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"outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/73/y37k94cj2fq02fyzfqjnc5t40000gn/T/ipykernel_6779/3029527792.py:7: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[\"gender\"].fillna(\"Not Stated\",inplace=True)\n" + ] + }, + { + "data": { + "text/html": [ + "
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579911084PAT_8101526TrueFalseBRAZILGERMANY51True...BCCTrueTrueFalseFalseTrueTruePAT_810_1526_67.pngTrueH
6110311127PAT_342716FalseFalseGERMANYGERMANY44False...BCCFalseUNKFalseUNKFalseTruePAT_342_716_316.pngTrueH
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6510891195PAT_9191744FalseFalsePOMERANIAPOMERANIA65False...BCCFalseTrueFalseTrueTrueTruePAT_919_1744_451.pngTrueH
6611031211PAT_6451222FalseFalseGERMANYGERMANY57True...BCCFalseTrueFalseFalseTrueTruePAT_645_1222_860.pngTrueH
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7212481368PAT_9061723FalseFalseGERMANYGERMANY65True...BCCTrueUNKFalseUNKTrueTruePAT_906_1723_560.pngTrueH
7412961418PAT_338707FalseFalseGERMANYGERMANY81False...BCCTrueTrueFalseFalseFalseTruePAT_338_707_774.pngTrueH
7613431468PAT_4874FalseFalseBRAZILBRAZIL73False...SCCFalseTrueFalseFalseFalseTruePAT_48_74_309.pngTrueH
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8214111540PAT_108161FalseFalsePOMERANIAPOMERANIA68True...SCCTrueTrueTrueTrueTrueTruePAT_108_161_575.pngTrueH
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8715461679PAT_132198FalseFalsePOMERANIAPOMERANIA58True...BCCTrueTrueTrueFalseTrueTruePAT_132_198_114.pngTrueH
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9115741708PAT_6297FalseFalsePOMERANIAPOMERANIA55True...BCCTrueTrueFalseFalseTrueTruePAT_62_97_868.pngTrueH
9315951729PAT_7961510TrueFalseITALYITALY83False...BCCTrueUNKTrueUNKTrueTruePAT_796_1510_361.pngTrueH
9516161752PAT_164255FalseFalseNETHERLANDSNETHERLANDS77False...SCCTrueFalseFalseFalseFalseFalsePAT_164_255_130.pngTrueH
9616181754PAT_177274FalseFalseGERMANYGERMANY51False...BCCFalseFalseFalseFalseFalseTruePAT_177_274_371.pngTrueH
9916691806PAT_9901860FalseFalsePOMERANIAPOMERANIA77True...SCCTrueUNKFalseUNKTrueTruePAT_990_1860_636.pngTrueH
10016851823PAT_5911126FalseFalsePOMERANIAPOMERANIA79True...SCCTrueUNKFalseUNKFalseTruePAT_591_1126_21.pngTrueH
10317561898PAT_5671078FalseFalseGERMANYGERMANY44False...BCCTrueUNKTrueUNKTrueTruePAT_567_1078_978.pngTrueH
10718682012PAT_4061542FalseFalseGERMANYNETHERLANDS63True...BCCTrueFalseTrueFalseTrueTruePAT_406_1542_754.pngTrueH
10919722121PAT_159245FalseFalsePOMERANIAPOMERANIA73True...BCCTrueTrueFalseFalseFalseFalsePAT_159_245_181.pngTrueH
11220162169PAT_1151138FalseFalsePOMERANIAPOMERANIA70False...MELFalseTrueFalseTrueFalseTruePAT_115_1138_970.pngTrueH
11320412201PAT_460894FalseFalsePOMERANIAPOMERANIA73False...BCCTrueFalseTrueFalseTrueTruePAT_460_894_429.pngTrueH
11420462210PAT_479917FalseFalsePOMERANIAPOMERANIA48True...BCCTrueTrueFalseFalseTrueTruePAT_479_917_598.pngTrueH
11520892276PAT_8301564TrueTrueITALYGERMANY84False...BCCTrueUNKFalseUNKFalseTruePAT_830_1564_740.pngTrueH
11620972287PAT_7541429FalseFalseITALYGERMANY75False...MELFalseTrueFalseFalseFalseFalsePAT_754_1429_380.pngTrueH
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59 rows × 29 columns

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MEL False \n", + "\n", + " grew hurt changed bleed elevation img_id biopsed \\\n", + "3 True False False True True PAT_944_1795_371.png True \n", + "6 UNK False UNK False False PAT_981_1848_906.png True \n", + "12 True False True False True PAT_192_295_164.png True \n", + "14 True True False True True PAT_561_1069_418.png True \n", + "16 True False False False True PAT_390_790_505.png True \n", + "17 UNK False UNK False False PAT_981_1848_486.png True \n", + "18 True False False False True PAT_726_1371_543.png True \n", + "20 True True False True True PAT_101_1041_651.png True \n", + "23 False False False False True PAT_270_416_398.png True \n", + "24 False False False False True PAT_326_690_797.png True \n", + "25 UNK True UNK True True PAT_431_850_472.png True \n", + "26 False True False True True PAT_227_347_19.png True \n", + "27 UNK False UNK True True PAT_761_1435_561.png True \n", + "28 UNK False UNK True True PAT_753_1428_451.png True \n", + "30 UNK True UNK True True PAT_204_310_975.png True \n", + "31 UNK False UNK False True PAT_812_1625_430.png True \n", + "33 True False False True True PAT_905_1721_327.png True \n", + "35 True True False True True PAT_86_1109_164.png True \n", + "37 UNK False UNK True True PAT_876_1664_579.png True \n", + "39 UNK False True False False PAT_884_1683_538.png True \n", + "44 UNK False UNK True True PAT_841_1603_667.png True \n", + "45 True True False True False PAT_56_86_802.png True \n", + "49 False True False False True PAT_330_698_302.png True \n", + "50 False False False False False PAT_59_93_342.png True \n", + "53 UNK True UNK False True PAT_150_1799_644.png True \n", + "54 False False False False True PAT_272_419_906.png True \n", + "55 True False False True True PAT_944_1795_666.png True \n", + "56 True False False False True PAT_788_1503_541.png True \n", + "57 True False False True True PAT_810_1526_67.png True \n", + "61 UNK False UNK False True PAT_342_716_316.png True \n", + "63 True False False True True PAT_406_808_975.png True \n", + "65 True False True True True PAT_919_1744_451.png True \n", + "66 True False False True True PAT_645_1222_860.png True \n", + "68 UNK False UNK True True PAT_938_1790_977.png True \n", + "69 UNK False UNK False True PAT_363_746_18.png True \n", + "71 True True False True True PAT_457_889_346.png True \n", + "72 UNK False UNK True True PAT_906_1723_560.png True \n", + "74 True False False False True PAT_338_707_774.png True \n", + "76 True False False False True PAT_48_74_309.png True \n", + "77 True False True False True PAT_361_1567_487.png True \n", + "82 True True True True True PAT_108_161_575.png True \n", + "83 True True False False True PAT_573_1090_660.png True \n", + "87 True True False True True PAT_132_198_114.png True \n", + "89 True True False True True PAT_120_183_623.png True \n", + "90 True True False True True PAT_711_1334_344.png True \n", + "91 True False False True True PAT_62_97_868.png True \n", + "93 UNK True UNK True True PAT_796_1510_361.png True \n", + "95 False False False False False PAT_164_255_130.png True \n", + "96 False False False False True PAT_177_274_371.png True \n", + "99 UNK False UNK True True PAT_990_1860_636.png True \n", + "100 UNK False UNK False True PAT_591_1126_21.png True \n", + "103 UNK True UNK True True PAT_567_1078_978.png True \n", + "107 False True False True True PAT_406_1542_754.png True \n", + "109 True False False False False PAT_159_245_181.png True \n", + "112 True False True False True PAT_115_1138_970.png True \n", + "113 False True False True True PAT_460_894_429.png True \n", + "114 True False False True True PAT_479_917_598.png True \n", + "115 UNK False UNK False True PAT_830_1564_740.png True \n", + "116 True False False False False PAT_754_1429_380.png True \n", + "\n", + " group_id \n", + "3 H \n", + "6 H \n", + "12 H \n", + "14 H \n", + "16 H \n", + "17 H \n", + "18 H \n", + "20 H \n", + "23 H \n", + "24 H \n", + "25 H \n", + "26 H \n", + "27 H \n", + "28 H \n", + "30 H \n", + "31 H \n", + "33 H \n", + "35 H \n", + "37 H \n", + "39 H \n", + "44 H \n", + "45 H \n", + "49 H \n", + "50 H \n", + "53 H \n", + "54 H \n", + "55 H \n", + "56 H \n", + "57 H \n", + "61 H \n", + "63 H \n", + "65 H \n", + "66 H \n", + "68 H \n", + "69 H \n", + "71 H \n", + "72 H \n", + "74 H \n", + "76 H \n", + "77 H \n", + "82 H \n", + "83 H \n", + "87 H \n", + "89 H \n", + "90 H \n", + "91 H \n", + "93 H \n", + "95 H \n", + "96 H \n", + "99 H \n", + "100 H \n", + "103 H \n", + "107 H \n", + "109 H \n", + "112 H \n", + "113 H \n", + "114 H \n", + "115 H \n", + "116 H \n", + "\n", + "[59 rows x 29 columns]" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\"metadata.csv\")\n", + "df_nafree = df.dropna().copy()\n", + "df_cancer = df[(df[\"diagnostic\"] == \"BCC\") | (df[\"diagnostic\"] == \"MEL\")| (df[\"diagnostic\"] == \"SCC\")]\n", + "df[\"gender\"].fillna(\"Not Stated\",inplace=True)\n", + "df_cancer" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "f629c670", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['ACK', 'NEV', 'BCC', 'SEK', 'SCC', 'MEL'], dtype=object)" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"diagnostic\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "b26b82a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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33634719PAT_9051721FalseFalsePOMERANIAPOMERANIA76True...BCCTrueTrueFalseFalseTrueTruePAT_905_1721_327.pngTrueH
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559201011PAT_9441795FalseTruePOMERANIAPOMERANIA31True...BCCFalseTrueFalseFalseTrueTruePAT_944_1795_666.pngTrueH
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6611031211PAT_6451222FalseFalseGERMANYGERMANY57True...BCCFalseTrueFalseFalseTrueTruePAT_645_1222_860.pngTrueH
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7713821511PAT_3611567FalseFalsePOMERANIAPOMERANIA71True...BCCFalseTrueFalseTrueFalseTruePAT_361_1567_487.pngTrueH
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8715461679PAT_132198FalseFalsePOMERANIAPOMERANIA58True...BCCTrueTrueTrueFalseTrueTruePAT_132_198_114.pngTrueH
9115741708PAT_6297FalseFalsePOMERANIAPOMERANIA55True...BCCTrueTrueFalseFalseTrueTruePAT_62_97_868.pngTrueH
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10818722016PAT_169694FalseTrueNETHERLANDSGERMANY44True...ACKTrueFalseFalseFalseFalseFalsePAT_169_694_411.pngFalseH
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11420462210PAT_479917FalseFalsePOMERANIAPOMERANIA48True...BCCTrueTrueFalseFalseTrueTruePAT_479_917_598.pngTrueH
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Unnamed: 0.1Unnamed: 0patient_idlesion_idsmokedrinkbackground_fatherbackground_motheragepesticide...diagnosticitchgrewhurtchangedbleedelevationimg_idbiopsedgroup_id
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30612695PAT_204310FalseTrueGERMANYPOMERANIA62False...BCCTrueUNKTrueUNKTrueTruePAT_204_310_975.pngTrueH
31618702PAT_8121625FalseFalseGERMANYGERMANY61False...BCCFalseUNKFalseUNKFalseTruePAT_812_1625_430.pngTrueH
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Unnamed: 0.1Unnamed: 0patient_idlesion_idsmokedrinkbackground_fatherbackground_motheragepesticide...diagnosticitchgrewhurtchangedbleedelevationimg_idbiopsedgroup_id
18409431PAT_7261371FalseFalsePOMERANIAPOMERANIA56False...BCCTrueTrueFalseFalseFalseTruePAT_726_1371_543.pngTrueH
20429451PAT_1011041TrueTrueGERMANYGERMANY59False...BCCTrueTrueTrueFalseTrueTruePAT_101_1041_651.pngTrueH
23491517PAT_270416FalseFalsePOMERANIAPOMERANIA90False...BCCTrueFalseFalseFalseFalseTruePAT_270_416_398.pngTrueH
25538595PAT_431850FalseFalseGERMANYGERMANY70False...BCCTrueUNKTrueUNKTrueTruePAT_431_850_472.pngTrueH
27544610PAT_7611435FalseFalseGERMANYGERMANY80False...BCCTrueUNKFalseUNKTrueTruePAT_761_1435_561.pngTrueH
28552628PAT_7531428FalseFalseGERMANYGERMANY61False...BCCTrueUNKFalseUNKTrueTruePAT_753_1428_451.pngTrueH
30612695PAT_204310FalseTrueGERMANYPOMERANIA62False...BCCTrueUNKTrueUNKTrueTruePAT_204_310_975.pngTrueH
31618702PAT_8121625FalseFalseGERMANYGERMANY61False...BCCFalseUNKFalseUNKFalseTruePAT_812_1625_430.pngTrueH
35660747PAT_861109FalseFalsePOMERANIAPOMERANIA53False...BCCTrueTrueTrueFalseTrueTruePAT_86_1109_164.pngTrueH
37738826PAT_8761664FalseFalseITALYGERMANY57False...BCCTrueUNKFalseUNKTrueTruePAT_876_1664_579.pngTrueH
44790879PAT_8411603FalseTruePOMERANIAPOMERANIA54False...BCCTrueUNKFalseUNKTrueTruePAT_841_1603_667.pngTrueH
45809898PAT_5686FalseFalseGERMANYGERMANY70False...SCCTrueTrueTrueFalseTrueFalsePAT_56_86_802.pngTrueH
49873964PAT_330698FalseFalseITALYITALY71False...BCCTrueFalseTrueFalseFalseTruePAT_330_698_302.pngTrueH
50884975PAT_5993TrueFalseBRAZILBRAZIL72False...BCCTrueFalseFalseFalseFalseFalsePAT_59_93_342.pngTrueH
549151006PAT_272419FalseFalsePOMERANIAPOMERANIA83False...BCCFalseFalseFalseFalseFalseTruePAT_272_419_906.pngTrueH
6110311127PAT_342716FalseFalseGERMANYGERMANY44False...BCCFalseUNKFalseUNKFalseTruePAT_342_716_316.pngTrueH
6510891195PAT_9191744FalseFalsePOMERANIAPOMERANIA65False...BCCFalseTrueFalseTrueTrueTruePAT_919_1744_451.pngTrueH
6811791297PAT_9381790FalseFalsePOMERANIAPOMERANIA75False...BCCTrueUNKFalseUNKTrueTruePAT_938_1790_977.pngTrueH
7112331353PAT_457889FalseFalsePOMERANIAPOMERANIA77False...BCCTrueTrueTrueFalseTrueTruePAT_457_889_346.pngTrueH
7412961418PAT_338707FalseFalseGERMANYGERMANY81False...BCCTrueTrueFalseFalseFalseTruePAT_338_707_774.pngTrueH
7613431468PAT_4874FalseFalseBRAZILBRAZIL73False...SCCFalseTrueFalseFalseFalseTruePAT_48_74_309.pngTrueH
8915711704PAT_120183FalseFalsePOMERANIAPOMERANIA53False...BCCTrueTrueTrueFalseTrueTruePAT_120_183_623.pngTrueH
9015731707PAT_7111334FalseFalseGERMANYGERMANY50False...BCCTrueTrueTrueFalseTrueTruePAT_711_1334_344.pngTrueH
9315951729PAT_7961510TrueFalseITALYITALY83False...BCCTrueUNKTrueUNKTrueTruePAT_796_1510_361.pngTrueH
9516161752PAT_164255FalseFalseNETHERLANDSNETHERLANDS77False...SCCTrueFalseFalseFalseFalseFalsePAT_164_255_130.pngTrueH
9616181754PAT_177274FalseFalseGERMANYGERMANY51False...BCCFalseFalseFalseFalseFalseTruePAT_177_274_371.pngTrueH
10317561898PAT_5671078FalseFalseGERMANYGERMANY44False...BCCTrueUNKTrueUNKTrueTruePAT_567_1078_978.pngTrueH
11220162169PAT_1151138FalseFalsePOMERANIAPOMERANIA70False...MELFalseTrueFalseTrueFalseTruePAT_115_1138_970.pngTrueH
11320412201PAT_460894FalseFalsePOMERANIAPOMERANIA73False...BCCTrueFalseTrueFalseTrueTruePAT_460_894_429.pngTrueH
11520892276PAT_8301564TrueTrueITALYGERMANY84False...BCCTrueUNKFalseUNKFalseTruePAT_830_1564_740.pngTrueH
11620972287PAT_7541429FalseFalseITALYGERMANY75False...MELFalseTrueFalseFalseFalseFalsePAT_754_1429_380.pngTrueH
\n", + "

31 rows × 29 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0.1 Unnamed: 0 patient_id lesion_id smoke drink \\\n", + "18 409 431 PAT_726 1371 False False \n", + "20 429 451 PAT_101 1041 True True \n", + "23 491 517 PAT_270 416 False False \n", + "25 538 595 PAT_431 850 False False \n", + "27 544 610 PAT_761 1435 False False \n", + "28 552 628 PAT_753 1428 False False \n", + "30 612 695 PAT_204 310 False True \n", + "31 618 702 PAT_812 1625 False False \n", + "35 660 747 PAT_86 1109 False False \n", + "37 738 826 PAT_876 1664 False False \n", + "44 790 879 PAT_841 1603 False True \n", + "45 809 898 PAT_56 86 False False \n", + "49 873 964 PAT_330 698 False False \n", + "50 884 975 PAT_59 93 True False \n", + "54 915 1006 PAT_272 419 False False \n", + "61 1031 1127 PAT_342 716 False False \n", + "65 1089 1195 PAT_919 1744 False False \n", + "68 1179 1297 PAT_938 1790 False False \n", + "71 1233 1353 PAT_457 889 False False \n", + "74 1296 1418 PAT_338 707 False False \n", + "76 1343 1468 PAT_48 74 False False \n", + "89 1571 1704 PAT_120 183 False False \n", + "90 1573 1707 PAT_711 1334 False False \n", + "93 1595 1729 PAT_796 1510 True False \n", + "95 1616 1752 PAT_164 255 False False \n", + "96 1618 1754 PAT_177 274 False False \n", + "103 1756 1898 PAT_567 1078 False False \n", + "112 2016 2169 PAT_115 1138 False False \n", + "113 2041 2201 PAT_460 894 False False \n", + "115 2089 2276 PAT_830 1564 True True \n", + "116 2097 2287 PAT_754 1429 False False \n", + "\n", + " background_father background_mother age pesticide ... diagnostic itch \\\n", + "18 POMERANIA POMERANIA 56 False ... BCC True \n", + "20 GERMANY GERMANY 59 False ... BCC True \n", + "23 POMERANIA POMERANIA 90 False ... BCC True \n", + "25 GERMANY GERMANY 70 False ... BCC True \n", + "27 GERMANY GERMANY 80 False ... BCC True \n", + "28 GERMANY GERMANY 61 False ... BCC True \n", + "30 GERMANY POMERANIA 62 False ... BCC True \n", + "31 GERMANY GERMANY 61 False ... BCC False \n", + "35 POMERANIA POMERANIA 53 False ... BCC True \n", + "37 ITALY GERMANY 57 False ... BCC True \n", + "44 POMERANIA POMERANIA 54 False ... BCC True \n", + "45 GERMANY GERMANY 70 False ... SCC True \n", + "49 ITALY ITALY 71 False ... BCC True \n", + "50 BRAZIL BRAZIL 72 False ... BCC True \n", + "54 POMERANIA POMERANIA 83 False ... BCC False \n", + "61 GERMANY GERMANY 44 False ... BCC False \n", + "65 POMERANIA POMERANIA 65 False ... BCC False \n", + "68 POMERANIA POMERANIA 75 False ... BCC True \n", + "71 POMERANIA POMERANIA 77 False ... BCC True \n", + "74 GERMANY GERMANY 81 False ... BCC True \n", + "76 BRAZIL BRAZIL 73 False ... SCC False \n", + "89 POMERANIA POMERANIA 53 False ... BCC True \n", + "90 GERMANY GERMANY 50 False ... BCC True \n", + "93 ITALY ITALY 83 False ... BCC True \n", + "95 NETHERLANDS NETHERLANDS 77 False ... SCC True \n", + "96 GERMANY GERMANY 51 False ... BCC False \n", + "103 GERMANY GERMANY 44 False ... BCC True \n", + "112 POMERANIA POMERANIA 70 False ... MEL False \n", + "113 POMERANIA POMERANIA 73 False ... BCC True \n", + "115 ITALY GERMANY 84 False ... BCC True \n", + "116 ITALY GERMANY 75 False ... MEL False \n", + "\n", + " grew hurt changed bleed elevation img_id biopsed \\\n", + "18 True False False False True PAT_726_1371_543.png True \n", + "20 True True False True True PAT_101_1041_651.png True \n", + "23 False False False False True PAT_270_416_398.png True \n", + "25 UNK True UNK True True PAT_431_850_472.png True \n", + "27 UNK False UNK True True PAT_761_1435_561.png True \n", + "28 UNK False UNK True True PAT_753_1428_451.png True \n", + "30 UNK True UNK True True PAT_204_310_975.png True \n", + "31 UNK False UNK False True PAT_812_1625_430.png True \n", + "35 True True False True True PAT_86_1109_164.png True \n", + "37 UNK False UNK True True PAT_876_1664_579.png True \n", + "44 UNK False UNK True True PAT_841_1603_667.png True \n", + "45 True True False True False PAT_56_86_802.png True \n", + "49 False True False False True PAT_330_698_302.png True \n", + "50 False False False False False PAT_59_93_342.png True \n", + "54 False False False False True PAT_272_419_906.png True \n", + "61 UNK False UNK False True PAT_342_716_316.png True \n", + "65 True False True True True PAT_919_1744_451.png True \n", + "68 UNK False UNK True True PAT_938_1790_977.png True \n", + "71 True True False True True PAT_457_889_346.png True \n", + "74 True False False False True PAT_338_707_774.png True \n", + "76 True False False False True PAT_48_74_309.png True \n", + "89 True True False True True PAT_120_183_623.png True \n", + "90 True True False True True PAT_711_1334_344.png True \n", + "93 UNK True UNK True True PAT_796_1510_361.png True \n", + "95 False False False False False PAT_164_255_130.png True \n", + "96 False False False False True PAT_177_274_371.png True \n", + "103 UNK True UNK True True PAT_567_1078_978.png True \n", + "112 True False True False True PAT_115_1138_970.png True \n", + "113 False True False True True PAT_460_894_429.png True \n", + "115 UNK False UNK False True PAT_830_1564_740.png True \n", + "116 True False False False False PAT_754_1429_380.png True \n", + "\n", + " group_id \n", + "18 H \n", + "20 H \n", + "23 H \n", + "25 H \n", + "27 H \n", + "28 H \n", + "30 H \n", + "31 H \n", + "35 H \n", + "37 H \n", + "44 H \n", + "45 H \n", + "49 H \n", + "50 H \n", + "54 H \n", + "61 H \n", + "65 H \n", + "68 H \n", + "71 H \n", + "74 H \n", + "76 H \n", + "89 H \n", + "90 H \n", + "93 H \n", + "95 H \n", + "96 H \n", + "103 H \n", + "112 H \n", + "113 H \n", + "115 H \n", + "116 H \n", + "\n", + "[31 rows x 29 columns]" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_pesticide_false = df_nafree[(df_nafree[\"pesticide\"] == False)&((df_nafree[\"diagnostic\"] == \"BCC\") | (df_nafree[\"diagnostic\"] == \"MEL\")| (df_nafree[\"diagnostic\"] == \"SCC\"))]\n", + "df_pesticide_false\n" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "a4e4afc3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df_nafree['gender'], bins= 2, edgecolor='black')\n", + "plt.xlabel('Gender')\n", + "plt.ylabel('Amount of Individuals')\n", + "plt.title('Gender Distribution')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "27743fed", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df['gender'],bins= 3, edgecolor='black')\n", + "plt.xlabel('Gender')\n", + "plt.ylabel('Amount of Individuals')\n", + "plt.title('Gender Distribution')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "c4bcc4de", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df['age'], bins=range(0, 100, 5), edgecolor='black')\n", + "plt.xlabel('Age')\n", + "plt.ylabel('Number of individuals')\n", + "plt.title('Age Distribution')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "d7f0b2a7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "plt.hist(df_nafree['background_father'], bins=15,edgecolor='black')\n", + "plt.xlabel('Country')\n", + "plt.ylabel('Number of individuals')\n", + "plt.title('Background of patient / Father side')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "63c077bc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "plt.hist(df_nafree['background_mother'], bins=15,edgecolor='black')\n", + "plt.xlabel('Country')\n", + "plt.ylabel('Number of individuals')\n", + "plt.title('Background of patient / Mother side')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4886c19", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "plt.hist(df_nafree['background_mother'], bins=15,edgecolor='black')\n", + "plt.xlabel('Country')\n", + "plt.ylabel('Number of individuals')\n", + "plt.title('Background of patient / Mother side')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9c7cfa9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Number of individuals')" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "true_all = df_pesticide_num_true.shape[0]\n", + "true_selected = df_pesticide_true.shape[0]\n", + "false_all = df_pesticide_num_false.shape[0]\n", + "false_selected = df_pesticide_false.shape[0]\n", + "x_true = [0, 1]\n", + "x_false = [3, 4] \n", + "plt.figure(figsize=(8, 5))\n", + "plt.bar(x_true, [true_all, true_selected])\n", + "plt.bar(x_false, [false_all, false_selected])\n", + "plt.xticks([0, 1, 3, 4],[\"All pesticide true\", \"BCC/MEL/SCC\", \"All pesticide false\", \"BCC/MEL/SCC\"])\n", + "plt.title(\"Pesticide Comparison\")\n", + "plt.ylabel(\"Number of individuals\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/results/reports/GroupH _Preliminary assignment/data exploration/Eduardo_graph_codes.ipynb b/results/reports/GroupH _Preliminary assignment/data exploration/Eduardo_graph_codes.ipynb new file mode 100644 index 0000000..21e28d1 --- /dev/null +++ b/results/reports/GroupH _Preliminary assignment/data exploration/Eduardo_graph_codes.ipynb @@ -0,0 +1,424 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "c3764c00", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Drink and Smoke vs Diagnostic\n", + "# NaN values replaced with \"Didn't Respond\"\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "df = pd.read_csv(\"metadata_with_group_H_anottated_eduardo.csv\", sep=\";\")\n", + "df.columns = df.columns.str.strip()\n", + "\n", + "df[\"diagnostic\"] = df[\"diagnostic\"].astype(str).str.strip().str.upper()\n", + "\n", + "df[\"drink\"] = df[\"drink\"].astype(str).str.strip()\n", + "df[\"smoke\"] = df[\"smoke\"].astype(str).str.strip()\n", + "\n", + "\n", + "df[\"drink\"] = df[\"drink\"].replace([\"\", \"nan\", \"NaN\", \"NONE\"], pd.NA)\n", + "df[\"smoke\"] = df[\"smoke\"].replace([\"\", \"nan\", \"NaN\", \"NONE\"], pd.NA)\n", + "\n", + "df[\"drink\"] = df[\"drink\"].fillna(\"Didn't Respond\")\n", + "df[\"smoke\"] = df[\"smoke\"].fillna(\"Didn't Respond\")\n", + "\n", + "df[\"drink\"] = df[\"drink\"].str.upper().replace(\"DIDN'T RESPOND\", \"Didn't Respond\")\n", + "df[\"smoke\"] = df[\"smoke\"].str.upper().replace(\"DIDN'T RESPOND\", \"Didn't Respond\")\n", + "\n", + "df[\"diagnostic_group\"] = df[\"diagnostic\"].apply(\n", + " lambda x: x if x in [\"BCC\", \"SCC\", \"MEL\"] else \"OTHER\"\n", + ")\n", + "\n", + "# GRAPH 1: DRINK\n", + "\n", + "drink_counts = (\n", + " df.groupby([\"drink\", \"diagnostic_group\"])\n", + " .size()\n", + " .unstack(fill_value=0)\n", + ")\n", + "\n", + "drink_counts.plot(kind=\"bar\", figsize=(10, 6))\n", + "\n", + "plt.title(\"Alcohol Consumption vs Skin Cancer Type\")\n", + "plt.xlabel(\"Drink\")\n", + "plt.ylabel(\"Number of Cases\")\n", + "plt.xticks(rotation=0)\n", + "plt.legend(title=\"Diagnostic\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "#SMOKE\n", + "smoke_counts = (\n", + " df.groupby([\"smoke\", \"diagnostic_group\"])\n", + " .size()\n", + " .unstack(fill_value=0)\n", + ")\n", + "\n", + "smoke_counts.plot(kind=\"bar\", figsize=(10, 6))\n", + "\n", + "plt.title(\"Smoking vs Skin Cancer Type\")\n", + "plt.xlabel(\"Smoke\")\n", + "plt.ylabel(\"Number of Cases\")\n", + "plt.xticks(rotation=0)\n", + "plt.legend(title=\"Diagnostic\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f041e402", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Grouping by region\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Read CSV\n", + "df = pd.read_csv(\"metadata_with_group_H_anottated_eduardo.csv\", sep=\";\")\n", + "df.columns = df.columns.str.strip()\n", + "\n", + "# Clean diagnostic and region columns\n", + "df[\"diagnostic\"] = df[\"diagnostic\"].astype(str).str.strip().str.upper()\n", + "df[\"region\"] = df[\"region\"].astype(str).str.strip()\n", + "\n", + "# Keep only BCC, SCC and MEL\n", + "df_filtered = df[df[\"diagnostic\"].isin([\"BCC\", \"SCC\", \"MEL\"])].copy()\n", + "\n", + "# Group by region and diagnostic\n", + "region_counts = (\n", + " df_filtered\n", + " .groupby([\"region\", \"diagnostic\"])\n", + " .size()\n", + " .unstack(fill_value=0)\n", + " .sort_index()\n", + ")\n", + "\n", + "region_counts.plot(kind=\"bar\", figsize=(12, 6))\n", + "\n", + "plt.title(\"Number of Cancer Cases by Region\")\n", + "plt.xlabel(\"Region\")\n", + "plt.ylabel(\"Number of Cases\")\n", + "plt.xticks(rotation=45, ha=\"right\")\n", + "\n", + "plt.legend(title=\"Diagnostic\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7d9d188", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\eduar\\AppData\\Local\\Temp\\ipykernel_2424\\2493061835.py:30: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", + " box = plt.boxplot(data, labels=diagnostics, patch_artist=True, showfliers=True)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Box-plot of areas (assuming eliptical)\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\n", + "df = pd.read_csv(\"metadata_with_group_H_anottated_eduardo.csv\", sep=\";\")\n", + "df.columns = df.columns.str.strip()\n", + "\n", + "\n", + "df[\"diagnostic\"] = df[\"diagnostic\"].astype(str).str.strip().str.upper()\n", + "\n", + "\n", + "df[\"diameter_1\"] = pd.to_numeric(df[\"diameter_1\"], errors=\"coerce\")\n", + "df[\"diameter_2\"] = pd.to_numeric(df[\"diameter_2\"], errors=\"coerce\")\n", + "\n", + "# Elliptical lesion area (mm²)\n", + "df[\"lesion_area\"] = (np.pi / 4) * df[\"diameter_1\"] * df[\"diameter_2\"]\n", + "\n", + "# Remove missing values\n", + "df = df.dropna(subset=[\"lesion_area\", \"diagnostic\"])\n", + "\n", + "diagnostics = sorted(df[\"diagnostic\"].unique())\n", + "\n", + "\n", + "data = [df[df[\"diagnostic\"] == d][\"lesion_area\"] for d in diagnostics]\n", + "\n", + "plt.figure(figsize=(14, 6))\n", + "box = plt.boxplot(data, labels=diagnostics, patch_artist=True, showfliers=True)\n", + "\n", + "for patch in box['boxes']:\n", + " patch.set_facecolor('#1f77b4')\n", + "\n", + "plt.title(\"Boxplot of Lesion Area by Diagnostic Type\")\n", + "plt.xlabel(\"Diagnostic\")\n", + "plt.ylabel(\"Lesion Area (mm²)\")\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9ccfd350", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Group_Number', 'Unnamed: 1', 'patient_id', 'lesion_id', 'smoke',\n", + " 'drink', 'background_father', 'background_mother', 'age',\n", + " 'pesticide', 'gender', 'skin_cancer_history', 'cancer_history',\n", + " 'has_piped_water', 'has_sewage_system', 'fitspatrick', 'region',\n", + " 'diameter_1', 'diameter_2', 'diagnostic', 'itch', 'grew', 'hurt',\n", + " 'changed', 'bleed', 'elevation', 'img_id', 'biopsed', 'group_id',\n", + " 'Hair', 'Pen', 'lesion_area'], dtype=object)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns.values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81e2caa7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " diagnostic img_id lesion_area extreme\n", + "0 ACK PAT_857_1628_916.png 117.809725 max\n", + "1 ACK PAT_108_162_526.png 11.780972 min\n", + "2 BCC PAT_810_1526_67.png 692.721180 max\n", + "3 BCC PAT_981_1848_906.png 7.068583 min\n", + "4 MEL PAT_754_1429_380.png 1068.141502 max\n", + "5 MEL PAT_115_1138_970.png 62.831853 min\n", + "6 NEV PAT_759_1433_973.png 176.714587 max\n", + "7 NEV PAT_297_4307_924.png 15.707963 min\n", + "8 SCC PAT_56_86_802.png 376.991118 max\n", + "9 SCC PAT_390_790_505.png 12.566371 min\n", + "10 SEK PAT_354_1814_726.png 18.849556 max\n", + "11 SEK PAT_939_1791_329.png 7.068583 min\n" + ] + } + ], + "source": [ + "#code for biggest areas\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "\n", + "df = pd.read_csv(\"metadata_with_group_H_anottated_eduardo.csv\", sep=\";\")\n", + "df.columns = df.columns.str.strip()\n", + "\n", + "df[\"diagnostic\"] = df[\"diagnostic\"].astype(str).str.strip().str.upper()\n", + "df[\"img_id\"] = df[\"img_id\"].astype(str).str.strip()\n", + "\n", + "\n", + "df[\"diameter_1\"] = pd.to_numeric(df[\"diameter_1\"], errors=\"coerce\")\n", + "df[\"diameter_2\"] = pd.to_numeric(df[\"diameter_2\"], errors=\"coerce\")\n", + "\n", + "\n", + "df[\"lesion_area\"] = (np.pi / 4) * df[\"diameter_1\"] * df[\"diameter_2\"] # ellipse area\n", + "\n", + "\n", + "df_valid = df.dropna(subset=[\"lesion_area\", \"diagnostic\", \"img_id\"]).copy()\n", + "\n", + "\n", + "idx_min = df_valid.groupby(\"diagnostic\")[\"lesion_area\"].idxmin()\n", + "idx_max = df_valid.groupby(\"diagnostic\")[\"lesion_area\"].idxmax()\n", + "\n", + "min_rows = df_valid.loc[idx_min, [\"diagnostic\", \"img_id\", \"lesion_area\"]].copy()\n", + "max_rows = df_valid.loc[idx_max, [\"diagnostic\", \"img_id\", \"lesion_area\"]].copy()\n", + "\n", + "min_rows[\"extreme\"] = \"min\"\n", + "max_rows[\"extreme\"] = \"max\"\n", + "\n", + "extremes = (\n", + " pd.concat([min_rows, max_rows], ignore_index=True)\n", + " .sort_values([\"diagnostic\", \"extreme\"])\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "print(extremes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcd89798", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Cancer and skin cancer history graphs\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = pd.read_csv(\"metadata_with_group_H_anottated_eduardo.csv\", sep=\";\")\n", + "df.columns = df.columns.str.strip()\n", + "\n", + "df[\"diagnostic\"] = df[\"diagnostic\"].astype(str).str.strip().str.upper()\n", + "cancerous_set = {\"BCC\", \"SCC\", \"MEL\"}\n", + "df[\"cancerous_case\"] = df[\"diagnostic\"].isin(cancerous_set)\n", + "\n", + "def to_true_false_missing(x):\n", + " if pd.isna(x):\n", + " return \"Didn't Respond\"\n", + " if isinstance(x, bool):\n", + " return \"True\" if x else \"False\"\n", + " s = str(x).strip().lower()\n", + " if s == \"true\":\n", + " return \"True\"\n", + " if s == \"false\":\n", + " return \"False\"\n", + " if s == \"\":\n", + " return \"Didn't Respond\"\n", + " return \"Didn't Respond\"\n", + "\n", + "for col in [\"cancer_history\", \"skin_cancer_history\"]:\n", + " df[col] = df[col].apply(to_true_false_missing)\n", + "\n", + "order_hist = [\"True\", \"False\", \"Didn't Respond\"]\n", + "order_case = [True, False] # True = cancerous, False = non-cancerous\n", + "\n", + "tab_cancer = (\n", + " df.groupby([\"cancer_history\", \"cancerous_case\"])\n", + " .size()\n", + " .unstack(fill_value=0)\n", + " .reindex(index=order_hist, columns=order_case, fill_value=0)\n", + ")\n", + "\n", + "tab_skin = (\n", + " df.groupby([\"skin_cancer_history\", \"cancerous_case\"])\n", + " .size()\n", + " .unstack(fill_value=0)\n", + " .reindex(index=order_hist, columns=order_case, fill_value=0)\n", + ")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "tab_cancer.plot(kind=\"bar\", ax=axes[0])\n", + "axes[0].set_title(\"Cancer History vs Cancerous Case\")\n", + "axes[0].set_xlabel(\"Cancer history\")\n", + "axes[0].set_ylabel(\"Number of lesions\")\n", + "axes[0].tick_params(axis=\"x\", rotation=0)\n", + "axes[0].legend([\"Cancerous (BCC/SCC/MEL)\", \"Non-cancerous\"], title=\"Case\")\n", + "\n", + "tab_skin.plot(kind=\"bar\", ax=axes[1])\n", + "axes[1].set_title(\"Skin Cancer History vs Cancerous Case\")\n", + "axes[1].set_xlabel(\"Skin cancer history\")\n", + "axes[1].set_ylabel(\"Number of lesions\")\n", + "axes[1].tick_params(axis=\"x\", rotation=0)\n", + "axes[1].legend([\"Cancerous (BCC/SCC/MEL)\", \"Non-cancerous\"], title=\"Case\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/results/reports/GroupH _Preliminary assignment/data exploration/Utku's graph codes.ipynb b/results/reports/GroupH _Preliminary assignment/data exploration/Utku's graph codes.ipynb new file mode 100644 index 0000000..31bc219 --- /dev/null +++ b/results/reports/GroupH _Preliminary assignment/data exploration/Utku's graph codes.ipynb @@ -0,0 +1,444 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 41, + "id": "c3764c00", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Unnamed: 0.1Unnamed: 0patient_idlesion_idsmokedrinkbackground_fatherbackground_motheragepesticide...grewhurtchangedbleedelevationimg_idbiopsedgroup_idHairPen
0910PAT_21404726NaNNaNNaNNaN45NaN...FALSEFalseFALSEFalseTruePAT_2140_4726_141.pngFalseH10
12829PAT_3594450FalseFalseITALYITALY54False...FALSEFalseFALSEFalseFalsePAT_359_4450_86.pngFalseH11
23132PAT_14531567NaNNaNNaNNaN52NaN...FALSEFalseFALSEFalseTruePAT_1453_1567_250.pngFalseH10
33839PAT_9441795FalseTruePOMERANIAPOMERANIA31True...TRUEFalseFALSETrueTruePAT_944_1795_371.pngTrueH00
44950PAT_20204174NaNNaNNaNNaN74NaN...FALSEFalseFALSEFalseFalsePAT_2020_4174_799.pngFalseH11
..................................................................
11220162169PAT_1151138FalseFalsePOMERANIAPOMERANIA70False...TRUEFalseTRUEFalseTruePAT_115_1138_970.pngTrueH01
11320412201PAT_460894FalseFalsePOMERANIAPOMERANIA73False...FALSETrueFALSETrueTruePAT_460_894_429.pngTrueH00
11420462210PAT_479917FalseFalsePOMERANIAPOMERANIA48True...TRUEFalseFALSETrueTruePAT_479_917_598.pngTrueH10
11520892276PAT_8301564TrueTrueITALYGERMANY84False...UNKFalseUNKFalseTruePAT_830_1564_740.pngTrueH00
11620972287PAT_7541429FalseFalseITALYGERMANY75False...TRUEFalseFALSEFalseFalsePAT_754_1429_380.pngTrueH10
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117 rows × 31 columns

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" + ], + "text/plain": [ + " Unnamed: 0.1 Unnamed: 0 patient_id lesion_id smoke drink \\\n", + "0 9 10 PAT_2140 4726 NaN NaN \n", + "1 28 29 PAT_359 4450 False False \n", + "2 31 32 PAT_1453 1567 NaN NaN \n", + "3 38 39 PAT_944 1795 False True \n", + "4 49 50 PAT_2020 4174 NaN NaN \n", + ".. ... ... ... ... ... ... \n", + "112 2016 2169 PAT_115 1138 False False \n", + "113 2041 2201 PAT_460 894 False False \n", + "114 2046 2210 PAT_479 917 False False \n", + "115 2089 2276 PAT_830 1564 True True \n", + "116 2097 2287 PAT_754 1429 False False \n", + "\n", + " background_father background_mother age pesticide ... grew hurt \\\n", + "0 NaN NaN 45 NaN ... FALSE False \n", + "1 ITALY ITALY 54 False ... FALSE False \n", + "2 NaN NaN 52 NaN ... FALSE False \n", + "3 POMERANIA POMERANIA 31 True ... TRUE False \n", + "4 NaN NaN 74 NaN ... FALSE False \n", + ".. ... ... ... ... ... ... ... \n", + "112 POMERANIA POMERANIA 70 False ... TRUE False \n", + "113 POMERANIA POMERANIA 73 False ... FALSE True \n", + "114 POMERANIA POMERANIA 48 True ... TRUE False \n", + "115 ITALY GERMANY 84 False ... UNK False \n", + "116 ITALY GERMANY 75 False ... TRUE False \n", + "\n", + " changed bleed elevation img_id biopsed group_id Hair \\\n", + "0 FALSE False True PAT_2140_4726_141.png False H 1 \n", + "1 FALSE False False PAT_359_4450_86.png False H 1 \n", + "2 FALSE False True PAT_1453_1567_250.png False H 1 \n", + "3 FALSE True True PAT_944_1795_371.png True H 0 \n", + "4 FALSE False False PAT_2020_4174_799.png False H 1 \n", + ".. ... ... ... ... ... ... ... \n", + "112 TRUE False True PAT_115_1138_970.png True H 0 \n", + "113 FALSE True True PAT_460_894_429.png True H 0 \n", + "114 FALSE True True PAT_479_917_598.png True H 1 \n", + "115 UNK False True PAT_830_1564_740.png True H 0 \n", + "116 FALSE False False PAT_754_1429_380.png True H 1 \n", + "\n", + " Pen \n", + "0 0 \n", + "1 1 \n", + "2 0 \n", + "3 0 \n", + "4 1 \n", + ".. .. \n", + "112 1 \n", + "113 0 \n", + "114 0 \n", + "115 0 \n", + "116 0 \n", + "\n", + "[117 rows x 31 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df=pd.read_csv('metadataUtku.csv')\n", + "df1=df[(df['diagnostic']=='BCC')| (df['diagnostic']=='MEL')| (df['diagnostic']=='SCC')\n", + "]\n", + "a=df1['background_mother'].value_counts()\n", + "a.plot(kind=\"bar\")\n", + "\n", + "plt.title(\"Number of Diognosis by the Different Mother Backgrounds\")\n", + "plt.xlabel(\"Background\")\n", + "plt.ylabel(\"Number of the Cases\")\n", + "plt.xticks(rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29d7c323", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/results/reports/GroupH _Preliminary assignment/data exploration/graph_codes_ofri .ipynb b/results/reports/GroupH _Preliminary assignment/data exploration/graph_codes_ofri .ipynb new file mode 100644 index 0000000..fa7727f --- /dev/null +++ b/results/reports/GroupH _Preliminary assignment/data exploration/graph_codes_ofri .ipynb @@ -0,0 +1,396 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "id": "c42a9159", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Unnamed: 0', 'patient_id', 'lesion_id', 'smoke', 'drink',\n", + " 'background_father', 'background_mother', 'age', 'pesticide', 'gender',\n", + " 'skin_cancer_history', 'cancer_history', 'has_piped_water',\n", + " 'has_sewage_system', 'fitspatrick', 'region', 'diameter_1',\n", + " 'diameter_2', 'diagnostic', 'itch', 'grew', 'hurt', 'changed', 'bleed',\n", + " 'elevation', 'img_id', 'biopsed', 'group_id'],\n", + " dtype='object')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df=pd.read_csv(r\"C:\\Users\\Ofri\\OneDrive - Birkerød Gymnasium - STX, HF, IB & Kostskole\\ITU\\DATA SCIENCE\\1ST year\\Projects in Data Science\\Lecture1\\metadata_with_group (2).csv\")\n", + "df.columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3df5ed5c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "# Filter patients with cancer history\n", + "df_ch = df[df[\"cancer_history\"] == True]\n", + "\n", + "# Count diagnoses within this group\n", + "diagnosis_counts = df_ch[\"diagnostic\"].value_counts()\n", + "\n", + "# Convert counts to probabilities (odds as proportions)\n", + "diagnosis_probs = diagnosis_counts / diagnosis_counts.sum()\n", + "\n", + "# Plot distribution\n", + "plt.figure()\n", + "plt.bar(diagnosis_probs.index, diagnosis_probs.values)\n", + "plt.title(\"Probability of Diagnosis Given Cancer History\")\n", + "plt.xlabel(\"Diagnosis\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd34f316", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = {\n", + " 'Symptom': ['Itching', 'Growing', 'Bleeding', 'Changing', 'Hurting', 'Elevation'],\n", + " 'BCC': [60, 75, 55, 30, 25, 70],\n", + " 'SCC': [65, 40, 35, 20, 30, 60],\n", + " 'MEL': [20, 80, 25, 50, 15, 40]\n", + "}\n", + "\n", + "df = pd.DataFrame(data)\n", + "df.set_index('Symptom').plot(kind='bar')\n", + "\n", + "plt.ylabel('Percentage of Patients')\n", + "plt.title('Symptom Frequency by Cancer Type')\n", + "plt.ylim(0, 100)\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b20bd659", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# List of symptom columns\n", + "symptom_cols = [\"itch\", \"grew\", \"hurt\", \"changed\", \"bleed\", \"elevation\"]\n", + "\n", + "# Filter ACK and SCC\n", + "df_ack = df[df[\"diagnostic\"] == \"ACK\"]\n", + "df_scc = df[df[\"diagnostic\"] == \"SCC\"]\n", + "\n", + "# Calculate percentage of patients with each symptom\n", + "ack_symptoms = df_ack[symptom_cols].mean() * 100\n", + "scc_symptoms = df_scc[symptom_cols].mean() * 100\n", + "\n", + "# Difference SCC - ACK\n", + "symptom_diff = scc_symptoms - ack_symptoms\n", + "\n", + "# Plot\n", + "plt.figure(figsize=(10,6))\n", + "bars = plt.bar(symptom_diff.index, symptom_diff.values, color=\"salmon\")\n", + "plt.axhline(0, color='black', linewidth=0.8) # baseline\n", + "\n", + "# Add values on top of bars\n", + "for bar in bars:\n", + " height = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width()/2, height + 1, f\"{height:.1f}%\", ha='center')\n", + "\n", + "plt.title(\"Symptom Increase from ACK → SCC\")\n", + "plt.ylabel(\"Difference in Percentage of Patients\")\n", + "plt.xlabel(\"Symptoms\")\n", + "plt.ylim(min(symptom_diff.values)-5, max(symptom_diff.values)+10)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "04f0a4fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Unnamed: 0', 'patient_id', 'lesion_id', 'smoke', 'drink',\n", + " 'background_father', 'background_mother', 'age', 'pesticide', 'gender',\n", + " 'skin_cancer_history', 'cancer_history', 'has_piped_water',\n", + " 'has_sewage_system', 'fitspatrick', 'region', 'diameter_1',\n", + " 'diameter_2', 'diagnostic', 'itch', 'grew', 'hurt', 'changed', 'bleed',\n", + " 'elevation', 'img_id', 'biopsed', 'group_id', 'group'],\n", + " dtype='object')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f859d3e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# --- 1. Load CSV ---\n", + "df = pd.read_csv(\n", + " r\"C:\\Users\\Ofri\\OneDrive - Birkerød Gymnasium - STX, HF, IB & Kostskole\\ITU\\DATA SCIENCE\\1ST year\\Projects in Data Science\\Lecture1\\metadata_with_group (2).csv\",\n", + " sep=\",\", # make sure your CSV uses commas; change to \";\" if needed\n", + " encoding=\"utf-8\"\n", + ")\n", + "\n", + "\n", + "# --- 2. Clean symptom columns ---\n", + "symptom_cols = [\"itch\", \"grew\", \"hurt\", \"changed\", \"bleed\", \"elevation\"]\n", + "\n", + "# Convert to numeric 0/1\n", + "for col in symptom_cols:\n", + " df[col] = df[col].astype(str).str.strip().map({\"False\": 0, \"True\": 1, \"UNK\": 0})\n", + "\n", + "# --- 3. Separate patients by itching ---\n", + "itch_yes = df[df[\"itch\"] == 1]\n", + "itch_no = df[df[\"itch\"] == 0]\n", + "\n", + "# Calculate average percentages for other symptoms (excluding itch itself)\n", + "other_symptoms = [\"grew\", \"hurt\", \"changed\", \"bleed\", \"elevation\"]\n", + "avg_with_itch = itch_yes[other_symptoms].mean() * 100\n", + "avg_without_itch = itch_no[other_symptoms].mean() * 100\n", + "\n", + "# --- 4. Plot side-by-side bar chart ---\n", + "x = range(len(other_symptoms))\n", + "width = 0.35\n", + "\n", + "plt.figure(figsize=(10,6))\n", + "bars1 = plt.bar([i - width/2 for i in x], avg_with_itch.values, width=width, color=\"orange\", label=\"With Itching\")\n", + "bars2 = plt.bar([i + width/2 for i in x], avg_without_itch.values, width=width, color=\"lightblue\", label=\"Without Itching\")\n", + "\n", + "# Add values on top of bars\n", + "for bars in [bars1, bars2]:\n", + " for bar in bars:\n", + " height = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width()/2, height + 1, f\"{height:.1f}%\", ha='center', fontsize=9)\n", + "\n", + "plt.xticks(x, other_symptoms)\n", + "plt.ylabel(\"Percentage of Patients\")\n", + "plt.title(\"Effect of Itching on Other Symptoms\")\n", + "plt.ylim(0, 100)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3e1265d1", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Column not found: lesion_area'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[7], line 23\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[38;5;66;03m# Keep only benign and malignant\u001b[39;00m\n\u001b[0;32m 21\u001b[0m df2 \u001b[38;5;241m=\u001b[39m df[df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmalignancy_group\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39misin([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBenign\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMalignant\u001b[39m\u001b[38;5;124m\"\u001b[39m])]\n\u001b[0;32m 22\u001b[0m summary \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m---> 23\u001b[0m df2\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmalignancy_group\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlesion_area\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 24\u001b[0m \u001b[38;5;241m.\u001b[39magg(n\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 25\u001b[0m median\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 26\u001b[0m mean\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 27\u001b[0m q1\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m s: s\u001b[38;5;241m.\u001b[39mquantile(\u001b[38;5;241m0.25\u001b[39m),\n\u001b[0;32m 28\u001b[0m q3\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m s: s\u001b[38;5;241m.\u001b[39mquantile(\u001b[38;5;241m0.75\u001b[39m))\n\u001b[0;32m 29\u001b[0m \u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m 30\u001b[0m )\n\u001b[0;32m 32\u001b[0m summary[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIQR\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m summary[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mq3\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m-\u001b[39m summary[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mq1\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 33\u001b[0m \u001b[38;5;28mprint\u001b[39m(summary)\n", + "File \u001b[1;32mc:\\Users\\Ofri\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\generic.py:1951\u001b[0m, in \u001b[0;36mDataFrameGroupBy.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1944\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(key) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 1945\u001b[0m \u001b[38;5;66;03m# if len == 1, then it becomes a SeriesGroupBy and this is actually\u001b[39;00m\n\u001b[0;32m 1946\u001b[0m \u001b[38;5;66;03m# valid syntax, so don't raise\u001b[39;00m\n\u001b[0;32m 1947\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 1948\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot subset columns with a tuple with more than one element. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1949\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse a list instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1950\u001b[0m )\n\u001b[1;32m-> 1951\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getitem__\u001b[39m(key)\n", + "File \u001b[1;32mc:\\Users\\Ofri\\anaconda3\\Lib\\site-packages\\pandas\\core\\base.py:244\u001b[0m, in \u001b[0;36mSelectionMixin.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 243\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj:\n\u001b[1;32m--> 244\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumn not found: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 245\u001b[0m ndim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj[key]\u001b[38;5;241m.\u001b[39mndim\n\u001b[0;32m 246\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gotitem(key, ndim\u001b[38;5;241m=\u001b[39mndim)\n", + "\u001b[1;31mKeyError\u001b[0m: 'Column not found: lesion_area'" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# ---- 1) Load your data ----\n", + "# Option A: CSV\n", + "df = pd.read_csv(\n", + " r\"C:\\Users\\Ofri\\OneDrive - Birkerød Gymnasium - STX, HF, IB & Kostskole\\ITU\\DATA SCIENCE\\1ST year\\Projects in Data Science\\Lecture1\\metadata_with_group (2).csv\",\n", + " sep=\",\", # make sure your CSV uses commas; change to \";\" if needed\n", + " encoding=\"utf-8\"\n", + ")\n", + "benign = {\"ACK\", \"NEV\", \"SEK\"}\n", + "malignant = {\"BCC\", \"SCC\", \"MEL\"}\n", + "\n", + "df[\"malignancy_group\"] = np.where(\n", + " df[\"diagnostic\"].isin(malignant), \"Malignant\",\n", + " np.where(df[\"diagnostic\"].isin(benign), \"Benign\", \"Other\")\n", + ")\n", + "\n", + "# Keep only benign and malignant\n", + "df2 = df[df[\"malignancy_group\"].isin([\"Benign\", \"Malignant\"])]\n", + "summary = (\n", + " df2.groupby(\"malignancy_group\")[\"lesion_area\"]\n", + " .agg(n=\"count\",\n", + " median=\"median\",\n", + " mean=\"mean\",\n", + " q1=lambda s: s.quantile(0.25),\n", + " q3=lambda s: s.quantile(0.75))\n", + " .reset_index()\n", + ")\n", + "\n", + "summary[\"IQR\"] = summary[\"q3\"] - summary[\"q1\"]\n", + "print(summary)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "827e11eb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Ofri\\AppData\\Local\\Temp\\ipykernel_33788\\2230813464.py:33: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", + " plt.boxplot([benign_vals, malig_vals], labels=[\"Benign - Not Cancer\", \"Malignant - Cancer\"], showfliers=True)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = pd.read_csv(\n", + " r\"C:\\Users\\Ofri\\OneDrive - Birkerød Gymnasium - STX, HF, IB & Kostskole\\ITU\\DATA SCIENCE\\1ST year\\Projects in Data Science\\Lecture1\\metadata_with_group (2).csv\",\n", + " sep=\",\", # make sure your CSV uses commas; change to \";\" if needed\n", + " encoding=\"utf-8\"\n", + ")\n", + "\n", + "# make sure diameter columns are numbers\n", + "df[\"diameter_1\"] = pd.to_numeric(df[\"diameter_1\"], errors=\"coerce\")\n", + "df[\"diameter_2\"] = pd.to_numeric(df[\"diameter_2\"], errors=\"coerce\")\n", + "# ellipse area\n", + "df[\"lesion_area\"] = np.pi * (df[\"diameter_1\"]/2) * (df[\"diameter_2\"]/2)\n", + "\n", + "# keep rows where we actually have area + diagnosis\n", + "d = df.dropna(subset=[\"lesion_area\", \"diagnostic\"]).copy()\n", + "benign = [\"ACK\", \"NEV\", \"SEK\"]\n", + "malignant = [\"BCC\", \"SCC\", \"MEL\"]\n", + "\n", + "def group_diag(x):\n", + " if x in malignant: return \"Malignant\"\n", + " if x in benign: return \"Benign\"\n", + " return None\n", + "\n", + "d[\"malignancy_group\"] = d[\"diagnostic\"].apply(group_diag)\n", + "d = d.dropna(subset=[\"malignancy_group\"])\n", + "\n", + "benign_vals = d.loc[d[\"malignancy_group\"]==\"Benign\", \"lesion_area\"].values\n", + "malig_vals = d.loc[d[\"malignancy_group\"]==\"Malignant\", \"lesion_area\"].values\n", + "\n", + "plt.figure(figsize=(7,5))\n", + "plt.boxplot([benign_vals, malig_vals], labels=[\"Benign - Not Cancer\", \"Malignant - Cancer\"], showfliers=True)\n", + "plt.ylabel(\"Lesion Area\")\n", + "plt.title(\"Lesion Area: Benign vs Malignant\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}