Hsu,647.0,France,Female,28.0,10.0,0.0,1.0,1.0,1.0,175749.36,0.0
-3584,15654409.0,Fokine,581.0,Spain,Female,42.0,7.0,143330.56,1.0,1.0,1.0,136983.78,0.0
-3585,15627385.0,Lucchese,635.0,France,Male,36.0,1.0,0.0,2.0,0.0,1.0,53817.23,0.0
-3586,15694188.0,Chialuka,578.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,0.0,10666.05,0.0
-3587,15652218.0,Ch'ien,663.0,France,Male,52.0,1.0,0.0,2.0,0.0,1.0,182055.36,0.0
-3588,15618774.0,Shih,669.0,France,Female,40.0,6.0,0.0,1.0,1.0,1.0,125445.8,1.0
-3589,15726487.0,Udinesi,663.0,France,Male,34.0,2.0,0.0,1.0,1.0,1.0,148087.62,0.0
-3590,15743543.0,Tuan,602.0,France,Male,35.0,3.0,0.0,2.0,0.0,1.0,183872.88,0.0
-3591,15773512.0,Nkemdilim,756.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,173870.39,1.0
-3592,15806945.0,Liao,595.0,Germany,Male,26.0,5.0,144837.79,1.0,1.0,1.0,127623.36,0.0
-3593,15696852.0,Ch'iu,651.0,France,Male,42.0,1.0,138653.51,1.0,1.0,0.0,178458.86,0.0
-3594,15641389.0,Hsieh,637.0,Germany,Male,34.0,10.0,119787.76,2.0,0.0,0.0,80701.72,0.0
-3595,15603594.0,Chu,583.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,1.0,148087.88,0.0
-3596,15726589.0,Nworie,493.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,0.0,158527.56,0.0
-3597,15795895.0,Manna,801.0,Spain,Female,34.0,4.0,0.0,2.0,1.0,0.0,131734.87,0.0
-3598,15645323.0,Nwora,670.0,France,Female,55.0,5.0,0.0,1.0,1.0,0.0,143397.14,1.0
-3599,15610156.0,Shih,561.0,Spain,Male,20.0,8.0,0.0,2.0,1.0,0.0,122433.09,0.0
-3600,15796835.0,Chibugo,774.0,Germany,Male,40.0,3.0,102016.38,1.0,0.0,0.0,120594.85,1.0
-3601,15702293.0,Ts'ao,627.0,Spain,Male,20.0,9.0,0.0,2.0,1.0,1.0,94570.52,0.0
-3602,15741180.0,Ch'en,642.0,France,Male,33.0,1.0,151855.33,1.0,1.0,0.0,74850.84,0.0
-3603,15644724.0,Maclean,747.0,France,Male,66.0,8.0,149940.04,1.0,1.0,1.0,126513.11,0.0
-3604,15662291.0,Y?an,674.0,Spain,Female,39.0,2.0,0.0,2.0,0.0,0.0,100556.98,0.0
-3605,15702300.0,K'ung,722.0,Spain,Male,26.0,2.0,0.0,2.0,0.0,0.0,123949.65,0.0
-3606,15762583.0,Chidozie,709.0,France,Female,39.0,5.0,0.0,1.0,1.0,0.0,152577.62,0.0
-3607,15777616.0,Chiemenam,628.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,101693.73,0.0
-3608,15672397.0,Nnamdi,603.0,Spain,Female,44.0,0.0,108432.88,1.0,0.0,0.0,180622.02,1.0
-3609,15780254.0,Estep,581.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,168034.9,0.0
-3610,15569410.0,Hsing,567.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,180344.69,0.0
-3611,15689425.0,Cremonesi,605.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,4035.46,0.0
-3612,15606096.0,Ch'ang,622.0,France,Female,40.0,3.0,0.0,2.0,0.0,1.0,111471.55,0.0
-3613,15731463.0,Macleod,656.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,14073.11,0.0
-3614,15650098.0,Ch'in,781.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,90558.24,0.0
-3615,15603096.0,Calabrese,569.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,96832.82,0.0
-3616,15648075.0,Maclean,592.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,64375.4,0.0
-3617,15585853.0,Chinwemma,738.0,France,Male,41.0,0.0,0.0,1.0,1.0,1.0,71709.12,0.0
-3618,15583480.0,Ch'en,584.0,Germany,Male,59.0,9.0,121898.82,1.0,1.0,0.0,19834.32,1.0
-3619,15795537.0,Chidozie,815.0,Germany,Male,27.0,1.0,123870.07,2.0,0.0,1.0,6078.46,0.0
-3620,15752626.0,Ch'ien,511.0,France,Female,45.0,4.0,98438.11,1.0,1.0,1.0,172437.12,0.0
-3621,15761950.0,Chukwuemeka,731.0,France,Male,30.0,2.0,113674.2,2.0,1.0,1.0,168895.06,0.0
-3622,15643908.0,Nnaife,678.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,1.0,35687.43,0.0
-3623,15775809.0,Ma,624.0,France,Male,39.0,9.0,151027.04,1.0,1.0,0.0,27330.59,0.0
-3624,15578098.0,Chin,730.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,92568.07,0.0
-3625,15674581.0,Hs?,683.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,6554.97,0.0
-3626,15781884.0,P'eng,569.0,France,Female,44.0,5.0,145988.65,1.0,1.0,0.0,188516.45,1.0
-3627,15577999.0,Toscani,754.0,France,Male,38.0,8.0,118640.24,1.0,1.0,0.0,90928.02,0.0
-3628,15776939.0,P'eng,590.0,Spain,Male,35.0,2.0,118022.06,1.0,1.0,1.0,143954.99,0.0
-3629,15604792.0,Pirozzi,607.0,Germany,Male,35.0,1.0,61297.05,1.0,1.0,0.0,107876.2,0.0
-3630,15628282.0,Tseng,709.0,France,Male,43.0,8.0,0.0,1.0,1.0,1.0,126535.92,0.0
-3631,15621822.0,Yeh,641.0,Germany,Female,41.0,4.0,157099.47,1.0,1.0,0.0,136910.19,0.0
-3632,15678460.0,Tsai,624.0,France,Female,28.0,6.0,0.0,2.0,1.0,0.0,49684.09,0.0
-3633,15572467.0,Pinto,667.0,France,Male,38.0,4.0,93694.22,1.0,0.0,0.0,94875.62,0.0
-3634,15662698.0,Ch'iu,597.0,France,Male,41.0,9.0,135096.05,1.0,1.0,1.0,83470.4,0.0
-3635,15741728.0,Nkemdilim,597.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,166516.84,0.0
-3636,15642725.0,P'an,712.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,57541.06,0.0
-3637,15765806.0,Ch'iu,598.0,Spain,Male,44.0,8.0,0.0,2.0,1.0,0.0,141364.62,0.0
-3638,15672908.0,Brown,648.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,172576.43,0.0
-3639,15678720.0,K?,766.0,France,Male,39.0,1.0,0.0,2.0,0.0,0.0,51380.9,0.0
-3640,15778936.0,Mazzanti,741.0,Germany,Male,23.0,4.0,84719.97,2.0,1.0,1.0,131662.47,0.0
-3641,15616276.0,Gentry,589.0,Germany,Female,56.0,3.0,122522.47,1.0,1.0,0.0,130404.53,1.0
-3642,15776467.0,Ch'in,678.0,France,Female,35.0,2.0,0.0,2.0,1.0,1.0,645.61,0.0
-3643,15692631.0,Chidiebere,712.0,France,Female,45.0,8.0,138089.97,3.0,1.0,0.0,143301.49,1.0
-3644,15577064.0,P'eng,712.0,France,Male,45.0,10.0,0.0,2.0,1.0,1.0,72876.43,0.0
-3645,15652674.0,Moore,565.0,Spain,Female,31.0,7.0,125169.26,1.0,0.0,0.0,77830.46,0.0
-3646,15617348.0,Ritchie,593.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,38196.24,0.0
-3647,15629150.0,Chiu,545.0,France,Male,49.0,8.0,0.0,2.0,1.0,0.0,143614.01,0.0
-3648,15791321.0,Genovese,689.0,Germany,Male,36.0,6.0,109646.83,2.0,0.0,1.0,55775.72,0.0
-3649,15749381.0,Chia,667.0,France,Male,46.0,3.0,0.0,2.0,1.0,1.0,164394.65,0.0
-3650,15698031.0,Chukwujamuike,679.0,Spain,Female,29.0,6.0,0.0,2.0,1.0,1.0,120770.75,0.0
-3651,15679760.0,Chiazagomekpere,767.0,France,Male,50.0,1.0,0.0,1.0,1.0,1.0,64716.08,0.0
-3652,15641359.0,Trevisano,655.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,172668.39,0.0
-3653,15752026.0,Nworie,677.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,91573.19,0.0
-3654,15626985.0,Lei,732.0,Germany,Female,39.0,4.0,135559.9,1.0,1.0,1.0,154460.18,1.0
-3655,15672930.0,Hsiao,622.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,133617.43,0.0
-3656,15644501.0,Hsieh,598.0,Germany,Male,36.0,5.0,168560.46,1.0,1.0,0.0,146041.45,0.0
-3657,15697574.0,L?,642.0,Germany,Male,30.0,6.0,133800.98,2.0,1.0,1.0,156858.2,0.0
-3658,15744789.0,Uspenskaya,586.0,France,Male,41.0,3.0,0.0,1.0,1.0,1.0,51362.82,0.0
-3659,15639552.0,Ch'eng,466.0,France,Male,35.0,3.0,0.0,1.0,1.0,0.0,109538.35,0.0
-3660,15594576.0,Chidubem,621.0,Spain,Female,40.0,4.0,0.0,2.0,1.0,1.0,61936.1,0.0
-3661,15807595.0,Chukwuhaenye,629.0,France,Male,36.0,7.0,0.0,1.0,1.0,1.0,61995.57,0.0
-3662,15754578.0,Teng,509.0,France,Male,55.0,1.0,0.0,2.0,1.0,1.0,15068.21,0.0
-3663,15708513.0,Maclean,675.0,Spain,Female,33.0,6.0,137227.04,1.0,1.0,1.0,126796.69,0.0
-3664,15592089.0,Ts'ui,551.0,France,Female,29.0,3.0,165969.26,1.0,1.0,0.0,71433.88,0.0
-3665,15735687.0,Lucciano,558.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,116547.31,0.0
-3666,15778861.0,Omeokachie,658.0,Germany,Female,58.0,5.0,152296.81,1.0,1.0,0.0,110371.84,1.0
-3667,15595039.0,Manna,704.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,102515.72,0.0
-3668,15602811.0,Chiemenam,712.0,Spain,Male,57.0,1.0,135120.72,1.0,1.0,1.0,153771.8,0.0
-3669,15640442.0,Chidi,769.0,France,Male,26.0,5.0,0.0,1.0,0.0,0.0,84760.32,0.0
-3670,15736431.0,Pirozzi,699.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,82970.69,0.0
-3671,15717984.0,Teng,850.0,France,Female,44.0,4.0,0.0,2.0,1.0,1.0,79559.02,0.0
-3672,15639669.0,Brown,718.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,84760.32,0.0
-3673,15734202.0,Dickson,738.0,France,Female,28.0,9.0,0.0,1.0,1.0,1.0,106977.53,0.0
-3674,15665963.0,Onyemere,589.0,France,Male,44.0,0.0,163943.89,2.0,1.0,1.0,110440.5,0.0
-3675,15802482.0,Mackey,548.0,Germany,Male,43.0,10.0,162036.92,1.0,1.0,0.0,158072.45,1.0
-3676,15784320.0,Genovese,602.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,130827.97,0.0
-3677,15586522.0,Chukwuemeka,811.0,Spain,Female,27.0,6.0,130114.39,1.0,1.0,1.0,149814.68,0.0
-3678,15697567.0,Folliero,633.0,France,Male,45.0,2.0,0.0,2.0,1.0,0.0,133086.32,0.0
-3679,15749123.0,Trevisani,588.0,Spain,Female,47.0,8.0,0.0,2.0,0.0,0.0,17095.49,1.0
-3680,15653741.0,Biryukov,677.0,France,Male,46.0,9.0,169824.46,1.0,1.0,1.0,151924.9,1.0
-3681,15784099.0,Scott,733.0,Spain,Male,44.0,9.0,0.0,2.0,1.0,0.0,161671.15,0.0
-3682,15611191.0,Fenton,699.0,France,Female,23.0,2.0,0.0,2.0,1.0,1.0,79919.13,0.0
-3683,15643359.0,Rizzo,584.0,France,Female,36.0,6.0,0.0,3.0,1.0,1.0,77866.91,0.0
-3684,15772896.0,Palerma,656.0,Spain,Male,59.0,6.0,118989.92,1.0,1.0,0.0,78436.36,1.0
-3685,15802823.0,Hs?,662.0,France,Male,31.0,7.0,0.0,1.0,1.0,1.0,179655.87,0.0
-3686,15641175.0,Yefremova,603.0,France,Male,33.0,6.0,0.0,1.0,0.0,1.0,160976.85,0.0
-3687,15592751.0,Ch'en,835.0,France,Male,34.0,8.0,0.0,1.0,1.0,1.0,125877.22,0.0
-3688,15658064.0,T'ao,559.0,Germany,Male,61.0,9.0,115617.24,1.0,0.0,0.0,71830.1,1.0
-3689,15702298.0,Nkemdirim,641.0,France,Female,32.0,1.0,0.0,2.0,0.0,0.0,152987.42,0.0
-3690,15658475.0,Trevisani,597.0,Germany,Female,41.0,9.0,113872.3,1.0,1.0,0.0,65986.48,0.0
-3691,15726720.0,Tseng,667.0,Germany,Male,33.0,1.0,125724.95,1.0,0.0,1.0,149614.81,1.0
-3692,15737080.0,Ozioma,569.0,Spain,Male,42.0,7.0,0.0,2.0,1.0,0.0,60905.51,0.0
-3693,15674868.0,Brown,595.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,91346.03,0.0
-3694,15750299.0,Akobundu,683.0,France,Female,44.0,4.0,0.0,2.0,1.0,0.0,94767.13,0.0
-3695,15810563.0,Kao,758.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,60901.72,0.0
-3696,15753092.0,Onyemere,752.0,France,Male,31.0,2.0,131355.6,1.0,1.0,1.0,52462.25,0.0
-3697,15806945.0,De Garis,686.0,France,Female,43.0,5.0,161766.87,1.0,1.0,1.0,129526.57,1.0
-3698,15581893.0,Ugochukwu,727.0,France,Female,32.0,9.0,74989.58,1.0,1.0,0.0,131660.31,0.0
-3699,15603851.0,Ch'ang,704.0,France,Male,49.0,6.0,0.0,1.0,1.0,0.0,102743.69,1.0
-3700,15620756.0,Ozioma,704.0,France,Male,42.0,4.0,154962.99,1.0,1.0,0.0,170819.01,1.0
-3701,15696834.0,Y?an,535.0,France,Male,41.0,2.0,0.0,1.0,1.0,1.0,60770.73,0.0
-3702,15653478.0,Milne,651.0,Germany,Female,40.0,7.0,130575.26,1.0,1.0,1.0,73334.43,0.0
-3703,15679550.0,Chidubem,653.0,France,Male,31.0,10.0,0.0,2.0,1.0,0.0,90537.97,0.0
-3704,15628583.0,H?,721.0,Spain,Female,37.0,3.0,0.0,2.0,0.0,1.0,161241.65,0.0
-3705,15663438.0,T'ang,559.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,51919.04,0.0
-3706,15590820.0,Y?an,723.0,France,Male,43.0,7.0,0.0,1.0,1.0,0.0,124045.65,0.0
-3707,15571571.0,Calabresi,724.0,France,Male,39.0,10.0,0.0,2.0,0.0,1.0,138527.56,0.0
-3708,15595440.0,Chia,605.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,1.0,141404.07,1.0
-3709,15662571.0,Chijindum,618.0,Germany,Female,34.0,1.0,131569.63,2.0,1.0,1.0,179436.6,0.0
-3710,15772573.0,Ritchie,554.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,1.0,156476.36,0.0
-3711,15618562.0,Fanucci,667.0,France,Female,64.0,2.0,122453.97,1.0,0.0,1.0,110468.73,0.0
-3712,15759436.0,H?,679.0,France,Male,38.0,7.0,97588.6,1.0,1.0,1.0,160523.33,0.0
-3713,15609977.0,Genovese,577.0,France,Male,58.0,8.0,0.0,2.0,1.0,1.0,85424.41,0.0
-3714,15602844.0,Chinagorom,762.0,Spain,Female,35.0,3.0,0.0,2.0,0.0,1.0,134589.58,0.0
-3715,15653547.0,Monds,703.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,0.0,89566.64,0.0
-3716,15772381.0,L?,706.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,101314.76,0.0
-3717,15722061.0,Fanucci,635.0,France,Male,37.0,6.0,124547.13,1.0,1.0,0.0,156424.4,0.0
-3718,15674811.0,Onwuamaeze,603.0,France,Female,53.0,9.0,0.0,2.0,0.0,1.0,158742.81,0.0
-3719,15646936.0,Hsiung,554.0,Spain,Female,39.0,5.0,123845.36,1.0,1.0,0.0,120540.83,1.0
-3720,15782736.0,P'eng,667.0,Spain,Female,31.0,1.0,107556.06,1.0,1.0,0.0,37688.31,0.0
-3721,15573318.0,Bogle,593.0,Germany,Female,34.0,4.0,150635.3,2.0,1.0,0.0,57558.95,0.0
-3722,15800688.0,Tuan,604.0,Germany,Female,39.0,2.0,98585.82,2.0,1.0,1.0,199357.24,0.0
-3723,15676966.0,Yegorov,633.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,80410.68,0.0
-3724,15735782.0,Cattaneo,572.0,Germany,Male,43.0,6.0,156848.13,2.0,0.0,0.0,52861.89,0.0
-3725,15578866.0,Chiazagomekpere,590.0,France,Male,19.0,4.0,0.0,2.0,1.0,1.0,143614.01,0.0
-3726,15583076.0,Ts'ai,653.0,France,Male,37.0,7.0,0.0,1.0,1.0,1.0,129525.58,0.0
-3727,15637753.0,Ikedinachukwu,565.0,Germany,Male,57.0,2.0,161889.79,1.0,1.0,0.0,104889.2,1.0
-3728,15763327.0,Hs?,599.0,Germany,Female,41.0,3.0,123823.3,2.0,0.0,1.0,51565.98,0.0
-3729,15806420.0,Dyer,766.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,0.0,107964.36,0.0
-3730,15600974.0,Ch'in,763.0,France,Male,42.0,7.0,117034.32,1.0,1.0,1.0,16929.41,0.0
-3731,15591344.0,K'ung,558.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,0.0,92888.52,0.0
-3732,15795586.0,Chiang,568.0,France,Female,58.0,8.0,0.0,2.0,1.0,1.0,123866.28,1.0
-3733,15586880.0,Chukwudi,716.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,113597.64,0.0
-3734,15696834.0,Ch'ang,629.0,France,Female,26.0,2.0,0.0,1.0,1.0,1.0,79577.4,0.0
-3735,15777045.0,Chidubem,711.0,France,Male,54.0,7.0,0.0,1.0,0.0,1.0,121592.5,1.0
-3736,15692671.0,Chiang,684.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,58854.88,0.0
-3737,15757912.0,Kaeppel,762.0,Spain,Male,27.0,2.0,0.0,2.0,0.0,1.0,182692.8,0.0
-3738,15732774.0,Chiedozie,661.0,Germany,Male,39.0,4.0,123863.64,2.0,1.0,0.0,1710.89,0.0
-3739,15677933.0,Chuang,755.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,83343.73,0.0
-3740,15572792.0,T'ien,683.0,Germany,Female,29.0,1.0,113599.55,2.0,0.0,0.0,104222.8,0.0
-3741,15665327.0,Chukwujekwu,689.0,Spain,Female,27.0,7.0,0.0,2.0,1.0,1.0,86413.11,0.0
-3742,15588928.0,Chidiegwu,635.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,59827.7,0.0
-3743,15743076.0,Li Fonti,559.0,France,Male,40.0,6.0,0.0,1.0,1.0,0.0,174435.48,0.0
-3744,15749019.0,Chukwujamuike,627.0,France,Male,32.0,8.0,0.0,1.0,1.0,1.0,134889.95,0.0
-3745,15590327.0,Dickson,703.0,Germany,Male,34.0,7.0,127209.0,1.0,1.0,1.0,164921.43,0.0
-3746,15645095.0,Chukwufumnanya,596.0,Germany,Female,39.0,4.0,128264.03,1.0,1.0,1.0,80552.11,1.0
-3747,15674573.0,Ritchie,743.0,France,Female,34.0,2.0,0.0,2.0,0.0,1.0,183682.09,0.0
-3748,15694566.0,Trevisan,641.0,Germany,Female,57.0,5.0,102724.38,2.0,1.0,0.0,123427.98,1.0
-3749,15750937.0,H?,629.0,Germany,Female,38.0,8.0,133141.34,1.0,0.0,0.0,171037.63,1.0
-3750,15682355.0,Onwubiko,556.0,Spain,Male,44.0,10.0,105770.7,1.0,1.0,0.0,179436.6,0.0
-3751,15680449.0,Chidumaga,671.0,Germany,Female,37.0,7.0,152303.8,1.0,1.0,0.0,96660.76,0.0
-3752,15774336.0,Onyemauchechukwu,589.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,152733.89,0.0
-3753,15734005.0,Chukwuemeka,655.0,France,Female,27.0,10.0,176914.8,1.0,1.0,0.0,102297.15,0.0
-3754,15651713.0,Obiajulu,709.0,France,Male,28.0,2.0,0.0,1.0,1.0,0.0,80190.36,0.0
-3755,15793424.0,Pinto,649.0,Germany,Female,39.0,9.0,118098.62,1.0,1.0,1.0,125877.22,1.0
-3756,15807433.0,Chinomso,673.0,France,Male,43.0,6.0,116471.43,1.0,1.0,0.0,124073.67,0.0
-3757,15751137.0,Chien,724.0,France,Female,45.0,5.0,0.0,2.0,0.0,1.0,176730.02,0.0
-3758,15655875.0,Frolov,571.0,France,Male,39.0,4.0,134950.19,1.0,1.0,0.0,163746.09,0.0
-3759,15749669.0,Fiorentino,718.0,France,Female,47.0,1.0,0.0,1.0,1.0,1.0,134491.09,1.0
-3760,15797964.0,Hsieh,598.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,98820.09,0.0
-3761,15723032.0,Milanesi,577.0,Spain,Male,33.0,5.0,0.0,1.0,0.0,1.0,66526.01,0.0
-3762,15631739.0,Nkemdilim,652.0,Germany,Male,28.0,3.0,136674.55,1.0,1.0,0.0,9984.4,0.0
-3763,15703731.0,Chukwujekwu,637.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,107867.38,0.0
-3764,15589475.0,Tuan,582.0,France,Male,32.0,6.0,0.0,2.0,0.0,1.0,27472.07,0.0
-3765,15689514.0,Onwumelu,596.0,Germany,Female,31.0,2.0,132763.49,2.0,1.0,0.0,126826.57,1.0
-3766,15748327.0,Ch'ien,632.0,France,Male,36.0,9.0,99513.87,1.0,1.0,1.0,79734.23,0.0
-3767,15784761.0,Ch'in,681.0,France,Female,62.0,7.0,0.0,1.0,1.0,0.0,136491.72,1.0
-3768,15690744.0,Chidiebere,679.0,Germany,Female,43.0,2.0,118889.66,2.0,1.0,0.0,141872.05,1.0
-3769,15593883.0,Onwuamaeze,619.0,France,Female,26.0,8.0,0.0,2.0,1.0,0.0,180329.83,0.0
-3770,15578722.0,Chukwufumnanya,573.0,France,Male,39.0,4.0,0.0,2.0,0.0,1.0,171774.5,0.0
-3771,15602034.0,Shao,651.0,France,Female,32.0,5.0,0.0,2.0,0.0,1.0,121232.58,0.0
-3772,15607537.0,Chukwukere,643.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,1.0,43821.86,0.0
-3773,15676526.0,Liao,682.0,France,Female,46.0,2.0,0.0,2.0,1.0,1.0,115818.51,0.0
-3774,15676966.0,Onyemachukwu,588.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,92220.09,0.0
-3775,15642545.0,Tsao,774.0,Spain,Male,36.0,6.0,137946.39,2.0,1.0,1.0,175094.62,0.0
-3776,15642997.0,Zikoranachidimma,537.0,France,Male,32.0,10.0,105791.06,2.0,1.0,1.0,140951.97,0.0
-3777,15770039.0,Lucchesi,651.0,Germany,Male,36.0,3.0,139848.17,1.0,1.0,0.0,106636.89,1.0
-3778,15649024.0,Iqbal,616.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,94862.93,0.0
-3779,15604544.0,H?,785.0,France,Male,41.0,8.0,123042.0,1.0,1.0,1.0,92914.67,0.0
-3780,15703413.0,Chibueze,647.0,Germany,Male,45.0,1.0,139825.34,1.0,1.0,1.0,102908.91,1.0
-3781,15782454.0,Toscani,592.0,Germany,Male,35.0,1.0,90124.64,2.0,1.0,0.0,136497.27,0.0
-3782,15649450.0,Nwankwo,607.0,France,Female,47.0,9.0,0.0,2.0,1.0,1.0,61977.82,0.0
-3783,15683544.0,Echezonachukwu,713.0,France,Male,34.0,2.0,96877.21,2.0,1.0,1.0,179258.68,0.0
-3784,15584037.0,Trevisani,687.0,France,Female,40.0,2.0,0.0,1.0,1.0,0.0,126013.58,1.0
-3785,15761763.0,Ch'ang,647.0,France,Female,48.0,8.0,0.0,1.0,1.0,0.0,184317.74,1.0
-3786,15586069.0,Brown,630.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,110440.5,0.0
-3787,15651581.0,Okwudiliolisa,757.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,143598.7,0.0
-3788,15663917.0,Nkemakonam,661.0,France,Female,32.0,1.0,100249.76,1.0,1.0,1.0,196261.64,0.0
-3789,15590344.0,Ch'ien,691.0,Spain,Female,42.0,4.0,0.0,2.0,1.0,1.0,18094.96,0.0
-3790,15596871.0,Chinedum,633.0,Germany,Female,38.0,2.0,104930.35,1.0,1.0,0.0,174777.98,0.0
-3791,15692540.0,Onwumelu,787.0,Germany,Male,42.0,6.0,101744.84,2.0,1.0,1.0,82463.69,0.0
-3792,15656484.0,Onwubiko,712.0,France,Male,38.0,2.0,0.0,1.0,1.0,0.0,129984.2,0.0
-3793,15783882.0,Tung,632.0,France,Female,29.0,2.0,0.0,2.0,0.0,1.0,35140.49,0.0
-3794,15792986.0,Pirozzi,662.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,72927.68,0.0
-3795,15684173.0,Teng,724.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,93901.61,0.0
-3796,15723488.0,Hs?,619.0,France,Female,43.0,3.0,0.0,1.0,0.0,1.0,66526.01,1.0
-3797,15806467.0,Chukwueloka,633.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,139946.68,0.0
-3798,15728474.0,Ch'en,651.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,1.0,143614.01,0.0
-3799,15672598.0,Shubin,587.0,Spain,Female,29.0,10.0,0.0,2.0,1.0,0.0,43821.56,0.0
-3800,15724296.0,Chukwuemeka,527.0,Germany,Female,39.0,5.0,140950.92,2.0,1.0,1.0,53328.7,0.0
-3801,15749177.0,Hsieh,619.0,Germany,Female,32.0,2.0,108914.17,1.0,0.0,0.0,172450.04,1.0
-3802,15669782.0,Echezonachukwu,716.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,95625.03,0.0
-3803,15568885.0,Tsao,773.0,Spain,Male,42.0,5.0,0.0,2.0,1.0,1.0,145936.21,0.0
-3804,15648067.0,Hsueh,641.0,France,Female,35.0,8.0,0.0,2.0,1.0,0.0,133598.8,0.0
-3805,15588933.0,Hsieh,739.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,1.0,86575.46,0.0
-3806,15604044.0,Yefremova,705.0,France,Female,34.0,2.0,0.0,2.0,0.0,0.0,109742.55,0.0
-3807,15733261.0,Onyekaozulu,685.0,France,Male,25.0,0.0,0.0,2.0,1.0,1.0,172668.39,0.0
-3808,15806901.0,Ch'iu,651.0,Germany,Female,39.0,1.0,120822.86,1.0,1.0,0.0,46811.77,1.0
-3809,15628999.0,Pinto,724.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,139297.71,0.0
-3810,15777901.0,Trevisani,612.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,138871.12,1.0
-3811,15599289.0,Hsieh,711.0,Spain,Male,37.0,7.0,0.0,2.0,0.0,0.0,180551.76,0.0
-3812,15779247.0,Lettiere,634.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,0.0,121525.48,0.0
-3813,15626457.0,Sun,619.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,123954.15,0.0
-3814,15746065.0,Chu,696.0,France,Male,43.0,1.0,0.0,2.0,1.0,0.0,53437.1,0.0
-3815,15806967.0,Scott,659.0,Spain,Female,32.0,5.0,0.0,1.0,0.0,0.0,84920.49,0.0
-3816,15688880.0,Gannon,639.0,France,Male,41.0,5.0,91536.97,1.0,1.0,1.0,93777.81,0.0
-3817,15589428.0,Chukwubuikem,629.0,Spain,Female,29.0,7.0,0.0,1.0,1.0,1.0,149384.43,0.0
-3818,15655341.0,Dickson,550.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,122960.98,0.0
-3819,15644977.0,Nkemdirim,685.0,France,Female,32.0,3.0,137482.19,1.0,1.0,1.0,58854.88,0.0
-3820,15771543.0,T'ang,584.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,57850.1,0.0
-3821,15756070.0,Chinweike,696.0,Germany,Female,36.0,1.0,131778.66,2.0,1.0,1.0,48242.11,0.0
-3822,15589948.0,Ochoa,757.0,France,Female,42.0,4.0,145079.72,2.0,1.0,0.0,113428.77,1.0
-3823,15694879.0,Onwuatuegwu,627.0,France,Male,43.0,1.0,0.0,2.0,1.0,1.0,134960.98,0.0
-3824,15725166.0,Rubeo,558.0,Germany,Female,43.0,3.0,78833.15,1.0,1.0,0.0,175893.53,1.0
-3825,15641655.0,Genovesi,712.0,Germany,Female,43.0,5.0,132623.6,1.0,1.0,0.0,122925.5,1.0
-3826,15749300.0,Hsueh,526.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,127569.8,0.0
-3827,15781665.0,Ritchie,753.0,France,Male,37.0,0.0,0.0,2.0,1.0,0.0,97734.3,0.0
-3828,15697042.0,Hsing,746.0,Germany,Male,36.0,7.0,94348.53,2.0,1.0,1.0,134901.56,0.0
-3829,15728006.0,Maclean,753.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,185541.2,0.0
-3830,15800326.0,Ch'in,629.0,France,Male,42.0,9.0,0.0,1.0,0.0,1.0,83681.2,0.0
-3831,15627665.0,Onyekachi,646.0,France,Female,28.0,8.0,0.0,1.0,1.0,0.0,82463.69,0.0
-3832,15676835.0,Ni,592.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,91943.94,0.0
-3833,15699195.0,Teng,673.0,Germany,Male,36.0,4.0,156848.23,2.0,1.0,0.0,60906.12,0.0
-3834,15618661.0,Chukwufumnanya,619.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,114448.77,0.0
-3835,15705313.0,Mironova,697.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,126726.33,0.0
-3836,15679429.0,Tsou,577.0,Spain,Male,41.0,7.0,0.0,2.0,1.0,1.0,58632.37,0.0
-3837,15698528.0,Shih,579.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,149772.61,0.0
-3838,15700696.0,Onyemachukwu,646.0,Spain,Female,45.0,9.0,0.0,1.0,1.0,0.0,179608.96,1.0
-3839,15744044.0,Ch'in,683.0,Germany,Male,37.0,2.0,116090.06,1.0,1.0,0.0,109614.57,0.0
-3840,15638355.0,T'ao,696.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,99298.8,0.0
-3841,15732265.0,Palerma,775.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,70956.75,0.0
-3842,15775235.0,O'Loghlin,516.0,France,Female,28.0,4.0,0.0,2.0,1.0,0.0,161407.48,0.0
-3843,15650098.0,Chukwueloka,613.0,France,Female,35.0,8.0,0.0,1.0,0.0,1.0,105570.39,0.0
-3844,15594812.0,Trevisani,775.0,France,Female,32.0,9.0,142862.21,1.0,1.0,1.0,137916.49,0.0
-3845,15689661.0,Y?,597.0,France,Male,28.0,7.0,0.0,1.0,0.0,0.0,127433.47,0.0
-3846,15699550.0,T'an,579.0,France,Male,37.0,7.0,113311.11,1.0,1.0,0.0,110230.4,0.0
-3847,15787550.0,T'ien,613.0,France,Male,43.0,10.0,0.0,1.0,1.0,1.0,101828.39,0.0
-3848,15712264.0,Chien,652.0,France,Male,40.0,5.0,0.0,4.0,1.0,0.0,72927.96,1.0
-3849,15705735.0,Toscani,683.0,Spain,Female,38.0,2.0,0.0,1.0,1.0,1.0,46775.29,0.0
-3850,15749119.0,K?,656.0,France,Female,44.0,9.0,91611.12,1.0,1.0,1.0,103832.58,0.0
-3851,15757837.0,Chibuzo,598.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,85422.14,0.0
-3852,15576706.0,Chukwuemeka,637.0,France,Female,31.0,4.0,135650.88,1.0,1.0,0.0,125440.11,0.0
-3853,15699225.0,Aksenov,642.0,France,Male,24.0,7.0,0.0,1.0,1.0,0.0,94977.3,0.0
-3854,15778915.0,P'an,669.0,France,Male,49.0,2.0,0.0,2.0,0.0,1.0,74580.8,0.0
-3855,15748552.0,Ch'ien,627.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,180862.26,0.0
-3856,15573876.0,Ch'iu,578.0,France,Female,36.0,3.0,119035.35,1.0,1.0,0.0,141446.01,0.0
-3857,15809057.0,Hsing,567.0,France,Male,41.0,8.0,0.0,2.0,0.0,0.0,158043.11,0.0
-3858,15734987.0,Chiemenam,688.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,1.0,113436.08,0.0
-3859,15709142.0,Chimezie,733.0,Spain,Male,31.0,3.0,0.0,2.0,1.0,1.0,166696.72,0.0
-3860,15591942.0,Smith,599.0,France,Male,44.0,6.0,0.0,2.0,1.0,0.0,161579.85,0.0
-3861,15597951.0,Zox,725.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,96680.16,0.0
-3862,15693737.0,T'ang,757.0,France,Male,25.0,8.0,0.0,2.0,1.0,0.0,126812.63,0.0
-3863,15732265.0,Ch'ien,590.0,Spain,Male,35.0,8.0,133636.04,1.0,1.0,0.0,151902.78,0.0
-3864,15589475.0,Kuo,727.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,175893.53,0.0
-3865,15799468.0,Ch'iu,735.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,161209.66,0.0
-3866,15747358.0,Palerma,588.0,France,Male,33.0,5.0,0.0,1.0,1.0,1.0,1795.93,0.0
-3867,15756975.0,Nnamdi,739.0,France,Female,41.0,5.0,0.0,2.0,0.0,0.0,131644.41,0.0
-3868,15673747.0,Ch'eng,684.0,France,Female,25.0,2.0,0.0,1.0,0.0,0.0,74965.61,1.0
-3869,15685920.0,Fanucci,678.0,France,Male,35.0,5.0,117641.43,1.0,1.0,1.0,154844.22,0.0
-3870,15572767.0,Tung,569.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,1.0,158750.14,0.0
-3871,15707189.0,Niu,602.0,France,Female,38.0,9.0,0.0,1.0,1.0,1.0,130189.72,0.0
-3872,15771323.0,H?,682.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,0.0,78854.57,0.0
-3873,15592021.0,Kao,683.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,74858.3,0.0
-3874,15665327.0,Ch'en,594.0,France,Male,25.0,3.0,0.0,2.0,1.0,0.0,123882.73,0.0
-3875,15672432.0,Tuan,593.0,France,Female,32.0,3.0,99546.25,1.0,1.0,1.0,164741.5,0.0
-3876,15758813.0,Ch'ang,850.0,France,Female,34.0,5.0,137851.31,1.0,0.0,0.0,74849.46,1.0
-3877,15621435.0,Chikelu,711.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,125848.88,0.0
-3878,15741643.0,Chiekwugo,785.0,France,Male,45.0,7.0,0.0,1.0,1.0,0.0,125424.42,1.0
-3879,15772423.0,Chukwudi,850.0,France,Male,38.0,3.0,102773.2,1.0,1.0,1.0,57929.9,0.0
-3880,15656835.0,Milano,559.0,Germany,Female,40.0,1.0,151912.49,2.0,1.0,0.0,120531.31,0.0
-3881,15721463.0,Gant,642.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,6076.62,0.0
-3882,15799358.0,Ch'ien,622.0,Spain,Female,39.0,3.0,144427.96,1.0,0.0,0.0,72861.84,0.0
-3883,15589230.0,Chukwumaobim,548.0,France,Male,31.0,6.0,117438.92,1.0,1.0,1.0,155915.24,0.0
-3884,15629885.0,Yefremov,543.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,166468.21,1.0
-3885,15597945.0,Nwankwo,684.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,107955.67,0.0
-3886,15570141.0,O'Donnell,446.0,France,Female,28.0,3.0,0.0,2.0,0.0,1.0,149913.94,0.0
-3887,15618243.0,Chin,668.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,0.0,152884.85,0.0
-3888,15752588.0,Chukwufumnanya,584.0,France,Male,30.0,6.0,0.0,2.0,1.0,0.0,107876.2,0.0
-3889,15690925.0,Okwuadigbo,589.0,Spain,Female,41.0,5.0,0.0,1.0,1.0,0.0,93536.38,1.0
-3890,15756560.0,Macleod,592.0,France,Male,35.0,10.0,114540.38,1.0,1.0,1.0,28707.69,0.0
-3891,15690731.0,Y?,812.0,Germany,Female,47.0,7.0,136724.69,1.0,1.0,0.0,126517.97,1.0
-3892,15782875.0,Hovell,635.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,136812.02,0.0
-3893,15761158.0,Dickson,607.0,France,Female,42.0,8.0,0.0,2.0,1.0,0.0,35450.53,0.0
-3894,15635277.0,Dickson,743.0,Spain,Male,37.0,7.0,0.0,2.0,0.0,1.0,86935.08,0.0
-3895,15794849.0,L?,743.0,France,Male,38.0,6.0,117300.02,1.0,1.0,1.0,59207.41,0.0
-3896,15711398.0,Li,733.0,France,Male,36.0,4.0,141952.07,1.0,1.0,0.0,173837.54,0.0
-3897,15665385.0,Liao,596.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,1.0,73873.65,0.0
-3898,15581388.0,Chidiebere,709.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,58440.43,0.0
-3899,15734850.0,Nkemdilim,679.0,Germany,Male,38.0,5.0,92895.56,2.0,1.0,0.0,158054.49,1.0
-3900,15733581.0,Chin,679.0,France,Female,46.0,8.0,0.0,1.0,1.0,1.0,18094.96,1.0
-3901,15729201.0,Chia,652.0,France,Female,30.0,8.0,0.0,2.0,1.0,1.0,131736.23,0.0
-3902,15726490.0,Chukwujekwu,736.0,Germany,Male,45.0,4.0,122486.11,1.0,1.0,0.0,121151.1,1.0
-3903,15672756.0,Nnaife,824.0,France,Female,48.0,8.0,0.0,3.0,1.0,1.0,10553.31,1.0
-3904,15773099.0,Ch'ien,602.0,France,Female,58.0,9.0,171646.76,1.0,1.0,0.0,136079.64,1.0
-3905,15800856.0,Chiang,723.0,Spain,Male,32.0,7.0,0.0,1.0,1.0,1.0,66463.62,0.0
-3906,15778089.0,White,578.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,126743.33,0.0
-3907,15577515.0,Nwachinemelu,667.0,France,Female,41.0,6.0,0.0,2.0,1.0,1.0,173870.39,0.0
-3908,15810563.0,Chukwukere,590.0,France,Male,36.0,6.0,97612.12,1.0,1.0,0.0,180439.75,0.0
-3909,15762588.0,Manna,565.0,France,Male,40.0,4.0,0.0,1.0,1.0,1.0,180551.76,1.0
-3910,15605672.0,Chukwualuka,673.0,France,Male,43.0,3.0,129120.64,1.0,0.0,0.0,2497.69,0.0
-3911,15691543.0,Chiang,651.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,1.0,97623.42,0.0
-3912,15605299.0,Lu,645.0,France,Male,43.0,1.0,0.0,1.0,0.0,0.0,61759.33,1.0
-3913,15762882.0,Mazzanti,629.0,France,Male,53.0,4.0,0.0,1.0,1.0,0.0,173464.43,1.0
-3914,15593854.0,Chiganu,735.0,Germany,Male,34.0,8.0,121898.58,1.0,0.0,1.0,84972.9,0.0
-3915,15694890.0,Maclean,758.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,90977.48,0.0
-3916,15582762.0,Lucchesi,576.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,0.0,109742.55,0.0
-3917,15772412.0,Piazza,646.0,Spain,Female,57.0,6.0,0.0,1.0,1.0,0.0,27862.58,1.0
-3918,15606841.0,Teng,726.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,46774.94,0.0
-3919,15698117.0,Macleod,705.0,Spain,Male,47.0,5.0,0.0,1.0,0.0,1.0,138881.02,1.0
-3920,15606841.0,Chiu,627.0,France,Male,45.0,9.0,0.0,1.0,1.0,0.0,93901.61,0.0
-3921,15594862.0,Nkemjika,709.0,Spain,Male,40.0,0.0,89696.84,1.0,1.0,1.0,143668.28,0.0
-3922,15680141.0,Tseng,584.0,Germany,Male,31.0,2.0,135927.03,1.0,0.0,1.0,108732.96,0.0
-3923,15764033.0,Scott,705.0,France,Female,27.0,2.0,0.0,2.0,1.0,0.0,132798.58,0.0
-3924,15745455.0,Hsu,758.0,France,Male,35.0,7.0,80728.15,1.0,1.0,0.0,80963.92,0.0
-3925,15589975.0,Ritchie,706.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,186885.01,0.0
-3926,15757897.0,Toscani,696.0,Germany,Female,64.0,7.0,124513.66,1.0,1.0,1.0,70563.9,0.0
-3927,15576680.0,Nwokike,842.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,62700.08,0.0
-3928,15758813.0,Bevan,730.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,139913.94,0.0
-3929,15602479.0,Ch'en,639.0,Germany,Male,32.0,3.0,73348.56,2.0,1.0,0.0,175820.91,0.0
-3930,15803526.0,Hs?,716.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,164741.5,0.0
-3931,15782530.0,Sun,616.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,1.0,148516.79,0.0
-3932,15782758.0,Genovesi,598.0,Germany,Female,43.0,10.0,79340.95,1.0,1.0,0.0,146511.2,1.0
-3933,15808784.0,Chinwenma,682.0,France,Male,51.0,5.0,0.0,2.0,0.0,0.0,141210.18,0.0
-3934,15609899.0,Ts'ui,678.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,170903.4,0.0
-3935,15641794.0,Chibueze,705.0,Spain,Female,33.0,10.0,0.0,2.0,0.0,1.0,181495.86,0.0
-3936,15613141.0,Toscani,607.0,France,Male,35.0,2.0,120875.23,1.0,1.0,0.0,171056.65,0.0
-3937,15672930.0,Manna,581.0,Germany,Male,40.0,2.0,128206.79,1.0,1.0,1.0,103792.53,1.0
-3938,15794056.0,Ts'ui,804.0,France,Male,38.0,0.0,0.0,2.0,1.0,0.0,120115.5,0.0
-3939,15627262.0,Ch'en,554.0,France,Male,40.0,9.0,0.0,1.0,1.0,0.0,81442.6,1.0
-3940,15706593.0,Shih,642.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,62368.33,0.0
-3941,15602034.0,P'eng,642.0,Spain,Male,24.0,2.0,0.0,1.0,1.0,0.0,91775.51,0.0
-3942,15617206.0,Nnamdi,660.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,1.0,114951.42,0.0
-3943,15633666.0,Chiu,653.0,Spain,Male,59.0,5.0,0.0,2.0,0.0,1.0,126688.83,0.0
-3944,15792489.0,Nucci,551.0,Germany,Female,40.0,6.0,131955.07,1.0,0.0,0.0,66463.62,1.0
-3945,15576680.0,P'eng,651.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,0.0,121541.56,0.0
-3946,15790442.0,Calabrese,596.0,France,Female,36.0,0.0,0.0,2.0,1.0,1.0,65612.12,0.0
-3947,15686983.0,Lo,629.0,France,Male,48.0,6.0,0.0,1.0,0.0,0.0,158887.09,1.0
-3948,15674727.0,Chiganu,564.0,France,Female,42.0,6.0,0.0,2.0,1.0,0.0,90905.28,0.0
-3949,15681068.0,Sun,708.0,France,Male,37.0,1.0,0.0,2.0,0.0,1.0,141821.32,0.0
-3950,15677906.0,Aitken,603.0,Germany,Female,29.0,5.0,96143.47,2.0,1.0,1.0,138350.79,0.0
-3951,15793723.0,Mazzanti,544.0,France,Female,35.0,1.0,0.0,1.0,1.0,1.0,189607.71,0.0
-3952,15794056.0,Ch'eng,636.0,Germany,Male,36.0,5.0,117206.3,4.0,1.0,0.0,52876.41,1.0
-3953,15777045.0,Tsao,607.0,Germany,Female,33.0,1.0,120124.35,2.0,1.0,0.0,18210.36,0.0
-3954,15755223.0,Shih,568.0,France,Male,36.0,8.0,0.0,1.0,0.0,1.0,121451.21,1.0
-3955,15594577.0,Efremov,642.0,France,Female,59.0,0.0,0.0,1.0,1.0,0.0,172749.65,1.0
-3956,15665811.0,Smith,697.0,Spain,Male,34.0,10.0,0.0,2.0,0.0,1.0,174825.57,0.0
-3957,15652999.0,T'ang,587.0,France,Male,46.0,5.0,0.0,1.0,0.0,0.0,179913.98,1.0
-3958,15765093.0,Chuang,632.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,129892.93,0.0
-3959,15702430.0,Yefimova,648.0,France,Female,27.0,2.0,0.0,1.0,1.0,1.0,74890.58,0.0
-3960,15589329.0,Chukwufumnanya,548.0,Germany,Female,43.0,3.0,99836.47,1.0,1.0,0.0,121440.8,0.0
-3961,15757577.0,Ch'ien,850.0,France,Male,35.0,5.0,0.0,2.0,0.0,0.0,120853.83,0.0
-3962,15592999.0,Ch'eng,709.0,France,Male,41.0,2.0,0.0,1.0,1.0,0.0,122221.33,0.0
-3963,15566633.0,Chidumaga,577.0,Germany,Male,51.0,5.0,143054.85,1.0,1.0,0.0,102908.91,1.0
-3964,15719649.0,Nkemakonam,775.0,France,Female,38.0,7.0,0.0,1.0,0.0,1.0,168918.47,0.0
-3965,15593737.0,Udinesi,746.0,France,Male,37.0,4.0,113743.37,1.0,1.0,1.0,72993.65,0.0
-3966,15612109.0,Hsia,681.0,Spain,Female,37.0,3.0,0.0,2.0,1.0,1.0,170968.99,0.0
-3967,15643679.0,Iqbal,646.0,Spain,Male,39.0,2.0,0.0,2.0,0.0,0.0,170984.97,0.0
-3968,15783029.0,Kao,553.0,Spain,Female,27.0,7.0,0.0,2.0,1.0,1.0,86950.72,0.0
-3969,15605827.0,Ozioma,665.0,France,Female,42.0,5.0,0.0,3.0,1.0,0.0,136054.45,1.0
-3970,15641640.0,Pinto,719.0,France,Female,40.0,2.0,0.0,1.0,1.0,1.0,79476.63,0.0
-3971,15663917.0,Tuan,707.0,Germany,Male,30.0,3.0,140733.74,1.0,1.0,1.0,177844.06,0.0
-3972,15645097.0,Ch'ang,597.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,175664.25,1.0
-3973,15724251.0,K?,586.0,Spain,Female,33.0,8.0,0.0,1.0,1.0,0.0,184524.65,0.0
-3974,15640074.0,Ch'ien,711.0,France,Male,29.0,10.0,0.0,2.0,1.0,0.0,156953.98,0.0
-3975,15797686.0,Trevisani,734.0,Germany,Male,37.0,7.0,115024.5,2.0,1.0,0.0,175893.53,0.0
-3976,15665416.0,Bellucci,753.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,1.0,111698.0,0.0
-3977,15738751.0,L?,654.0,France,Female,42.0,5.0,0.0,3.0,1.0,1.0,47981.54,1.0
-3978,15749381.0,Lombardo,542.0,Spain,Male,42.0,9.0,0.0,1.0,1.0,1.0,143683.75,0.0
-3979,15570141.0,T'ien,686.0,France,Male,58.0,7.0,0.0,2.0,1.0,1.0,147963.07,0.0
-3980,15775750.0,Chidubem,651.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,105986.01,0.0
-3981,15603465.0,Udinese,583.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,131662.47,0.0
-3982,15699450.0,Brown,708.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,173632.92,0.0
-3983,15593773.0,Calabresi,652.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,172363.41,0.0
-3984,15579112.0,Okwuadigbo,667.0,Spain,Male,35.0,0.0,116121.67,1.0,1.0,0.0,151401.33,0.0
-3985,15699095.0,Chukwubuikem,749.0,France,Female,30.0,6.0,0.0,1.0,1.0,1.0,176913.52,0.0
-3986,15684861.0,Maclean,638.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,79259.77,0.0
-3987,15691792.0,Galkin,790.0,France,Female,21.0,1.0,133467.77,1.0,1.0,1.0,141802.93,0.0
-3988,15803749.0,Hsia,584.0,France,Male,45.0,7.0,0.0,1.0,0.0,0.0,102021.39,1.0
-3989,15772381.0,McIntyre,645.0,France,Male,42.0,6.0,0.0,1.0,1.0,0.0,136491.72,1.0
-3990,15637550.0,Chiemela,695.0,Spain,Female,52.0,4.0,0.0,2.0,1.0,1.0,79005.95,0.0
-3991,15628308.0,Yobanna,684.0,France,Female,35.0,10.0,121874.89,1.0,0.0,1.0,189431.86,0.0
-3992,15637315.0,Hsiao,705.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,167033.83,0.0
-3993,15611669.0,Ugochukwu,743.0,France,Female,33.0,6.0,135984.15,1.0,1.0,1.0,183868.01,0.0
-3994,15671342.0,Tung,644.0,Germany,Male,44.0,5.0,130142.79,1.0,1.0,0.0,146511.6,1.0
-3995,15788686.0,Yefimova,520.0,France,Male,28.0,5.0,0.0,1.0,1.0,0.0,63218.85,0.0
-3996,15637593.0,Goliwe,707.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,95957.09,0.0
-3997,15757628.0,Chidiegwu,664.0,Germany,Male,24.0,5.0,121818.87,1.0,1.0,1.0,12093.61,0.0
-3998,15690796.0,Pinto,748.0,Spain,Female,29.0,3.0,0.0,2.0,1.0,0.0,87412.24,0.0
-3999,15738785.0,Yeh,743.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,158035.48,0.0
-4000,15690898.0,Chukwukere,733.0,France,Male,31.0,4.0,0.0,2.0,0.0,1.0,179833.45,0.0
-4001,15674812.0,Lai,591.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,0.0,94845.45,0.0
-4002,15771417.0,Ch'ang,774.0,Germany,Male,36.0,9.0,142880.28,1.0,1.0,1.0,99504.03,0.0
-4003,15682540.0,Pinto,753.0,France,Male,34.0,3.0,108711.57,1.0,0.0,1.0,80190.36,0.0
-4004,15796071.0,Yermakova,742.0,Spain,Female,26.0,1.0,149879.66,1.0,0.0,0.0,16081.62,0.0
-4005,15633840.0,Chiang,714.0,Germany,Male,39.0,10.0,120150.39,2.0,1.0,1.0,139410.08,0.0
-4006,15612455.0,Brown,659.0,France,Male,24.0,9.0,0.0,2.0,1.0,0.0,198193.75,0.0
-4007,15711378.0,Chiagoziem,679.0,France,Male,29.0,4.0,124576.65,1.0,1.0,1.0,140937.91,0.0
-4008,15722532.0,Ikedinachukwu,648.0,France,Male,47.0,2.0,0.0,2.0,1.0,1.0,125562.82,0.0
-4009,15620443.0,T'ao,684.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,6342.84,0.0
-4010,15599090.0,Maclean,496.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,142513.5,0.0
-4011,15793311.0,Hsing,720.0,Germany,Male,43.0,10.0,107233.85,2.0,1.0,1.0,173365.9,0.0
-4012,15623478.0,Pagnotto,658.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,172638.13,0.0
-4013,15589489.0,K'ung,724.0,Germany,Female,52.0,8.0,110112.55,2.0,1.0,0.0,151325.24,1.0
-4014,15705343.0,Chizuoke,592.0,France,Female,28.0,7.0,0.0,2.0,1.0,1.0,137079.86,0.0
-4015,15792525.0,Whitehead,679.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,171706.66,0.0
-4016,15622443.0,Chukwukadibia,651.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,181419.29,0.0
-4017,15763704.0,Cribb,644.0,France,Male,46.0,4.0,0.0,1.0,1.0,0.0,137961.41,0.0
-4018,15665896.0,Nkemdilim,757.0,Germany,Male,32.0,4.0,102994.04,2.0,0.0,1.0,115700.2,0.0
-4019,15701291.0,Ch'iu,636.0,France,Male,37.0,1.0,0.0,1.0,0.0,0.0,57320.92,0.0
-4020,15694829.0,Li Fonti,459.0,Spain,Male,43.0,0.0,0.0,1.0,1.0,0.0,121457.88,1.0
-4021,15604093.0,Nwankwo,719.0,France,Male,26.0,3.0,88758.93,1.0,1.0,1.0,60728.89,0.0
-4022,15722122.0,Eluemuno,725.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,0.0,158973.77,0.0
-4023,15795290.0,Ch'ang,706.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,132468.76,0.0
-4024,15798850.0,Shao,739.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,140941.47,0.0
-4025,15592461.0,Liao,597.0,Spain,Female,47.0,8.0,171770.55,1.0,1.0,0.0,137651.6,1.0
-4026,15673591.0,Bischof,624.0,France,Female,46.0,1.0,0.0,2.0,1.0,0.0,84440.5,0.0
-4027,15655274.0,Hsing,733.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,1.0,125962.02,0.0
-4028,15584091.0,Tsao,553.0,Spain,Male,41.0,4.0,0.0,2.0,1.0,0.0,136915.15,0.0
-4029,15646539.0,Hsueh,556.0,France,Female,44.0,3.0,0.0,1.0,0.0,0.0,160526.36,1.0
-4030,15783859.0,Obiajulu,538.0,France,Female,60.0,6.0,69413.44,3.0,1.0,1.0,157891.86,1.0
-4031,15658969.0,Milano,703.0,France,Male,48.0,8.0,0.0,2.0,1.0,1.0,86490.09,0.0
-4032,15746664.0,Clements,596.0,Spain,Female,32.0,6.0,0.0,2.0,1.0,0.0,162452.65,0.0
-4033,15624293.0,Genovesi,627.0,France,Male,46.0,5.0,0.0,2.0,1.0,0.0,173498.45,0.0
-4034,15734659.0,Chukwuebuka,639.0,France,Male,30.0,9.0,0.0,2.0,0.0,1.0,159835.94,0.0
-4035,15807595.0,Folliero,797.0,France,Female,39.0,3.0,0.0,2.0,1.0,0.0,38970.14,0.0
-4036,15691863.0,Lai,614.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,25310.82,0.0
-4037,15803965.0,Teng,755.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,81778.72,0.0
-4038,15625035.0,Calabresi,817.0,France,Female,42.0,8.0,0.0,2.0,0.0,1.0,91560.63,0.0
-4039,15626243.0,Chukwudi,716.0,France,Female,29.0,4.0,149379.66,1.0,1.0,0.0,75578.67,0.0
-4040,15660679.0,Chigozie,781.0,France,Male,43.0,4.0,162691.16,1.0,1.0,0.0,878.87,1.0
-4041,15790829.0,T'ang,679.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,118863.38,0.0
-4042,15589257.0,Ch'iu,501.0,Spain,Male,62.0,7.0,0.0,2.0,1.0,1.0,180427.24,0.0
-4043,15684440.0,Ozerova,732.0,Germany,Male,34.0,4.0,106414.57,2.0,1.0,1.0,185510.34,0.0
-4044,15709447.0,Tsai,705.0,Germany,Female,38.0,3.0,150635.3,2.0,1.0,0.0,124415.5,0.0
-4045,15628999.0,Nwachinemelu,704.0,France,Male,24.0,1.0,0.0,1.0,1.0,1.0,61312.15,0.0
-4046,15727299.0,Nwankwo,554.0,France,Male,27.0,8.0,0.0,2.0,1.0,1.0,148087.89,0.0
-4047,15752912.0,Ch'ang,652.0,Spain,Female,40.0,10.0,0.0,2.0,0.0,1.0,167969.15,0.0
-4048,15712176.0,Young,617.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,137510.41,0.0
-4049,15641640.0,Chukwudi,591.0,France,Female,36.0,4.0,123378.2,1.0,1.0,1.0,94738.54,0.0
-4050,15707968.0,T'ang,594.0,Germany,Male,32.0,3.0,119035.35,2.0,0.0,1.0,164741.5,0.0
-4051,15613197.0,Lucciano,688.0,Spain,Male,27.0,5.0,0.0,2.0,1.0,0.0,64421.02,0.0
-4052,15782468.0,Omeokachie,744.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,126761.1,0.0
-4053,15624995.0,Chiemela,756.0,Germany,Female,44.0,1.0,134571.5,2.0,1.0,1.0,127059.04,0.0
-4054,15741092.0,T'ao,544.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,90591.15,0.0
-4055,15736431.0,Onwughara,561.0,Germany,Female,33.0,1.0,125850.53,1.0,0.0,1.0,172749.4,0.0
-4056,15576928.0,Milne,712.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,121568.37,0.0
-4057,15724876.0,Chukwuemeka,590.0,France,Female,28.0,6.0,0.0,1.0,1.0,0.0,172524.51,0.0
-4058,15787830.0,Fomina,590.0,France,Male,25.0,7.0,0.0,2.0,1.0,1.0,132770.11,0.0
-4059,15578788.0,Hsu,718.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,1.0,164768.82,0.0
-4060,15793881.0,Ch'in,673.0,France,Female,37.0,2.0,0.0,2.0,1.0,1.0,10772.13,0.0
-4061,15771543.0,Mazzi,553.0,Germany,Female,46.0,5.0,130274.26,1.0,1.0,0.0,105720.09,1.0
-4062,15593943.0,Onyekachi,712.0,France,Female,34.0,4.0,0.0,2.0,0.0,0.0,136034.93,0.0
-4063,15592386.0,Mazzi,711.0,France,Male,35.0,3.0,0.0,1.0,1.0,0.0,139947.17,0.0
-4064,15691808.0,Y?an,571.0,France,Male,35.0,3.0,132088.59,1.0,1.0,1.0,193018.71,0.0
-4065,15746802.0,Mazzanti,675.0,France,Male,34.0,7.0,79340.95,1.0,1.0,0.0,87071.18,0.0
-4066,15674950.0,Frolov,607.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,145936.81,1.0
-4067,15702293.0,Eluemuno,663.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,179540.73,0.0
-4068,15724307.0,Fanucci,622.0,France,Male,38.0,8.0,0.0,2.0,0.0,0.0,121084.27,0.0
-4069,15596984.0,Lai,637.0,France,Female,46.0,5.0,0.0,2.0,1.0,1.0,121085.72,0.0
-4070,15764491.0,Scott,712.0,France,Male,33.0,1.0,69329.47,1.0,1.0,1.0,78698.04,0.0
-4071,15706799.0,Hsueh,553.0,France,Female,52.0,2.0,0.0,3.0,0.0,0.0,108764.69,1.0
-4072,15680430.0,Ts'ao,596.0,France,Male,42.0,9.0,61482.47,1.0,1.0,1.0,28238.25,0.0
-4073,15611287.0,Chukwukadibia,727.0,Spain,Female,32.0,7.0,113657.83,1.0,1.0,1.0,105817.63,1.0
-4074,15622443.0,T'ang,730.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,138780.93,0.0
-4075,15722533.0,Fiorentino,775.0,France,Male,45.0,8.0,0.0,2.0,1.0,1.0,86937.2,0.0
-4076,15612444.0,Folliero,466.0,France,Male,37.0,5.0,0.0,2.0,0.0,1.0,93739.71,0.0
-4077,15589924.0,Chiang,593.0,France,Female,24.0,2.0,0.0,2.0,1.0,1.0,84440.5,0.0
-4078,15643359.0,Ts'ao,603.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,1.0,80552.11,0.0
-4079,15685329.0,Hsia,525.0,Spain,Female,47.0,3.0,0.0,2.0,1.0,0.0,87822.14,1.0
-4080,15642545.0,Ritchie,707.0,France,Male,40.0,6.0,0.0,1.0,1.0,1.0,94951.27,0.0
-4081,15772781.0,Onwumelu,632.0,Spain,Female,39.0,9.0,0.0,2.0,1.0,1.0,82820.85,0.0
-4082,15605284.0,Yao,635.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,0.0,134589.58,0.0
-4083,15646764.0,Eluemuno,671.0,France,Male,25.0,5.0,0.0,2.0,1.0,0.0,137916.49,0.0
-4084,15762793.0,Chukwujamuike,542.0,France,Male,41.0,3.0,125495.64,1.0,1.0,0.0,1354.25,0.0
-4085,15707025.0,Zetticci,714.0,France,Female,38.0,9.0,0.0,2.0,1.0,0.0,141426.78,0.0
-4086,15581736.0,Ts'ui,730.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,141815.76,0.0
-4087,15665634.0,Mazzi,683.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,142223.94,0.0
-4088,15664539.0,Genovese,562.0,France,Male,23.0,5.0,0.0,2.0,1.0,1.0,151325.24,0.0
-4089,15620204.0,Palerma,646.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,146411.6,0.0
-4090,15642709.0,Hsueh,681.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,161519.79,0.0
-4091,15583576.0,Hsueh,624.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,48475.12,0.0
-4092,15578980.0,Lucciano,748.0,Spain,Male,49.0,8.0,0.0,1.0,1.0,0.0,52462.25,1.0
-4093,15791767.0,Chimezie,705.0,France,Male,31.0,3.0,81947.76,1.0,1.0,1.0,130789.6,0.0
-4094,15791535.0,Hsiao,683.0,France,Female,41.0,4.0,0.0,1.0,0.0,0.0,168895.06,0.0
-4095,15772073.0,Dyer,594.0,Germany,Female,34.0,5.0,143964.36,2.0,0.0,1.0,35279.74,0.0
-4096,15764033.0,Ts'ui,558.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,84133.48,0.0
-4097,15680752.0,Uvarov,636.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,113428.77,0.0
-4098,15670172.0,Ch'ang,642.0,France,Female,37.0,7.0,0.0,2.0,0.0,0.0,82709.8,0.0
-4099,15592230.0,Hs?,714.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,100423.88,0.0
-4100,15694829.0,Chibugo,637.0,Germany,Male,33.0,3.0,132412.38,2.0,1.0,0.0,155870.82,0.0
-4101,15750803.0,Obiajulu,706.0,France,Male,32.0,2.0,163760.82,1.0,1.0,0.0,145524.36,0.0
-4102,15598892.0,Okwudiliolisa,649.0,Spain,Male,50.0,8.0,0.0,1.0,1.0,1.0,136910.18,1.0
-4103,15610972.0,Rizzo,720.0,France,Female,25.0,7.0,148045.45,1.0,1.0,0.0,64945.57,0.0
-4104,15771857.0,Chukwuemeka,599.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,51454.06,0.0
-4105,15736420.0,Chikezie,627.0,France,Female,33.0,1.0,0.0,1.0,1.0,1.0,172668.39,0.0
-4106,15692991.0,Onyekachi,569.0,Spain,Male,30.0,3.0,0.0,2.0,0.0,0.0,140745.91,0.0
-4107,15641110.0,Ch'iu,540.0,France,Male,38.0,4.0,0.0,2.0,0.0,1.0,85421.48,0.0
-4108,15761141.0,DeRose,537.0,Germany,Female,35.0,9.0,153722.47,2.0,1.0,1.0,78700.02,0.0
-4109,15658577.0,Tuan,696.0,Germany,Female,37.0,9.0,121537.71,1.0,0.0,1.0,55775.84,1.0
-4110,15660144.0,Hsia,687.0,Spain,Female,37.0,2.0,109733.2,1.0,1.0,0.0,177718.17,0.0
-4111,15782688.0,Chiu,705.0,Germany,Male,31.0,2.0,141581.71,2.0,1.0,1.0,141404.07,0.0
-4112,15704081.0,Tsao,654.0,France,Male,34.0,7.0,0.0,1.0,1.0,1.0,131712.56,0.0
-4113,15790810.0,Hs?,634.0,France,Female,44.0,3.0,0.0,2.0,1.0,1.0,125154.57,0.0
-4114,15732540.0,T'ang,613.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,72350.47,0.0
-4115,15811200.0,Ts'ui,649.0,Spain,Male,32.0,2.0,0.0,2.0,0.0,0.0,133653.38,0.0
-4116,15706223.0,Nkemjika,766.0,Germany,Male,46.0,6.0,136623.19,1.0,1.0,0.0,165082.25,1.0
-4117,15714240.0,Onwumelu,537.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,133653.38,0.0
-4118,15589949.0,Onyekachukwu,714.0,Germany,Female,25.0,7.0,69712.84,2.0,1.0,1.0,123587.85,0.0
-4119,15793723.0,Ts'ai,682.0,Spain,Male,31.0,4.0,0.0,2.0,0.0,1.0,131886.46,0.0
-4120,15742210.0,White,612.0,Spain,Male,35.0,8.0,0.0,1.0,1.0,0.0,132798.26,0.0
-4121,15804919.0,Hsieh,762.0,France,Female,32.0,4.0,0.0,2.0,0.0,0.0,93524.19,0.0
-4122,15728352.0,Shih,638.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,0.0,38015.34,0.0
-4123,15783541.0,Hsiao,485.0,Spain,Female,54.0,2.0,145980.23,1.0,0.0,0.0,4272.0,1.0
-4124,15728773.0,Hsia,579.0,France,Male,41.0,7.0,0.0,2.0,0.0,0.0,132770.11,0.0
-4125,15602551.0,Shih,687.0,France,Female,31.0,3.0,0.0,1.0,1.0,0.0,146738.83,0.0
-4126,15788218.0,Kambinachi,539.0,France,Female,43.0,8.0,0.0,2.0,1.0,1.0,74835.65,0.0
-4127,15745250.0,Dyson,695.0,France,Male,42.0,4.0,131659.59,2.0,1.0,1.0,126794.56,0.0
-4128,15603764.0,Hsueh,521.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,186778.89,0.0
-4129,15752294.0,Pirozzi,710.0,France,Male,38.0,9.0,120599.38,1.0,1.0,0.0,162260.93,0.0
-4130,15780141.0,Ritchie,788.0,France,Male,34.0,1.0,151953.19,1.0,1.0,0.0,78854.58,0.0
-4131,15672598.0,Teng,599.0,Spain,Female,54.0,2.0,0.0,1.0,1.0,0.0,74854.97,1.0
-4132,15567339.0,Pisano,676.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,114442.66,0.0
-4133,15598894.0,Ubanwa,707.0,France,Female,35.0,5.0,0.0,3.0,1.0,0.0,143783.46,0.0
-4134,15764351.0,Scott,652.0,Spain,Female,23.0,5.0,0.0,2.0,1.0,0.0,75574.48,0.0
-4135,15738166.0,Lu,696.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,91086.74,0.0
-4136,15789924.0,Yefremova,559.0,France,Male,39.0,3.0,0.0,1.0,1.0,0.0,179833.45,1.0
-4137,15752588.0,Nkemdirim,683.0,Germany,Male,28.0,3.0,118064.85,2.0,1.0,0.0,131283.6,0.0
-4138,15798850.0,Chiu,683.0,France,Female,39.0,0.0,0.0,1.0,0.0,0.0,159629.64,1.0
-4139,15705620.0,Lo,655.0,France,Male,25.0,3.0,110738.89,1.0,1.0,0.0,138350.74,0.0
-4140,15719579.0,Hsia,575.0,France,Female,45.0,6.0,0.0,1.0,0.0,0.0,120880.72,1.0
-4141,15732704.0,Lu,683.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,136837.97,0.0
-4142,15713608.0,Ts'ui,700.0,France,Male,37.0,5.0,0.0,1.0,0.0,0.0,91359.79,0.0
-4143,15702293.0,Smith,850.0,Spain,Female,47.0,5.0,167949.48,1.0,1.0,1.0,141501.45,0.0
-4144,15655952.0,Martin,768.0,Spain,Female,44.0,7.0,107553.33,2.0,1.0,0.0,155711.64,0.0
-4145,15750248.0,McIntyre,705.0,France,Male,40.0,5.0,0.0,1.0,0.0,0.0,61251.25,1.0
-4146,15663410.0,T'ang,738.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,140931.57,0.0
-4147,15781451.0,Chukwudi,638.0,Germany,Female,29.0,5.0,152400.51,1.0,1.0,0.0,124964.82,0.0
-4148,15675328.0,Onyinyechukwuka,699.0,Spain,Male,33.0,7.0,0.0,1.0,1.0,1.0,49594.02,0.0
-4149,15608528.0,Ma,624.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,127734.16,0.0
-4150,15679294.0,Ozerova,743.0,France,Female,39.0,5.0,0.0,1.0,1.0,0.0,117323.31,1.0
-4151,15638231.0,Nkemdilim,776.0,France,Male,36.0,6.0,116393.59,1.0,1.0,0.0,93769.89,0.0
-4152,15620398.0,Chiang,772.0,France,Male,27.0,6.0,97612.12,1.0,0.0,0.0,60721.4,0.0
-4153,15701160.0,Ch'iu,468.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,105433.53,0.0
-4154,15727605.0,Palermo,540.0,Spain,Male,27.0,1.0,0.0,2.0,1.0,0.0,131772.51,0.0
-4155,15800515.0,Ndubuagha,574.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,61290.99,0.0
-4156,15693817.0,Chiang,705.0,Germany,Male,37.0,7.0,111837.78,2.0,1.0,1.0,91359.79,0.0
-4157,15658449.0,Iadanza,815.0,Spain,Female,46.0,7.0,0.0,2.0,0.0,1.0,82969.61,0.0
-4158,15752588.0,Scott,794.0,France,Female,26.0,4.0,98627.11,1.0,1.0,1.0,98015.85,0.0
-4159,15783619.0,Milanesi,711.0,Germany,Male,46.0,8.0,127974.07,1.0,1.0,1.0,119232.33,0.0
-4160,15749401.0,Lai,850.0,Germany,Female,45.0,5.0,122501.28,1.0,1.0,1.0,142977.05,1.0
-4161,15637461.0,Chigolum,596.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,1.0,60954.45,0.0
-4162,15610446.0,Cattaneo,641.0,France,Male,38.0,2.0,122522.32,2.0,1.0,1.0,149922.38,0.0
-4163,15797475.0,H?,645.0,France,Female,37.0,5.0,0.0,2.0,1.0,1.0,94767.77,0.0
-4164,15722692.0,Ts'ao,850.0,France,Male,33.0,6.0,133628.09,1.0,0.0,0.0,107876.2,0.0
-4165,15585379.0,Fiorentino,833.0,France,Male,29.0,3.0,122545.65,1.0,1.0,0.0,52709.55,0.0
-4166,15726557.0,Lo,679.0,France,Female,32.0,1.0,145512.5,1.0,0.0,0.0,121681.19,0.0
-4167,15647898.0,Wei,613.0,Germany,Female,38.0,2.0,94901.03,1.0,0.0,0.0,123587.85,0.0
-4168,15778025.0,Mazzanti,758.0,France,Male,36.0,2.0,0.0,1.0,1.0,1.0,120115.5,1.0
-4169,15651533.0,Ponomaryov,627.0,France,Male,35.0,4.0,121681.91,1.0,1.0,1.0,154971.64,0.0
-4170,15777922.0,Chidubem,850.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,35701.06,0.0
-4171,15661409.0,Ts'ai,581.0,France,Female,40.0,10.0,0.0,2.0,1.0,0.0,151871.84,0.0
-4172,15698664.0,Onwumelu,561.0,Spain,Female,41.0,2.0,160903.2,1.0,1.0,1.0,80619.17,0.0
-4173,15585738.0,Onyeorulu,581.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,167924.95,0.0
-4174,15672447.0,Tien,718.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,169435.3,0.0
-4175,15578835.0,Genovese,730.0,Germany,Female,36.0,6.0,132667.18,1.0,1.0,0.0,162599.51,1.0
-4176,15681671.0,Onyekachi,675.0,France,Female,23.0,8.0,0.0,1.0,1.0,0.0,168034.9,0.0
-4177,15737571.0,Onyekachukwu,668.0,France,Male,38.0,5.0,0.0,1.0,1.0,0.0,108419.41,0.0
-4178,15775809.0,Chiekwugo,646.0,France,Female,27.0,8.0,0.0,2.0,0.0,1.0,91943.94,0.0
-4179,15637315.0,Macleod,639.0,Germany,Male,32.0,3.0,125921.37,1.0,0.0,1.0,189683.7,1.0
-4180,15735339.0,Ugochukwu,581.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,123882.73,0.0
-4181,15584515.0,Yeh,589.0,Germany,Female,46.0,4.0,131365.37,1.0,0.0,0.0,164741.5,1.0
-4182,15761348.0,Calabrese,790.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,180712.28,0.0
-4183,15571666.0,Ting,494.0,Germany,Female,37.0,3.0,119496.25,1.0,0.0,0.0,166677.54,0.0
-4184,15782778.0,Mironova,686.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,143859.66,0.0
-4185,15807553.0,Folliero,732.0,France,Female,39.0,4.0,0.0,2.0,0.0,1.0,126565.9,0.0
-4186,15580623.0,Chukwuebuka,739.0,France,Male,60.0,4.0,0.0,1.0,1.0,0.0,164741.5,1.0
-4187,15721592.0,Y?,682.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,168683.57,0.0
-4188,15676904.0,Chidumaga,751.0,France,Male,47.0,5.0,0.0,1.0,0.0,0.0,158973.82,1.0
-4189,15708710.0,Chiekwugo,679.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,120853.83,0.0
-4190,15662426.0,Chiang,790.0,France,Male,32.0,9.0,81677.22,1.0,1.0,1.0,77906.87,0.0
-4191,15813422.0,Ts'ui,633.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,80134.65,0.0
-4192,15795990.0,Illingworth,641.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,61761.77,0.0
-4193,15659092.0,Ch'ien,633.0,Germany,Female,47.0,3.0,113501.72,1.0,1.0,1.0,99061.75,1.0
-4194,15784890.0,Chimezie,584.0,France,Female,38.0,3.0,151510.17,1.0,1.0,0.0,176760.9,0.0
-4195,15579826.0,Ch'iu,683.0,France,Male,47.0,9.0,151675.65,1.0,0.0,0.0,100717.39,1.0
-4196,15632365.0,Ugorji,686.0,France,Male,26.0,6.0,0.0,2.0,1.0,0.0,187905.12,0.0
-4197,15592029.0,Milanesi,705.0,France,Male,28.0,7.0,151510.17,2.0,0.0,1.0,93883.53,0.0
-4198,15670029.0,Onwudiwe,459.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,151325.24,0.0
-4199,15634602.0,Hsiao,594.0,Spain,Female,32.0,5.0,0.0,1.0,1.0,0.0,166957.82,0.0
-4200,15690175.0,Ch'in,586.0,Spain,Female,27.0,3.0,0.0,1.0,1.0,1.0,18761.92,0.0
-4201,15681332.0,Chukwufumnanya,653.0,Germany,Male,26.0,6.0,117412.19,1.0,1.0,1.0,120225.62,0.0
-4202,15798888.0,Hao,710.0,Spain,Female,37.0,5.0,98635.77,1.0,0.0,1.0,92664.09,0.0
-4203,15626156.0,Onwuamaeze,645.0,Germany,Female,61.0,8.0,121404.64,2.0,1.0,1.0,161241.65,0.0
-4204,15763922.0,Chiang,538.0,Spain,Female,39.0,4.0,0.0,2.0,1.0,0.0,116471.11,0.0
-4205,15797710.0,L?,554.0,Germany,Male,43.0,8.0,76671.06,1.0,1.0,0.0,116471.43,1.0
-4206,15802823.0,Baresi,632.0,Spain,Female,34.0,8.0,0.0,2.0,1.0,0.0,53478.02,0.0
-4207,15624347.0,Onyemaechi,602.0,France,Male,41.0,3.0,109922.61,1.0,1.0,1.0,168053.4,0.0
-4208,15571586.0,Chukwuebuka,556.0,France,Male,35.0,3.0,97378.54,1.0,0.0,0.0,132973.17,0.0
-4209,15763662.0,Tsao,663.0,Spain,Female,45.0,2.0,0.0,1.0,0.0,0.0,179673.11,1.0
-4210,15771702.0,Toscani,847.0,Germany,Male,55.0,9.0,92786.66,2.0,0.0,0.0,823.96,1.0
-4211,15603914.0,Kambinachi,704.0,France,Male,33.0,0.0,0.0,2.0,1.0,0.0,69055.45,0.0
-4212,15572390.0,Pinto,699.0,France,Male,26.0,0.0,0.0,2.0,0.0,0.0,169781.45,0.0
-4213,15594878.0,Onyemauchechukwu,652.0,France,Female,35.0,5.0,0.0,2.0,0.0,1.0,82222.86,0.0
-4214,15783875.0,Okwudiliolisa,719.0,Germany,Male,41.0,0.0,129895.19,1.0,0.0,1.0,138051.19,1.0
-4215,15692406.0,Onyekachukwu,682.0,France,Female,40.0,10.0,142946.13,2.0,1.0,0.0,122021.33,0.0
-4216,15649738.0,Calabresi,714.0,Spain,Female,42.0,2.0,0.0,2.0,1.0,1.0,121402.53,0.0
-4217,15797389.0,Fyodorova,579.0,France,Male,30.0,4.0,0.0,1.0,0.0,1.0,137791.18,0.0
-4218,15572038.0,T'an,850.0,France,Male,22.0,10.0,0.0,2.0,1.0,0.0,79577.4,0.0
-4219,15644849.0,Ts'ai,619.0,Germany,Male,52.0,9.0,102562.23,1.0,1.0,0.0,167873.49,1.0
-4220,15750447.0,Chidi,746.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,163666.85,0.0
-4221,15589949.0,Lettiere,628.0,Spain,Female,39.0,9.0,0.0,1.0,1.0,0.0,151401.33,1.0
-4222,15592229.0,Y?,508.0,Spain,Female,44.0,1.0,116442.39,2.0,1.0,1.0,46630.47,1.0
-4223,15784844.0,Hsieh,711.0,France,Female,37.0,8.0,113923.69,1.0,1.0,0.0,86701.4,0.0
-4224,15656597.0,Folliero,743.0,Germany,Female,33.0,8.0,128881.95,2.0,1.0,0.0,136812.02,0.0
-4225,15637678.0,Hsiung,667.0,France,Female,37.0,2.0,0.0,2.0,1.0,1.0,96864.46,0.0
-4226,15783883.0,Toscani,612.0,France,Male,54.0,5.0,0.0,1.0,0.0,0.0,1276.87,1.0
-4227,15791854.0,Ritchie,623.0,France,Male,31.0,3.0,152303.66,1.0,1.0,1.0,186948.35,0.0
-4228,15795540.0,Li Fonti,591.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,148811.49,0.0
-4229,15682955.0,Chiemela,588.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,112572.02,0.0
-4230,15723818.0,Yost,667.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,91098.76,0.0
-4231,15779947.0,Yuryeva,633.0,Spain,Female,29.0,4.0,0.0,1.0,0.0,0.0,161889.79,0.0
-4232,15596074.0,Mazzanti,625.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,138021.36,0.0
-4233,15675888.0,Chimaobim,708.0,Spain,Female,56.0,2.0,157832.15,2.0,0.0,1.0,156910.46,0.0
-4234,15671490.0,Shih,637.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,1.0,51999.5,0.0
-4235,15781347.0,Ch'in,707.0,France,Male,40.0,9.0,162560.32,1.0,1.0,1.0,113796.15,0.0
-4236,15743570.0,Illingworth,725.0,Spain,Male,30.0,4.0,126725.25,1.0,0.0,1.0,126726.33,0.0
-4237,15604544.0,Chiedozie,595.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,1.0,173899.42,0.0
-4238,15767231.0,Hsu,588.0,Germany,Female,33.0,1.0,91285.13,2.0,1.0,1.0,145558.42,0.0
-4239,15804787.0,Cattaneo,677.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,157097.09,0.0
-4240,15652693.0,Clements,579.0,Spain,Female,33.0,8.0,0.0,2.0,1.0,0.0,166526.26,0.0
-4241,15661903.0,Onyinyechukwuka,750.0,Germany,Male,51.0,5.0,83348.89,1.0,1.0,0.0,70323.68,0.0
-4242,15800482.0,Ogochukwu,679.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,169921.11,0.0
-4243,15671987.0,Hsia,682.0,Spain,Male,53.0,1.0,127864.4,1.0,0.0,1.0,187899.97,0.0
-4244,15747503.0,Lombardi,711.0,France,Male,30.0,8.0,0.0,1.0,1.0,1.0,131736.23,1.0
-4245,15775307.0,Okwuadigbo,706.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,179032.68,0.0
-4246,15733797.0,Hsing,591.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,1.0,156774.94,0.0
-4247,15713890.0,Brown,717.0,Spain,Male,18.0,8.0,0.0,2.0,1.0,0.0,146949.74,0.0
-4248,15699195.0,Nwabugwu,790.0,France,Female,31.0,3.0,121461.58,1.0,1.0,0.0,172601.62,0.0
-4249,15703923.0,Folliero,644.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,106826.92,0.0
-4250,15603035.0,Yeh,652.0,Spain,Male,56.0,8.0,125801.03,1.0,1.0,0.0,138399.67,1.0
-4251,15706534.0,O'Donnell,753.0,Germany,Male,28.0,9.0,93578.68,1.0,1.0,1.0,173486.39,0.0
-4252,15772573.0,Y?,710.0,Spain,Male,24.0,6.0,141749.74,1.0,0.0,1.0,172674.01,0.0
-4253,15797733.0,Nwachukwu,653.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,173899.42,0.0
-4254,15691817.0,Hsia,710.0,Germany,Female,51.0,3.0,108913.84,1.0,1.0,1.0,172601.62,1.0
-4255,15684594.0,Ch'in,684.0,France,Male,38.0,7.0,63663.93,1.0,1.0,0.0,72318.77,0.0
-4256,15700965.0,Tung,721.0,Spain,Male,59.0,5.0,0.0,2.0,0.0,1.0,92816.86,0.0
-4257,15702715.0,Chukwualuka,613.0,Germany,Male,36.0,10.0,134591.33,1.0,1.0,0.0,120790.02,0.0
-4258,15686913.0,Onwubiko,788.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,93901.61,0.0
-4259,15626900.0,Chien,593.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,149922.38,0.0
-4260,15772363.0,Ch'eng,683.0,France,Female,35.0,3.0,0.0,1.0,1.0,0.0,109545.56,0.0
-4261,15689955.0,Chuang,638.0,Germany,Female,61.0,2.0,101609.01,1.0,0.0,1.0,3232.82,1.0
-4262,15650066.0,K?,659.0,France,Male,43.0,5.0,127559.95,1.0,1.0,0.0,74858.3,1.0
-4263,15606554.0,Frolova,687.0,Germany,Female,38.0,8.0,125725.02,2.0,0.0,1.0,60954.45,0.0
-4264,15768451.0,Chiemenam,748.0,Spain,Male,25.0,9.0,0.0,2.0,0.0,0.0,131736.23,0.0
-4265,15771299.0,Pagnotto,628.0,France,Male,46.0,9.0,96166.88,2.0,1.0,0.0,91903.9,0.0
-4266,15593736.0,Ch'iu,648.0,Spain,Female,40.0,2.0,153639.11,1.0,0.0,1.0,86981.45,1.0
-4267,15807768.0,Ch'iu,850.0,Germany,Male,42.0,9.0,91760.46,2.0,0.0,1.0,147040.25,0.0
-4268,15698902.0,DeRose,628.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,130784.2,0.0
-4269,15702434.0,Hsieh,749.0,Spain,Female,26.0,10.0,81258.13,1.0,1.0,1.0,133535.75,0.0
-4270,15718773.0,Baresi,720.0,France,Female,37.0,1.0,120847.11,1.0,1.0,0.0,75632.78,0.0
-4271,15746410.0,Liao,569.0,France,Female,31.0,0.0,0.0,2.0,1.0,0.0,76549.64,0.0
-4272,15641786.0,Palerma,668.0,Germany,Male,38.0,0.0,134954.52,2.0,1.0,0.0,16081.62,0.0
-4273,15806964.0,Onyemauchechukwu,685.0,France,Female,40.0,6.0,0.0,1.0,1.0,0.0,177614.17,1.0
-4274,15704795.0,Tuan,538.0,Spain,Female,42.0,5.0,0.0,1.0,1.0,0.0,158545.25,1.0
-4275,15591279.0,Milani,695.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,121074.87,0.0
-4276,15737888.0,Chia,732.0,Germany,Female,32.0,3.0,128743.8,2.0,1.0,1.0,182738.0,0.0
-4277,15748552.0,T'ien,703.0,France,Female,32.0,8.0,0.0,1.0,1.0,1.0,160129.99,0.0
-4278,15585067.0,McIntyre,559.0,France,Male,43.0,1.0,0.0,2.0,1.0,1.0,38794.57,0.0
-4279,15569682.0,Ngozichukwuka,686.0,Spain,Female,45.0,4.0,142663.46,1.0,1.0,0.0,86786.41,0.0
-4280,15702300.0,Nwankwo,733.0,Germany,Female,35.0,9.0,125023.1,1.0,1.0,0.0,133535.29,1.0
-4281,15751177.0,Binder,667.0,Germany,Female,43.0,0.0,108308.0,2.0,1.0,0.0,141872.05,0.0
-4282,15783030.0,Onyinyechukwuka,685.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,138351.68,0.0
-4283,15700937.0,Ch'ang,573.0,Germany,Male,38.0,8.0,131754.11,1.0,1.0,1.0,82647.65,0.0
-4284,15662698.0,Mazzi,714.0,Germany,Male,40.0,1.0,102974.64,1.0,0.0,1.0,121681.19,0.0
-4285,15809772.0,Chukwufumnanya,699.0,Spain,Female,34.0,0.0,0.0,2.0,1.0,0.0,163667.56,0.0
-4286,15793881.0,Hsieh,477.0,France,Male,39.0,7.0,0.0,1.0,1.0,1.0,57517.71,0.0
-4287,15781804.0,Chidiegwu,713.0,Germany,Female,40.0,7.0,101070.69,2.0,1.0,1.0,108675.01,0.0
-4288,15683560.0,Onyekachi,544.0,France,Male,41.0,4.0,98675.74,1.0,0.0,0.0,171552.02,0.0
-4289,15702741.0,Shao,748.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,91943.94,0.0
-4290,15605916.0,Fanucci,713.0,Spain,Male,20.0,1.0,0.0,2.0,1.0,0.0,80178.54,0.0
-4291,15603574.0,Nwankwo,633.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,179908.71,0.0
-4292,15604792.0,Ijendu,615.0,France,Male,31.0,8.0,0.0,2.0,0.0,1.0,124486.28,0.0
-4293,15645323.0,Chidubem,646.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,123697.95,0.0
-4294,15709513.0,Ejimofor,681.0,Spain,Male,41.0,3.0,0.0,1.0,0.0,0.0,136301.1,0.0
-4295,15596863.0,Mott,584.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,0.0,172097.4,0.0
-4296,15776733.0,Tien,659.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,166476.95,0.0
-4297,15740175.0,Uspenskaya,741.0,France,Male,53.0,9.0,114084.6,1.0,1.0,0.0,167066.95,1.0
-4298,15680918.0,Kao,569.0,France,Male,52.0,8.0,0.0,1.0,1.0,0.0,162511.55,1.0
-4299,15701280.0,Shao,712.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,93336.3,0.0
-4300,15642785.0,Hsing,652.0,France,Male,42.0,3.0,0.0,2.0,0.0,1.0,73679.59,0.0
-4301,15778290.0,Fan,691.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,137985.69,0.0
-4302,15583736.0,Shao,584.0,Spain,Female,52.0,9.0,0.0,1.0,1.0,0.0,141558.04,1.0
-4303,15574743.0,Chin,687.0,France,Female,39.0,1.0,0.0,1.0,1.0,0.0,132975.36,0.0
-4304,15569249.0,Tien,731.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,1.0,48553.77,0.0
-4305,15597930.0,Onwuamaeze,657.0,France,Male,39.0,8.0,134766.85,1.0,1.0,1.0,83093.25,0.0
-4306,15686947.0,Chiu,683.0,France,Male,36.0,10.0,118569.03,2.0,0.0,0.0,89566.74,0.0
-4307,15713673.0,Fennell,579.0,Spain,Female,32.0,4.0,0.0,1.0,1.0,0.0,156858.2,0.0
-4308,15648876.0,Chukwueloka,496.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,176770.76,0.0
-4309,15792329.0,Ozerova,679.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,1.0,142400.27,0.0
-4310,15709639.0,Pethard,543.0,Spain,Male,42.0,5.0,0.0,1.0,1.0,0.0,53412.08,1.0
-4311,15641411.0,Chinwendu,715.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,177740.58,0.0
-4312,15659962.0,Kenechukwu,598.0,France,Male,29.0,3.0,141581.71,1.0,0.0,1.0,187718.26,0.0
-4313,15811588.0,Chiazagomekpere,645.0,Spain,Female,22.0,9.0,0.0,1.0,1.0,1.0,57876.05,0.0
-4314,15578722.0,Trevisani,595.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,109659.12,0.0
-4315,15586039.0,K'ung,653.0,Spain,Male,50.0,2.0,0.0,2.0,0.0,1.0,182624.93,0.0
-4316,15791323.0,Fanucci,735.0,Germany,Male,33.0,9.0,123699.28,1.0,1.0,0.0,175072.58,0.0
-4317,15766017.0,Iadanza,714.0,Germany,Female,25.0,2.0,129740.54,3.0,0.0,1.0,134878.19,0.0
-4318,15709741.0,Hsia,675.0,France,Male,40.0,2.0,0.0,1.0,1.0,1.0,162523.66,1.0
-4319,15663885.0,Maclean,749.0,Germany,Female,35.0,3.0,149777.0,1.0,1.0,0.0,110932.24,0.0
-4320,15582960.0,Ponomaryov,622.0,Germany,Female,45.0,1.0,138340.06,1.0,0.0,1.0,102930.46,1.0
-4321,15689517.0,Mancini,779.0,Germany,Female,46.0,0.0,163700.78,1.0,1.0,1.0,140938.95,1.0
-4322,15744529.0,Liao,678.0,Germany,Male,21.0,3.0,144550.62,1.0,1.0,1.0,116511.12,0.0
-4323,15602811.0,Shao,702.0,Germany,Female,27.0,4.0,137852.96,1.0,1.0,0.0,116696.77,1.0
-4324,15690925.0,Hsing,689.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,35177.31,0.0
-4325,15761168.0,Ch'ien,564.0,France,Male,49.0,4.0,0.0,2.0,1.0,0.0,134589.58,0.0
-4326,15632051.0,Biryukov,692.0,Germany,Female,42.0,6.0,57929.81,2.0,1.0,1.0,89566.64,0.0
-4327,15654700.0,Chukwukadibia,832.0,Spain,Female,71.0,9.0,0.0,2.0,1.0,1.0,87400.53,0.0
-4328,15622370.0,Chikezie,749.0,France,Male,41.0,5.0,110942.9,1.0,1.0,1.0,131736.23,0.0
-4329,15781127.0,Yobanna,595.0,France,Male,27.0,6.0,0.0,2.0,1.0,0.0,124596.63,0.0
-4330,15803764.0,Ts'ui,753.0,France,Male,33.0,4.0,0.0,2.0,0.0,0.0,152992.04,0.0
-4331,15658067.0,Ch'iu,684.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,0.0,122488.59,0.0
-4332,15787699.0,Dore,674.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,182697.24,0.0
-4333,15779947.0,Okonkwo,678.0,France,Female,66.0,3.0,0.0,1.0,1.0,1.0,88751.45,1.0
-4334,15598948.0,Oluchukwu,696.0,Germany,Male,30.0,8.0,147187.84,1.0,1.0,1.0,26318.73,0.0
-4335,15638806.0,Chukwudi,598.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,1.0,180769.55,0.0
-4336,15769955.0,Y?an,656.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,136983.77,0.0
-4337,15594864.0,Folliero,646.0,Spain,Female,32.0,2.0,0.0,2.0,0.0,1.0,126812.63,0.0
-4338,15809909.0,Olisaemeka,636.0,France,Male,36.0,1.0,84309.57,1.0,1.0,0.0,180769.55,0.0
-4339,15691170.0,Hsueh,649.0,France,Female,55.0,3.0,0.0,1.0,1.0,0.0,172490.78,1.0
-4340,15616380.0,Hs?,712.0,France,Female,22.0,8.0,0.0,2.0,1.0,1.0,144849.1,0.0
-4341,15628893.0,Ozerova,686.0,Germany,Male,33.0,6.0,164557.95,1.0,0.0,1.0,101455.07,0.0
-4342,15713346.0,Hsia,614.0,France,Male,29.0,2.0,144326.09,1.0,0.0,0.0,73881.68,0.0
-4343,15624695.0,T'ien,634.0,France,Male,37.0,1.0,123783.25,1.0,1.0,0.0,58467.08,0.0
-4344,15587066.0,Bancks,685.0,France,Male,37.0,6.0,111769.14,1.0,1.0,0.0,169920.92,0.0
-4345,15598070.0,Chienezie,669.0,France,Female,33.0,2.0,0.0,1.0,1.0,1.0,199761.29,0.0
-4346,15575581.0,Ubanwa,731.0,France,Male,29.0,3.0,85891.55,1.0,1.0,1.0,52820.13,0.0
-4347,15734044.0,Chidiegwu,646.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,70810.6,0.0
-4348,15656829.0,Macleod,733.0,France,Female,23.0,2.0,0.0,2.0,1.0,1.0,60728.89,0.0
-4349,15789611.0,Ejimofor,686.0,France,Female,46.0,8.0,0.0,2.0,1.0,1.0,10472.31,0.0
-4350,15749300.0,Pinto,635.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,93739.71,0.0
-4351,15803337.0,White,607.0,Spain,Male,49.0,7.0,0.0,2.0,1.0,0.0,138536.08,0.0
-4352,15603088.0,Nwabugwu,579.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,125870.2,0.0
-4353,15803908.0,P'eng,636.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,170819.01,0.0
-4354,15667971.0,Yobachukwu,644.0,France,Male,32.0,3.0,132951.92,1.0,1.0,1.0,75969.19,0.0
-4355,15631481.0,Chu,641.0,Spain,Male,24.0,5.0,0.0,2.0,1.0,0.0,170819.01,0.0
-4356,15623644.0,Nwankwo,676.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,116548.02,0.0
-4357,15607690.0,Milani,602.0,France,Male,21.0,9.0,0.0,2.0,1.0,0.0,181224.56,0.0
-4358,15641298.0,Yevseyev,554.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,172097.95,0.0
-4359,15700476.0,McGregor,636.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,105985.01,1.0
-4360,15797760.0,Nnachetam,642.0,France,Male,39.0,5.0,125476.31,1.0,1.0,1.0,172033.57,0.0
-4361,15783097.0,Chimezie,697.0,France,Male,25.0,2.0,99540.63,1.0,0.0,1.0,170634.49,0.0
-4362,15654851.0,Ch'in,671.0,France,Male,34.0,4.0,0.0,2.0,0.0,1.0,178076.47,0.0
-4363,15704954.0,Chibugo,538.0,Spain,Male,33.0,10.0,0.0,2.0,1.0,1.0,136983.77,0.0
-4364,15773876.0,Mazzanti,581.0,Germany,Female,37.0,6.0,104760.82,1.0,1.0,0.0,85412.54,1.0
-4365,15691446.0,Hsing,705.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,143598.7,0.0
-4366,15704053.0,Uchechukwu,687.0,France,Female,26.0,7.0,0.0,2.0,0.0,1.0,133535.52,0.0
-4367,15697460.0,Chiu,684.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,1.0,144880.81,0.0
-4368,15694192.0,H?,730.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,96420.58,0.0
-4369,15628292.0,Toscani,611.0,Spain,Female,40.0,8.0,0.0,1.0,0.0,0.0,140934.57,1.0
-4370,15662397.0,Yobachi,656.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,10792.42,0.0
-4371,15585100.0,Toscani,711.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,138399.67,0.0
-4372,15772892.0,Palermo,811.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,139947.17,0.0
-4373,15642997.0,Maclean,638.0,France,Male,39.0,8.0,0.0,1.0,1.0,0.0,127543.81,1.0
-4374,15784958.0,Okonkwo,704.0,France,Male,24.0,4.0,0.0,2.0,1.0,1.0,154491.79,0.0
-4375,15680683.0,Tung,684.0,France,Female,26.0,2.0,162923.85,1.0,0.0,1.0,156774.94,0.0
-4376,15668058.0,Chukwumaobim,677.0,France,Female,46.0,9.0,0.0,2.0,1.0,0.0,80700.86,0.0
-4377,15618661.0,Smith,632.0,Spain,Female,30.0,10.0,0.0,2.0,1.0,0.0,48131.92,0.0
-4378,15679029.0,Ts'ui,581.0,Spain,Male,35.0,2.0,0.0,1.0,1.0,0.0,136497.28,0.0
-4379,15673747.0,Chiu,850.0,France,Female,59.0,7.0,0.0,2.0,1.0,1.0,199493.38,0.0
-4380,15646578.0,Ch'eng,705.0,Germany,Male,33.0,3.0,150802.03,2.0,1.0,1.0,51695.41,0.0
-4381,15775238.0,McFarland,598.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,134421.32,0.0
-4382,15689953.0,Onyinyechukwuka,584.0,Germany,Male,41.0,1.0,140510.89,2.0,1.0,0.0,56577.0,0.0
-4383,15708003.0,Tsao,583.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,122964.18,0.0
-4384,15762048.0,Hsia,557.0,France,Male,56.0,2.0,0.0,1.0,1.0,0.0,53353.37,1.0
-4385,15654955.0,Cattaneo,730.0,France,Male,36.0,6.0,123815.86,1.0,0.0,0.0,176750.81,0.0
-4386,15812888.0,Chidiegwu,597.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,1.0,120512.08,0.0
-4387,15683560.0,Yefremova,716.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,115700.1,0.0
-4388,15602811.0,Obialo,611.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,73562.05,0.0
-4389,15644849.0,Nwachinemelu,699.0,France,Female,25.0,3.0,0.0,2.0,1.0,1.0,152790.92,0.0
-4390,15792726.0,P'eng,659.0,France,Male,21.0,3.0,0.0,2.0,1.0,1.0,143980.28,0.0
-4391,15603741.0,Hsing,597.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,166682.57,0.0
-4392,15593976.0,Shih,538.0,France,Female,32.0,1.0,0.0,1.0,1.0,0.0,49594.02,0.0
-4393,15636069.0,Chukwudi,671.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,104469.06,0.0
-4394,15733032.0,Chiang,663.0,Germany,Female,34.0,0.0,146466.22,1.0,1.0,1.0,72469.38,0.0
-4395,15662578.0,Hsieh,636.0,Germany,Female,39.0,6.0,92786.66,1.0,1.0,0.0,146723.72,1.0
-4396,15789548.0,Mairinger,773.0,France,Male,39.0,9.0,0.0,2.0,0.0,0.0,72577.33,0.0
-4397,15592751.0,Chiedozie,686.0,Germany,Male,44.0,1.0,142289.28,2.0,1.0,0.0,71638.0,0.0
-4398,15705313.0,Iloerika,489.0,Germany,Female,42.0,2.0,127772.1,2.0,1.0,1.0,186655.11,0.0
-4399,15798885.0,Chikezie,622.0,Germany,Female,44.0,9.0,128927.13,1.0,1.0,0.0,76637.38,1.0
-4400,15697574.0,Ts'ui,588.0,France,Male,36.0,0.0,0.0,2.0,1.0,1.0,143086.09,0.0
-4401,15659236.0,Ch'eng,588.0,France,Female,47.0,5.0,0.0,1.0,1.0,0.0,140941.47,1.0
-4402,15605037.0,Yobanna,599.0,France,Male,50.0,5.0,153537.57,1.0,1.0,0.0,38308.34,0.0
-4403,15682268.0,Okagbue,466.0,Spain,Female,35.0,9.0,95984.21,1.0,1.0,1.0,121408.55,0.0
-4404,15596982.0,Onyemauchechukwu,706.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,153482.82,0.0
-4405,15632665.0,Lettiere,705.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,166656.18,0.0
-4406,15570533.0,Trevisano,685.0,France,Female,29.0,4.0,0.0,2.0,1.0,1.0,145562.4,0.0
-4407,15583067.0,Chuang,571.0,France,Male,34.0,7.0,0.0,2.0,0.0,1.0,182055.36,0.0
-4408,15595537.0,K'ung,663.0,Spain,Male,33.0,4.0,133723.21,1.0,1.0,1.0,141827.83,0.0
-4409,15792329.0,Tien,753.0,France,Female,37.0,7.0,94598.48,2.0,1.0,1.0,136497.28,0.0
-4410,15792668.0,P'eng,735.0,France,Female,39.0,7.0,0.0,1.0,1.0,1.0,47722.79,1.0
-4411,15574886.0,Hsia,655.0,France,Female,22.0,9.0,0.0,2.0,1.0,1.0,66810.48,0.0
-4412,15804053.0,Oguejiofor,588.0,Germany,Female,45.0,1.0,127675.39,2.0,1.0,0.0,97767.63,0.0
-4413,15646091.0,Pirozzi,850.0,Spain,Male,34.0,9.0,0.0,2.0,0.0,1.0,125877.22,0.0
-4414,15635078.0,Brownless,623.0,France,Male,22.0,1.0,0.0,2.0,0.0,0.0,110932.24,0.0
-4415,15642570.0,Ritchie,695.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,177028.0,0.0
-4416,15649744.0,Chukwufumnanya,719.0,France,Male,36.0,5.0,0.0,1.0,1.0,0.0,93982.41,0.0
-4417,15802501.0,Palerma,758.0,Germany,Female,42.0,9.0,114552.6,1.0,0.0,1.0,176943.59,1.0
-4418,15593346.0,Kenechukwu,684.0,France,Male,35.0,2.0,164701.68,1.0,1.0,0.0,48984.95,0.0
-4419,15612444.0,Chukwuemeka,586.0,France,Male,36.0,4.0,81742.94,1.0,1.0,0.0,64421.02,0.0
-4420,15786800.0,Ch'iu,576.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,0.0,70849.47,0.0
-4421,15769005.0,Brown,632.0,France,Female,24.0,1.0,121206.96,1.0,0.0,1.0,38186.85,0.0
-4422,15732704.0,Ch'ien,622.0,France,Male,56.0,2.0,0.0,2.0,1.0,1.0,127059.04,0.0
-4423,15800412.0,Nnaife,707.0,Germany,Female,25.0,7.0,139022.06,1.0,1.0,1.0,156917.19,0.0
-4424,15590730.0,Ch'ang,662.0,Germany,Female,47.0,1.0,100116.67,1.0,1.0,1.0,125816.2,1.0
-4425,15614215.0,Fanucci,612.0,Spain,Female,45.0,1.0,0.0,2.0,1.0,0.0,705.18,0.0
-4426,15593468.0,Walker,707.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,0.0,68600.36,0.0
-4427,15704053.0,Nwachinemelu,577.0,Spain,Male,34.0,2.0,162485.8,2.0,1.0,1.0,122996.76,0.0
-4428,15792934.0,Bage,696.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,86634.3,0.0
-4429,15656901.0,McDonald,670.0,Spain,Female,53.0,4.0,121162.74,1.0,0.0,0.0,92914.67,1.0
-4430,15705512.0,Hales,644.0,Spain,Male,30.0,3.0,0.0,2.0,1.0,0.0,26327.42,0.0
-4431,15724663.0,Hsu,648.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,74588.41,0.0
-4432,15758726.0,Hsueh,740.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,171378.77,0.0
-4433,15807560.0,Sagese,705.0,France,Male,35.0,1.0,0.0,1.0,1.0,1.0,141896.74,1.0
-4434,15717191.0,McLean,559.0,France,Male,40.0,7.0,133628.09,1.0,0.0,1.0,184581.68,0.0
-4435,15769596.0,Liao,693.0,France,Female,39.0,8.0,0.0,2.0,0.0,0.0,185555.63,0.0
-4436,15757537.0,Lo Duca,671.0,France,Male,31.0,0.0,0.0,2.0,1.0,1.0,181208.47,0.0
-4437,15812338.0,T'ien,706.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,155047.11,0.0
-4438,15773868.0,Zetticci,742.0,Germany,Female,37.0,8.0,90218.9,2.0,1.0,1.0,152883.95,0.0
-4439,15787035.0,Onio,696.0,Spain,Female,28.0,4.0,0.0,2.0,0.0,1.0,141896.74,0.0
-4440,15781409.0,Nwankwo,662.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,177613.19,0.0
-4441,15676571.0,Tuan,586.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,92220.12,0.0
-4442,15648721.0,Nwankwo,686.0,France,Female,42.0,5.0,0.0,2.0,0.0,1.0,122964.88,0.0
-4443,15687431.0,H?,619.0,France,Female,42.0,2.0,0.0,2.0,1.0,1.0,71497.23,0.0
-4444,15694544.0,Ulyanova,651.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,138700.75,0.0
-4445,15576615.0,Walker,710.0,France,Female,21.0,3.0,0.0,2.0,1.0,0.0,85746.42,0.0
-4446,15576077.0,Yefremova,681.0,France,Male,40.0,8.0,0.0,2.0,0.0,0.0,170984.97,0.0
-4447,15737795.0,Chigozie,682.0,France,Female,33.0,3.0,99811.44,1.0,1.0,0.0,81297.75,0.0
-4448,15631512.0,Kao,713.0,France,Female,36.0,7.0,0.0,2.0,0.0,0.0,112465.19,0.0
-4449,15790489.0,Rishel,681.0,Spain,Female,53.0,6.0,0.0,2.0,1.0,1.0,151402.82,0.0
-4450,15689526.0,Yefremov,597.0,France,Male,57.0,4.0,0.0,1.0,1.0,0.0,123880.19,1.0
-4451,15642821.0,McGregor,693.0,Spain,Male,43.0,7.0,115465.98,1.0,0.0,0.0,174918.46,1.0
-4452,15637366.0,Smith,646.0,Germany,Male,41.0,2.0,137592.04,2.0,1.0,1.0,136507.69,0.0
-4453,15743893.0,Olisaemeka,748.0,France,Female,31.0,8.0,0.0,2.0,1.0,1.0,172939.3,0.0
-4454,15592707.0,Pirozzi,748.0,Germany,Male,43.0,8.0,139874.81,1.0,1.0,0.0,91936.1,1.0
-4455,15793046.0,Dickson,591.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,1375.41,0.0
-4456,15663478.0,Milanesi,662.0,Spain,Female,41.0,5.0,0.0,2.0,0.0,1.0,72375.03,0.0
-4457,15665621.0,H?,559.0,Germany,Female,32.0,1.0,107980.37,2.0,1.0,1.0,171070.55,0.0
-4458,15798960.0,P'eng,523.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,186418.85,0.0
-4459,15661349.0,Okwudiliolisa,676.0,Germany,Female,51.0,4.0,114512.59,1.0,0.0,0.0,147097.14,1.0
-4460,15683524.0,Tuan,526.0,Germany,Male,41.0,0.0,107363.28,1.0,1.0,0.0,103898.59,0.0
-4461,15772601.0,Manners,693.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,167773.55,0.0
-4462,15732643.0,Genovesi,630.0,France,Male,40.0,6.0,0.0,2.0,0.0,0.0,82709.8,1.0
-4463,15795690.0,O'Sullivan,637.0,Germany,Female,33.0,7.0,123544.0,2.0,1.0,0.0,176913.42,0.0
-4464,15702631.0,Hsing,669.0,Germany,Female,54.0,2.0,111837.78,1.0,1.0,0.0,104930.46,1.0
-4465,15603481.0,P'an,605.0,France,Male,46.0,10.0,94486.47,1.0,1.0,1.0,100557.03,0.0
-4466,15721377.0,Hao,754.0,France,Male,40.0,1.0,0.0,1.0,0.0,0.0,162523.66,1.0
-4467,15696733.0,Chukwumaobim,652.0,France,Female,38.0,1.0,101430.3,1.0,1.0,0.0,91347.76,0.0
-4468,15705544.0,Teng,569.0,Germany,Female,32.0,8.0,130170.82,2.0,1.0,1.0,54530.51,0.0
-4469,15722072.0,Chukwufumnanya,707.0,Germany,Male,49.0,1.0,127847.86,1.0,1.0,0.0,90896.06,1.0
-4470,15651336.0,Hsia,591.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,125518.32,0.0
-4471,15776850.0,Lucciano,714.0,France,Female,46.0,1.0,0.0,1.0,0.0,0.0,149402.54,1.0
-4472,15595588.0,Ch'iu,656.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,0.0,52861.89,0.0
-4473,15604576.0,Okwuoma,704.0,Spain,Female,44.0,7.0,0.0,1.0,1.0,0.0,167636.15,1.0
-4474,15633378.0,Ch'ang,594.0,France,Male,38.0,8.0,0.0,2.0,0.0,0.0,180595.02,0.0
-4475,15656613.0,Chibueze,769.0,France,Male,30.0,1.0,0.0,2.0,1.0,0.0,171413.66,0.0
-4476,15581548.0,Pagnotto,573.0,France,Male,33.0,8.0,0.0,2.0,0.0,0.0,88837.17,0.0
-4477,15743582.0,Chidubem,642.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,152790.92,0.0
-4478,15655774.0,Ebelegbulam,776.0,Spain,Female,33.0,4.0,0.0,2.0,1.0,1.0,63687.06,0.0
-4479,15680430.0,Hsia,751.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,104722.47,0.0
-4480,15603743.0,Lo,642.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,172490.78,0.0
-4481,15734005.0,Onwuamaeze,554.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,184544.59,0.0
-4482,15777955.0,Nwachukwu,735.0,France,Male,37.0,1.0,140694.78,1.0,1.0,0.0,141872.05,0.0
-4483,15724889.0,Ch'iu,715.0,France,Male,37.0,1.0,0.0,1.0,1.0,0.0,160548.12,0.0
-4484,15769902.0,Ch'en,731.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,85412.64,0.0
-4485,15610390.0,Pisano,705.0,Germany,Male,36.0,5.0,118466.85,2.0,0.0,0.0,71519.4,0.0
-4486,15718369.0,Ugochukwu,598.0,France,Male,36.0,1.0,122314.5,1.0,0.0,1.0,136859.55,0.0
-4487,15581198.0,T'ien,684.0,France,Female,43.0,8.0,0.0,2.0,0.0,1.0,46824.08,0.0
-4488,15642821.0,Eluemuno,656.0,France,Female,50.0,4.0,0.0,1.0,1.0,0.0,143357.05,1.0
-4489,15767804.0,Fan,639.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,164039.51,0.0
-4490,15659937.0,Dickson,581.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,113048.79,0.0
-4491,15657535.0,Onyeorulu,776.0,France,Female,29.0,7.0,0.0,2.0,0.0,1.0,112554.68,0.0
-4492,15741385.0,Galkin,652.0,France,Male,51.0,7.0,0.0,1.0,1.0,0.0,38652.96,1.0
-4493,15766288.0,Chien,724.0,France,Female,29.0,6.0,0.0,2.0,1.0,1.0,122996.76,0.0
-4494,15719377.0,Ts'ao,644.0,Spain,Female,37.0,4.0,0.0,2.0,1.0,1.0,146651.99,0.0
-4495,15592389.0,Hsu,753.0,Spain,Male,45.0,1.0,0.0,2.0,1.0,0.0,124499.98,0.0
-4496,15756975.0,Shih,749.0,France,Male,36.0,5.0,0.0,1.0,1.0,1.0,101314.65,0.0
-4497,15787322.0,Nnaife,753.0,Spain,Female,34.0,3.0,0.0,2.0,0.0,0.0,152883.95,0.0
-4498,15703394.0,Chuang,713.0,France,Female,61.0,5.0,0.0,1.0,1.0,1.0,126336.72,1.0
-4499,15675888.0,Ndukaku,692.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,165562.84,0.0
-4500,15622532.0,Chukwueloka,726.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,77832.41,0.0
-4501,15714391.0,Udegbulam,644.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,51636.7,0.0
-4502,15679770.0,T'ao,637.0,Germany,Female,40.0,9.0,104305.77,2.0,1.0,0.0,131736.72,0.0
-4503,15604551.0,Hsing,629.0,Germany,Male,47.0,5.0,131778.08,1.0,1.0,0.0,44374.44,1.0
-4504,15796787.0,Genovese,599.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,1.0,173629.78,0.0
-4505,15747174.0,Teng,431.0,France,Male,34.0,3.0,0.0,2.0,0.0,0.0,97413.0,0.0
-4506,15570002.0,Smith,735.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,113931.57,0.0
-4507,15652266.0,Fanucci,683.0,Germany,Male,23.0,1.0,152303.8,1.0,1.0,1.0,138527.56,0.0
-4508,15573893.0,Ch'eng,686.0,Germany,Male,46.0,9.0,132312.08,1.0,1.0,1.0,167773.55,0.0
-4509,15808228.0,Ch'en,629.0,Spain,Female,57.0,7.0,0.0,2.0,1.0,1.0,153917.41,0.0
-4510,15679048.0,Nnamdi,487.0,France,Male,27.0,10.0,0.0,2.0,0.0,1.0,64345.61,0.0
-4511,15623369.0,Lai,751.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,143317.42,0.0
-4512,15571059.0,Chukwukere,654.0,Germany,Female,47.0,2.0,131160.85,1.0,1.0,1.0,120594.85,1.0
-4513,15571550.0,Ts'ui,628.0,France,Male,30.0,7.0,134177.06,1.0,1.0,1.0,64345.61,0.0
-4514,15660403.0,Tsao,762.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,161802.82,0.0
-4515,15647782.0,T'ang,587.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,0.0,160941.78,0.0
-4516,15733777.0,Ejimofor,671.0,France,Male,43.0,7.0,0.0,2.0,0.0,1.0,141364.62,0.0
-4517,15719561.0,Yevseyev,538.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,173498.45,0.0
-4518,15635116.0,Kornilova,516.0,France,Male,39.0,9.0,0.0,2.0,0.0,0.0,72549.11,0.0
-4519,15589572.0,Chia,731.0,Germany,Female,37.0,3.0,137964.46,2.0,1.0,0.0,116471.43,0.0
-4520,15783029.0,Hs?eh,488.0,Spain,Male,24.0,2.0,0.0,2.0,0.0,0.0,82709.8,0.0
-4521,15692631.0,Onyekachi,850.0,Spain,Female,47.0,1.0,0.0,1.0,1.0,0.0,142516.35,1.0
-4522,15571586.0,Kao,535.0,France,Female,37.0,1.0,0.0,2.0,0.0,0.0,61290.99,0.0
-4523,15677955.0,Folliero,679.0,Spain,Female,43.0,7.0,0.0,1.0,1.0,0.0,57844.26,1.0
-4524,15607263.0,Pisano,779.0,France,Male,58.0,1.0,0.0,2.0,1.0,1.0,95611.47,0.0
-4525,15732570.0,Folliero,684.0,France,Female,38.0,2.0,109052.04,1.0,0.0,1.0,121056.29,0.0
-4526,15675896.0,D'Albertis,756.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,1.0,51380.9,0.0
-4527,15583090.0,Onochie,743.0,Spain,Male,34.0,10.0,0.0,2.0,0.0,1.0,156476.36,0.0
-4528,15704581.0,Chien,674.0,Germany,Male,39.0,7.0,104584.11,1.0,1.0,1.0,83022.61,0.0
-4529,15670898.0,Ch'eng,675.0,Germany,Male,33.0,9.0,103907.28,2.0,0.0,1.0,131736.23,0.0
-4530,15751774.0,Mistry,821.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,81884.8,0.0
-4531,15806945.0,Chin,620.0,France,Female,37.0,4.0,0.0,3.0,1.0,0.0,146723.72,1.0
-4532,15614687.0,L?,554.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,157072.91,0.0
-4533,15636999.0,Onyemachukwu,613.0,France,Female,40.0,4.0,108309.0,1.0,1.0,1.0,139919.38,0.0
-4534,15695585.0,Ch'en,653.0,Germany,Female,36.0,8.0,128718.93,2.0,0.0,1.0,136865.63,0.0
-4535,15593601.0,Mancini,636.0,Germany,Male,43.0,5.0,109949.05,1.0,0.0,0.0,143618.32,0.0
-4536,15672056.0,Nwoye,687.0,France,Male,34.0,8.0,0.0,2.0,0.0,0.0,122964.18,0.0
-4537,15643865.0,Chiganu,651.0,France,Male,31.0,2.0,131513.26,1.0,0.0,1.0,168834.38,0.0
-4538,15781598.0,Nnamutaezinwa,678.0,France,Male,45.0,6.0,129445.4,1.0,1.0,0.0,73334.43,1.0
-4539,15652914.0,Ting,558.0,Spain,Male,45.0,3.0,0.0,2.0,1.0,0.0,187961.87,0.0
-4540,15572547.0,Estrada,696.0,France,Female,55.0,1.0,0.0,1.0,1.0,0.0,174738.2,1.0
-4541,15653937.0,P'eng,624.0,France,Male,39.0,3.0,129818.61,1.0,1.0,0.0,140889.32,0.0
-4542,15788183.0,Chu,673.0,Germany,Male,35.0,6.0,125013.72,2.0,1.0,1.0,50783.1,0.0
-4543,15658052.0,Lo,627.0,Germany,Male,33.0,6.0,137986.47,2.0,1.0,1.0,9984.4,0.0
-4544,15644572.0,De Luca,798.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,86099.23,0.0
-4545,15598894.0,Hsieh,711.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,187985.85,0.0
-4546,15664001.0,Chigozie,713.0,Spain,Female,45.0,2.0,141359.37,1.0,1.0,0.0,62700.08,0.0
-4547,15806981.0,Y?an,832.0,Spain,Male,34.0,2.0,135939.99,1.0,0.0,0.0,181610.6,0.0
-4548,15594556.0,Nwachukwu,686.0,France,Male,33.0,1.0,145751.03,1.0,1.0,1.0,122073.48,0.0
-4549,15576760.0,Ts'ui,587.0,Germany,Male,36.0,9.0,91285.13,2.0,1.0,1.0,117036.06,0.0
-4550,15692991.0,Ch'ien,627.0,France,Female,44.0,1.0,0.0,2.0,1.0,0.0,143783.46,1.0
-4551,15627377.0,Eluemuno,774.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,176580.89,0.0
-4552,15792453.0,Hsiung,790.0,France,Male,37.0,0.0,0.0,2.0,1.0,0.0,99818.49,0.0
-4553,15694321.0,Okechukwu,706.0,Germany,Male,35.0,4.0,116077.48,2.0,1.0,1.0,123435.88,0.0
-4554,15760450.0,Ch'in,614.0,Germany,Male,35.0,3.0,126619.27,2.0,1.0,0.0,94790.34,0.0
-4555,15660505.0,Chien,749.0,France,Male,27.0,3.0,151027.05,2.0,1.0,1.0,116766.79,0.0
-4556,15721207.0,Chukwudi,567.0,Spain,Male,39.0,7.0,95120.59,1.0,1.0,0.0,162501.83,0.0
-4557,15751771.0,Shih,691.0,France,Male,35.0,10.0,172713.45,1.0,0.0,1.0,141137.52,0.0
-4558,15742291.0,Teng,678.0,Germany,Female,48.0,9.0,139756.12,3.0,0.0,0.0,173464.43,1.0
-4559,15779985.0,Mazzanti,615.0,France,Male,50.0,9.0,0.0,2.0,0.0,1.0,7900.46,0.0
-4560,15702801.0,Echezonachukwu,729.0,France,Male,25.0,1.0,0.0,2.0,1.0,1.0,27341.63,0.0
-4561,15667896.0,Lu,596.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,158545.25,0.0
-4562,15791550.0,De Bernales,535.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,166586.99,0.0
-4563,15784918.0,Ts'ui,732.0,Spain,Male,41.0,2.0,0.0,2.0,1.0,1.0,168521.72,0.0
-4564,15719561.0,Milne,849.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,176903.38,0.0
-4565,15756475.0,Ritchie,561.0,Spain,Male,45.0,9.0,0.0,1.0,1.0,0.0,113580.79,1.0
-4566,15734044.0,Onyemere,738.0,Spain,Female,46.0,9.0,160982.89,1.0,0.0,0.0,136079.64,1.0
-4567,15574523.0,Ch'in,571.0,France,Female,29.0,5.0,0.0,1.0,1.0,0.0,166692.03,0.0
-4568,15607065.0,Ch'iu,652.0,Germany,Female,52.0,4.0,126817.65,1.0,1.0,0.0,99294.22,1.0
-4569,15673747.0,Yermakova,728.0,Spain,Male,29.0,6.0,0.0,1.0,1.0,1.0,183893.31,0.0
-4570,15757911.0,Chien,830.0,France,Female,45.0,9.0,0.0,1.0,1.0,1.0,182650.15,1.0
-4571,15654577.0,Gorman,594.0,France,Male,50.0,8.0,132005.77,1.0,1.0,0.0,122218.27,1.0
-4572,15641158.0,Nnachetam,568.0,Germany,Female,40.0,4.0,123378.2,1.0,1.0,0.0,143882.25,1.0
-4573,15569098.0,Calabresi,603.0,France,Male,27.0,8.0,0.0,1.0,1.0,0.0,98820.09,0.0
-4574,15751524.0,Simmons,614.0,Spain,Female,30.0,3.0,0.0,2.0,1.0,0.0,131736.23,0.0
-4575,15643331.0,Ts'ai,763.0,Germany,Female,45.0,8.0,123345.85,2.0,1.0,1.0,173480.77,0.0
-4576,15729763.0,Hsing,745.0,Germany,Female,36.0,1.0,139914.7,2.0,1.0,0.0,129964.94,0.0
-4577,15771565.0,Manna,661.0,France,Female,31.0,6.0,0.0,2.0,0.0,1.0,170563.73,0.0
-4578,15645200.0,Cattaneo,564.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,98894.64,0.0
-4579,15645059.0,Niu,667.0,Spain,Male,46.0,7.0,117438.92,1.0,0.0,1.0,77866.92,1.0
-4580,15793856.0,Biryukova,584.0,France,Male,37.0,4.0,148962.76,2.0,0.0,1.0,143954.99,0.0
-4581,15792989.0,Iadanza,815.0,France,Male,32.0,6.0,139825.34,1.0,1.0,0.0,51117.06,0.0
-4582,15776824.0,Ch'en,652.0,France,Male,39.0,8.0,77959.39,1.0,1.0,1.0,91070.54,0.0
-4583,15673837.0,Chidumaga,743.0,France,Male,30.0,5.0,0.0,2.0,0.0,0.0,94498.81,0.0
-4584,15579548.0,Onwuamaeze,584.0,France,Female,38.0,0.0,0.0,2.0,1.0,0.0,122398.84,0.0
-4585,15790596.0,Milne,586.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,171575.54,0.0
-4586,15572073.0,Hsing,758.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,89543.25,0.0
-4587,15635905.0,Burt,651.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,45445.78,0.0
-4588,15709928.0,Ch'iu,665.0,Germany,Male,62.0,3.0,120787.18,2.0,1.0,1.0,138021.36,0.0
-4589,15771580.0,Ch'ien,712.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,166677.54,0.0
-4590,15694879.0,Manna,694.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,179833.43,1.0
-4591,15595588.0,Ting,619.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,176713.47,0.0
-4592,15775809.0,Onwuamaeze,716.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,81776.84,0.0
-4593,15636589.0,Ch'en,687.0,Germany,Female,34.0,7.0,81576.75,2.0,0.0,1.0,173882.54,0.0
-4594,15803685.0,Onyemere,622.0,France,Female,28.0,7.0,0.0,2.0,1.0,0.0,121061.89,0.0
-4595,15794560.0,Ts'ui,586.0,France,Female,72.0,4.0,153663.27,1.0,1.0,1.0,173792.42,0.0
-4596,15681476.0,Maclean,717.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,88724.69,0.0
-4597,15582591.0,T'ien,631.0,France,Female,36.0,1.0,128173.9,1.0,1.0,0.0,72950.14,0.0
-4598,15603674.0,Hs?eh,556.0,Spain,Female,32.0,0.0,0.0,1.0,1.0,1.0,129964.94,1.0
-4599,15745307.0,Hsueh,716.0,France,Male,61.0,9.0,0.0,2.0,1.0,0.0,85801.77,0.0
-4600,15690134.0,Hsing,545.0,Germany,Female,37.0,9.0,120145.96,1.0,0.0,0.0,120586.32,1.0
-4601,15697035.0,Chuang,752.0,France,Male,31.0,3.0,156847.29,1.0,0.0,1.0,88566.9,0.0
-4602,15747772.0,Y?,614.0,France,Male,30.0,6.0,91241.85,1.0,0.0,1.0,64945.57,0.0
-4603,15663625.0,Riddle,819.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,91903.9,0.0
-4604,15598884.0,Ch'in,686.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,184977.2,0.0
-4605,15662955.0,Chiang,597.0,France,Male,54.0,6.0,98765.84,1.0,1.0,1.0,153605.75,1.0
-4606,15641411.0,Hsia,687.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,121098.65,0.0
-4607,15783659.0,Genovesi,652.0,France,Female,46.0,2.0,0.0,1.0,1.0,0.0,73686.75,1.0
-4608,15643916.0,Yobachukwu,684.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,182336.45,0.0
-4609,15661532.0,Ts'ui,803.0,Germany,Female,47.0,7.0,106045.92,1.0,0.0,1.0,180736.24,1.0
-4610,15777033.0,Nnaife,577.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,164128.57,0.0
-4611,15610781.0,Li Fonti,644.0,France,Male,37.0,0.0,135139.33,1.0,1.0,1.0,134822.01,0.0
-4612,15643496.0,Kao,648.0,France,Female,33.0,3.0,141581.08,1.0,1.0,1.0,176450.19,0.0
-4613,15732265.0,Tsao,583.0,Germany,Female,30.0,5.0,126473.99,2.0,1.0,0.0,87071.18,0.0
-4614,15777893.0,Y?,687.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,128881.87,0.0
-4615,15746035.0,Mamelu,682.0,Germany,Male,37.0,8.0,132796.09,1.0,1.0,0.0,106564.88,0.0
-4616,15674442.0,Ritchie,681.0,Spain,Male,30.0,5.0,0.0,1.0,1.0,1.0,160122.66,0.0
-4617,15640034.0,Nwoye,562.0,Spain,Male,31.0,6.0,0.0,2.0,0.0,1.0,112465.19,0.0
-4618,15792721.0,Tuan,557.0,Germany,Female,54.0,7.0,146422.22,2.0,0.0,0.0,146723.72,1.0
-4619,15695097.0,H?,663.0,Spain,Male,27.0,7.0,0.0,2.0,1.0,1.0,25101.17,0.0
-4620,15752312.0,Yermakova,605.0,France,Female,40.0,1.0,0.0,2.0,1.0,1.0,126434.58,0.0
-4621,15763704.0,Milne,656.0,France,Male,33.0,9.0,89968.69,1.0,1.0,0.0,104220.5,0.0
-4622,15672610.0,Gentry,850.0,France,Female,22.0,6.0,0.0,2.0,1.0,0.0,94978.1,0.0
-4623,15770121.0,Hsia,699.0,Germany,Male,33.0,8.0,134760.21,1.0,1.0,0.0,187905.12,0.0
-4624,15591747.0,K?,650.0,Spain,Female,46.0,2.0,0.0,1.0,1.0,0.0,158043.11,1.0
-4625,15581252.0,Nkemjika,727.0,France,Female,25.0,2.0,0.0,2.0,1.0,0.0,75927.35,0.0
-4626,15694453.0,Milani,678.0,France,Male,40.0,1.0,0.0,2.0,1.0,0.0,167723.25,0.0
-4627,15589431.0,Folliero,559.0,Spain,Female,49.0,7.0,141136.62,1.0,1.0,1.0,98273.01,0.0
-4628,15628303.0,Maclean,678.0,Spain,Male,44.0,9.0,0.0,1.0,1.0,0.0,142763.51,1.0
-4629,15750476.0,Lucchesi,631.0,France,Female,49.0,4.0,0.0,2.0,1.0,1.0,131736.23,0.0
-4630,15653952.0,Palerma,604.0,France,Male,33.0,0.0,78892.23,1.0,1.0,0.0,147720.27,1.0
-4631,15665811.0,T'ien,550.0,Spain,Male,37.0,3.0,136422.41,1.0,1.0,0.0,14083.12,0.0
-4632,15581323.0,Koger,709.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,168816.03,0.0
-4633,15592583.0,Cremonesi,684.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,118029.08,0.0
-4634,15747345.0,Chidiebere,636.0,Spain,Female,34.0,5.0,0.0,2.0,0.0,0.0,182354.46,0.0
-4635,15769254.0,Ch'in,729.0,France,Female,33.0,3.0,0.0,1.0,1.0,0.0,82867.55,0.0
-4636,15658057.0,Fokine,660.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,1.0,156917.11,0.0
-4637,15806913.0,Chibugo,569.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,1.0,70810.6,0.0
-4638,15799211.0,Smith,594.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,121525.48,0.0
-4639,15808566.0,Hsia,648.0,France,Female,47.0,9.0,0.0,2.0,1.0,1.0,142513.5,0.0
-4640,15728885.0,Chineze,684.0,France,Male,35.0,7.0,0.0,2.0,0.0,1.0,143086.09,0.0
-4641,15658935.0,Smith,664.0,France,Male,41.0,2.0,0.0,1.0,1.0,0.0,15607.27,0.0
-4642,15723339.0,Nworie,645.0,Spain,Female,35.0,6.0,184552.12,1.0,1.0,1.0,102999.33,1.0
-4643,15656901.0,Hsia,613.0,Germany,Male,49.0,3.0,119961.48,3.0,1.0,0.0,116663.46,1.0
-4644,15757837.0,Trevisano,690.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,145764.4,0.0
-4645,15738267.0,T'an,696.0,Spain,Male,36.0,5.0,142658.07,1.0,1.0,1.0,72467.99,0.0
-4646,15799480.0,Chu,659.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,75860.01,0.0
-4647,15763029.0,Shih,555.0,Germany,Male,35.0,1.0,106036.52,1.0,1.0,1.0,172646.88,0.0
-4648,15706464.0,Chiang,671.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,158344.63,0.0
-4649,15761102.0,Okeke,573.0,Germany,Female,26.0,0.0,143262.04,2.0,0.0,0.0,171587.9,0.0
-4650,15656522.0,Fanucci,537.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,179540.73,0.0
-4651,15752426.0,Obialo,648.0,Germany,Female,34.0,7.0,92398.08,1.0,1.0,0.0,74564.41,0.0
-4652,15577985.0,Olisaemeka,712.0,France,Female,31.0,4.0,0.0,1.0,1.0,0.0,55868.97,1.0
-4653,15632439.0,Tsao,679.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,0.0,86099.23,0.0
-4654,15750121.0,Nkemakolam,584.0,Germany,Female,46.0,5.0,131647.01,2.0,0.0,0.0,167984.72,1.0
-4655,15778830.0,Pinto,545.0,Germany,Female,32.0,8.0,133537.1,1.0,1.0,0.0,29346.1,0.0
-4656,15652955.0,Rischbieth,572.0,France,Male,48.0,5.0,0.0,1.0,1.0,0.0,64448.36,1.0
-4657,15693526.0,Walker,648.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,0.0,60721.4,0.0
-4658,15732967.0,Chia,709.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,167873.55,0.0
-4659,15620756.0,P'eng,634.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,127569.8,0.0
-4660,15596011.0,Efremova,706.0,France,Male,40.0,5.0,129490.43,1.0,1.0,0.0,106977.53,0.0
-4661,15791501.0,Chukwujekwu,790.0,France,Male,37.0,8.0,0.0,1.0,1.0,1.0,105570.9,0.0
-4662,15795738.0,Yudina,588.0,Spain,Male,51.0,4.0,0.0,1.0,1.0,1.0,95007.8,0.0
-4663,15622443.0,Ubanwa,656.0,France,Male,37.0,6.0,0.0,2.0,0.0,1.0,100324.01,0.0
-4664,15723032.0,T'ien,707.0,France,Male,29.0,5.0,0.0,1.0,1.0,0.0,160976.85,0.0
-4665,15733169.0,Ch'ang,687.0,France,Male,25.0,5.0,0.0,2.0,1.0,1.0,93982.02,0.0
-4666,15742110.0,Hsieh,667.0,Germany,Female,42.0,5.0,121669.93,2.0,1.0,1.0,176750.81,0.0
-4667,15706185.0,Chidozie,605.0,Spain,Female,33.0,3.0,0.0,2.0,1.0,1.0,121061.89,0.0
-4668,15679531.0,Chidubem,757.0,Germany,Female,31.0,9.0,122028.65,1.0,1.0,1.0,133672.61,0.0
-4669,15598259.0,Chukwukere,792.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,0.0,95719.73,0.0
-4670,15677828.0,Hsia,850.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,103726.71,0.0
-4671,15606861.0,Lucciano,733.0,Spain,Male,56.0,7.0,0.0,1.0,1.0,0.0,135077.01,1.0
-4672,15685019.0,Kao,646.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,126722.57,0.0
-4673,15656427.0,Iadanza,731.0,Spain,Male,33.0,1.0,0.0,2.0,1.0,1.0,178028.96,0.0
-4674,15763665.0,Lai,639.0,Germany,Female,46.0,3.0,92366.04,1.0,0.0,1.0,115818.51,1.0
-4675,15782418.0,Genovese,704.0,Spain,Male,38.0,1.0,0.0,1.0,1.0,1.0,149913.84,0.0
-4676,15757912.0,Ting,590.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,0.0,72876.43,0.0
-4677,15690772.0,Genovesi,664.0,Spain,Male,36.0,2.0,175575.95,1.0,1.0,0.0,160976.83,1.0
-4678,15762797.0,Tsou,778.0,France,Male,32.0,5.0,0.0,2.0,0.0,1.0,66893.28,0.0
-4679,15715199.0,Yobachi,694.0,France,Male,35.0,9.0,0.0,1.0,1.0,1.0,170621.43,0.0
-4680,15690796.0,Okwudiliolisa,670.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,88890.75,0.0
-4681,15624592.0,Hsing,663.0,France,Female,36.0,4.0,0.0,1.0,1.0,0.0,90860.97,0.0
-4682,15672091.0,Nwabugwu,677.0,Germany,Female,40.0,6.0,102773.2,2.0,1.0,0.0,12092.04,1.0
-4683,15790659.0,McGregor,706.0,France,Female,45.0,6.0,0.0,1.0,0.0,1.0,184580.24,1.0
-4684,15686588.0,Lo,637.0,Germany,Male,23.0,4.0,109483.54,1.0,1.0,1.0,85937.48,0.0
-4685,15605968.0,Hsiung,652.0,Germany,Male,38.0,6.0,100252.18,1.0,1.0,1.0,171060.01,0.0
-4686,15769915.0,Calabresi,590.0,France,Female,45.0,2.0,132271.2,1.0,1.0,1.0,126726.33,0.0
-4687,15803032.0,Chiu,707.0,France,Female,51.0,8.0,0.0,1.0,1.0,0.0,162022.47,1.0
-4688,15768072.0,Calabresi,596.0,Germany,Male,35.0,9.0,146780.52,2.0,1.0,1.0,120540.83,0.0
-4689,15705001.0,Shih,662.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,136079.64,0.0
-4690,15606915.0,Hs?,726.0,Spain,Male,52.0,0.0,0.0,2.0,0.0,1.0,93832.33,0.0
-4691,15666032.0,Onyekachukwu,667.0,France,Male,37.0,2.0,125951.81,1.0,0.0,1.0,19998.31,0.0
-4692,15714397.0,Clements,738.0,Spain,Female,43.0,4.0,0.0,2.0,1.0,1.0,50485.4,0.0
-4693,15598097.0,K'ung,775.0,France,Female,41.0,5.0,0.0,2.0,0.0,1.0,133617.43,0.0
-4694,15608916.0,Tien,753.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,181429.87,0.0
-4695,15589431.0,Chibugo,709.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,169278.51,0.0
-4696,15574324.0,McFarland,668.0,Germany,Male,30.0,4.0,91955.61,2.0,1.0,0.0,144584.29,0.0
-4697,15586974.0,Shao,743.0,France,Female,32.0,2.0,0.0,1.0,1.0,1.0,143598.7,1.0
-4698,15736420.0,Cremonesi,695.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,67979.35,0.0
-4699,15654025.0,She,553.0,France,Male,45.0,5.0,0.0,1.0,0.0,1.0,150856.38,0.0
-4700,15627365.0,Hsia,652.0,France,Female,20.0,2.0,0.0,2.0,1.0,1.0,83056.22,0.0
-4701,15767793.0,Ts'ui,543.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,0.0,132498.39,0.0
-4702,15682574.0,Lu,706.0,France,Female,37.0,6.0,0.0,2.0,0.0,0.0,88827.67,0.0
-4703,15709621.0,Onyemachukwu,757.0,Spain,Female,42.0,1.0,0.0,2.0,1.0,1.0,183840.51,0.0
-4704,15693737.0,Chigozie,679.0,Spain,Male,33.0,10.0,0.0,2.0,1.0,1.0,138526.96,0.0
-4705,15759537.0,Cairns,478.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,106781.39,0.0
-4706,15597951.0,Hsiung,565.0,Germany,Male,52.0,6.0,91025.57,1.0,1.0,1.0,113505.93,1.0
-4707,15815560.0,Chidumaga,740.0,Spain,Female,43.0,8.0,0.0,2.0,1.0,1.0,126726.33,0.0
-4708,15752409.0,Johnstone,479.0,France,Male,39.0,1.0,155047.02,1.0,1.0,0.0,132547.02,1.0
-4709,15735270.0,Uwaezuoke,641.0,France,Male,48.0,6.0,141786.78,1.0,1.0,0.0,118913.53,1.0
-4710,15689492.0,Nkemakonam,589.0,France,Male,32.0,4.0,0.0,2.0,0.0,1.0,141872.05,0.0
-4711,15792989.0,Tipton,591.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,71179.31,0.0
-4712,15626233.0,Macleod,697.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,159483.76,0.0
-4713,15690733.0,Chiawuotu,729.0,France,Male,33.0,1.0,0.0,1.0,1.0,1.0,115441.66,0.0
-4714,15591747.0,Chikwado,718.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,60920.03,0.0
-4715,15651360.0,Chiazagomekpere,645.0,Germany,Female,37.0,7.0,116968.91,3.0,1.0,0.0,67979.35,1.0
-4716,15640268.0,Ch'ang,573.0,France,Male,25.0,3.0,0.0,2.0,1.0,0.0,140881.2,0.0
-4717,15652084.0,Ting,637.0,Spain,Male,34.0,9.0,139914.7,1.0,1.0,0.0,160968.44,0.0
-4718,15711750.0,Hs?,602.0,Spain,Male,29.0,1.0,125877.65,1.0,0.0,1.0,185855.21,0.0
-4719,15698902.0,Chibuzo,564.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,45959.86,0.0
-4720,15713483.0,McLean,714.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,57896.27,0.0
-4721,15595036.0,Kao,658.0,Germany,Female,58.0,2.0,123544.0,1.0,1.0,0.0,176943.59,1.0
-4722,15603764.0,Ch'iu,705.0,France,Male,39.0,0.0,0.0,2.0,0.0,1.0,71154.1,0.0
-4723,15711602.0,Manna,686.0,France,Female,28.0,9.0,0.0,2.0,1.0,1.0,183572.39,0.0
-4724,15617454.0,Hsia,714.0,France,Male,38.0,7.0,0.0,2.0,0.0,1.0,123587.93,0.0
-4725,15568429.0,Zito,806.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,0.0,27862.58,0.0
-4726,15757537.0,Chia,642.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,180598.66,0.0
-4727,15622985.0,Toscani,695.0,Germany,Male,35.0,2.0,102992.82,2.0,0.0,0.0,49418.87,0.0
-4728,15659710.0,Uspenskaya,602.0,France,Female,29.0,8.0,0.0,1.0,1.0,0.0,183598.77,1.0
-4729,15684469.0,Efimov,712.0,Germany,Female,61.0,6.0,113958.55,1.0,0.0,1.0,147055.87,1.0
-4730,15576676.0,Ts'ui,584.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,160532.88,0.0
-4731,15809906.0,Ch'iu,659.0,Spain,Male,39.0,1.0,0.0,1.0,1.0,0.0,133831.37,0.0
-4732,15604818.0,Chuang,603.0,Spain,Female,26.0,5.0,0.0,2.0,1.0,1.0,107499.58,0.0
-4733,15643439.0,Ts'ui,715.0,France,Female,33.0,9.0,0.0,1.0,1.0,0.0,160529.71,1.0
-4734,15737310.0,Ch'ang,628.0,France,Female,27.0,1.0,0.0,2.0,1.0,1.0,50488.91,0.0
-4735,15794566.0,Dickson,577.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,149139.13,0.0
-4736,15794849.0,Tuan,650.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,1.0,96528.0,0.0
-4737,15647570.0,Y?an,579.0,Germany,Female,32.0,6.0,70416.75,2.0,1.0,1.0,138360.29,0.0
-4738,15583548.0,Ch'ien,638.0,Germany,Female,40.0,7.0,135984.15,1.0,1.0,0.0,186984.34,1.0
-4739,15719508.0,Tsao,676.0,France,Male,37.0,0.0,0.0,1.0,1.0,1.0,179978.68,0.0
-4740,15774931.0,Nnonso,699.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,79919.13,0.0
-4741,15712776.0,Ozioma,538.0,Spain,Female,41.0,4.0,0.0,1.0,1.0,0.0,176913.42,0.0
-4742,15611579.0,Ma,581.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,123587.86,0.0
-4743,15796834.0,Hsia,572.0,France,Female,42.0,1.0,0.0,2.0,1.0,1.0,95009.6,0.0
-4744,15670172.0,Ozoemena,602.0,France,Female,29.0,2.0,0.0,1.0,1.0,0.0,44390.38,1.0
-4745,15653547.0,Hsieh,697.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,164768.82,0.0
-4746,15771749.0,Genovesi,665.0,France,Male,36.0,10.0,139825.26,1.0,1.0,1.0,166677.35,0.0
-4747,15773868.0,Toscani,638.0,Spain,Female,44.0,4.0,0.0,2.0,1.0,0.0,151757.81,0.0
-4748,15615352.0,O'Donnell,645.0,Germany,Male,30.0,5.0,144849.29,1.0,1.0,0.0,180622.03,0.0
-4749,15655341.0,Efremov,604.0,Spain,Male,37.0,9.0,0.0,1.0,1.0,1.0,107964.36,0.0
-4750,15774696.0,Chidozie,757.0,Germany,Male,31.0,8.0,172646.82,1.0,1.0,0.0,72924.56,0.0
-4751,15676526.0,Onwudiwe,739.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,1.0,102628.98,0.0
-4752,15777797.0,Maclean,645.0,France,Male,44.0,1.0,0.0,1.0,1.0,0.0,103931.49,1.0
-4753,15592765.0,Mazzi,590.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,150694.58,0.0
-4754,15675176.0,Yefremova,732.0,Spain,Male,40.0,7.0,209767.31,1.0,1.0,0.0,93783.0,1.0
-4755,15776850.0,Lattimore,607.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,179521.43,0.0
-4756,15711287.0,Yobachukwu,745.0,Spain,Female,33.0,3.0,117302.62,2.0,1.0,0.0,198802.9,0.0
-4757,15751203.0,Iweobiegbunam,682.0,France,Male,35.0,1.0,0.0,2.0,0.0,0.0,168043.6,0.0
-4758,15774262.0,Bazarova,537.0,Spain,Male,45.0,4.0,0.0,2.0,1.0,1.0,149991.32,0.0
-4759,15725679.0,Yeh,704.0,France,Male,31.0,5.0,0.0,1.0,1.0,1.0,111687.57,0.0
-4760,15681068.0,Chidiebere,758.0,Spain,Female,46.0,3.0,0.0,2.0,1.0,1.0,121866.87,0.0
-4761,15684461.0,Teng,634.0,Germany,Female,34.0,7.0,117866.92,1.0,1.0,0.0,83669.89,0.0
-4762,15775131.0,Ku,473.0,France,Male,52.0,1.0,0.0,1.0,0.0,1.0,183893.31,1.0
-4763,15700752.0,Chukwuhaenye,721.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,72496.05,0.0
-4764,15686941.0,Genovese,652.0,France,Female,49.0,9.0,0.0,2.0,1.0,0.0,165035.17,0.0
-4765,15662736.0,Yudin,599.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,126796.69,0.0
-4766,15712287.0,Hsueh,671.0,France,Male,32.0,4.0,122034.4,1.0,0.0,0.0,60420.07,0.0
-4767,15757837.0,Lo Duca,525.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,129562.74,0.0
-4768,15749688.0,Liao,569.0,France,Male,41.0,2.0,0.0,3.0,1.0,0.0,187926.6,1.0
-4769,15652658.0,Palerma,576.0,Germany,Female,41.0,8.0,132558.26,1.0,0.0,0.0,146738.82,0.0
-4770,15618647.0,Milano,733.0,France,Male,42.0,4.0,0.0,2.0,1.0,0.0,114566.47,0.0
-4771,15772329.0,Ch'ien,645.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,132770.11,0.0
-4772,15750056.0,Cattaneo,543.0,Germany,Female,52.0,1.0,106937.05,1.0,1.0,1.0,88391.97,1.0
-4773,15593900.0,Pagnotto,662.0,Spain,Male,49.0,2.0,0.0,2.0,1.0,0.0,74532.02,0.0
-4774,15708713.0,Chu,696.0,France,Female,23.0,2.0,0.0,2.0,1.0,0.0,140937.91,0.0
-4775,15633260.0,Nwokezuike,549.0,France,Male,56.0,8.0,0.0,1.0,1.0,1.0,107876.2,1.0
-4776,15646942.0,Tsou,749.0,Spain,Male,44.0,3.0,0.0,1.0,0.0,1.0,138526.96,0.0
-4777,15683658.0,Mazzi,675.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,147040.25,0.0
-4778,15720893.0,McLean,572.0,France,Male,31.0,6.0,145965.33,2.0,1.0,1.0,177844.06,0.0
-4779,15683980.0,Chinweuba,601.0,Germany,Male,40.0,4.0,96462.25,1.0,1.0,0.0,145796.49,0.0
-4780,15768151.0,Ikedinachukwu,581.0,Germany,Male,28.0,9.0,102290.56,2.0,1.0,0.0,116766.79,0.0
-4781,15584580.0,Tsai,578.0,France,Male,22.0,7.0,144962.74,1.0,0.0,1.0,121840.2,0.0
-4782,15735263.0,Lombardi,627.0,Germany,Female,47.0,1.0,137729.03,1.0,1.0,0.0,143681.83,1.0
-4783,15700813.0,Hsueh,605.0,France,Male,60.0,3.0,0.0,2.0,1.0,1.0,73334.43,0.0
-4784,15692631.0,Chiu,592.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,125877.22,0.0
-4785,15627212.0,Hs?,667.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,86937.2,0.0
-4786,15743067.0,Ndukaku,824.0,France,Male,31.0,9.0,0.0,1.0,1.0,1.0,115767.93,0.0
-4787,15765283.0,Dike,665.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,187987.01,0.0
-4788,15769945.0,Kolesnikova,576.0,Spain,Female,33.0,6.0,122088.67,1.0,1.0,0.0,149550.94,0.0
-4789,15589975.0,T'ien,676.0,Germany,Female,29.0,9.0,161274.05,1.0,1.0,1.0,121846.81,0.0
-4790,15679963.0,Yobachi,613.0,Germany,Male,47.0,2.0,113657.41,1.0,1.0,0.0,175296.76,1.0
-4791,15800811.0,K?,573.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,80701.72,0.0
-4792,15743076.0,K?,683.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,181757.19,0.0
-4793,15700511.0,Mazzanti,591.0,France,Female,43.0,4.0,0.0,1.0,1.0,1.0,161559.12,1.0
-4794,15731522.0,T'ien,632.0,Spain,Female,24.0,0.0,137781.65,1.0,1.0,0.0,102076.03,0.0
-4795,15754919.0,Maclean,737.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,181461.46,0.0
-4796,15763055.0,Chiu,646.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,173844.89,0.0
-4797,15583849.0,Hsia,607.0,Spain,Male,39.0,9.0,0.0,2.0,0.0,1.0,149474.69,0.0
-4798,15655339.0,Cattaneo,628.0,France,Male,41.0,2.0,0.0,1.0,1.0,1.0,114566.47,0.0
-4799,15592029.0,Ts'ao,558.0,France,Male,56.0,4.0,0.0,2.0,1.0,1.0,112777.38,0.0
-4800,15629551.0,Chikezie,690.0,France,Male,26.0,3.0,0.0,2.0,1.0,0.0,127630.88,0.0
-4801,15687069.0,Onyekaozulu,587.0,Germany,Female,36.0,9.0,86893.28,2.0,1.0,1.0,145796.49,0.0
-4802,15656300.0,T'ien,579.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,1.0,105898.79,0.0
-4803,15682472.0,Chiu,774.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,1801.77,0.0
-4804,15772573.0,Nwankwo,606.0,France,Male,47.0,8.0,0.0,2.0,1.0,1.0,175893.53,0.0
-4805,15694518.0,Hao,591.0,Spain,Female,25.0,1.0,0.0,2.0,1.0,1.0,88791.83,0.0
-4806,15578515.0,Tien,653.0,Germany,Male,52.0,6.0,111537.23,1.0,0.0,0.0,170950.48,1.0
-4807,15775293.0,Onwuamaeze,685.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,177772.03,0.0
-4808,15708534.0,Onyekachi,589.0,Germany,Female,30.0,7.0,106036.52,1.0,1.0,1.0,94544.88,0.0
-4809,15684516.0,Ritchie,581.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,121053.19,0.0
-4810,15590888.0,Chinwendu,584.0,France,Female,37.0,9.0,0.0,2.0,0.0,0.0,71830.1,0.0
-4811,15665077.0,Ojiofor,667.0,Germany,Male,31.0,1.0,99152.73,2.0,1.0,0.0,134732.99,0.0
-4812,15602914.0,Lombardi,683.0,France,Female,25.0,10.0,127763.24,1.0,1.0,0.0,126471.14,0.0
-4813,15632859.0,Pagnotto,705.0,France,Female,39.0,5.0,0.0,1.0,0.0,0.0,163427.18,1.0
-4814,15782236.0,Chin,754.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,121751.04,0.0
-4815,15664227.0,Hsia,662.0,France,Female,29.0,4.0,0.0,2.0,0.0,1.0,152996.89,0.0
-4816,15590730.0,Chiganu,649.0,Germany,Female,42.0,9.0,123971.51,1.0,0.0,1.0,59595.66,0.0
-4817,15772996.0,Zetticci,656.0,France,Female,33.0,6.0,0.0,2.0,0.0,0.0,199203.21,1.0
-4818,15761168.0,Brown,678.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,172668.39,0.0
-4819,15583595.0,Shih,557.0,France,Female,47.0,6.0,109730.22,1.0,1.0,1.0,140168.36,1.0
-4820,15744044.0,K?,589.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,173365.9,0.0
-4821,15572467.0,Chuang,756.0,France,Male,44.0,1.0,0.0,2.0,0.0,0.0,177738.98,0.0
-4822,15749482.0,Iadanza,704.0,France,Female,32.0,9.0,0.0,2.0,0.0,0.0,182728.8,0.0
-4823,15745455.0,Chiemezie,745.0,France,Female,38.0,7.0,105204.01,1.0,1.0,1.0,151924.9,0.0
-4824,15704586.0,Nwankwo,639.0,France,Male,37.0,5.0,121681.91,2.0,1.0,0.0,74564.41,0.0
-4825,15806967.0,Teng,755.0,France,Male,41.0,7.0,0.0,2.0,0.0,1.0,183569.25,0.0
-4826,15801293.0,Babbage,585.0,Spain,Female,30.0,1.0,160680.41,1.0,1.0,1.0,49848.86,0.0
-4827,15749472.0,Chukwuemeka,613.0,France,Female,62.0,5.0,0.0,1.0,1.0,1.0,145937.99,1.0
-4828,15680112.0,Chigozie,758.0,France,Female,44.0,7.0,69974.66,1.0,1.0,0.0,108543.21,0.0
-4829,15690164.0,Zetticci,554.0,France,Female,36.0,1.0,0.0,1.0,1.0,0.0,57762.0,1.0
-4830,15641114.0,Hsueh,609.0,Spain,Male,32.0,9.0,161814.64,1.0,1.0,1.0,182784.11,0.0
-4831,15688865.0,Shih,721.0,France,Female,35.0,2.0,107879.04,1.0,1.0,1.0,152992.03,0.0
-4832,15762268.0,Nwachinemelu,712.0,Germany,Female,44.0,9.0,161592.76,2.0,1.0,1.0,182692.8,1.0
-4833,15581526.0,Onuchukwu,719.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,145558.42,0.0
-4834,15682355.0,Chukwueloka,697.0,Spain,Female,49.0,4.0,0.0,2.0,1.0,0.0,14073.11,1.0
-4835,15763747.0,Hsueh,651.0,France,Male,46.0,1.0,0.0,1.0,1.0,0.0,183566.78,1.0
-4836,15679668.0,McLean,598.0,France,Male,38.0,4.0,0.0,1.0,0.0,1.0,62634.94,1.0
-4837,15765297.0,Hs?eh,652.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,125961.48,0.0
-4838,15656300.0,Chiazagomekpele,645.0,Spain,Male,40.0,2.0,0.0,1.0,1.0,0.0,101954.78,1.0
-4839,15755863.0,Hs?,679.0,Germany,Male,41.0,5.0,127095.14,1.0,1.0,0.0,123977.86,1.0
-4840,15779523.0,Nkemdilim,745.0,Germany,Female,34.0,3.0,129745.1,1.0,1.0,0.0,170152.02,1.0
-4841,15702464.0,Tung,594.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,28797.32,0.0
-4842,15791553.0,Nnanna,735.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,187905.12,0.0
-4843,15646969.0,Nkemakolam,653.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,145562.4,0.0
-4844,15757140.0,Mancini,642.0,Germany,Male,38.0,9.0,117271.8,2.0,1.0,1.0,183617.43,1.0
-4845,15788776.0,Fanucci,616.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,82996.47,0.0
-4846,15781298.0,Biryukov,471.0,Germany,Male,30.0,8.0,113674.2,1.0,1.0,1.0,78858.07,1.0
-4847,15632899.0,Chiemenam,726.0,Spain,Male,36.0,5.0,0.0,2.0,0.0,1.0,73371.65,0.0
-4848,15669611.0,Frolov,758.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,156424.4,0.0
-4849,15761043.0,Toscani,634.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,176902.83,0.0
-4850,15692452.0,Folliero,678.0,Germany,Male,31.0,5.0,98899.76,2.0,1.0,0.0,177026.59,0.0
-4851,15681476.0,Soto,579.0,Germany,Female,34.0,10.0,139781.06,1.0,0.0,1.0,28690.9,1.0
-4852,15644937.0,L?,651.0,France,Male,38.0,6.0,0.0,2.0,0.0,1.0,179032.68,0.0
-4853,15605918.0,Lo Duca,587.0,Spain,Female,34.0,1.0,0.0,2.0,0.0,1.0,152417.79,0.0
-4854,15752694.0,Ts'ui,571.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,0.0,140889.32,0.0
-4855,15594577.0,Young,577.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,146821.42,0.0
-4856,15584776.0,Ulyanova,645.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,73881.68,1.0
-4857,15773901.0,Fiorentino,617.0,Spain,Female,33.0,5.0,0.0,2.0,0.0,0.0,143315.57,0.0
-4858,15739576.0,Kao,682.0,France,Female,41.0,0.0,0.0,2.0,1.0,0.0,156774.94,0.0
-4859,15767681.0,Yefimova,624.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,51345.39,0.0
-4860,15807989.0,Chinedum,602.0,Spain,Male,37.0,1.0,0.0,1.0,1.0,0.0,92712.97,1.0
-4861,15723373.0,Aksyonova,663.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,113860.79,0.0
-4862,15713347.0,Chukwudi,577.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,88890.05,0.0
-4863,15804950.0,Ch'en,669.0,France,Female,55.0,1.0,0.0,1.0,1.0,1.0,125698.97,1.0
-4864,15775116.0,Smith,699.0,France,Male,28.0,9.0,141827.33,1.0,1.0,0.0,123876.22,0.0
-4865,15791114.0,Calabrese,637.0,Germany,Male,35.0,5.0,126550.7,1.0,1.0,1.0,109960.4,0.0
-4866,15809509.0,Chukwuemeka,602.0,Spain,Female,58.0,8.0,0.0,2.0,1.0,1.0,64831.36,0.0
-4867,15583716.0,Hsueh,587.0,France,Male,40.0,2.0,0.0,1.0,1.0,0.0,120593.85,1.0
-4868,15803764.0,Milanesi,709.0,France,Female,25.0,9.0,123042.0,1.0,1.0,0.0,169802.73,0.0
-4869,15691874.0,Chiemezie,516.0,Spain,Male,41.0,7.0,0.0,1.0,1.0,0.0,180962.32,0.0
-4870,15588566.0,Kao,683.0,France,Female,28.0,7.0,0.0,1.0,1.0,1.0,129188.18,0.0
-4871,15733888.0,Chiu,562.0,France,Female,26.0,0.0,0.0,2.0,0.0,1.0,127951.81,0.0
-4872,15733705.0,Shih,635.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,87731.02,0.0
-4873,15766044.0,Hsing,646.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,151903.6,0.0
-4874,15677052.0,Ozerova,661.0,France,Male,21.0,3.0,0.0,2.0,1.0,0.0,52831.13,0.0
-4875,15734987.0,Ugochukwu,710.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,103970.58,0.0
-4876,15762423.0,Chukwualuka,696.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,60920.03,0.0
-4877,15702571.0,Walker,554.0,Germany,Male,30.0,1.0,131569.63,2.0,0.0,1.0,147554.26,0.0
-4878,15665956.0,P'eng,590.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,138526.96,0.0
-4879,15583764.0,Okwudilichukwu,609.0,France,Male,42.0,2.0,143340.44,1.0,0.0,0.0,170821.43,0.0
-4880,15610446.0,Yobachi,679.0,France,Female,46.0,9.0,0.0,1.0,1.0,0.0,91711.66,1.0
-4881,15585893.0,Ch'in,673.0,France,Male,37.0,3.0,121073.23,1.0,1.0,1.0,44406.17,0.0
-4882,15763515.0,Bovee,598.0,Spain,Female,28.0,8.0,0.0,2.0,1.0,0.0,91574.92,0.0
-4883,15673584.0,Walker,697.0,Spain,Male,47.0,1.0,0.0,2.0,1.0,0.0,102763.69,0.0
-4884,15664754.0,Horsley,559.0,Spain,Female,40.0,0.0,0.0,2.0,1.0,1.0,96658.26,0.0
-4885,15736254.0,H?,599.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,185599.34,0.0
-4886,15639590.0,Udegbunam,638.0,Spain,Female,28.0,1.0,0.0,2.0,1.0,0.0,59650.42,0.0
-4887,15728505.0,Nnamdi,687.0,Germany,Male,49.0,5.0,110123.59,2.0,1.0,1.0,152845.99,0.0
-4888,15644296.0,Hsiung,781.0,France,Female,34.0,7.0,117038.96,1.0,1.0,1.0,180969.55,0.0
-4889,15791958.0,Manna,586.0,France,Female,37.0,8.0,0.0,1.0,1.0,1.0,60905.51,0.0
-4890,15721658.0,Shih,596.0,France,Female,22.0,3.0,0.0,1.0,1.0,1.0,9241.78,0.0
-4891,15641366.0,Tseng,776.0,France,Female,33.0,8.0,0.0,2.0,0.0,1.0,60728.89,0.0
-4892,15666884.0,T'ien,754.0,France,Male,27.0,7.0,0.0,2.0,1.0,0.0,54487.43,0.0
-4893,15593468.0,Ch'iu,673.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,173599.38,0.0
-4894,15570002.0,Shih,545.0,France,Female,33.0,5.0,166501.17,1.0,1.0,1.0,49463.44,0.0
-4895,15694829.0,Bibi,578.0,France,Female,38.0,3.0,0.0,3.0,1.0,0.0,107961.11,0.0
-4896,15679987.0,Chinomso,677.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,102799.14,0.0
-4897,15691952.0,Burtch,678.0,France,Male,43.0,8.0,0.0,2.0,0.0,1.0,159891.03,0.0
-4898,15661827.0,Hsieh,707.0,France,Female,31.0,4.0,94416.78,1.0,1.0,1.0,147530.06,0.0
-4899,15613886.0,Ch'iu,773.0,France,Female,56.0,9.0,0.0,1.0,0.0,0.0,156910.46,1.0
-4900,15630551.0,Chinwenma,708.0,France,Male,43.0,2.0,0.0,1.0,0.0,1.0,131043.2,1.0
-4901,15657778.0,Rowntree,730.0,France,Female,35.0,7.0,0.0,2.0,1.0,1.0,126794.56,0.0
-4902,15728005.0,Tung,602.0,France,Female,56.0,4.0,0.0,2.0,1.0,0.0,120801.65,1.0
-4903,15778089.0,Cremonesi,850.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,54448.22,0.0
-4904,15645029.0,Walker,719.0,France,Female,32.0,8.0,0.0,2.0,0.0,0.0,83419.15,0.0
-4905,15724223.0,Ndukaku,590.0,Spain,Male,46.0,10.0,0.0,1.0,1.0,0.0,141926.43,1.0
-4906,15740557.0,Palerma,850.0,France,Male,45.0,3.0,0.0,1.0,0.0,0.0,153566.97,0.0
-4907,15642885.0,Chibueze,729.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,91711.66,0.0
-4908,15661526.0,Hsiao,548.0,France,Male,40.0,1.0,0.0,1.0,0.0,0.0,110371.84,1.0
-4909,15681671.0,Cattaneo,646.0,Spain,Female,24.0,9.0,134562.8,2.0,1.0,1.0,128875.86,0.0
-4910,15605968.0,Y?,667.0,Germany,Female,29.0,8.0,121405.04,1.0,1.0,0.0,121232.51,0.0
-4911,15592222.0,Smith,678.0,France,Male,23.0,8.0,0.0,2.0,1.0,0.0,92985.78,1.0
-4912,15585823.0,Chuang,531.0,Spain,Female,44.0,9.0,0.0,1.0,0.0,1.0,138889.15,1.0
-4913,15640078.0,Ku,545.0,Spain,Female,60.0,8.0,0.0,1.0,1.0,1.0,119969.99,1.0
-4914,15730705.0,Shih,516.0,France,Male,44.0,2.0,0.0,2.0,1.0,1.0,123977.86,0.0
-4915,15751445.0,Y?an,644.0,France,Male,39.0,1.0,0.0,1.0,0.0,0.0,114093.73,0.0
-4916,15689671.0,Udinese,745.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,149913.94,0.0
-4917,15732672.0,Genovese,653.0,Germany,Female,37.0,6.0,81210.72,1.0,1.0,0.0,121747.98,1.0
-4918,15590730.0,Ch'ang,677.0,France,Female,41.0,0.0,0.0,2.0,0.0,0.0,132436.71,0.0
-4919,15754926.0,Milanesi,599.0,Spain,Male,44.0,2.0,0.0,2.0,1.0,0.0,153955.38,0.0
-4920,15747174.0,Macdonald,585.0,France,Male,32.0,8.0,0.0,1.0,1.0,0.0,177783.04,0.0
-4921,15577423.0,Macdonald,703.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,132770.11,0.0
-4922,15682355.0,Shih,684.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,121747.96,0.0
-4923,15681506.0,Hsieh,595.0,France,Male,27.0,8.0,155062.8,1.0,1.0,0.0,188203.58,0.0
-4924,15773792.0,Chin,638.0,France,Female,38.0,6.0,0.0,2.0,1.0,0.0,58854.88,0.0
-4925,15623062.0,Hs?,596.0,France,Male,62.0,3.0,0.0,1.0,1.0,1.0,88659.07,0.0
-4926,15642996.0,Chukwuhaenye,623.0,France,Female,33.0,7.0,0.0,2.0,0.0,0.0,138700.89,0.0
-4927,15633194.0,Hsu,726.0,Spain,Female,44.0,2.0,100549.43,1.0,1.0,0.0,131283.6,0.0
-4928,15671769.0,Bellucci,729.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,108411.66,0.0
-4929,15807609.0,Chiekwugo,718.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,47799.15,0.0
-4930,15600739.0,Chidubem,724.0,Germany,Female,34.0,6.0,146622.36,1.0,1.0,1.0,94898.1,0.0
-4931,15717290.0,Yermolayeva,637.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,0.0,55725.03,0.0
-4932,15764253.0,Genovese,850.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,96444.88,0.0
-4933,15658929.0,Chiang,583.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,93783.0,0.0
-4934,15662955.0,T'ien,678.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,167536.15,0.0
-4935,15771222.0,T'ien,696.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,28280.8,0.0
-4936,15806922.0,Wood,592.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,102260.98,0.0
-4937,15700813.0,Onyemauchechukwu,673.0,France,Female,36.0,10.0,110827.49,2.0,1.0,1.0,134790.61,0.0
-4938,15756975.0,Chibugo,631.0,Spain,Female,47.0,9.0,0.0,1.0,1.0,1.0,150856.38,1.0
-4939,15685564.0,Pisano,714.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,101311.95,0.0
-4940,15721469.0,Yermakova,711.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,179227.16,1.0
-4941,15798824.0,Hsu,619.0,Germany,Female,31.0,8.0,169824.46,2.0,0.0,0.0,142514.66,0.0
-4942,15587562.0,Chinwenma,633.0,Germany,Male,24.0,2.0,119799.27,1.0,1.0,0.0,147565.75,1.0
-4943,15776827.0,Chikezie,648.0,Spain,Male,46.0,9.0,0.0,1.0,1.0,1.0,182609.97,0.0
-4944,15572428.0,Udegbunam,705.0,France,Male,24.0,3.0,141786.78,1.0,1.0,0.0,180712.28,0.0
-4945,15578878.0,Cattaneo,711.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,146585.49,0.0
-4946,15677052.0,Hsueh,756.0,Germany,Male,33.0,9.0,129895.43,1.0,1.0,0.0,168822.01,0.0
-4947,15653572.0,Fennescey,707.0,France,Male,46.0,2.0,0.0,2.0,0.0,1.0,38276.84,0.0
-4948,15677550.0,Pinto,757.0,France,Male,29.0,6.0,0.0,1.0,1.0,0.0,135083.73,0.0
-4949,15578979.0,Ni,714.0,France,Male,42.0,10.0,0.0,2.0,0.0,0.0,167538.66,0.0
-4950,15694518.0,Ch'iu,684.0,France,Female,43.0,8.0,101438.0,1.0,1.0,0.0,172749.4,1.0
-4951,15781129.0,Okwudilichukwu,632.0,Germany,Male,28.0,5.0,61592.14,1.0,1.0,1.0,177655.69,0.0
-4952,15771059.0,Teng,738.0,France,Male,54.0,1.0,142876.88,1.0,1.0,1.0,172810.51,0.0
-4953,15588928.0,Ch'ien,542.0,France,Female,39.0,9.0,0.0,2.0,0.0,0.0,7905.06,0.0
-4954,15627262.0,De Luca,581.0,Spain,Female,34.0,1.0,0.0,2.0,1.0,1.0,122925.4,0.0
-4955,15632439.0,Chukwujamuike,727.0,Spain,Female,36.0,0.0,0.0,2.0,0.0,1.0,115525.13,0.0
-4956,15686885.0,Chuang,669.0,Germany,Male,37.0,3.0,108736.52,1.0,1.0,0.0,140136.1,0.0
-4957,15721921.0,Ndubueze,717.0,France,Male,28.0,9.0,126663.51,1.0,1.0,0.0,54416.18,0.0
-4958,15643916.0,Li Fonti,836.0,France,Male,29.0,2.0,124577.33,2.0,1.0,0.0,99800.87,0.0
-4959,15590876.0,Chibugo,782.0,Germany,Female,47.0,2.0,113657.83,1.0,1.0,1.0,182784.11,1.0
-4960,15633194.0,Lucchese,659.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,27231.26,0.0
-4961,15752912.0,L?,709.0,France,Female,22.0,1.0,130142.79,1.0,1.0,0.0,169935.46,0.0
-4962,15696812.0,Ts'ai,793.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,161051.75,0.0
-4963,15655007.0,Chuang,677.0,France,Male,38.0,3.0,122570.87,1.0,1.0,0.0,79577.38,0.0
-4964,15651581.0,Ejimofor,679.0,Germany,Male,49.0,5.0,116597.79,1.0,1.0,0.0,153372.39,1.0
-4965,15792360.0,Haddon,683.0,Spain,Female,33.0,8.0,0.0,2.0,1.0,0.0,145796.49,0.0
-4966,15661290.0,Nwachukwu,624.0,Germany,Female,54.0,5.0,152576.25,1.0,0.0,0.0,62370.67,1.0
-4967,15601263.0,O'Donnell,613.0,France,Male,33.0,9.0,103516.87,1.0,1.0,0.0,158978.79,0.0
-4968,15658889.0,Lo,516.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,123974.86,0.0
-4969,15694450.0,Ts'ui,653.0,France,Female,67.0,0.0,0.0,2.0,1.0,1.0,35140.49,0.0
-4970,15571816.0,Maclean,677.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,123866.28,0.0
-4971,15662494.0,Hs?,663.0,Spain,Female,39.0,6.0,0.0,2.0,1.0,0.0,91624.11,0.0
-4972,15702434.0,Hao,638.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,1.0,125696.26,0.0
-4973,15771580.0,Fanucci,762.0,Spain,Male,34.0,9.0,139432.37,1.0,1.0,1.0,79342.31,0.0
-4974,15689952.0,Okechukwu,628.0,France,Female,22.0,8.0,0.0,2.0,1.0,1.0,95583.45,0.0
-4975,15686941.0,Onwumelu,659.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,89040.61,0.0
-4976,15644934.0,Linger,665.0,France,Female,37.0,6.0,0.0,2.0,1.0,1.0,65864.4,0.0
-4977,15594556.0,Mazzanti,850.0,Germany,Male,41.0,7.0,115462.02,2.0,0.0,0.0,645.61,0.0
-4978,15803378.0,Ibezimako,577.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,49063.42,0.0
-4979,15670172.0,Ch'eng,645.0,France,Male,31.0,3.0,176913.52,2.0,1.0,1.0,58440.43,0.0
-4980,15791842.0,Nnanna,653.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,823.96,0.0
-4981,15567422.0,Huggins,553.0,Spain,Male,35.0,3.0,0.0,2.0,0.0,0.0,38308.34,0.0
-4982,15683523.0,Chukwufumnanya,556.0,Germany,Female,45.0,7.0,91092.52,2.0,1.0,0.0,51672.08,1.0
-4983,15573730.0,Kao,671.0,Germany,Female,35.0,2.0,143656.55,2.0,1.0,1.0,93524.19,0.0
-4984,15605000.0,Ch'ang,569.0,France,Male,31.0,5.0,117300.02,1.0,1.0,0.0,65612.01,1.0
-4985,15593795.0,Tretiakova,568.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,121747.98,0.0
-4986,15750808.0,T'ien,713.0,France,Female,27.0,4.0,0.0,2.0,1.0,0.0,58907.08,0.0
-4987,15615753.0,Chidimma,641.0,France,Male,44.0,3.0,0.0,2.0,1.0,1.0,139953.94,0.0
-4988,15646091.0,Genovesi,725.0,France,Male,25.0,3.0,0.0,2.0,1.0,1.0,26019.59,0.0
-4989,15753562.0,Echezonachukwu,652.0,France,Female,52.0,3.0,0.0,1.0,1.0,0.0,91565.25,1.0
-4990,15713347.0,Frolov,593.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,82709.8,0.0
-4991,15805637.0,Cremonesi,837.0,France,Male,38.0,1.0,147037.31,1.0,1.0,1.0,130483.19,0.0
-4992,15783025.0,Nwokeocha,594.0,Germany,Female,50.0,3.0,123311.39,1.0,0.0,0.0,174822.74,1.0
-4993,15734886.0,Mistry,741.0,France,Female,56.0,2.0,0.0,2.0,0.0,1.0,86767.48,0.0
-4994,15751943.0,Kincaid,850.0,France,Male,44.0,8.0,0.0,2.0,1.0,0.0,114957.22,0.0
-4995,15665943.0,Yashina,569.0,France,Female,50.0,7.0,0.0,4.0,1.0,0.0,166656.18,1.0
-4996,15679760.0,Chinweuba,635.0,Germany,Female,33.0,2.0,138825.19,1.0,0.0,1.0,138526.96,0.0
-4997,15806922.0,Hsing,706.0,France,Male,62.0,3.0,0.0,1.0,1.0,0.0,160176.47,1.0
-4998,15594902.0,Ts'ao,554.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,0.0,120830.73,0.0
-4999,15762729.0,Tuan,754.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,69080.46,0.0
-5000,15677522.0,Walker,588.0,France,Male,40.0,7.0,120867.18,1.0,0.0,1.0,141896.74,0.0
-5001,15743817.0,Padovano,553.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,127095.14,0.0
-5002,15607278.0,Chukwuemeka,602.0,France,Male,39.0,3.0,98443.15,1.0,1.0,1.0,174064.4,0.0
-5003,15584776.0,Zhirov,568.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,106040.97,0.0
-5004,15661034.0,Hsueh,594.0,Germany,Male,35.0,1.0,133800.98,1.0,1.0,1.0,166677.35,0.0
-5005,15754919.0,Bancks,681.0,France,Female,33.0,3.0,0.0,2.0,0.0,1.0,159742.33,0.0
-5006,15595557.0,Chukwuebuka,538.0,Spain,Male,39.0,1.0,0.0,1.0,1.0,0.0,62700.08,0.0
-5007,15690175.0,Chukwukere,661.0,Germany,Male,33.0,9.0,145747.67,1.0,1.0,1.0,103901.35,1.0
-5008,15661290.0,Kornilova,653.0,France,Male,36.0,2.0,0.0,2.0,0.0,1.0,14073.11,0.0
-5009,15582404.0,Cattaneo,850.0,Spain,Male,41.0,8.0,133467.77,1.0,1.0,0.0,87978.45,0.0
-5010,15725166.0,Eipper,642.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,142377.69,0.0
-5011,15697034.0,Bischof,537.0,Germany,Male,30.0,1.0,167545.32,1.0,0.0,1.0,69384.71,0.0
-5012,15682600.0,Onyekachi,656.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,151733.43,0.0
-5013,15641794.0,Trevisano,683.0,France,Female,35.0,9.0,121365.39,2.0,1.0,1.0,162719.69,0.0
-5014,15672945.0,Zikoranachidimma,713.0,Spain,Female,42.0,2.0,0.0,1.0,1.0,1.0,116885.59,0.0
-5015,15751336.0,Hsia,654.0,Germany,Female,51.0,6.0,90920.75,2.0,1.0,0.0,168034.83,1.0
-5016,15714046.0,Scott,594.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,129964.94,0.0
-5017,15704769.0,Chidiebele,558.0,France,Male,28.0,9.0,118897.1,1.0,1.0,1.0,82463.69,0.0
-5018,15597945.0,Fyodorov,687.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,101371.72,0.0
-5019,15722599.0,Okonkwo,662.0,Spain,Male,33.0,1.0,0.0,2.0,1.0,0.0,129918.43,0.0
-5020,15663478.0,Y?,687.0,France,Female,47.0,2.0,0.0,1.0,1.0,1.0,176678.72,1.0
-5021,15803681.0,Nwebube,577.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,111558.7,0.0
-5022,15669204.0,Gentry,653.0,France,Female,46.0,6.0,148046.39,1.0,1.0,0.0,103570.8,1.0
-5023,15795895.0,Nkemdilim,712.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,175072.47,0.0
-5024,15641674.0,Chinweuba,652.0,France,Female,38.0,4.0,0.0,2.0,0.0,0.0,4777.23,0.0
-5025,15765283.0,Chibugo,684.0,Spain,Male,30.0,2.0,96418.09,2.0,1.0,0.0,182784.11,0.0
-5026,15585901.0,O'Brien,553.0,Germany,Male,53.0,10.0,153610.61,1.0,1.0,1.0,73562.05,1.0
-5027,15797900.0,Manna,619.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,179834.45,0.0
-5028,15626806.0,Okonkwo,632.0,France,Female,35.0,4.0,0.0,1.0,1.0,0.0,180712.28,1.0
-5029,15789413.0,Maclean,716.0,Germany,Male,38.0,2.0,73348.56,2.0,1.0,0.0,149913.94,0.0
-5030,15798084.0,Hsiao,753.0,France,Male,37.0,7.0,0.0,2.0,0.0,0.0,176692.87,0.0
-5031,15685300.0,Onyemauchechukwu,769.0,Germany,Male,31.0,2.0,116324.53,1.0,0.0,0.0,25289.23,0.0
-5032,15585748.0,Chuang,663.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,118319.6,0.0
-5033,15744044.0,Pagnotto,687.0,Spain,Female,30.0,9.0,0.0,2.0,0.0,0.0,154700.61,0.0
-5034,15648032.0,Ch'en,592.0,Spain,Male,41.0,3.0,0.0,2.0,1.0,1.0,90177.3,0.0
-5035,15594812.0,Lombardi,585.0,Spain,Female,26.0,8.0,116933.11,1.0,0.0,1.0,118974.77,0.0
-5036,15707968.0,L?,733.0,France,Female,40.0,7.0,104982.57,1.0,1.0,1.0,28806.32,0.0
-5037,15709511.0,Tien,645.0,Germany,Male,30.0,1.0,132759.8,1.0,1.0,0.0,157891.86,0.0
-5038,15583736.0,P'eng,561.0,France,Female,43.0,5.0,0.0,2.0,1.0,0.0,140139.28,0.0
-5039,15790750.0,Ozoemena,794.0,France,Female,44.0,9.0,0.0,2.0,1.0,0.0,84569.13,0.0
-5040,15794916.0,Chukwujamuike,584.0,France,Female,33.0,3.0,0.0,2.0,0.0,1.0,168816.35,0.0
-5041,15771543.0,Pinto,845.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,137784.25,0.0
-5042,15690546.0,Iadanza,725.0,Germany,Male,39.0,9.0,115725.24,1.0,1.0,1.0,182025.95,0.0
-5043,15596962.0,Chiang,656.0,France,Female,29.0,7.0,0.0,1.0,1.0,1.0,181204.5,0.0
-5044,15692664.0,White,638.0,France,Male,36.0,10.0,0.0,2.0,0.0,1.0,98618.79,0.0
-5045,15794413.0,H?,696.0,France,Male,37.0,4.0,0.0,2.0,0.0,0.0,36315.03,0.0
-5046,15706593.0,Ijendu,516.0,France,Female,37.0,2.0,122295.17,1.0,0.0,0.0,115733.16,0.0
-5047,15579891.0,Ch'ien,648.0,Germany,Female,38.0,7.0,139914.67,2.0,1.0,1.0,162267.87,1.0
-5048,15581286.0,Ch'ien,586.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,163667.56,0.0
-5049,15690164.0,Folliero,696.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,0.0,149912.28,0.0
-5050,15739592.0,Onyeorulu,613.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,105784.85,0.0
-5051,15774536.0,Iheanacho,751.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,182081.45,0.0
-5052,15793049.0,Manna,577.0,France,Female,36.0,2.0,121404.64,1.0,1.0,1.0,16607.15,0.0
-5053,15775562.0,Lo Duca,668.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,0.0,175894.24,0.0
-5054,15680405.0,Chiu,587.0,Spain,Female,44.0,1.0,0.0,1.0,0.0,0.0,129892.93,1.0
-5055,15806938.0,Hsiung,683.0,France,Male,37.0,3.0,0.0,2.0,0.0,0.0,182784.11,0.0
-5056,15693902.0,T'ang,489.0,Spain,Female,47.0,4.0,91622.47,1.0,1.0,0.0,116848.79,0.0
-5057,15806967.0,Yuryeva,576.0,France,Male,35.0,4.0,175837.68,1.0,1.0,0.0,99800.87,0.0
-5058,15623210.0,Kambinachi,569.0,France,Male,27.0,10.0,139875.19,1.0,1.0,1.0,86414.41,0.0
-5059,15603883.0,Y?an,496.0,France,Female,68.0,4.0,156660.72,3.0,1.0,0.0,176091.31,1.0
-5060,15623536.0,Chiu,543.0,Germany,Female,33.0,2.0,131033.76,1.0,1.0,0.0,184842.08,0.0
-5061,15778936.0,Onyekachi,754.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,77951.84,0.0
-5062,15681695.0,Hsia,596.0,France,Female,43.0,7.0,0.0,1.0,1.0,0.0,113553.43,1.0
-5063,15634295.0,Uspensky,667.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,151315.76,0.0
-5064,15706071.0,Tsou,694.0,Germany,Male,39.0,3.0,114094.38,1.0,1.0,0.0,102908.91,0.0
-5065,15622289.0,Nwagugheuzo,678.0,France,Male,47.0,4.0,0.0,2.0,1.0,0.0,103799.71,0.0
-5066,15694890.0,Iloerika,710.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,130853.03,1.0
-5067,15754578.0,Kable,774.0,Germany,Male,41.0,5.0,108888.59,2.0,1.0,1.0,167034.83,0.0
-5068,15738672.0,Mazzi,629.0,Germany,Female,41.0,4.0,118827.35,1.0,0.0,1.0,70778.63,1.0
-5069,15682454.0,Toscani,687.0,Germany,Male,69.0,4.0,108862.76,1.0,1.0,1.0,73882.49,0.0
-5070,15607986.0,Mazzanti,681.0,France,Male,35.0,4.0,126815.87,1.0,1.0,1.0,161479.19,0.0
-5071,15640409.0,Pinto,733.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,131281.28,0.0
-5072,15684594.0,Fomin,535.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,127095.14,0.0
-5073,15781598.0,Hsueh,683.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,199409.19,0.0
-5074,15728669.0,Chien,681.0,France,Male,30.0,7.0,0.0,2.0,0.0,1.0,165038.27,0.0
-5075,15699225.0,Y?an,633.0,France,Female,48.0,5.0,139859.54,1.0,0.0,0.0,125553.52,0.0
-5076,15793949.0,Ch'iu,576.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,54949.73,0.0
-5077,15622454.0,Ijendu,637.0,Germany,Male,37.0,9.0,103549.74,1.0,1.0,0.0,121151.1,1.0
-5078,15804787.0,Ch'ien,731.0,France,Male,49.0,4.0,0.0,1.0,1.0,0.0,108411.66,1.0
-5079,15655192.0,Iloerika,567.0,France,Male,45.0,2.0,0.0,4.0,1.0,1.0,38960.59,1.0
-5080,15691823.0,Pisano,575.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,176570.28,0.0
-5081,15774105.0,Pagnotto,705.0,Spain,Female,41.0,9.0,0.0,2.0,1.0,0.0,74835.65,0.0
-5082,15802351.0,Hsiao,652.0,France,Male,34.0,2.0,96947.76,1.0,0.0,0.0,173837.54,0.0
-5083,15802362.0,Bibi,611.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,161064.29,0.0
-5084,15763980.0,Nebechukwu,704.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,1.0,133031.96,0.0
-5085,15625759.0,Chineze,635.0,France,Female,48.0,3.0,0.0,1.0,1.0,0.0,160696.72,1.0
-5086,15595842.0,Ch'ien,708.0,France,Female,29.0,7.0,131694.97,1.0,0.0,1.0,82222.86,0.0
-5087,15696733.0,White,838.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,113796.15,0.0
-5088,15780684.0,Hsieh,652.0,France,Female,28.0,4.0,0.0,2.0,1.0,0.0,177614.17,0.0
-5089,15634491.0,Tuan,815.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,92664.09,0.0
-5090,15751561.0,Chikelu,711.0,France,Male,36.0,5.0,0.0,2.0,0.0,0.0,64443.75,0.0
-5091,15746430.0,Nkemdilim,684.0,France,Male,46.0,7.0,0.0,1.0,0.0,1.0,35367.19,0.0
-5092,15635244.0,Milanesi,581.0,France,Female,56.0,3.0,0.0,3.0,1.0,0.0,73977.23,1.0
-5093,15679048.0,Shih,676.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,148516.79,0.0
-5094,15581626.0,Milano,683.0,Spain,Male,29.0,6.0,0.0,1.0,1.0,1.0,123663.63,0.0
-5095,15695097.0,Mironova,492.0,Germany,Male,40.0,7.0,134760.21,2.0,1.0,1.0,149853.89,0.0
-5096,15765532.0,Kao,681.0,France,Male,45.0,4.0,0.0,1.0,0.0,1.0,189366.03,1.0
-5097,15632859.0,Iloerika,607.0,France,Male,40.0,8.0,120905.03,1.0,0.0,1.0,147055.87,0.0
-5098,15725036.0,Pugh,656.0,France,Female,46.0,9.0,0.0,2.0,1.0,0.0,123981.36,0.0
-5099,15583595.0,Calabrese,604.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,121485.1,0.0
-5100,15737509.0,Palermo,682.0,France,Male,43.0,10.0,0.0,2.0,0.0,1.0,132468.76,0.0
-5101,15731543.0,Ulyanova,677.0,France,Female,37.0,9.0,0.0,2.0,0.0,1.0,143881.85,0.0
-5102,15629603.0,Chien,686.0,Germany,Male,40.0,2.0,147814.04,2.0,1.0,1.0,166038.27,0.0
-5103,15791326.0,Ritchie,726.0,France,Female,51.0,4.0,0.0,2.0,1.0,0.0,128841.31,1.0
-5104,15576065.0,Uchechukwu,582.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,171707.93,0.0
-5105,15736420.0,Folliero,569.0,Spain,Male,51.0,6.0,0.0,1.0,1.0,0.0,161519.77,1.0
-5106,15797787.0,Chiekwugo,667.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,185547.72,0.0
-5107,15772337.0,Fyodorova,633.0,France,Female,31.0,3.0,0.0,2.0,0.0,0.0,142331.85,0.0
-5108,15591386.0,Chiebuka,644.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,135925.72,0.0
-5109,15695097.0,Hsiao,653.0,France,Female,35.0,8.0,0.0,3.0,0.0,0.0,138051.19,1.0
-5110,15711687.0,Hsieh,717.0,Spain,Female,35.0,8.0,178074.33,1.0,1.0,0.0,67552.71,0.0
-5111,15679810.0,Ngozichukwuka,619.0,France,Male,36.0,4.0,0.0,2.0,0.0,1.0,167984.72,0.0
-5112,15583848.0,Y?,637.0,Spain,Male,35.0,5.0,0.0,1.0,1.0,1.0,179631.85,0.0
-5113,15631893.0,Smith,712.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,53584.03,0.0
-5114,15666096.0,Chiemenam,776.0,France,Male,35.0,4.0,0.0,2.0,0.0,1.0,85736.46,0.0
-5115,15658548.0,Ogg,810.0,France,Male,28.0,1.0,0.0,2.0,0.0,0.0,71862.79,0.0
-5116,15664547.0,Chinedum,589.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,103421.91,0.0
-5117,15790717.0,Chibugo,727.0,Germany,Female,39.0,7.0,145974.79,2.0,1.0,0.0,163471.01,0.0
-5118,15691627.0,Iloerika,724.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,97794.71,0.0
-5119,15625916.0,Nucci,559.0,France,Female,25.0,5.0,0.0,2.0,1.0,0.0,113428.77,0.0
-5120,15769216.0,Ch'in,640.0,France,Male,40.0,3.0,0.0,2.0,0.0,1.0,68621.98,0.0
-5121,15702656.0,Watson,622.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,92917.54,0.0
-5122,15582914.0,DeRose,684.0,France,Female,34.0,9.0,0.0,1.0,0.0,0.0,176984.34,0.0
-5123,15670587.0,Ts'ai,749.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,1.0,50560.15,0.0
-5124,15591747.0,Yermakova,675.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,127068.92,0.0
-5125,15810010.0,Onyemauchechukwu,685.0,France,Female,30.0,9.0,0.0,2.0,1.0,1.0,73334.41,0.0
-5126,15736271.0,Lu,689.0,France,Female,35.0,10.0,0.0,1.0,1.0,0.0,54474.48,0.0
-5127,15806945.0,Lucchese,722.0,Germany,Female,40.0,6.0,129810.6,2.0,1.0,1.0,169278.51,0.0
-5128,15629985.0,Kornilova,738.0,France,Male,43.0,1.0,182781.74,1.0,1.0,1.0,59595.66,0.0
-5129,15597905.0,Y?,597.0,France,Male,30.0,1.0,0.0,2.0,0.0,1.0,103713.93,0.0
-5130,15733387.0,Milani,804.0,France,Male,45.0,7.0,0.0,1.0,1.0,0.0,177738.98,1.0
-5131,15583394.0,Nwachinemelu,649.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,176743.51,0.0
-5132,15672930.0,Milne,577.0,Spain,Female,32.0,1.0,0.0,2.0,1.0,1.0,162697.93,0.0
-5133,15764444.0,Milani,616.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,118862.08,0.0
-5134,15709137.0,Chifley,813.0,France,Male,36.0,8.0,0.0,1.0,0.0,1.0,152577.62,0.0
-5135,15777639.0,Hs?,727.0,France,Female,29.0,7.0,171283.91,1.0,1.0,1.0,184842.21,0.0
-5136,15709780.0,Iloerika,749.0,France,Male,29.0,5.0,0.0,2.0,0.0,1.0,47952.45,0.0
-5137,15623063.0,Ponomaryova,687.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,108645.85,0.0
-5138,15651052.0,Li Fonti,604.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,143663.63,0.0
-5139,15790313.0,Smith,698.0,Germany,Female,57.0,2.0,144900.58,1.0,1.0,0.0,131389.39,1.0
-5140,15659325.0,Kao,708.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,1.0,93832.33,0.0
-5141,15642570.0,Y?,590.0,France,Male,39.0,9.0,109949.05,1.0,1.0,0.0,88890.05,0.0
-5142,15761158.0,Chibueze,684.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,54869.55,0.0
-5143,15756918.0,Smith,600.0,Germany,Male,60.0,3.0,106770.82,2.0,1.0,0.0,101828.39,1.0
-5144,15775886.0,Kao,699.0,Germany,Female,31.0,9.0,125437.86,2.0,1.0,0.0,164074.89,0.0
-5145,15794056.0,Smith,581.0,Spain,Male,41.0,5.0,0.0,2.0,0.0,0.0,93536.38,0.0
-5146,15782735.0,Ch'iu,596.0,France,Female,35.0,4.0,0.0,3.0,1.0,1.0,182372.35,1.0
-5147,15595883.0,Chien,579.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,102999.33,0.0
-5148,15726589.0,Teng,569.0,Spain,Female,52.0,10.0,0.0,1.0,1.0,1.0,52319.26,1.0
-5149,15668943.0,Pirozzi,627.0,France,Male,43.0,4.0,0.0,2.0,0.0,1.0,177528.92,0.0
-5150,15777045.0,Ch'ien,586.0,France,Male,38.0,7.0,0.0,1.0,1.0,1.0,116466.19,0.0
-5151,15617648.0,Oguejiofor,599.0,France,Female,36.0,4.0,0.0,2.0,0.0,0.0,162697.93,0.0
-5152,15598070.0,Ch'ien,686.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,122782.5,0.0
-5153,15659568.0,Ch'ang,735.0,France,Male,44.0,9.0,0.0,2.0,1.0,0.0,108419.41,1.0
-5154,15762745.0,Chidumaga,718.0,Germany,Female,43.0,8.0,118094.34,3.0,1.0,0.0,163650.53,1.0
-5155,15702434.0,Hsia,690.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,102515.57,0.0
-5156,15794413.0,T'ien,683.0,Spain,Male,39.0,5.0,148061.89,2.0,1.0,1.0,140937.91,0.0
-5157,15752885.0,Chiang,684.0,France,Male,36.0,8.0,0.0,2.0,0.0,1.0,187905.12,0.0
-5158,15742678.0,Chuang,809.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,67622.46,0.0
-5159,15652999.0,Chien,721.0,France,Male,27.0,2.0,0.0,2.0,0.0,1.0,131301.74,0.0
-5160,15737571.0,Ponomaryov,697.0,France,Male,27.0,8.0,0.0,2.0,1.0,0.0,163568.51,0.0
-5161,15691703.0,Macartney,717.0,France,Male,42.0,9.0,0.0,2.0,0.0,0.0,171706.66,0.0
-5162,15699247.0,Chukwuemeka,554.0,France,Male,45.0,4.0,0.0,2.0,1.0,0.0,62634.94,0.0
-5163,15784301.0,Nwokezuike,562.0,Spain,Female,33.0,9.0,144852.06,1.0,1.0,0.0,28805.09,0.0
-5164,15799358.0,Onyekachukwu,613.0,Spain,Male,39.0,6.0,0.0,1.0,1.0,1.0,116696.77,0.0
-5165,15705034.0,Chigolum,753.0,France,Female,46.0,9.0,128960.21,1.0,1.0,0.0,119357.93,1.0
-5166,15576723.0,Ch'iu,638.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,131886.46,0.0
-5167,15702788.0,T'ang,586.0,Germany,Female,36.0,9.0,90537.16,2.0,1.0,1.0,177069.24,0.0
-5168,15726589.0,Chukwudi,703.0,France,Male,25.0,4.0,0.0,2.0,0.0,0.0,140937.97,0.0
-5169,15660429.0,Zito,633.0,Germany,Male,29.0,5.0,63723.78,1.0,1.0,0.0,167058.75,1.0
-5170,15588928.0,Palermo,693.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,64907.48,0.0
-5171,15577078.0,Walker,643.0,France,Male,35.0,8.0,0.0,2.0,0.0,1.0,101314.76,0.0
-5172,15707962.0,Rowntree,714.0,France,Male,42.0,4.0,0.0,1.0,1.0,0.0,136859.13,1.0
-5173,15804919.0,Henty,594.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,0.0,71926.28,0.0
-5174,15641733.0,Chukwualuka,646.0,Spain,Male,26.0,7.0,0.0,2.0,1.0,0.0,134441.6,0.0
-5175,15643826.0,Hao,634.0,France,Male,45.0,8.0,0.0,1.0,1.0,0.0,115227.86,1.0
-5176,15743411.0,Chibugo,526.0,Spain,Male,50.0,1.0,0.0,2.0,1.0,1.0,114093.73,1.0
-5177,15679307.0,Tuan,814.0,France,Male,43.0,8.0,0.0,1.0,1.0,1.0,50213.81,0.0
-5178,15658675.0,Liao,706.0,France,Female,33.0,10.0,137948.51,1.0,1.0,1.0,142400.27,0.0
-5179,15692406.0,Nnamdi,663.0,Spain,Male,34.0,0.0,0.0,2.0,1.0,1.0,184399.76,0.0
-5180,15595047.0,Chinwendu,651.0,Spain,Female,40.0,8.0,0.0,2.0,1.0,1.0,83419.29,0.0
-5181,15697480.0,Chukwualuka,520.0,France,Female,34.0,4.0,93547.23,1.0,1.0,1.0,180890.4,0.0
-5182,15741032.0,Lu,539.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,110718.02,0.0
-5183,15680201.0,Hsia,639.0,France,Female,40.0,8.0,160736.63,1.0,0.0,0.0,60920.03,0.0
-5184,15762762.0,Brown,587.0,France,Male,36.0,4.0,115024.5,1.0,1.0,1.0,84920.49,0.0
-5185,15568326.0,Chukwujamuike,670.0,France,Male,53.0,8.0,0.0,2.0,1.0,1.0,89893.6,1.0
-5186,15660101.0,Hsia,732.0,Germany,Male,46.0,2.0,132602.88,1.0,1.0,0.0,148087.88,0.0
-5187,15725762.0,Macdonald,658.0,France,Female,47.0,8.0,0.0,2.0,0.0,1.0,181224.56,0.0
-5188,15713094.0,Yefimova,554.0,France,Female,34.0,9.0,83074.0,2.0,1.0,0.0,122783.04,0.0
-5189,15745012.0,Nucci,642.0,France,Female,38.0,3.0,73059.38,1.0,1.0,1.0,152417.97,0.0
-5190,15714883.0,Ch'in,609.0,Spain,Female,28.0,1.0,0.0,2.0,1.0,0.0,160548.12,0.0
-5191,15742210.0,Smith,589.0,Spain,Male,29.0,8.0,151607.56,1.0,1.0,0.0,90888.86,0.0
-5192,15652883.0,Udinese,636.0,Germany,Male,41.0,5.0,115465.98,1.0,1.0,0.0,52876.41,1.0
-5193,15664759.0,Chiu,687.0,France,Male,39.0,1.0,129860.37,1.0,1.0,0.0,183494.87,0.0
-5194,15574761.0,Tien,527.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,167830.26,0.0
-5195,15612505.0,Mamelu,745.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,96881.32,0.0
-5196,15589948.0,Fan,676.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,76544.9,0.0
-5197,15680998.0,Ch'ien,733.0,France,Male,41.0,1.0,0.0,2.0,0.0,0.0,88838.14,0.0
-5198,15723654.0,Chu,559.0,France,Male,33.0,2.0,0.0,2.0,0.0,1.0,80619.09,0.0
-5199,15661528.0,Ch'eng,749.0,France,Female,26.0,9.0,95710.11,1.0,1.0,0.0,94875.04,0.0
-5200,15806840.0,Y?,637.0,France,Female,41.0,1.0,0.0,3.0,1.0,0.0,102930.46,1.0
-5201,15791045.0,Chiawuotu,663.0,France,Male,54.0,8.0,0.0,1.0,0.0,1.0,182436.81,1.0
-5202,15745012.0,Genovese,741.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,61936.1,0.0
-5203,15741181.0,Lu,695.0,Germany,Male,39.0,7.0,109724.38,2.0,1.0,0.0,80792.83,0.0
-5204,15682048.0,Ts'ai,583.0,Germany,Male,44.0,4.0,133950.37,1.0,1.0,1.0,74836.64,1.0
-5205,15788442.0,Chia,525.0,France,Female,44.0,3.0,90547.48,1.0,1.0,1.0,66732.63,1.0
-5206,15761053.0,Hingston,687.0,France,Female,28.0,2.0,94486.47,2.0,1.0,1.0,124872.46,0.0
-5207,15592028.0,Lucchesi,682.0,France,Female,42.0,6.0,0.0,2.0,1.0,0.0,182528.44,0.0
-5208,15750728.0,K?,756.0,France,Male,34.0,5.0,0.0,1.0,1.0,0.0,84760.32,0.0
-5209,15682914.0,Ch'ang,614.0,Germany,Female,34.0,1.0,123544.0,2.0,1.0,0.0,74415.2,0.0
-5210,15612071.0,Nkemjika,611.0,France,Female,39.0,0.0,0.0,2.0,1.0,1.0,131778.58,0.0
-5211,15585067.0,Tien,581.0,France,Male,31.0,3.0,77967.5,1.0,1.0,1.0,53437.1,0.0
-5212,15747288.0,Efremova,769.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,176729.71,0.0
-5213,15589420.0,Chidozie,602.0,Germany,Female,42.0,5.0,109166.46,1.0,1.0,1.0,161668.15,1.0
-5214,15798895.0,Lu,632.0,Spain,Female,42.0,6.0,0.0,1.0,1.0,0.0,15068.18,1.0
-5215,15738220.0,Maclean,642.0,France,Male,45.0,8.0,136857.0,1.0,1.0,0.0,50485.7,1.0
-5216,15803479.0,Chukwufumnanya,684.0,France,Female,47.0,2.0,0.0,2.0,1.0,1.0,73066.71,0.0
-5217,15607988.0,Hsia,705.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,82870.75,0.0
-5218,15708534.0,Hsia,554.0,Spain,Female,39.0,9.0,0.0,1.0,1.0,0.0,158735.3,1.0
-5219,15676576.0,Ch'en,709.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,118862.08,0.0
-5220,15671353.0,Hsiung,726.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,183569.25,0.0
-5221,15624388.0,Nwankwo,725.0,Germany,Male,46.0,2.0,138054.59,1.0,1.0,1.0,171762.87,1.0
-5222,15571244.0,Toscani,595.0,Germany,Male,32.0,6.0,140681.68,2.0,1.0,1.0,124615.59,0.0
-5223,15569050.0,Onuoha,576.0,France,Male,49.0,0.0,135070.58,1.0,1.0,1.0,127095.14,1.0
-5224,15791373.0,Palerma,741.0,France,Female,34.0,4.0,0.0,2.0,0.0,1.0,163471.01,0.0
-5225,15761733.0,Piazza,652.0,France,Male,33.0,4.0,0.0,2.0,0.0,1.0,120586.32,0.0
-5226,15768282.0,Kao,645.0,Germany,Female,39.0,8.0,127343.4,1.0,1.0,1.0,138879.36,1.0
-5227,15679428.0,Kesteven,641.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,146895.86,0.0
-5228,15673539.0,Ch'eng,845.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,161479.19,0.0
-5229,15641185.0,Biryukov,567.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,151871.84,1.0
-5230,15626491.0,Fanucci,659.0,France,Female,40.0,4.0,80780.16,1.0,1.0,0.0,55701.62,1.0
-5231,15747757.0,Nnaife,684.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,102076.03,0.0
-5232,15784320.0,Chukwukere,592.0,Germany,Female,53.0,9.0,120530.32,1.0,1.0,0.0,62402.38,0.0
-5233,15668774.0,DeRose,634.0,Germany,Female,50.0,8.0,146566.01,1.0,1.0,0.0,143315.57,1.0
-5234,15783859.0,Tretiakova,695.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,146723.72,0.0
-5235,15778025.0,T'ang,748.0,France,Female,39.0,0.0,0.0,2.0,1.0,1.0,93769.89,0.0
-5236,15693381.0,Ngozichukwuka,687.0,France,Female,37.0,2.0,0.0,1.0,0.0,1.0,151481.86,1.0
-5237,15679550.0,Ch'ien,622.0,France,Male,53.0,7.0,0.0,1.0,1.0,0.0,171378.77,1.0
-5238,15753847.0,Hao,710.0,Spain,Male,22.0,1.0,0.0,2.0,1.0,1.0,126469.09,0.0
-5239,15623525.0,Yeh,706.0,Spain,Male,43.0,6.0,0.0,2.0,1.0,0.0,166495.2,0.0
-5240,15719309.0,Chien,649.0,France,Male,40.0,3.0,0.0,2.0,1.0,1.0,73334.43,0.0
-5241,15671630.0,Onyemauchechukwu,539.0,Spain,Male,30.0,1.0,0.0,2.0,1.0,1.0,142223.94,0.0
-5242,15603883.0,Ugonnatubelum,743.0,France,Male,61.0,3.0,0.0,2.0,1.0,1.0,143629.72,0.0
-5243,15627385.0,Kaur,679.0,France,Female,27.0,6.0,0.0,1.0,0.0,0.0,171706.66,0.0
-5244,15609928.0,Hsieh,775.0,France,Male,50.0,8.0,0.0,1.0,0.0,1.0,114066.77,0.0
-5245,15686907.0,Trevisano,575.0,France,Female,41.0,6.0,129731.64,1.0,0.0,0.0,185210.63,0.0
-5246,15674573.0,Tuan,572.0,France,Female,33.0,2.0,87612.64,2.0,1.0,0.0,171457.06,0.0
-5247,15661526.0,Hao,705.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,93718.35,0.0
-5248,15641158.0,Genovese,676.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,199505.53,0.0
-5249,15792736.0,K'ung,757.0,France,Female,39.0,3.0,0.0,2.0,0.0,0.0,182867.84,0.0
-5250,15761763.0,Ma,671.0,France,Female,46.0,2.0,0.0,2.0,0.0,0.0,120892.96,0.0
-5251,15667944.0,Nwebube,684.0,France,Female,36.0,0.0,0.0,2.0,1.0,0.0,55859.27,0.0
-5252,15641114.0,Kao,715.0,France,Female,31.0,7.0,0.0,1.0,1.0,0.0,149550.92,0.0
-5253,15747097.0,Ch'ang,739.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,74838.64,0.0
-5254,15694879.0,Ts'ui,648.0,Germany,Female,38.0,5.0,125437.86,2.0,1.0,1.0,99645.04,0.0
-5255,15687634.0,Li Fonti,738.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,122810.53,0.0
-5256,15656829.0,Babbage,710.0,Germany,Female,38.0,7.0,133964.4,2.0,0.0,1.0,167033.83,0.0
-5257,15761149.0,Hsueh,628.0,France,Female,35.0,7.0,122029.15,2.0,0.0,0.0,75837.58,0.0
-5258,15615444.0,Gboliwe,809.0,France,Male,28.0,1.0,121280.19,1.0,1.0,0.0,127587.22,0.0
-5259,15665802.0,P'eng,684.0,France,Female,52.0,3.0,0.0,2.0,1.0,0.0,114093.73,0.0
-5260,15638487.0,Trevisano,750.0,France,Female,26.0,8.0,0.0,2.0,0.0,1.0,66979.06,0.0
-5261,15581229.0,Chieloka,826.0,France,Female,48.0,8.0,0.0,3.0,1.0,0.0,179833.45,1.0
-5262,15785547.0,Tuan,644.0,France,Male,30.0,2.0,0.0,1.0,1.0,0.0,14073.11,0.0
-5263,15710390.0,Hs?,716.0,France,Female,39.0,6.0,0.0,2.0,1.0,0.0,62944.75,0.0
-5264,15689201.0,Chukwudi,652.0,Spain,Female,29.0,1.0,0.0,2.0,1.0,1.0,121840.2,0.0
-5265,15693771.0,Iadanza,762.0,Spain,Male,38.0,3.0,0.0,2.0,0.0,0.0,67976.57,0.0
-5266,15662091.0,Ngozichukwuka,683.0,Germany,Male,38.0,5.0,132405.52,2.0,1.0,1.0,16281.94,0.0
-5267,15711015.0,Chidiebere,743.0,France,Male,32.0,5.0,0.0,1.0,1.0,0.0,158977.6,0.0
-5268,15754929.0,Frolova,753.0,Germany,Female,33.0,5.0,103700.69,1.0,1.0,0.0,137980.58,0.0
-5269,15592222.0,Tuan,605.0,France,Female,39.0,3.0,0.0,2.0,1.0,1.0,83342.73,0.0
-5270,15588923.0,Chukwuemeka,684.0,France,Male,45.0,8.0,125512.98,1.0,1.0,1.0,74854.97,0.0
-5271,15662291.0,Hsu,571.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,132562.23,0.0
-5272,15690695.0,Mazzanti,595.0,France,Male,52.0,4.0,0.0,1.0,1.0,1.0,117060.3,1.0
-5273,15618363.0,Lucchese,579.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,115611.97,0.0
-5274,15671249.0,Piazza,598.0,France,Female,52.0,4.0,0.0,1.0,1.0,1.0,131919.48,1.0
-5275,15643361.0,Nwebube,565.0,France,Female,34.0,10.0,0.0,2.0,1.0,1.0,159742.33,0.0
-5276,15707488.0,Ch'in,584.0,Germany,Female,48.0,2.0,102989.83,1.0,1.0,0.0,176750.81,1.0
-5277,15681115.0,Y?,677.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,1.0,182038.6,0.0
-5278,15689328.0,Chiemenam,599.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,170819.01,0.0
-5279,15691483.0,Onuchukwu,696.0,Germany,Male,49.0,1.0,142765.24,1.0,1.0,0.0,160589.18,1.0
-5280,15737778.0,Hsiao,743.0,Spain,Male,35.0,9.0,0.0,1.0,0.0,1.0,172363.41,0.0
-5281,15629187.0,Ch'ang,737.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,48242.38,0.0
-5282,15656062.0,Nkemdirim,604.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,86935.08,0.0
-5283,15579914.0,Fiorentino,850.0,Germany,Female,55.0,0.0,92616.64,1.0,1.0,1.0,127875.1,0.0
-5284,15609823.0,Niu,742.0,France,Male,33.0,5.0,92674.2,1.0,0.0,1.0,172749.4,0.0
-5285,15787830.0,Udinese,665.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,180598.28,0.0
-5286,15777855.0,Chuang,713.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,60905.51,0.0
-5287,15793881.0,Okwudilichukwu,772.0,Germany,Female,51.0,3.0,123873.23,1.0,1.0,0.0,14917.09,1.0
-5288,15696722.0,Onyekaozulu,632.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,156774.75,0.0
-5289,15661528.0,Lucciano,806.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,1.0,118965.03,0.0
-5290,15575679.0,Chiekwugo,668.0,France,Female,61.0,5.0,120549.29,2.0,0.0,1.0,91957.39,0.0
-5291,15591512.0,Tuan,628.0,France,Male,31.0,4.0,0.0,1.0,1.0,1.0,51116.06,0.0
-5292,15671139.0,Linton,538.0,Spain,Female,39.0,5.0,0.0,1.0,1.0,1.0,116977.89,0.0
-5293,15568326.0,Pisani,637.0,France,Male,37.0,6.0,0.0,1.0,0.0,0.0,132973.17,0.0
-5294,15605950.0,Obiajulu,689.0,Spain,Female,29.0,5.0,0.0,2.0,1.0,1.0,121428.4,0.0
-5295,15636016.0,Tsao,569.0,France,Male,41.0,1.0,102703.62,1.0,0.0,0.0,19998.31,0.0
-5296,15730673.0,Cremonesi,733.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,126812.63,0.0
-5297,15732760.0,Onyemere,584.0,Germany,Female,39.0,3.0,113959.35,1.0,1.0,1.0,132038.65,1.0
-5298,15748327.0,Ch'ang,631.0,Spain,Female,30.0,1.0,0.0,2.0,0.0,1.0,25684.93,0.0
-5299,15754577.0,Lucchese,595.0,Germany,Male,39.0,3.0,124099.13,1.0,1.0,1.0,38196.24,0.0
-5300,15627395.0,Oluchi,682.0,Germany,Female,39.0,2.0,122446.81,2.0,1.0,0.0,46677.9,0.0
-5301,15781589.0,Cattaneo,667.0,France,Male,31.0,3.0,0.0,1.0,1.0,0.0,129891.66,0.0
-5302,15654901.0,Maclean,774.0,Germany,Male,25.0,6.0,122028.65,1.0,0.0,1.0,173586.63,0.0
-5303,15728474.0,P'eng,637.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,86634.3,0.0
-5304,15715888.0,Shih,718.0,France,Female,40.0,7.0,0.0,2.0,0.0,1.0,159800.16,0.0
-5305,15728352.0,Ting,667.0,Spain,Male,37.0,8.0,133658.24,1.0,1.0,0.0,47922.54,0.0
-5306,15787907.0,Hs?eh,731.0,France,Female,38.0,2.0,0.0,2.0,0.0,1.0,116971.05,0.0
-5307,15702432.0,Onyekachukwu,673.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,114532.68,0.0
-5308,15724453.0,Chizuoke,683.0,France,Female,38.0,1.0,0.0,1.0,1.0,0.0,128521.02,0.0
-5309,15619608.0,Hs?,796.0,Germany,Male,32.0,1.0,129974.79,2.0,1.0,1.0,141896.74,0.0
-5310,15790750.0,Chukwuebuka,526.0,Spain,Male,37.0,7.0,145980.23,1.0,1.0,1.0,164886.64,0.0
-5311,15749669.0,Hsiao,793.0,Spain,Female,30.0,8.0,0.0,2.0,0.0,1.0,83629.6,0.0
-5312,15778589.0,Chukwueloka,726.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,43932.54,0.0
-5313,15796910.0,Chin,602.0,France,Female,32.0,6.0,0.0,1.0,0.0,1.0,131660.31,0.0
-5314,15592914.0,Okwuoma,599.0,France,Male,31.0,8.0,151391.68,1.0,1.0,0.0,106095.82,0.0
-5315,15704597.0,Genovese,558.0,France,Female,48.0,8.0,0.0,1.0,0.0,1.0,136985.08,1.0
-5316,15701160.0,Ch'in,850.0,France,Male,32.0,2.0,130131.08,1.0,1.0,1.0,122381.02,0.0
-5317,15626012.0,Tsao,708.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,0.0,149614.81,0.0
-5318,15616152.0,Ch'in,604.0,France,Male,33.0,7.0,0.0,2.0,0.0,0.0,102628.56,0.0
-5319,15640034.0,Nwabugwu,634.0,France,Male,36.0,1.0,93522.3,1.0,1.0,1.0,94815.04,0.0
-5320,15677052.0,Chia,632.0,Spain,Male,43.0,5.0,0.0,1.0,0.0,1.0,123866.43,0.0
-5321,15727703.0,Pisani,633.0,France,Male,21.0,7.0,0.0,2.0,1.0,0.0,151146.67,0.0
-5322,15783398.0,Hsiung,714.0,France,Female,41.0,8.0,0.0,1.0,1.0,0.0,113481.02,0.0
-5323,15659710.0,Hao,663.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,96734.46,0.0
-5324,15733111.0,Rizzo,641.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,145558.42,0.0
-5325,15721207.0,Shih,742.0,France,Male,32.0,9.0,70417.79,1.0,1.0,1.0,38409.79,0.0
-5326,15641848.0,Frolova,545.0,France,Female,40.0,1.0,0.0,2.0,1.0,1.0,107876.2,0.0
-5327,15657540.0,Hargreaves,710.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,0.0,141501.45,0.0
-5328,15796813.0,Kao,631.0,Germany,Female,39.0,7.0,124043.8,2.0,1.0,1.0,126743.33,0.0
-5329,15802483.0,Hao,721.0,France,Male,36.0,1.0,0.0,2.0,0.0,1.0,54865.52,0.0
-5330,15773071.0,Niu,629.0,France,Female,27.0,6.0,0.0,2.0,1.0,1.0,187905.12,0.0
-5331,15791535.0,Chukwueloka,652.0,France,Male,24.0,2.0,0.0,2.0,1.0,0.0,150159.95,0.0
-5332,15722004.0,Chukwukere,516.0,France,Female,35.0,6.0,152685.4,1.0,1.0,0.0,172096.08,0.0
-5333,15618239.0,Chukwuemeka,595.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,141035.65,0.0
-5334,15707968.0,Ives,671.0,France,Male,31.0,5.0,138089.15,2.0,1.0,1.0,143086.49,0.0
-5335,15566292.0,Ch'ien,678.0,Spain,Female,32.0,5.0,0.0,2.0,1.0,1.0,82463.69,0.0
-5336,15642725.0,Udinese,516.0,Spain,Female,26.0,1.0,128843.67,1.0,1.0,1.0,176692.65,0.0
-5337,15605037.0,Ch'eng,662.0,Germany,Male,35.0,8.0,111369.79,1.0,0.0,0.0,177719.88,1.0
-5338,15717893.0,Ch'eng,592.0,France,Female,52.0,5.0,0.0,1.0,1.0,0.0,180829.99,1.0
-5339,15633108.0,Rizzo,634.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,163331.65,0.0
-5340,15621075.0,Tsao,651.0,Germany,Male,39.0,3.0,116536.43,1.0,0.0,0.0,52301.15,1.0
-5341,15632143.0,T'ien,599.0,France,Male,38.0,10.0,123971.51,1.0,0.0,1.0,166678.28,0.0
-5342,15652955.0,Chijindum,678.0,France,Male,38.0,10.0,128743.55,1.0,1.0,1.0,142995.32,0.0
-5343,15697043.0,Okechukwu,732.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,117832.88,0.0
-5344,15572428.0,Kao,686.0,France,Female,38.0,4.0,0.0,1.0,1.0,1.0,110782.88,1.0
-5345,15593470.0,Trevisani,726.0,France,Female,29.0,6.0,137811.75,1.0,1.0,1.0,163317.5,0.0
-5346,15662578.0,Cawthorne,677.0,Spain,Male,29.0,8.0,0.0,2.0,0.0,0.0,65816.53,0.0
-5347,15724444.0,H?,736.0,Spain,Male,44.0,7.0,0.0,1.0,1.0,1.0,60917.24,1.0
-5348,15762091.0,Onwughara,553.0,France,Female,38.0,3.0,141786.57,1.0,1.0,0.0,49833.5,0.0
-5349,15652069.0,Fanucci,678.0,Spain,Male,36.0,3.0,118560.0,1.0,1.0,1.0,48545.1,0.0
-5350,15808846.0,Shih,729.0,France,Female,32.0,3.0,136416.82,1.0,1.0,0.0,187985.77,0.0
-5351,15791674.0,Hsia,675.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,61531.86,0.0
-5352,15582797.0,Hsiung,678.0,France,Female,45.0,9.0,0.0,1.0,1.0,1.0,175072.47,1.0
-5353,15625944.0,P'an,628.0,Spain,Female,36.0,3.0,0.0,2.0,1.0,0.0,12761.16,0.0
-5354,15770576.0,Tien,576.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,92381.01,0.0
-5355,15714087.0,Lo Duca,642.0,France,Male,31.0,0.0,143965.52,1.0,1.0,1.0,54865.52,0.0
-5356,15651369.0,Yermakova,659.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,51554.58,0.0
-5357,15765173.0,Dike,639.0,France,Male,41.0,7.0,135095.41,1.0,0.0,1.0,162599.51,0.0
-5358,15576124.0,Yermakova,656.0,France,Male,21.0,8.0,0.0,2.0,1.0,1.0,60721.4,0.0
-5359,15661249.0,Hs?,634.0,Germany,Female,52.0,5.0,139872.53,1.0,1.0,1.0,135875.51,1.0
-5360,15648898.0,Pisani,724.0,Spain,Female,33.0,8.0,0.0,2.0,1.0,0.0,138526.96,0.0
-5361,15709861.0,Ozoemena,756.0,Spain,Female,26.0,6.0,0.0,1.0,1.0,1.0,46857.52,0.0
-5362,15587562.0,Manna,678.0,France,Male,37.0,7.0,0.0,1.0,0.0,1.0,45422.13,0.0
-5363,15771299.0,Mazzanti,718.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,153727.32,0.0
-5364,15777893.0,Iweobiegbulam,679.0,Germany,Female,40.0,1.0,132512.26,1.0,1.0,1.0,122964.18,0.0
-5365,15768006.0,Ch'en,483.0,Spain,Male,26.0,3.0,0.0,2.0,1.0,0.0,168034.9,0.0
-5366,15779886.0,Maclean,710.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,123587.83,0.0
-5367,15743673.0,Ts'ao,627.0,France,Female,47.0,3.0,0.0,2.0,0.0,0.0,115733.16,0.0
-5368,15702571.0,Ts'ui,744.0,France,Female,45.0,1.0,154495.74,1.0,1.0,0.0,119259.54,1.0
-5369,15780550.0,Chieloka,656.0,France,Female,34.0,5.0,0.0,1.0,1.0,1.0,155870.82,0.0
-5370,15590820.0,Nkemakolam,725.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,122976.73,0.0
-5371,15766137.0,Lucchesi,581.0,France,Female,27.0,5.0,0.0,1.0,1.0,0.0,58907.08,0.0
-5372,15709737.0,Chiu,651.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,146723.72,0.0
-5373,15571221.0,Kambinachi,596.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,1.0,177384.45,0.0
-5374,15773421.0,Nnanna,615.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,80552.11,0.0
-5375,15700383.0,Chibugo,683.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,180850.4,0.0
-5376,15788676.0,K'ung,597.0,France,Female,37.0,7.0,0.0,2.0,0.0,1.0,62634.94,0.0
-5377,15607568.0,L?,652.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,140889.32,0.0
-5378,15648881.0,Chukwujekwu,589.0,Germany,Female,31.0,7.0,136967.18,1.0,1.0,1.0,176780.69,0.0
-5379,15810944.0,P'an,575.0,France,Female,44.0,6.0,0.0,1.0,1.0,0.0,143598.7,1.0
-5380,15766044.0,Uwaezuoke,681.0,Spain,Male,27.0,5.0,107564.03,1.0,0.0,0.0,125302.64,0.0
-5381,15722072.0,Li Fonti,583.0,Spain,Male,48.0,6.0,174937.64,3.0,1.0,0.0,26287.21,1.0
-5382,15642934.0,P'eng,584.0,France,Male,40.0,5.0,0.0,2.0,0.0,1.0,151925.25,0.0
-5383,15572294.0,Ch'en,554.0,Spain,Female,51.0,7.0,0.0,1.0,0.0,1.0,170142.09,0.0
-5384,15596552.0,Nwachukwu,587.0,France,Female,29.0,1.0,174894.53,2.0,0.0,0.0,69697.74,0.0
-5385,15667871.0,Chuang,642.0,Germany,Female,38.0,4.0,126495.57,1.0,1.0,0.0,52327.57,1.0
-5386,15691840.0,Ritchie,586.0,France,Female,39.0,2.0,160818.08,1.0,1.0,1.0,79618.47,0.0
-5387,15736254.0,Genovese,745.0,France,Female,55.0,3.0,123599.49,1.0,0.0,1.0,126796.69,0.0
-5388,15641782.0,Nnachetam,492.0,France,Female,23.0,6.0,110939.3,2.0,1.0,0.0,77951.84,0.0
-5389,15650098.0,Onyeoruru,559.0,France,Female,30.0,2.0,102989.83,1.0,1.0,1.0,140937.91,0.0
-5390,15762392.0,Ch'iu,703.0,Germany,Female,33.0,1.0,116947.76,2.0,1.0,0.0,134600.94,0.0
-5391,15670039.0,Nnamutaezinwa,717.0,Spain,Male,36.0,5.0,91560.58,1.0,1.0,0.0,94544.88,0.0
-5392,15702061.0,Ch'ang,637.0,Germany,Male,34.0,8.0,108862.76,1.0,1.0,1.0,96463.25,0.0
-5393,15730397.0,Hsia,593.0,France,Male,34.0,6.0,142721.52,1.0,1.0,1.0,173052.12,0.0
-5394,15718588.0,Tseng,595.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,162734.31,0.0
-5395,15785865.0,Bazarova,769.0,Spain,Male,46.0,6.0,0.0,2.0,1.0,1.0,141533.19,0.0
-5396,15682369.0,Genovesi,711.0,France,Male,28.0,2.0,157731.6,1.0,1.0,0.0,137519.92,0.0
-5397,15722993.0,Milani,612.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,113079.19,0.0
-5398,15656471.0,Ch'iu,648.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,52894.01,0.0
-5399,15745012.0,T'ien,691.0,Spain,Male,28.0,8.0,113781.5,1.0,1.0,0.0,141230.07,0.0
-5400,15678910.0,Oguejiofor,778.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,70778.63,0.0
-5401,15685500.0,Maclean,633.0,France,Female,48.0,3.0,0.0,2.0,1.0,1.0,75937.14,1.0
-5402,15635972.0,Fan,686.0,Germany,Male,34.0,2.0,106648.98,1.0,1.0,1.0,170514.21,1.0
-5403,15751336.0,Gannon,635.0,France,Male,32.0,9.0,0.0,2.0,0.0,0.0,27330.59,0.0
-5404,15672237.0,Chinomso,678.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,120834.48,0.0
-5405,15607877.0,Echezonachukwu,628.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,80963.92,0.0
-5406,15667438.0,DeRose,758.0,Spain,Male,32.0,8.0,110939.3,1.0,0.0,1.0,183572.39,0.0
-5407,15652914.0,K'ung,709.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,108764.69,0.0
-5408,15603743.0,Dettmann,670.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,189016.24,0.0
-5409,15624793.0,Udobata,726.0,Germany,Female,36.0,9.0,109799.55,1.0,1.0,1.0,185827.74,1.0
-5410,15581748.0,Genovese,496.0,Germany,Male,32.0,7.0,143396.54,2.0,1.0,1.0,57553.02,0.0
-5411,15771856.0,Toscani,695.0,Germany,Female,43.0,5.0,107908.03,2.0,1.0,1.0,158054.49,0.0
-5412,15596863.0,Mazzi,538.0,Germany,Female,26.0,4.0,146780.52,2.0,1.0,0.0,133617.43,0.0
-5413,15617401.0,Uspenskaya,545.0,France,Female,63.0,2.0,113715.36,1.0,1.0,1.0,53767.1,0.0
-5414,15710456.0,Maclean,621.0,France,Male,31.0,7.0,0.0,1.0,1.0,1.0,150242.44,0.0
-5415,15769903.0,Hsueh,613.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,199478.05,0.0
-5416,15643865.0,Martin,648.0,Spain,Male,29.0,1.0,0.0,1.0,0.0,1.0,28797.32,0.0
-5417,15742668.0,Efremov,629.0,Germany,Female,59.0,6.0,90350.77,1.0,1.0,0.0,155823.2,1.0
-5418,15793883.0,Bell,801.0,Spain,Male,35.0,6.0,0.0,2.0,0.0,1.0,122373.22,0.0
-5419,15652700.0,Ibeabuchi,585.0,France,Male,31.0,1.0,88814.4,1.0,1.0,0.0,41970.72,0.0
-5420,15676835.0,Onyemauchechukwu,683.0,France,Female,38.0,6.0,0.0,2.0,1.0,0.0,61324.87,0.0
-5421,15682573.0,Chinweike,583.0,Germany,Male,28.0,3.0,128015.83,2.0,0.0,1.0,97640.2,0.0
-5422,15661629.0,Hsiung,790.0,France,Male,40.0,2.0,0.0,2.0,0.0,0.0,86791.9,0.0
-5423,15582573.0,Manna,553.0,France,Male,42.0,6.0,0.0,1.0,1.0,0.0,151411.2,0.0
-5424,15676521.0,Onyekachukwu,687.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,0.0,35408.77,0.0
-5425,15737051.0,Obiajulu,635.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,133535.29,0.0
-5426,15648032.0,Scott,710.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,54869.54,0.0
-5427,15784700.0,T'ien,619.0,Spain,Male,43.0,2.0,0.0,2.0,0.0,0.0,70533.93,0.0
-5428,15775192.0,Chizoba,678.0,Spain,Male,46.0,7.0,0.0,2.0,0.0,1.0,55470.78,0.0
-5429,15719579.0,Cremonesi,686.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,26257.01,0.0
-5430,15709639.0,Pisani,801.0,Germany,Female,43.0,5.0,131081.66,1.0,1.0,1.0,35356.46,0.0
-5431,15655339.0,Tien,592.0,France,Male,48.0,10.0,0.0,2.0,1.0,1.0,61326.43,0.0
-5432,15757632.0,White,521.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,0.0,93524.19,0.0
-5433,15782404.0,Hsia,636.0,Spain,Female,24.0,2.0,0.0,2.0,1.0,1.0,52795.15,0.0
-5434,15661349.0,Hsia,769.0,Germany,Female,46.0,8.0,144705.87,1.0,0.0,0.0,88842.8,1.0
-5435,15690540.0,Uspensky,669.0,France,Female,42.0,1.0,107868.14,3.0,1.0,1.0,176913.42,1.0
-5436,15620398.0,Genovese,679.0,Spain,Female,26.0,8.0,0.0,2.0,1.0,1.0,142377.69,0.0
-5437,15762392.0,Udinesi,712.0,France,Male,37.0,6.0,0.0,1.0,1.0,1.0,146663.43,0.0
-5438,15641312.0,Ozioma,787.0,Germany,Female,32.0,5.0,134796.05,2.0,1.0,1.0,152992.04,0.0
-5439,15772777.0,Nkemakonam,671.0,France,Male,46.0,6.0,0.0,1.0,1.0,0.0,176883.42,1.0
-5440,15580149.0,Onwumelu,576.0,Spain,Male,37.0,6.0,0.0,2.0,0.0,0.0,93983.04,0.0
-5441,15694576.0,Nnamutaezinwa,743.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,181204.5,0.0
-5442,15696054.0,Liao,706.0,France,Male,38.0,1.0,0.0,1.0,0.0,0.0,129975.54,0.0
-5443,15681342.0,Oluchukwu,678.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,173498.45,0.0
-5444,15636396.0,De Luca,695.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,0.0,100423.88,0.0
-5445,15696588.0,Chukwukere,597.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,0.0,91098.76,0.0
-5446,15763431.0,Chukwukadibia,693.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,134875.37,0.0
-5447,15762573.0,Trevisano,712.0,France,Male,35.0,4.0,137222.77,1.0,1.0,1.0,117034.32,0.0
-5448,15642394.0,Frolova,626.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,72927.68,0.0
-5449,15765518.0,Ch'ang,583.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,134589.58,0.0
-5450,15785819.0,Nnamdi,775.0,Spain,Female,35.0,8.0,0.0,2.0,1.0,0.0,46864.0,0.0
-5451,15681873.0,T'ang,681.0,France,Male,51.0,8.0,0.0,2.0,0.0,1.0,163830.64,0.0
-5452,15584545.0,Ponomarev,751.0,France,Male,39.0,3.0,124125.26,1.0,1.0,0.0,184936.77,0.0
-5453,15775709.0,Hs?,583.0,France,Female,50.0,9.0,0.0,2.0,1.0,1.0,10962.04,1.0
-5454,15771543.0,Liao,637.0,Germany,Female,23.0,4.0,135020.21,1.0,0.0,1.0,143598.7,1.0
-5455,15633043.0,Chukwuhaenye,694.0,France,Male,32.0,1.0,0.0,2.0,0.0,0.0,126726.33,0.0
-5456,15596074.0,Lucchesi,762.0,France,Female,35.0,7.0,0.0,1.0,0.0,0.0,173365.9,0.0
-5457,15614893.0,Onyemachukwu,653.0,France,Male,42.0,3.0,0.0,1.0,0.0,1.0,147964.99,1.0
-5458,15732644.0,Ch'eng,622.0,Spain,Female,39.0,8.0,0.0,1.0,0.0,0.0,57898.58,1.0
-5459,15661903.0,Onyemauchechukwu,683.0,France,Male,44.0,1.0,84445.68,2.0,0.0,0.0,93777.61,0.0
-5460,15576723.0,Nucci,763.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,26327.42,0.0
-5461,15780034.0,Ch'ien,704.0,Spain,Male,42.0,1.0,0.0,2.0,1.0,1.0,56794.73,0.0
-5462,15603914.0,Krichauff,624.0,France,Male,36.0,1.0,0.0,2.0,1.0,0.0,133086.45,0.0
-5463,15644572.0,Chiang,779.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,0.0,142995.32,0.0
-5464,15708048.0,Ts'ai,668.0,France,Male,26.0,2.0,0.0,2.0,0.0,0.0,157908.19,0.0
-5465,15772423.0,Ulyanov,662.0,France,Female,29.0,6.0,181768.32,1.0,1.0,1.0,10928.3,0.0
-5466,15684958.0,Hao,775.0,Germany,Male,28.0,9.0,148051.52,1.0,1.0,0.0,93536.38,0.0
-5467,15772423.0,Yobanna,577.0,France,Male,42.0,8.0,139541.58,1.0,1.0,0.0,142977.05,0.0
-5468,15769902.0,Pagnotto,558.0,Spain,Male,41.0,10.0,0.0,2.0,1.0,0.0,187699.76,0.0
-5469,15658057.0,Chin,684.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,0.0,147930.46,0.0
-5470,15627840.0,Ch'ang,493.0,Germany,Female,40.0,1.0,128135.99,3.0,1.0,1.0,9983.88,1.0
-5471,15724181.0,Martin,604.0,France,Male,45.0,1.0,0.0,2.0,1.0,1.0,85746.52,0.0
-5472,15625941.0,Kao,705.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,150720.39,0.0
-5473,15792489.0,T'ien,748.0,France,Male,38.0,2.0,84006.88,1.0,1.0,1.0,176763.27,0.0
-5474,15592448.0,Milani,775.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,124592.84,0.0
-5475,15645316.0,Chiang,614.0,France,Female,44.0,0.0,0.0,2.0,0.0,0.0,89937.65,0.0
-5476,15600792.0,Lo,679.0,Germany,Male,37.0,9.0,126550.7,2.0,1.0,0.0,79082.62,0.0
-5477,15617206.0,K?,762.0,Spain,Female,22.0,9.0,0.0,2.0,0.0,1.0,171413.66,0.0
-5478,15633260.0,Goliwe,679.0,Germany,Male,44.0,1.0,81564.1,1.0,1.0,0.0,121763.73,1.0
-5479,15723488.0,Yegorov,727.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,71682.54,0.0
-5480,15790067.0,Gannon,850.0,France,Male,31.0,2.0,0.0,2.0,0.0,1.0,59816.41,0.0
-5481,15787196.0,T'an,707.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,120880.72,0.0
-5482,15783338.0,Trevisani,613.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,1.0,154844.29,0.0
-5483,15668747.0,Chien,775.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,83679.66,0.0
-5484,15696588.0,Padovesi,730.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,179657.97,0.0
-5485,15597951.0,Okechukwu,592.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,163714.92,0.0
-5486,15796370.0,Onyemauchechukwu,822.0,France,Female,55.0,3.0,0.0,1.0,1.0,0.0,51112.8,1.0
-5487,15588933.0,Maughan,551.0,Spain,Female,45.0,5.0,0.0,1.0,0.0,0.0,125877.22,1.0
-5488,15795132.0,Ross,581.0,Spain,Female,32.0,7.0,0.0,2.0,0.0,1.0,72350.47,0.0
-5489,15770041.0,Iweobiegbunam,594.0,Germany,Female,54.0,0.0,120106.43,1.0,1.0,0.0,103921.41,1.0
-5490,15696733.0,Nwachinemelu,619.0,France,Male,47.0,3.0,0.0,2.0,1.0,0.0,134491.32,1.0
-5491,15643361.0,Ritchie,632.0,France,Female,37.0,9.0,0.0,1.0,1.0,1.0,101102.39,0.0
-5492,15794895.0,Ritchie,730.0,Spain,Male,39.0,4.0,0.0,1.0,1.0,0.0,67599.69,1.0
-5493,15791030.0,Yobanna,646.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,147910.64,0.0
-5494,15739662.0,K?,682.0,France,Female,29.0,8.0,0.0,2.0,1.0,1.0,136985.08,0.0
-5495,15750055.0,Zito,751.0,France,Female,31.0,6.0,0.0,1.0,1.0,0.0,135088.58,0.0
-5496,15641319.0,Ekechukwu,594.0,France,Male,36.0,9.0,123894.43,1.0,1.0,0.0,182566.76,0.0
-5497,15718281.0,Ch'ien,641.0,France,Male,47.0,6.0,0.0,1.0,1.0,1.0,82709.8,1.0
-5498,15631681.0,Chiazagomekpere,594.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,59249.83,0.0
-5499,15699753.0,Chinedum,694.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,10792.42,0.0
-5500,15791323.0,Chuang,646.0,France,Female,71.0,8.0,0.0,1.0,1.0,1.0,1276.87,1.0
-5501,15584897.0,Scott,655.0,France,Female,39.0,6.0,0.0,1.0,1.0,0.0,70774.97,0.0
-5502,15598435.0,Ugochukwu,657.0,Germany,Male,36.0,9.0,121558.42,1.0,0.0,0.0,101835.77,0.0
-5503,15797767.0,Chuang,556.0,Spain,Female,38.0,1.0,118633.08,1.0,1.0,0.0,151324.87,0.0
-5504,15603764.0,Maclean,661.0,France,Female,63.0,3.0,0.0,1.0,1.0,1.0,138536.09,1.0
-5505,15682576.0,H?,744.0,France,Female,39.0,2.0,0.0,1.0,1.0,0.0,155061.97,1.0
-5506,15763898.0,Chin,681.0,France,Female,42.0,9.0,0.0,3.0,1.0,0.0,175072.47,1.0
-5507,15782210.0,Chiang,638.0,Germany,Male,37.0,3.0,110827.49,1.0,1.0,1.0,183983.82,1.0
-5508,15682268.0,Fomin,594.0,Germany,Female,40.0,4.0,106625.07,1.0,0.0,0.0,85901.09,1.0
-5509,15797595.0,Onyemauchechukwu,549.0,France,Female,34.0,5.0,0.0,2.0,0.0,1.0,159823.16,0.0
-5510,15673929.0,Piazza,669.0,Germany,Female,44.0,9.0,134542.73,1.0,1.0,0.0,172524.51,1.0
-5511,15808689.0,Ch'iu,652.0,Germany,Female,31.0,9.0,102741.15,1.0,1.0,0.0,120853.85,1.0
-5512,15703413.0,Laidley,727.0,France,Male,28.0,8.0,0.0,2.0,0.0,0.0,90896.04,0.0
-5513,15808905.0,Ts'ui,581.0,France,Female,53.0,7.0,138881.04,1.0,0.0,1.0,182867.84,0.0
-5514,15632280.0,Onuora,567.0,France,Male,42.0,1.0,0.0,1.0,0.0,0.0,173784.04,1.0
-5515,15793896.0,Ugochukwutubelum,588.0,Spain,Female,43.0,1.0,0.0,1.0,1.0,0.0,103421.91,0.0
-5516,15592229.0,Ch'in,769.0,France,Male,48.0,9.0,0.0,1.0,1.0,0.0,9241.78,1.0
-5517,15794873.0,Ch'in,696.0,France,Male,29.0,4.0,109528.28,1.0,1.0,1.0,117572.59,0.0
-5518,15655648.0,Chinomso,651.0,France,Female,26.0,1.0,0.0,2.0,0.0,1.0,199304.74,0.0
-5519,15709396.0,Tuan,582.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,149604.18,0.0
-5520,15607537.0,T'ao,525.0,Spain,Male,37.0,9.0,0.0,2.0,0.0,1.0,151491.37,0.0
-5521,15662955.0,Zito,683.0,France,Male,23.0,7.0,0.0,2.0,1.0,0.0,73562.05,0.0
-5522,15733247.0,Onyemachukwu,695.0,Spain,Female,32.0,2.0,0.0,2.0,0.0,0.0,173683.0,0.0
-5523,15717164.0,Mironova,676.0,Spain,Male,24.0,7.0,0.0,2.0,1.0,1.0,170122.22,0.0
-5524,15591258.0,Niehaus,714.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,102750.8,0.0
-5525,15756475.0,Mironov,757.0,France,Female,46.0,6.0,129743.75,1.0,1.0,0.0,94499.06,0.0
-5526,15570192.0,Hao,739.0,Germany,Male,36.0,7.0,124620.01,1.0,0.0,1.0,136301.1,0.0
-5527,15577806.0,Genovese,634.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,73669.94,0.0
-5528,15706899.0,Ch'ang,654.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,1.0,138051.19,0.0
-5529,15669301.0,Nkemjika,592.0,France,Female,55.0,3.0,0.0,1.0,1.0,1.0,132673.23,1.0
-5530,15573599.0,Cattaneo,627.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,146891.07,0.0
-5531,15566289.0,Hsueh,627.0,Germany,Female,42.0,4.0,71996.09,1.0,0.0,1.0,9663.28,0.0
-5532,15672091.0,Calabresi,632.0,France,Female,39.0,8.0,0.0,1.0,1.0,0.0,103921.49,1.0
-5533,15683544.0,Okwuadigbo,587.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,151401.33,0.0
-5534,15754574.0,Ebelegbulam,709.0,Germany,Female,32.0,7.0,125193.96,1.0,1.0,0.0,140889.64,0.0
-5535,15643487.0,Lai,579.0,France,Male,40.0,9.0,0.0,1.0,0.0,1.0,129964.94,0.0
-5536,15592999.0,Lai,719.0,France,Female,38.0,6.0,0.0,1.0,1.0,0.0,173783.38,0.0
-5537,15733229.0,Onwuatuegwu,739.0,France,Female,53.0,4.0,0.0,2.0,0.0,1.0,141501.45,0.0
-5538,15700627.0,Macartney,651.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,145558.42,0.0
-5539,15601503.0,Chiu,642.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,120540.83,0.0
-5540,15671249.0,H?,727.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,175893.53,0.0
-5541,15765520.0,Young,639.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,164018.98,0.0
-5542,15675380.0,Chiang,757.0,France,Female,27.0,9.0,0.0,2.0,1.0,0.0,130590.35,0.0
-5543,15632446.0,Baresi,619.0,France,Female,51.0,9.0,0.0,1.0,1.0,0.0,40920.59,1.0
-5544,15692430.0,Chidubem,639.0,France,Female,47.0,3.0,0.0,2.0,1.0,1.0,152298.28,0.0
-5545,15802823.0,Mazzi,697.0,France,Male,50.0,1.0,0.0,2.0,1.0,1.0,138051.19,0.0
-5546,15729763.0,Fetherstonhaugh,575.0,France,Male,46.0,5.0,0.0,2.0,0.0,1.0,84475.13,0.0
-5547,15639133.0,Bazarova,679.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,95611.74,0.0
-5548,15742872.0,Onyemauchechi,635.0,France,Male,46.0,6.0,0.0,2.0,1.0,0.0,143960.72,0.0
-5549,15684929.0,Tuan,683.0,France,Male,48.0,4.0,0.0,1.0,1.0,0.0,161241.65,0.0
-5550,15644962.0,Nnachetam,627.0,Germany,Male,52.0,5.0,113034.72,3.0,1.0,0.0,102076.02,1.0
-5551,15733661.0,Nwachukwu,623.0,France,Female,25.0,8.0,0.0,2.0,1.0,0.0,116471.43,0.0
-5552,15593694.0,Yobanna,629.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,138871.12,0.0
-5553,15735106.0,Olisaemeka,794.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,161653.5,0.0
-5554,15727059.0,Chiemezie,573.0,France,Male,29.0,7.0,120599.38,1.0,0.0,0.0,72993.65,0.0
-5555,15642785.0,McLean,850.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,18092.75,1.0
-5556,15623102.0,Genovese,589.0,Germany,Female,45.0,6.0,126589.12,1.0,1.0,0.0,172363.41,1.0
-5557,15812720.0,Tikhonov,705.0,France,Male,35.0,4.0,0.0,1.0,1.0,0.0,160948.87,0.0
-5558,15758939.0,Onyemachukwu,697.0,Germany,Female,42.0,6.0,128362.59,1.0,1.0,0.0,102515.42,0.0
-5559,15589449.0,Ch'ang,670.0,France,Female,50.0,1.0,0.0,1.0,1.0,0.0,120138.27,1.0
-5560,15628308.0,Calabresi,681.0,France,Female,41.0,0.0,110829.52,1.0,1.0,0.0,126475.79,1.0
-5561,15673693.0,Iadanza,746.0,France,Female,32.0,6.0,91592.06,1.0,1.0,1.0,64872.33,0.0
-5562,15810167.0,Zito,850.0,France,Female,28.0,7.0,0.0,2.0,0.0,0.0,84760.32,0.0
-5563,15654760.0,Hsia,589.0,France,Male,44.0,3.0,0.0,1.0,1.0,0.0,88890.05,0.0
-5564,15572547.0,Y?,619.0,Spain,Male,36.0,4.0,137780.74,1.0,1.0,1.0,121751.04,0.0
-5565,15781307.0,Nwankwo,551.0,France,Male,31.0,9.0,0.0,1.0,1.0,0.0,147794.63,1.0
-5566,15673372.0,McDavid,644.0,Spain,Female,48.0,3.0,0.0,2.0,1.0,1.0,186655.11,0.0
-5567,15690169.0,Milne,699.0,France,Female,43.0,6.0,0.0,2.0,1.0,1.0,158958.7,0.0
-5568,15670066.0,Ojiofor,713.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,181600.72,0.0
-5569,15711254.0,Neumann,639.0,Spain,Female,52.0,6.0,0.0,1.0,0.0,0.0,177719.88,1.0
-5570,15571261.0,Lu,703.0,France,Female,35.0,6.0,116536.43,1.0,0.0,0.0,126728.28,0.0
-5571,15598884.0,Hsia,652.0,France,Male,22.0,2.0,0.0,2.0,1.0,0.0,130553.47,0.0
-5572,15684925.0,Mazzanti,731.0,France,Male,44.0,7.0,0.0,2.0,0.0,0.0,142977.05,0.0
-5573,15586522.0,White,718.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,0.0,136534.15,0.0
-5574,15747647.0,Chukwufumnanya,735.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,166370.81,0.0
-5575,15592232.0,Maclean,597.0,Germany,Male,36.0,8.0,121874.88,1.0,1.0,1.0,144849.1,0.0
-5576,15732266.0,Ch'ang,714.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,102908.95,0.0
-5577,15792064.0,Manna,743.0,Germany,Male,59.0,9.0,94762.16,1.0,0.0,1.0,181224.56,1.0
-5578,15790689.0,Onyinyechukwuka,757.0,France,Male,38.0,3.0,137657.25,1.0,0.0,0.0,103349.74,1.0
-5579,15796957.0,T'ang,773.0,Spain,Male,35.0,9.0,0.0,2.0,0.0,0.0,83473.82,0.0
-5580,15781702.0,Chien,629.0,France,Male,43.0,9.0,148283.94,1.0,1.0,0.0,121568.37,1.0
-5581,15692664.0,Chukwufumnanya,616.0,France,Female,35.0,8.0,0.0,2.0,0.0,0.0,168034.9,0.0
-5582,15692226.0,Hsing,753.0,France,Male,22.0,4.0,0.0,2.0,1.0,1.0,72330.46,0.0
-5583,15695301.0,Pisano,695.0,Germany,Female,42.0,3.0,123863.64,1.0,1.0,0.0,100566.81,1.0
-5584,15691817.0,Tien,774.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,101515.76,0.0
-5585,15748068.0,Brierly,668.0,Germany,Male,30.0,3.0,126732.85,2.0,1.0,0.0,128841.31,0.0
-5586,15597949.0,Ngozichukwuka,807.0,Spain,Female,30.0,7.0,102773.2,1.0,1.0,0.0,60917.24,0.0
-5587,15687558.0,Maclean,652.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,96442.86,0.0
-5588,15626474.0,Chukwueloka,678.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,54949.73,0.0
-5589,15716085.0,Smith,599.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,25554.4,0.0
-5590,15686835.0,De Luca,581.0,France,Female,54.0,8.0,0.0,1.0,1.0,1.0,91359.79,1.0
-5591,15742798.0,Ts'ao,639.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,184524.74,0.0
-5592,15689322.0,Hsing,648.0,France,Female,56.0,4.0,103891.47,1.0,1.0,0.0,151871.84,1.0
-5593,15572408.0,Ch'ang,588.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,155743.48,0.0
-5594,15709511.0,Trevisani,745.0,Germany,Female,30.0,2.0,132767.17,1.0,0.0,0.0,49848.86,0.0
-5595,15574072.0,Chiawuotu,815.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,170514.21,0.0
-5596,15692599.0,Martin,691.0,Germany,Female,28.0,10.0,146679.77,1.0,1.0,1.0,78854.57,1.0
-5597,15696047.0,Hsing,681.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,1.0,175894.24,0.0
-5598,15703394.0,Chukwualuka,539.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,166850.57,0.0
-5599,15762277.0,Piazza,738.0,France,Male,24.0,4.0,0.0,1.0,0.0,0.0,60778.11,0.0
-5600,15642725.0,Pardey,581.0,France,Male,47.0,1.0,0.0,1.0,1.0,1.0,94738.54,0.0
-5601,15757911.0,Folliero,634.0,France,Male,21.0,6.0,0.0,2.0,1.0,0.0,162858.29,0.0
-5602,15679770.0,Ibezimako,521.0,France,Female,58.0,2.0,0.0,2.0,1.0,1.0,121747.96,0.0
-5603,15593331.0,Cattaneo,649.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,91429.74,0.0
-5604,15806486.0,Scott,636.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,0.0,152944.39,0.0
-5605,15721469.0,Biryukov,595.0,Spain,Male,37.0,1.0,152462.79,1.0,1.0,0.0,119087.25,0.0
-5606,15746461.0,Folliero,629.0,France,Female,43.0,7.0,118907.6,1.0,0.0,1.0,136863.93,0.0
-5607,15791958.0,Wilson,608.0,Germany,Male,40.0,2.0,118890.81,1.0,1.0,0.0,113410.82,1.0
-5608,15584091.0,Tuan,655.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,130789.6,0.0
-5609,15742210.0,Obiajulu,535.0,France,Male,37.0,4.0,0.0,1.0,1.0,0.0,119127.06,1.0
-5610,15623644.0,Mazzanti,711.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,0.0,129984.2,0.0
-5611,15802351.0,Ebelechukwu,671.0,France,Male,41.0,6.0,0.0,2.0,1.0,0.0,183682.09,0.0
-5612,15726234.0,Piazza,678.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,107846.87,0.0
-5613,15593973.0,Clements,586.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,88695.19,0.0
-5614,15592761.0,Kaeppel,816.0,France,Male,32.0,3.0,0.0,2.0,0.0,1.0,74849.46,0.0
-5615,15589348.0,Chinwendu,639.0,Germany,Female,44.0,3.0,131038.16,1.0,1.0,1.0,59249.83,0.0
-5616,15746012.0,Chiebuka,645.0,France,Male,32.0,4.0,117094.02,1.0,1.0,0.0,153662.08,1.0
-5617,15741403.0,T'ang,716.0,France,Male,34.0,9.0,0.0,2.0,0.0,1.0,122488.59,0.0
-5618,15674932.0,Macleod,684.0,France,Male,40.0,7.0,95377.13,1.0,1.0,0.0,199644.2,1.0
-5619,15664311.0,Ch'in,619.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,0.0,6078.46,0.0
-5620,15587033.0,Trevisano,742.0,Germany,Female,44.0,1.0,117438.92,2.0,1.0,1.0,74048.1,0.0
-5621,15715199.0,Ch'ang,553.0,France,Male,43.0,4.0,0.0,2.0,0.0,0.0,83090.35,0.0
-5622,15636407.0,Chidimma,633.0,Spain,Female,27.0,9.0,0.0,2.0,1.0,1.0,84760.32,0.0
-5623,15795519.0,Yevseyev,627.0,Germany,Female,27.0,7.0,131935.51,1.0,1.0,0.0,97623.42,0.0
-5624,15665861.0,Chu,690.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,79577.38,0.0
-5625,15686957.0,Lu,738.0,France,Male,38.0,7.0,0.0,2.0,0.0,1.0,176984.34,0.0
-5626,15645136.0,P'eng,577.0,France,Male,40.0,8.0,0.0,2.0,1.0,1.0,136497.28,0.0
-5627,15782530.0,Nucci,793.0,France,Female,49.0,5.0,0.0,2.0,1.0,0.0,72927.68,0.0
-5628,15756850.0,Ndubueze,753.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,63889.1,0.0
-5629,15686588.0,Uspenskaya,709.0,France,Female,39.0,6.0,0.0,2.0,0.0,1.0,102299.81,0.0
-5630,15641640.0,Ch'ien,661.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,172618.52,0.0
-5631,15602841.0,Hsia,639.0,France,Male,31.0,5.0,141803.77,1.0,1.0,0.0,176091.31,0.0
-5632,15777822.0,Uspensky,668.0,Germany,Male,27.0,2.0,82373.94,2.0,1.0,1.0,146411.6,0.0
-5633,15657535.0,Manna,590.0,France,Female,41.0,6.0,0.0,2.0,1.0,1.0,49599.27,0.0
-5634,15750970.0,O'Brien,718.0,Spain,Male,38.0,7.0,130131.08,1.0,1.0,1.0,65464.66,0.0
-5635,15579914.0,Nwokeocha,595.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,120819.58,0.0
-5636,15583863.0,Tsai,739.0,France,Female,33.0,4.0,0.0,2.0,1.0,1.0,152665.85,0.0
-5637,15701160.0,Kao,644.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,181850.74,0.0
-5638,15798084.0,Chijindum,712.0,France,Male,21.0,6.0,0.0,2.0,1.0,1.0,73977.23,0.0
-5639,15713774.0,Hsia,645.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,59645.25,0.0
-5640,15768104.0,Chukwudi,565.0,France,Male,26.0,8.0,0.0,1.0,0.0,1.0,114774.35,0.0
-5641,15616530.0,Kao,556.0,Germany,Male,35.0,7.0,111379.55,1.0,1.0,0.0,177784.68,1.0
-5642,15705001.0,Clements,588.0,Germany,Female,38.0,7.0,120867.18,1.0,1.0,0.0,126253.31,1.0
-5643,15798053.0,Chia,749.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,1.0,186942.49,0.0
-5644,15775295.0,Folliero,667.0,Spain,Male,31.0,3.0,0.0,2.0,1.0,1.0,105570.39,0.0
-5645,15653715.0,Liao,642.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,199138.84,0.0
-5646,15690169.0,Chiu,748.0,France,Female,40.0,1.0,0.0,2.0,0.0,1.0,79577.38,0.0
-5647,15634262.0,Okwudilichukwu,707.0,Spain,Female,25.0,2.0,0.0,2.0,1.0,0.0,53954.24,0.0
-5648,15654025.0,Onio,706.0,Spain,Female,37.0,10.0,136980.23,1.0,1.0,1.0,64716.08,0.0
-5649,15572291.0,Buchi,602.0,France,Male,58.0,9.0,142084.04,1.0,1.0,1.0,169137.24,0.0
-5650,15685843.0,Hsia,672.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,112269.67,0.0
-5651,15581551.0,Uchechukwu,751.0,France,Male,21.0,6.0,112609.91,1.0,1.0,0.0,163550.58,0.0
-5652,15691823.0,Tsao,556.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,164741.81,0.0
-5653,15595047.0,Chukwufumnanya,696.0,France,Female,23.0,9.0,0.0,2.0,1.0,0.0,162022.87,0.0
-5654,15772892.0,Fiorentini,693.0,France,Female,34.0,1.0,152039.6,1.0,0.0,0.0,182624.56,0.0
-5655,15640667.0,Kable,434.0,Spain,Male,22.0,7.0,0.0,2.0,1.0,1.0,118545.49,0.0
-5656,15668818.0,Mazzanti,675.0,France,Female,35.0,2.0,98906.36,1.0,1.0,0.0,83470.4,0.0
-5657,15614215.0,Ch'iu,652.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,132298.28,1.0
-5658,15602845.0,Kolesnikov,727.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,54290.62,0.0
-5659,15654495.0,Mazzanti,687.0,Spain,Male,64.0,8.0,0.0,2.0,1.0,1.0,157891.86,1.0
-5660,15663826.0,Piazza,575.0,France,Female,37.0,6.0,0.0,1.0,1.0,1.0,158610.89,0.0
-5661,15636684.0,Onwumelu,683.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,139093.73,0.0
-5662,15767231.0,Walker,728.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,25885.72,0.0
-5663,15692919.0,Pagnotto,663.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,93953.84,0.0
-5664,15666032.0,Ndubueze,635.0,France,Female,62.0,4.0,102536.57,1.0,1.0,1.0,126336.72,0.0
-5665,15800061.0,Smith,705.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,138526.96,0.0
-5666,15774727.0,Ch'ang,592.0,France,Female,26.0,9.0,76914.21,2.0,1.0,0.0,62733.05,0.0
-5667,15664543.0,Chiang,713.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,86424.57,0.0
-5668,15651093.0,Nkemdirim,558.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,143913.32,0.0
-5669,15673877.0,Nwabugwu,684.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,158071.69,0.0
-5670,15589204.0,Lai,729.0,Spain,Female,30.0,1.0,0.0,2.0,1.0,1.0,109291.39,0.0
-5671,15610557.0,Ch'en,645.0,France,Male,35.0,3.0,0.0,1.0,1.0,0.0,130639.01,0.0
-5672,15687539.0,Macleod,579.0,France,Male,46.0,9.0,0.0,2.0,1.0,1.0,123850.58,0.0
-5673,15809847.0,T'ien,680.0,France,Male,29.0,7.0,0.0,2.0,0.0,1.0,127847.25,0.0
-5674,15804937.0,Ts'ui,756.0,France,Male,34.0,9.0,0.0,2.0,0.0,0.0,81298.09,0.0
-5675,15589431.0,White,684.0,Spain,Female,31.0,3.0,84521.48,1.0,0.0,1.0,48673.18,0.0
-5676,15605327.0,Sun,646.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,1.0,162599.51,0.0
-5677,15694879.0,Okwuadigbo,632.0,Germany,Female,31.0,4.0,125512.98,1.0,0.0,1.0,181224.56,0.0
-5678,15600738.0,Folliero,567.0,France,Male,38.0,3.0,0.0,1.0,0.0,0.0,168035.62,1.0
-5679,15760865.0,Liao,718.0,Spain,Male,39.0,5.0,0.0,1.0,1.0,1.0,52319.26,0.0
-5680,15577402.0,Young,466.0,France,Male,56.0,5.0,0.0,2.0,1.0,1.0,138399.67,0.0
-5681,15581811.0,Ch'in,641.0,France,Female,23.0,3.0,0.0,1.0,1.0,1.0,145562.4,0.0
-5682,15793331.0,Miah,600.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,182718.25,0.0
-5683,15665062.0,Hsieh,588.0,Germany,Female,42.0,1.0,169831.46,1.0,1.0,0.0,126728.27,1.0
-5684,15699492.0,Manna,643.0,France,Male,39.0,8.0,146466.46,1.0,1.0,1.0,180890.4,0.0
-5685,15618018.0,Liao,678.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,174431.01,0.0
-5686,15714993.0,Chukwufumnanya,632.0,Germany,Female,43.0,2.0,92887.13,1.0,1.0,1.0,101168.9,1.0
-5687,15746035.0,Chu,739.0,Spain,Male,23.0,9.0,0.0,1.0,0.0,0.0,96498.14,0.0
-5688,15777892.0,Lu,630.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,142903.44,0.0
-5689,15746662.0,Nnaife,653.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,54132.93,0.0
-5690,15698474.0,Hsieh,698.0,France,Male,53.0,3.0,0.0,1.0,1.0,0.0,105784.85,1.0
-5691,15699550.0,P'eng,559.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,141533.19,1.0
-5692,15746035.0,Chibueze,653.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,49361.84,0.0
-5693,15592765.0,H?,695.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,1.0,131778.67,0.0
-5694,15752508.0,Ebelechukwu,713.0,Germany,Female,29.0,9.0,151691.44,1.0,1.0,0.0,167553.06,0.0
-5695,15617454.0,Iweobiegbunam,632.0,France,Female,37.0,2.0,0.0,1.0,1.0,0.0,149892.79,0.0
-5696,15669611.0,Iloerika,564.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,1.0,113079.19,0.0
-5697,15690695.0,Onwumelu,646.0,France,Female,32.0,1.0,0.0,2.0,0.0,0.0,83084.33,0.0
-5698,15659325.0,Nwachukwu,526.0,France,Male,66.0,10.0,0.0,1.0,1.0,1.0,126812.63,0.0
-5699,15683522.0,Iweobiegbunam,632.0,France,Male,28.0,7.0,0.0,1.0,0.0,1.0,146875.76,0.0
-5700,15682533.0,Hsia,639.0,France,Male,36.0,5.0,123841.49,1.0,1.0,1.0,62905.29,0.0
-5701,15683947.0,Hs?,638.0,Germany,Female,46.0,4.0,141078.37,1.0,1.0,1.0,86043.27,1.0
-5702,15804052.0,Milano,753.0,France,Female,29.0,1.0,0.0,1.0,1.0,1.0,143683.75,0.0
-5703,15644962.0,Davison,577.0,Germany,Male,48.0,2.0,126732.85,1.0,1.0,0.0,53934.24,1.0
-5704,15595067.0,Rizzo,696.0,France,Male,44.0,0.0,96877.21,2.0,1.0,0.0,104393.78,1.0
-5705,15773098.0,Efremov,744.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,155870.82,0.0
-5706,15656901.0,Nkemdilim,613.0,Germany,Male,33.0,8.0,138653.51,1.0,1.0,1.0,177718.35,0.0
-5707,15692226.0,Ch'in,587.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,88592.8,0.0
-5708,15782530.0,Palerma,597.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,0.0,79691.4,0.0
-5709,15633586.0,Iadanza,684.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,98896.06,0.0
-5710,15611786.0,Ijendu,678.0,France,Female,35.0,4.0,0.0,2.0,1.0,0.0,141175.18,0.0
-5711,15740175.0,Chiang,651.0,France,Female,30.0,3.0,0.0,2.0,0.0,1.0,158937.55,0.0
-5712,15757911.0,Manna,794.0,France,Female,40.0,8.0,174400.36,1.0,0.0,0.0,122506.78,0.0
-5713,15657771.0,Hsueh,682.0,Germany,Female,29.0,8.0,137781.65,1.0,1.0,0.0,48778.46,0.0
-5714,15641490.0,Y?,649.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,171364.18,0.0
-5715,15651280.0,Uspensky,753.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,180622.02,0.0
-5716,15675380.0,Folliero,706.0,France,Female,26.0,0.0,0.0,2.0,1.0,0.0,118514.64,0.0
-5717,15770121.0,Aksenov,548.0,France,Female,27.0,4.0,0.0,2.0,1.0,0.0,121866.87,0.0
-5718,15746203.0,H?,696.0,France,Female,39.0,0.0,0.0,2.0,1.0,0.0,151316.37,0.0
-5719,15593976.0,Chiu,590.0,France,Male,35.0,5.0,0.0,1.0,1.0,1.0,52796.31,0.0
-5720,15686963.0,Lombardo,764.0,France,Female,57.0,9.0,0.0,1.0,1.0,0.0,66561.6,1.0
-5721,15632665.0,Chukwufumnanya,599.0,Germany,Male,26.0,5.0,125445.04,1.0,1.0,1.0,125435.81,0.0
-5722,15585255.0,Chien,693.0,France,Male,31.0,2.0,131752.71,1.0,1.0,1.0,87834.24,0.0
-5723,15778947.0,Manna,641.0,Spain,Female,35.0,2.0,0.0,1.0,1.0,0.0,9087.81,1.0
-5724,15594878.0,Yegorova,636.0,Germany,Female,37.0,7.0,129307.77,1.0,1.0,0.0,74048.1,0.0
-5725,15601550.0,Ts'ui,581.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,84460.18,0.0
-5726,15602841.0,K?,599.0,France,Female,30.0,10.0,0.0,2.0,1.0,1.0,101986.16,0.0
-5727,15699247.0,Hs?,562.0,Germany,Male,33.0,9.0,137964.55,2.0,0.0,0.0,123866.28,0.0
-5728,15793049.0,Smith,706.0,Spain,Female,42.0,1.0,0.0,3.0,0.0,0.0,160129.99,1.0
-5729,15592461.0,H?,769.0,France,Female,37.0,6.0,0.0,1.0,0.0,0.0,141449.86,0.0
-5730,15651585.0,Chukwudi,732.0,France,Male,36.0,10.0,0.0,1.0,1.0,1.0,58632.37,0.0
-5731,15605020.0,Tsao,651.0,France,Female,38.0,0.0,144427.96,1.0,1.0,1.0,92914.67,0.0
-5732,15724444.0,Uwaezuoke,667.0,France,Male,46.0,8.0,0.0,2.0,1.0,1.0,79919.13,0.0
-5733,15700366.0,Hsueh,730.0,Germany,Male,34.0,2.0,109052.04,1.0,1.0,1.0,115147.33,0.0
-5734,15585985.0,P'eng,635.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,179655.87,0.0
-5735,15802478.0,Tien,586.0,Germany,Male,38.0,2.0,95826.49,2.0,0.0,0.0,112979.6,0.0
-5736,15732917.0,Chin,471.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,177383.68,0.0
-5737,15745355.0,K'ung,637.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,176750.81,0.0
-5738,15632551.0,Fisk,637.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,135077.01,0.0
-5739,15646566.0,Uvarov,657.0,France,Male,36.0,0.0,0.0,2.0,1.0,1.0,167984.72,0.0
-5740,15737110.0,Ozerova,507.0,Germany,Male,27.0,3.0,105229.53,1.0,1.0,0.0,140941.47,1.0
-5741,15773949.0,Ts'ao,705.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,167066.95,0.0
-5742,15786418.0,Genovese,584.0,France,Female,32.0,2.0,117302.62,1.0,1.0,1.0,131609.48,0.0
-5743,15774277.0,Maclean,725.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,1.0,182855.21,0.0
-5744,15796496.0,Chien,614.0,Germany,Female,38.0,9.0,122530.18,1.0,0.0,0.0,101300.94,1.0
-5745,15766787.0,Iloerika,616.0,Germany,Male,41.0,3.0,101440.36,2.0,1.0,1.0,136491.72,0.0
-5746,15623566.0,T'ien,555.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,99449.86,0.0
-5747,15641675.0,Trevisano,633.0,France,Male,47.0,5.0,0.0,1.0,1.0,1.0,50899.91,1.0
-5748,15702571.0,Chin,629.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,65413.43,0.0
-5749,15597901.0,Bess,605.0,France,Male,41.0,2.0,0.0,1.0,1.0,1.0,130132.41,0.0
-5750,15682467.0,Chikwendu,728.0,Spain,Female,34.0,5.0,77526.66,2.0,1.0,1.0,159418.1,0.0
-5751,15766659.0,Iqbal,704.0,France,Female,38.0,1.0,133707.09,1.0,1.0,0.0,52331.15,0.0
-5752,15797733.0,Chukwuemeka,653.0,Spain,Male,38.0,9.0,0.0,1.0,1.0,1.0,100995.11,0.0
-5753,15704987.0,McIntyre,667.0,Germany,Female,54.0,4.0,113811.85,1.0,0.0,0.0,124218.53,1.0
-5754,15694518.0,Nwora,681.0,France,Male,23.0,5.0,0.0,2.0,0.0,1.0,167066.95,0.0
-5755,15771674.0,De Luca,729.0,France,Male,36.0,3.0,79932.41,1.0,1.0,1.0,126033.9,0.0
-5756,15641490.0,Hao,682.0,France,Male,61.0,9.0,0.0,1.0,1.0,0.0,189703.65,1.0
-5757,15792565.0,Ts'ui,640.0,France,Male,37.0,5.0,0.0,2.0,0.0,1.0,27239.28,0.0
-5758,15808905.0,Maclean,705.0,France,Female,22.0,3.0,0.0,2.0,0.0,1.0,151481.86,0.0
-5759,15602551.0,Ku,651.0,Germany,Male,71.0,1.0,118560.0,2.0,0.0,0.0,74858.3,1.0
-5760,15747222.0,Chiu,652.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,82868.34,0.0
-5761,15754569.0,Ritchie,748.0,Spain,Male,40.0,8.0,119755.0,2.0,1.0,0.0,86490.09,0.0
-5762,15654727.0,Ejimofor,595.0,Spain,Male,44.0,6.0,0.0,2.0,1.0,1.0,141872.05,0.0
-5763,15789936.0,Ewers,595.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,124592.83,0.0
-5764,15616454.0,Udegbulam,719.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,131283.6,1.0
-5765,15791501.0,Chinwendu,696.0,France,Male,37.0,9.0,130929.02,1.0,1.0,1.0,9551.49,0.0
-5766,15581736.0,Chidubem,628.0,Germany,Male,38.0,4.0,122265.24,2.0,1.0,1.0,86937.2,0.0
-5767,15786680.0,Ch'iu,581.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,75832.53,0.0
-5768,15791769.0,Onyemauchechi,580.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,0.0,93629.66,1.0
-5769,15622658.0,Chu,592.0,France,Female,41.0,6.0,0.0,2.0,1.0,0.0,99828.45,0.0
-5770,15590820.0,Lucchese,605.0,France,Male,28.0,9.0,0.0,2.0,1.0,1.0,150835.21,0.0
-5771,15592762.0,K'ung,709.0,France,Male,38.0,9.0,107959.39,1.0,0.0,0.0,137784.25,0.0
-5772,15628963.0,Haugh,574.0,Germany,Female,43.0,3.0,109341.87,1.0,1.0,0.0,115916.55,0.0
-5773,15692631.0,Lucchese,678.0,Germany,Female,31.0,8.0,116416.94,1.0,1.0,0.0,16657.68,1.0
-5774,15572428.0,Cawthorne,730.0,France,Male,29.0,6.0,0.0,2.0,0.0,0.0,12096.61,0.0
-5775,15598086.0,Ch'eng,712.0,Spain,Male,35.0,6.0,91592.06,1.0,1.0,1.0,175314.87,0.0
-5776,15622985.0,Okwudilichukwu,707.0,Spain,Female,43.0,9.0,0.0,1.0,1.0,0.0,184524.74,1.0
-5777,15674583.0,Y?an,699.0,Spain,Female,23.0,4.0,0.0,2.0,1.0,0.0,61811.1,0.0
-5778,15710671.0,Obijiaku,757.0,Spain,Female,47.0,2.0,0.0,2.0,1.0,1.0,142223.94,0.0
-5779,15595537.0,Y?an,769.0,Germany,Male,31.0,6.0,124447.68,2.0,0.0,0.0,180800.34,0.0
-5780,15757541.0,Chidimma,712.0,France,Female,41.0,8.0,0.0,2.0,1.0,0.0,115779.48,0.0
-5781,15807900.0,Ch'ang,776.0,France,Female,48.0,6.0,127864.4,1.0,1.0,1.0,96985.58,1.0
-5782,15691316.0,Fyodorov,661.0,France,Female,38.0,2.0,0.0,2.0,0.0,1.0,54724.03,0.0
-5783,15681081.0,Ponomaryova,620.0,Spain,Female,36.0,4.0,0.0,2.0,1.0,1.0,55868.97,0.0
-5784,15772423.0,Nieves,571.0,Germany,Male,39.0,5.0,133776.96,2.0,1.0,1.0,105780.18,0.0
-5785,15782531.0,Toscani,479.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,1.0,79616.37,0.0
-5786,15762615.0,Cisneros,675.0,Spain,Female,37.0,7.0,103711.56,1.0,0.0,0.0,141872.02,0.0
-5787,15631709.0,Chifley,638.0,Germany,Female,38.0,5.0,97445.49,2.0,1.0,1.0,109098.39,0.0
-5788,15763431.0,Tsao,676.0,France,Male,29.0,9.0,180520.29,1.0,1.0,0.0,15404.64,1.0
-5789,15719561.0,Fan,708.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,95583.45,0.0
-5790,15670866.0,Y?an,710.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,174311.3,0.0
-5791,15814022.0,Ch'ang,721.0,Germany,Male,36.0,9.0,138521.25,1.0,0.0,1.0,81718.37,0.0
-5792,15613673.0,Ku,682.0,Germany,Female,29.0,7.0,96486.57,1.0,1.0,0.0,127558.26,1.0
-5793,15788151.0,Ch'eng,577.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,25488.43,0.0
-5794,15744279.0,Eluemuno,659.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,0.0,129980.93,0.0
-5795,15692599.0,Trevisani,637.0,Germany,Female,60.0,8.0,117070.82,1.0,1.0,1.0,162719.69,1.0
-5796,15641480.0,Ch'iu,638.0,France,Female,54.0,9.0,0.0,1.0,1.0,1.0,155915.24,1.0
-5797,15631838.0,Sagese,639.0,France,Male,51.0,4.0,0.0,1.0,1.0,0.0,171378.77,1.0
-5798,15641662.0,Ugochukwutubelum,679.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,131718.12,0.0
-5799,15675787.0,Chidimma,653.0,Spain,Female,36.0,0.0,81576.75,1.0,0.0,1.0,172646.88,0.0
-5800,15665943.0,Fan,655.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,177784.68,0.0
-5801,15766776.0,P'eng,577.0,France,Female,44.0,1.0,87894.26,1.0,1.0,0.0,173498.45,0.0
-5802,15773921.0,De Luca,628.0,Spain,Female,33.0,4.0,0.0,2.0,1.0,1.0,115668.53,0.0
-5803,15644400.0,Chiang,752.0,Germany,Female,52.0,8.0,121577.33,1.0,1.0,0.0,184856.89,1.0
-5804,15683980.0,Nwebube,537.0,France,Female,27.0,2.0,153548.12,1.0,1.0,0.0,131718.12,0.0
-5805,15726358.0,Whitehead,724.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,112366.98,0.0
-5806,15576723.0,Ts'ui,641.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,1.0,96847.91,0.0
-5807,15687431.0,Brown,641.0,Germany,Male,31.0,8.0,128743.8,2.0,1.0,1.0,120138.27,0.0
-5808,15605275.0,Nkemdilim,826.0,Germany,Male,38.0,4.0,102025.32,2.0,1.0,1.0,153895.32,0.0
-5809,15674720.0,Miller,712.0,Germany,Female,48.0,2.0,107960.46,2.0,1.0,0.0,90876.95,1.0
-5810,15668767.0,Uwaezuoke,578.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,170968.99,0.0
-5811,15709639.0,Udinesi,704.0,France,Male,24.0,7.0,0.0,2.0,1.0,0.0,103832.58,0.0
-5812,15659501.0,Whitehouse,727.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,79577.4,0.0
-5813,15763612.0,Ch'iu,598.0,France,Male,38.0,4.0,0.0,1.0,0.0,1.0,138051.19,0.0
-5814,15641934.0,Pinto,612.0,France,Male,44.0,0.0,0.0,1.0,1.0,0.0,91406.62,1.0
-5815,15579345.0,Pinto,588.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,145796.49,0.0
-5816,15757916.0,Macleod,679.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,49722.34,0.0
-5817,15621227.0,Chukwudi,691.0,Spain,Female,42.0,3.0,170172.9,1.0,1.0,1.0,83052.03,0.0
-5818,15737071.0,Shih,639.0,France,Female,47.0,9.0,0.0,1.0,1.0,0.0,63736.17,0.0
-5819,15582910.0,Chidiebere,807.0,France,Male,28.0,7.0,0.0,2.0,0.0,1.0,170514.21,0.0
-5820,15581548.0,Ting,483.0,Spain,Male,32.0,7.0,0.0,2.0,1.0,0.0,142400.29,0.0
-5821,15627665.0,Fang,711.0,France,Female,36.0,3.0,0.0,1.0,1.0,0.0,72350.47,0.0
-5822,15594577.0,Lai,582.0,Spain,Male,54.0,8.0,0.0,2.0,0.0,1.0,130789.6,0.0
-5823,15703394.0,Ozerova,619.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,141896.74,0.0
-5824,15605968.0,DeRose,627.0,France,Female,41.0,6.0,0.0,1.0,1.0,0.0,67468.67,1.0
-5825,15668958.0,Lassetter,668.0,France,Male,49.0,3.0,123863.64,1.0,0.0,0.0,175893.53,1.0
-5826,15677404.0,Dickson,587.0,France,Female,29.0,9.0,0.0,2.0,0.0,0.0,161571.79,0.0
-5827,15814750.0,Ugochukwu,694.0,France,Male,35.0,7.0,0.0,1.0,1.0,1.0,103737.82,0.0
-5828,15743818.0,Onyemachukwu,614.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,127875.1,0.0
-5829,15761653.0,Ku,539.0,France,Male,38.0,1.0,129855.32,1.0,1.0,0.0,120858.18,0.0
-5830,15731553.0,Lombardo,753.0,Germany,Male,35.0,2.0,122295.17,1.0,1.0,1.0,138340.75,0.0
-5831,15657778.0,Iweobiegbulam,745.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,6936.27,0.0
-5832,15734438.0,Ch'ang,689.0,France,Male,38.0,6.0,0.0,2.0,0.0,0.0,151316.37,0.0
-5833,15581548.0,Ting,485.0,France,Male,50.0,1.0,0.0,2.0,1.0,1.0,70774.97,0.0
-5834,15726313.0,Onyemere,521.0,Germany,Male,40.0,2.0,99811.44,1.0,1.0,1.0,11525.72,1.0
-5835,15778947.0,Oluchi,749.0,Spain,Female,42.0,5.0,0.0,1.0,1.0,1.0,181794.86,1.0
-5836,15582841.0,Nwokezuike,591.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,131736.43,0.0
-5837,15768244.0,Lo Duca,584.0,France,Female,26.0,3.0,0.0,1.0,1.0,0.0,55829.25,1.0
-5838,15589715.0,Mazzanti,687.0,France,Male,44.0,5.0,127864.4,2.0,1.0,1.0,72924.29,1.0
-5839,15793901.0,Nnaife,712.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,102515.42,0.0
-5840,15651740.0,K'ung,663.0,France,Female,24.0,0.0,0.0,2.0,1.0,1.0,182728.8,0.0
-5841,15588928.0,Cattaneo,726.0,Spain,Female,57.0,4.0,0.0,2.0,1.0,1.0,121151.1,0.0
-5842,15611105.0,Shao,651.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,105868.96,0.0
-5843,15618363.0,Hsueh,589.0,France,Male,45.0,3.0,0.0,2.0,1.0,1.0,177747.03,0.0
-5844,15793424.0,Mazzanti,729.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,55725.04,0.0
-5845,15579892.0,Trevisan,699.0,France,Male,32.0,9.0,0.0,1.0,1.0,1.0,74564.41,0.0
-5846,15787907.0,P'eng,712.0,France,Female,51.0,6.0,136623.19,1.0,1.0,0.0,178267.07,1.0
-5847,15644765.0,Iloerika,719.0,Spain,Female,34.0,5.0,83888.54,1.0,0.0,0.0,134878.34,0.0
-5848,15792679.0,Bibi,489.0,France,Male,49.0,8.0,0.0,2.0,0.0,0.0,178705.45,0.0
-5849,15676521.0,Chukwueloka,697.0,France,Female,41.0,6.0,0.0,1.0,1.0,0.0,40357.39,1.0
-5850,15702669.0,Nwachinemelu,731.0,France,Male,31.0,2.0,0.0,1.0,1.0,1.0,143981.27,0.0
-5851,15659501.0,T'ang,684.0,France,Male,21.0,7.0,0.0,1.0,1.0,0.0,131647.31,1.0
-5852,15702095.0,Chu,705.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,51548.14,0.0
-5853,15772573.0,Martin,726.0,Spain,Female,62.0,8.0,133616.38,1.0,1.0,0.0,182270.88,1.0
-5854,15757628.0,Tsao,743.0,Spain,Male,35.0,2.0,0.0,2.0,1.0,0.0,117523.41,0.0
-5855,15672640.0,Nnamdi,586.0,Spain,Male,25.0,5.0,151510.17,1.0,1.0,0.0,132730.07,0.0
-5856,15603741.0,Nwankwo,743.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,175071.29,0.0
-5857,15645045.0,H?,717.0,Spain,Male,33.0,9.0,132876.23,1.0,0.0,1.0,47795.43,0.0
-5858,15796969.0,Chukwuebuka,696.0,Spain,Male,26.0,5.0,0.0,2.0,1.0,0.0,48201.64,0.0
-5859,15692631.0,Baresi,670.0,France,Female,39.0,8.0,0.0,2.0,0.0,1.0,169894.4,0.0
-5860,15731865.0,Yermakova,662.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,131736.43,0.0
-5861,15754084.0,K'ung,699.0,France,Male,29.0,5.0,0.0,1.0,1.0,1.0,168413.18,0.0
-5862,15711889.0,Chiu,769.0,France,Female,27.0,6.0,0.0,2.0,0.0,0.0,167023.95,0.0
-5863,15633950.0,Hsing,703.0,Germany,Female,29.0,2.0,120596.93,2.0,1.0,1.0,87071.18,0.0
-5864,15573926.0,Dike,708.0,France,Female,37.0,1.0,0.0,2.0,0.0,0.0,77837.63,0.0
-5865,15635972.0,T'ien,565.0,Spain,Male,27.0,7.0,0.0,2.0,1.0,1.0,75837.57,0.0
-5866,15587562.0,Tien,602.0,Spain,Male,40.0,9.0,145578.28,2.0,1.0,1.0,123850.58,0.0
-5867,15697042.0,Ives,590.0,France,Female,39.0,3.0,0.0,2.0,1.0,1.0,103931.49,1.0
-5868,15809057.0,Ngozichukwuka,699.0,France,Male,27.0,9.0,0.0,2.0,1.0,0.0,86490.09,0.0
-5869,15673372.0,Chiganu,749.0,Germany,Female,41.0,4.0,98630.31,2.0,0.0,1.0,163798.41,1.0
-5870,15764444.0,Fomin,714.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,121866.87,0.0
-5871,15795540.0,Ugochukwu,696.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,168816.03,0.0
-5872,15751595.0,T'ao,645.0,France,Male,63.0,5.0,0.0,1.0,0.0,1.0,121151.1,1.0
-5873,15683955.0,Tien,684.0,Spain,Male,39.0,4.0,125725.02,1.0,1.0,1.0,142797.5,0.0
-5874,15774936.0,Onyemauchechukwu,681.0,France,Female,32.0,7.0,127539.3,1.0,1.0,1.0,123982.14,0.0
-5875,15799785.0,Chukwuemeka,663.0,Spain,Male,43.0,2.0,0.0,1.0,1.0,0.0,138051.19,1.0
-5876,15800517.0,Bancks,679.0,Germany,Male,43.0,2.0,135903.33,2.0,1.0,1.0,67734.79,0.0
-5877,15656249.0,Greco,757.0,Germany,Female,32.0,5.0,155878.76,2.0,0.0,1.0,125061.97,0.0
-5878,15812594.0,Chukwumaobim,679.0,France,Male,29.0,10.0,0.0,2.0,1.0,1.0,147797.54,0.0
-5879,15658614.0,Ch'ang,658.0,Germany,Male,35.0,8.0,92855.02,2.0,1.0,1.0,131953.23,0.0
-5880,15582741.0,Mazzanti,588.0,France,Male,40.0,1.0,0.0,2.0,0.0,0.0,122996.76,0.0
-5881,15756825.0,Chinwendu,751.0,Spain,Female,40.0,6.0,0.0,2.0,1.0,1.0,166476.95,0.0
-5882,15775318.0,Trevisano,629.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,158750.58,0.0
-5883,15737385.0,Ch'en,711.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,0.0,141180.66,0.0
-5884,15799358.0,Ts'ai,586.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,94711.43,0.0
-5885,15652981.0,Ts'ui,714.0,Germany,Female,35.0,3.0,111213.64,3.0,0.0,0.0,51548.14,1.0
-5886,15704769.0,Ch'ien,583.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,98894.64,0.0
-5887,15682472.0,Tsao,725.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,177747.72,0.0
-5888,15752294.0,Nkemdirim,584.0,France,Female,26.0,5.0,107569.96,1.0,1.0,0.0,71154.1,0.0
-5889,15624451.0,Hsiung,764.0,Germany,Female,23.0,4.0,103772.45,2.0,1.0,0.0,141148.21,0.0
-5890,15797710.0,Chukwukadibia,693.0,France,Male,37.0,7.0,119496.75,1.0,1.0,0.0,91347.76,0.0
-5891,15585047.0,Trevisani,687.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,1.0,75927.35,0.0
-5892,15738148.0,T'ang,687.0,France,Female,43.0,4.0,0.0,1.0,1.0,0.0,158049.9,1.0
-5893,15671249.0,Lu,730.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,92647.94,0.0
-5894,15572467.0,Trevisano,669.0,France,Male,28.0,9.0,0.0,1.0,1.0,0.0,56905.38,0.0
-5895,15672610.0,Kuo,581.0,Germany,Female,44.0,3.0,116022.73,1.0,1.0,0.0,27341.63,1.0
-5896,15705765.0,Biryukov,752.0,France,Male,35.0,8.0,0.0,2.0,0.0,1.0,52319.26,0.0
-5897,15791131.0,Chukwualuka,597.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,64345.61,0.0
-5898,15782758.0,Yevseyev,728.0,France,Male,34.0,9.0,0.0,2.0,0.0,0.0,64872.33,0.0
-5899,15791174.0,Zaytseva,704.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,152992.04,0.0
-5900,15773512.0,Rowntree,850.0,Spain,Male,26.0,2.0,0.0,2.0,1.0,0.0,133051.64,0.0
-5901,15657529.0,Mancini,774.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,121311.12,0.0
-5902,15809823.0,Tuan,603.0,Spain,Male,33.0,7.0,0.0,1.0,1.0,1.0,168816.22,0.0
-5903,15642824.0,Chiedozie,623.0,Germany,Male,50.0,8.0,129177.01,1.0,1.0,0.0,154598.58,1.0
-5904,15679961.0,Tuan,745.0,Spain,Male,42.0,6.0,0.0,1.0,1.0,1.0,142838.64,0.0
-5905,15595160.0,Manna,642.0,Spain,Male,42.0,2.0,0.0,1.0,1.0,0.0,136079.64,1.0
-5906,15757916.0,Chin,714.0,France,Female,30.0,7.0,0.0,1.0,1.0,1.0,63687.06,0.0
-5907,15670354.0,Hsu,599.0,Germany,Male,46.0,2.0,131287.36,1.0,1.0,0.0,53817.23,1.0
-5908,15583364.0,Nwoye,543.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,141501.45,0.0
-5909,15612095.0,Omeokachie,636.0,Spain,Female,49.0,2.0,0.0,1.0,0.0,0.0,166476.95,1.0
-5910,15756538.0,K?,665.0,Germany,Male,36.0,7.0,126817.65,1.0,0.0,0.0,162858.29,0.0
-5911,15595324.0,Hsiao,639.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,109153.04,0.0
-5912,15592765.0,Tung,556.0,France,Female,55.0,8.0,0.0,1.0,1.0,0.0,144870.28,1.0
-5913,15721740.0,Aitken,678.0,France,Male,37.0,2.0,0.0,1.0,1.0,0.0,38019.74,0.0
-5914,15592773.0,Lettiere,547.0,Spain,Male,42.0,9.0,0.0,2.0,0.0,1.0,176692.87,0.0
-5915,15752588.0,Ch'in,697.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,161579.85,0.0
-5916,15605684.0,Chidumaga,756.0,France,Male,32.0,7.0,0.0,1.0,0.0,1.0,75578.67,0.0
-5917,15789946.0,Nnamutaezinwa,682.0,France,Female,33.0,5.0,91611.12,1.0,1.0,0.0,77511.45,0.0
-5918,15741032.0,Zaytseva,598.0,France,Male,39.0,5.0,0.0,1.0,1.0,1.0,138542.89,0.0
-5919,15635752.0,Yobanna,742.0,Germany,Female,33.0,5.0,122923.94,2.0,1.0,0.0,70399.71,0.0
-5920,15719479.0,Tsao,645.0,France,Male,41.0,5.0,0.0,2.0,0.0,0.0,128875.86,0.0
-5921,15610653.0,Chukwumaobim,706.0,France,Male,40.0,0.0,0.0,2.0,0.0,0.0,7077.67,0.0
-5922,15619130.0,Hsiung,663.0,France,Male,42.0,10.0,0.0,2.0,1.0,0.0,94780.9,0.0
-5923,15777045.0,Iadanza,717.0,Germany,Male,37.0,8.0,121805.05,1.0,1.0,1.0,122925.4,0.0
-5924,15623668.0,Martin,604.0,France,Female,45.0,3.0,0.0,1.0,1.0,0.0,179543.73,1.0
-5925,15736963.0,T'ien,721.0,France,Female,38.0,7.0,0.0,2.0,0.0,1.0,132741.13,0.0
-5926,15753861.0,Zikoranachidimma,807.0,Spain,Female,31.0,8.0,132311.71,1.0,1.0,1.0,103934.12,0.0
-5927,15745355.0,Macleod,684.0,France,Female,41.0,6.0,0.0,1.0,1.0,1.0,143913.32,0.0
-5928,15762762.0,Oluchukwu,628.0,France,Male,43.0,6.0,0.0,1.0,1.0,1.0,141860.62,0.0
-5929,15771674.0,Mistry,684.0,France,Female,38.0,9.0,114446.74,1.0,1.0,1.0,69586.27,0.0
-5930,15694381.0,Hiebner,586.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,77500.81,0.0
-5931,15796958.0,De Luca,635.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,126233.18,0.0
-5932,15771543.0,Trevisan,729.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,178268.36,0.0
-5933,15786463.0,Hs?,644.0,France,Female,34.0,1.0,0.0,1.0,1.0,1.0,180962.32,0.0
-5934,15717156.0,Chibuzo,663.0,Germany,Female,44.0,2.0,122552.48,3.0,0.0,0.0,51721.52,1.0
-5935,15726774.0,Mazzanti,725.0,Spain,Male,29.0,0.0,0.0,2.0,1.0,1.0,83093.25,0.0
-5936,15682773.0,Mazzanti,592.0,Germany,Female,40.0,0.0,147816.16,1.0,1.0,0.0,171837.06,1.0
-5937,15683955.0,Milani,603.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,1.0,48606.92,0.0
-5938,15641789.0,P'an,565.0,Germany,Female,41.0,9.0,105922.81,1.0,1.0,0.0,38761.61,1.0
-5939,15796764.0,Ozioma,704.0,France,Female,43.0,8.0,95947.76,1.0,1.0,0.0,162858.29,0.0
-5940,15716041.0,T'ien,738.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,163666.85,0.0
-5941,15757537.0,T'ang,669.0,Spain,Male,28.0,10.0,0.0,1.0,1.0,0.0,176692.87,0.0
-5942,15607988.0,Padovano,554.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,120853.83,0.0
-5943,15746802.0,Walker,667.0,France,Female,39.0,6.0,0.0,2.0,1.0,0.0,132905.43,0.0
-5944,15687558.0,Ting,605.0,Spain,Female,30.0,9.0,0.0,1.0,1.0,1.0,132702.64,0.0
-5945,15620204.0,Ogochukwu,583.0,France,Male,66.0,0.0,0.0,2.0,0.0,0.0,60905.51,1.0
-5946,15691119.0,Kao,512.0,Germany,Female,24.0,3.0,90544.0,2.0,1.0,1.0,170347.1,0.0
-5947,15614778.0,Tien,588.0,France,Female,39.0,7.0,149155.53,1.0,1.0,0.0,81402.02,0.0
-5948,15704788.0,Ch'iu,575.0,Germany,Male,36.0,3.0,114435.48,2.0,1.0,1.0,151325.24,0.0
-5949,15602845.0,Li Fonti,646.0,France,Female,34.0,1.0,108286.5,1.0,0.0,0.0,77878.62,0.0
-5950,15803801.0,H?,562.0,Spain,Male,48.0,2.0,0.0,1.0,1.0,1.0,103921.43,1.0
-5951,15773934.0,Chimezie,636.0,Germany,Male,46.0,6.0,121702.73,1.0,1.0,0.0,117832.88,1.0
-5952,15579996.0,Chinedum,642.0,France,Male,35.0,10.0,0.0,1.0,1.0,1.0,155743.48,0.0
-5953,15584090.0,K'ung,714.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,121085.72,0.0
-5954,15772995.0,Milne,653.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,159800.16,0.0
-5955,15792650.0,Ndubueze,684.0,France,Male,51.0,4.0,0.0,2.0,1.0,1.0,82547.01,0.0
-5956,15768151.0,Onwumelu,653.0,France,Female,61.0,7.0,0.0,2.0,1.0,1.0,73979.85,0.0
-5957,15617790.0,Hsu,744.0,France,Male,34.0,8.0,139986.66,1.0,0.0,1.0,106761.47,0.0
-5958,15731744.0,Foveaux,545.0,Spain,Male,22.0,9.0,155423.21,1.0,1.0,1.0,9241.78,0.0
-5959,15583392.0,Ch'ang,556.0,France,Female,37.0,5.0,0.0,1.0,0.0,1.0,104706.29,1.0
-5960,15694717.0,Ch'ang,582.0,France,Male,33.0,10.0,0.0,2.0,1.0,1.0,181461.46,0.0
-5961,15586479.0,Ritchie,605.0,France,Male,42.0,9.0,0.0,1.0,1.0,0.0,130404.53,0.0
-5962,15691808.0,Palerma,578.0,Germany,Female,45.0,1.0,137367.94,2.0,1.0,1.0,165574.54,0.0
-5963,15631054.0,Ch'in,705.0,Spain,Female,39.0,9.0,0.0,1.0,0.0,0.0,147557.71,1.0
-5964,15691808.0,Chiedozie,607.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,127825.25,0.0
-5965,15596971.0,Fanucci,744.0,France,Male,44.0,9.0,121827.43,1.0,1.0,0.0,123969.64,0.0
-5966,15583394.0,Shih,686.0,Germany,Male,36.0,3.0,133658.24,1.0,1.0,0.0,89566.74,0.0
-5967,15718893.0,Chiang,471.0,Germany,Female,36.0,3.0,139930.18,1.0,1.0,1.0,76946.3,0.0
-5968,15790829.0,Ts'ui,752.0,France,Female,47.0,7.0,0.0,2.0,1.0,0.0,149892.79,0.0
-5969,15622442.0,Chiawuotu,677.0,Germany,Male,25.0,2.0,121240.76,2.0,1.0,0.0,175743.92,0.0
-5970,15647975.0,Chien,683.0,Spain,Female,35.0,4.0,162442.35,1.0,1.0,1.0,96498.14,0.0
-5971,15578211.0,Gentry,667.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,1.0,168515.61,0.0
-5972,15792916.0,Lo Duca,753.0,France,Male,61.0,9.0,0.0,2.0,1.0,1.0,163736.09,0.0
-5973,15592768.0,Ch'ang,678.0,France,Male,41.0,2.0,0.0,2.0,0.0,1.0,45956.05,0.0
-5974,15587584.0,Onwughara,648.0,Germany,Female,41.0,9.0,167557.12,1.0,1.0,0.0,141756.32,1.0
-5975,15690540.0,Ch'ang,712.0,Spain,Female,43.0,10.0,0.0,1.0,0.0,0.0,167087.34,1.0
-5976,15591036.0,Toscani,599.0,Germany,Female,52.0,9.0,135096.77,2.0,1.0,1.0,51695.41,1.0
-5977,15667971.0,Yobanna,633.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,0.0,64345.61,0.0
-5978,15594862.0,Mazzi,547.0,Germany,Male,46.0,1.0,136576.02,1.0,1.0,1.0,179614.8,1.0
-5979,15651372.0,H?,703.0,Spain,Female,34.0,8.0,0.0,2.0,0.0,0.0,141533.19,0.0
-5980,15719302.0,Chukwuhaenye,779.0,Germany,Female,49.0,5.0,105795.9,1.0,1.0,0.0,83058.14,1.0
-5981,15791842.0,Shao,656.0,Spain,Male,37.0,2.0,134956.02,1.0,1.0,0.0,178028.96,0.0
-5982,15798975.0,Onyekachi,583.0,Spain,Male,46.0,8.0,0.0,2.0,1.0,1.0,182258.17,0.0
-5983,15654562.0,Fink,681.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,83419.49,0.0
-5984,15694253.0,Chiu,673.0,France,Female,47.0,5.0,0.0,1.0,1.0,0.0,158034.9,1.0
-5985,15764661.0,Aitken,707.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,66548.58,0.0
-5986,15709861.0,Nnaife,716.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,164864.67,0.0
-5987,15772216.0,Ch'en,733.0,France,Male,52.0,6.0,0.0,2.0,1.0,0.0,94774.12,1.0
-5988,15765322.0,Lo,850.0,Spain,Female,55.0,9.0,0.0,1.0,1.0,0.0,53512.16,1.0
-5989,15648047.0,Hsing,549.0,Spain,Female,38.0,10.0,98548.62,1.0,0.0,1.0,113679.75,0.0
-5990,15811032.0,O'Meara,598.0,France,Female,41.0,8.0,0.0,2.0,1.0,0.0,146700.22,0.0
-5991,15693771.0,Chuang,757.0,Germany,Female,58.0,5.0,126473.99,1.0,1.0,0.0,93769.89,1.0
-5992,15782758.0,Hsueh,717.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,131177.22,0.0
-5993,15594560.0,Ijendu,850.0,France,Female,50.0,2.0,0.0,1.0,1.0,1.0,176730.02,1.0
-5994,15681115.0,Rizzo,758.0,Spain,Male,24.0,9.0,118520.41,1.0,1.0,1.0,120225.62,0.0
-5995,15742285.0,P'eng,714.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,166650.32,0.0
-5996,15567993.0,T'ang,581.0,France,Male,29.0,2.0,114940.67,1.0,1.0,1.0,96660.76,0.0
-5997,15600626.0,Chiazagomekpere,678.0,France,Female,36.0,0.0,0.0,2.0,1.0,0.0,85901.09,0.0
-5998,15721383.0,Tien,627.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,148087.88,0.0
-5999,15623063.0,Maclean,673.0,Spain,Female,44.0,6.0,0.0,2.0,1.0,0.0,108675.01,0.0
-6000,15779743.0,Zikoranachukwudimma,728.0,France,Male,26.0,7.0,95829.13,1.0,1.0,0.0,96429.29,0.0
-6001,15727198.0,Ch'in,628.0,Spain,Male,50.0,7.0,0.0,2.0,1.0,1.0,161668.15,0.0
-6002,15778410.0,Onyemauchechukwu,718.0,France,Male,34.0,8.0,0.0,1.0,1.0,1.0,26215.85,0.0
-6003,15594039.0,Y?,558.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,140889.13,0.0
-6004,15771211.0,Lettiere,675.0,France,Male,25.0,4.0,0.0,2.0,1.0,1.0,120853.83,0.0
-6005,15659236.0,Onyemauchechukwu,660.0,France,Female,34.0,0.0,0.0,1.0,1.0,1.0,40652.33,0.0
-6006,15571529.0,McGregor,837.0,France,Female,60.0,4.0,0.0,1.0,1.0,0.0,170195.4,1.0
-6007,15693817.0,Galkin,722.0,France,Female,42.0,6.0,172463.45,1.0,0.0,0.0,167023.95,1.0
-6008,15690188.0,Trevisani,683.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,141872.05,0.0
-6009,15810563.0,Baresi,693.0,Spain,Female,31.0,7.0,107884.81,2.0,1.0,0.0,90977.48,0.0
-6010,15669987.0,Lucciano,605.0,Spain,Male,44.0,4.0,0.0,2.0,1.0,0.0,85890.75,0.0
-6011,15778975.0,Lombardi,664.0,France,Male,52.0,2.0,0.0,1.0,0.0,1.0,151481.86,1.0
-6012,15579548.0,Zikoranachidimma,648.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,89578.32,1.0
-6013,15633108.0,Ts'ui,713.0,France,Male,27.0,10.0,0.0,2.0,1.0,0.0,137519.92,0.0
-6014,15768777.0,Ch'ien,637.0,France,Female,28.0,4.0,68892.77,1.0,0.0,1.0,147521.35,0.0
-6015,15797910.0,Dike,682.0,France,Female,39.0,1.0,0.0,1.0,1.0,1.0,90964.54,0.0
-6016,15631693.0,Fyodorova,577.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,124592.84,0.0
-6017,15770214.0,Obialo,738.0,Germany,Male,44.0,0.0,127559.97,2.0,0.0,0.0,113580.79,1.0
-6018,15798053.0,Ch'ien,681.0,Germany,Male,37.0,2.0,125850.53,2.0,1.0,0.0,182059.28,0.0
-6019,15721921.0,T'ien,684.0,Spain,Female,33.0,9.0,142954.64,1.0,1.0,0.0,182532.23,0.0
-6020,15606944.0,Chinwendu,653.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,74858.3,0.0
-6021,15744109.0,Hsiao,733.0,France,Male,41.0,1.0,0.0,2.0,1.0,0.0,181718.73,0.0
-6022,15757588.0,Chibugo,627.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,181893.31,0.0
-6023,15783629.0,Okwudiliolisa,619.0,France,Female,32.0,4.0,152302.82,1.0,0.0,1.0,19150.14,0.0
-6024,15712264.0,Cremonesi,651.0,France,Male,25.0,7.0,121513.31,2.0,1.0,0.0,62762.12,0.0
-6025,15780498.0,Mazzanti,642.0,Germany,Male,48.0,7.0,141749.74,1.0,1.0,0.0,176570.55,0.0
-6026,15775430.0,Walker,516.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,92621.9,0.0
-6027,15777923.0,Teng,631.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,1.0,102057.86,0.0
-6028,15765322.0,Chiemenam,573.0,Spain,Male,34.0,8.0,0.0,1.0,1.0,1.0,104469.06,0.0
-6029,15659501.0,Tuan,745.0,France,Female,41.0,1.0,0.0,2.0,1.0,1.0,73881.68,0.0
-6030,15641733.0,Fomin,648.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,0.0,93739.71,0.0
-6031,15809616.0,Yobanna,653.0,France,Male,47.0,9.0,0.0,2.0,0.0,1.0,151871.84,0.0
-6032,15587553.0,Lueck,642.0,France,Female,37.0,2.0,117413.95,1.0,1.0,0.0,166953.62,0.0
-6033,15794122.0,Mancini,749.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,93901.61,0.0
-6034,15585928.0,Nkemjika,683.0,Germany,Female,40.0,1.0,122228.65,1.0,1.0,0.0,177695.67,0.0
-6035,15588928.0,Nwachukwu,666.0,France,Female,32.0,10.0,0.0,2.0,1.0,1.0,152723.23,0.0
-6036,15682070.0,Nnonso,758.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,95864.5,0.0
-6037,15767246.0,Tien,576.0,Spain,Female,47.0,4.0,0.0,2.0,1.0,0.0,74580.8,1.0
-6038,15572374.0,Sal,471.0,Germany,Female,43.0,6.0,123872.0,1.0,1.0,0.0,169386.81,1.0
-6039,15681402.0,Lo Duca,701.0,France,Female,33.0,6.0,0.0,1.0,1.0,0.0,59865.04,0.0
-6040,15792222.0,Chin,648.0,France,Male,58.0,3.0,0.0,1.0,1.0,0.0,91624.11,1.0
-6041,15782390.0,Tsou,646.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,70763.61,0.0
-6042,15763097.0,Ts'ui,730.0,Germany,Male,43.0,5.0,132730.17,1.0,1.0,0.0,158054.49,1.0
-6043,15774551.0,Ts'ai,646.0,Germany,Female,51.0,7.0,118560.0,1.0,1.0,0.0,163257.44,1.0
-6044,15718245.0,Hsueh,516.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,160589.12,0.0
-6045,15749300.0,Scott,741.0,Germany,Female,47.0,9.0,124976.19,2.0,1.0,1.0,66795.78,0.0
-6046,15806925.0,Young,711.0,France,Male,27.0,3.0,0.0,2.0,1.0,0.0,51380.9,0.0
-6047,15737267.0,Chidimma,602.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,151978.36,0.0
-6048,15623063.0,Milani,633.0,France,Male,38.0,3.0,0.0,2.0,0.0,1.0,161241.65,0.0
-6049,15672598.0,Tien,725.0,Spain,Female,31.0,3.0,123049.21,1.0,1.0,1.0,103921.43,0.0
-6050,15798737.0,P'eng,663.0,France,Female,40.0,8.0,0.0,1.0,1.0,1.0,62002.04,0.0
-6051,15607560.0,Tien,494.0,Germany,Female,41.0,5.0,134249.7,1.0,1.0,1.0,180862.26,1.0
-6052,15594404.0,Fyodorov,593.0,France,Female,28.0,5.0,70729.17,1.0,1.0,0.0,51548.14,0.0
-6053,15732674.0,Hooper,729.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,0.0,129892.93,0.0
-6054,15667932.0,Manna,506.0,France,Male,32.0,6.0,0.0,1.0,1.0,1.0,112366.98,0.0
-6055,15785559.0,Lucciano,683.0,France,Female,40.0,6.0,0.0,1.0,1.0,0.0,49063.42,1.0
-6056,15702337.0,Hsu,685.0,France,Female,49.0,1.0,79035.95,1.0,1.0,0.0,141501.45,1.0
-6057,15777892.0,Brown,593.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,95611.67,0.0
-6058,15584532.0,Ku,602.0,France,Female,44.0,5.0,0.0,1.0,1.0,1.0,124052.97,1.0
-6059,15775307.0,Ch'ien,556.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,70849.47,0.0
-6060,15805764.0,Nwankwo,597.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,166984.44,0.0
-6061,15602844.0,Baryshnikov,571.0,France,Female,26.0,7.0,0.0,2.0,1.0,1.0,142797.5,0.0
-6062,15673599.0,Lira,662.0,France,Female,40.0,7.0,141078.37,1.0,1.0,1.0,92708.1,0.0
-6063,15699450.0,Ewing,703.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,143783.46,0.0
-6064,15588928.0,Chibueze,697.0,Germany,Male,34.0,2.0,101487.18,2.0,1.0,0.0,130529.13,0.0
-6065,15689168.0,Chinweuba,546.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,1.0,104769.96,0.0
-6066,15786670.0,Tseng,661.0,Germany,Male,26.0,7.0,102088.68,2.0,1.0,1.0,74836.64,0.0
-6067,15674851.0,Onyemauchechukwu,806.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,60536.56,0.0
-6068,15645896.0,Nwankwo,681.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,1.0,94774.12,0.0
-6069,15693637.0,Lo,685.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,40807.27,0.0
-6070,15806923.0,Chiebuka,769.0,Spain,Male,44.0,1.0,0.0,1.0,1.0,1.0,86619.77,0.0
-6071,15661528.0,Fan,745.0,France,Male,43.0,3.0,0.0,2.0,0.0,1.0,74580.8,0.0
-6072,15680141.0,Hs?,598.0,France,Female,26.0,8.0,0.0,2.0,0.0,1.0,50578.4,0.0
-6073,15628308.0,Hsia,678.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,136826.32,0.0
-6074,15645248.0,Macdonald,719.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,132730.07,0.0
-6075,15807923.0,Kao,708.0,France,Male,40.0,9.0,119370.11,1.0,1.0,1.0,9241.52,0.0
-6076,15763515.0,Ch'eng,678.0,Germany,Male,59.0,8.0,121532.17,1.0,1.0,0.0,61230.83,1.0
-6077,15717010.0,Ch'iu,712.0,France,Female,55.0,6.0,116084.32,1.0,1.0,0.0,140763.18,0.0
-6078,15764033.0,Hsia,638.0,Germany,Female,62.0,10.0,152685.3,1.0,1.0,1.0,77500.81,1.0
-6079,15806901.0,Ch'ien,673.0,France,Male,23.0,4.0,0.0,2.0,0.0,0.0,152790.92,0.0
-6080,15653849.0,Pisano,667.0,France,Male,39.0,9.0,137963.07,1.0,1.0,1.0,52319.26,0.0
-6081,15652914.0,Chiang,850.0,Spain,Male,42.0,8.0,0.0,2.0,1.0,0.0,178038.17,0.0
-6082,15656683.0,Nwankwo,712.0,Germany,Female,43.0,6.0,116968.91,2.0,0.0,0.0,162858.29,1.0
-6083,15596962.0,Cisneros,521.0,France,Male,37.0,8.0,142072.02,1.0,1.0,0.0,141069.88,0.0
-6084,15709621.0,Clements,537.0,France,Male,47.0,6.0,0.0,2.0,0.0,1.0,120108.66,0.0
-6085,15724145.0,Teng,605.0,Spain,Female,37.0,2.0,0.0,1.0,0.0,0.0,199484.96,0.0
-6086,15705953.0,Lai,700.0,Germany,Male,46.0,5.0,132057.92,1.0,1.0,1.0,14073.11,1.0
-6087,15694444.0,Hsu,542.0,Germany,Male,46.0,7.0,103522.75,2.0,1.0,1.0,74836.64,0.0
-6088,15595350.0,Milanesi,613.0,France,Male,43.0,4.0,0.0,2.0,1.0,0.0,176984.34,0.0
-6089,15677049.0,Hsieh,644.0,Spain,Female,28.0,10.0,0.0,2.0,1.0,1.0,35279.74,0.0
-6090,15751185.0,Yashina,624.0,Spain,Male,67.0,6.0,0.0,2.0,1.0,1.0,166677.35,0.0
-6091,15806486.0,Calabresi,651.0,Germany,Male,32.0,8.0,122028.65,1.0,1.0,1.0,138399.67,0.0
-6092,15673578.0,T'ien,600.0,Spain,Male,33.0,1.0,0.0,2.0,0.0,1.0,171922.72,0.0
-6093,15778830.0,Udegbulam,652.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,183569.25,0.0
-6094,15772337.0,Hsueh,681.0,Spain,Male,29.0,7.0,0.0,2.0,1.0,0.0,158071.69,0.0
-6095,15737993.0,Chukwudi,636.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,102799.14,0.0
-6096,15691792.0,T'ien,697.0,Spain,Female,40.0,4.0,0.0,1.0,1.0,0.0,65868.28,0.0
-6097,15631222.0,Chukwumaobim,648.0,France,Female,34.0,1.0,0.0,2.0,1.0,1.0,127847.25,0.0
-6098,15759741.0,Onwumelu,645.0,France,Male,24.0,7.0,0.0,2.0,1.0,0.0,151822.66,0.0
-6099,15634792.0,Nwankwo,679.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,91094.46,0.0
-6100,15694566.0,Y?,598.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,71865.31,0.0
-6101,15765322.0,Manna,679.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,161889.79,0.0
-6102,15683928.0,Ch'ien,807.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,53534.8,0.0
-6103,15571476.0,Smith,635.0,France,Female,55.0,9.0,0.0,1.0,1.0,0.0,115068.99,1.0
-6104,15799031.0,Y?,645.0,France,Male,50.0,7.0,0.0,2.0,0.0,1.0,160128.98,0.0
-6105,15632263.0,T'ien,708.0,Spain,Female,43.0,7.0,0.0,2.0,1.0,1.0,142400.27,0.0
-6106,15603674.0,Hsiao,652.0,France,Male,25.0,3.0,0.0,2.0,1.0,1.0,187976.88,0.0
-6107,15746674.0,Chia,731.0,Spain,Female,36.0,7.0,122570.87,1.0,1.0,1.0,10063.75,0.0
-6108,15775206.0,Yeh,629.0,Germany,Female,44.0,1.0,131834.8,1.0,1.0,0.0,113677.53,1.0
-6109,15694039.0,Ch'ien,701.0,France,Male,50.0,6.0,0.0,1.0,1.0,1.0,132702.64,1.0
-6110,15693637.0,Mairinger,687.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,143783.46,0.0
-6111,15641366.0,Dickson,674.0,Spain,Male,43.0,2.0,0.0,2.0,0.0,1.0,141827.83,0.0
-6112,15742809.0,Hsu,599.0,Germany,Male,40.0,1.0,112957.26,1.0,0.0,1.0,122949.71,0.0
-6113,15589428.0,Ts'ai,615.0,Germany,Male,37.0,4.0,174701.05,2.0,1.0,1.0,55313.44,0.0
-6114,15692408.0,Ch'iu,687.0,Spain,Female,36.0,1.0,0.0,2.0,1.0,0.0,164825.03,0.0
-6115,15582519.0,Fanucci,637.0,France,Female,57.0,1.0,0.0,2.0,1.0,1.0,184564.67,0.0
-6116,15628298.0,Genovese,678.0,France,Male,37.0,2.0,0.0,1.0,1.0,1.0,105780.18,0.0
-6117,15672692.0,Rowntree,545.0,Germany,Male,46.0,7.0,109978.83,1.0,0.0,0.0,136837.97,1.0
-6118,15727605.0,Lucciano,756.0,Spain,Male,46.0,6.0,0.0,2.0,1.0,1.0,136610.02,1.0
-6119,15743532.0,Trevisano,850.0,France,Female,30.0,7.0,0.0,2.0,1.0,1.0,71692.8,0.0
-6120,15802570.0,Tien,731.0,Germany,Female,53.0,4.0,133379.41,1.0,1.0,0.0,49373.97,1.0
-6121,15656901.0,L?,776.0,Spain,Male,44.0,4.0,0.0,2.0,1.0,0.0,147720.01,0.0
-6122,15709991.0,Y?,756.0,Spain,Male,22.0,2.0,0.0,2.0,1.0,0.0,86792.81,0.0
-6123,15662662.0,Walker,644.0,Germany,Female,41.0,1.0,137781.65,1.0,0.0,1.0,95007.8,0.0
-6124,15745452.0,T'ang,619.0,France,Male,34.0,8.0,139246.22,1.0,1.0,0.0,64869.32,1.0
-6125,15640865.0,Cremonesi,641.0,Germany,Female,32.0,8.0,119315.75,1.0,1.0,1.0,86935.06,0.0
-6126,15658889.0,H?,639.0,France,Female,34.0,1.0,132005.77,1.0,0.0,0.0,142903.44,0.0
-6127,15750466.0,T'ien,667.0,France,Male,24.0,1.0,0.0,2.0,0.0,0.0,105568.4,0.0
-6128,15793331.0,K'ung,646.0,France,Female,26.0,3.0,136623.19,1.0,0.0,1.0,141210.5,0.0
-6129,15583848.0,Ulyanova,786.0,France,Male,43.0,4.0,0.0,2.0,1.0,0.0,50783.1,0.0
-6130,15781598.0,Nwachinemelu,554.0,France,Female,34.0,8.0,0.0,1.0,0.0,1.0,27380.99,0.0
-6131,15582944.0,H?,659.0,France,Male,43.0,10.0,0.0,2.0,1.0,0.0,145986.35,1.0
-6132,15733032.0,Hsia,695.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,93073.06,0.0
-6133,15699550.0,Maclean,653.0,France,Male,33.0,5.0,0.0,1.0,1.0,0.0,137651.6,1.0
-6134,15752694.0,Nkemdirim,711.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,69428.79,0.0
-6135,15623062.0,Chizuoke,619.0,France,Male,30.0,4.0,0.0,2.0,1.0,0.0,182855.42,0.0
-6136,15746802.0,Chiazagomekpere,695.0,France,Male,42.0,9.0,0.0,1.0,1.0,1.0,61312.15,0.0
-6137,15750839.0,Chukwuma,597.0,Germany,Male,53.0,5.0,118537.47,1.0,1.0,1.0,122218.57,1.0
-6138,15621870.0,Chikelu,637.0,France,Female,50.0,2.0,0.0,1.0,1.0,1.0,143598.7,1.0
-6139,15781495.0,McLean,543.0,France,Female,40.0,9.0,0.0,2.0,0.0,1.0,186489.95,0.0
-6140,15761047.0,Lo Duca,641.0,Germany,Male,36.0,3.0,125537.52,1.0,0.0,1.0,164864.67,0.0
-6141,15685314.0,Kao,703.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,163550.58,0.0
-6142,15763063.0,H?,729.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,0.0,142515.35,0.0
-6143,15636478.0,Pisano,627.0,France,Male,43.0,4.0,0.0,2.0,0.0,1.0,176680.16,0.0
-6144,15677310.0,Hsueh,733.0,Germany,Male,31.0,3.0,130854.59,2.0,1.0,0.0,172363.41,0.0
-6145,15789548.0,T'ien,652.0,Germany,Male,40.0,6.0,105770.7,1.0,1.0,1.0,103727.91,0.0
-6146,15698028.0,Chukwufumnanya,651.0,Germany,Female,55.0,4.0,139265.63,1.0,0.0,0.0,166694.93,1.0
-6147,15777616.0,Onyeorulu,711.0,Germany,Male,34.0,3.0,99167.54,2.0,1.0,0.0,161217.18,0.0
-6148,15733768.0,Chukwubuikem,591.0,Spain,Male,54.0,6.0,0.0,2.0,1.0,1.0,138399.67,0.0
-6149,15603914.0,Ruse,806.0,France,Male,28.0,5.0,0.0,1.0,1.0,0.0,146700.22,0.0
-6150,15630167.0,Chu,584.0,France,Female,57.0,8.0,93816.7,1.0,1.0,1.0,156768.45,0.0
-6151,15697042.0,Echezonachukwu,556.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,0.0,101694.67,0.0
-6152,15624781.0,Chukwualuka,605.0,France,Male,50.0,7.0,110812.91,1.0,1.0,1.0,82815.85,0.0
-6153,15700656.0,Chikelu,613.0,Germany,Female,51.0,8.0,105879.27,1.0,1.0,0.0,159823.16,1.0
-6154,15686588.0,O'Sullivan,598.0,France,Male,29.0,5.0,0.0,2.0,0.0,1.0,177747.03,0.0
-6155,15673820.0,Davison,622.0,France,Male,38.0,6.0,0.0,1.0,1.0,0.0,126726.33,1.0
-6156,15806913.0,K?,653.0,France,Male,31.0,3.0,120911.75,1.0,1.0,0.0,96670.2,0.0
-6157,15726289.0,Nwankwo,646.0,France,Female,30.0,4.0,0.0,1.0,1.0,0.0,159800.16,0.0
-6158,15676576.0,Hsueh,708.0,Spain,Male,30.0,10.0,0.0,1.0,1.0,1.0,96542.42,0.0
-6159,15642098.0,Nnanna,774.0,France,Male,57.0,1.0,0.0,2.0,1.0,1.0,141210.18,0.0
-6160,15722072.0,Ozerova,623.0,France,Male,30.0,0.0,102773.15,1.0,1.0,1.0,143980.29,0.0
-6161,15581733.0,Ugochukwu,703.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,1.0,149536.15,0.0
-6162,15670103.0,Obialo,659.0,France,Female,49.0,6.0,0.0,1.0,1.0,1.0,78744.19,1.0
-6163,15681316.0,Ch'en,616.0,Germany,Male,49.0,5.0,98710.89,1.0,1.0,1.0,94544.88,1.0
-6164,15654851.0,Shillito,577.0,Germany,Male,40.0,5.0,109730.22,2.0,1.0,0.0,188193.25,1.0
-6165,15665590.0,Tung,589.0,Spain,Female,38.0,8.0,0.0,1.0,0.0,0.0,119348.28,0.0
-6166,15737104.0,Ch'ang,678.0,France,Male,26.0,4.0,0.0,2.0,0.0,1.0,166038.27,0.0
-6167,15742442.0,K?,762.0,Germany,Male,56.0,2.0,119624.6,1.0,1.0,0.0,177099.71,1.0
-6168,15584776.0,Lo Duca,753.0,Spain,Male,43.0,10.0,0.0,2.0,1.0,1.0,174777.98,0.0
-6169,15595039.0,Ch'ang,684.0,France,Female,35.0,3.0,0.0,2.0,0.0,1.0,100727.68,0.0
-6170,15632280.0,Ch'iu,516.0,France,Female,40.0,7.0,0.0,2.0,1.0,0.0,56865.62,0.0
-6171,15594927.0,T'ao,723.0,France,Female,31.0,0.0,0.0,2.0,0.0,0.0,66463.62,0.0
-6172,15662291.0,Ndubueze,663.0,France,Female,50.0,1.0,0.0,2.0,1.0,0.0,120415.61,0.0
-6173,15654025.0,Uspenskaya,709.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,104902.68,0.0
-6174,15737194.0,Chukwudi,693.0,France,Female,31.0,7.0,96858.35,1.0,1.0,1.0,37264.11,0.0
-6175,15654489.0,Chienezie,582.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,182624.93,0.0
-6176,15747323.0,Udegbunam,635.0,France,Male,23.0,4.0,102097.62,1.0,1.0,1.0,51587.04,0.0
-6177,15654025.0,Ts'ai,695.0,Germany,Male,36.0,1.0,114440.24,1.0,1.0,1.0,113316.77,0.0
-6178,15666856.0,Aksenov,520.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,91561.91,0.0
-6179,15661290.0,T'ang,620.0,France,Male,38.0,10.0,0.0,1.0,1.0,0.0,185543.67,0.0
-6180,15643024.0,Mollison,696.0,Spain,Female,43.0,7.0,0.0,2.0,1.0,0.0,116957.6,0.0
-6181,15594902.0,Onwumelu,611.0,France,Female,46.0,2.0,0.0,2.0,1.0,0.0,173837.54,1.0
-6182,15672640.0,Tung,752.0,France,Male,33.0,2.0,124540.38,1.0,1.0,0.0,138340.12,0.0
-6183,15761487.0,Onyeoruru,681.0,Spain,Female,30.0,9.0,0.0,2.0,1.0,1.0,169920.92,0.0
-6184,15625080.0,O'Donnell,704.0,France,Male,34.0,6.0,117318.23,1.0,1.0,1.0,166850.57,0.0
-6185,15772423.0,Onyemauchechukwu,703.0,Germany,Female,31.0,4.0,135984.15,2.0,1.0,1.0,161559.12,0.0
-6186,15756950.0,Tsao,633.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,133076.44,0.0
-6187,15607526.0,Chidiegwu,709.0,Germany,Female,34.0,6.0,136082.0,1.0,1.0,1.0,94760.32,0.0
-6188,15639217.0,Galkin,476.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,141533.19,0.0
-6189,15572390.0,Ch'iu,571.0,Spain,Male,34.0,9.0,0.0,1.0,1.0,1.0,177092.16,0.0
-6190,15743375.0,Baresi,700.0,France,Male,61.0,3.0,0.0,2.0,1.0,1.0,113581.17,0.0
-6191,15649112.0,Tien,752.0,France,Male,37.0,3.0,0.0,1.0,1.0,1.0,174437.77,0.0
-6192,15588854.0,T'ao,628.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,54871.02,0.0
-6193,15573318.0,Cremonesi,706.0,France,Female,25.0,1.0,0.0,2.0,0.0,0.0,186826.22,0.0
-6194,15648800.0,Ch'ien,662.0,France,Male,23.0,1.0,125879.27,1.0,1.0,1.0,158036.72,0.0
-6195,15807345.0,Hsia,751.0,Germany,Male,30.0,2.0,85845.43,1.0,1.0,1.0,126565.94,0.0
-6196,15656822.0,Maclean,678.0,France,Male,19.0,5.0,0.0,2.0,0.0,1.0,91757.66,0.0
-6197,15738166.0,Genovese,763.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,177844.06,0.0
-6198,15752578.0,Ukaegbunam,691.0,Spain,Male,22.0,3.0,105303.73,2.0,1.0,0.0,55868.91,0.0
-6199,15697045.0,Nnaife,739.0,France,Female,36.0,7.0,0.0,1.0,0.0,0.0,97908.01,0.0
-6200,15737571.0,Onwumelu,725.0,Germany,Female,24.0,5.0,107959.39,1.0,0.0,1.0,113597.64,0.0
-6201,15642785.0,Clements,584.0,France,Female,33.0,4.0,125724.95,1.0,1.0,1.0,143980.29,0.0
-6202,15691543.0,Ch'ien,678.0,France,Female,30.0,8.0,0.0,2.0,0.0,0.0,48606.71,0.0
-6203,15635177.0,Calabrese,535.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,878.87,0.0
-6204,15672955.0,Li,652.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,145789.71,0.0
-6205,15722532.0,Chia,718.0,Germany,Male,41.0,5.0,153548.11,2.0,0.0,1.0,126551.65,0.0
-6206,15693637.0,Onyemere,639.0,Spain,Male,35.0,10.0,0.0,2.0,1.0,0.0,138526.96,0.0
-6207,15682048.0,Tuan,655.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,155700.22,0.0
-6208,15731989.0,Chiu,733.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,59195.05,0.0
-6209,15711687.0,Iadanza,605.0,Spain,Male,55.0,3.0,179960.2,1.0,1.0,1.0,176913.42,1.0
-6210,15649418.0,Onwuamaeze,663.0,Germany,Female,27.0,7.0,61687.33,1.0,1.0,1.0,72402.0,0.0
-6211,15778049.0,Aksenov,594.0,Germany,Male,33.0,2.0,132658.64,1.0,1.0,1.0,97939.4,0.0
-6212,15672754.0,Jamieson,742.0,France,Female,51.0,3.0,0.0,2.0,1.0,1.0,182531.74,0.0
-6213,15572792.0,Lucchesi,592.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,66728.81,0.0
-6214,15591915.0,Echezonachukwu,581.0,France,Female,46.0,2.0,0.0,1.0,1.0,1.0,131660.31,0.0
-6215,15575024.0,Galkin,652.0,France,Female,57.0,3.0,142319.03,3.0,1.0,0.0,115818.51,1.0
-6216,15729362.0,Sun,733.0,France,Female,26.0,7.0,0.0,2.0,0.0,1.0,106040.97,0.0
-6217,15593794.0,P'an,547.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,140947.05,0.0
-6218,15680430.0,Kao,633.0,Spain,Male,48.0,6.0,0.0,2.0,1.0,1.0,61977.82,0.0
-6219,15786463.0,Nnaife,633.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,177065.24,0.0
-6220,15599074.0,Nnanna,695.0,Spain,Male,45.0,6.0,0.0,2.0,1.0,1.0,182355.21,0.0
-6221,15708713.0,Yobanna,753.0,Germany,Male,59.0,7.0,127738.75,2.0,1.0,0.0,140941.47,1.0
-6222,15809302.0,Hs?,751.0,France,Female,35.0,7.0,0.0,2.0,0.0,1.0,131886.46,0.0
-6223,15659949.0,Yobachi,683.0,France,Female,47.0,2.0,0.0,1.0,1.0,0.0,151902.78,1.0
-6224,15576680.0,Ch'ang,629.0,France,Male,42.0,2.0,0.0,2.0,0.0,1.0,181605.85,1.0
-6225,15689953.0,Hsiung,629.0,France,Male,31.0,3.0,131698.57,1.0,0.0,1.0,91510.73,0.0
-6226,15594878.0,Chukwujamuike,712.0,Germany,Male,46.0,4.0,137998.2,1.0,1.0,0.0,109184.24,1.0
-6227,15594576.0,Hsueh,709.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,35608.88,0.0
-6228,15766163.0,Hsia,755.0,France,Female,23.0,7.0,80542.81,1.0,1.0,0.0,133051.64,0.0
-6229,15793894.0,Li,583.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,109742.55,0.0
-6230,15702601.0,Nnaife,638.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,187915.7,0.0
-6231,15584090.0,Nnachetam,794.0,Spain,Male,31.0,10.0,0.0,2.0,1.0,0.0,185830.72,0.0
-6232,15613786.0,Uspensky,644.0,Germany,Male,35.0,9.0,147531.82,1.0,1.0,0.0,78700.03,0.0
-6233,15624781.0,Chiang,727.0,France,Female,40.0,2.0,0.0,2.0,0.0,1.0,96660.76,0.0
-6234,15739168.0,Ponomaryov,686.0,France,Male,40.0,3.0,0.0,1.0,0.0,0.0,160941.78,0.0
-6235,15640865.0,Ch'iu,696.0,Germany,Male,38.0,3.0,161889.79,2.0,1.0,1.0,138350.75,0.0
-6236,15778975.0,Palerma,850.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,170819.01,0.0
-6237,15567834.0,Ts'ui,479.0,Spain,Male,42.0,3.0,0.0,2.0,1.0,1.0,113639.64,0.0
-6238,15773949.0,Ch'eng,579.0,France,Female,52.0,9.0,0.0,2.0,1.0,0.0,123587.83,1.0
-6239,15664523.0,Chia,619.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,1.0,108577.97,0.0
-6240,15793049.0,Chu,637.0,France,Female,32.0,2.0,0.0,2.0,0.0,1.0,121542.33,0.0
-6241,15793046.0,Tretiakova,704.0,France,Male,34.0,3.0,142872.36,1.0,0.0,1.0,129891.66,0.0
-6242,15616152.0,Fiorentino,494.0,Germany,Male,23.0,2.0,94722.88,2.0,1.0,1.0,140941.47,0.0
-6243,15672447.0,Dore,696.0,Germany,Male,26.0,7.0,129735.3,1.0,1.0,0.0,126336.72,0.0
-6244,15668958.0,Walker,492.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,59893.8,0.0
-6245,15660871.0,Chiu,725.0,Germany,Female,40.0,5.0,115074.02,1.0,1.0,0.0,138328.24,1.0
-6246,15745307.0,Ozioma,708.0,France,Male,37.0,0.0,0.0,2.0,1.0,0.0,180622.02,0.0
-6247,15788126.0,Maclean,679.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,1.0,96444.88,0.0
-6248,15593501.0,McGregor,773.0,Germany,Female,52.0,2.0,153590.31,1.0,1.0,0.0,70783.61,1.0
-6249,15787258.0,Ngozichukwuka,549.0,France,Male,44.0,7.0,0.0,1.0,1.0,0.0,103557.85,1.0
-6250,15703394.0,Toscani,745.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,937.15,0.0
-6251,15651581.0,Udinese,629.0,France,Female,49.0,2.0,0.0,1.0,1.0,0.0,153786.46,1.0
-6252,15644720.0,Groom,576.0,Spain,Male,38.0,6.0,0.0,2.0,0.0,1.0,11514.39,0.0
-6253,15687557.0,Mazzi,615.0,France,Female,37.0,9.0,100433.8,1.0,1.0,0.0,134441.6,1.0
-6254,15599090.0,Chukwumaobim,573.0,France,Female,52.0,1.0,163549.63,1.0,1.0,1.0,86622.47,0.0
-6255,15651075.0,Chukwuemeka,619.0,France,Male,45.0,4.0,0.0,2.0,0.0,1.0,186612.47,0.0
-6256,15604792.0,Shao,753.0,France,Male,29.0,5.0,0.0,2.0,0.0,0.0,89566.64,0.0
-6257,15626144.0,Yeh,637.0,France,Female,47.0,8.0,128876.71,1.0,1.0,1.0,94440.45,0.0
-6258,15597909.0,Ch'en,648.0,France,Female,32.0,5.0,0.0,1.0,1.0,0.0,169505.83,0.0
-6259,15596979.0,Pisano,516.0,Germany,Female,29.0,4.0,123599.49,1.0,1.0,0.0,58467.08,0.0
-6260,15789924.0,Chukwudi,695.0,Germany,Male,23.0,2.0,116318.23,1.0,0.0,1.0,78670.52,0.0
-6261,15806901.0,Nwachinemelu,669.0,France,Female,42.0,8.0,87070.23,1.0,1.0,0.0,176720.64,0.0
-6262,15572626.0,Chin,711.0,France,Female,44.0,7.0,0.0,2.0,1.0,1.0,162697.93,0.0
-6263,15771855.0,Chukwujamuike,619.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,106024.02,0.0
-6264,15574071.0,Sun,647.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,88958.49,0.0
-6265,15580462.0,Ch'ang,754.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,142517.31,0.0
-6266,15723886.0,Ch'ang,774.0,France,Female,46.0,4.0,0.0,1.0,1.0,0.0,66469.2,1.0
-6267,15608988.0,Nnanna,651.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,44904.26,0.0
-6268,15677243.0,Cattaneo,716.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,79919.13,0.0
-6269,15576680.0,Chiang,696.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,113098.58,0.0
-6270,15772383.0,Ugorji,807.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,130866.95,0.0
-6271,15570194.0,Macleod,637.0,France,Female,41.0,2.0,0.0,2.0,0.0,0.0,131807.14,0.0
-6272,15782467.0,Toth,681.0,France,Female,34.0,6.0,0.0,2.0,1.0,0.0,157937.35,0.0
-6273,15688876.0,Onyemauchechukwu,727.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,187776.49,0.0
-6274,15777772.0,Cremonesi,607.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,100127.71,0.0
-6275,15771742.0,Y?,707.0,Spain,Female,47.0,1.0,0.0,1.0,1.0,1.0,130189.53,1.0
-6276,15687431.0,Ch'ang,745.0,Spain,Female,34.0,7.0,132623.6,1.0,1.0,0.0,167522.19,0.0
-6277,15809906.0,Palerma,711.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,116581.1,0.0
-6278,15573741.0,Liao,613.0,Germany,Female,46.0,8.0,81710.46,1.0,1.0,0.0,66732.63,1.0
-6279,15683367.0,Chigbogu,544.0,Spain,Female,44.0,4.0,0.0,2.0,1.0,0.0,147090.9,0.0
-6280,15757537.0,Uvarova,682.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,98896.06,0.0
-6281,15701074.0,Shih,472.0,France,Male,56.0,8.0,0.0,1.0,1.0,0.0,51362.82,1.0
-6282,15583392.0,Kuo,753.0,Germany,Female,45.0,10.0,69974.66,2.0,1.0,1.0,176843.53,1.0
-6283,15644878.0,Ch'iu,714.0,France,Female,39.0,6.0,0.0,2.0,0.0,1.0,69414.13,0.0
-6284,15592078.0,Hs?,778.0,Spain,Female,47.0,6.0,104449.7,1.0,1.0,1.0,173486.39,1.0
-6285,15614103.0,Toscani,616.0,Spain,Female,24.0,3.0,0.0,1.0,0.0,1.0,145593.85,0.0
-6286,15624595.0,Fanucci,701.0,France,Male,38.0,7.0,122216.9,1.0,0.0,0.0,123663.63,0.0
-6287,15677087.0,Onwuamaeze,684.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,40807.27,0.0
-6288,15778515.0,Hsiao,627.0,France,Male,37.0,3.0,141434.04,1.0,0.0,0.0,171344.09,0.0
-6289,15671917.0,Goliwe,670.0,France,Male,32.0,2.0,125860.2,1.0,0.0,1.0,163558.16,0.0
-6290,15680430.0,Iloerika,543.0,France,Male,30.0,9.0,125552.96,1.0,1.0,0.0,57862.88,0.0
-6291,15762928.0,Y?,649.0,Spain,Male,43.0,6.0,0.0,1.0,1.0,1.0,26342.33,1.0
-6292,15658052.0,Brown,756.0,Spain,Female,28.0,8.0,145553.07,1.0,1.0,0.0,189683.7,0.0
-6293,15708904.0,Nwachukwu,677.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,115441.66,0.0
-6294,15782758.0,Mazzanti,695.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,96660.76,0.0
-6295,15793311.0,Ku,651.0,France,Female,50.0,7.0,0.0,2.0,1.0,1.0,36342.79,0.0
-6296,15772995.0,Nnamdi,726.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,157577.18,0.0
-6297,15587712.0,Cremonesi,683.0,Spain,Male,30.0,5.0,0.0,2.0,1.0,0.0,119647.6,0.0
-6298,15690647.0,Trevisano,706.0,Germany,Female,39.0,3.0,103499.08,2.0,1.0,0.0,60906.12,0.0
-6299,15712807.0,Chialuka,553.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,0.0,133692.88,0.0
-6300,15644765.0,Udinese,766.0,Spain,Male,41.0,0.0,0.0,2.0,1.0,0.0,126997.53,1.0
-6301,15685805.0,Baryshnikov,613.0,France,Female,28.0,4.0,0.0,2.0,1.0,0.0,141069.88,0.0
-6302,15569571.0,Chikelu,556.0,France,Male,41.0,3.0,133434.57,1.0,0.0,0.0,113079.19,0.0
-6303,15642816.0,Obidimkpa,709.0,France,Female,35.0,7.0,0.0,1.0,0.0,0.0,111839.71,1.0
-6304,15734005.0,Fennescey,685.0,France,Male,36.0,0.0,0.0,2.0,0.0,0.0,101617.64,0.0
-6305,15679611.0,Mazzanti,706.0,Germany,Female,39.0,1.0,176913.52,1.0,0.0,0.0,180934.48,0.0
-6306,15752488.0,Tuan,687.0,France,Female,43.0,4.0,0.0,2.0,0.0,1.0,88852.47,0.0
-6307,15578908.0,Yudin,706.0,Spain,Female,36.0,7.0,0.0,2.0,0.0,1.0,66463.62,0.0
-6308,15805627.0,Chiang,615.0,France,Male,41.0,4.0,0.0,2.0,1.0,1.0,132801.57,0.0
-6309,15776936.0,Chukwuemeka,646.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,152882.95,0.0
-6310,15657566.0,Ts'ui,695.0,France,Male,27.0,8.0,0.0,2.0,1.0,0.0,167984.72,0.0
-6311,15758813.0,Fomin,730.0,Spain,Male,38.0,8.0,112045.67,2.0,1.0,1.0,83093.25,0.0
-6312,15588517.0,Lu,639.0,France,Male,30.0,9.0,0.0,1.0,1.0,1.0,123437.05,0.0
-6313,15777553.0,Young,663.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,158043.11,0.0
-6314,15627830.0,Chidozie,624.0,Spain,Male,54.0,0.0,138577.34,1.0,1.0,0.0,166038.13,1.0
-6315,15781802.0,Tsao,722.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,174729.3,0.0
-6316,15792388.0,Tuan,694.0,France,Female,28.0,4.0,0.0,2.0,1.0,0.0,79377.45,0.0
-6317,15799479.0,Chukwumaobim,674.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,0.0,124874.23,0.0
-6318,15699550.0,T'ang,599.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,94670.77,0.0
-6319,15628145.0,Nnachetam,597.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,158071.69,0.0
-6320,15613141.0,K?,714.0,Spain,Male,36.0,4.0,0.0,1.0,1.0,1.0,160976.85,0.0
-6321,15764314.0,T'ien,589.0,France,Female,32.0,4.0,0.0,2.0,1.0,1.0,141039.51,0.0
-6322,15797710.0,Trevisano,821.0,France,Male,38.0,4.0,0.0,2.0,0.0,0.0,79577.38,0.0
-6323,15784099.0,Ts'ai,753.0,France,Male,21.0,1.0,141441.75,1.0,1.0,0.0,137537.32,0.0
-6324,15586699.0,Dickson,590.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,149550.95,0.0
-6325,15785975.0,Tien,676.0,Spain,Male,35.0,6.0,0.0,1.0,1.0,0.0,126494.82,0.0
-6326,15671778.0,Palermo,674.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,63795.8,0.0
-6327,15792250.0,Chiang,652.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,1.0,131173.9,0.0
-6328,15652218.0,Kosisochukwu,558.0,Spain,Female,47.0,8.0,0.0,2.0,0.0,1.0,91086.73,0.0
-6329,15682757.0,Uspensky,669.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,136869.31,0.0
-6330,15592412.0,Li Fonti,684.0,Germany,Female,38.0,4.0,102874.24,1.0,1.0,1.0,149614.81,0.0
-6331,15643048.0,Kao,744.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,83700.66,0.0
-6332,15750769.0,Onyekachukwu,734.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,0.0,28257.89,0.0
-6333,15662488.0,Ts'ui,586.0,Spain,Female,37.0,6.0,135120.72,1.0,1.0,1.0,111243.09,0.0
-6334,15765861.0,Nucci,687.0,Germany,Male,46.0,2.0,92340.61,1.0,0.0,0.0,131772.51,1.0
-6335,15676526.0,Ch'iu,597.0,France,Female,41.0,9.0,0.0,2.0,0.0,0.0,143439.38,1.0
-6336,15646521.0,Onyemauchechukwu,589.0,Germany,Female,45.0,7.0,142511.26,1.0,1.0,0.0,67789.99,1.0
-6337,15590094.0,Hsieh,663.0,France,Female,44.0,4.0,0.0,1.0,1.0,1.0,141872.05,1.0
-6338,15695499.0,Ives,634.0,France,Female,42.0,5.0,0.0,2.0,1.0,1.0,174918.46,1.0
-6339,15702912.0,Ndubueze,649.0,Germany,Female,30.0,2.0,131629.17,1.0,1.0,0.0,58883.91,0.0
-6340,15616555.0,Ni,683.0,France,Female,48.0,5.0,0.0,1.0,1.0,0.0,87412.24,1.0
-6341,15719276.0,P'eng,684.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,126761.2,0.0
-6342,15791323.0,Hsieh,571.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,181605.85,0.0
-6343,15595737.0,Chiu,596.0,France,Male,35.0,7.0,0.0,2.0,0.0,0.0,60427.3,0.0
-6344,15737509.0,Lucchesi,595.0,Spain,Female,29.0,5.0,0.0,2.0,1.0,1.0,124596.63,0.0
-6345,15690163.0,Chinonyelum,729.0,France,Female,44.0,5.0,0.0,1.0,1.0,0.0,143960.72,1.0
-6346,15676937.0,Walker,716.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,143773.07,0.0
-6347,15616152.0,Hao,628.0,France,Male,40.0,2.0,0.0,2.0,0.0,0.0,180427.24,0.0
-6348,15655952.0,Hs?,652.0,Germany,Female,36.0,4.0,103688.19,1.0,0.0,1.0,169921.11,1.0
-6349,15638487.0,Ugoji,613.0,France,Male,39.0,8.0,103060.63,1.0,1.0,1.0,100433.83,0.0
-6350,15690540.0,Shih,628.0,France,Female,42.0,10.0,0.0,1.0,1.0,0.0,145562.4,1.0
-6351,15613787.0,Olisaemeka,659.0,Spain,Female,26.0,9.0,0.0,1.0,0.0,1.0,56793.08,1.0
-6352,15692761.0,Shih,577.0,France,Female,42.0,6.0,0.0,1.0,1.0,0.0,120830.73,1.0
-6353,15725028.0,K'ung,641.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,168219.75,0.0
-6354,15778515.0,Onwughara,637.0,France,Male,42.0,3.0,0.0,2.0,0.0,0.0,69227.42,0.0
-6355,15683363.0,Mazzanti,544.0,France,Female,56.0,2.0,135962.9,1.0,1.0,0.0,124749.09,1.0
-6356,15754072.0,Tuan,567.0,Spain,Female,37.0,5.0,0.0,2.0,1.0,0.0,127068.91,0.0
-6357,15698927.0,Hsiao,598.0,France,Female,43.0,5.0,0.0,1.0,1.0,1.0,56585.2,1.0
-6358,15769246.0,Efremova,538.0,Germany,Female,52.0,2.0,141787.31,1.0,0.0,1.0,177357.17,1.0
-6359,15631512.0,Dickson,699.0,Spain,Female,41.0,8.0,0.0,1.0,1.0,1.0,80130.54,0.0
-6360,15653893.0,Nwokike,686.0,Germany,Male,34.0,1.0,111093.01,1.0,0.0,1.0,120250.58,0.0
-6361,15685500.0,Y?,713.0,France,Male,28.0,8.0,125561.97,1.0,1.0,1.0,110454.46,0.0
-6362,15671630.0,Chigolum,685.0,Spain,Female,41.0,2.0,0.0,1.0,1.0,0.0,177519.39,0.0
-6363,15699507.0,Chiu,619.0,Spain,Female,30.0,7.0,113680.53,1.0,1.0,1.0,135077.01,0.0
-6364,15632263.0,Calabrese,669.0,Spain,Female,42.0,6.0,130878.75,1.0,1.0,1.0,180622.05,0.0
-6365,15648881.0,Chuang,583.0,France,Male,25.0,4.0,0.0,2.0,1.0,0.0,116747.51,0.0
-6366,15686963.0,Naylor,663.0,France,Female,22.0,1.0,0.0,2.0,1.0,1.0,159823.16,0.0
-6367,15795872.0,Hao,683.0,Germany,Male,38.0,4.0,101979.16,2.0,1.0,0.0,106981.03,0.0
-6368,15663964.0,Ritchie,685.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,179421.84,0.0
-6369,15596972.0,Chukwumaobim,652.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,122964.18,0.0
-6370,15740175.0,Pirozzi,793.0,France,Male,41.0,10.0,0.0,1.0,1.0,0.0,82796.29,0.0
-6371,15615012.0,T'an,651.0,Spain,Female,29.0,6.0,110941.93,1.0,1.0,1.0,62746.55,0.0
-6372,15597930.0,De Bernales,709.0,Spain,Male,37.0,2.0,89175.06,1.0,1.0,1.0,5962.3,0.0
-6373,15661821.0,Chukwukadibia,708.0,France,Female,39.0,1.0,0.0,3.0,1.0,1.0,166370.81,1.0
-6374,15795511.0,Chikwado,564.0,Spain,Male,27.0,8.0,115285.85,1.0,1.0,0.0,179540.73,0.0
-6375,15742668.0,Chinweuba,716.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,65316.5,0.0
-6376,15742272.0,Mbanefo,543.0,France,Female,35.0,5.0,0.0,2.0,0.0,0.0,111471.55,0.0
-6377,15720706.0,Hao,678.0,France,Male,49.0,8.0,0.0,2.0,1.0,1.0,118711.75,0.0
-6378,15616507.0,Milanesi,539.0,Germany,Female,43.0,9.0,110022.74,1.0,1.0,0.0,161642.02,1.0
-6379,15703588.0,Ch'ien,705.0,France,Male,29.0,6.0,0.0,1.0,0.0,1.0,123954.15,0.0
-6380,15753955.0,Lockington,615.0,Germany,Male,42.0,2.0,105858.68,1.0,0.0,1.0,127939.18,1.0
-6381,15757310.0,Mays,611.0,Germany,Male,39.0,0.0,106901.91,2.0,1.0,1.0,71159.84,0.0
-6382,15592751.0,Nebechukwu,674.0,France,Female,40.0,8.0,0.0,1.0,1.0,0.0,51672.08,1.0
-6383,15757535.0,Chukwuemeka,703.0,Spain,Female,31.0,4.0,0.0,2.0,1.0,0.0,175324.74,0.0
-6384,15652320.0,Dyer,571.0,France,Male,21.0,5.0,0.0,2.0,1.0,1.0,25571.37,0.0
-6385,15646091.0,Laidley,569.0,France,Female,41.0,5.0,0.0,2.0,0.0,1.0,69320.97,0.0
-6386,15792453.0,Toscani,545.0,Germany,Male,39.0,3.0,129900.28,1.0,0.0,1.0,163641.53,0.0
-6387,15773973.0,Chidimma,603.0,Germany,Female,33.0,6.0,99774.5,2.0,1.0,1.0,63795.8,0.0
-6388,15804009.0,H?an,753.0,France,Male,30.0,6.0,0.0,2.0,1.0,0.0,185827.74,0.0
-6389,15695872.0,Hsing,709.0,France,Male,40.0,6.0,0.0,2.0,1.0,1.0,189657.18,0.0
-6390,15631406.0,Maclean,616.0,Spain,Male,27.0,4.0,101070.69,1.0,0.0,0.0,173486.39,0.0
-6391,15681446.0,Chidiebere,478.0,France,Male,34.0,0.0,0.0,1.0,1.0,1.0,86460.28,1.0
-6392,15701121.0,P'an,686.0,Spain,Male,36.0,3.0,0.0,2.0,0.0,1.0,163667.56,0.0
-6393,15768295.0,Ts'ui,687.0,Germany,Male,44.0,3.0,104533.15,1.0,1.0,0.0,131807.14,1.0
-6394,15741643.0,Fanucci,624.0,Germany,Male,37.0,10.0,137523.02,1.0,1.0,0.0,100447.53,0.0
-6395,15674929.0,Chinwenma,605.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,116471.43,0.0
-6396,15783026.0,P'eng,684.0,France,Male,39.0,6.0,0.0,2.0,0.0,0.0,74858.3,0.0
-6397,15770039.0,Ch'ien,749.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,93360.35,0.0
-6398,15774382.0,Chiemezie,633.0,Germany,Male,42.0,1.0,92436.86,2.0,1.0,1.0,88592.8,0.0
-6399,15807563.0,Hsiung,753.0,France,Male,49.0,7.0,0.0,1.0,1.0,0.0,77867.23,1.0
-6400,15702014.0,Lo,776.0,France,Female,22.0,4.0,90560.3,1.0,1.0,1.0,54477.51,0.0
-6401,15713374.0,Chiu,595.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,125244.18,0.0
-6402,15635364.0,Lu,607.0,Spain,Female,46.0,1.0,128306.7,1.0,1.0,1.0,60972.84,1.0
-6403,15682533.0,Lai,667.0,France,Female,25.0,2.0,0.0,2.0,1.0,1.0,107959.01,0.0
-6404,15720371.0,Fanucci,592.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,112239.03,0.0
-6405,15753362.0,Pagnotto,732.0,France,Male,33.0,10.0,0.0,2.0,1.0,0.0,187987.01,0.0
-6406,15702541.0,Ts'ai,667.0,France,Male,36.0,8.0,167557.12,1.0,1.0,1.0,146636.44,0.0
-6407,15664150.0,Lo,583.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,170338.35,0.0
-6408,15662294.0,Trevisano,585.0,Germany,Female,46.0,10.0,126606.63,2.0,1.0,1.0,130504.49,0.0
-6409,15768474.0,Ugochukwu,583.0,France,Male,28.0,2.0,0.0,2.0,1.0,1.0,157921.25,0.0
-6410,15615177.0,Mazzanti,589.0,France,Male,39.0,5.0,0.0,2.0,0.0,1.0,55400.17,0.0
-6411,15762882.0,Tuan,700.0,Germany,Female,47.0,2.0,87070.23,2.0,1.0,0.0,121866.87,0.0
-6412,15736397.0,Zito,582.0,France,Male,40.0,7.0,0.0,1.0,1.0,0.0,85547.33,1.0
-6413,15802758.0,Brient,569.0,Germany,Female,32.0,1.0,128262.72,1.0,1.0,0.0,137959.0,0.0
-6414,15772995.0,Fomin,687.0,France,Male,28.0,9.0,0.0,1.0,1.0,1.0,184399.77,1.0
-6415,15804586.0,Ch'ien,714.0,France,Male,52.0,4.0,0.0,2.0,1.0,1.0,72873.33,0.0
-6416,15696564.0,Burt,711.0,France,Male,44.0,1.0,0.0,1.0,1.0,1.0,64872.55,1.0
-6417,15786680.0,Okwuoma,685.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,57549.65,0.0
-6418,15777784.0,Hightower,748.0,France,Male,30.0,5.0,84445.68,2.0,0.0,1.0,180862.26,0.0
-6419,15639217.0,Y?an,516.0,France,Male,25.0,8.0,0.0,2.0,1.0,0.0,166682.57,0.0
-6420,15727281.0,Ugochukwutubelum,678.0,Spain,Male,34.0,7.0,0.0,1.0,0.0,1.0,132798.78,0.0
-6421,15757306.0,Okeke,681.0,France,Female,34.0,1.0,127864.4,1.0,1.0,1.0,147545.65,0.0
-6422,15648067.0,Shih,850.0,France,Male,26.0,2.0,0.0,2.0,1.0,0.0,170950.58,0.0
-6423,15682533.0,Farrar,682.0,France,Female,25.0,7.0,0.0,2.0,1.0,1.0,77326.78,0.0
-6424,15585255.0,Yermolayev,613.0,France,Male,48.0,8.0,0.0,1.0,1.0,1.0,157959.03,1.0
-6425,15711687.0,T'ien,635.0,Spain,Female,25.0,7.0,0.0,2.0,0.0,1.0,115676.61,0.0
-6426,15690695.0,Nnonso,632.0,France,Female,37.0,5.0,135907.65,1.0,1.0,1.0,73069.94,0.0
-6427,15780961.0,Maclean,683.0,France,Female,31.0,2.0,0.0,2.0,0.0,1.0,65765.22,0.0
-6428,15749482.0,P'eng,586.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,90162.35,0.0
-6429,15665062.0,Tien,694.0,France,Female,44.0,8.0,0.0,2.0,1.0,1.0,79633.38,0.0
-6430,15623357.0,Y?an,637.0,France,Male,29.0,1.0,121629.22,1.0,1.0,0.0,134732.99,0.0
-6431,15732265.0,Macleod,633.0,France,Female,65.0,1.0,131283.11,3.0,1.0,0.0,175072.47,1.0
-6432,15611331.0,Chiang,596.0,Spain,Female,36.0,6.0,126728.16,1.0,1.0,1.0,140179.68,0.0
-6433,15615091.0,Ritchie,665.0,Spain,Female,31.0,6.0,0.0,2.0,0.0,0.0,164769.13,0.0
-6434,15737047.0,Nwachinemelu,709.0,France,Male,48.0,3.0,0.0,2.0,1.0,0.0,163830.64,0.0
-6435,15741554.0,T'ang,637.0,France,Male,35.0,10.0,0.0,2.0,1.0,1.0,46824.08,0.0
-6436,15598883.0,Chidiebere,636.0,Germany,Male,47.0,2.0,116360.07,2.0,1.0,0.0,57541.08,0.0
-6437,15704586.0,Lo,681.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,72350.17,0.0
-6438,15626684.0,Chukwumaobim,624.0,Germany,Female,38.0,8.0,141349.43,2.0,1.0,0.0,184954.11,0.0
-6439,15737354.0,Chiganu,705.0,France,Male,27.0,1.0,0.0,1.0,1.0,0.0,96444.88,1.0
-6440,15705034.0,Zetticci,671.0,Germany,Female,28.0,7.0,97495.8,1.0,0.0,1.0,136902.01,0.0
-6441,15674928.0,Pirozzi,676.0,France,Female,37.0,9.0,0.0,2.0,1.0,1.0,143049.72,0.0
-6442,15700511.0,Onwughara,678.0,France,Male,37.0,6.0,127862.93,1.0,1.0,0.0,70783.61,0.0
-6443,15717185.0,Milano,678.0,France,Male,35.0,5.0,96888.39,1.0,1.0,0.0,166520.96,0.0
-6444,15803942.0,Ch'ang,653.0,France,Male,38.0,0.0,0.0,2.0,1.0,1.0,163666.85,0.0
-6445,15766017.0,Onyekaozulu,695.0,France,Male,47.0,1.0,0.0,2.0,1.0,1.0,172576.43,0.0
-6446,15654562.0,Ch'eng,554.0,Germany,Male,48.0,1.0,151922.94,1.0,0.0,0.0,66731.98,1.0
-6447,15751774.0,Hsia,707.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,116581.1,0.0
-6448,15769902.0,Lo Duca,679.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,51919.04,0.0
-6449,15731519.0,Tien,579.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,121840.2,0.0
-6450,15673591.0,P'eng,678.0,France,Male,56.0,1.0,0.0,1.0,1.0,0.0,7063.6,1.0
-6451,15773869.0,Obidimkpa,583.0,Spain,Female,28.0,9.0,0.0,2.0,1.0,0.0,139065.94,0.0
-6452,15725036.0,Ts'ui,704.0,France,Female,38.0,5.0,0.0,2.0,0.0,0.0,66463.62,0.0
-6453,15773973.0,Young,485.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,142566.75,0.0
-6454,15576723.0,Macfarlan,706.0,Germany,Female,45.0,3.0,123544.0,1.0,1.0,1.0,165562.84,1.0
-6455,15743817.0,Smith,667.0,France,Male,44.0,7.0,0.0,2.0,1.0,0.0,171519.06,1.0
-6456,15794868.0,Scott,665.0,France,Male,37.0,3.0,86616.35,1.0,0.0,1.0,81252.85,0.0
-6457,15783883.0,Ch'ien,687.0,France,Male,39.0,2.0,0.0,2.0,0.0,0.0,28690.9,0.0
-6458,15656627.0,Trevisano,446.0,Germany,Female,47.0,6.0,98775.23,1.0,1.0,1.0,176692.87,0.0
-6459,15732621.0,Smith,709.0,France,Female,40.0,7.0,0.0,2.0,0.0,0.0,131363.57,0.0
-6460,15795511.0,Kao,716.0,Germany,Male,23.0,5.0,138333.03,2.0,1.0,0.0,57553.02,0.0
-6461,15641411.0,Teng,682.0,Germany,Female,48.0,0.0,79731.91,1.0,1.0,0.0,143681.2,1.0
-6462,15602487.0,Pagnotto,743.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,1.0,447.73,0.0
-6463,15706021.0,Miah,665.0,France,Female,32.0,5.0,0.0,2.0,0.0,0.0,50906.65,0.0
-6464,15778368.0,Ch'en,742.0,Spain,Female,25.0,9.0,0.0,2.0,1.0,0.0,170563.73,0.0
-6465,15767954.0,Lai,614.0,France,Female,39.0,9.0,135923.39,1.0,1.0,1.0,158750.16,0.0
-6466,15750458.0,Tseng,591.0,Germany,Male,30.0,9.0,97544.29,1.0,1.0,1.0,123613.81,0.0
-6467,15595831.0,Yeh,637.0,France,Female,50.0,4.0,140595.45,1.0,1.0,0.0,70927.11,1.0
-6468,15674541.0,Chiemenam,625.0,France,Male,33.0,4.0,132934.89,1.0,1.0,0.0,162267.91,0.0
-6469,15685500.0,Chukwuebuka,660.0,France,Male,53.0,6.0,94443.77,1.0,1.0,1.0,26589.92,1.0
-6470,15660211.0,Ch'ien,639.0,France,Male,47.0,7.0,125397.21,1.0,1.0,1.0,87949.67,0.0
-6471,15801934.0,Chiu,730.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,88698.83,0.0
-6472,15720671.0,Onyekachi,581.0,France,Male,43.0,0.0,0.0,2.0,1.0,0.0,123882.73,1.0
-6473,15751208.0,Gentry,646.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,1.0,130461.09,0.0
-6474,15714087.0,O'Donnell,596.0,Spain,Male,39.0,5.0,0.0,2.0,1.0,0.0,139971.01,0.0
-6475,1581102.0,Chidumaga,602.0,France,Male,63.0,5.0,92466.21,1.0,1.0,0.0,176913.42,1.0
-6476,15616380.0,Y?an,678.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,0.0,105599.65,0.0
-6477,15602572.0,Hsing,630.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,1.0,108008.05,0.0
-6478,15810878.0,Uspenskaya,721.0,France,Male,25.0,2.0,127763.24,1.0,1.0,0.0,184800.27,0.0
-6479,15571917.0,Baryshnikov,650.0,Germany,Female,32.0,2.0,155413.08,2.0,1.0,1.0,94442.77,0.0
-6480,15692931.0,L?,597.0,France,Female,36.0,9.0,185734.75,1.0,1.0,0.0,165562.84,1.0
-6481,15654096.0,Maughan,683.0,France,Male,29.0,5.0,0.0,1.0,0.0,1.0,149558.83,0.0
-6482,15702416.0,Nwankwo,648.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,74870.99,0.0
-6483,15797710.0,Onyekachukwu,545.0,France,Male,31.0,8.0,128743.8,1.0,1.0,0.0,139048.0,0.0
-6484,15581548.0,Onuoha,593.0,France,Male,40.0,0.0,113226.47,1.0,1.0,1.0,53477.76,0.0
-6485,15690925.0,Wang,723.0,Spain,Female,42.0,4.0,88915.37,1.0,1.0,0.0,132439.31,0.0
-6486,15774133.0,Lettiere,564.0,Spain,Male,23.0,1.0,152367.21,1.0,1.0,1.0,86950.72,0.0
-6487,15735078.0,Uwaezuoke,602.0,Germany,Female,41.0,2.0,91592.06,2.0,1.0,1.0,94951.27,0.0
-6488,15802741.0,Yobachukwu,597.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,162699.7,0.0
-6489,15780128.0,Ch'in,765.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,1.0,182692.8,0.0
-6490,15794345.0,Pirozzi,714.0,Germany,Male,46.0,1.0,115764.32,4.0,1.0,1.0,72945.32,1.0
-6491,15656937.0,Kao,615.0,Spain,Male,32.0,5.0,0.0,2.0,1.0,0.0,111241.37,0.0
-6492,15613132.0,T'ien,774.0,France,Male,36.0,5.0,92961.61,1.0,0.0,0.0,78770.84,0.0
-6493,15702571.0,Chiedozie,709.0,Spain,Male,34.0,8.0,0.0,2.0,0.0,1.0,91045.46,0.0
-6494,15673529.0,Scott,592.0,Germany,Male,32.0,2.0,171325.98,1.0,1.0,0.0,65769.3,0.0
-6495,15775294.0,L?,696.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,0.0,188007.99,0.0
-6496,15785899.0,Onyemauchechukwu,645.0,Spain,Female,26.0,2.0,98640.77,1.0,1.0,1.0,125870.5,0.0
-6497,15582940.0,Ejimofor,850.0,France,Male,35.0,7.0,0.0,1.0,1.0,0.0,161574.19,0.0
-6498,15661249.0,Lo Duca,682.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,118862.08,0.0
-6499,15697028.0,Macleod,711.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,125518.32,0.0
-6500,15702464.0,Nwankwo,587.0,Spain,Female,34.0,5.0,137852.96,1.0,1.0,1.0,109843.16,1.0
-6501,15685320.0,Mazzanti,717.0,France,Male,45.0,2.0,0.0,2.0,1.0,1.0,93777.61,0.0
-6502,15602497.0,Chuter,688.0,Germany,Male,37.0,8.0,83600.24,2.0,1.0,0.0,166677.54,0.0
-6503,15611058.0,Pinto,756.0,France,Female,43.0,6.0,110765.68,1.0,0.0,0.0,77864.41,1.0
-6504,15729018.0,Tien,707.0,Germany,Female,27.0,3.0,118079.47,2.0,1.0,1.0,159418.1,0.0
-6505,15617301.0,Ch'eng,731.0,Spain,Female,37.0,7.0,0.0,1.0,0.0,1.0,97629.31,0.0
-6506,15642098.0,Martin,590.0,Germany,Female,44.0,2.0,92887.06,1.0,1.0,1.0,68600.66,1.0
-6507,15774551.0,Bancks,683.0,France,Male,26.0,5.0,0.0,2.0,1.0,1.0,157891.86,0.0
-6508,15703382.0,Scott,653.0,Spain,Male,29.0,5.0,0.0,1.0,0.0,1.0,171463.83,0.0
-6509,15721779.0,Kao,655.0,France,Male,64.0,5.0,0.0,2.0,1.0,1.0,62903.44,0.0
-6510,15684461.0,Tsou,623.0,France,Male,36.0,1.0,0.0,1.0,1.0,1.0,109208.23,0.0
-6511,15775231.0,Ch'ien,439.0,France,Female,26.0,5.0,0.0,2.0,1.0,1.0,44965.44,0.0
-6512,15745399.0,Ch'iu,697.0,France,Female,34.0,7.0,0.0,1.0,1.0,0.0,91036.74,0.0
-6513,15736413.0,Ch'ang,663.0,France,Female,53.0,1.0,0.0,1.0,0.0,1.0,185667.72,0.0
-6514,15661528.0,Dyson,638.0,Spain,Male,38.0,7.0,0.0,2.0,0.0,1.0,38152.01,0.0
-6515,15628112.0,T'ang,626.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,0.0,55313.51,0.0
-6516,15742986.0,Erskine,690.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,138835.78,0.0
-6517,15627829.0,White,569.0,France,Female,32.0,3.0,139528.23,1.0,1.0,1.0,151146.67,0.0
-6518,15588933.0,Ch'iu,582.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,56784.11,0.0
-6519,15649129.0,Lai,850.0,France,Male,29.0,3.0,0.0,2.0,1.0,0.0,126761.2,0.0
-6520,15705085.0,Nnonso,634.0,France,Female,24.0,3.0,0.0,2.0,0.0,1.0,56767.67,0.0
-6521,15796218.0,Tien,551.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,155700.15,0.0
-6522,15587573.0,Toscani,817.0,France,Male,56.0,8.0,96888.39,1.0,1.0,0.0,156618.41,1.0
-6523,15661827.0,Tien,641.0,Spain,Male,34.0,2.0,91057.37,1.0,0.0,0.0,53954.24,0.0
-6524,15768367.0,Lai,602.0,France,Female,53.0,7.0,0.0,2.0,1.0,0.0,90878.13,1.0
-6525,15612043.0,Genovese,712.0,Spain,Male,43.0,10.0,81571.05,1.0,0.0,1.0,79602.86,0.0
-6526,15733888.0,Shih,709.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,5983.8,0.0
-6527,15766172.0,Nnamutaezinwa,576.0,Germany,Male,38.0,3.0,84587.9,1.0,0.0,0.0,143598.7,1.0
-6528,15781665.0,Palerma,638.0,France,Female,35.0,4.0,0.0,1.0,0.0,0.0,56650.47,0.0
-6529,15777873.0,Trevisani,712.0,Spain,Male,45.0,7.0,0.0,1.0,1.0,1.0,118716.63,1.0
-6530,15694209.0,Nnamdi,592.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,111981.19,0.0
-6531,15644878.0,Tien,724.0,France,Male,41.0,3.0,69712.74,1.0,1.0,1.0,137763.93,0.0
-6532,15798895.0,Ch'en,622.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,87834.24,0.0
-6533,15586974.0,Chibueze,729.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,166850.57,0.0
-6534,15687913.0,Kao,595.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,51387.1,1.0
-6535,15603594.0,Hsia,601.0,Germany,Female,40.0,6.0,85935.82,1.0,1.0,0.0,89935.71,0.0
-6536,15603481.0,Nnonso,635.0,France,Male,44.0,0.0,0.0,1.0,1.0,1.0,83022.92,1.0
-6537,15637138.0,T'ien,706.0,France,Male,33.0,7.0,92566.54,1.0,1.0,1.0,142913.98,0.0
-6538,15792429.0,Aksenov,527.0,France,Female,32.0,4.0,0.0,2.0,1.0,1.0,79919.13,0.0
-6539,15763895.0,Chizuoke,730.0,France,Male,40.0,5.0,0.0,2.0,1.0,0.0,157932.75,0.0
-6540,15649853.0,Ch'ien,669.0,Spain,Male,41.0,8.0,0.0,2.0,0.0,1.0,64375.4,0.0
-6541,15635116.0,Lai,628.0,France,Male,40.0,6.0,0.0,2.0,1.0,1.0,135925.72,0.0
-6542,15581229.0,Tsao,633.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,88659.07,0.0
-6543,15635277.0,P'eng,635.0,France,Male,41.0,7.0,0.0,1.0,1.0,1.0,176570.28,0.0
-6544,15754599.0,Y?,757.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,1.0,50610.62,0.0
-6545,15665943.0,Yobachi,730.0,France,Male,30.0,6.0,0.0,2.0,1.0,0.0,148516.79,0.0
-6546,15799128.0,Manna,592.0,France,Female,41.0,1.0,0.0,2.0,1.0,0.0,93524.19,0.0
-6547,15595440.0,Lombardo,669.0,Spain,Female,52.0,6.0,0.0,1.0,1.0,0.0,98820.09,1.0
-6548,15793881.0,Ozoemena,711.0,Spain,Male,47.0,4.0,0.0,1.0,1.0,1.0,176779.46,0.0
-6549,15754747.0,Tuan,613.0,France,Female,31.0,1.0,0.0,2.0,0.0,0.0,126812.63,0.0
-6550,15595883.0,Frolova,611.0,Spain,Male,39.0,3.0,0.0,2.0,0.0,0.0,179445.28,0.0
-6551,15777892.0,Pirozzi,706.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,140941.47,0.0
-6552,15628145.0,Hsieh,629.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,103099.22,0.0
-6553,15796957.0,Lucciano,782.0,Spain,Female,34.0,3.0,0.0,1.0,1.0,1.0,152883.95,0.0
-6554,15724161.0,Chien,794.0,France,Female,28.0,8.0,0.0,2.0,1.0,1.0,166476.95,0.0
-6555,15581700.0,Toscani,532.0,Germany,Male,40.0,2.0,121453.63,2.0,0.0,1.0,56787.8,0.0
-6556,15671358.0,Okwudiliolisa,521.0,France,Female,32.0,6.0,86549.74,1.0,1.0,1.0,131093.99,0.0
-6557,15700964.0,Hsu,850.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,134778.02,0.0
-6558,15698619.0,Chukwuhaenye,646.0,Germany,Male,36.0,10.0,134796.05,1.0,1.0,1.0,102297.15,1.0
-6559,15771543.0,Aikenhead,774.0,Spain,Male,42.0,9.0,133525.1,2.0,1.0,0.0,119747.08,0.0
-6560,15765694.0,Onyinyechukwuka,616.0,France,Male,31.0,4.0,0.0,1.0,0.0,0.0,74580.8,0.0
-6561,15594796.0,Iadanza,708.0,France,Male,32.0,8.0,0.0,2.0,0.0,1.0,186489.95,0.0
-6562,15685920.0,Hsing,711.0,Spain,Female,52.0,5.0,0.0,1.0,1.0,0.0,94440.45,1.0
-6563,15776939.0,Cremonesi,745.0,France,Female,55.0,5.0,0.0,2.0,1.0,0.0,58974.0,1.0
-6564,15808846.0,Cawood,713.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,154908.52,0.0
-6565,15596962.0,Milne,719.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,145558.22,0.0
-6566,15752881.0,Ch'ang,678.0,Germany,Male,38.0,9.0,110624.99,3.0,0.0,0.0,185510.34,1.0
-6567,15783882.0,Tsou,656.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,90537.47,0.0
-6568,15679048.0,Ts'ui,730.0,France,Male,29.0,6.0,73023.26,1.0,1.0,0.0,9241.78,0.0
-6569,15730137.0,Onyemauchechukwu,676.0,France,Female,41.0,0.0,0.0,1.0,1.0,0.0,87834.89,1.0
-6570,15567446.0,Nwachukwu,829.0,France,Female,25.0,1.0,0.0,1.0,1.0,1.0,182612.38,0.0
-6571,15750658.0,Ts'ui,805.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,181208.47,0.0
-6572,15731760.0,T'ien,754.0,Germany,Male,22.0,3.0,104784.23,1.0,1.0,1.0,57972.45,1.0
-6573,15643487.0,Nnonso,670.0,Germany,Male,43.0,5.0,141787.31,2.0,1.0,1.0,169291.7,0.0
-6574,15593470.0,Lucchese,601.0,France,Female,52.0,6.0,0.0,1.0,1.0,1.0,173680.54,1.0
-6575,15659710.0,Chimezie,754.0,France,Female,43.0,5.0,155973.88,1.0,0.0,0.0,117632.63,1.0
-6576,15640078.0,Walker,605.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,1.0,70778.63,0.0
-6577,15684516.0,Ogochukwu,611.0,France,Male,30.0,7.0,169832.57,1.0,1.0,0.0,126812.63,0.0
-6578,15585066.0,P'an,584.0,Spain,Female,28.0,3.0,0.0,2.0,1.0,1.0,91346.03,0.0
-6579,15639357.0,Toscani,543.0,France,Male,32.0,7.0,0.0,2.0,0.0,0.0,125877.22,0.0
-6580,15641136.0,Frolov,578.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,64746.07,0.0
-6581,15589019.0,Hsia,633.0,Germany,Female,51.0,3.0,139914.13,1.0,1.0,1.0,70532.53,0.0
-6582,15623196.0,Kao,545.0,France,Female,37.0,1.0,90322.99,1.0,1.0,1.0,151325.24,1.0
-6583,15588859.0,T'ien,717.0,Germany,Female,46.0,6.0,117696.67,1.0,1.0,0.0,165675.01,1.0
-6584,15784736.0,Chukwualuka,627.0,Germany,Female,25.0,7.0,98640.95,1.0,1.0,0.0,44363.42,0.0
-6585,15770576.0,Chigolum,651.0,France,Male,39.0,8.0,141829.67,1.0,1.0,1.0,129975.54,0.0
-6586,15704795.0,Shih,850.0,Germany,Male,29.0,8.0,122724.45,2.0,1.0,1.0,71173.64,0.0
-6587,15744327.0,Iloerika,604.0,Spain,Male,32.0,5.0,0.0,1.0,1.0,0.0,101168.3,1.0
-6588,15777576.0,Chiawuotu,850.0,Spain,Male,35.0,2.0,0.0,2.0,0.0,0.0,51112.8,0.0
-6589,15738785.0,Ts'ui,669.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,132973.17,0.0
-6590,15696985.0,Chukwualuka,645.0,Germany,Female,60.0,9.0,164523.5,1.0,1.0,0.0,167773.55,1.0
-6591,15671172.0,Ch'ien,598.0,France,Female,52.0,6.0,0.0,1.0,1.0,0.0,5708.19,1.0
-6592,15581237.0,Iloerika,850.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,146723.72,0.0
-6593,15711759.0,Shih,595.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,184796.84,0.0
-6594,15766066.0,Martin,557.0,Spain,Female,31.0,9.0,0.0,1.0,0.0,1.0,61936.1,1.0
-6595,15672598.0,Ch'ang,718.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,73329.08,1.0
-6596,15668818.0,Chineze,611.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,152417.79,0.0
-6597,15582335.0,Frolov,653.0,Spain,Female,37.0,3.0,0.0,1.0,0.0,0.0,114522.68,1.0
-6598,15684951.0,Palermo,659.0,Spain,Male,39.0,3.0,0.0,2.0,0.0,1.0,141872.05,1.0
-6599,15655389.0,P'eng,667.0,Spain,Male,42.0,3.0,0.0,2.0,1.0,0.0,196257.67,0.0
-6600,15655961.0,Chiu,602.0,France,Male,40.0,5.0,0.0,2.0,0.0,1.0,92612.88,0.0
-6601,15590740.0,Fomin,725.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,1.0,166957.82,0.0
-6602,15808905.0,Genovese,730.0,France,Male,43.0,9.0,134378.96,1.0,1.0,1.0,119259.34,0.0
-6603,15795581.0,Nwabugwu,683.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,168816.23,0.0
-6604,15683553.0,De Luca,671.0,Spain,Female,47.0,0.0,0.0,2.0,0.0,1.0,147055.87,0.0
-6605,15652948.0,Hsiung,656.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,63736.14,0.0
-6606,15743456.0,K'ung,685.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,0.0,144870.28,0.0
-6607,15671450.0,Nkemdirim,740.0,Spain,Male,44.0,3.0,0.0,2.0,1.0,0.0,176750.81,0.0
-6608,15626383.0,Chien,634.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,134878.19,0.0
-6609,15638269.0,Dyer,648.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,104469.08,0.0
-6610,15793424.0,Chukwuemeka,725.0,France,Male,46.0,0.0,0.0,1.0,1.0,0.0,122965.14,1.0
-6611,15667632.0,De Luca,717.0,Spain,Female,44.0,3.0,123345.65,2.0,0.0,1.0,61531.86,0.0
-6612,15773487.0,P'eng,684.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,121592.5,0.0
-6613,15737506.0,Onyekaozulu,592.0,France,Male,40.0,0.0,0.0,2.0,1.0,0.0,91763.67,0.0
-6614,15710390.0,Koo,783.0,Germany,Male,37.0,7.0,144591.96,2.0,1.0,1.0,82393.06,0.0
-6615,15802220.0,Ejimofor,683.0,France,Male,40.0,1.0,0.0,2.0,0.0,0.0,173783.38,0.0
-6616,15806511.0,Chidubem,642.0,France,Male,29.0,8.0,0.0,2.0,0.0,0.0,171770.79,0.0
-6617,15582335.0,Yegorova,613.0,France,Female,36.0,7.0,0.0,1.0,1.0,1.0,172618.52,0.0
-6618,15586479.0,Chukwufumnanya,509.0,Spain,Male,59.0,10.0,0.0,2.0,1.0,1.0,180551.76,0.0
-6619,15775124.0,Pinto,649.0,France,Male,32.0,10.0,0.0,2.0,1.0,0.0,24207.86,0.0
-6620,15643908.0,Maclean,684.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,116977.89,0.0
-6621,15668058.0,Nnachetam,710.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,0.0,116781.71,0.0
-6622,15694349.0,Chibueze,627.0,Germany,Male,40.0,2.0,104349.45,1.0,1.0,1.0,165614.4,0.0
-6623,15581323.0,Iqbal,774.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,51553.43,0.0
-6624,15718779.0,Rizzo,657.0,France,Female,43.0,9.0,0.0,2.0,1.0,0.0,129890.94,0.0
-6625,15753343.0,Iqbal,567.0,France,Male,42.0,9.0,113858.95,1.0,1.0,1.0,162515.17,1.0
-6626,15591711.0,Chinweike,579.0,France,Male,29.0,9.0,77910.23,1.0,0.0,1.0,103934.12,0.0
-6627,15594556.0,Udinesi,599.0,Germany,Female,48.0,7.0,117154.9,2.0,1.0,1.0,179451.42,1.0
-6628,15663625.0,Chiawuotu,596.0,France,Female,59.0,5.0,0.0,1.0,1.0,1.0,199108.88,0.0
-6629,15775663.0,Trejo,703.0,France,Male,46.0,1.0,0.0,2.0,0.0,0.0,168816.22,0.0
-6630,15609562.0,Obialo,850.0,France,Male,44.0,6.0,111379.55,1.0,1.0,1.0,167723.25,0.0
-6631,15690169.0,Pinto,721.0,France,Male,38.0,3.0,158719.57,1.0,1.0,0.0,123866.37,0.0
-6632,15702715.0,Tung,692.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,64872.55,0.0
-6633,15760662.0,Hsueh,710.0,France,Male,34.0,1.0,0.0,1.0,1.0,0.0,105599.79,0.0
-6634,15658435.0,Ch'ien,705.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,157862.82,0.0
-6635,15607434.0,Chin,678.0,France,Female,35.0,6.0,106328.08,1.0,1.0,1.0,116973.48,0.0
-6636,15687079.0,Lettiere,704.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,172576.43,0.0
-6637,15592570.0,Chidiebele,520.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,185821.41,0.0
-6638,15732963.0,Yermakova,586.0,Germany,Female,33.0,2.0,171178.25,2.0,0.0,1.0,138192.98,0.0
-6639,15662901.0,Trevisani,531.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,93959.96,0.0
-6640,15578738.0,Udegbulam,572.0,Germany,Male,39.0,6.0,122552.34,1.0,1.0,0.0,84760.32,0.0
-6641,15769006.0,Chia,704.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,162889.1,0.0
-6642,15801414.0,Yeh,645.0,France,Female,62.0,1.0,0.0,2.0,1.0,0.0,48963.59,1.0
-6643,15641611.0,Phelan,461.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,55775.72,0.0
-6644,15699430.0,Chin,622.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,152733.89,0.0
-6645,15761854.0,T'ien,636.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,58814.41,0.0
-6646,15776467.0,Fanucci,718.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,138700.89,0.0
-6647,15770474.0,Eluemuno,615.0,France,Female,39.0,4.0,124577.33,1.0,0.0,1.0,823.96,0.0
-6648,15604314.0,Chinwenma,685.0,France,Female,48.0,9.0,0.0,1.0,0.0,1.0,136071.05,1.0
-6649,15788131.0,Lucciano,492.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,1.0,122823.84,0.0
-6650,15701160.0,Y?an,628.0,France,Male,34.0,10.0,0.0,2.0,1.0,1.0,28789.94,0.0
-6651,15802809.0,Onyemauchechukwu,596.0,France,Female,45.0,1.0,0.0,1.0,0.0,0.0,65801.44,1.0
-6652,15684928.0,K?,674.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,147720.27,0.0
-6653,15777784.0,Aksenov,778.0,France,Male,36.0,1.0,92716.96,2.0,0.0,0.0,143774.06,0.0
-6654,15706005.0,Chinwendu,706.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,124589.58,0.0
-6655,15629448.0,Chiang,592.0,France,Male,33.0,6.0,0.0,1.0,0.0,0.0,138380.12,0.0
-6656,15631159.0,Zetticci,749.0,France,Male,28.0,9.0,95994.98,2.0,1.0,0.0,175324.74,0.0
-6657,15591344.0,Yefremov,714.0,France,Male,32.0,10.0,146033.62,1.0,1.0,0.0,53534.6,0.0
-6658,15605741.0,Lucciano,773.0,France,Male,40.0,7.0,0.0,1.0,1.0,1.0,98949.85,0.0
-6659,15636562.0,Yobanna,593.0,France,Male,41.0,3.0,0.0,2.0,1.0,0.0,170661.02,0.0
-6660,15572294.0,Martin,666.0,Spain,Female,33.0,5.0,89047.14,1.0,1.0,0.0,168895.06,0.0
-6661,15649430.0,Ch'iu,718.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,83093.25,0.0
-6662,15776939.0,T'ang,826.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,199357.24,0.0
-6663,15766659.0,Ch'eng,589.0,France,Male,38.0,8.0,123049.21,1.0,1.0,0.0,159800.16,0.0
-6664,15794849.0,Pinto,682.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,0.0,149996.74,0.0
-6665,15646091.0,Ch'ang,726.0,Germany,Male,31.0,4.0,125789.69,2.0,1.0,1.0,26260.98,0.0
-6666,15589449.0,Tien,637.0,France,Female,44.0,4.0,0.0,1.0,1.0,0.0,67622.42,1.0
-6667,15689168.0,Pirozzi,696.0,Germany,Female,39.0,5.0,135095.58,1.0,0.0,0.0,78670.52,1.0
-6668,15793723.0,K'ung,669.0,France,Female,34.0,6.0,0.0,2.0,0.0,0.0,102515.43,0.0
-6669,15707861.0,Lo Duca,625.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,182867.84,0.0
-6670,15656683.0,P'eng,576.0,France,Male,58.0,3.0,0.0,2.0,0.0,0.0,86920.97,1.0
-6671,15613102.0,Nwankwo,621.0,France,Female,34.0,10.0,147910.3,1.0,1.0,1.0,153036.97,0.0
-6672,15760294.0,Ch'ang,754.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,115186.97,0.0
-6673,15778290.0,Tien,537.0,Spain,Male,35.0,9.0,0.0,3.0,0.0,0.0,119899.52,1.0
-6674,15763798.0,Zito,628.0,Germany,Male,50.0,4.0,92566.53,1.0,1.0,0.0,40333.94,1.0
-6675,15648069.0,Chibugo,744.0,Germany,Female,41.0,9.0,135937.94,1.0,1.0,1.0,134901.57,0.0
-6676,15652022.0,Nworie,724.0,France,Female,21.0,7.0,0.0,2.0,1.0,0.0,62830.97,0.0
-6677,15772423.0,Chukwukere,607.0,France,Male,39.0,7.0,115518.07,1.0,1.0,1.0,120853.87,0.0
-6678,15609637.0,Hayslett,584.0,France,Female,27.0,4.0,0.0,2.0,0.0,1.0,173784.04,0.0
-6679,15809847.0,Galkin,641.0,France,Male,26.0,2.0,130206.28,1.0,1.0,0.0,179631.85,0.0
-6680,15589401.0,Bair,602.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,122925.4,0.0
-6681,15597957.0,Hs?,659.0,France,Female,24.0,7.0,122311.59,1.0,1.0,0.0,181203.6,0.0
-6682,15736990.0,Hsiung,605.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,160929.52,0.0
-6683,15607986.0,Ts'ui,581.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,157567.37,0.0
-6684,15716284.0,Genovesi,724.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,60721.4,0.0
-6685,15754068.0,Pritchard,653.0,France,Female,45.0,3.0,0.0,2.0,0.0,0.0,126012.57,0.0
-6686,15809872.0,Chia,741.0,Germany,Male,43.0,0.0,133550.77,1.0,1.0,0.0,133086.45,1.0
-6687,15762762.0,Ch'iu,580.0,France,Male,42.0,7.0,161801.47,1.0,0.0,0.0,97627.25,0.0
-6688,15777573.0,Tuan,557.0,Germany,Female,42.0,6.0,105599.67,1.0,1.0,1.0,163799.52,1.0
-6689,15657565.0,Iadanza,577.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,136537.45,0.0
-6690,15796074.0,Chibugo,558.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,111184.67,0.0
-6691,15602851.0,Hsing,634.0,France,Female,33.0,1.0,0.0,1.0,1.0,0.0,175262.49,1.0
-6692,15761018.0,Hsieh,681.0,France,Female,34.0,6.0,0.0,2.0,0.0,1.0,93524.22,0.0
-6693,15789097.0,Ndubueze,729.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,18921.82,0.0
-6694,15806327.0,Hsueh,721.0,Spain,Female,39.0,0.0,133501.33,2.0,1.0,0.0,120115.5,0.0
-6695,15770101.0,Cattaneo,755.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,181534.04,0.0
-6696,15636407.0,Chidiebere,656.0,Germany,Male,36.0,3.0,113311.11,1.0,1.0,1.0,81259.25,0.0
-6697,15691875.0,White,597.0,Spain,Female,28.0,4.0,0.0,1.0,0.0,1.0,122221.71,0.0
-6698,15799452.0,Ch'ang,850.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,69080.46,0.0
-6699,15642619.0,Yermakov,753.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,82782.08,0.0
-6700,15641411.0,Scott,586.0,France,Male,31.0,10.0,84421.62,1.0,1.0,0.0,132973.21,0.0
-6701,15792360.0,Chukwujekwu,592.0,France,Female,36.0,1.0,0.0,2.0,1.0,1.0,63531.19,0.0
-6702,15798532.0,Chiemenam,682.0,France,Female,32.0,6.0,0.0,2.0,1.0,0.0,126434.61,0.0
-6703,15788676.0,L?,710.0,Germany,Male,40.0,7.0,85845.43,2.0,0.0,1.0,136859.55,0.0
-6704,15594762.0,Ch'iu,684.0,France,Male,48.0,0.0,0.0,1.0,1.0,0.0,138564.46,1.0
-6705,15700460.0,Ts'ui,704.0,France,Female,46.0,2.0,0.0,2.0,0.0,0.0,180598.32,0.0
-6706,15799720.0,Hs?eh,648.0,Spain,Male,25.0,7.0,0.0,2.0,1.0,0.0,82547.01,0.0
-6707,15702030.0,Tien,599.0,Spain,Female,35.0,8.0,0.0,2.0,1.0,1.0,86935.73,0.0
-6708,15586029.0,Hsia,546.0,Germany,Male,39.0,9.0,131769.04,1.0,1.0,1.0,131301.74,0.0
-6709,15573741.0,Okwudiliolisa,648.0,Germany,Male,42.0,6.0,109728.08,1.0,1.0,1.0,177613.19,1.0
-6710,15671534.0,Clements,605.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,167984.72,0.0
-6711,15705765.0,Chidiegwu,553.0,Spain,Male,57.0,9.0,0.0,1.0,1.0,0.0,51705.01,0.0
-6712,15759067.0,Fitts,694.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,1.0,28367.19,0.0
-6713,15672397.0,Lettiere,678.0,Germany,Female,55.0,1.0,101960.74,1.0,1.0,0.0,153356.8,1.0
-6714,15676908.0,L?,671.0,France,Male,30.0,2.0,0.0,2.0,0.0,1.0,142911.99,0.0
-6715,15761720.0,Chibueze,528.0,France,Male,33.0,8.0,0.0,1.0,1.0,1.0,49778.79,0.0
-6716,15699598.0,Manna,579.0,Germany,Male,38.0,5.0,93577.3,2.0,1.0,0.0,142906.75,0.0
-6717,15603328.0,Kao,676.0,Germany,Male,41.0,5.0,132406.86,1.0,1.0,0.0,25571.37,0.0
-6718,15743545.0,Ch'iu,687.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,143585.29,0.0
-6719,15584776.0,Hsiung,679.0,Germany,Male,51.0,2.0,137781.65,1.0,1.0,1.0,10908.33,1.0
-6720,15780984.0,Fetherstonhaugh,664.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,116978.19,0.0
-6721,15656426.0,Chukwueloka,691.0,France,Male,35.0,4.0,0.0,1.0,0.0,1.0,187826.11,1.0
-6722,15658852.0,Nnachetam,710.0,France,Female,48.0,4.0,0.0,2.0,1.0,0.0,140176.47,1.0
-6723,15712596.0,Rizzo,584.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,54290.62,0.0
-6724,15640056.0,Hs?,682.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,126494.82,0.0
-6725,15762803.0,Yeh,850.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,0.0,116973.48,0.0
-6726,15635244.0,Oguejiofor,554.0,Germany,Female,47.0,4.0,137794.18,1.0,1.0,0.0,88391.97,1.0
-6727,15810839.0,Lo,667.0,Spain,Female,42.0,7.0,0.0,2.0,1.0,0.0,170621.43,0.0
-6728,15805764.0,Onio,569.0,France,Female,28.0,2.0,0.0,1.0,0.0,0.0,146723.72,0.0
-6729,15588854.0,Cremonesi,719.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,147521.35,0.0
-6730,15589230.0,Mironov,697.0,France,Male,34.0,4.0,0.0,1.0,1.0,1.0,52319.26,0.0
-6731,15694946.0,Kao,756.0,Germany,Female,34.0,10.0,111098.85,1.0,1.0,0.0,60905.51,0.0
-6732,15746664.0,Chukwuraenye,850.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,85746.52,1.0
-6733,15684908.0,Nnanna,611.0,Germany,Male,33.0,4.0,88915.85,2.0,1.0,0.0,168895.06,0.0
-6734,15642099.0,H?,663.0,France,Male,23.0,4.0,0.0,2.0,1.0,1.0,176570.28,0.0
-6735,15609623.0,Nwankwo,629.0,Spain,Female,35.0,4.0,0.0,1.0,1.0,1.0,1485.27,1.0
-6736,15764293.0,Chiu,786.0,France,Female,40.0,8.0,0.0,2.0,0.0,1.0,7921.36,0.0
-6737,15805955.0,Martin,667.0,Germany,Male,50.0,4.0,96471.3,1.0,1.0,0.0,51143.43,1.0
-6738,15598884.0,Onyemauchechukwu,597.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,121402.53,1.0
-6739,15689633.0,Yefimov,648.0,France,Male,39.0,3.0,122029.15,1.0,0.0,1.0,141998.89,0.0
-6740,15585823.0,Chuang,592.0,France,Male,23.0,4.0,155059.1,1.0,0.0,1.0,123866.47,0.0
-6741,15677184.0,T'ang,659.0,France,Female,38.0,5.0,0.0,1.0,1.0,0.0,79919.13,0.0
-6742,15643523.0,Uchenna,850.0,France,Male,40.0,9.0,154007.21,1.0,0.0,1.0,94739.3,0.0
-6743,15643826.0,Ch'ien,674.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,138526.96,0.0
-6744,15770225.0,Onyekachukwu,710.0,France,Male,34.0,0.0,0.0,1.0,1.0,1.0,92727.43,0.0
-6745,15618363.0,K?,850.0,Spain,Male,61.0,7.0,0.0,2.0,1.0,1.0,86786.42,0.0
-6746,15777826.0,Teng,661.0,France,Male,25.0,1.0,0.0,2.0,1.0,1.0,94949.84,0.0
-6747,15800961.0,Chukwudi,826.0,Germany,Female,36.0,5.0,83739.32,1.0,1.0,0.0,163550.58,0.0
-6748,15756071.0,Ahern,624.0,Germany,Male,40.0,3.0,116220.5,1.0,1.0,0.0,152884.85,0.0
-6749,15809847.0,Chia,671.0,France,Female,48.0,4.0,0.0,2.0,1.0,0.0,62944.75,0.0
-6750,15740164.0,Pirozzi,590.0,France,Female,36.0,7.0,154737.88,1.0,1.0,0.0,152733.89,0.0
-6751,15773973.0,Crawford,697.0,Germany,Female,47.0,4.0,145081.73,1.0,1.0,1.0,116411.6,1.0
-6752,15696744.0,Chidubem,732.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,108732.96,0.0
-6753,15610900.0,Pinto,567.0,France,Female,45.0,5.0,0.0,4.0,1.0,0.0,101371.05,1.0
-6754,15573318.0,Okechukwu,597.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,131301.74,0.0
-6755,15772573.0,Fanucci,850.0,France,Male,63.0,3.0,91547.84,2.0,1.0,0.0,176750.81,1.0
-6756,15614893.0,Efremov,682.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,75832.53,0.0
-6757,15643824.0,T'ang,591.0,France,Female,47.0,1.0,146780.52,1.0,1.0,1.0,122925.5,0.0
-6758,15682868.0,Okoli,671.0,Spain,Female,36.0,8.0,0.0,2.0,1.0,1.0,150659.42,0.0
-6759,15788151.0,Mazzi,764.0,Germany,Female,40.0,7.0,80764.03,1.0,0.0,1.0,89891.4,1.0
-6760,15760456.0,Chikwendu,743.0,France,Female,49.0,6.0,0.0,2.0,1.0,0.0,184843.77,1.0
-6761,15717138.0,Ch'iu,850.0,France,Female,56.0,4.0,136622.55,1.0,1.0,0.0,93524.22,1.0
-6762,15772383.0,Hsia,498.0,Spain,Male,43.0,8.0,115265.21,1.0,1.0,1.0,162889.1,0.0
-6763,15766166.0,Pisano,686.0,Germany,Male,45.0,3.0,113938.27,1.0,1.0,0.0,51345.41,1.0
-6764,15735246.0,Chiang,682.0,France,Male,60.0,1.0,0.0,3.0,0.0,1.0,162889.1,1.0
-6765,15743628.0,T'an,628.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,1.0,159475.08,0.0
-6766,15675450.0,Chukwufumnanya,773.0,France,Female,50.0,3.0,0.0,1.0,1.0,1.0,86937.2,0.0
-6767,15726490.0,Yegorova,718.0,France,Female,35.0,10.0,0.0,1.0,1.0,0.0,171515.84,1.0
-6768,15757577.0,Baresi,739.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,0.0,121482.2,0.0
-6769,15809143.0,Chukwudi,684.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,0.0,88651.67,0.0
-6770,15635143.0,Ch'iu,683.0,France,Male,33.0,10.0,0.0,1.0,0.0,1.0,112366.98,0.0
-6771,15687558.0,Ts'ai,658.0,Germany,Male,36.0,4.0,157960.38,2.0,1.0,1.0,174098.28,0.0
-6772,15611579.0,Ch'ang,752.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,161849.15,0.0
-6773,15808187.0,Ritchie,679.0,Spain,Male,29.0,7.0,0.0,2.0,1.0,0.0,183868.21,0.0
-6774,15676937.0,T'ien,638.0,France,Female,33.0,5.0,0.0,1.0,1.0,0.0,169963.2,1.0
-6775,15702298.0,Hs?,648.0,France,Male,43.0,6.0,145081.73,1.0,0.0,0.0,159891.03,0.0
-6776,15683522.0,T'ang,706.0,France,Male,32.0,9.0,0.0,1.0,0.0,0.0,94815.04,1.0
-6777,15593365.0,Genovese,682.0,Spain,Female,30.0,3.0,0.0,2.0,0.0,0.0,1187.88,0.0
-6778,15790810.0,Eluemuno,657.0,Spain,Female,45.0,4.0,0.0,2.0,0.0,0.0,180598.86,0.0
-6779,15638774.0,Folliero,557.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,158344.63,0.0
-6780,15667046.0,Hightower,651.0,Spain,Male,43.0,9.0,146498.76,1.0,0.0,0.0,66526.01,0.0
-6781,15771543.0,Hsia,622.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,88721.84,0.0
-6782,15572626.0,Ewing,850.0,France,Male,35.0,10.0,0.0,2.0,1.0,0.0,152843.53,0.0
-6783,15783522.0,Dickson,682.0,Germany,Male,36.0,4.0,110112.54,2.0,0.0,1.0,117034.32,0.0
-6784,15595874.0,Kistler,711.0,Spain,Female,32.0,9.0,129378.43,1.0,1.0,0.0,172524.51,0.0
-6785,15615140.0,Hao,623.0,France,Female,49.0,5.0,0.0,1.0,1.0,1.0,121522.73,1.0
-6786,15689341.0,Tsao,706.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,163830.64,0.0
-6787,15768219.0,Ndubueze,535.0,Germany,Female,44.0,4.0,123428.69,1.0,1.0,0.0,131301.74,1.0
-6788,15641585.0,K'ung,554.0,Germany,Male,23.0,2.0,96341.0,2.0,0.0,1.0,122268.75,0.0
-6789,15727299.0,Ugoji,583.0,France,Male,26.0,5.0,0.0,2.0,1.0,0.0,49826.68,0.0
-6790,15790846.0,Chien,653.0,France,Female,45.0,5.0,0.0,1.0,1.0,1.0,24506.95,1.0
-6791,15737647.0,Ugoji,741.0,Germany,Male,30.0,4.0,131365.37,1.0,1.0,1.0,121894.31,0.0
-6792,15751137.0,Cremonesi,719.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,152479.19,0.0
-6793,15597913.0,Chin,724.0,Germany,Female,35.0,2.0,82498.14,2.0,1.0,0.0,176868.89,0.0
-6794,15575676.0,Ndukaku,724.0,Germany,Female,43.0,1.0,104592.89,2.0,1.0,1.0,82774.06,1.0
-6795,15746451.0,Nwabugwu,732.0,France,Female,26.0,9.0,0.0,2.0,0.0,1.0,96985.58,0.0
-6796,15709928.0,T'ien,714.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,123977.86,0.0
-6797,15702430.0,Nworie,695.0,France,Male,20.0,5.0,0.0,2.0,1.0,0.0,137565.57,0.0
-6798,15603914.0,Onuoha,790.0,France,Female,36.0,9.0,131240.24,1.0,1.0,1.0,89099.18,0.0
-6799,15593501.0,Teng,627.0,France,Male,39.0,10.0,0.0,2.0,1.0,1.0,185580.89,0.0
-6800,15734886.0,Chuang,652.0,Spain,Female,30.0,6.0,0.0,1.0,1.0,0.0,1710.89,1.0
-6801,15721383.0,Kudryashova,684.0,France,Male,29.0,9.0,97560.16,2.0,0.0,1.0,122268.75,0.0
-6802,15648032.0,Tuan,707.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,94820.85,0.0
-6803,15769346.0,Dickson,652.0,France,Male,43.0,9.0,161254.78,1.0,1.0,0.0,137537.22,0.0
-6804,15789924.0,Okwudilichukwu,733.0,France,Male,26.0,4.0,0.0,2.0,1.0,1.0,111590.35,0.0
-6805,15644857.0,Chimezie,657.0,France,Male,52.0,3.0,0.0,3.0,1.0,1.0,103560.98,1.0
-6806,15783541.0,Tien,767.0,France,Female,30.0,6.0,0.0,1.0,1.0,0.0,162734.31,0.0
-6807,15649414.0,Chukwukere,652.0,Germany,Male,33.0,2.0,93821.95,2.0,1.0,0.0,102298.79,0.0
-6808,15757911.0,Genovesi,661.0,France,Male,43.0,5.0,0.0,2.0,0.0,0.0,141364.62,0.0
-6809,15744240.0,Folliero,516.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,55893.37,0.0
-6810,15732903.0,Yobachi,710.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,108699.76,0.0
-6811,15665963.0,Ch'en,682.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,168886.39,0.0
-6812,15692226.0,Manna,693.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,122937.57,0.0
-6813,15701265.0,Tsou,656.0,France,Male,54.0,6.0,104857.19,1.0,1.0,1.0,51387.1,1.0
-6814,15605020.0,Y?,663.0,France,Male,29.0,8.0,0.0,1.0,1.0,1.0,183318.79,0.0
-6815,15758023.0,Cremonesi,684.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,130789.6,0.0
-6816,15628292.0,Tseng,685.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,90878.13,1.0
-6817,15809800.0,Li Fonti,710.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,141896.74,0.0
-6818,15743817.0,Ch'ien,668.0,France,Female,33.0,4.0,125973.17,2.0,1.0,0.0,50974.57,0.0
-6819,15597914.0,Ijendu,710.0,France,Male,28.0,5.0,0.0,2.0,1.0,0.0,123882.73,0.0
-6820,15724076.0,Chukwukadibia,669.0,Germany,Female,34.0,1.0,130024.87,1.0,1.0,0.0,123427.98,1.0
-6821,15792668.0,Milne,694.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,131167.98,0.0
-6822,15792649.0,Dike,679.0,France,Male,30.0,8.0,0.0,2.0,0.0,0.0,105720.09,0.0
-6823,15694319.0,Hsing,477.0,France,Female,37.0,4.0,0.0,1.0,1.0,1.0,184936.77,0.0
-6824,15792250.0,Tsou,738.0,France,Female,48.0,3.0,0.0,2.0,1.0,1.0,173948.4,0.0
-6825,15576723.0,Ritchie,712.0,France,Female,32.0,10.0,0.0,2.0,1.0,1.0,119882.7,0.0
-6826,15773071.0,O'Donnell,773.0,Germany,Male,24.0,2.0,131142.57,1.0,1.0,1.0,156791.37,0.0
-6827,15780256.0,Erskine,649.0,Germany,Male,35.0,9.0,125584.17,1.0,1.0,1.0,89935.73,0.0
-6828,15586959.0,Chiu,645.0,France,Male,36.0,7.0,0.0,1.0,1.0,0.0,68730.91,0.0
-6829,15580148.0,Udinesi,744.0,Germany,Male,30.0,6.0,135847.07,1.0,1.0,0.0,54439.53,1.0
-6830,15641604.0,Chukwujekwu,565.0,Spain,Male,61.0,7.0,0.0,2.0,1.0,1.0,66721.98,0.0
-6831,15735402.0,She,643.0,Germany,Female,42.0,6.0,125842.6,1.0,1.0,0.0,102517.83,0.0
-6832,15785831.0,Ch'in,732.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,51364.56,0.0
-6833,15650288.0,Chukwufumnanya,678.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,116747.63,0.0
-6834,15633866.0,Ugoji,706.0,France,Male,34.0,1.0,0.0,2.0,0.0,0.0,82796.29,0.0
-6835,15694628.0,Chijioke,714.0,France,Female,25.0,3.0,0.0,1.0,1.0,1.0,180150.49,0.0
-6836,15682463.0,White,732.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,0.0,72990.62,0.0
-6837,15780056.0,Paling,754.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,126743.33,0.0
-6838,15644962.0,Babbage,781.0,Germany,Male,32.0,6.0,96956.1,2.0,1.0,1.0,91957.39,0.0
-6839,15785547.0,Ch'iu,577.0,France,Male,31.0,8.0,0.0,1.0,1.0,0.0,93777.91,0.0
-6840,15796074.0,Lai,627.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,0.0,158835.49,0.0
-6841,15806049.0,Ch'iu,738.0,Germany,Female,48.0,9.0,129934.64,1.0,1.0,0.0,92624.62,1.0
-6842,15714241.0,Ch'ien,749.0,Spain,Female,58.0,7.0,88109.81,1.0,1.0,0.0,60905.51,1.0
-6843,15672754.0,Maclean,607.0,Germany,Female,33.0,6.0,118889.66,1.0,1.0,0.0,64345.61,0.0
-6844,15663410.0,Pisano,624.0,France,Male,42.0,9.0,0.0,1.0,1.0,1.0,141558.04,0.0
-6845,15667871.0,Hsia,687.0,Spain,Male,22.0,1.0,0.0,1.0,1.0,1.0,94888.6,0.0
-6846,15777907.0,Scott,651.0,France,Male,41.0,5.0,0.0,2.0,0.0,0.0,163365.4,0.0
-6847,15588923.0,Tsao,683.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,94977.3,0.0
-6848,15691823.0,Smith,739.0,France,Female,29.0,3.0,172463.45,1.0,1.0,1.0,73977.23,0.0
-6849,15679297.0,Ch'ien,567.0,Germany,Male,32.0,2.0,141349.43,1.0,0.0,0.0,71557.12,1.0
-6850,15672091.0,Chukwujamuike,649.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,160532.88,0.0
-6851,15686983.0,Mazzanti,590.0,Spain,Female,23.0,4.0,0.0,2.0,1.0,0.0,166587.9,0.0
-6852,15572038.0,Nebechukwu,597.0,Spain,Male,38.0,7.0,0.0,2.0,0.0,1.0,59115.77,0.0
-6853,15592925.0,Hsieh,677.0,Spain,Female,31.0,1.0,0.0,2.0,1.0,0.0,129892.93,0.0
-6854,15675185.0,Nwachinemelu,544.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,176680.16,1.0
-6855,15691813.0,Chukwufumnanya,649.0,France,Male,42.0,4.0,95858.37,1.0,1.0,1.0,149772.61,0.0
-6856,15735263.0,Yobachukwu,681.0,France,Female,37.0,6.0,0.0,2.0,0.0,1.0,73882.68,0.0
-6857,15789563.0,Chukwudi,636.0,France,Female,34.0,3.0,117052.82,1.0,0.0,0.0,114448.77,0.0
-6858,15713559.0,Tuan,685.0,France,Female,38.0,4.0,0.0,2.0,0.0,0.0,116046.22,0.0
-6859,15695872.0,Milani,712.0,France,Female,29.0,8.0,0.0,2.0,1.0,1.0,78744.39,0.0
-6860,15801152.0,Walker,678.0,France,Male,40.0,2.0,0.0,2.0,0.0,0.0,50400.32,0.0
-6861,15799294.0,Sagese,583.0,Germany,Male,44.0,1.0,72535.45,1.0,1.0,1.0,55893.37,0.0
-6862,15644962.0,Clements,683.0,France,Male,39.0,3.0,0.0,1.0,1.0,0.0,188533.11,0.0
-6863,15691823.0,Chiang,587.0,France,Male,29.0,3.0,0.0,1.0,1.0,0.0,152491.22,0.0
-6864,15702464.0,Iweobiegbunam,543.0,France,Female,34.0,9.0,0.0,1.0,1.0,1.0,158973.59,0.0
-6865,15749807.0,Ewers,709.0,Spain,Male,34.0,3.0,122325.56,1.0,1.0,0.0,86566.79,0.0
-6866,15588923.0,Tretyakova,614.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,55470.78,0.0
-6867,15642833.0,Cattaneo,850.0,France,Female,44.0,5.0,0.0,2.0,1.0,0.0,93777.15,0.0
-6868,15740147.0,Chukwumaobim,732.0,Spain,Female,37.0,3.0,123356.63,1.0,1.0,1.0,176720.28,0.0
-6869,15574743.0,Ch'en,731.0,France,Male,33.0,2.0,109339.17,1.0,0.0,1.0,51637.57,0.0
-6870,15566139.0,Chikere,659.0,France,Male,59.0,1.0,123943.18,1.0,1.0,1.0,135034.43,0.0
-6871,15634373.0,Fanucci,594.0,Spain,Female,28.0,9.0,0.0,2.0,0.0,1.0,86927.53,0.0
-6872,15631310.0,Pirozzi,714.0,Spain,Male,40.0,1.0,0.0,1.0,1.0,0.0,166310.82,0.0
-6873,15609510.0,Nwachukwu,683.0,France,Male,22.0,6.0,0.0,2.0,1.0,1.0,70760.69,0.0
-6874,15723884.0,Fanucci,605.0,Spain,Male,41.0,2.0,0.0,2.0,1.0,0.0,66893.28,0.0
-6875,15790314.0,Hs?eh,603.0,Germany,Male,31.0,2.0,130142.79,2.0,0.0,1.0,134589.34,0.0
-6876,15614365.0,Chu,711.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,119333.95,0.0
-6877,15682421.0,Udegbulam,673.0,Spain,Male,33.0,1.0,118046.88,2.0,0.0,0.0,158043.11,0.0
-6878,15705521.0,Fanucci,644.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,141756.32,0.0
-6879,15603743.0,Chinwemma,774.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,77837.63,0.0
-6880,15689955.0,Chidozie,569.0,France,Male,29.0,4.0,109799.55,2.0,1.0,1.0,127059.02,0.0
-6881,15768219.0,Chiazagomekpere,691.0,France,Female,41.0,10.0,0.0,1.0,0.0,0.0,120880.72,1.0
-6882,15593365.0,Chia,712.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,152843.53,0.0
-6883,15790750.0,Hsueh,749.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,0.0,156954.48,0.0
-6884,15659475.0,Rowntree,749.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,176576.71,0.0
-6885,15656683.0,Ch'iu,754.0,Spain,Female,32.0,10.0,0.0,2.0,1.0,0.0,53817.23,0.0
-6886,15593601.0,Nwankwo,683.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,138399.67,0.0
-6887,15635244.0,Fan,439.0,Germany,Female,28.0,7.0,119074.28,2.0,0.0,1.0,165576.55,0.0
-6888,15806880.0,T'ao,709.0,Germany,Male,50.0,8.0,104495.79,2.0,1.0,1.0,90212.38,0.0
-6889,15656426.0,Y?,549.0,France,Female,52.0,5.0,89364.91,1.0,0.0,1.0,153662.88,1.0
-6890,15627840.0,Yashina,615.0,France,Male,33.0,2.0,131762.94,1.0,1.0,0.0,67734.94,0.0
-6891,15604576.0,O'Donnell,732.0,France,Male,34.0,2.0,0.0,2.0,0.0,0.0,119647.6,0.0
-6892,15765520.0,Hs?,717.0,France,Male,39.0,10.0,0.0,2.0,0.0,0.0,180622.02,0.0
-6893,15783030.0,Chidubem,730.0,France,Male,39.0,7.0,91057.37,1.0,1.0,1.0,114793.45,0.0
-6894,15571940.0,Trevisani,707.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,56793.02,0.0
-6895,15651802.0,Trevisano,613.0,France,Male,34.0,5.0,180520.29,1.0,0.0,1.0,124954.56,0.0
-6896,15671779.0,Loyau,531.0,France,Male,54.0,1.0,0.0,2.0,1.0,0.0,130234.63,1.0
-6897,15685920.0,Hsieh,596.0,France,Female,32.0,9.0,0.0,2.0,0.0,0.0,62370.67,0.0
-6898,15672091.0,Hsia,809.0,France,Male,34.0,2.0,0.0,1.0,1.0,1.0,116973.48,0.0
-6899,15572467.0,T'ao,668.0,Germany,Female,38.0,5.0,106977.04,2.0,1.0,0.0,177670.57,0.0
-6900,15656592.0,Shih,692.0,France,Female,34.0,9.0,0.0,2.0,0.0,1.0,103799.76,0.0
-6901,15751445.0,Ch'eng,667.0,Germany,Male,37.0,9.0,129845.43,2.0,1.0,0.0,52319.26,0.0
-6902,15764661.0,Mazzanti,698.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,87067.73,0.0
-6903,15802478.0,Kornilova,516.0,France,Male,33.0,3.0,0.0,1.0,1.0,1.0,57589.29,0.0
-6904,15730353.0,Clements,598.0,France,Female,33.0,2.0,65848.36,1.0,1.0,0.0,38019.74,0.0
-6905,15751591.0,Dyer,687.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,93783.0,0.0
-6906,15659820.0,Macleod,646.0,Germany,Female,45.0,7.0,128306.7,1.0,0.0,0.0,171045.46,1.0
-6907,15704583.0,Shao,598.0,France,Female,33.0,9.0,0.0,2.0,1.0,1.0,86920.97,0.0
-6908,15755863.0,Smith,649.0,Germany,Female,40.0,8.0,127637.92,1.0,1.0,0.0,92794.61,1.0
-6909,15741032.0,Hsueh,622.0,Spain,Female,46.0,4.0,0.0,2.0,0.0,0.0,110525.6,0.0
-6910,15644878.0,She,725.0,France,Female,40.0,2.0,0.0,1.0,1.0,0.0,131736.43,0.0
-6911,15656840.0,Lombardo,598.0,Germany,Female,37.0,6.0,114446.84,1.0,1.0,0.0,8059.44,1.0
-6912,15589924.0,Ritchie,582.0,France,Female,25.0,4.0,0.0,2.0,1.0,0.0,70563.9,0.0
-6913,15791045.0,Kao,535.0,France,Female,38.0,4.0,0.0,1.0,1.0,0.0,164083.72,1.0
-6914,15754574.0,Ch'ien,773.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,97465.44,0.0
-6915,15569807.0,Brown,691.0,Germany,Male,34.0,8.0,127157.04,2.0,1.0,0.0,105715.01,0.0
-6916,15763544.0,Fanucci,632.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,91711.66,0.0
-6917,15672357.0,Onyemauchechukwu,779.0,Germany,Male,30.0,3.0,108862.76,1.0,1.0,1.0,102628.98,0.0
-6918,15669922.0,Maclean,764.0,Germany,Female,39.0,8.0,131283.11,1.0,1.0,1.0,57862.88,0.0
-6919,15794874.0,Tretiakova,583.0,France,Female,48.0,7.0,0.0,2.0,1.0,1.0,141706.43,0.0
-6920,15789924.0,Onyemachukwu,634.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,149892.79,0.0
-6921,15633194.0,Aksenov,653.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,106781.59,0.0
-6922,15785815.0,Greco,705.0,Germany,Female,38.0,7.0,110837.73,1.0,1.0,0.0,135851.3,0.0
-6923,15614491.0,Genovese,634.0,Spain,Female,27.0,5.0,149950.19,1.0,1.0,0.0,57553.98,1.0
-6924,15642937.0,Ch'in,685.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,87844.89,0.0
-6925,15636069.0,Ngozichukwuka,594.0,France,Female,41.0,6.0,0.0,2.0,1.0,1.0,179436.6,0.0
-6926,15681928.0,Cattaneo,697.0,France,Female,35.0,9.0,177624.59,1.0,1.0,0.0,173498.45,1.0
-6927,15791867.0,Shih,778.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,172667.39,0.0
-6928,15793042.0,Nkemjika,687.0,France,Male,47.0,10.0,0.0,2.0,1.0,1.0,186826.22,0.0
-6929,15682355.0,Chuang,642.0,Spain,Female,28.0,3.0,0.0,2.0,1.0,1.0,176743.51,0.0
-6930,15775888.0,H?,685.0,Germany,Female,30.0,8.0,145127.85,2.0,1.0,0.0,187985.85,0.0
-6931,15692593.0,Hsieh,700.0,France,Female,22.0,8.0,0.0,3.0,1.0,1.0,171021.44,1.0
-6932,15729579.0,Ugochukwutubelum,765.0,France,Female,63.0,5.0,139542.41,1.0,1.0,1.0,35671.45,0.0
-6933,15649523.0,Tien,686.0,France,Male,34.0,6.0,0.0,2.0,0.0,0.0,139065.94,0.0
-6934,15772632.0,Ugochukwu,655.0,France,Male,34.0,5.0,0.0,2.0,0.0,0.0,142223.94,0.0
-6935,15786539.0,T'ao,538.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,185561.89,0.0
-6936,15665088.0,Macleod,577.0,Germany,Male,41.0,2.0,98894.39,2.0,1.0,0.0,83022.92,0.0
-6937,15671249.0,Ch'ang,677.0,Germany,Male,50.0,9.0,128940.24,1.0,1.0,0.0,52876.41,1.0
-6938,15776096.0,Chiang,639.0,Spain,Male,32.0,9.0,98439.37,1.0,1.0,0.0,162599.51,0.0
-6939,15782899.0,Chukwuemeka,725.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,173576.71,0.0
-6940,15776433.0,Nickson,549.0,France,Male,28.0,9.0,120962.74,1.0,1.0,0.0,100862.36,0.0
-6941,15726796.0,Ahern,735.0,France,Female,37.0,6.0,131778.08,1.0,1.0,1.0,100722.72,0.0
-6942,15646726.0,Chibueze,681.0,France,Female,31.0,6.0,0.0,2.0,1.0,1.0,167984.72,0.0
-6943,15678882.0,P'eng,769.0,France,Male,38.0,1.0,0.0,1.0,1.0,0.0,83912.24,0.0
-6944,15680683.0,Chinweuba,687.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,1.0,94875.03,0.0
-6945,15597978.0,Kao,578.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,58815.25,0.0
-6946,15655952.0,Hao,636.0,Germany,Male,19.0,9.0,97786.08,2.0,1.0,0.0,142223.94,0.0
-6947,15800811.0,Gadsden,753.0,Spain,Female,45.0,3.0,0.0,1.0,1.0,1.0,162719.69,1.0
-6948,15723989.0,Ts'ui,656.0,France,Male,59.0,4.0,0.0,2.0,1.0,1.0,199357.24,0.0
-6949,15731582.0,Shubin,628.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,180439.75,0.0
-6950,15703793.0,Hsiao,556.0,Germany,Female,46.0,4.0,115285.85,2.0,1.0,0.0,171657.57,0.0
-6951,15763431.0,Lombardo,498.0,Germany,Female,48.0,5.0,161254.78,1.0,1.0,0.0,71178.09,1.0
-6952,15631162.0,Ts'ui,622.0,France,Female,27.0,4.0,0.0,2.0,1.0,0.0,126728.27,0.0
-6953,15673747.0,Nnachetam,644.0,Germany,Female,51.0,8.0,133903.12,1.0,1.0,0.0,177747.03,1.0
-6954,15737051.0,Hsieh,663.0,Germany,Male,40.0,10.0,123783.25,2.0,1.0,0.0,169921.11,0.0
-6955,15790829.0,Biryukov,743.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,151902.78,0.0
-6956,15596060.0,Ch'iu,811.0,Germany,Female,34.0,4.0,131365.37,2.0,1.0,1.0,131521.72,0.0
-6957,15674929.0,Ting,627.0,France,Male,28.0,7.0,0.0,2.0,0.0,0.0,133692.88,0.0
-6958,15761047.0,K?,649.0,France,Female,38.0,8.0,86047.74,1.0,1.0,1.0,138360.29,0.0
-6959,15589660.0,Ritchie,588.0,France,Female,29.0,1.0,0.0,2.0,1.0,1.0,113428.77,0.0
-6960,15583716.0,Shih,691.0,Spain,Female,32.0,6.0,0.0,2.0,0.0,1.0,98820.09,0.0
-6961,15791342.0,Shao,538.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,140937.91,1.0
-6962,15571529.0,Ch'ien,683.0,France,Male,29.0,3.0,0.0,1.0,1.0,0.0,155711.64,0.0
-6963,15582519.0,Toscani,634.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,162889.1,0.0
-6964,15731751.0,Bibi,667.0,Spain,Male,47.0,2.0,111432.77,1.0,1.0,0.0,132959.33,0.0
-6965,15744044.0,Ch'eng,741.0,Spain,Male,30.0,10.0,0.0,2.0,1.0,0.0,178074.04,0.0
-6966,15651022.0,Brown,811.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,170142.09,0.0
-6967,15752508.0,Hs?eh,807.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,180598.66,0.0
-6968,15651369.0,Y?,695.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,1.0,44904.26,0.0
-6969,15695792.0,Ch'en,710.0,France,Male,31.0,3.0,132405.52,1.0,1.0,1.0,86490.09,0.0
-6970,15653857.0,Cremonesi,773.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,91574.92,0.0
-6971,15601012.0,Ch'ien,691.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,82938.99,0.0
-6972,15711889.0,Ch'ang,684.0,France,Male,38.0,5.0,0.0,1.0,1.0,0.0,87938.85,0.0
-6973,15592846.0,Ch'en,752.0,France,Male,31.0,7.0,0.0,1.0,1.0,1.0,188516.45,0.0
-6974,15654891.0,Chin,605.0,Germany,Male,31.0,7.0,125552.96,1.0,1.0,0.0,151481.86,0.0
-6975,15611171.0,Tsao,639.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,97640.2,0.0
-6976,15691808.0,Fanucci,526.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,67840.81,0.0
-6977,15793693.0,Echezonachukwu,730.0,France,Male,31.0,4.0,96173.17,1.0,1.0,0.0,129525.5,0.0
-6978,15575694.0,Nkemakolam,710.0,Germany,Female,41.0,1.0,92887.06,2.0,1.0,1.0,172576.47,0.0
-6979,15802381.0,Sun,570.0,France,Male,27.0,8.0,0.0,2.0,1.0,1.0,48545.1,0.0
-6980,15656300.0,Ch'iu,726.0,Spain,Female,24.0,9.0,128743.55,1.0,1.0,0.0,167873.49,0.0
-6981,15727299.0,Chien,691.0,Germany,Male,38.0,1.0,71097.23,1.0,0.0,0.0,97624.02,0.0
-6982,15772798.0,Chijindum,682.0,France,Female,52.0,7.0,0.0,1.0,1.0,1.0,82820.85,1.0
-6983,15739046.0,Tien,572.0,France,Male,28.0,7.0,160980.04,1.0,1.0,0.0,82298.61,0.0
-6984,15739476.0,L?,656.0,France,Male,44.0,1.0,0.0,2.0,1.0,1.0,127068.92,0.0
-6985,15769412.0,Lucchese,726.0,France,Male,25.0,5.0,121260.19,1.0,1.0,0.0,168034.9,0.0
-6986,15651627.0,Pagnotto,839.0,France,Male,37.0,3.0,0.0,1.0,0.0,1.0,86920.97,0.0
-6987,15656062.0,Chigolum,642.0,Spain,Male,52.0,4.0,0.0,2.0,1.0,1.0,157567.37,0.0
-6988,15733229.0,Folliero,809.0,France,Male,20.0,8.0,0.0,1.0,1.0,1.0,100557.03,0.0
-6989,15659194.0,Scott,694.0,France,Male,23.0,9.0,0.0,2.0,1.0,1.0,125823.79,0.0
-6990,15579526.0,Folliero,705.0,France,Female,39.0,5.0,0.0,3.0,0.0,1.0,89801.9,1.0
-6991,15572467.0,Chiang,681.0,Germany,Male,43.0,6.0,118336.95,1.0,0.0,0.0,162858.24,1.0
-6992,15680224.0,Scott,708.0,France,Female,24.0,8.0,0.0,2.0,1.0,1.0,177519.39,0.0
-6993,15736578.0,Scott,595.0,France,Male,34.0,8.0,0.0,1.0,0.0,0.0,79329.7,0.0
-6994,15716608.0,Tsai,562.0,Germany,Male,38.0,1.0,116880.53,1.0,0.0,1.0,71047.51,0.0
-6995,15592222.0,Tien,773.0,Germany,Female,39.0,9.0,106603.74,1.0,0.0,1.0,122449.18,0.0
-6996,15656627.0,Maclean,678.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,143783.28,0.0
-6997,15588535.0,Ch'in,637.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,143614.01,0.0
-6998,15771742.0,Tseng,741.0,Spain,Female,41.0,10.0,0.0,1.0,1.0,0.0,152567.75,0.0
-6999,15770185.0,Frolov,674.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,0.0,86981.45,0.0
-7000,15646539.0,L?,749.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,46857.52,0.0
-7001,15646566.0,Pagnotto,599.0,France,Male,23.0,7.0,0.0,2.0,1.0,1.0,121840.2,0.0
-7002,15759499.0,Okwudiliolisa,554.0,Germany,Male,34.0,6.0,116936.81,1.0,1.0,0.0,72846.58,1.0
-7003,15594786.0,Liao,646.0,Germany,Female,40.0,4.0,108914.17,1.0,1.0,0.0,142782.25,1.0
-7004,15776743.0,Hs?,732.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,106655.88,0.0
-7005,15656613.0,Toscani,616.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,57929.8,0.0
-7006,15771543.0,Ch'ang,653.0,France,Female,34.0,4.0,0.0,2.0,0.0,0.0,153727.32,0.0
-7007,15598044.0,Oluchukwu,739.0,France,Male,31.0,3.0,0.0,1.0,1.0,1.0,100137.7,0.0
-7008,15700460.0,Trevisani,686.0,Spain,Female,54.0,5.0,0.0,1.0,1.0,0.0,172490.78,1.0
-7009,15790744.0,Hsieh,775.0,Germany,Female,37.0,10.0,99462.8,2.0,1.0,0.0,74580.8,0.0
-7010,15749167.0,Smith,741.0,Germany,Female,46.0,10.0,71842.91,3.0,1.0,0.0,61538.43,1.0
-7011,15771535.0,Lo,699.0,Spain,Male,39.0,2.0,118680.57,1.0,1.0,1.0,153535.38,0.0
-7012,15790689.0,Folliero,645.0,Germany,Male,35.0,8.0,153313.77,2.0,1.0,0.0,164741.5,0.0
-7013,15670080.0,Chien,751.0,France,Male,25.0,7.0,0.0,2.0,1.0,1.0,60906.12,0.0
-7014,15701054.0,Nieves,740.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,68600.36,0.0
-7015,15659366.0,O'Donnell,653.0,France,Male,23.0,8.0,0.0,2.0,1.0,0.0,74048.1,0.0
-7016,15774551.0,Scott,713.0,France,Male,24.0,6.0,183572.84,1.0,1.0,0.0,161217.18,0.0
-7017,15773773.0,Smith,678.0,Spain,Female,33.0,0.0,0.0,2.0,1.0,1.0,151623.91,0.0
-7018,15592078.0,McKay,663.0,France,Female,30.0,9.0,0.0,2.0,0.0,1.0,180551.76,0.0
-7019,15584897.0,Chikwado,732.0,Germany,Female,30.0,2.0,115309.75,2.0,1.0,1.0,26327.42,0.0
-7020,15806922.0,Mazzanti,640.0,France,Female,63.0,7.0,0.0,1.0,1.0,0.0,125962.02,1.0
-7021,15775042.0,Nkemakolam,763.0,Spain,Male,34.0,7.0,0.0,2.0,0.0,0.0,134732.99,0.0
-7022,15596074.0,H?,743.0,France,Female,39.0,3.0,0.0,2.0,1.0,1.0,58467.08,0.0
-7023,15682914.0,Chigolum,827.0,Spain,Male,56.0,6.0,0.0,1.0,1.0,1.0,143022.43,1.0
-7024,15810418.0,Chiazagomekpere,706.0,Spain,Male,62.0,9.0,97445.49,1.0,1.0,1.0,58879.8,0.0
-7025,15633378.0,Chu,634.0,France,Male,34.0,1.0,129728.5,1.0,1.0,1.0,47799.15,0.0
-7026,15662093.0,H?,673.0,France,Male,38.0,1.0,0.0,1.0,0.0,0.0,109895.16,0.0
-7027,15687310.0,Nnaife,653.0,Spain,Female,49.0,9.0,131354.53,1.0,1.0,0.0,123840.68,1.0
-7028,15628600.0,Yip,587.0,Spain,Male,67.0,4.0,0.0,2.0,0.0,1.0,166677.54,0.0
-7029,15714883.0,Kao,639.0,France,Female,38.0,1.0,0.0,1.0,1.0,0.0,86937.2,0.0
-7030,15667932.0,Lombardi,628.0,France,Female,60.0,4.0,0.0,1.0,1.0,1.0,125979.36,1.0
-7031,15591512.0,Nnachetam,573.0,France,Male,30.0,1.0,136526.26,1.0,1.0,0.0,96422.44,0.0
-7032,15723685.0,Udegbulam,595.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,62222.81,0.0
-7033,15783882.0,Obielumani,832.0,France,Male,36.0,8.0,92219.29,1.0,1.0,0.0,6078.46,0.0
-7034,15597901.0,P'eng,685.0,France,Male,33.0,3.0,141581.07,1.0,0.0,1.0,143783.98,0.0
-7035,15681081.0,Toscani,703.0,Germany,Female,37.0,1.0,106920.98,1.0,1.0,1.0,97640.2,0.0
-7036,15702434.0,Kao,593.0,France,Female,37.0,2.0,0.0,2.0,0.0,0.0,64907.48,0.0
-7037,15566543.0,Milani,686.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,138021.36,0.0
-7038,15602551.0,Hsiao,653.0,Spain,Male,31.0,5.0,0.0,2.0,1.0,0.0,136071.05,0.0
-7039,15752956.0,Okeke,592.0,Germany,Female,35.0,9.0,127723.25,1.0,1.0,0.0,140130.22,1.0
-7040,15628420.0,Chukwuemeka,516.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,48673.18,0.0
-7041,15782659.0,Nwankwo,803.0,Germany,Female,42.0,1.0,116546.59,3.0,1.0,0.0,174438.54,1.0
-7042,15644400.0,Efremov,623.0,France,Female,33.0,9.0,0.0,2.0,1.0,1.0,55601.44,0.0
-7043,15664793.0,Tisdall,651.0,France,Female,56.0,8.0,0.0,1.0,0.0,0.0,134417.31,1.0
-7044,15698716.0,Echezonachukwu,707.0,France,Female,46.0,6.0,0.0,1.0,1.0,0.0,121840.2,1.0
-7045,15634373.0,Onyekaozulu,597.0,France,Female,33.0,8.0,0.0,1.0,1.0,0.0,83090.35,1.0
-7046,15794345.0,Ch'in,712.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,124749.08,0.0
-7047,15602034.0,Tuan,614.0,France,Female,39.0,1.0,158718.13,1.0,1.0,1.0,71154.1,0.0
-7048,15709741.0,Aitken,714.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,88958.49,0.0
-7049,15677322.0,Chukwuemeka,706.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,91510.71,0.0
-7050,15684921.0,Cremonesi,717.0,Spain,Female,32.0,7.0,151236.96,2.0,1.0,0.0,152992.04,0.0
-7051,15715394.0,Y?an,742.0,Germany,Male,30.0,3.0,141074.59,1.0,0.0,1.0,146785.44,0.0
-7052,15684296.0,Lu,749.0,Spain,Male,39.0,5.0,131078.47,1.0,0.0,1.0,155870.79,0.0
-7053,15751177.0,Sagese,637.0,France,Female,48.0,5.0,0.0,1.0,1.0,1.0,152298.28,1.0
-7054,15749401.0,Ch'ien,773.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,148098.95,0.0
-7055,15596979.0,Palerma,556.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,166495.2,0.0
-7056,15639272.0,Burdekin,697.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,151822.66,0.0
-7057,15675888.0,Chukwukere,719.0,France,Female,29.0,7.0,68432.45,1.0,0.0,0.0,89048.46,0.0
-7058,15773394.0,O'Loughlin,768.0,Germany,Female,38.0,2.0,125470.81,1.0,0.0,0.0,173870.39,1.0
-7059,15705739.0,Chukwukadibia,565.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,136330.32,0.0
-7060,15802466.0,K?,649.0,Spain,Male,49.0,8.0,0.0,2.0,1.0,1.0,182059.28,0.0
-7061,15786739.0,Sagese,653.0,France,Male,41.0,2.0,0.0,1.0,1.0,0.0,164975.82,1.0
-7062,15630402.0,Ch'iu,526.0,France,Female,51.0,10.0,0.0,1.0,0.0,1.0,171378.57,1.0
-7063,15750731.0,Chia,748.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,111340.36,0.0
-7064,15722404.0,Ts'ui,644.0,France,Female,39.0,7.0,0.0,2.0,0.0,0.0,180862.26,0.0
-7065,15768359.0,Ch'ien,758.0,France,Male,25.0,4.0,0.0,2.0,0.0,1.0,163989.66,0.0
-7066,15766017.0,Rizzo,684.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,66797.79,0.0
-7067,15691703.0,Palerma,602.0,France,Male,38.0,5.0,0.0,2.0,0.0,0.0,145561.9,1.0
-7068,15576094.0,H?,709.0,France,Male,32.0,6.0,113781.1,1.0,1.0,0.0,179908.71,0.0
-7069,15696744.0,Chin,554.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,159800.16,0.0
-7070,15691457.0,Mazzanti,731.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,17848.3,0.0
-7071,15800524.0,Wright,622.0,France,Female,48.0,4.0,0.0,2.0,1.0,0.0,135650.29,0.0
-7072,15783029.0,Shao,850.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,83052.03,0.0
-7073,15636407.0,Macdonald,583.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,132468.76,0.0
-7074,15743490.0,Ch'iu,669.0,France,Female,27.0,7.0,0.0,2.0,0.0,0.0,163570.25,0.0
-7075,15769451.0,Chizoba,712.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,159737.56,0.0
-7076,15646726.0,Lucciano,605.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,122925.4,0.0
-7077,15589428.0,Iweobiegbunam,584.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,166475.44,0.0
-7078,15750874.0,Trevisani,634.0,France,Male,53.0,7.0,137148.68,1.0,0.0,0.0,115751.03,1.0
-7079,15809909.0,T'ang,716.0,Spain,Male,37.0,2.0,153316.14,1.0,0.0,0.0,73882.49,0.0
-7080,15585823.0,Hsueh,758.0,France,Female,52.0,1.0,0.0,1.0,1.0,1.0,5907.11,1.0
-7081,15796413.0,Ndubuagha,731.0,France,Male,44.0,2.0,0.0,2.0,1.0,1.0,170835.32,0.0
-7082,15697480.0,Scott,669.0,Germany,Female,32.0,10.0,104405.54,1.0,1.0,0.0,138882.98,0.0
-7083,15641158.0,Lassetter,755.0,France,Female,51.0,4.0,169831.46,1.0,0.0,1.0,185599.34,1.0
-7084,15712772.0,Okwudiliolisa,597.0,Germany,Male,28.0,0.0,141952.07,1.0,0.0,0.0,54871.02,0.0
-7085,15650889.0,Lu,734.0,France,Male,59.0,6.0,0.0,2.0,1.0,1.0,170487.4,0.0
-7086,15682834.0,Ch'in,659.0,Spain,Female,43.0,5.0,146034.74,1.0,1.0,1.0,173948.4,0.0
-7087,15761720.0,De Garis,683.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,107499.8,0.0
-7088,15783875.0,Smith,668.0,France,Female,49.0,8.0,0.0,2.0,1.0,0.0,172437.12,0.0
-7089,15595883.0,Chien,603.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,167773.55,0.0
-7090,15782236.0,Lu,678.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,0.0,167984.72,0.0
-7091,15667944.0,Hsing,634.0,France,Female,46.0,9.0,0.0,3.0,1.0,1.0,147794.63,1.0
-7092,15685314.0,Mironova,479.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,167087.34,1.0
-7093,15777639.0,Zetticci,594.0,Germany,Female,37.0,9.0,141788.63,2.0,0.0,1.0,176770.39,0.0
-7094,15684908.0,Chiawuotu,573.0,Spain,Female,42.0,2.0,0.0,2.0,1.0,0.0,106826.92,0.0
-7095,15709511.0,Hsia,706.0,Spain,Male,35.0,5.0,0.0,2.0,0.0,0.0,126223.84,0.0
-7096,15757777.0,Huddart,718.0,Germany,Female,38.0,1.0,145127.85,1.0,1.0,1.0,38962.94,1.0
-7097,15643908.0,Onwuamaeze,614.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,92381.01,0.0
-7098,15742378.0,Pisano,762.0,Germany,Female,35.0,3.0,145605.44,1.0,1.0,0.0,142516.35,0.0
-7099,15650034.0,Onyekachukwu,705.0,Germany,Male,34.0,3.0,117731.79,1.0,0.0,1.0,59636.43,0.0
-7100,15662641.0,De Luca,775.0,Spain,Male,41.0,8.0,0.0,2.0,1.0,1.0,170819.01,0.0
-7101,15623062.0,Aitken,614.0,France,Female,36.0,2.0,136624.6,1.0,0.0,1.0,119927.81,0.0
-7102,15576077.0,Olisanugo,727.0,France,Female,67.0,1.0,0.0,2.0,0.0,1.0,102036.82,0.0
-7103,15777076.0,Genovese,828.0,Spain,Female,50.0,4.0,0.0,1.0,1.0,1.0,88890.75,1.0
-7104,15681316.0,Genovese,750.0,France,Male,48.0,7.0,0.0,1.0,1.0,1.0,146949.74,1.0
-7105,15750056.0,Chu,786.0,Germany,Female,40.0,6.0,126473.33,2.0,1.0,0.0,60736.39,0.0
-7106,15793881.0,Chukwujamuike,724.0,France,Male,40.0,9.0,129745.1,1.0,1.0,1.0,197885.72,0.0
-7107,15571752.0,Molineux,609.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,72318.77,0.0
-7108,15682453.0,Rizzo,662.0,Spain,Female,40.0,1.0,132837.7,2.0,0.0,0.0,164967.21,0.0
-7109,15764195.0,Yefimova,671.0,France,Male,31.0,2.0,106059.4,1.0,1.0,1.0,187826.11,0.0
-7110,15635244.0,Ndukaku,705.0,France,Male,37.0,0.0,0.0,2.0,1.0,0.0,79495.01,0.0
-7111,15752994.0,Ndubuagha,613.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,111097.39,0.0
-7112,15778916.0,Tung,651.0,Spain,Male,49.0,0.0,0.0,2.0,1.0,0.0,106920.57,1.0
-7113,15585748.0,Yermolayev,585.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,159835.48,0.0
-7114,15628999.0,Ogochukwu,717.0,Spain,Male,45.0,5.0,0.0,2.0,1.0,1.0,179964.55,0.0
-7115,15571958.0,P'eng,769.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,193014.26,0.0
-7116,15749671.0,Shih,576.0,France,Male,25.0,8.0,0.0,2.0,1.0,0.0,91044.47,0.0
-7117,15606552.0,T'ao,581.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,0.0,143096.49,0.0
-7118,15763515.0,Onyekaozulu,705.0,France,Male,29.0,3.0,0.0,1.0,1.0,0.0,183515.13,0.0
-7119,15693902.0,Kao,682.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,172668.39,0.0
-7120,15604093.0,Hsu,683.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,143859.66,0.0
-7121,15794468.0,Fiorentino,731.0,Germany,Female,43.0,9.0,127924.95,1.0,1.0,0.0,109496.45,1.0
-7122,15699294.0,Ch'ien,663.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,129933.5,0.0
-7123,15572390.0,Onwuamaeze,653.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,122964.18,0.0
-7124,15694641.0,Ting,584.0,France,Male,27.0,8.0,123599.49,1.0,1.0,0.0,86977.53,0.0
-7125,15752912.0,Ponomaryova,687.0,Germany,Female,30.0,9.0,90218.11,1.0,1.0,1.0,168053.4,0.0
-7126,15733705.0,Manna,657.0,Spain,Female,24.0,2.0,0.0,2.0,1.0,0.0,79734.23,0.0
-7127,15592229.0,Chukwukadibia,669.0,France,Male,32.0,1.0,0.0,1.0,0.0,0.0,145562.4,0.0
-7128,15723658.0,Uspensky,526.0,Spain,Male,49.0,2.0,80728.15,1.0,0.0,1.0,149584.61,1.0
-7129,15657829.0,Ch'iu,634.0,France,Female,43.0,3.0,0.0,1.0,0.0,1.0,63795.62,1.0
-7130,15654562.0,Nkemakonam,681.0,France,Male,36.0,7.0,140700.61,1.0,1.0,0.0,161579.85,0.0
-7131,15689886.0,Ch'in,703.0,France,Female,35.0,10.0,132944.35,1.0,1.0,0.0,107874.39,0.0
-7132,15757931.0,Palerma,603.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,102908.91,0.0
-7133,15807433.0,Shao,585.0,France,Male,24.0,2.0,0.0,2.0,1.0,0.0,84870.23,0.0
-7134,15581551.0,H?,597.0,Spain,Male,27.0,7.0,0.0,2.0,1.0,1.0,66580.13,0.0
-7135,15645593.0,Ch'iu,741.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,113868.4,0.0
-7136,15631877.0,Watson,669.0,France,Female,42.0,1.0,0.0,1.0,1.0,0.0,17095.49,1.0
-7137,15615753.0,Shubin,633.0,Germany,Male,28.0,1.0,133658.24,1.0,1.0,0.0,140197.42,0.0
-7138,15616122.0,Scott,667.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,109135.11,0.0
-7139,15585892.0,Liao,613.0,France,Male,31.0,9.0,0.0,2.0,0.0,0.0,149575.18,0.0
-7140,15782531.0,Soto,633.0,Germany,Female,31.0,5.0,121461.63,1.0,1.0,0.0,44351.77,0.0
-7141,15684318.0,Chidozie,747.0,Germany,Male,39.0,6.0,153946.14,2.0,1.0,0.0,94949.84,0.0
-7142,15589212.0,Fomin,648.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,174094.24,0.0
-7143,15759717.0,Longstaff,584.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,134901.56,0.0
-7144,15724269.0,Milani,635.0,France,Male,29.0,8.0,0.0,1.0,1.0,0.0,55868.97,0.0
-7145,15795878.0,Ch'in,710.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,110525.6,0.0
-7146,15606945.0,Nwankwo,694.0,Germany,Female,40.0,8.0,129934.64,1.0,1.0,0.0,75562.47,0.0
-7147,15680303.0,Ch'en,679.0,Spain,Male,38.0,9.0,0.0,2.0,1.0,0.0,71154.1,0.0
-7148,15623989.0,Lockington,585.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,139607.71,0.0
-7149,15795511.0,O'Brien,638.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,157567.37,0.0
-7150,15783629.0,Nkemdilim,699.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,160129.99,0.0
-7151,15808831.0,Onuoha,590.0,Germany,Female,29.0,8.0,169831.46,2.0,1.0,0.0,35356.46,0.0
-7152,15642725.0,Hsueh,675.0,Spain,Female,41.0,7.0,0.0,2.0,0.0,0.0,151902.78,0.0
-7153,15605037.0,Onyekaozulu,628.0,France,Male,30.0,9.0,141827.33,1.0,1.0,1.0,124489.88,0.0
-7154,15713559.0,Tuan,648.0,France,Female,33.0,5.0,0.0,1.0,1.0,0.0,162022.47,1.0
-7155,15791533.0,Mamelu,711.0,France,Male,22.0,8.0,0.0,2.0,1.0,1.0,86022.09,0.0
-7156,15734659.0,Hsia,628.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,151335.24,0.0
-7157,15731555.0,Lucciano,724.0,Germany,Female,52.0,1.0,122398.08,1.0,1.0,1.0,131778.58,1.0
-7158,15696733.0,Yegorova,670.0,Spain,Female,39.0,7.0,0.0,2.0,0.0,1.0,144413.92,0.0
-7159,15777901.0,Nwora,656.0,France,Female,35.0,4.0,0.0,2.0,1.0,0.0,174027.3,0.0
-7160,15591135.0,Shih,642.0,France,Male,31.0,8.0,98795.61,1.0,1.0,0.0,113695.99,0.0
-7161,15744517.0,Toscani,651.0,Spain,Female,42.0,4.0,0.0,1.0,1.0,0.0,45445.78,1.0
-7162,15713578.0,Nucci,641.0,France,Female,56.0,8.0,139584.57,1.0,1.0,0.0,167984.72,1.0
-7163,15682211.0,Ch'ien,613.0,France,Male,36.0,2.0,128747.69,1.0,1.0,1.0,143783.28,0.0
-7164,15572374.0,Zikoranachidimma,659.0,France,Male,34.0,3.0,0.0,1.0,1.0,0.0,136910.18,0.0
-7165,15672970.0,Folliero,732.0,France,Male,44.0,3.0,0.0,3.0,0.0,0.0,105720.09,0.0
-7166,15633260.0,Chidubem,561.0,Spain,Female,27.0,8.0,0.0,2.0,1.0,1.0,107961.11,0.0
-7167,15796834.0,Nkemdilim,762.0,Spain,Female,41.0,8.0,0.0,1.0,1.0,0.0,140763.18,1.0
-7168,15698872.0,Macartney,645.0,France,Female,28.0,3.0,114084.6,2.0,1.0,0.0,74532.02,0.0
-7169,15764021.0,Nwebube,628.0,France,Male,52.0,1.0,0.0,1.0,1.0,0.0,133537.53,1.0
-7170,15705085.0,Rizzo,590.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,1.0,10789.3,0.0
-7171,15781210.0,Chiu,769.0,Spain,Female,34.0,1.0,0.0,2.0,1.0,1.0,82438.13,0.0
-7172,15708063.0,Fanucci,661.0,France,Male,26.0,3.0,0.0,2.0,1.0,1.0,101168.9,0.0
-7173,15695792.0,Fenton,716.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,95611.47,0.0
-7174,15640074.0,Dyer,667.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,56793.02,0.0
-7175,15624677.0,Nwokeocha,738.0,Spain,Female,35.0,8.0,0.0,2.0,1.0,1.0,122433.09,0.0
-7176,15658532.0,Chien,686.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,59601.15,0.0
-7177,15643826.0,T'ien,642.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,168080.53,0.0
-7178,15611644.0,Mazzanti,697.0,France,Male,39.0,0.0,83061.26,1.0,1.0,0.0,121894.31,0.0
-7179,15704954.0,Milanesi,714.0,Germany,Female,26.0,4.0,108432.88,1.0,1.0,1.0,91044.65,0.0
-7180,15652218.0,Mays,682.0,France,Male,30.0,1.0,131394.56,1.0,1.0,1.0,143952.24,0.0
-7181,15703394.0,Iadanza,638.0,France,Female,43.0,2.0,0.0,3.0,0.0,0.0,82820.85,1.0
-7182,15671630.0,Hs?,662.0,Spain,Female,71.0,6.0,0.0,2.0,1.0,1.0,61977.82,0.0
-7183,15769959.0,Chia,641.0,France,Male,40.0,8.0,127475.69,1.0,1.0,0.0,106061.47,0.0
-7184,15651958.0,Tuan,712.0,France,Female,53.0,0.0,0.0,2.0,1.0,1.0,56518.0,0.0
-7185,15619340.0,Chidubem,588.0,France,Male,39.0,0.0,127900.03,1.0,0.0,1.0,187811.71,0.0
-7186,15576824.0,Nkemdirim,544.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,118863.42,0.0
-7187,15662854.0,Tuan,716.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,101300.94,0.0
-7188,15593550.0,Uspenskaya,579.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,150475.14,0.0
-7189,15572390.0,Toscani,669.0,France,Female,72.0,4.0,0.0,1.0,1.0,1.0,142400.27,0.0
-7190,15737571.0,Chibugo,613.0,Spain,Female,25.0,9.0,0.0,2.0,0.0,1.0,68106.29,0.0
-7191,15784320.0,Ch'ang,605.0,Germany,Female,30.0,9.0,141952.07,1.0,1.0,1.0,164549.74,0.0
-7192,15579130.0,Onyemauchechi,710.0,France,Male,36.0,2.0,0.0,1.0,1.0,1.0,161479.19,0.0
-7193,15632256.0,Ts'ui,646.0,France,Male,36.0,1.0,0.0,1.0,1.0,1.0,173486.39,0.0
-7194,15733705.0,Hsueh,708.0,France,Male,47.0,4.0,0.0,2.0,1.0,1.0,155870.82,0.0
-7195,15672756.0,Ch'eng,706.0,France,Male,41.0,8.0,0.0,1.0,0.0,0.0,152490.79,1.0
-7196,15656300.0,Tsou,627.0,Spain,Male,34.0,2.0,0.0,1.0,1.0,0.0,137763.99,0.0
-7197,15780128.0,Bellucci,775.0,France,Male,35.0,5.0,0.0,2.0,0.0,1.0,162734.31,0.0
-7198,15651713.0,K?,648.0,France,Female,35.0,5.0,0.0,2.0,1.0,0.0,142400.27,0.0
-7199,15648047.0,Chibuzo,624.0,Germany,Male,42.0,2.0,137714.25,1.0,0.0,0.0,78416.14,1.0
-7200,15737509.0,L?,564.0,Spain,Male,29.0,3.0,0.0,2.0,1.0,1.0,152843.53,0.0
-7201,15578251.0,Pinto,656.0,Germany,Female,34.0,8.0,144974.55,1.0,1.0,1.0,58974.0,1.0
-7202,15664504.0,Onyekaozulu,774.0,Germany,Male,45.0,6.0,152582.2,1.0,0.0,0.0,46415.09,0.0
-7203,15631739.0,Omeokachie,773.0,France,Female,51.0,0.0,0.0,1.0,1.0,0.0,56583.88,1.0
-7204,15647800.0,Maclean,581.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,18082.06,0.0
-7205,15694530.0,Yobachukwu,773.0,France,Male,38.0,1.0,69423.52,1.0,1.0,0.0,162760.96,0.0
-7206,15787772.0,Genovesi,683.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,79691.4,0.0
-7207,15698932.0,Ritchie,572.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,75562.47,0.0
-7208,15571081.0,Macnamara,803.0,Germany,Female,46.0,4.0,135756.96,1.0,0.0,0.0,150664.97,1.0
-7209,15776047.0,Hsieh,558.0,France,Female,49.0,5.0,0.0,1.0,1.0,0.0,146636.44,1.0
-7210,15775131.0,Onyekaozulu,599.0,Spain,Male,29.0,6.0,0.0,2.0,0.0,0.0,121408.56,0.0
-7211,15724453.0,Baryshnikov,792.0,France,Female,33.0,2.0,0.0,2.0,1.0,0.0,132029.95,0.0
-7212,15661745.0,McGregor,684.0,Spain,Female,33.0,3.0,145105.64,1.0,1.0,0.0,122964.18,0.0
-7213,15617454.0,Teng,683.0,France,Male,26.0,8.0,0.0,1.0,1.0,1.0,74835.65,0.0
-7214,15759034.0,Lai,637.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,183358.21,0.0
-7215,15634606.0,Macleod,535.0,France,Female,60.0,5.0,0.0,1.0,1.0,1.0,167553.06,1.0
-7216,15664523.0,Ritchie,773.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,48962.59,0.0
-7217,15710257.0,T'an,549.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,52449.61,0.0
-7218,15638247.0,Udegbunam,705.0,France,Male,36.0,5.0,83348.89,1.0,0.0,0.0,179843.33,0.0
-7219,15663888.0,Chin,698.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,57896.27,0.0
-7220,15731555.0,Fanucci,682.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,1.0,182257.61,0.0
-7221,15681174.0,Ch'eng,599.0,France,Male,41.0,8.0,0.0,2.0,1.0,0.0,177092.16,0.0
-7222,15793369.0,Ch'in,719.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,74858.3,0.0
-7223,15762268.0,Chukwudi,710.0,France,Female,35.0,7.0,0.0,2.0,1.0,1.0,53420.53,0.0
-7224,15605039.0,Ugorji,614.0,Germany,Male,47.0,6.0,100549.42,1.0,1.0,0.0,152499.8,1.0
-7225,15781477.0,Ch'eng,757.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,146723.72,0.0
-7226,15713346.0,Niu,807.0,Germany,Female,43.0,7.0,127864.4,1.0,1.0,0.0,105732.9,1.0
-7227,15592877.0,T'ien,754.0,France,Female,45.0,8.0,0.0,2.0,1.0,1.0,143960.72,0.0
-7228,15742824.0,Uvarova,556.0,France,Female,25.0,6.0,0.0,2.0,1.0,0.0,164925.11,0.0
-7229,15596971.0,Walker,748.0,Spain,Male,36.0,10.0,0.0,2.0,1.0,0.0,109096.71,0.0
-7230,15621370.0,Hsiao,615.0,France,Female,47.0,3.0,0.0,4.0,1.0,0.0,171096.2,1.0
-7231,15585928.0,She,613.0,Germany,Female,44.0,9.0,152992.91,2.0,0.0,1.0,137674.55,0.0
-7232,15651460.0,Milanesi,718.0,Germany,Female,35.0,8.0,123953.31,1.0,1.0,0.0,11.58,1.0
-7233,15605037.0,Chigolum,554.0,Spain,Male,48.0,2.0,0.0,2.0,1.0,1.0,125979.36,0.0
-7234,15659710.0,Hsieh,604.0,France,Female,30.0,2.0,123311.39,1.0,1.0,0.0,73581.05,0.0
-7235,15780128.0,Pirozzi,487.0,France,Female,43.0,1.0,0.0,1.0,1.0,1.0,60427.3,0.0
-7236,15617648.0,Liao,606.0,France,Male,33.0,4.0,122793.96,1.0,0.0,0.0,106939.34,0.0
-7237,15687923.0,Y?,707.0,France,Male,23.0,5.0,0.0,1.0,1.0,0.0,155799.86,0.0
-7238,15774730.0,Yobachukwu,616.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,0.0,163477.22,0.0
-7239,15703399.0,Chiang,584.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,135875.51,0.0
-7240,15679997.0,Smith,731.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,58879.31,0.0
-7241,15683817.0,Ch'ang,850.0,Germany,Female,40.0,3.0,122021.92,2.0,1.0,1.0,126241.4,0.0
-7242,15642826.0,Ozioma,633.0,France,Male,33.0,6.0,123823.43,1.0,1.0,1.0,151146.67,0.0
-7243,15636061.0,Lo,684.0,Germany,Male,35.0,3.0,109733.2,2.0,1.0,0.0,173989.66,0.0
-7244,15782475.0,Tien,613.0,France,Female,33.0,1.0,121345.88,1.0,0.0,1.0,166475.79,0.0
-7245,15781298.0,Y?an,686.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,134901.58,0.0
-7246,15592028.0,Hsiao,676.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,102083.78,1.0
-7247,15708063.0,Chuang,599.0,Spain,Male,29.0,4.0,0.0,2.0,1.0,1.0,45610.63,0.0
-7248,15590094.0,Smith,636.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,57531.14,0.0
-7249,15652693.0,Ch'eng,591.0,Spain,Female,41.0,6.0,0.0,1.0,1.0,0.0,179634.69,1.0
-7250,15691119.0,K?,584.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,0.0,184514.01,0.0
-7251,15576077.0,T'ang,594.0,France,Male,36.0,2.0,0.0,2.0,0.0,1.0,57862.88,0.0
-7252,15591552.0,Chukwumaobim,642.0,France,Male,36.0,6.0,0.0,2.0,0.0,0.0,129526.57,0.0
-7253,15571550.0,Smith,768.0,France,Male,22.0,5.0,138162.81,1.0,1.0,1.0,83715.66,0.0
-7254,15583748.0,Chukwukadibia,567.0,France,Female,36.0,5.0,0.0,1.0,1.0,0.0,181297.65,1.0
-7255,15641389.0,Chin,593.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,187921.5,0.0
-7256,15583857.0,Hsieh,730.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,149853.89,0.0
-7257,15802479.0,Fisk,579.0,France,Male,45.0,5.0,0.0,2.0,0.0,0.0,176913.42,0.0
-7258,15774164.0,Okwudilichukwu,732.0,Spain,Female,27.0,4.0,0.0,2.0,1.0,0.0,149697.78,0.0
-7259,15746515.0,K'ung,714.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,164768.82,0.0
-7260,15787046.0,Rizzo,613.0,France,Female,45.0,4.0,0.0,2.0,0.0,0.0,52887.06,0.0
-7261,15741092.0,Chien,638.0,France,Male,25.0,2.0,0.0,2.0,1.0,1.0,72993.65,0.0
-7262,15794273.0,Yevseyev,718.0,Germany,Female,46.0,5.0,133618.01,1.0,1.0,0.0,80593.49,1.0
-7263,15743293.0,Chinwendu,730.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,97939.3,0.0
-7264,15793042.0,Ch'en,636.0,France,Female,35.0,10.0,0.0,1.0,1.0,0.0,95611.47,0.0
-7265,15625706.0,Nkemjika,628.0,Germany,Male,41.0,8.0,102576.06,1.0,1.0,0.0,141152.28,1.0
-7266,15623502.0,Ch'ien,681.0,Germany,Male,47.0,2.0,164701.68,1.0,1.0,0.0,72318.77,0.0
-7267,15605020.0,Martin,597.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,104889.3,1.0
-7268,15644833.0,Cattaneo,498.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,79559.02,0.0
-7269,15580249.0,Chiemenam,519.0,France,Male,33.0,9.0,71996.09,1.0,1.0,1.0,171463.83,0.0
-7270,15658946.0,Y?,542.0,France,Female,41.0,5.0,0.0,1.0,1.0,0.0,169915.02,0.0
-7271,15658935.0,Iadanza,651.0,France,Male,32.0,0.0,0.0,2.0,0.0,1.0,181224.56,0.0
-7272,15592761.0,Ch'ien,727.0,France,Male,51.0,6.0,0.0,2.0,0.0,1.0,79082.76,0.0
-7273,15602851.0,Aitken,704.0,France,Male,39.0,6.0,0.0,2.0,0.0,0.0,153831.6,0.0
-7274,15594450.0,Nwebube,706.0,France,Female,44.0,1.0,141787.31,1.0,1.0,1.0,607.49,0.0
-7275,15686835.0,Lo Duca,613.0,Spain,Male,36.0,9.0,0.0,1.0,1.0,0.0,11199.04,0.0
-7276,15738219.0,Genovesi,599.0,Germany,Female,45.0,2.0,73218.89,3.0,0.0,1.0,149331.01,1.0
-7277,15640667.0,Hs?,762.0,France,Female,26.0,3.0,0.0,2.0,1.0,0.0,171681.86,0.0
-7278,15792937.0,Nwachukwu,538.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,157921.25,0.0
-7279,15659325.0,Macdonald,738.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,84484.82,0.0
-7280,15631603.0,Chukwuemeka,637.0,Germany,Female,43.0,1.0,140694.78,2.0,1.0,0.0,90928.48,1.0
-7281,15586880.0,Chukwubuikem,646.0,Germany,Female,51.0,10.0,99811.44,2.0,0.0,0.0,149599.62,1.0
-7282,15687094.0,H?,644.0,Spain,Male,40.0,2.0,138912.19,1.0,1.0,1.0,50368.63,0.0
-7283,15741403.0,Bell,676.0,France,Female,26.0,9.0,63669.42,2.0,1.0,0.0,177065.24,0.0
-7284,15704954.0,Hsiao,755.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,65765.22,0.0
-7285,15606472.0,Chineze,745.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,74564.41,0.0
-7286,15687079.0,Ozerova,656.0,France,Male,49.0,3.0,0.0,1.0,1.0,0.0,139712.72,1.0
-7287,15641782.0,Bogle,637.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,0.0,136521.82,0.0
-7288,15576990.0,Tseng,573.0,Germany,Female,46.0,7.0,127864.4,1.0,0.0,0.0,181461.46,1.0
-7289,15807961.0,Maclean,714.0,France,Female,34.0,6.0,0.0,1.0,1.0,0.0,183515.13,1.0
-7290,15667093.0,Onwumelu,846.0,France,Female,38.0,10.0,0.0,2.0,0.0,0.0,40889.14,0.0
-7291,15684319.0,Chigbogu,604.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,108528.74,0.0
-7292,15739123.0,T'ao,693.0,Germany,Male,32.0,0.0,134542.73,1.0,1.0,1.0,62703.38,0.0
-7293,15796790.0,T'ien,707.0,France,Female,26.0,6.0,0.0,2.0,1.0,1.0,143598.7,0.0
-7294,15643487.0,L?,673.0,Germany,Male,35.0,4.0,102723.35,2.0,0.0,0.0,122960.98,0.0
-7295,15729771.0,Chizoba,535.0,Spain,Female,29.0,2.0,0.0,1.0,1.0,0.0,176962.31,0.0
-7296,15732660.0,Chidiebere,684.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,142670.61,1.0
-7297,15652069.0,Cattaneo,721.0,Spain,Male,35.0,5.0,0.0,1.0,1.0,1.0,47388.41,0.0
-7298,15719170.0,Nnaemeka,679.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,65464.66,0.0
-7299,15747772.0,Trevisani,708.0,France,Male,23.0,4.0,0.0,2.0,1.0,0.0,80582.36,0.0
-7300,15618239.0,Chukwudi,637.0,France,Female,36.0,1.0,122793.96,1.0,1.0,1.0,160948.87,0.0
-7301,15659800.0,Chiang,613.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,77951.84,0.0
-7302,15681446.0,Ts'ao,707.0,Spain,Male,41.0,5.0,125561.97,1.0,1.0,1.0,142963.29,0.0
-7303,15648075.0,Chidiegwu,539.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,88965.46,0.0
-7304,15579548.0,T'ien,585.0,Spain,Male,30.0,4.0,149995.72,1.0,1.0,1.0,184842.21,0.0
-7305,15608726.0,Ch'ien,683.0,France,Female,43.0,7.0,0.0,1.0,0.0,1.0,84760.32,1.0
-7306,15730590.0,Padovano,628.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,0.0,67840.81,0.0
-7307,15722072.0,Hsieh,699.0,France,Male,48.0,10.0,127580.93,1.0,1.0,1.0,86920.97,0.0
-7308,15724663.0,Lo Duca,535.0,France,Female,40.0,3.0,0.0,1.0,0.0,0.0,140947.05,1.0
-7309,15750014.0,Ch'ien,678.0,Spain,Male,44.0,9.0,0.0,2.0,1.0,1.0,91098.76,0.0
-7310,15748649.0,Whitehead,549.0,Germany,Female,44.0,2.0,92783.68,2.0,1.0,0.0,63736.17,0.0
-7311,15672598.0,Lucchesi,682.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,40257.79,0.0
-7312,15736397.0,Clements,645.0,France,Female,51.0,3.0,110976.23,1.0,0.0,1.0,182528.43,1.0
-7313,15807525.0,Chukwufumnanya,551.0,France,Female,29.0,1.0,0.0,1.0,1.0,1.0,121798.52,1.0
-7314,15588944.0,K?,611.0,France,Female,31.0,2.0,0.0,2.0,1.0,0.0,121207.66,0.0
-7315,15596863.0,Ts'ui,716.0,Germany,Male,32.0,7.0,108432.88,1.0,1.0,0.0,119903.2,0.0
-7316,15798885.0,Uwaezuoke,850.0,Spain,Male,23.0,5.0,126588.81,1.0,1.0,1.0,116971.05,0.0
-7317,15746028.0,Ch'in,679.0,Germany,Female,26.0,9.0,61811.23,2.0,1.0,1.0,158061.89,0.0
-7318,15769353.0,Maclean,527.0,France,Female,26.0,9.0,0.0,2.0,1.0,1.0,68730.91,0.0
-7319,15585388.0,K?,745.0,Spain,Female,25.0,5.0,135559.9,1.0,1.0,0.0,14083.12,0.0
-7320,15676571.0,Aiken,576.0,France,Female,31.0,10.0,0.0,2.0,1.0,1.0,81901.6,0.0
-7321,15781347.0,Kao,718.0,Spain,Male,55.0,7.0,0.0,1.0,1.0,1.0,18082.06,0.0
-7322,15722583.0,Ch'ang,619.0,France,Female,31.0,4.0,110219.14,1.0,1.0,0.0,90591.15,0.0
-7323,15752294.0,Cremonesi,632.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,121408.46,0.0
-7324,15727384.0,Ch'iu,549.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,171045.31,0.0
-7325,15654574.0,Hs?,673.0,Germany,Male,38.0,3.0,95727.05,2.0,0.0,0.0,82634.41,0.0
-7326,15693764.0,Nkemdilim,681.0,France,Male,30.0,6.0,142644.64,2.0,0.0,0.0,64732.82,0.0
-7327,15782688.0,Manna,632.0,France,Female,37.0,9.0,0.0,2.0,1.0,1.0,82526.92,0.0
-7328,15791525.0,L?,624.0,Germany,Female,33.0,7.0,88915.84,2.0,1.0,1.0,131736.23,0.0
-7329,15659475.0,Yevdokimova,682.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,132870.15,0.0
-7330,15692430.0,Chiu,758.0,France,Male,30.0,9.0,0.0,2.0,0.0,0.0,171922.72,0.0
-7331,15611157.0,Mazzanti,656.0,Germany,Male,30.0,5.0,112414.81,1.0,1.0,1.0,146700.22,0.0
-7332,15774127.0,Onyemauchechukwu,632.0,Germany,Male,42.0,0.0,113688.63,1.0,1.0,1.0,178822.32,1.0
-7333,15775809.0,Y?,662.0,France,Female,32.0,9.0,96399.31,2.0,1.0,0.0,131283.6,0.0
-7334,15630053.0,Lettiere,641.0,France,Female,42.0,8.0,0.0,2.0,1.0,0.0,92920.03,0.0
-7335,15611800.0,Echezonachukwu,582.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,122580.48,0.0
-7336,15694518.0,Lo,607.0,France,Male,46.0,6.0,0.0,2.0,1.0,0.0,183542.08,0.0
-7337,15704771.0,Chineze,738.0,Spain,Male,46.0,6.0,0.0,2.0,0.0,1.0,52795.15,0.0
-7338,15633141.0,Chukwukadibia,658.0,France,Male,33.0,2.0,94867.18,1.0,1.0,1.0,167181.77,0.0
-7339,15615176.0,Ch'iu,773.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,38190.78,0.0
-7340,15761693.0,Nwankwo,611.0,France,Male,41.0,4.0,102773.2,1.0,1.0,0.0,134878.19,0.0
-7341,15628893.0,Chibugo,628.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,48988.28,0.0
-7342,15700965.0,Scott,686.0,Spain,Female,43.0,6.0,0.0,2.0,1.0,1.0,82522.61,0.0
-7343,15593969.0,Ch'ang,613.0,Germany,Female,34.0,4.0,148061.49,1.0,1.0,1.0,174815.54,0.0
-7344,15593795.0,Shih,635.0,France,Female,40.0,10.0,0.0,2.0,1.0,1.0,125962.02,0.0
-7345,15754599.0,Tien,714.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,182270.88,0.0
-7346,15579914.0,Maclean,529.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,58879.31,0.0
-7347,15795878.0,Tretiakova,539.0,France,Male,38.0,9.0,88902.21,1.0,1.0,0.0,113481.02,0.0
-7348,15765192.0,Ch'ien,721.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,0.0,166476.95,0.0
-7349,15737521.0,Kolesnikov,684.0,France,Female,33.0,4.0,129855.63,2.0,1.0,1.0,47322.05,0.0
-7350,15632847.0,Tien,714.0,France,Male,29.0,6.0,0.0,1.0,1.0,0.0,179655.87,1.0
-7351,15625023.0,Fang,477.0,Germany,Female,32.0,6.0,125241.17,2.0,1.0,1.0,152698.24,0.0
-7352,15693690.0,Lettiere,684.0,France,Male,40.0,6.0,118829.03,1.0,1.0,0.0,166850.57,0.0
-7353,15711398.0,Chialuka,742.0,Germany,Female,29.0,7.0,112212.14,2.0,1.0,1.0,131718.12,0.0
-7354,15581748.0,Palermo,569.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,100101.06,0.0
-7355,15603883.0,Li Fonti,683.0,France,Female,52.0,9.0,0.0,1.0,1.0,0.0,63737.19,1.0
-7356,15671610.0,Ponomaryova,523.0,France,Male,29.0,4.0,134956.02,1.0,1.0,1.0,87835.43,0.0
-7357,15809663.0,Maclean,542.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,186884.04,0.0
-7358,15596671.0,Yobanna,722.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,119817.19,0.0
-7359,15687079.0,Lo Duca,682.0,France,Male,59.0,8.0,0.0,1.0,1.0,0.0,79623.28,1.0
-7360,15761717.0,Thompson,684.0,Germany,Female,36.0,6.0,95858.37,2.0,0.0,1.0,167564.82,0.0
-7361,15603764.0,Kao,670.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,85411.01,0.0
-7362,15609562.0,Hinton,705.0,France,Female,51.0,6.0,0.0,2.0,1.0,1.0,149705.25,0.0
-7363,15742210.0,Cremonesi,615.0,France,Female,41.0,8.0,0.0,2.0,1.0,1.0,61995.57,0.0
-7364,15650432.0,Palerma,706.0,Spain,Male,36.0,3.0,125822.21,2.0,1.0,1.0,168035.63,0.0
-7365,15650018.0,Cremonesi,716.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,51587.04,0.0
-7366,15659949.0,Ch'iu,716.0,Germany,Female,39.0,6.0,103399.87,1.0,1.0,1.0,86957.42,0.0
-7367,15768763.0,Chukwudi,589.0,Germany,Male,32.0,3.0,91523.84,1.0,1.0,0.0,28690.9,0.0
-7368,15681446.0,Genovesi,686.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,82566.46,0.0
-7369,15750141.0,Pugh,711.0,France,Male,42.0,5.0,132578.92,2.0,1.0,1.0,184954.11,1.0
-7370,15793956.0,K'ung,562.0,Germany,Female,31.0,5.0,91091.06,2.0,1.0,0.0,148516.72,0.0
-7371,15701354.0,Nwabugwu,707.0,France,Male,44.0,6.0,0.0,2.0,1.0,1.0,26796.73,1.0
-7372,15767794.0,Fyodorova,632.0,France,Female,31.0,1.0,0.0,2.0,1.0,1.0,131768.98,0.0
-7373,15590011.0,Pagnotto,718.0,Germany,Male,35.0,6.0,101851.8,2.0,1.0,0.0,158059.03,0.0
-7374,15592751.0,Mironova,582.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,0.0,48988.28,0.0
-7375,15784918.0,Tsai,686.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,182495.4,0.0
-7376,15643487.0,Fiorentini,598.0,Spain,Female,31.0,10.0,0.0,2.0,1.0,1.0,161479.19,0.0
-7377,15803633.0,Hsiao,633.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,113436.08,0.0
-7378,15586699.0,Johnson,684.0,France,Female,46.0,8.0,0.0,1.0,1.0,0.0,173779.4,1.0
-7379,15675337.0,Lai,646.0,Spain,Male,40.0,1.0,0.0,1.0,1.0,0.0,103381.47,0.0
-7380,15784323.0,Ejikemeifeuwa,646.0,Germany,Female,46.0,5.0,121365.39,1.0,1.0,0.0,51581.89,1.0
-7381,15594556.0,Tsao,703.0,Spain,Female,34.0,4.0,121702.73,1.0,1.0,1.0,81431.99,0.0
-7382,15589475.0,Dickson,565.0,France,Male,32.0,9.0,126629.58,1.0,1.0,1.0,162719.69,0.0
-7383,15758813.0,Kline,739.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,81442.6,0.0
-7384,15667554.0,Milani,828.0,Spain,Male,35.0,4.0,0.0,2.0,0.0,1.0,147720.27,0.0
-7385,15579996.0,Chizuoke,556.0,France,Male,43.0,6.0,0.0,3.0,1.0,0.0,187616.16,1.0
-7386,15784597.0,Ch'en,523.0,Germany,Female,33.0,8.0,102708.77,2.0,1.0,1.0,62722.44,0.0
-7387,15803352.0,Ch'ien,645.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,0.0,84928.4,0.0
-7388,15712264.0,Bancks,696.0,France,Male,51.0,4.0,132576.25,1.0,1.0,1.0,182738.0,0.0
-7389,15594761.0,T'an,662.0,Spain,Male,52.0,9.0,0.0,3.0,1.0,0.0,170980.86,1.0
-7390,15689344.0,Ebelechukwu,821.0,France,Male,51.0,0.0,131802.21,1.0,1.0,1.0,85801.77,1.0
-7391,15693526.0,Ekechukwu,706.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,28204.98,0.0
-7392,15761654.0,Hs?,657.0,France,Male,35.0,6.0,76595.08,2.0,1.0,1.0,179565.57,0.0
-7393,15797787.0,Fanucci,678.0,Germany,Male,46.0,7.0,160777.9,2.0,1.0,1.0,118971.74,0.0
-7394,15623082.0,Iadanza,679.0,France,Female,37.0,8.0,0.0,2.0,0.0,1.0,124592.84,0.0
-7395,15774854.0,Ibezimako,686.0,Germany,Female,27.0,4.0,117438.91,2.0,1.0,1.0,143481.85,0.0
-7396,15624793.0,Chiemenam,622.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,82709.8,0.0
-7397,15682773.0,White,663.0,France,Female,33.0,1.0,0.0,1.0,1.0,0.0,123850.58,0.0
-7398,15586870.0,Tsao,682.0,Spain,Male,37.0,8.0,123557.65,2.0,1.0,1.0,182627.25,0.0
-7399,15733444.0,Goliwe,648.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,99645.04,0.0
-7400,15629299.0,Pirozzi,608.0,France,Male,30.0,0.0,0.0,2.0,1.0,1.0,151902.78,0.0
-7401,15613886.0,Chinedum,594.0,Spain,Female,47.0,2.0,0.0,2.0,1.0,0.0,16897.19,0.0
-7402,15615245.0,Cattaneo,651.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,1.0,103603.65,0.0
-7403,15644765.0,Hsiung,581.0,Spain,Male,42.0,3.0,0.0,2.0,0.0,0.0,177784.68,0.0
-7404,15592386.0,Chinwenma,619.0,France,Male,22.0,6.0,0.0,2.0,1.0,0.0,180427.24,0.0
-7405,15642870.0,Iadanza,530.0,France,Male,45.0,1.0,0.0,2.0,1.0,1.0,90424.09,0.0
-7406,15777893.0,Ngozichukwuka,727.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,1.0,49594.02,0.0
-7407,15754926.0,Ch'ang,678.0,Germany,Male,38.0,5.0,139972.18,1.0,1.0,0.0,173784.04,0.0
-7408,15652658.0,T'ien,676.0,Germany,Female,33.0,7.0,108825.34,1.0,1.0,1.0,52469.06,0.0
-7409,15593694.0,McElhone,652.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,138775.69,1.0
-7410,15791867.0,Galkin,669.0,Germany,Female,29.0,6.0,125961.74,2.0,1.0,0.0,158887.09,0.0
-7411,15586870.0,Nkemdirim,571.0,Germany,Female,64.0,5.0,114084.6,2.0,1.0,0.0,119647.6,1.0
-7412,15629787.0,Y?,709.0,Germany,Male,46.0,10.0,108895.85,2.0,1.0,0.0,26387.72,0.0
-7413,15646764.0,Trevisani,731.0,France,Male,45.0,5.0,0.0,2.0,0.0,1.0,83053.97,0.0
-7414,15694340.0,Y?,637.0,France,Male,29.0,7.0,0.0,1.0,1.0,1.0,121365.45,0.0
-7415,15668889.0,Farrar,675.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,79633.38,0.0
-7416,15733444.0,Nwabugwu,716.0,France,Male,34.0,10.0,0.0,2.0,0.0,0.0,11525.72,0.0
-7417,15685826.0,K'ung,705.0,France,Female,41.0,2.0,0.0,1.0,0.0,0.0,117622.8,0.0
-7418,15639004.0,Sagese,713.0,France,Male,44.0,0.0,0.0,1.0,0.0,0.0,70548.96,1.0
-7419,15578077.0,Chukwukere,576.0,France,Male,42.0,1.0,68423.89,1.0,0.0,0.0,104469.06,1.0
-7420,15647842.0,Chukwujamuike,586.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,125877.22,0.0
-7421,15670343.0,Nebechukwu,678.0,Germany,Male,39.0,1.0,99842.5,2.0,1.0,1.0,177519.39,1.0
-7422,15668971.0,Ejimofor,613.0,Spain,Male,30.0,4.0,0.0,2.0,0.0,1.0,59981.62,0.0
-7423,15583692.0,Y?an,719.0,France,Male,47.0,0.0,0.0,2.0,1.0,1.0,140941.47,1.0
-7424,15612893.0,Ch'iu,757.0,Spain,Male,39.0,5.0,139825.34,1.0,0.0,1.0,163580.48,0.0
-7425,15773723.0,Shih,694.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,79919.13,0.0
-7426,15630113.0,Maclean,699.0,Spain,Male,39.0,8.0,0.0,1.0,1.0,0.0,121053.19,1.0
-7427,15614215.0,Lettiere,708.0,Spain,Female,33.0,5.0,0.0,1.0,1.0,0.0,133091.32,0.0
-7428,15768095.0,Echezonachukwu,598.0,Germany,Male,59.0,9.0,114739.92,1.0,1.0,1.0,72402.0,1.0
-7429,15716284.0,Chiemenam,577.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,93336.3,0.0
-7430,15753094.0,Liao,558.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,167538.66,0.0
-7431,15631310.0,H?,598.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,116097.26,0.0
-7432,15757537.0,Trevisani,721.0,France,Male,35.0,4.0,131834.76,1.0,1.0,0.0,115927.85,0.0
-7433,15715117.0,Ch'iu,565.0,France,Male,36.0,3.0,0.0,1.0,0.0,0.0,159823.16,0.0
-7434,15803415.0,Kaeppel,697.0,France,Female,36.0,3.0,0.0,2.0,0.0,1.0,183656.12,0.0
-7435,15667093.0,Fiorentino,682.0,Germany,Male,44.0,6.0,113383.07,1.0,1.0,0.0,147553.06,1.0
-7436,15604544.0,Chimezie,696.0,France,Female,40.0,7.0,133634.02,1.0,1.0,1.0,126471.14,0.0
-7437,15662089.0,Ch'iu,581.0,France,Female,37.0,9.0,0.0,1.0,1.0,1.0,114092.87,0.0
-7438,15775318.0,Chiazagomekpere,606.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,97938.59,0.0
-7439,15782418.0,Ch'ang,711.0,France,Male,38.0,10.0,0.0,2.0,1.0,0.0,157862.82,0.0
-7440,15756975.0,Chiazagomekpere,850.0,France,Male,42.0,2.0,0.0,2.0,0.0,1.0,89891.4,0.0
-7441,15717893.0,Ch'en,586.0,France,Male,47.0,9.0,0.0,2.0,1.0,1.0,102638.35,0.0
-7442,15662884.0,Chukwudi,591.0,France,Female,32.0,7.0,109327.65,1.0,1.0,1.0,69531.22,0.0
-7443,15633014.0,Chukwualuka,704.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,9241.83,0.0
-7444,15629150.0,Ch'ien,576.0,France,Female,28.0,2.0,0.0,2.0,0.0,0.0,152577.62,0.0
-7445,15711309.0,Genovesi,607.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,1.0,103560.98,0.0
-7446,15727059.0,Chidumaga,584.0,France,Male,48.0,3.0,0.0,1.0,1.0,1.0,56791.8,1.0
-7447,15642709.0,Eluemuno,706.0,Spain,Male,50.0,1.0,0.0,1.0,1.0,1.0,75927.05,1.0
-7448,15654611.0,Yegorova,712.0,Germany,Female,33.0,6.0,116546.29,1.0,1.0,0.0,121151.1,1.0
-7449,15804052.0,Onwumelu,678.0,Spain,Male,63.0,2.0,0.0,2.0,1.0,1.0,73881.68,0.0
-7450,15670172.0,Maclean,751.0,France,Female,35.0,5.0,123845.36,2.0,0.0,1.0,92568.05,0.0
-7451,15802593.0,Ratten,648.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,1.0,182354.46,0.0
-7452,15675141.0,Chidubem,642.0,France,Female,61.0,8.0,121276.32,1.0,1.0,1.0,61759.33,0.0
-7453,15579526.0,Rowntree,633.0,France,Male,31.0,7.0,133501.36,1.0,1.0,1.0,82970.69,0.0
-7454,15718789.0,Hsing,605.0,France,Male,38.0,1.0,0.0,1.0,1.0,1.0,103089.03,1.0
-7455,15605835.0,Young,724.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,162422.91,0.0
-7456,15683483.0,Ch'ien,850.0,Spain,Female,51.0,8.0,0.0,2.0,1.0,0.0,184936.77,1.0
-7457,15665062.0,Macleod,592.0,Germany,Female,33.0,2.0,127924.25,2.0,0.0,0.0,124626.57,0.0
-7458,15636592.0,Echezonachukwu,816.0,Germany,Male,33.0,4.0,90218.9,2.0,1.0,1.0,147090.9,0.0
-7459,15621815.0,T'ien,823.0,France,Female,44.0,10.0,0.0,1.0,0.0,0.0,142223.94,1.0
-7460,15725509.0,Ugochukwutubelum,667.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,142995.32,0.0
-7461,15673570.0,L?,673.0,Spain,Male,43.0,4.0,101648.5,1.0,1.0,0.0,103557.85,0.0
-7462,15754919.0,Chukwuemeka,573.0,France,Male,33.0,6.0,0.0,1.0,1.0,0.0,134526.87,0.0
-7463,15767359.0,Chukwudi,492.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,91957.39,0.0
-7464,15624072.0,Ch'iu,686.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,91830.75,0.0
-7465,15624293.0,Milanesi,715.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,145796.49,0.0
-7466,15792679.0,Pisani,712.0,France,Female,35.0,1.0,0.0,2.0,0.0,0.0,66977.68,0.0
-7467,15783526.0,Chukwuebuka,599.0,France,Female,39.0,1.0,0.0,1.0,1.0,1.0,175743.92,1.0
-7468,15718789.0,Obidimkpa,748.0,Spain,Female,26.0,5.0,0.0,2.0,0.0,1.0,60956.85,0.0
-7469,15632575.0,Pirozzi,694.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,64421.02,0.0
-7470,15790744.0,Nkemjika,659.0,France,Female,35.0,10.0,105245.34,1.0,1.0,0.0,163563.07,0.0
-7471,15583681.0,Brown,792.0,Germany,Female,33.0,6.0,137964.55,2.0,1.0,0.0,172495.83,0.0
-7472,15790067.0,Nnanna,597.0,France,Female,39.0,6.0,0.0,2.0,1.0,0.0,176843.53,0.0
-7473,15790599.0,K'ung,675.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,120511.01,0.0
-7474,15636428.0,Yeh,627.0,Germany,Female,43.0,4.0,118590.41,1.0,0.0,0.0,144880.81,1.0
-7475,15592230.0,Milanesi,698.0,France,Male,38.0,0.0,69712.74,1.0,1.0,1.0,170835.32,0.0
-7476,15574071.0,Ch'ien,683.0,Germany,Male,37.0,5.0,133970.21,2.0,0.0,1.0,147530.06,0.0
-7477,15583049.0,Ts'ao,671.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,86764.46,0.0
-7478,15654562.0,Chikwado,726.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,102297.15,0.0
-7479,15579914.0,Ozioma,582.0,France,Female,66.0,0.0,0.0,2.0,1.0,1.0,53353.36,0.0
-7480,15736118.0,Hargreaves,627.0,France,Male,28.0,7.0,79871.02,1.0,1.0,0.0,147964.99,0.0
-7481,15791925.0,Chiedozie,649.0,Germany,Female,56.0,2.0,106948.43,1.0,1.0,0.0,129964.94,1.0
-7482,15658067.0,Hsiung,593.0,France,Male,54.0,2.0,0.0,2.0,1.0,1.0,67976.57,0.0
-7483,15642821.0,Yobachukwu,632.0,France,Male,47.0,5.0,0.0,1.0,1.0,0.0,97640.2,1.0
-7484,15694641.0,Milano,685.0,Spain,Male,28.0,9.0,0.0,2.0,1.0,0.0,27149.27,0.0
-7485,15593636.0,Manna,628.0,France,Male,41.0,4.0,0.0,2.0,1.0,1.0,100153.75,0.0
-7486,15570134.0,T'ien,674.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,171045.35,0.0
-7487,15791842.0,Chidumaga,516.0,Germany,Female,32.0,7.0,152926.9,2.0,1.0,1.0,60212.51,0.0
-7488,15614782.0,Uspensky,542.0,France,Female,44.0,3.0,135870.01,1.0,1.0,1.0,172097.4,0.0
-7489,15803378.0,Chineze,710.0,France,Male,41.0,4.0,0.0,2.0,0.0,1.0,57844.69,0.0
-7490,15696733.0,Chidiegwu,716.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,60972.84,0.0
-7491,15787322.0,Okoli,676.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,86764.46,0.0
-7492,15618018.0,Aksenov,667.0,France,Male,25.0,6.0,0.0,2.0,0.0,1.0,172668.39,0.0
-7493,15603565.0,Yudina,705.0,Germany,Male,38.0,3.0,161807.18,2.0,0.0,1.0,92787.42,0.0
-7494,15641183.0,Teng,581.0,France,Male,36.0,7.0,0.0,2.0,0.0,1.0,152541.89,0.0
-7495,15804586.0,Pinto,583.0,Spain,Female,26.0,1.0,0.0,2.0,0.0,0.0,146033.52,0.0
-7496,15685829.0,Chiemenam,652.0,France,Male,39.0,3.0,0.0,1.0,0.0,0.0,145764.4,0.0
-7497,15673619.0,Nkemdilim,733.0,France,Male,36.0,2.0,0.0,2.0,0.0,0.0,88890.05,0.0
-7498,15608701.0,K?,753.0,France,Female,48.0,5.0,0.0,2.0,1.0,0.0,164741.5,1.0
-7499,15672447.0,Riggs,756.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,94780.9,0.0
-7500,15669987.0,Ritchie,656.0,France,Male,46.0,8.0,0.0,2.0,1.0,0.0,65762.02,0.0
-7501,15572728.0,Nnachetam,679.0,Spain,Female,38.0,0.0,0.0,2.0,1.0,0.0,67944.09,0.0
-7502,15731519.0,Chukwudi,706.0,France,Female,30.0,3.0,0.0,2.0,0.0,1.0,175743.92,0.0
-7503,15572374.0,Chukwuemeka,676.0,Germany,Male,40.0,2.0,117633.85,1.0,1.0,0.0,158948.63,1.0
-7504,15746664.0,Lettiere,759.0,France,Male,45.0,2.0,0.0,2.0,1.0,1.0,60552.44,0.0
-7505,15776596.0,Soto,731.0,Germany,Male,43.0,4.0,113690.85,1.0,1.0,1.0,60721.4,1.0
-7506,15588560.0,Ritchie,678.0,France,Male,40.0,4.0,0.0,2.0,1.0,1.0,182867.84,0.0
-7507,15665460.0,De Luca,710.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,124592.84,0.0
-7508,15800773.0,Nwankwo,679.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,114722.42,0.0
-7509,15737605.0,Johnstone,741.0,Germany,Male,46.0,4.0,133501.36,1.0,0.0,1.0,84760.32,1.0
-7510,15644551.0,Onwuamaeze,579.0,France,Male,31.0,9.0,117700.86,1.0,1.0,1.0,160968.44,0.0
-7511,15678412.0,Nwachukwu,683.0,France,Female,60.0,2.0,103907.28,1.0,1.0,0.0,186869.58,1.0
-7512,15793856.0,Chukwuemeka,652.0,Germany,Male,26.0,8.0,141827.33,2.0,0.0,1.0,63737.19,0.0
-7513,15603065.0,T'ien,516.0,Germany,Male,38.0,1.0,124043.8,1.0,1.0,1.0,180551.76,1.0
-7514,15621653.0,Tien,578.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,87071.18,0.0
-7515,15756481.0,Yefremova,713.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,182787.17,0.0
-7516,15790448.0,Liao,710.0,Spain,Female,46.0,7.0,0.0,2.0,1.0,0.0,65675.47,0.0
-7517,15624595.0,Toscani,659.0,France,Male,36.0,1.0,102773.2,1.0,0.0,0.0,55893.33,0.0
-7518,15588517.0,O'Toole,558.0,France,Male,36.0,0.0,101242.98,1.0,1.0,0.0,93779.66,0.0
-7519,15765532.0,Hayslett,705.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,62389.35,0.0
-7520,15753826.0,Brown,667.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,0.0,64443.75,0.0
-7521,15768072.0,Fan,710.0,France,Male,39.0,7.0,123599.49,1.0,1.0,0.0,187632.22,0.0
-7522,15760774.0,Koo,583.0,France,Male,40.0,3.0,0.0,1.0,0.0,1.0,72678.75,1.0
-7523,15736397.0,Estrada,792.0,Germany,Female,41.0,7.0,143595.87,2.0,1.0,1.0,164975.82,0.0
-7524,15766272.0,Chukwudi,595.0,Germany,Male,48.0,5.0,125724.95,1.0,0.0,0.0,107875.1,1.0
-7525,15652981.0,Ch'in,741.0,Spain,Male,51.0,3.0,0.0,1.0,1.0,0.0,49826.68,1.0
-7526,15705620.0,Yobanna,749.0,France,Male,44.0,5.0,121355.99,1.0,1.0,1.0,184317.74,0.0
-7527,15723028.0,Hovell,526.0,Germany,Female,36.0,3.0,121405.04,1.0,1.0,1.0,132450.09,1.0
-7528,15679810.0,Hao,709.0,Germany,Male,42.0,8.0,96340.81,2.0,1.0,0.0,30781.77,0.0
-7529,15757897.0,H?,602.0,Spain,Female,56.0,5.0,0.0,1.0,0.0,0.0,113034.81,1.0
-7530,15759499.0,Kolesnikov,771.0,France,Male,23.0,3.0,0.0,2.0,1.0,1.0,125445.8,0.0
-7531,15572038.0,Aksenov,587.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,120115.5,0.0
-7532,15655238.0,P'an,627.0,Germany,Female,43.0,6.0,152760.24,1.0,1.0,1.0,88872.33,1.0
-7533,15725997.0,McLean,588.0,France,Female,58.0,6.0,0.0,1.0,1.0,0.0,171707.93,1.0
-7534,15641582.0,K'ung,653.0,Germany,Female,32.0,9.0,108449.23,1.0,1.0,0.0,127939.18,1.0
-7535,15565706.0,Nwankwo,589.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,71972.95,0.0
-7536,15792668.0,Mironov,702.0,France,Female,27.0,6.0,101014.08,2.0,1.0,0.0,137985.69,0.0
-7537,15719352.0,Iloerika,633.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,164768.82,0.0
-7538,15632977.0,Lo Duca,644.0,Germany,Female,34.0,7.0,129969.68,1.0,0.0,0.0,175399.13,0.0
-7539,15765311.0,O'Brien,624.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,142331.85,0.0
-7540,15786717.0,Chidimma,758.0,Germany,Female,32.0,1.0,134919.35,2.0,0.0,0.0,146955.71,0.0
-7541,15776844.0,Yefremova,641.0,Germany,Female,40.0,8.0,131411.24,2.0,1.0,0.0,188997.08,0.0
-7542,15772329.0,Houghton,695.0,France,Female,32.0,8.0,111756.5,1.0,1.0,0.0,180622.44,0.0
-7543,15649724.0,Chiu,468.0,France,Female,45.0,5.0,0.0,1.0,1.0,0.0,177611.57,1.0
-7544,15592761.0,Dickson,727.0,Spain,Male,23.0,3.0,155413.08,1.0,1.0,1.0,141896.74,0.0
-7545,15661821.0,Nwokeocha,521.0,Spain,Female,29.0,7.0,0.0,2.0,1.0,1.0,25803.59,0.0
-7546,15579548.0,Brown,599.0,France,Male,33.0,6.0,123835.52,1.0,1.0,0.0,62634.94,0.0
-7547,15790594.0,Genovesi,729.0,Germany,Male,45.0,2.0,128226.37,1.0,1.0,0.0,99061.75,1.0
-7548,15591766.0,Yobanna,726.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,132498.39,0.0
-7549,15643048.0,Onio,678.0,Germany,Male,38.0,10.0,102570.37,2.0,1.0,1.0,141069.88,0.0
-7550,15599131.0,Trevisani,676.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,1.0,124489.88,0.0
-7551,15713378.0,Sun,737.0,France,Female,38.0,6.0,0.0,2.0,0.0,1.0,123882.73,0.0
-7552,15713845.0,Cattaneo,696.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,100950.48,0.0
-7553,15720508.0,Li Fonti,573.0,France,Male,44.0,3.0,0.0,2.0,1.0,1.0,171457.06,0.0
-7554,15742273.0,Cremonesi,678.0,Spain,Female,31.0,8.0,0.0,2.0,1.0,1.0,74588.41,0.0
-7555,15682974.0,Nwoye,632.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,114869.55,0.0
-7556,15667938.0,Lucchesi,793.0,France,Male,33.0,2.0,148659.48,1.0,1.0,1.0,72989.82,0.0
-7557,15693764.0,T'ien,677.0,France,Female,40.0,1.0,0.0,2.0,0.0,0.0,141449.86,0.0
-7558,15571244.0,K?,637.0,France,Female,39.0,1.0,170600.78,1.0,1.0,1.0,676.06,0.0
-7559,15760090.0,Chidiebere,679.0,Germany,Male,43.0,9.0,131078.42,2.0,0.0,1.0,69320.97,0.0
-7560,15653315.0,Tuan,592.0,France,Male,38.0,2.0,129289.99,1.0,1.0,1.0,70523.92,0.0
-7561,15583548.0,Onwumelu,559.0,France,Male,39.0,8.0,0.0,2.0,1.0,0.0,168034.9,0.0
-7562,15725028.0,Chin,659.0,France,Female,30.0,5.0,124923.35,1.0,1.0,1.0,85983.54,0.0
-7563,15723053.0,Ch'en,584.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,58363.94,0.0
-7564,15641582.0,Chukwueloka,663.0,Spain,Female,34.0,8.0,0.0,2.0,1.0,0.0,152567.75,0.0
-7565,15742886.0,Clements,712.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,1.0,153698.65,0.0
-7566,15661830.0,Ozerova,681.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,120880.72,0.0
-7567,15574795.0,Milne,696.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,62944.75,0.0
-7568,15593468.0,Mao,587.0,France,Female,52.0,1.0,134540.24,1.0,1.0,1.0,109572.58,1.0
-7569,15598088.0,Chiu,496.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,103934.22,0.0
-7570,15780359.0,Baresi,632.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,182855.24,0.0
-7571,15596969.0,H?,693.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,151843.54,0.0
-7572,15769783.0,Nwora,678.0,France,Male,24.0,8.0,0.0,2.0,0.0,0.0,180345.44,0.0
-7573,15605293.0,Ch'en,754.0,France,Male,31.0,9.0,0.0,1.0,1.0,0.0,62845.64,0.0
-7574,15636572.0,Yermolayev,595.0,France,Male,42.0,9.0,146777.44,2.0,1.0,0.0,172572.64,1.0
-7575,15642709.0,Y?,651.0,France,Male,50.0,7.0,0.0,2.0,1.0,0.0,120115.5,0.0
-7576,15637977.0,Tsao,743.0,France,Female,55.0,8.0,121874.89,1.0,1.0,0.0,181224.56,1.0
-7577,15589949.0,K'ung,635.0,France,Female,31.0,0.0,0.0,2.0,1.0,1.0,52795.15,0.0
-7578,15682928.0,Hsueh,598.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,121681.19,0.0
-7579,15774304.0,McIntyre,635.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,50743.83,1.0
-7580,15673584.0,Padovano,597.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,59816.41,0.0
-7581,15643582.0,Aitken,729.0,France,Female,31.0,6.0,0.0,2.0,1.0,1.0,160129.99,0.0
-7582,15656471.0,Mazzi,714.0,France,Female,28.0,5.0,0.0,2.0,1.0,1.0,27654.44,0.0
-7583,15705301.0,Teng,714.0,Germany,Male,37.0,1.0,86614.06,1.0,1.0,0.0,142287.65,0.0
-7584,15679024.0,Hinton,657.0,Germany,Female,47.0,5.0,122917.71,2.0,1.0,0.0,78650.95,1.0
-7585,15662085.0,Mazzanti,648.0,France,Male,46.0,5.0,137851.31,1.0,1.0,0.0,106841.12,0.0
-7586,15722993.0,Chukwufumnanya,666.0,Spain,Female,40.0,5.0,0.0,1.0,1.0,0.0,129891.66,1.0
-7587,15752266.0,Shih,704.0,France,Male,27.0,8.0,0.0,2.0,1.0,0.0,162223.66,0.0
-7588,15803378.0,Milanesi,549.0,Spain,Female,34.0,7.0,0.0,2.0,0.0,0.0,177738.98,0.0
-7589,15745012.0,Rios,622.0,France,Female,36.0,9.0,0.0,1.0,0.0,1.0,131609.48,1.0
-7590,15679075.0,Ch'in,605.0,France,Female,58.0,6.0,0.0,2.0,1.0,0.0,172495.81,1.0
-7591,15696859.0,T'ien,731.0,France,Male,32.0,3.0,0.0,2.0,0.0,0.0,143680.73,0.0
-7592,15673074.0,Chinedum,807.0,France,Female,37.0,4.0,0.0,1.0,1.0,0.0,151871.84,1.0
-7593,15752626.0,Smith,648.0,France,Female,26.0,9.0,0.0,2.0,1.0,0.0,38308.34,0.0
-7594,15792722.0,Bischof,559.0,France,Male,32.0,6.0,123863.64,1.0,1.0,0.0,93820.49,0.0
-7595,15733797.0,Miah,656.0,Spain,Male,59.0,9.0,0.0,1.0,0.0,1.0,66548.58,1.0
-7596,15756481.0,Onyemere,643.0,Germany,Male,36.0,6.0,110738.89,2.0,0.0,1.0,185821.41,0.0
-7597,15592386.0,Kaeppel,565.0,Spain,Female,28.0,6.0,0.0,1.0,0.0,1.0,129912.74,0.0
-7598,15780649.0,K?,667.0,Spain,Female,37.0,8.0,0.0,1.0,1.0,1.0,110142.41,0.0
-7599,15763460.0,Cattaneo,762.0,France,Male,37.0,1.0,151836.36,1.0,1.0,1.0,173870.43,0.0
-7600,15612140.0,Watson,601.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,55893.33,0.0
-7601,15750049.0,Chia,588.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,1.0,84790.34,0.0
-7602,15673837.0,DeRose,582.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,152992.04,0.0
-7603,15651868.0,Maclean,669.0,Spain,Female,37.0,7.0,0.0,1.0,1.0,0.0,79623.28,1.0
-7604,15796071.0,Chizuoke,706.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,125140.72,0.0
-7605,15797905.0,Ojiofor,593.0,Germany,Male,62.0,4.0,121542.57,1.0,1.0,1.0,82969.61,0.0
-7606,15744607.0,Tien,558.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,131768.98,0.0
-7607,15812191.0,Ts'ao,593.0,France,Female,30.0,8.0,0.0,1.0,1.0,1.0,114957.22,0.0
-7608,15732672.0,Tretiakov,622.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,84740.68,0.0
-7609,15653776.0,Ch'ien,738.0,Spain,Female,32.0,1.0,0.0,2.0,1.0,0.0,104786.58,0.0
-7610,15768474.0,Chikelu,569.0,France,Male,40.0,8.0,0.0,2.0,0.0,0.0,141822.5,0.0
-7611,15693326.0,Shih,588.0,France,Male,41.0,2.0,0.0,2.0,0.0,0.0,114522.68,0.0
-7612,15677903.0,Kerr,709.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,106977.8,1.0
-7613,15781495.0,Nkemdilim,644.0,France,Male,44.0,3.0,96947.19,1.0,1.0,1.0,152956.33,1.0
-7614,15713483.0,Trevisan,438.0,France,Female,30.0,6.0,0.0,2.0,0.0,1.0,82872.64,0.0
-7615,15674811.0,Chukwuma,719.0,France,Male,46.0,2.0,0.0,2.0,1.0,0.0,172459.39,0.0
-7616,15782467.0,Ugochukwutubelum,571.0,Germany,Male,38.0,8.0,119152.1,1.0,1.0,1.0,116912.45,1.0
-7617,15758513.0,Chibugo,597.0,France,Male,25.0,7.0,107764.75,1.0,1.0,1.0,28368.37,0.0
-7618,15717185.0,Maclean,705.0,France,Male,54.0,0.0,0.0,1.0,1.0,0.0,100995.11,1.0
-7619,15662337.0,Rizzo,667.0,France,Male,47.0,4.0,0.0,2.0,1.0,1.0,140941.47,0.0
-7620,15697034.0,Maclean,511.0,France,Female,39.0,1.0,106871.81,1.0,0.0,0.0,142913.67,0.0
-7621,15600583.0,Yefremova,683.0,France,Male,47.0,5.0,0.0,2.0,1.0,0.0,149912.28,1.0
-7622,15675787.0,Ch'iu,676.0,France,Male,40.0,3.0,0.0,1.0,1.0,0.0,116378.82,0.0
-7623,15655298.0,Tien,682.0,France,Female,38.0,8.0,169824.46,1.0,1.0,0.0,178229.04,0.0
-7624,15803415.0,Ulyanov,683.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,173952.5,0.0
-7625,15623471.0,Onuchukwu,684.0,France,Male,28.0,6.0,0.0,1.0,1.0,1.0,132973.17,1.0
-7626,15727499.0,Ch'in,611.0,France,Female,41.0,7.0,0.0,2.0,1.0,0.0,176730.02,0.0
-7627,15627840.0,Ebelegbulam,676.0,Spain,Male,33.0,10.0,0.0,2.0,1.0,0.0,53710.23,0.0
-7628,15737087.0,P'an,606.0,Germany,Female,40.0,4.0,113090.4,1.0,0.0,0.0,121409.06,1.0
-7629,15752467.0,Yeh,667.0,France,Female,37.0,1.0,127570.93,1.0,1.0,0.0,196257.67,0.0
-7630,15723720.0,Calabrese,683.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,152956.33,0.0
-7631,15730705.0,Chin,743.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,83056.22,0.0
-7632,15688251.0,Nnanna,618.0,France,Female,30.0,7.0,0.0,2.0,1.0,1.0,154775.7,0.0
-7633,15744044.0,Yeh,710.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,582.59,0.0
-7634,15723006.0,K'ung,579.0,France,Female,38.0,2.0,162560.32,2.0,1.0,0.0,71139.31,0.0
-7635,15659399.0,Nkemdilim,587.0,Spain,Male,40.0,7.0,144875.37,1.0,1.0,1.0,182355.21,0.0
-7636,15724848.0,Ch'iu,558.0,Germany,Male,35.0,2.0,111599.32,1.0,1.0,1.0,168460.12,1.0
-7637,15648032.0,Chukwualuka,803.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,162452.65,0.0
-7638,15802758.0,Chidi,721.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,151903.6,0.0
-7639,15736299.0,Chidimma,681.0,Germany,Female,35.0,6.0,139756.12,2.0,0.0,1.0,182495.7,0.0
-7640,15651533.0,Bancroft,710.0,France,Male,36.0,8.0,0.0,1.0,1.0,0.0,88890.05,0.0
-7641,15651315.0,Mazzi,646.0,France,Female,42.0,8.0,0.0,2.0,0.0,0.0,155721.15,0.0
-7642,15718106.0,Lai,850.0,France,Female,45.0,1.0,0.0,2.0,1.0,0.0,73977.23,0.0
-7643,15589969.0,Hsiao,708.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,154971.63,0.0
-7644,15665784.0,Hsieh,667.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,0.0,582.59,0.0
-7645,15728963.0,P'eng,682.0,Spain,Male,35.0,7.0,172646.82,1.0,1.0,0.0,182728.8,0.0
-7646,15594576.0,Chukwuebuka,579.0,Germany,Female,22.0,1.0,121231.39,1.0,1.0,1.0,161063.89,1.0
-7647,15796413.0,Chuang,776.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,0.0,10063.75,0.0
-7648,15678916.0,Liao,598.0,Spain,Male,37.0,6.0,124495.98,1.0,1.0,0.0,108872.45,0.0
-7649,15713890.0,Lai,640.0,France,Male,40.0,2.0,0.0,1.0,1.0,0.0,101828.39,0.0
-7650,15626452.0,Chiedozie,673.0,Germany,Male,40.0,6.0,122325.58,2.0,1.0,0.0,104889.3,0.0
-7651,15758035.0,Chuang,743.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,1.0,92888.52,0.0
-7652,15678725.0,Ts'ui,685.0,Germany,Female,33.0,6.0,121407.68,2.0,1.0,1.0,64516.8,0.0
-7653,15586479.0,Chukwukadibia,682.0,France,Male,31.0,7.0,122029.15,1.0,1.0,0.0,142911.99,0.0
-7654,15699325.0,Hs?,516.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,140800.66,0.0
-7655,15733444.0,Bell,583.0,France,Male,34.0,6.0,0.0,2.0,0.0,0.0,166650.9,0.0
-7656,15664752.0,Babbage,714.0,France,Female,31.0,8.0,142388.61,2.0,1.0,0.0,66463.62,1.0
-7657,15610755.0,Toscani,729.0,France,Male,55.0,1.0,0.0,1.0,1.0,0.0,77326.78,1.0
-7658,15680141.0,Kuo,634.0,France,Female,55.0,5.0,182364.53,1.0,1.0,0.0,167554.86,1.0
-7659,15714227.0,Ting,651.0,Spain,Female,30.0,3.0,135650.88,1.0,1.0,1.0,54726.43,0.0
-7660,15791216.0,Ewing,584.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,88703.04,0.0
-7661,15693637.0,P'an,634.0,France,Male,25.0,4.0,0.0,2.0,0.0,1.0,151411.4,0.0
-7662,15659931.0,Niu,611.0,France,Female,29.0,5.0,0.0,2.0,1.0,0.0,183358.21,0.0
-7663,15604576.0,Onwumelu,632.0,Germany,Female,43.0,2.0,100116.82,1.0,1.0,0.0,138399.67,1.0
-7664,15665385.0,Bibi,655.0,France,Female,26.0,9.0,0.0,1.0,0.0,1.0,86657.48,0.0
-7665,15594878.0,Hsia,733.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,88890.05,0.0
-7666,15634143.0,Chukwudi,590.0,Germany,Female,35.0,2.0,114007.5,3.0,1.0,0.0,121894.31,1.0
-7667,15732437.0,Lai,597.0,France,Male,34.0,4.0,0.0,2.0,0.0,0.0,48684.93,0.0
-7668,15675380.0,K'ung,793.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,148811.81,0.0
-7669,15762882.0,Cattaneo,711.0,Spain,Male,43.0,4.0,0.0,2.0,0.0,1.0,85890.75,0.0
-7670,15719579.0,Chibugo,721.0,Spain,Male,23.0,4.0,105000.23,1.0,1.0,1.0,94875.88,0.0
-7671,15605742.0,Ch'iu,668.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,71173.64,0.0
-7672,15717164.0,Chiang,562.0,France,Female,33.0,5.0,0.0,1.0,0.0,1.0,38276.84,1.0
-7673,15609510.0,Lo,583.0,France,Male,32.0,0.0,0.0,2.0,1.0,1.0,126794.56,0.0
-7674,15680683.0,P'an,588.0,France,Male,43.0,9.0,0.0,2.0,1.0,1.0,142223.94,0.0
-7675,15639552.0,Hsiung,579.0,Germany,Female,35.0,1.0,134599.33,1.0,1.0,1.0,60906.12,0.0
-7676,15778830.0,De Luca,699.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,131283.6,0.0
-7677,15795881.0,Teng,584.0,Spain,Female,29.0,1.0,0.0,1.0,1.0,0.0,27886.06,0.0
-7678,15585067.0,Ch'in,586.0,Spain,Male,62.0,9.0,0.0,2.0,1.0,1.0,150850.29,0.0
-7679,15672798.0,Okonkwo,614.0,France,Female,36.0,9.0,136659.75,1.0,1.0,0.0,89935.73,0.0
-7680,15591279.0,K?,814.0,Germany,Female,35.0,6.0,161887.81,1.0,1.0,0.0,126565.57,0.0
-7681,15684592.0,Chukwuhaenye,663.0,France,Female,29.0,5.0,0.0,1.0,1.0,1.0,19720.08,0.0
-7682,15701176.0,Okechukwu,804.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,62390.59,0.0
-7683,15782454.0,Ting,705.0,Spain,Male,27.0,10.0,0.0,2.0,1.0,0.0,124532.9,0.0
-7684,15572792.0,Pinto,619.0,Spain,Male,50.0,2.0,0.0,2.0,0.0,1.0,48606.71,0.0
-7685,15635197.0,Hsia,616.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,151316.37,0.0
-7686,15652667.0,Biryukov,713.0,France,Female,41.0,9.0,160838.13,1.0,1.0,0.0,133871.83,0.0
-7687,15641411.0,McGregor,645.0,Spain,Male,33.0,3.0,87541.06,1.0,1.0,0.0,135077.01,1.0
-7688,15670552.0,Chieloka,573.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,105969.05,0.0
-7689,15802758.0,K?,587.0,France,Male,36.0,10.0,0.0,1.0,1.0,0.0,147720.27,0.0
-7690,15679394.0,Teng,597.0,France,Female,41.0,1.0,92350.49,1.0,1.0,0.0,121504.18,0.0
-7691,15702464.0,Ts'ui,748.0,Spain,Male,40.0,6.0,0.0,1.0,1.0,1.0,121751.04,0.0
-7692,15686973.0,Toscani,637.0,Germany,Female,40.0,8.0,123048.38,1.0,0.0,0.0,65219.85,0.0
-7693,15799480.0,O'Connor,742.0,Germany,Male,58.0,6.0,124954.94,1.0,1.0,1.0,79919.13,0.0
-7694,15602554.0,Chukwumaobim,614.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,171482.56,0.0
-7695,15778287.0,Clements,680.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,1.0,138882.98,0.0
-7696,15793723.0,Ch'en,712.0,Germany,Male,33.0,1.0,95858.37,2.0,1.0,1.0,80552.11,0.0
-7697,15775335.0,Hsia,556.0,Spain,Male,42.0,2.0,112088.04,1.0,1.0,1.0,62944.75,0.0
-7698,15680449.0,Chibueze,656.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,140947.05,0.0
-7699,15793042.0,Maclean,667.0,France,Male,28.0,4.0,0.0,2.0,0.0,0.0,131778.58,0.0
-7700,15694566.0,Mazzanti,762.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,127569.8,0.0
-7701,15626135.0,Cattaneo,683.0,France,Female,27.0,0.0,0.0,1.0,0.0,0.0,132436.71,0.0
-7702,15652901.0,Chiu,565.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,88852.47,0.0
-7703,15804721.0,McIntyre,529.0,France,Male,24.0,8.0,0.0,1.0,0.0,0.0,114083.73,1.0
-7704,15675501.0,Chiganu,645.0,France,Male,33.0,7.0,129738.61,1.0,1.0,1.0,151871.84,0.0
-7705,15587584.0,Trevisani,677.0,France,Male,58.0,1.0,0.0,1.0,1.0,1.0,148087.62,1.0
-7706,15589230.0,Ozerova,597.0,France,Male,40.0,5.0,0.0,2.0,1.0,1.0,143463.28,0.0
-7707,15794356.0,Ritchie,695.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,1.0,145796.49,0.0
-7708,15684908.0,Chimezie,584.0,Spain,Female,34.0,5.0,0.0,1.0,1.0,0.0,71175.94,0.0
-7709,15587562.0,Toscani,705.0,France,Female,22.0,10.0,0.0,2.0,0.0,1.0,77326.78,0.0
-7710,15592089.0,Chizoba,652.0,Spain,Female,48.0,9.0,0.0,1.0,1.0,0.0,52449.62,0.0
-7711,15654891.0,T'ang,641.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,86657.48,0.0
-7712,15671390.0,Trevisan,651.0,France,Female,32.0,5.0,0.0,2.0,0.0,0.0,180969.55,0.0
-7713,15662067.0,L?,682.0,Spain,Male,30.0,1.0,114370.41,1.0,1.0,1.0,66825.33,0.0
-7714,15757981.0,Ch'ien,665.0,Germany,Male,25.0,8.0,95675.62,2.0,1.0,1.0,187839.9,0.0
-7715,15594515.0,K?,543.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,138871.12,0.0
-7716,15792473.0,Toscani,663.0,France,Male,56.0,8.0,0.0,1.0,1.0,1.0,55701.62,1.0
-7717,15684461.0,Cattaneo,649.0,France,Female,61.0,1.0,0.0,3.0,1.0,0.0,157549.6,1.0
-7718,15762045.0,Zetticci,806.0,Germany,Male,37.0,1.0,121093.28,1.0,0.0,0.0,167621.16,0.0
-7719,15665590.0,Ewing,752.0,Germany,Male,56.0,7.0,107944.39,1.0,0.0,0.0,153913.38,1.0
-7720,15659736.0,Ch'eng,818.0,Spain,Female,36.0,5.0,0.0,1.0,1.0,0.0,25161.61,1.0
-7721,15672237.0,Tien,709.0,France,Male,33.0,6.0,0.0,1.0,1.0,1.0,175025.28,0.0
-7722,15778975.0,Hs?,635.0,Germany,Female,70.0,9.0,131519.12,1.0,0.0,1.0,126812.63,1.0
-7723,15701602.0,Gell,739.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,182528.89,0.0
-7724,15690743.0,Ch'iu,578.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,130590.35,0.0
-7725,15777896.0,Rizzo,726.0,Germany,Female,49.0,1.0,127801.88,1.0,1.0,0.0,174027.3,1.0
-7726,15620276.0,Manna,592.0,France,Male,29.0,1.0,122378.88,2.0,1.0,1.0,114951.42,1.0
-7727,15654489.0,Chukwudi,553.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,123499.82,0.0
-7728,15686963.0,Pugh,850.0,France,Female,47.0,9.0,0.0,1.0,0.0,1.0,137028.51,1.0
-7729,15779886.0,Nucci,677.0,Spain,Female,36.0,3.0,0.0,2.0,1.0,1.0,175853.64,0.0
-7730,15773421.0,Lucciano,643.0,Germany,Male,36.0,3.0,119782.72,1.0,1.0,0.0,161574.01,0.0
-7731,15757911.0,Davison,749.0,France,Male,54.0,2.0,107720.64,1.0,1.0,0.0,103480.69,1.0
-7732,15800295.0,Chu,581.0,France,Male,25.0,4.0,0.0,1.0,1.0,0.0,130189.72,0.0
-7733,15692463.0,Tsai,545.0,France,Female,39.0,1.0,0.0,1.0,1.0,0.0,139431.0,0.0
-7734,15694821.0,Dyer,687.0,France,Male,25.0,5.0,0.0,2.0,1.0,0.0,132770.12,0.0
-7735,15603550.0,Tuan,577.0,France,Male,41.0,3.0,0.0,2.0,1.0,1.0,145796.49,0.0
-7736,15752294.0,Chukwualuka,589.0,Germany,Female,43.0,2.0,134956.02,1.0,1.0,1.0,172618.52,1.0
-7737,15690925.0,Ritchie,684.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,66588.6,0.0
-7738,15681075.0,Ting,749.0,France,Male,52.0,9.0,0.0,1.0,0.0,1.0,121408.06,1.0
-7739,15781619.0,Hsia,753.0,France,Male,52.0,6.0,0.0,2.0,1.0,0.0,185599.12,0.0
-7740,15766064.0,Chia,733.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,0.0,36292.94,0.0
-7741,15661290.0,Nwankwo,569.0,France,Female,64.0,5.0,0.0,1.0,1.0,0.0,107964.36,1.0
-7742,15764021.0,Chukwualuka,656.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,0.0,173632.92,0.0
-7743,15592461.0,Nnonso,629.0,France,Male,33.0,6.0,0.0,2.0,0.0,1.0,12577.21,0.0
-7744,15758031.0,Ma,769.0,Spain,Male,21.0,3.0,125561.97,1.0,1.0,1.0,38170.04,0.0
-7745,15605067.0,Nnamutaezinwa,722.0,France,Female,27.0,2.0,0.0,2.0,1.0,0.0,167931.81,0.0
-7746,15592583.0,Yobanna,637.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,49775.58,0.0
-7747,15614643.0,Chiang,654.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,102057.86,0.0
-7748,15571917.0,Kuznetsova,629.0,France,Female,66.0,4.0,0.0,2.0,1.0,1.0,60906.12,0.0
-7749,15754849.0,Longo,652.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,151902.78,0.0
-7750,15743817.0,Folliero,629.0,Spain,Male,23.0,2.0,135903.33,1.0,1.0,0.0,52796.31,1.0
-7751,15574324.0,Tung,663.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,125962.02,0.0
-7752,15807065.0,Kao,850.0,France,Female,35.0,1.0,0.0,2.0,0.0,1.0,82298.59,0.0
-7753,15753842.0,Shih,585.0,France,Female,44.0,4.0,0.0,2.0,1.0,1.0,66462.79,0.0
-7754,15691792.0,Yobachi,850.0,Germany,Male,48.0,2.0,133950.67,1.0,0.0,1.0,97623.42,1.0
-7755,15650086.0,P'eng,646.0,Germany,Male,37.0,3.0,141792.61,1.0,0.0,1.0,46884.1,0.0
-7756,15738785.0,Ogochukwu,596.0,Spain,Female,22.0,4.0,0.0,2.0,1.0,1.0,79919.13,0.0
-7757,15728885.0,Chiu,581.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,119882.86,0.0
-7758,15690731.0,Tuan,683.0,France,Female,38.0,2.0,0.0,1.0,1.0,0.0,186489.95,1.0
-7759,15697360.0,Maclean,613.0,France,Female,44.0,5.0,0.0,2.0,1.0,0.0,180197.74,0.0
-7760,15779973.0,Manna,694.0,France,Male,38.0,1.0,0.0,1.0,1.0,1.0,1713.1,1.0
-7761,15765806.0,Scott,588.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,97938.59,0.0
-7762,15639552.0,Hs?,567.0,Germany,Male,28.0,0.0,122031.55,1.0,1.0,0.0,182650.15,0.0
-7763,15757981.0,Cremonesi,581.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,52876.41,0.0
-7764,15766355.0,Kaur,593.0,Germany,Male,36.0,7.0,109681.61,2.0,1.0,0.0,70563.9,0.0
-7765,15653937.0,Naylor,667.0,France,Female,37.0,3.0,145729.71,1.0,1.0,1.0,163570.25,0.0
-7766,15593694.0,Lai,705.0,Spain,Male,28.0,4.0,0.0,2.0,0.0,1.0,62454.01,0.0
-7767,15716186.0,Okeke,695.0,Germany,Male,34.0,2.0,149961.99,2.0,0.0,1.0,82709.8,0.0
-7768,15631838.0,Ch'ien,707.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,184524.65,0.0
-7769,15613017.0,Padovesi,696.0,France,Male,41.0,6.0,142721.52,2.0,1.0,1.0,44244.49,0.0
-7770,15662085.0,De Luca,711.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,0.0,86797.41,0.0
-7771,15702293.0,Chiu,676.0,France,Female,41.0,9.0,0.0,1.0,1.0,0.0,56594.36,0.0
-7772,15625126.0,Lombardo,636.0,Germany,Male,41.0,4.0,99106.73,2.0,1.0,1.0,157549.6,0.0
-7773,15771535.0,Li Fonti,633.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,54448.36,0.0
-7774,15756875.0,Chidimma,633.0,Germany,Female,60.0,0.0,143903.12,1.0,1.0,1.0,134901.58,0.0
-7775,15747542.0,De Luca,713.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,93524.19,0.0
-7776,15782530.0,Chidiebele,717.0,Spain,Male,41.0,2.0,125265.61,1.0,1.0,1.0,46824.08,0.0
-7777,15767414.0,Hsiao,646.0,France,Male,33.0,1.0,91621.69,1.0,1.0,0.0,138565.96,0.0
-7778,15775803.0,Chiazagomekpere,625.0,Spain,Male,30.0,7.0,0.0,2.0,1.0,0.0,179978.68,0.0
-7779,15699450.0,Macnamara,721.0,Spain,Male,37.0,1.0,61297.05,1.0,1.0,0.0,45956.05,1.0
-7780,15793890.0,Uwaezuoke,726.0,Spain,Female,36.0,3.0,129837.72,1.0,1.0,0.0,106534.15,0.0
-7781,15640078.0,Chu,673.0,France,Male,20.0,7.0,0.0,2.0,1.0,1.0,158580.05,0.0
-7782,15572461.0,Okwuoma,484.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,99645.04,0.0
-7783,15702656.0,Chin,637.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,0.0,168886.18,0.0
-7784,15780359.0,Brown,759.0,Germany,Male,48.0,3.0,111379.55,1.0,1.0,0.0,64716.08,1.0
-7785,15598070.0,Yevseyev,681.0,France,Male,32.0,3.0,135814.7,1.0,1.0,1.0,134851.12,0.0
-7786,15610686.0,Tseng,753.0,France,Male,27.0,5.0,0.0,2.0,1.0,1.0,72876.65,0.0
-7787,15586699.0,Pinto,543.0,Germany,Female,37.0,9.0,120193.42,1.0,1.0,0.0,48606.71,0.0
-7788,15623210.0,Young,655.0,France,Female,44.0,2.0,0.0,2.0,1.0,0.0,184843.77,0.0
-7789,15690182.0,Chienezie,571.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,153400.24,0.0
-7790,15683363.0,Watson,683.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,64771.61,0.0
-7791,15723488.0,Maclean,722.0,France,Female,43.0,1.0,0.0,1.0,1.0,0.0,150422.32,1.0
-7792,15618182.0,Genovesi,710.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,71830.1,0.0
-7793,15647842.0,Mao,742.0,Germany,Male,42.0,6.0,122084.63,1.0,1.0,0.0,111241.37,1.0
-7794,15663885.0,White,678.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,170829.87,0.0
-7795,15682528.0,Nebechukwu,645.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,120107.1,0.0
-7796,15697360.0,Hsing,738.0,France,Male,55.0,3.0,0.0,1.0,1.0,1.0,183584.14,1.0
-7797,15802466.0,Niu,556.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,141827.83,0.0
-7798,15774536.0,Kepley,547.0,France,Female,31.0,8.0,0.0,2.0,0.0,0.0,56562.57,0.0
-7799,15581548.0,Milne,683.0,France,Male,33.0,6.0,125552.4,1.0,1.0,1.0,91652.43,0.0
-7800,15663883.0,Ts'ao,627.0,Germany,Male,39.0,1.0,112615.86,1.0,1.0,1.0,121551.93,0.0
-7801,15745399.0,Pisano,667.0,Germany,Female,24.0,5.0,152296.84,1.0,1.0,0.0,177084.04,0.0
-7802,15754526.0,Trevisani,848.0,Germany,Male,34.0,5.0,106901.42,1.0,0.0,1.0,152571.61,0.0
-7803,15677020.0,Chidiegwu,656.0,Spain,Male,54.0,10.0,0.0,1.0,1.0,0.0,5983.8,1.0
-7804,15663438.0,T'ao,768.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,79919.13,0.0
-7805,15792726.0,Pisano,589.0,Germany,Female,45.0,6.0,132951.92,2.0,1.0,1.0,159800.16,0.0
-7806,15806570.0,Ch'in,679.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,166495.2,1.0
-7807,15574910.0,Obiajulu,632.0,Spain,Female,28.0,2.0,0.0,1.0,0.0,1.0,62634.94,1.0
-7808,15641389.0,Ch'eng,619.0,Germany,Male,26.0,4.0,152880.97,2.0,1.0,0.0,113483.96,0.0
-7809,15592089.0,Fiorentini,633.0,Germany,Female,67.0,8.0,119327.93,1.0,1.0,1.0,16281.94,0.0
-7810,15779915.0,Hs?,738.0,France,Male,43.0,5.0,0.0,2.0,0.0,0.0,51637.67,0.0
-7811,15698528.0,Hsia,635.0,France,Female,40.0,6.0,0.0,1.0,0.0,1.0,100101.06,0.0
-7812,15751177.0,Ch'iu,588.0,Germany,Female,55.0,8.0,144961.97,1.0,1.0,0.0,171344.09,1.0
-7813,15677906.0,Hsia,651.0,Germany,Female,50.0,3.0,108724.98,1.0,1.0,0.0,143301.49,1.0
-7814,15796413.0,Nwebube,537.0,France,Male,37.0,8.0,122570.87,1.0,1.0,0.0,131363.57,0.0
-7815,15658432.0,Hsiung,576.0,France,Male,58.0,1.0,131342.53,1.0,1.0,1.0,175544.02,0.0
-7816,15589437.0,Toscani,684.0,France,Female,32.0,8.0,0.0,1.0,1.0,0.0,170634.49,1.0
-7817,15733476.0,Genovesi,597.0,Spain,Female,38.0,8.0,0.0,1.0,0.0,0.0,164967.21,1.0
-7818,15638159.0,Tien,682.0,France,Male,41.0,10.0,0.0,1.0,0.0,1.0,123866.28,0.0
-7819,15624229.0,Nwabugwu,651.0,France,Male,46.0,3.0,80793.58,1.0,1.0,1.0,165008.97,0.0
-7820,15690546.0,Chikezie,554.0,Germany,Male,39.0,8.0,122451.46,2.0,1.0,0.0,140941.47,1.0
-7821,15592937.0,Chuang,592.0,Spain,Female,40.0,6.0,0.0,2.0,0.0,0.0,72945.96,0.0
-7822,15809263.0,Chukwubuikem,667.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,121210.09,0.0
-7823,15771580.0,Nwachinemelu,615.0,France,Male,26.0,2.0,0.0,1.0,1.0,1.0,53710.23,0.0
-7824,15747534.0,Hsiung,752.0,Germany,Male,57.0,3.0,121152.05,2.0,1.0,1.0,82820.85,0.0
-7825,15794468.0,Mazzanti,652.0,France,Male,38.0,6.0,123544.0,1.0,1.0,1.0,9241.52,0.0
-7826,15715888.0,Ch'ien,711.0,Spain,Male,26.0,2.0,0.0,2.0,1.0,0.0,158264.62,0.0
-7827,15596074.0,Udinesi,745.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,140937.97,0.0
-7828,15606841.0,Zuev,663.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,182738.0,0.0
-7829,15793893.0,Chin,667.0,France,Male,41.0,9.0,0.0,2.0,1.0,1.0,166588.76,0.0
-7830,15701175.0,Chidubem,686.0,France,Female,43.0,7.0,0.0,2.0,1.0,0.0,79577.38,0.0
-7831,15704844.0,Chibugo,682.0,Germany,Female,40.0,8.0,71667.97,2.0,1.0,1.0,159823.16,0.0
-7832,15592773.0,O'Donnell,687.0,France,Male,33.0,9.0,0.0,2.0,0.0,0.0,128881.87,0.0
-7833,15585300.0,Lucchesi,686.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,169117.74,1.0
-7834,15731478.0,Hsiao,768.0,France,Female,40.0,9.0,0.0,1.0,1.0,0.0,149913.84,1.0
-7835,15719302.0,Ting,762.0,Germany,Male,54.0,4.0,123675.69,1.0,1.0,0.0,119830.08,1.0
-7836,15661036.0,Nkemjika,659.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,143463.18,0.0
-7837,15798960.0,Lucchesi,681.0,France,Female,41.0,10.0,139994.66,1.0,1.0,0.0,94820.85,0.0
-7838,15806945.0,Chukwudi,617.0,Spain,Male,27.0,10.0,0.0,2.0,0.0,0.0,162452.65,0.0
-7839,15680370.0,Chien,542.0,France,Male,39.0,5.0,0.0,2.0,0.0,0.0,163944.18,1.0
-7840,15623647.0,Chukwubuikem,599.0,France,Female,37.0,6.0,0.0,1.0,1.0,0.0,189268.81,1.0
-7841,15612478.0,Tsao,541.0,France,Male,24.0,5.0,0.0,2.0,0.0,1.0,180829.99,0.0
-7842,15771857.0,T'ao,556.0,France,Female,26.0,7.0,0.0,1.0,1.0,1.0,95393.43,0.0
-7843,15665087.0,Y?,711.0,France,Female,37.0,6.0,118627.07,1.0,1.0,1.0,161579.85,0.0
-7844,15802274.0,Hsieh,709.0,France,Male,31.0,0.0,0.0,2.0,1.0,0.0,56767.67,0.0
-7845,15612187.0,Lo Duca,729.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,1.0,170819.01,0.0
-7846,15737795.0,Trevisani,539.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,126837.72,0.0
-7847,15689614.0,Hsing,538.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,159800.16,0.0
-7848,15685826.0,Ku,738.0,France,Male,39.0,7.0,0.0,1.0,1.0,0.0,74850.84,0.0
-7849,15710408.0,Chiu,544.0,France,Female,40.0,1.0,81231.85,2.0,1.0,1.0,132959.33,0.0
-7850,15781702.0,Hs?,632.0,Germany,Female,41.0,9.0,122949.14,1.0,1.0,0.0,111032.79,1.0
-7851,15626141.0,Pagnotto,687.0,Germany,Female,35.0,7.0,111852.84,1.0,1.0,0.0,103557.97,0.0
-7852,15641480.0,Pirozzi,596.0,France,Male,38.0,9.0,0.0,2.0,0.0,1.0,168515.61,0.0
-7853,15779176.0,Chidozie,678.0,France,Male,33.0,3.0,0.0,1.0,1.0,1.0,168460.12,0.0
-7854,15692406.0,Maclean,592.0,France,Female,46.0,3.0,0.0,2.0,1.0,1.0,182266.01,0.0
-7855,15662085.0,Y?,543.0,Germany,Male,27.0,4.0,116318.23,1.0,1.0,0.0,164061.6,0.0
-7856,15733797.0,Lucchese,638.0,France,Female,47.0,4.0,0.0,1.0,1.0,1.0,164741.5,1.0
-7857,15572390.0,Nnaemeka,712.0,Germany,Male,39.0,3.0,119741.77,1.0,1.0,1.0,147059.16,1.0
-7858,15785831.0,Ku,667.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,137961.41,0.0
-7859,15604130.0,Clements,538.0,France,Female,28.0,6.0,74963.5,1.0,1.0,0.0,65986.48,0.0
-7860,15632264.0,Onyekachi,806.0,France,Female,33.0,7.0,0.0,1.0,1.0,1.0,121073.23,0.0
-7861,15771856.0,Chia,633.0,France,Male,48.0,10.0,0.0,2.0,1.0,1.0,121311.12,0.0
-7862,15757577.0,T'ien,593.0,Germany,Female,42.0,3.0,120905.03,1.0,1.0,0.0,51959.82,1.0
-7863,15690169.0,Chukwuma,589.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,89983.02,0.0
-7864,15660301.0,Chigbogu,641.0,Spain,Male,41.0,3.0,0.0,2.0,0.0,0.0,104469.06,0.0
-7865,15741875.0,Pirozzi,710.0,France,Female,38.0,4.0,0.0,1.0,1.0,0.0,184474.72,0.0
-7866,15723006.0,Hsia,704.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,35701.06,0.0
-7867,15603665.0,Tung,637.0,Germany,Male,33.0,9.0,157955.83,2.0,1.0,1.0,41970.72,0.0
-7868,15730273.0,Pisano,686.0,France,Male,59.0,5.0,0.0,2.0,1.0,1.0,176730.02,0.0
-7869,15612109.0,Manna,717.0,France,Male,25.0,1.0,0.0,2.0,1.0,0.0,174027.3,0.0
-7870,15780128.0,Ch'ien,683.0,France,Male,35.0,7.0,0.0,1.0,1.0,0.0,120853.83,0.0
-7871,15764537.0,Ch'iu,699.0,Germany,Female,44.0,0.0,82291.1,1.0,1.0,0.0,131304.45,0.0
-7872,15576085.0,Macleod,661.0,France,Female,37.0,10.0,153366.13,1.0,1.0,0.0,124596.63,1.0
-7873,15674543.0,L?,669.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,67830.2,0.0
-7874,15797733.0,Chukwudi,737.0,Germany,Male,38.0,4.0,109978.83,2.0,1.0,0.0,5791.85,0.0
-7875,15751561.0,Chukwualuka,652.0,Spain,Female,44.0,7.0,0.0,1.0,1.0,1.0,137519.92,1.0
-7876,15683553.0,Palerma,673.0,France,Male,22.0,2.0,0.0,1.0,1.0,1.0,176692.87,0.0
-7877,15574858.0,Zikoranachidimma,613.0,Spain,Female,47.0,3.0,100628.0,2.0,0.0,0.0,123982.14,1.0
-7878,15793723.0,L?,645.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,184520.0,0.0
-7879,15796834.0,P'eng,613.0,Spain,Female,34.0,9.0,0.0,2.0,0.0,1.0,141872.05,0.0
-7880,15806570.0,Yefimova,681.0,Spain,Female,38.0,4.0,0.0,1.0,1.0,0.0,158891.81,1.0
-7881,15576078.0,T'ien,668.0,France,Male,34.0,7.0,0.0,1.0,1.0,0.0,178393.68,0.0
-7882,15652981.0,Chibueze,651.0,France,Female,32.0,9.0,0.0,1.0,1.0,0.0,79919.13,0.0
-7883,15598046.0,Nnachetam,652.0,Germany,Female,49.0,4.0,115371.94,2.0,1.0,1.0,138880.24,1.0
-7884,15600882.0,Mazzi,663.0,France,Female,38.0,7.0,0.0,2.0,1.0,0.0,58883.91,0.0
-7885,15703437.0,Fanucci,778.0,Germany,Male,45.0,6.0,143391.07,1.0,0.0,1.0,181224.56,0.0
-7886,15612073.0,Onyemere,510.0,France,Female,44.0,1.0,0.0,2.0,0.0,1.0,178038.17,0.0
-7887,15587585.0,Uspenskaya,569.0,Spain,Female,41.0,1.0,0.0,2.0,1.0,0.0,79919.13,0.0
-7888,15642870.0,Mazzi,667.0,Spain,Male,46.0,9.0,0.0,2.0,1.0,1.0,85901.09,0.0
-7889,15685300.0,Mault,692.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,48963.59,0.0
-7890,15617320.0,Mazzanti,738.0,Germany,Female,48.0,6.0,145644.05,1.0,1.0,0.0,176712.59,1.0
-7891,15714642.0,Chin,749.0,France,Male,28.0,3.0,0.0,1.0,0.0,0.0,102547.56,0.0
-7892,15605835.0,Ts'ai,762.0,Germany,Male,41.0,2.0,125789.69,2.0,1.0,0.0,52861.89,0.0
-7893,15650575.0,Macleod,696.0,Spain,Female,63.0,1.0,0.0,2.0,1.0,1.0,79734.23,0.0
-7894,15802088.0,Folliero,632.0,France,Male,42.0,8.0,112708.2,1.0,1.0,1.0,74564.41,0.0
-7895,15807312.0,Okwudilichukwu,535.0,Germany,Female,58.0,3.0,121894.31,1.0,1.0,0.0,120892.72,1.0
-7896,15585892.0,H?,652.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,84760.32,0.0
-7897,15703544.0,O'Sullivan,649.0,Germany,Male,32.0,1.0,138053.69,1.0,1.0,0.0,129964.54,0.0
-7898,15642824.0,Baresi,619.0,France,Female,39.0,6.0,104944.1,1.0,1.0,1.0,71830.1,0.0
-7899,15624072.0,Chuang,581.0,Spain,Female,33.0,1.0,145537.21,1.0,1.0,0.0,158840.99,0.0
-7900,15732832.0,Yefimova,582.0,France,Female,42.0,9.0,0.0,1.0,0.0,0.0,148087.88,1.0
-7901,15652658.0,Tsao,714.0,Spain,Female,35.0,8.0,0.0,2.0,1.0,1.0,131660.31,0.0
-7902,15626243.0,Ozerova,590.0,Spain,Male,36.0,3.0,0.0,1.0,1.0,0.0,187905.12,0.0
-7903,15787830.0,Ch'iu,554.0,France,Male,25.0,5.0,133040.32,1.0,1.0,0.0,14917.09,0.0
-7904,15686909.0,Lo,704.0,Germany,Female,43.0,3.0,106328.08,1.0,1.0,0.0,171519.06,1.0
-7905,15733261.0,Kambinachi,707.0,Spain,Male,39.0,1.0,138053.79,1.0,1.0,0.0,142911.99,0.0
-7906,15588955.0,Nnaife,850.0,France,Male,39.0,2.0,0.0,1.0,1.0,0.0,116537.96,0.0
-7907,15800845.0,Chiawuotu,619.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,94875.09,0.0
-7908,15751175.0,Smith,653.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,70532.53,0.0
-7909,15633136.0,Ch'ien,598.0,France,Male,38.0,10.0,0.0,2.0,0.0,0.0,63889.1,1.0
-7910,15666032.0,Chiekwugo,632.0,France,Female,37.0,0.0,132730.17,1.0,1.0,0.0,159483.76,0.0
-7911,15683618.0,Uvarova,692.0,Spain,Female,39.0,4.0,0.0,2.0,1.0,1.0,87422.15,0.0
-7912,15676571.0,Tien,714.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,146895.86,0.0
-7913,15733261.0,Ch'iu,593.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,92920.04,0.0
-7914,15633586.0,Trevisani,714.0,France,Male,44.0,9.0,0.0,2.0,1.0,0.0,147913.56,0.0
-7915,15733777.0,Ch'iu,637.0,France,Male,21.0,5.0,71283.09,1.0,1.0,1.0,127847.25,0.0
-7916,15689612.0,Pisani,612.0,France,Male,30.0,1.0,121405.04,1.0,1.0,1.0,55829.25,0.0
-7917,15756538.0,Lettiere,535.0,Germany,Female,54.0,2.0,116969.28,1.0,0.0,0.0,199808.1,1.0
-7918,15802486.0,Ch'iu,630.0,Spain,Male,28.0,6.0,0.0,2.0,1.0,1.0,161653.5,0.0
-7919,15738267.0,Ts'ui,696.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,57549.65,0.0
-7920,15785547.0,Ts'ui,581.0,France,Male,47.0,1.0,0.0,1.0,0.0,0.0,158891.81,1.0
-7921,15706186.0,Okwuoma,679.0,Spain,Male,35.0,1.0,117524.72,1.0,1.0,1.0,148814.54,0.0
-7922,15583595.0,Trevisani,707.0,France,Male,42.0,9.0,121523.51,1.0,1.0,0.0,7953.62,0.0
-7923,15675256.0,Yefremova,685.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,96590.39,0.0
-7924,15573798.0,Okechukwu,691.0,Spain,Male,40.0,7.0,99169.67,1.0,1.0,1.0,152491.31,0.0
-7925,15656912.0,Martin,619.0,France,Female,49.0,0.0,0.0,1.0,1.0,0.0,145562.4,1.0
-7926,15686588.0,Nkemdirim,554.0,France,Male,39.0,5.0,0.0,2.0,0.0,1.0,126561.09,0.0
-7927,15645896.0,Y?,792.0,France,Female,44.0,2.0,0.0,2.0,0.0,1.0,182843.24,0.0
-7928,15577423.0,Hao,744.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,65986.48,0.0
-7929,15596074.0,Genovese,697.0,France,Male,35.0,4.0,0.0,2.0,0.0,1.0,198269.08,0.0
-7930,15811290.0,Trevisani,645.0,Germany,Female,38.0,7.0,144852.06,2.0,1.0,1.0,113557.7,0.0
-7931,15613282.0,Chidiebere,634.0,Germany,Male,44.0,6.0,124442.42,1.0,1.0,1.0,120830.73,1.0
-7932,15594928.0,Onyekachi,579.0,Germany,Male,42.0,7.0,111168.97,2.0,1.0,0.0,160526.36,0.0
-7933,15642530.0,Chidubem,653.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,131734.43,0.0
-7934,15683053.0,Cattaneo,697.0,France,Male,22.0,10.0,0.0,2.0,1.0,1.0,128841.31,0.0
-7935,15641690.0,Ch'eng,775.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,60721.4,0.0
-7936,15717290.0,Macleod,641.0,France,Female,31.0,0.0,0.0,2.0,1.0,0.0,112979.6,0.0
-7937,15776631.0,Grieve,575.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,0.0,72969.9,0.0
-7938,15671514.0,Scott,758.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,52318.66,0.0
-7939,15643908.0,Ch'ien,531.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,52710.7,0.0
-7940,15580148.0,Piazza,652.0,France,Female,39.0,4.0,123544.0,1.0,1.0,0.0,126469.09,0.0
-7941,15711889.0,Hsu,724.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,24991.92,0.0
-7942,15578073.0,Chiu,754.0,Germany,Female,42.0,2.0,118317.27,1.0,1.0,0.0,52795.56,1.0
-7943,15732967.0,Shao,762.0,France,Female,39.0,7.0,0.0,2.0,0.0,1.0,83679.46,0.0
-7944,15803750.0,Onwuamaeze,569.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,162858.29,0.0
-7945,15654574.0,Hsing,687.0,Germany,Female,32.0,2.0,113034.22,2.0,1.0,0.0,185547.71,0.0
-7946,15698732.0,Nwabugwu,592.0,Spain,Female,40.0,0.0,108477.49,1.0,1.0,1.0,59905.29,1.0
-7947,15693904.0,Ch'iu,561.0,France,Male,41.0,0.0,91760.11,1.0,0.0,1.0,144874.67,0.0
-7948,15707596.0,Cremonesi,673.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,159800.16,0.0
-7949,15690670.0,Pritchard,699.0,France,Male,48.0,5.0,0.0,2.0,1.0,1.0,145593.85,0.0
-7950,15771857.0,Biryukov,725.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,4395.45,0.0
-7951,15757952.0,T'ao,538.0,Spain,Male,22.0,2.0,0.0,2.0,0.0,1.0,127630.88,0.0
-7952,15685500.0,Manna,738.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,152835.3,0.0
-7953,15592991.0,Lucciano,721.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,185510.34,0.0
-7954,15662758.0,Ch'eng,641.0,France,Male,43.0,6.0,0.0,2.0,0.0,1.0,183569.25,0.0
-7955,15706029.0,Ch'eng,675.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,92915.84,0.0
-7956,15736413.0,Tuan,622.0,France,Male,41.0,0.0,0.0,2.0,1.0,1.0,73464.88,0.0
-7957,15658889.0,Lombardo,611.0,France,Male,37.0,1.0,137946.39,1.0,0.0,0.0,171657.57,0.0
-7958,15650266.0,Chukwualuka,644.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,131736.23,0.0
-7959,15686885.0,Onuora,763.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,79577.4,0.0
-7960,15580988.0,Kao,678.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,141872.05,0.0
-7961,15726153.0,Ch'in,684.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,1.0,131662.47,0.0
-7962,15690164.0,Maclean,711.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,111558.7,0.0
-7963,15735221.0,Obijiaku,619.0,France,Female,45.0,2.0,0.0,2.0,1.0,1.0,113931.57,1.0
-7964,15597901.0,Ritchie,714.0,France,Male,26.0,1.0,117294.12,1.0,0.0,1.0,51565.98,0.0
-7965,15610337.0,Chukwuemeka,705.0,France,Female,29.0,6.0,129855.72,1.0,1.0,1.0,155853.52,0.0
-7966,15665061.0,K?,722.0,France,Male,36.0,7.0,79954.61,1.0,1.0,0.0,134516.71,0.0
-7967,15783501.0,Ts'ai,649.0,Germany,Female,34.0,1.0,152462.79,2.0,0.0,1.0,167984.61,0.0
-7968,15729574.0,Ch'iu,739.0,France,Male,36.0,2.0,170184.99,2.0,0.0,0.0,155853.64,0.0
-7969,15750776.0,K'ung,644.0,France,Female,35.0,2.0,0.0,2.0,0.0,0.0,151581.79,0.0
-7970,15773421.0,Udegbunam,558.0,Germany,Female,52.0,5.0,129794.67,1.0,1.0,0.0,93073.09,1.0
-7971,15628999.0,Liao,679.0,Spain,Female,56.0,7.0,0.0,2.0,1.0,1.0,137674.55,0.0
-7972,15620557.0,Ugochukwu,605.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,174815.54,0.0
-7973,15571221.0,Shih,611.0,France,Female,44.0,7.0,0.0,2.0,1.0,0.0,138889.15,1.0
-7974,15729238.0,Calabresi,662.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,133591.36,0.0
-7975,15800229.0,Shih,683.0,Spain,Male,32.0,9.0,0.0,2.0,0.0,0.0,63795.62,0.0
-7976,15663933.0,Mazzanti,567.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,142538.64,0.0
-7977,15794360.0,Okechukwu,686.0,France,Male,34.0,7.0,141078.57,1.0,0.0,1.0,64907.48,0.0
-7978,15672447.0,Nebechukwu,850.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,91346.03,0.0
-7979,15766649.0,H?,613.0,Spain,Female,52.0,8.0,0.0,1.0,1.0,0.0,162858.29,1.0
-7980,15645371.0,Yermakov,834.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,161845.81,0.0
-7981,15811594.0,Lombardo,645.0,France,Female,33.0,2.0,125534.51,1.0,1.0,0.0,158028.96,0.0
-7982,15750574.0,Smith,622.0,France,Male,34.0,5.0,131038.38,1.0,1.0,1.0,135077.01,0.0
-7983,15627305.0,Palermo,656.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,1.0,94815.04,0.0
-7984,15600583.0,O'Donnell,743.0,France,Male,29.0,3.0,0.0,2.0,1.0,0.0,143598.7,0.0
-7985,15785819.0,Chiawuotu,579.0,Spain,Male,38.0,9.0,0.0,2.0,0.0,0.0,100722.72,0.0
-7986,15737354.0,Oguejiofor,652.0,France,Female,60.0,9.0,0.0,1.0,0.0,1.0,135875.51,1.0
-7987,15804075.0,Hsia,667.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,123969.64,0.0
-7988,15696822.0,Nnamdi,653.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,101057.95,0.0
-7989,15703793.0,Hsiao,667.0,France,Female,38.0,2.0,137452.09,1.0,1.0,1.0,51587.04,0.0
-7990,15716134.0,T'an,703.0,Germany,Female,46.0,2.0,102962.62,1.0,1.0,0.0,121207.66,1.0
-7991,15593973.0,Omeokachie,710.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,132734.87,0.0
-7992,15753831.0,Johnstone,730.0,Spain,Male,33.0,5.0,0.0,1.0,1.0,1.0,164768.82,1.0
-7993,15603846.0,Phelan,683.0,Germany,Female,47.0,4.0,122088.67,2.0,0.0,0.0,152883.95,1.0
-7994,15576256.0,Frolova,718.0,Spain,Female,26.0,6.0,0.0,2.0,0.0,0.0,149891.79,0.0
-7995,15741633.0,Chiu,682.0,Spain,Male,34.0,8.0,0.0,2.0,0.0,1.0,180595.02,0.0
-7996,15795519.0,Ch'iu,683.0,Germany,Female,44.0,9.0,120962.13,1.0,1.0,1.0,82900.25,1.0
-7997,15746451.0,Doyne,635.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,156774.75,0.0
-7998,15783097.0,Olisaemeka,781.0,Spain,Female,33.0,2.0,0.0,2.0,0.0,0.0,166588.76,0.0
-7999,15659355.0,Teng,697.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,144880.81,0.0
-8000,15654060.0,Ozerova,639.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,147963.07,0.0
-8001,15735246.0,Hsing,537.0,Germany,Female,35.0,7.0,151701.45,2.0,1.0,0.0,182531.74,1.0
-8002,15686436.0,Chineze,628.0,Germany,Female,48.0,3.0,90547.48,1.0,1.0,0.0,143049.72,1.0
-8003,15795895.0,Mazzi,559.0,France,Male,46.0,3.0,0.0,2.0,1.0,0.0,40463.03,0.0
-8004,15603065.0,Uvarova,684.0,Germany,Male,47.0,2.0,123959.97,1.0,1.0,0.0,136837.97,0.0
-8005,15763415.0,Ch'in,567.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,9085.0,0.0
-8006,15784700.0,Tien,725.0,Spain,Male,25.0,1.0,0.0,2.0,1.0,0.0,116696.77,0.0
-8007,15691446.0,Chukwukadibia,705.0,Germany,Female,53.0,1.0,160903.2,1.0,1.0,0.0,142903.38,1.0
-8008,15729238.0,Okwuadigbo,710.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,121537.91,0.0
-8009,15705512.0,Ch'iu,554.0,Spain,Female,34.0,3.0,136855.94,1.0,1.0,0.0,56770.76,0.0
-8010,15796071.0,Tretiakova,648.0,France,Male,42.0,3.0,118890.81,1.0,1.0,1.0,112181.08,0.0
-8011,15672597.0,Zetticci,678.0,France,Female,32.0,1.0,104474.97,1.0,0.0,1.0,144870.28,0.0
-8012,15566111.0,Onyemauchechukwu,663.0,France,Female,46.0,3.0,0.0,1.0,1.0,1.0,132973.18,0.0
-8013,15664547.0,Hsiung,644.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,38772.82,0.0
-8014,15567442.0,Hsia,683.0,Germany,Female,35.0,1.0,81828.22,2.0,1.0,1.0,26578.69,0.0
-8015,15683375.0,Pirozzi,758.0,Germany,Male,51.0,4.0,112119.48,3.0,1.0,0.0,115136.51,1.0
-8016,15682355.0,Nwachinemelu,587.0,France,Female,37.0,4.0,128718.78,1.0,0.0,0.0,147521.35,0.0
-8017,15627395.0,T'ao,659.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,140941.47,0.0
-8018,15586970.0,Uspensky,588.0,Germany,Female,39.0,5.0,154712.58,1.0,1.0,1.0,143774.06,1.0
-8019,15804586.0,Ogochukwu,571.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,64872.33,0.0
-8020,15692664.0,Ch'in,582.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,1.0,183494.87,0.0
-8021,15587584.0,Ting,753.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,180622.02,0.0
-8022,15633840.0,Chiazagomekpere,593.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,121365.45,0.0
-8023,15659937.0,Lueck,807.0,France,Male,38.0,3.0,0.0,1.0,0.0,0.0,142935.83,1.0
-8024,15708713.0,Lucchesi,806.0,Germany,Male,54.0,2.0,122227.79,1.0,1.0,0.0,151924.9,1.0
-8025,15773779.0,Nnonso,705.0,France,Female,65.0,0.0,0.0,1.0,0.0,1.0,171575.54,1.0
-8026,15658935.0,Chukwuhaenye,729.0,France,Male,29.0,4.0,0.0,1.0,1.0,1.0,79734.23,1.0
-8027,15668032.0,Smith,681.0,Germany,Female,30.0,9.0,98630.31,1.0,1.0,0.0,169964.12,0.0
-8028,15729019.0,Ch'ien,733.0,France,Male,34.0,3.0,137852.96,1.0,1.0,1.0,111243.09,0.0
-8029,15598884.0,Pagnotto,633.0,Germany,Male,39.0,6.0,127772.1,1.0,1.0,0.0,94816.45,1.0
-8030,15624892.0,Onochie,667.0,Spain,Female,31.0,6.0,0.0,2.0,0.0,0.0,187800.63,0.0
-8031,15658716.0,Palerma,709.0,France,Female,33.0,2.0,133936.99,1.0,1.0,1.0,167023.95,0.0
-8032,15778947.0,Onochie,646.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,86413.11,0.0
-8033,15656005.0,Milanesi,569.0,Spain,Male,45.0,8.0,0.0,2.0,1.0,0.0,71071.47,0.0
-8034,15648726.0,L?,706.0,France,Female,38.0,3.0,116084.32,2.0,1.0,1.0,107841.77,0.0
-8035,15654495.0,Milanesi,524.0,France,Male,43.0,0.0,0.0,2.0,1.0,0.0,48990.5,0.0
-8036,15579891.0,T'ien,649.0,France,Male,38.0,4.0,91621.69,1.0,1.0,1.0,168035.6,0.0
-8037,15687094.0,Ch'in,637.0,France,Female,43.0,5.0,181671.14,1.0,1.0,0.0,147913.56,0.0
-8038,15599433.0,Onyemauchechukwu,641.0,France,Male,33.0,7.0,131834.76,1.0,1.0,1.0,46675.51,0.0
-8039,15722072.0,Chia,574.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,0.0,180197.74,0.0
-8040,15720354.0,Chukwukadibia,494.0,Germany,Male,32.0,2.0,163715.84,2.0,1.0,0.0,153036.97,0.0
-8041,15723484.0,McLean,619.0,France,Male,45.0,9.0,95377.13,1.0,1.0,0.0,132424.69,0.0
-8042,15566689.0,Milanesi,521.0,France,Male,36.0,3.0,0.0,2.0,1.0,0.0,51380.9,0.0
-8043,15748123.0,Shih,835.0,France,Female,39.0,8.0,0.0,2.0,0.0,1.0,105760.01,0.0
-8044,15572461.0,Obidimkpa,645.0,France,Male,45.0,8.0,0.0,1.0,1.0,0.0,179866.01,1.0
-8045,15806913.0,T'ang,705.0,Spain,Male,40.0,9.0,0.0,2.0,1.0,1.0,25684.93,0.0
-8046,15675252.0,Chukwujamuike,540.0,Spain,Male,44.0,1.0,0.0,2.0,1.0,1.0,101491.23,0.0
-8047,15760216.0,Chukwualuka,692.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,125877.22,0.0
-8048,15596939.0,Nkemdirim,562.0,France,Male,29.0,1.0,0.0,1.0,0.0,0.0,88838.14,0.0
-8049,15791040.0,Ch'eng,725.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,6936.27,0.0
-8050,15703588.0,Mazzanti,582.0,France,Female,40.0,2.0,0.0,1.0,1.0,0.0,114439.66,1.0
-8051,15738662.0,Trevisani,593.0,France,Female,37.0,5.0,132351.29,1.0,0.0,1.0,187616.16,0.0
-8052,15723169.0,Onyekaozulu,730.0,Germany,Female,34.0,6.0,139799.54,1.0,1.0,0.0,64421.02,0.0
-8053,15766686.0,Smith,582.0,Spain,Female,30.0,7.0,0.0,1.0,1.0,1.0,161479.19,0.0
-8054,15780144.0,Atherton,677.0,France,Female,28.0,3.0,130233.14,1.0,1.0,0.0,146668.56,0.0
-8055,15656096.0,Trevisani,663.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,172576.43,0.0
-8056,15793046.0,Lu,683.0,France,Male,34.0,4.0,132838.07,2.0,1.0,0.0,95009.6,0.0
-8057,15806945.0,Chidumaga,753.0,Spain,Female,33.0,3.0,0.0,2.0,1.0,1.0,116969.89,0.0
-8058,15657535.0,K'ung,637.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,1.0,143665.44,0.0
-8059,15795586.0,Onyekachi,754.0,France,Female,36.0,6.0,178056.54,1.0,1.0,1.0,136521.82,0.0
-8060,15716186.0,Ch'eng,645.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,158887.09,0.0
-8061,15683963.0,Yobachi,652.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,109201.53,0.0
-8062,15777922.0,Chia,590.0,France,Female,37.0,0.0,0.0,2.0,1.0,1.0,152956.33,0.0
-8063,15729771.0,Chukwubuikem,673.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,151822.66,0.0
-8064,15691808.0,Chinwendu,656.0,France,Male,42.0,0.0,0.0,2.0,0.0,0.0,86044.98,0.0
-8065,15811947.0,Onwuamaeze,724.0,Spain,Female,23.0,4.0,0.0,2.0,0.0,1.0,49185.57,0.0
-8066,15782530.0,Chukwumaobim,645.0,Spain,Male,51.0,6.0,107467.56,1.0,1.0,0.0,163477.22,1.0
-8067,15648075.0,Chidiegwu,562.0,France,Female,37.0,8.0,0.0,2.0,0.0,1.0,136675.22,0.0
-8068,15630167.0,Tuan,639.0,Germany,Male,41.0,2.0,123378.2,1.0,1.0,0.0,74849.46,0.0
-8069,15572792.0,Tuan,605.0,France,Female,43.0,7.0,0.0,2.0,0.0,0.0,74850.84,0.0
-8070,15674950.0,Obialo,584.0,France,Female,40.0,3.0,0.0,1.0,1.0,0.0,119259.34,1.0
-8071,15579526.0,Ritchie,712.0,Germany,Female,29.0,1.0,103033.83,1.0,1.0,1.0,142797.5,1.0
-8072,15642093.0,Piazza,592.0,Spain,Male,23.0,8.0,146093.47,1.0,1.0,1.0,134794.02,0.0
-8073,15653547.0,Chidiebere,581.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,1.0,156660.21,0.0
-8074,15641359.0,Chukwuemeka,526.0,France,Female,56.0,1.0,0.0,2.0,1.0,0.0,26767.85,1.0
-8075,15767594.0,Tsao,577.0,Spain,Female,52.0,10.0,139825.53,2.0,1.0,0.0,142223.94,1.0
-8076,15809178.0,Ting,775.0,France,Male,37.0,10.0,0.0,2.0,1.0,1.0,63736.17,0.0
-8077,15697035.0,Lucciano,682.0,France,Male,42.0,6.0,0.0,2.0,0.0,1.0,160948.87,0.0
-8078,15794868.0,Chukwumaobim,763.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,0.0,84760.32,0.0
-8079,15673971.0,Wilson,599.0,Germany,Male,29.0,9.0,123894.35,1.0,1.0,0.0,180598.86,0.0
-8080,15632448.0,Kenechukwu,592.0,France,Female,49.0,3.0,0.0,2.0,1.0,1.0,168544.84,1.0
-8081,15572777.0,Mazzi,704.0,France,Male,38.0,6.0,0.0,2.0,0.0,0.0,159418.1,0.0
-8082,15693468.0,Toscani,695.0,France,Female,31.0,2.0,121457.88,1.0,1.0,1.0,123663.63,0.0
-8083,15592229.0,Ch'en,553.0,Germany,Male,51.0,9.0,142084.04,1.0,1.0,0.0,51454.08,1.0
-8084,15657778.0,Gadsdon,683.0,Germany,Male,36.0,6.0,145537.21,1.0,0.0,0.0,176570.55,1.0
-8085,15653776.0,Gilleland,635.0,France,Female,46.0,2.0,0.0,2.0,1.0,0.0,127587.22,0.0
-8086,15748625.0,Chiu,683.0,France,Female,29.0,3.0,0.0,2.0,0.0,1.0,132973.17,0.0
-8087,15673971.0,Ch'en,744.0,France,Female,46.0,3.0,0.0,1.0,0.0,0.0,111893.09,1.0
-8088,15717995.0,Chiemenam,719.0,France,Female,37.0,8.0,126645.13,1.0,1.0,0.0,79919.13,0.0
-8089,15661745.0,Chidubem,689.0,Germany,Female,45.0,1.0,125190.86,1.0,1.0,0.0,83983.54,1.0
-8090,15765457.0,Lucchese,746.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,164018.98,0.0
-8091,15737173.0,Ts'ui,850.0,France,Female,47.0,4.0,0.0,2.0,1.0,0.0,102999.33,0.0
-8092,15605918.0,Ch'ang,679.0,France,Male,43.0,6.0,0.0,2.0,1.0,0.0,156903.44,0.0
-8093,15567442.0,Hsing,692.0,France,Male,36.0,7.0,0.0,1.0,1.0,0.0,50368.63,0.0
-8094,15653253.0,Ts'ui,686.0,Germany,Male,56.0,9.0,102540.61,1.0,1.0,0.0,100127.71,1.0
-8095,15777784.0,Genovese,593.0,France,Female,40.0,6.0,0.0,1.0,1.0,0.0,121175.86,0.0
-8096,15678629.0,Whitehead,592.0,Spain,Male,40.0,1.0,0.0,2.0,0.0,0.0,146785.44,0.0
-8097,15654060.0,Yermakova,678.0,France,Male,22.0,1.0,121453.63,1.0,1.0,1.0,79927.28,0.0
-8098,15654074.0,Hsing,738.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,155915.24,0.0
-8099,15753969.0,Nwachukwu,586.0,France,Male,33.0,4.0,0.0,1.0,0.0,1.0,151335.24,0.0
-8100,15692583.0,Onyemere,709.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,94415.2,0.0
-8101,15686463.0,Pirozzi,663.0,Germany,Female,42.0,8.0,93320.8,3.0,1.0,0.0,182076.97,1.0
-8102,15678720.0,Fan,621.0,Germany,Female,46.0,2.0,103417.74,2.0,1.0,0.0,90106.94,0.0
-8103,15798895.0,Chin,602.0,France,Female,47.0,6.0,0.0,1.0,1.0,0.0,100101.06,1.0
-8104,15792453.0,Martin,706.0,Germany,Male,50.0,5.0,113220.2,1.0,1.0,0.0,126565.84,1.0
-8105,15580146.0,Hs?eh,684.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,79005.95,0.0
-8106,15671445.0,Ch'ien,683.0,France,Male,41.0,4.0,0.0,2.0,1.0,0.0,180932.46,0.0
-8107,15732704.0,Chiazagomekpere,634.0,France,Male,24.0,5.0,0.0,2.0,1.0,1.0,153015.42,0.0
-8108,15578865.0,Hsia,661.0,France,Male,33.0,10.0,0.0,1.0,1.0,1.0,143357.71,0.0
-8109,15652693.0,Watson,841.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,175082.2,0.0
-8110,15772421.0,P'eng,583.0,Spain,Male,55.0,1.0,121206.96,1.0,0.0,1.0,83700.66,0.0
-8111,15686776.0,Trevisani,647.0,Germany,Male,61.0,4.0,138032.15,1.0,1.0,1.0,53710.23,0.0
-8112,15651335.0,Chuang,682.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,105986.01,0.0
-8113,15579777.0,Aksyonova,572.0,France,Male,39.0,1.0,80780.16,1.0,1.0,1.0,152876.13,0.0
-8114,15670266.0,Manna,786.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,0.0,73881.68,0.0
-8115,15714689.0,Ch'ang,712.0,Spain,Female,50.0,2.0,0.0,1.0,1.0,0.0,148811.14,1.0
-8116,15609998.0,Chien,662.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,81776.84,0.0
-8117,15595588.0,Udinese,813.0,Spain,Female,51.0,3.0,0.0,2.0,1.0,0.0,82709.8,1.0
-8118,15727041.0,Chukwumaobim,704.0,Germany,Male,46.0,7.0,131284.46,1.0,1.0,1.0,105525.36,1.0
-8119,15587597.0,Hs?,635.0,Spain,Female,33.0,4.0,0.0,2.0,1.0,1.0,187632.22,0.0
-8120,15632665.0,K'ung,694.0,Spain,Male,20.0,6.0,0.0,2.0,1.0,1.0,58854.88,0.0
-8121,15589660.0,Chu,683.0,France,Female,33.0,8.0,101266.51,1.0,0.0,1.0,89048.46,0.0
-8122,15700710.0,Lu,619.0,France,Female,23.0,6.0,0.0,2.0,1.0,1.0,78418.85,0.0
-8123,15646521.0,Eluemuno,709.0,France,Female,33.0,6.0,0.0,1.0,1.0,1.0,90876.95,0.0
-8124,15735346.0,H?,702.0,France,Male,30.0,10.0,0.0,2.0,1.0,0.0,183572.84,0.0
-8125,15644501.0,McIntyre,803.0,France,Female,52.0,6.0,0.0,1.0,1.0,0.0,179866.01,1.0
-8126,15641733.0,Onyemauchechukwu,675.0,Spain,Female,63.0,3.0,0.0,1.0,1.0,0.0,58907.08,1.0
-8127,15691387.0,Niu,705.0,France,Male,31.0,10.0,113693.0,1.0,1.0,0.0,113079.19,0.0
-8128,15716164.0,Dickson,651.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,1.0,51387.1,0.0
-8129,15664772.0,Smith,637.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,10883.52,0.0
-8130,15720508.0,Genovese,627.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,48502.88,0.0
-8131,15592389.0,Chienezie,681.0,Germany,Female,26.0,9.0,120596.93,2.0,1.0,0.0,186612.47,0.0
-8132,15582910.0,Nwachinemelu,817.0,Spain,Male,36.0,2.0,0.0,1.0,1.0,1.0,83470.4,0.0
-8133,15600575.0,Mays,727.0,Spain,Female,33.0,3.0,0.0,2.0,0.0,1.0,80178.54,0.0
-8134,15672640.0,White,687.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,58814.41,0.0
-8135,15607988.0,Hsueh,537.0,Germany,Male,27.0,5.0,141581.71,2.0,0.0,1.0,164924.11,0.0
-8136,15783882.0,Iweobiegbunam,675.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,1.0,183572.39,0.0
-8137,15726791.0,Hao,562.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,158918.03,0.0
-8138,15676526.0,Genovese,651.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,1408.68,0.0
-8139,15790299.0,Milano,678.0,France,Female,56.0,5.0,0.0,1.0,1.0,0.0,173425.68,1.0
-8140,15761102.0,Nwankwo,636.0,France,Female,29.0,4.0,0.0,2.0,1.0,0.0,151315.76,0.0
-8141,15768449.0,Chia,679.0,France,Female,35.0,4.0,130170.82,1.0,1.0,0.0,152992.04,0.0
-8142,15773876.0,Chukwuemeka,597.0,France,Female,42.0,7.0,121523.51,1.0,0.0,1.0,177065.24,0.0
-8143,15585192.0,Hsia,621.0,France,Male,29.0,8.0,0.0,2.0,1.0,1.0,133535.29,0.0
-8144,15748696.0,Chukwufumnanya,609.0,Germany,Female,32.0,8.0,123939.36,1.0,1.0,0.0,54473.6,1.0
-8145,15732781.0,Uspensky,535.0,Spain,Female,42.0,5.0,0.0,2.0,1.0,0.0,107964.35,0.0
-8146,15683842.0,Ts'ai,640.0,Spain,Male,39.0,6.0,0.0,1.0,1.0,0.0,185543.34,1.0
-8147,15727467.0,Lucciano,649.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,82696.84,0.0
-8148,15782899.0,Ch'eng,653.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,175893.53,0.0
-8149,15626008.0,T'ien,741.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,90821.69,0.0
-8150,15696054.0,Yefimova,646.0,France,Female,28.0,7.0,0.0,2.0,1.0,0.0,176913.42,0.0
-8151,15795563.0,Ugochukwutubelum,632.0,Germany,Male,35.0,5.0,133800.98,2.0,1.0,1.0,159830.58,0.0
-8152,15797910.0,Chu,675.0,Spain,Female,28.0,2.0,93656.95,1.0,1.0,0.0,163471.01,0.0
-8153,15752026.0,Ch'en,696.0,Germany,Female,28.0,4.0,155973.88,2.0,1.0,0.0,142903.44,0.0
-8154,15635964.0,Ugochukwutubelum,678.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,154486.01,0.0
-8155,15732266.0,Ch'ien,727.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,46791.09,0.0
-8156,15762332.0,Hsueh,704.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,54949.73,0.0
-8157,15761674.0,Mazzi,659.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,162053.92,0.0
-8158,15619360.0,T'ien,850.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,140764.67,0.0
-8159,15583841.0,Chin,645.0,Spain,Female,46.0,9.0,150635.3,1.0,1.0,1.0,90928.48,1.0
-8160,15747795.0,Chidumaga,516.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,90925.72,0.0
-8161,15584545.0,Ch'eng,728.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,74838.64,0.0
-8162,15704771.0,Oluchukwu,643.0,France,Female,32.0,3.0,0.0,1.0,1.0,1.0,168895.06,1.0
-8163,15629010.0,Chukwubuikem,605.0,Germany,Male,32.0,2.0,124586.93,2.0,1.0,0.0,176984.34,0.0
-8164,15751788.0,Nnanna,593.0,Germany,Male,33.0,4.0,111565.91,1.0,1.0,0.0,133571.29,1.0
-8165,15707974.0,Tung,676.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,187800.63,0.0
-8166,15605425.0,Lo Duca,613.0,France,Male,32.0,2.0,129431.36,1.0,1.0,0.0,140134.43,0.0
-8167,15685829.0,Teng,697.0,France,Male,31.0,3.0,0.0,2.0,0.0,0.0,158071.69,0.0
-8168,15743153.0,Ch'in,529.0,Spain,Male,41.0,9.0,0.0,2.0,1.0,1.0,170819.01,0.0
-8169,15794849.0,Nkemdilim,710.0,France,Male,33.0,3.0,0.0,2.0,0.0,1.0,103570.8,0.0
-8170,15625641.0,Ku,648.0,France,Female,46.0,5.0,0.0,1.0,0.0,0.0,121525.48,1.0
-8171,15692259.0,Maclean,723.0,France,Female,31.0,5.0,0.0,2.0,1.0,0.0,122580.48,0.0
-8172,15773322.0,Ch'iu,638.0,France,Female,41.0,4.0,0.0,1.0,0.0,0.0,175324.74,1.0
-8173,15739803.0,Ch'en,628.0,France,Female,71.0,4.0,0.0,1.0,1.0,1.0,113666.48,1.0
-8174,15631339.0,O'Loughlin,576.0,Germany,Female,40.0,2.0,80931.75,1.0,1.0,1.0,162719.69,0.0
-8175,15615245.0,Liao,754.0,Germany,Male,47.0,2.0,150500.64,2.0,0.0,1.0,180622.02,0.0
-8176,15627395.0,Tretiakova,705.0,Germany,Female,35.0,3.0,152899.24,3.0,0.0,0.0,141896.74,1.0
-8177,15783338.0,Teng,693.0,France,Female,36.0,5.0,59893.85,1.0,1.0,0.0,106977.8,0.0
-8178,15766166.0,Goliwe,624.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,119446.64,0.0
-8179,15641837.0,Ch'in,634.0,France,Female,49.0,3.0,76515.71,1.0,0.0,0.0,645.61,1.0
-8180,15689094.0,Tung,663.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,60427.3,0.0
-8181,15785547.0,Hsiung,735.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,94820.85,0.0
-8182,15641585.0,Genovese,642.0,Germany,Female,62.0,2.0,152264.81,1.0,1.0,0.0,74838.64,1.0
-8183,15795586.0,Chidimma,671.0,France,Male,39.0,7.0,0.0,1.0,1.0,0.0,176943.59,0.0
-8184,15705735.0,P'eng,713.0,France,Male,46.0,4.0,131354.53,1.0,1.0,1.0,143357.03,0.0
-8185,15793688.0,Atherton,632.0,Spain,Male,26.0,10.0,0.0,2.0,1.0,0.0,177737.03,0.0
-8186,15809248.0,Ewing,473.0,France,Male,40.0,5.0,0.0,1.0,0.0,1.0,141210.5,0.0
-8187,15768887.0,Chukwudi,744.0,France,Male,25.0,6.0,0.0,2.0,0.0,1.0,50759.8,0.0
-8188,15685564.0,Eluemuno,763.0,France,Male,38.0,5.0,106259.63,1.0,1.0,0.0,65632.33,0.0
-8189,15790846.0,Nwachinemelu,707.0,Germany,Female,21.0,2.0,81571.05,2.0,1.0,1.0,182726.22,0.0
-8190,15772896.0,Ch'eng,681.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,9087.81,0.0
-8191,15662658.0,Iloerika,576.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,152699.8,0.0
-8192,15582944.0,Bischof,645.0,Germany,Male,43.0,7.0,126725.25,1.0,0.0,1.0,69320.97,0.0
-8193,15693326.0,Aitken,775.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,48226.76,0.0
-8194,15679963.0,Nwokezuike,774.0,Germany,Male,38.0,1.0,106089.3,1.0,1.0,1.0,71175.94,0.0
-8195,15573520.0,Lo Duca,653.0,Spain,Female,33.0,7.0,0.0,2.0,0.0,1.0,172618.52,0.0
-8196,15589435.0,Hs?,598.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,179521.24,0.0
-8197,15742369.0,Onwumelu,549.0,Spain,Female,33.0,3.0,0.0,1.0,1.0,1.0,88705.14,0.0
-8198,15802088.0,Ch'ang,594.0,France,Male,25.0,5.0,82373.94,1.0,1.0,1.0,184524.65,0.0
-8199,15685920.0,Dyer,572.0,France,Male,46.0,7.0,0.0,2.0,0.0,1.0,133617.83,0.0
-8200,15795881.0,Nnamutaezinwa,675.0,France,Male,48.0,7.0,0.0,1.0,1.0,0.0,139435.12,1.0
-8201,15585595.0,Hsiao,756.0,Germany,Female,34.0,1.0,97541.24,2.0,1.0,0.0,135851.3,1.0
-8202,15616270.0,O'Brien,595.0,France,Male,41.0,2.0,0.0,2.0,0.0,0.0,176733.72,0.0
-8203,15792228.0,Cremonesi,696.0,France,Male,26.0,2.0,0.0,2.0,1.0,0.0,6078.46,0.0
-8204,15734762.0,Hsing,562.0,Germany,Female,31.0,2.0,115930.87,1.0,0.0,1.0,141180.66,0.0
-8205,15682355.0,Hsia,717.0,France,Female,31.0,3.0,0.0,1.0,0.0,1.0,77500.81,0.0
-8206,15732672.0,Hs?,619.0,Spain,Female,42.0,4.0,0.0,2.0,1.0,0.0,180829.99,0.0
-8207,15701200.0,Shih,668.0,Spain,Female,37.0,4.0,0.0,1.0,1.0,0.0,52367.29,0.0
-8208,15754919.0,Hsing,651.0,Germany,Male,26.0,6.0,145059.97,2.0,1.0,0.0,170821.43,0.0
-8209,15702462.0,Chukwudi,667.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,171070.55,0.0
-8210,15742272.0,Chiemenam,687.0,France,Male,40.0,5.0,107960.06,1.0,1.0,0.0,178393.68,0.0
-8211,15691808.0,Lira,588.0,Germany,Male,39.0,8.0,106521.48,1.0,1.0,1.0,170122.22,1.0
-8212,15605067.0,Smith,572.0,France,Male,49.0,8.0,178032.52,1.0,1.0,0.0,149853.96,0.0
-8213,15777772.0,Onwuamaeze,782.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,131807.14,0.0
-8214,15567855.0,McElhone,708.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,187597.98,0.0
-8215,15605827.0,Chukwufumnanya,758.0,Germany,Female,45.0,5.0,122453.97,2.0,1.0,0.0,16081.62,1.0
-8216,15792365.0,Chidiebere,651.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,174921.92,0.0
-8217,15793331.0,Palerma,709.0,Spain,Male,36.0,3.0,0.0,2.0,0.0,0.0,172490.78,0.0
-8218,15662901.0,Onyemachukwu,539.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,64516.8,0.0
-8219,15718588.0,Pisani,686.0,Spain,Male,36.0,5.0,104305.74,1.0,1.0,0.0,80181.12,0.0
-8220,15604497.0,Fanucci,644.0,Spain,Male,30.0,5.0,105701.91,1.0,0.0,1.0,152417.79,0.0
-8221,15772573.0,Sazonova,628.0,France,Male,54.0,3.0,0.0,3.0,1.0,0.0,71682.54,1.0
-8222,15684474.0,Okagbue,775.0,Germany,Male,51.0,5.0,122486.47,1.0,1.0,0.0,84760.32,1.0
-8223,15636562.0,Onio,796.0,Spain,Female,44.0,4.0,133636.04,2.0,0.0,1.0,71173.64,0.0
-8224,15618391.0,Chiu,712.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,161064.29,0.0
-8225,15586629.0,Chiemenam,754.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,97938.59,0.0
-8226,15643908.0,Obialo,706.0,Germany,Male,29.0,1.0,161766.87,1.0,1.0,1.0,182566.77,0.0
-8227,15705918.0,Clements,659.0,France,Male,57.0,2.0,92674.94,1.0,1.0,1.0,187616.16,0.0
-8228,15692744.0,Ch'en,539.0,France,Male,49.0,9.0,0.0,2.0,1.0,1.0,71709.12,0.0
-8229,15746341.0,Y?,545.0,France,Female,27.0,4.0,156067.05,1.0,0.0,0.0,162878.96,0.0
-8230,15793901.0,Eluemuno,673.0,Spain,Female,24.0,8.0,0.0,2.0,1.0,1.0,180829.99,0.0
-8231,15775625.0,Ts'ui,531.0,Germany,Male,32.0,8.0,119960.29,2.0,0.0,1.0,187985.77,0.0
-8232,15664752.0,Ndubueze,678.0,France,Female,39.0,3.0,0.0,2.0,0.0,1.0,86480.29,0.0
-8233,15586870.0,Lettiere,726.0,Germany,Male,32.0,4.0,125889.3,2.0,1.0,1.0,66463.62,0.0
-8234,15570051.0,Fiorentini,521.0,Germany,Female,46.0,1.0,113905.48,1.0,1.0,1.0,96833.0,1.0
-8235,15692406.0,Tuan,686.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,18094.96,0.0
-8236,15662426.0,Chinonyelum,577.0,France,Female,29.0,1.0,0.0,1.0,0.0,0.0,179866.01,0.0
-8237,15702300.0,Ch'eng,850.0,France,Female,40.0,6.0,0.0,3.0,1.0,0.0,103570.28,1.0
-8238,15667934.0,Mancini,593.0,France,Male,34.0,5.0,0.0,2.0,0.0,0.0,90212.38,0.0
-8239,15689268.0,Okwuadigbo,667.0,France,Male,23.0,2.0,0.0,2.0,1.0,0.0,125848.88,0.0
-8240,15809302.0,Onyekachi,584.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,46857.52,0.0
-8241,15735388.0,Chukwunonso,686.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,84908.2,0.0
-8242,15738884.0,O'Brien,735.0,Spain,Male,44.0,6.0,0.0,1.0,1.0,0.0,175853.64,1.0
-8243,15750776.0,Nwokezuike,794.0,France,Male,35.0,8.0,0.0,1.0,1.0,1.0,152884.85,1.0
-8244,15651554.0,Ch'ien,598.0,France,Female,47.0,1.0,0.0,1.0,0.0,1.0,129891.66,1.0
-8245,15604345.0,Hsieh,599.0,Germany,Female,41.0,1.0,109960.06,1.0,1.0,0.0,735.79,1.0
-8246,15636590.0,Ting,724.0,France,Male,45.0,1.0,0.0,2.0,1.0,0.0,89566.64,0.0
-8247,15609083.0,Onio,755.0,Germany,Male,30.0,4.0,139973.65,1.0,1.0,1.0,118959.64,0.0
-8248,15707637.0,Chiekwugo,708.0,Germany,Male,56.0,0.0,120867.18,1.0,1.0,0.0,44719.5,1.0
-8249,15644977.0,Mazzi,727.0,France,Male,57.0,7.0,154733.97,1.0,1.0,1.0,102799.22,1.0
-8250,15741745.0,Trevisano,627.0,France,Female,36.0,9.0,0.0,1.0,0.0,0.0,146723.72,1.0
-8251,15731553.0,Lu,632.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,95611.47,0.0
-8252,15791767.0,Pinto,850.0,France,Female,37.0,2.0,0.0,1.0,1.0,0.0,158527.56,0.0
-8253,15688865.0,Tsai,678.0,France,Male,47.0,7.0,0.0,2.0,1.0,0.0,90876.95,0.0
-8254,15649430.0,Lucchese,619.0,Spain,Female,32.0,5.0,90721.52,1.0,1.0,0.0,177740.58,1.0
-8255,15646594.0,Hs?,717.0,Spain,Male,40.0,4.0,0.0,2.0,0.0,1.0,130827.97,0.0
-8256,15756538.0,Trevisano,713.0,France,Female,30.0,6.0,147972.19,1.0,1.0,0.0,141860.62,0.0
-8257,15674671.0,White,633.0,Germany,Male,32.0,9.0,125540.05,1.0,1.0,1.0,173792.77,1.0
-8258,15807568.0,Folliero,583.0,France,Female,42.0,7.0,0.0,2.0,1.0,1.0,102986.15,0.0
-8259,15796370.0,Chiang,713.0,France,Male,34.0,6.0,0.0,1.0,1.0,1.0,84469.13,0.0
-8260,15781689.0,Maclean,635.0,Spain,Male,33.0,9.0,0.0,1.0,1.0,1.0,73882.65,0.0
-8261,15791533.0,K'ung,678.0,Spain,Male,41.0,8.0,0.0,2.0,1.0,0.0,179540.73,0.0
-8262,15575060.0,Hsiung,703.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,158845.49,0.0
-8263,15679299.0,Toscani,696.0,France,Male,32.0,5.0,130627.66,1.0,1.0,1.0,86786.41,0.0
-8264,15565714.0,Okeke,516.0,Germany,Female,51.0,8.0,91285.13,1.0,0.0,0.0,164806.89,1.0
-8265,15805637.0,L?,671.0,Germany,Female,36.0,4.0,107556.06,1.0,1.0,1.0,181850.74,0.0
-8266,15768471.0,Chukwuemeka,696.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,100423.88,0.0
-8267,15805671.0,Mazzanti,693.0,Germany,Male,38.0,8.0,151532.4,2.0,0.0,1.0,103726.71,0.0
-8268,15706232.0,Chukwuemeka,724.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,94739.2,1.0
-8269,15579914.0,Walker,667.0,France,Female,32.0,1.0,90971.85,1.0,1.0,1.0,104889.2,0.0
-8270,15669293.0,Mazzanti,705.0,France,Female,39.0,0.0,0.0,1.0,0.0,0.0,175373.46,1.0
-8271,15732437.0,Y?an,677.0,France,Female,48.0,7.0,0.0,2.0,1.0,1.0,103557.93,0.0
-8272,15681697.0,Nkemdilim,656.0,France,Female,40.0,5.0,133845.26,1.0,1.0,0.0,94782.12,0.0
-8273,15609824.0,DeRose,769.0,France,Male,44.0,4.0,0.0,2.0,0.0,0.0,148087.62,0.0
-8274,15758856.0,Lo Duca,778.0,France,Female,41.0,3.0,0.0,2.0,0.0,1.0,144532.85,0.0
-8275,15682778.0,Nnamdi,744.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,109145.2,0.0
-8276,15684958.0,Chiazagomekpere,649.0,Spain,Male,46.0,1.0,126875.62,1.0,1.0,0.0,74800.99,0.0
-8277,15744127.0,Kao,767.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,159475.08,0.0
-8278,15794472.0,Onyekaozulu,662.0,France,Male,26.0,7.0,0.0,2.0,1.0,0.0,182076.97,0.0
-8279,15676521.0,Chukwubuikem,658.0,Spain,Female,61.0,3.0,0.0,1.0,1.0,1.0,61995.57,1.0
-8280,15777035.0,Pisano,695.0,Germany,Male,29.0,3.0,161806.09,1.0,1.0,0.0,176780.69,0.0
-8281,15635143.0,Chiabuotu,629.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,88890.75,0.0
-8282,15795895.0,Ch'in,659.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,0.0,157097.09,0.0
-8283,15618703.0,Kerr,587.0,France,Male,31.0,3.0,0.0,2.0,1.0,1.0,171867.08,0.0
-8284,15777615.0,Mazzi,658.0,Spain,Male,41.0,1.0,96654.72,1.0,0.0,0.0,161479.19,0.0
-8285,15731478.0,Lettiere,758.0,France,Male,36.0,5.0,93871.95,1.0,1.0,1.0,91291.2,0.0
-8286,15776921.0,White,648.0,France,Male,35.0,7.0,131778.08,1.0,1.0,1.0,167573.06,0.0
-8287,15602312.0,Hao,592.0,Spain,Male,42.0,2.0,0.0,2.0,1.0,1.0,126188.42,0.0
-8288,15783629.0,Biryukov,706.0,Germany,Female,29.0,0.0,145747.67,1.0,1.0,0.0,187987.01,0.0
-8289,15646969.0,Chibugo,646.0,Spain,Male,38.0,7.0,127837.52,1.0,1.0,0.0,168034.9,0.0
-8290,15796074.0,Aksenov,661.0,France,Female,33.0,1.0,165438.26,1.0,1.0,0.0,138344.7,0.0
-8291,15800784.0,Garmon,668.0,France,Female,56.0,1.0,0.0,2.0,1.0,0.0,177747.03,1.0
-8292,15791851.0,Hao,678.0,Germany,Female,31.0,6.0,137841.53,2.0,0.0,0.0,103787.36,0.0
-8293,15663921.0,Hsing,683.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,167066.95,0.0
-8294,15657535.0,P'eng,714.0,France,Male,26.0,3.0,0.0,2.0,1.0,1.0,122580.48,0.0
-8295,15733111.0,Hsieh,543.0,France,Male,31.0,2.0,89568.19,1.0,1.0,0.0,162452.65,0.0
-8296,15586069.0,Genovese,651.0,Germany,Male,24.0,9.0,145081.73,2.0,1.0,1.0,51581.07,0.0
-8297,15751167.0,Ch'ang,669.0,Germany,Female,46.0,5.0,81547.89,2.0,1.0,1.0,113553.43,0.0
-8298,15628674.0,Fadden,710.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,121408.55,0.0
-8299,15591036.0,Ch'iu,685.0,France,Male,29.0,6.0,0.0,2.0,0.0,1.0,64448.36,0.0
-8300,15578799.0,Smith,628.0,Spain,Male,45.0,5.0,0.0,1.0,1.0,0.0,141069.88,1.0
-8301,15686917.0,Nwebube,485.0,France,Male,43.0,2.0,136674.51,1.0,1.0,1.0,63795.8,0.0
-8302,15737571.0,Chukwukere,568.0,France,Female,42.0,8.0,0.0,1.0,1.0,1.0,177670.57,1.0
-8303,15590199.0,Teng,762.0,Germany,Male,53.0,6.0,132623.6,1.0,1.0,0.0,84932.4,1.0
-8304,15616380.0,Iweobiegbunam,526.0,Spain,Male,27.0,3.0,0.0,2.0,0.0,1.0,36315.03,0.0
-8305,15796910.0,Ch'ien,628.0,France,Female,49.0,6.0,0.0,1.0,1.0,0.0,93839.3,1.0
-8306,15664881.0,Walker,712.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,74858.3,0.0
-8307,15569410.0,Hs?,549.0,Germany,Female,51.0,0.0,134542.73,1.0,0.0,0.0,146511.2,1.0
-8308,15756976.0,Yudina,692.0,France,Male,30.0,3.0,0.0,1.0,1.0,0.0,107964.35,0.0
-8309,15648047.0,Toscani,706.0,Spain,Male,35.0,7.0,124379.14,1.0,1.0,1.0,169920.92,0.0
-8310,15702566.0,Chukwufumnanya,676.0,Spain,Male,34.0,5.0,0.0,1.0,1.0,1.0,167984.72,0.0
-8311,15593795.0,Calabrese,542.0,Spain,Male,29.0,4.0,0.0,2.0,0.0,1.0,141364.27,0.0
-8312,15682523.0,Trevisani,490.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,146875.86,0.0
-8313,15688637.0,Chu,575.0,Germany,Female,33.0,9.0,115378.94,1.0,1.0,1.0,71095.41,0.0
-8314,15769402.0,Baresi,581.0,France,Male,54.0,5.0,0.0,1.0,1.0,0.0,173498.45,1.0
-8315,15787151.0,Smith,637.0,Germany,Male,34.0,7.0,117183.74,1.0,1.0,0.0,78670.38,0.0
-8316,15614187.0,L?,662.0,Spain,Female,29.0,7.0,109949.05,2.0,1.0,1.0,154767.34,0.0
-8317,15664808.0,Napolitano,684.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,114093.73,0.0
-8318,15699415.0,Hsia,628.0,Spain,Male,26.0,1.0,0.0,2.0,0.0,0.0,143598.7,0.0
-8319,15646942.0,Palerma,599.0,Germany,Male,26.0,1.0,150685.79,1.0,1.0,0.0,49337.84,0.0
-8320,15788776.0,T'ien,584.0,Germany,Male,41.0,5.0,106328.08,1.0,1.0,1.0,133535.29,0.0
-8321,15734850.0,Yeh,637.0,Spain,Female,54.0,9.0,0.0,1.0,1.0,0.0,140889.12,1.0
-8322,15798532.0,Padovesi,651.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,86935.73,0.0
-8323,15632899.0,T'ao,824.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,161241.65,0.0
-8324,15586029.0,Y?,591.0,France,Female,33.0,4.0,0.0,1.0,0.0,1.0,88890.05,0.0
-8325,15743759.0,T'ang,606.0,Germany,Male,51.0,4.0,98630.31,1.0,1.0,0.0,161465.31,1.0
-8326,15590177.0,Tsai,682.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,0.0,142548.31,0.0
-8327,15773869.0,Shao,757.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,47777.15,0.0
-8328,15766378.0,Manna,744.0,Spain,Male,35.0,5.0,61592.14,1.0,1.0,0.0,155845.74,1.0
-8329,15689514.0,Manna,683.0,France,Male,46.0,2.0,121418.62,1.0,1.0,0.0,163989.66,1.0
-8330,15759717.0,Chidubem,709.0,Germany,Male,32.0,1.0,109387.9,2.0,1.0,1.0,188533.11,0.0
-8331,15603088.0,Uvarova,729.0,Spain,Female,29.0,4.0,0.0,1.0,1.0,1.0,183490.99,0.0
-8332,15671351.0,Ts'ai,657.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,166650.9,0.0
-8333,15714586.0,Ch'ien,582.0,France,Male,39.0,6.0,164557.95,1.0,1.0,1.0,162858.46,0.0
-8334,15602816.0,Aitken,603.0,France,Female,32.0,5.0,129855.72,1.0,1.0,0.0,180890.4,0.0
-8335,15610454.0,Cattaneo,587.0,France,Male,35.0,9.0,0.0,2.0,0.0,0.0,55829.25,0.0
-8336,15586479.0,Shao,798.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,193018.71,0.0
-8337,15692991.0,Chia,675.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,140934.57,0.0
-8338,15766066.0,Calabresi,667.0,Germany,Female,42.0,0.0,117565.74,1.0,1.0,0.0,120415.61,1.0
-8339,15752578.0,Chikelu,754.0,France,Male,30.0,8.0,133800.98,1.0,1.0,0.0,158750.53,0.0
-8340,15733229.0,Mazzanti,613.0,Spain,Male,34.0,3.0,0.0,2.0,0.0,1.0,51695.41,0.0
-8341,15644400.0,Chukwujamuike,537.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,180962.32,0.0
-8342,15775490.0,Okechukwu,707.0,Spain,Male,29.0,3.0,0.0,2.0,0.0,1.0,105969.05,0.0
-8343,15706463.0,H?,645.0,Spain,Male,38.0,7.0,143340.44,1.0,0.0,1.0,166586.99,0.0
-8344,15583692.0,Chibueze,648.0,Germany,Male,31.0,10.0,153610.61,1.0,0.0,0.0,162697.93,0.0
-8345,15576065.0,Milanesi,635.0,France,Female,41.0,1.0,0.0,2.0,1.0,0.0,181543.67,0.0
-8346,15671342.0,T'ien,778.0,Spain,Female,36.0,2.0,108914.17,2.0,1.0,0.0,121277.78,0.0
-8347,15673747.0,Walker,579.0,France,Female,27.0,10.0,0.0,2.0,1.0,0.0,44251.33,0.0
-8348,15622578.0,Nwankwo,704.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,92664.09,0.0
-8349,15697460.0,Kao,462.0,France,Female,29.0,3.0,0.0,2.0,1.0,1.0,47880.71,0.0
-8350,15652667.0,Y?an,648.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,1.0,156598.44,0.0
-8351,15690153.0,Tseng,697.0,France,Male,39.0,4.0,0.0,1.0,1.0,1.0,94898.1,0.0
-8352,15649354.0,Hargreaves,642.0,France,Male,43.0,5.0,0.0,2.0,1.0,1.0,60972.84,0.0
-8353,15791326.0,Chien,624.0,France,Female,36.0,3.0,143542.36,1.0,1.0,0.0,150687.65,0.0
-8354,15662533.0,Tsou,498.0,France,Female,30.0,6.0,0.0,2.0,0.0,0.0,182784.11,0.0
-8355,15775108.0,H?,726.0,Germany,Female,34.0,6.0,85845.43,2.0,0.0,0.0,117038.96,0.0
-8356,15762216.0,Trevisani,711.0,France,Male,42.0,4.0,0.0,1.0,1.0,0.0,116471.14,0.0
-8357,15608886.0,Shao,622.0,France,Female,66.0,9.0,0.0,1.0,1.0,0.0,80619.09,1.0
-8358,15725794.0,Liao,732.0,France,Female,59.0,5.0,0.0,2.0,1.0,1.0,54816.57,0.0
-8359,15603851.0,Frolova,706.0,Germany,Male,33.0,8.0,116968.91,2.0,1.0,1.0,137537.86,0.0
-8360,15571752.0,Ugochukwu,607.0,Spain,Male,41.0,7.0,0.0,1.0,1.0,0.0,135077.01,0.0
-8361,15768270.0,Nebechi,646.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,49503.5,0.0
-8362,15661409.0,Yefimova,612.0,France,Female,32.0,3.0,0.0,1.0,1.0,0.0,168834.43,0.0
-8363,15790336.0,Hsia,850.0,France,Female,37.0,7.0,0.0,1.0,0.0,0.0,83343.73,1.0
-8364,15763063.0,Ch'ang,478.0,Spain,Male,43.0,4.0,0.0,1.0,1.0,0.0,131281.28,0.0
-8365,15670343.0,Chukwujamuike,753.0,Spain,Male,40.0,9.0,0.0,1.0,1.0,0.0,122999.14,0.0
-8366,15690772.0,Walker,748.0,France,Female,39.0,0.0,0.0,2.0,0.0,0.0,149913.94,0.0
-8367,15641688.0,Chukwualuka,686.0,Germany,Male,32.0,1.0,108905.29,2.0,1.0,1.0,120835.65,0.0
-8368,15696733.0,Shih,725.0,Spain,Female,34.0,6.0,151914.93,2.0,1.0,0.0,89822.48,0.0
-8369,15788022.0,Chiu,635.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,118862.08,0.0
-8370,15741719.0,Chidubem,699.0,France,Male,39.0,3.0,0.0,1.0,1.0,0.0,171021.44,0.0
-8371,15744044.0,Pagnotto,644.0,France,Female,40.0,6.0,0.0,1.0,1.0,1.0,166650.32,0.0
-8372,15787597.0,Ch'iu,590.0,France,Male,45.0,9.0,0.0,2.0,1.0,0.0,180553.48,0.0
-8373,15808960.0,Hsiung,609.0,France,Female,35.0,7.0,0.0,1.0,1.0,0.0,178373.43,1.0
-8374,15813844.0,Hao,637.0,Spain,Male,29.0,3.0,0.0,1.0,1.0,1.0,62733.05,0.0
-8375,15680901.0,Genovese,590.0,France,Female,28.0,1.0,0.0,2.0,1.0,0.0,172097.4,0.0
-8376,15718289.0,Hs?,745.0,France,Female,36.0,7.0,161637.75,1.0,1.0,0.0,118977.43,0.0
-8377,15674833.0,Uspensky,679.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,101667.0,0.0
-8378,15626135.0,Chinwendu,651.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,159800.16,0.0
-8379,15668575.0,Nucci,712.0,France,Female,40.0,7.0,0.0,1.0,0.0,1.0,141210.18,0.0
-8380,15744240.0,T'ang,651.0,Germany,Male,41.0,5.0,99836.47,1.0,1.0,0.0,152884.85,0.0
-8381,15794580.0,H?,551.0,Germany,Male,38.0,2.0,138857.59,1.0,0.0,1.0,172363.41,0.0
-8382,15589435.0,Chukwuemeka,691.0,France,Male,42.0,4.0,0.0,2.0,1.0,1.0,77965.67,0.0
-8383,15727059.0,Tsao,663.0,Spain,Male,39.0,2.0,0.0,2.0,1.0,1.0,162643.15,0.0
-8384,15656330.0,Nnamdi,607.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,146700.22,0.0
-8385,15657829.0,Shih,709.0,France,Male,41.0,5.0,0.0,2.0,0.0,1.0,79919.13,0.0
-8386,15611551.0,Chidimma,747.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,44965.44,0.0
-8387,15806966.0,Chidimma,705.0,France,Male,39.0,10.0,0.0,1.0,0.0,0.0,139469.11,0.0
-8388,15757867.0,Hsia,711.0,Germany,Male,44.0,4.0,127785.16,1.0,1.0,1.0,134773.41,0.0
-8389,15658946.0,Kao,762.0,France,Male,38.0,4.0,0.0,2.0,0.0,0.0,113860.32,0.0
-8390,15800366.0,Chu,850.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,59103.13,0.0
-8391,15780561.0,Eiland,543.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,54871.02,0.0
-8392,15808760.0,Iweobiegbunam,710.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,151903.6,0.0
-8393,15665590.0,Obiajulu,642.0,Germany,Female,41.0,8.0,131938.92,1.0,0.0,0.0,66548.58,1.0
-8394,15662483.0,Chukwuhaenye,683.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,167636.15,0.0
-8395,15772996.0,Hsu,692.0,France,Male,31.0,6.0,0.0,1.0,0.0,0.0,70810.6,0.0
-8396,15649024.0,Tuan,668.0,Germany,Female,37.0,9.0,141136.89,1.0,1.0,0.0,93820.49,0.0
-8397,15736963.0,Chienezie,589.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,85470.45,0.0
-8398,15667588.0,Olisaemeka,561.0,France,Male,38.0,3.0,137060.88,1.0,0.0,1.0,103042.2,0.0
-8399,15743817.0,Ch'ien,549.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,83058.14,0.0
-8400,15575247.0,Rizzo,581.0,France,Female,33.0,1.0,0.0,1.0,1.0,1.0,84420.5,1.0
-8401,15653772.0,DeRose,711.0,France,Male,47.0,5.0,0.0,2.0,1.0,1.0,82709.8,0.0
-8402,15590828.0,Pirozzi,646.0,France,Male,37.0,3.0,139432.55,1.0,0.0,1.0,162763.01,0.0
-8403,15634564.0,Teng,656.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,118570.53,0.0
-8404,15603562.0,Hsiao,668.0,France,Male,35.0,6.0,0.0,1.0,1.0,1.0,151375.57,0.0
-8405,15780212.0,Chien,825.0,France,Male,30.0,1.0,0.0,2.0,0.0,1.0,138380.11,0.0
-8406,15773520.0,Chiu,721.0,Spain,Female,41.0,7.0,145553.07,1.0,1.0,1.0,88604.89,0.0
-8407,15731166.0,Yobachukwu,599.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,105732.9,0.0
-8408,15798960.0,Tseng,712.0,France,Male,26.0,7.0,0.0,2.0,1.0,1.0,176984.34,0.0
-8409,15612594.0,Mironova,850.0,Spain,Male,37.0,2.0,95675.62,1.0,1.0,1.0,121894.31,0.0
-8410,15675252.0,Tung,703.0,France,Female,34.0,5.0,0.0,1.0,1.0,1.0,93091.02,0.0
-8411,15763550.0,Ch'iu,588.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,182334.45,0.0
-8412,15786418.0,Eames,603.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,132029.95,0.0
-8413,15774133.0,Chin,535.0,France,Female,34.0,3.0,0.0,2.0,0.0,0.0,97734.4,0.0
-8414,15802220.0,Trevisani,741.0,Spain,Female,39.0,3.0,0.0,1.0,0.0,1.0,135875.51,0.0
-8415,15573045.0,Trevisan,762.0,France,Female,34.0,6.0,0.0,1.0,0.0,1.0,102763.69,0.0
-8416,15809847.0,Obidimkpa,721.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,0.0,141359.11,0.0
-8417,15782899.0,Mazzi,704.0,France,Male,44.0,9.0,0.0,2.0,0.0,1.0,140579.17,0.0
-8418,15652999.0,H?,537.0,France,Male,28.0,4.0,0.0,1.0,1.0,0.0,130404.53,1.0
-8419,15772383.0,Ozuoke,752.0,France,Male,50.0,3.0,108867.0,2.0,1.0,1.0,94440.45,0.0
-8420,15807900.0,Kao,559.0,Germany,Male,47.0,7.0,126442.56,2.0,1.0,1.0,131807.14,0.0
-8421,15600710.0,Chienezie,547.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,75884.65,0.0
-8422,15573852.0,Maclean,663.0,France,Male,34.0,5.0,0.0,1.0,0.0,1.0,73329.08,0.0
-8423,15600357.0,Liao,525.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,50368.63,0.0
-8424,15810723.0,Y?,641.0,France,Male,43.0,7.0,0.0,2.0,1.0,0.0,84863.85,0.0
-8425,15737169.0,O'Loghlin,686.0,France,Female,37.0,5.0,142631.87,1.0,1.0,1.0,91429.74,0.0
-8426,15605737.0,Li,598.0,Spain,Female,31.0,6.0,0.0,2.0,1.0,1.0,118522.73,0.0
-8427,15721719.0,Lucchese,652.0,Germany,Male,27.0,0.0,140931.57,1.0,0.0,1.0,139971.01,0.0
-8428,15642530.0,Chinwendu,739.0,France,Male,40.0,3.0,141525.55,1.0,1.0,1.0,131283.6,0.0
-8429,15690589.0,Folliero,592.0,France,Male,24.0,8.0,81732.88,1.0,1.0,1.0,115403.31,0.0
-8430,15693817.0,Iadanza,649.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,140169.38,1.0
-8431,15809800.0,Yashina,644.0,Spain,Female,39.0,5.0,98765.84,1.0,1.0,0.0,125961.48,0.0
-8432,15623450.0,T'ang,725.0,France,Male,36.0,4.0,0.0,2.0,0.0,0.0,82820.85,0.0
-8433,15773723.0,Ugochukwu,619.0,France,Female,30.0,4.0,0.0,2.0,0.0,0.0,126792.69,0.0
-8434,15788215.0,Hao,473.0,France,Female,35.0,8.0,0.0,1.0,1.0,0.0,164741.81,1.0
-8435,15602034.0,Hsiung,635.0,Spain,Female,30.0,9.0,0.0,2.0,1.0,0.0,77830.63,0.0
-8436,15628583.0,Ch'iu,587.0,France,Female,38.0,8.0,119703.1,1.0,1.0,0.0,68193.92,0.0
-8437,15801296.0,Tsao,684.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,170903.4,0.0
-8438,15622003.0,Chinagorom,679.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,187980.21,0.0
-8439,15718588.0,Hsing,599.0,France,Male,56.0,8.0,0.0,2.0,1.0,1.0,119127.06,0.0
-8440,15751177.0,Olisaemeka,632.0,Spain,Female,43.0,9.0,0.0,2.0,1.0,1.0,25448.31,1.0
-8441,15742668.0,Ch'iu,537.0,Spain,Male,41.0,1.0,0.0,2.0,0.0,0.0,171457.06,0.0
-8442,15690079.0,Iloerika,727.0,France,Male,48.0,1.0,0.0,1.0,1.0,0.0,50610.62,1.0
-8443,15756743.0,Tsao,636.0,Spain,Female,27.0,1.0,0.0,1.0,1.0,1.0,170770.44,0.0
-8444,15723858.0,Ch'iu,588.0,Germany,Female,36.0,2.0,119413.62,1.0,1.0,0.0,60972.84,1.0
-8445,15713505.0,Dancy,636.0,France,Female,34.0,6.0,122453.37,1.0,1.0,0.0,142916.43,0.0
-8446,15695097.0,Zaytseva,622.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,117832.88,0.0
-8447,15804009.0,Aitken,679.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,166694.93,0.0
-8448,15730397.0,Liao,637.0,France,Female,39.0,5.0,0.0,2.0,0.0,0.0,167778.62,0.0
-8449,15803801.0,Iweobiegbunam,641.0,Spain,Male,26.0,2.0,0.0,2.0,0.0,1.0,91775.51,0.0
-8450,15628600.0,O'Sullivan,521.0,France,Female,40.0,10.0,0.0,2.0,0.0,0.0,102908.91,0.0
-8451,15628936.0,Chukwufumnanya,556.0,Spain,Male,39.0,4.0,0.0,1.0,1.0,1.0,140800.66,0.0
-8452,15806420.0,Nwankwo,729.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,0.0,60536.56,0.0
-8453,15608408.0,Ugochukwu,664.0,France,Male,46.0,9.0,0.0,1.0,1.0,0.0,137985.69,1.0
-8454,15773789.0,Trevisano,469.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,57320.92,0.0
-8455,15570051.0,Chukwujekwu,684.0,Germany,Female,40.0,5.0,134760.21,1.0,1.0,1.0,73551.44,1.0
-8456,15718369.0,Pape,663.0,Spain,Female,36.0,8.0,0.0,2.0,1.0,1.0,58855.85,0.0
-8457,15591257.0,H?,706.0,Spain,Female,41.0,6.0,121513.31,1.0,1.0,1.0,149991.32,0.0
-8458,15696743.0,Rolon,531.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,142331.85,0.0
-8459,15740175.0,Biryukov,684.0,Germany,Female,37.0,8.0,131691.33,3.0,1.0,0.0,48486.25,1.0
-8460,15572767.0,Chinwendu,549.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,8982.75,0.0
-8461,15637593.0,Onio,634.0,France,Male,30.0,8.0,113599.42,1.0,1.0,0.0,162734.31,0.0
-8462,15747043.0,Mazzi,584.0,France,Male,40.0,10.0,119654.44,1.0,1.0,1.0,1002.39,0.0
-8463,15778418.0,Tsao,673.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,47832.82,0.0
-8464,15683625.0,Nkemjika,684.0,France,Male,43.0,1.0,0.0,1.0,1.0,0.0,131283.6,1.0
-8465,15714642.0,Manna,562.0,Germany,Female,24.0,5.0,99529.7,2.0,1.0,0.0,28461.29,0.0
-8466,15674442.0,Ch'eng,634.0,Germany,Female,35.0,4.0,123544.0,2.0,1.0,1.0,158937.64,0.0
-8467,15806486.0,K'ung,679.0,France,Female,35.0,4.0,132876.55,1.0,0.0,1.0,142963.27,0.0
-8468,15691723.0,Chukwukadibia,602.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,152956.33,0.0
-8469,15589195.0,Manna,699.0,Spain,Female,44.0,8.0,0.0,1.0,1.0,1.0,139410.08,1.0
-8470,15576935.0,Chiang,683.0,France,Female,27.0,2.0,0.0,1.0,0.0,0.0,193209.11,0.0
-8471,15778025.0,Maclean,682.0,France,Female,37.0,4.0,103960.38,1.0,1.0,1.0,178067.34,0.0
-8472,15614168.0,T'ien,704.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,138536.09,0.0
-8473,15653595.0,Ojiofor,705.0,Spain,Female,26.0,5.0,0.0,2.0,1.0,1.0,166585.9,0.0
-8474,15641640.0,Chimezie,850.0,Germany,Female,29.0,10.0,123783.25,2.0,1.0,1.0,74870.99,0.0
-8475,15592461.0,Hsiao,588.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,152956.33,0.0
-8476,15620505.0,Ch'in,587.0,France,Female,41.0,3.0,0.0,2.0,1.0,0.0,87412.74,0.0
-8477,15706021.0,Chiazagomekpere,652.0,Spain,Female,37.0,2.0,0.0,2.0,1.0,0.0,121789.3,0.0
-8478,15802071.0,DeRose,687.0,France,Female,42.0,9.0,0.0,1.0,1.0,0.0,48963.59,1.0
-8479,15728019.0,O'Brien,850.0,France,Female,32.0,4.0,0.0,2.0,1.0,0.0,80619.09,0.0
-8480,15681755.0,Lucchese,751.0,France,Male,37.0,3.0,0.0,2.0,0.0,0.0,131834.87,0.0
-8481,15733111.0,Nucci,713.0,France,Male,22.0,5.0,0.0,2.0,1.0,0.0,145562.4,0.0
-8482,15731815.0,Hs?,590.0,France,Female,30.0,5.0,121558.42,1.0,1.0,1.0,138380.12,0.0
-8483,15795519.0,Y?an,651.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,76569.64,0.0
-8484,15762594.0,Ting,554.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,96670.2,0.0
-8485,15638159.0,McIntyre,732.0,Germany,Male,40.0,3.0,92786.66,1.0,1.0,0.0,122949.68,1.0
-8486,15697360.0,Kaur,747.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,153068.96,1.0
-8487,15734850.0,Yeh,655.0,France,Female,44.0,10.0,0.0,1.0,1.0,0.0,145558.42,1.0
-8488,15599301.0,McGregor,769.0,Germany,Female,33.0,5.0,54980.81,1.0,0.0,1.0,156598.23,1.0
-8489,15609913.0,Babbage,586.0,France,Male,32.0,8.0,0.0,1.0,1.0,0.0,143635.83,0.0
-8490,15585067.0,Trevisano,463.0,Spain,Male,35.0,3.0,151421.44,1.0,0.0,0.0,93713.47,0.0
-8491,15741643.0,White,622.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,0.0,68830.71,0.0
-8492,15785406.0,Chibuzo,773.0,Germany,Male,54.0,0.0,107747.57,1.0,1.0,0.0,82868.34,1.0
-8493,15609899.0,Uvarov,656.0,Germany,Male,46.0,3.0,81732.88,2.0,0.0,0.0,187800.63,0.0
-8494,15716134.0,Rizzo,607.0,France,Male,39.0,9.0,0.0,1.0,1.0,1.0,94816.35,1.0
-8495,15681007.0,Chukwufumnanya,753.0,France,Female,40.0,5.0,108204.52,1.0,1.0,0.0,187946.55,0.0
-8496,15609987.0,Pirozzi,637.0,Germany,Male,47.0,7.0,105770.7,2.0,1.0,0.0,140941.47,1.0
-8497,15581551.0,Nkemakonam,721.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,161241.65,0.0
-8498,15665861.0,Ts'ai,577.0,France,Male,40.0,8.0,81231.85,1.0,1.0,1.0,112256.57,0.0
-8499,15754574.0,H?,598.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,180593.45,0.0
-8500,15750049.0,Tien,581.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,171060.01,0.0
-8501,15703394.0,Manna,613.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,56865.62,0.0
-8502,15682355.0,Maclean,707.0,France,Female,59.0,9.0,0.0,2.0,1.0,1.0,141533.19,0.0
-8503,15665088.0,Chikezie,627.0,France,Female,33.0,1.0,114514.71,1.0,1.0,0.0,122625.68,0.0
-8504,15648047.0,Sacco,677.0,Germany,Female,51.0,2.0,163894.35,2.0,1.0,0.0,103902.11,1.0
-8505,15792986.0,Y?an,754.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,54453.72,0.0
-8506,15803801.0,Trevisani,616.0,France,Male,29.0,1.0,102016.38,1.0,1.0,0.0,122996.76,1.0
-8507,15675888.0,Genovesi,592.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,51919.04,0.0
-8508,15592761.0,Rizzo,781.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,140889.32,0.0
-8509,15606472.0,Lu,850.0,France,Female,47.0,2.0,148636.21,1.0,1.0,0.0,89566.74,0.0
-8510,15662067.0,Milano,597.0,Spain,Male,45.0,6.0,0.0,1.0,1.0,0.0,96670.2,1.0
-8511,15651581.0,Smith,656.0,France,Male,51.0,4.0,174937.64,1.0,1.0,0.0,176984.34,1.0
-8512,15754599.0,Yermakov,599.0,Spain,Female,41.0,8.0,129605.99,1.0,1.0,1.0,180622.05,1.0
-8513,15803749.0,Hargreaves,667.0,Germany,Male,44.0,1.0,132406.43,3.0,1.0,0.0,171070.52,1.0
-8514,15682355.0,Ch'in,595.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,90877.76,0.0
-8515,15661162.0,McElhone,615.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,54487.43,0.0
-8516,15659931.0,K'ung,607.0,France,Male,32.0,7.0,0.0,2.0,0.0,1.0,79019.74,0.0
-8517,15620372.0,Onio,643.0,Germany,Female,32.0,2.0,127450.14,1.0,1.0,1.0,109742.55,0.0
-8518,15646521.0,Palerma,678.0,France,Male,46.0,3.0,0.0,1.0,1.0,0.0,161579.85,1.0
-8519,15768777.0,Chukwuemeka,627.0,Germany,Female,31.0,5.0,102723.35,2.0,1.0,0.0,157539.29,0.0
-8520,15782404.0,Ch'in,710.0,France,Male,41.0,2.0,131341.46,1.0,0.0,0.0,173365.9,0.0
-8521,15686835.0,Milani,727.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,140941.47,0.0
-8522,15776939.0,Chukwujamuike,596.0,France,Male,35.0,4.0,124577.17,1.0,1.0,0.0,139946.68,0.0
-8523,15644296.0,Miah,543.0,Germany,Male,43.0,2.0,133501.36,2.0,1.0,1.0,149379.1,0.0
-8524,15581552.0,Ch'ang,631.0,Germany,Male,37.0,0.0,138053.67,2.0,1.0,0.0,173632.92,0.0
-8525,15581548.0,Lai,735.0,France,Male,36.0,5.0,137781.65,1.0,1.0,0.0,70563.9,0.0
-8526,15752048.0,T'ien,703.0,France,Male,34.0,10.0,152365.21,1.0,1.0,0.0,5983.7,0.0
-8527,15803685.0,Hsing,599.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,147720.52,0.0
-8528,15621083.0,Onuora,773.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,181419.29,0.0
-8529,15787222.0,T'ao,612.0,France,Female,43.0,2.0,81076.8,1.0,1.0,1.0,121840.2,1.0
-8530,15704788.0,Chiedozie,590.0,Germany,Male,49.0,2.0,125445.04,2.0,1.0,0.0,125445.8,0.0
-8531,15583692.0,Tien,528.0,France,Female,47.0,3.0,0.0,1.0,0.0,1.0,104469.06,1.0
-8532,15748068.0,Okwudilichukwu,819.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,1.0,78899.23,0.0
-8533,15777596.0,Ting,678.0,France,Female,63.0,7.0,0.0,1.0,1.0,0.0,166476.95,1.0
-8534,15586517.0,Udinese,712.0,France,Male,28.0,6.0,117060.08,1.0,1.0,1.0,75927.35,0.0
-8535,15571586.0,Maclean,678.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,0.0,77965.44,0.0
-8536,15757895.0,Cattaneo,749.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,115611.97,0.0
-8537,15743532.0,Bock,638.0,Spain,Male,42.0,5.0,0.0,1.0,1.0,0.0,74042.0,0.0
-8538,15633378.0,Chia,646.0,Spain,Female,34.0,7.0,106518.52,1.0,1.0,1.0,101371.03,0.0
-8539,15794560.0,Hs?,756.0,Spain,Male,29.0,4.0,185966.64,1.0,0.0,1.0,16402.11,0.0
-8540,15589493.0,Onyemauchechi,599.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,109057.56,0.0
-8541,15779744.0,Trevisani,706.0,France,Male,52.0,7.0,0.0,1.0,0.0,1.0,181429.87,1.0
-8542,15738662.0,Shubin,526.0,France,Male,31.0,7.0,0.0,2.0,0.0,0.0,199409.19,0.0
-8543,15650098.0,Hsing,735.0,Germany,Male,36.0,4.0,127095.14,2.0,0.0,1.0,149913.92,0.0
-8544,15701164.0,Mazzanti,694.0,France,Male,32.0,1.0,141078.37,1.0,1.0,1.0,142287.65,0.0
-8545,15746333.0,Milne,733.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,1.0,114092.87,0.0
-8546,15598044.0,Nwankwo,692.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,90189.72,0.0
-8547,15799600.0,Ch'ang,749.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,115789.25,0.0
-8548,15772073.0,Nnamutaezinwa,619.0,Spain,Female,38.0,1.0,0.0,1.0,1.0,1.0,160810.85,0.0
-8549,15775307.0,Chiang,697.0,Spain,Male,23.0,1.0,0.0,2.0,1.0,1.0,69381.05,0.0
-8550,15792525.0,Chukwukere,700.0,France,Male,62.0,9.0,0.0,2.0,1.0,1.0,55857.48,0.0
-8551,15642448.0,Nwokike,567.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,47468.56,0.0
-8552,15777076.0,Ch'eng,615.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,124626.57,0.0
-8553,15572728.0,Chikezie,723.0,France,Female,47.0,6.0,139872.41,1.0,1.0,1.0,116969.89,1.0
-8554,15636423.0,Hs?eh,633.0,Germany,Female,44.0,4.0,127545.14,1.0,1.0,0.0,43932.54,1.0
-8555,15658233.0,Maclean,587.0,France,Male,33.0,7.0,0.0,2.0,0.0,1.0,161083.73,0.0
-8556,15756539.0,Ndubueze,439.0,France,Female,44.0,7.0,118627.8,1.0,1.0,1.0,100615.14,1.0
-8557,15681316.0,Ritchie,686.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,160979.66,0.0
-8558,15682355.0,Nkemakonam,526.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,186948.35,0.0
-8559,15589496.0,Baresi,651.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,182262.66,0.0
-8560,15612139.0,Ch'ien,677.0,France,Male,27.0,6.0,0.0,2.0,1.0,0.0,100843.82,0.0
-8561,15671172.0,White,535.0,Spain,Female,31.0,1.0,0.0,2.0,1.0,1.0,159742.33,0.0
-8562,15699325.0,Hsu,665.0,Spain,Female,35.0,5.0,141535.52,1.0,1.0,1.0,70563.9,0.0
-8563,15663438.0,Trevisani,699.0,Germany,Male,21.0,4.0,113973.23,1.0,1.0,1.0,168816.73,0.0
-8564,15794580.0,Toscani,714.0,France,Male,24.0,4.0,0.0,2.0,0.0,1.0,59595.66,0.0
-8565,15809309.0,Nnamutaezinwa,592.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,181499.77,0.0
-8566,15593636.0,Lucchesi,637.0,Germany,Male,42.0,6.0,128369.75,1.0,1.0,0.0,144870.28,1.0
-8567,15797329.0,Ulyanov,596.0,France,Female,36.0,2.0,0.0,2.0,0.0,1.0,59645.24,0.0
-8568,15702464.0,Tikhonov,602.0,France,Female,47.0,5.0,0.0,1.0,1.0,0.0,14081.64,1.0
-8569,15672637.0,Kambinachi,622.0,France,Female,47.0,2.0,0.0,1.0,1.0,0.0,118959.64,1.0
-8570,15762392.0,Obiajulu,582.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,125953.83,0.0
-8571,15591130.0,Padovesi,743.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,129892.93,0.0
-8572,15652667.0,Chukwuemeka,697.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,135077.01,0.0
-8573,15795564.0,Ch'ien,663.0,France,Female,44.0,9.0,0.0,2.0,1.0,0.0,151912.49,1.0
-8574,15752694.0,Maclean,616.0,France,Female,43.0,7.0,105443.68,2.0,1.0,1.0,136028.97,0.0
-8575,15756497.0,Fyodorov,663.0,Germany,Female,41.0,7.0,160139.59,2.0,1.0,1.0,80425.65,0.0
-8576,15686964.0,Tuan,706.0,Germany,Male,37.0,7.0,80923.75,2.0,1.0,0.0,124210.53,0.0
-8577,15744529.0,Chikezie,647.0,France,Male,43.0,1.0,0.0,1.0,1.0,0.0,172096.08,1.0
-8578,15641690.0,Nkemakonam,705.0,France,Male,38.0,7.0,102773.2,2.0,1.0,0.0,80130.54,0.0
-8579,15757559.0,Chukwudi,662.0,France,Female,39.0,5.0,113922.64,1.0,1.0,0.0,124218.33,0.0
-8580,15726403.0,Frolov,611.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,92381.01,0.0
-8581,15667460.0,Chukwudi,653.0,France,Male,52.0,2.0,0.0,1.0,1.0,0.0,182624.38,1.0
-8582,15729771.0,Chukwujamuike,669.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,86490.09,0.0
-8583,15714750.0,Macleod,608.0,France,Female,27.0,7.0,145260.23,1.0,1.0,0.0,156774.75,0.0
-8584,15779915.0,De Luca,663.0,Germany,Female,31.0,4.0,110150.43,2.0,1.0,1.0,110265.24,0.0
-8585,15622478.0,Udegbulam,602.0,Germany,Male,30.0,9.0,128882.49,2.0,1.0,0.0,158086.64,0.0
-8586,15790772.0,Onwuka,678.0,France,Male,43.0,1.0,0.0,2.0,1.0,1.0,170819.01,0.0
-8587,15578908.0,Pirozzi,807.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,123882.73,0.0
-8588,15678058.0,Burt,699.0,France,Male,33.0,2.0,106819.65,2.0,1.0,1.0,94820.85,0.0
-8589,15627042.0,Milne,731.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,91711.59,0.0
-8590,15682048.0,Chiazagomekpere,594.0,Spain,Male,34.0,8.0,0.0,1.0,0.0,1.0,145562.4,0.0
-8591,15701265.0,Ch'eng,714.0,Germany,Male,34.0,10.0,119859.52,2.0,1.0,0.0,158043.11,0.0
-8592,15612109.0,Onyemauchechi,637.0,Germany,Female,67.0,6.0,147511.26,2.0,1.0,1.0,161440.6,0.0
-8593,15723884.0,Ts'ao,775.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,86634.4,0.0
-8594,15662884.0,K'ung,648.0,France,Female,29.0,10.0,0.0,2.0,1.0,1.0,114774.35,0.0
-8595,15715673.0,Nwankwo,699.0,Spain,Male,50.0,1.0,0.0,1.0,1.0,0.0,101778.9,1.0
-8596,15803378.0,Nwachinemelu,568.0,France,Male,41.0,6.0,0.0,2.0,0.0,1.0,87071.18,0.0
-8597,15694376.0,Macleod,538.0,Germany,Male,37.0,9.0,123936.97,2.0,0.0,0.0,166984.44,0.0
-8598,15681550.0,Chiu,630.0,Germany,Female,31.0,5.0,79715.36,2.0,0.0,0.0,85522.84,0.0
-8599,15752953.0,Ch'iu,713.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,0.0,76614.04,0.0
-8600,15793042.0,Ndubueze,704.0,France,Male,23.0,7.0,0.0,2.0,1.0,1.0,114566.47,0.0
-8601,15807457.0,Eluemuno,711.0,France,Male,33.0,1.0,162482.76,1.0,0.0,0.0,159123.82,0.0
-8602,15764654.0,Hsiung,656.0,France,Female,50.0,4.0,155973.88,1.0,1.0,1.0,173365.89,0.0
-8603,15689168.0,Tseng,598.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,162884.4,0.0
-8604,15759915.0,Walker,732.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,47428.73,0.0
-8605,15644572.0,Okonkwo,630.0,Spain,Male,46.0,7.0,0.0,1.0,1.0,1.0,100843.82,1.0
-8606,15745455.0,Hsia,616.0,Germany,Male,51.0,7.0,121513.31,2.0,1.0,1.0,180716.1,1.0
-8607,15662751.0,Ch'iu,707.0,Spain,Female,57.0,6.0,121172.97,1.0,1.0,1.0,175749.36,0.0
-8608,15623072.0,Chidiebere,622.0,France,Female,30.0,7.0,161801.47,1.0,1.0,0.0,120115.5,0.0
-8609,15728352.0,Tuan,486.0,France,Male,44.0,2.0,0.0,1.0,0.0,0.0,58877.61,1.0
-8610,15792388.0,Chukwukere,705.0,Spain,Female,46.0,4.0,0.0,2.0,1.0,1.0,9684.52,0.0
-8611,15772896.0,Clements,699.0,France,Male,41.0,3.0,141136.79,1.0,1.0,1.0,167033.83,0.0
-8612,15600813.0,Pirozzi,597.0,Germany,Male,56.0,1.0,121438.65,1.0,1.0,1.0,165675.01,1.0
-8613,15794414.0,O'Sullivan,619.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,199203.21,0.0
-8614,15623450.0,Chibugo,653.0,Germany,Male,43.0,0.0,83600.24,1.0,1.0,0.0,140947.05,1.0
-8615,15797733.0,Chiang,706.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,735.59,0.0
-8616,15686963.0,Hsieh,721.0,Spain,Male,29.0,2.0,119670.18,1.0,1.0,0.0,102628.98,0.0
-8617,15657540.0,Palerma,597.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,1.0,131093.99,0.0
-8618,15615352.0,O'Donnell,635.0,France,Female,44.0,5.0,0.0,2.0,0.0,0.0,140889.32,0.0
-8619,15671345.0,Chigozie,642.0,Germany,Male,29.0,5.0,121263.62,2.0,1.0,1.0,67976.57,0.0
-8620,15705512.0,Mancini,728.0,Germany,Male,30.0,1.0,108286.5,1.0,1.0,0.0,944.41,0.0
-8621,15690767.0,Onyemauchechukwu,579.0,Germany,Male,49.0,8.0,149950.19,1.0,0.0,0.0,177099.71,0.0
-8622,15577771.0,Toscani,571.0,Germany,Male,30.0,0.0,143391.07,2.0,1.0,0.0,103931.49,0.0
-8623,15696812.0,Ch'ien,679.0,Germany,Female,49.0,3.0,108128.96,1.0,1.0,1.0,173516.71,1.0
-8624,15628292.0,Curnow,646.0,France,Male,25.0,9.0,0.0,2.0,1.0,0.0,54869.54,0.0
-8625,15771573.0,Kao,850.0,France,Female,43.0,2.0,0.0,2.0,0.0,1.0,176570.55,0.0
-8626,15588517.0,Mazzanti,700.0,Spain,Male,38.0,9.0,95331.83,1.0,1.0,0.0,184839.7,0.0
-8627,15728608.0,Chinedum,669.0,France,Female,41.0,1.0,0.0,1.0,0.0,1.0,170984.97,0.0
-8628,15704770.0,Chia,687.0,France,Male,41.0,7.0,0.0,2.0,0.0,0.0,177519.39,0.0
-8629,15809846.0,Rizzo,697.0,France,Male,34.0,10.0,121898.58,1.0,1.0,0.0,9221.78,0.0
-8630,15643826.0,Hsueh,602.0,France,Male,41.0,6.0,0.0,2.0,1.0,0.0,35644.79,0.0
-8631,15694444.0,Yobanna,718.0,France,Male,36.0,5.0,82498.14,1.0,0.0,1.0,118716.63,0.0
-8632,15757837.0,Ch'ang,644.0,Germany,Female,53.0,4.0,127545.94,2.0,1.0,0.0,184836.01,1.0
-8633,15804316.0,Ts'ai,721.0,Germany,Female,32.0,4.0,85845.43,2.0,1.0,0.0,119374.74,0.0
-8634,15682472.0,Yuriev,711.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,100618.17,0.0
-8635,15638231.0,McGregor,642.0,France,Male,36.0,6.0,0.0,3.0,1.0,0.0,123410.23,1.0
-8636,15737194.0,Chigozie,663.0,France,Female,38.0,2.0,102800.65,1.0,1.0,1.0,117616.29,0.0
-8637,15762418.0,Ibeabuchi,739.0,Spain,Female,43.0,0.0,0.0,1.0,1.0,0.0,54448.51,1.0
-8638,15635141.0,Chidiebele,528.0,Germany,Male,60.0,1.0,93578.68,1.0,1.0,1.0,63581.19,1.0
-8639,15759436.0,Chinagorom,637.0,France,Female,27.0,6.0,0.0,2.0,1.0,0.0,75825.57,0.0
-8640,15679307.0,Chidiebere,579.0,France,Female,36.0,8.0,0.0,2.0,1.0,0.0,124596.63,0.0
-8641,15672051.0,Onyekaozulu,613.0,Germany,Female,30.0,7.0,130170.82,2.0,1.0,0.0,158580.05,0.0
-8642,15575409.0,Yevseyev,592.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,174027.3,0.0
-8643,15601787.0,Tuan,734.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,130514.78,0.0
-8644,15797900.0,Okwuoma,850.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,141148.21,0.0
-8645,15646563.0,Ibezimako,651.0,France,Female,23.0,5.0,0.0,2.0,0.0,0.0,199203.21,0.0
-8646,15598046.0,Ku,707.0,France,Female,44.0,5.0,0.0,4.0,1.0,1.0,127059.03,1.0
-8647,15806936.0,K?,687.0,France,Male,35.0,8.0,0.0,2.0,0.0,0.0,151757.81,0.0
-8648,15782418.0,Boulger,850.0,France,Male,23.0,1.0,82453.96,1.0,1.0,0.0,163798.41,0.0
-8649,15747808.0,Nwabugwu,726.0,France,Male,33.0,7.0,0.0,2.0,1.0,0.0,161242.99,0.0
-8650,15594577.0,Ch'ang,684.0,France,Female,40.0,3.0,0.0,2.0,1.0,0.0,97908.01,0.0
-8651,15663943.0,Chidubem,597.0,France,Male,44.0,5.0,0.0,1.0,1.0,1.0,106969.98,1.0
-8652,15740557.0,Smith,613.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,1.0,146891.07,0.0
-8653,15609823.0,Nnonso,652.0,France,Male,35.0,9.0,127862.93,1.0,1.0,1.0,83470.4,0.0
-8654,15778368.0,T'ao,715.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,168512.64,0.0
-8655,15751412.0,McIntyre,587.0,France,Male,48.0,2.0,141528.88,1.0,1.0,0.0,65951.65,1.0
-8656,15777076.0,Liao,746.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,164741.5,0.0
-8657,15781589.0,Smith,681.0,France,Female,48.0,5.0,0.0,1.0,1.0,1.0,123693.97,0.0
-8658,15693906.0,Onnugo,733.0,Germany,Female,27.0,8.0,127660.46,2.0,1.0,1.0,141035.65,0.0
-8659,15620758.0,Chuang,548.0,France,Male,51.0,9.0,0.0,1.0,1.0,0.0,56787.8,1.0
-8660,15739189.0,Hs?,508.0,France,Female,42.0,4.0,0.0,1.0,1.0,1.0,126475.79,0.0
-8661,15774931.0,Hsueh,598.0,France,Male,21.0,1.0,0.0,1.0,1.0,1.0,166516.84,0.0
-8662,15692671.0,Lucchese,682.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,181224.56,0.0
-8663,15694581.0,P'an,710.0,France,Female,40.0,9.0,0.0,2.0,1.0,1.0,142996.89,0.0
-8664,15698872.0,Ch'ang,746.0,Spain,Female,39.0,8.0,0.0,1.0,1.0,1.0,167984.72,0.0
-8665,15753955.0,Hao,632.0,France,Male,37.0,6.0,0.0,1.0,1.0,1.0,51637.67,0.0
-8666,15784890.0,Haugh,669.0,France,Male,48.0,8.0,0.0,1.0,1.0,0.0,177670.57,1.0
-8667,15665629.0,Toscani,684.0,France,Female,25.0,3.0,108541.04,1.0,1.0,0.0,51387.1,0.0
-8668,15784323.0,Iweobiegbunam,653.0,France,Female,47.0,3.0,145984.02,1.0,1.0,0.0,79728.86,1.0
-8669,15690743.0,O'Donnell,543.0,France,Female,41.0,9.0,150461.07,1.0,0.0,1.0,9983.88,0.0
-8670,15793881.0,Hargreaves,697.0,France,Female,59.0,10.0,0.0,1.0,0.0,0.0,51364.56,1.0
-8671,15783671.0,Nwankwo,675.0,Germany,Male,41.0,6.0,130878.75,1.0,1.0,0.0,121537.91,0.0
-8672,15652626.0,Milano,605.0,Germany,Male,43.0,0.0,131082.89,1.0,1.0,0.0,60721.4,1.0
-8673,15689514.0,Ch'ang,570.0,France,Male,52.0,4.0,0.0,2.0,1.0,0.0,94450.97,0.0
-8674,15732299.0,Ngozichukwuka,521.0,Spain,Female,38.0,9.0,153861.1,2.0,0.0,0.0,100615.14,0.0
-8675,15621440.0,Hs?,559.0,Spain,Male,40.0,2.0,0.0,2.0,1.0,1.0,142331.85,0.0
-8676,15772423.0,Toscani,616.0,Germany,Male,31.0,3.0,141252.18,2.0,1.0,1.0,99645.04,0.0
-8677,15750868.0,Fiorentino,590.0,France,Male,45.0,4.0,0.0,1.0,0.0,0.0,78622.46,1.0
-8678,15774872.0,Hsueh,682.0,France,Male,25.0,4.0,0.0,2.0,1.0,0.0,125083.29,0.0
-8679,15631189.0,Mironova,697.0,Spain,Male,43.0,3.0,180520.29,1.0,1.0,0.0,132798.65,1.0
-8680,15612667.0,K'ung,727.0,Germany,Female,48.0,8.0,124976.19,1.0,1.0,0.0,187976.88,1.0
-8681,15758513.0,Ch'iu,539.0,Germany,Female,32.0,8.0,106901.42,1.0,0.0,0.0,62722.44,0.0
-8682,15606436.0,Fanucci,676.0,France,Male,38.0,4.0,96486.57,1.0,1.0,0.0,142963.29,0.0
-8683,15655192.0,Chieloka,699.0,Spain,Male,37.0,3.0,0.0,1.0,1.0,0.0,175310.38,0.0
-8684,15661591.0,Obidimkpa,731.0,France,Male,32.0,7.0,163546.14,1.0,0.0,1.0,115403.31,0.0
-8685,15646091.0,Skelton,648.0,France,Female,50.0,9.0,0.0,3.0,1.0,0.0,164741.5,1.0
-8686,15614302.0,Ts'ui,684.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,0.0,175324.74,0.0
-8687,15753831.0,T'ang,704.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,93091.02,0.0
-8688,15713819.0,Chukwunonso,622.0,France,Male,24.0,8.0,0.0,2.0,0.0,1.0,141400.51,0.0
-8689,15602497.0,Okwukwe,715.0,Germany,Female,37.0,7.0,103033.83,2.0,1.0,1.0,9241.52,0.0
-8690,15683544.0,Okagbue,707.0,Germany,Female,38.0,4.0,127726.33,1.0,1.0,0.0,103660.38,1.0
-8691,15644796.0,Nkemakonam,613.0,Spain,Male,45.0,5.0,0.0,2.0,1.0,1.0,185561.89,0.0
-8692,15806918.0,Ch'in,678.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,37294.88,0.0
-8693,15750055.0,Gresswell,656.0,France,Female,52.0,8.0,0.0,1.0,1.0,0.0,115288.24,1.0
-8694,15758035.0,Ts'ai,742.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,105760.01,0.0
-8695,15748123.0,Chin,631.0,Spain,Female,44.0,4.0,0.0,2.0,1.0,0.0,79323.61,0.0
-8696,15728608.0,Onyekachi,591.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,0.0,64872.33,0.0
-8697,15704763.0,White,520.0,France,Male,37.0,4.0,0.0,1.0,1.0,0.0,85746.52,0.0
-8698,15584812.0,Shih,588.0,France,Female,29.0,8.0,0.0,2.0,1.0,1.0,93901.61,0.0
-8699,15663770.0,Toscani,714.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,161519.77,0.0
-8700,15592761.0,Yevseyev,648.0,France,Female,38.0,4.0,0.0,2.0,0.0,0.0,131710.44,0.0
-8701,15749119.0,DeRose,729.0,France,Male,40.0,3.0,0.0,2.0,1.0,1.0,86099.23,0.0
-8702,15661532.0,Onyemauchechukwu,757.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,84760.9,0.0
-8703,15602851.0,Hsueh,682.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,94820.85,0.0
-8704,15683375.0,O'Sullivan,629.0,France,Male,42.0,2.0,0.0,2.0,1.0,0.0,125870.5,0.0
-8705,15746693.0,Hsieh,599.0,France,Female,33.0,1.0,0.0,2.0,0.0,1.0,167784.28,0.0
-8706,15614813.0,Milne,775.0,Spain,Male,28.0,6.0,0.0,2.0,1.0,1.0,95611.47,0.0
-8707,15751203.0,L?,742.0,France,Male,34.0,7.0,0.0,2.0,0.0,1.0,166317.71,0.0
-8708,15631457.0,Pagnotto,712.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,173878.87,0.0
-8709,15694518.0,Chukwualuka,792.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,155047.11,0.0
-8710,15777315.0,Hsueh,647.0,Germany,Male,33.0,2.0,71379.53,2.0,1.0,1.0,139919.38,0.0
-8711,15795511.0,Omeokachie,677.0,France,Male,34.0,4.0,86038.21,2.0,1.0,1.0,176843.53,0.0
-8712,15728885.0,Kao,741.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,135851.3,0.0
-8713,15776939.0,Udegbunam,661.0,Spain,Male,41.0,5.0,0.0,2.0,1.0,1.0,54487.43,0.0
-8714,15631685.0,Tsai,754.0,France,Male,30.0,5.0,0.0,2.0,0.0,1.0,59890.29,0.0
-8715,15794468.0,Cattaneo,559.0,Spain,Male,41.0,0.0,0.0,2.0,0.0,0.0,171060.01,0.0
-8716,15594878.0,Fiorentino,850.0,France,Female,41.0,3.0,0.0,2.0,1.0,1.0,136983.77,0.0
-8717,15583462.0,Chiemela,678.0,Germany,Male,30.0,8.0,141784.02,2.0,1.0,1.0,166586.99,0.0
-8718,15758710.0,Naylor,730.0,France,Male,32.0,6.0,0.0,2.0,0.0,0.0,151457.82,0.0
-8719,15644878.0,Trevisani,607.0,France,Male,44.0,2.0,0.0,1.0,1.0,0.0,136812.08,0.0
-8720,15582914.0,Ch'eng,738.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,49653.05,0.0
-8721,15673372.0,Iweobiegbunam,559.0,France,Female,35.0,0.0,0.0,2.0,1.0,1.0,161465.31,0.0
-8722,15584928.0,Hsiao,684.0,France,Female,37.0,10.0,0.0,1.0,1.0,0.0,84927.41,1.0
-8723,15733777.0,Teng,631.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,61977.82,0.0
-8724,15801316.0,Omeokachie,719.0,France,Male,39.0,0.0,0.0,2.0,1.0,0.0,92916.54,0.0
-8725,15713854.0,Yobachi,695.0,France,Male,43.0,3.0,0.0,2.0,1.0,1.0,128521.16,0.0
-8726,15702095.0,T'ien,647.0,Spain,Female,23.0,10.0,0.0,2.0,1.0,1.0,176913.42,0.0
-8727,15754053.0,Laidley,652.0,Germany,Female,33.0,2.0,131394.47,1.0,1.0,0.0,74850.84,1.0
-8728,15579183.0,Hsiung,637.0,France,Male,31.0,0.0,0.0,2.0,1.0,1.0,199242.65,0.0
-8729,15574071.0,Hs?,685.0,Spain,Female,34.0,6.0,0.0,1.0,0.0,0.0,132861.9,0.0
-8730,15592736.0,Chukwuemeka,704.0,France,Male,58.0,4.0,0.0,2.0,1.0,1.0,94810.88,0.0
-8731,15610755.0,Chibueze,628.0,France,Male,37.0,7.0,0.0,2.0,0.0,1.0,104786.59,0.0
-8732,15636016.0,Onyemere,656.0,Spain,Male,22.0,2.0,0.0,2.0,1.0,0.0,131778.58,0.0
-8733,15648933.0,Hs?,573.0,Germany,Female,35.0,3.0,129646.91,2.0,1.0,0.0,102517.54,1.0
-8734,15785078.0,Ch'en,587.0,France,Female,29.0,4.0,133598.7,1.0,0.0,1.0,133535.52,0.0
-8735,15651740.0,Chinwemma,637.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,174094.24,0.0
-8736,15662758.0,Ting,506.0,France,Female,26.0,9.0,0.0,2.0,0.0,0.0,133672.61,0.0
-8737,15593897.0,Chiawuotu,604.0,Spain,Female,31.0,5.0,0.0,2.0,1.0,1.0,132973.21,0.0
-8738,15578788.0,Maclean,584.0,Spain,Female,45.0,8.0,0.0,1.0,0.0,1.0,131710.59,1.0
-8739,15582519.0,Chinedum,561.0,France,Female,41.0,4.0,0.0,1.0,1.0,0.0,122518.5,0.0
-8740,15794048.0,Chukwukadibia,705.0,Germany,Female,39.0,9.0,121172.97,1.0,1.0,0.0,138399.67,1.0
-8741,15677739.0,Chukwueloka,572.0,Spain,Male,43.0,6.0,0.0,2.0,1.0,1.0,183542.08,0.0
-8742,15747232.0,Martin,790.0,Germany,Male,53.0,2.0,102278.85,2.0,1.0,1.0,120801.65,0.0
-8743,15790829.0,Yobachi,700.0,France,Male,42.0,7.0,0.0,2.0,1.0,0.0,100929.19,0.0
-8744,15775339.0,T'ien,705.0,Germany,Female,31.0,3.0,151659.52,2.0,1.0,1.0,140745.91,0.0
-8745,15576550.0,Ch'ang,564.0,France,Female,39.0,9.0,0.0,1.0,1.0,1.0,177362.45,0.0
-8746,15615477.0,Ch'ien,651.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,158043.11,0.0
-8747,15796790.0,Chiazagomekpere,662.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,45610.63,0.0
-8748,15793881.0,Nkemakonam,637.0,Germany,Female,53.0,5.0,161274.05,1.0,1.0,1.0,27474.81,1.0
-8749,15727041.0,Nkemakonam,726.0,Spain,Male,24.0,2.0,0.0,2.0,1.0,1.0,150850.29,0.0
-8750,15741094.0,Ojiofor,718.0,Germany,Male,42.0,5.0,122529.15,1.0,1.0,0.0,43932.56,1.0
-8751,15643903.0,O'Donnell,716.0,France,Female,23.0,7.0,144848.74,1.0,1.0,1.0,171463.83,0.0
-8752,15767231.0,Chien,744.0,Germany,Male,38.0,6.0,108117.1,1.0,1.0,0.0,151871.84,1.0
-8753,15595766.0,Macleod,585.0,Spain,Female,51.0,3.0,0.0,3.0,1.0,0.0,58855.85,1.0
-8754,15784320.0,Zetticci,739.0,Spain,Female,33.0,10.0,0.0,2.0,0.0,0.0,162599.51,0.0
-8755,15644551.0,Okwuadigbo,634.0,Germany,Female,28.0,6.0,129893.56,1.0,1.0,0.0,188203.58,1.0
-8756,15688760.0,Ch'ien,718.0,France,Male,32.0,5.0,142319.03,1.0,1.0,1.0,126012.57,0.0
-8757,15715117.0,Chiu,721.0,Spain,Male,39.0,1.0,0.0,2.0,0.0,0.0,119882.86,0.0
-8758,15779267.0,Foveaux,705.0,France,Male,32.0,8.0,0.0,1.0,1.0,0.0,116471.42,0.0
-8759,15765173.0,Hs?,604.0,Spain,Female,22.0,6.0,0.0,2.0,1.0,1.0,129563.86,0.0
-8760,15770185.0,Hazon,652.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,134443.83,0.0
-8761,15686463.0,Chinwenma,742.0,France,Female,30.0,4.0,92636.96,1.0,0.0,1.0,151335.24,0.0
-8762,15575060.0,Tien,714.0,France,Male,44.0,8.0,0.0,2.0,0.0,1.0,106969.98,0.0
-8763,15673074.0,K'ung,724.0,France,Female,31.0,7.0,0.0,1.0,1.0,0.0,77951.84,0.0
-8764,15668058.0,Wilson,748.0,France,Female,57.0,1.0,0.0,1.0,1.0,0.0,120853.87,1.0
-8765,15756918.0,Nucci,644.0,France,Male,32.0,4.0,113858.95,2.0,1.0,0.0,173586.63,0.0
-8766,15711602.0,Chiebuka,656.0,France,Female,28.0,9.0,0.0,2.0,0.0,1.0,139475.1,0.0
-8767,15621611.0,Hsia,685.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,82967.97,0.0
-8768,15610903.0,Chinomso,705.0,France,Male,36.0,1.0,0.0,2.0,0.0,1.0,132702.64,0.0
-8769,15600792.0,Chukwualuka,578.0,Spain,Female,38.0,0.0,167738.82,2.0,0.0,1.0,94450.97,0.0
-8770,15641794.0,Li,719.0,Spain,Female,45.0,9.0,0.0,1.0,1.0,0.0,183598.77,1.0
-8771,15774536.0,H?,587.0,Spain,Male,23.0,8.0,131778.66,1.0,1.0,1.0,166677.35,0.0
-8772,15661723.0,Trevisano,632.0,Germany,Male,37.0,5.0,133060.63,2.0,0.0,1.0,60905.51,0.0
-8773,15815690.0,Macleod,624.0,Spain,Female,36.0,10.0,0.0,2.0,0.0,1.0,127825.25,0.0
-8774,15752953.0,Pagnotto,681.0,France,Male,26.0,1.0,0.0,2.0,1.0,1.0,148887.69,0.0
-8775,15677178.0,Yobanna,757.0,Germany,Male,30.0,7.0,132512.26,1.0,1.0,0.0,53394.12,0.0
-8776,15596962.0,Tsai,705.0,France,Male,30.0,8.0,0.0,1.0,1.0,0.0,79919.13,0.0
-8777,15792329.0,Lueck,682.0,Germany,Male,47.0,1.0,119545.48,1.0,1.0,1.0,125877.22,1.0
-8778,15605918.0,P'eng,588.0,France,Female,55.0,4.0,161450.04,1.0,0.0,1.0,106761.47,1.0
-8779,15726313.0,Chukwuhaenye,574.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,50783.1,0.0
-8780,15615473.0,Cameron,668.0,Germany,Female,43.0,2.0,107764.75,1.0,1.0,0.0,55857.48,1.0
-8781,15658577.0,Hsu,596.0,Spain,Female,40.0,8.0,0.0,2.0,1.0,1.0,183893.31,0.0
-8782,15675184.0,Hsiung,685.0,Spain,Male,46.0,3.0,0.0,1.0,0.0,0.0,177528.92,1.0
-8783,15709387.0,Chien,583.0,France,Male,37.0,2.0,0.0,1.0,1.0,0.0,138882.98,0.0
-8784,15725676.0,Chinonyelum,646.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,126451.14,0.0
-8785,15706762.0,Ch'ien,656.0,France,Female,53.0,5.0,0.0,1.0,1.0,1.0,125762.95,1.0
-8786,15702656.0,Palerma,538.0,France,Male,34.0,7.0,0.0,1.0,1.0,1.0,57541.06,0.0
-8787,15634373.0,Ch'ang,710.0,France,Female,37.0,9.0,149571.94,1.0,1.0,1.0,67611.36,0.0
-8788,15592705.0,Ponomaryov,610.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,51454.06,0.0
-8789,15764708.0,Fitts,484.0,Spain,Female,39.0,8.0,0.0,2.0,1.0,1.0,176984.34,0.0
-8790,15756743.0,Chu,729.0,France,Male,31.0,3.0,0.0,2.0,1.0,0.0,84870.23,0.0
-8791,15569432.0,Macgroarty,582.0,Spain,Female,34.0,4.0,0.0,1.0,1.0,0.0,126477.41,1.0
-8792,15807989.0,Ch'ien,671.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,175310.23,0.0
-8793,15567778.0,Smith,758.0,France,Male,43.0,4.0,122314.5,1.0,1.0,0.0,187987.01,0.0
-8794,15806901.0,Kolesnikov,614.0,France,Male,40.0,10.0,0.0,2.0,1.0,1.0,159418.1,0.0
-8795,15664808.0,Genovesi,564.0,France,Male,33.0,7.0,0.0,1.0,1.0,0.0,170950.58,0.0
-8796,15658982.0,Mazzi,624.0,Germany,Female,33.0,2.0,101633.04,1.0,1.0,0.0,121747.96,0.0
-8797,15604551.0,Hsueh,671.0,Spain,Male,33.0,9.0,0.0,2.0,0.0,1.0,158887.09,0.0
-8798,15786078.0,Smith,679.0,France,Male,33.0,7.0,123760.68,1.0,0.0,1.0,172490.78,0.0
-8799,15605067.0,Hs?,652.0,France,Female,30.0,7.0,104568.41,1.0,1.0,0.0,177719.88,1.0
-8800,15706587.0,Calabrese,576.0,France,Male,49.0,1.0,0.0,1.0,1.0,0.0,171928.04,1.0
-8801,15602846.0,T'ien,543.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,96961.0,0.0
-8802,15744127.0,Yobachi,627.0,Spain,Male,29.0,1.0,137963.07,1.0,1.0,0.0,86957.42,0.0
-8803,15652948.0,Soto,635.0,France,Male,35.0,9.0,0.0,3.0,1.0,1.0,93524.19,1.0
-8804,15609913.0,Obiajulu,775.0,Spain,Male,33.0,10.0,0.0,1.0,1.0,1.0,149550.97,0.0
-8805,15739160.0,Teng,467.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,53949.98,0.0
-8806,15670039.0,Ch'ien,692.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,91573.18,0.0
-8807,15585100.0,Calabrese,806.0,Spain,Female,35.0,1.0,0.0,2.0,1.0,1.0,122580.48,0.0
-8808,15791674.0,Ch'in,572.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,60920.03,0.0
-8809,15772781.0,K?,706.0,Germany,Female,46.0,7.0,117413.95,1.0,1.0,0.0,139861.53,1.0
-8810,15678725.0,Mazzanti,528.0,France,Male,58.0,8.0,0.0,2.0,1.0,1.0,91947.74,0.0
-8811,15682048.0,Toscani,634.0,Spain,Female,36.0,4.0,0.0,1.0,0.0,1.0,69093.39,0.0
-8812,15589329.0,Nnonso,662.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,150319.49,0.0
-8813,15748673.0,Tsao,792.0,France,Male,42.0,10.0,73493.17,1.0,1.0,0.0,171575.06,0.0
-8814,15763063.0,Aitken,659.0,France,Female,44.0,7.0,0.0,2.0,0.0,1.0,100618.17,0.0
-8815,15807900.0,Uchenna,656.0,Germany,Female,48.0,2.0,132767.66,1.0,1.0,0.0,14217.77,1.0
-8816,15775294.0,Hsing,684.0,France,Male,44.0,10.0,0.0,2.0,1.0,0.0,111893.09,0.0
-8817,15588928.0,Manna,687.0,France,Female,61.0,1.0,0.0,2.0,0.0,1.0,131281.09,0.0
-8818,15766047.0,Ch'iu,642.0,France,Female,42.0,10.0,0.0,1.0,1.0,1.0,91903.9,0.0
-8819,15814930.0,Ch'en,773.0,France,Male,26.0,2.0,107556.06,2.0,1.0,0.0,92568.05,0.0
-8820,15798521.0,Trevisani,707.0,France,Male,29.0,7.0,0.0,2.0,0.0,0.0,94898.1,0.0
-8821,15585901.0,Onyemachukwu,634.0,Spain,Female,51.0,0.0,153946.14,1.0,1.0,1.0,101348.88,1.0
-8822,15643679.0,Onyemachukwu,683.0,France,Female,37.0,10.0,0.0,2.0,0.0,0.0,134878.19,0.0
-8823,15678460.0,Ts'ui,652.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,122960.98,0.0
-8824,15702300.0,K?,739.0,France,Male,30.0,5.0,0.0,2.0,1.0,0.0,116977.89,0.0
-8825,15648047.0,Bair,781.0,France,Male,28.0,2.0,0.0,2.0,1.0,1.0,43982.41,0.0
-8826,15684319.0,Chuang,677.0,Spain,Female,33.0,6.0,111827.27,1.0,1.0,1.0,97906.75,0.0
-8827,15589924.0,Onuora,509.0,France,Male,36.0,9.0,0.0,2.0,0.0,1.0,146891.07,0.0
-8828,15813363.0,Chidumaga,569.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,183682.09,0.0
-8829,15812198.0,Hsing,717.0,Germany,Female,42.0,6.0,132725.1,2.0,1.0,1.0,149614.81,0.0
-8830,15633059.0,Kistler,713.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,171681.86,0.0
-8831,15702801.0,Chuang,726.0,France,Male,44.0,2.0,91611.12,1.0,0.0,1.0,26589.56,0.0
-8832,15743828.0,Zetticci,678.0,France,Male,40.0,6.0,123086.39,1.0,0.0,0.0,55829.25,0.0
-8833,15712807.0,Tien,589.0,France,Male,32.0,1.0,0.0,2.0,1.0,0.0,878.87,0.0
-8834,15573741.0,Nnamutaezinwa,599.0,France,Female,51.0,6.0,0.0,1.0,0.0,0.0,105780.18,1.0
-8835,15613140.0,Calabrese,528.0,Spain,Female,38.0,5.0,83074.0,2.0,1.0,0.0,102950.79,0.0
-8836,15697567.0,Chukwualuka,576.0,France,Female,33.0,3.0,127726.33,1.0,1.0,0.0,114796.83,0.0
-8837,15693637.0,Lai,679.0,Spain,Female,47.0,5.0,0.0,2.0,1.0,1.0,58965.04,0.0
-8838,15571221.0,Chukwujamuike,445.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,176913.42,1.0
-8839,15733429.0,Pagnotto,838.0,France,Male,46.0,9.0,0.0,1.0,0.0,1.0,143955.72,1.0
-8840,15682442.0,Nnamutaezinwa,678.0,Spain,Male,33.0,2.0,0.0,2.0,0.0,0.0,145796.49,0.0
-8841,15573086.0,McIntyre,547.0,France,Female,34.0,7.0,0.0,2.0,0.0,1.0,69052.87,0.0
-8842,15702300.0,Chiu,727.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,0.0,139861.53,0.0
-8843,15762543.0,Genovese,705.0,Germany,Male,44.0,0.0,125920.98,1.0,1.0,1.0,59638.85,1.0
-8844,15573730.0,T'an,569.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,134901.56,1.0
-8845,15756538.0,Milne,676.0,Spain,Male,32.0,1.0,131769.04,1.0,1.0,0.0,129964.94,0.0
-8846,15784890.0,Ku,585.0,France,Female,26.0,0.0,121863.61,1.0,1.0,0.0,173498.45,1.0
-8847,15642570.0,Fanucci,642.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,127543.11,0.0
-8848,15582892.0,Zhirov,554.0,France,Male,41.0,5.0,0.0,2.0,0.0,1.0,175072.47,0.0
-8849,15723873.0,Hsu,611.0,France,Female,43.0,9.0,125837.86,1.0,1.0,0.0,121210.09,0.0
-8850,15623989.0,Maclean,682.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,104469.06,0.0
-8851,15714054.0,Chukwujamuike,677.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,149813.18,0.0
-8852,15702789.0,P'an,705.0,Germany,Male,29.0,1.0,162701.65,2.0,1.0,1.0,125877.25,0.0
-8853,15761364.0,Chukwudi,686.0,Germany,Male,41.0,0.0,129718.3,3.0,1.0,0.0,81622.52,1.0
-8854,15578788.0,Yudina,707.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,50783.1,0.0
-8855,15660271.0,Biryukov,739.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,97939.4,0.0
-8856,15583736.0,Chidubem,567.0,France,Female,52.0,7.0,109799.55,1.0,1.0,1.0,131304.45,0.0
-8857,15751561.0,Scott,619.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,82566.01,0.0
-8858,15692761.0,Chin,694.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,0.0,140889.12,0.0
-8859,15783030.0,Ts'ui,714.0,France,Male,41.0,5.0,0.0,1.0,1.0,1.0,183494.87,0.0
-8860,15757837.0,Teng,684.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,160968.44,0.0
-8861,15622470.0,Galkin,605.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,126517.97,0.0
-8862,15694456.0,K'ung,737.0,France,Male,36.0,7.0,111837.78,1.0,1.0,1.0,132468.76,0.0
-8863,15617166.0,Pisani,591.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,83022.92,0.0
-8864,15766166.0,Chiang,679.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,168369.37,0.0
-8865,15592761.0,Chukwuemeka,449.0,France,Male,39.0,4.0,95826.22,1.0,0.0,1.0,174098.24,0.0
-8866,15589715.0,Ting,753.0,France,Male,54.0,6.0,0.0,1.0,1.0,0.0,177065.24,1.0
-8867,15783025.0,Calabresi,678.0,France,Male,40.0,10.0,0.0,1.0,1.0,0.0,60988.21,0.0
-8868,15591287.0,Pirozzi,713.0,Spain,Female,28.0,3.0,0.0,2.0,1.0,0.0,121151.1,0.0
-8869,15789955.0,Milne,678.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,102280.29,0.0
-8870,15586039.0,Chiemenam,636.0,Germany,Male,40.0,4.0,129753.69,1.0,1.0,0.0,163471.01,1.0
-8871,15593973.0,Nnonso,745.0,France,Female,34.0,8.0,81132.21,1.0,1.0,0.0,134889.95,0.0
-8872,15684548.0,Chijindum,652.0,France,Male,42.0,1.0,104682.83,1.0,0.0,1.0,168035.63,0.0
-8873,15743076.0,Ozioma,647.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,183598.77,0.0
-8874,15736043.0,Yefremov,726.0,France,Female,43.0,9.0,151963.26,1.0,1.0,0.0,72993.65,0.0
-8875,15733661.0,Niu,584.0,France,Male,37.0,9.0,0.0,1.0,1.0,0.0,162889.1,1.0
-8876,15682778.0,Ch'ien,695.0,Spain,Male,42.0,10.0,0.0,2.0,1.0,1.0,80582.34,0.0
-8877,15647974.0,Hs?eh,762.0,France,Female,34.0,6.0,0.0,2.0,1.0,0.0,53420.53,0.0
-8878,15576680.0,O'Loghlen,681.0,France,Male,38.0,5.0,0.0,2.0,0.0,1.0,82463.69,0.0
-8879,15702298.0,Sagese,678.0,France,Male,35.0,2.0,137842.96,1.0,1.0,0.0,117020.07,0.0
-8880,15743838.0,Chiang,682.0,Spain,Male,36.0,5.0,0.0,1.0,1.0,1.0,141031.06,0.0
-8881,15669281.0,Ch'iu,638.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,104786.59,0.0
-8882,15704770.0,Chin,844.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,116977.89,0.0
-8883,15692761.0,Rizzo,709.0,France,Female,41.0,9.0,0.0,1.0,1.0,0.0,64495.78,1.0
-8884,15685829.0,Onwumelu,837.0,France,Male,35.0,5.0,135645.29,1.0,1.0,0.0,136816.37,0.0
-8885,15808849.0,Okwuadigbo,699.0,France,Male,46.0,3.0,0.0,3.0,1.0,0.0,166677.35,1.0
-8886,15579566.0,Li Fonti,559.0,France,Female,47.0,8.0,0.0,1.0,1.0,0.0,101319.76,1.0
-8887,15584518.0,Chinwendu,656.0,France,Female,49.0,9.0,0.0,2.0,1.0,1.0,79547.39,0.0
-8888,15626040.0,Fomin,731.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,1.0,173948.3,0.0
-8889,15806964.0,Ch'en,637.0,France,Male,35.0,2.0,98854.34,2.0,1.0,0.0,62972.13,1.0
-8890,15704844.0,Ch'en,651.0,Spain,Female,50.0,1.0,0.0,2.0,1.0,0.0,72876.88,0.0
-8891,15735221.0,Hsueh,735.0,France,Male,34.0,1.0,0.0,1.0,1.0,1.0,170563.73,0.0
-8892,15593969.0,Mironov,695.0,France,Male,45.0,1.0,0.0,2.0,1.0,1.0,107787.42,0.0
-8893,15803347.0,Oluchi,526.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,72849.47,0.0
-8894,15780359.0,Yudina,577.0,France,Male,49.0,5.0,127290.61,1.0,1.0,0.0,184524.74,0.0
-8895,15597914.0,Nock,526.0,Germany,Female,47.0,5.0,143773.07,3.0,0.0,0.0,91573.19,1.0
-8896,15683980.0,Hales,591.0,France,Male,29.0,3.0,0.0,1.0,1.0,1.0,161241.65,0.0
-8897,15617206.0,Ch'ang,719.0,Germany,Female,29.0,4.0,96471.4,2.0,0.0,1.0,171518.1,0.0
-8898,15694321.0,Onio,710.0,Germany,Male,37.0,4.0,133899.23,1.0,1.0,1.0,120511.03,0.0
-8899,15747264.0,Chukwudi,775.0,France,Male,44.0,9.0,0.0,2.0,1.0,1.0,102763.69,0.0
-8900,15793890.0,Ch'ang,646.0,Spain,Female,51.0,9.0,0.0,1.0,1.0,0.0,137985.67,1.0
-8901,15621426.0,Hsueh,619.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,173851.11,0.0
-8902,15682600.0,Aksenov,626.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,77330.35,0.0
-8903,15592222.0,Tuan,583.0,Spain,Male,44.0,3.0,0.0,2.0,1.0,0.0,122268.75,0.0
-8904,15674551.0,Hs?eh,758.0,Germany,Male,36.0,8.0,107714.73,1.0,1.0,0.0,146955.71,0.0
-8905,15755868.0,Chukwufumnanya,705.0,France,Female,41.0,1.0,0.0,2.0,0.0,1.0,55868.97,0.0
-8906,15763063.0,Yeh,652.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,180553.48,0.0
-8907,15651802.0,Oluchukwu,605.0,France,Female,59.0,3.0,110153.27,1.0,1.0,1.0,142995.32,1.0
-8908,15794868.0,Chiedozie,574.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,75578.67,0.0
-8909,15646513.0,Chidumaga,692.0,Spain,Female,39.0,9.0,0.0,1.0,0.0,1.0,83084.33,1.0
-8910,15769191.0,Chukwuma,637.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,1.0,85547.33,0.0
-8911,15747168.0,Hsiung,711.0,France,Male,38.0,9.0,0.0,1.0,0.0,1.0,177737.06,0.0
-8912,15807563.0,Tuan,841.0,Spain,Male,51.0,1.0,161757.87,1.0,1.0,1.0,163667.56,0.0
-8913,15767064.0,Shih,691.0,Spain,Male,32.0,9.0,0.0,2.0,1.0,1.0,121085.72,0.0
-8914,15738018.0,K'ung,730.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,62944.75,0.0
-8915,15636999.0,Olisaemeka,592.0,France,Female,35.0,3.0,0.0,1.0,1.0,0.0,151316.37,1.0
-8916,15772383.0,Nnachetam,669.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,182988.94,0.0
-8917,15774738.0,Kao,642.0,Germany,Male,24.0,9.0,122453.97,1.0,1.0,1.0,199249.29,0.0
-8918,15709142.0,Chinwenma,682.0,Spain,Male,36.0,3.0,0.0,2.0,0.0,1.0,184399.76,0.0
-8919,15570931.0,Hsia,652.0,France,Female,42.0,0.0,0.0,2.0,0.0,1.0,180769.55,0.0
-8920,15662901.0,Zetticci,542.0,France,Male,34.0,10.0,0.0,2.0,1.0,0.0,55867.38,0.0
-8921,15648069.0,Hsia,610.0,France,Male,27.0,9.0,0.0,1.0,1.0,1.0,80178.54,0.0
-8922,15655341.0,Hsu,630.0,France,Male,37.0,0.0,0.0,2.0,1.0,0.0,100995.11,0.0
-8923,15723605.0,Nwabugwu,611.0,France,Male,47.0,3.0,0.0,2.0,1.0,1.0,158023.1,0.0
-8924,15801488.0,Aitken,772.0,Germany,Female,28.0,1.0,105435.76,2.0,0.0,1.0,161574.19,0.0
-8925,15593501.0,P'eng,545.0,Germany,Male,44.0,1.0,127545.75,1.0,1.0,0.0,18094.96,1.0
-8926,15651020.0,K?,633.0,Spain,Female,39.0,7.0,0.0,2.0,1.0,1.0,93717.55,0.0
-8927,15626042.0,Chiawuotu,592.0,Spain,Female,44.0,3.0,0.0,1.0,1.0,1.0,80410.68,1.0
-8928,15746461.0,T'ien,584.0,Germany,Male,37.0,9.0,139825.34,1.0,1.0,0.0,61164.45,1.0
-8929,15593773.0,Macartney,538.0,Spain,Male,67.0,0.0,0.0,1.0,1.0,1.0,163365.85,1.0
-8930,15772329.0,Scott,581.0,France,Male,50.0,9.0,102282.61,1.0,1.0,0.0,82526.92,0.0
-8931,15682600.0,Okwuadigbo,804.0,France,Female,30.0,10.0,0.0,2.0,1.0,1.0,158054.49,0.0
-8932,15752846.0,Ch'eng,667.0,France,Male,28.0,9.0,123070.89,1.0,1.0,0.0,149913.84,0.0
-8933,15609558.0,Ch'iu,593.0,France,Male,49.0,6.0,0.0,1.0,1.0,0.0,67364.42,1.0
-8934,15674833.0,Fiorentini,693.0,France,Female,50.0,4.0,0.0,1.0,1.0,1.0,160546.58,0.0
-8935,15581736.0,Chukwuemeka,633.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,1.0,104889.3,0.0
-8936,15686963.0,Ch'en,749.0,France,Male,31.0,6.0,84475.67,1.0,1.0,1.0,104469.06,0.0
-8937,15624170.0,Iloerika,648.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,181850.74,0.0
-8938,15724153.0,H?,709.0,France,Female,19.0,7.0,0.0,2.0,1.0,1.0,160670.37,0.0
-8939,15568162.0,Scott,682.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,102325.19,0.0
-8940,15670755.0,Trevisani,685.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,162022.47,0.0
-8941,15777076.0,Tien,579.0,Spain,Female,32.0,5.0,74963.5,1.0,0.0,1.0,158036.72,0.0
-8942,15693888.0,Y?an,684.0,France,Male,41.0,4.0,161613.94,1.0,0.0,1.0,149913.84,0.0
-8943,15702416.0,Yobanna,697.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,63737.19,0.0
-8944,15672798.0,Onyemauchechukwu,562.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,50774.81,0.0
-8945,15631927.0,Hsueh,648.0,Germany,Male,68.0,3.0,116563.41,1.0,0.0,0.0,58854.88,1.0
-8946,15573854.0,Whitehead,632.0,France,Male,25.0,6.0,0.0,2.0,0.0,1.0,143619.52,0.0
-8947,15762268.0,Lo Duca,654.0,Spain,Female,47.0,5.0,0.0,1.0,1.0,1.0,136983.77,1.0
-8948,15591037.0,Hs?,715.0,Germany,Female,37.0,10.0,108822.5,2.0,1.0,1.0,121537.91,0.0
-8949,15662658.0,Macleod,588.0,France,Male,38.0,0.0,0.0,2.0,1.0,1.0,130853.03,0.0
-8950,15796958.0,Zito,716.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,95625.03,0.0
-8951,15731751.0,Dyer,743.0,France,Male,43.0,7.0,0.0,1.0,1.0,0.0,145796.49,0.0
-8952,15706071.0,Ts'ai,721.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,72318.77,0.0
-8953,15705313.0,Nwagugheuzo,735.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,88728.47,0.0
-8954,15587914.0,Hsia,699.0,Germany,Male,29.0,2.0,121513.34,2.0,1.0,0.0,113481.02,0.0
-8955,15622532.0,Okechukwu,714.0,Germany,Male,37.0,6.0,104584.11,2.0,0.0,1.0,175722.31,0.0
-8956,15682778.0,Hs?eh,754.0,France,Male,43.0,4.0,0.0,1.0,1.0,1.0,61843.73,0.0
-8957,15776047.0,Smith,730.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,60552.44,0.0
-8958,15750055.0,Mazzanti,658.0,Germany,Male,43.0,9.0,121898.58,1.0,1.0,0.0,163666.85,1.0
-8959,15662494.0,Genovesi,604.0,France,Female,30.0,6.0,0.0,1.0,1.0,0.0,177783.04,1.0
-8960,15648047.0,T'ao,628.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,71450.96,0.0
-8961,15776733.0,Ugoji,684.0,France,Male,27.0,6.0,0.0,2.0,1.0,1.0,180932.46,0.0
-8962,15735080.0,Nkemakolam,622.0,Germany,Female,46.0,6.0,108204.52,2.0,1.0,0.0,97734.3,1.0
-8963,15800326.0,Hsiung,681.0,Spain,Female,36.0,7.0,0.0,2.0,1.0,0.0,92888.52,0.0
-8964,15600992.0,T'ang,682.0,France,Female,35.0,2.0,0.0,2.0,0.0,0.0,48226.37,0.0
-8965,15807481.0,Yobanna,558.0,France,Male,33.0,4.0,89047.14,1.0,1.0,0.0,90537.47,0.0
-8966,15696054.0,T'ang,712.0,France,Male,44.0,5.0,106603.74,1.0,1.0,1.0,156036.19,0.0
-8967,15753092.0,Ch'eng,684.0,Germany,Female,32.0,4.0,126473.33,2.0,1.0,1.0,123775.15,0.0
-8968,15690134.0,Chidiegwu,691.0,Spain,Male,46.0,5.0,81811.71,1.0,1.0,1.0,164741.81,0.0
-8969,15704770.0,Lai,581.0,Germany,Male,35.0,1.0,118627.15,1.0,1.0,1.0,184514.01,0.0
-8970,15654574.0,Y?an,598.0,France,Female,29.0,6.0,86911.8,1.0,1.0,1.0,133535.29,0.0
-8971,15682640.0,Ch'ien,679.0,Spain,Male,36.0,7.0,157970.23,1.0,1.0,0.0,114323.66,0.0
-8972,15758048.0,Pisano,526.0,Spain,Male,42.0,1.0,0.0,2.0,1.0,1.0,157072.91,0.0
-8973,15571059.0,McIntosh,537.0,Spain,Male,41.0,6.0,0.0,2.0,1.0,0.0,59129.72,0.0
-8974,15617982.0,Galkin,679.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,81884.8,1.0
-8975,15579207.0,Chiu,671.0,France,Female,38.0,4.0,86837.95,1.0,1.0,0.0,141872.05,0.0
-8976,15578788.0,Nucci,526.0,France,Female,45.0,7.0,0.0,1.0,1.0,0.0,163427.18,1.0
-8977,15757933.0,White,646.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,0.0,167909.52,0.0
-8978,15788494.0,Onyemachukwu,645.0,Germany,Male,48.0,6.0,131834.76,1.0,0.0,1.0,143450.61,1.0
-8979,15757867.0,Fenton,731.0,Spain,Female,44.0,6.0,102962.62,1.0,0.0,1.0,142331.85,0.0
-8980,15642098.0,Kornilova,690.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,86460.28,0.0
-8981,15643496.0,Lucchese,579.0,France,Female,33.0,3.0,0.0,2.0,1.0,1.0,178291.09,0.0
-8982,15754577.0,H?,598.0,France,Female,44.0,9.0,0.0,1.0,1.0,0.0,63687.06,1.0
-8983,15641733.0,Nwokeocha,648.0,Germany,Male,50.0,3.0,125851.93,1.0,0.0,1.0,177747.72,1.0
-8984,15729359.0,Brown,717.0,Germany,Male,31.0,5.0,111100.98,2.0,0.0,1.0,160670.37,0.0
-8985,15728474.0,Milanesi,712.0,Germany,Female,30.0,9.0,118100.59,1.0,1.0,1.0,143463.28,0.0
-8986,15678067.0,Hs?,652.0,Spain,Female,39.0,3.0,111063.73,1.0,0.0,0.0,176780.2,0.0
-8987,15636396.0,Ts'ai,652.0,France,Male,45.0,7.0,0.0,3.0,1.0,0.0,4861.72,1.0
-8988,15591747.0,Nwankwo,572.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,103560.98,0.0
-8989,15693637.0,Chiedozie,580.0,Germany,Male,35.0,8.0,132693.56,2.0,1.0,1.0,167810.26,0.0
-8990,15702464.0,Nwachinemelu,731.0,France,Female,27.0,8.0,0.0,2.0,1.0,1.0,125870.5,0.0
-8991,15662291.0,Chidiebere,665.0,France,Male,51.0,2.0,0.0,2.0,1.0,1.0,38190.78,0.0
-8992,15706762.0,Wright,721.0,France,Female,41.0,9.0,103711.56,3.0,1.0,1.0,178822.07,1.0
-8993,15633666.0,Ch'in,850.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,1.0,94798.13,0.0
-8994,15621550.0,Nwankwo,712.0,France,Female,27.0,0.0,0.0,2.0,1.0,0.0,83419.49,1.0
-8995,15709511.0,Yeh,727.0,France,Male,49.0,2.0,0.0,1.0,1.0,0.0,177092.16,1.0
-8996,15727696.0,Olague,573.0,Spain,Male,21.0,9.0,0.0,1.0,0.0,1.0,140937.91,0.0
-8997,15810390.0,Chiu,753.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,176750.81,0.0
-8998,15752912.0,Lucchese,619.0,France,Male,44.0,5.0,0.0,2.0,1.0,1.0,89566.64,0.0
-8999,15796834.0,Okwudilichukwu,585.0,France,Female,37.0,9.0,0.0,2.0,1.0,1.0,136028.98,0.0
-9000,15751180.0,McGregor,539.0,Germany,Male,58.0,4.0,92578.14,1.0,1.0,1.0,131712.52,0.0
-9001,15615016.0,Chinomso,699.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,141364.67,0.0
-9002,15609735.0,Ts'ui,682.0,France,Male,32.0,1.0,0.0,2.0,0.0,1.0,181490.42,0.0
-9003,15647782.0,Uspensky,605.0,France,Female,35.0,10.0,0.0,2.0,1.0,1.0,129964.94,0.0
-9004,15591131.0,Hs?,592.0,Germany,Female,56.0,8.0,111756.5,1.0,1.0,0.0,173516.71,1.0
-9005,15604576.0,Ch'ang,704.0,Spain,Male,33.0,7.0,128398.38,2.0,1.0,1.0,155700.15,0.0
-9006,15588944.0,Ch'iu,602.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,198798.44,0.0
-9007,15605286.0,Chukwumaobim,726.0,France,Male,26.0,8.0,0.0,1.0,1.0,0.0,93777.61,0.0
-9008,15749050.0,Chukwueloka,724.0,France,Male,21.0,6.0,0.0,2.0,1.0,0.0,169952.82,0.0
-9009,15793901.0,Shih,521.0,France,Female,49.0,7.0,0.0,3.0,1.0,0.0,145986.35,1.0
-9010,15722532.0,Ch'ien,637.0,Germany,Female,52.0,3.0,122338.7,1.0,0.0,0.0,173837.54,1.0
-9011,15661629.0,Chiang,668.0,Spain,Male,22.0,9.0,0.0,2.0,1.0,1.0,48606.92,0.0
-9012,15677929.0,Ch'ien,610.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,0.0,89963.29,0.0
-9013,15804937.0,Hsiung,496.0,France,Male,35.0,3.0,0.0,2.0,1.0,0.0,131718.12,0.0
-9014,15683658.0,Chinwemma,727.0,France,Male,22.0,7.0,0.0,2.0,0.0,1.0,116442.42,0.0
-9015,15676521.0,Chin,705.0,Spain,Female,32.0,4.0,0.0,2.0,1.0,1.0,152855.96,0.0
-9016,15751771.0,Kenechukwu,651.0,France,Male,57.0,2.0,0.0,2.0,1.0,1.0,143739.21,0.0
-9017,15608968.0,Ijendu,714.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,153662.88,0.0
-9018,15762692.0,Tuan,714.0,Germany,Male,43.0,9.0,98415.37,2.0,0.0,0.0,118029.09,0.0
-9019,15640855.0,Ch'ien,471.0,France,Female,32.0,7.0,0.0,1.0,1.0,1.0,103787.28,1.0
-9020,15694188.0,Lupton,646.0,France,Female,46.0,9.0,0.0,1.0,1.0,1.0,93568.77,1.0
-9021,15579994.0,Chukwubuikem,627.0,Germany,Female,37.0,2.0,141787.31,1.0,1.0,0.0,59645.24,0.0
-9022,15690772.0,Chukwudi,614.0,Spain,Male,43.0,7.0,128264.03,1.0,1.0,0.0,114323.66,1.0
-9023,15640866.0,McLean,624.0,France,Male,62.0,4.0,0.0,2.0,1.0,0.0,62627.55,1.0
-9024,15641604.0,Fiorentini,699.0,Spain,Male,29.0,1.0,0.0,1.0,0.0,0.0,125518.32,0.0
-9025,15712596.0,Tuan,752.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,127543.81,0.0
-9026,15743522.0,Nickson,593.0,France,Female,51.0,8.0,125785.23,1.0,1.0,0.0,74838.64,0.0
-9027,15682860.0,Macleod,628.0,Spain,Female,42.0,9.0,0.0,1.0,1.0,0.0,124496.63,1.0
-9028,15757535.0,Chukwunonso,725.0,France,Female,36.0,4.0,0.0,1.0,1.0,0.0,72922.75,0.0
-9029,15685564.0,McIntyre,681.0,France,Male,47.0,2.0,0.0,4.0,1.0,1.0,105818.51,1.0
-9030,15705515.0,Onyemauchechukwu,543.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,155782.89,0.0
-9031,15746190.0,Pisano,637.0,France,Female,33.0,1.0,0.0,2.0,1.0,0.0,70523.92,0.0
-9032,15788483.0,Smith,651.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,97640.2,0.0
-9033,15665067.0,Smith,567.0,Germany,Female,49.0,8.0,100471.34,1.0,1.0,0.0,108645.85,1.0
-9034,15605968.0,Ndubinachi,579.0,Germany,Female,64.0,9.0,116537.6,1.0,1.0,0.0,176712.59,1.0
-9035,15674832.0,Chukwumaobim,730.0,Spain,Male,28.0,4.0,149950.19,1.0,0.0,0.0,114522.68,0.0
-9036,15673820.0,Smith,669.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,95611.47,0.0
-9037,15604551.0,Zito,553.0,France,Female,46.0,7.0,96781.39,1.0,1.0,1.0,110457.99,0.0
-9038,15587562.0,Manna,678.0,France,Female,38.0,7.0,0.0,1.0,1.0,0.0,167554.86,0.0
-9039,15794874.0,Dickson,710.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,176894.35,0.0
-9040,15643824.0,P'an,595.0,Germany,Male,38.0,5.0,143595.87,1.0,1.0,1.0,153392.28,0.0
-9041,15639552.0,Chigbogu,769.0,Germany,Female,37.0,8.0,104430.08,1.0,1.0,0.0,117832.88,1.0
-9042,15610643.0,Ch'iu,526.0,Spain,Male,33.0,8.0,108960.0,1.0,1.0,0.0,71552.04,0.0
-9043,15640409.0,T'ien,633.0,France,Male,28.0,3.0,151858.98,1.0,0.0,0.0,149913.92,0.0
-9044,15747649.0,Chukwudi,695.0,Spain,Female,38.0,3.0,149950.19,1.0,0.0,1.0,184800.31,0.0
-9045,15675794.0,Hsia,604.0,Spain,Male,46.0,5.0,0.0,1.0,1.0,1.0,62733.03,1.0
-9046,15702788.0,T'ao,614.0,Germany,Male,29.0,5.0,115046.74,1.0,1.0,0.0,25395.83,0.0
-9047,15716276.0,Smith,709.0,Spain,Female,37.0,1.0,0.0,1.0,1.0,1.0,116973.48,0.0
-9048,15680197.0,Chukwufumnanya,644.0,Germany,Female,46.0,1.0,96679.71,3.0,0.0,1.0,101515.76,1.0
-9049,15679284.0,Chukwueloka,558.0,France,Female,22.0,5.0,0.0,2.0,1.0,1.0,77961.41,0.0
-9050,15645316.0,Hsiao,628.0,Germany,Male,40.0,3.0,88815.56,1.0,0.0,0.0,166677.54,0.0
-9051,15661526.0,O'Loghlen,662.0,Germany,Female,36.0,3.0,111568.01,1.0,1.0,1.0,5791.85,0.0
-9052,15775294.0,Ts'ao,611.0,France,Female,34.0,6.0,0.0,2.0,0.0,1.0,122964.18,0.0
-9053,15659428.0,Milani,586.0,France,Male,47.0,6.0,0.0,1.0,1.0,0.0,162260.93,1.0
-9054,15595588.0,Nnanna,678.0,Spain,Female,43.0,0.0,155519.59,1.0,0.0,1.0,105570.69,0.0
-9055,15651369.0,Ch'ien,758.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,78854.57,0.0
-9056,15631681.0,Baresi,682.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,136497.28,0.0
-9057,15705657.0,Chidimma,556.0,Germany,Male,31.0,5.0,122314.5,1.0,1.0,1.0,133692.88,0.0
-9058,15607598.0,Hs?,727.0,France,Female,43.0,2.0,0.0,1.0,1.0,1.0,143954.99,0.0
-9059,15681873.0,Udegbulam,682.0,France,Male,42.0,4.0,0.0,3.0,1.0,0.0,52894.01,1.0
-9060,15734578.0,Tikhonov,596.0,France,Female,30.0,8.0,0.0,2.0,1.0,0.0,187811.71,0.0
-9061,15792922.0,Hsia,731.0,France,Female,30.0,9.0,0.0,2.0,1.0,0.0,60917.24,0.0
-9062,15754537.0,Chiu,634.0,Germany,Female,57.0,7.0,139859.54,2.0,1.0,0.0,164038.07,1.0
-9063,15673591.0,Doyne,850.0,France,Male,38.0,2.0,0.0,1.0,1.0,1.0,62845.64,0.0
-9064,15573926.0,Ch'ang,679.0,Germany,Male,33.0,7.0,148074.79,2.0,1.0,0.0,48224.72,0.0
-9065,15703923.0,Mazzi,717.0,Germany,Male,38.0,8.0,138053.67,1.0,1.0,0.0,152841.45,0.0
-9066,15633461.0,Aitken,618.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,86622.56,0.0
-9067,15795936.0,Chukwunonso,748.0,Spain,Male,42.0,3.0,104449.8,1.0,1.0,0.0,132547.03,1.0
-9068,15643496.0,Zikoranachidimma,676.0,France,Female,29.0,5.0,0.0,1.0,1.0,0.0,131807.14,0.0
-9069,15677906.0,Ch'ien,587.0,France,Female,41.0,8.0,121207.12,2.0,1.0,0.0,79957.95,0.0
-9070,15701555.0,Obialo,615.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,145528.22,0.0
-9071,15662232.0,Chukwufumnanya,809.0,France,Female,24.0,7.0,182025.95,1.0,1.0,1.0,182055.42,0.0
-9072,15732437.0,Chibueze,679.0,Spain,Female,35.0,8.0,135927.06,1.0,1.0,1.0,121151.1,0.0
-9073,15647898.0,Nwankwo,757.0,France,Male,33.0,5.0,137714.5,1.0,0.0,1.0,26387.71,0.0
-9074,15728693.0,Ts'ao,692.0,France,Male,28.0,1.0,0.0,2.0,0.0,1.0,78744.39,0.0
-9075,15641675.0,Ch'iu,644.0,Germany,Male,38.0,6.0,119496.25,1.0,0.0,0.0,175877.7,0.0
-9076,15626452.0,Hs?,727.0,France,Male,31.0,10.0,0.0,2.0,1.0,0.0,138399.67,0.0
-9077,15575060.0,Nwankwo,755.0,Germany,Male,41.0,1.0,144962.74,1.0,1.0,0.0,53420.53,0.0
-9078,15651868.0,Ch'en,577.0,France,Female,35.0,4.0,137725.34,1.0,1.0,1.0,38196.24,0.0
-9079,15738219.0,P'eng,677.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,135077.01,0.0
-9080,15598948.0,Yudina,682.0,France,Female,30.0,5.0,0.0,1.0,1.0,1.0,51620.87,0.0
-9081,15709621.0,Chiemenam,681.0,France,Male,26.0,2.0,0.0,2.0,1.0,0.0,170518.83,0.0
-9082,15628272.0,Fomin,705.0,Germany,Female,48.0,5.0,137204.77,1.0,1.0,0.0,183868.01,1.0
-9083,15638003.0,Chidubem,850.0,France,Male,49.0,3.0,0.0,2.0,1.0,1.0,55893.37,0.0
-9084,15593794.0,Yermakov,683.0,France,Male,44.0,8.0,0.0,1.0,1.0,1.0,86937.2,0.0
-9085,15613292.0,Lo Duca,565.0,Spain,Female,32.0,5.0,0.0,1.0,1.0,1.0,137791.18,0.0
-9086,15731463.0,Mao,434.0,Spain,Male,49.0,3.0,131354.25,1.0,0.0,0.0,182336.45,1.0
-9087,15757559.0,Iloerika,746.0,Spain,Female,50.0,8.0,0.0,1.0,1.0,0.0,149913.84,1.0
-9088,15758769.0,Trevisani,696.0,France,Female,27.0,6.0,0.0,1.0,1.0,1.0,115994.52,0.0
-9089,15614103.0,Nwankwo,758.0,France,Female,37.0,5.0,131834.76,1.0,0.0,0.0,96985.58,0.0
-9090,15670345.0,Ting,565.0,Spain,Female,33.0,6.0,154870.28,1.0,1.0,1.0,103914.4,1.0
-9091,15658432.0,Onwumelu,673.0,France,Male,34.0,1.0,108825.75,2.0,1.0,1.0,151425.82,0.0
-9092,15592571.0,Obidimkpa,662.0,France,Female,45.0,3.0,0.0,1.0,0.0,0.0,135851.3,1.0
-9093,15658972.0,Chidiebere,710.0,France,Male,28.0,8.0,0.0,1.0,1.0,1.0,171706.66,0.0
-9094,15693905.0,Blesing,656.0,Germany,Female,43.0,9.0,110641.18,1.0,0.0,0.0,172049.84,1.0
-9095,15585350.0,Obiora,663.0,France,Male,52.0,2.0,0.0,2.0,1.0,0.0,66810.48,1.0
-9096,15725094.0,Linger,628.0,Spain,Male,36.0,9.0,0.0,1.0,1.0,0.0,185630.76,0.0
-9097,15607301.0,O'Brien,709.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,0.0,168515.61,0.0
-9098,15687540.0,Maclean,528.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,142963.29,0.0
-9099,15801942.0,Nwabugwu,588.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,136909.85,0.0
-9100,15800412.0,Nwankwo,556.0,France,Male,38.0,6.0,141806.0,1.0,0.0,0.0,113089.43,0.0
-9101,15642099.0,Hsiung,597.0,France,Female,46.0,5.0,0.0,1.0,1.0,0.0,53511.74,1.0
-9102,15578878.0,O'Kane,635.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,147930.46,0.0
-9103,15573893.0,P'eng,667.0,Germany,Male,46.0,5.0,137847.41,1.0,0.0,0.0,164864.66,1.0
-9104,15609899.0,Nwachinemelu,636.0,Spain,Male,45.0,1.0,0.0,2.0,1.0,1.0,161889.15,0.0
-9105,15684419.0,Chiemenam,718.0,Germany,Male,40.0,3.0,134760.21,1.0,1.0,0.0,26318.73,1.0
-9106,15740458.0,Lo Duca,602.0,France,Male,26.0,8.0,0.0,2.0,1.0,1.0,85901.09,0.0
-9107,15693382.0,Kaleski,646.0,France,Male,41.0,5.0,92899.27,1.0,1.0,0.0,179435.31,0.0
-9108,15707918.0,Tung,705.0,Germany,Female,47.0,2.0,92887.06,1.0,1.0,0.0,82938.99,1.0
-9109,15766064.0,White,590.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,0.0,74900.9,0.0
-9110,15707974.0,Chiang,850.0,Spain,Male,29.0,2.0,0.0,1.0,1.0,1.0,162022.47,0.0
-9111,15709861.0,Ritchie,645.0,France,Female,37.0,2.0,0.0,1.0,1.0,0.0,177762.57,0.0
-9112,15578098.0,Genovese,589.0,Spain,Male,33.0,6.0,155059.1,1.0,1.0,0.0,131736.43,0.0
-9113,15665460.0,P'eng,583.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,78310.04,0.0
-9114,15605665.0,Nwachukwu,562.0,France,Male,42.0,6.0,0.0,2.0,1.0,1.0,103480.29,0.0
-9115,15783883.0,Hsueh,589.0,Spain,Female,41.0,7.0,105663.47,1.0,1.0,1.0,180962.32,0.0
-9116,15651022.0,Shao,674.0,Germany,Female,56.0,5.0,140956.99,1.0,1.0,1.0,141896.74,1.0
-9117,15601263.0,Iadanza,687.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,103832.58,0.0
-9118,15730076.0,Udegbunam,562.0,Spain,Male,37.0,8.0,112290.22,1.0,1.0,1.0,89566.64,0.0
-9119,15589210.0,Hs?,697.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,187816.59,0.0
-9120,15769959.0,Chijindum,769.0,France,Male,43.0,1.0,106804.26,1.0,1.0,1.0,1375.46,0.0
-9121,15773876.0,Fanucci,584.0,France,Male,48.0,9.0,0.0,1.0,1.0,1.0,181270.84,0.0
-9122,15805671.0,Efremov,743.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,130806.93,0.0
-9123,15774336.0,Genovesi,494.0,France,Female,33.0,2.0,163870.07,2.0,1.0,0.0,140800.66,0.0
-9124,15773591.0,Obialo,651.0,France,Female,37.0,8.0,0.0,1.0,0.0,1.0,74945.11,0.0
-9125,15696588.0,Nnamutaezinwa,623.0,Germany,Female,24.0,8.0,137946.39,2.0,1.0,1.0,11407.58,0.0
-9126,15631693.0,Hsia,581.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,140763.18,0.0
-9127,15582944.0,Lo Duca,631.0,France,Female,41.0,9.0,121823.7,1.0,0.0,1.0,167984.61,0.0
-9128,15690169.0,P'eng,755.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,131647.31,0.0
-9129,15806922.0,Hs?,711.0,Germany,Female,67.0,9.0,111168.97,1.0,1.0,1.0,100727.68,0.0
-9130,15803801.0,Ukaegbulam,607.0,France,Male,45.0,4.0,0.0,1.0,1.0,0.0,183542.08,1.0
-9131,15784092.0,Chibugo,693.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,95583.45,0.0
-9132,15652999.0,Onyemauchechukwu,560.0,France,Male,29.0,0.0,120135.55,1.0,1.0,0.0,60536.56,0.0
-9133,15791958.0,Calabrese,562.0,Spain,Female,34.0,0.0,0.0,2.0,0.0,1.0,9041.71,0.0
-9134,15788483.0,Mai,659.0,Germany,Female,39.0,5.0,85982.07,2.0,1.0,0.0,82662.73,0.0
-9135,15673908.0,Onwuatuegwu,664.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,176924.21,0.0
-9136,15773779.0,Sacco,699.0,Germany,Male,23.0,7.0,129828.58,1.0,1.0,1.0,79577.38,0.0
-9137,15656592.0,Ritchie,707.0,France,Male,42.0,7.0,0.0,2.0,0.0,0.0,1843.24,0.0
-9138,15726186.0,Chidozie,691.0,France,Male,26.0,5.0,0.0,2.0,1.0,0.0,170980.86,0.0
-9139,15603851.0,Pagnotto,707.0,Spain,Female,26.0,7.0,0.0,1.0,1.0,1.0,105570.39,0.0
-9140,15691808.0,Onyemauchechukwu,707.0,Germany,Male,32.0,9.0,149620.88,2.0,0.0,0.0,151481.86,0.0
-9141,15682868.0,Ts'ui,683.0,France,Female,35.0,3.0,0.0,2.0,1.0,0.0,143614.01,0.0
-9142,15760641.0,Chin,809.0,Spain,Male,44.0,8.0,0.0,2.0,0.0,1.0,155700.15,0.0
-9143,15693468.0,Hsiao,685.0,Germany,Female,34.0,2.0,97565.74,1.0,1.0,0.0,97913.67,0.0
-9144,15664897.0,P'eng,637.0,France,Male,33.0,3.0,0.0,2.0,0.0,0.0,91086.73,0.0
-9145,15683403.0,MacDevitt,637.0,France,Male,29.0,9.0,143330.56,1.0,0.0,1.0,146411.7,0.0
-9146,15748352.0,Chidubem,593.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,56787.8,0.0
-9147,15673908.0,Li,611.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,111241.37,0.0
-9148,15641480.0,Udinese,667.0,Germany,Male,32.0,4.0,136624.6,2.0,1.0,1.0,145792.01,0.0
-9149,15640266.0,Olisaemeka,559.0,France,Male,45.0,5.0,0.0,1.0,0.0,0.0,142963.29,1.0
-9150,15717164.0,Ch'iu,585.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,142238.54,0.0
-9151,15622443.0,Chukwuhaenye,672.0,France,Female,39.0,5.0,0.0,1.0,1.0,1.0,152992.04,0.0
-9152,15790717.0,Okwudilichukwu,683.0,Spain,Male,26.0,2.0,0.0,2.0,1.0,0.0,64375.4,0.0
-9153,15641640.0,Ch'in,635.0,France,Female,27.0,3.0,0.0,1.0,1.0,1.0,179093.26,0.0
-9154,15607884.0,Chiagoziem,538.0,Germany,Male,26.0,4.0,125734.2,2.0,0.0,0.0,115022.94,0.0
-9155,15575486.0,O'Sullivan,651.0,France,Female,30.0,3.0,0.0,1.0,1.0,0.0,9085.0,0.0
-9156,15806796.0,Nnaife,735.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,131660.31,0.0
-9157,15780144.0,Nwankwo,649.0,Germany,Female,27.0,8.0,108269.2,2.0,1.0,1.0,158086.33,0.0
-9158,15649033.0,Ugorji,844.0,Germany,Female,49.0,2.0,122453.97,2.0,1.0,1.0,126794.57,1.0
-9159,15690772.0,Loyau,651.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,137028.51,0.0
-9160,15814267.0,Onio,657.0,France,Male,34.0,10.0,0.0,2.0,0.0,0.0,105760.01,0.0
-9161,15683958.0,Teng,686.0,France,Female,36.0,7.0,0.0,2.0,0.0,1.0,170621.55,0.0
-9162,15702912.0,Eluemuno,498.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,82298.59,0.0
-9163,15752884.0,Chukwueloka,705.0,Spain,Male,39.0,7.0,0.0,2.0,0.0,1.0,128841.31,0.0
-9164,15635197.0,Tung,675.0,France,Male,35.0,3.0,0.0,1.0,0.0,0.0,150659.35,1.0
-9165,15631709.0,Yefremov,592.0,France,Female,25.0,2.0,0.0,2.0,0.0,0.0,80149.27,0.0
-9166,15640507.0,Tuan,732.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,180326.83,0.0
-9167,15685920.0,Aksyonova,790.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,121457.88,0.0
-9168,15603328.0,Yashina,663.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,27330.59,0.0
-9169,15589924.0,Walker,704.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,121151.1,0.0
-9170,15671140.0,Gartrell,638.0,Germany,Male,40.0,8.0,115961.58,1.0,1.0,0.0,184842.24,1.0
-9171,15791114.0,Ts'ao,682.0,Spain,Male,31.0,4.0,139875.7,1.0,1.0,0.0,158736.07,0.0
-9172,15598086.0,Mironova,558.0,Germany,Female,45.0,10.0,102702.69,1.0,1.0,0.0,80591.15,0.0
-9173,15690330.0,Hsieh,545.0,Germany,Male,49.0,2.0,97565.74,1.0,1.0,0.0,129372.94,1.0
-9174,15642063.0,Nnachetam,597.0,Spain,Female,27.0,4.0,0.0,1.0,1.0,1.0,152097.02,0.0
-9175,15777033.0,Folliero,709.0,Germany,Female,47.0,5.0,137715.66,1.0,1.0,0.0,176924.21,1.0
-9176,15638231.0,Hsueh,706.0,France,Female,39.0,5.0,0.0,2.0,1.0,0.0,160618.61,0.0
-9177,15576044.0,Kuo,651.0,Spain,Female,36.0,1.0,0.0,2.0,0.0,0.0,187905.12,0.0
-9178,15783025.0,Gaffney,657.0,France,Male,26.0,9.0,123981.31,1.0,1.0,0.0,157097.02,0.0
-9179,15750574.0,Ch'in,648.0,Spain,Female,41.0,1.0,0.0,2.0,0.0,1.0,175082.2,0.0
-9180,15705735.0,Chidiebele,582.0,Spain,Female,59.0,4.0,0.0,2.0,1.0,1.0,82900.25,0.0
-9181,15686696.0,Trevisano,635.0,France,Female,23.0,6.0,0.0,2.0,1.0,1.0,126837.72,0.0
-9182,15604093.0,T'ang,642.0,Spain,Female,61.0,3.0,0.0,2.0,1.0,1.0,113860.81,0.0
-9183,15588929.0,O'Meara,719.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,99654.03,0.0
-9184,15648047.0,Obialo,697.0,Germany,Female,50.0,2.0,133656.85,1.0,1.0,0.0,177065.24,1.0
-9185,15621201.0,Ch'in,850.0,France,Male,45.0,1.0,0.0,2.0,0.0,1.0,94440.45,0.0
-9186,15605826.0,Nucci,597.0,France,Female,39.0,4.0,134096.55,1.0,1.0,1.0,112946.67,0.0
-9187,15607748.0,Frolov,637.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,95007.8,0.0
-9188,15702412.0,Chiang,598.0,France,Male,36.0,2.0,113599.42,1.0,1.0,0.0,86927.53,0.0
-9189,15672447.0,Brown,571.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,1.0,145796.49,0.0
-9190,15593973.0,Tung,605.0,France,Male,31.0,8.0,0.0,1.0,1.0,1.0,38798.79,0.0
-9191,15773520.0,Ts'ai,659.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,0.0,145567.34,0.0
-9192,15752588.0,Martin,658.0,France,Female,47.0,3.0,145860.98,1.0,0.0,0.0,103970.58,1.0
-9193,15778947.0,Calabresi,751.0,France,Female,36.0,8.0,0.0,2.0,0.0,1.0,180800.42,0.0
-9194,15763431.0,Ch'eng,632.0,Germany,Female,40.0,1.0,131778.08,2.0,1.0,0.0,59385.24,0.0
-9195,15692819.0,Tsao,646.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,0.0,143357.03,0.0
-9196,15686696.0,Pacheco,597.0,Germany,Male,33.0,10.0,121729.42,1.0,1.0,0.0,119346.88,0.0
-9197,15668893.0,Uspenskaya,763.0,France,Male,30.0,3.0,137974.15,1.0,0.0,1.0,109538.35,0.0
-9198,15692570.0,Palermo,521.0,France,Female,58.0,5.0,141786.78,1.0,0.0,1.0,172792.43,1.0
-9199,15633064.0,Fink,786.0,Spain,Male,38.0,8.0,0.0,1.0,0.0,1.0,141364.62,0.0
-9200,15768137.0,Chiemeka,613.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,158750.58,0.0
-9201,15650258.0,Pinto,681.0,France,Female,29.0,3.0,130170.82,1.0,1.0,1.0,51380.9,0.0
-9202,15692599.0,Kenechukwu,641.0,France,Female,32.0,9.0,0.0,1.0,0.0,0.0,170903.4,0.0
-9203,15622171.0,Chukwudi,596.0,Spain,Male,32.0,10.0,0.0,2.0,1.0,1.0,129964.94,0.0
-9204,15588580.0,Onyeorulu,634.0,Spain,Male,23.0,5.0,0.0,2.0,0.0,0.0,80587.31,0.0
-9205,15752622.0,Tsao,642.0,France,Female,40.0,1.0,0.0,1.0,1.0,0.0,74048.1,0.0
-9206,15772363.0,Illingworth,685.0,France,Female,31.0,6.0,0.0,1.0,0.0,1.0,151912.49,0.0
-9207,15745355.0,Li Fonti,584.0,France,Male,46.0,2.0,0.0,2.0,1.0,1.0,134790.74,0.0
-9208,15579617.0,Obiajulu,729.0,Germany,Female,46.0,2.0,91938.71,1.0,0.0,1.0,107712.57,0.0
-9209,15801436.0,Ch'iu,616.0,France,Male,21.0,1.0,0.0,2.0,1.0,1.0,163562.25,0.0
-9210,15636016.0,Hsia,573.0,France,Female,42.0,3.0,0.0,2.0,1.0,1.0,141872.05,0.0
-9211,15770214.0,H?,674.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,138889.15,0.0
-9212,15643487.0,Macartney,658.0,Germany,Female,42.0,7.0,108269.2,1.0,1.0,0.0,111868.23,0.0
-9213,15785819.0,Chidozie,590.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,0.0,86799.49,0.0
-9214,15589475.0,Liao,733.0,Spain,Female,37.0,0.0,0.0,2.0,0.0,1.0,145558.42,0.0
-9215,15699340.0,Genovesi,739.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,72402.0,0.0
-9216,15723884.0,Shih,571.0,France,Male,38.0,2.0,0.0,1.0,0.0,0.0,73873.65,0.0
-9217,15696564.0,Nwachinemelu,598.0,France,Male,55.0,3.0,161608.81,1.0,0.0,0.0,141756.32,1.0
-9218,15652048.0,Nwoye,646.0,France,Male,32.0,2.0,0.0,2.0,0.0,0.0,86791.9,0.0
-9219,15745250.0,Chiang,581.0,France,Female,32.0,4.0,121205.15,1.0,1.0,0.0,164886.64,0.0
-9220,15792228.0,Hsieh,607.0,France,Female,21.0,8.0,0.0,2.0,1.0,0.0,71139.31,0.0
-9221,15627995.0,Tuan,592.0,France,Female,35.0,8.0,0.0,1.0,1.0,0.0,179634.69,1.0
-9222,15671410.0,Ch'in,590.0,France,Female,60.0,3.0,122524.61,1.0,0.0,1.0,147794.63,0.0
-9223,15645271.0,Y?,794.0,France,Female,42.0,8.0,95921.06,1.0,1.0,1.0,106761.47,0.0
-9224,15748797.0,Chimezie,732.0,France,Female,43.0,5.0,0.0,1.0,0.0,0.0,155799.86,0.0
-9225,15767488.0,O'Brien,584.0,France,Female,30.0,2.0,0.0,2.0,0.0,1.0,176943.59,0.0
-9226,15807457.0,Hsueh,806.0,France,Female,46.0,3.0,0.0,1.0,1.0,1.0,99128.13,1.0
-9227,15709639.0,Y?an,682.0,France,Male,29.0,9.0,0.0,2.0,0.0,1.0,187776.49,0.0
-9228,15648881.0,H?,663.0,France,Male,42.0,7.0,147069.78,1.0,1.0,0.0,170950.58,0.0
-9229,15593555.0,T'ang,711.0,Spain,Female,57.0,5.0,0.0,2.0,1.0,1.0,170886.01,0.0
-9230,15657779.0,Udinesi,681.0,Germany,Male,33.0,2.0,111543.27,2.0,1.0,1.0,113553.43,0.0
-9231,15590177.0,Y?,638.0,France,Male,26.0,7.0,0.0,2.0,1.0,0.0,111981.19,0.0
-9232,15718588.0,Hsieh,679.0,France,Male,29.0,7.0,0.0,2.0,0.0,0.0,93769.89,0.0
-9233,15579777.0,Tsao,712.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,145700.22,0.0
-9234,15585867.0,Mazzanti,614.0,France,Female,40.0,8.0,0.0,1.0,1.0,0.0,69157.93,1.0
-9235,15673984.0,Chidubem,635.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,55775.72,0.0
-9236,15608956.0,Mazzanti,612.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,156774.75,0.0
-9237,15588908.0,Chukwujamuike,619.0,France,Female,30.0,7.0,0.0,1.0,0.0,1.0,133897.32,0.0
-9238,15603597.0,Ch'in,705.0,Spain,Male,47.0,1.0,0.0,1.0,1.0,0.0,143859.66,1.0
-9239,15672930.0,Chukwualuka,749.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,48984.95,0.0
-9240,15606901.0,Chia,644.0,Germany,Female,40.0,9.0,113386.36,2.0,1.0,0.0,172586.89,1.0
-9241,15807525.0,Yefremova,695.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,10771.46,0.0
-9242,15581620.0,Fomin,545.0,France,Female,45.0,9.0,92889.91,1.0,0.0,0.0,163666.85,1.0
-9243,15684801.0,Chukwudi,567.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,1.0,155870.82,0.0
-9244,15579892.0,Tsai,613.0,France,Female,38.0,1.0,0.0,2.0,0.0,1.0,91433.38,0.0
-9245,15797710.0,Tien,582.0,France,Female,46.0,9.0,147521.35,1.0,0.0,0.0,114774.35,1.0
-9246,15715643.0,Tretiakova,627.0,Germany,Female,30.0,2.0,91625.15,2.0,0.0,1.0,46888.76,0.0
-9247,15585865.0,Yudina,724.0,Spain,Female,30.0,6.0,0.0,2.0,1.0,0.0,94780.9,0.0
-9248,15665760.0,Maclean,673.0,France,Male,58.0,2.0,104584.11,1.0,0.0,1.0,132798.78,0.0
-9249,15670822.0,L?,749.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,135851.3,0.0
-9250,15704788.0,Pisano,678.0,France,Male,62.0,8.0,0.0,2.0,0.0,1.0,182372.35,0.0
-9251,15583026.0,Nwachinemelu,748.0,France,Male,27.0,0.0,0.0,1.0,1.0,0.0,70916.0,1.0
-9252,15751203.0,Okeke,672.0,Spain,Male,48.0,4.0,0.0,1.0,1.0,0.0,82868.34,1.0
-9253,15752344.0,Hsueh,645.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,91903.9,0.0
-9254,15712607.0,L?,632.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,134875.37,0.0
-9255,15802857.0,Liao,652.0,Spain,Female,64.0,4.0,0.0,1.0,1.0,1.0,65636.92,0.0
-9256,15643330.0,Kibby,640.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,100601.73,0.0
-9257,15649112.0,Yeh,708.0,France,Male,35.0,7.0,93920.47,1.0,1.0,0.0,71178.09,0.0
-9258,15672930.0,Ting,773.0,France,Male,42.0,3.0,0.0,2.0,1.0,0.0,72467.99,0.0
-9259,15806918.0,P'eng,631.0,France,Male,40.0,7.0,0.0,1.0,1.0,0.0,102934.74,0.0
-9260,15737687.0,Mancini,724.0,Germany,Male,31.0,4.0,122947.92,1.0,1.0,1.0,79240.9,1.0
-9261,15719090.0,Hsiung,583.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,189428.75,0.0
-9262,15583681.0,Zikoranachidimma,712.0,Spain,Male,33.0,2.0,0.0,2.0,0.0,0.0,157097.09,0.0
-9263,15758813.0,Eluemuno,642.0,Germany,Male,28.0,10.0,166902.82,2.0,1.0,1.0,151902.78,0.0
-9264,15686963.0,Chiang,652.0,Germany,Male,43.0,1.0,142663.46,1.0,1.0,0.0,93953.84,1.0
-9265,15576000.0,Hsueh,559.0,France,Female,30.0,2.0,0.0,2.0,0.0,0.0,176780.13,0.0
-9266,15585892.0,Chinomso,627.0,Germany,Female,33.0,8.0,98899.91,1.0,1.0,0.0,479.54,0.0
-9267,15789974.0,Chidimma,648.0,France,Male,30.0,3.0,120777.88,1.0,1.0,0.0,167023.95,0.0
-9268,15642824.0,Milano,608.0,France,Male,55.0,6.0,113672.18,1.0,1.0,1.0,129984.2,0.0
-9269,15643361.0,Maclean,703.0,France,Male,33.0,10.0,0.0,1.0,1.0,1.0,51620.94,0.0
-9270,15809302.0,Hs?,591.0,Spain,Female,49.0,9.0,0.0,3.0,1.0,1.0,60905.51,1.0
-9271,15768474.0,Shih,681.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,1.0,126475.79,0.0
-9272,15737542.0,Kincaid,686.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,0.0,162719.69,0.0
-9273,15788215.0,Chinwenma,497.0,France,Female,47.0,9.0,0.0,2.0,1.0,0.0,51454.06,0.0
-9274,15680804.0,Rizzo,678.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,103737.82,0.0
-9275,15632264.0,Yeh,681.0,Germany,Male,44.0,4.0,120718.28,1.0,0.0,1.0,1354.25,1.0
-9276,15702789.0,Chuang,795.0,Germany,Male,31.0,1.0,120717.28,2.0,1.0,1.0,79577.38,0.0
-9277,15639087.0,Chukwubuikem,647.0,France,Male,30.0,4.0,95922.9,1.0,0.0,1.0,64849.74,0.0
-9278,15733138.0,Soto,812.0,Spain,Female,53.0,9.0,0.0,1.0,1.0,0.0,86044.98,1.0
-9279,15684512.0,Manna,687.0,Spain,Female,42.0,7.0,0.0,2.0,1.0,0.0,154844.22,0.0
-9280,15804787.0,Ch'ang,774.0,France,Male,28.0,9.0,0.0,1.0,1.0,1.0,170984.87,0.0
-9281,15680804.0,Mamelu,615.0,France,Male,40.0,2.0,71553.83,1.0,1.0,0.0,163911.32,0.0
-9282,15804482.0,Chukwukadibia,613.0,France,Female,44.0,8.0,69413.44,2.0,1.0,0.0,129964.94,0.0
-9283,15769902.0,Hightower,705.0,France,Male,26.0,7.0,96651.03,1.0,1.0,0.0,158887.09,0.0
-9284,15583090.0,Lucchesi,651.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,133535.29,0.0
-9285,15814553.0,Lucchesi,568.0,France,Male,54.0,6.0,0.0,3.0,1.0,0.0,147055.87,1.0
-9286,15793723.0,Bales,673.0,Spain,Female,32.0,10.0,0.0,1.0,1.0,0.0,119071.56,0.0
-9287,15769312.0,Nkemdirim,687.0,Germany,Male,48.0,2.0,123575.51,2.0,1.0,1.0,9983.88,0.0
-9288,15638774.0,Sun,634.0,France,Female,33.0,10.0,0.0,2.0,1.0,0.0,173870.39,0.0
-9289,15648047.0,Ts'ui,499.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,9984.4,0.0
-9290,15647898.0,De Luca,696.0,France,Female,51.0,6.0,0.0,2.0,1.0,1.0,131919.48,0.0
-9291,15783030.0,Frolova,565.0,Spain,Male,33.0,2.0,0.0,2.0,1.0,0.0,75578.67,0.0
-9292,15609510.0,Nnamdi,725.0,France,Female,35.0,2.0,0.0,2.0,0.0,0.0,24495.03,1.0
-9293,15650258.0,Mays,561.0,France,Male,46.0,8.0,0.0,2.0,1.0,1.0,182059.28,0.0
-9294,15661528.0,Chiekwugo,615.0,Germany,Male,44.0,3.0,106045.92,1.0,1.0,0.0,139861.53,1.0
-9295,15589296.0,Onwubiko,545.0,France,Female,36.0,2.0,90612.34,1.0,1.0,1.0,80613.54,0.0
-9296,15689061.0,Li,605.0,France,Male,52.0,5.0,107637.97,1.0,1.0,1.0,153723.28,0.0
-9297,15704053.0,Ch'ang,775.0,France,Male,64.0,9.0,0.0,1.0,1.0,0.0,105568.6,1.0
-9298,15572631.0,Hsia,718.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,164844.37,0.0
-9299,15752849.0,White,553.0,France,Female,35.0,2.0,146034.74,1.0,1.0,1.0,84475.13,0.0
-9300,15703059.0,Kao,594.0,Germany,Male,42.0,1.0,120962.59,1.0,1.0,0.0,56594.36,0.0
-9301,15689953.0,Nwachinemelu,622.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,142783.98,0.0
-9302,15783026.0,Zetticci,749.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,168035.6,0.0
-9303,15711015.0,Brown,659.0,Spain,Female,43.0,7.0,169312.13,2.0,1.0,1.0,170819.01,0.0
-9304,15790829.0,Yeh,683.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,4491.77,0.0
-9305,15783591.0,Macdonald,758.0,France,Male,38.0,6.0,0.0,2.0,0.0,1.0,56767.67,0.0
-9306,15757837.0,Chidozie,577.0,France,Female,37.0,2.0,155413.08,1.0,1.0,0.0,182784.11,0.0
-9307,15791958.0,Rounsevell,635.0,Germany,Female,39.0,8.0,105837.86,1.0,1.0,1.0,111175.09,1.0
-9308,15794345.0,Zhirov,634.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,35786.76,0.0
-9309,15699772.0,Chukwuhaenye,609.0,Spain,Male,48.0,1.0,0.0,2.0,1.0,0.0,150266.81,0.0
-9310,15746338.0,Oluchukwu,628.0,Spain,Female,41.0,9.0,0.0,2.0,1.0,0.0,106663.46,1.0
-9311,15579548.0,Kao,596.0,France,Female,40.0,1.0,0.0,1.0,1.0,1.0,93524.19,1.0
-9312,15781586.0,Chizuoke,591.0,Spain,Male,32.0,2.0,0.0,2.0,1.0,1.0,132908.52,0.0
-9313,15668893.0,T'ang,583.0,Germany,Female,34.0,6.0,95927.04,2.0,1.0,0.0,683.37,0.0
-9314,15631162.0,Chiedozie,714.0,Germany,Male,41.0,8.0,77910.23,2.0,1.0,1.0,160121.15,0.0
-9315,15643330.0,Shao,735.0,France,Male,59.0,1.0,0.0,1.0,1.0,1.0,135635.75,0.0
-9316,15797733.0,Chukwualuka,697.0,France,Male,45.0,6.0,0.0,1.0,0.0,0.0,142238.54,1.0
-9317,15769596.0,Ch'ang,839.0,Spain,Male,26.0,5.0,0.0,2.0,0.0,1.0,183542.08,0.0
-9318,15808849.0,Ch'en,619.0,Spain,Male,37.0,7.0,0.0,2.0,0.0,0.0,121098.65,0.0
-9319,15607312.0,Ojiofor,707.0,France,Male,25.0,9.0,0.0,1.0,1.0,0.0,100717.54,0.0
-9320,15665416.0,Cattaneo,745.0,France,Male,38.0,3.0,123042.0,1.0,1.0,1.0,12162.76,0.0
-9321,15572762.0,Hargreaves,639.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,97623.31,0.0
-9322,15792329.0,Yeh,588.0,Spain,Male,50.0,1.0,138089.97,1.0,1.0,0.0,110405.9,0.0
-9323,15723008.0,Dore,677.0,Germany,Female,40.0,7.0,123372.3,2.0,1.0,0.0,173783.38,0.0
-9324,15691150.0,P'eng,649.0,Germany,Female,33.0,2.0,157848.08,1.0,1.0,1.0,127939.19,0.0
-9325,15796313.0,Pinto,567.0,France,Female,27.0,2.0,0.0,2.0,1.0,1.0,134901.56,0.0
-9326,15671330.0,Ch'in,526.0,Germany,Female,51.0,3.0,156847.29,1.0,1.0,0.0,120531.31,1.0
-9327,15585066.0,Kesteven,627.0,Spain,Female,47.0,4.0,0.0,2.0,1.0,0.0,143665.44,0.0
-9328,15723654.0,Ch'iu,624.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,111346.22,0.0
-9329,15708904.0,Nwebube,595.0,Germany,Male,49.0,7.0,117561.49,1.0,1.0,0.0,86044.98,1.0
-9330,15801548.0,Chinweike,699.0,France,Male,42.0,9.0,0.0,2.0,1.0,1.0,103315.74,0.0
-9331,15664808.0,Chuang,754.0,France,Female,37.0,9.0,0.0,2.0,1.0,1.0,88849.89,0.0
-9332,15668580.0,McIntosh,686.0,France,Male,50.0,6.0,124442.58,1.0,1.0,0.0,15650.73,1.0
-9333,15682918.0,Hs?,687.0,France,Female,39.0,3.0,0.0,3.0,0.0,0.0,176780.39,1.0
-9334,15714391.0,Toscani,624.0,France,Female,35.0,10.0,0.0,2.0,0.0,0.0,160941.78,0.0
-9335,15702912.0,T'ao,638.0,Spain,Female,34.0,1.0,0.0,2.0,0.0,0.0,171021.44,0.0
-9336,15743828.0,De Luca,637.0,Germany,Male,52.0,7.0,131629.17,1.0,1.0,0.0,131660.31,1.0
-9337,15739292.0,Okwudilichukwu,656.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,176450.19,0.0
-9338,15793883.0,Chikelu,678.0,France,Male,47.0,7.0,0.0,2.0,1.0,0.0,73977.23,0.0
-9339,15603548.0,Chukwukadibia,678.0,Germany,Female,31.0,6.0,122781.51,1.0,1.0,0.0,178997.89,0.0
-9340,15792679.0,Hs?,489.0,Germany,Male,31.0,3.0,142721.52,2.0,1.0,1.0,149170.92,0.0
-9341,15785899.0,H?,678.0,Germany,Female,32.0,1.0,104763.41,2.0,1.0,0.0,94820.85,0.0
-9342,15570194.0,Lucchesi,614.0,Spain,Female,39.0,6.0,0.0,1.0,1.0,1.0,122925.4,0.0
-9343,15640407.0,Ndukaku,781.0,France,Female,29.0,3.0,0.0,1.0,1.0,0.0,96864.36,1.0
-9344,15679029.0,Cavenagh,584.0,France,Male,37.0,5.0,0.0,2.0,0.0,1.0,121073.23,0.0
-9345,15733581.0,Ts'ao,627.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,62002.04,0.0
-9346,15693737.0,Chiawuotu,615.0,Spain,Female,50.0,9.0,148556.74,1.0,1.0,0.0,141533.57,1.0
-9347,15761023.0,Tuan,627.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,55775.72,1.0
-9348,15761292.0,Nwankwo,752.0,France,Male,41.0,3.0,0.0,1.0,1.0,0.0,131736.23,0.0
-9349,15681409.0,Chiawuotu,793.0,Spain,Male,30.0,4.0,127631.62,1.0,1.0,1.0,92888.52,0.0
-9350,15689425.0,Toscani,713.0,Spain,Male,35.0,10.0,0.0,2.0,1.0,1.0,39789.62,0.0
-9351,15589329.0,Uspensky,485.0,France,Male,37.0,3.0,0.0,1.0,1.0,1.0,62944.75,0.0
-9352,15573926.0,Ts'ai,684.0,France,Male,38.0,1.0,0.0,2.0,0.0,1.0,86950.72,0.0
-9353,15754929.0,Cawood,543.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,94780.9,0.0
-9354,15801125.0,Y?an,706.0,Germany,Female,39.0,9.0,99713.87,2.0,1.0,1.0,130404.53,0.0
-9355,15783526.0,Bock,607.0,Germany,Male,43.0,2.0,108726.57,2.0,0.0,1.0,84932.4,0.0
-9356,15635034.0,Chukwuemeka,748.0,Spain,Male,40.0,4.0,0.0,2.0,1.0,0.0,88958.49,0.0
-9357,15729954.0,Chijioke,687.0,France,Female,33.0,9.0,0.0,1.0,0.0,0.0,25554.4,0.0
-9358,15732943.0,Hsiao,590.0,France,Female,40.0,4.0,132954.66,1.0,1.0,0.0,74854.97,1.0
-9359,15758713.0,Monds,636.0,Spain,Male,25.0,1.0,0.0,2.0,0.0,1.0,62703.38,0.0
-9360,15636413.0,Ponomaryova,545.0,France,Female,27.0,2.0,0.0,2.0,1.0,0.0,102233.73,0.0
-9361,15706463.0,Ting,725.0,France,Female,45.0,9.0,0.0,1.0,0.0,0.0,166520.96,1.0
-9362,15731463.0,Chiang,675.0,Spain,Female,39.0,3.0,0.0,1.0,0.0,0.0,185547.41,1.0
-9363,15628155.0,Lo Duca,588.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,153400.24,0.0
-9364,15621063.0,Onyemauchechukwu,732.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,172063.09,0.0
-9365,15758035.0,Nwankwo,619.0,Spain,Female,47.0,9.0,0.0,1.0,0.0,0.0,72873.33,1.0
-9366,15746664.0,H?,603.0,France,Male,32.0,1.0,0.0,2.0,1.0,1.0,171039.56,0.0
-9367,15695792.0,Chikelu,667.0,Germany,Female,63.0,0.0,122723.35,2.0,1.0,0.0,85901.09,1.0
-9368,15762927.0,Chidiebere,678.0,France,Female,48.0,5.0,180520.21,1.0,1.0,0.0,176943.59,1.0
-9369,15578835.0,Chukwumaobim,477.0,Spain,Male,35.0,9.0,0.0,2.0,0.0,1.0,100941.57,0.0
-9370,15571571.0,Scott,724.0,France,Male,35.0,8.0,0.0,2.0,0.0,0.0,167984.72,0.0
-9371,15592386.0,T'ien,619.0,France,Female,41.0,7.0,0.0,1.0,0.0,0.0,100809.99,1.0
-9372,15692991.0,Milani,696.0,France,Female,38.0,9.0,0.0,3.0,1.0,0.0,142963.29,1.0
-9373,15788151.0,Ku,649.0,France,Female,35.0,8.0,0.0,2.0,1.0,0.0,94538.54,0.0
-9374,15799358.0,Genovese,743.0,France,Female,52.0,8.0,0.0,1.0,1.0,0.0,154775.7,1.0
-9375,15793896.0,Liao,651.0,France,Male,25.0,7.0,123544.0,1.0,1.0,0.0,71333.44,0.0
-9376,15571144.0,Ting,642.0,Germany,Male,37.0,1.0,123906.55,1.0,1.0,1.0,125062.02,0.0
-9377,15602831.0,Shao,735.0,Spain,Female,39.0,3.0,0.0,2.0,1.0,0.0,166650.9,0.0
-9378,15753847.0,Calabresi,663.0,France,Male,36.0,1.0,96652.86,1.0,1.0,1.0,119368.11,0.0
-9379,15611171.0,Maclean,644.0,France,Male,40.0,5.0,0.0,1.0,1.0,0.0,62002.04,1.0
-9380,15644833.0,Nnachetam,749.0,France,Female,35.0,1.0,0.0,1.0,1.0,0.0,120165.83,0.0
-9381,15710689.0,Ch'eng,584.0,France,Male,43.0,8.0,0.0,1.0,0.0,0.0,142940.39,1.0
-9382,15745355.0,Sagese,696.0,France,Female,61.0,9.0,88963.31,1.0,1.0,1.0,106655.88,1.0
-9383,15690670.0,Yashina,694.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,121440.8,0.0
-9384,15592914.0,Chidimma,735.0,Spain,Female,37.0,0.0,0.0,1.0,0.0,0.0,177747.03,0.0
-9385,15692671.0,Chukwumaobim,850.0,Spain,Male,27.0,6.0,99095.73,1.0,1.0,0.0,93821.95,0.0
-9386,15775888.0,H?,686.0,Spain,Female,29.0,8.0,0.0,2.0,0.0,0.0,25662.27,0.0
-9387,15754068.0,Ebelechukwu,573.0,France,Female,43.0,1.0,0.0,2.0,1.0,0.0,179521.24,0.0
-9388,15605968.0,Shao,592.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,1.0,64872.33,0.0
-9389,15807239.0,Chukwueloka,589.0,France,Female,43.0,2.0,0.0,2.0,0.0,0.0,73069.9,0.0
-9390,15625023.0,Lo,711.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,133535.29,0.0
-9391,15806926.0,Macleod,594.0,France,Male,36.0,6.0,0.0,2.0,0.0,0.0,131736.46,0.0
-9392,15601589.0,Ch'iu,675.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,93336.3,0.0
-9393,15799031.0,Pisano,675.0,Spain,Male,35.0,8.0,0.0,2.0,0.0,0.0,43921.36,0.0
-9394,15596979.0,T'ang,674.0,Spain,Male,40.0,9.0,0.0,2.0,0.0,0.0,106841.12,0.0
-9395,15569624.0,Hsu,637.0,France,Female,46.0,0.0,0.0,1.0,1.0,1.0,124626.59,0.0
-9396,15778861.0,Chinedum,554.0,France,Female,25.0,9.0,0.0,2.0,1.0,0.0,162719.69,0.0
-9397,15680243.0,Chiekwugo,769.0,France,Male,34.0,1.0,104449.8,1.0,1.0,1.0,66580.13,0.0
-9398,15759966.0,Bales,713.0,Germany,Female,45.0,5.0,102989.84,1.0,1.0,0.0,171430.16,1.0
-9399,15806967.0,Macdonald,678.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,121408.55,0.0
-9400,15721251.0,Lucciano,742.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,162719.69,0.0
-9401,15655274.0,Ozerova,644.0,France,Male,36.0,4.0,97565.74,1.0,1.0,1.0,136497.28,0.0
-9402,15777784.0,Hsia,614.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,57817.87,0.0
-9403,15629273.0,Ponomaryov,479.0,Germany,Male,20.0,3.0,84903.33,2.0,1.0,1.0,84932.4,0.0
-9404,15682322.0,Onwuamaeze,706.0,Spain,Female,21.0,3.0,99557.94,1.0,1.0,1.0,25951.91,0.0
-9405,15737404.0,Bancroft,651.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,142838.57,0.0
-9406,15802088.0,Nwebube,635.0,Germany,Male,47.0,9.0,133598.3,2.0,1.0,0.0,125445.8,1.0
-9407,15690925.0,Pisani,713.0,Germany,Female,39.0,3.0,102088.29,1.0,1.0,0.0,160526.36,1.0
-9408,15788215.0,Chiazagomekpele,553.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,176576.62,0.0
-9409,15632859.0,Iweobiegbunam,684.0,France,Male,55.0,9.0,127480.53,1.0,0.0,1.0,1568.91,1.0
-9410,15702656.0,Chin,598.0,France,Male,33.0,6.0,0.0,2.0,0.0,0.0,103557.91,0.0
-9411,15602034.0,Hs?,744.0,Germany,Female,48.0,6.0,122334.26,3.0,1.0,0.0,86587.37,1.0
-9412,15662426.0,Tien,521.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,87822.14,0.0
-9413,15613463.0,Cremonesi,758.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,79704.12,0.0
-9414,15654901.0,Johnstone,684.0,France,Female,31.0,3.0,0.0,2.0,0.0,1.0,106781.59,0.0
-9415,15804962.0,Chu,771.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,0.0,72673.33,0.0
-9416,15805627.0,Chiu,722.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,180712.52,0.0
-9417,15752300.0,Lettiere,583.0,Germany,Female,24.0,5.0,105355.76,1.0,1.0,0.0,46857.52,1.0
-9418,15616280.0,H?,659.0,France,Male,39.0,3.0,0.0,2.0,0.0,0.0,126598.23,1.0
-9419,15747542.0,Nnonso,682.0,France,Male,23.0,9.0,97382.53,1.0,0.0,1.0,152290.28,0.0
-9420,15569572.0,Maclean,553.0,France,Female,31.0,1.0,164948.98,1.0,1.0,1.0,121751.04,0.0
-9421,15772573.0,Pirozzi,699.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,100615.14,0.0
-9422,15583552.0,Ulyanova,599.0,Spain,Female,32.0,1.0,140931.55,1.0,1.0,1.0,111448.92,0.0
-9423,15705735.0,Farrar,539.0,France,Female,52.0,4.0,0.0,1.0,1.0,1.0,175853.64,1.0
-9424,15735402.0,Goliwe,730.0,Spain,Female,44.0,9.0,0.0,2.0,1.0,0.0,94949.84,0.0
-9425,15786035.0,T'ien,705.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,70523.92,0.0
-9426,15716164.0,Yobachukwu,753.0,France,Male,37.0,5.0,140596.95,2.0,1.0,0.0,64420.5,0.0
-9427,15704763.0,Chiu,850.0,Germany,Female,40.0,8.0,92760.55,2.0,1.0,1.0,105898.79,0.0
-9428,15569187.0,Fan,594.0,Spain,Male,37.0,5.0,0.0,1.0,0.0,1.0,144584.29,0.0
-9429,15646937.0,Hsueh,637.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,94498.82,0.0
-9430,15774931.0,Cattaneo,615.0,France,Female,36.0,1.0,0.0,1.0,1.0,0.0,164967.21,0.0
-9431,15671934.0,Yobanna,572.0,France,Male,32.0,4.0,0.0,1.0,1.0,1.0,44114.88,0.0
-9432,15715199.0,Manna,713.0,France,Female,35.0,6.0,0.0,1.0,1.0,1.0,86935.08,0.0
-9433,15682355.0,Toscani,744.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,102628.98,0.0
-9434,15592765.0,Okechukwu,759.0,Germany,Female,46.0,2.0,121345.88,1.0,1.0,0.0,94815.04,1.0
-9435,15586039.0,Echezonachukwu,719.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,85746.52,0.0
-9436,15725882.0,Ch'ien,721.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,138360.29,0.0
-9437,15778861.0,Y?an,648.0,France,Male,49.0,2.0,0.0,1.0,0.0,0.0,131906.44,1.0
-9438,15579808.0,Onyeorulu,850.0,Germany,Male,32.0,3.0,63218.85,1.0,1.0,0.0,109794.31,0.0
-9439,15631359.0,Ch'ang,584.0,Germany,Male,38.0,9.0,121863.61,1.0,1.0,0.0,126837.72,0.0
-9440,15647800.0,Ndubueze,683.0,Spain,Female,45.0,4.0,122028.65,1.0,1.0,1.0,86786.41,0.0
-9441,15648034.0,Trevisano,643.0,Germany,Male,44.0,1.0,131938.91,1.0,1.0,1.0,180626.68,1.0
-9442,15771580.0,Gadsden,731.0,Germany,Female,38.0,9.0,129855.31,1.0,1.0,0.0,151978.36,0.0
-9443,15700394.0,Tsai,671.0,France,Female,41.0,9.0,0.0,2.0,1.0,1.0,72350.47,0.0
-9444,15691452.0,Yudina,689.0,France,Male,32.0,10.0,0.0,2.0,1.0,0.0,117977.75,0.0
-9445,15576077.0,Ogochukwu,741.0,Germany,Female,37.0,1.0,156974.26,2.0,1.0,1.0,84475.13,0.0
-9446,15583595.0,Tsao,753.0,Germany,Female,40.0,2.0,167738.82,1.0,1.0,1.0,49833.5,0.0
-9447,15681327.0,Aksenov,632.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,92277.53,0.0
-9448,15711299.0,Nwachukwu,621.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,52404.47,0.0
-9449,15784092.0,Onyemauchechukwu,559.0,France,Male,38.0,2.0,0.0,2.0,0.0,0.0,96424.57,0.0
-9450,15793883.0,Frolov,637.0,France,Female,34.0,5.0,0.0,2.0,1.0,0.0,184564.67,0.0
-9451,15617454.0,Uspensky,676.0,France,Male,35.0,8.0,0.0,2.0,0.0,0.0,706.5,0.0
-9452,15692929.0,Nnachetam,683.0,Spain,Male,35.0,1.0,0.0,2.0,0.0,1.0,181490.42,0.0
-9453,15745354.0,Chiganu,553.0,France,Female,30.0,1.0,111095.58,1.0,0.0,1.0,83679.46,0.0
-9454,15794049.0,Chinwenma,705.0,France,Male,34.0,8.0,0.0,1.0,0.0,0.0,149814.68,0.0
-9455,15740487.0,Tsao,711.0,Spain,Female,33.0,3.0,0.0,1.0,1.0,1.0,167973.63,0.0
-9456,15664204.0,Udegbunam,705.0,France,Male,31.0,2.0,94486.47,1.0,1.0,1.0,158059.95,0.0
-9457,15649280.0,Okwuadigbo,684.0,Spain,Female,34.0,8.0,0.0,2.0,1.0,0.0,142210.94,1.0
-9458,15737489.0,Ebelegbulam,709.0,France,Male,38.0,2.0,91621.69,1.0,0.0,1.0,160941.78,0.0
-9459,15809800.0,Udinesi,662.0,France,Male,59.0,1.0,152667.32,1.0,1.0,0.0,110299.77,1.0
-9460,15794566.0,O'Brien,613.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,102747.02,0.0
-9461,15789563.0,Okeke,813.0,Germany,Female,41.0,4.0,116803.8,2.0,0.0,1.0,126475.79,0.0
-9462,15772941.0,Fanucci,684.0,Germany,Female,47.0,1.0,169824.46,1.0,0.0,0.0,139761.25,0.0
-9463,15585853.0,Hsing,632.0,Spain,Male,33.0,7.0,133903.12,1.0,1.0,1.0,60721.4,0.0
-9464,15631481.0,Chien,801.0,France,Female,35.0,6.0,0.0,1.0,0.0,1.0,177784.68,1.0
-9465,15590730.0,Ch'iu,731.0,France,Male,33.0,3.0,0.0,1.0,1.0,0.0,134638.97,0.0
-9466,15772423.0,Fanucci,593.0,France,Male,33.0,8.0,0.0,2.0,0.0,1.0,64493.99,0.0
-9467,15690175.0,Milanesi,706.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,158840.99,0.0
-9468,15676959.0,Lucchesi,537.0,France,Female,50.0,9.0,0.0,3.0,1.0,1.0,182270.88,1.0
-9469,15744983.0,Aksenov,699.0,France,Female,38.0,4.0,133636.04,1.0,1.0,0.0,122433.09,0.0
-9470,15631838.0,Nnaife,633.0,France,Male,41.0,9.0,0.0,1.0,1.0,0.0,93716.38,0.0
-9471,15582892.0,Nnanna,809.0,France,Female,32.0,3.0,0.0,2.0,1.0,0.0,187905.12,0.0
-9472,15609510.0,Ugochukwutubelum,602.0,France,Male,32.0,9.0,134590.18,1.0,1.0,1.0,85902.39,0.0
-9473,15616954.0,Toscani,628.0,France,Male,42.0,8.0,0.0,1.0,1.0,1.0,177719.88,1.0
-9474,15585823.0,Kibby,679.0,France,Male,34.0,1.0,171937.27,1.0,1.0,1.0,70849.47,0.0
-9475,15721181.0,Zhirov,743.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,142513.5,0.0
-9476,15572398.0,T'ao,651.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,145986.35,0.0
-9477,15771483.0,Chukwudi,543.0,France,Male,35.0,4.0,114512.59,1.0,0.0,1.0,27278.42,0.0
-9478,15649414.0,Ritchie,636.0,France,Female,39.0,2.0,164557.95,1.0,1.0,0.0,174960.66,1.0
-9479,15803941.0,Pisani,705.0,Spain,Male,27.0,7.0,0.0,2.0,1.0,1.0,166677.35,0.0
-9480,15585855.0,Chiu,775.0,France,Female,48.0,0.0,151843.35,1.0,1.0,1.0,173843.21,1.0
-9481,15768340.0,Chukwudi,641.0,Spain,Female,27.0,1.0,0.0,2.0,1.0,1.0,113581.85,0.0
-9482,15649744.0,T'ien,679.0,France,Female,50.0,2.0,0.0,2.0,1.0,1.0,183542.08,0.0
-9483,15663625.0,Chiawuotu,678.0,Germany,Female,46.0,1.0,133744.26,1.0,1.0,0.0,158845.49,1.0
-9484,15585853.0,Smith,703.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,51548.14,0.0
-9485,15676938.0,Tung,645.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,1.0,182266.01,0.0
-9486,15749671.0,Sagese,598.0,Germany,Male,39.0,3.0,125951.82,2.0,1.0,0.0,168359.98,0.0
-9487,15640865.0,Trevisani,762.0,Spain,Male,33.0,2.0,0.0,2.0,0.0,0.0,116969.89,0.0
-9488,15641850.0,Trevisano,657.0,France,Female,40.0,3.0,0.0,1.0,1.0,1.0,131662.47,1.0
-9489,15657535.0,Chia,647.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,145765.83,0.0
-9490,15593954.0,Trevisano,601.0,France,Male,40.0,0.0,0.0,1.0,0.0,1.0,85801.77,1.0
-9491,15767064.0,Chikezie,687.0,Germany,Male,31.0,10.0,111096.91,2.0,1.0,1.0,90928.02,0.0
-9492,15666032.0,Chidi,560.0,Spain,Male,23.0,9.0,106596.29,2.0,1.0,1.0,79476.63,0.0
-9493,15711299.0,Onyekachi,633.0,France,Female,33.0,4.0,0.0,2.0,0.0,0.0,123537.05,0.0
-9494,15683955.0,Ch'ang,731.0,Spain,Female,45.0,0.0,133658.24,1.0,1.0,0.0,166513.49,1.0
-9495,15705403.0,Nwachukwu,586.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,108699.59,0.0
-9496,15683555.0,Tien,671.0,Germany,Male,37.0,2.0,98899.76,2.0,1.0,0.0,96444.88,0.0
-9497,15590097.0,Chiazagomekpere,565.0,Spain,Female,34.0,1.0,113348.2,1.0,1.0,0.0,182738.0,0.0
-9498,15594812.0,Ch'eng,526.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,168816.09,1.0
-9499,15671514.0,Hsing,663.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,92914.67,0.0
-9500,15719561.0,Chibugo,751.0,Spain,Female,30.0,7.0,0.0,2.0,1.0,0.0,109742.55,0.0
-9501,15620507.0,Maclean,735.0,France,Male,47.0,8.0,0.0,2.0,0.0,0.0,91347.76,0.0
-9502,15645059.0,Chiu,729.0,France,Male,30.0,8.0,0.0,2.0,1.0,0.0,28368.37,0.0
-9503,15616088.0,Chukwualuka,814.0,France,Male,42.0,10.0,0.0,2.0,1.0,1.0,68695.38,0.0
-9504,15764174.0,K'ung,756.0,Germany,Female,37.0,3.0,167735.69,2.0,0.0,1.0,18369.33,0.0
-9505,15651627.0,Chukwuemeka,632.0,France,Male,36.0,8.0,0.0,1.0,1.0,1.0,101954.78,0.0
-9506,15596624.0,McIntosh,577.0,Spain,Male,41.0,0.0,63723.78,1.0,1.0,0.0,91036.74,0.0
-9507,15726058.0,Okechukwu,729.0,France,Male,24.0,3.0,0.0,2.0,0.0,1.0,183515.13,0.0
-9508,15775104.0,Chukwuemeka,759.0,France,Male,29.0,8.0,133436.52,2.0,0.0,0.0,102628.98,0.0
-9509,15705899.0,Hsiung,603.0,Spain,Male,39.0,7.0,0.0,1.0,1.0,1.0,83700.66,0.0
-9510,15628298.0,Chidubem,707.0,France,Female,46.0,1.0,0.0,2.0,1.0,1.0,57541.07,0.0
-9511,15782360.0,Chimaobim,611.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,85522.84,0.0
-9512,15605742.0,Hs?,604.0,Germany,Female,45.0,8.0,114756.14,1.0,1.0,0.0,164040.94,1.0
-9513,15791942.0,Chinweike,549.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,148097.58,0.0
-9514,15643487.0,Chidiegwu,706.0,Spain,Male,23.0,6.0,88963.31,1.0,0.0,1.0,79957.95,0.0
-9515,15781451.0,Bibi,687.0,Germany,Male,45.0,6.0,154902.66,1.0,1.0,1.0,62944.75,0.0
-9516,15703923.0,Lueck,683.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,1.0,141364.27,0.0
-9517,15801915.0,Obiajulu,538.0,Germany,Female,43.0,9.0,157099.47,1.0,1.0,0.0,138051.19,1.0
-9518,15765093.0,Ndubueze,605.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,109523.88,0.0
-9519,15602851.0,Chiazagomekpele,633.0,Spain,Male,26.0,8.0,90560.3,1.0,1.0,0.0,158937.55,0.0
-9520,15642448.0,Kuo,603.0,France,Male,29.0,7.0,141581.08,1.0,1.0,1.0,125979.36,0.0
-9521,15656717.0,Hs?,612.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,142400.27,0.0
-9522,15673929.0,Chinedum,805.0,Spain,Female,38.0,3.0,87098.91,1.0,0.0,0.0,69039.43,0.0
-9523,15571244.0,Ch'ien,740.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,72985.61,0.0
-9524,15637354.0,Chukwuebuka,727.0,France,Male,28.0,1.0,124620.01,1.0,1.0,1.0,126794.56,0.0
-9525,15724307.0,Ma,648.0,Spain,Female,24.0,8.0,142872.36,1.0,1.0,0.0,133672.61,0.0
-9526,15741047.0,Onyinyechukwuka,596.0,Spain,Male,36.0,1.0,0.0,2.0,0.0,1.0,131924.38,0.0
-9527,15789924.0,Yegorova,728.0,Spain,Male,35.0,5.0,100116.67,1.0,0.0,1.0,168816.22,0.0
-9528,15605293.0,Hs?,531.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,1795.93,0.0
-9529,15684925.0,P'eng,644.0,France,Female,52.0,7.0,0.0,1.0,1.0,1.0,46786.92,1.0
-9530,15569678.0,Nwoye,677.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,1.0,49506.82,0.0
-9531,15799042.0,Hsing,682.0,Germany,Male,50.0,4.0,106011.59,1.0,1.0,0.0,103560.98,1.0
-9532,15638494.0,Ch'in,664.0,Germany,Male,55.0,8.0,161572.24,1.0,1.0,0.0,141558.04,1.0
-9533,15620505.0,Trevisano,677.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,86734.41,0.0
-9534,15588923.0,T'ien,729.0,Germany,Male,35.0,1.0,130830.22,1.0,1.0,1.0,160532.88,0.0
-9535,15686963.0,Walker,635.0,Germany,Male,26.0,2.0,121884.99,2.0,0.0,0.0,77519.62,0.0
-9536,15628112.0,Obidimkpa,571.0,France,Female,32.0,4.0,145984.92,1.0,1.0,0.0,141210.18,0.0
-9537,15746696.0,White,675.0,Germany,Male,29.0,4.0,98268.84,1.0,0.0,1.0,187900.12,0.0
-9538,15757310.0,Y?an,681.0,Spain,Female,46.0,6.0,0.0,1.0,1.0,1.0,164967.21,1.0
-9539,15642570.0,P'eng,646.0,France,Male,31.0,3.0,122552.51,1.0,1.0,0.0,121551.93,0.0
-9540,15772995.0,Lombardo,676.0,Spain,Female,38.0,8.0,110112.54,1.0,1.0,0.0,163477.22,0.0
-9541,15710671.0,O'Sullivan,564.0,France,Female,40.0,7.0,0.0,2.0,1.0,0.0,71167.17,0.0
-9542,15759034.0,Palerma,686.0,France,Male,45.0,5.0,0.0,2.0,1.0,1.0,96881.32,1.0
-9543,15722072.0,Burt,708.0,France,Female,41.0,7.0,0.0,2.0,0.0,0.0,134600.96,0.0
-9544,15804919.0,Piazza,581.0,France,Female,30.0,9.0,0.0,2.0,0.0,0.0,157862.82,0.0
-9545,15612667.0,Monds,614.0,Spain,Male,34.0,5.0,166833.86,1.0,1.0,0.0,126471.13,0.0
-9546,15762799.0,Chukwudi,537.0,Germany,Female,25.0,3.0,106687.76,2.0,1.0,1.0,138160.11,0.0
-9547,15806318.0,Hs?,721.0,Germany,Male,62.0,7.0,103210.27,1.0,0.0,1.0,47318.75,1.0
-9548,15803840.0,Lo Duca,619.0,Spain,Male,35.0,1.0,163760.82,1.0,0.0,1.0,122268.75,0.0
-9549,15773639.0,T'ao,564.0,France,Male,46.0,5.0,0.0,2.0,1.0,1.0,184524.74,0.0
-9550,15691627.0,Fyodorova,714.0,France,Male,47.0,7.0,0.0,1.0,0.0,0.0,74870.99,1.0
-9551,15593499.0,Y?an,565.0,Spain,Female,25.0,9.0,0.0,2.0,1.0,0.0,183494.87,0.0
-9552,15735784.0,Chinedum,758.0,France,Male,31.0,1.0,0.0,2.0,1.0,0.0,173498.45,0.0
-9553,15726267.0,T'ien,554.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,124489.88,0.0
-9554,15754929.0,K?,729.0,Germany,Female,29.0,2.0,90727.79,1.0,1.0,0.0,139946.68,0.0
-9555,15626144.0,Yefimova,561.0,France,Female,33.0,6.0,0.0,1.0,1.0,0.0,180318.6,0.0
-9556,15724689.0,Y?an,644.0,France,Female,59.0,2.0,123372.3,1.0,0.0,0.0,129988.56,1.0
-9557,15733597.0,Hsing,555.0,France,Male,39.0,4.0,141782.57,1.0,1.0,1.0,47428.73,0.0
-9558,15752626.0,Chukwuebuka,587.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,80178.54,0.0
-9559,15636572.0,Shih,593.0,France,Male,45.0,9.0,0.0,1.0,0.0,0.0,126794.56,1.0
-9560,15772421.0,Nkemdirim,678.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,175893.53,0.0
-9561,15721592.0,Ch'en,620.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,106837.06,0.0
-9562,15763898.0,Toth,751.0,France,Male,35.0,9.0,0.0,2.0,0.0,1.0,153920.27,0.0
-9563,15673790.0,K?,596.0,Germany,Male,44.0,4.0,152797.9,2.0,1.0,0.0,93873.13,0.0
-9564,15588854.0,Uchenna,667.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,38962.94,0.0
-9565,15632550.0,Chiedozie,648.0,France,Male,44.0,6.0,0.0,2.0,1.0,1.0,151401.33,0.0
-9566,15737521.0,Ch'en,709.0,France,Female,47.0,7.0,0.0,1.0,1.0,0.0,129590.18,1.0
-9567,15694946.0,Ch'in,619.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,136837.03,0.0
-9568,15659327.0,Whitehead,523.0,France,Female,29.0,8.0,0.0,1.0,1.0,0.0,152956.33,0.0
-9569,15699550.0,Chukwujamuike,699.0,France,Female,23.0,6.0,0.0,1.0,0.0,1.0,120834.46,0.0
-9570,15808846.0,Nwokike,648.0,France,Male,33.0,0.0,0.0,1.0,0.0,0.0,166513.49,1.0
-9571,15793346.0,Chiekwugo,683.0,Germany,Male,29.0,3.0,97588.6,2.0,1.0,0.0,82870.75,0.0
-9572,15782591.0,Hsia,651.0,Spain,Female,33.0,1.0,0.0,2.0,1.0,1.0,69055.45,0.0
-9573,15704466.0,K'ung,681.0,France,Male,31.0,9.0,154495.79,1.0,0.0,1.0,173516.71,0.0
-9574,15686878.0,McGregor,568.0,Germany,Male,39.0,2.0,107556.06,1.0,0.0,0.0,174770.14,1.0
-9575,15655232.0,Onyemauchechi,596.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,142913.43,0.0
-9576,15773789.0,Scott,593.0,Spain,Male,32.0,9.0,68423.69,1.0,1.0,1.0,136902.01,0.0
-9577,15595037.0,Chukwudi,743.0,France,Male,35.0,6.0,0.0,1.0,0.0,1.0,184544.59,0.0
-9578,15663933.0,Chiang,699.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,166038.27,0.0
-9579,15589660.0,Ch'in,627.0,Germany,Female,32.0,5.0,80780.16,1.0,1.0,0.0,145528.22,0.0
-9580,15594720.0,Fiorentini,775.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,67468.67,0.0
-9581,15662976.0,Ugochukwutubelum,716.0,Germany,Female,43.0,10.0,100262.88,1.0,1.0,0.0,167973.63,1.0
-9582,15604314.0,H?,567.0,France,Female,41.0,8.0,0.0,1.0,0.0,0.0,123437.05,1.0
-9583,15770995.0,Gell,642.0,France,Female,47.0,0.0,0.0,1.0,1.0,0.0,115091.38,1.0
-9584,15726178.0,Chiawuotu,708.0,France,Male,27.0,1.0,0.0,2.0,1.0,1.0,152883.95,0.0
-9585,15795519.0,Chu,709.0,France,Female,30.0,4.0,0.0,2.0,1.0,0.0,86410.28,0.0
-9586,15776467.0,Teng,590.0,Germany,Male,46.0,1.0,128743.55,1.0,1.0,0.0,6761.84,1.0
-9587,15702912.0,Lo,726.0,Spain,Female,47.0,7.0,0.0,2.0,1.0,0.0,172618.52,1.0
-9588,15600934.0,Onwumelu,721.0,France,Male,37.0,4.0,147510.34,1.0,1.0,0.0,75937.47,0.0
-9589,15729083.0,Lu,588.0,France,Female,33.0,0.0,86055.17,1.0,0.0,1.0,143049.72,0.0
-9590,15662641.0,Manna,598.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,130809.95,0.0
-9591,15646720.0,O'Sullivan,760.0,Germany,Female,43.0,9.0,117034.32,1.0,1.0,1.0,92320.37,1.0
-9592,15726058.0,Chu,691.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,1.0,143980.29,0.0
-9593,15576084.0,Chiu,496.0,France,Female,55.0,2.0,0.0,3.0,1.0,1.0,79342.31,1.0
-9594,15672983.0,Hsiung,637.0,Germany,Female,46.0,5.0,123681.32,1.0,1.0,0.0,130830.22,1.0
-9595,15693637.0,Chikezie,592.0,France,Female,44.0,4.0,0.0,1.0,1.0,1.0,123587.85,0.0
-9596,15697558.0,Shubin,531.0,France,Male,30.0,1.0,151191.31,2.0,1.0,1.0,187616.16,0.0
-9597,15632576.0,Folliero,682.0,France,Male,33.0,8.0,0.0,1.0,1.0,0.0,143393.24,0.0
-9598,15736578.0,Chukwujamuike,695.0,France,Female,28.0,3.0,0.0,2.0,1.0,1.0,105986.01,0.0
-9599,15608916.0,Tuan,745.0,Germany,Female,33.0,3.0,113988.53,1.0,1.0,0.0,171707.93,0.0
-9600,15612043.0,Chukwudi,686.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,1.0,145593.85,0.0
-9601,15797149.0,Uspenskaya,695.0,France,Male,32.0,1.0,130404.92,1.0,1.0,0.0,129964.94,0.0
-9602,15633650.0,Chin,663.0,Spain,Female,38.0,10.0,0.0,2.0,1.0,0.0,123583.85,0.0
-9603,15571940.0,Y?,678.0,France,Male,49.0,5.0,148061.89,1.0,1.0,0.0,126961.1,0.0
-9604,15794939.0,Chin,744.0,France,Male,28.0,5.0,0.0,2.0,1.0,0.0,167973.63,0.0
-9605,15676958.0,Chinweike,554.0,Spain,Male,35.0,2.0,0.0,2.0,0.0,1.0,88890.75,0.0
-9606,15591135.0,Y?,677.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,172967.87,0.0
-9607,15752801.0,Whitehead,558.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,88920.29,0.0
-9608,15578098.0,Dyer,714.0,Germany,Female,33.0,10.0,143330.56,2.0,1.0,1.0,19700.24,0.0
-9609,15624072.0,Nwankwo,567.0,France,Female,36.0,10.0,0.0,2.0,0.0,1.0,143022.31,0.0
-9610,15756820.0,P'eng,710.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,178393.68,0.0
-9611,15769353.0,Chinedum,681.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,95583.45,0.0
-9612,15638751.0,Fiorentino,717.0,France,Male,25.0,5.0,0.0,2.0,0.0,1.0,120830.73,0.0
-9613,15694192.0,Dickson,744.0,France,Male,42.0,4.0,0.0,2.0,0.0,0.0,121440.8,0.0
-9614,15685828.0,T'ang,627.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,0.0,70613.52,0.0
-9615,15648800.0,Chukwubuikem,586.0,Spain,Male,48.0,1.0,0.0,1.0,0.0,1.0,149946.43,1.0
-9616,15695819.0,Fiorentino,543.0,Germany,Male,42.0,6.0,184926.61,1.0,0.0,0.0,64849.74,0.0
-9617,15743076.0,Ts'ui,609.0,Spain,Female,52.0,6.0,0.0,2.0,1.0,1.0,123587.96,0.0
-9618,15641110.0,Chuang,687.0,Germany,Male,33.0,7.0,101629.3,2.0,0.0,1.0,121551.93,0.0
-9619,15698872.0,Chienezie,663.0,France,Male,31.0,9.0,131244.65,1.0,1.0,0.0,167873.55,0.0
-9620,15586475.0,Hsu,829.0,Spain,Female,28.0,7.0,0.0,2.0,1.0,0.0,14081.64,0.0
-9621,15623695.0,Brown,545.0,France,Male,36.0,8.0,0.0,1.0,1.0,1.0,164484.78,0.0
-9622,15662426.0,Uvarova,651.0,France,Female,34.0,10.0,0.0,1.0,1.0,0.0,74849.46,1.0
-9623,15689322.0,Nnanna,559.0,France,Male,31.0,7.0,0.0,1.0,1.0,1.0,113860.81,0.0
-9624,15615177.0,Trevisano,651.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,158887.09,0.0
-9625,15684592.0,Nwabugwu,645.0,Spain,Male,41.0,3.0,0.0,2.0,1.0,1.0,106799.49,0.0
-9626,15761681.0,Ch'eng,677.0,Spain,Female,38.0,9.0,0.0,2.0,1.0,0.0,119439.66,0.0
-9627,15766272.0,Chukwuebuka,696.0,Spain,Female,30.0,9.0,0.0,1.0,1.0,0.0,154767.34,1.0
-9628,15785865.0,Tsao,732.0,Germany,Male,43.0,7.0,121797.09,1.0,1.0,0.0,77330.35,0.0
-9629,15633213.0,Calabrese,695.0,France,Female,48.0,4.0,0.0,1.0,1.0,0.0,26215.85,1.0
-9630,15806837.0,Obialo,678.0,Germany,Male,34.0,5.0,98775.23,2.0,1.0,1.0,124596.63,0.0
-9631,15762443.0,Chiu,577.0,Germany,Male,32.0,5.0,104944.1,2.0,1.0,1.0,123499.98,0.0
-9632,15756610.0,Hsing,637.0,France,Female,38.0,5.0,0.0,2.0,0.0,1.0,173495.77,0.0
-9633,15653547.0,Nkemdilim,651.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,0.0,81468.99,0.0
-9634,15794257.0,Burt,731.0,Spain,Female,33.0,4.0,0.0,1.0,0.0,0.0,134467.08,0.0
-9635,15687913.0,Ts'ui,706.0,Spain,Male,31.0,3.0,0.0,1.0,1.0,0.0,187915.4,0.0
-9636,15797246.0,Okwuadigbo,639.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,178361.48,0.0
-9637,15707479.0,Hs?,717.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,171364.18,0.0
-9638,15593943.0,Lo Duca,667.0,Germany,Male,32.0,9.0,133970.23,2.0,1.0,0.0,93336.3,0.0
-9639,15593776.0,Chin,705.0,Spain,Male,40.0,5.0,0.0,1.0,1.0,0.0,138052.51,0.0
-9640,15600462.0,Kibby,684.0,Germany,Male,39.0,10.0,153366.13,1.0,1.0,0.0,115676.61,1.0
-9641,15675896.0,DeRose,669.0,France,Female,41.0,3.0,0.0,3.0,0.0,1.0,62830.97,1.0
-9642,15709920.0,Ch'iu,727.0,Spain,Male,23.0,5.0,0.0,2.0,1.0,1.0,121440.8,0.0
-9643,15671640.0,Trevisani,682.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,140934.57,0.0
-9644,15723886.0,Fanucci,687.0,France,Male,23.0,2.0,0.0,2.0,1.0,1.0,62612.88,0.0
-9645,15707962.0,Chukwujamuike,738.0,France,Female,26.0,8.0,134318.21,1.0,1.0,0.0,57972.43,0.0
-9646,15693326.0,Ts'ai,704.0,France,Male,42.0,8.0,0.0,1.0,1.0,1.0,166520.96,1.0
-9647,15803842.0,Onyekachukwu,684.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,181794.86,0.0
-9648,15777076.0,Kao,850.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,53477.76,0.0
-9649,15791533.0,Ritchie,565.0,France,Female,54.0,6.0,0.0,1.0,0.0,1.0,105887.04,0.0
-9650,15801417.0,Chukwudi,691.0,Germany,Female,52.0,9.0,126679.69,1.0,0.0,0.0,106837.06,1.0
-9651,15782941.0,Oguejiofor,614.0,France,Male,35.0,9.0,134811.3,1.0,0.0,1.0,120892.93,1.0
-9652,15786433.0,Hsia,651.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,0.0,103315.74,0.0
-9653,15732270.0,Yermakova,537.0,Spain,Male,63.0,9.0,0.0,1.0,1.0,0.0,128882.66,1.0
-9654,15632275.0,Chinwendu,722.0,France,Female,33.0,5.0,0.0,2.0,0.0,1.0,108419.41,0.0
-9655,15600688.0,Nnachetam,705.0,Germany,Female,36.0,4.0,127864.4,1.0,1.0,1.0,172930.28,0.0
-9656,15699415.0,Martin,695.0,Germany,Female,42.0,5.0,131772.57,2.0,0.0,0.0,94488.82,1.0
-9657,15665590.0,Ch'ien,545.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,142587.32,0.0
-9658,15647800.0,Mazzanti,748.0,France,Male,39.0,6.0,0.0,2.0,0.0,0.0,132979.43,0.0
-9659,15699430.0,Chiang,602.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,50390.98,0.0
-9660,15755868.0,Ch'ang,686.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,189366.04,0.0
-9661,15746012.0,Y?,614.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,27330.59,0.0
-9662,15737354.0,Onyemere,568.0,France,Female,29.0,1.0,0.0,2.0,1.0,1.0,99800.87,0.0
-9663,15768455.0,Avent,705.0,France,Male,34.0,4.0,0.0,1.0,1.0,0.0,143783.28,0.0
-9664,15768455.0,Ch'iu,687.0,France,Male,37.0,8.0,110245.13,1.0,1.0,1.0,179267.94,0.0
-9665,15601503.0,Pinto,545.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,74320.75,0.0
-9666,15591257.0,Chuang,653.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,153392.28,0.0
-9667,15724307.0,Yobachukwu,634.0,France,Female,35.0,9.0,180530.51,1.0,1.0,0.0,116971.05,0.0
-9668,15593365.0,Hsu,569.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,116893.87,0.0
-9669,15752801.0,Milanesi,773.0,Spain,Male,39.0,2.0,0.0,1.0,1.0,1.0,16081.62,0.0
-9670,15685500.0,Fennescey,743.0,France,Female,27.0,5.0,0.0,2.0,1.0,1.0,145894.9,0.0
-9671,15765297.0,Tsou,703.0,France,Male,34.0,4.0,169025.83,1.0,1.0,1.0,166677.35,0.0
-9672,15652999.0,Shih,488.0,Spain,Male,50.0,9.0,0.0,1.0,1.0,1.0,123826.83,1.0
-9673,15798844.0,Lombardi,577.0,France,Female,51.0,5.0,83772.95,1.0,0.0,0.0,138873.15,1.0
-9674,15641733.0,Hsueh,757.0,France,Female,46.0,2.0,0.0,2.0,1.0,0.0,141533.19,1.0
-9675,15732672.0,Chibueze,684.0,Germany,Female,38.0,7.0,103564.83,1.0,1.0,0.0,151925.25,0.0
-9676,15615091.0,Kao,597.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,90537.47,0.0
-9677,15593470.0,Y?an,741.0,France,Female,36.0,7.0,0.0,1.0,1.0,0.0,82298.59,0.0
-9678,15773869.0,Palerma,628.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,91406.62,0.0
-9679,15592846.0,O'Brien,651.0,France,Female,40.0,0.0,0.0,1.0,1.0,0.0,116547.31,1.0
-9680,15793901.0,Smith,717.0,France,Female,38.0,6.0,0.0,3.0,1.0,0.0,158973.77,0.0
-9681,15611186.0,Zito,661.0,France,Male,46.0,10.0,150844.94,1.0,1.0,1.0,158887.09,0.0
-9682,15635177.0,Hsueh,686.0,France,Male,54.0,5.0,133844.22,1.0,1.0,1.0,95611.74,0.0
-9683,15612455.0,Lucchesi,705.0,France,Female,32.0,5.0,160982.89,1.0,1.0,0.0,172718.35,0.0
-9684,15682355.0,Nkemakonam,814.0,France,Male,40.0,9.0,0.0,1.0,1.0,1.0,92320.37,0.0
-9685,15628308.0,Hsueh,556.0,France,Male,22.0,2.0,0.0,2.0,1.0,1.0,145593.85,0.0
-9686,15602914.0,Lo,724.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,133537.53,0.0
-9687,15697510.0,Nwankwo,669.0,Spain,Male,37.0,10.0,0.0,1.0,1.0,1.0,1235.49,0.0
-9688,15796467.0,Tung,596.0,France,Female,36.0,6.0,0.0,2.0,1.0,1.0,114774.35,0.0
-9689,15740406.0,Nnachetam,677.0,France,Male,54.0,1.0,180516.8,1.0,1.0,0.0,174431.01,1.0
-9690,15701687.0,Maclean,607.0,Germany,Female,41.0,3.0,110934.54,3.0,1.0,0.0,28698.17,1.0
-9691,15672894.0,Nnamdi,632.0,France,Male,33.0,8.0,129728.8,1.0,1.0,1.0,85901.09,0.0
-9692,15664547.0,Ch'iu,751.0,France,Male,46.0,8.0,0.0,2.0,0.0,0.0,125939.19,0.0
-9693,15757837.0,Chukwudi,724.0,France,Male,42.0,8.0,0.0,1.0,0.0,0.0,161388.22,0.0
-9694,15583840.0,Ritchie,587.0,France,Female,33.0,2.0,0.0,2.0,0.0,1.0,159344.43,0.0
-9695,15690829.0,Hao,705.0,France,Female,42.0,0.0,0.0,2.0,1.0,0.0,90905.94,0.0
-9696,15736778.0,Tsao,561.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,1.0,132424.69,0.0
-9697,15572408.0,Tien,671.0,France,Male,36.0,7.0,66101.89,1.0,0.0,0.0,176743.51,1.0
-9698,15692258.0,Piazza,667.0,Germany,Male,38.0,8.0,132876.23,1.0,1.0,1.0,18082.06,1.0
-9699,15695872.0,Obialo,593.0,Germany,Male,58.0,9.0,99095.73,1.0,1.0,0.0,167033.83,1.0
-9700,15736495.0,Ma,589.0,France,Female,63.0,9.0,161591.11,1.0,1.0,0.0,86927.53,1.0
-9701,15580956.0,Onyemauchechukwu,685.0,France,Female,47.0,9.0,102278.71,1.0,1.0,1.0,93536.38,0.0
-9702,15740147.0,Toscani,611.0,France,Male,49.0,7.0,0.0,1.0,1.0,0.0,161579.85,1.0
-9703,15747265.0,Hsu,613.0,Germany,Female,36.0,2.0,153058.64,1.0,0.0,0.0,49653.37,1.0
-9704,15643359.0,P'eng,719.0,France,Male,35.0,2.0,0.0,1.0,1.0,1.0,162865.25,0.0
-9705,15762573.0,Sun,652.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,94767.77,0.0
-9706,15733261.0,K?,583.0,France,Female,30.0,1.0,0.0,1.0,0.0,1.0,38652.96,0.0
-9707,15676909.0,Onyekaozulu,614.0,Spain,Male,21.0,7.0,123882.49,1.0,0.0,0.0,137037.97,0.0
-9708,15637324.0,Chukwueloka,785.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,152012.36,0.0
-9709,15652266.0,Ch'ang,535.0,France,Male,31.0,1.0,113909.69,1.0,1.0,1.0,174916.85,0.0
-9710,15705034.0,Chiu,785.0,Spain,Male,36.0,5.0,133801.35,1.0,1.0,1.0,153015.42,0.0
-9711,15666043.0,Nnachetam,605.0,France,Male,45.0,9.0,0.0,1.0,1.0,1.0,142513.5,1.0
-9712,15603594.0,Chiang,669.0,Spain,Female,43.0,3.0,0.0,1.0,1.0,0.0,78660.95,1.0
-9713,15756743.0,Uspensky,676.0,France,Female,38.0,2.0,0.0,2.0,1.0,1.0,101704.19,0.0
-9714,15653547.0,Chikwado,746.0,France,Female,43.0,3.0,95508.1,1.0,1.0,0.0,175674.01,1.0
-9715,15795519.0,P'eng,711.0,France,Male,29.0,8.0,125845.66,1.0,0.0,0.0,158845.49,0.0
-9716,15664793.0,Chuang,607.0,Germany,Female,37.0,7.0,121537.71,2.0,1.0,0.0,125518.32,0.0
-9717,15635388.0,Cone,667.0,France,Female,45.0,9.0,141749.74,1.0,1.0,0.0,118738.96,0.0
-9718,15589329.0,McIntyre,665.0,Germany,Female,43.0,4.0,135927.04,1.0,0.0,0.0,113645.8,1.0
-9719,15708534.0,Hsiung,683.0,Spain,Female,47.0,9.0,0.0,1.0,0.0,1.0,5791.85,1.0
-9720,15641359.0,Lo,691.0,Spain,Female,24.0,2.0,0.0,2.0,1.0,1.0,95181.06,0.0
-9721,15610271.0,Ndukaku,622.0,France,Female,35.0,9.0,0.0,1.0,0.0,1.0,199505.53,0.0
-9722,15597945.0,Hsueh,705.0,France,Male,33.0,6.0,97530.25,1.0,1.0,0.0,144290.25,0.0
-9723,15653777.0,Yashina,678.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,61793.47,0.0
-9724,15669491.0,Manna,605.0,Germany,Male,40.0,1.0,137714.25,1.0,1.0,1.0,116748.14,1.0
-9725,15658032.0,Iadanza,535.0,France,Female,30.0,2.0,0.0,2.0,0.0,0.0,60416.76,0.0
-9726,15751208.0,Aksenov,613.0,France,Male,35.0,4.0,137817.52,1.0,0.0,0.0,174770.14,0.0
-9727,15707596.0,P'an,587.0,Spain,Male,37.0,7.0,135962.0,1.0,1.0,1.0,161574.19,0.0
-9728,15641675.0,Ts'ui,613.0,Spain,Male,23.0,5.0,0.0,2.0,1.0,1.0,149575.13,0.0
-9729,15789611.0,Chukwujekwu,803.0,Germany,Male,46.0,1.0,141789.15,1.0,0.0,1.0,130189.72,0.0
-9730,15571261.0,T'ang,591.0,France,Male,22.0,8.0,0.0,2.0,1.0,1.0,171575.54,0.0
-9731,15756901.0,Ch'iu,757.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,133831.37,0.0
-9732,15688637.0,Chiemela,584.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,117977.45,0.0
-9733,15726589.0,Tretiakova,645.0,Germany,Female,43.0,3.0,120107.56,2.0,0.0,0.0,84920.49,1.0
-9734,15804822.0,Smith,581.0,Germany,Male,60.0,8.0,137841.53,1.0,1.0,1.0,181224.56,0.0
-9735,15667934.0,Lucchese,668.0,Spain,Female,24.0,4.0,113625.93,1.0,1.0,0.0,94739.2,0.0
-9736,15716605.0,Chidimma,573.0,France,Female,44.0,6.0,0.0,1.0,1.0,0.0,26015.8,1.0
-9737,15590820.0,Tien,520.0,Germany,Female,45.0,7.0,109570.21,1.0,0.0,0.0,15068.18,1.0
-9738,15726103.0,Ting,622.0,France,Female,36.0,6.0,136658.61,1.0,1.0,1.0,65316.5,0.0
-9739,15636551.0,Ts'ui,602.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,89801.9,0.0
-9740,15806918.0,Genovese,568.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,57549.65,0.0
-9741,15586880.0,Atherton,673.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,121053.19,0.0
-9742,15754929.0,Johnson,556.0,Spain,Male,55.0,2.0,0.0,1.0,1.0,0.0,131177.22,1.0
-9743,15632272.0,Ch'ien,729.0,France,Male,36.0,4.0,131583.59,1.0,1.0,0.0,198251.52,0.0
-9744,15803378.0,Ibragimova,673.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,69643.31,0.0
-9745,15808930.0,Mazzi,684.0,Germany,Female,55.0,1.0,147026.61,1.0,0.0,0.0,103832.58,1.0
-9746,15806511.0,Ma,683.0,Spain,Female,42.0,7.0,0.0,1.0,1.0,0.0,153064.85,1.0
-9747,15712584.0,Tsao,613.0,Germany,Male,30.0,6.0,108004.64,2.0,1.0,0.0,59497.01,0.0
-9748,15729771.0,Ch'iu,687.0,Spain,Female,46.0,3.0,153366.13,1.0,1.0,0.0,172668.38,1.0
-9749,15682563.0,Uspenskaya,707.0,Spain,Female,37.0,4.0,0.0,2.0,1.0,0.0,52301.15,0.0
-9750,15767894.0,Lombardo,497.0,France,Male,36.0,9.0,169743.83,1.0,1.0,0.0,52327.57,0.0
-9751,15709396.0,Hsueh,764.0,Germany,Female,54.0,9.0,118287.01,1.0,1.0,0.0,182055.36,1.0
-9752,15658716.0,Ts'ui,590.0,France,Male,36.0,1.0,0.0,1.0,1.0,0.0,135875.51,0.0
-9753,15582951.0,Ch'eng,574.0,Germany,Female,29.0,1.0,150844.94,1.0,0.0,1.0,89891.4,0.0
-9754,15758738.0,Toscani,667.0,Germany,Female,51.0,9.0,120782.7,1.0,1.0,1.0,138871.12,1.0
-9755,15629787.0,Hsueh,613.0,France,Male,41.0,9.0,0.0,2.0,1.0,1.0,98894.64,0.0
-9756,15792986.0,Hales,682.0,Germany,Female,40.0,4.0,142862.21,2.0,1.0,1.0,131903.6,0.0
-9757,15696900.0,Chukwukadibia,644.0,France,Male,46.0,3.0,146053.66,1.0,1.0,1.0,158973.77,1.0
-9758,15593715.0,Chidiebere,714.0,Germany,Male,59.0,4.0,135814.7,1.0,1.0,0.0,152298.28,1.0
-9759,15634177.0,P'eng,755.0,France,Male,43.0,3.0,0.0,2.0,0.0,1.0,97938.59,0.0
-9760,15770174.0,Hirst,733.0,Germany,Male,52.0,7.0,123082.08,1.0,1.0,0.0,143774.06,1.0
-9761,15626452.0,Ch'ien,679.0,France,Female,44.0,7.0,0.0,2.0,0.0,1.0,157959.02,0.0
-9762,15782530.0,Fitts,695.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,93719.87,0.0
-9763,15617507.0,Fanucci,725.0,France,Female,40.0,6.0,0.0,2.0,0.0,1.0,171364.15,0.0
-9764,15696852.0,Onyemauchechukwu,628.0,Germany,Female,41.0,5.0,111432.13,2.0,1.0,0.0,146851.73,0.0
-9765,15692226.0,Yeh,598.0,Spain,Male,45.0,5.0,0.0,1.0,1.0,0.0,56594.36,1.0
-9766,15583668.0,Ch'ang,769.0,Germany,Male,45.0,1.0,93798.42,1.0,0.0,1.0,137049.24,1.0
-9767,15782659.0,Chia,699.0,France,Male,34.0,8.0,0.0,1.0,1.0,1.0,113796.15,0.0
-9768,15746553.0,Folliero,468.0,France,Male,43.0,7.0,84904.45,1.0,0.0,1.0,66799.28,0.0
-9769,15732644.0,Kao,508.0,Germany,Female,42.0,10.0,121729.42,1.0,1.0,0.0,158344.63,1.0
-9770,15654901.0,Martin,535.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,136915.15,0.0
-9771,15697100.0,Yobachukwu,530.0,Spain,Male,38.0,2.0,109538.75,1.0,0.0,1.0,143938.27,0.0
-9772,15799217.0,P'eng,611.0,France,Male,39.0,10.0,150905.03,1.0,1.0,0.0,28368.37,0.0
-9773,15792463.0,Tuan,711.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,170893.87,0.0
-9774,15757916.0,H?,695.0,France,Female,35.0,3.0,0.0,1.0,1.0,0.0,177528.92,1.0
-9775,15583768.0,Nwabugwu,710.0,France,Male,32.0,7.0,141040.01,1.0,1.0,1.0,1235.49,0.0
-9776,15802351.0,Nnachetam,714.0,France,Male,40.0,6.0,0.0,2.0,1.0,1.0,136071.03,0.0
-9777,15603665.0,Onyekachi,632.0,Germany,Male,32.0,2.0,124695.72,2.0,1.0,0.0,132480.75,0.0
-9778,15747288.0,Yermakov,585.0,France,Male,35.0,4.0,95984.69,1.0,1.0,1.0,90177.3,0.0
-9779,15776947.0,De Luca,715.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,166317.71,0.0
-9780,15758056.0,Fomin,659.0,France,Female,61.0,9.0,145359.99,1.0,1.0,1.0,16081.62,0.0
-9781,15572022.0,Ch'ien,727.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,0.0,180598.32,0.0
-9782,15636397.0,Chukwujamuike,690.0,France,Male,44.0,4.0,121151.05,1.0,1.0,0.0,177896.87,0.0
-9783,15630551.0,Chidiebere,648.0,Germany,Female,43.0,1.0,133658.24,1.0,1.0,0.0,86957.42,1.0
-9784,15655175.0,Tsou,773.0,France,Female,37.0,10.0,0.0,2.0,1.0,0.0,143681.83,0.0
-9785,15644882.0,H?,850.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,153373.71,0.0
-9786,15793331.0,Hsiung,662.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,93844.67,0.0
-9787,15658813.0,Hsiao,588.0,France,Male,34.0,5.0,0.0,2.0,0.0,1.0,55313.51,0.0
-9788,15671165.0,Chinwendu,741.0,Spain,Female,24.0,8.0,0.0,2.0,1.0,1.0,169920.92,0.0
-9789,15671591.0,Hsing,850.0,Spain,Male,35.0,1.0,116576.45,1.0,1.0,1.0,160579.15,0.0
-9790,15769955.0,Lettiere,706.0,Germany,Male,41.0,8.0,88980.32,2.0,1.0,0.0,128528.83,0.0
-9791,15651352.0,Ndukaku,604.0,Spain,Male,55.0,6.0,169824.46,1.0,1.0,1.0,132798.78,1.0
-9792,15642997.0,Okwuoma,733.0,France,Female,50.0,6.0,0.0,1.0,1.0,0.0,139947.17,1.0
-9793,15681928.0,T'ien,628.0,Spain,Female,46.0,5.0,136416.82,2.0,1.0,0.0,179421.84,1.0
-9794,15772632.0,Gadsden,679.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,80587.27,0.0
-9795,15630661.0,Yobanna,632.0,France,Male,25.0,4.0,0.0,2.0,1.0,0.0,114094.73,0.0
-9796,15700854.0,Nnaife,588.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,1.0,65636.92,0.0
-9797,15582591.0,Nnamutaezinwa,474.0,France,Female,25.0,5.0,0.0,2.0,1.0,0.0,180591.32,0.0
-9798,15661380.0,Li Fonti,644.0,France,Female,52.0,1.0,0.0,1.0,1.0,1.0,158750.16,1.0
-9799,15576676.0,Chukwuemeka,599.0,Spain,Female,33.0,1.0,0.0,2.0,0.0,1.0,180800.42,0.0
-9800,15785576.0,Hs?,713.0,France,Female,56.0,7.0,117301.87,1.0,1.0,1.0,153895.33,1.0
-9801,15692971.0,Lo Duca,651.0,Spain,Female,41.0,4.0,0.0,2.0,1.0,1.0,173516.71,0.0
-9802,15603743.0,Hsiao,706.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,100995.68,0.0
-9803,15756535.0,Iadanza,550.0,France,Female,51.0,0.0,0.0,3.0,1.0,1.0,136034.32,1.0
-9804,15691543.0,Lo,557.0,Germany,Female,47.0,3.0,133616.39,1.0,1.0,0.0,166476.95,1.0
-9805,15757811.0,Toscani,687.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,85424.34,0.0
-9806,15693349.0,Hs?,716.0,France,Male,36.0,3.0,141334.43,2.0,1.0,0.0,157959.02,0.0
-9807,15621550.0,Pagnotto,577.0,France,Female,27.0,7.0,0.0,2.0,1.0,1.0,120893.07,0.0
-9808,15631436.0,Ch'ang,704.0,Germany,Female,35.0,5.0,170840.08,2.0,1.0,1.0,176743.51,0.0
-9809,15748649.0,Yeh,633.0,France,Male,31.0,7.0,79340.95,1.0,1.0,1.0,161326.73,1.0
-9810,15704770.0,Hsia,718.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,84760.32,0.0
-9811,15723169.0,Ozerova,571.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,71179.31,0.0
-9812,15686957.0,Nkemjika,731.0,Spain,Female,35.0,2.0,0.0,1.0,0.0,0.0,98413.1,1.0
-9813,15749050.0,Onyekachi,754.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,49848.86,0.0
-9814,15697948.0,Teng,626.0,Spain,Male,34.0,6.0,131778.66,1.0,1.0,1.0,142587.32,0.0
-9815,15711789.0,Tien,577.0,Germany,Male,44.0,2.0,105000.85,2.0,1.0,1.0,124489.88,0.0
-9816,15741032.0,Ch'eng,729.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,145558.42,0.0
-9817,15702715.0,Chiang,555.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,82870.75,0.0
-9818,15777315.0,Iqbal,718.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,92568.53,0.0
-9819,15708504.0,Pisano,673.0,France,Male,39.0,6.0,0.0,1.0,1.0,1.0,130789.15,0.0
-9820,15634245.0,Ch'iu,731.0,France,Male,29.0,7.0,0.0,2.0,0.0,1.0,161241.65,0.0
-9821,15789863.0,Hsieh,637.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,82967.97,0.0
-9822,15587562.0,T'ao,627.0,Spain,Male,34.0,7.0,0.0,1.0,1.0,0.0,93901.61,0.0
-9823,15807525.0,P'eng,684.0,Spain,Male,27.0,4.0,0.0,2.0,0.0,1.0,174918.46,0.0
-9824,15787322.0,Aikenhead,741.0,Germany,Male,35.0,1.0,141356.47,2.0,1.0,1.0,57589.29,0.0
-9825,15751697.0,Milani,714.0,France,Male,31.0,5.0,0.0,2.0,0.0,0.0,10054.51,0.0
-9826,15673908.0,Udinesi,705.0,France,Female,43.0,10.0,0.0,2.0,1.0,0.0,131609.48,1.0
-9827,15657535.0,Ponomaryov,573.0,France,Female,44.0,9.0,73493.17,3.0,0.0,0.0,83093.25,1.0
-9828,15753847.0,Maclean,678.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,83056.22,0.0
-9829,15764174.0,Hsia,681.0,Germany,Female,47.0,5.0,127570.93,1.0,1.0,0.0,83629.6,1.0
-9830,15698732.0,Hsing,638.0,France,Female,24.0,1.0,0.0,2.0,1.0,0.0,88838.14,0.0
-9831,15748595.0,Maughan,588.0,Spain,Male,23.0,5.0,141787.31,1.0,1.0,1.0,122964.18,0.0
-9832,15783859.0,Y?an,599.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,141756.32,0.0
-9833,15784092.0,Hsueh,659.0,France,Male,41.0,3.0,0.0,1.0,1.0,0.0,132480.75,1.0
-9834,15797760.0,Hsueh,611.0,Germany,Male,34.0,6.0,142717.84,1.0,0.0,1.0,92982.61,0.0
-9835,15759034.0,Hsia,681.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,103737.82,0.0
-9836,15656521.0,Ugochukwutubelum,449.0,France,Male,32.0,2.0,161889.79,1.0,1.0,1.0,61251.25,0.0
-9837,15635125.0,Chukwudi,758.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,116973.48,0.0
-9838,15667633.0,Hsu,599.0,France,Male,39.0,1.0,0.0,2.0,1.0,0.0,175518.31,0.0
-9839,15649086.0,Clements,659.0,Spain,Female,38.0,9.0,0.0,1.0,1.0,0.0,119882.7,1.0
-9840,15603764.0,Hsueh,595.0,France,Female,37.0,6.0,127739.35,1.0,1.0,0.0,67769.94,0.0
-9841,15796074.0,Ndukaku,686.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,83983.02,0.0
-9842,15595440.0,Mazzanti,716.0,France,Male,36.0,10.0,0.0,1.0,0.0,1.0,131807.14,0.0
-9843,15709639.0,Rowntree,706.0,Spain,Female,29.0,4.0,0.0,1.0,1.0,1.0,180862.26,0.0
-9844,15646726.0,Estrada,528.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,103511.96,0.0
-9845,15691840.0,Tsao,627.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,0.0,159418.1,0.0
-9846,15630195.0,Fanucci,721.0,Spain,Female,39.0,3.0,95252.72,1.0,1.0,1.0,61569.07,0.0
-9847,15680430.0,Mao,754.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,0.0,169763.89,1.0
-9848,15781298.0,Ch'in,649.0,France,Female,37.0,10.0,0.0,1.0,0.0,0.0,89519.47,1.0
-9849,15787655.0,Udinesi,584.0,France,Male,41.0,2.0,121702.73,1.0,1.0,1.0,120138.27,0.0
-9850,15799785.0,Nwachukwu,579.0,Germany,Male,38.0,7.0,112211.61,1.0,1.0,0.0,85431.37,0.0
-9851,15746749.0,Kao,754.0,France,Male,45.0,10.0,0.0,2.0,0.0,1.0,93883.53,0.0
-9852,15795936.0,Lo Duca,619.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,181177.9,0.0
-9853,15579891.0,Ch'iu,697.0,France,Male,60.0,9.0,0.0,1.0,1.0,0.0,89205.54,1.0
-9854,15684669.0,Hsueh,581.0,Spain,Male,32.0,7.0,0.0,2.0,0.0,0.0,151822.66,0.0
-9855,15613282.0,Fanucci,678.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,95611.47,0.0
-9856,15801699.0,Manna,842.0,Germany,Male,40.0,10.0,81742.84,2.0,1.0,1.0,163558.42,0.0
-9857,15572706.0,Tuan,569.0,France,Male,63.0,8.0,0.0,1.0,1.0,0.0,74048.1,1.0
-9858,15643819.0,Kao,535.0,Germany,Female,35.0,1.0,107712.57,2.0,1.0,0.0,151869.35,1.0
-9859,15681332.0,Nebeolisa,676.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,181419.29,0.0
-9860,15748696.0,Chimaobim,758.0,Spain,Male,42.0,1.0,0.0,1.0,1.0,0.0,147545.65,1.0
-9861,15608701.0,Tien,585.0,France,Male,27.0,3.0,0.0,2.0,1.0,1.0,96420.58,0.0
-9862,15785899.0,Hs?eh,730.0,France,Male,29.0,1.0,0.0,2.0,0.0,0.0,142548.33,0.0
-9863,15583462.0,Ugochukwu,695.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,0.0,78915.68,0.0
-9864,15723032.0,Lettiere,652.0,Germany,Female,32.0,8.0,132934.89,1.0,1.0,0.0,171706.66,0.0
-9865,15743617.0,K'ung,551.0,Germany,Male,42.0,5.0,87967.42,2.0,1.0,1.0,79957.95,0.0
-9866,15750545.0,Lu,733.0,Spain,Female,26.0,1.0,167738.82,2.0,1.0,0.0,64515.5,0.0
-9867,15737452.0,Kao,624.0,France,Male,31.0,6.0,0.0,2.0,0.0,0.0,180553.48,0.0
-9868,15679743.0,Udegbulam,697.0,France,Male,27.0,2.0,0.0,2.0,1.0,0.0,85937.47,0.0
-9869,15591130.0,Cremonesi,673.0,France,Male,36.0,4.0,0.0,2.0,0.0,1.0,124489.88,0.0
-9870,15752300.0,Ts'ai,653.0,France,Female,31.0,8.0,0.0,2.0,0.0,0.0,143783.46,0.0
-9871,15775625.0,Oluchukwu,669.0,Spain,Male,32.0,8.0,0.0,1.0,1.0,1.0,125870.5,0.0
-9872,15783097.0,Udinese,850.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,1187.88,0.0
-9873,15753969.0,Tretiakova,595.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,54865.92,0.0
-9874,15625175.0,Hs?,516.0,Germany,Male,33.0,6.0,98443.14,1.0,1.0,1.0,145551.6,0.0
-9875,15652981.0,Iloerika,649.0,France,Male,44.0,7.0,0.0,1.0,1.0,0.0,114793.45,1.0
-9876,15701280.0,Onyemauchechukwu,667.0,Spain,Male,38.0,1.0,0.0,2.0,0.0,1.0,27359.19,0.0
-9877,15706810.0,Chu,730.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,90977.48,0.0
-9878,15792729.0,Tsao,662.0,France,Female,31.0,3.0,165798.1,1.0,1.0,1.0,134901.34,0.0
-9879,15682355.0,Tien,571.0,France,Male,30.0,2.0,0.0,2.0,0.0,1.0,93081.87,0.0
-9880,15574584.0,Chinedum,844.0,France,Male,27.0,9.0,0.0,2.0,1.0,0.0,131807.14,0.0
-9881,15744368.0,Folliero,704.0,France,Female,43.0,1.0,0.0,1.0,1.0,1.0,74564.41,1.0
-9882,15656249.0,Trevisani,650.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,129890.94,0.0
-9883,15661526.0,O'Neill,707.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,79270.02,0.0
-9884,15585192.0,Nwachinemelu,535.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,14081.64,1.0
-9885,15709487.0,Nwachinemelu,542.0,Spain,Male,54.0,9.0,0.0,1.0,0.0,0.0,97767.63,1.0
-9886,15643908.0,Chienezie,662.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,167636.15,0.0
-9887,15777599.0,Ibezimako,599.0,France,Male,38.0,7.0,0.0,1.0,1.0,1.0,26265.18,0.0
-9888,15673837.0,Lai,644.0,France,Female,26.0,8.0,0.0,2.0,0.0,1.0,35644.79,0.0
-9889,15753037.0,Hsia,704.0,France,Female,39.0,4.0,0.0,2.0,1.0,0.0,51117.06,0.0
-9890,15672945.0,Ch'in,683.0,Spain,Male,35.0,8.0,127864.4,1.0,1.0,1.0,161083.53,0.0
-9891,15793331.0,Chukwuebuka,710.0,France,Male,37.0,9.0,0.0,2.0,0.0,0.0,126743.33,0.0
-9892,15682618.0,Hsia,613.0,France,Male,37.0,6.0,0.0,2.0,0.0,0.0,89048.46,0.0
-9893,15803749.0,Lu,667.0,Spain,Male,27.0,4.0,131852.81,1.0,1.0,1.0,131043.9,0.0
-9894,15649160.0,Walker,634.0,Germany,Male,37.0,9.0,121818.84,1.0,1.0,1.0,154631.35,0.0
-9895,15712176.0,T'ien,629.0,Germany,Male,41.0,1.0,139532.39,1.0,1.0,1.0,126812.63,0.0
-9896,15732269.0,Yuryeva,648.0,Spain,Male,43.0,4.0,0.0,2.0,0.0,0.0,168816.23,0.0
-9897,15773876.0,Hsueh,704.0,Germany,Female,58.0,7.0,122471.09,1.0,1.0,0.0,67311.12,1.0
-9898,15713774.0,Ch'ang,651.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,96498.14,0.0
-9899,15772479.0,Chiganu,679.0,France,Male,43.0,8.0,0.0,1.0,1.0,1.0,171463.83,0.0
-9900,15787772.0,Lettiere,669.0,France,Male,30.0,5.0,120961.5,1.0,1.0,0.0,56760.97,0.0
-9901,15648721.0,Genovesi,753.0,Germany,Male,31.0,3.0,137948.51,2.0,1.0,0.0,170886.77,0.0
-9902,15774727.0,Okeke,659.0,France,Male,19.0,9.0,0.0,2.0,1.0,0.0,170980.04,0.0
-9903,15699598.0,Lai,638.0,France,Male,33.0,7.0,126588.81,2.0,1.0,0.0,149535.97,0.0
-9904,15724444.0,Chukwuemeka,653.0,France,Male,35.0,3.0,0.0,1.0,0.0,0.0,121151.1,0.0
-9905,15585362.0,Ch'eng,735.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,199753.97,0.0
-9906,15612071.0,Milani,669.0,France,Female,39.0,8.0,0.0,2.0,0.0,1.0,138992.48,0.0
-9907,15705735.0,Ch'ien,589.0,Germany,Male,41.0,3.0,123599.49,1.0,1.0,0.0,183490.99,1.0
-9908,15677123.0,Chuang,754.0,France,Female,56.0,5.0,171069.39,1.0,0.0,1.0,124777.14,1.0
-9909,15730579.0,Chibugo,683.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,145796.49,0.0
-9910,15662488.0,Bogle,792.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,93832.33,0.0
-9911,15599251.0,White,723.0,Spain,Female,66.0,6.0,0.0,2.0,1.0,1.0,64515.5,0.0
-9912,15737047.0,Chukwueloka,697.0,Spain,Male,44.0,1.0,108465.65,1.0,1.0,1.0,173495.77,1.0
-9913,15681697.0,P'eng,725.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,125426.67,0.0
-9914,15761158.0,Chidumaga,585.0,Germany,Female,37.0,8.0,80542.81,1.0,1.0,1.0,83470.2,0.0
-9915,15566689.0,Yudina,627.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,62733.05,0.0
-9916,15710390.0,Y?,608.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,161064.29,0.0
-9917,15741032.0,Iweobiegbulam,718.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,118913.53,1.0
-9918,15603665.0,Ch'in,619.0,Spain,Male,30.0,9.0,0.0,2.0,0.0,0.0,64715.24,0.0
-9919,15621435.0,Onyemere,646.0,France,Male,39.0,9.0,0.0,2.0,0.0,0.0,85421.48,0.0
-9920,15583595.0,P'eng,678.0,Spain,Male,34.0,2.0,114330.95,1.0,0.0,1.0,153343.48,0.0
-9921,15696054.0,Tsai,496.0,France,Male,24.0,3.0,0.0,2.0,0.0,1.0,140881.2,0.0
-9922,15672826.0,Chien,678.0,Spain,Male,30.0,5.0,0.0,2.0,1.0,1.0,124964.82,1.0
-9923,15754577.0,Hsu,699.0,France,Male,32.0,3.0,111095.55,1.0,0.0,0.0,38190.78,0.0
-9924,15652693.0,Nnonso,730.0,Spain,Male,28.0,1.0,167026.61,1.0,1.0,0.0,177519.19,0.0
-9925,15596088.0,Hsiung,705.0,Spain,Male,47.0,9.0,0.0,1.0,1.0,0.0,71709.12,1.0
-9926,15643679.0,Fomin,697.0,Spain,Male,28.0,8.0,0.0,2.0,1.0,0.0,183572.39,0.0
-9927,15777576.0,Maclean,684.0,France,Female,43.0,10.0,0.0,2.0,0.0,1.0,9902.39,0.0
-9928,15718289.0,Dancy,707.0,Spain,Male,31.0,8.0,86977.96,2.0,1.0,0.0,84476.62,0.0
-9929,15646728.0,T'ien,641.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,1.0,56585.2,0.0
-9930,15768124.0,Loyau,678.0,Spain,Male,40.0,7.0,126689.71,1.0,1.0,1.0,182436.81,0.0
-9931,15784700.0,Zetticci,682.0,France,Male,27.0,3.0,0.0,2.0,1.0,0.0,148811.14,0.0
-9932,15662976.0,Zito,710.0,France,Female,62.0,3.0,0.0,2.0,0.0,1.0,65618.01,0.0
-9933,15796074.0,Trevisano,703.0,Spain,Female,32.0,6.0,122486.47,1.0,1.0,1.0,177065.24,0.0
-9934,15674583.0,Cawker,663.0,France,Female,30.0,5.0,0.0,2.0,0.0,0.0,94973.64,0.0
-9935,15755731.0,Nnaemeka,663.0,Spain,Male,35.0,10.0,0.0,3.0,1.0,0.0,38772.82,1.0
-9936,15658057.0,Kaeppel,572.0,Germany,Male,36.0,7.0,100480.53,1.0,1.0,1.0,100240.2,0.0
-9937,15672798.0,Liao,697.0,France,Male,37.0,9.0,0.0,2.0,1.0,0.0,80552.36,0.0
-9938,15595036.0,Manna,724.0,Germany,Male,48.0,3.0,118889.66,1.0,1.0,0.0,57876.05,1.0
-9939,15773869.0,Iadanza,572.0,Spain,Female,37.0,8.0,0.0,3.0,1.0,1.0,142903.44,1.0
-9940,15641715.0,Chukwufumnanya,603.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,118959.64,0.0
-9941,15578045.0,Brown,699.0,France,Female,36.0,2.0,0.0,1.0,1.0,1.0,131712.52,0.0
-9942,15643826.0,L?,676.0,France,Female,41.0,2.0,0.0,2.0,0.0,1.0,102397.22,0.0
-9943,15753861.0,Shao,721.0,Spain,Male,67.0,1.0,0.0,2.0,1.0,1.0,143317.18,0.0
-9944,15792453.0,Tien,711.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,85522.84,0.0
-9945,15763515.0,Hickey,683.0,Spain,Female,28.0,0.0,0.0,2.0,1.0,0.0,184524.65,0.0
-9946,15651713.0,T'an,850.0,Germany,Male,40.0,7.0,123575.51,1.0,1.0,0.0,160976.85,1.0
-9947,15708904.0,Maclean,624.0,France,Male,48.0,10.0,0.0,2.0,0.0,0.0,89081.41,1.0
-9948,15603879.0,Lo Duca,628.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,161579.85,0.0
-9949,15766417.0,Chuang,634.0,France,Male,46.0,5.0,108760.32,1.0,0.0,0.0,69454.24,0.0
-9950,15715208.0,Ch'ang,622.0,Spain,Male,35.0,7.0,161592.76,1.0,1.0,0.0,27149.27,0.0
-9951,15757535.0,Chukwudi,705.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,138527.56,0.0
-9952,15707025.0,Nwankwo,753.0,France,Male,36.0,0.0,0.0,2.0,1.0,1.0,49833.5,0.0
-9953,15777922.0,Tien,498.0,Spain,Male,35.0,6.0,104443.82,1.0,1.0,1.0,149991.32,0.0
-9954,15777826.0,Hsieh,594.0,Germany,Female,49.0,5.0,126436.29,1.0,0.0,0.0,122949.31,1.0
-9955,15800366.0,Teng,704.0,France,Male,30.0,7.0,0.0,2.0,0.0,0.0,183682.09,0.0
-9956,15786418.0,Nkemdilim,757.0,Germany,Female,38.0,2.0,92877.33,2.0,1.0,1.0,163798.41,0.0
-9957,15775234.0,Chukwufumnanya,684.0,Spain,Male,31.0,2.0,0.0,2.0,0.0,0.0,150720.39,0.0
-9958,15696539.0,Mazzi,659.0,Spain,Male,33.0,1.0,0.0,2.0,1.0,0.0,176743.51,0.0
-9959,15603096.0,Chien,559.0,France,Male,46.0,4.0,0.0,1.0,1.0,0.0,151335.24,1.0
-9960,15732437.0,Hsieh,683.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,141210.5,0.0
-9961,15653572.0,Chukwueloka,585.0,Spain,Male,59.0,1.0,104832.49,1.0,1.0,1.0,129964.94,0.0
-9962,15605327.0,Zetticci,576.0,France,Female,35.0,10.0,128509.63,1.0,0.0,1.0,103970.58,0.0
-9963,15753895.0,Chiganu,582.0,France,Male,28.0,4.0,98668.18,1.0,1.0,0.0,57589.29,0.0
-9964,15657566.0,Hsueh,714.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,119817.19,0.0
-9965,15803526.0,Onyekachukwu,544.0,France,Female,51.0,1.0,0.0,2.0,1.0,1.0,155700.15,0.0
-9966,15591698.0,Olisaemeka,619.0,Spain,Female,71.0,2.0,0.0,2.0,1.0,1.0,152235.3,0.0
-9967,15610226.0,Trevisano,711.0,Germany,Male,42.0,9.0,119825.75,1.0,1.0,0.0,124954.56,1.0
-9968,15806511.0,Kao,709.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,60420.07,0.0
-9969,15792646.0,Yobachi,751.0,Spain,Female,38.0,2.0,0.0,2.0,1.0,0.0,156774.75,0.0
-9970,15581229.0,Kao,794.0,France,Female,29.0,7.0,71283.09,2.0,1.0,0.0,49361.84,0.0
-9971,15600692.0,Buchi,697.0,France,Male,42.0,4.0,0.0,1.0,1.0,0.0,124071.71,1.0
-9972,15772030.0,Yefremova,651.0,Germany,Female,44.0,3.0,126442.59,1.0,1.0,0.0,155711.64,1.0
-9973,15610594.0,Li Fonti,644.0,France,Male,46.0,2.0,106511.85,1.0,1.0,1.0,143783.46,1.0
-9974,15815236.0,Hsia,543.0,Germany,Female,45.0,1.0,107714.74,1.0,1.0,0.0,152290.28,1.0
-9975,15604813.0,Macartney,724.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,171706.66,0.0
-9976,15672598.0,Calabrese,594.0,France,Male,37.0,6.0,0.0,1.0,1.0,1.0,125244.18,0.0
-9977,15798084.0,Hsia,712.0,France,Male,37.0,4.0,123835.52,1.0,1.0,0.0,75884.65,0.0
-9978,15691457.0,Ts'ui,623.0,France,Male,38.0,1.0,160971.87,1.0,1.0,1.0,87071.18,0.0
-9979,15772421.0,Genovese,587.0,France,Male,35.0,2.0,0.0,2.0,0.0,1.0,167032.99,0.0
-9980,15782089.0,Chidumaga,699.0,France,Male,43.0,4.0,0.0,2.0,1.0,0.0,116672.02,0.0
-9981,15773868.0,Onyekachi,655.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,143628.56,0.0
-9982,15668886.0,Pisano,726.0,Spain,Female,29.0,10.0,0.0,1.0,1.0,0.0,167066.95,0.0
-9983,15722583.0,Chin,715.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,0.0,171378.77,0.0
-9984,15603914.0,P'eng,653.0,France,Female,31.0,0.0,131344.52,1.0,1.0,0.0,51387.1,0.0
-9985,15677908.0,Nwachukwu,634.0,France,Male,29.0,1.0,97460.03,1.0,1.0,1.0,179797.79,0.0
-9986,15747772.0,Pisano,621.0,Germany,Female,27.0,7.0,116084.32,2.0,1.0,1.0,97623.42,0.0
-9987,15657535.0,Chiu,678.0,France,Male,28.0,2.0,0.0,2.0,0.0,0.0,126469.09,0.0
-9988,15658852.0,Chiang,705.0,Germany,Female,46.0,6.0,116537.6,1.0,1.0,0.0,102567.05,1.0
-9989,15736112.0,Kao,707.0,France,Male,30.0,8.0,0.0,1.0,1.0,0.0,118959.64,1.0
-9990,15734987.0,Chukwudi,687.0,France,Male,26.0,5.0,0.0,2.0,1.0,0.0,81753.92,0.0
-9991,15809907.0,Ch'ien,785.0,France,Male,39.0,0.0,0.0,2.0,0.0,1.0,173870.39,0.0
-9992,15634213.0,Young,645.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,128521.03,0.0
-9993,15782530.0,Hsia,684.0,France,Male,34.0,8.0,136067.82,1.0,1.0,0.0,114448.77,0.0
-9994,15585867.0,Mazzanti,545.0,France,Male,30.0,4.0,0.0,2.0,1.0,1.0,90280.7,0.0
-9995,15704910.0,Lockington,746.0,France,Male,28.0,7.0,0.0,1.0,0.0,1.0,139049.24,0.0
-9996,15768342.0,Eluemuno,745.0,France,Male,33.0,7.0,0.0,2.0,0.0,0.0,120512.08,0.0
-9997,15644878.0,Shao,516.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,92357.21,0.0
-9998,15790744.0,Ts'ui,735.0,Spain,Female,48.0,9.0,0.0,2.0,1.0,0.0,182355.21,0.0
-9999,15779947.0,Scott,712.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,72402.0,0.0
-10000,15808023.0,H?,635.0,France,Female,41.0,1.0,0.0,1.0,1.0,0.0,178779.52,1.0
-10001,15730038.0,Maclean,639.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,166957.82,0.0
-10002,15641877.0,Chukwukadibia,679.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,1.0,85890.55,0.0
-10003,15656319.0,Chimaobim,679.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,50974.57,0.0
-10004,15782096.0,Genovese,628.0,Spain,Female,35.0,1.0,0.0,1.0,1.0,1.0,28407.4,0.0
-10005,15627212.0,K'ung,731.0,France,Female,34.0,4.0,0.0,1.0,1.0,1.0,122449.15,0.0
-10006,15568897.0,Udinese,486.0,Germany,Female,29.0,6.0,131778.66,1.0,1.0,0.0,151146.67,0.0
-10007,15733661.0,Ndubueze,598.0,France,Male,31.0,9.0,0.0,2.0,0.0,1.0,80582.36,0.0
-10008,15692664.0,Chu,672.0,France,Female,33.0,8.0,0.0,2.0,1.0,1.0,51387.1,0.0
-10009,15776545.0,Chia,564.0,France,Male,47.0,1.0,0.0,3.0,1.0,0.0,171922.72,1.0
-10010,15806922.0,H?,573.0,Germany,Female,29.0,2.0,90432.92,2.0,1.0,0.0,71825.23,0.0
-10011,15774586.0,Fink,756.0,France,Female,43.0,1.0,112045.67,1.0,1.0,0.0,73977.23,1.0
-10012,15604345.0,Mollison,627.0,France,Male,61.0,2.0,0.0,1.0,1.0,1.0,48228.1,1.0
-10013,15662884.0,H?,692.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,60590.72,0.0
-10014,15797736.0,Teng,727.0,Spain,Male,35.0,5.0,143054.85,1.0,1.0,0.0,92708.1,0.0
-10015,15682600.0,Okeke,649.0,Germany,Male,32.0,3.0,132057.92,1.0,1.0,0.0,114514.64,0.0
-10016,15724892.0,Okwudilichukwu,659.0,Germany,Male,44.0,4.0,128960.21,1.0,1.0,1.0,82782.08,1.0
-10017,15700864.0,Padovesi,663.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,0.0,100816.29,0.0
-10018,15712121.0,Chukwualuka,592.0,Germany,Female,50.0,2.0,135096.05,2.0,1.0,0.0,152883.95,0.0
-10019,15674840.0,H?,762.0,Spain,Male,54.0,4.0,0.0,1.0,0.0,1.0,26327.42,1.0
-10020,15567383.0,Chukwujamuike,544.0,France,Female,43.0,5.0,0.0,2.0,1.0,1.0,78730.31,0.0
-10021,15790744.0,Chibueze,697.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,140941.47,0.0
-10022,15682540.0,Chikezie,447.0,France,Male,39.0,9.0,148061.49,1.0,0.0,0.0,169415.54,0.0
-10023,15655171.0,Chinweike,683.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,122925.44,0.0
-10024,15749472.0,Ugorji,588.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,151924.9,0.0
-10025,15772995.0,Moseley,597.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,171552.02,0.0
-10026,15675320.0,Ts'ui,613.0,France,Male,27.0,1.0,0.0,2.0,1.0,0.0,49503.5,0.0
-10027,15791268.0,Onwuatuegwu,729.0,Spain,Female,31.0,4.0,0.0,2.0,1.0,0.0,170621.55,0.0
-10028,15759913.0,Pinto,705.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,131736.23,0.0
-10029,15785815.0,P'eng,705.0,Spain,Female,46.0,1.0,0.0,1.0,0.0,0.0,133831.37,1.0
-10030,15593725.0,Manna,707.0,Germany,Female,26.0,4.0,127948.51,1.0,0.0,0.0,155700.15,1.0
-10031,15584518.0,Ting,850.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,162478.11,0.0
-10032,15690796.0,Chidumaga,539.0,Germany,Male,49.0,6.0,114233.18,1.0,0.0,1.0,170886.01,1.0
-10033,15774164.0,Ch'iu,583.0,France,Female,50.0,6.0,0.0,2.0,1.0,0.0,71179.31,0.0
-10034,15704583.0,Whitelegge,653.0,France,Female,31.0,3.0,0.0,2.0,0.0,0.0,180326.83,0.0
-10035,15637138.0,Chiemenam,516.0,Spain,Male,27.0,9.0,0.0,2.0,1.0,0.0,97767.38,0.0
-10036,15642787.0,Ritchie,719.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,1.0,93883.53,0.0
-10037,15668818.0,Ngozichukwuka,558.0,France,Male,32.0,4.0,101060.25,1.0,1.0,1.0,160574.09,0.0
-10038,15654851.0,Yermakova,656.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,158750.24,0.0
-10039,15709256.0,Hsia,712.0,Spain,Male,30.0,6.0,116465.53,1.0,1.0,1.0,110114.19,0.0
-10040,15578980.0,Kambinachi,662.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,86022.09,0.0
-10041,15683522.0,Trevisano,809.0,Germany,Male,35.0,4.0,93616.35,2.0,1.0,0.0,99097.67,0.0
-10042,15772479.0,Mao,675.0,France,Female,48.0,3.0,0.0,1.0,1.0,0.0,173599.38,1.0
-10043,15788776.0,Ch'ien,642.0,Spain,Female,43.0,5.0,152992.91,1.0,1.0,0.0,57553.02,0.0
-10044,15783030.0,Frolova,706.0,Spain,Male,48.0,2.0,0.0,2.0,0.0,0.0,121562.33,0.0
-10045,15594404.0,Tsao,648.0,Germany,Female,43.0,3.0,113909.67,2.0,1.0,1.0,186785.01,0.0
-10046,15570360.0,Onyemachukwu,686.0,France,Female,30.0,9.0,0.0,2.0,1.0,0.0,183585.01,0.0
-10047,15796790.0,Zaytseva,850.0,Spain,Male,24.0,7.0,0.0,2.0,1.0,1.0,122518.5,0.0
-10048,15807008.0,Hao,713.0,Germany,Female,40.0,4.0,127806.91,2.0,0.0,0.0,92865.46,0.0
-10049,15723013.0,Pardey,683.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,171096.2,0.0
-10050,15726403.0,Pagnotto,705.0,Spain,Female,31.0,6.0,96951.21,2.0,1.0,0.0,71492.28,0.0
-10051,15625905.0,Folliero,614.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,114566.47,0.0
-10052,15593470.0,Chukwueloka,605.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,48559.19,0.0
-10053,15665062.0,Horsley,669.0,France,Female,64.0,3.0,0.0,1.0,1.0,0.0,152577.62,1.0
-10054,15579062.0,Hsing,653.0,Germany,Male,37.0,7.0,119370.11,2.0,1.0,1.0,163365.85,0.0
-10055,15643048.0,Martin,678.0,Spain,Female,25.0,8.0,0.0,2.0,1.0,0.0,158037.76,0.0
-10056,15759991.0,Trentino,697.0,Germany,Male,43.0,4.0,151855.33,2.0,1.0,1.0,110593.62,1.0
-10057,15585595.0,Nwachukwu,647.0,Germany,Female,69.0,4.0,123210.55,1.0,1.0,1.0,175743.92,0.0
-10058,15583849.0,Nwankwo,614.0,Spain,Female,46.0,9.0,0.0,1.0,1.0,0.0,137793.35,1.0
-10059,15612043.0,Milanesi,836.0,Spain,Male,64.0,5.0,120599.21,1.0,1.0,1.0,56636.28,0.0
-10060,15636767.0,Fyans,652.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,180595.02,0.0
-10061,15744105.0,K?,678.0,France,Male,47.0,1.0,0.0,1.0,1.0,1.0,187597.98,1.0
-10062,15702789.0,L?,706.0,France,Female,48.0,6.0,0.0,1.0,1.0,0.0,143598.7,1.0
-10063,15695078.0,Ch'in,613.0,France,Male,41.0,5.0,0.0,2.0,1.0,0.0,182728.8,0.0
-10064,15724307.0,Chinweuba,628.0,Germany,Male,32.0,1.0,113736.27,1.0,1.0,0.0,181598.25,0.0
-10065,15714240.0,Lu,805.0,France,Female,33.0,8.0,0.0,2.0,0.0,0.0,100598.98,0.0
-10066,15798895.0,Genovese,709.0,Germany,Female,37.0,4.0,129855.72,2.0,1.0,0.0,122506.78,1.0
-10067,15720353.0,Okeke,559.0,France,Female,39.0,9.0,0.0,2.0,0.0,0.0,74878.12,0.0
-10068,15691875.0,Yobachi,588.0,Spain,Female,25.0,8.0,0.0,2.0,1.0,0.0,167087.34,0.0
-10069,15667588.0,Martin,724.0,Germany,Female,47.0,3.0,114754.08,2.0,1.0,0.0,156878.42,0.0
-10070,15777873.0,Hsueh,753.0,France,Female,42.0,3.0,0.0,1.0,0.0,0.0,93336.3,1.0
-10071,15593900.0,Chiedozie,727.0,Germany,Male,38.0,10.0,139762.13,2.0,1.0,0.0,131736.23,0.0
-10072,15702919.0,Manna,579.0,France,Male,33.0,9.0,160980.02,1.0,0.0,1.0,134878.34,0.0
-10073,15803639.0,Milne,593.0,France,Female,54.0,0.0,0.0,1.0,1.0,0.0,177738.98,1.0
-10074,15624995.0,Calabrese,774.0,France,Male,20.0,2.0,0.0,2.0,1.0,0.0,174815.54,0.0
-10075,15586870.0,Teng,696.0,France,Male,40.0,2.0,0.0,1.0,1.0,1.0,61312.15,0.0
-10076,15635388.0,Milano,579.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,96670.2,0.0
-10077,15709941.0,Goliwe,627.0,France,Female,30.0,9.0,0.0,2.0,1.0,0.0,93883.87,0.0
-10078,15787222.0,Nucci,535.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,167984.72,0.0
-10079,15701176.0,H?,850.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,181605.85,0.0
-10080,15632210.0,Yermakov,646.0,France,Female,32.0,4.0,0.0,2.0,1.0,1.0,63736.36,0.0
-10081,15779176.0,Hsu,591.0,France,Male,42.0,1.0,0.0,2.0,1.0,1.0,96498.14,0.0
-10082,15769955.0,Pirozzi,644.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,129890.94,0.0
-10083,15683625.0,Toscani,582.0,Germany,Male,22.0,6.0,143571.52,2.0,0.0,1.0,118862.08,0.0
-10084,15635502.0,T'ao,682.0,Germany,Female,32.0,3.0,113383.05,1.0,1.0,0.0,141872.05,0.0
-10085,15798943.0,Nwabugwu,531.0,France,Female,33.0,1.0,141749.68,1.0,1.0,1.0,129740.11,0.0
-10086,15659677.0,Yobachi,613.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,149913.84,0.0
-10087,15794056.0,Onwuamaeze,645.0,France,Male,52.0,6.0,0.0,1.0,0.0,1.0,110317.39,0.0
-10088,15665088.0,Shih,651.0,Spain,Female,71.0,4.0,0.0,2.0,1.0,1.0,174431.01,0.0
-10089,15717116.0,Ebelegbulam,758.0,Spain,Female,29.0,9.0,110949.41,1.0,1.0,0.0,80416.16,0.0
-10090,15688071.0,Kibby,593.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,52876.41,0.0
-10091,15625082.0,Ejimofor,745.0,France,Female,31.0,5.0,83456.79,1.0,1.0,1.0,164925.58,0.0
-10092,15636016.0,Kao,683.0,Spain,Male,34.0,2.0,0.0,2.0,0.0,0.0,121866.87,0.0
-10093,15769783.0,Aitken,627.0,Germany,Male,45.0,2.0,116270.72,1.0,0.0,1.0,162643.07,0.0
-10094,15725028.0,Hsiung,819.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,9983.88,0.0
-10095,15583073.0,Ts'ao,710.0,Germany,Male,40.0,6.0,108881.73,1.0,0.0,0.0,156911.35,0.0
-10096,15791323.0,Yuriev,479.0,Germany,Male,51.0,4.0,132412.34,1.0,1.0,0.0,8982.75,1.0
-10097,15574072.0,Hsu,633.0,France,Female,39.0,4.0,0.0,2.0,1.0,1.0,187718.26,0.0
-10098,15603743.0,Ch'ien,653.0,France,Female,43.0,6.0,0.0,1.0,1.0,0.0,161519.76,1.0
-10099,15616122.0,H?,656.0,Spain,Male,28.0,3.0,107556.06,1.0,1.0,0.0,160546.58,0.0
-10100,15615444.0,Maclean,646.0,France,Male,32.0,5.0,135096.58,1.0,1.0,1.0,61936.1,0.0
-10101,15572631.0,Folliero,565.0,France,Male,41.0,8.0,131513.26,1.0,1.0,0.0,84531.79,0.0
-10102,15585241.0,Pirozzi,778.0,Spain,Female,31.0,4.0,0.0,1.0,1.0,0.0,162820.07,0.0
-10103,15590177.0,Lucciano,619.0,Spain,Female,33.0,8.0,123971.16,1.0,1.0,0.0,119759.24,0.0
-10104,15576077.0,Galkina,719.0,Germany,Female,49.0,3.0,79562.98,2.0,1.0,1.0,171060.01,1.0
-10105,15774882.0,Yobachi,560.0,Spain,Male,44.0,9.0,87926.24,2.0,1.0,0.0,67926.18,0.0
-10106,15624641.0,Zikoranachidimma,627.0,France,Female,35.0,0.0,0.0,2.0,1.0,0.0,133086.45,0.0
-10107,15603481.0,Ts'ui,757.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,126554.54,0.0
-10108,15762927.0,Hsia,676.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,123880.19,0.0
-10109,15674928.0,DeRose,693.0,France,Female,38.0,10.0,0.0,2.0,1.0,0.0,15039.41,0.0
-10110,15736413.0,Chien,628.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,125426.67,0.0
-10111,15576676.0,Ch'ang,594.0,Spain,Male,23.0,7.0,0.0,2.0,1.0,0.0,80587.31,0.0
-10112,15594720.0,Chikezie,627.0,France,Female,32.0,2.0,131647.01,1.0,1.0,0.0,152698.24,0.0
-10113,15652999.0,Iadanza,611.0,Germany,Male,38.0,7.0,98986.28,1.0,1.0,1.0,189428.75,0.0
-10114,15685826.0,Okoli,693.0,Germany,Female,50.0,1.0,152674.51,1.0,1.0,1.0,86701.4,1.0
-10115,15764343.0,Tsai,731.0,France,Female,39.0,8.0,0.0,1.0,1.0,0.0,64350.6,1.0
-10116,15662091.0,Onio,634.0,Germany,Male,40.0,3.0,122388.38,1.0,1.0,0.0,179866.01,1.0
-10117,15778947.0,Chiawuotu,703.0,Germany,Female,37.0,5.0,122570.69,2.0,1.0,0.0,144290.97,1.0
-10118,15595071.0,Hs?eh,644.0,Germany,Male,50.0,5.0,96147.55,1.0,1.0,0.0,122996.76,1.0
-10119,15682357.0,Chu,581.0,Germany,Male,38.0,9.0,145536.9,2.0,1.0,1.0,138380.11,0.0
-10120,15682463.0,Teng,624.0,France,Male,36.0,1.0,0.0,1.0,0.0,1.0,16081.62,0.0
-10121,15723169.0,Nwachinemelu,745.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,143668.58,0.0
-10122,15702656.0,P'an,535.0,Spain,Female,24.0,3.0,0.0,2.0,1.0,1.0,129964.94,0.0
-10123,15603851.0,Tien,721.0,France,Male,44.0,8.0,0.0,1.0,1.0,1.0,110782.88,1.0
-10124,15804052.0,Shih,590.0,Spain,Female,24.0,2.0,0.0,1.0,1.0,0.0,104786.59,1.0
-10125,15791174.0,T'ien,710.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,0.0,120108.72,0.0
-10126,15784923.0,Fyodorov,678.0,France,Female,28.0,9.0,0.0,1.0,1.0,1.0,60416.76,0.0
-10127,15581299.0,T'ao,613.0,Spain,Male,34.0,1.0,0.0,1.0,1.0,0.0,158043.11,0.0
-10128,15651802.0,Onyekachukwu,751.0,Germany,Female,53.0,4.0,102085.72,2.0,1.0,1.0,123982.14,0.0
-10129,15640258.0,Chidiebere,572.0,France,Female,26.0,2.0,0.0,2.0,1.0,1.0,168515.61,0.0
-10130,15684516.0,Hsueh,602.0,Germany,Male,41.0,2.0,127818.52,1.0,1.0,0.0,136620.64,1.0
-10131,15688865.0,Toscani,643.0,Germany,Male,40.0,9.0,71026.77,2.0,1.0,1.0,117023.08,0.0
-10132,15763093.0,Tuan,593.0,France,Female,32.0,2.0,102768.42,1.0,1.0,1.0,71865.31,0.0
-10133,15757981.0,Scott,562.0,Germany,Male,39.0,5.0,142289.28,1.0,1.0,1.0,92814.92,0.0
-10134,15604588.0,T'ien,775.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,65612.12,0.0
-10135,15747168.0,Fiorentino,675.0,Spain,Female,33.0,8.0,0.0,1.0,0.0,1.0,193009.21,0.0
-10136,15756894.0,Nwabugwu,615.0,France,Male,24.0,4.0,62833.86,1.0,1.0,1.0,153342.38,0.0
-10137,15609977.0,Crawford,732.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,65765.22,0.0
-10138,15737795.0,Hs?,652.0,Spain,Male,37.0,1.0,102787.42,1.0,1.0,0.0,149991.32,0.0
-10139,15623202.0,Nkemdirim,738.0,Germany,Male,37.0,1.0,108926.02,2.0,1.0,1.0,138536.09,0.0
-10140,15738954.0,Dyer,559.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,43943.09,0.0
-10141,15782688.0,T'ien,587.0,France,Male,32.0,5.0,0.0,2.0,1.0,1.0,25183.01,0.0
-10142,15752731.0,Ch'ien,558.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,83910.4,0.0
-10143,15756871.0,Hsing,741.0,France,Male,40.0,6.0,0.0,2.0,0.0,0.0,167573.06,0.0
-10144,15782530.0,Chubb,731.0,Germany,Female,22.0,6.0,94493.03,2.0,1.0,1.0,47777.15,0.0
-10145,15651958.0,Palerma,603.0,France,Male,47.0,8.0,0.0,2.0,0.0,0.0,143022.31,0.0
-10146,15654506.0,Chiemenam,708.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,107964.35,0.0
-10147,15804862.0,Chukwumaobim,678.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,113557.7,0.0
-10148,15809837.0,Ts'ai,749.0,France,Male,35.0,3.0,91893.32,1.0,1.0,0.0,174777.98,0.0
-10149,15626233.0,Udegbunam,677.0,France,Male,27.0,5.0,0.0,2.0,1.0,1.0,151316.37,0.0
-10150,15566708.0,Palerma,636.0,Germany,Female,28.0,1.0,113034.63,2.0,1.0,0.0,100601.73,0.0
-10151,15763898.0,Chineze,711.0,France,Female,42.0,2.0,109949.05,1.0,1.0,1.0,26136.13,1.0
-10152,15760446.0,Chukwudi,657.0,France,Male,38.0,7.0,87739.08,1.0,0.0,0.0,152880.55,0.0
-10153,15791045.0,Uspenskaya,663.0,Spain,Female,40.0,3.0,0.0,1.0,1.0,0.0,166825.57,1.0
-10154,15611756.0,Ch'eng,689.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,160128.99,0.0
-10155,15739366.0,Kenechi,554.0,Spain,Female,38.0,3.0,0.0,2.0,1.0,1.0,66810.48,0.0
-10156,15662494.0,Chukwumaobim,686.0,France,Female,35.0,4.0,80764.03,2.0,1.0,1.0,126477.41,0.0
-10157,15804822.0,Palerma,741.0,Spain,Female,33.0,5.0,104339.56,1.0,1.0,1.0,60416.76,1.0
-10158,15692291.0,Hsia,732.0,France,Male,48.0,9.0,0.0,1.0,1.0,0.0,187841.89,1.0
-10159,15594915.0,Ugochukwutubelum,696.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,81450.97,0.0
-10160,15612095.0,Smith,543.0,Spain,Male,40.0,9.0,0.0,2.0,1.0,0.0,176780.69,0.0
-10161,15671779.0,Chukwualuka,686.0,Germany,Male,50.0,5.0,163723.41,1.0,1.0,0.0,28257.89,1.0
-10162,15573077.0,Efimov,710.0,Germany,Female,29.0,4.0,116927.89,1.0,1.0,1.0,60905.51,1.0
-10163,15643826.0,Nkemdirim,706.0,Germany,Male,53.0,4.0,106913.43,2.0,1.0,0.0,82393.08,1.0
-10164,15734461.0,Mazzanti,850.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,72950.14,0.0
-10165,15598883.0,Milanesi,673.0,Spain,Male,41.0,7.0,0.0,2.0,1.0,1.0,143952.24,0.0
-10166,15614361.0,Chukwuemeka,697.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,180551.76,0.0
-10167,15644962.0,Shih,659.0,Germany,Female,48.0,5.0,148045.35,1.0,0.0,0.0,114514.64,1.0
-10168,15784491.0,Chukwuebuka,704.0,France,Female,27.0,6.0,0.0,2.0,1.0,0.0,182609.97,0.0
-10169,15673055.0,P'eng,624.0,Germany,Male,40.0,4.0,127948.51,1.0,1.0,0.0,159800.16,0.0
-10170,15639004.0,Hsiung,807.0,France,Female,24.0,4.0,0.0,1.0,1.0,1.0,60941.27,1.0
-10171,15686885.0,Manna,743.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,160976.85,0.0
-10172,15792228.0,Nkemdirim,730.0,France,Female,44.0,8.0,155413.07,1.0,1.0,1.0,126475.79,1.0
-10173,15591429.0,Ch'iu,705.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,0.0,71672.86,0.0
-10174,15698732.0,Chidimma,724.0,Spain,Male,47.0,3.0,0.0,1.0,1.0,1.0,102233.73,0.0
-10175,15766017.0,Chukwualuka,613.0,France,Female,36.0,2.0,72948.58,2.0,0.0,0.0,167573.06,0.0
-10176,15739692.0,Chiu,682.0,France,Male,43.0,7.0,0.0,2.0,0.0,1.0,84932.4,0.0
-10177,15646563.0,Blythe,727.0,Spain,Male,33.0,9.0,123601.56,1.0,1.0,0.0,131647.31,0.0
-10178,15776824.0,O'Sullivan,649.0,Germany,Male,42.0,7.0,109949.05,2.0,1.0,1.0,176580.93,0.0
-10179,15645496.0,Yegorova,564.0,Germany,Male,52.0,8.0,104336.43,1.0,1.0,0.0,121747.96,1.0
-10180,15600575.0,Tsao,724.0,France,Male,36.0,3.0,0.0,1.0,1.0,0.0,103832.58,0.0
-10181,15684861.0,Hsieh,792.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,1.0,48502.88,0.0
-10182,15798975.0,Nwankwo,613.0,France,Female,58.0,8.0,171787.84,1.0,1.0,0.0,79270.02,1.0
-10183,15624374.0,Ch'in,521.0,France,Female,42.0,9.0,0.0,2.0,1.0,0.0,136909.87,0.0
-10184,15665629.0,Smith,850.0,France,Male,40.0,3.0,95507.12,1.0,1.0,1.0,161848.03,1.0
-10185,15741629.0,Atherton,600.0,Spain,Male,35.0,9.0,0.0,2.0,1.0,1.0,86957.42,0.0
-10186,15606076.0,Zetticci,704.0,Spain,Female,37.0,7.0,0.0,1.0,1.0,1.0,179908.71,0.0
-10187,15642570.0,Onwumelu,624.0,France,Male,32.0,3.0,0.0,1.0,1.0,1.0,131712.56,0.0
-10188,15661349.0,Chidiegwu,766.0,Spain,Male,46.0,6.0,0.0,2.0,1.0,1.0,64872.33,0.0
-10189,15649024.0,Ts'ai,807.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,0.0,171060.01,0.0
-10190,15697948.0,Chukwueloka,584.0,Germany,Female,36.0,8.0,110480.48,1.0,1.0,1.0,158891.02,1.0
-10191,15800440.0,Y?,636.0,Spain,Male,46.0,0.0,0.0,1.0,1.0,0.0,161243.09,1.0
-10192,15791535.0,K'ung,574.0,France,Female,34.0,2.0,57530.06,1.0,0.0,1.0,171020.09,0.0
-10193,15591607.0,Obiajulu,712.0,Spain,Male,66.0,2.0,0.0,2.0,1.0,1.0,51548.14,0.0
-10194,15692599.0,Nwoye,684.0,Germany,Male,30.0,8.0,112670.48,1.0,1.0,0.0,127569.8,0.0
-10195,15787884.0,Teng,516.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,163989.66,0.0
-10196,15625941.0,T'ien,714.0,France,Female,51.0,10.0,0.0,2.0,1.0,1.0,40318.33,0.0
-10197,15681081.0,Liao,652.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,163517.71,0.0
-10198,15605059.0,Ch'en,678.0,Spain,Female,52.0,3.0,0.0,1.0,1.0,1.0,176882.08,1.0
-10199,15731519.0,Iweobiegbunam,707.0,France,Male,36.0,10.0,143680.47,2.0,1.0,1.0,157871.55,1.0
-10200,15598510.0,Milne,635.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,166476.95,0.0
-10201,15585255.0,Shih,684.0,France,Female,38.0,6.0,0.0,1.0,0.0,1.0,97939.4,0.0
-10202,15790683.0,Fanucci,637.0,Germany,Female,39.0,5.0,137657.71,1.0,0.0,1.0,155722.66,1.0
-10203,15773394.0,Iheatu,711.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,38196.24,0.0
-10204,15592999.0,Li,718.0,Germany,Male,38.0,1.0,125437.88,1.0,1.0,1.0,86937.2,0.0
-10205,15788448.0,Chukwudi,678.0,Spain,Male,29.0,6.0,0.0,2.0,1.0,1.0,105618.14,0.0
-10206,15762682.0,Genovese,850.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,64716.08,0.0
-10207,15646615.0,Chukwukere,588.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,71178.09,0.0
-10208,15733114.0,Chiu,828.0,France,Female,27.0,2.0,0.0,2.0,1.0,1.0,109638.78,0.0
-10209,15743893.0,Ch'in,671.0,Spain,Female,38.0,10.0,0.0,2.0,1.0,0.0,143433.03,0.0
-10210,15679983.0,Niu,850.0,Spain,Female,40.0,6.0,142929.16,3.0,0.0,1.0,103570.8,1.0
-10211,15672965.0,Macleod,599.0,Germany,Female,45.0,0.0,114512.59,1.0,1.0,1.0,178291.09,0.0
-10212,15784923.0,Tsao,624.0,Spain,Male,51.0,5.0,0.0,2.0,0.0,1.0,88890.07,0.0
-10213,15658852.0,Uwakwe,745.0,Germany,Male,30.0,2.0,121276.32,2.0,1.0,0.0,154486.01,0.0
-10214,15732235.0,Onyemauchechukwu,675.0,France,Male,41.0,8.0,0.0,2.0,0.0,1.0,123982.14,0.0
-10215,15812647.0,Trevisano,710.0,Germany,Male,34.0,2.0,88815.56,1.0,0.0,1.0,172646.88,0.0
-10216,15770039.0,Gant,644.0,France,Male,39.0,8.0,0.0,1.0,1.0,1.0,48233.75,0.0
-10217,15716609.0,Chukwualuka,719.0,France,Female,63.0,6.0,0.0,2.0,1.0,1.0,172495.81,0.0
-10218,15583595.0,Doyne,682.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,161407.48,0.0
-10219,15787597.0,Lai,605.0,France,Male,37.0,3.0,124577.33,1.0,1.0,0.0,177524.19,0.0
-10220,15678886.0,Liao,696.0,Spain,Male,36.0,5.0,92833.89,1.0,1.0,1.0,105983.09,0.0
-10221,15749489.0,Nucci,597.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,61326.43,0.0
-10222,15698749.0,T'ang,648.0,France,Female,57.0,3.0,0.0,1.0,0.0,1.0,93844.82,1.0
-10223,15671390.0,Tretiakova,732.0,France,Female,36.0,9.0,0.0,2.0,0.0,1.0,182566.76,0.0
-10224,15711571.0,Ch'ang,774.0,France,Female,35.0,3.0,116270.72,1.0,1.0,0.0,179436.6,0.0
-10225,15686983.0,Maclean,651.0,France,Female,45.0,7.0,0.0,1.0,1.0,1.0,54495.82,1.0
-10226,15804919.0,Cremonesi,705.0,France,Female,38.0,9.0,143350.41,1.0,0.0,0.0,152855.96,0.0
-10227,15685920.0,Toscani,706.0,Germany,Male,40.0,5.0,117700.86,1.0,1.0,0.0,140000.29,0.0
-10228,15794560.0,Goliwe,652.0,France,Female,32.0,3.0,0.0,1.0,0.0,1.0,183682.09,1.0
-10229,15707968.0,Zito,712.0,Germany,Male,34.0,7.0,128306.7,1.0,0.0,0.0,146637.45,1.0
-10230,15780650.0,Yermolayev,637.0,Spain,Female,27.0,8.0,0.0,2.0,1.0,1.0,181276.22,0.0
-10231,15802362.0,Chien,596.0,France,Male,54.0,9.0,115743.6,1.0,1.0,0.0,151146.67,1.0
-10232,15575417.0,Brown,571.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,1.0,139953.94,0.0
-10233,15705890.0,Udinesi,535.0,Germany,Male,43.0,9.0,129717.3,1.0,0.0,0.0,53477.76,1.0
-10234,15594862.0,Mazzanti,678.0,France,Male,39.0,0.0,0.0,2.0,1.0,0.0,101778.9,0.0
-10235,15585835.0,Hsu,549.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,163798.41,0.0
-10236,15749130.0,Zito,619.0,France,Female,48.0,1.0,0.0,1.0,1.0,1.0,106920.57,1.0
-10237,15780144.0,Nkemakolam,748.0,Germany,Female,49.0,8.0,119787.76,1.0,1.0,1.0,160532.88,1.0
-10238,15709511.0,Chukwuebuka,590.0,France,Female,32.0,7.0,0.0,2.0,1.0,1.0,38570.13,0.0
-10239,15718789.0,Hs?,732.0,France,Female,37.0,9.0,145521.07,1.0,1.0,0.0,64448.36,1.0
-10240,15657439.0,Milani,656.0,Germany,Female,43.0,9.0,121543.26,1.0,1.0,0.0,100403.05,1.0
-10241,15571221.0,Chiganu,554.0,Spain,Male,44.0,4.0,115636.05,1.0,1.0,1.0,71682.54,0.0
-10242,15797767.0,Tuan,670.0,Spain,Female,38.0,2.0,122453.97,2.0,1.0,1.0,51919.04,0.0
-10243,15683677.0,Chidiebele,581.0,Germany,Female,39.0,9.0,107871.72,2.0,0.0,1.0,180142.42,0.0
-10244,15621210.0,Hs?,589.0,Germany,Female,40.0,1.0,142406.43,2.0,1.0,1.0,172096.08,0.0
-10245,15757425.0,Pinto,683.0,France,Male,41.0,7.0,105795.9,1.0,1.0,0.0,69079.85,0.0
-10246,15768574.0,Chukwuemeka,711.0,France,Male,33.0,8.0,0.0,2.0,0.0,0.0,131647.31,0.0
-10247,15802478.0,Hao,686.0,Germany,Female,52.0,1.0,130169.41,3.0,1.0,0.0,108699.59,1.0
-10248,15736397.0,Tuan,746.0,Spain,Male,58.0,8.0,81818.49,1.0,1.0,1.0,28184.7,0.0
-10249,15595537.0,Ch'iu,554.0,Spain,Female,46.0,2.0,0.0,2.0,1.0,0.0,100101.06,0.0
-10250,15625023.0,Udegbulam,713.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,0.0,16250.61,0.0
-10251,15578885.0,T'ien,810.0,France,Male,43.0,9.0,0.0,3.0,0.0,1.0,85547.33,1.0
-10252,15578515.0,Milne,675.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,163477.22,0.0
-10253,15752626.0,Yudin,597.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,143598.7,0.0
-10254,15762682.0,Chukwualuka,632.0,Germany,Male,41.0,2.0,130682.11,2.0,1.0,0.0,72318.77,0.0
-10255,15582335.0,Chiekwugo,684.0,France,Female,42.0,2.0,0.0,1.0,1.0,0.0,116969.89,1.0
-10256,15700174.0,Chukwuebuka,543.0,Spain,Male,38.0,2.0,98373.26,1.0,1.0,0.0,95727.31,0.0
-10257,15751203.0,Rizzo,590.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,83629.6,0.0
-10258,15614813.0,Lucchesi,850.0,France,Female,43.0,10.0,0.0,2.0,1.0,1.0,136526.12,0.0
-10259,15726167.0,Chia,792.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,81776.84,0.0
-10260,15596972.0,T'ao,753.0,Spain,Female,29.0,5.0,0.0,2.0,0.0,0.0,117038.96,0.0
-10261,15650391.0,Y?an,716.0,Spain,Male,43.0,3.0,129502.49,1.0,1.0,0.0,141404.08,0.0
-10262,15646616.0,Lo,652.0,Spain,Male,33.0,7.0,122038.34,1.0,1.0,0.0,141753.97,0.0
-10263,15622117.0,Oluchukwu,646.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,50695.97,0.0
-10264,15617476.0,Aitken,656.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,182784.11,0.0
-10265,15641640.0,Chizuoke,663.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,130789.15,0.0
-10266,15723320.0,Chidiegwu,579.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,162719.69,0.0
-10267,15598883.0,Chiu,753.0,France,Male,21.0,3.0,151963.26,1.0,1.0,1.0,164741.5,0.0
-10268,15794964.0,Lucchese,538.0,France,Male,48.0,5.0,0.0,1.0,1.0,0.0,91086.73,1.0
-10269,15690175.0,Nkemakonam,663.0,France,Female,38.0,2.0,0.0,2.0,1.0,0.0,143450.61,0.0
-10270,15621205.0,Yermolayev,711.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,176945.96,0.0
-10271,15791302.0,Walker,565.0,Germany,Male,40.0,8.0,124576.65,1.0,0.0,0.0,176768.59,1.0
-10272,15771483.0,P'eng,636.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,7900.46,0.0
-10273,15702541.0,Chuang,592.0,France,Male,32.0,5.0,0.0,1.0,0.0,1.0,75825.57,0.0
-10274,15798429.0,Tung,744.0,France,Male,30.0,1.0,0.0,1.0,1.0,1.0,96832.82,0.0
-10275,15761497.0,Yefremov,674.0,France,Female,36.0,4.0,102962.65,1.0,1.0,1.0,106912.35,0.0
-10276,15584536.0,Kao,545.0,Germany,Female,45.0,2.0,126619.27,3.0,0.0,1.0,104775.7,1.0
-10277,15757837.0,Ch'ang,619.0,France,Male,39.0,9.0,152086.15,2.0,1.0,0.0,115719.08,0.0
-10278,15581871.0,Aksyonov,628.0,France,Male,43.0,8.0,0.0,2.0,1.0,0.0,147040.25,0.0
-10279,15754952.0,Fanucci,627.0,Spain,Female,27.0,4.0,181671.16,1.0,1.0,0.0,88815.25,0.0
-10280,15761023.0,Ts'ui,553.0,France,Female,28.0,9.0,0.0,2.0,1.0,0.0,172097.95,0.0
-10281,15703614.0,Tung,667.0,France,Male,32.0,8.0,0.0,2.0,0.0,1.0,61636.22,0.0
-10282,15650670.0,Onyemere,743.0,France,Male,47.0,6.0,0.0,1.0,0.0,0.0,118862.08,1.0
-10283,15662658.0,Onwuamaeze,718.0,Germany,Male,41.0,6.0,139822.41,1.0,1.0,1.0,172459.39,0.0
-10284,15700627.0,Cremonesi,743.0,Germany,Female,36.0,8.0,71142.77,2.0,1.0,1.0,57975.8,0.0
-10285,15757837.0,Chukwuemeka,635.0,Spain,Male,36.0,6.0,0.0,1.0,1.0,0.0,134600.94,1.0
-10286,15592925.0,Chimaobim,564.0,Spain,Male,45.0,3.0,82697.28,1.0,1.0,0.0,134960.98,0.0
-10287,15791114.0,H?,627.0,France,Female,37.0,3.0,131033.76,1.0,0.0,0.0,151581.79,1.0
-10288,15794046.0,Yeh,575.0,France,Female,52.0,7.0,0.0,1.0,1.0,0.0,65636.92,1.0
-10289,15580249.0,Nnanna,725.0,France,Male,30.0,5.0,0.0,2.0,1.0,1.0,115676.61,0.0
-10290,15594717.0,Chukwumaobim,637.0,France,Male,27.0,3.0,0.0,1.0,0.0,1.0,64448.36,0.0
-10291,15775124.0,Young,603.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,47832.82,0.0
-10292,15731327.0,Martin,850.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,72851.74,0.0
-10293,15605067.0,Chijindum,547.0,Germany,Female,39.0,0.0,111379.55,2.0,0.0,1.0,19150.14,0.0
-10294,15672930.0,Yobanna,645.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,1.0,81409.0,0.0
-10295,15736399.0,Chukwudi,693.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,109742.55,0.0
-10296,15664543.0,Genovese,731.0,France,Female,36.0,8.0,0.0,2.0,1.0,1.0,55701.62,0.0
-10297,15768455.0,Shih,596.0,France,Male,38.0,3.0,0.0,2.0,0.0,0.0,129964.94,0.0
-10298,15782418.0,McWilliam,743.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,136329.96,0.0
-10299,15743478.0,Chukwuemeka,652.0,France,Male,25.0,2.0,0.0,2.0,1.0,1.0,98898.86,0.0
-10300,15792329.0,Yermolayev,660.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,71709.12,0.0
-10301,15576256.0,Nwachukwu,714.0,Germany,Female,46.0,4.0,139874.09,1.0,0.0,0.0,25669.1,1.0
-10302,15684960.0,Ting,576.0,Germany,Male,42.0,8.0,126473.99,1.0,1.0,0.0,37688.31,1.0
-10303,15661290.0,Mazzi,468.0,France,Male,23.0,3.0,116467.43,1.0,1.0,0.0,67311.12,0.0
-10304,15682533.0,Chukwudi,587.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,0.0,117946.3,0.0
-10305,15671243.0,Lai,694.0,France,Male,39.0,8.0,0.0,1.0,1.0,0.0,152567.75,0.0
-10306,15640407.0,Hsueh,538.0,France,Male,33.0,8.0,0.0,1.0,1.0,0.0,178709.98,0.0
-10307,15662434.0,Nwokike,633.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,100324.01,0.0
-10308,15807961.0,Kao,706.0,France,Male,45.0,4.0,0.0,2.0,1.0,0.0,142763.51,0.0
-10309,15594812.0,Mazzi,719.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,77864.41,0.0
-10310,15571081.0,Ch'en,609.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,137739.02,0.0
-10311,15614103.0,Chinwemma,553.0,Spain,Male,32.0,4.0,132944.55,1.0,1.0,1.0,126475.79,0.0
-10312,15758531.0,Chinwenma,619.0,France,Male,38.0,6.0,0.0,2.0,1.0,0.0,142903.44,0.0
-10313,15643392.0,Tsao,627.0,France,Female,32.0,3.0,0.0,2.0,1.0,1.0,93769.89,0.0
-10314,15573741.0,Yashina,786.0,France,Female,56.0,7.0,0.0,1.0,1.0,1.0,77906.87,1.0
-10315,15766183.0,Mancini,633.0,France,Male,56.0,2.0,0.0,1.0,1.0,0.0,93959.96,1.0
-10316,15595047.0,Fiorentino,727.0,France,Female,26.0,4.0,120962.13,1.0,1.0,0.0,99490.01,0.0
-10317,15687913.0,Udinesi,684.0,France,Female,40.0,8.0,131659.59,1.0,0.0,0.0,45613.75,0.0
-10318,15571244.0,K'ung,695.0,France,Female,31.0,8.0,121647.54,1.0,1.0,1.0,147545.65,0.0
-10319,15780961.0,Chikezie,697.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,119127.06,0.0
-10320,15725039.0,Ch'eng,661.0,Spain,Male,40.0,3.0,0.0,2.0,1.0,1.0,131301.74,0.0
-10321,15770405.0,Onyekaozulu,543.0,Germany,Female,41.0,3.0,132576.75,1.0,1.0,0.0,79871.16,0.0
-10322,15770405.0,T'ang,656.0,France,Male,52.0,8.0,0.0,1.0,0.0,0.0,143357.07,1.0
-10323,15682533.0,Onyemachukwu,621.0,France,Female,30.0,3.0,99816.46,1.0,1.0,0.0,62389.35,0.0
-10324,15616088.0,Y?,587.0,Spain,Female,50.0,5.0,0.0,1.0,1.0,0.0,158750.21,1.0
-10325,15682357.0,Nnaife,651.0,Spain,Female,33.0,8.0,0.0,3.0,1.0,0.0,82298.59,1.0
-10326,15643359.0,P'eng,628.0,Germany,Female,38.0,1.0,102290.53,2.0,1.0,1.0,58601.61,0.0
-10327,15689412.0,Manna,602.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,181879.56,0.0
-10328,15729771.0,Y?an,535.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,84760.32,0.0
-10329,15595388.0,Onyekachi,771.0,Germany,Male,42.0,7.0,133800.98,2.0,1.0,1.0,89893.6,0.0
-10330,15752650.0,Chiu,594.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,180712.28,0.0
-10331,15659820.0,Hsieh,684.0,Germany,Male,54.0,0.0,122822.0,1.0,1.0,1.0,92840.67,1.0
-10332,15643865.0,H?,708.0,Germany,Male,30.0,1.0,145177.84,1.0,1.0,1.0,171099.22,0.0
-10333,15684958.0,Maclean,579.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,95562.81,0.0
-10334,15692761.0,K'ung,713.0,France,Female,31.0,9.0,0.0,1.0,0.0,1.0,88947.62,0.0
-10335,15651802.0,Ritchie,584.0,Germany,Male,43.0,5.0,121394.42,1.0,0.0,0.0,71139.31,1.0
-10336,15700325.0,Smith,573.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,1.0,45959.86,0.0
-10337,15682470.0,Ngozichukwuka,544.0,France,Male,43.0,4.0,0.0,2.0,1.0,1.0,91346.03,0.0
-10338,15743456.0,Ijendu,662.0,France,Female,37.0,5.0,0.0,2.0,1.0,1.0,97985.2,0.0
-10339,15742285.0,Hsueh,775.0,Germany,Male,43.0,7.0,130786.92,2.0,1.0,1.0,162697.93,0.0
-10340,15652693.0,Kao,667.0,France,Female,25.0,9.0,0.0,2.0,1.0,0.0,152845.99,0.0
-10341,15793883.0,Lucchese,696.0,France,Male,23.0,9.0,0.0,2.0,0.0,0.0,126812.63,0.0
-10342,15750547.0,Ting,735.0,Germany,Male,40.0,0.0,121325.84,1.0,1.0,1.0,167522.19,0.0
-10343,15583724.0,Cremonesi,773.0,Spain,Female,37.0,3.0,0.0,2.0,0.0,0.0,162858.29,0.0
-10344,15785782.0,Nwabugwu,706.0,France,Female,31.0,1.0,0.0,1.0,1.0,0.0,73977.23,0.0
-10345,15702300.0,Baresi,471.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,122547.58,0.0
-10346,15802466.0,Fiorentini,687.0,France,Male,42.0,0.0,0.0,1.0,1.0,0.0,123428.98,0.0
-10347,15659710.0,Martin,672.0,Spain,Female,44.0,7.0,106070.89,1.0,1.0,0.0,91673.6,1.0
-10348,15725762.0,Hsiao,721.0,France,Male,46.0,2.0,0.0,2.0,0.0,0.0,140173.17,0.0
-10349,15597913.0,K?,575.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,171096.2,0.0
-10350,15684769.0,Chikwendu,646.0,Germany,Female,33.0,3.0,98848.19,2.0,0.0,0.0,102515.42,0.0
-10351,15711287.0,Hsing,711.0,Germany,Male,47.0,4.0,120777.88,1.0,1.0,0.0,180622.03,1.0
-10352,15581229.0,Chiu,615.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,171762.87,0.0
-10353,15776051.0,Olisanugo,682.0,France,Male,43.0,0.0,0.0,2.0,1.0,0.0,121409.06,0.0
-10354,15657540.0,Aksenov,755.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,0.0,172576.43,0.0
-10355,15641595.0,Chikelu,575.0,Germany,Male,29.0,10.0,145081.73,1.0,1.0,0.0,64907.48,1.0
-10356,15641640.0,Ozerova,635.0,France,Male,32.0,2.0,0.0,2.0,0.0,1.0,64872.55,0.0
-10357,15635177.0,Houghton,544.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,45445.78,0.0
-10358,15573318.0,Rizzo,712.0,Spain,Male,45.0,8.0,0.0,1.0,1.0,0.0,102930.46,0.0
-10359,15735080.0,Ch'ang,595.0,France,Female,34.0,7.0,121669.23,1.0,1.0,1.0,18094.96,0.0
-10360,15690526.0,Chukwudi,592.0,Spain,Female,38.0,6.0,137986.55,1.0,1.0,1.0,166495.2,0.0
-10361,15775318.0,Lo Duca,562.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,131718.12,0.0
-10362,15788676.0,Liao,644.0,France,Female,34.0,3.0,0.0,2.0,0.0,1.0,166682.57,0.0
-10363,15642996.0,Cremonesi,643.0,France,Male,43.0,2.0,0.0,2.0,0.0,0.0,138344.7,0.0
-10364,15664498.0,Henty,628.0,France,Female,34.0,10.0,0.0,2.0,1.0,0.0,133086.45,0.0
-10365,15692761.0,Chinwendu,696.0,France,Male,35.0,2.0,146033.62,1.0,0.0,1.0,98810.78,0.0
-10366,15745307.0,K?,624.0,France,Female,61.0,3.0,0.0,1.0,1.0,1.0,89566.64,1.0
-10367,15809823.0,Dettmann,711.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,1.0,79577.4,0.0
-10368,15733581.0,O'Donnell,703.0,France,Male,28.0,5.0,0.0,1.0,0.0,0.0,172363.41,1.0
-10369,15672912.0,Nnamutaezinwa,572.0,France,Male,32.0,5.0,0.0,1.0,1.0,1.0,159629.64,0.0
-10370,15752953.0,Chiawuotu,696.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,85424.34,0.0
-10371,15592877.0,Yevseyev,715.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,51362.82,0.0
-10372,15758813.0,Binder,649.0,Germany,Male,29.0,9.0,107556.06,1.0,1.0,0.0,71139.31,0.0
-10373,15805627.0,Y?,667.0,France,Female,41.0,5.0,0.0,2.0,1.0,0.0,129912.74,0.0
-10374,15749693.0,Smith,673.0,France,Female,47.0,9.0,120787.18,2.0,1.0,1.0,171928.04,0.0
-10375,15591100.0,Chigozie,628.0,Germany,Male,56.0,4.0,108916.1,2.0,1.0,0.0,177355.8,1.0
-10376,15700965.0,Lucchese,671.0,Spain,Female,40.0,6.0,0.0,1.0,1.0,1.0,147074.67,0.0
-10377,15644765.0,Shubin,691.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,61961.57,0.0
-10378,15571917.0,Burns,831.0,Germany,Female,34.0,9.0,106344.73,1.0,1.0,1.0,120834.58,1.0
-10379,15580213.0,Aksakova,646.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,70783.61,0.0
-10380,15771742.0,Trevisano,599.0,France,Female,49.0,2.0,0.0,2.0,0.0,0.0,181208.47,0.0
-10381,15772777.0,T'ao,577.0,France,Male,34.0,3.0,137811.75,1.0,0.0,1.0,172097.95,0.0
-10382,15598088.0,Maclean,644.0,France,Female,44.0,3.0,0.0,2.0,1.0,0.0,149614.81,0.0
-10383,15681332.0,Onwumelu,694.0,France,Female,46.0,1.0,0.0,1.0,0.0,0.0,120790.02,1.0
-10384,15579548.0,Lettiere,841.0,France,Male,39.0,2.0,0.0,1.0,1.0,1.0,76640.29,0.0
-10385,15803964.0,P'eng,714.0,Spain,Male,52.0,9.0,98877.95,1.0,0.0,1.0,131300.68,0.0
-10386,15637774.0,Lo,684.0,Spain,Male,45.0,8.0,0.0,1.0,1.0,0.0,129562.74,1.0
-10387,15595037.0,Ts'ao,611.0,France,Male,32.0,6.0,0.0,2.0,1.0,1.0,95611.47,0.0
-10388,15676521.0,Teng,736.0,Spain,Male,36.0,1.0,0.0,1.0,1.0,1.0,95611.47,0.0
-10389,15576560.0,Ch'iu,663.0,Germany,Male,24.0,4.0,121702.73,2.0,1.0,0.0,134441.6,0.0
-10390,15792726.0,Nucci,715.0,France,Male,42.0,0.0,0.0,1.0,1.0,1.0,132029.95,0.0
-10391,15692671.0,Palerma,614.0,France,Female,34.0,9.0,0.0,1.0,0.0,1.0,133814.28,0.0
-10392,15653547.0,Ch'ien,790.0,France,Female,52.0,3.0,0.0,1.0,0.0,0.0,166799.93,1.0
-10393,15592937.0,Ch'eng,684.0,France,Female,39.0,2.0,178058.06,1.0,1.0,0.0,145518.31,0.0
-10394,15668309.0,Hsiung,544.0,Spain,Male,38.0,8.0,129022.06,1.0,1.0,1.0,143913.32,0.0
-10395,15728683.0,Bibi,613.0,Spain,Female,38.0,5.0,0.0,2.0,1.0,0.0,95007.8,0.0
-10396,15582276.0,Ch'ang,563.0,France,Female,47.0,7.0,0.0,2.0,0.0,0.0,144532.85,0.0
-10397,15772073.0,Yobachi,648.0,Germany,Female,49.0,4.0,131023.76,1.0,1.0,1.0,170518.85,0.0
-10398,15661412.0,Chukwufumnanya,758.0,Germany,Male,44.0,8.0,92560.57,1.0,1.0,0.0,123427.98,0.0
-10399,15802501.0,Nkemakolam,663.0,Spain,Male,37.0,9.0,0.0,2.0,1.0,0.0,176614.86,0.0
-10400,15641850.0,Onyemauchechukwu,592.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,1.0,63687.06,0.0
-10401,15700554.0,Ch'ien,658.0,France,Female,27.0,3.0,0.0,1.0,1.0,1.0,91510.71,0.0
-10402,15612109.0,Shih,691.0,Germany,Female,35.0,8.0,96779.97,2.0,1.0,0.0,79577.38,0.0
-10403,15623649.0,Chukwujamuike,627.0,France,Female,38.0,6.0,0.0,2.0,1.0,1.0,152956.33,0.0
-10404,15732967.0,Piazza,641.0,France,Female,36.0,10.0,0.0,2.0,1.0,1.0,121846.81,0.0
-10405,15702571.0,Obialo,673.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,159483.76,0.0
-10406,15652674.0,Chukwuemeka,705.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,154775.7,0.0
-10407,15583073.0,Yermakova,599.0,Germany,Female,46.0,6.0,116574.85,1.0,1.0,0.0,161074.85,1.0
-10408,15704769.0,Chiu,543.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,180843.57,0.0
-10409,15682472.0,Baresi,585.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,140889.16,0.0
-10410,15731327.0,Chidubem,586.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,65801.44,0.0
-10411,15662955.0,Pisani,724.0,Germany,Female,43.0,4.0,115463.16,1.0,1.0,1.0,135925.72,1.0
-10412,15677906.0,Toscani,579.0,France,Male,61.0,1.0,0.0,2.0,1.0,1.0,38596.34,0.0
-10413,15687558.0,Hsueh,670.0,France,Male,35.0,7.0,148061.89,1.0,1.0,1.0,124592.84,0.0
-10414,15809800.0,Ch'in,679.0,Germany,Male,31.0,3.0,127738.35,2.0,1.0,0.0,88890.05,0.0
-10415,15591279.0,Chukwufumnanya,586.0,France,Female,37.0,1.0,0.0,1.0,1.0,1.0,58844.84,1.0
-10416,15679760.0,Onyemauchechukwu,669.0,France,Male,39.0,4.0,0.0,2.0,0.0,0.0,125083.29,0.0
-10417,15704770.0,Yobanna,667.0,France,Male,31.0,1.0,0.0,1.0,1.0,1.0,116977.89,0.0
-10418,15596863.0,Nwokeocha,676.0,France,Female,34.0,1.0,90544.0,1.0,0.0,1.0,73329.08,0.0
-10419,15691721.0,Mbanefo,603.0,Spain,Female,28.0,4.0,117565.03,1.0,1.0,0.0,113695.99,0.0
-10420,15700864.0,Frolov,686.0,France,Male,26.0,9.0,0.0,2.0,0.0,1.0,169862.01,0.0
-10421,15772363.0,O'Donnell,545.0,Spain,Male,59.0,4.0,0.0,1.0,0.0,1.0,179705.13,1.0
-10422,15583480.0,Tien,660.0,Germany,Male,36.0,6.0,92510.9,2.0,1.0,1.0,46635.11,0.0
-10423,15742638.0,Ch'ien,717.0,Germany,Female,74.0,3.0,156570.86,2.0,1.0,1.0,180553.48,0.0
-10424,15637753.0,Ndubueze,687.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,138516.1,0.0
-10425,15769596.0,Hs?,805.0,France,Female,30.0,7.0,141136.62,2.0,1.0,0.0,164967.21,0.0
-10426,15589563.0,Milani,704.0,France,Female,37.0,8.0,0.0,1.0,0.0,0.0,56562.57,1.0
-10427,15808960.0,Aitken,718.0,France,Male,40.0,1.0,133261.48,1.0,0.0,0.0,71682.54,0.0
-10428,15677522.0,Pye,622.0,Germany,Male,27.0,4.0,127543.11,1.0,0.0,1.0,180712.28,1.0
-10429,15725166.0,Biryukov,725.0,France,Female,34.0,7.0,138032.15,1.0,1.0,0.0,131662.47,0.0
-10430,15788683.0,Chukwudi,647.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,89543.25,0.0
-10431,15646513.0,Koo,767.0,Spain,Male,33.0,2.0,98894.39,1.0,0.0,0.0,67311.12,0.0
-10432,15645896.0,Ch'eng,729.0,France,Male,37.0,1.0,0.0,2.0,1.0,1.0,174027.3,0.0
-10433,15743538.0,Chiu,554.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,1.0,115818.51,0.0
-10434,15640078.0,Lucchesi,574.0,France,Male,40.0,9.0,0.0,2.0,0.0,1.0,176692.87,0.0
-10435,15801296.0,Ch'iu,611.0,France,Male,60.0,7.0,0.0,1.0,1.0,0.0,169669.33,1.0
-10436,15643908.0,Fries,577.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,142377.69,0.0
-10437,15753362.0,Chiemela,633.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,57553.02,0.0
-10438,15626040.0,Ndubuagha,696.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,55859.27,0.0
-10439,15614878.0,Chukwualuka,549.0,France,Male,46.0,8.0,0.0,1.0,1.0,0.0,87908.01,1.0
-10440,15735222.0,Ch'ang,659.0,Spain,Female,32.0,2.0,117866.92,1.0,1.0,0.0,94440.45,0.0
-10441,15694129.0,Chidimma,697.0,France,Male,30.0,2.0,0.0,2.0,1.0,1.0,141872.05,0.0
-10442,15573790.0,Logue,613.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,180890.4,0.0
-10443,15737556.0,Niu,628.0,France,Male,43.0,0.0,0.0,1.0,1.0,0.0,164741.5,1.0
-10444,15576517.0,T'ien,589.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,164975.82,0.0
-10445,15806945.0,Ch'eng,706.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,0.0,598.8,0.0
-10446,15658577.0,Palerma,656.0,France,Female,32.0,0.0,0.0,2.0,1.0,0.0,86410.28,0.0
-10447,15803790.0,Chinweike,619.0,Spain,Female,36.0,0.0,0.0,2.0,1.0,1.0,135650.18,0.0
-10448,15631406.0,Chukwudi,812.0,Spain,Female,41.0,6.0,123845.24,1.0,1.0,1.0,95857.18,0.0
-10449,15621520.0,Y?an,670.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,183572.39,0.0
-10450,15589380.0,K'ung,646.0,France,Male,35.0,3.0,0.0,2.0,0.0,0.0,120892.93,0.0
-10451,15702561.0,Hsiao,634.0,France,Male,35.0,1.0,0.0,1.0,1.0,0.0,6057.81,1.0
-10452,15631868.0,Ch'in,690.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,107959.01,0.0
-10453,15734987.0,Chin,588.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,132770.15,0.0
-10454,15574558.0,Udokamma,686.0,France,Female,46.0,9.0,0.0,1.0,1.0,0.0,58137.42,1.0
-10455,15802617.0,Hsia,564.0,France,Male,38.0,9.0,98263.46,1.0,0.0,0.0,133570.29,0.0
-10456,15624677.0,Chiekwugo,607.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,103726.71,0.0
-10457,15593470.0,Zikoranachidimma,837.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,156774.75,0.0
-10458,15693526.0,Hsing,718.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,147557.71,0.0
-10459,15620557.0,Mazzi,543.0,France,Female,22.0,6.0,0.0,2.0,1.0,1.0,188533.11,0.0
-10460,15603764.0,Chidiebere,699.0,France,Male,26.0,3.0,0.0,1.0,0.0,1.0,88890.05,0.0
-10461,15726167.0,Tuan,589.0,Spain,Male,40.0,3.0,111233.09,1.0,1.0,0.0,110916.15,0.0
-10462,15781495.0,Whitehouse,565.0,France,Female,34.0,2.0,0.0,2.0,0.0,1.0,158420.14,0.0
-10463,15692575.0,Chidiebele,735.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,62449.07,0.0
-10464,15642530.0,Chinedum,645.0,France,Female,42.0,1.0,0.0,1.0,1.0,1.0,127558.26,0.0
-10465,15602994.0,Lo,644.0,Spain,Male,39.0,0.0,0.0,1.0,1.0,0.0,97939.4,0.0
-10466,15809901.0,Smith,720.0,Germany,Female,40.0,3.0,127552.85,1.0,1.0,0.0,131647.31,1.0
-10467,15656330.0,K'ung,597.0,France,Female,39.0,10.0,0.0,2.0,1.0,1.0,76510.46,0.0
-10468,15748667.0,Lu,704.0,France,Female,49.0,9.0,152606.82,1.0,1.0,0.0,181757.19,0.0
-10469,15572390.0,K?,592.0,France,Male,40.0,1.0,148989.15,1.0,1.0,0.0,117791.06,1.0
-10470,15763381.0,Napolitani,642.0,France,Female,38.0,1.0,0.0,1.0,1.0,0.0,91346.03,1.0
-10471,15803526.0,Hsu,754.0,France,Female,52.0,4.0,0.0,2.0,1.0,1.0,47921.22,0.0
-10472,15668580.0,Rizzo,707.0,Spain,Female,57.0,3.0,0.0,1.0,1.0,0.0,126082.29,1.0
-10473,15636673.0,Nwebube,637.0,France,Male,31.0,4.0,0.0,2.0,1.0,1.0,171733.43,0.0
-10474,15772933.0,Shao,738.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,161428.25,0.0
-10475,15644878.0,Fiorentino,679.0,Germany,Female,59.0,5.0,125850.7,1.0,1.0,0.0,166376.54,1.0
-10476,15709741.0,Lettiere,578.0,France,Male,40.0,2.0,0.0,2.0,0.0,1.0,183566.85,0.0
-10477,15681075.0,Pagnotto,687.0,France,Male,40.0,3.0,154969.96,1.0,1.0,0.0,178045.97,0.0
-10478,15662662.0,Ugochukwu,581.0,France,Male,42.0,1.0,0.0,2.0,0.0,1.0,73882.49,0.0
-10479,15694556.0,Chukwufumnanya,757.0,Germany,Female,42.0,5.0,122028.65,1.0,1.0,0.0,134720.51,1.0
-10480,15802758.0,White,585.0,France,Male,43.0,8.0,0.0,2.0,0.0,0.0,113950.51,0.0
-10481,15702300.0,Ebelechukwu,571.0,France,Male,42.0,6.0,0.0,2.0,1.0,1.0,140937.91,0.0
-10482,15576683.0,K'ung,648.0,France,Male,46.0,5.0,0.0,1.0,1.0,0.0,179453.66,1.0
-10483,15752588.0,Hsu,678.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,78854.57,0.0
-10484,15761783.0,Pirozzi,697.0,Spain,Female,44.0,3.0,0.0,2.0,0.0,0.0,81622.51,0.0
-10485,15588535.0,Ch'en,613.0,Germany,Male,27.0,8.0,131834.7,1.0,1.0,1.0,53710.23,0.0
-10486,15682928.0,Uspenskaya,582.0,France,Female,60.0,5.0,0.0,1.0,1.0,1.0,136827.92,1.0
-10487,15723484.0,Mazzanti,699.0,Germany,Male,39.0,1.0,115569.21,1.0,1.0,1.0,26767.85,0.0
-10488,15692991.0,De Luca,703.0,Spain,Female,32.0,3.0,0.0,2.0,1.0,1.0,83343.73,0.0
-10489,15581198.0,Nwankwo,516.0,France,Male,32.0,0.0,0.0,2.0,0.0,0.0,173599.38,0.0
-10490,15757628.0,Mazzanti,581.0,France,Male,31.0,5.0,91955.61,1.0,1.0,0.0,74838.64,0.0
-10491,15749401.0,Chukwueloka,652.0,Spain,Female,39.0,3.0,0.0,1.0,0.0,0.0,171706.66,1.0
-10492,15742110.0,Nwankwo,749.0,Spain,Male,38.0,2.0,0.0,2.0,0.0,1.0,62413.01,0.0
-10493,15769955.0,Walker,594.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,0.0,180932.46,0.0
-10494,15809847.0,Liao,585.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,116978.19,0.0
-10495,15702298.0,Teng,635.0,Germany,Female,29.0,6.0,129837.72,1.0,1.0,1.0,86981.45,0.0
-10496,15637324.0,Hsieh,743.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,131804.86,0.0
-10497,15724251.0,Chukwujamuike,671.0,Spain,Female,33.0,7.0,97544.29,1.0,1.0,1.0,188203.58,0.0
-10498,15643907.0,Smith,707.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,0.0,162452.65,0.0
-10499,15710105.0,Ch'in,635.0,France,Male,41.0,3.0,0.0,1.0,1.0,0.0,18826.08,1.0
-10500,15722072.0,Dickson,697.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,47775.34,0.0
-10501,15802585.0,Bellucci,585.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,147964.99,0.0
-10502,15583692.0,H?,814.0,Germany,Male,36.0,2.0,129855.32,2.0,1.0,1.0,143960.72,0.0
-10503,15806420.0,Tung,667.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,62367.72,0.0
-10504,15641732.0,Ch'iu,681.0,France,Male,28.0,7.0,0.0,1.0,1.0,0.0,54487.43,0.0
-10505,15750121.0,Trevisani,735.0,France,Male,39.0,5.0,0.0,1.0,1.0,0.0,172576.43,0.0
-10506,15793949.0,P'eng,661.0,France,Female,40.0,8.0,0.0,2.0,0.0,0.0,160942.48,0.0
-10507,15688409.0,Yudin,704.0,France,Male,44.0,4.0,0.0,2.0,0.0,1.0,64448.36,0.0
-10508,15679048.0,Lo,731.0,Germany,Female,54.0,7.0,83765.35,1.0,0.0,0.0,81718.37,1.0
-10509,15792868.0,Chidiebere,738.0,Spain,Female,38.0,1.0,0.0,2.0,1.0,1.0,158071.69,0.0
-10510,15642967.0,Chiang,645.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,90877.76,0.0
-10511,15683677.0,Hsing,581.0,France,Male,31.0,7.0,0.0,2.0,0.0,1.0,110805.79,0.0
-10512,15758035.0,Scott,682.0,France,Female,31.0,9.0,0.0,2.0,1.0,0.0,185210.63,0.0
-10513,15690647.0,Lucchesi,586.0,France,Female,57.0,2.0,0.0,1.0,1.0,0.0,177065.24,1.0
-10514,15751748.0,Walker,641.0,France,Male,29.0,1.0,0.0,2.0,1.0,1.0,148087.88,0.0
-10515,15593636.0,Kaeppel,695.0,Germany,Female,44.0,8.0,108883.29,2.0,1.0,0.0,75574.48,1.0
-10516,15722993.0,H?,781.0,Germany,Female,52.0,7.0,102954.68,3.0,1.0,0.0,153662.88,1.0
-10517,15804787.0,Mazzi,762.0,France,Female,40.0,5.0,0.0,2.0,0.0,0.0,120108.56,0.0
-10518,15573741.0,Nnaife,686.0,France,Male,44.0,4.0,0.0,1.0,0.0,0.0,51535.19,0.0
-10519,15652320.0,Treacy,594.0,France,Female,22.0,2.0,0.0,2.0,1.0,0.0,48876.84,0.0
-10520,15694253.0,Kashiwagi,850.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,172450.04,0.0
-10521,15591130.0,Teng,605.0,France,Male,26.0,4.0,113637.62,1.0,1.0,1.0,104876.8,0.0
-10522,15734886.0,Zetticci,726.0,France,Male,32.0,8.0,0.0,1.0,1.0,1.0,161479.19,0.0
-10523,15796230.0,Kambinachi,730.0,France,Female,37.0,2.0,145975.78,2.0,1.0,1.0,91763.67,0.0
-10524,15782236.0,Okechukwu,605.0,Germany,Male,38.0,5.0,104631.67,2.0,1.0,1.0,171762.87,0.0
-10525,15765806.0,Lucchese,792.0,Germany,Male,54.0,9.0,92340.69,1.0,1.0,1.0,172096.08,0.0
-10526,15603065.0,Nucci,704.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,88566.9,0.0
-10527,15689612.0,Ch'iu,595.0,Spain,Female,41.0,6.0,129289.94,1.0,0.0,0.0,139947.17,0.0
-10528,15697028.0,Nkemdilim,604.0,Germany,Male,33.0,7.0,111201.41,1.0,0.0,1.0,178067.33,0.0
-10529,15746553.0,Ch'ien,739.0,France,Male,49.0,4.0,0.0,1.0,0.0,0.0,173783.38,1.0
-10530,15726354.0,Maclean,554.0,France,Male,51.0,8.0,0.0,1.0,0.0,1.0,91366.42,1.0
-10531,15694192.0,Chibugo,706.0,France,Male,27.0,6.0,0.0,2.0,1.0,0.0,60972.84,0.0
-10532,15705085.0,Naylor,663.0,France,Female,52.0,6.0,0.0,3.0,1.0,0.0,152883.95,1.0
-10533,15651713.0,Nebechukwu,637.0,France,Male,29.0,2.0,0.0,1.0,1.0,0.0,167067.34,1.0
-10534,15607877.0,Yobachi,692.0,Spain,Male,46.0,1.0,0.0,2.0,0.0,1.0,65868.28,0.0
-10535,15701376.0,Onyemere,682.0,France,Male,20.0,3.0,0.0,2.0,1.0,0.0,160810.85,0.0
-10536,15747168.0,Nwachinemelu,646.0,France,Male,44.0,7.0,0.0,2.0,0.0,0.0,122964.18,0.0
-10537,15686963.0,Smith,619.0,France,Male,40.0,3.0,0.0,2.0,0.0,0.0,102908.91,0.0
-10538,15617648.0,Chiawuotu,812.0,Germany,Female,43.0,6.0,113383.52,1.0,0.0,1.0,133672.61,0.0
-10539,15793683.0,Azikiwe,642.0,France,Male,35.0,7.0,109949.05,1.0,0.0,1.0,178252.73,0.0
-10540,15684924.0,Tuan,775.0,Germany,Female,33.0,5.0,130933.74,1.0,0.0,1.0,158028.36,1.0
-10541,15682868.0,Obialo,635.0,France,Male,46.0,3.0,0.0,2.0,1.0,0.0,187900.12,0.0
-10542,15776545.0,Ch'ang,801.0,France,Male,35.0,2.0,0.0,1.0,0.0,1.0,176925.69,0.0
-10543,15641411.0,Pagnotto,733.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,141872.05,0.0
-10544,15748691.0,K'ung,596.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,56594.36,0.0
-10545,15773804.0,Shih,731.0,France,Female,40.0,6.0,0.0,1.0,1.0,1.0,115800.1,1.0
-10546,15709354.0,Martin,683.0,France,Male,64.0,9.0,0.0,3.0,1.0,0.0,180345.44,1.0
-10547,15734365.0,Ch'eng,741.0,Germany,Female,40.0,8.0,128966.13,2.0,0.0,1.0,108764.69,0.0
-10548,15652674.0,Chineze,583.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,51980.25,0.0
-10549,15700476.0,Ch'ang,602.0,Germany,Male,33.0,3.0,120105.43,1.0,0.0,0.0,92320.37,0.0
-10550,15635388.0,Hsia,678.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,133535.29,0.0
-10551,15577274.0,Hsiung,564.0,France,Female,46.0,3.0,0.0,1.0,1.0,0.0,94878.14,1.0
-10552,15607990.0,Shih,633.0,France,Female,35.0,9.0,0.0,1.0,1.0,0.0,154844.22,0.0
-10553,15605276.0,Zetticci,625.0,Germany,Female,67.0,10.0,116197.65,1.0,0.0,1.0,140166.95,0.0
-10554,15591135.0,Bogle,616.0,France,Female,31.0,1.0,98205.77,1.0,1.0,1.0,79919.13,0.0
-10555,15759733.0,Ch'ien,567.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,72462.25,0.0
-10556,15673908.0,Y?an,599.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,523.0,0.0
-10557,15732967.0,Chia,753.0,France,Male,34.0,2.0,124577.33,1.0,0.0,1.0,121846.81,0.0
-10558,15806901.0,Ugochukwu,671.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,162699.7,0.0
-10559,15751561.0,Yudina,678.0,Germany,Male,48.0,3.0,121863.61,1.0,1.0,1.0,103747.51,0.0
-10560,15731519.0,Chukwudi,649.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,84760.32,0.0
-10561,15711901.0,Ch'ang,614.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,134594.99,0.0
-10562,15745080.0,Chien,811.0,France,Female,28.0,5.0,0.0,2.0,1.0,1.0,115818.51,0.0
-10563,15626507.0,Tuan,531.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,199409.19,0.0
-10564,15610972.0,Y?,695.0,France,Female,31.0,1.0,131834.76,1.0,1.0,1.0,177740.58,0.0
-10565,15680167.0,Eiland,740.0,France,Female,31.0,1.0,0.0,2.0,1.0,1.0,182059.28,0.0
-10566,15669922.0,Ch'en,535.0,France,Male,43.0,1.0,162485.8,1.0,1.0,1.0,90876.95,1.0
-10567,15808960.0,Y?,648.0,Spain,Female,41.0,2.0,0.0,1.0,1.0,0.0,145562.4,1.0
-10568,15681956.0,Chiedozie,596.0,France,Male,38.0,0.0,0.0,2.0,1.0,1.0,93832.33,0.0
-10569,15794468.0,Mancini,850.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,156048.64,0.0
-10570,15793049.0,Okwudilichukwu,516.0,Germany,Female,56.0,1.0,115029.4,1.0,1.0,0.0,123846.07,1.0
-10571,15723169.0,Chiedozie,632.0,Germany,Male,44.0,5.0,139432.37,1.0,1.0,1.0,151903.6,0.0
-10572,15761241.0,Hsueh,739.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,0.0,57320.92,0.0
-10573,15717995.0,Hs?,634.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,142287.65,0.0
-10574,15582892.0,Mamelu,685.0,France,Female,31.0,4.0,0.0,2.0,1.0,0.0,97939.4,0.0
-10575,15745201.0,T'ien,582.0,Germany,Female,29.0,0.0,129387.94,2.0,1.0,1.0,155850.9,0.0
-10576,15689953.0,Calabrese,606.0,France,Male,26.0,9.0,0.0,2.0,1.0,0.0,111558.7,0.0
-10577,15706537.0,Baresi,691.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,95611.47,0.0
-10578,15662758.0,Cattaneo,699.0,France,Male,26.0,9.0,0.0,2.0,1.0,0.0,111184.67,0.0
-10579,15604119.0,K'ung,701.0,France,Male,27.0,5.0,0.0,2.0,1.0,1.0,101300.94,0.0
-10580,15609083.0,Iweobiegbunam,693.0,France,Male,21.0,1.0,103818.41,1.0,1.0,0.0,171069.39,0.0
-10581,15755872.0,Achebe,662.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,4696.44,0.0
-10582,15605684.0,Kisch,849.0,Germany,Male,46.0,1.0,93716.72,2.0,1.0,0.0,164002.02,1.0
-10583,15586039.0,Chidubem,686.0,France,Male,43.0,2.0,0.0,3.0,0.0,0.0,143952.24,1.0
-10584,15624343.0,Fanucci,676.0,France,Male,23.0,8.0,127763.24,1.0,1.0,0.0,78854.57,0.0
-10585,15636089.0,Hsieh,584.0,Germany,Male,45.0,4.0,117307.44,1.0,0.0,1.0,113089.43,0.0
-10586,15640789.0,H?,690.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,143665.44,0.0
-10587,15586069.0,Chigolum,777.0,Germany,Male,34.0,10.0,141787.31,1.0,1.0,0.0,57553.02,0.0
-10588,15756560.0,Scott,615.0,France,Male,45.0,2.0,0.0,2.0,0.0,1.0,75969.19,0.0
-10589,15611551.0,Lo,549.0,Germany,Female,46.0,7.0,98955.84,1.0,1.0,0.0,182076.97,1.0
-10590,15773868.0,McGregor,803.0,Spain,Female,61.0,8.0,0.0,1.0,1.0,1.0,125920.54,1.0
-10591,15627999.0,Chukwueloka,662.0,Spain,Male,53.0,5.0,0.0,1.0,1.0,1.0,88827.67,0.0
-10592,15652955.0,Pinto,559.0,France,Female,24.0,4.0,0.0,2.0,1.0,1.0,120512.08,0.0
-10593,15631709.0,Fiorentini,586.0,Spain,Male,32.0,4.0,0.0,1.0,0.0,1.0,95936.84,0.0
-10594,15761348.0,Lo,567.0,Spain,Female,32.0,4.0,0.0,2.0,0.0,0.0,71405.17,0.0
-10595,15603088.0,Chin,667.0,France,Female,24.0,2.0,0.0,2.0,1.0,1.0,153662.88,0.0
-10596,15609913.0,Manna,738.0,France,Male,43.0,3.0,0.0,1.0,0.0,1.0,134417.31,1.0
-10597,15633666.0,Zikoranachidimma,659.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,156774.75,0.0
-10598,15635141.0,Chukwufumnanya,749.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,135482.26,0.0
-10599,15673074.0,Ku,679.0,France,Female,48.0,4.0,0.0,1.0,0.0,0.0,50610.62,1.0
-10600,15750099.0,T'ien,766.0,Germany,Female,20.0,9.0,104118.5,2.0,1.0,0.0,62811.03,0.0
-10601,15796370.0,Tuan,684.0,Germany,Male,31.0,7.0,111095.58,2.0,0.0,1.0,4798.12,0.0
-10602,15682467.0,Tung,557.0,Germany,Male,48.0,8.0,131659.52,1.0,1.0,0.0,155799.86,1.0
-10603,15771769.0,Biryukov,593.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,0.0,176576.71,0.0
-10604,15775803.0,Teng,594.0,France,Female,33.0,5.0,0.0,1.0,1.0,1.0,199505.53,1.0
-10605,15603914.0,Chiazagomekpere,616.0,France,Male,40.0,8.0,0.0,1.0,0.0,1.0,176743.51,1.0
-10606,15662434.0,Onuchukwu,673.0,France,Female,33.0,6.0,176913.52,1.0,1.0,1.0,114083.73,0.0
-10607,15703437.0,Ch'eng,539.0,Spain,Female,50.0,9.0,0.0,1.0,1.0,0.0,159334.93,1.0
-10608,15771855.0,Ts'ui,595.0,Spain,Female,53.0,7.0,0.0,2.0,0.0,1.0,165562.84,1.0
-10609,15682048.0,Lu,641.0,Germany,Male,47.0,8.0,113599.74,1.0,1.0,0.0,91830.75,1.0
-10610,15724145.0,Tretiakova,651.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,91366.41,0.0
-10611,15605835.0,Chukwukere,749.0,Spain,Female,41.0,7.0,0.0,1.0,1.0,1.0,72876.41,1.0
-10612,15700813.0,Palerma,603.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,1.0,149991.32,0.0
-10613,15627848.0,Hao,687.0,Germany,Female,40.0,1.0,76510.52,2.0,1.0,1.0,58974.0,0.0
-10614,15699467.0,Chiu,537.0,Spain,Male,35.0,3.0,0.0,1.0,1.0,1.0,53477.81,0.0
-10615,15746076.0,Iadanza,821.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,102547.56,0.0
-10616,15690164.0,Zetticci,696.0,Germany,Male,39.0,4.0,162261.01,2.0,1.0,0.0,111605.9,0.0
-10617,15663917.0,Young,611.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,172576.43,0.0
-10618,15723858.0,Scott,685.0,Spain,Male,29.0,3.0,126479.27,1.0,1.0,0.0,149853.89,0.0
-10619,15643496.0,Chidimma,562.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,89566.64,0.0
-10620,15777797.0,Tipton,627.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,100862.54,0.0
-10621,15702470.0,Chin,679.0,Germany,Female,48.0,4.0,113599.69,1.0,1.0,0.0,64448.36,1.0
-10622,15585580.0,Miah,754.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,115429.32,0.0
-10623,15594812.0,Lai,667.0,Germany,Male,62.0,3.0,127197.14,1.0,1.0,1.0,91573.19,0.0
-10624,15690829.0,Chukwumaobim,710.0,France,Female,55.0,9.0,0.0,1.0,1.0,0.0,177783.04,1.0
-10625,15593976.0,Fitts,683.0,Germany,Female,23.0,2.0,68345.86,2.0,1.0,1.0,176580.95,0.0
-10626,15671165.0,Ku,679.0,Germany,Female,36.0,7.0,102933.74,2.0,1.0,0.0,166929.43,1.0
-10627,15626233.0,Bergamaschi,675.0,Germany,Male,34.0,4.0,102085.32,1.0,1.0,1.0,158037.57,0.0
-10628,15575060.0,Chukwueloka,494.0,France,Female,32.0,10.0,91057.37,1.0,1.0,1.0,167554.86,0.0
-10629,15778089.0,Trevisano,527.0,France,Female,48.0,4.0,0.0,1.0,1.0,0.0,126726.33,1.0
-10630,15648858.0,Chidimma,641.0,Spain,Female,24.0,4.0,0.0,1.0,1.0,0.0,186869.58,0.0
-10631,15636562.0,Y?an,692.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,1.0,161407.48,0.0
-10632,15768104.0,Walker,676.0,Germany,Female,33.0,2.0,102773.2,1.0,1.0,0.0,173683.0,0.0
-10633,15673837.0,Chukwuebuka,646.0,Germany,Male,25.0,3.0,96019.5,2.0,1.0,0.0,162703.0,0.0
-10634,15677522.0,Uvarova,755.0,Germany,Female,29.0,1.0,160625.17,1.0,1.0,1.0,182627.25,0.0
-10635,15773934.0,Fomin,730.0,Germany,Female,46.0,5.0,126589.12,1.0,1.0,0.0,175043.69,1.0
-10636,15585284.0,H?,705.0,France,Male,40.0,4.0,0.0,1.0,1.0,1.0,142377.69,0.0
-10637,15727177.0,Y?an,645.0,France,Male,40.0,1.0,0.0,2.0,0.0,1.0,84760.32,0.0
-10638,15662658.0,Mironov,674.0,France,Male,44.0,6.0,0.0,2.0,0.0,1.0,150422.35,0.0
-10639,15740406.0,Udinesi,723.0,France,Male,30.0,1.0,117738.97,1.0,1.0,0.0,124095.69,0.0
-10640,15813168.0,Shih,633.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,161571.79,0.0
-10641,15737265.0,Brown,616.0,France,Male,34.0,0.0,0.0,2.0,1.0,1.0,82709.8,0.0
-10642,15758845.0,Hsia,731.0,France,Male,33.0,9.0,0.0,2.0,0.0,1.0,161668.15,0.0
-10643,15772030.0,Pirozzi,697.0,France,Male,40.0,6.0,127209.0,1.0,0.0,1.0,487.3,0.0
-10644,15612444.0,Ch'iu,712.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,2560.11,0.0
-10645,15696823.0,T'ien,755.0,France,Male,21.0,4.0,0.0,1.0,0.0,0.0,118716.63,0.0
-10646,15803801.0,Martin,744.0,France,Male,51.0,4.0,0.0,1.0,0.0,1.0,162443.05,1.0
-10647,15796764.0,Mancini,592.0,France,Male,38.0,8.0,0.0,2.0,1.0,1.0,10054.53,0.0
-10648,15689031.0,Nkemdilim,646.0,France,Female,36.0,3.0,134599.33,1.0,1.0,0.0,90876.95,0.0
-10649,15730673.0,Chin,651.0,Germany,Male,46.0,5.0,127864.42,2.0,1.0,1.0,55324.49,0.0
-10650,15808905.0,Zuyev,498.0,Spain,Female,32.0,8.0,0.0,1.0,1.0,1.0,92915.84,0.0
-10651,15795579.0,Smith,732.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,145562.4,0.0
-10652,15774738.0,Lucciano,663.0,France,Male,28.0,9.0,0.0,2.0,1.0,0.0,74898.8,0.0
-10653,15701885.0,Bellucci,686.0,France,Male,55.0,5.0,0.0,2.0,1.0,1.0,181534.04,0.0
-10654,15796958.0,Teng,539.0,France,Male,40.0,8.0,148962.76,1.0,1.0,1.0,61820.41,0.0
-10655,15757931.0,Iloerika,645.0,Spain,Female,43.0,7.0,127849.51,2.0,1.0,1.0,4035.46,0.0
-10656,15729954.0,Nnonso,636.0,Spain,Female,36.0,9.0,127771.35,1.0,0.0,1.0,129975.75,0.0
-10657,15782231.0,Hsiung,758.0,France,Female,31.0,5.0,0.0,2.0,0.0,0.0,68815.05,0.0
-10658,15589435.0,Cairns,652.0,France,Female,40.0,4.0,99835.17,1.0,0.0,0.0,95957.09,0.0
-10659,15711398.0,Ch'in,595.0,France,Female,38.0,3.0,0.0,1.0,0.0,1.0,66893.28,0.0
-10660,15795878.0,Chibueze,687.0,France,Male,60.0,6.0,0.0,2.0,0.0,1.0,148811.14,0.0
-10661,15694890.0,Balsillie,709.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,61820.41,0.0
-10662,15756539.0,Shubin,582.0,France,Female,72.0,4.0,121898.98,1.0,1.0,1.0,72993.67,0.0
-10663,15661380.0,Macleod,681.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,86424.57,0.0
-10664,15757617.0,Hsieh,579.0,France,Female,37.0,7.0,0.0,1.0,1.0,1.0,93835.64,0.0
-10665,15715888.0,Lu,590.0,Spain,Female,32.0,8.0,0.0,2.0,0.0,0.0,161811.23,0.0
-10666,15654075.0,T'ao,540.0,France,Male,29.0,6.0,0.0,2.0,1.0,0.0,148814.54,0.0
-10667,15594577.0,Udokamma,624.0,Spain,Male,44.0,3.0,142872.36,1.0,1.0,0.0,113921.64,1.0
-10668,15629985.0,Padovesi,679.0,France,Female,37.0,0.0,0.0,2.0,1.0,0.0,180716.1,0.0
-10669,15600724.0,Shih,593.0,France,Female,45.0,8.0,0.0,2.0,1.0,0.0,94774.12,0.0
-10670,15765537.0,Tien,711.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,0.0,66463.62,0.0
-10671,15607986.0,Chiu,674.0,Spain,Male,41.0,8.0,89182.09,1.0,1.0,1.0,102638.35,0.0
-10672,15693732.0,P'eng,581.0,Spain,Male,39.0,7.0,0.0,2.0,1.0,0.0,96670.2,0.0
-10673,15724453.0,Lucciano,781.0,Spain,Male,37.0,3.0,0.0,2.0,1.0,1.0,122960.98,0.0
-10674,15762682.0,Ritchie,588.0,France,Male,49.0,7.0,0.0,2.0,1.0,0.0,143425.56,0.0
-10675,15671934.0,Niu,679.0,France,Male,45.0,7.0,0.0,1.0,1.0,1.0,104769.96,1.0
-10676,15702631.0,Bellucci,575.0,France,Female,34.0,2.0,0.0,2.0,0.0,0.0,53584.03,0.0
-10677,15792463.0,Yeh,574.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,58907.08,0.0
-10678,15642099.0,Bess,651.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,1.0,51553.43,0.0
-10679,15651280.0,Fanucci,827.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,115950.96,0.0
-10680,15732778.0,Hsueh,667.0,Germany,Male,37.0,3.0,122394.0,2.0,1.0,1.0,35418.79,0.0
-10681,15692259.0,P'eng,641.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,64853.51,0.0
-10682,15588123.0,Chukwuemeka,642.0,Germany,Female,36.0,7.0,129855.32,1.0,1.0,0.0,110782.88,0.0
-10683,15702460.0,Ch'iu,591.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,152499.8,0.0
-10684,15599869.0,Ch'eng,718.0,France,Male,40.0,6.0,0.0,2.0,0.0,0.0,96823.32,0.0
-10685,15761487.0,Chukwudi,833.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,69303.15,0.0
-10686,15798885.0,Hsueh,635.0,Germany,Male,21.0,5.0,127661.69,2.0,1.0,1.0,141446.01,0.0
-10687,15579130.0,Calabresi,646.0,France,Female,35.0,2.0,0.0,2.0,1.0,0.0,78670.52,0.0
-10688,15766686.0,Chibugo,558.0,Germany,Female,35.0,5.0,104930.38,1.0,1.0,0.0,103787.28,1.0
-10689,15769312.0,Pisano,668.0,France,Female,31.0,8.0,0.0,2.0,0.0,0.0,174777.98,0.0
-10690,15707974.0,Ch'en,625.0,France,Female,41.0,6.0,0.0,2.0,1.0,0.0,181177.9,0.0
-10691,15632447.0,Hsueh,754.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,91775.51,0.0
-10692,15778947.0,Hsueh,668.0,France,Male,29.0,2.0,131834.8,1.0,1.0,1.0,126812.63,0.0
-10693,15644868.0,Pisano,582.0,Germany,Male,38.0,0.0,165413.81,2.0,1.0,0.0,149402.54,0.0
-10694,15653857.0,Fitts,651.0,France,Female,29.0,6.0,148874.01,2.0,1.0,0.0,137961.41,0.0
-10695,15627995.0,Chigolum,648.0,Germany,Male,22.0,2.0,170935.94,2.0,1.0,0.0,103713.93,0.0
-10696,15666975.0,Uwaezuoke,652.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,162960.05,0.0
-10697,15681220.0,Ts'ao,706.0,Spain,Female,26.0,7.0,153366.13,1.0,1.0,1.0,71519.4,0.0
-10698,15698902.0,Hsia,573.0,France,Female,45.0,5.0,0.0,1.0,1.0,1.0,152698.24,1.0
-10699,15742879.0,Chukwudi,679.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,180318.6,0.0
-10700,15806822.0,Ugoji,623.0,Spain,Male,38.0,10.0,0.0,2.0,1.0,1.0,108887.44,0.0
-10701,15769246.0,Mancini,608.0,France,Male,34.0,6.0,0.0,2.0,0.0,1.0,56984.58,0.0
-10702,15751137.0,Hsu,571.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,110405.9,0.0
-10703,15607986.0,Chidozie,602.0,France,Female,40.0,5.0,161371.5,1.0,1.0,0.0,101348.88,1.0
-10704,15763898.0,Chin,551.0,Spain,Female,48.0,2.0,0.0,2.0,0.0,1.0,50368.87,0.0
-10705,15665956.0,Nwankwo,666.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,108699.59,0.0
-10706,15773869.0,Genovese,733.0,France,Male,31.0,6.0,0.0,1.0,1.0,1.0,645.61,1.0
-10707,15763515.0,Chidozie,705.0,France,Male,40.0,3.0,137794.18,1.0,1.0,0.0,173495.77,1.0
-10708,15763515.0,Kuykendall,648.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,113410.41,0.0
-10709,15757981.0,Hs?,617.0,France,Female,44.0,9.0,0.0,2.0,0.0,0.0,142839.85,1.0
-10710,15746199.0,Fiorentino,545.0,Spain,Male,39.0,1.0,0.0,2.0,0.0,0.0,150679.65,0.0
-10711,15722409.0,Houghton,623.0,Spain,Male,38.0,9.0,0.0,1.0,0.0,0.0,90876.95,0.0
-10712,15674720.0,Onyekaozulu,648.0,Germany,Female,20.0,5.0,125445.04,1.0,1.0,1.0,107499.34,0.0
-10713,15805627.0,Ngozichukwuka,709.0,Spain,Male,44.0,5.0,0.0,2.0,1.0,1.0,110916.15,0.0
-10714,15633233.0,Okwuadigbo,653.0,France,Male,47.0,7.0,0.0,1.0,0.0,1.0,136491.72,1.0
-10715,15778838.0,P'eng,527.0,Spain,Male,27.0,5.0,142400.77,1.0,1.0,0.0,126812.63,0.0
-10716,15791371.0,Hargraves,553.0,France,Female,35.0,8.0,0.0,2.0,0.0,0.0,123866.28,0.0
-10717,15677522.0,De Luca,724.0,France,Female,39.0,10.0,0.0,1.0,1.0,0.0,93551.36,0.0
-10718,15583074.0,Yuryeva,620.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,121866.87,0.0
-10719,15723105.0,Aitken,575.0,France,Male,23.0,7.0,0.0,1.0,1.0,0.0,151902.78,0.0
-10720,15686885.0,Chiang,611.0,Germany,Female,40.0,2.0,114318.78,1.0,0.0,1.0,121551.58,1.0
-10721,15693771.0,Nnonso,587.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,64872.33,0.0
-10722,15790355.0,Hsia,636.0,Spain,Male,54.0,8.0,0.0,1.0,1.0,0.0,172096.08,1.0
-10723,15799435.0,P'eng,721.0,France,Female,32.0,4.0,0.0,2.0,0.0,1.0,150390.57,0.0
-10724,15603065.0,Chiu,652.0,France,Male,42.0,1.0,0.0,2.0,1.0,0.0,183566.78,0.0
-10725,15744553.0,Chiu,745.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,82872.64,0.0
-10726,15644856.0,Chikezie,719.0,Germany,Female,36.0,5.0,133628.09,1.0,1.0,0.0,55725.04,1.0
-10727,15792646.0,Chukwudi,653.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,1276.87,0.0
-10728,15774558.0,Omeokachie,704.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,151460.86,0.0
-10729,15663438.0,Hao,683.0,Germany,Male,32.0,8.0,108914.17,1.0,1.0,1.0,136610.02,0.0
-10730,15794941.0,Piazza,644.0,Germany,Male,36.0,9.0,91219.29,2.0,0.0,1.0,84487.62,0.0
-10731,15731246.0,Nwabugwu,652.0,Spain,Male,43.0,8.0,0.0,2.0,1.0,1.0,116885.59,0.0
-10732,15642530.0,Ch'ang,624.0,France,Male,24.0,1.0,0.0,2.0,1.0,1.0,48559.19,0.0
-10733,15583681.0,Nnaife,599.0,Spain,Male,33.0,3.0,0.0,2.0,0.0,0.0,51454.06,0.0
-10734,15637240.0,Nkemjika,773.0,Germany,Male,47.0,8.0,125890.49,1.0,1.0,0.0,82391.06,1.0
-10735,15579287.0,Tsao,607.0,Spain,Male,68.0,9.0,0.0,1.0,1.0,0.0,113906.55,1.0
-10736,15693871.0,Shao,649.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,184570.04,0.0
-10737,15667871.0,K?,544.0,France,Male,40.0,3.0,0.0,2.0,0.0,1.0,72350.47,0.0
-10738,15781589.0,Chikezie,706.0,France,Female,35.0,4.0,0.0,1.0,1.0,1.0,183542.08,0.0
-10739,15798521.0,Baresi,605.0,France,Male,52.0,8.0,0.0,1.0,1.0,0.0,167973.63,1.0
-10740,15593973.0,Olisanugo,667.0,Germany,Female,36.0,5.0,99640.89,2.0,1.0,0.0,122996.76,0.0
-10741,15583692.0,Ch'ien,678.0,France,Male,50.0,2.0,0.0,2.0,1.0,1.0,125079.65,0.0
-10742,15632263.0,Ch'ien,648.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,54949.73,0.0
-10743,15577695.0,Hsiao,683.0,Germany,Female,47.0,1.0,140431.98,1.0,1.0,0.0,103379.46,1.0
-10744,15656901.0,Hsueh,713.0,Spain,Male,40.0,10.0,0.0,2.0,1.0,1.0,107640.25,0.0
-10745,15593683.0,Maughan,744.0,France,Male,41.0,3.0,0.0,1.0,1.0,0.0,93718.44,1.0
-10746,15662494.0,Hsia,663.0,Spain,Male,33.0,6.0,171671.9,1.0,0.0,1.0,158049.9,0.0
-10747,15615050.0,Pisano,604.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,0.0,94767.77,0.0
-10748,15782659.0,K'ung,743.0,Spain,Male,36.0,5.0,0.0,2.0,0.0,0.0,115700.1,0.0
-10749,15802731.0,Hingston,516.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,161671.15,0.0
-10750,15591257.0,K?,583.0,Spain,Male,33.0,9.0,0.0,2.0,0.0,0.0,93739.71,0.0
-10751,15776824.0,Fanucci,679.0,France,Male,20.0,2.0,0.0,1.0,1.0,1.0,121525.48,0.0
-10752,15601271.0,Iadanza,682.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,0.0,115136.51,0.0
-10753,15632264.0,Ch'en,724.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,106920.57,0.0
-10754,15569423.0,Chien,579.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,0.0,141137.52,0.0
-10755,15677955.0,Onyeorulu,640.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,122925.44,0.0
-10756,15683841.0,Chia,613.0,Germany,Male,32.0,3.0,123374.46,1.0,1.0,1.0,103954.24,0.0
-10757,15577598.0,Cremonesi,604.0,Spain,Female,36.0,1.0,0.0,1.0,1.0,0.0,16081.62,0.0
-10758,15573851.0,Onyemauchechukwu,726.0,France,Male,31.0,6.0,123040.63,1.0,1.0,1.0,92977.95,0.0
-10759,15757952.0,Maclean,635.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,103480.69,1.0
-10760,15736146.0,Yobanna,608.0,Spain,Male,31.0,9.0,118082.89,1.0,1.0,1.0,129893.93,0.0
-10761,15628893.0,Nwankwo,603.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,116466.19,0.0
-10762,15604020.0,Hs?,577.0,Germany,Female,31.0,8.0,121827.43,1.0,1.0,0.0,140937.97,1.0
-10763,15795738.0,Chinomso,669.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,136859.55,0.0
-10764,15814275.0,Onwuamaegbu,687.0,Germany,Female,47.0,1.0,127570.93,1.0,1.0,0.0,143961.27,1.0
-10765,15618774.0,Chinomso,721.0,France,Male,29.0,2.0,121286.32,1.0,1.0,1.0,113931.57,0.0
-10766,15651802.0,Tien,629.0,France,Male,31.0,2.0,0.0,2.0,0.0,0.0,161465.31,0.0
-10767,15794413.0,Chuang,712.0,Spain,Male,42.0,4.0,0.0,1.0,1.0,0.0,116971.05,1.0
-10768,15793854.0,Maclean,554.0,Germany,Female,43.0,6.0,85982.07,1.0,1.0,1.0,80178.54,0.0
-10769,15694859.0,Chiu,679.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,97734.3,0.0
-10770,15789425.0,Pisano,567.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,158977.8,0.0
-10771,15736287.0,Chukwufumnanya,619.0,France,Male,28.0,8.0,0.0,1.0,1.0,0.0,75578.67,0.0
-10772,15672930.0,Chin,714.0,Germany,Male,33.0,6.0,117020.08,1.0,0.0,1.0,28805.09,0.0
-10773,15732020.0,Lucchesi,724.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,126336.72,0.0
-10774,15806945.0,Chukwudi,674.0,Germany,Male,48.0,5.0,116520.73,1.0,1.0,1.0,128875.86,0.0
-10775,15607786.0,T'ien,716.0,Spain,Male,43.0,4.0,125167.02,1.0,1.0,1.0,7861.68,0.0
-10776,15815070.0,Zuyeva,596.0,France,Female,32.0,5.0,0.0,2.0,0.0,1.0,151401.33,0.0
-10777,15795511.0,Chukwuemeka,600.0,Spain,Male,35.0,5.0,0.0,1.0,1.0,0.0,14073.11,0.0
-10778,15676905.0,Nkemdirim,579.0,France,Female,36.0,3.0,0.0,1.0,0.0,1.0,122518.5,1.0
-10779,15693690.0,Tsao,622.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,180800.42,0.0
-10780,15581526.0,Fiorentini,599.0,Spain,Male,30.0,3.0,0.0,2.0,1.0,1.0,180800.42,0.0
-10781,15652981.0,Liao,567.0,Spain,Female,22.0,2.0,0.0,1.0,1.0,1.0,108003.38,0.0
-10782,15587584.0,Ting,752.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,46824.08,0.0
-10783,15662426.0,Baresi,653.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,98896.06,0.0
-10784,15655175.0,McIntosh,657.0,France,Female,56.0,3.0,0.0,1.0,1.0,0.0,73679.59,1.0
-10785,15771211.0,Wilson,535.0,France,Female,51.0,7.0,126550.0,1.0,1.0,0.0,173792.49,1.0
-10786,15638247.0,McLean,652.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,177852.57,0.0
-10787,15757537.0,Tuan,731.0,Germany,Male,45.0,3.0,118627.53,2.0,0.0,0.0,118029.07,1.0
-10788,15717286.0,Mazzanti,682.0,France,Female,55.0,9.0,0.0,1.0,1.0,0.0,168080.53,1.0
-10789,15737404.0,Chigbogu,604.0,France,Male,39.0,5.0,0.0,2.0,0.0,0.0,126728.27,0.0
-10790,15662291.0,T'ien,645.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,184570.04,0.0
-10791,15732266.0,Hao,621.0,Spain,Male,25.0,5.0,138881.04,1.0,1.0,1.0,143022.31,0.0
-10792,15740164.0,Y?,576.0,France,Female,36.0,7.0,122314.5,1.0,1.0,1.0,73977.23,0.0
-10793,15632050.0,Hsiao,559.0,France,Female,56.0,10.0,0.0,2.0,1.0,1.0,158071.69,1.0
-10794,15641850.0,H?,668.0,Spain,Male,32.0,4.0,0.0,2.0,0.0,1.0,84475.13,0.0
-10795,15682421.0,Yermolayeva,679.0,France,Female,41.0,8.0,0.0,2.0,1.0,1.0,136675.22,0.0
-10796,15805062.0,Ch'ien,592.0,France,Female,28.0,5.0,96888.39,1.0,1.0,1.0,91094.46,0.0
-10797,15673582.0,Nnamutaezinwa,602.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,12761.16,0.0
-10798,15646936.0,Tsao,821.0,France,Female,52.0,10.0,0.0,1.0,1.0,1.0,103608.06,1.0
-10799,15594862.0,Hsia,717.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,123882.73,0.0
-10800,15720671.0,Chigbogu,697.0,Spain,Female,42.0,1.0,81677.22,1.0,1.0,0.0,134873.41,0.0
-10801,15781689.0,Mancini,659.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,113581.87,0.0
-10802,15680752.0,Chiemenam,703.0,France,Male,39.0,7.0,141203.68,1.0,1.0,0.0,169964.12,0.0
-10803,15741865.0,Fan,714.0,France,Male,47.0,5.0,0.0,2.0,0.0,1.0,173365.89,0.0
-10804,15684861.0,Chukwudi,730.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,57553.98,0.0
-10805,15628059.0,Shao,743.0,France,Female,40.0,4.0,0.0,1.0,1.0,1.0,74042.0,1.0
-10806,15753831.0,K?,639.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,80190.36,0.0
-10807,15697042.0,Ch'iu,624.0,France,Male,34.0,4.0,171740.69,1.0,1.0,0.0,121451.21,0.0
-10808,15728706.0,Tien,726.0,Spain,Male,29.0,7.0,0.0,2.0,1.0,1.0,102655.31,0.0
-10809,15780128.0,Shih,655.0,France,Female,42.0,3.0,0.0,2.0,1.0,1.0,167033.83,0.0
-10810,15752312.0,Mazzanti,619.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,53943.21,0.0
-10811,15758025.0,Yermolayeva,699.0,Spain,Female,34.0,5.0,0.0,1.0,0.0,1.0,171922.72,0.0
-10812,15703394.0,Ch'in,624.0,Spain,Male,36.0,2.0,0.0,2.0,1.0,0.0,117622.8,0.0
-10813,15712596.0,Genovese,662.0,France,Male,32.0,3.0,0.0,2.0,1.0,1.0,127068.92,0.0
-10814,15666856.0,Pirozzi,579.0,Spain,Male,31.0,4.0,0.0,2.0,1.0,1.0,178348.33,0.0
-10815,15695872.0,Goliwe,642.0,France,Female,45.0,3.0,0.0,2.0,1.0,1.0,24991.92,0.0
-10816,15695499.0,Ch'iu,806.0,France,Male,30.0,6.0,0.0,1.0,1.0,1.0,126726.33,0.0
-10817,15585047.0,Fiorentini,669.0,Spain,Female,35.0,4.0,0.0,1.0,0.0,0.0,56865.62,1.0
-10818,15642885.0,Nnonso,659.0,Germany,Female,29.0,3.0,121702.73,1.0,1.0,0.0,176580.89,0.0
-10819,15691446.0,Nkemakolam,521.0,France,Female,30.0,9.0,0.0,2.0,0.0,1.0,93717.55,0.0
-10820,15686963.0,Pagnotto,730.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,49133.45,0.0
-10821,15650098.0,Chidiegwu,611.0,Germany,Female,22.0,5.0,116695.27,1.0,1.0,1.0,137739.02,1.0
-10822,15642515.0,Zikoranachidimma,602.0,France,Female,41.0,5.0,0.0,2.0,0.0,0.0,47422.09,0.0
-10823,15576623.0,Chidiegwu,653.0,France,Female,37.0,2.0,0.0,2.0,0.0,1.0,155743.48,0.0
-10824,15728167.0,Ch'ien,754.0,France,Female,35.0,6.0,0.0,2.0,0.0,0.0,172049.84,0.0
-10825,15701175.0,Chiganu,684.0,Germany,Female,40.0,9.0,141827.33,1.0,1.0,1.0,52795.15,1.0
-10826,15585892.0,Chukwujekwu,544.0,France,Male,35.0,2.0,0.0,2.0,0.0,0.0,137029.97,0.0
-10827,15750447.0,Calabrese,678.0,France,Female,29.0,10.0,0.0,2.0,0.0,0.0,117946.3,0.0
-10828,15645593.0,Uwaezuoke,698.0,France,Female,46.0,1.0,0.0,1.0,0.0,0.0,145789.71,1.0
-10829,15654870.0,Ts'ai,543.0,France,Female,40.0,4.0,0.0,2.0,1.0,0.0,121551.93,0.0
-10830,15764892.0,Mazzi,699.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,92797.23,0.0
-10831,15656937.0,Kao,613.0,France,Male,33.0,8.0,102723.35,1.0,1.0,0.0,88827.67,0.0
-10832,15742229.0,Chukwufumnanya,707.0,Germany,Male,52.0,6.0,152685.4,1.0,1.0,0.0,86950.72,1.0
-10833,15636396.0,Mazzi,742.0,France,Female,48.0,5.0,133537.1,1.0,1.0,0.0,137674.55,1.0
-10834,15585892.0,Ts'ao,619.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,177357.16,0.0
-10835,15796218.0,Hsueh,656.0,France,Male,40.0,3.0,0.0,2.0,0.0,0.0,134600.94,0.0
-10836,15603465.0,Chiemenam,628.0,Germany,Male,43.0,1.0,119963.45,1.0,1.0,0.0,55775.72,1.0
-10837,15710365.0,Chuang,636.0,France,Female,32.0,10.0,0.0,1.0,1.0,0.0,151902.78,0.0
-10838,15574785.0,Chukwualuka,789.0,France,Male,40.0,10.0,0.0,2.0,1.0,0.0,85516.37,0.0
-10839,15636684.0,Nnanna,689.0,Spain,Male,24.0,2.0,0.0,2.0,1.0,1.0,132870.15,0.0
-10840,15583736.0,T'ien,685.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,110114.38,0.0
-10841,15661275.0,T'ang,703.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,169862.01,0.0
-10842,15632210.0,Nwabugwu,639.0,Germany,Male,43.0,3.0,141136.65,2.0,1.0,0.0,153698.65,1.0
-10843,15633650.0,Nwachinemelu,714.0,Spain,Male,28.0,9.0,0.0,1.0,1.0,0.0,115083.73,1.0
-10844,15586931.0,Pacheco,850.0,France,Female,40.0,7.0,0.0,2.0,0.0,0.0,61793.47,0.0
-10845,15570087.0,Hsing,611.0,France,Female,40.0,9.0,0.0,1.0,0.0,1.0,126726.33,0.0
-10846,15655574.0,T'ao,597.0,France,Male,30.0,4.0,0.0,2.0,0.0,0.0,100127.71,0.0
-10847,15806771.0,Calabresi,735.0,Germany,Female,35.0,4.0,109949.05,1.0,0.0,1.0,142514.66,0.0
-10848,15756871.0,Zetticci,577.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,28391.52,1.0
-10849,15581736.0,Ch'ien,621.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,0.0,187597.98,0.0
-10850,15672216.0,Hsieh,663.0,France,Male,31.0,5.0,128742.57,1.0,1.0,0.0,11950.29,0.0
-10851,15646936.0,K?,697.0,France,Female,22.0,7.0,0.0,1.0,1.0,0.0,162599.51,0.0
-10852,15721582.0,Okechukwu,716.0,France,Male,27.0,8.0,0.0,2.0,1.0,1.0,118913.53,0.0
-10853,15672966.0,Tung,749.0,France,Female,56.0,8.0,0.0,2.0,1.0,1.0,57549.65,0.0
-10854,15672640.0,Nwachinemelu,537.0,France,Female,30.0,5.0,106152.68,2.0,1.0,1.0,92916.53,0.0
-10855,15594577.0,Sagese,677.0,Germany,Male,36.0,4.0,134497.27,1.0,1.0,1.0,114093.73,1.0
-10856,15571928.0,Y?an,746.0,France,Female,47.0,5.0,0.0,2.0,1.0,1.0,145524.36,0.0
-10857,15751032.0,Chukwuemeka,679.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,75959.1,0.0
-10858,15761348.0,Genovese,676.0,Spain,Female,39.0,3.0,156848.13,1.0,1.0,1.0,173870.39,0.0
-10859,15756056.0,Ritchie,539.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,85547.33,0.0
-10860,15702571.0,Onwumelu,644.0,France,Female,42.0,3.0,0.0,1.0,1.0,0.0,83419.15,1.0
-10861,15756471.0,Chukwualuka,757.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,84760.32,0.0
-10862,15615797.0,Rizzo,660.0,France,Female,36.0,5.0,164850.54,1.0,1.0,1.0,155711.64,0.0
-10863,15631739.0,Tien,661.0,Spain,Male,44.0,4.0,121355.19,1.0,0.0,0.0,74637.57,0.0
-10864,15696674.0,Hs?,573.0,Spain,Male,32.0,5.0,0.0,1.0,1.0,1.0,50911.41,0.0
-10865,15782591.0,Ch'en,775.0,Germany,Male,29.0,2.0,154733.94,2.0,0.0,1.0,90928.02,0.0
-10866,15581252.0,Iheanacho,605.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,79342.31,0.0
-10867,15601172.0,Ting,717.0,France,Male,35.0,3.0,0.0,1.0,0.0,1.0,74836.64,0.0
-10868,15692929.0,Genovesi,632.0,Spain,Male,42.0,1.0,125724.95,2.0,0.0,1.0,125909.85,0.0
-10869,15649160.0,Hao,678.0,France,Male,34.0,7.0,187530.66,1.0,1.0,0.0,126812.63,0.0
-10870,15718673.0,Davide,682.0,France,Female,33.0,2.0,0.0,1.0,0.0,0.0,73371.65,1.0
-10871,15800811.0,Chinwendu,653.0,Germany,Male,29.0,5.0,143595.87,2.0,1.0,0.0,182436.81,0.0
-10872,15603764.0,Golubev,553.0,Germany,Male,38.0,9.0,102088.68,1.0,1.0,1.0,108577.97,1.0
-10873,15633866.0,Zuyeva,678.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,43486.73,0.0
-10874,15694143.0,Lu,619.0,Germany,Male,43.0,3.0,89086.31,2.0,0.0,1.0,142566.75,0.0
-10875,15807923.0,Chukwueloka,727.0,Germany,Female,41.0,9.0,152827.99,1.0,1.0,1.0,9085.0,0.0
-10876,15622454.0,Hsia,714.0,France,Female,31.0,2.0,0.0,1.0,1.0,1.0,110593.62,0.0
-10877,15774393.0,T'ien,535.0,France,Male,28.0,7.0,0.0,2.0,0.0,1.0,93783.0,0.0
-10878,15809847.0,Ts'ao,551.0,France,Female,37.0,4.0,0.0,2.0,1.0,1.0,56905.38,0.0
-10879,15625247.0,Hsueh,648.0,Germany,Male,32.0,1.0,99097.33,1.0,1.0,1.0,152097.02,0.0
-10880,15762715.0,Nwachukwu,708.0,Spain,Female,34.0,1.0,61688.61,2.0,1.0,1.0,177938.52,0.0
-10881,15581526.0,Smith,632.0,Spain,Male,36.0,2.0,0.0,2.0,0.0,0.0,26689.18,0.0
-10882,15703793.0,T'ang,539.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,57898.58,0.0
-10883,15586699.0,L?,703.0,Germany,Female,36.0,1.0,148507.24,2.0,1.0,0.0,91510.61,0.0
-10884,15587584.0,Tien,581.0,Germany,Male,36.0,10.0,140170.61,2.0,1.0,0.0,121904.14,0.0
-10885,15573888.0,Chu,675.0,Germany,Female,32.0,0.0,97266.1,2.0,1.0,0.0,61792.76,0.0
-10886,15674929.0,Chukwukadibia,677.0,France,Female,36.0,0.0,0.0,1.0,1.0,0.0,9983.88,0.0
-10887,15631739.0,Hsueh,706.0,Spain,Female,42.0,8.0,0.0,1.0,1.0,0.0,114020.06,1.0
-10888,15784597.0,Chinonyelum,809.0,Germany,Male,51.0,4.0,148071.69,1.0,1.0,1.0,161811.23,1.0
-10889,15633108.0,Onyekachukwu,661.0,France,Female,61.0,9.0,0.0,1.0,1.0,1.0,38550.4,1.0
-10890,15762708.0,Gartrell,785.0,France,Male,41.0,0.0,0.0,1.0,1.0,1.0,81409.0,1.0
-10891,15726490.0,Ch'ang,762.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,62634.94,0.0
-10892,15778025.0,Yefimova,585.0,France,Male,45.0,5.0,0.0,1.0,1.0,0.0,93360.37,1.0
-10893,15651823.0,Milano,643.0,France,Female,33.0,5.0,0.0,2.0,1.0,1.0,48849.89,1.0
-10894,15578799.0,Tsou,818.0,France,Male,28.0,7.0,0.0,2.0,1.0,0.0,102908.96,0.0
-10895,15585466.0,Chukwuebuka,662.0,Spain,Female,48.0,2.0,118627.06,3.0,0.0,0.0,46824.08,1.0
-10896,15576676.0,Obinna,648.0,Germany,Female,37.0,4.0,71497.79,1.0,0.0,0.0,19161.82,1.0
-10897,15757811.0,Ugochukwutubelum,754.0,Spain,Male,42.0,10.0,132319.26,1.0,1.0,1.0,74838.64,0.0
-10898,15769005.0,Linger,704.0,Germany,Male,41.0,6.0,106648.98,1.0,1.0,0.0,183569.25,0.0
-10899,15703068.0,Lettiere,592.0,France,Male,38.0,1.0,0.0,1.0,1.0,0.0,64350.8,0.0
-10900,15717995.0,Kao,549.0,Spain,Female,43.0,3.0,0.0,3.0,1.0,0.0,97734.3,1.0
-10901,15682533.0,Chukwuemeka,633.0,France,Male,34.0,9.0,0.0,2.0,1.0,1.0,70550.0,0.0
-10902,15586069.0,Ting,613.0,Spain,Male,36.0,6.0,0.0,1.0,1.0,1.0,102665.95,0.0
-10903,15589488.0,McIntosh,621.0,France,Male,46.0,3.0,0.0,1.0,1.0,0.0,133692.88,1.0
-10904,15704509.0,Hsieh,594.0,France,Female,39.0,6.0,116967.68,1.0,1.0,1.0,115700.2,0.0
-10905,15701200.0,Ch'iu,633.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,27380.99,0.0
-10906,15766047.0,Yeh,749.0,Germany,Female,37.0,7.0,135096.77,2.0,1.0,0.0,176868.89,0.0
-10907,15595592.0,Uwaezuoke,599.0,Spain,Female,34.0,4.0,0.0,2.0,0.0,1.0,131644.31,0.0
-10908,15682467.0,Lucciano,612.0,France,Female,37.0,7.0,106922.93,1.0,1.0,1.0,169415.54,0.0
-10909,15634375.0,Ch'iu,771.0,Spain,Female,44.0,9.0,0.0,2.0,0.0,0.0,153605.75,0.0
-10910,15631681.0,Clements,677.0,France,Female,42.0,9.0,81733.74,1.0,1.0,0.0,132959.55,0.0
-10911,15783629.0,Lai,662.0,Germany,Female,48.0,6.0,132558.26,1.0,1.0,1.0,23305.01,1.0
-10912,15745354.0,Fanucci,648.0,France,Female,33.0,4.0,0.0,2.0,1.0,1.0,86622.77,0.0
-10913,15775490.0,Brown,653.0,Germany,Female,52.0,9.0,106134.46,3.0,1.0,1.0,179365.1,1.0
-10914,15694209.0,Nkemdilim,679.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,82782.08,0.0
-10915,15605837.0,Chien,762.0,Germany,Female,35.0,6.0,122314.5,2.0,1.0,0.0,104902.68,0.0
-10916,15569438.0,Chuang,667.0,France,Female,44.0,9.0,0.0,1.0,1.0,0.0,142400.27,1.0
-10917,15702789.0,Chukwueloka,643.0,France,Female,39.0,8.0,0.0,2.0,1.0,0.0,167036.94,0.0
-10918,15798895.0,Hsia,769.0,France,Female,40.0,2.0,0.0,2.0,1.0,1.0,75632.78,0.0
-10919,15583548.0,Nwankwo,653.0,France,Female,31.0,3.0,0.0,1.0,1.0,0.0,65801.44,0.0
-10920,15697034.0,Chukwumaobim,646.0,France,Female,44.0,6.0,0.0,1.0,1.0,0.0,92916.54,1.0
-10921,15732643.0,Kao,716.0,Spain,Male,43.0,10.0,0.0,1.0,1.0,0.0,117632.63,1.0
-10922,15651264.0,Chukwufumnanya,627.0,Spain,Male,44.0,2.0,0.0,2.0,1.0,0.0,74860.99,0.0
-10923,15610355.0,Chiang,597.0,France,Male,56.0,3.0,0.0,4.0,1.0,0.0,102936.71,1.0
-10924,15659100.0,Nwora,681.0,Spain,Female,48.0,6.0,0.0,1.0,1.0,0.0,121053.19,1.0
-10925,15692408.0,Okechukwu,597.0,Germany,Female,30.0,6.0,153344.72,1.0,1.0,1.0,51380.9,0.0
-10926,15806360.0,Ch'ang,596.0,France,Male,36.0,7.0,0.0,1.0,1.0,0.0,94488.82,0.0
-10927,15683553.0,Chiang,707.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,124571.09,0.0
-10928,15586522.0,Balsillie,729.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,1.0,80582.34,0.0
-10929,15668830.0,Y?,758.0,Germany,Male,38.0,1.0,127475.24,1.0,0.0,1.0,155721.15,0.0
-10930,15579387.0,Hs?,758.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,1.0,174431.01,0.0
-10931,15704763.0,T'ien,687.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,24208.84,0.0
-10932,15773639.0,Eluemuno,644.0,Germany,Female,40.0,6.0,115532.52,1.0,0.0,1.0,91903.9,1.0
-10933,15654495.0,Shao,709.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,121789.3,0.0
-10934,15786800.0,Hsueh,607.0,France,Female,37.0,8.0,0.0,1.0,1.0,1.0,92887.06,0.0
-10935,15739803.0,Chukwuemeka,850.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,132468.76,0.0
-10936,15710365.0,Chiang,646.0,Spain,Female,32.0,6.0,0.0,2.0,1.0,1.0,167066.95,0.0
-10937,15776939.0,Mao,744.0,France,Female,45.0,5.0,0.0,1.0,1.0,0.0,75825.57,1.0
-10938,15761706.0,Nwachukwu,627.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,165614.4,0.0
-10939,15732643.0,Yeh,586.0,Spain,Female,41.0,9.0,142319.02,1.0,1.0,1.0,140650.19,0.0
-10940,15597896.0,Ozerova,748.0,France,Male,40.0,8.0,0.0,1.0,1.0,1.0,128521.17,0.0
-10941,15762682.0,H?,648.0,Spain,Male,47.0,2.0,129745.1,1.0,1.0,0.0,97629.31,1.0
-10942,15699005.0,Chukwuemeka,714.0,Spain,Female,55.0,2.0,0.0,2.0,0.0,1.0,163798.41,0.0
-10943,15806967.0,Aitken,648.0,Germany,Male,41.0,8.0,118560.0,1.0,1.0,1.0,92913.94,0.0
-10944,15671293.0,Ch'iu,645.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,1.0,141528.56,0.0
-10945,15609356.0,Genovese,646.0,Spain,Female,42.0,2.0,139914.17,4.0,1.0,0.0,104889.3,1.0
-10946,15672945.0,Teng,602.0,France,Male,39.0,1.0,0.0,2.0,0.0,1.0,86460.28,0.0
-10947,15593973.0,T'ien,633.0,France,Male,37.0,4.0,133377.8,1.0,1.0,1.0,123775.15,0.0
-10948,15804072.0,Hsueh,678.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,79304.38,0.0
-10949,15576085.0,Hsu,638.0,Spain,Female,41.0,2.0,0.0,1.0,1.0,1.0,149535.75,1.0
-10950,15733883.0,Ch'eng,687.0,France,Male,24.0,9.0,143930.92,2.0,1.0,1.0,165035.17,0.0
-10951,15666916.0,Iloerika,572.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,126172.11,0.0
-10952,15771483.0,Czajkowski,678.0,Spain,Male,36.0,1.0,137794.18,1.0,1.0,1.0,193502.9,0.0
-10953,15643826.0,Chidiebere,709.0,Spain,Male,46.0,4.0,0.0,1.0,1.0,1.0,182963.31,1.0
-10954,15634310.0,Wang,516.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,137580.11,0.0
-10955,15583857.0,Onwuamaeze,850.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,1.0,89566.64,0.0
-10956,15802758.0,Mazzanti,663.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,161465.31,0.0
-10957,15806945.0,Ch'iu,692.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,69079.85,0.0
-10958,15768193.0,K'ung,671.0,France,Female,31.0,6.0,0.0,1.0,1.0,0.0,155711.64,0.0
-10959,15589715.0,Chiazagomekpere,651.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,162719.69,0.0
-10960,15666884.0,Nwankwo,705.0,Germany,Male,35.0,5.0,124401.6,2.0,1.0,0.0,90876.95,0.0
-10961,15607986.0,Nucci,774.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,141180.66,0.0
-10962,15617197.0,Ma,775.0,Spain,Female,43.0,8.0,0.0,2.0,0.0,0.0,132498.39,1.0
-10963,15765311.0,Lucchesi,575.0,France,Male,28.0,7.0,160515.37,1.0,1.0,1.0,82709.8,0.0
-10964,15778915.0,Onwuamaegbu,651.0,Spain,Male,67.0,8.0,0.0,2.0,1.0,0.0,130132.54,0.0
-10965,15732917.0,Onochie,637.0,Spain,Male,29.0,3.0,140133.08,1.0,1.0,1.0,180551.76,0.0
-10966,15689671.0,Ukaegbunam,593.0,Spain,Male,47.0,4.0,0.0,2.0,1.0,1.0,137793.35,0.0
-10967,15766355.0,Onyemauchechukwu,683.0,Germany,Female,32.0,2.0,127864.26,2.0,0.0,0.0,158750.53,0.0
-10968,15787597.0,Nnaife,635.0,Spain,Male,32.0,8.0,0.0,2.0,0.0,0.0,92568.05,0.0
-10969,15700650.0,Chukwudi,605.0,Spain,Male,39.0,7.0,0.0,2.0,0.0,0.0,90896.04,0.0
-10970,15571221.0,K'ung,526.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,64443.75,1.0
-10971,15766295.0,Mazzanti,663.0,France,Female,52.0,6.0,98635.77,1.0,1.0,1.0,184524.65,1.0
-10972,15777873.0,Ts'ui,584.0,France,Female,29.0,8.0,91078.76,1.0,1.0,0.0,66937.6,0.0
-10973,15598044.0,Pirozzi,662.0,France,Male,37.0,8.0,137817.52,1.0,0.0,0.0,122964.18,0.0
-10974,15783816.0,Ebelegbulam,554.0,France,Male,52.0,6.0,135645.29,1.0,1.0,0.0,141706.43,1.0
-10975,15656829.0,Genovese,637.0,France,Male,33.0,2.0,0.0,1.0,1.0,0.0,141860.62,0.0
-10976,15574830.0,Ch'in,793.0,France,Male,31.0,2.0,133676.65,1.0,1.0,1.0,101728.46,0.0
-10977,15656426.0,Chukwueloka,597.0,France,Female,35.0,5.0,0.0,2.0,1.0,1.0,179436.6,0.0
-10978,15664521.0,Chijindum,516.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,49656.24,0.0
-10979,15798850.0,Uwaezuoke,609.0,Spain,Female,33.0,9.0,0.0,2.0,1.0,1.0,151368.37,0.0
-10980,15803947.0,Nwora,627.0,France,Female,33.0,8.0,120165.3,1.0,1.0,0.0,52411.99,0.0
-10981,15774936.0,Hsueh,710.0,France,Male,29.0,10.0,133501.36,1.0,0.0,0.0,125079.65,0.0
-10982,15808760.0,Shih,716.0,France,Male,26.0,3.0,0.0,2.0,1.0,0.0,72993.67,0.0
-10983,15791154.0,Yobachi,681.0,France,Male,36.0,2.0,155619.36,1.0,1.0,0.0,88890.05,0.0
-10984,15682355.0,H?,625.0,France,Male,31.0,8.0,0.0,2.0,1.0,1.0,87071.18,0.0
-10985,15719352.0,Chiu,603.0,Spain,Female,27.0,9.0,0.0,1.0,1.0,0.0,85736.46,0.0
-10986,15758813.0,H?,549.0,France,Female,38.0,0.0,0.0,2.0,1.0,1.0,129912.77,0.0
-10987,15628893.0,Brown,659.0,France,Male,33.0,1.0,0.0,2.0,1.0,0.0,74048.1,0.0
-10988,15749693.0,Hsu,456.0,France,Female,38.0,8.0,113882.31,1.0,1.0,1.0,89566.74,0.0
-10989,15677908.0,Chiekwugo,599.0,Germany,Female,55.0,9.0,114493.82,1.0,1.0,0.0,145894.9,1.0
-10990,15692761.0,Y?,663.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,0.0,85937.52,0.0
-10991,15770030.0,T'ang,565.0,France,Female,43.0,3.0,0.0,1.0,1.0,0.0,57553.02,0.0
-10992,15613140.0,Trevisani,586.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,0.0,134626.3,0.0
-10993,15769955.0,Tuan,745.0,France,Female,35.0,7.0,0.0,2.0,0.0,0.0,65464.66,0.0
-10994,15773017.0,Mazzi,687.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,93713.13,0.0
-10995,15682773.0,Nwokike,669.0,France,Male,35.0,3.0,0.0,1.0,1.0,1.0,138275.01,0.0
-10996,15803037.0,Yobanna,577.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,184843.77,0.0
-10997,15798824.0,Genovesi,719.0,Spain,Female,31.0,4.0,0.0,2.0,1.0,1.0,90876.95,0.0
-10998,15721921.0,Maclean,836.0,Spain,Male,40.0,1.0,0.0,2.0,0.0,1.0,130846.98,0.0
-10999,15643865.0,Ts'ui,573.0,France,Female,24.0,6.0,82498.14,1.0,0.0,0.0,130132.41,0.0
-11000,15729582.0,Onwuamaeze,545.0,Germany,Male,46.0,9.0,102278.71,1.0,1.0,0.0,136902.01,1.0
-11001,15716041.0,Milanesi,686.0,France,Male,24.0,6.0,0.0,2.0,1.0,1.0,184524.74,0.0
-11002,15584812.0,K'ung,662.0,Spain,Male,45.0,7.0,0.0,2.0,1.0,0.0,143585.29,0.0
-11003,15708713.0,Lo Duca,571.0,Germany,Female,33.0,5.0,122340.54,2.0,1.0,1.0,99136.49,0.0
-11004,15781802.0,Kao,762.0,Spain,Female,34.0,5.0,0.0,1.0,1.0,1.0,4791.8,0.0
-11005,15805909.0,Biryukov,494.0,France,Female,34.0,3.0,0.0,2.0,0.0,0.0,176570.28,0.0
-11006,15750121.0,Nwabugwu,678.0,Germany,Female,46.0,8.0,108722.57,1.0,1.0,0.0,176580.63,1.0
-11007,15750808.0,Chibugo,579.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,169763.89,0.0
-11008,15623053.0,Onyekachi,624.0,Germany,Female,55.0,3.0,130652.52,1.0,1.0,0.0,182336.45,1.0
-11009,15736146.0,Onyekachukwu,753.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,68400.14,0.0
-11010,15796480.0,Ch'ien,707.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,156424.4,0.0
-11011,15781689.0,Zuev,604.0,Spain,Male,47.0,9.0,0.0,2.0,1.0,0.0,117572.59,0.0
-11012,15611186.0,Hs?eh,632.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,0.0,180591.31,0.0
-11013,15794048.0,Tuan,531.0,France,Male,43.0,9.0,0.0,1.0,1.0,1.0,133537.57,0.0
-11014,15610936.0,Genovese,613.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,55775.72,0.0
-11015,15764294.0,Ch'in,729.0,Spain,Male,38.0,4.0,0.0,1.0,0.0,1.0,79005.95,0.0
-11016,15776545.0,Chu,544.0,Spain,Female,29.0,1.0,0.0,2.0,1.0,0.0,149170.92,0.0
-11017,15592089.0,Bibi,719.0,Spain,Female,26.0,8.0,0.0,1.0,1.0,1.0,125423.42,0.0
-11018,15789560.0,McLean,634.0,France,Male,28.0,9.0,0.0,2.0,1.0,1.0,103713.93,0.0
-11019,15707025.0,Yudina,637.0,Spain,Male,47.0,2.0,0.0,2.0,1.0,0.0,36251.0,0.0
-11020,15682773.0,Chiganu,692.0,France,Male,32.0,9.0,0.0,2.0,0.0,0.0,172810.52,0.0
-11021,15781589.0,Shao,559.0,Spain,Male,38.0,4.0,93920.47,1.0,1.0,0.0,140780.8,0.0
-11022,15723847.0,Hsueh,659.0,Spain,Female,35.0,1.0,0.0,2.0,0.0,1.0,142917.54,1.0
-11023,15591130.0,Chiawuotu,478.0,France,Female,42.0,4.0,0.0,2.0,1.0,0.0,158071.69,0.0
-11024,15571752.0,Kao,729.0,France,Male,52.0,9.0,122570.87,1.0,0.0,1.0,140136.1,0.0
-11025,15787035.0,Ch'in,745.0,France,Male,32.0,6.0,152576.25,1.0,1.0,0.0,120107.1,0.0
-11026,15804787.0,Ch'ien,578.0,France,Male,33.0,4.0,0.0,2.0,0.0,1.0,99645.04,0.0
-11027,15773520.0,Teng,599.0,Germany,Male,31.0,1.0,110654.02,2.0,1.0,1.0,53394.11,0.0
-11028,15651369.0,Iadanza,638.0,France,Female,46.0,1.0,0.0,2.0,1.0,1.0,57876.06,0.0
-11029,15675176.0,Ebelegbulam,605.0,France,Female,44.0,7.0,0.0,1.0,0.0,1.0,199505.53,0.0
-11030,15575391.0,Chia,721.0,Spain,Female,36.0,9.0,92716.95,1.0,1.0,0.0,81292.33,0.0
-11031,15642710.0,Chiang,584.0,Germany,Male,38.0,9.0,141806.46,1.0,1.0,1.0,163666.85,0.0
-11032,15681924.0,Pirozzi,794.0,Germany,Male,44.0,8.0,170184.99,1.0,1.0,0.0,104889.3,0.0
-11033,15783338.0,Chidubem,558.0,Spain,Female,44.0,0.0,93578.68,1.0,1.0,1.0,157822.54,1.0
-11034,15685300.0,Ugochukwu,611.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,1.0,27244.35,0.0
-11035,15713644.0,Oluchi,683.0,France,Male,24.0,4.0,0.0,2.0,1.0,1.0,98820.09,0.0
-11036,15690772.0,Ugochukwutubelum,656.0,France,Female,45.0,5.0,0.0,1.0,1.0,0.0,141180.66,1.0
-11037,15781598.0,Nkemdilim,539.0,France,Male,59.0,6.0,118897.1,1.0,1.0,0.0,67840.81,1.0
-11038,15656829.0,Milanesi,753.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,35300.08,0.0
-11039,15667971.0,Smith,582.0,France,Female,40.0,6.0,0.0,1.0,0.0,0.0,81251.24,1.0
-11040,15763683.0,Walker,527.0,Germany,Male,33.0,5.0,133501.33,2.0,1.0,0.0,159737.56,0.0
-11041,15684512.0,Obialo,678.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,101102.39,0.0
-11042,15798964.0,Ch'en,592.0,Germany,Male,33.0,7.0,109908.06,1.0,1.0,1.0,73881.68,0.0
-11043,15730161.0,Hsing,678.0,Spain,Male,34.0,3.0,123557.81,1.0,1.0,1.0,136079.64,0.0
-11044,15622171.0,Uspensky,586.0,Spain,Male,31.0,2.0,0.0,2.0,0.0,0.0,79957.95,0.0
-11045,15583090.0,H?,699.0,France,Male,35.0,0.0,0.0,2.0,0.0,0.0,136028.97,0.0
-11046,15570073.0,K?,683.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,177784.68,0.0
-11047,15745080.0,Chukwukere,722.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,1.0,166495.2,0.0
-11048,15569247.0,P'eng,686.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,158750.53,0.0
-11049,15636444.0,Yobanna,700.0,Spain,Female,60.0,6.0,0.0,3.0,1.0,0.0,60920.03,1.0
-11050,15748691.0,Udegbulam,757.0,France,Male,49.0,8.0,0.0,1.0,1.0,0.0,142935.83,1.0
-11051,15603088.0,Iweobiegbunam,712.0,France,Female,28.0,5.0,0.0,1.0,1.0,1.0,57844.26,0.0
-11052,15642725.0,Tien,726.0,France,Male,37.0,1.0,0.0,2.0,0.0,1.0,136497.28,0.0
-11053,15574324.0,Chizuoke,688.0,France,Female,37.0,3.0,65812.5,1.0,1.0,0.0,138564.48,1.0
-11054,15800517.0,Zuyev,723.0,Germany,Female,36.0,4.0,73481.05,1.0,1.0,1.0,150242.44,0.0
-11055,15616676.0,Burtch,602.0,Spain,Male,32.0,2.0,134766.89,1.0,1.0,1.0,182258.17,0.0
-11056,15584517.0,Martin,633.0,Germany,Male,29.0,9.0,71281.29,2.0,1.0,0.0,106851.6,0.0
-11057,15641091.0,Iadanza,774.0,Spain,Male,31.0,9.0,0.0,2.0,1.0,1.0,154491.39,0.0
-11058,15634373.0,Yermolayeva,822.0,France,Male,32.0,1.0,139872.41,1.0,1.0,1.0,56780.97,0.0
-11059,15804064.0,McWilliams,717.0,France,Male,43.0,2.0,0.0,2.0,1.0,1.0,81299.17,0.0
-11060,15775235.0,Wright,773.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,60584.33,0.0
-11061,15645095.0,Kaur,526.0,Germany,Male,53.0,3.0,104533.24,1.0,1.0,0.0,85902.83,1.0
-11062,15692671.0,Ch'iu,646.0,France,Male,37.0,9.0,0.0,1.0,1.0,1.0,71179.31,0.0
-11063,15606841.0,Nwora,627.0,Spain,Male,41.0,1.0,0.0,1.0,1.0,0.0,143981.27,1.0
-11064,15762091.0,Tuan,746.0,France,Female,35.0,8.0,0.0,2.0,1.0,1.0,115668.53,0.0
-11065,15728474.0,T'ien,686.0,France,Female,60.0,9.0,0.0,1.0,1.0,0.0,123977.86,1.0
-11066,15778395.0,McLean,614.0,France,Female,41.0,8.0,0.0,2.0,0.0,0.0,126554.96,0.0
-11067,15716085.0,Nwachukwu,592.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,142838.64,0.0
-11068,15585100.0,Onyemachukwu,653.0,France,Female,48.0,10.0,0.0,1.0,1.0,0.0,127413.0,1.0
-11069,15738220.0,Fanucci,720.0,Spain,Male,43.0,9.0,130486.57,1.0,1.0,0.0,51565.96,1.0
-11070,15675320.0,Liao,656.0,France,Male,29.0,1.0,0.0,1.0,1.0,0.0,97938.59,0.0
-11071,15702561.0,Ch'iu,574.0,Germany,Female,63.0,1.0,155519.59,1.0,1.0,0.0,116766.79,1.0
-11072,15671779.0,Chidozie,652.0,France,Female,23.0,3.0,0.0,2.0,1.0,0.0,143345.65,0.0
-11073,15762480.0,Lu,792.0,Spain,Female,43.0,0.0,108586.86,1.0,1.0,0.0,107876.2,0.0
-11074,15661828.0,Tuan,584.0,Spain,Female,41.0,9.0,151858.98,1.0,1.0,0.0,93835.64,0.0
-11075,15699450.0,Ch'iu,681.0,France,Male,39.0,2.0,0.0,2.0,0.0,0.0,173870.39,0.0
-11076,15648726.0,Chien,602.0,France,Male,43.0,6.0,0.0,1.0,1.0,1.0,113089.43,0.0
-11077,15792934.0,McIntyre,762.0,Germany,Female,49.0,3.0,122447.76,1.0,0.0,1.0,118716.63,1.0
-11078,15767414.0,Tardent,516.0,France,Male,60.0,1.0,0.0,1.0,1.0,1.0,108419.41,1.0
-11079,15581244.0,Hsia,708.0,Germany,Female,40.0,2.0,150725.53,2.0,1.0,0.0,14081.64,0.0
-11080,15671244.0,Ugorji,559.0,Spain,Female,33.0,3.0,0.0,1.0,1.0,1.0,142377.69,1.0
-11081,15600174.0,Ting,669.0,France,Male,25.0,4.0,0.0,2.0,1.0,0.0,162858.29,0.0
-11082,15747757.0,Ch'en,735.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,64872.55,0.0
-11083,15746410.0,McElhone,635.0,France,Male,39.0,1.0,0.0,2.0,0.0,0.0,140579.17,0.0
-11084,15790846.0,Fan,687.0,Germany,Female,45.0,7.0,92674.94,3.0,1.0,1.0,91633.53,1.0
-11085,15794479.0,Ting,709.0,Germany,Male,36.0,1.0,170214.23,2.0,1.0,1.0,85746.52,0.0
-11086,15656627.0,Nwankwo,730.0,Germany,Male,34.0,5.0,81732.88,1.0,1.0,0.0,135635.25,0.0
-11087,15685300.0,Palerma,569.0,Germany,Male,22.0,4.0,103914.17,1.0,0.0,1.0,135088.58,0.0
-11088,15789563.0,Ch'en,686.0,Spain,Female,31.0,7.0,138333.03,1.0,1.0,0.0,60905.51,0.0
-11089,15780128.0,Ijendu,683.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,167773.55,0.0
-11090,15626212.0,Dike,687.0,Germany,Male,32.0,4.0,125724.95,2.0,0.0,1.0,100557.03,0.0
-11091,15658258.0,H?,684.0,France,Male,27.0,6.0,0.0,2.0,1.0,0.0,180598.66,0.0
-11092,15592022.0,Ugochukwu,705.0,France,Male,31.0,7.0,95377.13,1.0,0.0,0.0,88695.19,0.0
-11093,15661528.0,Liao,583.0,Spain,Female,39.0,8.0,0.0,2.0,1.0,0.0,89891.9,0.0
-11094,15661036.0,Ch'ien,726.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,156048.64,0.0
-11095,15795519.0,Frolov,764.0,Germany,Male,42.0,9.0,86911.7,1.0,0.0,0.0,88965.46,1.0
-11096,15665861.0,Wilson,537.0,Germany,Female,51.0,1.0,103818.47,1.0,0.0,1.0,89566.64,1.0
-11097,15726588.0,Chukwubuikem,614.0,Spain,Male,67.0,2.0,0.0,2.0,1.0,1.0,164741.15,0.0
-11098,15696859.0,Mazzanti,671.0,France,Male,34.0,2.0,116099.82,2.0,1.0,1.0,143086.09,0.0
-11099,15623450.0,Genovese,751.0,France,Male,37.0,8.0,0.0,2.0,0.0,0.0,134901.56,0.0
-11100,15768342.0,K?,591.0,France,Female,40.0,7.0,0.0,2.0,1.0,0.0,6076.62,0.0
-11101,15733247.0,Fokine,708.0,France,Male,45.0,5.0,142872.36,1.0,1.0,0.0,142995.32,0.0
-11102,15646615.0,Teng,611.0,France,Male,35.0,8.0,0.0,1.0,1.0,0.0,142915.84,0.0
-11103,15677317.0,Chukwukere,645.0,Spain,Female,39.0,7.0,0.0,1.0,1.0,0.0,199256.98,1.0
-11104,15607986.0,Hsing,735.0,France,Female,46.0,4.0,0.0,2.0,1.0,1.0,86935.08,0.0
-11105,15579969.0,Chin,644.0,Germany,Female,38.0,4.0,102994.04,1.0,1.0,0.0,120834.81,0.0
-11106,15633043.0,Tuan,631.0,France,Male,28.0,8.0,0.0,2.0,1.0,1.0,79240.9,0.0
-11107,15783338.0,Chidiebele,605.0,France,Male,28.0,9.0,0.0,1.0,1.0,1.0,152843.53,0.0
-11108,15571940.0,Pisani,677.0,France,Male,33.0,3.0,0.0,2.0,0.0,1.0,92888.52,0.0
-11109,15753837.0,Tuan,591.0,France,Male,39.0,0.0,111098.85,1.0,0.0,1.0,74838.64,0.0
-11110,15799785.0,Mazzanti,525.0,France,Female,51.0,2.0,117035.89,1.0,1.0,0.0,49374.82,1.0
-11111,15597591.0,Lu,707.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,80963.92,0.0
-11112,15753747.0,Nwankwo,609.0,France,Female,33.0,9.0,0.0,1.0,0.0,1.0,140800.66,0.0
-11113,15593365.0,Ch'in,681.0,Spain,Female,42.0,6.0,0.0,2.0,1.0,0.0,118862.08,0.0
-11114,15671769.0,Nkemakonam,656.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,176763.27,0.0
-11115,15713374.0,Kable,773.0,France,Male,41.0,5.0,0.0,2.0,1.0,0.0,52331.15,0.0
-11116,15789413.0,Nkemakolam,685.0,France,Male,39.0,8.0,0.0,2.0,0.0,1.0,172495.81,0.0
-11117,15586870.0,Ch'ien,718.0,Germany,Female,54.0,2.0,101207.75,1.0,1.0,0.0,152499.8,1.0
-11118,15650086.0,Chuang,614.0,Spain,Male,35.0,10.0,61710.42,1.0,0.0,0.0,53394.24,0.0
-11119,15734461.0,Ting,692.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,16081.62,0.0
-11120,15616666.0,Y?an,467.0,Germany,Female,46.0,4.0,102955.14,1.0,1.0,0.0,85547.33,1.0
-11121,15690731.0,Chukwufumnanya,706.0,Germany,Male,45.0,2.0,105522.66,1.0,1.0,1.0,132770.15,0.0
-11122,15696812.0,Onwuamaeze,681.0,France,Male,28.0,4.0,0.0,2.0,1.0,1.0,162960.05,0.0
-11123,15641170.0,Sheppard,686.0,Germany,Female,38.0,6.0,130854.59,2.0,0.0,1.0,75562.47,0.0
-11124,15752312.0,Chu,730.0,France,Female,35.0,9.0,0.0,1.0,1.0,0.0,184524.65,0.0
-11125,15786463.0,Chikezie,624.0,France,Female,34.0,5.0,0.0,1.0,0.0,0.0,113931.57,0.0
-11126,15663923.0,Hsing,678.0,France,Female,41.0,8.0,0.0,2.0,1.0,1.0,48161.18,0.0
-11127,15635367.0,Lai,687.0,France,Female,27.0,3.0,129834.67,1.0,1.0,1.0,94780.9,0.0
-11128,15643496.0,Ch'ien,663.0,Spain,Male,43.0,9.0,0.0,2.0,1.0,0.0,47777.15,1.0
-11129,15713505.0,Shih,551.0,Spain,Male,43.0,9.0,0.0,2.0,1.0,0.0,150468.31,0.0
-11130,15622535.0,Nwankwo,595.0,France,Male,57.0,3.0,0.0,2.0,1.0,0.0,96463.25,1.0
-11131,15685829.0,Chizoba,825.0,Spain,Female,25.0,1.0,0.0,1.0,1.0,0.0,180591.32,0.0
-11132,15772554.0,Kao,583.0,France,Female,40.0,4.0,0.0,2.0,1.0,1.0,100834.22,0.0
-11133,15742632.0,Rizzo,681.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,171430.16,0.0
-11134,15630698.0,Y?,724.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,97623.42,0.0
-11135,15782941.0,Chigozie,646.0,Spain,Male,26.0,5.0,0.0,2.0,0.0,1.0,142548.64,0.0
-11136,15719309.0,Hsiung,684.0,Germany,Male,41.0,1.0,115341.55,1.0,1.0,0.0,53511.85,0.0
-11137,15615032.0,Chu,804.0,France,Male,24.0,9.0,125830.74,1.0,1.0,0.0,155879.57,0.0
-11138,15803378.0,Ogochukwu,648.0,Germany,Female,53.0,3.0,119961.48,1.0,1.0,0.0,159718.16,1.0
-11139,15781347.0,Zuyeva,717.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,63981.37,0.0
-11140,15743582.0,Lai,656.0,France,Male,40.0,8.0,0.0,1.0,1.0,1.0,177655.69,0.0
-11141,15627801.0,Ch'in,733.0,France,Female,31.0,5.0,0.0,2.0,1.0,1.0,172618.52,0.0
-11142,15696900.0,Pinto,587.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,159800.16,0.0
-11143,15756969.0,King,773.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,67979.35,0.0
-11144,15731781.0,Y?,743.0,France,Male,35.0,1.0,0.0,1.0,1.0,0.0,93714.29,0.0
-11145,15737506.0,Clements,684.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,180716.1,0.0
-11146,15659557.0,Zetticci,571.0,Spain,Female,38.0,5.0,136733.24,1.0,1.0,0.0,102299.14,0.0
-11147,15736295.0,O'Donnell,708.0,France,Male,28.0,4.0,0.0,2.0,0.0,0.0,146891.07,0.0
-11148,15621267.0,Chiu,729.0,Spain,Male,22.0,3.0,0.0,2.0,1.0,0.0,151733.43,0.0
-11149,15803689.0,L?,564.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,180890.4,0.0
-11150,15693906.0,Zetticci,572.0,France,Male,28.0,6.0,0.0,1.0,1.0,0.0,130846.82,0.0
-11151,15654878.0,Dike,612.0,Germany,Male,33.0,5.0,148897.03,1.0,1.0,0.0,136497.28,0.0
-11152,15727489.0,Nnachetam,741.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,158973.04,0.0
-11153,15722061.0,Hs?,687.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,1.0,176692.65,0.0
-11154,15688849.0,Hsu,707.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,160529.71,0.0
-11155,15632789.0,Chukwukere,607.0,France,Female,39.0,1.0,0.0,1.0,1.0,1.0,56650.47,0.0
-11156,15597945.0,Ts'ao,645.0,Spain,Female,32.0,2.0,68423.89,2.0,1.0,0.0,138889.15,1.0
-11157,15596979.0,T'an,644.0,Spain,Male,33.0,4.0,0.0,2.0,0.0,1.0,186841.71,0.0
-11158,15667460.0,Chukwualuka,677.0,France,Female,29.0,7.0,0.0,2.0,1.0,1.0,79866.73,0.0
-11159,15751180.0,Chukwufumnanya,594.0,Germany,Male,21.0,2.0,123971.51,2.0,1.0,1.0,61251.25,0.0
-11160,15626008.0,Chia,745.0,France,Male,28.0,6.0,0.0,2.0,0.0,0.0,128881.87,0.0
-11161,15666055.0,Chien,811.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,145789.71,0.0
-11162,15682600.0,Iloerika,735.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,123866.43,0.0
-11163,15705343.0,Uchechukwu,562.0,France,Male,38.0,8.0,108722.57,1.0,1.0,1.0,165008.97,0.0
-11164,15728885.0,Chukwualuka,577.0,Spain,Male,32.0,6.0,88915.85,1.0,0.0,1.0,72993.67,0.0
-11165,15632275.0,Chikelu,711.0,Spain,Male,35.0,5.0,88902.21,1.0,1.0,0.0,103931.49,0.0
-11166,15751180.0,Trevisano,755.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,86657.48,0.0
-11167,15585855.0,Lai,603.0,France,Male,33.0,7.0,77599.23,1.0,1.0,1.0,131625.14,0.0
-11168,15808846.0,Chinomso,632.0,France,Female,37.0,7.0,0.0,2.0,1.0,1.0,94420.5,0.0
-11169,15708048.0,Macdonald,599.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,88878.15,0.0
-11170,15690958.0,Nwebube,615.0,France,Male,33.0,5.0,150139.59,2.0,1.0,1.0,79919.97,0.0
-11171,15633194.0,White,678.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,582.59,0.0
-11172,15637646.0,Folliero,705.0,France,Male,51.0,8.0,0.0,2.0,1.0,0.0,118959.64,1.0
-11173,15662494.0,Walker,613.0,France,Male,40.0,6.0,108797.63,1.0,1.0,1.0,159800.16,0.0
-11174,15686916.0,Toscani,648.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,122433.09,0.0
-11175,15791045.0,Chukwuebuka,790.0,Spain,Female,34.0,0.0,0.0,2.0,1.0,0.0,151842.31,0.0
-11176,15682467.0,Zito,663.0,Germany,Male,37.0,5.0,95560.04,1.0,0.0,1.0,146411.6,0.0
-11177,15675380.0,Ugoji,544.0,France,Female,49.0,5.0,0.0,1.0,0.0,0.0,131718.12,1.0
-11178,15802071.0,Nnaife,753.0,Germany,Female,31.0,1.0,108432.88,1.0,0.0,1.0,82298.59,0.0
-11179,15704844.0,Hsiao,726.0,Germany,Female,51.0,2.0,104305.77,2.0,1.0,0.0,123955.83,1.0
-11180,15807593.0,Douglas,633.0,Germany,Male,39.0,1.0,117468.67,1.0,0.0,1.0,173844.89,0.0
-11181,15790659.0,Tien,690.0,France,Female,39.0,6.0,91285.22,1.0,1.0,0.0,937.15,0.0
-11182,15652626.0,T'ao,585.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,186489.97,0.0
-11183,15726796.0,Young,686.0,France,Female,33.0,6.0,0.0,2.0,0.0,1.0,53437.1,0.0
-11184,15633870.0,Chidiegwu,602.0,France,Female,35.0,9.0,153663.13,1.0,0.0,0.0,93777.15,0.0
-11185,15681703.0,Hs?,727.0,France,Male,44.0,5.0,0.0,1.0,1.0,1.0,148527.56,0.0
-11186,15664808.0,Aikenhead,777.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,141776.62,0.0
-11187,15576623.0,P'an,477.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,108732.96,0.0
-11188,15751531.0,Lucciano,679.0,Germany,Male,32.0,9.0,111096.91,2.0,1.0,0.0,187597.98,0.0
-11189,15630106.0,Pirozzi,719.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,152883.95,0.0
-11190,15611759.0,Nnanna,613.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,131312.56,0.0
-11191,15777797.0,Tung,756.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,141533.19,0.0
-11192,15593501.0,Ch'ang,806.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,10928.3,0.0
-11193,15684960.0,Olisanugo,577.0,France,Male,41.0,9.0,120916.52,1.0,1.0,1.0,70783.61,0.0
-11194,15695792.0,Trevisano,586.0,France,Female,46.0,6.0,116880.53,1.0,0.0,0.0,161241.65,1.0
-11195,15626452.0,Chukwufumnanya,628.0,France,Female,39.0,9.0,0.0,2.0,0.0,1.0,64833.08,0.0
-11196,15642870.0,Onwumelu,699.0,France,Female,37.0,3.0,0.0,1.0,1.0,1.0,126812.63,0.0
-11197,15756680.0,Nnaife,754.0,France,Male,22.0,3.0,0.0,2.0,1.0,1.0,152883.95,0.0
-11198,15699238.0,Golibe,679.0,France,Male,46.0,6.0,0.0,2.0,0.0,1.0,108469.2,0.0
-11199,15732643.0,Chinedum,684.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,107640.25,0.0
-11200,15672610.0,Ogochukwu,585.0,Germany,Male,27.0,1.0,137761.55,1.0,1.0,1.0,171430.16,0.0
-11201,15656822.0,Nnonso,594.0,Spain,Male,24.0,2.0,0.0,2.0,0.0,1.0,143301.49,0.0
-11202,15757837.0,Yermolayeva,711.0,Spain,Male,43.0,8.0,0.0,1.0,0.0,1.0,51364.56,1.0
-11203,15781802.0,Milne,628.0,France,Female,40.0,6.0,0.0,2.0,1.0,0.0,71830.1,0.0
-11204,15794890.0,Lombardo,581.0,France,Male,37.0,7.0,63723.78,1.0,1.0,1.0,25951.91,1.0
-11205,15645571.0,Ch'ang,824.0,France,Male,41.0,8.0,0.0,1.0,1.0,0.0,180329.83,0.0
-11206,15608968.0,P'eng,749.0,Germany,Female,38.0,5.0,108907.8,2.0,1.0,0.0,63889.1,0.0
-11207,15781702.0,Chukwualuka,743.0,Germany,Female,31.0,9.0,103391.38,1.0,1.0,1.0,73979.85,0.0
-11208,15781409.0,Shih,713.0,Germany,Female,48.0,7.0,107715.84,1.0,0.0,0.0,121406.62,1.0
-11209,15673372.0,T'ao,785.0,Spain,Female,42.0,10.0,0.0,2.0,1.0,0.0,114774.35,0.0
-11210,15770121.0,Kao,580.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,105887.04,0.0
-11211,15750121.0,Ukaegbunam,597.0,France,Male,39.0,2.0,140614.15,1.0,1.0,0.0,193018.71,0.0
-11212,15699340.0,Iloerika,577.0,France,Female,58.0,2.0,0.0,2.0,1.0,1.0,98640.78,0.0
-11213,15711457.0,Ch'ang,688.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,120906.83,0.0
-11214,15587565.0,Houghton,588.0,Germany,Female,42.0,3.0,121730.49,1.0,1.0,0.0,51565.89,1.0
-11215,15632118.0,Ch'eng,660.0,France,Female,28.0,2.0,0.0,2.0,1.0,1.0,157091.34,0.0
-11216,15626044.0,Udinesi,711.0,France,Female,43.0,0.0,171671.9,1.0,1.0,0.0,171364.22,1.0
-11217,15691817.0,Nnonso,646.0,France,Male,35.0,1.0,0.0,1.0,1.0,0.0,187976.88,1.0
-11218,15641490.0,O'Loughlin,516.0,France,Female,26.0,1.0,0.0,2.0,1.0,1.0,81861.1,0.0
-11219,15758035.0,Fanucci,488.0,Spain,Male,33.0,2.0,87637.35,1.0,1.0,0.0,120830.73,0.0
-11220,15592461.0,Ts'ao,695.0,Spain,Male,44.0,10.0,0.0,2.0,1.0,0.0,60905.51,0.0
-11221,15773789.0,Zuyev,727.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,0.0,96832.82,0.0
-11222,15578908.0,McIntosh,833.0,France,Male,45.0,3.0,0.0,3.0,1.0,0.0,169152.76,1.0
-11223,15647725.0,Milanesi,705.0,Germany,Female,31.0,3.0,87772.52,1.0,0.0,1.0,130610.78,0.0
-11224,15777045.0,H?,739.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,55775.72,0.0
-11225,15698031.0,Kornilova,594.0,France,Male,53.0,1.0,0.0,2.0,1.0,0.0,187616.16,1.0
-11226,15787830.0,Goliwe,653.0,France,Female,34.0,10.0,0.0,2.0,0.0,0.0,170829.87,0.0
-11227,15648858.0,Ch'in,757.0,Germany,Female,34.0,4.0,106434.94,1.0,1.0,0.0,59755.14,0.0
-11228,15662232.0,Ni,487.0,Germany,Female,28.0,7.0,124577.33,1.0,1.0,0.0,86786.42,1.0
-11229,15767391.0,Nnonso,739.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,185599.34,0.0
-11230,15607877.0,Mazzanti,698.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,9087.81,0.0
-11231,15793883.0,Chukwufumnanya,684.0,Germany,Female,30.0,7.0,114944.0,1.0,0.0,0.0,181294.39,0.0
-11232,15648876.0,Lucchese,655.0,France,Male,59.0,7.0,0.0,1.0,1.0,0.0,175015.12,1.0
-11233,15581740.0,Chiang,651.0,Germany,Female,54.0,3.0,137523.02,1.0,1.0,1.0,137537.13,0.0
-11234,15765266.0,Fanucci,603.0,France,Female,33.0,2.0,0.0,1.0,1.0,0.0,129563.86,0.0
-11235,15778526.0,Teng,686.0,Germany,Female,56.0,6.0,132405.52,1.0,1.0,1.0,107876.2,1.0
-11236,15673367.0,Onyeorulu,676.0,France,Female,38.0,1.0,0.0,2.0,1.0,0.0,122662.98,0.0
-11237,15616172.0,Hargreaves,746.0,Germany,Female,40.0,2.0,116499.43,2.0,1.0,1.0,182059.28,0.0
-11238,15723008.0,Nwachinemelu,596.0,France,Male,35.0,5.0,111827.27,1.0,1.0,1.0,38131.77,0.0
-11239,15793893.0,Chinedum,549.0,France,Male,51.0,8.0,0.0,1.0,1.0,1.0,132673.23,1.0
-11240,15652693.0,Shih,605.0,France,Female,46.0,4.0,0.0,1.0,1.0,0.0,121189.3,1.0
-11241,15583582.0,Lai,748.0,Spain,Male,30.0,4.0,0.0,2.0,0.0,0.0,131736.23,0.0
-11242,15758031.0,Ndubuagha,687.0,France,Male,25.0,2.0,0.0,2.0,1.0,0.0,172576.43,0.0
-11243,15705890.0,Chimaobim,564.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,126551.65,0.0
-11244,15693322.0,Hsiung,730.0,Spain,Male,33.0,1.0,0.0,3.0,1.0,1.0,152992.04,0.0
-11245,15589488.0,Galkin,624.0,Spain,Male,35.0,9.0,178058.06,2.0,0.0,1.0,25684.93,0.0
-11246,15752984.0,P'an,667.0,France,Male,39.0,3.0,0.0,2.0,0.0,0.0,174094.24,0.0
-11247,15796218.0,Ponomarev,662.0,Germany,Female,32.0,8.0,114193.24,1.0,1.0,1.0,51554.58,0.0
-11248,15619304.0,Kornilova,738.0,France,Male,50.0,2.0,143391.07,1.0,1.0,1.0,184514.01,1.0
-11249,15704910.0,Ch'in,642.0,Spain,Male,35.0,4.0,0.0,2.0,1.0,0.0,177099.71,0.0
-11250,15733169.0,Maclean,678.0,France,Female,53.0,3.0,0.0,2.0,1.0,1.0,27145.73,1.0
-11251,15745012.0,Onio,651.0,Spain,Male,44.0,5.0,151607.56,2.0,0.0,1.0,76544.9,1.0
-11252,15749489.0,Kincaid,752.0,France,Female,31.0,2.0,0.0,1.0,1.0,1.0,171345.02,0.0
-11253,15763625.0,Chukwujamuike,596.0,France,Female,42.0,5.0,0.0,2.0,1.0,1.0,75732.25,0.0
-11254,15731511.0,Chien,592.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,47322.05,0.0
-11255,15631681.0,Calabresi,744.0,France,Male,38.0,7.0,86847.19,1.0,1.0,1.0,107876.2,0.0
-11256,15761706.0,Lettiere,738.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,0.0,134878.19,0.0
-11257,15688079.0,Mazzanti,582.0,France,Male,31.0,8.0,110349.82,1.0,0.0,1.0,125710.53,0.0
-11258,15597957.0,Ch'eng,757.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,54474.48,0.0
-11259,15793046.0,Ch'eng,721.0,France,Male,29.0,10.0,0.0,2.0,1.0,1.0,142839.81,0.0
-11260,15661349.0,Onyeorulu,704.0,Spain,Male,40.0,5.0,0.0,1.0,1.0,0.0,162515.17,1.0
-11261,15780140.0,Fanucci,826.0,Germany,Male,54.0,6.0,151139.65,3.0,1.0,0.0,124964.82,1.0
-11262,15692991.0,Lueck,593.0,Germany,Female,36.0,5.0,83624.55,2.0,1.0,0.0,143096.49,1.0
-11263,15767339.0,Toscani,678.0,Spain,Female,34.0,1.0,0.0,1.0,1.0,1.0,186489.95,0.0
-11264,15781465.0,Nkemdirim,643.0,Germany,Female,44.0,6.0,119496.85,1.0,1.0,0.0,166513.49,1.0
-11265,15662532.0,Ch'in,574.0,France,Female,42.0,6.0,0.0,1.0,1.0,1.0,150944.41,1.0
-11266,15615473.0,Macleod,724.0,France,Male,29.0,3.0,140733.74,1.0,0.0,1.0,96929.24,0.0
-11267,15682585.0,Chiu,696.0,Spain,Female,38.0,4.0,0.0,2.0,1.0,0.0,152884.85,0.0
-11268,15649033.0,Onyemauchechi,681.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,1099.95,0.0
-11269,15800440.0,Folliero,704.0,Germany,Male,29.0,6.0,114430.16,1.0,1.0,0.0,72993.65,0.0
-11270,15688074.0,Nnachetam,637.0,France,Female,44.0,5.0,0.0,2.0,1.0,0.0,1285.48,0.0
-11271,15777047.0,Ch'iu,710.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,163516.16,0.0
-11272,15770995.0,Chiganu,619.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,56984.58,0.0
-11273,15789611.0,Mironova,556.0,Germany,Male,54.0,9.0,153060.63,1.0,0.0,1.0,134778.01,1.0
-11274,15602844.0,Lu,627.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,167931.81,0.0
-11275,15572472.0,Shih,643.0,France,Female,40.0,9.0,0.0,2.0,1.0,0.0,182988.94,0.0
-11276,15639133.0,Chien,685.0,France,Female,67.0,2.0,0.0,1.0,1.0,1.0,127875.1,0.0
-11277,15671534.0,Smith,665.0,Germany,Male,44.0,1.0,104488.19,1.0,1.0,1.0,57817.84,1.0
-11278,15723720.0,Onwuamaeze,577.0,France,Female,34.0,2.0,0.0,2.0,0.0,0.0,107985.4,0.0
-11279,15691792.0,T'an,751.0,France,Female,26.0,2.0,0.0,1.0,0.0,1.0,123872.0,0.0
-11280,15775335.0,Manna,584.0,Spain,Male,30.0,9.0,0.0,2.0,1.0,0.0,113950.51,0.0
-11281,15593973.0,Brownless,678.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,1.0,151481.86,0.0
-11282,15596972.0,Ch'ien,688.0,France,Female,36.0,4.0,0.0,2.0,0.0,1.0,84902.4,0.0
-11283,15783865.0,Tung,749.0,Germany,Male,30.0,9.0,135889.3,1.0,1.0,0.0,171039.56,0.0
-11284,15625916.0,Oluchukwu,805.0,France,Male,39.0,10.0,0.0,2.0,1.0,0.0,131177.17,0.0
-11285,15573318.0,Hsia,627.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,645.61,0.0
-11286,15673599.0,Palerma,689.0,Germany,Female,40.0,3.0,103469.69,1.0,1.0,0.0,77500.81,1.0
-11287,15696900.0,Chiemenam,652.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,66580.13,0.0
-11288,15763613.0,Palerma,605.0,France,Male,48.0,7.0,0.0,2.0,1.0,1.0,177613.17,0.0
-11289,15683524.0,Fanucci,665.0,France,Male,40.0,7.0,0.0,2.0,1.0,0.0,52327.57,0.0
-11290,15655952.0,Hsia,686.0,France,Female,42.0,4.0,134919.54,2.0,1.0,0.0,145551.6,1.0
-11291,15683522.0,Dufresne,758.0,Spain,Male,32.0,4.0,0.0,2.0,1.0,1.0,93844.89,0.0
-11292,15694366.0,T'ao,755.0,France,Female,24.0,5.0,0.0,2.0,1.0,1.0,47777.15,0.0
-11293,15586699.0,Nwora,663.0,Germany,Male,52.0,1.0,108891.7,1.0,1.0,0.0,194808.51,1.0
-11294,15681115.0,Chiu,613.0,Germany,Female,29.0,5.0,128747.69,2.0,1.0,1.0,161465.31,1.0
-11295,15784700.0,Liao,577.0,France,Male,33.0,5.0,92612.88,1.0,1.0,0.0,61811.1,0.0
-11296,15703544.0,O'Donnell,599.0,France,Male,40.0,3.0,108128.96,1.0,1.0,1.0,102640.52,0.0
-11297,15679961.0,Piazza,850.0,Germany,Female,42.0,1.0,131394.56,1.0,1.0,1.0,79476.63,0.0
-11298,15631261.0,Hsueh,466.0,Spain,Female,43.0,1.0,0.0,2.0,1.0,1.0,142223.94,0.0
-11299,15706899.0,Olisanugo,678.0,Germany,Female,36.0,6.0,178718.19,2.0,0.0,1.0,170195.4,0.0
-11300,15755722.0,Padovesi,593.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,148087.62,0.0
-11301,15766066.0,Onyemauchechukwu,710.0,France,Male,37.0,3.0,0.0,2.0,1.0,0.0,24218.65,0.0
-11302,15626212.0,Zito,535.0,France,Female,30.0,5.0,166082.18,2.0,1.0,1.0,144584.29,0.0
-11303,15642570.0,Ch'ang,614.0,France,Male,40.0,3.0,0.0,2.0,0.0,0.0,146680.28,0.0
-11304,15671172.0,Onyekachi,637.0,Germany,Male,42.0,5.0,115221.36,1.0,1.0,1.0,6761.84,0.0
-11305,15790829.0,McIntyre,678.0,Germany,Male,35.0,2.0,152805.05,1.0,0.0,1.0,90908.95,1.0
-11306,15751203.0,Nwoye,718.0,Spain,Male,36.0,8.0,95120.59,1.0,0.0,1.0,53437.1,0.0
-11307,15665524.0,Palerma,611.0,Spain,Female,37.0,8.0,0.0,2.0,1.0,0.0,107876.2,0.0
-11308,15634143.0,Ch'iu,758.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,86678.48,0.0
-11309,15763093.0,Chukwuemeka,627.0,France,Female,26.0,1.0,0.0,1.0,1.0,1.0,93959.96,0.0
-11310,15791268.0,Macdonald,719.0,France,Male,32.0,1.0,0.0,1.0,0.0,1.0,123969.64,0.0
-11311,15583692.0,Mazzanti,605.0,Germany,Female,40.0,6.0,81564.1,2.0,1.0,1.0,126812.63,0.0
-11312,15613097.0,Tien,686.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,131712.52,0.0
-11313,15809800.0,Palermo,653.0,France,Female,31.0,5.0,0.0,2.0,0.0,1.0,90878.13,0.0
-11314,15781777.0,L?,699.0,Spain,Female,34.0,5.0,148069.69,1.0,0.0,0.0,79866.73,0.0
-11315,15734246.0,T'ien,807.0,France,Male,29.0,0.0,108632.48,2.0,1.0,0.0,131048.24,0.0
-11316,15690731.0,Chiang,646.0,Spain,Female,42.0,5.0,0.0,1.0,0.0,0.0,151903.6,1.0
-11317,15665327.0,Ugochukwutubelum,637.0,France,Male,30.0,9.0,145981.87,1.0,0.0,1.0,106040.97,0.0
-11318,15782360.0,O'Brien,709.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,145562.4,0.0
-11319,15663883.0,Yobanna,659.0,Spain,Male,41.0,8.0,121519.59,1.0,1.0,1.0,81299.17,0.0
-11320,15736413.0,Shen,714.0,France,Female,46.0,6.0,122314.5,1.0,1.0,1.0,64511.44,1.0
-11321,15662263.0,Tsou,678.0,France,Male,36.0,10.0,0.0,2.0,1.0,1.0,121866.87,0.0
-11322,15705001.0,Udegbunam,749.0,Spain,Male,31.0,1.0,146622.35,1.0,0.0,0.0,152887.23,0.0
-11323,15682585.0,Hsiung,565.0,Germany,Male,46.0,2.0,122314.5,1.0,1.0,1.0,117832.88,1.0
-11324,15782530.0,Ibezimako,602.0,France,Male,40.0,1.0,137781.65,1.0,1.0,0.0,162501.83,0.0
-11325,15792986.0,Lucchesi,633.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,116973.48,0.0
-11326,15803378.0,Ts'ui,708.0,Spain,Male,28.0,5.0,0.0,2.0,1.0,0.0,65765.22,0.0
-11327,15589019.0,Onwuamaeze,653.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,61977.82,0.0
-11328,15777907.0,Ch'in,684.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,1.0,136869.31,0.0
-11329,15594878.0,Palerma,678.0,Germany,Male,59.0,5.0,108437.89,1.0,0.0,1.0,66893.28,1.0
-11330,15593976.0,Kistler,835.0,Spain,Female,47.0,7.0,0.0,1.0,1.0,1.0,58854.88,1.0
-11331,15726494.0,Toscani,679.0,Germany,Female,34.0,3.0,126663.51,2.0,1.0,1.0,179861.79,0.0
-11332,15641582.0,Chukwuemeka,677.0,France,Female,36.0,5.0,0.0,2.0,0.0,1.0,79458.12,0.0
-11333,15716347.0,Onio,778.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,181490.42,0.0
-11334,15643824.0,Mancini,621.0,Germany,Male,36.0,9.0,79857.64,1.0,0.0,1.0,176984.34,0.0
-11335,15808960.0,Genovese,667.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,71433.88,0.0
-11336,15609510.0,Ch'ien,558.0,Germany,Female,42.0,2.0,92636.96,2.0,0.0,1.0,183963.34,0.0
-11337,15643826.0,Garmon,730.0,France,Female,35.0,1.0,0.0,1.0,0.0,0.0,106837.06,1.0
-11338,15668058.0,Chiang,692.0,France,Male,36.0,2.0,145974.79,1.0,1.0,0.0,183566.78,0.0
-11339,15802466.0,Trevisani,598.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,165082.25,0.0
-11340,15805769.0,Scott,727.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,58879.11,0.0
-11341,15782630.0,Lucciano,697.0,France,Female,56.0,1.0,0.0,1.0,1.0,0.0,154957.46,1.0
-11342,15684958.0,Chukwufumnanya,546.0,France,Male,43.0,8.0,140576.11,1.0,1.0,0.0,96794.95,0.0
-11343,15605076.0,Cremonesi,653.0,France,Male,25.0,2.0,0.0,2.0,1.0,0.0,72008.61,0.0
-11344,15737571.0,Chidubem,704.0,France,Female,37.0,4.0,0.0,1.0,1.0,1.0,161811.23,1.0
-11345,15662532.0,Milanesi,756.0,France,Female,38.0,7.0,157556.06,1.0,0.0,0.0,100940.91,0.0
-11346,15783541.0,Ogochukwu,669.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,83910.4,0.0
-11347,15697510.0,Nkemdilim,637.0,France,Male,59.0,8.0,0.0,2.0,1.0,1.0,85901.09,0.0
-11348,15693382.0,Hsu,710.0,France,Female,58.0,10.0,0.0,2.0,1.0,1.0,52327.57,0.0
-11349,15570932.0,Chijioke,684.0,France,Male,32.0,9.0,112712.17,1.0,1.0,0.0,103603.65,0.0
-11350,15631071.0,Ch'iu,535.0,France,Female,33.0,1.0,0.0,2.0,0.0,0.0,120810.63,0.0
-11351,15756850.0,Onwumelu,743.0,France,Male,30.0,10.0,0.0,2.0,1.0,1.0,84760.32,0.0
-11352,15643658.0,Ch'eng,535.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,173629.66,0.0
-11353,15732621.0,Ts'ui,721.0,France,Male,33.0,4.0,146710.76,1.0,0.0,0.0,102628.98,0.0
-11354,15707025.0,Hsing,516.0,France,Male,40.0,2.0,145536.9,1.0,1.0,1.0,185547.71,0.0
-11355,15677317.0,Fanucci,687.0,France,Male,51.0,8.0,121523.51,1.0,1.0,0.0,160574.06,0.0
-11356,15651369.0,Ch'eng,762.0,Germany,Male,45.0,8.0,132450.24,1.0,1.0,0.0,28789.94,0.0
-11357,15651883.0,Chiang,642.0,Germany,Male,35.0,10.0,90425.15,1.0,1.0,1.0,100834.22,0.0
-11358,15643976.0,Nkemakonam,735.0,France,Male,44.0,0.0,0.0,2.0,1.0,0.0,176984.34,1.0
-11359,15586039.0,Mazzanti,611.0,France,Female,34.0,3.0,0.0,1.0,1.0,1.0,186489.95,0.0
-11360,15683625.0,Kao,553.0,Spain,Female,55.0,2.0,0.0,2.0,1.0,1.0,143829.54,0.0
-11361,15732270.0,T'ien,607.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,74832.46,0.0
-11362,15724161.0,Biryukova,726.0,France,Female,20.0,3.0,0.0,1.0,1.0,1.0,77965.44,0.0
-11363,15743709.0,Ch'in,523.0,France,Male,55.0,10.0,0.0,1.0,1.0,1.0,153012.13,1.0
-11364,15726354.0,Rizzo,684.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,0.0,52449.62,0.0
-11365,15659937.0,Mazzanti,713.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,149458.61,0.0
-11366,15744273.0,Chukwuebuka,710.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,62347.71,0.0
-11367,15609998.0,Fyodorova,634.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,175894.24,0.0
-11368,15655410.0,Y?,664.0,Spain,Female,48.0,9.0,0.0,2.0,1.0,0.0,160129.99,0.0
-11369,15777784.0,Milanesi,616.0,France,Male,49.0,5.0,0.0,1.0,0.0,0.0,93524.19,1.0
-11370,15813067.0,Ch'iu,699.0,Spain,Male,40.0,9.0,0.0,2.0,0.0,0.0,142238.54,0.0
-11371,15808845.0,Scott,597.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,94774.12,0.0
-11372,15761076.0,Chuang,738.0,Spain,Female,32.0,2.0,0.0,2.0,1.0,1.0,67611.36,0.0
-11373,15659710.0,Ch'iu,592.0,France,Female,30.0,4.0,0.0,1.0,1.0,1.0,158853.98,1.0
-11374,15583079.0,Smith,633.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,100556.98,0.0
-11375,15743545.0,Chukwuebuka,682.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,178373.93,0.0
-11376,15576085.0,Lu,588.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,138526.96,0.0
-11377,15761497.0,Chukwuemeka,646.0,France,Female,27.0,1.0,0.0,2.0,1.0,1.0,145558.42,0.0
-11378,15722072.0,Folliero,581.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,129975.54,0.0
-11379,15794857.0,Hs?,611.0,France,Male,58.0,1.0,121355.99,1.0,1.0,0.0,74850.93,1.0
-11380,15618562.0,Chukwufumnanya,732.0,France,Male,45.0,1.0,0.0,1.0,1.0,1.0,115566.57,1.0
-11381,15642099.0,Hs?,703.0,France,Female,40.0,8.0,145988.65,1.0,1.0,0.0,131501.72,0.0
-11382,15609899.0,Goliwe,705.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,116748.14,0.0
-11383,15686957.0,Hsiao,659.0,France,Female,48.0,9.0,0.0,2.0,0.0,1.0,186869.86,0.0
-11384,15708505.0,Smith,679.0,Germany,Female,55.0,8.0,118890.75,1.0,1.0,0.0,138350.74,1.0
-11385,15658449.0,Dyer,721.0,France,Male,30.0,7.0,131287.36,1.0,1.0,1.0,59325.21,0.0
-11386,15658946.0,Hsing,619.0,Spain,Male,41.0,4.0,141786.78,1.0,1.0,0.0,88890.05,1.0
-11387,15593683.0,Ts'ui,711.0,Spain,Female,37.0,9.0,0.0,2.0,0.0,1.0,177613.19,0.0
-11388,15613463.0,Nwabugwu,651.0,Spain,Male,33.0,7.0,121263.62,2.0,1.0,0.0,62733.05,0.0
-11389,15746708.0,Hs?,556.0,Germany,Male,49.0,9.0,141099.43,1.0,1.0,1.0,170528.63,1.0
-11390,15585455.0,Nwabugwu,656.0,Spain,Female,53.0,7.0,0.0,2.0,1.0,1.0,49848.86,0.0
-11391,15703394.0,Ch'en,662.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,16911.66,0.0
-11392,15752622.0,Hsu,531.0,Germany,Male,34.0,4.0,126492.57,2.0,1.0,1.0,115468.84,0.0
-11393,15745804.0,Ch'ien,588.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,151369.31,1.0
-11394,15771674.0,P'an,639.0,France,Female,43.0,7.0,0.0,1.0,0.0,1.0,187632.23,1.0
-11395,15582404.0,P'eng,692.0,France,Male,29.0,5.0,0.0,2.0,1.0,1.0,69409.37,0.0
-11396,15593976.0,Tien,582.0,France,Female,42.0,2.0,0.0,1.0,1.0,1.0,131753.94,1.0
-11397,15583857.0,Toscani,643.0,France,Male,32.0,7.0,97565.74,1.0,0.0,0.0,167036.94,0.0
-11398,15671148.0,Shih,581.0,France,Male,31.0,4.0,134919.34,1.0,1.0,0.0,95611.47,0.0
-11399,15792228.0,Niu,543.0,France,Female,35.0,3.0,128173.9,1.0,0.0,1.0,103799.77,0.0
-11400,15681180.0,Chiang,479.0,Spain,Female,32.0,7.0,0.0,2.0,1.0,0.0,151349.35,0.0
-11401,15676835.0,Chidozie,817.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,136869.31,0.0
-11402,15642824.0,Nkemakolam,724.0,France,Female,41.0,4.0,0.0,2.0,1.0,1.0,81523.06,0.0
-11403,15572390.0,Chiemenam,648.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,133020.9,0.0
-11404,15602034.0,Chukwudi,602.0,Spain,Female,47.0,4.0,0.0,4.0,1.0,0.0,126477.41,1.0
-11405,15641626.0,Ch'ien,632.0,France,Female,60.0,4.0,0.0,2.0,1.0,1.0,166588.61,0.0
-11406,15592999.0,Mancini,675.0,Germany,Female,26.0,3.0,131647.01,2.0,1.0,1.0,130504.49,0.0
-11407,15682668.0,Lo Duca,645.0,France,Male,58.0,7.0,0.0,1.0,1.0,0.0,82870.75,1.0
-11408,15694444.0,Chukwualuka,641.0,Germany,Female,66.0,2.0,141535.55,3.0,0.0,0.0,136620.64,1.0
-11409,15748595.0,Brown,730.0,Spain,Male,44.0,7.0,0.0,2.0,1.0,1.0,130789.6,0.0
-11410,15677522.0,Mazzanti,554.0,France,Male,43.0,6.0,0.0,2.0,1.0,0.0,80190.36,0.0
-11411,15753874.0,Nnaife,615.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,176576.62,0.0
-11412,15634141.0,Uchenna,678.0,Germany,Male,46.0,10.0,140197.18,1.0,1.0,1.0,61820.41,0.0
-11413,15632264.0,Chinwenma,651.0,Germany,Female,36.0,4.0,132557.26,2.0,1.0,0.0,141756.32,0.0
-11414,15600974.0,Chu,641.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,0.0,112239.03,0.0
-11415,15656417.0,Chu,613.0,Spain,Female,52.0,1.0,0.0,1.0,1.0,1.0,143450.61,1.0
-11416,15730579.0,Mazzi,571.0,France,Female,46.0,4.0,0.0,1.0,0.0,1.0,125961.48,0.0
-11417,15654074.0,Yobachi,623.0,France,Female,59.0,1.0,0.0,2.0,1.0,1.0,113048.79,0.0
-11418,15689152.0,Goliwe,705.0,France,Male,29.0,9.0,0.0,2.0,0.0,0.0,94620.0,0.0
-11419,15771856.0,Hsiung,645.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,53630.9,0.0
-11420,15654495.0,Yevdokimova,586.0,Spain,Male,51.0,4.0,91893.32,1.0,1.0,1.0,177783.04,0.0
-11421,15609824.0,T'ien,634.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,1.0,94845.45,0.0
-11422,15752694.0,Chukwuemeka,705.0,France,Male,50.0,0.0,0.0,2.0,1.0,1.0,158580.05,0.0
-11423,15673372.0,Sagese,619.0,France,Female,46.0,4.0,0.0,2.0,0.0,0.0,110953.33,1.0
-11424,15777893.0,Onyemaechi,693.0,France,Male,36.0,1.0,139432.37,1.0,1.0,1.0,183569.25,0.0
-11425,15771565.0,Yobachi,628.0,Germany,Male,33.0,9.0,134760.21,1.0,1.0,0.0,146700.22,0.0
-11426,15738889.0,Calabresi,695.0,France,Female,47.0,9.0,79259.99,1.0,1.0,0.0,130489.37,1.0
-11427,15578908.0,Calabrese,762.0,France,Female,25.0,2.0,0.0,2.0,1.0,1.0,138380.12,0.0
-11428,15791030.0,Ch'eng,717.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,87834.24,1.0
-11429,15751788.0,Ch'iu,725.0,Spain,Female,36.0,3.0,0.0,2.0,0.0,0.0,175894.24,0.0
-11430,15723658.0,Lombardo,520.0,France,Female,32.0,3.0,0.0,1.0,1.0,1.0,126722.57,0.0
-11431,15754574.0,Lo Duca,667.0,France,Female,46.0,1.0,0.0,1.0,0.0,1.0,142513.5,1.0
-11432,15802362.0,Zhirov,735.0,France,Female,24.0,5.0,0.0,2.0,1.0,0.0,80619.17,0.0
-11433,15781875.0,Toscani,593.0,Germany,Male,29.0,6.0,119714.25,1.0,1.0,1.0,160526.36,0.0
-11434,15772632.0,Smith,579.0,France,Female,37.0,5.0,0.0,1.0,0.0,1.0,72993.67,0.0
-11435,15647898.0,Ch'ien,711.0,France,Male,31.0,1.0,0.0,2.0,0.0,1.0,161249.24,0.0
-11436,15685564.0,Ting,678.0,Germany,Female,47.0,5.0,98141.57,2.0,1.0,0.0,124964.82,1.0
-11437,15603065.0,Onwuamaeze,588.0,France,Female,38.0,8.0,0.0,2.0,0.0,1.0,96985.58,0.0
-11438,15670866.0,Dickson,553.0,Spain,Male,38.0,9.0,76467.16,1.0,1.0,1.0,182627.25,0.0
-11439,15782390.0,Onwuamaeze,678.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,123882.73,0.0
-11440,15723299.0,Chinedum,716.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,125920.54,0.0
-11441,15649450.0,Hs?eh,559.0,Spain,Female,24.0,7.0,0.0,2.0,1.0,1.0,122925.44,0.0
-11442,15690526.0,Yevdokimova,685.0,France,Female,26.0,5.0,0.0,1.0,1.0,0.0,179655.87,1.0
-11443,15686885.0,Chiu,755.0,Germany,Male,36.0,1.0,126816.18,1.0,1.0,0.0,107292.91,0.0
-11444,15622518.0,Lucciano,699.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,139994.68,0.0
-11445,15763898.0,Nkemdilim,686.0,Germany,Male,41.0,6.0,131033.76,2.0,0.0,1.0,107955.67,0.0
-11446,15750929.0,Maughan,631.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,199304.74,0.0
-11447,15797900.0,Fanucci,653.0,Germany,Male,43.0,10.0,154640.41,2.0,0.0,1.0,4175.68,0.0
-11448,15663885.0,Nkemjika,689.0,France,Male,35.0,6.0,0.0,2.0,1.0,0.0,84908.2,0.0
-11449,15673929.0,Chiu,651.0,Germany,Female,35.0,9.0,102723.35,2.0,1.0,1.0,122373.42,0.0
-11450,15781574.0,Ch'eng,663.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,26116.82,0.0
-11451,15642578.0,Fang,687.0,Spain,Male,46.0,2.0,0.0,2.0,1.0,1.0,131736.23,0.0
-11452,15767358.0,Zetticci,678.0,Germany,Male,37.0,9.0,133384.55,1.0,0.0,1.0,50610.62,0.0
-11453,15803801.0,P'eng,694.0,France,Male,38.0,6.0,0.0,1.0,0.0,0.0,83611.45,0.0
-11454,15631838.0,Hsueh,575.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,47848.56,0.0
-11455,15667042.0,Yobanna,713.0,France,Male,31.0,2.0,110791.97,1.0,1.0,0.0,143360.56,0.0
-11456,15635116.0,Pagnotto,730.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,104502.04,0.0
-11457,15775203.0,Baresi,584.0,France,Male,30.0,7.0,0.0,2.0,1.0,0.0,122964.18,0.0
-11458,15810444.0,Tsao,449.0,France,Female,39.0,9.0,0.0,2.0,1.0,0.0,82968.14,0.0
-11459,15740264.0,Palerma,678.0,France,Female,28.0,7.0,0.0,1.0,1.0,0.0,131097.9,1.0
-11460,15733581.0,Milani,598.0,France,Male,35.0,9.0,143330.56,1.0,1.0,1.0,136886.86,0.0
-11461,15637522.0,Mazzanti,681.0,Spain,Female,38.0,4.0,141827.33,1.0,0.0,1.0,38276.84,0.0
-11462,15742504.0,Ch'ang,706.0,France,Female,42.0,5.0,0.0,2.0,1.0,0.0,127413.0,0.0
-11463,15672437.0,Tsao,739.0,Spain,Male,37.0,10.0,0.0,2.0,1.0,0.0,72849.29,0.0
-11464,15683927.0,Iweobiegbunam,545.0,Germany,Female,46.0,10.0,135095.78,1.0,1.0,0.0,57843.75,1.0
-11465,15795878.0,Ch'ang,572.0,Spain,Male,28.0,8.0,114430.18,1.0,1.0,1.0,91775.51,0.0
-11466,15663478.0,Chia,478.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,188516.45,0.0
-11467,15681316.0,Uspenskaya,711.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,131177.22,0.0
-11468,15582892.0,Hsueh,657.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,176963.17,0.0
-11469,15770309.0,Pirozzi,593.0,France,Female,48.0,2.0,66582.81,1.0,1.0,0.0,160976.85,1.0
-11470,15641114.0,Chukwukere,719.0,Spain,Female,40.0,1.0,0.0,2.0,1.0,1.0,173516.71,0.0
-11471,15727605.0,Folliero,705.0,France,Female,49.0,8.0,0.0,1.0,1.0,1.0,67552.76,1.0
-11472,15765650.0,Chiganu,658.0,France,Female,29.0,5.0,0.0,2.0,0.0,1.0,92888.52,0.0
-11473,15809817.0,P'eng,729.0,Germany,Female,40.0,9.0,128276.83,1.0,1.0,1.0,75585.48,1.0
-11474,15681115.0,Chibugo,762.0,Germany,Female,64.0,0.0,116322.28,1.0,1.0,1.0,97734.3,1.0
-11475,15623989.0,Hsueh,593.0,France,Female,28.0,2.0,0.0,1.0,1.0,1.0,60584.33,1.0
-11476,15751943.0,Sagese,685.0,France,Male,38.0,4.0,117060.03,1.0,1.0,0.0,74838.64,0.0
-11477,15604792.0,Nwokeocha,562.0,Germany,Female,42.0,3.0,162701.65,1.0,1.0,0.0,152992.89,1.0
-11478,15807525.0,Lu,584.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,91346.03,0.0
-11479,15794868.0,Walker,614.0,Spain,Male,28.0,8.0,109679.72,1.0,1.0,0.0,149991.32,0.0
-11480,15723250.0,Shaver,597.0,France,Male,24.0,3.0,0.0,2.0,1.0,0.0,120115.3,0.0
-11481,15631882.0,Walker,724.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,107837.55,0.0
-11482,15764293.0,Chimaobim,678.0,Germany,Female,48.0,8.0,129847.26,1.0,1.0,0.0,55324.49,1.0
-11483,15786078.0,Okagbue,596.0,Germany,Male,32.0,8.0,134796.89,1.0,1.0,0.0,898.51,1.0
-11484,15765461.0,Okonkwo,478.0,Spain,Male,31.0,2.0,0.0,1.0,1.0,1.0,145562.4,0.0
-11485,15773520.0,Ch'iu,583.0,France,Male,30.0,1.0,0.0,2.0,0.0,1.0,93835.64,0.0
-11486,15766787.0,Chia,583.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,162858.29,0.0
-11487,15675896.0,Ijendu,683.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,61994.2,0.0
-11488,15642093.0,Ch'iu,706.0,France,Male,24.0,4.0,101713.84,1.0,1.0,1.0,102515.34,0.0
-11489,15672821.0,Trevisani,682.0,France,Female,39.0,7.0,153697.53,1.0,1.0,0.0,149892.79,1.0
-11490,15623210.0,Baryshnikov,686.0,France,Male,27.0,1.0,0.0,2.0,0.0,0.0,162760.96,0.0
-11491,15672945.0,Mironova,705.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,1.0,86911.49,0.0
-11492,15790750.0,Chibueze,556.0,Spain,Female,55.0,5.0,0.0,1.0,1.0,1.0,164844.37,1.0
-11493,15796959.0,Udegbulam,689.0,France,Female,31.0,8.0,0.0,1.0,0.0,1.0,102021.39,0.0
-11494,15804075.0,Onyeorulu,498.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,1.0,120512.08,0.0
-11495,15702293.0,Tsao,582.0,Spain,Female,39.0,6.0,121863.61,1.0,1.0,1.0,179436.6,0.0
-11496,15782875.0,Y?,586.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,185599.12,0.0
-11497,15597591.0,Yost,585.0,Spain,Male,33.0,9.0,131307.11,1.0,1.0,1.0,121151.1,0.0
-11498,15598884.0,Ulyanov,569.0,France,Male,45.0,5.0,0.0,2.0,1.0,0.0,171706.66,1.0
-11499,15634602.0,Onyekaozulu,656.0,Germany,Male,36.0,2.0,81370.07,2.0,0.0,1.0,116469.19,0.0
-11500,15795872.0,Chukwudi,633.0,France,Female,42.0,6.0,0.0,4.0,1.0,0.0,131647.31,1.0
-11501,15709928.0,Chukwuemeka,622.0,France,Male,44.0,3.0,0.0,2.0,1.0,0.0,93478.95,0.0
-11502,15631217.0,Mancini,670.0,Spain,Female,41.0,3.0,120599.21,1.0,1.0,1.0,91817.38,0.0
-11503,15668818.0,Oluchukwu,646.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,53630.9,0.0
-11504,15663917.0,Chizoba,585.0,France,Male,30.0,6.0,0.0,1.0,0.0,0.0,73329.08,0.0
-11505,15715199.0,Ch'eng,583.0,France,Male,28.0,5.0,128920.75,1.0,0.0,0.0,79577.4,0.0
-11506,15586870.0,Ts'ao,682.0,Germany,Female,39.0,10.0,122523.21,1.0,1.0,0.0,74850.84,1.0
-11507,15793331.0,Ch'ang,637.0,France,Female,35.0,7.0,99564.22,1.0,1.0,0.0,82500.84,0.0
-11508,15677317.0,Yashina,486.0,France,Female,37.0,5.0,0.0,1.0,1.0,0.0,61257.83,0.0
-11509,15639882.0,Trevisani,673.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,97629.31,0.0
-11510,15778049.0,Eames,568.0,Germany,Male,41.0,4.0,146587.3,2.0,0.0,1.0,4292.94,0.0
-11511,15783859.0,H?,559.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,88724.49,0.0
-11512,15694450.0,Onuora,590.0,Spain,Male,42.0,7.0,0.0,2.0,0.0,0.0,72643.95,0.0
-11513,15802351.0,Shih,590.0,France,Male,24.0,2.0,0.0,2.0,1.0,0.0,116977.89,0.0
-11514,15670103.0,Chien,571.0,France,Male,37.0,0.0,0.0,1.0,1.0,1.0,52876.41,0.0
-11515,15705403.0,Udegbulam,614.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,81602.02,0.0
-11516,15772996.0,T'ang,756.0,France,Female,31.0,1.0,0.0,2.0,1.0,0.0,172646.88,0.0
-11517,15571940.0,Chukwufumnanya,748.0,Germany,Male,51.0,7.0,133384.66,1.0,1.0,1.0,182787.17,1.0
-11518,15570508.0,Bogle,651.0,France,Female,26.0,9.0,0.0,1.0,1.0,1.0,62339.64,0.0
-11519,15567442.0,Trevisano,521.0,Germany,Male,36.0,5.0,116363.37,1.0,1.0,0.0,67734.79,0.0
-11520,15693947.0,Martin,716.0,Germany,Female,40.0,2.0,129974.79,2.0,1.0,1.0,138700.86,0.0
-11521,15595588.0,Hsia,636.0,Spain,Female,47.0,2.0,0.0,1.0,0.0,0.0,177783.04,1.0
-11522,15618661.0,Tien,632.0,Germany,Female,30.0,7.0,125537.16,1.0,0.0,0.0,141069.88,0.0
-11523,15783026.0,Ch'iu,695.0,Germany,Female,38.0,8.0,71481.79,2.0,1.0,1.0,121151.1,0.0
-11524,15718773.0,Pugh,571.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,88838.14,0.0
-11525,15743076.0,Hsueh,559.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,139972.01,0.0
-11526,15745012.0,Nkemakonam,621.0,France,Female,31.0,2.0,0.0,2.0,1.0,1.0,48606.92,0.0
-11527,15794468.0,Ch'iu,584.0,France,Female,41.0,9.0,0.0,1.0,1.0,1.0,176692.65,1.0
-11528,15612444.0,H?,650.0,Spain,Male,43.0,4.0,134954.53,1.0,1.0,1.0,123850.58,0.0
-11529,15591150.0,Udegbulam,707.0,Germany,Female,54.0,9.0,150561.02,1.0,0.0,1.0,122398.84,0.0
-11530,15792489.0,Genovesi,721.0,France,Female,32.0,1.0,0.0,2.0,0.0,0.0,139048.0,1.0
-11531,15796969.0,Ni,748.0,France,Female,27.0,3.0,0.0,2.0,1.0,0.0,121681.19,0.0
-11532,15759733.0,Chinedum,577.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,173779.29,0.0
-11533,15665309.0,Fiorentino,561.0,Germany,Female,45.0,5.0,121730.49,1.0,1.0,1.0,48849.89,1.0
-11534,15571286.0,Ch'ang,592.0,Germany,Male,45.0,1.0,125437.86,1.0,1.0,0.0,78833.15,1.0
-11535,15790067.0,Ch'ien,685.0,France,Female,40.0,4.0,0.0,1.0,1.0,0.0,147794.63,0.0
-11536,15730488.0,Yobanna,586.0,France,Male,27.0,7.0,100565.94,1.0,1.0,1.0,71830.1,0.0
-11537,15713578.0,Calabrese,622.0,France,Female,33.0,1.0,61319.63,2.0,1.0,0.0,139971.01,0.0
-11538,15708714.0,Y?an,605.0,Germany,Male,33.0,10.0,163894.35,2.0,0.0,0.0,57904.22,0.0
-11539,15593973.0,Onyekachi,554.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,1.0,9984.4,0.0
-11540,15662067.0,McIntyre,676.0,France,Male,50.0,8.0,0.0,1.0,1.0,1.0,166698.18,1.0
-11541,15777901.0,Ch'ang,703.0,Spain,Female,35.0,4.0,0.0,2.0,1.0,0.0,99818.41,0.0
-11542,15603743.0,Ch'iu,715.0,Spain,Male,41.0,6.0,0.0,1.0,1.0,0.0,158043.11,1.0
-11543,15772030.0,Chidubem,543.0,Germany,Female,42.0,2.0,107712.57,1.0,0.0,0.0,101433.76,1.0
-11544,15672246.0,Hsueh,762.0,France,Male,37.0,2.0,0.0,2.0,1.0,0.0,116467.35,0.0
-11545,15735240.0,Hsia,609.0,Spain,Male,37.0,10.0,148021.12,1.0,1.0,0.0,92664.09,0.0
-11546,15632185.0,T'ao,603.0,France,Female,31.0,8.0,99844.71,1.0,1.0,1.0,197264.35,0.0
-11547,15770309.0,Shih,748.0,Spain,Male,38.0,9.0,0.0,2.0,0.0,0.0,131660.31,0.0
-11548,15751593.0,Clements,685.0,Germany,Male,30.0,4.0,102278.52,1.0,1.0,0.0,167032.49,0.0
-11549,15775335.0,Ngozichukwuka,762.0,Germany,Female,45.0,4.0,115290.27,1.0,1.0,0.0,176576.62,1.0
-11550,15794895.0,Lucchese,544.0,France,Male,34.0,2.0,0.0,2.0,0.0,1.0,121789.8,0.0
-11551,15700801.0,Ngozichukwuka,705.0,France,Female,43.0,2.0,0.0,1.0,1.0,0.0,56636.28,1.0
-11552,15745232.0,Tien,655.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,15859.95,0.0
-11553,15806796.0,Hsueh,633.0,Germany,Female,52.0,8.0,113522.64,1.0,0.0,0.0,92712.97,1.0
-11554,15696744.0,Iloerika,732.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,60721.4,0.0
-11555,15686958.0,Chigozie,613.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,0.0,104077.51,0.0
-11556,15582951.0,Hsia,663.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,138540.09,0.0
-11557,15614103.0,Ch'ien,691.0,France,Female,31.0,1.0,141581.71,1.0,1.0,1.0,132973.17,0.0
-11558,15769974.0,Ch'iu,678.0,Spain,Male,39.0,9.0,94488.17,1.0,0.0,1.0,173586.63,0.0
-11559,15778897.0,Hs?,687.0,France,Male,28.0,1.0,120952.74,1.0,1.0,0.0,53353.37,0.0
-11560,15591512.0,Ch'ang,521.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,154882.79,0.0
-11561,15789863.0,Hsieh,535.0,France,Female,56.0,2.0,111756.5,1.0,0.0,0.0,102076.03,1.0
-11562,15617482.0,Chukwumaobim,589.0,Spain,Female,25.0,9.0,0.0,1.0,1.0,0.0,160529.71,0.0
-11563,15737051.0,Yobanna,487.0,France,Female,51.0,6.0,0.0,2.0,0.0,1.0,122662.98,1.0
-11564,15658935.0,Uwakwe,684.0,France,Male,42.0,2.0,153323.1,1.0,1.0,0.0,61811.1,0.0
-11565,15769216.0,O'Sullivan,678.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,104722.43,0.0
-11566,15798960.0,Smith,725.0,France,Male,46.0,4.0,0.0,2.0,1.0,0.0,173989.66,0.0
-11567,15694510.0,Chibugo,695.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,62678.53,0.0
-11568,15724443.0,Chikezie,588.0,France,Female,44.0,7.0,145974.79,1.0,1.0,0.0,105618.14,1.0
-11569,15655238.0,Ch'iu,706.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,179631.85,0.0
-11570,15696733.0,Hsia,669.0,Germany,Male,35.0,6.0,109387.9,2.0,1.0,0.0,131804.86,0.0
-11571,15679294.0,Chukwueloka,753.0,Germany,Female,36.0,2.0,130274.22,2.0,1.0,1.0,55775.72,0.0
-11572,15692406.0,Y?,520.0,Germany,Male,31.0,9.0,148074.79,2.0,1.0,0.0,148848.76,0.0
-11573,15658935.0,P'eng,711.0,Spain,Male,34.0,3.0,0.0,2.0,0.0,1.0,71047.51,0.0
-11574,15600575.0,Onwumelu,714.0,Spain,Male,42.0,3.0,0.0,1.0,1.0,1.0,130260.11,1.0
-11575,15756850.0,Milano,633.0,France,Female,38.0,3.0,91286.51,1.0,1.0,1.0,143317.19,0.0
-11576,15787229.0,K?,663.0,France,Female,41.0,9.0,0.0,1.0,1.0,0.0,183584.14,1.0
-11577,15638231.0,Shih,611.0,France,Male,42.0,1.0,0.0,1.0,1.0,0.0,64907.48,1.0
-11578,15624892.0,Ts'ai,659.0,France,Female,31.0,4.0,0.0,2.0,0.0,0.0,59938.81,0.0
-11579,15593973.0,Ch'ang,681.0,Germany,Female,54.0,7.0,129490.33,2.0,1.0,0.0,140881.2,1.0
-11580,15665088.0,Young,592.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,133617.73,0.0
-11581,15715184.0,Sagese,753.0,Germany,Female,39.0,1.0,141334.95,1.0,1.0,1.0,65765.22,1.0
-11582,15729582.0,Oluchukwu,682.0,France,Female,66.0,9.0,0.0,2.0,1.0,1.0,101617.43,0.0
-11583,15652835.0,Onyemauchechukwu,597.0,France,Female,42.0,8.0,0.0,3.0,1.0,0.0,147557.71,1.0
-11584,15751336.0,Lucciano,662.0,Spain,Male,39.0,9.0,154525.5,1.0,0.0,0.0,138555.46,0.0
-11585,15778884.0,Tung,551.0,France,Female,43.0,6.0,0.0,2.0,1.0,1.0,151843.54,0.0
-11586,15592999.0,Shubina,586.0,France,Female,60.0,1.0,0.0,2.0,1.0,1.0,108977.5,0.0
-11587,15754568.0,Ts'ai,587.0,France,Female,32.0,9.0,101543.51,2.0,1.0,0.0,110359.68,0.0
-11588,15709222.0,Chukwualuka,632.0,France,Female,24.0,6.0,0.0,2.0,1.0,0.0,161465.31,0.0
-11589,15690772.0,Tsai,706.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,67972.45,0.0
-11590,15706021.0,Ch'en,724.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,127569.8,0.0
-11591,15780804.0,Chu,762.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,143317.19,0.0
-11592,15777923.0,Chidiegwu,685.0,France,Female,42.0,10.0,0.0,2.0,1.0,1.0,86791.9,0.0
-11593,15803965.0,Chikezie,521.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,131736.23,0.0
-11594,15616380.0,P'an,644.0,France,Female,37.0,10.0,0.0,2.0,1.0,1.0,145551.6,0.0
-11595,15655410.0,Fan,685.0,Spain,Female,39.0,2.0,73844.26,1.0,1.0,0.0,133021.32,0.0
-11596,15689612.0,Chinedum,710.0,Germany,Female,40.0,3.0,130169.79,1.0,0.0,1.0,153662.88,0.0
-11597,15647975.0,Mazzi,545.0,Spain,Male,36.0,0.0,0.0,2.0,1.0,1.0,149772.61,0.0
-11598,15753550.0,Pinto,604.0,France,Male,23.0,1.0,0.0,2.0,0.0,0.0,167984.72,0.0
-11599,15742681.0,Rizzo,602.0,France,Male,37.0,2.0,0.0,1.0,1.0,1.0,177524.19,0.0
-11600,15642099.0,Nnaife,593.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,81898.81,0.0
-11601,15575060.0,McIntosh,599.0,Germany,Female,39.0,5.0,111098.85,1.0,1.0,1.0,9983.88,1.0
-11602,15645896.0,Ch'ien,667.0,France,Female,27.0,5.0,0.0,2.0,1.0,1.0,117060.2,0.0
-11603,15633233.0,Lucciano,741.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,152422.91,0.0
-11604,15674720.0,Obidimkpa,667.0,France,Female,29.0,4.0,0.0,2.0,1.0,1.0,131363.57,0.0
-11605,15701164.0,Chizoba,634.0,France,Male,38.0,7.0,144953.63,1.0,1.0,0.0,175458.21,1.0
-11606,15678173.0,Y?an,842.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,177065.24,0.0
-11607,15595883.0,K?,684.0,Germany,Female,42.0,5.0,122417.19,1.0,1.0,0.0,172459.39,0.0
-11608,15791700.0,Ch'ien,649.0,Spain,Male,35.0,2.0,129209.03,1.0,1.0,0.0,60920.03,0.0
-11609,15686463.0,Tien,581.0,France,Female,34.0,6.0,0.0,2.0,0.0,0.0,90876.95,0.0
-11610,15755262.0,Hsu,671.0,Germany,Female,37.0,5.0,116886.59,1.0,0.0,0.0,103396.08,0.0
-11611,15628298.0,Calabrese,637.0,Germany,Female,43.0,9.0,117103.26,1.0,1.0,0.0,182785.01,1.0
-11612,15603851.0,Hs?,644.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,0.0,121894.31,0.0
-11613,15617982.0,Chuang,667.0,France,Male,57.0,2.0,141581.07,2.0,1.0,1.0,62944.75,0.0
-11614,15682912.0,Chukwujamuike,521.0,Spain,Female,39.0,1.0,127864.4,1.0,1.0,0.0,163798.41,1.0
-11615,15592448.0,Houghton,671.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,131712.52,0.0
-11616,15772650.0,Tien,684.0,France,Male,62.0,3.0,0.0,2.0,1.0,1.0,89983.02,0.0
-11617,15605918.0,Onyeorulu,678.0,Germany,Male,39.0,6.0,137714.25,1.0,1.0,0.0,179435.21,0.0
-11618,15813504.0,Liao,633.0,Spain,Female,37.0,6.0,0.0,2.0,1.0,0.0,120512.08,0.0
-11619,15651582.0,Udobata,711.0,Spain,Female,25.0,3.0,0.0,2.0,1.0,1.0,167810.26,0.0
-11620,15722563.0,Ritchie,588.0,Spain,Female,40.0,7.0,0.0,1.0,1.0,1.0,158610.89,0.0
-11621,15772781.0,Udinese,743.0,Germany,Female,48.0,9.0,130878.75,1.0,0.0,0.0,145518.31,1.0
-11622,15595136.0,Maccallum,678.0,France,Female,51.0,3.0,135096.05,1.0,0.0,1.0,51554.58,1.0
-11623,15667438.0,Sagese,684.0,France,Male,47.0,5.0,153590.89,1.0,1.0,0.0,59890.29,1.0
-11624,15589320.0,Okwudiliolisa,506.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,51554.58,0.0
-11625,15626787.0,Ts'ai,607.0,Germany,Male,31.0,2.0,121525.52,1.0,1.0,0.0,117622.8,0.0
-11626,15722090.0,Chien,696.0,France,Female,39.0,2.0,125566.9,1.0,1.0,1.0,113410.89,0.0
-11627,15603665.0,Mazzanti,710.0,Spain,Female,38.0,0.0,114655.58,1.0,1.0,0.0,159418.1,0.0
-11628,15629244.0,Chukwuebuka,668.0,France,Male,25.0,8.0,133467.77,1.0,1.0,1.0,107964.36,0.0
-11629,15624995.0,Ch'iu,593.0,Spain,Male,46.0,7.0,78314.63,1.0,1.0,0.0,124592.84,0.0
-11630,15778142.0,Ch'ang,645.0,Germany,Female,41.0,1.0,108432.88,1.0,1.0,1.0,161579.85,1.0
-11631,15624592.0,Chin,734.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,35669.31,0.0
-11632,15774931.0,Ts'ui,676.0,France,Male,48.0,2.0,0.0,1.0,1.0,1.0,162699.7,0.0
-11633,15661528.0,Maclean,743.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,55724.24,0.0
-11634,15654577.0,O'Loughlin,705.0,Germany,Male,44.0,4.0,148045.45,2.0,1.0,0.0,94499.06,1.0
-11635,15696733.0,Hs?eh,684.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,86460.28,0.0
-11636,15761158.0,Trevisano,659.0,France,Male,41.0,7.0,151858.98,1.0,1.0,1.0,83629.4,0.0
-11637,15627195.0,De Luca,841.0,France,Female,49.0,1.0,0.0,2.0,1.0,1.0,143668.58,0.0
-11638,15628292.0,Foveaux,554.0,France,Male,48.0,1.0,151766.71,1.0,0.0,0.0,74564.41,1.0
-11639,15574372.0,Lucchesi,619.0,France,Female,40.0,5.0,0.0,1.0,1.0,0.0,162515.17,1.0
-11640,15782454.0,Kao,594.0,France,Male,34.0,1.0,72788.03,1.0,1.0,1.0,171922.72,0.0
-11641,15717893.0,Chizoba,730.0,Spain,Male,24.0,8.0,102520.37,1.0,0.0,0.0,67769.94,0.0
-11642,15804002.0,Clements,588.0,France,Male,35.0,4.0,0.0,2.0,1.0,0.0,38409.79,0.0
-11643,15765415.0,Onyeoruru,577.0,Germany,Male,32.0,4.0,125547.51,1.0,1.0,1.0,161671.15,0.0
-11644,15574865.0,Lo Duca,555.0,France,Male,42.0,6.0,0.0,2.0,1.0,0.0,59981.62,0.0
-11645,15723299.0,Hs?eh,550.0,France,Male,55.0,9.0,0.0,2.0,0.0,1.0,168515.61,0.0
-11646,15757912.0,Onyemachukwu,850.0,France,Male,30.0,3.0,0.0,2.0,1.0,1.0,168034.9,0.0
-11647,15596870.0,Yeh,605.0,Germany,Female,47.0,8.0,110635.32,1.0,0.0,1.0,51117.06,1.0
-11648,15618182.0,Yefimova,653.0,Spain,Female,41.0,3.0,0.0,2.0,0.0,1.0,181499.77,0.0
-11649,15650068.0,Cattaneo,669.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,163427.18,0.0
-11650,15793723.0,Lucchesi,679.0,France,Female,50.0,4.0,0.0,2.0,1.0,1.0,80410.68,0.0
-11651,15569247.0,Ch'ang,687.0,France,Female,41.0,3.0,0.0,1.0,0.0,0.0,180712.28,0.0
-11652,15651740.0,Chukwumaobim,648.0,France,Male,38.0,3.0,0.0,2.0,0.0,0.0,94738.54,0.0
-11653,15612092.0,K'ung,714.0,Spain,Female,59.0,6.0,0.0,1.0,0.0,1.0,162858.29,1.0
-11654,15808023.0,Olisaemeka,704.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,1.0,117572.59,0.0
-11655,15674542.0,Blesing,584.0,Spain,Male,28.0,7.0,0.0,2.0,0.0,1.0,153064.87,0.0
-11656,15692819.0,Cattaneo,588.0,Spain,Female,34.0,7.0,0.0,3.0,1.0,0.0,134626.3,1.0
-11657,15585823.0,Stevenson,693.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,113645.4,0.0
-11658,15678727.0,Nwachukwu,663.0,Germany,Male,44.0,9.0,168085.98,2.0,1.0,0.0,131807.16,0.0
-11659,15792232.0,Nnaife,738.0,France,Female,61.0,7.0,0.0,2.0,1.0,1.0,48161.18,0.0
-11660,15732430.0,T'ang,719.0,Germany,Female,24.0,2.0,122029.15,2.0,1.0,1.0,172437.12,0.0
-11661,15598892.0,Tuan,678.0,Germany,Male,38.0,5.0,130404.92,1.0,1.0,1.0,150580.88,0.0
-11662,15672966.0,Nwachukwu,684.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,172601.62,0.0
-11663,15605020.0,Pinto,595.0,France,Female,33.0,6.0,0.0,1.0,1.0,1.0,166586.99,0.0
-11664,15777892.0,Nwokeocha,628.0,Germany,Female,40.0,6.0,61319.63,1.0,1.0,0.0,189366.04,1.0
-11665,15591100.0,Chiang,682.0,Germany,Female,38.0,2.0,122947.92,1.0,1.0,1.0,147040.25,0.0
-11666,15704509.0,Chidubem,585.0,France,Male,32.0,7.0,0.0,2.0,1.0,0.0,105746.94,0.0
-11667,15697567.0,Hs?,599.0,Germany,Male,41.0,9.0,142289.28,1.0,0.0,0.0,60778.11,1.0
-11668,15589401.0,Tseng,639.0,France,Male,39.0,3.0,137714.5,1.0,1.0,0.0,90820.04,0.0
-11669,15595440.0,Palerma,596.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,166513.49,0.0
-11670,15699225.0,Chiabuotu,588.0,France,Male,28.0,5.0,71816.74,1.0,1.0,1.0,53394.24,0.0
-11671,15762218.0,Scott,564.0,Germany,Male,36.0,3.0,145974.79,2.0,1.0,0.0,60552.44,0.0
-11672,15682890.0,Teng,727.0,France,Male,33.0,7.0,0.0,2.0,0.0,1.0,84870.23,0.0
-11673,15655574.0,Nwankwo,676.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,116973.48,0.0
-11674,15672798.0,T'ien,685.0,France,Female,55.0,7.0,0.0,1.0,1.0,0.0,119882.86,1.0
-11675,15713774.0,Onwuka,604.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,120112.5,0.0
-11676,15803048.0,Sagese,636.0,Germany,Male,28.0,1.0,117094.02,1.0,0.0,1.0,174431.01,0.0
-11677,15571917.0,Tsou,629.0,France,Male,48.0,3.0,0.0,1.0,0.0,0.0,149535.75,1.0
-11678,15614491.0,Zikoranachidimma,713.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,118862.08,0.0
-11679,15707132.0,Trevisani,595.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,1710.89,0.0
-11680,15682211.0,Zetticci,651.0,Spain,Male,38.0,7.0,109342.82,1.0,1.0,0.0,80552.11,0.0
-11681,15641640.0,Onwughara,678.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,86410.29,0.0
-11682,15665524.0,Yip,659.0,Spain,Male,35.0,1.0,0.0,2.0,0.0,1.0,150694.58,0.0
-11683,15686099.0,Fyodorova,554.0,Spain,Male,33.0,3.0,91718.2,1.0,0.0,0.0,116944.3,0.0
-11684,15602312.0,Manna,592.0,France,Female,30.0,1.0,0.0,2.0,1.0,0.0,86981.45,0.0
-11685,15576723.0,P'eng,666.0,Germany,Male,50.0,5.0,99824.45,1.0,1.0,0.0,123722.15,1.0
-11686,15627854.0,Kao,683.0,Spain,Female,51.0,10.0,108632.48,1.0,1.0,1.0,60778.12,1.0
-11687,15768346.0,Chidubem,542.0,Germany,Male,42.0,6.0,128131.53,3.0,1.0,0.0,114957.22,1.0
-11688,15669411.0,Muecke,667.0,France,Male,35.0,10.0,0.0,1.0,1.0,1.0,175894.24,0.0
-11689,15650034.0,T'ang,653.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,109895.16,0.0
-11690,15619340.0,Cremonesi,792.0,France,Male,40.0,5.0,65612.55,1.0,1.0,0.0,165038.41,0.0
-11691,15592091.0,T'ang,704.0,France,Female,48.0,3.0,0.0,2.0,1.0,0.0,152097.02,0.0
-11692,15662291.0,Chiawuotu,714.0,Spain,Female,37.0,7.0,0.0,2.0,0.0,0.0,168460.12,0.0
-11693,15673529.0,Shubina,611.0,France,Female,41.0,5.0,0.0,1.0,1.0,1.0,152883.95,1.0
-11694,15677783.0,Hs?,683.0,France,Male,30.0,6.0,102025.35,1.0,1.0,0.0,176317.71,0.0
-11695,15800515.0,Ponomarev,712.0,France,Male,38.0,9.0,137851.31,1.0,0.0,0.0,24765.53,1.0
-11696,15808838.0,Ginn,738.0,France,Female,44.0,9.0,0.0,2.0,1.0,0.0,124964.82,0.0
-11697,15677052.0,Iweobiegbulam,593.0,Germany,Male,43.0,6.0,96651.03,2.0,1.0,1.0,72852.74,0.0
-11698,15801488.0,Hs?,641.0,France,Female,38.0,6.0,0.0,2.0,0.0,1.0,170638.98,0.0
-11699,15718572.0,Ts'ao,598.0,Germany,Female,46.0,2.0,93616.35,2.0,1.0,0.0,157072.91,0.0
-11700,15730044.0,Tretiakova,542.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,92381.01,0.0
-11701,15636089.0,Onwuatuegwu,651.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,82709.8,0.0
-11702,15669411.0,Mazzanti,645.0,Spain,Female,28.0,7.0,0.0,2.0,0.0,0.0,86678.48,0.0
-11703,15677146.0,Ogochukwu,699.0,France,Male,66.0,10.0,0.0,2.0,1.0,1.0,83084.33,0.0
-11704,15792989.0,O'Brien,707.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,88827.67,0.0
-11705,15649024.0,Onyekachukwu,653.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,101617.64,0.0
-11706,15612108.0,Iloerika,586.0,France,Male,27.0,9.0,0.0,2.0,1.0,0.0,162760.96,0.0
-11707,15686936.0,Nwabugwu,641.0,France,Male,35.0,10.0,0.0,2.0,1.0,0.0,645.61,0.0
-11708,15696812.0,Chukwuhaenye,581.0,Spain,Male,38.0,3.0,0.0,1.0,1.0,1.0,74965.61,0.0
-11709,15770995.0,Chukwualuka,758.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,1.0,67734.89,0.0
-11710,15716609.0,Hs?,667.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,1.0,132498.39,0.0
-11711,15740458.0,Chukwudi,656.0,Germany,Male,30.0,8.0,131765.72,1.0,1.0,1.0,123614.01,0.0
-11712,15737778.0,Pinto,595.0,France,Male,30.0,7.0,146033.62,1.0,0.0,0.0,125062.02,0.0
-11713,15775307.0,Hsueh,730.0,France,Female,37.0,3.0,0.0,1.0,1.0,0.0,123535.04,1.0
-11714,15727353.0,Onwuamaeze,559.0,Spain,Female,41.0,4.0,0.0,2.0,1.0,0.0,136983.77,0.0
-11715,15682618.0,Chien,535.0,Spain,Male,55.0,3.0,0.0,1.0,0.0,0.0,60721.4,1.0
-11716,15692406.0,T'ien,562.0,Spain,Male,28.0,7.0,0.0,2.0,1.0,1.0,98912.69,0.0
-11717,15646513.0,Nwabugwu,652.0,France,Male,30.0,6.0,0.0,2.0,1.0,1.0,94775.54,0.0
-11718,15800890.0,Ch'ien,628.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,144870.28,0.0
-11719,15783859.0,Chin,678.0,Spain,Female,38.0,5.0,161608.81,2.0,1.0,1.0,110114.39,0.0
-11720,15724858.0,Mao,677.0,Germany,Female,58.0,7.0,141952.07,1.0,1.0,0.0,114869.51,1.0
-11721,15773731.0,Milanesi,652.0,Spain,Female,57.0,3.0,152341.55,2.0,1.0,0.0,75732.25,1.0
-11722,15763665.0,Chinweuba,754.0,Spain,Female,34.0,2.0,145553.97,2.0,1.0,0.0,135067.02,0.0
-11723,15626608.0,Okechukwu,575.0,Spain,Female,33.0,2.0,0.0,2.0,1.0,1.0,95655.16,0.0
-11724,15792668.0,Obiajulu,592.0,France,Female,38.0,9.0,0.0,1.0,1.0,0.0,59961.81,0.0
-11725,15734765.0,Zito,637.0,France,Female,32.0,6.0,0.0,2.0,0.0,0.0,169469.3,0.0
-11726,15689886.0,Baryshnikov,549.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,1.0,151325.24,0.0
-11727,15642063.0,Pagnotto,627.0,France,Female,34.0,0.0,0.0,2.0,1.0,1.0,85890.75,0.0
-11728,15615444.0,Okagbue,659.0,France,Male,45.0,5.0,0.0,1.0,1.0,0.0,142331.85,1.0
-11729,15800287.0,Ts'ao,587.0,France,Male,30.0,5.0,0.0,2.0,0.0,0.0,133871.83,0.0
-11730,15797767.0,Niu,491.0,Spain,Female,56.0,8.0,0.0,1.0,1.0,1.0,89891.4,1.0
-11731,15590320.0,Chukwuemeka,553.0,France,Female,30.0,2.0,166824.59,2.0,1.0,0.0,134491.32,1.0
-11732,15597951.0,Mazzi,612.0,France,Female,61.0,3.0,152462.79,1.0,1.0,1.0,61666.87,1.0
-11733,15700964.0,Hsueh,574.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,199761.29,0.0
-11734,15583076.0,Hazon,738.0,France,Female,32.0,7.0,0.0,2.0,1.0,0.0,75946.4,0.0
-11735,15757827.0,Onwuamaeze,651.0,France,Female,32.0,9.0,0.0,2.0,1.0,0.0,106655.88,0.0
-11736,15748696.0,Ts'ao,748.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,124596.63,0.0
-11737,15692761.0,T'ien,615.0,Germany,Male,52.0,6.0,123894.22,1.0,1.0,1.0,141446.01,1.0
-11738,15781533.0,Nwankwo,756.0,Germany,Male,42.0,4.0,135536.7,2.0,1.0,1.0,100496.84,0.0
-11739,15743075.0,Nkemdilim,670.0,France,Male,60.0,1.0,0.0,1.0,1.0,1.0,173870.39,0.0
-11740,15634896.0,Onwuatuegwu,724.0,Germany,Male,52.0,1.0,116891.72,1.0,1.0,0.0,142331.85,1.0
-11741,15583548.0,Hao,583.0,Germany,Male,43.0,9.0,119961.48,2.0,1.0,1.0,153810.41,0.0
-11742,15718247.0,Chiazagomekpele,663.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,182765.14,0.0
-11743,15774536.0,K?,565.0,France,Male,37.0,10.0,84945.58,1.0,1.0,1.0,106663.46,0.0
-11744,15622494.0,Ch'iu,656.0,Germany,Male,62.0,8.0,132953.96,1.0,1.0,1.0,118863.38,1.0
-11745,15682928.0,Kao,637.0,France,Female,37.0,10.0,0.0,1.0,1.0,1.0,182059.28,0.0
-11746,15654891.0,Chidozie,579.0,France,Male,39.0,5.0,0.0,1.0,1.0,1.0,125977.22,0.0
-11747,15612207.0,Scott,724.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,81452.29,0.0
-11748,15752626.0,Chia,757.0,Spain,Female,39.0,9.0,93920.47,1.0,1.0,0.0,172749.4,0.0
-11749,15756852.0,Nwebube,710.0,Spain,Male,38.0,8.0,0.0,2.0,1.0,0.0,135067.02,0.0
-11750,15697028.0,Mazzanti,717.0,France,Male,34.0,0.0,0.0,2.0,0.0,0.0,174815.54,0.0
-11751,15797246.0,Lo,733.0,France,Female,55.0,5.0,0.0,1.0,1.0,1.0,161653.5,1.0
-11752,15592925.0,Sun,609.0,France,Male,41.0,10.0,61297.05,1.0,1.0,1.0,167060.49,0.0
-11753,15682084.0,Toscani,477.0,Germany,Male,52.0,2.0,123870.07,1.0,0.0,0.0,27448.33,1.0
-11754,15605960.0,Chukwukadibia,516.0,Spain,Female,48.0,6.0,0.0,2.0,1.0,1.0,53954.24,0.0
-11755,15635078.0,Hsiung,724.0,Germany,Male,38.0,3.0,92889.91,1.0,0.0,1.0,84321.03,1.0
-11756,15753895.0,Y?an,570.0,France,Male,35.0,5.0,0.0,1.0,1.0,0.0,171056.65,0.0
-11757,15728006.0,Ts'ui,619.0,France,Male,36.0,2.0,0.0,2.0,1.0,1.0,6554.97,0.0
-11758,15663438.0,Kao,712.0,Germany,Male,38.0,3.0,99799.76,1.0,1.0,0.0,124210.53,0.0
-11759,15668818.0,Ch'ang,539.0,France,Female,55.0,1.0,0.0,2.0,1.0,0.0,78896.59,1.0
-11760,15591100.0,Yobachi,642.0,Spain,Male,37.0,7.0,0.0,1.0,0.0,0.0,78730.95,0.0
-11761,15566790.0,Lu,581.0,France,Male,39.0,6.0,92599.55,1.0,1.0,1.0,151623.91,0.0
-11762,15644833.0,Manna,567.0,Germany,Male,71.0,8.0,104449.4,1.0,1.0,1.0,79919.13,0.0
-11763,15644295.0,Fenton,763.0,France,Male,32.0,6.0,110132.55,1.0,1.0,1.0,183542.08,0.0
-11764,15751167.0,Cremonesi,683.0,Germany,Male,60.0,1.0,121730.49,1.0,0.0,0.0,63584.55,1.0
-11765,15623471.0,Baresi,613.0,France,Male,35.0,5.0,0.0,2.0,0.0,0.0,105715.01,0.0
-11766,15771749.0,Onyemauchechukwu,753.0,France,Male,46.0,1.0,0.0,1.0,1.0,0.0,130189.53,0.0
-11767,15806467.0,H?,597.0,France,Male,40.0,7.0,0.0,2.0,0.0,1.0,151677.31,0.0
-11768,15662337.0,Bock,542.0,France,Female,48.0,6.0,0.0,3.0,1.0,0.0,133042.33,1.0
-11769,15783025.0,Lo Duca,628.0,Germany,Male,32.0,1.0,71996.09,2.0,1.0,1.0,152417.97,0.0
-11770,15676513.0,Fanucci,710.0,France,Male,38.0,8.0,0.0,1.0,1.0,1.0,151738.54,0.0
-11771,15690134.0,McFarland,729.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,170980.86,0.0
-11772,15604818.0,Nnonso,687.0,France,Male,41.0,7.0,122917.71,1.0,1.0,0.0,64771.61,0.0
-11773,15777033.0,Chibugo,781.0,France,Male,35.0,3.0,0.0,1.0,1.0,0.0,67979.35,0.0
-11774,15684461.0,Tsao,686.0,Germany,Female,46.0,9.0,168094.01,1.0,1.0,0.0,57320.92,1.0
-11775,15728362.0,Chidozie,554.0,Spain,Male,33.0,2.0,0.0,1.0,1.0,1.0,124749.03,0.0
-11776,15778936.0,Baresi,684.0,France,Female,42.0,3.0,0.0,2.0,1.0,0.0,149913.84,0.0
-11777,15781451.0,Hsieh,712.0,France,Male,44.0,8.0,0.0,2.0,1.0,0.0,121485.1,0.0
-11778,15752300.0,Tuan,616.0,Germany,Male,35.0,10.0,92347.96,2.0,1.0,0.0,60416.76,0.0
-11779,15690695.0,Chikezie,749.0,France,Male,36.0,3.0,0.0,2.0,0.0,1.0,48965.03,0.0
-11780,15757933.0,Hsieh,587.0,Germany,Male,33.0,3.0,71264.02,2.0,1.0,1.0,181270.84,0.0
-11781,15738751.0,Ting,739.0,Spain,Male,46.0,1.0,0.0,1.0,1.0,0.0,149535.91,1.0
-11782,15671137.0,Lo Duca,721.0,Germany,Male,41.0,1.0,124577.11,3.0,1.0,1.0,41967.03,1.0
-11783,15702298.0,H?,686.0,France,Male,39.0,6.0,0.0,2.0,1.0,1.0,121053.19,0.0
-11784,15671148.0,H?,660.0,France,Female,57.0,1.0,0.0,1.0,1.0,1.0,177028.0,1.0
-11785,15693326.0,Chidozie,542.0,France,Male,37.0,8.0,0.0,1.0,1.0,0.0,173516.71,1.0
-11786,15578546.0,Udegbunam,674.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,0.0,18082.06,0.0
-11787,15644501.0,Toscani,762.0,Spain,Female,41.0,6.0,0.0,2.0,0.0,1.0,61240.83,0.0
-11788,15752588.0,Ch'in,644.0,France,Female,44.0,9.0,0.0,1.0,1.0,0.0,110371.84,1.0
-11789,15656901.0,Pirozzi,663.0,France,Male,31.0,7.0,128747.69,1.0,1.0,0.0,159418.1,0.0
-11790,15622578.0,Chu,543.0,France,Female,26.0,6.0,118945.08,1.0,1.0,1.0,157891.86,0.0
-11791,15652914.0,Chukwuemeka,850.0,France,Male,34.0,4.0,0.0,2.0,1.0,0.0,183556.12,0.0
-11792,15803522.0,Chidiebere,537.0,France,Male,41.0,3.0,0.0,1.0,1.0,1.0,82222.86,0.0
-11793,15605872.0,Iadanza,663.0,France,Female,35.0,7.0,0.0,2.0,0.0,1.0,76776.36,0.0
-11794,15684930.0,Pisano,649.0,France,Female,42.0,6.0,0.0,2.0,1.0,1.0,99503.19,0.0
-11795,15593795.0,Ch'in,558.0,France,Female,29.0,1.0,118336.95,1.0,0.0,0.0,139048.0,0.0
-11796,15577683.0,Milanesi,520.0,Germany,Male,44.0,7.0,132876.23,2.0,1.0,0.0,131473.31,1.0
-11797,15649430.0,Chien,669.0,Germany,Male,44.0,6.0,137592.89,1.0,1.0,0.0,102889.84,0.0
-11798,15581323.0,Chialuka,713.0,Spain,Male,33.0,10.0,0.0,2.0,1.0,0.0,179435.21,0.0
-11799,15735024.0,Tsao,521.0,France,Male,29.0,1.0,0.0,2.0,0.0,1.0,148814.54,0.0
-11800,15616240.0,Ts'ui,579.0,France,Female,43.0,4.0,0.0,1.0,0.0,0.0,126192.54,0.0
-11801,15796958.0,T'ien,635.0,France,Male,28.0,2.0,0.0,1.0,1.0,1.0,86935.08,1.0
-11802,15606097.0,Maclean,683.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,82855.99,0.0
-11803,15571586.0,Uspenskaya,684.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,123499.98,0.0
-11804,15800524.0,Pagnotto,535.0,Germany,Male,41.0,9.0,148874.01,1.0,1.0,1.0,175750.21,0.0
-11805,15751175.0,Chiu,651.0,France,Male,31.0,6.0,88814.4,2.0,1.0,0.0,125422.66,0.0
-11806,15780561.0,Ritchie,681.0,France,Male,31.0,8.0,81576.75,1.0,1.0,1.0,112157.31,0.0
-11807,15706593.0,Ch'eng,636.0,Germany,Male,59.0,4.0,134796.05,4.0,1.0,0.0,147075.69,1.0
-11808,15605059.0,Mazzanti,721.0,France,Male,27.0,7.0,0.0,2.0,0.0,1.0,72350.47,0.0
-11809,15719508.0,Ch'iu,586.0,Germany,Male,30.0,4.0,150213.71,2.0,0.0,1.0,69056.26,0.0
-11810,15692744.0,Smith,615.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,148087.62,0.0
-11811,15651868.0,Zaytseva,687.0,France,Female,39.0,5.0,0.0,2.0,1.0,1.0,77466.72,0.0
-11812,15772423.0,Tretiakova,748.0,France,Female,23.0,6.0,0.0,2.0,0.0,1.0,121311.12,0.0
-11813,15577587.0,Ch'ang,651.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,170984.97,0.0
-11814,15654964.0,Uspensky,686.0,Germany,Female,35.0,3.0,171325.98,1.0,0.0,0.0,109538.35,1.0
-11815,15676895.0,Kambinachi,635.0,France,Female,37.0,9.0,0.0,2.0,0.0,1.0,121537.91,0.0
-11816,15658852.0,Hs?,627.0,France,Male,35.0,5.0,0.0,2.0,0.0,0.0,105557.78,0.0
-11817,15716134.0,Chu,592.0,Spain,Female,28.0,4.0,0.0,2.0,1.0,0.0,52796.31,0.0
-11818,15714586.0,Hsing,646.0,Spain,Female,40.0,5.0,0.0,1.0,1.0,0.0,143882.25,1.0
-11819,15653952.0,Chidiebele,706.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,1.0,60988.21,0.0
-11820,15761487.0,Hsia,683.0,France,Female,29.0,2.0,0.0,2.0,1.0,0.0,8996.79,0.0
-11821,15623196.0,Piazza,549.0,Germany,Male,46.0,1.0,116537.6,2.0,1.0,0.0,171045.49,1.0
-11822,15667085.0,Palermo,705.0,Spain,Male,28.0,1.0,80728.15,1.0,1.0,1.0,158948.63,0.0
-11823,15684512.0,T'ien,588.0,Spain,Female,29.0,4.0,0.0,2.0,1.0,1.0,147937.91,0.0
-11824,15787597.0,Fiorentini,602.0,Spain,Male,31.0,4.0,0.0,1.0,1.0,1.0,152499.8,0.0
-11825,15749679.0,Lucchese,588.0,France,Female,35.0,1.0,0.0,2.0,1.0,0.0,83084.33,0.0
-11826,15783026.0,Hsueh,671.0,France,Male,34.0,6.0,0.0,2.0,0.0,0.0,173989.66,0.0
-11827,15589475.0,Chikelu,715.0,France,Female,50.0,3.0,0.0,2.0,1.0,1.0,166830.71,0.0
-11828,15717185.0,Hsiung,684.0,France,Female,42.0,5.0,0.0,2.0,1.0,1.0,161076.92,0.0
-11829,15603319.0,Milani,758.0,France,Female,26.0,10.0,0.0,2.0,1.0,0.0,111241.37,0.0
-11830,15706021.0,Toscani,592.0,Germany,Female,44.0,2.0,104982.57,1.0,1.0,1.0,126082.18,0.0
-11831,15572735.0,Ch'ang,705.0,France,Male,43.0,6.0,0.0,1.0,1.0,0.0,132418.59,1.0
-11832,15803801.0,Ch'ang,577.0,Germany,Male,34.0,1.0,131326.47,2.0,1.0,1.0,73656.38,0.0
-11833,15654891.0,Mazzanti,676.0,Germany,Male,44.0,5.0,140931.83,1.0,1.0,1.0,131304.45,0.0
-11834,15667554.0,Tuan,682.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,129563.86,0.0
-11835,15705657.0,Nwebube,754.0,Germany,Female,53.0,2.0,92398.08,1.0,1.0,0.0,147090.9,1.0
-11836,15736154.0,Chikezie,615.0,France,Male,49.0,7.0,0.0,2.0,1.0,0.0,86622.56,0.0
-11837,15672970.0,Pinto,596.0,Germany,Female,41.0,8.0,108914.17,1.0,1.0,0.0,134778.01,0.0
-11838,15649744.0,H?,632.0,France,Male,35.0,5.0,0.0,2.0,1.0,0.0,72861.94,0.0
-11839,15590094.0,Eluemuno,715.0,France,Female,29.0,3.0,0.0,1.0,1.0,1.0,823.96,1.0
-11840,15751167.0,P'eng,739.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,131834.91,0.0
-11841,15757931.0,Yermolayeva,539.0,France,Male,38.0,5.0,0.0,2.0,1.0,0.0,125962.02,0.0
-11842,15585823.0,Yobachi,712.0,Spain,Male,64.0,8.0,0.0,1.0,0.0,0.0,44937.02,1.0
-11843,15795511.0,Bancks,662.0,France,Female,30.0,1.0,0.0,2.0,1.0,0.0,118716.63,0.0
-11844,15793854.0,Onyeorulu,684.0,France,Female,34.0,8.0,81210.72,1.0,1.0,1.0,133535.92,0.0
-11845,15810845.0,Ritchie,725.0,France,Male,37.0,1.0,0.0,2.0,0.0,1.0,173365.9,0.0
-11846,15790594.0,Hsia,769.0,Spain,Female,38.0,8.0,0.0,1.0,1.0,0.0,62402.38,0.0
-11847,15573045.0,Hao,694.0,France,Female,36.0,8.0,0.0,2.0,1.0,0.0,141872.05,0.0
-11848,15714256.0,Ch'ien,459.0,Germany,Male,39.0,6.0,99141.86,1.0,1.0,0.0,72993.65,1.0
-11849,15702656.0,Mazzanti,678.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,137028.51,0.0
-11850,15627190.0,Hs?,850.0,Germany,Male,34.0,8.0,111091.01,1.0,1.0,0.0,179670.31,1.0
-11851,15773338.0,Okonkwo,594.0,Germany,Female,39.0,7.0,119545.48,1.0,1.0,1.0,160122.66,0.0
-11852,15582797.0,Aksenov,656.0,Germany,Female,24.0,2.0,123971.16,1.0,1.0,0.0,121798.52,0.0
-11853,15645248.0,Iloerika,591.0,Germany,Male,37.0,6.0,124580.69,2.0,1.0,1.0,131734.43,0.0
-11854,15659428.0,Onyekachi,592.0,France,Male,41.0,2.0,0.0,2.0,1.0,1.0,35356.46,0.0
-11855,15742285.0,Okwudilichukwu,709.0,Germany,Male,37.0,9.0,140576.11,1.0,1.0,1.0,122218.57,1.0
-11856,15736274.0,Ch'iu,567.0,Spain,Female,27.0,3.0,0.0,2.0,1.0,0.0,117632.63,0.0
-11857,15776733.0,Genovesi,705.0,France,Female,31.0,1.0,0.0,2.0,0.0,1.0,119374.74,1.0
-11858,15697035.0,Fetherstonhaugh,602.0,Germany,Female,42.0,1.0,119782.72,1.0,1.0,0.0,180800.42,1.0
-11859,15757577.0,Nwebube,850.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,89519.47,0.0
-11860,15683441.0,Pinto,562.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,90547.94,0.0
-11861,15797710.0,Naylor,576.0,Germany,Female,35.0,5.0,161807.18,2.0,1.0,1.0,171706.66,0.0
-11862,15763662.0,Nwankwo,531.0,France,Female,36.0,6.0,0.0,2.0,1.0,0.0,180553.48,0.0
-11863,15745355.0,Ts'ai,679.0,Germany,Female,37.0,2.0,161572.24,1.0,0.0,0.0,142223.94,0.0
-11864,15582090.0,Ts'ao,661.0,France,Female,44.0,10.0,0.0,2.0,1.0,1.0,96658.26,0.0
-11865,15746035.0,Padovano,741.0,France,Male,39.0,8.0,91216.0,1.0,1.0,0.0,97938.39,0.0
-11866,15583595.0,Yobachukwu,608.0,Spain,Male,30.0,7.0,147531.82,1.0,0.0,1.0,56585.2,0.0
-11867,15572361.0,P'an,559.0,Germany,Male,57.0,3.0,118788.57,1.0,1.0,0.0,139366.58,1.0
-11868,15676958.0,Onwuamaeze,528.0,France,Female,32.0,1.0,106901.42,1.0,0.0,1.0,158887.09,0.0
-11869,15764174.0,Milano,721.0,Spain,Male,34.0,0.0,0.0,1.0,0.0,0.0,134778.01,0.0
-11870,15794048.0,H?,716.0,Spain,Male,29.0,2.0,0.0,1.0,1.0,1.0,138849.06,0.0
-11871,15657085.0,Maclean,666.0,France,Male,35.0,5.0,124730.26,1.0,0.0,0.0,175082.2,0.0
-11872,15672594.0,Yuryeva,597.0,France,Female,35.0,3.0,0.0,1.0,0.0,1.0,71519.4,0.0
-11873,15634792.0,Kao,826.0,Germany,Female,35.0,8.0,94920.75,1.0,1.0,0.0,158036.72,1.0
-11874,15611186.0,Obidimkpa,604.0,Spain,Female,36.0,4.0,0.0,2.0,1.0,0.0,114448.77,0.0
-11875,15677307.0,Okwudiliolisa,677.0,Germany,Male,52.0,5.0,144848.74,1.0,0.0,0.0,131083.73,1.0
-11876,15795936.0,Nwagugheuzo,691.0,France,Male,33.0,1.0,0.0,1.0,1.0,0.0,128882.31,0.0
-11877,15731751.0,Zuyev,677.0,France,Male,33.0,9.0,0.0,2.0,1.0,1.0,101694.67,0.0
-11878,15654851.0,Ts'ai,728.0,France,Male,24.0,3.0,121898.59,1.0,1.0,1.0,82796.29,0.0
-11879,15691794.0,Shao,651.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,137763.93,0.0
-11880,15678067.0,Fang,733.0,Spain,Male,33.0,5.0,0.0,2.0,1.0,1.0,130664.79,0.0
-11881,15777033.0,Chigbogu,543.0,Spain,Female,35.0,5.0,0.0,2.0,0.0,0.0,81299.17,0.0
-11882,15757912.0,Chialuka,685.0,Spain,Female,44.0,8.0,121520.39,1.0,1.0,0.0,90591.15,0.0
-11883,15805764.0,Onyekaozulu,564.0,France,Male,28.0,7.0,0.0,2.0,1.0,1.0,162599.51,0.0
-11884,15771674.0,Liao,577.0,France,Male,46.0,9.0,0.0,1.0,1.0,0.0,49089.36,0.0
-11885,15732563.0,White,732.0,France,Female,40.0,6.0,0.0,2.0,0.0,0.0,1408.68,0.0
-11886,15672970.0,Ch'iu,710.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,117832.88,0.0
-11887,15753955.0,Zetticci,587.0,Spain,Male,41.0,4.0,108691.95,1.0,0.0,1.0,163365.85,0.0
-11888,15603764.0,Tuan,599.0,France,Female,34.0,5.0,0.0,1.0,1.0,0.0,91903.9,0.0
-11889,15681342.0,Kibble,685.0,France,Female,33.0,8.0,0.0,2.0,1.0,0.0,91574.92,0.0
-11890,15669611.0,Eluemuno,637.0,Spain,Male,40.0,7.0,0.0,1.0,1.0,1.0,121525.48,0.0
-11891,15640953.0,Ejimofor,819.0,Spain,Female,41.0,4.0,0.0,2.0,1.0,1.0,126794.56,0.0
-11892,15645323.0,Ikedinachukwu,632.0,Germany,Female,62.0,6.0,130334.37,1.0,1.0,0.0,151822.66,1.0
-11893,15661380.0,Kao,749.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,0.0,75825.57,0.0
-11894,15780220.0,Nnachetam,652.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,1.0,103764.55,0.0
-11895,15576094.0,Uvarova,596.0,France,Male,41.0,4.0,0.0,2.0,1.0,1.0,77822.13,0.0
-11896,15768282.0,White,733.0,France,Male,33.0,7.0,135095.47,1.0,1.0,0.0,113931.57,0.0
-11897,15727556.0,Manna,497.0,Spain,Male,43.0,7.0,0.0,2.0,1.0,0.0,120531.31,0.0
-11898,15782096.0,Chuang,707.0,Spain,Female,31.0,4.0,0.0,1.0,1.0,1.0,94888.6,0.0
-11899,15742848.0,Tsao,627.0,France,Female,24.0,3.0,71996.09,1.0,1.0,1.0,138825.19,0.0
-11900,15682463.0,Manna,681.0,Spain,Female,66.0,7.0,0.0,2.0,1.0,1.0,77830.36,0.0
-11901,15667554.0,Ch'in,637.0,France,Male,45.0,4.0,0.0,1.0,1.0,0.0,143463.28,1.0
-11902,15672574.0,Hsu,588.0,Germany,Female,28.0,6.0,126265.88,2.0,1.0,0.0,50249.16,0.0
-11903,15610355.0,Chidiebere,577.0,France,Female,36.0,8.0,106603.74,1.0,0.0,1.0,125083.29,0.0
-11904,15729771.0,Palerma,730.0,Germany,Male,32.0,7.0,117300.02,2.0,1.0,0.0,140000.29,0.0
-11905,15581229.0,Onwumelu,635.0,Spain,Female,33.0,4.0,0.0,1.0,1.0,0.0,69113.14,0.0
-11906,15661626.0,Nnonso,682.0,France,Male,33.0,5.0,0.0,2.0,1.0,1.0,126675.62,0.0
-11907,15782360.0,Ch'ang,629.0,Spain,Female,28.0,3.0,71902.52,1.0,1.0,1.0,147530.06,0.0
-11908,15750569.0,Ts'ai,769.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,178067.33,0.0
-11909,15697597.0,H?,547.0,Germany,Female,41.0,2.0,139561.46,1.0,1.0,1.0,142223.94,1.0
-11910,15680278.0,Okwudilichukwu,753.0,France,Female,34.0,0.0,103499.09,1.0,0.0,0.0,130189.53,0.0
-11911,15770039.0,Milne,597.0,Spain,Female,29.0,5.0,0.0,2.0,0.0,1.0,100127.71,0.0
-11912,15656426.0,Udegbulam,697.0,France,Female,66.0,4.0,100194.44,1.0,0.0,0.0,127560.46,0.0
-11913,15585914.0,Tsou,596.0,Spain,Female,40.0,6.0,0.0,2.0,1.0,1.0,164741.8,0.0
-11914,15726234.0,T'ien,705.0,Germany,Female,37.0,4.0,113693.0,2.0,0.0,0.0,171021.44,0.0
-11915,15649150.0,Smith,713.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,143683.75,0.0
-11916,15757897.0,Toscani,713.0,France,Male,59.0,5.0,0.0,2.0,0.0,1.0,107839.03,0.0
-11917,15736431.0,Chiemela,447.0,Germany,Male,51.0,7.0,142663.46,1.0,1.0,0.0,113655.48,1.0
-11918,15790782.0,Yermakova,676.0,Spain,Female,31.0,3.0,0.0,2.0,1.0,0.0,132498.39,0.0
-11919,15775797.0,Hsueh,709.0,France,Male,40.0,8.0,0.0,2.0,0.0,0.0,87711.61,0.0
-11920,15762238.0,Chukwuemeka,535.0,Germany,Female,35.0,1.0,108348.66,2.0,1.0,0.0,141926.43,0.0
-11921,15712608.0,P'eng,681.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,55835.3,1.0
-11922,15775318.0,Lettiere,526.0,France,Female,43.0,8.0,0.0,1.0,1.0,1.0,145538.35,1.0
-11923,15591822.0,Chienezie,712.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,182059.28,0.0
-11924,15640074.0,Tuan,635.0,France,Female,44.0,3.0,0.0,2.0,0.0,0.0,90928.02,0.0
-11925,15782875.0,Yermolayev,634.0,France,Female,37.0,9.0,0.0,1.0,1.0,0.0,150684.23,0.0
-11926,15781598.0,Ibezimako,597.0,Spain,Male,28.0,4.0,0.0,2.0,1.0,0.0,163562.08,0.0
-11927,15732061.0,Ch'eng,721.0,Spain,Male,40.0,3.0,0.0,2.0,1.0,1.0,86575.46,0.0
-11928,15641110.0,Fiorentini,745.0,Germany,Female,43.0,8.0,104982.57,1.0,1.0,0.0,167969.15,0.0
-11929,15754849.0,T'ang,763.0,France,Male,41.0,5.0,124043.8,1.0,1.0,1.0,134878.19,0.0
-11930,15806049.0,Okwudilichukwu,581.0,France,Male,31.0,5.0,0.0,2.0,1.0,0.0,71139.31,0.0
-11931,15644724.0,Milani,669.0,Germany,Female,44.0,1.0,155903.82,1.0,1.0,0.0,109041.53,1.0
-11932,15607837.0,Pagnotto,562.0,France,Male,34.0,7.0,0.0,2.0,1.0,1.0,131281.29,0.0
-11933,15679810.0,Chioke,585.0,France,Female,61.0,2.0,0.0,1.0,1.0,1.0,52327.57,0.0
-11934,15704819.0,Ch'in,713.0,Spain,Female,38.0,0.0,98400.61,1.0,0.0,1.0,93894.41,0.0
-11935,15648067.0,Chukwumaobim,595.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,1.0,80591.15,0.0
-11936,15805627.0,Yeh,624.0,Spain,Male,48.0,9.0,116967.68,1.0,1.0,1.0,91346.03,1.0
-11937,15572390.0,Mazzi,744.0,Germany,Female,30.0,9.0,135650.88,1.0,1.0,1.0,166692.03,0.0
-11938,15802486.0,Chiemenam,624.0,France,Female,52.0,9.0,0.0,1.0,1.0,1.0,51703.09,1.0
-11939,15679024.0,Kao,545.0,Germany,Female,45.0,1.0,122311.21,1.0,1.0,0.0,140765.44,1.0
-11940,15689594.0,Lai,569.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,173948.4,0.0
-11941,15806964.0,Tseng,569.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,0.0,138516.1,0.0
-11942,15682576.0,Genovese,506.0,Spain,Female,52.0,3.0,0.0,2.0,0.0,1.0,170819.01,1.0
-11943,15799925.0,T'ien,579.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,92381.01,0.0
-11944,15701160.0,Tuan,652.0,Spain,Male,30.0,2.0,0.0,2.0,0.0,1.0,160976.85,0.0
-11945,15773789.0,Nwachinemelu,633.0,Spain,Female,25.0,9.0,0.0,2.0,0.0,1.0,159845.68,0.0
-11946,15673599.0,Chibugo,646.0,France,Male,35.0,0.0,0.0,2.0,1.0,0.0,176768.91,0.0
-11947,15773804.0,Palermo,769.0,Spain,Male,40.0,3.0,82453.96,1.0,1.0,1.0,174032.0,0.0
-11948,15702465.0,Ch'ang,642.0,France,Female,42.0,3.0,0.0,1.0,0.0,0.0,92888.52,1.0
-11949,15785819.0,Ts'ui,723.0,Spain,Male,38.0,5.0,76495.04,1.0,1.0,1.0,152298.28,0.0
-11950,15576065.0,Yermakova,683.0,France,Male,42.0,5.0,0.0,1.0,0.0,1.0,126475.79,0.0
-11951,15718280.0,Trevisani,633.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,183494.87,0.0
-11952,15571752.0,Chukwufumnanya,561.0,Germany,Female,41.0,3.0,123356.63,2.0,0.0,1.0,111243.09,0.0
-11953,15585066.0,Ch'eng,579.0,Germany,Male,56.0,6.0,118547.74,1.0,1.0,0.0,183963.34,1.0
-11954,15773098.0,K'ung,697.0,Germany,Male,38.0,2.0,82697.28,2.0,1.0,0.0,50839.85,0.0
-11955,15759991.0,Mamelu,850.0,France,Male,31.0,2.0,0.0,2.0,1.0,0.0,151902.78,0.0
-11956,15605872.0,Chien,724.0,France,Male,24.0,10.0,0.0,2.0,1.0,1.0,121529.59,0.0
-11957,15687828.0,Onyekachukwu,633.0,Germany,Male,61.0,7.0,119452.03,1.0,0.0,0.0,170829.87,1.0
-11958,15647975.0,Nwabugwu,748.0,France,Female,43.0,8.0,0.0,2.0,1.0,0.0,68814.23,0.0
-11959,15650026.0,O'Sullivan,651.0,Germany,Female,42.0,2.0,148962.76,2.0,1.0,0.0,83084.33,0.0
-11960,15684319.0,Milanesi,605.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,142331.85,0.0
-11961,15766044.0,Nwankwo,544.0,France,Female,31.0,10.0,0.0,1.0,1.0,0.0,96670.2,0.0
-11962,15658475.0,Onyemere,573.0,Spain,Male,44.0,8.0,0.0,2.0,1.0,0.0,130504.49,0.0
-11963,15811486.0,Ting,752.0,France,Male,36.0,9.0,0.0,2.0,0.0,0.0,67789.99,0.0
-11964,15611365.0,Aksenov,707.0,France,Male,45.0,9.0,0.0,2.0,1.0,1.0,108301.45,0.0
-11965,15689953.0,Pirozzi,607.0,France,Female,32.0,2.0,0.0,2.0,1.0,0.0,145792.01,0.0
-11966,15686696.0,Mazzanti,573.0,France,Male,23.0,1.0,0.0,1.0,1.0,1.0,138542.87,0.0
-11967,15682453.0,Yermolayev,628.0,France,Female,36.0,3.0,0.0,4.0,0.0,0.0,97623.42,1.0
-11968,15571284.0,Ulyanova,609.0,France,Male,47.0,3.0,125712.2,1.0,0.0,1.0,133672.61,1.0
-11969,15654074.0,Nwankwo,744.0,Spain,Female,40.0,2.0,0.0,2.0,1.0,0.0,131097.9,0.0
-11970,15632826.0,Chigolum,738.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,0.0,105720.09,0.0
-11971,15641794.0,Eluemuno,678.0,Spain,Male,36.0,8.0,0.0,2.0,1.0,0.0,97468.44,0.0
-11972,15761141.0,Chiemezie,743.0,France,Female,44.0,5.0,116803.8,1.0,1.0,1.0,62972.13,0.0
-11973,15741643.0,Hsueh,686.0,France,Female,28.0,5.0,0.0,2.0,1.0,1.0,103799.77,0.0
-11974,15691910.0,Hao,632.0,Germany,Male,30.0,4.0,121345.86,1.0,0.0,0.0,93917.68,0.0
-11975,15761497.0,Ponomarev,687.0,France,Male,28.0,4.0,0.0,2.0,1.0,0.0,159815.68,0.0
-11976,15682563.0,Martin,545.0,France,Female,52.0,1.0,133384.66,1.0,1.0,0.0,145796.49,1.0
-11977,15642710.0,K?,652.0,Germany,Male,37.0,7.0,83347.25,1.0,1.0,1.0,173629.78,0.0
-11978,15647898.0,Pagnotto,604.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,142513.5,0.0
-11979,15804787.0,Fanucci,727.0,Spain,Male,34.0,8.0,0.0,2.0,1.0,0.0,26170.08,0.0
-11980,15587735.0,Nnamutaezinwa,553.0,Spain,Male,28.0,3.0,179904.0,1.0,1.0,0.0,126469.09,0.0
-11981,15783714.0,Pirozzi,712.0,France,Male,51.0,5.0,152324.66,2.0,1.0,1.0,164064.44,1.0
-11982,15766017.0,Kuo,753.0,France,Female,38.0,1.0,82498.14,1.0,1.0,0.0,103099.22,1.0
-11983,15737506.0,Chiemenam,677.0,France,Male,30.0,2.0,0.0,2.0,1.0,0.0,63736.19,0.0
-11984,15761149.0,Yirawala,749.0,France,Male,33.0,1.0,0.0,1.0,1.0,0.0,143317.42,0.0
-11985,15771873.0,Okwudilichukwu,537.0,Germany,Female,31.0,5.0,132412.13,1.0,1.0,1.0,80552.11,0.0
-11986,15705315.0,Bischof,635.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,132702.64,0.0
-11987,15774872.0,Chikwendu,850.0,France,Female,45.0,8.0,0.0,3.0,1.0,1.0,94912.78,1.0
-11988,15579183.0,Hsueh,644.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,51345.44,0.0
-11989,15698872.0,Onyemauchechukwu,754.0,France,Female,31.0,7.0,102994.03,1.0,0.0,0.0,147822.54,1.0
-11990,15646521.0,Chiemezie,696.0,France,Male,42.0,9.0,179960.2,1.0,1.0,1.0,122763.95,0.0
-11991,15662085.0,P'eng,769.0,France,Male,45.0,9.0,0.0,2.0,1.0,1.0,161845.81,0.0
-11992,15804009.0,Palermo,681.0,Spain,Female,37.0,1.0,0.0,2.0,1.0,0.0,123587.83,0.0
-11993,15592025.0,Hsiao,579.0,Spain,Male,31.0,7.0,0.0,2.0,1.0,0.0,170142.09,0.0
-11994,15592979.0,Shih,708.0,France,Female,55.0,8.0,0.0,3.0,1.0,0.0,88407.51,1.0
-11995,15593937.0,Ch'in,684.0,France,Male,49.0,7.0,0.0,4.0,1.0,1.0,142963.18,1.0
-11996,15777076.0,Chukwufumnanya,687.0,France,Female,51.0,8.0,0.0,1.0,1.0,0.0,115818.51,1.0
-11997,15736287.0,Y?an,629.0,France,Male,33.0,3.0,0.0,1.0,1.0,1.0,181449.51,0.0
-11998,15769346.0,Hsia,648.0,France,Male,31.0,7.0,0.0,2.0,0.0,0.0,141533.19,0.0
-11999,15662854.0,Chinedum,590.0,Spain,Male,34.0,9.0,0.0,2.0,1.0,1.0,138380.11,0.0
-12000,15696061.0,Ch'ien,635.0,Germany,Female,43.0,3.0,122313.88,1.0,1.0,1.0,117632.63,1.0
-12001,15594560.0,Toscani,648.0,Germany,Female,51.0,0.0,123835.52,1.0,1.0,0.0,121551.58,1.0
-12002,15768006.0,Nwabugwu,685.0,France,Male,55.0,7.0,82231.51,1.0,1.0,0.0,95983.54,1.0
-12003,15786063.0,Hsing,713.0,Spain,Female,28.0,6.0,0.0,2.0,1.0,1.0,86699.27,0.0
-12004,15584928.0,Hsing,711.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,26342.33,0.0
-12005,15769703.0,Tien,714.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,180622.42,0.0
-12006,15747174.0,Hs?,602.0,France,Male,36.0,4.0,132623.6,1.0,1.0,1.0,181204.5,0.0
-12007,15589563.0,Tien,705.0,France,Female,43.0,5.0,0.0,2.0,1.0,1.0,125909.87,0.0
-12008,15650391.0,Nwabugwu,576.0,France,Male,24.0,4.0,0.0,2.0,1.0,0.0,121151.1,0.0
-12009,15652084.0,Pinto,638.0,France,Male,29.0,6.0,0.0,2.0,0.0,0.0,62703.38,0.0
-12010,15627377.0,Chiang,685.0,Spain,Female,33.0,2.0,0.0,1.0,1.0,0.0,91936.1,1.0
-12011,15815271.0,Yashina,608.0,France,Male,25.0,3.0,0.0,2.0,1.0,1.0,51721.52,0.0
-12012,15756975.0,Chidiegwu,553.0,Germany,Male,34.0,8.0,130147.96,2.0,1.0,1.0,172749.65,0.0
-12013,15592222.0,Chinedum,697.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,36557.55,0.0
-12014,15617507.0,Ch'ien,673.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,103660.31,1.0
-12015,15603088.0,Rieke,775.0,Germany,Female,43.0,9.0,108632.48,1.0,1.0,1.0,63795.8,1.0
-12016,15724756.0,Shao,602.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,0.0,76637.38,0.0
-12017,15697597.0,Ch'en,594.0,Germany,Male,33.0,7.0,111432.77,1.0,1.0,0.0,49374.82,0.0
-12018,15691808.0,Folliero,645.0,France,Female,35.0,5.0,98899.76,1.0,1.0,0.0,143628.56,0.0
-12019,15681755.0,Trevisano,667.0,France,Male,29.0,8.0,0.0,1.0,1.0,1.0,168515.61,0.0
-12020,15770309.0,Uvarova,569.0,France,Male,39.0,2.0,0.0,2.0,1.0,0.0,86622.48,0.0
-12021,15663917.0,Ojiofor,717.0,Germany,Male,43.0,7.0,124043.8,2.0,1.0,0.0,80344.96,0.0
-12022,15645794.0,Chukwufumnanya,667.0,France,Female,49.0,6.0,0.0,1.0,1.0,1.0,182336.45,1.0
-12023,15693637.0,Chuang,775.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,87566.97,0.0
-12024,15802570.0,Henty,633.0,France,Female,25.0,5.0,0.0,2.0,0.0,1.0,122518.5,0.0
-12025,15756680.0,Onyenachiya,804.0,France,Female,26.0,10.0,0.0,2.0,1.0,1.0,91718.12,0.0
-12026,15581229.0,Hargreaves,543.0,Germany,Female,58.0,10.0,109137.12,1.0,1.0,1.0,171867.08,0.0
-12027,15672756.0,Ch'eng,431.0,France,Male,36.0,5.0,0.0,1.0,1.0,0.0,147055.87,1.0
-12028,15572735.0,Hsia,592.0,France,Female,29.0,9.0,0.0,2.0,0.0,0.0,70954.41,0.0
-12029,15727556.0,Iheanacho,555.0,France,Male,27.0,1.0,91897.8,1.0,0.0,0.0,164040.94,0.0
-12030,15579706.0,Nkemjika,637.0,France,Male,31.0,9.0,130142.79,1.0,1.0,0.0,176450.19,0.0
-12031,15671915.0,Cattaneo,706.0,France,Male,30.0,10.0,0.0,2.0,1.0,1.0,90876.95,0.0
-12032,15611905.0,Okechukwu,756.0,Spain,Male,35.0,3.0,0.0,1.0,0.0,0.0,80552.11,0.0
-12033,15674929.0,K'ung,624.0,Spain,Male,42.0,8.0,0.0,2.0,1.0,0.0,183542.08,0.0
-12034,15773520.0,Milne,594.0,France,Female,45.0,4.0,0.0,2.0,1.0,0.0,55857.48,0.0
-12035,15578738.0,Chiu,850.0,Germany,Male,34.0,5.0,81677.22,2.0,0.0,0.0,59981.62,0.0
-12036,15572022.0,Mazzi,705.0,Germany,Male,35.0,7.0,108913.84,1.0,1.0,0.0,172967.87,1.0
-12037,15591723.0,Yermolayeva,531.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,0.0,74849.46,0.0
-12038,15744691.0,Cremonesi,537.0,Spain,Male,41.0,10.0,0.0,2.0,0.0,1.0,163440.8,0.0
-12039,15672598.0,Kosisochukwu,681.0,France,Male,45.0,1.0,0.0,2.0,1.0,0.0,36754.13,0.0
-12040,15682868.0,Nkemdilim,604.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,173912.45,0.0
-12041,15737542.0,Ch'in,579.0,France,Male,36.0,10.0,0.0,2.0,0.0,1.0,178373.93,0.0
-12042,15742369.0,Chiang,651.0,France,Female,46.0,6.0,0.0,1.0,1.0,0.0,1187.88,1.0
-12043,15659557.0,Chibugo,678.0,France,Female,41.0,7.0,0.0,2.0,1.0,1.0,97640.2,0.0
-12044,15686936.0,Mancini,653.0,France,Female,47.0,7.0,0.0,2.0,1.0,0.0,148087.62,0.0
-12045,15732672.0,Uspenskaya,665.0,Spain,Female,47.0,4.0,0.0,1.0,1.0,0.0,54447.71,1.0
-12046,15576065.0,Chukwukadibia,732.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,149772.61,0.0
-12047,15616709.0,Fanucci,716.0,Germany,Male,33.0,2.0,105858.71,2.0,1.0,1.0,157891.86,0.0
-12048,15641582.0,Gadsdon,712.0,France,Female,51.0,3.0,0.0,1.0,1.0,0.0,59864.56,1.0
-12049,15720338.0,Efremov,714.0,Spain,Female,31.0,8.0,83348.89,1.0,0.0,1.0,65464.66,0.0
-12050,15605872.0,P'an,605.0,Germany,Male,30.0,5.0,114991.45,2.0,1.0,0.0,73857.94,0.0
-12051,15808621.0,P'eng,641.0,France,Male,33.0,7.0,0.0,2.0,1.0,1.0,51919.04,0.0
-12052,15750772.0,Shih,724.0,Spain,Female,41.0,9.0,116536.43,1.0,1.0,1.0,175006.78,0.0
-12053,15609623.0,L?,664.0,France,Female,53.0,5.0,0.0,1.0,1.0,0.0,181870.24,1.0
-12054,15637354.0,Ch'eng,748.0,France,Male,35.0,7.0,0.0,2.0,0.0,1.0,107876.2,0.0
-12055,15631512.0,Ugochukwu,648.0,France,Male,38.0,6.0,113657.83,1.0,0.0,1.0,143747.68,1.0
-12056,15777904.0,Ch'ang,725.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,36342.33,0.0
-12057,15742834.0,Fanucci,719.0,France,Female,35.0,9.0,123365.85,1.0,1.0,0.0,142963.39,0.0
-12058,15642736.0,Chin,691.0,France,Female,36.0,3.0,0.0,2.0,1.0,0.0,153036.97,0.0
-12059,15665064.0,Fyodorov,718.0,France,Female,46.0,9.0,0.0,1.0,0.0,0.0,103799.77,1.0
-12060,15640454.0,Ebelechukwu,642.0,Spain,Male,34.0,4.0,0.0,2.0,1.0,0.0,147554.26,0.0
-12061,15773904.0,Chidiegwu,678.0,Spain,Female,66.0,2.0,0.0,3.0,1.0,1.0,55725.04,1.0
-12062,15631220.0,Zikoranachidimma,648.0,France,Male,27.0,5.0,144773.29,1.0,0.0,0.0,71830.1,0.0
-12063,15768474.0,Oguejiofor,652.0,France,Male,25.0,6.0,0.0,2.0,1.0,1.0,122584.29,0.0
-12064,15768346.0,Baresi,699.0,France,Male,35.0,8.0,122486.11,1.0,1.0,0.0,77965.67,0.0
-12065,15709199.0,Udegbunam,755.0,France,Male,54.0,5.0,134105.01,1.0,1.0,0.0,159654.0,1.0
-12066,15614643.0,Chizuoke,721.0,France,Male,42.0,7.0,0.0,2.0,1.0,1.0,136934.91,0.0
-12067,15731553.0,Shih,586.0,France,Male,36.0,8.0,0.0,1.0,1.0,0.0,171575.54,0.0
-12068,15641582.0,Chukwufumnanya,646.0,France,Male,32.0,9.0,0.0,2.0,0.0,0.0,79577.38,0.0
-12069,15592028.0,Nwokike,677.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,113645.4,0.0
-12070,15644446.0,Chinweike,687.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,144493.07,0.0
-12071,15662955.0,Ts'ui,705.0,Germany,Female,25.0,2.0,121056.63,2.0,1.0,0.0,75937.47,0.0
-12072,15696852.0,Onyemachukwu,637.0,France,Male,30.0,7.0,0.0,1.0,1.0,1.0,145796.49,0.0
-12073,15610105.0,Echezonachukwu,675.0,Germany,Male,43.0,6.0,153535.43,3.0,0.0,1.0,116957.6,1.0
-12074,15638247.0,Ch'ien,695.0,France,Female,35.0,7.0,0.0,2.0,1.0,0.0,126812.85,0.0
-12075,15689980.0,Iqbal,584.0,France,Male,50.0,3.0,0.0,2.0,1.0,0.0,84760.32,0.0
-12076,15790744.0,Frolov,590.0,France,Male,40.0,6.0,132623.6,1.0,1.0,0.0,186489.95,1.0
-12077,15639252.0,Biryukova,583.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,0.0,163558.66,0.0
-12078,15757952.0,Milne,693.0,France,Male,36.0,1.0,116574.79,1.0,1.0,1.0,179066.58,0.0
-12079,15635277.0,Li,585.0,France,Female,32.0,8.0,0.0,2.0,0.0,1.0,74593.84,0.0
-12080,15624993.0,Hs?,688.0,Germany,Female,47.0,3.0,108449.23,1.0,1.0,0.0,113553.42,1.0
-12081,15601116.0,Onwuamaeze,559.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,187987.01,0.0
-12082,15776051.0,Pisani,705.0,Germany,Male,25.0,9.0,124619.33,1.0,1.0,0.0,162683.06,1.0
-12083,15751774.0,Wright,706.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,149550.93,0.0
-12084,15698031.0,Chukwuemeka,581.0,Spain,Female,48.0,6.0,0.0,1.0,1.0,1.0,99792.82,1.0
-12085,15798895.0,Chukwumaobim,724.0,France,Male,48.0,1.0,106425.75,1.0,1.0,1.0,100816.29,0.0
-12086,15584540.0,Trevisani,725.0,France,Male,38.0,9.0,113599.42,1.0,1.0,1.0,177065.24,0.0
-12087,15780128.0,Tretiakova,741.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,147794.63,0.0
-12088,15691793.0,Maclean,498.0,France,Male,26.0,7.0,0.0,2.0,1.0,0.0,88827.67,0.0
-12089,15682600.0,Ibezimako,616.0,France,Female,43.0,6.0,0.0,2.0,1.0,0.0,85522.84,1.0
-12090,15779915.0,Mazzi,544.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,121866.87,0.0
-12091,15810660.0,Chiemezie,721.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,145553.4,0.0
-12092,15803479.0,Yermolayev,516.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,64872.33,0.0
-12093,15798885.0,Yobanna,751.0,France,Female,25.0,9.0,0.0,2.0,1.0,0.0,94738.54,0.0
-12094,15742369.0,Tung,676.0,France,Female,27.0,9.0,152303.8,1.0,1.0,0.0,96658.26,0.0
-12095,15694859.0,Manna,537.0,France,Female,38.0,1.0,0.0,2.0,1.0,1.0,130132.54,0.0
-12096,15771543.0,Ch'eng,640.0,France,Male,43.0,1.0,0.0,1.0,0.0,1.0,139024.31,0.0
-12097,15747772.0,Piazza,642.0,Germany,Male,47.0,2.0,129743.75,1.0,1.0,1.0,61995.57,0.0
-12098,15724444.0,Nwachinemelu,731.0,Germany,Female,31.0,10.0,119825.75,2.0,0.0,1.0,134901.57,0.0
-12099,15721779.0,Teng,594.0,Germany,Female,30.0,5.0,110646.54,2.0,1.0,0.0,70899.27,0.0
-12100,15652999.0,Ts'ui,683.0,France,Female,31.0,6.0,129745.1,1.0,1.0,0.0,72924.56,1.0
-12101,15679810.0,Tien,704.0,Germany,Female,53.0,3.0,161637.75,1.0,1.0,0.0,163798.41,1.0
-12102,15596984.0,Onyekachi,537.0,Germany,Female,41.0,1.0,146720.98,1.0,1.0,0.0,160526.36,0.0
-12103,15777784.0,Kuo,732.0,France,Male,50.0,3.0,0.0,1.0,1.0,0.0,27239.28,1.0
-12104,15743828.0,Smith,641.0,France,Female,45.0,6.0,0.0,2.0,1.0,0.0,96832.82,1.0
-12105,15572631.0,Kaleski,633.0,Spain,Female,40.0,4.0,0.0,2.0,1.0,0.0,70563.9,0.0
-12106,15783501.0,Ch'ien,682.0,Germany,Female,51.0,3.0,127837.54,1.0,1.0,1.0,38772.45,1.0
-12107,15661734.0,Lombardo,619.0,Germany,Male,54.0,1.0,122265.24,1.0,1.0,0.0,157564.75,1.0
-12108,15591512.0,Iadanza,712.0,Germany,Female,41.0,4.0,116932.53,1.0,1.0,1.0,140745.91,0.0
-12109,15711750.0,Lettiere,681.0,Spain,Male,42.0,5.0,0.0,2.0,1.0,0.0,81299.17,0.0
-12110,15643487.0,Ch'in,578.0,Spain,Female,29.0,2.0,0.0,1.0,1.0,1.0,174032.0,0.0
-12111,15778955.0,Li,607.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,18082.06,0.0
-12112,15737506.0,Clements,748.0,France,Male,31.0,7.0,0.0,1.0,1.0,0.0,100720.02,0.0
-12113,15671351.0,Hsiao,559.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,142839.82,0.0
-12114,15630053.0,Hs?,739.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,181419.29,0.0
-12115,15668958.0,Tien,547.0,France,Male,50.0,8.0,0.0,2.0,1.0,1.0,94875.03,0.0
-12116,15682454.0,Pirozzi,554.0,Spain,Female,35.0,10.0,0.0,2.0,1.0,0.0,147538.26,0.0
-12117,15655252.0,Maclean,667.0,France,Male,37.0,8.0,0.0,2.0,1.0,1.0,94544.88,0.0
-12118,15629582.0,Zhirov,742.0,France,Female,41.0,10.0,0.0,3.0,1.0,0.0,150218.11,1.0
-12119,15753831.0,Folliero,714.0,Germany,Male,46.0,6.0,121525.33,1.0,0.0,1.0,166792.71,1.0
-12120,15778023.0,Onyekaozulu,724.0,France,Male,38.0,4.0,0.0,1.0,1.0,0.0,51536.99,0.0
-12121,15773792.0,Tsai,555.0,France,Female,33.0,8.0,0.0,2.0,0.0,1.0,52367.29,1.0
-12122,15743818.0,Brown,633.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,166850.57,0.0
-12123,15770185.0,Fanucci,667.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,89566.64,0.0
-12124,15697567.0,K'ung,662.0,Spain,Female,35.0,1.0,0.0,2.0,1.0,1.0,127095.14,0.0
-12125,15684469.0,Ch'ien,598.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,95625.03,0.0
-12126,15782404.0,Chu,593.0,Germany,Male,29.0,1.0,146053.66,2.0,0.0,1.0,93717.46,0.0
-12127,15671789.0,Manna,707.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,143617.43,0.0
-12128,15663438.0,Yefimova,712.0,Spain,Female,29.0,8.0,0.0,1.0,1.0,1.0,89566.64,0.0
-12129,15606861.0,Chiemenam,589.0,France,Male,46.0,6.0,132084.66,1.0,1.0,0.0,136079.64,0.0
-12130,15771742.0,Cone,708.0,France,Female,58.0,9.0,0.0,2.0,1.0,1.0,133030.96,0.0
-12131,15651581.0,Udegbunam,591.0,Spain,Male,36.0,6.0,128966.13,1.0,1.0,0.0,187921.3,0.0
-12132,15813095.0,Hs?,730.0,Spain,Female,38.0,1.0,0.0,1.0,0.0,0.0,164864.66,0.0
-12133,15718572.0,Ch'ien,850.0,France,Male,33.0,10.0,0.0,2.0,1.0,0.0,66463.62,0.0
-12134,15791342.0,Ch'iu,732.0,France,Male,40.0,7.0,0.0,1.0,1.0,0.0,121485.1,1.0
-12135,15773520.0,Hsiung,708.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,183490.99,0.0
-12136,15782360.0,Yobachi,710.0,France,Female,41.0,9.0,103544.0,1.0,1.0,1.0,175893.53,0.0
-12137,15635939.0,Chukwujamuike,615.0,France,Male,41.0,6.0,187841.99,1.0,1.0,1.0,164996.68,0.0
-12138,15587562.0,Nnamdi,677.0,Germany,Female,37.0,4.0,138089.97,2.0,1.0,1.0,123587.85,0.0
-12139,15763747.0,Chukwufumnanya,615.0,Spain,Male,32.0,7.0,133776.65,1.0,1.0,0.0,160546.58,0.0
-12140,15697034.0,Onyemauchechukwu,684.0,Germany,Female,38.0,1.0,113599.82,1.0,1.0,1.0,177624.01,0.0
-12141,15772601.0,Onwubiko,607.0,France,Male,38.0,6.0,0.0,2.0,0.0,0.0,95611.47,0.0
-12142,15753337.0,Nkemjika,828.0,Germany,Female,46.0,1.0,107534.79,1.0,1.0,0.0,74898.8,1.0
-12143,15647800.0,H?,613.0,Germany,Male,31.0,1.0,122451.46,1.0,1.0,1.0,176925.69,0.0
-12144,15781598.0,Onuchukwu,592.0,Germany,Female,33.0,1.0,153313.66,2.0,1.0,0.0,40257.79,0.0
-12145,15797381.0,Chiemenam,594.0,France,Male,30.0,3.0,129697.69,1.0,1.0,1.0,187910.6,0.0
-12146,15791747.0,Galkin,794.0,Spain,Female,60.0,3.0,88594.93,1.0,0.0,1.0,173365.89,0.0
-12147,15617982.0,Nwabugwu,681.0,Spain,Male,38.0,9.0,0.0,2.0,0.0,1.0,181605.85,0.0
-12148,15678058.0,Chu,699.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,0.0,177783.04,0.0
-12149,15658972.0,Lo,599.0,France,Female,49.0,7.0,0.0,1.0,1.0,0.0,162668.33,1.0
-12150,15753840.0,Ch'iu,726.0,France,Male,25.0,4.0,0.0,2.0,1.0,1.0,141364.62,0.0
-12151,15654878.0,Yobanna,810.0,France,Male,58.0,3.0,0.0,1.0,1.0,0.0,151822.66,1.0
-12152,15804721.0,Lu,611.0,France,Male,24.0,7.0,0.0,2.0,0.0,0.0,61581.2,0.0
-12153,15794468.0,Chien,571.0,Germany,Male,50.0,10.0,139879.41,2.0,0.0,0.0,57975.8,0.0
-12154,15744398.0,Ch'ang,577.0,Spain,Female,42.0,9.0,108541.04,1.0,1.0,0.0,147794.63,1.0
-12155,15636478.0,Sagese,730.0,Germany,Female,40.0,6.0,116497.31,1.0,1.0,1.0,173495.77,1.0
-12156,15568240.0,Hsia,652.0,Spain,Female,25.0,7.0,0.0,2.0,1.0,1.0,60954.45,0.0
-12157,15599329.0,Trevisani,704.0,France,Female,34.0,0.0,0.0,2.0,1.0,0.0,85547.33,0.0
-12158,15683471.0,Chiu,713.0,France,Female,54.0,7.0,103818.41,1.0,1.0,0.0,126702.41,1.0
-12159,15593365.0,T'ang,679.0,Germany,Male,37.0,1.0,128960.3,1.0,0.0,0.0,101371.72,0.0
-12160,15593555.0,Hsiao,582.0,Spain,Female,35.0,6.0,0.0,2.0,1.0,1.0,51387.1,0.0
-12161,15600110.0,Onyinyechukwuka,578.0,Germany,Female,31.0,5.0,154983.98,2.0,0.0,1.0,97939.4,0.0
-12162,15615477.0,K'ung,593.0,Germany,Female,41.0,6.0,77526.66,2.0,1.0,1.0,102628.98,0.0
-12163,15589437.0,Nwokeocha,739.0,France,Female,28.0,4.0,0.0,2.0,1.0,1.0,126555.74,0.0
-12164,15594812.0,Hsueh,578.0,France,Female,46.0,2.0,128289.6,1.0,1.0,0.0,71167.17,0.0
-12165,15742404.0,Ojiofor,624.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,84871.99,0.0
-12166,15665088.0,Hsiao,456.0,France,Female,36.0,7.0,0.0,2.0,0.0,0.0,130664.79,0.0
-12167,15574072.0,Hsueh,646.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,96670.2,0.0
-12168,15592745.0,Piazza,591.0,France,Male,28.0,1.0,0.0,2.0,1.0,0.0,88890.75,0.0
-12169,15582335.0,Liao,648.0,Spain,Female,41.0,9.0,0.0,2.0,1.0,0.0,71323.23,0.0
-12170,15674551.0,Tung,719.0,Germany,Male,32.0,2.0,163894.35,1.0,1.0,1.0,163714.82,0.0
-12171,15586029.0,Tseng,741.0,France,Male,48.0,5.0,0.0,1.0,0.0,0.0,127875.1,1.0
-12172,15569394.0,Chinweike,706.0,France,Male,37.0,5.0,0.0,2.0,1.0,0.0,153698.65,0.0
-12173,15600575.0,Chuang,615.0,Spain,Female,51.0,6.0,76914.21,1.0,1.0,1.0,132649.85,0.0
-12174,15587597.0,Chinweuba,589.0,France,Male,35.0,9.0,0.0,2.0,1.0,1.0,93901.61,0.0
-12175,15756026.0,T'ien,633.0,Spain,Female,50.0,8.0,0.0,3.0,0.0,0.0,9149.96,1.0
-12176,15585367.0,Chiang,570.0,Spain,Male,44.0,5.0,81733.74,1.0,1.0,0.0,27330.59,0.0
-12177,15737571.0,Hsueh,671.0,France,Female,31.0,2.0,0.0,2.0,0.0,1.0,167984.72,0.0
-12178,15645896.0,Dennys,706.0,Germany,Male,30.0,2.0,97473.61,2.0,0.0,1.0,156906.15,0.0
-12179,15619932.0,Chukwualuka,585.0,Germany,Male,36.0,8.0,77992.97,2.0,1.0,1.0,85746.52,0.0
-12180,15589475.0,Tung,678.0,France,Male,28.0,10.0,0.0,2.0,1.0,0.0,163550.58,0.0
-12181,15778861.0,Eluemuno,683.0,Germany,Female,35.0,7.0,132571.67,2.0,1.0,1.0,181224.56,0.0
-12182,15743490.0,Chukwualuka,464.0,Germany,Male,57.0,1.0,151958.19,1.0,1.0,0.0,86797.41,1.0
-12183,15779985.0,H?,616.0,Germany,Male,47.0,10.0,108867.0,1.0,1.0,0.0,78416.14,1.0
-12184,15631053.0,Ugochukwu,568.0,Germany,Male,29.0,8.0,93903.68,2.0,1.0,1.0,163774.01,0.0
-12185,15635502.0,Ritchie,634.0,France,Male,30.0,7.0,0.0,1.0,1.0,0.0,185561.89,0.0
-12186,15799494.0,Nkemdirim,505.0,Germany,Male,28.0,6.0,113988.35,2.0,1.0,1.0,124218.82,0.0
-12187,15600710.0,Iloerika,529.0,France,Female,43.0,9.0,129895.57,1.0,1.0,1.0,62390.59,1.0
-12188,15593694.0,Hsia,697.0,France,Female,39.0,5.0,176666.62,3.0,0.0,0.0,94670.77,1.0
-12189,15684596.0,Chidimma,656.0,Spain,Female,27.0,6.0,0.0,2.0,1.0,1.0,60972.84,0.0
-12190,15576774.0,Maclean,732.0,Spain,Male,40.0,2.0,0.0,2.0,0.0,0.0,163317.5,0.0
-12191,15809462.0,Ch'ien,584.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,72849.29,0.0
-12192,15783097.0,Ch'ang,628.0,France,Female,47.0,3.0,138901.61,1.0,1.0,0.0,111421.77,1.0
-12193,15652914.0,Lo Duca,624.0,France,Female,54.0,8.0,97585.32,2.0,0.0,1.0,142956.48,1.0
-12194,15814816.0,Lassetter,714.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,171364.18,0.0
-12195,15594720.0,Lucchese,699.0,Germany,Female,33.0,10.0,122384.22,2.0,1.0,0.0,159463.8,0.0
-12196,15635116.0,Kao,641.0,France,Female,61.0,6.0,0.0,1.0,0.0,1.0,91943.94,1.0
-12197,15746076.0,Pipes,583.0,France,Male,24.0,5.0,0.0,2.0,0.0,0.0,44506.09,0.0
-12198,15771942.0,Hao,705.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,83983.54,0.0
-12199,15605835.0,Udobata,659.0,Germany,Male,43.0,9.0,116360.07,1.0,1.0,1.0,76637.38,1.0
-12200,15766896.0,Hs?,564.0,France,Male,29.0,0.0,79450.09,1.0,1.0,0.0,164081.42,0.0
-12201,15806964.0,Hao,708.0,Spain,Male,47.0,6.0,0.0,1.0,1.0,0.0,176883.42,1.0
-12202,15772030.0,Mazzanti,703.0,France,Male,35.0,7.0,126473.33,1.0,1.0,1.0,47764.99,0.0
-12203,15790810.0,Nnachetam,771.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,160979.66,0.0
-12204,15729083.0,Chukwufumnanya,817.0,Germany,Male,48.0,7.0,150840.03,2.0,1.0,1.0,78616.92,0.0
-12205,15688072.0,Chidumaga,682.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,1.0,72924.56,0.0
-12206,15771086.0,Fanucci,636.0,Germany,Female,58.0,3.0,121161.57,2.0,0.0,1.0,90189.72,0.0
-12207,15682778.0,Doyne,639.0,France,Male,58.0,9.0,152309.41,1.0,0.0,1.0,183868.01,0.0
-12208,15656829.0,Ritchie,684.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,111180.3,0.0
-12209,15790455.0,Shih,771.0,Spain,Female,38.0,8.0,0.0,2.0,1.0,1.0,94778.12,0.0
-12210,15772030.0,Lo,705.0,France,Male,52.0,7.0,126728.77,1.0,1.0,0.0,139947.17,0.0
-12211,15675176.0,Okechukwu,655.0,Spain,Male,32.0,3.0,170426.13,1.0,1.0,0.0,187987.01,0.0
-12212,15656829.0,Cattaneo,652.0,France,Male,34.0,3.0,132412.13,1.0,0.0,0.0,125977.25,0.0
-12213,15668893.0,Macleod,778.0,Spain,Male,51.0,10.0,115576.44,1.0,0.0,0.0,83419.29,1.0
-12214,15743760.0,Ch'iu,724.0,France,Male,47.0,1.0,0.0,2.0,0.0,1.0,136540.09,0.0
-12215,15784958.0,Hsu,599.0,France,Male,37.0,3.0,0.0,2.0,1.0,1.0,91347.76,0.0
-12216,15652955.0,Kao,678.0,France,Male,35.0,6.0,0.0,1.0,1.0,1.0,152996.89,0.0
-12217,15592583.0,Ts'ao,681.0,Spain,Female,50.0,1.0,0.0,2.0,1.0,0.0,161378.02,1.0
-12218,15578098.0,Trevisani,754.0,Germany,Male,29.0,4.0,80790.74,2.0,1.0,1.0,112331.19,0.0
-12219,15652674.0,Chiu,607.0,Germany,Female,33.0,1.0,93616.45,1.0,1.0,0.0,187980.21,0.0
-12220,15795895.0,Fanucci,704.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,47799.15,0.0
-12221,15692406.0,Martin,644.0,Germany,Female,36.0,5.0,153344.72,2.0,1.0,0.0,56784.11,0.0
-12222,15734588.0,Tsou,653.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,94774.12,0.0
-12223,15805627.0,Pirozzi,627.0,Spain,Female,42.0,4.0,0.0,1.0,1.0,0.0,147963.07,0.0
-12224,15596074.0,Lo Duca,585.0,Germany,Male,40.0,3.0,115532.52,2.0,1.0,1.0,93901.61,1.0
-12225,15770174.0,Frolov,692.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,172531.23,0.0
-12226,15711446.0,Chukwualuka,718.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,106977.8,0.0
-12227,15682454.0,Maclean,654.0,France,Female,38.0,9.0,0.0,2.0,1.0,0.0,944.41,0.0
-12228,15763693.0,Chijindum,665.0,Germany,Female,41.0,0.0,111065.93,1.0,1.0,0.0,132450.19,1.0
-12229,15758813.0,Chukwudi,757.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,152845.99,0.0
-12230,15602844.0,Ejimofor,681.0,France,Male,71.0,8.0,0.0,2.0,1.0,1.0,75825.43,0.0
-12231,15687094.0,Iweobiegbunam,526.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,149853.89,0.0
-12232,15738291.0,Brown,678.0,Spain,Male,40.0,6.0,119370.11,1.0,1.0,0.0,70810.6,0.0
-12233,15721719.0,Calabresi,708.0,France,Male,37.0,9.0,68423.89,1.0,0.0,0.0,124592.84,0.0
-12234,15795738.0,Hsueh,590.0,France,Male,33.0,9.0,144974.55,1.0,0.0,0.0,120853.83,0.0
-12235,15706593.0,Akabueze,639.0,Spain,Female,39.0,8.0,0.0,2.0,1.0,1.0,156774.75,0.0
-12236,15642099.0,Trevisani,707.0,France,Male,21.0,3.0,0.0,2.0,1.0,1.0,148564.76,0.0
-12237,15572801.0,Hs?eh,724.0,Spain,Male,49.0,4.0,0.0,1.0,1.0,1.0,47318.75,1.0
-12238,15681554.0,Pirozzi,677.0,France,Female,30.0,4.0,0.0,1.0,1.0,1.0,136887.58,0.0
-12239,15702641.0,Ch'iu,743.0,France,Female,22.0,3.0,0.0,2.0,1.0,0.0,72678.75,0.0
-12240,15622454.0,Chukwuhaenye,659.0,France,Male,39.0,4.0,0.0,3.0,0.0,0.0,142377.69,1.0
-12241,15616172.0,P'eng,601.0,Spain,Female,43.0,6.0,114631.23,1.0,1.0,1.0,111687.57,0.0
-12242,15634310.0,Nwabugwu,616.0,France,Female,66.0,6.0,0.0,1.0,1.0,1.0,162699.7,0.0
-12243,15584580.0,Hao,711.0,Germany,Male,39.0,7.0,81153.82,2.0,0.0,1.0,64448.36,0.0
-12244,15737051.0,Lu,556.0,Germany,Male,25.0,9.0,123942.0,1.0,1.0,0.0,97985.4,0.0
-12245,15656322.0,Lo Duca,707.0,France,Female,43.0,7.0,0.0,2.0,1.0,1.0,160176.47,0.0
-12246,15795934.0,Okwudilichukwu,597.0,Germany,Male,28.0,0.0,137843.8,1.0,1.0,0.0,131736.23,1.0
-12247,15748352.0,Yobanna,638.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,94767.77,0.0
-12248,15590820.0,Trevisani,538.0,France,Female,27.0,7.0,0.0,1.0,1.0,1.0,133086.45,0.0
-12249,15643359.0,Chidimma,531.0,Germany,Male,47.0,4.0,138857.59,1.0,1.0,0.0,96862.56,1.0
-12250,15682355.0,Chikezie,755.0,France,Male,30.0,2.0,0.0,1.0,1.0,0.0,86634.3,0.0
-12251,15797760.0,Ritchie,689.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,119174.88,0.0
-12252,15756850.0,Ch'ien,659.0,France,Female,33.0,9.0,0.0,1.0,0.0,1.0,149892.79,0.0
-12253,15643865.0,Fang,844.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,79734.23,0.0
-12254,15763612.0,Nnaife,611.0,France,Female,41.0,8.0,0.0,1.0,1.0,1.0,83084.33,0.0
-12255,15793046.0,Fyodorova,682.0,France,Male,40.0,9.0,0.0,2.0,1.0,1.0,72876.43,0.0
-12256,15758031.0,Hsieh,587.0,Spain,Female,41.0,2.0,0.0,1.0,1.0,0.0,182855.42,0.0
-12257,15604818.0,L?,711.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,0.0,174094.24,0.0
-12258,15644878.0,Fan,602.0,France,Male,32.0,6.0,0.0,2.0,0.0,1.0,81622.49,0.0
-12259,15713774.0,Onyemauchechukwu,568.0,France,Male,44.0,2.0,0.0,1.0,1.0,0.0,142839.64,1.0
-12260,15598097.0,Burt,681.0,France,Female,40.0,4.0,0.0,1.0,1.0,1.0,147074.67,1.0
-12261,15723488.0,Tretiakova,651.0,France,Male,34.0,3.0,0.0,2.0,0.0,1.0,16250.61,0.0
-12262,15685839.0,Chukwubuikem,727.0,Spain,Male,30.0,5.0,0.0,2.0,1.0,1.0,153538.51,0.0
-12263,15768474.0,Y?,635.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,82867.55,0.0
-12264,15592229.0,Calabrese,585.0,France,Male,31.0,9.0,0.0,2.0,1.0,1.0,149043.78,0.0
-12265,15646632.0,Onio,645.0,Germany,Male,46.0,6.0,113348.14,1.0,1.0,1.0,50299.14,1.0
-12266,15673599.0,Y?an,696.0,Germany,Male,37.0,9.0,152926.2,1.0,1.0,0.0,170819.01,0.0
-12267,15769980.0,Ts'ai,686.0,Spain,Female,33.0,2.0,0.0,2.0,0.0,0.0,136435.75,0.0
-12268,15607827.0,Udokamma,581.0,France,Female,35.0,2.0,0.0,2.0,1.0,1.0,116973.48,0.0
-12269,15756847.0,Chukwuebuka,684.0,France,Male,39.0,7.0,0.0,1.0,1.0,0.0,161435.02,0.0
-12270,15590820.0,T'ien,708.0,Germany,Male,28.0,0.0,126645.13,2.0,1.0,0.0,47416.44,0.0
-12271,15813659.0,Pirozzi,624.0,France,Female,39.0,9.0,133866.23,1.0,1.0,1.0,56780.97,0.0
-12272,15781598.0,Chiemela,730.0,France,Male,45.0,9.0,0.0,2.0,1.0,1.0,131807.15,0.0
-12273,15692761.0,Nwachinemelu,691.0,France,Female,39.0,7.0,0.0,2.0,1.0,0.0,89983.02,0.0
-12274,15794468.0,Chiu,697.0,France,Male,46.0,3.0,0.0,2.0,1.0,1.0,143617.83,0.0
-12275,15645353.0,Chu,628.0,Germany,Male,22.0,5.0,139914.27,1.0,1.0,0.0,79927.99,0.0
-12276,15722533.0,Ts'ai,554.0,Spain,Male,33.0,5.0,152264.81,1.0,1.0,0.0,72814.31,0.0
-12277,15756926.0,McClinton,665.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,155711.64,0.0
-12278,15591279.0,O'Sullivan,644.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,103914.4,0.0
-12279,15703437.0,Onio,746.0,France,Male,32.0,3.0,0.0,1.0,1.0,0.0,122998.28,0.0
-12280,15584928.0,Walker,641.0,Germany,Female,33.0,2.0,116968.91,1.0,1.0,1.0,111249.62,0.0
-12281,15566563.0,Calabresi,479.0,Germany,Female,42.0,2.0,102721.49,3.0,1.0,0.0,122547.58,1.0
-12282,15806822.0,Chiemenam,681.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,0.0,85547.33,0.0
-12283,15661409.0,T'an,676.0,France,Male,36.0,1.0,0.0,1.0,1.0,1.0,108008.65,0.0
-12284,15707559.0,Pinto,559.0,France,Male,51.0,8.0,0.0,2.0,1.0,0.0,174770.14,1.0
-12285,15725698.0,Mazzanti,598.0,France,Female,29.0,7.0,0.0,1.0,1.0,0.0,153917.41,0.0
-12286,15691792.0,Chiu,642.0,France,Male,41.0,7.0,0.0,2.0,1.0,0.0,168418.35,0.0
-12287,15664539.0,Lo Duca,648.0,Germany,Male,31.0,2.0,142263.45,2.0,1.0,1.0,123415.05,0.0
-12288,15683339.0,Chidiegwu,523.0,Spain,Female,42.0,1.0,0.0,1.0,1.0,1.0,146723.72,0.0
-12289,15631693.0,Okwudilichukwu,497.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,1.0,97767.63,0.0
-12290,15774586.0,Ibeamaka,461.0,France,Female,30.0,3.0,121172.97,1.0,1.0,1.0,137916.49,0.0
-12291,15573319.0,Shih,758.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,105732.9,0.0
-12292,15763415.0,Fanucci,697.0,France,Female,36.0,4.0,0.0,2.0,1.0,0.0,64420.5,0.0
-12293,15618446.0,Trevisani,711.0,France,Male,37.0,2.0,112414.81,1.0,1.0,1.0,105525.16,0.0
-12294,15672432.0,Liao,585.0,France,Female,42.0,5.0,0.0,3.0,1.0,0.0,165580.93,1.0
-12295,15664555.0,Eluemuno,554.0,France,Male,36.0,4.0,0.0,2.0,0.0,1.0,131736.23,0.0
-12296,15732967.0,Onyemauchechukwu,604.0,Germany,Male,32.0,2.0,118627.7,2.0,1.0,1.0,163714.92,0.0
-12297,15672216.0,Ch'ang,479.0,Germany,Female,40.0,8.0,111363.1,1.0,1.0,0.0,83499.89,0.0
-12298,15659495.0,Maclean,591.0,France,Male,38.0,8.0,121730.49,1.0,1.0,1.0,87412.24,0.0
-12299,15615140.0,Tsao,724.0,France,Male,42.0,3.0,0.0,1.0,0.0,0.0,62634.94,0.0
-12300,15771580.0,Chukwuemeka,850.0,Germany,Female,41.0,8.0,72535.45,2.0,1.0,0.0,27341.63,0.0
-12301,15682600.0,Ting,684.0,France,Female,32.0,10.0,0.0,2.0,1.0,1.0,50404.72,0.0
-12302,15739850.0,Chienezie,590.0,Spain,Male,32.0,5.0,0.0,2.0,0.0,1.0,53584.03,0.0
-12303,15787699.0,O'Donnell,562.0,Germany,Male,30.0,2.0,103515.39,2.0,1.0,1.0,88698.83,0.0
-12304,15690175.0,Nwachinemelu,611.0,France,Female,57.0,8.0,113563.46,1.0,1.0,1.0,145558.42,0.0
-12305,15762169.0,Martin,590.0,France,Female,29.0,7.0,0.0,1.0,1.0,0.0,131312.56,1.0
-12306,15612109.0,Ch'ang,711.0,Germany,Male,32.0,7.0,102773.2,1.0,0.0,1.0,157072.91,0.0
-12307,15774738.0,Hsu,663.0,France,Female,37.0,3.0,0.0,2.0,1.0,0.0,167036.94,0.0
-12308,15742272.0,Ch'iu,582.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,157959.02,0.0
-12309,15640268.0,Chidiegwu,585.0,Germany,Female,36.0,7.0,135842.89,1.0,1.0,1.0,82452.06,0.0
-12310,15786463.0,Ch'ang,619.0,Spain,Male,33.0,4.0,0.0,2.0,1.0,0.0,132770.11,0.0
-12311,15730044.0,Onwubiko,679.0,Spain,Male,72.0,7.0,0.0,1.0,1.0,1.0,162428.05,0.0
-12312,15652069.0,K'ung,590.0,France,Female,40.0,8.0,143582.96,1.0,1.0,0.0,4175.68,0.0
-12313,15656683.0,H?,578.0,France,Female,37.0,1.0,0.0,2.0,0.0,0.0,164061.4,0.0
-12314,15726153.0,Onyemere,584.0,France,Male,33.0,3.0,0.0,2.0,0.0,0.0,38550.4,0.0
-12315,15704053.0,Ch'eng,592.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,49418.87,0.0
-12316,15624781.0,Chiu,573.0,France,Male,27.0,1.0,147069.78,1.0,0.0,1.0,161083.73,0.0
-12317,15647838.0,Nnaife,633.0,France,Male,29.0,2.0,0.0,2.0,0.0,1.0,153036.96,0.0
-12318,15668309.0,Uspenskaya,542.0,Germany,Female,47.0,2.0,111099.79,1.0,1.0,1.0,143913.16,1.0
-12319,15693637.0,Chukwukadibia,681.0,Spain,Female,40.0,1.0,0.0,1.0,0.0,0.0,102280.29,1.0
-12320,15793425.0,Walker,678.0,Germany,Male,33.0,6.0,76665.71,1.0,1.0,0.0,129892.93,0.0
-12321,15804052.0,Chukwuemeka,658.0,Germany,Male,39.0,3.0,141859.12,1.0,1.0,0.0,82351.06,1.0
-12322,15747647.0,Lai,757.0,France,Male,38.0,1.0,0.0,2.0,1.0,1.0,86837.32,0.0
-12323,15732437.0,Chinwenma,699.0,France,Male,40.0,8.0,0.0,2.0,1.0,0.0,129569.92,0.0
-12324,15705890.0,Y?an,549.0,Spain,Female,38.0,9.0,0.0,1.0,0.0,1.0,162599.51,0.0
-12325,15746203.0,Milanesi,554.0,France,Male,33.0,8.0,82228.67,1.0,0.0,1.0,161407.48,0.0
-12326,15627484.0,Onwumelu,732.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,183568.55,0.0
-12327,15641877.0,Ngozichukwuka,706.0,Germany,Male,37.0,1.0,80790.74,2.0,1.0,0.0,27012.0,0.0
-12328,15650442.0,Sazonova,564.0,Spain,Female,46.0,5.0,0.0,1.0,1.0,0.0,153917.54,1.0
-12329,15685920.0,Maclean,641.0,France,Male,44.0,2.0,0.0,2.0,1.0,0.0,102999.13,0.0
-12330,15769596.0,Hs?,651.0,Germany,Female,35.0,4.0,99263.09,1.0,0.0,1.0,159069.37,1.0
-12331,15762762.0,Maclean,687.0,France,Female,45.0,7.0,0.0,1.0,1.0,1.0,159840.58,1.0
-12332,15736851.0,Hsia,679.0,France,Male,34.0,2.0,112708.2,1.0,1.0,0.0,68815.03,0.0
-12333,15656683.0,Ch'ang,747.0,France,Female,34.0,2.0,124923.35,1.0,1.0,1.0,176763.27,0.0
-12334,15680683.0,P'eng,583.0,France,Female,37.0,2.0,88963.31,1.0,1.0,0.0,115183.92,0.0
-12335,15791053.0,Chidiebere,590.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,74850.84,0.0
-12336,15615896.0,Nwoye,652.0,France,Male,35.0,1.0,0.0,2.0,0.0,1.0,51703.09,0.0
-12337,15652955.0,L?,850.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,171070.54,0.0
-12338,15632576.0,Onwuatuegwu,635.0,France,Female,39.0,3.0,0.0,1.0,1.0,1.0,77965.44,0.0
-12339,15669805.0,Chidiebele,730.0,Germany,Female,44.0,2.0,148962.46,2.0,1.0,0.0,173495.77,0.0
-12340,15661528.0,Onyemachukwu,581.0,France,Male,45.0,7.0,0.0,1.0,1.0,0.0,151169.85,1.0
-12341,15642991.0,Uspenskaya,612.0,Spain,Female,56.0,9.0,0.0,1.0,1.0,0.0,180551.76,1.0
-12342,15682600.0,Genovesi,634.0,Spain,Female,48.0,1.0,0.0,1.0,1.0,1.0,28228.35,1.0
-12343,15748481.0,Nnamutaezinwa,585.0,France,Male,33.0,8.0,146781.24,1.0,1.0,1.0,115818.51,0.0
-12344,15769451.0,Macleod,642.0,Germany,Male,37.0,1.0,141789.15,2.0,1.0,1.0,181429.87,0.0
-12345,15696812.0,Nucci,703.0,France,Female,51.0,9.0,0.0,2.0,1.0,1.0,80592.8,1.0
-12346,15624391.0,Hsia,609.0,France,Male,28.0,8.0,0.0,2.0,0.0,1.0,122949.71,0.0
-12347,15661412.0,Okonkwo,721.0,Germany,Female,38.0,3.0,183572.84,1.0,0.0,0.0,64477.25,1.0
-12348,15650442.0,Onyemauchechukwu,656.0,Germany,Male,41.0,4.0,104568.41,2.0,1.0,0.0,105599.65,0.0
-12349,15670374.0,Ugoji,494.0,France,Female,31.0,6.0,0.0,2.0,1.0,1.0,147093.84,0.0
-12350,15744327.0,Genovese,711.0,Spain,Male,29.0,8.0,0.0,2.0,1.0,1.0,176984.34,0.0
-12351,15642725.0,Tsao,593.0,Spain,Female,34.0,6.0,0.0,2.0,1.0,0.0,60552.44,0.0
-12352,15800773.0,Lombardi,775.0,France,Female,35.0,2.0,142550.52,1.0,1.0,0.0,165626.6,0.0
-12353,15732299.0,Ts'ui,640.0,Germany,Female,27.0,9.0,104678.62,1.0,1.0,1.0,137028.51,0.0
-12354,15798895.0,Ch'in,628.0,France,Male,36.0,4.0,160625.17,1.0,1.0,1.0,53420.43,0.0
-12355,15586029.0,Mancini,542.0,France,Male,35.0,1.0,0.0,2.0,0.0,0.0,122449.15,0.0
-12356,15595588.0,Chukwudi,738.0,France,Male,40.0,4.0,0.0,2.0,1.0,0.0,161388.22,0.0
-12357,15702298.0,Onwuamaeze,758.0,France,Male,30.0,7.0,0.0,1.0,1.0,0.0,92277.47,0.0
-12358,15759035.0,Ibezimako,749.0,France,Female,49.0,6.0,0.0,2.0,1.0,0.0,65612.12,0.0
-12359,15780359.0,Nwebube,583.0,Spain,Male,57.0,9.0,0.0,1.0,1.0,0.0,158071.69,1.0
-12360,15728012.0,Chukwuemeka,561.0,Spain,Male,33.0,8.0,0.0,2.0,1.0,1.0,84424.57,0.0
-12361,15785042.0,Liao,686.0,Germany,Male,38.0,9.0,123971.51,2.0,0.0,1.0,168816.73,0.0
-12362,15789974.0,Trevisani,779.0,Germany,Male,46.0,4.0,103421.91,1.0,0.0,0.0,86797.92,1.0
-12363,15678944.0,L?,615.0,France,Male,33.0,9.0,127070.73,1.0,0.0,0.0,78854.57,0.0
-12364,15632345.0,Walker,605.0,Germany,Male,22.0,4.0,143680.47,1.0,1.0,1.0,171706.66,1.0
-12365,15740147.0,Chukwujamuike,597.0,France,Male,34.0,3.0,0.0,1.0,1.0,1.0,135034.43,0.0
-12366,15808930.0,Ch'ien,699.0,France,Female,26.0,4.0,0.0,2.0,1.0,1.0,86950.72,0.0
-12367,15641675.0,Ndubueze,556.0,Germany,Female,35.0,7.0,98765.84,1.0,1.0,1.0,161653.5,0.0
-12368,15745201.0,Pagnotto,562.0,France,Female,34.0,5.0,0.0,2.0,1.0,1.0,136329.96,0.0
-12369,15594917.0,Hsing,710.0,France,Female,35.0,3.0,0.0,2.0,1.0,1.0,93959.96,0.0
-12370,15583076.0,Chimaobim,643.0,Spain,Male,49.0,4.0,125739.26,1.0,0.0,0.0,161241.65,0.0
-12371,15576680.0,Logue,775.0,France,Male,28.0,7.0,0.0,1.0,1.0,0.0,166650.32,0.0
-12372,15807889.0,Davison,579.0,France,Male,47.0,4.0,0.0,1.0,1.0,0.0,116971.05,1.0
-12373,15689031.0,Yermolayev,628.0,France,Female,40.0,9.0,0.0,2.0,1.0,1.0,151733.43,0.0
-12374,15586674.0,Ts'ai,582.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,0.0,126796.69,0.0
-12375,15673529.0,Lai,639.0,France,Male,34.0,1.0,0.0,2.0,1.0,1.0,131302.74,0.0
-12376,15784744.0,Nkemakolam,684.0,Germany,Male,31.0,3.0,108432.88,2.0,1.0,0.0,126082.18,0.0
-12377,15780572.0,P'eng,648.0,France,Male,29.0,4.0,0.0,2.0,1.0,0.0,185561.89,0.0
-12378,15784597.0,Chu,526.0,France,Male,40.0,5.0,0.0,2.0,0.0,0.0,173632.92,0.0
-12379,15618581.0,Onyemere,601.0,France,Female,35.0,6.0,125860.2,1.0,1.0,1.0,152845.99,0.0
-12380,15721469.0,Ugochukwu,552.0,Germany,Female,52.0,1.0,100471.34,1.0,1.0,1.0,58854.88,1.0
-12381,15669011.0,Fang,765.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,76515.79,0.0
-12382,15784923.0,Chin,635.0,France,Male,40.0,7.0,81804.64,1.0,1.0,1.0,171096.2,0.0
-12383,15681327.0,Onyinyechukwuka,691.0,France,Male,35.0,0.0,138577.34,1.0,0.0,1.0,151677.31,0.0
-12384,15624453.0,De Salis,695.0,Spain,Female,36.0,2.0,0.0,2.0,1.0,1.0,151903.6,0.0
-12385,15606901.0,Smith,684.0,Germany,Male,46.0,4.0,102773.15,4.0,1.0,0.0,183566.85,1.0
-12386,15579548.0,Lo Duca,676.0,France,Female,55.0,8.0,0.0,1.0,0.0,0.0,180693.61,1.0
-12387,15666032.0,Chia,635.0,Germany,Male,45.0,7.0,116498.22,1.0,1.0,0.0,140937.91,0.0
-12388,15626452.0,Maclean,652.0,France,Female,42.0,4.0,0.0,1.0,1.0,0.0,103901.28,1.0
-12389,15800811.0,Hao,673.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,153810.41,0.0
-12390,15665088.0,Hsia,572.0,France,Male,42.0,1.0,118899.45,1.0,1.0,0.0,70613.52,0.0
-12391,15788483.0,Watson,757.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,6761.84,0.0
-12392,15763612.0,Nwachinemelu,733.0,Spain,Male,32.0,6.0,0.0,2.0,1.0,0.0,127528.79,0.0
-12393,15663885.0,Pisani,802.0,France,Male,36.0,9.0,0.0,2.0,1.0,1.0,74042.0,0.0
-12394,15616367.0,Chinweike,710.0,France,Male,34.0,0.0,0.0,2.0,1.0,0.0,174825.57,0.0
-12395,15641490.0,Macleod,572.0,France,Male,19.0,7.0,0.0,2.0,1.0,0.0,86767.48,0.0
-12396,15598493.0,Trevisano,850.0,Spain,Male,50.0,4.0,98800.0,1.0,1.0,1.0,152479.64,0.0
-12397,15583499.0,Onwuka,579.0,France,Male,39.0,6.0,0.0,2.0,0.0,0.0,72950.14,0.0
-12398,15809837.0,Cattaneo,756.0,Germany,Male,34.0,6.0,132670.53,2.0,0.0,1.0,155722.69,0.0
-12399,15592736.0,T'ien,732.0,France,Female,36.0,7.0,0.0,2.0,1.0,1.0,27011.8,0.0
-12400,15778838.0,Chimezie,633.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,141796.39,0.0
-12401,15739292.0,Boulger,711.0,Spain,Male,43.0,5.0,0.0,2.0,1.0,0.0,116951.1,0.0
-12402,15593365.0,Chidimma,669.0,France,Male,41.0,1.0,0.0,2.0,1.0,0.0,103713.93,0.0
-12403,15652400.0,Goliwe,544.0,Germany,Female,31.0,5.0,115518.07,1.0,1.0,0.0,171482.56,0.0
-12404,15780954.0,Okwudilichukwu,646.0,Spain,Female,35.0,9.0,0.0,1.0,1.0,0.0,173843.21,1.0
-12405,15695078.0,Trevisano,663.0,France,Male,33.0,0.0,143542.36,1.0,1.0,1.0,56518.0,0.0
-12406,15747043.0,Nwokezuike,628.0,Germany,Female,37.0,2.0,97612.12,2.0,1.0,1.0,86797.41,0.0
-12407,15729083.0,Hsia,590.0,France,Male,39.0,6.0,0.0,2.0,0.0,0.0,177524.19,0.0
-12408,15631359.0,Chien,733.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,178393.68,0.0
-12409,15783882.0,Onyeoruru,663.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,18094.96,0.0
-12410,15650751.0,Ch'ien,676.0,France,Female,29.0,3.0,0.0,2.0,1.0,0.0,103921.43,0.0
-12411,15582910.0,Tien,614.0,France,Female,41.0,7.0,0.0,1.0,1.0,0.0,177028.0,0.0
-12412,15742442.0,Ko,571.0,France,Male,56.0,6.0,0.0,1.0,1.0,0.0,120892.96,1.0
-12413,15662397.0,Ugoji,652.0,Spain,Male,36.0,9.0,0.0,2.0,0.0,0.0,151481.86,0.0
-12414,15698522.0,Lucchese,589.0,Germany,Female,32.0,2.0,125537.52,2.0,0.0,0.0,122518.5,0.0
-12415,15584576.0,Chidubem,609.0,France,Female,51.0,4.0,0.0,1.0,1.0,0.0,187911.52,1.0
-12416,15644977.0,Molineux,597.0,Spain,Male,25.0,3.0,113811.75,1.0,1.0,1.0,1843.24,0.0
-12417,15745080.0,Lucciano,663.0,Germany,Female,34.0,6.0,105489.31,2.0,1.0,0.0,173498.45,0.0
-12418,15773520.0,Milani,610.0,France,Male,29.0,3.0,98400.61,1.0,1.0,1.0,186612.47,0.0
-12419,15576723.0,Ugoji,753.0,France,Male,36.0,3.0,148071.39,1.0,1.0,0.0,136050.44,0.0
-12420,15682928.0,Ni,479.0,Spain,Female,66.0,8.0,144470.77,1.0,1.0,1.0,166468.21,0.0
-12421,15670681.0,Nwachinemelu,679.0,France,Male,27.0,7.0,0.0,2.0,1.0,1.0,187905.12,0.0
-12422,15623210.0,Chiemenam,535.0,France,Female,42.0,7.0,0.0,1.0,1.0,0.0,172792.43,1.0
-12423,15781589.0,Chizuoke,682.0,France,Male,43.0,5.0,0.0,1.0,1.0,0.0,28266.9,1.0
-12424,15794868.0,T'ang,681.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,188426.12,0.0
-12425,15607986.0,Chu,496.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,165562.84,0.0
-12426,15608701.0,Teng,516.0,Spain,Male,19.0,5.0,0.0,2.0,1.0,0.0,96660.76,0.0
-12427,15712303.0,Hsing,613.0,Germany,Male,28.0,3.0,114543.27,2.0,0.0,0.0,156036.19,0.0
-12428,15766378.0,Liao,744.0,France,Male,41.0,9.0,110837.73,1.0,1.0,0.0,141210.5,0.0
-12429,15631639.0,Onyeoruru,686.0,France,Female,36.0,4.0,0.0,2.0,1.0,1.0,11517.39,0.0
-12430,15617657.0,Ch'ien,755.0,France,Female,45.0,2.0,0.0,2.0,1.0,0.0,131644.31,1.0
-12431,15593499.0,Milanesi,561.0,France,Male,37.0,10.0,131834.76,1.0,1.0,1.0,143317.31,0.0
-12432,15666055.0,Chibueze,709.0,France,Female,32.0,8.0,0.0,2.0,1.0,1.0,152273.59,0.0
-12433,15713346.0,Chibueze,651.0,France,Female,29.0,7.0,0.0,1.0,1.0,0.0,62830.97,0.0
-12434,15658980.0,Horsley,539.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,107876.2,0.0
-12435,15643048.0,Ts'ao,637.0,France,Male,45.0,6.0,0.0,1.0,1.0,0.0,96792.49,1.0
-12436,15762682.0,Ch'iu,599.0,France,Female,43.0,7.0,0.0,2.0,0.0,1.0,162960.05,0.0
-12437,15586931.0,Miller,850.0,Germany,Female,51.0,4.0,93560.55,1.0,1.0,0.0,120852.85,1.0
-12438,15719356.0,Chiemezie,678.0,France,Female,40.0,2.0,142987.46,1.0,1.0,1.0,77866.92,0.0
-12439,15725141.0,Nwora,678.0,France,Male,41.0,4.0,132578.92,1.0,1.0,0.0,141533.19,0.0
-12440,15776824.0,O'Loughlin,708.0,France,Female,38.0,3.0,0.0,2.0,0.0,0.0,140745.91,0.0
-12441,15690188.0,Aitken,724.0,Spain,Male,33.0,5.0,138162.41,1.0,1.0,0.0,105720.09,0.0
-12442,15698856.0,Manna,714.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,183868.01,0.0
-12443,15756680.0,Pagnotto,686.0,France,Male,32.0,0.0,0.0,2.0,1.0,0.0,121681.19,0.0
-12444,15736250.0,Hsueh,749.0,France,Male,51.0,8.0,0.0,2.0,1.0,0.0,160979.66,0.0
-12445,15812706.0,Shih,648.0,France,Female,43.0,1.0,0.0,2.0,1.0,1.0,52337.97,0.0
-12446,15748649.0,Ch'ien,663.0,Germany,Male,41.0,7.0,71460.67,1.0,1.0,0.0,162668.33,0.0
-12447,15703541.0,Manna,729.0,France,Female,35.0,9.0,0.0,1.0,1.0,0.0,48478.47,0.0
-12448,15655238.0,Ch'iu,591.0,France,Female,40.0,4.0,151702.72,1.0,1.0,1.0,126812.63,0.0
-12449,15794493.0,Tsao,653.0,France,Female,42.0,1.0,113781.95,1.0,1.0,0.0,93777.81,0.0
-12450,15576550.0,Olisaemeka,633.0,France,Female,34.0,10.0,0.0,1.0,1.0,1.0,78854.57,0.0
-12451,15602841.0,Ku,724.0,France,Female,46.0,1.0,0.0,1.0,1.0,0.0,107977.53,1.0
-12452,15631838.0,Ch'in,683.0,Spain,Female,29.0,9.0,0.0,2.0,1.0,0.0,94739.2,0.0
-12453,15709941.0,Ch'iu,642.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,56767.67,0.0
-12454,15667438.0,Smith,622.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,45613.75,0.0
-12455,15617226.0,T'ien,678.0,France,Male,36.0,3.0,0.0,1.0,1.0,0.0,93777.15,0.0
-12456,15650751.0,Chin,633.0,Spain,Female,34.0,4.0,0.0,2.0,1.0,0.0,147040.25,0.0
-12457,15651389.0,Young,598.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,149517.71,0.0
-12458,15691875.0,T'ao,571.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,0.0,149604.18,0.0
-12459,15696812.0,Chikezie,670.0,France,Male,34.0,4.0,0.0,2.0,1.0,1.0,94570.58,0.0
-12460,15782736.0,Hsia,733.0,France,Male,35.0,9.0,0.0,1.0,1.0,1.0,59864.96,0.0
-12461,15593973.0,Ch'iu,766.0,France,Female,31.0,2.0,132351.29,1.0,1.0,1.0,67789.99,0.0
-12462,15804721.0,Yudina,687.0,Germany,Male,29.0,7.0,133658.24,2.0,0.0,1.0,52861.89,0.0
-12463,15609804.0,T'ao,633.0,France,Female,30.0,3.0,0.0,1.0,1.0,1.0,121138.77,0.0
-12464,15703614.0,Fiorentini,554.0,Spain,Male,26.0,9.0,141462.13,1.0,1.0,1.0,74878.12,0.0
-12465,15726403.0,Walker,576.0,France,Female,30.0,7.0,0.0,2.0,0.0,1.0,108977.5,0.0
-12466,15658670.0,Chia,575.0,Spain,Male,30.0,1.0,0.0,2.0,1.0,1.0,180318.6,0.0
-12467,15797246.0,Hsueh,616.0,France,Male,34.0,3.0,0.0,2.0,0.0,1.0,62347.71,0.0
-12468,15632439.0,Tuan,658.0,France,Female,35.0,1.0,0.0,2.0,1.0,0.0,48963.59,0.0
-12469,15788483.0,Tung,526.0,Germany,Female,42.0,3.0,108528.07,1.0,1.0,1.0,134417.37,0.0
-12470,15668575.0,Onio,669.0,Spain,Male,28.0,2.0,114948.76,2.0,1.0,0.0,181208.47,0.0
-12471,15597951.0,Chien,546.0,Spain,Male,38.0,6.0,139994.66,1.0,0.0,0.0,152883.95,1.0
-12472,15579826.0,Chien,684.0,Spain,Female,40.0,3.0,0.0,2.0,1.0,0.0,184843.77,0.0
-12473,15724663.0,Iadanza,694.0,Germany,Female,41.0,8.0,137222.77,1.0,0.0,1.0,145562.4,0.0
-12474,15682890.0,Chidubem,738.0,France,Female,43.0,2.0,0.0,2.0,1.0,0.0,124486.28,0.0
-12475,15684319.0,Tien,707.0,France,Male,37.0,10.0,123966.21,2.0,1.0,0.0,115452.51,0.0
-12476,15709511.0,Ch'iu,681.0,France,Male,31.0,5.0,0.0,1.0,1.0,1.0,112256.57,0.0
-12477,15626452.0,Chiu,590.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,88604.89,0.0
-12478,15611905.0,Ch'iu,613.0,France,Female,40.0,3.0,0.0,2.0,1.0,1.0,161064.29,0.0
-12479,15581626.0,Ku,715.0,France,Male,57.0,10.0,0.0,2.0,1.0,0.0,162719.69,1.0
-12480,15762048.0,Trevisani,561.0,France,Female,38.0,5.0,76989.97,1.0,0.0,1.0,66795.78,0.0
-12481,15723884.0,Lu,622.0,Germany,Male,45.0,4.0,137648.41,1.0,0.0,0.0,136031.01,1.0
-12482,15753074.0,Palerma,594.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,65765.22,0.0
-12483,15777896.0,H?,717.0,France,Male,29.0,9.0,0.0,1.0,1.0,0.0,75927.35,0.0
-12484,15796787.0,P'eng,637.0,France,Male,40.0,2.0,132436.58,1.0,1.0,1.0,121440.8,0.0
-12485,15646521.0,T'ang,785.0,Spain,Male,39.0,4.0,0.0,2.0,0.0,0.0,179908.71,0.0
-12486,15596074.0,Ijendu,608.0,Germany,Male,33.0,8.0,152303.8,1.0,1.0,1.0,121151.1,0.0
-12487,15766458.0,Chiu,668.0,France,Male,38.0,7.0,0.0,1.0,0.0,0.0,199689.65,0.0
-12488,15665763.0,Okwudiliolisa,605.0,Germany,Female,44.0,3.0,124099.13,1.0,1.0,0.0,92887.06,1.0
-12489,15593365.0,Hs?,706.0,Spain,Male,36.0,3.0,0.0,2.0,1.0,1.0,163581.67,0.0
-12490,15594404.0,Onyemauchechukwu,722.0,France,Female,32.0,1.0,113922.21,1.0,1.0,0.0,86791.9,0.0
-12491,15691396.0,Nnaife,732.0,France,Male,42.0,4.0,0.0,2.0,1.0,0.0,166513.49,0.0
-12492,15583043.0,Galkin,681.0,France,Female,36.0,7.0,0.0,1.0,1.0,1.0,47729.33,0.0
-12493,15572051.0,Chigolum,615.0,France,Female,31.0,9.0,139432.37,1.0,1.0,1.0,142754.71,1.0
-12494,15751336.0,Pisani,683.0,Germany,Female,28.0,9.0,149961.99,1.0,0.0,0.0,67468.67,0.0
-12495,15669957.0,Mancini,558.0,France,Male,51.0,5.0,0.0,1.0,1.0,1.0,76611.33,1.0
-12496,15752300.0,Chukwujekwu,583.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,0.0,176984.34,0.0
-12497,15580149.0,Lucciano,707.0,France,Male,34.0,1.0,0.0,2.0,1.0,1.0,162878.96,0.0
-12498,15762583.0,Iloerika,594.0,France,Male,32.0,4.0,0.0,2.0,1.0,0.0,187622.42,0.0
-12499,15809847.0,Trevisani,685.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,138889.15,0.0
-12500,15726695.0,Y?,629.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,158041.12,0.0
-12501,15617320.0,Chukwukadibia,683.0,Spain,Male,38.0,1.0,128743.55,1.0,0.0,1.0,64490.67,0.0
-12502,15662884.0,K'ung,646.0,Spain,Male,42.0,4.0,0.0,2.0,0.0,0.0,53843.71,0.0
-12503,15777892.0,Tsao,683.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,143393.24,0.0
-12504,15567114.0,Shao,596.0,Germany,Male,27.0,10.0,127076.68,1.0,0.0,0.0,40889.14,0.0
-12505,15763980.0,Ch'in,681.0,Spain,Male,34.0,1.0,0.0,2.0,1.0,0.0,152298.28,0.0
-12506,15651581.0,Houghton,619.0,France,Male,38.0,8.0,124547.13,1.0,1.0,1.0,163427.18,0.0
-12507,15668943.0,Chin,777.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,0.0,126469.09,0.0
-12508,15752543.0,Niu,646.0,France,Male,37.0,3.0,178095.55,1.0,0.0,1.0,95611.47,0.0
-12509,15678747.0,Hsia,706.0,Spain,Female,60.0,1.0,106967.18,1.0,1.0,1.0,63736.17,0.0
-12510,15756472.0,H?,679.0,France,Male,45.0,7.0,0.0,3.0,1.0,0.0,179634.69,1.0
-12511,15636551.0,Chukwufumnanya,696.0,Spain,Female,32.0,2.0,0.0,2.0,1.0,1.0,132628.98,0.0
-12512,15594878.0,Kudryashova,586.0,France,Male,38.0,4.0,0.0,2.0,1.0,1.0,123639.64,0.0
-12513,15614215.0,Nnamdi,643.0,Germany,Female,37.0,8.0,126815.97,1.0,1.0,0.0,119327.75,1.0
-12514,15589329.0,Pirozzi,651.0,Spain,Male,29.0,2.0,89688.94,2.0,1.0,1.0,131167.98,0.0
-12515,15622289.0,Nwankwo,687.0,France,Male,31.0,8.0,120599.38,1.0,1.0,1.0,153955.38,0.0
-12516,15658614.0,Maitland,682.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,98820.09,0.0
-12517,15655175.0,Hsia,551.0,France,Male,40.0,10.0,0.0,1.0,1.0,1.0,70954.41,1.0
-12518,15751491.0,Ponomarev,632.0,Germany,Male,51.0,1.0,107594.11,1.0,0.0,1.0,53379.52,1.0
-12519,15789611.0,Narelle,745.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,152054.33,0.0
-12520,15775295.0,Nkemdirim,749.0,Spain,Male,24.0,5.0,121669.23,1.0,1.0,0.0,47880.71,0.0
-12521,15677324.0,Chukwuemeka,651.0,Germany,Male,40.0,9.0,122356.39,1.0,1.0,0.0,122547.58,1.0
-12522,15742834.0,Cattaneo,732.0,Spain,Male,39.0,8.0,0.0,1.0,1.0,1.0,55857.48,0.0
-12523,15602551.0,Maclean,662.0,France,Male,46.0,4.0,127157.04,1.0,1.0,0.0,56787.8,1.0
-12524,15792668.0,Nwachinemelu,562.0,Spain,Male,40.0,6.0,0.0,2.0,1.0,0.0,102683.0,0.0
-12525,15791342.0,Iheatu,599.0,Spain,Male,26.0,6.0,0.0,2.0,1.0,1.0,88827.67,0.0
-12526,15593601.0,Ts'ui,730.0,France,Male,49.0,6.0,0.0,2.0,0.0,1.0,27330.27,0.0
-12527,15792934.0,T'an,704.0,Spain,Female,37.0,5.0,152310.55,1.0,0.0,1.0,156660.33,0.0
-12528,15761670.0,Ch'eng,678.0,France,Male,33.0,5.0,0.0,2.0,0.0,0.0,165562.84,0.0
-12529,15808044.0,Mazzanti,703.0,France,Male,37.0,9.0,0.0,2.0,1.0,1.0,143628.56,0.0
-12530,15625092.0,Ch'ang,659.0,France,Male,37.0,5.0,0.0,1.0,1.0,0.0,176750.72,0.0
-12531,15568562.0,Biryukova,584.0,Germany,Male,25.0,8.0,123378.2,2.0,1.0,0.0,84869.64,0.0
-12532,15683625.0,Chidiebere,744.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,144870.28,0.0
-12533,15783883.0,Ch'iu,593.0,France,Male,49.0,2.0,0.0,2.0,1.0,0.0,180622.01,0.0
-12534,15803633.0,Yudina,663.0,France,Female,42.0,0.0,94937.77,1.0,1.0,0.0,126469.8,0.0
-12535,15595071.0,Hs?,642.0,France,Male,32.0,5.0,106431.97,1.0,1.0,0.0,74425.41,1.0
-12536,15771087.0,Pagnotto,710.0,France,Male,29.0,9.0,110938.34,1.0,1.0,1.0,158071.69,0.0
-12537,15678058.0,K?,753.0,Spain,Male,33.0,6.0,0.0,1.0,1.0,0.0,62402.38,1.0
-12538,15572767.0,K'ung,589.0,France,Female,35.0,4.0,154495.74,1.0,1.0,0.0,126561.07,0.0
-12539,15732299.0,Hsueh,774.0,Germany,Male,56.0,10.0,102025.32,1.0,1.0,1.0,155743.48,1.0
-12540,15592025.0,Teng,675.0,Germany,Male,42.0,8.0,137774.55,1.0,0.0,0.0,142400.27,1.0
-12541,15794849.0,Olisaemeka,807.0,Spain,Female,40.0,2.0,108711.57,1.0,1.0,0.0,189558.45,1.0
-12542,15809847.0,Chioke,705.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,106977.8,0.0
-12543,15755978.0,Martin,569.0,France,Female,40.0,3.0,142872.36,1.0,0.0,0.0,175082.2,0.0
-12544,15629885.0,Ngozichukwuka,695.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,130567.02,0.0
-12545,15683560.0,Obijiaku,576.0,France,Male,31.0,10.0,0.0,2.0,1.0,1.0,158840.99,0.0
-12546,15764170.0,Ch'eng,637.0,France,Male,31.0,4.0,98382.72,1.0,1.0,0.0,71738.56,0.0
-12547,15782311.0,Walker,579.0,France,Male,41.0,10.0,0.0,2.0,1.0,0.0,60906.12,0.0
-12548,15779204.0,Ch'en,678.0,Spain,Male,25.0,10.0,0.0,1.0,1.0,0.0,62933.03,0.0
-12549,15631063.0,Ibeamaka,794.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,55816.2,0.0
-12550,15642996.0,Aksenov,554.0,Spain,Male,43.0,9.0,0.0,1.0,1.0,1.0,74564.41,0.0
-12551,15790455.0,Pinto,667.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,151843.54,0.0
-12552,15691817.0,Ch'ien,707.0,France,Male,29.0,5.0,0.0,2.0,1.0,0.0,135088.58,0.0
-12553,15734886.0,T'ien,516.0,France,Female,46.0,5.0,0.0,1.0,1.0,1.0,82438.34,0.0
-12554,15785576.0,Iweobiegbunam,713.0,France,Female,26.0,7.0,111852.84,1.0,1.0,0.0,94670.77,0.0
-12555,15777907.0,Biryukov,706.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,172490.78,0.0
-12556,15682600.0,Smith,668.0,Germany,Female,29.0,5.0,106365.29,2.0,1.0,1.0,159938.82,0.0
-12557,15792222.0,Ch'ang,757.0,Spain,Female,30.0,9.0,0.0,2.0,1.0,1.0,103901.31,0.0
-12558,15694125.0,McEwan,763.0,France,Female,40.0,9.0,0.0,2.0,1.0,1.0,40980.87,0.0
-12559,15705890.0,Chiawuotu,710.0,France,Female,29.0,3.0,0.0,2.0,0.0,0.0,113553.43,1.0
-12560,15672894.0,Chien,579.0,France,Male,23.0,1.0,0.0,2.0,1.0,0.0,117946.3,0.0
-12561,15679963.0,Fanucci,678.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,55893.33,0.0
-12562,15654096.0,T'ao,562.0,Germany,Female,35.0,5.0,129748.54,1.0,0.0,0.0,86981.45,0.0
-12563,15775295.0,Tien,637.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,98640.15,0.0
-12564,15665327.0,Ts'ai,543.0,Spain,Male,25.0,9.0,90425.15,1.0,1.0,1.0,146895.87,0.0
-12565,15699294.0,Trevisan,679.0,Germany,Female,48.0,5.0,139859.23,1.0,1.0,0.0,173683.0,1.0
-12566,15799128.0,Tuan,699.0,France,Male,29.0,2.0,0.0,1.0,1.0,0.0,9241.78,0.0
-12567,15662263.0,Fetherstonhaugh,588.0,France,Female,33.0,4.0,0.0,2.0,1.0,0.0,93881.75,0.0
-12568,15793723.0,Hsueh,618.0,France,Female,31.0,0.0,135645.43,1.0,1.0,1.0,73329.08,0.0
-12569,15661433.0,Hao,611.0,Germany,Female,40.0,5.0,143391.07,1.0,1.0,1.0,145792.01,1.0
-12570,15652674.0,K'ung,579.0,France,Female,33.0,9.0,0.0,2.0,1.0,0.0,128528.83,0.0
-12571,15592765.0,Ch'en,641.0,France,Female,49.0,2.0,0.0,1.0,0.0,0.0,168890.33,1.0
-12572,15706005.0,Shih,644.0,Spain,Male,29.0,2.0,0.0,2.0,0.0,1.0,155700.22,0.0
-12573,15673055.0,H?,715.0,France,Male,40.0,8.0,0.0,1.0,1.0,0.0,73873.65,0.0
-12574,15580134.0,Onyekachukwu,584.0,Spain,Female,36.0,5.0,0.0,2.0,1.0,1.0,122433.09,0.0
-12575,15769902.0,Fanucci,516.0,France,Female,35.0,9.0,0.0,1.0,1.0,1.0,151887.16,1.0
-12576,15579820.0,Hao,678.0,France,Female,21.0,7.0,0.0,2.0,0.0,0.0,102076.02,0.0
-12577,15602797.0,Nwabugwu,643.0,Spain,Male,31.0,9.0,0.0,2.0,0.0,1.0,120107.1,0.0
-12578,15782210.0,Chiazagomekpere,667.0,Spain,Female,32.0,1.0,0.0,2.0,1.0,0.0,25950.49,0.0
-12579,15656683.0,Chiemenam,734.0,France,Male,35.0,2.0,0.0,1.0,1.0,0.0,68860.3,0.0
-12580,15729804.0,Tien,652.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,150468.35,0.0
-12581,15760662.0,Hsia,588.0,France,Male,24.0,5.0,0.0,2.0,1.0,1.0,102517.35,0.0
-12582,15742292.0,Ch'iu,714.0,France,Male,37.0,2.0,125510.82,1.0,1.0,1.0,121053.19,0.0
-12583,15662501.0,Yobachi,624.0,France,Female,53.0,1.0,0.0,1.0,0.0,0.0,163550.58,1.0
-12584,15635125.0,Macleod,708.0,France,Male,26.0,3.0,0.0,2.0,0.0,0.0,97782.92,0.0
-12585,15747323.0,Manna,652.0,France,Female,40.0,8.0,0.0,2.0,1.0,1.0,139469.11,0.0
-12586,15687540.0,Hao,774.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,149593.61,0.0
-12587,15752955.0,Nwankwo,765.0,Spain,Female,42.0,9.0,144953.31,1.0,1.0,0.0,137028.51,1.0
-12588,15792934.0,Chukwumaobim,809.0,France,Female,49.0,6.0,0.0,2.0,1.0,1.0,97623.31,0.0
-12589,15676966.0,Cattaneo,556.0,France,Male,41.0,9.0,0.0,2.0,1.0,0.0,148564.76,0.0
-12590,15665590.0,Clements,682.0,Spain,Female,38.0,5.0,130788.71,1.0,1.0,1.0,131048.35,0.0
-12591,15736327.0,Ch'iu,850.0,Spain,Male,35.0,1.0,107749.03,1.0,1.0,0.0,182988.94,0.0
-12592,15699772.0,Ch'eng,539.0,France,Female,29.0,7.0,102286.9,1.0,1.0,1.0,95957.09,0.0
-12593,15772383.0,Yobachi,663.0,France,Male,29.0,9.0,0.0,2.0,0.0,0.0,140723.04,0.0
-12594,15642099.0,Ponomaryova,693.0,France,Female,47.0,10.0,0.0,1.0,1.0,0.0,178342.63,1.0
-12595,15738088.0,Nnonso,567.0,France,Male,39.0,7.0,122453.97,1.0,1.0,1.0,47728.0,0.0
-12596,15767894.0,Chinonyelum,662.0,France,Male,29.0,5.0,0.0,1.0,0.0,0.0,94949.84,0.0
-12597,15690620.0,Hsieh,558.0,Spain,Male,41.0,7.0,0.0,2.0,1.0,1.0,126812.63,0.0
-12598,15644856.0,Chien,684.0,Germany,Male,25.0,5.0,96147.55,1.0,1.0,0.0,95611.47,0.0
-12599,15706163.0,T'ao,521.0,Germany,Male,33.0,10.0,133467.77,2.0,1.0,1.0,65763.66,0.0
-12600,15673908.0,Ch'iu,850.0,France,Male,33.0,7.0,161450.03,1.0,1.0,0.0,110900.43,0.0
-12601,15803750.0,Brazenor,693.0,Germany,Female,32.0,4.0,152827.99,1.0,1.0,1.0,184842.21,0.0
-12602,15757425.0,Hsieh,688.0,Germany,Male,34.0,9.0,130170.82,2.0,1.0,0.0,133672.61,0.0
-12603,15778947.0,Zikoranachidimma,644.0,Spain,Male,40.0,7.0,0.0,2.0,0.0,1.0,163714.92,0.0
-12604,15579914.0,Ritchie,745.0,France,Female,38.0,4.0,0.0,1.0,1.0,0.0,164741.5,1.0
-12605,15779915.0,Trevisani,652.0,Germany,Female,38.0,1.0,131765.72,2.0,1.0,1.0,135635.75,0.0
-12606,15682773.0,Milano,745.0,Spain,Female,30.0,8.0,0.0,2.0,1.0,1.0,68600.36,0.0
-12607,15713374.0,Nkemdirim,695.0,France,Male,44.0,7.0,0.0,2.0,1.0,0.0,79871.16,0.0
-12608,15738785.0,Binder,686.0,France,Male,35.0,9.0,0.0,2.0,1.0,0.0,133086.45,0.0
-12609,15757577.0,Smith,719.0,France,Female,18.0,4.0,80793.58,2.0,1.0,1.0,86569.13,0.0
-12610,15786463.0,Frolova,624.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,129826.89,0.0
-12611,15577683.0,Ch'ang,588.0,France,Male,37.0,6.0,0.0,2.0,0.0,1.0,130567.02,0.0
-12612,15744295.0,Cattaneo,688.0,France,Female,41.0,3.0,0.0,2.0,1.0,0.0,58601.61,0.0
-12613,15811690.0,Hsiao,543.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,147040.25,0.0
-12614,15743490.0,Chiu,663.0,France,Male,30.0,1.0,0.0,2.0,1.0,1.0,101677.46,0.0
-12615,15737521.0,Tsou,679.0,France,Female,39.0,9.0,0.0,2.0,0.0,0.0,62389.35,0.0
-12616,15789425.0,Iweobiegbulam,714.0,Spain,Male,64.0,7.0,0.0,2.0,0.0,1.0,123499.13,0.0
-12617,15722533.0,Pisani,735.0,Spain,Male,36.0,10.0,0.0,1.0,1.0,1.0,86764.46,0.0
-12618,15736287.0,Chikwendu,667.0,France,Male,26.0,5.0,0.0,2.0,1.0,0.0,183487.98,0.0
-12619,15662698.0,Ch'en,739.0,Spain,Female,35.0,2.0,0.0,2.0,0.0,1.0,82782.08,0.0
-12620,15789611.0,Macleod,683.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,141706.43,0.0
-12621,15679075.0,Y?,682.0,France,Male,35.0,5.0,0.0,1.0,1.0,0.0,77830.6,0.0
-12622,15688337.0,Erskine,758.0,France,Male,66.0,8.0,96473.57,1.0,1.0,1.0,63732.9,0.0
-12623,15792922.0,Ponce,681.0,France,Female,38.0,8.0,108632.46,1.0,0.0,1.0,63736.17,0.0
-12624,15597930.0,Hs?,683.0,France,Female,35.0,9.0,0.0,2.0,1.0,1.0,126477.41,0.0
-12625,15768777.0,Ejikemeifeuwa,724.0,France,Female,52.0,1.0,126645.13,1.0,0.0,0.0,109742.8,1.0
-12626,15665416.0,L?,498.0,Germany,Female,24.0,2.0,123894.22,2.0,1.0,0.0,168544.84,0.0
-12627,15674442.0,Obidimkpa,569.0,Germany,Male,42.0,5.0,107701.85,2.0,1.0,1.0,177668.55,0.0
-12628,15660263.0,Lo,584.0,France,Male,39.0,8.0,147803.48,1.0,1.0,1.0,178331.36,1.0
-12629,15778089.0,Chiu,613.0,France,Female,38.0,9.0,0.0,2.0,1.0,1.0,79342.31,0.0
-12630,15694566.0,Chuang,837.0,France,Female,43.0,8.0,0.0,1.0,1.0,0.0,71159.84,0.0
-12631,15673570.0,Chin,708.0,France,Male,38.0,7.0,128644.46,1.0,0.0,0.0,82970.69,0.0
-12632,15685826.0,Trevisani,479.0,Germany,Female,62.0,4.0,108632.48,1.0,1.0,0.0,104469.06,1.0
-12633,15741875.0,Onuoha,679.0,France,Male,33.0,6.0,0.0,2.0,1.0,1.0,97939.4,0.0
-12634,15671209.0,Shih,596.0,Spain,Female,34.0,7.0,0.0,2.0,1.0,0.0,38798.79,0.0
-12635,15617486.0,Chukwuebuka,629.0,France,Female,28.0,1.0,106851.81,1.0,1.0,1.0,130529.13,0.0
-12636,15756901.0,K?,675.0,France,Female,52.0,2.0,0.0,1.0,0.0,0.0,172665.21,1.0
-12637,15734987.0,O'Sullivan,581.0,Germany,Male,32.0,4.0,125890.72,2.0,1.0,1.0,132959.3,0.0
-12638,15783883.0,Lo Duca,678.0,France,Male,30.0,9.0,116622.73,1.0,0.0,0.0,164768.82,0.0
-12639,15730868.0,Pagnotto,679.0,France,Male,34.0,7.0,0.0,2.0,0.0,0.0,133020.9,0.0
-12640,15644296.0,T'ien,719.0,France,Female,46.0,4.0,0.0,2.0,1.0,0.0,121747.96,1.0
-12641,15692575.0,Clements,711.0,Spain,Female,47.0,5.0,96950.21,1.0,1.0,0.0,143618.47,1.0
-12642,15725028.0,Walker,721.0,France,Male,39.0,5.0,0.0,2.0,1.0,1.0,65864.4,0.0
-12643,15626383.0,Ch'en,588.0,France,Female,49.0,6.0,0.0,1.0,1.0,0.0,182728.8,1.0
-12644,15671165.0,Ch'iu,633.0,France,Male,30.0,2.0,0.0,1.0,1.0,1.0,125806.3,0.0
-12645,15752300.0,Onwuamaeze,686.0,France,Female,29.0,6.0,0.0,2.0,1.0,0.0,158021.09,0.0
-12646,15777033.0,Pagnotto,714.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,28750.67,0.0
-12647,15791501.0,Chukwukadibia,732.0,France,Female,46.0,10.0,0.0,2.0,1.0,0.0,147521.35,0.0
-12648,15650098.0,Ch'iu,567.0,Germany,Female,33.0,1.0,81564.1,2.0,1.0,1.0,124749.08,0.0
-12649,15583049.0,Lettiere,676.0,France,Female,30.0,9.0,0.0,1.0,0.0,0.0,143598.5,0.0
-12650,15591724.0,Onuchukwu,668.0,France,Female,47.0,5.0,84719.13,1.0,0.0,1.0,70774.97,0.0
-12651,15585901.0,Chiu,726.0,France,Female,35.0,2.0,116974.26,1.0,1.0,1.0,164864.67,0.0
-12652,15681956.0,Mazzi,715.0,France,Female,30.0,1.0,0.0,1.0,0.0,1.0,91711.66,0.0
-12653,15704987.0,Lo Duca,743.0,France,Female,44.0,4.0,0.0,2.0,1.0,0.0,105780.18,0.0
-12654,15721207.0,Outhwaite,667.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,138052.51,0.0
-12655,15726484.0,Aitken,633.0,France,Female,42.0,1.0,0.0,2.0,1.0,0.0,48986.18,0.0
-12656,15782530.0,Yobachukwu,592.0,Germany,Male,45.0,7.0,141829.67,2.0,1.0,1.0,138504.28,0.0
-12657,15684461.0,Iweobiegbunam,684.0,Spain,Male,36.0,2.0,0.0,2.0,0.0,1.0,180716.1,0.0
-12658,15636016.0,Tseng,695.0,France,Female,43.0,4.0,0.0,2.0,0.0,0.0,105570.39,0.0
-12659,15579892.0,Nwankwo,673.0,France,Female,30.0,2.0,0.0,2.0,1.0,1.0,162760.96,0.0
-12660,15644765.0,Hsieh,758.0,France,Male,40.0,6.0,116537.6,1.0,1.0,0.0,71830.1,0.0
-12661,15803886.0,Ch'in,520.0,France,Male,31.0,6.0,0.0,1.0,1.0,0.0,141706.43,0.0
-12662,15729454.0,Onyemauchechukwu,634.0,Germany,Male,48.0,7.0,135979.01,2.0,0.0,1.0,116977.89,0.0
-12663,15643679.0,Palermo,685.0,France,Male,33.0,2.0,0.0,2.0,0.0,1.0,161428.25,0.0
-12664,15674840.0,H?,726.0,France,Male,36.0,5.0,0.0,2.0,0.0,0.0,167564.82,0.0
-12665,15667938.0,T'ien,709.0,France,Female,34.0,2.0,0.0,2.0,1.0,1.0,95957.09,0.0
-12666,15627665.0,Sagese,587.0,France,Male,47.0,3.0,65942.26,1.0,1.0,0.0,140765.57,1.0
-12667,15581620.0,Uspenskaya,683.0,France,Male,26.0,4.0,0.0,2.0,0.0,1.0,74425.41,0.0
-12668,15668971.0,Hsing,663.0,France,Male,43.0,2.0,0.0,1.0,1.0,0.0,169719.35,1.0
-12669,15758510.0,Ts'ai,749.0,Germany,Female,43.0,3.0,137580.69,1.0,1.0,0.0,171922.72,1.0
-12670,15738166.0,Echezonachukwu,668.0,Germany,Male,37.0,7.0,114731.76,1.0,1.0,0.0,135083.73,1.0
-12671,15573741.0,Ritchie,685.0,France,Female,23.0,9.0,116465.53,1.0,1.0,0.0,147553.06,0.0
-12672,15622527.0,Chukwukadibia,563.0,France,Male,37.0,5.0,155973.13,1.0,1.0,1.0,64448.36,0.0
-12673,15795519.0,Trevisani,689.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,130862.11,0.0
-12674,15616367.0,Fanucci,627.0,France,Female,37.0,1.0,0.0,1.0,0.0,1.0,121408.55,0.0
-12675,15751777.0,Okwuadigbo,604.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,167522.19,0.0
-12676,15649423.0,Maclean,632.0,France,Female,40.0,2.0,0.0,2.0,1.0,0.0,180134.88,0.0
-12677,15732299.0,Ting,583.0,France,Male,28.0,3.0,0.0,1.0,1.0,1.0,61324.87,0.0
-12678,15692664.0,Zito,561.0,Spain,Male,35.0,2.0,0.0,1.0,1.0,1.0,141035.65,0.0
-12679,15773973.0,Toscani,632.0,Germany,Male,32.0,5.0,91285.22,2.0,1.0,1.0,168034.83,0.0
-12680,15787071.0,Mazzanti,717.0,Spain,Male,32.0,4.0,0.0,2.0,0.0,1.0,54726.43,0.0
-12681,15772995.0,Ch'ang,733.0,Spain,Female,39.0,8.0,0.0,2.0,1.0,1.0,73329.08,0.0
-12682,15699507.0,Palermo,593.0,France,Female,35.0,6.0,125889.3,1.0,1.0,0.0,1528.4,0.0
-12683,15662501.0,Ch'ien,637.0,Spain,Male,29.0,2.0,0.0,1.0,1.0,0.0,151239.65,0.0
-12684,15679810.0,Frolova,565.0,Germany,Female,31.0,3.0,121411.9,2.0,1.0,1.0,176903.35,0.0
-12685,15703394.0,Chukwujamuike,692.0,Spain,Female,38.0,6.0,0.0,1.0,0.0,1.0,131662.47,0.0
-12686,15756820.0,Maclean,587.0,Spain,Male,37.0,2.0,0.0,2.0,1.0,1.0,152855.96,0.0
-12687,15633922.0,Chibugo,637.0,Spain,Male,33.0,2.0,0.0,2.0,0.0,0.0,172646.88,0.0
-12688,15591512.0,Chukwukadibia,743.0,Germany,Male,29.0,5.0,130483.95,1.0,1.0,0.0,106040.97,1.0
-12689,15727281.0,Tsai,751.0,Spain,Male,38.0,5.0,0.0,1.0,1.0,1.0,118716.63,0.0
-12690,15771543.0,Hsia,743.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,93881.75,0.0
-12691,15739357.0,T'ien,692.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,176750.81,0.0
-12692,15743538.0,Lo Duca,752.0,Spain,Male,48.0,4.0,109848.6,1.0,1.0,0.0,116052.92,0.0
-12693,15766179.0,Uspenskaya,714.0,Germany,Female,36.0,3.0,107569.93,1.0,0.0,0.0,119348.88,1.0
-12694,15603550.0,Oluchukwu,652.0,France,Male,37.0,8.0,139723.9,1.0,1.0,1.0,100940.91,0.0
-12695,15782995.0,Ch'ang,721.0,Germany,Female,31.0,7.0,133040.32,2.0,1.0,1.0,180934.67,0.0
-12696,15645496.0,Chidiebele,642.0,France,Female,47.0,7.0,112544.65,1.0,1.0,0.0,127543.83,1.0
-12697,15778947.0,Tuan,592.0,France,Male,28.0,1.0,0.0,1.0,1.0,0.0,95583.45,1.0
-12698,15778936.0,Chikere,769.0,Germany,Male,35.0,0.0,98675.74,1.0,0.0,0.0,121151.1,1.0
-12699,15782939.0,Hs?,648.0,France,Male,36.0,9.0,0.0,2.0,0.0,1.0,161076.92,0.0
-12700,15786078.0,Ngozichukwuka,694.0,Spain,Female,38.0,7.0,0.0,2.0,1.0,0.0,108699.59,0.0
-12701,15684934.0,Chin,707.0,Germany,Male,42.0,4.0,134956.02,1.0,1.0,0.0,80619.09,1.0
-12702,15724451.0,Chukwualuka,673.0,Germany,Male,43.0,6.0,105239.1,1.0,0.0,0.0,158973.77,0.0
-12703,15776921.0,McWilliams,577.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,92220.12,0.0
-12704,15721719.0,Pinto,725.0,Germany,Female,37.0,1.0,133793.89,1.0,0.0,0.0,47722.79,1.0
-12705,15646563.0,Knepper,685.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,138882.98,0.0
-12706,15595036.0,Maclean,663.0,France,Female,35.0,3.0,0.0,3.0,1.0,0.0,161971.42,1.0
-12707,15671610.0,Calabrese,638.0,Germany,Male,31.0,9.0,144287.6,1.0,0.0,0.0,137565.54,0.0
-12708,15619360.0,Chien,667.0,France,Female,29.0,0.0,0.0,2.0,1.0,0.0,177747.72,0.0
-12709,15677522.0,Oluchi,656.0,France,Male,32.0,5.0,0.0,3.0,1.0,1.0,135875.51,1.0
-12710,15752826.0,Udinese,809.0,France,Female,32.0,0.0,144974.52,1.0,1.0,0.0,127413.0,0.0
-12711,15781574.0,Dickson,644.0,France,Male,31.0,6.0,0.0,2.0,0.0,0.0,93524.19,0.0
-12712,15802486.0,De Luca,594.0,Germany,Male,35.0,5.0,121365.39,1.0,0.0,0.0,135851.4,1.0
-12713,15694192.0,Lucciano,753.0,Spain,Female,33.0,4.0,0.0,2.0,1.0,0.0,84710.65,0.0
-12714,15654531.0,Yevseyev,703.0,France,Male,41.0,7.0,0.0,1.0,1.0,0.0,164967.22,1.0
-12715,15757577.0,Hs?,738.0,Spain,Female,54.0,3.0,142646.68,1.0,1.0,1.0,96598.16,1.0
-12716,15615444.0,T'ao,603.0,France,Male,32.0,6.0,131244.65,1.0,0.0,0.0,176768.59,0.0
-12717,15774510.0,Whitehouse,611.0,France,Female,47.0,4.0,127763.24,1.0,1.0,0.0,160576.55,1.0
-12718,15785899.0,Palermo,562.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,44203.55,0.0
-12719,15786035.0,Milanesi,577.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,84927.42,0.0
-12720,15802220.0,Ch'iu,687.0,France,Female,34.0,2.0,153572.61,1.0,1.0,1.0,143913.27,0.0
-12721,15641389.0,Ch'in,589.0,Spain,Female,63.0,1.0,0.0,1.0,1.0,1.0,161465.31,1.0
-12722,15763625.0,T'ao,716.0,Germany,Female,35.0,4.0,124616.23,2.0,1.0,0.0,102517.83,0.0
-12723,15571843.0,Okwukwe,730.0,France,Female,34.0,1.0,116576.45,1.0,1.0,1.0,143598.7,0.0
-12724,15592583.0,Hsu,572.0,France,Female,48.0,8.0,144875.71,1.0,1.0,1.0,94810.84,0.0
-12725,15803801.0,Brown,581.0,Spain,Male,46.0,4.0,0.0,2.0,1.0,1.0,71830.1,0.0
-12726,15792232.0,Ts'ui,678.0,Spain,Female,31.0,3.0,0.0,1.0,1.0,0.0,159823.16,0.0
-12727,15798905.0,Nwankwo,572.0,France,Female,23.0,8.0,0.0,2.0,1.0,0.0,107499.55,0.0
-12728,15651020.0,Hsing,682.0,Germany,Female,29.0,1.0,124616.23,3.0,1.0,0.0,131736.43,1.0
-12729,15593365.0,Lucchese,681.0,Germany,Male,46.0,4.0,120568.41,1.0,1.0,0.0,147530.06,1.0
-12730,15605059.0,Okonkwo,663.0,Spain,Female,44.0,2.0,0.0,2.0,1.0,0.0,172576.43,0.0
-12731,15682914.0,Chimaobim,633.0,France,Female,55.0,8.0,0.0,1.0,1.0,0.0,157549.6,1.0
-12732,15678067.0,Lettiere,760.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,101839.77,0.0
-12733,15592222.0,Chinwemma,676.0,France,Female,28.0,6.0,0.0,2.0,0.0,1.0,141053.85,0.0
-12734,15780144.0,Hs?eh,663.0,Germany,Female,48.0,7.0,141356.46,1.0,1.0,0.0,135851.3,1.0
-12735,15800268.0,Chukwufumnanya,716.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,0.0,81492.6,0.0
-12736,15793723.0,Chiang,699.0,Spain,Male,29.0,5.0,118433.94,1.0,1.0,0.0,187866.21,0.0
-12737,15649616.0,Shao,713.0,France,Male,40.0,6.0,0.0,2.0,1.0,0.0,94488.82,0.0
-12738,15773731.0,Folliero,639.0,Spain,Female,46.0,6.0,0.0,1.0,1.0,1.0,183490.99,1.0
-12739,15628008.0,Ch'in,675.0,Germany,Male,37.0,8.0,133845.34,2.0,1.0,0.0,153036.97,0.0
-12740,15591512.0,H?,716.0,France,Male,36.0,1.0,0.0,2.0,0.0,0.0,56984.58,0.0
-12741,15771728.0,Trevisani,697.0,France,Male,44.0,8.0,0.0,2.0,1.0,0.0,168823.33,0.0
-12742,15785865.0,Efimov,648.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,89099.61,0.0
-12743,15634606.0,Ch'ang,730.0,Spain,Male,32.0,7.0,0.0,1.0,1.0,0.0,117547.76,0.0
-12744,15709447.0,Ch'ien,679.0,France,Male,52.0,4.0,129735.3,1.0,1.0,0.0,16081.62,1.0
-12745,15659820.0,Martin,509.0,Spain,Female,37.0,7.0,113348.5,2.0,1.0,0.0,103726.71,1.0
-12746,15777599.0,Ndubueze,841.0,France,Female,37.0,3.0,0.0,3.0,1.0,1.0,170821.43,1.0
-12747,15646513.0,Folliero,593.0,France,Male,38.0,6.0,0.0,1.0,1.0,1.0,155876.06,0.0
-12748,15775886.0,Folliero,678.0,France,Male,28.0,4.0,0.0,1.0,1.0,1.0,176750.81,0.0
-12749,15585367.0,McLean,577.0,Spain,Female,45.0,6.0,0.0,2.0,1.0,0.0,122518.5,1.0
-12750,15798960.0,Ch'in,687.0,France,Female,29.0,6.0,0.0,2.0,1.0,1.0,166650.9,0.0
-12751,15651533.0,Nkemdirim,744.0,France,Male,40.0,3.0,0.0,1.0,1.0,0.0,123499.98,1.0
-12752,15640266.0,Chidubem,668.0,Spain,Female,34.0,3.0,0.0,2.0,1.0,0.0,70763.69,0.0
-12753,15682479.0,Chin,635.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,182692.8,0.0
-12754,15724076.0,Hargreaves,669.0,Spain,Female,33.0,2.0,131434.96,1.0,1.0,1.0,138880.24,0.0
-12755,15692744.0,Galkin,585.0,Spain,Male,45.0,4.0,0.0,2.0,1.0,0.0,175893.53,0.0
-12756,15636572.0,Ts'ao,599.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,143030.09,0.0
-12757,15754084.0,Yermakov,661.0,Germany,Female,51.0,0.0,141786.78,2.0,1.0,1.0,101839.77,0.0
-12758,15603883.0,Hsieh,850.0,Spain,Female,34.0,5.0,92618.62,1.0,1.0,1.0,164083.72,0.0
-12759,15759499.0,Tuan,838.0,France,Male,38.0,9.0,0.0,1.0,0.0,0.0,28750.67,0.0
-12760,15775590.0,Palermo,590.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,167033.83,0.0
-12761,15680167.0,Ch'ang,627.0,France,Female,57.0,3.0,0.0,2.0,0.0,1.0,95500.98,0.0
-12762,15655339.0,Olisaemeka,614.0,France,Female,33.0,8.0,0.0,1.0,1.0,1.0,110916.15,0.0
-12763,15781554.0,Hsiao,671.0,France,Male,34.0,1.0,0.0,1.0,0.0,0.0,141210.5,0.0
-12764,15706557.0,Chiu,612.0,France,Male,58.0,4.0,0.0,2.0,0.0,1.0,181605.85,0.0
-12765,15794479.0,Nnamutaezinwa,651.0,Spain,Male,32.0,6.0,121580.42,1.0,1.0,1.0,129892.93,0.0
-12766,15582762.0,Ibezimako,653.0,Germany,Female,52.0,8.0,122265.21,1.0,0.0,1.0,105720.09,1.0
-12767,15723484.0,Chidumaga,679.0,Spain,Female,33.0,3.0,145988.65,1.0,1.0,0.0,115676.61,0.0
-12768,15693637.0,Ts'ui,821.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,135034.57,0.0
-12769,15764033.0,Hsueh,746.0,France,Male,31.0,4.0,0.0,2.0,0.0,0.0,184350.78,0.0
-12770,15677136.0,Lo Duca,586.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,113050.92,0.0
-12771,15707473.0,Manna,786.0,Germany,Male,35.0,3.0,106074.89,2.0,0.0,1.0,91574.92,0.0
-12772,15711571.0,Chidiebere,739.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,89566.64,0.0
-12773,15595350.0,Hsia,749.0,France,Male,22.0,7.0,0.0,2.0,1.0,1.0,142935.83,0.0
-12774,15772479.0,Hsia,697.0,France,Female,39.0,1.0,0.0,2.0,1.0,0.0,93524.19,0.0
-12775,15571823.0,Fiorentino,607.0,France,Female,57.0,7.0,0.0,1.0,0.0,0.0,187905.12,1.0
-12776,15612494.0,Nnamdi,521.0,France,Male,48.0,4.0,117265.21,1.0,0.0,0.0,172321.21,1.0
-12777,15803378.0,T'ien,581.0,Spain,Male,39.0,4.0,0.0,1.0,1.0,1.0,153723.73,0.0
-12778,15674720.0,Gentry,586.0,Spain,Female,54.0,5.0,0.0,2.0,0.0,1.0,114006.35,0.0
-12779,15590344.0,Shao,605.0,Germany,Female,41.0,8.0,125685.79,4.0,1.0,0.0,138399.67,1.0
-12780,15745375.0,Lai,678.0,France,Female,36.0,8.0,65848.36,1.0,1.0,0.0,61164.45,0.0
-12781,15672437.0,Nwabugwu,757.0,France,Male,36.0,4.0,0.0,1.0,0.0,0.0,159483.76,0.0
-12782,15780056.0,Chinedum,640.0,France,Male,37.0,7.0,152582.2,1.0,0.0,1.0,107839.03,0.0
-12783,15582903.0,McIntyre,562.0,France,Female,39.0,4.0,0.0,1.0,1.0,0.0,182612.38,1.0
-12784,15723008.0,Chizoba,579.0,France,Female,32.0,5.0,102773.2,1.0,1.0,0.0,177783.04,0.0
-12785,15608701.0,Hsiao,731.0,France,Female,33.0,8.0,0.0,1.0,1.0,1.0,135630.32,0.0
-12786,15777045.0,Ch'ang,770.0,Spain,Male,26.0,2.0,0.0,2.0,1.0,1.0,96590.39,0.0
-12787,15682533.0,White,592.0,Spain,Female,47.0,7.0,0.0,2.0,1.0,0.0,177613.19,0.0
-12788,15642710.0,Chuang,718.0,Spain,Male,29.0,6.0,0.0,1.0,0.0,0.0,4497.55,0.0
-12789,15651802.0,Hargreaves,682.0,Germany,Female,42.0,1.0,122314.5,1.0,1.0,1.0,49722.36,1.0
-12790,15793726.0,Ch'iu,679.0,Spain,Male,33.0,3.0,0.0,2.0,1.0,1.0,152698.24,0.0
-12791,15684921.0,Shih,749.0,Germany,Female,45.0,9.0,119278.44,1.0,1.0,0.0,48242.18,0.0
-12792,15642883.0,Lucchesi,641.0,Germany,Male,43.0,6.0,100900.5,2.0,0.0,1.0,131919.48,0.0
-12793,15701175.0,Obiajulu,639.0,Germany,Female,37.0,2.0,81877.38,1.0,1.0,0.0,162668.33,1.0
-12794,15642545.0,Pagnotto,718.0,France,Male,39.0,9.0,0.0,1.0,1.0,0.0,100862.36,1.0
-12795,15757577.0,Chidubem,582.0,Spain,Male,31.0,8.0,0.0,2.0,0.0,0.0,158545.25,0.0
-12796,15600879.0,Cremonesi,684.0,France,Female,52.0,4.0,0.0,1.0,0.0,1.0,140941.47,1.0
-12797,15571940.0,Tikhonov,652.0,France,Male,67.0,1.0,166969.28,1.0,1.0,1.0,149913.92,0.0
-12798,15800326.0,Manna,491.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,50783.1,0.0
-12799,15689294.0,Ch'in,709.0,France,Male,23.0,1.0,0.0,2.0,1.0,0.0,93777.61,0.0
-12800,15597909.0,Udegbunam,755.0,Germany,Male,45.0,7.0,76915.4,1.0,0.0,1.0,125553.51,1.0
-12801,15567013.0,Nkemakolam,553.0,France,Male,30.0,9.0,109844.81,1.0,1.0,1.0,157932.75,0.0
-12802,15634564.0,Tsao,751.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,147527.02,0.0
-12803,15795913.0,Ch'in,659.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,125961.48,0.0
-12804,15672882.0,Onyemauchechukwu,595.0,France,Male,32.0,9.0,0.0,2.0,0.0,0.0,138775.75,0.0
-12805,15807563.0,Ts'ui,713.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,57898.57,0.0
-12806,15750874.0,Oluchukwu,571.0,Germany,Female,38.0,8.0,118899.45,1.0,1.0,0.0,29346.1,0.0
-12807,15777923.0,Chidiebere,636.0,France,Female,37.0,4.0,115625.07,1.0,1.0,1.0,172668.39,0.0
-12808,15592740.0,Hsieh,733.0,France,Female,41.0,4.0,0.0,1.0,1.0,1.0,121210.09,0.0
-12809,15604963.0,Zito,754.0,Germany,Female,66.0,0.0,111097.87,3.0,0.0,1.0,26287.21,1.0
-12810,15789526.0,Erskine,645.0,Spain,Male,29.0,1.0,0.0,1.0,1.0,1.0,172097.4,0.0
-12811,15745375.0,Wang,697.0,France,Male,46.0,7.0,0.0,2.0,1.0,0.0,120511.03,0.0
-12812,15651022.0,Uvarova,580.0,France,Male,55.0,8.0,0.0,1.0,0.0,0.0,52861.89,1.0
-12813,15645029.0,T'ien,728.0,Germany,Female,44.0,4.0,116980.53,1.0,1.0,0.0,72876.43,1.0
-12814,15618363.0,Cremonesi,671.0,France,Male,22.0,7.0,0.0,2.0,1.0,0.0,49594.02,0.0
-12815,15652914.0,Chinedum,585.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,173837.54,0.0
-12816,15589017.0,Efremov,661.0,Spain,Male,37.0,5.0,0.0,2.0,0.0,1.0,61312.15,0.0
-12817,15678882.0,Hsueh,438.0,Germany,Male,40.0,1.0,145731.83,2.0,0.0,0.0,81898.81,0.0
-12818,15761706.0,Chibugo,717.0,France,Male,43.0,1.0,122453.97,1.0,0.0,1.0,142783.98,0.0
-12819,15641794.0,Chidumaga,704.0,Spain,Female,35.0,9.0,0.0,2.0,0.0,1.0,180712.52,0.0
-12820,15774738.0,Chukwudi,573.0,France,Male,38.0,10.0,103769.26,1.0,1.0,1.0,120790.02,0.0
-12821,15594917.0,Tien,627.0,Germany,Female,29.0,1.0,117412.18,2.0,1.0,0.0,177737.07,0.0
-12822,15627190.0,Hsu,651.0,Germany,Female,44.0,9.0,106756.01,2.0,0.0,1.0,199953.33,0.0
-12823,15578738.0,Chiu,695.0,Germany,Male,55.0,5.0,116537.6,1.0,1.0,1.0,80619.09,0.0
-12824,15780128.0,Ts'ai,633.0,France,Male,35.0,2.0,129855.32,1.0,1.0,0.0,162865.35,0.0
-12825,15602909.0,Nweke,713.0,Germany,Male,48.0,8.0,111432.77,1.0,1.0,1.0,163736.09,0.0
-12826,15750545.0,Rowntree,594.0,Germany,Female,38.0,4.0,102773.2,2.0,1.0,0.0,141230.07,1.0
-12827,15679770.0,Chuang,684.0,Germany,Male,33.0,3.0,123403.85,1.0,1.0,0.0,131083.56,0.0
-12828,15698522.0,McLean,521.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,140745.81,0.0
-12829,15682946.0,Ndubueze,738.0,France,Female,33.0,2.0,145843.82,1.0,0.0,0.0,62733.03,0.0
-12830,15762218.0,Ch'ang,592.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,121763.68,0.0
-12831,15668894.0,Ugochukwu,850.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,48502.88,0.0
-12832,15690898.0,Frolov,715.0,France,Female,30.0,1.0,0.0,2.0,1.0,1.0,85891.02,0.0
-12833,15730793.0,Chiekwugo,577.0,France,Female,42.0,2.0,0.0,2.0,1.0,1.0,182354.46,0.0
-12834,15705383.0,Brown,662.0,France,Male,25.0,6.0,0.0,2.0,0.0,1.0,98015.85,0.0
-12835,15655213.0,Ting,691.0,France,Male,39.0,7.0,0.0,1.0,1.0,0.0,125877.22,0.0
-12836,15665058.0,Nwachinemelu,633.0,France,Male,43.0,7.0,0.0,2.0,1.0,1.0,179453.66,1.0
-12837,15593477.0,Trevisani,665.0,Germany,Male,43.0,2.0,116646.76,2.0,1.0,1.0,125553.52,0.0
-12838,15792986.0,Ch'ien,588.0,Spain,Male,38.0,9.0,141547.26,1.0,0.0,1.0,141152.28,0.0
-12839,15777062.0,Okwudilichukwu,722.0,Germany,Female,43.0,7.0,109978.83,1.0,0.0,1.0,199336.63,0.0
-12840,15743617.0,Trevisano,651.0,France,Male,29.0,2.0,0.0,1.0,1.0,1.0,66548.58,0.0
-12841,15649793.0,Walker,850.0,France,Female,30.0,4.0,0.0,1.0,1.0,1.0,103787.28,0.0
-12842,15705918.0,Chioke,559.0,France,Male,38.0,7.0,127132.96,1.0,1.0,0.0,139435.75,0.0
-12843,15649121.0,Palerma,749.0,Germany,Male,19.0,10.0,79468.96,2.0,1.0,0.0,171457.06,0.0
-12844,15707042.0,Cremonesi,850.0,France,Male,38.0,9.0,0.0,1.0,1.0,0.0,146875.86,0.0
-12845,15699550.0,Moore,646.0,France,Female,67.0,5.0,0.0,2.0,0.0,1.0,155870.79,0.0
-12846,15810864.0,Tsai,592.0,France,Female,29.0,9.0,0.0,2.0,0.0,1.0,184524.65,0.0
-12847,15654074.0,Cremonesi,708.0,Spain,Female,36.0,6.0,0.0,2.0,1.0,1.0,104889.3,0.0
-12848,15566689.0,T'ien,579.0,France,Male,36.0,7.0,143390.51,1.0,1.0,0.0,119817.19,0.0
-12849,15596671.0,Lucciano,589.0,Spain,Male,33.0,1.0,110153.33,1.0,1.0,0.0,125823.79,0.0
-12850,15678720.0,Erskine,646.0,Germany,Female,39.0,6.0,115462.44,2.0,0.0,0.0,174822.74,0.0
-12851,15798850.0,Hargreaves,683.0,Spain,Female,43.0,6.0,0.0,1.0,0.0,0.0,54776.64,1.0
-12852,15717290.0,Toscani,598.0,Germany,Female,46.0,8.0,91068.56,1.0,1.0,0.0,102681.16,1.0
-12853,15639238.0,Ts'ai,613.0,France,Male,37.0,4.0,0.0,2.0,0.0,1.0,70529.0,0.0
-12854,15576256.0,Chukwubuikem,496.0,France,Female,21.0,2.0,0.0,2.0,1.0,1.0,87822.14,0.0
-12855,15793042.0,Ch'eng,687.0,Spain,Female,33.0,6.0,0.0,2.0,1.0,1.0,170142.09,0.0
-12856,15660211.0,Okeke,661.0,France,Male,36.0,2.0,122924.75,1.0,1.0,0.0,11.58,0.0
-12857,15682453.0,Yegorov,597.0,France,Female,41.0,3.0,0.0,1.0,1.0,0.0,64375.4,0.0
-12858,15654858.0,Yermakov,727.0,France,Female,66.0,9.0,0.0,2.0,1.0,1.0,143960.72,0.0
-12859,15609653.0,Ebelegbulam,542.0,France,Male,32.0,3.0,97408.03,1.0,1.0,1.0,113860.81,0.0
-12860,15659962.0,Hsu,589.0,Spain,Female,38.0,3.0,0.0,1.0,1.0,1.0,127413.0,0.0
-12861,15617197.0,Chiekwugo,778.0,Germany,Male,32.0,5.0,155973.88,2.0,1.0,0.0,122449.18,0.0
-12862,15786042.0,Ts'ai,438.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,108577.97,0.0
-12863,15731519.0,Pomeroy,531.0,France,Female,43.0,9.0,125903.22,1.0,0.0,1.0,142903.44,0.0
-12864,15606435.0,Milne,678.0,France,Male,39.0,7.0,0.0,1.0,1.0,1.0,103970.58,0.0
-12865,15615176.0,Hsueh,635.0,Spain,Male,40.0,7.0,0.0,1.0,1.0,0.0,65675.36,1.0
-12866,15682890.0,Calabrese,683.0,Spain,Male,35.0,4.0,0.0,1.0,1.0,1.0,86786.41,0.0
-12867,15743236.0,Cremonesi,531.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,168816.73,0.0
-12868,15790247.0,Manna,605.0,Spain,Male,31.0,9.0,106920.92,1.0,1.0,0.0,172810.45,0.0
-12869,15754068.0,Chin,632.0,Spain,Male,23.0,3.0,0.0,2.0,1.0,0.0,113048.79,0.0
-12870,15783338.0,Ch'ang,706.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,1.0,163666.85,0.0
-12871,15747571.0,Zikoranachidimma,600.0,France,Male,37.0,0.0,121399.98,1.0,1.0,0.0,74800.99,0.0
-12872,15598097.0,Genovesi,738.0,Germany,Male,55.0,7.0,91219.29,1.0,0.0,1.0,194998.34,1.0
-12873,15637366.0,T'ao,688.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,94499.06,0.0
-12874,15663410.0,Fomin,688.0,France,Male,41.0,1.0,0.0,2.0,1.0,1.0,122449.21,0.0
-12875,15759733.0,Cremonesi,774.0,France,Male,28.0,10.0,0.0,2.0,1.0,1.0,162878.96,0.0
-12876,15758025.0,Chien,717.0,France,Female,33.0,4.0,123372.3,1.0,1.0,1.0,161465.31,0.0
-12877,15685826.0,Ch'iu,735.0,France,Female,36.0,2.0,0.0,2.0,0.0,0.0,182650.15,0.0
-12878,15757895.0,T'ien,613.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,86043.27,0.0
-12879,15700813.0,Nkemdilim,586.0,France,Male,39.0,7.0,133040.32,1.0,1.0,0.0,80552.11,0.0
-12880,15593954.0,Chizuoke,556.0,France,Female,57.0,7.0,0.0,1.0,1.0,1.0,167066.95,1.0
-12881,15737509.0,Sagese,676.0,France,Male,33.0,7.0,122314.5,1.0,1.0,0.0,156774.75,0.0
-12882,15661708.0,Ts'ui,565.0,France,Female,46.0,2.0,0.0,2.0,1.0,0.0,150580.88,0.0
-12883,15697460.0,Chukwuebuka,628.0,Germany,Female,31.0,6.0,153313.67,2.0,1.0,0.0,127847.25,0.0
-12884,15691457.0,Chidiebere,623.0,Spain,Male,29.0,9.0,0.0,2.0,1.0,1.0,5983.8,0.0
-12885,15605286.0,Bibi,707.0,France,Male,63.0,9.0,0.0,2.0,1.0,1.0,165562.84,0.0
-12886,15704442.0,Oluchukwu,725.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,166677.54,0.0
-12887,15613471.0,McIntosh,579.0,France,Female,47.0,1.0,0.0,1.0,1.0,0.0,100130.95,1.0
-12888,15602354.0,Toscani,652.0,France,Female,31.0,1.0,0.0,1.0,1.0,0.0,27330.27,0.0
-12889,15775192.0,Chidubem,591.0,France,Female,31.0,3.0,0.0,2.0,1.0,1.0,79919.13,0.0
-12890,15784761.0,Smith,706.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,152290.28,0.0
-12891,15758813.0,Kambinachi,659.0,Spain,Male,32.0,1.0,0.0,2.0,1.0,0.0,83473.82,0.0
-12892,15772423.0,Young,707.0,France,Male,37.0,4.0,0.0,2.0,0.0,1.0,148527.56,0.0
-12893,15722543.0,Okechukwu,633.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,158947.58,0.0
-12894,15621868.0,Ting,556.0,Germany,Male,55.0,4.0,136678.52,1.0,1.0,0.0,161559.12,1.0
-12895,15731543.0,Hsieh,639.0,France,Male,42.0,5.0,0.0,2.0,1.0,0.0,176580.89,0.0
-12896,15640442.0,Pugh,639.0,France,Male,43.0,9.0,0.0,2.0,1.0,0.0,143585.29,0.0
-12897,15589230.0,Ch'ang,488.0,France,Male,42.0,2.0,106968.96,1.0,0.0,0.0,147759.84,0.0
-12898,15643657.0,Nnamutaezinwa,617.0,France,Male,40.0,7.0,0.0,1.0,0.0,0.0,158973.45,1.0
-12899,15605684.0,Hsia,724.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,171519.06,0.0
-12900,15592707.0,Nwankwo,703.0,France,Female,26.0,4.0,0.0,2.0,1.0,1.0,158973.78,0.0
-12901,15779414.0,Ugoji,678.0,Germany,Male,39.0,1.0,123675.69,2.0,1.0,0.0,107876.2,0.0
-12902,15623346.0,T'ang,674.0,France,Female,41.0,2.0,0.0,2.0,1.0,1.0,141872.05,0.0
-12903,15791834.0,Ch'ien,714.0,Spain,Female,43.0,5.0,117805.21,1.0,0.0,0.0,151349.37,1.0
-12904,15648858.0,Cheng,471.0,France,Female,27.0,3.0,0.0,2.0,1.0,1.0,99818.45,0.0
-12905,15607988.0,Ch'en,762.0,Spain,Male,32.0,7.0,0.0,2.0,1.0,0.0,130789.6,0.0
-12906,15763415.0,Ngozichukwuka,597.0,Spain,Female,33.0,1.0,0.0,1.0,1.0,1.0,57929.9,0.0
-12907,15707962.0,Uwaezuoke,714.0,France,Female,33.0,6.0,0.0,2.0,0.0,0.0,167038.51,0.0
-12908,15690188.0,Hao,635.0,Germany,Male,47.0,5.0,106301.85,4.0,1.0,0.0,147794.63,1.0
-12909,15694928.0,Ts'ui,705.0,France,Female,52.0,1.0,99824.45,1.0,1.0,1.0,137565.87,0.0
-12910,15611058.0,Obidimkpa,693.0,Spain,Male,38.0,1.0,0.0,1.0,0.0,1.0,50368.63,0.0
-12911,15666916.0,Hsia,781.0,France,Male,34.0,7.0,83543.37,1.0,0.0,1.0,59981.62,0.0
-12912,15782879.0,Baryshnikov,793.0,France,Male,36.0,4.0,0.0,1.0,0.0,0.0,180345.44,0.0
-12913,15783097.0,Mazzi,521.0,France,Female,37.0,6.0,120787.18,1.0,1.0,0.0,171922.72,0.0
-12914,15770375.0,Olisanugo,678.0,Spain,Male,36.0,3.0,97565.74,1.0,0.0,1.0,103764.55,0.0
-12915,15732791.0,Oluchukwu,681.0,France,Male,39.0,10.0,0.0,1.0,1.0,0.0,131753.97,0.0
-12916,15691703.0,O'Meara,516.0,France,Male,35.0,1.0,0.0,2.0,1.0,0.0,63337.19,0.0
-12917,15614168.0,De Salis,644.0,France,Male,56.0,4.0,0.0,2.0,1.0,1.0,74580.8,1.0
-12918,15636016.0,Pisani,681.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,121408.55,0.0
-12919,15585853.0,Nwachinemelu,809.0,Spain,Male,49.0,9.0,0.0,1.0,1.0,1.0,173629.78,0.0
-12920,15624072.0,Shih,589.0,France,Female,41.0,5.0,0.0,2.0,1.0,1.0,158021.43,0.0
-12921,15655389.0,Lai,619.0,France,Male,43.0,7.0,0.0,2.0,0.0,0.0,138700.75,0.0
-12922,15690744.0,Iweobiegbunam,727.0,France,Female,36.0,5.0,0.0,2.0,1.0,1.0,73881.68,0.0
-12923,15806901.0,Ijendu,732.0,Germany,Male,48.0,4.0,164948.98,1.0,1.0,1.0,60920.03,1.0
-12924,15632789.0,Tien,671.0,France,Male,43.0,6.0,0.0,2.0,1.0,1.0,51919.04,0.0
-12925,15671610.0,Chiu,645.0,Spain,Female,42.0,9.0,0.0,2.0,0.0,1.0,676.06,0.0
-12926,15664311.0,Nkemjika,663.0,Germany,Female,38.0,10.0,59532.18,2.0,1.0,1.0,146875.86,0.0
-12927,15639303.0,Yeh,715.0,France,Male,54.0,9.0,0.0,2.0,1.0,1.0,170835.32,0.0
-12928,15694890.0,Chien,661.0,France,Male,36.0,5.0,0.0,2.0,1.0,1.0,88890.05,0.0
-12929,15672692.0,T'ao,792.0,France,Male,27.0,9.0,121827.43,1.0,1.0,1.0,161241.65,0.0
-12930,15803378.0,Kambinachi,614.0,Spain,Male,35.0,3.0,0.0,2.0,1.0,0.0,137037.97,0.0
-12931,15775756.0,Ch'ien,695.0,Spain,Male,21.0,7.0,114493.79,1.0,0.0,1.0,146585.79,0.0
-12932,15691703.0,Gorman,586.0,Germany,Male,47.0,3.0,142663.46,1.0,1.0,1.0,50783.1,0.0
-12933,15682021.0,Mazzanti,531.0,Spain,Female,44.0,5.0,0.0,1.0,1.0,0.0,47160.01,1.0
-12934,15748649.0,Lai,640.0,Spain,Male,41.0,6.0,96798.79,1.0,1.0,0.0,113580.79,0.0
-12935,15627196.0,Yobanna,644.0,France,Male,50.0,4.0,0.0,2.0,1.0,1.0,152479.64,1.0
-12936,15602497.0,Chibuzo,599.0,Germany,Female,34.0,5.0,95808.19,2.0,0.0,1.0,81451.21,0.0
-12937,15651336.0,Chien,581.0,France,Male,46.0,3.0,131513.26,1.0,1.0,0.0,54949.73,1.0
-12938,15724076.0,Tretiakova,602.0,France,Male,33.0,2.0,0.0,1.0,1.0,1.0,94820.85,0.0
-12939,15808960.0,K'ung,671.0,Spain,Male,38.0,5.0,0.0,2.0,0.0,1.0,163798.41,0.0
-12940,15700255.0,Knepper,644.0,France,Female,39.0,3.0,113858.95,2.0,1.0,0.0,158918.03,0.0
-12941,15803378.0,Onwuamaeze,682.0,Germany,Female,43.0,2.0,96888.39,1.0,0.0,0.0,57841.89,1.0
-12942,15801395.0,Okwudilichukwu,706.0,France,Male,39.0,4.0,0.0,2.0,1.0,0.0,161407.48,0.0
-12943,15772941.0,Hsing,634.0,Germany,Male,51.0,8.0,126588.81,3.0,1.0,0.0,170621.55,1.0
-12944,15743582.0,T'ien,495.0,France,Female,36.0,5.0,0.0,1.0,1.0,1.0,185543.68,0.0
-12945,15633112.0,Chukwuhaenye,691.0,France,Male,33.0,6.0,0.0,2.0,1.0,0.0,84870.23,0.0
-12946,15671917.0,Mazzanti,753.0,Germany,Male,40.0,9.0,114435.47,1.0,1.0,0.0,55313.51,1.0
-12947,15682533.0,Ch'ang,579.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,140941.47,0.0
-12948,15806922.0,Ch'iu,523.0,France,Female,35.0,2.0,0.0,1.0,1.0,1.0,114515.28,0.0
-12949,15671124.0,Okwudilichukwu,732.0,Germany,Male,36.0,5.0,115171.71,1.0,0.0,1.0,25289.23,0.0
-12950,15796721.0,Milano,656.0,Spain,Female,37.0,7.0,0.0,2.0,1.0,1.0,144532.85,0.0
-12951,15716041.0,Lucchese,644.0,France,Male,40.0,1.0,0.0,2.0,1.0,1.0,171768.89,0.0
-12952,15702416.0,Chukwualuka,724.0,Spain,Male,47.0,7.0,0.0,1.0,0.0,0.0,59650.42,1.0
-12953,15795511.0,Nwabugwu,567.0,France,Male,37.0,7.0,0.0,2.0,0.0,0.0,142913.98,0.0
-12954,15585855.0,Kosisochukwu,687.0,France,Female,57.0,5.0,0.0,1.0,1.0,0.0,112222.64,1.0
-12955,15603594.0,Hs?,681.0,Germany,Male,44.0,9.0,141334.57,1.0,1.0,1.0,94862.93,1.0
-12956,15731553.0,Ch'ang,651.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,160529.71,0.0
-12957,15793881.0,Chien,712.0,France,Female,33.0,3.0,108465.65,1.0,0.0,1.0,51999.5,1.0
-12958,15662494.0,Tsai,707.0,France,Female,32.0,1.0,0.0,2.0,1.0,1.0,53394.12,0.0
-12959,15737509.0,Ch'ien,727.0,France,Male,29.0,1.0,124663.54,1.0,1.0,0.0,118716.63,1.0
-12960,15598044.0,Ch'in,660.0,France,Female,42.0,5.0,0.0,2.0,0.0,1.0,152577.62,0.0
-12961,15808838.0,Chukwualuka,551.0,Spain,Female,40.0,2.0,0.0,1.0,1.0,1.0,166513.49,0.0
-12962,15784280.0,H?,604.0,Germany,Male,34.0,5.0,114023.64,1.0,1.0,1.0,171707.93,0.0
-12963,15700366.0,Ch'ien,671.0,France,Female,41.0,8.0,0.0,2.0,1.0,0.0,121409.06,0.0
-12964,15682453.0,Mazzanti,773.0,France,Female,48.0,8.0,0.0,1.0,1.0,0.0,129590.18,1.0
-12965,15689061.0,Chiang,633.0,Germany,Male,55.0,4.0,107963.23,1.0,0.0,0.0,151336.96,1.0
-12966,15661409.0,Chinonyelum,835.0,Spain,Female,44.0,8.0,0.0,1.0,1.0,0.0,185561.89,1.0
-12967,15645045.0,Lucchesi,607.0,France,Female,27.0,3.0,0.0,2.0,0.0,1.0,71167.17,0.0
-12968,15585595.0,Lucciano,682.0,Spain,Male,40.0,1.0,0.0,1.0,1.0,0.0,172033.57,1.0
-12969,15630106.0,Yevdokimova,677.0,France,Male,41.0,3.0,0.0,1.0,0.0,1.0,51871.95,0.0
-12970,15627042.0,Eluemuno,611.0,Germany,Male,28.0,7.0,111432.13,1.0,1.0,0.0,124720.68,0.0
-12971,15598892.0,Ch'iu,713.0,Spain,Male,38.0,6.0,0.0,2.0,0.0,1.0,126281.83,0.0
-12972,15810864.0,Chibueze,719.0,Germany,Female,34.0,6.0,111348.18,2.0,1.0,0.0,94949.84,0.0
-12973,15648858.0,Tu,553.0,France,Male,42.0,8.0,0.0,1.0,1.0,1.0,4993.94,0.0
-12974,15773487.0,Manna,556.0,Germany,Female,19.0,2.0,118680.57,1.0,1.0,1.0,130567.02,0.0
-12975,15576945.0,T'ien,578.0,France,Female,43.0,1.0,0.0,2.0,1.0,1.0,180862.31,0.0
-12976,15654574.0,Milne,705.0,Spain,Female,31.0,4.0,132077.48,1.0,1.0,0.0,106977.53,0.0
-12977,15589425.0,T'ao,640.0,France,Male,42.0,4.0,0.0,1.0,1.0,1.0,175544.02,0.0
-12978,15626012.0,T'ien,627.0,France,Male,32.0,8.0,0.0,2.0,1.0,0.0,74898.6,0.0
-12979,15803750.0,Iweobiegbunam,526.0,Spain,Male,29.0,10.0,0.0,1.0,1.0,0.0,102747.02,0.0
-12980,15665088.0,Macvitie,651.0,Spain,Male,54.0,9.0,123863.64,1.0,0.0,1.0,138350.74,1.0
-12981,15706464.0,Fanucci,721.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,178028.96,0.0
-12982,15804787.0,Chiemenam,675.0,Spain,Female,44.0,3.0,0.0,2.0,1.0,1.0,171345.02,0.0
-12983,15781702.0,Nwachinemelu,651.0,France,Female,31.0,7.0,0.0,2.0,0.0,1.0,66721.98,0.0
-12984,15565701.0,Trevisani,609.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,167553.06,0.0
-12985,15620758.0,Mironova,648.0,Germany,Male,43.0,2.0,119545.43,1.0,1.0,1.0,161653.5,1.0
-12986,15692744.0,Ch'ien,588.0,France,Male,35.0,2.0,0.0,2.0,0.0,1.0,85901.09,0.0
-12987,15794396.0,Nwokezuike,564.0,France,Female,37.0,5.0,0.0,2.0,1.0,1.0,44251.33,0.0
-12988,15774179.0,Chigolum,678.0,France,Male,28.0,5.0,0.0,2.0,1.0,1.0,59249.83,0.0
-12989,15693893.0,Ch'eng,683.0,France,Female,40.0,7.0,0.0,2.0,1.0,1.0,186942.49,0.0
-12990,15706904.0,Folliero,593.0,France,Male,26.0,8.0,0.0,2.0,1.0,0.0,132210.53,0.0
-12991,15797733.0,Brown,603.0,France,Male,32.0,2.0,0.0,2.0,1.0,0.0,96847.91,0.0
-12992,15668943.0,Ch'in,704.0,France,Male,27.0,4.0,107569.96,1.0,1.0,1.0,64716.08,0.0
-12993,15649160.0,Fan,733.0,Spain,Male,45.0,3.0,121823.7,1.0,1.0,0.0,136869.98,1.0
-12994,15646082.0,Ogochukwu,705.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,179908.71,0.0
-12995,15703394.0,Fanucci,659.0,Spain,Female,32.0,1.0,0.0,1.0,1.0,1.0,10054.53,1.0
-12996,15782468.0,Hs?eh,554.0,Germany,Male,52.0,1.0,125445.04,1.0,1.0,1.0,109545.56,1.0
-12997,15780215.0,Lombardo,745.0,Spain,Male,33.0,5.0,94435.45,1.0,1.0,1.0,148087.62,0.0
-12998,15757531.0,L?,651.0,France,Female,45.0,8.0,0.0,2.0,0.0,0.0,55313.44,0.0
-12999,15671390.0,Tien,569.0,France,Male,30.0,10.0,0.0,1.0,1.0,1.0,61240.83,0.0
-13000,15775294.0,Ulyanova,648.0,France,Male,35.0,10.0,0.0,2.0,1.0,1.0,19116.97,0.0
-13001,15797910.0,Nwachinemelu,651.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,91086.73,0.0
-13002,15583692.0,Yuryeva,721.0,France,Female,32.0,9.0,0.0,2.0,1.0,1.0,104181.78,0.0
-13003,15572762.0,Hsueh,698.0,France,Male,34.0,6.0,142644.64,1.0,1.0,0.0,59249.83,0.0
-13004,15641299.0,Maclean,667.0,France,Male,39.0,2.0,0.0,2.0,1.0,1.0,128521.16,0.0
-13005,15678886.0,Chia,682.0,France,Female,32.0,1.0,0.0,2.0,1.0,0.0,45985.52,0.0
-13006,15689886.0,Ch'ang,656.0,Germany,Male,32.0,8.0,117412.18,1.0,1.0,1.0,58814.41,0.0
-13007,15771668.0,Macvitie,565.0,France,Female,33.0,9.0,0.0,1.0,1.0,0.0,57962.41,1.0
-13008,15576676.0,Ch'en,579.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,120790.02,0.0
-13009,15709136.0,Chigolum,583.0,Germany,Female,30.0,1.0,74896.92,2.0,1.0,1.0,166677.54,0.0
-13010,15672499.0,Watson,726.0,Spain,Female,46.0,4.0,0.0,1.0,0.0,0.0,172668.39,1.0
-13011,15782941.0,Ch'en,584.0,France,Female,42.0,3.0,0.0,1.0,1.0,1.0,81602.02,0.0
-13012,15702801.0,Onio,724.0,Germany,Male,37.0,5.0,142905.51,1.0,1.0,1.0,153786.46,0.0
-13013,15768359.0,Hsueh,572.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,0.0,149613.77,0.0
-13014,15758035.0,Ch'ang,727.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,161076.92,0.0
-13015,15705707.0,Lu,573.0,France,Female,40.0,8.0,0.0,3.0,1.0,1.0,88695.19,1.0
-13016,15777784.0,Chukwuemeka,686.0,France,Male,38.0,3.0,0.0,2.0,1.0,0.0,71095.41,0.0
-13017,15654901.0,Chukwualuka,705.0,France,Female,33.0,7.0,0.0,2.0,0.0,1.0,169894.4,0.0
-13018,15691808.0,Shih,635.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,139947.17,0.0
-13019,15782659.0,Teng,573.0,Spain,Male,41.0,5.0,96196.24,1.0,0.0,0.0,129964.94,0.0
-13020,15686917.0,Onyeoruru,646.0,Germany,Male,32.0,4.0,106938.11,2.0,1.0,1.0,124045.65,0.0
-13021,15594862.0,Maclean,643.0,France,Male,42.0,0.0,103772.73,1.0,1.0,0.0,187866.21,1.0
-13022,15604891.0,P'an,648.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,61513.87,0.0
-13023,15579781.0,Shand,708.0,Spain,Female,33.0,9.0,0.0,2.0,0.0,1.0,103379.31,0.0
-13024,15593496.0,Mazzanti,771.0,Germany,Male,39.0,7.0,141803.77,2.0,1.0,1.0,179521.24,0.0
-13025,15604020.0,Hsing,676.0,France,Male,48.0,5.0,0.0,1.0,0.0,1.0,152417.79,1.0
-13026,15667554.0,Hsing,615.0,Germany,Female,37.0,10.0,133658.24,1.0,1.0,0.0,66810.48,0.0
-13027,15631162.0,Ts'ui,757.0,Spain,Male,39.0,2.0,0.0,2.0,0.0,1.0,122488.59,0.0
-13028,15750264.0,Chukwumaobim,685.0,Spain,Male,32.0,2.0,0.0,1.0,1.0,1.0,136859.55,0.0
-13029,15667588.0,Fenton,686.0,France,Female,32.0,8.0,129974.79,1.0,1.0,1.0,129964.94,0.0
-13030,15695816.0,Miah,705.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,94450.97,0.0
-13031,15620756.0,Chin,544.0,Spain,Male,35.0,7.0,151191.31,2.0,1.0,1.0,122580.48,0.0
-13032,15713250.0,Chukwufumnanya,595.0,France,Female,41.0,1.0,0.0,3.0,0.0,1.0,48876.84,1.0
-13033,15629133.0,Tsao,717.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,162501.83,0.0
-13034,15686963.0,Hsia,648.0,France,Male,26.0,9.0,0.0,2.0,1.0,0.0,167723.25,0.0
-13035,15723008.0,Chizuoke,729.0,France,Male,30.0,6.0,0.0,2.0,0.0,1.0,124795.84,0.0
-13036,15636423.0,Mazzanti,719.0,France,Male,32.0,8.0,62717.84,1.0,1.0,1.0,138882.98,0.0
-13037,15791452.0,P'eng,775.0,France,Female,27.0,6.0,0.0,2.0,1.0,1.0,161845.81,0.0
-13038,15659949.0,Shih,609.0,Spain,Female,59.0,7.0,0.0,1.0,1.0,0.0,126812.85,1.0
-13039,15677371.0,Kao,725.0,Germany,Male,50.0,2.0,118287.01,1.0,0.0,0.0,138360.74,1.0
-13040,15635597.0,Chukwumaobim,647.0,Germany,Male,46.0,8.0,132944.53,1.0,0.0,0.0,185827.74,1.0
-13041,15697042.0,Maclean,598.0,France,Female,28.0,9.0,0.0,2.0,1.0,0.0,90876.95,0.0
-13042,15594917.0,Ugorji,637.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,103760.53,0.0
-13043,15730059.0,McIntyre,696.0,France,Female,46.0,9.0,105264.88,1.0,1.0,1.0,119924.8,1.0
-13044,15793881.0,Ch'eng,706.0,France,Female,42.0,6.0,0.0,2.0,0.0,0.0,12761.16,0.0
-13045,15780804.0,Rowntree,645.0,France,Male,32.0,1.0,0.0,1.0,1.0,0.0,169952.82,1.0
-13046,15735400.0,Hsueh,658.0,France,Female,50.0,6.0,0.0,1.0,1.0,0.0,60954.45,1.0
-13047,15662662.0,Nwachukwu,636.0,France,Female,48.0,9.0,0.0,1.0,0.0,1.0,51117.06,1.0
-13048,15627854.0,Ch'eng,583.0,France,Female,34.0,2.0,0.0,2.0,1.0,0.0,158974.84,0.0
-13049,15724851.0,Hs?,683.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,103396.06,0.0
-13050,15803337.0,Chibueze,812.0,France,Female,31.0,10.0,0.0,1.0,1.0,1.0,176945.96,0.0
-13051,15618562.0,Ch'eng,706.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,44965.54,0.0
-13052,15658364.0,T'ien,698.0,Spain,Female,24.0,2.0,0.0,2.0,1.0,1.0,54954.51,0.0
-13053,15672598.0,Ch'ien,547.0,France,Female,37.0,4.0,113090.4,1.0,0.0,0.0,124777.14,0.0
-13054,15709604.0,Nkemjika,721.0,France,Female,36.0,4.0,0.0,2.0,0.0,0.0,16402.11,0.0
-13055,15756871.0,Nkemdirim,521.0,France,Female,41.0,1.0,0.0,2.0,1.0,1.0,52795.15,0.0
-13056,15696812.0,Shih,686.0,France,Male,22.0,7.0,0.0,2.0,1.0,1.0,163666.85,0.0
-13057,15811978.0,Hsu,694.0,Germany,Male,44.0,8.0,141604.76,1.0,1.0,1.0,62944.75,0.0
-13058,15581286.0,McGregor,638.0,Spain,Female,40.0,10.0,0.0,1.0,0.0,0.0,141756.32,1.0
-13059,15768451.0,Hsia,659.0,France,Male,29.0,6.0,0.0,2.0,1.0,1.0,141860.62,0.0
-13060,15637235.0,Frolov,837.0,Spain,Female,48.0,6.0,0.0,1.0,1.0,0.0,112946.67,1.0
-13061,15566380.0,Lucciano,634.0,France,Male,31.0,8.0,0.0,1.0,0.0,1.0,141872.05,0.0
-13062,15785519.0,Lo,578.0,Spain,Female,43.0,1.0,0.0,1.0,1.0,0.0,163473.07,1.0
-13063,15621205.0,Lo,652.0,Germany,Male,47.0,8.0,123692.0,1.0,1.0,0.0,158043.11,1.0
-13064,15581733.0,Ni,568.0,Germany,Male,49.0,4.0,131468.44,1.0,1.0,0.0,186655.11,1.0
-13065,15798964.0,H?,663.0,France,Female,28.0,6.0,0.0,2.0,1.0,1.0,129562.74,0.0
-13066,15743893.0,Gilleland,647.0,France,Male,29.0,5.0,90544.0,2.0,0.0,1.0,87412.24,0.0
-13067,15656029.0,Fanucci,607.0,Spain,Female,45.0,3.0,0.0,1.0,1.0,0.0,4993.94,1.0
-13068,15662494.0,Ch'eng,713.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,107839.03,0.0
-13069,15768006.0,Ts'ui,814.0,France,Male,35.0,2.0,0.0,1.0,1.0,1.0,74850.84,0.0
-13070,15771674.0,Mazzi,731.0,Spain,Male,33.0,5.0,119492.88,1.0,1.0,0.0,177719.88,0.0
-13071,15698028.0,Obielumani,628.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,174921.91,0.0
-13072,15764444.0,Nwankwo,707.0,France,Female,36.0,6.0,0.0,2.0,0.0,1.0,119830.08,0.0
-13073,15697035.0,K'ung,758.0,France,Female,40.0,6.0,96763.89,1.0,1.0,1.0,162960.05,0.0
-13074,15768387.0,Tretiakova,671.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,122960.98,0.0
-13075,15635240.0,Sun,753.0,Germany,Male,29.0,2.0,137657.71,1.0,1.0,1.0,62463.01,0.0
-13076,15808389.0,Zaytseva,516.0,France,Female,23.0,9.0,0.0,2.0,1.0,1.0,160596.93,0.0
-13077,15682758.0,Chikezie,709.0,France,Female,26.0,2.0,0.0,2.0,1.0,0.0,106841.12,0.0
-13078,15684952.0,Nkemjika,754.0,France,Male,60.0,5.0,0.0,1.0,1.0,0.0,184524.65,1.0
-13079,15802368.0,Mazzanti,727.0,Spain,Male,49.0,1.0,0.0,1.0,0.0,0.0,151335.45,1.0
-13080,15806901.0,Obiajulu,713.0,France,Female,41.0,5.0,0.0,1.0,1.0,1.0,14081.64,0.0
-13081,15733111.0,Ch'ang,578.0,Germany,Female,29.0,6.0,141528.88,3.0,0.0,0.0,185561.89,1.0
-13082,15765287.0,Onyekachi,849.0,France,Male,30.0,7.0,0.0,2.0,1.0,1.0,91429.74,0.0
-13083,15785547.0,K?,679.0,Spain,Male,30.0,4.0,0.0,2.0,0.0,1.0,127847.25,0.0
-13084,15770174.0,Nwabugwu,638.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,151335.96,0.0
-13085,15653773.0,Ojiofor,678.0,Germany,Male,35.0,8.0,134571.9,2.0,0.0,1.0,143598.7,0.0
-13086,15771483.0,Pipes,539.0,France,Male,45.0,7.0,0.0,2.0,1.0,0.0,106977.06,1.0
-13087,15776947.0,Onwuamaeze,565.0,France,Female,45.0,10.0,0.0,1.0,1.0,1.0,145558.42,1.0
-13088,15757981.0,Nnaife,638.0,France,Female,35.0,5.0,0.0,1.0,0.0,0.0,136910.18,1.0
-13089,15701765.0,Ugochukwutubelum,574.0,France,Female,34.0,6.0,0.0,2.0,0.0,0.0,142331.85,0.0
-13090,15756936.0,P'eng,589.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,109629.59,0.0
-13091,15716984.0,Chiang,526.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,138526.96,0.0
-13092,15661827.0,Pisano,850.0,France,Female,31.0,4.0,0.0,2.0,1.0,1.0,185547.71,0.0
-13093,15632859.0,H?,554.0,Germany,Male,34.0,3.0,152086.15,2.0,1.0,1.0,48571.14,0.0
-13094,15746662.0,Chia,648.0,France,Female,38.0,4.0,150186.92,1.0,0.0,1.0,130827.97,0.0
-13095,15695097.0,Shih,678.0,France,Female,35.0,6.0,0.0,2.0,1.0,1.0,67830.2,0.0
-13096,15674868.0,Hsiao,676.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,114732.62,0.0
-13097,15751185.0,Nucci,811.0,France,Male,34.0,4.0,0.0,2.0,0.0,1.0,68814.23,0.0
-13098,15671630.0,Ting,593.0,France,Male,27.0,9.0,0.0,2.0,1.0,0.0,95957.09,0.0
-13099,15766183.0,Lucchesi,706.0,France,Male,38.0,8.0,138881.04,1.0,1.0,0.0,172495.81,0.0
-13100,15789548.0,Ch'iu,692.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,123583.85,0.0
-13101,15723884.0,Hsiung,653.0,France,Male,41.0,1.0,138876.06,1.0,1.0,0.0,164816.6,0.0
-13102,15684592.0,Hsing,696.0,France,Male,38.0,0.0,0.0,2.0,1.0,0.0,71433.88,0.0
-13103,15635240.0,Mistry,773.0,France,Male,39.0,9.0,0.0,2.0,1.0,0.0,158086.33,0.0
-13104,15592762.0,Tien,659.0,France,Male,38.0,7.0,0.0,2.0,1.0,1.0,143668.88,0.0
-13105,15637461.0,Onwuamaeze,685.0,France,Male,41.0,9.0,0.0,2.0,0.0,0.0,129826.73,0.0
-13106,15700511.0,Cremonesi,581.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,121681.19,0.0
-13107,15803032.0,Chiang,708.0,Germany,Male,38.0,4.0,99564.22,1.0,1.0,1.0,151822.66,0.0
-13108,15741652.0,Udegbulam,824.0,France,Female,33.0,9.0,125338.8,1.0,1.0,1.0,171035.38,0.0
-13109,15668894.0,T'ao,607.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,1.0,139537.43,0.0
-13110,15632069.0,Nwankwo,586.0,France,Male,35.0,6.0,123545.14,1.0,0.0,0.0,88890.05,0.0
-13111,15587584.0,Hsieh,650.0,France,Male,31.0,9.0,133950.19,1.0,1.0,1.0,158870.28,0.0
-13112,15574886.0,Chiang,841.0,Germany,Female,38.0,9.0,61636.97,2.0,1.0,1.0,108732.96,0.0
-13113,15735078.0,Pisano,537.0,Germany,Male,62.0,1.0,85859.76,1.0,1.0,0.0,44965.54,1.0
-13114,15757926.0,Obiajulu,673.0,France,Male,36.0,7.0,0.0,2.0,0.0,0.0,140745.91,0.0
-13115,15623489.0,Maclean,692.0,Germany,Female,39.0,4.0,103772.45,1.0,1.0,1.0,102908.91,0.0
-13116,15696852.0,Chibugo,581.0,Spain,Female,36.0,6.0,122453.97,1.0,1.0,1.0,176924.21,0.0
-13117,15634310.0,Chiang,766.0,Spain,Female,32.0,1.0,0.0,1.0,1.0,1.0,142516.35,1.0
-13118,15683553.0,Chikezie,721.0,Germany,Male,20.0,6.0,153663.23,1.0,1.0,0.0,51380.9,0.0
-13119,15659736.0,Chin,705.0,France,Female,29.0,9.0,0.0,2.0,1.0,0.0,171457.06,0.0
-13120,15668580.0,Gartrell,569.0,France,Male,36.0,8.0,0.0,2.0,1.0,1.0,182372.35,0.0
-13121,15714586.0,Yermakov,659.0,France,Female,42.0,9.0,0.0,1.0,1.0,0.0,160979.66,1.0
-13122,15689344.0,Kaeppel,594.0,France,Female,39.0,2.0,0.0,1.0,1.0,1.0,62762.03,1.0
-13123,15693764.0,Pugh,730.0,France,Female,60.0,0.0,118097.82,1.0,1.0,0.0,176580.86,1.0
-13124,15636590.0,Biryukova,664.0,France,Male,32.0,5.0,0.0,1.0,1.0,0.0,188426.13,0.0
-13125,15783030.0,Chiu,755.0,France,Male,55.0,7.0,0.0,1.0,1.0,1.0,49553.38,1.0
-13126,15592751.0,P'eng,696.0,Spain,Male,55.0,7.0,0.0,2.0,1.0,1.0,82820.85,0.0
-13127,15586535.0,Pinto,724.0,Spain,Female,40.0,4.0,135962.4,1.0,1.0,0.0,66721.98,1.0
-13128,15635776.0,Ukaegbunam,707.0,France,Male,32.0,5.0,118616.4,1.0,1.0,0.0,87067.73,0.0
-13129,15709256.0,Hs?,596.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,62825.03,0.0
-13130,15694860.0,Chukwubuikem,686.0,Germany,Female,45.0,7.0,129855.32,2.0,1.0,1.0,41970.72,0.0
-13131,15591047.0,Hsing,635.0,France,Male,21.0,8.0,0.0,2.0,0.0,0.0,185210.63,0.0
-13132,15734659.0,O'Brien,604.0,Germany,Female,46.0,4.0,145974.79,1.0,0.0,1.0,179833.45,0.0
-13133,15583480.0,Bazarova,695.0,Germany,Male,41.0,7.0,92526.15,1.0,1.0,1.0,65986.48,0.0
-13134,15658935.0,Hsiung,584.0,Spain,Female,47.0,3.0,0.0,2.0,1.0,0.0,160529.71,1.0
-13135,15786035.0,Cavenagh,641.0,France,Female,32.0,2.0,74323.2,1.0,1.0,0.0,161479.19,0.0
-13136,15757577.0,Ch'ien,659.0,France,Male,42.0,1.0,117325.56,1.0,1.0,1.0,146585.29,1.0
-13137,15690620.0,Smith,521.0,Spain,Male,40.0,8.0,0.0,2.0,1.0,1.0,102284.2,0.0
-13138,15680901.0,Tien,621.0,France,Male,29.0,6.0,0.0,2.0,0.0,0.0,159835.53,0.0
-13139,15659399.0,Walker,643.0,Germany,Male,24.0,3.0,125889.3,1.0,1.0,1.0,147059.16,0.0
-13140,15675316.0,Ojiofor,686.0,France,Female,31.0,9.0,0.0,2.0,1.0,1.0,121747.96,0.0
-13141,15665521.0,Chinweike,651.0,Germany,Male,36.0,5.0,132767.66,2.0,1.0,0.0,168080.53,0.0
-13142,15749583.0,Iadanza,758.0,Spain,Female,60.0,1.0,0.0,1.0,1.0,1.0,167032.99,1.0
-13143,15778385.0,McElhone,613.0,Germany,Male,42.0,5.0,137841.53,2.0,1.0,1.0,138536.09,0.0
-13144,15782659.0,Hsia,709.0,France,Female,59.0,4.0,0.0,1.0,1.0,0.0,55775.72,1.0
-13145,15728082.0,Chiemenam,691.0,France,Male,43.0,8.0,0.0,2.0,1.0,1.0,121747.92,0.0
-13146,15694300.0,Smith,562.0,France,Male,41.0,4.0,0.0,2.0,1.0,1.0,10656.89,0.0
-13147,15647301.0,Hsiung,618.0,Spain,Female,37.0,2.0,104930.38,1.0,1.0,0.0,61820.41,0.0
-13148,15772383.0,Fiorentini,525.0,France,Female,26.0,1.0,0.0,2.0,0.0,0.0,136859.62,0.0
-13149,15694958.0,Onwuamaeze,557.0,Germany,Female,65.0,3.0,113882.73,1.0,1.0,1.0,100538.8,1.0
-13150,15696733.0,Ch'ien,807.0,France,Female,63.0,5.0,0.0,2.0,1.0,1.0,47764.99,0.0
-13151,15775293.0,Udinesi,581.0,France,Female,39.0,6.0,139542.86,1.0,1.0,1.0,70774.97,0.0
-13152,15592386.0,Ch'ang,689.0,France,Male,31.0,6.0,0.0,2.0,1.0,1.0,122449.18,0.0
-13153,15733261.0,Ndukaku,751.0,France,Female,30.0,5.0,0.0,2.0,1.0,0.0,171096.2,0.0
-13154,15704053.0,Zaytseva,632.0,France,Female,41.0,9.0,0.0,2.0,1.0,0.0,170819.01,0.0
-13155,15602497.0,Palerma,692.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,65769.3,0.0
-13156,15798885.0,Onwuamaeze,588.0,France,Female,35.0,9.0,0.0,2.0,1.0,0.0,96444.88,0.0
-13157,15644724.0,Hung,738.0,France,Male,46.0,7.0,0.0,1.0,1.0,0.0,93769.89,1.0
-13158,15675320.0,Cremonesi,577.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,95860.03,0.0
-13159,15691457.0,Fiorentini,639.0,France,Female,25.0,9.0,121927.92,1.0,1.0,1.0,110273.84,0.0
-13160,15588695.0,Milanesi,592.0,France,Male,31.0,4.0,0.0,1.0,1.0,0.0,151335.24,0.0
-13161,15683544.0,Hsu,629.0,Germany,Male,40.0,3.0,140694.78,2.0,1.0,1.0,157547.71,0.0
-13162,15672610.0,Hsueh,682.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,47775.34,0.0
-13163,15609562.0,Chienezie,599.0,Spain,Male,26.0,7.0,0.0,2.0,1.0,1.0,154844.22,0.0
-13164,15624641.0,Nnonso,772.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,84760.32,0.0
-13165,15797733.0,Ch'iu,587.0,Spain,Male,36.0,4.0,116473.65,1.0,1.0,1.0,136859.86,0.0
-13166,15679909.0,Ch'in,575.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,71548.96,0.0
-13167,15577126.0,Chiu,687.0,Germany,Male,33.0,6.0,125845.66,2.0,1.0,0.0,147963.07,0.0
-13168,15804937.0,Chiang,633.0,Germany,Female,32.0,7.0,118028.36,1.0,0.0,1.0,175015.12,1.0
-13169,15604797.0,Yobanna,705.0,Germany,Female,41.0,9.0,90727.79,2.0,1.0,1.0,735.79,0.0
-13170,15612103.0,Genovesi,549.0,France,Female,29.0,7.0,93723.28,1.0,1.0,1.0,108665.89,1.0
-13171,15620284.0,P'an,756.0,Spain,Male,27.0,7.0,0.0,2.0,0.0,1.0,93769.89,0.0
-13172,15770309.0,Chiang,616.0,Germany,Female,37.0,9.0,81564.1,1.0,1.0,1.0,51959.82,0.0
-13173,15782591.0,Iadanza,749.0,France,Male,28.0,9.0,0.0,2.0,0.0,0.0,79792.4,0.0
-13174,15686963.0,Chukwumaobim,679.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,131646.36,0.0
-13175,15578980.0,Nwachukwu,598.0,France,Male,45.0,5.0,0.0,2.0,1.0,0.0,119969.99,0.0
-13176,15692406.0,Okwukwe,706.0,Germany,Male,35.0,3.0,127924.25,1.0,1.0,1.0,82696.84,0.0
-13177,15627830.0,Fanucci,668.0,France,Female,28.0,4.0,0.0,2.0,1.0,0.0,49826.68,0.0
-13178,15661409.0,Cattaneo,850.0,France,Male,31.0,6.0,0.0,2.0,1.0,0.0,26257.01,0.0
-13179,15622985.0,Ch'iu,730.0,Spain,Female,43.0,8.0,0.0,1.0,1.0,0.0,143315.57,0.0
-13180,15682547.0,Milanesi,687.0,France,Male,27.0,2.0,0.0,2.0,1.0,0.0,36108.5,0.0
-13181,15707128.0,Ch'ang,645.0,Germany,Male,36.0,6.0,151933.63,1.0,0.0,0.0,167033.83,0.0
-13182,15753337.0,Chukwudi,712.0,Germany,Female,32.0,4.0,105261.76,1.0,1.0,1.0,53717.02,0.0
-13183,15677955.0,Ch'ien,738.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,74838.64,0.0
-13184,15665088.0,Wright,667.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,0.0,135630.32,0.0
-13185,15792525.0,Ukaegbunam,551.0,France,Male,45.0,3.0,0.0,2.0,1.0,1.0,100183.05,0.0
-13186,15768072.0,Ch'ien,684.0,France,Male,41.0,10.0,0.0,2.0,1.0,1.0,58440.45,0.0
-13187,15623649.0,Chu,617.0,Germany,Female,65.0,9.0,120849.29,1.0,1.0,1.0,152298.28,1.0
-13188,15614302.0,Trevisani,718.0,Germany,Female,38.0,6.0,129949.49,2.0,1.0,0.0,92811.2,0.0
-13189,15760090.0,L?,694.0,France,Male,35.0,10.0,0.0,2.0,1.0,0.0,181224.56,0.0
-13190,15756472.0,Sagese,648.0,Germany,Male,28.0,8.0,108128.96,2.0,1.0,0.0,62810.91,0.0
-13191,15612092.0,Folliero,622.0,France,Female,41.0,10.0,0.0,1.0,1.0,0.0,63795.8,0.0
-13192,15671514.0,Palermo,535.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,131281.28,0.0
-13193,15682022.0,Ndubueze,684.0,France,Female,48.0,4.0,0.0,1.0,0.0,0.0,75969.19,1.0
-13194,15596713.0,Hsiung,567.0,Spain,Male,35.0,0.0,155973.88,1.0,1.0,1.0,100834.22,0.0
-13195,15672091.0,Palermo,731.0,France,Male,31.0,0.0,0.0,2.0,0.0,1.0,93814.41,0.0
-13196,15672826.0,Rowntree,584.0,France,Male,42.0,9.0,0.0,1.0,1.0,0.0,137898.57,1.0
-13197,15742210.0,Kao,813.0,France,Female,39.0,8.0,0.0,1.0,1.0,0.0,177092.16,1.0
-13198,15643496.0,Onyekaozulu,553.0,Germany,Male,37.0,6.0,146034.74,1.0,1.0,1.0,133814.28,0.0
-13199,15609998.0,Chiu,738.0,Germany,Female,51.0,5.0,134919.85,1.0,1.0,0.0,93739.71,1.0
-13200,15771087.0,Ritchie,652.0,France,Female,47.0,6.0,0.0,1.0,1.0,0.0,163666.85,1.0
-13201,15712403.0,P'an,703.0,France,Male,29.0,5.0,141803.77,1.0,1.0,0.0,171587.9,0.0
-13202,15597901.0,Genovese,753.0,Germany,Male,49.0,1.0,107949.31,1.0,1.0,1.0,645.61,0.0
-13203,15796074.0,Nkemdirim,705.0,Spain,Female,29.0,5.0,109542.29,1.0,1.0,0.0,125083.29,0.0
-13204,15761158.0,Mazzanti,695.0,Germany,Male,37.0,4.0,122021.92,2.0,1.0,1.0,94047.75,0.0
-13205,15807961.0,Martin,762.0,France,Female,51.0,10.0,0.0,1.0,1.0,0.0,136519.94,1.0
-13206,15593973.0,Hsia,713.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,142839.94,0.0
-13207,15649078.0,Bess,624.0,Spain,Male,34.0,0.0,0.0,1.0,1.0,1.0,175894.24,0.0
-13208,15718247.0,Yobanna,632.0,France,Male,43.0,3.0,0.0,2.0,1.0,0.0,62733.05,0.0
-13209,15592999.0,Onyekachi,545.0,France,Male,45.0,3.0,0.0,2.0,1.0,1.0,126153.24,0.0
-13210,15682533.0,Okwudiliolisa,708.0,France,Female,48.0,4.0,0.0,2.0,1.0,1.0,167723.25,1.0
-13211,15662397.0,Hs?,642.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,57517.71,0.0
-13212,15722090.0,Onuora,645.0,Germany,Female,33.0,2.0,84560.04,1.0,0.0,1.0,142911.99,1.0
-13213,15632448.0,Ch'eng,613.0,France,Male,46.0,7.0,169831.46,1.0,1.0,0.0,152733.89,1.0
-13214,15762048.0,Ts'ao,607.0,France,Male,32.0,7.0,0.0,2.0,1.0,1.0,83052.03,0.0
-13215,15634146.0,Tsou,714.0,France,Female,36.0,9.0,0.0,2.0,0.0,0.0,10963.04,0.0
-13216,15693322.0,Ewen,681.0,Germany,Female,48.0,0.0,150186.22,1.0,1.0,1.0,178822.31,0.0
-13217,15589329.0,Lai,635.0,France,Female,26.0,1.0,0.0,2.0,1.0,1.0,173321.65,0.0
-13218,15723786.0,Ch'iu,598.0,Germany,Female,45.0,6.0,111900.14,1.0,1.0,0.0,121084.27,1.0
-13219,15626383.0,T'ao,577.0,France,Female,41.0,7.0,0.0,2.0,0.0,0.0,127543.81,0.0
-13220,15596074.0,Udinesi,758.0,Germany,Female,55.0,9.0,116923.81,1.0,1.0,0.0,170360.11,1.0
-13221,15749365.0,DeRose,693.0,France,Male,23.0,8.0,0.0,2.0,1.0,0.0,175893.53,0.0
-13222,15699195.0,Kambinachi,605.0,Spain,Female,30.0,2.0,0.0,2.0,1.0,0.0,137136.29,0.0
-13223,15592999.0,Young,641.0,France,Male,44.0,6.0,0.0,2.0,0.0,0.0,77906.87,0.0
-13224,15662091.0,Nucci,537.0,Germany,Male,40.0,9.0,157780.93,1.0,1.0,0.0,5983.7,1.0
-13225,15665579.0,Chikelu,710.0,France,Female,31.0,7.0,0.0,1.0,1.0,1.0,158036.72,0.0
-13226,15744983.0,Mancini,707.0,France,Male,37.0,2.0,0.0,1.0,1.0,0.0,80134.65,0.0
-13227,15659710.0,Walker,637.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,51536.99,0.0
-13228,15757837.0,Chiekwugo,724.0,France,Female,58.0,3.0,0.0,1.0,1.0,0.0,126307.25,1.0
-13229,15739271.0,Ch'iu,641.0,France,Female,43.0,4.0,0.0,2.0,1.0,0.0,162599.51,0.0
-13230,15777896.0,Onwuamaeze,686.0,France,Female,34.0,5.0,0.0,2.0,0.0,0.0,73365.41,0.0
-13231,15780128.0,Lo Duca,683.0,France,Female,40.0,8.0,0.0,2.0,1.0,0.0,170903.4,0.0
-13232,15643658.0,Goliwe,668.0,France,Female,37.0,9.0,0.0,1.0,1.0,1.0,51959.82,0.0
-13233,15691875.0,Mazzi,646.0,France,Male,45.0,5.0,0.0,1.0,1.0,1.0,180932.46,1.0
-13234,15591135.0,Chin,677.0,France,Male,47.0,4.0,0.0,2.0,1.0,0.0,88827.67,1.0
-13235,15694852.0,Tuan,594.0,Germany,Female,39.0,5.0,109740.62,1.0,1.0,1.0,137794.63,1.0
-13236,15705514.0,Nwankwo,683.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,183566.78,0.0
-13237,15803749.0,Palerma,594.0,Spain,Male,22.0,5.0,0.0,2.0,0.0,1.0,100929.59,0.0
-13238,15723884.0,Chinedum,639.0,Germany,Male,33.0,2.0,123674.28,1.0,1.0,1.0,94875.04,0.0
-13239,15589435.0,Chikelu,729.0,France,Male,46.0,1.0,0.0,2.0,1.0,1.0,177065.24,0.0
-13240,15752912.0,Yobanna,614.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,0.0,161409.66,0.0
-13241,15759480.0,Macleod,644.0,France,Male,37.0,6.0,108862.76,1.0,1.0,0.0,61792.76,0.0
-13242,15804052.0,T'ien,678.0,France,Female,31.0,7.0,0.0,2.0,1.0,0.0,161671.15,0.0
-13243,15692317.0,Teng,608.0,France,Male,39.0,2.0,0.0,2.0,0.0,1.0,177092.16,0.0
-13244,15686461.0,Nucci,588.0,Spain,Male,31.0,1.0,100116.82,1.0,1.0,1.0,60552.44,0.0
-13245,15795878.0,Iloerika,614.0,Spain,Female,40.0,3.0,91622.42,1.0,1.0,1.0,126812.63,0.0
-13246,15588955.0,Kuo,697.0,Germany,Male,63.0,6.0,133889.35,1.0,1.0,1.0,119818.17,0.0
-13247,15589969.0,Nnaife,683.0,France,Female,37.0,8.0,139432.37,1.0,1.0,1.0,19244.58,0.0
-13248,15793049.0,Ch'en,582.0,France,Male,39.0,3.0,0.0,2.0,1.0,1.0,133831.37,0.0
-13249,15793856.0,Nwabugwu,628.0,Germany,Male,43.0,4.0,121161.57,1.0,1.0,0.0,166468.21,1.0
-13250,15683544.0,Hs?eh,537.0,Germany,Male,37.0,9.0,148363.38,1.0,1.0,0.0,70899.27,0.0
-13251,15743871.0,Chia,596.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,160129.99,0.0
-13252,15799031.0,T'ang,710.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,124592.84,0.0
-13253,15763063.0,Ch'ien,648.0,Spain,Female,27.0,6.0,0.0,2.0,1.0,1.0,67734.79,0.0
-13254,15730137.0,Ch'eng,635.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,163587.9,0.0
-13255,15673929.0,Hsueh,651.0,France,Female,29.0,4.0,0.0,2.0,0.0,1.0,141035.65,0.0
-13256,15688963.0,Chukwukere,644.0,Germany,Female,46.0,3.0,123971.51,1.0,1.0,0.0,180594.34,1.0
-13257,15749489.0,Aitken,850.0,France,Female,33.0,6.0,0.0,2.0,1.0,1.0,96658.26,0.0
-13258,15674541.0,Ting,704.0,France,Female,37.0,3.0,0.0,2.0,1.0,1.0,100816.29,0.0
-13259,15597949.0,Tsai,649.0,France,Male,43.0,1.0,0.0,3.0,1.0,0.0,178458.86,1.0
-13260,15762708.0,Liao,549.0,France,Male,39.0,5.0,123935.35,1.0,1.0,0.0,199661.5,0.0
-13261,15652948.0,Chiu,706.0,Spain,Male,38.0,9.0,0.0,1.0,1.0,0.0,180553.48,0.0
-13262,15610594.0,Chin,598.0,France,Male,38.0,0.0,0.0,2.0,1.0,0.0,171070.43,0.0
-13263,15600379.0,Cattaneo,684.0,France,Male,36.0,5.0,0.0,2.0,1.0,0.0,35179.18,0.0
-13264,15584766.0,Hsueh,690.0,Germany,Male,45.0,3.0,145731.83,1.0,1.0,1.0,135650.72,1.0
-13265,15739168.0,Tung,695.0,Germany,Male,42.0,4.0,79715.36,1.0,1.0,1.0,7698.6,0.0
-13266,15701166.0,Mazzanti,725.0,France,Male,35.0,2.0,89750.21,1.0,0.0,1.0,118324.75,0.0
-13267,15738219.0,Nkemakolam,596.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,175025.27,0.0
-13268,15690743.0,Teng,691.0,Spain,Female,48.0,5.0,0.0,2.0,1.0,0.0,71139.31,0.0
-13269,15673342.0,O'Loughlin,697.0,France,Male,37.0,2.0,0.0,2.0,1.0,1.0,142963.26,0.0
-13270,15797960.0,Okwuoma,667.0,France,Male,40.0,7.0,178056.54,1.0,1.0,1.0,99298.8,0.0
-13271,15794566.0,Shubina,653.0,France,Female,28.0,4.0,0.0,1.0,1.0,1.0,122218.81,0.0
-13272,15694518.0,Genovese,710.0,France,Female,41.0,0.0,0.0,2.0,0.0,1.0,182738.0,0.0
-13273,15733883.0,T'ien,685.0,France,Female,47.0,4.0,0.0,2.0,1.0,1.0,78858.07,0.0
-13274,15776985.0,Ch'in,597.0,Spain,Male,37.0,4.0,0.0,2.0,1.0,1.0,4696.44,0.0
-13275,15729019.0,Pirozzi,619.0,France,Male,41.0,0.0,0.0,1.0,0.0,0.0,65720.92,1.0
-13276,15628558.0,Ch'iu,710.0,Spain,Female,30.0,3.0,0.0,2.0,0.0,0.0,107839.03,0.0
-13277,15694518.0,Chinomso,681.0,Germany,Female,39.0,9.0,91955.61,1.0,1.0,0.0,183535.24,0.0
-13278,15813192.0,Scott,679.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,58854.88,0.0
-13279,15759717.0,Frolov,845.0,France,Male,36.0,2.0,0.0,1.0,1.0,0.0,118469.38,0.0
-13280,15723894.0,T'ien,592.0,France,Female,44.0,4.0,53772.73,1.0,1.0,1.0,71519.4,0.0
-13281,15790958.0,Onyeorulu,643.0,France,Female,40.0,5.0,0.0,2.0,1.0,1.0,807.06,0.0
-13282,15803764.0,Fennell,695.0,Germany,Male,36.0,1.0,108262.84,2.0,1.0,0.0,183646.41,0.0
-13283,15662021.0,Padovesi,565.0,France,Female,31.0,8.0,0.0,1.0,0.0,1.0,4497.55,0.0
-13284,15626143.0,Nwabugwu,605.0,Spain,Male,23.0,8.0,0.0,2.0,1.0,1.0,184842.37,0.0
-13285,15716800.0,Y?an,678.0,France,Male,43.0,3.0,0.0,1.0,1.0,0.0,56908.41,0.0
-13286,15651664.0,Toscani,669.0,France,Male,21.0,0.0,152264.81,1.0,1.0,1.0,119817.19,0.0
-13287,15699195.0,Chuang,748.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,93835.64,0.0
-13288,15591248.0,Obidimkpa,633.0,France,Female,29.0,7.0,122373.52,1.0,1.0,1.0,172363.41,0.0
-13289,15777873.0,Y?,682.0,France,Male,47.0,1.0,0.0,1.0,0.0,1.0,104079.62,0.0
-13290,15756481.0,Ibezimako,713.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,178393.68,0.0
-13291,15593468.0,Ch'iu,693.0,France,Male,39.0,7.0,0.0,2.0,1.0,1.0,162443.05,0.0
-13292,15572767.0,Chukwudi,775.0,France,Female,33.0,6.0,121355.99,1.0,1.0,0.0,141152.28,0.0
-13293,15632859.0,Chienezie,699.0,France,Female,33.0,8.0,125437.86,1.0,1.0,1.0,119374.74,0.0
-13294,15605742.0,T'ien,628.0,France,Female,49.0,4.0,0.0,2.0,1.0,0.0,126281.83,0.0
-13295,15710366.0,Macleod,705.0,France,Female,24.0,4.0,0.0,2.0,0.0,0.0,173989.67,0.0
-13296,15652693.0,Rizzo,682.0,Germany,Male,34.0,10.0,122570.87,2.0,1.0,0.0,130805.54,0.0
-13297,15633260.0,Onuchukwu,619.0,France,Female,31.0,6.0,0.0,2.0,1.0,0.0,77511.45,0.0
-13298,15796834.0,Frolov,488.0,France,Male,49.0,7.0,0.0,1.0,1.0,0.0,134878.19,1.0
-13299,15576623.0,Udegbulam,707.0,France,Male,46.0,5.0,0.0,3.0,1.0,1.0,147964.99,1.0
-13300,15784594.0,Ch'ien,850.0,Spain,Male,31.0,3.0,0.0,2.0,1.0,0.0,140889.95,0.0
-13301,15738174.0,Oluchukwu,583.0,France,Male,30.0,5.0,0.0,1.0,1.0,1.0,122433.09,0.0
-13302,15684198.0,Toscani,498.0,France,Male,29.0,2.0,0.0,1.0,0.0,0.0,199484.96,1.0
-13303,15700212.0,Chiu,554.0,France,Male,39.0,3.0,0.0,1.0,1.0,0.0,76515.79,0.0
-13304,15756560.0,Teng,713.0,France,Male,72.0,2.0,0.0,1.0,1.0,1.0,66561.6,0.0
-13305,15795527.0,Ch'iu,565.0,France,Male,38.0,2.0,137936.94,1.0,1.0,1.0,113909.66,0.0
-13306,15790717.0,Toscani,586.0,Spain,Male,36.0,7.0,0.0,2.0,1.0,0.0,172097.4,0.0
-13307,15662291.0,Palermo,644.0,France,Female,46.0,2.0,0.0,1.0,0.0,0.0,138526.96,1.0
-13308,15752081.0,Tien,743.0,France,Male,56.0,5.0,0.0,1.0,1.0,0.0,40333.94,1.0
-13309,15775238.0,Onochie,497.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,151925.25,0.0
-13310,15581548.0,Folliero,587.0,France,Female,37.0,0.0,0.0,2.0,0.0,0.0,93883.53,0.0
-13311,15737051.0,Tsou,613.0,Germany,Female,27.0,1.0,151607.56,2.0,1.0,0.0,103931.49,0.0
-13312,15728505.0,Okorie,654.0,France,Male,38.0,5.0,0.0,1.0,0.0,0.0,173365.89,0.0
-13313,15762351.0,Hsia,703.0,Spain,Male,28.0,5.0,0.0,1.0,0.0,0.0,180595.02,0.0
-13314,15806822.0,Folliero,698.0,Spain,Female,39.0,1.0,0.0,1.0,1.0,0.0,60916.56,0.0
-13315,15778089.0,Zetticci,642.0,France,Male,46.0,4.0,122471.09,1.0,1.0,1.0,137720.56,0.0
-13316,15572073.0,Ma,563.0,France,Male,44.0,4.0,0.0,1.0,1.0,0.0,136079.64,1.0
-13317,15641110.0,O'Donnell,667.0,Spain,Male,31.0,8.0,0.0,1.0,0.0,0.0,105618.14,0.0
-13318,15752588.0,Nwachinemelu,753.0,Spain,Male,33.0,9.0,0.0,2.0,1.0,0.0,55775.84,0.0
-13319,15777076.0,Teng,595.0,France,Male,33.0,8.0,0.0,2.0,1.0,0.0,151924.9,0.0
-13320,15602851.0,Clements,731.0,France,Female,45.0,4.0,0.0,2.0,1.0,0.0,103726.71,1.0
-13321,15585067.0,Trevisani,576.0,Spain,Male,38.0,9.0,152683.54,1.0,0.0,1.0,63736.17,0.0
-13322,15702442.0,Yobanna,805.0,Spain,Female,35.0,7.0,0.0,1.0,1.0,0.0,177065.24,0.0
-13323,15761706.0,Chia,632.0,France,Female,44.0,4.0,0.0,1.0,1.0,0.0,112269.67,0.0
-13324,15781495.0,Babbage,743.0,France,Male,35.0,5.0,0.0,1.0,1.0,0.0,126675.62,0.0
-13325,15615473.0,Nnamdi,681.0,Germany,Male,35.0,2.0,135953.97,2.0,0.0,1.0,173486.39,0.0
-13326,15742009.0,Ch'iu,678.0,Germany,Female,43.0,2.0,110153.33,1.0,1.0,1.0,179206.92,1.0
-13327,15640161.0,Tuan,535.0,Spain,Male,26.0,5.0,0.0,2.0,1.0,0.0,125010.24,0.0
-13328,15791053.0,Ch'iu,663.0,Germany,Male,33.0,7.0,152212.88,2.0,1.0,0.0,132298.52,0.0
-13329,15794566.0,Hsieh,673.0,France,Male,47.0,3.0,0.0,2.0,1.0,1.0,124489.88,0.0
-13330,15592336.0,K?,632.0,France,Male,26.0,10.0,173590.61,1.0,0.0,1.0,168544.85,0.0
-13331,15662578.0,Shih,683.0,France,Female,57.0,4.0,0.0,2.0,1.0,0.0,167984.72,1.0
-13332,15743617.0,Chibugo,658.0,Spain,Male,23.0,8.0,0.0,2.0,1.0,0.0,73669.94,0.0
-13333,15620505.0,Young,630.0,France,Female,34.0,9.0,0.0,2.0,0.0,0.0,90876.95,0.0
-13334,15804009.0,Chiang,794.0,France,Female,42.0,4.0,0.0,2.0,1.0,1.0,141180.66,0.0
-13335,15716085.0,Palerma,614.0,Spain,Female,40.0,2.0,0.0,1.0,1.0,1.0,143360.56,0.0
-13336,15589401.0,H?,738.0,France,Female,36.0,3.0,0.0,1.0,1.0,1.0,174738.2,0.0
-13337,15732644.0,Ch'ien,579.0,France,Male,40.0,9.0,0.0,1.0,1.0,1.0,93826.31,0.0
-13338,15628963.0,Frolova,556.0,Germany,Female,52.0,2.0,102800.65,1.0,1.0,0.0,176883.42,1.0
-13339,15771086.0,Maclean,735.0,France,Male,43.0,4.0,0.0,2.0,1.0,0.0,93835.64,0.0
-13340,15793331.0,Onyekachi,692.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,138889.15,0.0
-13341,15782879.0,Ts'ui,559.0,France,Male,39.0,7.0,145988.65,1.0,1.0,0.0,80416.16,0.0
-13342,15778915.0,Napolitano,679.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,97465.44,0.0
-13343,15662494.0,T'ien,642.0,France,Female,34.0,0.0,141359.37,1.0,1.0,1.0,126761.2,0.0
-13344,15731760.0,Smith,607.0,Spain,Female,53.0,3.0,0.0,1.0,0.0,1.0,82900.25,1.0
-13345,15597909.0,Yeh,721.0,Spain,Female,40.0,5.0,63723.53,1.0,1.0,0.0,142963.29,1.0
-13346,15624186.0,Rolon,706.0,Germany,Female,46.0,4.0,129433.38,3.0,0.0,0.0,175310.38,1.0
-13347,15794414.0,Ndubuagha,646.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,164816.6,0.0
-13348,15759499.0,Tretiakova,735.0,Germany,Male,59.0,3.0,121540.26,3.0,1.0,1.0,94739.2,1.0
-13349,15622083.0,P'eng,629.0,France,Female,60.0,1.0,0.0,2.0,1.0,0.0,160589.15,1.0
-13350,15696900.0,Lai,838.0,Germany,Male,28.0,8.0,102785.16,2.0,1.0,1.0,47951.12,0.0
-13351,15667971.0,Dickson,579.0,Germany,Male,31.0,2.0,141356.47,2.0,1.0,0.0,184856.89,0.0
-13352,15801256.0,Hsiung,586.0,France,Male,49.0,5.0,0.0,1.0,1.0,1.0,134626.2,1.0
-13353,15668289.0,Chizoba,730.0,France,Male,35.0,1.0,127286.04,1.0,1.0,0.0,127549.6,0.0
-13354,15793331.0,Macleod,562.0,Spain,Female,31.0,6.0,0.0,2.0,1.0,1.0,112781.5,0.0
-13355,15568104.0,Baresi,577.0,Germany,Male,44.0,6.0,121063.8,1.0,1.0,1.0,85411.01,0.0
-13356,15694556.0,T'ang,632.0,France,Male,38.0,5.0,0.0,2.0,0.0,0.0,148087.62,0.0
-13357,15769353.0,Ch'iu,628.0,Germany,Female,43.0,6.0,133537.7,1.0,0.0,1.0,185547.71,1.0
-13358,15792360.0,Nnanna,700.0,Spain,Female,29.0,7.0,0.0,2.0,1.0,1.0,176962.41,0.0
-13359,15746662.0,Shih,637.0,Germany,Male,42.0,9.0,133432.59,1.0,1.0,0.0,49653.01,0.0
-13360,15635942.0,Palerma,557.0,France,Male,56.0,5.0,0.0,2.0,1.0,0.0,80552.11,1.0
-13361,15671630.0,Palerma,636.0,Spain,Male,44.0,6.0,89416.99,1.0,1.0,1.0,18364.19,1.0
-13362,15754068.0,Chukwufumnanya,596.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,117038.96,0.0
-13363,15578788.0,Ritchie,579.0,Spain,Male,38.0,7.0,0.0,1.0,1.0,0.0,55763.66,0.0
-13364,15793883.0,Ts'ao,505.0,France,Male,20.0,6.0,0.0,2.0,1.0,1.0,59249.83,0.0
-13365,15603743.0,Kao,679.0,Germany,Male,35.0,7.0,99092.45,2.0,0.0,1.0,123587.85,0.0
-13366,15704910.0,Lo,571.0,France,Female,37.0,1.0,0.0,2.0,1.0,1.0,152956.33,0.0
-13367,15654074.0,Fan,581.0,Germany,Female,31.0,5.0,121669.93,2.0,1.0,0.0,123977.86,0.0
-13368,15796467.0,Ch'eng,632.0,France,Male,45.0,1.0,98897.1,1.0,1.0,0.0,187985.85,1.0
-13369,15679963.0,O'Loghlen,683.0,France,Female,38.0,4.0,0.0,2.0,1.0,0.0,57762.0,0.0
-13370,15772781.0,Ch'in,596.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,35786.76,0.0
-13371,15570194.0,Mazzanti,698.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,92320.37,0.0
-13372,15671137.0,Maclean,734.0,Germany,Male,32.0,3.0,169902.92,1.0,1.0,0.0,126535.92,0.0
-13373,15756875.0,Piazza,648.0,Spain,Female,35.0,7.0,0.0,2.0,1.0,0.0,167984.72,0.0
-13374,15578825.0,Lo Duca,683.0,France,Female,38.0,10.0,0.0,1.0,1.0,0.0,181718.73,1.0
-13375,15636756.0,Chukwujamuike,581.0,Spain,Male,47.0,9.0,0.0,1.0,1.0,0.0,125986.18,0.0
-13376,15771855.0,Uspenskaya,682.0,Spain,Female,39.0,5.0,0.0,2.0,0.0,0.0,180969.55,0.0
-13377,15691808.0,Chin,597.0,France,Female,28.0,5.0,0.0,2.0,1.0,1.0,82796.29,0.0
-13378,15674868.0,Nnaife,748.0,Germany,Female,49.0,3.0,143258.85,1.0,0.0,0.0,158974.45,1.0
-13379,15795519.0,Li,648.0,France,Male,31.0,9.0,0.0,2.0,0.0,1.0,79240.9,0.0
-13380,15574068.0,Shubin,687.0,France,Male,29.0,2.0,0.0,2.0,1.0,0.0,167873.49,0.0
-13381,15626114.0,Oluchukwu,724.0,France,Female,32.0,9.0,0.0,2.0,0.0,1.0,118313.36,0.0
-13382,15694444.0,Onwumelu,584.0,France,Male,28.0,6.0,0.0,1.0,1.0,0.0,171413.66,0.0
-13383,15779581.0,Chimaoke,595.0,France,Female,39.0,2.0,0.0,2.0,1.0,0.0,176450.19,0.0
-13384,15756475.0,Lucchesi,646.0,France,Male,31.0,7.0,119963.45,1.0,1.0,1.0,157097.09,0.0
-13385,15810563.0,T'ao,637.0,France,Male,37.0,5.0,106171.68,1.0,1.0,1.0,54141.5,0.0
-13386,15668521.0,Hs?,652.0,France,Female,33.0,4.0,111098.79,1.0,0.0,1.0,120540.83,0.0
-13387,15800746.0,Chibuzo,634.0,France,Male,40.0,9.0,0.0,1.0,1.0,1.0,162697.93,0.0
-13388,15794048.0,Zetticci,709.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,172576.56,0.0
-13389,15715003.0,Milano,733.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,114514.64,0.0
-13390,15592078.0,Chu,683.0,Spain,Female,39.0,9.0,0.0,2.0,1.0,0.0,187915.7,0.0
-13391,15812197.0,Iloerika,767.0,France,Male,31.0,8.0,0.0,1.0,1.0,0.0,183566.85,0.0
-13392,15705458.0,Chinedum,646.0,France,Female,29.0,4.0,131354.37,1.0,1.0,0.0,182038.6,0.0
-13393,15586974.0,Lu,706.0,France,Female,52.0,9.0,0.0,1.0,1.0,0.0,52301.15,1.0
-13394,15632439.0,Iweobiegbunam,526.0,France,Male,38.0,9.0,128226.37,1.0,1.0,1.0,83629.6,0.0
-13395,15783522.0,Folliero,607.0,Germany,Male,39.0,8.0,106070.89,3.0,1.0,0.0,118059.19,1.0
-13396,15771674.0,Nnamutaezinwa,739.0,Germany,Male,39.0,7.0,131778.08,1.0,1.0,0.0,180736.24,0.0
-13397,15593468.0,Ch'iu,730.0,Spain,Male,28.0,2.0,0.0,2.0,1.0,1.0,108008.65,0.0
-13398,15736250.0,Chiang,697.0,France,Male,36.0,4.0,0.0,1.0,1.0,1.0,121551.58,0.0
-13399,15646616.0,Yevseyev,646.0,Spain,Male,21.0,7.0,0.0,2.0,1.0,1.0,82565.01,0.0
-13400,15578878.0,K'ung,635.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,1.0,131473.31,0.0
-13401,15661409.0,Ch'eng,543.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,55725.04,0.0
-13402,15749669.0,Macdonald,714.0,France,Male,52.0,7.0,0.0,1.0,1.0,0.0,111590.35,1.0
-13403,15706232.0,Hsueh,724.0,France,Male,27.0,4.0,0.0,2.0,1.0,0.0,120880.72,0.0
-13404,15588948.0,Mancini,697.0,Spain,Male,38.0,7.0,0.0,2.0,1.0,1.0,93881.75,0.0
-13405,15656426.0,Okeke,774.0,Spain,Female,36.0,4.0,0.0,2.0,1.0,0.0,127749.61,0.0
-13406,15702801.0,Cattaneo,597.0,France,Female,39.0,9.0,0.0,1.0,1.0,0.0,132436.71,1.0
-13407,15793723.0,Chukwudi,714.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,153917.54,0.0
-13408,15704586.0,Calabrese,652.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,129991.66,0.0
-13409,15638247.0,Maclean,661.0,France,Male,38.0,0.0,0.0,2.0,1.0,1.0,152567.75,0.0
-13410,15636562.0,Rizzo,543.0,France,Male,35.0,7.0,0.0,1.0,1.0,0.0,10771.46,1.0
-13411,15663817.0,Hao,651.0,Germany,Female,34.0,5.0,92636.96,2.0,0.0,0.0,75860.01,0.0
-13412,15767794.0,Harker,582.0,Germany,Male,30.0,8.0,122453.97,1.0,1.0,0.0,167984.61,0.0
-13413,15757895.0,Kudryashova,681.0,France,Female,26.0,3.0,127638.34,1.0,0.0,1.0,162734.31,0.0
-13414,15743759.0,Yobanna,644.0,France,Male,41.0,1.0,0.0,2.0,1.0,0.0,152740.16,0.0
-13415,15711219.0,Ritchie,743.0,Germany,Female,64.0,4.0,131766.71,1.0,0.0,0.0,123982.14,1.0
-13416,15576935.0,Nnanna,657.0,France,Female,26.0,0.0,0.0,1.0,0.0,1.0,164741.5,1.0
-13417,15672397.0,Clements,584.0,Spain,Male,38.0,6.0,0.0,2.0,1.0,0.0,71179.31,0.0
-13418,15667971.0,Walker,615.0,Germany,Male,31.0,5.0,145973.88,2.0,1.0,1.0,65612.12,0.0
-13419,15757537.0,Lu,616.0,France,Male,41.0,6.0,0.0,2.0,1.0,0.0,131301.74,0.0
-13420,15673584.0,Tuan,850.0,Germany,Female,39.0,6.0,103391.38,2.0,1.0,0.0,60905.51,0.0
-13421,15633474.0,Nwachukwu,628.0,Germany,Male,32.0,2.0,161525.96,1.0,1.0,1.0,100919.19,0.0
-13422,15595588.0,Koo,712.0,Germany,Female,42.0,2.0,111669.68,1.0,1.0,1.0,83478.12,0.0
-13423,15648800.0,Nkemdilim,748.0,Spain,Female,36.0,8.0,0.0,2.0,1.0,0.0,105965.25,0.0
-13424,15613363.0,Chukwujamuike,596.0,France,Male,43.0,8.0,0.0,1.0,0.0,1.0,152876.13,0.0
-13425,15622230.0,Ch'iu,603.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,143481.85,0.0
-13426,15732266.0,Henty,659.0,France,Female,55.0,6.0,0.0,1.0,1.0,0.0,10883.52,1.0
-13427,15579891.0,Teng,657.0,Spain,Female,26.0,4.0,0.0,2.0,1.0,1.0,171070.58,0.0
-13428,15763029.0,Y?,743.0,France,Male,35.0,6.0,0.0,1.0,1.0,1.0,66893.28,0.0
-13429,15664214.0,Tung,600.0,Germany,Female,34.0,3.0,102994.04,1.0,0.0,1.0,14073.11,0.0
-13430,15598838.0,Y?an,619.0,France,Male,66.0,5.0,0.0,2.0,1.0,1.0,106534.15,0.0
-13431,15677105.0,Hsiao,573.0,France,Male,30.0,9.0,0.0,2.0,1.0,1.0,26116.82,0.0
-13432,15740104.0,Pinto,635.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,170705.53,0.0
-13433,15666032.0,Hargrave,756.0,France,Female,28.0,2.0,0.0,2.0,1.0,0.0,44251.33,0.0
-13434,15785547.0,Nnanna,659.0,France,Male,39.0,8.0,0.0,2.0,0.0,1.0,51380.9,1.0
-13435,15684960.0,Nkemdilim,714.0,France,Female,34.0,4.0,0.0,2.0,1.0,1.0,136540.09,0.0
-13436,15649858.0,Kao,633.0,Spain,Male,40.0,7.0,95377.13,2.0,1.0,0.0,163534.6,0.0
-13437,15647570.0,Zikoranachidimma,675.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,179908.71,0.0
-13438,15688868.0,Hsing,677.0,France,Female,37.0,3.0,0.0,2.0,0.0,0.0,92888.52,0.0
-13439,15658032.0,T'ien,790.0,France,Male,64.0,0.0,0.0,2.0,0.0,1.0,47799.15,0.0
-13440,15804950.0,Scott,624.0,France,Female,32.0,9.0,91955.61,1.0,1.0,1.0,132973.21,0.0
-13441,15652948.0,White,718.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,132973.17,0.0
-13442,15751167.0,P'an,714.0,Spain,Female,45.0,7.0,154533.51,1.0,1.0,0.0,142977.05,1.0
-13443,15665579.0,Manna,562.0,France,Male,43.0,10.0,0.0,2.0,1.0,0.0,115842.1,0.0
-13444,15808905.0,Chiazagomekpere,699.0,Germany,Male,35.0,2.0,103391.38,1.0,1.0,0.0,84487.62,0.0
-13445,15709447.0,Chidiebele,641.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,149705.25,0.0
-13446,15720509.0,Kao,639.0,Spain,Male,41.0,8.0,0.0,1.0,1.0,0.0,123954.15,1.0
-13447,15636562.0,Genovese,679.0,France,Male,31.0,8.0,0.0,2.0,1.0,0.0,102640.52,0.0
-13448,15610557.0,Kornilova,589.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,161579.85,0.0
-13449,15733777.0,Lo,751.0,France,Male,31.0,10.0,0.0,2.0,0.0,1.0,96847.91,0.0
-13450,15674526.0,Ponomaryov,778.0,Germany,Male,36.0,3.0,91547.84,1.0,0.0,0.0,94951.27,0.0
-13451,15740356.0,P'an,709.0,France,Male,29.0,3.0,0.0,2.0,0.0,1.0,97640.2,0.0
-13452,15607598.0,Nnachetam,653.0,France,Male,30.0,8.0,144772.69,1.0,0.0,0.0,180932.46,0.0
-13453,15658972.0,Ponomaryov,719.0,Germany,Female,47.0,1.0,129855.72,1.0,1.0,0.0,97921.25,1.0
-13454,15780254.0,Chukwujamuike,615.0,France,Male,34.0,10.0,92895.56,1.0,1.0,0.0,167553.06,0.0
-13455,15626457.0,Hsueh,717.0,Germany,Male,43.0,1.0,124449.85,2.0,1.0,1.0,179655.87,0.0
-13456,15658969.0,Chidiebele,545.0,France,Male,43.0,6.0,128718.78,1.0,1.0,0.0,79259.77,0.0
-13457,15671591.0,Walker,551.0,Germany,Male,42.0,1.0,130334.35,2.0,0.0,1.0,89893.6,0.0
-13458,15686696.0,Piazza,850.0,France,Female,56.0,5.0,0.0,1.0,1.0,0.0,175874.68,1.0
-13459,15614365.0,Chukwudi,648.0,Germany,Female,50.0,3.0,129855.32,2.0,1.0,0.0,171922.72,1.0
-13460,15780212.0,Hsia,752.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,82662.73,0.0
-13461,15669987.0,Ch'eng,543.0,Spain,Male,40.0,10.0,0.0,2.0,0.0,0.0,147090.9,0.0
-13462,15583090.0,Okeke,812.0,Germany,Female,55.0,1.0,110153.58,1.0,0.0,1.0,82351.06,1.0
-13463,15719276.0,Lu,602.0,France,Male,41.0,5.0,0.0,2.0,1.0,1.0,141756.32,0.0
-13464,15662232.0,Ch'iu,644.0,Spain,Male,34.0,5.0,127552.85,1.0,0.0,1.0,62733.05,0.0
-13465,15669922.0,Hao,631.0,Spain,Female,46.0,2.0,148106.84,1.0,0.0,1.0,172699.54,1.0
-13466,15803681.0,Hsueh,567.0,France,Male,37.0,5.0,0.0,1.0,0.0,1.0,95655.16,1.0
-13467,15782591.0,Ch'eng,738.0,Spain,Female,27.0,3.0,0.0,2.0,0.0,0.0,121408.55,0.0
-13468,15652674.0,Chiu,689.0,Germany,Female,40.0,4.0,95710.11,1.0,1.0,0.0,78650.95,1.0
-13469,15697691.0,Maclean,757.0,France,Male,44.0,8.0,0.0,2.0,1.0,0.0,57844.84,0.0
-13470,15599403.0,Ch'iu,719.0,Spain,Female,32.0,0.0,0.0,2.0,1.0,0.0,88724.84,0.0
-13471,15573971.0,Onyemauchechukwu,726.0,France,Female,52.0,4.0,106301.85,2.0,0.0,0.0,151310.16,1.0
-13472,15780038.0,Palermo,754.0,France,Male,27.0,4.0,0.0,2.0,0.0,0.0,146723.72,0.0
-13473,15580149.0,Hao,554.0,France,Male,55.0,8.0,123894.22,1.0,0.0,1.0,79304.38,1.0
-13474,15625247.0,Chukwujamuike,650.0,Spain,Male,26.0,2.0,82453.96,2.0,1.0,0.0,92612.52,0.0
-13475,15706464.0,Hayslett,751.0,France,Female,31.0,1.0,119948.09,1.0,1.0,1.0,180598.86,0.0
-13476,15769412.0,Pisani,705.0,France,Female,56.0,8.0,0.0,1.0,1.0,1.0,3560.0,0.0
-13477,15783629.0,Onwumelu,731.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,171552.02,0.0
-13478,15758813.0,Chinwendu,567.0,Germany,Female,48.0,9.0,154776.42,1.0,1.0,0.0,107876.91,0.0
-13479,15693526.0,Davidson,571.0,Germany,Male,28.0,4.0,82531.11,2.0,0.0,0.0,132973.18,0.0
-13480,15621116.0,Lettiere,597.0,Spain,Male,39.0,7.0,0.0,1.0,1.0,0.0,157862.82,0.0
-13481,15736302.0,Teng,554.0,France,Male,41.0,5.0,84427.58,1.0,1.0,1.0,104079.53,0.0
-13482,15646720.0,Tseng,678.0,Germany,Male,42.0,9.0,161889.79,1.0,1.0,0.0,79323.61,0.0
-13483,15701265.0,Hsia,633.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,118738.81,0.0
-13484,15703728.0,Zito,634.0,Spain,Female,42.0,9.0,0.0,2.0,1.0,0.0,124749.08,0.0
-13485,15791867.0,Lo Duca,731.0,France,Male,36.0,3.0,0.0,2.0,0.0,0.0,72846.58,1.0
-13486,15576065.0,Hsiao,714.0,France,Male,52.0,8.0,0.0,1.0,1.0,0.0,24218.65,1.0
-13487,15782311.0,Whitehead,607.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,0.0,142903.44,0.0
-13488,15600357.0,Brown,663.0,France,Male,46.0,8.0,152265.43,2.0,1.0,0.0,119333.38,0.0
-13489,15609823.0,Ch'iu,619.0,France,Male,46.0,2.0,0.0,2.0,1.0,0.0,73464.88,0.0
-13490,15626608.0,Chidubem,611.0,France,Female,37.0,7.0,0.0,2.0,0.0,1.0,84760.32,0.0
-13491,15730793.0,Ritchie,729.0,Spain,Female,51.0,8.0,0.0,2.0,1.0,0.0,88890.05,1.0
-13492,15648725.0,Obiajulu,731.0,Spain,Male,39.0,6.0,140418.81,1.0,1.0,1.0,117946.3,1.0
-13493,15618582.0,H?,748.0,Germany,Female,32.0,6.0,151607.56,1.0,1.0,0.0,138882.98,0.0
-13494,15711455.0,Chiang,660.0,Spain,Male,34.0,7.0,0.0,2.0,1.0,1.0,152987.42,0.0
-13495,15641733.0,Y?,543.0,Germany,Male,30.0,2.0,112808.18,1.0,1.0,1.0,53478.02,0.0
-13496,15573741.0,Genovesi,629.0,France,Female,48.0,2.0,0.0,1.0,1.0,1.0,73679.76,1.0
-13497,15682778.0,Manna,526.0,France,Male,37.0,2.0,153366.13,1.0,0.0,0.0,10908.33,0.0
-13498,15743040.0,Tuan,521.0,France,Male,41.0,2.0,101827.07,1.0,1.0,0.0,185599.86,0.0
-13499,15673372.0,Yobachukwu,637.0,France,Female,31.0,7.0,0.0,2.0,1.0,1.0,134851.12,0.0
-13500,15688876.0,Ewers,711.0,Spain,Female,39.0,2.0,0.0,2.0,1.0,0.0,91366.42,0.0
-13501,15690733.0,Lucchese,745.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,126551.65,0.0
-13502,15682355.0,Hales,559.0,Germany,Male,39.0,7.0,132558.26,1.0,1.0,1.0,127543.81,0.0
-13503,15661528.0,Chiang,598.0,France,Female,22.0,10.0,112572.39,1.0,1.0,1.0,149550.95,0.0
-13504,15636353.0,Ts'ui,663.0,France,Male,30.0,9.0,0.0,2.0,0.0,0.0,11783.1,0.0
-13505,15772559.0,Chukwueloka,689.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,183617.49,0.0
-13506,15761506.0,Nweke,706.0,France,Male,24.0,4.0,0.0,2.0,1.0,0.0,182855.42,0.0
-13507,15786539.0,Nwokeocha,782.0,France,Male,43.0,3.0,113978.97,1.0,1.0,1.0,176913.42,0.0
-13508,15711130.0,Hs?,461.0,Germany,Female,46.0,9.0,103417.73,1.0,1.0,0.0,157097.09,1.0
-13509,15664214.0,Trevisano,550.0,France,Female,32.0,4.0,80780.16,1.0,1.0,0.0,170835.32,0.0
-13510,15667460.0,Nwankwo,658.0,Germany,Male,27.0,4.0,102945.01,1.0,0.0,0.0,175399.14,0.0
-13511,15694556.0,Chiu,676.0,Germany,Female,47.0,9.0,153819.58,2.0,1.0,0.0,100995.11,1.0
-13512,15729362.0,Pinto,559.0,France,Female,27.0,3.0,0.0,1.0,0.0,1.0,172097.4,0.0
-13513,15624397.0,Nwankwo,769.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,133692.88,0.0
-13514,15742238.0,Y?an,595.0,France,Female,24.0,5.0,0.0,1.0,1.0,0.0,170195.4,0.0
-13515,15769110.0,Ugochukwutubelum,682.0,France,Male,28.0,10.0,0.0,2.0,1.0,0.0,82972.65,0.0
-13516,15588918.0,O'Donnell,591.0,Germany,Male,33.0,3.0,130554.51,2.0,1.0,1.0,81778.72,0.0
-13517,15776518.0,Baryshnikov,790.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,138700.75,1.0
-13518,15674929.0,Obidimkpa,663.0,Germany,Male,41.0,4.0,81576.75,2.0,1.0,0.0,100941.57,0.0
-13519,15672757.0,Bazarova,828.0,France,Male,40.0,3.0,118050.79,1.0,1.0,0.0,121151.1,0.0
-13520,15674932.0,Mazzi,807.0,France,Female,38.0,10.0,0.0,1.0,1.0,1.0,74945.11,1.0
-13521,15753639.0,Goliwe,705.0,Germany,Female,45.0,3.0,111235.05,1.0,1.0,0.0,175750.21,1.0
-13522,15571261.0,Lo,724.0,France,Female,28.0,6.0,0.0,1.0,0.0,1.0,187985.85,0.0
-13523,15707634.0,Chiawuotu,682.0,Spain,Female,28.0,2.0,0.0,2.0,1.0,1.0,173899.42,0.0
-13524,15631451.0,Dreyer,638.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,173516.71,0.0
-13525,15573741.0,Pagnotto,731.0,France,Male,43.0,4.0,128509.63,1.0,1.0,0.0,73464.88,1.0
-13526,15776936.0,Hsia,637.0,France,Male,59.0,5.0,135923.16,1.0,1.0,1.0,111190.53,0.0
-13527,15771222.0,Goliwe,535.0,Germany,Female,59.0,3.0,115532.52,1.0,1.0,0.0,97939.4,1.0
-13528,15792726.0,Uspensky,706.0,France,Male,37.0,6.0,129745.1,1.0,1.0,1.0,110510.28,0.0
-13529,15782939.0,Chuang,754.0,France,Male,29.0,2.0,0.0,2.0,1.0,1.0,80619.09,0.0
-13530,15761655.0,Ch'iu,487.0,France,Male,31.0,7.0,0.0,2.0,1.0,0.0,64516.8,0.0
-13531,15753952.0,Obiajulu,718.0,Germany,Female,37.0,7.0,121063.85,2.0,1.0,1.0,174882.5,0.0
-13532,15572408.0,Ponomaryov,671.0,Germany,Male,31.0,3.0,141078.37,1.0,1.0,0.0,161203.6,0.0
-13533,15757827.0,Ch'en,641.0,France,Female,36.0,7.0,0.0,1.0,1.0,1.0,93624.55,0.0
-13534,15798824.0,Y?an,675.0,France,Female,33.0,7.0,150635.3,1.0,1.0,0.0,133086.45,0.0
-13535,15757933.0,Mironova,721.0,France,Male,35.0,8.0,138523.2,1.0,1.0,0.0,86587.37,0.0
-13536,15773512.0,Rueda,729.0,Germany,Female,38.0,8.0,90432.92,1.0,1.0,1.0,167065.94,0.0
-13537,15791501.0,Ch'en,553.0,France,Male,47.0,8.0,162658.18,2.0,0.0,1.0,52331.15,0.0
-13538,15654574.0,Ewing,688.0,Spain,Male,40.0,1.0,0.0,2.0,1.0,1.0,52462.25,0.0
-13539,15803880.0,Teng,850.0,Germany,Male,23.0,8.0,101100.22,2.0,1.0,1.0,58885.88,0.0
-13540,15696834.0,Hsiung,624.0,France,Female,29.0,2.0,0.0,2.0,1.0,1.0,167984.72,0.0
-13541,15760873.0,Horsley,697.0,Germany,Male,30.0,9.0,115046.74,2.0,1.0,1.0,54474.48,0.0
-13542,15794566.0,Pinto,727.0,Spain,Female,33.0,5.0,0.0,2.0,1.0,1.0,159475.08,0.0
-13543,15709324.0,Ukaegbunam,826.0,Germany,Female,54.0,5.0,103549.24,1.0,1.0,1.0,113597.64,1.0
-13544,15671137.0,Zito,590.0,Germany,Female,44.0,8.0,110559.47,2.0,0.0,1.0,88724.49,0.0
-13545,15734999.0,Pagnotto,663.0,France,Female,35.0,4.0,152992.91,1.0,0.0,0.0,85890.05,0.0
-13546,15602497.0,Ts'ai,529.0,Spain,Male,29.0,5.0,0.0,2.0,1.0,1.0,134442.79,0.0
-13547,15805312.0,Shao,690.0,France,Male,33.0,5.0,121681.91,1.0,0.0,0.0,73329.08,0.0
-13548,15669411.0,Fenton,613.0,Spain,Male,43.0,4.0,0.0,2.0,1.0,1.0,61759.33,0.0
-13549,15774192.0,Chukwufumnanya,624.0,Spain,Male,35.0,2.0,108019.27,1.0,1.0,1.0,55829.25,0.0
-13550,15757551.0,Hsieh,616.0,Germany,Male,38.0,8.0,132351.29,1.0,1.0,0.0,54816.57,0.0
-13551,15780389.0,Shih,611.0,France,Female,27.0,9.0,110738.89,2.0,1.0,1.0,147545.65,0.0
-13552,15626452.0,Lo,662.0,France,Male,31.0,6.0,0.0,2.0,0.0,1.0,41970.72,0.0
-13553,15736656.0,Hsia,706.0,France,Female,43.0,4.0,0.0,1.0,1.0,0.0,177519.39,1.0
-13554,15782941.0,Oluchukwu,554.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,182867.84,0.0
-13555,15596939.0,Tsao,633.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,123866.43,0.0
-13556,15656592.0,Ritchie,649.0,Spain,Male,31.0,1.0,0.0,2.0,1.0,1.0,198490.33,0.0
-13557,15806945.0,Lettiere,590.0,Spain,Female,28.0,8.0,0.0,2.0,1.0,1.0,126307.25,0.0
-13558,15583368.0,Chuang,712.0,France,Male,36.0,8.0,0.0,1.0,1.0,1.0,145936.28,0.0
-13559,15663826.0,Mazzanti,774.0,France,Female,34.0,3.0,0.0,2.0,1.0,0.0,147055.87,0.0
-13560,15583841.0,Smith,641.0,France,Female,52.0,4.0,97541.24,1.0,1.0,1.0,137964.45,0.0
-13561,15780038.0,Ewing,691.0,Germany,Female,30.0,3.0,102954.68,2.0,1.0,1.0,117832.88,0.0
-13562,15795936.0,Ulyanov,584.0,France,Female,29.0,5.0,0.0,2.0,1.0,1.0,180622.43,0.0
-13563,15578980.0,Li Fonti,704.0,Germany,Female,35.0,6.0,137781.65,2.0,1.0,0.0,66893.28,0.0
-13564,15571917.0,Ugorji,731.0,Spain,Male,32.0,5.0,0.0,2.0,0.0,1.0,173365.89,0.0
-13565,15773852.0,T'an,585.0,France,Male,59.0,4.0,88141.1,2.0,1.0,0.0,5669.31,1.0
-13566,15682563.0,Chidubem,538.0,France,Female,32.0,5.0,0.0,2.0,1.0,1.0,582.59,0.0
-13567,15751788.0,Bevan,713.0,France,Male,35.0,6.0,0.0,1.0,1.0,0.0,48606.71,0.0
-13568,15723873.0,Yudin,673.0,France,Female,69.0,7.0,0.0,2.0,1.0,1.0,131736.23,0.0
-13569,15574072.0,White,758.0,France,Male,39.0,6.0,0.0,2.0,1.0,0.0,104469.06,0.0
-13570,15741094.0,Hsueh,659.0,France,Female,26.0,2.0,118022.73,1.0,1.0,1.0,168034.9,0.0
-13571,15642570.0,Ch'in,735.0,Spain,Female,45.0,7.0,123571.77,4.0,1.0,1.0,187811.71,1.0
-13572,15793046.0,Tsou,521.0,France,Male,42.0,5.0,153344.72,1.0,1.0,1.0,139953.94,1.0
-13573,15737354.0,Kumm,792.0,France,Female,35.0,5.0,0.0,1.0,1.0,0.0,179655.87,0.0
-13574,15581840.0,Mai,624.0,Germany,Female,42.0,5.0,152325.21,2.0,1.0,0.0,89972.25,0.0
-13575,15641094.0,Ukaegbunam,682.0,France,Female,43.0,6.0,0.0,2.0,0.0,0.0,100153.43,0.0
-13576,15771573.0,O'Brien,707.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,60552.44,0.0
-13577,15786680.0,Ch'ien,679.0,Germany,Female,41.0,0.0,92786.66,1.0,0.0,0.0,48984.18,1.0
-13578,15641640.0,T'ien,545.0,Spain,Male,53.0,4.0,0.0,2.0,1.0,1.0,82526.92,0.0
-13579,15658935.0,Chien,597.0,Germany,Male,30.0,10.0,143129.41,1.0,0.0,0.0,141827.83,1.0
-13580,15752731.0,Kashiwagi,743.0,Spain,Male,40.0,8.0,162448.69,1.0,1.0,1.0,183575.01,0.0
-13581,15606436.0,T'ien,549.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,88729.22,0.0
-13582,15672640.0,Nnamdi,725.0,France,Male,34.0,2.0,0.0,2.0,1.0,1.0,96833.0,0.0
-13583,15690796.0,Smith,680.0,France,Male,35.0,5.0,0.0,2.0,1.0,1.0,115916.55,0.0
-13584,15733581.0,Y?,577.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,0.0,25292.53,0.0
-13585,15704581.0,Deleon,714.0,Germany,Male,41.0,8.0,134542.73,1.0,0.0,0.0,137041.24,1.0
-13586,15700209.0,Nwebube,662.0,France,Female,28.0,6.0,0.0,2.0,0.0,1.0,181461.46,0.0
-13587,15676576.0,Ch'en,676.0,France,Male,33.0,3.0,0.0,1.0,1.0,1.0,72402.0,0.0
-13588,15676094.0,Martin,636.0,Spain,Female,34.0,8.0,133868.21,1.0,0.0,0.0,167522.19,0.0
-13589,15622478.0,Nwokezuike,607.0,France,Male,48.0,5.0,0.0,2.0,1.0,1.0,132973.21,0.0
-13590,15676835.0,Tsao,469.0,Spain,Female,31.0,7.0,0.0,2.0,1.0,1.0,109742.55,0.0
-13591,15751026.0,Nnonso,679.0,Germany,Female,58.0,8.0,107594.11,1.0,1.0,0.0,70400.86,1.0
-13592,15686963.0,Pagnotto,678.0,France,Female,69.0,8.0,123936.54,1.0,1.0,0.0,156787.34,1.0
-13593,15632859.0,Udinesi,629.0,France,Male,42.0,7.0,0.0,1.0,0.0,0.0,99843.95,1.0
-13594,15737404.0,Uwaezuoke,677.0,France,Male,32.0,9.0,0.0,2.0,1.0,1.0,167033.83,0.0
-13595,15751561.0,Aksenov,743.0,Spain,Female,37.0,3.0,0.0,2.0,0.0,1.0,117038.96,0.0
-13596,15679693.0,Tsai,679.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,61164.15,0.0
-13597,15763662.0,Hsieh,648.0,France,Male,46.0,5.0,0.0,2.0,0.0,1.0,86490.09,0.0
-13598,15643361.0,Ch'eng,678.0,France,Female,50.0,6.0,0.0,1.0,1.0,0.0,175235.99,1.0
-13599,15615207.0,Hsing,751.0,France,Female,38.0,4.0,0.0,2.0,1.0,1.0,60729.41,0.0
-13600,15585853.0,Ch'in,769.0,Spain,Male,30.0,4.0,0.0,2.0,1.0,1.0,153400.24,0.0
-13601,15624641.0,Okwudiliolisa,850.0,France,Male,36.0,8.0,136496.29,1.0,1.0,0.0,90189.72,0.0
-13602,15579189.0,Ting,529.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,0.0,123587.97,0.0
-13603,15790442.0,Udegbulam,641.0,Spain,Male,25.0,9.0,0.0,2.0,1.0,0.0,149170.92,0.0
-13604,15676526.0,T'ien,687.0,France,Male,31.0,1.0,124447.65,1.0,1.0,1.0,184976.76,0.0
-13605,15664752.0,K'ung,677.0,Spain,Male,64.0,6.0,0.0,2.0,0.0,1.0,102925.72,0.0
-13606,15608406.0,Chiang,598.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,56583.88,0.0
-13607,15687634.0,Zikoranachidimma,709.0,France,Female,33.0,5.0,0.0,2.0,1.0,0.0,73656.38,1.0
-13608,15656592.0,Ogochukwu,614.0,France,Female,40.0,4.0,0.0,1.0,1.0,1.0,71492.28,1.0
-13609,15757588.0,Chiu,633.0,France,Female,46.0,6.0,0.0,2.0,1.0,0.0,91573.19,1.0
-13610,15596962.0,Onwudiwe,663.0,France,Female,35.0,10.0,0.0,2.0,1.0,0.0,62746.54,0.0
-13611,15769222.0,Nwebube,669.0,France,Female,36.0,7.0,0.0,2.0,1.0,0.0,62757.33,0.0
-13612,15783816.0,Chigbogu,746.0,Germany,Male,35.0,3.0,152283.39,1.0,1.0,0.0,138360.29,0.0
-13613,15766355.0,Pagnotto,590.0,France,Male,42.0,2.0,0.0,1.0,1.0,0.0,172749.72,0.0
-13614,15808582.0,Chialuka,571.0,France,Female,40.0,6.0,0.0,2.0,1.0,1.0,86935.08,0.0
-13615,15644896.0,Zikoranachidimma,584.0,Spain,Female,39.0,5.0,0.0,1.0,1.0,0.0,180326.83,1.0
-13616,15592229.0,Chinedum,651.0,Spain,Male,40.0,3.0,0.0,2.0,1.0,1.0,164825.03,0.0
-13617,15634359.0,Lombardi,675.0,France,Female,34.0,4.0,119278.49,1.0,0.0,0.0,148611.52,0.0
-13618,15596761.0,Hsia,595.0,Spain,Female,56.0,9.0,144550.84,1.0,1.0,0.0,106040.97,1.0
-13619,15780056.0,Chigozie,683.0,France,Male,38.0,7.0,179960.2,1.0,1.0,0.0,185837.08,0.0
-13620,15754574.0,Y?,681.0,France,Female,41.0,5.0,0.0,2.0,1.0,1.0,91573.19,0.0
-13621,15600879.0,Ting,656.0,Spain,Male,34.0,3.0,0.0,2.0,1.0,1.0,140579.17,0.0
-13622,15643633.0,T'ien,553.0,France,Female,41.0,5.0,0.0,2.0,1.0,0.0,53353.36,0.0
-13623,15667971.0,Ch'iu,535.0,Germany,Male,40.0,3.0,127616.56,1.0,1.0,1.0,131660.31,1.0
-13624,15721715.0,Chukwualuka,835.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,0.0,81753.92,0.0
-13625,15585879.0,Trevisani,753.0,France,Female,48.0,7.0,139979.07,1.0,1.0,1.0,67979.35,0.0
-13626,15767598.0,Zetticci,598.0,Spain,Female,36.0,5.0,0.0,1.0,0.0,0.0,77906.85,0.0
-13627,15800366.0,Chien,587.0,Spain,Female,33.0,5.0,0.0,2.0,0.0,0.0,131662.47,0.0
-13628,15766163.0,Kao,705.0,France,Male,26.0,1.0,0.0,2.0,1.0,1.0,91829.17,0.0
-13629,15682472.0,Chien,605.0,France,Male,39.0,8.0,0.0,2.0,1.0,1.0,165082.25,0.0
-13630,15791172.0,Chiang,713.0,France,Female,40.0,7.0,123971.16,1.0,1.0,1.0,131970.86,0.0
-13631,15608968.0,Tsao,678.0,France,Male,43.0,2.0,0.0,2.0,1.0,0.0,67944.09,0.0
-13632,15591360.0,McIntyre,578.0,France,Female,37.0,4.0,0.0,2.0,1.0,0.0,63736.17,1.0
-13633,15598522.0,Mazzanti,599.0,France,Female,52.0,7.0,0.0,3.0,0.0,0.0,141756.32,1.0
-13634,15697042.0,Manna,736.0,France,Male,38.0,9.0,0.0,2.0,1.0,1.0,99645.04,0.0
-13635,15615797.0,Chukwumaobim,707.0,Germany,Male,46.0,1.0,107174.58,1.0,0.0,0.0,158035.58,0.0
-13636,15739046.0,Chiwetelu,584.0,Spain,Female,40.0,4.0,0.0,1.0,1.0,0.0,129563.86,1.0
-13637,15721815.0,Ndubuagha,602.0,France,Male,30.0,5.0,0.0,1.0,1.0,0.0,103099.22,0.0
-13638,15690546.0,Horsley,602.0,France,Female,36.0,3.0,0.0,2.0,1.0,1.0,73977.23,0.0
-13639,15785406.0,T'ao,537.0,Spain,Male,34.0,1.0,114543.43,1.0,1.0,1.0,54869.54,0.0
-13640,15643487.0,Ch'ien,607.0,France,Male,32.0,9.0,0.0,2.0,1.0,0.0,149853.89,0.0
-13641,15656613.0,Tuan,614.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,126517.97,0.0
-13642,15591036.0,Milanesi,583.0,France,Female,29.0,4.0,0.0,1.0,1.0,0.0,27448.33,0.0
-13643,15690772.0,T'ien,841.0,France,Female,34.0,9.0,0.0,1.0,1.0,0.0,100919.19,0.0
-13644,15595037.0,Ch'ien,663.0,Spain,Male,27.0,7.0,0.0,1.0,1.0,0.0,86786.41,0.0
-13645,15783883.0,Chuang,498.0,Spain,Male,39.0,10.0,0.0,2.0,1.0,1.0,98301.61,0.0
-13646,15584930.0,Chibugo,733.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,79329.7,0.0
-13647,15759021.0,Tuan,714.0,France,Female,28.0,6.0,0.0,2.0,0.0,0.0,143598.7,0.0
-13648,15737782.0,H?,728.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,91086.46,0.0
-13649,15766017.0,Chiu,637.0,Spain,Female,31.0,2.0,0.0,2.0,1.0,1.0,162599.51,0.0
-13650,15592994.0,H?,582.0,Spain,Female,28.0,8.0,0.0,2.0,1.0,1.0,92888.52,0.0
-13651,15757912.0,Mazzanti,594.0,Spain,Female,32.0,9.0,0.0,2.0,1.0,1.0,180329.83,0.0
-13652,15699598.0,Kao,699.0,Spain,Female,43.0,6.0,0.0,2.0,1.0,0.0,62222.81,0.0
-13653,15746139.0,Mazzanti,712.0,Spain,Male,29.0,7.0,0.0,2.0,1.0,0.0,91340.02,0.0
-13654,15793425.0,T'ao,769.0,France,Female,32.0,1.0,121461.87,1.0,1.0,0.0,167973.63,0.0
-13655,15789425.0,Shih,723.0,Spain,Female,34.0,1.0,136492.92,1.0,1.0,0.0,100557.03,0.0
-13656,15595592.0,Mancini,579.0,France,Male,33.0,6.0,125689.29,1.0,1.0,1.0,86701.4,0.0
-13657,15642710.0,Chukwuemeka,717.0,France,Male,27.0,9.0,0.0,2.0,1.0,0.0,138350.94,0.0
-13658,15566211.0,Iweobiegbunam,718.0,France,Female,30.0,2.0,0.0,2.0,0.0,1.0,121537.91,0.0
-13659,15593973.0,Pirozzi,656.0,Germany,Female,37.0,10.0,123974.96,1.0,1.0,1.0,178074.04,0.0
-13660,15678725.0,H?,656.0,France,Male,35.0,10.0,113574.61,1.0,0.0,0.0,179521.24,0.0
-13661,15733361.0,Yeh,611.0,Spain,Male,36.0,9.0,121778.71,1.0,1.0,1.0,73856.28,0.0
-13662,15781689.0,Uvarov,669.0,Germany,Male,42.0,1.0,71264.02,2.0,1.0,1.0,93336.3,0.0
-13663,15765283.0,Calabrese,608.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,171706.66,0.0
-13664,15761168.0,Ritchie,575.0,Germany,Male,45.0,9.0,116886.59,2.0,1.0,1.0,131736.23,0.0
-13665,15731519.0,Calabresi,543.0,France,Male,37.0,3.0,141827.33,1.0,0.0,0.0,43496.36,0.0
-13666,15578825.0,Okoli,635.0,France,Male,34.0,3.0,149542.25,1.0,1.0,1.0,140889.32,0.0
-13667,15648775.0,Chiemenam,613.0,France,Male,38.0,9.0,0.0,2.0,0.0,0.0,86927.53,0.0
-13668,15681206.0,Chukwuhaenye,678.0,France,Female,38.0,3.0,128760.39,1.0,1.0,0.0,140889.72,0.0
-13669,15714260.0,K?,545.0,France,Male,46.0,7.0,0.0,2.0,1.0,1.0,161579.19,0.0
-13670,15675141.0,Ts'ui,584.0,Germany,Female,52.0,1.0,123544.0,2.0,0.0,0.0,143463.28,1.0
-13671,15794870.0,Zikoranachidimma,636.0,Germany,Female,42.0,10.0,138171.1,2.0,1.0,1.0,117431.1,0.0
-13672,15709142.0,Chijindum,556.0,Germany,Male,42.0,8.0,151739.65,2.0,1.0,0.0,126812.63,0.0
-13673,15698872.0,Chuang,850.0,Spain,Male,29.0,9.0,0.0,2.0,0.0,1.0,169415.54,0.0
-13674,15703068.0,Ch'in,633.0,France,Male,29.0,8.0,0.0,2.0,1.0,0.0,645.61,0.0
-13675,15708513.0,Shih,635.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,1.0,162452.65,0.0
-13676,15704910.0,Illingworth,627.0,Spain,Female,29.0,2.0,0.0,2.0,1.0,0.0,54869.54,0.0
-13677,15621659.0,Cattaneo,726.0,Spain,Female,36.0,6.0,0.0,2.0,0.0,0.0,115818.51,0.0
-13678,15701265.0,Lucchesi,709.0,France,Female,41.0,3.0,151191.31,2.0,1.0,0.0,143317.18,0.0
-13679,15714485.0,De Luca,593.0,France,Male,31.0,1.0,118680.57,1.0,1.0,1.0,92898.17,0.0
-13680,15576044.0,Ngozichukwuka,708.0,France,Male,36.0,8.0,0.0,1.0,1.0,0.0,87411.49,0.0
-13681,15628274.0,Ndubueze,738.0,France,Female,20.0,5.0,0.0,2.0,1.0,0.0,74838.64,0.0
-13682,15679307.0,Pisani,614.0,France,Female,34.0,9.0,0.0,2.0,0.0,0.0,73679.64,0.0
-13683,15581286.0,Yobachukwu,703.0,Spain,Female,35.0,5.0,0.0,2.0,1.0,1.0,133672.61,0.0
-13684,15659325.0,Scott,755.0,France,Female,40.0,5.0,0.0,2.0,1.0,0.0,121894.31,0.0
-13685,15617206.0,Shih,687.0,Germany,Male,38.0,10.0,158718.13,2.0,1.0,1.0,98463.99,0.0
-13686,15678057.0,Milani,679.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,85547.33,0.0
-13687,15643072.0,Trevisani,773.0,France,Female,40.0,0.0,81732.88,1.0,1.0,1.0,74870.99,0.0
-13688,15768471.0,K'ung,567.0,France,Female,49.0,3.0,0.0,2.0,1.0,0.0,183717.94,1.0
-13689,15726046.0,Ma,758.0,France,Male,41.0,9.0,0.0,2.0,1.0,1.0,122073.48,0.0
-13690,15750639.0,Brown,752.0,France,Male,33.0,3.0,0.0,2.0,1.0,0.0,175892.04,0.0
-13691,15713890.0,Kao,605.0,France,Male,39.0,9.0,0.0,2.0,1.0,1.0,142995.32,0.0
-13692,15579526.0,Ch'iu,498.0,Spain,Male,42.0,7.0,0.0,2.0,1.0,1.0,160176.47,0.0
-13693,15701109.0,Smith,696.0,France,Female,34.0,6.0,121827.43,1.0,0.0,1.0,99818.15,0.0
-13694,15585198.0,De Luca,585.0,France,Female,38.0,5.0,0.0,2.0,1.0,1.0,153920.22,0.0
-13695,15794878.0,Yobachukwu,710.0,Germany,Male,31.0,2.0,109564.1,2.0,1.0,1.0,173844.89,0.0
-13696,15591512.0,Ch'ien,732.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,179631.85,0.0
-13697,15774164.0,Obiajulu,715.0,Spain,Male,38.0,3.0,0.0,1.0,1.0,1.0,94912.78,0.0
-13698,15666032.0,Aksenov,586.0,France,Male,60.0,3.0,0.0,1.0,1.0,1.0,80619.17,0.0
-13699,15644882.0,Watson,711.0,France,Male,33.0,2.0,0.0,2.0,0.0,1.0,176963.17,0.0
-13700,15583076.0,Dike,756.0,Germany,Female,34.0,4.0,157829.12,2.0,1.0,0.0,51536.99,0.0
-13701,15652914.0,Okechukwu,646.0,France,Female,32.0,5.0,0.0,2.0,1.0,0.0,105886.77,0.0
-13702,15802088.0,Mancini,633.0,France,Male,34.0,8.0,0.0,2.0,1.0,1.0,140579.17,0.0
-13703,15650237.0,Chiang,545.0,France,Male,38.0,9.0,0.0,2.0,1.0,0.0,130664.79,0.0
-13704,15782591.0,Ndubueze,726.0,France,Male,48.0,10.0,0.0,1.0,1.0,0.0,62222.81,0.0
-13705,15684592.0,Tseng,684.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,175043.69,0.0
-13706,15786680.0,Hao,703.0,France,Female,56.0,9.0,0.0,1.0,1.0,0.0,156906.15,1.0
-13707,15724153.0,Chukwufumnanya,755.0,Spain,Female,33.0,8.0,0.0,2.0,0.0,1.0,121797.14,0.0
-13708,15652266.0,Chiu,651.0,Germany,Female,67.0,0.0,124043.8,1.0,1.0,1.0,113048.79,1.0
-13709,15643696.0,Ch'ien,572.0,Spain,Female,29.0,1.0,0.0,1.0,0.0,0.0,51959.82,0.0
-13710,15685300.0,Ch'ang,521.0,France,Female,26.0,10.0,126817.65,1.0,1.0,1.0,149614.81,0.0
-13711,15571526.0,Nwankwo,717.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,1.0,122925.44,0.0
-13712,15631693.0,Yermakov,695.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,149599.62,0.0
-13713,15680666.0,Chukwueloka,745.0,France,Male,39.0,3.0,136528.94,1.0,1.0,1.0,86927.53,0.0
-13714,15706071.0,Trevisano,638.0,Spain,Male,30.0,2.0,0.0,2.0,1.0,1.0,160976.85,0.0
-13715,15757541.0,Yermolayev,745.0,France,Female,36.0,5.0,0.0,1.0,0.0,0.0,186489.95,0.0
-13716,15702468.0,Ts'ui,593.0,France,Male,28.0,2.0,0.0,2.0,1.0,0.0,123627.96,0.0
-13717,15591135.0,Pirozzi,607.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,152733.89,0.0
-13718,15672432.0,Bibi,535.0,France,Male,33.0,3.0,0.0,2.0,0.0,0.0,116672.02,0.0
-13719,15668057.0,Ives,668.0,Germany,Male,33.0,2.0,123770.68,1.0,1.0,0.0,125083.29,0.0
-13720,15738174.0,Milanesi,850.0,Spain,Male,40.0,0.0,0.0,2.0,1.0,0.0,89566.64,0.0
-13721,15695097.0,Hs?,756.0,France,Male,36.0,9.0,0.0,2.0,1.0,0.0,94738.54,0.0
-13722,15732438.0,Chia,578.0,France,Male,24.0,5.0,0.0,2.0,1.0,1.0,172576.43,0.0
-13723,15796413.0,Lai,652.0,France,Male,33.0,9.0,0.0,2.0,1.0,0.0,82298.59,0.0
-13724,15634359.0,Tien,604.0,Spain,Male,34.0,4.0,168078.83,1.0,1.0,0.0,167078.97,0.0
-13725,15682868.0,Brown,696.0,France,Female,27.0,6.0,0.0,1.0,1.0,1.0,153356.8,0.0
-13726,15751788.0,Lu,773.0,Spain,Male,37.0,8.0,0.0,2.0,1.0,1.0,70550.0,0.0
-13727,15776824.0,Lai,675.0,France,Male,26.0,7.0,0.0,2.0,1.0,0.0,69237.21,0.0
-13728,15631310.0,McLean,554.0,France,Female,49.0,3.0,0.0,1.0,1.0,0.0,163666.85,1.0
-13729,15758856.0,Chukwuemeka,710.0,Germany,Female,25.0,6.0,107963.11,1.0,1.0,1.0,126469.09,1.0
-13730,15678460.0,Chigolum,562.0,Spain,Female,31.0,6.0,0.0,2.0,0.0,0.0,59697.14,0.0
-13731,15790067.0,P'eng,526.0,France,Male,33.0,3.0,0.0,2.0,1.0,1.0,107839.03,0.0
-13732,15746341.0,T'ao,679.0,Germany,Female,41.0,8.0,111973.97,2.0,1.0,0.0,63736.17,1.0
-13733,15622507.0,Eluemuno,612.0,Spain,Female,47.0,3.0,0.0,1.0,1.0,0.0,162260.93,1.0
-13734,15797736.0,T'an,674.0,Germany,Male,44.0,8.0,116074.89,1.0,0.0,0.0,67468.67,1.0
-13735,15674541.0,Ch'eng,579.0,France,Male,39.0,3.0,178095.55,1.0,1.0,1.0,4459.5,0.0
-13736,15809509.0,Trevisani,706.0,Germany,Female,39.0,8.0,121513.31,1.0,0.0,0.0,175072.47,1.0
-13737,15789413.0,Lucchese,691.0,France,Male,20.0,7.0,0.0,2.0,1.0,1.0,151887.16,0.0
-13738,15699446.0,Ch'ien,641.0,France,Male,37.0,5.0,0.0,2.0,1.0,1.0,152097.02,0.0
-13739,15570931.0,Lucciano,667.0,France,Female,28.0,9.0,89743.05,1.0,1.0,1.0,131304.45,0.0
-13740,15811947.0,Chukwudi,675.0,France,Male,66.0,9.0,0.0,2.0,1.0,1.0,63795.8,0.0
-13741,15720354.0,Ch'in,659.0,Germany,Female,40.0,4.0,134796.05,3.0,1.0,0.0,132798.78,1.0
-13742,15726695.0,Frolov,748.0,France,Female,46.0,8.0,0.0,2.0,1.0,1.0,86044.98,0.0
-13743,15713374.0,Pacheco,592.0,Spain,Female,35.0,3.0,0.0,2.0,1.0,0.0,131609.48,0.0
-13744,15758531.0,Onuora,739.0,France,Female,41.0,10.0,0.0,2.0,1.0,1.0,72330.46,0.0
-13745,15637169.0,Tsao,537.0,Spain,Male,31.0,5.0,121270.17,1.0,1.0,1.0,96885.19,0.0
-13746,15774277.0,Chimezie,605.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,16081.62,0.0
-13747,15632575.0,Lu,754.0,France,Male,58.0,7.0,148045.45,1.0,1.0,1.0,72969.9,0.0
-13748,15661830.0,Lucchesi,653.0,France,Male,29.0,3.0,0.0,2.0,1.0,1.0,171045.38,0.0
-13749,15744606.0,Hsiung,535.0,Germany,Male,48.0,2.0,141038.57,1.0,0.0,0.0,117632.63,1.0
-13750,15665521.0,Ponomarev,558.0,Spain,Female,40.0,10.0,0.0,2.0,1.0,1.0,121408.55,0.0
-13751,15661827.0,Manna,648.0,France,Male,44.0,9.0,0.0,2.0,1.0,0.0,125062.02,0.0
-13752,15742854.0,Chu,592.0,France,Male,35.0,0.0,0.0,2.0,0.0,0.0,182372.35,0.0
-13753,15583736.0,Lettiere,649.0,Germany,Male,37.0,6.0,139180.97,1.0,1.0,0.0,79866.73,0.0
-13754,15730793.0,Lucchese,748.0,France,Male,38.0,7.0,0.0,2.0,1.0,0.0,91359.79,0.0
-13755,15690744.0,Mairinger,562.0,France,Female,23.0,5.0,0.0,2.0,1.0,0.0,125870.5,0.0
-13756,15617454.0,Kao,572.0,Spain,Female,39.0,8.0,72948.58,1.0,0.0,1.0,170819.01,0.0
-13757,15581554.0,Ch'eng,686.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,47775.34,0.0
-13758,15594917.0,Milne,748.0,Germany,Male,32.0,0.0,138847.9,2.0,0.0,1.0,178827.3,0.0
-13759,15588566.0,Trevisano,658.0,Spain,Female,45.0,2.0,123544.0,1.0,0.0,1.0,159618.17,0.0
-13760,15768233.0,Chiu,531.0,France,Female,38.0,8.0,0.0,2.0,1.0,0.0,71830.1,0.0
-13761,15684556.0,Ch'iu,652.0,Germany,Female,37.0,5.0,121685.31,1.0,1.0,0.0,93839.3,1.0
-13762,15689633.0,Genovesi,757.0,Spain,Female,36.0,5.0,139825.04,1.0,1.0,0.0,182055.36,1.0
-13763,15595039.0,Ts'ui,587.0,France,Male,32.0,2.0,124435.54,1.0,1.0,0.0,161217.18,0.0
-13764,15803078.0,Chidiegwu,599.0,Germany,Female,23.0,4.0,123544.0,2.0,0.0,0.0,144849.1,0.0
-13765,15744044.0,Shih,745.0,France,Male,30.0,1.0,129717.3,1.0,0.0,1.0,86733.61,0.0
-13766,15595713.0,Chiemenam,543.0,Spain,Female,38.0,0.0,125860.2,1.0,1.0,0.0,143980.29,0.0
-13767,15617790.0,Kao,598.0,France,Male,37.0,5.0,154902.66,1.0,1.0,0.0,122925.4,0.0
-13768,15582914.0,Smith,763.0,France,Female,54.0,3.0,0.0,1.0,1.0,0.0,71323.23,0.0
-13769,15593776.0,Onio,516.0,Germany,Male,43.0,6.0,131762.94,1.0,1.0,0.0,79259.77,1.0
-13770,15588839.0,Ch'iu,577.0,France,Male,42.0,10.0,131698.57,1.0,1.0,0.0,136435.75,1.0
-13771,15701687.0,Chimezie,714.0,Spain,Female,46.0,10.0,0.0,1.0,0.0,0.0,125920.54,1.0
-13772,15605037.0,Ts'ui,628.0,Spain,Female,37.0,1.0,160139.59,1.0,1.0,1.0,48606.71,0.0
-13773,15678317.0,Chukwueloka,703.0,France,Female,26.0,9.0,0.0,2.0,1.0,1.0,64477.25,0.0
-13774,15593973.0,Ch'en,809.0,France,Male,35.0,2.0,91897.8,1.0,1.0,1.0,72873.33,0.0
-13775,15788291.0,Ch'in,472.0,France,Male,41.0,4.0,0.0,1.0,1.0,0.0,170893.85,0.0
-13776,15792934.0,Teng,711.0,Germany,Female,47.0,6.0,91908.36,1.0,1.0,0.0,102908.96,1.0
-13777,15762729.0,Hsiung,652.0,France,Male,37.0,8.0,0.0,2.0,1.0,0.0,16897.19,1.0
-13778,15578878.0,Milanesi,822.0,Germany,Female,51.0,3.0,141541.25,1.0,0.0,1.0,172618.52,1.0
-13779,15808831.0,Ch'iu,679.0,Spain,Female,39.0,5.0,0.0,2.0,1.0,0.0,182913.95,0.0
-13780,15752626.0,Biryukov,696.0,Spain,Female,40.0,4.0,0.0,2.0,1.0,1.0,112503.24,0.0
-13781,15571666.0,Nucci,587.0,France,Male,51.0,9.0,0.0,2.0,1.0,1.0,53534.8,0.0
-13782,15605918.0,Horsley,629.0,Spain,Male,25.0,2.0,0.0,2.0,1.0,1.0,149853.89,0.0
-13783,15672905.0,Ts'ui,706.0,Spain,Male,34.0,5.0,141806.0,1.0,1.0,1.0,548.52,0.0
-13784,15679983.0,Folliero,652.0,Spain,Male,34.0,5.0,0.0,2.0,1.0,1.0,48478.47,0.0
-13785,15641114.0,Tretiakova,637.0,France,Male,23.0,5.0,0.0,2.0,1.0,0.0,157097.09,0.0
-13786,15702566.0,Chidalu,751.0,France,Female,32.0,8.0,0.0,2.0,1.0,0.0,174531.27,0.0
-13787,15641478.0,Onyemere,705.0,France,Female,45.0,6.0,0.0,2.0,1.0,0.0,178045.97,0.0
-13788,15583076.0,Fyodorova,579.0,France,Male,33.0,4.0,0.0,2.0,1.0,0.0,140800.66,0.0
-13789,15591550.0,Ugorji,619.0,Germany,Male,62.0,6.0,171922.72,1.0,1.0,1.0,143952.24,0.0
-13790,15756896.0,Mazzanti,597.0,Spain,Female,40.0,5.0,0.0,2.0,1.0,0.0,57549.65,0.0
-13791,15591915.0,Clements,714.0,France,Male,28.0,8.0,0.0,2.0,1.0,0.0,152872.39,0.0
-13792,15570947.0,Kao,663.0,Spain,Male,41.0,9.0,0.0,2.0,0.0,1.0,27654.44,0.0
-13793,15767231.0,Udinese,684.0,Germany,Male,30.0,1.0,111568.01,2.0,0.0,0.0,132702.64,0.0
-13794,15806822.0,Hsia,576.0,Spain,Male,52.0,8.0,0.0,1.0,1.0,0.0,124448.77,1.0
-13795,15726153.0,Chibuzo,646.0,Germany,Male,27.0,8.0,102954.68,2.0,1.0,0.0,179206.92,0.0
-13796,15803526.0,Martin,651.0,Germany,Female,40.0,5.0,121729.42,1.0,1.0,0.0,130132.41,1.0
-13797,15763612.0,Chigozie,698.0,France,Female,40.0,1.0,0.0,2.0,1.0,0.0,79623.28,0.0
-13798,15734577.0,Nwachinemelu,748.0,France,Female,38.0,5.0,0.0,2.0,1.0,0.0,82762.03,0.0
-13799,15607634.0,Scott,682.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,1.0,181270.84,0.0
-13800,15804722.0,Zikoranachidimma,637.0,France,Male,42.0,3.0,0.0,2.0,1.0,1.0,85421.48,0.0
-13801,15722993.0,McIntyre,714.0,France,Female,48.0,8.0,0.0,2.0,0.0,1.0,68730.91,1.0
-13802,15607637.0,Lo,646.0,Spain,Female,41.0,6.0,0.0,2.0,1.0,1.0,136079.64,0.0
-13803,15745354.0,Chukwuemeka,583.0,Spain,Female,41.0,5.0,0.0,1.0,1.0,0.0,79616.37,0.0
-13804,15743817.0,Ts'ao,556.0,Germany,Male,37.0,6.0,113865.23,1.0,0.0,1.0,173365.89,1.0
-13805,15684461.0,Hsiung,710.0,France,Male,28.0,3.0,0.0,2.0,1.0,1.0,38962.94,0.0
-13806,15673820.0,Hsueh,565.0,France,Female,34.0,3.0,0.0,2.0,1.0,1.0,144880.81,0.0
-13807,15601184.0,Lucciano,705.0,France,Female,41.0,3.0,0.0,1.0,1.0,0.0,130189.72,0.0
-13808,15761497.0,Ch'ang,581.0,France,Male,31.0,3.0,0.0,2.0,0.0,1.0,143317.42,0.0
-13809,15595067.0,Brown,638.0,Germany,Female,41.0,2.0,139859.54,3.0,1.0,0.0,12761.92,1.0
-13810,15682442.0,T'ien,710.0,Germany,Female,40.0,0.0,122793.96,1.0,0.0,0.0,88791.83,1.0
-13811,15723884.0,Nkemakolam,628.0,Spain,Male,29.0,1.0,133379.41,1.0,1.0,1.0,71830.1,0.0
-13812,15602731.0,De Luca,667.0,France,Male,35.0,8.0,143390.51,1.0,0.0,0.0,143598.7,0.0
-13813,15596013.0,Nwachukwu,667.0,Germany,Male,43.0,0.0,118342.26,1.0,1.0,0.0,74564.41,1.0
-13814,15756070.0,Gartrell,684.0,France,Female,31.0,3.0,0.0,2.0,1.0,0.0,161579.85,0.0
-13815,15769902.0,T'ang,588.0,Spain,Female,29.0,1.0,0.0,2.0,0.0,1.0,157891.11,0.0
-13816,15677764.0,Ugochukwu,663.0,France,Male,36.0,7.0,0.0,2.0,1.0,1.0,103564.25,0.0
-13817,15768124.0,Tseng,751.0,Germany,Female,35.0,8.0,90547.48,1.0,1.0,0.0,88604.89,1.0
-13818,15708710.0,Chiawuotu,516.0,France,Male,32.0,5.0,0.0,2.0,1.0,0.0,95500.98,0.0
-13819,15637650.0,Obiajulu,589.0,Spain,Female,32.0,9.0,0.0,1.0,0.0,1.0,46422.36,0.0
-13820,15743709.0,Chukwufumnanya,619.0,France,Male,31.0,9.0,0.0,2.0,1.0,0.0,120115.5,0.0
-13821,15660263.0,Kao,757.0,France,Female,45.0,7.0,0.0,2.0,1.0,0.0,122925.4,0.0
-13822,15716155.0,Mazzanti,722.0,France,Female,32.0,3.0,0.0,2.0,1.0,1.0,164608.98,0.0
-13823,15604073.0,Burt,661.0,France,Female,53.0,3.0,146710.76,1.0,1.0,0.0,138510.01,0.0
-13824,15663927.0,Iweobiegbulam,763.0,France,Female,48.0,8.0,0.0,1.0,1.0,1.0,138021.36,0.0
-13825,15783026.0,Nucci,696.0,Germany,Female,40.0,7.0,124532.68,1.0,1.0,0.0,158059.04,1.0
-13826,15770474.0,Tien,814.0,France,Male,24.0,8.0,0.0,2.0,1.0,1.0,119439.66,0.0
-13827,15692406.0,Onwuamaeze,738.0,France,Male,26.0,9.0,88815.56,1.0,1.0,1.0,139919.38,0.0
-13828,15699430.0,Y?,678.0,France,Female,42.0,0.0,0.0,1.0,0.0,1.0,142839.82,0.0
-13829,15632210.0,Trevisani,613.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,58469.98,0.0
-13830,15572792.0,T'ien,603.0,France,Male,24.0,9.0,0.0,2.0,1.0,0.0,144880.81,0.0
-13831,15646091.0,Calabresi,616.0,Germany,Female,44.0,4.0,109528.44,2.0,1.0,1.0,94815.04,0.0
-13832,15615020.0,Ch'eng,632.0,France,Female,35.0,2.0,0.0,2.0,0.0,1.0,177528.92,0.0
-13833,15631310.0,Mazzanti,682.0,Germany,Male,38.0,8.0,165438.26,1.0,1.0,1.0,148516.71,0.0
-13834,15602354.0,Y?an,767.0,France,Female,38.0,4.0,0.0,1.0,1.0,1.0,14073.11,0.0
-13835,15682465.0,Mazzanti,586.0,Germany,Male,35.0,7.0,122314.5,1.0,1.0,0.0,53954.24,1.0
-13836,15770121.0,Chukwueloka,553.0,France,Female,21.0,9.0,0.0,2.0,1.0,0.0,100637.07,0.0
-13837,15651063.0,Nwachinemelu,686.0,Germany,Male,42.0,7.0,148061.89,2.0,0.0,0.0,143317.42,0.0
-13838,15644833.0,Iadanza,613.0,France,Female,25.0,7.0,0.0,2.0,1.0,0.0,54869.54,0.0
-13839,15732265.0,Chukwuemeka,850.0,France,Male,50.0,8.0,0.0,2.0,0.0,1.0,67840.81,0.0
-13840,15641110.0,Kao,739.0,France,Male,48.0,7.0,90537.16,1.0,1.0,1.0,176882.08,0.0
-13841,15584785.0,Chien,714.0,Spain,Male,38.0,2.0,0.0,2.0,1.0,1.0,64849.74,0.0
-13842,15794868.0,Chukwujekwu,793.0,France,Female,35.0,6.0,0.0,2.0,0.0,0.0,140934.87,0.0
-13843,15573926.0,Okwuoma,614.0,France,Male,50.0,6.0,0.0,1.0,1.0,1.0,178642.73,1.0
-13844,15591179.0,Smith,739.0,France,Male,25.0,9.0,0.0,2.0,0.0,1.0,83983.39,0.0
-13845,15566269.0,Gboliwe,596.0,Germany,Male,57.0,4.0,121532.17,2.0,0.0,1.0,74854.97,1.0
-13846,15665062.0,Onyemauchechi,687.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,180593.45,0.0
-13847,15669411.0,Naylor,599.0,France,Male,28.0,6.0,0.0,2.0,1.0,1.0,137028.51,0.0
-13848,15597957.0,Ijendu,567.0,France,Male,26.0,6.0,107852.81,1.0,0.0,0.0,155061.97,0.0
-13849,15741554.0,Ch'ang,645.0,France,Male,32.0,8.0,0.0,2.0,1.0,1.0,149913.84,0.0
-13850,15715079.0,Chukwuemeka,641.0,France,Female,37.0,1.0,0.0,1.0,0.0,0.0,145558.36,0.0
-13851,15583724.0,Ch'en,651.0,France,Male,29.0,2.0,0.0,1.0,1.0,0.0,123885.88,0.0
-13852,15782531.0,Mancini,663.0,France,Male,38.0,3.0,0.0,1.0,1.0,0.0,121151.1,0.0
-13853,15655007.0,Hsiao,557.0,France,Male,34.0,1.0,0.0,2.0,0.0,1.0,97628.08,0.0
-13854,15572547.0,T'ien,653.0,France,Male,40.0,3.0,0.0,2.0,0.0,1.0,155047.11,0.0
-13855,15657812.0,Shih,764.0,Spain,Female,32.0,6.0,89900.24,2.0,0.0,0.0,187905.12,0.0
-13856,15704788.0,Onyekachi,728.0,France,Female,32.0,4.0,120167.42,2.0,1.0,1.0,121681.19,0.0
-13857,15671590.0,Nwachukwu,628.0,Spain,Male,36.0,10.0,0.0,2.0,1.0,0.0,140934.95,0.0
-13858,15711398.0,Hsieh,554.0,Germany,Female,23.0,6.0,113625.93,2.0,1.0,1.0,182266.01,0.0
-13859,15642636.0,Chukwufumnanya,599.0,Spain,Male,35.0,5.0,84327.77,1.0,1.0,0.0,175894.24,0.0
-13860,15729954.0,Onyemere,584.0,France,Male,70.0,5.0,0.0,2.0,1.0,1.0,25662.27,1.0
-13861,15681562.0,Chinwendu,677.0,France,Male,47.0,2.0,0.0,2.0,0.0,1.0,86587.37,0.0
-13862,15794468.0,Nnamdi,661.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,140168.36,0.0
-13863,15709136.0,Kaeppel,598.0,France,Female,61.0,6.0,0.0,1.0,1.0,1.0,121428.4,0.0
-13864,15773520.0,Chiu,712.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,132467.54,0.0
-13865,15714355.0,Ch'ien,657.0,France,Male,47.0,7.0,0.0,2.0,1.0,0.0,102076.02,1.0
-13866,15649858.0,Chukwumaobim,675.0,France,Female,23.0,4.0,0.0,2.0,1.0,1.0,97623.42,0.0
-13867,15731751.0,Shih,645.0,France,Male,36.0,5.0,0.0,1.0,1.0,0.0,89937.71,0.0
-13868,15798960.0,Chikezie,710.0,Germany,Male,49.0,6.0,79035.95,1.0,1.0,0.0,136675.22,1.0
-13869,15592877.0,Shih,628.0,France,Male,33.0,5.0,0.0,2.0,0.0,0.0,86935.08,0.0
-13870,15718289.0,Ch'ang,551.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,178122.71,0.0
-13871,15572415.0,Ch'ang,590.0,France,Female,66.0,5.0,123372.3,1.0,0.0,1.0,167558.26,0.0
-13872,15603328.0,Chinagorom,746.0,France,Female,32.0,1.0,0.0,1.0,0.0,1.0,143463.28,0.0
-13873,15789563.0,Nweke,544.0,France,Male,24.0,1.0,0.0,2.0,0.0,1.0,109626.01,0.0
-13874,15799480.0,Manna,757.0,France,Female,43.0,8.0,0.0,2.0,1.0,1.0,187905.12,0.0
-13875,15627220.0,Shih,730.0,France,Female,24.0,2.0,0.0,2.0,1.0,1.0,108716.16,0.0
-13876,15676966.0,Chiang,637.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,0.0,36706.56,0.0
-13877,15745794.0,Lu,516.0,France,Female,46.0,2.0,0.0,1.0,1.0,1.0,51907.72,1.0
-13878,15582797.0,Padovesi,731.0,Spain,Female,42.0,3.0,0.0,2.0,1.0,0.0,139435.73,0.0
-13879,15682528.0,Udegbunam,712.0,France,Male,33.0,2.0,0.0,2.0,1.0,0.0,155879.57,0.0
-13880,15761047.0,O'Sullivan,715.0,France,Male,61.0,2.0,0.0,2.0,1.0,1.0,90212.38,0.0
-13881,15766552.0,Lujan,649.0,Spain,Male,58.0,3.0,110132.55,1.0,0.0,1.0,49816.46,0.0
-13882,15659820.0,Nwora,586.0,Germany,Female,45.0,6.0,144428.87,1.0,0.0,0.0,101039.53,1.0
-13883,15631481.0,Hs?eh,657.0,France,Male,38.0,2.0,0.0,2.0,1.0,1.0,153605.68,0.0
-13884,15726087.0,Chukwufumnanya,697.0,France,Male,35.0,8.0,0.0,2.0,1.0,0.0,157959.02,0.0
-13885,15775203.0,De Luca,609.0,France,Male,64.0,5.0,0.0,2.0,1.0,1.0,179056.23,0.0
-13886,15771299.0,Rizzo,668.0,France,Female,41.0,5.0,0.0,1.0,1.0,1.0,169921.11,0.0
-13887,15649033.0,Fyodorov,571.0,France,Male,27.0,4.0,0.0,2.0,1.0,1.0,94889.2,0.0
-13888,15771222.0,Ritchie,705.0,Spain,Male,45.0,6.0,0.0,2.0,1.0,1.0,164741.5,0.0
-13889,15750772.0,Kenechukwu,677.0,France,Male,29.0,5.0,0.0,2.0,0.0,0.0,40893.61,0.0
-13890,15610147.0,Nucci,714.0,France,Male,60.0,2.0,0.0,2.0,0.0,1.0,26149.25,0.0
-13891,15762993.0,Eluemuno,704.0,France,Male,26.0,8.0,117300.02,1.0,1.0,1.0,180622.02,0.0
-13892,15697574.0,Chiemela,697.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,95957.09,0.0
-13893,15704053.0,Ch'ang,569.0,France,Male,30.0,9.0,0.0,2.0,1.0,0.0,109089.04,0.0
-13894,15703923.0,Maclean,775.0,Germany,Male,31.0,7.0,124328.88,1.0,1.0,1.0,172363.41,0.0
-13895,15714586.0,Onwumelu,697.0,France,Female,34.0,1.0,0.0,2.0,1.0,0.0,6409.55,0.0
-13896,15721189.0,Gilleland,757.0,Spain,Female,47.0,3.0,137811.75,1.0,1.0,0.0,152417.79,1.0
-13897,15661670.0,Y?an,678.0,France,Female,37.0,8.0,121398.42,1.0,1.0,1.0,123499.98,0.0
-13898,15618446.0,Mazzi,638.0,Spain,Male,55.0,1.0,0.0,2.0,1.0,1.0,186885.01,0.0
-13899,15613471.0,Tseng,815.0,Spain,Female,61.0,5.0,0.0,2.0,1.0,1.0,175894.24,0.0
-13900,15674833.0,Brown,641.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,107787.42,0.0
-13901,15576085.0,Ch'ang,678.0,France,Male,39.0,9.0,0.0,1.0,1.0,0.0,26136.13,0.0
-13902,15794413.0,Trevisano,682.0,France,Male,25.0,7.0,0.0,2.0,1.0,1.0,77981.85,0.0
-13903,15727299.0,Ts'ai,641.0,France,Male,39.0,7.0,0.0,2.0,0.0,0.0,102628.98,0.0
-13904,15684319.0,Fanucci,632.0,France,Male,47.0,8.0,0.0,2.0,1.0,1.0,121866.87,0.0
-13905,15581323.0,Nwabugwu,602.0,Germany,Female,46.0,6.0,130274.22,3.0,1.0,0.0,125823.79,1.0
-13906,15598892.0,Chiang,724.0,Germany,Female,38.0,8.0,103907.28,1.0,0.0,0.0,177844.06,1.0
-13907,15794964.0,Nwachinemelu,686.0,France,Female,30.0,4.0,0.0,2.0,1.0,1.0,186869.58,0.0
-13908,15779497.0,Ts'ai,615.0,Germany,Male,33.0,5.0,133476.05,1.0,1.0,1.0,119127.06,1.0
-13909,15732265.0,Ndukaku,632.0,France,Male,42.0,8.0,0.0,1.0,1.0,1.0,47468.56,0.0
-13910,15652626.0,T'ao,684.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,79577.4,0.0
-13911,15809909.0,Oluchukwu,714.0,Spain,Male,39.0,2.0,0.0,2.0,0.0,0.0,131097.9,0.0
-13912,15654577.0,Chigozie,529.0,France,Male,37.0,1.0,0.0,1.0,1.0,1.0,105720.09,0.0
-13913,15736990.0,Ch'en,678.0,France,Male,41.0,3.0,0.0,2.0,0.0,1.0,56905.42,1.0
-13914,15681671.0,Ts'ai,817.0,Germany,Female,39.0,4.0,141787.31,1.0,1.0,1.0,149184.15,1.0
-13915,15773491.0,Nucci,645.0,Spain,Male,35.0,9.0,0.0,1.0,1.0,0.0,129936.53,0.0
-13916,15752300.0,O'Sullivan,714.0,France,Male,38.0,5.0,0.0,2.0,1.0,1.0,160979.66,0.0
-13917,15751412.0,Ch'eng,714.0,France,Male,31.0,8.0,0.0,2.0,0.0,0.0,44390.38,0.0
-13918,15621818.0,Nwankwo,729.0,France,Female,31.0,0.0,121516.9,1.0,1.0,0.0,101699.77,0.0
-13919,15601012.0,Udinesi,598.0,Germany,Female,40.0,5.0,87772.52,1.0,1.0,0.0,108008.65,1.0
-13920,15595440.0,Ritchie,506.0,France,Male,71.0,8.0,0.0,2.0,1.0,1.0,114448.77,0.0
-13921,15595713.0,Hs?,724.0,France,Female,39.0,8.0,0.0,2.0,1.0,1.0,184524.65,0.0
-13922,15614687.0,Genovese,595.0,France,Female,45.0,9.0,0.0,2.0,0.0,1.0,170528.63,0.0
-13923,15644833.0,Chukwufumnanya,719.0,Germany,Male,30.0,2.0,156652.13,2.0,1.0,0.0,55868.97,0.0
-13924,15687565.0,Nwachinemelu,587.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,68600.36,0.0
-13925,15588854.0,Nucci,724.0,France,Female,47.0,5.0,0.0,3.0,1.0,0.0,9677.0,1.0
-13926,15683471.0,Toscani,610.0,France,Male,37.0,6.0,0.0,2.0,1.0,0.0,163989.66,0.0
-13927,15783865.0,Hsu,744.0,Spain,Male,28.0,5.0,0.0,2.0,0.0,0.0,157547.71,0.0
-13928,15775234.0,Foveaux,592.0,France,Female,36.0,2.0,0.0,1.0,1.0,1.0,73977.23,0.0
-13929,15734649.0,Hs?,622.0,France,Female,40.0,5.0,0.0,1.0,1.0,1.0,149991.32,0.0
-13930,15750049.0,Trevisani,705.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,70563.9,0.0
-13931,15773869.0,Ogochukwu,595.0,France,Female,48.0,2.0,0.0,2.0,1.0,0.0,182055.36,0.0
-13932,15592765.0,Walker,712.0,France,Male,35.0,5.0,128504.76,1.0,1.0,0.0,160529.71,0.0
-13933,15617676.0,Pagnotto,748.0,France,Male,50.0,5.0,0.0,1.0,1.0,1.0,97629.31,0.0
-13934,15796418.0,Tien,564.0,France,Male,52.0,2.0,141784.02,1.0,1.0,1.0,62232.6,1.0
-13935,15681755.0,Walker,663.0,Germany,Male,42.0,9.0,137814.5,1.0,1.0,1.0,96961.0,0.0
-13936,15571940.0,Udegbunam,703.0,Germany,Female,29.0,6.0,114446.74,1.0,1.0,0.0,181598.25,1.0
-13937,15691952.0,Onyemere,599.0,Spain,Male,28.0,5.0,0.0,2.0,0.0,0.0,93739.71,0.0
-13938,15771543.0,Onio,651.0,France,Male,42.0,8.0,0.0,1.0,1.0,0.0,151903.6,1.0
-13939,15736228.0,Smith,733.0,France,Female,54.0,9.0,0.0,3.0,1.0,0.0,92664.09,1.0
-13940,15794580.0,Pacheco,687.0,France,Female,22.0,4.0,0.0,2.0,1.0,0.0,91098.76,0.0
-13941,15590199.0,Sun,634.0,France,Male,27.0,1.0,0.0,2.0,1.0,0.0,93073.04,0.0
-13942,15643496.0,Ch'ang,594.0,Spain,Male,30.0,5.0,0.0,2.0,1.0,0.0,117031.2,0.0
-13943,15748649.0,Tikhonov,605.0,France,Male,31.0,1.0,0.0,2.0,1.0,1.0,125553.83,0.0
-13944,15775886.0,Uwaezuoke,556.0,France,Female,47.0,5.0,0.0,2.0,0.0,1.0,70954.41,0.0
-13945,15725429.0,Onyemauchechukwu,585.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,161887.84,0.0
-13946,15781574.0,Chia,686.0,France,Male,41.0,0.0,0.0,2.0,1.0,1.0,166476.95,0.0
-13947,15765266.0,Ts'ao,634.0,Spain,Female,40.0,7.0,0.0,2.0,1.0,1.0,72993.67,0.0
-13948,15572777.0,L?,691.0,Spain,Male,37.0,9.0,0.0,2.0,0.0,1.0,100995.68,0.0
-13949,15731553.0,Ejikemeifeuwa,687.0,France,Female,40.0,1.0,51116.26,1.0,1.0,0.0,85421.48,1.0
-13950,15784597.0,Hsueh,727.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,1.0,103920.51,0.0
-13951,15792525.0,Ch'en,584.0,Spain,Male,36.0,7.0,124023.18,1.0,1.0,1.0,137791.18,0.0
-13952,15608528.0,Pagnotto,598.0,Spain,Male,29.0,1.0,0.0,2.0,1.0,1.0,65986.48,0.0
-13953,15629985.0,Clements,709.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,147913.56,0.0
-13954,15806967.0,Hsia,724.0,Germany,Female,23.0,10.0,152759.0,2.0,0.0,1.0,60590.72,0.0
-13955,15777054.0,Yobachukwu,762.0,France,Female,49.0,4.0,0.0,1.0,1.0,0.0,118929.64,1.0
-13956,15792650.0,Chineze,686.0,Germany,Male,38.0,1.0,115730.71,2.0,1.0,0.0,163584.85,0.0
-13957,15761674.0,Ritchie,597.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,100566.81,0.0
-13958,15795527.0,Hsia,706.0,Spain,Female,50.0,4.0,131078.42,1.0,1.0,0.0,50908.48,1.0
-13959,15787602.0,T'ien,589.0,France,Female,46.0,0.0,0.0,1.0,1.0,0.0,172668.39,1.0
-13960,15771217.0,Hsia,706.0,France,Male,34.0,2.0,159364.66,1.0,1.0,1.0,126675.62,0.0
-13961,15606397.0,Chikwado,595.0,France,Male,27.0,7.0,0.0,2.0,1.0,0.0,89935.73,0.0
-13962,15590146.0,Cattaneo,595.0,France,Male,38.0,10.0,0.0,2.0,1.0,1.0,173365.9,0.0
-13963,15761525.0,Pagnotto,774.0,France,Female,49.0,6.0,0.0,2.0,1.0,0.0,114514.64,0.0
-13964,15808544.0,Uspenskaya,718.0,Germany,Male,38.0,7.0,80958.36,2.0,0.0,1.0,187905.12,0.0
-13965,15731751.0,Mao,629.0,Germany,Female,56.0,4.0,131852.81,1.0,1.0,0.0,91573.19,1.0
-13966,15697360.0,Yefimov,741.0,Germany,Male,37.0,6.0,134497.27,2.0,1.0,1.0,118855.26,0.0
-13967,15774336.0,Rizzo,731.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,91947.74,0.0
-13968,15745266.0,Genovese,627.0,Spain,Female,44.0,8.0,0.0,1.0,1.0,0.0,161889.95,1.0
-13969,15690731.0,Onyemaechi,679.0,Spain,Male,35.0,7.0,121863.61,1.0,1.0,1.0,172668.39,0.0
-13970,15621882.0,Ch'eng,741.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,738.88,0.0
-13971,15654574.0,Lucciano,594.0,France,Male,34.0,3.0,93920.47,1.0,0.0,1.0,160529.71,0.0
-13972,15598088.0,Uchenna,624.0,Spain,Male,47.0,2.0,133964.3,1.0,1.0,1.0,135939.3,0.0
-13973,15669414.0,Ts'ai,594.0,France,Male,23.0,3.0,0.0,2.0,1.0,0.0,160976.85,0.0
-13974,15744517.0,Olisaemeka,669.0,France,Female,30.0,9.0,71026.77,1.0,1.0,0.0,144880.81,0.0
-13975,15666884.0,Chukwujekwu,628.0,France,Male,40.0,7.0,0.0,2.0,1.0,1.0,26260.98,0.0
-13976,15743817.0,Zetticci,573.0,Spain,Male,33.0,4.0,80764.03,1.0,0.0,1.0,91830.75,0.0
-13977,15746737.0,Onochie,648.0,Germany,Female,45.0,6.0,154495.79,1.0,0.0,1.0,142513.5,0.0
-13978,15731117.0,Tikhonov,602.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,44363.43,0.0
-13979,15809862.0,Kao,754.0,Spain,Female,44.0,6.0,0.0,1.0,1.0,0.0,120511.08,0.0
-13980,15690958.0,Ibezimako,539.0,France,Male,41.0,4.0,80922.75,1.0,1.0,0.0,125979.19,0.0
-13981,15752312.0,Okwudiliolisa,697.0,Germany,Female,44.0,4.0,117412.19,1.0,0.0,0.0,92898.17,1.0
-13982,15641170.0,Toscani,648.0,Spain,Male,32.0,7.0,0.0,2.0,1.0,0.0,166678.28,0.0
-13983,15679550.0,Biryukova,753.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,116748.14,0.0
-13984,15693947.0,DeRose,746.0,Germany,Female,54.0,7.0,134571.5,1.0,1.0,0.0,163534.75,1.0
-13985,15795872.0,Lucciano,527.0,Spain,Female,46.0,4.0,111095.58,1.0,0.0,0.0,52795.56,1.0
-13986,15670481.0,Chiu,682.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,127569.8,0.0
-13987,15654075.0,Fanucci,737.0,France,Male,40.0,6.0,0.0,2.0,0.0,0.0,146875.86,0.0
-13988,15627360.0,Nnachetam,537.0,France,Female,33.0,9.0,0.0,2.0,0.0,1.0,171345.02,0.0
-13989,15758035.0,Chukwubuikem,594.0,France,Male,43.0,9.0,0.0,1.0,0.0,0.0,100478.6,0.0
-13990,15586757.0,Ponomarev,758.0,France,Male,39.0,6.0,110941.93,1.0,1.0,1.0,145593.85,0.0
-13991,15733032.0,Chukwuemeka,624.0,France,Male,43.0,2.0,125903.22,1.0,0.0,0.0,67788.49,0.0
-13992,15656912.0,Chukwudi,633.0,France,Male,44.0,7.0,0.0,2.0,1.0,1.0,131281.29,0.0
-13993,15753837.0,Chinonyelum,640.0,Germany,Male,37.0,10.0,116927.89,1.0,1.0,0.0,182988.94,1.0
-13994,15789939.0,Chukwuemeka,667.0,France,Female,36.0,9.0,0.0,2.0,1.0,0.0,124550.88,0.0
-13995,15668575.0,K?,747.0,France,Female,42.0,9.0,0.0,3.0,0.0,0.0,158043.11,1.0
-13996,15668893.0,Hsia,610.0,France,Female,45.0,6.0,0.0,2.0,1.0,0.0,172601.62,0.0
-13997,15779985.0,McNeil,671.0,France,Male,30.0,5.0,0.0,2.0,0.0,1.0,171345.02,0.0
-13998,15597930.0,Onyekaozulu,736.0,Germany,Male,46.0,3.0,106091.97,2.0,1.0,1.0,159823.16,0.0
-13999,15566251.0,Chukwualuka,744.0,France,Male,26.0,6.0,124547.13,1.0,1.0,1.0,168823.31,0.0
-14000,15775490.0,Fetherstonhaugh,694.0,France,Male,33.0,10.0,0.0,1.0,0.0,1.0,162452.65,0.0
-14001,15576676.0,Harker,790.0,Spain,Female,36.0,3.0,0.0,2.0,1.0,1.0,87412.24,0.0
-14002,15645605.0,Iadanza,632.0,Spain,Male,50.0,4.0,0.0,2.0,1.0,0.0,91981.85,0.0
-14003,15761783.0,Chiekwugo,633.0,Spain,Male,38.0,3.0,145330.43,1.0,1.0,0.0,168035.62,0.0
-14004,15702377.0,Macleod,629.0,France,Male,49.0,8.0,137592.61,1.0,1.0,1.0,57844.26,0.0
-14005,15627995.0,Hsieh,762.0,Germany,Male,23.0,8.0,150412.14,1.0,1.0,1.0,155846.69,0.0
-14006,15633108.0,Milanesi,749.0,France,Male,57.0,2.0,0.0,2.0,1.0,0.0,121525.48,0.0
-14007,15765192.0,Teng,593.0,Germany,Male,42.0,2.0,102286.2,1.0,1.0,1.0,146723.72,0.0
-14008,15802560.0,Yefimova,629.0,Spain,Male,29.0,6.0,0.0,4.0,1.0,0.0,180551.76,0.0
-14009,15695034.0,H?,730.0,France,Male,38.0,8.0,0.0,2.0,1.0,0.0,24802.77,0.0
-14010,15789941.0,Hao,678.0,Spain,Female,34.0,5.0,0.0,2.0,1.0,0.0,151411.2,0.0
-14011,15684198.0,Folliero,697.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,72929.84,0.0
-14012,15606901.0,McIntosh,644.0,France,Female,47.0,3.0,0.0,1.0,1.0,0.0,136491.72,1.0
-14013,15641342.0,Chigbogu,613.0,Germany,Female,60.0,9.0,114668.89,1.0,1.0,0.0,174032.0,1.0
-14014,15713578.0,De Bernales,707.0,Spain,Male,44.0,9.0,0.0,2.0,1.0,0.0,187905.12,0.0
-14015,15734886.0,Hsiao,586.0,Spain,Male,63.0,9.0,0.0,1.0,0.0,1.0,179436.6,1.0
-14016,15574858.0,Chuang,739.0,France,Male,41.0,2.0,0.0,1.0,1.0,1.0,156048.64,0.0
-14017,15639882.0,Maclean,663.0,France,Female,56.0,6.0,92930.96,1.0,1.0,0.0,65612.12,1.0
-14018,15716781.0,Udobata,579.0,Spain,Male,35.0,5.0,141136.62,1.0,1.0,0.0,50400.32,1.0
-14019,15604544.0,Nucci,651.0,France,Male,36.0,6.0,0.0,1.0,1.0,1.0,157921.25,1.0
-14020,15663938.0,Chidiegwu,709.0,France,Female,29.0,3.0,0.0,2.0,0.0,0.0,79919.13,0.0
-14021,15619304.0,De Luca,738.0,Germany,Female,47.0,1.0,83772.95,1.0,1.0,0.0,14073.11,1.0
-14022,15581198.0,Chidiegwu,549.0,France,Female,39.0,7.0,0.0,2.0,1.0,1.0,86791.9,0.0
-14023,15703923.0,Kao,684.0,Spain,Female,25.0,3.0,0.0,2.0,1.0,0.0,50777.77,0.0
-14024,15583076.0,De Luca,553.0,France,Male,36.0,6.0,0.0,2.0,1.0,0.0,131534.96,0.0
-14025,15776939.0,Ugorji,587.0,France,Female,29.0,8.0,0.0,2.0,1.0,0.0,88852.11,0.0
-14026,15809736.0,Ch'iu,681.0,Germany,Male,40.0,3.0,137983.07,2.0,1.0,1.0,74838.64,0.0
-14027,15709639.0,Uspensky,594.0,Spain,Male,55.0,10.0,0.0,2.0,1.0,1.0,169719.35,0.0
-14028,15648005.0,Shillito,850.0,Spain,Male,43.0,2.0,0.0,2.0,1.0,1.0,164975.82,0.0
-14029,15603482.0,Bibi,727.0,France,Female,34.0,7.0,0.0,2.0,1.0,0.0,152290.28,0.0
-14030,15707025.0,McIntyre,587.0,France,Male,62.0,9.0,0.0,1.0,1.0,0.0,157931.39,1.0
-14031,15723873.0,White,703.0,France,Female,37.0,2.0,0.0,2.0,1.0,0.0,126551.65,0.0
-14032,15646810.0,Nnaife,644.0,France,Female,30.0,9.0,0.0,2.0,1.0,1.0,138536.09,0.0
-14033,15808849.0,Omeokachie,707.0,Spain,Male,34.0,1.0,113829.45,2.0,1.0,1.0,162267.91,0.0
-14034,15733261.0,Hsia,651.0,Spain,Male,34.0,2.0,0.0,2.0,1.0,0.0,88658.73,0.0
-14035,15577647.0,Nnamutaezinwa,597.0,France,Female,37.0,9.0,137827.33,1.0,1.0,1.0,60928.51,0.0
-14036,15631481.0,Chiang,663.0,France,Male,40.0,2.0,0.0,2.0,1.0,1.0,179908.71,0.0
-14037,15783883.0,K'ung,622.0,France,Male,37.0,7.0,0.0,2.0,1.0,1.0,142935.83,0.0
-14038,15742272.0,Kaur,619.0,France,Female,42.0,8.0,0.0,2.0,1.0,1.0,160122.66,0.0
-14039,15807432.0,Ting,627.0,Spain,Male,30.0,3.0,113383.07,1.0,1.0,1.0,101491.23,0.0
-14040,15609987.0,Chibuzo,705.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,38691.34,0.0
-14041,15795895.0,Smith,683.0,France,Female,28.0,7.0,105957.88,1.0,1.0,1.0,132029.95,0.0
-14042,15658935.0,Chinedum,641.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,9984.4,0.0
-14043,15776733.0,Uvarov,850.0,France,Male,45.0,3.0,0.0,2.0,1.0,0.0,161653.5,0.0
-14044,15646810.0,Hsueh,677.0,Spain,Male,36.0,9.0,0.0,2.0,1.0,1.0,73564.44,0.0
-14045,15802060.0,T'ang,712.0,Spain,Female,52.0,2.0,115532.52,1.0,1.0,1.0,169347.56,0.0
-14046,15692406.0,Chinwemma,671.0,Germany,Female,37.0,8.0,116893.28,2.0,1.0,1.0,145551.6,1.0
-14047,15797964.0,Ts'ai,616.0,France,Female,38.0,3.0,126473.33,1.0,1.0,0.0,120512.08,0.0
-14048,15673790.0,Manna,575.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,1.0,65769.3,0.0
-14049,15804017.0,Ch'iu,652.0,France,Male,35.0,8.0,0.0,2.0,1.0,1.0,105887.04,0.0
-14050,15656592.0,Onyekachukwu,556.0,France,Female,35.0,7.0,0.0,1.0,1.0,1.0,101693.73,1.0
-14051,15665524.0,Uwakwe,596.0,France,Male,33.0,9.0,142987.46,1.0,0.0,0.0,148087.88,0.0
-14052,15807167.0,Chiazagomekpere,685.0,Spain,Female,63.0,8.0,0.0,2.0,1.0,1.0,180593.45,0.0
-14053,15632882.0,Liao,667.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,177847.03,0.0
-14054,15687094.0,Chiemenam,685.0,Germany,Male,41.0,2.0,127738.94,2.0,1.0,1.0,152883.95,0.0
-14055,15642394.0,P'eng,596.0,Spain,Male,36.0,1.0,0.0,2.0,1.0,0.0,95857.18,0.0
-14056,15643487.0,Toth,704.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,119903.2,0.0
-14057,15627298.0,Chia,844.0,France,Male,24.0,6.0,0.0,2.0,1.0,0.0,115068.99,0.0
-14058,15587584.0,Nwabugwu,691.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,127528.84,0.0
-14059,15730793.0,Fang,562.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,35795.85,0.0
-14060,15568819.0,Ch'ien,611.0,France,Male,49.0,2.0,119817.33,1.0,1.0,1.0,44965.44,1.0
-14061,15701364.0,Hs?eh,748.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,83470.4,0.0
-14062,15712584.0,Chiemenam,806.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,143859.66,0.0
-14063,15706463.0,Ugochukwu,592.0,France,Male,28.0,5.0,0.0,2.0,1.0,0.0,93713.12,0.0
-14064,15628274.0,Mazzanti,582.0,Germany,Male,46.0,4.0,130306.34,2.0,0.0,0.0,64872.33,1.0
-14065,15752622.0,Milne,683.0,France,Male,42.0,8.0,0.0,2.0,1.0,1.0,138882.98,0.0
-14066,15663883.0,Iloerika,670.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,0.0,151903.6,0.0
-14067,15744041.0,Shao,649.0,France,Male,43.0,5.0,0.0,2.0,0.0,0.0,109071.71,0.0
-14068,15737509.0,Chienezie,537.0,France,Male,31.0,5.0,0.0,2.0,0.0,1.0,120892.96,0.0
-14069,15640034.0,Ch'ang,850.0,France,Male,31.0,3.0,80957.88,1.0,1.0,0.0,126561.05,0.0
-14070,15602034.0,Ts'ui,744.0,France,Male,34.0,9.0,0.0,2.0,1.0,0.0,165562.84,0.0
-14071,15649738.0,Chukwukere,730.0,Germany,Female,48.0,9.0,90544.0,1.0,1.0,1.0,164741.5,1.0
-14072,15716085.0,Palerma,642.0,Spain,Female,37.0,6.0,119787.76,1.0,0.0,0.0,122662.98,0.0
-14073,15696054.0,Chukwueloka,684.0,France,Male,37.0,6.0,0.0,2.0,1.0,1.0,125608.74,0.0
-14074,15603764.0,Kosisochukwu,659.0,France,Male,44.0,3.0,0.0,2.0,1.0,1.0,172576.43,0.0
-14075,15571856.0,Onyemachukwu,538.0,Spain,Male,40.0,7.0,0.0,2.0,1.0,0.0,172618.52,0.0
-14076,15609998.0,Chizoba,564.0,France,Female,36.0,2.0,0.0,2.0,1.0,1.0,141756.32,0.0
-14077,15648032.0,Chin,658.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,0.0,198490.33,0.0
-14078,15738166.0,Shao,535.0,France,Male,37.0,6.0,0.0,2.0,0.0,1.0,64746.07,0.0
-14079,15676869.0,Lettiere,545.0,France,Female,64.0,8.0,164902.43,1.0,1.0,0.0,103573.96,1.0
-14080,15757911.0,T'ien,621.0,France,Male,44.0,7.0,125801.03,1.0,0.0,1.0,120311.07,0.0
-14081,15665088.0,Okeke,594.0,France,Female,31.0,5.0,0.0,2.0,1.0,0.0,138526.96,0.0
-14082,15773868.0,Yobanna,803.0,France,Male,30.0,5.0,95927.04,1.0,0.0,0.0,91565.63,0.0
-14083,15701765.0,Mazzanti,748.0,France,Female,31.0,4.0,0.0,1.0,1.0,0.0,88724.67,0.0
-14084,15766289.0,McLean,661.0,France,Male,21.0,5.0,0.0,1.0,0.0,0.0,125962.02,0.0
-14085,15781689.0,Onyekachi,645.0,France,Female,42.0,7.0,0.0,2.0,0.0,1.0,68343.08,0.0
-14086,15671342.0,Tien,576.0,Germany,Male,35.0,3.0,121260.19,1.0,0.0,0.0,15404.64,0.0
-14087,15607877.0,Nwabugwu,722.0,France,Male,49.0,0.0,181821.96,1.0,1.0,0.0,26589.56,1.0
-14088,15699486.0,Palfreyman,718.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,126469.09,0.0
-14089,15642996.0,Udegbulam,721.0,Spain,Male,32.0,8.0,0.0,2.0,1.0,1.0,123882.73,0.0
-14090,15615091.0,Cattaneo,762.0,Spain,Male,24.0,4.0,0.0,2.0,1.0,1.0,122925.4,0.0
-14091,15805523.0,Fanucci,601.0,Spain,Male,30.0,4.0,0.0,1.0,1.0,1.0,90928.02,0.0
-14092,15633352.0,Okwuadigbo,684.0,Spain,Male,36.0,9.0,0.0,1.0,1.0,1.0,158516.48,0.0
-14093,15704770.0,L?,548.0,France,Male,45.0,5.0,0.0,1.0,1.0,0.0,58854.88,0.0
-14094,15616088.0,Hsiao,671.0,Germany,Male,39.0,2.0,181461.46,2.0,1.0,1.0,85901.09,0.0
-14095,15574733.0,Nwachukwu,656.0,France,Male,35.0,2.0,167924.95,1.0,1.0,0.0,180852.12,0.0
-14096,15654060.0,K'ung,556.0,France,Male,39.0,1.0,0.0,2.0,1.0,1.0,147963.07,0.0
-14097,15789425.0,Chiang,602.0,Spain,Female,34.0,8.0,74896.92,1.0,1.0,1.0,138775.86,0.0
-14098,15725429.0,Lucchese,648.0,France,Male,31.0,5.0,127675.39,1.0,0.0,0.0,103421.91,0.0
-14099,15796764.0,Hs?,674.0,Germany,Female,46.0,7.0,123860.18,2.0,1.0,0.0,130529.13,1.0
-14100,15694125.0,Yermakova,659.0,Spain,Male,34.0,2.0,124788.18,2.0,1.0,1.0,57549.65,0.0
-14101,15570069.0,Frolov,621.0,France,Female,34.0,9.0,0.0,2.0,1.0,1.0,68860.3,0.0
-14102,15627185.0,Chinwendu,711.0,France,Male,27.0,0.0,0.0,1.0,0.0,1.0,162699.7,0.0
-14103,15789865.0,Chinedum,613.0,Germany,Male,28.0,8.0,71097.23,2.0,0.0,0.0,53420.53,0.0
-14104,15574858.0,Yermakova,758.0,Germany,Male,33.0,1.0,113599.48,2.0,1.0,0.0,178187.28,0.0
-14105,15581548.0,Smith,516.0,Spain,Female,33.0,7.0,0.0,2.0,1.0,0.0,65951.28,0.0
-14106,15662291.0,Chikezie,724.0,Germany,Male,46.0,3.0,73314.04,1.0,1.0,1.0,58214.79,0.0
-14107,15762902.0,Trevisano,752.0,Germany,Female,42.0,2.0,114435.47,2.0,1.0,1.0,180622.02,0.0
-14108,15566563.0,K'ung,659.0,France,Male,32.0,8.0,104488.16,1.0,0.0,1.0,136902.01,0.0
-14109,15764343.0,Hsieh,543.0,Germany,Male,47.0,9.0,104449.8,1.0,0.0,1.0,121504.18,1.0
-14110,15665521.0,Chia,479.0,Germany,Female,39.0,5.0,123873.19,2.0,1.0,1.0,183963.34,1.0
-14111,15752881.0,Ch'iu,584.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,118749.58,0.0
-14112,15776631.0,Okechukwu,707.0,France,Male,28.0,1.0,0.0,1.0,0.0,0.0,171060.01,0.0
-14113,15748649.0,Teng,627.0,France,Female,30.0,5.0,0.0,2.0,0.0,0.0,87400.53,0.0
-14114,15592999.0,Li Fonti,542.0,France,Female,30.0,5.0,0.0,2.0,0.0,1.0,178074.04,0.0
-14115,15667460.0,Nnachetam,651.0,France,Male,25.0,2.0,0.0,2.0,1.0,0.0,181204.5,0.0
-14116,15761606.0,Chiu,748.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,123978.86,0.0
-14117,15603328.0,P'eng,707.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,1.0,57553.98,0.0
-14118,15777772.0,Ch'in,547.0,France,Male,30.0,2.0,86977.96,2.0,0.0,1.0,115818.51,0.0
-14119,15654495.0,Manna,602.0,France,Male,57.0,6.0,0.0,1.0,1.0,0.0,171762.87,1.0
-14120,15641389.0,Shao,678.0,Germany,Female,40.0,3.0,57784.22,1.0,0.0,0.0,52796.31,1.0
-14121,15749177.0,Lucchesi,596.0,France,Male,34.0,9.0,155619.58,1.0,0.0,1.0,81778.72,0.0
-14122,15600882.0,Fanucci,652.0,France,Female,63.0,6.0,0.0,2.0,1.0,1.0,143665.44,0.0
-14123,15650029.0,Ch'ien,648.0,France,Female,43.0,10.0,0.0,2.0,1.0,0.0,140941.47,0.0
-14124,15574071.0,Lu,750.0,France,Female,39.0,5.0,163549.63,1.0,1.0,0.0,147530.06,0.0
-14125,15809823.0,Uwaezuoke,591.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,182372.35,0.0
-14126,15810829.0,Ozerova,641.0,France,Male,41.0,6.0,0.0,2.0,1.0,1.0,168816.22,0.0
-14127,15662658.0,Frolov,677.0,Spain,Female,53.0,10.0,109044.6,1.0,1.0,1.0,118982.51,0.0
-14128,15796721.0,Chikelu,773.0,Spain,Female,38.0,1.0,0.0,1.0,1.0,1.0,89801.9,0.0
-14129,15668594.0,Udegbulam,559.0,France,Male,42.0,5.0,0.0,1.0,1.0,1.0,135875.51,1.0
-14130,15789432.0,Yermakov,636.0,France,Female,30.0,8.0,0.0,2.0,0.0,1.0,131301.74,0.0
-14131,15737267.0,Tseng,726.0,France,Female,29.0,1.0,0.0,2.0,1.0,1.0,38570.13,0.0
-14132,15719606.0,L?,631.0,Spain,Female,34.0,6.0,0.0,1.0,1.0,1.0,118029.09,0.0
-14133,15701265.0,Nwachinemelu,745.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,0.0,104469.05,0.0
-14134,15652693.0,Chukwueloka,709.0,Spain,Male,44.0,9.0,0.0,2.0,1.0,1.0,64849.74,0.0
-14135,15741921.0,Lettiere,578.0,Spain,Male,34.0,7.0,0.0,2.0,0.0,1.0,151648.66,0.0
-14136,15594815.0,Toscani,714.0,France,Male,31.0,5.0,152347.01,2.0,1.0,1.0,151401.33,0.0
-14137,15600912.0,Chidiebere,726.0,Germany,Male,30.0,10.0,121325.84,2.0,1.0,1.0,56905.38,0.0
-14138,15723169.0,Onyemachukwu,683.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,73329.08,0.0
-14139,15646082.0,Lo Duca,706.0,France,Female,40.0,9.0,0.0,2.0,1.0,1.0,184936.77,0.0
-14140,15803378.0,Palerma,687.0,France,Female,39.0,7.0,0.0,1.0,1.0,1.0,51454.06,0.0
-14141,15653776.0,Fiorentini,593.0,France,Female,31.0,3.0,0.0,1.0,1.0,1.0,136491.72,1.0
-14142,15784097.0,P'eng,682.0,Spain,Male,43.0,8.0,0.0,1.0,1.0,0.0,199255.32,0.0
-14143,15699446.0,Onwumelu,652.0,Spain,Male,66.0,6.0,0.0,1.0,1.0,1.0,121562.33,1.0
-14144,15749130.0,Fadden,584.0,France,Female,30.0,1.0,0.0,1.0,0.0,1.0,51364.56,0.0
-14145,15703763.0,K'ung,679.0,France,Male,51.0,6.0,0.0,1.0,0.0,1.0,24113.91,1.0
-14146,15740164.0,Nwabugwu,642.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,5791.85,0.0
-14147,15766355.0,Goliwe,686.0,Germany,Female,45.0,9.0,92887.13,1.0,0.0,0.0,163717.16,1.0
-14148,15681562.0,Tuan,653.0,France,Female,34.0,7.0,0.0,2.0,0.0,0.0,104722.47,0.0
-14149,15743192.0,Yermakova,676.0,Spain,Female,45.0,4.0,0.0,2.0,1.0,1.0,156774.75,0.0
-14150,15725024.0,T'ang,662.0,France,Male,31.0,2.0,0.0,2.0,1.0,1.0,180594.85,0.0
-14151,15717995.0,Hsiao,583.0,France,Female,36.0,1.0,123385.72,1.0,0.0,0.0,108872.45,0.0
-14152,15728505.0,Ch'eng,611.0,France,Female,37.0,6.0,0.0,2.0,1.0,0.0,144870.28,0.0
-14153,15810834.0,Ting,707.0,France,Female,61.0,6.0,0.0,3.0,1.0,1.0,84870.23,1.0
-14154,15670753.0,Gboliwe,569.0,Germany,Female,39.0,7.0,123959.97,2.0,1.0,0.0,89938.07,0.0
-14155,15691627.0,Chiawuotu,637.0,France,Male,29.0,4.0,0.0,2.0,0.0,1.0,85901.09,0.0
-14156,15638159.0,Lo,692.0,France,Male,47.0,2.0,0.0,1.0,1.0,1.0,127825.25,0.0
-14157,15589572.0,Ku,685.0,Spain,Male,36.0,9.0,0.0,1.0,0.0,0.0,70954.41,1.0
-14158,15773731.0,Iloerika,741.0,Spain,Male,32.0,3.0,0.0,2.0,1.0,0.0,121408.55,0.0
-14159,15598521.0,Onwumelu,581.0,Germany,Male,39.0,8.0,132951.92,1.0,1.0,1.0,74849.46,1.0
-14160,15595766.0,Genovesi,706.0,Spain,Female,47.0,1.0,0.0,1.0,1.0,0.0,89566.64,1.0
-14161,15683560.0,Gartrell,651.0,Germany,Female,49.0,6.0,108004.64,1.0,1.0,0.0,83700.66,1.0
-14162,15586518.0,Tseng,838.0,Germany,Female,44.0,1.0,106376.85,1.0,1.0,0.0,156439.56,1.0
-14163,15715750.0,Fanucci,705.0,France,Female,56.0,1.0,0.0,1.0,1.0,1.0,91510.71,1.0
-14164,15645463.0,Ch'en,850.0,Germany,Female,35.0,7.0,120171.58,2.0,1.0,1.0,113046.81,0.0
-14165,15735358.0,T'ien,561.0,France,Male,41.0,6.0,0.0,2.0,0.0,1.0,71830.1,0.0
-14166,15760456.0,Hsiung,714.0,France,Female,36.0,7.0,94108.54,1.0,1.0,1.0,109538.35,0.0
-14167,15599195.0,Pirozzi,656.0,Germany,Female,43.0,5.0,114435.43,1.0,1.0,0.0,153983.99,1.0
-14168,15632850.0,Onuchukwu,719.0,France,Female,36.0,5.0,124053.03,1.0,0.0,0.0,160532.88,0.0
-14169,15586880.0,Chidiebere,550.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,90174.83,0.0
-14170,15793834.0,Pirozzi,584.0,France,Male,38.0,10.0,0.0,2.0,1.0,0.0,88890.05,0.0
-14171,15581252.0,Thompson,699.0,Germany,Female,33.0,7.0,129499.42,2.0,0.0,0.0,86951.6,1.0
-14172,15718231.0,Hs?eh,697.0,France,Female,24.0,8.0,0.0,2.0,1.0,0.0,94739.2,0.0
-14173,15766458.0,Onyemauchechukwu,605.0,France,Male,35.0,4.0,0.0,1.0,0.0,1.0,96881.32,0.0
-14174,15769974.0,Ch'eng,669.0,France,Female,34.0,8.0,0.0,1.0,1.0,0.0,178373.43,0.0
-14175,15692463.0,Scott,535.0,France,Male,46.0,1.0,0.0,1.0,1.0,1.0,177065.24,0.0
-14176,15679963.0,K?,678.0,Spain,Male,57.0,2.0,0.0,1.0,1.0,1.0,172063.09,1.0
-14177,15614168.0,Ch'ien,667.0,France,Male,29.0,4.0,0.0,2.0,1.0,1.0,58974.0,0.0
-14178,15600708.0,Genovesi,577.0,Spain,Female,41.0,8.0,0.0,1.0,1.0,1.0,156792.89,1.0
-14179,15774720.0,Chikezie,850.0,Germany,Female,46.0,8.0,92474.05,2.0,1.0,0.0,118024.1,1.0
-14180,15631739.0,Manna,535.0,Germany,Female,49.0,6.0,136674.51,1.0,1.0,0.0,26900.63,1.0
-14181,15771483.0,Ts'ui,633.0,France,Male,28.0,8.0,0.0,1.0,1.0,1.0,177028.0,0.0
-14182,15733361.0,K?,662.0,France,Female,29.0,1.0,0.0,2.0,1.0,0.0,182266.01,0.0
-14183,15662091.0,Dike,841.0,France,Male,31.0,1.0,113599.74,1.0,1.0,1.0,59872.3,0.0
-14184,15729224.0,Nwachukwu,638.0,France,Female,36.0,5.0,0.0,2.0,1.0,0.0,153343.48,0.0
-14185,15672756.0,Kudryashova,565.0,France,Female,33.0,4.0,0.0,2.0,1.0,1.0,187900.12,0.0
-14186,15607746.0,Lucciano,651.0,France,Male,38.0,1.0,0.0,1.0,0.0,0.0,105618.14,0.0
-14187,15591037.0,Chukwuemeka,687.0,France,Female,43.0,1.0,0.0,1.0,1.0,0.0,149073.88,1.0
-14188,15703544.0,Chien,757.0,France,Male,40.0,4.0,118879.35,1.0,1.0,1.0,185599.74,0.0
-14189,15684921.0,Onwumelu,724.0,France,Male,29.0,1.0,0.0,2.0,1.0,1.0,147040.25,0.0
-14190,15734765.0,Ting,611.0,France,Male,35.0,7.0,0.0,2.0,1.0,0.0,99792.82,0.0
-14191,15786463.0,Zetticci,706.0,Spain,Male,42.0,8.0,96950.41,1.0,1.0,0.0,142963.36,0.0
-14192,15674929.0,Ojiofor,683.0,Germany,Male,37.0,4.0,137301.87,1.0,0.0,1.0,28257.89,0.0
-14193,15661528.0,T'ien,667.0,France,Female,45.0,9.0,0.0,1.0,1.0,0.0,56794.73,1.0
-14194,15582616.0,Aksenov,703.0,Germany,Male,39.0,3.0,135871.5,2.0,0.0,0.0,38773.74,0.0
-14195,15770030.0,Nkemdirim,667.0,Spain,Female,34.0,9.0,0.0,2.0,1.0,0.0,143301.49,0.0
-14196,15794468.0,Tien,701.0,Spain,Male,44.0,4.0,0.0,2.0,0.0,1.0,29132.43,0.0
-14197,15734609.0,Chinweike,635.0,France,Female,41.0,9.0,0.0,1.0,0.0,0.0,140929.98,1.0
-14198,15769312.0,Hs?eh,663.0,France,Female,44.0,10.0,0.0,1.0,1.0,1.0,142754.71,0.0
-14199,15804862.0,Kuo,632.0,Germany,Female,32.0,4.0,119825.75,1.0,1.0,0.0,177737.04,1.0
-14200,15753362.0,Ugoji,645.0,Spain,Male,46.0,0.0,0.0,1.0,0.0,0.0,141180.66,1.0
-14201,15609998.0,Chukwuebuka,749.0,France,Female,23.0,4.0,0.0,2.0,1.0,0.0,67468.67,0.0
-14202,15782798.0,Uspenskaya,753.0,Germany,Male,47.0,9.0,90450.0,1.0,0.0,0.0,152499.8,1.0
-14203,15744044.0,Lucchese,669.0,France,Male,23.0,9.0,0.0,2.0,1.0,1.0,140586.08,0.0
-14204,15672798.0,Chukwudi,651.0,Spain,Male,39.0,6.0,0.0,2.0,0.0,0.0,122925.4,0.0
-14205,15583595.0,Hsieh,607.0,France,Female,39.0,10.0,0.0,2.0,1.0,0.0,147963.07,0.0
-14206,15793049.0,L?,686.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,1.0,60954.45,0.0
-14207,15679909.0,Shih,588.0,Spain,Male,36.0,4.0,0.0,2.0,0.0,1.0,87067.73,0.0
-14208,15655335.0,Hargreaves,731.0,Spain,Male,33.0,4.0,0.0,2.0,0.0,0.0,103557.85,0.0
-14209,15743016.0,Ch'iu,678.0,France,Male,33.0,8.0,0.0,2.0,1.0,1.0,90106.94,0.0
-14210,15698872.0,Smith,609.0,France,Female,43.0,4.0,0.0,1.0,1.0,1.0,160131.58,1.0
-14211,15720509.0,Uspenskaya,624.0,Spain,Female,30.0,5.0,0.0,2.0,1.0,0.0,120853.83,0.0
-14212,15648881.0,Goliwe,580.0,Spain,Male,32.0,10.0,0.0,1.0,1.0,0.0,138051.19,0.0
-14213,15633574.0,Ndubuagha,721.0,France,Male,32.0,1.0,0.0,1.0,1.0,0.0,134901.34,0.0
-14214,15641575.0,Ndubuagha,616.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,0.0,85523.84,0.0
-14215,15609510.0,Shao,579.0,Germany,Female,23.0,7.0,127801.88,2.0,1.0,0.0,116957.6,0.0
-14216,15771674.0,White,651.0,France,Female,37.0,5.0,0.0,2.0,1.0,0.0,177524.19,0.0
-14217,15578835.0,Chiemenam,681.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,107839.03,0.0
-14218,15610226.0,Onuora,607.0,Germany,Female,47.0,10.0,137104.47,1.0,1.0,1.0,103557.85,1.0
-14219,15653760.0,DeRose,621.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,1.0,69052.87,0.0
-14220,15631912.0,Hsieh,634.0,Germany,Female,42.0,0.0,90218.9,1.0,1.0,1.0,53954.24,0.0
-14221,15770121.0,Tien,679.0,Spain,Male,29.0,2.0,0.0,2.0,1.0,1.0,51387.1,0.0
-14222,15703793.0,Clements,683.0,France,Male,42.0,9.0,0.0,2.0,1.0,0.0,130726.96,0.0
-14223,15810834.0,Tsao,679.0,Germany,Female,57.0,6.0,109628.44,2.0,1.0,0.0,131284.93,1.0
-14224,15651581.0,Gidney,658.0,France,Male,34.0,2.0,0.0,1.0,1.0,1.0,93536.38,0.0
-14225,15686957.0,Fanucci,642.0,France,Male,28.0,6.0,0.0,2.0,0.0,1.0,186785.01,0.0
-14226,15592877.0,Hsia,639.0,Germany,Male,29.0,9.0,132730.17,2.0,1.0,1.0,165576.55,1.0
-14227,15773869.0,Tien,582.0,Germany,Female,42.0,1.0,119839.69,2.0,1.0,0.0,93713.47,0.0
-14228,15623102.0,Chidiebele,721.0,France,Female,31.0,8.0,0.0,2.0,0.0,1.0,137791.18,0.0
-14229,15642991.0,Chibuzo,697.0,Spain,Male,35.0,6.0,0.0,2.0,0.0,0.0,109496.69,0.0
-14230,15706647.0,Chukwujamuike,753.0,France,Male,31.0,2.0,123082.08,1.0,1.0,1.0,130504.49,0.0
-14231,15785078.0,Chu,701.0,Spain,Male,35.0,6.0,117593.48,1.0,1.0,1.0,146636.44,0.0
-14232,15626900.0,Chigozie,645.0,Germany,Male,43.0,10.0,108528.07,1.0,1.0,0.0,115779.48,1.0
-14233,15761554.0,Chimaoke,554.0,Germany,Female,54.0,6.0,104449.8,1.0,1.0,1.0,133091.32,1.0
-14234,15779985.0,Uspenskaya,537.0,France,Male,24.0,3.0,0.0,2.0,0.0,1.0,141756.32,0.0
-14235,15597909.0,O'Brien,709.0,Spain,Male,34.0,6.0,0.0,2.0,1.0,0.0,167984.72,0.0
-14236,15800806.0,Chinedum,684.0,France,Male,34.0,1.0,0.0,1.0,1.0,1.0,152992.04,0.0
-14237,15589210.0,Ch'ang,718.0,Germany,Female,51.0,4.0,126537.52,1.0,0.0,1.0,46555.15,1.0
-14238,15698716.0,Hs?,562.0,Spain,Male,39.0,3.0,133800.98,1.0,1.0,1.0,187915.4,0.0
-14239,15716334.0,Ch'eng,718.0,Spain,Female,47.0,3.0,0.0,1.0,1.0,0.0,173365.89,1.0
-14240,15757467.0,Chinonyelum,569.0,Germany,Female,56.0,7.0,124573.33,1.0,1.0,0.0,151411.4,1.0
-14241,15803526.0,Milano,686.0,France,Male,34.0,8.0,0.0,1.0,1.0,0.0,143913.48,0.0
-14242,15753847.0,Nwankwo,749.0,France,Female,47.0,7.0,0.0,1.0,1.0,0.0,73856.28,1.0
-14243,15700511.0,Calabresi,496.0,France,Female,41.0,4.0,0.0,2.0,1.0,0.0,35786.76,1.0
-14244,15763612.0,Iadanza,567.0,Germany,Male,49.0,6.0,106301.85,2.0,1.0,0.0,123882.73,1.0
-14245,15584518.0,Nwankwo,710.0,Spain,Male,39.0,8.0,0.0,2.0,1.0,0.0,124592.84,0.0
-14246,15754569.0,Calabresi,466.0,Germany,Female,22.0,4.0,74988.59,1.0,1.0,0.0,113557.7,0.0
-14247,15642710.0,Ch'ien,758.0,France,Male,44.0,4.0,0.0,2.0,1.0,1.0,187987.01,0.0
-14248,15636388.0,Mazzi,670.0,France,Male,41.0,7.0,0.0,2.0,1.0,1.0,179655.83,0.0
-14249,15751771.0,Rizzo,686.0,France,Female,38.0,3.0,0.0,2.0,1.0,1.0,109614.57,0.0
-14250,15750778.0,Ts'ao,645.0,Spain,Female,22.0,9.0,0.0,2.0,1.0,0.0,51919.04,0.0
-14251,15625080.0,Mazzanti,850.0,France,Male,64.0,8.0,108477.49,1.0,1.0,0.0,120801.65,1.0
-14252,15733261.0,Onyemachukwu,628.0,France,Female,30.0,6.0,0.0,2.0,1.0,0.0,127734.16,0.0
-14253,15641860.0,T'ien,686.0,France,Male,39.0,7.0,0.0,2.0,1.0,0.0,176433.6,0.0
-14254,15679024.0,Gomes,730.0,France,Male,41.0,0.0,0.0,2.0,0.0,1.0,142963.29,0.0
-14255,15586674.0,Hixson,686.0,France,Male,36.0,0.0,0.0,1.0,0.0,1.0,72991.62,0.0
-14256,15640407.0,Chialuka,749.0,France,Male,40.0,5.0,152341.55,1.0,1.0,1.0,88463.63,0.0
-14257,15592571.0,Macleod,762.0,France,Male,34.0,0.0,97460.2,1.0,0.0,1.0,134600.94,0.0
-14258,15734886.0,Ch'ien,523.0,Germany,Male,49.0,3.0,88902.21,1.0,1.0,1.0,157097.09,0.0
-14259,15724663.0,Yobachukwu,704.0,Spain,Male,33.0,7.0,0.0,2.0,1.0,1.0,185541.6,0.0
-14260,15656484.0,Yobachi,686.0,Spain,Male,50.0,9.0,125876.95,1.0,1.0,0.0,162599.51,1.0
-14261,15725166.0,Iadanza,750.0,France,Male,29.0,5.0,110791.97,1.0,1.0,1.0,134600.94,0.0
-14262,15735222.0,Ch'ang,717.0,Spain,Male,49.0,5.0,0.0,2.0,1.0,1.0,180622.03,0.0
-14263,15616172.0,Ejimofor,589.0,France,Female,57.0,7.0,0.0,2.0,1.0,1.0,141453.62,0.0
-14264,15649024.0,Ku,569.0,France,Female,51.0,7.0,0.0,1.0,1.0,0.0,103570.8,1.0
-14265,15769353.0,Chiu,619.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,0.0,161253.08,0.0
-14266,15632575.0,Chukwualuka,494.0,France,Male,29.0,9.0,137060.88,1.0,1.0,0.0,136886.86,1.0
-14267,15582841.0,Houghton,583.0,France,Female,34.0,5.0,0.0,2.0,0.0,1.0,70618.0,0.0
-14268,15599195.0,P'eng,775.0,France,Male,35.0,2.0,0.0,2.0,1.0,1.0,180598.66,0.0
-14269,15641640.0,Chidiebele,661.0,France,Male,31.0,8.0,0.0,1.0,0.0,1.0,115462.27,0.0
-14270,15781665.0,Pugh,512.0,France,Male,42.0,3.0,0.0,1.0,1.0,0.0,90212.38,1.0
-14271,15786078.0,Lucchese,707.0,France,Female,31.0,9.0,125167.74,1.0,1.0,0.0,162697.93,0.0
-14272,15744109.0,Ch'iu,646.0,France,Female,50.0,4.0,0.0,1.0,1.0,0.0,172586.89,1.0
-14273,15576928.0,Onyemauchechi,686.0,Spain,Male,37.0,6.0,0.0,2.0,0.0,1.0,142538.31,0.0
-14274,15683544.0,Hsia,609.0,France,Female,39.0,3.0,92636.96,1.0,1.0,0.0,107825.08,0.0
-14275,15662494.0,Hs?eh,646.0,France,Male,33.0,7.0,0.0,1.0,1.0,1.0,153810.51,0.0
-14276,15620557.0,Y?an,523.0,Spain,Male,27.0,8.0,0.0,2.0,1.0,0.0,181610.6,0.0
-14277,15801466.0,Kao,611.0,France,Male,34.0,1.0,0.0,2.0,1.0,0.0,105890.55,0.0
-14278,15680197.0,Hsia,633.0,Spain,Male,37.0,9.0,86911.8,1.0,1.0,0.0,62454.01,0.0
-14279,15605872.0,Ugochukwu,577.0,France,Male,39.0,2.0,93816.7,1.0,1.0,1.0,96832.82,0.0
-14280,15592925.0,Lucchesi,710.0,France,Female,37.0,7.0,167557.12,1.0,0.0,0.0,9984.4,0.0
-14281,15774931.0,Yobanna,719.0,Spain,Male,38.0,1.0,0.0,1.0,1.0,1.0,93716.38,0.0
-14282,15576022.0,Hsueh,708.0,France,Male,41.0,4.0,0.0,2.0,0.0,0.0,27802.0,0.0
-14283,15648898.0,Fanucci,535.0,Germany,Female,37.0,1.0,124445.58,1.0,1.0,1.0,129980.93,0.0
-14284,15570932.0,T'ao,587.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,79866.73,0.0
-14285,15585961.0,Lo,632.0,Germany,Female,45.0,8.0,137148.68,1.0,1.0,0.0,174825.57,1.0
-14286,15573854.0,Teng,762.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,75837.58,0.0
-14287,15743817.0,Chinedum,741.0,France,Male,37.0,10.0,0.0,2.0,1.0,0.0,79082.62,0.0
-14288,15572051.0,Lai,685.0,France,Male,39.0,5.0,0.0,3.0,1.0,0.0,86935.08,1.0
-14289,15796790.0,Ch'eng,763.0,France,Male,48.0,7.0,91522.06,1.0,1.0,0.0,937.15,0.0
-14290,15774857.0,Cremonesi,573.0,Germany,Male,36.0,8.0,111359.55,1.0,1.0,0.0,181224.56,1.0
-14291,15677382.0,Yobachukwu,677.0,France,Male,48.0,2.0,132596.92,1.0,1.0,0.0,58974.0,1.0
-14292,15683338.0,Chin,582.0,France,Female,34.0,8.0,0.0,1.0,1.0,1.0,73979.85,0.0
-14293,15583392.0,Trevisani,744.0,Germany,Female,46.0,7.0,118311.06,1.0,1.0,0.0,129890.94,1.0
-14294,15772262.0,Trentini,684.0,Germany,Male,30.0,1.0,95853.39,2.0,1.0,1.0,51620.94,0.0
-14295,15648898.0,Chinedum,634.0,Spain,Male,40.0,5.0,0.0,2.0,1.0,0.0,91830.75,0.0
-14296,15741180.0,Pisano,684.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,141872.05,0.0
-14297,15725024.0,Scott,739.0,Germany,Male,32.0,2.0,116338.68,2.0,1.0,1.0,69531.22,0.0
-14298,15656937.0,Shih,543.0,Germany,Male,64.0,7.0,152341.55,1.0,1.0,1.0,132798.95,1.0
-14299,15585599.0,Gaffney,583.0,France,Female,56.0,7.0,0.0,1.0,1.0,1.0,159629.64,1.0
-14300,15681412.0,Obidimkpa,718.0,France,Male,39.0,4.0,0.0,2.0,1.0,1.0,118711.75,0.0
-14301,15778696.0,Palermo,683.0,Spain,Male,40.0,8.0,96674.2,1.0,0.0,1.0,175070.94,0.0
-14302,15644724.0,Onwubiko,667.0,France,Female,59.0,6.0,0.0,1.0,1.0,0.0,167036.94,1.0
-14303,15759537.0,Brown,635.0,Spain,Male,30.0,7.0,0.0,2.0,1.0,1.0,142377.69,0.0
-14304,15644724.0,Chidubem,633.0,Spain,Female,21.0,4.0,0.0,2.0,1.0,0.0,182691.31,0.0
-14305,15676521.0,Pisano,721.0,Germany,Female,46.0,6.0,142721.52,1.0,1.0,0.0,126013.58,1.0
-14306,15591740.0,Yobachi,636.0,Spain,Male,30.0,5.0,0.0,1.0,0.0,0.0,183318.79,0.0
-14307,15618018.0,Maclean,691.0,France,Female,34.0,6.0,0.0,1.0,1.0,0.0,187816.59,0.0
-14308,15567486.0,Pirozzi,634.0,Spain,Female,36.0,9.0,104584.11,1.0,1.0,1.0,48108.72,0.0
-14309,15732774.0,DeRose,431.0,France,Male,37.0,1.0,0.0,1.0,0.0,1.0,180598.86,0.0
-14310,15623102.0,Ch'in,629.0,France,Male,31.0,2.0,153316.14,1.0,1.0,1.0,88920.68,0.0
-14311,15698028.0,Chiawuotu,642.0,France,Male,43.0,8.0,0.0,1.0,1.0,0.0,139919.38,0.0
-14312,15731267.0,Whitelegge,676.0,Spain,Female,35.0,2.0,0.0,2.0,1.0,1.0,62368.33,0.0
-14313,15602844.0,Chinwenma,572.0,France,Male,31.0,2.0,0.0,1.0,1.0,0.0,121834.81,0.0
-14314,15766787.0,Ritchie,675.0,France,Female,36.0,1.0,0.0,2.0,1.0,1.0,56780.97,0.0
-14315,15809817.0,Liao,615.0,Germany,Female,36.0,6.0,131644.26,1.0,1.0,1.0,141478.55,1.0
-14316,15807553.0,Chidubem,591.0,France,Male,36.0,6.0,96858.35,1.0,1.0,1.0,100995.11,0.0
-14317,15583499.0,Nnaife,706.0,Spain,Male,27.0,7.0,116084.32,1.0,1.0,0.0,181419.29,0.0
-14318,15659428.0,O'Kane,732.0,France,Male,43.0,3.0,0.0,2.0,1.0,1.0,125518.32,0.0
-14319,15680797.0,Walker,733.0,France,Female,44.0,2.0,0.0,1.0,0.0,0.0,185599.34,1.0
-14320,15803966.0,Iadanza,691.0,Germany,Male,39.0,10.0,146780.52,2.0,1.0,0.0,167916.35,0.0
-14321,15651369.0,Hsiao,673.0,Germany,Male,43.0,2.0,161806.09,1.0,1.0,1.0,175853.64,0.0
-14322,15676576.0,Chikwendu,685.0,France,Female,51.0,6.0,0.0,2.0,1.0,1.0,131718.12,0.0
-14323,15730488.0,P'eng,708.0,Spain,Female,55.0,2.0,0.0,3.0,0.0,1.0,121537.91,1.0
-14324,15796910.0,Nnachetam,653.0,France,Female,54.0,6.0,0.0,1.0,1.0,0.0,158845.49,1.0
-14325,15697424.0,Onyekachukwu,676.0,France,Male,36.0,1.0,0.0,1.0,1.0,1.0,72873.33,0.0
-14326,15686885.0,Y?an,582.0,France,Female,43.0,9.0,0.0,1.0,1.0,1.0,25881.0,0.0
-14327,15679991.0,Lo Duca,824.0,France,Male,40.0,1.0,0.0,1.0,1.0,0.0,182059.28,0.0
-14328,15683544.0,Y?,732.0,France,Male,37.0,5.0,115532.52,1.0,1.0,0.0,137037.97,0.0
-14329,15655339.0,Nwachukwu,706.0,France,Female,44.0,1.0,0.0,1.0,1.0,0.0,182355.21,1.0
-14330,15751175.0,Kline,684.0,France,Male,37.0,8.0,0.0,2.0,0.0,1.0,118716.63,0.0
-14331,15685329.0,Onio,695.0,Spain,Male,35.0,7.0,0.0,2.0,1.0,0.0,95957.09,0.0
-14332,15567147.0,Chukwumaobim,653.0,Germany,Male,43.0,4.0,104783.81,1.0,1.0,1.0,4777.23,0.0
-14333,15733261.0,Zikoranachidimma,619.0,Spain,Female,38.0,6.0,0.0,2.0,1.0,0.0,62449.07,0.0
-14334,15666884.0,Chiang,605.0,Germany,Female,41.0,1.0,104822.45,1.0,1.0,0.0,179964.55,1.0
-14335,15749050.0,Nnonso,649.0,Spain,Male,35.0,8.0,0.0,2.0,1.0,0.0,91906.0,0.0
-14336,15742272.0,Ch'ang,715.0,France,Female,55.0,5.0,174852.89,1.0,1.0,0.0,57898.58,1.0
-14337,15700710.0,Ch'in,712.0,Germany,Male,47.0,9.0,118235.7,1.0,1.0,0.0,184514.01,0.0
-14338,15753837.0,Nwabugwu,712.0,Germany,Female,54.0,0.0,121629.22,1.0,1.0,1.0,167522.19,1.0
-14339,15719302.0,Chidozie,559.0,Germany,Male,37.0,7.0,132876.88,1.0,0.0,0.0,180593.45,0.0
-14340,15696904.0,Chukwumaobim,678.0,France,Male,60.0,7.0,0.0,2.0,1.0,1.0,132973.18,0.0
-14341,15665524.0,Piazza,554.0,France,Male,39.0,0.0,0.0,2.0,1.0,1.0,148098.95,0.0
-14342,15687558.0,Ting,653.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,132973.17,0.0
-14343,15618258.0,Dike,704.0,France,Male,22.0,4.0,0.0,1.0,1.0,0.0,126106.4,0.0
-14344,15742369.0,Linger,661.0,France,Male,35.0,8.0,128743.8,1.0,1.0,1.0,66926.83,0.0
-14345,15766185.0,Iloerika,581.0,France,Male,33.0,2.0,0.0,2.0,0.0,0.0,92357.21,0.0
-14346,15651664.0,Calabresi,571.0,Spain,Male,32.0,2.0,134562.2,1.0,1.0,1.0,62868.33,0.0
-14347,15668073.0,Palerma,700.0,Spain,Male,31.0,9.0,131593.85,1.0,1.0,1.0,67622.46,0.0
-14348,15611287.0,Chidiebere,605.0,France,Female,41.0,9.0,0.0,2.0,0.0,0.0,86786.41,0.0
-14349,15760456.0,Chiang,687.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,89935.73,0.0
-14350,15771087.0,Shih,733.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,170634.49,0.0
-14351,15643679.0,Ch'in,696.0,Germany,Male,25.0,5.0,152462.79,1.0,0.0,1.0,114935.21,0.0
-14352,15649418.0,Ts'ai,685.0,France,Female,36.0,3.0,0.0,1.0,1.0,1.0,62282.99,0.0
-14353,15668679.0,Chizuoke,850.0,Germany,Female,39.0,7.0,114997.42,1.0,0.0,0.0,175072.47,1.0
-14354,15589475.0,Zikoranachidimma,745.0,France,Female,36.0,1.0,0.0,2.0,1.0,0.0,166650.32,0.0
-14355,15775490.0,Chia,673.0,France,Male,41.0,1.0,0.0,2.0,0.0,1.0,76611.45,0.0
-14356,15668893.0,Lucciano,684.0,Spain,Female,31.0,3.0,0.0,1.0,0.0,0.0,173498.45,0.0
-14357,15618401.0,Nwora,633.0,France,Male,44.0,9.0,0.0,2.0,1.0,0.0,121276.36,0.0
-14358,15690733.0,Narelle,644.0,France,Male,49.0,4.0,0.0,2.0,0.0,0.0,94790.34,1.0
-14359,15773869.0,O'Donnell,622.0,France,Male,26.0,9.0,0.0,2.0,1.0,0.0,74870.99,0.0
-14360,15583681.0,L?,756.0,France,Female,59.0,8.0,0.0,2.0,0.0,1.0,169862.01,1.0
-14361,15660646.0,Smith,637.0,France,Male,32.0,8.0,0.0,2.0,0.0,0.0,18094.96,0.0
-14362,15621093.0,Iweobiegbulam,801.0,Germany,Female,43.0,6.0,127864.4,1.0,1.0,0.0,113089.43,1.0
-14363,15614491.0,Zikoranachidimma,588.0,France,Male,42.0,10.0,0.0,2.0,1.0,1.0,63736.17,0.0
-14364,15798834.0,Chukwubuikem,552.0,France,Male,32.0,8.0,0.0,2.0,0.0,1.0,122488.59,0.0
-14365,15642725.0,Chidimma,611.0,France,Male,34.0,8.0,0.0,2.0,0.0,1.0,179883.04,0.0
-14366,15680303.0,Chidiebele,590.0,Spain,Male,32.0,5.0,125584.17,1.0,1.0,1.0,26265.18,0.0
-14367,15708003.0,H?,615.0,Spain,Male,29.0,5.0,0.0,2.0,0.0,1.0,28407.4,0.0
-14368,15637324.0,Chukwuemeka,590.0,Spain,Male,30.0,6.0,0.0,1.0,1.0,0.0,94790.34,0.0
-14369,15772933.0,Fiorentino,553.0,France,Female,31.0,8.0,0.0,2.0,1.0,0.0,122810.53,0.0
-14370,15742738.0,T'ang,718.0,France,Male,28.0,10.0,0.0,2.0,1.0,1.0,103599.67,0.0
-14371,15579212.0,K?,596.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,182624.93,0.0
-14372,15691705.0,Fyans,634.0,Germany,Male,29.0,3.0,142646.68,1.0,1.0,0.0,180716.1,0.0
-14373,15791045.0,Manna,605.0,Spain,Male,30.0,5.0,0.0,1.0,1.0,1.0,166526.26,0.0
-14374,15628893.0,Pirozzi,547.0,France,Female,30.0,7.0,0.0,2.0,1.0,0.0,177065.24,0.0
-14375,15735572.0,Chukwufumnanya,601.0,France,Male,24.0,3.0,132944.48,1.0,1.0,0.0,129964.94,0.0
-14376,15696054.0,Hsiao,605.0,France,Male,47.0,5.0,0.0,3.0,1.0,0.0,100995.68,1.0
-14377,15733476.0,Nwankwo,691.0,France,Female,30.0,3.0,0.0,2.0,1.0,0.0,166677.54,0.0
-14378,15674750.0,Chiang,678.0,France,Male,59.0,5.0,106968.96,1.0,1.0,0.0,3825.37,1.0
-14379,15583736.0,Udinese,644.0,Germany,Male,37.0,2.0,123276.69,1.0,0.0,0.0,91633.53,1.0
-14380,15605666.0,H?,684.0,Spain,Female,32.0,6.0,0.0,2.0,1.0,0.0,162719.69,0.0
-14381,15802478.0,Nwankwo,693.0,France,Male,26.0,9.0,0.0,2.0,1.0,0.0,126688.83,0.0
-14382,15743007.0,Nwachukwu,659.0,France,Female,37.0,4.0,0.0,1.0,1.0,0.0,153955.38,1.0
-14383,15696900.0,T'ang,712.0,Germany,Male,59.0,6.0,167557.12,1.0,1.0,1.0,167558.26,1.0
-14384,15752984.0,Hsueh,711.0,France,Female,33.0,6.0,0.0,2.0,1.0,0.0,155786.39,0.0
-14385,15630709.0,Pye,646.0,France,Male,47.0,3.0,0.0,1.0,1.0,0.0,131736.23,1.0
-14386,15664204.0,Hao,689.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,101192.08,0.0
-14387,15794048.0,Ch'ang,756.0,France,Male,29.0,9.0,0.0,2.0,1.0,1.0,126192.54,0.0
-14388,15736495.0,Mazzanti,704.0,Spain,Male,37.0,1.0,0.0,2.0,1.0,0.0,151924.9,0.0
-14389,15677955.0,Shih,614.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,120540.83,0.0
-14390,15592745.0,Chiemenam,774.0,France,Male,26.0,1.0,0.0,2.0,1.0,0.0,136837.97,0.0
-14391,15636330.0,Mancini,554.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,124592.84,0.0
-14392,15686461.0,Chiedozie,691.0,France,Female,32.0,3.0,0.0,1.0,1.0,0.0,166513.49,0.0
-14393,15702293.0,Onyemere,775.0,France,Male,37.0,4.0,0.0,2.0,1.0,1.0,100442.22,0.0
-14394,15746035.0,Hovell,709.0,Spain,Male,31.0,2.0,0.0,2.0,1.0,1.0,121151.1,0.0
-14395,15693902.0,L?,645.0,Spain,Female,28.0,8.0,100683.54,1.0,0.0,0.0,178032.93,0.0
-14396,15656613.0,Dyer,717.0,France,Female,46.0,7.0,0.0,2.0,1.0,0.0,176883.42,0.0
-14397,15682473.0,Ch'eng,579.0,France,Female,43.0,1.0,0.0,2.0,1.0,0.0,71830.1,0.0
-14398,15769412.0,Iadanza,635.0,France,Male,43.0,5.0,177069.24,2.0,1.0,0.0,150836.93,0.0
-14399,15593555.0,Gadsden,697.0,Germany,Male,51.0,8.0,103819.57,1.0,1.0,1.0,180716.1,1.0
-14400,15751392.0,T'an,753.0,France,Male,36.0,8.0,0.0,1.0,1.0,1.0,159835.16,1.0
-14401,15754858.0,Lo Duca,730.0,France,Male,34.0,5.0,0.0,2.0,1.0,0.0,101348.88,0.0
-14402,15791836.0,Horsley,568.0,Spain,Female,48.0,4.0,74483.97,3.0,0.0,0.0,171457.06,1.0
-14403,15752388.0,Chukwumaobim,629.0,Spain,Male,32.0,2.0,96651.03,1.0,1.0,0.0,86791.9,0.0
-14404,15689412.0,Hsieh,629.0,Spain,Male,37.0,2.0,110802.03,1.0,1.0,1.0,147794.63,0.0
-14405,15593365.0,Hsia,599.0,France,Male,36.0,4.0,0.0,2.0,1.0,1.0,60533.96,0.0
-14406,15617790.0,Ch'ang,691.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,72318.77,0.0
-14407,15614818.0,Nucci,565.0,France,Male,38.0,4.0,0.0,2.0,1.0,0.0,120853.83,0.0
-14408,15591740.0,Pinto,632.0,France,Male,49.0,3.0,0.0,2.0,1.0,1.0,143859.01,0.0
-14409,15631451.0,K'ung,675.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,124874.23,0.0
-14410,15679693.0,Hsieh,579.0,Germany,Female,42.0,6.0,113588.69,1.0,1.0,1.0,90876.95,1.0
-14411,15772363.0,P'eng,601.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,78955.8,0.0
-14412,15644296.0,Iqbal,706.0,France,Female,38.0,4.0,155879.07,1.0,0.0,1.0,100557.03,0.0
-14413,15705521.0,Aitken,624.0,Germany,Female,27.0,7.0,127837.86,2.0,1.0,1.0,161812.84,0.0
-14414,15659710.0,Ch'in,697.0,France,Female,34.0,4.0,0.0,2.0,0.0,1.0,149550.95,0.0
-14415,15706116.0,Chukwualuka,697.0,France,Female,33.0,7.0,0.0,2.0,1.0,1.0,105735.92,0.0
-14416,15746035.0,Okeke,617.0,Germany,Female,25.0,3.0,99353.42,2.0,0.0,1.0,174770.14,0.0
-14417,15797786.0,Hsu,710.0,Spain,Male,34.0,6.0,96863.13,1.0,1.0,1.0,91094.46,0.0
-14418,15770041.0,Ritchie,646.0,France,Male,54.0,6.0,0.0,2.0,0.0,1.0,56984.58,0.0
-14419,15728605.0,Brown,686.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,15068.18,0.0
-14420,15645571.0,Milanesi,745.0,France,Male,34.0,2.0,0.0,2.0,0.0,0.0,136902.01,0.0
-14421,15691627.0,Y?,732.0,Spain,Female,41.0,5.0,110479.48,1.0,0.0,0.0,143049.72,0.0
-14422,15739189.0,Hsia,718.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,0.0,171482.56,0.0
-14423,15681332.0,Ives,636.0,France,Male,39.0,3.0,0.0,2.0,1.0,0.0,147554.26,0.0
-14424,15781674.0,Nwachinemelu,699.0,France,Female,41.0,2.0,0.0,2.0,1.0,0.0,64833.28,0.0
-14425,15692408.0,Chukwufumnanya,701.0,Spain,Male,39.0,3.0,0.0,2.0,0.0,0.0,144290.97,0.0
-14426,15718507.0,Chinedum,550.0,France,Female,36.0,10.0,0.0,2.0,1.0,1.0,143783.77,0.0
-14427,15752534.0,Onyinyechukwuka,648.0,France,Male,38.0,4.0,0.0,1.0,1.0,1.0,92816.86,0.0
-14428,15763029.0,Nnonso,573.0,France,Female,30.0,1.0,0.0,2.0,1.0,0.0,93777.15,0.0
-14429,15602841.0,Onyekaozulu,686.0,Spain,Male,39.0,6.0,0.0,2.0,1.0,1.0,180598.86,0.0
-14430,15774586.0,H?,632.0,France,Female,37.0,7.0,0.0,2.0,1.0,0.0,125302.64,0.0
-14431,15690372.0,Lucchesi,695.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,118959.64,0.0
-14432,15622478.0,Sun,685.0,France,Female,29.0,4.0,0.0,2.0,0.0,1.0,141871.84,0.0
-14433,15673907.0,Fenton,684.0,France,Female,45.0,6.0,0.0,1.0,1.0,0.0,143635.33,1.0
-14434,15650288.0,Onyemauchechukwu,730.0,France,Male,45.0,10.0,120105.43,1.0,1.0,0.0,171706.66,0.0
-14435,15680804.0,Chuang,685.0,France,Female,40.0,7.0,0.0,1.0,1.0,0.0,132973.17,1.0
-14436,15587712.0,Chiang,709.0,Spain,Male,39.0,3.0,0.0,2.0,1.0,1.0,166957.82,0.0
-14437,15655341.0,Pagnotto,659.0,Spain,Male,35.0,4.0,0.0,2.0,0.0,1.0,90877.76,0.0
-14438,15657771.0,Nebeolisa,605.0,France,Female,41.0,8.0,0.0,1.0,1.0,0.0,158750.53,1.0
-14439,15701765.0,Chinwemma,684.0,France,Male,45.0,4.0,0.0,2.0,1.0,1.0,79355.87,0.0
-14440,15741875.0,Hsieh,655.0,France,Female,62.0,7.0,0.0,2.0,1.0,1.0,123982.29,0.0
-14441,15635141.0,Eluemuno,598.0,France,Male,35.0,6.0,142387.31,1.0,1.0,0.0,184954.11,0.0
-14442,15771270.0,Yermakov,677.0,France,Male,37.0,5.0,81536.93,1.0,1.0,1.0,183584.14,0.0
-14443,15709920.0,Aksenov,726.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,64448.36,0.0
-14444,15704081.0,Laidley,850.0,Germany,Male,31.0,9.0,125023.1,2.0,1.0,1.0,136909.87,0.0
-14445,15723886.0,Hsiung,762.0,Germany,Male,31.0,5.0,128882.49,2.0,1.0,1.0,117034.32,0.0
-14446,15620750.0,Ch'ang,633.0,France,Male,51.0,7.0,0.0,1.0,1.0,0.0,120892.96,1.0
-14447,15593976.0,Chidiebere,707.0,France,Female,28.0,1.0,0.0,2.0,1.0,0.0,107874.39,0.0
-14448,15605037.0,Chiekwugo,639.0,Spain,Male,24.0,4.0,0.0,2.0,1.0,1.0,158545.25,0.0
-14449,15589210.0,Nucci,745.0,France,Female,30.0,8.0,0.0,1.0,1.0,0.0,180326.83,0.0
-14450,15671139.0,Mancini,614.0,France,Female,53.0,10.0,171913.87,1.0,1.0,0.0,115719.08,1.0
-14451,15730394.0,Tuan,516.0,Spain,Male,38.0,6.0,111395.53,1.0,1.0,0.0,105715.01,0.0
-14452,15703779.0,Curnow,561.0,France,Female,35.0,8.0,115462.44,1.0,1.0,0.0,142516.35,0.0
-14453,15760450.0,Chiang,434.0,Spain,Male,52.0,5.0,116536.43,1.0,1.0,1.0,94715.24,1.0
-14454,15677087.0,Lo,586.0,Germany,Female,41.0,8.0,115315.04,1.0,0.0,1.0,5791.85,1.0
-14455,15748691.0,Ukaegbunam,709.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,64420.5,0.0
-14456,15583394.0,Pethard,642.0,France,Female,26.0,3.0,0.0,2.0,1.0,0.0,52327.57,0.0
-14457,15689633.0,Li,733.0,France,Male,42.0,1.0,89087.89,1.0,1.0,0.0,154491.39,1.0
-14458,15583576.0,Nwankwo,667.0,France,Female,50.0,1.0,0.0,1.0,1.0,1.0,35367.19,1.0
-14459,15798888.0,Uwaezuoke,667.0,France,Male,32.0,4.0,0.0,2.0,1.0,1.0,123969.64,0.0
-14460,15682485.0,Nwachinemelu,714.0,Spain,Male,60.0,8.0,128306.7,2.0,1.0,1.0,183569.25,0.0
-14461,15677907.0,Chidubem,598.0,Spain,Male,28.0,8.0,0.0,2.0,1.0,1.0,125079.65,0.0
-14462,15658577.0,Tung,684.0,France,Female,37.0,8.0,0.0,2.0,1.0,0.0,53483.21,0.0
-14463,15792250.0,Chuter,749.0,France,Female,38.0,7.0,0.0,2.0,1.0,1.0,167984.61,0.0
-14464,15608918.0,Ch'eng,559.0,France,Female,30.0,8.0,0.0,1.0,1.0,1.0,92647.95,0.0
-14465,15807473.0,Ts'ai,557.0,France,Male,30.0,1.0,0.0,2.0,0.0,1.0,85902.14,0.0
-14466,15765520.0,Y?,638.0,Spain,Male,42.0,4.0,0.0,2.0,1.0,0.0,138770.96,0.0
-14467,15610337.0,Yashina,710.0,Spain,Male,38.0,4.0,0.0,2.0,1.0,0.0,114083.73,0.0
-14468,15723373.0,K?,678.0,France,Female,42.0,7.0,0.0,2.0,1.0,0.0,108699.59,0.0
-14469,15724443.0,H?,692.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,175893.49,0.0
-14470,15581871.0,Onwuka,819.0,Germany,Female,28.0,8.0,102723.35,1.0,1.0,0.0,152097.02,0.0
-14471,15576000.0,Shih,563.0,France,Male,37.0,3.0,131390.75,1.0,1.0,1.0,96548.88,0.0
-14472,15607314.0,Hsueh,690.0,Spain,Female,36.0,0.0,127841.52,1.0,1.0,1.0,92916.53,0.0
-14473,15649410.0,Fetherstonhaugh,682.0,France,Male,32.0,3.0,0.0,2.0,1.0,0.0,126565.11,0.0
-14474,15767794.0,Olisaemeka,594.0,Spain,Male,40.0,3.0,0.0,1.0,1.0,1.0,121681.19,1.0
-14475,15707551.0,Aitken,731.0,Spain,Male,34.0,2.0,0.0,2.0,0.0,0.0,128882.66,0.0
-14476,15679983.0,Pirozzi,678.0,Germany,Male,33.0,7.0,118469.38,1.0,1.0,1.0,105887.08,0.0
-14477,15640789.0,Milne,713.0,Germany,Female,36.0,2.0,132623.6,1.0,1.0,1.0,133961.19,1.0
-14478,15635244.0,Ts'ui,572.0,Spain,Male,26.0,5.0,0.0,2.0,0.0,0.0,140941.47,0.0
-14479,15752626.0,O'Donnell,730.0,France,Female,35.0,8.0,0.0,2.0,1.0,0.0,125870.5,0.0
-14480,15772383.0,Tung,659.0,Germany,Female,41.0,8.0,116536.43,1.0,1.0,0.0,125167.92,1.0
-14481,15635396.0,K'ung,637.0,Germany,Female,34.0,2.0,116114.93,1.0,1.0,0.0,102057.86,1.0
-14482,15656468.0,H?,663.0,Germany,Female,37.0,2.0,131753.41,1.0,1.0,0.0,120853.83,0.0
-14483,15626042.0,Ch'ien,781.0,France,Male,58.0,4.0,104430.08,1.0,1.0,1.0,126761.2,0.0
-14484,15605279.0,Okwudilichukwu,579.0,France,Female,37.0,5.0,0.0,1.0,1.0,0.0,151925.25,0.0
-14485,15573893.0,Onyekachukwu,756.0,Spain,Male,39.0,9.0,0.0,2.0,0.0,0.0,116692.8,0.0
-14486,15667934.0,Chukwudi,656.0,Germany,Male,28.0,9.0,111911.63,2.0,1.0,0.0,138344.3,0.0
-14487,15644856.0,Chidozie,696.0,Germany,Female,37.0,3.0,81070.34,1.0,0.0,1.0,184524.74,0.0
-14488,15593365.0,T'ien,803.0,France,Female,37.0,2.0,0.0,2.0,1.0,1.0,149503.51,0.0
-14489,15724181.0,Okwudilichukwu,726.0,Germany,Male,51.0,0.0,104911.35,1.0,1.0,1.0,85746.52,1.0
-14490,15711130.0,Cremonesi,703.0,Spain,Female,61.0,8.0,0.0,1.0,1.0,1.0,140941.47,1.0
-14491,15771529.0,T'ao,537.0,Germany,Male,31.0,1.0,149641.53,2.0,1.0,1.0,131300.68,0.0
-14492,15674620.0,Udinese,741.0,Germany,Male,27.0,5.0,121159.65,2.0,1.0,1.0,149740.22,0.0
-14493,15704770.0,Chidiegwu,669.0,France,Male,41.0,2.0,0.0,2.0,1.0,0.0,136914.56,0.0
-14494,15704053.0,Frolov,574.0,France,Male,33.0,1.0,0.0,2.0,1.0,1.0,78970.58,0.0
-14495,15681554.0,Chukwuhaenye,581.0,France,Male,52.0,3.0,0.0,1.0,0.0,0.0,61315.37,1.0
-14496,15724307.0,Pinto,474.0,France,Male,42.0,3.0,0.0,2.0,0.0,1.0,153662.88,0.0
-14497,15735080.0,Manna,607.0,Germany,Female,55.0,8.0,98675.74,1.0,1.0,0.0,119969.99,1.0
-14498,15583392.0,Ebelegbulam,628.0,France,Male,55.0,4.0,0.0,1.0,0.0,1.0,187811.71,1.0
-14499,15674678.0,Chiemenam,573.0,Germany,Female,33.0,7.0,121132.26,2.0,0.0,0.0,185827.74,0.0
-14500,15624013.0,Nwokezuike,651.0,France,Female,48.0,8.0,0.0,3.0,1.0,1.0,170528.63,1.0
-14501,15784923.0,Fanucci,611.0,France,Male,42.0,2.0,0.0,2.0,0.0,1.0,126761.2,0.0
-14502,15574072.0,Mazzi,663.0,Germany,Male,42.0,4.0,104796.54,1.0,0.0,0.0,165083.92,1.0
-14503,15769125.0,Onyekachukwu,593.0,France,Male,49.0,7.0,0.0,1.0,1.0,1.0,151844.81,1.0
-14504,15592597.0,T'ien,678.0,France,Female,40.0,6.0,0.0,1.0,1.0,0.0,123585.89,0.0
-14505,15737605.0,Lucchesi,712.0,Germany,Male,26.0,4.0,82291.1,2.0,0.0,1.0,91574.92,0.0
-14506,15693817.0,Chukwualuka,571.0,Spain,Female,39.0,9.0,0.0,2.0,1.0,0.0,64872.55,0.0
-14507,15592877.0,Ch'iu,605.0,France,Male,47.0,9.0,0.0,2.0,1.0,0.0,104889.2,0.0
-14508,15605264.0,Luffman,571.0,France,Male,32.0,8.0,0.0,1.0,1.0,0.0,121846.81,0.0
-14509,15641690.0,Okwudiliolisa,675.0,France,Female,35.0,3.0,0.0,1.0,0.0,0.0,63736.17,0.0
-14510,15799211.0,Maclean,627.0,France,Female,37.0,8.0,0.0,2.0,1.0,1.0,147545.65,0.0
-14511,15723004.0,Ndubueze,561.0,France,Female,30.0,4.0,115007.08,1.0,1.0,1.0,184842.21,0.0
-14512,15625141.0,Chukwumaobim,684.0,Germany,Male,46.0,4.0,87637.28,1.0,1.0,0.0,184564.67,1.0
-14513,15701206.0,Teng,526.0,France,Male,31.0,5.0,0.0,2.0,1.0,1.0,129891.66,0.0
-14514,15587735.0,Ts'ui,769.0,Germany,Female,34.0,7.0,141788.63,1.0,1.0,0.0,177852.57,0.0
-14515,15579207.0,Mazzi,587.0,Germany,Female,47.0,1.0,123681.32,1.0,1.0,0.0,56784.11,1.0
-14516,15777904.0,Ch'ang,719.0,Germany,Male,38.0,6.0,74896.92,1.0,0.0,0.0,110498.1,0.0
-14517,15682355.0,Pisani,667.0,France,Male,40.0,2.0,0.0,2.0,1.0,0.0,61569.07,0.0
-14518,15791513.0,Young,707.0,Spain,Male,30.0,1.0,0.0,2.0,0.0,0.0,81442.6,0.0
-14519,15642291.0,Chibueze,628.0,Spain,Female,49.0,4.0,0.0,3.0,0.0,1.0,70550.0,1.0
-14520,15771211.0,Ts'ao,669.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,93336.3,0.0
-14521,15593976.0,T'ien,706.0,France,Female,59.0,3.0,0.0,1.0,1.0,0.0,181893.31,1.0
-14522,15609356.0,Chukwujamuike,682.0,France,Male,35.0,9.0,121152.05,1.0,1.0,0.0,186489.15,0.0
-14523,15695585.0,Shao,729.0,France,Female,41.0,3.0,0.0,2.0,1.0,0.0,147055.87,0.0
-14524,15726494.0,Pugh,663.0,France,Female,51.0,3.0,0.0,3.0,1.0,0.0,109783.29,1.0
-14525,15744368.0,Chiang,712.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,168023.6,0.0
-14526,15655238.0,Udegbulam,597.0,France,Female,41.0,4.0,109909.06,1.0,1.0,0.0,66721.98,1.0
-14527,15661473.0,Dreyer,706.0,Spain,Male,35.0,1.0,123971.16,1.0,1.0,1.0,35300.08,0.0
-14528,15763029.0,Tseng,659.0,Spain,Male,34.0,5.0,104760.82,2.0,1.0,0.0,84760.32,0.0
-14529,15662337.0,Shih,724.0,Spain,Male,42.0,3.0,126066.26,2.0,0.0,0.0,100720.02,0.0
-14530,15692977.0,Maclean,706.0,France,Male,31.0,7.0,0.0,2.0,1.0,1.0,147931.81,0.0
-14531,15667554.0,Dyer,644.0,France,Female,33.0,7.0,0.0,2.0,1.0,0.0,86799.28,0.0
-14532,15651336.0,Hsueh,605.0,France,Female,44.0,5.0,0.0,1.0,1.0,1.0,73564.44,0.0
-14533,15779492.0,Uspensky,656.0,France,Male,45.0,6.0,133776.86,1.0,1.0,1.0,116511.12,0.0
-14534,15780498.0,Milne,708.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,180829.99,0.0
-14535,15578835.0,Chiekwugo,660.0,France,Male,37.0,1.0,0.0,2.0,1.0,0.0,79476.63,0.0
-14536,15809830.0,Tseng,694.0,France,Female,26.0,1.0,142501.97,1.0,1.0,0.0,43954.21,0.0
-14537,15784761.0,Mamelu,733.0,France,Female,40.0,2.0,141806.0,2.0,1.0,0.0,162599.51,1.0
-14538,15699294.0,Lucciano,635.0,Spain,Female,46.0,4.0,129934.64,1.0,0.0,1.0,163798.41,0.0
-14539,15604130.0,O'Donnell,627.0,Spain,Male,36.0,4.0,0.0,2.0,1.0,1.0,59129.72,0.0
-14540,15815095.0,Ifeanyichukwu,624.0,France,Female,26.0,7.0,0.0,2.0,1.0,0.0,12510.53,0.0
-14541,15772781.0,Ch'ien,516.0,France,Male,41.0,8.0,0.0,2.0,1.0,1.0,168515.61,0.0
-14542,15604497.0,Onyemauchechi,709.0,Germany,Male,25.0,6.0,161805.95,2.0,1.0,1.0,138375.31,0.0
-14543,15643359.0,Y?an,602.0,France,Female,32.0,5.0,0.0,1.0,0.0,0.0,132038.65,0.0
-14544,15588902.0,Onyemere,548.0,France,Male,32.0,6.0,0.0,2.0,1.0,0.0,82647.73,0.0
-14545,15667083.0,Lung,611.0,France,Male,39.0,0.0,0.0,2.0,1.0,1.0,77866.91,0.0
-14546,15734044.0,Milano,677.0,France,Male,38.0,4.0,119912.52,1.0,1.0,1.0,172450.04,0.0
-14547,15591628.0,Hs?,656.0,Spain,Male,56.0,2.0,0.0,1.0,1.0,0.0,177624.01,1.0
-14548,15771856.0,Udegbunam,691.0,France,Female,39.0,8.0,0.0,1.0,0.0,0.0,134901.56,0.0
-14549,15795874.0,Kao,701.0,France,Male,34.0,5.0,0.0,2.0,1.0,1.0,89972.25,0.0
-14550,15593365.0,Maclean,624.0,France,Female,39.0,3.0,0.0,2.0,1.0,0.0,159584.61,0.0
-14551,15795954.0,Clements,706.0,France,Male,34.0,6.0,0.0,2.0,1.0,1.0,48226.76,0.0
-14552,15598883.0,McLean,645.0,France,Male,29.0,10.0,0.0,2.0,1.0,0.0,179540.73,0.0
-14553,15733229.0,Onyekachi,731.0,France,Female,27.0,4.0,0.0,2.0,1.0,1.0,143463.28,0.0
-14554,15572728.0,White,508.0,Spain,Male,36.0,6.0,0.0,2.0,0.0,0.0,161574.19,0.0
-14555,15759991.0,Ugochukwutubelum,699.0,Germany,Female,41.0,6.0,105599.67,1.0,1.0,1.0,90878.13,1.0
-14556,15571529.0,Lo,675.0,France,Male,42.0,1.0,0.0,1.0,1.0,1.0,136983.77,1.0
-14557,15783865.0,Watson,439.0,Germany,Female,36.0,3.0,116969.35,1.0,0.0,0.0,59872.9,1.0
-14558,15748752.0,Ponomaryov,738.0,France,Female,39.0,2.0,0.0,2.0,1.0,1.0,67734.79,0.0
-14559,15752912.0,Hsueh,625.0,France,Female,35.0,1.0,0.0,2.0,0.0,1.0,55775.72,0.0
-14560,15688865.0,Chukwuma,767.0,Germany,Female,34.0,7.0,142987.46,1.0,1.0,0.0,62232.6,0.0
-14561,15631756.0,K'ung,682.0,Spain,Male,33.0,10.0,119624.6,1.0,1.0,1.0,151401.33,0.0
-14562,15758081.0,Hsia,635.0,France,Male,30.0,5.0,0.0,1.0,1.0,1.0,163714.92,0.0
-14563,15693906.0,Okeke,694.0,France,Male,40.0,9.0,0.0,2.0,1.0,0.0,47729.33,0.0
-14564,15623478.0,Hsu,571.0,France,Male,22.0,2.0,106036.52,1.0,1.0,0.0,71173.64,0.0
-14565,15664543.0,Chiang,696.0,Germany,Female,32.0,6.0,116968.91,1.0,1.0,1.0,120770.76,0.0
-14566,15776096.0,Hsieh,579.0,France,Female,28.0,8.0,0.0,2.0,1.0,0.0,166650.9,0.0
-14567,15703068.0,Hs?,813.0,France,Male,29.0,7.0,0.0,2.0,1.0,0.0,10883.52,0.0
-14568,15656627.0,Chidimma,637.0,France,Male,37.0,4.0,116662.96,1.0,1.0,0.0,121210.09,0.0
-14569,15676519.0,Milanesi,669.0,Germany,Female,41.0,2.0,134984.66,3.0,1.0,0.0,185900.88,1.0
-14570,15572801.0,Chia,559.0,Germany,Female,53.0,4.0,114512.59,1.0,0.0,1.0,87067.73,1.0
-14571,15772601.0,Tuan,608.0,France,Male,33.0,4.0,0.0,2.0,1.0,1.0,95655.16,0.0
-14572,15757628.0,Udegbulam,773.0,France,Male,42.0,8.0,0.0,2.0,1.0,0.0,86022.09,1.0
-14573,15729377.0,Ch'ang,659.0,Spain,Female,35.0,1.0,0.0,2.0,1.0,0.0,95864.5,0.0
-14574,15724648.0,Ting,576.0,France,Female,53.0,7.0,0.0,1.0,0.0,0.0,185821.41,1.0
-14575,15715951.0,Nucci,721.0,Germany,Male,37.0,9.0,82498.14,2.0,1.0,0.0,115337.74,0.0
-14576,15682355.0,Chiawuotu,815.0,Germany,Male,27.0,7.0,123870.07,1.0,1.0,0.0,91981.85,0.0
-14577,15698872.0,Mancini,692.0,Spain,Male,30.0,8.0,0.0,2.0,1.0,0.0,82298.59,0.0
-14578,15672894.0,Clements,594.0,France,Female,35.0,6.0,0.0,2.0,1.0,0.0,116969.98,0.0
-14579,15751561.0,Maclean,732.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,91359.79,0.0
-14580,15803689.0,Nucci,719.0,France,Male,37.0,1.0,0.0,2.0,0.0,1.0,71519.4,0.0
-14581,15686588.0,Chukwuebuka,677.0,France,Male,51.0,1.0,131694.97,1.0,1.0,0.0,183494.87,1.0
-14582,15683483.0,Lettiere,756.0,France,Male,24.0,2.0,0.0,2.0,1.0,0.0,27474.81,0.0
-14583,15741416.0,H?,850.0,France,Female,46.0,7.0,0.0,1.0,1.0,0.0,161241.65,1.0
-14584,15634891.0,Ch'iu,648.0,France,Male,34.0,8.0,0.0,2.0,1.0,0.0,121408.55,0.0
-14585,15672960.0,Loyau,635.0,France,Female,29.0,7.0,0.0,2.0,1.0,0.0,80698.51,0.0
-14586,15652999.0,Okeke,697.0,Germany,Female,41.0,0.0,86006.21,2.0,1.0,1.0,184524.74,0.0
-14587,15651369.0,Koger,602.0,Spain,Female,30.0,7.0,131778.08,1.0,0.0,1.0,88827.67,0.0
-14588,15794849.0,Calabrese,684.0,France,Female,45.0,3.0,0.0,2.0,1.0,0.0,182266.01,0.0
-14589,15702561.0,Ts'ao,628.0,France,Male,37.0,7.0,0.0,2.0,1.0,0.0,10072.55,0.0
-14590,15631406.0,Chiang,616.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,79886.06,0.0
-14591,15783029.0,Nnonso,729.0,France,Male,39.0,5.0,0.0,1.0,0.0,1.0,173843.21,0.0
-14592,15590314.0,Onyeorulu,757.0,France,Male,27.0,10.0,0.0,2.0,0.0,1.0,136902.01,0.0
-14593,15635143.0,Linger,594.0,France,Male,31.0,8.0,143390.51,2.0,1.0,0.0,74870.99,0.0
-14594,15585853.0,Trevisani,756.0,France,Female,25.0,0.0,0.0,2.0,1.0,1.0,171021.44,0.0
-14595,15644788.0,Chukwujamuike,614.0,Spain,Male,22.0,9.0,0.0,1.0,1.0,1.0,70523.92,0.0
-14596,15723671.0,Nwankwo,684.0,France,Male,52.0,7.0,0.0,1.0,0.0,1.0,128521.17,0.0
-14597,15648735.0,Yuriev,614.0,France,Female,48.0,8.0,111233.09,1.0,0.0,1.0,56793.02,0.0
-14598,15682955.0,L?,679.0,France,Male,27.0,6.0,133800.98,1.0,1.0,1.0,183494.87,0.0
-14599,15774744.0,Fiorentino,724.0,France,Female,44.0,9.0,0.0,1.0,0.0,1.0,163581.85,1.0
-14600,15702801.0,McLean,648.0,France,Female,37.0,9.0,0.0,2.0,1.0,0.0,181419.29,0.0
-14601,15661528.0,Iadanza,756.0,Spain,Female,33.0,8.0,0.0,2.0,1.0,1.0,66963.15,0.0
-14602,15803790.0,Chibuzo,683.0,Spain,Male,31.0,6.0,0.0,2.0,1.0,1.0,58844.88,0.0
-14603,15737408.0,Tung,749.0,France,Female,55.0,9.0,0.0,2.0,1.0,1.0,180345.44,0.0
-14604,15681174.0,Udegbulam,573.0,Germany,Male,41.0,6.0,148863.31,2.0,0.0,1.0,104886.65,0.0
-14605,15798844.0,Nwankwo,841.0,Germany,Female,34.0,4.0,130170.82,3.0,0.0,0.0,134531.96,1.0
-14606,15803790.0,Tien,805.0,France,Male,39.0,3.0,85845.43,1.0,0.0,0.0,68730.91,0.0
-14607,15803378.0,Chin,682.0,France,Male,35.0,4.0,0.0,2.0,1.0,1.0,170980.86,0.0
-14608,15721779.0,Onyeorulu,711.0,Germany,Male,48.0,9.0,121166.89,1.0,1.0,1.0,153955.38,1.0
-14609,15673591.0,Trevisani,613.0,France,Female,34.0,7.0,0.0,2.0,1.0,1.0,173495.77,0.0
-14610,15813132.0,Yeh,571.0,Germany,Female,46.0,5.0,162532.14,1.0,0.0,1.0,79577.38,0.0
-14611,15707078.0,Mazzanti,686.0,France,Male,29.0,7.0,0.0,2.0,1.0,1.0,92612.88,0.0
-14612,15743826.0,Hsueh,752.0,France,Male,34.0,6.0,0.0,1.0,1.0,0.0,132547.02,0.0
-14613,15621353.0,Chukwudi,741.0,Spain,Female,59.0,4.0,113737.07,1.0,1.0,0.0,182547.04,1.0
-14614,15653572.0,Chiang,599.0,France,Female,45.0,9.0,133800.98,1.0,1.0,1.0,107841.77,0.0
-14615,15675794.0,Chia,643.0,Germany,Male,38.0,4.0,116546.23,1.0,1.0,0.0,96847.91,1.0
-14616,15715673.0,Tuan,679.0,France,Female,34.0,9.0,0.0,2.0,1.0,0.0,88890.05,0.0
-14617,15806901.0,Pisani,762.0,Germany,Female,40.0,3.0,106901.42,1.0,1.0,0.0,64477.25,0.0
-14618,15794413.0,Krichauff,666.0,France,Female,30.0,3.0,0.0,2.0,1.0,1.0,145558.42,0.0
-14619,15581332.0,Fanucci,667.0,Spain,Female,45.0,3.0,0.0,2.0,0.0,0.0,141856.33,0.0
-14620,15690182.0,Nwabugwu,551.0,France,Male,47.0,6.0,0.0,2.0,1.0,0.0,94774.12,0.0
-14621,15802741.0,Cremonesi,699.0,France,Male,24.0,9.0,0.0,2.0,1.0,1.0,121299.09,0.0
-14622,15756610.0,Trevisano,762.0,Spain,Female,38.0,5.0,150302.84,1.0,1.0,0.0,58467.08,0.0
-14623,15782735.0,Clements,722.0,France,Female,33.0,1.0,0.0,2.0,1.0,1.0,131712.52,0.0
-14624,15809227.0,T'ang,568.0,Germany,Male,40.0,4.0,99513.87,1.0,1.0,0.0,135629.28,0.0
-14625,15658852.0,Nebechi,850.0,Germany,Male,44.0,5.0,137714.5,1.0,0.0,0.0,92865.41,1.0
-14626,15690540.0,Pagnotto,694.0,France,Female,58.0,9.0,137555.05,1.0,0.0,1.0,123587.83,1.0
-14627,15672640.0,Chukwubuikem,662.0,Spain,Female,33.0,7.0,0.0,1.0,1.0,1.0,56585.2,0.0
-14628,15684769.0,Chukwuebuka,602.0,France,Male,33.0,5.0,0.0,2.0,1.0,0.0,582.59,0.0
-14629,15720687.0,Kepley,633.0,Spain,Female,46.0,1.0,0.0,3.0,1.0,0.0,183617.32,1.0
-14630,15738889.0,P'an,521.0,Spain,Female,32.0,8.0,0.0,2.0,1.0,0.0,176925.19,0.0
-14631,15696834.0,K?,598.0,Germany,Male,36.0,5.0,111213.64,2.0,0.0,0.0,7953.62,1.0
-14632,15777076.0,Nnabuife,705.0,France,Female,51.0,0.0,0.0,1.0,1.0,0.0,159840.58,1.0
-14633,15677796.0,Trevisano,704.0,Germany,Female,40.0,5.0,129974.79,1.0,1.0,0.0,131768.98,1.0
-14634,15663917.0,Hsiung,681.0,Germany,Male,44.0,8.0,118629.53,3.0,1.0,1.0,175071.29,1.0
-14635,15693764.0,Tsao,850.0,France,Female,33.0,10.0,0.0,2.0,1.0,1.0,82469.06,0.0
-14636,15585599.0,Trevisani,678.0,France,Male,33.0,3.0,0.0,1.0,1.0,0.0,133516.51,1.0
-14637,15771299.0,Fiorentino,661.0,France,Male,51.0,5.0,0.0,1.0,1.0,1.0,75937.47,1.0
-14638,15600781.0,Ch'ang,730.0,Spain,Female,26.0,5.0,0.0,1.0,0.0,1.0,73073.68,0.0
-14639,15781665.0,Hs?,591.0,France,Male,42.0,0.0,0.0,2.0,1.0,1.0,185541.2,0.0
-14640,15740160.0,Mazzanti,611.0,France,Male,67.0,7.0,0.0,2.0,0.0,1.0,155711.64,0.0
-14641,15743617.0,Maclean,652.0,France,Male,38.0,2.0,0.0,2.0,1.0,0.0,183569.25,0.0
-14642,15661526.0,P'eng,624.0,France,Male,43.0,5.0,0.0,2.0,1.0,0.0,110887.14,0.0
-14643,15656683.0,Onwumelu,709.0,France,Female,34.0,0.0,0.0,2.0,0.0,0.0,152577.62,0.0
-14644,15807107.0,Pisani,579.0,France,Female,71.0,3.0,0.0,2.0,1.0,0.0,181419.29,0.0
-14645,15810390.0,Onyemauchechukwu,703.0,Spain,Male,35.0,5.0,0.0,2.0,1.0,1.0,163944.18,0.0
-14646,15790744.0,Hsia,624.0,Spain,Female,41.0,8.0,0.0,2.0,1.0,0.0,131778.87,0.0
-14647,15777907.0,Chukwufumnanya,636.0,Spain,Male,36.0,7.0,0.0,1.0,1.0,1.0,131804.86,1.0
-14648,15662758.0,Folliero,646.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,47799.15,0.0
-14649,15702968.0,Hao,597.0,France,Female,42.0,7.0,0.0,2.0,1.0,1.0,91406.62,0.0
-14650,15592999.0,Nnanna,713.0,Spain,Male,35.0,1.0,0.0,2.0,1.0,1.0,158887.09,0.0
-14651,15716276.0,Chukwudi,637.0,France,Male,42.0,5.0,0.0,2.0,1.0,1.0,155870.82,0.0
-14652,15681924.0,K?,613.0,Germany,Female,37.0,8.0,127120.62,1.0,1.0,0.0,134901.58,1.0
-14653,15609824.0,Fyodorov,588.0,France,Female,43.0,2.0,0.0,2.0,1.0,1.0,53931.05,1.0
-14654,15718245.0,Kaur,681.0,Spain,Male,42.0,9.0,0.0,2.0,1.0,1.0,82996.47,0.0
-14655,15745452.0,Ch'iu,591.0,Spain,Male,36.0,6.0,0.0,2.0,1.0,1.0,121504.18,0.0
-14656,15741745.0,Y?,748.0,Germany,Male,43.0,5.0,133537.1,1.0,0.0,1.0,84749.96,0.0
-14657,15773456.0,Fanucci,573.0,France,Female,29.0,1.0,126815.71,1.0,1.0,0.0,71405.17,0.0
-14658,15632263.0,Liao,597.0,France,Female,34.0,4.0,0.0,2.0,0.0,0.0,140929.98,0.0
-14659,15654296.0,Ritchie,678.0,France,Male,43.0,5.0,0.0,2.0,1.0,1.0,151903.6,0.0
-14660,15790829.0,Genovesi,538.0,Germany,Female,49.0,5.0,129855.32,1.0,1.0,0.0,35356.46,1.0
-14661,15710671.0,Chukwuemeka,652.0,France,Male,28.0,3.0,0.0,2.0,1.0,0.0,154767.34,0.0
-14662,15679642.0,Obialo,701.0,France,Female,45.0,1.0,148580.12,1.0,1.0,1.0,99061.75,0.0
-14663,15688868.0,Ibeamaka,687.0,France,Male,27.0,2.0,0.0,2.0,1.0,1.0,141069.88,0.0
-14664,15746012.0,O'Loughlin,773.0,France,Male,38.0,6.0,0.0,2.0,1.0,1.0,51587.04,0.0
-14665,15642710.0,Nkemakolam,689.0,Spain,Male,37.0,5.0,0.0,2.0,1.0,1.0,140723.09,0.0
-14666,15709941.0,Trevisano,676.0,France,Male,39.0,0.0,0.0,2.0,1.0,1.0,170980.86,0.0
-14667,15631838.0,Hsu,633.0,Spain,Female,42.0,9.0,0.0,2.0,1.0,0.0,86634.3,0.0
-14668,15706812.0,Uspenskaya,695.0,Spain,Male,51.0,1.0,0.0,2.0,1.0,1.0,84872.66,0.0
-14669,15762588.0,Ch'ien,850.0,Spain,Male,42.0,1.0,0.0,2.0,1.0,0.0,26318.73,0.0
-14670,15593954.0,Okechukwu,635.0,France,Male,31.0,8.0,0.0,2.0,0.0,0.0,146511.2,0.0
-14671,15802351.0,Nwebube,639.0,France,Male,37.0,4.0,0.0,2.0,1.0,0.0,102908.91,0.0
-14672,15645103.0,Folliero,588.0,France,Male,38.0,7.0,101090.27,1.0,1.0,0.0,47848.56,0.0
-14673,15643359.0,Teng,573.0,France,Female,29.0,10.0,0.0,1.0,1.0,1.0,182436.81,0.0
-14674,15677785.0,Hsiao,753.0,France,Male,45.0,4.0,0.0,2.0,1.0,0.0,181605.85,0.0
-14675,15763093.0,Ts'ai,589.0,France,Female,39.0,6.0,0.0,2.0,1.0,1.0,176773.59,0.0
-14676,15609735.0,Tien,659.0,Germany,Male,25.0,2.0,117456.72,1.0,1.0,0.0,163666.85,1.0
-14677,15754849.0,Pirozzi,693.0,France,Male,34.0,1.0,0.0,1.0,1.0,0.0,185821.41,0.0
-14678,15732674.0,P'eng,694.0,France,Male,34.0,6.0,0.0,2.0,0.0,1.0,163471.01,0.0
-14679,15772363.0,Nkemjika,632.0,France,Female,39.0,1.0,0.0,2.0,1.0,1.0,140166.95,0.0
-14680,15655368.0,Hsueh,639.0,France,Male,33.0,2.0,153895.33,1.0,0.0,0.0,180345.44,0.0
-14681,15697318.0,Chukwuhaenye,731.0,Germany,Female,50.0,2.0,118079.47,1.0,1.0,0.0,119313.74,1.0
-14682,15581552.0,K'ung,642.0,Spain,Female,37.0,9.0,153340.89,1.0,0.0,1.0,173365.89,1.0
-14683,15578825.0,Onwuatuegwu,709.0,France,Male,29.0,8.0,0.0,2.0,0.0,0.0,129964.94,0.0
-14684,15688880.0,Ugochukwutubelum,658.0,Spain,Female,61.0,4.0,0.0,1.0,0.0,1.0,81718.37,1.0
-14685,15706131.0,T'ao,633.0,France,Male,35.0,3.0,0.0,2.0,1.0,1.0,131501.72,0.0
-14686,15611794.0,Ku,699.0,France,Male,30.0,7.0,0.0,2.0,0.0,1.0,145986.35,0.0
-14687,15752312.0,H?,704.0,France,Male,45.0,1.0,0.0,2.0,1.0,0.0,180622.09,0.0
-14688,15747174.0,P'eng,516.0,Germany,Female,51.0,6.0,132057.92,2.0,1.0,0.0,142763.51,1.0
-14689,15588839.0,Ibeamaka,653.0,Germany,Male,31.0,4.0,117056.46,2.0,1.0,0.0,176692.87,0.0
-14690,15658732.0,Martin,686.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,116973.27,0.0
-14691,15588854.0,Mamelu,597.0,France,Male,30.0,4.0,123971.51,2.0,1.0,0.0,48228.1,0.0
-14692,15661626.0,Ewing,607.0,Spain,Male,44.0,9.0,0.0,1.0,1.0,1.0,53437.1,1.0
-14693,15593470.0,Chiang,706.0,France,Male,37.0,3.0,111096.91,1.0,1.0,0.0,112256.57,0.0
-14694,15679760.0,Bogle,624.0,France,Male,28.0,3.0,99745.95,1.0,1.0,1.0,47832.82,0.0
-14695,15633378.0,Shih,684.0,Spain,Female,38.0,10.0,0.0,2.0,1.0,1.0,141364.27,0.0
-14696,15776433.0,Kao,729.0,Spain,Female,33.0,9.0,0.0,2.0,1.0,0.0,139861.53,0.0
-14697,15743456.0,Ritchie,648.0,France,Male,38.0,6.0,138162.81,1.0,0.0,0.0,89519.47,0.0
-14698,15747168.0,Onochie,567.0,France,Male,35.0,7.0,0.0,2.0,0.0,0.0,123866.28,0.0
-14699,15792453.0,Ch'in,663.0,France,Male,31.0,5.0,153611.83,1.0,1.0,0.0,141763.26,0.0
-14700,15710365.0,Ijendu,681.0,Germany,Male,38.0,8.0,109869.32,2.0,1.0,0.0,172096.08,0.0
-14701,15669414.0,Goliwe,707.0,Germany,Female,60.0,7.0,152805.05,1.0,0.0,1.0,49594.02,1.0
-14702,15653762.0,Chien,725.0,France,Female,31.0,1.0,105799.32,1.0,1.0,1.0,177747.03,0.0
-14703,15775192.0,Hsia,719.0,France,Male,39.0,5.0,0.0,2.0,1.0,0.0,55775.84,0.0
-14704,15590097.0,Nnamdi,850.0,France,Male,51.0,1.0,0.0,2.0,0.0,1.0,155823.2,0.0
-14705,15807961.0,Folliero,615.0,France,Male,29.0,3.0,155619.58,1.0,1.0,0.0,176712.59,0.0
-14706,15809837.0,Henty,526.0,France,Female,31.0,4.0,0.0,2.0,0.0,1.0,152843.53,0.0
-14707,15715643.0,T'ao,543.0,Germany,Male,32.0,6.0,123763.61,2.0,0.0,0.0,100187.43,0.0
-14708,15690526.0,H?,579.0,Spain,Female,37.0,4.0,103960.38,1.0,1.0,1.0,161653.5,0.0
-14709,15776850.0,Ch'ang,726.0,France,Male,31.0,4.0,0.0,2.0,1.0,0.0,168544.84,0.0
-14710,15661703.0,Cisneros,674.0,Spain,Female,41.0,3.0,0.0,1.0,1.0,1.0,172495.81,0.0
-14711,15571871.0,Ch'en,691.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,93524.19,0.0
-14712,15722090.0,Mazzi,619.0,Germany,Female,50.0,7.0,134919.85,3.0,1.0,0.0,115116.35,1.0
-14713,15749952.0,T'ien,680.0,France,Female,44.0,9.0,0.0,2.0,1.0,0.0,123866.28,0.0
-14714,15623346.0,Horsfall,569.0,Germany,Male,33.0,8.0,98993.02,2.0,1.0,0.0,161574.19,0.0
-14715,15689671.0,Macleod,753.0,Spain,Female,49.0,8.0,0.0,1.0,1.0,0.0,48505.1,1.0
-14716,15731519.0,Liao,595.0,Spain,Female,56.0,2.0,0.0,1.0,1.0,0.0,180344.69,1.0
-14717,15784099.0,Chiazagomekpere,714.0,France,Female,36.0,1.0,0.0,2.0,0.0,0.0,143433.33,0.0
-14718,15600839.0,De Bernales,683.0,France,Male,35.0,7.0,0.0,2.0,1.0,1.0,147545.65,0.0
-14719,15576680.0,Obijiaku,599.0,France,Male,44.0,4.0,120847.11,1.0,1.0,0.0,77951.84,1.0
-14720,15598044.0,Cattaneo,652.0,France,Male,46.0,5.0,0.0,2.0,1.0,1.0,134423.31,0.0
-14721,15649523.0,Chinonyelum,719.0,France,Male,29.0,9.0,0.0,2.0,1.0,0.0,103379.31,0.0
-14722,15703794.0,Kao,689.0,France,Male,43.0,9.0,91711.59,1.0,0.0,1.0,113645.4,0.0
-14723,15674727.0,Hsia,749.0,Germany,Female,30.0,3.0,141786.78,1.0,0.0,1.0,173783.38,0.0
-14724,15625494.0,Tien,608.0,France,Male,35.0,3.0,0.0,1.0,1.0,0.0,179258.49,0.0
-14725,15794493.0,Chiekwugo,695.0,France,Male,56.0,6.0,0.0,1.0,1.0,0.0,81602.02,1.0
-14726,15700752.0,Udegbulam,704.0,France,Male,32.0,2.0,0.0,2.0,1.0,1.0,71047.51,0.0
-14727,15672945.0,Shih,547.0,Germany,Male,48.0,5.0,141749.74,2.0,1.0,0.0,90905.94,1.0
-14728,15752912.0,Nnanna,628.0,France,Female,36.0,4.0,0.0,2.0,0.0,1.0,176778.52,0.0
-14729,15576550.0,Aksenov,656.0,France,Male,36.0,7.0,0.0,2.0,1.0,0.0,60721.4,1.0
-14730,15671358.0,Hs?,639.0,France,Female,38.0,2.0,0.0,1.0,0.0,1.0,133814.37,1.0
-14731,15659820.0,Pacheco,850.0,Germany,Female,31.0,9.0,115463.16,1.0,1.0,0.0,143481.79,0.0
-14732,15642833.0,Lucciano,637.0,France,Male,44.0,0.0,0.0,2.0,1.0,0.0,102986.15,0.0
-14733,15737782.0,Chukwuemeka,545.0,Spain,Male,26.0,5.0,0.0,2.0,1.0,1.0,85547.33,0.0
-14734,15576676.0,Chiang,578.0,France,Female,34.0,4.0,0.0,2.0,1.0,0.0,140763.07,0.0
-14735,15651052.0,Chiang,735.0,France,Male,34.0,3.0,0.0,2.0,1.0,0.0,64516.8,0.0
-14736,15721730.0,O'Donnell,596.0,France,Male,37.0,1.0,136855.24,1.0,1.0,0.0,64872.55,0.0
-14737,15685300.0,Hsiung,705.0,Germany,Male,36.0,5.0,167795.6,2.0,1.0,0.0,120790.02,0.0
-14738,15614778.0,Chiu,558.0,Germany,Female,48.0,3.0,118879.35,1.0,1.0,0.0,136902.01,1.0
-14739,15648881.0,Milano,686.0,France,Male,40.0,5.0,125921.37,1.0,0.0,0.0,179631.85,0.0
-14740,15671209.0,Ebelegbulam,731.0,Germany,Female,47.0,9.0,106301.85,1.0,0.0,0.0,109137.3,1.0
-14741,15592583.0,Milani,590.0,France,Male,40.0,3.0,0.0,3.0,1.0,0.0,149457.81,1.0
-14742,15600991.0,Goliwe,543.0,Spain,Male,42.0,1.0,0.0,2.0,1.0,1.0,143481.85,0.0
-14743,15630661.0,Chin,624.0,France,Male,32.0,3.0,0.0,2.0,0.0,0.0,75837.58,0.0
-14744,15691808.0,Ugochukwu,612.0,Germany,Male,48.0,0.0,135422.19,1.0,1.0,1.0,184936.77,1.0
-14745,15711398.0,Piazza,562.0,France,Female,34.0,8.0,0.0,2.0,1.0,0.0,53584.03,0.0
-14746,15752312.0,Calabrese,661.0,France,Female,26.0,6.0,0.0,2.0,1.0,0.0,35671.45,0.0
-14747,15749117.0,Onwudiwe,634.0,France,Female,47.0,4.0,0.0,2.0,0.0,0.0,126796.69,0.0
-14748,15672996.0,Shih,606.0,France,Female,37.0,1.0,0.0,1.0,1.0,1.0,159830.58,0.0
-14749,15694566.0,Frolov,651.0,France,Male,30.0,3.0,0.0,2.0,1.0,0.0,80552.36,0.0
-14750,15809585.0,T'ien,567.0,Spain,Male,35.0,2.0,114947.67,1.0,0.0,1.0,131283.6,0.0
-14751,15615032.0,Nwokezuike,711.0,France,Female,41.0,7.0,0.0,2.0,1.0,0.0,131501.72,0.0
-14752,15635097.0,Ndubuisi,729.0,Spain,Female,44.0,5.0,0.0,2.0,0.0,0.0,60941.27,0.0
-14753,15576085.0,Ch'en,724.0,Germany,Female,42.0,9.0,150635.3,1.0,1.0,1.0,86786.41,1.0
-14754,15723685.0,Udegbunam,554.0,Spain,Female,37.0,5.0,0.0,2.0,0.0,0.0,151345.49,0.0
-14755,15595557.0,Oluchukwu,758.0,France,Male,40.0,9.0,0.0,2.0,0.0,0.0,121189.3,0.0
-14756,15791842.0,Nkemjika,738.0,Spain,Female,42.0,1.0,0.0,1.0,1.0,1.0,91947.74,1.0
-14757,15621267.0,Frolov,638.0,Germany,Male,32.0,8.0,131835.88,1.0,1.0,1.0,4395.45,0.0
-14758,15594812.0,Ch'ien,653.0,France,Male,23.0,3.0,89087.89,1.0,0.0,1.0,184843.77,0.0
-14759,15798943.0,Ch'eng,758.0,Spain,Female,57.0,9.0,0.0,2.0,0.0,0.0,162643.15,1.0
-14760,15760244.0,Frolov,704.0,France,Female,62.0,6.0,116498.22,3.0,1.0,0.0,119903.2,1.0
-14761,15756481.0,Onyekachi,556.0,Spain,Male,39.0,1.0,0.0,2.0,1.0,1.0,152417.79,0.0
-14762,15771636.0,Okwuadigbo,714.0,France,Female,56.0,8.0,148045.45,1.0,1.0,0.0,70974.13,1.0
-14763,15585350.0,Rowntree,850.0,Spain,Female,31.0,4.0,0.0,2.0,1.0,0.0,158937.55,0.0
-14764,15724451.0,Dyer,676.0,Spain,Male,29.0,3.0,112291.34,1.0,1.0,0.0,173683.0,0.0
-14765,15728362.0,T'ien,850.0,France,Female,55.0,8.0,0.0,1.0,1.0,0.0,44946.29,1.0
-14766,15708904.0,Lettiere,614.0,France,Male,38.0,1.0,0.0,2.0,1.0,0.0,130452.88,0.0
-14767,15692991.0,Chukwuemeka,634.0,Spain,Female,35.0,9.0,0.0,1.0,1.0,1.0,84300.4,0.0
-14768,15686964.0,Chialuka,553.0,Spain,Male,28.0,1.0,0.0,2.0,1.0,1.0,163830.64,0.0
-14769,15789611.0,Chukwualuka,597.0,Spain,Female,32.0,0.0,161591.11,1.0,0.0,0.0,75937.47,0.0
-14770,15698716.0,Zito,611.0,France,Female,45.0,10.0,0.0,2.0,1.0,0.0,73686.75,0.0
-14771,15696989.0,Mazzi,636.0,Germany,Female,36.0,5.0,155973.88,1.0,1.0,1.0,173952.5,0.0
-14772,15661936.0,Shih,687.0,Germany,Female,31.0,6.0,129472.33,3.0,1.0,0.0,96670.2,1.0
-14773,15647572.0,T'ien,597.0,Spain,Female,36.0,8.0,0.0,2.0,1.0,0.0,64321.02,0.0
-14774,15787848.0,Oluchukwu,660.0,Spain,Male,41.0,3.0,166070.18,1.0,1.0,1.0,176576.62,0.0
-14775,15657565.0,Pisano,738.0,Germany,Male,33.0,2.0,141528.88,1.0,1.0,0.0,136915.15,0.0
-14776,15761043.0,Chukwumaobim,584.0,France,Female,32.0,6.0,0.0,2.0,1.0,1.0,163558.18,0.0
-14777,15704771.0,Okorie,628.0,France,Male,35.0,2.0,0.0,2.0,1.0,0.0,65720.92,0.0
-14778,15605531.0,Chien,593.0,Spain,Male,44.0,6.0,148384.64,1.0,0.0,0.0,59659.53,0.0
-14779,15592461.0,Udinese,663.0,France,Female,25.0,2.0,0.0,2.0,1.0,1.0,176763.27,0.0
-14780,15790596.0,Nucci,756.0,France,Male,33.0,2.0,0.0,2.0,1.0,1.0,173498.45,0.0
-14781,15772250.0,Walker,596.0,France,Female,40.0,0.0,0.0,2.0,0.0,1.0,161853.12,0.0
-14782,15641629.0,Eluemuno,638.0,France,Female,43.0,6.0,145974.79,1.0,1.0,0.0,77965.44,1.0
-14783,15713094.0,Kuo,738.0,Germany,Male,36.0,4.0,144976.5,1.0,0.0,1.0,106564.88,0.0
-14784,15708713.0,Milne,569.0,Spain,Male,37.0,1.0,0.0,1.0,1.0,0.0,163625.56,0.0
-14785,15638068.0,Nnachetam,777.0,France,Male,31.0,1.0,99824.46,1.0,0.0,1.0,51670.82,0.0
-14786,15682618.0,Yobachi,721.0,France,Female,33.0,3.0,0.0,2.0,1.0,0.0,16081.62,0.0
-14787,15589793.0,Aksenova,696.0,France,Male,36.0,6.0,0.0,2.0,1.0,1.0,103560.98,0.0
-14788,15796370.0,Cremonesi,735.0,France,Female,38.0,3.0,0.0,1.0,1.0,1.0,71139.31,0.0
-14789,15718507.0,Hsieh,684.0,Germany,Female,56.0,1.0,118520.26,1.0,1.0,0.0,120736.04,1.0
-14790,15585066.0,Uvarov,554.0,France,Female,48.0,10.0,0.0,2.0,0.0,1.0,174738.2,1.0
-14791,15643056.0,Trevisan,731.0,France,Male,26.0,1.0,0.0,2.0,1.0,0.0,79775.19,0.0
-14792,15595388.0,Kuo,659.0,France,Male,51.0,2.0,0.0,1.0,1.0,0.0,150836.98,1.0
-14793,15723006.0,Chuang,598.0,Spain,Male,30.0,6.0,0.0,2.0,1.0,0.0,55829.25,0.0
-14794,15645600.0,Lombardi,681.0,Germany,Female,40.0,4.0,109342.82,2.0,1.0,1.0,12092.03,1.0
-14795,15716357.0,Chukwumaobim,748.0,France,Female,33.0,2.0,0.0,2.0,1.0,1.0,61666.87,0.0
-14796,15582892.0,Hsiung,585.0,Spain,Male,35.0,10.0,0.0,2.0,1.0,0.0,46635.11,0.0
-14797,15635939.0,Trevisani,637.0,Spain,Male,41.0,3.0,0.0,1.0,1.0,0.0,119348.28,0.0
-14798,15568328.0,Walker,578.0,Germany,Female,42.0,5.0,120599.38,1.0,1.0,1.0,49848.86,0.0
-14799,15671345.0,Lucciano,770.0,Spain,Male,62.0,5.0,107720.64,3.0,0.0,0.0,154347.95,1.0
-14800,15696054.0,Lai,584.0,France,Male,38.0,5.0,128743.8,1.0,1.0,0.0,86460.28,0.0
-14801,15798024.0,Olisaemeka,578.0,France,Male,45.0,4.0,0.0,2.0,1.0,0.0,126517.97,1.0
-14802,15791326.0,Ch'ang,438.0,France,Male,32.0,6.0,0.0,1.0,1.0,1.0,121440.8,0.0
-14803,15811594.0,Milanesi,572.0,France,Female,41.0,4.0,0.0,1.0,1.0,0.0,111421.77,1.0
-14804,15643361.0,Kambinachi,587.0,France,Female,34.0,4.0,0.0,2.0,0.0,1.0,90876.95,0.0
-14805,15777901.0,Chukwuebuka,675.0,France,Male,28.0,6.0,0.0,2.0,1.0,0.0,185543.69,0.0
-14806,15728333.0,Ts'ui,661.0,Spain,Female,58.0,3.0,0.0,2.0,1.0,1.0,124872.42,0.0
-14807,15795953.0,Pisano,660.0,France,Female,39.0,9.0,0.0,1.0,1.0,1.0,89566.74,0.0
-14808,15621611.0,Hao,603.0,France,Male,36.0,1.0,0.0,2.0,1.0,1.0,94440.45,0.0
-14809,15769412.0,Smith,613.0,Germany,Male,29.0,5.0,116465.24,1.0,0.0,1.0,159268.67,1.0
-14810,15705726.0,Hsia,602.0,France,Male,40.0,9.0,0.0,1.0,0.0,0.0,75860.01,1.0
-14811,15689412.0,Chiu,823.0,Spain,Male,40.0,8.0,121461.87,1.0,1.0,0.0,67740.08,0.0
-14812,15669954.0,Chung,644.0,Germany,Female,42.0,8.0,11582.9,1.0,1.0,0.0,144824.45,1.0
-14813,15693908.0,Olisaemeka,593.0,France,Male,34.0,10.0,0.0,1.0,1.0,1.0,91092.84,0.0
-14814,15784597.0,Pirozzi,678.0,France,Female,39.0,8.0,0.0,1.0,1.0,0.0,131093.99,1.0
-14815,15593969.0,Hsia,850.0,France,Male,57.0,3.0,0.0,2.0,1.0,1.0,172531.23,0.0
-14816,15724444.0,Ts'ui,644.0,Spain,Female,42.0,4.0,0.0,2.0,1.0,1.0,61666.87,0.0
-14817,15690169.0,Fanucci,635.0,Spain,Male,35.0,6.0,0.0,2.0,1.0,0.0,88890.05,0.0
-14818,15797905.0,Padovano,653.0,France,Female,42.0,4.0,0.0,2.0,0.0,0.0,145792.01,0.0
-14819,15756425.0,Folliero,753.0,France,Female,43.0,3.0,0.0,2.0,1.0,1.0,185210.63,0.0
-14820,15602844.0,Martin,668.0,Germany,Male,33.0,5.0,105858.71,2.0,1.0,0.0,109394.62,0.0
-14821,15777599.0,Ubanwa,633.0,France,Male,28.0,1.0,124580.69,1.0,1.0,0.0,120892.96,0.0
-14822,15662066.0,Brown,686.0,France,Female,29.0,5.0,0.0,1.0,1.0,1.0,177622.38,0.0
-14823,15757577.0,Teng,495.0,France,Female,33.0,9.0,123835.46,1.0,1.0,0.0,157577.71,0.0
-14824,15762392.0,Ch'in,562.0,Spain,Male,33.0,8.0,0.0,1.0,0.0,1.0,51380.9,0.0
-14825,15673929.0,Shih,621.0,France,Female,33.0,7.0,162390.52,1.0,1.0,0.0,147090.9,0.0
-14826,15636592.0,Ts'ui,704.0,France,Female,30.0,8.0,0.0,1.0,0.0,0.0,160176.47,0.0
-14827,15651264.0,Cattaneo,752.0,France,Female,38.0,7.0,0.0,1.0,1.0,1.0,151401.33,0.0
-14828,15609987.0,Tsao,648.0,France,Male,48.0,2.0,0.0,2.0,1.0,1.0,88890.75,0.0
-14829,15611157.0,Onyeorulu,707.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,27286.1,0.0
-14830,15681924.0,Hao,592.0,France,Female,36.0,2.0,0.0,2.0,1.0,0.0,90558.24,0.0
-14831,15579915.0,Fan,741.0,France,Female,41.0,2.0,129870.61,1.0,1.0,1.0,74637.57,0.0
-14832,15734461.0,Baresi,684.0,France,Female,61.0,5.0,0.0,2.0,0.0,1.0,187985.85,1.0
-14833,15701200.0,K?,653.0,France,Female,42.0,2.0,0.0,2.0,1.0,1.0,121537.91,0.0
-14834,15661409.0,Macdonald,605.0,France,Female,42.0,4.0,0.0,1.0,1.0,0.0,94916.68,1.0
-14835,15584091.0,Galkin,705.0,France,Female,34.0,8.0,0.0,2.0,1.0,1.0,88721.84,0.0
-14836,15803801.0,Chinedum,651.0,Germany,Female,44.0,6.0,155870.93,2.0,1.0,1.0,90177.3,0.0
-14837,15594594.0,Ts'ui,544.0,France,Female,34.0,10.0,113090.4,1.0,1.0,1.0,106534.15,0.0
-14838,15765806.0,Udegbunam,850.0,Spain,Female,34.0,3.0,163700.78,1.0,1.0,1.0,199493.38,0.0
-14839,15691808.0,Udegbunam,584.0,France,Male,45.0,2.0,0.0,1.0,0.0,1.0,152577.62,1.0
-14840,15596671.0,Chukwuebuka,727.0,Spain,Female,33.0,0.0,120962.59,1.0,1.0,1.0,89972.25,0.0
-14841,15725794.0,Piazza,641.0,Germany,Male,37.0,8.0,116800.25,1.0,1.0,0.0,171733.43,1.0
-14842,15802483.0,Ch'ien,743.0,France,Male,48.0,8.0,0.0,1.0,1.0,0.0,176576.71,0.0
-14843,15572408.0,Ch'iu,661.0,Spain,Male,46.0,1.0,0.0,1.0,0.0,1.0,57876.05,1.0
-14844,15776596.0,Lombardo,642.0,Germany,Female,49.0,7.0,129750.68,1.0,1.0,0.0,97938.59,1.0
-14845,15783591.0,Yermolayeva,595.0,France,Male,26.0,5.0,0.0,1.0,0.0,1.0,132798.78,0.0
-14846,15742798.0,Trevisan,485.0,France,Male,38.0,3.0,0.0,2.0,1.0,1.0,80587.37,0.0
-14847,15787097.0,Outhwaite,667.0,France,Male,34.0,3.0,0.0,2.0,1.0,1.0,132973.17,0.0
-14848,15693877.0,Chia,679.0,Spain,Male,39.0,9.0,0.0,2.0,1.0,0.0,134790.74,0.0
-14849,15628303.0,Onwumelu,627.0,France,Male,36.0,2.0,0.0,2.0,1.0,0.0,86791.9,0.0
-14850,15802381.0,Ting,790.0,Germany,Male,33.0,3.0,96776.39,2.0,1.0,0.0,126722.57,0.0
-14851,15571529.0,Hs?,663.0,France,Male,29.0,1.0,0.0,2.0,1.0,0.0,173899.42,0.0
-14852,15682048.0,Nucci,582.0,France,Male,42.0,2.0,0.0,1.0,0.0,0.0,75837.58,1.0
-14853,15679810.0,Frolov,653.0,France,Male,42.0,7.0,0.0,2.0,0.0,0.0,138344.7,0.0
-14854,15791174.0,Cattaneo,742.0,France,Male,50.0,3.0,0.0,2.0,0.0,0.0,182944.14,1.0
-14855,15632050.0,Lo,712.0,Germany,Male,44.0,7.0,134497.27,2.0,0.0,0.0,166526.26,0.0
-14856,15776824.0,Chizoba,637.0,Spain,Female,34.0,3.0,0.0,2.0,0.0,1.0,106061.47,0.0
-14857,15688963.0,Nwebube,687.0,France,Male,45.0,7.0,0.0,2.0,1.0,1.0,73888.65,0.0
-14858,15671779.0,Lucchese,473.0,France,Female,46.0,5.0,0.0,2.0,1.0,1.0,126743.33,0.0
-14859,15718247.0,Kerr,619.0,Spain,Female,27.0,9.0,0.0,1.0,0.0,0.0,161854.98,0.0
-14860,15673367.0,Chiekwugo,725.0,France,Female,49.0,0.0,0.0,2.0,0.0,1.0,5791.85,0.0
-14861,15692581.0,Baresi,709.0,France,Male,45.0,6.0,0.0,2.0,1.0,1.0,146723.72,0.0
-14862,15803526.0,Ts'ui,628.0,France,Male,37.0,5.0,0.0,2.0,0.0,0.0,82696.84,0.0
-14863,15769254.0,Blesing,742.0,France,Female,34.0,6.0,0.0,2.0,1.0,1.0,199805.63,0.0
-14864,15588123.0,Palerma,803.0,France,Female,55.0,4.0,0.0,3.0,1.0,0.0,89602.86,1.0
-14865,15671293.0,Ch'ang,716.0,Germany,Female,43.0,8.0,112972.57,1.0,1.0,1.0,71167.79,1.0
-14866,15714087.0,White,595.0,Spain,Female,58.0,1.0,0.0,2.0,1.0,1.0,47468.67,0.0
-14867,15703923.0,Yocum,497.0,France,Female,28.0,6.0,0.0,2.0,1.0,0.0,165082.25,0.0
-14868,15692566.0,Chiu,583.0,France,Male,45.0,2.0,0.0,2.0,1.0,1.0,75732.25,0.0
-14869,15776096.0,Onyemachukwu,676.0,Spain,Male,37.0,5.0,0.0,1.0,1.0,0.0,160979.66,0.0
-14870,15632859.0,Nwokeocha,633.0,Germany,Male,35.0,2.0,125584.17,1.0,1.0,1.0,83084.33,0.0
-14871,15616954.0,Lucciano,850.0,Spain,Male,31.0,8.0,0.0,2.0,1.0,0.0,157527.6,0.0
-14872,15640074.0,Baddeley,695.0,Spain,Male,37.0,6.0,0.0,2.0,1.0,0.0,81425.61,0.0
-14873,15631681.0,Manna,710.0,France,Male,34.0,6.0,0.0,2.0,1.0,0.0,131662.47,0.0
-14874,15732051.0,Yudina,696.0,France,Male,20.0,7.0,120549.29,1.0,1.0,0.0,38153.19,0.0
-14875,15581528.0,Ch'en,647.0,France,Male,42.0,6.0,0.0,1.0,1.0,0.0,171045.31,1.0
-14876,15591386.0,Fiorentini,635.0,France,Male,26.0,0.0,81828.22,1.0,0.0,1.0,75860.01,0.0
-14877,15679148.0,Ch'ien,679.0,France,Female,27.0,2.0,0.0,2.0,0.0,0.0,93835.64,0.0
-14878,15802088.0,Nkemjika,628.0,Spain,Female,35.0,4.0,0.0,2.0,0.0,1.0,125961.48,0.0
-14879,15793723.0,Chidiebere,641.0,Germany,Female,34.0,5.0,106937.05,1.0,1.0,1.0,41967.05,0.0
-14880,15700826.0,Pagnotto,663.0,France,Male,45.0,10.0,0.0,2.0,1.0,0.0,116969.89,0.0
-14881,15791501.0,Burtch,699.0,France,Female,36.0,9.0,0.0,2.0,1.0,1.0,141830.42,0.0
-14882,15732270.0,Maitland,528.0,France,Female,55.0,7.0,0.0,1.0,1.0,1.0,136915.15,1.0
-14883,15797710.0,Hs?,567.0,France,Male,55.0,2.0,0.0,2.0,1.0,1.0,151581.79,0.0
-14884,15703979.0,Martin,599.0,France,Male,41.0,6.0,73070.18,1.0,1.0,0.0,142235.75,0.0
-14885,15740557.0,Chukwuemeka,619.0,France,Male,34.0,8.0,113599.71,1.0,0.0,0.0,136910.18,0.0
-14886,15773421.0,Yobachi,637.0,France,Male,36.0,4.0,0.0,2.0,1.0,0.0,96590.39,0.0
-14887,15663987.0,Chibueze,679.0,France,Male,33.0,8.0,97259.25,1.0,1.0,0.0,187811.71,0.0
-14888,15755642.0,Nkemakolam,640.0,Germany,Female,50.0,7.0,135919.33,1.0,1.0,0.0,179797.79,1.0
-14889,15697460.0,Chinonyelum,695.0,Germany,Female,35.0,1.0,123971.51,2.0,0.0,1.0,171045.35,0.0
-14890,15768367.0,Johnstone,635.0,France,Female,41.0,4.0,0.0,1.0,0.0,1.0,166677.35,1.0
-14891,15716134.0,Nwankwo,629.0,France,Male,37.0,2.0,0.0,2.0,0.0,0.0,176680.16,0.0
-14892,15626452.0,Milano,778.0,France,Male,37.0,5.0,0.0,1.0,1.0,1.0,74425.41,0.0
-14893,15675185.0,Chien,689.0,France,Female,38.0,8.0,0.0,2.0,1.0,1.0,75860.01,0.0
-14894,15675562.0,Y?an,679.0,France,Male,38.0,8.0,0.0,2.0,0.0,0.0,82647.65,0.0
-14895,15612893.0,Hs?,678.0,Spain,Male,47.0,3.0,0.0,2.0,1.0,1.0,139919.38,0.0
-14896,15761693.0,Fanucci,648.0,France,Female,26.0,6.0,0.0,2.0,1.0,0.0,25669.1,0.0
-14897,15777893.0,Chukwubuikem,678.0,Spain,Male,31.0,3.0,0.0,2.0,0.0,0.0,189428.75,0.0
-14898,15792726.0,Ch'iu,806.0,France,Male,30.0,6.0,0.0,2.0,0.0,0.0,94670.77,0.0
-14899,15694300.0,Hsu,661.0,Spain,Female,31.0,2.0,0.0,1.0,1.0,1.0,183584.04,0.0
-14900,15687540.0,Manna,705.0,Germany,Female,45.0,7.0,134954.35,2.0,1.0,1.0,175094.62,0.0
-14901,15803479.0,Kao,602.0,France,Male,23.0,2.0,0.0,2.0,1.0,1.0,53954.24,0.0
-14902,15583681.0,Chiang,573.0,France,Male,31.0,4.0,107712.57,1.0,1.0,1.0,158750.53,0.0
-14903,15593694.0,Ch'ien,681.0,France,Female,29.0,7.0,138033.55,1.0,0.0,1.0,121525.48,0.0
-14904,15690182.0,Shih,589.0,Spain,Male,38.0,3.0,0.0,2.0,1.0,0.0,131097.9,0.0
-14905,15796370.0,Onwuatuegwu,642.0,Spain,Male,48.0,2.0,0.0,2.0,1.0,1.0,937.15,0.0
-14906,15644297.0,Galkin,575.0,France,Male,42.0,1.0,120787.18,1.0,1.0,1.0,58854.88,1.0
-14907,15699238.0,Lu,598.0,France,Female,36.0,2.0,0.0,2.0,0.0,0.0,84760.32,0.0
-14908,15748381.0,P'an,711.0,Germany,Male,29.0,2.0,88915.68,2.0,0.0,0.0,122022.89,0.0
-14909,15736069.0,Hsing,526.0,France,Female,49.0,4.0,137843.8,1.0,1.0,0.0,126812.63,1.0
-14910,15680399.0,Onyemauchechi,678.0,Germany,Male,44.0,2.0,98899.91,1.0,1.0,1.0,126160.24,0.0
-14911,15603594.0,Hsieh,707.0,France,Male,33.0,1.0,121355.99,1.0,1.0,1.0,177524.19,0.0
-14912,15665802.0,Folliero,587.0,France,Male,30.0,3.0,155973.88,2.0,0.0,0.0,167066.95,0.0
-14913,15685500.0,Manna,582.0,France,Female,49.0,6.0,0.0,3.0,1.0,0.0,184856.89,1.0
-14914,15604093.0,Chiu,745.0,France,Male,34.0,2.0,0.0,2.0,1.0,0.0,66926.83,0.0
-14915,15711309.0,Martin,521.0,Germany,Male,41.0,1.0,122311.21,1.0,0.0,0.0,60590.72,1.0
-14916,15727177.0,Ts'ai,535.0,France,Male,48.0,3.0,92476.88,1.0,0.0,0.0,134421.32,1.0
-14917,15597983.0,Ritchie,543.0,Spain,Female,34.0,2.0,0.0,2.0,1.0,0.0,151815.76,0.0
-14918,15697310.0,Maconochie,516.0,France,Male,40.0,3.0,0.0,2.0,1.0,0.0,134878.19,0.0
-14919,15714642.0,Toscani,599.0,Germany,Male,39.0,4.0,81556.89,2.0,1.0,0.0,149602.54,0.0
-14920,15576824.0,Hsing,588.0,Germany,Female,26.0,0.0,130652.52,2.0,1.0,1.0,105898.79,0.0
-14921,15757777.0,Ts'ai,571.0,Spain,Female,43.0,3.0,0.0,2.0,1.0,1.0,166850.57,0.0
-14922,15603851.0,Frolov,710.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,1.0,3560.0,0.0
-14923,15810457.0,Young,667.0,Spain,Male,49.0,4.0,0.0,2.0,0.0,0.0,71692.8,0.0
-14924,15583079.0,Fiorentini,598.0,Germany,Male,39.0,4.0,117879.32,1.0,1.0,1.0,53718.28,1.0
-14925,15637366.0,McLean,775.0,Spain,Male,38.0,1.0,0.0,2.0,1.0,1.0,149139.13,0.0
-14926,15602312.0,Chin,604.0,France,Male,36.0,3.0,0.0,2.0,1.0,1.0,46422.46,0.0
-14927,15646091.0,Maclean,604.0,France,Male,46.0,8.0,0.0,1.0,1.0,0.0,1710.89,1.0
-14928,15808760.0,Tuan,684.0,Spain,Male,46.0,1.0,0.0,2.0,1.0,1.0,138889.15,0.0
-14929,15644400.0,Chiazagomekpere,677.0,France,Male,31.0,3.0,0.0,2.0,0.0,0.0,86619.77,0.0
-14930,15674868.0,Cattaneo,612.0,Spain,Female,25.0,8.0,136855.94,1.0,1.0,0.0,92887.06,0.0
-14931,15687558.0,Hovell,755.0,Germany,Female,60.0,1.0,90560.3,1.0,1.0,0.0,145764.4,1.0
-14932,15656627.0,Milano,699.0,France,Male,35.0,6.0,0.0,2.0,1.0,1.0,162878.96,0.0
-14933,15597957.0,Ugochukwu,599.0,Spain,Female,35.0,1.0,0.0,2.0,0.0,0.0,182867.84,0.0
-14934,15583724.0,Toscani,695.0,France,Male,31.0,4.0,108004.64,1.0,1.0,1.0,162960.05,0.0
-14935,15681476.0,Nnachetam,605.0,Germany,Female,34.0,6.0,148897.03,2.0,1.0,1.0,187976.88,0.0
-14936,15651581.0,Shih,699.0,Spain,Male,36.0,5.0,0.0,2.0,1.0,1.0,131249.46,0.0
-14937,15663817.0,Smith,679.0,France,Male,37.0,1.0,119706.22,2.0,1.0,1.0,65219.85,0.0
-14938,15589948.0,Onwuamaeze,594.0,Germany,Male,33.0,5.0,102773.2,2.0,1.0,0.0,166694.93,0.0
-14939,15808930.0,Ch'eng,683.0,France,Male,35.0,1.0,0.0,2.0,1.0,1.0,51565.98,0.0
-14940,15763381.0,Trevisan,646.0,France,Male,35.0,3.0,0.0,1.0,0.0,0.0,127543.81,0.0
-14941,15713479.0,Macleod,699.0,France,Female,35.0,4.0,0.0,2.0,1.0,1.0,126812.85,0.0
-14942,15577018.0,Lo,716.0,Germany,Female,27.0,5.0,135095.58,1.0,1.0,1.0,143668.58,0.0
-14943,15737542.0,K?,699.0,France,Male,34.0,2.0,133800.98,1.0,1.0,0.0,169669.33,0.0
-14944,15795519.0,Ch'ang,554.0,France,Male,29.0,2.0,0.0,2.0,0.0,1.0,93524.19,0.0
-14945,15792228.0,Dike,605.0,France,Female,41.0,3.0,0.0,2.0,1.0,1.0,173498.45,0.0
-14946,15792360.0,Chukwuemeka,737.0,Spain,Male,43.0,10.0,0.0,2.0,0.0,1.0,126743.33,0.0
-14947,15794560.0,Chiu,572.0,Spain,Female,29.0,8.0,0.0,2.0,0.0,1.0,645.61,0.0
-14948,15654891.0,Chiu,516.0,Germany,Male,36.0,7.0,140931.57,2.0,0.0,0.0,177065.24,0.0
-14949,15691792.0,Maclean,681.0,France,Female,41.0,6.0,0.0,1.0,0.0,1.0,152992.04,0.0
-14950,15638884.0,Hsieh,579.0,France,Male,26.0,8.0,0.0,1.0,1.0,1.0,104722.45,0.0
-14951,15654489.0,Udobata,651.0,Spain,Male,47.0,8.0,117799.28,2.0,0.0,0.0,142838.64,1.0
-14952,15631240.0,Chukwudi,639.0,Spain,Female,46.0,7.0,93901.61,1.0,0.0,1.0,142903.44,0.0
-14953,15751549.0,Hsiung,565.0,France,Male,33.0,5.0,0.0,1.0,1.0,0.0,186976.6,1.0
-14954,15807008.0,Lucciano,653.0,Spain,Female,35.0,3.0,0.0,2.0,0.0,0.0,63736.17,1.0
-14955,15771299.0,White,553.0,France,Male,43.0,5.0,0.0,2.0,0.0,1.0,162223.67,0.0
-14956,15777892.0,Ebelechukwu,693.0,Spain,Female,41.0,2.0,0.0,2.0,1.0,0.0,135875.78,0.0
-14957,15612615.0,Aksenov,553.0,Germany,Male,43.0,2.0,124043.8,2.0,1.0,1.0,136902.01,0.0
-14958,15620981.0,Chukwukadibia,603.0,France,Male,23.0,7.0,0.0,2.0,1.0,0.0,174815.54,0.0
-14959,15594898.0,Ch'en,596.0,France,Female,28.0,7.0,0.0,2.0,1.0,0.0,79888.78,0.0
-14960,15732265.0,Hsia,627.0,Germany,Male,25.0,4.0,121532.17,2.0,0.0,0.0,117948.0,0.0
-14961,15636388.0,Hs?,733.0,Germany,Male,36.0,8.0,81733.74,1.0,1.0,1.0,180862.26,0.0
-14962,15695097.0,Fiorentino,659.0,Germany,Female,29.0,4.0,118613.34,2.0,1.0,0.0,103379.31,0.0
-14963,15771543.0,Hsing,642.0,France,Male,31.0,10.0,0.0,2.0,1.0,1.0,82469.06,0.0
-14964,15776947.0,Ts'ui,629.0,France,Female,28.0,3.0,0.0,2.0,1.0,0.0,111241.37,0.0
-14965,15575024.0,Padovano,703.0,France,Male,36.0,8.0,0.0,2.0,1.0,0.0,141426.78,0.0
-14966,15771299.0,Chiebuka,521.0,Germany,Female,40.0,10.0,103741.28,2.0,1.0,0.0,181257.84,1.0
-14967,15684196.0,Mazzanti,636.0,Germany,Female,44.0,8.0,114519.57,1.0,1.0,0.0,171681.86,1.0
-14968,15571550.0,Mazzi,573.0,Spain,Male,38.0,5.0,0.0,2.0,1.0,1.0,147534.81,0.0
-14969,15777896.0,Chukwualuka,773.0,France,Female,38.0,3.0,0.0,2.0,1.0,0.0,81861.1,0.0
-14970,15669411.0,Chiedozie,689.0,France,Male,36.0,6.0,0.0,1.0,1.0,0.0,127947.19,0.0
-14971,15673372.0,Wright,645.0,Germany,Male,28.0,3.0,128100.28,2.0,1.0,0.0,152577.62,0.0
-14972,15807523.0,Chiu,489.0,France,Male,49.0,8.0,0.0,2.0,1.0,1.0,58855.85,0.0
-14973,15665087.0,Folliero,646.0,France,Female,25.0,4.0,0.0,2.0,1.0,1.0,119782.89,0.0
-14974,15682463.0,H?,708.0,Spain,Male,52.0,6.0,0.0,1.0,1.0,0.0,54947.51,1.0
-14975,15585985.0,Deleon,644.0,France,Male,34.0,7.0,0.0,2.0,1.0,0.0,167554.86,0.0
-14976,15633213.0,Tuan,605.0,France,Male,52.0,0.0,0.0,2.0,1.0,1.0,94739.2,0.0
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diff --git a/bank_customer_churn_prediction/submission_cat.csv b/bank_customer_churn_prediction/0_submission_cat.csv
similarity index 100%
rename from bank_customer_churn_prediction/submission_cat.csv
rename to bank_customer_churn_prediction/0_submission_cat.csv
diff --git a/bank_customer_churn_prediction/submission_lgbm.csv b/bank_customer_churn_prediction/0_submission_lgbm.csv
similarity index 100%
rename from bank_customer_churn_prediction/submission_lgbm.csv
rename to bank_customer_churn_prediction/0_submission_lgbm.csv
diff --git a/bank_customer_churn_prediction/submission_rf.csv b/bank_customer_churn_prediction/0_submission_rf.csv
similarity index 100%
rename from bank_customer_churn_prediction/submission_rf.csv
rename to bank_customer_churn_prediction/0_submission_rf.csv
diff --git a/bank_customer_churn_prediction/submission_xgb.csv b/bank_customer_churn_prediction/0_submission_xgb.csv
similarity index 100%
rename from bank_customer_churn_prediction/submission_xgb.csv
rename to bank_customer_churn_prediction/0_submission_xgb.csv
diff --git a/bank_customer_churn_prediction/1_submission_cat_1.csv b/bank_customer_churn_prediction/1_submission_cat_1.csv
new file mode 100644
index 0000000..6bb8851
--- /dev/null
+++ b/bank_customer_churn_prediction/1_submission_cat_1.csv
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diff --git a/bank_customer_churn_prediction/1_submission_lgbm.csv b/bank_customer_churn_prediction/1_submission_lgbm.csv
new file mode 100644
index 0000000..4478c25
--- /dev/null
+++ b/bank_customer_churn_prediction/1_submission_lgbm.csv
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diff --git a/bank_customer_churn_prediction/1_submission_rf.csv b/bank_customer_churn_prediction/1_submission_rf.csv
new file mode 100644
index 0000000..6415a94
--- /dev/null
+++ b/bank_customer_churn_prediction/1_submission_rf.csv
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diff --git a/bank_customer_churn_prediction/1_submission_xgb.csv b/bank_customer_churn_prediction/1_submission_xgb.csv
new file mode 100644
index 0000000..2db99a9
--- /dev/null
+++ b/bank_customer_churn_prediction/1_submission_xgb.csv
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diff --git a/bank_customer_churn_prediction/2_submission_cat.csv b/bank_customer_churn_prediction/2_submission_cat.csv
new file mode 100644
index 0000000..feeab91
--- /dev/null
+++ b/bank_customer_churn_prediction/2_submission_cat.csv
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diff --git a/bank_customer_churn_prediction/2_submission_lgbm.csv b/bank_customer_churn_prediction/2_submission_lgbm.csv
new file mode 100644
index 0000000..1035bad
--- /dev/null
+++ b/bank_customer_churn_prediction/2_submission_lgbm.csv
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diff --git a/bank_customer_churn_prediction/2_submission_rf.csv b/bank_customer_churn_prediction/2_submission_rf.csv
new file mode 100644
index 0000000..9e55089
--- /dev/null
+++ b/bank_customer_churn_prediction/2_submission_rf.csv
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diff --git a/bank_customer_churn_prediction/2_submission_xgb.csv b/bank_customer_churn_prediction/2_submission_xgb.csv
new file mode 100644
index 0000000..3de3e45
--- /dev/null
+++ b/bank_customer_churn_prediction/2_submission_xgb.csv
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diff --git a/bank_customer_churn_prediction/catboost_info/catboost_training.json b/bank_customer_churn_prediction/catboost_info/catboost_training.json
index e7d979b..aa46fab 100644
--- a/bank_customer_churn_prediction/catboost_info/catboost_training.json
+++ b/bank_customer_churn_prediction/catboost_info/catboost_training.json
@@ -1,9004 +1,9004 @@
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diff --git a/bank_customer_churn_prediction/catboost_info/learn_error.tsv b/bank_customer_churn_prediction/catboost_info/learn_error.tsv
index 8987181..4badc9b 100644
--- a/bank_customer_churn_prediction/catboost_info/learn_error.tsv
+++ b/bank_customer_churn_prediction/catboost_info/learn_error.tsv
@@ -1,9001 +1,9001 @@
 iter	Logloss
-0	0.6920457827
-1	0.6909410077
-2	0.6897623239
-3	0.6886752554
-4	0.6874720569
-5	0.686309514
-6	0.6851459213
-7	0.6839901567
-8	0.6828499314
-9	0.6816592065
-10	0.6804766486
-11	0.6793005801
-12	0.6782186636
-13	0.6771573373
-14	0.6761268651
-15	0.6750350365
-16	0.6738623868
-17	0.6727724665
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diff --git a/bank_customer_churn_prediction/catboost_info/test/events.out.tfevents b/bank_customer_churn_prediction/catboost_info/test/events.out.tfevents
index 0b93fb074a4d54faed7497bf246b7f7cce3f0626..47d64ea44cf38b6f1c4351e98fa4f4d1d43c8bb3 100644
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diff --git a/bank_customer_churn_prediction/catboost_info/test_error.tsv b/bank_customer_churn_prediction/catboost_info/test_error.tsv
index 4dd1023..4a8a390 100644
--- a/bank_customer_churn_prediction/catboost_info/test_error.tsv
+++ b/bank_customer_churn_prediction/catboost_info/test_error.tsv
@@ -1,9001 +1,9001 @@
 iter	AUC	Logloss
-0	0.8822837063	0.6920829161
-1	0.9072186428	0.6909989676
-2	0.9109268998	0.6898466692
-3	0.9120740409	0.6887898044
-4	0.9137109862	0.6876166145
-5	0.9134021171	0.6864919104
-6	0.9144487387	0.6853584655
-7	0.9148983352	0.6842402162
-8	0.9140369132	0.683148823
-9	0.9149129562	0.6819954348
-10	0.9159735896	0.6808497411
-11	0.9172736422	0.6796965073
-12	0.9182087785	0.6786220784
-13	0.9190202452	0.6775675691
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diff --git a/bank_customer_churn_prediction/catboost_info/time_left.tsv b/bank_customer_churn_prediction/catboost_info/time_left.tsv
index 48f6625..d2e9ef8 100644
--- a/bank_customer_churn_prediction/catboost_info/time_left.tsv
+++ b/bank_customer_churn_prediction/catboost_info/time_left.tsv
@@ -1,9001 +1,9001 @@
 iter	Passed	Remaining
-0	4	44365
-1	9	42194
-2	13	40781
-3	17	39297
-4	21	38946
-5	25	38367
-6	29	38171
-7	33	37719
-8	37	37550
-9	41	37274
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-11	49	36851
-12	53	36728
-13	57	36645
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-15	65	36643
-16	69	36661
-17	73	36529
-18	77	36433
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-20	85	36385
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-22	92	36175
-23	96	36153
-24	100	36118
-25	104	36015
-26	108	35939
-27	112	35898
-28	115	35796
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-31	127	35698
-32	131	35617
-33	134	35538
-34	138	35425
-35	142	35370
-36	145	35289
-37	149	35228
-38	153	35168
-39	156	35093
-40	160	35060
-41	164	35052
-42	168	35011
-43	171	34978
-44	175	34940
-45	179	34895
-46	183	34862
-47	186	34835
-48	190	34825
-49	194	34825
-50	198	34800
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-52	206	34776
-53	209	34747
-54	213	34754
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-56	221	34733
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-59	233	34736
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-61	240	34723
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-63	248	34691
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-65	256	34675
-66	260	34697
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diff --git a/bank_customer_churn_prediction/kaggle_competition.ipynb b/bank_customer_churn_prediction/kaggle_competition.ipynb
index c3de5b8..5f36bcc 100644
--- a/bank_customer_churn_prediction/kaggle_competition.ipynb
+++ b/bank_customer_churn_prediction/kaggle_competition.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:19.855016Z",
-     "start_time": "2025-06-20T18:11:19.850020Z"
+     "end_time": "2025-06-24T18:34:47.316360Z",
+     "start_time": "2025-06-24T18:34:47.312360Z"
     }
    },
    "source": [
@@ -33,29 +33,30 @@
     "\n",
     "import warnings\n",
     "\n",
-    "warnings.filterwarnings('ignore')"
+    "warnings.filterwarnings('ignore')\n",
+    "iteration = '2'"
    ],
    "outputs": [],
-   "execution_count": 70
+   "execution_count": 276
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:19.977463Z",
-     "start_time": "2025-06-20T18:11:19.960524Z"
+     "end_time": "2025-06-24T18:34:47.477373Z",
+     "start_time": "2025-06-24T18:34:47.460133Z"
     }
    },
    "cell_type": "code",
-   "source": "df = pd.read_csv('../assets/BCCP/train.csv')",
+   "source": "df = pd.read_csv('../assets/hw_2_2/train.csv')",
    "id": "db2bbff7858a1e68",
    "outputs": [],
-   "execution_count": 71
+   "execution_count": 277
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:20.087037Z",
-     "start_time": "2025-06-20T18:11:20.076948Z"
+     "end_time": "2025-06-24T18:34:47.629896Z",
+     "start_time": "2025-06-24T18:34:47.619629Z"
     }
    },
    "cell_type": "code",
@@ -212,18 +213,18 @@
        "</div>"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 278,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 72
+   "execution_count": 278
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:20.231642Z",
-     "start_time": "2025-06-20T18:11:20.226378Z"
+     "end_time": "2025-06-24T18:34:47.810713Z",
+     "start_time": "2025-06-24T18:34:47.805809Z"
     }
    },
    "cell_type": "code",
@@ -250,18 +251,18 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 279,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 73
+   "execution_count": 279
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:20.386391Z",
-     "start_time": "2025-06-20T18:11:20.379915Z"
+     "end_time": "2025-06-24T18:34:48.009562Z",
+     "start_time": "2025-06-24T18:34:48.002805Z"
     }
    },
    "cell_type": "code",
@@ -296,13 +297,13 @@
      ]
     }
    ],
-   "execution_count": 74
+   "execution_count": 280
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:20.702354Z",
-     "start_time": "2025-06-20T18:11:20.698071Z"
+     "end_time": "2025-06-24T18:34:48.317174Z",
+     "start_time": "2025-06-24T18:34:48.313381Z"
     }
    },
    "cell_type": "code",
@@ -315,31 +316,31 @@
        "array([0., 1.])"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 281,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 75
+   "execution_count": 281
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:20.888178Z",
-     "start_time": "2025-06-20T18:11:20.885718Z"
+     "end_time": "2025-06-24T18:34:48.598434Z",
+     "start_time": "2025-06-24T18:34:48.595920Z"
     }
    },
    "cell_type": "code",
    "source": "df['IsActiveMember'] = df['IsActiveMember'].astype(int)",
    "id": "ad36fb614843627e",
    "outputs": [],
-   "execution_count": 76
+   "execution_count": 282
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:20.993200Z",
-     "start_time": "2025-06-20T18:11:20.984158Z"
+     "end_time": "2025-06-24T18:34:48.751748Z",
+     "start_time": "2025-06-24T18:34:48.743824Z"
     }
    },
    "cell_type": "code",
@@ -352,18 +353,18 @@
        "0"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 283,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 77
+   "execution_count": 283
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:21.096528Z",
-     "start_time": "2025-06-20T18:11:21.091617Z"
+     "end_time": "2025-06-24T18:34:48.909924Z",
+     "start_time": "2025-06-24T18:34:48.904716Z"
     }
    },
    "cell_type": "code",
@@ -390,18 +391,18 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 284,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 78
+   "execution_count": 284
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:21.220429Z",
-     "start_time": "2025-06-20T18:11:21.216950Z"
+     "end_time": "2025-06-24T18:34:49.085200Z",
+     "start_time": "2025-06-24T18:34:49.081199Z"
     }
    },
    "cell_type": "code",
@@ -414,31 +415,31 @@
        "8652"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 285,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 79
+   "execution_count": 285
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:21.340004Z",
-     "start_time": "2025-06-20T18:11:21.336982Z"
+     "end_time": "2025-06-24T18:34:49.252158Z",
+     "start_time": "2025-06-24T18:34:49.249212Z"
     }
    },
    "cell_type": "code",
    "source": "df = df.drop(columns=['CustomerId'])",
    "id": "50a48bd3bf1a4a97",
    "outputs": [],
-   "execution_count": 80
+   "execution_count": 286
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:21.528463Z",
-     "start_time": "2025-06-20T18:11:21.522308Z"
+     "end_time": "2025-06-24T18:34:49.539941Z",
+     "start_time": "2025-06-24T18:34:49.533859Z"
     }
    },
    "cell_type": "code",
@@ -472,13 +473,13 @@
      ]
     }
    ],
-   "execution_count": 81
+   "execution_count": 287
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:21.635734Z",
-     "start_time": "2025-06-20T18:11:21.631721Z"
+     "end_time": "2025-06-24T18:34:49.696207Z",
+     "start_time": "2025-06-24T18:34:49.692352Z"
     }
    },
    "cell_type": "code",
@@ -494,18 +495,18 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 288,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 82
+   "execution_count": 288
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:21.798409Z",
-     "start_time": "2025-06-20T18:11:21.755373Z"
+     "end_time": "2025-06-24T18:34:49.906492Z",
+     "start_time": "2025-06-24T18:34:49.865588Z"
     }
    },
    "cell_type": "code",
@@ -535,13 +536,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 83
+   "execution_count": 289
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:21.916350Z",
-     "start_time": "2025-06-20T18:11:21.912054Z"
+     "end_time": "2025-06-24T18:34:50.076445Z",
+     "start_time": "2025-06-24T18:34:50.071519Z"
     }
    },
    "cell_type": "code",
@@ -566,18 +567,18 @@
        "Name: count, Length: 764, dtype: int64"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 290,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 84
+   "execution_count": 290
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:22.240676Z",
-     "start_time": "2025-06-20T18:11:22.128025Z"
+     "end_time": "2025-06-24T18:34:50.503645Z",
+     "start_time": "2025-06-24T18:34:50.391400Z"
     }
    },
    "cell_type": "code",
@@ -599,13 +600,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 85
+   "execution_count": 291
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:22.483003Z",
-     "start_time": "2025-06-20T18:11:22.356055Z"
+     "end_time": "2025-06-24T18:34:50.799853Z",
+     "start_time": "2025-06-24T18:34:50.671068Z"
     }
    },
    "cell_type": "code",
@@ -627,13 +628,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 86
+   "execution_count": 292
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:22.715863Z",
-     "start_time": "2025-06-20T18:11:22.710351Z"
+     "end_time": "2025-06-24T18:34:50.989385Z",
+     "start_time": "2025-06-24T18:34:50.984637Z"
     }
    },
    "cell_type": "code",
@@ -650,18 +651,18 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 293,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 87
+   "execution_count": 293
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:22.880283Z",
-     "start_time": "2025-06-20T18:11:22.835520Z"
+     "end_time": "2025-06-24T18:34:51.363995Z",
+     "start_time": "2025-06-24T18:34:51.318682Z"
     }
    },
    "cell_type": "code",
@@ -698,13 +699,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 88
+   "execution_count": 294
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:23.096397Z",
-     "start_time": "2025-06-20T18:11:22.995875Z"
+     "end_time": "2025-06-24T18:34:51.644599Z",
+     "start_time": "2025-06-24T18:34:51.540678Z"
     }
    },
    "cell_type": "code",
@@ -728,13 +729,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 89
+   "execution_count": 295
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:23.446696Z",
-     "start_time": "2025-06-20T18:11:23.279822Z"
+     "end_time": "2025-06-24T18:34:52.152792Z",
+     "start_time": "2025-06-24T18:34:51.927605Z"
     }
    },
    "cell_type": "code",
@@ -758,13 +759,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 90
+   "execution_count": 296
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:23.694957Z",
-     "start_time": "2025-06-20T18:11:23.564797Z"
+     "end_time": "2025-06-24T18:34:53.217853Z",
+     "start_time": "2025-06-24T18:34:53.084647Z"
     }
    },
    "cell_type": "code",
@@ -786,13 +787,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 91
+   "execution_count": 297
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:24.181585Z",
-     "start_time": "2025-06-20T18:11:23.795977Z"
+     "end_time": "2025-06-24T18:34:53.553685Z",
+     "start_time": "2025-06-24T18:34:53.222858Z"
     }
    },
    "cell_type": "code",
@@ -825,13 +826,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 92
+   "execution_count": 298
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:25.284668Z",
-     "start_time": "2025-06-20T18:11:25.279860Z"
+     "end_time": "2025-06-24T18:34:53.838569Z",
+     "start_time": "2025-06-24T18:34:53.833569Z"
     }
    },
    "cell_type": "code",
@@ -856,18 +857,18 @@
        "Name: count, Length: 3395, dtype: int64"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 299,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 93
+   "execution_count": 299
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:25.517041Z",
-     "start_time": "2025-06-20T18:11:25.404630Z"
+     "end_time": "2025-06-24T18:34:54.130204Z",
+     "start_time": "2025-06-24T18:34:54.010295Z"
     }
    },
    "cell_type": "code",
@@ -890,13 +891,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 94
+   "execution_count": 300
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:25.636697Z",
-     "start_time": "2025-06-20T18:11:25.633089Z"
+     "end_time": "2025-06-24T18:34:54.303988Z",
+     "start_time": "2025-06-24T18:34:54.300174Z"
     }
    },
    "cell_type": "code",
@@ -912,18 +913,18 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 301,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 95
+   "execution_count": 301
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:25.968094Z",
-     "start_time": "2025-06-20T18:11:25.883097Z"
+     "end_time": "2025-06-24T18:34:54.741554Z",
+     "start_time": "2025-06-24T18:34:54.654334Z"
     }
    },
    "cell_type": "code",
@@ -952,13 +953,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 96
+   "execution_count": 302
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:26.505561Z",
-     "start_time": "2025-06-20T18:11:26.082885Z"
+     "end_time": "2025-06-24T18:34:55.189547Z",
+     "start_time": "2025-06-24T18:34:54.909797Z"
     }
    },
    "cell_type": "code",
@@ -985,13 +986,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 97
+   "execution_count": 303
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:27.451175Z",
-     "start_time": "2025-06-20T18:11:27.446451Z"
+     "end_time": "2025-06-24T18:34:55.488119Z",
+     "start_time": "2025-06-24T18:34:55.483616Z"
     }
    },
    "cell_type": "code",
@@ -1008,18 +1009,18 @@
        "Name: Exited, dtype: float64"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 304,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
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+   "execution_count": 304
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:28.296219Z",
-     "start_time": "2025-06-20T18:11:27.572192Z"
+     "end_time": "2025-06-24T18:34:56.605215Z",
+     "start_time": "2025-06-24T18:34:55.662415Z"
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@@ -1054,13 +1055,13 @@
      "output_type": "display_data"
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    ],
-   "execution_count": 99
+   "execution_count": 305
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:29.545893Z",
-     "start_time": "2025-06-20T18:11:29.247046Z"
+     "end_time": "2025-06-24T18:34:57.736541Z",
+     "start_time": "2025-06-24T18:34:57.365714Z"
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@@ -1083,13 +1084,13 @@
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+   "execution_count": 306
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   {
    "metadata": {
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-     "start_time": "2025-06-20T18:11:30.762926Z"
+     "end_time": "2025-06-24T18:34:58.813721Z",
+     "start_time": "2025-06-24T18:34:58.659607Z"
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@@ -1119,13 +1120,13 @@
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    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:31.046919Z",
-     "start_time": "2025-06-20T18:11:31.033617Z"
+     "end_time": "2025-06-24T18:34:58.990118Z",
+     "start_time": "2025-06-24T18:34:58.977054Z"
     }
    },
    "cell_type": "code",
@@ -1135,13 +1136,13 @@
    ],
    "id": "8cbecb04cd348ce1",
    "outputs": [],
-   "execution_count": 102
+   "execution_count": 308
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:32.741397Z",
-     "start_time": "2025-06-20T18:11:31.144759Z"
+     "end_time": "2025-06-24T18:35:00.619103Z",
+     "start_time": "2025-06-24T18:34:59.137577Z"
     }
    },
    "cell_type": "code",
@@ -1175,13 +1176,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 103
+   "execution_count": 309
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:32.848810Z",
-     "start_time": "2025-06-20T18:11:32.843440Z"
+     "end_time": "2025-06-24T18:35:00.775038Z",
+     "start_time": "2025-06-24T18:35:00.769976Z"
     }
    },
    "cell_type": "code",
@@ -1199,18 +1200,18 @@
        "Name: Exited, dtype: float64"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 310,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 104
+   "execution_count": 310
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:33.086391Z",
-     "start_time": "2025-06-20T18:11:32.969207Z"
+     "end_time": "2025-06-24T18:35:01.066224Z",
+     "start_time": "2025-06-24T18:35:00.947706Z"
     }
    },
    "cell_type": "code",
@@ -1240,26 +1241,26 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 105
+   "execution_count": 311
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:33.207710Z",
-     "start_time": "2025-06-20T18:11:33.204198Z"
+     "end_time": "2025-06-24T18:35:01.094827Z",
+     "start_time": "2025-06-24T18:35:01.091798Z"
     }
    },
    "cell_type": "code",
    "source": "df['ActiveCrCard'] = df['HasCrCard'] + (df['IsActiveMember'])",
    "id": "16a3fcb5f0e9014e",
    "outputs": [],
-   "execution_count": 106
+   "execution_count": 312
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:33.625479Z",
-     "start_time": "2025-06-20T18:11:33.387057Z"
+     "end_time": "2025-06-24T18:35:01.735855Z",
+     "start_time": "2025-06-24T18:35:01.530254Z"
     }
    },
    "cell_type": "code",
@@ -1281,26 +1282,26 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 107
+   "execution_count": 313
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:34.567536Z",
-     "start_time": "2025-06-20T18:11:34.563516Z"
+     "end_time": "2025-06-24T18:35:02.595481Z",
+     "start_time": "2025-06-24T18:35:02.592330Z"
     }
    },
    "cell_type": "code",
    "source": "df['TenureAgeRatio'] = df['Tenure'] / df['Age']",
    "id": "84c08a8cb08a8c98",
    "outputs": [],
-   "execution_count": 108
+   "execution_count": 314
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:34.668317Z",
-     "start_time": "2025-06-20T18:11:34.663910Z"
+     "end_time": "2025-06-24T18:35:02.750274Z",
+     "start_time": "2025-06-24T18:35:02.745934Z"
     }
    },
    "cell_type": "code",
@@ -1325,18 +1326,18 @@
        "Name: Exited, dtype: float64"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 315,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 109
+   "execution_count": 315
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:35.161745Z",
-     "start_time": "2025-06-20T18:11:34.870393Z"
+     "end_time": "2025-06-24T18:35:03.233507Z",
+     "start_time": "2025-06-24T18:35:02.922393Z"
     }
    },
    "cell_type": "code",
@@ -1360,13 +1361,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 110
+   "execution_count": 316
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:35.294321Z",
-     "start_time": "2025-06-20T18:11:35.281184Z"
+     "end_time": "2025-06-24T18:35:04.374684Z",
+     "start_time": "2025-06-24T18:35:04.363586Z"
     }
    },
    "cell_type": "code",
@@ -1380,13 +1381,13 @@
    ],
    "id": "f0910a78d32f7c09",
    "outputs": [],
-   "execution_count": 111
+   "execution_count": 317
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:35.481366Z",
-     "start_time": "2025-06-20T18:11:35.477012Z"
+     "end_time": "2025-06-24T18:35:04.523731Z",
+     "start_time": "2025-06-24T18:35:04.518970Z"
     }
    },
    "cell_type": "code",
@@ -1437,13 +1438,13 @@
      ]
     }
    ],
-   "execution_count": 112
+   "execution_count": 318
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:35.604398Z",
-     "start_time": "2025-06-20T18:11:35.599225Z"
+     "end_time": "2025-06-24T18:35:04.685679Z",
+     "start_time": "2025-06-24T18:35:04.679053Z"
     }
    },
    "cell_type": "code",
@@ -1471,19 +1472,42 @@
     "    d['TenureAgeRatio'] = d['Tenure'] / d['Age']\n",
     "    return d[['TenureAgeRatio']]\n",
     "\n",
+    "def HasBalanceFunc(d):\n",
+    "    d['HasBalance'] = (d['Balance'] > 0).astype(int)\n",
+    "    return d[['HasBalance']]\n",
+    "\n",
+    "def AgeGroupFunc(df):\n",
+    "    df = df.copy()\n",
+    "    df['AgeGroup'] = pd.cut(\n",
+    "        df['Age'],\n",
+    "        bins=[0, 30, 40, 50, 60, np.inf],\n",
+    "        labels=[0, 1, 2, 3, 4]\n",
+    "    ).astype(int)\n",
+    "    return df[['AgeGroup']]\n",
+    "\n",
+    "def SurnameFreqFunc(df):\n",
+    "    df = df.copy()\n",
+    "    freq_map = df['Surname'].value_counts().to_dict()\n",
+    "    df['SurnameFreq'] = df['Surname'].map(freq_map)\n",
+    "    return df[['SurnameFreq']]\n",
+    "\n",
     "\n",
     "ActiveCrCard_pipeline = make_pipeline(num_pipeline, FunctionTransformer(ActiveCrCardFunc, validate=False))\n",
     "TenureAge_pipeline = make_pipeline(num_pipeline, FunctionTransformer(TenurebyAgeFunc, validate=False))\n",
+    "HasBalance_pipeline = make_pipeline(num_pipeline, FunctionTransformer(HasBalanceFunc, validate=False))\n",
+    "AgeGroup_pipeline = make_pipeline(num_pipeline, FunctionTransformer(AgeGroupFunc, validate=False))\n",
+    "SurnameFreq_pipeline = make_pipeline(target_enc_pipeline, FunctionTransformer(SurnameFreqFunc, validate=False))\n",
     "\n",
     "preprocessing = ColumnTransformer([\n",
     "    ('N', num_pipeline, ['CreditScore', 'Balance', 'EstimatedSalary', 'Tenure']),\n",
     "    ('ActiveCrCard', ActiveCrCard_pipeline, ['IsActiveMember', 'HasCrCard']),\n",
-    "    #     ('TenureAgeR',TenureAge_pipeline,['Age','Tenure']),\n",
+    "    ('TenureAgeR', TenureAge_pipeline, ['Age', 'Tenure']),\n",
     "    ('Age', make_pipeline(num_pipeline, FunctionTransformer(func=np.log)), ['Age']),\n",
+    "    ('AgeGroup', AgeGroup_pipeline, ['Age']),\n",
+    "    ('HasBalance', HasBalance_pipeline, ['Balance']),\n",
+    "    ('SurnameFreq', SurnameFreq_pipeline, ['Surname']),\n",
     "    ('Cat', cat_pipeline, ['Geography', 'Gender', 'NumOfProducts']),\n",
-    "    ('Tar', target_enc_pipeline, ['Surname',\n",
-    "                                  #                                 'NumOfProducts','Gender','Geography','Tenure'\n",
-    "                                  ]),\n",
+    "    ('Tar', target_enc_pipeline, ['Surname', 'NumOfProducts', 'Gender', 'Geography']),\n",
     "], remainder='drop', verbose_feature_names_out=False)\n",
     "\n",
     "preprocessing = Pipeline([\n",
@@ -1506,13 +1530,13 @@
    ],
    "id": "ddd5da4a7d2c42c0",
    "outputs": [],
-   "execution_count": 113
+   "execution_count": 319
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:35.778548Z",
-     "start_time": "2025-06-20T18:11:35.707980Z"
+     "end_time": "2025-06-24T18:35:05.001957Z",
+     "start_time": "2025-06-24T18:35:04.853883Z"
     }
    },
    "cell_type": "code",
@@ -1529,16 +1553,24 @@
      "data": {
       "text/plain": [
        "       CreditScore   Balance  EstimatedSalary    Tenure  ActiveCrCard  \\\n",
-       "1438      0.287594 -0.710855         0.333994 -1.089897     -0.865896   \n",
-       "12579     1.051194 -0.710855        -1.071925 -1.089897     -0.865896   \n",
+       "7401     -0.892516 -0.710855        -2.211410 -1.089897     -0.865896   \n",
+       "14724    -0.698145 -0.710855         1.348969 -0.728787     -0.865896   \n",
+       "\n",
+       "       TenureAgeRatio       Age  AgeGroup  HasBalance  SurnameFreq  \\\n",
+       "7401        -1.129539  1.136507  0.883292   -0.729964    -0.843948   \n",
+       "14724       -0.711800 -0.246380 -0.267113   -0.729964     0.547766   \n",
+       "\n",
+       "       Geography_Germany  Geography_Spain  Gender_Male  NumOfProducts_2.0  \\\n",
+       "7401            -0.45821         1.880329    -1.159202           0.892244   \n",
+       "14724           -0.45821        -0.531822     0.862662          -1.120769   \n",
        "\n",
-       "           Age  Geography_Germany  Geography_Spain  Gender_Male  \\\n",
-       "1438   0.49584           -0.45821        -0.531822     0.862662   \n",
-       "12579 -0.24638           -0.45821        -0.531822     0.862662   \n",
+       "       NumOfProducts_3.0  NumOfProducts_4.0   Surname  NumOfProducts  \\\n",
+       "7401           -0.121601          -0.047864 -1.020319       -0.83344   \n",
+       "14724          -0.121601          -0.047864 -0.091110        0.94223   \n",
        "\n",
-       "       NumOfProducts_2.0  NumOfProducts_3.0  NumOfProducts_4.0  Surname  \n",
-       "1438            0.892244          -0.121601          -0.047864  0.57261  \n",
-       "12579          -1.120769          -0.121601          -0.047864 -1.14718  "
+       "         Gender  Geography  \n",
+       "7401   1.159202  -0.502280  \n",
+       "14724 -0.862662  -0.441993  "
       ],
       "text/html": [
        "<div>\n",
@@ -1564,7 +1596,11 @@
        "      <th>EstimatedSalary</th>\n",
        "      <th>Tenure</th>\n",
        "      <th>ActiveCrCard</th>\n",
+       "      <th>TenureAgeRatio</th>\n",
        "      <th>Age</th>\n",
+       "      <th>AgeGroup</th>\n",
+       "      <th>HasBalance</th>\n",
+       "      <th>SurnameFreq</th>\n",
        "      <th>Geography_Germany</th>\n",
        "      <th>Geography_Spain</th>\n",
        "      <th>Gender_Male</th>\n",
@@ -1572,58 +1608,75 @@
        "      <th>NumOfProducts_3.0</th>\n",
        "      <th>NumOfProducts_4.0</th>\n",
        "      <th>Surname</th>\n",
+       "      <th>NumOfProducts</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Geography</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1438</th>\n",
-       "      <td>0.287594</td>\n",
+       "      <th>7401</th>\n",
+       "      <td>-0.892516</td>\n",
        "      <td>-0.710855</td>\n",
-       "      <td>0.333994</td>\n",
+       "      <td>-2.211410</td>\n",
        "      <td>-1.089897</td>\n",
        "      <td>-0.865896</td>\n",
-       "      <td>0.49584</td>\n",
+       "      <td>-1.129539</td>\n",
+       "      <td>1.136507</td>\n",
+       "      <td>0.883292</td>\n",
+       "      <td>-0.729964</td>\n",
+       "      <td>-0.843948</td>\n",
        "      <td>-0.45821</td>\n",
-       "      <td>-0.531822</td>\n",
-       "      <td>0.862662</td>\n",
+       "      <td>1.880329</td>\n",
+       "      <td>-1.159202</td>\n",
        "      <td>0.892244</td>\n",
        "      <td>-0.121601</td>\n",
        "      <td>-0.047864</td>\n",
-       "      <td>0.57261</td>\n",
+       "      <td>-1.020319</td>\n",
+       "      <td>-0.83344</td>\n",
+       "      <td>1.159202</td>\n",
+       "      <td>-0.502280</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12579</th>\n",
-       "      <td>1.051194</td>\n",
+       "      <th>14724</th>\n",
+       "      <td>-0.698145</td>\n",
        "      <td>-0.710855</td>\n",
-       "      <td>-1.071925</td>\n",
-       "      <td>-1.089897</td>\n",
+       "      <td>1.348969</td>\n",
+       "      <td>-0.728787</td>\n",
        "      <td>-0.865896</td>\n",
-       "      <td>-0.24638</td>\n",
+       "      <td>-0.711800</td>\n",
+       "      <td>-0.246380</td>\n",
+       "      <td>-0.267113</td>\n",
+       "      <td>-0.729964</td>\n",
+       "      <td>0.547766</td>\n",
        "      <td>-0.45821</td>\n",
        "      <td>-0.531822</td>\n",
        "      <td>0.862662</td>\n",
        "      <td>-1.120769</td>\n",
        "      <td>-0.121601</td>\n",
        "      <td>-0.047864</td>\n",
-       "      <td>-1.14718</td>\n",
+       "      <td>-0.091110</td>\n",
+       "      <td>0.94223</td>\n",
+       "      <td>-0.862662</td>\n",
+       "      <td>-0.441993</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 320,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 114
+   "execution_count": 320
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:37.330446Z",
-     "start_time": "2025-06-20T18:11:35.805989Z"
+     "end_time": "2025-06-24T18:35:06.582072Z",
+     "start_time": "2025-06-24T18:35:05.198833Z"
     }
    },
    "cell_type": "code",
@@ -1639,7 +1692,7 @@
        "RandomForestClassifier(max_depth=10, n_estimators=200, random_state=3)"
       ],
       "text/html": [
-       "<style>#sk-container-id-5 {\n",
+       "<style>#sk-container-id-9 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: #000;\n",
        "  --sklearn-color-text-muted: #666;\n",
@@ -1670,15 +1723,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 {\n",
+       "#sk-container-id-9 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 pre {\n",
+       "#sk-container-id-9 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "#sk-container-id-9 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1690,7 +1743,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1698,7 +1751,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-container {\n",
+       "#sk-container-id-9 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1708,7 +1761,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1724,14 +1777,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel {\n",
+       "#sk-container-id-9 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1739,28 +1792,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "#sk-container-id-9 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-5 div.sk-serial {\n",
+       "#sk-container-id-9 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1778,14 +1831,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable {\n",
+       "#sk-container-id-9 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "#sk-container-id-9 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: flex;\n",
        "  width: 100%;\n",
@@ -1798,13 +1851,13 @@
        "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "#sk-container-id-9 label.sk-toggleable__label .caption {\n",
        "  font-size: 0.6rem;\n",
        "  font-weight: lighter;\n",
        "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1812,25 +1865,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "#sk-container-id-9 div.sk-toggleable__content {\n",
        "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1838,79 +1891,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  display: block;\n",
        "  width: 100%;\n",
        "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-5 div.sk-label label {\n",
+       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-9 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-5 div.sk-label label {\n",
+       "#sk-container-id-9 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label-container {\n",
+       "#sk-container-id-9 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-5 div.sk-estimator {\n",
+       "#sk-container-id-9 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1920,18 +1973,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "#sk-container-id-9 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "#sk-container-id-9 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2019,7 +2072,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "#sk-container-id-9 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -2034,21 +2087,21 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
@@ -2116,7 +2169,7 @@
        "    height: 14px;\n",
        "    cursor: pointer;\n",
        "}\n",
-       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, n_estimators=200, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "</style><body><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, n_estimators=200, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
        "        <div class=\"estimator-table\">\n",
        "            <details>\n",
        "                <summary>Parameters</summary>\n",
@@ -2361,18 +2414,18 @@
        "</script></body>"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 321,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 115
+   "execution_count": 321
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:38.106393Z",
-     "start_time": "2025-06-20T18:11:37.818055Z"
+     "end_time": "2025-06-24T18:35:07.067247Z",
+     "start_time": "2025-06-24T18:35:06.780655Z"
     }
    },
    "cell_type": "code",
@@ -2397,22 +2450,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.9417142857142857\n",
-      "Validation Accuracy 0.8982222222222223\n",
+      "Train Accuracy 0.9458095238095238\n",
+      "Validation Accuracy 0.8977777777777778\n",
       "\n",
       "\n",
-      "Train AUC Score 0.9818998109429211\n",
-      "Validation AUC Score 0.9274699781659389\n"
+      "Train AUC Score 0.983604772638936\n",
+      "Validation AUC Score 0.9234089890049907\n"
      ]
     }
    ],
-   "execution_count": 116
+   "execution_count": 322
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:38.392348Z",
-     "start_time": "2025-06-20T18:11:38.204252Z"
+     "end_time": "2025-06-24T18:35:07.617798Z",
+     "start_time": "2025-06-24T18:35:07.398979Z"
     }
    },
    "cell_type": "code",
@@ -2441,19 +2494,19 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 117
+   "execution_count": 323
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:38.531736Z",
-     "start_time": "2025-06-20T18:11:38.495049Z"
+     "end_time": "2025-06-24T18:35:07.826711Z",
+     "start_time": "2025-06-24T18:35:07.790749Z"
     }
    },
    "cell_type": "code",
@@ -2467,41 +2520,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test AUC Score 0.9280755322052402\n"
+      "Test AUC Score 0.9249624727074236\n"
      ]
     }
    ],
-   "execution_count": 118
+   "execution_count": 324
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:38.760831Z",
-     "start_time": "2025-06-20T18:11:38.626345Z"
+     "end_time": "2025-06-24T18:35:08.153247Z",
+     "start_time": "2025-06-24T18:35:07.997266Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "test_data = pd.read_csv('../assets/BCCP/test.csv')\n",
+    "test_data = pd.read_csv('../assets/hw_2_2/test.csv')\n",
     "CustomerID = test_data['id']\n",
     "test_data = preprocessing.transform(test_data)\n",
     "preds = rfmodel.predict_proba(test_data)[:, 1]"
    ],
    "id": "7bfb79c5c9fe66d5",
    "outputs": [],
-   "execution_count": 119
+   "execution_count": 325
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:38.887034Z",
-     "start_time": "2025-06-20T18:11:38.853703Z"
+     "end_time": "2025-06-24T18:35:08.360956Z",
+     "start_time": "2025-06-24T18:35:08.326625Z"
     }
    },
    "cell_type": "code",
    "source": [
     "submission = pd.DataFrame({'id': CustomerID, 'Exited': preds})\n",
-    "submission.to_csv('submission_rf.csv', index=False)\n",
+    "submission.to_csv(iteration + '_submission_rf.csv', index=False)\n",
     "submission"
    ],
    "id": "81c59f3776bdbbef",
@@ -2510,17 +2563,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.166806\n",
-       "1     15001  0.022604\n",
-       "2     15002  0.043870\n",
-       "3     15003  0.695240\n",
-       "4     15004  0.031620\n",
+       "0     15000  0.157467\n",
+       "1     15001  0.014333\n",
+       "2     15002  0.022945\n",
+       "3     15003  0.736975\n",
+       "4     15004  0.023328\n",
        "...     ...       ...\n",
-       "9995  24995  0.016138\n",
-       "9996  24996  0.009818\n",
-       "9997  24997  0.010606\n",
-       "9998  24998  0.101138\n",
-       "9999  24999  0.178523\n",
+       "9995  24995  0.017799\n",
+       "9996  24996  0.022879\n",
+       "9997  24997  0.009138\n",
+       "9998  24998  0.063574\n",
+       "9999  24999  0.150443\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -2551,27 +2604,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.166806</td>\n",
+       "      <td>0.157467</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.022604</td>\n",
+       "      <td>0.014333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.043870</td>\n",
+       "      <td>0.022945</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.695240</td>\n",
+       "      <td>0.736975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.031620</td>\n",
+       "      <td>0.023328</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2581,27 +2634,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.016138</td>\n",
+       "      <td>0.017799</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.009818</td>\n",
+       "      <td>0.022879</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.010606</td>\n",
+       "      <td>0.009138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.101138</td>\n",
+       "      <td>0.063574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.178523</td>\n",
+       "      <td>0.150443</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2609,18 +2662,18 @@
        "</div>"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 326,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 120
+   "execution_count": 326
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:40.168441Z",
-     "start_time": "2025-06-20T18:11:39.008441Z"
+     "end_time": "2025-06-24T18:35:08.744050Z",
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@@ -2634,326 +2687,60 @@
      "name": "stdout",
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+      "[21:35:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0fdc6d574b9c0d168-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
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+      "[48]\tvalidation_0-auc:0.91380\n",
+      "[49]\tvalidation_0-auc:0.91386\n",
+      "[50]\tvalidation_0-auc:0.91395\n"
      ]
     },
     {
@@ -2972,7 +2759,7 @@
        "              predictor=None, ...)"
       ],
       "text/html": [
-       "<style>#sk-container-id-6 {\n",
+       "<style>#sk-container-id-10 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: #000;\n",
        "  --sklearn-color-text-muted: #666;\n",
@@ -3003,15 +2790,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 {\n",
+       "#sk-container-id-10 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 pre {\n",
+       "#sk-container-id-10 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "#sk-container-id-10 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -3023,7 +2810,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -3031,7 +2818,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-container {\n",
+       "#sk-container-id-10 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -3041,7 +2828,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -3057,14 +2844,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel {\n",
+       "#sk-container-id-10 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -3072,28 +2859,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "#sk-container-id-10 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-6 div.sk-serial {\n",
+       "#sk-container-id-10 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -3111,14 +2898,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable {\n",
+       "#sk-container-id-10 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "#sk-container-id-10 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: flex;\n",
        "  width: 100%;\n",
@@ -3131,13 +2918,13 @@
        "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
        "  font-size: 0.6rem;\n",
        "  font-weight: lighter;\n",
        "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -3145,25 +2932,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "#sk-container-id-10 div.sk-toggleable__content {\n",
        "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -3171,79 +2958,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  display: block;\n",
        "  width: 100%;\n",
        "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-6 div.sk-label label {\n",
+       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-10 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-6 div.sk-label label {\n",
+       "#sk-container-id-10 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-label-container {\n",
+       "#sk-container-id-10 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-6 div.sk-estimator {\n",
+       "#sk-container-id-10 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3253,18 +3040,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "#sk-container-id-10 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "#sk-container-id-10 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3352,7 +3139,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "#sk-container-id-10 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -3367,21 +3154,21 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
@@ -3449,7 +3236,7 @@
        "    height: 14px;\n",
        "    cursor: pointer;\n",
        "}\n",
-       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "</style><body><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
        "              colsample_bylevel=None, colsample_bynode=None,\n",
        "              colsample_bytree=None, early_stopping_rounds=None,\n",
        "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
@@ -3459,7 +3246,7 @@
        "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
        "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
        "              n_estimators=1500, n_jobs=None, njobs=-1, num_parallel_tree=None,\n",
-       "              predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "              predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
        "        <div class=\"estimator-table\">\n",
        "            <details>\n",
        "                <summary>Parameters</summary>\n",
@@ -3934,18 +3721,18 @@
        "</script></body>"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 327,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 121
+   "execution_count": 327
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:40.353587Z",
-     "start_time": "2025-06-20T18:11:40.316800Z"
+     "end_time": "2025-06-24T18:35:08.984228Z",
+     "start_time": "2025-06-24T18:35:08.966863Z"
     }
    },
    "cell_type": "code",
@@ -3956,22 +3743,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.9218095238095239\n",
-      "Validation Accuracy 0.9013333333333333\n",
+      "Train Accuracy 0.9117142857142857\n",
+      "Validation Accuracy 0.8937777777777778\n",
       "\n",
       "\n",
-      "Train AUC Score 0.9605690665094028\n",
-      "Validation AUC Score 0.9249003333593262\n"
+      "Train AUC Score 0.9379888471100507\n",
+      "Validation AUC Score 0.9174996588427947\n"
      ]
     }
    ],
-   "execution_count": 122
+   "execution_count": 328
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:40.902069Z",
-     "start_time": "2025-06-20T18:11:40.616069Z"
+     "end_time": "2025-06-24T18:35:09.605901Z",
+     "start_time": "2025-06-24T18:35:09.399199Z"
     }
    },
    "cell_type": "code",
@@ -3983,26 +3770,26 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 123
+   "execution_count": 329
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:41.918377Z",
-     "start_time": "2025-06-20T18:11:41.872680Z"
+     "end_time": "2025-06-24T18:35:09.826863Z",
+     "start_time": "2025-06-24T18:35:09.787515Z"
     }
    },
    "cell_type": "code",
    "source": [
     "preds = xgmodel.predict_proba(test_data)[:, 1]\n",
     "submission = pd.DataFrame({'id': CustomerID, 'Exited': preds})\n",
-    "submission.to_csv('submission_xgb.csv', index=False)\n",
+    "submission.to_csv(iteration + '_submission_xgb.csv', index=False)\n",
     "submission"
    ],
    "id": "f0255d9dc4675132",
@@ -4011,17 +3798,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.132331\n",
-       "1     15001  0.054556\n",
-       "2     15002  0.051230\n",
-       "3     15003  0.792428\n",
-       "4     15004  0.068198\n",
+       "0     15000  0.451203\n",
+       "1     15001  0.415478\n",
+       "2     15002  0.416244\n",
+       "3     15003  0.573613\n",
+       "4     15004  0.416244\n",
        "...     ...       ...\n",
-       "9995  24995  0.042877\n",
-       "9996  24996  0.047073\n",
-       "9997  24997  0.042877\n",
-       "9998  24998  0.127413\n",
-       "9999  24999  0.196946\n",
+       "9995  24995  0.415478\n",
+       "9996  24996  0.415478\n",
+       "9997  24997  0.415478\n",
+       "9998  24998  0.428972\n",
+       "9999  24999  0.428972\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -4052,27 +3839,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.132331</td>\n",
+       "      <td>0.451203</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.054556</td>\n",
+       "      <td>0.415478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.051230</td>\n",
+       "      <td>0.416244</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.792428</td>\n",
+       "      <td>0.573613</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.068198</td>\n",
+       "      <td>0.416244</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -4082,27 +3869,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.042877</td>\n",
+       "      <td>0.415478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.047073</td>\n",
+       "      <td>0.415478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.042877</td>\n",
+       "      <td>0.415478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.127413</td>\n",
+       "      <td>0.428972</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.196946</td>\n",
+       "      <td>0.428972</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -4110,18 +3897,18 @@
        "</div>"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 330,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 124
+   "execution_count": 330
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:47.509255Z",
-     "start_time": "2025-06-20T18:11:42.056845Z"
+     "end_time": "2025-06-24T18:35:13.077486Z",
+     "start_time": "2025-06-24T18:35:10.062522Z"
     }
    },
    "cell_type": "code",
@@ -4136,11 +3923,11 @@
      "output_type": "stream",
      "text": [
       "[LightGBM] [Info] Number of positive: 2136, number of negative: 8364\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000240 seconds.\n",
+      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000253 seconds.\n",
       "You can set `force_row_wise=true` to remove the overhead.\n",
       "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 1084\n",
-      "[LightGBM] [Info] Number of data points in the train set: 10500, number of used features: 13\n",
+      "[LightGBM] [Info] Total Bins 1449\n",
+      "[LightGBM] [Info] Number of data points in the train set: 10500, number of used features: 20\n",
       "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203429 -> initscore=-1.365002\n",
       "[LightGBM] [Info] Start training from score -1.365002\n"
      ]
@@ -4152,7 +3939,7 @@
        "               num_leaves=16, random_state=3)"
       ],
       "text/html": [
-       "<style>#sk-container-id-7 {\n",
+       "<style>#sk-container-id-11 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: #000;\n",
        "  --sklearn-color-text-muted: #666;\n",
@@ -4183,15 +3970,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 {\n",
+       "#sk-container-id-11 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 pre {\n",
+       "#sk-container-id-11 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "#sk-container-id-11 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -4203,7 +3990,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -4211,7 +3998,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-container {\n",
+       "#sk-container-id-11 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -4221,7 +4008,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -4237,14 +4024,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "#sk-container-id-11 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel {\n",
+       "#sk-container-id-11 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -4252,28 +4039,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "#sk-container-id-11 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-7 div.sk-serial {\n",
+       "#sk-container-id-11 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -4291,14 +4078,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable {\n",
+       "#sk-container-id-11 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "#sk-container-id-11 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: flex;\n",
        "  width: 100%;\n",
@@ -4311,13 +4098,13 @@
        "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
+       "#sk-container-id-11 label.sk-toggleable__label .caption {\n",
        "  font-size: 0.6rem;\n",
        "  font-weight: lighter;\n",
        "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -4325,25 +4112,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "#sk-container-id-11 div.sk-toggleable__content {\n",
        "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -4351,79 +4138,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  display: block;\n",
        "  width: 100%;\n",
        "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-7 div.sk-label label {\n",
+       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-11 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-7 div.sk-label label {\n",
+       "#sk-container-id-11 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label-container {\n",
+       "#sk-container-id-11 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-7 div.sk-estimator {\n",
+       "#sk-container-id-11 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -4433,18 +4220,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "#sk-container-id-11 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "#sk-container-id-11 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -4532,7 +4319,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "#sk-container-id-11 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -4547,21 +4334,21 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
@@ -4629,8 +4416,8 @@
        "    height: 14px;\n",
        "    cursor: pointer;\n",
        "}\n",
-       "</style><body><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(learning_rate=0.0088, max_depth=16, n_estimators=2000,\n",
-       "               num_leaves=16, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "</style><body><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(learning_rate=0.0088, max_depth=16, n_estimators=2000,\n",
+       "               num_leaves=16, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
        "        <div class=\"estimator-table\">\n",
        "            <details>\n",
        "                <summary>Parameters</summary>\n",
@@ -4875,18 +4662,18 @@
        "</script></body>"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 331,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 125
+   "execution_count": 331
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:47.952452Z",
-     "start_time": "2025-06-20T18:11:47.650664Z"
+     "end_time": "2025-06-24T18:35:13.580002Z",
+     "start_time": "2025-06-24T18:35:13.285694Z"
     }
    },
    "cell_type": "code",
@@ -4897,22 +4684,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.943047619047619\n",
-      "Validation Accuracy 0.8982222222222223\n",
+      "Train Accuracy 0.9461904761904761\n",
+      "Validation Accuracy 0.8835555555555555\n",
       "\n",
       "\n",
-      "Train AUC Score 0.9813163401379552\n",
-      "Validation AUC Score 0.9260736704616345\n"
+      "Train AUC Score 0.9840109744455012\n",
+      "Validation AUC Score 0.9206480524797255\n"
      ]
     }
    ],
-   "execution_count": 126
+   "execution_count": 332
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:48.380159Z",
-     "start_time": "2025-06-20T18:11:48.200631Z"
+     "end_time": "2025-06-24T18:35:14.198910Z",
+     "start_time": "2025-06-24T18:35:13.977462Z"
     }
    },
    "cell_type": "code",
@@ -4924,25 +4711,25 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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"
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3brzzzjt07tyZd999F3t7e5YsWcIvv/zCt99++0Ry8SCbNm2iYMGCeHt7s3//fhYsWEDPnj3JlSsXZcuWpUWLFkyePJm4uDiqVavGn3/+yeTJk6lWrRq1a9d+ZN/58uXjjz/+YP/+/bz++uspzvPjFChQgPfee4958+ZhZ2dn3Xznhx9+oFevXuTIkSNF33cRefaocBMRkYc6f/48I0eOTHa8RYsW1KpVi6ZNm3L79m1mzZrF8uXLKVeuHP3796d///7WUYN7RowYwZgxYzh37hwvvfQSM2bMsI5apJWNjQ0zZsxg3LhxBAUFcfv2bV5++WVmzpyZZEOK++MOCwtj+fLlLFy4kMKFC+Pt7U2bNm346quvOHXqFGXLlmXmzJlMmjSJ/v37ExsbS7ly5QgKCsLLywu4u63/+PHjmTRpElFRURQpUoRPPvkkxbs9/luuXLlYsGAB48aNY+zYsdy6dYtixYrx+eef8+GHH6YrR49Sq1YtZs2axeTJk+nevTu2tra8+uqrzJkzx7oBRt++fUlISGDixInEx8dTvHhxunbtyqlTp/j111+tUyG7dOnC1KlT6dixIxs2bKB06dKMHj2a4OBgOnXqRJkyZRg2bBjDhg17bFwvv/wyCxYsYMKECXz55ZdYLBZeeuklpkyZ8thXIGSkHj16sH//fpYsWUKRIkUYOHAg7777rvX8iBEjKFWqFMuXL2fWrFkUKlSIwMBAPv7448euu/zwww/5+uuv6dChA3PmzElRnlPqiy++oGDBgixatIjZs2dTvHhx+vXrR5s2bYCUfd9F5NljsGTUymIREcl21q1bR/ny5ZOspdm2bRudO3dm9erVvPzyy5kYnUjahIWFUa9ePUaOHIm/v39mhyMiAmjETURE0mHNmjVMmDCBzz77jCJFinD27Fm+/fZbqlatqqJNREQkA6lwExGRNBs9erR1el9kZCQFCxakcePGdO/ePbNDExERea5oqqSIiIiIiMgzTq8DEBERERERecapcBMREREREXnGqXATERERERF5xqlwExERERERecapcBMREREREXnG6XUAkqVERcVkdghZkpNTbuUuHZS/tFPu0k65Sx/lL+2Uu/RR/tIuM3JnsVgwmzNvk32DAZyd86asrV4HICIiIiIi2ZEp0cT1G7GZVrwZDFCwYMoKN424SZaypOeXhB09ltlhiIiIiEgWV/ilsrQL/haj0ZCpo24ppcJNspQrp04T/rsKNxERERHJXlS4iYiIZDALsC+PHbnMFireTkhyLsrGyL68djS+Hpek/V8OOQi3z0GiAfKYLLwcm4BzojlZ33855CDczgbfm3esx+4Y4PdcdlzLYcQAFIs38UpsgnUHsigbA3/ksuOWjQE7s4WycYmUjDc9NP7T9jk465CDBAPkTzRT4XYCef73t9Fn7G3428EWI1AuNoFS/+vHDOzOa497TLy1rYiIZBztKikiIpLBTjrkIDJH0v/FWoALdjaE5LXDbDAkOXfezobLdjbUvBlHw+txFIlP5EAeO/5dWl3NYeS0Q/K/cz2c2w4bi4X6N+KodesOV22NnLG/2y7BAAfy2FMsPpGG1+N4/XYCf+ay5bqNIVk/AGF2Npx1yEHVW3docD2O/CYzh/LYYQESgT9z2uJ16w5Vo+9wPJctif+77ox9DlwSTCraRESeEBVukioLFizAzc2NuXPnZnYoIiLPpKs5jFyys6FIQtKy62guW87b5+Cl2MRk10TbGLEAFu4WUwbA5l/1zx3D3T5K30l6fYzRQKStDa/EJmAD5DJbKBebyDl7GwD+sbXBzmLhhTsmjEDBRDNF402ctX/wpJvz9jaUupNIXrMFG+Dl2ETijAbraJ41wPu+jDVAuL0N5eKSP5uIiGQMFW6SKgsWLODdd99l/vz5JCbqf9AiIve7V1y5x8QnK7xeik2g5q075Dcln/5Y8k4iZuBXRwc2OjrwV05bKsfEY/O/8xYgNLcdZeISyWNK2vEtGwO2ZgsO9x3OYzITa2MkwXC3KMz7r2vymMzcsnnwrwC3jEby3hejEchlsnDLxoAN8OrtBA7kseNgbjtev323WPwjlx1u/yscRUTkyVDhJim2d+9erl27Rt++fTGbzWzevNl6Lioqip49e1KlShXq1avH999/T/ny5QkLCwPg/PnzdOnShWrVquHj48OECROIj4/PrEcREclwFuBIbjtK30kknyn5dMGcj5hBaDZAgUQz3jfiaHQ9jjJxiRzObUfc/0a2TjnkwNaCdT3Z/RINBmxI2vm9AioRSDSAzb/e/GNjuXv8QUyG5KN9NhYLif+b3lkq3kS9G3fwvXmH4vEmInLcHS0skGjmUG47dua151hOW5KXpyIikh4q3CTFvv/+ewICAnBwcKBNmzbMnj3beq53797cunWLLVu2sHTpUrZu3YrJdPcXjNu3b9O+fXvKlSvHjh07WLhwIXv27CEoKCizHkVEJMOdcsiB0QKl7zx804+H+S23HYUSzOT53/TEcnGJ5LBY+MfOhms5jFyws+H12w/+y64cFgsmklZhJuu5u0WY6V9r6kyGu+cexOZ/55O2N5DjAa99NQEnctpSPjaBUw45sLNYqHXrDrdtDFyw0/ibiEhGUuEmKRIeHs7OnTtp27YtAAEBAZw6dYr9+/dz+fJldu3aRb9+/XB0dKRAgQL069fPeu22bduIj4+nV69e2NvbU6RIEXr06MGCBQsy63FERDJcuJ0NkbZGNud3YHN+B8LtbAi3s2FzfofHXhtrNGD+V7FkBIyWu/3GGw38mu9uv8dy2RJrNLA5vwOR/5sGmWA0cOe+66NtjDiYzdgCeU1mbv1rI5K70ycfPCaW12Qm2vj/vx6Ygds2hmTTLQH+65CDIgkmcpkt3LIxkj/RjAHIl/jwqZgiIpI2eh2ApMjChQtJTEykefPm1mOJiYnMnj2bLl26AFC8eHHruRIlSli/Dg8PJzIyEk9PT+sxi8VCQkIC165dw9nZ+Sk8gYjIk1X3vu35AX7LZQuQ7HUAD1I43sTfDjkokGjGwWzhnL0NcQYDhRNMlIo38fp9fVyws+FvhxxJXgfglGDij1y2VIhJIN5o4O+cOSjxv5E/1wQTf+a05Yy9DaXumIjMYeSinQ1Voh88glf8jom/c9rikmgit8nCXzlzYGe2UOBfrya4bTRwydaGmrfuxpHbZCEqh5Hi8Sau5zBSKCH1I48iIvJwKtzkse7cucOyZcsYMWIENWrUsB4/efIknTp1onPnzsDdAq106dLWr+9xdXWlZMmSbNq0yXosOjqaa9euUaBAgaf0FCIiz67XbifwV05b9ua1JxHIZzJTLfpOkg1HHqVKTDzHctnxa36H/73HLdG6w6OdBapF3+GPXLaczGmLndlC+dsJFPxfIRaZw8j+PHZ437hDTouFEvEmEg0GDua2I95owDHRjGd0fLIpOsdz2iZ5V1yZuARCc9vxs6MDBRPMlErDlFEREXk4g8XygEnrIvdZtmwZ33zzDTt37sTW1jbJuWbNmlGxYkUiIiIwGo2MHDkSgD59+rB161a2bNmCo6MjjRs35v333+e9994jLi6O//znP/zzzz+sWLEiVbEENW3JmZADGfZsIiIiIpI9FavwGp9v2UBUVAyJiZmzpZLBAAUL5k1RW01Al8dauHAhTZs2TVa0AbRu3ZrVq1czYsQIDAYDdevWpUWLFpQvXx4AW1tb8uTJw9y5cwkJCaFOnTrUr18fo9FIcHDw034UEREREZEsSSNukiF2795NlSpVcHC4uwj/r7/+ws/PjyNHjmBvb59h99GIm4iIiIhkBI24SbY0evRogoODSUxMJDo6muDgYGrUqJGhRZuIiIiISHalwk0yxLhx4zhy5AheXl74+vpiY2PDmDFjMjssEREREZHngnaVlAxRrlw55s2b98TvU6hsGeJvxz7x+4iIiIjI863wS2UzO4RU0Ro3ERERERHJlkyJJq7fiMVszpySKDVr3DTiJllKVFRMZoeQJTk55Vbu0kH5SzvlLu2Uu/RR/tJOuUsf5S/tMiN3ZrMl04q21FLhJlmK2WzGnDmb/mRZBsPdf5pMZjS+nnrKX9opd2mn3KWP8pd2yl36KH9pp9w9ngo3yVKMRiNGbamTJjY2Slx6KH9pp9ylnXKXPspf2il36aP8pV1G5C4rjaKlhta4iYiIiIjIc8OcaCIqE9etpYbWuMlza+OX/+Hy8T8yOwwREREReQY5ly1D00njMRoNWaJwSw0VbpKlRP73DJePHc/sMEREREREnipNwBUREREREXnGacQti7hx4wYTJkxg69at3Lhxgzx58lCzZk169uyJq6trZocnIiIiIiJPkEbcsoiePXsSFRXFsmXLOHLkCKtWrSI+Pp4PPviAxMTEzA5PRERERESeIBVuWcShQ4do0KABLi4uABQsWJB+/fpRsWJFbt68ia+vLytWrLC2DwkJwc3NDYCwsDDc3NwYNWoUnp6eDBkyhKCgILp3707v3r3x8PCgTp06jBs3znr95cuX+eyzz/D19aVixYrUq1ePZcuWWc+7ubmxZMkSGjVqRMWKFenSpQvHjh3jnXfewd3dnZYtW3Lu3Dlr+/Xr19O0aVOqVKmCv78/u3btetIpExERERF5bqhwyyKaNGnCoEGDGDx4MBs2bCA8PBwXFxdGjRpFgQIFUtRHTEwMu3fvpmfPngD89NNP1KpVi5CQEIYNG8aMGTM4cuQIAAMGDMDW1pb169dz+PBh2rVrx7Bhw4iJ+f+32a9du5YlS5bw888/c+jQIbp168aIESPYvXs3dnZ2TJs2DYDt27czaNAgBg4cyP79+/n000/59NNP+fvvvzM2SSIiIiIizykVblnE8OHDGThwIP/88w8DBw7E19eXBg0asGbNmhT34efnh52dHfny5QPghRdewM/PDxsbG7y9vXFxceHs2bPW+w0aNAhbW1suXrxI7ty5iYuL48aNG9b+2rVrh6OjI4UKFaJcuXI0bNiQMmXKkCtXLry8vAgPDwfghx9+4N1338XT0xMbGxt8fHzw9fVl8eLFGZcgEREREZHnmDYnySKMRiPNmzenefPmWCwWTp8+zerVq/nyyy+t0ycfp1ChQkk+//s6W1tbzGYzABcuXGDMmDGcPXuWF154gVKlSgFYzwM4Ojpav7axsSF//vxJ4r33bvfw8HD279/PokWLrOdNJhNeXl4piltEREREJLtT4ZYF7Ny5k+7du7N161YcHR0xGAyULVuWzz//nN27d/PHH39gNBpJSEiwXhMVFZWsH4PBkKL7JSQk0LlzZ3r16kWbNm0wGAwcO3Ys2eheSvtzdXXFz8+PTp06WY9dvHgRBweHFF0vIiIiIpLdaapkFuDp6YmzszP/+c9/+Ouvv0hISCA6Opo1a9Zw9uxZ6tatS5kyZdiyZQtxcXFEREQwf/78NN8vISGBuLg4HBwcMBgMXLx4kbFjx1rPpVZAQADz58/n6NGjAPz+++/4+/uzbt26NMcoIiIiIpKdaMQtC3BwcGDhwoVMnjyZrl27cu3aNWxtbalUqRJz5syhTJky9O7dm8GDB1OzZk0KFSrE+++/z6FDh9J0v1y5cvH1118zadIkhg8fjrOzMwEBAZw6dYqTJ09SunTpVPX3xhtvcPv2bfr168fFixdxdHSkffv2BAYGpik+EREREZHsxmC5txBJJAtY0Oodwg4czOwwREREROQZVPi1V2m/fjVRUTEkJpoff0EmMxigYMG8KWqrqZIiIiIiIiLPOBVuIiIiIiIizzitcZMspcCLpUmIjc3sMERERETkGeRctkxmh/DEaI2biIiIiIg8N8yJJqJuxGI2P/tlTmrWuGnETbKUqKiYzA4hS3Jyyq3cpYPyl3bKXdopd+mj/KWdcpc+yl/aZVTuzGZLlijaUkuFm2QpZrMZ87O/QdAz5d570k0mMxpfTz3lL+2Uu7RT7tJH+Us75S59lL+0U+4eT4WbZClGoxGjttRJExsbJS49lL+0U+7STrlLH+Uv7ZS79FH+0u5e7p7XUbP00Bo3ERERERF5pmSldWrpoTVu8tza0e8rrv3xR2aHISIiIiJPiGOZMviMG4PRaHjuC7fUUOEmWcqNM2e49sefmR2GiIiIiMhTpQm4IiIiIiIizzgVbqlkMpm4cOHCU7nX2bNnn8p9RERERETk2ZYtCzdfX18qVKiAu7t7sj8HDx585LU9e/Zk1apVAFy8eBF3d3cuXryY4TEuWLCAr776Ks3XBwUFERgYaP38999/8/HHH1OtWjXc3d1p2LAhEyZMID4+PkX9rVixAl9f3zTHIyIiIiIiaZdt17gNGTIEf3//VF8XFRVl/bpo0aKEhoZmZFhWkZGRGdZXdHQ0gYGBfPjhh3zzzTc4ODhw6tQpevXqxdWrVxkxYkSG3UtERERERDJethxxe5zNmzfTpEkTqlSpQuPGjZk6dSoA/fv35+DBg0yfPp0uXboQFhaGm5sbYWFhALi5ubFkyRIaNWpExYoV6dKlC8eOHeOdd97B3d2dli1bcu7cOQDi4+MZPXo0jRs3xt3dnerVqzNs2DAsFgsrV65k+vTpHDx4EA8PD+Bu8TV06FC8vb2pXr06PXv25OrVq9aYDx8+TMuWLalUqRLvvPOONSaA//73v0RFReHn50fOnDkxGAyUK1eO/v37ky9fPmu7X3/9lXfeeYfq1atTsWJF2rVr99DpmsuWLcPf3986gte5c2drsRkUFMSHH35Iy5YtqVq1KpMnT6ZKlSrcuXPHev2mTZvw8fFBb6MQEREREXk8FW7/EhcXxxdffMHAgQM5dOgQ48aNY8aMGRw9epQRI0bg4eFB586dmTZt2gOvX7t2LUuWLOHnn3/m0KFDdOvWjREjRrB7927s7Oys182bN4+dO3cyb948QkNDmTp1KosXL2bfvn20aNGCzp074+HhYZ262a9fP86dO8eKFSv45ZdfyJMnD5988gkWi4WoqCg6d+5Mo0aNOHDgAF988QW//PKLNaaXX36ZMmXK8O677zJhwgR27NjB9evX8fLyok+fPgBcunSJHj160KlTJ/bu3cu2bduwWCxMmTIl2TMePXqU4cOHM3jwYEJCQti4cSNnz55l/vz51jZ79+6ld+/ebN26lU6dOmFjY8OWLVus51etWkWLFi0wGAzp/6aJiIiIiDznsvVUya+//jrJsSJFirB06VIcHBxYtmwZZrOZypUrc+jQIYzGlNW47dq1w9HREYBy5cpRvnx5ypQpA4CXlxeHDh0CICAggBYtWuDs7MyVK1eIi4sjd+7cXL58OVmf165dY/PmzWzcuBFnZ2fgbiHn4eHB8ePH+fvvv8mZMycdO3bEYDBQpUoVWrZsyZ9/3t02387Ojh9//JGFCxfy66+/MmvWLBITE6lcuTJ9+/bl9ddfp0CBAqxfv56SJUsSHR3NpUuXcHJyemA8L730EuvWraN48eLcuHGDK1euUKBAgSRtS5QoQfXq1a2f33rrLVavXs2bb77JtWvX2LVrF/37909RTkVEREREsrtsW7gNGjTooWvcFi1axNSpU/n888+Jjo6mUaNGDBgwgPz58z+233tFG4CNjU2Sa4xGo3VqYGxsLEOHDuXAgQO4urpSvnx5LBYLZrM5WZ/h4eHA3WLvfjY2NoSFhXH58mWKFCmSZPSqZMmS1sINIE+ePHTq1IlOnToRHx/PsWPHmDFjBh988AG//vor+fLlY926dSxevBiDwcBLL71EdHQ0OXIk/xExGo3Mnz+ftWvXkitXLtzc3IiOjk4y7bFQoUJJrvH396d169Zcu3aNNWvWULlyZUqUKPHYfIqIiIiISDYu3B4mOjqaK1euMG7cOAD+/PNPevXqxbRp06zTCh8lpVP/7hWCu3btwt7eHrPZjKen5wPbFi5cGICNGzfi4uJiPX7q1ClKlCjBxo0bCQ8Px2w2W0cGL126ZG03YcIE9uzZw9KlS4G7I3CVK1dm7NixVKlShfPnz3PhwgV++OEHFi1aRKlSpQAYNmwYJ0+eTBbP3Llz2b17N2vXrqVgwYIAdOnS5ZF5eO211yhbtiybN29m/fr1SXa8FBERERGRR9Mat3+JiYmhY8eOrF27FovFQqFChTAajTg5OQF3i55bt26l+z7R0dHY29tjNBqJjo5mzJgxREdHk5CQAIC9vb11FKtw4cLUrVuXESNGEBUVRUJCAsHBwbRq1YqbN2/i6+uLxWIhKCjIOpp2r0gDaNy4MX/99RcTJ04kPDwci8XC1atXmTJlCqVKlcLNzY1bt25hNBpxcHDAYrGwY8cOVq1aZY3n37HnyJEDW1tbEhMTWb16NTt37nxg2/v5+/vz448/cvbsWRo2bJjuHIqIiIiIZBfZtnAbNGjQA9/jtmbNGr799ltmzJhB5cqVeeutt/Dy8qJ9+/YA+Pn5sXz5ctq0aZOu+w8YMIATJ05QtWpV3njjDaKjo6ldu7Z1hMvHx4fr169TpUoVbt68yZgxY8iXLx9+fn54eXmxfft2Zs6ciYuLC/ny5WPWrFns3buXqlWr0r9/fxo1amS918svv8wPP/zAyZMnadWqFRUrVsTPz4/r16/z/fffY2dnR4sWLahRowZNmjTBy8uL4OBg3n//fc6cOZPsXW8ffvghRYoUwcfHh9q1a7NmzRratGnzwNG5+zVt2pRTp07x5ptvkjNnznTlT0REREQkOzFYtB+7PCUmk4latWoxbdo0KlasmKY+1r7bjsuHDmdwZCIiIiLyrHAu/wotVi0nKiqGxMTk+z88TwwGKFgwb4raao2bPBV///03GzduxNXVNc1Fm4iIiIhIdqXCTZ6Kzp07A/Dtt9+mq5/8pUuTGBubESGJiIiIyDPI8X+v0pKkNFVSRERERESeKeZEE1E3YjGbn+9SRVMl5bkVFRWT2SFkSU5OuZW7dFD+0k65SzvlLn2Uv7RT7tJH+Uu7+3NnNlue+6IttVS4SZZiNpt5wDvK5RHuvVLPZDKj8fXUU/7STrlLO+UufZS/tFPu0kf5Szvl7vGy7esAREREREREsgqNuEmWYjQaMeqvG9LExkaJSw/lL+2Uu7RT7tJH+Uu75yl3mnInzwttTiIiIiIizy2zyUTU9aezycW9jSauXr2l6X6plF1zp81J5Ll14KvBRP35Z2aHISIiIllAvhdfxGvMSIxGg0bdJMtT4SZZys2zZ7n+54nMDkNERERE5Kl6fiYwyyOdPXs2s0MQEREREZE0UuH2lJw5c4Y+ffpQp04d3N3dqV+/Pt988w0xMRn3ng83NzdCQkIAaNKkCWvWrAFgwYIFfPXVV9Z2ZrOZGTNm8Oabb+Lu7o6npycdOnQgNDQ0w2IREREREZGMo8LtKTh8+DAtWrSgWLFirFq1itDQUGbMmMFvv/3Ghx9+iMlkyvB7rl+/nmbNmgEQGRmZ5Nx3333HypUr+fbbbzl8+DA7duzAy8uL999/n3PnzmV4LCIiIiIikj4q3J6CgQMH4ufnR/fu3SlQoAAApUuXZsKECTg7O3PhwgXc3NwYPnw41apVo0uXLgDs2bOHVq1a4eHhkWQEDSAhIYGRI0dSrVo1vLy8mDlzZpJ7+vr6smLFClauXMn06dM5ePAgHh4eABw6dAgPDw/Kli2LwWAgZ86cdOzYkYCAAK5evQpAYmIikyZNwtvbm8qVK9O2bVtOnLi7tiwuLo4xY8bg7e2Np6cngYGBHD161Hrv1D6LiIiIiIg8mjYnecLOnz/P33//zeDBg5OdK1iwIFOnTk3Sdtu2bSQkJHDixAm6du3K2LFjqVevHr/99hvdunXDycmJ2rVrM3XqVLZt28ayZctwdnZ+YP8ALVq0ICwsjP379/P9998Dd6dR9uvXj+vXr1O7dm1ef/11ypUrx4ABA6zXBQcHs27dOmbNmkXp0qWZPHkynTt35tdff2Xw4MH88ccfzJ8/nyJFirBo0SLat2/PunXrKFq0aKqfRUREREREHk0jbk/YvWmKBQsWfGzbt956i5w5c5IvXz4WL15MvXr1aNiwITY2NlSuXJmAgAAWLFgAwOrVq+nQoQMlSpQgV65cDBgwAIPBkKKY/Pz8mD9/Pg4ODkyePJlmzZpRvXp1xo8fT2JiIgArV67ko48+omzZstjY2NC1a1cmTZpEXFwc69at4/PPP6dUqVLY2dnx/vvv8+KLL7Ju3bo0PYuIiIiIiDyaRtyeMBcXFwAiIiJ44YUXkp2/evWqtagrVKiQ9Xh4eDj79u2zTm8EMJlMlCxZEoArV65QpEgR67l8+fKRP3/+FMfl4eFh7fvy5cts27aNsWPHYjQa+eyzz4iIiLCOngHY2dlRqVIlrly5QkJCAsWLF0/SX/HixQkLC7N+Ts2ziIiIiIjIo6lwe8KKFSvGSy+9xIYNG/D09Exy7tq1a/j4+DBy5EiAJCNmrq6utGjRgqFDh1qPXblyBcv/XiXv6urKhQsXrOdu377NrVu3HhtPTEwMNWvWZMKECfj4+ABQuHBhWrduzZkzZ/jzfy+3LlKkCP/884/1uoSEBMaOHUuHDh2wt7fnwoULlClTxnr+/Pnz+Pr6Wj+n5llEREREROTRNFXyKfjqq69Yvnw5kydPJioqCovFwp9//kmXLl149dVXadSoUbJrWrVqxbp169i1axdms5mzZ8/Srl07Zs+eDcDbb7/NzJkzOX36NHfu3GHUqFEP3Z3S3t6e6OhoLBYLuXPnpl69eowZM4aQkBBu375NfHw8hw4dYvPmzTRs2BAAf39/Zs2axZkzZ0hMTGT69On88ssvFChQgJYtWzJ+/HjOnTtHfHw88+bN49SpUzRp0uSB93/cs4iIiIiIyKNpxO0pqFq1Kj/88APTpk2jSZMmxMbGUrBgQd544w06d+6Mra1tsmsqVqzI+PHjGT9+PD169CBnzpy89dZb9OrVC4COHTsSGxtLu3btSExMJCAgAEdHxwfe38fHh0WLFlGlShW2bdvGyJEjmTlzJsOHD7dOb3zxxRf57LPPaN68OQAfffQRiYmJdOjQgRs3blChQgVmzJiBra0tX375JUFBQbRv357r16/j5uZm3cTkQR73LCIiIiIi8mgGi+arSRayJbA91w7rReEiIiLyeI6vvEzDZUuIioohMdH8xO9nMEDBgnm5evUW+g07dbJr7u49d0poqqSIiIiIiMgzToWbiIiIiIjIM05r3CRLyffCC5hiYzM7DBEREckC8r34YmaHIJJhVLhJluI5bHBmhyAiIiJZiNlkwmzORoum5Lmlwk2ylKiomMwOIUtycsqt3KWD8pd2yl3aKXfpo/yl3fOWO7PZosJNngsq3CRLMZvNmJ/8plDPlXvvQjeZzNlql6aMovylnXKXdspd+ih/aafciTy7VLhJlmI0GjFqS500sbFR4tJD+Us75S7tlLv0Uf7SLqW502iWyNOj97iJiIiISJqYTSairseqePuf7PousoyQXXOXmve4acRNspRjw4Zz88SJzA5DREQk28tTujSvDx+G0WhQ4SbyFKhwkywl5tw5bp34K7PDEBERERF5qjT5WzLUuXPnMjsEEREREZHnTrYdcRs4cCBr164FIDExkYSEBHLmzGk9P2PGDDw8PDIrvAdq1aoVf/31F7/++isuLi4Z1m9QUBBTp07FwcHBesxsNlOoUCHatm1L+/btU9TP6NGjiYqKYtSoUQC4u7s/k3kUEREREclqsm3hNnToUIYOHQrAihUrmDx5Mr/++msmR/Vwv/32G5cuXaJOnTr88MMP9OzZM0P79/Dw4Pvvv7d+TkhIYNWqVQwYMAA3NzeqV6/+2D6ioqKSfA4NDc3QGEVEREREsitNlXyA8+fP06VLF6pVq4aPjw8TJkwgPj4euFvkvfvuuwwfPhwvLy+qV69O//79SUhIAKBv37707ds3SX9ubm6EhIQA4Ovry8CBA6lZsyZ+fn6YzWaOHz9OYGAgnp6eNGzYkLlz5/LvzT5/+OEH3njjDdq0acPixYuJjY1Ncn7Pnj34+flRuXJl3nnnHcaOHUtgYKD1/Pr162natClVqlTB39+fXbt2PTIHtra2vP322zg6OvLHH38AEB0dzYABA2jYsCGVKlWidu3aTJs2DYApU6awdu1a1q5dS7NmzZI9d1RUFF999RW1atWiWrVqdO7cmbNnz6b4eyIiIiIikp2pcPuX27dv0759e8qVK8eOHTtYuHAhe/bsISgoyNrm8OHDODs7s3PnTqZPn86GDRv46aefUnyPo0ePsnHjRubPn09ERATvv/8+b7zxBnv27GHq1KksXLiQJUuWWNtfu3aNzZs38+6771KjRg2cnZ1Zvny59XxYWBhdunTh3XffZf/+/fTu3TvJ9du3b2fQoEEMHDiQ/fv38+mnn/Lpp5/y999/PzTGuLg4vv/+e2JiYqhZsyYA33zzDWFhYSxbtozQ0FAGDBjAhAkTOHfuHB9//DFNmzaladOmrFmzJll/3bt35/z586xcuZLt27fz4osv0r59e6Kjo1OcNxERERGR7EqF279s27aN+Ph4evXqhb29PUWKFKFHjx4sWLDA2sbBwYEuXbpga2vL66+/jpubG2fOnEnxPRo1akS+fPnIly8fa9asoUyZMrRt2xZbW1vKli1Lhw4dktxvyZIlVK1alTJlymAwGAgMDGTu3LmYzWYA1q5dyyuvvELr1q3JkSMHHh4eBAQEWK//4YcfePfdd/H09MTGxgYfHx98fX1ZvHixtc2hQ4fw8PCgcuXKVKhQAS8vL/bv38+8efN4+eWXAfj000+ZOHEiefLk4dKlS9jb2wNw5cqVRz7vhQsX2L9/P1999RUuLi44ODjQu3dvEhMT2b59e4rzJiIiIiKSXWXbNW4PEx4eTmRkJJ6entZjFouFhIQErl27BoCzszMGg8F63tbWNtnUxkcpVKhQkvsdP348yQYeZrMZGxsb4O7GKYsXL+bmzZtUq1bNev7mzZv8/PPPNGrUiH/++YdixYoluUeJEiX4/fffrffYv38/ixYtsp43mUx4eXlZP1epUsW6xu3IkSP06NEDFxcXqlSpYm1z7do1RowYwR9//EHx4sV57bXXrPE8ytWrV60x3WNjY0ORIkUIDw9/XLpERERERLI9FW7/4urqSsmSJdm0aZP1WHR0NNeuXaNAgQKPvd5oNHLnzh3r58jIyGRt7i/6XF1dqVatGrNmzbIei4qKIiYmBoCff/6Z+Ph41q9fby3mACZNmsScOXNo1KgRxYoVY+vWrUnucfHixST38PPzo1OnTknO37+L5P0qVarEtGnTaN26Nfnz56dHjx4A9OjRA19fX2bNmkWOHDmIiorixx9/fGxO7hWV58+fp1y5csDdwvHixYsZujumiIiIiMjzSlMl/8XHx4eYmBhmzpxJfHw8N2/epE+fPvTs2TNJwfUwZcqU4eDBg1y+fJm4uDimTJnyyOuaNm3KkSNHWLNmDYmJiVy5coUuXbpYt9T/4YcfaNq0KcWKFcPV1dX6JzAwkNDQUA4fPkzz5s35888/WbVqFSaTid9++y1JQRUQEMD8+fM5evQoAL///jv+/v6sW7fuoXG98sor9O3bl2nTprFv3z4Abt26hYODAzY2NkRGRjJ8+HAA68YsdnZ23Lp1K1lfhQoVwtvbm+HDhxMREUFcXBzffPMNJpMJHx+fx+ZURERERCS7U+H2L3ny5GHu3LmEhIRQp04d6tevj9FoJDg4OEXXt27dGnd3d5o1a0aDBg0oUqQIRYsWfWj7YsWKMXPmTJYsWUKNGjVo3rw5L774IqNGjeLEiRMcPHgQf3//ZNeVL1+e1157jdmzZ+Pq6sq3335rfWfa6NGjqVWrFra2tgC88cYb9OrVi379+lG5cmV69OhB+/btk+w6+SBt2rTB29ubPn36cOPGDUaOHMmGDRuoXLky/v7+FC5cmPLly3Py5EkA3nzzTQ4fPkzdunWT9TVmzBhKlChBixYtqFGjBn/99Rfz5s3D0dExRXkVEREREcnODJbULM6SZ9I///xDVFQU5cuXtx4bNWoUERERjBs3LhMjy3ghH3XkeuiRzA5DREQk28v7shs1FvxAVFQMiYmPXu+eXRgMULBgXq5evYV+w06d7Jq7e8+dEhpxew5ERUXRpk0bjh07BsCJEydYs2aNpiGKiIiIiDwntDnJc6B8+fL079+fXr16ERERQcGCBenUqRNvvfVWZocmIiIiIiIZQIXbc+Ltt9/m7bffzuwwnrjcpUphio3N7DBERESyvTylS2d2CCLZigo3yVJe+2pAZocgIiIi/2M2mTCbs9GCJJFMpMJNspSoqJjMDiFLcnLKrdylg/KXdspd2il36aP8pV1qcmc2W1S4iTwlKtwkSzGbzZi1cVWq3HuNoMlkzla7NGUU5S/tlLu0U+7SR/lLO+VO5Nmlwk2yFKPRiFF7oaaJjY0Slx7KX9opd2mn3KWP8pdyGjkTefapcJMsxckpd2aHkGUpd+mj/KWdcpd2yl36KH8pZzaZiLoei17vK/LsUuEmWcrpsaOI+fvvzA5DRETkuZGzVCnK9R+I0WjAZFLhJvKsUuEmWUrshQvE/H0ys8MQEREREXmqNPlbRERERETkGafCTURERERE5BmnqZJpdOPGDSZMmMDWrVu5ceMGefLkoWbNmvTs2RNXV9fMDi9dgoKCmDp1Kg4ODsnOrVmzhhIlSmRCVCIiIiIi2ZcKtzTq2bMnefPmZdmyZbi4uHD16lVGjBjBBx98wNq1a8mRI2un1sPDg++//z6zwxARERERETRVMs0OHTpEgwYNcHFxAaBgwYL069ePihUrcvPmTXx9fVmxYoW1fUhICG5ubgCEhYXh5ubGqFGj8PT0ZMiQIQQFBdG9e3d69+6Nh4cHderUYdy4cdbrL1++zGeffYavry8VK1akXr16LFu2zHrezc2NJUuW0KhRIypWrEiXLl04duwY77zzDu7u7rRs2ZJz585Z269fv56mTZtSpUoV/P392bVrV4qffcWKFfj7+/Phhx/i4eHB2rVriY+PZ9KkSdSrV4+qVavSsWPHJPc7ffo0gYGBuLu706RJE6ZPn46vr2/qEy8iIiIikg2pcEujJk2aMGjQIAYPHsyGDRsIDw/HxcWFUaNGUaBAgRT1ERMTw+7du+nZsycAP/30E7Vq1SIkJIRhw4YxY8YMjhw5AsCAAQOwtbVl/fr1HD58mHbt2jFs2DBiYmKs/a1du5YlS5bw888/c+jQIbp168aIESPYvXs3dnZ2TJs2DYDt27czaNAgBg4cyP79+/n000/59NNP+TsV2+wfP36cpk2bsmfPHho0aMCECRPYtm0bc+fOZefOnVSsWJEPP/yQO3fukJCQQKdOnShbtiz79u1j3LhxLFy4MMX3EhERERHJ7lS4pdHw4cMZOHAg//zzDwMHDsTX15cGDRqwZs2aFPfh5+eHnZ0d+fLlA+CFF17Az88PGxsbvL29cXFx4ezZs9b7DRo0CFtbWy5evEju3LmJi4vjxo0b1v7atWuHo6MjhQoVoly5cjRs2JAyZcqQK1cuvLy8CA8PB+CHH37g3XffxdPTExsbG3x8fPD19WXx4sXWvg4dOoSHh0eSPz/++KP1vK2tLc2bN8fOzg57e3sWL15Mr169KFGiBPb29nz88cckJCSwbds2Dh06xKVLl+jTpw/29va8/PLLfPjhh+lJv4iIiIhItpK1F2JlIqPRSPPmzWnevDkWi4XTp0+zevVqvvzyS+v0yccpVKhQks//vs7W1haz2QzAhQsXGDNmDGfPnuWFF16gVKlSANbzAI6OjtavbWxsyJ8/f5J4LZa7L9UMDw9n//79LFq0yHreZDLh5eVl/VylSpVHrnFzcXHBaLxb90dGRnL79m169OhhPQaQkJBAeHg4JpMJJyenJJudlC5d+qF9i4iIiIhIUirc0mDnzp10796drVu34ujoiMFgoGzZsnz++efs3r2bP/74A6PRSEJCgvWaqKioZP0YDIYU3S8hIYHOnTvTq1cv2rRpg8Fg4NixY8lG91Lan6urK35+fnTq1Ml67OLFiw/cRfJh7r+Xk5MT9vb2zJ49m0qVKlmP//e//6Vw4cKcPn2ayMhIYmJiyJ07N3B3zZ6IiIiIiKSMpkqmgaenJ87OzvznP//hr7/+IiEhgejoaNasWcPZs2epW7cuZcqUYcuWLcTFxREREcH8+fPTfL+EhATi4uJwcHDAYDBw8eJFxo4daz2XWgEBAcyfP5+jR48C8Pvvv+Pv78+6devSFJ/RaKRVq1aMGzeOS5cuYTabWblyJW+99Rbnzp2jQoUKvPbaawwfPpzbt29z/vx5Zs6cmaZ7iYiIiIhkRxpxSwMHBwcWLlzI5MmT6dq1K9euXcPW1pZKlSoxZ84cypQpQ+/evRk8eDA1a9akUKFCvP/++xw6dChN98uVKxdff/01kyZNYvjw4Tg7OxMQEMCpU6c4efJkqqcdvvHGG9y+fZt+/fpx8eJFHB0dad++PYGBgWmKD6BPnz4EBQXRpk0brl+/TokSJfj2228pX748AFOmTGHYsGH4+Pjg4uKCh4cHe/fuTfP9RERERESyE4Pl3sInkadoxYoVTJ48mV9//TVV1x3r/jG3fj/6hKISERHJfnKXe4nXv5tFVFQMJpOZggXzcvXqLfQbYuoZDCh/aZRdc3fvuVNCUyVFRERERESecSrcREREREREnnFa4yaZwt/fH39//1Rfl7NECcxxcU8gIhERkewp5/9eMSQizzYVbpKllPmib2aHICIi8twxm0yYzdloYZFIFqTCTbKUqKiYzA4hS3Jyyq3cpYPyl3bKXdopd+mj/KWO2WzBbLaQwlfCikgmUOEmWYrZbMZszuwospZ7/xM2mczZapemjKL8pZ1yl3bKXfoofyLyPFLhJlmK0WjEqC110sTGRolLD+Uv7ZS7tFPu0kf5+3/3RtREJOtS4SZZipNT7swOIctS7tJH+Us75S7tlLv0Uf7+n9lkIup6rIo3kSxMhZtkKeGTv+HO6VOZHYaIiEiWYVeiJMV79cNoNKhwE8nCVLhJlhIfFkbcf1W4iYiIiEj2osnfIiIiIiIizziNuGVxbm5u2NvbY2Njg8ViwdbWFg8PDwYOHEiRIkUee31YWBj16tVjy5YtFC9e/ClELCIiIiIiqaURt+fAjBkzCA0N5ciRI2zduhWLxcIXX3yR2WGJiIiIiEgGUeH2nMmTJw8BAQEcO3bMeuz06dN07tyZunXr8vrrr/Pmm2+ydevWB15/+PBh3nvvPWrVqkWFChXw9/fnyJEjAISEhODr60twcDC1a9ematWqfPrpp0RHR1uvnzdvHg0aNMDd3R1/f3/27t0LgMViYf78+TRq1AgPDw/atGmTJEYREREREXk4FW7PmRs3brB+/XoaNmxoPfbpp5/y0ksv8fPPP3Pw4EFq1arF4MGDk10bFxdH165dadSoETt27CAkJISSJUsyZswYa5vw8HAuX77Mzz//zNKlSwkNDWXhwoUArFixgqlTpzJmzBgOHTrEu+++S9euXbl+/ToLFy5kzpw5TJo0ib179+Lv788HH3zA1atXn3hORERERESyOq1xew506dIFGxsbzGYzMTEx5M2bl+nTp1vPT58+ncKFC2OxWAgPDydfvnxcvnw5WT+2trYsWbKEUqVKcefOHcLDw3F0dOT3339P0u7jjz/GwcGBUqVKUa1aNc6cOQPAypUrad26Ne7u7gC8/fbblClTBgcHBxYsWEDnzp15+eWXAWjVqhXLli1jzZo1fPjhh08qNSIiIiIizwUVbs+BadOmUa1aNeDuqNmCBQt4//33WbJkCa+++ionTpygW7duREREUKZMGQoUKIDFkvw9LjY2NoSEhNCxY0du375N2bJlyZEjR7K2Li4u1q9tbW2t5yMiIihatGiStpUrVwbujtSNHj2ab775xnouMTGR1157LWOSICIiIiLyHFPh9pxxcHCgQ4cOfPfdd+zZs4eCBQvSo0cPJk+ejK+vLwCbN2/mp59+Snbtb7/9xrBhw1i8eLG1oJo9e7Z1RO1xihQpwj///JPk2IQJE2jWrBmurq50796dJk2aWM+dP38eR0fHND6piIiIiEj2oTVuz5nExESWL1/OzZs3qVKlCjExMZhMJnLmzAnAqVOnmDJlCgDx8fFJrr116xZGoxEHBwcAjhw5wvz585O1exh/f3+WLFnC0aNHMZvNLF++nAULFuDk5ERAQADBwcGcPn0agJ07d9KkSRMOHDiQUY8uIiIiIvLc0ojbc6Bjx47Y2NgAYDAYeOGFFxg/frx1muKXX37JF198QWxsLK6urgQEBDB27FhOnjyZZMSrZs2atGnThrZt22I2mylevDiBgYGMGzcuRZuING3alJs3b/LFF18QERFB2bJlmTFjBgUKFKB9+/ZYLBa6devGlStXKFy4MAMHDqRevXpPJCciIiIiIs8Tg+VBi51EnlFn+n5G7J96jYCIiEhKObxYlhcnTCMqKobERPMj2xoMULBgXq5evYV+Q0w95S/tsmvu7j13SmiqpIiIiIiIyDNOhZuIiIiIiMgzTmvcJEuxK14cy524zA5DREQky7ArUTKzQxCRDKDCTbKUYp/0zuwQREREshyzyYTZnI0WDok8h1S4SZYSFRWT2SFkSU5OuZW7dFD+0k65SzvlLn2Uv6TMZosKN5EsToWbZClmsxnzozfEkn8xGO7+02QyZ6tdmjKK8pd2yl3aKXfpo/yJyPNIhZtkKUajEaO21EkTGxslLj2Uv7RLae40IiAiIvJwKtwkS3Fyyp3ZIWRZyl36KH9pl9LcmU0moq7HqngTERF5ABVukqVcmTWZO+dOZ3YYIpLB7IqWoHCXXhiNBhVuIiIiD6DCTbKU+EthxJ/7b2aHISIiIiLyVGnRRiYymUxcuHAhs8MQEREREZFnnAq3DLJgwQLc3NyYO3duiq/p2bMnq1atAuDixYu4u7tz8eLFDInn6NGjdO/enRo1alC5cmUaN27M9OnTSUxMfOy169evJzAwkGrVquHp6Unr1q3ZtGlThsR1z4oVK/D19c3QPkVEREREnlcq3DLIggULePfdd5k/f36KiiOAqKgo69dFixYlNDSUokWLpjuWTZs28d577+Hp6clPP/3EoUOHGDduHGvXruXzzz9/5LXDhw9nzJgxfPTRR+zcuZO9e/fSsWNH+vfvz4IFC9Idm4iIiIiIpJ4Ktwywd+9erl27Rt++fTGbzWzevNl6LjIykt69e+Pp6Um1atXo2bMnN27coH///hw8eJDp06fTpUsXwsLCcHNzIywsjEmTJvHOO+8kucfYsWPp1KkTAFevXqV3797UrFmTWrVqMXDgQKKjowG4c+cOgwYN4uOPPyYwMJA8efJgMBgoX74833zzDRaLhevXr1vvN2rUKDw9PRkyZAhHjx7l+++/59tvv8Xb2xs7Ozty5MhB/fr1+eqrrzh37hwAFouF7777jqZNm+Lh4YGnpyeff/45cXFxAPTt25fu3bvTuHFjvLy8OH/+PKdPnyYwMBB3d3eaNm3KH3/88TS+NSIiIiIizwUVbhng+++/JyAgAAcHB9q0acPs2bOt53r06EF0dDQ//fQTW7Zs4ebNmwwZMoQRI0bg4eFB586dmTZtWpL+WrVqxW+//cbZs2eBu2vh1qxZQ6tWrTCbzXTr1g2j0cjmzZtZu3YtV65cYeDAgQAcPnyY69ev89ZbbyWL8+WXX+bbb7/F0dHReiwmJobdu3fTs2dPfv31V0qUKEHFihWTXevn50e/fv0A2LhxI/PnzycoKIiDBw+yePFidu3axdq1a63td+7cyaRJk/jpp58oUqQInTt3ply5cuzbt4/x48fzyy+/pDnfIiIiIiLZjQq3dAoPD2fnzp20bdsWgICAAE6dOsX+/fsJDw9n//799OnTBycnJ/LkycOoUaPo2rXrI/ssVqwYNWrUsK5/27VrFyaTCR8fH44dO8bx48cZNGgQefLkwcnJiT59+rB+/XqioqKIjIwEoGDBgimK38/PDzs7O/Lly0dkZGSKrqtTpw7Lli3jhRdeIDIykqioKBwdHbl8+bK1TaVKlXjppZfIly8foaGh/PPPP3z55ZfY29tTrlw5PvjggxTFJyIiIiIieh1Aui1cuJDExESaN29uPZaYmMjs2bPp0qULcLcQu8fFxQUXF5fH9vv2228zZswYevTowcqVK2nevDm2traEhYVhMpnw9vZO0t7Ozo4LFy5Y+46IiHjgermIiIgk9y9UqFCSr3fv3v3AeO7cuUN8fDx58+bFYrEwYcIEtm7dSoECBXjllVdISEjAYrEk6euey5cv4+TkhIODg/VYyZIlH5sDERERERG5SyNu6XDnzh2WLVvGiBEjWL16tfVPcHAw27Ztw2QyASTZKfLUqVNMnDjxsX3Xq1eP2NhYduzYwa+//kqrVq0AcHV1xcHBgZCQEA4ePMjBgwfZs2cPq1atonz58lSqVAlHR0c2bNiQrM8TJ05Qq1YtQkNDrccMBoP167p16xIWFsbRo0eTXbtkyRJ8fX2JjY3lm2++4eLFi/z6669s2rSJCRMmkDt37iTt7++3SJEiREZGEhMTYz126dKlx+ZARERERETuUuGWDmvXrsVgMNC0aVNcXV2tf+rUqcNLL73EqlWrqFmzJmPGjOHmzZtER0czduxY67vb7OzsuHXr1gP7trW1xc/PjyFDhvDqq69SpkwZAF5//XVKlSrFqFGjiImJIS4ujq+//pr27dtjMpmws7NjwIABTJ48mQULFhATE4PJZOLgwYP06NGDRo0a4e7u/sB7vvbaa7Ru3ZoePXqwY8cOEhMTuXPnDqtXr2b8+PF0796dnDlzEh0djb29PTY2Nty5c4fZs2dz8uRJEhISHtivu7s7pUuXZvjw4cTGxnLu3Lkk6wBFREREROTRVLilw8KFC2natCm2trbJzrVu3ZrVq1czZswY8uTJQ+PGjalXrx4FChRgyJAhwN31ZcuXL6dNmzYP7P/tt98mPDzcOtoGkCNHDqZPn87Vq1dp2LAhtWrV4vz588yZMwd7e3sAmjZtyuTJk9myZQv16tXDw8ODgQMH0rJlS8aNG/fIZxoyZAgfffQREydOpHr16tSsWZNFixYxevRoAgMDAfjss8+Ii4ujRo0a+Pr6cuTIEZo3b87Jkycf2KeNjQ3fffcdV65coUaNGnz00UfUq1fv8QkWEREREREADJb7FyaJPOPCRvTlzsk/MzsMEclgdqVepMTQCURFxZCYaM7scDKdwQAFC+bl6tVb6P/Sqaf8pZ1ylz7KX9pl19zde+6U0IibiIiIiIjIM06Fm4iIiIiIyDNOrwOQLMXOtTiWO3cyOwwRyWB2RUtkdggiIiLPNBVukqUU6vBJZocgIk+I2WTCbM5GCxtERERSQYWbZClRUTGPbyTJODnlVu7SQflLu9Tkzmy2qHATERF5CBVukqWYzWbM2nAuVe69C91kMmerXZoyivKXdsqdiIhIxknz5iQ3btzg2LFjmM1m4uPjMzImERERERERuU+qR9xiYmIYOHAg69evx8HBgRUrVvDBBx8wZ84cXnzxxScRo4iV0WjEqL1Q08TGRolLj4zKn6YDioiISFqkunAbM2YMt2/fZuPGjQQEBFCiRAl8fHwYMWIEs2bNehIxilg5OeXO7BCyLOUufTIqf2aTiajrsSreREREJFVSXbht3bqVtWvXkj9/fgwGA7a2tvTt25c6deo8ifhEkohc/B3x4ecyOwyRNLEtVBTnwI8xGg0q3ERERCRVUl24mc1m7OzsALD8b7X5/cdEnqSEiEskhJ3N7DBERERERJ6qVC/a8PLyYujQocTGxmL435ZhEydOpGrVqhkenGQ8k8nEhQsXMjsMERERERFJhVQXbv/5z384ffo0np6e3Lp1C3d3dw4cOECfPn0yJCA3Nzc6depkHc27Z8WKFfj6+mbIPR4kIiKCoUOH4uvrS8WKFalVqxaff/45J0+eTNJuw4YNVK9enSpVqrB161Z8fX2pUKEC7u7uuLu7U6lSJWrVqsXo0aMxZ+C+9YGBgQQFBaW7n549e7Jq1ar0BwQsWrSIRo0a4e7uTqNGjViwYMFD25pMJkaPHk2NGjVwd3ena9euXLlyJUPiEBERERF53qW6cLt9+zZLlixh4cKFjB8/ntmzZ7Ns2TIKFy6cYUFt376dmTNnZlh/jxMWFkaLFi2IiIhg+vTphIaGsnr1akqUKEFAQAC7d++2tl26dClNmjTh0KFD+Pj4ADBkyBBCQ0MJDQ3lyJEjzJo1i1WrVjF58uSn9gwpFRUVlSH9/PLLL4wfP57Ro0dz+PBhRo0axcSJE9m8efMD2wcHB7N7926WL1/Ozp07cXBwYMCAARkSi4iIiIjI8y7VhVvr1q2JiYnh9ddfp3Hjxri7u2NjY5OhQQUGBjJp0iQOHz78wPNhYWG4ubkRFhZmPRYUFERgYCBwd3SuTZs2jB49mqpVq+Ll5cX333/Pjz/+iI+PD1WqVGHgwIHWa0eOHEm5cuX49ttvKVeuHEajEWdnZz777DMCAwPp27cviYmJtGrVin379rF48WLq16//0Pjd3Nzw9PTkjz/+sD5P37598fHxoW7dukRHR/PXX3/RsWNHqlatSp06dRg8eDC3bt2y9rF06VLq1auHu7s7ffr0ITY21nqub9++9O3bN9k9Q0JCAIiMjKR37954enpSrVo1evbsyY0bN+jfvz8HDx5k+vTpdOnSxZo3b29vqlatSsuWLdmyZUuKvkeXL1+mY8eOVKpUCYPBgLu7O9WqVePAgQMPbL906VI6duxIkSJFyJMnD/3792fHjh2atikiIiIikgKpLtwcHR25fPnyk4jFqkGDBrRu3ZpevXpx/fr1NPVx6NAhChcuzL59++jevTsjR44kJCSEDRs2MHfuXJYtW8aBAweIj49n+/btNG/e3Lpm734BAQFcuXKF0NBQli1bhoeHB507d+aXX3554H0TEhIICQlh37591KxZ03p8z549LF68mDVr1pCQkMB7771H2bJl2bFjB8uXL+fMmTN8+eWXAOzdu5ehQ4cyfPhwDhw4QMWKFfn9999T/Ow9evQgOjqan376iS1btnDz5k2GDBnCiBEjrPFPmzaNffv2sWTJEpYuXUpISAhvv/02/fv3JyEh4bH3aNu2LZ06dbJ+vnbtGgcOHOC1115L1vbWrVtcunSJl156yXqsYMGC5M+fn7/++ivFzyUiIiIikl2lelfJcuXKERAQQKVKlShUqFCScyNHjsywwPr06UNoaCh9+/YlODg41dfnypWL999/H4PBQK1atTCZTHTo0IGcOXNSoUIFChUqRHh4OCVLliQhIYGCBQs+sJ97z/io9VhDhgzh66+/tn52dXXlgw8+oF27dtZjderUsU4nXbZsGba2tvTu3RsbGxscHBz46quvaNKkCREREaxZs4aGDRtSvXp1ANq0acPSpUtT9Nzh4eHs37+fTZs24eTkBMCoUaMeWADb29tz48YN60jk22+/TevWrR9YwD5KREQEnTt35rXXXuOtt95Kdj4mJga4+z25n4ODg/WciIiIiIg8XKoLt1y5ctGwYcMnEUsSdnZ2TJw4kRYtWjB79mxrEZJSjo6O1gLEaLw7sJgvXz7reaPRiNlsxsnJiRw5cnDx4sUH9nNvOqaLi8tD7zVo0CD8/f0fGc/9Re61a9coWrRokimmxYsXB+4WXpcvX+bVV19Ncn2JEiUe2f89ERERABQrVsx6zMXF5YHxu7u7ExQUxPfff8/MmTNxcHAgMDCQrl27WnP2OEeOHKFHjx54eHgwcuRIcuRI/iOVM2dOgCTTPQHi4uLInVsvhRYREREReZxUF24ZOar2OCVLlmTYsGF8+eWXSQqjewXP/VP6/r3pRkpHjezs7KhXrx4rVqygVatWyQqWpUuX4uLigru7e1ofI1k8xYoV4+LFi5hMJuuznD9/HrhbZLm6uiZb+3Xp0iXKlSsH3C0679y5Yz0XGRlp/bpIkSIAXLx4kRdeeAGAU6dOsW7dOj777LMkfV68eBFnZ2dmzZpFfHw8e/fu5ZNPPuHVV1+lbt26j32mZcuWMXz4cLp3786HH3740Hb58+encOHCnDp1yjpdMiIiguvXryeZPikiIiIiIg+W6jVukydPfuifJ+HNN9+kZcuWLFmyxHrM2dmZ/Pnzs379eiwWC8ePH2fTpk1pvkf//v25ePEi3bt35/Tp05jNZi5fvsz48eNZuHAhX3/9Nba2thnxOAB4e3sD8M033xAXF0dERAQjRozAy8uLYsWK0bJlS3755Re2bt1KYmIiK1eu5LfffrNeX6ZMGQ4ePMjly5eJi4tjypQp1sKwcOHC1KxZkzFjxnDz5k2io6MZO3astRC0s7OzboLy+++/89FHH3HixAns7OxwdnYGSNHo5ubNmxk8eDBBQUGPLNru8ff3Jzg4mAsXLhAdHc3XX39N1apVKVmyZOqSJyIiIiKSDaV6xO3ezoX3XL9+ndOnT/PGG29kWFD/1q9fP3777Tdu3rwJ3C0+hg0bxrfffsusWbN47bXXCAgI4NChQ2nqv3DhwqxatYrg4GA6derEtWvXyJMnD1WrVuXHH3/k5ZdfzsjHIW/evMyZM4dRo0ZZi7h69epZNyepUqUKY8aMYdSoUfTs2RMvL68kG520bt2a33//nWbNmmFnZ8f7779P0aJFree/+eYbRo0aRePGjUlMTMTX15f+/fsD4Ofnx+DBgzl27BgLFy7k7NmzdO3alaioKJydnenXrx8VK1Z87DNMnjwZk8lE9+7dkxxv2rQpQ4cOZc2aNQwaNIjQ0FAAPv74YxITE2nbti0xMTFUq1aNiRMnpiuPIiIiIiLZhcHy7zddp8Hq1asJCQlJskGHyJNwOWgo8f/VTpSSNdkWfwHXz0cQFRVDYqI5s8N54gwGKFgwL1ev3iL9/6fJXpS79FH+0k65Sx/lL+2ya+7uPXdKpHqq5IM0b948xe//EhERERERkdRJ9VTJB9m/f3+yrd4l69q8eXOyF3zfr0qVKsycOfMpRiQiIiIikr2lunDz9fVNskNiQkICV69epWvXrhkamGSeRo0a0ahRo8wO44FsXVyxxN95fEORZ5BtoaKPbyQiIiLyAKku3D799NMkn41GI2XKlOG1117LsKBEHqbAO50yOwSRdDGbTJjN2WjyvoiIiGSIVBdukZGRdOjQIdnxiRMnJntPmEhGi4qKyewQsiQnp9zKXTpkZP7MZosKNxEREUm1FBVukZGRnD59GoCgoCAqVqzI/ZtR3rp1i3nz5qlwkyfObDZjfv4348tQ92Y2m0zmbLVLU0ZR/kRERORZkKLCzc7Oju7duxMVFQVAu3btkp1v3bp1xkcn8i9GoxFjhuyFmv3Y2Dy7idMolIiIiMijpahwy5MnD3v37gXgjTfeYNOmTU80KJGHcXLKndkhZFnPcu4sJhOR12NVvImIiIg8RKrXuD2saIuMjKRAgQLpDkjkUaJWf0/ipfOZHYZkoBwFi+DU8kOMRoMKNxEREZGHSHXhdvToUcaMGcPly5cx/2+xUUJCApGRkRw7dizDAxS5n+naJRL/uZDZYYiIiIiIPFWpXvQydOhQXFxcqFWrFqVLl6Zdu3bY2Njw+eefP4n4REREREREsr1UF25///03I0eOpG3btphMJj744AMmTJjA2rVrU9XPxYsXGTRoEL6+vlSqVImqVavSoUMHdu/endqQMl1YWBhubm6EhYU9sXusWrWKdu3aUbVqVSpVqkSjRo0YO3Ys0dHRT+yeIiIiIiLybEh14ZYvXz4cHBwoUaIEf//9NwCVKlUiPDw8xX2cPHmSZs2aER8fz4wZMzh06BA//fQTzZo14+OPP2b79u2pDeu51r9/fyZMmEDbtm356aefOHjwIJMnT+bkyZN89NFHSV7NICIiIiIiz59UF24vvvgiixYtwt7enly5cvHnn39y+vRpDPdedpQCAwcOpGbNmowcOZIyZcpgY2ODo6MjzZs3Z9CgQSQkJACwZ88eWrVqhYeHB02aNGHNmjXWPsxmM9999x3169enSpUqtGrVip07d1rPR0VF0bNnT6pUqUK9evX4/vvvKV++PGFhYdYRslGjRuHp6cmQIUOIj49n9OjRNG7cGHd3d6pXr86wYcOsRVFgYCCjRo3C39+fSpUq4e/vz8GDB5M819q1a2ncuDGVKlWiffv2XL58GYDGjRszbdq0JG2bNm3KsmXLHpur3bt3s3LlSr777jsaN26Mo6MjOXLkoFy5cowZMwZ3d3du3boFQHR0NEOHDsXb25vq1avTs2dPrl69CvDAZw4KCqJHjx706dOHypUrU6dOHTZu3MiUKVOoUaMGVatWZerUqdZYDh8+zHvvvUetWrWoUKEC/v7+HDlyBICQkBB8fX0JDg6mdu3aVK1alU8//ZTo6Gju3LmDp6dnklHZ+Ph4qlWrZt2tVEREREREHi7VhVuPHj2YOHEi58+fp0OHDgQEBNCyZUtatGiRousvXbpEaGgo77zzzgPPt2jRgvr163PixAm6du1Kp06dCAkJYdiwYXz99dfW4mzKlCksWLCASZMmERISwocffki3bt04evQoAL179+bWrVts2bKFpUuXsnXrVkwmU5J7xcTEsHv3bnr27Mm8efPYuXMn8+bNIzQ0lKlTp7J48WL27dtnbb9kyRK+/PJL9u/fT4MGDejatav13XYAx48f58cff2T79u3cuHGDKVOmAODv78/q1aut7Y4dO0ZYWBiNGzd+bL42bNiAu7s7bm5uyc45OTnRp08f8uXLB0C/fv04d+4cK1as4JdffiFPnjx88sknSUbk7n9mgM2bN+Pj48OhQ4do1qwZn3/+OdHR0Wzfvp2vv/6aSZMmER4eTlxcHF27dqVRo0bs2LGDkJAQSpYsyZgxY6x9h4eHc/nyZX7++WeWLl1KaGgoCxcuxN7eniZNmiTJwdatW8mdOzdeXl6PzYGIiIiISHaX6l0lK1euzI4dO7Czs6NkyZK88sor3Lp1i5o1a6bo+kuXLgHg6upqPbZ3714+/fRTAEwmE4UKFaJ69erUq1ePhg0bWu8bEBDAggULqF27NsuXL6dTp068+uqrALz55pts3ryZZcuWUbhwYXbt2sXGjRtxdHQE7hY1TZo0SRKLn58fdnZ22NnZERAQQIsWLXB2dubKlSvExcWRO3du66gZQMuWLa2FRpcuXVi0aBFbt26latWq1mN58+YFoHbt2tYi0s/Pj4kTJ/L7779ToUIFVq1axRtvvEHu3I9/r9alS5eS5Arggw8+4PfffwfujlwNHTqU2rVrs3nzZjZu3Iizs7P1mT08PDh+/Lg1D/c/M0DZsmV54403AKhZsyYzZsygS5cu2Nra4uvrC9xdj+jq6sqSJUsoVaoUd+7cITw8HEdHR2sc93z88cc4ODhQqlQpqlWrxpkzZ6y5a926NREREbi4uLBy5Ur8/f1TNVIrIiIiIpJdpbpwAzAYDPzyyy+Eh4fTunVrzp07l+JrXVxcALh8+TKlS5cGoHr16tZphytWrGDy5MmEh4ezb98+PDw8rNeaTCZKliwJwNWrVylRokSSvosXL86JEyf4559/rJ/v+XdbgEKFClm/jo2NZejQoRw4cABXV1fKly+PxWKxvvIA4IUXXkiSA1dXVyIiIqzH7hVHALa2ttYRPhcXF2rXrs3q1at5+eWXWbduHUFBQSnI1t0YL1xIuv39nDlzrF/7+vpiNputawwDAgKStLWxsSEsLMwa2/3P/O+Yjca7A7D58+dP8tlsNmNjY0NISAgdO3bk9u3blC1blhw5ciRbX3fv+3svB/fOV6hQgTJlyrB+/XqaNm3Krl27GDBgQIpyICIiIiKS3aW6cDt//jwffvghCQkJ3Lx5E29vb1q2bMnkyZPx8fF57PXFihWjQoUKLF269JHT5FxdXWnRogVDhw61Hrty5Yq1EChWrFiygubChQsUKlSIokWLAnen7t0rDh+0ecr9oz0DBgwgf/787Nq1C3t7e8xmM56enkna3z/6ZjabuXjxIkWKFHnsM8PdEachQ4ZQs2ZN8ubNm6zvh2nUqBHdunXj9OnTlClT5qHtChcuDMDGjRuTFE+nTp2iRIkS1gLz3yNcKR3x+u233xg2bBiLFy/mtddeA2D27NnWEbWUaNmyJevXr8fW1hYPD48khbWIiIiIiDxcqte4jRgxAn9/f7Zt20aOHDkoXbo0w4cP59tvv01xH/fWqn311VecOXMGi8VCdHQ0q1atIigoiEKFCtGqVSvWrVvHrl27MJvNnD17lnbt2jF79mwA3n77bb777juOHz+OyWRi48aN/Prrr7Ro0YJChQrh4+PD2LFjuXHjBjdu3EiyFutBoqOjsbe3x2g0Eh0dzZgxY4iOjrZulAKwdOlSjh07Rnx8PFOmTMFisaSoWAWoW7cuJpOJb7/9Fn9//xTnqm7duvj7+9OhQwc2bdpEbGwsFouFkydP8p///IdLly7h7OxM4cKFqVu3LiNGjCAqKoqEhASCg4Np1aoVN2/eTPH9HubWrVsYjUYcHBwAOHLkCPPnzyc+Pj7FfTRr1owTJ06wdOnSVOVARERERCS7S3XhduTIET766CMMBoN1tKZ58+bJRr8e5aWXXmLdunU4ODjQpUsXqlSpgre3Nz/++CMfffQR8+fPp2LFiowfP57x48fj6elJu3bt8PX1tb7o+4MPPqBt27b07NkTDw8Ppk+fzvjx463rzUaMGIHBYKBu3bq0aNGC8uXLA3en7z3IgAEDOHHiBFWrVuWNN94gOjqa2rVrc/LkSWubqlWrMnToULy8vAgJCWH27NnWNW2PY2tray1cUrqRyz3Dhw+nT58+/Pjjj/j4+ODu7k6XLl0wGo2sWLECb29vAMaMGUO+fPnw8/PDy8uL7du3M3PmzCQjcGlVs2ZN2rRpQ9u2ba27UgYGBhIZGWndufJxChQogLe3N2FhYda1iyIiIiIi8ngGSypfAla/fn3mz59P0aJFqVq1Kvv37+fy5cu0adOGLVu2PKk4U2337t1UqVLFOkL0119/4efnx5EjR7C3t091f4GBgdYt7tNq/vz57Nixg5kzZ6a5j6xu5MiRxMXFMWTIkDRdf3X2WBLOn87gqCQz5ShSApfO/YmKiiEx0fz4C54ygwEKFszL1au30CsTU0e5SzvlLn2Uv7RT7tJH+Uu77Jq7e8+dEqkecWvatCmffPIJu3fvxmw2c/ToUXr37p1sx8bMNnr0aIKDg0lMTCQ6Oprg4GBq1KiRpqItvSIiIjh69Cjz5s3j3Xfffer3fxb8888/7N27l1WrVj30VRAiIiIiIvJgqd6cpFu3bsTFxfHJJ58QGxvLe++9R6tWrfjkk0+eRHxpNm7cOIYPH46XlxdGo5HatWs/dp3bk7Jt2zaGDx9O8+bNqVevnvX4nDlzHrk2sGnTpkk2Z8nKfvzxR+bOnctHH33EK6+8ktnhiIiIiIhkKSmeKtmhQwdmzZpl/RwbG0tsbCxOTk56F5c8NVGrvyfx0vnMDkMyUI6CRXBq+aGmSj6HlLu0U+7SR/lLO+UufZS/tMuuuUvNVMkUj7iFhoYm+ezt7c3+/ftTF5lIOjk1D8zsEOQJsJhMmM3Z6L/SIiIiIqmUphdwA8levCzyNERFxWR2CFmSk1PuZzp3ZrNFhZuIiIjII6S5cNP0SMkMZrMZ87M3m+6Zdu9fVZPJnK2mHoiIiIg8T9JcuIlkBqPRiDHVe6EKgI3N00ucRtBEREREMlaKC7fExERWrVpl/ZyQkJDkM4Cfn18GhSXyYE5OuTM7hCzraebOYjYTGXVbxZuIiIhIBklx4VawYMEkW9c7OTkl+WwwGFS4yRN34+dlmK5czOww5BFsChQif+N3MBoNKtxEREREMkiKC7dff/31ScYhkiKmyKskRqhwExEREZHsRauF5IkxmUxcuHAhs8MQEREREcnysnXh5ubmRqdOnZK92mDFihX4+vo+sftGREQwdOhQfH19qVixIrVq1eLzzz/n5MmTSdpt2LCB6tWrU6VKFbZu3Yqvry8VKlTA3d0dd3d3KlWqRK1atRg9ejTmDNxqMTAwkKCgoHT307Nnz2TrIEVEREREJPWydeEGsH37dmbOnPnU7hcWFkaLFi2IiIhg+vTphIaGsnr1akqUKEFAQAC7d++2tl26dClNmjTh0KFD+Pj4ADBkyBBCQ0MJDQ3lyJEjzJo1i1WrVjF58uSn9gwpFRUVldkhiIiIiIg8F7J94RYYGMikSZM4fPjwA8+HhYXh5uZGWFiY9VhQUBCBgYHA3dG5Nm3aMHr0aKpWrYqXlxfff/89P/74Iz4+PlSpUoWBAwdarx05ciTlypXj22+/pVy5chiNRpydnfnss88IDAykb9++JCYm0qpVK/bt28fixYupX7/+Q+N3c3PD09OTP/74w/o8ffv2xcfHh7p16xIdHc1ff/1Fx44dqVq1KnXq1GHw4MHcunXL2sfSpUupV68e7u7u9OnTh9jYWOu5vn370rdv32T3DAkJASAyMpLevXvj6elJtWrV6NmzJzdu3KB///4cPHiQ6dOn06VLF2vevL29qVq1Ki1btmTLli0p+h6JiIiIiGR32b5wa9CgAa1bt6ZXr15cv349TX0cOnSIwoULs2/fPrp3787IkSMJCQlhw4YNzJ07l2XLlnHgwAHi4+PZvn07zZs3f+ALzAMCArhy5QqhoaEsW7YMDw8POnfuzC+//PLA+yYkJBASEsK+ffuoWbOm9fiePXtYvHgxa9asISEhgffee4+yZcuyY8cOli9fzpkzZ/jyyy8B2Lt3L0OHDmX48OEcOHCAihUr8vvvv6f42Xv06EF0dDQ//fQTW7Zs4ebNmwwZMoQRI0ZY4582bRr79u1jyZIlLF26lJCQEN5++2369+9PQkJCKrMtIiIiIpL96AXcQJ8+fQgNDaVv374EBwen+vpcuXLx/vvvYzAYqFWrFiaTiQ4dOpAzZ04qVKhAoUKFCA8Pp2TJkiQkJFCwYMEH9lOoUCEArly58tB7DRkyhK+//tr62dXVlQ8++IB27dpZj9WpU4fChQsDsGzZMmxtbenduzc2NjY4ODjw1Vdf0aRJEyIiIlizZg0NGzakevXqALRp04alS5em6LnDw8PZv38/mzZtwsnJCYBRo0Y9sAC2t7fnxo0b1pHIt99+m9atWz+wgBURERERkaRUuAF2dnZMnDiRFi1aMHv2bGsRklKOjo7WAsRovDuImS9fPut5o9GI2WzGycmJHDlycPHig7ezvzcd08XF5aH3GjRoEP7+/o+M514BCHDt2jWKFi2KjY2N9Vjx4sWBu4XX5cuXefXVV5NcX6JEiUf2f09ERAQAxYoVsx5zcXF5YPzu7u4EBQXx/fffM3PmTBwcHAgMDKRr167WnImIiIiIyIPpN+b/KVmyJMOGDWPChAkcOXLEevxewXP/lL5/b7qR0lEjOzs76tWrx4oVKx64C+TSpUtxcXHB3d09DU/w4HiKFSvGxYsXMZlM1mPnz58H7hZZrq6uybbsv3TpkvVro9GY5NkjIyOtXxcpUgQgSSF66tQpJk6cmCymixcv4uzszKxZs9i/fz+jR49m2rRp7NixI41PKSIiIiKSfahwu8+bb75Jy5YtWbJkifWYs7Mz+fPnZ/369VgsFo4fP86mTZvSfI/+/ftz8eJFunfvzunTpzGbzVy+fJnx48ezcOFCvv76a2xtbTPicQDw9vYG4JtvviEuLo6IiAhGjBiBl5cXxYoVo2XLlvzyyy9s3bqVxMREVq5cyW+//Wa9vkyZMhw8eJDLly8TFxfHlClTrIVh4cKFqVmzJmPGjOHmzZtER0czduxYayFoZ2dn3QTl999/56OPPuLEiRPY2dnh7OwMkOrRTRERERGR7EiF27/069ePV155xfrZzs6OYcOGsXHjRipXrsyoUaMICAhIc/+FCxdm1apVFClShE6dOlG5cmVatmxJWFgYP/74I3Xq1MmIx7DKmzcvc+bM4eTJk3h7e/PWW29RrFgxJk2aBECVKlUYM2YMo0aNwsPDg82bNyfZ6KR169a4u7vTrFkzGjRoQJEiRShatKj1/DfffEOePHlo3Lgx9erVo0CBAgwZMgQAPz8/li9fTps2bWjUqBEffvghXbt2pVKlSvTo0YN+/fpRsWLFDH1eEREREZHnkcHy77dPizzDIpdMI/Gfs5kdhjxCDpeiFGjbnaioGBITM+7F8JnFYICCBfNy9eot9F/L1FHu0k65Sx/lL+2Uu/RR/tIuu+bu3nOnhEbcREREREREnnEq3ERERERERJ5xeh2AZCk2BQpCYnxmhyGPYFOg0OMbiYiIiEiqqHCTLCV/g1aZHYKkgMVsxmzORhPURURERJ4wFW6SpURFxWR2CFmSk1Pup5o7s9miwk1EREQkA6lwkyzFbDbzgHeXyyPcex+7yWTOVrs0iYiIiDxPtDmJiIiIiIjIM04jbpKlGI1GjPrrhjSxscnYxGk6pIiIiMjTo8JNshQnp9yZHUKWldG5s5jNREbdVvEmIiIi8hSocJMs5dbO9SRGXsrsMLK9HPkLkrduc4xGgwo3ERERkadAhZtkKYk3r2G6djmzwxAREREReaq0WkhEREREROQZp8JNRERERETkGafCLZ0uXrzIoEGD8PX1pVKlSlStWpUOHTqwe/fuJ3K/kJAQ3NzcnkjfAL6+vri5ubFz585k53766Sfc3Nzo27dvivoKDAwkKCgoo0MUEREREcl2VLilw8mTJ2nWrBnx8fHMmDGDQ4cO8dNPP9GsWTM+/vhjtm/fntkhpomTkxMrV65Mdnz58uXkyZMnEyISEREREcnetDlJOgwcOJCaNWsycuRI6zFHR0eaN2+O2WwmISEBgD179jB+/HjOnj1L4cKF6dy5M82aNQOgb9++2NnZceXKFUJCQihQoADvv/8+7733HgBXrlxh4MCB7N+/HycnJ5o0aZIkhvPnz/P1118TGhpKrly5rEWjnZ0dK1as4IcffsDR0ZGjR48yaNAgmjZt+tjnatq0KUuWLOHWrVvkzZvXGseRI0eoXbu2tV18fDwTJkxg27ZtXLp0CQcHB958800GDBiAwWBI0qfFYuH7779nwYIFXLt2jZdeeol+/frx2muvpSHzIiIiIiLZi0bc0ujSpUuEhobyzjvvPPB8ixYtqF+/PidOnKBr16506tSJkJAQhg0bxtdff51kKuKKFSsIDAzkwIEDdOzYkVGjRnH58t2dE3v27EmOHDnYsWMHP/zwAzt27LBed/v2bdq3b0+5cuXYsWMHCxcuZM+ePUmmJx4/fpymTZuyZ88eGjRokKJne+WVVyhdujQbNmywHlu1ahWNGzfGwcHBemzevHns3LmTefPmERoaytSpU1m8eDH79u1L1ufChQuZM2cOkyZNYu/evfj7+/PBBx9w9erVFMUkIiIiIpKdqXBLo0uX7r5LzNXV1Xps7969eHh44OHhgbu7O40aNWLx4sXUq1ePhg0bYmNjQ+XKlQkICGDBggXW66pVq0bNmjXJkSMHLVu2xGQycf78ecLDwzl48CC9e/cmT548FClShE8++cR63bZt24iPj6dXr17Y29tTpEgRevTokaRvW1tbmjdvjp2dXZKi63H8/f2TTJdcvnw5LVu2TNImICCAuXPn4uLiwpUrV4iLiyN37tzWovN+CxYsoHPnzrz88svY2trSqlUrypQpw5o1a1Ick4iIiIhIdqWpkmnk4uICwOXLlyldujQA1atX5+DBg8DdUbTJkycTHh7Ovn378PDwsF5rMpkoWbJksr7gbqEFYDabrQVQ0aJFrefvvy48PJzIyEg8PT2txywWCwkJCVy7ds3at9GY+vq8adOmjB07ljNnznDt2jXs7e2pUKFCkjaxsbEMHTqUAwcO4OrqSvny5bFYLJjN5mT9hYeHM3r0aL755hvrscTERE2VFBERERFJARVuaVSsWDEqVKjA0qVL8fLyemg7V1dXWrRowdChQ63Hrly5gsVieew97o3mXbhwgTJlygD/P9J373zJkiXZtGmT9Vh0dDTXrl2jQIECAMnWmqVUgQIFqFu3LqtWreLKlSu0atUqWZsBAwaQP39+du3ahb29PWazOUkR+e9n6d69e5I1eufPn8fR0TFN8YmIiIiIZCeaKpkO99aqffXVV5w5cwaLxUJ0dDSrVq0iKCiIQoUK0apVK9atW8euXbswm82cPXuWdu3aMXv27Mf2X7RoUWrVqsXIkSO5ceMGERERTJ482Xrex8eHmJgYZs6cSXx8PDdv3qRPnz707NkzzQXb/fz9/Vm7di1bt2594KYm0dHR2NvbYzQaiY6OZsyYMURHR1s3ZblfQEAAwcHBnD59GoCdO3fSpEkTDhw4kO44RURERESedxpxS4eXXnqJdevWMWPGDLp06UJERAQGgwE3Nzc++ugj3n77bezs7Bg/fjzjx4+nR48e5MyZk7feeotevXql6B7jxo1jyJAh+Pj4kCdPHvz9/fntt98AyJMnD3PnzmXUqFHMnDkTs9lMtWrVCA4OzpDnq1OnDnfu3MHLywsnJ6dk5wcMGMDAgQOpWrUquXPnpm7dutSuXZuTJ08ma9u+fXssFgvdunXjypUrFC5cmIEDB1KvXr0MiVVERERE5HlmsKRkzp7IMyJq/XxMl8MyO4xsz8a5ME7NOxAVFUNiYvI1jc8TgwEKFszL1au30H8tU0e5SzvlLn2Uv7RT7tJH+Uu77Jq7e8+dEpoqKSIiIiIi8ozTVMlsxN/fnzNnzjz0/IwZM5LsfvksypHPGRKTr6GTpytH/oKZHYKIiIhItqLCLRtZsWJFZoeQbnlrN3l8I3kqLGYzZnM2mssgIiIikolUuEmWEhUVk9khZElOTrkzPHdms0WFm4iIiMhTosJNshSz2cwD3u8tj3DvzRAmkzlbLfYVEREReZ6ocJMsxWg0YswmW+poREtERERE7lHhJlmKk1PuzA7hqbGYzURG3VbxJiIiIiIq3CRriTm0DdONq5kdxhNnk9eJ3J71MBoNKtxERERERIWbZC2m6OuYrj//hZuIiIiIyP2yyWohERERERGRrEsjbs+xgQMHsnbtWgASExNJSEggZ86c1vNZ4YXbIiIiIiKiwu25NnToUIYOHQrcffn25MmT+fXXXzM5KhERERERSS1Nlcymzp8/T5cuXahWrRo+Pj5MmDCB+Ph44G6R9+677zJ8+HC8vLyoXr06/fv3JyEhAYC+ffvSt2/fJP25ubkREhICgK+vLwMHDqRmzZr4+flhNps5fvw4gYGBeHp60rBhQ+bOnYtFLxUTEREREUkRFW7Z0O3bt2nfvj3lypVjx44dLFy4kD179hAUFGRtc/jwYZydndm5cyfTp09nw4YN/PTTTym+x9GjR9m4cSPz588nIiKC999/nzfeeIM9e/YwdepUFi5cyJIlS57E44mIiIiIPHdUuGVD27ZtIz4+nl69emFvb0+RIkXo0aMHCxYssLZxcHCgS5cu2Nra8vrrr+Pm5saZM2dSfI9GjRqRL18+8uXLx5o1ayhTpgxt27bF1taWsmXL0qFDhyT3ExERERGRh9Mat2woPDycyMhIPD09rccsFgsJCQlcu3YNAGdnZwwGg/W8ra1tqqY2FipUKMn9jh8/nmQjFLPZjI2NTXoeQ0REREQk21Dhlg25urpSsmRJNm3aZD0WHR3NtWvXKFCgwGOvNxqN3Llzx/o5MjIyWZv7iz5XV1eqVavGrFmzrMeioqKIiYlJ6yOIiIiIiGQrmiqZDfn4+BATE8PMmTOJj4/n5s2b9OnTh549eyYpuB6mTJkyHDx4kMuXLxMXF8eUKVMeeV3Tpk05cuQIa9asITExkStXrtClSxdGjRqVkY8lIiIiIvLcUuGWDeXJk4e5c+cSEhJCnTp1qF+/PkajkeDg4BRd37p1a9zd3WnWrBkNGjSgSJEiFC1a9KHtixUrxsyZM1myZAk1atSgefPmvPjiiyrcRERERERSyGDRnuyShdzcvgrTtUuZHcYTZ+NYkHy+rYiKiiEx0ZyuvgwGKFgwL1ev3kL/tqee8pd2yl3aKXfpo/ylnXKXPspf2mXX3N177pTQiJuIiIiIiMgzToWbiIiIiIjIM067SkqWYpPHEUyJmR3GE2eT1ymzQxARERGRZ4gKN8lSclepm9khPDUWsxmzORtN8hYRERGRh1LhJllKVFT2efeb2WxR4SYiIiIigAo3yWLMZjPm9G2yKCIiIiKS5WhzEhERERERkWecRtwkSzEajRjT8dcNmn4oIiIiIlmRCjfJUpyccqfreovZTGTUbRVvIiIiIpKlqHCTLOX2HwcwRUel6VqbXPnI9ZoXRqNBhZuIiIiIZCkq3CRLMd2+iflW2go3EREREZGsSpuTiIiIiIiIPOOeu8Lt4sWLDBo0CF9fXypVqkTVqlXp0KEDu3fvzuzQUi0sLAw3NzfCwsKeSP9btmzhnXfeoXLlylSuXBl/f39WrlyZYf27u7tz8ODBDOtPRERERCS7eq6mSp48eZI2bdrQoEEDZsyYwQsvvMCtW7fYvn07H3/8MZMmTcLb2zuzw3wmHDx4kN69ezNx4kRq1aoFwK5du+jZsydGo5HmzZun+x6hoaHp7kNERERERJ6zEbeBAwdSs2ZNRo4cSZkyZbCxscHR0ZHmzZszaNAgEhISANizZw+tWrXCw8ODJk2asGbNGmsfZrOZ7777jvr161OlShVatWrFzp07reejoqLo2bMnVapUoV69enz//feUL1+esLAw6wjZqFGj8PT0ZMiQIcTHxzN69GgaN26Mu7s71atXZ9iwYVgsdzfHCAwMZNSoUfj7+1OpUiX8/f2TjVKtXbuWxo0bU6lSJdq3b8/ly5cBaNy4MdOmTUvStmnTpixbtuyxuQoNDcXV1ZU6depgY2ODjY0N3t7efP7559Y8rVixgoCAAAYOHEjlypWpVasWU6dOtcZ++fJlPvvsM3x9falYsSL16tVLcm83NzdCQkIA8PX1Zfr06fj5+eHu7o6fnx/79u1L2TdWRERERCSbe24Kt0uXLhEaGso777zzwPMtWrSgfv36nDhxgq5du9KpUydCQkIYNmwYX3/9tbU4mzJlCgsWLGDSpEmEhITw4Ycf0q1bN44ePQpA7969uXXrFlu2bGHp0qVs3boVk8mU5F4xMTHs3r2bnj17Mm/ePHbu3Mm8efMIDQ1l6tSpLF68OEnRsmTJEr788kv2799PgwYN6Nq1K1FR/78Bx/Hjx/nxxx/Zvn07N27cYMqUKQD4+/uzevVqa7tjx44RFhZG48aNH5svHx8fLl26xLvvvsvs2bM5dOgQd+7coW3btrRq1cra7rfffiNnzpzs3buX4OBg5s2bZy3OBgwYgK2tLevXr+fw4cO0a9eOYcOGERMT88B7Ll++nEmTJrFnzx5efvllBg8e/Ng4RURERETkOSvcAFxdXa3H9u7di4eHBx4eHri7u9OoUSMWL15MvXr1aNiwITY2NlSuXJmAgAAWLFgA3C0uOnXqxKuvvkqOHDl488038fX1ZdmyZVy+fJldu3bRr18/HB0dKVCgAP369UsWi5+fH3Z2duTLl4+AgADmzp2Li4sLV65cIS4ujty5c1tHzQBatmyJl5cXdnZ2dOnShZw5c7J161br+S5dupA3b17y589P7dq1OX/+vPU+58+f5/fffwdg1apVvPHGG+TO/fh3nZUtW5Y1a9ZQqVIlVqxYQdu2bfHw8KBXr15ERERY2zk6OtK7d2/s7e2pUKECrVu3to5QDh8+nEGDBmFra8vFixfJnTs3cXFx3Lhx44H3bNWqFaVKlSJnzpw0bdqUs2fPPjZOERERERF5jta4ubi4AHen75UuXRqA6tWrW6cdrlixgsmTJxMeHs6+ffvw8PCwXmsymShZsiQAV69epUSJEkn6Ll68OCdOnOCff/6xfr7n320BChUqZP06NjaWoUOHcuDAAVxdXSlfvjwWiwWz2Wxt88ILL1i/NhgMuLq6Jiue7rG1tbWO8Lm4uFC7dm1Wr17Nyy+/zLp16wgKCkpBtv4/9r59+wJw69Yt9u/fz4QJE+jRowcLFy4EoFixYtja2lqvKVKkCJs3bwbgwoULjBkzhrNnz/LCCy9QqlQpgCTPdr+CBQtav86RI4d1yqWIiIiIiDzac1O4FStWjAoVKrB06VK8vLwe2s7V1ZUWLVowdOhQ67ErV65Yi4hixYpx4cKFJNdcuHCBQoUK/V97dx5XdbXvf/y1NzI4JXgFUUsbsaxUFJFETXDKDAccKoeTZiZWaqileUqPWo5JJRY382LicCzNNC3Lc5wHxDTNq+ei2c2DYiICKhvTDez1+8Pr/kUOIKZs4P18PHw8Nmt9h/V9nwXn8Wl9v99N7dq1AUhNTXUWh6mpqVecw2KxOD+/+eabVKtWjW3btuHp6YnD4aBZs2YFtv/96pvD4eDEiRPUqlWrSNfdo0cPJk6cSGhoKFWrVr3i2NfSt29fGjZsyJgxYwCoWrUqbdu2xWKxMGrUKOd2l7O5fE3Hjx+ndu3a5ObmMmTIEEaOHEmfPn2wWCwcOHCgwPOCIiIiIiLy5ygzt0oCzmfV3nrrLX755ReMMdhsNlauXElsbCx+fn707NmTNWvWsG3bNhwOB0ePHqVfv37Ex8cD0KtXL+bOncvBgwfJz89n7dq1bNiwge7du+Pn50dYWBgzZ87k7NmznD17lhkzZlx3TDabDU9PT6xWKzabjRkzZmCz2ZwvAAFYtmwZBw4cwG638+GHH2KMISwsrEjX3KZNG/Lz85k9ezaRkZFFzqpLly4sXbqUVatWkZmZicPh4JdffmHhwoV06NDBuV16ejpz584lNzeX/fv3s2zZMnr16kVubi4XLlzAy8sLi8XCiRMnmDlzJkCBaxMRERERkZtXZlbcAAICAlizZg2ffPIJUVFRpKenY7FYqF+/Pi+88AK9evXCw8ODmJgYYmJiGDFiBBUrVuSpp55i5MiRAAwcOBCHw0F0dDTp6enUq1ePmJgYgoODAXjnnXcYP348bdq0wcfHh27durFx40bc3d2vWrC8+eabjB8/nuDgYCpXrkybNm1o1aoVhw8fdm4THBzMpEmTOHLkCA0aNCA+Pp6qVate81mx33N3d6dLly4kJCQQFxdX5KyefvppqlSpwqJFi5g0aRJ5eXnUrFmTp556iqioKOd2vr6+HD9+nJYtW1K5cmVGjBjBk08+CVwqlD/44APefvtt/uM//oPevXtz5MgRDh8+7FyRFBERERGRm2cxetDohmzfvp2mTZvi5eUFwKFDh+jWrRv79u3D09Pzho/Xv39/goODGTZsWLHHlJCQwJYtW5g3b16xj3E1l58L3LBhw5963JuRvXs9jrOni7WvtaoPVYM7kJWVQ17e1Z/DK4ssFqhRoyqnT2ej3/Ybp/yKT9kVn7K7Ocqv+JTdzVF+xVdes7t83UVRpm6VvB2mT59OXFwceXl52Gw24uLiaNGiRbGKtpuVnp7O/v37WbBgAc8+++xtP7+IiIiIiNweZepWydth1qxZvP3224SEhGC1WmnVqlWhz7ndKps2beLtt9+ma9eutG3b1tk+f/58Zs+efc39IiIiCrycRUREREREXJtulZRS5fy/viffllX4hlfhVukOKj0Solsl5YYov+JTdsWn7G6O8is+ZXdzlF/xldfsbuRWSa24SalSqUHRvu7gWozDgcNRjv4aiIiIiEiZoMJNSpWsrJyb2t/hMCrcRERERKTUUeEmpYrD4cBRfu5yFBEREREBVLhJKWO1WrEW812oWm0TERERkdJKhZuUKj4+lYu9r3E4yMw6r+JNREREREodFW5Sqlz45V84fsu+4f2sXpXxuvcRrFaLCjcRERERKXVUuEmp4rh4Hsf5Gy/cRERERERKs2I+LSQiIiIiIiK3iwq3EnTixAkmTJhAeHg4jRs3Jjg4mEGDBrF9+/aSHtoNO378OPXr1+f48eMlPRQRERERkTJHhVsJOXz4MF26dMFut/PJJ5+wZ88e1q1bR5cuXXj55ZfZvHlzSQ9RRERERERchAq3EjJ+/HhCQ0OZOnUq9913H25ubnh7e9O1a1cmTJhAbm4uADt27KBnz54EBQXRuXNnvvrqK+cxHA4Hc+fOpV27djRt2pSePXuydetWZ39WVhbR0dE0bdqUtm3bsnDhQho0aMDx48edK2TTpk2jWbNmTJw4EbvdzvTp0+nUqROBgYE89thjTJ48GWMuvcyjf//+TJs2jcjISBo3bkxkZCS7d+8ucF2rV6+mU6dONG7cmAEDBpCWlgZAp06d+M///M8C20ZERLB8+fJbkq+IiIiISFmiwq0EnDx5kr179/LMM89ctb979+60a9eO5ORkhg4dyosvvkhSUhKTJ09mypQpzuLsww8/ZPHixXzwwQckJSXx/PPP89JLL7F//34ARo8eTXZ2NuvXr2fZsmVs3LiR/Pz8AufKyclh+/btREdHs2DBArZu3cqCBQvYu3cvH330EUuXLmXnzp3O7T/77DNef/11du3aRfv27Rk6dChZWVnO/oMHD/L555+zefNmzp49y4cffghAZGQkq1atcm534MABjh8/TqdOnf6cUEVEREREyjAVbiXg5MmTAPj7+zvbEhMTCQoKIigoiMDAQDp27MjSpUtp27YtHTp0wM3NjSZNmtC7d28WL14MwBdffMGLL77Iww8/TIUKFXjyyScJDw9n+fLlpKWlsW3bNsaNG4e3tzfVq1dn3LhxV4ylW7dueHh4cMcdd9C7d28+/fRTfH19OXXqFBcuXKBy5crOVTOAHj16EBISgoeHB1FRUVSsWJGNGzc6+6OioqhatSrVqlWjVatWpKSkOM+TkpLCf//3fwOwcuVKnnjiCSpXLv73somIiIiIlBf6OoAS4OvrC0BaWhr33HMPAI899pjztsMVK1YwZ84cUlNT2blzJ0FBQc598/PzqVu3LgCnT5/mrrvuKnDsO++8k+TkZH799Vfnz5f9cVsAPz8/5+fffvuNSZMm8f333+Pv70+DBg0wxuBwOJzb3H333c7PFosFf39/0tPTnW3e3t7Oz+7u7s4VPl9fX1q1asWqVat48MEHWbNmDbGxsUVIS0REREREVLiVgDp16vDoo4+ybNkyQkJCrrmdv78/3bt3Z9KkSc62U6dOOZ85q1OnDseOHSuwz7Fjx/Dz86N27doApKamOovD1NTUK85hsVicn998802qVavGtm3b8PT0xOFw0KxZswLb/371zeFwcOLECWrVqlWk6+7RowcTJ04kNDSUqlWrXnFsERERERG5Ot0qWUIuP6v21ltv8csvv2CMwWazsXLlSmJjY/Hz86Nnz56sWbOGbdu24XA4OHr0KP369SM+Ph6AXr16MXfuXA4ePEh+fj5r165lw4YNdO/eHT8/P8LCwpg5cyZnz57l7NmzzJgx47pjstlseHp6YrVasdlszJgxA5vN5nxRCsCyZcs4cOAAdrudDz/8EGMMYWFhRbrmNm3akJ+fz+zZs4mMjCx+eCIiIiIi5YwKtxISEBDAmjVr8PLyIioqiqZNm/L444/z+eef88ILL5CQkECjRo2IiYkhJiaGZs2a0a9fP8LDwxk1ahQAAwcOpG/fvkRHRxMUFMTHH39MTEwMwcHBALzzzjtYLBbatGlD9+7dadCgAXDpFsarefPNN0lOTiY4OJgnnngCm81Gq1atOHz4sHOb4OBgJk2aREhICElJScTHx1O1atUiXbO7uztdunQhOTmZ7t2730x8IiIiIiLlisVcvu9Oypzt27fTtGlTvLy8ADh06BDdunVj3759eHp63vDx+vfvT3BwMMOGDSv2mBISEtiyZQvz5s0r1v7nk3fjsJ254f2slapSqUFzsrJyyMtzFL5DGWKxQI0aVTl9Ohv9tt845Vd8yq74lN3NUX7Fp+xujvIrvvKa3eXrLgqtuJVh06dPJy4ujry8PGw2G3FxcbRo0aJYRdvNSk9PZ//+/SxYsIBnn332tp9fRERERKQ0U+FWhs2aNYt9+/YREhJCeHg4bm5uhT7ndqts2rSJ/v37ExoaStu2bUtkDCIiIiIipZVulZRS5cIv/8LxW/YN72f1qozXvY/oVkn9tt8w5Vd8yq74lN3NUX7Fp+xujvIrvvKa3Y3cKqmvA5BSxeueBsXe1zgcOBzl6C+BiIiIiJQZKtykVMnKyin2vg6HUeEmIiIiIqWSCjcpVRwOB47ydaejiIiIiIgKNyldrFYr1v97pY5W0ERERESkvFDhJqWKj09l52fjMGRm5ah4ExEREZEyT4WblCr2k0dxXPwNq4cXHrXuwWq1qHATERERkTJPhZuUKg77RczF39BjbiIiIiJSnugLuEVERERERFycCjcXtXjxYurXr8+nn376px97z549DBs2jJYtW9KoUSNatWrF66+/ztGjR//0c4mIiIiIyM1T4eaiFi9ezLPPPktCQgJ5eXl/2nG//PJLBg0aRMOGDVm+fDn79u1j2bJl+Pn58fTTT5OWlvannUtERERERP4cKtxcUGJiIhkZGYwdOxaHw8F3333n7MvKyiI6OpqmTZvStm1bFi5cSIMGDTh+/DgAKSkpREVF0bx5c8LCwnjvvfew2+0AnDt3jokTJ/L6668zePBg/P39sVgs+Pv7M3r0aPr160dWVhYAY8eOZfjw4XTq1ImQkBBSUlJITU3l1Vdf5bHHHiM0NJRRo0Zx6tQpAJKSkqhfv36B6xg7dixjx44FIDY2lpdeeolhw4bRuHFjwsPD+eyzz255liIiIiIiZYEKNxe0cOFCevfujZeXF3369CE+Pt7ZN3r0aLKzs1m/fj3Lli1j48aN5OfnA3D+/HkGDBjAAw88wJYtW1iyZAk7duwgNjYWgI0bN5KXl0dkZORVzzts2DAefPBB589bt27lgw8+YN26ddSqVYvnn38eNzc31q1bx9q1awGIiooq8org+vXradKkCd9//z2TJk1i8uTJJCYmFisjEREREZHyRIWbi0lNTWXr1q307dsXgN69e3PkyBF27dpFWloa27ZtY9y4cXh7e1O9enXGjRvn3HfTpk3Y7XZGjhyJp6cntWrVYsSIESxevBiAtLQ0vL298fLycu4zZ84cgoKCCAoKonHjxrz55pvOvsaNGxMQEMAdd9zB7t27OXbsGBMnTqRq1arccccdTJw4keTkZA4cOFCka6tfvz4DBw7E3d2dli1b0rFjR1atWvVnxCYiIiIiUqbp6wBczJIlS8jLy6Nr167Otry8POLj44mKigLgzjvvdPbdddddzs+pqalkZmbSrFkzZ5sxhtzcXDIyMvD19eXMmTPY7XY8PDwAeOWVV3jllVeAS7c2/n71zM/Pz/k5IyMDHx8fqlSp4myrUqUK3t7epKamUqNGjUKv7e677y7wc61atfif//mfQvcTERERESnvVLi5kIsXL7J8+XLeeecdWrRo4Ww/fPgwL774IkOGDAEuFWj33HOP8/Nl/v7+1K1bl2+//dbZZrPZyMjIoHr16rRp0war1crKlSvp3bt3oeOxWCzOz3Xq1CErKwubzeYs3rKzs8nKysLX1xer9dLi7e+LwqysLHx8fJzH+OOLT44fP06tWrWKFo6IiIiISDmmWyVdyOrVq7FYLERERODv7+/817p1awICAlixYgVhYWHMnDmTs2fPcvbsWWbMmOHcPywsjJycHObNm4fdbufcuXOMGTOG6OhoLBYLPj4+TJo0ialTp/Jf//VfZGRkAJcKqri4ONauXVtgle33Hn30Ue6//34mTJhAdnY22dnZ/O1vf6Nu3bo0adKEunXrUqFCBb7++msAduzYwc6dOwscY9++faxatYr8/Hw2b97M+vXr6dGjxy1KU0RERESk7FDh5kKWLFlCREQE7u7uV/Q9/fTTrFq1infeeQeLxUKbNm3o3r07DRo0AMDd3Z0qVarw6aefkpSUROvWrWnXrh1Wq5W4uDjncbp160ZCQgL/+te/6NatG40bN6Zbt27s37+f999/n5EjR151bBUqVODjjz8mLy+Pjh07EhYWRm5uLvPnz6dChQr4+fkxbtw4PvroI5o0acKiRYuueAnKQw89xPr16wkJCWHatGnMnDmTwMDAPzFBEREREZGyyWKMMSU9CCm67du307RpU+cLRg4dOkS3bt3Yt28fnp6eJTy6a4uNjWXXrl0sXLjwpo5zIeUQ5kIOFs+KeNV7iKysHPLyHH/SKMsmiwVq1KjK6dPZ6Lf9xim/4lN2xafsbo7yKz5ld3OUX/GV1+wuX3dRaMWtlJk+fTpxcXHk5eVhs9mIi4ujRYsWLl20iYiIiIjIzVHhVsrMmjWLffv2ERISQnh4OG5ubgWecxMRERERkbJHb5UsZR544AEWLFhQ0sO4YcOGDftTjmP18MRhHFg9vArfWERERESkjFDhJqWKh//dzs/GYXA4ytFN0CIiIiJSbqlwk1IlKyvH+dmhwk1EREREygkVblKqOBwOHHqJpIiIiIiUM3o5iYiIiIiIiIvTipuUKlarFatVt0mKiIiISPmiwk1KFR+fygAYY8jMzFHxJiIiIiLlgm6VlFIlL+sUeVlpWCwWrFZLSQ9HREREROS20IqblCom3w5Gq2wiIiIiUr5oxU1ERERERMTFqXC7hcLDw1mxYsUV7StWrCA8PPymjr1ixQoefPBBAgMDCQwMpFGjRoSFhTF79mxMEVekYmNj6d+//02NQ0REREREbj3dKlmK1a5dmw0bNjh/PnDgAIMGDaJOnTr06NGjBEcmIiIiIiJ/Jq24lbANGzbwzDPP8Nhjj9GoUSP69evH0aNHAbDZbERHR9O8eXNCQ0MZNGgQP//88zWP9cgjjxAcHMyBAwecbcuXLycyMpLmzZsTGBjIkCFDyMzMvGJfYwxz584lIiKCoKAgmjVrxqhRo7hw4QIAY8eOZfz48URFRREYGEjbtm1JSEhw7p+Zmcno0aNp1qwZzZs3Jzo6mrNnzwJw+vRpRo8eTWhoKC1btmT8+PHYbLY/Iz4RERERkXJBhdstNnHiRIKCggr8mzhxIgAnT55kxIgRvPjiiyQmJrJp0yaMMXz44YcAxMfHY7PZ2Lx5Mxs3bsTX15d33333mudKTk5m3759tG/fHoD9+/fz9ttv87e//Y2kpCTWrl3L0aNHCxRcl61du5aEhARiY2PZvXs3S5cuZdu2baxevdq5zYoVK+jfvz/ff/89gwcPZtq0aaSlpQEwYsQIbDYb69atY/369Zw7d46JEyficDh46aWXsFqtfPfdd6xevZpTp04xfvz4Py1jEREREZGyTrdK3mITJkwgMjKyQNuKFSuYM2cO1atX5+uvv6Zu3brYbDZOnjyJj4+Psxjy8vIiOTmZlStXEhoaypQpU7Ba/3+tfeLECYKCggC4ePEidrud0NBQHnnkEQACAgJYs2YNd955J2fPnuXUqVNUr17defzfa926NU2aNMHf35/MzEyysrLw9vYusO3llT+AHj16MGHCBFJSUsjLy2PXrl18++23+Pj4ADBt2jTOnDnDgQMHOHjwIPPnz6dy5UvfwTZmzBieeOIJ3nrrLef2IiIiIiJybSrcSpC7uztr1qxh6dKlWCwWAgICsNlsVKhw6X+WwYMH4+HhwfLly5k0aRJ33XUXo0aNokOHDsCVz7ilpqbyxhtvMHDgQL744gusVisJCQmsXr2aSpUqUb9+fWw221VfXmKM4b333mPjxo1Ur16dhx56iNzc3ALb+vr6Fhg7gMPhID09HYA6deoU2NbX15dvvvmG/Px8Hn/88QLn8/Dw4NixYyrcRERERESKQIVbCVq7di2LFi3i73//O/Xq1QNg8uTJHD58GIBDhw4RHh7OgAEDyM7OZsmSJURHR7Nz586rHq9OnToMGDCAoUOHkpGRwRdffMH27dtZvXo1NWrUACAqKuqq+7777rucOHGCDRs2UKVKFQAiIiKKdB21atUCLq0A3n333QAcOXKENWvW0Lp1a7y8vEhKSsLNzQ0Au93OsWPHnNcsIiIiIiLXp2fcSlB2djZWqxUvLy+MMWzZsoWVK1eSm5sLwLJly3j99dfJyMigSpUqVKlShUqVKuHh4XHV42VmZvL5559z33334ePj41y9c3d3Jy8vj1WrVrF161bn8X/PZrPh6emJm5sbFy9eJD4+nsOHD1912z+qWbMmoaGhzJgxg3PnzmGz2Zg5cybHjh2jYcOG1KtXj2nTppGTk8OFCxeYMmUKAwYMID8//+YCFBEREREpJ7TiVoK6d+/Onj176Ny5M25ubtx7770899xzLF68GLvdzsiRI5k0aRKdO3fm4sWL3HvvvXz00Ud4enoCl1a4AgMDncfz8PCgefPmfPzxx1itVp5//nkOHz5MWFgYnp6eNGjQgD59+lx1xe7VV1/ljTfeoEWLFlSqVImmTZvStWtX5+pfYd59912mTZtGp06dyMvLIzw8nL/+9a9UqFCBjz/+mOnTp9OhQwcuXrxIw4YNmT9/vvM6RERERETk+iymqN/WLOICck8fB2Nw972LrKwc8vIcJT0kl2exQI0aVTl9Ohv9tt845Vd8yq74lN3NUX7Fp+xujvIrvvKa3eXrLgrdKikiIiIiIuLiVLiJiIiIiIi4OD3jJqWKxc0DKEfr5yIiIiIiqHCTUqaCjx9w6XvnHA4VcCIiIiJSPqhwk1IlKysHAIdDhZuIiIiIlB8q3KRUcTgcOPQiSREREREpZ/RyEilVrFYrVqulpIchIiIiInJbqXCTUsXHpzLVq1dW8SYiIiIi5YoKNylVHL/ZsFgsKtxEREREpFxR4SalinHkl/QQRERERERuOxVuIiIiIiIiLk6FWxl14sQJJkyYQHh4OI0bNyY4OJhBgwaxffv2W3K+pKQk6tevf0uOLSIiIiJS3qlwK4MOHz5Mly5dsNvtfPLJJ+zZs4d169bRpUsXXn75ZTZv3lzSQxQRERERkRug73Erg8aPH09oaChTp051tnl7e9O1a1ccDge5ubkA7Nixg5iYGI4ePUrNmjUZMmQIXbp0AWDs2LF4eHhw6tQpkpKSqF69Os899xx/+ctfADh16hTjx49n165d+Pj40Llz5wJjSElJYcqUKezdu5dKlSo5i0YPDw9WrFjBokWL8Pb2Zv/+/UyYMIGIiIjblI6IiIiISOmjFbcy5uTJk+zdu5dnnnnmqv3du3enXbt2JCcnM3ToUF588UWSkpKYPHkyU6ZMYevWrc5tV6xYQf/+/fn+++8ZPHgw06ZNIy0tDYDo6GgqVKjAli1bWLRoEVu2bHHud/78eQYMGMADDzzAli1bWLJkCTt27CA2Nta5zcGDB4mIiGDHjh20b9/+FqUhIiIiIlI2qHArY06ePAmAv7+/sy0xMZGgoCCCgoIIDAykY8eOLF26lLZt29KhQwfc3Nxo0qQJvXv3ZvHixc79mjdvTmhoKBUqVKBHjx7k5+eTkpJCamoqu3fvZvTo0VSpUoVatWrxyiuvOPfbtGkTdrudkSNH4unpSa1atRgxYkSBY7u7u9O1a1c8PDzw8vK6DcmIiIiIiJReulWyjPH19QUgLS2Ne+65B4DHHnuM3bt3A5dW0ebMmUNqaio7d+4kKCjIuW9+fj5169a94lhwqdACcDgczlW32rVrO/t/v19qaiqZmZk0a9bM2WaMITc3l4yMDOexrVb9dwMRERERkaJQ4VbG1KlTh0cffZRly5YREhJyze38/f3p3r07kyZNcradOnUKY0yh57i8mnfs2DHuu+8+4P+v9F3ur1u3Lt9++62zzWazkZGRQfXq1QGwWPQF2iIiIiIiRaUljzLo8rNqb731Fr/88gvGGGw2GytXriQ2NhY/Pz969uzJmjVr2LZtGw6Hg6NHj9KvXz/i4+MLPX7t2rVp2bIlU6dO5ezZs6SnpzNnzhxnf1hYGDk5OcybNw+73c65c+cYM2YM0dHRKthERERERIpBhVsZFBAQwJo1a/Dy8iIqKoqmTZvy+OOP8/nnn/PCCy+QkJBAo0aNiImJISYmhmbNmtGvXz/Cw8MZNWpUkc4xa9YsqlatSlhYGD169KBFixbOvipVqvDpp5+SlJRE69atadeuHVarlbi4uFt1ySIiIiIiZZrFFOXeOBEXkZ9zFrfK1cjKyiEvz1HSwykVLBaoUaMqp09no9/2G6f8ik/ZFZ+yuznKr/iU3c1RfsVXXrO7fN1FoRU3ERERERERF6fCTURERERExMWpcJNSxWJ1K+khiIiIiIjcdircpFSxVqyCMQaHoxzd/CwiIiIi5Z6+x01KlaysHIwxGGPQNwsUzeWclFfxKL/iU3bFp+xujvIrPmV3c5Rf8ZXX7G7kevVWSRERERERERenWyVFRERERERcnAo3ERERERERF6fCTURERERExMWpcBMREREREXFxKtxERERERERcnAo3ERERERERF6fCTURERERExMWpcBMREREREXFxKtxERERERERcnAo3cXkZGRm89NJLBAUF0bx5c9555x3y8vJKeli3xTfffEODBg0IDAx0/nvttdcA+PHHH+nVqxeBgYGEh4ezbNmyAvt++eWXtG/fnsaNGxMZGcnevXudffn5+UyfPp0WLVoQGBjI0KFDOXXqlLO/tGeemZlJ+/btSUpKcraVZF6FnduVXC27CRMm8MgjjxSYh5999pmzX9lBcnIyAwcOJDg4mNDQUF5//XUyMzMBzb3CXC87zb3CJSYm0qtXL5o0aUJoaCiTJ0/mwoULgOZeYa6XneZe0eTn59O/f3/Gjh3rbNO8u4WMiIvr16+fGTVqlDl//rxJSUkxnTt3Np988klJD+u2mDZtmhk7duwV7WfOnDHBwcFm0aJFJjc31+zYscMEBgaaH3/80RhjzM6dO01gYKDZvXu3sdvtZv78+aZ58+bm/PnzxhhjYmNjTUREhDlx4oTJzs42r776qhk8eLDz+KU58927d5t27dqZgIAAs3PnTmNMyeZV2LldydWyM8aY7t27mxUrVlx1H2VnzG+//WZCQ0PNBx98YC5evGgyMzPN4MGDzZAhQzT3CnG97IzR3CtMRkaGefTRR80XX3xh8vPzTVpamnnqqafMBx98oLlXiOtlZ4zmXlG9//775sEHHzRjxowxxuj/b281FW7i0o4ePWoCAgLMyZMnnW1ff/21adOmTQmO6vbp27evWbRo0RXtn3/+uenQoUOBtvHjx5vXX3/dGGPMqFGjzJtvvlmg/4knnjDLly83xhjTunVr89VXXzn70tPTTf369U1KSkqpznzFihWmTZs25uuvvy5QfJRkXoWd21VcK7uLFy+ahx9+2Bw+fPiq+yk7Y37++WczaNAgk5eX52z75z//aZo0aaK5V4jrZae5VzTZ2dnGGGMcDoc5dOiQad++vVm4cKHmXhFcKzvNvaLZsWOHefLJJ83w4cOdhZvm3a2lWyXFpf300094e3tTs2ZNZ9t9993HiRMnOHfuXAmO7NZzOBwcPHiQTZs2ERYWRuvWrXnrrbc4e/YsP/30EwEBAQW2v//++0lOTgbgyJEj1+zPzs7m5MmTBfpr1KhBtWrVOHToUKnOvGXLlvzjH//gySefLNBeknkVdm5Xca3skpOTycvLY/bs2bRo0YKOHTsyd+5cHA4HoOwA7r33XubNm4ebm5uz7bvvvuPhhx/W3CvE9bLT3CuaKlWqAPD4448TERGBr68vkZGRmntFcK3sNPcKl5GRwV//+ldmzZpFxYoVne2ad7eWCjdxaTk5OQX+IADOn8+fP18SQ7ptMjMzadCgAR07duSbb75h6dKlHD16lNdee+2quXh5eTkzuV5/Tk4OAJUqVbqiPycnp1Rn7uvrS4UKFa5oL8m8Cju3q7hWdtnZ2QQHB9O/f382b97MzJkzWbhwIfHx8YCy+yNjDO+99x4bN27kr3/9q+beDfhjdpp7N2bdunVs2bIFq9XK8OHDNfduwB+z09y7PofDwWuvvcbAgQN58MEHC/Rp3t1aKtzEpVWqVInffvutQNvlnytXrlwSQ7ptatSoweLFi+nZsycVK1akdu3avPbaa2zZsgVjjPMB6ssuXLjgzKRixYrX7L/8R+2PuV7uL4uZXy+PwvpvNq/Czu3qQkNDSUhIIDg4GHd3dxo2bMhzzz3HN998Ayi737PZbAwfPpzVq1ezaNEi6tevr7lXRFfLTnPvxnh5eVGzZk1ee+01tm7dqrl3A/6Y3SOPPKK5dx0ff/wxHh4e9O/f/4o+zbtbS4WbuLQHHniAM2fOcPr0aWfbzz//jL+/P1WrVi3Bkd16ycnJvPvuuxhjnG12ux2r1UrDhg356aefCmx/5MgRHnjgAeBSbtfqr1atGjVr1uTIkSPOvvT0dM6cOUNAQECZzDwgIKDE8irs3K7un//8J0uXLi3QZrfb8fLyApTdZSkpKfTo0QObzcby5cupX78+oLlXFNfKTnOvcD/88ANPPPEEdrvd2Wa323F3d+f+++/X3LuO62W3fft2zb3rWLVqFbt27SIoKIigoCDWrFnDmjVrCAoK0t+8W61kH7ETKdyzzz5roqOjTXZ2tvMNQrNnzy7pYd1yv/76q2ncuLGZO3euyc3NNampqaZ3795m3LhxJjMz0wQFBZn58+cbu91uEhMTTWBgoElMTDTGGOeblBITE51vbWrWrJnJysoyxhjz3nvvmaeeesqkpKQ439rUr18/57nLQua/f8FGSeZV2Lld0e+zW7dunWnYsKHZsWOHcTgc5ocffjDNmzc3K1euNMYoO2MuvcmsTZs2ZuzYsSY/P79An+be9V0vO829wtlsNvP444+bKVOmmIsXL5rjx4+bnj17mgkTJmjuFeJ62Wnu3ZgxY8Y4X06ieXdrqXATl5eenm6GDRtmgoODTUhIiJk2bVqBN5CVZUlJSebpp582gYGBJiQkxEyePNlcuHDBGGPM/v37nX1t27Y1X3zxRYF9V65caTp27GgaN25sevbsafbt2+fss9vtZubMmaZVq1amSZMmZujQoeb06dPO/rKQ+R9faV+SeRV2blfzx+z+/ve/mw4dOphGjRqZtm3bXvGm0/KeXXx8vAkICDCNGjUyjRs3LvDPGM296yksO829wv30009m4MCBJigoyISFhZmYmBhz8eJFY4zmXmGul53mXtH9vnAzRvPuVrIY87v7sERERERERMTl6Bk3ERERERERF6fCTURERERExMWpcBMREREREXFxKtxERERERERcnAo3ERERERERF6fCTURERERExMWpcBMREREREXFxKtxERETkqv7973+X9BBEROT/qHATERFxIf379yc2Nrakh8H06dOJi4sr6WGIiMj/UeEmIiIiV8jKyirpIYiIyO+ocBMREXFBK1asoE+fPkyfPp3g4GBCQkJYuHAhn3/+OWFhYTRt2pTx48c7tw8PD2fOnDl07NiRwMBA+vbty5EjR5z9u3fvpm/fvgQFBREeHs7777+P3W4HIDY2lueff54ePXoQHBzMnDlzWL16NatXr6ZLly4A/PDDD/zlL3+hZcuWPProo0RGRrJv3z4AkpKSCA8PJy4ujlatWhEcHMywYcOw2WzO8y9YsID27dsTGBhIZGQkiYmJABhjSEhIoGPHjgQFBdGnTx8OHDhwq+MVESl1VLiJiIi4qD179lCzZk127tzJ8OHDmTp1KklJSXzzzTd8+umnLF++nO+//965/Weffcb7779PYmIi9913H1FRUeTm5vK///u/DBw4kA4dOrBjxw7mz5/Phg0bmDFjhnPfxMRERo8ezcaNG4mKiiIiIoKIiAi++uorLly4wNChQ+nYsSNbtmwhKSmJunXrFtg/NTWVtLQ0/vGPf7Bs2TL27t3LkiVLgEtF6EcffcSMGTPYs2cPzz77LEOHDuXMmTMsWbKE+fPn88EHH5CYmEhkZCQDBw7k9OnTty9oEZFSQIWbiIiIi6pUqRLPPfccVquVli1bkp+fz6BBg6hYsSKPPvoofn5+pKamOrcfNGgQDz30EF5eXrzxxhv8+uuv/PDDD6xevZr69evz3HPP4eHhQb169Rg1ahTLli3D4XAAcNddd/HYY49RuXJlKlSoUGAc7u7ufPbZZ/Tp0we73U5qaire3t6kpaUV2O7ll1/Gy8uLevXq0bx5c3755RcAvvzyS55++mkCAwOxWq306tWL+Ph4vLy8WLx4MUOGDOHBBx/E3d2dnj17ct999/HVV1/d4nRFREqXCoVvIiIiIiXB29sbi8UCgNV66b+13nHHHc5+q9XqLLwA6tWr5/xcsWJFvL29SU9PJyMjg7vuuqvAse+8804uXLhARkYGAH5+ftcch5ubG0lJSQwePJjz589z//33U6FCBYwxBbbz9fV1fnZ3d3f2p6enU7t27QLbNmnSBLi0Ujd9+nTeffddZ19eXh6PPPLINccjIlIeqXATERFxUZeLtqL6/QpYTk4OWVlZ1KpVizp16rBu3boC26akpODh4UG1atUKPdePP/7I5MmTWbp0qbOgio+Pd66oFaZWrVr8+uuvBdree+89unTpgr+/P8OHD6dz584Fxubt7V2kY4uIlBe6VVJERKSMmD9/Pv/+97/57bffmDp1Kvfeey+BgYF07tyZn3/+mQULFmC320lJSSEmJoaIiAg8PDyueiwPDw+ys7MByM7Oxmq14uXlBcC+fftISEhwvtykMJGRkXz22Wfs378fh8PBF198weLFi/Hx8aF3797ExcXx888/A7B161Y6d+5c4Nk9ERHRipuIiEiZ0bRpU15++WVOnDhBs2bNmDt3LlarlTvvvJN58+YRExNDbGwsXl5ePPXUU7z66qvXPNaTTz5JdHQ0bdq0YePGjfTp04e+ffvicDi488476d+/P7NmzSrSS0QiIiI4d+4cr732Gunp6dx///188sknVK9enQEDBmCM4aWXXuLUqVPUrFmT8ePH07Zt2z8xGRGR0s9i/niDuoiIiJQ64eHhvPLKK0RGRpb0UERE5BbQrZIiIiIiIiIuToWbiIiIiIiIi9OtkiIiIiIiIi5OK24iIiIiIiIuToWbiIiIiIiIi1PhJiIiIiIi4uJUuImIiIiIiLg4FW4iIiIiIiIuToWbiIiIiIiIi1PhJiIiIiIi4uJUuImIiIiIiLg4FW4iIiIiIiIu7v8Bi8AWpRtie+EAAAAASUVORK5CYII="
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 127
+   "execution_count": 333
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:48.508201Z",
-     "start_time": "2025-06-20T18:11:48.475680Z"
+     "end_time": "2025-06-24T18:35:14.401811Z",
+     "start_time": "2025-06-24T18:35:14.367865Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "tt_preds = lgmodel.predict_proba(test_prepared)[:,1]\n",
-    "print('Test AUC Score',roc_auc_score(y_test,tt_preds))"
+    "tt_preds = lgmodel.predict_proba(test_prepared)[:, 1]\n",
+    "print('Test AUC Score', roc_auc_score(y_test, tt_preds))"
    ],
    "id": "40f267953aebaa12",
    "outputs": [
@@ -4950,24 +4737,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test AUC Score 0.9329918512164692\n"
+      "Test AUC Score 0.9252025011696818\n"
      ]
     }
    ],
-   "execution_count": 128
+   "execution_count": 334
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:11:48.780475Z",
-     "start_time": "2025-06-20T18:11:48.629476Z"
+     "end_time": "2025-06-24T18:35:14.752533Z",
+     "start_time": "2025-06-24T18:35:14.599290Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "preds = lgmodel.predict_proba(test_data)[:,1]\n",
+    "preds = lgmodel.predict_proba(test_data)[:, 1]\n",
     "submission = pd.DataFrame({'id': CustomerID, 'Exited': preds})\n",
-    "submission.to_csv('submission_lgbm.csv', index=False)\n",
+    "submission.to_csv(iteration + '_submission_lgbm.csv', index=False)\n",
     "submission"
    ],
    "id": "76848a327e743af9",
@@ -4976,17 +4763,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.079817\n",
-       "1     15001  0.027843\n",
-       "2     15002  0.019557\n",
-       "3     15003  0.827549\n",
-       "4     15004  0.020200\n",
+       "0     15000  0.118829\n",
+       "1     15001  0.034504\n",
+       "2     15002  0.018889\n",
+       "3     15003  0.768910\n",
+       "4     15004  0.023811\n",
        "...     ...       ...\n",
-       "9995  24995  0.015970\n",
-       "9996  24996  0.009182\n",
-       "9997  24997  0.000322\n",
-       "9998  24998  0.055128\n",
-       "9999  24999  0.316030\n",
+       "9995  24995  0.017133\n",
+       "9996  24996  0.018456\n",
+       "9997  24997  0.000448\n",
+       "9998  24998  0.060574\n",
+       "9999  24999  0.218695\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -5017,27 +4804,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.079817</td>\n",
+       "      <td>0.118829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.027843</td>\n",
+       "      <td>0.034504</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.019557</td>\n",
+       "      <td>0.018889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.827549</td>\n",
+       "      <td>0.768910</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.020200</td>\n",
+       "      <td>0.023811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -5047,27 +4834,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.015970</td>\n",
+       "      <td>0.017133</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.009182</td>\n",
+       "      <td>0.018456</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.000322</td>\n",
+       "      <td>0.000448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.055128</td>\n",
+       "      <td>0.060574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.316030</td>\n",
+       "      <td>0.218695</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -5075,18 +4862,18 @@
        "</div>"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 335,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 129
+   "execution_count": 335
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:12:24.252510Z",
-     "start_time": "2025-06-20T18:11:48.938905Z"
+     "end_time": "2025-06-24T18:35:48.440842Z",
+     "start_time": "2025-06-24T18:35:14.956412Z"
     }
    },
    "cell_type": "code",
@@ -5098,7 +4885,7 @@
     "    random_seed=3\n",
     ")\n",
     "\n",
-    "cat_model.fit(train_prepared,y_train,eval_set=(valid_prepared,y_valid),verbose=1000)"
+    "cat_model.fit(train_prepared, y_train, eval_set=(valid_prepared, y_valid), verbose=1000)"
    ],
    "id": "3559eabf9c960157",
    "outputs": [
@@ -5106,41 +4893,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:\ttest: 0.8822837\tbest: 0.8822837 (0)\ttotal: 4.93ms\tremaining: 44.4s\n",
-      "1000:\ttest: 0.9282924\tbest: 0.9282924 (1000)\ttotal: 3.62s\tremaining: 28.9s\n",
-      "2000:\ttest: 0.9304307\tbest: 0.9304368 (1995)\ttotal: 7.59s\tremaining: 26.5s\n",
-      "3000:\ttest: 0.9312081\tbest: 0.9312605 (2954)\ttotal: 12s\tremaining: 24s\n",
-      "4000:\ttest: 0.9313592\tbest: 0.9313787 (3659)\ttotal: 16.8s\tremaining: 20.9s\n",
-      "5000:\ttest: 0.9312337\tbest: 0.9313787 (3659)\ttotal: 20.4s\tremaining: 16.3s\n",
-      "6000:\ttest: 0.9311764\tbest: 0.9313787 (3659)\ttotal: 23.9s\tremaining: 11.9s\n",
-      "7000:\ttest: 0.9310984\tbest: 0.9313787 (3659)\ttotal: 27.3s\tremaining: 7.8s\n",
-      "8000:\ttest: 0.9310022\tbest: 0.9313787 (3659)\ttotal: 30.8s\tremaining: 3.84s\n",
-      "8999:\ttest: 0.9309266\tbest: 0.9313787 (3659)\ttotal: 34.2s\tremaining: 0us\n",
+      "0:\ttest: 0.8975749\tbest: 0.8975749 (0)\ttotal: 4.27ms\tremaining: 38.5s\n",
+      "1000:\ttest: 0.9289577\tbest: 0.9289638 (999)\ttotal: 3.58s\tremaining: 28.6s\n",
+      "2000:\ttest: 0.9307439\tbest: 0.9307548 (1999)\ttotal: 7.18s\tremaining: 25.1s\n",
+      "3000:\ttest: 0.9308913\tbest: 0.9310302 (2575)\ttotal: 10.8s\tremaining: 21.5s\n",
+      "4000:\ttest: 0.9302017\tbest: 0.9310302 (2575)\ttotal: 14.3s\tremaining: 17.9s\n",
+      "5000:\ttest: 0.9297557\tbest: 0.9310302 (2575)\ttotal: 18s\tremaining: 14.4s\n",
+      "6000:\ttest: 0.9292525\tbest: 0.9310302 (2575)\ttotal: 21.5s\tremaining: 10.8s\n",
+      "7000:\ttest: 0.9286896\tbest: 0.9310302 (2575)\ttotal: 25.1s\tremaining: 7.17s\n",
+      "8000:\ttest: 0.9281974\tbest: 0.9310302 (2575)\ttotal: 28.8s\tremaining: 3.59s\n",
+      "8999:\ttest: 0.9278245\tbest: 0.9310302 (2575)\ttotal: 32.3s\tremaining: 0us\n",
       "\n",
-      "bestTest = 0.931378665\n",
-      "bestIteration = 3659\n",
+      "bestTest = 0.9310301973\n",
+      "bestIteration = 2575\n",
       "\n",
-      "Shrink model to first 3660 iterations.\n"
+      "Shrink model to first 2576 iterations.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<catboost.core.CatBoostClassifier at 0x157eb0ccad0>"
+       "<catboost.core.CatBoostClassifier at 0x1670fbcf0b0>"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 336,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 130
+   "execution_count": 336
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:12:24.397799Z",
-     "start_time": "2025-06-20T18:12:24.372049Z"
+     "end_time": "2025-06-24T18:35:48.842051Z",
+     "start_time": "2025-06-24T18:35:48.815879Z"
     }
    },
    "cell_type": "code",
@@ -5151,22 +4938,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.9151428571428571\n",
-      "Validation Accuracy 0.9026666666666666\n",
+      "Train Accuracy 0.9122857142857143\n",
+      "Validation Accuracy 0.8982222222222223\n",
       "\n",
       "\n",
-      "Train AUC Score 0.9516252102375617\n",
-      "Validation AUC Score 0.9313786650031193\n"
+      "Train AUC Score 0.9491507768266711\n",
+      "Validation AUC Score 0.9310301972863382\n"
      ]
     }
    ],
-   "execution_count": 131
+   "execution_count": 337
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:12:24.803347Z",
-     "start_time": "2025-06-20T18:12:24.622872Z"
+     "end_time": "2025-06-24T18:35:49.421553Z",
+     "start_time": "2025-06-24T18:35:49.213533Z"
     }
    },
    "cell_type": "code",
@@ -5178,25 +4965,31 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 132
+   "execution_count": 338
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "",
+   "id": "c0805dca6642c526"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:12:24.904779Z",
-     "start_time": "2025-06-20T18:12:24.897949Z"
+     "end_time": "2025-06-24T18:35:49.598089Z",
+     "start_time": "2025-06-24T18:35:49.590576Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "tt_preds = cat_model.predict_proba(test_prepared)[:,1]\n",
-    "print('Test AUC Score',roc_auc_score(y_test,tt_preds))"
+    "tt_preds = cat_model.predict_proba(test_prepared)[:, 1]\n",
+    "print('Test AUC Score', roc_auc_score(y_test, tt_preds))"
    ],
    "id": "a0c5a11e54d12b91",
    "outputs": [
@@ -5204,24 +4997,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test AUC Score 0.9309997368215847\n"
+      "Test AUC Score 0.9302321331097941\n"
      ]
     }
    ],
-   "execution_count": 133
+   "execution_count": 339
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-20T18:12:25.052118Z",
-     "start_time": "2025-06-20T18:12:25.011027Z"
+     "end_time": "2025-06-24T18:35:49.812575Z",
+     "start_time": "2025-06-24T18:35:49.772641Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "preds = cat_model.predict_proba(test_data)[:,1]\n",
+    "preds = cat_model.predict_proba(test_data)[:, 1]\n",
     "submission = pd.DataFrame({'id': CustomerID, 'Exited': preds})\n",
-    "submission.to_csv('submission_cat.csv', index=False)\n",
+    "submission.to_csv(iteration + '_submission_cat.csv', index=False)\n",
     "submission"
    ],
    "id": "7193aafa5f76efa4",
@@ -5230,17 +5023,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.086600\n",
-       "1     15001  0.029949\n",
-       "2     15002  0.046532\n",
-       "3     15003  0.779406\n",
-       "4     15004  0.043621\n",
+       "0     15000  0.108353\n",
+       "1     15001  0.032000\n",
+       "2     15002  0.057833\n",
+       "3     15003  0.732829\n",
+       "4     15004  0.050659\n",
        "...     ...       ...\n",
-       "9995  24995  0.008009\n",
-       "9996  24996  0.011243\n",
-       "9997  24997  0.005530\n",
-       "9998  24998  0.137327\n",
-       "9999  24999  0.184573\n",
+       "9995  24995  0.011100\n",
+       "9996  24996  0.015169\n",
+       "9997  24997  0.008829\n",
+       "9998  24998  0.144934\n",
+       "9999  24999  0.207669\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -5271,27 +5064,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.086600</td>\n",
+       "      <td>0.108353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.029949</td>\n",
+       "      <td>0.032000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.046532</td>\n",
+       "      <td>0.057833</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.779406</td>\n",
+       "      <td>0.732829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.043621</td>\n",
+       "      <td>0.050659</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -5301,27 +5094,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.008009</td>\n",
+       "      <td>0.011100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.011243</td>\n",
+       "      <td>0.015169</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.005530</td>\n",
+       "      <td>0.008829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.137327</td>\n",
+       "      <td>0.144934</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.184573</td>\n",
+       "      <td>0.207669</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -5329,12 +5122,12 @@
        "</div>"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 340,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 134
+   "execution_count": 340
   }
  ],
  "metadata": {

From 628fd5d50889d6cabd6b62df8e83a10e5eabf068 Mon Sep 17 00:00:00 2001
From: Dmytro Chechenets <demavend12345@gmail.com>
Date: Wed, 25 Jun 2025 20:37:20 +0300
Subject: [PATCH 3/4] Add polinomial features

---
 .../3_submission_cat.csv                      | 10001 +++++++++
 .../3_submission_lgbm.csv                     | 10001 +++++++++
 .../3_submission_rf.csv                       | 10001 +++++++++
 .../3_submission_xgb.csv                      | 10001 +++++++++
 .../catboost_info/catboost_training.json      | 18000 ++++++++--------
 .../catboost_info/learn/events.out.tfevents   |   Bin 494870 -> 494870 bytes
 .../catboost_info/learn_error.tsv             | 18000 ++++++++--------
 .../catboost_info/test/events.out.tfevents    |   Bin 917740 -> 917740 bytes
 .../catboost_info/test_error.tsv              | 18000 ++++++++--------
 .../catboost_info/time_left.tsv               | 18000 ++++++++--------
 .../kaggle_competition.ipynb                  |  2677 ++-
 11 files changed, 78058 insertions(+), 36623 deletions(-)
 create mode 100644 bank_customer_churn_prediction/3_submission_cat.csv
 create mode 100644 bank_customer_churn_prediction/3_submission_lgbm.csv
 create mode 100644 bank_customer_churn_prediction/3_submission_rf.csv
 create mode 100644 bank_customer_churn_prediction/3_submission_xgb.csv

diff --git a/bank_customer_churn_prediction/3_submission_cat.csv b/bank_customer_churn_prediction/3_submission_cat.csv
new file mode 100644
index 0000000..8d7bee9
--- /dev/null
+++ b/bank_customer_churn_prediction/3_submission_cat.csv
@@ -0,0 +1,10001 @@
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diff --git a/bank_customer_churn_prediction/3_submission_lgbm.csv b/bank_customer_churn_prediction/3_submission_lgbm.csv
new file mode 100644
index 0000000..502f850
--- /dev/null
+++ b/bank_customer_churn_prediction/3_submission_lgbm.csv
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diff --git a/bank_customer_churn_prediction/3_submission_rf.csv b/bank_customer_churn_prediction/3_submission_rf.csv
new file mode 100644
index 0000000..1939583
--- /dev/null
+++ b/bank_customer_churn_prediction/3_submission_rf.csv
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diff --git a/bank_customer_churn_prediction/3_submission_xgb.csv b/bank_customer_churn_prediction/3_submission_xgb.csv
new file mode 100644
index 0000000..91d1994
--- /dev/null
+++ b/bank_customer_churn_prediction/3_submission_xgb.csv
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diff --git a/bank_customer_churn_prediction/catboost_info/catboost_training.json b/bank_customer_churn_prediction/catboost_info/catboost_training.json
index aa46fab..5d1d857 100644
--- a/bank_customer_churn_prediction/catboost_info/catboost_training.json
+++ b/bank_customer_churn_prediction/catboost_info/catboost_training.json
@@ -1,9004 +1,9004 @@
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diff --git a/bank_customer_churn_prediction/catboost_info/learn_error.tsv b/bank_customer_churn_prediction/catboost_info/learn_error.tsv
index 4badc9b..146f61c 100644
--- a/bank_customer_churn_prediction/catboost_info/learn_error.tsv
+++ b/bank_customer_churn_prediction/catboost_info/learn_error.tsv
@@ -1,9001 +1,9001 @@
 iter	Logloss
-0	0.6919708698
-1	0.6908603123
-2	0.6897772126
-3	0.6886133334
-4	0.6874890469
-5	0.6863851364
-6	0.685336131
-7	0.6842170428
-8	0.6830360312
-9	0.6819821617
-10	0.6808680867
-11	0.679693641
-12	0.6786420373
-13	0.677493188
-14	0.6763391914
-15	0.6752804184
-16	0.6741652896
-17	0.6730993403
-18	0.6719729264
-19	0.6708822806
-20	0.6697568665
-21	0.6687001422
-22	0.6675416037
-23	0.6664642212
-24	0.6653318996
-25	0.6641961902
-26	0.6630960578
-27	0.6620858704
-28	0.6609012111
-29	0.6598418425
-30	0.6587854138
-31	0.6576924907
-32	0.6565381602
-33	0.6555544275
-34	0.6544789791
-35	0.6533482885
-36	0.6522781091
-37	0.6512435024
-38	0.6502156411
-39	0.6491189185
-40	0.6481098112
-41	0.6469800674
-42	0.6459122341
-43	0.6448894076
-44	0.6438785415
-45	0.6428947784
-46	0.6418986891
-47	0.6408908878
-48	0.6399085478
-49	0.6389064271
-50	0.6378450592
-51	0.6367967897
-52	0.6357663322
-53	0.6347974147
-54	0.6337890127
-55	0.6327305435
-56	0.6317605103
-57	0.6308117077
-58	0.6298083658
-59	0.6287608007
-60	0.6277362914
-61	0.6267488513
-62	0.6257408974
-63	0.6247505236
-64	0.62394
-65	0.6229256723
-66	0.6218563181
-67	0.6209269954
-68	0.6199860844
-69	0.6190272876
-70	0.618050915
-71	0.6170847708
-72	0.6161223752
-73	0.6151438038
-74	0.614148647
-75	0.6132331213
-76	0.6122685653
-77	0.611312944
-78	0.6103086986
-79	0.6094315036
-80	0.6085025117
-81	0.607582815
-82	0.606642725
-83	0.6057656666
-84	0.6047684985
-85	0.6037729883
-86	0.6028783527
-87	0.6019436852
-88	0.6010358058
-89	0.6000975989
-90	0.5991577477
-91	0.5982752917
-92	0.5974013299
-93	0.5964689594
-94	0.5955623013
-95	0.5946272204
-96	0.5938106095
-97	0.592870534
-98	0.5920022135
-99	0.5911793979
-100	0.5903478596
-101	0.5894406626
-102	0.5885818175
-103	0.5876775448
-104	0.5868178078
-105	0.5859076645
-106	0.585008289
-107	0.5841328407
-108	0.5832696313
-109	0.58242731
-110	0.5816117716
-111	0.5807570404
-112	0.5800077874
-113	0.5792050338
-114	0.578332641
-115	0.5774927702
-116	0.5766287291
-117	0.5758630977
-118	0.5751208126
-119	0.5742758695
-120	0.5734500418
-121	0.5726382457
-122	0.5717986074
-123	0.5709664604
-124	0.5700725567
-125	0.5692468334
-126	0.5684092097
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diff --git a/bank_customer_churn_prediction/catboost_info/test/events.out.tfevents b/bank_customer_churn_prediction/catboost_info/test/events.out.tfevents
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diff --git a/bank_customer_churn_prediction/catboost_info/test_error.tsv b/bank_customer_churn_prediction/catboost_info/test_error.tsv
index 4a8a390..032df0c 100644
--- a/bank_customer_churn_prediction/catboost_info/test_error.tsv
+++ b/bank_customer_churn_prediction/catboost_info/test_error.tsv
@@ -1,9001 +1,9001 @@
 iter	AUC	Logloss
-0	0.8975748596	0.6920019298
-1	0.9081446409	0.6909091622
-2	0.9100337989	0.6898442972
-3	0.914772838	0.6888232462
-4	0.91508719	0.6877207778
-5	0.9191524437	0.6866318684
-6	0.9190379123	0.6855885234
-7	0.9200583379	0.6844769823
-8	0.9216794438	0.6833064773
-9	0.9224982211	0.6822575778
-10	0.9220407049	0.6811717015
-11	0.9224567949	0.6800317015
-12	0.9229813241	0.6789821329
-13	0.9237745146	0.67784004
-14	0.9235539808	0.6767326774
-15	0.9235484979	0.6756953826
-16	0.9222374795	0.6747701373
-17	0.9220711654	0.6737258573
-18	0.9227029154	0.6726157296
-19	0.9225585328	0.6715430294
-20	0.9231409369	0.6704355103
-21	0.9234796573	0.6693758818
-22	0.9234967151	0.6682471936
-23	0.923484531	0.6671785384
-24	0.9234297021	0.666098418
-25	0.9239633695	0.6649783308
-26	0.923903667	0.6638878508
-27	0.9241741559	0.6628689899
-28	0.9245177499	0.6617021099
-29	0.9243362056	0.6606637608
-30	0.9241217639	0.6596310051
-31	0.9243825055	0.6585401139
-32	0.9248686545	0.6574043917
-33	0.9247516863	0.6564197798
-34	0.9246164418	0.6553767506
-35	0.9244129659	0.6542950104
-36	0.9245019105	0.6532493999
-37	0.9246615233	0.6522215213
-38	0.9247687442	0.6512000124
-39	0.9245482104	0.650132502
-40	0.9246797996	0.6491237203
-41	0.9249490701	0.6480095658
-42	0.9250221752	0.6469772578
-43	0.9249563806	0.6459829799
-44	0.9248747466	0.6449900014
-45	0.9246627417	0.6441455342
-46	0.9247870204	0.643153379
-47	0.924771181	0.6421556335
-48	0.9247236627	0.6412163466
-49	0.9248698729	0.6402247029
-50	0.9249819674	0.6391663147
-51	0.9250977172	0.6381284184
-52	0.9251720407	0.637114322
-53	0.925062383	0.6361683701
-54	0.9252244327	0.6351723507
-55	0.9253645508	0.6341286179
-56	0.9254400928	0.6331640922
-57	0.9254266902	0.6322199143
-58	0.9255351294	0.6312217264
-59	0.9255314742	0.6302017871
-60	0.9255875214	0.629199605
-61	0.925594832	0.628222951
-62	0.925653316	0.6272344595
-63	0.9257118001	0.6262565059
-64	0.9256789028	0.6254315425
-65	0.925624074	0.6244361777
-66	0.9257215475	0.6233814902
-67	0.9255777741	0.6224855189
-68	0.9255119795	0.6215656821
-69	0.9254157244	0.6206389112
-70	0.9254425296	0.6196955889
-71	0.9255594978	0.618784025
-72	0.9254717717	0.6178467854
-73	0.9255594978	0.6168731424
-74	0.9256874318	0.6158791284
-75	0.9256886502	0.6149748733
-76	0.9255387847	0.6140474486
-77	0.925594832	0.6131123156
-78	0.9255972688	0.6121344557
-79	0.9256301661	0.6112561888
-80	0.925714237	0.6103708296
-81	0.9256874318	0.6094802274
-82	0.9257349501	0.6085583342
-83	0.925848263	0.6076731526
-84	0.9258555735	0.6067036955
-85	0.9258519183	0.6057388348
-86	0.9257739395	0.6048657456
-87	0.9257861237	0.6039479125
-88	0.9257922158	0.6030516886
-89	0.9258019631	0.6021428507
-90	0.9258945629	0.6012160607
-91	0.9259335523	0.6003417495
-92	0.9259274602	0.5994793916
-93	0.9258775051	0.5985727897
-94	0.9258360788	0.5976715086
-95	0.9259518286	0.5967475756
-96	0.9259895996	0.5959736306
-97	0.9259274602	0.5950591034
-98	0.9259542654	0.5941945362
-99	0.9259286786	0.5933675924
-100	0.9259798522	0.5925376892
-101	0.9259396444	0.5916406122
-102	0.9259055287	0.5907979147
-103	0.9259835075	0.5899177694
-104	0.925976197	0.5890703564
-105	0.9259981285	0.5881732329
-106	0.9259055287	0.5873038162
-107	0.9260042206	0.586432124
-108	0.9260103127	0.5855731991
-109	0.9259567023	0.5847527254
-110	0.9259286786	0.5839663202
-111	0.9259359892	0.5831238461
-112	0.9258799419	0.5823782517
-113	0.9258519183	0.5815724256
-114	0.9258494814	0.580703439
-115	0.9258665393	0.5798727304
-116	0.9258762867	0.5790214646
-117	0.925862884	0.5782624043
-118	0.9259262418	0.5775062324
-119	0.9259079655	0.5766846993
-120	0.925915276	0.5758618781
-121	0.9259347707	0.5750819922
-122	0.925967668	0.5742515721
-123	0.9259944733	0.5734210155
-124	0.9260968204	0.5725330051
-125	0.9260505205	0.5717202762
-126	0.9260748889	0.5708999181
-127	0.9261321546	0.5700943943
-128	0.9261406835	0.5692659195
-129	0.9261808913	0.5684880381
-130	0.9262552148	0.5676716369
-131	0.9263002963	0.566972016
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diff --git a/bank_customer_churn_prediction/catboost_info/time_left.tsv b/bank_customer_churn_prediction/catboost_info/time_left.tsv
index d2e9ef8..e6933ca 100644
--- a/bank_customer_churn_prediction/catboost_info/time_left.tsv
+++ b/bank_customer_churn_prediction/catboost_info/time_left.tsv
@@ -1,9001 +1,9001 @@
 iter	Passed	Remaining
-0	4	38464
-1	8	37534
-2	12	37373
-3	16	36952
-4	20	36633
-5	24	36468
-6	28	36542
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-9	39	35692
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-11	47	35258
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-13	54	34911
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-15	61	34681
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-18	72	34471
-19	76	34350
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-22	87	34146
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-24	95	34124
-25	98	34098
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-34	131	33655
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-38	145	33542
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-40	153	33484
-41	156	33439
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-54	205	33450
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diff --git a/bank_customer_churn_prediction/kaggle_competition.ipynb b/bank_customer_churn_prediction/kaggle_competition.ipynb
index 5f36bcc..3021279 100644
--- a/bank_customer_churn_prediction/kaggle_competition.ipynb
+++ b/bank_customer_churn_prediction/kaggle_competition.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:47.316360Z",
-     "start_time": "2025-06-24T18:34:47.312360Z"
+     "end_time": "2025-06-25T17:23:09.758958Z",
+     "start_time": "2025-06-25T17:23:08.368478Z"
     }
    },
    "source": [
@@ -29,34 +29,35 @@
     "from sklearn.model_selection import GridSearchCV, train_test_split\n",
     "\n",
     "from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix, ConfusionMatrixDisplay\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
     "import os\n",
     "\n",
     "import warnings\n",
     "\n",
     "warnings.filterwarnings('ignore')\n",
-    "iteration = '2'"
+    "iteration = '3'"
    ],
    "outputs": [],
-   "execution_count": 276
+   "execution_count": 1
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:47.477373Z",
-     "start_time": "2025-06-24T18:34:47.460133Z"
+     "end_time": "2025-06-25T17:23:09.806088Z",
+     "start_time": "2025-06-25T17:23:09.769964Z"
     }
    },
    "cell_type": "code",
    "source": "df = pd.read_csv('../assets/hw_2_2/train.csv')",
    "id": "db2bbff7858a1e68",
    "outputs": [],
-   "execution_count": 277
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:47.629896Z",
-     "start_time": "2025-06-24T18:34:47.619629Z"
+     "end_time": "2025-06-25T17:23:09.905687Z",
+     "start_time": "2025-06-25T17:23:09.892386Z"
     }
    },
    "cell_type": "code",
@@ -213,18 +214,18 @@
        "</div>"
       ]
      },
-     "execution_count": 278,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 278
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:47.810713Z",
-     "start_time": "2025-06-24T18:34:47.805809Z"
+     "end_time": "2025-06-25T17:23:10.143812Z",
+     "start_time": "2025-06-25T17:23:10.138296Z"
     }
    },
    "cell_type": "code",
@@ -251,18 +252,18 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 279,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 279
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:48.009562Z",
-     "start_time": "2025-06-24T18:34:48.002805Z"
+     "end_time": "2025-06-25T17:23:10.360844Z",
+     "start_time": "2025-06-25T17:23:10.347380Z"
     }
    },
    "cell_type": "code",
@@ -297,13 +298,13 @@
      ]
     }
    ],
-   "execution_count": 280
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:48.317174Z",
-     "start_time": "2025-06-24T18:34:48.313381Z"
+     "end_time": "2025-06-25T17:23:10.552227Z",
+     "start_time": "2025-06-25T17:23:10.547292Z"
     }
    },
    "cell_type": "code",
@@ -316,31 +317,31 @@
        "array([0., 1.])"
       ]
      },
-     "execution_count": 281,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 281
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:48.598434Z",
-     "start_time": "2025-06-24T18:34:48.595920Z"
+     "end_time": "2025-06-25T17:23:10.651858Z",
+     "start_time": "2025-06-25T17:23:10.648181Z"
     }
    },
    "cell_type": "code",
    "source": "df['IsActiveMember'] = df['IsActiveMember'].astype(int)",
    "id": "ad36fb614843627e",
    "outputs": [],
-   "execution_count": 282
+   "execution_count": 7
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:48.751748Z",
-     "start_time": "2025-06-24T18:34:48.743824Z"
+     "end_time": "2025-06-25T17:23:10.727389Z",
+     "start_time": "2025-06-25T17:23:10.716634Z"
     }
    },
    "cell_type": "code",
@@ -353,18 +354,18 @@
        "0"
       ]
      },
-     "execution_count": 283,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 283
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:48.909924Z",
-     "start_time": "2025-06-24T18:34:48.904716Z"
+     "end_time": "2025-06-25T17:23:10.809739Z",
+     "start_time": "2025-06-25T17:23:10.803955Z"
     }
    },
    "cell_type": "code",
@@ -391,18 +392,18 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 284,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 284
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:49.085200Z",
-     "start_time": "2025-06-24T18:34:49.081199Z"
+     "end_time": "2025-06-25T17:23:10.904822Z",
+     "start_time": "2025-06-25T17:23:10.900401Z"
     }
    },
    "cell_type": "code",
@@ -415,31 +416,31 @@
        "8652"
       ]
      },
-     "execution_count": 285,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 285
+   "execution_count": 10
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:49.252158Z",
-     "start_time": "2025-06-24T18:34:49.249212Z"
+     "end_time": "2025-06-25T17:23:11.073272Z",
+     "start_time": "2025-06-25T17:23:11.066621Z"
     }
    },
    "cell_type": "code",
    "source": "df = df.drop(columns=['CustomerId'])",
    "id": "50a48bd3bf1a4a97",
    "outputs": [],
-   "execution_count": 286
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:49.539941Z",
-     "start_time": "2025-06-24T18:34:49.533859Z"
+     "end_time": "2025-06-25T17:23:11.182972Z",
+     "start_time": "2025-06-25T17:23:11.176122Z"
     }
    },
    "cell_type": "code",
@@ -473,13 +474,13 @@
      ]
     }
    ],
-   "execution_count": 287
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:49.696207Z",
-     "start_time": "2025-06-24T18:34:49.692352Z"
+     "end_time": "2025-06-25T17:23:11.243132Z",
+     "start_time": "2025-06-25T17:23:11.238010Z"
     }
    },
    "cell_type": "code",
@@ -495,18 +496,18 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 288,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 288
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:49.906492Z",
-     "start_time": "2025-06-24T18:34:49.865588Z"
+     "end_time": "2025-06-25T17:23:11.403985Z",
+     "start_time": "2025-06-25T17:23:11.315230Z"
     }
    },
    "cell_type": "code",
@@ -536,13 +537,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 289
+   "execution_count": 14
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:50.076445Z",
-     "start_time": "2025-06-24T18:34:50.071519Z"
+     "end_time": "2025-06-25T17:23:11.507982Z",
+     "start_time": "2025-06-25T17:23:11.501420Z"
     }
    },
    "cell_type": "code",
@@ -567,18 +568,18 @@
        "Name: count, Length: 764, dtype: int64"
       ]
      },
-     "execution_count": 290,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 290
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:50.503645Z",
-     "start_time": "2025-06-24T18:34:50.391400Z"
+     "end_time": "2025-06-25T17:23:11.721169Z",
+     "start_time": "2025-06-25T17:23:11.592641Z"
     }
    },
    "cell_type": "code",
@@ -600,13 +601,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 291
+   "execution_count": 16
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:50.799853Z",
-     "start_time": "2025-06-24T18:34:50.671068Z"
+     "end_time": "2025-06-25T17:23:11.943934Z",
+     "start_time": "2025-06-25T17:23:11.806470Z"
     }
    },
    "cell_type": "code",
@@ -628,13 +629,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 292
+   "execution_count": 17
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:50.989385Z",
-     "start_time": "2025-06-24T18:34:50.984637Z"
+     "end_time": "2025-06-25T17:23:12.003850Z",
+     "start_time": "2025-06-25T17:23:11.998633Z"
     }
    },
    "cell_type": "code",
@@ -651,18 +652,18 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 293,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 293
+   "execution_count": 18
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:51.363995Z",
-     "start_time": "2025-06-24T18:34:51.318682Z"
+     "end_time": "2025-06-25T17:23:12.117726Z",
+     "start_time": "2025-06-25T17:23:12.070782Z"
     }
    },
    "cell_type": "code",
@@ -699,13 +700,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 294
+   "execution_count": 19
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:51.644599Z",
-     "start_time": "2025-06-24T18:34:51.540678Z"
+     "end_time": "2025-06-25T17:23:12.355547Z",
+     "start_time": "2025-06-25T17:23:12.243743Z"
     }
    },
    "cell_type": "code",
@@ -729,13 +730,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 295
+   "execution_count": 20
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:52.152792Z",
-     "start_time": "2025-06-24T18:34:51.927605Z"
+     "end_time": "2025-06-25T17:23:12.541553Z",
+     "start_time": "2025-06-25T17:23:12.370023Z"
     }
    },
    "cell_type": "code",
@@ -759,13 +760,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 296
+   "execution_count": 21
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:53.217853Z",
-     "start_time": "2025-06-24T18:34:53.084647Z"
+     "end_time": "2025-06-25T17:23:12.699735Z",
+     "start_time": "2025-06-25T17:23:12.556736Z"
     }
    },
    "cell_type": "code",
@@ -787,13 +788,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 297
+   "execution_count": 22
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:53.553685Z",
-     "start_time": "2025-06-24T18:34:53.222858Z"
+     "end_time": "2025-06-25T17:23:13.131994Z",
+     "start_time": "2025-06-25T17:23:12.712253Z"
     }
    },
    "cell_type": "code",
@@ -826,13 +827,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 298
+   "execution_count": 23
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:53.838569Z",
-     "start_time": "2025-06-24T18:34:53.833569Z"
+     "end_time": "2025-06-25T17:23:13.150811Z",
+     "start_time": "2025-06-25T17:23:13.145669Z"
     }
    },
    "cell_type": "code",
@@ -857,18 +858,18 @@
        "Name: count, Length: 3395, dtype: int64"
       ]
      },
-     "execution_count": 299,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 299
+   "execution_count": 24
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:54.130204Z",
-     "start_time": "2025-06-24T18:34:54.010295Z"
+     "end_time": "2025-06-25T17:23:13.320198Z",
+     "start_time": "2025-06-25T17:23:13.196136Z"
     }
    },
    "cell_type": "code",
@@ -891,13 +892,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 300
+   "execution_count": 25
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:54.303988Z",
-     "start_time": "2025-06-24T18:34:54.300174Z"
+     "end_time": "2025-06-25T17:23:13.387254Z",
+     "start_time": "2025-06-25T17:23:13.382239Z"
     }
    },
    "cell_type": "code",
@@ -913,18 +914,18 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 301,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 301
+   "execution_count": 26
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:54.741554Z",
-     "start_time": "2025-06-24T18:34:54.654334Z"
+     "end_time": "2025-06-25T17:23:13.556722Z",
+     "start_time": "2025-06-25T17:23:13.463363Z"
     }
    },
    "cell_type": "code",
@@ -953,13 +954,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 302
+   "execution_count": 27
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:55.189547Z",
-     "start_time": "2025-06-24T18:34:54.909797Z"
+     "end_time": "2025-06-25T17:23:13.945364Z",
+     "start_time": "2025-06-25T17:23:13.639276Z"
     }
    },
    "cell_type": "code",
@@ -986,13 +987,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 303
+   "execution_count": 28
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:55.488119Z",
-     "start_time": "2025-06-24T18:34:55.483616Z"
+     "end_time": "2025-06-25T17:23:13.967522Z",
+     "start_time": "2025-06-25T17:23:13.962405Z"
     }
    },
    "cell_type": "code",
@@ -1009,18 +1010,18 @@
        "Name: Exited, dtype: float64"
       ]
      },
-     "execution_count": 304,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 304
+   "execution_count": 29
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:56.605215Z",
-     "start_time": "2025-06-24T18:34:55.662415Z"
+     "end_time": "2025-06-25T17:23:14.603767Z",
+     "start_time": "2025-06-25T17:23:14.010321Z"
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    },
    "cell_type": "code",
@@ -1055,13 +1056,13 @@
      "output_type": "display_data"
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    ],
-   "execution_count": 305
+   "execution_count": 30
   },
   {
    "metadata": {
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-     "end_time": "2025-06-24T18:34:57.736541Z",
-     "start_time": "2025-06-24T18:34:57.365714Z"
+     "end_time": "2025-06-25T17:23:14.927156Z",
+     "start_time": "2025-06-25T17:23:14.636051Z"
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    },
    "cell_type": "code",
@@ -1084,13 +1085,13 @@
      "output_type": "display_data"
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    ],
-   "execution_count": 306
+   "execution_count": 31
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   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:58.813721Z",
-     "start_time": "2025-06-24T18:34:58.659607Z"
+     "end_time": "2025-06-25T17:23:15.117903Z",
+     "start_time": "2025-06-25T17:23:14.943858Z"
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    },
    "cell_type": "code",
@@ -1120,13 +1121,13 @@
      "output_type": "display_data"
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    ],
-   "execution_count": 307
+   "execution_count": 32
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:34:58.990118Z",
-     "start_time": "2025-06-24T18:34:58.977054Z"
+     "end_time": "2025-06-25T17:23:15.149519Z",
+     "start_time": "2025-06-25T17:23:15.131899Z"
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    },
    "cell_type": "code",
@@ -1136,13 +1137,13 @@
    ],
    "id": "8cbecb04cd348ce1",
    "outputs": [],
-   "execution_count": 308
+   "execution_count": 33
   },
   {
    "metadata": {
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-     "end_time": "2025-06-24T18:35:00.619103Z",
-     "start_time": "2025-06-24T18:34:59.137577Z"
+     "end_time": "2025-06-25T17:23:16.660968Z",
+     "start_time": "2025-06-25T17:23:15.163600Z"
     }
    },
    "cell_type": "code",
@@ -1176,13 +1177,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 309
+   "execution_count": 34
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:00.775038Z",
-     "start_time": "2025-06-24T18:35:00.769976Z"
+     "end_time": "2025-06-25T17:23:16.685649Z",
+     "start_time": "2025-06-25T17:23:16.680320Z"
     }
    },
    "cell_type": "code",
@@ -1200,18 +1201,18 @@
        "Name: Exited, dtype: float64"
       ]
      },
-     "execution_count": 310,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 310
+   "execution_count": 35
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:01.066224Z",
-     "start_time": "2025-06-24T18:35:00.947706Z"
+     "end_time": "2025-06-25T17:23:16.864648Z",
+     "start_time": "2025-06-25T17:23:16.740264Z"
     }
    },
    "cell_type": "code",
@@ -1241,26 +1242,26 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 311
+   "execution_count": 36
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:01.094827Z",
-     "start_time": "2025-06-24T18:35:01.091798Z"
+     "end_time": "2025-06-25T17:23:16.901737Z",
+     "start_time": "2025-06-25T17:23:16.898815Z"
     }
    },
    "cell_type": "code",
    "source": "df['ActiveCrCard'] = df['HasCrCard'] + (df['IsActiveMember'])",
    "id": "16a3fcb5f0e9014e",
    "outputs": [],
-   "execution_count": 312
+   "execution_count": 37
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:01.735855Z",
-     "start_time": "2025-06-24T18:35:01.530254Z"
+     "end_time": "2025-06-25T17:23:17.142800Z",
+     "start_time": "2025-06-25T17:23:16.943720Z"
     }
    },
    "cell_type": "code",
@@ -1282,26 +1283,26 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 313
+   "execution_count": 38
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:02.595481Z",
-     "start_time": "2025-06-24T18:35:02.592330Z"
+     "end_time": "2025-06-25T17:23:17.163479Z",
+     "start_time": "2025-06-25T17:23:17.159480Z"
     }
    },
    "cell_type": "code",
    "source": "df['TenureAgeRatio'] = df['Tenure'] / df['Age']",
    "id": "84c08a8cb08a8c98",
    "outputs": [],
-   "execution_count": 314
+   "execution_count": 39
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:02.750274Z",
-     "start_time": "2025-06-24T18:35:02.745934Z"
+     "end_time": "2025-06-25T17:23:17.189487Z",
+     "start_time": "2025-06-25T17:23:17.184626Z"
     }
    },
    "cell_type": "code",
@@ -1326,18 +1327,18 @@
        "Name: Exited, dtype: float64"
       ]
      },
-     "execution_count": 315,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 315
+   "execution_count": 40
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:03.233507Z",
-     "start_time": "2025-06-24T18:35:02.922393Z"
+     "end_time": "2025-06-25T17:23:17.540919Z",
+     "start_time": "2025-06-25T17:23:17.243941Z"
     }
    },
    "cell_type": "code",
@@ -1361,13 +1362,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 316
+   "execution_count": 41
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:04.374684Z",
-     "start_time": "2025-06-24T18:35:04.363586Z"
+     "end_time": "2025-06-25T17:23:17.586567Z",
+     "start_time": "2025-06-25T17:23:17.573002Z"
     }
    },
    "cell_type": "code",
@@ -1381,13 +1382,13 @@
    ],
    "id": "f0910a78d32f7c09",
    "outputs": [],
-   "execution_count": 317
+   "execution_count": 42
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:04.523731Z",
-     "start_time": "2025-06-24T18:35:04.518970Z"
+     "end_time": "2025-06-25T17:23:17.638002Z",
+     "start_time": "2025-06-25T17:23:17.632767Z"
     }
    },
    "cell_type": "code",
@@ -1438,13 +1439,13 @@
      ]
     }
    ],
-   "execution_count": 318
+   "execution_count": 43
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:04.685679Z",
-     "start_time": "2025-06-24T18:35:04.679053Z"
+     "end_time": "2025-06-25T17:23:17.722745Z",
+     "start_time": "2025-06-25T17:23:17.716922Z"
     }
    },
    "cell_type": "code",
@@ -1456,6 +1457,10 @@
     "num_pipeline = Pipeline([\n",
     "    ('impute', SimpleImputer(strategy='median')),\n",
     "])\n",
+    "num_poly_pipeline = Pipeline([\n",
+    "    ('impute', SimpleImputer(strategy='median')),\n",
+    "    ('poly', PolynomialFeatures(degree=2, include_bias=False))\n",
+    "])\n",
     "target_enc_pipeline = Pipeline([\n",
     "    ('impute', SimpleImputer(strategy='most_frequent')),\n",
     "    ('encode', TargetEncoder())\n",
@@ -1472,40 +1477,15 @@
     "    d['TenureAgeRatio'] = d['Tenure'] / d['Age']\n",
     "    return d[['TenureAgeRatio']]\n",
     "\n",
-    "def HasBalanceFunc(d):\n",
-    "    d['HasBalance'] = (d['Balance'] > 0).astype(int)\n",
-    "    return d[['HasBalance']]\n",
-    "\n",
-    "def AgeGroupFunc(df):\n",
-    "    df = df.copy()\n",
-    "    df['AgeGroup'] = pd.cut(\n",
-    "        df['Age'],\n",
-    "        bins=[0, 30, 40, 50, 60, np.inf],\n",
-    "        labels=[0, 1, 2, 3, 4]\n",
-    "    ).astype(int)\n",
-    "    return df[['AgeGroup']]\n",
-    "\n",
-    "def SurnameFreqFunc(df):\n",
-    "    df = df.copy()\n",
-    "    freq_map = df['Surname'].value_counts().to_dict()\n",
-    "    df['SurnameFreq'] = df['Surname'].map(freq_map)\n",
-    "    return df[['SurnameFreq']]\n",
-    "\n",
     "\n",
     "ActiveCrCard_pipeline = make_pipeline(num_pipeline, FunctionTransformer(ActiveCrCardFunc, validate=False))\n",
     "TenureAge_pipeline = make_pipeline(num_pipeline, FunctionTransformer(TenurebyAgeFunc, validate=False))\n",
-    "HasBalance_pipeline = make_pipeline(num_pipeline, FunctionTransformer(HasBalanceFunc, validate=False))\n",
-    "AgeGroup_pipeline = make_pipeline(num_pipeline, FunctionTransformer(AgeGroupFunc, validate=False))\n",
-    "SurnameFreq_pipeline = make_pipeline(target_enc_pipeline, FunctionTransformer(SurnameFreqFunc, validate=False))\n",
     "\n",
     "preprocessing = ColumnTransformer([\n",
-    "    ('N', num_pipeline, ['CreditScore', 'Balance', 'EstimatedSalary', 'Tenure']),\n",
+    "    ('N', num_poly_pipeline, ['CreditScore', 'Balance', 'EstimatedSalary', 'Tenure']),\n",
     "    ('ActiveCrCard', ActiveCrCard_pipeline, ['IsActiveMember', 'HasCrCard']),\n",
     "    ('TenureAgeR', TenureAge_pipeline, ['Age', 'Tenure']),\n",
     "    ('Age', make_pipeline(num_pipeline, FunctionTransformer(func=np.log)), ['Age']),\n",
-    "    ('AgeGroup', AgeGroup_pipeline, ['Age']),\n",
-    "    ('HasBalance', HasBalance_pipeline, ['Balance']),\n",
-    "    ('SurnameFreq', SurnameFreq_pipeline, ['Surname']),\n",
     "    ('Cat', cat_pipeline, ['Geography', 'Gender', 'NumOfProducts']),\n",
     "    ('Tar', target_enc_pipeline, ['Surname', 'NumOfProducts', 'Gender', 'Geography']),\n",
     "], remainder='drop', verbose_feature_names_out=False)\n",
@@ -1530,13 +1510,13 @@
    ],
    "id": "ddd5da4a7d2c42c0",
    "outputs": [],
-   "execution_count": 319
+   "execution_count": 44
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:05.001957Z",
-     "start_time": "2025-06-24T18:35:04.853883Z"
+     "end_time": "2025-06-25T17:23:17.997461Z",
+     "start_time": "2025-06-25T17:23:17.854069Z"
     }
    },
    "cell_type": "code",
@@ -1552,25 +1532,27 @@
     {
      "data": {
       "text/plain": [
-       "       CreditScore   Balance  EstimatedSalary    Tenure  ActiveCrCard  \\\n",
-       "7401     -0.892516 -0.710855        -2.211410 -1.089897     -0.865896   \n",
-       "14724    -0.698145 -0.710855         1.348969 -0.728787     -0.865896   \n",
+       "       CreditScore   Balance  EstimatedSalary    Tenure  CreditScore^2  \\\n",
+       "12280    -0.248350  1.243009        -0.142896 -1.089564      -0.298182   \n",
+       "12672    -1.327882  1.896069        -1.166256 -0.009100      -1.279125   \n",
+       "\n",
+       "       CreditScore Balance  CreditScore EstimatedSalary  CreditScore Tenure  \\\n",
+       "12280             1.180445                    -0.200157           -1.081623   \n",
+       "12672             1.503946                    -1.311482           -0.265030   \n",
        "\n",
-       "       TenureAgeRatio       Age  AgeGroup  HasBalance  SurnameFreq  \\\n",
-       "7401        -1.129539  1.136507  0.883292   -0.729964    -0.843948   \n",
-       "14724       -0.711800 -0.246380 -0.267113   -0.729964     0.547766   \n",
+       "       Balance^2  Balance EstimatedSalary  ...  Geography_Germany  \\\n",
+       "12280   1.029133                 1.021896  ...           2.151399   \n",
+       "12672   2.350924                 0.642598  ...          -0.464814   \n",
        "\n",
-       "       Geography_Germany  Geography_Spain  Gender_Male  NumOfProducts_2.0  \\\n",
-       "7401            -0.45821         1.880329    -1.159202           0.892244   \n",
-       "14724           -0.45821        -0.531822     0.862662          -1.120769   \n",
+       "       Geography_Spain  Gender_Male  NumOfProducts_2.0  NumOfProducts_3.0  \\\n",
+       "12280        -0.531394    -1.148057          -1.117531          -0.122566   \n",
+       "12672        -0.531394     0.871037          -1.117531          -0.122566   \n",
        "\n",
-       "       NumOfProducts_3.0  NumOfProducts_4.0   Surname  NumOfProducts  \\\n",
-       "7401           -0.121601          -0.047864 -1.020319       -0.83344   \n",
-       "14724          -0.121601          -0.047864 -0.091110        0.94223   \n",
+       "       NumOfProducts_4.0   Surname  NumOfProducts    Gender  Geography  \n",
+       "12280          -0.044766 -2.023555       0.930251  1.148057   2.151078  \n",
+       "12672          -0.044766  0.970214       0.930251 -0.871037  -0.453224  \n",
        "\n",
-       "         Gender  Geography  \n",
-       "7401   1.159202  -0.502280  \n",
-       "14724 -0.862662  -0.441993  "
+       "[2 rows x 27 columns]"
       ],
       "text/html": [
        "<div>\n",
@@ -1595,12 +1577,13 @@
        "      <th>Balance</th>\n",
        "      <th>EstimatedSalary</th>\n",
        "      <th>Tenure</th>\n",
-       "      <th>ActiveCrCard</th>\n",
-       "      <th>TenureAgeRatio</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>AgeGroup</th>\n",
-       "      <th>HasBalance</th>\n",
-       "      <th>SurnameFreq</th>\n",
+       "      <th>CreditScore^2</th>\n",
+       "      <th>CreditScore Balance</th>\n",
+       "      <th>CreditScore EstimatedSalary</th>\n",
+       "      <th>CreditScore Tenure</th>\n",
+       "      <th>Balance^2</th>\n",
+       "      <th>Balance EstimatedSalary</th>\n",
+       "      <th>...</th>\n",
        "      <th>Geography_Germany</th>\n",
        "      <th>Geography_Spain</th>\n",
        "      <th>Gender_Male</th>\n",
@@ -1615,68 +1598,71 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>7401</th>\n",
-       "      <td>-0.892516</td>\n",
-       "      <td>-0.710855</td>\n",
-       "      <td>-2.211410</td>\n",
-       "      <td>-1.089897</td>\n",
-       "      <td>-0.865896</td>\n",
-       "      <td>-1.129539</td>\n",
-       "      <td>1.136507</td>\n",
-       "      <td>0.883292</td>\n",
-       "      <td>-0.729964</td>\n",
-       "      <td>-0.843948</td>\n",
-       "      <td>-0.45821</td>\n",
-       "      <td>1.880329</td>\n",
-       "      <td>-1.159202</td>\n",
-       "      <td>0.892244</td>\n",
-       "      <td>-0.121601</td>\n",
-       "      <td>-0.047864</td>\n",
-       "      <td>-1.020319</td>\n",
-       "      <td>-0.83344</td>\n",
-       "      <td>1.159202</td>\n",
-       "      <td>-0.502280</td>\n",
+       "      <th>12280</th>\n",
+       "      <td>-0.248350</td>\n",
+       "      <td>1.243009</td>\n",
+       "      <td>-0.142896</td>\n",
+       "      <td>-1.089564</td>\n",
+       "      <td>-0.298182</td>\n",
+       "      <td>1.180445</td>\n",
+       "      <td>-0.200157</td>\n",
+       "      <td>-1.081623</td>\n",
+       "      <td>1.029133</td>\n",
+       "      <td>1.021896</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.151399</td>\n",
+       "      <td>-0.531394</td>\n",
+       "      <td>-1.148057</td>\n",
+       "      <td>-1.117531</td>\n",
+       "      <td>-0.122566</td>\n",
+       "      <td>-0.044766</td>\n",
+       "      <td>-2.023555</td>\n",
+       "      <td>0.930251</td>\n",
+       "      <td>1.148057</td>\n",
+       "      <td>2.151078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14724</th>\n",
-       "      <td>-0.698145</td>\n",
-       "      <td>-0.710855</td>\n",
-       "      <td>1.348969</td>\n",
-       "      <td>-0.728787</td>\n",
-       "      <td>-0.865896</td>\n",
-       "      <td>-0.711800</td>\n",
-       "      <td>-0.246380</td>\n",
-       "      <td>-0.267113</td>\n",
-       "      <td>-0.729964</td>\n",
-       "      <td>0.547766</td>\n",
-       "      <td>-0.45821</td>\n",
-       "      <td>-0.531822</td>\n",
-       "      <td>0.862662</td>\n",
-       "      <td>-1.120769</td>\n",
-       "      <td>-0.121601</td>\n",
-       "      <td>-0.047864</td>\n",
-       "      <td>-0.091110</td>\n",
-       "      <td>0.94223</td>\n",
-       "      <td>-0.862662</td>\n",
-       "      <td>-0.441993</td>\n",
+       "      <th>12672</th>\n",
+       "      <td>-1.327882</td>\n",
+       "      <td>1.896069</td>\n",
+       "      <td>-1.166256</td>\n",
+       "      <td>-0.009100</td>\n",
+       "      <td>-1.279125</td>\n",
+       "      <td>1.503946</td>\n",
+       "      <td>-1.311482</td>\n",
+       "      <td>-0.265030</td>\n",
+       "      <td>2.350924</td>\n",
+       "      <td>0.642598</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.464814</td>\n",
+       "      <td>-0.531394</td>\n",
+       "      <td>0.871037</td>\n",
+       "      <td>-1.117531</td>\n",
+       "      <td>-0.122566</td>\n",
+       "      <td>-0.044766</td>\n",
+       "      <td>0.970214</td>\n",
+       "      <td>0.930251</td>\n",
+       "      <td>-0.871037</td>\n",
+       "      <td>-0.453224</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>2 rows × 27 columns</p>\n",
        "</div>"
       ]
      },
-     "execution_count": 320,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 320
+   "execution_count": 45
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:06.582072Z",
-     "start_time": "2025-06-24T18:35:05.198833Z"
+     "end_time": "2025-06-25T17:25:55.359172Z",
+     "start_time": "2025-06-25T17:25:52.022783Z"
     }
    },
    "cell_type": "code",
@@ -1692,7 +1678,7 @@
        "RandomForestClassifier(max_depth=10, n_estimators=200, random_state=3)"
       ],
       "text/html": [
-       "<style>#sk-container-id-9 {\n",
+       "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: #000;\n",
        "  --sklearn-color-text-muted: #666;\n",
@@ -1723,15 +1709,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 {\n",
+       "#sk-container-id-1 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 pre {\n",
+       "#sk-container-id-1 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 input.sk-hidden--visually {\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1743,7 +1729,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1751,7 +1737,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-container {\n",
+       "#sk-container-id-1 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1761,7 +1747,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1777,14 +1763,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel {\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1792,28 +1778,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item {\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-9 div.sk-serial {\n",
+       "#sk-container-id-1 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1831,14 +1817,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable {\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-9 label.sk-toggleable__label {\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: flex;\n",
        "  width: 100%;\n",
@@ -1851,13 +1837,13 @@
        "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 label.sk-toggleable__label .caption {\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
        "  font-size: 0.6rem;\n",
        "  font-weight: lighter;\n",
        "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1865,25 +1851,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content {\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
        "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1891,79 +1877,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  display: block;\n",
        "  width: 100%;\n",
        "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-9 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-9 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-label-container {\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-9 div.sk-estimator {\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1973,18 +1959,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-estimator.fitted {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-9 div.sk-estimator:hover {\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2072,7 +2058,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-9 a.estimator_doc_link {\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -2087,21 +2073,21 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
@@ -2169,7 +2155,7 @@
        "    height: 14px;\n",
        "    cursor: pointer;\n",
        "}\n",
-       "</style><body><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, n_estimators=200, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, n_estimators=200, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
        "        <div class=\"estimator-table\">\n",
        "            <details>\n",
        "                <summary>Parameters</summary>\n",
@@ -2414,18 +2400,18 @@
        "</script></body>"
       ]
      },
-     "execution_count": 321,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 321
+   "execution_count": 47
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:07.067247Z",
-     "start_time": "2025-06-24T18:35:06.780655Z"
+     "end_time": "2025-06-25T17:26:25.021561Z",
+     "start_time": "2025-06-25T17:26:24.653399Z"
     }
    },
    "cell_type": "code",
@@ -2450,22 +2436,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.9458095238095238\n",
-      "Validation Accuracy 0.8977777777777778\n",
+      "Train Accuracy 0.9390666666666667\n",
+      "Validation Accuracy 0.9391111111111111\n",
       "\n",
       "\n",
-      "Train AUC Score 0.983604772638936\n",
-      "Validation AUC Score 0.9234089890049907\n"
+      "Train AUC Score 0.97756974192668\n",
+      "Validation AUC Score 0.9750504425296319\n"
      ]
     }
    ],
-   "execution_count": 322
+   "execution_count": 49
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:07.617798Z",
-     "start_time": "2025-06-24T18:35:07.398979Z"
+     "end_time": "2025-06-25T17:26:31.828506Z",
+     "start_time": "2025-06-25T17:26:31.606625Z"
     }
    },
    "cell_type": "code",
@@ -2494,19 +2480,19 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 323
+   "execution_count": 50
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:07.826711Z",
-     "start_time": "2025-06-24T18:35:07.790749Z"
+     "end_time": "2025-06-25T17:26:46.380385Z",
+     "start_time": "2025-06-25T17:26:46.342506Z"
     }
    },
    "cell_type": "code",
@@ -2520,17 +2506,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test AUC Score 0.9249624727074236\n"
+      "Test AUC Score 0.9750321662507797\n"
      ]
     }
    ],
-   "execution_count": 324
+   "execution_count": 51
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:08.153247Z",
-     "start_time": "2025-06-24T18:35:07.997266Z"
+     "end_time": "2025-06-25T17:26:54.021610Z",
+     "start_time": "2025-06-25T17:26:53.875845Z"
     }
    },
    "cell_type": "code",
@@ -2542,13 +2528,13 @@
    ],
    "id": "7bfb79c5c9fe66d5",
    "outputs": [],
-   "execution_count": 325
+   "execution_count": 52
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:08.360956Z",
-     "start_time": "2025-06-24T18:35:08.326625Z"
+     "end_time": "2025-06-25T17:27:00.495234Z",
+     "start_time": "2025-06-25T17:27:00.453894Z"
     }
    },
    "cell_type": "code",
@@ -2563,17 +2549,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.157467\n",
-       "1     15001  0.014333\n",
-       "2     15002  0.022945\n",
-       "3     15003  0.736975\n",
-       "4     15004  0.023328\n",
+       "0     15000  0.148913\n",
+       "1     15001  0.013209\n",
+       "2     15002  0.022483\n",
+       "3     15003  0.769789\n",
+       "4     15004  0.032218\n",
        "...     ...       ...\n",
-       "9995  24995  0.017799\n",
-       "9996  24996  0.022879\n",
-       "9997  24997  0.009138\n",
-       "9998  24998  0.063574\n",
-       "9999  24999  0.150443\n",
+       "9995  24995  0.023759\n",
+       "9996  24996  0.016029\n",
+       "9997  24997  0.011792\n",
+       "9998  24998  0.060347\n",
+       "9999  24999  0.183835\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -2604,27 +2590,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.157467</td>\n",
+       "      <td>0.148913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.014333</td>\n",
+       "      <td>0.013209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.022945</td>\n",
+       "      <td>0.022483</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.736975</td>\n",
+       "      <td>0.769789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.023328</td>\n",
+       "      <td>0.032218</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2634,27 +2620,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.017799</td>\n",
+       "      <td>0.023759</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.022879</td>\n",
+       "      <td>0.016029</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.009138</td>\n",
+       "      <td>0.011792</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.063574</td>\n",
+       "      <td>0.060347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.150443</td>\n",
+       "      <td>0.183835</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2662,18 +2648,18 @@
        "</div>"
       ]
      },
-     "execution_count": 326,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 326
+   "execution_count": 53
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:08.744050Z",
-     "start_time": "2025-06-24T18:35:08.552515Z"
+     "end_time": "2025-06-25T17:27:30.801057Z",
+     "start_time": "2025-06-25T17:27:22.515229Z"
     }
    },
    "cell_type": "code",
@@ -2687,60 +2673,1509 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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+      "[20:27:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0fdc6d574b9c0d168-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
       "Parameters: { \"njobs\", \"verbose\" } are not used.\n",
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+      "[1399]\tvalidation_0-auc:0.97783\n",
+      "[1400]\tvalidation_0-auc:0.97783\n",
+      "[1401]\tvalidation_0-auc:0.97783\n",
+      "[1402]\tvalidation_0-auc:0.97786\n",
+      "[1403]\tvalidation_0-auc:0.97786\n",
+      "[1404]\tvalidation_0-auc:0.97787\n",
+      "[1405]\tvalidation_0-auc:0.97789\n",
+      "[1406]\tvalidation_0-auc:0.97793\n",
+      "[1407]\tvalidation_0-auc:0.97793\n",
+      "[1408]\tvalidation_0-auc:0.97793\n",
+      "[1409]\tvalidation_0-auc:0.97794\n",
+      "[1410]\tvalidation_0-auc:0.97797\n",
+      "[1411]\tvalidation_0-auc:0.97799\n",
+      "[1412]\tvalidation_0-auc:0.97800\n",
+      "[1413]\tvalidation_0-auc:0.97801\n",
+      "[1414]\tvalidation_0-auc:0.97801\n",
+      "[1415]\tvalidation_0-auc:0.97802\n",
+      "[1416]\tvalidation_0-auc:0.97802\n",
+      "[1417]\tvalidation_0-auc:0.97804\n",
+      "[1418]\tvalidation_0-auc:0.97805\n",
+      "[1419]\tvalidation_0-auc:0.97805\n",
+      "[1420]\tvalidation_0-auc:0.97808\n",
+      "[1421]\tvalidation_0-auc:0.97809\n",
+      "[1422]\tvalidation_0-auc:0.97810\n",
+      "[1423]\tvalidation_0-auc:0.97811\n",
+      "[1424]\tvalidation_0-auc:0.97811\n",
+      "[1425]\tvalidation_0-auc:0.97812\n",
+      "[1426]\tvalidation_0-auc:0.97812\n",
+      "[1427]\tvalidation_0-auc:0.97816\n",
+      "[1428]\tvalidation_0-auc:0.97818\n",
+      "[1429]\tvalidation_0-auc:0.97819\n",
+      "[1430]\tvalidation_0-auc:0.97818\n",
+      "[1431]\tvalidation_0-auc:0.97820\n",
+      "[1432]\tvalidation_0-auc:0.97820\n",
+      "[1433]\tvalidation_0-auc:0.97821\n",
+      "[1434]\tvalidation_0-auc:0.97822\n",
+      "[1435]\tvalidation_0-auc:0.97822\n",
+      "[1436]\tvalidation_0-auc:0.97822\n",
+      "[1437]\tvalidation_0-auc:0.97824\n",
+      "[1438]\tvalidation_0-auc:0.97825\n",
+      "[1439]\tvalidation_0-auc:0.97826\n",
+      "[1440]\tvalidation_0-auc:0.97828\n",
+      "[1441]\tvalidation_0-auc:0.97830\n",
+      "[1442]\tvalidation_0-auc:0.97831\n",
+      "[1443]\tvalidation_0-auc:0.97834\n",
+      "[1444]\tvalidation_0-auc:0.97834\n",
+      "[1445]\tvalidation_0-auc:0.97839\n",
+      "[1446]\tvalidation_0-auc:0.97840\n",
+      "[1447]\tvalidation_0-auc:0.97840\n",
+      "[1448]\tvalidation_0-auc:0.97844\n",
+      "[1449]\tvalidation_0-auc:0.97844\n",
+      "[1450]\tvalidation_0-auc:0.97846\n",
+      "[1451]\tvalidation_0-auc:0.97846\n",
+      "[1452]\tvalidation_0-auc:0.97847\n",
+      "[1453]\tvalidation_0-auc:0.97847\n",
+      "[1454]\tvalidation_0-auc:0.97848\n",
+      "[1455]\tvalidation_0-auc:0.97851\n",
+      "[1456]\tvalidation_0-auc:0.97855\n",
+      "[1457]\tvalidation_0-auc:0.97855\n",
+      "[1458]\tvalidation_0-auc:0.97856\n",
+      "[1459]\tvalidation_0-auc:0.97857\n",
+      "[1460]\tvalidation_0-auc:0.97859\n",
+      "[1461]\tvalidation_0-auc:0.97861\n",
+      "[1462]\tvalidation_0-auc:0.97862\n",
+      "[1463]\tvalidation_0-auc:0.97863\n",
+      "[1464]\tvalidation_0-auc:0.97863\n",
+      "[1465]\tvalidation_0-auc:0.97863\n",
+      "[1466]\tvalidation_0-auc:0.97866\n",
+      "[1467]\tvalidation_0-auc:0.97866\n",
+      "[1468]\tvalidation_0-auc:0.97869\n",
+      "[1469]\tvalidation_0-auc:0.97870\n",
+      "[1470]\tvalidation_0-auc:0.97870\n",
+      "[1471]\tvalidation_0-auc:0.97873\n",
+      "[1472]\tvalidation_0-auc:0.97874\n",
+      "[1473]\tvalidation_0-auc:0.97874\n",
+      "[1474]\tvalidation_0-auc:0.97875\n",
+      "[1475]\tvalidation_0-auc:0.97877\n",
+      "[1476]\tvalidation_0-auc:0.97877\n",
+      "[1477]\tvalidation_0-auc:0.97877\n",
+      "[1478]\tvalidation_0-auc:0.97880\n",
+      "[1479]\tvalidation_0-auc:0.97886\n",
+      "[1480]\tvalidation_0-auc:0.97889\n",
+      "[1481]\tvalidation_0-auc:0.97893\n",
+      "[1482]\tvalidation_0-auc:0.97893\n",
+      "[1483]\tvalidation_0-auc:0.97893\n",
+      "[1484]\tvalidation_0-auc:0.97893\n",
+      "[1485]\tvalidation_0-auc:0.97895\n",
+      "[1486]\tvalidation_0-auc:0.97895\n",
+      "[1487]\tvalidation_0-auc:0.97897\n",
+      "[1488]\tvalidation_0-auc:0.97899\n",
+      "[1489]\tvalidation_0-auc:0.97903\n",
+      "[1490]\tvalidation_0-auc:0.97903\n",
+      "[1491]\tvalidation_0-auc:0.97903\n",
+      "[1492]\tvalidation_0-auc:0.97907\n",
+      "[1493]\tvalidation_0-auc:0.97907\n",
+      "[1494]\tvalidation_0-auc:0.97909\n",
+      "[1495]\tvalidation_0-auc:0.97909\n",
+      "[1496]\tvalidation_0-auc:0.97911\n",
+      "[1497]\tvalidation_0-auc:0.97912\n",
+      "[1498]\tvalidation_0-auc:0.97912\n",
+      "[1499]\tvalidation_0-auc:0.97913\n"
      ]
     },
     {
@@ -2759,7 +4194,7 @@
        "              predictor=None, ...)"
       ],
       "text/html": [
-       "<style>#sk-container-id-10 {\n",
+       "<style>#sk-container-id-2 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: #000;\n",
        "  --sklearn-color-text-muted: #666;\n",
@@ -2790,15 +4225,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 {\n",
+       "#sk-container-id-2 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 pre {\n",
+       "#sk-container-id-2 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 input.sk-hidden--visually {\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -2810,7 +4245,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -2818,7 +4253,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-container {\n",
+       "#sk-container-id-2 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -2828,7 +4263,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -2844,14 +4279,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel {\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2859,28 +4294,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item {\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-10 div.sk-serial {\n",
+       "#sk-container-id-2 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -2898,14 +4333,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable {\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-10 label.sk-toggleable__label {\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: flex;\n",
        "  width: 100%;\n",
@@ -2918,13 +4353,13 @@
        "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
        "  font-size: 0.6rem;\n",
        "  font-weight: lighter;\n",
        "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2932,25 +4367,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content {\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
        "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2958,79 +4393,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  display: block;\n",
        "  width: 100%;\n",
        "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-10 div.sk-label label {\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-10 div.sk-label label {\n",
+       "#sk-container-id-2 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-label-container {\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-10 div.sk-estimator {\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3040,18 +4475,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-estimator.fitted {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-10 div.sk-estimator:hover {\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3139,7 +4574,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-10 a.estimator_doc_link {\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -3154,21 +4589,21 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
@@ -3236,7 +4671,7 @@
        "    height: 14px;\n",
        "    cursor: pointer;\n",
        "}\n",
-       "</style><body><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
        "              colsample_bylevel=None, colsample_bynode=None,\n",
        "              colsample_bytree=None, early_stopping_rounds=None,\n",
        "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
@@ -3246,7 +4681,7 @@
        "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
        "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
        "              n_estimators=1500, n_jobs=None, njobs=-1, num_parallel_tree=None,\n",
-       "              predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "              predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
        "        <div class=\"estimator-table\">\n",
        "            <details>\n",
        "                <summary>Parameters</summary>\n",
@@ -3721,18 +5156,18 @@
        "</script></body>"
       ]
      },
-     "execution_count": 327,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 327
+   "execution_count": 54
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:08.984228Z",
-     "start_time": "2025-06-24T18:35:08.966863Z"
+     "end_time": "2025-06-25T17:27:37.246177Z",
+     "start_time": "2025-06-25T17:27:37.116740Z"
     }
    },
    "cell_type": "code",
@@ -3743,22 +5178,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.9117142857142857\n",
-      "Validation Accuracy 0.8937777777777778\n",
+      "Train Accuracy 0.9470666666666666\n",
+      "Validation Accuracy 0.9435555555555556\n",
       "\n",
       "\n",
-      "Train AUC Score 0.9379888471100507\n",
-      "Validation AUC Score 0.9174996588427947\n"
+      "Train AUC Score 0.981989670397849\n",
+      "Validation AUC Score 0.9791260527136618\n"
      ]
     }
    ],
-   "execution_count": 328
+   "execution_count": 55
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:09.605901Z",
-     "start_time": "2025-06-24T18:35:09.399199Z"
+     "end_time": "2025-06-25T17:27:39.004054Z",
+     "start_time": "2025-06-25T17:27:38.790024Z"
     }
    },
    "cell_type": "code",
@@ -3770,19 +5205,19 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 329
+   "execution_count": 56
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:09.826863Z",
-     "start_time": "2025-06-24T18:35:09.787515Z"
+     "end_time": "2025-06-25T17:27:49.694389Z",
+     "start_time": "2025-06-25T17:27:49.615007Z"
     }
    },
    "cell_type": "code",
@@ -3798,17 +5233,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.451203\n",
-       "1     15001  0.415478\n",
-       "2     15002  0.416244\n",
-       "3     15003  0.573613\n",
-       "4     15004  0.416244\n",
+       "0     15000  0.086987\n",
+       "1     15001  0.033294\n",
+       "2     15002  0.021645\n",
+       "3     15003  0.921273\n",
+       "4     15004  0.034494\n",
        "...     ...       ...\n",
-       "9995  24995  0.415478\n",
-       "9996  24996  0.415478\n",
-       "9997  24997  0.415478\n",
-       "9998  24998  0.428972\n",
-       "9999  24999  0.428972\n",
+       "9995  24995  0.017658\n",
+       "9996  24996  0.012540\n",
+       "9997  24997  0.000907\n",
+       "9998  24998  0.058854\n",
+       "9999  24999  0.155139\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -3839,27 +5274,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.451203</td>\n",
+       "      <td>0.086987</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.415478</td>\n",
+       "      <td>0.033294</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.416244</td>\n",
+       "      <td>0.021645</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.573613</td>\n",
+       "      <td>0.921273</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.416244</td>\n",
+       "      <td>0.034494</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -3869,27 +5304,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.415478</td>\n",
+       "      <td>0.017658</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.415478</td>\n",
+       "      <td>0.012540</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.415478</td>\n",
+       "      <td>0.000907</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.428972</td>\n",
+       "      <td>0.058854</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.428972</td>\n",
+       "      <td>0.155139</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3897,18 +5332,18 @@
        "</div>"
       ]
      },
-     "execution_count": 330,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 330
+   "execution_count": 57
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:13.077486Z",
-     "start_time": "2025-06-24T18:35:10.062522Z"
+     "end_time": "2025-06-25T17:28:03.542484Z",
+     "start_time": "2025-06-25T17:27:57.942584Z"
     }
    },
    "cell_type": "code",
@@ -3922,14 +5357,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[LightGBM] [Info] Number of positive: 2136, number of negative: 8364\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000253 seconds.\n",
+      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
+      "[LightGBM] [Info] Number of positive: 3052, number of negative: 11948\n",
+      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000836 seconds.\n",
       "You can set `force_row_wise=true` to remove the overhead.\n",
       "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 1449\n",
-      "[LightGBM] [Info] Number of data points in the train set: 10500, number of used features: 20\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203429 -> initscore=-1.365002\n",
-      "[LightGBM] [Info] Start training from score -1.365002\n"
+      "[LightGBM] [Info] Total Bins 3671\n",
+      "[LightGBM] [Info] Number of data points in the train set: 15000, number of used features: 27\n",
+      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203467 -> initscore=-1.364767\n",
+      "[LightGBM] [Info] Start training from score -1.364767\n"
      ]
     },
     {
@@ -3939,7 +5376,7 @@
        "               num_leaves=16, random_state=3)"
       ],
       "text/html": [
-       "<style>#sk-container-id-11 {\n",
+       "<style>#sk-container-id-3 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: #000;\n",
        "  --sklearn-color-text-muted: #666;\n",
@@ -3970,15 +5407,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 {\n",
+       "#sk-container-id-3 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 pre {\n",
+       "#sk-container-id-3 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-hidden--visually {\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -3990,7 +5427,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -3998,7 +5435,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-container {\n",
+       "#sk-container-id-3 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -4008,7 +5445,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -4024,14 +5461,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel {\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -4039,28 +5476,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item {\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-11 div.sk-serial {\n",
+       "#sk-container-id-3 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -4078,14 +5515,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable {\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-11 label.sk-toggleable__label {\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: flex;\n",
        "  width: 100%;\n",
@@ -4098,13 +5535,13 @@
        "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 label.sk-toggleable__label .caption {\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
        "  font-size: 0.6rem;\n",
        "  font-weight: lighter;\n",
        "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -4112,25 +5549,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content {\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
        "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -4138,79 +5575,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  display: block;\n",
        "  width: 100%;\n",
        "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-11 div.sk-label label {\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-11 div.sk-label label {\n",
+       "#sk-container-id-3 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label-container {\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-11 div.sk-estimator {\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -4220,18 +5657,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-11 div.sk-estimator:hover {\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -4319,7 +5756,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link {\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -4334,21 +5771,21 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
@@ -4416,8 +5853,8 @@
        "    height: 14px;\n",
        "    cursor: pointer;\n",
        "}\n",
-       "</style><body><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(learning_rate=0.0088, max_depth=16, n_estimators=2000,\n",
-       "               num_leaves=16, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(learning_rate=0.0088, max_depth=16, n_estimators=2000,\n",
+       "               num_leaves=16, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
        "        <div class=\"estimator-table\">\n",
        "            <details>\n",
        "                <summary>Parameters</summary>\n",
@@ -4662,18 +6099,18 @@
        "</script></body>"
       ]
      },
-     "execution_count": 331,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 331
+   "execution_count": 58
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:13.580002Z",
-     "start_time": "2025-06-24T18:35:13.285694Z"
+     "end_time": "2025-06-25T17:28:06.723767Z",
+     "start_time": "2025-06-25T17:28:06.328407Z"
     }
    },
    "cell_type": "code",
@@ -4684,22 +6121,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.9461904761904761\n",
-      "Validation Accuracy 0.8835555555555555\n",
+      "Train Accuracy 0.9378666666666666\n",
+      "Validation Accuracy 0.936\n",
       "\n",
       "\n",
-      "Train AUC Score 0.9840109744455012\n",
-      "Validation AUC Score 0.9206480524797255\n"
+      "Train AUC Score 0.9766497987566041\n",
+      "Validation AUC Score 0.9741512496101061\n"
      ]
     }
    ],
-   "execution_count": 332
+   "execution_count": 59
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:14.198910Z",
-     "start_time": "2025-06-24T18:35:13.977462Z"
+     "end_time": "2025-06-25T17:28:08.814843Z",
+     "start_time": "2025-06-25T17:28:08.607812Z"
     }
    },
    "cell_type": "code",
@@ -4711,19 +6148,19 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 333
+   "execution_count": 60
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:14.401811Z",
-     "start_time": "2025-06-24T18:35:14.367865Z"
+     "end_time": "2025-06-25T17:28:13.557360Z",
+     "start_time": "2025-06-25T17:28:13.523859Z"
     }
    },
    "cell_type": "code",
@@ -4737,17 +6174,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test AUC Score 0.9252025011696818\n"
+      "Test AUC Score 0.9748762086712413\n"
      ]
     }
    ],
-   "execution_count": 334
+   "execution_count": 61
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:14.752533Z",
-     "start_time": "2025-06-24T18:35:14.599290Z"
+     "end_time": "2025-06-25T17:28:20.549335Z",
+     "start_time": "2025-06-25T17:28:20.401488Z"
     }
    },
    "cell_type": "code",
@@ -4763,17 +6200,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.118829\n",
-       "1     15001  0.034504\n",
-       "2     15002  0.018889\n",
-       "3     15003  0.768910\n",
-       "4     15004  0.023811\n",
+       "0     15000  0.060546\n",
+       "1     15001  0.033435\n",
+       "2     15002  0.023444\n",
+       "3     15003  0.834804\n",
+       "4     15004  0.033128\n",
        "...     ...       ...\n",
-       "9995  24995  0.017133\n",
-       "9996  24996  0.018456\n",
-       "9997  24997  0.000448\n",
-       "9998  24998  0.060574\n",
-       "9999  24999  0.218695\n",
+       "9995  24995  0.009217\n",
+       "9996  24996  0.009822\n",
+       "9997  24997  0.001068\n",
+       "9998  24998  0.051219\n",
+       "9999  24999  0.228998\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -4804,27 +6241,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.118829</td>\n",
+       "      <td>0.060546</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.034504</td>\n",
+       "      <td>0.033435</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.018889</td>\n",
+       "      <td>0.023444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.768910</td>\n",
+       "      <td>0.834804</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.023811</td>\n",
+       "      <td>0.033128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -4834,27 +6271,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.017133</td>\n",
+       "      <td>0.009217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.018456</td>\n",
+       "      <td>0.009822</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.000448</td>\n",
+       "      <td>0.001068</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.060574</td>\n",
+       "      <td>0.051219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.218695</td>\n",
+       "      <td>0.228998</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -4862,18 +6299,18 @@
        "</div>"
       ]
      },
-     "execution_count": 335,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 335
+   "execution_count": 62
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:48.440842Z",
-     "start_time": "2025-06-24T18:35:14.956412Z"
+     "end_time": "2025-06-25T17:29:11.741077Z",
+     "start_time": "2025-06-25T17:28:33.174629Z"
     }
    },
    "cell_type": "code",
@@ -4893,41 +6330,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:\ttest: 0.8975749\tbest: 0.8975749 (0)\ttotal: 4.27ms\tremaining: 38.5s\n",
-      "1000:\ttest: 0.9289577\tbest: 0.9289638 (999)\ttotal: 3.58s\tremaining: 28.6s\n",
-      "2000:\ttest: 0.9307439\tbest: 0.9307548 (1999)\ttotal: 7.18s\tremaining: 25.1s\n",
-      "3000:\ttest: 0.9308913\tbest: 0.9310302 (2575)\ttotal: 10.8s\tremaining: 21.5s\n",
-      "4000:\ttest: 0.9302017\tbest: 0.9310302 (2575)\ttotal: 14.3s\tremaining: 17.9s\n",
-      "5000:\ttest: 0.9297557\tbest: 0.9310302 (2575)\ttotal: 18s\tremaining: 14.4s\n",
-      "6000:\ttest: 0.9292525\tbest: 0.9310302 (2575)\ttotal: 21.5s\tremaining: 10.8s\n",
-      "7000:\ttest: 0.9286896\tbest: 0.9310302 (2575)\ttotal: 25.1s\tremaining: 7.17s\n",
-      "8000:\ttest: 0.9281974\tbest: 0.9310302 (2575)\ttotal: 28.8s\tremaining: 3.59s\n",
-      "8999:\ttest: 0.9278245\tbest: 0.9310302 (2575)\ttotal: 32.3s\tremaining: 0us\n",
+      "0:\ttest: 0.9192103\tbest: 0.9192103 (0)\ttotal: 152ms\tremaining: 22m 47s\n",
+      "1000:\ttest: 0.9384077\tbest: 0.9384077 (1000)\ttotal: 5.37s\tremaining: 42.9s\n",
+      "2000:\ttest: 0.9424529\tbest: 0.9424529 (2000)\ttotal: 9.3s\tremaining: 32.5s\n",
+      "3000:\ttest: 0.9452309\tbest: 0.9452309 (3000)\ttotal: 13.2s\tremaining: 26.5s\n",
+      "4000:\ttest: 0.9471011\tbest: 0.9471011 (4000)\ttotal: 17.4s\tremaining: 21.8s\n",
+      "5000:\ttest: 0.9485571\tbest: 0.9485571 (4999)\ttotal: 21.4s\tremaining: 17.1s\n",
+      "6000:\ttest: 0.9498206\tbest: 0.9498243 (5999)\ttotal: 25.4s\tremaining: 12.7s\n",
+      "7000:\ttest: 0.9509087\tbest: 0.9509087 (7000)\ttotal: 29.3s\tremaining: 8.36s\n",
+      "8000:\ttest: 0.9519870\tbest: 0.9519894 (7998)\ttotal: 33.2s\tremaining: 4.15s\n",
+      "8999:\ttest: 0.9529118\tbest: 0.9529130 (8996)\ttotal: 37.2s\tremaining: 0us\n",
       "\n",
-      "bestTest = 0.9310301973\n",
-      "bestIteration = 2575\n",
+      "bestTest = 0.9529129952\n",
+      "bestIteration = 8996\n",
       "\n",
-      "Shrink model to first 2576 iterations.\n"
+      "Shrink model to first 8997 iterations.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<catboost.core.CatBoostClassifier at 0x1670fbcf0b0>"
+       "<catboost.core.CatBoostClassifier at 0x25616ec6360>"
       ]
      },
-     "execution_count": 336,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 336
+   "execution_count": 63
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:48.842051Z",
-     "start_time": "2025-06-24T18:35:48.815879Z"
+     "end_time": "2025-06-25T17:29:15.959502Z",
+     "start_time": "2025-06-25T17:29:15.899996Z"
     }
    },
    "cell_type": "code",
@@ -4938,22 +6375,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.9122857142857143\n",
-      "Validation Accuracy 0.8982222222222223\n",
+      "Train Accuracy 0.9194\n",
+      "Validation Accuracy 0.9182222222222223\n",
       "\n",
       "\n",
-      "Train AUC Score 0.9491507768266711\n",
-      "Validation AUC Score 0.9310301972863382\n"
+      "Train AUC Score 0.9557166079222285\n",
+      "Validation AUC Score 0.952912995165315\n"
      ]
     }
    ],
-   "execution_count": 337
+   "execution_count": 64
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:49.421553Z",
-     "start_time": "2025-06-24T18:35:49.213533Z"
+     "end_time": "2025-06-25T17:29:18.355221Z",
+     "start_time": "2025-06-25T17:29:18.146404Z"
     }
    },
    "cell_type": "code",
@@ -4965,25 +6402,19 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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uu+9Ss2bNe/YXKFAAuDuf6ty5cyxbtozq1avj4ODA7du3Wbdu3T/WbzKZ7ukVTs/iF3nz5sVkMtGlSxdatGhxz/70JFwZJTXJSZU6Z7NQoUIUKFCA2NhYUlJS0iRvqYlP6j2dXo/azn+V+rnGxMSkWdn0t99+IyYmxvhcH/S5i0jm0uIkIiJPia5du3L9+vU088pSXbt2jSVLlvDMM8/g6enJb7/9xvXr13n99depUKGCMdRy3759AMYKeKnb/+rQoUNphlTt2LGDlJQUatWqRdmyZXFzc7vnwcuXLl3i5MmTxrwcHx8fdu/enaYXwmKxsG3bNqpUqYKDgwOTJ0+mffv22Gw2cufOjZ+fn/Gw7dQVKv8ao7e3N5cuXeLnn382tiUlJdGvXz8+++yze67lxIkTlC9fnvbt2xtJW2RkJGfPnjXaYfny5fj7+5OUlISDgwN16tRh7NixRhxhYWHUrVuXU6dOYTKZeP755wkJCaFixYpcuXLlvm34IDVr1uT8+fOULVuWKlWqGD+bN29m3bp1xjyvjPTss89SqFAhwsPD05zT3d2d6dOnG72lJ06coFmzZtSuXdtIIP9679wvvrx58xrzMVP9dZGN+3F2dqZSpUr89ttvaeKqUKECc+fONYbfPg6pw4lTffXVV5QoUYLSpUtTs2ZNLBYLX375ZZoyqUNbU3vL/s5f7+X0tHN6pJ73m2++SbN95syZjB07Nt2fu4hkLvW4iYg8JTw9PRkwYACzZs3i3LlztG3bFldXV3755ReWLl1KQkICixYtwmQyUbZsWZydnVmwYAG5cuUiV65c7Nixw1iFLnUYXupf6r/++msaNGhgLOxx9epV+vXrR3BwMBcuXGDGjBn4+vpSp04dTCYTAwcO5L333iMkJIQ2bdoQGxvL3LlzKVCggLEgxNtvv82+fft4/fXX6dGjBw4ODnz66adcunSJJUuWAHcXUFi2bBlDhw6lVatWJCcns2TJElxcXIzVE/Pnz09YWBiHDx+mUqVKBAYGsnLlSnr37s2AAQMoWLAgq1atIjExMc1CLKmqVq3KRx99xKJFi/D09OT3339n4cKFJCUlGe1Qu3Ztpk2bRt++fencuTN2dnasWbMGBwcH/Pz8KFGiBE5OTrz77rv069ePwoULc+jQIf73v/8Zi0E8rC5durBp0ya6dOlC165dcXV15csvv+Szzz77V3Pn/omdnR0hISGMGDECOzs7/Pz8uHHjBh999BGRkZHGsM2qVauyZcsWKleujLu7O2FhYSxcuBCTyWS0WWoSfPjwYcqVK0e1atXw8/Nj5cqVDBs2jFdeecW4N9OThA4cOJAePXowaNAgWrVqhcViYenSpXz//ffG6qePw/Lly3FycsLT05OdO3eye/duY75ogwYNqFWrFiNHjiQqKopKlSpx9OhRFi9eTNu2bY35bX8n9V4+duyYMYfyQe2cHs899xwvvvgi06ZNIzExkcqVK3PgwAG+/vprZs2ale7PXUQylxI3EZGnSO/evalUqRKrVq1i4sSJXL9+HXd3dxo0aECvXr0oXrw4cPdL9UcffcSUKVMYMGAAefPm5fnnn+fTTz+le/fuHD9+HH9/f2rVqkXdunWZPn06hw8fZtGiRQAEBQWRmJhI3759cXBwoGXLlvznP/8xHiQdGBhI3rx5WbhwIX379sXZ2Zn69eszcOBAY75PhQoVWL16NTNmzGDYsGGYTCaqVq3KihUr8Pb2Bu5+EZ42bRpLly7l7bffxmQyUaNGDVasWGEMyezUqROnT5+me/fuTJw4kZYtW/Lpp58yZcoUxo8fT0pKCtWqVWPlypXGKpl/1rNnT2JjY1mxYgXz5s2jWLFitG7dGpPJxMKFC4mLi+O5555jwYIFzJs3j4EDB2KxWHjhhRdYunQpzz77LABLly5l+vTpjB8/nhs3blCmTBnGjBlDYGDgI32WRYsWZc2aNUyfPp1Ro0Zx584dypQpw/jx49O1guSjeuWVV8ibNy9Llixh7dq15MmTh+rVqzNt2jRKlSoFwKRJkxg7dqzR61imTBlGjx7N5s2bjYUwnJ2defPNN1m7di179uzh4MGD+Pr6MmTIEFauXMnOnTupXLkyc+fO5dVXX31gXPXq1ePjjz9m7ty59O/fH3t7eypXrsyyZcse60PFhw0bxhdffMHChQt59tln+fDDD2nWrBmAcc98+OGHrFixgpiYGEqWLElISIjxB4t/0qtXLz766CO6d+/Ol19+ma52Tq+pU6cyd+5cVq5cSWxsLGXLlmXWrFnGyp3p+dxFJHOZbHr4hoiIiMi/Ehoayuuvv86KFSuoVatWVocjIk8gzXETERERERHJ5pS4iYiIiIiIZHMaKikiIiIiIpLNqcdNREREREQkm1PiJiIiIiIiks0pcRMREREREcnmlLiJiIiIiIhkc0rcREREREREsrlcWR2AyMOIjU3I6hDkCeTqmlf3lmQ43VeSWXRvSWZ5Gu8tm82G1Zp1i+ybTFCoUL70ldXjAERERERE5GlkSbFwPe52liVvJhMULpy+xE09bpKjrA15l/BTp7M6DBERERHJ4YpWLE/n+R9iNpuytNctvZS4SY4S9es5In5Q4iYiIiIiTxclbiIiIo/JDTsT/8ttT5ydGTNQONlCpdvJONjgD3szvzrZc8vOhL3VRskkCxUSUzD937HhDnb84pSLO2YTzhYblW8l42qxAnDLbOLH3PbE5Lq75lihFCuVbieT5wF/QU4BDuR3pHiShYqJKQAkmuA7Zwdu2JkpkGKlekISjv9XTYS9HdH2ZjxvJWdC64iIyD/RqpIiIiKPgQU46uyIa4qVxnGJNLiRSJLZxPd5HIizM3EyrwMVE5Npej0Rn/gkwh3sOO949++r13KZ+TGPPdUS7u4vnpTCcWcHLP9X94m8DjjZbDSOS6RxXCK5bDa+z2P/wJhO57EnwWxKs+28Uy6crDaaXE/E0WbjvNPdGJKBX3Pn4vnbStpERLKCEjd5KKtWrcLDw4Ply5dndSgiIjnKbbOJ/BYrFRJTMAMONnjmTgox9mZumU08c8dC0WQrJiCf1YZ7stXoQbvoYEexJAsFLVbMwLN3LNjbbFx2sAOg7s07VL6VjB2QbIIUkwmHB0zXuORgx22ziYIp1jTbTf93nHH4/704m9ueMokpRu+biIg8Xkrc5KGsWrWK1157jRUrVpCSkpLV4YiI5BjOVhs145P4c//WH/Z2FEixUiz57tDGVBYgyt5Mgf8bChlvZyafJW2Clc9i44bd3f/G7bj7H3pYHnt2FXDiei4zHv/QM3bTbOIXp1x4JSTds6/snRSSTCa+LeBEisnEs3dSuGFn4nouM6WTLPepTUREHgclbpJuhw8f5tq1awwdOhSr1cqOHTuMfbGxsYSEhFCjRg0CAgJYuXIllSpVIjw8HICLFy/Sq1cvatWqhZ+fHzNnziQp6d4vDCIiTwMb8LNTLiLt7aj0l/liKcBxZwfsbFD2/+adpZgg1196uuxsNixpRzlS9VYyza4nUjzJwpF8jtwvdbMAYXkdqHw7Gaf79J452qB2fBJN4xKpGZ+EvQ1+zG1P5VtJ/GFvx/58jhzK50CMnb5CiIg8TvqtK+m2cuVKgoKCcHJyomPHjixdutTYN3jwYG7evMmuXbtYt24du3fvxmK5+5fZW7du0aVLFypUqMC+fftYvXo1hw4dYs6cOVl1KSIiWSYZ+C6vAxEOdtS5eYf8f1pAJN5s4mB+R2xA7fg7xgpidjbuSdIsJtO9yRx3Vx17/nYyKSa4Zn/vf/M/5rGnUIqVosnWe/bdT7iDHfksNvJYbfyQ1x7v+CSeu5XCd84OaNSkiMjjo8RN0iUiIoL9+/fTqVMnAIKCgvj11185evQokZGRHDhwgGHDhuHi4kLBggUZNmyYceyePXtISkpi4MCBODo6UqxYMQYMGMCqVauy6nJERLJEwv8lZikmqPeXpC0ql5mD+R1xS7YYPV2p8lms3PxLD9dNOxPOFisWYE9+R67b/f/MzvZ/P/b3yawiHOwId7RjRwEndhRwIjaXmXNOudiXz/Gessmmu4uVeCQmk2A2YWeD3DYbLhYrd8wmkkz31i8iIplDjwOQdFm9ejUpKSm0bt3a2JaSksLSpUvp1asXACVLljT2lSpVyngdERFBTEwMPj4+xjabzUZycjLXrl2jUKFCj+EKRESyVrIJQp0dKJRipeqt5DRz3WLtTJxwduCFW8mUus88slJJFk44O9xdoCTFyu+OdiSZTLgnW7ADnC02/pfbnhoJSZht8FMee5wtNlxT7u1Va349Mc37w/8XU+rjAP7sTG57nk1Mwd4Geaw2kk13k89E891HFjxoARQREck4Stzkge7cucP69esZP348devWNbafPXuWHj160LNnT+Bugla2bFnjdSp3d3dKly7NV199ZWyLj4/n2rVrFCxY8DFdhYhI1rrkkIvbdmb+MJv44/9Wg0xVKNmKlbvDGH/80zL+BVOs1IxPonCKlcq3kjmdx55Es4l8Fhs+8XeMxKnarSR+ym3P3vxOABROseATf8cYVnPU2YHcVhtVHuL5a9ftTMSbTVT5v0TS0XZ3COahfI7Y2WxUu5V2oRUREclcStzkgbZs2YLJZKJly5bY2///LxTu7u5UrFiRDRs24Ofnx9SpU5k4cSIAU6ZMMcr5+fkxZcoUlixZwuuvv05iYiLvvfcef/zxBxs2bHjs1yMikhWevZPCs3cefTXekkkWSv7Nqo72Nqh2KxnuuxwJ1Iz/+8Wg6vzNPheL7Z59Ze5YKHNHK0uKiGQFzXGTB1q9evU9SVuqDh06sGnTJsaPH4/JZKJRo0a0bduWSpUqAWBvb4+zszPLly8nNDSUBg0a0LhxY8xmM/Pnz3/clyIiIiIikiOZbDabRqjLv3bw4EFq1KiBk9PdYTo///wzbdq04eTJkzg63jvh/VHNadmO86HHMqw+EREREXk6lajyAoN2fUlsbAIp95kT/DiYTFC4cL50lVWPm2SIyZMnM3/+fFJSUoiPj2f+/PnUrVs3Q5M2EREREZGnlRI3yRDTp0/n5MmT1K5dG39/f+zs7NLMcxMRERERkUenxUkkQ1SoUIFPPvkk089TpHw5km7dzvTziIiIiMiTrWjF8lkdwkPRHDcREREREXkqWVIsXI+7jdWaNSnRw8xxU4+b5CixsQlZHYI8gVxd8+rekgyn+0oyi+4tySxP471ltdqyLGl7WErcJEexWq1Ys2bRH3lCmf7vCcIWixWNP5CMovtKMovuLcksureyPyVukqOYzWbMWlJHMoGdnW4syXi6rySz6N6Sf5KTepEk/TTHTURERETkCWJNsRD7kPO2UudaXb16Uz1uj5HmuMkTa/u77xH5409ZHYaIiIhItlSofDlazp6B2WxSr9sTRomb5Cgxv50n8vSPWR2GiIiIiMhjpQHSTxmLxcKlS5eyOgwREREREXkI2S5x8/DwoEePHvx16t2GDRvw9/fPtPNGR0czZswY/P39qVatGvXq1WPQoEGcPXs2Tbkvv/ySOnXqUKNGDXbv3o2/vz9VqlTBy8sLLy8vPD09qVevHpMnT8aagcsfBgcHM2fOnH9dT0hICBs3bvz3AQH//e9/adasGV5eXjRr1oxVq1b9bVmLxcLkyZOpW7cuXl5e9O7dm6ioqAyJQ0RERETkSZftEjeAvXv3smTJksd2vvDwcNq2bUt0dDQLFy4kLCyMTZs2UapUKYKCgjh48KBRdt26dbRo0YITJ07g5+cHwOjRowkLCyMsLIyTJ0/y8ccfs3HjRubOnfvYriG9YmNjM6Seb775hhkzZjB58mS+++47Jk2axKxZs9ixY8d9y8+fP5+DBw/y+eefs3//fpycnBg+fHiGxCIiIiIi8qTLlolbcHAws2fP5rvvvrvv/vDwcDw8PAgPDze2zZkzh+DgYOBu71zHjh2ZPHkyNWvWpHbt2qxcuZLPPvsMPz8/atSowYgRI4xjJ06cSIUKFfjwww+pUKECZrOZQoUK8c477xAcHMzQoUNJSUmhffv2HDlyhDVr1tC4ceO/jd/DwwMfHx9++ukn43qGDh2Kn58fjRo1Ij4+np9//pnu3btTs2ZNGjRowKhRo7h586ZRx7p16wgICMDLy4shQ4Zw+/ZtY9/QoUMZOnToPecMDQ0FICYmhsGDB+Pj40OtWrUICQkhLi6O999/n+PHj7Nw4UJ69epltFvDhg2pWbMm7dq1Y9euXen6jCIjI+nevTuenp6YTCa8vLyoVasWx44du2/5devW0b17d4oVK4azszPvv/8++/bt07BNEREREZF0yJaJW5MmTejQoQMDBw7k+vXrj1THiRMnKFq0KEeOHKF///5MnDiR0NBQvvzyS5YvX8769es5duwYSUlJ7N27l9atW2NKffLgnwQFBREVFUVYWBjr16/H29ubnj178s0339z3vMnJyYSGhnLkyBF8fX2N7YcOHWLNmjVs3ryZ5ORkXn/9dcqXL8++ffv4/PPPOX/+PO+++y4Ahw8fZsyYMYwbN45jx45RrVo1fvjhh3Rf+4ABA4iPj2fnzp3s2rWLGzduMHr0aMaPH2/Ev2DBAo4cOcLatWtZt24doaGhvPLKK7z//vskJyc/8BydOnWiR48exvtr165x7NgxXnjhhXvK3rx5kytXrlCxYkVjW+HChSlQoAA///xzuq9LRERERORplW1XlRwyZAhhYWEMHTqU+fPnP/TxefLk4Y033sBkMlGvXj0sFgvdunUjd+7cVKlShSJFihAREUHp0qVJTk6mcOHC962nSJEiAP84H2v06NFMmDDBeO/u7s6bb75J586djW0NGjSgaNGiAKxfvx57e3sGDx6MnZ0dTk5OfPDBB7Ro0YLo6Gg2b95M06ZNqVOnDgAdO3Zk3bp16bruiIgIjh49yldffYWrqysAkyZNum8C7OjoSFxcnNET+corr9ChQ4f7JrD/JDo6mp49e/LCCy/w8ssv37M/ISEBuPuZ/JmTk5OxT0RERERE/l62TdwcHByYNWsWbdu2ZenSpUYSkl4uLi5GAmI23+1YzJ8/v7HfbDZjtVpxdXUlV65cXL58+b71pA7HdHNz+9tzjRw5ksDAwH+MJzUBhLu9U8WLF8fOzs7YVrJkSeBu4hUZGUnlypXTHF+qVKl/rD9VdHQ0ACVKlDC2ubm53Td+Ly8v5syZw8qVK1myZAlOTk4EBwfTu3dvo80e5OTJkwwYMABvb28mTpxIrlz33lK5c+cGSDPcEyAxMZG8efOm6zwiIiIiIk+zbDlUMlXp0qUZO3YsM2fO5OTJk8b21ITnz0P6/rroRnp7jRwcHAgICGDDhg33XQVy3bp1uLm54eXl9QhXcP94SpQoweXLl7FYLMa2ixcvAneTLHd393vmfl25csV4bTab01x7TEyM8bpYsWIAaRLRX3/9lVmzZt0T0+XLlylUqBAff/wxR48eZfLkySxYsIB9+/al65rWr19Ply5deOONN5g+fToODg73LVegQAGKFi3Kr7/+amyLjo7m+vXraYZPioiIiIjI/WXrxA3gpZdeol27dqxdu9bYVqhQIQoUKMC2bduw2Wz8+OOPfPXVV498jvfff5/Lly/Tv39/zp07h9VqJTIykhkzZrB69WomTJiAvb19RlwOAA0bNgRg2rRpJCYmEh0dzfjx46lduzYlSpSgXbt2fPPNN+zevZuUlBS++OILvv/+e+P4cuXKcfz4cSIjI0lMTGTevHlGYli0aFF8fX2ZMmUKN27cID4+nqlTpxqJoIODg7EIyg8//MBbb73FmTNncHBwoFChQgDp6t3csWMHo0aNYs6cOXTt2vWB5QMDA5k/fz6XLl0iPj6eCRMmULNmTUqXLv1wjSciIiIi8hTK9okbwLBhw3j++eeN9w4ODowdO5bt27dTvXp1Jk2aRFBQ0CPXX7RoUTZu3EixYsXo0aMH1atXp127doSHh/PZZ5/RoEGDjLgMQ758+Vi2bBlnz56lYcOGvPzyy5QoUYLZs2cDUKNGDaZMmcKkSZPw9vZmx44daRY66dChA15eXrRq1YomTZpQrFgxihcvbuyfNm0azs7ONG/enICAAAoWLMjo0aMBaNOmDZ9//jkdO3akWbNmdO3ald69e+Pp6cmAAQMYNmwY1apVe+A1zJ07F4vFQv/+/Y1n2Hl5eRmrdW7evDlNL2Xfvn1p2LAhnTp1omHDhty5c+e+vYAiIiIiInIvk+2vT7oWycZWtX+V8GPHszoMERERkWyp6AuV6bJtE7GxCaSk3DsN6O+YTFC4cD6uXr2JsoPHJ7Xd0yNH9LiJiIiIiIg8zbLtqpKSdXbs2HHPA77/rEaNGixZsuQxRiQiIiIi8nRT4ib3aNasGc2aNcvqMO6r4LNlSf7LYwVERERE5K5C5ctldQiSSTTHTURERETkCWJNsRAbdxurNf1f8zXHLWs8zBw39bhJjhIbm5DVIcgTyNU1r+4tyXC6rySz6N6SB7FabQ+VtEnOoMRNchSr1cp9npMu8sj+7xGIWCxW/YVRMozuK8ksurdEnl5K3CRHMZvNmLUWqmQCOzvdWJLxdF9JZtG99eRQ75ikl+a4iYiIiIhkkUeZj5YZNMcta2iOmzyx9g37gGs//ZTVYYiIiIj8ay7lyuE3fQpmsynLEzfJ/pS4SY4Sd/481376X1aHISIiIiLyWGmAtGQai8XCpUuXsjoMEREREZEc76lO3Dw8POjRowd/nea3YcMG/P39M+280dHRjBkzBn9/f6pVq0a9evUYNGgQZ8+eTVPuyy+/pE6dOtSoUYPdu3fj7+9PlSpV8PLywsvLC09PT+rVq8fkyZOxZuBSi8HBwcyZM+df1xMSEsLGjRv/fUAiIiIiIk+5pzpxA9i7dy9Llix5bOcLDw+nbdu2REdHs3DhQsLCwti0aROlSpUiKCiIgwcPGmXXrVtHixYtOHHiBH5+fgCMHj2asLAwwsLCOHnyJB9//DEbN25k7ty5j+0a0is2NjarQxAREREReSI89YlbcHAws2fP5rvvvrvv/vDwcDw8PAgPDze2zZkzh+DgYOBu71zHjh2ZPHkyNWvWpHbt2qxcuZLPPvsMPz8/atSowYgRI4xjJ06cSIUKFfjwww+pUKECZrOZQoUK8c477xAcHMzQoUNJSUmhffv2HDlyhDVr1tC4ceO/jd/DwwMfHx9++r8FO1Lr8PPzo1GjRsTHx/Pzzz/TvXt3atasSYMGDRg1ahQ3b9406li3bh0BAQF4eXkxZMgQbt++bewbOnQoQ4cOveecoaGhAMTExDB48GB8fHyoVasWISEhxMXF8f7773P8+HEWLlxIr169jHZr2LAhNWvWpF27duzatStdn5GIiIiIyNPuqU/cmjRpQocOHRg4cCDXr19/pDpOnDhB0aJFOXLkCP3792fixImEhoby5Zdfsnz5ctavX8+xY8dISkpi7969tG7dGlPqEzT/JCgoiKioKMLCwli/fj3e3t707NmTb7755r7nTU5OJjQ0lCNHjuDr62tsP3ToEGvWrGHz5s0kJyfz+uuvU758efbt28fnn3/O+fPneffddwE4fPgwY8aMYdy4cRw7doxq1arxww8/pPvaBwwYQHx8PDt37mTXrl3cuHGD0aNHM378eCP+BQsWcOTIEdauXcu6desIDQ3llVde4f333yc5OfkhW1tERERE5OmjVSWBIUOGEBYWxtChQ5k/f/5DH58nTx7eeOMNTCYT9erVw2Kx0K1bN3Lnzk2VKlUoUqQIERERlC5dmuTkZAoXLnzfeooUKQJAVFTU355r9OjRTJgwwXjv7u7Om2++SefOnY1tDRo0oGjRogCsX78ee3t7Bg8ejJ2dHU5OTnzwwQe0aNGC6OhoNm/eTNOmTalTpw4AHTt2ZN26dem67oiICI4ePcpXX32Fq6srAJMmTbpvAuzo6EhcXJzRE/nKK6/QoUOH+yawIiIiIiKSlhI3wMHBgVmzZtG2bVuWLl1qJCHp5eLiYiQgZvPdTsz8+fMb+81mM1arFVdXV3LlysXly5fvW0/qcEw3N7e/PdfIkSMJDAz8x3hSE0CAa9euUbx4cezs7IxtJUuWBO4mXpGRkVSuXDnN8aVKlfrH+lNFR0cDUKJECWObm5vbfeP38vJizpw5rFy5kiVLluDk5ERwcDC9e/c22kxERERERO5P35j/T+nSpRk7diwzZ87k5MmTxvbUhOfPQ/r+uuhGenuNHBwcCAgIYMOGDfddBXLdunW4ubnh5eX1CFdw/3hKlCjB5cuXsVgsxraLFy8Cd5Msd3f3e5bsv3LlivHabDanufaYmBjjdbFixQDSJKK//vors2bNuiemy5cvU6hQIT7++GOOHj3K5MmTWbBgAfv27XvEqxQREREReXoocfuTl156iXbt2rF27VpjW6FChShQoADbtm3DZrPx448/8tVXXz3yOd5//30uX75M//79OXfuHFarlcjISGbMmMHq1auZMGEC9vb2GXE5ADRs2BCAadOmkZiYSHR0NOPHj6d27dqUKFGCdu3a8c0337B7925SUlL44osv+P77743jy5Urx/Hjx4mMjCQxMZF58+YZiWHRokXx9fVlypQp3Lhxg/j4eKZOnWokgg4ODsYiKD/88ANvvfUWZ86cwcHBgUKFCgE8dO+miIiIiMjTSInbXwwbNoznn3/eeO/g4MDYsWPZvn071atXZ9KkSQQFBT1y/UWLFmXjxo0UK1aMHj16UL16ddq1a0d4eDifffYZDRo0yIjLMOTLl49ly5Zx9uxZGjZsyMsvv0yJEiWYPXs2ADVq1GDKlClMmjQJb29vduzYkWahkw4dOuDl5UWrVq1o0qQJxYoVo3jx4sb+adOm4ezsTPPmzQkICKBgwYKMHj0agDZt2vD555/TsWNHmjVrRteuXenduzeenp4MGDCAYcOGUa1atQy9XhERERGRJ5HJ9tenT4tkY1te60zkifs/ukFEREQkJylU6Xnabvyc2NgEUlLunUbzOJlMULhwPq5evYmyg8cntd3TQz1uIiIiIiIi2ZwSNxERERERkWxOjwOQHKVA2bKk3L6d1WGIiIiI/Gsu5cpldQiSg2iOm4iIiIhIFrGmWIiNu43VmrVfyTXHLWs8zBw39bhJjhIbm5DVIcgTyNU1r+4tyXC6rySz6N56slittixP2iRnUOImOYrVauU+zy4XeWSpz6u3WKz6C6NkGN1Xkll0b4k8vbQ4iYiIiIiISDanHjfJUcxmM2b9uUEygZ2dbqzsSEOIRERE7lLiJjmKq2verA5BnlC6t7Inq8VC7PWsn7QvIiKS1ZS4SY5y7INRxP7vf1kdhog8BvmffZbaUyZiNpuUuImIyFNPiZvkKDcuXOD6/85kdRgiIiIiIo+VJnWIiIiIiIhkc+pxyyHi4uKYOXMmu3fvJi4uDmdnZ3x9fQkJCcHd3T2rwxMRERERkUykHrccIiQkhNjYWNavX8/JkyfZuHEjSUlJvPnmm6SkpGR1eCIiIiIikomUuOUQJ06coEmTJri5uQFQuHBhhg0bRrVq1bhx4wb+/v5s2LDBKB8aGoqHhwcA4eHheHh4MGnSJHx8fBg9ejRz5syhf//+DB48GG9vbxo0aMD06dON4yMjI3nnnXfw9/enWrVqBAQEsH79emO/h4cHa9eupVmzZlSrVo1evXpx+vRpXn31Vby8vGjXrh2///67UX7btm20bNmSGjVqEBgYyIEDBzK7yUREREREnhhK3HKIFi1aMHLkSEaNGsWXX35JREQEbm5uTJo0iYIFC6arjoSEBA4ePEhISAgAO3fupF69eoSGhjJ27FgWL17MyZMnARg+fDj29vZs27aN7777js6dOzN27FgSEhKM+rZs2cLatWv5+uuvOXHiBH369GH8+PEcPHgQBwcHFixYAMDevXsZOXIkI0aM4OjRo/Tr149+/frxyy+/ZGwjiYiIiIg8oZS45RDjxo1jxIgR/PHHH4wYMQJ/f3+aNGnC5s2b011HmzZtcHBwIH/+/ACUKVOGNm3aYGdnR8OGDXFzc+PChQvG+UaOHIm9vT2XL18mb968JCYmEhcXZ9TXuXNnXFxcKFKkCBUqVKBp06aUK1eOPHnyULt2bSIiIgD49NNPee211/Dx8cHOzg4/Pz/8/f1Zs2ZNxjWQiIiIiMgTTIuT5BBms5nWrVvTunVrbDYb586dY9OmTbz77rvG8MkHKVKkSJr3fz3O3t4eq9UKwKVLl5gyZQoXLlygTJkyPPPMMwDGfgAXFxfjtZ2dHQUKFEgTr81297lLERERHD16lP/+97/GfovFQu3atdMVt4iIiIjI006JWw6wf/9++vfvz+7du3FxccFkMlG+fHkGDRrEwYMH+emnnzCbzSQnJxvHxMbG3lOPyWRK1/mSk5Pp2bMnAwcOpGPHjphMJk6fPn1P715663N3d6dNmzb06NHD2Hb58mWcnJzSdbyIiIiIyNNOQyVzAB8fHwoVKsR7773Hzz//THJyMvHx8WzevJkLFy7QqFEjypUrx65du0hMTCQ6OpoVK1Y88vmSk5NJTEzEyckJk8nE5cuXmTp1qrHvYQUFBbFixQpOnToFwA8//EBgYCBbt2595BhFRERERJ4m6nHLAZycnFi9ejVz586ld+/eXLt2DXt7ezw9PVm2bBnlypVj8ODBjBo1Cl9fX4oUKcIbb7zBiRMnHul8efLkYcKECcyePZtx48ZRqFAhgoKC+PXXXzl79ixly5Z9qPpefPFFbt26xbBhw7h8+TIuLi506dKF4ODgR4pPRERERORpY7KlTkQSyQF2BXfh2ndhWR2GiDwGLs8/R9P1a4mNTSAlxfrgA7IRkwkKF87H1as30f+ykpF0b0lm0b2VNVLbPT00VFJERERERCSbU+ImIiIiIiKSzWmOm+Qo+cuUwXL7dlaHISKPQf5nn83qEERERLINJW6So/iMHZXVIYjIY2S1WLBaNdlCREREiZvkKLGxCVkdgjyBXF3z6t7KpqxWmxI3ERERlLhJDmO1WrHmrMXlJJtLfY68xWLVKloiIiKSbSlxkxzFbDZj1pI6kgns7HRjPW7qTRMREUk/JW6So7i65s3qEOQJpXvr8bNaLMRev63kTUREJB2UuEmOcnrsOG6cOZPVYYjIv+RctixVx43FbDYpcRMREUkHJW6SoyT8/js3z/yc1WGIiIiIiDxWmtSRhSwWC5cuXcrqMEREREREJJtT4pZBVq1ahYeHB8uXL0/3MSEhIWzcuBGAy5cv4+XlxeXLlzMknlOnTtG/f3/q1q1L9erVad68OQsXLiQlJeWBx27bto3g4GBq1aqFj48PHTp04KuvvsqQuFJt2LABf3//DK1TRERERORJpcQtg6xatYrXXnuNFStWpCs5AoiNjTVeFy9enLCwMIoXL/6vY/nqq694/fXX8fHxYefOnZw4cYLp06ezZcsWBg0a9I/Hjhs3jilTpvDWW2+xf/9+Dh8+TPfu3Xn//fdZtWrVv45NREREREQenhK3DHD48GGuXbvG0KFDsVqt7Nixw9gXExPD4MGD8fHxoVatWoSEhBAXF8f777/P8ePHWbhwIb169SI8PBwPDw/Cw8OZPXs2r776appzTJ06lR49egBw9epVBg8ejK+vL/Xq1WPEiBHEx8cDcOfOHUaOHEnfvn0JDg7G2dkZk8lEpUqVmDZtGjabjevXrxvnmzRpEj4+PowePZpTp06xcuVKPvzwQxo2bIiDgwO5cuWicePGfPDBB/z+++8A2Gw2Fi1aRMuWLfH29sbHx4dBgwaRmJgIwNChQ+nfvz/Nmzendu3aXLx4kXPnzhEcHIyXlxctW7bkp59+ehwfjYiIiIjIE0GJWwZYuXIlQUFBODk50bFjR5YuXWrsGzBgAPHx8ezcuZNdu3Zx48YNRo8ezfjx4/H29qZnz54sWLAgTX3t27fn+++/58KFC8DduXCbN2+mffv2WK1W+vTpg9lsZseOHWzZsoWoqChGjBgBwHfffcf169d5+eWX74nzueee48MPP8TFxcXYlpCQwMGDBwkJCeHbb7+lVKlSVKtW7Z5j27Rpw7BhwwDYvn07K1asYM6cORw/fpw1a9Zw4MABtmzZYpTfv38/s2fPZufOnRQrVoyePXtSoUIFjhw5wowZM/jmm28eub1FRERERJ42Stz+pYiICPbv30+nTp0ACAoK4tdff+Xo0aNERERw9OhRhgwZgqurK87OzkyaNInevXv/Y50lSpSgbt26xvy3AwcOYLFY8PPz4/Tp0/z444+MHDkSZ2dnXF1dGTJkCNu2bSM2NpaYmBgAChcunK7427Rpg4ODA/nz5ycmJiZdxzVo0ID169dTpkwZYmJiiI2NxcXFhcjISKOMp6cnFStWJH/+/ISFhfHHH3/w7rvv4ujoSIUKFXjzzTfTFZ+IiIiIiOhxAP/a6tWrSUlJoXXr1sa2lJQUli5dSq9evYC7iVgqNzc33NzcHljvK6+8wpQpUxgwYABffPEFrVu3xt7envDwcCwWCw0bNkxT3sHBgUuXLhl1R0dH33e+XHR0dJrzFylSJM3rgwcP3jeeO3fukJSURL58+bDZbMycOZPdu3dTsGBBnn/+eZKTk7HZbGnqShUZGYmrqytOTk7GttKlSz+wDURERERE5C71uP0Ld+7cYf369YwfP55NmzYZP/Pnz2fPnj1YLBaANCtF/vrrr8yaNeuBdQcEBHD79m327dvHt99+S/v27QFwd3fHycmJ0NBQjh8/zvHjxzl06BAbN26kUqVKeHp64uLiwpdffnlPnWfOnKFevXqEhYUZ20wmk/G6UaNGhIeHc+rUqXuOXbt2Lf7+/ty+fZtp06Zx+fJlvv32W7766itmzpxJ3rx505T/c73FihUjJiaGhIQEY9uVK1ce2AYiIiIiInKXErd/YcuWLZhMJlq2bIm7u7vx06BBAypWrMjGjRvx9fVlypQp3Lhxg/j4eKZOnWo8u83BwYGbN2/et257e3vatGnD6NGjqVy5MuXKlQOgatWqPPPMM0yaNImEhAQSExOZMGECXbp0wWKx4ODgwPDhw5k7dy6rVq0iISEBi8XC8ePHGTBgAM2aNcPLy+u+53zhhRfo0KEDAwYMYN++faSkpHDnzh02bdrEjBkz6N+/P7lz5yY+Ph5HR0fs7Oy4c+cOS5cu5ezZsyQnJ9+3Xi8vL8qWLcu4ceO4ffs2v//+e5p5gCIiIiIi8s+UuP0Lq1evpmXLltjb29+zr0OHDmzatIkpU6bg7OxM8+bNCQgIoGDBgowePRq4O7/s888/p2PHjvet/5VXXiEiIsLobQPIlSsXCxcu5OrVqzRt2pR69epx8eJFli1bhqOjIwAtW7Zk7ty57Nq1i4CAALy9vRkxYgTt2rVj+vTp/3hNo0eP5q233mLWrFnUqVMHX19f/vvf/zJ58mSCg4MBeOedd0hMTKRu3br4+/tz8uRJWrduzdmzZ+9bp52dHYsWLSIqKoq6devy1ltvERAQ8OAGFhERERERAEy2P09MEsnmQt/qzvWwk1kdhoj8S/me86Duqk+JjU0gJcWa1eFkOJMJChfOx9WrN9H/spKRdG9JZtG9lTVS2z091OMmIiIiIiKSzSlxExERERERyeb0OADJUfI+8wyW27ezOgwR+Zecy5bN6hBERERyFCVukqO88MHwrA5BRDKI1WLBatVEChERkfRQ4iY5SmxswoMLiTwkV9e8ureygNVqU+ImIiKSTkrcJEexWq1Yn7wF6CQLpT4r3mKxahUtERERybaUuEmOYjabMWtJnSeWemBERERE7k+Jm+Qorq55szoEyURWi4XY67eVvImIiIj8hRI3yVHOTZ1Ewi+/ZHUYkglyP/MMFd4fgdlsUuImIiIi8hdK3CRHuX3pEgm/nM3qMEREREREHivNFhIREREREcnmlLiJiIiIiIhkc0rcsqlVq1bh4eHB8uXLM7zuEydO0K9fP+rVq0e1atWoX78+7777LhcuXMjwc4mIiIiIyL+nxC2bWrVqFa+99horVqwgJSUlw+r94osv6NatG1WrVmX9+vWcPHmSdevWUaRIETp06EBkZGSGnUtERERERDKGErds6PDhw1y7do2hQ4ditVrZsWOHsS82NpaQkBBq1KhBQEAAK1eupFKlSoSHhwNw8eJFevXqRa1atfDz82PmzJkkJSUBcOPGDUaPHs27775L9+7dcXd3x2Qy4e7uzuDBg+ncuTOxsbEADB06lP79+9O8eXNq167NxYsXiYiI4J133qFOnTr4+voyaNAgoqKiAAgNDcXDwyPNdQwdOpShQ4cCMGfOHPr06UO/fv3w9PTE39+ftWvXZnpbioiIiIg8CZS4ZUMrV64kKCgIJycnOnbsyNKlS419gwcP5ubNm+zatYt169axe/duLBYLALdu3aJLly5UqFCBffv2sXr1ag4dOsScOXMA2L17NykpKQQGBt73vP369eO5554z3u/fv5/Zs2ezc+dOihUrRteuXbGzs2Pnzp1s374dgF69eqW7R3DXrl1Ur16dY8eOMWbMGMaOHcvhw4cfqY1ERERERJ4mStyymYiICPbv30+nTp0ACAoK4tdff+Xo0aNERkZy4MABhg0bhouLCwULFmTYsGHGsXv27CEpKYmBAwfi6OhIsWLFGDBgAKtWrQIgMjISFxcXnJycjGPmzp2Lt7c33t7eeHp6Mnz4cGOfp6cnFStWJH/+/Bw/fpxLly4xevRo8uXLR/78+Rk9ejRnzpzh9OnT6bo2Dw8P3nzzTezt7alXrx7NmjVj06ZNGdFsIiIiIiJPND3HLZtZvXo1KSkptG7d2tiWkpLC0qVL6dWrFwAlS5Y09pUqVcp4HRERQUxMDD4+PsY2m81GcnIy165dw83NjevXr5OUlISDgwMAb7/9Nm+//TZwd2jjn3vPihQpYry+du0arq6uODs7G9ucnZ1xcXEhIiKCwoULP/DaypQpk+Z9sWLF+N///vfA40REREREnnZK3LKRO3fusH79esaPH0/dunWN7WfPnqVHjx707NkTuJuglS1b1nidyt3dndKlS/PVV18Z2+Lj47l27RoFCxakUaNGmM1mNm7cSFBQ0APjMZlMxusSJUoQGxtLfHy8kbzdvHmT2NhY3NzcMJvvdt7+OSmMjY3F1dXVqOOvC5+Eh4dTrFix9DWOiIiIiMhTTEMls5EtW7ZgMplo2bIl7u7uxk+DBg2oWLEiGzZswM/Pj6lTpxIXF0dcXBxTpkwxjvfz8yMhIYElS5aQlJTEjRs3GDJkCCEhIZhMJlxdXRkzZgwTJ07k448/5tq1a8DdhGr+/Pls3749TS/bn1WpUoXy5cszcuRIbt68yc2bNxk1ahSlS5emevXqlC5dmly5crFt2zYADh06xJEjR9LUcfLkSTZt2oTFYmHv3r3s2rWLdu3aZVJrioiIiIg8OZS4ZSOrV6+mZcuW2Nvb37OvQ4cObNq0ifHjx2MymWjUqBFt27alUqVKANjb2+Ps7Mzy5csJDQ2lQYMGNG7cGLPZzPz584162rRpw4oVK/jpp59o06YNnp6etGnThlOnTjFr1iwGDhx439hy5crFwoULSUlJoVmzZvj5+ZGcnMyyZcvIlSsXRYoUYdiwYXz00UdUr16dTz/99J5FUJ5//nl27dpF7dq1mTRpElOnTsXLyysDW1BERERE5MlkstlstqwOQtLv4MGD1KhRw1hg5Oeff6ZNmzacPHkSR0fHLI7u782ZM4ejR4+ycuXKf1XP6f59ufnDqQyKSrKTvBUqUnXRx8TGJpCSYn1s5zWZoHDhfFy9ehP9NpSMovtKMovuLcksureyRmq7p4d63HKYyZMnM3/+fFJSUoiPj2f+/PnUrVs3WydtIiIiIiLy7yhxy2GmT5/OyZMnqV27Nv7+/tjZ2aWZ5yYiIiIiIk8erSqZw1SoUIFPPvkkq8N4aP369cuQenKXKoU1MTFD6pLsJfczz2R1CCIiIiLZlhI3yVHK/WdoVocgmchqsWC1amC9iIiIyF8pcZMcJTY2IatDkExktdqUuImIiIjchxI3yVGsVivWx7fgoIiIiIhItqDETXIUs9mMWUvq5EjqTRMRERF5dErcJEdxdc2b1SHII7JaLMRev63kTUREROQRKHGTHCVi7jTunPs1q8OQh+RQqjQlBw7DbDYpcRMRERF5BErcJEdJCg8n8TclbiIiIiLydNFsIRERERERkWxOPW45nIeHB46OjtjZ2WGz2bC3t8fb25sRI0ZQrFixBx4fHh5OQEAAu3btomTJko8hYhEREREReVjqcXsCLF68mLCwME6ePMnu3bux2Wz85z//yeqwREREREQkgyhxe8I4OzsTFBTE6dOnjW3nzp2jZ8+eNGrUiKpVq/LSSy+xe/fu+x7/3Xff8frrr1OvXj2qVKlCYGAgJ0+eBCA0NBR/f3/mz59P/fr1qVmzJv369SM+Pt44/pNPPqFJkyZ4eXkRGBjI4cOHAbDZbKxYsYJmzZrh7e1Nx44d08QoIiIiIiJ/T4nbEyYuLo5t27bRtGlTY1u/fv2oWLEiX3/9NcePH6devXqMGjXqnmMTExPp3bs3zZo1Y9++fYSGhlK6dGmmTJlilImIiCAyMpKvv/6adevWERYWxurVqwHYsGEDH330EVOmTOHEiRO89tpr9O7dm+vXr7N69WqWLVvG7NmzOXz4MIGBgbz55ptcvXo109tERERERCSn0xy3J0CvXr2ws7PDarWSkJBAvnz5WLhwobF/4cKFFC1aFJvNRkREBPnz5ycyMvKeeuzt7Vm7di3PPPMMd+7cISIiAhcXF3744Yc05fr27YuTkxPPPPMMtWrV4vz58wB88cUXdOjQAS8vLwBeeeUVypUrh5OTE6tWraJnz54899xzALRv357169ezefNmunbtmllNIyIiIiLyRFDi9gRYsGABtWrVAu72mq1atYo33niDtWvXUrlyZc6cOUOfPn2Ijo6mXLlyFCxYEJvt3mdp2dnZERoaSvfu3bl16xbly5cnV65c95R1c3MzXtvb2xv7o6OjKV68eJqy1atXB+721E2ePJlp06YZ+1JSUnjhhRcyphFERERERJ5gStyeME5OTnTr1o1FixZx6NAhChcuzIABA5g7dy7+/v4A7Nixg507d95z7Pfff8/YsWNZs2aNkVAtXbrU6FF7kGLFivHHH3+k2TZz5kxatWqFu7s7/fv3p0WLFsa+ixcv4uLi8ohXKiIiIiLy9NActydMSkoKn3/+OTdu3KBGjRokJCRgsVjInTs3AL/++ivz5s0DICkpKc2xN2/exGw24+TkBMDJkydZsWLFPeX+TmBgIGvXruXUqVNYrVY+//xzVq1ahaurK0FBQcyfP59z584BsH//flq0aMGxY8cy6tJFRERERJ5Y6nF7AnTv3h07OzsATCYTZcqUYcaMGcYwxXfffZf//Oc/3L59G3d3d4KCgpg6dSpnz55N0+Pl6+tLx44d6dSpE1arlZIlSxIcHMz06dPTtYhIy5YtuXHjBv/5z3+Ijo6mfPnyLF68mIIFC9KlSxdsNht9+vQhKiqKokWLMmLECAICAjKlTUREREREniQm2/0mO4lkU+eHvsPt/+kxAjmN07PleXbmAmJjE0hJsWZ1OGmYTFC4cD6uXr2JfhtKRtF9JZlF95ZkFt1bWSO13dNDQyVFRERERESyOSVuIiIiIiIi2ZzmuEmO4lCyJLY7iVkdhjwkh1KlszoEERERkRxNiZvkKCXeHpzVIcgjslosWK0aNC8iIiLyKJS4SY4SG5uQ1SHII7JabUrcRERERB6REjfJUaxWK9bstSihiIiIiEimU+ImOYrZbMasJXVyHPW2iYiIiPw7StwkR3F1zZvVIcgjsFosxF6/reRNRERE5BEpcZMcJerjudz5/VxWhyEPwaF4KYr2GojZbFLiJiIiIvKIlLhJjpJ0JZyk33/L6jBERERERB4rzRYSERERERHJ5pS4ZaHLly8zcuRI/P398fT0pGbNmnTr1o2DBw9mdWgPLTw8HA8PD8LDw7M6FBERERGRJ44Styxy9uxZWrVqRVJSEosXL+bEiRPs3LmTVq1a0bdvX/bu3ZvVIYqIiIiISDahxC2LjBgxAl9fXyZOnEi5cuWws7PDxcWF1q1bM3LkSJKTkwE4dOgQ7du3x9vbmxYtWrB582ajDqvVyqJFi2jcuDE1atSgffv27N+/39gfGxtLSEgINWrUICAggJUrV1KpUiXCw8ONHrJJkybh4+PD6NGjSUpKYvLkyTRv3hwvLy/q1KnD2LFjsdnuLigRHBzMpEmTCAwMxNPTk8DAQI4fP57murZs2ULz5s3x9PSkS5cuREZGAtC8eXMWLFiQpmzLli1Zv359prSviIiIiMiTRIlbFrhy5QphYWG8+uqr993ftm1bGjduzJkzZ+jduzc9evQgNDSUsWPHMmHCBCM5mzdvHqtWrWL27NmEhobStWtX+vTpw6lTpwAYPHgwN2/eZNeuXaxbt47du3djsVjSnCshIYGDBw8SEhLCJ598wv79+/nkk08ICwvjo48+Ys2aNRw5csQov3btWt59912OHj1KkyZN6N27N7Gxscb+H3/8kc8++4y9e/cSFxfHvHnzAAgMDGTTpk1GudOnTxMeHk7z5s0zplFFRERERJ5gStyywJUrVwBwd3c3th0+fBhvb2+8vb3x8vKiWbNmrFmzhoCAAJo2bYqdnR3Vq1cnKCiIVatWAfD555/To0cPKleuTK5cuXjppZfw9/dn/fr1REZGcuDAAYYNG4aLiwsFCxZk2LBh98TSpk0bHBwcyJ8/P0FBQSxfvhw3NzeioqJITEwkb968Rq8ZQLt27ahduzYODg706tWL3Llzs3v3bmN/r169yJcvHwUKFKB+/fpcvHjROM/Fixf54YcfANi4cSMvvvgiefPquWwiIiIiIg+ixwFkATc3NwAiIyMpW7YsAHXq1DGGHW7YsIG5c+cSERHBkSNH8Pb2No61WCyULl0agKtXr1KqVKk0dZcsWZIzZ87wxx9/GO9T/bUsQJEiRYzXt2/fZsyYMRw7dgx3d3cqVaqEzWbDarUaZcqUKWO8NplMuLu7Ex0dbWxzcXExXtvb2xs9fG5ubtSvX59Nmzbx3HPPsXXrVubMmZOO1hIRERERESVuWaBEiRJUqVKFdevWUbt27b8t5+7uTtu2bRkzZoyxLSoqyphzVqJECS5dupTmmEuXLlGkSBGKFy8OQEREhJEcRkRE3HMOk8lkvB4+fDgFChTgwIEDODo6YrVa8fHxSVP+z71vVquVy5cvU6xYsXRdd7t27Rg9ejS+vr7ky5fvnrpFREREROT+NFQyi6TOVfvggw84f/48NpuN+Ph4Nm7cyJw5cyhSpAjt27dn69atHDhwAKvVyoULF+jcuTNLly4F4JVXXmHRokX8+OOPWCwWtm/fzrfffkvbtm0pUqQIfn5+TJ06lbi4OOLi4pgyZco/xhQfH4+joyNms5n4+HimTJlCfHy8sVAKwLp16zh9+jRJSUnMmzcPm82Gn59fuq65UaNGWCwWPvzwQwIDAx+98UREREREnjJK3LJIxYoV2bp1K05OTvTq1YsaNWrQsGFDPvvsM9566y1WrFhBtWrVmDFjBjNmzMDHx4fOnTvj7+/PoEGDAHjzzTfp1KkTISEheHt7s3DhQmbMmEHNmjUBGD9+PCaTiUaNGtG2bVsqVaoE3B3CeD/Dhw/nzJkz1KxZkxdffJH4+Hjq16/P2bNnjTI1a9ZkzJgx1K5dm9DQUJYuXUq+fPnSdc329va0atWKM2fO0LZt23/TfCIiIiIiTxWTLXXcnTxxDh48SI0aNXBycgLg559/pk2bNpw8eRJHR8eHri84OJiaNWvSr1+/R45pxYoV7Nu3jyVLljzS8eHjh3Ln7P8e+fzy+Dk88yylxswkNjaBlBTrgw94zEwmKFw4H1ev3kS/DSWj6L6SzKJ7SzKL7q2skdru6aEetyfY5MmTmT9/PikpKcTHxzN//nzq1q37SEnbvxUdHc2pU6f45JNPeO211x77+UVEREREcjIlbk+w6dOnc/LkSWrXro2/vz92dnYPnOeWWfbs2UNwcDC+vr4EBARkSQwiIiIiIjmVhkpKjhL18Vzu/H4uq8OQh+BQvBRFew3UUEl5qui+ksyie0syi+6trPEwQyX1OADJUYp0ezurQ5BHYLVYsFr1v4CIiIjIo1LiJjlKbGxCVocgj8BqtSlxExEREfkXlLhJjmK1WrFmv9F2IiIiIiKZ6pEXJ4mLi+P06dNYrVaSkpIyMiYRERERERH5k4fucUtISGDEiBFs27YNJycnNmzYwJtvvsmyZct49tlnMyNGEYPZbMastVAzjYY0ioiIiGRPD524TZkyhVu3brF9+3aCgoIoVaoUfn5+jB8/no8//jgzYhQxuLrmzeoQnmhWi4XY67eVvImIiIhkMw+duO3evZstW7ZQoEABTCYT9vb2DB06lAYNGmRGfCJpxKxZRFLE71kdxhPJvkhxCgX3xWw2KXETERERyWYeOnGzWq04ODgAkPoIuD9vE8lMydFXSA6/kNVhiIiIiIg8Vg89W6h27dqMGTOG27dvYzKZAJg1axY1a9bM8OBERERERETkERK39957j3PnzuHj48PNmzfx8vLi2LFjDBkyJDPiExEREREReeo9dOJ269Yt1q5dy+rVq5kxYwZLly5l/fr1FC1aNDPiy/YuX77MyJEj8ff3x9PTk5o1a9KtWzcOHjyYKecLDQ3Fw8MjU+oG8Pf3x8PDg/3799+zb+fOnXh4eDB06NB01RUcHMycOXMyOkQRERERkafOQyduHTp0ICEhgapVq9K8eXO8vLyws7PLjNiyvbNnz9KqVSuSkpJYvHgxJ06cYOfOnbRq1Yq+ffuyd+/erA7xkbi6uvLFF1/cs/3zzz/H2dk5CyISEREREXm6PfTiJC4uLkRGRuoLPDBixAh8fX2ZOHGisc3FxYXWrVtjtVpJTk4G4NChQ8yYMYMLFy5QtGhRevbsSatWrQAYOnQoDg4OREVFERoaSsGCBXnjjTd4/fXXAYiKimLEiBEcPXoUV1dXWrRokSaGixcvMmHCBMLCwsiTJ4+RNDo4OLBhwwY+/fRTXFxcOHXqFCNHjqRly5YPvK6WLVuydu1abt68Sb58+Yw4Tp48Sf369Y1ySUlJzJw5kz179nDlyhWcnJx46aWXGD58uDH/MZXNZmPlypWsWrWKa9euUbFiRYYNG8YLL7zwCC0vIiIiIvJ0eejErUKFCgQFBeHp6UmRIkXS7PtzAvOku3LlCmFhYSxfvvy++9u2bQvAmTNn6N27N1OnTiUgIIDvv/+ePn364OrqaiRBGzZsYOHChcydO5f169czZswYmjVrRtGiRQkJCcHV1ZV9+/Zx8+ZNevfubZzj1q1bdOnShRYtWjB79mxiYmLo378/VquVQYMGAfDjjz8yadIkFixYgNVqTde1Pf/885QtW5Yvv/ySDh06ALBx40aaN29OYmKiUe6TTz5h//79fPLJJxQpUoSwsDA6d+5M48aNqVOnTpo6V69ezbJly5g/fz7lypVj06ZNvPnmm2zfvp3ChQunr9FFRERERJ5SDz1UMk+ePDRt2vSepO1pc+XKFQDc3d2NbYcPH8bb2xtvb2+8vLxo1qwZa9asISAggKZNm2JnZ0f16tUJCgpi1apVxnG1atXC19eXXLly0a5dOywWCxcvXiQiIoLjx48zePBgnJ2dKVasGG+//bZx3J49e0hKSmLgwIE4OjpSrFgxBgwYkKZue3t7WrdujYODA05OTum+vsDAwDTDJT///HPatWuXpkxQUBDLly/Hzc2NqKgoEhMTyZs3L5GRkffUt2rVKnr27Mlzzz2Hvb097du3p1y5cmzevDndMYmIiIiIPK0eusftaepV+ydubm4AREZGUrZsWQDq1KnD8ePHgbu9aHPnziUiIoIjR47g7e1tHGuxWChduvQ9dcHdRAvuPhsvNQEqXry4sf/Px0VERBATE4OPj4+xzWazkZyczLVr14y6zeaHzs9p2bIlU6dO5fz581y7dg1HR0eqVKmSpszt27cZM2YMx44dw93dnUqVKmGz2e7bsxcREcHkyZOZNm2asS0lJUVDJUVERERE0uGhE7e5c+f+7b4/9wY96UqUKEGVKlVYt24dtWvX/tty7u7utG3bljFjxhjboqKijIeX/5PU3rxLly5Rrlw54P/39KXuL126NF999ZWxLT4+nmvXrlGwYEGAe+aapVfBggVp1KgRGzduJCoqivbt299TZvjw4RQoUIADBw7g6OiI1WpNk0T+9Vr69++fZo7exYsXcXFxeaT4RERERESeJg/dFRMaGprmZ8eOHXz00Uf89ttvmRFftjZhwgT279/PBx98wPnz57HZbMTHx7Nx40bmzJlDkSJFaN++PVu3buXAgQNYrVYuXLhA586dWbp06QPrL168OPXq1WPixInExcURHR2dJnH28/MjISGBJUuWkJSUxI0bNxgyZAghISGPnLD9WWBgIFu2bGH37t33XdQkPj4eR0dHzGYz8fHxTJkyhfj4eGNRlj8LCgpi/vz5nDt3DoD9+/fTokULjh079q/jFBERERF50j10j9vKlSvv2bZp0yZCQ0MzJKCcpGLFimzdupXFixfTq1cvoqOjMZlMeHh48NZbb/HKK6/g4ODAjBkzmDFjBgMGDCB37ty8/PLLDBw4MF3nmD59OqNHj8bPzw9nZ2cCAwP5/vvvAXB2dmb58uVMmjSJJUuWYLVaqVWrFvPnz8+Q62vQoAF37tyhdu3auLq63rN/+PDhjBgxgpo1a5I3b14aNWpE/fr1OXv27D1lu3Tpgs1mo0+fPkRFRVG0aFFGjBhBQEBAhsQqIiIiIvIkM9nSM2YvHWrVqvVUJm/yeEXOGUPSbz9ndRhPJPuSZXAfNJ7Y2ARSUtK3AumTwGSCwoXzcfXqTTLmt6GI7ivJPLq3JLPo3soaqe2eHg+/asV9HD16lDx58mREVSIiIiIiIvIXDz1U0t/fP838qeTkZK5evZrm+WKSPQUGBnL+/Pm/3b948eI0q19mR/Zu7tiS7mR1GE8k+yLFH1xIRERERLLEQydu/fr1S/PebDZTrlw5LeueA2zYsCGrQ/jXCr7aI6tDeKJZLRasVo2PEBEREcluHjpxi4mJoVu3bvdsnzVrFu+8805GxCTyt2JjE7I6hCea1WpT4iYiIiKSDaUrcYuJiTGWcZ8zZw7VqlVL8xyymzdv8sknnyhxk0xntVq5z/O9RURERESeaOlK3BwcHOjfvz+xsbEAdO7c+Z79HTp0yPjoRP7CbDZjzpAldZ4+6k0TERERybnSlbg5Oztz+PBhAF588UW++uqrTA1K5O+4uubN6hByLJvFQsz120reRERERHKgh57j9ndJW0xMDAULFvzXAYn8k9hNK0m5cjGrw8hxchUuhmu7rpjNJiVuIiIiIjnQQydup06dYsqUKURGRmL9v8lGycnJxMTEcPr06QwPUOTPLNeukPLHpawOQ0RERETksXro2UJjxozBzc2NevXqUbZsWTp37oydnR2DBg3KjPhERERERESeeg+duP3yyy9MnDiRTp06YbFYePPNN5k5cyZbtmzJjPjkEV2+fJmRI0fi7++Pp6cnNWvWpFu3bhw8eDBTzhcaGoqHh0em1C0iIiIi8rR76MQtf/78ODk5UapUKX755RcAPD09iYiIyPDg5NGcPXuWVq1akZSUxOLFizlx4gQ7d+6kVatW9O3bl71792Z1iCIiIiIi8hAeeo7bs88+y3//+19ee+018uTJw//+9z8cHBwwmUyZEZ88ghEjRuDr68vEiRONbS4uLrRu3Rqr1UpycjIAhw4dYsaMGVy4cIGiRYvSs2dPWrVqBcDQoUNxcHAgKiqK0NBQChYsyBtvvMHrr78OQFRUFCNGjODo0aO4urrSokWLNDFcvHiRCRMmEBYWRp48eYyk0cHBgQ0bNvDpp5/i4uLCqVOnGDlyJC1btnxMrSMiIiIikvM8dI/bgAEDmDVrFhcvXqRbt24EBQXRrl072rZtmxnxyUO6cuUKYWFhvPrqq/fd37ZtWxo3bsyZM2fo3bs3PXr0IDQ0lLFjxzJhwgT2799vlN2wYQPBwcEcO3aM7t27M2nSJCIjIwEICQkhV65c7Nu3j08//ZR9+/YZx926dYsuXbpQoUIF9u3bx+rVqzl06BBz5swxyvz444+0bNmSQ4cO0aRJk0xqDRERERGRJ8ND97hVr16dffv24eDgQOnSpXn++ee5efMmvr6+mRGfPKQrV64A4O7ubmw7fPgw/fr1A8BisVCkSBHq1KlDQEAATZs2Be5+rkFBQaxatYr69esDUKtWLeNzbdeuHSNHjuTixYukpKRw/PhxduzYgbOzM87Ozrz99tv07dsXgD179pCUlMTAgQMxmUwUK1aMAQMG0L9/f2MRG3t7e1q3bo1ZT9MWEREREXmgh07cAEwmE9988w0RERF06NCB33//PaPjkkfk5uYGQGRkJGXLlgWgTp06HD9+HLjbizZ37lwiIiI4cuQI3t7exrEWi4XSpUvfUxfcTbQArFar0etWvHhxY/+fj4uIiCAmJgYfHx9jm81mIzk5mWvXrhl1K2kTEREREUmfh07cLl68SNeuXUlOTubGjRs0bNiQdu3aMXfuXPz8/DIjRnkIJUqUoEqVKqxbt47atWv/bTl3d3fatm3LmDFjjG1RUVHYbA9+OHNqb96lS5coV64c8P97+lL3ly5dOs3D2uPj47l27ZrxkHbNiRQRERERSb+H7vIYP348gYGB7Nmzh1y5clG2bFnGjRvHhx9+mBnxySNInav2wQcfcP78eWw2G/Hx8WzcuJE5c+ZQpEgR2rdvz9atWzlw4ABWq5ULFy7QuXNnli5d+sD6ixcvTr169Zg4cSJxcXFER0czd+5cY7+fnx8JCQksWbKEpKQkbty4wZAhQwgJCVHCJiIiIiLyCB46cTt58iRvvfUWJpPJ+BLeunVrLl26lOHByaOpWLEiW7duxcnJiV69elGjRg0aNmzIZ599xltvvcWKFSuoVq0aM2bMYMaMGfj4+NC5c2f8/f3T/SD16dOnky9fPvz8/GjXrh1169Y19jk7O7N8+XJCQ0Np0KABjRs3xmw2M3/+/My6ZBERERGRJ5rJlp6xcX/SuHFjVqxYQfHixalZsyZHjx4lMjKSjh07smvXrsyKUwSAq0unknzxXFaHkePkKlYKt57vExubQEqKNavDyVZMJihcOB9Xr97k4X4bivw93VeSWXRvSWbRvZU1Uts9PR66x61ly5a8/fbbHDx4EKvVyqlTpxg8ePA9z/ESERERERGRjPHQi5P06dOHxMRE3n77bW7fvs3rr79O+/btefvttzMjPhERERERkadeuhO3bt268fHHH2Nvb8+QIUPo378/t2/fxtXVVQtOyGNjV8gdW3JSVoeR4+QqXCyrQxARERGRfyHdiVtYWFia9w0bNuTo0aMZHpDIP3FtHZzVIeRYNosFq1WD1kVERERyokd6ADeQrud9iWS02NiErA4hx7JabUrcRERERHKoR07cNDxSsoLVasWqRRFFRERE5CnzyImbSFYwm82YH3ot1JxNPWUiIiIiku7ELSUlhY0bNxrvk5OT07wHaNOmTQaFJXJ/rq55szqEx85mtRITe0vJm4iIiMhTLN0P4Pb39//nikwmPYBbMl3c1+uxRF3O6jAeG7uCRSjQ/FU9ODsT6YGjkhl0X0lm0b0lmUX3VtZ4mAdwp7vH7dtvv33kgEQyiiXmKinRT0/iJiIiIiIC8JTNFhIREREREcl5lLhlIn9/fzZs2HDP9g0bNjxw6OmDbNiwgeeeew4vLy+8vLyoVq0afn5+fPjhh+l+VMOcOXMIDtZz0UREREREsjutKpmDFS9ePM0Q1tOnT9OtWzdKlChBu3btsjAyERERERHJSOpxy2Lffvstr776KnXq1KFatWp07tyZCxcuABAfH09ISAi1atXC19eXbt26ce7cub+t64UXXqBmzZqcPn3a2LZ+/XoCAwOpVasWXl5e9OzZk5iYmHuOtdlsLFq0iJYtW+Lt7Y2Pjw+DBg0iMTERgKFDhzJixAh69eqFl5cXAQEBrFixwjg+JiaGwYMH4+PjQ61atQgJCSEuLg6Aq1evMnjwYHx9falXrx4jRowgPj4+I5pPREREROSpoMQtk40ePRpvb+80P6NHjwbgypUrDBgwgB49enD48GH27NmDzWZj3rx5ACxdupT4+Hj27t3L7t27cXNzY9q0aX97rjNnznDy5EmaNGkCwKlTpxg3bhyjRo0iNDSU7du3c+HChTQJV6rt27ezYsUK5syZw/Hjx1mzZg0HDhxgy5YtRpkNGzYQHBzMsWPH6N69O5MmTSIyMhKAAQMGEB8fz86dO9m1axc3btxg9OjRWK1W+vTpg9lsZseOHWzZsoWoqChGjBiRYW0sIiIiIvKk01DJTDZy5EgCAwPTbNuwYQNz586lYMGCbNu2jdKlSxMfH8+VK1dwdXU1kiEnJyfOnDnDxo0b8fX1ZcKECZj/9PTpy5cv4+3tDcCdO3dISkrC19eXF154AYCKFSuydetWSpYsSVxcHFFRURQsWNCo/88aNGhA9erVcXd3JyYmhtjYWFxcXNKUTe35A2jXrh0jR47k4sWLpKSkcPToUb766itcXV0BmDRpEtevX+f06dP8+OOPLFu2jLx57z6DbciQIbz44ot88MEHRnkREREREfl7StyykL29PVu3bmXNmjWYTCYqVqxIfHw8uXLd/Vi6d++Og4MD69evZ8yYMZQqVYpBgwbRtGlT4N45bhEREbz33nu8+eabfP7555jNZlasWMGWLVvIkycPHh4exMfH33fxEpvNxsyZM9m9ezcFCxbk+eefJzk5OU1ZNze3NLEDWK1WoqOjAShRokSasm5ubnz55ZdYLBYaNmyY5nwODg5cunRJiZuIiIiISDoocctC27dv59NPP+W///0vzzzzDABjx47l7NmzAPz888/4+/vTpUsXbt68yerVqwkJCeHIkSP3ra9EiRJ06dKF3r17c+3aNT7//HMOHjzIli1bKFy4MAC9evW677HTpk3j8uXLfPvttzg7OwPQsmXLdF1HsWLFgLs9gGXKlAHg119/ZevWrTRo0AAnJydCQ0Oxs7MDICkpiUuXLhnXLCIiIiIi/0xz3LLQzZs3MZvNODk5YbPZ2LdvHxs3biQ5ORmAdevW8e6773Lt2jWcnZ1xdnYmT548ODg43Le+mJgYPvvsM8qVK4erq6vRe2dvb09KSgqbNm1i//79Rv1/Fh8fj6OjI3Z2dty5c4elS5dy9uzZ+5b9q6JFi+Lr68uUKVO4ceMG8fHxTJ06lUuXLlG1alWeeeYZJk2aREJCAomJiUyYMIEuXbpgsVj+XQOKiIiIiDwl1OOWhdq2bcuJEydo0aIFdnZ2PPvss7zxxhusWrWKpKQkBg4cyJgxY2jRogV37tzh2Wef5aOPPsLR0RG428Pl5eVl1Ofg4ECtWrVYuHAhZrOZrl27cvbsWfz8/HB0dKRSpUp07Njxvj1277zzDu+99x5169YlT5481KhRg9atWxu9fw8ybdo0Jk2aRPPmzUlJScHf35/333+fXLlysXDhQiZPnkzTpk25c+cOVatWZdmyZcZ1iIiIiIjIPzPZ0vu0ZpFsIGbtAlL+uJDVYTw2udyKU7BTf2JjE0hJsWZ1OE8kkwkKF87H1as30W9DySi6rySz6N6SzKJ7K2uktnt6aKikiIiIiIhINqfETUREREREJJvTHDfJUewKFoaUpKwO47GxK1gkq0MQERERkWxAiZvkKAWatM/qEB47m9WK1arB5iIiIiJPMyVukqPExiZkdQiPndVqU+ImIiIi8pRT4iY5itVqxarFFUVERETkKaPFSURERERERLI59bhJjmI2mzHnwD83aLijiIiIiPwbStwkR3F1zZvVITwSm9VKTOwtJW8iIiIi8kiUuEmOcnP/NlJirmR1GA8lV4HC5GvUGrPZpMRNRERERB6JEjfJUVJuXMNyLTKrwxAREREReaxy4GwhERERERGRp0uW9bhdvnyZhQsXsn//fmJiYnBwcKBKlSp07doVX1/frArrkYSHhxMQEMCuXbsoWbJkppxj48aNrF+/nrNnz5KUlETRokVp3LgxvXv3xtnZOVPOKSIiIiIi2UOW9LidPXuWVq1akZSUxOLFizlx4gQ7d+6kVatW9O3bl71792ZFWNnW+++/z8yZM+nUqRM7d+7k+PHjzJ07l7Nnz/LWW29hs2nelIiIiIjIkyxLErcRI0bg6+vLxIkTKVeuHHZ2dri4uNC6dWtGjhxJcnIyAIcOHaJ9+/Z4e3vTokULNm/ebNRhtVpZtGgRjRs3pkaNGrRv3579+/cb+2NjYwkJCaFGjRoEBASwcuVKKlWqRHh4OOHh4Xh4eDBp0iR8fHwYPXo0SUlJTJ48mebNm+Pl5UWdOnUYO3askRQFBwczadIkAgMD8fT0JDAwkOPHj6e5ri1bttC8eXM8PT3p0qULkZF352I1b96cBQsWpCnbsmVL1q9f/8C2OnjwIF988QWLFi2iefPmuLi4kCtXLipUqMCUKVPw8vLi5s2bAMTHxzNmzBgaNmxInTp1CAkJ4erVqwD3veY5c+YwYMAAhgwZQvXq1WnQoAHbt29n3rx51K1bl5o1a/LRRx8ZsXz33Xe8/vrr1KtXjypVqhAYGMjJkycBCA0Nxd/fn/nz51O/fn1q1qxJv379iI+P586dO/j4+LBlyxajrqSkJGrVqsXhw4cf2AYiIiIiIk+7x564XblyhbCwMF599dX77m/bti2NGzfmzJkz9O7dmx49ehAaGsrYsWOZMGGCkZzNmzePVatWMXv2bEJDQ+natSt9+vTh1KlTAAwePJibN2+ya9cu1q1bx+7du7FYLGnOlZCQwMGDBwkJCeGTTz5h//79fPLJJ4SFhfHRRx+xZs0ajhw5YpRfu3Yt7777LkePHqVJkyb07t2b2NhYY/+PP/7IZ599xt69e4mLi2PevHkABAYGsmnTJqPc6dOnCQ8Pp3nz5g9sry+//BIvLy88PDzu2efq6sqQIUPInz8/AMOGDeP3339nw4YNfPPNNzg7O/P222+n6ZH78zUD7NixAz8/P06cOEGrVq0YNGgQ8fHx7N27lwkTJjB79mwiIiJITEykd+/eNGvWjH379hEaGkrp0qWZMmWKUXdERASRkZF8/fXXrFu3jrCwMFavXo2joyMtWrRI0wa7d+8mb9681K5d+4FtICIiIiLytMuSxA3A3d3d2Hb48GG8vb3x9vbGy8uLZs2asWbNGgICAmjatCl2dnZUr16doKAgVq1aBcDnn39Ojx49qFy5Mrly5eKll17C39+f9evXExkZyYEDBxg2bBguLi4ULFiQYcOG3RNLmzZtcHBwIH/+/AQFBbF8+XLc3NyIiooiMTGRvHnzGr1mAO3ataN27do4ODjQq1cvcufOze7du439vXr1Il++fBQoUID69etz8eJF4zwXL17khx9+AO7OV3vxxRfJm/fBzyS7cuVKmrYCePPNN432qlq1Khs3buTatWvs2LGD999/n0KFCpE3b16GDRvGDz/8wI8//njfawYoX748L774IiaTCV9fXywWC7169cLe3h5/f3/g7nxEe3t71q5dS8eOHUlKSiIiIgIXF5c07QPQt29fnJyceOaZZ6hVqxbnz5832u7QoUNER0cD8MUXXxAYGIjJZHpgG4iIiIiIPO0e++Ikbm5uAERGRlK2bFkA6tSpYww73LBhA3PnziUiIoIjR47g7e1tHGuxWChdujQAV69epVSpUmnqLlmyJGfOnOGPP/4w3qf6a1mAIkWKGK9v377NmDFjOHbsGO7u7lSqVAmbzYbVajXKlClTxnhtMplwd3c3EhEAFxcX47W9vb3Rw+fm5kb9+vXZtGkTzz33HFu3bmXOnDnpaK27MV66dCnNtmXLlhmv/f39sVqtREREABAUFJSmrJ2dHeHh4UZsf77mv8ZsNt/N4wsUKJDmvdVqxc7OjtDQULp3786tW7coX748uXLlumd+Xernm9oGqfurVKlCuXLl2LZtGy1btuTAgQMMHz48XW0gIiIiIvK0e+yJW4kSJahSpQrr1q37x2Fy7u7utG3bljFjxhjboqKijESgRIkS9yQ0ly5dokiRIhQvXhy4O3QvNTlMTWz+7M+9PcOHD6dAgQIcOHAAR0dHrFYrPj4+acr/uXfJarVy+fJlihUrlq7rbteuHaNHj8bX15d8+fLdU/ffadasGX369OHcuXOUK1fub8sVLVoUgO3bt6dJnn799VdKlSplJJh/7eFKb4/X999/z9ixY1mzZg0vvPACAEuXLjV61NKjXbt2bNu2DXt7e7y9vTNtBU4RERERkSdNlixOkjpX7YMPPuD8+fPYbDbi4+PZuHEjc+bMoUiRIrRv356tW7dy4MABrFYrFy5coHPnzixduhSAV155hUWLFvHjjz9isVjYvn073377LW3btqVIkSL4+fkxdepU4uLiiIuLSzMX637i4+NxdHTEbDYTHx/PlClTiI+PNxZKAVi3bh2nT58mKSmJefPmYbPZ8PPzS9c1N2rUCIvFwocffkhgYGC626pRo0YEBgbSrVs3vvrqK27fvo3NZuPs2bO89957XLlyhUKFClG0aFEaNWrE+PHjiY2NJTk5mfnz59O+fXtu3LiR7vP9nZs3b2I2m3FycgLg5MmTrFixgqSkpHTX0apVK86cOcO6deseqg1ERERERJ52WfIct4oVK7J161YWL15Mr169iI6OxmQy4eHhwVtvvcUrr7yCg4MDM2bMYMaMGQwYMIDcuXPz8ssvM3DgQODuPC+r1UpISAjR0dE888wzzJgxg5o1awIwfvx4RowYQaNGjXB1daVNmzbs3r0be3v7NMlYquHDhzNixAhq1qxJ3rx5adSoEfXr1+fs2bNGmZo1azJmzBh+/fVXKlWqxNKlS8mXLx9xcXEPvGZ7e3tatWrFihUrmD9//kO117hx49i+fTufffYZo0aNIjExkYIFC1KnTh02bNjAc889B8CUKVOYPn06bdq0IT4+ngoVKrBkyRLc3NwIDw9/qHP+la+vLx07dqRTp05YrVZKlixJcHAw06dPN1aufJCCBQvSsGFDjhw5QtOmTf9VPCIiIiIiTxOT7Ql9CNjBgwepUaOG0UP0888/06ZNG06ePImjo+ND1xccHGwscf+oVqxYwb59+1iyZMkj15HTTZw4kcTEREaPHv1Ix8duW4El8t8loY+bXaGiuLbuRmxsAikp1gcfII+VyQSFC+fj6tWbPJm/DSUr6L6SzKJ7SzKL7q2skdru6ZElQyUfh8mTJzN//nxSUlKIj49n/vz51K1b95GStn8rOjqaU6dO8cknn/Daa6899vNnB3/88QeHDx9m48aNf/soCBERERERub8sGSr5OEyfPp1x48ZRu3ZtzGYz9evXf+A8t8yyZ88exo0bR+vWrQkICDC2L1u2jA8//PBvj2vZsmWaxVlyss8++4zly5fz1ltv8fzzz2d1OCIiIiIiOcoTO1RSnkw3928jJeZKVofxUHIVKEy+Rq01VDKb0tAQyQy6rySz6N6SzKJ7K2s8zFDJJ7bHTZ5M+eq3yOoQHonNasVq1W9BEREREXk0StwkR4mNTcjqEB6J1WpT4iYiIiIij0yJm+QoVqsVq0YbioiIiMhTRomb5ChmsxlzBq+Fqt4wEREREcnulLhJjuLqmjfD67RZrcTE3lLyJiIiIiLZlhI3yVESTuzBEnc1w+qzy+dKXp8AzGaTEjcRERERybaUuEmOYom/juV6xiVuIiIiIiI5QQbPFhIREREREZGMph63RxQXF8fMmTPZvXs3cXFxODs74+vrS0hICO7u7lkd3r8yZ84cPvroI5ycnO7Zt3nzZkqVKpUFUYmIiIiIPL2UuD2ikJAQ8uXLx/r163Fzc+Pq1auMHz+eN998ky1btpArV85uWm9vb1auXJnVYYiIiIiICBoq+chOnDhBkyZNcHNzA6Bw4cIMGzaMatWqcePGDfz9/dmwYYNRPjQ0FA8PDwDCw8Px8PBg0qRJ+Pj4MHr0aObMmUP//v0ZPHgw3t7eNGjQgOnTpxvHR0ZG8s477+Dv70+1atUICAhg/fr1xn4PDw/Wrl1Ls2bNqFatGr169eL06dO8+uqreHl50a5dO37//Xej/LZt22jZsiU1atQgMDCQAwcOpPvaN2zYQGBgIF27dsXb25stW7aQlJTE7NmzCQgIoGbNmnTv3j3N+c6dO0dwcDBeXl60aNGChQsX4u/v//ANLyIiIiLyFFLi9ohatGjByJEjGTVqFF9++SURERG4ubkxadIkChYsmK46EhISOHjwICEhIQDs3LmTevXqERoaytixY1m8eDEnT54EYPjw4djb27Nt2za+++47OnfuzNixY0lISDDq27JlC2vXruXrr7/mxIkT9OnTh/Hjx3Pw4EEcHBxYsGABAHv37mXkyJGMGDGCo0eP0q9fP/r168cvv/yS7uv/8ccfadmyJYcOHaJJkybMnDmTPXv2sHz5cvbv30+1atXo2rUrd+7cITk5mR49elC+fHmOHDnC9OnTWb16dbrPJSIiIiLytFPi9ojGjRvHiBEj+OOPPxgxYgT+/v40adKEzZs3p7uONm3a4ODgQP78+QEoU6YMbdq0wc7OjoYNG+Lm5saFCxeM840cORJ7e3suX75M3rx5SUxMJC4uzqivc+fOuLi4UKRIESpUqEDTpk0pV64cefLkoXbt2kRERADw6aef8tprr+Hj44OdnR1+fn74+/uzZs0ao64TJ07g7e2d5uezzz4z9tvb29O6dWscHBxwdHRkzZo1DBw4kFKlSuHo6Ejfvn1JTk5mz549nDhxgitXrjBkyBAcHR157rnn6Nq1679pfhERERGRp0rOnoiVhcxmM61bt6Z169bYbDbOnTvHpk2bePfdd43hkw9SpEiRNO//epy9vT1WqxWAS5cuMWXKFC5cuECZMmV45plnAIz9AC4uLsZrOzs7ChQokCZem+3uc8oiIiI4evQo//3vf439FouF2rVrG+9r1Kjxj3Pc3NzcMJvv5v0xMTHcunWLAQMGGNsAkpOTiYiIwGKx4Orqmmaxk7Jly/5t3SIiIiIikpYSt0ewf/9++vfvz+7du3FxccFkMlG+fHkGDRrEwYMH+emnnzCbzSQnJxvHxMbG3lOPyWRK1/mSk5Pp2bMnAwcOpGPHjphMJk6fPn1P715663N3d6dNmzb06NHD2Hb58uX7riL5d/58LldXVxwdHVm6dCmenp7G9t9++42iRYty7tw5YmJiSEhIIG/evMDdOXsiIiIiIpI+Gir5CHx8fChUqBDvvfceP//8M8nJycTHx7N582YuXLhAo0aNKFeuHLt27SIxMZHo6GhWrFjxyOdLTk4mMTERJycnTCYTly9fZurUqca+hxUUFMSKFSs4deoUAD/88AOBgYFs3br1keIzm820b9+e6dOnc+XKFaxWK1988QUvv/wyv//+O1WqVOGFF15g3Lhx3Lp1i4sXL7JkyZJHOpeIiIiIyNNIPW6PwMnJidWrVzN37lx69+7NtWvXsLe3x9PTk2XLllGuXDkGDx7MqFGj8PX1pUiRIrzxxhucOHHikc6XJ08eJkyYwOzZsxk3bhyFChUiKCiIX3/9lbNnzz70sMMXX3yRW7duMWzYMC5fvoyLiwtdunQhODj4keIDGDJkCHPmzKFjx45cv36dUqVK8eGHH1KpUiUA5s2bx9ixY/Hz88PNzQ1vb28OHz78yOcTEREREXmamGypE59EHqMNGzYwd+5cvv3224c67sbejViuXcmwOOxcCpPfvz2xsQmkpFgffIA8cUwmKFw4H1ev3kS/DSWj6L6SzKJ7SzKL7q2skdru6aGhkiIiIiIiItmcEjcREREREZFsTnPcJEsEBgYSGBj40MfZObuAJSXD4rDL55phdYmIiIiIZBYlbpKj5K3RKMPrtFmtWK0azC0iIiIi2ZcSN8lRYmMTMrxOq9WmxE1EREREsjUlbpKjWK1WrFr8UURERESeMlqcREREREREJJtTj5vkKGazGXMG/blBQyRFREREJKdQ4iY5iqtr3gyry2a1EhN7S8mbiIiIiGR7StwkR7n10zEs8bH/uh67PPnJ80JtzGaTEjcRERERyfaUuEmOYrl1A+vNf5+4iYiIiIjkJFqc5Clx4cKFrA5BREREREQekRK3x+T8+fMMGTKEBg0a4OXlRePGjZk2bRoJCRn3XDIPDw9CQ0MBaNGiBZs3bwZg1apVfPDBB0Y5q9XK4sWLeemll/Dy8sLHx4du3boRFhaWYbGIiIiIiEjGUeL2GHz33Xe0bduWEiVKsHHjRsLCwli8eDHff/89Xbt2xWKxZPg5t23bRqtWrQCIiYlJs2/RokV88cUXfPjhh3z33Xfs27eP2rVr88Ybb/D7779neCwiIiIiIvLvKHF7DEaMGEGbNm3o378/BQsWBKBs2bLMnDmTQoUKcenSJTw8PBg3bhy1atWiV69eABw6dIj27dvj7e2dpgcNIDk5mYkTJ1KrVi1q167NkiVL0pzT39+fDRs28MUXX7Bw4UKOHz+Ot7c3ACdOnMDb25vy5ctjMpnInTs33bt3JygoiKtXrwKQkpLC7NmzadiwIdWrV6dTp06cOXMGgMTERKZMmULDhg3x8fEhODiYU6dOGed+2GsREREREZF/psVJMtnFixf55ZdfGDVq1D37ChcuzEcffZSm7J49e0hOTubMmTP07t2bqVOnEhAQwPfff0+fPn1wdXWlfv36fPTRR+zZs4f169dTqFCh+9YP0LZtW8LDwzl69CgrV64E7g6jHDZsGNevX6d+/fpUrVqVChUqMHz4cOO4+fPns3XrVj7++GPKli3L3Llz6dmzJ99++y2jRo3ip59+YsWKFRQrVoz//ve/dOnSha1bt1K8ePGHvhYREREREfln6nHLZKnDFAsXLvzAsi+//DK5c+cmf/78rFmzhoCAAJo2bYqdnR3Vq1cnKCiIVatWAbBp0ya6detGqVKlyJMnD8OHD8dkMqUrpjZt2rBixQqcnJyYO3curVq1ok6dOsyYMYOUlBQAvvjiC9566y3Kly+PnZ0dvXv3Zvbs2SQmJrJ161YGDRrEM888g4ODA2+88QbPPvssW7dufaRrERERERGRf6Yet0zm5uYGQHR0NGXKlLln/9WrV42krkiRIsb2iIgIjhw5YgxvBLBYLJQuXRqAqKgoihUrZuzLnz8/BQoUSHdc3t7eRt2RkZHs2bOHqVOnYjabeeedd4iOjjZ6zwAcHBzw9PQkKiqK5ORkSpYsmaa+kiVLEh4ebrx/mGsREREREZF/psQtk5UoUYKKFSvy5Zdf4uPjk2bftWvX8PPzY+LEiQBpeszc3d1p27YtY8aMMbZFRUVhs9mM/ZcuXTL23bp1i5s3bz4wnoSEBHx9fZk5cyZ+fn4AFC1alA4dOnD+/Hn+97//AVCsWDH++OMP47jk5GSmTp1Kt27dcHR05NKlS5QrV87Yf/HiRfz9/Y33D3MtIiIiIiLyzzRU8jH44IMP+Pzzz/l/7d15WFXV4v/xNwdBUFMcEHDq+3OIMk0PIDilQSmmYgoOqXHVvCrVTdO0/IbZzTSHTCscrzgPQZgZlGaDppWKE5pZTl1nUhFQOSgynP37w+v5RqByU+MAn9fz+Dyctdbee+3Nes7jh7322rNmzSI9PR3DMPjll1+IiIjg4YcfJjg4uMA2PXv25LPPPuP777/HarVy/PhxnnnmGRYtWgRAr169iI6O5tdff+XatWtMmTLlpqtTli9fHovFgmEYVKxYkccff5xp06aRmJjIlStXyM7OZvfu3WzYsIGOHTsCEBoaysKFCzl27Bi5ubnMnz+fr7/+mmrVqhEWFsaMGTM4ceIE2dnZLF26lKNHj9KlS5dCj3+7cxERERERkVvTHbe/gL+/PytWrGDevHl06dKFq1evUqNGDTp16sSwYcNwcnIqsE2zZs2YMWMGM2bMYMSIEbi6utK1a1dGjRoFwJAhQ7h69SrPPPMMubm59O7dGzc3t0KPHxgYyIcffoivry/ffvstkydPJjo6mokTJ9qmN9avX5+XXnqJp556CoC///3v5ObmMnjwYC5dukTTpk1ZsGABTk5OvPLKK0RFRTFw4EAuXryIt7e3bRGTwtzuXERERERE5NYcDM1XkxIkY9c3WC9duOP9mO6ryn3+HUlPzyQ313oXeiYllYMD1KhxHxcuZKBvQ7lbNK7kXtHYkntFY6t43LjuRaGpkiIiIiIiInZOwU1ERERERMTO6Rk3KVEcK1QGa+GLsPzX+xERERERKSEU3KREqdC4xe0bFZFhtWK1ahK3iIiIiNg/BTcpUdLTM+/avqxWQ8FNREREREoEBTcpUaxWK1YtAikiIiIiZYyCm5QoJpMJ002W1NEdNBEREREprRTcpESpWrXiTesMq5W09CsKbyIiIiJS6ii4SYmSdexnrFczCpSbXCriUr8JJpODgpuIiIiIlDoKblKiWK9dwXqlYHATERERESnN9ALuMiYvL49Tp04VdzdEREREROS/YHfBzdvbm6FDh2IY+ae7rVmzhqCgoHt23JSUFCZMmEBQUBDNmjWjbdu2vPzyyxw+fDhfu3Xr1tGqVSt8fX3ZtGkTQUFBNG3aFLPZjNlspnnz5rRt25apU6divYvLH4aHhxMVFXXH+xk5ciRr16694/1YrVaioqJo3749ZrOZkJAQ1q1bd9P2eXl5TJ06ldatW2M2m3nuuec4f/78HfdDRERERKQssLvgBrB582aio6P/suOdPn2aHj16kJKSwvz580lKSuLTTz+lbt269O7dmx9++MHWNi4uji5durB7924CAwMBePPNN0lKSiIpKYm9e/eycOFC1q5dy6xZs/6ycyiq9PT0u7KflStXsnbtWpYvX05SUhKjRo3i5Zdf5uTJk4W2nzt3Lj/88AMff/wx3333HS4uLowbN+6u9EVEREREpLSzy+AWHh7O+++/z549ewqtP336NN7e3pw+fdpWFhUVRXh4OHD97ly/fv2YOnUq/v7+tGzZkuXLl/PRRx8RGBiIr68v48ePt207efJkGjVqxAcffECjRo0wmUxUr16dl156ifDwcMaOHUtubi49e/Zk+/btxMTE8MQTT9y0/97e3rRo0YKff/7Zdj5jx44lMDCQxx57DIvFwqFDhxgyZAj+/v60a9eOf/7zn2Rk/N+zW3FxcTz++OOYzWZeffVVrl69aqsbO3YsY8eOLXDMxMREANLS0hg9ejQtWrQgICCAkSNHcunSJSIjI9m1axfz588nIiLCdt3at2+Pv78/YWFhfPPNN0X6HfXv35+EhATq1atHdnY2aWlpuLq64uLiUmj7uLg4hgwZgpeXF5UqVSIyMpItW7Zo2qaIiIiISBHYZXDr0KEDffr0YdSoUVy8ePFP7WP37t14eHiwfft2hg8fzuTJk0lMTGTdunUsWbKE1atXs3PnTrKzs9m8eTNPPfUUDg4OBfbTu3dvzp8/T1JSEqtXr8bPz49hw4bx9ddfF3rcnJwcEhMT2b59O23atLGVb926lZiYGOLj48nJyeFvf/sbDRs2ZMuWLXz88cccO3aMV155BYBt27YxYcIEJk6cyM6dO2nWrBn79+8v8rmPGDECi8XCl19+yTfffMPly5d58803mTRpkq3/8+bNY/v27cTGxhIXF0diYiK9evUiMjKSnJyc2x7DZDJRoUIFvv/+e5o1a0ZkZCQjRoygZs2aBdpmZGRw9uxZHnjgAVtZjRo1qFKlCocOHSryeYmIiIiIlFV2u6rkq6++SlJSEmPHjmXu3Ln/9fYVKlRgwIABODg40LZtW/Ly8hg8eDCurq40bdqUmjVrcubMGerVq0dOTg41atQodD83gsitnsd68803efvtt22fPT09GTRoEM8884ytrF27dnh4eACwevVqnJycGD16NI6Ojri4uPD666/TpUsXUlJSiI+Pp2PHjrRq1QqAfv36ERcXV6TzPnPmDDt27OCLL76gatWqAEyZMqXQAFy+fHkuXbpkuxPZq1cv+vTpU2iAvRl/f3/279/Pzp07ef7553F3d6dz58752mRmZgLXfye/5+LiYqsTEREREZGbs9vg5uzszHvvvUePHj1YtGiRLYQUlZubmy2AmEzXbyxWrlzZVm8ymbBarVStWpVy5cqRnJxc6H5uTMd0d3e/6bHeeOMNQkNDb9mf39+JSk1NpVatWjg6OtrK6tSpA1wPXufOnePhhx/Ot33dunVvuf8bUlJSAKhdu7atzN3dvdD+m81moqKiWL58OdHR0bi4uBAeHs5zzz1nu2a34+zsDECrVq146qmnSEhIKBDcXF1dAfJN9wTIysqiYsWbv1BbRERERESus8upkjfUq1ePt956i5kzZ7J3715b+Y3A8/spfX9cdKOod42cnZ15/PHHWbNmTaGrQMbFxeHu7o7ZbP4TZ1B4f2rXrk1ycjJ5eXm2shuLeri7u+Pp6Vng2a+zZ8/afjaZTPnOPS0tzfazl5cXQL4gevToUd57770CfUpOTqZ69eosXLiQHTt2MHXqVObNm8eWLVtuez5TpkxhypQp+cqys7Nxc3Mr0LZKlSp4eHhw9OhRW1lKSgoXL17MN31SREREREQKZ9fBDaBz586EhYURGxtrK6tevTpVqlTh888/xzAMDhw4wBdffPGnjxEZGUlycjLDhw/n119/xWq1cu7cOWbMmMGqVat4++23cXJyuhunA0D79u0BmD59OllZWaSkpDBp0iRatmxJ7dq1CQsL4+uvv2bTpk3k5ubyySefsG/fPtv2DRo0YNeuXZw7d46srCxmz55tC4YeHh60adOGadOmcfnyZSwWC++8844tCDo7O9sWQdm/fz9///vfOXjwIM7OzlSvXh2gSHc3/fz8iImJYefOnVitVjZu3Mi6devo1atXoe1DQ0OZO3cup06dwmKx8Pbbb+Pv70+9evX+/IUUERERESkj7D64Abz22ms89NBDts/Ozs689dZbrF+/Hh8fH6ZMmULv3r3/9P49PDxYu3YtXl5eDB06FB8fH8LCwjh9+jQfffQR7dq1uxunYXPfffexePFiDh8+TPv27enatSu1a9fm/fffB8DX15dp06YxZcoU/Pz82LBhQ76FTvr06YPZbKZbt2506NABLy8vatWqZaufPn06lSpV4sknn+Txxx+nWrVqvPnmmwB0796djz/+mH79+hEcHMyzzz7Lc889R/PmzRkxYgSvvfYazZo1u+05PPHEE4wbN45x48bRokULZs+eTVRUFD4+PgDEx8fnu0v5wgsv0L59e/r370/79u25du1aoXcBRURERESkIAfjj2+6FrFjVw7uwmq5WKDcVOE+KjQOID09k9zcu/ficyn9HBygRo37uHAhA30byt2icSX3isaW3CsaW8XjxnUvihJxx01ERERERKQss9tVJaX4bNiwocALvn/P19eX6Ojov7BHIiIiIiJlm4KbFBAcHExwcHBxd6NQpvIVwJpXsNxFrxUQERERkdJLwU1KFJf/1/imdYbVitWqSdkiIiIiUvoouEmJkp6eedM6q9VQcBMRERGRUknBTUoUq9VKIe9JFxEREREp1RTcpEQxmUyYbrIWqu64iYiIiEhppeAmJUrVqjdfhMSwGqSlZyq8iYiIiEipo+AmJUr22eNYr10tUG5ydsHZ6/9hMjkouImIiIhIqaPgJiWKNfsaRiHBTY+9iYiIiEhpdpOnhURERERERMReKLj9l/Ly8jh16tRfcqzjx4//JccRERERERH7ViaDW1BQEE2bNsVsNhf4t2vXrltuO3LkSNauXQtAcnIyZrOZ5OTku97HlStX8vrrr//p7aOioggPD7d9PnLkCC+88AIBAQGYzWY6duzIzJkzyc7OLtL+1qxZQ1BQ0J/uj4iIiIiI/Hll9hm3N998k9DQ0P96u/T0dNvPtWrVIikp6W52yyYtLe2u7ctisRAeHs6zzz7L9OnTcXFx4ejRo4waNYoLFy4wadKku3YsERERERG5+8rkHbfb2bBhA126dMHX15cnn3ySOXPmABAZGcmuXbuYP38+ERERnD59Gm9vb06fPg2At7c3sbGxBAcH06xZMyIiIvjpp594+umnMZvNhIWFceLECQCys7OZOnUqTz75JGazmVatWvHWW29hGAaffPIJ8+fPZ9euXfj5+QHXw9eECRNo3749rVq1YuTIkVy4cMHW5z179hAWFkbz5s15+umnbX0C+Pe//016ejrdu3fH1dUVBwcHGjVqRGRkJJUrV7a127hxI08//TStWrWiWbNmPPPMMzedrrl69WpCQ0Ntd/CGDRtmC5tRUVE8++yzhIWF4e/vz6xZs/D19eXatWu27b/44gsCAwMxDK0AKSIiIiJyOwpuf5CVlcWYMWMYP348u3fv5t1332XBggX8+OOPTJo0CT8/P4YNG8a8efMK3T4hIYHY2Fi++uordu/ezfPPP8+kSZP44YcfcHZ2tm23dOlSvvvuO5YuXUpSUhJz5swhJiaG7du306NHD4YNG4afn59t6uZrr73GiRMnWLNmDV9//TWVKlXiH//4B4ZhkJ6ezrBhwwgODmbnzp2MGTOGr7/+2tanBx98kAYNGtC3b19mzpzJli1buHjxIi1btuTVV18F4OzZs4wYMYKhQ4eybds2vv32WwzDYPbs2QXO8ccff2TixIn885//JDExkfXr13P8+HGWLVtma7Nt2zZGjx7Npk2bGDp0KI6OjnzzzTe2+rVr19KjRw8cHBzu/JcmIiIiIlLKlempkm+//Xa+Mi8vL+Li4nBxcWH16tVYrVZ8fHzYvXs3JlPRMu4zzzyDm5sbAI0aNaJx48Y0aNAAgJYtW7J7924AevfuTY8ePahevTrnz58nKyuLihUrcu7cuQL7TE1NZcOGDaxfv57q1asD14Ocn58fBw4c4MiRI7i6ujJkyBAcHBzw9fUlLCyMX375BQBnZ2c++ugjVq1axcaNG1m4cCG5ubn4+PgwduxYHnnkEapVq8bnn39OvXr1sFgsnD17lqpVqxbanwceeIDPPvuMOnXqcOnSJc6fP0+1atXyta1bty6tWrWyfe7atSuffvopnTt3JjU1le+//57IyMgiXVMRERERkbKuzAa3N95446bPuH344YfMmTOHl19+GYvFQnBwMOPGjaNKlSq33e+N0Abg6OiYbxuTyWSbGnj16lUmTJjAzp078fT0pHHjxhiGgdVa8I1kZ86cAa6Hvd9zdHTk9OnTnDt3Di8vr3x3r+rVq2cLbgCVKlVi6NChDB06lOzsbH766ScWLFjAoEGD2LhxI5UrV+azzz4jJiYGBwcHHnjgASwWC+XKFRwiJpOJZcuWkZCQQIUKFfD29sZiseSb9lizZs1824SGhtKnTx9SU1OJj4/Hx8eHunXr3vZ6ioiIiIhIGQ5uN2OxWDh//jzvvvsuAL/88gujRo1i3rx5tmmFt1LUqX83guD3339P+fLlsVqttGjRotC2Hh4eAKxfvx53d3db+dGjR6lbty7r16/nzJkzWK1W253Bs2fP2trNnDmTrVu3EhcXB1y/A+fj48M777yDr68vJ0+e5NSpU6xYsYIPP/yQ+++/H4C33nqLw4cPF+jPkiVL+OGHH0hISKBGjRoARERE3PI6NGnShIYNG7JhwwY+//zzfCteioiIiIjIrekZtz/IzMxkyJAhJCQkYBgGNWvWxGQyUbVqVeB66MnIyLjj41gsFsqXL4/JZMJisTBt2jQsFgs5OTkAlC9f3nYXy8PDg8cee4xJkyaRnp5OTk4Oc+fOpWfPnly+fJmgoCAMwyAqKsp2N+1GSAN48sknOXToEO+99x5nzpzBMAwuXLjA7Nmzuf/++/H29iYjIwOTyYSLiwuGYbBlyxbWrl1r688f+16uXDmcnJzIzc3l008/5bvvviu07e+Fhoby0Ucfcfz4cTp27HjH11BEREREpKwos8HtjTfeKPQ9bvHx8XzwwQcsWLAAHx8funbtSsuWLRk4cCAA3bt35+OPP6Zfv353dPxx48Zx8OBB/P396dSpExaLhUcffdR2hyswMJCLFy/i6+vL5cuXmTZtGpUrV6Z79+60bNmSzZs3Ex0djbu7O5UrV2bhwoVs27YNf39/IiMjCQ4Oth3rwQcfZMWKFRw+fJiePXvSrFkzunfvzsWLF1m+fDnOzs706NGD1q1b06VLF1q2bMncuXMZMGAAx44dK/Cut2effRYvLy8CAwN59NFHiY+Pp1+/foXenfu9kJAQjh49SufOnXF1db2j6yciIiIiUpY4GFqPXf4ieXl5tG3blnnz5tGsWbM/tY+sk4cwsjILlDuUd8Xl/odIT88kN7fgc4IiN+PgADVq3MeFCxno21DuFo0ruVc0tuRe0dgqHjeue1HoGTf5Sxw5coT169fj6en5p0ObiIiIiEhZpeAmf4lhw4YB8MEHH9zRfkzO5bEaBe+omZxd7mi/IiIiIiL2TMFN/hIbN268K/tx9vyfm9YZVgOrVff2RURERKT0UXCTEiU9veDzbTdYFdxEREREpJRScJMSxWq1Usg7ykVERERESrUy+zoAERERERGRkkJ33KREMZlMmP7w5wZNkRQRERGR0k7BTUqUqlUrFigzDIO0tEyFNxEREREptRTcpETJTT+PkXvN9tmhnDPlqnpgMjkouImIiIhIqaXgJiWKkZeNkZtd3N0QEREREflLaXESuatOnDhR3F0QERERESl1yuwdt/Hjx5OQkABAbm4uOTk5uLq62uoXLFiAn59fcXWvUD179uTQoUNs3LgRd3f3u7bfqKgo5syZg4uLi63MarVSs2ZN+vfvz8CBA4u0n6lTp5Kens6UKVMAMJvNdnkdRURERERKmjIb3CZMmMCECRMAWLNmDbNmzWLjxo3F3Kub27dvH2fPnqVdu3asWLGCkSNH3tX9+/n5sXz5ctvnnJwc1q5dy7hx4/D29qZVq1a33Ud6enq+z0lJSXe1jyIiIiIiZZWmShbi5MmTREREEBAQQGBgIDNnziQ7+/pzVWvWrKFv375MnDiRli1b0qpVKyIjI8nJyQFg7NixjB07Nt/+vL29SUxMBCAoKIjx48fTpk0bunfvjtVq5cCBA4SHh9OiRQs6duzIkiVLMIz8C22sWLGCTp060a9fP2JiYrh69Wq++q1bt9K9e3d8fHx4+umneeeddwgPD7fVf/7554SEhODr60toaCjff//9La+Bk5MTvXr1ws3NjZ9//hkAi8XCuHHj6NixI82bN+fRRx9l3rx5AMyePZuEhAQSEhLo1q1bgfNOT0/n9ddfp23btgQEBDBs2DCOHz9e5N+JiIiIiEhZpuD2B1euXGHgwIE0atSILVu2sGrVKrZu3UpUVJStzZ49e6hevTrfffcd8+fPZ926dXz55ZdFPsaPP/7I+vXrWbZsGSkpKQwYMIBOnTqxdetW5syZw6pVq4iNjbW1T01NZcOGDfTt25fWrVtTvXp1Pv74Y1v96dOniYiIoG/fvuzYsYPRo0fn237z5s288cYbjB8/nh07dvDiiy/y4osvcuTIkZv2MSsri+XLl5OZmUmbNm0AmD59OqdPn2b16tUkJSUxbtw4Zs6cyYkTJ3jhhRcICQkhJCSE+Pj4AvsbPnw4J0+e5JNPPmHz5s3Ur1+fgQMHYrFYinzdRERERETKKgW3P/j222/Jzs5m1KhRlC9fHi8vL0aMGMHKlSttbVxcXIiIiMDJyYlHHnkEb29vjh07VuRjBAcHU7lyZSpXrkx8fDwNGjSgf//+ODk50bBhQwYPHpzveLGxsfj7+9OgQQMcHBwIDw9nyZIlWK1WABISEnjooYfo06cP5cqVw8/Pj969e9u2X7FiBX379qVFixY4OjoSGBhIUFAQMTExtja7d+/Gz88PHx8fmjZtSsuWLdmxYwdLly7lwQcfBODFF1/kvffeo1KlSpw9e5by5csDcP78+Vue76lTp9ixYwevv/467u7uuLi4MHr0aHJzc9m8eXORr5uIiIiISFlVZp9xu5kzZ86QlpZGixYtbGWGYZCTk0NqaioA1atXx8HBwVbv5ORUYGrjrdSsWTPf8Q4cOJBvAQ+r1YqjoyNwfeGUmJgYLl++TEBAgK3+8uXLfPXVVwQHB/Pbb79Ru3btfMeoW7cu+/fvtx1jx44dfPjhh7b6vLw8WrZsafvs6+tre8Zt7969jBgxAnd3d3x9fW1tUlNTmTRpEj///DN16tShSZMmtv7cyoULF2x9usHR0REvLy/OnDlzu8slIiIiIlLmKbj9gaenJ/Xq1eOLL76wlVksFlJTU6lWrdpttzeZTFy79n8viE5LSyvQ5vehz9PTk4CAABYuXGgrS09PJzMzE4CvvvqK7OxsPv/8c1uYA3j//fdZvHgxwcHB1K5dm02bNuU7RnJycr5jdO/enaFDh+ar//0qkr/XvHlz5s2bR58+fahSpQojRowAYMSIEQQFBbFw4ULKlStHeno6H3300W2vyY1QefLkSRo1agRcD47Jycl3dXVMEREREZHSSlMl/yAwMJDMzEyio6PJzs7m8uXLvPrqq4wcOTJf4LqZBg0asGvXLs6dO0dWVhazZ8++5XYhISHs3buX+Ph4cnNzOX/+PBEREbYl9VesWEFISAi1a9fG09PT9i88PJykpCT27NnDU089xS+//MLatWvJy8tj3759+QJV7969WbZsGT/++CMA+/fvJzQ0lM8+++ym/XrooYcYO3Ys8+bNY/v27QBkZGTg4uKCo6MjaWlpTJw4EcC2MIuzszMZGRkF9lWzZk3at2/PxIkTSUlJISsri+nTp5OXl0dgYOBtr6mIiIiISFmn4PYHlSpVYsmSJSQmJtKuXTueeOIJTCYTc+fOLdL2ffr0wWw2061bNzp06ICXlxe1atW6afvatWsTHR1NbGwsrVu35qmnnqJ+/fpMmTKFgwcPsmvXLkJDQwts17hxY5o0acKiRYvw9PTkgw8+sL0zberUqbRt2xYnJycAOnXqxKhRo3jttdfw8fFhxIgRDBw4MN+qk4Xp168f7du359VXX+XSpUtMnjyZdevW4ePjQ2hoKB4eHjRu3JjDhw8D0LlzZ/bs2cNjjz1WYF/Tpk2jbt269OjRg9atW3Po0CGWLl2Km5tbka6riIiIiEhZ5mD8Nw9niV367bffSE9Pp3HjxrayKVOmkJKSwrvvvluMPbv7ci6cxsj5v6moDuWccXKvS3p6Jrm5t37WTqQwDg5Qo8Z9XLiQgb4N5W7RuJJ7RWNL7hWNreJx47oXhe64lQLp6en069ePn376CYCDBw8SHx+vaYgiIiIiIqWEFicpBRo3bkxkZCSjRo0iJSWFGjVqMHToULp27VrcXRMRERERkbtAwa2U6NWrF7169SrubtxzDo7O/P7+vUM552LsjYiIiIjIX0PBTUqUclVrFigzDAOrVZOxRURERKT0UnCTEiU9PbNAmdWq4CYiIiIipZuCm5QoVqsVqxaPFBEREZEyRsFNShSTyYTpd2uh6m6biIiIiJQFCm5SolStWjHfZ8MwSEvLVHgTERERkVJNwU1KlLzMixh5uQA4OJbDsaIbJpODgpuIiIiIlGoKblKiGHl58J/gpqgmIiIiImWF6fZNREREREREpDjpjlspNn78eBISEgDIzc0lJycHV1dXW/2CBQvw8/Mrru6JiIiIiEgRORiGoRlnZcCaNWuYNWsWGzduLO6u3JHcy6mQl3P9g2M5ylWuQXp6Jrm5ekeA/DkODlCjxn1cuJCBvg3lbtG4kntFY0vuFY2t4nHjuheFpkqWUSdPniQiIoKAgAACAwOZOXMm2dnZwPWQ17dvXyZOnEjLli1p1aoVkZGR5ORcD0xjx45l7Nix+fbn7e1NYmIiAEFBQYwfP542bdrQvXt3rFYrBw4cIDw8nBYtWtCxY0eWLFmC/mYgIiIiIlI0Cm5l0JUrVxg4cCCNGjViy5YtrFq1iq1btxIVFWVrs2fPHqpXr853333H/PnzWbduHV9++WWRj/Hjjz+yfv16li1bRkpKCgMGDKBTp05s3bqVOXPmsGrVKmJjY+/F6YmIiIiIlDoKbmXQt99+S3Z2NqNGjaJ8+fJ4eXkxYsQIVq5caWvj4uJCREQETk5OPPLII3h7e3Ps2LEiHyM4OJjKlStTuXJl4uPjadCgAf3798fJyYmGDRsyePDgfMcTEREREZGb0+IkZdCZM2dIS0ujRYsWtjLDMMjJySE1NRWA6tWr4+DgYKt3cnL6r6Y21qxZM9/xDhw4kG8hFKvViqOj452choiIiIhImaHgVgZ5enpSr149vvjiC1uZxWIhNTWVatWq3XZ7k8nEtWvXbJ/T0tIKtPl96PP09CQgIICFCxfaytLT08nMzPyzpyAiIiIiUqZoqmQZFBgYSGZmJtHR0WRnZ3P58mVeffVVRo4cmS9w3UyDBg3YtWsX586dIysri9mzZ99yu5CQEPbu3Ut8fDy5ubmcP3+eiIgIpkyZcjdPS0RERESk1FJwK4MqVarEkiVLSExMpF27djzxxBOYTCbmzp1bpO379OmD2WymW7dudOjQAS8vL2rVqnXT9rVr1yY6OprY2Fhat27NU089Rf369RXcRERERESKSO9xkxJF73GTu03vrZF7QeNK7hWNLblXNLaKh97jJiIiIiIiUooouImIiIiIiNg5rSopJYqDoyMGxn9+1vAVERERkbJB//OVEsWxolu+z4ZhYLVqIraIiIiIlG4KblKipKfnf/ebYRgYhkER3mIgUqgbY0djSO4mjSu5VzS25F7R2Coe/8311qqSIiIiIiIidk6Lk4iIiIiIiNg5BTcRERERERE7p+AmIiIiIiJi5xTcRERERERE7JyCm4iIiIiIiJ1TcBMREREREbFzCm4iIiIiIiJ2TsFNRERERETEzim4iYiIiIiI2DkFN7F7qampPP/88/j5+REQEMCkSZPIzc0t7m5JCbBu3ToaN26M2Wy2/RszZgwA+/bto1evXpjNZoKCgoiLi8u37SeffEKHDh1o3rw5oaGhJCUlFccpiB1JS0ujQ4cOJCYm2sruZBzl5eUxdepUWrdujdls5rnnnuP8+fN/2fmI/ShsbL3xxhs0adIk3/dXbGysrV5jS27l4MGDDBo0CH9/f9q0acMrr7xCWloaoO+tEs0QsXPPPPOM8fLLLxtXrlwxTp48aXTp0sVYsGBBcXdLSoApU6YYY8eOLVB+8eJFw9/f31ixYoWRk5NjbN261TCbzca+ffsMwzCM7du3G2az2di1a5eRnZ1tLF682AgICDCuXLnyV5+C2Ildu3YZTzzxhPHAAw8Y27dvNwzjzsdRVFSUERISYiQnJxsZGRnGSy+9ZAwZMqTYzlGKR2FjyzAMo0ePHsaaNWsK3UZjS27l6tWrRps2bYz333/fuHbtmpGWlmYMGTLEGDZsmL63SjjdcRO7duLECXbs2MGYMWNwdXWlbt26PP/886xcubK4uyYlwP79+2nSpEmB8i+//BI3Nzf69+9PuXLlaNWqFSEhIbZxFRcXR5cuXfD19cXJyYmBAwdStWpV1q1b91efgtiBTz75hNGjRzNy5Mh85Xc6juLi4hgyZAheXl5UqlSJyMhItmzZwqlTp/7yc5TicbOxlZ2dzeHDhwv9/gKNLbm15ORkHnzwQV544QWcnZ2pWrUqffr0YefOnfreKuEU3MSuHTlyBDc3Nzw8PGxlDRo0IDk5mcuXLxdjz8TeWa1WDhw4wLfffktgYCDt2rXj9ddf59KlSxw5coQHHnggX/uGDRty8OBBAI4ePXrLeilb2rZty1dffUXnzp3zld/JOMrIyODs2bP56mvUqEGVKlU4dOjQPToTsTc3G1sHDx4kNzeXDz74gNatWxMcHMy//vUvrFYroLElt1a/fn2io6NxdHS0lW3YsIGHH35Y31slnIKb2LXMzExcXV3zld34fOXKleLokpQQaWlpNG7cmODgYNatW0dMTAzHjx9nzJgxhY4rFxcX25i6Xb2ULe7u7pQrV65A+Z2Mo8zMTAAqVKhQoP5GnZR+NxtbGRkZ+Pv7Ex4ezubNm3nnnXdYvnw5ixYtAjS2pOgMw2DmzJls2rSJyMhIfW+VcApuYtcqVKjA1atX85Xd+FyxYsXi6JKUEDVq1GDlypX07NkTV1dXatWqxZgxY9iyZQuGYZCVlZWvfVZWlm1Mubq63rJeBG4/Tm5Vf+M/Rn/8ftM4E4A2bdqwbNky/P39cXJy4pFHHmHAgAG26WoaW1IUFouF4cOHk5CQwIoVK/D29tb3Vgmn4CZ2rVGjRly8eJELFy7Yyn799Vc8PT257777irFnYu8OHjzI9OnTMQzDVpadnY3JZOKRRx7hyJEj+dofPXqURo0aAdfH3a3qRQAeeOCBPz2OqlSpgoeHB0ePHrXVpaSkcPHixQLTlKTs+frrr4mJiclXlp2djYuLC6CxJbd38uRJwsLCsFgsrF69Gm9vb0DfWyWdgpvYtf/5n//B19eXt99+G4vFwqlTp5gzZw49e/Ys7q6JnXNzc2PlypVER0eTm5tLcnIy77zzDj169CA4OJgLFy6wZMkScnJy2L59OwkJCYSFhQHQs2dPEhIS2L59Ozk5OSxZsoTU1FQ6dOhQzGcl9qRDhw53NI5CQ0OZO3cup06dwmKx8Pbbb+Pv70+9evWK87TEDhiGweTJk9m2bRuGYZCUlMSyZcvo06cPoLElt3bp0iUGDBiAj48PCxcupFq1arY6fW+VbA7G7/8cLWKHLly4wIQJE0hMTMRkMtG9e3dGjx6d76FbkcLs2LGDGTNmcPjwYcqXL0+XLl0YM2YM5cuXZ//+/UyaNInDhw9TrVo1nn/+eUJDQ23bfvrpp8ydO5dz587RsGFDxo0bR7NmzYrxbMQeeHt7s2zZMgICAgDuaBzl5OTw/vvvEx8fT2ZmJgEBAbz11ltUr169WM5Nitcfx1ZMTAyLFy/m3Llz1KhRg0GDBtG/f39be40tuZnFixczZcoUXF1dcXBwyFeXlJSk760STMFNRERERETEzmmqpIiIiIiIiJ1TcBMREREREbFzCm4iIiIiIiJ2TsFNRERERETEzim4iYiIiIiI2DkFNxERERERETun4CYiIiIiImLnFNxERESkUCdOnCjuLoiIyH8ouImIiNiR8PBwoqKiirsbTJ06lblz5xZ3N0RE5D8U3ERERKSA9PT04u6CiIj8joKbiIiIHVqzZg39+vVj6tSp+Pv707JlS5YvX85HH31EYGAgvr6+jB8/3tY+KCiIWbNmERwcjNlspn///hw9etRWv2vXLvr374+fnx9BQUG89957ZGdnAxAVFcWzzz5LWFgY/v7+zJo1i4SEBBISEujWrRsAe/bs4W9/+xtt27aladOmhIaGsnfvXgASExMJCgpi7ty5PProo/j7+/Piiy9isVhsx1+6dCkdOnTAbDYTGhrKtm3bADAMg2XLlhEcHIyfnx/9+vXjp59+uteXV0SkxFFwExERsVO7d+/Gw8OD7du3M3z4cCZPnkxiYiLr1q1jyZIlrF69mp07d9rax8bG8t5777Ft2zYaNGhAREQEOTk5/Pvf/2bQoEF07NiRrVu3snjxYjZu3Mi0adNs227bto3Ro0ezadMmIiIiCAkJISQkhPj4eLKysnjuuecIDg5my5YtJCYmUq9evXzbnzlzhnPnzvHVV18RFxdHUlISq1atAq6H0Dlz5jBt2jR2795N3759ee6557h48SKrVq1i8eLFvP/++2zbto3Q0FAGDRrEhQsX/roLLSJSAii4iYiI2KkKFSowYMAATCYTbdu2JS8vj8GDB+Pq6krTpk2pWbMmZ86csbUfPHgwDz30EC4uLvzv//4vv/32G3v27CEhIQFvb28GDBiAs7Mz999/Py+//DJxcXFYrVYA6tatS6tWrahYsSLlypXL1w8nJydiY2Pp168f2dnZnDlzBjc3N86dO5ev3QsvvICLiwv3338/AQEBHDt2DIBPPvmEPn36YDabMZlM9OrVi0WLFuHi4sLKlSsZNmwYDz74IE5OTvTs2ZMGDRoQHx9/j6+uiEjJUu72TURERKQ4uLm54eDgAIDJdP1vrZUrV7bVm0wmW/ACuP/++20/u7q64ubmRkpKCqmpqdStWzffvuvUqUNWVhapqakA1KxZ86b9cHR0JDExkSFDhnDlyhUaNmxIuXLlMAwjXzt3d3fbz05OTrb6lJQUatWqla+tj48PcP1O3dSpU5k+fbqtLjc3lyZNmty0PyIiZZGCm4iIiJ26EdqK6vd3wDIzM0lPT8fLy4vatWvz5Zdf5mt78uRJnJ2dqVKlym2PtW/fPt566y1iYmJsgWrRokW2O2q34+XlxW+//ZavbObMmXTr1g1PT0+GDx9Oly5d8vXNzc2tSPsWESkrNFVSRESklFi8eDEnTpzg6tWrTJ48mfr162M2m+nSpQu//vorS5cuJTs7m5MnTzJjxgxCQkJwdnYudF/Ozs5kZGQAkJGRgclkwsXFBYC9e/eybNky2+ImtxMaGkpsbCw//vgjVquVjz/+mJUrV1K1alV69+7N3Llz+fXXXwH47rvv6NKlS75n90RERHfcRERESg1fX19eeOEFkpOTadGiBf/6178wmUzUqVOH6OhoZsyYQVRUFC4uLnTt2pWXXnrppvvq3LkzI0eO5LHHHmPTpk3069eP/v37Y7VaqVOnDuHh4bz77rtFWkQkJCSEy5cvM2bMGFJSUmjYsCELFiygWrVqDBw4EMMweP755zl//jweHh6MHz+exx9//C5eGRGRks/B+OMEdRERESlxgoKC+Mc//kFoaGhxd0VERO4BTZUUERERERGxcwpuIiIiIiIidk5TJUVEREREROyc7riJiIiIiIjYOQU3ERERERERO6fgJiIiIiIiYucU3EREREREROycgpuIiIiIiIidU3ATERERERGxcwpuIiIiIiIidk7BTURERERExM4puImIiIiIiNi5/w83Gfwi7MTFtAAAAABJRU5ErkJggg=="
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 338
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "",
-   "id": "c0805dca6642c526"
+   "execution_count": 65
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:49.598089Z",
-     "start_time": "2025-06-24T18:35:49.590576Z"
+     "end_time": "2025-06-25T17:29:21.507779Z",
+     "start_time": "2025-06-25T17:29:21.497312Z"
     }
    },
    "cell_type": "code",
@@ -4997,17 +6428,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test AUC Score 0.9302321331097941\n"
+      "Test AUC Score 0.9520162390829696\n"
      ]
     }
    ],
-   "execution_count": 339
+   "execution_count": 66
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-24T18:35:49.812575Z",
-     "start_time": "2025-06-24T18:35:49.772641Z"
+     "end_time": "2025-06-25T17:29:28.891491Z",
+     "start_time": "2025-06-25T17:29:28.841389Z"
     }
    },
    "cell_type": "code",
@@ -5023,17 +6454,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.108353\n",
-       "1     15001  0.032000\n",
-       "2     15002  0.057833\n",
-       "3     15003  0.732829\n",
-       "4     15004  0.050659\n",
+       "0     15000  0.054398\n",
+       "1     15001  0.020858\n",
+       "2     15002  0.028986\n",
+       "3     15003  0.857300\n",
+       "4     15004  0.033321\n",
        "...     ...       ...\n",
-       "9995  24995  0.011100\n",
-       "9996  24996  0.015169\n",
-       "9997  24997  0.008829\n",
-       "9998  24998  0.144934\n",
-       "9999  24999  0.207669\n",
+       "9995  24995  0.004218\n",
+       "9996  24996  0.005933\n",
+       "9997  24997  0.004155\n",
+       "9998  24998  0.081669\n",
+       "9999  24999  0.155663\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -5064,27 +6495,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.108353</td>\n",
+       "      <td>0.054398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.032000</td>\n",
+       "      <td>0.020858</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.057833</td>\n",
+       "      <td>0.028986</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.732829</td>\n",
+       "      <td>0.857300</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.050659</td>\n",
+       "      <td>0.033321</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -5094,27 +6525,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.011100</td>\n",
+       "      <td>0.004218</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.015169</td>\n",
+       "      <td>0.005933</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.008829</td>\n",
+       "      <td>0.004155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.144934</td>\n",
+       "      <td>0.081669</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.207669</td>\n",
+       "      <td>0.155663</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -5122,12 +6553,12 @@
        "</div>"
       ]
      },
-     "execution_count": 340,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 340
+   "execution_count": 67
   }
  ],
  "metadata": {

From 3305e9c340957f0554deeba99232114e73986aa9 Mon Sep 17 00:00:00 2001
From: Dmytro Chechenets <demavend12345@gmail.com>
Date: Mon, 25 Aug 2025 18:08:10 +0300
Subject: [PATCH 4/4] Lasst try

---
 ...226\321\210\320\265\320\275\321\214.ipynb" |   164 +-
 .../custom_features.cpython-312.pyc           |   Bin 5575 -> 5575 bytes
 HW_2_3/__pycache__/plot_utils.cpython-312.pyc |   Bin 5086 -> 5086 bytes
 .../process_bank_churn.cpython-312.pyc        |   Bin 7853 -> 7853 bytes
 ...202\320\270\320\275\320\263\321\203.ipynb" |  1307 +-
 .../hw_2_3/submission_decision_tree_proba.csv | 10001 +++++++++
 ...n_soft_params_tree_depth6_leaf30_proba.csv | 10001 +++++++++
 ...on_tree_full_train_depth8_leaf44_proba.csv | 10001 +++++++++
 assets/hw_2_4/submission_cb_hyperopt.csv      | 10001 +++++++++
 .../submission_cb_hyperopt_cross_val.csv      | 10001 +++++++++
 .../hw_2_4/submission_cb_hyperopt_proba.csv   | 10001 +++++++++
 ...submission_cb_hyperopt_proba_cross_val.csv | 10001 +++++++++
 .../hw_2_4/submission_ensemble_oof_refit.csv  | 10001 +++++++++
 assets/hw_2_4/submission_ensemble_rank.csv    | 10001 +++++++++
 .../0_submission_ensemble.csv                 | 10001 +++++++++
 .../1_submission_ensemble.csv                 | 10001 +++++++++
 .../4_1_submission_cat.csv                    | 10001 +++++++++
 .../4_1_submission_xgb.csv                    | 10001 +++++++++
 .../4_2_submission_cat.csv                    | 10001 +++++++++
 .../4_submission_cat.csv                      | 10001 +++++++++
 .../4_submission_lgbm.csv                     | 10001 +++++++++
 .../4_submission_xgb.csv                      | 10001 +++++++++
 .../Bank Churn Prediction.ipynb               |  4735 +++++
 .../catboost_info/catboost_training.json      | 14855 ++++++--------
 .../catboost_info/learn/events.out.tfevents   |   Bin 494870 -> 321785 bytes
 .../catboost_info/learn_error.tsv             |  3147 ---
 .../catboost_info/test/events.out.tfevents    |   Bin 917740 -> 800950 bytes
 .../catboost_info/test_error.tsv              | 16855 +++++++---------
 .../catboost_info/time_left.tsv               | 14853 ++++++--------
 .../kaggle_competition.ipynb                  |  3931 +---
 30 files changed, 195671 insertions(+), 34193 deletions(-)
 create mode 100644 assets/hw_2_3/submission_decision_tree_proba.csv
 create mode 100644 assets/hw_2_3/submission_soft_params_tree_depth6_leaf30_proba.csv
 create mode 100644 assets/hw_2_3/submission_tree_full_train_depth8_leaf44_proba.csv
 create mode 100644 assets/hw_2_4/submission_cb_hyperopt.csv
 create mode 100644 assets/hw_2_4/submission_cb_hyperopt_cross_val.csv
 create mode 100644 assets/hw_2_4/submission_cb_hyperopt_proba.csv
 create mode 100644 assets/hw_2_4/submission_cb_hyperopt_proba_cross_val.csv
 create mode 100644 assets/hw_2_4/submission_ensemble_oof_refit.csv
 create mode 100644 assets/hw_2_4/submission_ensemble_rank.csv
 create mode 100644 bank_customer_churn_prediction/0_submission_ensemble.csv
 create mode 100644 bank_customer_churn_prediction/1_submission_ensemble.csv
 create mode 100644 bank_customer_churn_prediction/4_1_submission_cat.csv
 create mode 100644 bank_customer_churn_prediction/4_1_submission_xgb.csv
 create mode 100644 bank_customer_churn_prediction/4_2_submission_cat.csv
 create mode 100644 bank_customer_churn_prediction/4_submission_cat.csv
 create mode 100644 bank_customer_churn_prediction/4_submission_lgbm.csv
 create mode 100644 bank_customer_churn_prediction/4_submission_xgb.csv
 create mode 100644 bank_customer_churn_prediction/Bank Churn Prediction.ipynb

diff --git "a/HW_2_3/HW 2.3 \320\224\320\265\321\200\320\265\320\262\320\260 \320\277\321\200\320\270\320\271\320\275\321\217\321\202\321\202\321\217 \321\200\321\226\321\210\320\265\320\275\321\214.ipynb" "b/HW_2_3/HW 2.3 \320\224\320\265\321\200\320\265\320\262\320\260 \320\277\321\200\320\270\320\271\320\275\321\217\321\202\321\202\321\217 \321\200\321\226\321\210\320\265\320\275\321\214.ipynb"
index fc9f098..df1a774 100644
--- "a/HW_2_3/HW 2.3 \320\224\320\265\321\200\320\265\320\262\320\260 \320\277\321\200\320\270\320\271\320\275\321\217\321\202\321\202\321\217 \321\200\321\226\321\210\320\265\320\275\321\214.ipynb"	
+++ "b/HW_2_3/HW 2.3 \320\224\320\265\321\200\320\265\320\262\320\260 \320\277\321\200\320\270\320\271\320\275\321\217\321\202\321\202\321\217 \321\200\321\226\321\210\320\265\320\275\321\214.ipynb"	
@@ -18,8 +18,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:22:40.813589Z",
-     "start_time": "2025-07-13T17:22:39.378492Z"
+     "end_time": "2025-08-05T19:43:23.510740Z",
+     "start_time": "2025-08-05T19:43:23.167746Z"
     }
    },
    "cell_type": "code",
@@ -96,8 +96,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:22:40.959722Z",
-     "start_time": "2025-07-13T17:22:40.933226Z"
+     "end_time": "2025-08-05T19:43:23.643951Z",
+     "start_time": "2025-08-05T19:43:23.605270Z"
     }
    },
    "cell_type": "code",
@@ -158,8 +158,8 @@
    "metadata": {
     "id": "gJKP4iL38AEL",
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:22:41.089604Z",
-     "start_time": "2025-07-13T17:22:40.995381Z"
+     "end_time": "2025-08-05T19:43:23.913431Z",
+     "start_time": "2025-08-05T19:43:23.772368Z"
     }
    },
    "outputs": [
@@ -229,8 +229,8 @@
    "metadata": {
     "id": "v2U2dJIaF_W4",
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:22:41.270784Z",
-     "start_time": "2025-07-13T17:22:41.224500Z"
+     "end_time": "2025-08-05T19:43:24.083102Z",
+     "start_time": "2025-08-05T19:43:24.020967Z"
     }
    },
    "outputs": [
@@ -668,8 +668,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:22:41.564505Z",
-     "start_time": "2025-07-13T17:22:41.322653Z"
+     "end_time": "2025-08-05T19:43:24.691855Z",
+     "start_time": "2025-08-05T19:43:24.248923Z"
     }
    },
    "cell_type": "code",
@@ -702,7 +702,7 @@
       "text/plain": [
        "<Figure size 600x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -712,7 +712,7 @@
       "text/plain": [
        "<Figure size 600x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -723,8 +723,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:22:41.633103Z",
-     "start_time": "2025-07-13T17:22:41.629102Z"
+     "end_time": "2025-08-05T19:43:24.783213Z",
+     "start_time": "2025-08-05T19:43:24.779212Z"
     }
    },
    "cell_type": "code",
@@ -743,8 +743,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:22:42.020057Z",
-     "start_time": "2025-07-13T17:22:41.741913Z"
+     "end_time": "2025-08-05T19:43:25.337915Z",
+     "start_time": "2025-08-05T19:43:24.924604Z"
     }
    },
    "cell_type": "code",
@@ -759,7 +759,7 @@
       "text/plain": [
        "<Figure size 1400x700 with 1 Axes>"
       ],
-      "image/png": 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      },
      "metadata": {},
      "output_type": "display_data"
@@ -770,8 +770,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:22:42.233306Z",
-     "start_time": "2025-07-13T17:22:42.077268Z"
+     "end_time": "2025-08-05T19:43:25.696166Z",
+     "start_time": "2025-08-05T19:43:25.422913Z"
     }
    },
    "cell_type": "code",
@@ -808,7 +808,7 @@
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -926,8 +926,8 @@
    "metadata": {
     "id": "z4PK69GAGCsc",
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:22:43.819752Z",
-     "start_time": "2025-07-13T17:22:42.288723Z"
+     "end_time": "2025-08-05T19:43:27.912601Z",
+     "start_time": "2025-08-05T19:43:25.779777Z"
     }
    },
    "outputs": [
@@ -954,8 +954,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:22:44.087936Z",
-     "start_time": "2025-07-13T17:22:43.876464Z"
+     "end_time": "2025-08-05T19:43:28.405120Z",
+     "start_time": "2025-08-05T19:43:27.994825Z"
     }
    },
    "cell_type": "code",
@@ -993,7 +993,7 @@
       "text/plain": [
        "<Figure size 600x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1003,7 +1003,7 @@
       "text/plain": [
        "<Figure size 600x500 with 1 Axes>"
       ],
-      "image/png": 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"
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mTejcuTMxMTEsXryYDh06EBER4bp/y5YtREVF0aNHD1coWLt2LVC0Q5iNGzembt26LFmypMDyVatWFbi9ZcsWunfvzuDBg12h6ffffycpKanAfi42kB+gQ4cOeHp6XnCm4C+//MKJEycuOx6rMCkpKWRlZVGvXr1C769fv36B1+NKJ+Pctm0bFouFBg0aFOm1zs7OJioqynUWXb169Zg0aRIjRoxwvf8udciwNOQf3k1OTi5QZ0pKCm+++SYpKSklet+KXIp6nETO89hjjzF69Gief/555s6di9VqpW/fvjzyyCO88sor7Nq1i3HjxlG7dm2OHTvGF198we7du5k+fTqtWrVybWf48OH8+OOPTJ8+nV9//ZXo6Gj8/PzYsWMHH374IQEBAZecHBDyBu3u2LGDv/3tb66Zw+12O+vWrePrr7+mX79+rrAWGBhIZGQkn376KY0aNaJmzZr873//Izs7u8B4pksZO3Ys//znP3E4HIwePdq13GKxMHXqVJ566iksFgsDBgwgNTWVWbNmER8fX+iA43y9e/fm888/xzCMQnvexo8fz4svvojVamXq1KkF7mvfvj0LFiygTZs2hIWFsW3bNt577z1MJhNZWVmXfT4mk4kHH3yQBx54gCeeeILo6Gi2b9/OF198ccF+Fi9ezBdffEGzZs3Ys2cP77777gX7CQgIYNu2bWzevJkuXboU2EZQUBC33347b7/9Nh4eHgwaNIhjx47xr3/9i+bNm3PNNddctt4/27JlC5B3OK00pKens337dtdtm83GihUrWLBgAX/5y19chzQv91p7e3vTpk0b13Nt2bIlhw4dYu7cuURFRQF5QRzyBrY3a9aMDh06lMpzyBcREcHo0aN58sknOX78OG3btuXQoUPMmDGD+vXr07hx4xK9b0UuRcFJ5DxNmzblxhtv5MMPP+TTTz/l5ptvBuDvf/87kZGRfPLJJ7z88sskJSURGhpK7969mT59Os2bN79gW88//zzdu3fn66+/5umnnyY9PZ3w8HCuvfZabr311gvG3vxZQEAA//vf//j0009ZtGgRX375JYZh0KhRIx599FEmTJhQYHzTSy+9xHPPPceTTz6Jv78/48ePJzIyssiHtvr27UtgYCBhYWEXjKuaMGECfn5+fPDBB3z11Vf4+vrSqVMnXnvttUsehho6dCjvvPMOO3bsoH379hfcHx0dzfPPP4/dbmf48OEF7st/Ps899xyQ14M0bdo05s+fzy+//FKk5zRy5EjMZjOzZs3i+++/JyIigmeffZb777/ftc4jjzyCzWbjzTffJDc3l/r163PXXXdx4MABVq5cicPhwGKxcOeddzJr1ixuu+02Fi1adMG+7r77bkJCQvj000/55ptvCAoKIjo6mvvuu6/QcV6Xs3btWtq3b3/J2eWLY9euXfzlL39x3fby8qJhw4ZMnTqVW265xbW8KK/1s88+y5tvvsmHH37ImTNnCA4OZvz48dx7771AXg/o3//+d7766itWr17Nhg0bSn2G+xdffJH33nuPL7/8klOnThEcHMzw4cO57777XD1eV/q+FbkUk6HJLESkDN15553UqlWLF154wd2lVBoZGRn07duXV155xXWWoYhUDBrjJCJlaurUqcTExBQ6Y7cU7vPPPyciIuKCqRRExP3U4yQiZe79999nz549vPHGG+4upcJLSkpizJgxrvFqIlKxKDiJiIiIFJEO1YmIiIgUkYKTiIiISBEpOImIiIgUkYKTiIiISBFVmwkwExPTKO1h8CYTBAfXKJNty+Wp/d1Hbe8+anv3Udu7T1m3ff72i6LaBCfDoMze6GW5bbk8tb/7qO3dR23vPmp796kIba9DdSIiIiJFpOAkIiIiUkQKTiIiIiJFpOAkIiIiUkQKTiIiIiJFpOAkIiIiUkQKTiIiIiJFpOAkIiIiUkQKTiIiIiJFpOAkIiIiUkQKTiIiIiJFpOAkIiIiUkQKTiIiIiJF5NbglJSUxJAhQ9i4ceNF11mzZg2jRo2iY8eODBs2jFWrVpVjhSIiIiJ/cFtw2rJlC3/5y184cuTIRdc5fPgwd999N/feey+//PILd999N/fddx/x8fHlWKmIiIhIHqs7djp37lxmzpzJQw89xNSpUy+5XpcuXRg8eDAAw4cPZ86cOXz11Vfcc889xdqnyVSiki+5zbLYtlye2t991Pbuo7Z3n+re9oZh4HAaZNud5Nqd5Nid5DjO+7/dSa7jj//n5K/n+OP/uXZn3uMd5/3f7iTH7iDXYZx7nOPc4wxy7A6w52IYBrVrBfLhX9tTw8uj1J9bcV5TtwSnPn36MGrUKKxW6yWD04EDB4iIiCiwrHnz5uzZs6fY+wwOrlHsx1SEbcvlqf3dR23vPmp793F32zudeQEj2+ZwBY1sW8F/c2xOss/9e/66hT0mx+4k58/3F3j8H+s6jfJ9rjVNmQz0jCXLsLI+vQ1+Ab6E1PAu3yL+xC3BKTQ0tEjrZWRk4OPjU2CZt7c3mZmZxd5nYmIaRim/4CZT3i9QWWxbLk/t7z5qe/dR27vP+W3vdBrYHAa5jj96Tc7vTckLJIarNyWvZ8ZwBZVcR144KfD4c8sK9OKct15+L469vNPLRXhaTHhazXhZLXid/3+rGS+rGU+LGU+rGW9r3r+elvP+f24dL8u5dfNvn1vPy2om6Xgsu7Zsx+mwU9ffkydv7Iol10ZCgq3Un0v+a1sUbglOReXj40N2dnaBZdnZ2fj5+RV7W4ZBmX3IlOW25fLU/u6jtncftX0ex7nel7zQkne4p2BY+dOhI0fBw0gXHFoq5P7zg02uwyDblncoqSI0v8VswstSePDwvEwwudz95y8vsOzc/sxldMzS4XDw449r+H3HdgAaNGjE0KHDqVs7hIQE9//BUKGDU0REBDt37iyw7MCBA7Rt29ZNFYmIyJ8ZhnFufIrjgjEsFwskOYWEkj/fX2BszEXud1SQ3pcrCiVFDC1/BCMLnhbTufsteFrNWM1Va8BVRkY6MTE/cOrUCQC6dOlOly49sVgqzuxJFTo4jR49mo8++ohFixYxdOhQli5dyqZNm3j88cfdXZqISIWSP3D3/LBxqdByfm/M5e4vsK6rN8coMKi3IrCaTUUPJX/qpSk0tFjyA4rJdQgqLNSfzLRsPCx/BCMPiwlTdR0xXspWrFjCqVMn8PT0YvDgaBo3bubuki5Q4YJTZGQk06ZNY/To0TRr1ox33nmH1157jccff5zw8HDeeustmjRp4u4yRUQK5TSMS4eWPwebP4cSx58Cit1wjZnJP9PIYUBmju2CfVSEzhcTFAwjfwokhQUUT0v+2BiTqyfFy/LH/4syNsbTYsZSxr0vJhOEhNQgwazDpGWlX79BrFq1lIEDhxIYWNPd5RTKZBjV4+Uvi+Oirl+iCnDMtTpS+7tPRW97wzCwOw1XsCg0lFzkFGlXQDk3kLdA8Llcb47Dia2C9L7kD9wtEDrOCyjnH/rxPi+geHmYC46ZOS+guAb6XmRMjde5Q0dVtfelor/vKyObzcbx40cK9CwZhnHBe6is2z5/+0VR4XqcRKR6szsNUjJzScywkZCZS1JGLokZuSRm2kjKyCXz3MDcwgb1nj8vTEX4XjOb8ntfzgUUD0vB0HHe/wsNJYUEFG8PM6G1/MnOzM5b9yJjZspq4K5IaUlJSWbJkvkkJycxatS11K/fEKDCB28FJxEpF07D4MTZbE6czSYxM5ekDNu5QHQuGGXYSMrMJTnTVuqhp9AxL1d4JpLr/x6F9eac/zhLmQzcVa+HVAWHDsWyYsVicnNz8fX1w2KxuLukIlNwEpFSl2VzEJuQwb4zGew7nc7+MxkcOJNBps1RpMebTVDT15Navh4E+3nm/fh6Euznga+H5YLDSX+ElvMG8lryA03VPXQkUtk4nU42b/6JLVvyrlEbFlaPqKiR+Pn5u7myolNwEpErZhgG8Wm57D+TF472nc5g35l0jiZnFdpr5GkxER7oQ7C/J8H5ocj3XDDy86DWuf8H+XiU+UBfESlf2dlZLFu2iKNH4wBo1y6SXr36VareJlBwEpEisjmcHEzMdIWkwynZ7Dx+lrPZ9kLXD/bzpEWoHxGhfrQI9Seith8Na/pWuXlnRKRoDh2K5ejROKxWK1dfPYSIiKvcXdIVUXASkQskZ+ay70wG+89kuILSwcTMQicbtJigUS1fImr7nwtJeUEp2M/TDZWLSEXVqlUbUlPP0rx5BMHBRbv0WkWk4CRSjTmcBkeTs9h3Jv1cUMoLSWfScwtdv4aXlRahfrSs7U9k02Dq+VhoXMsPL2vFmdVXRCoGh8POL79spGPHznh5eWMymejevbe7yyoxBSeRaiI9x86BM+cGbJ8LSLEJGeTYnYWu3yDImxah/nmH2871JtWp4YXJZNKZXSJySWlpacTEzOf06XiSkhIYNmyMu0sqNQpOIlWMYRicSM1m/+m8Q235vUknzmYXur631ew6vNbi3KG25qF++Hnq40FEiu/YsSMsXbqQ7OwsvLy8aNOmvbtLKlX6ZBSpxLJtDmITM9l/7pT//edCUkZu4af91/b3PG8sUl5Qqh/kozPYRKTEDMNg+/Zf+Pnn9RiGQUhIbaKjRxEQEOju0kqVgpNIJWAYBokZuQXmRdp/JoO45MxCr0/mYTHRpJYvLc6FpIhQf5qH+hHk41H+xYtIlZebm8vKlTEcPLgfyBsI3q/fQKzWqveZo+AkUsHYHU4OJ50bsH36jwHbyVm2Qtev6ePhOtQWUTsvJDWu5YPVogHbIlI+HA47p0+fwmw207fvQFq3bldlJ55VcBJxI8MwiEvO4ufDyew9nc6+0+kcSsos9EKxZhM0qunrGoeU35sU4udZZT+gRKRy8PHxJTp6NIbhpE6duu4up0wpOImUM5vDydZjZ1l/MIkNBxM5mnLhoG0/T8u5ySP9XSGpWbAv3h6Va4ZdEamanE4nP/+8npo1a3HVVW0BqF27jpurKh8KTiLlIDEjlw2Hklh/MIlNcckFBm9bzSY61Q+kQ3hAXlCq7Ue9AG/1IolIhZSZmcmyZQs5fvwoFouFhg0bV6przZWUgpNIGXAaBntPp7P+YF5Y2nUqrcD9tXw96NO0Fn2aBtOtUZBO/ReRSiE+/iRLliwgIyMdDw8PBg6MqlahCRScREpNZq6DTXHJrD+UxIaDSSRkFJx9+6o6/q6w1KqOP2b1KIlIJWEYBrt27WDdulU4nQ6CgmoSHT2aWrWC3V1auVNwEimB42ezWB+bxPpDSWw5mlJgULePh5nujWrSp2ktejepRYi/lxsrFRG5MoZhsHr1Mnbv/h2Apk2bM3BgFJ6e1fMzTcFJpBjsToPfTpxlw8Ek1h1M4lBiZoH76wV607dpLfo0rUWn+kF46hpuIlLJmUwm/Pz8MZlM9OjRh44du1TrMZgKTiKX4TQMfj2eytI9p1mxL6HAfEoWE3QID3Qdgmtcy6daf6CISNXhcDiwWPLO5O3atSeNGzerNmfOXYqCk0ghDMNgd3w6S/ecYdne05xO/2O8UqC3lV5N8nqVejSuSYB31ZsZV0SqL8Mw2LJlI4cOxTJu3HVYrR6YTCaFpnMUnETOE5uQwdI9p1m69wzHzptfyc/TwoAWIQxtFUrXhjWx6tpuIlIF5eRks2LFEg4fPghAbOx+WrZs7eaqKhYFJ6n2jqVksXTPGZbuPU1swh9jlrysZvo1C2Zoy1B6NqmFl8YriUgVlph4hiVLFnD2bAoWi4V+/QYpNBVCwUmqpYSMXGJ25/UsnT/HktVsoleTWkS1CqVP02B8PTVTt4hUffv372HVqqXY7XZq1AggKmqUDs1dhIKTVDs/HU7i0QW7XbN3m03QtWEQQ1vV5urmwRqzJCLVyq+/bmXDhtUA1K/fiCFDhuPj4+PeoiowBSepVub8dpJXlu/HYUBEqB9j29dlUEQItXw93V2aiIhbNGnSjC1bNtK6dTu6deuF2axhCZei4CTVgtMweGfdIf67+RgAw1vX5omhEXhY9AEhItVPenoa/v41AAgICGTixJvx9lYvU1HoW0OqvGybg8d/2O0KTbf3bMQz0S0VmkSk2jEMg99+28qnn/6Hw4djXcsVmopOPU5SpSVn5vLAvF3sOJmK1WziyagIhrfWgEcRqX5sNhurVy9j//49ABw+fJDGjZu5uarKR8FJqqzDSZlMnfs7x1KyqeFl5dUxrencIMjdZYmIlLuzZ5NZvHgBSUkJmEwmevXqT/v2ke4uq1JScJIqaeuxFB76fhep2XbqBXrz5ri2NAn2dXdZIiLl7vDhgyxfvpjc3Bx8fHyJihpJvXr13V1WpaXgJFXOkt2neTZmLzaHQdu6NXh9bBudNSci1VJCwhkWLZoHQJ06dYmKGukaFC5XRsFJqgzDMPhw4xH+vSEOgAEtQnh2WEu8PTSJpYhUTyEhobRp0wGTCXr3vtp10V65cgpOUiXYHU5eWLafBTvjAZjUuT739G+C2aRryolI9XLmzGn8/f3x8ckbntCv30BM+iwsNQpOUumlZdv5v/m72HwkBbMJHhrYnPEd67m7LBGRcrdnz07WrFlOWFg9Ro26FrPZrNBUyhScpFI7lpzJLV9s52BiJj4eZl4c2ZreTWu5uywRkXLlcNhZv341O3f+BoDVasVut+PpqfGdpU3BSSqtXafSuH/eLhLScwj192TG2La0rOPv7rJERMpVenoaMTELiI8/BUDXrj3p0qWHeprKiIKTVEprDiTwxMI9ZNudtAj1Y8a4ttSp4eXuskREytXx40dYunQhWVlZeHl5MXjwMBo1aurusqo0BSepdL7YepwZq2IxgH4RoTwb1QI/T72VRaR6cTqdrF27kqysLIKDQ4mOHkVgYJC7y6ry9G0jlYbDaTBjdSxfbTsBwLj2Ybzyl0jOJmdgGG4uTkSknJnNZoYOHcmOHdvo3ftqPDw83F1StaDgJJVCls3BEwv3sDY2EYC7+zbhpm71daFeEalWkpOTOHMmnoiIqwAIDg7h6quHuLmq6kXBSSq8hPQc7p+3k93x6XhaTEwb1orBLUPRuEcRqU5iY/ezcmUMDoedGjUCqFs33N0lVUsKTlKhHUjIYOqc3zmVlkOQjwevjWlNh/BAd5clIlJunE4nGzduYNu2zQDUq1dfY5ncSMFJKqyNcck8PH8XGbkOGtb04V/XtKV+kI+7yxIRKTdZWZksXbqQ48ePAtChQ2d69OijS6e4kYKTVEjzd5ziheX7cTgNIsMDeGVMG4J8NPBRRKqP+PhTxMQsID09DavVgwEDhtKiRUt3l1XtKThJhWIYBv/ecJgPN+b9dRXVKpSnolriadUgcBGpXk6ePEZ6ehqBgUEMGzaaWrVC3F2SoOAkFUiO3clzMXuJ2XMGgH/0aMidvRpp9lsRqZY6dOgMmLjqqrZ4eWmC34pCf8ZLhZCSZWPKt78Rs+cMFrOJJ4dGcFfvxgpNIlJtpKaeZdmyRdhsuQCYTCY6duys0FTBqMdJ3O5ochb3zf2dI8lZ+HlaeHl0a7o3qunuskREys2RI4dZtmwROTnZeHh4aG6mCkzBSdzq1+NneWDeTs5m2wmr4cWb17SlWYifu8sSESkXhmGwdesmNm7cAEBoaB06d+7u5qrkUhScxG2W7T3DM4v3kOswuKqOP2+Ma0uIn6e7yxIRKRc5OTmsXLmEQ4diAbjqqrb07TsQq1VfzRWZXh0pd4Zh8N/Nx3h73SEA+jUL5vkRrfDx0LwkIlI9JCcnsWjRPM6eTcFsttCv30Bat27n7rKkCBScpFzZHU5eXnGAeTtOAXB9p3Du698Ui1mDwEWk+vD09MRms+HvX4OoqFHUqRPm7pKkiBScpNyk59h5dMFufo5LxgTcP6AZ13fStZZEpHowDMN1prCfnz8jRozD398fHx9fN1cmxaHgJOXiVGo2U+fu5EBCBt5WM8+PuIr+zYPdXZaISLnIzMxg6dIfaNOmAy1atAIgNLS2m6uSK6HgJGVuT3waU+fuJCEjl2A/T94Y24bWYTXcXZaISLk4efIES5cuICMjg5SUFJo0aYbVqktIVVYKTlKm1h9M5LEfdpNlc9I02Jc3r2lL3QBvd5clIlLmDMPg99+3s2HDGpxOJzVrBhMdPUqhqZJTcJIyYRgGX207wYzVsTgN6NYwiJdHt8bfS285Ean6bDYba9YsZ9++3QA0axbBwIFD8fDQlCuVnb7FpNTFJmTw8vL9bDueCsDotnV4dHALrBZd4UdEqj673cbcuV+SkHAGk8lEz5596dChsy4hVUUoOEmpybI5+OCnOD7bchyH08DbauauPo35a6dwfWCISLVhtXpQv34jMjIyGDp0BOHhDdxdkpQiBScpFWsOJPLaygOcSssB4OrmwTwwoBlhGs8kItWAYRjk5OTg7Z33mdejRx86dOiEn5+/myuT0qbgJCVyMjWb11bGsjY2EYC6AV48OLA5/ZppqgERqR6ys7NYvnwxOTk5jB17HRaLBbPZrNBURSk4yRWxO5x8vuU4s3+KI9vuxGI2cUOX+tzSo6EunSIi1UZCwmmWLFlAaupZLBYLZ87EExZWz91lSRlScJJiOZWazYLf45m34ySn03MBiKwfyCODm9M02M/N1YmIlJ89e3axZs0yHA4HAQGBREePIiREk1pWdW4JTomJiTz55JNs2rQJi8XC6NGjefjhhwu9IvQnn3zCJ598QkpKCuHh4UyZMoWoqCg3VF192Z0GGw4mMW/HSX48lITTyFtey9eDe/o1ZXjr2hr8LSLVhsPhYP361fz++68ANGzYmMGDh+Ht7ePmyqQ8uCU43XfffdSpU4d169aRkJDAXXfdxccff8ytt95aYL01a9bw3nvv8emnn9K0aVNiYmK47777WLZsGfXr13dH6dXKibPZfP/7KRb8fooz53qXALo0CGRsu7pc3SIEL6umGBCR6mXNmuXs3r0TgC5detC1a0/98ViNlHtwiouLY9OmTaxduxYfHx8aNGjA5MmTefXVVy8ITgcPHsQwDNePxWLBw8Oj0J4pKT1n0nOYvnQ/Px5K4lznEkE+HoxqU4cx7cJoVEsXpBSR6qtTp24cO3aUvn0H0rhxU3eXI+Ws3BPI/v37CQoKok6dOq5lzZo148SJE6SmphIQEOBaPmLECObMmcPw4cOxWCyYTCZeffVVwsLCir3fsvhjIH+bVekPDcMweHrxXjYfSQHyZvwe174u/ZsH41nBepeqYvtXFmp791Hblz/DMDh9Ot713VOzZk0mTfo7FotOhCkvZf2+L852yz04ZWRk4ONT8Dhw/u3MzMwCwclms9GqVSumT59Oq1atWLBgAY8//jjNmjWjZcuWxdpvcHDZXVS2LLdd3j7feITNR1Lw9jAz567etK4XcPkHuVlVav/KRm3vPmr78pGbm8v8+fPZtWsXN9xwA8HBNdT2blQR2r7cg5Ovry9ZWVkFluXf9vMreFbWc889R6dOnWjfvj0A1157LT/88ANz587lkUceKdZ+ExPTMIzLr1ccJlPei1gW23aHU6nZTF+4C4C7ejemtqeJhIQ0N1d1cVWt/SsTtb37qO3LT0pKMosXzycpKRGz2czx46do2rSp2t4Nyvp9n7/9oij34NSiRQtSUlJISEggJCQEgNjYWMLCwqhRo2DRJ06coG3btgWWWa1WPDyKf2Vpw6DM3uhlue3yYhgG05fuJyPXQbu6AfwlMrzSPKeq0P6VldrefdT2ZevQoQOsWLGE3NxcfH39iIoaSb164YDa3p0qQtuX+6CVxo0b07lzZ1544QXS09M5evQos2bNYvz48ResO3DgQD799FN27tyJ0+lkyZIlbNy4keHDh5d32VXeDzvj+elwMp4WE09FRWAxawCFiFQ/TqeTn39ez+LF88nNzaVu3XCuu+4G6tYNd3dpUkG45fS0mTNn8uyzzzJo0CDMZjNjx45l8uTJAERGRjJt2jRGjx7NlClTsFgs3H333Zw9e5ZGjRrxzjvvcNVVV7mj7CrrTHoOM1YfBOD2Xo1pHKyz5kSkeoqLO8jWrZsAaN++Ez179tUgcCnAZBju7vQqHwkJZTPGKSSkRplsu7wYhsGD3+9ibWwiV9Xx58OJkVgrSW9TVWj/ykpt7z5q+7JlGAbr1q2kbt1wWrRoVeA+tb37lHXb52+/KDQhUjW3dM8Z1sYmYjWbeCq6ZaUJTSIipWXfvt00bNgEb29vTCYT/foNcndJUoFVrIl5pFwlZuTy6soDANzSoyHNQ3StORGpPux2O6tXL2P58sUsX76YanIARkpIPU7V2KsrD3A2205EqB83d2vg7nJERMpNWloqS5Ys4MyZeADq1q3n5oqkslBwqqZW7DvDin0JWMwmnopqidWizkcRqR6OHo1j2bKFZGdn4+XlzZAhw2nYsLG7y5JKQsGpGkrJtPHKirxDdH/r1oCWdfzdXJGISNkzDINt2zazceMGDMMgNLQ2UVGjCAgIdHdpUokoOFVDr6+OJSnTRtNgX27p3tDd5YiIlIvc3Bx27vwNwzBo1aoN/foNxGot/oTKUr0pOFUza2MTWbL7NGYTPBXdssJduFdEpKx4eXkTFTWKM2fiad26HSZdKVmugIJTNZKabePFZfsBuKFLfdqEuf9iiSIiZenAgX3Y7XZatWoNQO3adahdu46bq5LKTMGpGnlz9UESMnJpWNOH23o2cnc5IiJlJu/SKevYvn0LFouF2rVrU6tWiLvLkipAwama+PFQEgt2xmMCnoqKwNtDlxAQkaopMzODpUsXcuLEMQDat48kKKiWm6uSqkLBqRpIz7HzwrlDdH/pFE6HcJ1BIiJV06lTJ4iJ+YGMjHQ8PDwYODCKZs0i3F2WVCEKTtXAW2sPEZ+WQ3igN5P7NHZ3OSIiZeL3339l/fpVOJ1OgoJqMWzYaGrWVE+TlC4Fpypu85Fk5vx2EoAnoyLw0SE6EamisrIycTqdNG3agoEDo/D09HR3SVIFKThVYZm5Dp5fmneI7toOdencIMi9BYmIlDLDMFzTCnTp0oOaNYNp1qyFphqQMqNJfKqwWesPceJsNmE1vLi7XxN3lyMiUqri4g6xYMF32O02AEwmE82bRyg0SZlScKqith87y9fbTgDw+NAW+Hmqc1FEqgbDMNi8+ScWLpzLsWNH2L59i7tLkmpE36ZVULbNwXNL92EAo9vWoUdjDY4UkaohJyeb5cuXEBd3EIA2bdoTGdnFzVVJdaLgVAXN/ukIR5KzCPX35L7+zdxdjohIqUhIOMOSJfNJTT2LxWKhf//BtGrVxt1lSTWj4FTF7I5P47NfjgLw8KAW1PDWSywilV9c3EFiYn7AbrdTo0YA0dGjCA3VpVOk/OlbtQqxOZw8F7MPhwFDWobSv3mwu0sSESkVtWqFYLVaqVs3nCFDhuPt7ePukqSaUnCqQj7ZdJT9ZzII9Lby0EAdohORys1ms+Hh4QFAjRoBXHPNXwkICMRs1nlN4j5691URsQkZ/OfnIwA8OLA5NX018ZuIVF4nThzjs88+5NChA65lQUE1FZrE7fQOrAIcToPnl+7D7jTo07QWUa1C3V2SiMgVMQyDX3/dyvz535KZmcH27VswDMPdZYm46FBdFRCz5zS/n0zDz9PCo4M1Y66IVE42m43Vq5eyf/9eAFq0aMnVVw/VZ5pUKApOlVhyZi5fbD3ORxvzzqIbFBFC7Rpebq5KRKT4UlKSWbJkPklJiZjNZnr16ke7dpEKTVLhKDhVUhvjknlw3k6y7U4Amgb78tdO9d1clYhI8WVkpPPtt5+Tm5uDr68fUVEjqVs33N1liRRKwakSMgyDf605SLbdScva/tzaoyH9mgdj1l9mIlIJ+fn5ExHRioSEM0RFjcTPz9/dJYlclIJTJbQpLoX9ZzLw8TDzzvh2BPp4uLskEZFiyc7OwjAMfHx8Aejd+2oALBaLG6sSuTydVVcJfbrlGACj24YpNIlIpXPmTDzffPMZS5cuxOnMG25gsVgUmqRSUI9TJbP/TDo/H07GbIK/dtYYABGpXHbv/p21a1fgcDgwmUxkZmbg71/D3WWJFJmCUyXz2ZbjAAxsEUp4oC45ICKVg8NhZ926VezatQOARo2aMnhwNF5e3m6uTKR4FJwqkdNpOcTsPg3ADV11Bp2IVA5paWnExCzg9OlTAHTr1ovOnbtrqgGplBScKpGvtp3A7jSIrB9ImzB1bYtIxWcYBsuXL+L06VN4eXkxZMhwGjZs4u6yRK6YBodXEhm5dub8dgKASZ3V2yQilYPJZKJ//0HUrRvOhAk3KDRJpafgVEl8v+MU6TkOGtb0oW+zWu4uR0TkonJzc4mLO+i6XatWCGPHXkdAQKAbqxIpHQpOlYDd4eSLc4PCJ3Wpr4kuRaTCSk5O4ttvP2fx4vmcPHnctVzjmaSq0BinSmDZvjOcSsuhlq8HI1rXcXc5IiKFio3dx8qVMdhsNvz8/DGb9be5VD0KThWcYRj8b3PehJfXdwrHy6oPIhGpWJxOJz//vJ7t238BoF69+gwdOhJfX183VyZS+hScKriNccmuy6tc26Guu8sRESkgMzOTZcsWcvz4UQA6duxMjx591dskVZaCUwX333O9TWPb1SXAW5dXEZGK5eDBfRw/fhSr1YOBA6No3jzC3SWJlCkFpwpsd3wam4+kYNHlVUSkgmrTpgNpaam0bNmGWrWC3V2OSJlTX2oFlj+2aUir2tQN0GUJRMT97HYbGzduIDc3F8g7W65nz34KTVJtqMepgjpxNpsV+84AcGMXTXgpIu6XmnqWmJgFnDlzmrNnUxg6dIS7SxIpdwpOFdRPh5NwGhBZP5CI2v7uLkdEqrkjRw6zbNkicnKy8fb2oXXrdu4uScQtFJwqqEOJmQC0rqNr0omI+xiGwZYtG9m06UcAateuQ1TUKGrUCHBzZSLuoeBUQeUHp6bBmgdFRNwjJyebFSuWcPhw3uVTWrduR9++A7BY9NUh1Zfe/RXUoaS84NREwUlE3MThcHD6dDwWi4V+/QZx1VVt3V2SiNspOFVAKVk2zqTnnbGi4CQi7uLr60d09CjMZgu1a+tyTyKg4FQhfbwxbwbeJsG++HvpJRKR8uFwOPjpp3XUqRNGixatAAgLq+fmqkQqFn0rVzAHEjL4cmve/E339m/q5mpEpLrIzMwgJuYHTp48jtXqQf36DfHxUY+3yJ8pOFUghmHw6ooDOAy4unkwvZvUcndJIlINnDx5nJiYH8jMzMDT05NBg6IVmkQuotjBKSkpifnz53P8+HHuvfdeNm/ezIABA8qitmonZs8Zth47i5fVzNSrm7m7HBGp4gzDYMeO7fz44xqcTie1agUTHT2aoKCa7i5NpMIq1iVXdu7cSXR0NEuWLOHbb78lOTmZe++9l++++66s6qs20nPsvLkm75Tff3RvSL1AXWJFRMqOYRisWLGE9etX4XQ6ad68Jdde+1eFJpHLKFZwevHFF3nkkUf48ssvsVqtNGjQgHfeeYf//Oc/ZVVftTH7pzgSM3JpEOTNDbrEioiUMZPJhJ+fPyaTid69r2bIkOF4eHi6uyyRCq9YwWnfvn2MGTMGyPulA+jbty/x8fGlX1k1YncafPfrSQAeGNgcT6uuvSwiZcPhcLj+3717b8aPn0iHDp1cn+kicmnF+oauVasWBw8eLLDs4MGDhISElGpR1c2Js9nk2J14Wc30bKxuchEpfU6nk02bNvD999/gcNgBMJvNhIZqfiaR4ijW4PCJEydyxx13cOedd2K321m0aBHvvvsuf/nLX8qqvmrhUGIGAE1q+WLWX30iUsqys7NYvnwxR44cBuDQoViaN2/p3qJEKqliBaebbroJi8XCJ598gtPpZObMmVx33XX8/e9/L6v6qoWDibq8ioiUjTNnTrNkyXzS0lKxWq307z9YoUmkBIoVnH799VcmTZrEpEmTCixfu3Yt/fr1K9XCqpN9p9MBXdBXRErXnj07WbNmOQ6Hg4CAQKKjRxMSEuruskQqtWKNcSqsZyk9PZ1777231AqqbgzDYOuxswB0CA90czUiUlVs3bqJlStjcDgcNGzYhPHjJyk0iZSCy/Y4xcXFMWLECBwOB4ZhcNVVV12wTqdOncqkuOrgSHIWSZk2PC0mWofVcHc5IlJFNGvWgm3bNtO+fSe6dOmhs+ZESsllg1OjRo345ptvSE1N5fbbb2f27NkF7vfy8iIiIqLMCqzq8nub2tYNwEvTEIhICaSnp+Hvn/cHWGBgTSZN+gfe3j5urkqkainSGKf8XqYffviBBg0alGlB1c22c8Epsr4O04nIlTEMg19/3cLPP29g+PAxNGzYGEChSaQMFGtwuL+/PzNnziQ+Ph6n0wmAzWZj3759zJ8/v0wKrMrOH9+k4CQiVyI3N5dVq5YSG7sPgLi4Q67gJCKlr1jB6dFHH+Xw4cPUqlWLjIwM6taty/r16y84y06KJiXLRnxaDiagfb0Ad5cjIpVMcnISS5bMJzk5CbPZTO/eV9O2bQd3lyVSpRUrOG3evJlFixYRHx/P+++/z9tvv83333/PDz/8UKydJiYm8uSTT7Jp0yYsFgujR4/m4Ycfxmq9sJxNmzbx6quvcuDAAQICAlyTcFYFR5KzAAgL8MLHw+LmakSkMomN3c+KFTHYbLn4+fkxdOgo6tat5+6yRKq8Yo1Gtlqt1KlTh8aNG7N3714ARowYwa5du4q10/vuuw9fX1/WrVvHt99+y08//cTHH398wXqxsbHcfvvtTJw4ka1bt/Lee+/x4YcfsmTJkmLtr6KKOxecGtbUOAQRKbrjx4+zZMkCbLZc6tULZ8KEGxSaRMpJsXqcwsPD+f3332nbti0ZGRkkJSVhtVrJzs4u8jbi4uLYtGkTa9euxcfHhwYNGjB58mReffVVbr311gLrfv755wwaNIhx48YB0KpVK7788kv8/f2LUzYAZXEmbv42r3TbR13BybdM6qvqStr+cuXU9u5jMkG9evW46qo2eHp607NnHywW9ViXB73v3aes27442y32tepuvPFGFi5cyMiRI/nb3/6G1Wqla9euRd7G/v37CQoKok6dPy4s2axZM06cOEFqaioBAX+M9fntt9/o1asX999/Pxs2bKBWrVrcfPPNV3RtvODgspsj6Uq3fSrDBkDrBkGEhGgOpytVlq+tXJravvycOHGCmjVr4uOT10M9YcK1mpvJTfS+d5+K0PbFCk7jx48nIiKCkJAQHnroIT766CMyMjK45ZZbiryNjIwM1y9+vvzbmZmZBYLT2bNn+e9//8uMGTN45ZVX2LZtG3fccQeBgYFER0cXp3QSE9MwjGI95LJMprwX8Uq3vT8+FYBgTzMJCWmlW1w1UNL2lyunti9fO3f+xtq1q2jQoCEjR44lJCSApKR0tX050/vefcq67fO3XxTFCk4A7du3d/3/9ttvByAmJoaoqKgiPd7X15esrKwCy/Jv+/n5FVju6enJoEGDuPrqqwHo2rUrY8aMYfHixcUOToZBmb3Rr2TbTsNwHaprEOSjX8ISKMvXVi5NbV+27HY769atZPfu3wEwm83YbHZAbe9Oanv3qQhtX6TB4ampqTz88MOMGjWK119/3TWHU2ZmJo899hj33XdfkXfYokULUlJSSEhIcC2LjY0lLCyMGjUKpr1mzZqRm5tbYFn+pV8qu/i0HHIdBlazibAAb3eXIyIVTFpaKnPnfsXu3b9jMpno3r0P0dGj8fDwcHdpItVakYLT008/zdatW+nTpw8LFy7ks88+4/Tp01x33XWsWLGCf/3rX0XeYePGjencuTMvvPAC6enpHD16lFmzZjF+/PgL1r3++utZsWIF33//PYZhsHnzZhYsWMCYMWOK/gwrqCNJeb1N9YO8sZo1TkFE/nD0aBzffPMpZ87E4+3tzciR19C5czeNaRKpAIoUnH7++Wf+/e9/8/DDDzNz5kwWLFjAzTffTFBQEPPnz2fo0KHF2unMmTOx2+0MGjSI6667jr59+zJ58mQAIiMjXbOQ9+zZk1mzZvHf//6Xzp078+ijj/Lwww8zaNCgYj7NiifuvDPqRETyORwO1qxZTnZ2NqGhdZgw4QYaNGjk7rJE5JwijXHKzs6mWbNmALRt25bff/+dYcOG8fLLLxc6aeXlhISEMHPmzELv27ZtW4Hb/fv3p3///sXeR0V3JDkT0BxOIlKQxWIhKmoku3b9Tu/e/a/oM1ZEyk6RfiP/3D3s6enJk08+qV/oEohL0uSXIpInKSmBpKREmjdvCUBoaB36969zmUeJiDtcUfLx9PQkKCiolEupXg4n5fU4NamlQ3Ui1dn+/XtZtSoGp9MgICCQ2rXD3F2SiFxCkYKTYRicPHnSdTab0+kscBvyZrKVosmyOTiVlgNAYwUnkWrJ4XDw88/r+PXXrQDUr9+QGjV0sW+Riq5IwSkrK4uBAwe6bhuG4bptGAYmk4ndu3eXTYVVUP4ZdYHeVoJ8dWqxSHWTmZnB0qU/cOLEcQAiI7vSvXtvzOZiXT5URNygSMFpxYoVZV1HteI6TBes3iaR6ubkyRMsXbqAjIwMPDw8GTQoiqZNW7i7LBEpoiIFp/Dw8LKuo1rJD06NdJhOpNo5fvwoGRkZ1KxZi+jo0dSsWcvdJYlIMei0ODfID04a3yRS/XTu3A2LxULbtu3x8PB0dzkiUkw6oO4Gh8+NcWpcS1MRiFR1Z8+msHz5Ymw2G5A3vUtkZBeFJpFKSj1O5czhNFyTX6rHSaRqO3z4ICtWLCYnJwcvLy/69h14+QeJSIV2RcHp7NmzHD16lNatW2O32/H01F9ORXUyNZtch4GnxURdXdxXpErKu7bmT/zyy88A1KlTl8jIrm6uSkRKQ7EO1WVkZPDAAw/QvXt3brjhBg4fPsyQIUM4ePBgWdVX5fwxY7gvFl3cV6TKyc7OYuHCea7Q1LZtB8aOnYC/fw03VyYipaFYwemVV14hMzOTxYsX4+HhQYMGDRgwYADTp08vq/qqnEOugeEa3yRS1SQlJfDtt59z5MghLBYLAwdG06/fICwWjYoQqSqK9du8atUqFixYQGBgICaTCQ8PDx555BH69etXVvVVOTqjTqTq8vDwIjc3l4CAQKKjRxESUtvdJYlIKStWcHI6na7xTOdffkVjnIouTsFJpErJv3oCQI0aNRg5chwBAYF4e6tXWaQqKtahuh49evDss8+SlZXl+qB488036datW5kUVxX9MRWBgpNIZZeRkc7cuV9x8OAB17LatcMUmkSqsGIFp0cffZTY2Fi6du1KWloakZGRbN68mYcffris6qtSUjJtpGTlzeXSUGOcRCq1EyeO8fXXn3Lq1AnWr1+Fw+Fwd0kiUg6KdaguODiYr776ih07dnD8+HHCwsJo3749FoulrOqrUvLHN9UN8MLHQ20mUhkZhsFvv23lxx/XYhgGwcEhREeP0uegSDVRrOB05513Mn78eAYMGED79u3LqqYqS9eoE6ncbLZcVq1axoEDewFo0aIVV189BA8PDzdXJiLlpViH6po1a8azzz5L//79efXVVzl06FBZ1VUlaXyTSOVls+Xy3XdfcODAXsxmM336DGDw4GEKTSLVTLGC00MPPcTq1auZPn06J06cYOzYsUyaNIl58+aVUXlVS1yy5nASqaw8PDwJD2+Ar68fY8ZMoH37SNdJMiJSfRT7Ir9ms5n+/fszY8YMZs6cycmTJ3n00UfLorYqR3M4iVQuTqeTnJwc1+1evfpz3XU3ULduuBurEhF3KvZ0tkeOHGHevHnMnz+frKwsxowZw4QJE8qitiolx+7kxNlsQMFJpDLIyspi2bJFOJ0ORo8ej9lsxmKx4Ovr5+7SRMSNihWcrr/+enbs2EGPHj146KGHGDRoEFarLiVQFEeTs3AaUMPLSi1fjYkQqchOn45nyZL5pKenYbVaSUg4Q+3addxdlohUAMVKPb169eKNN96gXr16ZVVPlXX4vGvUaVyESMW1a9cO1q1bicPhIDAwiOjoUQQHh7q7LBGpIIoUnE6dOkVYWBjjx48H4MSJExesozB1aZqKQKRiczjsrFu3il27dgDQuHEzBg2KxsvLy82ViUhFUqTgNHz4cLZu3crAgQMxmUwFrs2U///du3eXaaGVXX5waqLgJFIhrVy5lP379wDQrVtvOnfupt5hEblAkYLTwoULAVixYkWZFlOV5c/hpB4nkYqpc+funDp1gv79B9OwYWN3lyMiFVSRpiOoW7cuAM8//zzh4eEX/OhadZfmNAzikjSHk0hFYhgGp0+fct2uVSuYiRP/rtAkIpd02R6nY8eOuSa4XL9+PW+//XaB+9PT09m7d2+ZFFdVnErNIdvuxMNiIjxIwUnE3XJzc1ixIobDh2MZM2YC9erVB9D15kTksi4bnOrVq8f+/ftJSkrC4XCwcePGAvd7eXnx9NNPl1mBVcGhxLzepoY1fbCaNWZCxJ2SkhJZsmQ+KSnJmM1m0tJS3V2SiFQilw1OZrOZf/3rXwA88cQTPP/882VeVFVzyDUwXBPnibjTgQP7WLkyBrvdhp+fP9HRo6hTp667yxKRSqRY0xFMnjy50KkIQNMRXMqhxAwAmgZrYLiIOzidTn7+eR3bt28BIDy8AUOGjMDXV7+TIlI8Vzwdwfk0HcGl5R+qa6zgJOIWsbH7XaEpMrIL3bv3wWwu9qU6RUQ0HUFZMwyDg+eCUxMFJxG3aN48gmPH4mjYsDHNmkW4uxwRqcSKNR1BeHg4Xl5ehIeHExoaypo1a9ixYwfh4bpS+MUkZOSSkevAYoKGOqNOpFwYhsHevbvIzc0B8nrFBwwYqtAkIiVWrGvVffPNN0yfPp3t27fz6quvsmjRIkwmEwcPHmTy5MllVWOllt/bFB7kg6dVhwZEyprdbmPNmhXs3buLpk1bEBU1UjOAi0ipKdY3+aeffso777yDw+Fgzpw5vPXWW3zxxRd8/fXXZVVfpZc/vkkDw0XKXmrqWebM+ZK9e3dhMpl0xpyIlLpi9TidPHmS3r17s3XrVqxWK506dQIgNVXzoFzMIY1vEikXR44cYtmyReTk5ODj48PQoSMID2/o7rJEpIopVnAKDAwkLi6OmJgYunXrBsDPP/9MaGhomRRXFRxyXWpFwUmkLBiGwZYtG9m06UcAatcOIypqFDVq1HBzZSJSFRUrOP39739n1KhRAPzvf/9jy5Yt3HHHHZo5/BJ0qE6kbOXkZLNz568AtGnTnj59rsZiKdZHm4hIkRXr02XixIn07dsXq9VK3bp1SUpK4rPPPqNt27ZlVV+llpyZS0qWDRPqcRIpK97ePkRFjSIlJZlWrdq4uxwRqeKK/WeZh4cHCxYs4Pjx44SGhjJy5MiyqKtKyD9MVzfAC28PXTxUpLTs27cbk8lEixatAAgLq0dYmK5eICJlr1hn1e3YsYMRI0awdOlSzp49y8qVKxkzZgxbtmwpq/oqtfi0vDlk6gV6u7kSkarB4XCwbt1Kli9fzKpVS0lJSXZ3SSJSzRSrx+nVV1/l3nvv5aabbnIt++STT3jttdf44osvSr24yi4t2w5AgLeHmysRqfwyMtKJifmBU6fyrpfZoUNnAgIC3VyViFQ3xepx2rt3LxMnTiywbOLEiezbt69Ui6oqUs8FpxreGqgqUhInTx7nm28+49SpE3h6ejJs2Bi6d++t682JSLkr1je6j48PJ0+epEGDBq5lJ0+eJDBQf/UVJi3nXI+Tl4KTyJX67bdt/PjjGpxOJ7VqBRMdPZqgoJruLktEqqlifaMPHz6cu+++mwceeID69etz5MgRZsyYwfDhw8uqvkpNPU4iJZeRkY7T6aRFi5ZcffVQPDx06FtE3KdY3+j33nsvSUlJTJ48GZvNhpeXF9deey133313WdVXqf0xxknBSaQ4DMNwXV+ue/fe1K5dh6ZNW+iacyLidsX6Rvfy8uKll17i2Wef5ezZs4SEhOiD7BJSczQ4XKS4Dh+OZceO7QwfPgaLxYrZbKZZswh3lyUiAhQjOL399tvs3LmTPn36MGnSJF1mpQhcPU4a4yRyWU6nk82bf2LLlo0A/PbbdiIju7i5KhGRgop0Ssorr7zC559/joeHBzNnzuT9998v67qqhNRsG6AxTiKXk52dxcKFc12hqV27SNq3j3RzVSIiFyrSN/oPP/zAJ598QosWLdi4cSPPP/88t99+e1nXVumlaoyTyGWdORPPkiULSEtLxWq1cvXVQ4iIuMrdZYmIFKpI3+hpaWm0aNECgM6dOxMfH1+mRVUFNoeTbLsTgBo6VCdSqIMHD7Bs2UIcDgcBAYFER48mJETDAESk4irSN/r5k8xZrQoBRZHf2wTgr+AkUqjg4BCsViv16zdi8OBovLx0eSIRqdiK9I1uGEZZ11Hl5A8Mr+FlxWLWmYci+Ww2m2supsDAIK69diKBgUE6Q1dEKoUiBSe73c68efNct202W4HbAGPHji3Fsiq//KkINDBc5A/Hjh1h2bJFDBoUTcOGjQE0C7iIVCpF+lYPCQlh5syZrts1a9YscNtkMik4/YmmIhD5g2EYbN/+Cz//vN71/wYNGqmXSUQqnSJ9q69cubKs66hyUnM0FYEIQG5uLqtWxRAbux+Ali1b07//IIUmEamU9K1eRnS5FRFITk5i8eL5pKQkYTab6dNnAG3atFdoEpFKS9/qZST1vMHhItVRaupZvv32M2w2G35+/kRFjSQsrJ67yxIRKRF9q5eRtBz1OEn1VqNGAM2aRZCaepahQ0fg6+vn7pJEREpM3+pl5HRaDgDBfp5urkSk/GRmZmI2m/D29sFkMp0by2QuMBeciEhlVuxPs9zcXJYtW8bHH39MVlYWe/bsKYu6Kr245CwAGtb0cXMlIuUjPv4k33zzKcuXL8bpzJs132KxKjSJSJVSrB6nI0eO8I9//AObzUZqair9+/fn2muv5e2332bAgAFlVWOl4zQMjpwLTo1q+rq5GpGyZRgGu3btYN26VTidDjw8PMjKysTPz9/dpYmIlLpi/Sk4ffp0rrnmGlavXo3VaqVJkyY8//zzBeZ0KorExEQmT55Mly5d6N69O9OnT8dut1/yMfv27aNDhw5s3LixWPtyh/i0HHLsTqxmE3UDdQkJqbrsdhurVi1lzZrlOJ0OmjZtzvjxExWaRKTKKlZw2r59O7feeismk8l1OvGYMWM4evRosXZ633334evry7p16/j222/56aef+Pjjjy+6flZWFg888ADZ2dnF2o+7xCVlAtAgyAerLrciVVRKSgpz5nzFnj07MZlM9OjRh6ioUXh6erm7NBGRMlOs4FSjRg0SEhIKLDtz5gyBgYFF3kZcXBybNm3ioYcewsfHhwYNGjB58mQ+++yziz5m2rRpDB48uDilulVc0rnDdLU0vkmqJsMw+Oabbzhz5jTe3j6MGnUtnTp10/xMIlLlFWuM06hRo5gyZQoPPPAATqeT3377jVdffZURI0YUeRv79+8nKCiIOnXquJY1a9aMEydOkJqaSkBAQIH1582bR1xcHNOnT2fWrFnFKbeAsvg8z9/mn7edPzC8US3fMtmv5LlY+0vZM5lMjBw5kh9+WMjgwcOoUSPg8g+SUqH3vfuo7d2nrNu+ONstVnCaPHky2dnZTJkyhaysLG688UbGjx/PlClTiryNjIwMfHwK9sTk387MzCwQnGJjY5kxYwZffPEFFoulOKVeIDi4RokeX5xtn0zPBaBNw5qEhJTdfiVPWb628ofs7GyOHTtG8+bNzy2pwa233qJeJjfR+9591PbuUxHavljBycPDg4cffpiHH36YpKQkatasWewPTV9fX7Kysgosy7/t5/fHBHk5OTlMnTqVxx57jHr1Sj7bcGJiGoZR4s0UYDLlvYh/3vb++DQAgj1MJCSkle5OxeVi7S+lLzExgcWL55OWlsY111xHWFhdgoNrkJSUrrYvZ3rfu4/a3n3Kuu3zt18UxQpO8+bNu+h9Y8eOLdI2WrRoQUpKCgkJCYSEhAB5PUthYWHUqPFH0Tt27ODw4cM8/vjjPP74467ld955J2PGjOGZZ54pTukYBmX2Rj9/21k2B/HnJr9sWNNXv1zloCxfW4H9+/ewatVS7HY7/v41ALOrvdX27qO2dx+1vftUhLYvVnD687QDZ8+eJSsri86dOxc5ODVu3JjOnTvzwgsv8Oyzz5KcnMysWbMYP358gfW6dOnCb7/9VmBZy5Yt+fe//0337t2LU3a5yp+/KdDbSpCPh5urEblyDoeDn35ax2+/bQWgfv2GDBky4oJD7SIi1UmxgtPKlSsL3DYMg9mzZ5OSklKsnc6cOZNnn32WQYMGYTabGTt2LJMnTwYgMjKSadOmMXr06GJts6LIn4qgoSa+lEosMzODmJgfOHnyOACdOnWjW7demgVcRKq9El2rzmQyccstt9CvXz/+7//+r8iPCwkJueikmdu2bbvo4/bu3VvsGsvbH2fU6a9yqbz27dvDyZPH8fDwZNCgaJo2bX75B4mIVAMlvsjvoUOHdFbNefJ7nBrpGnVSiXXo0ImMjDRat25PzZq13F2OiEiFUazgdOONNxYISTabjb1791baw2pl4ch5cziJVBY2m42tWzfRqVM3PDw8MJlM9O59tbvLEhGpcIoVnP48KNtsNnPzzTdXqlm9y1pCRt4cTmEBuuyEVA5nzyazePECkpISSE9PY9CgaHeXJCJSYRUrOCUnJzN16lT8/XUBz8IYhkFSpg2AmjqjTiqBw4cPsnz5YnJzc/Dx8eWqq9q6uyQRkQqtWKfILFiwQKciX0Jqth2HM2+CiVq+nm6uRuTinE4nmzZtYNGieeTm5lCnTl0mTJhEvXr13V2aiEiFVqwep2uvvZZp06ZxzTXXEBoaWmC8U2nM7l3ZJZ/rbfL3suBp1WnbUjFlZ2exfPlijhw5DEDbth3o3fvqEl/WSESkOihWcProo48A+Prrr12hyTAMTCYTu3fvLv3qKpnEzLzxTeptkorMbrdz5kw8FouFq68eQsuWrd1dkohIpVGk4LRlyxY6d+7MihUryrqeSi2/x6mWr8Y3ScXl71+DqKhReHp6EhJS293liIhUKkUKTrfddhtbt24lPDy8rOup1JLU4yQVkMPhYMOG1YSHN6RZsxYAGsskInKFihScDHdfUa+ScJ1Rpx4nqSDS09OIifmB+PiT7Nu3h/DwBnh7e7u7LBGRSqtIwUkzgxdNfo9TsHqcpAI4fvwoS5cuJCsrEy8vLwYPHqbQJCJSQkUKTllZWQwaNOiS62j80x9jnNTjJO5kGAa//rqFn35ah2EYBAeHEh09isDAIHeXJiJS6RUpOHl4eDBlypSyrqXSS8w4NzjcTz1O4h5Op5NlyxYRG7sPgIiIq+jffzAeHgrzIiKloUjByWq1Mm7cuLKupdJLzso/VKcvKXEPs9mMr68fZrOZ3r2vpm3bDjrULiJSijQ4vBQlZeQfqlOPk5Qvh8PhmsCyV69+tGrVmtDQOm6uSkSk6inS9NajR48u6zoqvWybg0ybA9A8TlJ+nE4nP/20jgULvsPhyHv/WSwWhSYRkTJSpB6nadOmlXUdlV7+VASeFhN+nrp0hZS9rKxMli1bxLFjRwA4cuQwTZo0c3NVIiJVW7EuuSIXl3ze5JcaUyJlLT7+FDExC0hPT8Nq9WDAgKEKTSIi5UDBqZQkaioCKSe7dv3G2rWrcDodBAYGMWzYaGrVCnF3WSIi1YKCUylJ1uVWpBxs3vwTmzf/BECTJs0YODAaLy8vN1clIlJ9FGlwuFze2Sw7AEHqcZIy1KxZBJ6eXnTv3ofo6NEKTSIi5Uw9TqXkbHZecArwUpNK6UpPT8PfvwYAtWoFc8MN/8Db28fNVYmIVE/qcSolaTl5Y5wCvBWcpHQYhsGWLZv49NMPOX78qGu5QpOIiPvoW76UpOb3OCk4SSnIyclh5colHDoUC0Bc3CHCwxu4uSoREdG3fClxHarz1hgnKZmkpASWLFlASkoyZrOFfv0G0Lp1e3eXJSIiKDiVmrRzwamGepykBPbv38uqVUux2234+9cgKmokderUdXdZIiJyjr7lS0lqdt4Yp0AFJ7lCJ04cY9myhQDUr9+QIUOG4+Pj6+aqRETkfPqWLyX5Y5xq6Kw6uUJ164bTokVL/P0D6N69N2azzt0QEalo9C1fCuwOJxm5eRdYDdQYJymG+PiT1KxZC09PL0wmE4MHD9cle0REKjD9SVsKMm0O1//9vHSBX7k8wzDYsWM7c+d+xYoVMRiGAaDQJCJSwanHqRTkOgzX/61mffHJpdlsNtasWc6+fbsBMJvNOBwOrFb9OoqIVHT6pC4FNrsTAE+LST0Gcklnz6awZMkCEhPPYDKZ6NmzLx06dNb7RkSkklBwKgU2Z16Pk4dFRz7l4uLiDrJ8+WJycnLw8fFh6NCRmtRSRKSSUXAqBbmuHicFJymc3W5nzZoV5OTkUKdOXaKiRrquPyciIpWHglMpsDnygpOHRYdbpHBWq5WhQ0ewf/8eevXqh8WiXz0RkcpIn96lIPdccPK0qsdJ/pCQcIbU1BSaNm0BQFhYPcLC6rm5KhERKQkFp1Jgc2iMkxS0d+8u1qxZDsA11wQREhLq5opERKQ0KDiVAlePk4JTtedwONiwYTW///4rAA0bNsbf39/NVYmISGlRcCoFfwQnjXGqzjIy0omJ+YFTp04A0KVLd7p06alLp4iIVCEKTqXAZtehuuruxIljxMT8QFZWJp6eXgwePIzGjZu6uywRESllCk6lQIfq5OjROLKyMqlVK4Rhw0YRGFjT3SWJiEgZUHAqBZqOQLp27Ymnpydt23bEw0MXehYRqarURVIK8q9Vp+kIqo+UlGSWL1+M3W4H8q43FxnZVaFJRKSKU49TKch19TgpOFUHBw8eYMWKJdhsufj6+tGrVz93lyQiIuVEwakUuA7VmXWoripzOp1s2vQjW7duAqBu3XA6duzs5qpERKQ8KTiVAte16nSorsrKyspi2bJFHDsWB0D79pH07NkPi8Xi5spERKQ8KTiVAs0cXrUlJJxh0aJ5pKenYbVaGTBgKC1atHJ3WSIi4gYKTqVAE2BWbR4eHthsuQQGBhEdPYrgYF0+RUSkulJwKgU2DQ6vcgzDwGTKC8KBgUGMHHkNQUE18fLydnNlIiLiTvqmLwX5h+o0AWbVkJaWxpw5XxIXd8i1rE6dugpNIiKiHqfSkKsJMKuMY8eOsHTpQrKzs1i/fhUNGjTSteZERMRFwakUqMep8jMMg23bNrNx4wYMwyAkpDbR0aMUmkREpAAFp1Lg6nHSdASVUm5uDitXxnDw4AEAWrVqQ79+A7FaNQu4iIgUpOBUCmx2nVVXWeXkZPPdd1+QkpKM2Wymb9+BtG7dzjUwXERE5HwKTqXgj+kI1ONU2Xh5eVO3bjg2m43o6FHUqVPX3SWJiEgFpuBUCnI1AWal4nQ6sdtteHp6AdC370Byc3Px9fV1c2UiIlLRKTiVApt6nCqNzMxMli79AYvFwogR4zCbzVitVqxW/SqIiMjl6duiFGg6gsrh1KkTxMT8QEZGOh4eHiQnJ2oWcBERKRYFp1Kg6QgqNsMw2LnzN9avX4XT6SQoqBbR0aOoVSvY3aWJiEglo+BUCnLtmo6gorLbbaxZs4K9e3cB0LRpCwYOjMLT09PNlYmISGWk4FQKbLrIb4W1fPliDh48gMlkokePPnTs2EVTDYiIyBVTcCoFOquu4urSpQenT8czcGAU9es3dHc5IiJSySk4lQLN41RxGIbBmTOnqV27DgAhIbWZNOkfWCwWN1cmIiJVgb7pS4EO1VUMOTnZLF78PXPmfEF8/EnXcoUmEREpLepxKiG7w4kz70idDtW5UWLiGZYsWcDZsylYLBZSU1M1C7iIiJQ6t3zTJyYmMnnyZLp06UL37t2ZPn06dru90HW/+OILoqKiiIyMJCoqis8++6ycq720/KkIADx1Vp1b7Nu3m++++4KzZ1OoUSOAa665nhYtWrq7LBERqYLc0uN03333UadOHdatW0dCQgJ33XUXH3/8MbfeemuB9ZYvX84bb7zB7Nmz6dChA9u3b+f2228nJCSEqKgod5R+gfypCEA9TuXN4XCwePFiNm3aBECDBo0YMmQ43t4+bq5MRESqqnIPTnFxcWzatIm1a9fi4+NDgwYNmDx5Mq+++uoFwSk+Pp7bbruNjh07AhAZGUn37t3ZvHlzsYNTWZyBbjJBjsMBgNmkmcPL2/79e1yhqXPn7nTr1hOzWeG1POT/Pmlmh/Kntncftb37lHXbF2e75R6c9u/fT1BQEHXq1HEta9asGSdOnCA1NZWAgADX8kmTJhV4bGJiIps3b+bRRx8t9n6Dg2tcedGXcCw5E8g7TBcSUjb7kML17t2dhIRTtGrVilatWrm7nGqprH6v5PLU9u6jtnefitD25R6cMjIy8PEpeCgl/3ZmZmaB4HS+M2fOcMcdd9C2bVtGjhxZ7P0mJqZhGJdfrzhMJsh15sVUD7OZhIS00t2BFGAYBnv37qJZswg8PDwwmWDs2LEkJqap7cuZyZT3AVYWv1dyaWp791Hbu09Zt33+9oui3IOTr68vWVlZBZbl3/bz8yv0Mdu3b+fee++lS5cuvPjii1d0JXvDoEwaOzM371Cdl9WsX6QyZLPZWL16Gfv37+HIkTgGDx6G2ZwXWsvqtZXLU9u7j9refdT27lMR2r7cB4S0aNGClJQUEhISXMtiY2MJCwujRo0L0963337LzTffzN/+9jdef/31CneNsbNZNgACfTSzQ1lJSUnmu+8+Z//+PZjNZmrXDnN3SSIiUk2Ve3Bq3LgxnTt35oUXXiA9PZ2jR48ya9Ysxo8ff8G6MTExPPPMM7z11lv84x//KO9SiyQ5MxeAAG8PN1dSNR0+HMu3335OUlIivr5+jB49ng4dOul6cyIi4hZuOQVp5syZ2O12Bg0axHXXXUffvn2ZPHkykHfm3Pz58wF4++23cTgc3HPPPURGRrp+nnrqKXeUXaiUzHM9Tt7qcSpNTqeTjRs3sGjR9+Tm5hAWVo8JEyZRr159d5cmIiLVmFu+7UNCQpg5c2ah923bts31/wULFpRXSVcs/1BdDS8Fp9KUnZ3Frl07AGjXLpJevfrp0ikiIuJ2+rYvIfu5mcM1+WXp8vX1Y+jQEWRkpBMRcZW7yxEREQEUnErM4cybOdxq1pibktqzZyceHp40a9YCgPDwBm6uSEREpCAFpxKynbvCr0XB6Yo5HHbWr1/Nzp2/4eHhQWhobQICAt1dloiIyAUUnErIcS44qcfpyqSnpxETs4D4+FMAREZ2pUaNwidBFRERcTcFpxLKH+OkHqfiO378CEuXLiQrKwsvLy8GDx5Oo0ZN3F2WiIjIRSk4lVD+GCcFp+LZtu0Xfv55HYZhEBISSlTUKAIDg9xdloiIyCUpOJWQxjhdmYyMdAzDoGXL1vTrNwgPD00gKiIiFZ+CUwk5HBrjVFSGYbhm/O7Zsy9169ajadMWmgVcREQqDU0+VEJ2DQ4vktjYfSxaNA+HI++iyBaLhWbNIhSaRESkUlGPUwlpjNOl5V06ZT3btv0CwK5dv9GuXaSbqxIREbkyCk4llN/jZFHPyQUyMzNZtmwhx48fBaBjx860adPBzVWJiIhcOQWnEtJ0BIWLjz/JkiULyMhIx2r1YODAKJo3j3B3WSIiIiWi4FRCdp1Vd4EDB/ayfPkSnE4HQUE1iY4eTa1awe4uS0REpMQUnEpIY5wuFBwcisViplGjJgwaFIWnp5e7SxIRESkVCk4lpLPq8thsNtdcTDVr1mL8+EkEBdXUWXMiIlKlaDqCEtK16uDIkcN8+ul/XIPAIS88KTSJiEhVox6nEqrOg8MNw2DLlk1s2rQBgO3bfyE8vIGbqxIRESk7Ck4lZM8f41TNeldycnJYuXIJhw7FAtC6dTv69Bng5qpERETKloJTCTmq4Vl1iYkJLFkyn7NnU7BYLPTtO5DWrdu5uywREZEyp+BUQq7B4ZbqEZxSUpL57rvPsdvt+PvXIDp6FLVrh7m7LBERkXKh4FRCjmo2c3hgYBBNmjQnKyuTIUNG4OPj4+6SREREyo2CUwnZHFV/HqfMzAwsFiteXl6YTCYGDBiK2WzGbNZJmSIiUr3om6+Eqvp0BCdPHufrrz9l5colGMa552q1KjSJiEi1pB6nEqqql1wxDIMdO7bz449rcDqdnD2bQnZ2Fj4+vu4uTURExG0UnEqoKvY42Ww2Vq9exv79ewBo3rwlAwYMwcPD082ViYiIuJeCUwlVtR6ns2eTWbJkAYmJCZhMJnr16k/79pGaBVxERAQFpxKzV6HB4YZhsHjxApKSEvDx8SUqaiT16tV3d1kiIiIVhkb4lpC9Ck1HkHfG3BDq1avPhAmTFJpERET+RMGphCr7zOHZ2VkcPRrnul2nTl3GjJmAv38NN1YlIiJSMSk4lZC9Eg8OT0g4zbfffs6iRfNISDjtWq7xTCIiIoXTGKcSyh/jVNmC0549u1izZhkOh4OAgECgctUvIiLiDgpOJeA0DM51OFWaQ3UOh4MNG1bz+++/AtCwYRMGDx6Gt7e3mysTERGp+BScSsCZn5qoHMEpPT2NmJgfiI8/CUCXLj3o2rWnDs2JiIgUkYJTCdgrWXDau3cX8fEn8fLyYtCgYTRu3NTdJYmIiFQqCk4l4DD+CE7WSnDttsjIrmRlZdKuXSSBgUHuLkdERKTSqfjf9hWY3VGxe5xstlw2btyA3W4HwGw206fPAIUmERGRK6QepxJwnH+oroLlpuTkJJYsmU9ychLZ2Vn07z/Y3SWJiIhUegpOJZB/qM5iqlhzHx08uJ8VK2Kw2XLx9fUjIqK1u0sSERGpEhScSqCizRrudDrZuHED27ZtBqBevXCGDh2Jr6+fmysTERGpGhScSuCPWcPdP1QsKyuTZcsWcezYEQA6dOhMjx59sFgsbq5MRESk6lBwKgF7BepxstlsnDkTj9XqwYABQ2nRoqW7SxIREalyFJxKoCIdqgsICCQ6ehQ+Pr7UqhXi7nJERESqJPcfY6rE3Bmc7HY7q1cvIy7uoGtZeHhDhSYREZEypOBUAg7XGKfy3W9aWipz537Frl07WLFiCbm5ueVbgIiISDWlQ3Ul8Md0BOXX43T0aBzLli0kOzsbLy9vBg8ejqenZ7ntX0REpDpTcCqB/JnDrZay73IyDINt2zazceMGDMMgNLQOUVEjCQgILPN9i4iISB4FpxJwnVVXxj1ODoeDpUt/4NChWABatWpDv36DsFr18omIiJQnffOWgOtQXRkPDrdYLPj4+GI2W+jXbwCtW7cv0/2JiIhI4RScSqCsz6pzOByuCSz79h1A27YdCAmpXSb7EhERkcvTWXUlUFYTYDocDjZsWM2iRfNwOp15+7BYFZpERETcTD1OJfDHdASlF5wyMzNYunQhJ04cA+DYsSM0bNi41LYvIiIiV07BqQRKOzidPHmCpUsXkJGRgYeHJwMHRik0iYiIVCAKTiVQWofqDMPg999/ZcOG1TidTmrWrEV09Ghq1qxVGmWKiIhIKVFwKoHSGhy+ceMGtm7dBECzZhEMGDBUk1qKiIhUQBocXgKlNY9T8+Yt8fDwpFevfgwdOkKhSUREpIJSj1MJuMY4WYofnNLT0/D3rwFASEgoN954C97ePqVan4hUD4ZhYLfb3F1GlWcyQXZ2NjZbLuem8ZNyUtK2t1ismM2l01ek4FQCjivocTIMg19++ZmtWzcxZswEwsLqASg0icgVsdttJCaewjCc7i6lWkhKMrumiZHyVdK29/HxJyCgFqYSHiVScCqB4s4cnp2dzYoVi4mLOwTAkSOHXMFJRKS4DMPg7NkkzGYzgYGhmEwafVHWLBYTDoe6m9zhStveMAxyc3NIT08GIDAwuER1KDiVgL0Y0xEkJJxhyZL5pKaexWKx0L//YFq1alPWJYpIFeZ0OrDZsgkMDMHT09vd5VQLVqsZu109Tu5Qkrb39PQCID09mRo1apbosJ2CUwnYHUXrcdq7dxdr1izHbrdTo0YA0dGjCQ3VLOAiUjLnX1lARC4tPzw5HHbM5is/CUu/bSWQf6juUj1OR4/GsWLFEgAaNmzM4MHDNJ5JREpVScdsiFQHpfV7ouBUAkWZx6l+/YY0bdqCWrVq0aVLz1Ib1S8iIiLlT8GpBC4WnE6dOkFwcCgeHh6YTCaiokbqL0IREZEqQMGpBP4cnAzD4LfftvHjj2to3jyCwYOHYzKZFJpERM4zfvwokpISsVgsQN5np9lsoUWLCO699wEiIlq51j127Cgff/wBmzdvJCMjnYCAQHr06MVNN91CWFhYge3u2vU7n3/+X379dTs5OTmEhoYSHT2Cv/71RqzWyvd1t2bNSjZu/In/+7/HXcvOnk3hmmtG0KBBIz7++PMC62/d+gv33HMn69f/csG2pky5ncjIztxyyx2uZcuXxzBv3nccPBiL0+mkUaPGXH/9JAYMGFxqz+Gnn9bz7rtvceLEcerUCWPy5Hvp3btvoeump6fzzjtvsm7dGgzDSe/e/bj77vupUSNvzsP9+/fxr3+9wd69e/Dw8KBr1+7cfff9BAUFcfz4MaZPf4aZM/9d5q+1jhuVgOusOpMJmy2XZcsWsWHDagzDAEya60NEyp1hGGTZHOX6Y1zBjIQPPvgoy5atY9mydSxfvp4vv5yDv78/jz32kOuzc8+eXfzjHzfg6enJu+/+h2XL1jFr1geYTCZuvvmvxMYecG1v1arl3HPPnXTs2Ikvv5xDTMxqnn76eZYuXcy0aU+UWvuWl+TkZN5++1/cfvs/CyxfsGAePXr0IjExgc2bf77i7b/55mvMmjWTSZNu4vvvl/DDD8uYNOlvvPTSc8yZ801Jywfg6NEjPP74w9x6610sWbKaf/zjDp566hHOnDld6PovvDCNAwf285///I9vvlmAzWbjscceBCAnJ5upU6fQrl0H5s+P4X//+4rU1LO88MI0AMLD69OxYyc+/viDUqn9UtwSwRMTE3nyySfZtGkTFouF0aNH8/DDDxeaEtesWcNrr73G0aNHqVu3Lv/3f//HgAED3FD1hVwzh9sz+O67L0hKSsRsNtOrV3/ateuoniYRKVeGYXDrl7/y24nUct1vh3oBzL6+Q4k+82rVCmb06Gt4+OGppKamEhQUxMsvP8/AgYML9LiEhdXloYceIz09nZdeeo7Zsz8hJyeH1157kb///TbGj7/etW5ERCuefno6H388m9TUswQEBF6w382bf+b992dx+PAhgoJqcv31k7j22r+waNECPvzwfb79doFr3SlTbqdz5y78/e+3M336M2RlZXHoUCxnz6bQvXtPjh8/zr///aFr/VmzZnLoUCyvvvovkpISefvtN/nll02YTCb69OnHP/95L76+foW2x+ef/5fu3XsQFBTkWuZ0Opk37zvuvnsqDRs25osvPqNr1x7Fbuvdu3fy7bdf8t57H9OmTVvX8n79riYj4yH2799b6ONeffUFli5dXOh9y5atu2DZ4sU/0KFDR/r1uxqAQYOGsGjRAubPn1ug5wvy5jlcv34NM2f+mzp18noS7757KqNHR3H48CHMZhPNm0dw8823YrFYCAwMYsyYa3juuadc27jmmglMnDiea6/9CzVr1ixWmxSHW3qc7rvvPnx9fVm3bh3ffvstP/30Ex9//PEF6x0+fJi7776be++9l19++YW7776b++67j/j4+PIvuhB2p0FDczLsX0VSUiK+vn6MGTOB9u0jFZpExC0q6ydPfPwpvvvuK666qjVBQUGcPHmC/fv3ER09otD1R48ex+7dOzl16hQ7dvzK2bNnGTw46oL1mjdvwfPPv1JoaDpyJI6HH76fMWOuZfHiVTz33Mu8994sNm78qUg1b9z4E8899xJffDGX226bzK5dv3P06BEAHA4HS5cuZuTIMTidTh555AFMJhNffjmH//73S86cOcPLL08vdLt2u50FC+YyZEh0geXr16/F6cw7hDVu3Hi2bt1coNetqNavX0u9euEFQlO+YcNGcs89DxT6uIceeszVS/jnn8IcOnSQpk2bF1jWuHETDhzYd8G6TqcTwzAKnHWeP6FrXNxhGjZszJtvvu06vAuwatUKWra8ynU7JCSUVq2uYvHiHy7x7Euu3Huc4uLi2LRpE2vXrsXHx4cGDRowefJkXn31VW699dYC686dO5cuXboweHDe8dbhw4czZ84cvvrqK+65555i7bcscozTYaOn5xFw2qlbN5yoqBH4+fmX/o6kUPmvqTJq+VPbu8/5bf/n9jeZTMy+vgPZ5TxBo7fVXOw/Fl9//SVmznwdu92OzWajdu0w+vW7mptu+juQN2kw5PVEFSYkJNS1XkpK3ozQwcEhxaph+fIYIiJaMXLkGABatbqKWbM+IDg4hJ9+Wn/Zx7dp09YVDGrUqEGXLt1YsmQht912F5s2/YzD4aB3737s2bOLvXt38+abs/D19QVgypT7mDjxWu6///8IDAwqsN29e/eQlZXFVVcVnCT5u+++5pprJmC1Wqlduw79+w/gq68+47HHni7W805JSSY4uGSzZxdFZmYmPj4Fp9/x9vYmMzPrgnV9fX3p2rUH77//Do8//gw+Pj7MmvUvLBYLOTk5BdY1DIPZs99lw4Z1vPPO7AL3tW3bni1bNjNx4o0Xravw352iP69yD0779+8nKCiIOnXquJY1a9aMEydOkJqaSkBAgGv5gQMHiIiIKPD45s2bs2fPnmLvNzi4xpUXfRFdWoTx1q5m/KOtJ3+/bnSBJCzlpyxeWykatb37BAfXIDs7m6QkMxaLCav1jwMIHh4V/7PooYceZeTI0eTm5vL111/w8cf/oU+fPgQH1wKgTp28SYLPnImnadMmFzw+Pv7kufVCcTrtAKSkJBIWVveCdRMTEwoNVcnJidStW7dA27Vq1RIA87mTfs6/Lz8cWs8Fxdq1axe4f+zYa3jrrTe5887JLFnyA8OHj8Db25PTp0/hdDq55prhBfbv6elJfPxJ13POl5AQT2BgEL6+f8wGf+jQQbZs2cTevbv44otPAcjNzcFutzN58hRCQkLPW995wdAXp9OJj483VquZ0NBQNm/eWKD2fDk5Odhsua6L0J/vlVdevOihuuXL116wzNfXh9zcnAL7yc3Nwd/fr9B9T5v2PG+++Ro33zwRf39/Jk68gQ0b1hEUFOhaPycnk+eee4Y9e3bz739/QPPmLQpsIywsjPXr1xS6fafThNlspmZNP7y9r3ym/XIPThkZGRck0PzbmZmZBYJTYevmpdXMYu83MTGt1K9mPbRZTcY9M56M1EySk4tfk5SMyZT35VEWr61cmtrefc5v+9zcXJxOJw6HUekuA+J05tVsNlu5/vobSUk5y//93wPMmvUBLVpEULt2XVq2vIr58+fSuXO3Cx7//fdzaNnyKkJDwwgKCiYwMJClS2OYOPGmAusdOLCfm2/+K//+94e0bdu+wH0hIbU5cGB/gbZbuHA+NWvWAkzYbLYC9+X3bNntTteA+PPv79WrH6+88iLr169j3bq1fPTRZ9jtToKDQ/Hy8mLhwhWuP7Bzc3M5efIE4eH1L3jtDCMv6Jy//Ouvv6Jnz9489NBjBdadOvWffPXVl9xxxz8JDs4Lm8eP5233j+0ZHD9+lNDQOtjtTnr06MMHH7zHjh07LujVmjPnO/7zn/eYN2/xBeHi/vsf5v77H/7zS3FBO+Rr3Lgp+/btLXDfwYMHadXqqkLXP336DPfe+5ArBxw+fIi0tFRatGiJ3e7k1KnjTJ16N3XqhPHBB/8jKCjogu3YbDZMpsIvzeJwGDidTpKTM/DwsBW4L//3qijKfYyTr68vWVkFu+nyb/v5FRwk5+PjQ3Z2doFl2dnZF6xXFIZRNj/eHpYy27Z+Lv9Tlq+tftT2FfXn/LavKm677S6aN2/OtGmPk5OT97n/yCNPsnHjT7z66gucPHkCp9PJiRPHefnl59m8eROPPJJ3tpyHhwf33vsQH374PnPmfENmZiYOh4Nff93OE088zNVXD7wgNAEMHhzF3r17Wbz4BxwOB3v27Oatt2ZgtVpp3LgJSUmJbN36C4ZhEBOziLi4w5d8DlarlWHDRvL66y/TsmUrGjVqDMBVV7Whfv2GvP32m2RmZpKTk83MmW9w77134XA4LthOWFhdUlPPug5RZWSks2TJQkaNGkft2nUK/IwaNZZ5874jKyuL0NDadOrUhRkzXiE+/hSQN33Bu+++hWFAjx69gbxDkmPGXMMTTzzMzz//iN1uJycnh5iYRbz33tvceusdJeqRyRcdPYJt27awYsUy7HY7K1YsY9u2LURFDS90/VmzZvL22zOw2WwkJJzhjTdeZvDgKGrWrEVqaipTptxBu3YdeOONtwsMmj9fQkKCa3D5xVzq96ooyj04tWjRgpSUFBISElzLYmNjCQsLc83VkC8iIoL9+/cXWHbgwAFatCjYNSciIpWbxWLhySef48yZ07z99r8AaNEigg8//Ay73c4//3kbQ4b0ZcqU2wH45JMvaNGipevxQ4dG88ILr7Fu3Wquu2400dEDePXV6YwcOZpnnnmh0H2Gh9fntdf+xZw53zB8+ECeeeYx7r57Kt269aBVq9b87W+38PzzTzNs2EC2bNnM1VcPuuzzGDVqDKdOnXSNm4K8QPXKKzNISkrg+uvHMWZMNMePH2XGjHfw8vK6YBstWrQkICCQnTt3ALBo0Q94eXnRq1efC9YdNmwkOTnZ/PDD9wA8//zLhIXV5a67bmHIkL5MmjSe48eP8s47swt8xz744KNMnHgT778/i5EjhzBmTBRz537LE09MK3BmYkk0atSYF198jf/97yOGDRvIxx/PZvr0l2nYsBEAS5cuZsiQP+Z0evjhx0lOTmLUqCHcfPNEGjdu4grHixbN59SpU6xcuYyoqP4MGdLX9XO+337bTvfuPUul/osxGVcyAUcJTZw4kbCwMJ599lmSk5O56667iIqK4u677y6wXmxsLOPGjeOll15i6NChLF26lEceeYTvv/+eJk0uPOZ9KQkJpX9IwWSCkJAaZbJtuTy1v/uo7d3n/LbPzc0lMfEkwcF18fC48ouWStFZrYUfBiptb7/9JtnZWTz44KNlvq/K4nJtf/p0PDfddD1ffjm30B4pm+3ivy/5v1dF4ZbpCGbOnIndbmfQoEFcd9119O3bl8mTJwMQGRnJ/PnzgbxB4++88w7vvfceXbt2ZdasWbz11lvFDk0iIiKVyQ033MyGDetISUlxdymVxrfffsmECddf9DBeaXFLj5M7qMep6lH7u4/a3n3U4+Re5dXjBLBixTJ++WUjDz9c+WY+LwuXavvjx4/x3HNP8dZb7+Hh4VHoOqXV41T5Lt4jIiJSDQwaNIRBg4a4u4xKITy8foFZ28uSrlUnIiIiUkQKTiIilVw1GXEhUiKl9Xui4CQiUkmZzXkf4Q6H3c2ViFR8ubl582JZLCUbpaQxTiIilZTZbMHDw5v09BQsFovroqhSdpxOEw6Hevjc4Urb3jAMcnNzSE9PxsfH3/UHx5VScBIRqaRMJhOBgbVITDxFUlK8u8upFsxmM05n5bq8TVVR0rb38fEnIKDW5Ve8DAUnEZFKzGr1oHbt+tjttsuvLCViMkHNmn4kJ2doGo5yVtK2t1isJe5pyqfgJCJSyZlMJs3jVA5MprwLzXt42BScyllFansdEBcREREpIgUnERERkSJScBIREREpomozxslkKrttlsW25fLU/u6jtncftb37qO3dp6zbvjjbrTYX+RUREREpKR2qExERESkiBScRERGRIlJwEhERESkiBScRERGRIlJwEhERESkiBScRERGRIlJwEhERESkiBScRERGRIlJwEhERESkiBafLSExMZPLkyXTp0oXu3bszffp07HZ7oeuuWbOGUaNG0bFjR4YNG8aqVavKudqqpzjt/8UXXxAVFUVkZCRRUVF89tln5Vxt1VKcts+3b98+OnTowMaNG8upyqqpOG2/adMmJkyYQGRkJP379+e9994r52qrluK0/SeffMLAgQPp1KkTo0aNIiYmppyrrZqSkpIYMmTIJT9H3Pp9a8gl3XDDDcYDDzxgZGZmGkeOHDFGjBhhzJ49+4L1Dh06ZLRr185YtmyZYbPZjIULFxrt27c3Tp065Yaqq46itv+yZcuMLl26GNu2bTOcTqexdetWo0uXLsaSJUvcUHXVUNS2z5eZmWmMHDnSiIiIMH7++edyrLTqKWrbHzhwwOjQoYMxZ84cw+l0Grt37za6detmLF682A1VVw1FbfvVq1cbPXv2NGJjYw3DMIwlS5YYrVq1Mo4ePVreJVcpv/zyizF48OBLfo64+/tWPU6XEBcXx6ZNm3jooYfw8fGhQYMGTJ48udCejLlz59KlSxcGDx6M1Wpl+PDhdO3ala+++soNlVcNxWn/+Ph4brvtNjp27IjJZCIyMpLu3buzefNmN1Re+RWn7fNNmzaNwYMHl2OVVVNx2v7zzz9n0KBBjBs3DpPJRKtWrfjyyy/p3LmzGyqv/IrT9gcPHsQwDNePxWLBw8MDq9Xqhsqrhrlz5/Lggw8yderUy67nzu9bBadL2L9/P0FBQdSpU8e1rFmzZpw4cYLU1NQC6x44cICIiIgCy5o3b86ePXvKpdaqqDjtP2nSJG6//XbX7cTERDZv3kzbtm3Lrd6qpDhtDzBv3jzi4uKYMmVKeZZZJRWn7X/77Tfq16/P/fffT/fu3Rk2bBibNm0iNDS0vMuuEorT9iNGjCAkJIThw4fTpk0b7r33Xl566SXCwsLKu+wqo0+fPixbtozhw4dfcj13f98qOF1CRkYGPj4+BZbl387MzLzsut7e3hesJ0VXnPY/35kzZ7jtttto27YtI0eOLNMaq6ritH1sbCwzZszg9ddfx2KxlFuNVVVx2v7s2bP897//ZfTo0WzYsIFnn32Wl19+mSVLlpRbvVVJcdreZrPRqlUrvvnmG7Zv386zzz7L448/zt69e8ut3qomNDS0SD127v6+VXC6BF9fX7Kysgosy7/t5+dXYLmPjw/Z2dkFlmVnZ1+wnhRdcdo/3/bt2xk/fjxNmjTh3XffVbf5FSpq2+fk5DB16lQee+wx6tWrV641VlXFed97enoyaNAgrr76aqxWK127dmXMmDEsXry43OqtSorT9s899xwtWrSgffv2eHp6cu2119KxY0fmzp1bbvVWV+7+vlVwuoQWLVqQkpJCQkKCa1lsbCxhYWHUqFGjwLoRERHs37+/wLIDBw7QokWLcqm1KipO+wN8++233Hzzzfztb3/j9ddfx9PTszzLrVKK2vY7duzg8OHDPP7443Tp0oUuXboAcOedd/LMM8+Ud9lVQnHe982aNSM3N7fAMofDgWEY5VJrVVOctj9x4sQFbW+1WvHw8CiXWqszt3/flssQ9Ersr3/9qzF16lQjLS3NdYbFzJkzL1jvwIEDRrt27YyFCxe6Rvm3a9fOOHjwoBuqrjqK2v5Lliwx2rRpY6xdu9YNVVZNRW37P9NZdSVX1Lb/8ccfjdatWxvz5s0znE6nsWnTJqNjx47G8uXL3VB11VDUtp8xY4bRvXt34/fffzccDoexePFio127dsauXbvcUHXVc6nPEXd/3yo4XcaZM2eMu+++2+jWrZvRo0cP46WXXjLsdrthGIbRsWNH4/vvv3etu3btWmP06NFGx44djREjRhirV692V9lVRlHbf+TIkUarVq2Mjh07Fvh58skn3Vl+pVac9/75FJxKrjhtv3r1auOaa64xIiMjjUGDBhlffPGFu8quEora9jabzZg5c6YxYMAAo1OnTsa4ceP0h1sp+vPnSEX6vjUZhvp0RURERIpCY5xEREREikjBSURERKSIFJxEREREikjBSURERKSIFJxEREREikjBSURERKSIFJxEREREikjBSUTknLS0NJKSktxdRpEdPnzY3SWIVDsKTiICwMCBA2nXrh2RkZEFfv7xj38U6fEtW7Zk48aNpVrTsWPHaNmyJR07dnTV07FjR6Kiopg3b16Jtz9//nxGjBjhuj1kyBDXNbD+fF9pmTNnDq1atSrQxh06dGDAgAG8/vrrRb7O3GeffcaTTz5Z6vWJyKXp0vEi4jJt2jSuueYad5dxgR9++IH69esDYBgGixYt4sEHH6ROnTr07Nnzirc7evRoRo8e7bqdnJx80ftKU7169Vi5cqXrtsPhYM2aNdxzzz00bNiQCRMmXHYblalnTKQqUY+TiBRJfHw89913HwMHDqRDhw4MGjSIb7/9ttB1Y2JiGDFiBJ07d2bYsGHMmjXLdV9CQgIPPvggvXv3pk+fPjz11FOkp6cXuQ6TycSIESMICAhg9+7dABw/fpz77ruPnj170rt3bx544AFOnz4NgN1u55lnnqF37950796diRMnsmXLFiCv92fgwIEAREVFAXDbbbcxe/bsAvdNmjSJN954o0AdEyZM4IMPPgBg586d3HjjjXTt2pWhQ4fy8ccfF7nnCMBisTBw4EAiIiJczyk3N5eXX36ZYcOGERkZSc+ePXnuuecwDIO5c+fy3nvv8csvv9ClSxcA0tPTefbZZ+nfvz89e/Zk6tSpJCQkFLkGESkaBScRKZInnngCDw8PFi5cyNatW7nhhht47rnnyMjIKLBednY2Dz30EE899RRbtmzh9ddfZ/bs2fz22284nU4mT56M2WwmJiaGBQsWcPr0aZ566qki15GVlcXnn39Oeno6PXr0wGaz8Y9//AOLxcLSpUtZvHgxAHfeeSd2u53vv/+ebdu2sXjxYn788Ue6du3KtGnTLthuTEwMALNnz+a2224rcN+ECROYP38+TqcTgNjYWHbv3s3YsWOJj4/nb3/7G9HR0fz444/MmjWLzz//nK+++qrIzyk3N5fFixezd+9e+vfvD8Ann3zCunXr+OSTT9i2bRuzZs3iyy+/5Oeff2bcuHHccccddOnShV9++QWAxx57jLi4OObMmcPy5cvx9/dnypQpxQpwInJ5OlQnIi7Tpk3jhRdeKLBs7dq1+Pr68vzzz+Pn54eHhwcnTpzAz8+P7Oxszp49i5+fX4HHeHt78+233+J0OunUqRNbtmzBbDbz22+/sXPnTj766CPXYx5++GGio6N58sknqVmzZqF1jR49GrM57+88i8VCkyZNmDFjBq1bt+ann37i6NGjfPfdd/j7+7ueR7du3fj999/x9vbm2LFjfPvtt/Tr1497772XqVOnFqtdoqOjmT59Ohs3bqRnz57MmTOH/v37ExISwuzZs2nWrBmTJk0CoHnz5txyyy18+umnXH/99YVu78SJE3Tp0gXDMMjNzcUwDHr16sVbb73lCk7XXXcd48aNIzg4mNOnT5OdnY2fnx/x8fEXbC8xMZGYmBgWL15McHAwkBekunTpws6dO2nbtm2xnq+IXJyCk4i4PP300xcd43T06FFeeeUVDh8+TOPGjWnUqBGAqxcmn7e3N1988QWzZs3igQceID09naioKJ544gmOHTuGw+FwhYN8np6eHD169KLBaf78+a4xTn+WmJhIzZo1XaEJwN/fn6CgII4fP86IESOw2Wx88803vPHGGwQHB3PnnXfy17/+tcjt4u3tzahRo5g3bx7dunVj/vz5PPfcc0DeYcKdO3e6Dpnlt4nFYrno9s4f4xQbG8t9992Hp6cnffv2da2TlZXFs88+y+bNmwkLC6N169YYhnFBe+fXAHlh63wWi4Vjx44pOImUIgUnEbksm83GHXfcwf3338/EiRMxmUz8/vvvzJ8//4J109PTOX36NK+//joAu3fv5v777+ff//43Q4YMwdvbm40bN7qCRW5uLkePHnUFseIKDw8nOTmZ9PR0V3hKS0sjOTmZ0NBQDh06RJs2bRg7dizZ2dksWbKEhx9+uEDQKYrrrruOv/71rwwZMgSTyeQKOWFhYXTv3p3//Oc/rnWTk5MvOIR5Mc2aNWP27NmMGzeOxx9/nFdeeQXIOzQaGBjI+vXr8fLywul00rVr10K3UadOHQAWL15MaGioa/mBAwdo0KBBsZ6niFyaxjiJyGXZbDays7Px9vbGZDJx4sQJXn31Vdd958vIyOC2225jwYIFGIZB7dq1MZvN1KxZk/bt29OoUSNeeuklMjIyyM7O5oUXXuDmm2/G4XBcUW3t2rWjefPmPP3006SlpZGWlsYzzzxDw4YN6dSpE6tWrWLKlCkcO3YMb29vgoKCsFqt1KhR44JteXp6kpaWVuh+WrVqRdOmTXnhhRcYN26cK/iNGjWK7du3M3/+fOx2O6dPn+bOO+/kpZdeKvJzCAsL46WXXuL7779n7ty5QF4A9fLywmw2k56eziuvvEJ6erqrvb28vEhPT8cwDOrUqcPVV1/N9OnTSU5Oxmaz8e677zJ+/HhSU1OL26QicgkKTiJyWb6+vrzwwgu88847REZGctNNN9G7d29CQkLYt29fgXXr1KnDzJkzmT17Np06dWLkyJH06NGDm2++GavVynvvvUdCQgJDhw6lT58+HDlyhI8++ggvL68rqi1/m3a7naioKAYMGIDNZuOjjz7CarVy0003cfXVV3P99dfTsWNHXn31VWbMmEFYWNgF2/rLX/7CAw88wIwZMwrd13XXXceJEycYP368a1l4eDgffPABX331Fb169WLMmDE0bdq0WMEJoH///q4B90ePHuWJJ55gz549dOvWjejoaNLT0+nbt6+rvQcMGEBKSgqdO3cmNTWVV155hYCAAMaOHUuPHj1Ys2YNH3zwQYEeKBEpOZOhUy5EREREikQ9TiIiIiJFpOAkIiIiUkQKTiIiIiJFpOAkIiIiUkQKTiIiIiJFpOAkIiIiUkQKTiIiIiJFpOAkIiIiUkQKTiIiIiJFpOAkIiIiUkQKTiIiIiJF9P9gr1VOn5nX3AAAAABJRU5ErkJggg=="
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1080,8 +1080,8 @@
    "metadata": {
     "id": "R6RVg0TvGDu1",
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:22:45.068537Z",
-     "start_time": "2025-07-13T17:22:44.155610Z"
+     "end_time": "2025-08-05T19:43:29.708393Z",
+     "start_time": "2025-08-05T19:43:28.422126Z"
     }
    },
    "outputs": [
@@ -1090,7 +1090,7 @@
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1101,8 +1101,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:23:41.163538Z",
-     "start_time": "2025-07-13T17:22:45.072542Z"
+     "end_time": "2025-08-05T19:44:46.457445Z",
+     "start_time": "2025-08-05T19:43:29.864483Z"
     }
    },
    "cell_type": "code",
@@ -1140,7 +1140,7 @@
       "text/plain": [
        "<Figure size 1200x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1151,8 +1151,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:23:41.500129Z",
-     "start_time": "2025-07-13T17:23:41.287538Z"
+     "end_time": "2025-08-05T19:44:46.964589Z",
+     "start_time": "2025-08-05T19:44:46.553401Z"
     }
    },
    "cell_type": "code",
@@ -1190,7 +1190,7 @@
       "text/plain": [
        "<Figure size 600x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1200,7 +1200,7 @@
       "text/plain": [
        "<Figure size 600x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1267,7 +1267,7 @@
     "\n",
     "X_test_processed = preprocess_new_data(test_raw_df, preprocessor)\n",
     "\n",
-    "test_preds = best_tree.predict(X_test_processed)\n",
+    "test_preds = best_tree.predict_proba(X_test_processed)[:, 1]\n",
     "\n",
     "test_raw_df[\"Exited\"] = test_preds\n",
     "\n",
@@ -1275,27 +1275,27 @@
     "\n",
     "submission_df[\"Exited\"] = test_raw_df[\"Exited\"]\n",
     "\n",
-    "submission_df.to_csv(\"../assets/hw_2_3/submission_decision_tree.csv\", index=False)\n",
+    "submission_df.to_csv(\"../assets/hw_2_3/submission_decision_tree_proba.csv\", index=False)\n",
     "\n",
     "display(submission_df.head())"
    ],
    "metadata": {
     "id": "ht6fOjbQGH2D",
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:23:41.625551Z",
-     "start_time": "2025-07-13T17:23:41.566670Z"
+     "end_time": "2025-08-05T19:44:47.158376Z",
+     "start_time": "2025-08-05T19:44:47.059033Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "      id  Exited\n",
-       "0  15000     0.0\n",
-       "1  15001     0.0\n",
-       "2  15002     0.0\n",
-       "3  15003     1.0\n",
-       "4  15004     0.0"
+       "      id    Exited\n",
+       "0  15000  0.058824\n",
+       "1  15001  0.014736\n",
+       "2  15002  0.014736\n",
+       "3  15003  0.663636\n",
+       "4  15004  0.014736"
       ],
       "text/html": [
        "<div>\n",
@@ -1324,27 +1324,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.058824</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.014736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.014736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.663636</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.014736</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1360,8 +1360,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:23:41.750538Z",
-     "start_time": "2025-07-13T17:23:41.689616Z"
+     "end_time": "2025-08-05T19:44:47.366356Z",
+     "start_time": "2025-08-05T19:44:47.257652Z"
     }
    },
    "cell_type": "code",
@@ -1375,13 +1375,13 @@
     "\n",
     "X_test_processed = preprocess_new_data(test_raw_df, preprocessor)\n",
     "\n",
-    "test_preds = soft_params_tree.predict(X_test_processed)\n",
+    "test_preds = soft_params_tree.predict_proba(X_test_processed)[:, 1]\n",
     "\n",
     "test_raw_df[\"Exited\"] = test_preds\n",
     "\n",
     "submission_df[\"Exited\"] = test_raw_df[\"Exited\"]\n",
     "\n",
-    "submission_df.to_csv(\"../assets/hw_2_3/submission_soft_params_tree_depth6_leaf30.csv\", index=False)\n",
+    "submission_df.to_csv(\"../assets/hw_2_3/submission_soft_params_tree_depth6_leaf30_proba.csv\", index=False)\n",
     "\n",
     "display(submission_df.head())"
    ],
@@ -1389,12 +1389,12 @@
     {
      "data": {
       "text/plain": [
-       "      id  Exited\n",
-       "0  15000     0.0\n",
-       "1  15001     0.0\n",
-       "2  15002     0.0\n",
-       "3  15003     1.0\n",
-       "4  15004     0.0"
+       "      id    Exited\n",
+       "0  15000  0.153646\n",
+       "1  15001  0.014736\n",
+       "2  15002  0.014736\n",
+       "3  15003  0.629174\n",
+       "4  15004  0.014736"
       ],
       "text/html": [
        "<div>\n",
@@ -1423,27 +1423,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.153646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.014736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.014736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.629174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.014736</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1459,8 +1459,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-13T17:29:05.770402Z",
-     "start_time": "2025-07-13T17:29:05.665831Z"
+     "end_time": "2025-08-05T19:44:47.673334Z",
+     "start_time": "2025-08-05T19:44:47.489239Z"
     }
    },
    "cell_type": "code",
@@ -1483,12 +1483,12 @@
     "\n",
     "X_test_processed = preprocess_new_data(test_raw_df, preprocessor)\n",
     "\n",
-    "test_preds = final_soft_params_tree.predict(X_test_processed)\n",
+    "test_preds = final_soft_params_tree.predict_proba(X_test_processed)[:, 1]\n",
     "\n",
     "test_raw_df[\"Exited\"] = test_preds\n",
     "submission_df[\"Exited\"] = test_raw_df[\"Exited\"]\n",
     "\n",
-    "submission_df.to_csv(\"../assets/hw_2_3/submission_tree_full_train_depth8_leaf44.csv\", index=False)\n",
+    "submission_df.to_csv(\"../assets/hw_2_3/submission_tree_full_train_depth8_leaf44_proba.csv\", index=False)\n",
     "\n",
     "display(submission_df.head())\n"
    ],
@@ -1504,12 +1504,12 @@
     {
      "data": {
       "text/plain": [
-       "      id  Exited\n",
-       "0  15000     0.0\n",
-       "1  15001     0.0\n",
-       "2  15002     0.0\n",
-       "3  15003     1.0\n",
-       "4  15004     0.0"
+       "      id    Exited\n",
+       "0  15000  0.186842\n",
+       "1  15001  0.013561\n",
+       "2  15002  0.013561\n",
+       "3  15003  0.671329\n",
+       "4  15004  0.013561"
       ],
       "text/html": [
        "<div>\n",
@@ -1538,27 +1538,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.186842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.013561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.013561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.671329</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.013561</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1569,7 +1569,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 17
+   "execution_count": 16
   }
  ]
 }
diff --git a/HW_2_3/__pycache__/custom_features.cpython-312.pyc b/HW_2_3/__pycache__/custom_features.cpython-312.pyc
index 555243f0420d5791f8675df58cb795496d81aaf2..7573f8d679d7e14c583475cee16ccdaa5d363102 100644
GIT binary patch
delta 20
acmX@EeO#OSG%qg~0}zC$PTI)5M-%`&j0JZ9

delta 20
acmX@EeO#OSG%qg~0}vE@mu=+UBMJaLy#=5E

diff --git a/HW_2_3/__pycache__/plot_utils.cpython-312.pyc b/HW_2_3/__pycache__/plot_utils.cpython-312.pyc
index c5406928b150e112ab71af4fc82c4dfefd9b5e7a..7f692e5c1580c4d9ef53a0da18f1410c27ca51dc 100644
GIT binary patch
delta 20
acmcboeovkIG%qg~0}zC$PTI(QSr`C6k_EB=

delta 20
acmcboeovkIG%qg~0}vE@mu=*}EDQiZ!v)&_

diff --git a/HW_2_3/__pycache__/process_bank_churn.cpython-312.pyc b/HW_2_3/__pycache__/process_bank_churn.cpython-312.pyc
index 6bd93e15803aa26f5ce53e656672c488bb4d93c9..2e153717f73fcb865a50450067749083279d2c0a 100644
GIT binary patch
delta 20
acmZ2$yVjQbG%qg~0}zC$PTI)5NDcry4Fy&J

delta 20
acmZ2$yVjQbG%qg~0}vE@mu=);BnJRIJ_UaO

diff --git "a/HW_2_4/HW 2.4.2 \320\220\320\273\320\263\320\276\321\200\320\270\321\202\320\274\320\270 \320\261\321\203\321\201\321\202\320\270\320\275\320\263\321\203.ipynb" "b/HW_2_4/HW 2.4.2 \320\220\320\273\320\263\320\276\321\200\320\270\321\202\320\274\320\270 \320\261\321\203\321\201\321\202\320\270\320\275\320\263\321\203.ipynb"
index a963a5b..5b04900 100644
--- "a/HW_2_4/HW 2.4.2 \320\220\320\273\320\263\320\276\321\200\320\270\321\202\320\274\320\270 \320\261\321\203\321\201\321\202\320\270\320\275\320\263\321\203.ipynb"	
+++ "b/HW_2_4/HW 2.4.2 \320\220\320\273\320\263\320\276\321\200\320\270\321\202\320\274\320\270 \320\261\321\203\321\201\321\202\320\270\320\275\320\263\321\203.ipynb"	
@@ -31,8 +31,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-03T15:23:33.179428Z",
-     "start_time": "2025-08-03T15:23:30.767709Z"
+     "end_time": "2025-08-10T07:43:14.544392Z",
+     "start_time": "2025-08-10T07:43:13.667963Z"
     }
    },
    "cell_type": "code",
@@ -60,7 +60,7 @@
     "from HW_2_3.plot_utils import plot_roc_curve, print_main_metrics, plot_feature_importances"
    ],
    "outputs": [],
-   "execution_count": 1
+   "execution_count": 2
   },
   {
    "cell_type": "markdown",
@@ -112,12 +112,12 @@
    "metadata": {
     "id": "cKE8RTPf6CRD",
     "ExecuteTime": {
-     "end_time": "2025-08-03T15:23:33.252323Z",
-     "start_time": "2025-08-03T15:23:33.248897Z"
+     "end_time": "2025-08-10T07:43:47.252755Z",
+     "start_time": "2025-08-10T07:43:47.248838Z"
     }
    },
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 4
   },
   {
    "cell_type": "code",
@@ -138,177 +138,10 @@
     "raw_df.head()"
    ],
    "metadata": {
-    "id": "-bHdMJVB4xQR",
-    "ExecuteTime": {
-     "end_time": "2025-08-03T15:23:33.294792Z",
-     "start_time": "2025-08-03T15:23:33.259609Z"
-    }
+    "id": "-bHdMJVB4xQR"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train shape: (12000, 10) (12000,)\n",
-      "Val shape: (3000, 10) (3000,)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "   id  CustomerId   Surname  CreditScore Geography  Gender   Age  Tenure  \\\n",
-       "0   0  15779985.0   Nwankwo        678.0    France    Male  29.0     4.0   \n",
-       "1   1  15650086.0     Ch'in        687.0    France  Female  34.0     1.0   \n",
-       "2   2  15733602.0  Thompson        682.0    France  Female  52.0     6.0   \n",
-       "3   3  15645794.0   Macleod        753.0   Germany    Male  44.0     6.0   \n",
-       "4   4  15633840.0      Hsia        544.0   Germany  Female  55.0     0.0   \n",
-       "\n",
-       "     Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
-       "0       0.00            3.0        1.0             0.0        180626.36   \n",
-       "1       0.00            2.0        0.0             1.0         63736.17   \n",
-       "2       0.00            3.0        0.0             0.0        179655.87   \n",
-       "3   83347.25            2.0        1.0             0.0        161407.48   \n",
-       "4  107747.57            1.0        1.0             0.0        176580.86   \n",
-       "\n",
-       "   Exited  \n",
-       "0     0.0  \n",
-       "1     0.0  \n",
-       "2     1.0  \n",
-       "3     0.0  \n",
-       "4     1.0  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>CustomerId</th>\n",
-       "      <th>Surname</th>\n",
-       "      <th>CreditScore</th>\n",
-       "      <th>Geography</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Tenure</th>\n",
-       "      <th>Balance</th>\n",
-       "      <th>NumOfProducts</th>\n",
-       "      <th>HasCrCard</th>\n",
-       "      <th>IsActiveMember</th>\n",
-       "      <th>EstimatedSalary</th>\n",
-       "      <th>Exited</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>15779985.0</td>\n",
-       "      <td>Nwankwo</td>\n",
-       "      <td>678.0</td>\n",
-       "      <td>France</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>180626.36</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>15650086.0</td>\n",
-       "      <td>Ch'in</td>\n",
-       "      <td>687.0</td>\n",
-       "      <td>France</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>63736.17</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>15733602.0</td>\n",
-       "      <td>Thompson</td>\n",
-       "      <td>682.0</td>\n",
-       "      <td>France</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>179655.87</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>15645794.0</td>\n",
-       "      <td>Macleod</td>\n",
-       "      <td>753.0</td>\n",
-       "      <td>Germany</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>83347.25</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>161407.48</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>15633840.0</td>\n",
-       "      <td>Hsia</td>\n",
-       "      <td>544.0</td>\n",
-       "      <td>Germany</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>107747.57</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>176580.86</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 3
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -333,25 +166,10 @@
     "print(train_inputs.dtypes[categorical_cols])"
    ],
    "metadata": {
-    "id": "UPmqo-Mr4yUO",
-    "ExecuteTime": {
-     "end_time": "2025-08-03T15:23:33.332415Z",
-     "start_time": "2025-08-03T15:23:33.325260Z"
-    }
+    "id": "UPmqo-Mr4yUO"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Geography        category\n",
-      "Gender           category\n",
-      "NumOfProducts    category\n",
-      "dtype: object\n"
-     ]
-    }
-   ],
-   "execution_count": 4
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -399,57 +217,10 @@
     "plot_roc_curve(val_targets, val_preds_proba, title=\"ROC Curve - Validation\")"
    ],
    "metadata": {
-    "id": "_5rDqdDP41hb",
-    "ExecuteTime": {
-     "end_time": "2025-08-03T15:23:33.898299Z",
-     "start_time": "2025-08-03T15:23:33.440267Z"
-    }
+    "id": "_5rDqdDP41hb"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train AUROC: 0.9349\n",
-      "Validation AUROC: 0.9334\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Dmity\\AppData\\Roaming\\Python\\Python312\\site-packages\\xgboost\\core.py:729: UserWarning: [18:23:33] WARNING: C:\\actions-runner\\_work\\xgboost\\xgboost\\src\\common\\error_msg.cc:58: Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: cuda:0, while the input data is on: cpu.\n",
-      "Potential solutions:\n",
-      "- Use a data structure that matches the device ordinal in the booster.\n",
-      "- Set the device for booster before call to inplace_predict.\n",
-      "\n",
-      "This warning will only be shown once.\n",
-      "\n",
-      "  return func(**kwargs)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x500 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x500 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 5
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -556,45 +327,10 @@
     "plot_roc_curve(val_targets, val_preds_proba, title=\"ROC Curve - Validation\")"
    ],
    "metadata": {
-    "id": "WhR1g9B4433r",
-    "ExecuteTime": {
-     "end_time": "2025-08-03T15:23:43.894037Z",
-     "start_time": "2025-08-03T15:23:33.924830Z"
-    }
+    "id": "WhR1g9B4433r"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 20/20 [00:09<00:00,  2.21trial/s, best loss: -0.9363570889635778]\n",
-      "🔧 Best hyperparameters: {'colsample_bytree': 0.7660374925657433, 'gamma': 0.29607485901814845, 'learning_rate': 0.2569555426252916, 'max_depth': 4, 'min_child_weight': 5, 'n_estimators': 225, 'reg_alpha': 0.5497102361783437, 'reg_lambda': 0.9868252866055883, 'subsample': 0.6438128826245918}\n",
-      "✅ Train AUROC: 0.9714\n",
-      "✅ Validation AUROC: 0.9238\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x500 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x500 with 1 Axes>"
-      ],
-      "image/png": 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Kgkqlsvn5qWxsd+mw7aXBdpcG210a9m737OxshISEPHYx8GI1LjgVX55zc3OzW3BycnKCm5sb/1NVILa7dNj20mC7S4PtLo2KandLhvBwcDgRERGRhRiciIiIiCzE4ERERERkIQYnIiIiIgsxOBERERFZiMGJiIiIyEIMTkREREQWYnAiIiIishCDExEREZGFGJyIiIiILMTgRERERGQhSYPTvn370L9/fwQGBkImk2H9+vVPPGbv3r1o3bo1HBwcUKdOHcybN8/+hRIRERFB4uCUl5eH5s2b49tvv7Vo/8TERERHR6NLly44deoU/vKXv2DixIlYs2aNnSslIiIiApRSvnjfvn3Rt29fi/efN28eQkNDMXv2bABAo0aNcPz4cXzxxRcYMmSInaokIiIiKiJpcLLW4cOHERUVVWJbnz59sHDhQuj1eqhUKokqIyKqeYQQyMrXIzkzHzqjSepyKoTBYMDVbOB40j0olVXqI7RK0+v1uJoNpN4vRKiPtJ/1VeqrnpaWBj8/vxLb/Pz8YDAYkJ6ejoCAgFLHaLVaaLVa8+Ps7GwARV8EvV5v8xqLz2mPc9Ojsd2lw7aXRkW1u8kkcDtHi+TMfCRn5iMpo6Do3/fykZxZgJxCg11fv3JS4pvzx6QuosZwgg5d1ddwXB8Mhd9NTOhRz+avYc3/oyoVnABAJpOVeCyEKHN7sZkzZ2LGjBmltu/YsQNOTk62L/CB+Ph4u52bHo3tLh22ffkk5QDJeWX//HoyGfb/9ItN6zGYgHtaGdK1QHqhDBmFgEE8vj43lYCDwqZlEAEAHKFFJ/wOjcyAbvLrSEl0QFzBJZu/Tn5+vsX7Vqng5O/vj7S0tBLb7ty5A6VSCW9v7zKP+eijjzBlyhTz4+zsbISEhCAqKgpubm42r1Gv1yM+Ph69e/fmpcMKxHaXDtveejmFehy9fg/7L2dg2bkbUpfzREq5DEEejgj1ckSolxPCvJ0Q6ln072BPRziqa05q4vd7xRJCYPVqLbKystDIxwfPP2+fdi++GmWJKhWcOnTogE2bNpXYtmPHDrRp0+aRDanRaKDRaEptV6lUdv2mt/f5qWxsd+nUxLY3mgTydAbka43I1RqQrzMgT2tEntZQtF334N9aI/J1BuRoDTh36z7O3boPk/jfeZzVCnRvUMuq1zaZTEhNS0WAfwDkctvdIC2TAYEe/wtI4d7OCHB3gFLBaf8eVhO/3yuKTqcDAKjVagDAkCFDYDQaER8fb7d2t+ackgan3NxcXLlyxfw4MTERp0+fhpeXF0JDQ/HRRx/h1q1bWLp0KQBg/Pjx+PbbbzFlyhSMGzcOhw8fxsKFC7FixQqp3gIRVQN6owmJ6XnYdi4NPx66jow8nd1fs7aPM9rX8UbHut54LtIfKiuDiV6vR1zcLURHN+cHOFUb6enpWLlyJfz9/TFo0CDIZDJoNJpKNYZS0uB0/Phx9OjRw/y4+JLaqFGjsGTJEqSmpiI5Odn8fO3atREXF4f33nsP3333HQIDA/HNN99wKgIiAlB0CezLHZdw6kaWxcdo9UZcu5v3VHeFKeUyOGuUcFYr4FT8t1pZtE3z4N8Pngv3dkKHut4IcHcs9+sRVUcXLlzA+vXrodPpUFBQgNzcXLi6ukpdVimSBqfu3bubB3eXZcmSJaW2devWDSdPnrRjVURUldwv0GPX77ex/dxt7L10FwV6Y7nO46xWoIG/KzrX88HL7cOgkD95wLZCJoOTRgG1Qv7IG1SI6PFMJhN27dqFgwcPAgBCQ0MxbNgwuLi4SFxZ2arUGCciqllyCvXYe+kudIbSvUHZBXrs/P0ODl/NgOGhAUNh3k54s2td+LmVHttYFoVchrq+LgjycITcgrBERLaTl5eHtWvX4tq1awCA9u3bo1evXlAoKu8NBwxORFRhhBDIyNPhTrYWOYV65GoNyCksGjSdU6hHbuGDxw+eO5mchUwLxhvVr+WC5yL90aeJP5oEurH3h6gKEEJg+fLlSElJgUqlwoABAxAZGSl1WU/E4EREdnH4agb2XLwDkxDIKTTgyp1cXL6Ti/sF1g3y9HBSoWmQe6kwpJTL8Ey4F/o08UMd38rZpU9EjyaTydCrVy/ExcVh2LBhqFXLujtLpcLgREQ2kZGrxelbGTh2PRPHrmfi7M37Ze4nkwE+Lhq4OijhqlHC1UEFF40Srg5KuDgUPXZ98NjDSYXO9X3houGPKqLqwGAw4M6dOwgMDARQdNPXW2+9ZdMpNeyNP42IyGpCCNzILMDR65n49Vo69p5X4M7hvSX2kcuAgS2CUMtVAweVAnVruaCerwvq+DrDQVV5xy8QkX1kZWVh1apVSE9Px7hx4+Dj4wMAVSo0AQxORGQhIQTWnLyF3Rfv4FhiJu7kaB96tugyWoSfC54J98Iz4V5oV8eLt9wTEQDg2rVrWL16NQoKCuDo6Ii8vDxzcKpqGJyIyKxQb8S6U7ew7NckpN0vLPFcem7JQdoqhQxNg9zROtQDuHsVbwzuBV93+63/SERVjxACBw8exK5duyCEQGBgIIYNGwYPDw+pSys3BieiGu5+vh6Hr2Xg0NV0bDqTgnv5Tx68/fMb7dEixAMOKsWDGayvwMOJs1cT0f8UFhZiw4YN+P333wEALVu2RHR0NJTKqh09qnb1RFQuBTojEtPzsPviHfx7+8USzwV7OuK1juHoXN8HMpS8k00mA+r6ulg0OSQR1WxHjx7F77//DoVCgejoaLRq1UrqkmyCwYmomsku1OPsjfswPpiV32gy4ea9Aly7m4erd3Nx7W4ebmUVlDpuZPswdI3wRY8GvlzQlYieWqdOnZCeno527dohKChI6nJshsGJqJq4m6PFooOJ+O/hJORoDU/c38NJhbq+RXe6je9eF7V9nCugSiKqroxGI06cOIHWrVtDoVBAoVBg8ODBUpdlcwxORFWUEAJX7uTi4JV07Pz9Do5cy4DeWNTLFOThaB5zJJMB/m6OqOvrjDq+zqjr64I6vi7wclZLWT4RVSO5ublYvXo1kpKSkJmZieeee07qkuyGwYmoCsnI1WL7+ds4dDUdR65lIj1XW+L5FiEemNC9Lno18uO6a0RUIW7cuIFVq1YhJycHarUaYWFhUpdkVwxORFVATqEeu36/g2kbzyProbveHFRytAnzQtcIH/RqxKVHiKjiCCFw7NgxbN++HSaTCb6+voiJiamy8zNZisGJqJJKySrAzgu3sSPhdonLcG4OSrzeuTY61PFGi1APaJSchZuIKpZer8fmzZtx9uxZAEDjxo0xcOBAqNXVfwgAgxNRJaE3mnAy6R72XrqLvZfu4nxKdonn6/g4o3djP4zpUhu1XB0kqpKICMjOzsbvv/8OmUyG3r17o3379qUW4q6uGJyIJHQjMx97L93Fvkt3cehqBnIfuhtOJgNah3qiV2M/9G7sh7q8DEdElYS3tzcGDx4MjUaD8PBwqcupUAxORBXs3K37WH3iJvZduotr6XklnvNyVqNLfR90re+Lbg184eOikahKIqL/EUJg7969CA8PNwelBg0aSFuURBiciJ5C2v1CvBd7GjnaJy9TAgAGo8DvaTnmxwq5DK1CPdAtwhddI3wRGejOu+GIqFIpKCjA2rVrceXKFRw/fhzvvPMOHBxq7nABBieiR8jXGbDoQCLSsgvLfL5AZ8KakzfLdW65DPhuRCt0qu8DNweu8UZElVNaWhpiY2ORlZUFpVKJ3r171+jQBDA4EZUp9X4Bxiw5joTU7CfvDGBwqyD0bx5o8fmbBrnzMhwRVWpnzpzB5s2bYTAY4OnpiZiYGPj7+0tdluQYnIj+4PSNLIxbehx3c7Twdlbj5XahZV4+k8tkaBrkjg51veGg4pQARFQ9mEwmbN26FcePHwcA1K9fH4MGDYKjo6PElVUODE5ED9l0JgXvrzoDrcGEBn6uWDCqDUK8nKQui4iowshkMmi1RasSdOvWDd26dasxUw1YgsGJaqT0XC2OXMtAZp4O19PzkZyZh+sZ+bhyJxcA8GzDWvj6pZZw0fC/CBHVDEIIyGQyyGQyPP/882jRogXq1KkjdVmVDj8VqEYRQmDV8Zv4+5YE5BQaytxnXJfamNq3ERS8u42IagAhBA4fPoxbt25h6NChkMlkUKvVDE2PwOBENcaNzHz8Zd1v2H85HQAQ7OmIRgFuCPd2Qqi3M8K9nVCvlgsC3Hkdn4hqBq1Wi40bNyIhIQEAcOnSpRo7P5OlGJyo2jOZBH48fB3/3n4R+TojNEo5/hQVgdc71YZSIZe6PCIiSaSnpyM2Nhbp6emQy+V47rnnEBERIXVZlR6DE1UrOYV6aPP/dwkuPUeH6ZvO40TSPQBA29pe+OeQZqjt4yxViUREkrtw4QLWr18PnU4HV1dXDBs2DCEhIVKXVSUwOFG1YDIJbL8pw59m7oHBJEo976xWYGp0I7zctuypBYiIaoqDBw/il19+AQCEhYVh6NChcHHhWpiWYnCiKi9Pa8B7sWew44YCgIBSLkPxnbMyyNA1whczBjZBkAfHLhERhYWFQaFQ4JlnnkGvXr2gUHAeOmswOFGVdT9fj7TsQkxccQoXb+dAIRP4ZEATvNyhttSlERFVKoWFhealUoKDg/H222/D09NT4qqqJgYnqpK+3HER/9l1xfzYx0WNV8LzEdMmWMKqiIgqnxMnTuCXX37BqFGjzEumMDSVH28poipnw+lbJUJT+zpeWPdWe9R2lbAoIqJKxmAwYOPGjdi8eTMKCwtx6tQpqUuqFtjjRFWG1mDEPzZfwE9HkgAAnep5Y9Frz0CjVECv10tcHRFR5ZGVlYWVK1ciNTUVMpkMPXv2RKdOnaQuq1pgcKJKr1BvxKrjN7D86A1cSM0GAEzsWQ+TekVwdm8ioj+4evUq1qxZg4KCAjg5OWHIkCGcBdyGGJyo0vt86+9Ycug6AMDDSYWvhrdAjwa1pC2KiKgSun79Ov773/8CAAIDAxETEwN3d3eJq6peGJyoUruYlmO+NNezYS18MrAJgj2dJK6KiKhyCg0NRd26deHu7o6+fftCqeTHvK2xRalSyszT4erdXPxr2+8wmgSea+KPeSNbS10WEVGlk56eDg8PDyiVSsjlcrz44osMTHbElqVK43p6Ht786QRu5xQiK/9/g701Sjk+7tdIwsqIiCqn3377DZs2bULTpk3Rv39/AGBosjO2LknOYDRh5tbfsfBAYontQR6OqOPrjNc710aIFy/PEREVMxqNiI+Px6+//gqg6C46g8HA0FQB2MIkqYxcLd5ZfgqHr2UAKApLbWt74bNBTeGo5jIARER/lJOTg9WrVyM5ORkA0LlzZ/To0QNyOadmrAgMTiSZc7fu482fTuBWVgGc1Qp8GdMcz0UGSF0WEVGllZycjFWrViE3NxcajQYvvPACGjZsKHVZNQqDE0liw+lb+HD1WWgNJoR7O+GHV9ugvh+n/iYiehS9Xo+VK1ciLy8Pvr6+GD58OLy9vaUuq8ZhcKIKJYTAV79cxjc7LwMAujfwxdcvtoS7o0riyoiIKjeVSoWBAwfi7Nmz6N+/P9RqtdQl1UgMTlRhCvVGfLj6LDaeSQEAvNmtDj7s05CzfxMRPUJmZiays7MRHh4OAKhfvz7q168vbVE1HIMTVYgrd3Lx5zVncSLpHpRyGf7xQiRebBsqdVlERJXWpUuXsHbtWshkMrzxxhvw9PSUuiQCgxPZ2fHrmfh+3zXEJ9wGALg6KDHvldboVM9H4sqIiConk8mEvXv3Yt++fQCAkJAQTjNQifArQTZnNAnEJ6Thh/2JOJF0z7w9qrEf/ty3Ier6ukhYHRFR5VVQUIC1a9fiypUrAIBnnnkGffr0gULB6VkqCwYnshkhBH5Py8G0jedxNDETAKBWyDG4VRDGdqmDerUYmIiIHiU1NRUrV65EVlYWlEol+vfvj2bNmkldFv0BgxPZRKHeiNGLj5knsnRUKfB653C82iEcfm4OEldHRFT5nTx5EllZWfD09ERMTAz8/f2lLonKwOBENrH25C0cvpYBtUKObg18MenZ+ogMcpe6LCKiKqNPnz5QqVTo0qULHB0dpS6HHoHBiZ7aoSvpmP3LJQDAh881wNgudSSuiIio8svOzsaRI0fQq1cvyOVyKJVKREVFSV0WPQGDEz2VS7dzMHrJMfMM4DHPhEhdEhFRpZeYmIjVq1cjPz8fGo0G3bp1k7okshCDE5Vbgc6Id5afhNZgQpf6Ppj3Sms4a/gtRUT0KEIIHDp0CDt37oQQAv7+/hwAXsXwU47K7eudl3Hpdi58XTX4angLhiYiosfQarXYsGEDLly4AABo3rw5+vXrB5WKS05VJfyko3LbeaFoUsu/9msEHxeNxNUQEVVe6enpiI2NRXp6OuRyOZ577jm0adMGMhmXnKpqGJyoXLIL9bhyNxcAOAs4EdETGAwGZGVlwdXVFTExMQgODpa6JConBicql7M37kMIIMTLkb1NRERP4O/vj+HDh8Pf3x8uLpwMuCqTS10AVU27L94BALQM4aKTRER/lJeXh2XLluHmzZvmbfXq1WNoqgYYnMgqQggs/zUZPx1JAgAMahUkcUVERJXLzZs3MX/+fFy5cgUbNmyAyWSSuiSyIV6qI4vdL9Djo7VnEfdbGgDguSb+6B7hK3FVRESVgxACJ06cwLZt22A0GuHt7Y2YmBjI5eyjqE4YnOiJ7uXpcPhaBj7dcgG3sgqglMuKZgjvXId3hBARAdDr9YiLi8Pp06cBAI0aNcLAgQOh0XAMaHXD4ESPdCMzH//YkoCdF+7AYBIAgFAvJ3zzUku0CPGQtjgiokqioKAAP/30E1JTUyGTydCzZ0906tSJv1hWU5L3H86ZMwe1a9eGg4MDWrdujf379z92/2XLlqF58+ZwcnJCQEAARo8ejYyMjAqqtmbI1RrQ/z8H0OVfu7H9/G0YTAK+rhoMbxOCLRM7MzQRET3EwcEB7u7ucHJywiuvvILOnTszNFVjkvY4xcbGYvLkyZgzZw46deqE77//Hn379kVCQgJCQ0NL7X/gwAG8+uqr+Oqrr9C/f3/cunUL48ePx9ixY7Fu3ToJ3kH19M3Oy/jt1n0AgI+LBgtGtWFYIiJ6iBACBoMBSqUSMpkMAwcOhFarhbu7u9SlkZ1J2uM0a9YsjBkzBmPHjkWjRo0we/ZshISEYO7cuWXuf+TIEYSHh2PixImoXbs2OnfujDfffBPHjx+v4MqrryPXMjB/3zUAwCvtQ3Fwag+GJiKihxgMBqxevRobNmyAEEXDGIp7naj6kyw46XQ6nDhxAlFRUSW2R0VF4dChQ2Ue07FjR9y8eRNxcXEQQuD27dtYvXo1+vXrVxEl1wgzt/4OAGgb7oW/D4yERqmQuCIiosrjzp07uHTpEi5fvowLFy7g7t27UpdEFUyyS3Xp6ekwGo3w8/Mrsd3Pzw9paWllHtOxY0csW7YMw4cPR2FhIQwGAwYMGID//Oc/j3wdrVYLrVZrfpydnQ2g6A4IvV5vg3dSUvE57XFue7ubo8WZG1kAgL9GN4DBYJC2ICtU5Xav6tj20mC7V7zz589jy5YtMBgMcHNzw5AhQ+Dp6cmvQQWw9/e7NeeV/K66Pw6gE0I8clBdQkICJk6ciL/97W/o06cPUlNT8cEHH2D8+PFYuHBhmcfMnDkTM2bMKLV9x44dcHJyevo38Ajx8fF2O7e9HLkjA6BAiLNA4qn9SDwldUXWq4rtXl2w7aXBdrc/k8mElJQUpKenAwBcXV0RGhqKU6dO4dSpKviDsgqz1/d7fn6+xfvKRPEF2gqm0+ng5OSEVatWYdCgQebtkyZNwunTp7F3795Sx4wcORKFhYVYtWqVeduBAwfQpUsXpKSkICAgoNQxZfU4hYSEID09HW5ubjZ+V0WpNT4+Hr1794ZKpbL5+e1pwvLTiL9wBxN71MW7PetKXY5VqnK7V3Vse2mw3SvOmjVrcPHiRQBA+/btUVBQgKioKLZ7BbL393t2djZ8fHxw//79J2YDyXqc1Go1Wrdujfj4+BLBKT4+HgMHDizzmPz8fCiVJUtWKIrG4Dwq/2k0mjInIFOpVHb9prf3+W1NazDi4NWiaR16NfGvUrU/rKq1e3XCtpcG293+2rdvj+TkZAwcOBB16tRBXFwc210i9mp3a84p6aW6KVOmYOTIkWjTpg06dOiA+fPnIzk5GePHjwcAfPTRR7h16xaWLl0KAOjfvz/GjRuHuXPnmi/VTZ48GW3btkVgYKCUb6XKO5qYiXydEb6uGkQG8s4QIqq5hBC4d+8evLy8AADh4eGYNGkSNBoNxzORtMFp+PDhyMjIwCeffILU1FRERkYiLi4OYWFhAIDU1FQkJyeb93/ttdeQk5ODb7/9Fn/605/g4eGBnj174p///KdUb6FayNMaMObHoikdejaoBbmcE7cRUc2k0+mwefNmXLx4EWPHjoWvb9F6nFw6hYpJPjh8woQJmDBhQpnPLVmypNS2d999F++++66dq6o5hBD4y7rfoDMUrd49sCV77oioZsrMzERsbCzu3LkDuVyOlJQUc3AiKiZ5cKKKd/BKOk4/mHbg7M0sbD9/Gwq5DN++1BId6/pIWxwRkQQuXryIdevWQavVwsXFBUOHDjVf/SB6GINTDXM9PQ+vLjoKo+l/g+llMuD/+jVC36al70okIqrOTCYT9uzZY14nNSQkBMOGDYOrq6vElVFlxeBUw8zZcwVGk0D9Wi5oFeoJJ40CLz4Tigb+/CFBRDXPyZMnzaGpbdu2iIqKMt+tTVQWBqca5EZmPtaevAUA+HxIM7QO85S4IiIiabVs2RIXL15E06ZN0axZM6nLoSqAwakGmbf3KgwmgU71vBmaiKjGunjxIurVqweFQgGFQoERI0Y8csUKoj+SbJFfqlhp9wux6vhNAMC7PetLXA0RUcUzGAzYtGkTfv755xJLdzA0kTXY41RDzNt7FTqjCW3DvdC+jrfU5RARVaj79+9j1apVuHWraLiCk5PTY9dGJXoUBqca4E5OIVYcLZpI9N1n60lcDRFRxUpMTMTq1auRn58PBwcHDBkyBPXq8WchlQ+DUw3ww75r0BpMaBHigc71OE8TEdUMQggcOnQIO3fuhBAC/v7+iImJgacnx3hS+TE4VXMXUrPxw/5EAMDEZ+uxW5qIaozs7Gzs27cPQgi0aNEC0dHRXJiXnhqDUzW3/NeiS3SuDkr0aFBL4mqIiCqOu7s7XnjhBeTl5aF169b8xZFsgsGpGjOZBLafTwMAfP1iC/7QIKJqLyEhAU5OTggPDwcANGrUSNqCqNrhdATV2Mnke7iTo4WrRolOHNtERNWYyWRCfHw8Vq1ahVWrViEnJ0fqkqiaYo9TNbbmwSzhvRr7QaPkEgJEVD3l5eVh9erVuH79OgCgRYsWcHZ2lrYoqrYYnKqhS7dz8O/tFxGfcBsAMKB5oMQVERHZx82bN7Fy5Urk5ORArVZj4MCBaNy4sdRlUTXG4FTNmEwCby87ict3cgEAf+odge4NfCWuiojItoQQOHHiBLZu3QqTyQRvb28MHz4cvr78eUf2xeBUzey5dMccmua83ArRTQMkroiIyD6Sk5NhMpnQqFEjDBw4EBqNRuqSqAZgcKoGcgr1WHo4CQU6I365UHR57o2udRiaiKjakslkeP755xEeHo6WLVvyrmGqMAxO1cC/tl3ET0eSzI+VchlGdwqXriAiIju4fPkyzp8/j4EDB0Imk0GtVqNVq1ZSl0U1DINTFZd2vxCxx24AAGLaBMNJXTT1QIC7o8SVERHZhhAC+/btw549ewAAoaGhDEwkGQanKu4/uy5DZzShbbgX/jW0udTlEBHZVGFhIdatW4dLly4BAFq3bo1mzZpJXBXVZAxOVdix65lY9mBJlfd6R0hcDRGRbd2+fRuxsbG4d+8eFAoF+vXrh5YtW0pdFtVwDE5V1M17+Ri9+BiAokt0Hep6S1wREZHtXLhwAWvXroXBYICHhwdiYmIQEMAbXkh6DE5VkBACY388jlytAbVcNfi4Hyd7I6Lqxc3NDUII1K1bF4MHD4aTk5PUJREBYHCqkvJ1RvyeVrQO0/cjW8PdUSVxRURET89oNEKhKFoeKigoCKNHj0ZAQADkci6rSpUHvxurIK3BZP53s2AP6QohIrKRpKQkfPvtt0hNTTVvCwoKYmiiSoffkVWQ7kFwUsplUMg56RsRVV1CCBw5cgQ//vgjsrKysHfvXqlLInosXqqrgrQGIwBArWTuJaKqS6fTYdOmTTh37hwAoGnTpnj++eclroro8RicqqDiS3UaBiciqqIyMjKwcuVK3LlzB3K5HFFRUWjbti2XTqFKj8GpCkpMzwPAHiciqpru3r2LhQsXQqvVwsXFBcOGDUNoaKjUZRFZhMGpCpq+8TwAwEnNLx8RVT3e3t4IDg6GXq/H0KFD4erqKnVJRBbjJ28Vk6c14HZ2IQBgbJfaEldDRGSZ/Px8qNVqKJVKyOVyDB06FCqVyjz9AFFVwWs9VczZm/dhEkCguwNebhcmdTlERE+UkpKC+fPnIy4uzrzNwcGBoYmqJPY4VTGnb2QBAFqEekhaBxGRJU6ePIm4uDgYjUYkJSWhoKAAjo6OUpdFVG4MTlWIEAKbz6YAANqEeUlcDRHRoxkMBmzduhUnT54EAERERGDQoEFwcHCQuDKip8PgVIUcvpaB8ynZ0CjlGNQySOpyiIjKdP/+faxcuRIpKUW/6PXo0QNdunThVANULTA4VRF6owkjfvgVADC4VRA8ndUSV0REVJrJZMJPP/2EjIwMODo6YvDgwahXr57UZRHZDAeHVwGp9wvQ8P+2mR+/+AznOyGiykkul+O5555DYGAg3njjDYYmqnbY41QFzNl9FUaTAAC0q+2FZsHuEldERPQ/Wq0W6enpCAoqGkJQr1491K1bl5fmqFpicKoCjiZmAgC+G9EK/ZoFSFwNEdH/3L17F7GxscjLy8O4cePg5VV04wpDE1VXDE6V3P18PS7ezgEAtKvDO+mIqPI4f/48NmzYAL1eDzc3N2i1WqlLIrI7BqdK7mTyPQBAHR9n+LhoJK6GiKhoAPgvv/yCw4cPAwBq166NIUOGwNnZWeLKiOyPwamSO55UdJmudZinxJUQEQG5ublYvXo1kpKSAAAdO3bEs88+C7mc9xpRzcDgVIkZTQLf7b4KAIgM4oBwIpLekSNHkJSUBLVajYEDB6Jx48ZSl0RUocoVnAwGA/bs2YOrV69ixIgRcHV1RUpKCtzc3ODi4mLrGmus3b/fMf+7a4SvhJUQERXp3r07cnJy0KVLF/j4+EhdDlGFszo4JSUl4bnnnkNycjK0Wi169+4NV1dX/Otf/0JhYSHmzZtnjzprpLTsQgCAq0aJ2j4cO0BEFU+v1+PYsWNo37495HI5lEolBg0aJHVZRJKx+qL0pEmT0KZNG9y7d6/EQo2DBg3Czp07bVpcTVc8DUGfSH+JKyGimujevXtYuHAh4uPjsXv3bqnLIaoUrO5xOnDgAA4ePAi1uuSSH2FhYbh165bNCiNg45midZ5cNByKRkQV6/Lly1i7di0KCwvh5OSEOnXqSF0SUaVg9SeyyWSC0Wgstf3mzZtwdXW1SVEECCGgkMtgNAkMaBEodTlEVEMIIbB3717s3bsXABAcHIxhw4bBzc1N4sqIKgerL9X17t0bs2fPNj+WyWTIzc3FtGnTEB0dbcvaarRCvcm8zEoDPwZSIrK/goICrFixwhya2rRpg1GjRjE0ET3E6h6nr776Cj169EDjxo1RWFiIESNG4PLly/Dx8cGKFSvsUWONlF2oBwDIZICTWiFxNURUE2RnZyMxMRFKpRL9+vVDixYtpC6JqNKxOjgFBgbi9OnT+Pnnn3HixAmYTCaMGTMGL7/8conB4vR0Zv9yCQAQ6uXENZ+IqEL4+flh8ODB8PDwQEAA18UkKovVwWnfvn3o2LEjRo8ejdGjR5u3GwwG7Nu3D127drVpgTXRLwm3seLoDchkwKcvNJW6HCKqpoxGI3bs2IHIyEiEhIQAABo1aiRxVUSVm9VjnHr06IHMzMxS2+/fv48ePXrYpKia7sCVdADAi8+EonN9TjBHRLaXnZ2NJUuW4OjRo1i9ejX0er3UJRFVCVb3OAkhyrx0lJGRwQUebSTtftHEl40COCiciGzv+vXrWL16NfLy8uDg4IB+/fpBpVJJXRZRlWBxcBo8eDCAorvoXnvtNWg0GvNzRqMRZ8+eRceOHW1fYQ2U+mDGcD83B4krIaLqRAiBI0eOID4+HkII+Pn5ISYmBl5eXlKXRlRlWByc3N2LFpkVQsDV1bXEQHC1Wo327dtj3Lhxtq+whhFC4Na9fABAgDuDExHZhsFgwPr163H+/HkAQLNmzfD888+zp4nIShYHp8WLFwMAwsPD8f777/OynJ0kZ+YjPVcHlUKGCM7fREQ2olAoYDQaIZfL0adPHzzzzDO8Y5eoHKwe4zRt2jR71EEP/HqtaOB982APOKg4fxMRPZ3icakymQwvvPAC7t69i+DgYKnLIqqyyrUI2urVq7Fy5UokJydDp9OVeO7kyZM2Kaym+vXBwr7t6nDMARGVn8lkwq5du3D//n0MHjwYMpkMGo2GoYnoKVk9HcE333yD0aNHo1atWjh16hTatm0Lb29vXLt2DX379rVHjTXKr4kZAIC2tb0lroSIqqq8vDwsW7YMBw8exLlz55CUlCR1SUTVhtXBac6cOZg/fz6+/fZbqNVqfPjhh4iPj8fEiRNx//59e9RYY2Tl63DzXgEAoHWYp8TVEFFVdOvWLcyfPx/Xrl2DSqXCkCFDEB4eLnVZRNWG1cEpOTnZPO2Ao6MjcnJyAAAjR47kWnVPKfbYDQCAQi6Di6ZcV1GJqAY7ceIEFi9ejOzsbHh5eWHs2LGIjIyUuiyiasXq4OTv74+MjKLLSWFhYThy5AgAIDExEUIIqwuYM2cOateuDQcHB7Ru3Rr79+9/7P5arRYff/wxwsLCoNFoULduXSxatMjq161srt7NxRc7LgIAhj8TInE1RFTV/PLLL9i8eTOMRiMaNGiAcePGoVatWlKXRVTtWN2t0bNnT2zatAmtWrXCmDFj8N5772H16tU4fvy4eZJMS8XGxmLy5MmYM2cOOnXqhO+//x59+/ZFQkICQkNDyzwmJiYGt2/fxsKFC1GvXj3cuXMHBoPB2rdR6Ww4dQt6o0Cnet749AX+hkhE1qlfvz6OHDmCbt26oXPnzpxqgMhOrA5O8+fPh8lkAgCMHz8eXl5eOHDgAPr374/x48dbda5Zs2ZhzJgxGDt2LABg9uzZ2L59O+bOnYuZM2eW2n/btm3Yu3cvrl27Zp7ptjpcuy/QGbHm5C0AwNDWwfyBR0QWefiXxrCwMEycOBFubm4SVkRU/Vl9qU4ul0Op/F/eiomJwTfffIOJEyfi7t27Fp9Hp9PhxIkTiIqKKrE9KioKhw4dKvOYjRs3ok2bNvjXv/6FoKAgRERE4P3330dBQYG1b6NSWbD/Gm5lFSDA3QFRjf2lLoeIKjkhBA4dOoSEhIQSP3cZmojszyYjkNPS0vDpp59iwYIFFoeY9PR0GI1G+Pn5ldju5+eHtLS0Mo+5du0aDhw4AAcHB6xbtw7p6emYMGECMjMzHznOSavVQqvVmh9nZ2cDAPR6vV1WAy8+pzXn/j2tqKaR7UOglguuUl4O5Wl3sg22fcUqLCzE5s2bcenSJQDA+fPn4evrK3FVNQe/36Vh73a35rwWB6esrCy8/fbb2LFjB1QqFaZOnYp33nkH06dPxxdffIEmTZqUa5D2Hy9LFc9yWxaTyQSZTIZly5aZ186bNWsWhg4diu+++67E+nnFZs6ciRkzZpTavmPHDjg5OVldr6Xi4+Mt3jc1RQ5AjisXf0dc9gW71VQTWNPuZFtse/srKCjA9evXodVqIZPJEBwcjPz8fMTFxUldWo3D73dp2Kvd8/PzLd7X4uD0l7/8Bfv27cOoUaOwbds2vPfee9i2bRsKCwuxdetWdOvWzaoifXx8oFAoSvUu3blzp1QvVLGAgAAEBQWZQxMANGrUCEII3Lx5E/Xr1y91zEcffYQpU6aYH2dnZyMkJARRUVF26dbW6/WIj49H7969LV48c3PWaSDjDppGNkF0u7IHxdPjlafdyTbY9hUjISEBW7ZsgV6vh5ubGwYMGIBz586x3SsYv9+lYe92L74aZQmLg9OWLVuwePFi9OrVCxMmTEC9evUQERGB2bNnl6dGqNVqtG7dGvHx8Rg0aJB5e3x8PAYOHFjmMZ06dcKqVauQm5sLFxcXAMClS5cgl8sfuYyARqOBRqMptV2lUtn1m96a819LzwMABHg48z/iU7L315UejW1vPxcvXsT69esBALVr18aQIUOgVqtx7tw5trtE2O7SsFe7W3NOiweHp6SkoHHjxgCAOnXqwMHBwXw3XHlNmTIFCxYswKJFi3DhwgW89957SE5ONt+d99FHH+HVV1817z9ixAh4e3tj9OjRSEhIwL59+/DBBx/g9ddfL/MyXVVgNAlcvVsUnLyc1RJXQ0SVUb169RAWFoZOnTrhlVdegbOzs9QlEdVYFvc4mUymEolMoVA89X/e4cOHIyMjA5988glSU1MRGRmJuLg4hIWFAQBSU1ORnJxs3t/FxQXx8fF499130aZNG3h7eyMmJgb/+Mc/nqoOKRXojeZ/N/BzlbASIqpMUlNTUatWLSgUCigUCowcORIKhULqsohqPIuDkxACr732mvmyV2FhIcaPH18qPK1du9aqAiZMmIAJEyaU+dySJUtKbWvYsGG1GpR3817RgDRntQKuDlxmhaimE0Lg2LFj2L59O9q0aWNePJ2hiahysPiTetSoUSUev/LKKzYvpiY6e6NoYeRmwR6QyznxJVFNptfrsXnzZpw9exYAkJeXB5PJBLnc6in3iMhOLA5OixcvtmcdNVZ2YdHcEX5upQewE1HNkZmZiZUrV+L27duQyWTo3bs32rdvz5UEiCoZXhuS2P2CouDkpOGXgqimunTpEtauXQutVgtnZ2cMHTq0WiwnRVQd8dNaYsmZRWOcwrzsNxknEVVehYWF5tAUHByMYcOGcekUokqMwUliSRlFwSmUwYmoRnJwcMALL7yAa9euoU+fPhwETlTJMThJ7MaDHqdQbwYnopoiLS0NWq3WPPVKw4YN0bBhQ4mrIiJLMDhJKFdrQEaeDgAQwh4nohrhzJkz2Lx5M9RqNd54440SS0gRUeVXrntcf/rpJ3Tq1AmBgYFISkoCAMyePRsbNmywaXHVXfKDy3SeTiq4OXDqfqLqzGg0YsuWLVi/fj0MBgOCgoKgVnO1AKKqxurgNHfuXEyZMgXR0dHIysqC0Vg087WHh0e5162rqZLNl+m4fAJRdZadnY0lS5bg+PHjAIBu3brhpZdeqrJLRRHVZFYHp//85z/44Ycf8PHHH5cYxNimTRv89ttvNi2uujOPb+JlOqJq6/r165g/fz5u3rwJBwcHjBgxAt27d+f8TERVlNVjnBITE9GyZctS2zUaDfLy8mxSVE2RlFnUXqFe/K2TqLo6c+YM8vLy4Ofnh5iYGHh5eUldEhE9BauDU+3atXH69Gnz3SDFtm7disaNG9ussJogObMAAHuciKqz6OhouLq6okuXLiUWSieiqsnq4PTBBx/g7bffRmFhIYQQOHr0KFasWIGZM2diwYIF9qix2rqTXQgACHBnjxNRdZGeno7jx4+jT58+kMlkUKlU6Nmzp9RlEZGNWB2cRo8eDYPBgA8//BD5+fkYMWIEgoKC8PXXX+PFF1+0R43VkskkcPNeUY9TLa5TR1QtXLhwAevXr4dOp4Obmxs6duwodUlEZGPlmsdp3LhxGDduHNLT02EymVCrVi1b11XtJWXmI1drgEYpRz1fF6nLIaKnYDKZsGvXLhw8eBAAEBYWhmbNmklcFRHZg9V31c2YMQNXr14FAPj4+DA0ldO5W/cBAA0D3KBUlGs6LSKqBPLy8vDf//7XHJrat2+PkSNHwsWFvxARVUdWf2KvWbMGERERaN++Pb799lvcvXvXHnVVe+dSioJTZCAX8ySqqlJSUjB//nwkJiZCpVJhyJAhXG+OqJqzOjidPXsWZ8+eRc+ePTFr1iwEBQUhOjoay5cvR35+vj1qrJbO38oGAEQGcbkFoqpKCIG8vDx4e3tj7NixiIyMlLokIrKzcl0jatKkCT777DNcu3YNu3fvRu3atTF58mT4+/vbur5qSQjxUI8TgxNRVSKEMP87KCgIL730EsaOHcthC0Q1xFMPrnF2doajoyPUajX0er0taqr2hACy8ovaKtDDQeJqiMhSWVlZWLJkCVJTU83b6tatCwcH/j8mqinKFZwSExPx6aefonHjxmjTpg1OnjyJ6dOnIy0tzdb1VXtcdoGoarh69Srmz5+P5ORkbN68uUTPExHVHFZPR9ChQwccPXoUTZs2xejRo83zOJHl+OOWqOoQQuDAgQPYtWsXACAwMBAxMTH8pYeohrI6OPXo0QMLFixAkyZN7FFPjZBbaDD/20nNu2+IKqvCwkKsX78eFy9eBAC0bNkS0dHRUCrLNQUeEVUDVv/v/+yzz+xRR42S9mCpFU8nFRxUDE5ElVFOTg6WLFmCzMxMKBQKREdHo1WrVlKXRUQSsyg4TZkyBX//+9/h7OyMKVOmPHbfWbNm2aSw6iz1ftFSK35uHFBKVFm5uLjAy8sLRqMRMTExCAwMlLokIqoELApOp06dMt8xd+rUKbsWVBPcftDj5O/O4ERUmRiNRgghoFQqIZPJMHjwYAgh4OTkJHVpRFRJWBScdu/eXea/qXxyHoxxcndUSVwJERXLzc3FqlWr4OXlhQEDBkAmk8HR0VHqsoiokrF6OoLXX38dOTk5pbbn5eXh9ddft0lR1V2+zggAcFJzgClRZZCcnIzvv/8eycnJuHDhAu7fvy91SURUSVkdnH788UcUFBSU2l5QUIClS5fapKjqLk9X1OPkzDvqiCQlhMCvv/6KH3/8Ebm5ufD19cW4cePg4eEhdWlEVElZ3OWRnZ0NIQSEEMjJySkxU67RaERcXByXHLBQvra4x4nBiUgqOp0Omzdvxm+//QagaCmpAQMGQK1WS1wZEVVmFgcnDw8PyGQyyGQyRERElHpeJpNhxowZNi2uujKYiqbAVCmeesUbIioHIQRWrFiB69evQyaTISoqCu3ateOklkT0RBYHp927d0MIgZ49e2LNmjXw8vIyP6dWqxEWFsbbdS2kN5oAAPwZTSQNmUyGzp07IyMjA0OGDEFYWJjUJRFRFWFxcOrWrRuAonXqQkND+ZvZUzh7MwsAUNfXRdpCiGoQk8mEzMxM+Pj4AChanPfdd9+FSsW7W4nIchYFp7NnzyIyMhJyuRz37983jwkoS7NmzWxWXHV0N0eLS7dzAQDt63hLXA1RzVBQUIC1a9fi5s2bGDdunLnHnKGJiKxlUXBq0aIF0tLSUKtWLbRo0QIymazMlcFlMhmMRqPNi6xOjlzLAAA0CnCDpzMHoRLZW2pqKlauXImsrCwolUrcvXu3xFADIiJrWBScEhMT4evra/43ld+hq0XBqWNd9jYR2dvp06exZcsWGAwGeHp6IiYmBv7+/lKXRURVmEXB6eGBkxxE+XSKe5wYnIjsx2AwYNu2bThx4gQAoH79+hg0aBBnAieip1auCTC3bNlifvzhhx/Cw8MDHTt2RFJSkk2Lq25S7xcgMT0PchnwTG1eKiCyl19//dUcmrp164aXXnqJoYmIbMLq4PTZZ5+ZfwAdPnwY3377Lf71r3/Bx8cH7733ns0LrE4OP7hM1zTYA24OHJRKZC/t2rVDvXr1MGLECHTv3p13ARORzVi9WNqNGzdQr149AMD69esxdOhQvPHGG+jUqRO6d+9u6/qqlVnxlwAAHXg3HZFNCSFw7tw5NGnSBHK5HEqlEi+//LLUZRFRNWR1j5OLiwsyMop6Tnbs2IFevXoBABwcHMpcw47+JyNXBwBoFeohbSFE1YhWq8WqVauwdu1a7Nq1S+pyiKias7rHqXfv3hg7dixatmyJS5cuoV+/fgCA8+fPIzw83Nb1VSumB1M4NAlyl7gSourh7t27WLlyJdLT0yGXy+Huzv9bRGRfVvc4fffdd+jQoQPu3r2LNWvWwNu76LLTiRMn8NJLL9m8QCKisiQkJGDBggVIT0+Hq6srRo8ejWeeeUbqsoiomrO6x8nDwwPffvttqe1c4PfxCvVG6B6sUeeg5OK+ROVlMpmwc+dOHDp0CAAQHh6OIUOGwMWFSxgRkf1ZHZwAICsrCwsXLsSFCxcgk8nQqFEjjBkzht3kj3H1bi6EADycVPDijOFE5Xbv3j0cO3YMANChQwf06tULcjl/GSGiimF1cDp+/Dj69OkDR0dHtG3bFkIIfPXVV/jss8+wY8cOtGrVyh51VnlX7hStT1fP14W3RhM9BW9vbwwcOBAA0KRJE4mrIaKaxurg9N5772HAgAH44YcfoFQWHW4wGDB27FhMnjwZ+/bts3mR1UHcb6kAgHq1eDmByBpCCJw8eRK1atVCSEgIAAYmIpJOuXqcHg5NAKBUKvHhhx+iTZs2Ni2uOjl78z4AwMdFI3ElRFWHXq9HXFwcTp8+DVdXV4wfPx5OTk5Sl0VENZjVAwPc3NyQnJxcavuNGzfg6upqk6KqmwKdEem5WgBAVBM/iashqhqysrKwePFinD59GjKZDG3btuWyKUQkOat7nIYPH44xY8bgiy++QMeOHSGTyXDgwAF88MEHnI7gEdaeugm9UcDfzQFNOYcT0RNduXIFa9euRUFBAZycnDBkyBDUqVNH6rKIiKwPTl988QVkMhleffVVGAwGAIBKpcJbb72Fzz//3OYFVgdJGfkAgN6N/TgwnOgxhBDYv38/du/eDQAIDAxETEwM79glokrD6uCkVqvx9ddfY+bMmbh69SqEEKhXrx7HHTxGTqEeADgNAZEF0tLSAACtWrVC3759S4ynJCKSmsU/kfLz8/HBBx9g/fr10Ov16NWrF7755hv4+PjYs75q4ea9ojX8gjw5PoPocWQyGQYOHIjGjRsjMjJS6nKIiEqxeHD4tGnTsGTJEvTr1w8vvvgi4uPj8dZbb9mztmrjVlZRcAr2YHAi+qPffvsNGzZsgHiwlqNGo2FoIqJKy+Iep7Vr12LhwoV48cUXAQCvvPIKOnXqBKPRCIVCYbcCqzohBG496HEK9uTlTKJiRqMRO3bswNGjRwEA9erV4/xMRFTpWRycbty4gS5dupgft23bFkqlEikpKeZJ6ai09FwdtAYTZDLA391B6nKIKoWcnBysWrUKN27cAAB06dIFjRo1krgqIqInszg4GY1GqNUlBzcrlUrznXVUtnv5OgCAh6MKai7uS4SkpCSsXr0aubm50Gg0GDRoEBo0aCB1WUREFrE4OAkh8Nprr0Gj+d/M14WFhRg/fjycnZ3N29auXWvbCqu4nMKiYOnqoJK4EiLpnTx5Elu2bIHJZEKtWrUQExMDb29vqcsiIrKYxcFp1KhRpba98sorNi2mOsrVFgUnFw1vqSYqvgs3MjIS/fv3L9WLTURU2Vn8ab548WJ71lFt5RYyOFHNZjAYzHMxhYaGYty4cfDz42SwRFQ1cdCNneVqiya/dHFgcKKa5+LFi/jmm29w9+5d8zZ/f3+GJiKqshic7CyHPU5UA5lMJuzatQs///wzcnJycPDgQalLIiKyCX6a21me1giAPU5Uc+Tn52Pt2rW4evUqgKKpS6KioiSuiojINiTvcZozZw5q164NBwcHtG7dGvv377fouIMHD0KpVKJFixb2LfApZT9Yp86VwYlqgNTUVMyfPx9Xr16FUqnEoEGD0LdvX06SS0TVhqTBKTY2FpMnT8bHH3+MU6dOoUuXLujbty+Sk5Mfe9z9+/fx6quv4tlnn62gSsvvXl7RPE5eTrx7iKq3lJQULFy4EPfv34enpyfGjh2LZs2aSV0WEZFNlSs4/fTTT+jUqRMCAwORlJQEAJg9ezY2bNhg1XlmzZqFMWPGYOzYsWjUqBFmz56NkJAQzJ0797HHvfnmmxgxYgQ6dOhQnvIrVOaDCTA9nRmcqHrz9/dHSEgIIiIi8MYbb8DPz0/qkoiIbM7q60dz587F3/72N0yePBmffvopjMaiMTweHh6YPXs2Bg4caNF5dDodTpw4galTp5bYHhUVhUOHDj3yuMWLF+Pq1av473//i3/84x9PfB2tVgutVmt+nJ2dDQDQ6/XQ6/UW1WqN4nMW/52ZW/Tabhq5XV6Pivyx3ali5OTkQKUqmtzVaDRiyJAhUKvVkMlk/FrYGb/npcF2l4a9292a81odnP7zn//ghx9+wAsvvIDPP//cvL1NmzZ4//33LT5Peno6jEZjqd9K/fz8kJaWVuYxly9fxtSpU7F//37zvDBPMnPmTMyYMaPU9h07dsDJyX6L7sbHxwMAbqUrAMjw+5nj0F6z28vRA8XtTvaXk5OD69evw9PTE8HBwWx7ibDdpcF2l4a92j0/P9/ifa0OTomJiWjZsmWp7RqNBnl5edaertR8LkKIMud4MRqNGDFiBGbMmIGIiAiLz//RRx9hypQp5sfZ2dkICQlBVFQU3NzcrK73SfR6PeLj49G7d2+oVCp8fHIXAAOin+2G2j7OTzyeyueP7U72I4TAkSNHcObMGQghIJfLYTQa8dxzz7HtKxC/56XBdpeGvdu9+GqUJawOTrVr18bp06cRFhZWYvvWrVvRuHFji8/j4+MDhUJRqnfpzp07ZY6NyMnJwfHjx3Hq1Cm88847AIrmihFCQKlUYseOHejZs2ep4zQaTYn19YqpVCq7ftOrVCoImcK85Eotdyf+J6sA9v661nRarRYbNmzAhQsXAAAtWrRA7969ER8fz7aXCNtdGmx3adir3a05p9XB6YMPPsDbb7+NwsJCCCFw9OhRrFixAjNnzsSCBQssPo9arUbr1q0RHx+PQYMGmbfHx8eXOU7Kzc0Nv/32W4ltc+bMwa5du7B69WrUrl3b2rdid1kPBobLZYAbF/mlKu7u3buIjY1FRkYG5HI5+vbti9atW8NgMEhdGhFRhbE6OI0ePRoGgwEffvgh8vPzMWLECAQFBeHrr7/Giy++aNW5pkyZgpEjR6JNmzbo0KED5s+fj+TkZIwfPx5A0WW2W7duYenSpZDL5YiMjCxxfK1ateDg4FBqe2VhvqPOSQ25nEtMUNVlMBjw008/IScnB66uroiJiUFwcLDUZRERVbhyzco4btw4jBs3Dunp6TCZTKhVq1a5Xnz48OHIyMjAJ598gtTUVERGRiIuLs58GTA1NfWJczpVZpkP5nDycGJvE1VtSqUS0dHROHr0KIYMGQJnZ47XI6Ka6amms/bx8XnqAiZMmIAJEyaU+dySJUsee+z06dMxffr0p67BXu7lFd3e6MU5nKgKysvLw71798w9Sw0bNkSDBg24QC8R1WjlGhz+uB+c167xnvtiD1+qI6pKbt68iVWrVsFgMOCNN96Au7s7gNJ3wRIR1TRWB6fJkyeXeKzX63Hq1Cls27YNH3zwga3qqhbMy62wx4mqCCEETpw4ga1bt8JkMsHb25sT/RERPcTq4DRp0qQyt3/33Xc4fvz4UxdUnVy9mwsACPZ0lLgSoifT6/XYsmULzpw5AwBo1KgRBg4cWOZ0HkRENZXNFvnt27cv1qxZY6vTVQvnbt0HAEQGuUtcCdHj3bt3D4sWLcKZM2cgk8nQq1cvDBs2jKGJiOgPnmpw+MNWr14NLy8vW52uysvXGXAtvWgm9SaBDE5UuR06dAhpaWlwcnLC0KFDK+W8aERElYHVwally5YlBogKIZCWloa7d+9izpw5Ni2uKssuNEAIQCmXwdeVv7VT5RYVFQWDwYDu3bubB4ITEVFpVgenF154ocRjuVwOX19fdO/eHQ0bNrRVXdUGb0KiyqiwsBDHjh1D586dIZPJoFKpypyxn4iISrIqOBkMBoSHh6NPnz7w9/e3V03VgtEkAPD2bap8bt++jdjYWNy7dw9CCHTt2lXqkoiIqgyrgpNSqcRbb71lXuCTHi23sGj9LleNzYaRET21s2fPYtOmTTAYDHB3d0f9+vWlLomIqEqx+lO9Xbt2OHXqlHlZFCpbjvZBcHJgcCLpGY1GbN++HceOHQMA1K1bF4MHD4aTk5PElRERVS1Wf6pPmDABf/rTn3Dz5k20bt261JpVzZo1s1lxVVnOgx4nN0euU0fSysnJwapVq3Djxg0AQNeuXdGtWzfI5TabjYSIqMawODi9/vrrmD17NoYPHw4AmDhxovk5mUwGIQRkMhmMRqPtq6yCioMTe5xIajk5OUhJSYFGo8GgQYPQoEEDqUsiIqqyLP5U//HHH/H5558jMTHRnvVUGzmFRctUuGrY40TSCgwMxJAhQ+Dn58e51oiInpLFwUmIorvEOLbJMuxxIqnodDrExcWhbdu2CAwMBFC0fAoRET09qwY58NZ6y+mMJgCARsVxJFRxMjIysHDhQpw5cwZr1qzhpXMiIhuzqjskIiLiieEpMzPzqQqqbmRg2KSKcfHiRaxbtw5arRYuLi4YOHAgFAqF1GUREVUrVgWnGTNmcDkGokrGZDJhz5492L9/PwAgNDQUQ4cOhaurq8SVERFVP1YFpxdffBG1atWyVy3VyoMhYUR2pdPpsHLlSly9ehUA0LZtW0RFRbGniYjITiwOThzfZJ3iMU5qJcc4kf2oVCrI5XKoVCr0798fTZs2lbokIqJqzeq76sgyhfqi4OTAweFkByaTCXK5HDKZDIMGDUJ2djb8/PykLouIqNqzODiZTCZ71lHtaA1FdzM5KHnJhGzHYDBg69atMBqNGDhwIGQyGRwdHeHo6Ch1aURENQInGbKT//U4MTiRbdy/fx8rV65ESkoKgKJ1IwMCAiSuioioZmFwspNC/YMeJ16qIxu4du0a1qxZg/z8fDg6OmLw4MEMTUREEmBwspNCQ/EEmOxxovITQuDgwYPYtWsXhBAICAhATEwMPDw8pC6NiKhGYnCyE625x4nBicpv06ZNOHXqFACgRYsWiI6OhkrF9Q+JiKTC60h2Utzj5MDpCOgpREZGQqlU4vnnn8eAAQMYmoiIJMYeJzvh4HAqr9zcXLi4uAAA6tSpg8mTJ8PZ2VniqoiICGCPk93wUh1Zy2QyYceOHfj2229LrPnI0EREVHkwONmJ+VId76ojC+Tm5mLp0qU4fPgwtFotLl++LHVJRERUBl6qs5Pi6Qg0nACTnuDmzZtYuXIlcnJyoFarMXDgQDRu3FjqsoiIqAwMTnaiZY8TPYEQAsePH8e2bdtgMpng4+OD4cOHw8fHR+rSiIjoERic7ECIhyfAZI8Tle3s2bOIi4sDADRu3BgDBgyARqORuCoiInocBic7MArA9GBNZK5VR48SGRmJkydPokGDBujQoQNkMpnUJRER0RMwONmB/qH1kDW8VEcPSU5ORnBwMORyORQKBUaNGgW5nN8jRERVBX9i20FxcJLJAA0nwCQUjWfas2cPFi9ejJ07d5q3MzQREVUt7HGyg4Ki4U1w0Sh5+YVQUFCAdevWmacY0Ol0EELwe4OIqApicLKDAkPR3+6OXB6jpktLS8PKlStx7949KJVK9OvXDy1atJC6LCIiKicGJzvINxT1JDA41WxnzpzB5s2bYTAY4OHhgZiYGAQEBEhdFhERPQUGJzsovlTn5sDgVFPl5uZiy5YtMBgMqFevHgYPHgxHR0epyyIioqfE4GQH93VFf7PHqeZycXHBgAEDcPfuXXTr1o2DwImIqgkGJzs4m1n0IcmpCGqW69evQ6FQICQkBEDRPE1ERFS98JPdDop7nFwdmEtrAiEEDh8+jKVLl2LlypXIzc2VuiQiIrITfrLbQXFwivBzlbYQsjudToeNGzfi/PnzAIDatWtz2RQiomqMwckONAog3wC0CPGQuhSyo4yMDMTGxuLu3buQy+WIiopC27ZtOT8TEVE1xuBkBw/W94WXs1raQshufv/9d6xbtw46nQ4uLi4YNmwYQkNDpS6LiIjsjMHJxowmAb0o6nFwUrN5q6tz585Bp9MhNDQUw4YNg4uLi9QlERFRBeAnu40VFnc3AXBUKSSshOypf//+8PPzQ8eOHaFQ8OtMRFRT8K46G9MaTOZ/c4Hf6uPWrVvYunUrhBAAAI1Ggy5dujA0ERHVMOxxsiOOEa4eTpw4ga1bt8JoNMLX1xdt2rSRuiQiIpIIgxPRIxgMBsTFxeHUqVMAgAYNGnBSSyKiGo7BiagMWVlZWLlyJVJTUyGTydCjRw907tyZUw0QEdVwDE5Ef5CYmIhVq1ahoKAAjo6OGDJkCOrWrSt1WUREVAkwOBH9gUKhgFarRUBAAGJiYuDh4SF1SUREVEkwOBGhaL254stwoaGhePnllxEaGgqlkv9FiIjof3i/PNV4d+7cwfz583Hnzh3ztjp16jA0ERFRKQxOVKOdO3cOCxYsQFpaGrZt2yZ1OUREVMnxV2qqkYxGI3755RccOXIEAFC7dm0MGTJE4qqIiKiyY3CiGic3NxerV69GUlISAKBTp07o2bMn5HJ2wBIR0eMxOFGNcu/ePSxevBg5OTlQq9V44YUX0KhRI6nLIiKiKoLBiWoUd3d3eHt7w8HBATExMfDx8ZG6JCIiqkIYnGzM9GARWKo89Ho95HI5FAoF5HI5hg0bBqVSCbVaLXVpRERUxXBQh40ZTEXBSSmXcXmOSiAzMxMLFy4scceck5MTQxMREZULg5ONGYuDk4KhSWqXLl3CDz/8gNu3b+PChQvIy8uTuiQiIqriJA9Oc+bMQe3ateHg4IDWrVtj//79j9x37dq16N27N3x9feHm5oYOHTpg+/btFVjtkxmMRcFJIWdwkooQAnv27MGKFStQWFiI4OBgvPHGG3B2dpa6NCIiquIkDU6xsbGYPHkyPv74Y5w6dQpdunRB3759kZycXOb++/btQ+/evREXF4cTJ06gR48e6N+/P06dOlXBlT9a8aU6FW9tl0RBQQGWL1+OvXv3AgCeeeYZvPbaa3Bzc5O4MiIiqg4kHRw+a9YsjBkzBmPHjgUAzJ49G9u3b8fcuXMxc+bMUvvPnj27xOPPPvsMGzZswKZNm9CyZcuKKPmJDEYTAPY4SUEIgeXLl+P27dtQKpV4/vnn0bx5c6nLIiKiakSy4KTT6XDixAlMnTq1xPaoqCgcOnTIonOYTCbk5OTAy8vrkftotVpotVrz4+zsbABFd1rp9fpyVP54hbqicyrksMv5qWx6vR4ymQwdO3bEnj17MHjwYPj5+fFrUAGK25htXbHY7tJgu0vD3u1uzXklC07p6ekwGo3w8/Mrsd3Pzw9paWkWnePLL79EXl4eYmJiHrnPzJkzMWPGjFLbd+zYAScnJ+uKtkBSLgAoYdBpERcXZ/PzU0kmkwlarRaOjo4AgMTERISEhODEiRMSV1bzxMfHS11CjcR2lwbbXRr2avf8/HyL95V8Hqc/3rIvhLDoNv4VK1Zg+vTp2LBhA2rVqvXI/T766CNMmTLF/Dg7OxshISGIioqyy7iXo9fSgd9OwtnJCdHRXWx+fvqfnJwcrF27FpmZmRg5ciSOHj2K3r17Q6VSSV1ajaLX6xEfH8+2r2Bsd2mw3aVh73YvvhplCcmCk4+PDxQKRanepTt37pTqhfqj2NhYjBkzBqtWrUKvXr0eu69Go4FGoym1XaVS2eeb/sGgcJVCzv9UdnT9+nWsXr0aeXl5cHBwME81YLevKz0R214abHdpsN2lYa92t+ackt36pVar0bp161LdbvHx8ejYseMjj1uxYgVee+01LF++HP369bN3mVYrno5AycHhdiGEwKFDh7B06VLk5eXBz88P48aNQ1hYmNSlERFRDSDppbopU6Zg5MiRaNOmDTp06ID58+cjOTkZ48ePB1B0me3WrVtYunQpgKLQ9Oqrr+Lrr79G+/btzb1Vjo6OcHd3l+x9PKx4AkzeVWd7Wq0WGzduREJCAgCgWbNmeP7556FSqThQk4iIKoSkwWn48OHIyMjAJ598gtTUVERGRiIuLs7ce5CamlpiTqfvv/8eBoMBb7/9Nt5++23z9lGjRmHJkiUVXX6ZzPM4ceZwmzt48CASEhIgl8vRp08fPPPMM1zWhoiIKpTkg8MnTJiACRMmlPncH8PQnj177F/QU+LM4fbTpUsXpKWloUuXLggJCZG6HCIiqoE4vbWNGUxFE2AqFWzap2UymXDq1CkI8aAXT6XCiBEjGJqIiEgykvc4VTfFl+o4OPzp5OXlYc2aNUhMTMT9+/fRvXt3qUsiIiJicLI1Dg5/erdu3cLKlSuRnZ0NlUoFHx8fqUsiIiICwOBkc+xxejonTpzA1q1bYTQa4e3tjZiYmMdOcEpERFSRGJxsjPM4lY/BYEBcXBxOnToFAGjYsCEGDhwIBwcHiSsjIiL6HwYnGzNycHi5ZGRk4OzZs5DJZOjZsyc6derEqQaIiKjSYXCyMT3HOJWLn58fBgwYABcXF9SpU0fqcoiIiMrEbhEbKx4crmJweiwhBPbv34+UlBTztmbNmjE0ERFRpcbgZGPmCTA5c/gjFRYWIjY2Frt27cLKlSuh1WqlLomIiMgivFRnY7yr7vHu3LmD2NhYZGZmQqFQoGvXrtBoNFKXRUREZBEGJxszDw6XszPvj86dO4eNGzdCr9fD3d0dw4YNQ1BQkNRlERERWYzByca4Vl1pJpMJO3bswK+//goAqFOnDoYMGQInJyeJKyMiIrIOg5ONFV+qU3GMk5lMJkNmZiYAoHPnzujRowfk7JEjIqIqiMHJxrjkSmkymQyDBg3CjRs3EBERIXU5RERE5cZf+22M8zgVTTXw66+/YuPGjRCiqD0cHR0ZmoiIqMpjj5ONFQ8OV9XQS1F6vR6bNm3Cb7/9BgBo3Lgx6tWrJ3FVREREtsHgZGM1eXB4ZmYmVq5cidu3b0MmkyEqKgp169aVuiwiIiKbYXCyMfM8TjVscPilS5ewdu1aaLVaODs7Y9iwYQgLC5O6LCIiIpticLIxYw2cAPPQoUOIj48HAAQHB2PYsGFwc3OTuCoiIiLbY3CysZp4qc7f3x8ymQxt2rRBnz59oFAopC6JiIjILhicbMxgnjm8egcnvV4PlUoFoGhCy7feegu+vr4SV0VERGRfNfPWr4ogq77B6fTp0/j666+RkZFh3sbQRERENQGDE1nMYDBgy5Yt2LBhA/Ly8nDs2DGpSyIiIqpQvFRHFsnOzsbKlStx69YtAEC3bt3QrVs3iasiIiKqWAxO9ESJiYlYvXo18vPz4eDggMGDB6N+/fpSl0VERFThGJzosa5du4b//ve/EELAz88Pw4cPh6enp9RlERERSYLBiR4rLCwMwcHB8PT0xPPPP2++k46IiKgmYnCiUjIzM+Hh4QG5XA6FQoFXXnkFKpUKsmp8pyAREZEleFcdlZCQkIDvv/8ev/zyi3mbWq1maCIiIgJ7nOgBk8mEnTt34tChQwCA1NRUGI1GzgJORET0EAYnQl5eHtasWYPExEQAQIcOHdCrVy/I5eyQJCIiehiDUw1369YtrFy5EtnZ2VCpVBg4cCCaNGkidVlERESVEoOTjZmK1vhFVRgRpNPpsGzZMhQUFMDb2xvDhw/n0ilERESPweBkYyZRlJwUVWCRX7VajX79+uH8+fMYOHAgNBqN1CURERFVagxONmY0Ve7glJWVhby8PAQFBQEAmjRpgsaNG/OuOSIiIgswONmYOThVwiBy5coVrF27FgqFAm+88QZcXV0BgKGJiIjIQgxONmZ8cKlOXol6nIQQ2L9/P3bv3g0ACAwMhHhQJxEREVmOwcnGTOYeJ4kLeaCwsBDr1q3DpUuXAACtWrVC3759oVTyS09ERGQtfnramPFBR05lGON0+/ZtrFy5EpmZmVAoFOjXrx9atmwpdVlERERVFoOTjRX3OMkrwbihw4cPIzMzE+7u7oiJiUFgYKDUJREREVVpDE42ZqxE0xFER0dDpVKhR48ecHJykrocIiKiKo9ratiYScLpCHJycrBnzx7zwO/ieZoYmoiIiGyDPU42ZpAoOCUnJ2PVqlXIzc2FRqNBhw4dKvT1iYiIagIGJxur6DFOQggcPXoUO3bsgMlkQq1atRAREVEhr01ERFTTMDjZ2P/GONn/tXQ6HTZv3ozffvsNABAZGYn+/ftDrVbb/8WJiIhqIAYnGzOaiv62d49TZmYmYmNjcefOHcjlcvTu3Rvt2rXjLOBERER2xOBkYxW1yG9eXh7S09Ph7OyMYcOGISwszK6vR0RERAxONldRa9WFhIRg6NChCA4ONq85R0RERPbF6QhszF49Tvn5+YiNjcXt27fN2xo1asTQREREVIEYnGzMHtMRpKam4ocffsDvv/+OdevWcYFeIiIiifBSnY3ZejqCU6dOYcuWLTAajfD09MSgQYM4AJyIiEgiDE42ZqvpCAwGA7Zu3YqTJ08CACIiIjBo0CA4ODg8bYlERERUTgxONmaywXQE+fn5WL58OW7dugUA6N69O7p27cqeJiIiIokxONmYLRb5dXBwgFqthoODA4YMGYJ69erZqjwiqoSEEDAYDDAajRYfo9froVQqUVhYaNVx9HTY7tJ42nZXKBRQKpU26YBgcLIx8xgnK4OTEAImkwkKhQJyuRxDhgyBTqeDp6enPcokokpCp9MhNTUV+fn5Vh0nhIC/vz9u3LjB3ugKxHaXhi3a3cnJCQEBAU+9ugaDk40V31WntCI4abVabNiwAc7OzujXrx8AwNnZGc7OznapkYgqB5PJhMTERCgUCgQGBkKtVlv8oWAymZCbmwsXFxfI5bxBuqKw3aXxNO0uhIBOp8Pdu3eRmJiI+vXrP9XXjsHJxorncbJ0jNPdu3cRGxuLjIwMKBQKtG/fHt7e3vYskYgqCZ1OB5PJhJCQEDg5OVl1rMlkgk6ng4ODAz/AKxDbXRpP2+6Ojo5QqVRISkoyn6e8GJxszGjFPE4JCQnYsGEDdDodXF1dERMTw9BEVAPxA5jI/mz1/4zByYaEEHiQm6B4TG4ymUzYuXMnDh06BAAIDw/H0KFDeWmOiIiokmNwsiHTQxN6P25w+OrVq3HhwgUAQMeOHfHss8/yN04iIqIqgJ/WNmR8KDk9bpHfFi1aQKPRYNiwYejduzdDExFRDZKRkYFatWrh+vXrUpdSbfz2228IDg5GXl6e3V+Ln9g2ZHpoDbmHe5yEEMjOzjY/joiIwKRJk9C4ceMKrY+IyFZee+01yGQyyGQyKJVKhIaG4q233sK9e/dK7Xvo0CFER0fD09MTDg4OaNq0Kb788ssy5+PZvXs3oqOj4e3tDScnJzRu3Bh/+tOfzBMCVwczZ85E//79ER4eXuq5qKgoKBQKHDlypNRz3bt3x+TJk0ttX79+fam7MXU6Hf71r3+hefPmcHJygo+PDzp16oTFixdDr9fb6q2UkpycjP79+8PZ2Rk+Pj6YOHEidDrdY4+5evUqBg0aBF9fX7i5uSEmJqbEgvYAMHDgQERGRpqnFBg5ciRSUlLMzzdt2hRt27bFV199ZZf39TAGJxsyPNTjVDwdgV6vx4YNGzBv3jxkZWWZn3d0dKzo8oiIbOq5555Damoqrl+/jgULFmDTpk2YMGFCiX3WrVuHbt26ITg4GLt378bvv/+OSZMm4dNPP8WLL75YYtHy77//Hr169YK/vz/WrFmDhIQEzJs3D/fv38eXX35ZYe/rSR/0T6OgoAALFy7E2LFjSz2XnJyMw4cP45133sHChQvL/Ro6nQ59+vTB559/jjfeeAOHDh3C0aNH8fbbb+M///kPzp8//zRv4ZGMRiP69euHvLw8HDhwAD///DPWrFmDP/3pT488Ji8vD1FRUZDJZNi1axcOHjwInU6H/v37w1S8FAeAHj16YPHixbhw4QLWrFmDq1evYujQoSXONXr0aMydO9f+E5MKiX333XciPDxcaDQa0apVK7Fv377H7r9nzx7RqlUrodFoRO3atcXcuXOter379+8LAOL+/ftPU3aZsvJ1IuzPm0XYnzeL3PxCkZmZKebNmyemT58uZsyYIc6cOWPz16QiOp1OrF+/Xuh0OqlLqXHY9uVXUFAgEhISREFBgXmbyWQSeVr9E//kFGhFyu10kVOgtWh/S/6YTCaLax81apQYOHBgiW1TpkwRXl5e5se5ubnC29tbDB48uNTxGzduFADEzz//LIQQ4saNG0KtVovJkyeX+Xr37t17ZC337t0T48aNE7Vq1RIajUY0adJEbNq0SQghxLRp00Tz5s1L7P/VV1+JsLCwUu/ls88+EwEBASIsLExMnTpVtGvXrtRrNW3aVHz44YfCaDQKIYRYtGiRaNiwodBoNKJBgwbiu+++e2SdQgixZs0a4ePjU+Zz06dPFy+++KK4cOGCcHV1Fbm5uSWe79atm5g0aVKp49atWyce/jj/5z//KeRyuTh58mSpfXU6Xanz2kpcXJyQy+Xi1q1b5m0rVqwQGo3mkZ+527dvF3K5vMTzmZmZAoCIj483bzMajeLevXvmdt+wYYOQyWQlfu5otVqh0WjEzp07y3ytsv6/FbMmG0g6ODw2NhaTJ0/GnDlz0KlTJ3z//ffo27cvEhISEBoaWmr/xMREREdHY9y4cfjvf/+LgwcPYsKECfD19cWQIUMkeAd/8NDg8OuJ17B500YUFhbCyckJQ4cORe3ataWrjYiqhAK9EY3/tl2S1074pA+c1OX7WLh27Rq2bdsGlUpl3rZjxw5kZGTg/fffL7V///79ERERgRUrVmD48OFYtWoVdDodPvzwwzLP7+HhUeZ2k8mEvn37IicnB//9739Rt25dJCQkQKFQWFX/zp074ebmhvj4eHMv2Oeff46rV6+ibt26AIDz58/jt99+w6JFiwAAP/zwA6ZNm4Zvv/0WLVu2xKlTpzBu3Dg4Oztj1KhRZb7Ovn370KZNm1LbhRBYvHgxvvvuOzRs2BARERFYuXIlRo8ebdX7AIBly5ahV69eaNmyZannVCpVia/Rw5KTk584hOSVV17BvHnzynzu8OHDiIyMRGBgoHlbnz59oNVqceLECfTo0aPUMVqtFjKZDBqNxryteK6mAwcOoFevXqWOyczMxLJly9CxY8cS70WtVqN58+bYv38/evbs+dj38TQkDU6zZs3CmDFjzF2Ws2fPxvbt2zF37lzMnDmz1P7z5s1DaGgoZs+eDQBo1KgRjh8/ji+++KJyBCcAgEBzZSpWrzoOAAgKCsKwYcPg7u4ucV1ERLa1efNmuLi4wGg0orCwEEDRz/Vily5dAlD0s7osDRs2NO9z+fJluLm5ISAgwKoafvnlFxw9ehQXLlxAREQEAKBOnTpWvxdnZ2csWLCgxHIczZo1w/Lly/F///d/AIoCyTPPPGNeP/Tvf/87vvzySwwePBgAULt2bSQkJOD7779/ZHC6fv16iWDx8PvIz89Hnz59ABQFlIULF5YrOF2+fBndu3e3+rjAwECcPn36sfu4ubk98rm0tDT4+fmV2Obp6Qm1Wo20tLQyj2nfvj2cnZ3x5z//GZ999hmEEPjzn/8Mk8mE1NTUEvtOmzYNCxYsQH5+Ptq3b4/NmzeXOl9QUJDdB91LFpx0Oh1OnDiBqVOnltgeFRVlnt/ojw4fPoyoqKgS2/r06YOFCxdCr9eXmaK1Wi20Wq35cfEgbb1eb/MBcnqDHo0Ud9BKVTRgrWXLlujduzeUSqVdB+MRzO3Ldq54bPvy0+v15nUqi8dzaBQynJve+4nHCiGQm5MLF1cXm62ZplHISowredLrd+/eHXPmzEF+fj4WLlyIS5cu4e233zafo7jnxmg0lnlek8kEmUxmfv/F/7bGqVOnEBwcjHr16pV5bHENDz/3x21CCERGRkKpVJbYb8SIEVi8eDE+/vhjCCGwYsUKTJw4EQBw584d3LhxA2PGjMG4cePMxxgMBri7uz/yfeTn5yMwMLDU8wsWLEBMTAzkcjlMJhOGDx+ODz74ABcuXECDBg1K1P7HY4sf/7HdrW1LuVxuUeh81Hkf9bpCiDLrBgBvb2/Exsbi7bffxjfffAO5XI4XX3wRrVq1MrdF8TkmTpyI8ePHIzk5GX//+98xcuRIbNq0qcT3v4ODA/Ly8h75/SaEgF6vL9Ujac3PL8mCU3p6OoxGY6l06ufn98hkWlaa9fPzg8FgQHp6epm/qcycORMzZswotX3Hjh1WL3HwJPkG4JLRF7WN99Aq3BNCCOzYscOmr0GPFx8fL3UJNRbb3npKpRL+/v7Izc0t14BkR7UCRm2BzerJKbR8X71eD41Gg1q1agEo6n3p378/Pv74Y3z88ccAgODgYADA8ePH0a5du1LnKA4F2dnZCA0Nxf3793Hp0iX4+/tbXEdx2Hr4zuU/1mkwGEo8n5OTU+KY4vfyx3M8//zz+Oijj7B//34UFBTgxo0b5vVEi/edPXt2qUtvCoXikfW4u7vjzp07JZ6/d+8eNmzYAL1eX+IymNFoxLx588yfYY6OjkhPTy917rS0NLi6upq3161bF+fOnXtkDY9y48YNdOjQ4bH7DBs27JF3rnl6euLw4cMlXjcrKwt6vb5EfX/Uvn17nDhxAhkZGVAqlXB3d0eDBg0wcODAEscUr6wREBCA77//HpGRkdi5cyfatm1r3ufOnTuoXbt2ma+l0+lQUFCAffv2wWAwlHjOmkW2JZ8A84+/KQkhHvvbU1n7l7W92EcffYQpU6aYH2dnZyMkJARRUVGP7XIsD73RBI+6t3HylAkvxvSC5ilXYCbL6fV6xMfHo3fv3o+8fk/2wbYvv8LCQty4cQMuLi5Wr50lhEBOTg5cXV1t1uNkDZVKBaVSWeLn6IwZM9CvXz9MmjQJgYGBGDhwILy8vPD999+jd++SvWgbN27E1atX8fe//x1ubm54+eWXMWPGDMybN6/E5b5iWVlZZY5zeuaZZ5CSkoK0tDTzpbqHBQUF4e7duyXa6ffff4dcLjfXXtZ7AYouS3Xt2hUbNmxAQUEBnn32WdStWxc5OTmoW7cugoKCkJaWhhYtWljcbm3btsWyZctKvNbSpUsRHByMtWvXlth3165d+Pzzz/Hvf/8bSqUSkZGR2LZtW6k6z507h4YNG5q3v/LKK/j4449x9erVUuOcDAYDtFptmStVNGjQACdPnnxs/W5ubo/87OzWrRu+/PJL5OXlmTsytm7dCo1Ggy5dujzxM7f4+V27duHu3bsYNmyYedsfv9/v378PAKW+bhcvXsTw4cPLfK3CwkI4Ojqia9eupf6/WRUynzh83E60Wq1QKBRi7dq1JbZPnDhRdO3atcxjunTpIiZOnFhi29q1a4VSqbT4jh573lUnBO8wkgrbXTps+/J73F0+T/LHu4wqWll31QkhROvWrcXbb79tfrxq1SqhUCjEuHHjxJkzZ0RiYqJYsGCB8PT0FEOHDi1xJ993330nZDKZeP3118WePXvE9evXxYEDB8Qbb7whpkyZ8shaunfvLiIjI8WOHTvEtWvXRFxcnNi6dasQQoiEhAQhk8nE559/Lq5cuSK+/fZb4enpWeZddWWZP3++CAwMFD4+PuKnn34q0e4//PCDcHR0FLNnzxYXL14UZ8+eFYsWLRJffvnlI2s9e/asUCqVIjMz07ytefPm4s9//nOpfbOzs4VGoxHr168XQgiRmJgoHB0dxYQJE8Tp06fFxYsXxbfffis0Go1YuXKl+bjCwkLRpUsX4enpKb799ltx+vRpcfXqVREbGytatWolTp069cj6nobBYBCRkZHi2WefFSdPnhS//PKLCA4OFu+88455n5s3b4oGDRqIX3/91bxt0aJF4vDhw+LKlSvip59+El5eXiW+3r/++qv45ptvxL59+8S1a9fErl27ROfOnUXdunVFYWGheb/ExEQhk8nE9evXy6zPVnfVSTodQdu2bcVbb71VYlujRo3E1KlTy9z/ww8/FI0aNSqxbfz48aJ9+/YWvyaDU/XEdpcO2778qmNwWrZsmVCr1SI5Odm8bd++feK5554T7u7uQq1Wi8aNG4svvvhCGAyGUsfHx8eLPn36CE9PT+Hg4CAaNmwo3n//fZGSkvLIWjIyMsTo0aOFt7e3cHBwEJGRkWLz5s3m5+fOnStCQkKEs7OzePXVV8Wnn35qcXC6d++e0Gg0wsnJSeTk5JRq92XLlokWLVoItVotPD09RdeuXUt1CPxR+/btxbx584QQQhw/flwAEEePHi1z3/79+4v+/fubHx8/flz06dNH1KpVS7i5uYk2bdqIFStWlDqusLBQzJw5UzRt2lQ4ODgILy8v0alTJ7FkyRKh1+sfW9/TSEpKEv369ROOjo7Cy8tLvPPOO6XCDQCxe/du87Y///nPws/PT6hUKlG/fn3x5ZdflgjUZ8+eFT169BCenp5Co9GI8PBwMX78eHHz5s0Sr/3ZZ5+JPn36PLK2ahGcfv75Z6FSqcTChQtFQkKCmDx5snB2djanxalTp4qRI0ea97927ZpwcnIS7733nkhISBALFy4UKpVKrF692uLXZHCqntju0mHbl19VDk41lS3afcuWLaJRo0b82lnhSe1eWFgoQkJCxIEDBx55jmoxj9Pw4cORkZGBTz75BKmpqYiMjERcXBzCwsIAAKmpqUhOTjbvX7t2bcTFxeG9997Dd999h8DAQHzzzTeVaCoCIiKix4uOjsbly5dx69YthISESF1OtZCUlISPP/4YnTp1svtrST44fMKECaWm6C+2ZMmSUtu6dev2xMFrREREldmkSZOkLqFaiYiIKPPmAHvgWnVEREREFmJwIiIiIrIQgxMRkcSEEE/eiYieiq3+nzE4ERFJpHjCUGtmLSai8in+f/a0E/VKPjiciKimUigU8PDwwJ07dwAATk5OFs8CbjKZoNPpUFhYCLmcvwNXFLa7NJ6m3YUQyM/Px507d+Dh4VFqnTprMTgREUmoeF224vBkKSEECgoK4OjoKMmSKzUV210atmh3Dw8Pq9ZBfBQGJyIiCclkMgQEBKBWrVpWrdCu1+uxb98+dO3alWsEViC2uzSett1VKtVT9zQVY3AiIqoEFAqFVT/YFQoFDAYDHBwc+AFegdju0qhM7c4LtEREREQWYnAiIiIishCDExEREZGFatwYp+IJsLKzs+1yfr1ej/z8fGRnZ0t+HbYmYbtLh20vDba7NNju0rB3uxdnAksmyaxxwSknJwcAuCI1ERERlZCTkwN3d/fH7iMTNWyuf5PJhJSUFLi6utplDo7s7GyEhITgxo0bcHNzs/n5qWxsd+mw7aXBdpcG210a9m53IQRycnIQGBj4xAk2a1yPk1wuR3BwsN1fx83Njf+pJMB2lw7bXhpsd2mw3aVhz3Z/Uk9TMQ4OJyIiIrIQgxMRERGRhRicbEyj0WDatGnQaDRSl1KjsN2lw7aXBttdGmx3aVSmdq9xg8OJiIiIyos9TkREREQWYnAiIiIishCDExEREZGFGJzKYc6cOahduzYcHBzQunVr7N+//7H77927F61bt4aDgwPq1KmDefPmVVCl1Ys17b527Vr07t0bvr6+cHNzQ4cOHbB9+/YKrLb6sPb7vdjBgwehVCrRokUL+xZYjVnb9lqtFh9//DHCwsKg0WhQt25dLFq0qIKqrT6sbfdly5ahefPmcHJyQkBAAEaPHo2MjIwKqrZ62LdvH/r374/AwEDIZDKsX7/+icdI9tkqyCo///yzUKlU4ocffhAJCQli0qRJwtnZWSQlJZW5/7Vr14STk5OYNGmSSEhIED/88INQqVRi9erVFVx51WZtu0+aNEn885//FEePHhWXLl0SH330kVCpVOLkyZMVXHnVZm27F8vKyhJ16tQRUVFRonnz5hVTbDVTnrYfMGCAaNeunYiPjxeJiYni119/FQcPHqzAqqs+a9t9//79Qi6Xi6+//lpcu3ZN7N+/XzRp0kS88MILFVx51RYXFyc+/vhjsWbNGgFArFu37rH7S/nZyuBkpbZt24rx48eX2NawYUMxderUMvf/8MMPRcOGDUtse/PNN0X79u3tVmN1ZG27l6Vx48ZixowZti6tWitvuw8fPlz89a9/FdOmTWNwKidr237r1q3C3d1dZGRkVER51Za17f7vf/9b1KlTp8S2b775RgQHB9utxurOkuAk5WcrL9VZQafT4cSJE4iKiiqxPSoqCocOHSrzmMOHD5fav0+fPjh+/Dj0er3daq1OytPuf2QymZCTkwMvLy97lFgtlbfdFy9ejKtXr2LatGn2LrHaKk/bb9y4EW3atMG//vUvBAUFISIiAu+//z4KCgoqouRqoTzt3rFjR9y8eRNxcXEQQuD27dtYvXo1+vXrVxEl11hSfrbWuLXqnkZ6ejqMRiP8/PxKbPfz80NaWlqZx6SlpZW5v8FgQHp6OgICAuxWb3VRnnb/oy+//BJ5eXmIiYmxR4nVUnna/fLly5g6dSr2798PpZI/XsqrPG1/7do1HDhwAA4ODli3bh3S09MxYcIEZGZmcpyThcrT7h07dsSyZcswfPhwFBYWwmAwYMCAAfjPf/5TESXXWFJ+trLHqRxkMlmJx0KIUtuetH9Z2+nxrG33YitWrMD06dMRGxuLWrVq2au8asvSdjcajRgxYgRmzJiBiIiIiiqvWrPme95kMkEmk2HZsmVo27YtoqOjMWvWLCxZsoS9Tlaypt0TEhIwceJE/O1vf8OJEyewbds2JCYmYvz48RVRao0m1WcrfyW0go+PDxQKRanfPO7cuVMq+Rbz9/cvc3+lUglvb2+71VqdlKfdi8XGxmLMmDFYtWoVevXqZc8yqx1r2z0nJwfHjx/HqVOn8M477wAo+jAXQkCpVGLHjh3o2bNnhdRe1ZXnez4gIABBQUElVnhv1KgRhBC4efMm6tevb9eaq4PytPvMmTPRqVMnfPDBBwCAZs2awdnZGV26dME//vEPXlWwEyk/W9njZAW1Wo3WrVsjPj6+xPb4+Hh07NixzGM6dOhQav8dO3agTZs2UKlUdqu1OilPuwNFPU2vvfYali9fzvEG5WBtu7u5ueG3337D6dOnzX/Gjx+PBg0a4PTp02jXrl1FlV7lled7vlOnTkhJSUFubq5526VLlyCXyxEcHGzXequL8rR7fn4+5PKSH6UKhQLA/3pAyPYk/Wy1+/Dzaqb4VtWFCxeKhIQEMXnyZOHs7CyuX78uhBBi6tSpYuTIkeb9i2+ZfO+990RCQoJYuHAhpyMoB2vbffny5UKpVIrvvvtOpKammv9kZWVJ9RaqJGvb/Y94V135Wdv2OTk5Ijg4WAwdOlScP39e7N27V9SvX1+MHTtWqrdQJVnb7osXLxZKpVLMmTNHXL16VRw4cEC0adNGtG3bVqq3UCXl5OSIU6dOiVOnTgkAYtasWeLUqVPmaSAq02crg1M5fPfddyIsLEyo1WrRqlUrsXfvXvNzo0aNEt26dSux/549e0TLli2FWq0W4eHhYu7cuRVccfVgTbt369ZNACj1Z9SoURVfeBVn7ff7wxicno61bX/hwgXRq1cv4ejoKIKDg8WUKVNEfn5+BVdd9Vnb7t98841o3LixcHR0FAEBAeLll18WN2/erOCqq7bdu3c/9md2ZfpslQnBvkQiIiIiS3CMExEREZGFGJyIiIiILMTgRERERGQhBiciIiIiCzE4EREREVmIwYmIiIjIQgxORERERBZicCIiIiKyEIMTEZXbkiVL4OHhIXUZ5RYeHo7Zs2c/dp/p06ejRYsWFVIPEVV+DE5ENdxrr70GmUxW6s+VK1ekLg1LliwpUVNAQABiYmKQmJhok/MfO3YMb7zxhvmxTCbD+vXrS+zz/vvvY+fOnTZ5vUf54/v08/ND//79cf78eavPU5WDLFFVwOBERHjuueeQmppa4k/t2rWlLgsA4ObmhtTUVKSkpGD58uU4ffo0BgwYAKPR+NTn9vX1hZOT02P3cXFxgbe391O/1pM8/D63bNmCvLw89OvXDzqdzu6vTUSWY3AiImg0Gvj7+5f4o1AoMGvWLDRt2hTOzs4ICQnBhAkTkJub+8jznDlzBj169ICrqyvc3NzQunVrHD9+3Pz8oUOH0LVrVzg6OiIkJAQTJ05EXl7eY2uTyWTw9/dHQEAAevTogWnTpuHcuXPmHrG5c+eibt26UKvVaNCgAX766acSx0+fPh2hoaHQaDQIDAzExIkTzc89fKkuPDwcADBo0CDIZDLz44cv1W3fvh0ODg7Iysoq8RoTJ05Et27dbPY+27Rpg/feew9JSUm4ePGieZ/HfT327NmD0aNH4/79++aeq+nTpwMAdDodPvzwQwQFBcHZ2Rnt2rXDnj17HlsPEZWNwYmIHkkul+Obb77BuXPn8OOPP2LXrl348MMPH7n/yy+/jODgYBw7dgwnTpzA1KlToVKpAAC//fYb+vTpg8GDB+Ps2bOIjY3FgQMH8M4771hVk6OjIwBAr9dj3bp1mDRpEv70pz/h3LlzePPNNzF69Gjs3r0bALB69Wp89dVX+P7773H58mWsX78eTZs2LfO8x44dAwAsXrwYqamp5scP69WrFzw8PLBmzRrzNqPRiJUrV+Lll1+22fvMysrC8uXLAcDcfsDjvx4dO3bE7NmzzT1XqampeP/99wEAo0ePxsGDB/Hzzz/j7NmzGDZsGJ577jlcvnzZ4pqI6AFBRDXaqFGjhEKhEM7OzuY/Q4cOLXPflStXCm9vb/PjxYsXC3d3d/NjV1dXsWTJkjKPHTlypHjjjTdKbNu/f7+Qy+WioKCgzGP+eP4bN26I9u3bi+DgYKHVakXHjh3FuHHjShwzbNgwER0dLYQQ4ssvvxQRERFCp9OVef6wsDDx1VdfmR8DEOvWrSuxz7Rp00Tz5s3NjydOnCh69uxpfrx9+3ahVqtFZmbmU71PAMLZ2Vk4OTkJAAKAGDBgQJn7F3vS10MIIa5cuSJkMpm4detWie3PPvus+Oijjx57fiIqTSltbCOiyqBHjx6YO3eu+bGzszMAYPfu3fjss8+QkJCA7OxsGAwGFBYWIi8vz7zPw6ZMmYKxY8fip59+Qq9evTBs2DDUrVsXAHDixAlcuXIFy5YtM+8vhIDJZEJiYiIaNWpUZm3379+Hi4sLhBDIz89Hq1atsHbtWqjValy4cKHE4G4A6NSpE77++msAwLBhwzB79mzUqVMHzz33HKKjo9G/f38oleX/0ffyyy+jQ4cOSElJQWBgIJYtW4bo6Gh4eno+1ft0dXXFyZMnYTAYsHfvXvz73//GvHnzSuxj7dcDAE6ePAkhBCIiIkps12q1FTJ2i6i6YXAiIjg7O6NevXoltiUlJSE6Ohrjx4/H3//+d3h5eeHAgQMYM2YM9Hp9meeZPn06RowYgS1btmDr1q2YNm0afv75ZwwaNAgmkwlvvvlmiTFGxUJDQx9ZW3GgkMvl8PPzKxUQZDJZicdCCPO2kJAQXLx4EfHx8fjll18wYcIE/Pvf/8bevXtLXAKzRtu2bVG3bl38/PPPeOutt7Bu3TosXrzY/Hx536dcLjd/DRo2bIi0tDQMHz4c+/btA1C+r0dxPQqFAidOnIBCoSjxnIuLi1XvnYgYnIjoEY4fPw6DwYAvv/wScnnRcMiVK1c+8biIiAhERETgvffew0svvYTFixdj0KBBaNWqFc6fP18qoD3Jw4Hijxo1aoQDBw7g1VdfNW87dOhQiV4dR0dHDBgwAAMGDMDbb7+Nhg0b4rfffkOrVq1KnU+lUll0t96IESOwbNkyBAcHQy6Xo1+/fubnyvs+/+i9997DrFmzsG7dOgwaNMiir4darS5Vf8uWLWE0GnHnzh106dLlqWoiIg4OJ6JHqFu3LgwGA/7zn//g2rVr+Omnn0pdOnpYQUEB3nnnHezZswdJSUk4ePAgjh07Zg4xf/7zn3H48GG8/fbbOH36NC5fvoyNGzfi3XffLXeNH3zwAZYsWYJ58+bh8uXLmDVrFtauXWseFL1kyRIsXLgQ586dM78HR0dHhIWFlXm+8PBw7Ny5E2lpabh3794jX/fll1/GyZMn8emnn2Lo0KFwcHAwP2er9+nm5oaxY8di2rRpEEJY9PUIDw9Hbm4udu7cifT0dOTn5yMiIgIvv/wyXn31VaxduxaJiYk4duwY/vnPfyIuLs6qmogIHBxOVNONGjVKDBw4sMznZs2aJQICAoSjo6Po06ePWLp0qQAg7t27J4QoORhZq9WKF198UYSEhAi1Wi0CAwPFO++8U2JA9NGjR0Xv3r2Fi4uLcHZ2Fs2aNROffvrpI2sra7DzH82ZM0fUqVNHqFQqERERIZYuXWp+bt26daJdu3bCzc1NODs7i/bt24tffvnF/PwfB4dv3LhR1KtXTyiVShEWFiaEKD04vNgzzzwjAIhdu3aVes5W7zMpKUkolUoRGxsrhHjy10MIIcaPHy+8vb0FADFt2jQhhBA6nU787W9/E+Hh4UKlUgl/f38xaNAgcfbs2UfWRERlkwkhhLTRjYiIiKhq4KU6IiIiIgsxOBERERFZiMGJiIiIyEIMTkREREQWYnAiIiIishCDExEREZGFGJyIiIiILMTgRERERGQhBiciIiIiCzE4EREREVmIwYmIiIjIQgxORERERBb6fwaDZnkC9M4NAAAAAElFTkSuQmCC"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 6
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -653,106 +389,10 @@
     "plot_roc_curve(val_targets, val_preds_proba, title=\"ROC Curve - Validation\")"
    ],
    "metadata": {
-    "id": "C-9aZn4d45No",
-    "ExecuteTime": {
-     "end_time": "2025-08-03T15:23:44.315896Z",
-     "start_time": "2025-08-03T15:23:43.947836Z"
-    }
+    "id": "C-9aZn4d45No"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[LightGBM] [Warning] Unknown parameter: missing\n",
-      "[LightGBM] [Warning] Unknown parameter: missing\n",
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000277 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842\n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Warning] Unknown parameter: missing\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561\n",
-      "[LightGBM] [Info] Start training from score -1.364561\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Unknown parameter: missing\n",
-      "[LightGBM] [Warning] Unknown parameter: missing\n",
-      "Train AUROC: 0.9391\n",
-      "Validation AUROC: 0.9362\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x500 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x500 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 7
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -862,286 +502,10 @@
     "plot_roc_curve(val_targets, val_preds_proba, title=\"ROC Curve - Validation\")"
    ],
    "metadata": {
-    "id": "cfMQKA4D47Rq",
-    "ExecuteTime": {
-     "end_time": "2025-08-03T15:23:45.868344Z",
-     "start_time": "2025-08-03T15:23:44.362283Z"
-    }
+    "id": "cfMQKA4D47Rq"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000301 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842                      \n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561\n",
-      "[LightGBM] [Info] Start training from score -1.364561 \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "Training until validation scores don't improve for 10 rounds\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "Did not meet early stopping. Best iteration is:       \n",
-      "[73]\tvalid_0's auc: 0.936623\tvalid_0's binary_logloss: 0.246681\n",
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558             \n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000317 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842                                                 \n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561  \n",
-      "[LightGBM] [Info] Start training from score -1.364561                            \n",
-      "Training until validation scores don't improve for 10 rounds                     \n",
-      "Early stopping, best iteration is:                                               \n",
-      "[12]\tvalid_0's auc: 0.935786\tvalid_0's binary_logloss: 0.266039\n",
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558             \n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000298 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842                                                 \n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561  \n",
-      "[LightGBM] [Info] Start training from score -1.364561                            \n",
-      "Training until validation scores don't improve for 10 rounds                     \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "Early stopping, best iteration is:                                               \n",
-      "[43]\tvalid_0's auc: 0.935611\tvalid_0's binary_logloss: 0.251649\n",
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558             \n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000277 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842                                                 \n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561  \n",
-      "[LightGBM] [Info] Start training from score -1.364561                            \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "Training until validation scores don't improve for 10 rounds                     \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "Early stopping, best iteration is:                                               \n",
-      "[20]\tvalid_0's auc: 0.93539\tvalid_0's binary_logloss: 0.248763\n",
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558             \n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000291 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842                                                 \n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561  \n",
-      "[LightGBM] [Info] Start training from score -1.364561                            \n",
-      "Training until validation scores don't improve for 10 rounds                     \n",
-      "Early stopping, best iteration is:                                               \n",
-      "[9]\tvalid_0's auc: 0.934635\tvalid_0's binary_logloss: 0.278613\n",
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558             \n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000290 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842                                                 \n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561  \n",
-      "[LightGBM] [Info] Start training from score -1.364561                            \n",
-      "Training until validation scores don't improve for 10 rounds                     \n",
-      "Early stopping, best iteration is:                                               \n",
-      "[48]\tvalid_0's auc: 0.934689\tvalid_0's binary_logloss: 0.313308\n",
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558             \n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000297 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842                                                 \n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561  \n",
-      "[LightGBM] [Info] Start training from score -1.364561                            \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "Training until validation scores don't improve for 10 rounds                     \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "Early stopping, best iteration is:                                               \n",
-      "[5]\tvalid_0's auc: 0.934731\tvalid_0's binary_logloss: 0.308404\n",
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558             \n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000260 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842                                                 \n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561  \n",
-      "[LightGBM] [Info] Start training from score -1.364561                            \n",
-      "Training until validation scores don't improve for 10 rounds                     \n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf       \n",
-      "Early stopping, best iteration is:                                               \n",
-      "[26]\tvalid_0's auc: 0.935555\tvalid_0's binary_logloss: 0.261675\n",
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558             \n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000287 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842                                                 \n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561  \n",
-      "[LightGBM] [Info] Start training from score -1.364561                            \n",
-      "Training until validation scores don't improve for 10 rounds                     \n",
-      "Early stopping, best iteration is:                                               \n",
-      "[15]\tvalid_0's auc: 0.936678\tvalid_0's binary_logloss: 0.25662\n",
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558             \n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000296 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842                                                 \n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561  \n",
-      "[LightGBM] [Info] Start training from score -1.364561                            \n",
-      "Training until validation scores don't improve for 10 rounds                     \n",
-      "Early stopping, best iteration is:                                               \n",
-      "[9]\tvalid_0's auc: 0.932144\tvalid_0's binary_logloss: 0.278419\n",
-      "100%|██████████| 10/10 [00:01<00:00,  8.97trial/s, best loss: -0.9366777556759722]\n",
-      "Best hyperparameters: {'colsample_bytree': 0.7369806219407662, 'learning_rate': 0.18380774683882223, 'max_depth': 10, 'min_child_weight': 1, 'min_split_gain': 0.04856790120802634, 'n_estimators': 100, 'num_leaves': 21, 'reg_alpha': 0.2477642986940356, 'reg_lambda': 0.918704447001374, 'subsample': 0.7239571822061349}\n",
-      "[LightGBM] [Info] Number of positive: 2442, number of negative: 9558\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000282 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 842\n",
-      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203500 -> initscore=-1.364561\n",
-      "[LightGBM] [Info] Start training from score -1.364561\n",
-      "Train AUROC: 0.9726\n",
-      "Validation AUROC: 0.9320\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x500 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x500 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 8
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -1235,297 +599,364 @@
     "display(submission_lgb_proba.head())"
    ],
    "metadata": {
-    "id": "COIjJH9f5SSp",
+    "id": "COIjJH9f5SSp"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-03T15:28:18.342674Z",
-     "start_time": "2025-08-03T15:28:17.421611Z"
+     "end_time": "2025-08-10T09:55:17.474547Z",
+     "start_time": "2025-08-10T08:20:46.815356Z"
     }
    },
+   "cell_type": "code",
+   "source": [
+    "from hyperopt import fmin, tpe, hp, Trials, STATUS_OK\n",
+    "from catboost import CatBoostClassifier, Pool\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "raw_df = pd.read_csv('../assets/hw_2_2/train.csv')\n",
+    "\n",
+    "drop_cols = ['id', 'CustomerId', 'Exited']\n",
+    "input_cols = [col for col in raw_df.columns if col not in drop_cols]\n",
+    "target_col = 'Exited'\n",
+    "\n",
+    "train_df, val_df = split_train_val(raw_df, target_col=target_col)\n",
+    "\n",
+    "train_inputs, train_targets = separate_inputs_targets(train_df, input_cols, target_col)\n",
+    "val_inputs, val_targets = separate_inputs_targets(val_df, input_cols, target_col)\n",
+    "\n",
+    "X = raw_df[input_cols].copy()\n",
+    "y = raw_df[target_col].copy()\n",
+    "\n",
+    "cat_cols = train_inputs.select_dtypes(include=[\"object\", \"category\"]).columns.tolist()\n",
+    "\n",
+    "train_pool = Pool(data=train_inputs, label=train_targets, cat_features=cat_cols)\n",
+    "val_pool   = Pool(data=val_inputs,   label=val_targets,   cat_features=cat_cols)\n",
+    "\n",
+    "def objective(params):\n",
+    "    params_casted = {\n",
+    "        \"iterations\": 9000,\n",
+    "        \"learning_rate\": float(params[\"learning_rate\"]),\n",
+    "        \"depth\": int(params[\"depth\"]),\n",
+    "        \"l2_leaf_reg\": float(params[\"l2_leaf_reg\"]),\n",
+    "        \"rsm\": float(params[\"rsm\"]),\n",
+    "        \"bagging_temperature\": float(params[\"bagging_temperature\"]),\n",
+    "        \"random_strength\": float(params[\"random_strength\"]),\n",
+    "        \"min_data_in_leaf\": int(params[\"min_data_in_leaf\"]),\n",
+    "        \"loss_function\": \"Logloss\",\n",
+    "        \"eval_metric\": \"AUC\",\n",
+    "        \"bootstrap_type\": \"Bayesian\",\n",
+    "        \"od_type\": \"Iter\",\n",
+    "        \"od_wait\": int(params[\"od_wait\"]),\n",
+    "        \"random_seed\": 3,\n",
+    "        \"use_best_model\": True,\n",
+    "        \"task_type\": \"CPU\",\n",
+    "        \"verbose\": False,\n",
+    "        \"allow_writing_files\": False\n",
+    "    }\n",
+    "\n",
+    "    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+    "    aucs = []\n",
+    "\n",
+    "    for tr_idx, va_idx in skf.split(X, y):\n",
+    "        X_tr, X_va = X.iloc[tr_idx], X.iloc[va_idx]\n",
+    "        y_tr, y_va = y.iloc[tr_idx], y.iloc[va_idx]\n",
+    "\n",
+    "        train_pool = Pool(X_tr, y_tr, cat_features=cat_cols)\n",
+    "        val_pool   = Pool(X_va, y_va, cat_features=cat_cols)\n",
+    "\n",
+    "        clf = CatBoostClassifier(**params_casted)\n",
+    "        clf.fit(train_pool, eval_set=val_pool)\n",
+    "\n",
+    "        preds_proba = clf.predict_proba(val_pool)[:, 1]\n",
+    "        aucs.append(roc_auc_score(y_va, preds_proba))\n",
+    "\n",
+    "    return {\"loss\": -float(np.mean(aucs)), \"status\": STATUS_OK}\n",
+    "\n",
+    "space = {\n",
+    "    \"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 0.12),\n",
+    "    \"depth\": hp.quniform(\"depth\", 4, 10, 1),\n",
+    "    \"l2_leaf_reg\": hp.loguniform(\"l2_leaf_reg\", np.log(1e-2), np.log(1e2)),\n",
+    "    \"rsm\": hp.uniform(\"rsm\", 0.5, 1.0),\n",
+    "    \"bagging_temperature\": hp.uniform(\"bagging_temperature\", 0.0, 5.0),\n",
+    "    \"random_strength\": hp.loguniform(\"random_strength\", np.log(1e-8), np.log(5.0)),\n",
+    "    \"min_data_in_leaf\": hp.quniform(\"min_data_in_leaf\", 1, 128, 1),\n",
+    "    \"od_wait\": hp.quniform(\"od_wait\", 300, 1200, 50),\n",
+    "}\n",
+    "\n",
+    "trials = Trials()\n",
+    "best_cb_params = fmin(\n",
+    "    fn=objective,\n",
+    "    space=space,\n",
+    "    algo=tpe.suggest,\n",
+    "    max_evals=50,\n",
+    "    trials=trials,\n",
+    "    rstate=np.random.default_rng(3)\n",
+    ")\n",
+    "\n",
+    "print(best_cb_params)\n"
+   ],
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[LightGBM] [Info] Number of positive: 3052, number of negative: 11948\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000467 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 844\n",
-      "[LightGBM] [Info] Number of data points in the train set: 15000, number of used features: 10\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203467 -> initscore=-1.364767\n",
-      "[LightGBM] [Info] Start training from score -1.364767\n"
+      "100%|██████████| 50/50 [1:34:30<00:00, 113.41s/trial, best loss: -0.9396079611341601]\n",
+      "{'bagging_temperature': 0.5213171872760074, 'depth': 4.0, 'l2_leaf_reg': 4.2861986065252395, 'learning_rate': 0.05652004079491241, 'min_data_in_leaf': 20.0, 'od_wait': 700.0, 'random_strength': 0.3192262765891149, 'rsm': 0.560533173632469}\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "      id  Exited\n",
-       "0  15000       0\n",
-       "1  15001       0\n",
-       "2  15002       0\n",
-       "3  15003       1\n",
-       "4  15004       0"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>Exited</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>15000</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>15001</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>15002</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>15003</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>15004</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
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-     "data": {
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-       "      id    Exited\n",
-       "0  15000  0.054826\n",
-       "1  15001  0.018405\n",
-       "2  15002  0.036041\n",
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-     "metadata": {},
-     "output_type": "display_data"
-    },
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-10T09:59:11.244349Z",
+     "start_time": "2025-08-10T09:55:29.601032Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "test_raw_df = pd.read_csv(\"../assets/hw_2_2/test.csv\")\n",
+    "submission_df = pd.read_csv(\"../assets/hw_2_2/sample_submission.csv\")\n",
+    "\n",
+    "input_cols_all = [col for col in raw_df.columns if col not in [\"id\", \"CustomerId\", \"Exited\"]]\n",
+    "X_all = raw_df[input_cols_all].copy()\n",
+    "y_all = raw_df[\"Exited\"].copy()\n",
+    "\n",
+    "cat_cols = X_all.select_dtypes(include=[\"object\", \"category\"]).columns.tolist()\n",
+    "for c in cat_cols:\n",
+    "    X_all[c] = X_all[c].astype(\"category\")\n",
+    "    test_raw_df[c] = test_raw_df[c].astype(\"category\")\n",
+    "\n",
+    "X_test_processed = test_raw_df[input_cols_all].copy()\n",
+    "\n",
+    "cb_params_final = {\n",
+    "    \"iterations\": 9000,\n",
+    "    \"learning_rate\": float(best_cb_params[\"learning_rate\"]),\n",
+    "    \"depth\": int(best_cb_params[\"depth\"]),\n",
+    "    \"l2_leaf_reg\": float(best_cb_params[\"l2_leaf_reg\"]),\n",
+    "    \"rsm\": float(best_cb_params[\"rsm\"]),\n",
+    "    \"bagging_temperature\": float(best_cb_params[\"bagging_temperature\"]),\n",
+    "    \"random_strength\": float(best_cb_params[\"random_strength\"]),\n",
+    "    \"min_data_in_leaf\": int(best_cb_params[\"min_data_in_leaf\"]),\n",
+    "    \"loss_function\": \"Logloss\",\n",
+    "    \"eval_metric\": \"AUC\",\n",
+    "    \"bootstrap_type\": \"Bayesian\",\n",
+    "    \"use_best_model\": False,\n",
+    "    \"random_seed\": 3,\n",
+    "    \"task_type\": \"CPU\",\n",
+    "    \"verbose\": False,\n",
+    "    \"allow_writing_files\": False\n",
+    "}\n",
+    "\n",
+    "cb_best = CatBoostClassifier(**cb_params_final)\n",
+    "\n",
+    "train_pool_all = Pool(X_all, y_all, cat_features=cat_cols)\n",
+    "cb_best.fit(train_pool_all)\n",
+    "\n",
+    "cb_preds_proba = cb_best.predict_proba(X_test_processed)[:, 1]\n",
+    "cb_preds = (cb_preds_proba >= 0.5).astype(int)\n",
+    "\n",
+    "submission_cb = submission_df.copy()\n",
+    "submission_cb_proba = submission_df.copy()\n",
+    "submission_cb[\"Exited\"] = cb_preds\n",
+    "submission_cb_proba[\"Exited\"] = cb_preds_proba\n",
+    "\n",
+    "submission_cb.to_csv(\"../assets/hw_2_4/submission_cb_hyperopt_cross_val.csv\", index=False)\n",
+    "submission_cb_proba.to_csv(\"../assets/hw_2_4/submission_cb_hyperopt_proba_cross_val.csv\", index=False)"
+   ],
+   "outputs": [],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-10T10:29:57.750765Z",
+     "start_time": "2025-08-10T10:29:52.742878Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# GBC: base vs +poly (degree=2). Comments in English.\n",
+    "import numpy as np, pandas as pd, time\n",
+    "from sklearn.model_selection import StratifiedKFold, cross_validate\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "\n",
+    "raw_df = pd.read_csv('../assets/hw_2_2/train.csv')\n",
+    "drop_cols = ['id','CustomerId','Exited']\n",
+    "y = raw_df['Exited'].copy()\n",
+    "X = raw_df[[c for c in raw_df.columns if c not in drop_cols]].copy()\n",
+    "\n",
+    "cat_cols = X.select_dtypes(include=['object','category']).columns.tolist()\n",
+    "num_cols = X.columns.difference(cat_cols).tolist()\n",
+    "\n",
+    "# split low/high-card; OHE only low-card\n",
+    "LOW_CARD_TH = 20\n",
+    "low_card = [c for c in cat_cols if X[c].nunique(dropna=False) <= LOW_CARD_TH]\n",
+    "# high-card example: Surname -> use TE later if нужно\n",
+    "if 'Surname' in low_card: low_card.remove('Surname')\n",
+    "\n",
+    "# engineered features (keeps pandas DataFrame)\n",
+    "class FeatureMaker(BaseEstimator, TransformerMixin):\n",
+    "    def fit(self, X, y=None): return self\n",
+    "    def transform(self, X):\n",
+    "        Z = X.copy()\n",
+    "        if 'Age' in Z:\n",
+    "            Z['Age_log'] = np.log(np.clip(Z['Age'].astype(float), 1e-9, None))\n",
+    "        if {'Tenure','Age'}.issubset(Z.columns):\n",
+    "            a = np.log(np.clip(Z['Age'].astype(float), 1e-9, None))\n",
+    "            Z['TenureAgeRatio'] = Z['Tenure'].astype(float) / a\n",
+    "        if {'HasCrCard','IsActiveMember'}.issubset(Z.columns):\n",
+    "            Z['ActiveCrCard'] = Z['HasCrCard'].astype(float) + 4*Z['IsActiveMember'].astype(float) + 1\n",
+    "        return Z\n",
+    "\n",
+    "# choose a SMALL poly set (don’t explode dims)\n",
+    "poly_cols = [c for c in ['CreditScore','Balance','EstimatedSalary','Tenure'] if c in X.columns]\n",
+    "\n",
+    "# common pieces\n",
+    "numeric_base = ['Age','Balance','CreditScore','EstimatedSalary','Tenure','NumOfProducts',\n",
+    "                'Age_log','TenureAgeRatio','ActiveCrCard']\n",
+    "numeric_base = [c for c in numeric_base if c in (set(num_cols)|{'Age_log','TenureAgeRatio','ActiveCrCard'})]\n",
+    "\n",
+    "num_tf_base = Pipeline([\n",
+    "    ('impute', SimpleImputer(strategy='median')),\n",
+    "])\n",
+    "\n",
+    "cat_tf_low = Pipeline([\n",
+    "    ('impute', SimpleImputer(strategy='most_frequent')),\n",
+    "    ('ohe', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n",
+    "])\n",
+    "\n",
+    "# BASE pipeline\n",
+    "preprocess_base = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('num', num_tf_base, numeric_base),\n",
+    "        ('cat_low', cat_tf_low, low_card),\n",
+    "    ],\n",
+    "    remainder='drop'\n",
+    ")\n",
+    "\n",
+    "pipe_base = Pipeline([\n",
+    "    ('feat', FeatureMaker()),\n",
+    "    ('prep', preprocess_base),\n",
+    "    ('clf', GradientBoostingClassifier(\n",
+    "        n_estimators=300, learning_rate=0.08, max_depth=3,\n",
+    "        subsample=0.8, min_samples_leaf=20, max_features='sqrt',\n",
+    "        random_state=42\n",
+    "    ))\n",
+    "])\n",
+    "\n",
+    "# +POLY(d2) on a small subset\n",
+    "num_without_poly = [c for c in numeric_base if c not in poly_cols]\n",
+    "preprocess_poly = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('num', num_tf_base, num_without_poly),\n",
+    "        ('poly', Pipeline([\n",
+    "            ('impute', SimpleImputer(strategy='median')),\n",
+    "            ('poly', PolynomialFeatures(degree=2, include_bias=False))\n",
+    "        ]), poly_cols),\n",
+    "        ('cat_low', cat_tf_low, low_card),\n",
+    "    ],\n",
+    "    remainder='drop'\n",
+    ")\n",
+    "\n",
+    "pipe_poly = Pipeline([\n",
+    "    ('feat', FeatureMaker()),\n",
+    "    ('prep', preprocess_poly),\n",
+    "    ('clf', GradientBoostingClassifier(\n",
+    "        n_estimators=300, learning_rate=0.08, max_depth=3,\n",
+    "        subsample=0.8, min_samples_leaf=20, max_features='sqrt',\n",
+    "        random_state=42\n",
+    "    ))\n",
+    "])\n",
+    "\n",
+    "# run quick CV\n",
+    "def eval_pipe(name, pipe):\n",
+    "    skf = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n",
+    "    t0 = time.time()\n",
+    "    cv = cross_validate(pipe, X, y, cv=skf, scoring='roc_auc', n_jobs=-1, return_train_score=False)\n",
+    "    return {\n",
+    "        'setup': name,\n",
+    "        'mean_auc': cv['test_score'].mean(),\n",
+    "        'std_auc': cv['test_score'].std(),\n",
+    "        'fit_time_sum': cv['fit_time'].sum(),\n",
+    "        'wall_s': time.time()-t0\n",
+    "    }\n",
+    "\n",
+    "res = [eval_pipe('BASE (log+ratios, OHE low-card)', pipe_base),\n",
+    "       eval_pipe('+POLY d2 on few nums', pipe_poly)]\n",
+    "print(pd.DataFrame(res).sort_values('mean_auc', ascending=False))\n"
+   ],
+   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "      id  Exited\n",
-       "0  15000     0.0\n",
-       "1  15001     0.0\n",
-       "2  15002     0.0\n",
-       "3  15003     1.0\n",
-       "4  15004     0.0"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>Exited</th>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>15001</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>15002</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>15003</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>15004</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                             setup  mean_auc   std_auc  fit_time_sum    wall_s\n",
+      "0  BASE (log+ratios, OHE low-card)  0.934829  0.006140      3.135735  2.429239\n",
+      "1             +POLY d2 on few nums  0.934098  0.005451      4.589873  2.547923\n"
+     ]
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-10T10:32:17.580380Z",
+     "start_time": "2025-08-10T10:32:02.495020Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Paired CV comparison of two pipelines (Wilcoxon)\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from scipy.stats import wilcoxon\n",
+    "import numpy as np\n",
+    "\n",
+    "def cv_scores(pipe, X, y, skf):\n",
+    "    scores = []\n",
+    "    for tr, va in skf.split(X, y):\n",
+    "        pipe.fit(X.iloc[tr], y.iloc[tr])\n",
+    "        p = pipe.predict_proba(X.iloc[va])[:,1]\n",
+    "        scores.append(roc_auc_score(y.iloc[va], p))\n",
+    "    return np.array(scores)\n",
+    "\n",
+    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+    "s_base = cv_scores(pipe_base, X, y, skf)\n",
+    "s_poly = cv_scores(pipe_poly, X, y, skf)\n",
+    "\n",
+    "print(\"BASE:\", s_base.mean(), s_base)\n",
+    "print(\"POLY:\", s_poly.mean(), s_poly)\n",
+    "print(\"Δ (base - poly):\", (s_base - s_poly).mean())\n",
+    "print(\"Wilcoxon p-value:\", wilcoxon(s_base, s_poly).pvalue)"
+   ],
+   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "      id    Exited\n",
-       "0  15000  0.118807\n",
-       "1  15001  0.014791\n",
-       "2  15002  0.044229\n",
-       "3  15003  0.645772\n",
-       "4  15004  0.031068"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
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-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>Exited</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>15000</td>\n",
-       "      <td>0.118807</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>15001</td>\n",
-       "      <td>0.014791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>15002</td>\n",
-       "      <td>0.044229</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>15003</td>\n",
-       "      <td>0.645772</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>15004</td>\n",
-       "      <td>0.031068</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BASE: 0.935675578939222 [0.92275293 0.93856163 0.9368681  0.93813229 0.94206295]\n",
+      "POLY: 0.9347167262481024 [0.92317374 0.938076   0.93379313 0.93674465 0.94179611]\n",
+      "Δ (base - poly): 0.0009588526911196382\n",
+      "Wilcoxon p-value: 0.1875\n"
+     ]
     }
    ],
-   "execution_count": 11
+   "execution_count": 17
   }
  ]
 }
diff --git a/assets/hw_2_3/submission_decision_tree_proba.csv b/assets/hw_2_3/submission_decision_tree_proba.csv
new file mode 100644
index 0000000..ec70dc1
--- /dev/null
+++ b/assets/hw_2_3/submission_decision_tree_proba.csv
@@ -0,0 +1,10001 @@
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diff --git a/assets/hw_2_3/submission_soft_params_tree_depth6_leaf30_proba.csv b/assets/hw_2_3/submission_soft_params_tree_depth6_leaf30_proba.csv
new file mode 100644
index 0000000..eaff705
--- /dev/null
+++ b/assets/hw_2_3/submission_soft_params_tree_depth6_leaf30_proba.csv
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diff --git a/assets/hw_2_3/submission_tree_full_train_depth8_leaf44_proba.csv b/assets/hw_2_3/submission_tree_full_train_depth8_leaf44_proba.csv
new file mode 100644
index 0000000..2b7c2ba
--- /dev/null
+++ b/assets/hw_2_3/submission_tree_full_train_depth8_leaf44_proba.csv
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diff --git a/assets/hw_2_4/submission_cb_hyperopt.csv b/assets/hw_2_4/submission_cb_hyperopt.csv
new file mode 100644
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--- /dev/null
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diff --git a/assets/hw_2_4/submission_cb_hyperopt_cross_val.csv b/assets/hw_2_4/submission_cb_hyperopt_cross_val.csv
new file mode 100644
index 0000000..abd0cce
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new file mode 100644
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--- /dev/null
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index 0000000..d535218
--- /dev/null
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diff --git a/assets/hw_2_4/submission_ensemble_oof_refit.csv b/assets/hw_2_4/submission_ensemble_oof_refit.csv
new file mode 100644
index 0000000..f04cbc0
--- /dev/null
+++ b/assets/hw_2_4/submission_ensemble_oof_refit.csv
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diff --git a/assets/hw_2_4/submission_ensemble_rank.csv b/assets/hw_2_4/submission_ensemble_rank.csv
new file mode 100644
index 0000000..046b5ff
--- /dev/null
+++ b/assets/hw_2_4/submission_ensemble_rank.csv
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diff --git a/bank_customer_churn_prediction/0_submission_ensemble.csv b/bank_customer_churn_prediction/0_submission_ensemble.csv
new file mode 100644
index 0000000..30f8d69
--- /dev/null
+++ b/bank_customer_churn_prediction/0_submission_ensemble.csv
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diff --git a/bank_customer_churn_prediction/1_submission_ensemble.csv b/bank_customer_churn_prediction/1_submission_ensemble.csv
new file mode 100644
index 0000000..e0f7aa7
--- /dev/null
+++ b/bank_customer_churn_prediction/1_submission_ensemble.csv
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diff --git a/bank_customer_churn_prediction/4_1_submission_cat.csv b/bank_customer_churn_prediction/4_1_submission_cat.csv
new file mode 100644
index 0000000..ac57205
--- /dev/null
+++ b/bank_customer_churn_prediction/4_1_submission_cat.csv
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diff --git a/bank_customer_churn_prediction/4_1_submission_xgb.csv b/bank_customer_churn_prediction/4_1_submission_xgb.csv
new file mode 100644
index 0000000..59d55e3
--- /dev/null
+++ b/bank_customer_churn_prediction/4_1_submission_xgb.csv
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diff --git a/bank_customer_churn_prediction/4_2_submission_cat.csv b/bank_customer_churn_prediction/4_2_submission_cat.csv
new file mode 100644
index 0000000..c4567f0
--- /dev/null
+++ b/bank_customer_churn_prediction/4_2_submission_cat.csv
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diff --git a/bank_customer_churn_prediction/4_submission_cat.csv b/bank_customer_churn_prediction/4_submission_cat.csv
new file mode 100644
index 0000000..1274512
--- /dev/null
+++ b/bank_customer_churn_prediction/4_submission_cat.csv
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diff --git a/bank_customer_churn_prediction/4_submission_lgbm.csv b/bank_customer_churn_prediction/4_submission_lgbm.csv
new file mode 100644
index 0000000..00f32ea
--- /dev/null
+++ b/bank_customer_churn_prediction/4_submission_lgbm.csv
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diff --git a/bank_customer_churn_prediction/4_submission_xgb.csv b/bank_customer_churn_prediction/4_submission_xgb.csv
new file mode 100644
index 0000000..0db5f3f
--- /dev/null
+++ b/bank_customer_churn_prediction/4_submission_xgb.csv
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diff --git a/bank_customer_churn_prediction/Bank Churn Prediction.ipynb b/bank_customer_churn_prediction/Bank Churn Prediction.ipynb
new file mode 100644
index 0000000..4ab2589
--- /dev/null
+++ b/bank_customer_churn_prediction/Bank Churn Prediction.ipynb	
@@ -0,0 +1,4735 @@
+{
+ "cells": [
+  {
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:43.322833Z",
+     "iopub.status.idle": "2024-01-31T04:04:43.341846Z",
+     "iopub.execute_input": "2024-01-31T04:04:43.323772Z",
+     "shell.execute_reply": "2024-01-31T04:04:43.340473Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.045614,
+     "end_time": "2024-01-31T04:04:43.344842",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:43.299228"
+    },
+    "cell_id": "0d784f82fd4b4b1aa33ce3ae8a35aa58",
+    "deepnote_cell_type": "code"
+   },
+   "cell_type": "code",
+   "source": [
+    "# import os\n",
+    "# for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+    "#     for filenaprme in filenames:\n",
+    "#         print(os.path.join(dirname, filename))"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:43.398740Z",
+     "iopub.status.idle": "2024-01-31T04:04:45.211969Z",
+     "iopub.execute_input": "2024-01-31T04:04:43.399107Z",
+     "shell.execute_reply": "2024-01-31T04:04:45.211182Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 1.840824,
+     "end_time": "2024-01-31T04:04:45.214345",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:43.373521"
+    },
+    "cell_id": "2422509b2b9a402bb4b8d6892cc74e02",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:29:00.395975Z",
+     "start_time": "2025-08-06T06:29:00.390971Z"
+    }
+   },
+   "source": "import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom prettytable import PrettyTable\n\n%matplotlib inline\n\nsns.set_theme(style=\"whitegrid\", palette=\"muted\", context=\"talk\", font_scale=1.2)\n\nplt.rcParams.update({\n    'figure.figsize': (10, 6),\n    'axes.titlesize': 18,\n    'axes.labelsize': 16,\n    'xtick.labelsize': 14,\n    'ytick.labelsize': 14,\n    'legend.fontsize': 12\n})\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")",
+   "block_group": "c5390187314949ae9e7fdd03f8e433f1",
+   "outputs_reference": "dbtable:cell_outputs/ffac4361-d127-43b2-be23-c29612702645",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 89
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:45.251991Z",
+     "iopub.status.idle": "2024-01-31T04:04:45.341515Z",
+     "iopub.execute_input": "2024-01-31T04:04:45.252896Z",
+     "shell.execute_reply": "2024-01-31T04:04:45.340500Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.11071,
+     "end_time": "2024-01-31T04:04:45.343634",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:45.232924"
+    },
+    "cell_id": "98df9bb42bbc443389597c38b569fcd3",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:29:02.294611Z",
+     "start_time": "2025-08-06T06:29:02.281601Z"
+    }
+   },
+   "source": [
+    "def reduce_mem_usage(df, verbose=True):\n",
+    "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
+    "    start_mem = df.memory_usage().sum() / 1024**2    \n",
+    "    for col in df.columns:\n",
+    "        col_type = df[col].dtypes\n",
+    "        if col_type in numerics:\n",
+    "            c_min = df[col].min()\n",
+    "            c_max = df[col].max()\n",
+    "            if str(col_type)[:3] == 'int':\n",
+    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
+    "                    df[col] = df[col].astype(np.int8)\n",
+    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
+    "                    df[col] = df[col].astype(np.int16)\n",
+    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
+    "                    df[col] = df[col].astype(np.int32)\n",
+    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
+    "                    df[col] = df[col].astype(np.int64)  \n",
+    "            else:\n",
+    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
+    "                    df[col] = df[col].astype(np.float16)\n",
+    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
+    "                    df[col] = df[col].astype(np.float32)\n",
+    "                else:\n",
+    "                    df[col] = df[col].astype(np.float64)    \n",
+    "    end_mem = df.memory_usage().sum() / 1024**2\n",
+    "    if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))\n",
+    "    return df\n",
+    "\n",
+    "## EDA\n",
+    "\n",
+    "def print_missing_table(train_copy, test_copy, target):\n",
+    "    table = PrettyTable()\n",
+    "    table.field_names = ['Feature', 'Data Type', 'Train Missing %', 'Test Missing %']\n",
+    "\n",
+    "    for column in train_copy.columns:\n",
+    "        data_type = str(train_copy[column].dtype)\n",
+    "        non_null_count_train = np.round(100 - train_copy[column].count() / train_copy.shape[0] * 100, 1)\n",
+    "        non_null_count_test = np.round(100 - test_copy[column].count() / test_copy.shape[0] * 100, 1) if column != target else \"NA\"\n",
+    "        table.add_row([column, data_type, non_null_count_train, non_null_count_test])\n",
+    "    \n",
+    "    print(table)\n",
+    "\n",
+    "\n",
+    "def plot_categorical(data, column_name):\n",
+    "    f, ax = plt.subplots(1, 2, figsize=(18, 8))\n",
+    "    data[column_name].value_counts().plot.pie(explode=[0, 0.1], autopct='%1.1f%%', ax=ax[0], shadow=True)\n",
+    "    ax[0].set_title(column_name)\n",
+    "    ax[0].set_ylabel('')\n",
+    "    sns.countplot(x=column_name, data=data, ax=ax[1])\n",
+    "    ax[1].set_title(column_name)\n",
+    "    plt.show()\n",
+    "    \n",
+    "def plot_correlation_heatmap(data, numerical_cols, target_col):\n",
+    "    df = data=data[numerical_cols + [target_col]]\n",
+    "    corr = df.corr()\n",
+    "    mask = np.triu(corr)\n",
+    "    plt.figure(figsize=(15, 11))\n",
+    "    sns.heatmap(corr, mask=mask, annot=True, fmt=\".4f\")\n",
+    "    plt.show()\n",
+    "    \n",
+    "    \n",
+    "def plot_pairplot(data, numerical_cols, target_col):\n",
+    "    pairplot = sns.pairplot(data=data[numerical_cols + [target_col]], \n",
+    "                            hue=target_col, \n",
+    "                            corner=True, \n",
+    "                            plot_kws={'alpha': 0.7, 's': 50, 'edgecolor': 'k'},\n",
+    "                            palette='Set1', \n",
+    "                            diag_kws={'edgecolor':'k'})\n",
+    "    pairplot.fig.suptitle(\"Pairplot of Numerical Variables\", y=1.02)\n",
+    "    plt.show()\n",
+    "    \n",
+    "def plot_violin_plots(data, numerical_cols, target):\n",
+    "    n_rows = len(numerical_cols)\n",
+    "    fig, axs = plt.subplots(n_rows, 1, figsize=(12, 4 * n_rows))\n",
+    "\n",
+    "    for i, col in enumerate(numerical_cols):\n",
+    "        sns.violinplot(x=target, y=col, data=data, ax=axs[i])\n",
+    "        axs[i].set_title(f'{col.title()} Distribution by Target (Train)', fontsize=14)\n",
+    "        axs[i].set_xlabel(target.title(), fontsize=12)\n",
+    "        axs[i].set_ylabel(col.title(), fontsize=12)\n",
+    "        sns.despine()\n",
+    "\n",
+    "    fig.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "def plot_histograms(data, continuous_vars, target_col):\n",
+    "    for column in continuous_vars:\n",
+    "        if data[column].dtype == 'float16':\n",
+    "            data[column] = data[column].astype('float32')\n",
+    "\n",
+    "        fig, ax = plt.subplots(figsize=(18, 4))\n",
+    "        sns.histplot(data=data, x=column, hue=target_col, bins=50, kde=True)\n",
+    "        plt.show()\n",
+    "\n",
+    "def plot_countplot(data, column_name):\n",
+    "    sns.countplot(x=column_name, data=data)\n",
+    "    plt.title(f'Count Plot of {column_name}')\n",
+    "    plt.show()"
+   ],
+   "block_group": "25656b65b5644a0aa19b707b46f52303",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 90
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.017418,
+     "end_time": "2024-01-31T04:04:45.378969",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:45.361551"
+    },
+    "cell_id": "80e12d43a28a468fbec00d57bc8d2fae",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "## Read data",
+   "block_group": "1b0179835a1947559892470a1b677cb2"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:45.414975Z",
+     "iopub.status.idle": "2024-01-31T04:04:46.356579Z",
+     "iopub.execute_input": "2024-01-31T04:04:45.415348Z",
+     "shell.execute_reply": "2024-01-31T04:04:46.355639Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.962401,
+     "end_time": "2024-01-31T04:04:46.358839",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:45.396438"
+    },
+    "allow_embed": true,
+    "cell_id": "6e35d70ab7334758b9c471702edd146f",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:29:06.672304Z",
+     "start_time": "2025-08-06T06:29:06.634433Z"
+    }
+   },
+   "source": [
+    "train = pd.read_csv(\"../assets/hw_2_2/train.csv\")\n",
+    "# original = pd.read_csv(\"/kaggle/input/bank-customer-churn-prediction/Churn_Modelling.csv\")\n",
+    "test = pd.read_csv(\"../assets/hw_2_2/test.csv\")\n",
+    "sample = pd.read_csv(\"../assets/hw_2_2/sample_submission.csv\")\n",
+    "\n",
+    "\n",
+    "train = train.drop(columns=[\"id\"])\n",
+    "test = test.drop(columns=[\"id\"])\n",
+    "# original.drop(columns=[\"RowNumber\"], inplace=True)\n",
+    "\n",
+    "# train = pd.concat([train, original.dropna()], axis=0)\n",
+    "# train.reset_index(inplace=True, drop=True)\n",
+    "\n",
+    "target_col = \"Exited\""
+   ],
+   "block_group": "9cc4b69a2e594f32ae9d6d68855aac7c",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 91
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:46.395993Z",
+     "iopub.status.idle": "2024-01-31T04:04:46.403055Z",
+     "iopub.execute_input": "2024-01-31T04:04:46.396368Z",
+     "shell.execute_reply": "2024-01-31T04:04:46.402160Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.027692,
+     "end_time": "2024-01-31T04:04:46.405018",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:46.377326"
+    },
+    "allow_embed": true,
+    "cell_id": "d313758d69b94981a2a75e77fd9c7e80",
+    "deepnote_cell_type": "code"
+   },
+   "source": "train.shape",
+   "block_group": "2ec26598ed2b4d52b140d407a76a0a2f",
+   "outputs_reference": "dbtable:cell_outputs/58e5437e-a66d-49a1-b118-988060dd967d",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:46.448218Z",
+     "iopub.status.idle": "2024-01-31T04:04:46.556541Z",
+     "iopub.execute_input": "2024-01-31T04:04:46.448685Z",
+     "shell.execute_reply": "2024-01-31T04:04:46.555371Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.135501,
+     "end_time": "2024-01-31T04:04:46.559512",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:46.424011"
+    },
+    "cell_id": "b0b6a3be2ab74edcb6806eda45d896a9",
+    "deepnote_cell_type": "code"
+   },
+   "source": "train.duplicated().sum()",
+   "block_group": "fd80b50010ed4d76a12f9bdc80c16b8d",
+   "outputs_reference": "dbtable:cell_outputs/070336cb-3296-4a47-bbb4-39c2c6aed292",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:46.607045Z",
+     "iopub.status.idle": "2024-01-31T04:04:46.732510Z",
+     "iopub.execute_input": "2024-01-31T04:04:46.608161Z",
+     "shell.execute_reply": "2024-01-31T04:04:46.731467Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.154971,
+     "end_time": "2024-01-31T04:04:46.735025",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:46.580054"
+    },
+    "cell_id": "e2bdf91588874578bb088f5c09faf703",
+    "deepnote_cell_type": "code"
+   },
+   "source": "train = train.drop_duplicates()",
+   "block_group": "f1f64a85c779444f881b560238833155",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:46.833314Z",
+     "iopub.status.idle": "2024-01-31T04:04:46.936266Z",
+     "iopub.execute_input": "2024-01-31T04:04:46.834006Z",
+     "shell.execute_reply": "2024-01-31T04:04:46.935170Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.184772,
+     "end_time": "2024-01-31T04:04:46.939793",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:46.755021"
+    },
+    "allow_embed": true,
+    "cell_id": "2c525aaae7a84cf986a0d1dc3952f588",
+    "deepnote_cell_type": "code"
+   },
+   "source": "print_missing_table(train, test, target_col)",
+   "block_group": "530e96db1a48429f841f686df6dbf1f5",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/ce048774-cb22-488f-838b-2232e77975ad",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:46.983093Z",
+     "iopub.status.idle": "2024-01-31T04:04:47.007398Z",
+     "iopub.execute_input": "2024-01-31T04:04:46.983529Z",
+     "shell.execute_reply": "2024-01-31T04:04:47.006003Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.048232,
+     "end_time": "2024-01-31T04:04:47.009606",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:46.961374"
+    },
+    "allow_embed": true,
+    "cell_id": "ed391f3fceab4246b1f7be529639479a",
+    "deepnote_cell_type": "code"
+   },
+   "source": "# train = reduce_mem_usage(train)",
+   "block_group": "5c39b3d28c7a4f6fa82c156427e1d5da",
+   "outputs_reference": "dbtable:cell_outputs/b21046f2-68c9-4995-ac48-e985c8580ffd",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:47.048553Z",
+     "iopub.status.idle": "2024-01-31T04:04:47.067457Z",
+     "iopub.execute_input": "2024-01-31T04:04:47.049489Z",
+     "shell.execute_reply": "2024-01-31T04:04:47.066422Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.040597,
+     "end_time": "2024-01-31T04:04:47.069562",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:47.028965"
+    },
+    "cell_id": "35fd88d81e4245cc943c0b534134f84f",
+    "deepnote_cell_type": "code"
+   },
+   "source": "# test = reduce_mem_usage(test)",
+   "block_group": "224e3a03820d4a85bb0adcf8a90cb0ee",
+   "outputs_reference": "dbtable:cell_outputs/2b0a73c2-bff2-4270-944f-98dab57a9888",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:47.107447Z",
+     "iopub.status.idle": "2024-01-31T04:04:47.127855Z",
+     "iopub.execute_input": "2024-01-31T04:04:47.108209Z",
+     "shell.execute_reply": "2024-01-31T04:04:47.126986Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.041202,
+     "end_time": "2024-01-31T04:04:47.129837",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:47.088635"
+    },
+    "allow_embed": true,
+    "cell_id": "233ce03d0e654ed795b603f325c7d460",
+    "deepnote_cell_type": "code"
+   },
+   "source": "train.head()",
+   "block_group": "8cffc24c9c1f4d479fd8ffefeee98948",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/3b95a1f0-dafe-4df0-b951-81361cf4ce68",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:47.168352Z",
+     "iopub.status.idle": "2024-01-31T04:04:47.235570Z",
+     "iopub.execute_input": "2024-01-31T04:04:47.168949Z",
+     "shell.execute_reply": "2024-01-31T04:04:47.234439Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.088971,
+     "end_time": "2024-01-31T04:04:47.237741",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:47.148770"
+    },
+    "cell_id": "34c53cf9443049b491755a58df823fa2",
+    "deepnote_cell_type": "code"
+   },
+   "source": "train.info()",
+   "block_group": "b15958e4afdf4619ab120fe18a9a1531",
+   "outputs_reference": "dbtable:cell_outputs/8591f9f4-497d-4da5-85a3-813c95b7703e",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.021038,
+     "end_time": "2024-01-31T04:04:47.468539",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:47.447501"
+    },
+    "cell_id": "b056dea1c6424595b7bb278b3e76500d",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "## EDA",
+   "block_group": "498e4c228d32411db2ebb86ae05a6984"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:47.507797Z",
+     "iopub.status.idle": "2024-01-31T04:04:47.585703Z",
+     "iopub.execute_input": "2024-01-31T04:04:47.508612Z",
+     "shell.execute_reply": "2024-01-31T04:04:47.584424Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.100392,
+     "end_time": "2024-01-31T04:04:47.588099",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:47.487707"
+    },
+    "allow_embed": true,
+    "cell_id": "8f1cb95ee560463abe50eae82923436b",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:29:15.782484Z",
+     "start_time": "2025-08-06T06:29:15.771813Z"
+    }
+   },
+   "source": "# Unique value counts for each column\nunique_counts = train.nunique()\n\n# Threshold to distinguish continuous and categorical\nthreshold = 12\n\n# Select only numeric columns for continuous variables\nnumeric_cols = train.select_dtypes(include=[np.number]).columns.tolist()\n\ncontinuous_vars = unique_counts[(unique_counts > threshold) & unique_counts.index.isin(numeric_cols)].index.tolist()\ncategorical_vars = unique_counts[(unique_counts <= threshold) | ~unique_counts.index.isin(numeric_cols)].index.tolist()\n\ntarget_col = 'Exited'\nid_col = ['id', 'CustomerId']\n\nif target_col in categorical_vars:\n    categorical_vars.remove(target_col)\n\nfor col in id_col:\n    if col in continuous_vars:\n        continuous_vars.remove(col)\n\nprint(f\"Categorical Variables: {categorical_vars}\")\nprint(f\"Continuous/Numerical Variables: {continuous_vars}\")",
+   "block_group": "00eddfd1f3b94b8c9d0ce1d4c02842a2",
+   "outputs_reference": "dbtable:cell_outputs/b665703f-60d3-4de7-9cdf-1bd6dc580110",
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Categorical Variables: ['Surname', 'Geography', 'Gender', 'Tenure', 'NumOfProducts', 'HasCrCard', 'IsActiveMember']\n",
+      "Continuous/Numerical Variables: ['CreditScore', 'Age', 'Balance', 'EstimatedSalary']\n"
+     ]
+    }
+   ],
+   "execution_count": 92
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:47.629086Z",
+     "iopub.status.idle": "2024-01-31T04:04:48.097901Z",
+     "iopub.execute_input": "2024-01-31T04:04:47.629460Z",
+     "shell.execute_reply": "2024-01-31T04:04:48.096799Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.491573,
+     "end_time": "2024-01-31T04:04:48.099967",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:47.608394"
+    },
+    "allow_embed": true,
+    "cell_id": "223be7e721b84dc59596e24ca4231af0",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:29:17.761220Z",
+     "start_time": "2025-08-06T06:29:17.599101Z"
+    }
+   },
+   "source": [
+    "# train['Exited'] = train['Exited'].astype(int)\n",
+    "plot_categorical(train, column_name='Exited')"
+   ],
+   "block_group": "bedb838d0c50486e8e51eb6b43e5744f",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/96f3c08e-c085-4af6-a86d-6325b090ff7e",
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1800x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 93
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:48.141961Z",
+     "iopub.status.idle": "2024-01-31T04:04:51.164848Z",
+     "iopub.execute_input": "2024-01-31T04:04:48.142776Z",
+     "shell.execute_reply": "2024-01-31T04:04:51.163890Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 3.050154,
+     "end_time": "2024-01-31T04:04:51.170441",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:48.120287"
+    },
+    "allow_embed": true,
+    "cell_id": "79f41a6db1674987846f7b16162bb8ad",
+    "deepnote_cell_type": "code"
+   },
+   "source": "plot_violin_plots(train, continuous_vars, target_col)",
+   "block_group": "44661de7fc8a493b8c153382673f5202",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/7e9c4c3f-fcf4-44fd-88af-995755230029",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.024437,
+     "end_time": "2024-01-31T04:04:51.220061",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:51.195624"
+    },
+    "cell_id": "35dd5d7a544947ad91010853293d471a",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "age seems to have a different distribution",
+   "block_group": "125e229bcd174e3a8af304a8c0bc5924"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:51.269460Z",
+     "iopub.status.idle": "2024-01-31T04:04:56.819922Z",
+     "iopub.execute_input": "2024-01-31T04:04:51.269820Z",
+     "shell.execute_reply": "2024-01-31T04:04:56.818683Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 5.578598,
+     "end_time": "2024-01-31T04:04:56.822519",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:51.243921"
+    },
+    "allow_embed": true,
+    "cell_id": "630b82274fd54f5da6fb3f5da142be94",
+    "deepnote_cell_type": "code"
+   },
+   "source": "plot_histograms(train, continuous_vars, target_col)",
+   "block_group": "075b632dd8664c6aa4822707f2aea1b5",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/f7f53239-68ad-47fb-8b0e-f1d861e873a4",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:56.879344Z",
+     "iopub.status.idle": "2024-01-31T04:04:57.355112Z",
+     "iopub.execute_input": "2024-01-31T04:04:56.880147Z",
+     "shell.execute_reply": "2024-01-31T04:04:57.354126Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.508674,
+     "end_time": "2024-01-31T04:04:57.359318",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:56.850644"
+    },
+    "allow_embed": true,
+    "cell_id": "d77cf890436d49169fbdfb63ac99d260",
+    "deepnote_cell_type": "code"
+   },
+   "source": "plot_correlation_heatmap(train, continuous_vars, target_col)",
+   "block_group": "cf79a13b2f5540c297e706d8bcc3e108",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/1a088b39-d3a0-438e-9bce-10fb445f9bd8",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:04:57.418097Z",
+     "iopub.status.idle": "2024-01-31T04:06:12.492569Z",
+     "iopub.execute_input": "2024-01-31T04:04:57.418481Z",
+     "shell.execute_reply": "2024-01-31T04:06:12.491581Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 75.154009,
+     "end_time": "2024-01-31T04:06:12.541910",
+     "exception": false,
+     "start_time": "2024-01-31T04:04:57.387901"
+    },
+    "allow_embed": true,
+    "cell_id": "2b4c64380d654179879cf941cb5c5292",
+    "deepnote_cell_type": "code"
+   },
+   "source": "plot_pairplot(train, continuous_vars, target_col)",
+   "block_group": "ad47d0a70bc444bcaca6a08649fa8ab1",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/faec401d-699f-403e-85c4-32b9d4e2b783",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.040515,
+     "end_time": "2024-01-31T04:06:12.624587",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:12.584072"
+    },
+    "cell_id": "9284c9e9c21c4d5ab9b8265d6d37e210",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "## Feature engineering\n\n\nsource: \n\n- [🚀Churn Prediction: Ensemble w CB | XGB | LGBM | Kaggle](https://www.kaggle.com/code/yorkyong/churn-prediction-ensemble-w-cb-xgb-lgbm/notebook)\n- [💰PS4E1 | Advanced Feature Engineering | Ensemble | Kaggle](https://www.kaggle.com/code/arunklenin/ps4e1-advanced-feature-engineering-ensemble#TFIDF-PCA-(Text-Transformation)\n- [Playground S4E1|🏦Bank Churn💸 Prediction|Ensemble | Kaggle](https://www.kaggle.com/code/ashishkumarak/playground-s4e1-bank-churn-prediction-ensemble?scriptVersionId=157911965#%F0%9F%92%BD-Importing-the-data)\n\nPipeline resources\n\n- [Understand and use a Pipeline | Kaggle](https://www.kaggle.com/code/lucabasa/understand-and-use-a-pipeline)\n- [Pipelines for Preprocessing: A tutorial | Kaggle](https://www.kaggle.com/code/nnjjpp/pipelines-for-preprocessing-a-tutorial)",
+   "block_group": "e1c39d074f4a461db9efe2a493f31771"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:12.706519Z",
+     "iopub.status.idle": "2024-01-31T04:06:12.712745Z",
+     "iopub.execute_input": "2024-01-31T04:06:12.706851Z",
+     "shell.execute_reply": "2024-01-31T04:06:12.711886Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.049639,
+     "end_time": "2024-01-31T04:06:12.714632",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:12.664993"
+    },
+    "cell_id": "890102aa08244e14a54f9af19cc29160",
+    "deepnote_cell_type": "code"
+   },
+   "source": "train.columns",
+   "block_group": "d1c13f08aa6747ec9a9f6472023c713c",
+   "outputs_reference": "dbtable:cell_outputs/c3731ee0-2b4f-45e9-95db-c7e5838a19f2",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.042372,
+     "end_time": "2024-01-31T04:06:12.797157",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:12.754785"
+    },
+    "cell_id": "3811d8e96a7b41d6bc7b73105b09dc10",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "### writing transformers\n\n",
+   "block_group": "09c2c1b4f38441269f2bdb4cd70b17e8"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:12.883033Z",
+     "iopub.status.idle": "2024-01-31T04:06:13.236715Z",
+     "iopub.execute_input": "2024-01-31T04:06:12.883423Z",
+     "shell.execute_reply": "2024-01-31T04:06:13.235940Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.399858,
+     "end_time": "2024-01-31T04:06:13.239149",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:12.839291"
+    },
+    "cell_id": "cdca0526b00c4aa3957e94e78e4cbb38",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:29:32.950687Z",
+     "start_time": "2025-08-06T06:29:32.936207Z"
+    }
+   },
+   "source": "from sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.decomposition import TruncatedSVD\n\n# surname\n\nclass TFIDFTransformer(BaseEstimator, TransformerMixin):\n    def __init__(self, column, max_features, n_components):\n        self.column = column\n        self.max_features = max_features\n        self.n_components = n_components\n        self.vectorizer = TfidfVectorizer(max_features = max_features)\n        self.svd = TruncatedSVD(n_components = n_components)\n        \n    def fit(self, X, y=None):\n        vectors = self.vectorizer.fit_transform(X[self.column])\n        self.svd.fit(vectors)\n        return self\n    \n    def transform(self, X):\n        vectors = self.vectorizer.transform(X[self.column])\n        svd_result = self.svd.transform(vectors)\n        \n        tfidf_df = pd.DataFrame(svd_result, columns = [f\"{self.column}_tfidf_{i}\" for i in range(self.n_components)])\n        X = pd.concat([X, tfidf_df], axis=\"columns\")\n        return X\n\n# bin age, estimated salary and credit score\n\nclass VariableBinning(BaseEstimator, TransformerMixin):\n    def __init__(self, n_bins, column_name):\n        self.n_bins = n_bins\n        self.column_name = column_name\n    \n    def fit(self, X, y=None):\n        return self\n    \n    def transform(self, X, y=None):\n        if self.column_name not in X:\n            raise ValueError(f\"Column '{self.column_name}' not found in input data\")\n        transformed = pd.qcut(X[self.column_name], self.n_bins, labels=False, duplicates='drop')\n        transformed[transformed.isna()] = 0\n        transformed_series = pd.Series(transformed,\n                                       name=f'QCut{self.n_bins}_{self.column_name}',\n                                       index=X.index)\n        X_copy = X.copy()\n        return pd.concat([X_copy, transformed_series], axis=1)\n    \n    \n# geography gender\n\nclass GeoGenderTransformer(BaseEstimator, TransformerMixin):\n    def fit(self, X, y=None):\n        return self\n    \n    def transform(self, X):\n        X_copy = X.copy()\n        X_copy['Geo_Gender'] = X_copy['Geography'] + '_' + X_copy['Gender']\n        return X_copy\n    \n\n# balance / salary\n\nclass BalanceSalaryRatioTransformer(BaseEstimator, TransformerMixin):\n    def fit(self, X, y=None):\n        return self\n    \n    def transform(self, X):\n        X_copy = X.copy()\n        X_copy['Balance_Salary_Ratio'] = X_copy['Balance'] / X_copy['EstimatedSalary']\n        return X_copy\n    \n# total products\n\nclass BalanceSalaryRatioTransformer(BaseEstimator, TransformerMixin):\n    def fit(self, X, y=None):\n        return self\n    \n    def transform(self, X):\n        X_copy = X.copy()\n        X_copy['Total_Products_Used'] = X_copy['NumOfProducts'] + X_copy['HasCrCard']\n        return X_copy\n    \n    \n# gender and total product interaction\n\nclass TpGenderTransformer(BaseEstimator, TransformerMixin):\n    def __init__(self, total_products_field='Total_Products_Used', gender_field='Gender'):\n        self.total_products_field = total_products_field\n        self.gender_field = gender_field\n    \n    def fit(self, X, y=None):\n        return self\n    \n    def transform(self, X):\n        X_copy = X.copy()\n        X_copy['Tp_Gender'] = X_copy[self.total_products_field].astype('str') + X_copy[self.gender_field]\n        return X_copy\n\n    \nclass IsSeniorTransformer(BaseEstimator, TransformerMixin):\n    def fit(self, X, y=None):\n        return self\n    \n    def transform(self, X):\n        X_copy = X.copy()\n        X_copy['IsSenior'] = X_copy['Age'].apply(lambda x: 1 if x >= 60 else 0)\n        return X_copy\n\n\nclass QualityOfBalanceTransformer(BaseEstimator, TransformerMixin):\n    def fit(self, X, y=None):\n        return self\n    \n    def transform(self, X):\n        X_copy = X.copy()\n        quality_labels = [0, 1, 2, 3, 4]\n        bins = [-1, 100, 1000, 10000, 50000, 1000000]\n        quality_series = pd.cut(X_copy['Balance'], bins=bins, labels=quality_labels)\n        X_copy['QualityOfBalance'] = quality_series\n        return X_copy\n\nclass CreditScoreTierTransformer(BaseEstimator, TransformerMixin):\n    def fit(self, X, y=None):\n        return self\n    \n    def transform(self, X):\n        X_copy = X.copy()\n        score_labels = [0, 1, 2]\n        bins = [0, 650, 750, 850]\n        score_series = pd.cut(X_copy['CreditScore'], bins=bins, labels=score_labels)\n        X_copy['CreditScoreTier'] = score_series\n        return X_copy\n    \nclass IsActiveByCreditCardTransformer(BaseEstimator, TransformerMixin):\n    def fit(self, X, y=None):\n        return self\n    \n    def transform(self, X):\n        X_copy = X.copy()\n        X_copy['IsActive_by_CreditCard'] = X_copy['HasCrCard'] * X_copy['IsActiveMember']\n        return X_copy\n    \n\nclass ProductsPerTenureTransformer(BaseEstimator, TransformerMixin):\n    def fit(self, X, y=None):\n        return self\n    \n    def transform(self, X):\n        X_copy = X.copy()\n        X_copy['Products_Per_Tenure'] = X_copy['Tenure'] / X_copy['NumOfProducts']\n        return X_copy\n\n\nclass CustomerStatusTransformer(BaseEstimator, TransformerMixin):\n    def fit(self, X, y=None):\n        return self\n    \n    def transform(self, X):\n        X_copy = X.copy()\n        X_copy['Customer_Status'] = X_copy['Tenure'].apply(lambda x: 0 if x < 2 else 1)\n        return X_copy",
+   "block_group": "437b83192c7042d39c66a57e4d69d36d",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 95
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:13.320336Z",
+     "iopub.status.idle": "2024-01-31T04:06:28.045162Z",
+     "iopub.execute_input": "2024-01-31T04:06:13.321035Z",
+     "shell.execute_reply": "2024-01-31T04:06:28.044192Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 14.767878,
+     "end_time": "2024-01-31T04:06:28.047386",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:13.279508"
+    },
+    "allow_embed": true,
+    "cell_id": "9f71f30df0ea4e93950f54b9aeedb63d",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:29:40.209828Z",
+     "start_time": "2025-08-06T06:29:39.705606Z"
+    }
+   },
+   "source": "from sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nfrom sklearn.cluster import KMeans\n\n\nclass KMeansClusterer(BaseEstimator, TransformerMixin):\n    def __init__(self, features, n_clusters=20, random_state=0, n_components=None):\n        self.features = features\n        self.n_clusters = n_clusters\n        self.random_state = random_state\n        self.n_components = n_components\n        self.kmeans = KMeans(n_clusters=n_clusters, n_init=50, random_state=random_state)\n        self.scaler = StandardScaler()\n        self.pca = PCA(n_components=n_components)\n        \n    def fit(self, X, y=None):\n        X_scaled = self.scaler.fit_transform(X.loc[:, self.features])\n        if self.n_components is not None:\n            X_scaled = self.pca.fit_transform(X_scaled)\n        self.kmeans.fit(X_scaled)\n        \n        return self\n    \n    def transform(self, X):\n        X_scaled = self.scaler.transform(X.loc[:, self.features])\n        \n        # check for NaN and replace with zero\n        if np.isnan(X_scaled).any():\n            X_scaled = np.nan_to_num(X_scaled)\n            \n        if self.n_components is not None:\n            X_scaled = self.pca.transform(X_scaled)\n            \n        X_new = pd.DataFrame()\n        X_new[\"Cluster\"] = self.kmeans.predict(X_scaled)\n        \n        X_copy = X.copy()\n        \n        # convert to dense format\n        X_new[\"Cluster\"] = X_new[\"Cluster\"].values\n        \n        return pd.concat([X_copy.reset_index(drop=True), X_new.reset_index(drop=True)], axis=1)\n    \nclusterer_with_pca = KMeansClusterer(features=[\"CustomerId\",\"EstimatedSalary\",\"Balance\"], n_clusters=10, random_state=123, n_components=3)\nclusterer_with_pca.fit_transform(train)",
+   "block_group": "11d622283e4e4049aa387f06aa7ab183",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/48ece69e-182d-4664-a5cc-34908f5ae3b3",
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       CustomerId   Surname  CreditScore Geography  Gender   Age  Tenure  \\\n",
+       "0      15779985.0   Nwankwo        678.0    France    Male  29.0     4.0   \n",
+       "1      15650086.0     Ch'in        687.0    France  Female  34.0     1.0   \n",
+       "2      15733602.0  Thompson        682.0    France  Female  52.0     6.0   \n",
+       "3      15645794.0   Macleod        753.0   Germany    Male  44.0     6.0   \n",
+       "4      15633840.0      Hsia        544.0   Germany  Female  55.0     0.0   \n",
+       "...           ...       ...          ...       ...     ...   ...     ...   \n",
+       "14995  15807989.0     Ch'iu        585.0    France    Male  33.0     3.0   \n",
+       "14996  15731781.0    Ch'ang        678.0    France    Male  34.0     6.0   \n",
+       "14997  15667093.0    Goliwe        678.0    France  Female  54.0     4.0   \n",
+       "14998  15732644.0   Fanucci        705.0     Spain  Female  40.0     7.0   \n",
+       "14999  15713890.0  Milanesi        648.0    France  Female  42.0     3.0   \n",
+       "\n",
+       "         Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
+       "0           0.00            3.0        1.0             0.0        180626.36   \n",
+       "1           0.00            2.0        0.0             1.0         63736.17   \n",
+       "2           0.00            3.0        0.0             0.0        179655.87   \n",
+       "3       83347.25            2.0        1.0             0.0        161407.48   \n",
+       "4      107747.57            1.0        1.0             0.0        176580.86   \n",
+       "...          ...            ...        ...             ...              ...   \n",
+       "14995       0.00            1.0        1.0             0.0         54191.38   \n",
+       "14996       0.00            2.0        1.0             1.0         53437.10   \n",
+       "14997       0.00            1.0        1.0             0.0        147720.29   \n",
+       "14998       0.00            2.0        1.0             0.0        149550.95   \n",
+       "14999       0.00            3.0        1.0             0.0        112442.63   \n",
+       "\n",
+       "       Exited  Cluster  \n",
+       "0         0.0        6  \n",
+       "1         0.0        4  \n",
+       "2         1.0        6  \n",
+       "3         0.0        8  \n",
+       "4         1.0        8  \n",
+       "...       ...      ...  \n",
+       "14995     0.0        1  \n",
+       "14996     0.0        1  \n",
+       "14997     1.0        0  \n",
+       "14998     0.0        6  \n",
+       "14999     1.0        7  \n",
+       "\n",
+       "[15000 rows x 14 columns]"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CustomerId</th>\n",
+       "      <th>Surname</th>\n",
+       "      <th>CreditScore</th>\n",
+       "      <th>Geography</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Tenure</th>\n",
+       "      <th>Balance</th>\n",
+       "      <th>NumOfProducts</th>\n",
+       "      <th>HasCrCard</th>\n",
+       "      <th>IsActiveMember</th>\n",
+       "      <th>EstimatedSalary</th>\n",
+       "      <th>Exited</th>\n",
+       "      <th>Cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15779985.0</td>\n",
+       "      <td>Nwankwo</td>\n",
+       "      <td>678.0</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>180626.36</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15650086.0</td>\n",
+       "      <td>Ch'in</td>\n",
+       "      <td>687.0</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>63736.17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15733602.0</td>\n",
+       "      <td>Thompson</td>\n",
+       "      <td>682.0</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>179655.87</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15645794.0</td>\n",
+       "      <td>Macleod</td>\n",
+       "      <td>753.0</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>83347.25</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>161407.48</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15633840.0</td>\n",
+       "      <td>Hsia</td>\n",
+       "      <td>544.0</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107747.57</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>176580.86</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14995</th>\n",
+       "      <td>15807989.0</td>\n",
+       "      <td>Ch'iu</td>\n",
+       "      <td>585.0</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>54191.38</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14996</th>\n",
+       "      <td>15731781.0</td>\n",
+       "      <td>Ch'ang</td>\n",
+       "      <td>678.0</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>53437.10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14997</th>\n",
+       "      <td>15667093.0</td>\n",
+       "      <td>Goliwe</td>\n",
+       "      <td>678.0</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>147720.29</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14998</th>\n",
+       "      <td>15732644.0</td>\n",
+       "      <td>Fanucci</td>\n",
+       "      <td>705.0</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>149550.95</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14999</th>\n",
+       "      <td>15713890.0</td>\n",
+       "      <td>Milanesi</td>\n",
+       "      <td>648.0</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>112442.63</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15000 rows × 14 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 96
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:28.130723Z",
+     "iopub.status.idle": "2024-01-31T04:06:28.136402Z",
+     "iopub.execute_input": "2024-01-31T04:06:28.131538Z",
+     "shell.execute_reply": "2024-01-31T04:06:28.135472Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.048885,
+     "end_time": "2024-01-31T04:06:28.138288",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:28.089403"
+    },
+    "allow_embed": true,
+    "cell_id": "847453e392a9412a916d6c28625d98f3",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:29:43.550726Z",
+     "start_time": "2025-08-06T06:29:43.546409Z"
+    }
+   },
+   "source": "class DropColumn(BaseEstimator, TransformerMixin):\n    def __init__(self, cols):\n        self.cols = cols\n\n    def fit(self, X, y=None):\n        return self\n\n    def transform(self, X):\n        return X.drop(self.cols, axis=1)",
+   "block_group": "f240bd022802457a91a9f23662233661",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 97
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.041957,
+     "end_time": "2024-01-31T04:06:28.221465",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:28.179508"
+    },
+    "cell_id": "328d881055be4b3ab752cbc69d82d760",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "## making the pipeline",
+   "block_group": "b7b9870879874d63b70f40572930d88e"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:28.303142Z",
+     "iopub.status.idle": "2024-01-31T04:06:28.381635Z",
+     "iopub.execute_input": "2024-01-31T04:06:28.303498Z",
+     "shell.execute_reply": "2024-01-31T04:06:28.380613Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.121891,
+     "end_time": "2024-01-31T04:06:28.383558",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:28.261667"
+    },
+    "allow_embed": true,
+    "cell_id": "189bf767514643a694daff1a2b8207a3",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:29:46.164178Z",
+     "start_time": "2025-08-06T06:29:46.124533Z"
+    }
+   },
+   "source": "from sklearn.pipeline import Pipeline\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder\n\n\npreprocessing_pipeline = Pipeline([\n    ('kmeans', KMeansClusterer(features=[\"CustomerId\", \"EstimatedSalary\", \"Balance\"], n_clusters=10, random_state=123, n_components=3)),\n    ('surname_tfid', TFIDFTransformer(column=\"Surname\", max_features=1000, n_components=5)),\n    ('age_binning', VariableBinning(n_bins=5, column_name=\"Age\")),\n    ('salary_binning', VariableBinning(n_bins=10, column_name=\"EstimatedSalary\")),\n    ('balance_salary_ratio', BalanceSalaryRatioTransformer()),\n    ('geo_gender', GeoGenderTransformer()),\n    ('total_products', BalanceSalaryRatioTransformer()),  # Note: Should be TotalProductsTransformer, but not defined above\n    ('tp_gender', TpGenderTransformer()),\n    ('is_senior', IsSeniorTransformer()),\n    ('quality_of_balance', QualityOfBalanceTransformer()),\n    ('credit_score_tier', CreditScoreTierTransformer()),\n    ('is_active_by_credit_card', IsActiveByCreditCardTransformer()),\n    ('products_per_tenure', ProductsPerTenureTransformer()),\n    ('customer_status', CustomerStatusTransformer()),\n    ('drop', DropColumn(cols=['CustomerId', 'Surname'])),\n    ('prep', ColumnTransformer([\n        ('encode', OneHotEncoder(handle_unknown='ignore', sparse_output=False), \n         ['Gender', 'Geography', 'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'Geo_Gender', 'Tp_Gender']),\n        ],\n        remainder='passthrough').set_output(transform='pandas')),\n])\n\npreprocessing_pipeline",
+   "block_group": "199e818c524d46ba9fc65a7842902711",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/16f8d6bc-12ff-4482-a0ff-0f0740f92b40",
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('kmeans',\n",
+       "                 KMeansClusterer(features=['CustomerId', 'EstimatedSalary',\n",
+       "                                           'Balance'],\n",
+       "                                 n_clusters=10, n_components=3,\n",
+       "                                 random_state=123)),\n",
+       "                ('surname_tfid',\n",
+       "                 TFIDFTransformer(column='Surname', max_features=1000,\n",
+       "                                  n_components=5)),\n",
+       "                ('age_binning', VariableBinning(column_name='Age', n_bins=5)),\n",
+       "                ('salary_binning',\n",
+       "                 VariableBinning(column_name='EstimatedSalary'...\n",
+       "                ('products_per_tenure', ProductsPerTenureTransformer()),\n",
+       "                ('customer_status', CustomerStatusTransformer()),\n",
+       "                ('drop', DropColumn(cols=['CustomerId', 'Surname'])),\n",
+       "                ('prep',\n",
+       "                 ColumnTransformer(remainder='passthrough',\n",
+       "                                   transformers=[('encode',\n",
+       "                                                  OneHotEncoder(handle_unknown='ignore',\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  ['Gender', 'Geography',\n",
+       "                                                   'NumOfProducts', 'HasCrCard',\n",
+       "                                                   'IsActiveMember',\n",
+       "                                                   'Geo_Gender',\n",
+       "                                                   'Tp_Gender'])]))])"
+      ],
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
+       "                 KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;,\n",
+       "                                           &#x27;Balance&#x27;],\n",
+       "                                 n_clusters=10, n_components=3,\n",
+       "                                 random_state=123)),\n",
+       "                (&#x27;surname_tfid&#x27;,\n",
+       "                 TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000,\n",
+       "                                  n_components=5)),\n",
+       "                (&#x27;age_binning&#x27;, VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)),\n",
+       "                (&#x27;salary_binning&#x27;,\n",
+       "                 VariableBinning(column_name=&#x27;EstimatedSalary&#x27;...\n",
+       "                (&#x27;products_per_tenure&#x27;, ProductsPerTenureTransformer()),\n",
+       "                (&#x27;customer_status&#x27;, CustomerStatusTransformer()),\n",
+       "                (&#x27;drop&#x27;, DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])),\n",
+       "                (&#x27;prep&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;encode&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [&#x27;Gender&#x27;, &#x27;Geography&#x27;,\n",
+       "                                                   &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;,\n",
+       "                                                   &#x27;IsActiveMember&#x27;,\n",
+       "                                                   &#x27;Geo_Gender&#x27;,\n",
+       "                                                   &#x27;Tp_Gender&#x27;])]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-114\" type=\"checkbox\" ><label for=\"sk-estimator-id-114\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
+       "                 KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;,\n",
+       "                                           &#x27;Balance&#x27;],\n",
+       "                                 n_clusters=10, n_components=3,\n",
+       "                                 random_state=123)),\n",
+       "                (&#x27;surname_tfid&#x27;,\n",
+       "                 TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000,\n",
+       "                                  n_components=5)),\n",
+       "                (&#x27;age_binning&#x27;, VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)),\n",
+       "                (&#x27;salary_binning&#x27;,\n",
+       "                 VariableBinning(column_name=&#x27;EstimatedSalary&#x27;...\n",
+       "                (&#x27;products_per_tenure&#x27;, ProductsPerTenureTransformer()),\n",
+       "                (&#x27;customer_status&#x27;, CustomerStatusTransformer()),\n",
+       "                (&#x27;drop&#x27;, DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])),\n",
+       "                (&#x27;prep&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;encode&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [&#x27;Gender&#x27;, &#x27;Geography&#x27;,\n",
+       "                                                   &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;,\n",
+       "                                                   &#x27;IsActiveMember&#x27;,\n",
+       "                                                   &#x27;Geo_Gender&#x27;,\n",
+       "                                                   &#x27;Tp_Gender&#x27;])]))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-115\" type=\"checkbox\" ><label for=\"sk-estimator-id-115\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>KMeansClusterer</div></div></label><div class=\"sk-toggleable__content \"><pre>KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;, &#x27;Balance&#x27;],\n",
+       "                n_clusters=10, n_components=3, random_state=123)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-116\" type=\"checkbox\" ><label for=\"sk-estimator-id-116\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>TFIDFTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000, n_components=5)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-117\" type=\"checkbox\" ><label for=\"sk-estimator-id-117\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>VariableBinning</div></div></label><div class=\"sk-toggleable__content \"><pre>VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-118\" type=\"checkbox\" ><label for=\"sk-estimator-id-118\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>VariableBinning</div></div></label><div class=\"sk-toggleable__content \"><pre>VariableBinning(column_name=&#x27;EstimatedSalary&#x27;, n_bins=10)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-119\" type=\"checkbox\" ><label for=\"sk-estimator-id-119\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>BalanceSalaryRatioTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>BalanceSalaryRatioTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-120\" type=\"checkbox\" ><label for=\"sk-estimator-id-120\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>GeoGenderTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>GeoGenderTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-121\" type=\"checkbox\" ><label for=\"sk-estimator-id-121\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>BalanceSalaryRatioTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>BalanceSalaryRatioTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-122\" type=\"checkbox\" ><label for=\"sk-estimator-id-122\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>TpGenderTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>TpGenderTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-123\" type=\"checkbox\" ><label for=\"sk-estimator-id-123\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>IsSeniorTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>IsSeniorTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-124\" type=\"checkbox\" ><label for=\"sk-estimator-id-124\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>QualityOfBalanceTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>QualityOfBalanceTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-125\" type=\"checkbox\" ><label for=\"sk-estimator-id-125\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CreditScoreTierTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>CreditScoreTierTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-126\" type=\"checkbox\" ><label for=\"sk-estimator-id-126\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>IsActiveByCreditCardTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>IsActiveByCreditCardTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-127\" type=\"checkbox\" ><label for=\"sk-estimator-id-127\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ProductsPerTenureTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>ProductsPerTenureTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-128\" type=\"checkbox\" ><label for=\"sk-estimator-id-128\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CustomerStatusTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>CustomerStatusTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-129\" type=\"checkbox\" ><label for=\"sk-estimator-id-129\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>DropColumn</div></div></label><div class=\"sk-toggleable__content \"><pre>DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])</pre></div> </div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-130\" type=\"checkbox\" ><label for=\"sk-estimator-id-130\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>prep: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for prep: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                  transformers=[(&#x27;encode&#x27;,\n",
+       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                               sparse_output=False),\n",
+       "                                 [&#x27;Gender&#x27;, &#x27;Geography&#x27;, &#x27;NumOfProducts&#x27;,\n",
+       "                                  &#x27;HasCrCard&#x27;, &#x27;IsActiveMember&#x27;, &#x27;Geo_Gender&#x27;,\n",
+       "                                  &#x27;Tp_Gender&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-131\" type=\"checkbox\" ><label for=\"sk-estimator-id-131\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>encode</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Gender&#x27;, &#x27;Geography&#x27;, &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;, &#x27;IsActiveMember&#x27;, &#x27;Geo_Gender&#x27;, &#x27;Tp_Gender&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-132\" type=\"checkbox\" ><label for=\"sk-estimator-id-132\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-133\" type=\"checkbox\" ><label for=\"sk-estimator-id-133\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content \"><pre></pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-134\" type=\"checkbox\" ><label for=\"sk-estimator-id-134\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>passthrough</div></div></label><div class=\"sk-toggleable__content \"><pre>passthrough</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 98
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:28.469971Z",
+     "iopub.status.idle": "2024-01-31T04:06:45.697362Z",
+     "iopub.execute_input": "2024-01-31T04:06:28.470776Z",
+     "shell.execute_reply": "2024-01-31T04:06:45.696170Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 17.273524,
+     "end_time": "2024-01-31T04:06:45.699702",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:28.426178"
+    },
+    "allow_embed": true,
+    "cell_id": "6dc4d8f188404d9489a8070e833e3de2",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:29:57.167797Z",
+     "start_time": "2025-08-06T06:29:56.460197Z"
+    }
+   },
+   "source": "df_train = preprocessing_pipeline.fit_transform(train.drop(['Exited'], axis=1))\ndf_train.info()",
+   "block_group": "17b358c4346644578a0f262fc0fdfe54",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/139da677-d9db-46cc-80fb-e61cc334ba90",
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 15000 entries, 0 to 14999\n",
+      "Data columns (total 49 columns):\n",
+      " #   Column                             Non-Null Count  Dtype   \n",
+      "---  ------                             --------------  -----   \n",
+      " 0   encode__Gender_Female              15000 non-null  float64 \n",
+      " 1   encode__Gender_Male                15000 non-null  float64 \n",
+      " 2   encode__Geography_France           15000 non-null  float64 \n",
+      " 3   encode__Geography_Germany          15000 non-null  float64 \n",
+      " 4   encode__Geography_Spain            15000 non-null  float64 \n",
+      " 5   encode__NumOfProducts_1.0          15000 non-null  float64 \n",
+      " 6   encode__NumOfProducts_2.0          15000 non-null  float64 \n",
+      " 7   encode__NumOfProducts_3.0          15000 non-null  float64 \n",
+      " 8   encode__NumOfProducts_4.0          15000 non-null  float64 \n",
+      " 9   encode__HasCrCard_0.0              15000 non-null  float64 \n",
+      " 10  encode__HasCrCard_1.0              15000 non-null  float64 \n",
+      " 11  encode__IsActiveMember_0.0         15000 non-null  float64 \n",
+      " 12  encode__IsActiveMember_1.0         15000 non-null  float64 \n",
+      " 13  encode__Geo_Gender_France_Female   15000 non-null  float64 \n",
+      " 14  encode__Geo_Gender_France_Male     15000 non-null  float64 \n",
+      " 15  encode__Geo_Gender_Germany_Female  15000 non-null  float64 \n",
+      " 16  encode__Geo_Gender_Germany_Male    15000 non-null  float64 \n",
+      " 17  encode__Geo_Gender_Spain_Female    15000 non-null  float64 \n",
+      " 18  encode__Geo_Gender_Spain_Male      15000 non-null  float64 \n",
+      " 19  encode__Tp_Gender_1.0Female        15000 non-null  float64 \n",
+      " 20  encode__Tp_Gender_1.0Male          15000 non-null  float64 \n",
+      " 21  encode__Tp_Gender_2.0Female        15000 non-null  float64 \n",
+      " 22  encode__Tp_Gender_2.0Male          15000 non-null  float64 \n",
+      " 23  encode__Tp_Gender_3.0Female        15000 non-null  float64 \n",
+      " 24  encode__Tp_Gender_3.0Male          15000 non-null  float64 \n",
+      " 25  encode__Tp_Gender_4.0Female        15000 non-null  float64 \n",
+      " 26  encode__Tp_Gender_4.0Male          15000 non-null  float64 \n",
+      " 27  encode__Tp_Gender_5.0Female        15000 non-null  float64 \n",
+      " 28  encode__Tp_Gender_5.0Male          15000 non-null  float64 \n",
+      " 29  remainder__CreditScore             15000 non-null  float64 \n",
+      " 30  remainder__Age                     15000 non-null  float64 \n",
+      " 31  remainder__Tenure                  15000 non-null  float64 \n",
+      " 32  remainder__Balance                 15000 non-null  float64 \n",
+      " 33  remainder__EstimatedSalary         15000 non-null  float64 \n",
+      " 34  remainder__Cluster                 15000 non-null  int32   \n",
+      " 35  remainder__Surname_tfidf_0         15000 non-null  float64 \n",
+      " 36  remainder__Surname_tfidf_1         15000 non-null  float64 \n",
+      " 37  remainder__Surname_tfidf_2         15000 non-null  float64 \n",
+      " 38  remainder__Surname_tfidf_3         15000 non-null  float64 \n",
+      " 39  remainder__Surname_tfidf_4         15000 non-null  float64 \n",
+      " 40  remainder__QCut5_Age               15000 non-null  int64   \n",
+      " 41  remainder__QCut10_EstimatedSalary  15000 non-null  int64   \n",
+      " 42  remainder__Total_Products_Used     15000 non-null  float64 \n",
+      " 43  remainder__IsSenior                15000 non-null  int64   \n",
+      " 44  remainder__QualityOfBalance        15000 non-null  category\n",
+      " 45  remainder__CreditScoreTier         15000 non-null  category\n",
+      " 46  remainder__IsActive_by_CreditCard  15000 non-null  float64 \n",
+      " 47  remainder__Products_Per_Tenure     15000 non-null  float64 \n",
+      " 48  remainder__Customer_Status         15000 non-null  int64   \n",
+      "dtypes: category(2), float64(42), int32(1), int64(4)\n",
+      "memory usage: 5.4 MB\n"
+     ]
+    }
+   ],
+   "execution_count": 99
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:45.791618Z",
+     "iopub.status.idle": "2024-01-31T04:06:47.067617Z",
+     "iopub.execute_input": "2024-01-31T04:06:45.791985Z",
+     "shell.execute_reply": "2024-01-31T04:06:47.066686Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 1.324638,
+     "end_time": "2024-01-31T04:06:47.070285",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:45.745647"
+    },
+    "cell_id": "57f448c5524a45bb978a2b06cdbbad3c",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:29:59.077300Z",
+     "start_time": "2025-08-06T06:29:58.978090Z"
+    }
+   },
+   "source": "df_test = pd.DataFrame(preprocessing_pipeline.transform(test))\ndf_test.info()",
+   "block_group": "e97b0f2dcd3d432ea15bdcf5a4665267",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/c5034e93-2d3c-44e0-9984-a285b63cc85d",
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10000 entries, 0 to 9999\n",
+      "Data columns (total 49 columns):\n",
+      " #   Column                             Non-Null Count  Dtype   \n",
+      "---  ------                             --------------  -----   \n",
+      " 0   encode__Gender_Female              10000 non-null  float64 \n",
+      " 1   encode__Gender_Male                10000 non-null  float64 \n",
+      " 2   encode__Geography_France           10000 non-null  float64 \n",
+      " 3   encode__Geography_Germany          10000 non-null  float64 \n",
+      " 4   encode__Geography_Spain            10000 non-null  float64 \n",
+      " 5   encode__NumOfProducts_1.0          10000 non-null  float64 \n",
+      " 6   encode__NumOfProducts_2.0          10000 non-null  float64 \n",
+      " 7   encode__NumOfProducts_3.0          10000 non-null  float64 \n",
+      " 8   encode__NumOfProducts_4.0          10000 non-null  float64 \n",
+      " 9   encode__HasCrCard_0.0              10000 non-null  float64 \n",
+      " 10  encode__HasCrCard_1.0              10000 non-null  float64 \n",
+      " 11  encode__IsActiveMember_0.0         10000 non-null  float64 \n",
+      " 12  encode__IsActiveMember_1.0         10000 non-null  float64 \n",
+      " 13  encode__Geo_Gender_France_Female   10000 non-null  float64 \n",
+      " 14  encode__Geo_Gender_France_Male     10000 non-null  float64 \n",
+      " 15  encode__Geo_Gender_Germany_Female  10000 non-null  float64 \n",
+      " 16  encode__Geo_Gender_Germany_Male    10000 non-null  float64 \n",
+      " 17  encode__Geo_Gender_Spain_Female    10000 non-null  float64 \n",
+      " 18  encode__Geo_Gender_Spain_Male      10000 non-null  float64 \n",
+      " 19  encode__Tp_Gender_1.0Female        10000 non-null  float64 \n",
+      " 20  encode__Tp_Gender_1.0Male          10000 non-null  float64 \n",
+      " 21  encode__Tp_Gender_2.0Female        10000 non-null  float64 \n",
+      " 22  encode__Tp_Gender_2.0Male          10000 non-null  float64 \n",
+      " 23  encode__Tp_Gender_3.0Female        10000 non-null  float64 \n",
+      " 24  encode__Tp_Gender_3.0Male          10000 non-null  float64 \n",
+      " 25  encode__Tp_Gender_4.0Female        10000 non-null  float64 \n",
+      " 26  encode__Tp_Gender_4.0Male          10000 non-null  float64 \n",
+      " 27  encode__Tp_Gender_5.0Female        10000 non-null  float64 \n",
+      " 28  encode__Tp_Gender_5.0Male          10000 non-null  float64 \n",
+      " 29  remainder__CreditScore             10000 non-null  float64 \n",
+      " 30  remainder__Age                     10000 non-null  float64 \n",
+      " 31  remainder__Tenure                  10000 non-null  float64 \n",
+      " 32  remainder__Balance                 10000 non-null  float64 \n",
+      " 33  remainder__EstimatedSalary         10000 non-null  float64 \n",
+      " 34  remainder__Cluster                 10000 non-null  int32   \n",
+      " 35  remainder__Surname_tfidf_0         10000 non-null  float64 \n",
+      " 36  remainder__Surname_tfidf_1         10000 non-null  float64 \n",
+      " 37  remainder__Surname_tfidf_2         10000 non-null  float64 \n",
+      " 38  remainder__Surname_tfidf_3         10000 non-null  float64 \n",
+      " 39  remainder__Surname_tfidf_4         10000 non-null  float64 \n",
+      " 40  remainder__QCut5_Age               10000 non-null  int64   \n",
+      " 41  remainder__QCut10_EstimatedSalary  10000 non-null  int64   \n",
+      " 42  remainder__Total_Products_Used     10000 non-null  float64 \n",
+      " 43  remainder__IsSenior                10000 non-null  int64   \n",
+      " 44  remainder__QualityOfBalance        10000 non-null  category\n",
+      " 45  remainder__CreditScoreTier         10000 non-null  category\n",
+      " 46  remainder__IsActive_by_CreditCard  10000 non-null  float64 \n",
+      " 47  remainder__Products_Per_Tenure     10000 non-null  float64 \n",
+      " 48  remainder__Customer_Status         10000 non-null  int64   \n",
+      "dtypes: category(2), float64(42), int32(1), int64(4)\n",
+      "memory usage: 3.6 MB\n"
+     ]
+    }
+   ],
+   "execution_count": 100
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:47.156378Z",
+     "iopub.status.idle": "2024-01-31T04:06:47.227071Z",
+     "iopub.execute_input": "2024-01-31T04:06:47.156723Z",
+     "shell.execute_reply": "2024-01-31T04:06:47.226169Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.115152,
+     "end_time": "2024-01-31T04:06:47.229226",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:47.114074"
+    },
+    "cell_id": "1010eeb21fe647598c21cf9b7fad47b6",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:30:02.768226Z",
+     "start_time": "2025-08-06T06:30:02.730242Z"
+    }
+   },
+   "source": "from sklearn.preprocessing import RobustScaler\n\nclass RobustScalerNamed(RobustScaler, TransformerMixin):\n    def get_feature_names_out(self, X, y=None):\n        return X.columns.tolist()\n\n    def transform(self, X, y=None):\n        transformed = super().transform(X, y)\n        return pd.DataFrame(transformed, columns=X.columns)\n    \nmodelling_pipeline = Pipeline(preprocessing_pipeline.steps + [('scale',RobustScaler().set_output(transform='pandas')),])\nmodelling_pipeline",
+   "block_group": "a2184e81c1444824a91340582126e6c9",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/23fbc2c3-edb5-49a7-944c-9bb90aa36959",
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('kmeans',\n",
+       "                 KMeansClusterer(features=['CustomerId', 'EstimatedSalary',\n",
+       "                                           'Balance'],\n",
+       "                                 n_clusters=10, n_components=3,\n",
+       "                                 random_state=123)),\n",
+       "                ('surname_tfid',\n",
+       "                 TFIDFTransformer(column='Surname', max_features=1000,\n",
+       "                                  n_components=5)),\n",
+       "                ('age_binning', VariableBinning(column_name='Age', n_bins=5)),\n",
+       "                ('salary_binning',\n",
+       "                 VariableBinning(column_name='EstimatedSalary'...\n",
+       "                ('customer_status', CustomerStatusTransformer()),\n",
+       "                ('drop', DropColumn(cols=['CustomerId', 'Surname'])),\n",
+       "                ('prep',\n",
+       "                 ColumnTransformer(remainder='passthrough',\n",
+       "                                   transformers=[('encode',\n",
+       "                                                  OneHotEncoder(handle_unknown='ignore',\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  ['Gender', 'Geography',\n",
+       "                                                   'NumOfProducts', 'HasCrCard',\n",
+       "                                                   'IsActiveMember',\n",
+       "                                                   'Geo_Gender',\n",
+       "                                                   'Tp_Gender'])])),\n",
+       "                ('scale', RobustScaler())])"
+      ],
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
+       "                 KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;,\n",
+       "                                           &#x27;Balance&#x27;],\n",
+       "                                 n_clusters=10, n_components=3,\n",
+       "                                 random_state=123)),\n",
+       "                (&#x27;surname_tfid&#x27;,\n",
+       "                 TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000,\n",
+       "                                  n_components=5)),\n",
+       "                (&#x27;age_binning&#x27;, VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)),\n",
+       "                (&#x27;salary_binning&#x27;,\n",
+       "                 VariableBinning(column_name=&#x27;EstimatedSalary&#x27;...\n",
+       "                (&#x27;customer_status&#x27;, CustomerStatusTransformer()),\n",
+       "                (&#x27;drop&#x27;, DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])),\n",
+       "                (&#x27;prep&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;encode&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [&#x27;Gender&#x27;, &#x27;Geography&#x27;,\n",
+       "                                                   &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;,\n",
+       "                                                   &#x27;IsActiveMember&#x27;,\n",
+       "                                                   &#x27;Geo_Gender&#x27;,\n",
+       "                                                   &#x27;Tp_Gender&#x27;])])),\n",
+       "                (&#x27;scale&#x27;, RobustScaler())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-135\" type=\"checkbox\" ><label for=\"sk-estimator-id-135\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
+       "                 KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;,\n",
+       "                                           &#x27;Balance&#x27;],\n",
+       "                                 n_clusters=10, n_components=3,\n",
+       "                                 random_state=123)),\n",
+       "                (&#x27;surname_tfid&#x27;,\n",
+       "                 TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000,\n",
+       "                                  n_components=5)),\n",
+       "                (&#x27;age_binning&#x27;, VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)),\n",
+       "                (&#x27;salary_binning&#x27;,\n",
+       "                 VariableBinning(column_name=&#x27;EstimatedSalary&#x27;...\n",
+       "                (&#x27;customer_status&#x27;, CustomerStatusTransformer()),\n",
+       "                (&#x27;drop&#x27;, DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])),\n",
+       "                (&#x27;prep&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;encode&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [&#x27;Gender&#x27;, &#x27;Geography&#x27;,\n",
+       "                                                   &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;,\n",
+       "                                                   &#x27;IsActiveMember&#x27;,\n",
+       "                                                   &#x27;Geo_Gender&#x27;,\n",
+       "                                                   &#x27;Tp_Gender&#x27;])])),\n",
+       "                (&#x27;scale&#x27;, RobustScaler())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-136\" type=\"checkbox\" ><label for=\"sk-estimator-id-136\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>KMeansClusterer</div></div></label><div class=\"sk-toggleable__content \"><pre>KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;, &#x27;Balance&#x27;],\n",
+       "                n_clusters=10, n_components=3, random_state=123)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-137\" type=\"checkbox\" ><label for=\"sk-estimator-id-137\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>TFIDFTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000, n_components=5)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-138\" type=\"checkbox\" ><label for=\"sk-estimator-id-138\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>VariableBinning</div></div></label><div class=\"sk-toggleable__content \"><pre>VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-139\" type=\"checkbox\" ><label for=\"sk-estimator-id-139\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>VariableBinning</div></div></label><div class=\"sk-toggleable__content \"><pre>VariableBinning(column_name=&#x27;EstimatedSalary&#x27;, n_bins=10)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-140\" type=\"checkbox\" ><label for=\"sk-estimator-id-140\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>BalanceSalaryRatioTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>BalanceSalaryRatioTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-141\" type=\"checkbox\" ><label for=\"sk-estimator-id-141\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>GeoGenderTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>GeoGenderTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-142\" type=\"checkbox\" ><label for=\"sk-estimator-id-142\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>BalanceSalaryRatioTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>BalanceSalaryRatioTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-143\" type=\"checkbox\" ><label for=\"sk-estimator-id-143\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>TpGenderTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>TpGenderTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-144\" type=\"checkbox\" ><label for=\"sk-estimator-id-144\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>IsSeniorTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>IsSeniorTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-145\" type=\"checkbox\" ><label for=\"sk-estimator-id-145\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>QualityOfBalanceTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>QualityOfBalanceTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-146\" type=\"checkbox\" ><label for=\"sk-estimator-id-146\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CreditScoreTierTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>CreditScoreTierTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-147\" type=\"checkbox\" ><label for=\"sk-estimator-id-147\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>IsActiveByCreditCardTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>IsActiveByCreditCardTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-148\" type=\"checkbox\" ><label for=\"sk-estimator-id-148\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ProductsPerTenureTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>ProductsPerTenureTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-149\" type=\"checkbox\" ><label for=\"sk-estimator-id-149\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CustomerStatusTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>CustomerStatusTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-150\" type=\"checkbox\" ><label for=\"sk-estimator-id-150\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>DropColumn</div></div></label><div class=\"sk-toggleable__content \"><pre>DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])</pre></div> </div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-151\" type=\"checkbox\" ><label for=\"sk-estimator-id-151\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>prep: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for prep: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                  transformers=[(&#x27;encode&#x27;,\n",
+       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                               sparse_output=False),\n",
+       "                                 [&#x27;Gender&#x27;, &#x27;Geography&#x27;, &#x27;NumOfProducts&#x27;,\n",
+       "                                  &#x27;HasCrCard&#x27;, &#x27;IsActiveMember&#x27;, &#x27;Geo_Gender&#x27;,\n",
+       "                                  &#x27;Tp_Gender&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-152\" type=\"checkbox\" ><label for=\"sk-estimator-id-152\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>encode</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Gender&#x27;, &#x27;Geography&#x27;, &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;, &#x27;IsActiveMember&#x27;, &#x27;Geo_Gender&#x27;, &#x27;Tp_Gender&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-153\" type=\"checkbox\" ><label for=\"sk-estimator-id-153\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-154\" type=\"checkbox\" ><label for=\"sk-estimator-id-154\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;CreditScore&#x27;, &#x27;Age&#x27;, &#x27;Tenure&#x27;, &#x27;Balance&#x27;, &#x27;EstimatedSalary&#x27;, &#x27;Cluster&#x27;, &#x27;Surname_tfidf_0&#x27;, &#x27;Surname_tfidf_1&#x27;, &#x27;Surname_tfidf_2&#x27;, &#x27;Surname_tfidf_3&#x27;, &#x27;Surname_tfidf_4&#x27;, &#x27;QCut5_Age&#x27;, &#x27;QCut10_EstimatedSalary&#x27;, &#x27;Total_Products_Used&#x27;, &#x27;IsSenior&#x27;, &#x27;QualityOfBalance&#x27;, &#x27;CreditScoreTier&#x27;, &#x27;IsActive_by_CreditCard&#x27;, &#x27;Products_Per_Tenure&#x27;, &#x27;Customer_Status&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-155\" type=\"checkbox\" ><label for=\"sk-estimator-id-155\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>passthrough</div></div></label><div class=\"sk-toggleable__content \"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-156\" type=\"checkbox\" ><label for=\"sk-estimator-id-156\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>RobustScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.RobustScaler.html\">?<span>Documentation for RobustScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>RobustScaler()</pre></div> </div></div></div></div></div></div>"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 101
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.040807,
+     "end_time": "2024-01-31T04:06:47.312400",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:47.271593"
+    },
+    "cell_id": "7335250402434da2b028847342a5b169",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "## Modeling",
+   "block_group": "43d08906c6264cf39b3611e2a75d4f90"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:47.396095Z",
+     "iopub.status.idle": "2024-01-31T04:06:48.404953Z",
+     "iopub.execute_input": "2024-01-31T04:06:47.396465Z",
+     "shell.execute_reply": "2024-01-31T04:06:48.404088Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 1.05307,
+     "end_time": "2024-01-31T04:06:48.407311",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:47.354241"
+    },
+    "cell_id": "e24780e7f284412482797a98f8cf960d",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:30:06.347328Z",
+     "start_time": "2025-08-06T06:30:06.343821Z"
+    }
+   },
+   "source": "from sklearn.pipeline import make_pipeline\nfrom sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import  confusion_matrix\nfrom sklearn.metrics import precision_score, f1_score\nfrom sklearn.metrics import log_loss\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import cross_val_score, StratifiedKFold\n\nimport optuna",
+   "block_group": "338d726922084537b6f2a594dec2c36e",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 102
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:48.495607Z",
+     "iopub.status.idle": "2024-01-31T04:06:48.512721Z",
+     "iopub.execute_input": "2024-01-31T04:06:48.496266Z",
+     "shell.execute_reply": "2024-01-31T04:06:48.511737Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.063893,
+     "end_time": "2024-01-31T04:06:48.514756",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:48.450863"
+    },
+    "cell_id": "dcc5073906e84061971c002f3365b6bb",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:30:08.389439Z",
+     "start_time": "2025-08-06T06:30:08.385575Z"
+    }
+   },
+   "source": "X = train.drop(['Exited'], axis=1)\ny = train['Exited']",
+   "block_group": "a0ab1d2d8bd54a8780f8819c4df5acb3",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 103
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.040872,
+     "end_time": "2024-01-31T04:06:48.598781",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:48.557909"
+    },
+    "cell_id": "00f87414d02445a7a5bec0407ae7fb03",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "### XGBoost",
+   "block_group": "093e81b12abb4c4699c9cbd52c7201a0"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:48.681002Z",
+     "iopub.status.idle": "2024-01-31T04:06:48.838789Z",
+     "iopub.execute_input": "2024-01-31T04:06:48.681401Z",
+     "shell.execute_reply": "2024-01-31T04:06:48.837827Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.201898,
+     "end_time": "2024-01-31T04:06:48.841263",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:48.639365"
+    },
+    "cell_id": "a079f7132467441789ca2845c6b29209",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T06:30:10.298932Z",
+     "start_time": "2025-08-06T06:30:10.296348Z"
+    }
+   },
+   "source": "import xgboost as xgb\nfrom xgboost import XGBClassifier",
+   "block_group": "8c624300cbdd4396b28ba65032325c1f",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 104
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:48.930107Z",
+     "iopub.status.idle": "2024-01-31T04:06:48.937059Z",
+     "iopub.execute_input": "2024-01-31T04:06:48.930920Z",
+     "shell.execute_reply": "2024-01-31T04:06:48.936170Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.055664,
+     "end_time": "2024-01-31T04:06:48.939464",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:48.883800"
+    },
+    "allow_embed": true,
+    "cell_id": "52dcb9b4ffad4436b4425ed017d7eb7b",
+    "deepnote_cell_type": "code"
+   },
+   "source": [
+    "# Assuming 'skf' is your StratifiedKFold object\n",
+    "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
+    "\n",
+    "def objective_xgb(trial):\n",
+    "    \"\"\"Define the objective function for XGBClassifier\"\"\"\n",
+    "\n",
+    "    params = {\n",
+    "        'max_depth': trial.suggest_int('max_depth', 3, 10),\n",
+    "        'min_child_weight': trial.suggest_int('min_child_weight', 1, 10),\n",
+    "        'learning_rate': trial.suggest_float('learning_rate', 0.0001, 1.0),\n",
+    "        'n_estimators': trial.suggest_int('n_estimators', 150, 5000),\n",
+    "        'subsample': trial.suggest_float('subsample', 0.01, 1.0),\n",
+    "        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.01, 1.0),\n",
+    "        'random_state': trial.suggest_categorical('random_state', [42]),\n",
+    "        'tree_method': 'hist',\n",
+    "        'device' : 'cuda', # Use GPU for training\n",
+    "        'eval_metric': 'auc',  # Evaluation metric\n",
+    "        'verbosity': 2,  # Set verbosity to 0 for less output\n",
+    "    }\n",
+    "\n",
+    "    xgb_model = xgb.XGBClassifier(**params)\n",
+    "    xgb_pipeline = make_pipeline(modelling_pipeline, xgb_model)\n",
+    "\n",
+    "    cv = abs(cross_val_score(xgb_pipeline, X, y, cv=skf, scoring='roc_auc').mean())\n",
+    "\n",
+    "    return cv\n",
+    "\n",
+    "\n",
+    "\n",
+    "study = optuna.create_study(direction='maximize')\n",
+    "study.optimize(objective_xgb, n_trials=100)\n",
+    "\n",
+    "# Get the best parameters\n",
+    "best_params_xgb = study.best_params\n",
+    "print(\"Best Hyperparameters for XGBoost:\", best_params_xgb)"
+   ],
+   "block_group": "28638dc13ff3406c9a69a7729d6da097",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:49.048505Z",
+     "iopub.status.idle": "2024-01-31T04:06:49.054836Z",
+     "iopub.execute_input": "2024-01-31T04:06:49.049675Z",
+     "shell.execute_reply": "2024-01-31T04:06:49.053845Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.055577,
+     "end_time": "2024-01-31T04:06:49.057152",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:49.001575"
+    },
+    "cell_id": "e56c9e4477c54268a7fb449e1bda3666",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T09:25:33.130847Z",
+     "start_time": "2025-08-06T09:25:33.127341Z"
+    }
+   },
+   "source": "best_params_xgb = {'max_depth': 3, 'min_child_weight': 10, 'learning_rate': 0.016884596813970348, 'n_estimators': 1624, 'subsample': 0.924465633057611, 'colsample_bytree': 0.1755975053777089, 'random_state': 42} # 0.9364344753993251",
+   "block_group": "1a77e41f93c647d6956945f51e3c87b0",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 116
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:49.152326Z",
+     "iopub.status.idle": "2024-01-31T04:06:49.157204Z",
+     "iopub.execute_input": "2024-01-31T04:06:49.152694Z",
+     "shell.execute_reply": "2024-01-31T04:06:49.156186Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.057033,
+     "end_time": "2024-01-31T04:06:49.159706",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:49.102673"
+    },
+    "cell_id": "5f8e59dadf76472391ccabbb1c925085",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T09:25:35.295467Z",
+     "start_time": "2025-08-06T09:25:35.292449Z"
+    }
+   },
+   "source": "xgb_model_optimized = xgb.XGBClassifier(**best_params_xgb)\nxgb_pipeline_optimized = make_pipeline(modelling_pipeline, xgb_model_optimized)",
+   "block_group": "ac036be3d5bd4423ad1a0ff8aa799856",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 117
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:49.251495Z",
+     "iopub.status.idle": "2024-01-31T04:06:49.257717Z",
+     "iopub.execute_input": "2024-01-31T04:06:49.252421Z",
+     "shell.execute_reply": "2024-01-31T04:06:49.256840Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.051921,
+     "end_time": "2024-01-31T04:06:49.259637",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:49.207716"
+    },
+    "cell_id": "43da3f9586474543842a56b6e15d3445",
+    "deepnote_cell_type": "code"
+   },
+   "source": "# # number of folds\n# n_splits = 10\n\n# # StratifiedKFold\n# stratkf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n\n# # cross-validation results\n# cv_results = []\n\n# # stratified k-fold cross-validation\n# for fold, (train_idx, val_idx) in enumerate(stratkf.split(X, y)):\n#     X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]\n#     y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]\n\n#     xgb_pipeline_optimized.fit(X_train, y_train)\n\n#     # predictions on the validation set\n#     y_val_pred_prob = xgb_pipeline_optimized.predict_proba(X_val)[:, 1]\n#     y_pred = xgb_pipeline_optimized.predict(X_val)\n        \n#     f1 = f1_score(y_val, y_pred, average='weighted')\n\n#     # Evaluating the model\n#     roc_auc = roc_auc_score(y_val, y_val_pred_prob)\n#     print(f'Fold {fold + 1}, AUC Score on Validation Set: {roc_auc}')\n#     print(f'Fold {fold + 1}, F1 Score on Validation Set: {f1}')\n#     print('-'*70)\n\n#     # results\n#     cv_results.append(roc_auc)\n\n# # average cross-validation result\n# average_cv_result = sum(cv_results) / n_splits\n# print(f'\\nAverage AUC-score across {n_splits} folds: {average_cv_result}')",
+   "block_group": "8a5282a5164b475d8cbfb875044c818a",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:06:49.356383Z",
+     "iopub.status.idle": "2024-01-31T04:07:23.459705Z",
+     "iopub.execute_input": "2024-01-31T04:06:49.357191Z",
+     "shell.execute_reply": "2024-01-31T04:07:23.458857Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 34.199829,
+     "end_time": "2024-01-31T04:07:23.506043",
+     "exception": false,
+     "start_time": "2024-01-31T04:06:49.306214"
+    },
+    "cell_id": "8578e6a188244135a276d1e8c6555ca6",
+    "deepnote_cell_type": "code"
+   },
+   "source": "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\nxgb_pipeline_optimized.fit(X_train, y_train)\npredictions_xgb = xgb_pipeline_optimized.predict(X_val)\ncm_xgb = confusion_matrix(y_val, predictions_xgb)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm_xgb, display_labels=['Not Churn', 'Churn'])\ndisp.plot()\nplt.show()",
+   "block_group": "fba365cd0f314a358246cd1e28d37595",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/19db88b3-7b33-41a8-b520-ead5e59ea273",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:16:13.280458Z",
+     "iopub.status.idle": "2024-01-31T04:16:32.300755Z",
+     "iopub.execute_input": "2024-01-31T04:16:13.281178Z",
+     "shell.execute_reply": "2024-01-31T04:16:32.299829Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 19.070268,
+     "end_time": "2024-01-31T04:16:32.304864",
+     "exception": false,
+     "start_time": "2024-01-31T04:16:13.234596"
+    },
+    "cell_id": "951842cf705e44debc5c17d29d05a389",
+    "deepnote_cell_type": "code"
+   },
+   "source": "xgb_feature_importance = xgb_pipeline_optimized.named_steps['xgbclassifier'].feature_importances_\ntrain_X = modelling_pipeline.fit_transform(train.drop(['Exited'], axis=1))\n\nsorted_idx = np.argsort(xgb_feature_importance)\nfig = plt.figure(figsize=(18, 16))\nplt.barh(range(len(sorted_idx)), xgb_feature_importance[sorted_idx], align='center')\nplt.yticks(range(len(sorted_idx)), np.array(train_X.columns)[sorted_idx])\nplt.title('XGB_Feature Importance')\nplt.show()",
+   "block_group": "8c457117a3a648c9a8c3c01d41e1bd70",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/83fc191b-5b98-4fe3-9a70-5e720af6fa81",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-06T07:52:47.973350Z",
+     "start_time": "2025-08-06T07:52:44.811269Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Fit the xgb final model\n",
+    "xgb_pipeline_optimized_full = make_pipeline(modelling_pipeline, xgb_model_optimized)\n",
+    "xgb_pipeline_optimized_full.fit(X=train.drop(['Exited'], axis=1), y=train['Exited'])\n",
+    "\n",
+    "# Create submission file with probability predictions\n",
+    "predictions = xgb_pipeline_optimized_full.predict_proba(test)[:, 1]  # Use the probabilities of class 1\n",
+    "\n",
+    "sample['Exited'] = predictions\n",
+    "sample.to_csv('4_1_submission_xgb.csv', index=False)"
+   ],
+   "outputs": [],
+   "execution_count": 108
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.041041,
+     "end_time": "2024-01-31T04:07:23.588279",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:23.547238"
+    },
+    "cell_id": "4783f40e73814054b7db09969bfae28a",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "### lightgbm",
+   "block_group": "07ac22708eae4f02baf747184cc295c3"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:23.671808Z",
+     "iopub.status.idle": "2024-01-31T04:07:25.820548Z",
+     "iopub.execute_input": "2024-01-31T04:07:23.672188Z",
+     "shell.execute_reply": "2024-01-31T04:07:25.819578Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 2.194017,
+     "end_time": "2024-01-31T04:07:25.823016",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:23.628999"
+    },
+    "cell_id": "ce0a53ef415245cc947adbd30afacf4a",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T08:02:07.255851Z",
+     "start_time": "2025-08-06T08:02:07.252340Z"
+    }
+   },
+   "source": "import warnings\n# Suppress LightGBM warnings\nwarnings.filterwarnings(\"ignore\", category=UserWarning, message=\".*num_leaves.*\")\nwarnings.filterwarnings(\"ignore\", category=UserWarning, message=\".*No further splits with positive gain.*\")\n\nimport lightgbm as lgb\nfrom lightgbm import LGBMClassifier",
+   "block_group": "62f83258c17941358f8c7f7f6da89ed6",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 109
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:25.909311Z",
+     "iopub.status.idle": "2024-01-31T04:07:25.914470Z",
+     "iopub.execute_input": "2024-01-31T04:07:25.909648Z",
+     "shell.execute_reply": "2024-01-31T04:07:25.913696Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.050122,
+     "end_time": "2024-01-31T04:07:25.916389",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:25.866267"
+    },
+    "cell_id": "1cbf15dd94f74b20aa98af2cf49b3af9",
+    "deepnote_cell_type": "code"
+   },
+   "source": [
+    "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
+    "\n",
+    "def objective_lgbm(trial):\n",
+    "    \"\"\"Define the objective function for LGBMClassifier\"\"\"\n",
+    "\n",
+    "    params = {\n",
+    "        'objective': 'binary',  # Objective for binary classification\n",
+    "        'boosting_type': 'gbdt',\n",
+    "        'device': 'gpu', # Use GPU for training\n",
+    "        'metric': 'auc',  # Evaluation metric\n",
+    "        'max_depth': trial.suggest_int('max_depth', 3, 20),\n",
+    "        'min_child_samples': trial.suggest_int('min_child_samples', 1, 20),\n",
+    "        'learning_rate': trial.suggest_float('learning_rate', 0.0001, 1.0),\n",
+    "        'n_estimators': trial.suggest_int('n_estimators', 150, 5000),\n",
+    "        'subsample': trial.suggest_float('subsample', 0.1, 1.0),\n",
+    "        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.1, 1.0),\n",
+    "        'reg_alpha': trial.suggest_float('reg_alpha', 0.0, 1.0),\n",
+    "        'reg_lambda': trial.suggest_float('reg_lambda', 0.0, 1.0),\n",
+    "        'random_state': 42,\n",
+    "    }\n",
+    "\n",
+    "    lgb_model = lgb.LGBMClassifier(**params)\n",
+    "    lgb_pipeline = make_pipeline(modelling_pipeline, lgb_model)\n",
+    "\n",
+    "    cv = abs(cross_val_score(lgb_pipeline, X, y, cv=skf, scoring='roc_auc').mean())\n",
+    "\n",
+    "    return cv\n",
+    "\n",
+    "# Create an Optuna study\n",
+    "study = optuna.create_study(direction='maximize')\n",
+    "study.optimize(objective_lgbm, n_trials=100)\n",
+    "\n",
+    "# Get the best parameters\n",
+    "best_params_lgb = study.best_params\n",
+    "print(\"Best Hyperparameters for LGBM:\", best_params_lgb)"
+   ],
+   "block_group": "3632d6240fe047c4af9a9d3f3f0533c3",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:26.002445Z",
+     "iopub.status.idle": "2024-01-31T04:07:26.007491Z",
+     "iopub.execute_input": "2024-01-31T04:07:26.002752Z",
+     "shell.execute_reply": "2024-01-31T04:07:26.006551Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.050188,
+     "end_time": "2024-01-31T04:07:26.009397",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:25.959209"
+    },
+    "cell_id": "c9eba4c758d14b6db02538f310c38755",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T09:25:41.295269Z",
+     "start_time": "2025-08-06T09:25:41.292250Z"
+    }
+   },
+   "source": "best_params_lgb = {'max_depth': 3, 'min_child_samples': 17, 'learning_rate': 0.06933030702848553, 'n_estimators': 162, 'subsample': 0.8951097160631081, 'colsample_bytree': 0.7870705172001088, 'reg_alpha': 0.7730796425793625, 'reg_lambda': 0.3309686355313894} # 0.9367646807126482",
+   "block_group": "cf6d768103e4455a8dfbcbb6331305f4",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 118
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:26.101581Z",
+     "iopub.status.idle": "2024-01-31T04:07:26.107185Z",
+     "iopub.execute_input": "2024-01-31T04:07:26.101959Z",
+     "shell.execute_reply": "2024-01-31T04:07:26.106198Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.055014,
+     "end_time": "2024-01-31T04:07:26.109311",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:26.054297"
+    },
+    "cell_id": "f9814ba5c72342038f25d9b5f80a8354",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T09:25:42.328565Z",
+     "start_time": "2025-08-06T09:25:42.325270Z"
+    }
+   },
+   "source": "lgb_model_optimized = lgb.LGBMClassifier(**best_params_lgb, random_state=42)\nlgb_pipeline_optimized = make_pipeline(modelling_pipeline, lgb_model_optimized)",
+   "block_group": "217a9a2add794abf956ce3578d1c063b",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 119
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:26.198656Z",
+     "iopub.status.idle": "2024-01-31T04:07:26.203534Z",
+     "iopub.execute_input": "2024-01-31T04:07:26.199011Z",
+     "shell.execute_reply": "2024-01-31T04:07:26.202660Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.051048,
+     "end_time": "2024-01-31T04:07:26.205559",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:26.154511"
+    },
+    "cell_id": "d4076885f17e4896832b81dc3e1f884e",
+    "deepnote_cell_type": "code"
+   },
+   "source": "# n_splits = 10\n# stratkf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n# cv_results = []\n\n# for fold, (train_idx, val_idx) in enumerate(stratkf.split(X, y)):\n#     X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]\n#     y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]\n\n#     lgb_pipeline_optimized.fit(X_train, y_train)\n#     y_val_pred_prob = lgb_pipeline_optimized.predict_proba(X_val)[:, 1]\n#     y_pred = lgb_pipeline_optimized.predict(X_val)\n#     f1 = f1_score(y_val, y_pred, average='weighted')\n\n#     # Evaluating the model\n#     roc_auc = roc_auc_score(y_val, y_val_pred_prob)\n#     print(f'Fold {fold + 1}, AUC-Score on Validation Set: {roc_auc}')\n#     print(f'Fold {fold + 1}, F1 Score on Validation Set: {f1}')\n#     print('-'*70)\n\n#     cv_results.append(roc_auc)\n\n# average_cv_result = sum(cv_results) / n_splits\n# print(f'\\nAverage AUC-SCORE across {n_splits} folds: {average_cv_result}')",
+   "block_group": "820de9503a0d48e9b7f0c564dbd03c86",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:26.291696Z",
+     "iopub.status.idle": "2024-01-31T04:07:45.470109Z",
+     "iopub.execute_input": "2024-01-31T04:07:26.292048Z",
+     "shell.execute_reply": "2024-01-31T04:07:45.469284Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 19.224032,
+     "end_time": "2024-01-31T04:07:45.472623",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:26.248591"
+    },
+    "cell_id": "d27fb5951a91497ab7cee0cbc338b690",
+    "deepnote_cell_type": "code"
+   },
+   "source": "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\nlgb_pipeline_optimized.fit(X_train, y_train)\npredictions_lgb = lgb_pipeline_optimized.predict(X_val)\ncm_lgb = confusion_matrix(y_val, predictions_lgb)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm_lgb, display_labels=['Not Churn', 'Churn'])\ndisp.plot()\nplt.show()",
+   "block_group": "3be11dd1159e45a1b49d9179a1bc612f",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/e87fdc5f-873a-4fa6-8900-25d82c439b46",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:16:32.408050Z",
+     "iopub.status.idle": "2024-01-31T04:16:33.331515Z",
+     "iopub.execute_input": "2024-01-31T04:16:32.408652Z",
+     "shell.execute_reply": "2024-01-31T04:16:33.330645Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.97965,
+     "end_time": "2024-01-31T04:16:33.336233",
+     "exception": false,
+     "start_time": "2024-01-31T04:16:32.356583"
+    },
+    "cell_id": "5202fe47504940388bb8974cfcd1167c",
+    "deepnote_cell_type": "code"
+   },
+   "source": "lgbm_feature_importance = lgb_pipeline_optimized.named_steps['lgbmclassifier'].feature_importances_\nsorted_idx = np.argsort(lgbm_feature_importance)\nfig = plt.figure(figsize=(18, 16))\nplt.barh(range(len(sorted_idx)), lgbm_feature_importance[sorted_idx], align='center')\nplt.yticks(range(len(sorted_idx)), np.array(train_X.columns)[sorted_idx])\nplt.title('LGBM_Feature Importance')\nplt.show()",
+   "block_group": "814127203e374195aea06b790c4558ea",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/5a672026-3f32-4256-a0b3-5d2ba791c725",
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-06T09:23:57.765218Z",
+     "start_time": "2025-08-06T09:23:56.818219Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "lgb_pipeline_optimized_full = make_pipeline(modelling_pipeline, lgb_model_optimized)\n",
+    "lgb_pipeline_optimized_full.fit(X=train.drop(['Exited'], axis=1), y=train['Exited'])\n",
+    "\n",
+    "# Create submission file with probability predictions\n",
+    "predictions = lgb_pipeline_optimized_full.predict_proba(test)[:, 1]  # Use the probabilities of class 1\n",
+    "\n",
+    "sample['Exited'] = predictions\n",
+    "sample.to_csv('4_submission_lgbm.csv', index=False)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Info] Number of positive: 3052, number of negative: 11948\n",
+      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000529 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 2164\n",
+      "[LightGBM] [Info] Number of data points in the train set: 15000, number of used features: 47\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203467 -> initscore=-1.364767\n",
+      "[LightGBM] [Info] Start training from score -1.364767\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
+     ]
+    }
+   ],
+   "execution_count": 115
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.041723,
+     "end_time": "2024-01-31T04:07:45.558472",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:45.516749"
+    },
+    "cell_id": "27524e236d4745a08fcf1679892c657f",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "### catboost",
+   "block_group": "f4ba46f996c849f18137b0056ff5aabf"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:45.643115Z",
+     "iopub.status.idle": "2024-01-31T04:07:45.823658Z",
+     "iopub.execute_input": "2024-01-31T04:07:45.643792Z",
+     "shell.execute_reply": "2024-01-31T04:07:45.822912Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.225324,
+     "end_time": "2024-01-31T04:07:45.825791",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:45.600467"
+    },
+    "allow_embed": true,
+    "cell_id": "2829d26d9a6148b284310195fef51996",
+    "deepnote_cell_type": "code"
+   },
+   "source": "from catboost import CatBoostClassifier",
+   "block_group": "ad3fc56c3082421083922c9c926ece51",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:45.916094Z",
+     "iopub.status.idle": "2024-01-31T04:07:45.921078Z",
+     "iopub.execute_input": "2024-01-31T04:07:45.916953Z",
+     "shell.execute_reply": "2024-01-31T04:07:45.920195Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.053002,
+     "end_time": "2024-01-31T04:07:45.923068",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:45.870066"
+    },
+    "allow_embed": true,
+    "cell_id": "5a38eae8f2b041ae8a3bcc32e919410f",
+    "deepnote_cell_type": "code"
+   },
+   "source": [
+    "# Suppress FutureWarnings related to is_sparse\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"sklearn.utils.validation\")\n",
+    "\n",
+    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+    "\n",
+    "def objective(trial):\n",
+    "    params = {\n",
+    "        'iterations': trial.suggest_int('iterations', 500, 10000),\n",
+    "        'depth': trial.suggest_int('depth', 10, 16),\n",
+    "        'min_data_in_leaf': trial.suggest_int('min_data_in_leaf', 2, 20),\n",
+    "        'learning_rate': trial.suggest_float('learning_rate', 1e-4, 0.2, log=True),\n",
+    "    }\n",
+    "\n",
+    "    cb_model = CatBoostClassifier(**params, random_state=42, grow_policy='Lossguide', verbose=1000)\n",
+    "    cb_pipeline = make_pipeline(modelling_pipeline, cb_model)\n",
+    "    \n",
+    "\n",
+    "    cv = abs(cross_val_score(cb_pipeline, X, y, cv=skf, scoring='roc_auc').mean())\n",
+    "    return cv\n",
+    "\n",
+    "study = optuna.create_study(direction='maximize')\n",
+    "study.optimize(objective, n_trials=100)\n",
+    "\n",
+    "best_params_cb = study.best_params\n",
+    "print(\"Best Hyperparameters for CatBoost:\", best_params_cb)"
+   ],
+   "block_group": "bd1e5727d5bc4fd6992158297c4da2da",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:46.011634Z",
+     "iopub.status.idle": "2024-01-31T04:07:46.016002Z",
+     "iopub.execute_input": "2024-01-31T04:07:46.012182Z",
+     "shell.execute_reply": "2024-01-31T04:07:46.015151Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.050211,
+     "end_time": "2024-01-31T04:07:46.017956",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:45.967745"
+    },
+    "cell_id": "65bc5574208448b18c602ab08e1d56d7",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T09:25:48.957812Z",
+     "start_time": "2025-08-06T09:25:48.954746Z"
+    }
+   },
+   "source": "best_params_cb = {'iterations': 3402, 'depth': 10, 'min_data_in_leaf': 6, 'learning_rate': 0.0033287476370021897} # 0.9356086346674015",
+   "block_group": "b8f52b6e6a4542a5b4a5f6602da9a053",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 120
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:46.103638Z",
+     "iopub.status.idle": "2024-01-31T04:07:46.110254Z",
+     "iopub.execute_input": "2024-01-31T04:07:46.104346Z",
+     "shell.execute_reply": "2024-01-31T04:07:46.109347Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.051389,
+     "end_time": "2024-01-31T04:07:46.112098",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:46.060709"
+    },
+    "allow_embed": true,
+    "cell_id": "214e9da2e29548f788a8cf0d64b82114",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T09:25:50.237338Z",
+     "start_time": "2025-08-06T09:25:50.234336Z"
+    }
+   },
+   "source": [
+    "cb_model = CatBoostClassifier(**best_params_cb, random_state=42, verbose=1000)\n",
+    "cb_pipeline_optimized = make_pipeline(modelling_pipeline, cb_model)"
+   ],
+   "block_group": "e33eb15731e34f20a752d43e3ea150cb",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 121
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:46.201164Z",
+     "iopub.status.idle": "2024-01-31T04:07:46.205979Z",
+     "iopub.execute_input": "2024-01-31T04:07:46.201463Z",
+     "shell.execute_reply": "2024-01-31T04:07:46.205123Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.05069,
+     "end_time": "2024-01-31T04:07:46.207952",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:46.157262"
+    },
+    "allow_embed": true,
+    "cell_id": "f3a2ab208ba44a6685fb787d9aaad19a",
+    "deepnote_cell_type": "code"
+   },
+   "source": [
+    "# n_splits = 10\n",
+    "# stratkf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
+    "#\n",
+    "# cv_results = []\n",
+    "#\n",
+    "# for fold, (train_idx, val_idx) in enumerate(stratkf.split(X, y)):\n",
+    "#     X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]\n",
+    "#     y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]\n",
+    "#\n",
+    "#     cb_pipeline_optimized.fit(X_train, y_train)\n",
+    "#\n",
+    "#     y_val_pred_prob = cb_pipeline_optimized.predict_proba(X_val)[:, 1]\n",
+    "#     y_pred = cb_pipeline_optimized.predict(X_val)\n",
+    "#     f1 = f1_score(y_val, y_pred, average='weighted')\n",
+    "#\n",
+    "#     # Evaluating the model\n",
+    "#     logloss = log_loss(y_val, y_val_pred_prob)\n",
+    "#     roc_auc = roc_auc_score(y_val, y_val_pred_prob)\n",
+    "#     print(f'Fold {fold + 1}, AUC-Score on Validation Set: {roc_auc}')\n",
+    "#     print(f'Fold {fold + 1}, F1 Score on Validation Set: {f1}')\n",
+    "#     print(f'Fold {fold + 1}, Log Loss Score on Validation Set: {logloss}')\n",
+    "#     print('-'*70)\n",
+    "#\n",
+    "#     cv_results.append(logloss)\n",
+    "#\n",
+    "# average_cv_result = sum(cv_results) / n_splits\n",
+    "# print(f'\\nAverage Logarithmic Loss across {n_splits} folds: {average_cv_result}')\n"
+   ],
+   "block_group": "d036a90af0ff4a86ae0765504e80194c",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:07:46.293058Z",
+     "iopub.status.idle": "2024-01-31T04:11:32.819948Z",
+     "iopub.execute_input": "2024-01-31T04:07:46.293342Z",
+     "shell.execute_reply": "2024-01-31T04:11:32.819054Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 226.616168,
+     "end_time": "2024-01-31T04:11:32.866945",
+     "exception": false,
+     "start_time": "2024-01-31T04:07:46.250777"
+    },
+    "allow_embed": true,
+    "cell_id": "7c81481a485646829d404f09ca4a9479",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-05T17:54:42.981553Z",
+     "start_time": "2025-08-05T17:49:20.470654Z"
+    }
+   },
+   "source": "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n\ncb_pipeline_optimized.fit(X = X_train,\n                y = y_train)\n\npredictions_cb = cb_pipeline_optimized.predict(X_val)\n\ncm_cb = confusion_matrix(y_val, predictions_cb)\n\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm_cb, display_labels=['Not Churn', 'Churn'])\ndisp.plot()\nplt.show()\n",
+   "block_group": "70f7c2877a7247038a2fa2ab5e15178f",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/5e191275-ef2b-4b32-b15d-033e7abbe5d9",
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0:\tlearn: 0.6652814\ttotal: 401ms\tremaining: 5m 15s\n",
+      "100:\tlearn: 0.1613241\ttotal: 36.7s\tremaining: 4m 9s\n",
+      "200:\tlearn: 0.1049289\ttotal: 1m 14s\tremaining: 3m 35s\n",
+      "300:\tlearn: 0.0719672\ttotal: 1m 51s\tremaining: 2m 59s\n",
+      "400:\tlearn: 0.0518641\ttotal: 2m 31s\tremaining: 2m 25s\n",
+      "500:\tlearn: 0.0396832\ttotal: 3m 11s\tremaining: 1m 49s\n",
+      "600:\tlearn: 0.0328704\ttotal: 3m 56s\tremaining: 1m 13s\n",
+      "700:\tlearn: 0.0278857\ttotal: 4m 41s\tremaining: 34.5s\n",
+      "786:\tlearn: 0.0244222\ttotal: 5m 20s\tremaining: 0us\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1800x1600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 65
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:16:33.449278Z",
+     "iopub.status.idle": "2024-01-31T04:16:34.391209Z",
+     "iopub.execute_input": "2024-01-31T04:16:33.449833Z",
+     "shell.execute_reply": "2024-01-31T04:16:34.390288Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 1.002703,
+     "end_time": "2024-01-31T04:16:34.395051",
+     "exception": false,
+     "start_time": "2024-01-31T04:16:33.392348"
+    },
+    "allow_embed": true,
+    "cell_id": "7f70f907b0064821b4e67776b7f05246",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-05T17:56:31.503383Z",
+     "start_time": "2025-08-05T17:56:31.114090Z"
+    }
+   },
+   "source": "cb_feature_importance = cb_pipeline_optimized.named_steps['catboostclassifier'].feature_importances_\nsorted_idx = np.argsort(cb_feature_importance)\nfig = plt.figure(figsize=(18, 16))\nplt.barh(range(len(sorted_idx)), cb_feature_importance[sorted_idx], align='center')\nplt.yticks(range(len(sorted_idx)), np.array(train_X.columns)[sorted_idx])\nplt.title('CB_Feature Importance')\nplt.show()",
+   "block_group": "c86cb489bb524b4bbd865f919ebea22c",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/69739eb5-3ccc-446c-9a83-e5ee65b7508a",
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1800x1600 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 66
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# Fit the xgb final model\n",
+    "cb_pipeline_optimized_full = make_pipeline(modelling_pipeline, cb_model)\n",
+    "cb_pipeline_optimized_full.fit(X=train.drop(['Exited'], axis=1), y=train['Exited'])\n",
+    "\n",
+    "# Create submission file with probability predictions\n",
+    "predictions = cb_pipeline_optimized_full.predict_proba(test)[:, 1]  # Use the probabilities of class 1\n",
+    "\n",
+    "sample['Exited'] = predictions\n",
+    "sample.to_csv('4_2_submission_cat.csv', index=False)"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Random forest"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "from sklearn.ensemble import RandomForestClassifier"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.043184,
+     "end_time": "2024-01-31T04:11:32.953155",
+     "exception": false,
+     "start_time": "2024-01-31T04:11:32.909971"
+    },
+    "cell_id": "b4c5ed0313304f6c80ad65e2055fc2e7",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "## ensemble",
+   "block_group": "fa9a1a7ae41a4329a26d632c73f5bf8e"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:11:33.045419Z",
+     "iopub.status.idle": "2024-01-31T04:11:33.101331Z",
+     "iopub.execute_input": "2024-01-31T04:11:33.046054Z",
+     "shell.execute_reply": "2024-01-31T04:11:33.100547Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.105647,
+     "end_time": "2024-01-31T04:11:33.103379",
+     "exception": false,
+     "start_time": "2024-01-31T04:11:32.997732"
+    },
+    "cell_id": "c8e7b78a648542a9b6b0d694dae980ee",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-06T09:26:00.341053Z",
+     "start_time": "2025-08-06T09:26:00.338036Z"
+    }
+   },
+   "source": "from sklearn.ensemble import VotingClassifier",
+   "block_group": "a99c20e16c1f43739f718ce3fbce2aa0",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": 122
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:11:33.192027Z",
+     "iopub.status.idle": "2024-01-31T04:11:33.198443Z",
+     "iopub.execute_input": "2024-01-31T04:11:33.192376Z",
+     "shell.execute_reply": "2024-01-31T04:11:33.197545Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.052524,
+     "end_time": "2024-01-31T04:11:33.200285",
+     "exception": false,
+     "start_time": "2024-01-31T04:11:33.147761"
+    },
+    "cell_id": "f4022095a3684785bc3ecfcc5d9a0652",
+    "deepnote_cell_type": "code"
+   },
+   "source": [
+    "# Define the parameter search space\n",
+    "def objective(trial):\n",
+    "    weights = [\n",
+    "        trial.suggest_float('weight_xgb', 0, 1),\n",
+    "        trial.suggest_float('weight_lgb', 0, 1),\n",
+    "        trial.suggest_float('weight_cb', 0, 1)\n",
+    "    ]\n",
+    "\n",
+    "    ensemble_model = VotingClassifier(\n",
+    "        estimators=[\n",
+    "            ('xgb', xgb_pipeline_optimized),\n",
+    "            ('lgb', lgb_pipeline_optimized),\n",
+    "            ('cb', cb_pipeline_optimized)\n",
+    "        ], voting='soft', weights=weights)\n",
+    "\n",
+    "    cv = abs(cross_val_score(ensemble_model, X, y, cv=skf, scoring='roc_auc').mean())\n",
+    "\n",
+    "    return cv\n",
+    "\n",
+    "# Assuming 'skf' is your StratifiedKFold object\n",
+    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+    "\n",
+    "# Use the 'sampler' parameter for parallelization\n",
+    "study = optuna.create_study(direction='maximize', sampler=optuna.samplers.TPESampler(seed=42))\n",
+    "study.optimize(objective, n_trials=75)\n",
+    "\n",
+    "best_weights = study.best_params\n",
+    "print(\"Best Weights for Ensemble:\", best_weights)\n"
+   ],
+   "block_group": "adf26591df89468baa2cce567ddc8c1e",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:11:33.287907Z",
+     "iopub.status.idle": "2024-01-31T04:11:34.706887Z",
+     "iopub.execute_input": "2024-01-31T04:11:33.288738Z",
+     "shell.execute_reply": "2024-01-31T04:11:34.705893Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 1.46584,
+     "end_time": "2024-01-31T04:11:34.709804",
+     "exception": false,
+     "start_time": "2024-01-31T04:11:33.243964"
+    },
+    "allow_embed": true,
+    "cell_id": "e9359290e2454cdba3ead8ba4f723111",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-07T06:22:19.013031Z",
+     "start_time": "2025-08-07T06:22:18.308789Z"
+    }
+   },
+   "source": [
+    "ensemble_model = VotingClassifier(estimators=[\n",
+    "    ('xgb', xgb_pipeline_optimized),\n",
+    "    ('lgb', lgb_pipeline_optimized),\n",
+    "    ('cb', cb_pipeline_optimized)\n",
+    "], voting='soft', weights = [0.3027242642994131, 0.7881985146021274, 0.13131663446625]) # 0.9371697485746712\n",
+    "\n",
+    "ensemble_model"
+   ],
+   "block_group": "423f4d0423fe49aeb7154e05b8a27c43",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/1621fe41-b6d5-4c08-a379-5900c2f74833",
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VotingClassifier(estimators=[('xgb',\n",
+       "                              Pipeline(steps=[('pipeline',\n",
+       "                                               Pipeline(steps=[('kmeans',\n",
+       "                                                                KMeansClusterer(features=['CustomerId',\n",
+       "                                                                                          'EstimatedSalary',\n",
+       "                                                                                          'Balance'],\n",
+       "                                                                                n_clusters=10,\n",
+       "                                                                                n_components=3,\n",
+       "                                                                                random_state=123)),\n",
+       "                                                               ('surname_tfid',\n",
+       "                                                                TFIDFTransformer(column='Surname',\n",
+       "                                                                                 max_features=1000,\n",
+       "                                                                                 n_components=5)),\n",
+       "                                                               ('age_binning',\n",
+       "                                                                VariableBinning(column_name='Age',\n",
+       "                                                                                n_bins=5))...\n",
+       "                                                                                                 OneHotEncoder(handle_unknown='ignore',\n",
+       "                                                                                                               sparse_output=False),\n",
+       "                                                                                                 ['Gender',\n",
+       "                                                                                                  'Geography',\n",
+       "                                                                                                  'NumOfProducts',\n",
+       "                                                                                                  'HasCrCard',\n",
+       "                                                                                                  'IsActiveMember',\n",
+       "                                                                                                  'Geo_Gender',\n",
+       "                                                                                                  'Tp_Gender'])])),\n",
+       "                                                               ('scale',\n",
+       "                                                                RobustScaler())])),\n",
+       "                                              ('catboostclassifier',\n",
+       "                                               <catboost.core.CatBoostClassifier object at 0x00000132831A2720>)]))],\n",
+       "                 voting='soft',\n",
+       "                 weights=[0.3027242642994131, 0.7881985146021274,\n",
+       "                          0.13131663446625])"
+      ],
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;xgb&#x27;,\n",
+       "                              Pipeline(steps=[(&#x27;pipeline&#x27;,\n",
+       "                                               Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
+       "                                                                KMeansClusterer(features=[&#x27;CustomerId&#x27;,\n",
+       "                                                                                          &#x27;EstimatedSalary&#x27;,\n",
+       "                                                                                          &#x27;Balance&#x27;],\n",
+       "                                                                                n_clusters=10,\n",
+       "                                                                                n_components=3,\n",
+       "                                                                                random_state=123)),\n",
+       "                                                               (&#x27;surname_tfid&#x27;,\n",
+       "                                                                TFIDFTransformer(column=&#x27;Surname&#x27;,\n",
+       "                                                                                 max_features=1000,\n",
+       "                                                                                 n_components=5)),\n",
+       "                                                               (&#x27;age_binning&#x27;,\n",
+       "                                                                VariableBinning(column_name=&#x27;Age&#x27;,\n",
+       "                                                                                n_bins=5))...\n",
+       "                                                                                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                                                                                               sparse_output=False),\n",
+       "                                                                                                 [&#x27;Gender&#x27;,\n",
+       "                                                                                                  &#x27;Geography&#x27;,\n",
+       "                                                                                                  &#x27;NumOfProducts&#x27;,\n",
+       "                                                                                                  &#x27;HasCrCard&#x27;,\n",
+       "                                                                                                  &#x27;IsActiveMember&#x27;,\n",
+       "                                                                                                  &#x27;Geo_Gender&#x27;,\n",
+       "                                                                                                  &#x27;Tp_Gender&#x27;])])),\n",
+       "                                                               (&#x27;scale&#x27;,\n",
+       "                                                                RobustScaler())])),\n",
+       "                                              (&#x27;catboostclassifier&#x27;,\n",
+       "                                               &lt;catboost.core.CatBoostClassifier object at 0x00000132831A2720&gt;)]))],\n",
+       "                 voting=&#x27;soft&#x27;,\n",
+       "                 weights=[0.3027242642994131, 0.7881985146021274,\n",
+       "                          0.13131663446625])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-227\" type=\"checkbox\" ><label for=\"sk-estimator-id-227\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>VotingClassifier</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.VotingClassifier.html\">?<span>Documentation for VotingClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>VotingClassifier(estimators=[(&#x27;xgb&#x27;,\n",
+       "                              Pipeline(steps=[(&#x27;pipeline&#x27;,\n",
+       "                                               Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
+       "                                                                KMeansClusterer(features=[&#x27;CustomerId&#x27;,\n",
+       "                                                                                          &#x27;EstimatedSalary&#x27;,\n",
+       "                                                                                          &#x27;Balance&#x27;],\n",
+       "                                                                                n_clusters=10,\n",
+       "                                                                                n_components=3,\n",
+       "                                                                                random_state=123)),\n",
+       "                                                               (&#x27;surname_tfid&#x27;,\n",
+       "                                                                TFIDFTransformer(column=&#x27;Surname&#x27;,\n",
+       "                                                                                 max_features=1000,\n",
+       "                                                                                 n_components=5)),\n",
+       "                                                               (&#x27;age_binning&#x27;,\n",
+       "                                                                VariableBinning(column_name=&#x27;Age&#x27;,\n",
+       "                                                                                n_bins=5))...\n",
+       "                                                                                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                                                                                               sparse_output=False),\n",
+       "                                                                                                 [&#x27;Gender&#x27;,\n",
+       "                                                                                                  &#x27;Geography&#x27;,\n",
+       "                                                                                                  &#x27;NumOfProducts&#x27;,\n",
+       "                                                                                                  &#x27;HasCrCard&#x27;,\n",
+       "                                                                                                  &#x27;IsActiveMember&#x27;,\n",
+       "                                                                                                  &#x27;Geo_Gender&#x27;,\n",
+       "                                                                                                  &#x27;Tp_Gender&#x27;])])),\n",
+       "                                                               (&#x27;scale&#x27;,\n",
+       "                                                                RobustScaler())])),\n",
+       "                                              (&#x27;catboostclassifier&#x27;,\n",
+       "                                               &lt;catboost.core.CatBoostClassifier object at 0x00000132831A2720&gt;)]))],\n",
+       "                 voting=&#x27;soft&#x27;,\n",
+       "                 weights=[0.3027242642994131, 0.7881985146021274,\n",
+       "                          0.13131663446625])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><label>xgb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-228\" type=\"checkbox\" ><label for=\"sk-estimator-id-228\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>pipeline: Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for pipeline: Pipeline</span></a></div></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
+       "                 KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;,\n",
+       "                                           &#x27;Balance&#x27;],\n",
+       "                                 n_clusters=10, n_components=3,\n",
+       "                                 random_state=123)),\n",
+       "                (&#x27;surname_tfid&#x27;,\n",
+       "                 TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000,\n",
+       "                                  n_components=5)),\n",
+       "                (&#x27;age_binning&#x27;, VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)),\n",
+       "                (&#x27;salary_binning&#x27;,\n",
+       "                 VariableBinning(column_name=&#x27;EstimatedSalary&#x27;...\n",
+       "                (&#x27;customer_status&#x27;, CustomerStatusTransformer()),\n",
+       "                (&#x27;drop&#x27;, DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])),\n",
+       "                (&#x27;prep&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;encode&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [&#x27;Gender&#x27;, &#x27;Geography&#x27;,\n",
+       "                                                   &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;,\n",
+       "                                                   &#x27;IsActiveMember&#x27;,\n",
+       "                                                   &#x27;Geo_Gender&#x27;,\n",
+       "                                                   &#x27;Tp_Gender&#x27;])])),\n",
+       "                (&#x27;scale&#x27;, RobustScaler())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-229\" type=\"checkbox\" ><label for=\"sk-estimator-id-229\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>KMeansClusterer</div></div></label><div class=\"sk-toggleable__content \"><pre>KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;, &#x27;Balance&#x27;],\n",
+       "                n_clusters=10, n_components=3, random_state=123)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-230\" type=\"checkbox\" ><label for=\"sk-estimator-id-230\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>TFIDFTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000, n_components=5)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-231\" type=\"checkbox\" ><label for=\"sk-estimator-id-231\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>VariableBinning</div></div></label><div class=\"sk-toggleable__content \"><pre>VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-232\" type=\"checkbox\" ><label for=\"sk-estimator-id-232\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>VariableBinning</div></div></label><div class=\"sk-toggleable__content \"><pre>VariableBinning(column_name=&#x27;EstimatedSalary&#x27;, n_bins=10)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-233\" type=\"checkbox\" ><label for=\"sk-estimator-id-233\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>BalanceSalaryRatioTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>BalanceSalaryRatioTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-234\" type=\"checkbox\" ><label for=\"sk-estimator-id-234\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>GeoGenderTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>GeoGenderTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-235\" type=\"checkbox\" ><label for=\"sk-estimator-id-235\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>BalanceSalaryRatioTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>BalanceSalaryRatioTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-236\" type=\"checkbox\" ><label for=\"sk-estimator-id-236\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>TpGenderTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>TpGenderTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-237\" type=\"checkbox\" ><label for=\"sk-estimator-id-237\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>IsSeniorTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>IsSeniorTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-238\" type=\"checkbox\" ><label for=\"sk-estimator-id-238\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>QualityOfBalanceTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>QualityOfBalanceTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-239\" type=\"checkbox\" ><label for=\"sk-estimator-id-239\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CreditScoreTierTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>CreditScoreTierTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-240\" type=\"checkbox\" ><label for=\"sk-estimator-id-240\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>IsActiveByCreditCardTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>IsActiveByCreditCardTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-241\" type=\"checkbox\" ><label for=\"sk-estimator-id-241\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ProductsPerTenureTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>ProductsPerTenureTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-242\" type=\"checkbox\" ><label for=\"sk-estimator-id-242\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CustomerStatusTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>CustomerStatusTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-243\" type=\"checkbox\" ><label for=\"sk-estimator-id-243\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>DropColumn</div></div></label><div class=\"sk-toggleable__content \"><pre>DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])</pre></div> </div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-244\" type=\"checkbox\" ><label for=\"sk-estimator-id-244\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>prep: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for prep: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                  transformers=[(&#x27;encode&#x27;,\n",
+       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                               sparse_output=False),\n",
+       "                                 [&#x27;Gender&#x27;, &#x27;Geography&#x27;, &#x27;NumOfProducts&#x27;,\n",
+       "                                  &#x27;HasCrCard&#x27;, &#x27;IsActiveMember&#x27;, &#x27;Geo_Gender&#x27;,\n",
+       "                                  &#x27;Tp_Gender&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-245\" type=\"checkbox\" ><label for=\"sk-estimator-id-245\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>encode</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Gender&#x27;, &#x27;Geography&#x27;, &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;, &#x27;IsActiveMember&#x27;, &#x27;Geo_Gender&#x27;, &#x27;Tp_Gender&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-246\" type=\"checkbox\" ><label for=\"sk-estimator-id-246\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-247\" type=\"checkbox\" ><label for=\"sk-estimator-id-247\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;CreditScore&#x27;, &#x27;Age&#x27;, &#x27;Tenure&#x27;, &#x27;Balance&#x27;, &#x27;EstimatedSalary&#x27;, &#x27;Cluster&#x27;, &#x27;Surname_tfidf_0&#x27;, &#x27;Surname_tfidf_1&#x27;, &#x27;Surname_tfidf_2&#x27;, &#x27;Surname_tfidf_3&#x27;, &#x27;Surname_tfidf_4&#x27;, &#x27;QCut5_Age&#x27;, &#x27;QCut10_EstimatedSalary&#x27;, &#x27;Total_Products_Used&#x27;, &#x27;IsSenior&#x27;, &#x27;QualityOfBalance&#x27;, &#x27;CreditScoreTier&#x27;, &#x27;IsActive_by_CreditCard&#x27;, &#x27;Products_Per_Tenure&#x27;, &#x27;Customer_Status&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-248\" type=\"checkbox\" ><label for=\"sk-estimator-id-248\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>passthrough</div></div></label><div class=\"sk-toggleable__content \"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-249\" type=\"checkbox\" ><label for=\"sk-estimator-id-249\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>RobustScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.RobustScaler.html\">?<span>Documentation for RobustScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>RobustScaler()</pre></div> </div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-250\" type=\"checkbox\" ><label for=\"sk-estimator-id-250\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.0.0/python/python_api.html#xgboost.XGBClassifier\">?<span>Documentation for XGBClassifier</span></a></div></label><div class=\"sk-toggleable__content \"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=0.1755975053777089, device=None,\n",
+       "              early_stopping_rounds=None, enable_categorical=False,\n",
+       "              eval_metric=None, feature_types=None, feature_weights=None,\n",
+       "              gamma=None, grow_policy=None, importance_type=None,\n",
+       "              interaction_constraints=None, learning_rate=0.016884596813970348,\n",
+       "              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
+       "              min_child_weight=10, missing=nan, monotone_constraints=None,\n",
+       "              multi_strategy=None, n_estimators=1624, n_jobs=None,\n",
+       "              num_parallel_tree=None, ...)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><label>lgb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-251\" type=\"checkbox\" ><label for=\"sk-estimator-id-251\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>pipeline: Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for pipeline: Pipeline</span></a></div></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
+       "                 KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;,\n",
+       "                                           &#x27;Balance&#x27;],\n",
+       "                                 n_clusters=10, n_components=3,\n",
+       "                                 random_state=123)),\n",
+       "                (&#x27;surname_tfid&#x27;,\n",
+       "                 TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000,\n",
+       "                                  n_components=5)),\n",
+       "                (&#x27;age_binning&#x27;, VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)),\n",
+       "                (&#x27;salary_binning&#x27;,\n",
+       "                 VariableBinning(column_name=&#x27;EstimatedSalary&#x27;...\n",
+       "                (&#x27;customer_status&#x27;, CustomerStatusTransformer()),\n",
+       "                (&#x27;drop&#x27;, DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])),\n",
+       "                (&#x27;prep&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;encode&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [&#x27;Gender&#x27;, &#x27;Geography&#x27;,\n",
+       "                                                   &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;,\n",
+       "                                                   &#x27;IsActiveMember&#x27;,\n",
+       "                                                   &#x27;Geo_Gender&#x27;,\n",
+       "                                                   &#x27;Tp_Gender&#x27;])])),\n",
+       "                (&#x27;scale&#x27;, RobustScaler())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-252\" type=\"checkbox\" ><label for=\"sk-estimator-id-252\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>KMeansClusterer</div></div></label><div class=\"sk-toggleable__content \"><pre>KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;, &#x27;Balance&#x27;],\n",
+       "                n_clusters=10, n_components=3, random_state=123)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-253\" type=\"checkbox\" ><label for=\"sk-estimator-id-253\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>TFIDFTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000, n_components=5)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-254\" type=\"checkbox\" ><label for=\"sk-estimator-id-254\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>VariableBinning</div></div></label><div class=\"sk-toggleable__content \"><pre>VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-255\" type=\"checkbox\" ><label for=\"sk-estimator-id-255\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>VariableBinning</div></div></label><div class=\"sk-toggleable__content \"><pre>VariableBinning(column_name=&#x27;EstimatedSalary&#x27;, n_bins=10)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-256\" type=\"checkbox\" ><label for=\"sk-estimator-id-256\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>BalanceSalaryRatioTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>BalanceSalaryRatioTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-257\" type=\"checkbox\" ><label for=\"sk-estimator-id-257\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>GeoGenderTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>GeoGenderTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-258\" type=\"checkbox\" ><label for=\"sk-estimator-id-258\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>BalanceSalaryRatioTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>BalanceSalaryRatioTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-259\" type=\"checkbox\" ><label for=\"sk-estimator-id-259\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>TpGenderTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>TpGenderTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-260\" type=\"checkbox\" ><label for=\"sk-estimator-id-260\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>IsSeniorTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>IsSeniorTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-261\" type=\"checkbox\" ><label for=\"sk-estimator-id-261\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>QualityOfBalanceTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>QualityOfBalanceTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-262\" type=\"checkbox\" ><label for=\"sk-estimator-id-262\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CreditScoreTierTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>CreditScoreTierTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-263\" type=\"checkbox\" ><label for=\"sk-estimator-id-263\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>IsActiveByCreditCardTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>IsActiveByCreditCardTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-264\" type=\"checkbox\" ><label for=\"sk-estimator-id-264\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ProductsPerTenureTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>ProductsPerTenureTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-265\" type=\"checkbox\" ><label for=\"sk-estimator-id-265\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CustomerStatusTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>CustomerStatusTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-266\" type=\"checkbox\" ><label for=\"sk-estimator-id-266\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>DropColumn</div></div></label><div class=\"sk-toggleable__content \"><pre>DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])</pre></div> </div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-267\" type=\"checkbox\" ><label for=\"sk-estimator-id-267\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>prep: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for prep: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                  transformers=[(&#x27;encode&#x27;,\n",
+       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                               sparse_output=False),\n",
+       "                                 [&#x27;Gender&#x27;, &#x27;Geography&#x27;, &#x27;NumOfProducts&#x27;,\n",
+       "                                  &#x27;HasCrCard&#x27;, &#x27;IsActiveMember&#x27;, &#x27;Geo_Gender&#x27;,\n",
+       "                                  &#x27;Tp_Gender&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-268\" type=\"checkbox\" ><label for=\"sk-estimator-id-268\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>encode</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Gender&#x27;, &#x27;Geography&#x27;, &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;, &#x27;IsActiveMember&#x27;, &#x27;Geo_Gender&#x27;, &#x27;Tp_Gender&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-269\" type=\"checkbox\" ><label for=\"sk-estimator-id-269\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-270\" type=\"checkbox\" ><label for=\"sk-estimator-id-270\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;CreditScore&#x27;, &#x27;Age&#x27;, &#x27;Tenure&#x27;, &#x27;Balance&#x27;, &#x27;EstimatedSalary&#x27;, &#x27;Cluster&#x27;, &#x27;Surname_tfidf_0&#x27;, &#x27;Surname_tfidf_1&#x27;, &#x27;Surname_tfidf_2&#x27;, &#x27;Surname_tfidf_3&#x27;, &#x27;Surname_tfidf_4&#x27;, &#x27;QCut5_Age&#x27;, &#x27;QCut10_EstimatedSalary&#x27;, &#x27;Total_Products_Used&#x27;, &#x27;IsSenior&#x27;, &#x27;QualityOfBalance&#x27;, &#x27;CreditScoreTier&#x27;, &#x27;IsActive_by_CreditCard&#x27;, &#x27;Products_Per_Tenure&#x27;, &#x27;Customer_Status&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-271\" type=\"checkbox\" ><label for=\"sk-estimator-id-271\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>passthrough</div></div></label><div class=\"sk-toggleable__content \"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-272\" type=\"checkbox\" ><label for=\"sk-estimator-id-272\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>RobustScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.RobustScaler.html\">?<span>Documentation for RobustScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>RobustScaler()</pre></div> </div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-273\" type=\"checkbox\" ><label for=\"sk-estimator-id-273\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>LGBMClassifier</div></div></label><div class=\"sk-toggleable__content \"><pre>LGBMClassifier(colsample_bytree=0.7870705172001088,\n",
+       "               learning_rate=0.06933030702848553, max_depth=3,\n",
+       "               min_child_samples=17, n_estimators=162, random_state=42,\n",
+       "               reg_alpha=0.7730796425793625, reg_lambda=0.3309686355313894,\n",
+       "               subsample=0.8951097160631081)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><label>cb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-274\" type=\"checkbox\" ><label for=\"sk-estimator-id-274\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>pipeline: Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for pipeline: Pipeline</span></a></div></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
+       "                 KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;,\n",
+       "                                           &#x27;Balance&#x27;],\n",
+       "                                 n_clusters=10, n_components=3,\n",
+       "                                 random_state=123)),\n",
+       "                (&#x27;surname_tfid&#x27;,\n",
+       "                 TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000,\n",
+       "                                  n_components=5)),\n",
+       "                (&#x27;age_binning&#x27;, VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)),\n",
+       "                (&#x27;salary_binning&#x27;,\n",
+       "                 VariableBinning(column_name=&#x27;EstimatedSalary&#x27;...\n",
+       "                (&#x27;customer_status&#x27;, CustomerStatusTransformer()),\n",
+       "                (&#x27;drop&#x27;, DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])),\n",
+       "                (&#x27;prep&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;encode&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [&#x27;Gender&#x27;, &#x27;Geography&#x27;,\n",
+       "                                                   &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;,\n",
+       "                                                   &#x27;IsActiveMember&#x27;,\n",
+       "                                                   &#x27;Geo_Gender&#x27;,\n",
+       "                                                   &#x27;Tp_Gender&#x27;])])),\n",
+       "                (&#x27;scale&#x27;, RobustScaler())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-275\" type=\"checkbox\" ><label for=\"sk-estimator-id-275\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>KMeansClusterer</div></div></label><div class=\"sk-toggleable__content \"><pre>KMeansClusterer(features=[&#x27;CustomerId&#x27;, &#x27;EstimatedSalary&#x27;, &#x27;Balance&#x27;],\n",
+       "                n_clusters=10, n_components=3, random_state=123)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-276\" type=\"checkbox\" ><label for=\"sk-estimator-id-276\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>TFIDFTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>TFIDFTransformer(column=&#x27;Surname&#x27;, max_features=1000, n_components=5)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-277\" type=\"checkbox\" ><label for=\"sk-estimator-id-277\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>VariableBinning</div></div></label><div class=\"sk-toggleable__content \"><pre>VariableBinning(column_name=&#x27;Age&#x27;, n_bins=5)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-278\" type=\"checkbox\" ><label for=\"sk-estimator-id-278\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>VariableBinning</div></div></label><div class=\"sk-toggleable__content \"><pre>VariableBinning(column_name=&#x27;EstimatedSalary&#x27;, n_bins=10)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-279\" type=\"checkbox\" ><label for=\"sk-estimator-id-279\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>BalanceSalaryRatioTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>BalanceSalaryRatioTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-280\" type=\"checkbox\" ><label for=\"sk-estimator-id-280\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>GeoGenderTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>GeoGenderTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-281\" type=\"checkbox\" ><label for=\"sk-estimator-id-281\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>BalanceSalaryRatioTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>BalanceSalaryRatioTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-282\" type=\"checkbox\" ><label for=\"sk-estimator-id-282\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>TpGenderTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>TpGenderTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-283\" type=\"checkbox\" ><label for=\"sk-estimator-id-283\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>IsSeniorTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>IsSeniorTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-284\" type=\"checkbox\" ><label for=\"sk-estimator-id-284\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>QualityOfBalanceTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>QualityOfBalanceTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-285\" type=\"checkbox\" ><label for=\"sk-estimator-id-285\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CreditScoreTierTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>CreditScoreTierTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-286\" type=\"checkbox\" ><label for=\"sk-estimator-id-286\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>IsActiveByCreditCardTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>IsActiveByCreditCardTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-287\" type=\"checkbox\" ><label for=\"sk-estimator-id-287\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ProductsPerTenureTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>ProductsPerTenureTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-288\" type=\"checkbox\" ><label for=\"sk-estimator-id-288\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CustomerStatusTransformer</div></div></label><div class=\"sk-toggleable__content \"><pre>CustomerStatusTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-289\" type=\"checkbox\" ><label for=\"sk-estimator-id-289\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>DropColumn</div></div></label><div class=\"sk-toggleable__content \"><pre>DropColumn(cols=[&#x27;CustomerId&#x27;, &#x27;Surname&#x27;])</pre></div> </div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-290\" type=\"checkbox\" ><label for=\"sk-estimator-id-290\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>prep: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for prep: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                  transformers=[(&#x27;encode&#x27;,\n",
+       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
+       "                                               sparse_output=False),\n",
+       "                                 [&#x27;Gender&#x27;, &#x27;Geography&#x27;, &#x27;NumOfProducts&#x27;,\n",
+       "                                  &#x27;HasCrCard&#x27;, &#x27;IsActiveMember&#x27;, &#x27;Geo_Gender&#x27;,\n",
+       "                                  &#x27;Tp_Gender&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-291\" type=\"checkbox\" ><label for=\"sk-estimator-id-291\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>encode</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Gender&#x27;, &#x27;Geography&#x27;, &#x27;NumOfProducts&#x27;, &#x27;HasCrCard&#x27;, &#x27;IsActiveMember&#x27;, &#x27;Geo_Gender&#x27;, &#x27;Tp_Gender&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-292\" type=\"checkbox\" ><label for=\"sk-estimator-id-292\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-293\" type=\"checkbox\" ><label for=\"sk-estimator-id-293\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;CreditScore&#x27;, &#x27;Age&#x27;, &#x27;Tenure&#x27;, &#x27;Balance&#x27;, &#x27;EstimatedSalary&#x27;, &#x27;Cluster&#x27;, &#x27;Surname_tfidf_0&#x27;, &#x27;Surname_tfidf_1&#x27;, &#x27;Surname_tfidf_2&#x27;, &#x27;Surname_tfidf_3&#x27;, &#x27;Surname_tfidf_4&#x27;, &#x27;QCut5_Age&#x27;, &#x27;QCut10_EstimatedSalary&#x27;, &#x27;Total_Products_Used&#x27;, &#x27;IsSenior&#x27;, &#x27;QualityOfBalance&#x27;, &#x27;CreditScoreTier&#x27;, &#x27;IsActive_by_CreditCard&#x27;, &#x27;Products_Per_Tenure&#x27;, &#x27;Customer_Status&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-294\" type=\"checkbox\" ><label for=\"sk-estimator-id-294\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>passthrough</div></div></label><div class=\"sk-toggleable__content \"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-295\" type=\"checkbox\" ><label for=\"sk-estimator-id-295\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>RobustScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.RobustScaler.html\">?<span>Documentation for RobustScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>RobustScaler()</pre></div> </div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-296\" type=\"checkbox\" ><label for=\"sk-estimator-id-296\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CatBoostClassifier</div></div></label><div class=\"sk-toggleable__content \"><pre>&lt;catboost.core.CatBoostClassifier object at 0x00000132831A2720&gt;</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 129
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:11:34.800569Z",
+     "iopub.status.idle": "2024-01-31T04:11:34.805774Z",
+     "iopub.execute_input": "2024-01-31T04:11:34.800903Z",
+     "shell.execute_reply": "2024-01-31T04:11:34.804882Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 0.051873,
+     "end_time": "2024-01-31T04:11:34.807655",
+     "exception": false,
+     "start_time": "2024-01-31T04:11:34.755782"
+    },
+    "cell_id": "083af8955a444c14901918adbd00ca8c",
+    "deepnote_cell_type": "code"
+   },
+   "source": "# # number of folds\n# n_splits = 10\n\n# # StratifiedKFold\n# stratkf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n\n# # cross-validation results\n# cv_results = []\n\n# # stratified k-fold cross-validation\n# for fold, (train_idx, val_idx) in enumerate(stratkf.split(X, y)):\n#     X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]\n#     y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]\n\n#     ensemble_model.fit(X_train, y_train)\n\n#     # predictions on the validation set\n#     y_val_pred_prob = ensemble_model.predict_proba(X_val)[:, 1]\n#     y_pred = ensemble_model.predict(X_val)\n        \n#     f1 = f1_score(y_val, y_pred, average='weighted')\n\n#     # Evaluating the model\n#     roc_auc = roc_auc_score(y_val, y_val_pred_prob)\n#     print(f'Fold {fold + 1}, AUC Score on Validation Set: {roc_auc}')\n#     print(f'Fold {fold + 1}, F1 Score on Validation Set: {f1}')\n#     print('-'*70)\n\n#     # results\n#     cv_results.append(roc_auc)\n\n# # average cross-validation result\n# average_cv_result = sum(cv_results) / n_splits\n# print(f'\\nAverage AUC-score across {n_splits} folds: {average_cv_result}')\n",
+   "block_group": "879cd4e8c2e24b78a4324048ebc03366",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:11:34.899109Z",
+     "iopub.status.idle": "2024-01-31T04:16:13.049757Z",
+     "iopub.execute_input": "2024-01-31T04:11:34.899459Z",
+     "shell.execute_reply": "2024-01-31T04:16:13.048798Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 278.24576,
+     "end_time": "2024-01-31T04:16:13.099098",
+     "exception": false,
+     "start_time": "2024-01-31T04:11:34.853338"
+    },
+    "allow_embed": true,
+    "cell_id": "cb3091ef24b649dd9dd22dc46d911c2c",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-05T18:03:35.483635Z",
+     "start_time": "2025-08-05T17:58:00.096215Z"
+    }
+   },
+   "source": "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n\nensemble_model.fit(X = X_train, y = y_train)\n\npredictions_ensemble = ensemble_model.predict(X_val)\n\ncm_ensemble = confusion_matrix(y_val, predictions_ensemble)\n\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm_ensemble, display_labels=['Not Churn', 'Churn'])\ndisp.plot()\nplt.show()",
+   "block_group": "3800bebdc49a489baed1e09156a7c8f7",
+   "outputs_reference": "s3:deepnote-cell-outputs-production/60356584-ceba-4282-8771-5d594aaf5218",
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Info] Number of positive: 2423, number of negative: 9577\n",
+      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000409 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 2157\n",
+      "[LightGBM] [Info] Number of data points in the train set: 12000, number of used features: 47\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.201917 -> initscore=-1.374358\n",
+      "[LightGBM] [Info] Start training from score -1.374358\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "0:\tlearn: 0.6652659\ttotal: 369ms\tremaining: 4m 50s\n",
+      "100:\tlearn: 0.1618852\ttotal: 36.8s\tremaining: 4m 10s\n",
+      "200:\tlearn: 0.1045442\ttotal: 1m 14s\tremaining: 3m 36s\n",
+      "300:\tlearn: 0.0703044\ttotal: 1m 53s\tremaining: 3m 2s\n",
+      "400:\tlearn: 0.0509417\ttotal: 2m 37s\tremaining: 2m 31s\n",
+      "500:\tlearn: 0.0388118\ttotal: 3m 22s\tremaining: 1m 55s\n",
+      "600:\tlearn: 0.0322758\ttotal: 4m 8s\tremaining: 1m 16s\n",
+      "700:\tlearn: 0.0277256\ttotal: 4m 51s\tremaining: 35.8s\n",
+      "786:\tlearn: 0.0243375\ttotal: 5m 31s\tremaining: 0us\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 70
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.062002,
+     "end_time": "2024-01-31T04:16:34.520049",
+     "exception": false,
+     "start_time": "2024-01-31T04:16:34.458047"
+    },
+    "cell_id": "a3fa9201cac64b569f71400c56dd1155",
+    "deepnote_cell_type": "markdown"
+   },
+   "source": "## Submit",
+   "block_group": "4c286edbc7814ec0a7b386815f253f12"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "execution": {
+     "iopub.status.busy": "2024-01-31T04:16:34.644097Z",
+     "iopub.status.idle": "2024-01-31T04:21:45.353277Z",
+     "iopub.execute_input": "2024-01-31T04:16:34.644478Z",
+     "shell.execute_reply": "2024-01-31T04:21:45.352435Z"
+    },
+    "papermill": {
+     "status": "completed",
+     "duration": 310.774105,
+     "end_time": "2024-01-31T04:21:45.355781",
+     "exception": false,
+     "start_time": "2024-01-31T04:16:34.581676"
+    },
+    "cell_id": "140507200e8745feb048cee6767de275",
+    "deepnote_cell_type": "code",
+    "ExecuteTime": {
+     "end_time": "2025-08-07T06:25:50.793960Z",
+     "start_time": "2025-08-07T06:22:28.102576Z"
+    }
+   },
+   "source": [
+    "# Fit the ensemble final model\n",
+    "ensemble_model.fit(X=train.drop(['Exited'], axis=1), y=train['Exited'])\n",
+    "\n",
+    "# Create submission file with probability predictions\n",
+    "predictions = ensemble_model.predict_proba(test)[:, 1]  # Use the probabilities of class 1\n",
+    "\n",
+    "sample['Exited'] = predictions\n",
+    "sample.to_csv('1_submission_ensemble.csv', index=False)"
+   ],
+   "block_group": "e4bd44e4c3014db584343824962fe266",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Info] Number of positive: 3052, number of negative: 11948\n",
+      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003131 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 2153\n",
+      "[LightGBM] [Info] Number of data points in the train set: 15000, number of used features: 47\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203467 -> initscore=-1.364767\n",
+      "[LightGBM] [Info] Start training from score -1.364767\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "0:\tlearn: 0.6885124\ttotal: 54ms\tremaining: 3m 3s\n",
+      "1000:\tlearn: 0.1961344\ttotal: 52.3s\tremaining: 2m 5s\n",
+      "2000:\tlearn: 0.1530857\ttotal: 1m 51s\tremaining: 1m 17s\n",
+      "3000:\tlearn: 0.1222988\ttotal: 2m 56s\tremaining: 23.6s\n",
+      "3401:\tlearn: 0.1127655\ttotal: 3m 17s\tremaining: 0us\n"
+     ]
+    }
+   ],
+   "execution_count": 130
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "tags": [],
+    "papermill": {
+     "status": "completed",
+     "duration": 0.065815,
+     "end_time": "2024-01-31T04:21:45.486182",
+     "exception": false,
+     "start_time": "2024-01-31T04:21:45.420367"
+    },
+    "cell_id": "7135fc91a6ad4d5987c579d195f35b79",
+    "deepnote_cell_type": "code"
+   },
+   "source": "",
+   "block_group": "1f46712e6c3745de9bb688f919a486a6",
+   "outputs_reference": null,
+   "content_dependencies": null,
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": "<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=a605a3e6-1564-47b2-94e7-842290ba7692' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHN2ZyB3aWR0aD0iODBweCIgaGVpZ2h0PSI4MHB4IiB2aWV3Qm94PSIwIDAgODAgODAiIHZlcnNpb249IjEuMSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIiB4bWxuczp4bGluaz0iaHR0cDovL3d3dy53My5vcmcvMTk5OS94bGluayI+CiAgICA8IS0tIEdlbmVyYXRvcjogU2tldGNoIDU0LjEgKDc2NDkwKSAtIGh0dHBzOi8vc2tldGNoYXBwLmNvbSAtLT4KICAgIDx0aXRsZT5Hcm91cCAzPC90aXRsZT4KICAgIDxkZXNjPkNyZWF0ZWQgd2l0aCBTa2V0Y2guPC9kZXNjPgogICAgPGcgaWQ9IkxhbmRpbmciIHN0cm9rZT0ibm9uZSIgc3Ryb2tlLXdpZHRoPSIxIiBmaWxsPSJub25lIiBmaWxsLXJ1bGU9ImV2ZW5vZGQiPgogICAgICAgIDxnIGlkPSJBcnRib2FyZCIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoLTEyMzUuMDAwMDAwLCAtNzkuMDAwMDAwKSI+CiAgICAgICAgICAgIDxnIGlkPSJHcm91cC0zIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgxMjM1LjAwMDAwMCwgNzkuMDAwMDAwKSI+CiAgICAgICAgICAgICAgICA8cG9seWdvbiBpZD0iUGF0aC0yMCIgZmlsbD0iIzAyNjVCNCIgcG9pbnRzPSIyLjM3NjIzNzYyIDgwIDM4LjA0NzY2NjcgODAgNTcuODIxNzgyMiA3My44MDU3NTkyIDU3LjgyMTc4MjIgMzIuNzU5MjczOSAzOS4xNDAyMjc4IDMxLjY4MzE2ODMiPjwvcG9seWdvbj4KICAgICAgICAgICAgICAgIDxwYXRoIGQ9Ik0zNS4wMDc3MTgsODAgQzQyLjkwNjIwMDcsNzYuNDU0OTM1OCA0Ny41NjQ5MTY3LDcxLjU0MjI2NzEgNDguOTgzODY2LDY1LjI2MTk5MzkgQzUxLjExMjI4OTksNTUuODQxNTg0MiA0MS42NzcxNzk1LDQ5LjIxMjIyODQgMjUuNjIzOTg0Niw0OS4yMTIyMjg0IEMyNS40ODQ5Mjg5LDQ5LjEyNjg0NDggMjkuODI2MTI5Niw0My4yODM4MjQ4IDM4LjY0NzU4NjksMzEuNjgzMTY4MyBMNzIuODcxMjg3MSwzMi41NTQ0MjUgTDY1LjI4MDk3Myw2Ny42NzYzNDIxIEw1MS4xMTIyODk5LDc3LjM3NjE0NCBMMzUuMDA3NzE4LDgwIFoiIGlkPSJQYXRoLTIyIiBmaWxsPSIjMDAyODY4Ij48L3BhdGg+CiAgICAgICAgICAgICAgICA8cGF0aCBkPSJNMCwzNy43MzA0NDA1IEwyNy4xMTQ1MzcsMC4yNTcxMTE0MzYgQzYyLjM3MTUxMjMsLTEuOTkwNzE3MDEgODAsMTAuNTAwMzkyNyA4MCwzNy43MzA0NDA1IEM4MCw2NC45NjA0ODgyIDY0Ljc3NjUwMzgsNzkuMDUwMzQxNCAzNC4zMjk1MTEzLDgwIEM0Ny4wNTUzNDg5LDc3LjU2NzA4MDggNTMuNDE4MjY3Nyw3MC4zMTM2MTAzIDUzLjQxODI2NzcsNTguMjM5NTg4NSBDNTMuNDE4MjY3Nyw0MC4xMjg1NTU3IDM2LjMwMzk1NDQsMzcuNzMwNDQwNSAyNS4yMjc0MTcsMzcuNzMwNDQwNSBDMTcuODQzMDU4NiwzNy43MzA0NDA1IDkuNDMzOTE5NjYsMzcuNzMwNDQwNSAwLDM3LjczMDQ0MDUgWiIgaWQ9IlBhdGgtMTkiIGZpbGw9IiMzNzkzRUYiPjwvcGF0aD4KICAgICAgICAgICAgPC9nPgogICAgICAgIDwvZz4KICAgIDwvZz4KPC9zdmc+' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>",
+   "metadata": {
+    "created_in_deepnote_cell": true,
+    "deepnote_cell_type": "markdown"
+   }
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "deepnote_notebook_id": "01dcfa69b00540c5a5f5392ba8c52623"
+ }
+}
diff --git a/bank_customer_churn_prediction/catboost_info/catboost_training.json b/bank_customer_churn_prediction/catboost_info/catboost_training.json
index 5d1d857..448af3a 100644
--- a/bank_customer_churn_prediction/catboost_info/catboost_training.json
+++ b/bank_customer_churn_prediction/catboost_info/catboost_training.json
@@ -1,9004 +1,5857 @@
 {
-"meta":{"test_sets":["test"],"test_metrics":[{"best_value":"Max","name":"AUC"},{"best_value":"Min","name":"Logloss"}],"learn_metrics":[{"best_value":"Min","name":"Logloss"}],"launch_mode":"Train","parameters":"","iteration_count":9000,"learn_sets":["learn"],"name":"experiment"},
+"meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"Logloss"}],"launch_mode":"Train","parameters":"","iteration_count":5853,"learn_sets":["learn"],"name":"experiment"},
 "iterations":[
-{"learn":[0.6919771973],"iteration":0,"passed_time":0.1520067389,"remaining_time":1367.908643,"test":[0.9192103185,0.691978304]},
-{"learn":[0.6908364258],"iteration":1,"passed_time":0.1573348546,"remaining_time":707.849511,"test":[0.9205304751,0.6908449065]},
-{"learn":[0.6897222738],"iteration":2,"passed_time":0.1627142991,"remaining_time":487.9801831,"test":[0.9226761102,0.6897386766]},
-{"learn":[0.6885702759],"iteration":3,"passed_time":0.1672284819,"remaining_time":376.0968559,"test":[0.9250307041,0.688596029]},
-{"learn":[0.6873564575],"iteration":4,"passed_time":0.1723849953,"remaining_time":310.1206066,"test":[0.9293311126,0.6873782851]},
-{"learn":[0.686272762],"iteration":5,"passed_time":0.1771153361,"remaining_time":265.4958888,"test":[0.9292062247,0.6863028042]},
-{"learn":[0.6851098145],"iteration":6,"passed_time":0.1817269901,"remaining_time":233.4672603,"test":[0.930615935,0.6851343431]},
-{"learn":[0.6839749919],"iteration":7,"passed_time":0.1861172417,"remaining_time":209.1957797,"test":[0.9307280295,0.6840067813]},
-{"learn":[0.6828633423],"iteration":8,"passed_time":0.1906089566,"remaining_time":190.4183477,"test":[0.9302546739,0.6828947419]},
-{"learn":[0.6817465698],"iteration":9,"passed_time":0.1948431307,"remaining_time":175.1639745,"test":[0.9299677363,0.6817849368]},
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diff --git a/bank_customer_churn_prediction/catboost_info/learn_error.tsv b/bank_customer_churn_prediction/catboost_info/learn_error.tsv
index 146f61c..4b7c609 100644
--- a/bank_customer_churn_prediction/catboost_info/learn_error.tsv
+++ b/bank_customer_churn_prediction/catboost_info/learn_error.tsv
@@ -5852,3150 +5852,3 @@ iter	Logloss
 5850	0.2154889821
 5851	0.2154850677
 5852	0.2154803843
-5853	0.2154763
-5854	0.2154743774
-5855	0.2154706045
-5856	0.2154665639
-5857	0.2154627431
-5858	0.2154595093
-5859	0.2154548777
-5860	0.2154514099
-5861	0.2154473022
-5862	0.215444105
-5863	0.215441687
-5864	0.215438564
-5865	0.2154356445
-5866	0.2154318858
-5867	0.2154299581
-5868	0.2154246826
-5869	0.2154206502
-5870	0.2154163157
-5871	0.2154123444
-5872	0.2154092021
-5873	0.2154052266
-5874	0.2154014648
-5875	0.215397879
-5876	0.2153945557
-5877	0.2153905039
-5878	0.2153874054
-5879	0.2153829132
-5880	0.2153783152
-5881	0.2153741404
-5882	0.2153719401
-5883	0.2153687602
-5884	0.2153666941
-5885	0.2153630391
-5886	0.2153599091
-5887	0.2153562327
-5888	0.2153529561
-5889	0.2153494029
-5890	0.2153456746
-5891	0.2153413472
-5892	0.2153382365
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diff --git a/bank_customer_churn_prediction/catboost_info/test/events.out.tfevents b/bank_customer_churn_prediction/catboost_info/test/events.out.tfevents
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diff --git a/bank_customer_churn_prediction/catboost_info/test_error.tsv b/bank_customer_churn_prediction/catboost_info/test_error.tsv
index 032df0c..878055d 100644
--- a/bank_customer_churn_prediction/catboost_info/test_error.tsv
+++ b/bank_customer_churn_prediction/catboost_info/test_error.tsv
@@ -1,9001 +1,7856 @@
 iter	AUC	Logloss
-0	0.9192103185	0.691978304
-1	0.9205304751	0.6908449065
-2	0.9226761102	0.6897386766
-3	0.9250307041	0.688596029
-4	0.9293311126	0.6873782851
-5	0.9292062247	0.6863028042
-6	0.930615935	0.6851343431
-7	0.9307280295	0.6840067813
-8	0.9302546739	0.6828947419
-9	0.9299677363	0.6817849368
-10	0.9298684352	0.6807353584
-11	0.9301364873	0.6795843169
-12	0.930659798	0.6784561817
-13	0.9310387262	0.6773461523
-14	0.9310222776	0.6763362193
-15	0.9312489034	0.6751943784
-16	0.9313792742	0.6740901932
-17	0.9316863157	0.6729247053
-18	0.9319598507	0.6718151203
-19	0.9321663726	0.6706669896
-20	0.9317582024	0.6695973874
-21	0.9318252154	0.6685508526
-22	0.9319330455	0.6674872694
-23	0.9318051115	0.6664040152
-24	0.9317173854	0.6653386754
-25	0.9320561057	0.6642610617
-26	0.932036611	0.6631886151
-27	0.9321097162	0.6621222659
-28	0.9320816925	0.6610694744
-29	0.9321998791	0.6599635896
-30	0.9319208613	0.6589346674
-31	0.9322595816	0.6578501273
-32	0.9322437422	0.6567208884
-33	0.9322145002	0.6556500234
-34	0.932007369	0.6546012929
-35	0.9319196429	0.6535337258
-36	0.9317965826	0.6525209231
-37	0.9318940561	0.6515166525
-38	0.9317466274	0.6504666065
-39	0.9317837892	0.649412912
-40	0.9319446204	0.6483463684
-41	0.9320372203	0.6472377425
-42	0.9320195532	0.6461926546
-43	0.9322778579	0.6451764535
-44	0.9321767292	0.644192674
-45	0.9322151094	0.6432523231
-46	0.9322394777	0.6422065073
-47	0.9321749016	0.6412239298
-48	0.9321596713	0.6402592292
-49	0.9321133714	0.639250889
-50	0.932231558	0.6382141785
-51	0.932179166	0.6371803689
-52	0.9320963136	0.6362108967
-53	0.9320475768	0.6351986996
-54	0.9319878743	0.6342135024
-55	0.9319586323	0.6332191412
-56	0.9318453193	0.6322807804
-57	0.9319720349	0.6312647125
-58	0.9319659428	0.6302696601
-59	0.9319915296	0.6292332157
-60	0.9321182451	0.6282814846
-61	0.9322071896	0.6272607906
-62	0.932298571	0.6262310678
-63	0.9322120633	0.6252733066
-64	0.9322815132	0.6242527682
-65	0.9324179761	0.6232429461
-66	0.9323936077	0.6222710283
-67	0.9323704577	0.6213206443
-68	0.9323692393	0.620356693
-69	0.9324374708	0.6193817216
-70	0.9324472181	0.6183605889
-71	0.9323643656	0.6174623207
-72	0.9323716762	0.6165505735
-73	0.9323765498	0.6155371357
-74	0.9323131921	0.6145708985
-75	0.9322352133	0.6136745829
-76	0.9323412157	0.612698644
-77	0.9323570551	0.6117527118
-78	0.9324606207	0.6107697139
-79	0.9324204129	0.6098571988
-80	0.9325239785	0.6089303101
-81	0.9325727152	0.6080139817
-82	0.9326141415	0.6070795912
-83	0.9325751521	0.6061378804
-84	0.9325422548	0.6052783672
-85	0.932493518	0.6043716119
-86	0.9326860282	0.6034346629
-87	0.9327652254	0.6024538785
-88	0.9327566964	0.6015546323
-89	0.9328773199	0.6006038878
-90	0.9329857591	0.599734801
-91	0.9329528618	0.598794947
-92	0.9330223117	0.5978818388
-93	0.9330783589	0.596979808
-94	0.9330369327	0.5960979348
-95	0.9330150012	0.5952441782
-96	0.9330052538	0.5943418609
-97	0.9330576458	0.5934661315
-98	0.9330296222	0.5926124258
-99	0.9329857591	0.5917518247
-100	0.9330381511	0.5908489057
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-102	0.9331490272	0.589113005
-103	0.9330418064	0.5883590236
-104	0.9329991617	0.5875010455
-105	0.9330686116	0.5866233729
-106	0.9330357143	0.585798783
-107	0.9330661748	0.5849394725
-108	0.9330040354	0.5840517887
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-110	0.9330832326	0.5822648818
-111	0.9331002905	0.5814666808
-112	0.9330820142	0.5806344136
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-117	0.9330734853	0.5763919285
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-124	0.9331587746	0.5706358252
-125	0.9330734853	0.5698280066
-126	0.9331502456	0.5689571508
-127	0.9331344062	0.5680966103
-128	0.9331392799	0.5672739657
-129	0.9332684322	0.5664460123
-130	0.9332611217	0.5656078353
-131	0.9333378821	0.5647485192
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-133	0.9333147321	0.5631488801
-134	0.9333878373	0.5623239591
-135	0.9333732162	0.5615060423
-136	0.9333951478	0.5606652312
-137	0.9333975846	0.5598464009
-138	0.9333866188	0.5589917625
-139	0.9333829636	0.5582018693
-140	0.9333683426	0.5574093296
-141	0.9334353556	0.5566112329
-142	0.9335060239	0.5558162802
-143	0.9335194265	0.5550374634
-144	0.9334572871	0.5543286276
-145	0.933384182	0.5535768498
-146	0.9334231714	0.5527648417
-147	0.9334048951	0.5519319956
-148	0.9334426661	0.551149605
-149	0.9335023686	0.5503653253
-150	0.9335425764	0.549615063
-151	0.9334914028	0.5488554677
-152	0.9334621608	0.5480843004
-153	0.9334122056	0.5473546551
-154	0.933459724	0.5465423264
-155	0.9334207346	0.5457995955
-156	0.9333914925	0.5450507652
-157	0.9334170793	0.544275836
-158	0.9333646873	0.54353558
-159	0.9334024583	0.5427393637
-160	0.9334463214	0.5419584456
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-162	0.933451195	0.5404450938
-163	0.933407332	0.5397006756
-164	0.9333634689	0.5389993851
-165	0.9334182977	0.5382687245
-166	0.9334572871	0.5374807779
-167	0.9335377027	0.5367155814
-168	0.9335425764	0.5359952393
-169	0.9335718185	0.5352791826
-170	0.9336156815	0.5345573538
-171	0.9336022789	0.5338524389
-172	0.9336144631	0.5331141074
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-175	0.9336802577	0.5309176702
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-179	0.9336705104	0.5281329856
-180	0.9336473604	0.527424331
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-182	0.9335681632	0.52606642
-183	0.9336169	0.5253413528
-184	0.9336437052	0.5246441476
-185	0.9336217736	0.5239681606
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-187	0.9336290841	0.5225693803
-188	0.9336766025	0.5218514144
-189	0.9336863498	0.5211219966
-190	0.9336558893	0.5205049975
-191	0.9336522341	0.519859152
-192	0.9336095894	0.5191910801
-193	0.9335998421	0.5185581447
-194	0.933646142	0.5178511204
-195	0.9336802577	0.5171608418
-196	0.9336997524	0.5164825323
-197	0.9336619814	0.5158072412
-198	0.9336851314	0.5151411848
-199	0.9336473604	0.5144704852
-200	0.9336619814	0.5138150553
-201	0.9336351762	0.5131684058
-202	0.933683913	0.5124973874
-203	0.9336900051	0.5118229041
-204	0.9336583262	0.5111869217
-205	0.933683913	0.5105385237
-206	0.9336875682	0.5098799374
-207	0.9336522341	0.5092220019
-208	0.9336705104	0.5085323864
-209	0.933713155	0.5079309524
-210	0.9336960972	0.5072719092
-211	0.9337180287	0.5066277739
-212	0.9337302129	0.505972684
-213	0.9337338681	0.5052946372
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-215	0.9337533628	0.5040579292
-216	0.9337618918	0.5033812507
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-218	0.9338167206	0.5020762424
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-222	0.9339239414	0.4995016968
-223	0.9339202862	0.4988585318
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diff --git a/bank_customer_churn_prediction/catboost_info/time_left.tsv b/bank_customer_churn_prediction/catboost_info/time_left.tsv
index e6933ca..7d3e31d 100644
--- a/bank_customer_churn_prediction/catboost_info/time_left.tsv
+++ b/bank_customer_churn_prediction/catboost_info/time_left.tsv
@@ -1,9001 +1,5854 @@
 iter	Passed	Remaining
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-2	162	487980
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-4	172	310120
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diff --git a/bank_customer_churn_prediction/kaggle_competition.ipynb b/bank_customer_churn_prediction/kaggle_competition.ipynb
index 3021279..fa65660 100644
--- a/bank_customer_churn_prediction/kaggle_competition.ipynb
+++ b/bank_customer_churn_prediction/kaggle_competition.ipynb
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-     "start_time": "2025-06-25T17:23:16.943720Z"
+     "end_time": "2025-08-10T10:38:22.316009Z",
+     "start_time": "2025-08-10T10:38:22.108630Z"
     }
    },
    "cell_type": "code",
@@ -1288,8 +1288,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:23:17.163479Z",
-     "start_time": "2025-06-25T17:23:17.159480Z"
+     "end_time": "2025-08-10T10:38:22.332322Z",
+     "start_time": "2025-08-10T10:38:22.328323Z"
     }
    },
    "cell_type": "code",
@@ -1301,8 +1301,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:23:17.189487Z",
-     "start_time": "2025-06-25T17:23:17.184626Z"
+     "end_time": "2025-08-10T10:38:22.355333Z",
+     "start_time": "2025-08-10T10:38:22.350588Z"
     }
    },
    "cell_type": "code",
@@ -1337,8 +1337,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:23:17.540919Z",
-     "start_time": "2025-06-25T17:23:17.243941Z"
+     "end_time": "2025-08-10T10:38:22.702926Z",
+     "start_time": "2025-08-10T10:38:22.400677Z"
     }
    },
    "cell_type": "code",
@@ -1367,8 +1367,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:23:17.586567Z",
-     "start_time": "2025-06-25T17:23:17.573002Z"
+     "end_time": "2025-08-10T10:38:22.747525Z",
+     "start_time": "2025-08-10T10:38:22.735248Z"
     }
    },
    "cell_type": "code",
@@ -1387,8 +1387,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:23:17.638002Z",
-     "start_time": "2025-06-25T17:23:17.632767Z"
+     "end_time": "2025-08-10T10:38:22.834704Z",
+     "start_time": "2025-08-10T10:38:22.829084Z"
     }
    },
    "cell_type": "code",
@@ -1444,8 +1444,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:23:17.722745Z",
-     "start_time": "2025-06-25T17:23:17.716922Z"
+     "end_time": "2025-08-10T10:38:22.906961Z",
+     "start_time": "2025-08-10T10:38:22.901578Z"
     }
    },
    "cell_type": "code",
@@ -1515,8 +1515,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:23:17.997461Z",
-     "start_time": "2025-06-25T17:23:17.854069Z"
+     "end_time": "2025-08-10T10:38:23.198431Z",
+     "start_time": "2025-08-10T10:38:23.066761Z"
     }
    },
    "cell_type": "code",
@@ -1532,25 +1532,25 @@
     {
      "data": {
       "text/plain": [
-       "       CreditScore   Balance  EstimatedSalary    Tenure  CreditScore^2  \\\n",
-       "12280    -0.248350  1.243009        -0.142896 -1.089564      -0.298182   \n",
-       "12672    -1.327882  1.896069        -1.166256 -0.009100      -1.279125   \n",
+       "      CreditScore   Balance  EstimatedSalary    Tenure  CreditScore^2  \\\n",
+       "9523     1.134496 -0.710855        -0.981462 -0.367678       1.144536   \n",
+       "9121    -1.031352 -0.710855         1.393098  1.437868      -1.022805   \n",
        "\n",
-       "       CreditScore Balance  CreditScore EstimatedSalary  CreditScore Tenure  \\\n",
-       "12280             1.180445                    -0.200157           -1.081623   \n",
-       "12672             1.503946                    -1.311482           -0.265030   \n",
+       "      CreditScore Balance  CreditScore EstimatedSalary  CreditScore Tenure  \\\n",
+       "9523            -0.704643                    -0.749083           -0.184195   \n",
+       "9121            -0.704643                     0.903563            1.044435   \n",
        "\n",
-       "       Balance^2  Balance EstimatedSalary  ...  Geography_Germany  \\\n",
-       "12280   1.029133                 1.021896  ...           2.151399   \n",
-       "12672   2.350924                 0.642598  ...          -0.464814   \n",
+       "      Balance^2  Balance EstimatedSalary  ...  Geography_Germany  \\\n",
+       "9523  -0.665538                -0.641046  ...           -0.45821   \n",
+       "9121  -0.665538                -0.641046  ...           -0.45821   \n",
        "\n",
-       "       Geography_Spain  Gender_Male  NumOfProducts_2.0  NumOfProducts_3.0  \\\n",
-       "12280        -0.531394    -1.148057          -1.117531          -0.122566   \n",
-       "12672        -0.531394     0.871037          -1.117531          -0.122566   \n",
+       "      Geography_Spain  Gender_Male  NumOfProducts_2.0  NumOfProducts_3.0  \\\n",
+       "9523        -0.531822     0.862662           0.892244          -0.121601   \n",
+       "9121        -0.531822     0.862662          -1.120769          -0.121601   \n",
        "\n",
-       "       NumOfProducts_4.0   Surname  NumOfProducts    Gender  Geography  \n",
-       "12280          -0.044766 -2.023555       0.930251  1.148057   2.151078  \n",
-       "12672          -0.044766  0.970214       0.930251 -0.871037  -0.453224  \n",
+       "      NumOfProducts_4.0   Surname  NumOfProducts    Gender  Geography  \n",
+       "9523          -0.047864 -0.193367       -0.83344 -0.862662  -0.441993  \n",
+       "9121          -0.047864 -1.015003        0.94223 -0.862662  -0.441993  \n",
        "\n",
        "[2 rows x 27 columns]"
       ],
@@ -1598,52 +1598,52 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>12280</th>\n",
-       "      <td>-0.248350</td>\n",
-       "      <td>1.243009</td>\n",
-       "      <td>-0.142896</td>\n",
-       "      <td>-1.089564</td>\n",
-       "      <td>-0.298182</td>\n",
-       "      <td>1.180445</td>\n",
-       "      <td>-0.200157</td>\n",
-       "      <td>-1.081623</td>\n",
-       "      <td>1.029133</td>\n",
-       "      <td>1.021896</td>\n",
+       "      <th>9523</th>\n",
+       "      <td>1.134496</td>\n",
+       "      <td>-0.710855</td>\n",
+       "      <td>-0.981462</td>\n",
+       "      <td>-0.367678</td>\n",
+       "      <td>1.144536</td>\n",
+       "      <td>-0.704643</td>\n",
+       "      <td>-0.749083</td>\n",
+       "      <td>-0.184195</td>\n",
+       "      <td>-0.665538</td>\n",
+       "      <td>-0.641046</td>\n",
        "      <td>...</td>\n",
-       "      <td>2.151399</td>\n",
-       "      <td>-0.531394</td>\n",
-       "      <td>-1.148057</td>\n",
-       "      <td>-1.117531</td>\n",
-       "      <td>-0.122566</td>\n",
-       "      <td>-0.044766</td>\n",
-       "      <td>-2.023555</td>\n",
-       "      <td>0.930251</td>\n",
-       "      <td>1.148057</td>\n",
-       "      <td>2.151078</td>\n",
+       "      <td>-0.45821</td>\n",
+       "      <td>-0.531822</td>\n",
+       "      <td>0.862662</td>\n",
+       "      <td>0.892244</td>\n",
+       "      <td>-0.121601</td>\n",
+       "      <td>-0.047864</td>\n",
+       "      <td>-0.193367</td>\n",
+       "      <td>-0.83344</td>\n",
+       "      <td>-0.862662</td>\n",
+       "      <td>-0.441993</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12672</th>\n",
-       "      <td>-1.327882</td>\n",
-       "      <td>1.896069</td>\n",
-       "      <td>-1.166256</td>\n",
-       "      <td>-0.009100</td>\n",
-       "      <td>-1.279125</td>\n",
-       "      <td>1.503946</td>\n",
-       "      <td>-1.311482</td>\n",
-       "      <td>-0.265030</td>\n",
-       "      <td>2.350924</td>\n",
-       "      <td>0.642598</td>\n",
+       "      <th>9121</th>\n",
+       "      <td>-1.031352</td>\n",
+       "      <td>-0.710855</td>\n",
+       "      <td>1.393098</td>\n",
+       "      <td>1.437868</td>\n",
+       "      <td>-1.022805</td>\n",
+       "      <td>-0.704643</td>\n",
+       "      <td>0.903563</td>\n",
+       "      <td>1.044435</td>\n",
+       "      <td>-0.665538</td>\n",
+       "      <td>-0.641046</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.464814</td>\n",
-       "      <td>-0.531394</td>\n",
-       "      <td>0.871037</td>\n",
-       "      <td>-1.117531</td>\n",
-       "      <td>-0.122566</td>\n",
-       "      <td>-0.044766</td>\n",
-       "      <td>0.970214</td>\n",
-       "      <td>0.930251</td>\n",
-       "      <td>-0.871037</td>\n",
-       "      <td>-0.453224</td>\n",
+       "      <td>-0.45821</td>\n",
+       "      <td>-0.531822</td>\n",
+       "      <td>0.862662</td>\n",
+       "      <td>-1.120769</td>\n",
+       "      <td>-0.121601</td>\n",
+       "      <td>-0.047864</td>\n",
+       "      <td>-1.015003</td>\n",
+       "      <td>0.94223</td>\n",
+       "      <td>-0.862662</td>\n",
+       "      <td>-0.441993</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1661,8 +1661,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:25:55.359172Z",
-     "start_time": "2025-06-25T17:25:52.022783Z"
+     "end_time": "2025-08-10T10:38:25.748283Z",
+     "start_time": "2025-08-10T10:38:23.338264Z"
     }
    },
    "cell_type": "code",
@@ -1858,7 +1858,9 @@
        "/* Toggleable content - dropdown */\n",
        "\n",
        "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
@@ -1884,9 +1886,9 @@
        "\n",
        "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
        "}\n",
        "\n",
        "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
@@ -2091,327 +2093,21 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, n_estimators=200, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">200</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">10</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('bootstrap',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('oob_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">oob_score&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_samples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_samples&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, n_estimators=200, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=10, n_estimators=200, random_state=3)</pre></div> </div></div></div></div>"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 47
+   "execution_count": 46
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:26:25.021561Z",
-     "start_time": "2025-06-25T17:26:24.653399Z"
+     "end_time": "2025-08-10T10:38:26.182229Z",
+     "start_time": "2025-08-10T10:38:25.898084Z"
     }
    },
    "cell_type": "code",
@@ -2436,22 +2132,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.9390666666666667\n",
-      "Validation Accuracy 0.9391111111111111\n",
+      "Train Accuracy 0.95\n",
+      "Validation Accuracy 0.8973333333333333\n",
       "\n",
       "\n",
-      "Train AUC Score 0.97756974192668\n",
-      "Validation AUC Score 0.9750504425296319\n"
+      "Train AUC Score 0.9853216567525888\n",
+      "Validation AUC Score 0.926681661338116\n"
      ]
     }
    ],
-   "execution_count": 49
+   "execution_count": 47
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:26:31.828506Z",
-     "start_time": "2025-06-25T17:26:31.606625Z"
+     "end_time": "2025-08-10T10:38:26.632751Z",
+     "start_time": "2025-08-10T10:38:26.398005Z"
     }
    },
    "cell_type": "code",
@@ -2480,19 +2176,19 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 50
+   "execution_count": 48
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:26:46.380385Z",
-     "start_time": "2025-06-25T17:26:46.342506Z"
+     "end_time": "2025-08-10T10:38:26.791326Z",
+     "start_time": "2025-08-10T10:38:26.752002Z"
     }
    },
    "cell_type": "code",
@@ -2506,17 +2202,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test AUC Score 0.9750321662507797\n"
+      "Test AUC Score 0.9275759805832813\n"
      ]
     }
    ],
-   "execution_count": 51
+   "execution_count": 49
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:26:54.021610Z",
-     "start_time": "2025-06-25T17:26:53.875845Z"
+     "end_time": "2025-08-10T10:38:27.048329Z",
+     "start_time": "2025-08-10T10:38:26.904810Z"
     }
    },
    "cell_type": "code",
@@ -2528,13 +2224,13 @@
    ],
    "id": "7bfb79c5c9fe66d5",
    "outputs": [],
-   "execution_count": 52
+   "execution_count": 50
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:27:00.495234Z",
-     "start_time": "2025-06-25T17:27:00.453894Z"
+     "end_time": "2025-08-10T10:38:27.211371Z",
+     "start_time": "2025-08-10T10:38:27.177878Z"
     }
    },
    "cell_type": "code",
@@ -2549,17 +2245,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.148913\n",
-       "1     15001  0.013209\n",
-       "2     15002  0.022483\n",
-       "3     15003  0.769789\n",
-       "4     15004  0.032218\n",
+       "0     15000  0.213633\n",
+       "1     15001  0.014378\n",
+       "2     15002  0.014053\n",
+       "3     15003  0.806698\n",
+       "4     15004  0.011883\n",
        "...     ...       ...\n",
-       "9995  24995  0.023759\n",
-       "9996  24996  0.016029\n",
-       "9997  24997  0.011792\n",
-       "9998  24998  0.060347\n",
-       "9999  24999  0.183835\n",
+       "9995  24995  0.026844\n",
+       "9996  24996  0.006901\n",
+       "9997  24997  0.015474\n",
+       "9998  24998  0.076145\n",
+       "9999  24999  0.236198\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -2590,27 +2286,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.148913</td>\n",
+       "      <td>0.213633</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.013209</td>\n",
+       "      <td>0.014378</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.022483</td>\n",
+       "      <td>0.014053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.769789</td>\n",
+       "      <td>0.806698</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.032218</td>\n",
+       "      <td>0.011883</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2620,27 +2316,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.023759</td>\n",
+       "      <td>0.026844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.016029</td>\n",
+       "      <td>0.006901</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.011792</td>\n",
+       "      <td>0.015474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.060347</td>\n",
+       "      <td>0.076145</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.183835</td>\n",
+       "      <td>0.236198</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2648,24 +2344,24 @@
        "</div>"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 53
+   "execution_count": 51
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:27:30.801057Z",
-     "start_time": "2025-06-25T17:27:22.515229Z"
+     "end_time": "2025-08-10T10:38:28.161406Z",
+     "start_time": "2025-08-10T10:38:27.358976Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "xgmodel = XGBClassifier(n_estimators=1500, verbose=0, learning_rate=.009, njobs=-1, random_state=3)\n",
-    "xgmodel.fit(train_prepared, y_train, eval_set=[(valid_prepared, y_valid)], eval_metric='auc', early_stopping_rounds=30)"
+    "xgmodel = XGBClassifier(n_estimators=1500, verbose=0, learning_rate=.009, eval_metric='auc', njobs=-1, early_stopping_rounds=30, random_state=3)\n",
+    "xgmodel.fit(train_prepared, y_train, eval_set=[(valid_prepared, y_valid)], )"
    ],
    "id": "1344df5295a992cd",
    "outputs": [
@@ -2673,1509 +2369,308 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[20:27:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0fdc6d574b9c0d168-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
-      "Parameters: { \"njobs\", \"verbose\" } are not used.\n",
-      "\n",
-      "[0]\tvalidation_0-auc:0.92293\n",
-      "[1]\tvalidation_0-auc:0.92631\n",
-      "[2]\tvalidation_0-auc:0.92603\n",
-      "[3]\tvalidation_0-auc:0.92641\n",
-      "[4]\tvalidation_0-auc:0.92617\n",
-      "[5]\tvalidation_0-auc:0.92642\n",
-      "[6]\tvalidation_0-auc:0.92613\n",
-      "[7]\tvalidation_0-auc:0.92651\n",
-      "[8]\tvalidation_0-auc:0.92618\n",
-      "[9]\tvalidation_0-auc:0.92653\n",
-      "[10]\tvalidation_0-auc:0.92664\n",
-      "[11]\tvalidation_0-auc:0.92976\n",
-      "[12]\tvalidation_0-auc:0.93062\n",
-      "[13]\tvalidation_0-auc:0.93061\n",
-      "[14]\tvalidation_0-auc:0.93102\n",
-      "[15]\tvalidation_0-auc:0.93123\n",
-      "[16]\tvalidation_0-auc:0.93123\n",
-      "[17]\tvalidation_0-auc:0.93122\n",
-      "[18]\tvalidation_0-auc:0.93135\n",
-      "[19]\tvalidation_0-auc:0.93124\n",
-      "[20]\tvalidation_0-auc:0.93157\n",
-      "[21]\tvalidation_0-auc:0.93151\n",
-      "[22]\tvalidation_0-auc:0.93170\n",
-      "[23]\tvalidation_0-auc:0.93151\n",
-      "[24]\tvalidation_0-auc:0.93198\n",
-      "[25]\tvalidation_0-auc:0.93198\n",
-      "[26]\tvalidation_0-auc:0.93233\n",
-      "[27]\tvalidation_0-auc:0.93221\n",
-      "[28]\tvalidation_0-auc:0.93222\n",
-      "[29]\tvalidation_0-auc:0.93245\n",
-      "[30]\tvalidation_0-auc:0.93243\n",
-      "[31]\tvalidation_0-auc:0.93244\n",
-      "[32]\tvalidation_0-auc:0.93246\n",
-      "[33]\tvalidation_0-auc:0.93287\n",
-      "[34]\tvalidation_0-auc:0.93296\n",
-      "[35]\tvalidation_0-auc:0.93321\n",
-      "[36]\tvalidation_0-auc:0.93332\n",
-      "[37]\tvalidation_0-auc:0.93333\n",
-      "[38]\tvalidation_0-auc:0.93356\n",
-      "[39]\tvalidation_0-auc:0.93372\n",
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     },
     {
@@ -4183,15 +2678,15 @@
       "text/plain": [
        "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
        "              colsample_bylevel=None, colsample_bynode=None,\n",
-       "              colsample_bytree=None, early_stopping_rounds=None,\n",
-       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
-       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.009, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
-       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=1500, n_jobs=None, njobs=-1, num_parallel_tree=None,\n",
-       "              predictor=None, ...)"
+       "              colsample_bytree=None, device=None, early_stopping_rounds=30,\n",
+       "              enable_categorical=False, eval_metric='auc', feature_types=None,\n",
+       "              feature_weights=None, gamma=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.009, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
+       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
+       "              monotone_constraints=None, multi_strategy=None, n_estimators=1500,\n",
+       "              n_jobs=None, njobs=-1, ...)"
       ],
       "text/html": [
        "<style>#sk-container-id-2 {\n",
@@ -4374,7 +2869,9 @@
        "/* Toggleable content - dropdown */\n",
        "\n",
        "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  display: none;\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
@@ -4400,9 +2897,9 @@
        "\n",
        "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
        "}\n",
        "\n",
        "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
@@ -4607,567 +3104,41 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=None, device=None, early_stopping_rounds=30,\n",
+       "              enable_categorical=False, eval_metric=&#x27;auc&#x27;, feature_types=None,\n",
+       "              feature_weights=None, gamma=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.009, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
+       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
+       "              monotone_constraints=None, multi_strategy=None, n_estimators=1500,\n",
+       "              n_jobs=None, njobs=-1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.0.0/python/python_api.html#xgboost.XGBClassifier\">?<span>Documentation for XGBClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
        "              colsample_bylevel=None, colsample_bynode=None,\n",
-       "              colsample_bytree=None, early_stopping_rounds=None,\n",
-       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
-       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.009, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
-       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=1500, n_jobs=None, njobs=-1, num_parallel_tree=None,\n",
-       "              predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('objective',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">objective&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;binary:logistic&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('use_label_encoder',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">use_label_encoder&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('base_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">base_score&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('booster',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">booster&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('callbacks',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">callbacks&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('colsample_bylevel',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">colsample_bylevel&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('colsample_bynode',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">colsample_bynode&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('early_stopping_rounds',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">early_stopping_rounds&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('enable_categorical',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">enable_categorical&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('eval_metric',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">eval_metric&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('feature_types',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">feature_types&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('gamma',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">gamma&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('gpu_id',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">gpu_id&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('grow_policy',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">grow_policy&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('importance_type',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">importance_type&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('interaction_constraints',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">interaction_constraints&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('learning_rate',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
-       "            <td class=\"value\">0.009</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_bin',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_bin&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_cat_threshold',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_cat_threshold&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_cat_to_onehot',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_cat_to_onehot&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_delta_step',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_delta_step&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaves',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaves&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_child_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('missing',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">missing&nbsp;</td>\n",
-       "            <td class=\"value\">nan</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotone_constraints',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotone_constraints&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">1500</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('num_parallel_tree',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">num_parallel_tree&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('predictor',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">predictor&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('reg_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('reg_lambda',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('sampling_method',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">sampling_method&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('scale_pos_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">scale_pos_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('subsample',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">subsample&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tree_method',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tree_method&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('validate_parameters',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">validate_parameters&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbosity',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbosity&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('njobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">njobs&nbsp;</td>\n",
-       "            <td class=\"value\">-1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
+       "              colsample_bytree=None, device=None, early_stopping_rounds=30,\n",
+       "              enable_categorical=False, eval_metric=&#x27;auc&#x27;, feature_types=None,\n",
+       "              feature_weights=None, gamma=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.009, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
+       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
+       "              monotone_constraints=None, multi_strategy=None, n_estimators=1500,\n",
+       "              n_jobs=None, njobs=-1, ...)</pre></div> </div></div></div></div>"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 54
+   "execution_count": 52
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:27:37.246177Z",
-     "start_time": "2025-06-25T17:27:37.116740Z"
+     "end_time": "2025-08-10T10:38:28.593808Z",
+     "start_time": "2025-08-10T10:38:28.563148Z"
     }
    },
    "cell_type": "code",
@@ -5178,22 +3149,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.9470666666666666\n",
-      "Validation Accuracy 0.9435555555555556\n",
+      "Train Accuracy 0.924\n",
+      "Validation Accuracy 0.8973333333333333\n",
       "\n",
       "\n",
-      "Train AUC Score 0.981989670397849\n",
-      "Validation AUC Score 0.9791260527136618\n"
+      "Train AUC Score 0.9632359658031477\n",
+      "Validation AUC Score 0.9259518286026202\n"
      ]
     }
    ],
-   "execution_count": 55
+   "execution_count": 53
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:27:39.004054Z",
-     "start_time": "2025-06-25T17:27:38.790024Z"
+     "end_time": "2025-08-10T10:38:29.008584Z",
+     "start_time": "2025-08-10T10:38:28.783692Z"
     }
    },
    "cell_type": "code",
@@ -5205,19 +3176,19 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 56
+   "execution_count": 54
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:27:49.694389Z",
-     "start_time": "2025-06-25T17:27:49.615007Z"
+     "end_time": "2025-08-10T10:38:29.175472Z",
+     "start_time": "2025-08-10T10:38:29.130324Z"
     }
    },
    "cell_type": "code",
@@ -5233,17 +3204,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.086987\n",
-       "1     15001  0.033294\n",
-       "2     15002  0.021645\n",
-       "3     15003  0.921273\n",
-       "4     15004  0.034494\n",
+       "0     15000  0.116020\n",
+       "1     15001  0.047011\n",
+       "2     15002  0.042478\n",
+       "3     15003  0.719505\n",
+       "4     15004  0.050922\n",
        "...     ...       ...\n",
-       "9995  24995  0.017658\n",
-       "9996  24996  0.012540\n",
-       "9997  24997  0.000907\n",
-       "9998  24998  0.058854\n",
-       "9999  24999  0.155139\n",
+       "9995  24995  0.034848\n",
+       "9996  24996  0.029164\n",
+       "9997  24997  0.021878\n",
+       "9998  24998  0.104312\n",
+       "9999  24999  0.176992\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -5274,27 +3245,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.086987</td>\n",
+       "      <td>0.116020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.033294</td>\n",
+       "      <td>0.047011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.021645</td>\n",
+       "      <td>0.042478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.921273</td>\n",
+       "      <td>0.719505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.034494</td>\n",
+       "      <td>0.050922</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -5304,27 +3275,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.017658</td>\n",
+       "      <td>0.034848</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.012540</td>\n",
+       "      <td>0.029164</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.000907</td>\n",
+       "      <td>0.021878</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.058854</td>\n",
+       "      <td>0.104312</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.155139</td>\n",
+       "      <td>0.176992</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -5332,18 +3303,18 @@
        "</div>"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 57
+   "execution_count": 55
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:28:03.542484Z",
-     "start_time": "2025-06-25T17:27:57.942584Z"
+     "end_time": "2025-08-10T10:38:32.578288Z",
+     "start_time": "2025-08-10T10:38:29.342932Z"
     }
    },
    "cell_type": "code",
@@ -5358,15 +3329,15 @@
      "output_type": "stream",
      "text": [
       "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
-      "[LightGBM] [Info] Number of positive: 3052, number of negative: 11948\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000836 seconds.\n",
+      "[LightGBM] [Info] Number of positive: 2136, number of negative: 8364\n",
+      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000676 seconds.\n",
       "You can set `force_row_wise=true` to remove the overhead.\n",
       "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 3671\n",
-      "[LightGBM] [Info] Number of data points in the train set: 15000, number of used features: 27\n",
+      "[LightGBM] [Info] Total Bins 3653\n",
+      "[LightGBM] [Info] Number of data points in the train set: 10500, number of used features: 27\n",
       "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203467 -> initscore=-1.364767\n",
-      "[LightGBM] [Info] Start training from score -1.364767\n"
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203429 -> initscore=-1.365002\n",
+      "[LightGBM] [Info] Start training from score -1.365002\n"
      ]
     },
     {
@@ -5556,7 +3527,9 @@
        "/* Toggleable content - dropdown */\n",
        "\n",
        "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  display: none;\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
@@ -5582,9 +3555,9 @@
        "\n",
        "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
        "}\n",
        "\n",
        "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
@@ -5789,328 +3762,23 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(learning_rate=0.0088, max_depth=16, n_estimators=2000,\n",
-       "               num_leaves=16, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('boosting_type',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">boosting_type&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gbdt&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('num_leaves',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">num_leaves&nbsp;</td>\n",
-       "            <td class=\"value\">16</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">16</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('learning_rate',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
-       "            <td class=\"value\">0.0088</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">2000</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('subsample_for_bin',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">subsample_for_bin&nbsp;</td>\n",
-       "            <td class=\"value\">200000</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('objective',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">objective&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_split_gain',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_split_gain&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_child_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
-       "            <td class=\"value\">0.001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_child_samples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_child_samples&nbsp;</td>\n",
-       "            <td class=\"value\">20</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('subsample',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">subsample&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('subsample_freq',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">subsample_freq&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('reg_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('reg_lambda',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('importance_type',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">importance_type&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;split&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(learning_rate=0.0088, max_depth=16, n_estimators=2000,\n",
+       "               num_leaves=16, random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(learning_rate=0.0088, max_depth=16, n_estimators=2000,\n",
+       "               num_leaves=16, random_state=3)</pre></div> </div></div></div></div>"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 58
+   "execution_count": 56
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:28:06.723767Z",
-     "start_time": "2025-06-25T17:28:06.328407Z"
+     "end_time": "2025-08-10T10:38:33.036957Z",
+     "start_time": "2025-08-10T10:38:32.740898Z"
     }
    },
    "cell_type": "code",
@@ -6121,22 +3789,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.9378666666666666\n",
-      "Validation Accuracy 0.936\n",
+      "Train Accuracy 0.9497142857142857\n",
+      "Validation Accuracy 0.8982222222222223\n",
       "\n",
       "\n",
-      "Train AUC Score 0.9766497987566041\n",
-      "Validation AUC Score 0.9741512496101061\n"
+      "Train AUC Score 0.9849315753980409\n",
+      "Validation AUC Score 0.9236648569089208\n"
      ]
     }
    ],
-   "execution_count": 59
+   "execution_count": 57
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:28:08.814843Z",
-     "start_time": "2025-06-25T17:28:08.607812Z"
+     "end_time": "2025-08-10T10:38:33.640520Z",
+     "start_time": "2025-08-10T10:38:33.432399Z"
     }
    },
    "cell_type": "code",
@@ -6148,19 +3816,19 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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"
+      "image/png": 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MmzaN7NmzPzGuLl268P777xMUFMQHH3yAg4MDy5cv58cff2Tq1KkZci9SsmXLFlxcXKhZsyYHDhxg8eLF9OzZk2zZslGyZEkaN27MtGnTiIuLw9vbmxMnTjBt2jS8vb2pXr36Y+vOmTMnv//+OwcOHKBcuXKpvs9P4uzszIcffsiCBQuwt7e3LYC1aNEievXqRZYsWVL13EXk30fJpoiIPFcuXrzI6NGjH9reuHFjqlWrRsOGDbl79y5z585l1apVlCpVigEDBjBgwABb78x9o0aNYty4cVy4cIHSpUsze/ZsW+/Q0zKZTMyePZuJEycSHBzM3bt3efXVV5kzZ06yRWEejDssLIxVq1axZMkS8ufPT82aNWnRogWDBg3izJkzlCxZkjlz5jBlyhQGDBhAbGwspUqVIjg4GB8fH+DeK0wmTZrElClTiIyMpECBAnzyySepXiX2r7Jly8bixYuZOHEi48ePJyoqikKFCvHpp5/Stm3bv3WPHqdatWrMnTuXadOm0a1bN+zs7Hj99df5+uuvbYvQ9OvXj8TERL744gsSEhIoXLgwnTt35syZM2zbts02TLZTp05Mnz6dDh068N133/HKK68wduxYZsyYQceOHSlRogQjRoxgxIgRT4zr1VdfZfHixUyePJk+ffpgtVopXbo0X3755RNf95KeunfvzoEDB1i+fDkFChRg8ODBfPDBB7b9o0aNolixYqxatYq5c+eSL18+AgMD+fjjj584j7ht27Z8/vnntGvXjq+//jpV9zm1PvvsM1xcXFi6dCnz5s2jcOHC9O/fnxYtWgCpe+4i8u9jsKbXTHkREZFnwMaNGylTpkyyuWE7duwgKCiIdevW8eqrr2ZidCJPJywsjNq1azN69GgCAgIyOxwREUA9myIi8oJZv349kydPpkePHhQoUIDz588zdepUKlWqpERTREQkHSnZFBGRF8rYsWNtQz8jIiJwcXHh7bffplu3bpkdmoiIyHNFw2hFREREREQk3enVJyIiIiIiIpLulGyKiIiIiIhIulOyKSIiIiIiIulOyaaIiIiIiIikOyWbIiIiIiIiku706hORDBYZGZPZIchzwMkpu9qSpBu1J0kvakuSntSeUsdqtWKxZN4LRQwGyJPnpdSV1atPREREREREng3mJDO3bsdmWsJpMICLS+qSTfVsimSw5T37EHbseGaHISIiIiLPuPylS9JqxlSMRkOm9m6mlpJNkQx27cxZwn9VsikiIiIiLxYlmyIiIoIV2J/DnmwWK+XvJgLwazY7wuxNGB4oV+ZuIkUTzACE2Zs47ZiFeKOBHGYrr99NxMlsASDeAD/mzorpgdk69hYrfnfibft/zWbPzSxGDEChBDOvxSbaVi6MNBn4PZs9USYD9hYrJeOSbOdNyVmHLJx3zEKiAXIlWSh7N5Ec//vW/5yDidOOdhiBUrGJFPtfPRZgz0sOuMck2MqKiEj6UbIpIiIinHLMQkQWI9keSOhum4yUvZtI4RSSvJtZjPyWzQ6vqARymy2cdzBxKIc9frfjMAG3shjJarbYksu/OpzdHkeLlTq344g3GjiYw55zliyUiE8i0QAHczhQOi6RovFmIrIYCc1hT06zhdzmh5PCMHsT5x2zUCkqnmwWK39kzUJoDntq3InHDJzIake1O/FggN0vOVAowUwW4JxDFvImmpVoiohkEL36RDLM7du3GTp0KDVr1qRChQpUq1aNvn37cuXKlcwOTUREHnAji5Er9iYKJP5/UmkGokwGciVZUjzmor2JAglmnM0WjEDxeDN2ViuX7U3AvUQ1VwqJIUCM0UCEnYnXYhMxAdksVkrFJnHB4d6xf9qZsLdaeTnejBFwSbJQMMHMeYeUvyO/6GCiWHwSL1msmIBXY5OIMxpsvaYAD3bPGoBYA4Q7mCgVl5Ta2yQiImmkZFMyTM+ePYmMjGTlypUcPXqUtWvXkpCQwEcffURSkv7nLiLybxBvgGPZ7HCPScD0QG4YZTJgAU5lteOHXI7syOnAWYcs3C8SbTLykjl5IvqS2cod070/LW5lMRJnNPBzTgd+yOXIgRz2RBkNtrrtLFYcHzhfDrOFWJORRMP9upMnqjnMFqJMKf/ZEmVMHosRyGa2EmUyYAJev5vIwRz2HMpuT7m79xLc37PZ4/a/ZFdERDKGkk3JMKGhobz55pvkzZsXABcXF/r370/58uW5c+cOfn5+rF692lY+JCQENzc3AMLCwnBzc2PMmDF4eXkxbNgwgoOD6datG71798bT05MaNWowceJE2/FXr16lR48e+Pn5Ub58eWrXrs3KlStt+93c3Fi+fDn16tWjfPnydOrUiePHj/P+++/j7u5OkyZNuHDhgq38pk2baNiwIR4eHgQEBLB79+6MvmUiIv8oK3A0uz2vxCeR8y/JXaLBQJ4kCy/HJ1H7dhwVYhI455iF//6vdzHJAFn+0nFpslox/68H0c5qxTnJTOWoeHxvx5HdbCXkJQcSgSSDARPJD76f9CX9r27TX97MZrLe254Ss4FkifL9WJIM9w4olmCm9u14/O7EUzjBzPUsRqyAc5KF0Oz27HrJgeNZ7Ui5D1dERJ6Wkk3JMPXr12fIkCEMHTqU7777jvDwcPLmzcuYMWNwdnZOVR0xMTHs2bOHnj17AvD9999TrVo1QkJCGDFiBLNnz+bo0aMADBw4EDs7OzZt2sThw4dp1aoVI0aMICbm/18OvGHDBpYvX84PP/xAaGgoXbp0YdSoUezZswd7e3tmzpwJwM8//8yQIUMYPHgwBw4coGvXrnTt2pXTp0+n700SEclEZxyzYLTCK/EPz8nMm2TBJzqBPEn3hsnmNlt5JS6JP/83TNZkxZZY3mc2GGwJqHtMIq/FJmFvvbdARJnYRMwGiLAzksVqxUzyg+9HkMV6v+6/7E8hub3PxKNiefgAM3Ayqx1lYhM545gFe6uValHx3DUZuGSvfk4RkfSkBYIkw4wcORJvb2++++47Bg8eTFRUFEWLFqVr1668++67qarD398fe3t77O3tAXj55Zfx9/cHoGbNmuTNm5fz589ToUIFRo4cSfbs2bGzs+Py5ctkz56duLg4bt++Tfbs2QFo1aoVuXPnBqBUqVKUKVOGEiVKAODj40NoaCgAixYt4oMPPsDLywsAX19f/Pz8WLZsGYMGDUqvWyQikqnC7U3EGw1szeUI/H/CdsXORPm7CcQbDLaVWwEsBjD+L4F7yTas9f/3R5kM5E00kwScypqFl+PNZPvf4jtW7q3+arJCVouVRKOBeAM4/C8fjDYZcbRYsPtf3dftkv+JktKw3fteMluINhrJ/7++SQtw12R4aCguwH8ds1Ag8V5cUSYjrgn30t6cSQ9fj4iI/D1KNiXDGI1GGjVqRKNGjbBarZw9e5Z169bRp08f29DaJ8mXL1+yz389zs7ODovl3h8Xly5dYty4cZw/f56XX36ZYsWKAdj2A7ZEE8BkMpErV65k8Vr/90dUeHg4Bw4cYOnSpbb9ZrMZHx+fVMUtIvIsqPWXlWJ/yWYHQPm7iVyxM/J7NjuyW6zkSbJwy2TkvEMWXou991qUIglmQnPY31skKMnCBQcTCQYDron3Vnq9mcVErNFIuZgEDMCJbHZks1hx/l9PqVOimd+z2VE2JpEEo4HTWbNQ5H89rK6JZk5kteOcg4li/1uN9rK9CY/ohBSvo3C8mdNZ7cibZCa7+d5qtPb/O9eD7hoNXLEzUTXq3nVnN1uJzGKkcIKZW1mM5EtUoikikp6UbEqG2LVrF926dWP79u3kzp0bg8FAyZIl+fTTT9mzZw+///47RqORxMRE2zGRkZEP1WMwPGKCzl8kJiYSFBREr169aNGiBQaDgePHj7N+/fqnqs/V1RV/f386duxo23b58mUcHR1TdbyIyLPONdFCmbuJHM9mR5zRgIPFSqm4/38NikuShdcf2P+S2YpXdDz2/+tM9IhJ4PesdmzP5YjVAHkSLXhFJ9jm73jEJHA8mz3bcjn+7z2bSbaVYe2t4B0dz+/Z7DiV1Q57i5UydxNx+V/yGJHFyIEc9tS8HU9Wq5UiCWaSDAYOZbcnwWggd1Lyc933W1a7ZO/yLBGXyJHs9vyQ2xGXRAvFUhhOLCIiT0/JpmQILy8v8uTJw3/+8x969OhB8eLFiY+PZ9u2bZw/f55atWpx4MABfvrpJxo1akRUVBQLFy586vMlJiYSFxeHo6MjBoOBy5cvM378eNu+tGrWrBkjR47Ex8eHcuXK8euvv9KhQwe6dOnChx9++NRxioj8m5W/m/y/l8USzMmG0f5V4QRziu/ghHuvM/GMSbknEu4Nn/V4zP7cZitVolLe75xk4a1bcbbPBqB4fBLF4x+/0rnXX87naIXKj+gtFRGRv0/JpmQIR0dHlixZwrRp0+jcuTM3b97Ezs6OChUq8PXXX1OiRAl69+7N0KFDqVq1Kvny5aN169a2OZNplS1bNj7//HOmTJnCyJEjyZMnD82aNePMmTOcOnWKV155JU31vfXWW9y9e5f+/ftz+fJlcufOTZs2bQgMDHyq+EREREREXjQGqzWFpdpEJN0EN2zCuZCDmR2GiIiIiDzjCpV9g09/+o7IyBiSkjLnhU0GA7i4vJSqsnr1iYiIiIiIiKQ7JZsiIiIiIiKS7jRnUySD5StZgoS7sZkdhoiIiIg84/KXLpnZIaSJ5myKiIiIiIg8I8xJZm7djsViyZw0Li1zNtWzKZLBIiNjMjsEeQ44OWVXW5J0o/Yk6UVtSdKT2lPqWCzWTEs000rJpkgGs1gsWDJnsTB5ThgM9/5pNlvQWBT5u9SeJL2oLUl6Unt6PinZFMlgRqMRo5biknRgMqkhSfpRe5L0orYkqfUs9chJ+tCcTRERERERyXCWJDORj5hreH8e4I0bUerZ/JfTnE2Rf5HNff7D1d9+z+wwRERERDJNnpIlaDhlEkajQb2bLxAlmyIZLOK/57h6/LfMDkNERERE5B+lQfYiIiIiIiKS7pRsynNv8eLFuLm5MX/+/MwORURERETkhaFkU557ixcv5oMPPmDhwoUkJSVldjgiIiIiIi8EJZvyXNu3bx83b96kX79+WCwWtm7datsXGRlJz5498fDwoHbt2nzzzTeUKVOGsLAwAC5evEinTp3w9vbG19eXyZMnk5CQkFmXIiIiIiLyTFGyKc+1b775hmbNmuHo6EiLFi2YN2+ebV/v3r2Jiorip59+YsWKFWzfvh2z2QzA3bt3adOmDaVKlWLnzp0sWbKEvXv3EhwcnFmXIiIiIiLyTFGyKc+t8PBwdu3aRcuWLQFo1qwZZ86c4cCBA1y9epXdu3fTv39/cufOjbOzM/3797cdu2PHDhISEujVqxcODg4UKFCA7t27s3jx4sy6HBERERGRZ4pefSLPrSVLlpCUlESjRo1s25KSkpg3bx6dOnUCoHDhwrZ9RYoUsf0eHh5OREQEXl5etm1Wq5XExERu3rxJnjx5/oErEBERERF5dinZlOdSfHw8K1euZNSoUVSpUsW2/dSpU3Ts2JGgoCDgXlL5yiuv2H6/z9XVlaJFi7JlyxbbtujoaG7evImzs/M/dBUiIiIiIs8uDaOV59KGDRswGAw0bNgQV1dX20+NGjUoXbo0q1evxtfXl/Hjx3P79m1u377NuHHjbMf7+voSExPDnDlzSEhI4M6dO/Tt25eePXtiMBgy8cpERERERJ4NSjblubRkyRIaNmyInZ3dQ/uaN2/OunXrGDVqFAaDgVq1atG4cWPKlCkDgJ2dHTly5GD+/PmEhIRQo0YN6tSpg9FoZMaMGf/0pYiIiIiIPJMMVqvVmtlBiGSGPXv24OHhgaOjIwB//PEH/v7+HD16FAcHh3Q7z+Km7xN28FC61SciIiLyrMn/xuu02bSOyMgYkpIsD+03GMDF5SVu3IhC2cm/2/1nlRrq2ZQX1tixY5kxYwZJSUlER0czY8YMqlSpkq6JpoiIiIjIi0rJprywJk6cyNGjR/Hx8cHPzw+TyZRs3qaIiIiIiDw9rUYrL6xSpUqxYMGCDD+Pc/FXSIyNzfDziIiIiPxb5SlZIrNDkEygOZsiIiIiIpLhLElmIm/HYrE8nH5ozuazIy1zNtWzKZLBIiNjMjsEeQ44OWVXW5J0o/Yk6UVtSdLCYrGmmGjK80vJpkgGs1gsWB5edE0k1e6/2tVstujbXvnb1J4kvagticiTKNkUyWBGoxGjluKSdGAyqSFJ+lF7ejz1wIiI/H1KNkUymJNT9swOQZ4TakuSntSeHu9xc8tERCR1lGyKZLCd/Qdx8/ffMzsMERFJpdwlSuA7cRxGo0HJpojI36BkUySD3T53jpu/n8jsMERERERE/lGasCH/WufPn8/sEERERERE5Ckp2ZRHOnfuHH379qVGjRq4u7tTp04dJkyYQExM+i1x7ubmRkhICAD169dn/fr1ACxevJhBgwbZylksFmbPns0777yDu7s7Xl5etGvXjiNHjqRbLCIiIiIikn6UbEqKDh8+TOPGjSlUqBBr167lyJEjzJ49m19++YW2bdtiNpvT/ZybNm3i3XffBSAiIiLZvq+++oo1a9YwdepUDh8+zM6dO/Hx8aF169ZcuHAh3WMREREREZG/R8mmpGjw4MH4+/vTrVs3nJ2dAXjllVeYPHkyefLk4dKlS7i5uTFy5Ei8vb3p1KkTAHv37qVp06Z4enom66kESExMZPTo0Xh7e+Pj48OcOXOSndPPz4/Vq1ezZs0aZs2axaFDh/D09AQgNDQUT09PSpYsicFgIGvWrHTo0IFmzZpx48YNAJKSkpgyZQo1a9akYsWKtGzZkpMnTwIQFxfHuHHjqFmzJl5eXgQGBnLs2DHbudN6LSIiIiIi8nhaIEgecvHiRU6fPs3QoUMf2ufi4sL06dOTld2xYweJiYmcPHmSzp07M378eGrXrs0vv/xCly5dcHJyonr16kyfPp0dO3awcuVK8uTJk2L9AI0bNyYsLIwDBw7wzTffAPeG2Pbv359bt25RvXp1ypUrR6lSpRg4cKDtuBkzZrBx40bmzp3LK6+8wrRp0wgKCmLbtm0MHTqU33//nYULF1KgQAGWLl1KmzZt2LhxIwULFkzztYiIiIiIyOOpZ1Mecn8Iq4uLyxPLNmjQgKxZs5IzZ06WLVtG7dq1qVu3LiaTiYoVK9KsWTMWL14MwLp162jXrh1FihQhW7ZsDBw4EIPBkKqY/P39WbhwIY6OjkybNo13332XypUrM2nSJJKSkgBYs2YN7du3p2TJkphMJjp37syUKVOIi4tj48aNfPrppxQrVgx7e3tat25N8eLF2bhx41Ndi4iIiIiIPJ56NuUhefPmBeD69eu8/PLLD+2/ceOGLRHNly+fbXt4eDj79++3DX0FMJvNFC1aFIBr165RoEAB276cOXOSK1euVMfl6elpq/vq1avs2LGD8ePHYzQa6dGjB9evX7f1UgLY29tToUIFrl27RmJiIoULF05WX+HChQkLC7N9Tsu1iIiIiIjI4ynZlIcUKlSI0qVL89133+Hl5ZVs382bN/H19WX06NEAyXomXV1dady4McOHD7dtu3btGlar1bb/0qVLtn13794lKirqifHExMRQtWpVJk+ejK+vLwD58+enefPmnDt3jhMn7r3DskCBAvz555+24xITExk/fjzt2rXDwcGBS5cuUaJECdv+ixcv4ufnZ/uclmsREREREZHH0zBaSdGgQYNYtWoV06ZNIzIyEqvVyokTJ+jUqROvv/469erVe+iYpk2bsnHjRnbv3o3FYuH8+fO0atWKefPmAfDee+8xZ84czp49S3x8PGPGjHnkqrYODg5ER0djtVrJnj07tWvXZty4cYSEhHD37l0SEhIIDQ1l69at1K1bF4CAgADmzp3LuXPnSEpKYtasWfz44484OzvTpEkTJk2axIULF0hISGDBggWcOXOG+vXrp3j+J12LiIiIiIg8nno2JUWVKlVi0aJFzJw5k/r16xMbG4uLiwtvvfUWQUFB2NnZPXRM+fLlmTRpEpMmTaJ79+5kzZqVBg0a0KtXLwA6dOhAbGwsrVq1IikpiWbNmpE7d+4Uz+/r68vSpUvx8PBgx44djB49mjlz5jBy5Ejb0NfixYvTo0cPGjVqBED79u1JSkqiXbt23L59m7JlyzJ79mzs7Ozo06cPwcHBtGnThlu3buHm5mZbSCglT7oWERERERF5PINV4wJFMtSGD1pxNfRwZochIiKplKfMazReu4rIyBiSkiyZHc6/lsEALi4vceNGFPprUv4utadnx/1nlRoaRisiIiIiIiLpTsmmiIiIiIiIpDvN2RTJYLleeYWk2NjMDkNERFIp9wMrl4uIyNNTsimSwWp8PiKzQxARkTSyJJmxWDRxTETk71CyKZLBIiNjMjsEeQ44OWVXW5J0o/b0ZBaLVcmmiMjfpGRTJINZLBYsWsxQ/gaD4d4/zWaLVuiTv03tSURE/ilaIEhERERERETSnXo2RTKY0WjEqK91JB2YTGpIknoaBioiIplNyaZIBnNyyp7ZIchzQm1J0sJiNhN5K1YJp4iIZBolmyIZ7OCgoUSeOJHZYYjICyRn8eL4jBuN0WhQsikiIplGyaZIBrtz/jy3TpzM7DBERERERP5RmgD0AjCbzVy6dOkfOdf58+f/kfOIiIiIiMi/m5LNZ4Sfnx9ly5bF3d39oZ9Dhw499tiePXuydu1aAC5fvoy7uzuXL19O9xgXL17MoEGDnvr44OBgAgMDbZ9Pnz7Nxx9/jLe3N+7u7tStW5fJkyeTkJCQqvpWr16Nn5/fU8cjIiIiIiJPT8NonyHDhg0jICAgzcdFRkbafi9YsCBHjhxJz7BsIiIi0q2u6OhoAgMDadu2LRMmTMDR0ZEzZ87Qq1cvbty4wahRo9LtXCIiIiIikv7Us/mc2Lp1K/Xr18fDw4O3336b6dOnAzBgwAAOHTrErFmz6NSpE2FhYbi5uREWFgaAm5sby5cvp169epQvX55OnTpx/Phx3n//fdzd3WnSpAkXLlwAICEhgbFjx/L222/j7u5O5cqVGTFiBFarlTVr1jBr1iwOHTqEp6cncC9hHD58ODVr1qRy5cr07NmTGzdu2GI+fPgwTZo0oUKFCrz//vu2mAD++9//EhkZib+/P1mzZsVgMFCqVCkGDBhAzpw5beW2bdvG+++/T+XKlSlfvjytWrV65FDelStXEhAQYOspDQoKsiXIwcHBtG3bliZNmlCpUiWmTZuGh4cH8fHxtuO3bNmCr68vVr0FXURERETkiZRsPgfi4uL47LPPGDx4MKGhoUycOJHZs2dz7NgxRo0ahaenJ0FBQcycOTPF4zds2MDy5cv54YcfCA0NpUuXLowaNYo9e/Zgb29vO27BggXs2rWLBQsWcOTIEaZPn86yZcvYv38/jRs3JigoCE9PT9uw3v79+3PhwgVWr17Njz/+SI4cOfjkk0+wWq1ERkYSFBREvXr1OHjwIJ999hk//vijLaZXX32VEiVK8MEHHzB58mR27tzJrVu38PHxoW/fvgBcuXKF7t2707FjR/bt28eOHTuwWq18+eWXD13jsWPHGDlyJEOHDiUkJITNmzdz/vx5Fi5caCuzb98+evfuzfbt2+nYsSMmk4mffvrJtn/t2rU0btwYg8Hw9x+aiIiIiMhzTsNonyHDhg3j888/T7atQIECrFixAkdHR1auXInFYqFixYqEhoZiNKbuu4RWrVqRO3duAEqVKkWZMmUoUaIEAD4+PoSGhgLQrFkzGjduTJ48ebh27RpxcXFkz56dq1evPlTnzZs32bp1K5s3byZPnjzAveTT09OT3377jdOnT5M1a1Y6dOiAwWDAw8ODJk2acOJ/rwixt7fn22+/ZcmSJWzbto25c+eSlJRExYoV6devH+XKlcPZ2ZlNmzZRtGhRoqOjuXLlCk5OTinGU7p0aTZu3EjhwoW5ffs2165dw9nZOVnZIkWKULlyZdvnBg0asG7dOt555x1u3rzJ7t27GTBgQKruqYiIiIjIi07J5jNkyJAhj5yzuXTpUqZPn86nn35KdHQ09erVY+DAgeTKleuJ9d5PNAFMJlOyY4xGo23YaGxsLMOHD+fgwYO4urpSpkwZrFYrFovloTrDw8OBewnqg0wmE2FhYVy9epUCBQok6yUsWrSoLdkEyJEjBx07dqRjx44kJCRw/PhxZs+ezUcffcS2bdvImTMnGzduZNmyZRgMBkqXLk10dDRZsjzcrI1GIwsXLmTDhg1ky5YNNzc3oqOjkw2JzZcvX7JjAgICaN68OTdv3mT9+vVUrFiRIkWKPPF+ioiIiIiIks3nQnR0NNeuXWPixIkAnDhxgl69ejFz5kzbkNPHSe2w0PvJ6+7du3FwcMBiseDl5ZVi2fz58wOwefNm8ubNa9t+5swZihQpwubNmwkPD8disdh6YK9cuWIrN3nyZPbu3cuKFSuAez2dFStWZPz48Xh4eHDx4kUuXbrEokWLWLp0KcWKFQNgxIgRnDp16qF45s+fz549e9iwYQMuLi4AdOrU6bH34Y033qBkyZJs3bqVTZs2JVspV0REREREHk9zNp8DMTExdOjQgQ0bNmC1WsmXLx9GoxEnJyfgXqIWFRX1t88THR2Ng4MDRqOR6Ohoxo0bR3R0NImJiQA4ODjYegvz589PrVq1GDVqFJGRkSQmJjJjxgyaNm3KnTt38PPzw2q1EhwcbOu1vJ9YArz99tv88ccffPHFF4SHh2O1Wrlx4wZffvklxYoVw83NjaioKIxGI46OjlitVnbu3MnatWtt8fw19ixZsmBnZ0dSUhLr1q1j165dKZZ9UEBAAN9++y3nz5+nbt26f/seioiIiIi8KJRsPkOGDBmS4ns2169fz9SpU5k9ezYVK1akQYMG+Pj40KZNGwD8/f1ZtWoVLVq0+FvnHzhwICdPnqRSpUq89dZbREdHU716dVtPoq+vL7du3cLDw4M7d+4wbtw4cubMib+/Pz4+Pvz888/MmTOHvHnzkjNnTubOncu+ffuoVKkSAwYMoF69erZzvfrqqyxatIhTp07RtGlTypcvj7+/P7du3eKbb77B3t6exo0bU6VKFerXr4+Pjw8zZsygdevWnDt37qF3cbZt25YCBQrg6+tL9erVWb9+PS1atEixF/RBDRs25MyZM7zzzjtkzZr1b90/EREREZEXicGq9ziIPJLZbKZatWrMnDmT8uXLP1UdPwW24ebhjHm3qYhISnK/9ip1Vy4nMjKGpKTk8+oNBnBxeYkbN6LQXwDyd6gtSXpSe3p23H9WqaE5myKPcPr0aTZv3oyrq+tTJ5oiIiIiIi8qJZsijxAUFATA1KlTMzkSEREREZFnj5JNkUfYtm1butST8+WXMcfGpktdIiKpkbN48cwOQURERMmmSEbzGjE0s0MQkReQxWzGYtHEJxERyTxKNkUyWGRkTGaHIM8BJ6fsakuSJhaLVcmmiIhkKiWbIhnMYrFgsTy5nMijGAz3/mk2W7RCn4iIiDwzlGyKZDCj0YhRb7SVdGAyqSHJ/1PPpYiI/Nsp2RTJYE5O2TM7BHlOqC3JgyxmM5G3YpVwiojIv5aSTZEMdnzESO6cPJnZYYjIcyTHK69QbuQIjEaDkk0REfnXUrIpksFiLlwg6uQfmR2GiIiIiMg/ShOA0shsNnPp0qV/5Fznz5//R84jIiIiIiKS3l64nk0/Pz+uX79OliwPX/rs2bPx9PR87PE9e/akVKlSdO3alcuXL1O/fn02bdpEwYIF0zXOxYsXs2XLFr755punOj44OJgDBw7Yjj99+jRffPEFhw4dIiEhgbx58/L222/z8ccfY29v/8T6Vq9ezbRp09i2bdtTxZNa7du3JzQ0FIDExETMZjOOjo62/Rlxr0VEREREJP29cMkmwLBhwwgICHiqYyMjI22/FyxYkCNHjqRXWMlERESkW13R0dEEBgbStm1bJkyYgKOjI2fOnKFXr17cuHGDUaNGpdu5/q45c+bYfv9rwiwiIiIiIs8ODaNNwdatW6lfvz4eHh68/fbbTJ8+HYABAwZw6NAhZs2aRadOnQgLC8PNzY2wsDAA3NzcWL58OfXq1aN8+fJ06tSJ48eP8/777+Pu7k6TJk24cOECAAkJCYwdO5a3334bd3d3KleuzIgRI7BaraxZs4ZZs2Zx6NAhW09rdHQ0w4cPp2bNmlSuXJmePXty48YNW8yHDx+mSZMmVKhQgffff98WE8B///tfIiMj8ff3J2vWrBgMBkqVKsWAAQPImTOnrdy2bdt4//33qVy5MuXLl6dVq1aPHMq7cuVKAgIC8Pb2xt3dnaCgIFuCHBwcTNu2bWnSpAmVKlVi2rRpeHh4EB8fbzt+y5Yt+Pr6Yk3jSwN/++03AgMD8fLyom7dusyfP99WR3BwMN26daN37954enpSo0YNJk6caDs2MDCQ4OBg2+eUnt/IkSPx9vamU6dOAOzdu5emTZvi6elJ/fr1Wb9+fZriFRERERF5USnZ/Iu4uDg+++wzBg8eTGhoKBMnTmT27NkcO3aMUaNG4enpSVBQEDNnzkzx+A0bNrB8+XJ++OEHQkND6dKlC6NGjWLPnj3Y29vbjluwYAG7du1iwYIFHDlyhOnTp7Ns2TL2799P48aNCQoKwtPTk0OHDgHQv39/Lly4wOrVq/nxxx/JkSMHn3zyCVarlcjISIKCgqhXrx4HDx7ks88+48cff7TF9Oqrr1KiRAk++OADJk+ezM6dO7l16xY+Pj707dsXgCtXrtC9e3c6duzIvn372LFjB1arlS+//PKhazx27BgjR45k6NChhISEsHnzZs6fP8/ChQttZfbt20fv3r3Zvn07HTt2xGQy8dNPP9n2r127lsaNG2O4/7b6VLh69SqtW7fmrbfeYu/evUyfPp0lS5awfPlyW5nvv/+eatWqERISwogRI5g9ezZHjx5N9TkuXrzIjh07GDduHCdPnqRz58507NjRVt/nn3/Orl27Ul2fiIiIiMiL6oVMNocNG4anp2eyn4YNG9r2Ozo6snLlSvbt20eJEiUIDQ2lXLlyqaq7VatW5M6dm3z58lGqVCnq1q1LiRIlyJYtGz4+PoSHhwPQrFkz5s+fT968ebl27RpxcXFkz56dq1evPlTnzZs32bp1KwMGDCBPnjxkz56d/v378+uvv/Lbb7+xY8cOsmbNSocOHbCzs8PDw4MmTZrYjre3t+fbb7+lefPmhISE0KVLF3x8fGjRogXHjh0DwNnZmU2bNuHn50d0dDRXrlzByckpxXhKly7Nxo0bKVeuHLdv3+batWs4OzsnK1ukSBEqV65M9uzZsbe3p0GDBqxbt852Pbt376Zx48apuqf3rV+/nhIlStCyZUvs7OwoWbIk7dq1Y/HixbYyL7/8Mv7+/phMJmrWrEnevHnTtNBSgwYNyJo1Kzlz5mTZsmXUrl2bunXrYjKZqFixIs2aNUt2PhERERERSdkLOWdzyJAhj5yz6ejoyNKlS5k+fTqffvop0dHR1KtXj4EDB5IrV64n1p07d27b7yaTKdkxRqPRNuQzNjaW4cOHc/DgQVxdXSlTpgxWqxWLxfJQnQ8mqA8ymUyEhYVx9epVChQokKyXsGjRopw4ccL2OUeOHHTs2JGOHTuSkJDA8ePHmT17Nh999BHbtm0jZ86cbNy4kWXLlmEwGChdujTR0dEpLqRkNBpZuHAhGzZsIFu2bLi5uREdHZ1sSGy+fPmSHRMQEEDz5s25efMm69evp2LFihQpUuSJ9/Ov9+G3335LtoiTxWLBZDLZPufNmzfZMXZ2dine00d5MO7w8HD279+f7Hxms5miRYumKW4RERERkRfRC5lsPk50dDTXrl2zzfU7ceIEvXr1YubMmbYhp4+T2mGh95PX3bt34+DggMViwcvLK8Wy+fPnB2Dz5s3JkqkzZ85QpEgRNm/eTHh4OBaLBaPxXmf1lStXbOUmT57M3r17WbFiBXCvp7NixYqMHz8eDw8PLl68yKVLl1i0aBFLly6lWLFiAIwYMYJTp049FM/8+fPZs2cPGzZswMXFBcA2x/FR9+GNN96gZMmSbN26lU2bNhEYGJiq+/QgV1dXvL29mTt3rm1bZGQkMTExqTreaDSSmJiY7Ni/ejBuV1dXGjduzPDhw23brl27luZ5piIiIiIiL6IXchjt48TExNChQwc2bNiA1WolX758GI1GnJycgHuJWlRU1N8+T3R0NA4ODhiNRqKjoxk3bhzR0dG2ZMjBwcHWW5g/f35q1arFqFGjiIyMJDExkRkzZtC0aVPu3LmDn58fVquV4OBgW6/l/cQS4O233+aPP/7giy++IDw8HKvVyo0bN/jyyy8pVqwYbm5uREVFYTQacXR0xGq1snPnTtauXZssOXsw9ixZsmBnZ0dSUhLr1q1j165dKZZ9UEBAAN9++y3nz5+nbt26ab5nDRs25OjRo6xfv56kpCSuXbtGp06dGDNmTKqOL1GiBLt27eLOnTtERUUxe/bsx5Zv2rQpGzduZPfu3VgsFs6fP0+rVq2YN29emmMXEREREXnRvJDJ5pAhQ3B3d3/oZ/bs2eTPn5+pU6cye/ZsKlasSIMGDfDx8aFNmzYA+Pv7s2rVKlq0aPG3Yhg4cCAnT56kUqVKvPXWW0RHR1O9enVbT6Kvry+3bt3Cw8ODO3fuMG7cOHLmzIm/vz8+Pj78/PPPzJkzh7x585IzZ07mzp3Lvn37qFSpEgMGDKBevXq2c7366qssWrSIU6dO0bRpU8qXL4+/vz+3bt3im2++wd7ensaNG1OlShXq16+Pj48PM2bMoHXr1pw7d46EhIRksbdt25YCBQrg6+tL9erVWb9+PS1atEixF/RBDRs25MyZM7zzzjtkzZo1zfesUKFCzJkzh+XLl1OlShUaNWpE8eLFU51sBgUFkSdPHmrXrk2jRo3w8/N7bPny5cszadIkJk2ahJeXF61atcLPz49PP/00zbGLiIiIiLxoDFaNCZR/iNlsplq1asycOZPy5ctndjj/mJD2Hbh15GhmhyEiz5GXXnWjyuJFREbGkJSU+nnpAAYDuLi8xI0bUegvAPk71JYkPak9PTvuP6vU0JxN+UecPn2azZs34+rq+kIlmiIiIiIiLyolm/KPCAoKAmDq1KmZHImIiIiIiPwTlGzKP2Lbtm2ZHUKmyV6sGObY2MwOQ0SeIzleeSWzQxAREXkiJZsiGeyNQQMzOwQReQ5ZzGYsFk1sEhGRfy8lmyIZLDIyde8BFXkcJ6fsakuSjMViVbIpIiL/ako2RTKYxWLBkrbFIkWSMRju/dNstmiFPhEREXlmKNkUyWBGoxHjC/lGW0lvJpMa0vNMPZUiIvK8UbIpksGcnLJndgjynFBber5ZzGYib8Uq4RQRkeeGkk2RDHZ2/BhiTp/O7DBE5F8sa7FilBowGKPRoGRTRESeG0o2RTJY7KVLxJw+ldlhiIiIiIj8ozQBSERERERERNKdks0XwPnz5zM7BBERERERecEo2cxg586do2/fvtSoUQN3d3fq1KnDhAkTiIlJ3/flubm5ERISAkD9+vVZv349AIsXL2bQoEG2chaLhdmzZ/POO+/g7u6Ol5cX7dq148iRI+kaz9OYOXMm7u7uuLu7U65cOdzc3Gyf3d3dbdckIiIiIiL/fpqzmYEOHz5M27Ztadu2LWvXrsXZ2Zlz584xePBg2rZty5IlSzCZTOl+3k2bNtl+j4iISLbvq6++Yv369UydOpUSJUoQFxfHokWLaN26NRs2bKBYsWLpHk9qderUiU6dOgEQEhLChx9++K9IgkVEREREJO3Us5mBBg8ejL+/P926dcPZ2RmAV155hcmTJ5MnTx4uXboE3OuVHDlyJN7e3rZka+/evTRt2hRPT89kPZUAiYmJjB49Gm9vb3x8fJgzZ06y8/r5+bF69WrWrFnDrFmzOHToEJ6engCEhobi6elJyZIlMRgMZM2alQ4dOtCsWTNu3LgBQFJSElOmTKFmzZpUrFiRli1bcvLkSQDi4uIYN24cNWvWxMvLi8DAQI4dO2Y7d1qvJS1u3LhB7969qVq1KtWqVWPw4MFER0cD95JTPz8/ZsyYQfXq1alUqRJdu3a17Q8ODiYwMDDF+wQQGBhIv3798PX1pVatWkRHR3Px4kU6deqEt7c3vr6+TJ48mYSEhKeKXURERETkRaNkM4NcvHiR06dP06BBg4f2ubi4MH36dF5++eVk5Xfs2MG4ceM4efIknTt3pmPHjoSEhDBixAg+//xzdu3aBcD06dPZsWMHK1euZNu2bZw6lfJKp40bNyYoKAhPT08OHToE3Btiu3LlSrp168aKFSv4448/sFgsDBw4EA8PDwBmzJjBxo0bmTt3LgcPHqRSpUoEBQVhNpsZOnQou3fvZuHChezZs4c6derQpk0bLl++/FTXkloWi4UuXbpgNBrZunUrGzZs4Nq1awwePNhWJjw8nKtXr/LDDz+wYsUKjhw5wpIlS1J9jr1797Js2TLWr1+P0WikTZs2lCpVip07d7JkyRL27t1LcHBwmuIWEREREXlRKdnMIPeHr7q4uKSqfIMGDciaNSs5c+Zk2bJl1K5dm7p162IymahYsSLNmjVj8eLFAKxbt4527dpRpEgRsmXLxsCBAzEYDKk6j7+/PwsXLsTR0ZFp06bx7rvvUrlyZSZNmkRSUhIAa9asoX379pQsWRKTyUTnzp2ZMmUKcXFxbNy4kU8//ZRixYphb29P69atKV68OBs3bnyqa0mt48eP89tvvzFkyBBy5MiBk5MTffv2ZdOmTURGRtrKffzxxzg6OlKsWDG8vb05d+5cqs9Ro0YN8ufPT86cOdmxYwcJCQn06tULBwcHChQoQPfu3dMct4iIiIjIi0pzNjNI3rx5Abh+/XqyHsz7bty4kSwRzZcvn+338PBw9u/fbxv6CmA2mylatCgA165do0CBArZ9OXPmJFeuXKmOzdPT01b31atX2bFjB+PHj8doNNKjRw+uX79OwYIFbeXt7e2pUKEC165dIzExkcKFCyerr3DhwoSFhT3VtaRWWFgYZrOZmjVrJttub29vG44M/3/fAezs7LBaU/9y9L/GHRERgZeXl22b1WolMTGRmzdvkidPnjTFLyIiIiLyolGymUEKFSpE6dKl+e6775IlLAA3b97E19eX0aNH24bZPtgz6erqSuPGjRk+fLht27Vr12yJk6ura7IE6+7du0RFRT0xppiYGKpWrcrkyZPx9fUFIH/+/DRv3pxz585x4sQJAAoUKMCff/5pOy4xMZHx48fTrl07HBwcuHTpEiVKlLDtv3jxIn5+frbPabmW1HJ1dcXR0ZGQkBDbokoJCQlcunSJYsWKERoa+tjjjUYjiYmJts8Wi4Vbt24lK/PXuIsWLcqWLVts26Kjo7l586Zt/q2IiIiIiDyahtFmoEGDBrFq1SqmTZtGZGQkVquVEydO0KlTJ15//XXq1auX4nFNmzZl48aN7N69G4vFwvnz52nVqhXz5s0D4L333mPOnDmcPXuW+Ph4xowZg9lsTrEuBwcHoqOjsVqtZM+endq1azNu3DhCQkK4e/cuCQkJhIaGsnXrVurWrQtAQEAAc+fO5dy5cyQlJTFr1ix+/PFHnJ2dadKkCZMmTeLChQskJCSwYMECzpw5Q/369Z/qWlKrXLlyFCtWjDFjxhATE0NcXByff/45bdq0eeS1P6hEiRL88ccfnD59mqSkJObMmcPdu3cfWd7X15eYmBjmzJlDQkICd+7coW/fvvTs2TPVQ5ZFRERERF5k6tnMQJUqVWLRokXMnDmT+vXrExsbi4uLC2+99RZBQUHY2dmleFz58uWZNGkSkyZNonv37mTNmpUGDRrQq1cvADp06EBsbCytWrUiKSmJZs2akTt37hTr8vX1ZenSpXh4eLBjxw5Gjx7NnDlzGDlypG3oa/HixenRoweNGjUCoH379iQlJdGuXTtu375N2bJlmT17NnZ2dvTp04fg4GDatGnDrVu3cHNzY+7cubzyyitPdS2plSVLFmbNmsXYsWOpW7cu8fHxlCtXjq+//hoHB4cnHl+nTh327t1LmzZtsFgs+Pv72xZESkmOHDmYP38+Y8aMYc6cOVgsFry9vZkxY0aa4hYREREReVEZrGkdzygiaXK828dE/XrsyQVF5IWVvVRpyn01l8jIGJKSLBl6LoMBXFxe4saNKPQXgPwdakuSntSenh33n1VqaBitiIiIiIiIpDslmyIiIiIiIpLuNGdTJINlLVIES1xcZochIv9iWYsVy+wQRERE0p2STZEMVuKzfpkdgog8AyxmMxaLJiqJiMjzQ8mmSAaLjIzJ7BDkOeDklF1t6TlnsViVbIqIyHNFyaZIBrNYLFgydnFJec7df7Wr2WzRCn0iIiLyzFCyKZLBjEYjRi3FJenAZFJDelaol1JERETJpkiGc3LKntkhyHNCbenZYTGbibwVq4RTREReaEo2RTJY+LQJxJ89k9lhiMg/xL5IUQr36o/RaFCyKSIiLzQlmyIZLCEsjLj/KtkUERERkReLJgDJC+/ChQuZHYKIiIiIyHNHPZvPkMGDB7NhwwYAkpKSSExMJGvWrLb9s2fPxtPTM7PCS1HTpk35448/2LZtG3nz5k23eoODg5k+fTqOjo62bRaLhXz58tGyZUvatGmTqnrGjh1LZGQkY8aMAcDd3f1feR9FRERERJ41SjafIcOHD2f48OEArF69mmnTprFt27ZMjurRfvnlF65cuUKNGjVYtGgRPXv2TNf6PT09+eabb2yfExMTWbt2LQMHDsTNzY3KlSs/sY7IyMhkn48cOZKuMYqIiIiIvKg0jPY5cfHiRTp16oS3tze+vr5MnjyZhIQE4F5i+sEHHzBy5Eh8fHyoXLkyAwYMIDExEYB+/frRr1+/ZPW5ubkREhICgJ+fH4MHD6Zq1ar4+/tjsVj47bffCAwMxMvLi7p16zJ//nysf3kB4KJFi3jrrbdo0aIFy5YtIzY2Ntn+vXv34u/vT8WKFXn//fcZP348gYGBtv2bNm2iYcOGeHh4EBAQwO7dux97D+zs7HjvvffInTs3v//+OwDR0dEMHDiQunXrUqFCBapXr87MmTMB+PLLL9mwYQMbNmzg3Xfffei6IyMjGTRoENWqVcPb25ugoCDOnz+f6mciIiIiIvIiU7L5HLh79y5t2rShVKlS7Ny5kyVLlrB3716Cg4NtZQ4fPkyePHnYtWsXs2bN4rvvvuP7779P9TmOHTvG5s2bWbhwIdevX6d169a89dZb7N27l+nTp7NkyRKWL19uK3/z5k22bt3KBx98QJUqVciTJw+rVq2y7Q8LC6NTp0588MEHHDhwgN69eyc7/ueff2bIkCEMHjyYAwcO0LVrV7p27crp06cfGWNcXBzffPMNMTExVK1aFYAJEyYQFhbGypUrOXLkCAMHDmTy5MlcuHCBjz/+mIYNG9KwYUPWr1//UH3dunXj4sWLrFmzhp9//pnixYvTpk0boqOjU33fREREREReVEo2nwM7duwgISGBXr164eDgQIECBejevTuLFy+2lXF0dKRTp07Y2dlRrlw53NzcOHfuXKrPUa9ePXLmzEnOnDlZv349JUqUoGXLltjZ2VGyZEnatWuX7HzLly+nUqVKlChRAoPBQGBgIPPnz8disQCwYcMGXnvtNZo3b06WLFnw9PSkWbNmtuMXLVrEBx98gJeXFyaTCV9fX/z8/Fi2bJmtTGhoKJ6enlSsWJGyZcvi4+PDgQMHWLBgAa+++ioAXbt25YsvviBHjhxcuXIFBwcHAK5du/bY67106RIHDhxg0KBB5M2bF0dHR3r37k1SUhI///xzqu+biIiIiMiLSnM2nwPh4eFERETg5eVl22a1WklMTOTmzZsA5MmTB4PBYNtvZ2f30LDXx8mXL1+y8/3222/JFtGxWCyYTCbg3uJFy5Yt486dO3h7e9v237lzhx9++IF69erx559/UqhQoWTnKFKkCL/++qvtHAcOHGDp0qW2/WazGR8fH9tnDw8P25zNo0eP0r17d/LmzYuHh4etzM2bNxk1ahS///47hQsX5o033rDF8zg3btywxXSfyWSiQIEChIeHP+l2iYiIiIi88JRsPgdcXV0pWrQoW7ZssW2Ljo7m5s2bODs7P/F4o9FIfHy87XNERMRDZR5MVF1dXfH29mbu3Lm2bZGRkcTExADwww8/kJCQwKZNm2wJKMCUKVP4+uuvqVevHoUKFWL79u3JznH58uVk5/D396djx47J9j+4+uyDKlSowMyZM2nevDm5cuWie/fuAHTv3h0/Pz/mzp1LlixZiIyM5Ntvv33iPbmfCF+8eJFSpUoB95Ldy5cvp+uquiIiIiIizysNo30O+Pr6EhMTw5w5c0hISODOnTv07duXnj17JksSH6VEiRIcOnSIq1evEhcXx5dffvnY4xo2bMjRo0dZv349SUlJXLt2jU6dOtleH7Jo0SIaNmxIoUKFcHV1tf0EBgZy5MgRDh8+TKNGjThx4gRr167FbDbzyy+/JEsCmzVrxsKFCzl27BgAv/76KwEBAWzcuPGRcb322mv069ePmTNnsn//fgCioqJwdHTEZDIRERHByJEjAWyLI9nb2xMVFfVQXfny5aNmzZqMHDmS69evExcXx4QJEzCbzfj6+j7xnoqIiIiIvOiUbD4HcuTIwfz58wkJCaFGjRrUqVMHo9HIjBkzUnV88+bNcXd359133+XNN9+kQIECFCxY8JHlCxUqxJw5c1i+fDlVqlShUaNGFC9enDFjxnDy5EkOHTpEQEDAQ8eVKVOGN954g3nz5uHq6srUqVNt77QcO3Ys1apVw87ODoC33nqLXr160b9/fypWrEj37t1p06ZNstVqU9KiRQtq1qxJ3759uX37NqNHj+a7776jYsWKBAQEkD9/fsqUKcOpU6cAeOeddzh8+DC1atV6qK5x48ZRpEgRGjduTJUqVfjjjz9YsGABuXPnTtV9FRERERF5kRmsaZm4J5JO/vzzTyIjIylTpoxt25gxY7h+/ToTJ07MxMjS37l+PYg9cTyzwxCRf4hj8ZIUnzyTyMgYkpIePz88MxgM4OLyEjduRKG/AOTvUFuS9KT29Oy4/6xSQz2bkikiIyNp0aIFx4/fS8JOnjzJ+vXrNURVREREROQ5oQWCJFOUKVOGAQMG0KtXL65fv46LiwsdO3akQYMGmR2aiIiIiIikAyWbkmnee+893nvvvcwOI8PZFy6MNT4us8MQkX+IfZGimR2CiIjIv4KSTZEMVuiT3pkdgoj8wyxmMxaLJh2JiMiLTcmmSAaLjIzJ7BDkOeDklF1t6RlisViVbIqIyAtPyaZIBrNYLFj+fQtSyjPk/mtvzWaLVugTERGRZ4aSTZEMZjQaMWrdZ0kHJpMa0tNQL6OIiEjmULIpksGcnLJndgjynFBbejoWs5nIW7FKOEVERP5hSjZFMti1udOIv3A2s8MQeSHZFyxC/k69MBoNSjZFRET+YUo2RTJYwpUwEi78N7PDEBERERH5R2kCkIiIiIiIiKS7FzrZPH/+fGaH8I/6p6732rVr3L179x85l4iIiIiI/DtlWrJ57tw5+vbtS40aNXB3d6dOnTpMmDCBmJj0fY+cm5sbISEhANSvX5/169cDsHjxYgYNGmQrZ7FYmD17Nu+88w7u7u54eXnRrl07jhw5kq7xPK3g4GBee+013N3dH/oZPHjwE4/ftm0b7dq1s31u3749M2fOTPc4b9y4Qb169YiIiHiq48PCwnBzcyMsLAyAhIQEJk6cSJ06dXB3d8fHx4euXbty9mzq50A+2AZEREREROSfkSlzNg8fPkzbtm1p27Yta9euxdnZmXPnzjF48GDatm3LkiVLMJlM6X7eTZs22X7/azL01VdfsX79eqZOnUqJEiWIi4tj0aJFtG7dmg0bNlCsWLF0jyetPD09+eabb57q2Fu3bmF94AV9c+bMSa+wkomLi0vXXs0RI0Zw7tw55s+fT+HChblz5w7BwcG0bNmS77//npw5c6bbuUREREREJP1kSs/m4MGD8ff3p1u3bjg7OwPwyiuvMHnyZPLkycOlS5eAez1SI0eOxNvbm06dOgGwd+9emjZtiqenZ7KeSoDExERGjx6Nt7c3Pj4+DyVUfn5+rF69mjVr1jBr1iwOHTqEp6cnAKGhoXh6elKyZEkMBgNZs2alQ4cONGvWjBs3bgCQlJTElClTqFmzJhUrVqRly5acPHkSuJdkjRs3jpo1a+Ll5UVgYCDHjh2znTut1/I0rl69Svv27alUqRI1atTgk08+4dq1a4SEhDBkyBAuX76Mu7s7V69eJTAwkODgYAD69evH8OHDCQoKwt3dnTfffJN9+/YxYsQIvLy8qFq1KitWrLCdZ9u2bbz//vtUrlyZ8uXL06pVK86fP4/ZbKZBgwYANGjQgO+++w64l+Q3bNgQDw8PAgIC2L17t62u6Oho+vbti4eHB9WrV2fdunXJrik0NJTq1atTuHBhAHLmzEmfPn3w9fXl+vXrtuvu0aMHfn5+lC9fntq1a7Ny5coU79HZs2cJCgqiVq1alCtXjnfeeYft27cD/9+rOmbMGLy8vOjfvz9eXl5s2LDBdnxCQgLe3t7s27fvbz0rEREREZHn3T+ebF68eJHTp0/bkpIHubi4MH36dF5++eVk5Xfs2MG4ceM4efIknTt3pmPHjoSEhDBixAg+//xzdu3aBcD06dPZsWMHK1euZNu2bZw6dSrFGBo3bkxQUBCenp4cOnQIuDfEduXKlXTr1o0VK1bwxx9/YLFYGDhwIB4eHgDMmDGDjRs3MnfuXA4ePEilSpUICgrCbDYzdOhQdu/ezcKFC9mzZw916tShTZs2XL58+amu5WlMmjQJV1dX9uzZw3fffcfdu3f56quv8Pb2ZtiwYRQsWJAjR46QP3/+h45dtWoVHTp0IDQ0lHLlytGuXTtefvll9u3bR1BQEMOHDychIYErV67QvXt3OnbsyL59+9ixYwdWq5Uvv/wSk8nExo0bAdi4cSPvvPMOP//8M0OGDGHw4MEcOHCArl270rVrV06fPg3A8OHDuXDhAt9//z3r168nNDQ0WVz169dn2rRp9OvXj7Vr13Lu3Dns7OwYPXo0JUqUAGDgwIHY2dmxadMmDh8+TKtWrRgxYkSKQ7K7du1K6dKl+eGHHzh06BDVqlVj6NChycrExMSwZ88e+vXrR/369ZMlwNu3byd79uz4+Pg89XMSEREREXkR/OPJ5v3hqy4uLqkq36BBA7JmzUrOnDlZtmwZtWvXpm7duphMJipWrEizZs1YvHgxAOvWraNdu3YUKVKEbNmyMXDgQAwGQ6rO4+/vz8KFC3F0dGTatGm8++67VK5cmUmTJpGUlATAmjVraN++PSVLlsRkMtG5c2emTJlCXFwcGzdu5NNPP6VYsWLY29vTunVrihcvbku+0notKbnf+/rXn/sJs4ODA6GhoWzatImYmBjmzJnDwIEDU3X9Pj4+eHp6YjQa8fHxIVu2bAQGBpIlSxZ8fX1JSEjgxo0bODs7s2nTJvz8/IiOjubKlSs4OTlx9erVFOtdtGgRH3zwAV5eXphMJnx9ffHz82PZsmUkJCSwefNmunbtSp48eXBycqJPnz7Jjv/444+ZMmUKd+/eZezYsbz11ltUr16d+fPn28qMHDmSIUOGYGdnx+XLl8mePTtxcXHcvn37oXhmzZpF165dsVqthIeHkzNnzodi9/f3x97enpw5c9KkSRP27t1r60Vds2YNAQEBqW5XIiIiIiIvqn98zmbevHkBuH79erIezPtu3LiRLBHNly+f7ffw8HD2799vG/oKYDabKVq0KHBvFdQCBQrY9uXMmZNcuXKlOrb7yRvcG5q5Y8cOxo8fj9FopEePHly/fp2CBQvaytvb21OhQgWuXbtGYmKibajnfYULF7YtdJPWa0mJh4fHY+dsDhw4kFmzZjF37lz69evHq6++ysCBA5Od41Fy585t+91kMiWbC3k/sbJYLNjZ2bFx40aWLVuGwWCgdOnSREdHkyVLyk0pPDycAwcOsHTp0mTX6ePjQ2RkJAkJCcmeWZEiRR6qw8/PDz8/P+Be7/D333/PhAkTyJ49O++99x6XLl1i3LhxnD9/npdfftk2v9ZisTxU18mTJ+nSpQvXr1+nRIkSODs7J5vLCsmfU9myZSlRooRtKPDu3btTncCLiIiIiLzI/vFks1ChQpQuXZrvvvsOLy+vZPtu3ryJr68vo0ePtg2zfbAHydXVlcaNGzN8+HDbtmvXrtmSBVdXV9t8T4C7d+8SFRX1xJhiYmKoWrUqkydPxtfXF4D8+fPTvHlzzp07x4kTJwAoUKAAf/75p+24xMRExo8fT7t27XBwcODSpUu2oZ1wLzG6nySl9Vqexu+//07z5s3p2rUrERERfPnll3zyySfs37//icemtqdu8+bNLFq0iKVLl9qSuhEjRjxyyLKrqyv+/v507NjRtu3y5cs4OjqSI0cO230rXrw4AFeuXLGVO3v2LP7+/qxatYrSpUsDULRoUdq3b88vv/zCiRMnSExMJCgoiF69etGiRQsMBgPHjx9Pcf7r1atX6d69O9OmTbM9l61bt/L9998/9l40adKETZs2YWdnh6en50NfKoiIiIiIyMMyZYGgQYMGsWrVKqZNm0ZkZCRWq5UTJ07QqVMnXn/9derVq5ficU2bNmXjxo3s3r0bi8XC+fPnadWqFfPmzQPgvffeY86cOZw9e5b4+HjGjBmD2WxOsS4HBweio6OxWq1kz56d2rVrM27cOEJCQrh79y4JCQmEhoaydetW6tatC0BAQABz587l3LlzJCUlMWvWLH788UecnZ1p0qQJkyZN4sKFCyQkJLBgwQLOnDlD/fr1n+pansbMmTMZMWIE0dHR5MyZk6xZs+Lk5GS73tjYWNuQ4KcVFRWF0WjE0dERq9XKzp07Wbt2LYmJibbzwL2FfwCaNWvGwoULbYsl/frrrwQEBLBx40bs7e3x9/dnypQpXLlyhaioKMaPH287V/HixXn99dcZPHgwx44dIz4+ntjYWH7++WdCQkJ48803SUxMJC4uDkdHRwwGA5cvX7bVcT+m+2JiYjCbzWTNmhWAM2fO8OWXXwL3Fv55lHfffZeTJ0+yYsUKAgIC/tb9ExERERF5UWTKq08qVarEokWLmDlzJvXr1yc2NhYXFxfeeustgoKCsLOzS/G48uXLM2nSJCZNmkT37t3JmjUrDRo0oFevXgB06NCB2NhYWrVqRVJSEs2aNUs2PPRBvr6+LF26FA8PD3bs2MHo0aOZM2cOI0eOtA19LV68OD169KBRo0bAvXdTJiUl0a5dO27fvk3ZsmWZPXs2dnZ29OnTh+DgYNq0acOtW7dwc3Nj7ty5vPLKK091LSk5dOgQ7u7uD20vUqQI69evZ/jw4QwbNozatWuTkJDAG2+8wZQpUwDw8vIiT548eHl5sWzZskee40kaN25MaGgo9evXx2QyUbx4cVq3bs3ixYtJSEjAxcWFN998k+bNm9OvXz8++OAD7t69S//+/bl8+TK5c+emTZs2BAYGAjBgwABGjx5Nw4YNyZIlCx9++KFtdViDwcDs2bOZPn06n332GVevXsVoNPLaa68xfvx4KleuDMDnn3/OlClTGDlyJHny5KFZs2acOXOGU6dOJbv/xYsXp0+fPnz22WfExsbi6upKs2bNGD9+PKdOnXpkW3F2dqZmzZrs37/f9sWDiIiIiIg8nsH6d8ZtirwgRo8eTVxcHMOGDUvzsWGj+hF/6kQGRCUiT2JfrDhFhk8mMjKGpKSH53G/iAwGcHF5iRs3otBfAPJ3qC1JelJ7enbcf1apkSk9myLPij///JPz58+zdu3aZCvgioiIiIjI4ynZFHmMb7/9lvnz59O+fXtee+21p6rD3rUw1vj4dI5MRFLDvuDDK1yLiIjIP0PDaEVE5LlmMZuJvBWLxaL/3YGGqkn6UVuS9KT29OzQMFqRf5HIyJjMDkGeA05O2dWWnpLFYlWiKSIikgmUbIpkMIvFgkXrksjfcP/Vr2azRd/2ioiIyDPjqd+zefv2bY4fP47FYnnsOwpFRERERETkxZPmns2YmBgGDx7Mpk2bcHR0ZPXq1Xz00Ud8/fXXFC9ePCNiFHmmGY1GjE/9tY7I/zOZXuyGpOGwIiIiz5Y0J5vjxo3j7t27bN68mWbNmlGkSBF8fX0ZNWoUc+fOzYgYRZ5pTk7ZMzsEeU686G1JC/2IiIg8W9KcbG7fvp0NGzaQK1cuDAYDdnZ29OvXjxo1amREfCLPvIhlX5EQfiGzwxB5ptnlK0iewI8xGg1KNkVERJ4RaU42LRYL9vb2ANx/a8qD20QkucTrV0gMO5/ZYYiIiIiI/KPSPAHIx8eH4cOHExsbi+F/SyR+8cUXVKpUKd2Dk/R14cI/07t2/vz5f+Q8IiIiIiLy75XmZPM///kPZ8+excvLi6ioKNzd3Tl48CB9+/bNiPjSzM3NjXLlyuHu7k6FChXw8vKic+fO/Pnnn6k6PiwsDDc3N8LCwjI40rQJDAzkjTfewN3d/aGf9evXP/H4sWPHMmPGDNtnd3d3Dh06lO5xbtu2jXbt2j318atXr8bPz8/2+c8//+Szzz6jSpUqVKhQAV9fX0aMGMGdO3dSVV9ISAhubm5PHY+IiIiIiDydNA+jvXv3LsuXL+fXX38lPDwcV1dXypUrh8lkyoj4nsrs2bPx9vYGIDo6mt69e/PZZ5+xaNGiTI7s7wkKCqJr165PdWxkZGSyz0eOHEmPkB5y69Yt2/Dqv8tisdC2bVuqVq3Kli1byJkzJ5cuXaJ///5069aN+fPnp8t5REREREQk/aW5Z7N58+bExMRQrlw53n77bdzd3f9VieZf5ciRg2bNmnH8+HHbtrNnzxIUFEStWrUoV64c77zzDtu3b0/x+MOHD/Phhx9SrVo1ypYtS0BAAEePHgXu9Zr5+fkxY8YMqlevTqVKlejatSvR0dG24xcsWMCbb76Ju7s7AQEB7Nu3D7g333XhwoXUq1cPT09PWrRokSzGp3Hw4EECAgLw9PTkzTffZNSoUSQlJfHll1+yYcMGNmzYwLvvvgvc6wEOCQkBwM/Pj6+//pp3332X8uXL88EHH/Dbb7/RoUMH3N3deeeddzh27Jgt7q+++oqGDRvi6emJl5cXn376KXFxcYSEhDBkyBAuX76Mu7s7V69eJSEhgSlTplC7dm0qVapEhw4dkg3nPXv2LIGBgbi7u9OwYUN+//13277IyEj++9//Ur9+fXLmzAlAkSJFGDhwIAULFsRsNj/xGf3Vtm3beP/996lcuTLly5enVatWtmG/q1evJiAggLZt2+Lp6cnMmTN57bXXuHLliu34X3/9lQoVKiR7xiIiIiIi8rA0J5u5c+fm6tWrGRFLhrh9+zabNm2ibt26tm1du3aldOnS/PDDDxw6dIhq1aoxdOjQh46Ni4ujc+fO1KtXj507dxISEkLRokUZN26crUx4eDhXr17lhx9+YMWKFRw5coQlS5YA95KX6dOnM27cOEJDQ/nggw/o3Lkzt27dYsmSJXz99ddMmTKFffv2ERAQwEcffcSNGzee+lr79OlDYGAghw4d4uuvv2bLli389NNPfPzxxzRs2JCGDRs+csjtihUr+Oqrr9izZw8REREEBgbSpUsXQkJCKF26NBMmTABg8+bNLFy4kODgYA4dOsSyZcvYvXs3GzZswNvbm2HDhlGwYEGOHDlC/vz5mTx5Mjt27GD+/Pns2rWL8uXL07ZtW+Lj40lMTCQoKIhSpUqxf/9+Jk2axI8//miLKU+ePPj4+PDJJ58wZswYfvzxR65fv46bmxuff/45JpMpVc/ovitXrtC9e3c6duzIvn372LFjB1arlS+//NJW5rfffqNhw4bs3buXNm3aULx48WT3bO3atdSrV48cOXI89XMSEREREXkRpHkYbalSpWjWrBkVKlQgX758yfaNHj063QL7Ozp16oTJZMJisRATE8NLL73ErFmzbPtnzZpF/vz5sVqthIeHkzNnzhQTaDs7O5YvX06xYsWIj48nPDyc3Llz8+uvvyYr9/HHH+Po6EixYsXw9vbm3LlzAKxZs4bmzZvj7u4OwHvvvUeJEiVwdHRk8eLFBAUF8eqrrwLQtGlTVq5cyfr162nbtm2K1/XVV1+xYMGCh7bfn3vp4ODA5s2byZ07N15eXvz8888Yjan7PqFJkya4uroCUK5cOaKjo21xV6tWzTbfs0aNGlSsWBFXV1ciIiKIjIx85BcQVquVZcuWMXXqVIoUKWK7V99++y07duzAycmJP//8kz59+uDg4ECpUqX46KOPkl3j7NmzWbFiBT/88APLli0jNjaWV199ld69e1O9evVUPyMAZ2dnNm3aRNGiRYmOjubKlSs4OTkli93Ozo5GjRrZ7ltAQABr1qyhY8eOJCYmsnHjRoKDg1N1T0VEREREXmRpTjazZcuWrJfw32jmzJm2OZtxcXEsXryY1q1bs3z5cl5//XVOnjxJly5duH79OiVKlMDZ2TnFeYYmk4mQkBA6dOjA3bt3KVmyJFmyZHmobN68eW2/29nZ2fZfv36dggULJitbsWJF4F6P6NixY209hgBJSUm88cYbj7yujh07PnbO5oIFCwgODmbYsGFcv36d6tWrM3ToUFsS+Ti5c+dOdt25cuWyfTYajbZrslqtTJ48me3bt+Ps7Mxrr71GYmJiivcvIiKCu3fv0r1792RJb2JiIuHh4SQkJODk5ISjo6NtX9GiRZPVYW9vT8uWLWnZsiVms5mTJ0+yZMkSOnXqxIYNGyhevHiqnhHcezYbN25k2bJlGAwGSpcuTXR0NFmy/P+/Bnnz5k0Wa6NGjZg0aRK///47YWFhvPTSS3h5eT3xfoqIiIiIvOjSnGz+W3ovU8vR0ZF27drx1VdfsXfvXlxcXOjevTvTpk2zrXq6detWvv/++4eO/eWXXxgxYgTLli2zJYHz5s2z9Vw+SYECBR5aBXfy5Mm8++67uLq60q1bN+rXr2/bd/HixWRJX1rEx8dz5swZhg4dSpYsWTh37hwDBw7k888/Z+rUqU88/v5rbJ5kwoQJXL58mW3bttmGkjZs2DDFsk5OTjg4ODBv3jwqVKhg2/7f//6X/Pnzc+LECSIiIoiJiSF79uwAyeZHfvvtt0ybNo3t27djMpkwmUy8/vrrjBo1ih9//JFTp04RFRWV6me0efNmFi1axNKlSylWrBgAI0aM4NSpU4+8Dy4uLtSoUYNNmzYRFhZGQEBAqu+ViIiIiMiLLM1zNqdNm/bIn3+jpKQkVq1axZ07d/Dw8CAmJgaz2UzWrFkBOHPmjG3OXkJCQrJjo6KiMBqNtp63o0ePsnDhwofKPUpAQADLly/n2LFjWCwWVq1axeLFi3FycqJZs2bMmDGDs2fPArBr1y7q16/PwYMHn+o6DQYDvXr1Yt68eSQlJZE3b16yZMmCk5MTcK+HMCoq6qnqflB0dDQODg6YTCbi4+OZN28ep06dIjExEbg3lDc2NpakpCSMRiNNmzZl4sSJXLlyBYvFwpo1a2jQoAEXLlzA3d2dV155hZEjRxIbG8uFCxeYN2+e7Vy1atUiPj6eIUOGcP78ecxmM7du3eLrr78GoFKlSml6Rg+WtVqt7Ny5k7Vr19pif5QmTZrwww8/sHfvXho3bvy376GIiIiIyIsgzT2b91cwve/WrVucPXuWt956K92C+rs6dOhgWyHXYDDw8ssvM2nSJNsQ1j59+vDZZ58RGxuLq6srzZo1Y/z48Zw6dSpZz2LVqlVp0aIFLVu2xGKxULhwYQIDA5k4cWKqFvJp2LAhd+7c4bPPPuP69euULFmS2bNn4+zsTJs2bbBarXTp0oVr166RP39+Bg8eTO3atR9Z36xZs5IlY/c1btyYwYMHM2PGDMaOHcusWbMwmUzUqFGD3r17A/DOO+/Qs2dPatWqxY4dO9JwN5Pr0aMH//nPf6hSpQrZsmXDw8ODRo0a2XoHvby8yJMnD15eXixbtoy+ffsSHBxMixYtuHXrFkWKFGHq1KmUKVMGuDcPdfDgwVSpUgUXFxdq165t62XOly8fy5YtY9q0aQQGBnL79m2yZs2Kt7c3S5YswdnZOU3PqHHjxoSGhlK/fn1MJhPFixendevWLF68+LFfINSqVYshQ4ZQrlw5ChQo8NT3TkRERETkRWKwpsNLEdetW0dISAiff/55esQk8q/TuHFjOnTowDvvvJPmY68GDyfhv39kQFQiLw67wi/j+ukoIiNjSEqyZHY4zzSDAVxcXuLGjSjS6bXI8oJSW5L0pPb07Lj/rFIjzcNoU9KoUSN++umn9KhK5F/l3LlzLFu2jOvXr1OnTp3MDkdERERE5JmR5mG0KTlw4ADZsmVLj6pE/lUGDRrE2bNnGTNmDPb29pkdjoiIiIjIMyPNyaafn1+y1TgTExO5ceMGnTt3TtfARP4NFi1a9LfrsMvrijUhPh2iEXlx2eUr+ORCIiIi8q+S5mTzr+95NBqNlChR4rHvhxR5kTm/3zGzQxB5LljMZiwWTeQRERF5VqQ52YyIiKBdu3YPbf/iiy/o0aNHesQk8lyJjIzJ7BDkOeDklP2Fb0sWi1XJpoiIyDMkVclmRESE7X2QwcHBlC9fngcXsY2KimLBggVKNkVSYLFYsGjxTPkb7s9cMJstWqFPREREnhmpSjbt7e3p1q0bkZGRALRq1eqh/c2bN0//6ESeA0ajEWO6rPssLzqT6fltSOq1FBERef6kKtnMkSMH+/btA+Ctt95iy5YtGRqUyPPEySl7Zocgz4nnuS1ZzWYibsUq4RQREXmOpHnO5qMSzYiICJydnf92QCLPm8h135B05WJmhyHyr5XFpQBOTdpiNBqUbIqIiDxH0pxsHjt2jHHjxnH16lUs/5uIlpiYSEREBMePH0/3AEWedeabV0j681JmhyEiIiIi8o9K8wSg4cOHkzdvXqpVq8Yrr7xCq1atMJlMfPrppxkRn/zLmM1mLl1S4iQiIiIiIo+X5mTz9OnTjB49mpYtW2I2m/noo4+YPHkyGzZsyIj4JAMtXrwYNzc35s+fn+pjevbsydq1awG4fPky7u7uXL58OV3iOXbsGN26daNKlSpUrFiRt99+m1mzZpGUlPTEYzdt2kRgYCDe3t54eXnRvHnzdJ9bvHr1avz8/NK1ThERERGR51Wak82cOXPi6OhIkSJFOH36NAAVKlQgPDw83YOTjLV48WI++OADFi5cmKqEDrCtSAxQsGBBjhw5QsGCBf92LFu2bOHDDz/Ey8uL77//ntDQUCZOnMiGDRue2Gs+cuRIxo0bR/v27dm1axf79u2jQ4cODBgwgMWLF//t2EREREREJO3SnGwWL16cpUuX4uDgQLZs2Thx4gRnz57FcP9FcPJM2LdvHzdv3qRfv35YLBa2bt1q2xcREUHv3r3x8vLC29ubnj17cvv2bQYMGMChQ4eYNWsWnTp1IiwsDDc3N8LCwpgyZQrvv/9+snOMHz+ejh07AnDjxg169+5N1apVqVatGoMHDyY6OhqA+Ph4hgwZwscff0xgYCA5cuTAYDBQpkwZJkyYgNVq5datW7bzjRkzBi8vL4YNG8axY8f45ptvmDp1KjVr1sTe3p4sWbJQp04dBg0axIULFwCwWq189dVXNGzYEE9PT7y8vPj000+Ji4sDoF+/fnTr1o23334bHx8fLl68yNmzZwkMDMTd3Z2GDRvy+++//xOPRkRERETkuZDmZLN79+588cUXXLx4kXbt2tGsWTOaNGlC48aNMyI+ySDffPMNzZo1w9HRkRYtWjBv3jzbvu7duxMdHc3333/PTz/9xJ07dxg2bBijRo3C09OToKAgZs6cmay+pk2b8ssvv3D+/Hng3tzO9evX07RpUywWC126dMFoNLJ161Y2bNjAtWvXGDx4MACHDx/m1q1bNGjQ4KE4X331VaZOnUru3Llt22JiYtizZw89e/Zk27ZtFClShPLlyz90rL+/P/379wdg8+bNLFy4kODgYA4dOsSyZcvYvXt3suHfu3btYsqUKXz//fcUKFCAoKAgSpUqxf79+5k0aRI//vjjU99vEREREZEXTZpXo61YsSI7d+7E3t6eokWL8tprrxEVFUXVqlUzIj7JAOHh4ezatcuW7DVr1owvv/ySAwcOUKhQIQ4cOMCWLVtwcnICYMyYMdy6deuxdRYqVIgqVaqwdu1aevTowe7duzGbzfj6+nL8+HF+++03vv76a7Jnv/eewL59+/LWW28xaNAgIiIiAHBxcUlV/P7+/tjb22Nvb09ERESqjqtRowYVK1bE1dWViIgIIiMjyZ07N1evXrWVqVChAqVLlwbgwIED/Pnnn/Tp0wcHBwdKlSrFRx99xIIFC1IVo4iIiIjIiy7NySaAwWDgxx9/JDw8nObNm9uGKsqzYcmSJSQlJdGoUSPbtqSkJObNm0enTp2Ae8njfXnz5iVv3rxPrPe9995j3LhxdO/enTVr1tCoUSPs7OwICwvDbDZTs2bNZOXt7e25dOmSre7r16+nOP/z+vXryc6fL1++ZL/v2bMnxXji4+NJSEjgpZdewmq1MnnyZLZv346zszOvvfYaiYmJWK3WZHXdd/XqVZycnHB0dLRtK1q06BPvgYiIiIiI3JPmYbQXL17knXfeYeTIkUyZMoUrV67QpEkTtm/fnhHxSTqLj49n5cqVjBo1inXr1tl+ZsyYwY4dOzCbzQDJVpg9c+YMX3zxxRPrrl27NrGxsezcuZNt27bRtGlTAFxdXXF0dCQkJIRDhw5x6NAh9u7dy9q1aylTpgwVKlQgd+7cfPfddw/VefLkSapVq8aRI0ds2x6cH1yrVi3CwsI4duzYQ8cuX74cPz8/YmNjmTBhApcvX2bbtm1s2bKFyZMn23pZU6q3QIECREREEBMTY9t25cqVJ94DERERERG5J83J5qhRowgICGDHjh1kyZKFV155hZEjRzJ16tSMiE/S2YYNGzAYDDRs2BBXV1fbT40aNShdujRr166latWqjBs3jjt37hAdHc348eNt79a0t7cnKioqxbrt7Ozw9/dn2LBhvP7665QoUQKAcuXKUaxYMcaMGUNMTAxxcXF8/vnntGnTBrPZjL29PQMHDmTatGksXryYmJgYzGYzhw4donv37tSrVw93d/cUz/nGG2/QvHlzunfvzs6dO0lKSiI+Pp5169YxadIkunXrRtasWYmOjsbBwQGTyUR8fDzz5s3j1KlTJCYmplivu7u7rW3HxsZy4cKFZPNaRURERETk8dKcbB49epT27dtjMBhsPUGNGjWyJSPy77ZkyRIaNmyInZ3dQ/uaN2/OunXrGDduHDly5ODtt9+mdu3aODs7M2zYMODefMlVq1bRokWLFOt/7733CA8Pt/VqAmTJkoVZs2Zx48YN6tatS7Vq1bh48SJff/01Dg4OADRs2JBp06bx008/Ubt2bTw9PRk8eDBNmjRh4sSJj72mYcOG0b59e7744gsqV65M1apVWbp0KWPHjiUwMBCAHj16EBcXR5UqVfDz8+Po0aM0atSIU6dOpVinyWTiq6++4tq1a1SpUoX27dtTu3btJ99gEREREREBwGB9cNJaKtSpU4eFCxdSsGBBKlWqxIEDB7h69SotWrTgp59+yqg4RZ5ZN+aNJ/Hi2cwOQ+RfK0uBIuQNGkBkZAxJSZbMDue5ZzCAi8tL3LgRRdr+AhBJTm1J0pPa07Pj/rNKjTT3bDZs2JBPPvmEPXv2YLFYOHbsGL1796Z+/fppDlRERERERESeT2lejbZLly7ExcXxySefEBsby4cffkjTpk355JNPMiI+EREREREReQalOtls164dc+fOxc7Ojr59+9KtWzdiY2NxcnJKtoqniCRnyuOKNTEhs8MQ+dfK4lIgs0MQERGRDJDqZPPBV08A1KxZkwMHDqR7QCLPG6dGgZkdgsi/ntVsxmLRJB0REZHnSZqH0d6XxnWFRF5YkZExTy4k8gROTtmf67ZksViVbIqIiDxnnjrZ1NBZkdSxWCxYtMCm/A33/3NrNlu0Qp+IiIg8M5462RSR1DEajRjTvO6zyMNMpvRrSOpJFBERkYyW6mQzKSmJtWvX2j4nJiYm+wzg7++fTmGJPD+cnLJndgjynEjPtmS1WIiIvKuEU0RERDKMwZrKyZd+fn6Pr8hg4KeffkqXoESeJ7d/WIn52uXMDkPExuScj1xvv09kZAxJSRrj/aLRi9MlvagtSXpSe3p23H9WqZHqns1t27Y9dUAiLzJzxA2SrivZFBEREZEXi2aSiYiIiIiISLpTsin/emazmUuXLmV2GCIiIiIikgZKNtOBm5sbHTt2fOjdo6tXr37iXNe/4/r16wwfPhw/Pz/Kly9PtWrV+PTTTzl16lSyct999x2VK1fGw8OD7du34+fnR9myZXF3d8fd3Z0KFSpQrVo1xo4diyUd39ERGBhIcHDw366nZ8+eDy1G9bSWLl1KvXr1cHd3p169eixevPiRZc1mM2PHjqVKlSq4u7vTuXNnrl27li5xiIiIiIg875RsppOff/6ZOXPm/GPnCwsLo3Hjxly/fp1Zs2Zx5MgR1q1bR5EiRWjWrBl79uyxlV2xYgX169cnNDQUX19fAIYNG8aRI0c4cuQIR48eZe7cuaxdu5Zp06b9Y9eQWpGRkelSz48//sikSZMYO3Yshw8fZsyYMXzxxRds3bo1xfIzZsxgz549rFq1il27duHo6MjAgQPTJRYRERERkeedks10EhgYyJQpUzh8+HCK+8PCwnBzcyMsLMy2LTg4mMDAQOBeL2iLFi0YO3YslSpVwsfHh2+++YZvv/0WX19fPDw8GDx4sO3Y0aNHU6pUKaZOnUqpUqUwGo3kyZOHHj16EBgYSL9+/UhKSqJp06bs37+fZcuWUadOnUfG7+bmhpeXF7///rvtevr164evry+1atUiOjqaP/74gw4dOlCpUiVq1KjB0KFDiYqKstWxYsUKateujbu7O3379iU2Nta2r1+/fvTr1++hc4aEhAAQERFB79698fLywtvbm549e3L79m0GDBjAoUOHmDVrFp06dbLdt5o1a1KpUiWaNGmS6lWQr169SocOHahQoQIGgwF3d3e8vb05ePBgiuVXrFhBhw4dKFCgADly5GDAgAHs3LlTQ3pFRERERFJByWY6efPNN2nevDm9evXi1q1bT1VHaGgo+fPnZ//+/XTr1o3Ro0cTEhLCd999x/z581m5ciUHDx4kISGBn3/+mUaNGmEwGB6qp1mzZly7do0jR46wcuVKPD09CQoK4scff0zxvImJiYSEhLB//36qVq1q2753716WLVvG+vXrSUxM5MMPP6RkyZLs3LmTVatWce7cOfr06QPAvn37GD58OCNHjuTgwYOUL1+eX3/9NdXX3r17d6Kjo/n+++/56aefuHPnDsOGDWPUqFG2+GfOnMn+/ftZvnw5K1asICQkhPfee48BAwaQmJj4xHO0bNmSjh072j7fvHmTgwcP8sYbbzxUNioqiitXrlC6dGnbNhcXF3LlysUff/yR6usSEREREXlRpfrVJ/Jkffv25ciRI/Tr148ZM2ak+fhs2bLRunVrDAYD1apVw2w2065dO7JmzUrZsmXJly8f4eHhFC1alMTERFxcXFKsJ1++fACPnV84bNgwPv/8c9tnV1dXPvroI1q1amXbVqNGDfLnzw/AypUrsbOzo3fv3phMJhwdHRk0aBD169fn+vXrrF+/nrp161K5cmUAWrRowYoVK1J13eHh4Rw4cIAtW7bg5OQEwJgxY1JM2h0cHLh9+7atx/e9996jefPmKSbdj3P9+nWCgoJ44403aNCgwUP7Y2JigHvP5EGOjo62fSIiIiIi8mhKNtORvb09X3zxBY0bN2bevHm2xCm1cufObUuajMZ7nc45c+a07TcajVgsFpycnMiSJQuXL6f87sb7Q3Xz5s37yHMNGTKEgICAx8ZzP2mFe72ABQsWxGQy2bYVLlwYuJcsXr16lddffz3Z8UWKFHls/fddv34dgEKFCtm25c2bN8X43d3dCQ4O5ptvvmHOnDk4OjoSGBhI586dbffsSY4ePUr37t3x9PRk9OjRZMny8L8GWbNmBUg2FBggLi6O7Nmzp+o8IiIiIiIvMg2jTWdFixZlxIgRTJ48maNHj9q230/SHhzu+deFb1LbO2dvb0/t2rVZvXp1iqvHrlixgrx58+Lu7v4UV5ByPIUKFeLy5cuYzWbbtosXLwL3EkNXV9eH5jJeuXLF9rvRaEx27REREbbfCxQoAJAseT5z5gxffPHFQzFdvnyZPHnyMHfuXA4cOMDYsWOZOXMmO3fuTNU1rVy5kjZt2tC6dWsmTpyIvb19iuVy5cpF/vz5OXPmjG3b9evXuXXrVrKhtSIiIiIikjIlmxngnXfeoUmTJixfvty2LU+ePOTKlYtNmzZhtVr57bff2LJly1OfY8CAAVy+fJlu3bpx9uxZLBYLV69eZdKkSSxZsoTPP/8cOzu79LgcAGrWrAnAhAkTiIuL4/r164waNQofHx8KFSpEkyZN+PHHH9m+fTtJSUmsWbOGX375xXZ8iRIlOHToEFevXiUuLo4vv/zSlszmz5+fqlWrMm7cOO7cuUN0dDTjx4+3Ja/29va2hYh+/fVX2rdvz8mTJ7G3tydPnjwAqepF3rp1K0OHDiU4OJi2bds+sXxAQAAzZszg0qVLREdH8/nnn1OpUiWKFi2atpsnIiIiIvICUrKZQfr3789rr71m+2xvb8+IESPYvHkzFStWZMyYMTRr1uyp68+fPz9r166lQIECdOzYkYoVK9KkSRPCwsL49ttvqVGjRnpchs1LL73E119/zalTp6hZsyYNGjSgUKFCTJkyBQAPDw/GjRvHmDFj8PT0ZOvWrckWG2revDnu7u68++67vPnmmxQoUICCBQva9k+YMIEcOXLw9ttvU7t2bZydnRk2bBgA/v7+rFq1ihYtWlCvXj3atm1L586dqVChAt27d6d///6UL1/+idcwbdo0zGYz3bp1s71j1N3d3bbK7/r165P1Bn/88cfUrFmTli1bUrNmTeLj41PsbRURERERkYcZrFarNbODEHmeRSyfSdKf5zM7DBGbLHkL4tyyG5GRMSQlPTwUX55vBgO4uLzEjRtR6C8A+TvUliQ9qT09O+4/q9RQz6aIiIiIiIikO61GK8+FrVu30q9fv0fu9/DwYM6cOf9gRP/P5OwCSQmZcm6RlJic8z25kIiIiMjfpGG0IiIvIKvFQkTkXSwW/S/gRaOhapJe1JYkPak9PTvSMoxWPZsiGSwyMiazQ5DngJNT9nRtSxaLVYmmiIiIZCglmyIZzGKxkMLrUEVS7f4rb81mi77tFRERkWeGFggSERERERGRdKeeTZEMZjQaMeprHUkHJlP6NSQNoxUREZGMpmRTJIM5OWXP7BDkOZGebUkLBImIiEhGU7IpksGidm0iKeJKZochYpMllwsv1WqE0WhQsikiIiIZRsmmSAZLunMT882rmR2GiIiIiMg/SjPJREREREREJN2pZ1P+cW5ubjg4OGAymbBardjZ2eHp6cngwYMpUKDAE48PCwujdu3a/PTTTxQuXPgfiFhERERERNJKPZuSKWbPns2RI0c4evQo27dvx2q18tlnn2V2WCIiIiIikk6UbEqmy5EjB82aNeP48eO2bWfPniUoKIhatWpRrlw53nnnHbZv357i8YcPH+bDDz+kWrVqlC1bloCAAI4ePQpASEgIfn5+zJgxg+rVq1OpUiW6du1KdHS07fgFCxbw5ptv4u7uTkBAAPv27QPAarWycOFC6tWrh6enJy3+r727j+v57v//f+vdiZqzQsrJ2BZyDJuS4sCkhpGMJBtrmBlxYGxOjjHHnMxJyBxMLOdbDs3pnGxzsoNhaCJsdjiMj60VKoSKdPJ+/f7w9f6txSHUQvfr5XJcVq/n8/V6PV6vnkfcPZ+v17tnz3w1ioiIiIjInSlsSom7cuUKW7ZsoV27dpZtQ4YMoV69emzfvp24uDhatmzJBx98UGDfrKwswsLCaN++Pbt37yY2NpZatWoRHh5u6ZOUlERycjLbt29n9erVxMfHs3LlSgDWrVvH/PnzCQ8P59ChQ7z66quEhYVx+fJlVq5cydKlS5kzZw779+8nKCiIvn37cuHChWK/JyIiIiIijzo9syklYuDAgVhbW2M2m8nMzKR8+fIsXLjQ0r5w4UJcXFwwDIOkpCQqVKhAcnLBN7ra2toSExND7dq1uXHjBklJSTg6OvLDDz/k6zd48GDs7e2pXbs2Pj4+nDlzBoD169fTo0cPPDw8AOjevTtubm7Y29sTHR3NgAEDqF+/PgDBwcGsWbOGjRs38sYbbxTXrREREREReSwobEqJWLBgAT4+PsDN2cno6Gh69+5NTEwMDRo04MSJEwwaNIjU1FTc3NyoVKkShlHw8wCtra2JjY2lf//+XLt2jTp16mBjY1Ogr7Ozs+VrW1tbS3tqairVq1fP19fT0xO4OSM6ffp0Zs6caWnLzc2lYcOGRXMTREREREQeYwqbUuLs7e3p168fn3zyCfv27aNKlSoMGzaMefPm4efnB8DWrVvZtm1bgX2PHj3KpEmTWLVqlSUELlmyxDJzeTfVqlXj3Llz+bbNnj2bzp074+rqytChQwkICLC0JSQk4OjoeJ9XKiIiIiJSeuiZTSlxubm5rF27lqtXr9KkSRMyMzPJy8vDwcEBgFOnTvHxxx8DkJ2dnW/f9PR0TCYT9vb2ABw5coQVK1YU6HcnQUFBxMTEcOzYMcxmM2vXriU6OhonJydCQkKIjIzk9OnTAOzZs4eAgAAOHjxYVJcuIiIiIvLY0symlIj+/ftjbW0NgJWVFU899RQRERGWJayjRo1i5MiRXL9+HVdXV0JCQpgxYwYnT57MN7PYokULevbsSa9evTCbzdSsWZPQ0FBmzZpVqBf5BAYGcvXqVUaOHElqaip16tQhKiqKSpUq0adPHwzDYNCgQaSkpODi4sL48ePx9/cvlnsiIiIiIvI4sTJu9yCciBSZtC0ryEtOLOkyRCysK7vg9HI/0tIyyc01l3Q58iezsoIqVcpz4UI6+huAPAiNJSlKGk+Pjls/q8LQMloREREREREpcgqbIiIiIiIiUuT0zKZIMbOpUBlyc0q6DBELm4pVSroEERERKQUUNkWKWflWAXfvJPInM8xmzGY9FCMiIiLFR2FTpJilpWWWdAnyGHByKlukY8lsNhQ2RUREpFgpbIoUM7PZjFkv/JQHYGV18795eWa9oU9EREQeGQqbIsXMZDJh0qu4pAhYWxfdQNLMpoiIiBQ3hU2RYubkVLakS5DHRFGOJcNs5lLaNQVOERERKTYKmyLFLPPQLvKuXCjpMkQsrMs7UbapPyaTlcKmiIiIFBuFTZFilpdxmbzLCpsiIiIiUrroSbLHxC+//FLSJYiIiIiIiFgobBaBM2fOMHr0aF544QU8PDx48cUXmTlzJpmZRfuRF+7u7sTGxgIQEBDAxo0bAYiOjub999+39DObzURFRdGxY0c8PDxo2rQp/fr1Iz4+vkjruV9jxoyhQYMGeHh44OHhQePGjQkMDGTr1q2FPkZoaChz584txipFRERERORBKGw+oMOHD9O1a1dq1KjBhg0biI+PJyoqiqNHj/LGG2+Ql5dXLOfdsmULnTt3BuDSpUv52j755BPWr1/PP//5Tw4fPszu3btp1qwZvXv35tdffy2Weu5VYGAg8fHxxMfHc/jwYfr168eIESMemvpEREREROTBKGw+oPHjx9OlSxeGDh1KpUqVAHj66aeZPXs2lStX5rfffgNuzkpOnjwZHx8fBg4cCMC+ffsIDg7Gy8sr30wlQE5ODlOnTsXHx4dmzZqxaNGifOf18/Nj3bp1rF+/noULFxIXF4eXlxcAhw4dwsvLizp16mBlZYWDgwP9+/cnJCSECxduPjuYm5vLnDlzaN26NZ6envTq1YsTJ04AkJWVRXh4OK1bt6Zp06aEhoZy7Ngxy7nv9VruxmQy0aVLF8qVK8dPP/0EQHZ2NtOnT6dDhw54eHjQvHlzJk2ahHGbDxnMyMhg3LhxtGvXjsaNG9OqVSsWLFiQ714tXLiQLl264OHhQZcuXThw4ICl/fjx44SGhuLh4UHLli2ZM2eO5Ty32po2bUq7du1YtmzZbWsQEREREZH89IKgB5CQkMDPP//MBx98UKCtSpUqzJ8/v0D/Xbt2kZOTw4kTJwgLC2PGjBn4+/tz9OhRBg0ahJOTE61atWL+/Pns2rWLNWvWULly5dueA6Br164kJiby/fff8+mnnwI3l9i+9957XL58mVatWvHcc89Rt25dxo0bZ9kvMjKSzZs3s3jxYp5++mnmzZvHgAED+Pe//80HH3zATz/9xIoVK6hWrRr/+te/6NOnD5s3b6Z69er3fC13k5uby44dO7CyssLb2xuA5cuXs2fPHpYvX07VqlWJj4/ntdde48UXX6R58+b59p85cyaJiYmsWbOG8uXLs23bNoYOHUqHDh2oXbs2AGvXriUqKoqqVasyYcIEPvjgA77++msuX77MG2+8QWhoKIsXL+b8+fOEhobi4uJCmzZt6N27N8OHD2fJkiX8+uuvDBo0CHt7e1555ZW7XpeIiIiISGmmmc0HcGv5apUqVQrVv1OnTjg4OFChQgVWrVqFv78/7dq1w9raGk9PT0JCQoiOjgbgiy++oF+/fjz55JM88cQTjBs3Disrq0Kdp0uXLqxYsQJ7e3vmzZtH586dad68OREREeTm5gKwfv163nzzTerUqYO1tTVhYWHMmTOHrKwsNm/ezDvvvEPt2rWxs7Ojd+/ePPPMM2zevPm+ruV2Nm/ejJeXF15eXjz//PMMGzaMbt264eTkBEBISAjLli3D2dmZlJQUsrKyKFu2LMnJyQWONWTIED766CPKlSvH+fPnKVOmDAApKSmWPsHBwdSuXRsHBwcCAwMtL1TauXMnZcqUYfDgwdjZ2VGrVi2WLl2Kr68vGzduxM3NjV69emFra0udOnXo16/f/7wuERERERG5STObD8DZ2RmA1NRUnnrqqQLtFy5cyBdEq1atavk6KSmJAwcOWJa+AuTl5VGrVi3gZlCqVq2apa1ChQpUrFix0LXdCnIAycnJ7Nq1ixkzZmAymXj77bdJTU21zFIC2NnZ0bhxY1JSUsjJyaFmzZr5jlezZk0SExPv61pup1OnTkybNg0AwzCIj49nxIgRmM1mRo8ezfXr15k4cSIHDx7E1dWVZ599FsMwMJvNBY518eJFPvzwQ3766Sdq1qxJw4YNAfL1/f3PwcbGxrIUNjU1lWrVquUL8s8884zluo4fP57vusxmM9bW1ne8LhERERERuUlh8wHUqFGDevXq8eWXX9K0adN8bRcvXqRNmzZMnTqVTp06AeQLNK6urnTt2pWJEydatqWkpFhCkKurq+V5T4Br166Rnp5+15oyMzNp0aIFs2fPpk2bNgC4uLjQo0cPzpw5w3/+8x8AqlWrxrlz5yz75eTkMGPGDPr160eZMmX47bffcHNzs7QnJCTg5+dn+f5eruVurKys8PT05KWXXuLbb79l9OjRjBs3jooVK7J3717KlCmD2WwucI9vGTZsGH5+fixevBgbGxvS0tL4/PPPC3VuV1dXzp07h2EYlmvasWMHGRkZuLq64uPjw+LFiy3909LSivwtwyIiIiIijyMto31A77//PmvXrmXevHmkpaVhGAb/+c9/GDhwIA0aNKB9+/a33S84OJjNmzezd+9ezGYzv/zyC6+99hpLliwBoHv37ixatIjTp09z48YNpk2bdsc325YpU4aMjAwMw6Bs2bL4+/sTHh5ObGws165dIzs7m0OHDrF161batWsHQFBQEIsXL+bMmTPk5uaycOFCduzYQaVKlejWrRsRERH8+uuvZGdns3z5ck6dOkVAQMB9XUthnD59mh07dlhmETMyMihTpgwmk4mMjAzCw8PJyMggJyenwL7p6enY29tjbW3NpUuXmDx5MsBt+/6Rr68vubm5LFiwgOzsbBISEpgyZQo3btwgMDCQI0eOsHHjRnJzc0lJSWHgwIGWGVkREREREbkzzWw+IG9vbz777DMWLFhAQEAA169fp0qVKrz00ksMGDAAW1vb2+73/PPPExERQUREBMOGDcPBwYFOnToxYsQIAPr378/169d57bXXyM3NJSQkBEdHx9seq02bNvzrX/+iSZMm7Nq1i6lTp7Jo0SImT55sWfr6zDPP8Pbbb/Pyyy8D8Oabb5Kbm0u/fv24cuUKjRo1IioqCltbW0aNGsXcuXPp06cPly9fxt3d3fIiofu5ltvZtGlTvs/VLF++PO3bt+fdd98FYNy4cYwfPx5vb2/Kli2Lr68vrVq14uTJkwWONXXqVKZMmcKSJUuoWLEiHTt25Nlnn+XkyZO0bNnyjjXAzeXJixcvZurUqSxduhQHBwd69epFjx49AFi0aBEzZ85k8uTJWFtb4+vry9ixY//nMUVEREREBKwMfY6DSLG6+u0G8i6eL+kyRCysHatQwS+YtLRMcnMLPgctjzcrK6hSpTwXLqSjvwHIg9BYkqKk8fTouPWzKgwtoxUREREREZEip7ApIiIiIiIiRU7PbIoUM+tyjpCXW9JliFhYl3cq6RJERESkFFDYFClmZZv4lnQJIgUYZjNmsx6KERERkeKjsClSzNLS9Lmc8uCcnMoW6Vgymw2FTRERESlWCpsixcxsNmPWCz/lAVhZ3fxvXp5Zb+gTERGRR4ZeECQiIiIiIiJFTjObIsXMZDJh0j/rPJa0FFVERETkzhQ2RYqZk1PZki5BiolhNnMp7ZoCp4iIiMhtKGyKFLNrPx0kLyOtpMuQImb9RAWeaNgMk8lKYVNERETkNhQ2RYpZ3rWrmNMVNkVERESkdNGTZCIiIiIiIlLkNLP5CHN3d6dMmTJYW1tjGAa2trZ4eXkxfvx4qlWrdtf9ExMT8ff355tvvqFmzZp/QsV39+abb3Lo0CEAcnJyyMvLw97e3tK+ZcsWqlevXlLliYiIiIhIISlsPuKioqLw8fEBICMjg3fffZeRI0fy2WeflXBl92fRokWWr+fOncv333/Pp59+WoIViYiIiIjI/dAy2sdIuXLlCAkJ4ccff7RsO336NAMGDMDX15fnnnuOjh07snPnztvuf/jwYV5//XVatmxJo0aNCAoK4siRIwDExsbi5+dHZGQkrVq1wtvbmyFDhpCRkWHZf/ny5bRt2xYPDw+CgoLYv38/AIZhsGLFCtq3b4+Xlxc9e/bMV+O9OH78OKGhoTRt2pR27dqxbNkyjP/3Kfdz585l6NChvPvuu3h5efHCCy8wa9Ysy76hoaHMnTvX8n1iYiLu7u4kJiYCN2eKJ0+ejI+PDwMHDgRg3759BAcH4+XlRUBAABs3bryvukVEREREShuFzcfIlStX2LJlC+3atbNsGzJkCPXq1WP79u3ExcXRsmVLPvjggwL7ZmVlERYWRvv27dm9ezexsbHUqlWL8PBwS5+kpCSSk5PZvn07q1evJj4+npUrVwKwbt065s+fT3h4OIcOHeLVV18lLCyMy5cvs3LlSpYuXcqcOXPYv38/QUFB9O3blwsXLtzT9SUnJ9O7d29eeukl9u3bx/z581m5ciUxMTGWPtu2baNly5bExsYyadIkoqKiLIG5MBISEti1axfh4eGcOHGCsLAw3nrrLcvxpkyZwp49e+6pbhERERGR0kjLaB9xAwcOxNraGrPZTGZmJuXLl2fhwoWW9oULF+Li4oJhGCQlJVGhQgWSk5MLHMfW1paYmBhq167NjRs3SEpKwtHRkR9++CFfv8GDB2Nvb0/t2rXx8fHhzJkzAKxfv54ePXrg4eEBQPfu3XFzc8Pe3p7o6GgGDBhA/fr1AQgODmbNmjVs3LiRN954o9DXunHjRtzc3OjVqxcAderUoV+/fnz22We88sorADz11FN06dIFgNatW+Ps7Mwvv/xC48aNC3WOTp064eDggIODAxEREfj7+1vCu6enJyEhIURHR9OqVatC1y0iIiIiUhopbD7iFixYYHlmMysri+joaHr37k1MTAwNGjTgxIkTDBo0iNTUVNzc3KhUqZJl2envWVtbExsbS//+/bl27Rp16tTBxsamQF9nZ2fL17a2tpb21NTUAi/u8fT0BG7OiE6fPp2ZM2da2nJzc2nYsOE9XWtSUhLHjx/Hy8vLss1sNmNtbX3b+m7VaDabC32OqlWr5jvfgQMH8p0vLy+PWrVq3VPdIiIiIiKlkcLmY8Te3p5+/frxySefsG/fPqpUqcKwYcOYN28efn5+AGzdupVt27YV2Pfo0aNMmjSJVatWWULgkiVLLDOXd1OtWjXOnTuXb9vs2bPp3Lkzrq6uDB06lICAAEtbQkICjo6O93R9rq6u+Pj4sHjxYsu2tLQ0MjMzC7W/yWQiJycn375/ZGVlle98Xbt2ZeLEiZZtKSkptw3rIiIiIiKSn57ZfIzk5uaydu1arl69SpMmTcjMzCQvLw8HBwcATp06xccffwxAdnZ2vn3T09MxmUyWjxk5cuQIK1asKNDvToKCgoiJieHYsWOYzWbWrl1LdHQ0Tk5OhISEEBkZyenTpwHYs2cPAQEBHDx48J6uLzAwkCNHjrBx40Zyc3NJSUlh4MCBTJs2rVD7u7m5sWfPHq5evUp6ejpRUVH/s39wcDCbN29m7969mM1mfvnlF1577TWWLFlyT3WLiIiIiJRGmtl8xPXv39+yjNTKyoqnnnqKiIgIyxLWUaNGMXLkSK5fv46rqyshISHMmDGDkydP5ptZbNGiBT179qRXr16YzWZq1qxJaGgos2bNKtSLfAIDA7l69SojR44kNTWVOnXqEBUVRaVKlejTpw+GYTBo0CBSUlJwcXFh/Pjx+Pv739O11qhRg0WLFjFz5kwmT56MtbU1vr6+jB07tlD7DxgwgLFjx+Lv70/58uUZOnQoW7duvWP/559/noiICCIiIhg2bBgODg506tSJESNG3FPdIiIiIiKlkZWhNYEixSo97hvMV+7tzbvy8DOVd6K8dzvS0jLJzS38c8H3w8oKqlQpz4UL6eg3tjwojScpKhpLUpQ0nh4dt35WhaFltCIiIiIiIlLkFDZFRERERESkyOmZTZFiZv1EBTDnlXQZUsSsn6hQ0iWIiIiIPNQUNkWK2RPPNi3pEqSYGGYzZrMeLBERERG5HYVNkWKWlla4zwGVR4/ZbChsioiIiNyBwqZIMTObzZiL92WlIiIiIiIPHYVNkWJmMpkw6VVcDyXNTIqIiIgUH4VNkWLm5FS2pEuQOzDMZi6lXVPgFBERESkGCpsixSzrzE+Yr6eXdBnyByb7stg/0xCTyUphU0RERKQYKGyKFDPzjWuYrylsioiIiEjpoifJREREREREpMhpZvMenD17loULF7Jnzx4uXbqEnZ0djRo14o033qBFixYlXd49SUxMxN/fn2+++YaaNWsWyzk2bNjAmjVrOHnyJNnZ2bi4uPDiiy8SFhZGuXLliuWcIiIiIiLycNDMZiGdPHmSzp07k52dTVRUFIcOHWLbtm107tyZwYMH8+2335Z0iQ+VsWPHMnv2bHr16sW2bduIi4tj3rx5nDx5kjfffBPD0DNyIiIiIiKPM4XNQho/fjwtWrRg6tSpuLm5YW1tjaOjIy+//DL/+Mc/yMnJAWDfvn0EBwfj5eVFQEAAGzdutBzDbDbzySef8OKLL9KkSROCg4PZs2ePpT0tLY3hw4fTpEkT/P39+fTTT3n22WdJTEwkMTERd3d3pk2bRtOmTZkwYQLZ2dlMnz6dDh064OHhQfPmzZk0aZIlyIWGhjJt2jSCgoJo3LgxQUFBxMXF5buuTZs20aFDBxo3bkyfPn1ITk4GoEOHDixYsCBf38DAQNasWXPXe/Xdd9+xfv16PvnkEzp06ICjoyM2NjbUrVuX8PBwPDw8SE+/+QxjRkYGEydOpHXr1jRv3pzhw4dz4cIFgNte89y5cxk2bBijR4/G09OTF154ga+++oqPP/6Yv/71r3h7ezN//nxLLYcPH+b111+nZcuWNGrUiKCgII4cOQJAbGwsfn5+REZG0qpVK7y9vRkyZAgZGRncuHGDpk2bsmnTJsuxsrOz8fHxYf/+/Xe9ByIiIiIipZ3CZiGcP3+e+Ph4Xnnlldu2d+3alRdffJETJ04QFhbGW2+9RWxsLJMmTWLKlCmWQPnxxx8THR3NnDlziI2N5Y033mDQoEEcO3YMgHfffZf09HS++eYbVq9ezc6dO8nLy8t3rszMTL777juGDx/O8uXL2bNnD8uXLyc+Pp758+ezatUqDhw4YOkfExPDqFGj+P7772nbti1hYWGkpaVZ2o8fP87nn3/Ot99+y5UrV/j4448BCAoK4osvvrD0+/HHH0lMTKRDhw53vV9ffvklHh4euLu7F2hzcnJi9OjRVKhQAYD33nuPX3/9lXXr1rFjxw7KlSvH3/72t3wzn7+/ZoCtW7fSpk0bDh06ROfOnXnnnXfIyMjg22+/ZcqUKcyZM4ekpCSysrIICwujffv27N69m9jYWGrVqkV4eLjl2ElJSSQnJ7N9+3ZWr15NfHw8K1eupEyZMgQEBOS7Bzt37qRs2bI0a9bsrvdARERERKS0U9gshPPnzwPg6upq2bZ//368vLzw8vLCw8OD9u3bs2rVKvz9/WnXrh3W1tZ4enoSEhJCdHQ0AGvXruWtt96iQYMG2NjY0LFjR/z8/FizZg3Jycns3buX9957D0dHRypVqsR7771XoJYuXbpgZ2dHhQoVCAkJYdmyZTg7O5OSkkJWVhZly5a1zE4CdOvWjWbNmmFnZ8fAgQNxcHBg586dlvaBAwdSvnx5KlasSKtWrUhISLCcJyEhgR9++AG4+fzlSy+9RNmyd//MyPPnz+e7VwB9+/a13K/nnnuODRs2cPHiRbZu3crYsWOpXLkyZcuW5b333uOHH37g+PHjt71mgDp16vDSSy9hZWVFixYtyMvLY+DAgdja2uLn5wfcfL7W1taWmJgYevbsSXZ2NklJSTg6Oua7PwCDBw/G3t6e2rVr4+Pjw5kzZyz3bt++faSmpgKwfv16goKCsLKyuus9EBEREREp7fSCoEJwdnYGIDk5maeffhqA5s2bW5akrlu3jnnz5pGUlMSBAwfw8vKy7JuXl0etWrUAuHDhAk8++WS+Y9esWZMTJ05w7tw5y/e3/LEvQNWqVS1fX79+nYkTJ3Lw4EFcXV159tlnMQwDs9ls6fPUU09ZvrayssLV1dUSngAcHR0tX9va2lpmUp2dnWnVqhVffPEF9evXZ/PmzcydO7cQd+tmjb/99lu+bUuXLrV87efnh9lsJikpCYCQkJB8fa2trUlMTLTU9vtr/mPNJtPNfy+pWLFivu/NZjPW1tbExsbSv39/rl27Rp06dbCxsSnwvOitn++te3CrvVGjRri5ubFlyxYCAwPZu3cv48aNK9Q9EBEREREp7RQ2C6FGjRo0atSI1atX/88llK6urnTt2pWJEydatqWkpFjCS40aNQqEsN9++42qVatSvXp14OayzluB9lYY+73fz6qNGzeOihUrsnfvXsqUKYPZbKZp06b5+v9+Fs9sNnP27FmqVatWqOvu1q0bEyZMoEWLFpQvX77Ase+kffv2DBo0iNOnT+Pm5nbHfi4uLgB89dVX+QLfqVOnePLJJy2h+I8ziYWdWTx69CiTJk1i1apVNGzYEIAlS5ZYZi4Lo1u3bmzZsgVbW1u8vLyK7c29IiIiIiKPGy2jLaRbz16+//77nDlzBsMwyMjIYMOGDcydO5eqVasSHBzM5s2b2bt3L2azmV9++YXXXnuNJUuWANC9e3c++eQTjh8/Tl5eHl999RX//ve/6dq1K1WrVqVNmzbMmDGDK1eucOXKlXzPFt5ORkYGZcqUwWQykZGRQXh4OBkZGZaXFQGsXr2aH3/8kezsbD7++GMMw6BNmzaFumZfX1/y8vL45z//SVBQUKHvla+vL0FBQfTr14+vv/6a69evYxgGJ0+e5O9//zvnz5+ncuXKuLi44Ovry4cffkhaWho5OTlERkYSHBzM1atXC32+O0lPT8dkMmFvbw/AkSNHWLFiBdnZ2YU+RufOnTlx4gSrV6++p3sgIiIiIlLaaWazkOrVq8fmzZuJiopi4MCBpKamYmVlhbu7O2+++Sbdu3fHzs6OiIgIIiIiGDZsGA4ODnTq1IkRI0YAN59bNJvNDB8+nNTUVGrXrk1ERATe3t4AfPjhh4wfPx5fX1+cnJzo0qULO3fuxNbWNl+AvGXcuHGMHz8eb29vypYti6+vL61ateLkyZOWPt7e3kycOJFTp07x7LPPsmTJEsqXL8+VK1fues22trZ07tyZFStWEBkZeU/3a/LkyXz11Vd8/vnnfPDBB2RlZVGpUiWaN2/OunXrqF+/PgDh4eHMmjWLLl26kJGRQd26dVm0aBHOzs4kJibe0zn/qEWLFvTs2ZNevXphNpupWbMmoaGhzJo1y/LG27upVKkSrVu35sCBA7Rr1+6B6hERERERKU2sDH3g4UPju+++o0mTJpaZuP/+97906dKFI0eOUKZMmXs+XmhoqOXjPO7XihUr2L17N4sWLbrvYzzqpk6dSlZWFhMmTLiv/a+diMOccbloi5IHZnqiPE8860NaWia5uea771CCrKygSpXyXLiQjn5jy4PSeJKiorEkRUnj6dFx62dVGFpG+xCZPn06kZGR5ObmkpGRQWRkJH/961/vK2g+qNTUVI4dO8by5ct59dVX//TzPwzOnTvH/v372bBhwx0/9kZERERERG5Py2gfIrNmzWLy5Mk0a9YMk8lEq1at7vrcZnHZtWsXkydP5uWXX8bf39+yfenSpfzzn/+8436BgYH5XpD0KPv8889ZtmwZb775Jn/5y19KuhwRERERkUeKltGKFLOsMz9hvp5e0mXIH5jsy2L/TEMto5VSR+NJiorGkhQljadHx70so9XMpkgxs3/62ZIuQe7AMJsxm/UnmoiIiEhxUNgUKWZpaZklXYLcgdlsKGyKiIiIFBOFTZFiZjabMT/cqzRFRERERIqcwqZIMTOZTJj03ucSo9lLERERkZKhsClSzJycypZ0CaWaYTa4lJapwCkiIiLyJ1PYFClm2ed/wXzjekmXUSqZ7Oyxq/Y0JpOVwqaIiIjIn0xhU6SYmbNvYChslgg9KisiIiJScvQkmYiIiIiIiBQ5hU0REREREREpcgqbpdDZs2f5xz/+gZ+fH40bN8bb25t+/frx3XffFcv5YmNjcXd3L5ZjA/j5+eHu7s6ePXsKtG3btg13d3fGjBlTqGOFhoYyd+7coi5RRERERKTUUdgsZU6ePEnnzp3Jzs4mKiqKQ4cOsW3bNjp37szgwYP59ttvS7rE++Lk5MT69esLbF+7di3lypUrgYpEREREREo3vSColBk/fjwtWrRg6tSplm2Ojo68/PLLmM1mcnJyANi3bx8RERH88ssvuLi4MGDAADp37gzAmDFjsLOzIyUlhdjYWCpVqkTv3r15/fXXAUhJSWH8+PF8//33ODk5ERAQkK+GhIQEpkyZQnx8PE888YQl6NrZ2bFu3To+++wzHB0dOXbsGP/4xz8IDAy863UFBgYSExNDeno65cuXt9Rx5MgRWrVqZemXnZ3N7Nmz2bVrF+fPn8fe3p6OHTsybtw4rKys8h3TMAw+/fRToqOjuXjxIvXq1eO9996jYcOG93HnRURERERKF81sliLnz58nPj6eV1555bbtXbt25cUXX+TEiROEhYXx1ltvERsby6RJk5gyZUq+Zarr1q0jNDSUgwcP0r9/f6ZNm0ZycjIAw4cPx8bGht27d/PZZ5+xe/duy37Xrl2jT58+1K1bl927d7Ny5Ur27duXb+nq8ePHCQwMZN++fbRt27ZQ1/aXv/yFp59+mi+//NKybcOGDXTo0AF7e3vLtuXLl7Nnzx6WL19OfHw88+fPZ9WqVRw4cKDAMVeuXMnSpUuZM2cO+/fvJygoiL59+3LhwoVC1SQiIiIiUpopbJYi58+fB8DV1dWybf/+/Xh5eeHl5YWHhwft27dn1apV+Pv7065dO6ytrfH09CQkJITo6GjLfj4+PrRo0QIbGxu6detGXl4eCQkJJCUlERcXx7vvvku5cuWoVq0af/vb3yz77dq1i+zsbEaMGEGZMmWoVq0aw4YNy3dsW1tbXn75Zezs7PIFxbsJCgrKt5R27dq1dOvWLV+fkJAQli1bhrOzMykpKWRlZVG2bFlLUP696OhoBgwYQP369bG1tSU4OBg3Nzc2btxY6JpEREREREorLaMtRZydnQFITk7m6aefBqB58+bExcUBN2cr582bR1JSEgcOHMDLy8uyb15eHrVq1SpwLLgZDgHMZrMltFWvXt3S/vv9kpKSuHTpEk2bNrVsMwyDnJwcLl68aDm2yXTv/w4SGBjIjBkzOHPmDBcvXqRMmTI0atQoX5/r168zceJEDh48iKurK88++yyGYWA2F/xExqSkJKZPn87MmTMt23Jzc7WMVkRERESkEBQ2S5EaNWrQqFEjVq9eTbNmze7Yz9XVla5duzJx4kTLtpSUFAzDuOs5bs2a/vbbb7i5uQH//4zqrfZatWrx9ddfW7ZlZGRw8eJFKlWqBFDg2cnCqlSpEr6+vmzYsIGUlBSCg4ML9Bk3bhwVK1Zk7969lClTBrPZnC/4/vFahg4dmu+Z04SEBBwdHe+rPhERERGR0kTLaEuZW89evv/++5w5cwbDMMjIyGDDhg3MnTuXqlWrEhwczObNm9m7dy9ms5lffvmF1157jSVLltz1+NWrV6dly5ZMnTqVK1eukJqayrx58yztbdq0ITMzk0WLFpGdnc3Vq1cZPXo0w4cPv++Q+XtBQUFs2rSJnTt33vbFQhkZGZQpUwaTyURGRgbh4eFkZGRYXoz0eyEhIURGRnL69GkA9uzZQ0BAAAcPHnzgOkVEREREHnea2Sxl6tWrx+bNm4mKimLgwIGkpqZiZWWFu7s7b775Jt27d8fOzo6IiAgiIiIYNmwYDg4OdOrUiREjRhTqHLNmzWLChAm0adOGcuXKERQUxNGjRwEoV64cy5YtY9q0aSxatAiz2YyPjw+RkZFFcn0vvPACN27coFmzZjg5ORVoHzduHOPHj8fb25uyZcvi6+tLq1atOHnyZIG+ffr0wTAMBg0aREpKCi4uLowfPx5/f/8iqVVERERE5HFmZRRmbaSI3LeshP9iZGWWdBmlklUZB+xr/4W0tExycws+l/uosLKCKlXKc+FCOvqNLQ9K40mKisaSFCWNp0fHrZ9VYWgZrYiIiIiIiBQ5LaOVh1pQUBBnzpy5Y3tUVFS+t+Y+jEx2ZTAbj+6s2qPMZFf4j84RERERkaKlsCkPtXXr1pV0CQ/MzvWpki6hVDPMBmaz1uOIiIiI/NkUNkWKWVqantcsSWaFTREREZESobApUszMZjNmraIVERERkVJGLwgSERERERGRIqeZTZFiZjKZMOmfdf50Wj4rIiIiUrIUNkWKmZNT2ZIuoVQyDINLlzIVOEVERERKiMKmSDHLTUvByL1R0mWUKlY2dtg4uWAyWSlsioiIiJQQhU2RYmbkZWPkZpd0GSIiIiIifyo9SSbyO3l5efz2228lXYaIiIiIyCNPYfMR4+7uzltvvYVh5F8auG7dOvz8/IrtvKmpqUycOBE/Pz+ef/55WrZsyTvvvMPJkyfz9fvyyy9p3rw5TZo0YefOnfj5+dGoUSM8PDzw8PCgcePGtGzZkunTp2Muws8DCQ0NZe7cuQ98nOHDh7Nhw4YHL0hEREREpJRT2HwEffvttyxatOhPO19iYiJdu3YlNTWVhQsXEh8fzxdffMGTTz5JSEgI3333naXv6tWrCQgI4NChQ7Rp0waACRMmEB8fT3x8PEeOHGHx4sVs2LCBefPm/WnXUFhpaWklXYKIiIiIyGNBYfMRFBoaypw5czh8+PBt2xMTE3F3dycxMdGybe7cuYSGhgI3Z0F79uzJ9OnT8fb2plmzZnz66ad8/vnntGnThiZNmjB+/HjLvlOnTqVu3br885//pG7duphMJipXrszbb79NaGgoY8aMITc3l+DgYA4cOMCqVat48cUX71i/u7s7TZs25aeffrJcz5gxY2jTpg2+vr5kZGTw3//+l/79++Pt7c0LL7zABx98QHp6uuUYq1evxt/fHw8PD0aPHs3169ctbWPGjGHMmDEFzhkbGwvApUuXePfdd2natCk+Pj4MHz6cK1euMHbsWOLi4li4cCEDBw603LfWrVvj7e1Nt27d+Oabbwr1MxIRERERKe0UNh9Bbdu2pUePHowYMYLLly/f1zEOHTqEi4sLBw4cYOjQoUydOpXY2Fi+/PJLli1bxpo1azh48CDZ2dl8++23vPzyy1hZWRU4TkhICCkpKcTHx7NmzRq8vLwYMGAAO3bsuO15c3JyiI2N5cCBA7Ro0cKyfd++faxatYqNGzeSk5PD66+/Tp06ddi9ezdr167lzJkzjBo1CoD9+/czceJEJk+ezMGDB3n++ef54YcfCn3tw4YNIyMjg23btvHNN99w9epVJkyYwIcffmipf8GCBRw4cICYmBhWr15NbGws3bt3Z+zYseTk5Nzj3RYRERERKX30NtpH1OjRo4mPj2fMmDFERkbe8/5PPPEEvXv3xsrKipYtW5KXl0e/fv1wcHCgUaNGVK1alaSkJGrVqkVOTg5VqlS57XGqVq0KQEpKyh3PNWHCBKZMmWL53tXVlb59+/Laa69Ztr3wwgu4uLgAsGbNGmxtbXn33XextrbG3t6e999/n4CAAFJTU9m4cSPt2rWjefPmAPTs2ZPVq1cX6rqTkpL4/vvv+frrr3FycgJg2rRptw3tZcqU4cqVK5YZ3+7du9OjR4/bhm4REREREclPYfMRZWdnx0cffUTXrl1ZsmSJJTgVlqOjoyU0mUw3J7grVKhgaTeZTJjNZpycnLCxseHs2bO3Pc6tpbrOzs53PNc//vEPgoKC/mc9t0IrwMWLF6levTrW1taWbTVr1gRuhsXk5GQaNGiQb/8nn3zyfx7/ltTUVABq1Khh2ebs7Hzb+j08PJg7dy6ffvopixYtwt7entDQUMLCwiz3TEREREREbk9/Y36E1apVi0mTJjF79myOHDli2X4rpP1+uecfX3xT2Nk5Ozs7/P39Wbdu3W3fHrt69WqcnZ3x8PC4jyu4fT01atTg7Nmz5OXlWbYlJCQAN4Ohq6trgY8nOX/+vOVrk8mU79ovXbpk+bpatWoA+cLzqVOn+OijjwrUdPbsWSpXrszixYv5/vvvmT59OgsWLGD37t33eZUiIiIiIqWHwuYjrmPHjnTr1o2YmBjLtsqVK1OxYkW2bNmCYRgcP36cr7/++r7PMXbsWM6ePcvQoUM5ffo0ZrOZ5ORkIiIiWLlyJVOmTMHW1rYoLgeA1q1bAzBz5kyysrJITU3lww8/pFmzZtSoUYNu3bqxY8cOdu7cSW5uLuvXr+fo0aOW/d3c3IiLiyM5OZmsrCw+/vhjS5h1cXGhRYsWhIeHc/XqVTIyMpgxY4YlvNrZ2VleRPTDDz/w5ptvcuLECezs7KhcuTLAPc8ii4iIiIiURgqbj4H33nuPv/zlL5bv7ezsmDRpEl999RWenp5MmzaNkJCQ+z6+i4sLGzZsoFq1arz11lt4enrSrVs3EhMT+fzzz3nhhReK4jIsypcvz9KlSzl58iStW7emU6dO1KhRgzlz5gDQpEkTwsPDmTZtGl5eXmzdujXfy4Z69OiBh4cHnTt3pm3btlSrVo3q1atb2mfOnEm5cuXo0KED/v7+VKpUiQkTJgDQpUsX1q5dS8+ePWnfvj1vvPEGYWFhNG7cmGHDhvHee+/x/PPPF+n1ioiIiIg8jqwMwzBKugiRx1nOhUSMnBslXUapYmVjh63zk6SlZZKbW3D596PGygqqVCnPhQvp6De2PCiNJykqGktSlDSeHh23flaFoZlNERERERERKXIKmyIiIiIiIlLk9NEnIsXMytoOrQf5c1nZ2JV0CSIiIiKlnsKmSDGzcap6905S5AzDwGxWyBcREREpKQqbIsUsLS2zpEsolcxmhU0RERGRkqSwKVLMzGYz5kf/hagiIiIiIvdEYVOkmJlMJkx6FVcBmnkUERERebwpbIoUMyensiVdwkPJMAwuXcpU4BQRERF5TClsihSzvMzLGHm5JV3GQ8XK2gbrso6YTFYKmyIiIiKPKYVNkWJm5OWBwmY+ipciIiIijz89SSYiIiIiIiJFTmFT7pu7uzvPPfccHh4eNG7cmKZNmxIWFsa5c+cKtX9iYiLu7u4kJiYWc6V3Fx8fj7u7O3Fxcfm2b926lZdffhlPT0/8/PyYN28eZr1aVkRERETkrhQ25YFERUURHx/PkSNH2LlzJ4ZhMHLkyJIu655FRkbSpEkTIiMjLdt+/PFHRo0axdtvv01cXBxRUVGsW7eOZcuWlVyhIiIiIiKPCIVNKTLlypUjJCSEH3/80bLt9OnTDBgwAF9fX5577jk6duzIzp07b7v/4cOHef3112nZsiWNGjUiKCiII0eOABAbG4ufnx+RkZG0atUKb29vhgwZQkZGhmX/5cuX07ZtWzw8PAgKCmL//v3Azbeerlixgvbt2+Pl5UXPnj3z1fif//yHo0ePMn/+fH766Sd++OEHAJKSknjllVdo06YNJpMJNzc32rZty8GDB4v61omIiIiIPHYUNqXIXLlyhS1bttCuXTvLtiFDhlCvXj22b99OXFwcLVu25IMPPiiwb1ZWFmFhYbRv357du3cTGxtLrVq1CA8Pt/RJSkoiOTmZ7du3s3r1auLj41m5ciUA69atY/78+YSHh3Po0CFeffVVwsLCuHz5MitXrmTp0qXMmTOH/fv3ExQURN++fblw4QJwc1azR48eODo60rNnTxYuXAhA+/bt+fvf/56vxl27dtGgQYPiuH0iIiIiIo8VvY1WHsjAgQOxtrbGbDaTmZlJ+fLlLWENYOHChbi4uGAYBklJSVSoUIHk5OQCx7G1tSUmJobatWtz48YNkpKScHR0tMwy3jJ48GDs7e2pXbs2Pj4+nDlzBoD169fTo0cPPDw8AOjevTtubm7Y29sTHR3NgAEDqF+/PgDBwcGsWbOGjRs30rp1a/7973+zY8cOAHr16kWbNm34+eefqVu3ruW8GRkZDBs2DHt7e/r06VOk91BERERE5HGksCkPZMGCBfj4+AA3Z/6io6Pp3bs3MTExNGjQgBMnTjBo0CBSU1Nxc3OjUqVKGEbBD76wtrYmNjaW/v37c+3aNerUqYONjU2Bvs7OzpavbW1tLe2pqalUr149X19PT0/g5ozo9OnTmTlzpqUtNzeXhg0b4ubmlm9JbaVKlTh69Gi+4/zf//0fQ4cOpXLlyqxYsYJy5crdz60SERERESlVFDalyNjb29OvXz8++eQT9u3bR5UqVRg2bBjz5s3Dz88PuPl2123bthXY9+jRo0yaNIlVq1bRsGFDAJYsWWKZubybatWqFXgL7uzZs+ncuTOurq4MHTqUgIAAS1tCQgKOjo53Pe63337LiBEjCAkJ4Z133sHGRv+XEREREREpDD2zKUUmNzeXtWvXcvXqVZo0aUJmZiZ5eXk4ODgAcOrUKT7++GMAsrOz8+2bnp6OyWTC3t4egCNHjrBixYoC/e4kKCiImJgYjh07htlsZu3atURHR+Pk5ERISAiRkZGcPn0agD179hAQEHDXF/0cOXKEwYMH8/e//53Ro0craIqIiIiI3AP97VkeSP/+/bG2tgbAysqKp556ioiICMsS1lGjRjFy5EiuX7+Oq6srISEhzJgxg5MnT+abWWzRogU9e/akV69emM1matasSWhoKLNmzbK8yOd/CQwM5OrVq4wcOZLU1FTq1KlDVFQUlSpVok+fPhiGwaBBg0hJScHFxYXx48fj7+//P4+5YMECcnNz+fDDD/nwww8t25s0acKiRYvu426JiIiIiJQeVsbtHqATkSKTe/Ui5OWUdBkPF2sbbCpUIS0tk9xcc0lX89CzsoIqVcpz4UI6+o0tD0rjSYqKxpIUJY2nR8etn1VhaBmtiIiIiIiIFDmFTRERERERESlyemZTpJhZWVtjoPUgv2dlrV89IiIiIo87/Y1PpJhZl3Us6RIeSoZhYDYrhIuIiIg8rhQ2RYpZWlpmSZfwUDIMA8MwsLIq6Uoefrfuke6VFAWNJykqGktSlDSeHh338jPS22hFRERERESkyOkFQSIiIiIiIlLkFDZFRERERESkyClsioiIiIiISJFT2BQREREREZEip7ApIiIiIiIiRU5hU0RERERERIqcwqaIiIiIiIgUOYVNERERERERKXIKmyIiIiIiIlLkFDZFisHFixcZNGgQXl5e+Pj48OGHH5Kbm1vSZcmf7NKlS7Rt25bY2FjLtqNHj9K9e3c8PDzw8/Nj9erV+fZZv349bdu2pXHjxgQFBREfH29py8vLY/r06fz1r3/Fw8ODsLAwUlJSLO13G3d3O7c8fE6cOEHfvn3x9vamRYsWjBo1ikuXLgEaS3Lv9u/fT/fu3fH09KRFixZMmjSJrKwsQONJ7k9eXh6hoaGMGTPGsk1jSfIxRKTIvfbaa8Y777xjXLt2zUhISDACAgKMqKioki5L/kRxcXHGiy++aNSrV884cOCAYRiGcfnyZcPb29v47LPPjJycHGPfvn2Gh4eHcfToUcMwDOPAgQOGh4eHERcXZ2RnZxtLly41fHx8jGvXrhmGYRhz5841AgMDjbNnzxrp6enG22+/bfTv399yzv817u52bnn4XL9+3WjRooUxZ84c48aNG8alS5eM/v37GwMGDNBYknt28eJFo1GjRsbatWuNvLw8Izk52ejUqZMxZ84cjSe5bx999JFRv359Y/To0YZh6M85KUhhU6SI/fLLL0a9evWM8+fPW7Zt2bLF8PX1LcGq5M+0bt06w9fX19iyZUu+sPn5558b7dq1y9d3/PjxxqhRowzDMIx33nnHGDduXL72l156yVizZo1hGIbxwgsvGBs3brS0paamGu7u7kZCQsJdx93dzi0Pn9OnTxv9+vUzcnNzLdt27NhheHp6aizJfUlPTzcMwzDMZrPx3//+12jbtq3x6aefajzJfdm3b5/RsWNHY+jQoZawqbEkf6RltCJF7Oeff8bR0REXFxfLNjc3N86ePcvVq1dLsDL5s7Rs2ZLt27fTsWPHfNt//vln6tWrl29bnTp1OHHiBACnTp26Y3t6ejrnz5/P116lShUqVqzIf//737uOu7udWx4+zzzzDIsWLcLa2tqybevWrTRo0EBjSe5LuXLlAGjdujWBgYE4OzsTFBSk8ST37OLFi4wdO5ZZs2bh4OBg2a6xJH+ksClSxDIzM/P94gUs31+7dq0kSpI/mbOzMzY2NgW2325s2NvbW8bF/2rPzMwE4IknnijQnpmZeddxd7dzy8PNMAxmz57Nzp07GTt2rMaSPJBt27axe/duTCYTQ4cO1XiSe2I2mxk5ciR9+/alfv36+do0luSPFDZFitgTTzzB9evX82279X3ZsmVLoiR5SDg4OFhexnFLVlaWZVz8r/Zbf4D+cWzdar/buLvbueXhlZGRwdChQ9m0aROfffYZ7u7uGkvyQOzt7XFxcWHkyJHs2bNH40nuycKFC7GzsyM0NLRAm8aS/JHCpkgRq1u3LpcvX+bChQuWbadPn8bV1ZXy5cuXYGVS0urVq8fPP/+cb9upU6eoW7cucHPs3Km9YsWKuLi4cOrUKUtbamoqly9fpl69encdd3c7tzycEhIS6NatGxkZGaxZswZ3d3dAY0nu3eHDh3nppZfIzs62bMvOzsbW1pY6depoPEmhffHFF3z//fd4eXnh5eXF5s2b2bx5M15eXvrdJAUobIoUsaeeeoomTZowZcoUMjIy+O2335g/fz7BwcElXZqUsLZt23LhwgWWLVtGTk4OBw4cYNOmTXTr1g2A4OBgNm3axIEDB8jJyWHZsmVcvHiRtm3bAhAUFERkZCS//fYbGRkZTJkyBW9vb2rVqnXXcXe3c8vD58qVK/Tu3RtPT08WL15MpUqVLG0aS3Kv3N3dycrKYtasWWRnZ5OUlMT06dMJDg6mffv2Gk9SaF9//TWHDx8mLi6OuLg4OnXqRKdOnYiLi9PvJimopN9QJPI4Sk1NNYYMGWJ4e3sbzZo1M6ZNm5bvjZJSevz+bbSGYRjHjh0zevToYXh4eBj+/v7G2rVr8/XfsGGD0b59e6Nx48ZGcHCwceTIEUtbdna2MWPGDKNVq1aGp6enERYWZly4cMHSfrdxd7dzy8NlyZIlRr169Yznn3/eaNy4cb7/GYbGkty7n3/+2ejbt6/h5eVltGnTxoiIiDBu3LhhGIbGk9y/0aNHW95GaxgaS5KflWEYRkkHXhEREREREXm8aBmtiIiIiIiIFDmFTRERERERESlyCpsiIiIiIiJS5BQ2RUREREREpMgpbIqIiIiIiEiRU9gUERERERGRIqewKSIiIiIiIkVOYVNEREQeG7/++mtJlyAiIv+PwqaIiIg8kNDQUObOnVvSZTB9+nQiIyNLugwREfl/FDZFRETksZCWllbSJYiIyO8obIqIiEiRWLduHT179mT69Ol4e3vTrFkzPv30Uz7//HPatGlDkyZNGD9+vKW/n58f8+bNo3379nh4eNCrVy9OnTplaY+Li6NXr154eXnh5+fHRx99RHZ2NgBz587ljTfeoFu3bnh7ezNv3jw2bdrEpk2b6Ny5MwCHDx/m9ddfp2XLljRq1IigoCCOHDkCQGxsLH5+fkRGRtKqVSu8vb0ZMmQIGRkZlvMvX76ctm3b4uHhQVBQEPv37wfAMAxWrFhB+/bt8fLyomfPnvz444/FfXtFRB45CpsiIiJSZA4dOoSLiwsHDhxg6NChTJ06ldjYWL788kuWLVvGmjVrOHjwoKV/TEwMH330Efv378fNzY2BAweSk5PD//3f/9G3b1/atWvHvn37WLp0Kf/+978JDw+37Lt//37effdddu7cycCBAwkMDCQwMJCNGzeSlZVFWFgY7du3Z/fu3cTGxlKrVq18+yclJZGcnMz27dtZvXo18fHxrFy5ErgZnOfPn094eDiHDh3i1VdfJSwsjMuXL7Ny5UqWLl3KnDlz2L9/P0FBQfTt25cLFy78eTdaROQRoLApIiIiReaJJ56gd+/emEwmWrZsSV5eHv369cPBwYFGjRpRtWpVkpKSLP379evHX/7yF+zt7fn73//OuXPnOHz4MJs2bcLd3Z3evXtjZ2dH7dq1eeedd1i9ejVmsxmAJ598kubNm1O2bFlsbGzy1WFra0tMTAw9e/YkOzubpKQkHB0dSU5Oztdv8ODB2NvbU7t2bXx8fDhz5gwA69evp0ePHnh4eGAymejevTtLlizB3t6e6OhoBgwYQP369bG1tSU4OBg3Nzc2btxYzHdXROTRYnP3LiIiIiKF4+joiJWVFQAm081/065QoYKl3WQyWcIiQO3atS1fOzg44OjoSGpqKhcvXuTJJ5/Md+yaNWuSlZXFxYsXAahateod67C2tiY2Npb+/ftz7do16tSpg42NDYZh5Ovn7Oxs+drW1tbSnpqaSvXq1fP19fT0BG7OiE6fPp2ZM2da2nJzc2nYsOEd6xERKY0UNkVERKTI3AqahfX7mcbMzEzS0tKoVq0aNWrUYNu2bfn6JiQkYGdnR8WKFe96rqNHjzJp0iRWrVplCYFLliyxzFzeTbVq1Th37ly+bbNnz6Zz5864uroydOhQAgIC8tXm6OhYqGOLiJQWWkYrIiIiJWbp0qX8+uuvXL9+nalTp/LMM8/g4eFBQEAAp0+fZvny5WRnZ5OQkEBERASBgYHY2dnd9lh2dnakp6cDkJ6ejslkwt7eHoAjR46wYsUKywuG7iYoKIiYmBiOHTuG2Wxm7dq1REdH4+TkREhICJGRkZw+fRqAPXv2EBAQkO9ZVBER0cymiIiIlKAmTZowePBgzp49S9OmTfnkk08wmUzUrFmTRYsWERERwdy5c7G3t6dTp068/fbbdzxWx44dGT58OL6+vuzcuZOePXvSq1cvzGYzNWvWJDQ0lFmzZhXqRT6BgYFcvXqVkSNHkpqaSp06dYiKiqJSpUr06dMHwzAYNGgQKSkpuLi4MH78ePz9/YvwzoiIPPqsjD8+vCAiIiLyJ/Dz8+Nvf/sbQUFBJV2KiIgUAy2jFRERERERkSKnsCkiIiIiIiJFTstoRUREREREpMhpZlNERERERESKnMKmiIiIiIiIFDmFTRERERERESlyCpsiIiIiIiJS5BQ2RUREREREpMgpbIqIiIiIiEiRU9gUERERERGRIqewKSIiIiIiIkVOYVNERERERESK3P8HQ+PS783aI8IAAAAASUVORK5CYII="
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 60
+   "execution_count": 58
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:28:13.557360Z",
-     "start_time": "2025-06-25T17:28:13.523859Z"
+     "end_time": "2025-08-10T10:38:33.805166Z",
+     "start_time": "2025-08-10T10:38:33.772217Z"
     }
    },
    "cell_type": "code",
@@ -6174,17 +3842,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test AUC Score 0.9748762086712413\n"
+      "Test AUC Score 0.9323582735495944\n"
      ]
     }
    ],
-   "execution_count": 61
+   "execution_count": 59
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:28:20.549335Z",
-     "start_time": "2025-06-25T17:28:20.401488Z"
+     "end_time": "2025-08-10T10:38:34.100528Z",
+     "start_time": "2025-08-10T10:38:33.947064Z"
     }
    },
    "cell_type": "code",
@@ -6200,17 +3868,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.060546\n",
-       "1     15001  0.033435\n",
-       "2     15002  0.023444\n",
-       "3     15003  0.834804\n",
-       "4     15004  0.033128\n",
+       "0     15000  0.080228\n",
+       "1     15001  0.034488\n",
+       "2     15002  0.012899\n",
+       "3     15003  0.863150\n",
+       "4     15004  0.019281\n",
        "...     ...       ...\n",
-       "9995  24995  0.009217\n",
-       "9996  24996  0.009822\n",
-       "9997  24997  0.001068\n",
-       "9998  24998  0.051219\n",
-       "9999  24999  0.228998\n",
+       "9995  24995  0.008383\n",
+       "9996  24996  0.006655\n",
+       "9997  24997  0.000636\n",
+       "9998  24998  0.049413\n",
+       "9999  24999  0.248039\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -6241,27 +3909,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.060546</td>\n",
+       "      <td>0.080228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.033435</td>\n",
+       "      <td>0.034488</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.023444</td>\n",
+       "      <td>0.012899</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.834804</td>\n",
+       "      <td>0.863150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.033128</td>\n",
+       "      <td>0.019281</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -6271,27 +3939,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.009217</td>\n",
+       "      <td>0.008383</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.009822</td>\n",
+       "      <td>0.006655</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.001068</td>\n",
+       "      <td>0.000636</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.051219</td>\n",
+       "      <td>0.049413</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.228998</td>\n",
+       "      <td>0.248039</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -6299,18 +3967,18 @@
        "</div>"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 62
+   "execution_count": 60
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:29:11.741077Z",
-     "start_time": "2025-06-25T17:28:33.174629Z"
+     "end_time": "2025-08-10T10:39:09.418260Z",
+     "start_time": "2025-08-10T10:38:34.252459Z"
     }
    },
    "cell_type": "code",
@@ -6330,41 +3998,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:\ttest: 0.9192103\tbest: 0.9192103 (0)\ttotal: 152ms\tremaining: 22m 47s\n",
-      "1000:\ttest: 0.9384077\tbest: 0.9384077 (1000)\ttotal: 5.37s\tremaining: 42.9s\n",
-      "2000:\ttest: 0.9424529\tbest: 0.9424529 (2000)\ttotal: 9.3s\tremaining: 32.5s\n",
-      "3000:\ttest: 0.9452309\tbest: 0.9452309 (3000)\ttotal: 13.2s\tremaining: 26.5s\n",
-      "4000:\ttest: 0.9471011\tbest: 0.9471011 (4000)\ttotal: 17.4s\tremaining: 21.8s\n",
-      "5000:\ttest: 0.9485571\tbest: 0.9485571 (4999)\ttotal: 21.4s\tremaining: 17.1s\n",
-      "6000:\ttest: 0.9498206\tbest: 0.9498243 (5999)\ttotal: 25.4s\tremaining: 12.7s\n",
-      "7000:\ttest: 0.9509087\tbest: 0.9509087 (7000)\ttotal: 29.3s\tremaining: 8.36s\n",
-      "8000:\ttest: 0.9519870\tbest: 0.9519894 (7998)\ttotal: 33.2s\tremaining: 4.15s\n",
-      "8999:\ttest: 0.9529118\tbest: 0.9529130 (8996)\ttotal: 37.2s\tremaining: 0us\n",
+      "0:\ttest: 0.9122800\tbest: 0.9122800 (0)\ttotal: 147ms\tremaining: 22m 4s\n",
+      "1000:\ttest: 0.9290271\tbest: 0.9290271 (1000)\ttotal: 3.83s\tremaining: 30.6s\n",
+      "2000:\ttest: 0.9308158\tbest: 0.9308243 (1999)\ttotal: 7.59s\tremaining: 26.5s\n",
+      "3000:\ttest: 0.9314810\tbest: 0.9315237 (2840)\ttotal: 11.2s\tremaining: 22.5s\n",
+      "4000:\ttest: 0.9315529\tbest: 0.9316016 (3637)\ttotal: 14.9s\tremaining: 18.6s\n",
+      "5000:\ttest: 0.9313616\tbest: 0.9316016 (3637)\ttotal: 18.6s\tremaining: 14.9s\n",
+      "6000:\ttest: 0.9313043\tbest: 0.9316016 (3637)\ttotal: 22.4s\tremaining: 11.2s\n",
+      "7000:\ttest: 0.9311204\tbest: 0.9316016 (3637)\ttotal: 26.3s\tremaining: 7.5s\n",
+      "8000:\ttest: 0.9310022\tbest: 0.9316016 (3637)\ttotal: 30s\tremaining: 3.75s\n",
+      "8999:\ttest: 0.9308864\tbest: 0.9316016 (3637)\ttotal: 33.8s\tremaining: 0us\n",
       "\n",
-      "bestTest = 0.9529129952\n",
-      "bestIteration = 8996\n",
+      "bestTest = 0.9316016356\n",
+      "bestIteration = 3637\n",
       "\n",
-      "Shrink model to first 8997 iterations.\n"
+      "Shrink model to first 3638 iterations.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<catboost.core.CatBoostClassifier at 0x25616ec6360>"
+       "<catboost.core.CatBoostClassifier at 0x2bf23650800>"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 63
+   "execution_count": 61
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:29:15.959502Z",
-     "start_time": "2025-06-25T17:29:15.899996Z"
+     "end_time": "2025-08-10T10:39:09.589328Z",
+     "start_time": "2025-08-10T10:39:09.561972Z"
     }
    },
    "cell_type": "code",
@@ -6375,22 +4043,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.9194\n",
-      "Validation Accuracy 0.9182222222222223\n",
+      "Train Accuracy 0.9153333333333333\n",
+      "Validation Accuracy 0.8995555555555556\n",
       "\n",
       "\n",
-      "Train AUC Score 0.9557166079222285\n",
-      "Validation AUC Score 0.952912995165315\n"
+      "Train AUC Score 0.9528160526565609\n",
+      "Validation AUC Score 0.9316016356051154\n"
      ]
     }
    ],
-   "execution_count": 64
+   "execution_count": 62
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:29:18.355221Z",
-     "start_time": "2025-06-25T17:29:18.146404Z"
+     "end_time": "2025-08-10T10:39:10.083548Z",
+     "start_time": "2025-08-10T10:39:09.867494Z"
     }
    },
    "cell_type": "code",
@@ -6402,19 +4070,19 @@
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 65
+   "execution_count": 63
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:29:21.507779Z",
-     "start_time": "2025-06-25T17:29:21.497312Z"
+     "end_time": "2025-08-10T10:39:10.215672Z",
+     "start_time": "2025-08-10T10:39:10.207589Z"
     }
    },
    "cell_type": "code",
@@ -6428,17 +4096,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test AUC Score 0.9520162390829696\n"
+      "Test AUC Score 0.9314858858390516\n"
      ]
     }
    ],
-   "execution_count": 66
+   "execution_count": 64
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-25T17:29:28.891491Z",
-     "start_time": "2025-06-25T17:29:28.841389Z"
+     "end_time": "2025-08-10T10:39:10.386435Z",
+     "start_time": "2025-08-10T10:39:10.347364Z"
     }
    },
    "cell_type": "code",
@@ -6454,17 +4122,17 @@
      "data": {
       "text/plain": [
        "         id    Exited\n",
-       "0     15000  0.054398\n",
-       "1     15001  0.020858\n",
-       "2     15002  0.028986\n",
-       "3     15003  0.857300\n",
-       "4     15004  0.033321\n",
+       "0     15000  0.094564\n",
+       "1     15001  0.030259\n",
+       "2     15002  0.045016\n",
+       "3     15003  0.793228\n",
+       "4     15004  0.041639\n",
        "...     ...       ...\n",
-       "9995  24995  0.004218\n",
-       "9996  24996  0.005933\n",
-       "9997  24997  0.004155\n",
-       "9998  24998  0.081669\n",
-       "9999  24999  0.155663\n",
+       "9995  24995  0.008090\n",
+       "9996  24996  0.010796\n",
+       "9997  24997  0.005723\n",
+       "9998  24998  0.125824\n",
+       "9999  24999  0.191667\n",
        "\n",
        "[10000 rows x 2 columns]"
       ],
@@ -6495,27 +4163,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>15000</td>\n",
-       "      <td>0.054398</td>\n",
+       "      <td>0.094564</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15001</td>\n",
-       "      <td>0.020858</td>\n",
+       "      <td>0.030259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>15002</td>\n",
-       "      <td>0.028986</td>\n",
+       "      <td>0.045016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>15003</td>\n",
-       "      <td>0.857300</td>\n",
+       "      <td>0.793228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15004</td>\n",
-       "      <td>0.033321</td>\n",
+       "      <td>0.041639</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -6525,27 +4193,27 @@
        "    <tr>\n",
        "      <th>9995</th>\n",
        "      <td>24995</td>\n",
-       "      <td>0.004218</td>\n",
+       "      <td>0.008090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>24996</td>\n",
-       "      <td>0.005933</td>\n",
+       "      <td>0.010796</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>24997</td>\n",
-       "      <td>0.004155</td>\n",
+       "      <td>0.005723</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>24998</td>\n",
-       "      <td>0.081669</td>\n",
+       "      <td>0.125824</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>24999</td>\n",
-       "      <td>0.155663</td>\n",
+       "      <td>0.191667</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -6553,12 +4221,225 @@
        "</div>"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 67
+   "execution_count": 65
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-10T11:23:14.438540Z",
+     "start_time": "2025-08-10T11:18:20.911127Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# ===== OOF blending of rfmodel / xgmodel / lgmodel / cat_model =====\n",
+    "# Uses your existing `preprocessing` (ColumnTransformer+Pipeline)\n",
+    "# 1) OOF CV for each model with fold-wise preprocessing (no leakage)\n",
+    "# 2) Weight search on OOF (maximize AUC, weights sum to 1)\n",
+    "# 3) Refit each model on ALL train data using best iterations\n",
+    "# 4) Blend test predictions and save submissions\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from sklearn.base import clone\n",
+    "from sklearn import set_config\n",
+    "import numpy as np, pandas as pd\n",
+    "from catboost import CatBoostClassifier\n",
+    "from lightgbm import LGBMClassifier, early_stopping, log_evaluation\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "set_config(transform_output='pandas')  # ensure pandas DataFrame from transformers\n",
+    "\n",
+    "# ---------- Data ----------\n",
+    "df = pd.read_csv('../assets/hw_2_2/train.csv')\n",
+    "test_df_raw = pd.read_csv('../assets/hw_2_2/test.csv')\n",
+    "sub_template = pd.read_csv('../assets/hw_2_2/sample_submission.csv')\n",
+    "\n",
+    "drop_cols = ['id', 'CustomerId', 'Exited']  # Surname is TE’ed inside preprocessing already\n",
+    "feat_cols = [c for c in df.columns if c not in drop_cols]\n",
+    "X_full = df[feat_cols].copy()\n",
+    "y_full = df['Exited'].copy()\n",
+    "\n",
+    "X_test_raw = test_df_raw[feat_cols].copy()\n",
+    "test_ids = test_df_raw['id'].values\n",
+    "\n",
+    "# ---------- Model configs (taken from your notebook; tiny fixes like n_jobs) ----------\n",
+    "rf_cfg = dict(n_estimators=200, max_depth=10, random_state=3)\n",
+    "xgb_cfg = dict(\n",
+    "    n_estimators=1500, learning_rate=0.009, eval_metric='auc',\n",
+    "    n_jobs=-1, random_state=3, tree_method='hist'\n",
+    ")\n",
+    "lgb_cfg = dict(\n",
+    "    learning_rate=0.0088, max_depth=16, n_estimators=2000,\n",
+    "    num_leaves=16, random_state=3, n_jobs=-1\n",
+    ")\n",
+    "cat_cfg = dict(\n",
+    "    eval_metric='AUC', learning_rate=0.0009, iterations=9000,\n",
+    "    random_seed=3, verbose=False\n",
+    ")\n",
+    "\n",
+    "# helpers to build new instances\n",
+    "def build_models():\n",
+    "    return {\n",
+    "        'rf': RandomForestClassifier(**rf_cfg),\n",
+    "        'xgb': XGBClassifier(**xgb_cfg),\n",
+    "        'lgb': LGBMClassifier(**lgb_cfg),\n",
+    "        'cat': CatBoostClassifier(**cat_cfg),\n",
+    "    }\n",
+    "\n",
+    "# ---------- K-Fold OOF ----------\n",
+    "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+    "\n",
+    "oof = {k: np.zeros(len(X_full), dtype=float) for k in ['rf','xgb','lgb','cat']}\n",
+    "test_fold_preds = {k: [] for k in ['rf','xgb','lgb','cat']}\n",
+    "best_iters = {k: [] for k in ['xgb','lgb','cat']}  # rf has no best_iteration\n",
+    "\n",
+    "for fold, (tr, va) in enumerate(cv.split(X_full, y_full), start=1):\n",
+    "    X_tr, X_va = X_full.iloc[tr], X_full.iloc[va]\n",
+    "    y_tr, y_va = y_full.iloc[tr], y_full.iloc[va]\n",
+    "\n",
+    "    # fold-wise preprocessing (fit on train fold with y for TE)\n",
+    "    prep = clone(preprocessing)\n",
+    "    Xtr_p = prep.fit_transform(X_tr, y_tr).replace(0, -1)\n",
+    "    Xva_p = prep.transform(X_va).replace(0, -1)\n",
+    "    Xte_p = prep.transform(X_test_raw).replace(0, -1)\n",
+    "\n",
+    "    models = build_models()\n",
+    "\n",
+    "    # --- RF ---\n",
+    "    models['rf'].fit(Xtr_p, y_tr)\n",
+    "    oof['rf'][va] = models['rf'].predict_proba(Xva_p)[:, 1]\n",
+    "    test_fold_preds['rf'].append(models['rf'].predict_proba(Xte_p)[:, 1])\n",
+    "\n",
+    "    # --- XGB (with early stopping on the fold) ---\n",
+    "    models['xgb'].set_params(early_stopping_rounds=50)\n",
+    "    models['xgb'].fit(Xtr_p, y_tr, eval_set=[(Xva_p, y_va)], verbose=False)\n",
+    "    oof['xgb'][va] = models['xgb'].predict_proba(Xva_p)[:, 1]\n",
+    "    test_fold_preds['xgb'].append(models['xgb'].predict_proba(Xte_p)[:, 1])\n",
+    "    if hasattr(models['xgb'], 'best_iteration') and models['xgb'].best_iteration is not None:\n",
+    "        best_iters['xgb'].append(int(models['xgb'].best_iteration))\n",
+    "\n",
+    "    # --- LGBM (with early stopping on the fold) ---\n",
+    "    models['lgb'].set_params(n_estimators=lgb_cfg['n_estimators'], verbosity=-1)\n",
+    "    models['lgb'].fit(\n",
+    "        Xtr_p, y_tr,\n",
+    "        eval_set=[(Xva_p, y_va)],\n",
+    "        eval_metric='auc',\n",
+    "        callbacks=[early_stopping(100, verbose=False), log_evaluation(period=0)]\n",
+    "    )\n",
+    "    oof['lgb'][va] = models['lgb'].predict_proba(Xva_p)[:, 1]\n",
+    "    test_fold_preds['lgb'].append(models['lgb'].predict_proba(Xte_p)[:, 1])\n",
+    "    if getattr(models['lgb'], 'best_iteration_', None) is not None:\n",
+    "        best_iters['lgb'].append(int(models['lgb'].best_iteration_))\n",
+    "\n",
+    "    # --- CatBoost (with early stopping on the fold) ---\n",
+    "    models['cat'].set_params(use_best_model=True, od_type='Iter', od_wait=800)\n",
+    "    models['cat'].fit(Xtr_p, y_tr, eval_set=(Xva_p, y_va), verbose=False)\n",
+    "    oof['cat'][va] = models['cat'].predict_proba(Xva_p)[:, 1]\n",
+    "    test_fold_preds['cat'].append(models['cat'].predict_proba(Xte_p)[:, 1])\n",
+    "    if getattr(models['cat'], 'best_iteration_', None) is not None:\n",
+    "        best_iters['cat'].append(int(models['cat'].best_iteration_))\n",
+    "\n",
+    "# OOF AUCs\n",
+    "for k in oof:\n",
+    "    print(f\"{k.upper()} OOF AUC: {roc_auc_score(y_full, oof[k]):.6f}\")\n",
+    "\n",
+    "# mean best iterations for full-data refit (fallback to config if not found)\n",
+    "def mean_or_default(lst, default_val):\n",
+    "    return int(np.clip(int(np.round(np.mean(lst))) if len(lst) else default_val, 50, 10000))\n",
+    "\n",
+    "best_n = {\n",
+    "    'xgb': mean_or_default(best_iters['xgb'], xgb_cfg['n_estimators']),\n",
+    "    'lgb': mean_or_default(best_iters['lgb'], lgb_cfg['n_estimators']),\n",
+    "    'cat': mean_or_default(best_iters['cat'], cat_cfg['iterations']),\n",
+    "}\n",
+    "\n",
+    "print(\"Best iterations (avg over folds):\", best_n)\n",
+    "\n",
+    "# ---------- Weight search on OOF ----------\n",
+    "# Constrained grid w_rf + w_xgb + w_lgb + w_cat = 1, maximize AUC on OOF\n",
+    "W = None; best_auc = -1.0\n",
+    "weights_grid = np.linspace(0, 1, 21)  # 0.00..1.00 step 0.05\n",
+    "for w_rf in weights_grid:\n",
+    "    for w_xgb in weights_grid:\n",
+    "        for w_lgb in weights_grid:\n",
+    "            w_cat = 1.0 - w_rf - w_xgb - w_lgb\n",
+    "            if w_cat < 0 or w_cat > 1:\n",
+    "                continue\n",
+    "            blend = (w_rf*oof['rf'] + w_xgb*oof['xgb'] + w_lgb*oof['lgb'] + w_cat*oof['cat'])\n",
+    "            auc = roc_auc_score(y_full, blend)\n",
+    "            if auc > best_auc:\n",
+    "                best_auc = auc; W = (w_rf, w_xgb, w_lgb, w_cat)\n",
+    "print(f\"Best OOF blend AUC={best_auc:.6f}, weights (RF, XGB, LGB, CAT)={W}\")\n",
+    "\n",
+    "# ---------- Refit on ALL data with best iterations ----------\n",
+    "prep_full = clone(preprocessing)\n",
+    "Xfull_p = prep_full.fit_transform(X_full, y_full).replace(0, -1)\n",
+    "Xtest_p = prep_full.transform(X_test_raw).replace(0, -1)\n",
+    "\n",
+    "rf_full  = RandomForestClassifier(**rf_cfg).fit(Xfull_p, y_full)\n",
+    "\n",
+    "xgb_full = XGBClassifier(**{**xgb_cfg, 'n_estimators': best_n['xgb']})\n",
+    "xgb_full.fit(Xfull_p, y_full, verbose=False)\n",
+    "\n",
+    "lgb_full = LGBMClassifier(**{**lgb_cfg, 'n_estimators': best_n['lgb'], 'verbosity': -1})\n",
+    "try:\n",
+    "    from lightgbm import log_evaluation\n",
+    "    lgb_full.fit(Xfull_p, y_full, callbacks=[log_evaluation(period=0)])\n",
+    "except Exception:\n",
+    "    lgb_full.fit(Xfull_p, y_full)\n",
+    "\n",
+    "cat_full = CatBoostClassifier(**{**cat_cfg, 'iterations': best_n['cat'], 'use_best_model': False})\n",
+    "cat_full.fit(Xfull_p, y_full, verbose=False)\n",
+    "\n",
+    "# ---------- Blend test predictions ----------\n",
+    "pred_rf  = rf_full.predict_proba(Xtest_p)[:, 1]\n",
+    "pred_xgb = xgb_full.predict_proba(Xtest_p)[:, 1]\n",
+    "pred_lgb = lgb_full.predict_proba(Xtest_p)[:, 1]\n",
+    "pred_cat = cat_full.predict_proba(Xtest_p)[:, 1]\n",
+    "\n",
+    "w_rf, w_xgb, w_lgb, w_cat = W\n",
+    "test_blend = w_rf*pred_rf + w_xgb*pred_xgb + w_lgb*pred_lgb + w_cat*pred_cat\n",
+    "\n",
+    "sub = sub_template.copy()\n",
+    "sub['Exited'] = test_blend\n",
+    "sub.to_csv('../assets/hw_2_4/submission_ensemble_oof_refit.csv', index=False)\n",
+    "print(\"Saved: ../assets/hw_2_4/submission_ensemble_oof_refit.csv  (AUC OOF:\", f\"{best_auc:.6f})\")\n",
+    "\n",
+    "# (Optional) rank-averaging (often safer on LB)\n",
+    "from scipy.stats import rankdata\n",
+    "rank_blend = (rankdata(pred_rf)/len(pred_rf) +\n",
+    "              rankdata(pred_xgb)/len(pred_xgb) +\n",
+    "              rankdata(pred_lgb)/len(pred_lgb) +\n",
+    "              rankdata(pred_cat)/len(pred_cat)) / 4.0\n",
+    "sub_rank = sub_template.copy()\n",
+    "sub_rank['Exited'] = rank_blend\n",
+    "sub_rank.to_csv('../assets/hw_2_4/submission_ensemble_rank.csv', index=False)\n",
+    "print(\"Saved: ../assets/hw_2_4/submission_ensemble_rank.csv\")\n"
+   ],
+   "id": "6b7c2c1e9e2cf025",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RF OOF AUC: 0.931004\n",
+      "XGB OOF AUC: 0.930837\n",
+      "LGB OOF AUC: 0.932829\n",
+      "CAT OOF AUC: 0.931109\n",
+      "Best iterations (avg over folds): {'xgb': 437, 'lgb': 600, 'cat': 5853}\n",
+      "Best OOF blend AUC=0.933877, weights (RF, XGB, LGB, CAT)=(0.35000000000000003, 0.0, 0.5, 0.1499999999999999)\n",
+      "Saved: ../assets/hw_2_4/submission_ensemble_oof_refit.csv  (AUC OOF: 0.933877)\n",
+      "Saved: ../assets/hw_2_4/submission_ensemble_rank.csv\n"
+     ]
+    }
+   ],
+   "execution_count": 72
   }
  ],
  "metadata": {