From 8564b268d40b4ce5e69790339283c4c26dd34859 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Fri, 22 May 2020 00:27:55 +0200 Subject: [PATCH 01/17] first datasets --- your-project/Data Gathering.ipynb | 2022 +++++++++++++++++++++++++++++ 1 file changed, 2022 insertions(+) create mode 100644 your-project/Data Gathering.ipynb diff --git a/your-project/Data Gathering.ipynb b/your-project/Data Gathering.ipynb new file mode 100644 index 00000000..aed1202c --- /dev/null +++ b/your-project/Data Gathering.ipynb @@ -0,0 +1,2022 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import datetime" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CO annual emissions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_annual = pd.read_csv('')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CO Emissions per capita (1800-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearPer capita COâ‚‚ emissions (tonnes per capita)
0AfghanistanAFG18000.0
1AfghanistanAFG18010.0
2AfghanistanAFG18020.0
3AfghanistanAFG18030.0
4AfghanistanAFG18040.0
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Per capita COâ‚‚ emissions (tonnes per capita)\n", + "0 Afghanistan AFG 1800 0.0\n", + "1 Afghanistan AFG 1801 0.0\n", + "2 Afghanistan AFG 1802 0.0\n", + "3 Afghanistan AFG 1803 0.0\n", + "4 Afghanistan AFG 1804 0.0" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gas = pd.read_csv(\"data//co_emissions_per_capita.csv\")\n", + "gas.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "gas.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "gas = gas.rename(columns={'Per capita COâ‚‚ emissions (tonnes per capita)':'COâ‚‚', 'Entity':'Country'})" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearCOâ‚‚
0Afghanistan18000.0
1Afghanistan18010.0
2Afghanistan18020.0
3Afghanistan18030.0
4Afghanistan18040.0
\n", + "
" + ], + "text/plain": [ + " Country Year COâ‚‚\n", + "0 Afghanistan 1800 0.0\n", + "1 Afghanistan 1801 0.0\n", + "2 Afghanistan 1802 0.0\n", + "3 Afghanistan 1803 0.0\n", + "4 Afghanistan 1804 0.0" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gas.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "gas = gas[gas.Country != 'World']" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "#gas.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 42723 entries, 0 to 42843\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 42723 non-null object \n", + " 1 Year 42723 non-null int64 \n", + " 2 COâ‚‚ 42723 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 1.3+ MB\n" + ] + } + ], + "source": [ + "#gas.Year.unique()\n", + "gas.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Share of deaths by pollution (1990-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearShare of deaths from air pollution (%)
0AfghanistanAFG199013.56
1AfghanistanAFG199113.19
2AfghanistanAFG199213.05
3AfghanistanAFG199312.88
4AfghanistanAFG199412.80
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Share of deaths from air pollution (%)\n", + "0 Afghanistan AFG 1990 13.56\n", + "1 Afghanistan AFG 1991 13.19\n", + "2 Afghanistan AFG 1992 13.05\n", + "3 Afghanistan AFG 1993 12.88\n", + "4 Afghanistan AFG 1994 12.80" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deaths = pd.read_csv('data//share_deaths_air_pollution.csv')\n", + "deaths.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Entity 0\n", + "Code 980\n", + "Year 0\n", + "Share of deaths from air pollution (%) 0\n", + "dtype: int64" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deaths.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "deaths.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "deaths.rename(columns={'Share of deaths from air pollution (%)':'Pollution_deaths', 'Entity':'Country'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "deaths = deaths[deaths.Country != 'World']" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearPollution_deaths
0Afghanistan199013.56
1Afghanistan199113.19
2Afghanistan199213.05
3Afghanistan199312.88
4Afghanistan199412.80
\n", + "
" + ], + "text/plain": [ + " Country Year Pollution_deaths\n", + "0 Afghanistan 1990 13.56\n", + "1 Afghanistan 1991 13.19\n", + "2 Afghanistan 1992 13.05\n", + "3 Afghanistan 1993 12.88\n", + "4 Afghanistan 1994 12.80" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deaths.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000,\n", + " 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,\n", + " 2012, 2013, 2014, 2015, 2016, 2017], dtype=int64)" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#deaths.info()\n", + "deaths.Year.unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global temperatures by country" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "temperature = pd.read_csv('GlobalLandTemperaturesByCountry.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtAverageTemperatureAverageTemperatureUncertaintyCountry
01743-11-014.3842.294Ã…land
11743-12-01NaNNaNÃ…land
21744-01-01NaNNaNÃ…land
31744-02-01NaNNaNÃ…land
41744-03-01NaNNaNÃ…land
\n", + "
" + ], + "text/plain": [ + " dt AverageTemperature AverageTemperatureUncertainty Country\n", + "0 1743-11-01 4.384 2.294 Åland\n", + "1 1743-12-01 NaN NaN Åland\n", + "2 1744-01-01 NaN NaN Åland\n", + "3 1744-02-01 NaN NaN Åland\n", + "4 1744-03-01 NaN NaN Åland" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "temperature['dt']= pd.to_datetime(temperature['dt']) " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 577462 entries, 0 to 577461\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 dt 577462 non-null datetime64[ns]\n", + " 1 AverageTemperature 544811 non-null float64 \n", + " 2 AverageTemperatureUncertainty 545550 non-null float64 \n", + " 3 Country 577462 non-null object \n", + "dtypes: datetime64[ns](1), float64(2), object(1)\n", + "memory usage: 17.6+ MB\n" + ] + } + ], + "source": [ + "temperature.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "577457 False\n", + "577458 False\n", + "577459 False\n", + "577460 False\n", + "577461 False\n", + "Name: Country, Length: 577462, dtype: bool" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature['Country'] == 'Spain'" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Åland', 'Afghanistan', 'Africa', 'Albania', 'Algeria',\n", + " 'American Samoa', 'Andorra', 'Angola', 'Anguilla', 'Antarctica',\n", + " 'Antigua And Barbuda', 'Argentina', 'Armenia', 'Aruba', 'Asia',\n", + " 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',\n", + " 'Baker Island', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium',\n", + " 'Belize', 'Benin', 'Bhutan', 'Bolivia',\n", + " 'Bonaire, Saint Eustatius And Saba', 'Bosnia And Herzegovina',\n", + " 'Botswana', 'Brazil', 'British Virgin Islands', 'Bulgaria',\n", + " 'Burkina Faso', 'Burma', 'Burundi', \"Côte D'Ivoire\", 'Cambodia',\n", + " 'Cameroon', 'Canada', 'Cape Verde', 'Cayman Islands',\n", + " 'Central African Republic', 'Chad', 'Chile', 'China',\n", + " 'Christmas Island', 'Colombia', 'Comoros',\n", + " 'Congo (Democratic Republic Of The)', 'Congo', 'Costa Rica',\n", + " 'Croatia', 'Cuba', 'Curaçao', 'Cyprus', 'Czech Republic',\n", + " 'Denmark (Europe)', 'Denmark', 'Djibouti', 'Dominica',\n", + " 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador',\n", + " 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Ethiopia', 'Europe',\n", + " 'Falkland Islands (Islas Malvinas)', 'Faroe Islands',\n", + " 'Federated States Of Micronesia', 'Fiji', 'Finland',\n", + " 'France (Europe)', 'France', 'French Guiana', 'French Polynesia',\n", + " 'French Southern And Antarctic Lands', 'Gabon', 'Gambia',\n", + " 'Gaza Strip', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland',\n", + " 'Grenada', 'Guadeloupe', 'Guam', 'Guatemala', 'Guernsey',\n", + " 'Guinea Bissau', 'Guinea', 'Guyana', 'Haiti',\n", + " 'Heard Island And Mcdonald Islands', 'Honduras', 'Hong Kong',\n", + " 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\n", + " 'Ireland', 'Isle Of Man', 'Israel', 'Italy', 'Jamaica', 'Japan',\n", + " 'Jersey', 'Jordan', 'Kazakhstan', 'Kenya', 'Kingman Reef',\n", + " 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon',\n", + " 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n", + " 'Luxembourg', 'Macau', 'Macedonia', 'Madagascar', 'Malawi',\n", + " 'Malaysia', 'Mali', 'Malta', 'Martinique', 'Mauritania',\n", + " 'Mauritius', 'Mayotte', 'Mexico', 'Moldova', 'Monaco', 'Mongolia',\n", + " 'Montenegro', 'Montserrat', 'Morocco', 'Mozambique', 'Namibia',\n", + " 'Nepal', 'Netherlands (Europe)', 'Netherlands', 'New Caledonia',\n", + " 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Niue',\n", + " 'North America', 'North Korea', 'Northern Mariana Islands',\n", + " 'Norway', 'Oceania', 'Oman', 'Pakistan', 'Palau', 'Palestina',\n", + " 'Palmyra Atoll', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n", + " 'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',\n", + " 'Reunion', 'Romania', 'Russia', 'Rwanda', 'Saint Barthélemy',\n", + " 'Saint Kitts And Nevis', 'Saint Lucia', 'Saint Martin',\n", + " 'Saint Pierre And Miquelon', 'Saint Vincent And The Grenadines',\n", + " 'Samoa', 'San Marino', 'Sao Tome And Principe', 'Saudi Arabia',\n", + " 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',\n", + " 'Sint Maarten', 'Slovakia', 'Slovenia', 'Solomon Islands',\n", + " 'Somalia', 'South Africa', 'South America',\n", + " 'South Georgia And The South Sandwich Isla', 'South Korea',\n", + " 'Spain', 'Sri Lanka', 'Sudan', 'Suriname',\n", + " 'Svalbard And Jan Mayen', 'Swaziland', 'Sweden', 'Switzerland',\n", + " 'Syria', 'Taiwan', 'Tajikistan', 'Tanzania', 'Thailand',\n", + " 'Timor Leste', 'Togo', 'Tonga', 'Trinidad And Tobago', 'Tunisia',\n", + " 'Turkey', 'Turkmenistan', 'Turks And Caicas Islands', 'Uganda',\n", + " 'Ukraine', 'United Arab Emirates', 'United Kingdom (Europe)',\n", + " 'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan',\n", + " 'Venezuela', 'Vietnam', 'Virgin Islands', 'Western Sahara',\n", + " 'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature.Country.unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global Temperature anomaly\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearMedian (℃)Upper (℃)Lower (℃)
0GlobalNaN1850-0.373-0.339-0.425
1GlobalNaN1851-0.218-0.184-0.274
2GlobalNaN1852-0.228-0.196-0.280
3GlobalNaN1853-0.269-0.239-0.321
4GlobalNaN1854-0.248-0.218-0.301
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Median (℃) Upper (℃) Lower (℃)\n", + "0 Global NaN 1850 -0.373 -0.339 -0.425\n", + "1 Global NaN 1851 -0.218 -0.184 -0.274\n", + "2 Global NaN 1852 -0.228 -0.196 -0.280\n", + "3 Global NaN 1853 -0.269 -0.239 -0.321\n", + "4 Global NaN 1854 -0.248 -0.218 -0.301" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a = pd.read_csv('temperature_anomaly.csv')\n", + "temperature_a.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 676 entries, 0 to 675\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Entity 676 non-null object \n", + " 1 Code 0 non-null float64\n", + " 2 Year 676 non-null int64 \n", + " 3 Median (℃) 676 non-null float64\n", + " 4 Upper (℃) 676 non-null float64\n", + " 5 Lower (℃) 676 non-null float64\n", + "dtypes: float64(4), int64(1), object(1)\n", + "memory usage: 31.8+ KB\n" + ] + } + ], + "source": [ + "temperature_a.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "temperature_a.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860,\n", + " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", + " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", + " 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893,\n", + " 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904,\n", + " 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915,\n", + " 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926,\n", + " 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937,\n", + " 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948,\n", + " 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", + " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", + " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", + " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", + " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", + " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", + " 2015, 2016, 2017, 2018], dtype=int64)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.Year.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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oJWiPqeSYCOo6rXiE6pZaWpx2yT7fQKSFIZFIJOeB1eFiZ1UHAD944wgAP3nrGD9+8xjhoSE8fcdsfnvXHAAUBdpMqkBkJkSRFhdJm8mG2yP48EQry6ekERkWGnTeRXJMBFkJah3HaMcxpIUhkUgk58H28nYcLg8ADrf6d0lBEj+8aSpXFCShKAoH67oANZ02OkINYGclGEiLi+JQXTeH67to77WzckYmAB29gYKRFB2B2yPITTJwbJTjGNLCkEgkkiC8c7SZH795NGC7xe7ir7tq+KcXDwTse+3rV1FSmKzFH7ISogA1/uAr4MtMiCQ9LpIOi4O3j7QQHqqwfIrqZurwWhgTdNlSybHqHIwZ2fGcGGULQwqGRCKRBOGD4y38bX8DvtKxmnYLP9t4nEW/2MxP/nFcO+7kEzeSFhfJhNQYQkP8OyWlxUYSokBLT59Wj5GZYNC60P79UANXFaVqLT98Qe2iNJ1gREdolsyZdgtm2+hN3ZMuKYlEIgmCr5nge8daeH1/PVtOGwkPVbh5VhYzsxP493dO8t/3zMMQEcqTa2fQ5wwMWIeFhpAeF0Vzj42YyDDiIsOIjQwj3SsY3VYnK6f3Z0f5qsKL0mNxuj3sqGjnl++eAuD9462j2ngQpGBIJBJJAGabk1JvQPsbLx0kLS6Sf1kxmXsX5pMeF8UP3igjOiJUcyXdOHPw+ReZCVHUdlhpNdsoSo8FID0+Stt/g04wKlrVau5nt1Xz2r56uq391sS/3jiVB5dODLBiLiVSMCQSicRLlbGXF0preONAg7bta0sm8MiqqUSEqR78nj4nG8qa+My8HOKG0T02Mz5Ka0T489tmAmguqbl5iaTHRXKwrouNZU28dbi/b+vy4jRunZNNbGQYdz23m6lZcaMqFiAFQyKRfMrxeARbTrexrrSGTyraiQgNYeWMDDYdaQZgyeQ0TSwA3jrUiM3p4d4FBcO6fqY38L1yegZLJqdx35/3MCUjDoCqtl6WPLWFhq4+wkP7xeCP95dolkejd56Gfq7GaCEFQyKRfCrp6XPyt/31/HV3LbUdVjLiI/neDcXcszAfm9OtCUaXtT/VVQjBS3tqmZObwKzchGG9z9TMOGIiQnls9XT2nunkk4p2PqloB8DqdDM/LYnvrCgmLzmazz2rNvP2ZVcBZCdEERsZxumWy6T5oEQikYwXKlrNrCut4c1DjVgdbkoKknhk1RRWzcgkPFS1JHz1E+BfG1HTYaW8tZcnva6lgfz2w3KmZ8ezyltXAbCkOI2vLpnI0qe3+B3789tmcvOsLK31x1929k/XS4wOx+0RfPOlA3xmXi7FGbHanIzRRAqGRCK57HF7BJtPtrJ+Vw07KzuICAth7ZxsHlhcyMycQEtBP9xIPwCp2esWmpQWG3COy+3hf7ZWkpVgYF5+Iu8da2HD4Sb213YFHAtwz4J8v5jEnupO7eek6Ag2n2zl/eOtGMJDmZIZz7vHmhFCaDUeo4EUDIlEctnSbXXw2j7V7dTQ1UdWQhSPrJrCPQvytSf7YLTpBKOxqz924BOPjPjIgHOON5lwugV1nVYW/PtmQHVHPbJqCrfMzuKRvx1hb02/KBjNdi2+4fEIbV94qEJ0RCh/3qFaHBVtvXyuJI9X9tbRZraTocuwutRIwZBIJJcdp1pMrPe6nWxODwsnJPPjm6dxw/QMwkLPXq/cprMqKtv6mwi2evtB+Rbtnj4nfz/YwM7KDj466T+D+4PvLKXYG9wG/MQC1GC2TzAq2nq1PlLJMREcbzKx50wnCYZwqoy92nVOtZilYEgkEsmF4nJ7+OhkK3/ZWcOeM51EhYdw29wcHlhcyLSs+HO6VoWu0+zp1v7YQavJRkRYCDsr29lQ1qQFxlNj+62Vexbk8/r+ehIMwVNu46LCMNtc3sFK6gjWTyqMgBrgzkkysK60huiIUB66tohfvHOKuCh1qT7dYmLZKHarlYIhkUjGNZ0WB6/uq+PFXbU09djISTTw6E1TuevKPBKjB3c7DcXJFv+eTS63h9KqDtaV1gDw4F/9+0j99q65/ONwE9vKjfzTsom8vr+er72wn+fuK9GsCB+Li1J4/3irX5rsm4camZ2bQJvJTn5yDPtrOlk+JY15+aqgGM12MuIjRz3wLXtJSSSSccnxph5+8EYZV/1yM0+9d5rC1Bieve8Ktv/gWr6+rOi8xQKgtsPq93rhLzZz//N7tde+YUg+nt9xhipjL0aznY9PtfGHL1xBVVsva36/g45eu9aPCtBalTd1q26vUy0mjjeZuHlWFi0mG7lJBhq6+ihMidGC65VtvUzJjB/11NoREQxFUW5UFOW0oiiViqL8MMj+LyqKYlQU5bD3z1d1+x5QFKXC++eBkbgfiURyeeJ0e9h0pIk7/1DK6t/tYGNZM3dckcsH31nKy19bxKoZmRdUDS2E4EhDd8D2RUUpPHvfFaTGRvDZ+Tm4PaoAFKSoE/a2nDZyqE4973ebK1gyOZXn7i+hzWxn75lOLI7+PlM+95KvIO/Ng42EhSjMyU0EICIsBJdHUJASTVJMBKmxEVS29TI1M46Ktl5c3lbqo8EFu6QURQkFngFuABqAfYqibBBCnBhw6GtCiIcHnJsMPA6UAAI44D03eB6aRCL5VNLea+fVvXW8uLuOFpON/ORoHls9jTuvyCMh+uztOc5GRauZDWVNbCxromaAdQHwmzvnEBEawkMvHSQ6IpQ9Z9QA9gNXFfLEJv+lrsuqtg65ZbbaX6qyrVdzS83MiScmUl12q4y9uD2CNw81snxKekAX2vxktWNtUVoslcZe7l2Qj8PloabDyqT0wLTeS8FIxDAWAJVCiGoARVFeBdYCAwUjGKuAD4UQnd5zPwRuBF4ZgfuSSCTjnCMN3awrrWFTWTMOt4clk1P598/MZPmU9Avuq1TXYWXjEVUkTrWYCVFgcVEqd12Zz3+8d8rv2G6rk7BQBZdH+AXEb5yZGSAYUzLiWLezhjuvyCUn0UClsZdEr6jNykkg1isY1UYLOyrbaTPbuX1+DnWdqlD5yix81svkjFg2HG7imsmp/PmBwJjIpWQkBCMHqNe9bgAWBjnudkVRlgLlwHeEEPWDnJszAvckkUjGKQ6Xh3ePNbO+tIaDdd3ERIRy94I87r+q8IKfrFtNNjYdaWZjWROH61UX0vx81RX01B1zuOOKXE61mAIEo8vq0NxQJ5vVgHh4qEJmfBRfurqQv+ysAWBBYTK3zcvhR28eZX9tF0XpsVS29RLp7UU1IztBc0kBvLynlgRDONdNS+fJTSdIMITTY3USERZCpjd9dlJaLCabC0WB66dlMJqMhGAEk3kx4PVG4BUhhF1RlH8C1gPXDfNc9U0U5UHgQYD8/Pzzv1uJRDImaTPbeHlPHS/tqcNotlOYEs3jt07n9itytQFD50OXxcE7x1SR2HOmEyFgRnY8P7xpKrfMziI2Moy5T3xItVG1HPRV3vpr2FxqHMJkcwEwMTWWkBCFzy/M1wTj9ityuGV2No+9dZRPKtqZlBbL3jMdmthMyYzD1Nfvenr/eCtfWJRPZFgotR1WClKiqe2wkpdkIMRrQU1KV2swKlp7SY8bPesCRkYwGoA83etcoEl/gBCiQ/fyj8B/6M5dPuDcrcHeRAjxHPAcQElJSVBRkUgk449DdV2sL63h7aPNON2C5VPSeGBxIcsmp2mL5rlitjn58EQrG8qa2FHRjssjmJgWwz9fP5lb52RTpGvt4SvM67WrQhBUMKxOevr8Yww+l9HE1P5rXTM5jZjIMApTYzjVbGL5lHRsTo+WDpsUHY7H4798fXZ+LqBmZs3JS6SyrZf85Ght/6ycBOIiw/jvjyu4amLKeX8nI8FICMY+YLKiKBOARuBu4F79AYqiZAkhmr0v1wAnvT+/D/xCUZQk7+uVwKMjcE8SiWQMY3e5eedoM+tKaymr7yY2MozPLyzg/qsKmBikT9NwsDndfHyqjQ2Hm/j4dBsOl4ecRANfXTKRNXOymZYVF7QPk6/C2icY+qFF2jFWB+0DhKTQO3c7JEShpCCJxu4+bSLetMx4jjb28NUlE/3O2VbezqKJydrrCakxzMtLxOn20Njdx5o52Xx8spWFE/qPSYgO57FbpvGv/3eUl/bWcd+i4bVVvxhcsGAIIVyKojyMuviHAs8LIY4rivIEsF8IsQH4tqIoawAX0Al80Xtup6IoT6KKDsATvgC4RCK5/Gg12Xhpdy0v762jvddBUVoMT6ydwWfn52rB4HPB4fKwo9LIxrJmPjjegsXhJi0uknsX5HPrnGzm5yeetVmfb452r9fV5BMOPd0WB626diHQb2EA/P7e+Th16a5TMuN4+2izX5tygCc3neCVry3SXn9+YT6KotDYZcXtEcRFhWFxuP0sjIYuKy6PICo8hF+9c5I75udiiAg921dzURiRSm8hxDvAOwO2/Zvu50cZxHIQQjwPPD8S9yGRSMYeQggO1nWxrrSWd4824xaC66ak88WrC7lmUuo5d191ewR7qjvYeKSJd4+10G11kmAIZ83cbG6dnc3CiSnnlEHV4bUwLA5VKHwWh54uqzNAMF7f38Bn56mL98DMpamZatzB2GsnOSbC75qv7K0D4Ac3TuEr10wAoHZAhlRNh4WfbTzO9nIjVUYLoLYNuXFmFmGh49slJZFIJAHYnG42ljWxflcNxxpNxEWF8cXFhdx3VQEFKTHndC0hBIfqu9lwuIm3jzZjNNuJjghl5fQM1szN5ppJ/lPxfHg8gp+/fZJ7F+ZpweOBaC4pr4VxrLEn4Jguq0NrPOijrL6bP31SzbeunxxwvK931almsxr4tvQ7TjYdafJ+JlAUBSEEG8vUbb94R83OemFXLZFhISycmMK9CwtYVpxKUVrsqLY2BykYEolkhGnu6ePF3bW8sreeTouDyemx/Py2mXxmXo5WtDYchBCcbDZrtRINXX1EhIVw3ZR0bp2TzXVT08/qmmkz23l+5xmiI0L5/qopQY/RXFJeV1Sw+RXvHG3WMp30/GFbFfcszCc11r/deU6igdjIME61mChKj9U61V4zKZXSKnXa3qv76qjtsLC9vN1v5gaoI1qXTE4lKnx0XE+DIQVDIpFcMEII9tV0sa70DO8fb0UIwYppGXxxcSFXFaWc05NxtbGXjWXNbChrpMpoITREYcnkVL6zopgbZmScU4qtyVs9rW9RPpCOAUHvYCyfkkZoiMI7R1u0bTNz4jnZbOa/N1fws7X+E/hCQhR1Sl6zmfsXF2huqB2V7dox9Z199FhbKClMpsVk44bpGdS0WxCgzfMea0jBkEgk543N6eYfhxtZV1rLyWYTCYZwvnrNBL6wqIA8XeD2bDR297GprImNR5o41mhCUdQiuC9fM4GbZmYNOexoKHw1D5XGIQTDO4LVYncPesyPb55Ofko085/8UHNhrZmTzYwsC6/sredHq6cRGRbKo38/wqycRO5dmE9idAQfn2ojNS74vecnR/Px95axs6qDj0+1YbG7qGjr5U/3l5zXZ70USMGQSCTnTEOXlb/uruW1ffV0W51MzYzjV5+dxdq5OcPO4DGa7bxzVC2o87mB5uQl8tjqadwyO3tEWmD4LIzaDgtOt0eb2a1Hn1arr5GYmRPPsUa1qrunz4nN6fYLXs/LTyI70cBr++upaO0lJ9HAK3vreYV6fv3Bae1YvVXy5G0zsdpd/PLdU2TERxIWGsL2cnUWxq7qDlZMy2DFGLUuQAqGRCIZJkIIdlV3sL60hg9PtKIoCiunZ/DA4kIWTkgeltupx+rk/eMtbChrorSqHY/wH2N6rsHws2HqU91MvtGpRUFqPDos/cFsq7PfypiXl+QnGAOzpGZmJ9DlFYVb/nuH375Oi4OV0zP44EQrf36ghK+s3w9AUWqM1rn2SIMaXN/mFYywEIXHb51+/h/2EiAFQyKRDInV4eKtQ02sL63hdKuZpOhw/mlZEV9YVEC2t1BtKCx2Fx+dbGVjmTpgyOlWW3c/dO0kbp2T7TfG9EJo7O7jcF03q71dYgG/DrCVbb0BguHxCDotDlJjI2jvdWiZUgDFmf331dPnJKzHXxBX/Oc2rUU5QIgCHgFLJqdSWtXBQ9dO4oMTrX5Dj5JiImlpqXwAACAASURBVMhMUK0cu8tDc0+fFl+5YXrGObnxRgMpGBKJJCj1nVZe2FXDa/vqMdlczMiO56k7ZrNmTvZZs3fsLjdbTxvZWNbE5pNt9DndZMZH8cXFhdw6J5tZOQkjniL6yp46ntlayfXTbtTuz6QTgMq2XlbN8D+nu8+JR6jxhPZeB732foHJSex3if387RM09wywMHLieejaSfy/j8qZkBpDfnI0W0638Z+fm8vCX3zERydbyUk0aM0KAVJiIkjSxWN8FgzA3VeO/R55UjAkEomGEIKdlR2sKz3D5lNthCgKN87M5IuLCykpSBpykXe5Peys6mBjWRPvH2/BbHORHBPB7VfksGZODiUFSRe1D5LZ5kQINTbie1I39TmJDAshKTqCqiCZUp1ed1RBSgwH67pp09Va/Ord/o61A8Xim8uL+MGNUwE41tTDxrImr6gmkBYXycycBPZUdzI1M46DujTdpJgIvziKr+FhgiGcayalXuhXcNGRgiGRSLDYXfz9YAPrd9VS2dZLSkwED187ic8vLBgy+OzxCPbXdrGhrJF3j7bQYXEQFxnGqpmZ3Donm6uLUggLEmi+GPR6s5zazLZ+wbA5iTeEMyk9NmimlC9DavPJVgDu/dMebZ9+TOvnSnIxhIeyflctAFfqej1Nz4rn5T11nGw2cd3UIgAWTkhmfWkt911VwOZTbdqxA4Puvn1fWzJhVJsKDhcpGBLJp5iadgsv7Krlb/vrMdtdzM5N4Dd3zmH17KxB3U5CCI42qk/Vm44009xjIyo8hBXTMrh1TjbLitNGpeDM6m3toa/INvW5iI8KY1J6LH/bX6/N1j7TbmF7uZGfblSHH+ldVz5OPXkjEx5VOx65PMLPyijSdaidnh2v/TwzOwGARRNT+OMnZ7A5B0/VBdjrndz3uSvzhjxurCAFQyL5lOHxCLZXGFlfWsPWciOhisLq2Vk8sLiQeXmDN+srbzWzUTfGNDxUYVlxGj+8aSorpmWcUxX3xcBXeNemy2byWRiZCVFYHG6+9sJ+Treaqe/sC3qN2+fn8n8HGwC1bUd6XCRtZntA4V9GQn9l99TMOBRFbfUxM0cVjJLCZBRFjZEEY1pWvF9sY7TnXAwXKRgSyacEs83J/x1o4IVdtVS3W0iNjeTb103m8wvzSY8PvmANNsb0G8uLuHFG1ojM0x4pLF7BaDXb8XgEJ5pNfFKhVlYfqlOn6310so0V0zJ4cMlElhan8eahRv7fRxXaNXZXd/hdMyM+ijazXUuB9REZ1m9BRUeEMSElhvZeO7lJatZYgiGc6VnxtJlsRISG4HB7SI/rF5m5eQmaYNy7cOwHu31IwZBILnOqjL28UFrDGwcasDjczM1L5L/unstNM7OCNuxr6bHx9tFmNpQ1UeYdY1pSkMTP1szg5llZpMVFBpwzFvDNxP7frVW8vq9ea/kBanZSh8XB7++dxy2zs7XtAzvT6tNkATLiIznaCIuLUogMC2HLaWPQ975tXg7dVqefdbZoYgov7q4lPyWayrZev3bos3MTeWWvOp36uinp5/mJLz1SMCSSyxCPR7C1vI11pbVsLzcSERrCLV6305y8xIDjOy0O3j3WzIbDTeyt6R9j+uhNU1k9O4vcpLFXH+BweThQ28X2CiPby4209/Yv/ksmp7K0OI3vvl4GqJZCh8WBdUD7j47ewFbmenyW102zsrhvUQGFP3w76HHfDtKxdtHEFP684wxWr+WjL0rM032fE9NGtljxYiIFQyK5jOjpc/K3/fX8dXcttR1WMuIj+d4Nxdy9ID/AMjDbnHxwvJWNR84+xnSsoHZ3NbKtvJ1dVe1YHG7CQhTmFyRpx0xKj+X/3T0PIYQmGCe87h+TzT+moK/y1lPivV6GN7bQZrIFjFY9G4uLUogIDcHo7YarF4bYKHXpDQtRxnyxnh4pGBLJZUBFq5n1u2r4+8FGrA43JQVJfH/lFG6cmemXymlzutl8so2NZec2xnS0sNhd7Krq0KyIGm+qa16ygdvm5bC0OI3FRSnERYVT/ON3cbg92kxum9MTcL2Bc7kHszBunqVWi2fEqyLbarJpLq+cYVS3A8REhrF4Ugp7z3TidLs18QG06YL5ydFB+1uNVaRgSCTjFLdH8PGpNtaX1rCjsp2IsBDWzMnmi4sLtWwd6B9juuFwEx+eaPUbY7pmbvaQmVGXGt8MjG3lqkDsr+3E6RYYwkO5qiiFLy4uZNmUdApTov3u2eHy4HB7iAoP0RoF6q2JnEQDjd19AYJR32UlGNdMVovo0r2C0Wa2c9Q7WGmW7rs9GyumZbDVG/dIju2v8PYJxnhyR4EUDIlk3NFjdfLa/jr+uruW+s4+shKieGTVFO6+Mo8U7yCfkR5jejHp6LWzo7KdbeVGPqlo1yyEqZlxfPnqCSwrTuOKwiS/zKSB+GowJqTGcrLZhNFs96uBeHDpRB7fcJzSqv4sKIvdFdQKAbSZ2r5011aTTjByhy8Y109L57G31J+To3WCEeUTjLHn9hsKKRgSySjy3rEW0uMjmZ+fdNZjT7eYWVdaw1uHGulzulkwIZlHb5rGyukZhIWGIITgQG0XG8v6x5jGRISyckYmt87JGnSM6aXG6fZwuL6bbaeNbK8wcrSxByEgKTqcayansdQbsM4YJNU3GL4ajImpMZxsNtFm9m/lscBbma2vpxg45U6Pr/AwwaCmDbeZbBxtOHcLIyvBoLVJ18/0iI0M49Gbpo7pVubBGBHBUBTlRuC/gFDgT0KIXw3Y/13gq4ALMAJfFkLUeve5gaPeQ+uEEGtG4p4udx58YT93luSN2clckuHx2FvHmJ+fyHODDM1xuT18dLKVdaU17K7uJDIshM/My+H+qwqZnh2vuXA2eAvqGrv7x5iumZvNtVPOPsZ0JBBC8OLuWm6dk01idODAoPpOqxaHKK3swGx3ERqiMC8vke+sKGZpcRqzchLO2+rxDT+akKq6eFpNdgy6avPMIOLToqvcnp2bEFBrAWjfXYfFoVkYvnndw+WmmVlUtvUGJB18fVnROV1nLHDBgqEoSijwDHAD0ADsUxRlgxDihO6wQ0CJEMKqKMo3gKeAu7z7+oQQcy/0Pj5NWB0uPjjRSlpcpBSMcYzJ5qS91x60GrjL4uDVffW8uLuWxu4+chIN/PCmqdxVkkdSTATVxl7+30flbCxr8htj+t0bilk5I4O4cxhjOhI0dPXxk38cJyRE4fMLC+hzuNld3aHGIiqMVBstgBpLuGVOFksnp7F4Uqr2BH+h+CyM32+pBNQgte+JPi4qjMQgBYZ6wZiXlxhcMHSi43uPlHOc/vf1pROH1eF3PDASFsYCoFIIUQ2gKMqrwFpAEwwhxBbd8buBL4zA+35q8f1Df2lPHf/+mVmjfDeS88W3iPZY+wXjeFMP60tr+MfhJuwuD4uLUvi3W6ezYloGLSYbr++vZ0NZE8eb1DGmCydc+BjTkcDijSH8+v3TvHu0hb01nThcHiLDQlg0MYXPLyxgWXEqRWmx5xRgF0LQa3edVQB9MQwfP9t4gifXqr3MJ6bGBH1PvUtqsDqTYIv8uTYJDAsNGVeps0MxEoKRA9TrXjcAC4c4/ivAu7rXUYqi7Ed1V/1KCPFWsJMURXkQeBAgP3/8lNJfDHymsWR8c6Zd9ae399p5+0gz60tr2FvTiSE8lNuvyOWBqwpJjongnaPNfO7ZXRzQjTH9yS3TWT0ra0TGmJ4v3VaHGqw+beRvB9T+S11WJ21mG/cvKmDZlDSuLEy+oCfrLafb+OZLB9n1w+tJiongy+v2EaLAnx640u84X1sQPb7geVlDD4t/uVnbLoRAURQ/C8PX0mMgYyUxYKwwEoIR7BsNWuGiKMoXgBJgmW5zvhCiSVGUicDHiqIcFUJUBVxQiOeA5wBKSkrOrYLmMkPfX18yftlXo/4eOywOHnr5IPnJ0Ty2ehqrZmRSWtXOk5tOBIwxvXV2Nvkpo/O06nJ7KGvo0VJejzR04xEQHxVGUnQ4XVYny4rTWP/lBSP2nqdberE5PbSYbCTFRPCxrlW4nl57YFdY3+hTUIXMh83pwRARysG6/v9HQ00OTDCEk5No0Ir/Ps2MhGA0APrevLlA08CDFEVZAfwYWCaE0MorhRBN3r+rFUXZCswDAgRD0s+BOikY45mjDT2sK63RuqICPHXHbMJDFd4+0szP3z4JQOF5jjG1Olzc9sxOfn7bLC076Hxp7unzVlYb2VHRjsnmIkRRrZxvXTeZpcVpzMlNYMtpI197YT/RIxxg92U7mYO0H9cTzMIo08Uk+nQptj19TgwRoRxv6heAf9twfNBrG8JDmZWTQKvJxtXjYMjRxWQkBGMfMFlRlAlAI3A3cK/+AEVR5gHPAjcKIdp025MAqxDCrihKKnA1akBcMgT6sY6S8YHT7eHdYy2s23mGg3XdAQvrD944AkBWQpTWKvv1r181aBfZoahpt1Le2suRhu5zFgyb083eM52aFVHhTUPNjI/ixpmZLC1O45pJqQGZUL4YwsDCuAvF51YyeYvxfPjcSj4sjqEFRU9dpzUgc8zXZBFgfr5/ry1DRChHG3vo6XMOa4b55cwFC4YQwqUoysPA+6hptc8LIY4rivIEsF8IsQF4GogF/ub9JfvSZ6cBzyqK4gFCUGMYJ4K+kUQyDjGa7by8p46X9tTSZlbbX187JY3w0BA+ONGqHbegMJnvr5pCSUES3371EJuONNNisp2XYLR6n8qDDQUaiBCCKmMvW08b2V7Rzp7qDuwuDxFhISyckMznSvJYWpxGccbQwWqrQ13ML5ZgmO1OvxqKTotDK1IEaDcP3UTwkVVTePr90wCcajHxb/845rc/JSaCn66ZwbdeOcRNM7P89kWFh3Ki2UROooEvX114IR9n3DMidRhCiHeAdwZs+zfdzysGOa8UkGk+ksuOw/XdrC+tYdORJpxuQXREKLlJBix2l1+L7LioMMw2F99dWaxZA8e8SQ3tvcEb450N3wAhsy344t3T56TUW1m9vdxIkzf4W5QWw70L81lanMaiCSnnVL9xsQXD1OfSvheAirZeP8Go67QMeZ25ug69j284jhgQBb2zJE9LIBhoRcRHhREfFca6L115XgJ+OSErvSWSEcLucvPO0WbWldb6uThAXVA9wq6NMQ0PVfjyuv388/WT+fnbJ+n2BmVNNqfWYM+3WJ4rvhGlpj7VwnB71JGqvsrqw/XduD2CuMgwrp6UysPXpbG0OPWCWpj3XWSXlNnmpEKXBlveambRxBTt9Zn2oQVjVm4Cq2dn8faR5gCxALh3QT6JMeEsn5LG/AJ/l9QvPjsLhfHXxuNiIAVjnNFtHdr0llx6Wk02XtpTx8t76gKsAnWMaTq3zsnyG2P61101AMzztgTp6VN/r/qn6PMXDHVh/b+DDdhdbnZUtnuH+8DsnAS+ubyIpcVplNV3c+PMzBGZdeGzMMw2F26PGJF01D6HG7M3mG2yuSir7/9uTreY/Y6t7QjeRNBHfFQ4X1hYwNtHmgP2XTc1Xcs8W/elwAyvsdjmfbSQgjHO8LVYlowuQggO1nWxrrSWjWX+SYGKAlcXpbJmTjarZmQGHWNaZbQQHRHKlEw1+8lnYegFw5d2OxzsLjf7a7rYXm7kpT112vY9Zzq5fmoGS4tTWTI5TSvuazPbuPMPu+iyOnhk1dRBr7ujop3izNizzpz2CQao1kCw9iDnil58zTanX/2Rb+SqD5duVsWKael8dDIw/Xag5QCw5fvL/UanSoZGCsY4o6K19+wHSS4aNqebTUea+Y/3TgVYACUFSdw6J3tYY0ybe/rITTIQExFKeKiitQc5qsuAOzhE+rQQgjPtFi3ldXd1J31ON+Gh/U/2igJ7f3R90GB1Y5c6irSmffAHkC6Lg/uf38PD107iuyunDPl59JXWPX0jIxj6BoI+95qPoWoiVs/OCioYwbrdDmyTLhkaKRjjjEP1/ouIxyPOuVWB5Nxp7unjd5sreWVvnd/2iakx3HVlHrfMyR72YB3wLqqGCBRFIcEQobka9RbGwNoDs81JaVWHFqxu8C76E1Jj+FxJrhqsnpjCtb/eSpvZTk6iYdDF0De7unoI3/+eMx14xPDiEnoLYyTiGEII3jjQqL0eOClvKBZOSDn7QajfmxSLc0MKxjjjQK2/KW5zuYmOkL/Gi4EQgi2n2/jyuv0B+/5lxYWNMTX1uchOVN08SdHhdFudmGzOgODt0YYetlcY2XbayMG6LlweQUxEKIsnpfL1ZUUsm5zmV/ntcns0V45piIW7qdtnYVgCahp87PLOjghWRT2QvhEWjE8q2v3EeaALajAmpMYEtEUfTBP0QXPJ8JArzTjj5ABTvM8hBWOk6bY6+Parh9muay0BcMvsLL65fNKIjDHt6XMyNUuNXyR6BeN4kILMW3+/A4CZOfE8uHQiS4vTmJ+fNOhciw6LA49Q21mYbM5BLdD6TlUw+pxuWk32oD2pfMOGglVRD8Rn7fg+24Wir4KH/k6xZ2NefmJAwH1ObmDsAmBZ8ae7avt8kCvNOCUsRMHlEZhsLr98dMn54XB5eG1fHT/5h3+LCEN4KH9+oISrilIuSCQ6LQ6e3VbFP6+YTHREGCabk/iocBwuD6eazZjtLnZVdwSc98vPzmLFtIyzxkR8+DKkfIt2r8NFfJBOr7t171Xd3hsgGEazXavyHk4VtT4ZI5hg2F1uIkJDhvUdPrnpBP84HNBdaFjMyA4cbjRHNyHPN8woRIHFn/I2H+eDFIxxyqT0WE61mOm0OLShMZJzw+0R7K7u4MdvHtVqH3z88/WT+fb1kwdND/3XN46wamYG100d3jyS//qonPW7apmVm8CM7ATMNhfrSmv42/56LI7BXT7XTEodtliA/4wHUOMgwQSjQlc1fbC2i8VF/ounT7wSo8OHZWEM7NXkY93OM/x+SxXtvXYeurZoyIwsUIPnf95x5qzv5zs2OiIMp7t/zGqwrrP6QrzpWapgzM9PCvq9SIZGCsY4JSshilMtZlmXcY6o6bDdvL6vntf21/vtm5oZx7P3XUFBytACbHO6eW1/PaGhyrAEo7LNzPpdtQA8/PIhv32fmZ/DJxXt1HZYmZgag8nm8ksnrW63+M1SEEKw8UgzK6dnBG0b/qM3/VtemPqcZw3G//fHlTx83WS/bbuqOoiNDGNObiLNPX2DnBkc/XyPj08bCVEgNTbCr7XHYJytxYeeNpOdwtQwv+/LJxhzchO05oNJuowt38CmpcVpw34fST9SMMYpvv8E+rbNkuAIITjRbGJjWTN/2BbYCPmJtTP4XEnesOc2+J7iOwZp3eF7v+3l7Wwrb2N3daff/q9eM4E/7TjD03fM5s6SPH7/cQW//qCc6nYLUzPj/BbAt480sUy3uO0908m3XznEr++cwx1X5Ppd1+0RAYWDwQLfA1uG2F0ev9cdvXbePdbMVUUpfKjrdzVc9BaG2eZkSmacWoQ3jN5Wxt7B52wHHmunMDXGr2gvN1EV10npcZpg6OtgfFaFFIzzQwrGOMWX5y4tjMGpMvaysayJDWVN2nQ7H8uK03j4ukmUFCQF+NU3HWliQWHyoH2Dmr2C0d7b/9139NrVYULlRraXt2sLt+/pfmJaDDfPzOKZrZVcNzWdP+04oz3tJuiegFNjI4H+KubX9zfw1B1ztNc+V5Fv+JIe/QQ5H8EaEPpSavXUdlg0y+rnb5/EYnfxlWsmnJdg6FN1zTYXWQlRhIUoft/XYJxLnVGbyU5Dl9UvOSHeoC5pyTH9IpGoGwO7amYmPX1OZuUExjokZ0cKxjhC6JrgvH+8BYCuMSoY/zjc6NcK41LR0GVl05FmNnrHmOoJUeCbyyfx+UX5ZCUEd9P0Odw8/PIhvn3d4MVqLSZ1wT1Q28Wv3z/NtnIjx5p6EEJNkV0yOY2lxWksnZxKc4+Ntc/s5NGbphEbGcbvt1RqQ4DivQuZfkFLjQ0seLM53Zr14wtWDyy4c7g8VAVx+QRrQOhLqZ2SEcfkjFg2HWnmwxOtfHXJRD6pMPLmoUa+ff3kYc/b9nj8mzMdaehPgTXbnBjN9iGr1nvtLqx2F+nxUec0TfI3H5wOqCPxib++cFD/c3FGHI/dMn3Y7yHxRwrGOELvOvA9JY5Fl1Rdh5V/fvUwv/jMLO5dePHH6RrNdt4+0sTGI83aGFM9s3IS+OLiQlbPzjqr28lXIBbsab2+08r2CiM/1sUJ/ndbFfPzE/nuimKWFqcxMyfBL1C+54zqjspLNlCYEkN4qMJHJ9Wn9gRDOL/54DR7z/S7rJyewM54h+u7WTQxBZvTzUFvPUJNh/9C+YM3yngrSGZRMJeUL323ytjLqhlqDOb1/fV8dclEnt1WTU6igW8uL/K7L7vLrVVKV7b1cryph7VzcwD/gDeoE+18tJrsWjPEwXj6vVPsqGxn8/eW+xUuno3qdgv3X1VAeas5wO2X5CcYMrg9UkjBGEfYnIHZNGPRJdXpvadzDZaeCz1WJ+8db2ZDWRO7qtSKZD1hIQo3z8rigcWFzM9PHHZKrG+BbTHZsTpc7KnuHyYUrCp696PXD5nFVN+lWgK5SdFEhYcyOzdREzWrw8Xvt1T6dU89YwzyHtUdLJqYwoHaLhwuD3nJBmo7rH4Fd4NVbAdzSX1S0Q6o/Zf+ulsNxpe39tLea2dXdQcPLp1IVHgo+3Xia7X3C8afd1TzfwcaWTMnG0VR/Kq89biDiF8wTjSbqDJacLg8WtzBN0RqKH62ZgYPLC7ksbeOBgiG3iU1XEtJcnakYIwjBj7JAXRZRtfCsDpc3myV/swin4i1BnlKvxAsdhcfnWxlY1kT28qNON2C1NhIYiPDtIUxNTaSzy/M5/ML8895doEQgr016sKzvdzI3J99iMPtISo8hEUTU/jCogKWFqfxi3dOam4lh9sz1CU56l0AN59sZe3cHBZMSNYE4782q2Lha5Y3KT2Wky3q078hPFT7fe+u7qDX7uLzf9oDwJ1X5PGfH5bT3uvQxKp7EEszmIXh+4zgb6H+9sNy3B7BB8dbeGTlFP774wptX6/dRZK3cWFNuxWH24PZrqbsWgep0+gNIlZ695oPn7tKnxIshJrxpC8IBHXx9wXV873ZY+8cVd2zt83N1o7zuaEM4aHDTmaQnB0pGOOIviBPcqMZw3C5PTzw/F5qOqzs+3H/jKwe3VP6hWJzutl62sjGI01sPtmKzekhKyGKZcVpdFmdnG4x02t3MTcvkS8uLuTmWVmDVkEHo8viYEdlO9vL1VkRevfJA4tVgbiyMNlv0fGJBajB7qHSVrecVo890Wxi7dwc7r4yj//dqmZq+YK1sd44z21zs/n1B+WAWmfj8+fvru5k5uPva9ec5S1Eq+2waIIx2L+DTsvg/z6mZsbR3GPTfl++VhxVRgsv763ze8LXF+/5ivRae2xEhoUEtTDcHhG0/5PZ5hp0AT/d6t+y/OqiVM50WPxcY8UZsZrAVLdbuFb3Ga+f1p/i7HNJSXfUyCIFYxwRrIJ2sCfLS8GvPyhnX00XIYr/jGXffbadp4XhdHsorepgw+EmPjjegtnuIiUmgtvn55JgCOdoYw8fnWwjPFTh1tnZXDM5leunZQzL9eByeyhr6GZbuSoSZQ3dCG8rjWsmp9JlcWgtMb63cspZn047zpL54/PnN3hbcQSr8fDFHk7qZjwUpcUMGgDO9QrUmXYLJYXJuNyeQVNWfS6xYNw2LweHy8N/fqiKlN6D9PgG/4p3X22F3eWmyetqvOG327luajoPXTsp4NpmmzNoOw+zzTmoC29nZbvf68WT1Kl/esGYnBGnCcbATLEcXdFektclJd1RI4sUjHFEsKfF0bIwDtZ18YdtVcRHqe4gu8ujLa4+ETsXl5THI9hX08mGsibePdZCp8VBXFQYN87MZPmUdBq7rby8p46aDivpcZF894Zi7lmQT2psBPOe/JBqo4Xvrwqe1dTU3adZEDsq2jHZXIQo6tjOf75+MkuL05iTq/YgemFXjSYYbSa7X2M/CIwj6VtwD8Slc1c16Bbua6ek+Y1p9aEf7lOUFss3lhdp1gjAksmpfFLRTnefk9AQRas/GBin+Mo1E7Rq6YEV7HpWTlez2HyCoUcMCCD4RKK+s8/P8iir7w5q+TZ09QW1PIaqxXjjgH//qMVFqQGCPCktlojQEBxuD9VGi9/vI0vX3iTRoFoYSSPQZl3SjxSMcURHEMEYWHR1qTjuffq976oCntlShdXR75v2WRhdVmdQn7UPIQRHGnrYWNbEpiPNtJhsRIWHsGJaBmvmZJOTZODVvfU88kYZVoebKwqS+N7KKdw4M5PwUNXtZLG76LY6/b4bm9PNnjOdqkiUG7U2GJnxUdw0M4ulxWlcPSkl6MwGvcXWarYFCMaGAZlIZQ093HVl8O+oWeeTr9f54vXCf8vsLDYFmQJXlB6LITyU/0UVjAUTkvnN5+aw4N83c6iui9wkA2e8mVIDHxpWTs/QBGOoqX2+kaPpcZG0DTjum8sn8fstldprn4U0cHZ2h8VBY3egKB1v6vHWlPgzUDD0gXG9RTIlI460uEhumZNFr92liVp6fCTTs+M5XN9NtdHil1Wlf7+IsBDiIsOkS2qEGRHBUBTlRuC/gFDgT0KIXw3YHwm8AFwBdAB3CSFqvPseBb4CuIFvCyHeRxIU/UIznCySi4lPFHx9eix2lzbNTb/oGs12v9YWoI7X3FjWxMYjTdR2WLUxpo/ePJXrpqazu7qT9aU17KhsJyIshDVzsvni4kJmBim28j2BHmno5k+fVLO9op091R3YXR4iwkJYOCGZu67MY2lxGpPTY8+aLaUf/TmwLxPA8zvVhfjLV0/g+Z1n/NwlA9G7gzotDq77zVbWf2mBlgl03dR0Fk5M8ROMiLAQHC4PE1Jj/IR23ZeuJDoijMKUaPbVdFGYEkOtVzAGwXgXZQAAIABJREFUuiWzh4ip6PsuPfTSQf5pmTqudeDTfUFKtF/g3Rd81ldVLy5KobSqg8P1ga6z6nZL0IFFHRZ/YQoWGAfVHQWQHhfFt6+fzHvHWjjRbCI5JoLClGgO13fTYrLx3rEW7RzfQ4SPq4pSuKIgKej1JefHBQuGoiihwDPADUADsE9RlA1CiBO6w74CdAkhJimKcjfwH8BdiqJMB+4GZgDZwEeKohQLIc7egP9TiF4witPjAoKEl5KePieG8FDN9PdvPtd/ny0mG3nJ0dR2WFSRKGvmdKuZEAWunpTKQ8snsWpGJqDWAtz8u0+o7+wjKyGKR1ZN4e4r84J24+3pc7Kzsp3/+kjN5DneZOJ4k4mitBg+v7CApcWpLJyg+sDPhcP1/UVnwVxqp7yCcv20dJ7feWbI/kj1A8bpVhstfm27r52SxlUTk/2OcXgtxoz4KL96juiIMGxON1My49hzppM1c7I5UNuFEMLv+waGTPPVi8snFUYO13fzlWsmBBy3q6rD73fqq/vRC4bv91JWHzirorbdSlqQ39vArKfBBiMNbIYYF+Wr4I7wm0G+oWzwrrbP3V8y6D7J+TESFsYCoFIIUQ2gKMqrwFpALxhrgZ96f34D+L2iPuqtBV4VQtiBM4qiVHqvt2sE7uuyQy8Ys3ITRl0w4g1hRHsXZH1HU/2i9It3TuLxCO2puqQgiSfWzuCmmeoY09MtZn713ineOtRIn9PNggnJPHrTNFZOzyBM98To9giONHSzvbyd7RVGDtV1BdRe7Pzhdec09S4Y+rYZQ8VgZmTHn/Vax4LMt9DP/y5IiaEoLZbU2AgsdrffAp1oCPcb/GNzulny1BbNxbSjop1eu4v2XkdAavVAF6A+IUH/+f7ypQXc+YdS3jvewkAGtgTxxWBqdQWDEaEhxEaG+Y1LzUk00NjdR02HJcCdBwRUow+WxbVwgJD6quKTYyL83Ew+V9rsXNnq41IwEoKRA+jbfjYACwc7RgjhUhSlB0jxbt894NycYG+iKMqDwIMA+fkXv3r4YiCE4H+2VvGZeTlDug0GQ/+fa2Z2PG8cGLl7M9ucxEaGDbvArafPSYIhXBMMX+Czo9fuV/B1qK6bmTnx/OjmqayerY4xdbk9fHSyjfWlNeyq7iAyLITb5ubwwOJCpusW4laTTSua21HZTrfViaLA7JwEHr52EkuL0zjVYuaxt44xPz/xgsViILVDBIyDZd8cqO3i41OtWgvvgVk/xRmxlOt6JaXFRaIoCgsmJGu1BD4GDj16fX89RrOd66ems/lUm1ao19jdp80Dh34/vm/hBtX68w3ZOt3Sv7hfUZDEN5YX8cyWwIaMZu8DQElBEvtru7TgeZWusLDL6mBSeqyfVXb7Fbn8bnMFp1rMFGfEBVy30ugvGM1B3H5AQOtxn4WRFB0RtC35tMyzC7jkwhkJwQi2wgz0rg92zHDOVTcK8RzwHEBJSckoeu/Pn4auPp5+/zSVbb389q6553y+PrAbrCjtxd21vLavno3fuuacrtvU3ccN/7mNJ9bO5PYBHVAHw9Tn8gqG+k/or7treXZ7NTsGLJJLJqfy16+ozw9dFgf/u7WKF3fX0tjdR06igR/eNJW7SvJIionA7nKzo0K1ILaXGzX3T1pcJCumZbC0OI1rJqVqsRLob70xWLXxhaAvcBtIMGHdWNbEutIa7lmQT25SdED19czsBD/B8C3uiyamBAjGQH63uZKshCieu7+Eoh+9o21v6u6jRxf0npCqPtVPTIvRBMPU59J+TwNbp3zlmolBBcOHRxcoc3uE36CkDouDYp1gREeEUqKLGQRrrzKwuWCwRogrpqUHbMtNiiYn0UB4aIjWYFBPRrwcInYpGAnBaADydK9zgYGORd8xDYqihAEJQOcwz70kvHesmd98UM7b315yToVf54LPVbOxrIkf3Dhl0AZ4g9HpDRhmxkdpT1x6DtV1c7Sxx6/vz3B4bns1Fof7nFp5tJptVBstfPf1wwC8e6yF3CQDDy6d6JcK+klFOyeaTKwvreGtw43Y/3975x0eR3W2/ftsX61WfdUlq1iSLRfZQu7d2GAMxoBpAUIPJSEJ5P0CJgnvmwQIoYRUEkJIQkkIJUAwLYBNsXEB27hX2bIt2ZKsXlfSarXn+2PmzJ6ZnZV2JVmS0fldly6NZmd3z45mzzPnKffj9WFWTjweuKgQS8Yn4kSDG//ZeQrrD9diS1kDOrqlzmwlWbG4/4JxmJ/vwrjk4C1RmSrsmTAY2mCyVmSP0drZDafNrGQrbT5aj0unqg16WowdbV1eTEiNUkQRmeGbkR28t3R2ggPH6tpR19aFOxfmwmggyioDkAwGX63NjFB2gkORAGnt7FY66mkNRpzDoryHHl9xvbR5AxDvsKCx3YO8JH9P87GJkRjDuaH0XHpatYL9lYFuu+tnZQXs+/bCXFw/awwA/+qDz+4Kt6pf0D8Gw2BsBZBHCMkGcApSEPsazTFrANwAKTZxOYCPKaWUELIGwEuEkCchBb3zAHw5CGMKm8+P1KG0pg0nG91KuuFg0yQHJ70+iuc2Hsf9y8eH9fwGOSMox+WAU2dZzmoCGtu7kRwdmsGoa+tSKnx50Tg9PF4fNpTWquTCWcrqlSXpeHTVZHR5fSqDAQDLf7cBNrMBlxWn4/Jz0lDb6sH60lo89O5+JQianeCQs5kSMDMnPuQ+5SxLajAMhkcnRZn3/7s1k920rFhsPd6InRVNmJfnUlyGm8vqVS7HeIcFBclOVDR24JrpGUobWBbUzkuMREacXemzzTMhNUqZzFcVS97aCyalKAbjYHWrahJmBoO/GdlyrAF5snuIuZQiORXhqRkxaO30IjshAm5PDxKdVt06kROcUclKcOBQdSvyEv1uJ6vJoFIIDqUOZ0d5oFjk9Oy4gH02TuKDxTMKkp1+gxFGV0JB/xmwwZBjEncB+ABSWu3fKKX7CCE/B7CNUroGwF8BvCgHtRsgGRXIx70KKUDuBfCd4cqQYl/K8oYzZzDYnWBhShRe+qIcdy0eqzvxB4O18kx0WnVXGCwgWt/eFdCjORh//fwYPD0+GA1EV9yQtTF9Wy6oa+7oVoKO45KdeOiSibj86c0oSI4CIQRHawOzhqZkxGBaVix2VTTjqj9vgddHEWk1YVZuPO5YkIsF+a6A1NtQYWmaHSH0ne4L5m7h/f8tnV4lXsEC+3Z54irJisPW443YdrwR8/Jcyopk89F6JbYDABlxEciItWPrsQZVjIZhMBBcO2MMfvn+wYDHmAGYkhGDP356FBdNTsHsXP+KZN2B06oVMcuQ4sX3/r7xGK6dnqmKi/DpplMzY/DGjlPo8vZg7tgELCpI1DUYfPyBrVRSYvzX2dbjjSoZ875uQAC/aKLDYlSu776q69n/Y0JqtLKKShIrjCFhUOowKKXvAXhPs+9/ue1OAFcEee7DAB4ejHGEy66KJhQkO2EzGxWVUG0q5GDCfM13LMzF9/61A9uON2LRuEB/bV/ERFh0DQa72+pLroLnvT1VWJDvwqeHarFVngSkNqaNeHtXFd7ZXYW6ti44LEacNyEZFxelYlZuPMY98F9QCnzjL1LOwpfH6nGgqiUgnx+QUlV3VkjB79sX5GB+ngvFY2ID8ub7g7LC6O5RrQb6w75KKZOrKCNaMRinWzqVCapG1pnKSnDgd+tK0SVPiAfkLKGGdg/MRoKq5k68utV/HjLiIpAeG4HWLq+yitAGza8syVAMRmxEYH+Mn1w4Hpc/vRmN7R78/abpiLAY4fb0BMjbMwMT5/DfcZfVtuOz0losKvBfa9OyeIMhbbd2epEZF4GlhUkwvklQlB6NIzVtSiX5jvLA9Nn7Xt+jbI9LduL2BTm455VdAcf1RX6yEwerWhWdrN5gLimX04pvTM/Ev74sFwZjiBi1ld5ltW1Y+dRGPHBRIa6ZnolKOVuj/AwaDPblniPfIR6sbu2XwXBYjQGZIh6vT3GJ9CY4p4WvvN1V0YRH3j+Ad3ZV4VRTBywmA84dl4gVRalYPC6Rk/6QXp9P6/1gX/DObPPzXXjyyiLdyt+Bwrq4USrd0fZWd+Hx+nDL81tx95J83YIuFrwtSo9B6ek2lNa0YUd5o5LtwyqrCYAnPzqsSFEwhdlGtwdzxybgk0O1KhXbzDi7soJ6c8cpAJJbkYcP5OdyK1w28bPU1S+ONaC7x4dLpqbhpS/KAz4DMzD8CiMpyoq/fX5MZTCKM/2ff1yyEzazAZ3dPmTERSDWYcHV0zKQEm1Dl9enuJl499GY+AicqHerajD+/M1zFEXbcBnrisSb354T0rGR8s1SS0c3Ep1WEKLfeEow+JyZ6O5ZAMs9336iQdWM5kwajCa3lLoaH2lFarQNB6sDA36hEGk1w8q5IXw+ilqul7OehEgwGto9+JRzP/x1wzHkJ0XiySuLsP0nS/Cn687B8knqxkNHdXo2aMmKj8APluYDAAqSIs+IsfD5KBrauxAlTyDBZLYZlU0d2FBah89L63QfZwbjkfcPIkL28d/3+h5FfuK47D5hhpKlhFY0dKCzuwduTw9KsuICCucyYiOwsMCFGdlx+ExWqM3VcXveJYv48TUIzJCw/5Hb04OdFU2YnhXo5weABPm9eQ2ly4rTsaG0TqX5ND7F7xozGQ2YnB4DwC8Z/vClk3DX4jxkcEVyvC7VEk4ZlpEUZdNNeQ2F3MTQ3cBGA0G8w4KKBjdqWjsR77CqanYEZ45Re5bZHfGuimYlfpEcZUO5TuBxsGhye/z+/5QolQxFOERajSrXS5fXp1KGbWjvXVb8ZKMbT392FMt+sz7gsS9/vAR/v2k6LitOV8VX2rq8+NWHh5C1+l2s+tMm3dfd+7Pzcdv8HADAFSUZmJsnVeuuO1Cje/xAaeroho/6xff6CnyzjCrWYlULf7dczWWM3fbCNnR29ygGo0eOwajGIq8eYyMsKM6UJt/zCqVJNTNOap70wi3TMU8+J3k6E+Q1MzKRFmPHqmJ/anO8fOe86Wgd0mPtIESq7/jogP6KjlVXx3MuqTjZeNRxNxVajaWpmWqDwQgWCztXJ/XV2kt2oc3c+1SjZ0B7Y0GBCx8dOI2TjR0ipXYIGZUuqermTuyqaEJKtA2nmjrwhdwneX5+At7bUz1gXzgPE1czGgiauIBxQbIT6w/XwiNrHoWDw2pSJDEAKVWRF4/Tc0nVtHbivd1Vqjam+UmBX1J2R+vzURyobsHa/TX49dpANVPGhnsXYd5jnwCQMm/Y3fiE1ChMlrWfgnWDGyhaw6jXYIqHTZjBisX4rNlouxlN7m74KEVlcyeO1LQpLikAuH1+Dv6yoUwxVuycxznMiLSa4bAYMT07Dh/uP43kaBu2HW9AWV07Vl8wDhtKP0eKTpFhaowdG1cvVu1j/4/Obh/m5CbgQHUL/ru3OmiVP1vd8DEuk1G6lvlrRHt9XzM9E0ZCAgxGMLXXcXKhXEq0TTmfwb4zZiNBfpITu+Vqf71rPjUmvBjEismpeOOrU9h0tB7z8xL6foJgUBiVBuPD/ZI76p4l+bj39d14e3cVUqJtKEiOwqvbTqLR3a3yKYdCj4/iN2sP4+Y52So/7g9e3YkOTw+eub4EjW6P8gUcl+yE10dRVtemfPlC5VB1K/68vkz5u5MzGLERZsWv3+T24L97q/H2bn8b03HJTvzw/AKsmJwKu8WIaQ+vhYH4J8v/7JBqIt6Qfe08yyYk4xeXTcKmo3W466UdAAJ1i5i/e1xylMpN0B/D2Bd1muB+u07/BR6WRaYnKqh9bkuHFzfOzlLOc3mDW1X5fVFRKkpr2hQto9NySvP60jqsO3ga509IVuoyVv1pkxK/YqsPPZ0lPRK4lcKk9GjEOix4+rOjUg8SzbGRVpPiOuQzotjKqzfl2jHxDty7bFzAfj21V7ORIFqWLikeE6uSZdcjymZGZlyEYjBaO7sD9MHCdVnOGZugSOuLgPfQMSoNxgf7qpEUZcWD70hyVw3tHszOjVfursob3GEbjCM1bfj9x0eQneDAZbJLweej+ORgjeK+aHZ3K/IVzEgcrGpVtjcfrUdCpEXJmQ/Ga5pMJLfHi9qWThAiNZH5aP9p3PLcVqwvldqYZsVH4K5FY7GiKFX12iwFlr+zvvuVnarXnpQWjf93fgHmjU1QJqGWDmlyTYuxB6RAMhVbbV789hONmJUbvECtP2izwfT6MvDUtrFe44EGg49jAVKR2g2zs/DMhjJQKsmEsFVERpwd2QkOLClMUgzGV/KqjQWiF41LVPSYGt3duHtJHqqaOvHKNklFx+UM7fqKsptgMhB4fRST06ORFe/A058dxVXTMpX6GUYw0UG26uNdUqGiZzAumpwKo4EgNsKCtfuDJzswPF6fyiC0dnol+XHO5emwhjcVWUwGnD8hGa9tPymK9oaQUWkw9pxsRmqMHadb/Mv6SKtJZTCmZMSE9ZqshoH3o5fVSSmJbV1erPrTJpTVtWNcijRh57gcMBuJIn8BAPe9vhsFyU78pQ+VTa3Lqbq5Cy9uOQFK/YJ3+6tacOPsLFxclIaJaVGKu6Ciwa3oM30Y5MseaTXhqmkZ+ObMMape3QxmFLISgtdOMOPCahrWl9YOvsHQuKT0YhgHq1vg9vSgODNWmTCbO7rh9nhVxYF6lc5N7m58d3EefreuFNtP+GVCJqZKrrYZXIHZh5ossfl5LqUnBQCcV5iM7AQHtpc34khNG1yRoU1yhBDEOixocntQkOyEyWDA/60oxGVT07H7ZJOqUC5LR+wPkJpdEdL7CiMYeppZb+44hZ0VTXDaTMq1yAfhk6Ksqla3rV1e1etsPd6A+17fjTV3+SVsIvrRd/vCySl4bftJEcMYQkadwejy9qCl0wuHPOk5rSa0dnlRWtOGjDjp7r8/tRiskRGfqcNiBT7q335vTzVe334SK4pSkeuKVGVK1bd14VhdaG6bJeOTsFYOfF731y8CHt9432IYDARujxefHKqRVF4P1yrxhLQYO+IclgDj8/OVE7CqOL3XOz5mMEKRNlEMxuFa3Kfj8hgIWpeUthIbAB565wD2VTZj8/3noo6bMKubO1UFmse4zK+xiZFSzKKuXVGl/YLre8FcjrxbSS+mUMrty4yPgN1ixLPXl2BDaS2iw2jsE++wICnKqsi93DRHkiO/cHKKymD87OKJquexa/t0Sxei7WZVJl2o6DWZyoqPUAxsQqQVNrNBFTOZMzYBb3yldmnyBuPLYw3wUWAHl2SgFVsMhXl5LjxwUSGWT0wJ+7mC/jHqsqSYG6Oh3QOHxYj/XVEIQDISERYTEiKtKO9FpTQYbIXR3uWftLS6PYz/eW0XFj3xqSLvDUjL9nZPD8rr3aouZMFYq5MlMysnHjfNyQIA/Omzo7j22S2Y8rOPcPNz2/Dy1nJkxkfgfy8qxGOrJmNKRoxucHxlUVqf7gFmML4qb1TpK2nbegJQhOL2Vbagyxs4oVc0uDH/sU9ULUxDpV4zAbp1YhifH6lDo7sb/91bjbq2LiWOoo1j8CuMRQUueV+b4krh61Vi5Mmvt0nu0Q8Owt3dg6mZMYh3WBS3ZFaCA9/U0UrqjXuXFeBHFwTKyORwq78clyNATpxvONXc0T1oK4wP71mAnAQH4h0WvHr7TBgNRKmNACQlg95eZ7tcz7HnZGAhYDgYDQS3zM3ud+2HIHxGncFgbokurw9jk5y4YJL/7sTj9SEzzt6vWoxgKwxt1skTVxTht1dPwammDtS0dKGquRP/2XEKv10nZSJ5enyo5BQ8KaXYVdGkxFsY/B3dt+ZJd5yby+rx943HAQCPf3AIda0e3DB7DP5xywx88aMlWD4xBf/efhL3vr4bG0prA4rHgNB6hLMU3rLadjz83gFlP98cp1VujMPn5esZqB0VTShvcAeomIaC1ievdUnx3eVe3HICdW0epf5AG8fgM7kKU6OQEm1DWV27bkEYnzkULG7w0hfluLgoFREWY79lTxiLxyVh9tjATCDeXXjH/NyAxyemqSfucHqsM2J0DIbFZMD1s8agvt2Djw/W4HRLp+p61LuuorjXYTpkr24LVAUQjGxGrcEAgPzESERaTXj6unPg9UlyGOmxEbqSy32hjWE0uT04WtuOiyarl8txDjMunJQCq8mgqMM++M5+lcT0sbp2HKpuxRMfHMLCJz7Fyqc2Kq1BAWDllFQ8cGGh8vdfNvgfY/z9xmn44J75uHFONjYercPCxz/Bva/vRo+P4pHLJuGLHy3B98/NC3ge31shGHx6Ke+nZ6uejDg7Sh5ai/f2VKkmCj3JklOykWntI8NJD20di9Yg8YZ3+4lGlDf4K5O10tt80Ds7IRJZ8ZKCq163Pz4QXJQeGOtKirLCZCD4wdJ8lDe4VQqug8mYOP/EnKjjx9e6DFmWUjhE6RgMQOp74bAY8dC7B5AcZVMpzEbbA42s3kpFcPYx6mIYda1cm1M5Y2j22HiYDATrD9ciPtIS4Opg7KpowqP/PYi/3TgtIDvIv8KQDAbT3ZmX58IfOfXWaLsFJqMB41OilMpibWX29X+TBHuVNqaLxiI12q7EKt7aWYm3dvauAn/Tc1uxbEKykkJ8XmEybpidhZk5cUoAXC+rqK+iP0DdWGjJ+ESslQvzXpPvGGPsFlQ0dOCj/aeREeuftPR86GxSbw3SqrM3jmtch4c1cYSDvRRG8lLuje0elZR5drwD2S4H3ttTBYeO1Ai/wihMcarcg89eXwJ3dw9aO7uRGmNHZVMnLplyZgyG3WJU6iASnYFB9PhBkMswatxuyXJGktNmxkOXTsSpxg7cOi9H9X3Q0zkTBuPrwagzGPyklZ8sGYwomxnFmbFYX1qLCyamoN3Tg87ungCjsKWsHpuO1uNEvRsFyerUV38MQ7pT/tnbkoS1VkyNCctNTo9WdSrT8qPl4xDvsGJfZQueWV8W0Dv6j9cW49v//AqAJPNwoCpQZmRzWT1um5+L62ZmqvogM/Syiqqb+zYYfIyleEysYjCY3hGLcWw6WodvzctRjtVbYTCD0dYZ3gpDL16yQ3M+t8gFmXrwMYwPNC1KD9e0IsFhQZO7G43ubpiNBN09/veLiTDjp2v2obzBjY8PqqvYsxIcGCtXcZ+ob0ePjwa4JQeT7AQHqpo7dV1jekZkoCwt9EuCXDpVv9kWXwXPRBKFwfh6MOoMhsolxVU6Lyhw4fEPDuHccdIXoqHdE9BGtcHN8vg7ghoMVm3M7n61bpOYCGkFs6sP98CvPjyMLrnYbUZ2HLLiI5SJ+eFLJ2I5F3vRMxaAZFTm6Pi+GXqV0Xpd0oJhMxt0K4GZa+h0S5cqk0mvDuCUssIIz2C06xg7bVB381HJYFhNBmUFyGCfs8PTg9Vv+BVX85MicfUzW5TsqAWPf6IyFoC0Inxu03HdlrB8xTKLhZ1pg/HlsQbduiHtNToYjNcJaGvhA+DspkSvS57g7GMUxjD8E1gM52tlE/Cmo5Iwnd7dcEMvhV9sQtJWDL+sKa4qfvAjnPPQWpVukR5MTfSNO2fjxVtmqO7mnDaz7h02ALxy20xlW0/NlEdPrK+6uQN7TzXj2me36GZr8VlRua5IlRQ3o63Li8nyyooVjQGBWU2A323Um/uIf2+2Mggl44e9pp7uUVWT9DpPfHhI2RdlM8FACHp8FNF2MxYVuBTDwfOlnGL77ztnBTzG13YoBuMMxTAA4NZ5Ofj1VVMCXEcAwi4+DYW02L5TqR2a5ldmI1F6iAjObkafweAmmkue2qj4srMTHJicHq00gNEWhQH+DCJdg6FTuAcEVmUzepPJMBqIElhlkw4fW1izsxLn/TpQOPCT/7cQM3L8xXHv7qlSZQpp0XVJtXThrpe+wsYj9brNkNo4I1Ne7w6qNbRsYjLSYuzYW+k3GNq6idfkqmfAb6h748P91Zj32Meoae1UiS2eVxionMqjp6xa3+7BT9fsU4L2MRFmtHR6lcZHta1d+PtN03G/TjrrlrJ6FKVHIyXajnGau/ibn9uqnPPyejcsJgOSzoBriJGd4MCKotSwnqPNnuoLfiWlt6rSYjQQVexHkhFRG7RwxyAYGYw6g8F3DTtW344/f+bXZLqY++LppYCyfUzJtLO7R6ktYCuMJnd3SHIJ1l7kmHt8FO/vlfzqH+yrxvLfbsCmo35//NoDp2EzGzGTk8G+ZW42snWqsjceCT4R68UNqps7FHeanqurhcuiau3yBk1BnpAajVm58apgMu+Sau/y4of/3q38HUqL1fIGN7p7KI7WtKtiUYt1eorwKzBDEFG85zYdBwA8cFEhfiS3y2U1KMfr2+HzUaXnBc++yhacNyEZAAI66H18sAavyzcJ5Q1uZMTa+1WUNljM1XFJlozRl0YPxiSuniMUgwGopT5YphVfN3LJlLSwxiAYGYw6g8FcGTNz4rBU1gJivZxXhGgw2Arj9he3Y/Xre9Djo0qP5eqWTtz6wrY+x3HxlFSlqT2rMOdhc8xbOyuxv6pFMSAA8OiqSVhz1xzcvUTqN5GXGIkfnl+gPM4XTmmDsjyHawJXEMfr/AbgqM7jzZq02690ejKzMczKUUuB8CuMcLoCat+7osGtdL8D1AVqeuPka0W0XFachlvmZuNUYwcMxC/R3dntQ2Vzh25aLQCcLxuMsToy5b//+Ag6u3tQVtt+RuMXocDccbzLqlineVRv5MmxvniHpdcGVTwsjnH1tAx8d7HU5yOdOxeTuXTk1BDbCQuGn1EVieLdM0vGJyHH5cC7u6vw2eFanKhvxwubTyAnwYGyuvYA9wmgNhg+H1Wa4bypo+wajAX5LiRH2fD6Vyfxl+tL8MLmE4i0mgGoaz96K/aen+8CIQST0qJxXmES7l6Sr8rompkTr2Qs6WkkMfQqbflOcUd0XFJNmpagetXsLqcVLqcV2ZoCLj6GoTU8oaAYjEa3KoitVyjGr3z0KsAZrPd8Yn+RAAAgAElEQVTEvspm5LoiUdXcqUhqlNW262aX5br8mVDaPg4L8l347HAtzv3VZzjV1IGVU8NzFw0202SNJz4eNSZMI8YynEKJXzCc8grj4qJUpeiQF6Tk9Z/CaZ4kGF4GtMIghMQRQj4ihJTKvwNuXQghUwghmwkh+wghuwkhV3GPPUcIOUYI2Sn/TBnIePqCXzUsyHdhXp4LCZEWPPHBIfzivQMob3ArFb+82BwgGRvW++BITRtyfqRqYa7iWU488NKp6qX3pLRo3LYgBwQEj39wCBdOTgmp9oGHqXw6rCY8c31JgFtkSqb/7k2bjsvTlwKJ3nO1BuOwToU2W+Eka1RE69s9StC8PwaDvXdFgxt7uCyzCEvgfU8Zpw11RUmGbkvWhEgrZsqroL2nWjAhNQrH6tqxQJYGKdMxmID67li7wrhr8VjMzIlDu8eLxy+fjDsXBFZgDyV5Oj1PwlWGZXpS6WEYDLbCiODeixmMaLtZpV6b34c6s2DkMFCX1GoA6yileQDWyX9rcQO4nlI6AcAyAL8hhPDlsT+klE6Rf3bqPH/Q4DNrMuIiYDYacHFRGg6dbkVWvAP3X+AXx2PBbwA4UtMatMscADx0iVr0bSanyvrwpRNVk9XEtCjkuiLxh2umYu+pZry7u0ql7Mn4z3fmBFUf1Ssm45nKKe1WNXf2GvjuDT2DEYp0CEvndDmt4N33PT6qGIqBrTA6sLmXGgtAUkRlnDs+EXVtXQEZT06bCbe/uB3H6tpR3SLVMrg9PZiRHYdIqwllde26tTL8xKl1OU1IjcJzN03H5tXn4oqSjEFrxNVfmGAhj8MaXsYSkwcJNX4B+Gsx+Gs1UWkfa1YZrUk6LkXByGSgBmMlgOfl7ecBXKI9gFJ6mFJaKm9XAqgB4Brg+/YLPujKfODXzxqDWTnxeOraYtw2P0cVSF765GfIWv0uljy5PkBWgU0USVFWXDdzjMpHzLteIiwmlKgMhvTlmJYdhzhHoH98Qb50ahIiLUFlGfqahLR3gpX9kDoB9Fcg/ESfFmPXlb1g7282GpQ7SYsc5Gf/g6aOQMPj8fZu2PgYBkPbKpXBG4zJaTGoaHBjUUGikgUFSO66tQek3iEAYJdXKjmuSOS4HCirbcdvdboN8n2uzVzyQqLTigiL1MQoVF//cBDuCoMp64ZnMKTnqFYY8opTq4Cr13NDMDIZqMFIopRWAYD8OzBdhYMQMh2ABcBRbvfDsqvq14SQoML2hJDbCCHbCCHbamtr+zVYvibg0f8eBCBV5v7rtpnIdUViza5KbCnzTzSl3B02UzBldGjSaHkfsVayokT2I8dEmNHZ3YMH/rMXs36xTreQjcVFtp9o7Hd1rNagaMcD6FdKhwIvYDc2MVI3C4efWFiWkVluE8piQ3orjL5EH9lz+FajszU9NpjLi7nKxiU7UVrTCh8FpmTEqArPjAaCy6amKW5IViuQ43IgJ8GBXRVN+PRw4LWmNcjnT5DSdhcV9Hr5Dxu5coyHxRXC7T2RneBAQqQ1rGA5kwfh34udt3malqrhGjDB8NGnwSCErCWE7NX5WRnOGxFCUgC8COAmSim7lbwfwDgA0wDEAbgv2PMppc9QSksopSUuV/8WKJ8e8n/5d5Q34nhdO363rhRZq99F/k/ex/dfVnvELpycgrJfLMfxX16Iy8/JUD3G3Ft6eky87/vaZ7fgu/+SJDya3N1Y8uR6vLK1AssmpuBtroGMlu0nGoOuMEJhKhfHKK9vx8HqFsx77GPl7rw/UteAOoie43LoSlLwwVHWPpPVnTAj2aCTVFAapE81o8ndHWBE52omH231+nUzxyirw0np0aoMslvnZuPxK4owMycO45KdqG3tgt1sRHKUDTmuSLR2eXVTcrXqsyyOMTljZLpW3vj2HABSGrTVZFC1zg2FhEgrtv1kiSp20xds5RfBub/YvhyXA41cPFFb6CcYufR55VBKl1BKJ+r8vAXgtGwImEHQzeEkhEQBeBfATyilW7jXrqISXQD+DmD6YHyoYGzjMnoqmzux8IlP8eRHfpfD+JQoVcDaZCBKDn2Dju+eEMDrowGuFD7guvFIPTq7/Y//8PwCbL5/MX51ZREmpUcHXUW8sPmESho8XFZM9mfnnKh34/PSOlQ0dCgtRbXxiVCqguvaurCh1F/X4XJadceov8KQLjXmrtPLwNJrQsTw+ShaOrsDCr5m56oNhraeY9nEZOw51YxEpxVJUTYlQeDWudm4Z2k+jAaC52+ejtfumIVjdW3ITnCAEKJkXukVBSZr0kDZqkUvsD4SiLabcetcSQJ/qO7mLy1Ow/+tKFTFUFhygtvTo6rxCTemIhg+BuqSWgPgBnn7BgBvaQ8ghFgAvAngBUrpa5rHmLEhkOIfewc4nrA5rzAJb981F8ceWY73vz8PS7gJgr+o9e6IWXqidpXB10wAUvvKhEgLLCYDbp2Xrcrt15OeYOjdCEbpKIHqcSEnq368vh0HqqTJ+PEPDuHiP3yuGA5GUXrvd8eUUlz2R3XgP95hCVAmdVpNcNrMONnohs9HkSRPrp3dPTAQv0vqsI4UiFZtlqe1ywtKA2sucjUptW6PVxXkT4i0YvfJJkWqhK0wSrJi4fb04L97q2E1GRFpNWFfZYuSVTQ1MxaJTiu+NT8n4D3Mmn/MBfJqkfVmH4ncNDcbRgNRxXDOJLmuSKUzIMNpM4EQqW/Kfu67pZflJhiZDNRg/BLAUkJIKYCl8t8ghJQQQp6Vj7kSwHwAN+qkz/6TELIHwB4ACQAeGuB4ekXrb79zYS6eub4Ek9KjdQPJ/EpBLzuI3Wm6u9V5/nxs4t3vzcWrd8zCo6smw+P1Ydtxdd1Cb21O95wKrLTWCiIGI4lLad12olFl/HafbMbLWytUxxcEmeyY3lRzR3dAjCE2IjAwnx4XgSM1bVjw+KdYs6tSWWG0dHoR57AqkiuVOvIqWqFGnmY5pTbXFakU1wGB8ZqKhg6Vu62ty4uyunZMSouRP6cTBiJVaz/y3gHc8Y/tOFLTin2VLahp7cK8PMndmRZjx5c/XoLizFiV3IrehGs0kABV4pFGWowdV0/LCEk88EzhsJqwIN+F17efxB45nvjthbm6TaoEI5MBGQxKaT2l9FxKaZ78u0Hev41Sequ8/Q9KqZlLnVXSZymliymlk2QX13WU0vDbroXBai5ttjAlKqgA4LQsybXg5QLZepXfTD76Nx+VBn3PCanSRDIzJx4Wo0EJajOO1fk/sstpVX2h9candYeEQpO7G6U1/sl4+aRkzMhWy0Pk6+TrA/7+IXoxj/jIwBVGWowdb+08hR4fxRfHGpAc5TdwXd09qG0NPI/xsjvsaG3wIkMW8I6NsPTawW77iUaV4u6+U82gFMoKw2Y2ItcVifWldUpPkTW7qvDxwRoQAiwsCIyPFXB1AnpV5WcLD10yEX/hXK7DwbUzxqCmtQvv7anC/HwX7l02bthTjwWhM6qkQWpa/RNJ8ZgY7D7ZrFJfZehJgusZDObWeWVbRcBjWhxWE6Zlx+IzLvDe46OKoiohwL/vmIWXbp3R6+voaRuFAi/RXdPShSeuKFI9Hmzlwrrr6TU/inMExjBSY2zKRLz7ZJPKwLV2eQMyw5K5uEJvsDTcaLtZ1ZRJy9bjDTjNrV4efFdqbctP9BNSpZsFT48PuS4H3tlViY8P1mByeoyqoIzBa3SdzTUDI2FiXlTgQkq0Dd09FONTRMHe2caoMhisQC4zLgJTMmLR1uXVVWTlK5SZQemtH/KDKyfo7tcW2C3Id+HQ6VZFIfdEfbsSpI22mzEm3tFnQ3v+jr0vlsl6RzxXlWRge3mjqstfjsuh2yUN8AfH9VYYcQ5LgMGoa+uSRPfi7DhY3Rogf67NhCpIdiIrPlDaQwtbYUTbzUHdZwDw5fEGVUB/76kWpETbVNlczEAtHpeIW+flKAV6i4OkxfLSI1p1WkF4mIwGXD0tE4Ba80xwdjCqDAbrpTAlIwZT5Grod/dUqY5xe7x4Zr1fwba1y4uGdo+qJkNLMI2daRq3z0J5QmLpvSwQnZPgUOns9EaCM3R/r1bF1WY2YEGBC5QC+yv9MY3UaHvQjKzeDEaUzRTQGGfb8UZYTAbcfW4+enwUR2vbEW03KxlErJshIz8pUlX8F6xVKzMYMRFm3DwnK+Bxlj3l8frwJVe09/R15+AXl05SHVuSFQcDAb41LwfLJiTDJGfC6aneAtL5YQy3mODXgW/OGoNvTM/EwvyRWbciCM6oMhis4nlMfARyXQ4sn5SM364rxfuc0fjN2lLFDQMAt72wDTMfWad6Ha0+lFZfiTElQ523npcYifRYOz6S5c+3lNXDajLgbzdOw1PXFCvHBateBvw6UqGQrlHBLUiOUvpI7OGKGJOibEFXGEyxVs8lRQgJGGtNaxcWFyQqbr3dJ5uQEm1Tpe3y2VBj4h0o5fSogoklsnMcbTfrFvjxHQj51N9lE5OxSGMIijNj8dUDSzErNx6xDgsWym6SYBlrBgNRMqXShcEYMHEOCx65bJJSQS44exhVBoMVcKXG2EEIwZNXTkFxZiy+/8pOlMvV0Gv3nwZfBP3FsQZcxE1GAHD5Oepexv/6Ur+zndZ9QQjBeYXJ+PxIHZo7uvHeniosKUxCVoIDearAavClerCJXY8MHaVVJvXNjzkt1h4w8bMVwcFqaSWiXWEwJRST0RDgehuX4kRytA2JTit2n2xGUpQN1c2dmC5XvPMKtzvKm1QxoLIgge+Wjm5YTQbYzEbVCoJxwcQUnWcFh5eneOzyIrx6+6xe+1bkJTphMpAAQUWBYDQxqgwGk5pmhWU2sxG/vnIKPF4fXttegYff3a/IRPDcszRf9bc28Lz1uH5PiLGJgf7upYVJ8Hh9eOS9A6hv9+g2kpmYGjywGmodht44d1U0BSm0swVU/7JJnSn0ag3GTC7VVLvqYYHjyekx2FUhrTCqmjuxVK5xYY2LpPdRT/56MSVAXeW96Ui9/Pr+88T30mYwXa6+iHP0nnkFAKvOScctci2DQDBaGVUGg02gfEYQq6/4/cdH8JcNUrvOW+Zmq1woVz+zBTwJIcYb9DKapmXFIibCjJe3ViDabtad1HpL3QzHJaUnAfGn684J2JcW07ebhU/xddpMqvOjjWOwvPopGdEoq2uH02ZCfXuXYmROcNpWWp0rfoXR46NY/fpu/Pzt/fj4UI1iMDbK7Vz5Km89Vda+ihHDYWlhEu5fHtiuVSAYTYwqg1HZJPnvXU4r3tp5Cpc8tRErn9qoPL6iKBWEAHcvyVME5QApq4Yv2HKGKK+gJ8NgMhqU4OryScm6vb0HyyUF+EX/GNNl6W4evbtzLS1cO9ceH1UZDGbE8mRNJbbCOEduBdro7galQLtHv5ERn8HErzDe31uFl7dW4PnNx1Hb2oXSmjY8t/GY4jKc3IdBCCVdVyAQhM6oMhgn5TacS578DN9/eSdaOrrx0xWFeFDuZ7HteAMKkpxw2sxYMt5vMP5yfQnO5f4eaD77RbJsx2XF6bqP95ZmGs4KA1C7ZXJdDvz182No03Sg66t6/Lpnv1C2LSYD3J4e9QrDZsKktGjcuVBqFsSkT6ZnxyEh0qrEh/ZoJOIBKYX5yhL/eSira0ePj4JSiqc+OYoclwNfPbBUefynb+9XtvU67fEMZ1WzQPB1ZFQZDI/XBwqp+Or5m6dj7Q8W4MY52VgqG4Oq5k5MzZSCvdouYMwlw9xMVp2VQagsHpeE9T9cpLTP1BJMTdRA+m6epIV9HkCqpH7wnf2qx+MdFlV7Vz0+P+LPOmKmMl6zwmjp7FaK8phLymgguHBSMnadbEK03YxdOi1hL5iUjMIU/0rB4/WhsqkDnxyqwYGqFty5IDeoQKO2yE7bryFUGRWBQBAao8pg/N/Fhdi0ejH+duM0LMh3KVkxydE2pZq3WJYFj9fo27BUzmJ5Ah5oxW9mkG56vRFpNYW9umE9CHJcDuS6HLhZIwgXrIYkL0ifZdZLm5+so+wmtHZ6Ud/mgcVkULm8VhSlosvrQ3NHN3adbAqQQy9Kjwmo+P3pmn348Zt7kRZjxyVTA5MCGDEaQzJfEw/SigQKBIKBMaq+UVE2c1Cxv1lyIx7WJIZX0OSbDbE+E7xcBKO32MNAmTM2vtfJMxis0OyCiclY9z8L8cPzC8DbnNQg5+OqaRm6+wEgI86umpydNjNaOrpR29YFV6RVZdSKM2ORKq/KKho6FJ0uRkq0DWM0Lrh1B2uUrCqz0RDQYjYh0oJEpzVgJTZL00xJIBAMLqPKYPTGTbOz8L1z85CjYwh4HSkmYa2XKTU968xNWLfOy8HPV07s+0ANY+ViwTlyRpHdYlR9xmBum4unpOruB4BHL5usCuhH2czw+ihONnYErMwMBoILuDqWeE1b2tQYe9BU1U8O1YBSihZNd76ESCv+eG1xwPElI7QfhUDwdUEYDJm8JCd+sDRf1+Wzl5PRYC4VPZG6wRRT006iLp33CwWnzYzP71uM2ZygYmGqfv0C31s50WkL6paarRFnZJlbx+radc9LAVfA2K4JuCdpCuFY97rYCDNO1Ltx+HQbmjQG493vzVPa3vKIojqB4MwiDEYIvLj5hLLNArp6Gv6DmZUzkUsJNRnIoAZwedE3PlCsNUp3LMgNeO49S/ID9rGeGLWtXapgOIPXX9JKjLC0YpYiSyBpa71y+ywAwIf7qrFBIwkfbEXSW6W2QCAYOMJghMDaA6eV7VhZUkLvjl9PiqO/sOK95ZOS8f7354XUQjVU+JUQH/TWBqRXnZOOhy7xu8HykyJxw+wxAa/H14bouepYFbWBSPLjyntzxurWeTkApOr7D++Zj/wkJ6ZkxODdPVV4+rMyZMSFZzC1XfIEAsHAEQYjBNbcNUfZZnex8ToGw2r2n06+RWp/uGaGJAF94+xslc7UYMAXtPErF2Yw+BjH1Vzw+8VbZqg0mBi83IjeCiM5ygazkWBmTryqvzkv9seqsvecalbcgksLk3CwuhXVLZ1Kj3Jt6mwwRA2GQDD4CIPRC6wwjFd2Zei5pKwmgyI4eHFR8KBxKExIjcbBB5dherZ+rcZASHTakBBphcVkUE3wLFtpRo7/PU1GAy6anIJvL8wNiDcweH0r7SoFkFxIaTF2xEZYVO4w3ljxhmBLmaQVxcc7WCOmooze05mZXpW2Ha9AIBg4wmD0Qq5LCsD++TOpPwYvRREbYQnwpRNCFCltvYkzXPoqqBsIE9OikBkXoQrysyyjRZpGQn+4phj3LhuHYPB9vbVZUIyMuAhUNLrxjen+FQsfcDcZDUpR5GvbKuD2ePHGV6eUx1n/Dj1BRx6miNuXmKBAIAgfYTB6gWXssKI9ZkAAyTWlF1dgxwSrTh4p/OziCfjt1VNU+zLiImA2Et0ak95QxzD0Yy0ZcREob3BjJVdLoq2JYauz/+ysxMLHP0V1Sye+u3gsAODlrZIEel9qvawgMthqSCAQ9J8BGQxCSBwh5CNCSKn8WzcRnhDSQwjZKf+s4fZnE0K+kJ//CiFk8CK7gwBvIIBANxSfQspu1FcUpeBP1xYHPHekMSbegQkaGfXsBAcO/HxZ2DETu9modK3TS6sFpEypJnc3CKBoTmlFD7MSHLAYDfjFpZNgNRuwqjgd312cpzJIUX0Y4qXjk/Dmt2crxl4gEAweA11hrAawjlKaB2Cd/LceHZTSKfLPxdz+RwH8Wn5+I4BbBjieQUWbaaOdDO9dVoAVcqyCZUhZTUZVodrZRjAdq94ghMBpM8FA/FlkWtj5qWjoUORKtCuM7AQHPD0+nDs+ERvuXYxfXVkEi8mgap0arJUsw2AgKv0sgUAweAzUYKwE8Ly8/TyAS0J9IpGc54sB/Ls/zx8KtJIVWoOxqCAR58qTmc08ur17UXYz4hyBcR0Gq8Uob3CjqqkTBoKAPuZMpfe4ponV+ROSufcJT95dIBAMHgOd5ZIopVUAIP8O1tXdRgjZRgjZQghhRiEeQBOllKXCnAQQVCyJEHKb/Brbamtrgx02qMTYzZg7NgHjU6JgNBBVxTLDLqvH6jXwGU04baagAW/AbzBONrpR2dyBpKjALn+KwahXG4ylhUmYJTdf6muFIRAIzhx93q4RQtYCSNZ56MdhvE8mpbSSEJID4GNCyB4ALTrHUZ190gOUPgPgGQAoKSkJetxgYjAQ/OPWGQCA7h6frvopa6w02lcYk9NjVCKNWqIjzHDaTMoKQ68bYWqMVK9xrE7dhc9sNGDVOenYXFYvDIZAMIz0aTAopUuCPUYIOU0ISaGUVhFCUgDUBHmNSvl3GSHkUwBTAbwOIIYQYpJXGekAKvvxGYaEYFLZfoMxulcYv7h0Up/HZMZF4IN91Wjp8GLx+MDFqMloQEZchOKS8vb4cOc/v8J1M8coAoTCJSUQDB8DvS1eA+AGefsGAG9pDyCExBJCrPJ2AoA5APZT6Xb0EwCX9/b8kY7dLE1gA2moNFooyohBc0c35ucn4I75gTpVAJAd78ARuU3rp4dq8dH+05KR6ZQMhra9rEAgGDoG+u37JYBXCSG3ACgHcAUAEEJKANxBKb0VwHgAfyaE+CAZqF9SSlnbt/sAvEwIeQjADgB/HeB4hhy2wrCO8hVGKDx8yUT87OIJvTY2mpeXgHVv78eWsnq8uk2qvThW2w6byYhIq6lfWVwCgWBwGJDBoJTWAzhXZ/82ALfK25sA6PorKKVlAKYPZAzDTYSVBb3FRNYXhBCYjb0ryl49PRNPfXoUv3jvAPZXtoAQoKyuDemxdlU9hkAgGHrELDdAWGe+0R7DGCxsZiNun5+D3Seb4fVRXDo1DadbulDV3CkC3gLBMCMMxgCxm8UKY7C5dsYYJERaUTImFufJYoK7TzaJgLdAMMyIb+AAMRoIMuLsqiZBgoFhtxjx7ztmwW4xosktBbtbOr1ihSEQDDPCYAwCa3+wAGaDWGEMJlmyAGK03QxCAEohYhgCwTAjZrlBwGoyivagZwib2aj0yuhLeFAgEJxZhMEQjHhyZOVf4ZISCIYXYTAEIx7WMlYEvQWC4UUYDMGIh7XKdYoVhkAwrAiDIRjx5CRILikR9BYIhhdhMAQjnpKsWNw2PwfzxrqGeygCwahG3LIJRjw2sxE/Wj5+uIchEIx6xApDIBAIBCEhDIZAIBAIQkIYDIFAIBCEhDAYAoFAIAgJYTAEAoFAEBLCYAgEAoEgJITBEAgEAkFICIMhEAgEgpAglNLhHkPYEEJqAZzo59MTANQN4nCGgrNxzMDZOW4x5qHjbBz32T7mMZTSfksmnJUGYyAQQrZRSkuGexzhcDaOGTg7xy3GPHScjeMe7WMWLimBQCAQhIQwGAKBQCAIidFoMJ4Z7gH0g7NxzMDZOW4x5qHjbBz3qB7zqIthCAQCgaB/jMYVhkAgEAj6gTAYAoFAIAiJr4XBIIT8jRBSQwjZy+2bQgjZQgjZSQjZRgiZLu9fSAhplvfvJIT8L/ecZYSQQ4SQI4SQ1SNozD/kxruXENJDCImTHztOCNnDnjMMYy4ihGyWx/A2ISSKe+x++VweIoScz+0f7vOsO2ZCyFJCyHZ5/3ZCyGLuOZ/KY2b/h8QRNO4sQkgHN7anueecIx9/hBDyO0IIGSFjvpYb705CiI8QMkV+bMjONSEkgxDyCSHkACFkHyHk+/L+OELIR4SQUvl3rLyfyOfxCCFkNyGkmHutG+TjSwkhN4ygMV8rj3U3IWQTIaSIe63w5g9K6Vn/A2A+gGIAe7l9HwK4QN5eDuBTeXshgHd0XsMI4CiAHAAWALsAFI6EMWuetwLAx9zfxwEkDON53gpggbx9M4AH5e1C+RxaAWTL59Y4Qs5zsDFPBZAqb08EcIp7zqcASob5mg427iz+OM3rfAlgFgAC4H12fQ33mDXPmwSgbDjONYAUAMXythPAYfnafQzAann/agCPytvL5fNIAMwE8IW8Pw5Amfw7Vt6OHSFjns3GAuACNmb577Dmj6/FCoNSuh5Ag3Y3AHa3Gw2gso+XmQ7gCKW0jFLqAfAygJWDOlB+cP0f8zcA/OtMjas3goy5AMB6efsjAKvk7ZUAXqaUdlFKjwE4Aukcj4TzrDtmSukOSik75/sA2Agh1jM1tt4I81zrQghJARBFKd1MpdnhBQCXDPZYGQMY83Be01WU0q/k7VYABwCkQbomn5cPex7+87YSwAtUYguAGPk8nw/gI0ppA6W0EdJnXTYSxkwp3SSPCQC2AEjv73t/LQxGEO4G8DghpALAEwDu5x6bRQjZRQh5nxAyQd6XBqCCO+akvG8o6W3MIIREQLoIX+d2UwAfyi6U24ZspH72ArhY3r4CQIa8Hex8joTzHGzMPKsA7KCUdnH7/i4v3R84k66dXuht3NmEkB2EkM8IIfPkfWmQzi9jpJ7rqxBoMIb8XBNCsiCtMr8AkEQprQKkCRoAc4uNqOs6xDHz3AJphcQIa/74OhuMOwHcQynNAHAPgL/K+7+CpKdSBOD3AP4j79e7KIc65zjYmBkrAGyklPJ3cXMopcWQlprfIYTMH5qhKtwsv+92SMtjj7w/2PkcCec52JgBAPJNxKMAbud2X0spnQRgnvzzzSEaK0+wcVcByKSUTgXwAwAvybGCs+FczwDgppTu5XYP+bkmhERCuhG7m1La0tuhOvuG5boOY8zs+EWQDMZ93O6w5o+vs8G4AcAb8vZrkFwhoJS2UErb5O33AJgJIQmQ7gj4u5909O3GGmx0x8xxNTR3YsyFQimtAfCmznPOKJTSg5TS8yil58hjOyo/FOx8Dvt57mXMIISkQzqP11NKj3LPOSX/bgXwEob4PMvvrTtu2e1XL29vl/fnQzrXvPthRJ1rGb1rekjPNSHEDGni/SellH3/TsuuJubaq5H3j4jrOswxgxAyGcCzAFayawUIf/74OhuMSgAL5O3FAEoBgBCSzL63pQwAAANpSURBVJa4RMpCMgCohxScyyOEZBNCLJAu5DUjYczyWKPlx97i9jkIIU62DeA8SC6AIYNlsBBCDAB+AoBl6KwBcDUhxEoIyQaQBykAO+znOdiYCSExAN4FcD+ldCN3vEm+qWBf1IswxOe5j3G7CCFGeTsH0rkuk90SrYSQmfI1fz2462c4x8ztuwJSHIvtG9JzLZ+XvwI4QCl9kntoDaQbOMi/3+L2Xy9nS80E0Cyf5w8AnEcIiZWzk86T9w37mAkhmZBuRL9JKT3MvU7488eZiOIP9Q+kO5QqAN2QLP0tAOYC2A4pC+cLAOfIx94FKaC5C1IAaDb3OsshZRwcBfDjkTJm+fgbIQWR+dfIkY/dJX+m4Rjz9+VzdhjALyGrB8jH/1g+l4fAZeeMgPOsO2ZIE1o7gJ3cTyIAh/x/2S2f598CMI6gca/irumvAKzgXqcE0iRwFMAf+P/PCLg+FgLYonmNIT3X8neOyu/H/ufLAcQDWAfppm0dgDj5eALgKfl87gGXzQXJ/XZE/rlpBI35WQCN3LHb5P1hzx9CGkQgEAgEIfF1dkkJBAKBYBARBkMgEAgEISEMhkAgEAhCQhgMgUAgEISEMBgCgUAgCAlhMASCPpBz7j8nhFzA7buSEPLf4RyXQDDUiLRagSAECCETIVXfT4WkuLsTwDLKVYP34zVNlFLvIA1RIDjjCIMhEIQIIeQxSIV9DgCtlNIHidT34DuQpNo3AbiLUuojhDwDSerbDuAVSunP5dc4CeDPkEQkf0MpfW0YPopA0C9Mwz0AgeAs4meQKqk9AErkVcelkNQCvLKRuBqS/tFqSmkDIcQE4BNCyL8ppfvl12mnlM4Zjg8gEAwEYTAEghChlLYTQl4B0EYp7SKELAEwDcA2WZ7MDr/E9TcIIbdA+o6lQmpwwwzGK0M7coFgcBAGQyAID5/8A0i6Qn+jlD7AH0AIyYOkoTSdUtpECPkHABt3SPuQjFQgGGRElpRA0H/WAriSU1eNl5VBowC0AmjhurEJBGc9YoUhEPQTSukeQsjPAKyVpbq7AdwBYBsk99NeSL2dNwZ/FYHg7EFkSQkEAoEgJIRLSiAQCAQhIQyGQCAQCEJCGAyBQCAQhIQwGAKBQCAICWEwBAKBQBASwmAIBAKBICSEwRAIBAJBSPx/bT8f/9L4XuYAAAAASUVORK5CYII=\n", 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Country NameCountry CodeIndicator NameIndicator Code196019611962196319641965...201120122013201420152016201720182019Unnamed: 64
0ArubaABWProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1AfghanistanAFGProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...27.727.727.727.727.727.727.727.727.87NaN
2AngolaAGOProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...38.234.134.136.836.836.838.230.530.00NaN
3AlbaniaALBProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...15.715.717.920.020.722.927.927.929.51NaN
4AndorraANDProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...50.050.050.050.039.332.132.132.150.00NaN
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" + ], + "text/plain": [ + " Country Name Country Code \\\n", + "0 Aruba ABW \n", + "1 Afghanistan AFG \n", + "2 Angola AGO \n", + "3 Albania ALB \n", + "4 Andorra AND \n", + "\n", + " Indicator Name Indicator Code 1960 \\\n", + "0 Proportion of seats held by women in national ... SG.GEN.PARL.ZS NaN \n", + "1 Proportion of seats held by women in national ... SG.GEN.PARL.ZS NaN \n", + "2 Proportion of seats held by women in national ... SG.GEN.PARL.ZS NaN \n", + "3 Proportion of seats held by women in national ... SG.GEN.PARL.ZS NaN \n", + "4 Proportion of seats held by women in national ... SG.GEN.PARL.ZS NaN \n", + "\n", + " 1961 1962 1963 1964 1965 ... 2011 2012 2013 2014 2015 2016 \\\n", + "0 NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN ... 27.7 27.7 27.7 27.7 27.7 27.7 \n", + "2 NaN NaN NaN NaN NaN ... 38.2 34.1 34.1 36.8 36.8 36.8 \n", + "3 NaN NaN NaN NaN NaN ... 15.7 15.7 17.9 20.0 20.7 22.9 \n", + "4 NaN NaN NaN NaN NaN ... 50.0 50.0 50.0 50.0 39.3 32.1 \n", + "\n", + " 2017 2018 2019 Unnamed: 64 \n", + "0 NaN NaN NaN NaN \n", + "1 27.7 27.7 27.87 NaN \n", + "2 38.2 30.5 30.00 NaN \n", + "3 27.9 27.9 29.51 NaN \n", + "4 32.1 32.1 50.00 NaN \n", + "\n", + "[5 rows x 65 columns]" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "women_in_parliment.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',\n", + " '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n", + " '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n", + " '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n", + " '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n", + " '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',\n", + " '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n", + " '2014', '2015', '2016', '2017', '2018', '2019', 'Unnamed: 64'],\n", + " dtype='object')" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "women_in_parliment.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 264 entries, 0 to 263\n", + "Data columns (total 65 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country Name 264 non-null object \n", + " 1 Country Code 264 non-null object \n", + " 2 Indicator Name 264 non-null object \n", + " 3 Indicator Code 264 non-null object \n", + " 4 1960 13 non-null object \n", + " 5 1961 0 non-null float64\n", + " 6 1962 0 non-null float64\n", + " 7 1963 0 non-null float64\n", + " 8 1964 0 non-null float64\n", + " 9 1965 0 non-null float64\n", + " 10 1966 0 non-null float64\n", + " 11 1967 0 non-null float64\n", + " 12 1968 0 non-null float64\n", + " 13 1969 0 non-null float64\n", + " 14 1970 0 non-null float64\n", + " 15 1971 0 non-null float64\n", + " 16 1972 0 non-null float64\n", + " 17 1973 0 non-null float64\n", + " 18 1974 0 non-null float64\n", + " 19 1975 0 non-null float64\n", + " 20 1976 0 non-null float64\n", + " 21 1977 0 non-null float64\n", + " 22 1978 0 non-null float64\n", + " 23 1979 0 non-null float64\n", + " 24 1980 0 non-null float64\n", + " 25 1981 0 non-null float64\n", + " 26 1982 0 non-null float64\n", + " 27 1983 0 non-null float64\n", + " 28 1984 0 non-null float64\n", + " 29 1985 0 non-null float64\n", + " 30 1986 0 non-null float64\n", + " 31 1987 0 non-null float64\n", + " 32 1988 0 non-null float64\n", + " 33 1989 0 non-null float64\n", + " 34 1990 160 non-null float64\n", + " 35 1991 10 non-null float64\n", + " 36 1992 0 non-null float64\n", + " 37 1993 0 non-null float64\n", + " 38 1994 0 non-null float64\n", + " 39 1995 0 non-null float64\n", + " 40 1996 0 non-null float64\n", + " 41 1997 191 non-null float64\n", + " 42 1998 204 non-null float64\n", + " 43 1999 191 non-null float64\n", + " 44 2000 198 non-null float64\n", + " 45 2001 209 non-null float64\n", + " 46 2002 208 non-null float64\n", + " 47 2003 219 non-null float64\n", + " 48 2004 226 non-null float64\n", + " 49 2005 232 non-null float64\n", + " 50 2006 235 non-null float64\n", + " 51 2007 234 non-null float64\n", + " 52 2008 233 non-null float64\n", + " 53 2009 233 non-null float64\n", + " 54 2010 233 non-null float64\n", + " 55 2011 235 non-null float64\n", + " 56 2012 233 non-null float64\n", + " 57 2013 233 non-null float64\n", + " 58 2014 234 non-null float64\n", + " 59 2015 235 non-null float64\n", + " 60 2016 237 non-null float64\n", + " 61 2017 239 non-null float64\n", + " 62 2018 239 non-null float64\n", + " 63 2019 238 non-null float64\n", + " 64 Unnamed: 64 11 non-null float64\n", + "dtypes: float64(60), object(5)\n", + "memory usage: 134.2+ KB\n" + ] + } + ], + "source": [ + "women_in_parliment.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Proportion of seats held by women in national parliaments (%)',\n", + " 'BHS', 'COD', 'COG', 'EGY', 'FSM', 'GMB', 'HKG', 'IRN', 'KOR',\n", + " 'MAC', 'PRK', 'VEN', 'YEM'], dtype=object)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "women_in_parliment['Indicator Name'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "women_in_parliment = women_in_parliment.where(women_in_parliment['Indicator Name']=='Proportion of seats held by women in national parliaments (%)')" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Proportion of seats held by women in national parliaments (%)',\n", + " nan], dtype=object)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "women_in_parliment['Indicator Name'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "women_in_parliment.drop(['Country Code', 'Indicator Name', 'Indicator Code',\n", + " '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n", + " '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n", + " '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n", + " '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n", + " '1996','Unnamed: 64'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 264 entries, 0 to 263\n", + "Data columns (total 24 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country Name 251 non-null object \n", + " 1 1997 191 non-null float64\n", + " 2 1998 196 non-null float64\n", + " 3 1999 181 non-null float64\n", + " 4 2000 188 non-null float64\n", + " 5 2001 200 non-null float64\n", + " 6 2002 198 non-null float64\n", + " 7 2003 211 non-null float64\n", + " 8 2004 217 non-null float64\n", + " 9 2005 221 non-null float64\n", + " 10 2006 224 non-null float64\n", + " 11 2007 223 non-null float64\n", + " 12 2008 222 non-null float64\n", + " 13 2009 222 non-null float64\n", + " 14 2010 222 non-null float64\n", + " 15 2011 224 non-null float64\n", + " 16 2012 223 non-null float64\n", + " 17 2013 222 non-null float64\n", + " 18 2014 224 non-null float64\n", + " 19 2015 225 non-null float64\n", + " 20 2016 227 non-null float64\n", + " 21 2017 228 non-null float64\n", + " 22 2018 228 non-null float64\n", + " 23 2019 227 non-null float64\n", + "dtypes: float64(23), object(1)\n", + "memory usage: 49.6+ KB\n" + ] + } + ], + "source": [ + "women_in_parliment.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1AfghanistanNaNNaNNaNNaNNaNNaNNaNNaN27.3...27.727.727.727.727.727.727.727.727.727.87
2Angola9.515.515.515.515.515.515.515.015.0...38.638.234.134.136.836.836.838.230.530.00
3AlbaniaNaNNaN5.25.25.75.75.76.47.1...16.415.715.717.920.020.722.927.927.929.51
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5 rows × 24 columns

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" + ], + "text/plain": [ + " Country Name 1997 1998 1999 2000 2001 \\\n", + "2 Angola 9.500000 15.500000 15.500000 15.500000 15.500000 \n", + "4 Andorra 7.100000 7.100000 7.100000 7.100000 14.300000 \n", + "5 Arab World 3.757835 3.474235 3.623432 3.768103 4.610598 \n", + "6 United Arab Emirates 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "8 Armenia 6.300000 6.300000 3.100000 3.100000 3.100000 \n", + "\n", + " 2002 2003 2004 2005 ... 2010 2011 \\\n", + "2 15.500000 15.500000 15.000000 15.000000 ... 38.600000 38.200000 \n", + "4 14.300000 14.300000 14.300000 28.600000 ... 35.700000 50.000000 \n", + "5 6.132352 6.043918 6.702671 7.041782 ... 11.923926 11.335266 \n", + "6 0.000000 0.000000 0.000000 0.000000 ... 22.500000 17.500000 \n", + "8 3.100000 4.600000 5.300000 5.300000 ... 9.200000 8.400000 \n", + "\n", + " 2012 2013 2014 2015 2016 2017 2018 \\\n", + "2 34.100000 34.100000 36.80000 36.800000 36.80000 38.200000 30.500000 \n", + "4 50.000000 50.000000 50.00000 39.300000 32.10000 32.100000 32.100000 \n", + "5 14.872018 17.492445 17.79397 18.779151 18.85265 18.821034 19.017979 \n", + "6 17.500000 17.500000 17.50000 22.500000 22.50000 22.500000 22.500000 \n", + "8 10.700000 10.700000 10.70000 10.700000 10.70000 18.100000 18.100000 \n", + "\n", + " 2019 \n", + "2 30.000000 \n", + "4 50.000000 \n", + "5 18.001945 \n", + "6 22.500000 \n", + "8 24.240000 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "women_in_parliment_clean.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 115 entries, 2 to 263\n", + "Data columns (total 24 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country Name 115 non-null object \n", + " 1 1997 115 non-null float64\n", + " 2 1998 115 non-null float64\n", + " 3 1999 115 non-null float64\n", + " 4 2000 115 non-null float64\n", + " 5 2001 115 non-null float64\n", + " 6 2002 115 non-null float64\n", + " 7 2003 115 non-null float64\n", + " 8 2004 115 non-null float64\n", + " 9 2005 115 non-null float64\n", + " 10 2006 115 non-null float64\n", + " 11 2007 115 non-null float64\n", + " 12 2008 115 non-null float64\n", + " 13 2009 115 non-null float64\n", + " 14 2010 115 non-null float64\n", + " 15 2011 115 non-null float64\n", + " 16 2012 115 non-null float64\n", + " 17 2013 115 non-null float64\n", + " 18 2014 115 non-null float64\n", + " 19 2015 115 non-null float64\n", + " 20 2016 115 non-null float64\n", + " 21 2017 115 non-null float64\n", + " 22 2018 115 non-null float64\n", + " 23 2019 115 non-null float64\n", + "dtypes: float64(23), object(1)\n", + "memory usage: 22.5+ KB\n" + ] + } + ], + "source": [ + "women_in_parliment_clean.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'women_in_power = women_in_parliment_clean.pivot_table(values=women_in_parliment_clean.values).reset_index()\\nwomen_in_power'" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''women_in_power = women_in_parliment_clean.pivot_table(values=women_in_parliment_clean.values).reset_index()\n", + "women_in_power'''" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 04d2a682327eb192926930352f222936987a82c7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Fri, 22 May 2020 21:23:10 +0200 Subject: [PATCH 02/17] just tobe save 4datasets ready --- your-project/Data Gathering.ipynb | 351 +++++++++++++++++++++++++++++- 1 file changed, 339 insertions(+), 12 deletions(-) diff --git a/your-project/Data Gathering.ipynb b/your-project/Data Gathering.ipynb index aed1202c..98dbc8e7 100644 --- a/your-project/Data Gathering.ipynb +++ b/your-project/Data Gathering.ipynb @@ -16,45 +16,339 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### CO annual emissions" + "### CO annual emissions (1949-2017)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes)
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes)\n", + "0 Afghanistan AFG 1949 14656.0\n", + "1 Afghanistan AFG 1950 84272.0\n", + "2 Afghanistan AFG 1951 91600.0\n", + "3 Afghanistan AFG 1952 91600.0\n", + "4 Afghanistan AFG 1953 106256.0" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_annual = pd.read_csv('data//annual_co2_emissions_per_country.csv')\n", + "co_annual.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 133, "metadata": {}, "outputs": [], "source": [ - "co_annual = pd.read_csv('')" + "co_annual.drop(['Code'], axis=1, inplace=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 134, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "co_annual = co_annual.rename(columns={'Annual COâ‚‚ emissions (tonnes)':'Annual COâ‚‚', 'Entity':'Country'})" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 135, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "co_annual = co_annual[co_annual.Country != 'World']" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 20586 entries, 0 to 20852\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 20586 non-null object \n", + " 1 Year 20586 non-null int64 \n", + " 2 Annual COâ‚‚ 20586 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 643.3+ KB\n" + ] + } + ], + "source": [ + "#co_annual.Year.unique()\n", + "co_annual.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Annual share of CO per country (1949-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearShare of global COâ‚‚ emissions (%)
0AfghanistanAFG19490.0003
1AfghanistanAFG19500.0015
2AfghanistanAFG19510.0015
3AfghanistanAFG19520.0015
4AfghanistanAFG19530.0016
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Share of global COâ‚‚ emissions (%)\n", + "0 Afghanistan AFG 1949 0.0003\n", + "1 Afghanistan AFG 1950 0.0015\n", + "2 Afghanistan AFG 1951 0.0015\n", + "3 Afghanistan AFG 1952 0.0015\n", + "4 Afghanistan AFG 1953 0.0016" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_share = pd.read_csv('data//annual_share_of_co2_emissions.csv')\n", + "co_share.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 145, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "co_share.drop(['Code'], axis=1, inplace=True)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 149, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "co_share = co_share.rename(columns={'Share of global COâ‚‚ emissions (%)b':'Annual COâ‚‚', 'Entity':'Country'})" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", + " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", + " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", + " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", + " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", + " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", + " 2015, 2016, 2017, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758,\n", + " 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769,\n", + " 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780,\n", + " 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791,\n", + " 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802,\n", + " 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813,\n", + " 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824,\n", + " 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835,\n", + " 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846,\n", + " 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857,\n", + " 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868,\n", + " 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879,\n", + " 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890,\n", + " 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901,\n", + " 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912,\n", + " 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923,\n", + " 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934,\n", + " 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945,\n", + " 1946, 1947, 1948], dtype=int64)" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_share.Year.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20586 entries, 0 to 20585\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 20586 non-null object \n", + " 1 Year 20586 non-null int64 \n", + " 2 Share of global COâ‚‚ emissions (%) 20586 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 482.6+ KB\n" + ] + } + ], + "source": [ + "co_share.info()" + ] }, { "cell_type": "markdown", @@ -151,9 +445,16 @@ ], "source": [ "gas = pd.read_csv(\"data//co_emissions_per_capita.csv\")\n", - "gas.head()" + "gas.head()\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 111, @@ -557,6 +858,32 @@ "deaths.Year.unique()" ] }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 6384 entries, 0 to 6411\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 6384 non-null object \n", + " 1 Year 6384 non-null int64 \n", + " 2 Pollution_deaths 6384 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 199.5+ KB\n" + ] + } + ], + "source": [ + "deaths.info()" + ] + }, { "cell_type": "markdown", "metadata": {}, From 38a76abae01322966505ad426bc9ce50f1b12e45 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Sat, 23 May 2020 10:34:02 +0200 Subject: [PATCH 03/17] 3 more datasets --- your-project/Data Gathering.ipynb | 3437 ++++++++++++++++++++--------- 1 file changed, 2346 insertions(+), 1091 deletions(-) diff --git a/your-project/Data Gathering.ipynb b/your-project/Data Gathering.ipynb index 98dbc8e7..4e0b5438 100644 --- a/your-project/Data Gathering.ipynb +++ b/your-project/Data Gathering.ipynb @@ -2,14 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 37, + "execution_count": 178, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "import datetime" + "import datetime\n", + "import re" ] }, { @@ -359,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 250, "metadata": {}, "outputs": [ { @@ -438,14 +439,14 @@ "4 Afghanistan AFG 1804 0.0" ] }, - "execution_count": 110, + "execution_count": 250, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "gas = pd.read_csv(\"data//co_emissions_per_capita.csv\")\n", - "gas.head()\n" + "co_per_c = pd.read_csv(\"data//co_emissions_per_capita.csv\")\n", + "co_per_c.head()\n" ] }, { @@ -457,25 +458,25 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 251, "metadata": {}, "outputs": [], "source": [ - "gas.drop(['Code'], axis=1, inplace=True)" + "co_per_c.drop(['Code'], axis=1, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 252, "metadata": {}, "outputs": [], "source": [ - "gas = gas.rename(columns={'Per capita COâ‚‚ emissions (tonnes per capita)':'COâ‚‚', 'Entity':'Country'})" + "co_per_c = co_per_c.rename(columns={'Per capita COâ‚‚ emissions (tonnes per capita)':'COâ‚‚', 'Entity':'Country'})" ] }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 253, "metadata": {}, "outputs": [ { @@ -548,22 +549,22 @@ "4 Afghanistan 1804 0.0" ] }, - "execution_count": 113, + "execution_count": 253, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "gas.head()" + "co_per_c.head()" ] }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 254, "metadata": {}, "outputs": [], "source": [ - "gas = gas[gas.Country != 'World']" + "co_per_c = co_per_c[co_per_c.Country != 'World']" ] }, { @@ -572,12 +573,12 @@ "metadata": {}, "outputs": [], "source": [ - "#gas.Country.unique()" + "#co_per_c.Country.unique()" ] }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 255, "metadata": {}, "outputs": [ { @@ -598,20 +599,20 @@ } ], "source": [ - "#gas.Year.unique()\n", - "gas.info()" + "#co_per_c.Year.unique()\n", + "co_per_c.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Share of deaths by pollution (1990-2017)" + "### Total greenhouse gas emissions (1990-2016)" ] }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 248, "metadata": {}, "outputs": [ { @@ -638,7 +639,7 @@ " Entity\n", " Code\n", " Year\n", - " Share of deaths from air pollution (%)\n", + " Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", " \n", " \n", " \n", @@ -647,113 +648,122 @@ " Afghanistan\n", " AFG\n", " 1990\n", - " 13.56\n", + " 15140000.0\n", " \n", " \n", " 1\n", " Afghanistan\n", " AFG\n", " 1991\n", - " 13.19\n", + " 15060000.0\n", " \n", " \n", " 2\n", " Afghanistan\n", " AFG\n", " 1992\n", - " 13.05\n", + " 13600000.0\n", " \n", " \n", " 3\n", " Afghanistan\n", " AFG\n", " 1993\n", - " 12.88\n", + " 13430000.0\n", " \n", " \n", " 4\n", " Afghanistan\n", " AFG\n", " 1994\n", - " 12.80\n", + " 13240000.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Entity Code Year Share of deaths from air pollution (%)\n", - "0 Afghanistan AFG 1990 13.56\n", - "1 Afghanistan AFG 1991 13.19\n", - "2 Afghanistan AFG 1992 13.05\n", - "3 Afghanistan AFG 1993 12.88\n", - "4 Afghanistan AFG 1994 12.80" + " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", + "0 Afghanistan AFG 1990 15140000.0\n", + "1 Afghanistan AFG 1991 15060000.0\n", + "2 Afghanistan AFG 1992 13600000.0\n", + "3 Afghanistan AFG 1993 13430000.0\n", + "4 Afghanistan AFG 1994 13240000.0" ] }, - "execution_count": 117, + "execution_count": 248, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "deaths = pd.read_csv('data//share_deaths_air_pollution.csv')\n", - "deaths.head()" + "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", + "ghg.head()" ] }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 249, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Entity 0\n", - "Code 980\n", - "Year 0\n", - "Share of deaths from air pollution (%) 0\n", - "dtype: int64" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "deaths.isna().sum()" + "ghg.drop(['Code'], axis=1, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 256, "metadata": {}, "outputs": [], "source": [ - "deaths.drop(['Code'], axis=1, inplace=True)" + "ghg = ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )', 'Entity':'Country'})" ] }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 257, "metadata": {}, "outputs": [], "source": [ - "deaths.rename(columns={'Share of deaths from air pollution (%)':'Pollution_deaths', 'Entity':'Country'}, inplace=True)" + "ghg = ghg[ghg.Country != 'World']" ] }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 259, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 5157 entries, 0 to 5183\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 5157 non-null object \n", + " 1 Year 5157 non-null int64 \n", + " 2 GHG(tonnes COâ‚‚e ) 5157 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 161.2+ KB\n" + ] + } + ], "source": [ - "deaths = deaths[deaths.Country != 'World']" + "ghg.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global COâ‚‚ atmospheric concentration" ] }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 260, "metadata": {}, "outputs": [ { @@ -777,132 +787,211 @@ " \n", " \n", " \n", - " Country\n", + " Entity\n", + " Code\n", " Year\n", - " Pollution_deaths\n", + " COâ‚‚ concentration (parts per million)\n", " \n", " \n", " \n", " \n", " 0\n", - " Afghanistan\n", - " 1990\n", - " 13.56\n", + " World\n", + " OWID_WRL\n", + " 803719 BCE\n", + " 207.29\n", " \n", " \n", " 1\n", - " Afghanistan\n", - " 1991\n", - " 13.19\n", + " World\n", + " OWID_WRL\n", + " 803182 BCE\n", + " 202.23\n", " \n", " \n", " 2\n", - " Afghanistan\n", - " 1992\n", - " 13.05\n", + " World\n", + " OWID_WRL\n", + " 802573 BCE\n", + " 204.86\n", " \n", " \n", " 3\n", - " Afghanistan\n", - " 1993\n", - " 12.88\n", + " World\n", + " OWID_WRL\n", + " 802060 BCE\n", + " 207.50\n", " \n", " \n", " 4\n", - " Afghanistan\n", - " 1994\n", - " 12.80\n", + " World\n", + " OWID_WRL\n", + " 801975 BCE\n", + " 202.92\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 1866\n", + " World\n", + " OWID_WRL\n", + " 2014\n", + " 398.65\n", + " \n", + " \n", + " 1867\n", + " World\n", + " OWID_WRL\n", + " 2015\n", + " 400.83\n", + " \n", + " \n", + " 1868\n", + " World\n", + " OWID_WRL\n", + " 2016\n", + " 404.24\n", + " \n", + " \n", + " 1869\n", + " World\n", + " OWID_WRL\n", + " 2017\n", + " 406.55\n", + " \n", + " \n", + " 1870\n", + " World\n", + " OWID_WRL\n", + " 2018\n", + " 408.52\n", " \n", " \n", "\n", + "

1871 rows × 4 columns

\n", "" ], "text/plain": [ - " Country Year Pollution_deaths\n", - "0 Afghanistan 1990 13.56\n", - "1 Afghanistan 1991 13.19\n", - "2 Afghanistan 1992 13.05\n", - "3 Afghanistan 1993 12.88\n", - "4 Afghanistan 1994 12.80" + " Entity Code Year COâ‚‚ concentration (parts per million)\n", + "0 World OWID_WRL 803719 BCE 207.29\n", + "1 World OWID_WRL 803182 BCE 202.23\n", + "2 World OWID_WRL 802573 BCE 204.86\n", + "3 World OWID_WRL 802060 BCE 207.50\n", + "4 World OWID_WRL 801975 BCE 202.92\n", + "... ... ... ... ...\n", + "1866 World OWID_WRL 2014 398.65\n", + "1867 World OWID_WRL 2015 400.83\n", + "1868 World OWID_WRL 2016 404.24\n", + "1869 World OWID_WRL 2017 406.55\n", + "1870 World OWID_WRL 2018 408.52\n", + "\n", + "[1871 rows x 4 columns]" ] }, - "execution_count": 122, + "execution_count": 260, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "deaths.head()" + "co_concentration = pd.read_csv('data//global-co-concentration-ppm.csv')\n", + "co_concentration" ] }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 261, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration = co_concentration.rename(columns={'COâ‚‚ concentration (parts per million)':'COâ‚‚ concentration', 'Entity':'Global'})" + ] + }, + { + "cell_type": "code", + "execution_count": 281, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000,\n", - " 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,\n", - " 2012, 2013, 2014, 2015, 2016, 2017], dtype=int64)" + "'1900'" ] }, - "execution_count": 130, + "execution_count": 281, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#deaths.info()\n", - "deaths.Year.unique()" + "co_concentration.Year[1768]" ] }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 282, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 6384 entries, 0 to 6411\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 6384 non-null object \n", - " 1 Year 6384 non-null int64 \n", - " 2 Pollution_deaths 6384 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 199.5+ KB\n" - ] + "data": { + "text/plain": [ + "Text(0, 0.5, 'CO atmospheric concentration')" + ] + }, + "execution_count": 282, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "deaths.info()" + "x = co_concentration.Year[1768:]\n", + "y = co_concentration['CO₂ concentration'][1768:]\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('CO atmospheric concentration')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Global temperatures by country" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "temperature = pd.read_csv('GlobalLandTemperaturesByCountry.csv')" + "### CO emissions in imported goods as a share of domestic emissions (1990-2016)" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 284, "metadata": {}, "outputs": [ { @@ -926,230 +1015,181 @@ " \n", " \n", " \n", - " dt\n", - " AverageTemperature\n", - " AverageTemperatureUncertainty\n", - " Country\n", + " Entity\n", + " Code\n", + " Year\n", + " CO₂ emissions embedded in trade as a share of territorial emissions (%)\n", " \n", " \n", " \n", " \n", " 0\n", - " 1743-11-01\n", - " 4.384\n", - " 2.294\n", - " Åland\n", + " Africa\n", + " NaN\n", + " 1990\n", + " -17.07\n", " \n", " \n", " 1\n", - " 1743-12-01\n", - " NaN\n", + " Africa\n", " NaN\n", - " Åland\n", + " 1991\n", + " -16.34\n", " \n", " \n", " 2\n", - " 1744-01-01\n", - " NaN\n", + " Africa\n", " NaN\n", - " Åland\n", + " 1992\n", + " -16.78\n", " \n", " \n", " 3\n", - " 1744-02-01\n", - " NaN\n", + " Africa\n", " NaN\n", - " Åland\n", + " 1993\n", + " -18.40\n", " \n", " \n", " 4\n", - " 1744-03-01\n", - " NaN\n", + " Africa\n", " NaN\n", - " Åland\n", + " 1994\n", + " -17.49\n", " \n", " \n", "\n", "" ], "text/plain": [ - " dt AverageTemperature AverageTemperatureUncertainty Country\n", - "0 1743-11-01 4.384 2.294 Åland\n", - "1 1743-12-01 NaN NaN Åland\n", - "2 1744-01-01 NaN NaN Åland\n", - "3 1744-02-01 NaN NaN Åland\n", - "4 1744-03-01 NaN NaN Åland" + " Entity Code Year \\\n", + "0 Africa NaN 1990 \n", + "1 Africa NaN 1991 \n", + "2 Africa NaN 1992 \n", + "3 Africa NaN 1993 \n", + "4 Africa NaN 1994 \n", + "\n", + " CO₂ emissions embedded in trade as a share of territorial emissions (%) \n", + "0 -17.07 \n", + "1 -16.34 \n", + "2 -16.78 \n", + "3 -18.40 \n", + "4 -17.49 " ] }, - "execution_count": 20, + "execution_count": 284, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "temperature.head()" + "co_imported = pd.read_csv('data//co-emissions-in-imported-goods-as-a-share-of-domestic-emissions.csv')\n", + "co_imported.head()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 40, + "execution_count": 286, "metadata": {}, "outputs": [], "source": [ - "temperature['dt']= pd.to_datetime(temperature['dt']) " - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 577462 entries, 0 to 577461\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 dt 577462 non-null datetime64[ns]\n", - " 1 AverageTemperature 544811 non-null float64 \n", - " 2 AverageTemperatureUncertainty 545550 non-null float64 \n", - " 3 Country 577462 non-null object \n", - "dtypes: datetime64[ns](1), float64(2), object(1)\n", - "memory usage: 17.6+ MB\n" - ] - } - ], - "source": [ - "temperature.info()" + "#CO₂ emissions embedded in trade as a share of territorial emissions (%)\n", + "co_imported = co_imported.rename(columns={'CO₂ emissions embedded in trade as a share of territorial emissions (%)':'CO₂ imported', 'Entity':'Country'})" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 287, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0 False\n", - "1 False\n", - "2 False\n", - "3 False\n", - "4 False\n", - " ... \n", - "577457 False\n", - "577458 False\n", - "577459 False\n", - "577460 False\n", - "577461 False\n", - "Name: Country, Length: 577462, dtype: bool" + "array(['Africa', 'Albania', 'Argentina', 'Armenia', 'Asia', 'Australia',\n", + " 'Austria', 'Azerbaijan', 'Bahrain', 'Bangladesh', 'Belarus',\n", + " 'Belgium', 'Benin', 'Bolivia', 'Botswana', 'Brazil', 'Brunei',\n", + " 'Bulgaria', 'Burkina Faso', 'Cambodia', 'Cameroon', 'Canada',\n", + " 'Central America', 'Chile', 'China', 'Colombia', 'Costa Rica',\n", + " \"Cote d'Ivoire\", 'Croatia', 'Cyprus', 'Czech Republic', 'Denmark',\n", + " 'Dominican Republic', 'EU-28', 'Ecuador', 'Egypt', 'El Salvador',\n", + " 'Estonia', 'Ethiopia', 'Europe', 'Finland', 'France', 'Georgia',\n", + " 'Germany', 'Ghana', 'Greece', 'Guatemala', 'Guinea', 'Honduras',\n", + " 'Hong Kong', 'Hungary', 'India', 'Indonesia', 'Iran', 'Ireland',\n", + " 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'KP Annex B',\n", + " 'Kazakhstan', 'Kenya', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia',\n", + " 'Lithuania', 'Luxembourg', 'Madagascar', 'Malawi', 'Malaysia',\n", + " 'Malta', 'Mauritius', 'Mexico', 'Middle East', 'Mongolia',\n", + " 'Morocco', 'Mozambique', 'Namibia', 'Nepal', 'Netherlands',\n", + " 'New Zealand', 'Nicaragua', 'Nigeria', 'Non KP Annex B',\n", + " 'Non-OECD', 'North America', 'Norway', 'OECD', 'Oceania', 'Oman',\n", + " 'Pakistan', 'Panama', 'Paraguay', 'Peru', 'Philippines', 'Poland',\n", + " 'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda', 'Saudi Arabia',\n", + " 'Senegal', 'Singapore', 'Slovakia', 'Slovenia', 'South Africa',\n", + " 'South America', 'South Korea', 'Spain', 'Sri Lanka', 'Sweden',\n", + " 'Switzerland', 'Taiwan', 'Tanzania', 'Thailand', 'Togo',\n", + " 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Uganda', 'Ukraine',\n", + " 'United Arab Emirates', 'United Kingdom', 'United States',\n", + " 'Uruguay', 'Venezuela', 'Vietnam', 'Zambia', 'Zimbabwe'],\n", + " dtype=object)" ] }, - "execution_count": 23, + "execution_count": 287, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "temperature['Country'] == 'Spain'" + "co_imported.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "metadata": {}, + "outputs": [], + "source": [ + "regions = ['Africa','Asia','Central America','EU-28','Europe','KP Annex B','Middle East','Non KP Annex B','Non-OECD','North America','OECD','Oceania','South America']\n", + "for region in regions:\n", + " co_imported = co_imported[co_imported.Country != region]" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 301, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array(['Åland', 'Afghanistan', 'Africa', 'Albania', 'Algeria',\n", - " 'American Samoa', 'Andorra', 'Angola', 'Anguilla', 'Antarctica',\n", - " 'Antigua And Barbuda', 'Argentina', 'Armenia', 'Aruba', 'Asia',\n", - " 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',\n", - " 'Baker Island', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium',\n", - " 'Belize', 'Benin', 'Bhutan', 'Bolivia',\n", - " 'Bonaire, Saint Eustatius And Saba', 'Bosnia And Herzegovina',\n", - " 'Botswana', 'Brazil', 'British Virgin Islands', 'Bulgaria',\n", - " 'Burkina Faso', 'Burma', 'Burundi', \"Côte D'Ivoire\", 'Cambodia',\n", - " 'Cameroon', 'Canada', 'Cape Verde', 'Cayman Islands',\n", - " 'Central African Republic', 'Chad', 'Chile', 'China',\n", - " 'Christmas Island', 'Colombia', 'Comoros',\n", - " 'Congo (Democratic Republic Of The)', 'Congo', 'Costa Rica',\n", - " 'Croatia', 'Cuba', 'Curaçao', 'Cyprus', 'Czech Republic',\n", - " 'Denmark (Europe)', 'Denmark', 'Djibouti', 'Dominica',\n", - " 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador',\n", - " 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Ethiopia', 'Europe',\n", - " 'Falkland Islands (Islas Malvinas)', 'Faroe Islands',\n", - " 'Federated States Of Micronesia', 'Fiji', 'Finland',\n", - " 'France (Europe)', 'France', 'French Guiana', 'French Polynesia',\n", - " 'French Southern And Antarctic Lands', 'Gabon', 'Gambia',\n", - " 'Gaza Strip', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland',\n", - " 'Grenada', 'Guadeloupe', 'Guam', 'Guatemala', 'Guernsey',\n", - " 'Guinea Bissau', 'Guinea', 'Guyana', 'Haiti',\n", - " 'Heard Island And Mcdonald Islands', 'Honduras', 'Hong Kong',\n", - " 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\n", - " 'Ireland', 'Isle Of Man', 'Israel', 'Italy', 'Jamaica', 'Japan',\n", - " 'Jersey', 'Jordan', 'Kazakhstan', 'Kenya', 'Kingman Reef',\n", - " 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon',\n", - " 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n", - " 'Luxembourg', 'Macau', 'Macedonia', 'Madagascar', 'Malawi',\n", - " 'Malaysia', 'Mali', 'Malta', 'Martinique', 'Mauritania',\n", - " 'Mauritius', 'Mayotte', 'Mexico', 'Moldova', 'Monaco', 'Mongolia',\n", - " 'Montenegro', 'Montserrat', 'Morocco', 'Mozambique', 'Namibia',\n", - " 'Nepal', 'Netherlands (Europe)', 'Netherlands', 'New Caledonia',\n", - " 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Niue',\n", - " 'North America', 'North Korea', 'Northern Mariana Islands',\n", - " 'Norway', 'Oceania', 'Oman', 'Pakistan', 'Palau', 'Palestina',\n", - " 'Palmyra Atoll', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n", - " 'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',\n", - " 'Reunion', 'Romania', 'Russia', 'Rwanda', 'Saint Barthélemy',\n", - " 'Saint Kitts And Nevis', 'Saint Lucia', 'Saint Martin',\n", - " 'Saint Pierre And Miquelon', 'Saint Vincent And The Grenadines',\n", - " 'Samoa', 'San Marino', 'Sao Tome And Principe', 'Saudi Arabia',\n", - " 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',\n", - " 'Sint Maarten', 'Slovakia', 'Slovenia', 'Solomon Islands',\n", - " 'Somalia', 'South Africa', 'South America',\n", - " 'South Georgia And The South Sandwich Isla', 'South Korea',\n", - " 'Spain', 'Sri Lanka', 'Sudan', 'Suriname',\n", - " 'Svalbard And Jan Mayen', 'Swaziland', 'Sweden', 'Switzerland',\n", - " 'Syria', 'Taiwan', 'Tajikistan', 'Tanzania', 'Thailand',\n", - " 'Timor Leste', 'Togo', 'Tonga', 'Trinidad And Tobago', 'Tunisia',\n", - " 'Turkey', 'Turkmenistan', 'Turks And Caicas Islands', 'Uganda',\n", - " 'Ukraine', 'United Arab Emirates', 'United Kingdom (Europe)',\n", - " 'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan',\n", - " 'Venezuela', 'Vietnam', 'Virgin Islands', 'Western Sahara',\n", - " 'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 3213 entries, 27 to 3563\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 3213 non-null object \n", + " 1 Code 3213 non-null object \n", + " 2 Year 3213 non-null int64 \n", + " 3 CO₂ imported 3213 non-null float64\n", + "dtypes: float64(1), int64(1), object(2)\n", + "memory usage: 125.5+ KB\n" + ] } ], "source": [ - "temperature.Country.unique()" + "co_imported.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Global Temperature anomaly\n" + "### Share of deaths by pollution (1990-2017)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 117, "metadata": {}, "outputs": [ { @@ -1176,207 +1216,123 @@ " Entity\n", " Code\n", " Year\n", - " Median (℃)\n", - " Upper (℃)\n", - " Lower (℃)\n", + " Share of deaths from air pollution (%)\n", " \n", " \n", " \n", " \n", " 0\n", - " Global\n", - " NaN\n", - " 1850\n", - " -0.373\n", - " -0.339\n", - " -0.425\n", + " Afghanistan\n", + " AFG\n", + " 1990\n", + " 13.56\n", " \n", " \n", " 1\n", - " Global\n", - " NaN\n", - " 1851\n", - " -0.218\n", - " -0.184\n", - " -0.274\n", + " Afghanistan\n", + " AFG\n", + " 1991\n", + " 13.19\n", " \n", " \n", " 2\n", - " Global\n", - " NaN\n", - " 1852\n", - " -0.228\n", - " -0.196\n", - " -0.280\n", + " Afghanistan\n", + " AFG\n", + " 1992\n", + " 13.05\n", " \n", " \n", " 3\n", - " Global\n", - " NaN\n", - " 1853\n", - " -0.269\n", - " -0.239\n", - " -0.321\n", + " Afghanistan\n", + " AFG\n", + " 1993\n", + " 12.88\n", " \n", " \n", " 4\n", - " Global\n", - " NaN\n", - " 1854\n", - " -0.248\n", - " -0.218\n", - " -0.301\n", + " Afghanistan\n", + " AFG\n", + " 1994\n", + " 12.80\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Entity Code Year Median (℃) Upper (℃) Lower (℃)\n", - "0 Global NaN 1850 -0.373 -0.339 -0.425\n", - "1 Global NaN 1851 -0.218 -0.184 -0.274\n", - "2 Global NaN 1852 -0.228 -0.196 -0.280\n", - "3 Global NaN 1853 -0.269 -0.239 -0.321\n", - "4 Global NaN 1854 -0.248 -0.218 -0.301" + " Entity Code Year Share of deaths from air pollution (%)\n", + "0 Afghanistan AFG 1990 13.56\n", + "1 Afghanistan AFG 1991 13.19\n", + "2 Afghanistan AFG 1992 13.05\n", + "3 Afghanistan AFG 1993 12.88\n", + "4 Afghanistan AFG 1994 12.80" ] }, - "execution_count": 24, + "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "temperature_a = pd.read_csv('temperature_anomaly.csv')\n", - "temperature_a.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 676 entries, 0 to 675\n", - "Data columns (total 6 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Entity 676 non-null object \n", - " 1 Code 0 non-null float64\n", - " 2 Year 676 non-null int64 \n", - " 3 Median (℃) 676 non-null float64\n", - " 4 Upper (℃) 676 non-null float64\n", - " 5 Lower (℃) 676 non-null float64\n", - "dtypes: float64(4), int64(1), object(1)\n", - "memory usage: 31.8+ KB\n" - ] - } - ], - "source": [ - "temperature_a.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "temperature_a.drop(['Code'], axis=1, inplace=True)" + "deaths = pd.read_csv('data//share_deaths_air_pollution.csv')\n", + "deaths.head()" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860,\n", - " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", - " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", - " 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893,\n", - " 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904,\n", - " 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915,\n", - " 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926,\n", - " 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937,\n", - " 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948,\n", - " 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", - " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", - " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", - " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", - " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", - " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", - " 2015, 2016, 2017, 2018], dtype=int64)" + "Entity 0\n", + "Code 980\n", + "Year 0\n", + "Share of deaths from air pollution (%) 0\n", + "dtype: int64" ] }, - "execution_count": 31, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "temperature_a.Year.unique()" + "deaths.isna().sum()" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 119, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "temperature_a.plot(x='Year', y='Upper (℃)')" + "deaths.drop(['Code'], axis=1, inplace=True)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 120, "metadata": {}, + "outputs": [], "source": [ - "### Women in parliment by country" + "deaths.rename(columns={'Share of deaths from air pollution (%)':'Pollution_deaths', 'Entity':'Country'}, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 121, "metadata": {}, "outputs": [], "source": [ - "women_in_parliment = pd.read_csv('women_in_parliment.csv')" + "deaths = deaths[deaths.Country != 'World']" ] }, { "cell_type": "code", - "execution_count": 71, - "metadata": { - "collapsed": true - }, + "execution_count": 122, + "metadata": {}, "outputs": [ { "data": { @@ -1399,432 +1355,132 @@ " \n", " \n", " \n", - " Country Name\n", - " Country Code\n", - " Indicator Name\n", - " Indicator Code\n", - " 1960\n", - " 1961\n", - " 1962\n", - " 1963\n", - " 1964\n", - " 1965\n", - " ...\n", - " 2011\n", - " 2012\n", - " 2013\n", - " 2014\n", - " 2015\n", - " 2016\n", - " 2017\n", - " 2018\n", - " 2019\n", - " Unnamed: 64\n", + " Country\n", + " Year\n", + " Pollution_deaths\n", " \n", " \n", " \n", " \n", " 0\n", - " Aruba\n", - " ABW\n", - " Proportion of seats held by women in national ...\n", - " SG.GEN.PARL.ZS\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " Afghanistan\n", + " 1990\n", + " 13.56\n", " \n", " \n", " 1\n", " Afghanistan\n", - " AFG\n", - " Proportion of seats held by women in national ...\n", - " SG.GEN.PARL.ZS\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.87\n", - " NaN\n", + " 1991\n", + " 13.19\n", " \n", " \n", " 2\n", - " Angola\n", - " AGO\n", - " Proportion of seats held by women in national ...\n", - " SG.GEN.PARL.ZS\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 38.2\n", - " 34.1\n", - " 34.1\n", - " 36.8\n", - " 36.8\n", - " 36.8\n", - " 38.2\n", - " 30.5\n", - " 30.00\n", - " NaN\n", + " Afghanistan\n", + " 1992\n", + " 13.05\n", " \n", " \n", " 3\n", - " Albania\n", - " ALB\n", - " Proportion of seats held by women in national ...\n", - " SG.GEN.PARL.ZS\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 15.7\n", - " 15.7\n", - " 17.9\n", - " 20.0\n", - " 20.7\n", - " 22.9\n", - " 27.9\n", - " 27.9\n", - " 29.51\n", - " NaN\n", + " Afghanistan\n", + " 1993\n", + " 12.88\n", " \n", " \n", " 4\n", - " Andorra\n", - " AND\n", - " Proportion of seats held by women in national ...\n", - " SG.GEN.PARL.ZS\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 50.0\n", - " 50.0\n", - " 50.0\n", - " 50.0\n", - " 39.3\n", - " 32.1\n", - " 32.1\n", - " 32.1\n", - " 50.00\n", - " NaN\n", + " Afghanistan\n", + " 1994\n", + " 12.80\n", " \n", " \n", "\n", - "

5 rows × 65 columns

\n", "" ], "text/plain": [ - " Country Name Country Code \\\n", - "0 Aruba ABW \n", - "1 Afghanistan AFG \n", - "2 Angola AGO \n", - "3 Albania ALB \n", - "4 Andorra AND \n", - "\n", - " Indicator Name Indicator Code 1960 \\\n", - "0 Proportion of seats held by women in national ... SG.GEN.PARL.ZS NaN \n", - "1 Proportion of seats held by women in national ... SG.GEN.PARL.ZS NaN \n", - "2 Proportion of seats held by women in national ... SG.GEN.PARL.ZS NaN \n", - "3 Proportion of seats held by women in national ... SG.GEN.PARL.ZS NaN \n", - "4 Proportion of seats held by women in national ... SG.GEN.PARL.ZS NaN \n", - "\n", - " 1961 1962 1963 1964 1965 ... 2011 2012 2013 2014 2015 2016 \\\n", - "0 NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN ... 27.7 27.7 27.7 27.7 27.7 27.7 \n", - "2 NaN NaN NaN NaN NaN ... 38.2 34.1 34.1 36.8 36.8 36.8 \n", - "3 NaN NaN NaN NaN NaN ... 15.7 15.7 17.9 20.0 20.7 22.9 \n", - "4 NaN NaN NaN NaN NaN ... 50.0 50.0 50.0 50.0 39.3 32.1 \n", - "\n", - " 2017 2018 2019 Unnamed: 64 \n", - "0 NaN NaN NaN NaN \n", - "1 27.7 27.7 27.87 NaN \n", - "2 38.2 30.5 30.00 NaN \n", - "3 27.9 27.9 29.51 NaN \n", - "4 32.1 32.1 50.00 NaN \n", - "\n", - "[5 rows x 65 columns]" + " Country Year Pollution_deaths\n", + "0 Afghanistan 1990 13.56\n", + "1 Afghanistan 1991 13.19\n", + "2 Afghanistan 1992 13.05\n", + "3 Afghanistan 1993 12.88\n", + "4 Afghanistan 1994 12.80" ] }, - "execution_count": 71, + "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "women_in_parliment.head()" + "deaths.head()" ] }, { "cell_type": "code", - "execution_count": 72, - "metadata": { - "collapsed": true - }, + "execution_count": 130, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Index(['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',\n", - " '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n", - " '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n", - " '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n", - " '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n", - " '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',\n", - " '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n", - " '2014', '2015', '2016', '2017', '2018', '2019', 'Unnamed: 64'],\n", - " dtype='object')" + "array([1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000,\n", + " 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,\n", + " 2012, 2013, 2014, 2015, 2016, 2017], dtype=int64)" ] }, - "execution_count": 72, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "women_in_parliment.columns" + "#deaths.info()\n", + "deaths.Year.unique()" ] }, { "cell_type": "code", - "execution_count": 73, - "metadata": { - "collapsed": true - }, + "execution_count": 143, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", - "RangeIndex: 264 entries, 0 to 263\n", - "Data columns (total 65 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country Name 264 non-null object \n", - " 1 Country Code 264 non-null object \n", - " 2 Indicator Name 264 non-null object \n", - " 3 Indicator Code 264 non-null object \n", - " 4 1960 13 non-null object \n", - " 5 1961 0 non-null float64\n", - " 6 1962 0 non-null float64\n", - " 7 1963 0 non-null float64\n", - " 8 1964 0 non-null float64\n", - " 9 1965 0 non-null float64\n", - " 10 1966 0 non-null float64\n", - " 11 1967 0 non-null float64\n", - " 12 1968 0 non-null float64\n", - " 13 1969 0 non-null float64\n", - " 14 1970 0 non-null float64\n", - " 15 1971 0 non-null float64\n", - " 16 1972 0 non-null float64\n", - " 17 1973 0 non-null float64\n", - " 18 1974 0 non-null float64\n", - " 19 1975 0 non-null float64\n", - " 20 1976 0 non-null float64\n", - " 21 1977 0 non-null float64\n", - " 22 1978 0 non-null float64\n", - " 23 1979 0 non-null float64\n", - " 24 1980 0 non-null float64\n", - " 25 1981 0 non-null float64\n", - " 26 1982 0 non-null float64\n", - " 27 1983 0 non-null float64\n", - " 28 1984 0 non-null float64\n", - " 29 1985 0 non-null float64\n", - " 30 1986 0 non-null float64\n", - " 31 1987 0 non-null float64\n", - " 32 1988 0 non-null float64\n", - " 33 1989 0 non-null float64\n", - " 34 1990 160 non-null float64\n", - " 35 1991 10 non-null float64\n", - " 36 1992 0 non-null float64\n", - " 37 1993 0 non-null float64\n", - " 38 1994 0 non-null float64\n", - " 39 1995 0 non-null float64\n", - " 40 1996 0 non-null float64\n", - " 41 1997 191 non-null float64\n", - " 42 1998 204 non-null float64\n", - " 43 1999 191 non-null float64\n", - " 44 2000 198 non-null float64\n", - " 45 2001 209 non-null float64\n", - " 46 2002 208 non-null float64\n", - " 47 2003 219 non-null float64\n", - " 48 2004 226 non-null float64\n", - " 49 2005 232 non-null float64\n", - " 50 2006 235 non-null float64\n", - " 51 2007 234 non-null float64\n", - " 52 2008 233 non-null float64\n", - " 53 2009 233 non-null float64\n", - " 54 2010 233 non-null float64\n", - " 55 2011 235 non-null float64\n", - " 56 2012 233 non-null float64\n", - " 57 2013 233 non-null float64\n", - " 58 2014 234 non-null float64\n", - " 59 2015 235 non-null float64\n", - " 60 2016 237 non-null float64\n", - " 61 2017 239 non-null float64\n", - " 62 2018 239 non-null float64\n", - " 63 2019 238 non-null float64\n", - " 64 Unnamed: 64 11 non-null float64\n", - "dtypes: float64(60), object(5)\n", - "memory usage: 134.2+ KB\n" + "Int64Index: 6384 entries, 0 to 6411\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 6384 non-null object \n", + " 1 Year 6384 non-null int64 \n", + " 2 Pollution_deaths 6384 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 199.5+ KB\n" ] } ], "source": [ - "women_in_parliment.info()" + "deaths.info()" ] }, { - "cell_type": "code", - "execution_count": 74, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Proportion of seats held by women in national parliaments (%)',\n", - " 'BHS', 'COD', 'COG', 'EGY', 'FSM', 'GMB', 'HKG', 'IRN', 'KOR',\n", - " 'MAC', 'PRK', 'VEN', 'YEM'], dtype=object)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "women_in_parliment['Indicator Name'].unique()" + "### Global temperatures by country (not convinced yet)" ] }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "women_in_parliment = women_in_parliment.where(women_in_parliment['Indicator Name']=='Proportion of seats held by women in national parliaments (%)')" + "temperature = pd.read_csv('GlobalLandTemperaturesByCountry.csv')" ] }, { "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Proportion of seats held by women in national parliaments (%)',\n", - " nan], dtype=object)" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "women_in_parliment['Indicator Name'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "women_in_parliment.drop(['Country Code', 'Indicator Name', 'Indicator Code',\n", - " '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n", - " '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n", - " '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n", - " '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n", - " '1996','Unnamed: 64'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 264 entries, 0 to 263\n", - "Data columns (total 24 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country Name 251 non-null object \n", - " 1 1997 191 non-null float64\n", - " 2 1998 196 non-null float64\n", - " 3 1999 181 non-null float64\n", - " 4 2000 188 non-null float64\n", - " 5 2001 200 non-null float64\n", - " 6 2002 198 non-null float64\n", - " 7 2003 211 non-null float64\n", - " 8 2004 217 non-null float64\n", - " 9 2005 221 non-null float64\n", - " 10 2006 224 non-null float64\n", - " 11 2007 223 non-null float64\n", - " 12 2008 222 non-null float64\n", - " 13 2009 222 non-null float64\n", - " 14 2010 222 non-null float64\n", - " 15 2011 224 non-null float64\n", - " 16 2012 223 non-null float64\n", - " 17 2013 222 non-null float64\n", - " 18 2014 224 non-null float64\n", - " 19 2015 225 non-null float64\n", - " 20 2016 227 non-null float64\n", - " 21 2017 228 non-null float64\n", - " 22 2018 228 non-null float64\n", - " 23 2019 227 non-null float64\n", - "dtypes: float64(23), object(1)\n", - "memory usage: 49.6+ KB\n" - ] - } - ], - "source": [ - "women_in_parliment.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 79, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1848,194 +1504,82 @@ " \n", " \n", " \n", - " Country Name\n", - " 1997\n", - " 1998\n", - " 1999\n", - " 2000\n", - " 2001\n", - " 2002\n", - " 2003\n", - " 2004\n", - " 2005\n", - " ...\n", - " 2010\n", - " 2011\n", - " 2012\n", - " 2013\n", - " 2014\n", - " 2015\n", - " 2016\n", - " 2017\n", - " 2018\n", - " 2019\n", + " dt\n", + " AverageTemperature\n", + " AverageTemperatureUncertainty\n", + " Country\n", " \n", " \n", " \n", " \n", " 0\n", - " Aruba\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 1743-11-01\n", + " 4.384\n", + " 2.294\n", + " Ã…land\n", " \n", " \n", " 1\n", - " Afghanistan\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 1743-12-01\n", " NaN\n", " NaN\n", - " 27.3\n", - " ...\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.7\n", - " 27.87\n", + " Ã…land\n", " \n", " \n", " 2\n", - " Angola\n", - " 9.5\n", - " 15.5\n", - " 15.5\n", - " 15.5\n", - " 15.5\n", - " 15.5\n", - " 15.5\n", - " 15.0\n", - " 15.0\n", - " ...\n", - " 38.6\n", - " 38.2\n", - " 34.1\n", - " 34.1\n", - " 36.8\n", - " 36.8\n", - " 36.8\n", - " 38.2\n", - " 30.5\n", - " 30.00\n", + " 1744-01-01\n", + " NaN\n", + " NaN\n", + " Ã…land\n", " \n", " \n", " 3\n", - " Albania\n", + " 1744-02-01\n", " NaN\n", " NaN\n", - " 5.2\n", - " 5.2\n", - " 5.7\n", - " 5.7\n", - " 5.7\n", - " 6.4\n", - " 7.1\n", - " ...\n", - " 16.4\n", - " 15.7\n", - " 15.7\n", - " 17.9\n", - " 20.0\n", - " 20.7\n", - " 22.9\n", - " 27.9\n", - " 27.9\n", - " 29.51\n", + " Ã…land\n", " \n", " \n", " 4\n", - " Andorra\n", - " 7.1\n", - " 7.1\n", - " 7.1\n", - " 7.1\n", - " 14.3\n", - " 14.3\n", - " 14.3\n", - " 14.3\n", - " 28.6\n", - " ...\n", - " 35.7\n", - " 50.0\n", - " 50.0\n", - " 50.0\n", - " 50.0\n", - " 39.3\n", - " 32.1\n", - " 32.1\n", - " 32.1\n", - " 50.00\n", + " 1744-03-01\n", + " NaN\n", + " NaN\n", + " Ã…land\n", " \n", " \n", "\n", - "

5 rows × 24 columns

\n", "" ], "text/plain": [ - " Country Name 1997 1998 1999 2000 2001 2002 2003 2004 2005 ... \\\n", - "0 Aruba NaN NaN NaN NaN NaN NaN NaN NaN NaN ... \n", - "1 Afghanistan NaN NaN NaN NaN NaN NaN NaN NaN 27.3 ... \n", - "2 Angola 9.5 15.5 15.5 15.5 15.5 15.5 15.5 15.0 15.0 ... \n", - "3 Albania NaN NaN 5.2 5.2 5.7 5.7 5.7 6.4 7.1 ... \n", - "4 Andorra 7.1 7.1 7.1 7.1 14.3 14.3 14.3 14.3 28.6 ... \n", - "\n", - " 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 \n", - "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.87 \n", - "2 38.6 38.2 34.1 34.1 36.8 36.8 36.8 38.2 30.5 30.00 \n", - "3 16.4 15.7 15.7 17.9 20.0 20.7 22.9 27.9 27.9 29.51 \n", - "4 35.7 50.0 50.0 50.0 50.0 39.3 32.1 32.1 32.1 50.00 \n", - "\n", - "[5 rows x 24 columns]" + " dt AverageTemperature AverageTemperatureUncertainty Country\n", + "0 1743-11-01 4.384 2.294 Ã…land\n", + "1 1743-12-01 NaN NaN Ã…land\n", + "2 1744-01-01 NaN NaN Ã…land\n", + "3 1744-02-01 NaN NaN Ã…land\n", + "4 1744-03-01 NaN NaN Ã…land" ] }, - "execution_count": 79, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "women_in_parliment.head()" + "temperature.head()" ] }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 219, "metadata": {}, "outputs": [], "source": [ - "women_in_parliment_clean = women_in_parliment.dropna()" + "temperature['dt']= pd.to_datetime(temperature['dt']) " ] }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 227, "metadata": {}, "outputs": [ { @@ -2059,199 +1603,161 @@ " \n", " \n", " \n", - " Country Name\n", - " 1997\n", - " 1998\n", - " 1999\n", - " 2000\n", - " 2001\n", - " 2002\n", - " 2003\n", - " 2004\n", - " 2005\n", - " ...\n", - " 2010\n", - " 2011\n", - " 2012\n", - " 2013\n", - " 2014\n", - " 2015\n", - " 2016\n", - " 2017\n", - " 2018\n", - " 2019\n", - " \n", - " \n", - " \n", + " dt\n", + " AverageTemperature\n", + " AverageTemperatureUncertainty\n", + " Country\n", + " year\n", + " \n", + " \n", + " \n", " \n", - " 2\n", - " Angola\n", - " 9.500000\n", - " 15.500000\n", - " 15.500000\n", - " 15.500000\n", - " 15.500000\n", - " 15.500000\n", - " 15.500000\n", - " 15.000000\n", - " 15.000000\n", - " ...\n", - " 38.600000\n", - " 38.200000\n", - " 34.100000\n", - " 34.100000\n", - " 36.80000\n", - " 36.800000\n", - " 36.80000\n", - " 38.200000\n", - " 30.500000\n", - " 30.000000\n", + " 0\n", + " 1743-11-01\n", + " 4.384\n", + " 2.294\n", + " Ã…land\n", + " 1743\n", " \n", " \n", - " 4\n", - " Andorra\n", - " 7.100000\n", - " 7.100000\n", - " 7.100000\n", - " 7.100000\n", - " 14.300000\n", - " 14.300000\n", - " 14.300000\n", - " 14.300000\n", - " 28.600000\n", - " ...\n", - " 35.700000\n", - " 50.000000\n", - " 50.000000\n", - " 50.000000\n", - " 50.00000\n", - " 39.300000\n", - " 32.10000\n", - " 32.100000\n", - " 32.100000\n", - " 50.000000\n", + " 1\n", + " 1743-12-01\n", + " NaN\n", + " NaN\n", + " Ã…land\n", + " 1743\n", " \n", " \n", - " 5\n", - " Arab World\n", - " 3.757835\n", - " 3.474235\n", - " 3.623432\n", - " 3.768103\n", - " 4.610598\n", - " 6.132352\n", - " 6.043918\n", - " 6.702671\n", - " 7.041782\n", - " ...\n", - " 11.923926\n", - " 11.335266\n", - " 14.872018\n", - " 17.492445\n", - " 17.79397\n", - " 18.779151\n", - " 18.85265\n", - " 18.821034\n", - " 19.017979\n", - " 18.001945\n", + " 2\n", + " 1744-01-01\n", + " NaN\n", + " NaN\n", + " Ã…land\n", + " 1744\n", " \n", " \n", - " 6\n", - " United Arab Emirates\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " ...\n", - " 22.500000\n", - " 17.500000\n", - " 17.500000\n", - " 17.500000\n", - " 17.50000\n", - " 22.500000\n", - " 22.50000\n", - " 22.500000\n", - " 22.500000\n", - " 22.500000\n", + " 3\n", + " 1744-02-01\n", + " NaN\n", + " NaN\n", + " Ã…land\n", + " 1744\n", " \n", " \n", - " 8\n", - " Armenia\n", - " 6.300000\n", - " 6.300000\n", - " 3.100000\n", - " 3.100000\n", - " 3.100000\n", - " 3.100000\n", - " 4.600000\n", - " 5.300000\n", - " 5.300000\n", - " ...\n", - " 9.200000\n", - " 8.400000\n", - " 10.700000\n", - " 10.700000\n", - " 10.70000\n", - " 10.700000\n", - " 10.70000\n", - " 18.100000\n", - " 18.100000\n", - " 24.240000\n", + " 4\n", + " 1744-03-01\n", + " NaN\n", + " NaN\n", + " Ã…land\n", + " 1744\n", " \n", " \n", "\n", - "

5 rows × 24 columns

\n", "" ], "text/plain": [ - " Country Name 1997 1998 1999 2000 2001 \\\n", - "2 Angola 9.500000 15.500000 15.500000 15.500000 15.500000 \n", - "4 Andorra 7.100000 7.100000 7.100000 7.100000 14.300000 \n", - "5 Arab World 3.757835 3.474235 3.623432 3.768103 4.610598 \n", - "6 United Arab Emirates 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "8 Armenia 6.300000 6.300000 3.100000 3.100000 3.100000 \n", - "\n", - " 2002 2003 2004 2005 ... 2010 2011 \\\n", - "2 15.500000 15.500000 15.000000 15.000000 ... 38.600000 38.200000 \n", - "4 14.300000 14.300000 14.300000 28.600000 ... 35.700000 50.000000 \n", - "5 6.132352 6.043918 6.702671 7.041782 ... 11.923926 11.335266 \n", - "6 0.000000 0.000000 0.000000 0.000000 ... 22.500000 17.500000 \n", - "8 3.100000 4.600000 5.300000 5.300000 ... 9.200000 8.400000 \n", - "\n", - " 2012 2013 2014 2015 2016 2017 2018 \\\n", - "2 34.100000 34.100000 36.80000 36.800000 36.80000 38.200000 30.500000 \n", - "4 50.000000 50.000000 50.00000 39.300000 32.10000 32.100000 32.100000 \n", - "5 14.872018 17.492445 17.79397 18.779151 18.85265 18.821034 19.017979 \n", - "6 17.500000 17.500000 17.50000 22.500000 22.50000 22.500000 22.500000 \n", - "8 10.700000 10.700000 10.70000 10.700000 10.70000 18.100000 18.100000 \n", + " dt AverageTemperature AverageTemperatureUncertainty Country year\n", + "0 1743-11-01 4.384 2.294 Ã…land 1743\n", + "1 1743-12-01 NaN NaN Ã…land 1743\n", + "2 1744-01-01 NaN NaN Ã…land 1744\n", + "3 1744-02-01 NaN NaN Ã…land 1744\n", + "4 1744-03-01 NaN NaN Ã…land 1744" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature['year'] = pd.DatetimeIndex(temperature['dt']).year\n", + "temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
AverageTemperature
yearCountry
1743Albania8.620
Andorra7.556
Austria2.482
Belarus0.767
Belgium7.106
\n", + "
" + ], + "text/plain": [ + " AverageTemperature\n", + "year Country \n", + "1743 Albania 8.620\n", + " Andorra 7.556\n", + " Austria 2.482\n", + " Belarus 0.767\n", + " Belgium 7.106" ] }, - "execution_count": 81, + "execution_count": 231, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "women_in_parliment_clean.head()" + "annual_temperature = temperature[['year', 'Country','AverageTemperature']].groupby(['year','Country']).agg({'AverageTemperature':'mean'})\n", + "annual_temperature.head()" ] }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 234, "metadata": {}, "outputs": [ { @@ -2259,32 +1765,1205 @@ "output_type": "stream", "text": [ "\n", - "Int64Index: 115 entries, 2 to 263\n", - "Data columns (total 24 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country Name 115 non-null object \n", - " 1 1997 115 non-null float64\n", - " 2 1998 115 non-null float64\n", - " 3 1999 115 non-null float64\n", - " 4 2000 115 non-null float64\n", - " 5 2001 115 non-null float64\n", - " 6 2002 115 non-null float64\n", - " 7 2003 115 non-null float64\n", - " 8 2004 115 non-null float64\n", - " 9 2005 115 non-null float64\n", - " 10 2006 115 non-null float64\n", - " 11 2007 115 non-null float64\n", - " 12 2008 115 non-null float64\n", - " 13 2009 115 non-null float64\n", - " 14 2010 115 non-null float64\n", - " 15 2011 115 non-null float64\n", - " 16 2012 115 non-null float64\n", - " 17 2013 115 non-null float64\n", - " 18 2014 115 non-null float64\n", - " 19 2015 115 non-null float64\n", - " 20 2016 115 non-null float64\n", - " 21 2017 115 non-null float64\n", + "MultiIndex: 48243 entries, (1743, 'Albania') to (2013, 'Ã…land')\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 AverageTemperature 45915 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 569.5+ KB\n" + ] + } + ], + "source": [ + "annual_temperature.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global Temperature anomaly 1850-2018\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearMedian (℃)Upper (℃)Lower (℃)
0GlobalNaN1850-0.373-0.339-0.425
1GlobalNaN1851-0.218-0.184-0.274
2GlobalNaN1852-0.228-0.196-0.280
3GlobalNaN1853-0.269-0.239-0.321
4GlobalNaN1854-0.248-0.218-0.301
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Median (℃) Upper (℃) Lower (℃)\n", + "0 Global NaN 1850 -0.373 -0.339 -0.425\n", + "1 Global NaN 1851 -0.218 -0.184 -0.274\n", + "2 Global NaN 1852 -0.228 -0.196 -0.280\n", + "3 Global NaN 1853 -0.269 -0.239 -0.321\n", + "4 Global NaN 1854 -0.248 -0.218 -0.301" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a = pd.read_csv('temperature_anomaly.csv')\n", + "temperature_a.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 676 entries, 0 to 675\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Entity 676 non-null object \n", + " 1 Code 0 non-null float64\n", + " 2 Year 676 non-null int64 \n", + " 3 Median (℃) 676 non-null float64\n", + " 4 Upper (℃) 676 non-null float64\n", + " 5 Lower (℃) 676 non-null float64\n", + "dtypes: float64(4), int64(1), object(1)\n", + "memory usage: 31.8+ KB\n" + ] + } + ], + "source": [ + "temperature_a.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "temperature_a.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": {}, + "outputs": [], + "source": [ + "temperature_a.rename(columns={'Entity':'Country'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Global', 'Northern Hemisphere', 'Southern Hemisphere', 'Tropics'],\n", + " dtype=object)" + ] + }, + "execution_count": 245, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860,\n", + " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", + " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", + " 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893,\n", + " 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904,\n", + " 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915,\n", + " 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926,\n", + " 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937,\n", + " 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948,\n", + " 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", + " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", + " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", + " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", + " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", + " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", + " 2015, 2016, 2017, 2018], dtype=int64)" + ] + }, + "execution_count": 243, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.Year.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" + ] + }, + "execution_count": 247, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "x = temperature_a.Year\n", + "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='Temperature Anomaly', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('increse ordescrease in Temperature(℃)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Women in parliment by country" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "women_in_parliment = pd.read_csv('women_in_parliment.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Country NameCountry CodeIndicator NameIndicator Code196019611962196319641965...201120122013201420152016201720182019Unnamed: 64
0ArubaABWProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1AfghanistanAFGProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...27.727.727.727.727.727.727.727.727.87NaN
2AngolaAGOProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...38.234.134.136.836.836.838.230.530.00NaN
3AlbaniaALBProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...15.715.717.920.020.722.927.927.929.51NaN
4AndorraANDProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...50.050.050.050.039.332.132.132.150.00NaN
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5 rows × 65 columns

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5 rows × 24 columns

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" + ], + "text/plain": [ + " Country Name 1997 1998 1999 2000 2001 2002 2003 2004 2005 ... \\\n", + "0 Aruba NaN NaN NaN NaN NaN NaN NaN NaN NaN ... \n", + "1 Afghanistan NaN NaN NaN NaN NaN NaN NaN NaN 27.3 ... \n", + "2 Angola 9.5 15.5 15.5 15.5 15.5 15.5 15.5 15.0 15.0 ... \n", + "3 Albania NaN NaN 5.2 5.2 5.7 5.7 5.7 6.4 7.1 ... \n", + "4 Andorra 7.1 7.1 7.1 7.1 14.3 14.3 14.3 14.3 28.6 ... \n", + "\n", + " 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 \n", + "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.87 \n", + "2 38.6 38.2 34.1 34.1 36.8 36.8 36.8 38.2 30.5 30.00 \n", + "3 16.4 15.7 15.7 17.9 20.0 20.7 22.9 27.9 27.9 29.51 \n", + "4 35.7 50.0 50.0 50.0 50.0 39.3 32.1 32.1 32.1 50.00 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "women_in_parliment.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [], + "source": [ + "columns = list(women_in_parliment.columns)\n", + "columns.remove('Country Name')" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Country NameYearWomen in Parliment
0Aruba1997NaN
1Afghanistan1997NaN
2Angola19979.5
3Albania1997NaN
4Andorra19977.1
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" + ], + "text/plain": [ + " Country Name Year Women in Parliment\n", + "0 Aruba 1997 NaN\n", + "1 Afghanistan 1997 NaN\n", + "2 Angola 1997 9.5\n", + "3 Albania 1997 NaN\n", + "4 Andorra 1997 7.1" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "women_in_power = pd.melt(women_in_parliment, id_vars=['Country Name'], value_vars=columns, var_name=\"Year\", value_name=\"Women in Parliment\")\n", + "women_in_power.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Country Name 299\n", + "Year 0\n", + "Women in Parliment 1128\n", + "dtype: int64" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "women_in_power.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 6072 entries, 0 to 6071\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country Name 5773 non-null object \n", + " 1 Year 6072 non-null object \n", + " 2 Women in Parliment 4944 non-null float64\n", + "dtypes: float64(1), object(2)\n", + "memory usage: 142.4+ KB\n" + ] + } + ], + "source": [ + "women_in_power.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GDP per capita (1981-2018)" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0CountryYearGDPPopulationGDP_per_capita
00Canada1981808.68817124900000.032477.436602
11United States19816698.570801229465714.029192.033459
22Cuba198135.8826419705971.03696.965594
33Mexico1981589.54895069930971.08430.441359
44Antigua Barbuda19810.40892768264.05990.381182
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" + ], + "text/plain": [ + " Unnamed: 0 Country Year GDP Population GDP_per_capita\n", + "0 0 Canada 1981 808.688171 24900000.0 32477.436602\n", + "1 1 United States 1981 6698.570801 229465714.0 29192.033459\n", + "2 2 Cuba 1981 35.882641 9705971.0 3696.965594\n", + "3 3 Mexico 1981 589.548950 69930971.0 8430.441359\n", + "4 4 Antigua Barbuda 1981 0.408927 68264.0 5990.381182" + ] + }, + "execution_count": 236, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdp = pd.read_csv('data//GDP_P.csv')\n", + "gdp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n", + " 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n", + " 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,\n", + " 2014, 2015, 2016, 2017, 2018], dtype=int64)" + ] + }, + "execution_count": 237, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdp.Year.unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tertiary education (1970-2010)" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0CountryYearTertiary_education
00Afghanistan19701.08
11Afghanistan19751.48
22Afghanistan19801.96
33Afghanistan19852.49
44Afghanistan19902.91
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" + ], + "text/plain": [ + " Unnamed: 0 Country Year Tertiary_education\n", + "0 0 Afghanistan 1970 1.08\n", + "1 1 Afghanistan 1975 1.48\n", + "2 2 Afghanistan 1980 1.96\n", + "3 3 Afghanistan 1985 2.49\n", + "4 4 Afghanistan 1990 2.91" + ] + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "education = pd.read_csv('data//education_df.csv')\n", + "education.head()" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 240, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'women_in_power = women_in_parliment_clean.pivot_table(values=women_in_parliment_clean.values).reset_index()\\nwomen_in_power'" + "array([1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010], dtype=int64)" ] }, - "execution_count": 88, + "execution_count": 240, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "'''women_in_power = women_in_parliment_clean.pivot_table(values=women_in_parliment_clean.values).reset_index()\n", - "women_in_power'''" + "education.Year.unique()" ] }, { From a629054e6cfe689293efeb9a978d325ff8ec4d9b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Sat, 23 May 2020 10:51:45 +0200 Subject: [PATCH 04/17] two more datasets --- your-project/Data Gathering.ipynb | 295 +++++++++++++++++++++++++++++- 1 file changed, 287 insertions(+), 8 deletions(-) diff --git a/your-project/Data Gathering.ipynb b/your-project/Data Gathering.ipynb index 4e0b5438..c8b6a079 100644 --- a/your-project/Data Gathering.ipynb +++ b/your-project/Data Gathering.ipynb @@ -1676,7 +1676,98 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 303, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Åland', 'Afghanistan', 'Africa', 'Albania', 'Algeria',\n", + " 'American Samoa', 'Andorra', 'Angola', 'Anguilla', 'Antarctica',\n", + " 'Antigua And Barbuda', 'Argentina', 'Armenia', 'Aruba', 'Asia',\n", + " 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',\n", + " 'Baker Island', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium',\n", + " 'Belize', 'Benin', 'Bhutan', 'Bolivia',\n", + " 'Bonaire, Saint Eustatius And Saba', 'Bosnia And Herzegovina',\n", + " 'Botswana', 'Brazil', 'British Virgin Islands', 'Bulgaria',\n", + " 'Burkina Faso', 'Burma', 'Burundi', \"Côte D'Ivoire\", 'Cambodia',\n", + " 'Cameroon', 'Canada', 'Cape Verde', 'Cayman Islands',\n", + " 'Central African Republic', 'Chad', 'Chile', 'China',\n", + " 'Christmas Island', 'Colombia', 'Comoros',\n", + " 'Congo (Democratic Republic Of The)', 'Congo', 'Costa Rica',\n", + " 'Croatia', 'Cuba', 'Curaçao', 'Cyprus', 'Czech Republic',\n", + " 'Denmark (Europe)', 'Denmark', 'Djibouti', 'Dominica',\n", + " 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador',\n", + " 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Ethiopia', 'Europe',\n", + " 'Falkland Islands (Islas Malvinas)', 'Faroe Islands',\n", + " 'Federated States Of Micronesia', 'Fiji', 'Finland',\n", + " 'France (Europe)', 'France', 'French Guiana', 'French Polynesia',\n", + " 'French Southern And Antarctic Lands', 'Gabon', 'Gambia',\n", + " 'Gaza Strip', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland',\n", + " 'Grenada', 'Guadeloupe', 'Guam', 'Guatemala', 'Guernsey',\n", + " 'Guinea Bissau', 'Guinea', 'Guyana', 'Haiti',\n", + " 'Heard Island And Mcdonald Islands', 'Honduras', 'Hong Kong',\n", + " 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\n", + " 'Ireland', 'Isle Of Man', 'Israel', 'Italy', 'Jamaica', 'Japan',\n", + " 'Jersey', 'Jordan', 'Kazakhstan', 'Kenya', 'Kingman Reef',\n", + " 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon',\n", + " 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n", + " 'Luxembourg', 'Macau', 'Macedonia', 'Madagascar', 'Malawi',\n", + " 'Malaysia', 'Mali', 'Malta', 'Martinique', 'Mauritania',\n", + " 'Mauritius', 'Mayotte', 'Mexico', 'Moldova', 'Monaco', 'Mongolia',\n", + " 'Montenegro', 'Montserrat', 'Morocco', 'Mozambique', 'Namibia',\n", + " 'Nepal', 'Netherlands (Europe)', 'Netherlands', 'New Caledonia',\n", + " 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Niue',\n", + " 'North America', 'North Korea', 'Northern Mariana Islands',\n", + " 'Norway', 'Oceania', 'Oman', 'Pakistan', 'Palau', 'Palestina',\n", + " 'Palmyra Atoll', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n", + " 'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',\n", + " 'Reunion', 'Romania', 'Russia', 'Rwanda', 'Saint Barthélemy',\n", + " 'Saint Kitts And Nevis', 'Saint Lucia', 'Saint Martin',\n", + " 'Saint Pierre And Miquelon', 'Saint Vincent And The Grenadines',\n", + " 'Samoa', 'San Marino', 'Sao Tome And Principe', 'Saudi Arabia',\n", + " 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',\n", + " 'Sint Maarten', 'Slovakia', 'Slovenia', 'Solomon Islands',\n", + " 'Somalia', 'South Africa', 'South America',\n", + " 'South Georgia And The South Sandwich Isla', 'South Korea',\n", + " 'Spain', 'Sri Lanka', 'Sudan', 'Suriname',\n", + " 'Svalbard And Jan Mayen', 'Swaziland', 'Sweden', 'Switzerland',\n", + " 'Syria', 'Taiwan', 'Tajikistan', 'Tanzania', 'Thailand',\n", + " 'Timor Leste', 'Togo', 'Tonga', 'Trinidad And Tobago', 'Tunisia',\n", + " 'Turkey', 'Turkmenistan', 'Turks And Caicas Islands', 'Uganda',\n", + " 'Ukraine', 'United Arab Emirates', 'United Kingdom (Europe)',\n", + " 'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan',\n", + " 'Venezuela', 'Vietnam', 'Virgin Islands', 'Western Sahara',\n", + " 'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)" + ] + }, + "execution_count": 303, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "metadata": {}, + "outputs": [], + "source": [ + "non_wanted = ['Åland','Africa','Asia','Baker Island','Denmark','Federated States Of Micronesia','France', 'French Guiana', 'French Polynesia',\n", + " 'French Southern And Antarctic Lands','Gaza Strip','Guadeloupe', 'Guam','Guernsey','Heard Island And Mcdonald Islands','Isle Of Man','Jersey','Kingman Reef',\n", + " 'Montserrat','Netherlands (Europe)','Niue','North America','New Caledonia','Northern Mariana Islands','Oceania',\n", + " 'Palmyra Atoll','Saint Barthélemy','American Samoa','South America','South Georgia And The South Sandwich Isla',\n", + " 'Svalbard And Jan Mayen','Turks And Caicas Islands','United Kingdom','Western Sahara']\n", + "for region in non_wanted:\n", + " temperature = temperature[temperature.Country != region]" + ] + }, + { + "cell_type": "code", + "execution_count": 307, "metadata": {}, "outputs": [ { @@ -1745,7 +1836,7 @@ " Belgium 7.106" ] }, - "execution_count": 231, + "execution_count": 307, "metadata": {}, "output_type": "execute_result" } @@ -1757,7 +1848,193 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 309, + "metadata": {}, + "outputs": [], + "source": [ + "annual_temperature.reset_index(level=0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yearAverageTemperature
Country
Albania17438.620
Andorra17437.556
Austria17432.482
Belarus17430.767
Belgium17437.106
\n", + "
" + ], + "text/plain": [ + " year AverageTemperature\n", + "Country \n", + "Albania 1743 8.620\n", + "Andorra 1743 7.556\n", + "Austria 1743 2.482\n", + "Belarus 1743 0.767\n", + "Belgium 1743 7.106" + ] + }, + "execution_count": 310, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 311, + "metadata": {}, + "outputs": [], + "source": [ + "annual_temperature.reset_index(level=0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryyearAverageTemperature
0Albania17438.620
1Andorra17437.556
2Austria17432.482
3Belarus17430.767
4Belgium17437.106
\n", + "
" + ], + "text/plain": [ + " Country year AverageTemperature\n", + "0 Albania 1743 8.620\n", + "1 Andorra 1743 7.556\n", + "2 Austria 1743 2.482\n", + "3 Belarus 1743 0.767\n", + "4 Belgium 1743 7.106" + ] + }, + "execution_count": 312, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 313, "metadata": {}, "outputs": [ { @@ -1765,13 +2042,15 @@ "output_type": "stream", "text": [ "\n", - "MultiIndex: 48243 entries, (1743, 'Albania') to (2013, 'Ã…land')\n", - "Data columns (total 1 columns):\n", + "RangeIndex: 41931 entries, 0 to 41930\n", + "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 AverageTemperature 45915 non-null float64\n", - "dtypes: float64(1)\n", - "memory usage: 569.5+ KB\n" + " 0 Country 41931 non-null object \n", + " 1 year 41931 non-null int64 \n", + " 2 AverageTemperature 39793 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 982.9+ KB\n" ] } ], From ea4ee8a4288d031d4741695b5ed22e751339d24b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Sun, 24 May 2020 00:04:13 +0200 Subject: [PATCH 05/17] saving --- your-project/Data Gathering.ipynb | 2105 +++++++++++++++++++++++++++-- 1 file changed, 2006 insertions(+), 99 deletions(-) diff --git a/your-project/Data Gathering.ipynb b/your-project/Data Gathering.ipynb index c8b6a079..5c32fe68 100644 --- a/your-project/Data Gathering.ipynb +++ b/your-project/Data Gathering.ipynb @@ -1471,11 +1471,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 316, "metadata": {}, "outputs": [], "source": [ - "temperature = pd.read_csv('GlobalLandTemperaturesByCountry.csv')" + "temperature = pd.read_csv('data//GlobalLandTemperaturesByCountry.csv')" ] }, { @@ -2067,7 +2067,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 315, "metadata": {}, "outputs": [ { @@ -2158,13 +2158,13 @@ "4 Global NaN 1854 -0.248 -0.218 -0.301" ] }, - "execution_count": 24, + "execution_count": 315, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "temperature_a = pd.read_csv('temperature_anomaly.csv')\n", + "temperature_a = pd.read_csv('data//temperature_anomaly.csv')\n", "temperature_a.head()" ] }, @@ -2322,11 +2322,11 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 314, "metadata": {}, "outputs": [], "source": [ - "women_in_parliment = pd.read_csv('women_in_parliment.csv')" + "women_in_parliment = pd.read_csv('data//women_in_parliment.csv')" ] }, { @@ -2550,9 +2550,7 @@ { "cell_type": "code", "execution_count": 72, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -3551,6 +3549,15 @@ "women_in_power.head()" ] }, + { + "cell_type": "code", + "execution_count": 344, + "metadata": {}, + "outputs": [], + "source": [ + "women_in_power.rename(columns={'Country Name':'Country'}, inplace=True)" + ] + }, { "cell_type": "code", "execution_count": 176, @@ -3600,16 +3607,254 @@ "women_in_power.info()" ] }, + { + "cell_type": "code", + "execution_count": 346, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Aruba', 'Afghanistan', 'Angola', 'Albania', 'Andorra',\n", + " 'Arab World', 'United Arab Emirates', 'Argentina', 'Armenia',\n", + " 'American Samoa', 'Antigua and Barbuda', 'Australia', 'Austria',\n", + " 'Azerbaijan', 'Burundi', 'Belgium', 'Benin', 'Burkina Faso',\n", + " 'Bangladesh', 'Bulgaria', 'Bahrain', nan, 'Bosnia and Herzegovina',\n", + " 'Belarus', 'Belize', 'Bermuda', 'Bolivia', 'Brazil', 'Barbados',\n", + " 'Brunei Darussalam', 'Bhutan', 'Botswana',\n", + " 'Central African Republic', 'Canada',\n", + " 'Central Europe and the Baltics', 'Switzerland', 'Channel Islands',\n", + " 'Chile', 'China', \"Cote d'Ivoire\", 'Cameroon', 'Colombia',\n", + " 'Comoros', 'Cabo Verde', 'Costa Rica', 'Caribbean small states',\n", + " 'Cuba', 'Curacao', 'Cayman Islands', 'Cyprus', 'Czech Republic',\n", + " 'Germany', 'Djibouti', 'Dominica', 'Denmark', 'Dominican Republic',\n", + " 'Algeria', 'East Asia & Pacific (excluding high income)',\n", + " 'Early-demographic dividend', 'East Asia & Pacific',\n", + " 'Europe & Central Asia (excluding high income)',\n", + " 'Europe & Central Asia', 'Ecuador', 'Euro area', 'Eritrea',\n", + " 'Spain', 'Estonia', 'Ethiopia', 'European Union',\n", + " 'Fragile and conflict affected situations', 'Finland', 'Fiji',\n", + " 'France', 'Faroe Islands', 'Gabon', 'United Kingdom', 'Georgia',\n", + " 'Ghana', 'Gibraltar', 'Guinea', 'Guinea-Bissau',\n", + " 'Equatorial Guinea', 'Greece', 'Grenada', 'Greenland', 'Guatemala',\n", + " 'Guam', 'Guyana', 'High income', 'Honduras',\n", + " 'Heavily indebted poor countries (HIPC)', 'Croatia', 'Haiti',\n", + " 'Hungary', 'IBRD only', 'IDA & IBRD total', 'IDA total',\n", + " 'IDA blend', 'Indonesia', 'IDA only', 'Isle of Man', 'India',\n", + " 'Not classified', 'Ireland', 'Iraq', 'Iceland', 'Israel', 'Italy',\n", + " 'Jamaica', 'Jordan', 'Japan', 'Kazakhstan', 'Kenya',\n", + " 'Kyrgyz Republic', 'Cambodia', 'Kiribati', 'St. Kitts and Nevis',\n", + " 'Kuwait', 'Latin America & Caribbean (excluding high income)',\n", + " 'Lao PDR', 'Lebanon', 'Liberia', 'Libya', 'St. Lucia',\n", + " 'Latin America & Caribbean',\n", + " 'Least developed countries: UN classification', 'Low income',\n", + " 'Liechtenstein', 'Sri Lanka', 'Lower middle income',\n", + " 'Low & middle income', 'Lesotho', 'Late-demographic dividend',\n", + " 'Lithuania', 'Luxembourg', 'Latvia', 'St. Martin (French part)',\n", + " 'Morocco', 'Monaco', 'Moldova', 'Madagascar', 'Maldives',\n", + " 'Middle East & North Africa', 'Mexico', 'Marshall Islands',\n", + " 'Middle income', 'North Macedonia', 'Mali', 'Malta', 'Myanmar',\n", + " 'Middle East & North Africa (excluding high income)', 'Montenegro',\n", + " 'Mongolia', 'Northern Mariana Islands', 'Mozambique', 'Mauritania',\n", + " 'Mauritius', 'Malawi', 'Malaysia', 'North America', 'Namibia',\n", + " 'New Caledonia', 'Niger', 'Nigeria', 'Nicaragua', 'Netherlands',\n", + " 'Norway', 'Nepal', 'Nauru', 'New Zealand', 'OECD members', 'Oman',\n", + " 'Other small states', 'Pakistan', 'Panama', 'Peru', 'Philippines',\n", + " 'Palau', 'Papua New Guinea', 'Poland', 'Pre-demographic dividend',\n", + " 'Puerto Rico', 'Portugal', 'Paraguay', 'West Bank and Gaza',\n", + " 'Pacific island small states', 'Post-demographic dividend',\n", + " 'French Polynesia', 'Qatar', 'Romania', 'Russian Federation',\n", + " 'Rwanda', 'South Asia', 'Saudi Arabia', 'Sudan', 'Senegal',\n", + " 'Singapore', 'Solomon Islands', 'Sierra Leone', 'El Salvador',\n", + " 'San Marino', 'Somalia', 'Serbia',\n", + " 'Sub-Saharan Africa (excluding high income)', 'South Sudan',\n", + " 'Sub-Saharan Africa', 'Small states', 'Sao Tome and Principe',\n", + " 'Suriname', 'Slovak Republic', 'Slovenia', 'Sweden', 'Eswatini',\n", + " 'Sint Maarten (Dutch part)', 'Seychelles', 'Syrian Arab Republic',\n", + " 'Turks and Caicos Islands', 'Chad',\n", + " 'East Asia & Pacific (IDA & IBRD countries)',\n", + " 'Europe & Central Asia (IDA & IBRD countries)', 'Togo', 'Thailand',\n", + " 'Tajikistan', 'Turkmenistan',\n", + " 'Latin America & the Caribbean (IDA & IBRD countries)',\n", + " 'Timor-Leste', 'Middle East & North Africa (IDA & IBRD countries)',\n", + " 'Tonga', 'South Asia (IDA & IBRD)',\n", + " 'Sub-Saharan Africa (IDA & IBRD countries)', 'Trinidad and Tobago',\n", + " 'Tunisia', 'Turkey', 'Tuvalu', 'Tanzania', 'Uganda', 'Ukraine',\n", + " 'Upper middle income', 'Uruguay', 'United States', 'Uzbekistan',\n", + " 'St. Vincent and the Grenadines', 'British Virgin Islands',\n", + " 'Virgin Islands (U.S.)', 'Vietnam', 'Vanuatu', 'World', 'Samoa',\n", + " 'Kosovo', 'South Africa', 'Zambia', 'Zimbabwe'], dtype=object)" + ] + }, + "execution_count": 346, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "women_in_power['Country'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 347, + "metadata": {}, + "outputs": [], + "source": [ + "Countries = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n", + " 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n", + " 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh',\n", + " 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',\n", + " 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n", + " 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia',\n", + " 'Cameroon', 'Canada', 'Cape Verde', 'Central African Republic',\n", + " 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'Congo',\n", + " 'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus',\n", + " 'Czech Republic', 'Democratic Republic of Congo', 'Denmark',\n", + " 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',\n", + " 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',\n", + " 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon', 'Gambia',\n", + " 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada', 'Guatemala',\n", + " 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Honduras',\n", + " 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\n", + " 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan',\n", + " 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos',\n", + " 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',\n", + " 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macedonia',\n", + " 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta',\n", + " 'Marshall Islands', 'Mauritania', 'Mauritius', 'Mexico',\n", + " 'Micronesia (country)', 'Moldova', 'Monaco', 'Mongolia',\n", + " 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\n", + " 'Nauru', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua',\n", + " 'Niger', 'Nigeria', 'North Korea', 'Norway', 'Oman', 'Pakistan',\n", + " 'Palau', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n", + " 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia',\n", + " 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia',\n", + " 'Saint Vincent and the Grenadines', 'Samoa', 'San Marino',\n", + " 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n", + " 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',\n", + " 'Solomon Islands', 'Somalia', 'South Africa', 'South Korea',\n", + " 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Swaziland', 'Sweden',\n", + " 'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand',\n", + " 'Timor', 'Togo', 'Tonga', 'Trinidad and Tobago', 'Tunisia',\n", + " 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine',\n", + " 'United Arab Emirates', 'United Kingdom', 'United States',\n", + " 'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam',\n", + " 'Yemen', 'Zambia', 'Zimbabwe']" + ] + }, + { + "cell_type": "code", + "execution_count": 353, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aruba\n", + "Arab World\n", + "American Samoa\n", + "nan\n", + "Bermuda\n", + "Brunei Darussalam\n", + "Central Europe and the Baltics\n", + "Channel Islands\n", + "Cabo Verde\n", + "Caribbean small states\n", + "Curacao\n", + "Cayman Islands\n", + "East Asia & Pacific (excluding high income)\n", + "Early-demographic dividend\n", + "East Asia & Pacific\n", + "Europe & Central Asia (excluding high income)\n", + "Europe & Central Asia\n", + "Euro area\n", + "European Union\n", + "Fragile and conflict affected situations\n", + "Faroe Islands\n", + "Gibraltar\n", + "Greenland\n", + "Guam\n", + "High income\n", + "Heavily indebted poor countries (HIPC)\n", + "IBRD only\n", + "IDA & IBRD total\n", + "IDA total\n", + "IDA blend\n", + "IDA only\n", + "Isle of Man\n", + "Not classified\n", + "Kyrgyz Republic\n", + "St. Kitts and Nevis\n", + "Latin America & Caribbean (excluding high income)\n", + "Lao PDR\n", + "St. Lucia\n", + "Latin America & Caribbean\n", + "Least developed countries: UN classification\n", + "Low income\n", + "Lower middle income\n", + "Low & middle income\n", + "Late-demographic dividend\n", + "St. Martin (French part)\n", + "Middle East & North Africa\n", + "Middle income\n", + "North Macedonia\n", + "Middle East & North Africa (excluding high income)\n", + "Northern Mariana Islands\n", + "North America\n", + "New Caledonia\n", + "OECD members\n", + "Other small states\n", + "Pre-demographic dividend\n", + "Puerto Rico\n", + "West Bank and Gaza\n", + "Pacific island small states\n", + "Post-demographic dividend\n", + "French Polynesia\n", + "Russian Federation\n", + "South Asia\n", + "Sub-Saharan Africa (excluding high income)\n", + "South Sudan\n", + "Sub-Saharan Africa\n", + "Small states\n", + "Slovak Republic\n", + "Eswatini\n", + "Sint Maarten (Dutch part)\n", + "Syrian Arab Republic\n", + "Turks and Caicos Islands\n", + "East Asia & Pacific (IDA & IBRD countries)\n", + "Europe & Central Asia (IDA & IBRD countries)\n", + "Latin America & the Caribbean (IDA & IBRD countries)\n", + "Timor-Leste\n", + "Middle East & North Africa (IDA & IBRD countries)\n", + "South Asia (IDA & IBRD)\n", + "Sub-Saharan Africa (IDA & IBRD countries)\n", + "Upper middle income\n", + "St. Vincent and the Grenadines\n", + "British Virgin Islands\n", + "Virgin Islands (U.S.)\n", + "World\n", + "Kosovo\n" + ] + } + ], + "source": [ + "for country in women_in_power['Country'].unique():\n", + " if country not in Countries:\n", + " print(country)" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### GDP per capita (1981-2018)" + "### Proportion of women in ministerial positions (2005, 2008, 2010, 2012, 2014, 2015, 2016)" ] }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 317, "metadata": {}, "outputs": [ { @@ -3633,116 +3878,286 @@ " \n", " \n", " \n", - " Unnamed: 0\n", - " Country\n", + " Entity\n", + " Code\n", " Year\n", - " GDP\n", - " Population\n", - " GDP_per_capita\n", + " Proportion of women in ministerial level positions (%) (%)\n", " \n", " \n", " \n", " \n", " 0\n", - " 0\n", - " Canada\n", - " 1981\n", - " 808.688171\n", - " 24900000.0\n", - " 32477.436602\n", + " Afghanistan\n", + " AFG\n", + " 2005\n", + " 10.0\n", " \n", " \n", " 1\n", - " 1\n", - " United States\n", - " 1981\n", - " 6698.570801\n", - " 229465714.0\n", - " 29192.033459\n", + " Afghanistan\n", + " AFG\n", + " 2008\n", + " 3.7\n", " \n", " \n", " 2\n", - " 2\n", - " Cuba\n", - " 1981\n", - " 35.882641\n", - " 9705971.0\n", - " 3696.965594\n", + " Afghanistan\n", + " AFG\n", + " 2010\n", + " 7.4\n", " \n", " \n", " 3\n", - " 3\n", - " Mexico\n", - " 1981\n", - " 589.548950\n", - " 69930971.0\n", - " 8430.441359\n", + " Afghanistan\n", + " AFG\n", + " 2012\n", + " 12.0\n", " \n", " \n", " 4\n", - " 4\n", - " Antigua Barbuda\n", - " 1981\n", - " 0.408927\n", - " 68264.0\n", - " 5990.381182\n", + " Afghanistan\n", + " AFG\n", + " 2014\n", + " 11.1\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Unnamed: 0 Country Year GDP Population GDP_per_capita\n", - "0 0 Canada 1981 808.688171 24900000.0 32477.436602\n", - "1 1 United States 1981 6698.570801 229465714.0 29192.033459\n", - "2 2 Cuba 1981 35.882641 9705971.0 3696.965594\n", - "3 3 Mexico 1981 589.548950 69930971.0 8430.441359\n", - "4 4 Antigua Barbuda 1981 0.408927 68264.0 5990.381182" + " Entity Code Year \\\n", + "0 Afghanistan AFG 2005 \n", + "1 Afghanistan AFG 2008 \n", + "2 Afghanistan AFG 2010 \n", + "3 Afghanistan AFG 2012 \n", + "4 Afghanistan AFG 2014 \n", + "\n", + " Proportion of women in ministerial level positions (%) (%) \n", + "0 10.0 \n", + "1 3.7 \n", + "2 7.4 \n", + "3 12.0 \n", + "4 11.1 " ] }, - "execution_count": 236, + "execution_count": 317, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "gdp = pd.read_csv('data//GDP_P.csv')\n", - "gdp.head()" + "women_in_ministeries = pd.read_csv('data//proportion-of-women-in-ministerial-positions.csv')\n", + "women_in_ministeries.head()" ] }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 318, + "metadata": {}, + "outputs": [], + "source": [ + "women_in_ministeries.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "metadata": {}, + "outputs": [], + "source": [ + "women_in_ministeries.rename(columns={'Proportion of women in ministerial level positions (%) (%)':'Women in Ministeries(%)','Entity':'Country'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 320, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n", - " 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n", - " 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,\n", - " 2014, 2015, 2016, 2017, 2018], dtype=int64)" + "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n", + " 'Antigua and Barbuda', 'Arab World', 'Argentina', 'Armenia',\n", + " 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',\n", + " 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin',\n", + " 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana',\n", + " 'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi',\n", + " 'Cambodia', 'Cameroon', 'Canada', 'Cape Verde',\n", + " 'Caribbean small states', 'Central African Republic',\n", + " 'Central Europe and the Baltics', 'Chad', 'Chile', 'China',\n", + " 'Colombia', 'Comoros', 'Congo', 'Costa Rica', \"Cote d'Ivoire\",\n", + " 'Croatia', 'Cuba', 'Cyprus', 'Czech Republic',\n", + " 'Democratic Republic of Congo', 'Denmark', 'Djibouti', 'Dominica',\n", + " 'Dominican Republic', 'Early-demographic dividend',\n", + " 'East Asia & Pacific', 'East Asia & Pacific (IDA & IBRD)',\n", + " 'East Asia & Pacific (excluding high income)', 'Ecuador', 'Egypt',\n", + " 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',\n", + " 'Ethiopia', 'Euro area', 'Europe & Central Asia',\n", + " 'Europe & Central Asia (IDA & IBRD)',\n", + " 'Europe & Central Asia (excluding high income)', 'European Union',\n", + " 'Fiji', 'Finland', 'Fragile and conflict affected situations',\n", + " 'France', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana',\n", + " 'Greece', 'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau',\n", + " 'Guyana', 'Haiti', 'Heavily indebted poor countries (HIPC)',\n", + " 'High income', 'Honduras', 'Hungary', 'IBRD only',\n", + " 'IDA & IBRD total', 'IDA blend', 'IDA only', 'IDA total',\n", + " 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland',\n", + " 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan',\n", + " 'Kenya', 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos',\n", + " 'Late-demographic dividend', 'Latin America & Caribbean',\n", + " 'Latin America & Caribbean (IDA & IBRD)',\n", + " 'Latin America & Caribbean (excluding high income)', 'Latvia',\n", + " 'Least developed countries: UN classification', 'Lebanon',\n", + " 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n", + " 'Low & middle income', 'Low income', 'Lower middle income',\n", + " 'Luxembourg', 'Macedonia', 'Madagascar', 'Malawi', 'Malaysia',\n", + " 'Maldives', 'Mali', 'Malta', 'Marshall Islands', 'Mauritania',\n", + " 'Mauritius', 'Mexico', 'Micronesia (country)',\n", + " 'Middle East & North Africa',\n", + " 'Middle East & North Africa (IDA & IBRD)',\n", + " 'Middle East & North Africa (excluding high income)',\n", + " 'Middle income', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro',\n", + " 'Morocco', 'Mozambique', 'Myanmar', 'Namibia', 'Nauru', 'Nepal',\n", + " 'Netherlands', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria',\n", + " 'North America', 'North Korea', 'Norway', 'OECD members', 'Oman',\n", + " 'Other small states', 'Pacific island small states', 'Pakistan',\n", + " 'Palau', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n", + " 'Philippines', 'Poland', 'Portugal', 'Post-demographic dividend',\n", + " 'Pre-demographic dividend', 'Qatar', 'Romania', 'Russia', 'Rwanda',\n", + " 'Saint Kitts and Nevis', 'Saint Lucia',\n", + " 'Saint Vincent and the Grenadines', 'Samoa', 'San Marino',\n", + " 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n", + " 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',\n", + " 'Small states', 'Solomon Islands', 'Somalia', 'South Africa',\n", + " 'South Asia', 'South Asia (IDA & IBRD)', 'South Korea',\n", + " 'South Sudan', 'Spain', 'Sri Lanka', 'Sub-Saharan Africa',\n", + " 'Sub-Saharan Africa (IDA & IBRD)',\n", + " 'Sub-Saharan Africa (excluding high income)', 'Sudan', 'Suriname',\n", + " 'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Tajikistan',\n", + " 'Tanzania', 'Thailand', 'Timor', 'Togo', 'Tonga',\n", + " 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Turkmenistan',\n", + " 'Tuvalu', 'Uganda', 'Ukraine', 'United Arab Emirates',\n", + " 'United Kingdom', 'United States', 'Upper middle income',\n", + " 'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam',\n", + " 'World', 'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)" ] }, - "execution_count": 237, + "execution_count": 320, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "gdp.Year.unique()" + "women_in_ministeries.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 323, + "metadata": {}, + "outputs": [], + "source": [ + "no_wanted = ['Arab World','Caribbean small states','Central Europe and the Baltics','Early-demographic dividend','East Asia & Pacific', \n", + " 'East Asia & Pacific (IDA & IBRD)','East Asia & Pacific (excluding high income)','Euro area', 'Europe & Central Asia',\n", + " 'Europe & Central Asia (IDA & IBRD)','Europe & Central Asia (excluding high income)', 'European Union','Fragile and conflict affected situations',\n", + " 'Heavily indebted poor countries (HIPC)','High income','IBRD only','IDA & IBRD total', 'IDA blend', 'IDA only', 'IDA total',\n", + " 'Late-demographic dividend', 'Latin America & Caribbean','Latin America & Caribbean (IDA & IBRD)','Latin America & Caribbean (excluding high income)',\n", + " 'Least developed countries: UN classification','Low & middle income', 'Low income', 'Lower middle income','Middle East & North Africa',\n", + " 'Middle East & North Africa (IDA & IBRD)','Middle East & North Africa (excluding high income)','Middle income','North America',\n", + " 'OECD members','Other small states', 'Pacific island small states','Post-demographic dividend','Pre-demographic dividend',\n", + " 'Small states','South Asia', 'South Asia (IDA & IBRD)','South Sudan','Sub-Saharan Africa','Sub-Saharan Africa (IDA & IBRD)',\n", + " 'Sub-Saharan Africa (excluding high income)','Upper middle income','World']\n", + "for region in no_wanted:\n", + " women_in_ministeries = women_in_ministeries[women_in_ministeries.Country != region]" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2005, 2008, 2010, 2012, 2014, 2015, 2016], dtype=int64)" + ] + }, + "execution_count": 327, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "women_in_ministeries.Year.unique()" ] }, + { + "cell_type": "code", + "execution_count": 342, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n", + " 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n", + " 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh',\n", + " 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',\n", + " 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n", + " 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia',\n", + " 'Cameroon', 'Canada', 'Cape Verde', 'Central African Republic',\n", + " 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'Congo',\n", + " 'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus',\n", + " 'Czech Republic', 'Democratic Republic of Congo', 'Denmark',\n", + " 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',\n", + " 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',\n", + " 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon', 'Gambia',\n", + " 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada', 'Guatemala',\n", + " 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Honduras',\n", + " 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\n", + " 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan',\n", + " 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos',\n", + " 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',\n", + " 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macedonia',\n", + " 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta',\n", + " 'Marshall Islands', 'Mauritania', 'Mauritius', 'Mexico',\n", + " 'Micronesia (country)', 'Moldova', 'Monaco', 'Mongolia',\n", + " 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\n", + " 'Nauru', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua',\n", + " 'Niger', 'Nigeria', 'North Korea', 'Norway', 'Oman', 'Pakistan',\n", + " 'Palau', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n", + " 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia',\n", + " 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia',\n", + " 'Saint Vincent and the Grenadines', 'Samoa', 'San Marino',\n", + " 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n", + " 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',\n", + " 'Solomon Islands', 'Somalia', 'South Africa', 'South Korea',\n", + " 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Swaziland', 'Sweden',\n", + " 'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand',\n", + " 'Timor', 'Togo', 'Tonga', 'Trinidad and Tobago', 'Tunisia',\n", + " 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine',\n", + " 'United Arab Emirates', 'United Kingdom', 'United States',\n", + " 'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam',\n", + " 'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)" + ] + }, + "execution_count": 342, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Tertiary education (1970-2010)" + "### Share of top Female Managers (2008, 2014, 2013, 2010, 2017, 2009, 2007, 2016, 2015, 2011, 2012)" ] }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 340, "metadata": {}, "outputs": [ { @@ -3766,89 +4181,1581 @@ " \n", " \n", " \n", - " Unnamed: 0\n", - " Country\n", + " Entity\n", + " Code\n", " Year\n", - " Tertiary_education\n", + " Firms with female top manager (% of firms) (% of firms)\n", " \n", " \n", " \n", " \n", " 0\n", - " 0\n", " Afghanistan\n", - " 1970\n", - " 1.08\n", + " AFG\n", + " 2008\n", + " 0.7\n", " \n", " \n", " 1\n", - " 1\n", " Afghanistan\n", - " 1975\n", - " 1.48\n", + " AFG\n", + " 2014\n", + " 4.7\n", " \n", " \n", " 2\n", - " 2\n", - " Afghanistan\n", - " 1980\n", - " 1.96\n", + " Albania\n", + " ALB\n", + " 2013\n", + " 11.7\n", " \n", " \n", " 3\n", - " 3\n", - " Afghanistan\n", - " 1985\n", - " 2.49\n", + " Angola\n", + " AGO\n", + " 2010\n", + " 13.5\n", " \n", " \n", " 4\n", - " 4\n", - " Afghanistan\n", - " 1990\n", - " 2.91\n", + " Antigua and Barbuda\n", + " ATG\n", + " 2010\n", + " 17.5\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Unnamed: 0 Country Year Tertiary_education\n", - "0 0 Afghanistan 1970 1.08\n", - "1 1 Afghanistan 1975 1.48\n", - "2 2 Afghanistan 1980 1.96\n", - "3 3 Afghanistan 1985 2.49\n", - "4 4 Afghanistan 1990 2.91" + " Entity Code Year \\\n", + "0 Afghanistan AFG 2008 \n", + "1 Afghanistan AFG 2014 \n", + "2 Albania ALB 2013 \n", + "3 Angola AGO 2010 \n", + "4 Antigua and Barbuda ATG 2010 \n", + "\n", + " Firms with female top manager (% of firms) (% of firms) \n", + "0 0.7 \n", + "1 4.7 \n", + "2 11.7 \n", + "3 13.5 \n", + "4 17.5 " ] }, - "execution_count": 239, + "execution_count": 340, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "education = pd.read_csv('data//education_df.csv')\n", - "education.head()" + "female_managers = pd.read_csv('data//share-firms-top-female-manager.csv')\n", + "female_managers.head()" ] }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 329, + "metadata": {}, + "outputs": [], + "source": [ + "female_managers.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [], + "source": [ + "female_managers.rename(columns={'Firms with female top manager (% of firms) (% of firms)':'Female top manager (%)','Entity':'Country'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [], + "source": [ + "for region in no_wanted:\n", + " female_managers = female_managers[female_managers.Country != region]" + ] + }, + { + "cell_type": "code", + "execution_count": 333, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010], dtype=int64)" + "array(['Afghanistan', 'Albania', 'Angola', 'Antigua and Barbuda',\n", + " 'Argentina', 'Armenia', 'Azerbaijan', 'Bahamas', 'Bangladesh',\n", + " 'Barbados', 'Belarus', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\n", + " 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Bulgaria',\n", + " 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon', 'Cape Verde',\n", + " 'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',\n", + " 'Congo', 'Costa Rica', \"Cote d'Ivoire\", 'Croatia',\n", + " 'Czech Republic', 'Democratic Republic of Congo', 'Djibouti',\n", + " 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',\n", + " 'El Salvador', 'Eritrea', 'Estonia', 'Ethiopia', 'Gabon',\n", + " 'Georgia', 'Ghana', 'Grenada', 'Guatemala', 'Guinea', 'Guyana',\n", + " 'Honduras', 'Hungary', 'India', 'Indonesia', 'Iraq', 'Israel',\n", + " 'Jamaica', 'Jordan', 'Kazakhstan', 'Kenya', 'Kosovo', 'Kyrgyzstan',\n", + " 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Lithuania',\n", + " 'Macedonia', 'Madagascar', 'Malawi', 'Malaysia', 'Mali',\n", + " 'Mauritania', 'Mexico', 'Moldova', 'Mongolia', 'Montenegro',\n", + " 'Morocco', 'Myanmar', 'Namibia', 'Nepal', 'Nicaragua', 'Niger',\n", + " 'Nigeria', 'Pakistan', 'Palestine', 'Panama', 'Papua New Guinea',\n", + " 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Romania', 'Russia',\n", + " 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia',\n", + " 'Saint Vincent and the Grenadines', 'Senegal', 'Serbia',\n", + " 'Sierra Leone', 'Slovakia', 'Slovenia', 'Solomon Islands',\n", + " 'Sri Lanka', 'Sudan', 'Suriname', 'Swaziland', 'Sweden',\n", + " 'Syrian Arab Republic', 'Tajikistan', 'Tanzania', 'Thailand',\n", + " 'Timor', 'Togo', 'Trinidad and Tobago', 'Tunisia', 'Turkey',\n", + " 'Uganda', 'Ukraine', 'Uruguay', 'Uzbekistan', 'Venezuela',\n", + " 'Vietnam', 'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)" ] }, - "execution_count": 240, + "execution_count": 333, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "education.Year.unique()" + "female_managers.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2008, 2014, 2013, 2010, 2017, 2009, 2007, 2016, 2015, 2011, 2012],\n", + " dtype=int64)" + ] + }, + "execution_count": 334, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "female_managers.Year.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 337, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 339, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "Name: Country, dtype: bool" + ] + }, + "execution_count": 339, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### another set for women in parliment" + ] + }, + { + "cell_type": "code", + "execution_count": 355, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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LOCATIONINDICATORSUBJECTMEASUREFREQUENCYTIMEValueFlag Codes
0AUSWOMENPOLVOICEPOLREPRESPCA200225.3NaN
1AUSWOMENPOLVOICEPOLREPRESPCA201224.7NaN
2AUSWOMENPOLVOICEPOLREPRESPCA201426.0NaN
3AUSWOMENPOLVOICEPOLREPRESPCA201526.7NaN
4AUSWOMENPOLVOICEPOLREPRESPCA201728.7NaN
\n", + "
" + ], + "text/plain": [ + " LOCATION INDICATOR SUBJECT MEASURE FREQUENCY TIME Value \\\n", + "0 AUS WOMENPOLVOICE POLREPRES PC A 2002 25.3 \n", + "1 AUS WOMENPOLVOICE POLREPRES PC A 2012 24.7 \n", + "2 AUS WOMENPOLVOICE POLREPRES PC A 2014 26.0 \n", + "3 AUS WOMENPOLVOICE POLREPRES PC A 2015 26.7 \n", + "4 AUS WOMENPOLVOICE POLREPRES PC A 2017 28.7 \n", + "\n", + " Flag Codes \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 355, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "female_parlamentaries = pd.read_csv('data//DP_LIVE_23052020174320282.csv')\n", + "female_parlamentaries.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 382, + "metadata": {}, + "outputs": [], + "source": [ + "countries_dict = {'ABW':'Aruba','AFG':'Afghanistan','AGO':'Angola','AIA':'Anguilla','ALA':'Åland Islands','ALB':'Albania',\n", + " 'AND':'Andorra','ARE':'United Arab Emirates','ARG':'Argentina','ARM':'Armenia','ASM':'American Samoa',\n", + " 'ATA':'Antarctica','ATF':'French Southern Territories','ATG':'Antigua and Barbuda','AUS':'Australia',\n", + " 'AUT':'Austria','AZE':'Azerbaijan','BDI':'Burundi','BEL':'Belgium','BEN':'Benin','BES':'Bonaire','BFA':'Burkina Faso',\n", + " 'BGD':'Bangladesh','BGR':'Bulgaria','BHR':'Bahrain','BHS':'Bahamas','BIH':'Bosnia and Herzegovina',\n", + " 'BLM':'Saint Barthélemy','BLR':'Belarus','BLZ':'Belize','BMU':'Bermuda','BOL':'Bolivia','BRA':'Brazil',\n", + " 'BRB':'Barbados','BRN':'Brunei Darussalam','BTN':'Bhutan','BVT':'Bouvet Island','BWA':'Botswana',\n", + " 'CAF':'Central African Republic','CAN':'Canada','CCK':'Cocos Islands','CHE':'Switzerland','CHL':'Chile',\n", + " 'CHN':'China','CIV':\"Côte d'Ivoire\",'CMR':'Cameroon','COG':'Congo','COK':'Cook Islands','COL':'Colombia',\n", + " 'COM':'Comoros','CPV':'Cabo Verde','CRI':'Costa Rica','CUB':'Cuba','CUW':'Curaçao','CXR':'Christmas Island',\n", + " 'CYM':'Cayman Islands','CYP':'Cyprus','CZE':'Czechia','DEU':'Germany','DJI':'Djibouti','DMA':'Dominica',\n", + " 'DNK':'Denmark','DOM':'Dominican Republic','DZA':'Algeria','ECU':'Ecuador','EGY':'Egypt','ERI':'Eritrea',\n", + " 'ESH':'Western Sahara','ESP':'Spain','EST':'Estonia','ETH':'Ethiopia','FIN':'Finland','FJI':'Fiji',\n", + " 'FLK':'Falkland Islands (Malvinas)','FRA':'France','FRO':'Faroe Islands','FSM':'Micronesia','GAB':'Gabon',\n", + " 'GBR':'United Kingdom','GEO':'Georgia','GGY':'Guernsey','GHA':'Ghana','GIB':'Gibraltar','GIN':'Guinea',\n", + " 'GLP':'Guadeloupe','GMB':'Gambia','GNB':'Guinea Bissau','GNQ':'Equatorial Guinea','GRC':'Greece',\n", + " 'GRD':'Grenada','GRL':'Greenland','GTM':'Guatemala','GUF':'French Guiana','GUM':'Guam','GUY':'Guyana',\n", + " 'HKG':'Hong Kong','HMD':'Heard Island and McDonald Islands','HND':'Honduras','HRV':'Croatia','HTI':'Haiti',\n", + " 'HUN':'Hungary','IDN':'Indonesia','IMN':'Isle of Man','IND':'India','IRL':'Ireland','IRN':'Iran','IRQ':'Iraq',\n", + " 'ISL':'Iceland','ISR':'Israel','ITA':'Italy','JAM':'Jamaica','JEY':'Jersey','JOR':'Jordan','JPN':'Japan',\n", + " 'KAZ':'Kazakhstan','KEN':'Kenya','KGZ':'Kyrgyzstan','KHM':'Cambodia','KIR':'Kiribati',\n", + " 'KNA':'Saint Kitts and Nevis','KOR':'South Korea','KWT':'Kuwait','LBN':'Lebanon','LBR':'Liberia','LBY':'Libya',\n", + " 'LCA':'Saint Lucia','LIE':'Liechtenstein','LKA':'Sri Lanka','LSO':'Lesotho','LTU':'Lithuania',\n", + " 'LUX':'Luxembourg','LVA':'Latvia','MAC':'Macao','MAF':'Saint Martin','MAR':'Morocco','MCO':'Monaco',\n", + " 'MDA':'Moldova','MDG':'Madagascar','MDV':'Maldives','MEX':'Mexico','MHL':'Marshall Islands',\n", + " 'MKD':'North Macedonia','MLI':'Mali','MLT':'Malta','MMR':'Myanmar','MNE':'Montenegro','MNG':'Mongolia',\n", + " 'MNP':'Northern Mariana Islands','MOZ':'Mozambique','MRT':'Mauritania','MSR':'Montserrat','MTQ':'Martinique',\n", + " 'MUS':'Mauritius','MWI':'Malawi','MYS':'Malaysia','MYT':'Mayotte','NAM':'Namibia','NCL':'New Caledonia',\n", + " 'NER':'Niger','NFK':'Norfolk Island','NGA':'Nigeria','NIC':'Nicaragua','NIU':'Niue','NLD':'Netherlands',\n", + " 'NOR':'Norway','NPL':'Nepal','NRU':'Nauru','NZL':'New Zealand','OMN':'Oman','PAK':'Pakistan','PAN':'Panama',\n", + " 'PCN':'Pitcairn','PER':'Peru','PHL':'Philippines','PLW':'Palau','PNG':'Papua New Guinea','POL':'Poland',\n", + " 'PRI':'Puerto Rico','PRT':'Portugal','PRY':'Paraguay','PSE':'Palestine','PYF':'French Polynesia','QAT':'Qatar',\n", + " 'REU':'Réunion','ROU':'Romania','RUS':'Russia','RWA':'Rwanda','SAU':'Saudi Arabia','SDN':'Sudan',\n", + " 'SEN':'Senegal','SGP':'Singapore','SGS':'South Georgia and the South Sandwich Islands',\n", + " 'SHN':'Saint Helena, Ascension and Tristan da Cunha','SJM':'Svalbard and Jan Mayen','SLB':'Solomon Islands',\n", + " 'SLE':'Sierra Leone','SLV':'El Salvador','SMR':'San Marino','SOM':'Somalia','SPM':'Saint Pierre and Miquelon',\n", + " 'SRB':'Serbia','SSD':'South Sudan','STP':'Sao Tome and Principe','SUR':'Suriname','SVK':'Slovakia','SVN':'Slovenia',\n", + " 'SWE':'Sweden','SWZ':'Eswatini','SXM':'Sint Maarten','SYC':'Seychelles','SYR':'Syrian Arab Republic',\n", + " 'TCA':'Turks and Caicos Islands','TCD':'Chad','TGO':'Togo','THA':'Thailand','TJK':'Tajikistan',\n", + " 'TKL':'Tokelau','TKM':'Turkmenistan','TLS':'Timor Leste','TON':'Tonga','TTO':'Trinidad and Tobago',\n", + " 'TUN':'Tunisia','TUR':'Turkey','TUV':'Tuvalu','TZA':'Tanzania','UGA':'Uganda','UKR':'Ukraine','URY':'Uruguay',\n", + " 'USA':'United States','UZB':'Uzbekistan','VCT':'Saint Vincent and the Grenadines','VEN':'Venezuela',\n", + " 'VIR':'Virgin Islands','VNM':'Vietnam','VUT':'Vanuatu','WLF':'Wallis and Futuna','WSM':'Samoa',\n", + " 'YEM':'Yemen','ZAF':'South Africa','ZMB':'Zambia','ZWE':'Zimbabwe'}" + ] + }, + { + "cell_type": "code", + "execution_count": 406, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "2 columns passed, passed data had 241 columns", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36m_list_to_arrays\u001b[1;34m(data, columns, coerce_float, dtype)\u001b[0m\n\u001b[0;32m 496\u001b[0m result = _convert_object_array(\n\u001b[1;32m--> 497\u001b[1;33m \u001b[0mcontent\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 498\u001b[0m )\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36m_convert_object_array\u001b[1;34m(content, columns, coerce_float, dtype)\u001b[0m\n\u001b[0;32m 580\u001b[0m raise AssertionError(\n\u001b[1;32m--> 581\u001b[1;33m \u001b[1;34mf\"{len(columns)} columns passed, passed data had \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 582\u001b[0m \u001b[1;34mf\"{len(content)} columns\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mAssertionError\u001b[0m: 2 columns passed, passed data had 241 columns", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcountries_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcountries_dict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcountries_dict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Code'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Country'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m 472\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_named_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 473\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fields\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 474\u001b[1;33m \u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 475\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36mto_arrays\u001b[1;34m(data, columns, coerce_float, dtype)\u001b[0m\n\u001b[0;32m 483\u001b[0m \u001b[1;31m# last ditch effort\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 484\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 485\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_list_to_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 486\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 487\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36m_list_to_arrays\u001b[1;34m(data, columns, coerce_float, dtype)\u001b[0m\n\u001b[0;32m 498\u001b[0m )\n\u001b[0;32m 499\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mAssertionError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 500\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 501\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 502\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: 2 columns passed, passed data had 241 columns" + ] + } + ], + "source": [ + "countries_df = pd.DataFrame(data=[countries_dict.keys(),countries_dict.values()], columns=['Code','Country'])" + ] + }, + { + "cell_type": "code", + "execution_count": 401, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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482 rows × 2 columns

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" + ], + "text/plain": [ + " Code Country\n", + "0 ABW NaN\n", + "1 AFG NaN\n", + "2 AGO NaN\n", + "3 AIA NaN\n", + "4 ALA NaN\n", + ".. ... ...\n", + "236 NaN Samoa\n", + "237 NaN Yemen\n", + "238 NaN South Africa\n", + "239 NaN Zambia\n", + "240 NaN Zimbabwe\n", + "\n", + "[482 rows x 2 columns]" + ] + }, + "execution_count": 405, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "codes_df.append(countries_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 395, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m female_parlamentaries['Country'] = (lambda countries_dict.values(): if countries_dict.key() in female_parlamentaries['LOCATION'])\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "female_parlamentaries['Country'] = (lambda countries_dict.values(): if countries_dict.key() in female_parlamentaries['LOCATION'])" + ] + }, + { + "cell_type": "code", + "execution_count": 394, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_values(['Aruba', 'Afghanistan', 'Angola', 'Anguilla', 'Åland Islands', 'Albania', 'Andorra', 'United Arab Emirates', 'Argentina', 'Armenia', 'American Samoa', 'Antarctica', 'French Southern Territories', 'Antigua and Barbuda', 'Australia', 'Austria', 'Azerbaijan', 'Burundi', 'Belgium', 'Benin', 'Bonaire', 'Burkina Faso', 'Bangladesh', 'Bulgaria', 'Bahrain', 'Bahamas', 'Bosnia and Herzegovina', 'Saint Barthélemy', 'Belarus', 'Belize', 'Bermuda', 'Bolivia', 'Brazil', 'Barbados', 'Brunei Darussalam', 'Bhutan', 'Bouvet Island', 'Botswana', 'Central African Republic', 'Canada', 'Cocos Islands', 'Switzerland', 'Chile', 'China', \"Côte d'Ivoire\", 'Cameroon', 'Congo', 'Cook Islands', 'Colombia', 'Comoros', 'Cabo Verde', 'Costa Rica', 'Cuba', 'Curaçao', 'Christmas Island', 'Cayman Islands', 'Cyprus', 'Czechia', 'Germany', 'Djibouti', 'Dominica', 'Denmark', 'Dominican Republic', 'Algeria', 'Ecuador', 'Egypt', 'Eritrea', 'Western Sahara', 'Spain', 'Estonia', 'Ethiopia', 'Finland', 'Fiji', 'Falkland Islands (Malvinas)', 'France', 'Faroe Islands', 'Micronesia', 'Gabon', 'United Kingdom', 'Georgia', 'Guernsey', 'Ghana', 'Gibraltar', 'Guinea', 'Guadeloupe', 'Gambia', 'Guinea Bissau', 'Equatorial Guinea', 'Greece', 'Grenada', 'Greenland', 'Guatemala', 'French Guiana', 'Guam', 'Guyana', 'Hong Kong', 'Heard Island and McDonald Islands', 'Honduras', 'Croatia', 'Haiti', 'Hungary', 'Indonesia', 'Isle of Man', 'India', 'Ireland', 'Iran', 'Iraq', 'Iceland', 'Israel', 'Italy', 'Jamaica', 'Jersey', 'Jordan', 'Japan', 'Kazakhstan', 'Kenya', 'Kyrgyzstan', 'Cambodia', 'Kiribati', 'Saint Kitts and Nevis', 'South Korea', 'Kuwait', 'Lebanon', 'Liberia', 'Libya', 'Saint Lucia', 'Liechtenstein', 'Sri Lanka', 'Lesotho', 'Lithuania', 'Luxembourg', 'Latvia', 'Macao', 'Saint Martin', 'Morocco', 'Monaco', 'Moldova', 'Madagascar', 'Maldives', 'Mexico', 'Marshall Islands', 'North Macedonia', 'Mali', 'Malta', 'Myanmar', 'Montenegro', 'Mongolia', 'Northern Mariana Islands', 'Mozambique', 'Mauritania', 'Montserrat', 'Martinique', 'Mauritius', 'Malawi', 'Malaysia', 'Mayotte', 'Namibia', 'New Caledonia', 'Niger', 'Norfolk Island', 'Nigeria', 'Nicaragua', 'Niue', 'Netherlands', 'Norway', 'Nepal', 'Nauru', 'New Zealand', 'Oman', 'Pakistan', 'Panama', 'Pitcairn', 'Peru', 'Philippines', 'Palau', 'Papua New Guinea', 'Poland', 'Puerto Rico', 'Portugal', 'Paraguay', 'Palestine', 'French Polynesia', 'Qatar', 'Réunion', 'Romania', 'Russia', 'Rwanda', 'Saudi Arabia', 'Sudan', 'Senegal', 'Singapore', 'South Georgia and the South Sandwich Islands', 'Saint Helena, Ascension and Tristan da Cunha', 'Svalbard and Jan Mayen', 'Solomon Islands', 'Sierra Leone', 'El Salvador', 'San Marino', 'Somalia', 'Saint Pierre and Miquelon', 'Serbia', 'South Sudan', 'Sao Tome and Principe', 'Suriname', 'Slovakia', 'Slovenia', 'Sweden', 'Eswatini', 'Sint Maarten', 'Seychelles', 'Syrian Arab Republic', 'Turks and Caicos Islands', 'Chad', 'Togo', 'Thailand', 'Tajikistan', 'Tokelau', 'Turkmenistan', 'Timor Leste', 'Tonga', 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Tuvalu', 'Tanzania', 'Uganda', 'Ukraine', 'Uruguay', 'United States', 'Uzbekistan', 'Saint Vincent and the Grenadines', 'Venezuela', 'Virgin Islands', 'Vietnam', 'Vanuatu', 'Wallis and Futuna', 'Samoa', 'Yemen', 'South Africa', 'Zambia', 'Zimbabwe'])" + ] + }, + "execution_count": 394, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries_dict.values()" + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "metadata": {}, + "outputs": [], + "source": [ + "for code in female_parlamentaries['LOCATION']:\n", + " female_parlamentaries.rename({code : 'countries_dict.values'}, inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 385, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LOCATIONINDICATORSUBJECTMEASUREFREQUENCYTIMEValueFlag CodesCountry
0AUSWOMENPOLVOICEPOLREPRESPCA200225.3NaN<built-in method values of dict object at 0x00...
1AUSWOMENPOLVOICEPOLREPRESPCA201224.7NaN<built-in method values of dict object at 0x00...
2AUSWOMENPOLVOICEPOLREPRESPCA201426.0NaN<built-in method values of dict object at 0x00...
3AUSWOMENPOLVOICEPOLREPRESPCA201526.7NaN<built-in method values of dict object at 0x00...
4AUSWOMENPOLVOICEPOLREPRESPCA201728.7NaN<built-in method values of dict object at 0x00...
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" + ], + "text/plain": [ + " LOCATION INDICATOR SUBJECT MEASURE FREQUENCY TIME Value \\\n", + "0 AUS WOMENPOLVOICE POLREPRES PC A 2002 25.3 \n", + "1 AUS WOMENPOLVOICE POLREPRES PC A 2012 24.7 \n", + "2 AUS WOMENPOLVOICE POLREPRES PC A 2014 26.0 \n", + "3 AUS WOMENPOLVOICE POLREPRES PC A 2015 26.7 \n", + "4 AUS WOMENPOLVOICE POLREPRES PC A 2017 28.7 \n", + "\n", + " Flag Codes Country \n", + "0 NaN \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0CountryYearGDPPopulationGDP_per_capita
00Canada1981808.68817124900000.032477.436602
11United States19816698.570801229465714.029192.033459
22Cuba198135.8826419705971.03696.965594
33Mexico1981589.54895069930971.08430.441359
44Antigua Barbuda19810.40892768264.05990.381182
\n", + "" + ], + "text/plain": [ + " Unnamed: 0 Country Year GDP Population GDP_per_capita\n", + "0 0 Canada 1981 808.688171 24900000.0 32477.436602\n", + "1 1 United States 1981 6698.570801 229465714.0 29192.033459\n", + "2 2 Cuba 1981 35.882641 9705971.0 3696.965594\n", + "3 3 Mexico 1981 589.548950 69930971.0 8430.441359\n", + "4 4 Antigua Barbuda 1981 0.408927 68264.0 5990.381182" + ] + }, + "execution_count": 236, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdp = pd.read_csv('data//GDP_P.csv')\n", + "gdp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n", + " 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n", + " 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,\n", + " 2014, 2015, 2016, 2017, 2018], dtype=int64)" + ] + }, + "execution_count": 237, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdp.Year.unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tertiary education (1970-2010)" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0CountryYearTertiary_education
00Afghanistan19701.08
11Afghanistan19751.48
22Afghanistan19801.96
33Afghanistan19852.49
44Afghanistan19902.91
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Country Year Tertiary_education\n", + "0 0 Afghanistan 1970 1.08\n", + "1 1 Afghanistan 1975 1.48\n", + "2 2 Afghanistan 1980 1.96\n", + "3 3 Afghanistan 1985 2.49\n", + "4 4 Afghanistan 1990 2.91" + ] + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "education = pd.read_csv('data//education_df.csv')\n", + "education.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010], dtype=int64)" + ] + }, + "execution_count": 240, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "education.Year.unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Merging datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CO merged " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_merged = pd.merge(co_annual, co_share, on=['Country','Year']).merge(co_per_c, on=['Country','Year'])\n", + "co_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 410, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 16541 entries, 0 to 16540\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 16541 non-null object \n", + " 1 Year 16541 non-null int64 \n", + " 2 Annual CO₂ 16541 non-null float64\n", + " 3 Share of global CO₂ emissions (%) 16541 non-null float64\n", + " 4 CO₂ 16541 non-null float64\n", + "dtypes: float64(3), int64(1), object(1)\n", + "memory usage: 775.4+ KB\n" + ] + } + ], + "source": [ + "co_merged.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 411, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", + " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", + " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", + " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", + " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", + " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", + " 2015, 2016, 2017, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940,\n", + " 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1900, 1901, 1902,\n", + " 1903, 1904, 1905, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923,\n", + " 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1887, 1888,\n", + " 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899,\n", + " 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1860,\n", + " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", + " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", + " 1883, 1884, 1885, 1886, 1807, 1819, 1820, 1821, 1822, 1823, 1824,\n", + " 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835,\n", + " 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846,\n", + " 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857,\n", + " 1858, 1859, 1802, 1800, 1801, 1803, 1804, 1805, 1806, 1808, 1809,\n", + " 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818], dtype=int64)" + ] + }, + "execution_count": 411, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_merged.Year.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 416, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Country 0\n", + "Year 0\n", + "Annual CO₂ 0\n", + "Share of global CO₂ emissions (%) 0\n", + "CO₂ 0\n", + "dtype: int64" + ] + }, + "execution_count": 416, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_merged.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 412, + "metadata": {}, + "outputs": [], + "source": [ + "years = [1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", + " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", + " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", + " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", + " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", + " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", + " 2015, 2016, 2017, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940,\n", + " 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1900, 1901, 1902,\n", + " 1903, 1904, 1905, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923,\n", + " 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1887, 1888,\n", + " 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899,\n", + " 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1860,\n", + " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", + " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", + " 1883, 1884, 1885, 1886, 1807, 1819, 1820, 1821, 1822, 1823, 1824,\n", + " 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835,\n", + " 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846,\n", + " 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857,\n", + " 1858, 1859, 1802, 1800, 1801, 1803, 1804, 1805, 1806, 1808, 1809,\n", + " 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818]" + ] + }, + { + "cell_type": "code", + "execution_count": 413, + "metadata": {}, + "outputs": [], + "source": [ + "years.sort()" + ] + }, + { + "cell_type": "code", + "execution_count": 414, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1800,\n", + " 1801,\n", + " 1802,\n", + " 1803,\n", + " 1804,\n", + " 1805,\n", + " 1806,\n", + " 1807,\n", + " 1808,\n", + " 1809,\n", + " 1810,\n", + " 1811,\n", + " 1812,\n", + " 1813,\n", + " 1814,\n", + " 1815,\n", + " 1816,\n", + " 1817,\n", + " 1818,\n", + " 1819,\n", + " 1820,\n", + " 1821,\n", + " 1822,\n", + " 1823,\n", + " 1824,\n", + " 1825,\n", + " 1826,\n", + " 1827,\n", + " 1828,\n", + " 1829,\n", + " 1830,\n", + " 1831,\n", + " 1832,\n", + " 1833,\n", + " 1834,\n", + " 1835,\n", + " 1836,\n", + " 1837,\n", + " 1838,\n", + " 1839,\n", + " 1840,\n", + " 1841,\n", + " 1842,\n", + " 1843,\n", + " 1844,\n", + " 1845,\n", + " 1846,\n", + " 1847,\n", + " 1848,\n", + " 1849,\n", + " 1850,\n", + " 1851,\n", + " 1852,\n", + " 1853,\n", + " 1854,\n", + " 1855,\n", + " 1856,\n", + " 1857,\n", + " 1858,\n", + " 1859,\n", + " 1860,\n", + " 1861,\n", + " 1862,\n", + " 1863,\n", + " 1864,\n", + " 1865,\n", + " 1866,\n", + " 1867,\n", + " 1868,\n", + " 1869,\n", + " 1870,\n", + " 1871,\n", + " 1872,\n", + " 1873,\n", + " 1874,\n", + " 1875,\n", + " 1876,\n", + " 1877,\n", + " 1878,\n", + " 1879,\n", + " 1880,\n", + " 1881,\n", + " 1882,\n", + " 1883,\n", + " 1884,\n", + " 1885,\n", + " 1886,\n", + " 1887,\n", + " 1888,\n", + " 1889,\n", + " 1890,\n", + " 1891,\n", + " 1892,\n", + " 1893,\n", + " 1894,\n", + " 1895,\n", + " 1896,\n", + " 1897,\n", + " 1898,\n", + " 1899,\n", + " 1900,\n", + " 1901,\n", + " 1902,\n", + " 1903,\n", + " 1904,\n", + " 1905,\n", + " 1906,\n", + " 1907,\n", + " 1908,\n", + " 1909,\n", + " 1910,\n", + " 1911,\n", + " 1912,\n", + " 1913,\n", + " 1914,\n", + " 1915,\n", + " 1916,\n", + " 1917,\n", + " 1918,\n", + " 1919,\n", + " 1920,\n", + " 1921,\n", + " 1922,\n", + " 1923,\n", + " 1924,\n", + " 1925,\n", + " 1926,\n", + " 1927,\n", + " 1928,\n", + " 1929,\n", + " 1930,\n", + " 1931,\n", + " 1932,\n", + " 1933,\n", + " 1934,\n", + " 1935,\n", + " 1936,\n", + " 1937,\n", + " 1938,\n", + " 1939,\n", + " 1940,\n", + " 1941,\n", + " 1942,\n", + " 1943,\n", + " 1944,\n", + " 1945,\n", + " 1946,\n", + " 1947,\n", + " 1948,\n", + " 1949,\n", + " 1950,\n", + " 1951,\n", + " 1952,\n", + " 1953,\n", + " 1954,\n", + " 1955,\n", + " 1956,\n", + " 1957,\n", + " 1958,\n", + " 1959,\n", + " 1960,\n", + " 1961,\n", + " 1962,\n", + " 1963,\n", + " 1964,\n", + " 1965,\n", + " 1966,\n", + " 1967,\n", + " 1968,\n", + " 1969,\n", + " 1970,\n", + " 1971,\n", + " 1972,\n", + " 1973,\n", + " 1974,\n", + " 1975,\n", + " 1976,\n", + " 1977,\n", + " 1978,\n", + " 1979,\n", + " 1980,\n", + " 1981,\n", + " 1982,\n", + " 1983,\n", + " 1984,\n", + " 1985,\n", + " 1986,\n", + " 1987,\n", + " 1988,\n", + " 1989,\n", + " 1990,\n", + " 1991,\n", + " 1992,\n", + " 1993,\n", + " 1994,\n", + " 1995,\n", + " 1996,\n", + " 1997,\n", + " 1998,\n", + " 1999,\n", + " 2000,\n", + " 2001,\n", + " 2002,\n", + " 2003,\n", + " 2004,\n", + " 2005,\n", + " 2006,\n", + " 2007,\n", + " 2008,\n", + " 2009,\n", + " 2010,\n", + " 2011,\n", + " 2012,\n", + " 2013,\n", + " 2014,\n", + " 2015,\n", + " 2016,\n", + " 2017]" + ] + }, + "execution_count": 414, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "years" + ] + }, + { + "cell_type": "code", + "execution_count": 427, + "metadata": {}, + "outputs": [], + "source": [ + "test = (co_merged['Year']== 1850).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 428, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "17" + ] + }, + "execution_count": 428, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test" ] }, { From 2927163aa8870f0e504bf3eac675b0c0c3686401 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Sun, 24 May 2020 21:18:49 +0200 Subject: [PATCH 06/17] more datasets and merged tables --- your-project/Data Gathering.ipynb | 577 +- your-project/Final Dataset notebook.ipynb | 6064 +++++++++++++++++++++ 2 files changed, 6616 insertions(+), 25 deletions(-) create mode 100644 your-project/Final Dataset notebook.ipynb diff --git a/your-project/Data Gathering.ipynb b/your-project/Data Gathering.ipynb index 5c32fe68..7a08fc66 100644 --- a/your-project/Data Gathering.ipynb +++ b/your-project/Data Gathering.ipynb @@ -944,7 +944,9 @@ { "cell_type": "code", "execution_count": 282, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -2067,7 +2069,7 @@ }, { "cell_type": "code", - "execution_count": 315, + "execution_count": 430, "metadata": {}, "outputs": [ { @@ -2158,7 +2160,7 @@ "4 Global NaN 1854 -0.248 -0.218 -0.301" ] }, - "execution_count": 315, + "execution_count": 430, "metadata": {}, "output_type": "execute_result" } @@ -2170,7 +2172,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 431, "metadata": {}, "outputs": [ { @@ -2199,7 +2201,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 432, "metadata": {}, "outputs": [], "source": [ @@ -2208,7 +2210,7 @@ }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 433, "metadata": {}, "outputs": [], "source": [ @@ -2217,7 +2219,7 @@ }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 434, "metadata": {}, "outputs": [ { @@ -2227,7 +2229,7 @@ " dtype=object)" ] }, - "execution_count": 245, + "execution_count": 434, "metadata": {}, "output_type": "execute_result" } @@ -2238,7 +2240,7 @@ }, { "cell_type": "code", - "execution_count": 243, + "execution_count": 435, "metadata": {}, "outputs": [ { @@ -2262,7 +2264,7 @@ " 2015, 2016, 2017, 2018], dtype=int64)" ] }, - "execution_count": 243, + "execution_count": 435, "metadata": {}, "output_type": "execute_result" } @@ -2273,7 +2275,7 @@ }, { "cell_type": "code", - "execution_count": 247, + "execution_count": 436, "metadata": {}, "outputs": [ { @@ -2282,7 +2284,7 @@ "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" ] }, - "execution_count": 247, + "execution_count": 436, "metadata": {}, "output_type": "execute_result" }, @@ -2313,6 +2315,15 @@ "plt.ylabel('increse ordescrease in Temperature(℃)')" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temperature_a.Entity." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -5355,17 +5366,98 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 437, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)
0Afghanistan194914656.00.0003
1Afghanistan195084272.00.0015
2Afghanistan195191600.00.0015
3Afghanistan195291600.00.0015
4Afghanistan1953106256.00.0016
\n", + "
" + ], + "text/plain": [ + " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%)\n", + "0 Afghanistan 1949 14656.0 0.0003\n", + "1 Afghanistan 1950 84272.0 0.0015\n", + "2 Afghanistan 1951 91600.0 0.0015\n", + "3 Afghanistan 1952 91600.0 0.0015\n", + "4 Afghanistan 1953 106256.0 0.0016" + ] + }, + "execution_count": 437, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "co_merged = pd.merge(co_annual, co_share, on=['Country','Year']).merge(co_per_c, on=['Country','Year'])\n", + "co_merged = pd.merge(co_annual, co_share, on=['Country','Year'])\n", "co_merged.head()" ] }, { "cell_type": "code", - "execution_count": 410, + "execution_count": 438, "metadata": {}, "outputs": [ { @@ -5373,17 +5465,16 @@ "output_type": "stream", "text": [ "\n", - "Int64Index: 16541 entries, 0 to 16540\n", - "Data columns (total 5 columns):\n", + "Int64Index: 20586 entries, 0 to 20585\n", + "Data columns (total 4 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 Country 16541 non-null object \n", - " 1 Year 16541 non-null int64 \n", - " 2 Annual COâ‚‚ 16541 non-null float64\n", - " 3 Share of global COâ‚‚ emissions (%) 16541 non-null float64\n", - " 4 COâ‚‚ 16541 non-null float64\n", - "dtypes: float64(3), int64(1), object(1)\n", - "memory usage: 775.4+ KB\n" + " 0 Country 20586 non-null object \n", + " 1 Year 20586 non-null int64 \n", + " 2 Annual COâ‚‚ 20586 non-null float64\n", + " 3 Share of global COâ‚‚ emissions (%) 20586 non-null float64\n", + "dtypes: float64(2), int64(1), object(1)\n", + "memory usage: 804.1+ KB\n" ] } ], @@ -5758,6 +5849,442 @@ "test" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### global Temperature historical" + ] + }, + { + "cell_type": "code", + "execution_count": 442, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAverageTemperatureUncertaintyLandMaxTemperatureLandMaxTemperatureUncertaintyLandMinTemperatureLandMinTemperatureUncertaintyLandAndOceanAverageTemperatureLandAndOceanAverageTemperatureUncertainty
31872015-08-0114.7550.07220.6990.1109.0050.17017.5890.057
31882015-09-0112.9990.07918.8450.0887.1990.22917.0490.058
31892015-10-0110.8010.10216.4500.0595.2320.11516.2900.062
31902015-11-017.4330.11912.8920.0932.1570.10615.2520.063
31912015-12-015.5180.10010.7250.1540.2870.09914.7740.062
\n", + "
" + ], + "text/plain": [ + " dt LandAverageTemperature LandAverageTemperatureUncertainty \\\n", + "3187 2015-08-01 14.755 0.072 \n", + "3188 2015-09-01 12.999 0.079 \n", + "3189 2015-10-01 10.801 0.102 \n", + "3190 2015-11-01 7.433 0.119 \n", + "3191 2015-12-01 5.518 0.100 \n", + "\n", + " LandMaxTemperature LandMaxTemperatureUncertainty LandMinTemperature \\\n", + "3187 20.699 0.110 9.005 \n", + "3188 18.845 0.088 7.199 \n", + "3189 16.450 0.059 5.232 \n", + "3190 12.892 0.093 2.157 \n", + "3191 10.725 0.154 0.287 \n", + "\n", + " LandMinTemperatureUncertainty LandAndOceanAverageTemperature \\\n", + "3187 0.170 17.589 \n", + "3188 0.229 17.049 \n", + "3189 0.115 16.290 \n", + "3190 0.106 15.252 \n", + "3191 0.099 14.774 \n", + "\n", + " LandAndOceanAverageTemperatureUncertainty \n", + "3187 0.057 \n", + "3188 0.058 \n", + "3189 0.062 \n", + "3190 0.063 \n", + "3191 0.062 " + ] + }, + "execution_count": 442, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_temperature = pd.read_csv('data//GlobalTemperatures.csv')\n", + "global_temperature.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 443, + "metadata": {}, + "outputs": [], + "source": [ + "global_temperature.dropna(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 444, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 1992 entries, 1200 to 3191\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 dt 1992 non-null object \n", + " 1 LandAverageTemperature 1992 non-null float64\n", + " 2 LandAverageTemperatureUncertainty 1992 non-null float64\n", + " 3 LandMaxTemperature 1992 non-null float64\n", + " 4 LandMaxTemperatureUncertainty 1992 non-null float64\n", + " 5 LandMinTemperature 1992 non-null float64\n", + " 6 LandMinTemperatureUncertainty 1992 non-null float64\n", + " 7 LandAndOceanAverageTemperature 1992 non-null float64\n", + " 8 LandAndOceanAverageTemperatureUncertainty 1992 non-null float64\n", + "dtypes: float64(8), object(1)\n", + "memory usage: 155.6+ KB\n" + ] + } + ], + "source": [ + "global_temperature.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 449, + "metadata": {}, + "outputs": [], + "source": [ + "global_temperature = global_temperature[['dt','LandAverageTemperature','LandAndOceanAverageTemperature']]" + ] + }, + { + "cell_type": "code", + "execution_count": 450, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAndOceanAverageTemperature
12001850-01-010.74912.833
12011850-02-013.07113.588
12021850-03-014.95414.043
12031850-04-017.21714.667
12041850-05-0110.00415.507
............
31872015-08-0114.75517.589
31882015-09-0112.99917.049
31892015-10-0110.80116.290
31902015-11-017.43315.252
31912015-12-015.51814.774
\n", + "

1992 rows × 3 columns

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" + ], + "text/plain": [ + " dt LandAverageTemperature LandAndOceanAverageTemperature\n", + "1200 1850-01-01 0.749 12.833\n", + "1201 1850-02-01 3.071 13.588\n", + "1202 1850-03-01 4.954 14.043\n", + "1203 1850-04-01 7.217 14.667\n", + "1204 1850-05-01 10.004 15.507\n", + "... ... ... ...\n", + "3187 2015-08-01 14.755 17.589\n", + "3188 2015-09-01 12.999 17.049\n", + "3189 2015-10-01 10.801 16.290\n", + "3190 2015-11-01 7.433 15.252\n", + "3191 2015-12-01 5.518 14.774\n", + "\n", + "[1992 rows x 3 columns]" + ] + }, + "execution_count": 450, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 453, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LandAverageTemperatureLandAndOceanAverageTemperature
Year
18431.91000013.210500
18447.99391714.880083
18458.17666714.989000
18468.02500015.003417
18477.97583314.932333
\n", + "
" + ], + "text/plain": [ + " LandAverageTemperature LandAndOceanAverageTemperature\n", + "Year \n", + "1843 1.910000 13.210500\n", + "1844 7.993917 14.880083\n", + "1845 8.176667 14.989000\n", + "1846 8.025000 15.003417\n", + "1847 7.975833 14.932333" + ] + }, + "execution_count": 453, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_temperature['dt']= pd.to_datetime(temperature['dt']) \n", + "global_temperature['Year'] = pd.DatetimeIndex(global_temperature['dt']).year\n", + "annual_global_temp = global_temperature[['Year','LandAverageTemperature','LandAndOceanAverageTemperature']].groupby(['Year']).agg({'LandAverageTemperature':'mean',\n", + " 'LandAndOceanAverageTemperature':'mean'})\n", + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 454, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LandAverageTemperature 0\n", + "LandAndOceanAverageTemperature 0\n", + "dtype: int64" + ] + }, + "execution_count": 454, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/your-project/Final Dataset notebook.ipynb b/your-project/Final Dataset notebook.ipynb new file mode 100644 index 00000000..fc35e8bf --- /dev/null +++ b/your-project/Final Dataset notebook.ipynb @@ -0,0 +1,6064 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import datetime\n", + "import re\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CO annual emissions (1949-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes)
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes)\n", + "0 Afghanistan AFG 1949 14656.0\n", + "1 Afghanistan AFG 1950 84272.0\n", + "2 Afghanistan AFG 1951 91600.0\n", + "3 Afghanistan AFG 1952 91600.0\n", + "4 Afghanistan AFG 1953 106256.0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_annual = pd.read_csv('data//annual_co2_emissions_per_country.csv')\n", + "co_annual.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "co_annual.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "co_annual = co_annual.rename(columns={'Annual COâ‚‚ emissions (tonnes)':'Annual COâ‚‚', 'Entity':'Country'})" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "co_annual = co_annual[co_annual.Country != 'World']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 20586 entries, 0 to 20852\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 20586 non-null object \n", + " 1 Year 20586 non-null int64 \n", + " 2 Annual COâ‚‚ 20586 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 643.3+ KB\n" + ] + } + ], + "source": [ + "#co_annual.Year.unique()\n", + "co_annual.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Annual share of CO per country (1949-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearShare of global COâ‚‚ emissions (%)
0AfghanistanAFG19490.0003
1AfghanistanAFG19500.0015
2AfghanistanAFG19510.0015
3AfghanistanAFG19520.0015
4AfghanistanAFG19530.0016
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Share of global COâ‚‚ emissions (%)\n", + "0 Afghanistan AFG 1949 0.0003\n", + "1 Afghanistan AFG 1950 0.0015\n", + "2 Afghanistan AFG 1951 0.0015\n", + "3 Afghanistan AFG 1952 0.0015\n", + "4 Afghanistan AFG 1953 0.0016" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_share = pd.read_csv('data//annual_share_of_co2_emissions.csv')\n", + "co_share.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "co_share.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "co_share = co_share.rename(columns={'Share of global COâ‚‚ emissions (%)b':'Annual COâ‚‚', 'Entity':'Country'})" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", + " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", + " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", + " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", + " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", + " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", + " 2015, 2016, 2017, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758,\n", + " 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769,\n", + " 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780,\n", + " 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791,\n", + " 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802,\n", + " 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813,\n", + " 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824,\n", + " 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835,\n", + " 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846,\n", + " 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857,\n", + " 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868,\n", + " 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879,\n", + " 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890,\n", + " 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901,\n", + " 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912,\n", + " 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923,\n", + " 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934,\n", + " 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945,\n", + " 1946, 1947, 1948], dtype=int64)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_share.Year.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20586 entries, 0 to 20585\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 20586 non-null object \n", + " 1 Year 20586 non-null int64 \n", + " 2 Share of global COâ‚‚ emissions (%) 20586 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 482.6+ KB\n" + ] + } + ], + "source": [ + "co_share.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CO Emissions per capita (1800-2017) (no using it for now)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearPer capita COâ‚‚ emissions (tonnes per capita)
0AfghanistanAFG18000.0
1AfghanistanAFG18010.0
2AfghanistanAFG18020.0
3AfghanistanAFG18030.0
4AfghanistanAFG18040.0
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Per capita COâ‚‚ emissions (tonnes per capita)\n", + "0 Afghanistan AFG 1800 0.0\n", + "1 Afghanistan AFG 1801 0.0\n", + "2 Afghanistan AFG 1802 0.0\n", + "3 Afghanistan AFG 1803 0.0\n", + "4 Afghanistan AFG 1804 0.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_per_c = pd.read_csv(\"data//co_emissions_per_capita.csv\")\n", + "co_per_c.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "co_per_c.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "co_per_c = co_per_c.rename(columns={'Per capita COâ‚‚ emissions (tonnes per capita)':'COâ‚‚', 'Entity':'Country'})" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearCOâ‚‚
0Afghanistan18000.0
1Afghanistan18010.0
2Afghanistan18020.0
3Afghanistan18030.0
4Afghanistan18040.0
\n", + "
" + ], + "text/plain": [ + " Country Year COâ‚‚\n", + "0 Afghanistan 1800 0.0\n", + "1 Afghanistan 1801 0.0\n", + "2 Afghanistan 1802 0.0\n", + "3 Afghanistan 1803 0.0\n", + "4 Afghanistan 1804 0.0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_per_c.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "co_per_c = co_per_c[co_per_c.Country != 'World']" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#co_per_c.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 42723 entries, 0 to 42843\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 42723 non-null object \n", + " 1 Year 42723 non-null int64 \n", + " 2 COâ‚‚ 42723 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 1.3+ MB\n" + ] + } + ], + "source": [ + "#co_per_c.Year.unique()\n", + "co_per_c.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Annual CO emissions globally " + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", + "0 Afghanistan AFG 1949 14656.0\n", + "1 Afghanistan AFG 1950 84272.0\n", + "2 Afghanistan AFG 1951 91600.0\n", + "3 Afghanistan AFG 1952 91600.0\n", + "4 Afghanistan AFG 1953 106256.0" + ] + }, + "execution_count": 301, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('data//annual-co-emissions-by-region.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 302, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
20353WorldOWID_WRL17519350528.0
20354WorldOWID_WRL17529354192.0
20355WorldOWID_WRL17539354192.0
20356WorldOWID_WRL17549357856.0
20357WorldOWID_WRL17559361520.0
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" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", + "20353 World OWID_WRL 1751 9350528.0\n", + "20354 World OWID_WRL 1752 9354192.0\n", + "20355 World OWID_WRL 1753 9354192.0\n", + "20356 World OWID_WRL 1754 9357856.0\n", + "20357 World OWID_WRL 1755 9361520.0" + ] + }, + "execution_count": 302, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_co = data[data.Entity == 'World']\n", + "global_co.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + } + ], + "source": [ + "global_co.drop(['Entity','Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 267 entries, 20353 to 20619\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Year 267 non-null int64 \n", + " 1 Annual COâ‚‚ emissions (tonnes ) 267 non-null float64\n", + "dtypes: float64(1), int64(1)\n", + "memory usage: 6.3 KB\n" + ] + } + ], + "source": [ + "global_co.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Total greenhouse gas emissions (1990-2016)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
0AfghanistanAFG199015140000.0
1AfghanistanAFG199115060000.0
2AfghanistanAFG199213600000.0
3AfghanistanAFG199313430000.0
4AfghanistanAFG199413240000.0
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" + ], + "text/plain": [ + " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", + "0 Afghanistan AFG 1990 15140000.0\n", + "1 Afghanistan AFG 1991 15060000.0\n", + "2 Afghanistan AFG 1992 13600000.0\n", + "3 Afghanistan AFG 1993 13430000.0\n", + "4 Afghanistan AFG 1994 13240000.0" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", + "ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "ghg.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "ghg = ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )', 'Entity':'Country'})" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "ghg = ghg[ghg.Country != 'World']" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 5157 entries, 0 to 5183\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 5157 non-null object \n", + " 1 Year 5157 non-null int64 \n", + " 2 GHG(tonnes COâ‚‚e ) 5157 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 161.2+ KB\n" + ] + } + ], + "source": [ + "ghg.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global COâ‚‚ atmospheric concentration" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearCOâ‚‚ concentration (parts per million)
0WorldOWID_WRL803719 BCE207.29
1WorldOWID_WRL803182 BCE202.23
2WorldOWID_WRL802573 BCE204.86
3WorldOWID_WRL802060 BCE207.50
4WorldOWID_WRL801975 BCE202.92
...............
1866WorldOWID_WRL2014398.65
1867WorldOWID_WRL2015400.83
1868WorldOWID_WRL2016404.24
1869WorldOWID_WRL2017406.55
1870WorldOWID_WRL2018408.52
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1871 rows × 4 columns

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" + ], + "text/plain": [ + " Entity Code Year COâ‚‚ concentration (parts per million)\n", + "0 World OWID_WRL 803719 BCE 207.29\n", + "1 World OWID_WRL 803182 BCE 202.23\n", + "2 World OWID_WRL 802573 BCE 204.86\n", + "3 World OWID_WRL 802060 BCE 207.50\n", + "4 World OWID_WRL 801975 BCE 202.92\n", + "... ... ... ... ...\n", + "1866 World OWID_WRL 2014 398.65\n", + "1867 World OWID_WRL 2015 400.83\n", + "1868 World OWID_WRL 2016 404.24\n", + "1869 World OWID_WRL 2017 406.55\n", + "1870 World OWID_WRL 2018 408.52\n", + "\n", + "[1871 rows x 4 columns]" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_concentration = pd.read_csv('data//global-co-concentration-ppm.csv')\n", + "co_concentration" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration.drop(['Code', 'Entity'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration = co_concentration.rename(columns={'COâ‚‚ concentration (parts per million)':'COâ‚‚ concentration'})" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1846'" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_concentration.Year[1740]" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'CO atmospheric concentration')" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = co_concentration.Year[1768:]\n", + "y = co_concentration['COâ‚‚ concentration'][1768:]\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('CO atmospheric concentration')" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearCOâ‚‚ concentration
0803719 BCE207.29
1803182 BCE202.23
2802573 BCE204.86
3802060 BCE207.50
4801975 BCE202.92
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" + ], + "text/plain": [ + " Year COâ‚‚ concentration\n", + "0 803719 BCE 207.29\n", + "1 803182 BCE 202.23\n", + "2 802573 BCE 204.86\n", + "3 802060 BCE 207.50\n", + "4 801975 BCE 202.92" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_concentration.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global temperatures by country" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "temperature = pd.read_csv('data//GlobalLandTemperaturesByCountry.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtAverageTemperatureAverageTemperatureUncertaintyCountry
01743-11-014.3842.294Ã…land
11743-12-01NaNNaNÃ…land
21744-01-01NaNNaNÃ…land
31744-02-01NaNNaNÃ…land
41744-03-01NaNNaNÃ…land
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" + ], + "text/plain": [ + " dt AverageTemperature AverageTemperatureUncertainty Country\n", + "0 1743-11-01 4.384 2.294 Ã…land\n", + "1 1743-12-01 NaN NaN Ã…land\n", + "2 1744-01-01 NaN NaN Ã…land\n", + "3 1744-02-01 NaN NaN Ã…land\n", + "4 1744-03-01 NaN NaN Ã…land" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "temperature['dt']= pd.to_datetime(temperature['dt']) " + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtAverageTemperatureAverageTemperatureUncertaintyCountryyearYear
32391838-04-0113.0082.586Afghanistan18381838
32401838-05-01NaNNaNAfghanistan18381838
32411838-06-0123.9502.510Afghanistan18381838
32421838-07-0126.8772.883Afghanistan18381838
32431838-08-0124.9382.992Afghanistan18381838
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" + ], + "text/plain": [ + " dt AverageTemperature AverageTemperatureUncertainty \\\n", + "3239 1838-04-01 13.008 2.586 \n", + "3240 1838-05-01 NaN NaN \n", + "3241 1838-06-01 23.950 2.510 \n", + "3242 1838-07-01 26.877 2.883 \n", + "3243 1838-08-01 24.938 2.992 \n", + "\n", + " Country year Year \n", + "3239 Afghanistan 1838 1838 \n", + "3240 Afghanistan 1838 1838 \n", + "3241 Afghanistan 1838 1838 \n", + "3242 Afghanistan 1838 1838 \n", + "3243 Afghanistan 1838 1838 " + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature['Year'] = pd.DatetimeIndex(temperature['dt']).year\n", + "temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n", + " 'Anguilla', 'Antarctica', 'Antigua And Barbuda', 'Argentina',\n", + " 'Armenia', 'Aruba', 'Australia', 'Austria', 'Azerbaijan',\n", + " 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus',\n", + " 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\n", + " 'Bonaire, Saint Eustatius And Saba', 'Bosnia And Herzegovina',\n", + " 'Botswana', 'Brazil', 'British Virgin Islands', 'Bulgaria',\n", + " 'Burkina Faso', 'Burma', 'Burundi', \"Côte D'Ivoire\", 'Cambodia',\n", + " 'Cameroon', 'Canada', 'Cape Verde', 'Cayman Islands',\n", + " 'Central African Republic', 'Chad', 'Chile', 'China',\n", + " 'Christmas Island', 'Colombia', 'Comoros',\n", + " 'Congo (Democratic Republic Of The)', 'Congo', 'Costa Rica',\n", + " 'Croatia', 'Cuba', 'Curaçao', 'Cyprus', 'Czech Republic',\n", + " 'Denmark (Europe)', 'Djibouti', 'Dominica', 'Dominican Republic',\n", + " 'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea',\n", + " 'Estonia', 'Ethiopia', 'Europe',\n", + " 'Falkland Islands (Islas Malvinas)', 'Faroe Islands', 'Fiji',\n", + " 'Finland', 'France (Europe)', 'Gabon', 'Gambia', 'Georgia',\n", + " 'Germany', 'Ghana', 'Greece', 'Greenland', 'Grenada', 'Guatemala',\n", + " 'Guinea Bissau', 'Guinea', 'Guyana', 'Haiti', 'Honduras',\n", + " 'Hong Kong', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran',\n", + " 'Iraq', 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan',\n", + " 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos',\n", + " 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',\n", + " 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macau', 'Macedonia',\n", + " 'Madagascar', 'Malawi', 'Malaysia', 'Mali', 'Malta', 'Martinique',\n", + " 'Mauritania', 'Mauritius', 'Mayotte', 'Mexico', 'Moldova',\n", + " 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique',\n", + " 'Namibia', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua',\n", + " 'Niger', 'Nigeria', 'North Korea', 'Norway', 'Oman', 'Pakistan',\n", + " 'Palau', 'Palestina', 'Panama', 'Papua New Guinea', 'Paraguay',\n", + " 'Peru', 'Philippines', 'Poland', 'Portugal', 'Puerto Rico',\n", + " 'Qatar', 'Reunion', 'Romania', 'Russia', 'Rwanda',\n", + " 'Saint Kitts And Nevis', 'Saint Lucia', 'Saint Martin',\n", + " 'Saint Pierre And Miquelon', 'Saint Vincent And The Grenadines',\n", + " 'Samoa', 'San Marino', 'Sao Tome And Principe', 'Saudi Arabia',\n", + " 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',\n", + " 'Sint Maarten', 'Slovakia', 'Slovenia', 'Solomon Islands',\n", + " 'Somalia', 'South Africa', 'South Korea', 'Spain', 'Sri Lanka',\n", + " 'Sudan', 'Suriname', 'Swaziland', 'Sweden', 'Switzerland', 'Syria',\n", + " 'Taiwan', 'Tajikistan', 'Tanzania', 'Thailand', 'Timor Leste',\n", + " 'Togo', 'Tonga', 'Trinidad And Tobago', 'Tunisia', 'Turkey',\n", + " 'Turkmenistan', 'Uganda', 'Ukraine', 'United Arab Emirates',\n", + " 'United Kingdom (Europe)', 'United States', 'Uruguay',\n", + " 'Uzbekistan', 'Venezuela', 'Vietnam', 'Virgin Islands', 'Yemen',\n", + " 'Zambia', 'Zimbabwe'], dtype=object)" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "non_wanted = ['Åland','Africa','Asia','Baker Island','Denmark','Federated States Of Micronesia','France', 'French Guiana', 'French Polynesia',\n", + " 'French Southern And Antarctic Lands','Gaza Strip','Guadeloupe', 'Guam','Guernsey','Heard Island And Mcdonald Islands','Isle Of Man','Jersey','Kingman Reef',\n", + " 'Montserrat','Netherlands (Europe)','Niue','North America','New Caledonia','Northern Mariana Islands','Oceania',\n", + " 'Palmyra Atoll','Saint Barthélemy','American Samoa','South America','South Georgia And The South Sandwich Isla',\n", + " 'Svalbard And Jan Mayen','Turks And Caicas Islands','United Kingdom','Western Sahara']\n", + "for region in non_wanted:\n", + " temperature = temperature[temperature.Country != region]" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AverageTemperature
yearCountry
1743Albania8.620
Andorra7.556
Austria2.482
Belarus0.767
Belgium7.106
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" + ], + "text/plain": [ + " AverageTemperature\n", + "year Country \n", + "1743 Albania 8.620\n", + " Andorra 7.556\n", + " Austria 2.482\n", + " Belarus 0.767\n", + " Belgium 7.106" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_temp = temperature[['year', 'Country','AverageTemperature']].groupby(['year','Country']).agg({'AverageTemperature':'mean'})\n", + "annual_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "annual_temp.reset_index(level=0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yearAverageTemperature
Country
Albania17438.620
Andorra17437.556
Austria17432.482
Belarus17430.767
Belgium17437.106
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" + ], + "text/plain": [ + " year AverageTemperature\n", + "Country \n", + "Albania 1743 8.620\n", + "Andorra 1743 7.556\n", + "Austria 1743 2.482\n", + "Belarus 1743 0.767\n", + "Belgium 1743 7.106" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "annual_temp.reset_index(level=0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryyearAverageTemperature
0Albania17438.620
1Andorra17437.556
2Austria17432.482
3Belarus17430.767
4Belgium17437.106
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" + ], + "text/plain": [ + " Country year AverageTemperature\n", + "0 Albania 1743 8.620\n", + "1 Andorra 1743 7.556\n", + "2 Austria 1743 2.482\n", + "3 Belarus 1743 0.767\n", + "4 Belgium 1743 7.106" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 41931 entries, 0 to 41930\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 41931 non-null object \n", + " 1 year 41931 non-null int64 \n", + " 2 AverageTemperature 39793 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 982.9+ KB\n" + ] + } + ], + "source": [ + "annual_temp.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global Temperature anomaly 1850-2018\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearMedian (℃)Upper (℃)Lower (℃)
0GlobalNaN1850-0.373-0.339-0.425
1GlobalNaN1851-0.218-0.184-0.274
2GlobalNaN1852-0.228-0.196-0.280
3GlobalNaN1853-0.269-0.239-0.321
4GlobalNaN1854-0.248-0.218-0.301
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" + ], + "text/plain": [ + " Entity Code Year Median (℃) Upper (℃) Lower (℃)\n", + "0 Global NaN 1850 -0.373 -0.339 -0.425\n", + "1 Global NaN 1851 -0.218 -0.184 -0.274\n", + "2 Global NaN 1852 -0.228 -0.196 -0.280\n", + "3 Global NaN 1853 -0.269 -0.239 -0.321\n", + "4 Global NaN 1854 -0.248 -0.218 -0.301" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a = pd.read_csv('data//temperature_anomaly.csv')\n", + "temperature_a.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 676 entries, 0 to 675\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Entity 676 non-null object \n", + " 1 Code 0 non-null float64\n", + " 2 Year 676 non-null int64 \n", + " 3 Median (℃) 676 non-null float64\n", + " 4 Upper (℃) 676 non-null float64\n", + " 5 Lower (℃) 676 non-null float64\n", + "dtypes: float64(4), int64(1), object(1)\n", + "memory usage: 31.8+ KB\n" + ] + } + ], + "source": [ + "temperature_a.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "temperature_a.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "temperature_a.rename(columns={'Entity':'Country'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Global', 'Northern Hemisphere', 'Southern Hemisphere', 'Tropics'],\n", + " dtype=object)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860,\n", + " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", + " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", + " 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893,\n", + " 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904,\n", + " 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915,\n", + " 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926,\n", + " 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937,\n", + " 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948,\n", + " 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", + " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", + " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", + " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", + " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", + " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", + " 2015, 2016, 2017, 2018], dtype=int64)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.Year.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "x = temperature_a.Year\n", + "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='Temperature Anomaly', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('increse ordescrease in Temperature(℃)')" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearMedian (℃)Upper (℃)Lower (℃)
0Global1850-0.373-0.339-0.425
1Global1851-0.218-0.184-0.274
2Global1852-0.228-0.196-0.280
3Global1853-0.269-0.239-0.321
4Global1854-0.248-0.218-0.301
\n", + "
" + ], + "text/plain": [ + " Country Year Median (℃) Upper (℃) Lower (℃)\n", + "0 Global 1850 -0.373 -0.339 -0.425\n", + "1 Global 1851 -0.218 -0.184 -0.274\n", + "2 Global 1852 -0.228 -0.196 -0.280\n", + "3 Global 1853 -0.269 -0.239 -0.321\n", + "4 Global 1854 -0.248 -0.218 -0.301" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryYearMedian (℃)Upper (℃)Lower (℃)
0Global1850.0-0.373-0.339-0.425
1Global1851.0-0.218-0.184-0.274
2Global1852.0-0.228-0.196-0.280
3Global1853.0-0.269-0.239-0.321
4Global1854.0-0.248-0.218-0.301
..................
164Global2014.00.5790.6160.545
165Global2015.00.7630.8020.730
166Global2016.00.7970.8350.762
167Global2017.00.6770.7160.636
168Global2018.00.5950.6340.558
\n", + "

169 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Country Year Median (℃) Upper (℃) Lower (℃)\n", + "0 Global 1850.0 -0.373 -0.339 -0.425\n", + "1 Global 1851.0 -0.218 -0.184 -0.274\n", + "2 Global 1852.0 -0.228 -0.196 -0.280\n", + "3 Global 1853.0 -0.269 -0.239 -0.321\n", + "4 Global 1854.0 -0.248 -0.218 -0.301\n", + ".. ... ... ... ... ...\n", + "164 Global 2014.0 0.579 0.616 0.545\n", + "165 Global 2015.0 0.763 0.802 0.730\n", + "166 Global 2016.0 0.797 0.835 0.762\n", + "167 Global 2017.0 0.677 0.716 0.636\n", + "168 Global 2018.0 0.595 0.634 0.558\n", + "\n", + "[169 rows x 5 columns]" + ] + }, + "execution_count": 270, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_a = temperature_a.where(temperature_a['Country']=='Global')\n", + "global_a.dropna(inplace=True)\n", + "global_a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ozone depletion crisis" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearOzone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)
0Natural emissionsNaN1961165000
1Natural emissionsNaN1962165000
2Natural emissionsNaN1963165000
3Natural emissionsNaN1964165000
4Natural emissionsNaN1965165000
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" + ], + "text/plain": [ + " Entity Code Year \\\n", + "0 Natural emissions NaN 1961 \n", + "1 Natural emissions NaN 1962 \n", + "2 Natural emissions NaN 1963 \n", + "3 Natural emissions NaN 1964 \n", + "4 Natural emissions NaN 1965 \n", + "\n", + " Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents) \n", + "0 165000 \n", + "1 165000 \n", + "2 165000 \n", + "3 165000 \n", + "4 165000 " + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozone = pd.read_csv('data//ozone-depleting-substance-emissions.csv')\n", + "ozone.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [], + "source": [ + "ozone.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [], + "source": [ + "ozone.rename(columns={'Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)':'Ozone-depleting emissions'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Natural emissions', 'Total emissions'], dtype=object)" + ] + }, + "execution_count": 187, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozone.Entity.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(17,6))\n", + "plt.plot(ozone.Year,ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Natural emissions'), label='Natural emissions')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Tonnes CFC11-equivalents')\n", + "plt.plot(ozone.Year, ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Total emissions'), label='Total emissions')\n", + "plt.legend(['Natural emissions', 'Total emissions'], loc='upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityYearOzone-depleting emissions
0Natural emissions1961165000
1Natural emissions1962165000
2Natural emissions1963165000
3Natural emissions1964165000
4Natural emissions1965165000
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" + ], + "text/plain": [ + " Entity Year Ozone-depleting emissions\n", + "0 Natural emissions 1961 165000\n", + "1 Natural emissions 1962 165000\n", + "2 Natural emissions 1963 165000\n", + "3 Natural emissions 1964 165000\n", + "4 Natural emissions 1965 165000" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozone.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GDP per capita (1981-2018)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0CountryYearGDPPopulationGDP_per_capita
00Canada1981808.68817124900000.032477.436602
11United States19816698.570801229465714.029192.033459
22Cuba198135.8826419705971.03696.965594
33Mexico1981589.54895069930971.08430.441359
44Antigua Barbuda19810.40892768264.05990.381182
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Country Year GDP Population GDP_per_capita\n", + "0 0 Canada 1981 808.688171 24900000.0 32477.436602\n", + "1 1 United States 1981 6698.570801 229465714.0 29192.033459\n", + "2 2 Cuba 1981 35.882641 9705971.0 3696.965594\n", + "3 3 Mexico 1981 589.548950 69930971.0 8430.441359\n", + "4 4 Antigua Barbuda 1981 0.408927 68264.0 5990.381182" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdp = pd.read_csv('data//GDP_P.csv')\n", + "gdp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "gdp.drop(['Unnamed: 0'],axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tertiary education (1970-2010)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0CountryYearTertiary_education
00Afghanistan19701.08
11Afghanistan19751.48
22Afghanistan19801.96
33Afghanistan19852.49
44Afghanistan19902.91
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Country Year Tertiary_education\n", + "0 0 Afghanistan 1970 1.08\n", + "1 1 Afghanistan 1975 1.48\n", + "2 2 Afghanistan 1980 1.96\n", + "3 3 Afghanistan 1985 2.49\n", + "4 4 Afghanistan 1990 2.91" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "education = pd.read_csv('data//education_df.csv')\n", + "education.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "education.drop(['Unnamed: 0'],axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global average temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAverageTemperatureUncertaintyLandMaxTemperatureLandMaxTemperatureUncertaintyLandMinTemperatureLandMinTemperatureUncertaintyLandAndOceanAverageTemperatureLandAndOceanAverageTemperatureUncertainty
01750-01-013.0343.574NaNNaNNaNNaNNaNNaN
11750-02-013.0833.702NaNNaNNaNNaNNaNNaN
21750-03-015.6263.076NaNNaNNaNNaNNaNNaN
31750-04-018.4902.451NaNNaNNaNNaNNaNNaN
41750-05-0111.5732.072NaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " dt LandAverageTemperature LandAverageTemperatureUncertainty \\\n", + "0 1750-01-01 3.034 3.574 \n", + "1 1750-02-01 3.083 3.702 \n", + "2 1750-03-01 5.626 3.076 \n", + "3 1750-04-01 8.490 2.451 \n", + "4 1750-05-01 11.573 2.072 \n", + "\n", + " LandMaxTemperature LandMaxTemperatureUncertainty LandMinTemperature \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " LandMinTemperatureUncertainty LandAndOceanAverageTemperature \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " LandAndOceanAverageTemperatureUncertainty \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_temperature = pd.read_csv('data//GlobalTemperatures.csv')\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAndOceanAverageTemperature
12001850-01-010.74912.833
12011850-02-013.07113.588
12021850-03-014.95414.043
12031850-04-017.21714.667
12041850-05-0110.00415.507
\n", + "
" + ], + "text/plain": [ + " dt LandAverageTemperature LandAndOceanAverageTemperature\n", + "1200 1850-01-01 0.749 12.833\n", + "1201 1850-02-01 3.071 13.588\n", + "1202 1850-03-01 4.954 14.043\n", + "1203 1850-04-01 7.217 14.667\n", + "1204 1850-05-01 10.004 15.507" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_temperature.dropna(inplace=True)\n", + "global_temperature = global_temperature[['dt','LandAverageTemperature','LandAndOceanAverageTemperature']]\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAndOceanAverageTemperatureYear
12001850-01-010.74912.8331850
12011850-02-013.07113.5881850
12021850-03-014.95414.0431850
12031850-04-017.21714.6671850
12041850-05-0110.00415.5071850
\n", + "
" + ], + "text/plain": [ + " dt LandAverageTemperature LandAndOceanAverageTemperature Year\n", + "1200 1850-01-01 0.749 12.833 1850\n", + "1201 1850-02-01 3.071 13.588 1850\n", + "1202 1850-03-01 4.954 14.043 1850\n", + "1203 1850-04-01 7.217 14.667 1850\n", + "1204 1850-05-01 10.004 15.507 1850" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#global_temperature['dt']= pd.to_datetime(global_temperature['dt']) \n", + "global_temperature['Year'] = pd.DatetimeIndex(global_temperature['dt']).year\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LandAverageTemperatureLandAndOceanAverageTemperature
Year
18507.90066714.867167
18518.17858314.991833
18528.10016715.006500
18538.04183314.955167
18548.21050014.991000
\n", + "
" + ], + "text/plain": [ + " LandAverageTemperature LandAndOceanAverageTemperature\n", + "Year \n", + "1850 7.900667 14.867167\n", + "1851 8.178583 14.991833\n", + "1852 8.100167 15.006500\n", + "1853 8.041833 14.955167\n", + "1854 8.210500 14.991000" + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_global_temp = global_temperature[['Year','LandAverageTemperature','LandAndOceanAverageTemperature']].groupby(['Year']).agg({'LandAverageTemperature':'mean',\n", + " 'LandAndOceanAverageTemperature':'mean'})\n", + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexYearLandAverageTemperatureLandAndOceanAverageTemperature
0018507.90066714.867167
1118518.17858314.991833
2218528.10016715.006500
3318538.04183314.955167
4418548.21050014.991000
\n", + "
" + ], + "text/plain": [ + " index Year LandAverageTemperature LandAndOceanAverageTemperature\n", + "0 0 1850 7.900667 14.867167\n", + "1 1 1851 8.178583 14.991833\n", + "2 2 1852 8.100167 15.006500\n", + "3 3 1853 8.041833 14.955167\n", + "4 4 1854 8.210500 14.991000" + ] + }, + "execution_count": 202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_global_temp.reset_index(level=0, inplace=True)\n", + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [], + "source": [ + "annual_global_temp.drop(['index'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
YearLandAverageTemperatureLandAndOceanAverageTemperature
018507.90066714.867167
118518.17858314.991833
218528.10016715.006500
318538.04183314.955167
418548.21050014.991000
\n", + "
" + ], + "text/plain": [ + " Year LandAverageTemperature LandAndOceanAverageTemperature\n", + "0 1850 7.900667 14.867167\n", + "1 1851 8.178583 14.991833\n", + "2 1852 8.100167 15.006500\n", + "3 1853 8.041833 14.955167\n", + "4 1854 8.210500 14.991000" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 166 entries, 0 to 165\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Year 166 non-null int64 \n", + " 1 LandAverageTemperature 166 non-null float64\n", + " 2 LandAndOceanAverageTemperature 166 non-null float64\n", + "dtypes: float64(2), int64(1)\n", + "memory usage: 4.0 KB\n" + ] + } + ], + "source": [ + "annual_global_temp.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Merging datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CO merged " + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)
0Afghanistan194914656.00.0003
1Afghanistan195084272.00.0015
2Afghanistan195191600.00.0015
3Afghanistan195291600.00.0015
4Afghanistan1953106256.00.0016
\n", + "
" + ], + "text/plain": [ + " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%)\n", + "0 Afghanistan 1949 14656.0 0.0003\n", + "1 Afghanistan 1950 84272.0 0.0015\n", + "2 Afghanistan 1951 91600.0 0.0015\n", + "3 Afghanistan 1952 91600.0 0.0015\n", + "4 Afghanistan 1953 106256.0 0.0016" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_merged = pd.merge(co_annual, co_share, on=['Country','Year'])\n", + "co_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 20586 entries, 0 to 20585\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 20586 non-null object \n", + " 1 Year 20586 non-null int64 \n", + " 2 Annual COâ‚‚ 20586 non-null float64\n", + " 3 Share of global COâ‚‚ emissions (%) 20586 non-null float64\n", + "dtypes: float64(2), int64(1), object(1)\n", + "memory usage: 804.1+ KB\n" + ] + } + ], + "source": [ + "co_merged.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### total emissions (1990-2016)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearGHG(tonnes COâ‚‚e )
0Afghanistan199015140000.0
1Afghanistan199115060000.0
2Afghanistan199213600000.0
3Afghanistan199313430000.0
4Afghanistan199413240000.0
\n", + "
" + ], + "text/plain": [ + " Country Year GHG(tonnes COâ‚‚e )\n", + "0 Afghanistan 1990 15140000.0\n", + "1 Afghanistan 1991 15060000.0\n", + "2 Afghanistan 1992 13600000.0\n", + "3 Afghanistan 1993 13430000.0\n", + "4 Afghanistan 1994 13240000.0" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )
0Afghanistan19902602407.590.011715140000.0
1Afghanistan19912426486.160.010815060000.0
2Afghanistan19921381521.840.006213600000.0
3Afghanistan19931333837.110.006013430000.0
4Afghanistan19941282488.380.005713240000.0
\n", + "
" + ], + "text/plain": [ + " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", + "0 Afghanistan 1990 2602407.59 0.0117 \n", + "1 Afghanistan 1991 2426486.16 0.0108 \n", + "2 Afghanistan 1992 1381521.84 0.0062 \n", + "3 Afghanistan 1993 1333837.11 0.0060 \n", + "4 Afghanistan 1994 1282488.38 0.0057 \n", + "\n", + " GHG(tonnes COâ‚‚e ) \n", + "0 15140000.0 \n", + "1 15060000.0 \n", + "2 13600000.0 \n", + "3 13430000.0 \n", + "4 13240000.0 " + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_emissions = pd.merge(co_merged,ghg, on=(['Country','Year']))\n", + "total_emissions.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 5074 entries, 0 to 5073\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 5074 non-null object \n", + " 1 Year 5074 non-null int64 \n", + " 2 Annual COâ‚‚ 5074 non-null float64\n", + " 3 Share of global COâ‚‚ emissions (%) 5074 non-null float64\n", + " 4 GHG(tonnes COâ‚‚e ) 5074 non-null float64\n", + "dtypes: float64(3), int64(1), object(1)\n", + "memory usage: 237.8+ KB\n" + ] + } + ], + "source": [ + "total_emissions.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging Temperature with Emissions (1990-2013)" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearAverageTemperature
0Albania17438.620
1Andorra17437.556
2Austria17432.482
3Belarus17430.767
4Belgium17437.106
\n", + "
" + ], + "text/plain": [ + " Country Year AverageTemperature\n", + "0 Albania 1743 8.620\n", + "1 Andorra 1743 7.556\n", + "2 Austria 1743 2.482\n", + "3 Belarus 1743 0.767\n", + "4 Belgium 1743 7.106" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "annual_temp.rename(columns={'year':'Year'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperature
0Afghanistan19902602407.590.011715140000.014.993333
1Afghanistan19912426486.160.010815060000.014.370750
2Afghanistan19921381521.840.006213600000.014.056083
3Afghanistan19931333837.110.006013430000.014.439250
4Afghanistan19941282488.380.005713240000.014.754750
\n", + "
" + ], + "text/plain": [ + " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", + "0 Afghanistan 1990 2602407.59 0.0117 \n", + "1 Afghanistan 1991 2426486.16 0.0108 \n", + "2 Afghanistan 1992 1381521.84 0.0062 \n", + "3 Afghanistan 1993 1333837.11 0.0060 \n", + "4 Afghanistan 1994 1282488.38 0.0057 \n", + "\n", + " GHG(tonnes COâ‚‚e ) AverageTemperature \n", + "0 15140000.0 14.993333 \n", + "1 15060000.0 14.370750 \n", + "2 13600000.0 14.056083 \n", + "3 13430000.0 14.439250 \n", + "4 13240000.0 14.754750 " + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_impact = pd.merge(total_emissions, annual_temp, on=(['Country','Year']))\n", + "co_impact.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging co_impact with demographic and economic indicators" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperatureGDPPopulationGDP_per_capita
0Afghanistan19902602407.590.011715140000.014.9933334.59266513568282.0338.485353
1Afghanistan19912426486.160.010815060000.014.3707504.62940613671918.0338.606915
2Afghanistan19921381521.840.006213600000.014.0560834.67570015023317.0311.229518
3Afghanistan19931333837.110.006013430000.014.4392504.53075317003437.0266.461018
4Afghanistan19941282488.380.005713240000.014.7547504.39483118486041.0237.737799
\n", + "
" + ], + "text/plain": [ + " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", + "0 Afghanistan 1990 2602407.59 0.0117 \n", + "1 Afghanistan 1991 2426486.16 0.0108 \n", + "2 Afghanistan 1992 1381521.84 0.0062 \n", + "3 Afghanistan 1993 1333837.11 0.0060 \n", + "4 Afghanistan 1994 1282488.38 0.0057 \n", + "\n", + " GHG(tonnes COâ‚‚e ) AverageTemperature GDP Population GDP_per_capita \n", + "0 15140000.0 14.993333 4.592665 13568282.0 338.485353 \n", + "1 15060000.0 14.370750 4.629406 13671918.0 338.606915 \n", + "2 13600000.0 14.056083 4.675700 15023317.0 311.229518 \n", + "3 13430000.0 14.439250 4.530753 17003437.0 266.461018 \n", + "4 13240000.0 14.754750 4.394831 18486041.0 237.737799 " + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indicators_merged = pd.merge(co_impact,gdp, on=(['Country','Year']))\n", + "indicators_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 3663 entries, 0 to 3662\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 3663 non-null object \n", + " 1 Year 3663 non-null int64 \n", + " 2 Annual COâ‚‚ 3663 non-null float64\n", + " 3 Share of global COâ‚‚ emissions (%) 3663 non-null float64\n", + " 4 GHG(tonnes COâ‚‚e ) 3663 non-null float64\n", + " 5 AverageTemperature 3663 non-null float64\n", + " 6 GDP 3663 non-null float64\n", + " 7 Population 3663 non-null float64\n", + " 8 GDP_per_capita 3663 non-null float64\n", + "dtypes: float64(7), int64(1), object(1)\n", + "memory usage: 286.2+ KB\n" + ] + } + ], + "source": [ + "indicators_merged.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperatureGDPPopulationGDP_per_capita
0Afghanistan19902602407.590.011715140000.014.9933334.59266513568282.0338.485353
1Afghanistan19912426486.160.010815060000.014.3707504.62940613671918.0338.606915
2Afghanistan19921381521.840.006213600000.014.0560834.67570015023317.0311.229518
3Afghanistan19931333837.110.006013430000.014.4392504.53075317003437.0266.461018
4Afghanistan19941282488.380.005713240000.014.7547504.39483118486041.0237.737799
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" + ], + "text/plain": [ + " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", + "0 Afghanistan 1990 2602407.59 0.0117 \n", + "1 Afghanistan 1991 2426486.16 0.0108 \n", + "2 Afghanistan 1992 1381521.84 0.0062 \n", + "3 Afghanistan 1993 1333837.11 0.0060 \n", + "4 Afghanistan 1994 1282488.38 0.0057 \n", + "\n", + " GHG(tonnes COâ‚‚e ) AverageTemperature GDP Population GDP_per_capita \n", + "0 15140000.0 14.993333 4.592665 13568282.0 338.485353 \n", + "1 15060000.0 14.370750 4.629406 13671918.0 338.606915 \n", + "2 13600000.0 14.056083 4.675700 15023317.0 311.229518 \n", + "3 13430000.0 14.439250 4.530753 17003437.0 266.461018 \n", + "4 13240000.0 14.754750 4.394831 18486041.0 237.737799 " + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indicators_merged_outer = pd.merge(co_impact,gdp, on=(['Country','Year']), how='outer')\n", + "indicators_merged_outer.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 7131 entries, 0 to 7130\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 7131 non-null object \n", + " 1 Year 7131 non-null int64 \n", + " 2 Annual COâ‚‚ 4030 non-null float64\n", + " 3 Share of global COâ‚‚ emissions (%) 4030 non-null float64\n", + " 4 GHG(tonnes COâ‚‚e ) 4030 non-null float64\n", + " 5 AverageTemperature 4030 non-null float64\n", + " 6 GDP 6764 non-null float64\n", + " 7 Population 6764 non-null float64\n", + " 8 GDP_per_capita 6764 non-null float64\n", + "dtypes: float64(7), int64(1), object(1)\n", + "memory usage: 557.1+ KB\n" + ] + } + ], + "source": [ + "indicators_merged_outer.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging global co concentration, temp anomally and annual_global_temp" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearLandAverageTemperatureLandAndOceanAverageTemperature
018507.90066714.867167
118518.17858314.991833
218528.10016715.006500
318538.04183314.955167
418548.21050014.991000
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" + ], + "text/plain": [ + " Year LandAverageTemperature LandAndOceanAverageTemperature\n", + "0 1850 7.900667 14.867167\n", + "1 1851 8.178583 14.991833\n", + "2 1852 8.100167 15.006500\n", + "3 1853 8.041833 14.955167\n", + "4 1854 8.210500 14.991000" + ] + }, + "execution_count": 231, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 166 entries, 0 to 165\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Year 166 non-null int64 \n", + " 1 LandAverageTemperature 166 non-null float64\n", + " 2 LandAndOceanAverageTemperature 166 non-null float64\n", + "dtypes: float64(2), int64(1)\n", + "memory usage: 4.0 KB\n" + ] + } + ], + "source": [ + "annual_global_temp.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearCOâ‚‚ concentration
0803719 BCE207.29
1803182 BCE202.23
2802573 BCE204.86
3802060 BCE207.50
4801975 BCE202.92
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" + ], + "text/plain": [ + " Year COâ‚‚ concentration\n", + "0 803719 BCE 207.29\n", + "1 803182 BCE 202.23\n", + "2 802573 BCE 204.86\n", + "3 802060 BCE 207.50\n", + "4 801975 BCE 202.92" + ] + }, + "execution_count": 233, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_concentration.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearCOâ‚‚ concentration
17401846283.62
17411847286.84
17421850284.00
17431851287.13
17441854288.05
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" + ], + "text/plain": [ + " Year COâ‚‚ concentration\n", + "1740 1846 283.62\n", + "1741 1847 286.84\n", + "1742 1850 284.00\n", + "1743 1851 287.13\n", + "1744 1854 288.05" + ] + }, + "execution_count": 234, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_concentration_modern = co_concentration[1740:]\n", + "co_concentration_modern.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 131 entries, 1740 to 1870\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Year 131 non-null object \n", + " 1 COâ‚‚ concentration 131 non-null float64\n", + "dtypes: float64(1), object(1)\n", + "memory usage: 2.2+ KB\n" + ] + } + ], + "source": [ + "co_concentration_modern.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + } + ], + "source": [ + "co_concentration_modern[\"Year\"] = co_concentration_modern[\"Year\"].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 131 entries, 1740 to 1870\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Year 131 non-null int32 \n", + " 1 COâ‚‚ concentration 131 non-null float64\n", + "dtypes: float64(1), int32(1)\n", + "memory usage: 1.7 KB\n" + ] + } + ], + "source": [ + "co_concentration_modern.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearLandAverageTemperatureLandAndOceanAverageTemperatureCOâ‚‚ concentration
018507.90066714.867167284.00
118518.17858314.991833287.13
218548.21050014.991000288.05
318558.11075015.021083285.57
418577.75816714.758250283.16
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" + ], + "text/plain": [ + " Year LandAverageTemperature LandAndOceanAverageTemperature \\\n", + "0 1850 7.900667 14.867167 \n", + "1 1851 8.178583 14.991833 \n", + "2 1854 8.210500 14.991000 \n", + "3 1855 8.110750 15.021083 \n", + "4 1857 7.758167 14.758250 \n", + "\n", + " COâ‚‚ concentration \n", + "0 284.00 \n", + "1 287.13 \n", + "2 288.05 \n", + "3 285.57 \n", + "4 283.16 " + ] + }, + "execution_count": 265, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_global_impact = pd.merge(annual_global_temp,co_concentration_modern, on='Year')\n", + "co_global_impact.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 126 entries, 0 to 125\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Year 126 non-null int64 \n", + " 1 LandAverageTemperature 126 non-null float64\n", + " 2 LandAndOceanAverageTemperature 126 non-null float64\n", + " 3 COâ‚‚ concentration 126 non-null float64\n", + "dtypes: float64(3), int64(1)\n", + "memory usage: 4.9 KB\n" + ] + } + ], + "source": [ + "co_global_impact.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [], + "source": [ + "co_impact_temp = pd.merge(co_global_impact,global_a, on='Year')" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [], + "source": [ + "co_impact_temp.drop(['Country'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearLandAverageTemperatureLandAndOceanAverageTemperatureCO₂ concentrationMedian (℃)Upper (℃)Lower (℃)
018507.90066714.867167284.00-0.373-0.339-0.425
118518.17858314.991833287.13-0.218-0.184-0.274
218548.21050014.991000288.05-0.248-0.218-0.301
318558.11075015.021083285.57-0.272-0.241-0.324
418577.75816714.758250283.16-0.461-0.431-0.512
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" + ], + "text/plain": [ + " Year LandAverageTemperature LandAndOceanAverageTemperature \\\n", + "0 1850 7.900667 14.867167 \n", + "1 1851 8.178583 14.991833 \n", + "2 1854 8.210500 14.991000 \n", + "3 1855 8.110750 15.021083 \n", + "4 1857 7.758167 14.758250 \n", + "\n", + " CO₂ concentration Median (℃) Upper (℃) Lower (℃) \n", + "0 284.00 -0.373 -0.339 -0.425 \n", + "1 287.13 -0.218 -0.184 -0.274 \n", + "2 288.05 -0.248 -0.218 -0.301 \n", + "3 285.57 -0.272 -0.241 -0.324 \n", + "4 283.16 -0.461 -0.431 -0.512 " + ] + }, + "execution_count": 275, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_impact_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 267 entries, 20353 to 20619\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Year 267 non-null object \n", + " 1 Annual CO₂ emissions (tonnes ) 267 non-null float64\n", + "dtypes: float64(1), object(1)\n", + "memory usage: 6.3+ KB\n" + ] + } + ], + "source": [ + "global_co.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 308, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearLandAverageTemperatureLandAndOceanAverageTemperatureCO₂ concentrationMedian (℃)Upper (℃)Lower (℃)Annual CO₂ emissions (tonnes )
018507.90066714.867167284.00-0.373-0.339-0.425196896032.0
118518.17858314.991833287.13-0.218-0.184-0.274198742688.0
218548.21050014.991000288.05-0.248-0.218-0.301255010736.0
318558.11075015.021083285.57-0.272-0.241-0.324260034080.0
418577.75816714.758250283.16-0.461-0.431-0.512279680448.0
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" + ], + "text/plain": [ + " Year LandAverageTemperature LandAndOceanAverageTemperature \\\n", + "0 1850 7.900667 14.867167 \n", + "1 1851 8.178583 14.991833 \n", + "2 1854 8.210500 14.991000 \n", + "3 1855 8.110750 15.021083 \n", + "4 1857 7.758167 14.758250 \n", + "\n", + " CO₂ concentration Median (℃) Upper (℃) Lower (℃) \\\n", + "0 284.00 -0.373 -0.339 -0.425 \n", + "1 287.13 -0.218 -0.184 -0.274 \n", + "2 288.05 -0.248 -0.218 -0.301 \n", + "3 285.57 -0.272 -0.241 -0.324 \n", + "4 283.16 -0.461 -0.431 -0.512 \n", + "\n", + " Annual CO₂ emissions (tonnes ) \n", + "0 196896032.0 \n", + "1 198742688.0 \n", + "2 255010736.0 \n", + "3 260034080.0 \n", + "4 279680448.0 " + ] + }, + "execution_count": 308, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_temp_global = pd.merge(co_impact_temp,global_co, on='Year')\n", + "co_temp_global.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(17,6))\n", + "plt.plot(co_global_impact.Year,co_global_impact['LandAverageTemperature'], label='LandAverageTemperature')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Tonnes CFC11-equivalents')\n", + "plt.plot(co_global_impact.Year,co_global_impact['LandAndOceanAverageTemperature'], label='LandAndOceanAverageTemperature')\n", + "plt.legend(['LandAverageTemperature', 'LandAndOceanAverageTemperature'], loc='upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": 307, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9149133329199667" + ] + }, + "execution_count": 307, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_conc_temp_corr = co_global_impact[\"LandAverageTemperature\"].corr(co_global_impact[\"COâ‚‚ concentration\"])\n", + "co_conc_temp_corr" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8796467057646229" + ] + }, + "execution_count": 310, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_temp_corr = co_temp_global[\"LandAverageTemperature\"].corr(co_temp_global[\"Annual COâ‚‚ emissions (tonnes )\"])\n", + "co_temp_corr" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [], + "source": [ + "co_global_impact[\"Year\"] = co_global_impact[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": 311, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n" + ] + }, + { + "data": { + "image/png": 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YearLandAverageTemperatureLandAndOceanAverageTemperatureCO₂ concentrationMedian (℃)Upper (℃)Lower (℃)Annual CO₂ emissions (tonnes )
018507.90066714.867167284.00-0.373-0.339-0.425196896032.0
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" + ], + "text/plain": [ + " Year LandAverageTemperature LandAndOceanAverageTemperature \\\n", + "0 1850 7.900667 14.867167 \n", + "1 1851 8.178583 14.991833 \n", + "2 1854 8.210500 14.991000 \n", + "3 1855 8.110750 15.021083 \n", + "4 1857 7.758167 14.758250 \n", + "\n", + " CO₂ concentration Median (℃) Upper (℃) Lower (℃) \\\n", + "0 284.00 -0.373 -0.339 -0.425 \n", + "1 287.13 -0.218 -0.184 -0.274 \n", + "2 288.05 -0.248 -0.218 -0.301 \n", + "3 285.57 -0.272 -0.241 -0.324 \n", + "4 283.16 -0.461 -0.431 -0.512 \n", + "\n", + " Annual CO₂ emissions (tonnes ) \n", + "0 196896032.0 \n", + "1 198742688.0 \n", + "2 255010736.0 \n", + "3 260034080.0 \n", + "4 279680448.0 " + ] + }, + "execution_count": 312, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_temp_global.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearLandAverageTemperatureCO₂ concentrationMedian (℃)Annual CO₂ emissions (tonnes )
018507.900667284.00-0.373196896032.0
118518.178583287.13-0.218198742688.0
218548.210500288.05-0.248255010736.0
318558.110750285.57-0.272260034080.0
418577.758167283.16-0.461279680448.0
\n", + "
" + ], + "text/plain": [ + " Year LandAverageTemperature CO₂ concentration Median (℃) \\\n", + "0 1850 7.900667 284.00 -0.373 \n", + "1 1851 8.178583 287.13 -0.218 \n", + "2 1854 8.210500 288.05 -0.248 \n", + "3 1855 8.110750 285.57 -0.272 \n", + "4 1857 7.758167 283.16 -0.461 \n", + "\n", + " Annual CO₂ emissions (tonnes ) \n", + "0 196896032.0 \n", + "1 198742688.0 \n", + "2 255010736.0 \n", + "3 260034080.0 \n", + "4 279680448.0 " + ] + }, + "execution_count": 322, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_temp_global_no_colin = co_temp_global.drop(['LandAndOceanAverageTemperature','Upper (℃)','Lower (℃)'], axis=1)\n", + "co_temp_global_no_colin.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 324, + "metadata": {}, + "outputs": [], + "source": [ + "co_temp_global_no_colin[\"Year\"] = co_temp_global_no_colin[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": 325, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 325, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(co_temp_global_no_colin)" + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Median (℃)')" + ] + }, + "execution_count": 317, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x=co_temp_global['CO₂ concentration'], y=co_temp_global['Median (℃)'])\n", + "plt.xlabel('CO₂ concentration')\n", + "plt.ylabel('Median (℃)')" + ] + }, + { + "cell_type": "code", + "execution_count": 318, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Median (℃)')" + ] + }, + "execution_count": 318, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperatureGDPPopulationGDP_per_capita
0Afghanistan19902602407.590.011715140000.014.9933334.59266513568282.0338.485353
1Afghanistan19912426486.160.010815060000.014.3707504.62940613671918.0338.606915
2Afghanistan19921381521.840.006213600000.014.0560834.67570015023317.0311.229518
3Afghanistan19931333837.110.006013430000.014.4392504.53075317003437.0266.461018
4Afghanistan19941282488.380.005713240000.014.7547504.39483118486041.0237.737799
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" + ], + "text/plain": [ + " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", + "0 Afghanistan 1990 2602407.59 0.0117 \n", + "1 Afghanistan 1991 2426486.16 0.0108 \n", + "2 Afghanistan 1992 1381521.84 0.0062 \n", + "3 Afghanistan 1993 1333837.11 0.0060 \n", + "4 Afghanistan 1994 1282488.38 0.0057 \n", + "\n", + " GHG(tonnes COâ‚‚e ) AverageTemperature GDP Population GDP_per_capita \n", + "0 15140000.0 14.993333 4.592665 13568282.0 338.485353 \n", + "1 15060000.0 14.370750 4.629406 13671918.0 338.606915 \n", + "2 13600000.0 14.056083 4.675700 15023317.0 311.229518 \n", + "3 13430000.0 14.439250 4.530753 17003437.0 266.461018 \n", + "4 13240000.0 14.754750 4.394831 18486041.0 237.737799 " + ] + }, + "execution_count": 250, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indicators_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged[\"Year\"] = indicators_merged[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Annual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperatureGDPPopulationGDP_per_capita
Annual COâ‚‚1.0000000.9857080.971729-0.2249290.8386000.6760940.150583
Share of global COâ‚‚ emissions (%)0.9857081.0000000.957867-0.2345630.8368880.6665550.154708
GHG(tonnes COâ‚‚e )0.9717290.9578671.000000-0.1936020.7916030.7248910.109097
AverageTemperature-0.224929-0.234563-0.1936021.000000-0.208351-0.068548-0.386509
GDP0.8386000.8368880.791603-0.2083511.0000000.3766200.304585
Population0.6760940.6665550.724891-0.0685480.3766201.000000-0.048032
GDP_per_capita0.1505830.1547080.109097-0.3865090.304585-0.0480321.000000
\n", + "
" + ], + "text/plain": [ + " Annual COâ‚‚ \\\n", + "Annual COâ‚‚ 1.000000 \n", + "Share of global COâ‚‚ emissions (%) 0.985708 \n", + "GHG(tonnes COâ‚‚e ) 0.971729 \n", + "AverageTemperature -0.224929 \n", + "GDP 0.838600 \n", + "Population 0.676094 \n", + "GDP_per_capita 0.150583 \n", + "\n", + " Share of global COâ‚‚ emissions (%) \\\n", + "Annual COâ‚‚ 0.985708 \n", + "Share of global COâ‚‚ emissions (%) 1.000000 \n", + "GHG(tonnes COâ‚‚e ) 0.957867 \n", + "AverageTemperature -0.234563 \n", + "GDP 0.836888 \n", + "Population 0.666555 \n", + "GDP_per_capita 0.154708 \n", + "\n", + " GHG(tonnes COâ‚‚e ) AverageTemperature \\\n", + "Annual COâ‚‚ 0.971729 -0.224929 \n", + "Share of global COâ‚‚ emissions (%) 0.957867 -0.234563 \n", + "GHG(tonnes COâ‚‚e ) 1.000000 -0.193602 \n", + "AverageTemperature -0.193602 1.000000 \n", + "GDP 0.791603 -0.208351 \n", + "Population 0.724891 -0.068548 \n", + "GDP_per_capita 0.109097 -0.386509 \n", + "\n", + " GDP Population GDP_per_capita \n", + "Annual COâ‚‚ 0.838600 0.676094 0.150583 \n", + "Share of global COâ‚‚ emissions (%) 0.836888 0.666555 0.154708 \n", + "GHG(tonnes COâ‚‚e ) 0.791603 0.724891 0.109097 \n", + "AverageTemperature -0.208351 -0.068548 -0.386509 \n", + "GDP 1.000000 0.376620 0.304585 \n", + "Population 0.376620 1.000000 -0.048032 \n", + "GDP_per_capita 0.304585 -0.048032 1.000000 " + ] + }, + "execution_count": 253, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indicators_merged.corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [], + "source": [ + "ind_no_colin = indicators_merged.drop(['Share of global COâ‚‚ emissions (%)','GDP','Annual COâ‚‚'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Country', 'Year', 'Annual COâ‚‚', 'Share of global COâ‚‚ emissions (%)',\n", + " 'GHG(tonnes COâ‚‚e )', 'AverageTemperature', 'GDP', 'Population',\n", + " 'GDP_per_capita'],\n", + " dtype='object')" + ] + }, + "execution_count": 262, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indicators_merged.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.10909684577014037" + ] + }, + "execution_count": 264, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_ghg_corr = indicators_merged[\"GDP_per_capita\"].corr(indicators_merged[\"GHG(tonnes COâ‚‚e )\"])\n", + "temp_ghg_corr" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# here we are using seaborn to show the heatmap of the correlation between our final datasets' parameters\n", + "corr = ind_no_colin.corr()\n", + "plt.figure(figsize=(12, 6))\n", + "sns.set(style=\"white\")\n", + "\n", + "mask = np.zeros_like(corr, dtype=np.bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "cmap = sns.diverging_palette(200, 10, as_cmap=True)\n", + "\n", + "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From c95c190cf296ccd86d89e530854e75dfa4458be3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Sun, 24 May 2020 23:55:11 +0200 Subject: [PATCH 07/17] some more datasets and charts --- your-project/Final Dataset notebook.ipynb | 881 +++++++++++++++++----- 1 file changed, 673 insertions(+), 208 deletions(-) diff --git a/your-project/Final Dataset notebook.ipynb b/your-project/Final Dataset notebook.ipynb index fc35e8bf..86fb85c3 100644 --- a/your-project/Final Dataset notebook.ipynb +++ b/your-project/Final Dataset notebook.ipynb @@ -2,10 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 177, + "execution_count": 326, "metadata": {}, "outputs": [], "source": [ + "#importing the needed libraries\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -23,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 327, "metadata": {}, "outputs": [ { @@ -102,12 +103,13 @@ "4 Afghanistan AFG 1953 106256.0" ] }, - "execution_count": 2, + "execution_count": 327, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#importing CO annual emissions dataset\n", "co_annual = pd.read_csv('data//annual_co2_emissions_per_country.csv')\n", "co_annual.head()" ] @@ -118,6 +120,7 @@ "metadata": {}, "outputs": [], "source": [ + "#cleaning the data set\n", "co_annual.drop(['Code'], axis=1, inplace=True)" ] }, @@ -175,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 328, "metadata": {}, "outputs": [ { @@ -254,12 +257,13 @@ "4 Afghanistan AFG 1953 0.0016" ] }, - "execution_count": 7, + "execution_count": 328, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#importing Annual share of CO per country (1949-2017)\n", "co_share = pd.read_csv('data//annual_share_of_co2_emissions.csv')\n", "co_share.head()" ] @@ -270,6 +274,7 @@ "metadata": {}, "outputs": [], "source": [ + "#cleaning the data set\n", "co_share.drop(['Code'], axis=1, inplace=True)" ] }, @@ -361,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 329, "metadata": {}, "outputs": [ { @@ -440,29 +445,24 @@ "4 Afghanistan AFG 1804 0.0" ] }, - "execution_count": 12, + "execution_count": 329, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#importing CO Emissions per capita\n", "co_per_c = pd.read_csv(\"data//co_emissions_per_capita.csv\")\n", "co_per_c.head()\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": 330, "metadata": {}, "outputs": [], "source": [ + "#cleaning the data set\n", "co_per_c.drop(['Code'], axis=1, inplace=True)" ] }, @@ -698,13 +698,14 @@ } ], "source": [ + "#importing Annual CO emissions globally\n", "data = pd.read_csv('data//annual-co-emissions-by-region.csv')\n", "data.head()" ] }, { "cell_type": "code", - "execution_count": 302, + "execution_count": 331, "metadata": {}, "outputs": [ { @@ -783,19 +784,20 @@ "20357 World OWID_WRL 1755 9361520.0" ] }, - "execution_count": 302, + "execution_count": 331, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#keeping only the global data\n", "global_co = data[data.Entity == 'World']\n", "global_co.head()" ] }, { "cell_type": "code", - "execution_count": 303, + "execution_count": 332, "metadata": {}, "outputs": [ { @@ -811,6 +813,7 @@ } ], "source": [ + "#cleaning the dataset\n", "global_co.drop(['Entity','Code'], axis=1, inplace=True)" ] }, @@ -848,7 +851,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 339, "metadata": {}, "outputs": [ { @@ -927,28 +930,30 @@ "4 Afghanistan AFG 1994 13240000.0" ] }, - "execution_count": 19, + "execution_count": 339, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#importing Total greenhouse gas emissions\n", "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", "ghg.head()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 340, "metadata": {}, "outputs": [], "source": [ + "#cleaning the dataset\n", "ghg.drop(['Code'], axis=1, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 341, "metadata": {}, "outputs": [], "source": [ @@ -957,16 +962,7 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "ghg = ghg[ghg.Country != 'World']" - ] - }, - { - "cell_type": "code", - "execution_count": 23, + "execution_count": 346, "metadata": {}, "outputs": [ { @@ -999,7 +995,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 347, "metadata": {}, "outputs": [ { @@ -1129,28 +1125,30 @@ "[1871 rows x 4 columns]" ] }, - "execution_count": 165, + "execution_count": 347, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#importing Global COâ‚‚ atmospheric concentration dataset\n", "co_concentration = pd.read_csv('data//global-co-concentration-ppm.csv')\n", "co_concentration" ] }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 348, "metadata": {}, "outputs": [], "source": [ + "#cleaning the data\n", "co_concentration.drop(['Code', 'Entity'], axis=1, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 349, "metadata": {}, "outputs": [], "source": [ @@ -1159,7 +1157,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 350, "metadata": {}, "outputs": [ { @@ -1168,7 +1166,7 @@ "'1846'" ] }, - "execution_count": 214, + "execution_count": 350, "metadata": {}, "output_type": "execute_result" } @@ -1179,7 +1177,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 352, "metadata": {}, "outputs": [ { @@ -1188,26 +1186,25 @@ "Text(0, 0.5, 'CO atmospheric concentration')" ] }, - "execution_count": 170, + "execution_count": 352, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "x = co_concentration.Year[1768:]\n", - "y = co_concentration['CO₂ concentration'][1768:]\n", + "#plotting the Global CO₂ atmospheric concentration increasement from 1864-2018\n", + "x = co_concentration.Year[1740:]\n", + "y = co_concentration['CO₂ concentration'][1740:]\n", "plt.figure(figsize=(17,6))\n", "\n", "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", @@ -1305,16 +1302,17 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 363, "metadata": {}, "outputs": [], "source": [ + "#importing Global temperatures by country data set\n", "temperature = pd.read_csv('data//GlobalLandTemperaturesByCountry.csv')" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 364, "metadata": {}, "outputs": [ { @@ -1393,7 +1391,7 @@ "4 1744-03-01 NaN NaN Åland" ] }, - "execution_count": 30, + "execution_count": 364, "metadata": {}, "output_type": "execute_result" } @@ -1404,16 +1402,17 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 365, "metadata": {}, "outputs": [], "source": [ + "#cleaning the dataset\n", "temperature['dt']= pd.to_datetime(temperature['dt']) " ] }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 366, "metadata": {}, "outputs": [ { @@ -1441,77 +1440,64 @@ " AverageTemperature\n", " AverageTemperatureUncertainty\n", " Country\n", - " year\n", " Year\n", " \n", " \n", " \n", " \n", - " 3239\n", - " 1838-04-01\n", - " 13.008\n", - " 2.586\n", - " Afghanistan\n", - " 1838\n", - " 1838\n", + " 0\n", + " 1743-11-01\n", + " 4.384\n", + " 2.294\n", + " Åland\n", + " 1743\n", " \n", " \n", - " 3240\n", - " 1838-05-01\n", + " 1\n", + " 1743-12-01\n", " NaN\n", " NaN\n", - " Afghanistan\n", - " 1838\n", - " 1838\n", + " Åland\n", + " 1743\n", " \n", " \n", - " 3241\n", - " 1838-06-01\n", - " 23.950\n", - " 2.510\n", - " Afghanistan\n", - " 1838\n", - " 1838\n", + " 2\n", + " 1744-01-01\n", + " NaN\n", + " NaN\n", + " Åland\n", + " 1744\n", " \n", " \n", - " 3242\n", - " 1838-07-01\n", - " 26.877\n", - " 2.883\n", - " Afghanistan\n", - " 1838\n", - " 1838\n", + " 3\n", + " 1744-02-01\n", + " NaN\n", + " NaN\n", + " Åland\n", + " 1744\n", " \n", " \n", - " 3243\n", - " 1838-08-01\n", - " 24.938\n", - " 2.992\n", - " Afghanistan\n", - " 1838\n", - " 1838\n", + " 4\n", + " 1744-03-01\n", + " NaN\n", + " NaN\n", + " Åland\n", + " 1744\n", " \n", " \n", "\n", "" ], "text/plain": [ - " dt AverageTemperature AverageTemperatureUncertainty \\\n", - "3239 1838-04-01 13.008 2.586 \n", - "3240 1838-05-01 NaN NaN \n", - "3241 1838-06-01 23.950 2.510 \n", - "3242 1838-07-01 26.877 2.883 \n", - "3243 1838-08-01 24.938 2.992 \n", - "\n", - " Country year Year \n", - "3239 Afghanistan 1838 1838 \n", - "3240 Afghanistan 1838 1838 \n", - "3241 Afghanistan 1838 1838 \n", - "3242 Afghanistan 1838 1838 \n", - "3243 Afghanistan 1838 1838 " + " dt AverageTemperature AverageTemperatureUncertainty Country Year\n", + "0 1743-11-01 4.384 2.294 Åland 1743\n", + "1 1743-12-01 NaN NaN Åland 1743\n", + "2 1744-01-01 NaN NaN Åland 1744\n", + "3 1744-02-01 NaN NaN Åland 1744\n", + "4 1744-03-01 NaN NaN Åland 1744" ] }, - "execution_count": 115, + "execution_count": 366, "metadata": {}, "output_type": "execute_result" } @@ -1523,75 +1509,20 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 367, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n", - " 'Anguilla', 'Antarctica', 'Antigua And Barbuda', 'Argentina',\n", - " 'Armenia', 'Aruba', 'Australia', 'Austria', 'Azerbaijan',\n", - " 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus',\n", - " 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\n", - " 'Bonaire, Saint Eustatius And Saba', 'Bosnia And Herzegovina',\n", - " 'Botswana', 'Brazil', 'British Virgin Islands', 'Bulgaria',\n", - " 'Burkina Faso', 'Burma', 'Burundi', \"Côte D'Ivoire\", 'Cambodia',\n", - " 'Cameroon', 'Canada', 'Cape Verde', 'Cayman Islands',\n", - " 'Central African Republic', 'Chad', 'Chile', 'China',\n", - " 'Christmas Island', 'Colombia', 'Comoros',\n", - " 'Congo (Democratic Republic Of The)', 'Congo', 'Costa Rica',\n", - " 'Croatia', 'Cuba', 'Curaçao', 'Cyprus', 'Czech Republic',\n", - " 'Denmark (Europe)', 'Djibouti', 'Dominica', 'Dominican Republic',\n", - " 'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea',\n", - " 'Estonia', 'Ethiopia', 'Europe',\n", - " 'Falkland Islands (Islas Malvinas)', 'Faroe Islands', 'Fiji',\n", - " 'Finland', 'France (Europe)', 'Gabon', 'Gambia', 'Georgia',\n", - " 'Germany', 'Ghana', 'Greece', 'Greenland', 'Grenada', 'Guatemala',\n", - " 'Guinea Bissau', 'Guinea', 'Guyana', 'Haiti', 'Honduras',\n", - " 'Hong Kong', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran',\n", - " 'Iraq', 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan',\n", - " 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos',\n", - " 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',\n", - " 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macau', 'Macedonia',\n", - " 'Madagascar', 'Malawi', 'Malaysia', 'Mali', 'Malta', 'Martinique',\n", - " 'Mauritania', 'Mauritius', 'Mayotte', 'Mexico', 'Moldova',\n", - " 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique',\n", - " 'Namibia', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua',\n", - " 'Niger', 'Nigeria', 'North Korea', 'Norway', 'Oman', 'Pakistan',\n", - " 'Palau', 'Palestina', 'Panama', 'Papua New Guinea', 'Paraguay',\n", - " 'Peru', 'Philippines', 'Poland', 'Portugal', 'Puerto Rico',\n", - " 'Qatar', 'Reunion', 'Romania', 'Russia', 'Rwanda',\n", - " 'Saint Kitts And Nevis', 'Saint Lucia', 'Saint Martin',\n", - " 'Saint Pierre And Miquelon', 'Saint Vincent And The Grenadines',\n", - " 'Samoa', 'San Marino', 'Sao Tome And Principe', 'Saudi Arabia',\n", - " 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',\n", - " 'Sint Maarten', 'Slovakia', 'Slovenia', 'Solomon Islands',\n", - " 'Somalia', 'South Africa', 'South Korea', 'Spain', 'Sri Lanka',\n", - " 'Sudan', 'Suriname', 'Swaziland', 'Sweden', 'Switzerland', 'Syria',\n", - " 'Taiwan', 'Tajikistan', 'Tanzania', 'Thailand', 'Timor Leste',\n", - " 'Togo', 'Tonga', 'Trinidad And Tobago', 'Tunisia', 'Turkey',\n", - " 'Turkmenistan', 'Uganda', 'Ukraine', 'United Arab Emirates',\n", - " 'United Kingdom (Europe)', 'United States', 'Uruguay',\n", - " 'Uzbekistan', 'Venezuela', 'Vietnam', 'Virgin Islands', 'Yemen',\n", - " 'Zambia', 'Zimbabwe'], dtype=object)" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "temperature.Country.unique()" + "#temperature.Country.unique()" ] }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 368, "metadata": {}, "outputs": [], "source": [ + "#keeping only the desired countries, and removing regions\n", "non_wanted = ['Åland','Africa','Asia','Baker Island','Denmark','Federated States Of Micronesia','France', 'French Guiana', 'French Polynesia',\n", " 'French Southern And Antarctic Lands','Gaza Strip','Guadeloupe', 'Guam','Guernsey','Heard Island And Mcdonald Islands','Isle Of Man','Jersey','Kingman Reef',\n", " 'Montserrat','Netherlands (Europe)','Niue','North America','New Caledonia','Northern Mariana Islands','Oceania',\n", @@ -1603,7 +1534,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 370, "metadata": {}, "outputs": [ { @@ -1631,7 +1562,7 @@ " AverageTemperature\n", " \n", " \n", - " year\n", + " Year\n", " Country\n", " \n", " \n", @@ -1664,7 +1595,7 @@ ], "text/plain": [ " AverageTemperature\n", - "year Country \n", + "Year Country \n", "1743 Albania 8.620\n", " Andorra 7.556\n", " Austria 2.482\n", @@ -1672,28 +1603,29 @@ " Belgium 7.106" ] }, - "execution_count": 118, + "execution_count": 370, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "annual_temp = temperature[['year', 'Country','AverageTemperature']].groupby(['year','Country']).agg({'AverageTemperature':'mean'})\n", + "annual_temp = temperature[['Year', 'Country','AverageTemperature']].groupby(['Year','Country']).agg({'AverageTemperature':'mean'})\n", "annual_temp.head()" ] }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 371, "metadata": {}, "outputs": [], "source": [ + "#reseting index to adapt it for the future merge\n", "annual_temp.reset_index(level=0, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 372, "metadata": {}, "outputs": [ { @@ -1717,7 +1649,7 @@ " \n", " \n", " \n", - " year\n", + " Year\n", " AverageTemperature\n", " \n", " \n", @@ -1757,7 +1689,7 @@ "" ], "text/plain": [ - " year AverageTemperature\n", + " Year AverageTemperature\n", "Country \n", "Albania 1743 8.620\n", "Andorra 1743 7.556\n", @@ -1766,7 +1698,7 @@ "Belgium 1743 7.106" ] }, - "execution_count": 120, + "execution_count": 372, "metadata": {}, "output_type": "execute_result" } @@ -1777,7 +1709,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 373, "metadata": {}, "outputs": [], "source": [ @@ -1786,7 +1718,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 374, "metadata": {}, "outputs": [ { @@ -1811,7 +1743,7 @@ " \n", " \n", " Country\n", - " year\n", + " Year\n", " AverageTemperature\n", " \n", " \n", @@ -1851,7 +1783,7 @@ "" ], "text/plain": [ - " Country year AverageTemperature\n", + " Country Year AverageTemperature\n", "0 Albania 1743 8.620\n", "1 Andorra 1743 7.556\n", "2 Austria 1743 2.482\n", @@ -1859,7 +1791,7 @@ "4 Belgium 1743 7.106" ] }, - "execution_count": 122, + "execution_count": 374, "metadata": {}, "output_type": "execute_result" } @@ -1870,7 +1802,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 375, "metadata": {}, "outputs": [ { @@ -1883,7 +1815,7 @@ " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Country 41931 non-null object \n", - " 1 year 41931 non-null int64 \n", + " 1 Year 41931 non-null int64 \n", " 2 AverageTemperature 39793 non-null float64\n", "dtypes: float64(1), int64(1), object(1)\n", "memory usage: 982.9+ KB\n" @@ -1903,7 +1835,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 379, "metadata": {}, "outputs": [ { @@ -1994,19 +1926,20 @@ "4 Global NaN 1854 -0.248 -0.218 -0.301" ] }, - "execution_count": 41, + "execution_count": 379, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#importing Global Temperature anomaly (increase-decreage on previous average temperature)\n", "temperature_a = pd.read_csv('data//temperature_anomaly.csv')\n", "temperature_a.head()" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 380, "metadata": {}, "outputs": [ { @@ -2035,16 +1968,17 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 381, "metadata": {}, "outputs": [], "source": [ + "#cleaning the dataset\n", "temperature_a.drop(['Code'], axis=1, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 382, "metadata": {}, "outputs": [], "source": [ @@ -2053,7 +1987,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 383, "metadata": {}, "outputs": [ { @@ -2063,7 +1997,7 @@ " dtype=object)" ] }, - "execution_count": 45, + "execution_count": 383, "metadata": {}, "output_type": "execute_result" } @@ -2074,7 +2008,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 384, "metadata": {}, "outputs": [ { @@ -2098,7 +2032,7 @@ " 2015, 2016, 2017, 2018], dtype=int64)" ] }, - "execution_count": 46, + "execution_count": 384, "metadata": {}, "output_type": "execute_result" } @@ -2109,7 +2043,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 385, "metadata": {}, "outputs": [ { @@ -2118,25 +2052,33 @@ "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" ] }, - "execution_count": 74, + "execution_count": 385, "metadata": {}, "output_type": "execute_result" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n" + ] + }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "\n", + "#plotting the median temperature anomaly over time to see how it's actually ingreasing \n", "x = temperature_a.Year\n", "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", "plt.figure(figsize=(17,6))\n", @@ -2247,7 +2189,7 @@ }, { "cell_type": "code", - "execution_count": 270, + "execution_count": 386, "metadata": {}, "outputs": [ { @@ -2389,12 +2331,13 @@ "[169 rows x 5 columns]" ] }, - "execution_count": 270, + "execution_count": 386, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#keeping only the global data to compare it with the CO emissions and the CO concentration\n", "global_a = temperature_a.where(temperature_a['Country']=='Global')\n", "global_a.dropna(inplace=True)\n", "global_a" @@ -2409,7 +2352,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 390, "metadata": {}, "outputs": [ { @@ -2495,28 +2438,30 @@ "4 165000 " ] }, - "execution_count": 184, + "execution_count": 390, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# importing Ozone depleting emissions dataset\n", "ozone = pd.read_csv('data//ozone-depleting-substance-emissions.csv')\n", "ozone.head()" ] }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 391, "metadata": {}, "outputs": [], "source": [ + "# cleaning the dataset\n", "ozone.drop(['Code'], axis=1, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 392, "metadata": {}, "outputs": [], "source": [ @@ -2525,7 +2470,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 393, "metadata": {}, "outputs": [ { @@ -2534,7 +2479,7 @@ "array(['Natural emissions', 'Total emissions'], dtype=object)" ] }, - "execution_count": 187, + "execution_count": 393, "metadata": {}, "output_type": "execute_result" } @@ -2545,16 +2490,16 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 394, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 188, + "execution_count": 394, "metadata": {}, "output_type": "execute_result" }, @@ -2570,6 +2515,7 @@ } ], "source": [ + "#ploting natural and total emissions to see that we have nearly stopped using CFC\n", "plt.figure(figsize=(17,6))\n", "plt.plot(ozone.Year,ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Natural emissions'), label='Natural emissions')\n", "plt.xlabel('Year')\n", @@ -2580,7 +2526,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 400, "metadata": {}, "outputs": [ { @@ -2653,7 +2599,7 @@ "4 Natural emissions 1965 165000" ] }, - "execution_count": 84, + "execution_count": 400, "metadata": {}, "output_type": "execute_result" } @@ -2662,6 +2608,340 @@ "ozone.head()" ] }, + { + "cell_type": "code", + "execution_count": 403, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + } + ], + "source": [ + "caused_ozone = ozone[ozone.Entity == 'Total emissions']\n", + "caused_ozone.drop(['Entity'],axis=1, inplace=True )" + ] + }, + { + "cell_type": "code", + "execution_count": 404, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Entity Code Year Maximum ozone hole area (square kilometres) \\\n", + "0 World OWID_WRL 1979 1100000 \n", + "1 World OWID_WRL 1980 3300000 \n", + "2 World OWID_WRL 1981 3100000 \n", + "3 World OWID_WRL 1982 10800000 \n", + "4 World OWID_WRL 1983 12200000 \n", + "\n", + " Mean ozone hole area (square kilometres) \n", + "0 100000 \n", + "1 1400000 \n", + "2 600000 \n", + "3 4800000 \n", + "4 7900000 " + ] + }, + "execution_count": 409, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Antartica Ozone Hole area dataset\n", + "oz_hole = pd.read_csv('data//antarctic-ozone-hole-area.csv')\n", + "oz_hole.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 410, + "metadata": {}, + "outputs": [], + "source": [ + "oz_hole.drop(['Entity', 'Code','Maximum ozone hole area (square kilometres)'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 411, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMean ozone hole area (square kilometres)
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" + ], + "text/plain": [ + " Year Mean ozone hole area (square kilometres)\n", + "0 1979 100000\n", + "1 1980 1400000\n", + "2 1981 600000\n", + "3 1982 4800000\n", + "4 1983 7900000" + ] + }, + "execution_count": 411, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oz_hole.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 412, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 412, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting natural and total emissionsMean ozone hole area (square kilometres) to see if it has reduced\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(oz_hole.Year,oz_hole['Mean ozone hole area (square kilometres)'], label='Mean ozone hole area (square kilometres)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Mean ozone hole area (square kilometres)')\n", + "plt.legend(['Mean ozone hole area (square kilometres)'], loc='upper right')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -4403,6 +4683,193 @@ "indicators_merged_outer.info()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging ozone datasets 1979-2014" + ] + }, + { + "cell_type": "code", + "execution_count": 415, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
YearOzone-depleting emissionsMean ozone hole area (square kilometres)
019791110000100000
1198010800001400000
219811040000600000
3198211000004800000
4198310900007900000
\n", + "
" + ], + "text/plain": [ + " Year Ozone-depleting emissions Mean ozone hole area (square kilometres)\n", + "0 1979 1110000 100000\n", + "1 1980 1080000 1400000\n", + "2 1981 1040000 600000\n", + "3 1982 1100000 4800000\n", + "4 1983 1090000 7900000" + ] + }, + "execution_count": 415, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozone_merged = pd.merge(caused_ozone, oz_hole, on='Year')\n", + "ozone_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 416, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.5597422292909034" + ] + }, + "execution_count": 416, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oz_hole_emiss_corr = ozone_merged[\"Ozone-depleting emissions\"].corr(ozone_merged[\"Mean ozone hole area (square kilometres)\"])\n", + "oz_hole_emiss_corr" + ] + }, + { + "cell_type": "code", + "execution_count": 418, + "metadata": {}, + "outputs": [], + "source": [ + "ozone_merged[\"Year\"] = ozone_merged[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": 420, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 35 entries, 0 to 34\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Year 35 non-null object\n", + " 1 Ozone-depleting emissions 35 non-null int64 \n", + " 2 Mean ozone hole area (square kilometres) 35 non-null int64 \n", + "dtypes: int64(2), object(1)\n", + "memory usage: 2.3+ KB\n" + ] + } + ], + "source": [ + "ozone_merged.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 427, + "metadata": {}, + "outputs": [], + "source": [ + "ozone_merged = ozone_merged.set_index('Year')" + ] + }, + { + "cell_type": "code", + "execution_count": 428, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 428, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(ozone_merged)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -6033,10 +6500,8 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [] } ], From 6371362b98aef88d1f5b3e37ec29c705d695bcf8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Tue, 26 May 2020 23:42:45 +0200 Subject: [PATCH 08/17] saving notebooks --- your-project/Final Dataset notebook.ipynb | 500 +- .../Global climate change datasets.ipynb | 5711 +++++++++++++++++ 2 files changed, 5968 insertions(+), 243 deletions(-) create mode 100644 your-project/Global climate change datasets.ipynb diff --git a/your-project/Final Dataset notebook.ipynb b/your-project/Final Dataset notebook.ipynb index 86fb85c3..1f34d48e 100644 --- a/your-project/Final Dataset notebook.ipynb +++ b/your-project/Final Dataset notebook.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 326, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 327, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -103,7 +103,7 @@ "4 Afghanistan AFG 1953 106256.0" ] }, - "execution_count": 327, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 328, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -257,7 +257,7 @@ "4 Afghanistan AFG 1953 0.0016" ] }, - "execution_count": 328, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -366,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 329, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -445,7 +445,7 @@ "4 Afghanistan AFG 1804 0.0" ] }, - "execution_count": 329, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -458,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 330, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -613,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 301, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -692,7 +692,7 @@ "4 Afghanistan AFG 1953 106256.0" ] }, - "execution_count": 301, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -705,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 331, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -784,7 +784,7 @@ "20357 World OWID_WRL 1755 9361520.0" ] }, - "execution_count": 331, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -797,7 +797,7 @@ }, { "cell_type": "code", - "execution_count": 332, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -819,7 +819,7 @@ }, { "cell_type": "code", - "execution_count": 304, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -851,7 +851,7 @@ }, { "cell_type": "code", - "execution_count": 339, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -930,7 +930,7 @@ "4 Afghanistan AFG 1994 13240000.0" ] }, - "execution_count": 339, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -943,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": 340, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -953,7 +953,7 @@ }, { "cell_type": "code", - "execution_count": 341, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -962,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 346, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -970,15 +970,15 @@ "output_type": "stream", "text": [ "\n", - "Int64Index: 5157 entries, 0 to 5183\n", + "RangeIndex: 5184 entries, 0 to 5183\n", "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 Country 5157 non-null object \n", - " 1 Year 5157 non-null int64 \n", - " 2 GHG(tonnes COâ‚‚e ) 5157 non-null float64\n", + " 0 Country 5184 non-null object \n", + " 1 Year 5184 non-null int64 \n", + " 2 GHG(tonnes COâ‚‚e ) 5184 non-null float64\n", "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 161.2+ KB\n" + "memory usage: 121.6+ KB\n" ] } ], @@ -995,7 +995,7 @@ }, { "cell_type": "code", - "execution_count": 347, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1125,7 +1125,7 @@ "[1871 rows x 4 columns]" ] }, - "execution_count": 347, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1138,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 348, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1148,7 +1148,7 @@ }, { "cell_type": "code", - "execution_count": 349, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1157,7 +1157,7 @@ }, { "cell_type": "code", - "execution_count": 350, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1166,7 +1166,7 @@ "'1846'" ] }, - "execution_count": 350, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1177,7 +1177,7 @@ }, { "cell_type": "code", - "execution_count": 352, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1186,18 +1186,20 @@ "Text(0, 0.5, 'CO atmospheric concentration')" ] }, - "execution_count": 352, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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9ah+4cM9sz3R9XiFH1PcAjkgpHQI0BJqSjbA3TyltkRtVbwvMzJ0/A9gRmJFS2gJoBsxf/6IRcRNwE0D//v0/V8hLkiRJkqqPd+bDaQ/ClPmwVSlcvh8cvnOxU1VvBZujHhE/j4i2EdEBOAF4IiKGA6OBY3OnnQw8kHs+Ktcmd/yJiLAQlyRJkqQa6oG34Yi7syK9yzYw6gSL9HwUeo56Zc4H7k4pXQq8DNyc678ZuDOlNIVsJP2EImSTJEmSJH1FK8vg0qeyOekAR+4Cvx0KW9Uvbq6aYrMU6hExBhiTe/4uMLCSc1YAx22OPJIkSZKkwvjwEzjjIZg4O5uD/su94cSe4J5e+SvGiLokSZIkqRZ6chqc/QgsWAFtmsANh0DvHYqdquaxUJckSZIkfSXlFXDt83DdC9nWXfu0hz8cCFs3KnaymslCXZIkSZL0pa0sg7MegUemZntu/2R3OHMAlHir+5dmoS5JkiRJ+lKWr4YfPJjd8t60Afz5YNirfbFT1XwW6pIkSZKkL2zJKvjOKHjuQ9imEfz30dC9ZbFT1Q4W6pIkSZKkL2TRCvjW/dnK7ttvBSOPyfZJ16ZhoS5JkiRJytvHy+Ck++CNj6FtU/j7MdCuWbFT1S4W6pIkSZKkvMxaDMPvg6kLoPPWMPJoaNWk2KlqHwt1SZIkSdJGvf0xfPv/YMYnsOu22Zz0bbcsdqrayUJdkiRJklSlUW/Dzx6D5WXQZ3u4/Sho3rDYqWovC3VJkiRJUqVWl8Nvn4abJ2btY7rCZUOhUWlxc9V2FuqSJEmSpM+ZsxR++BC8MBNKS+CXe8NJvSClYier/SzUJUmSJEmfMX4mnP5QVqxvvxXccCj0a1XsVHWHhbokSZIkCYDyCrhlIvzuGSirgK+1gesPhu22KnayusVCXZIkSZLEpDnw88fh1TlZ+7t94YI9oLRecXPVRRbqkiRJklSHLV0FVz0Ht06EioBWjeGSfWH/TsVOVndZqEuSJElSHfXoVLh4DMxcAiUJvtMXzh0EjesXO1ndZqEuSZIkSXXM3KVw0Wj499Ss3XM7+O2w7KuKz0JdkiRJkuqQqQvg5Pth+iewVSmcNxi+1QvqlRQ7mdawUJckSZKkOuLFmfDd/4OFK6D39nDjodCqSbFTaX0W6pIkSZJUBzz0Dpzzb1hZDvt1hD8eDFuWFjuVKuPNDZIkSZJUy938MpzxUFakn9gTbjzMIr06c0RdkiRJkmqpioBLn8oKdYDzB8Pp/SGl4uZS1SzUJUmSJKkWWrQCzn8cHp4CpSXw+/3h6K7FTqV8WKhLkiRJUi3zyBT4r9Ewdxk0qZ/d6r7HjsVOpXxZqEuSJElSLTFnKVw8Bh6akrX7t8pG0jttXdRY+oIs1CVJkiSphouA/30Tfj0WFq3M9kc/fw84qReUOB+9xrFQlyRJkqQabPoncOHjMPaDrD2kPVw2FNo2LW4ufXkW6pIkSZJUA1UE3P4KXDEOlq2G5g3h4r2zBeNc1b1ms1CXJEmSpBrmnflw/mMwYVbWPqwLjBgCLbcqbi5tGhbqkiRJklRDrC6HGyfAtS/AqnJouSX8Zigc2LnYybQpWahLkiRJUg3w2hz42X/gjY+z9vHd4aI9oVnD4ubSpmehLkmSJEnV2MoyuO4FuGE8lAfs2BR+Nwz2bFfsZCoUC3VJkiRJqqYmzYGfPApvzYMEfKcP/HQwbFla7GQqJAt1SZIkSapmVpfDn16EP74IZRXQvhlctT8MaFPsZNocLNQlSZIkqRp562M491F4fW7WPqU3nL+Ho+h1iYW6JEmSJFUD5RXZiu5XPwerK6BtU7hyf9i9bbGTaXOzUJckSZKkIpu7FM75Nzw9PWuf2BN+vic0rl/cXCqOghXqKaWGwFigQe597omIi1NKw4DfAyXAEuCUiJiSUmoA3AH0A+YBx0fE+4XKJ0mSJEnVwbjpcNYjMHcZtGgEVx8A+3QodioVU0kBr70SGBoRvYE+wEEppUHADcDwiOgD3AX8V+787wALImIn4Brg8gJmkyRJkqSiKq+Aa5+H4fdlRfrX2sDD37RIVwFH1CMiyEbMAUpzj8g9mub6mwEzc8+PBEbknt8DXJ9SSrnrSJIkSVKtMXcpnP1veGZ6tu3ajwbCOV+DLQo5lKoao6Bz1FNK9YAJwE7AnyLi+ZTSd4GHUkrLgU+AQbnT2wDTASKiLKW0CGgBfFzIjJIkSZK0OT03A858+NNb3f9wIOzdvtipVJ0U9Pc1EVGeu8W9LTAwpdQD+DFwSES0BW4Frs6dniq7xPodKaXvp5TGp5TGz507t1DRJUmSJGmTioDbX4Fv3psV6YNyt7pbpGt9m+XGiohYCIwBDgZ6R8TzuUP/AwzOPZ8B7AiQUtqC7Lb4+ZVc66aI6B8R/Vu2bFno6JIkSZL0la0qh58/Ab8cA+UBP+gHI4+B7RsXO5mqo4IV6imlliml5rnnjYD9gDeBZimlnXOn7Z/rAxgFnJx7fizwhPPTJUmSJNV0c5fCN/4X/j4JGtTLbnW/cE/no2vDCjlHvRVwe26eegnwj4j4V0rpe8D/ppQqgAXAt3Pn3wzcmVKaQjaSfkIBs0mSJElSwb02G773L5i1BFo1hpsOg17bFzuVqrtCrvr+KtC3kv77gPsq6V8BHFeoPJIkSZK0Od3/FvzsMVhZDv1awV8Ohe22KnYq1QQFXfVdkiRJkuqa2Uvg12PhX+9k7RO6w6/3gQZWX8pTXj8qKaU2QPt1z4+IsYUKJUmSJEk1TVkF3PEKXPUcLFkFjbaAi/aCE3tCqmyPK2kDNlqop5QuB44H3gDKc90BWKhLkiRJEvDyR3DRE/B6bgfpAzrBxUOgbdPi5lLNlM+I+lHALhGxstBhJEmSJKkmWbQCLn8G7pqUjWa2bQK/2gf261TsZKrJ8inU3wVKAQt1SZIkScqZPA9Ovh9mLsm2Wvv+bvCjgbBlabGTqabLp1BfBkxMKT3OOsV6RJxVsFSSJEmSVI29OBO+MwoWrYS+O8AV+8HOLYqdSrVFPoX6qNxDkiRJkuq8f0+FHz2cbbt2YGe47iBo6Iru2oQ2+uMUEbenlOoDO+e63o6I1YWNJUmSJEnVz8jX4L9GQ0XA8B5wyb5Qr6TYqVTb5LPq+z7A7cD7QAJ2TCmd7PZskiRJkuqKCPjD89kD4NxBcNZAt11TYeRzg8ZVwAER8TZASmln4O9Av0IGkyRJkqTqoKwiG0X/+yQoSXDZUPhGj2KnUm2WT6FeuqZIB4iIySkl1zGUJEmSVOt9vAzOfgSeng4N6sGfDoH93XpNBZZPoT4+pXQzcGeuPRyYULhIkiRJklR8L34IP3wYZi+FFo3gxsNgQOtip1JdkE+hfjrwQ+AssjnqY4E/FzKUJEmSJBVLBNz0Elz+DJRHVpxffzDs0LjYyVRX5LPq+0rg6txDkiRJkmqtRSvhp4/Co+9m7R/0g/N2h9J6xc2lumWDhXpK6R8R8fWU0mtArH88InoVNJkkSZIkbUavzYHTH4Tpn0DT+nDVAXBA52KnUl1U1Yj62bmvh22OIJIkSZJUDItWwh9fgNsmwuoK6N4S/nIotGtW7GSqqzZYqEfErNzTMyLi/HWPpZQuB87//KskSZIkqWYoq4C7XoNrnof5y7O+E3vCL/aGhvms5iUVSD4/fvvz+aL84Er6JEmSJKlGGPM+XPoUvDM/aw9sDb/cG3puX9RYElD1HPXTgTOATimlV9c51AR4ptDBJEmSJGlTe2Nutpr7mGlZu10z+PkecPBOkFJxs0lrVDWifhfwMPBb4IJ1+hdHxPyCppIkSZKkTWRVOTwyBe54FV6cmfU1qQ9nDoRTe0MDb3NXNVPVHPVFwCLgGwAppe2AhkDjlFLjiPhg80SUJEmSpC/uoyUw8jX4+ySYuyzra1wfjusGZw6Abbcsbj5pQzb6u6OU0uFke6i3BuYA7YE3ge6FjSZJkiRJX9zbH2cLxD06FcpzG03v3AK+1QuO7poV61J1ls9NHpcCg4DHIqJvSmlfcqPskiRJklRdLF6ZFei3TcwK9C1K4NCdsgL9a22cg66aI59CfXVEzEsplaSUSiJidG57NkmSJEkqugi4/234zVPZLe6JbJu1swbC9o2LnU764vIp1BemlBoDY4GRKaU5QFlhY0mSJEnSxr31MfxyDDz/YdbuuwNcsi/03K6osaSvJJ9C/UhgOfBjYDjQDPh1IUNJkiRJUlXKK+DycfC3l7Lb3LdplG2zdmw3KPEWd9VwVRbqKaV6wAMRsR9QAdy+WVJJkiRJ0gZEwEWjs9XcS1I2B/2nu0OzhsVOJm0aVRbqEVGeUlqWUmqW265NkiRJkoomAi59KivSG9SD246EwTsWO5W0aeVz6/sK4LWU0n+ApWs6I+KsgqWSJEmSpEpc9wL87WUoLYEbD7NIV+2UT6H+YO6xrihAFkmSJEnaoL+9BFc/l93ufu1BsG+HYieSCiOfQr15RFy7bkdK6ewC5ZEkSZKkz/n7JLjkqez55cPg0C7FzSMVUkke55xcSd8pmziHJEmSJFXq/ybDzx/Pno8YAl/vXtw8UqFtcEQ9pfQN4JtAx5TSqHUONQHmFTqYJEmSpLpt+Wq4500Y8WQ29/Ynu8OpfYqdSiq8qm59HwfMArYFrlqnfzHwaiFDSZIkSaq73voYRr4G978Fn6zK+n7QD340oLi5pM1lg4V6REwDpgG7b744kiRJkuqiZavhX5Phrknw8kef9vfdAU7qBcd0hZSKl0/anDa6mFxK6RjgcmA7IOUeERFNC5xNkiRJUi1XXgF3vgZXPQufrMz6mtSHo7rCN3tAt5bFzScVQz6rvl8BHB4RbxY6jCRJkqS6Y/I8+Nljn46g990hK84P2xm2LC1uNqmY8inUZ1ukS5IkSdpUVpTBn16EG8bD6grYbiu4ZB84aKdiJ5Oqh3wK9fEppf8B7gdWrumMiHsLlkqSJElSrfTCh3DB4zB1QdYe3gPO3xOaNShuLqk6yadQbwosAw5Ypy+AKgv1lFJDYCzQIPc+90TExSmlBFwKHAeUAzdExHW5/muBQ3Lvd0pEvPQFP48kSZKkamjZavjNU/Dfr2XtzlvD74bBwDbFzSVVRxst1CPi1C957ZXA0IhYklIqBZ5OKT0M7ArsCHSNiIqU0na58w8GuuQeXwNuyH2VJEmSVIO9OhvOfgTeXQilJXBGf/jhAGiQz7ChVAfls+r7zmRF8/YR0SOl1As4IiIurep1ERHAklyzNPcI4HTgmxFRkTtvTu6cI4E7cq97LqXUPKXUKiJmfZkPJkmSJKm4yivgLxPg6uegrAJ2aQHXHgi7upK7VKWSPM75K/BzYDVARLwKnJDPxVNK9VJKE4E5wH8i4nmgM3B8Sml8SunhlFKX3OltgOnrvHxGrm/9a34/99rxc+fOzSeGJEmSpM3sw0/gG/fCFeOyIv3bfWDUCRbpUj7yKdS3jIgX1usry+fiEVEeEX2AtsDAlFIPsjnrKyKiP9kvAW7JnZ4qu0Ql17wpIvpHRP+WLf1TLkmSJFU3o96Gg0bC8x9Cyy3hjqPg4iHQ0Fvdpbzk80fl45RSZ3JFc0rpWOAL3Y4eEQtTSmOAg8hGyv83d+g+4Nbc8xlkc9fXaAvM/CLvI0mSJKl4Zi+BX42FB9/J2vt3gsuHQYsti5tLqmnyGVH/IXAj0DWl9CFwDtk88yqllFqmlJrnnjcC9gPeItvmbWjutCHA5NzzUcC3UmYQsMj56ZIkSVL1V14Bt06EoXdmRXqjLeCyofDXwyzSpS8jn1Xf3wX2SyltBZRExOI8r90KuD2lVI/sFwL/iIh/pZSeBkamlH5Mttjcd3PnP0S2NdsUsu3Zvuxq85IkSZI2k1dnw4VPwGu5JaL37wQjhkDbpsXNJdVk+az6fhlwRUQszLW3Bn4SEf9V1etyi871raR/IXBoJf1BNnovSZIkqZr7ZCVc+Szc8Uo2R7Z1Y/jVPnBA52Ink2q+fOaoHxwRF65pRMSClNIhQJWFuiRJkqTao6wC3pwL42fBizNh3HRYsALqJfjubnD2QNiqfrFTSrVDPoV6vZRSg4hYCWvnmzcobCxJkiRJxbS6HCbMygry8bPg5Y9g2erPnrNbK7hsX06ZPXQAACAASURBVLdckza1fAr1/wYeTyndSnZXy7eB2wuaSpIkSdJmt2gFjH4fHn8PxkzLbm9fV/tm0L81DGgN/VpBl20gVbbJsqSvJJ/F5K5IKb0GDCPb6/ySiPh3wZNJkiRJKrg5S+H+t+Hxd7Nb2svj02Odt4Z92sOANllhvt1Wxcsp1SX5jKgTEQ8DDxc4iyRJkqTN5M25cPPL8MBkWFWe9W1RAoPbwLCOsF8n6NC8uBmluiqfVd+PAS4HtiMbUU9ki7S74YIkSZJUg1QEPDkN/vYSPD0960vAAZ3giF1g7/bQzNWopKLLZ0T9CuDwiHiz0GEkSZIkbXoryuDeN+FvL8PUBVnflqXw9W7w7T7Q3pFzqVrJp1CfbZEuSZIk1TxzlsIdr8J/v5ptpQbQqjGc0ge+0cPRc6m6yqdQH59S+h/gfmDtuo8RcW/BUkmSJEn60t7IzT8ftc78817bwfd2g4N3gtJ6xc0nqWr5FOpNgWXAAev0BWChLkmSJFUjT38Afx4Pz6wz//ygzvDdvtm2am6lJtUM+WzPdurmCCJJkiTpy3n7Y/jN09lCcQBblcLXu8OpvZ1/LtVE+az63hb4I7AH2Uj608DZETGjwNkkSZIkVWH2ErjqOfjnG9mK7k3qw2n94KTezj+XarJ8bn2/FbgLOC7XPjHXt3+hQkmSJEnasKWr4MYJcNNLsLws2//8pF5w9kBosWWx00n6qvIp1FtGxK3rtG9LKZ1TqECSJEmSNmzsNDj3UZi7LGsf1BnO3wM6bV3cXJI2nXwK9Y9TSicCf8+1vwHMK1wkSZIkSZW581W4eAyUB/TdAS7aCwa0LnYqSZtaPoX6t4HrgWvI5qiPy/VJkiRJ2gzKK+A3T8HNE7P2mQPgJ7tDiau4S7VSPqu+fwAcsRmySJIkSVrPklVw1iPw+HtQWgK/GwbHdit2KkmFVLKxE1JKt6eUmq/T3jqldEthY0mSJEmauRiO/WdWpDdvCCOPsUiX6oJ8bn3vFREL1zQiYkFKqW8BM0mSJEl13quz4dujskXjOjWHW4+EDu6JLtUJ+RTqJSmlrSNiAUBKaZs8XydJkiTpC1pZBjeMhz+Nh1XlsHtb+Muh2Yi6pLohn4L7KmBcSukessXkvg78pqCpJEmSpDrohQ/hgsdh6oKsfVIv+OXeUL9ecXNJ2rzyWUzujpTSeGAokIBjIuKNgieTJEmS6ohFK+Cyp+Hu17N2563ht8Pga22Km0tSceR1C3uuMLc4lyRJkjahCPjXO/CrJ7O56KUl8MMBcEZ/aOBkU6nO8o+/JEmStJlVBIx+D26YAC/OzPoGtobLhkGXbYqbTVLxWahLkiRJm8mqchj1Ntz4Ekyel/U1awA/3xOO7w4lqbj5JFUPGy3UU0odgVkRsSLXbgRsHxHvFzibJEmSVCssWQV/nwQ3vwyzlmR9OzSG7/aFb/SAxvWLm09S9ZLPiPo/gcHrtMtzfQMKkkiSJEmqJZathlsnwo0TYNHKrK/LNnBaPzhiF1dzl1S5fAr1LSJi1ZpGRKxKKfk7P0mSJGkDVpbBXZPgTy9mi8QBDGgNp/eDfTt6i7ukquVTqM9NKR0REaMAUkpHAh8XNpYkSZJU85RVwH1vwR+egxmLs74+28N5g2HPdsXNJqnmyKdQPw0YmVK6nmwf9enAtwqaSpIkSapBIuDhKXDlszB1Qda3cws4b3fYvxMkR9AlfQEbLdQjYiowKKXUGEgRsbjwsSRJkqSa4bU5cMlYeP7DrN2uGZw7CI7YGeqVFDebpJppg4V6SunEiPjvlNK56/UDEBFXFzibJEmSVG3NXgpXjoN/vgEBbNMIfvw1OKGHi8RJ+mqqGlHfKve1yeYIIkmSJNUEK8qybdb+9CIsXQ2lJXBKH/jRwGxPdEn6qjZYqEfEjSmlesAnEXHNZswkSZIkVTsR8NAUuOxpmPFJ1ndAJ7hwT+i4dXGzSapdqpyjHhHlKaUjAAt1SZIk1VmvzYZfjYUXZ2btri3gF3u7krukwshn1fdxuRXf/wdYuqYzIl4qWCpJkiSpGpi9FH4/Du7JzUNv0Qh+ujsc392F4iQVTj6F+uDc11+v0xfA0E0fR5IkSSq+FWXwt5fgT+NhWW4e+qm5eehNnYcuqcDy2Z5t380RRJIkSSq2FWXZ6Pmfx8OHuU2JD+yczUPv0Ly42STVHRst1FNK2wOXAa0j4uCUUjdg94i4eSOvawiMBRrk3ueeiLh4neN/BE6NiMa5dgPgDqAfMA84PiLe/1KfSpIkSfoClq2Gka/BTS/BnNxkz123zeah77FjcbNJqnvyufX9NuBW4KJcezLZfPUqC3VgJTA0IpaklEqBp1NKD0fEcyml/sD6v5P8DrAgInZKKZ0AXA4cn+fnkCRJkr6wRSvh9lfglpdhwYqsr9u2cOZAOKiz89AlFUc+hfq2EfGPlNLPASKiLKVUvrEXRUQAS3LN0twjclu+/R74JnD0Oi85EhiRe34PcH1KKeWuI0mSJG0y85bBzRPhjldg8aqsr+8O2Rz0oR0gpaLGk1TH5VOoL00ptSBbQI6U0iBgUT4XzxXlE4CdgD9FxPMppbOBURExK332b8A2wHRY+8uARUAL4ON8P4wkSZJUlY+WwE0T4K5JsLws6xvcNhtBH9zWAl1S9ZBPoX4uMAronFJ6BmgJHJvPxSOiHOiTUmoO3JdS2hs4DtinktMr+2vxc6PpKaXvA98HaNfOjSslSZK0cR8sgr+Mh3++Caty94YO6whnDoDdWhU3myStL59V319KKQ0BdiErpt+OiNVf5E0iYmFKaQywL9no+pTcaPqWKaUpEbETMAPYEZiRUtoCaAbMr+RaNwE3AfTv39/b4iVJkrRBMxdn+6A/8DaUR/Y/s4d2gR8OgO4ti51OkiqXz4g6wECgQ+783VJKRMQdVb0gpdQSWJ0r0hsB+wGXR8QO65yzJFekQzZqfzLwLNmI/RPOT5ckSdKXEQH3vAm/ejKbg14vwbG7wun9Yadtip1OkqqWz/ZsdwKdgYnAmkXkgmwrtaq0Am7PzVMvAf4REf+q4vybgTtTSlPIRtJP2Fg2SZIkaX1zlsKFT8B/3s3aB3TKtllr16y4uSQpX/mMqPcHun3R0e2IeBXou5FzGq/zfAXZ/HVJkiTpS3nwHbjoiWyrtab1YcQ+cExXF4mTVLPkU6hPAnYAZhU4iyRJkvSlLFwBvxgNoyZn7b3awRX7Qesmxc0lSV/GBgv1lNL/kd3i3gR4I6X0ArByzfGIOKLw8SRJkqQNW1EGf58E178AHy+HLUvhoj1heE9H0SXVXFWNqF+52VJIkiRJX8CqcvjnG/DHF2DWkqxvYGu4cn9o37y42STpq9pgoR4RT655nlLagWzl9wBejIiPNkM2SZIk6TPKKuC+t+APz8OMT7K+ri3gJ7vD/p0cRZdUO+Sz6vt3gV8CT5BtPfnHlNKvI+KWQoeTJEmSINtu7cF34Kpn4d2FWV/nreHcQXBIFyixQJdUi+SzmNx5QN+ImAeQUmoBjAMs1CVJklRwE2bBpU/BS7mljds3g3O+BkfuAvVKiptNkgohn0J9BrB4nfZiYHph4kiSJEmZDxbBFePg/3IruW/bCM7dHb7eDUrrFTebJBVSPoX6h8DzKaUHyOaoHwm8kFI6FyAiri5gPkmSJNUxn6yEP70It0zMFo1rUA++txuc3h8a1y92OkkqvHwK9am5xxoP5L66K6UkSZI2mQi450247GmYvzzrO7or/Gyw+6FLqls2WqhHxK/WPE8plQCNI+KTgqaSJElSnTJtIfz8CXgmN8FyYGv4xd7Qa/vi5pKkYshn1fe7gNOAcmAC0CyldHVE/L7Q4SRJklS7lVXA316Ca56HFWWwdUP45RA4ehe3WpNUd+WzTma33Aj6UcBDQDvgpIKmkiRJUq332hw44m747TNZkX50V3j8JDimq0W6pLotnznqpSmlUrJC/fqIWJ1SigLnkiRJUi0UAS9/BP/7Jtw1CSoC2jaBy4bBkPbFTidJ1UM+hfqNwPvAK8DYlFJ7wDnqkiRJyktFrjh/8B14+B2YuSTrL0nwnb7wk0Gwlau5S9Ja+Swmdx1w3Tpd01JK+xYukiRJkmq6ioCXZsFD78BDU2DWkk+P7dAYDtkJjusG3VoWL6MkVVf5LCbXDLgY2DvX9STwa2BRAXNJkiSphqkImDATHpwCD0+Bj9Ypzls3hoO7wKFdoO8O2Wi6JKly+dz6fgswCfh6rn0ScCtwTKFCSZIkqWZYvBImzYVHcsX57KWfHmvTBA7eKSvO+1icS1Le8inUO0fE/1un/auU0sRCBZIkSVL1U14B7y2Etz6GNz+Gt+dlX2est3JRmyZwSG7kvM/2rt4uSV9GPoX68pTSnhHxNEBKaQ9geWFjSZIkqbp4Zz6c9iBMmf/5Y/XrwU5bw57tsuK8t8W5JH1l+RTqpwO35+aqJ2A+cEohQ0mSJKl6eHgK/ORRWLoaWm4JvbaHrttC1xbZ147NobResVNKUu2Sz6rvE4HeKaWmubZbs0mSJNVy5RVw1XPwpxez9hE7w+X7wZalxc0lSXVBPqu+Nwe+BXQAtki5e5ki4qyCJpMkSVJRLFwBZz0CT07LFoD7+Z7wvb7e0i5Jm0s+t74/BDwHvAZUFDaOJEmSiunNufD9B+GDRbB1Q7j+4Gz+uSRp88mnUG8YEecWPIkkSZKKZtZiuPt1uHECLC+D7i3hpsOgbdNiJ5OkuiefQv3OlNL3gH8BK9d0RkQl635KkiSppiivyG5vv2sSPP4eVETW//92hcuGQsN8/k9RkrTJ5fPX7yrg98BFQO6vbwLoVKhQkiRJKpzZS+Afb8Ddk2DG4qyvtAQO2QmG94Td2zofXZKKKZ9C/Vxgp4j4uNBhJEmStGlVBExdAONnwoRZMGEmvLvw0+PtmsE3usNx3aDlVsXLKUn6VD6F+uvAskIHkSRJ0qaxYDk8MBmefD8rzhet/OzxhlvAPu2z0fM922Uru0uSqo98CvVyYGJKaTSfnaPu9mySJEnVRFkFjJ0G/3wDHnsPVpV/emyHxtC/FfRrBf1bw67bQmm94mWVJFUtn0L9/txjXVHZiZIkSdq8pszPivN734I5S7O+kgRD2sNRu8DX2kDrJs45l6SaJJ9CvXlEXLtuR0rp7ALlkSRJUh6mLYTfPgMPT/m0r2PzbK75/9s1G0WXJNVM+RTqJwPXrtd3SiV9kiRJKrBFK+H6F+C2V7Lb2xtukY2cH9stu73dkXNJqvk2WKinlL4BfBPomFIatc6hJsC8QgeTJEnSp8oqYORrcM1zsGBF1nfsrvDT3aFVk+JmkyRtWlWNqI8DZgHbAlet078YeLWQoSRJkpRZWQaj34ffP5vNR4ds3vkv9oKe2xc1miSpQDZYqEfENGAasPvmiyNJkqRPVsKY9+HRd7MifcmqrL99M7hwTziws7e4S1JtttE56imlQcAfgV2B+kA9YGlENC1wNkmSpDrjoyXw+Hvw76kwbjqsrvj0WLdtsznoJ/aEBvmsMCRJqtHy+av+euAE4J9Af+BbwE6FDCVJklTbrSiDFz6EJ6fB2A9g8jorAJWk7Pb2AzrBAZ2hXbPi5ZQkbX55/U42IqaklOpFRDlwa0ppXIFzSZIk1TqLVsK9b2a3sz//YVasr7FlKeyxI+zfCfbrCC22LFpMSVKR5VOoL0sp1QcmppSuIFtgbquNvSil1BAYCzTIvc89EXFxSmkk2cj8auAF4AcRsTqllMi2fDsEWAacEhEvfZkPJUmSVJ3MXQq3TIQ7Xv10vjlAt5YwpB0M6QD9WkH9ekWLKEmqRvIp1E8CSoAzgR8DOwL/L4/XrQSGRsSSlFIp8HRK6WFgJHBi7py7gO8CNwAHA11yj6/l+r6W/0eRJEmqXj78BG58Ce6eBCvLs77BbbP55nu1g+02OvQhSaqLNlqo51Z/B1gB/CrfC0dEAEtyzdLcIyLioTXnpJReANrmmkcCd+Re91xKqXlKqVVEzMr3PSVJkoptySp4Z3625/l9b2X7n0M23/yMAdB3h+LmkyRVfwVdNzSlVA+YQLb43J8i4vl1jpWSjdafnetqA0xf5+Uzcn0W6pIkqdqZvQRemwPvLoT3FsC7C7Lnc5Z+ek5JgiN3gTP6Q9dti5dVklSzFLRQzy0+1yel1By4L6XUIyIm5Q7/GRgbEU/l2pXtBhrrd6SUvg98H6Bdu3YFSC1JkvRZq8vhzY9hwix4KfeYsbjycxvUg/bNYWBr+P5u2XNJkr6IvAr1lFJjstvWl2705EpExMKU0hjgIGBSSulioCXwg3VOm0E2/32NtsDMSq51E3ATQP/+/T9XyEuSJH0VZRUwZT68Phden5ONmr8657MrtAM0rg+9toMuLaBjc+i0dfZo3RjqlRQnuySpdqiyUE8pnQFcQLbKe0opLQYuj4g/b+zCKaWWwOpckd4I2A+4PKX0XeBAYFhEVKzzklHAmSmlu8kWkVvk/HRJklRoc5dme5lPmJUV5299/OnCb+vq2DxbmX23VtnXLttYkEuSCmODhXpK6b+AwcA+EfFurq8TcG1KaZuIuHQj124F3J6bp14C/CMi/pVSKgOmAc9mO7Jxb0T8GniIbGu2KWTbs5361T6aJEnS55VXZCPko9/L9jN/dc7nz2nXDLq3hB4toft22ci5+5pLkjaXlC2yXsmBlN4GekfEivX6GwGvRMTOmyFflfr37x/jx48vdgxJklTNzVoMz0yHpz6AsR/A/OWfHmtQD3ZvC3u2g57bZXubN21QvKySpLojpTQhIvqv31/lre/rF+m5vuUppYrKzpckSaoO5i+HZ2fAuOlZgf7ews8eb9sUhnaAfTtkRXqj0iKElCRpA6oq1GeklIZFxOPrdqaUhuKWaZIkqZp5fyE8+A48NAUmrXc7e+P68LU2MLgt7NMBOm8NqbL9ZiRJqgaqKtTPAh5IKT1Nthd6AAOAPYAjN0M2SZKkKn2wKCvO//XOZ4vzBvWyBd/22BEG7wi9toctXPhNklRDbLBQj4jXU0o9gG8C3cn2OR8L/KCyW+IlSZIKbdFKeDm3j/kT72dbp63RuD7s3wkO7QJ7tYOGeW1CK0lS9VPVqu87AdtHxC3r9e+VUpoZEVMLnk6SJNUKq8qzBd0qX8J2w5avzlZln5Arzt+Z/9njW5XCfp3gsC6wd3uLc0lS7VDVP2d/AC6spH957tjhBUkkSZJqvNlLs8L6pVlZkT1pTuV7k39R9etBj+2y29oHtrY4lyTVTlX909YhIl5dvzMixqeUOhQskSRJqpE+WATXPg/PfQgzPvn88TZNvvg88XoJum4Lu7XKivPuLaGBhbkkqZar6p+6hlUca7Spg0iSpJopAu57C34xBpasyvq2KoW+O3xaYPfdAZpV9X8WkiRpraoK9RdTSt+LiL+u25lS+g7ZKvCSJKmOW7QCLnwiW3Ud4OCd4KyBsEsLqOcq65IkfSlVFernAPellIbzaWHeH6gPHF3oYJIkqXp7Zjr85FGYtSQbQR8xBI7r5v7kkiR9VVVtzzYbGJxS2hfoket+MCKe2CzJJElSwc1cDFvVh2YN8n/NyjK48ln460vZKu59d4BrD4T2zQsWU5KkOmWjy7FExGhg9GbIIklSjTV7KZRXQCIbUU4Aua/bNPrii6htDqPehrP/nc0x37kF9G8F/dtkX3dsmn2OiOyzTZqTe8yFiR/B3GXZQm9nDYQzB1bPzydJUk3luqmSJH0Fy1bDmQ/D4+9t+JzttoIz+sM3emx8K7HJ8+CmCdl+4T22gwGtoX/rbMX0TXlL+TPT4dxHoSKyIvvtedlj5KRPM3faGqbOz4ry9XVsDlcdkC0UJ0mSNq0UEcXO8KX1798/xo8fX+wYkqQ6atEKOHVUtk94wy2geW5V84jslvAIWF0BC1dk/Ts0hjMHwNe7fX6LsZdmwQ3j4dF3K3+vHRpnRXu/VtC6CWyRssXa6uW+blGS3cK+67ZQspGC/o258PV7YPEq+HYfOH8PeG0OjJ+Ze8z6NDNA0wbQo2X2i4M1j47NN/4+kiSpaimlCRHR/3P9FuqSpNqqvCIrJguxuNnspf+/vfMOt6q4+vC76b0IqEgRaxQbKnbsGnvvGnua8TMYoyYmsUZj1MQWTew99h57BxsiXXovlyKdC1y4dX9/rDXuuYeD1AsH+L3Pc55zzp49a6/ps6ZtOO81GD4LNmsCz5wMW7Vc8r40NeP7rl4wbKZda9fUDPZTO8PXRfDvPtCryNzq14bTd4DDt7Sl5n2m2EDAvNLl02vf9nD74bZ0PR9FxXDSizB9IRy7DfzrqCUN7qoUxsyx96Jvs1G2DF4IIYQQqxcZ6kIIITYohs2AM1+193pv1BBaN4RWjaCVf2/cCLZoCVtvBJs3X7E91hPnwTmv2fdWLeGZk2yW+8eoSuG90XDXN7a8HaBRXVs6D9C0Hpy3M1zYBdo0XtLv6NlmtPebCnNLoaLKrldU2YBEZWrL5ecuthPY/7w/nL1jdQN7ziI45SUzwvduB0+euOyl+EIIIYSoOWSoCyGE2GAoLoXjnoPx85bv/rq1oFMLM9q33siWj3fZxIzv3JnkETPhZ6/bjPROG8OTJ5jhv7xUpfD2KLj7GzO+2zSCi3eFc3ayJearwswS+Mun8O5o+79/R7jtUGjXDBZXwNmv2uz8dq3gxdNW7KR3IYQQQqx+ZKgLIYTYIEhTuOQdM1a3bw0vnGKz1jMXwawSmLXIPtPmw+g5dlha0fz8sjZubK8eC5+qFH79ti1D37s9PHIsNF1JY7eyyma2OzZfvbPaaQpvjYJrP4U5i6FJPfjL/vDpeHh/jC3Tf+0M2/MuhBBCiLWLDHUhhBAbBI/2h5t6moH61pm2vH1ZlJSb0Tx6ti0fHzzdXkG2tH3hh28J9x1V2MvGZyyEP39qxnmgWX145TR7FZsQQggh1j5LM9QLuIshhBBCrBh9psDfvrDfdxy2fEY62F7xnTa2TyBNYdxc6D/N9oUP+N72vZ/aGf52SOG/N7xNY3jwGHhjBFz3GZRWwqPHyUgXQggh1gVkqAshhFgvmFUCl75rh6tdvCscvc2qyUsSe4/4li3hlO3tWpquW6efJwmcuB0ctiUsKl/ykDohhBBCFCYFPh8ghBCikOk/Db6ZbAbs8lBaAS8PhX9+bQe+LS8zFsLzg+0gtKo8z6qsgu7vw7QF9p7xa/ZbftkrwrpkpMc0qScjXQghhFiX0Iy6EEKIleKJgXDDZ5ACW7awU8tP7QwtGix578wS+O938NRAO9QN4NXhcO+RZlj/GB+Nhas/sgPgwA54++mWcORWdqBb3dpwb2/4fKK9eu3fR9s1IYQQQoh1FR0mJ4QQYoVIU7jja7j/W/vfumFmfNevDcdvCz/bGXbZxN4X/ugAeH247ZEG6NzaZqaHzIDaCVy+N1zaFWrnrPEqKYebe8J/B9v/XTYxg39ydEJ7s/qwT3v4wA9Me+Yk6Nax5sIuhBBCCLE60anvQgghfliivrJLuMsr4ZqP4aVhZmTfdhictB18PA6eGQQ9J2b3tm8GRcX+PODQLWzv+D7tobwK/vE1PNjX3PduB3cfAW2b2v9B30P392DsXKhXG67e1/wmwOAZdpL5+2NsICBwxd7Qfa+VC5cQQgghxNpAhroQQmzgDJsBV3wA0xfC2TvZrPcmK7BvuaQcLnkbPpsADevYEvNDtqh+z/i5tsT9xaEwd7Hdd2pnuKiLHcqWS88JptOMElsy//dD7b3md31jh8Jt2wruOQI6t8mv09g52Wz6L3eHWuvoHnIhhBBCbJjIUBdCiA2UqhSeGAB//zJbfg5QtxYcuy1c3AV22uTHZcwqgYvetFeUtWwAj58Au2669PsXV9is+Lat8u9Zj5lZAr//wAYAYi7qAn/Yr7DfVS6EEEIIsSrIUBdCiA2Q6Qvhyg+hhxvBZ+0Ix24DTw2CD8dmJ6h3bQsX7QqdWtjM+aJy+y6psN8P97N3irdvBk+fmH92fFWoSuFxH0xoXh/u/CkcsPnqfYYQQgghRKEhQ10IITYwPh4LV34EsxfZrPZth8KRW2fuE+fBkwPhxSFQXLZseZ1bwxMnrthy+RVlVgk0qgsN69bcM4QQQgghCoWlGepaUCiEEOswaWoz3wvKYUEZLCiF+WXw7mh45ju7Z78ONkO9aZPqfjs2h2sPsEPYXh4Grwyz95w3rGvGcqO6tse8UV3YrClc2MVOWa9JWjWqWflCCCGEEOsCMtSFEGItUVYJfafa+7+HzoBf727vBV8eRs2GS9+x76qlLIyqWwuu3g9+vuuPH7LWuB6cv4t9hBBCCCHE2keGuhBCrCHS1Azrzyfap1cRLKrI3L+aBA8fBwcuY2/2qNlw1it2UjrYYWtN60GTemZ0N6kHbRqZ4b/jxjUXHiGEEEIIUTPIUBdCiDVAnylw9UcwZk7169u2gv07wKxF8PoI+Pn/4IGj4dAt88sZHRnp3TqYYd9I+7mFEEIIIdYrZKgLIUQNUloBd38DD/S1JeqtG0K3jnaiebcOsInvG69Kbf/3U4PgV2/DfUdVP/gNzEg/0430/TrAI8fp0DUhhBBCiPURGepCCFFDDJsBv/sAhs20PeKXdoXue0H9PDVvrQRuOgjq1YZH+sNv3oF7joTjtjX3XCP9URnpQgghhBDrLTLUhRBiNVNZZe8d/2cvOzBu8+bwz5/CHpv9uL8kgb/sD/Vrw/194LfvQXkV7LJJZqTv215GuhBCCCHE+o4MdSGEWI2MnAXXfAx9ptr/n+0Ef+pmh7wtD0kCV+1rM+t3fQNXvA/NG8DcxWakP3a8jHQhhBBCiPUdGepCiPWG8XPNoO2y6Zp9blUKn42HxwdAz4l2bePGcPthcHCnFZeXJHD53lC3Ntz+lYVpHxnpQgghhBAbDDLUhRDrBX2mwM9es9edPXbc0k9NvkS/kQAAIABJREFUX17ml8Kl70JRMey8iS0/33VT2L51tsd8YRm8PAyeGABj59q1BnXg1O3hyn2gZcNV0+HSPaBtExg6E36/t4x0IYQQQogNhSRN05oRnCQNgJ5AfWxA4OU0Ta9PkmQL4HlgI6AfcG6apmVJktQHngJ2B2YBZ6RpOv7HntG1a9e0T58+NaK/EGLdYdgMOP0VKC61/83qw9tnQcfmKydvUTmc9zr0nrKkW73asEMb23f+yTgoLrPrmzWB83eBM3eEFg1W7rlCCCGEEGLDIkmSvmmads29XpMz6qXAIWmaLkiSpC7wRZIk7wJXAHelafp8kiQPABcD//HvOWmabp0kyZnAbcAZNaifEGI9YMJcOPd1M9KP2MqWoX84Fn79Nrx6us1wrwilFfDLt8xI37QJ3HEYTJgHA6ZB/2n2HvT+/huga1u4aFd7dp1aqz98QgghhBBiw6PGDPXUpuoX+N+6/kmBQ4Cz/fqTwA2YoX6C/wZ4GbgvSZIkrakpfyHEOs/3C+Cc17LT0O89Ekor4djnYMgMuP4zuO2w5ZdXXgn/967tM2/dEJ49GbZqaW7n7mzf80rhu+9h1GzYva0tixdCCCGEEGJ1UqPzP0mS1E6SZAAwHfgQGAPMTdO0wm8pAtr573bAJAB3nwe0yiPzl0mS9EmSpM+MGTNqUn0hRAEzd7HNpE8qtv3jDx9ns+fN68MDx9grzp4fAi8MWT55lVVwxQfwwViT8cxJmZEe07w+dOsIF3aRkS6EEEIIIWqGGjXU0zStTNO0C9Ae2BPYPt9t/p38iFss86E0Tbumadq1TZs2q09ZIUTBUJXCM4PsFPXek+1gt5iFZXDBGzBiFmy9ETx5AjSJXn+2Qxu45RD7fe2nNru+rOdd8wm8OdLkPH0ibK/qRQghhBBCrCXWyKnvaZrOTZLkM2BvoEWSJHV81rw9EI5rKgI6AEVJktQBmgOz14R+QojCIU3hpp5mpMd0ag47bmxG+BeTbI94+6bwzIn5T1c/rbOdBP/8ENuv/tZZNhu+tOe9MMRm5B87HnZZw693E0IIIYQQIqbGDPUkSdoA5W6kNwQOww6I+xQ4FTv5/XzgDffypv//2t0/0f50ITY87u1tRnq92nDsNjByNoycBePn2eetUXZf64a2PL1t06XLuvEgGDwDBk+HK96HB4+FSfNg+CwYMdNm5IfNhHFz7XkPHwt7tVu6PCGEEEIIIdYENTmj3hZ4MkmS2tgS+xfTNH0rSZKhwPNJktwM9Ace9fsfBZ5OkmQ0NpN+Zg3qJoQoQJ4YCHf2glqJHQx31NZ2vawSRs82g3vIDJi6AH63F2yRZw95TIM68J+j4Zjn4KNxsP2/TVYujevC3UfAAZuv/jAJIYQQQgixotTYe9TXBHqPuhDrD68Ph+7v2+/bDrX3ka8uPh4Hv/gfVKbQtgls2wq2aw3btYKftLZD41b0NW5CCCGEEEKsKmvjPepCCLFcfDIOfv+h/b6m2+o10gEO3QK+vshPhW+wemULIYQQQgixupGhLoRYq3w72Q57q6iCS3aHX+9eM8/ZpEnNyBVCCCGEEGJ1U6OvZxNCrF+8Ogx+9dayX3e2LEorbM/526PgojehtBLO3AH+sN/q0VMIIYQQQoh1Gc2oCyGWi4f6wS2f2++Px8Hv9rbZ79rLGO77fiG8NhzGzIaJ8+wzdQHEp2MctTX87RBIkhpTXwghhBBCiHUGGepCrMfMXmSz1glQv469gqxebajv31u1/PHXm4G9Z/yeb+Cub+z/gZtDjwlw+1dmsN/1U9i8xZL+pi2AB/rCs9/ZjHlMrcTegd6xOezWFi7bY9kGvxBCCCGEEBsKMtSFWE+pSm3v9zeTl35PnVpw/s7Qfa/8h6ylKfztC5tNr5XAHYfBqZ3hs/Fw1UfQdyoc+Sxcd4AtXU8SmDof/t0HXhiSGehHbAX7d4TNm5tx3q4p1K1dI8EWQgghhBBinUevZxOigFhcAe+MhgM7QqtGqybriYFw/WfQqqEZymWVZjiXVdpzSsqhzxRbgt6iAVyxN5yzkxnvYIb+tZ/CM9/ZtXuPhGO2yeTPWQR/+RTeGmX/D93CXn324tDsXeVHbw2/3RO2b7NqYRFCCCGEEGJ9ZGmvZ5OhLkQB8ZdP4elB0KYR/POntsx8ZZg4D474rxnjDxxje8DzMWQG3NQTehXZ/202gmsPgP06wFUfwqvDbZn8f44xQzwfb4wwvYtL7X+CGfS/3dPeUS6EEEIIIYTIjwx1IQqc2Ytg70er7+e+uAtcvZ+9/3t5SVM45zX4chIcuw3cf/Sy739/DNzyhRn4YPvHi+ZDo7rwyHFmuP8YU+fDzZ/bPvhf7w7btlp+fYUQQgghhNhQWZqhrj3qQhQITw8yI/3AzWGvdnBnL3h0AHxVZMvOl9f4fW6wGekbNYSbDlr2/UkCR24NB3eCxwfCv3qbkd6sHjxxIuzedtky2jZd9oCAEEIIIYQQYvmQoS7EKpKmq/5ascUV8NRA+/2r3W0Gu1sH+O17MGwmHPsc/Gl/O/jtx541udhmxsGM9BXZ5x5mw0/ZDl4ZDod00sy4EEIIIYQQawO9EEmIVeDrItjrUbi396rJeW04zFwEO7SBfdvbtV02hXfOhtM720z79Z/B+W/A8Jn5ZaQpXPMJLCizw+OO3Sb/fcuiTWMtXxdCCCGEEGJtIkNdiJVkcQX84SP4fiH882v4z0oel1CVwsP97Pcvdqs+Y964HtxxOPz7aGhe395ffsR/4TfvwMhZ1eW8NNTcm9eHmw9e9Vl+IYQQQgghxNpBhrrYoJhUDKe9ZIZ1ZdWqyXqoH0yYZye0J8Dfv8yWr68In46HMXPs1WZLmwU/Zhv46Fy4sIudwv72KPjpM3DZuzB6NkxbAH/taffecCBs3HhlQyWEEEIIIYRY28hQFxsUf+0JvafYUvXz37B3ga8MRcVw/7f2+94j4ZZD7Pe1n8Grw1ZM1kM+m35hF6hbe+n3bdzYjPAe58O5O9u7zd8cCYc/A6e8CMVltq/8pO1WNDRCCCGEEEKIQkKGuthg+HKSvYasUV1o1RA+nwjHPm/vEl9RbuppS9+P2xb27QDn7AR/6mZuV34I741ePjnffW/vMG9SD87acfn8tG1qS9t7nA/n7Ai1EjulvWk9uPVQLXkXQgghhBBiXUeGutggqKyCm3rY70u7wltnwc4b28z4yS/aYW7LS48JmcH/527Z9V/tDr/dEypTuOw9+HzCsmWF2fQzd4Bm9ZdfB4B2zeBvh8Jn58Nle9r7zjdtsmIyhBBCCCGEEIWHDHWxQfDCEBg+C9o3hZ/vBps1hZdOg9M628z45e+bIV+xjH3rpRV2+jpA971sdjvmir1tCXtZJfziLfh2ytJlTS62vea1E7ioy8qHrUMzuHIf2Lv9yssQQgghhBBCFA4y1MV6T3Ep/ONr+/2n/aFBHfvdoA7ccZgtI69bCx4dAOe8BhPmLl3WI/1h3FzYqmV+4zpJ4LoDbABgUQWc/zrc8w3MK13y3scG2Oz7MdvY7LgQQgghhBBCgAx1sQ4wfi7MW7zy/v/VG2Ytgj03g6O3ru6WJHYw23On2OntvYrg0Kdtdn1uzjMnF5ssgJsOgnpLOfitVgK3HQon/gQWlsOdvWC/x+COr2C2H15XXArPD7Hfv9xt5cMmhBBCCCGEWP+QoS4Kmo/HwcFPwX6Pw4N9ben5ijBuDjw+wF6fdt2BSz9obY/N4J2zbSa8ospm1/d/wt5vHp558+c2S37MNtCt448/t3YtuOdIeOEU2Lc9zC+D+761cPztC3igDywos+XqO22yYmESQgghhBBCrN8kaZqubR1Wmq5du6Z9+vRZ22oUBJVVth+65wQ4qBPs2W5ta7TqTC6Go5+rPrO9eXP48/7w0y2X73TzX/wPPhgLp3eGOw5fvucOmQG3fG6nxAN0bG6z4/f2hoZ14JPzbI/7itDHXwnXI+eAuceOg0O3XDFZQgghhBBCiPWDJEn6pmnadYnrMtTXXSqqbKn2u6PtdWAzfVl1nVq29/rk7deufqtCWSWc/jL0n2bvBj9vF5vRHj3b3PdpD9cfANu3WbqMLybanvPGde1k9I0bL//z0xQ+HW+z36NmZ9f/sC/8Zo+VCZExcJrNrH8wFnZoY6fP19Lr1IQQQgghhNggkaG+HjF4Ojw9yF4RNieabe7YHH7SCj4ca/+v2gcu3WP1vle7rBJGzjIdKlM4eTtoWHf1yQ/c1BMe7Q+bNbEl6S0bQnkl/Hcw3NXLZtlrJbZU/dAtYLtW0KF5ZvRWVMHRz8KIWatmXFdU2Ynxd39jurx02tL3pq8IU/y9501X8JVsQgghhBBCiPUHGeoFSEk53PYlbN8aztxx+fz0mwpnvWqvFAPYogUcvY0dkrZDGzPKHx8AN/aAFPjZTnDjQTbLvjIMng59ptr3kBkwahaUR68w69jcDk7bt8OyZZVVwnfTYaeNf9zYfW80/Opt0/mlU2G3ttXd5y62k9SfGlT9dWqN6tpAxfatoSq1w9o6NIOPzs1Oel9Z0tTiU7PfQgghhBBCiNXF0gz1VTRfxMpSWQWXvQsfjbP/cxfDr5dInuqMmQMXvWlG+jHbwG/3NMM0d8b8wi6wSWN7N/gz38G0BfCvo8yQXREe7W8z2zEJsGUL2HFjm1kfPssGDs7eEa7pBs3yzBAvKIPnBpu8qQugk+8zPzzPPvOJ8+CqD+33NfstaaQDtGgA1x8I5+wELw2FYTNh+Ez4fqEtle8/Lbv3T91W3UgH01M2uhBCCCGEEGJNoBn1tUCawvU94MmB0KQeLCyz2do/77/0V3V9vxBOfgGK5sPBneDhY6HuMpZgfzsFfv4/GwTosgk8ejy0brR8On4+Ac57w2amT94Odt7EjPPOraFxPbunrBIe6GuvLCurtMGBWw4xAxxgxkJ4fKAt0y/294g3qmsrCcBOQ78u2mdeWgGnvGSz7kdsBQ8es2LL9mcvMoN9xCwz3jdrAt33Wr1L/4UQQgghhBBidaGl7wXEw/3sYLR6teG/J8HYOfCHj83t2v3h5znGenEpnPEyDJ1pBvdzpyz/7Pjo2XD+G1BUbCemP3kCbNHyx/1MmAvHPQ/zSuGyPeHKfX78/pGz4OqPspns47aF5vVttru00q7tsRlcsjvsvzk8m7PP/Mwd4Pf7ZMvZOzSDt882GUIIIYQQQgixviJDvUB4ZxT85h2bQb/vKDNqAZ79Dq75xH7fcKAtXwebZb7gDfiqyPajv3IatFrOWfHA9IVw4Zu2z7xFA5uNX9rr2xaWwUkv2qz0YVvAw8ct377syiozsm/70t41Hjh8S/j17tB1s+r35+4zDzPt9WpbGHfWu8WFEEIIIYQQ6zky1AuAvlPhrFdslvmP+8ElOcnx9CD4y6f2+68Hwc92hu7vwZsjoU0jePV0O7xtZVhYBv/3Lnwy3ozhOw6DE7erfk9VCpe8De+Nga1awhtnrPip5BPnwe1f2SvRfr4bbLPRj98/era9s/yT8fb/poPg/F1W7JlCCCGEEEIIsS4iQ30tM36uzVTPXgTn7Gh7ufPtnX5yIFz3mf3eu729J71JPXjhFNsjvipUVNnhcE8OtP9X7G0H0gU97vkG7uxlrw1740wz1tcUX02CGSVw/LbaUy6EEEIIIYTYMNCp72uR2Yvg/Nft+6DN4aaDl26Mnr+LzWzf0MOM9Lq17FC1VTXSwV53dtNBtoT+xh5mlE+YB38/FHpMsP8JcO+Ra9ZIh+V7vZsQQgghhBBCbAjIUK9hFlfYyevj59l7zu8/etnvNL+wixnyD/W114t167h6dbqwC7RvZq+He2WYzfaPmGVuV+0Lh2yxep8nhBBCCCGEEGL5kaFew9RObHZ66nx4/Hhbxr48XLCLfWqKw7eEl06Fi/5ne+cBjt0GfrOMd7kLIYQQQgghhKhZZKjXMHVrw+2HwcwSaNN4bWtTnZ02sQPjLn/fD5g7XPvDhRBCCCGEEGJtI0N9DZAkhWekBzZrCi+eura1EEIIIYQQQggRWMZuaSGEEEIIIYQQQqxJZKgLIYQQQgghhBAFRI0Z6kmSdEiS5NMkSYYlSTIkSZLufr1LkiS9kiQZkCRJnyRJ9vTrSZIk9yZJMjpJkkFJkuxWU7oJIYQQQgghhBCFSk3uUa8Afp+mab8kSZoCfZMk+RC4HbgxTdN3kyQ52v8fBBwFbOOfvYD/+LcQQgghhBBCCLHBUGMz6mmaTk3TtJ//ng8MA9oBKdDMb2sOTPHfJwBPpUYvoEWSJG1rSj8hhBBCCCGEEKIQWSOnvidJ0gnYFfgGuBx4P0mSf2ADBfv6be2ASZG3Ir82NUfWL4FfAnTs2LEm1RZCCCGEEEIIIdY4NX6YXJIkTYBXgMvTNC0GLgF+l6ZpB+B3wKPh1jze0yUupOlDaZp2TdO0a5s2bWpKbSGEEEIIIYQQYq1Qo4Z6kiR1MSP9v2mavuqXzwfC75eAPf13EdAh8t6ebFm8EEIIIYQQQgixQVCTp74n2Gz5sDRN74ycpgAH+u9DgFH++03gPD/9fW9gXpqm1Za9CyGEEEIIIYQQ6zs1uUd9P+Bc4LskSQb4tT8BvwDuSZKkDrAY328OvAMcDYwGSoALa1A3IYQQQgghhBCiIKkxQz1N0y/Iv+8cYPc896fApTWljxBCCCGEEEIIsS5Q44fJCSGEEEIIIYQQYvmRoS6EEEIIIYQQQhQQia04XzdJkmQGMGFt67GctAZmrqDbyvjZ0OQVgg6rW14h6FDo8gpBh0KXVwg6FLq8QtBhdcsrBB0KXV4h6FDo8gpBh9UtrxB0KHR5haBDocsrBB1Wt7xC0GFNyitENk/TdMn3jqdpqs8a+AB9VtRtZfxsaPIKQQeFSXFUiPIKQYdCl1cIOihMiqNClFcIOihMiqNClFcIOihMq+62rny09F0IIYQQQgghhCggZKgLIYQQQgghhBAFhAz1NcdDK+G2Mn42NHmFoMPqllcIOhS6vELQodDlFYIOhS6vEHRY3fIKQYdCl1cIOhS6vELQYXXLKwQdCl1eIehQ6PIKQYfVLa8QdFiT8tYZ1unD5IQQQgghhBBCiPUNzagLIYQQQgghhBCFxNo+zW5d/gCPAdOBwdG1Ln6tHFgE7Bld7wXMA1JgWuTnSGC+X0+xV84dDIwGKqPrFZG8u6LrKTDbrx/kzw7XyyM/P3cZ+fxU+Se4jQF+kkeHykje7cDiyG2RX+8ADM8jrwHQG5i4FHldgKF55P3E/cTyZrrbmzk6pMBh7vZqjp8U2Ai4xtMmdhvnfq4CRuS4HeY6jMyJv/nup7mHt1o6AZv79djP9MhPD6DEr1cBY9xtB2B2bhpieSJXh3nupyWWb4LelZG8U4GFkZ/FLq8D0C8nfSe5n03J8mpu3usGzIrTNsrLPXJ0GBrl8ThMJUAz16E31fPshEjePZ5WIY6CvPHYKzeqyYv0i8M7za+38jiK46FZVDYq8zynFTA58lMauX0cpV8KFEfyDsiJv1jet2TlrSqK81/kyJsfyXsoJ50m+PXDgbFLifNvo7jLjaPcPD4pklcUXa8EOrnbveTPK3sCcyN55Vg57gD08TgLbiHdG7Bk3RLLGx6lYZweUzzdqslbSv32fZT/F+c8K+SJu3LkLYrk3ebyK2N57nZJ9KyKyM9rHv7KHB1CPg/p8UM6ufuukbw4X072eKjIo0M3YE4UpnFYPTWA6vVOeSTvjnxhAjr5fSEe4jBdFj0jJWs3tnM5wU+cTp1y4rUskjcoR97cHD9xXTAJaIrlp7iNqozk3ZpPnrvdmaNfG79+JtXLxmKggbu9kuNnGNAIeNvTI9YhyPvyR+SdipXl4G+YX//sR+TtTvU6tgwrM009neO8HNLjLJZsA4K853L8pFh7ew5Wl8Z1wX7u55OceAh+LswJTwqMdT8Dc9I9lncNlpdjt6BDSc718e7nPKrn8RTYz+Nhyo+EaXKOW5B3NtXr5bis/R/52+q6VG83Ksjqt8+wer+aPE+rwVTPy7G8NyO3OC9P/hEd3slxi8v7PPKX3X8sRV494Lvoelw+z8nRe3zkJ243Qj3fyHWL4yGul78iT/vu8l6nep4I9UcPquexWN5eVC9rIUwbYe1QtbIBdAd+huWj4LYYK9fdgTOwshbql0WR2/l5wvXHqP2Py9o49xNsgOCnFPgj1h8c4HEdylTwkwAPRuGqiHSoZMm+U9BhJ6r3fye5n4Ox/lFcporc7XaPp3zynoriqDzSYTtP+zi/BLdXovStABZ4eEP7X8mSYdrZ74vl/TFK31BGq6iuw+LoemXktkcURxVYW9sSKx/DyPLYYuDyqH34HTAEK6vP4XV2oX3WugLr8gfrjO9GdUP9A8zY2w1rAD+Lrh8D9PWMsTjyMxvrtH4NPAks9OsHATOA6zzjTsAahtqe4QYAh3gBLIn8zMU6oKNzdPjWZXyIVdLxcyqxCusArGOWq8PVWMEvch129II20f0Np7phXYI1ske4/xKsMmrn/m51Hb6P9PvE4+LzHP1auJ/zXJ/5Lq8dVtF95HHeDyvwIY5KsE7FIa57pctLsEr2KOBPWOW4KEqPES5rsLsF/UYBL7oOofKuB/wVq6iuA47361+622j3cwRZRVLPnzsOa6h6uP8Q3rHANA/TMZ42PXJ0OIbMkKmHVZYlfv1i13uhx8NcLN3/6M9c7HHUFuvsPO36xXF0h+txFXAaVsn1c7epwDdYPhoHlPn14/2+Y7BOYxnWANbG8ski4Er3UwHc7DqM9s+xZI1pbaCzh+k/wBcuLzTKk93td+6vArjZ3eZgDeWhwL8ieXt42F8CXvDf97ufhVin5h/uPxgPjbEy9B9PoxsiHTbFGoHnsQZyNvBXd+uLGdfDXM/pkbwZ2KDdq8CNHu91XOeRLm8Ylsf+ihn3ZcAFWPkcDlS5vF1d90uwTkV5pHs3rI64zJ9XGem30NP2AODfZOne1WVcQ5buPVyHUuAk9zPY3epgHbXhmKE8ASur0z1t98XqnSs9jiqxdE+A/ljZPBjLU7G8b4H7yMpGiL8J7u8y4KdBnrvNxPLtAVj9lyvvQOBkvz4Lqz/Ge1pdRjbIdDNWv5X4M0KnMZTPUP8OxeqeSuA/7jbM42EBVs8v8Ott/X8PoKfH/6yozpmI5Z8ZWF0f0qkUK1c9YnlReEcAn2JlcUbk9q2nfSlZPmrn/w93efPwdsjDWQX8nqychLJRgpW3A7D8GfLXdi7vQE/HCVQ3ukuxjtporN0IYVrg/w+gennq5Gn9lLvdSDYg28n1uMzjfYaHqY77meB+vozSaWMP071Ye1wCPJOjw4UepnmernU87L/z+2/D8nIjrD4pwTq6/bA8FPJeP2ww+kLXrziSV4Ll5Q9d7xDeHv6sv7t+UyJ5k7H8cKHHw6wceRd5eKdi9VWoPxa5vCexPHszWdk9nqz+qIzyygLgn8DpHl/P+vWhWHkvoXodEcr7gVhbE9c5zbC8l6/O+Rb4i8fDrBwdioFfYnXcnCiObvR4uQzrZ1TkyDvQwzQ90i/kieNdXmgLW2FlZo7Le9v1u9fjthzLP4dg7W8llu/Odv1+jrVdc1z/tlg7Pgur3yZG8hK/92OsfpucI28WVv+/73F+r+tejtUPBwC35PgpxfLe4a5DGKAJ8rp7+Cpch3Yu71OXFwa7OmH5ogIr76Hs3x/VOV+5n7F+Xy3gUqwvcZLH6RSysnE3lrcvxwbi4nSaiBnyof6ojOTNwPJEbj0/MkqnQ7H2L8ibi9UpB2DtdXmOvJPcTynwX5e10MN/C9b2F5P1kWdh7cAtWN9zeuQ2A2u3HvQ0DOWys+s8xdPrfqwsjsT6gUWeBqFvMgfYBeunjcYGm4qx/u9I4Fce17097ue7DiOxOmKCy9ubrK/R2XWdjNUfD2H5NpZ3C/Absnw50Z8/AesbfecyZgO/dvfHsfpjLlZvjfS4no/V5/d4WJ90t0uxMl2ClYPrXN4BWF/mMb93QeSnG5b/e2O2xSSPj87+zF6u5/dRPHRzGb382h8jt3c93j4B/oC1K7dF7cAewN/8GZv79WA/NPT/LwIXrG27Mt9HS99XgTRNe2IZstplsk5Xbawgh+unYZl2PlZwSJKkMzb62gPrdFVgFU8srz1W2dZyeXtilVBlmqafYJ2EJPJThnUagoESdKiLjTLd6vfHzwFo6mEijw7NscosdXnb+z190jR9AStUgUOxglyapun7/rsqtdJwAlb4hkY6B/3ae/ivz9HvbPdTihml0z2MYI1yglWMm/k9IY5KsAGMT8hG+XE9KrBOxS5+XwVAkiTtgdbAW/4dGqQQDy1chzCDWAFs7c8dQWaMk6Zpmd/TAqvgakV+UqAh0Ab4n4e13PNDLQ/fRLKR8sk5OrTzOA/yOmMNTpM0TR/1exOPh3KsAh+HVUZ1sUZ+qv8e5ek0xaIg2cTlzXAdvnNZ81xuGyxf3ep61XE/Z7h+76Rp+pynWXPXYbHL+KfrUAWc4zq0Bx5N0/QtrJEPep/gz9kWq/wXeNjBOge1sQ7CYI//c5Ikaeb33JWm6cfAs37/nlgHpRbWQVuMlcNTkyRpi5XBq1zWCGzGhjRNF2Ij9d942N7H8g1pmk7zdPjQn7EYOMV/b+Nx+LTHf7NIXiusHpgCvAc08TAnWOfvPdezxOVt6f+f9PL5gqdTfU/XBsADmIFTFun+hft9wOMObHYPoD7wqct7Gqjl8gZ6vD6LdYTx+N/Sr7/ufj52fUnTtAQrex95HJRisx9T0zT9Chu4+ydWfwCc7GVwK6xe+4ZsIC/I2w6rYx7E8koz95ti5e0+stUHJ3u6bwRc7fp9niPvJ1iHqjFWXhq7vNqeVv/B6pIUM+a3JzPye2N5ooH72RMrN2+TDead7G7tPK0WYQOvDVyHqcAWLusTT7em8EOdE/LRfI+/kI/qYmWodywvCu/cLUOEAAAbNElEQVSbWNsxLMhzdsA6MDOpni/rAptgaTvP/+NxmmAz0DNYsmzMifJKeM5ij6OeXsf2iuK1EVZPnoe1QwsiHRpgAwQ9icqTUwe4xN1C2QjU92stsLruFNc5DJx8jtU59fz+s/y7O1YvzwOO9jA1wgZgH/cwNUnTtBIzWmph9UoVNpDazPNQfdfvD9ggSDlZum+Hpc8zrlvDSF5dbJatO77qxv20dN2vcf2KI3ltgfddv/eAxpG8Olhneqrr2tDjIeh3jX8vdHmh7P7P43W634/HRUOsg32g+znKdWjv4a/y+ArtWijvPbEyPApP9zRNi7Gy9gCWz0Idhl8/0uOhVpDnNML6MduGOPfrF2F54D6yVUqxvJ4eD40ieSFPDHR5E7A8saXf2wQr71/5/T/FynQtT4eemGFZi2xAviFWV4V2o4mX6XGu691Yn6sK+KnXbyH8ffw7ltccy8evRDrgei9y3Xp6WIqxMhMG+Pu5DqGspa7DCMzgqcqRV4rVscWRvK7ufifZKr2zPD/UA/7teeVxl9EVa8/qe/pUYm1/C88PdT1e7yGbRT3Hn7Ep1p58jZXPJJLXBMsTl2DlKbTvjT3O7/N4LY/kNQdudf1eydGviesXVm3ugw0q1cHa3fswo/l7rGyEFS313O0h/30c1scrcfk3YH3C2u7eDus7Pu/xGQbExpLlpVu9T1Ph8bYx1jf+p+tUjNUBY/15YZVIWD13HFav1/H4vTVN015k/eB9sDbg5jRN+2H95o0ieQ08TBUejqOxNri1+7/LZS32/7t5nIdJpLEeR8Ow+qsWNsEzCcsXR7vbSKxslXj6POz61scmEv7s8oqwQbBh/pwdsDxem2ywsR1W/4RBs3nRcxq6zL6ebk+62yjXrxXZCtEHgRMB0jSdnqbpt9gM/6w0TSeQUQdomCRJaOunUIis7ZGCdf2Ddf7jGfXtsUpiCpbZNvfrB2IFYhI+Q+HXLyKrECr9nhHudhCZQViGFbjNsc52D7+/zJ9TnOMnLGOsiHQIS27D0sxxkZ+whLEKawRydah0f0GH7bFCtIDMCA4zGXdjFfNi168SMwbBKvJgzFbm6Dce6ygEA3dqHnmVWEMW9OvufuLlOiGOXva0KHJ58ZLIHciWIC2M5L2MdQSmx/LcbfcorClQ5Nf/TDZbGOIwzIwcHd0f+2nm8RcvxRqBVS7vUn2pUxxHQYdcecEwnhTpPtvjYQDwhMdF6Cgc4/4e9GvxMsLdsdHHJ6O0iP2UYBX2QWQN4u7Y6HUZtsysEdnSplOx2cCRmPH9O4/XsAJjKtZh6ES25OlUrJGZD1zhz5pHNpNc7PEwGuuYlmD5oovrMASbee0fydve73kg0rUMa+Tnum4X4NsvorzyFZZvy7BZvHgmaI7rMdbDEbZDTMEGMq503cJsTwuscezn6TQ0cjuVbOnYtCAPa8xLsaXxdfCl6e4n1v1Uf26ao98CzNArjfSbh5W3AZixGfsZ4TqHQaJYh7953Fbm+BlENmv0Yk4cBbcq13V+FK/BT0VI20i/qWQDZSGOxrlupf6Z4/p18e+w3L8yR94gLK+We9wHeWFpYajfgryQV0ZhA1Pzojg/x/PCGVjdtIhsVUkvrGM5FJ+h9+uNXW7sJ+jwGlYH3Yjlo+Iojhb7vf2x8hPk7e06f+bxVBTJC8/61D+xvPHubwZWXwR5h+LLXMm2ooSyEcpafyxfhnjo5LLGYiteiiMdHvX0nujpNSvSISxpLfH4Djrs7DrM9XvGRfLCs2Zj+SGWN4asrl4cybvG/QwmW4Zc4WFa5HHzPr6Fxv1cjpXL0OH7KNIhuA3E8t6CSIdxrlvY8lYV+SnxOBrs4QryRpN1or8P6YTVERVk9XWuvKDfFMwIDfIeJ2vfF0TycuuPUvLXHxP8d0VUPk9wWbOpXp6C2zi8HctT5yzAB31y6sRO5C/v41233h73LbD2bIqn8cKcdA86XBene04dVkHWtrXEykkYtAmTIvOxuvN7bLBnhl8PcVQXyyPFHr9vRXGe6xaXta/J2sAfwut+Qv4JWz3i8j6fLK9WRekUymQlPnufo0OoE+dRPV9WkC0VDvKu8//nkZX9UDZKMCOpDtYfSrE+0S/9OSd5WsRlLbj9jKyuD+37GCxPzCFbPh3khRUg3dxPVZR+FVi5eTjIw/JEuT+jH1Y/59PvTrL6YCJWH6VkqwHnehgmka24qIMZ/mGgcg5W54a4vNbjMwwcl2Mz/m+5e5gQeS/yEyaMYj+j/dqlOX4qMGPyAtdrPtmWz0qsbTnR06YKG4gMK976Y6vJ8unwCdbOB3ljPS7mYTPuQd6JrkMjbOXGLI+bicC5WN66AevThP8TPVzFWL10JVa+Q3jDSpu7PT7nRX7muq6l2MBZ8FOG9SefwNrR2E8lWXv3S7J+Qh/3F9K3MTa4HNtq/YHXcq51J6un/hu7FdJHM+qrn0swQ2RfLNOE2c1HgBvSNO2AdRpb+fWfYwXj99ho/BTM0ASriN7ACtglWOF6FBuR3Bg4L03TelhnrWnk5zmsApkS/CRJciw2InUutnRqYs5znsFm/M/BRuI6Rm4vYBXZ74O8NE3D0tw6WCHpDTRKkmRbv3aw6zzU9W/n8sII7y9cNsBzrl8TrAI+Hhtx3CRH3i+wwr0A6JAkSUusoX4WW9Yzw3UKcbS9p8XvsYIdRqDBlgb9C6vQilzesVhlc6DH6Uyqp+FfsY7bbKxS3Mxntn7i4Trd9UyxPZBg+97e9vBPjvzc6eE4HZuhSf2eOmRLGk8nG1V/ItLhc9ftr5G8X5HliwpPrzBTOxBrMCrJ9phd6/JSrAJrQTYjUIE1nnu4zm/7fX/zOHoLm4UMs1u4n7nYaPa/yVZApGSzxQ9jjdMV/r/C/X6CLb0f4uENgxCdsNn8I7EBhYRs1uQVD0MR1umo7fLq+Ke+hyWQep69GVsifprfX+lyh7puN2AdnzTyexHWmanjcRq77ePx1g4rN2VJknTxeN4d61jHetfBRq9b+/8Z2Ox4Xay8HIV1WJtjZbosTdM5WINyB5a/W0fygu5XY4MP/fLo9x3ZAE9YiXIc1gmpj806pZgidbH8PczDUwrUjnT4DdnZCyRJEmaZTyeb+dg/R4cLPCxTsTIYdLgIS/Pvonho4Hnsbb8+DZslCPL2w0b+v/b/TV1eHax+q/L4GBHkRfqNINt+k0b1RzcsDy6O4nwYVq+1wzo2i6LwnIGN6l+MdZYSfIWUP7sMmz2vF/m5Ecuvv/Q4HOs6HIvl8z9jg3rtXF6Io9ewdKqFzWIGLvXvhlTPC+FZN3hc7RjkeXjrYWk30eMs8AXWadsI2/dY2+MywdLoa/8/CH6Y0Z/qzxiDDZTU8zB1wWbR9sXycm2yDjZYhzCsIFtk4pJmWJ2G+ynCZpVDmMKz6pEZceWeX8NWkkGYsRnklWN5uSPZqpRQH03D9kJugtX1JEkSyngZ2eqhzlEcBbf2LjeuY17EysswrM5MXF5Hf16YxakfyXvW9eiD9QEakW3XqO26jcUGn5I8+rV0P6HcdsPq3S+wctsYM3zi+iOs5sutP/7kzwsDzWDl81J/RiXV69OL3E9HssHQwD4us2HQN6dO/Ihsn2ngONe9CmvDQny3x9qimVjaN6J6/XEpVnbCmTNxHTYSywPfY284mkO2LD0MCIV2I8TD1ljZ6OmytsTazq/JBmj2jfTeE+v7gPW3GpG1a/di8T0QS+fE5V3u937t12tHfq7A+gXDXL/gZxe/rz824LMxWdnYE8v/IZ81Biq8vIfBjQFkZWNLbMD1a2xwJyy/D+V9oMd7MTYgiOv3GNb2P0k2+xjS/TGszn4C69uWk5X3411WfbI6rAKrQ2di9eE7ZPucwfqoz2CDpBeQDaSG9r0utool7n/E+nXH6oy6WP9vCyzffO8yp2PltbvHZwOsXzXe5TXCVivF+boeVictTm3lyA8kSdLE9foH2WrZth6GNMfPBVg+uzDy0x4beJkURLoOl2NtUwm2svQeLC+EAZ4EW3a/B9Yny6fDTlh90QgrS02wfNEOO3+pi+v3usv8Chv4mISl2eX+vJj6HreX58TFD9sww3WPm9Ox/NE4x88D2Pain2Fl+ACsr9Y3iofYzwKsL/gIZuM0wfrSXbCy+SC+3z1WNkmSelg/fWB0LbT/W2D5snGSJD+jEFnbIwXr+oclZ9TnYZmrE1YBhJnuKqwSGE+2t/hEsspuPNkoeBrJW4QthemEZcD5WENYTvZ6vdtcRutIh05Ywxj83BrpMI3s0IrYT5BXTPXR6YXYHvsfdPDri7HBB7DKIsUMoD+62/tYw/MC2UhpcEuwpTcl/gkHAoVRtkV55J3gerzgYTkNM6LnYR3ap7FGqTiOI3f7X06Ygp+zyfaz3ko2+x6W8M6N0jDs3Q1xkWKNZP/oWZ389+LIz/7++6XITxh1Hk+2r6wKaxhnYXkhIZt9XhDJu4csT6Rkh7yFvJeQHZ6xj6dDcLsNq8SLIz8hnYK8ZnnycljtEeJovKdTmN1v5nFzTRTHYVXJPlin6Zoov5aRHRzzZ3e/AhvoqHQ/n5HNtIQ9g2EW4ZpI3t1BHmYgzIncwkFY+0R6XYA15rNddltgeOTWG59VyvFT5vqV5HH7AssrvTGDe4rLnp2jd0ibC7EVA2Ff6B7Ax5G89/y+3tFz2ns8PQuU+7Wwx38kZsjcFOse+flX0C+P29Nks2h7eFhGkjXWc+Pw+n3/cL275taFWHkI+b8u1fNXCVF9GfkNs4FdWTKP/RB/OX5Cug/2dB8fuYV0j/UL9Ud/LN+ehg06BvcrsHI42P+Hjtt4skGn/8M6vYv8ephJmxP5KSKrd4OfIGuRf4r9nltddhg0CgbM+GXo0Mv9jCebHaqK/HwfPafS7zvN4zPIC3Xs/+Vp08qxPPxD2XC3m/Kk+2OYURLiNc7/IS4ql5Luf4/SPZSNWu42hOp19i5YvuyE5yOicuP3/DakOzZY+kQUptC+tsXq3yeiMFViW19iPws8bGG1xJnYDN69Lm8mMDFyC/5ujeQ94rqGuIhnSWM/V5LNXiUej8HtdA/TD/pFcfGYx+8eHh+xn7KgXxQ/d2H5I64/hvv12zHjZnqOnzJskDW33huMderz1Yl3YR3zfHXifI+Hyjx+/uR5IhjPC7EBlruwWbdSqtdhu2D1/Q/6hTwRyfstWf17P3Bu1IaUYvXnPh73wS3M4p4e/JDVb+9g2/pwnQaR1W/zgNF5nvW454nTMQM1tD0zPG2L8/h5IvLzONXPoOhFlo/u97CGfsk8D9NpWJsY5IU+4OlLKe+TWXp575zjZ4HHRXzWUqgHOmF5eVCe8h506MyS9URVbp6IykYF2ba4uI4IZaNzdH8oG2GlYMgPIzx8B3t4Z/n9D5LVdWEJ/kxsEHaou3Ug20cd0ncuZiS+ja3OqXAZD2LlaBDWVszM8XMJ1i5O9fRti9UrFf6McEhuSY5+HTyNij1993Y/QV44g6Bt5OdavGy43Kuwgb8gb5SnZdAvxNFNWDkNM+L7kJ3jczU2gBiflzKG7MyU68lWqI7wuPnAdZ4e+Rnh8RfumY7lj3KPhwVEKzMiP0GHca7jplgeCm73Y4OBI3La/jHAldG13Pb/PGzLx1q3K5coA2tbgXX9w5KG+jBsmW4nz0h94+v+uwdZRR/2152AVcxDyDoGm2IV3uXuHkYhw2EpZ2IjWMHYT8gOuDoTG0GtiHQY6bodRHYqe4KNKI3w6wdgDXNppMM8rPE8IUfeeNdnC9ehCpvBCfvXX8ZG8SZ5GNtgRmrYB9fH5Q1xeWNc3qFYpy+WtxAbWfxVJG8vsiXVxdhs7qwojsqwUffZWCMcjIc2WAVVjI3+DgvhdfcR/rzPctJwjv+/EDt4owqb3XwMq3CPJZtRH4wZQnM8XVtinefU/fwHa6QPwjoSYYlrbazCm+Vub7rsAZEOM/w5sQ4bRel7A1bJLfJ4GO9xe5in00KPo8TT9hWX/TCZwbZVJO8vrkOozLfG8t3BuJHg13fA8lV9bHan0p9Xx9NsJDYCGvbWX+w6vOthakC2BKy2yxvi8s4i60w0xhrfIR6v3wV5rsdA16MBls5V2KwwWAfhAixvVuJGCpbf9va0XYgfYuVuG5MZ6q9gnYomWIO2MZbHxuGHPAU//n0V1hksiuS9hg1W3Ee2f3EzrOHe3p8VTk8O8vb0a+d7nIdTy1u4/z9iHdppWP5pgo0gD8EapP8F/Tw99nB5Z3h4gqG5ncfzhVi5XoztlQWb5aiD5bVgALb2+9qSDQ6G5eoJdmDMQ+4/LG/8LVYGDyLbBz47V577OZMsHzXG6oO2/juk+2/93n6evrXI0j3W73lPj6DfXlie3MJ1fSdHXmf/3tzTMHSmQ5naAuuYVgF/ifJKJ6z8DyV7K0OCzbrejc14D6J6ntjY4yPM3l/kYdzC3Zt4+i2M/PTyNL4Rq1vm5+SxC8mW2V/k4R3u8V0Ly0dheWobYAf/fRpWT13t/7/DZhFrYeW0EssDbTAD4RWsTlgETInkbepxEVbqXOZxtw/ZXs9p7hbkvY7tY9zK4zzW718e1gMieZthHbxNXL/RkbyWWP5vhK3CSMkM2UEeF41dhzLsEMyW7ha27kwnO9DuHx5njT285R6/TTx++mEDlkXulisv+AmHR27lbs2wOrASuNuf9RaWNxuT7fmN5f0D60xPd3mbeThCeP+VIy+Ui9DRj98609/lHOD63RXlyXCuyntUz693Yvlxa5asEzfx/x+TUye6vKmuQ1FUnrZ3Pz+hen30PDZgWIR14sty5P3dZb0XydsMKw9TPI6HkU0u3IStOGzp1yuxFVl1sDroJay9CUvjd8ZWvD2H5eUhZIeAJVg7Otx/d47ktcEGPh739JiZR16C1eep+2ns4Xnc43JWjp/pHj9NsLIR2t0/YPXRhTk67OV+nvJ4n+huO7tOu7v/7kQDdli7cBDZnv0wqNOI7A0gi7C6IMT538n29Z/reofzBfbwsIXVLxV55J2L5b0iT4sOHt91saXMlWTp/ia2+qgWfjhdLM91CVslLiJrWx/E2smxHn+3u7+z/dqNnrYLPe02IhvYHEh2AGXw9wrZ1sqBZFs1T8Dy/SSyg2ZvJzvzYYRfL8UHX7CyPdb1G4Tl89uxfHqCuw3EDNOp7lYbq49GeRrPJmtvgryJWDsx3/2cgZX3cVh9Nc3TJMj7l+swwvW7Iyqjc7FB/L4eR7GR+yDZQYh/dHlhNdYXrvu4KO7aYG3nVHcfR9YPvcNljA5p4dcbY/XtR657MfCSu32ODYp+hA0K9Aj+onrkdaob6sF+aOS6PglctrZtynyfta7AuvzBKtupZBXMxVhnNOxVq8Iq9HC9L9mp1pWRn7CPLSw/qnC31/13mG1NI3mvRtfSyM9/qf7ag9Qz9MVY4xr2b6Y5zymN5IVlRkWewZemw0Cqv3YjhCnMjlfluH2PDR7Er9WK5XVz/5U58i7GKoJwLewtK8Iq7fAaivAJ8q6M0iEO0/VYBRXCFbtdjC0BLc8j7/RIVqzfFWT7fuIw/ZMlX6UT++kdpUdVpMMDVH9NVxxHvyDLI7G8MEtdFcmK0zBOpxCmpaVTEWa0l5Jfh7vyPCvEXfxKodLI7W6q58sqqueVCpbU4WKswxqeFdJpKtb4zcsj72KskYjDWxG5xddTLB9cjHV806W4Lcq5HgzHcTl6x366U/11V7HbnBw/Qb+nfkRe/Iq4IHMKZpCV5TwnzIbOzrke/FxC9dezpDnylqbDuEheXE89kieOKqh+zkNuOl1P9kqcfPKmkp1JEaf7rKXIC3klX534iIchpN0isnzZLye8sbwx+KGdZOd6BLcHqF6uw/W+S5EX8nmoZ+PwhvyyMMfPHzzdgw6hrorzeTgzI67DLnFZ86NnBT/vkc1wx+3Q/VQv77GfN5aSTk9E13LdwinIlTnyLqH6a4Ny/UyP3OIw3R+FP1e/L8mf7iHPxvkyXA+nReercwbn+An7IlOWLDdF2KqnCT8i7+koDUN5mIwZAHFdEIfpBrIzVPLJC9fCGTWh/pi7FHl9I/0WU73OfmEpcfQxWV4OeawIa1fjayFvhjpxZh4/IY9PonqZDnni++j+OExXU73uy5UX8sSCnDB9GsmL+wvfUv2MlziOHqV6vgzP+g356+xQv+W21aF+i18RlyuvP0vWH3+g+lkxuX7C5Epu2YjjITdMH+Q8J8i7mur5K/bzUE48xGmxMMdPXDby1Xu/o/orCHPlhS1yFZHbZLLBrnzpfjf5y3uQF67PxGa0B2BGWFwGS7AByAHYhMycSGbsdhfV+y0L3e1obOtAHLdT3M8deeJ8FLZycEgUrirMGB3g8h4hy5vl/pyRWD0R93ViHU7OiacxkbznlxKmd6j+yrkg7wSyAe7gVoYNHJxD9b5d6mEZgK2qqczjFtcTcVoNwgzx3L7YENd7e6q3RUGHy6meZ2MdrslxW0x2rtMWrl8xVj8Wka0avRErV4OxerX+2rYr833CUmchhBBCCCGEEEIUADpMTgghhBBCCCGEKCBkqAshhBBCCCGEEAWEDHUhhBBCCCGEEKKAkKEuhBBCCCGEEEIUEDLUhRBCCCGEEEKIAkKGuhBCCLEBkRhfJElyVHTt9CRJ3lubegkhhBAiQ69nE0IIITYwkiTZEXgJ2BWojb2P9sg0Tcesgsw6aZpWrCYVhRBCiA0aGepCCCHEBkiSJLcDC4HGwPw0Tf+aJMn5wKVAPeAr4P/SNK1KkuQhYDegIfBCmqY3uYwi4EHgSODuNE1fWgtBEUIIIdY76qxtBYQQQgixVrgR6AeUAV19lv0kYN80TSvcOD8TeBb4Y5qms5MkqQN8miTJy2maDnU5C9M03W9tBEAIIYRYX5GhLoQQQmyApGm6MEmSF4AFaZqWJklyGLAH0CdJErDZ80l++1lJklyM9Rs2AzoDwVB/Yc1qLoQQQqz/yFAXQgghNlyq/AOQAI+laXptfEOSJNsA3YE90zSdmyTJM0CD6JaFa0RTIYQQYgNCp74LIYQQAuAj4PQkSVoDJEnSKkmSjkAzYD5QnCRJW+CItaijEEIIsUGgGXUhhBBCkKbpd0mS3Ah8lCRJLaAc+DXQB1vmPhgYC3y59rQUQgghNgx06rsQQgghhBBCCFFAaOm7EEIIIYQQQghRQMhQF0IIIYQQQgghCggZ6kIIIYQQQgghRAEhQ10IIYQQQgghhCggZKgLIYQQQgghhBAFhAx1IYQQQgghhBCigJChLoQQQgghhBBCFBAy1IUQQgghhBBCiALi/wERwSWca7uCEgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1217,7 +1219,7 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1284,7 +1286,7 @@ "4 801975 BCE 202.92" ] }, - "execution_count": 172, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1302,7 +1304,7 @@ }, { "cell_type": "code", - "execution_count": 363, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1312,7 +1314,7 @@ }, { "cell_type": "code", - "execution_count": 364, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1391,7 +1393,7 @@ "4 1744-03-01 NaN NaN Åland" ] }, - "execution_count": 364, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1402,7 +1404,7 @@ }, { "cell_type": "code", - "execution_count": 365, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1412,7 +1414,7 @@ }, { "cell_type": "code", - "execution_count": 366, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1497,7 +1499,7 @@ "4 1744-03-01 NaN NaN Åland 1744" ] }, - "execution_count": 366, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1509,7 +1511,7 @@ }, { "cell_type": "code", - "execution_count": 367, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1518,7 +1520,7 @@ }, { "cell_type": "code", - "execution_count": 368, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1534,7 +1536,7 @@ }, { "cell_type": "code", - "execution_count": 370, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1603,7 +1605,7 @@ " Belgium 7.106" ] }, - "execution_count": 370, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1615,7 +1617,7 @@ }, { "cell_type": "code", - "execution_count": 371, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1625,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 372, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1698,7 +1700,7 @@ "Belgium 1743 7.106" ] }, - "execution_count": 372, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1709,7 +1711,7 @@ }, { "cell_type": "code", - "execution_count": 373, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1718,7 +1720,7 @@ }, { "cell_type": "code", - "execution_count": 374, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1791,7 +1793,7 @@ "4 Belgium 1743 7.106" ] }, - "execution_count": 374, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1802,7 +1804,7 @@ }, { "cell_type": "code", - "execution_count": 375, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1835,7 +1837,7 @@ }, { "cell_type": "code", - "execution_count": 379, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1926,7 +1928,7 @@ "4 Global NaN 1854 -0.248 -0.218 -0.301" ] }, - "execution_count": 379, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1939,7 +1941,7 @@ }, { "cell_type": "code", - "execution_count": 380, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1968,7 +1970,7 @@ }, { "cell_type": "code", - "execution_count": 381, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1978,7 +1980,7 @@ }, { "cell_type": "code", - "execution_count": 382, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1987,7 +1989,7 @@ }, { "cell_type": "code", - "execution_count": 383, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1997,7 +1999,7 @@ " dtype=object)" ] }, - "execution_count": 383, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -2008,7 +2010,7 @@ }, { "cell_type": "code", - "execution_count": 384, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -2032,7 +2034,7 @@ " 2015, 2016, 2017, 2018], dtype=int64)" ] }, - "execution_count": 384, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -2043,7 +2045,7 @@ }, { "cell_type": "code", - "execution_count": 385, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -2052,28 +2054,20 @@ "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" ] }, - "execution_count": 385, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0, flags=flags)\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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9wjFfNMYsM8YsNcY8nuk1ioiIiIiISOakovwegn31f1sPza3JnSuXZS2oN8bkA/cBZwHjgMuMMeM6HFMN/CtwgrV2PPDPGV+oiIiIiIiIZEyqMvXDe0NVL9jTCAu3Jr+uXJXNTP00YJW1do21tgl4AjivwzHXA/dZa3cBWGu3Z3iNIiIiIiIikiGhe9QnG9QDnFzlbt88gkvwsxnUVwAbQh5vDDwXajQw2hjzjjFmjjHmzHAnMsbcYIyZb4yZX1tbm6blioiIiIiISDrVHYSDzdCrOPE96kO17Ve/Nvlz5apsBvXhdhy0HR4XANXAycBlwEPGmN6HfZK1D1hra6y1Nf3790/5QkVERERERCT9UpmlBzcBvzgfVtTB3sbUnDPXFGTxvTcCQ0MeVwKbwxwzx1p7CPjEGLMCF+TPy8wSRUREREREJFNSHdSXFsLvL4Yx5dCtMDXnzDXZzNTPA6qNMSOMMUXApcDzHY55FjgFwBjTD1eOvyajqxQREREREZGMSNXk+1BTBh25AT1kMai31jYDNwMvA8uBJ621S40xdxtjzg0c9jJQZ4xZBrwB3GatrcvOikVERERERCSdVu10t6nK1HcF2Sy/x1r7EvBSh+e+G3LfAt8IfIiIiIiIiMgRqqEZXlnt7p8wNPqxEuQrqDfG9AQGAweBDYFgW0RERERERCQl3lgLe5pgfH8YXZ7t1XQeEYN6Y0wZ8BXgcqAHsAMoAcqNMW8D91tr/56RVYqIiIiIiEjOaWiGonzIC7e3WZye/cjdnn908ufqSqJl6p8BHgNOC+1jN8YYYBrwJWNMtbX2kTSvUURERERERHJM7X4463E4ZiA8fG7s46Opb4C/rnX7np83OhWr6zoiBvXW2tMjPG+B9wIfIiIiIiIi0gU99iHUHoC/rYdDLVCYn/i5XloFTS2ul35gj9StsSuIOP3eGDPIGHNdmOdPNsaMT++yREREREREJFc1tcDvPgzeX1uf3Pm80vsLVHoft2hb2n0TaAjz/CbgJ+lZjoiIiIiIiCTriSVw859hb2N6zv/iSpel93ycxMbjm/bAnE1QnA9njkx+bV1NtKD+OOCPHZ+01q4EKtK2IhERERERkU5m7W74wzLIhX3CrIUf/wP+9DH8NE1N079Z6G4rytztih2Jn+v5j93tGUdBWXFy6+qKogX1AP0jPF+W6oWIiIiIiIh0Vt99E775KszdnO2VuAsMXhb9NwuTy6KH88FWWLgNepfALdPccyuSeA9NvU9OtKD+BeAHxph24w6MMd8BtJWdiIiIiIhIwPJApnpDkr3lqTAvcGEhz0CLdRccUllB8OtAlv6y8TB5oLuf6IWD5bXwUZ27QHDS8NSsr6uJFtT/BGgB1hhj/mCMmW2MWQ6cDHw9E4sTERERERHJdXsaYft+d3/b/uyuBYLVAjfVQJ8S+MdGeGFlas69bZ/rp88z8KVJcFQfyDduUF5Dc/zne3aFuz2n2u13L/GLGNRba5uttbOA04EncZn7y621Z4TuWy8iIiIiItKVrdkVvL89B4L6+YGgfsZR8K3j3f0f/B32NyV/7sc+hOZWmDkSKnpCcQGM6AOtFlbtjO9crRaeCwT1549Jfm1dVbQt7WYYYx4D/gNYb619zFr7QeaWJiIiIiIikvtWhwT12c7Ub98Pn+yGboUwfgBcMh4mDYCt++AX85I7d2OzC+oBZk0OPj+m3N3GW4L/3ibYsg8qe8KnhiS3tq4sWvn9VdbaK4B/wm1vJyIiIiIiIh2sDslQZztT7/XTTxkEBXmQnwd3n+Kee3BB+6qCeL24EnYchHH9YFpIEO4F9fEOy/vjcnd73hhXzi+JiRbU/80Ycx/wAPB0htYjIiIiIiLSqazKoUy9V3ofGnRPGeQy9oda4a63EhuaZy08EhiQ90+TwYQE4aMTCOrX18MzH4EBLh4b/3okqCDSC9baB4wxvYBWa+3eDK5JRERERESk0wjNftfudwGwSXHmefNeeGqZ60Ef3x9+flb447wheVMr2j//7ePhz6vgrXXwjVfgu5+FPqX+339JLXy43Q3eO69D/3si5fc/e8/15l94tBu2J4mLuk+9tbZeAb2IiIiIiEh4za1uX3iAkgJobIH6xtScu6nFlbxf9Swc/wj8ZI7r33/+Y/hw2+HH72uCZbWu7H7KoPavlXeD/zgNivPh6Y/g9N/Cnz72n7Wfu8ndnnGU+zpDDe/lzrtpL+z18bV/XOey9AV5cOt0f+8vkUUblPeGMeYrxpghHZ4vMMZ81hjzsDFmVvqXKCIiIiIikps21Luy9ooy9wGp6auvO+AC75tectn1wny37dvMke71x5Yc/jkLtriJ8hP6u0F5HZ1dDS9fAdMrXG/8zX+G619wQ/RiWRS4iNDxYgG4vv1Rfd39j31MwP/JHLfOS8fDsF6xj5foomXqzwYKgWeMMRuNMYuNMR8Da4BZwC+ttb/OxCJFRERERERykTf5fmQfGNDd3d/mI0iO5Xcfwrp6GNoT7vwszL0W7vsc3BbYou65FS4zH8orva+JMkl+RB+YfRH8+6lQVgSvrnEXD5bWRl/Poq3udtLA8K/7LcH/cJtrAyjOh1umRT9W/Im2T/0Ba+3/WGuPA0bigvzjrbXDrLWzrLXzM7ZKERERERGRHOQNyRvZNxjUbz+Q3DkPtbigHlzJ/DVTgv3v1X3huAo4cCi4x7vHm3w/rUM/fUd5Bi6fCK99CU4YCnub4PEPIx+/uwHW1rtA3AveO/I7Af/ef7jbq4+BQT2iHyv+RO2pDzEFOMVau8MY09cYMyydixIREREREekMvO3sRvaBgSnK1L+82pXwj+zjgu6OLpvgbh8PKcFvaoEPtrj7NYP9vc+gHsGedi/LH87iQOn9+AGuDSCctgn4OyKfZ+4m10rQowi+UuNvjRJbzKDeGHMHcCdwR+CpUuDxdC5KRERERESkMwhXfp9spv7RRe726mPCT9E/axT0LoEl24MB95LtbkjfyD5uKJ5fkwa4DPzHdbDzYPhjvH76yRFK7yF2+b21cO+77v51U6BvHJP3JTo/mfqLgc8B+wGstZuAnulclIiIiIiISK6zFlalOFO/rNZlzXsUwUUR9m8vKQi+5mXr5/osve+ouACmBDL78yJk672gPlI/PcCQMrfmHQdhR5iLGn9b79bYu8QF9ZI6foL6RmutBSyAMSaO6z4iIiIiIiJHpp0H3fZ1PYpclr4tU5/E9Pv/W+xuLxrrzhuJV4L/3Aq3jdy8wJZz06IMyYvkuMDnvLfp8NeshYWBIXnRMvXGhJTgd8jWh2bpv1IDZcXxr1Ei8xPUP22MuQ/oFdjC7hXgkfQuS0REREREJLetCSm9NyYkU59gUF/f4PZvB7hqUvRjQwfmPbsC5nv99AkE9V52f26YoH7rPqg9AD2Loap39PNEKsH/y2r4cDv07wZXx/i6JH4FsQ6w1v7IGHMW0AQcA/zQWvvntK9MREREREQkh60KCeqhfabe2vD98NE8uQwamuEzw4L7vkdz2QSXXf/v99yE+kE93BZ48Tp2MBTkuW3t9ja2z6R7pffHDIz99YSbgN/SCj8OTLz/2jQoLYx/fRJd1Ey9MSbfGPOytfbP1tpbrbX/rIBeRERERESk/ZA8gO5FrmS+sQX2NMZ3rlYbLL2/+hh/n+MNzKsN9LBPHRL/hQSAboUwcYBbw/tb2r8WGtTHEq78/rkVbu5AZU+4dEL8a5PYogb11toWoMkYo8F4IiIiIiIiIdq2swvJqg8ITCCLtwT/zbWwvh4qy+DUKn+fEzowD1xQn6i2EvwOw/LiCepDy++tddvs/WSOe+7W46AownZ4khw/PfX7gEXGmF8ZY37ifaR7YSIiIiIiIrmsY6YeYEAPdxvvsDxvG7svTYJ8P1FawOUh2e9EhuR5vGF5oX31rTa4Zd4xg2Kfo183KC+FfU2weS88uRQ27HGtBBccnfjaJLqYPfXAa4EPERERERERwfW+b9gDeQaG9wo+72Xq4wnq1+2GN9e5/eIvGR/fOkb1ha/WuK3kxvSL73ND1QwBg8vMNzS7KoBPdsPeJter7w0BjGV0OfxjozvPf891z31jenwXKiQ+fgblPZyJhYiIiIiIiHQW63a7THZVL7fXu2dgIFMfT/n9Oxvc7RlHQZ/S+NfyrRPi/5yOepXA2H6wbIfbwm56JSwKbGXnp/TeMyYQ1N/ztruwMWGA6/2X9IkZ1BtjVhLYoz6UtXZ0WlYkIiIiIiKS49om33eYUp/IXvXeucb1T35dyZhW4YL69zYFgvo4+uk9Xl/9hj3u9l8+7aoZJH38lN+fGHK/BPgC0CvCsSIiIiIiIke8cP30kNhe9asCA/f8bGOXTtMq4DeLgn31iQT13gR8cIP7Th6euvVJeH7K77d1eOrHxpi307QeERERERGRnLcmQlCfSKY+0gWCTPMG7b2/BQ4cgmW17vHEBIP6245PbIs9iY+f8vtJIQ/zgBqUqRcRERERkS4s3HZ2EH+m/uAh2LQHCvLaD9zLhv7d3YWF1bvgD8ugscU97lXs/xw9i+Guk9x2dsdVpG+tEuSn/P6+kPvNwCfAJelZjoiIiIiISG6zNphdHxUlU29t7Ez1mt1ugNnwXlCYA/u4T6twX9vDH7jH8ZTee2ZNTu2aJDo/Gwtcaa39TODjFGvtNbi965NmjDnTGLPCGLPKGHN7lOMuNsZYY0xNKt5XREREREQkUdv2w/5D0Lf08Gn1PYqge6HbFm5PU+xzrc6RfnqPV4K/tt7dTkogqJfM8hPUPxPmuWeTfWNjTD6uCuAsYBxwmTFmXJjjyoCvAe8l+54iIiIiIiLJ8gbbHdU7/Ovx9NW3DcnLcj+9Z1qHkvnJg7KzDvEvYlBvjBltjDkP6GWMOTfk40rcFPxkTQNWWWvXWGubgCeA88Ic933gP4GGFLyniIiIiIhIUlZH2M7O09ZX76O+Oda5Mq2yJ1SUufsFeW7veslt0TL144GLgd64bey8j+OBG1Pw3hXAhpDHGwPPtTHGTAGGWmtfiHYiY8wNxpj5xpj5tbW1KViaiIiIiIhIeLGm1XfmTD0Es/Vj+0GJnylsklUR/4qstc8AzxhjTrTWpmMLu3AjI2zbi8bkAT8F/inWiay1DwAPANTU1NgYh4uIiIiIiCRsdYxA3G9Q39IKn+x294/KoaD+lCp45iM4YWi2VyJ++LnuMs8YcyMuc99Wdm+tvSHJ994IhH6bVAKbQx6XAROAN40bGTkIeN4Yc661dn6S7y0iIiIiIpKQtYFAfESMoD7Wtnab9rpt4wb1gLI4to1Lt3NHw5AeMGFAtlcifvgZlPd/QBVwDm5Y3UhS098+D6g2xowwxhQBlwLPey9aa+uttf2stVXW2ipgDqCAXkREREREsqalFbYEeuWHlIU/ZqDPTP1Kb6/7HMrSg9uGb2oFlBZmeyXih5+gfrS19l+Bfdbah4EzcRn0pFhrm4GbgZeB5cCT1tqlxpi7jTHnJnt+ERERERGRVNtxAFoslJdG7jf3m6lv2+s+R4bkSefkp/z+UOB2tzFmLLANGJ6KN7fWvgS81OG570Y49uRUvKeIiIiIiEiiNgey9IMjZOkBBvZwt7Ey9atyNFMvnYufoP5hY0wf4E5cVr0bEDbwFhEREREROZJt3utuB/eIfMyAbu52236w1pWzh7NaQb2kQNSg3hiTD+yw1u4C3gCGZWRVIiIiIiIiOWirl6mPEtT3KIJuhXDgEOxtgp4RhuB55ffVKr+XJETtqbfWtgD/nKG1iIiIiIiI5DQvUx9pSB64zHxotj6cugOwq8FdAPB68EUS4WdQ3svGmH82xgw2xvT0PtK+MhERERERkRyzxUemHmL31bcNyesTuTxfxA8/PfU3Bm6/CVjABG5Vii8iIiIiIl3KFh+D8iD2BPy2IXkqvZckxQzqrbVDM7EQERERERGRXLfFx6A8CAb1kTL1qwKZeg3Jk2TFLL83xpQaY243xvwy8HiUMeas9C9NREREREQkdzS3BjPvg/wG9fvCv+5Nvtce9ZIsPz31jwSO+0zg8WbgnrStSEREREREJAdt3w+tFvp3g6L86McO9IL6A+FfV6ZeUsVPUF9trb0HOARgrT2A66sXEf8jOKgAACAASURBVBERERHpMvwOyYOQnvowmfqDh2DTHijIg+G9Urc+6Zr8BPVNxpgS3HA8jDEjgKa0rkpERERERCTDDhyCppbIr7f108cYkgchmfowPfVrdrvgangvKIyR8ReJxU9QfzfwF6DSGPMo8Abwr2ldlYiIiIiISAbVN8JnfwM3vhD5mM2BrHu0Peo9odPvrW3/mvrpJZX8TL//izHmfeB4XNn9bdba7WlfmYiIiIiISIYs2AK1B+Dv691AvIIw6c+tgUx9rCF5AGVFUFoAB5thXxOUFQdfC92jXiRZfjL1AJ8GTgjcHpe+5YiIiIiIiGTeh4G05aFWWFcf/pi2TL2PoN6YyHvVa496SSU/W9r9HPg6sBJYBXwt8JyIiIiIiMgRYUlILfLKuvDHxNNTD5H76pWpl1SKWX4PnApMsNZ6g/IeARandVUiIiIiIiIZ1C6o3wlnhjlmSxyZeghm6v+4HMb1h94l0NIKawJB/VEK6iUF/JTffwxUhjweDCxJz3JEREREREQya+dB2LQ3+HjlzsOPOdTiMu6GYLAey4nD3O0flsOJv4afzYGP6qCxxfXlh/bZiyTKT1DfC1hujHnNGPMasBzobYx52hjzdHqXJyIiIiIiXd2LK+HfXnf7u6eD10/fo8jdhgvqt+1329AN6O5/G7rLJsBzl8Bnh8HeJvjpe3DeE+61kcrSS4r4Kb//YdpXISIiIiIiEsFP5rjhcgN7wNfTMLb7w23u9qyR8NRyt+VcSyvkh6RAt8SxnV2oyYPgtxfAe5vg3ndh3mb3fLWG5EmK+NnS7nUAY0y30OOttXvSuC4RERERERFaLWwITKP/3/dd9ttv+btfS2rd7aeHwt/Wu6z8xj0wvHfwmC1xbGcXznEV8NTFbsu8V9fADccmt2YRj5/p99caY7bgeuuXAEtRT72IiIiIiGRA7X7Xgw5w4JDrS081r/x+4oBgBn1VhxL8eLazi8QY+Oxw+P4pUNEz8fOIhPLTU387cIy1ttJaO8xaO9RaOyzdCxMREREREVkfqA8e1APyDTyxFD6OsOVcInYddFn50gLX515d7p7v2Fcf73Z2IpniJ6hfA6jUXkREREREMm5DIBKZOsSV3rdY+I93Und+byu7cf1dD72XqT8sqE+wp14k3fwMyrsdeMcYMwdo9J601n4jbasSEREREREh2E8/rCfMmgzPfASvfwLvboDjhyZ/fq/0fsIAdxsxqE+yp14kXfxk6v8XeAdYiOun9z5ERERERETSygvqh/aC/t3hyzXu8Q/fdkP0khXaTw/tg3obcv4tKeipF0kHP5n6Vmvt19K+EhERERERkQ688vuhgcFy10+B3y12ZfPPrYALjk7u/N7key+o71MK/Uphx0HYvNcNtGtqgdoDrqc/1ZP3RZLlJ1P/ujHmGmNMf2NMT+8j7SsTEREREZEub71Xft/L3ZYWwr982t2/911oaE783PUN7vzF+TAqZN/4UR1K8LcGsvQDurffu14kF/j5lrwa+B6wgGDpvba0ExERERGRtGpqcWXveQYGh5S9XzQWji6HTXtdf32iPgwZklcQEhl1DOq90ntNvpdcFLP83lqbgvETIiIiIiIi8dm0ByxQ0QMK84PP5+fBqSPgozpYWQdUJ3b+JR2G5Hk6bmvnDclTP73kopiZemNMqTHmdmPMLwOPRxljzkr/0kREREREpCtr66fvdfhrI/u429W7Ej9/xyF5Hm9Y3ipl6qUT8FN+/0jguM8EHm8G7knbikRERERERDh8SF6okV7gnURQvyRGUO9NwN8cyNQPVqZecpCfoL7aWnsPcAjAWnsAMGldlYiIiIiIdHkdh+SFOiqQqV+zK7Gt7fY0wtrAkLzqvu1f698NehW7Y7YfCMnUK6iXHOQnqG8yxpTg2lkwxowAmtK6KhERERER6fKiZep7Fbvgu6E5mEmPh5elP7pf+359AGNCsvV1IXvUq/xecpCfoP5u4C9ApTHmUeAN4F/TuioREREREenyvEx9uJ56CJbgJ9JXH6mf3hM6LM8blKeeeslFEYN6Y8wwAGvtX4AvANcDzwDTrLWvZ2Z5IiIiIiLSVXmZ+mFhMvUAo7xheTvjP3ekyfcdz710O9QddFve9SuN/31E0i3alnbPAscCWGtrgecysiIREREREeny9jTC7gYoLYB+3cIfk8wE/EhD8jxepv7vG9ztoO5uKz2RXBPt2zLtw/CMMWcaY1YYY1YZY24P8/o3jDHLjDGLjTGvG2OGp3tNIiIiIiKSfaHb2ZkIkYk3LC/eCfh7G2HNbijKh9Hl4Y/xeuq3ajs7yXHRMvUVxpj/ifSitfZrybyxMSYfuA84A9gIzDPGPG+tXRZy2AdAjbX2gDHmK8B/Apck874iIiIiIpL7Nnj99BFK7yHYU78mzqB+wVZ3O6bcBfbhDO4B3Qth/6HgY5FcFC2oPwi8n8b3ngasstauATDGPAGcB7QF9dbaN0KOnwNcmcb1iIiIiIhIBh1qcb3q4TLx66NMvvdUlLkt6bbvd+X6PYv9ve8fAhHH6SMiH+NNwF+4zT1Wpl5yVbSgvs5a+2ga37sC2BDyeCNwXJTjrwX+HO4FY8wNwA0Aw4YNS9X6RERERES6pOZWNxxuYPfUnrepBRZuhXc3wLsbYcEWmDIInrz48MB+Q5Q96j15xvXVL9vhsvWTB8VeQ30DvLza9Rp/YVz0Y9sF9crUS46KFtSney/6cJ0xNuyBxlwJ1AAnhXvdWvsA8ABATU1N2HOIiIiIiIg/3/kr/H4pvPqlYG95Mppa4NaX4fVP4GBz+9fmboZPdgf74z3R9qgPNbKvC+pX+wzqn1kBjS3w2WFQEePco0L67bVHveSqiIPyrLXT0/zeG4GhIY8rgc0dDzLGnA58BzjXWtuY5jWJiIiIiHR5C7a6bJs3IT5Z8zfDCytdQF/dF64+Bh44B2aOdK+/sfbwz1nvo6ceQibg+9zW7vdL3e0l42MfWx1yoUGZeslV0TL16TYPqDbGjAA2AZcCl4ceYIyZAvwKONNam6L/UkREREREJBJrYWMgS75lb2rO6W05d/FY+K8ZwecPHnKl8H/9BK6dEny+NWQNQ6OU30MwqPczAf/D7bCsFvqUwBlHxT6+OiRTr6BeclXWgnprbbMx5mbgZSAfeMRau9QYczcw31r7PHAv0AN4yrgmm/XW2nOztWYRERERkSPdzoNwIDDxfcu+1JzTC+o7lvKfNNz15L63CfY3Qfci93ztflci37cUehRFP7c3Ad/PXvVelv6Co6HYRyRU2ROG9HBVC+XdYh8vkg2+gvrA9nMDQ4+31q5P9s2ttS8BL3V47rsh909P9j1ERERERMQ/L0MOqQvqVwVK40d1COr7lMKUwW5g3jsbYEagHN+bfD8sRuk9wFG93e263W7AX0GEBuOGZnjuI3ffT+k9uEF8f74ieF8kF0XsqfcYY24BtgGvAi8GPl5I87pERERERCQLNqQhqPf63Uf2Ofy1U6rc7V/XHr6GWKX3AKWFUFkGh1qDE/PD+fMq2NMExwyEo/v5WHRA7xL3IZKrYgb1wNeBMdba8dbaiYGPSelemIiIiIiIZF67TH0Keur3N8HmfVCUHz5IP7XK3b6x1vXzQzA4jzUkz+OnBD+eAXkinYmfoH4DEOWal4iIiIiIHClCM/V1B13ZejLWBALtqt7hS+PH94cB3WHrPvhoR2ANPvaoD3VUjGF563bDPzZCSQF8frT/tYt0Bn566tcAbxpjXgTatpSz1v4kbasSEREREZGsCA3qAbbtg+G9Ez/fykCgHa70HsAYOHk4PLnMleCP7R/sqfedqY+xrd2Ty9zt2dXQs9jfOUU6Cz+Z+vW4fvoioCzkQ0REREREjjBe+b0X/G5Osq9+dYQheaFOqXK33n71cZffe0F9mEx9Syv8Ybm7r9J7ORLFzNRba7+XiYWIiIiIZFJzq/tl38+2ViJdRege9TWDXeY82WF5q2Nk6gE+M8yV5r+/xW1nt2WfmzY/xGcq0eupX7XTfQ0mZFL9X9e60v4RvWHakIS+BJGcFjFTb4z5WeD2T8aY5zt+ZG6JIiIiIql35TNw6m+T7xcW6Uxq98OOA5Ff337A7Q/fpwSqy91zyQ7Li7SdXaiyYpg6BFotzF7q9oUf0gMK8/29x4BuUFYE9Y2w82Dw+VYL//UPd//Kie2DfZEjRbRr078N3P44EwsRERERyZQDh9zQLICltfCpwdldj0gm7GmEsx6H/Dx4Z1b4oXVelr6yJwzu4e4nk6lvboW1u939aJl6cCX4/9gIj33oHvvZzs5jjDv/wm2uMqC8m3v+2Y9g+Q53geBK7d8lR6iImXpr7fuB27fCfWRuiSIiIiKptSak73bJ9uytQyST/m8R1B5wpeif7A5/zMaQAXVtQX0Smfr19W7/+Ioy6FYY/dhTqtzt1n3BNcSj47Z2Dc3w40CW/pufdpPvRY5EfgbliYiIiBxRViuoly7mwCF46IPgY2/ruI68AXWhmfpkBuX56af3VPd17+uJJ1Mf+h7ee/5uMWzaC2PK4YKj4zuXSGeioF5ERES6nNBtr5bUZm8dIpny2IewqyH4eHmEoH5jICs/tCcMDgyp25pEUO/104+M0k/vMSaYrQcYFm+mPmRbu/pG+Pk89/jbJ7iWA5Ejle9vb2NM93QuRERERCRTVoVk6j+ug0YNy5MjWEMzPLDA3b9orLv1k6nv1w0K89zguUQHSrZtZ+cjUw/tg/q4M/Uh5fe/mg+7G2B6BZxaFfXTRDq9mEG9MeZ4Y8wyYHng8THGmPvTvjIRERGRNPECjcI8N8jr47rsrkcknZ5aBtv3w7j+8JUa91ykoD50UF6egYGBEvxEs/XeBTQ/mXqA4ytd77sBhscZ1A/vBfkGNuyBhxe6524/URPv5cjnJ1P/U2AmUAdgrV0EfDadixIRERFJl5bW4JCwk4a7W5Xgy5HqUAv8cr67f/NUt1d7cb7rNa9vbH9sS6t7HoJD6oZ4ffUJDMuzNv5MfWkh/Ops+NlMVykQj6J8GNbLbWPX0AxnjYIpg+I7h0hn5Kv83lq7ocNTLWlYi4iIiEjabdrr9uEe1AOOq3TPaVieHKme+ch9z4/q64LcgjwYHdh/fkWHbP32/W5Sfb9SF1yD+3cCiWXqaw/AniboWRxfgH5yFZyf4GA7r68+38Btxyd2DpHOxk9Qv8EYczxgjTFFxph/IVCKLyIiItLZtA3u6gMT+rv7CurlSNTSCvcHsvRfnerK6QHG9nO3HYflbfC2swspex8SGJaXSKbe+7c2qm/mSuDHBf5NXzre38R9kSOBn90avwz8N1ABbAReAb6azkWJiIiIpEvoFlvjB7j7y3e4MuXC/Oyty6+31sELH8P3T9G+2xLdiytdq8mwXnDu6ODzXlDfsa8+tJ/e42XqtySQqY9nO7tUuf5Y11t/zujYx4ocKWL+KLDW7gCuyMBaRERERNJudcjgrl7FLuBZX++eP7pfdtcWi7VwxxtuvTNGwhlHZXtFkqtaLfwisKXbTTWu7N5zdKxMfUhQ72XqEwnqV8XZT58KPYvh4nGZez+RXOBn+v1/GmN6GmMKjTGvG2N2GGOuzMTiRERERFKtY6DRmUrwl2x3AT0kt3e4HPkeXQQr6mBwD7iwQ3+6F9SvqHPBv2dDmEz9YC9Tn0D5vXcBbZTPyfcikhg/PfUzrLV7gHNw5fejgdvSuioRERGRNOkYaEwIlOB3hgn4L6wM3ldQL5Gs2w3/8Y67f/fJUNyhNrdvKQzsDgcOBS8SQfhM/eBkyu9D5leISPr4CeoDsy/5HDDbWrszjesRERERSZudB91H90IX1EBIUJ/jmXpr2wf12/Znby2Su1otfOs1t6XbeWNcm0Y44Urww/XUl3eDwjzY1QAHD/lfx/4m2LzPbTM3NM795kUkPn6C+j8ZYz4CaoDXjTH9gYb0LktEREQk9UIHd3nTuMcHyu+X1rYvRc41i7YFgy5w24+JdPTbxTBnk9uW7nsnRT6u47C85tZgiX1FWfC4PJPYtnZrAv/Wqnq37+cXkdSL+U/MWns78Gmgxlp7CNgPnJfuhYmIiIikWls5cEiPb79ursT4wCE3KTxXeVn64yrcrcrvpaP19cGy+x+eCn1KIx/bMVO/ZR+0WFfB0rFc3yvB3xzH99xKr81Fpfciaef3ulkFcJEx5irgYmBG+pYkIiIikh6RttjK9RJ8a+HFj939a6e4W5Xf5x5r3Uc2tFq47TV3cerzo+HMUdGP75ip3xDorQ/tp/cMDmTu47mQFO4Cmoikh5/p93cCPw98nAL8J3BumtclIiIiknKrIgzuyvUJ+B9sdVnSwT3g9BGux3l3g+ubltzQ3ArnPgE3vpid93/sQ5izEcpjlN17jurjvo/W1bv+93D99J4hXqY+jgn4kf6tiUjq+cnUXwycBmy11s4CjgGK07oqERERkTSItMWWl6lfmqMT8L3S+89VQ34eDAgM+VNffe5YtRMWb4fX1mR+NsO2/XDP2+7+909xw+1iKcoP/jtYURcy+T7MULtB3l71cQT12s5OJHP8BPUHrbWtQLMxpiewHTgqvcsSERERSa2GZhe45BkY3iFwCS2/z1b5dCStFl4MBPXnVLtbL6jfpr76nOH1prdY2NOY2fd+Z70ruz9xKJxd7f/zQkvw2zL1ZYcfNyTObe2aW2FtYD6FMvUi6ecnqJ9vjOkNPAi8DywA5qZ1VSIiIiIptm63C5CH9zp8ENjA7m5aeH0jbIwjG5kJ729xvcwVZTBlkHvOm0aezr76v62DuZvSd36AxdvgrMfh1TXpfZ9MCN0abseB1Jxz4x74zl+hNsbfs9c2Mr0yvvOHDsuLlqmPd6/69fVwqNV9z3YrjH28iCTHz/T7m6y1u621/wucAVwdKMMXERER6TRWRRiSB257u/E5OizvxZDSe28bvoFepj5NQf2aXXDVs3DZ0zAvTYH9gUNwy59hWS08vyI975FJy0NaN3YeTM05f7sYfvchPPhB9OOWBN7bqzjxK1ymPtqgPL9BvTd8T1l6kczwMyjPGGOuNMZ811q7FthtjJmW/qWJiIiIpE7b5PsIPb65OAE/XOk9BIP6dG1r98hCsLgy6q+8FF8vNcC8zfCt16J/3n+8A2sDE9frUhQEZ1M6MvW1gfMs3Br5mFYbnAXhDXz0y8vUL9vhvpfyTDArH6pvqevB390ABw/FPu8ba93t1Ir41iMiifFTfn8/bp/6ywKP9wL3pW1FIhLT/86Hmgfj/yVLRKQrWx1jGncuTsCfv9kNw6vsCccMDD6fzvL7+gb4wzJ3f3x/F1h++cX4Ju3/+F34/VK4/Onww/zeXg+PLgo+rktREByP/U3w93WpmaFQuz8YgEPqMvXen8vibe4CSzjrdsO+Jnehp3/3+M7fv5ublr+vyV3EGdwDCvMPPy7PBL/nYmXrW1rh9U/c/RmawiWSEX6C+uOstV8FGgCstbuAorSuSkSienKZ++Vh7uZsr0REpPOIVn4PuTks74WQLL1Xeg8hg/LSENTPXgoHm93Qtd9d4AanLdwG/+8Nf38uBw65OQAAa3bDFc+0D3L3NMJtr7r7V050t9nI1P/PXLjyWfi/xcmf66Md7R+n6uvZ1eBuDzbDyrrwx3hZ+vFxZunBfU952XoIX3rvGexzW7sFW9zXP7QnjCmPf00iEj8/Qf0hY0w+7gIexpj+QIRrhSKSbvWNwRLSbGQ2REQ6o1YbO1M/tCf0LIYdB9M7gM4va+Evq9z9jhPNB3qZ+hSX3ze3wqML3f1rpriy61+dAyUF7oLy7z6MfY65m9yQtOq+7uPjOhfY1wcC1Lvfgs37XOXB//use27nwcxvA+eVtP92cfIXcZYFgvrCwG/WqQrqQ8+zcFv4Y7zKknj76T2hQX24Peo9foflvRIYejhjZPsLUSKSPn6C+v8BngEGGGN+CLwN3JPWVYlIRB+G/FBPVc+eiEhn0tLqypH/dz7c9JIroY5l6z6X7SwvhT6l4Y8xBiYFAqMFW1K33kQt2+EuLgzsDhM7BGyhg/JSWVXw8moXcB/VG06pcs9NGAA/Os3dv+ut2IPz/r7e3c44Ch6/0J1rWS186Vl4+iN4ajkU58NPZriLBb2K3TZwXtCfKasCF3lW7gxWFiTK66f/1BB3m6qL7qEVDpH66hMdkucZG2emPlpQby28strdV+m9SOb4mX7/GPAt4N+BLcD51tqn0r0wEQkv9If6jiNgsJCIiB8Nza4H+/o/weRfweefgH9/xw2Ru+01aIzR7+1l6UdFGJLnmRYY7JUL7U1/DfQln1J1eMazrMhtFXbgkOuHTpWHA1PWZ012fdSe84+G66YEB+dFG5b2zgZ3e+Iw1ybw+IUuWFy0DW592b327ROCfxf9urnbTP5M293Q/v2eWJrc+bzJ9ycOdbepyNQ3NLu/X8+iMJl6a0My9QmU30McmXpvAn6U8vuVO93ww94lUDMksfWISPyiBvXGmDxjzBJr7UfW2vustb+w1i5P1ZsbY840xqwwxqwyxtwe5vViY8zvA6+/Z4ypStV7i3RWi5SpF5Eu6JEP4LtvutLePU0wrBdcOt5lgbfsg6eWRf/8WP30nragPs37s/vhTRA/perw14wJmYCfolaBhVtdxrpnMVw09vDX//VEGBcYnPfXteHPUbvfZa1LCuBTg91zg8tg9kUwJJDpnV7pLhp4ygOVE5lsKVsZuMjTP3BB4YWPYW9jYudqbHbfXwY4PoVBvZel710C+QZW1LUP8sH1t+9qgD4lMKQssfep7uvOD+H3qPcM8bGt3auB0vvTRkCBn3pgEUmJqP/crLWtwCJjzLBUv3GgT/8+4CxgHHCZMWZch8OuBXZZa0cBPwV+lOp1iHQm1sIHIZl69dSLSFdgLfzxI3f/G9Ph7Vnw93+CH50O3/y0e/7++dDUEvkcq30G9VMGub7oZbVuoFu27Dro/r8vzHMZ73DaSvBT1FfvZekvmwDdw4xELsiDLwZ+U3v2o/Dn8LL0x1VAcUHw+aE94ckvwG2fhvvOal8FUB4IrDM5LM8rvT9xGEyvcK0Zz61I7Fyrd7kKhqrewUx3Kn4+e38eg3u4bHqrhQ877MwQWnqfaP96SYG7mFVWBKOjVLK0Tb+Pkqn3gnqV3otklp9raIOBpcaY140xz3sfKXjvacAqa+0aa20T8ARwXodjzgMeDdz/A3CaMRq5IV3X1n0uQ+L9I1D5vYh0Bct3uCCsTwncVNO+7/dz1S7TuGkv/DFKLaEXxEXao95TUuAGuFncdnLZ8tY6F8RNq4AeEfYc8oblhdsyLl5b9sJLq1zG9upJkY87u9oF5G+sDd8D/3YgqD9h6OGvDe0JN08Lltt7vEx9JqvPvO+HUX3g0gnu/uwES/C9fvqx/aBvibu/q8HNfkjGrsDP+L6l7mITHN5Xn2zpveeBc+D1qyLPm4BgpUWkTP22/e5CVHE+fHZ4cusRkfj4Ceq/B5wD3A38V8hHsiqADSGPNwaeC3uMtbYZqAe0OYZ0Wd7k2ymBksYdB3Jn2yURkXTxMqhnVx++h3aegVumufu/mAeHImTr/WbqwWWZIbsl+NFK7z0DU7it3W8Xu2zzmaOgIkpf9YDuLmA/1OouAoSy1u0/D5GrC8LxgvxMVp+tCpmxcNYoN6xvyfbDM+F+LAtky4/u574/exW7CzK7kxz8VxcS1B8z0N2PGNQnOCTP07M4+P0USd9SF7DXNx7eBgDwWiBLf+IwN+9BRDLHT1C/HnjPWvuWtfYtYC7gY85sTOEy7h3DEz/HYIy5wRgz3xgzv7a2NgVLE8lNiwI/zI+vdNmkjkN0REQ6k8Zm+LfX4fTfwrrd4Y9ptfB8IKg/f0z4Y86pdsH6xj1uunpHv13sstndCqHCR9/x1EBQ/16WMvUtrS5TD3DqiMjHtfXUJ1l+39AMjy1x96+dEvv48wJ/Dx3L1dfsdlnc8tL2E9Vjaeupz2T5feAiz6i+7ufphYEZAk8sif9cXqZ+XCBb3i9F7QShmfrJgUx9x2F5yU6+j4cxwRL8cHvVe1Pvz1DpvUjG+Qnqn6L9vvQtgeeStREILc6qBDr++Gw7xhhTAPQCdnY8kbX2AWttjbW2pn//JOuPRHLYB16mfhD0y0K5oohIquxugKuedcHkyp3w43+EP27+ZrfFWkVZcLuwjvLzXFk3uGx9c8hvLY8ugjvecPdvP8EdG8unBrsKgMXbok95T5dF21z59rBebhBgJG171SeZqV++w/19jOoLxw6KffzMkS5jO2dj+wsKXpb+hKHte+Zj8XrqM/Xz7OAh2LTHzQgYHhgMd+l4d/vcivgullvbvvweXBAOyQf13ueXl7qLVj2KXJuJ126xbb+7X1bkvlcywdvWruOFpH1N8O5Gl407PcqFKBFJDz9BfUGg5x2AwP0I3V1xmQdUG2NGGGOKgEuBjr36zwNXB+5fDPzVWhUbS9fU0hrco37SwOxsASQikgrrdsOFT8KcTa6cuzAP/vQxfFx3+LFeNvjc0dEDxXNHw4jesL4++Dm/Xugm5gPcdRJcfYy/9fUsdlnX5lZYEGFv8HTyJsufUhV9+NmgFJXf1wY+f1hPf8PWeha7CgKL+3vzhAb18UhVZtuv1bvc2qt6B9s5ju7nLpjvbYKXVvo/1/YDbkp9z6JgFYhXebAzya/H+/w+Je5i1KRANt7L1i8NlN6P7x/fRZRkeBPwH/qgfbvEW+vcoMpjB0P/GGX8IpJ6foL6WmPMud4DY8x5wI5k3zjQI38z8DKwHHjSWrvUGHN3yPs9DJQbY1YB3wAO2/ZOpKtYvQv2H3K/NAzonvnMhohIKry/Bc5/0v2fdnQ5PHcJXDLeBVn/M7f9sU0t8EIgwDovQum9pyAPbp7q7v98Ljy4AO56yz2+++T2W6j5cVygKiAbffVeP/2pVdGPaJGzYgAAIABJREFUa8vUJ1l+7/0c6TjALpqOJfjNrfCPje7+Z+LcM6lvhre0Cx2SF8rL1s+OowR/eUg/vXdBpF+Kfj57Qb33894rwfd2wfFK78dnoPTec+UkVzHwxlqY+Ri8Huij90rvNfVeJDv8BPVfBv7NGLPeGLMB+DZwYyre3Fr7krV2tLV2pLX2h4HnvmutfT5wv8Fa+wVr7Shr7TRr7ZpUvK9IZ+QNx/GG5WRjsJCISDJeWwOX/dEFKycNhz98wWX+vjoVivLdXuGh2fq/r3Nl4aPLXdAUy/lHuzLkT3bDD/7unvv+yf4z9KG8/erfy3BQv22/G35WUuD2c49mQCAjun2/mz2QqNrAz5H+cQT1p1S5su8Pt7sLNIu3uSz3iN7RB+2F09ZOlqFMfWg/fajPj4buhTB/S/iqkXDaSu9Duj/7pjhT752vra/eC+pTNCQvHp8aDC9f4b43aw/ANX+Cb70WrC45Y2Tm1iIiQTGDemvtamvtdNxe8uOstcdba1fF+jwRSS2v3O6YwA/1TP8SJCKSrDvfgsYWuGICPHIulBW754eUuSypBX72XvD4ZwNZ4PNG+ysLD83WA9xzKlyVQEAPMDWQqV+wxVUMZMqba92tNxA1mpIC6F3iJtHvSuJngVf2Hk+mvqTATcoHN8jw7wmW3gP0KnFb6e1pzMyfdejk+1Ddi4IVCKFtBdF4k+9DBwOmqpKubfp9YJu8yYGL+ou2uYs4S1O0nV28KnvC7Avhjs+42Qq/X+r+7kb28be7hIikXsyg3hjzdWNMT2A/8FNjzAJjzIz0L01EQnnb2Xk/1FV+LyKdya6Dbjp9aQF8/xQXgIf66lQXILy4Ej7aAfub4NVAfd65MUrvQ100Fr51PDz0ebhiYuLrLe8G1X3dRYjF22Ifnyptpfc+h421TcBPoq8+kfJ7CO5G8NyKYD99vKX34PrBU5Xd9iNSUA/B6oh4M/XjQoP6FH0tuwJb4nl/NgN7uEF1e5tcG8vGve7iSjYC6TwD1x8LL1zmevohdouMiKRPjGvAAFxjrf1vY8xMYAAwC/g18EpaVyYibRqa3S+5eQYmBsrsVH4vIp3JUi+j2T/8BPpBPeCyCfCbRfDTOW7v8IPNbvBWPJO9C/LcBYJUOK7CTeafuwlqOkzef32NG8JnjNsqr1uhu2DRp9RVC4xLIHt6qCWY8T65yt/nDOwOK+pcX/34BDO2XlBfHmdQ/+lKV7L/yW73kWfcc4no182Vc+84ENw2LR2aW2FtYPvEcMHwiMBuA2sjbLEYqqEZ1uxyX/fo8uDzqdiiryWk+qJPafD5Ywa6bQMf+9A9HtvP344O6TK6HJ69xFUsTMxgG4CItOfnvwGv4O1zwK+ttYsIv3+8iKTJ0lr3i0h1X1ceCMFfGpSpF5HOYEnIpO5Ibqpx2fq/rHZb00Hkvekzweurn9thw92t++DWV1ymdMMeF1R/sNVt6fXiSrj2edjbGP/7zdvstgar7gtDffalD0zBBPwdCfTUgwsmPz86+HjSAFdKn4hM7VW/rt61K1SUuQsxHVV5QX197DkFK3dCi3UXAkpDzpWKr6W+0bWj9CpuX9UyJdCC503oz2Q/fSRF+a7fP5sXF0S6Oj///N43xryCC+pfNsaU0X7fehFJs0UdhuRB8Jcv9dSLSGfgZeqjBfUDe8CVgZL5lTtdn/XZ1elfWyTTAtn5+Ztd5hRcoPfNV1zQdfJw+NvV8JfL4ekvwmMXuP+nN++De96O//280vtTqvx/Tiom4NcmWH4P7UuuT0yg9N5TnqFt7aKV3oPbrq9fqcvCd9yLvaPlYfrpIeRrSeKie+ge9aG8uTqNgdkDme6nF5Hc5Ceovxa3ldxUa+0B3B71s9K6KhFpp62fflDwuVT80iAikileUB8rs/jlmuCAuBOHJRZopsrgMpcx39sU7J3+9UJ4e4Prc773DBje27UUfGqwW++Pz3CZy8eXBPvM/Yq3nx6Sz9Q3NrshZ/nGDd2L1zEDgyXrJw1PbA0QHP6a7p9pkbazC+Vl6z+JUYLfNvm+Q1Dfp8SVtO5ucFV2idgZpvQeXDVE6J70uZCpF5Hs8xPUW9zk+68FHncHEiyuEpFEhMvU9ylxP9h3Nbg+TBGRXLW/yfUeF+TB6AgZUs+A7nDDse7+lUkMukuV47wS/E1utsmP3nGPf3RacEu5UKPL4evT3P1vvebK6f3Y3eCqE0oKoGaw//W1ZeoTDOpD90LPS6C50hj41dnws5nBdoVEZOpCdaxMPcCIQMAfq69+WZjt7MCVofcpdb9AJ7orwf9n777D4yqv/IF/j7pc5CLJcu8FGxt3OqYbU4INhCQEQgmEQHryS7LJbtomuxvS26ZBCm0JGEIN1fRe3HDvvfduS5b0/v4499W9Gk25d+ZO03w/z6NnxjOj0ZWnaM57znvOnhiZ+o5luj0DAEqLWu/lJ6LC5Seo/wOA0wBc4/z7IIDfp+2IiKiVfcd0b195MTDC88e7uMgdc7PnWHaOjYjIj6W7NMAZ3h0o99Gi92unAu/fAkzNgZnXNlB9fQPw5ee07Pma0fGP7bZJ2jRs80Hgxz7L8G3PgZE1QGmx/+PrmWKmPtnO914jaoArTkj++wFPn5gsl98DbuXBmr2xb2OMLvIAbTP1QOrd/CNn1HvZqr0RNVoVQkTkJ6g/xRjzeQDHAMAYsxdagk9EGWDn04/u0faDHsfaEVE+8LOf3kvELSvPNpupf2UdsGw3MLAL8N2z4n9PSZGW4ZcWAfcv9FeGH/T/yEp1T/3OJJvkhS3ZiS7bD+v4uURN7QANxFc7gbqfoD5epn7bIV1071KuY+Yi1aS4SLE7TlB/ujNh4OTeba8josLkZ6TdcREphi6yQ0RqwUZ5RBkz3ym9H1fX9rqaDtp1mfvqiSiXtQSsebj/d0AXLbPfcVj3nf96mjuFJJ4TaoAvngz88l3g314Enr8O6BTn+/z2HIhU00H3b+86oluxgmT5Ac84uyjBYyYl0yivvhG49AFdmOheqeP0Tu8LnN5PA3OJ2E6w9RBw+LjeNlqwbA3ysad+iWdEY+TPAdz7T/bv8944Qf30Edr/IHLMIhEVLj+Z+t8CeAxADxH5bwBvAviftB4VEbWwmfqxPdtel6lyRSKiVCSbhc4FIsAUp6v7l09xR4r58blJOq9+00HgV+/Gv22y/0clRRrYGyRXtRVG+X0YqpMIgt/cqAG9QMvVn14J/McrwLn3Ah95ENgfsTXNT5M8wG2Ut2F/7EZ3i+wiTIzHK9Vu/rG63wP6nDxnYPxFIiIqLAmDemPM/wH4JoAfA9gKYIYx5uF0HxgRKfshJNqevVqW3xNRjmtoApbH2XucD747BbhvBvClk4N9X2kx8KNz9Pxzq2Lf7uhx3b9dLMk1PuvplH9vS2Jffa4F9buOapm8H886/6dfPRV49Xrgf84DLhumJfELd2iVhJef/fSAzpzv1Unn2W85GP02iapPUp1VvzdG93siomhiBvUi0t1+AdgB4B8AHgCw3bmMiDLA/mGPtt+RY+2IKChj/AdNYVi5R4OjgV2AzuWZ+7lh6loBTBkQvcw6kfE9gaoyzdbHChCX7tI94cO6u+P8gmgZa5fEvvpc2VPfoVR/92ONwJHjiW/f2AzMWqPnLx6qHeuvHQP8/hLgoau0k/+9C9wyecB/UA+42fpYzfIWO40NY2bqbaO8JP8+x8vUExFFipepnwNgtnO6E8AKACud83PSf2hE1NgMHGjQ0sKqKB+Ga5ipJ6KA/vcDYMTvgXnbMvPzbPCTj/vpw1BcBExwRtTN3hL9Nqn2HEhlVr0NHrOdqRfxzKr3kd1+d5M2qhvSzR3xZo2sBa4/SRdKvvequ4i1ykeTPGtwnH31+47pIk15MTA4Ril/SyPbNHS/JyKKFDOoN8YMMsYMBvA8gI8YY2qMMdUALgPwaKYOkKiQ7XP2A3ap0A+GkVq66zKoJyIf9h0D/jBbx7Il2uMdlnzeTx+WyU5Dsw9iBPWLEmR9E0mlA36ulN8DwSa6PLdaT6cNjV5B8bXTNMv9wRbg8eV6md899QAwMM6sevucHlmjPQ2iqU5hpJ0xDOqJKBg/jfImG2Oesf8wxjwL4Oz0HRIRWS1/1CuiX59qJoCICsu9C9zS5tfWu8FJOiXb1b09meyMxcvFTH1OBfU+M/XNBnjeCeovHhL9Nl3KgW+doef/+w1terf7qJb59+6c+FjidcD3s1CVyva4I8d14a2iRI+XiCgRP0H9LhH5jogMFJEBIvIfAHan+8CIyM3Ud42xUp/sXF8iKjzHGoG75+t5Ww7+p9np/ZnNxt3TXMiZ+rF1OrN+6S7gQH3r64436WhSABiVZCPBlkx9wKC+sVkXjwW5kRH2m6mfu1VHDPbtHH+x6KOjtKfBziPAV5/Xy4Z089cbIW5Qbysr4vzsVBrlcT89EQXlJ6i/BkAtdKzdY875a9J5UESk9jpBfbcYmfoaz8icTDa+IqLwGQO8tDZ922lmLtb3ipN6AP87TcuGn16pGcx0Wb9P54LXdcyNTHC2VJQAY+p07Nzcra2vW7VHJwQMSKGRYLKZ+j1H9Zi6VcYuI8+kGp9j7WzX+4tilN5bRaLTBwTAbOf/3c9+egDo30W/f9MBfXy8/GTqu1bo9++v14WbINj5noiCivsWLiLFAL5tjPmyMWa8MWaCMeYrxpg9GTo+ooLW8oc9RlBfUaJzahuatKEeEeWvV9YBn34S+P6r4d93YzNw51w9f9skoE8VcPlwoMkAf5kX/s+zuJ/eFWtffRj/Ry0j7QLuqc+l0nvA32x3Y9zxgBcPTXyfY+q0K77lZz89AJQVA306a7WJd+Hr6HFgtTN+cEScyooicbfO7Tnm72dazNQTUVBxg3pjTBOAiRk6FiKK0JKpj/OHvdpnZoOIcts7m/T0rY3hV948sxLYeEDHyk1z9iDf5vx1f2hx+t4/Ut0r3p7YoD5yX30YQX23Ci3vP1CvQadfLUF9jgSPfsrvF+3UzvO1HYCJvfzd79dP08w5AAyv9n88g6M0y7PjB4f6GD+Y7L56+7c/F7ZEEFF+8FNsNU9EnhSRT4nIlfYr7UdGRAnL7wGOtSNqL+yIub3Hou/jTZYxwB+dQbS3TnQnaYyoAc4bqHvt7/kwvJ/nZbu6M1MPTHIC0HnbgPpG9/IwFj5E3BL8HQFK8HflyDg7y89Iu5bS+yGaDfejWyXw50uBW8YD5w70fzwts+o9r8cgizDdk+yAvztBk1wiokh+gvru0MZ45wH4iPN1WToPiohUovJ7gEE9UXtwvAlYuMP995ytsW8b1BsbtFldbQfgqpGtr7t9kp7e4+mKHxZjWH7v1a1S56nXN2m2GQi3kaBtlrctSFDv/N2ozZGg3ma24wXBQUrvvU7tC3x3ClBa7P97bLO8dXvdy4I8p5P9+7zHuT0z9UTkV8Kg3hhzU5SvT2fi4IgKXUv3+zhBfSoddokoNyzbrRlzK9bos2T80elwf9O4tuXCk3trJ/x9x4AHF/m7v/nbgP0+9ghvP6zvS1XlQL+qYMfcXkXuq9+wHzjYoEF1j46p3bfN1AfZV2+DzepcCeqdv2exguAVu3U/e9cK4JQ+6T+eQdEy9bb6xEdlRbKZ+j0svyeigBIG9SLSV0QeE5EdIrJdRP4pIn0zcXBEhc7Pvjpm6ony33yn9N4GEWFl6hdsB97eBHQsBa47qe31Iu7e+r/MS9yle/FOYPpDwI1PJt73b4OfUbX+RogVgsh99WFWMtjnzvub/X9PrjXK8wbBzVGeX885s+kvHBQs456slky9E9S3Gj/o4zFrWaQIGtTb8nsG9UTkk5/y+78DeBJAbwB9ADzlXEZEabbHR/m937m+RPmuqVm7uLdHdj/9tWOA8mJg5R5/2fBEbGf7T44BusQYl3bhYJ3dvfkg8MyqBMfpLDbM3Qq8tj7+bRex9L6NSZ6gvtkEy/omMn2Enj65vHXVRzy5FtSXFWtlR5OJ/vx/NsnS+2T1qdIGhFsPuV3v65t03F2s15NXso1s2f2eiILyE9TXGmP+boxpdL7uhs6qJ6I0aym/j/OHvZZBPRWI77wCjPszsDPgLO58YDP1p/QBxjgB3txtqd/vwu16etUJsW9TJMDHTtTzibK8qzx7i3/9Xvxsvc1Cj2bn+xb9qrRMfu8xDRDDXPgYUQOc1EPHmz6XYHHG2plje+oBt1leZHZ70wHtP9CxFDijf2aOpaRIA3hAs/VBKyv89AiIhnPqiSgoP0H9LhG5TkSKna/roI3ziCiNjHGD+riZeu6ppwLQbICnVuj+YxsItRf7nQCvvBg4oQaYGGP0WVDGaIYR0IxjPKOcedsr98S/3UrPX/9524A3N0S/XbNxO9+PZhqghUjrEnzbJC+s/yO7ODNzib/b51qmHog9Bs5WhpzVP/EouTDZEvy1+zyVFX6D+jjl9zsPAx/GWLhjpp6IgvIT1H8awMcAbAOwFcBHncuIKI0ONGgJYqcyLUmMpSbJObhE+WTlbg3ogfaXqZ/vZNNH99DXup29neq++gP1wNFGzWx2Lot/Wzu7e/nu+Nl3G/RfM1pPY2Xr/zxHy/lrKt1Z36RsCf7TKzVTXlXmZoNTdflwXRx6e6NmtuNpNm4GOZeCx1gL1TaoP2dgRg+nZaxdSpn6KH+fP/Mv7U+xbFfry4836Wu3SIAuHGlHRD756X6/wRhzuTGm1hjTwxgzwxiTYCcdEaXKlt/F63wPsFEeFQZvgBtkDnc+sPvUx/fUUxvUz9+WWg8Bm6Xv2Slxo7q6jrqXed8xYEeM95L99drRvqIE+Pcz9b1p9lZtxOf1wWbgZ2/r+Z9eqCXM5LKZ+jecKocwGwl2qdD57QbAP5fGv+3+Y/r8qioHyjOY+U4k2t+0403AWxv1/JQMld5bLR3w93qCep9bSmpiLFCs2K2VLgbAqxGfqPd6KvSK2GCSiHzin1qiHLXXR+k9oB/ISoo0s1/vszkSUb7xBvU729kClt07b4P6mg7AwC6aZV+6K/b3JWKD+l6dEt9WBBjhZOtXxthgt8rJ0g/upu87t4zXf//2Pfc2e44CX3hOq4xumwicPyi5Y2/PTqjRCiwr7EaCHxulpzOXRO8gb9nXUU0OZemB6M3l5m4DDjUAQ7sn3koStkFOpckbG93xg3U+xw/av88HI/4+P77MPf9uxKIYO98TUTIY1BPlqL0+Ot8DupLPffXU3rXXTL0xbpM8G9QD4eyr3xYgqAfcoH55jKDeBvvDuuvpDWM1aHl3swYmzQb42gv6cyf2Ar5+WvLH3p6VFAETPI91GJ3vvU7vB/TprOX3kQGjVy7upwc8e+o9f89eW6enZ2c4Sw+4mXr7egqyCCPSdlZ9swGeWO7e5oMtrStycnFLBBHlPgb1RDmqpfO9jz11sRoL5StjgIeXuJlBKmx7jmqTKqs9ZerX7tOy9toOQO/O7uW2BH9uCvvqbRDSs3P821nDEgX1zuvRBvXebP1v3gPunAO8sk7fs343LTNzxPOV3VcPhJ+pLy4Crhqp5+M1zMvVoD5a+f1rzlaFswdm/HDQs5P2KbCCLsJELrrP3gJsOgj07qQVOYcagIU73NvvYed7IkpCwqBeROpE5K8i8qzz71EicnP6D42osAUpwbPjiNpLsDN7C/D1WcD3X832kVAusIGtLXltT5l6m6Wf0Kv1vupJITTLC1J+D7iZ+hUJyu9tUA8AN47TRm9vbwJ+4uyj/+WFmS+Rzjd2X315MTAkDY0Er3ZK8J9dpU3XosnVoD4yCN55WCcplBfryMdMKxI3Ww8EX4SJ/H1s6f30EVpVAbSuqGDneyJKhp9M/d0Angdg15VXAPhKug6IiJTfPfVA+yu/tyPLEnVvpsJgA9uLhujpjsPxO7TnE7ufflxd68uHVWvH+s0Hga0Hk7vvbZ5GeX4Md4L1FTE64LcE9dXuZV3KgZvG6flmA9w6ATh/cHLHW0gm9gLOHqCLIumoaOjfBTi1L3CsUbvsR2PHrOVcUB9RefaG0yDv1L6ZHWXnNdAT1AcdP9jd8/e5oQn4l/N4zDhBfycAeMcT1O/lnnoiSoKfoL7GGDMTQDMAGGMaATSl9aiIyC2/9/GHvb2NtbOZwlyqPNh1JH7TKUofm6k/e4B+qD/aCBw+nt1jCkvLfvperS8vEs3eA8ln64Nm6qs7aNO0w8d1McHrcIOWDJcWAQMixq/dPB7oVwWc2Q/45unJHWuhKS8B7p2hUwTSxTbMe2hx9Ottpr4214L6iNnuLfvpB2TlcAC4Yxk7lwH9Ao4f9P59fnWdbrcZWaMNE21Q/8EW7fAPuIvzDOqJKAg/Qf1hEamGTt6AiJwKYH8qP1REuovILBFZ6Zy2KT4TkXEi8o6ILBaRBSLy8VR+JlG+CZSpb2fl93ZP7+HjGkxk2+KdwKS7gKsebj8LJ/nieJM7x31CL6BHFkrwjQFufxq49V/hVggcc7rbFwlwUpR9uqnOq9/mBOZ+M/VA63n1Xqv36umgbm1H1HWpAN64Ebj/Cu6jzyUXD9Uu+/O2uf0QvHK1/L6rM8rtQL2+RuzovylZDOptpn5UbfAxc95GeY85pfczRuhpXUfdfnHkOLDA2Vdv//YzqCeiIPwE9V8D8CSAISLyFoB7AXwxxZ/7LQAvGWOGAXjJ+XekIwCuN8acCGAagF+LSNcotyNql1r21PsI6mPNws1HxrTe05sLCxULtuuq5tytwIyZbOCXSUt36Qf7wV31Q242+kd8sAV4ZhXw/GpgY4hbQhbu0K7XI6qBjmVtr08lqD/UoGMuy4v9LQxaI2r0NHKsXWSTvEgi4c1ap3B0KAUuG6bnH4sys94G9dU5FtR7J7q8sUH/rvXpDAxNQ+8Bv6YN1e0/n58c/Hvtosm6fcBLawEBcPkI9/rTbAm+s83ALhxzTz0RBZEwqDfGzAVwNoDTAXwWwInGmAUp/tzpAO5xzt8DYEaUn7vCGLPSOb8FwA4AIfeIJcpdyZTf7/IZ6BzP4Q002w7pTF9rZw40RdviZDxLioAN+4ErZ7beA0npYwNaG+BmI1PvLV/2dqlO1Ty7n75n9OvH9dQAZ/FO4GjA7Qbe/fRBgu1YY+0ix9lRfvjIcD2dtbbtdblafg+4Ae0/ncWIswdkd9GoSzlw52XJbQGwGfdZa4D6JuCUvq0nXdig/t3Nesru90SUDD/d768GUGmMWQwNvh8SkQkp/tw6Y8xWAHBO4w4IEZGTAZQBWJ3izyXKG8mU3/spDX9lHTDmT8CMh1KbgZ0ukcHEjhzI1NtGZf9xJjB1sO6J/NRj7gfOZGw+ALy5IZzja8/sfno7t70lU5+hoP5gfetGYwu2h3ff0ebTe3Uq0723jc3AhwF/btAZ9ZYN2tsE9Qky9ZSbTu6j+8BX7NYFScuY3C2/B9y/aS85ixHZLL1PlV2gOO7Mor9iROvrbUf/2Vu0kd6eY62/j4jIDz/l9981xhwUkTMBXATNrP8x0TeJyIsisijK1/QgBygivQDcB+AmY0xzjNvcKiKzRWT2zp07g9w9Uc6yHXD9BPU20NmVoPx+yU7g889oo7F523SP+O1Pt/6wl21tgvocyNTbpmGDuwF/uhS4eZx+QPvaC8A9HyZ3n198Drj2MQb2idhM/QQn8M10pv6pFfp66VCq/w4zqJ/n/G6xgnrArVB4Z5O+fh9bBvzkLeCWp4C/zI39fUE731t2T/2qPUCT5y8ug/r8VFbsZpdfXONefrBBs8YdSt3ndi6xAW1DE1AswBn9sns8qfAG52XFwMXDWl9f21FfV0cbdaEvyN9+IiLLT1BvC3UvBfBHY8wT0Kx5XMaYC4wxo6N8PQFguxOs26A9akGjiFQBeBrAd4wx78b5WXcaYyYZYybV1rJCn/Lf0eP6gau82N8HrpaROXE6tG87BNz0pDafu3w48KWTtZP4M6uA8+8D/uuN2POMM8mW+dqZ5Lmwp952Ee/dGSguAr53NvCds/Syv84Lfn97j7oZ6GQXBQrBtkO6oNK5zB2j1iPDzwtbev+lk/V00Y5wmuVtPwRsOaS/29A4gbIN6n/9HnDxA8BXngf+MFtLef/nTQ16ogna+d6qKgd6d9L3H7vYd6xRewkUS+vRXpQfLnBGDL7oKcHfncNZeqD1cU3spc/LfOXtWXD+IC3lj2S74L+wRheMO5XphAQiIr/8BPWbReTPAD4G4BkRKff5ffE8CeAG5/wNAJ6IvIGIlAF4DMC9xpiHU/x5eaPZtJ/5y5Q8u6eua4W/fYRlxfpBockA+4+1vf5wA/DpJzVImtwb+PmFwP87DXj1euCqkRoY3DUX+MKz4f4eybCZepuZyfaeemPcPfXefZDXjtHTrYeCv2bf2uiME4F+0N4cYvO19mSOJ5NtO07bqpRMZOqX79LO+53LgBvH6s8+0ACsD6Gyxe6nP6kufjftM/sDVWV6m8FdgWlDgK+coosbTUabb0XTkqnvHP36eIZF7Ktfs1f/Ng3sykAjH507UBdk3tvsLtzmcuk90Dq7nc+l94C+f5Q6n5pnjIh+G7uv/llnqw873xNRUH6C848BeB7ANGPMPgDdAXwjxZ97B4ALRWQlgAudf0NEJonIXzw/dwqAG0VkvvM1LsWfm9PW7QNG/gE4917g7/O1ezEVpiD76a2aGF3Bm5q11HvxTmBQV+Cuy9wP5r06A7+cCvzrE7ow8Pp6t/QvG5oNsMIp8z2zv55mu/x+7zHNWlaVafbE6lCqCykNTcGnDtgRTeXF+jv/36Lwjrc9iWySB2S2/N5m6aePACpLNQAHwinBf9tptDixV/zb1XQAPvgMsPRzwCs3AH++DPjqqcAYpxNNtFFlQPKZesBtlmdfi/ZnxKsooNzVtQKY1Ft7M7y2Xi+zfydqcjR49Ga3z8nzoF5Eu91P6qULLNGc6uyr3+QsIPuZekNE5OWn+/0RaHn8mc5Zifl8AAAgAElEQVRFjQBWxv6OxIwxu40x5xtjhjmne5zLZxtjbnHO32+MKTXGjPN8zU/l5+a6NzdomePafcAPXgNO+Svwn6/FzsRQ9qWri3yQzvdWdZSxdk3NwA9f12ZDXSuAv18evaPumDrN4Bu4AWc2bNyvr4G6ju7e3WyX39ssfa8oGU+bubeN9Pwwxt1H/29n6OlDi4D6xuSPsb2aGyWor81Q+X19I/CoM1P64yfqqQ2kF6TYAd8Y4GWnFPqcgYlvX1GiX142wI41XjGUoN7J1DOoz3/nD9JTu68+1zP19riqK4ET47ZSzg+/nAr882OxK12qO7ivO4CZeiIKzk/3++8D+DcA33YuKgVwfzoPqlCtcYL3i4YAJ/fWTP3f5gPn3APc8WZ2j43aemYlMPR/9TRsNlMfZLXefgj6zXvAJx8FzrobGP574O4PNQt/12XAoDhzfm0zpdfXJ3XIobCZweHV2ZlHHk284MhetuWQ//tbt0+zMV0rtKT7hGptcPgcZ3u0cqxR968LWo98q6nUUvTdR9M7mvHFtfo6HFnjBvMnOaepjrVbtVf3qHevBMbVJXcfdmZ3rKA+2UZ5QNuxdhxnl//svvpX1mnGPteD+km9gCHdgFvGx9+e0p7YEnyAne+JKDg/5fdXALgcwGGgZWZ8Erv0KJG1e/X0ihOAh68Gnr4GuHqUNub64xx3/BHlhn85wfwDaSid3pNE91ubNX5nk+7Z3rBfP7z16Qz85iIdbRSPDepf25C9vg7Ld+np8OrWY/qaos69yIxo++mtlqA+QKbeVkKc2U9f29eP1X/ftyD5Y2yPFu7QhlEjqoHOnsZSxUXRq1LCZkvvP36i29ditBOAL9oRuyGlH3ZM1zkD9PdJxjBPl/pIxxr1PaSkKLmgbWh3XUxZs1e3l7Dzff4b0k17Muyv120tuR7Ud6sEXr4e+NzkbB9J5pzqCeo5o56IgvLT8qbBGGNExACAiHRM8zEVrDVOUD/EycCM7qENzWoqNaj/4evAP6/21ziN2rpzrq5i3Tw+nP9Du8jy7iadZd05xO68yZTf3zoB6FKh+7z7VQH9uwB9q9qW7cYyolrL3rcfBpbtAkZmYZCEzQyOqNbqgu6VGpzsOeqWXWdavPJ7e9nWAJn6Nzfqqe0ZMGOEdjH/YAuwdGd2/t9zUbT99FZtR63g2HE4uUx0IpsPaMVKWXHrxlZ1Hd3XyNp97nt1UK84Qf15g5I/RvuzV+/VRS/v4sD2Q+7xJpPlrCzV94/1+7UEf90+DfKT/X0pN5w/GFgzVycn2PGnuRrUF6JT++jrzICZeiIKzk+OYKbT/b6riHwGwIsA7krvYRWehiYtxxTohymvz0/WwH7OVjc7TMFs2A/89xvAj94A7ngr9Uz0jsPu7PLjzcDrIe9DT6ZRXl0nHbt1w1gNFoZ29x/QA7rQYbsMv5alEnybEbTlv7lQgm+D+j4h7KlvbAbedoL6s5ygvmOZTiAAgPsXJn+c7Y3dTz8hSlDfwz4v0tQs75Gl+sH6oiFtM2a2Wd7CJJvl7a/XBZxiSa2rd1W5Bu31Te57kbXN+X9JZcHDvgZfWK1d9vt10WCf8tcFziLSS2vcTH0tg/qc0a1St/sA3FNPRMH5aZT3cwCPAPgngBEAvmeM+V26D6zQbDygH5z6RMmsdi4Hvn66nr/jTS2tjGb+Nv1jTW15g9Q/zdGZz6mI3Aphy2nDsjeJ8vsw2C7Dr2YhqD/epFlHwC3zzYWg3s+eer+Z+g+3AwcbdApB3yr38uuc8XiPLtOqD3JfY9GCelu1sSMNz4vjTcDMJXr+46PaXp9qs7w31ut7/aTe0edVB2Eb19k975ZdZEqmSZ5ly/vtQvJQZunznn3OrdkHLNmplzFTn1s+PV63SdhKLiIiv+IG9SJSLCIvGmNmGWO+YYz5ujFmVqYOrpBElt5H+tgoXcHddBD467y2189cDFw5E/j0U8AfPkjfceaaTQd0/nqiZnW2+du0IVqO+uv3gN+n8P9kA46Lhujpy2vD3ffdkqnP8Gr9mf31/2f2Fp1tn0nr9mvFSt8qzV4Dnk7nWRxrtzXOnvqgmXrb9T7yA9vwat1PeeS423G9kB09riXupUXAgC5tr0/nWLsfvq7vK/2qgDOifLC2QX2ymfqXQii9t+zi18q9rS9PpUmeZTP1LQtt1bFvS/mhpMidtnDUSQ5UM6jPKVeP0tGV0f7eEBHFEzeoN8Y0ATgiIlE+VlGYbFA/qGv064uLgO+cped//4H7YdYY/fc3XtTsDwD85O3ogX97c7gBuOUp/ZD8kzgl9Q1N2jgOAL5/NvCLqbrN4acp/D/Nc4L6q0YCA7toEG7LhcOQrUx91wpgbJ1uKXhnU2Z/9grPfnorkzPJo2lqjl/KbC/bdtjfoo5dXDorSrB4/Ul6eu+C7DUqzBUbD+hpn87RG8nZCo6wnxf3LdD//7Ji4DfTou9Ht0H94p3BF/Kamt0qmPMGpnSoAGKPtUtlnJ01IiKIZ5O89uHCwe75smKgqix7x0JEROHxs6f+GICFIvJXEfmt/Ur3gRWatc44u3gjx87sr3viDh8HfvGOdl/+wWsanAqAH54D3HG+3vaHrwP3t+Nu2s0G+OoLwFKnW/q6/cCSXdFvO3er/p8Nr9bV7ytPaP3/dPeHwYKopma39HZcT20+BOgIrLAks6c+LNkqwbdB/XBP8JDt8vtdR3QffHVl9P4EFSW699E7IiqWg/W6GFQsrUcXWVMH6yLGqj2657qQ2aC+X4zl5B5pmFX/1kbg+6/q+TvOj96gD9Dqkd6d9D3FjiH168Pt2vSxb1U4QXLCoD6FbN/gbprZtRjUtw9nD3Af15pKNt4lImov/AT1TwP4LoDXAczxfFGIWsrvY2Tqrf84S/8gP7QYuOFxdwb57y/RBmnXjAb+8xzntq9oWX579PN3gOdXa7MoG4Q+HaME3+6nn+LJjn5itC6CAPpB/tNPajM9P1bvBQ416Af7uo5u86Ewg/p9WSq/B9zmXZmeV2873w+vcS/LdlC/xUdw5HdW/bubtZpmbE993kYqLdbtIQAwt8DHV7YE9VXRr+8RcqZ+3T7g9qf18bltotu4MJYxSTbLe3mdnp43MJxgyjur3rswGUb5fVkxMNDz92gog/p2oaocOLm3nud+eiKi9sNPUP8IgPuNMfcYY+4BcD+Ah9N7WIWnpfw+QTOiwd20TNdAO653KgPumQ5cOsy9zY1jNfgHgG++CDzezvboPr5MtxwUC/CHi4HPTNDLn1kZPeNuM87nRHSavmEs8NMLgM5l+mH7gvuAX78buxGhZUvvx/XU08m9tYRx1R5gfcDMXTQNTdpMrUiiB3/pNrZOmymt36/BTqZEK7/P9p76lhn1cYIjv/vq7Xz6KXEaII2IM3u8kGx0FthiBvUhPi8O1AM3P6Vd6c8fBHzz9MTf07KvPmCzPLuf/vwQ9tMDGpR1rdD3C+8CRxjl94D7fOzdSf/WUPtwgVNd1oMDiomI2g0/Qf1LALz5wkroWDsKycF6zUSWF/trjvLlU3SvaV1HYOZHgdP7tb3NrROA/3eaBv9fe8FdNMh387fpQgUAfO9s4KwB2mCse6VuYVgWUYK/47B2+a0oASb3aXt/Hz8RePl6LcmvbwJ+9R5w4f3AK+viHwPgBvWlxW7zoTCy9S0z6iuSmzGdquIiN1ufqRL8Y426gFAkrZtFZntP/ZY4TfIsvx3wYzXJ84pVTl1oEpXf13rK71PpP9DUDHzxWf3/HlEN/HZa9D38kexYuwVRMvXHGoHlUbYCbTuk70WVJfqeFQYRN1tvx0Eeb9LFjiJJfVyZDerZJK99+cSJOnHj9knZPhIiIgqLn6C+whjT8nHVOc+irRC17Kfv6i+I61oBvPgp4K2bgBNrY9/uSycDlwzVktIwS8PD9vRK4NpH3WA2lh2Hgc/8S4Pv68YANziNxUqK3LLlp1e1/h6bHT21T+yZ7T06Ar+6SBdIhldrGf5NT0T/wA64Qf34nu5lNvM2K4SRgvuyuJ/estnkTJXgr9mrz9OBXVo/Tj2yXH7fMhosTlBvA/4tcTL1Ww7qto1OZcC4uti3a+lmvqewm+UlKr/vUKr/l/VNmmlP1nOrdeGqWwXwl4/4z0bHapZ3sB64YiYw9f908dFb9WMXCs/oF/u9KBlDPc8ZQN8nDTSgLy1O7b6nDtaqHW8lGOW/jmXAf5+nI+6IiKh98BPUHxaRCfYfIjIRwNH0HVLh8Vt679Wh1N8HtqlOsGuzhGF4dJk75igM9y8A3tyYOIB8Yrl+YJ3cG/jB2a33pNoPnZEl+HY/vc2kx3NKH+CZa4CrR+qH4vuiNBo8chxYtltL/+0HewA4d6Be9sEWLeNNxV5Ppj5bznYy9W9vBOoTbEcIQ0uTvIiMYFW57u092KBjzjLN7pOPV37vJ1NvF5dO6xv/dVvdQatODjXoSLd0+ft84K656bv/VG1KUH4PePbVp7Dg88BCPf3yKUD/ADNeulcCfTvrWDD7XtjQBHz2aXf+90OLgasednt1vBziKDuvYRHVHXZaQ6ql9wAwshZYcJtWNBEREVHu8hPUfwXAwyLyhoi8AeAhAF9I72EVFpupH5ygSV4yznRK89/bnHivuB/ztgFffR74zFPhZRJtFnZbgiBms5MJnTqkbWBkS/BX73UbrjU1uwsFUyL208dSWgx8brKef2qFZt68Fu7QzvsjaoDKUvfyLhVa3t/YDLy2zt/PisWOs+uehSZ5Vl0n4IRqDVoy0Yk92n56QBdustksL6xMvZ/Se6ulnHp34tsmY+9RnZrxX2+kvgCVDvuPAQcadOEy3mugNsWtGev36WJiebFuvwlqjKcEv9kAX5+lHfRrOwB3XaaLBIt2AJf9A3hulbuwc+7A5I43lpYtG87iQhhN8oiIiCi/JAzqjTEfADgBwO0APgdgpDGG3e9DZDP1gwNk6v2q7QiMrNGAfk4Ic9RtNmj13vCCvZagPsGe5G1xmj+VFAEX2RJ8pwv+oh2a9e5bFWzBZHA3Ldc/2qiBvZdtkjc+Sgl1WF3wcyFTDwBnD9TT10Os8ohleYxMPZDdffV+MvU9E2TqjQHe2aTnz4zS/yLSsIggLWzvbXbPbzqQnp+Rig2e0vt4HeJTfV78Y5GeXjZcF+WCOsmp1FmwA/jxm1pJ1LEUuHu6Ljz+6xp9T9hfrxn8o43AqBp/fVOCaAnqndeQXYhiUE9ERFQ4/GTqAWAygJMAjAdwjYhcn75DKjxrfMyoT4XNDoZRgu8NAuyH4lTUN7p7yP0G9T1jdOy9dKiePu2U4L/m/L5n9w8+Purjo/U08neMbJLnZYP6V9Zps6pk2Ux9NvfUA24JfqqVB37EC+qzlalv8DQcq0sQ1As0uGxsbnv9jiN67FXlrZsAxtKyRzpNmXpvUL/R5xjHTErU+d5K5XnR0AQ8vETPf3J08O8H3O03jywB7pyrC4t/vhQY7VzepRy46yPAN05ze6WcG3LpPaCLBJUlwK6j+l4aVud7IiIiyh8Jg3oRuQ/AzwGcCQ3uJwNgz9SQGAOstZn6NJTfA8BZtulZCEH9Rk9Q//RKLZVNxS7PB/JEQb39sNozRqbrtH4aCK/eq+XcNhi1GecgLhmqQdiCHdoMy4oX1A/qpkHbgXpgdgpVEXtzoFEeAEzqpaXJy3ant0z7yHF9XpUWabPISC3BW4Yz9dudhmN1HTVgi6WsWEeLNZvoWePFztizUbX+FpfSnal/d5N7fmMOZuoTdb63UsnUz1qjQfDwamBir+DfD7jl94edXg8/u1CncXgVCfCFk4H7r9B96TeOTe5nxVMkrZvlsfyeiIio8PjJ1E8CcIYx5nPGmC86X19K94EVih2H9UNhtwqgW5r2UJ/cWwOPxTuAPSm2OLSZ+qpy7Tz9+PLU7m+nz6C+sdkN6upiZOq9JfgPLtZS+ZIi4PQkxkdVlLj7bB90svXbD+nCQucy90N0pDC64LcE9VncUw8A5SW6dQMAFsaYBBAGm5Ee3C16E7mWvdMZztRv8bGf3oq3r942Tjuxxt/PjexmHqb9x4ClnnFruVh+n6jzvZXKrHrbIO+To4NX8VhdK9zKi2+fEX9f/hn9gJ9ekL654N5RiC2Z+pDL/ImIiCh3+QnqFwGIkpekMKS79B7Qhm6TemnW8a2Nqd2XLY29baKePrgotYZ53qB++2HNdkaz64iOPKup1AWKWC5xuuDf86HefmIvoHN5csf2Cafj8+PLtPO63U8/ti726MFpzhaAf61oPeoqiFwpvwfcedwLd6TvZ9ggM7JJnpVK8JaKlhn1PjKe8TrgL3F+v1Fxxk969eyko9X2HAV2h7yQ8f4WfR8odp6/OZmpD1h+HzRTn2qDPK8/XqJN8T47MbX7SZV3Vn283iNERETUPvkJ6msALBGR50XkSfuV7gMrFGvSXHpv2RL8N1IowW9o0g71RaJlpN0qNGCJNc/dD2+g1tjcuhzfa1uC0nvr9L6aQWtyFgfO9tn1PpqRtTpT/EAD8Myq+KX31oSeOmt9+2EdB5eMXMnUA7qAAQAfpjFTbxcMRveIfn229tT76XxvxcvU2/L7E30G9SJukBZ2Cb4tvbevi5wM6oNm6gM+L2yfjI8k2SDPa0SNNsVLNtsfFm8fBjsKMV1VAURERJR7/AT1PwAwA8D/APiF54tC0DKjPt1BvfMh/s0NyWfWtx7UTHrPjkDHMuCqkXr5PxYnf1yRH8i3xyjB99v8qbQYmDrY/bffUXaxfMLTMG+eE9iOjxPUiwBXONm/R5cl9zP35cieesDN1KeycJPIfOe+Yy2W1GYrU++j871lA//ITP2hBmDdfq0uGRJjy0Y0w9LULM82yfvoKD3ddCC80ZRhMMbdEpBoT30ymfpWDfLGBD++XDXMqXKZvVUXR6srdQsRERERFQY/I+1ei/aViYMrBC0z6tNYfg9olrB7pc56tz8zqMgGVjbgfXI5cLghufuM/EAeayyYzdTH2k/vdalTgl9T6T87Gsvlw3VM1QdbgLlO87t4mXrADeqfW61N4ILKlUZ5gO4Z7lCqz5tYVRSpONYILNul1R+jYzxWLcFbDmfq7WJTZKbebi0Y1j3+tpFIkbPHw3CgXps+lhbprPTOZfr8TLXPRph2HNFeHd0qdAtCPN0qtWfG/nqdouGHbZA3olqratqLAV30cbXvN2ySR0REVFhiBvUi8qZzelBEDni+DopIDhZt5qd0zqj3KhJt1gQkX4JvM2h9nbLYYd11r/7h423nuftlM/U2gI3VLC/IPtEpA3SM1M+nxt777lfHMi3TBTTL17dKO53HM6ArMKGXfsB+bnWwn9ds3Ex9tufUA0BxkRtspyNbv2SnZhaHddf/62hsUL/rSPSeCxv2A+uSXKiKJ1Cm3rlN5PPXTk4IuriUjkz9B1v0/++kOl2oseXtudQsz+9+ekBf2zUBt2a0NMgbk/2S+TCVFAEDPdVe3E9PRERUWGIG9caYM53TzsaYKs9XZ2OMj49clMjxJg1IBK0/kKXLmU5Qn+y8epup7+vJXF7jZOsfTLIE32bq7cznWEF9kI7OdozUuQOTO6ZI13jmWMcrvfeyDbgeWxrsZx2o18Crc1n0TvDZkM5meXavvv0Z0ZSX6MzvxmZ3wcNqaAKunAnMeEiz/mGymfreKeypt53v/TbJs2w5dZiZelt6f2ofPbUVN7m0r97vODurR4ASfNsgr6LEraZpT4Z5tncwU09ERFRY/OyppzTZeEAbuvXpnJn9j7ZZ3jubNEAKKtpe10uHaQA6b5uWUQdlM2x25vO2GB/Og5Tfh21snTvabVyc4NPrsmFaDvvmRrdxlR+2830uZOmtk9LYLM/eZ6L/11j76udt0+fQ3mPhZrWPHtf7LCsGqhNUZgDalKxItJqgocm9PNlMfZ/O2p192yFd6AnDe06TvFOcEY92cS4ng3qfy8ZBmuW9vE5PLxqii0TtjXfMJjP1REREhYVBfRa1NMlLc+m91adKu+wfbHA7uQexMaL8HtBxedNH6HnbVdovY9wgzWbqt0bpHg54yu+zMHtZBPjRObq/3jYYS6RbJXDeIM26P7nc/8/Kpf301lhPs7ywm6p96BkTGE+spmjerSRLklhUisWW3vfs5G8LR0mRLjgZuM/V403ACmehYaTPGfVWcZHbWG91CNn6Qw1aaVEsOuYRcBfn8rX8HnAXe/xk6pc7j4Xfapt8M5SZeiIiooLFoD6LWmbUZ6D03jrTydYnU4K/KUYWzZanP7bMf8MqQBcX6pu0Ed0QZ2EjWlbbGM9IuyyNaZrcB/jdxcEy6Ml0wW+ZUZ8D4+ysAV2AqnLNhsbaHpGM/fX6Gigv1tFg8cTKyHqfx7bUPQxbA8yotyI74K/eq1n7AV2Azklkhltmj4dQgTB7i1YFjenhNqCzr+OczNQHLL/3MxnBBvXDq4MfVz5gUE9ERFS4GNRn0doMNcnzSnZe/bFGDbiLpe0HxtE9NBO5vx54ZZ3/+7TZtdoOQJ1zn1sPtc0G7z2mwX9VWexmarnovIFa5rtkJ7DcZxY5FzP1IsBJTiVFmCX4C537GlWbuDN8tA74+4+1Pp6lYWbqA3S+t3pHdMBfnOR+eivMffUt++n7upfZihubHc8FQcvvWzL1CcrvjXGrJka006B+SDftzwJkp6KJiIiIsodBfRZlqvO916l9NTCftw04GGCv7hZP07CSKM+aZLLSNuvao6MG7B1KtWP8wYjxeC1Z+jz7oFpeonvrAf//Ly1BfQ5l6gFPCX6IzfJsQJ6o9B6Ivqf+rY26vcFWeSzdGd72gK0BOt9bPSM64Kca1IeZqX/XCepP6eNeZgPnzQejTxXItMZmrZAQaE8BP3rE6LUQactB3YJQXZl4ekW+qijRyR8DuvhfFCEiIqL2gUF9FmWj/L6qXOesNxn3g74f0fbTe80YoXuPX17btkN5LPaDeG1HzQbb0vrIEu+tWS69T8UVI/X08eVAk4/mhLmYqQfcRoZhjrULEtT3iLJ32pbezzgBqKkEDjQAm2L0ZAgqqUx9RAf8JUk2ybPCytQfOa6PW5EAk3u7l3csA7pXahWMn/L1dNtyUN+X6jrpgpgfsXotRGrvpffWPdOBl69PXPlCRERE7QuD+iw51KAfRMuL/WelwnKaU4I7e4v/74m1n96q6wSc0Q843gw8vdLffdpMvf1gHpnptLYdan19PpnUC+jfRX+HdzYlvv0+u6c+x4L6ZJvlHWqIfV1LUO+jcVmPKPPI39iop2f1B0Y6gXNY++pbMvUBXpu9IraQJNv53hrYRatiNu7XbvzJmrtVs+An1rbd22874G/IgX31QUvvAf/d79t76b0lEr2SioiIiNo3/vnPEruffkBX7XSdSbb79Zyt/r/H7ruNlakHPCX4Pmez7/TsqQfcoH1rjKA+H8c0iQBXONMB7luQOCDek6Pl97066eO0vx7Y4GMP9rFG4JangPF3Rm/KuP2QPq6dy/xVqtRGBPUb9utXVbnu9x8VclC/OYlGed5M/eaDOoque2XyYxhLi4GBXbWjfiod8O1ikrf03sqlDvhBO98DrZ8X8bYQLC+QoJ6IiIgKE4P6LMlG6b01wQnqF2xvPVM7HlvWHK8r9bQhQGUJMHurv8DPNrey+6V7xcjUb83jTD2gY/DKi4HnVgN/nhv/trlafi/ijh1MtK/+UANwwxPArDX6/PrNe21vY7P0J9X5GxkXObrMNno8va8uio1yuueH0SzPGE/3+yCZek/3e2/pvfj4/WIZ5nQ0X7Un+fuwTfJO69v2ulzqgJ9Mpr68RJtRNja7kyOiKZTyeyIiIipMDOqzxGbqh2SwSZ7VtUJ/bn2T/8xmSxYtTpDTsQy4aIief8xHY7jITH1djKB+e54H9f27AL+6SM//+M342xNs+X2Q0XmZYkvw43XA33cMuPZR4N1NbgPE97doY0av+c59jPOxnx7Q/4/SIs1+H2t0g/opA/TUZuoXh5CpP9AAHD6ujRurAoyiq+2gx7jnqJa8e48rWXZM2cokg/pjjfp4CXQsYyRbeZMTmfqA4+ysRCX4Tc3uogiDeiIiImqPGNRnye2TgFnXAdeNyc7PD1qCv8nnB+4rncZwjy5NXGru7X4PJM7U52P5vXXpMODbZ+j5rz7fNsgF9P/Llt93z7HyeyBxs7ydh4GPP6IBe98q4J9XA9edpNf9eU7r2y7wZOr9KBK3a/mOw8Dbnv30gE6QKC/W5+n+AFMdorFZ+l6dgmXZi8RdmHppnZ7aCoJkpZqpX7lbqyWGdteMdqR+OTTWLplMPZC4Wd76/bqA2btTsEUaIiIionzBoD5Lyks0a9QnS6OHggT1R44Du45qR+UeCfYHn9FPP2Sv2x89cPXydr8H2mejPK/PTgSuGa0Bxi1PulsUmg3w3Cpg+kMamAhyM6i3mfpFO9p28t9yELj6EWDZbq0CeeSjWqFw41jNXj+3CljvbDlpNm62f5yPJnmWDd5eWquBe/8u+gVoczCbhV2WYgn+liRK7y278GQbs53YI7VjSTVTv9KpCLKLA5HsIl1OlN8nsacecBuNxuo7wNJ7IiIiau+yEtSLSHcRmSUiK53TmEXoIlIlIptF5H8zeYztnd1XP9dHUG+z9H06J97/XFIETHcaw8UrwW9sBnYf1QC22glgW4J6T8btUIPOrS8vzs2S9CBEgB+dA0zpr4skNz0JPLgIuPB+4LNPa6DbvRL4z3N05nSuqemgz4HDx1sHUEt2AlfMBNbu08z0zI+6+8vrOunIOQPgrnl62bp9Wkbfo2OwhRq7+GOfV2f1a319GM3yjAFeXa/nk6kM8X5PRQkwOMWeGUO66Wtk/X7//S+8VjkBbaygvo+nD0Cjj5GL6XKsUSt3SouCL96d4vQKiNaQEfB0vk+xaoKIiIgoV2UrU/8tAC8ZY4YBeMn5d9NSk80AABqvSURBVCw/AvBaRo6qgAztrqWoWw+5mclYNiWYUR/JdsF/akXsQGT3UQ30qivdEUw1HfT8nqP6IR9o3fk+lYZjuaK0GPj9JdqFe9Ue4N9e0tPenYAfnA28fRNww9hsH2VstlneQqdZ3strgY8+rI/T5N7Ag1e5ZfLWrRP09OEl+th+GHA/vWWrROz3n9m/9fUjnaAt2aC+oQn4+izgng81kL5kWPD78Gb3T6hOfbJFRYlm0xubdTEkKJvhHxojqK8o0f/Xxua2FTKZZCsFencO/n9mt2C8syn6+w073xMREVF7l62gfjqAe5zz9wCYEe1GIjIRQB2AFzJ0XAWjSIAJTulzohL8oHtdT6zVUte9x4DX1ke/zY6I0nt7THURXc7bS+m9V1U58PfLgQFdNIP68wuB124EbhoHVJZm++ji8zbLu28BcPNTmrmfPgL4vyuALlGqKYZXA+cO1IWa+xa07nwfRK1nsaBIdKuHl50HvySJ8vv9x4DrHwceWaoTHP7yET3moHp5gvpk59NHSmVfvf2eWJl6IDc64NvS+/4Bm+QB+t4wrLs+D6Nt+WH5PREREbV32Qrq64wxWwHAOW2z81REigD8AsA3Et2ZiNwqIrNFZPbOnSENqi4Afkvwg2bqRYArE8ysj+x8b9VFzKpvj0E9oL0UXrsBePFTwNWjtF9BPrCB+MzFwHde0f3xXzoZ+M1F2iciFputv+dD4H1nxFrQTL33uXJSj7YLCCc4mfqVu4HjAUrVN+zX7QPvbNKf8fBHgQsGBzs2yzvXPtXO95YNyBcFfGurb9TeFkUCDIozZSMXOuAn2yTPmuJk61+PWESsb9RJI4L4CxtERERE+SxtQb2IvCgii6J8Tfd5F58D8IwxZmOiGxpj7jTGTDLGTKqtDemTdAHw2yxvY8CgHgBmjNAP0i+tdUvpvSI731uRHfDzfUZ9PPm4ncCW3x9t1K0SP78Q+H+nJf5dTuur37v7qDt2LnCm3vNciSy9B7QCol+VNiJcE6NpWqRlu4AZD2mPgBOqgSc+7nb5T4Y3Ux9WUG/ny7+yNtj3rduniy79q+L3aMiFDvipBvVnOaMN34jYV79mL9BkgIFdc7NPBREREVEY0hbUG2MuMMaMjvL1BIDtItILAJzTHVHu4jQAXxCRdQB+DuB6EbkjXcdbiMb11Cze4p3RA29rUxIfuHt1BoZVa4AVrRt5rEx9zwLJ1OerLhXAqX20aeG9M7TKwA8RN1sPAIO6Ri/Vj8e7AHRWlKAe8DTL81mC/9O3daFhSn/gkatTn0ZhG88J3MqBVJ3aF+hQqr/T5gDZdLuffliCsvNcKr8POqPeOqWPVrss2A7sPepeztJ7IiIiKgTZKr9/EsANzvkbADwReQNjzLXGmP7GmIEAvg7gXmNMvIZ6FFCnMs1ONjbHnj0OJJepB9o2VfOymfraiEy9Dd63RwT1+Tyjvr154ErgvZvb7mlP5JJhQF8n6A2apQeAns5zpUOpu3Uk0qgAzfJ2HQFeXacVB7+6COgcwgzz7pXAl08G/v0sPc4wVJS4ixgvBcjW2/30Q+OU3gO5UX6/IcVMfYdSYFIvbb759ib38hVskkdEREQFIFtB/R0ALhSRlQAudP4NEZkkIn/J0jEVpAkJSvAP1gP7julIuciseiKjnaxp3KA+4j57RWTq23P5fb4qLkqulLmkyCnVB3DJ0ODf36cK+N4U4FdTY/cgsJn6pT4y9U8s19Lscwa07difiq+d1roqIQwXOnv8XwwQ1PvN1PfP8qz6+kZgU5Iz6r2mOCX43n317HxPREREhSAruwyNMbsBnB/l8tkAboly+d0A7k77gRWgib2B+xfGDuq9TfKC7gGPl6mP1v0ecLOx25ipb5euHAlcNjz5xoA3j49//UjPrHpj4j9n/7nUPaZcd+5AXQx5ZxNwuAHoWJb4e/xm6nt10m042w7pSLhMNW00BnhmFfDjN4EDDbrA170y+fs7qz9wx1u6r94+9iy/JyIiokKQrUw95YhJnky9MW2v3+TMsE8mgzaqVgORFbvb7tmPuafeKc/edkgzeLuPAsUSbiaVsiudQWPfzkBVmT5v7MJRNMt2aS+JqnLggkHpO56w1HQAxvfSoDuyGVw0jc3AGmeu/ZAEXd9LizWwNwC2HEz5UH2ZuxW46mHgc89ohcDwauBPl6bWPHJULVBdCWw+qL/74Qa979Ii7eFARERE1F4xqC9w/aqAmkpgz1FgfZTu17aBVdD99IBmE4d01wDD7m21YnW/986pt6X3PTpqyTdRIiL+muXZLP3lw+OP4ssldvHhxTWJb7thvy4A9OmsvTMSyVQH/GYDfGOWjhCcs1Xfe358HvDsJ4FJvVO77yJxpyK8vt7dfjCkmy5cEBEREbVXDJUKnIiW4APRS/BbOt8n2ZU6Wgn+4Qbg8HHdp985IuAoK9YP+k0GWOR8D/fTUxDeEvxoGpuBx5bp+XwovbdsUP/yOqCpOf5tW0rvfc5mt0H9pjRn6j/YAsxcoq/9z08GXr0B+OQY7bcQBjuv/o0Nnv30IU0hICIiIspVDOrJbZa3pe11qc6PjtYsb5en8320cts6J4ift01PuZ+egrAd8GM1y3tzg1aKDOoKTOiZueNK1fBqrZjZfRSYH2daBeBmqf0G9X0zlKmf5ywcXj0K+Obp4Uwc8LJTAt7Z5C4KskkeERERtXcM6gkTncBm7ra21yUzo95rjDO6zBvU74jR+d6yQfx853iYqacgEmXqvQ3yUtnDnWkibhf8lxKU4NtM/TC/mfoMdcC37zHj07SYUtdJg/gjx3W6AcCgnoiIiNo/BvWEMXXaTGrZLh1h57UpyRn11olOs7zlu7TxHeA2MIvcT2/ZIH4hy+8pCcO6azn3mr3aK8LrYD3w/Go9f8UJmT+2VLXsq08w2i5opr5lT30ag3pj3Oqb8b3S93Psvvr9znsZO98TERFRe8egnlBRAozuod2vX13vdsHff0xHTXUsBbpVJHffncqAwd2A483ACifQiDWj3rJBfH2TnrL8noKoKAHG1enz+fIH3TJsAHh6pT6vTu2b2kz0bDm5j/ahWL5bm+FF02yA1Xv1vN9MvV2025TGoH7rIV3QqypPbzd6u68eADqUJr8gSURERJQvGNQTAHdf/ReeBU75K/CV54G/zNPLkplR7zXaaZZng6udPjP1sf5NlMhvpmmTxo0HgCtnAg8t1ssfdRrkXZWHWXpAG0lOGaDnX4qRrd9yUMvPazsAXX0uxtV11GqdnUeAo8fDOdZI8zyl90Vp3PZwSh93bOLw7un9WURERES5gEE9AQBuGQ9MH6Gd57cf1u7gv31fr0s1o2mD+oVOc69EmfrIzDyDegqqbxXwyNXANaM1M//NF4HPPwO8t1kz+RcPzfYRJi/RaLugne8BHRnZ39lX/97m5I8tnnlp3k9vVZYCk52JHiy9JyIiokLAoJ4AAL07A7+dBsz+DPDCtcAPzgamDtaAfkaKWc3IsXY2U18bI1NfFxHE18W4HVE8FSXAHecDP7tAR6j9a6VePm1I+F3XM+ncgZp9fm9z2x4YQPD99NZHnfF+v3nf3YITJtv4clwGJg7Y3+Xcgen/WURERETZVpLtA6DcIqJznUfUADeNC+c+T3S6kS/bDTQ0BcvUV1cC5XyWUgo+diIwqha4/RndM37tmGwfUWq6VQKTegHvbwFe3wBcOqz19SsDdr63bhgL3DUPmLsVeG09cM7AUA4XAHC8CVjgVOqMqwvvfmO5ciRw/iCgS5K9QIiIiIjyCTP1lHa2MVZDkwYctvt9rKC+U5l+ASy9p3CM7gHMug549XptNpfvzndG29leAV7JlN8DQMcy4LMT9fyv3g03W79st26DGNRVFyUygQE9ERERFQoG9ZQRtgR/wXZgl5Opr4kR1ANuMM+gnsJSUQIMSGPX9Uy6eiRQVaYZ9VfXuZcbk3ymHgCuP0n7aszfDryyLuHNfZu3VU/TvZ+eiIiIqBAxqKeMONEJ6l9fDzQZ7codr6y+p7OPnuPsiNqq7gB84WQ9/19vAI3Nen7nEeBAPdClPHYlTDwdSoHPTtLzQbL1r68HTv8b8Nyq6NfPt6X3DOqJiIiIQsegnjLCZurf2KCniQKOXszUE8V141hgQBfNzD+wUC/zNslLdgzlp8bo63PBjthj87y2HwK+9Byw+SDw07ejLwTYTP0EBvVEREREoWNQTxlhx9odbNDTREH9ZcOBEdXa7IqI2iovAb59pp7/5bvA/np3P30ypfdWZSlwm91b/178bH1TM/CV54G9x/Tfq/cCb25ofZt9x4A1+3QCwQk1yR8XEREREUXHoJ4yoku5ZhWtWOPsrHMGAi9cp13LiSi6aUOAU/toUP2795MfZxfpupN04W3RDmDWmti3+/Mc4O1Nug/fTsv42/zWt7Gj7Mb0AEqLUzsuIiIiImqLQT1ljM3WA8nt9yWi1kSA70wBBMDd84G3nCx5Kpl6QJsKfn6ynv/Vu5qRjzR3K/Dzd/T8L6YCX5ys2fiX1wFr97q3s0H9+F6pHRMRERERRcegnjJmjDeoT5CpJyJ/xvQArhoJHG/WMncg9Uw9AFwzGqjrCCzZBVxwn47Pa2jS6w7U6z76JgPcMl4ra6o7AJeP0OvvXeDez1wb1HM/PREREVFaMKinjPEG9T0Y1BOF5punA5XONIkOpUDvzqnfZ0UJ8KuLgL5VuljwzReBKXcDf50H/PvLwMYDWn3zzdPd77lxrJ7OXAIcatD9+PMZ1BMRERGlFYN6yhhv+X0Plt8ThaauE3C7M4puWHegKMnO95HO6Ae8er0G98O6A1sPAT98HXhqhS4e/G5a69GUo3sAJ/fWgP6RpcDafdrAr0dHjqckIiIiSpc4k8KJwtW1AhjcVbN+fULIJBKR67MTgfomYEr/cO+3tBi48gRgxgjgxTXA7z8AFu4A/uc8YHC3tre/aRzw/hbgnvlAp1K9bHzP5EfsEREREVF8DOopo347TYP6QVGCASJKXkVJ61L4sBUJMHUIcOFg4Fijjr6LZuoQoHcnfZ3/YbZextJ7IiIiovRh+T1l1Jg6YPqIbB8FESVLJHZADwAlRcD1zt761U4XfAb1REREROnDoJ6IiEL1iRN1vB2gGf6T6rJ7PERERETtGYN6IiIKVbdK4IoT9PwJ1dpUj4iIiIjSg0E9ERGF7vZJwJBuwCfHZPtIiIiIiNo3NsojIqLQDewKvHx9to+CiIiIqP1jpp6IiIiIiIgoTzGoJyIiIiIiIspTDOqJiIiIiIiI8hSDeiIiIiIiIqI8xaCeiIiIiIiIKE8xqCciIiIiIiLKUwzqiYiIiIiIiPJUVoJ6EekuIrNEZKVz2i3G7fqLyAsislRElojIwMweKREREREREVHuylam/lsAXjLGDAPwkvPvaO4F8DNjzEgAJwPYkaHjIyIiIiIiIsp52QrqpwO4xzl/D4AZkTcQkVEASowxswDAGHPIGHMkc4dIRERERERElNuyFdTXGWO2AoBz2iPKbYYD2Ccij4rIPBH5mYgUR7szEblVRGaLyOydO3em8bCJiIiIiIiIckdJuu5YRF4E0DPKVf/h8y5KAJwFYDyADQAeAnAjgL9G3tAYcyeAOwFg0qRJJonDJSIiIiIiIso7aQvqjTEXxLpORLaLSC9jzFYR6YXoe+U3AZhnjFnjfM/jAE5FlKCeiIiIiIiIqBClLahP4EkANwC4wzl9IsptPgDQTURqjTE7AZwHYHaiO54zZ84uEVkf5sGmWQ2AXdk+CMoaPv6FjY8/8TlQ2Pj4FzY+/sTnQGGL9/gPCHJHYkzmq9VFpBrATAD9oaX1Vxtj9ojIJAC3GWNucW53IYBfABAAcwDcaoxpyPgBp5GIzDbGTMr2cVB28PEvbHz8ic+BwsbHv7Dx8Sc+BwpbmI9/VjL1xpjdAM6PcvlsALd4/j0LwEkZPDQiIiIiIiKivJGt7vdERERERERElCIG9dl3Z7YPgLKKj39h4+NPfA4UNj7+hY2PP/E5UNhCe/yzsqeeiIiIiIiIiFLHTD0RERERERFRnmJQHzIR+ZuI7BCRRZ7LxonIuyIyX0Rmi8jJzuXniMh+5/L5IvI9z/dME5HlIrJKRL6Vjd+Fggv4+H/D89gvEpEmEenuXLdORBba78nW70PBxXgOjBWRd5zH9CkRqfJc923ndb5cRC7yXM73gDwU5PEXkQtFZI5z+RwROc/zPa86j799j+iRjd+Hggn4+A8UkaOex/hPnu+Z6Nx+lYj8VkQkG78PBRfwOXCt5/GfLyLNIjLOuY7vAXlIRPqJyCsislREFovIl53Lu4vILBFZ6Zx2cy4X5zW+SkQWiMgEz33d4Nx+pYjckK3fifxL4vG/1nncF4jI2yIy1nNfwWIBYwy/QvwCMAXABACLPJe9AOBi5/wlAF51zp8D4F9R7qMYwGoAgwGUAfgQwKhs/278Cvfxj/i+jwB42fPvdQBqsv378Cu058AHAM52zn8awI+c86Oc13c5gEHO676Y7wH5+xXw8R8PoLdzfjSAzZ7veRXApGz/PvxK6+M/0Hu7iPt5H8Bp0JG+z9q/IfzK/a8gz4GI7xsDYI3n33wPyMMvAL0ATHDOdwawwvlb/1MA33Iu/xaAnzjnL3Fe4wLgVADvOZd3B7DGOe3mnO+W7d+PX6E//qfbxxXAxfbxd/4dKBZgpj5kxpjXAeyJvBiAzcx1AbAlwd2cDGCVMWaNMaYBwIMApod6oJQWKTz+1wD4RxoPjTIkxnNgBIDXnfOzAFzlnJ8O4EFjTL0xZi2AVdDXP98D8lSQx98YM88YY98PFgOoEJHyjBwopUXA139UItILQJUx5h2jn+zuBTAj7GOl9EjhOcDPAe2AMWarMWauc/4ggKUA+kD/ht/j3OweuK/p6QDuNepdAF2d94CLAMwyxuwxxuyFPm+mZfBXoSQEffyNMW87jy8AvAugb7I/m0F9ZnwFwM9EZCOAnwP4tue600TkQxF5VkROdC7rA2Cj5zabnMsoP8V7/CEiHaBv1P/0XGwAvOCU5N6asSOldFkE4HLn/NUA+jnnY73W+R7QvsR6/L2uAjDPGFPvuezvTtndd1l+ndfiPf6DRGSeiLwmImc5l/WBvuYtvv7zn5/3gI+jbVDP94A8JiIDoRVZ7wGoM8ZsBTTwA2C3U/BzQDvl8/H3uhlatWEFigUY1GfG7QC+aozpB+CrAP7qXD4XwABjzFgAvwPwuHN5tDdujinIX7Eef+sjAN4yxnhX9s8wxkyAluJ8XkSmZOZQKU0+DX0c50DLsRqcy2O91vke0L7EevwBAM6C7k8AfNZz8bXGmDEAznK+PpWhY6XwxXr8twLob4wZD+BrAB5w9lrz9d/+JHoPOAXAEWPMIs/FfA/IYyLSCZqs+Yox5kC8m0a5jJ8D8lyAx9/e/lxoUP9vnosDxQIM6jPjBgCPOucfhpbWwhhzwBhzyDn/DIBSEamBrsZ5V3H7InHJPuWuqI+/xycQsTpvS3KNMTsAPBbleyiPGGOWGWOmGmMmQh/r1c5VsV7rfA9oR+I8/hCRvtDX+PXGmNWe79nsnB4E8AD4HpC3Yj3+zrab3c75Oc7lw6Gvf28JJl//eS7ee4Aj2ucAvgfkKREphQZ0/2eMsZ//tjtl9XaLzQ7ncn4OaGcCPv4QkZMA/AXAdPs3AQgeCzCoz4wtAM52zp8HYCUAiEhPW04l2hG9CMBuaEOVYSIySETKoG/2T2b8qCksUR9/ABCRLs51T3gu6ygine15AFOhpXuUp2zXYhEpAvAdALbL9ZMAPiEi5SIyCMAwaIMsvge0I7EefxHpCuBpAN82xrzluX2Js8BrPxxcBr4H5K04j3+tiBQ75wdDX/9rnNLMgyJyqvMZ4Xp4/kZQ/onzN8BedjW0d4q9jO8Becp5zf4VwFJjzC89Vz0JTfLAOX3Cc/n1Thf8UwHsd94DngcwVUS6OZ3SpzqXUQ4L+viLSH9o4u9TxpgVnvsJHAuUhPVLkBKRf0C72teIyCYA3wfwGQC/EZESAMcA2H0RHwVwu4g0AjgK4BNOU5xGEfkC9MVbDOBvxpjFmf1NKBkBH38AuALAC8aYw57L6gA85qz3lAB4wBjzXAYOn0IQ4znQSUQ+79zkUQB/BwBjzGIRmQlgCYBGAJ83xjQ598P3gDwU5PEH8AUAQwF8V0S+61w2FcBhAM87H+aLAbwI4K7M/AaUioCP/xQAP3Q+AzQBuM2zDet2AHcDqITusfTus6QcFvA5AOjzYJMxZo3nsnLwPSBfnQHdKrFQROY7l/07gDsAzBSRmwFsgC7kAMAz0A74qwAcAXATABhj9ojIj6CL/ADww4htmpSbgj7+3wNQDeAPzuf+RmPMJCQRC4jTMp+IiIiIiIiI8gzL74mIiIiIiIjyFIN6IiIiIiIiojzFoJ6IiIiIiIgoTzGoJyIiIiIiIspTDOqJiIiIiIiI8hSDeiIiogLnzEh+U0Qu9lz2MRHhOE0iIqIcx5F2REREBBEZDeBhAOOhs7HnA5hmjFmdwn2WGGMaQzpEIiIiioJBPREREQEAROSnAA4D6AjgoDHmRyJyA4DPAygD8DaALxhjmkXkTgATAFQCeMgY80PnPjYB+DOAaQB+bYx5OAu/ChERUcEoyfYBEBERUc74TwBzATQAmORk768AcLoxptEJ5D8B4AEA3zLG7BGREgCviMgjxpglzv0cNsackY1fgIiIqNAwqCciIiIAgDHmsIg8BOCQMaZeRC4AMBnAbBEBNCu/0bn5NSJyM/SzRG8AowDYoP6hzB45ERFR4WJQT0RERF7NzhcACIC/GWO+672BiAwD8GUAJxtj9onI/QAqPDc5nJEjJSIiIna/JyIiopheBPAxEakBABGpFpH+AKoAHARwQER6Abgoi8dIRERU0JipJyIioqiMMQtF5D8BvCgiRQCOA7gNwGxoqf0iAGsAvJW9oyQiIips7H5PRERERERElKdYfk9ERERERESUpxjUExEREREREeUpBvVEREREREREeYpBPREREREREVGeYlBPRERERERElKcY1BMRERERERH9//brgAQAAABA0P/X7Qj0hVNSDwAAAFNSDwAAAFMBTtLbGesPrb8AAAAASUVORK5CYII=\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -2093,7 +2087,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -2178,7 +2172,7 @@ "4 Global 1854 -0.248 -0.218 -0.301" ] }, - "execution_count": 64, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -2189,7 +2183,7 @@ }, { "cell_type": "code", - "execution_count": 386, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -2331,7 +2325,7 @@ "[169 rows x 5 columns]" ] }, - "execution_count": 386, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -2352,7 +2346,7 @@ }, { "cell_type": "code", - "execution_count": 390, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -2438,7 +2432,7 @@ "4 165000 " ] }, - "execution_count": 390, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -2451,7 +2445,7 @@ }, { "cell_type": "code", - "execution_count": 391, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -2461,7 +2455,7 @@ }, { "cell_type": "code", - "execution_count": 392, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -2470,7 +2464,7 @@ }, { "cell_type": "code", - "execution_count": 393, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -2479,7 +2473,7 @@ "array(['Natural emissions', 'Total emissions'], dtype=object)" ] }, - "execution_count": 393, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -2490,27 +2484,29 @@ }, { "cell_type": "code", - "execution_count": 394, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 394, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -2526,7 +2522,7 @@ }, { "cell_type": "code", - "execution_count": 400, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -2599,7 +2595,7 @@ "4 Natural emissions 1965 165000" ] }, - "execution_count": 400, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -2610,7 +2606,7 @@ }, { "cell_type": "code", - "execution_count": 403, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -2632,7 +2628,7 @@ }, { "cell_type": "code", - "execution_count": 404, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -2699,7 +2695,7 @@ "58 1965 510000" ] }, - "execution_count": 404, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -2717,7 +2713,7 @@ }, { "cell_type": "code", - "execution_count": 409, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -2809,7 +2805,7 @@ "4 7900000 " ] }, - "execution_count": 409, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -2822,7 +2818,7 @@ }, { "cell_type": "code", - "execution_count": 410, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -2831,7 +2827,7 @@ }, { "cell_type": "code", - "execution_count": 411, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -2898,7 +2894,7 @@ "4 1983 7900000" ] }, - "execution_count": 411, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -2909,27 +2905,29 @@ }, { "cell_type": "code", - "execution_count": 412, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 412, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -2951,7 +2949,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -3042,7 +3040,7 @@ "4 4 Antigua Barbuda 1981 0.408927 68264.0 5990.381182" ] }, - "execution_count": 95, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -3054,7 +3052,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -3070,7 +3068,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -3149,7 +3147,7 @@ "4 4 Afghanistan 1990 2.91" ] }, - "execution_count": 96, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -3161,7 +3159,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -3177,7 +3175,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -3307,7 +3305,7 @@ "4 NaN " ] }, - "execution_count": 197, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -3319,7 +3317,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -3392,7 +3390,7 @@ "1204 1850-05-01 10.004 15.507" ] }, - "execution_count": 198, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -3405,7 +3403,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -3484,7 +3482,7 @@ "1204 1850-05-01 10.004 15.507 1850" ] }, - "execution_count": 199, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -3497,7 +3495,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -3570,7 +3568,7 @@ "1854 8.210500 14.991000" ] }, - "execution_count": 200, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -3583,7 +3581,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -3607,7 +3605,6 @@ " \n", " \n", " \n", - " index\n", " Year\n", " LandAverageTemperature\n", " LandAndOceanAverageTemperature\n", @@ -3616,35 +3613,30 @@ " \n", " \n", " 0\n", - " 0\n", " 1850\n", " 7.900667\n", " 14.867167\n", " \n", " \n", " 1\n", - " 1\n", " 1851\n", " 8.178583\n", " 14.991833\n", " \n", " \n", " 2\n", - " 2\n", " 1852\n", " 8.100167\n", " 15.006500\n", " \n", " \n", " 3\n", - " 3\n", " 1853\n", " 8.041833\n", " 14.955167\n", " \n", " \n", " 4\n", - " 4\n", " 1854\n", " 8.210500\n", " 14.991000\n", @@ -3654,15 +3646,15 @@ "" ], "text/plain": [ - " index Year LandAverageTemperature LandAndOceanAverageTemperature\n", - "0 0 1850 7.900667 14.867167\n", - "1 1 1851 8.178583 14.991833\n", - "2 2 1852 8.100167 15.006500\n", - "3 3 1853 8.041833 14.955167\n", - "4 4 1854 8.210500 14.991000" + " Year LandAverageTemperature LandAndOceanAverageTemperature\n", + "0 1850 7.900667 14.867167\n", + "1 1851 8.178583 14.991833\n", + "2 1852 8.100167 15.006500\n", + "3 1853 8.041833 14.955167\n", + "4 1854 8.210500 14.991000" ] }, - "execution_count": 202, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -3674,16 +3666,7 @@ }, { "cell_type": "code", - "execution_count": 203, - "metadata": {}, - "outputs": [], - "source": [ - "annual_global_temp.drop(['index'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 204, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -3756,7 +3739,7 @@ "4 1854 8.210500 14.991000" ] }, - "execution_count": 204, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -3767,7 +3750,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -3807,7 +3790,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -3886,7 +3869,7 @@ "4 Afghanistan 1953 106256.0 0.0016" ] }, - "execution_count": 147, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -3898,7 +3881,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -3932,7 +3915,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -4005,7 +3988,7 @@ "4 Afghanistan 1994 13240000.0" ] }, - "execution_count": 107, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -4016,7 +3999,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -4108,7 +4091,7 @@ "4 13240000.0 " ] }, - "execution_count": 109, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -4120,7 +4103,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -4155,7 +4138,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -4228,7 +4211,7 @@ "4 Belgium 1743 7.106" ] }, - "execution_count": 126, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -4239,7 +4222,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -4248,7 +4231,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -4346,7 +4329,7 @@ "4 13240000.0 14.754750 " ] }, - "execution_count": 128, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -4365,7 +4348,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -4481,7 +4464,7 @@ "4 13240000.0 14.754750 4.394831 18486041.0 237.737799 " ] }, - "execution_count": 149, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -4493,7 +4476,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -4525,7 +4508,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -4641,7 +4624,7 @@ "4 13240000.0 14.754750 4.394831 18486041.0 237.737799 " ] }, - "execution_count": 141, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -4653,7 +4636,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -4683,6 +4666,15 @@ "indicators_merged_outer.info()" ] }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged_outer.to_csv('country_indicators')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -4692,7 +4684,7 @@ }, { "cell_type": "code", - "execution_count": 415, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -4765,7 +4757,7 @@ "4 1983 1090000 7900000" ] }, - "execution_count": 415, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -4777,7 +4769,7 @@ }, { "cell_type": "code", - "execution_count": 416, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -4786,7 +4778,7 @@ "-0.5597422292909034" ] }, - "execution_count": 416, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -4798,7 +4790,7 @@ }, { "cell_type": "code", - "execution_count": 418, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -4807,7 +4799,7 @@ }, { "cell_type": "code", - "execution_count": 420, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -4823,7 +4815,7 @@ " 1 Ozone-depleting emissions 35 non-null int64 \n", " 2 Mean ozone hole area (square kilometres) 35 non-null int64 \n", "dtypes: int64(2), object(1)\n", - "memory usage: 2.3+ KB\n" + "memory usage: 1.1+ KB\n" ] } ], @@ -4833,7 +4825,7 @@ }, { "cell_type": "code", - "execution_count": 427, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -4842,27 +4834,29 @@ }, { "cell_type": "code", - "execution_count": 428, + "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 428, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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cDmzKFZrZrTWLyjnnMszHICWXJMkMAjYQdR3OMcCTjHPOuZKS9C47vjMCcc451/0UTTKS/svMfirpVxSYesXMTqtpZM4557q8Ulcyucb++Z0RiHPOue6naJIxszvD3Q1mdlP8OUlH1TQq55xz3UKScTLfSVjmnHPOtVGqTeYg4GBgmKQLYk8NApprHZhzzmVNS4uxen2Tj4+pQKk2mdeJ2mMOAxbEyhuBb9QyKOecy5rc7Mu5yTFzI/1322mgJ5oSSrXJPA08LenasN1IM1vSaZE551yGFJt9+bZTJvnAzBKStMlMARYC9wBIGi9pTk2jcs65jPHZl9snSZI5C9iTsECZmS0ERtUuJOecyx6ffbl9kiSZZjNbW/NInHMuw3z25fZJMnfZs5I+D/SSNAY4DfhLbcPquXy2ZueyyWdfbp8kVzJfBT5ANAPzdcA64OvtPaGk3SQtjN3WSfq6pLMkvRYrPzi2z3ckLZW0RNKnYuVTQtlSSWfEyneR9New2NoNkvqG8n7h8dLw/Kj2/h7OuZ4nN/vysCHbUj+wnyeYBMomGTPbYGbfNbMPm9nEcP/t9p7QzJaY2XgzGw9MIJrh+bbw9Pm558xsLoCkscAxRIluCnCRpF6SegG/Bg4CxgLHhm0BzgvHGgOsAU4M5ScCa8zs34Dzw3bOOedqpNRgzDspMDFmjpkdVoXzHwC8aGb/kIp+IzgcuN7MNgEvS1pK1BEBYKmZvRTivR44XNLzwCeAz4dtriLqvHBxONZZofxm4EJJMrOiv6dzzrn2K9Um8/NOOP8xRFVwOadKmkY0CPQ/zWwNMAx4LLZNQygDeDWvfC9gB+BNM2susP2w3D5m1ixpbdh+VdV+I+ecc62KVpeZ2Z9yN+BRomqnN4BHQ1mHhHaSw4Dc5JsXA6OB8cAy4Be5TQuF147yUsfKj22GpPmS5q9cubLo7+BcZ/P3putqyrbJSPo08CJwAXAhsDTMa9ZRBwFPmtlyADNbbmabzawFuJwtVWINwIjYfsOJprwpVr4KGCypd155m2OF57cjSpxtmNllof1pYn19fYd/Ueeqxd+brqtJ0rvsF8D+ZrafmX0c2J+o0byjjiVWVSZp59hzU4Fnw/05wDGhZ9guwBjgceAJYEzoSdaXqOptTmhfeQA4Muw/Hbgjdqzp4f6RwB+9PcY5V0pLi7GycROvrdnAysZNtLT4R0YlkoyTWWFmS2OPXwJWdOSkkrYFPgmcFCv+qaTxRNVXr+SeM7PnJN0ILCaa/XmmmW0OxzkVuBfoBcwys+fCsU4Hrpf0Y+Ap4IpQfgVwdeg88AZRYnLOuYJ8UsyOS5JknpM0F7iRKAEcBTwh6bMAZnZrpSc1sw1EDe7xsi+U2P4c4JwC5XOBuQXKX2JLdVu8/O0Qv3POleWTYnZckiSzDbAc+Hh4vBLYHjiUKOlUnGScc64r8EkxO65skjGz4zsjEOecy5rcpJjxROOTYlYmSe+yXSXdL+nZ8HicpO/VPjTnnEuXT4rZcUmqyy4Hvg1cCmBmi8JCZj+uZWDOOZem3FLLg7bpzY0n7UMvQV1dnU+KWaEkSWZbM3s8b9qX5mIbO1fNmaR9VmqXBu9VVj1JxsmskjSaMDJe0pFEI/Kdc65bKtarbPX6ppQj63qSXMnMBC4D3i/pNeBl4LiaRuWccynyXmXVk6R32UvAgZIGAHVm1lj7sJxzLj0d6VWWa8vxhc0ipab6/2aRcgDM7Jc1isk551KV61UWb5OZfcKeGMZrazYUTR7elrO1UlcyA8PP3YAPE837BdEgzIdqGZRzzqUpf6nl/n17sXzdJqZd9JeSycNnCNhaqan+f2hmPwR2BPYws/80s/8kWs1yeGcF6JxzaYgvtby5hUQdAbwtZ2tJepeNBOKvZBMwqibROOdcBpVKHvFZmiUxeezQNtv19BkCkvQuuxp4XNJt4fFniJY0ds65HqFYR4D+fXtt1QZzyXETALhv8QqfIYBkvcvOkXQ38NFQdLyZPVXbsJxzLjsKdQS4fNpEmltsq2q0k69ZwI0n7cOZh5r3LiPZlQxm9iTwpKQZnmCccz1NfkeAXPJYtnZjwWo0M2PYkG1TijZbEiWZmJOJBmY651yPkusIENe3dy8mjx3KERNGMLh/H97c+A63LHi1R7fB5Ks0yfTcaz7nnMszpH8fTjtgV06+ZkGbNpkh/fukHVpmJOldFndotU4s6RVJz0haKGl+KNte0jxJL4SfQ0K5JF0gaamkRZL2iB1netj+BUnTY+UTwvGXhn1V6hzOOVepNRvfaU0wsKVNZs3Gd1KOLDvKXslI2gn4CfAeMztI0lhgHzO7ogrn39/MVsUenwHcb2bnSjojPD4dOAgYE257ARcDe0naHjgTmEg0gecCSXPMbE3YZgbwGNESzVOAu0ucw3WypDMsO5dVPi6mvCRXMlcC9wLvCY//Dny9RvEczpbu0VcRdZfOlc+2yGPAYEk7A58C5pnZGyGxzAOmhOcGmdmjZmbA7LxjFTqHcy4F8XEmKxs30dJiaYeUWK5rc1xPHxeTL0mS2dHMbgRaAMysGahGmjbgPkkLJM0IZTuZ2bJwnmVAblTTMODV2L4NoaxUeUOB8lLncM51stxcX1MveoRJ5z3A1IseYcnyxi6TaPJXzpw8dijXfmkvmpo3d7mEWStJGv7XS9qBLevJ7A2srcK5J5nZ65KGAvMk/a3EtoU6HFg7yhMJSW8GwMiRI5Pu5lzNdbf3ZhpzfVVzluR41+aWlhZWrW/i87/5q0+OGZPkSuabRJNjjpb0CFHV01c7emIzez38XAHcBuwJLA9VXYSfK8LmDcCI2O7DgdfLlA8vUE6Jc8Rju8zMJprZxPr6+o78ms5VVXd7b3Z2m0YtrpxyXZvr6uo46eoFZec362nKJpkwEPPjwEeAk4APmNmijpxU0gBJA3P3gcnAs0TJLNdDbDpwR7g/B5gWepntDawNVV33ApMlDQm9xCYD94bnGiXtHXqVTcs7VqFzOOc6WWe3aVRzxcut25JavBNAAUnHyexJNClmb2APSZjZ7A6cdyfgttCruDdwrZndI+kJ4EZJJwL/BI4K288FDgaWAhuA4wHM7A1JPwKeCNudbWZvhPtfIeq00J+oV9ndofzcIudwznWyYtO11Gqur2pdORVaN+bSL0xg8tih3Ld4S+WIdwJI1oX5amA0sJAtDf65HlvtElbb/GCB8tXAAQXKjWgZ6ELHmgXMKlA+H9g96Tmcc52v2HQttWrD6MiKl3GFrohOunoB135pLxYva6RhzUYmjx3K9z49trUTQE+dwyzJlcxEYGz4oHfOuaoqNF1LrVTryqnYFVGvOnkngDxJksyzwLuBZTWOxTnnairplVO5HmilrojqB/ZjZeOmgp0AeuIKmUmSzI7AYkmPA5tyhWZ2WM2ics65Gil35VSovSX/KiT/iii/asw7AWyRJMmcVesgnHMuK1avb+L8eUv4/iFjW2dWPn/eEs6ZOq41OZUbH+OdALZIsmjZn8L8ZR8ORY+HsS3OOdfllKsKa2lpYfpHduH0Wxa1Jo3zjhhHS0tLm+PkrogKVY3ldwLoyStkJulddjTwM+BBopH0v5L0bTO7ucaxuRJ8cknnKpekKmyz0ZpgIEoap9+yiBtP2qfg8ZqaN/OLoz7Imxvf4ZIHX+SpV99s0wmgM3rNZVmS6rLvAh/OXb1Iqgf+AHiScc51KUmmsTGzoqtdxhVKWOcdMY6f37uElW9tau0E0NMlSTJ1edVjq6l8HRrnnOtUharFkgzGTDqWplDCOv2WRfzo8N1593bb9MiqsUKSJJl7JN0LXBcef44to+edcy5zilWL7TSoX9EEEk9K135pL35812LuW7yiaHtKsYQ1eui7GD64f4+sGiskScP/tyV9FtiXqE3mMjO7reaROedcOxWrFrv1lI8UHIw5pH+fgtPE/Ojw3amrqyvYnlLsiqd/n16eYGKSNPx/H7jSzG6Nlc0ws8tqGplzzrVTsauMd5pbCg7GfHNjE/9a+3abBvyTrl5QcvBkZ8+71lUlqS77KnCspJlm9kAoOxnwJOOcy6RS7Sr5gzFbWoxlb77N9+94dqsG/FKDJzt73rWuKkkD/mvAFOBcSd8OZf4qOucyK3/FylJXGavXN3HSNQu2asA/7YAxZQdP5hLWsCHbhjVl/KMxX6Kp/s3sn5I+Dlws6Sai6fOdcy6TKrnKKFa1tsuOA7zqqwqSXMnMBzCzt83seKJBmf7KO+cyLelVRrGF07bt5w341ZAkyXxV0u6SPiBpGzP7tZm9r+aROedcJyhWtbbjAB9IWQ1Fq8sk9QZ+QrQK5T+JEtJwSb8Fvmtm73ROiM45VzvegF9bpdpkfgYMBN5nZo0AkgYBPw+3r9U+POecq73OXDitpylVXXYI8OVcggEws3XAV4CDax2Yc865rk/FVlWW9Hcz27XS57obSSuBf9TwFDsCq2p4/GrqyrGuMrMpaQVTCwnem13p75WmtF+nbvfejCtVXbZY0jQzmx0vlHQc8LfahpUdZlZfy+NLmm9mE2t5jmrxWLOl3HuzJ7wG1eCvU22VSjIzgVslnQAsAIxo4bL+wNROiM0551wXVzTJmNlrwF6SPgF8gGiU/91mdn9nBeecc65rSzIL8x+BP3ZCLD1VV5oDzmPtWvw1SMZfpxoq2vDvnHPOdZSvcOmcc65mPMk455yrGU8yzjnnasaTjHPOuZrxJOOcc65mEi1a1pkk1QEfBN4DbASeM7Pl6UblnHOuPTLThVnSaOB04EDgBWAlsA2wK7ABuBS4ysxaUgvSOedcRbKUZK4DLgYetrygJA0FPg+sMbOr0ojPOedc5TKTZJxzznU/mWv4l3SUpIHh/vck3Sppj7Tjcs45V7nMJRng+2bWKGlf4FPAVUTVaM4557qYLCaZzeHnp4GLzewOoG+K8TjnnGunLCaZ1yRdChwNzJXUj2zG6ZxzrozMNfxL2haYAjxjZi9I2hn4dzO7L+XQnHPOVShzVwhmtgFYAewbipqJxs0455zrYrJ4JXMmMBHYzcx2lfQe4CYzm5RyaM455yqUuSsZYCpwGLAewMxeBwamGpFzzrl2yWKSaQoj/g1A0oCU43HOOddOWUwyN4beZYMlfRn4A3B5yjE555xrh8y1yQBI+iQwGRBwr5nNSzkk55xz7ZCpJCOpF1FSOTDtWJxzznVcpqrLzGwzsEHSdmnH4pxzruMyt2gZ8DbwjKR5hB5mAGZ2WnohOeeca48sJpm7wi0uO3V6zjnnEstikhlsZv8XL5D0tbSCcc45136ZapMJphco+2JnB+Gcc67jMnMlI+lYoiWWd5E0J/bUQGB1OlE555zriMwkGeAvwDJgR+AXsfJGYFEqETnnnOuQTI2TyZH0XmCMmf1BUn+gt5k1ph2Xc865ymSuTSZMJXMzcGkoGg7cnl5Ezjnn2itzSQaYCUwC1gGY2QvA0FQjcs451y5ZTDKbzKwp90BSb3ycjHPOdUlZTDJ/kvTfQP8wUeZNwJ0px+Scc64dMtfwL6kOOJHYLMzAbyxrgTrnnCsrc0kmiyTNAg4BVpjZ7mW2PR/YPzzcFhhqZoNrHKJzzmVS5pKMpEOAHwHvJRrHI8DMbFCKMX0MeAuYXS7J5O33VeBDZnZCzYJzzrkMy2KbzP8STS2zg5kNMrOBaSYYADN7CHgjXiZptKR7JC2Q9LCk9xfY9Vjguk4J0jnnMihLI/5zXgWe7QJtMJcBJ5vZC5L2Ai4CPpF7Mgwo3QX4Y0rxOedc6rKYZP4LmCvpT8CmXKGZ/TK9kNqS9C7gI8BNknLF/fI2Owa4OSzE5pxzPVIWk8w5RO0f2wB9U46lmDrgTTMbX2KbY4gGljrnXI+VxSSzvZlNTjuIUsxsnaSXJR1lZjcpupwZZ2ZPA0jaDRgCPJpqoM45l7IsNvz/QVKmkoyk64gSxm6SGiSdCPwHcKKkp4HngMNjuxwLXN8F2pWcc66mstiFuREYADQB74TiVLswO+eca5/MJRnnnHPdRxary5B0mKSfh9shacYyZcoUI5qg029d+9bt+Huz29y6tcwlGUnnAl8DFofb10JZKlatWpXWqZ0ryd+brivIYu+yg4HxZtYCIEAZkbEAACAASURBVOkq4CngjFSjcs45V7EsJhmAwWyZxmW7NAPpqJYWY/X6JpqaN9O3dy92GNCXujqV39E557qBLCaZ/wGekvQA0eSYHwO+k25I7dPSYixZ3siXZ8+nYc1Ghg/pz+XTJrLbTgM90TjneoTMtcmY2XXA3sCt4baPmV2fblTts3p9U2uCAWhYs5Evz57P6vVNZfZ0zrnuITNXMpL2yCtqCD/fI+k9ZvZkZ8fUUU3Nm1sTTE7Dmo00NZefzsyr2ZzLtlFn3JVou1fO/XSNI8m2zCQZ4BclnjNiMxx3FX1792L4kP5tEs3wIf2RxGtrNhRNHl7N5pzrLjKTZMxs//JbdS07DOjL5dMmtkkWlxw3gbPmPMt9i1cUTR7FqtluO2US9QPzJ3t2zrnsykySkfQJM/ujpM8Wet7Mbu3smNorXtW1w7v6MufUSWxs2oyk1gQDxZNHR6rZnHMuSzKTZICPEy3wdWiB54yoE0BRkkYAs4F3Ay3AZWb2f3nb7AfcAbwcim41s7M7FnZbpaq6lq3d2JpgchrWbGTjO5vbVJ8Vq2br27tXNUN1zrmay0zvMjM7M9w92cyOj9+AbyU4RDPwn2b2/4h6p82UNLbAdg+b2fhwq3qC+de6t1m/qZnvHzKWD40Y3KZHWS55xA0f0p8XV7zFpPMeYOpFj/D8v9bR0tLCtV/ai8ljh7Zuc/m0iewwIKvL6zjnXGGZSTIxt0pqvcKS9G5gXrmdzGxZrgeamTUCzwPDahZlntwVzNGXPsqRlzzKj36/mG99arfWRNPUvLm1jSaXaIYP6c/PjhzHBfe/AERXNSddvYCFDWv5/G/+ytcO3JW/fucT3HbKJG/0d851SVlMMrcDN0vqJWkUcB8VDsYM+30I+GuBp/eR9LSkuyV9oMj+MyTNlzR/5cqVic5ZqLH+9FsWcfJ+o1t7lC1bu7G1jeaR0/fnhhl789N7lvDUq2+2HqdhzUYG9+/TmnDq6uqoH9jPE4wD2vfedC5NWWqTAcDMLpfUlyjZjAJOMrO/JN1f0ruAW4Cvm9m6vKefBN5rZm9JOjicY0yBGC4DLgOYOHFiollSizXW7zCgb9EeZavXN7HyrU1t9hk+pD9vbnyndf80Gvt9jE52tee96VyaMnMlI+mbuRuwDTACWAjsHcqSHKMPUYL5XaHeaGa2zszeCvfnAn0k7ViN+Iu1t+y83TZccP/ft+pRtnp9U8Hqs/OOGMclD77Y+rizG/tz1X5TL3qktZ1oyfJGWlr888w5V7ksXckMzHt8W5HygiQJuAJ43sx+WWSbdwPLzcwk7UmUZFe3M942Co2JuXzaRHrXqWCPsqbmzdTVid12Gshtp0yiqXkzm1uMH9+1mKdefbOqjf2VXJn4GB3nXDVlJsmY2Q87eIhJwBeAZyQtDGX/DYwMx78EOBL4iqRmYCNwjFVpadD8hJH7MF+9vqlkd+S6OrV+eLe0GOdMHceZh1avmqrS2QN8jI5zrpoyk2Q6ysz+TDRrc6ltLgQurFUM8YSRU+wKp9AVSn7CqUa7SKVXJj5GxzlXTd0myWRVsSucUgmjmnOXVXplUklSdM65cjzJdIJCVzilJL36SHK1U+mVSXuSonNdhc+c3Pky07ssR9Kuku6X9Gx4PE7S99KOqzMlufpI2gusUA+2clcmuaQ4bMi2PkbHOdchWbySuRz4NnApgJktknQt8ONUo+pESa4+kl7t1PrKxMfUOOdKyWKS2dbMHo96JLdqTiuYNBRrF+lVR+tEmi0tLYnbWiqtrkvK171xzpWTxSSzStJoopmXkXQksCzdkJKLf7OXRC9BXV1dRd/w868++vSu4623mznswkdaP8wv/cIEJo8d2mYMTmf3AvMxNc65crKYZGYSTZvxfkmvEU3L/x/phpRMoW/25x0xjqv+8jLf+ORuFX3Dj199rGzcxLRZj7f5MD/p6gVc+6W9WLysMbVeYD6mxjlXTqaSjKQ6YKKZHShpAFAXZlTuEopNkvn9Q8Z26Bt+sQ/zXnVKtReYj6lxzpWTqd5lZtYCnBrur+9KCQaKJ4PcrMrt/YZfbF60vr17pdoLrD0915xzPUumrmSCeZK+BdwArM8Vmtkb6YW0tUJtL5IKtpO8ufGdDn3Dz+oASR9T45wrJ4tJ5oTwc2aszID3pRBLQaXaXk47YFeA1mn9c+UdSQpZ/jCvVc8151z3kLkkY2a7pB1DOaXaXk6+ZgE3nrQPZx5qrVc450wd1+Gk4B/mzrmuKHNJBkDS7sBYonVlADCz2elF1Fa5thczY9iQbVOKzjnnsiNTDf8Aks4EfhVu+wM/BQ5LsN8ISQ9Iel7Sc5K+VmAbSbpA0lJJiyTt0Z4YizXEd7TtxTnnupvMJRmiNV8OAP5lZscDHwSS1BM1A/9pZv8P2BuYKWls3jYHES23PAaYAVzcngCLrWh5y4JXM9Eg75xzWZHF6rKNZtYiqVnSIGAFCRr9zWwZYWYAM2uU9DwwDFgc2+xwYHZYqOwxSYMl7Rz2TSy/Ib6abS/OufQlna3ZlZfFJDNf0mCiiTIXAG8Bj1dyAEmjgA8Bf817ahjwauxxQyireNqaQg3x5SaL9MkknXM9TeaSjJmdEu5eIukeYJCZLUq6v6R3AbcAXzezdflPFzplgWPMIKpOY+TIkYnOW26ySJ9MMpn8RDykfx/WbHzHE3PQnvemc2nKXJuMpI/lbsBIYHC4n2TfPkQJ5ndmdmuBTRqAEbHHw4HX8zcys8vMbKKZTayvr08Ud7HJIlevb0r0vCu8Rs7fljfy3dsWlVwzpydpz3vTuTRlLskQrSWTu30fuBM4q9xOitYGuAJ43sx+WWSzOcC00Mtsb2Btpe0xxZSbLLInTybZ0mKsbNzEa2s2sLJxU9EkUSgRn3zNAo6YMKL18fnzlvCvdW+XPZZzLhuyWF12aPyxpBFE3ZjLmQR8AXhG0sJQ9t9EV0OY2SXAXOBgYCmwATi+SmGXnSyyp04mWUk1YanxRwAfGjGY6R/ZhaMvfdSrHJ3rIrJ4JZOvAdi93EZm9mczk5mNM7Px4TbXzC4JCQaLzDSz0Wb272Y2v1pBlpsssqdOJllJNWGp8UcAJ+83mtNvWeRVjs51IZm7kpH0K7Y0xtcB44Gn04somXLzi2V5/rFaqqSasNBEoJccN4EL7v976/M9tcrRua4qc0kGiF9dNAPXmdkjaQWTRH6PqJ23618wefTE+ccqqSYslIiH9O/DOVPHceah0Xiknljl6FxXlrkkY2ZXpR1DJbxrcmmVLlNQKBHnHre0WCaXPHDOFZe5JCPpGQqMXSEa42JmNq6TQyrJ17kvrZrVhD21ytG5rixzSQa4O/y8Ovz8D6KeYJm8wunJXZOTqmY1YU+scnSuK8tikplkZpNij8+Q9IiZnZ1aRCX01K7JzjmXRBa7MA+QtG/ugaSPAANSjKeknto12TnnksjilcyJwCxJ24XHb7JlSebM8XYC55wrLnNJxswWAB8M0/zLzNamHVM53k7gnHOFZa66TNLXQoJpBH4h6UlJk9OOyznnXOUyl2SAE8IU/ZOBoUTzi52bbkjOOefaI4tJJteYcTDwWzN7msLrwDjnnMu4LCaZBZLuI0oy90oaCLSkHJNzzrl2yGKSORE4A/iwmW0A+pJgSn5JsyStkPRskef3k7RW0sJw+0F1w3bOOZcvM73LJI0ys1fMrAV4MlduZquB1WFRsmFm1lDkEFcCFwKzS5zmYTM7pFoxO+ecKy0zSQb4maQ64A5gAbAS2Ab4N2B/4ADgTKL1ZbZiZg9JGtUpkTrnnEskM0nGzI6SNJZorrITgJ2J5ix7nmhFy3PM7O0OnmYfSU8DrwPfMrPnOng855xzJWQmyQCY2WLguzU6/JPAe83sLUkHA7cDYwptKGkGMANg5MiRNQqnZ8hfa8dnQ+gYf2+6riaLDf81YWbrzOytcH8u0EfSjkW2vczMJprZxPr6+k6NszvJrbUz9aJHmHTeA0y96BGWLG+kpaXQSg4uCX9vuq6mxyQZSe8OnQeQtCfR77463ai6t2Jr7axe35RyZM65zpKp6rKOkHQdsB+wo6QGok4CfQDM7BLgSOArkpqBjcAxZuZfqWvI19pxzmUyyUgaQtResk2uzMweKrWPmR1b5vkLibo4u07ia+045zJXXSbpS8BDwL3AD8PPs9KMybWPr7XjnMvilczXgA8Dj5nZ/pLeT5RsXBfja+0457KYZN42s7clIamfmf1N0m5pB+Xax9faca5ny2KSaZA0mGgcyzxJa4gGTzrnnOtiMpdkzGxquHuWpAeA7YB7UgzJOedcO2UuyQBI2hcYY2a/lVQPDANeTjmsqvFR8M65niJzSUbSmcBEYDfgt0RjXa4BJqUZV7XkRsHnBinmelztttNATzTOuW4nc12YganAYcB6ADN7HRiYakRV5KPgnXM9SeauZIAmMzNJBiBpQNoBVZOPgt+aVx86131lMcncKOlSYLCkLxNN+395yjFVjY+Cb8urD53r3jJXXWZmPwduBm4hapf5gZn9Kt2oqsdHwbfl1YfOdW+ZupKR1Au418wOBOalHU8t+Cj4trz60LnuLVNJxsw2S9ogaTszW5t2PLXio+C38OpD57q3zFWXAW8Dz0i6QtIFuVvaQbna8OpD57q3TF3JBHeFW8UkzQIOAVaY2e4Fnhfwf8DBwAbgi2b2ZAdidUUk7THW0epD75nmXLZlLsmY2VUd2P1KojVjZhd5/iCidWrGAHsBF4efrooq7THW3upD75nmXPZlrrpM0hhJN0taLOml3C3JvmFhszdKbHI4MNsijxF1k965GnG7LTqrx5j3THMu+zKXZIimkrkYaAb2J7oqubpKxx4GvBp73BDK2pA0Q9J8SfNXrlxZpVP3HJ3VY6wn9kzz96brarKYZPqb2f2AzOwfZnYW8IkqHbtQHYptVWB2mZlNNLOJ9fX1VTp1z5HrMRZXix5jnXWeLPH3putqsphk3pZUB7wg6VRJU4GhVTp2AzAi9ng4vlZN1XVWjzHvmeZc9mWu4R/4OrAtcBrwI6Iqs+lVOvYc4FRJ1xM1+K81s2VVOrYLOmvAqQ9sdS77MpdkzOyJcPct4PhK9pV0HbAfsKOkBuBMoqUCMLNLgLlE3ZeXEnVhruj4LrnOGnDqA1udy7bMJZmOMLNjyzxvwMxOCsc553q8LLbJOOec6ya61ZWM63w+4t45V0rmrmQk7SrpfknPhsfjJH0v7bjc1nIj7qde9AiTznuAqRc9wpLljbS0bNUr3DnXQ2XxSuZy4NvApQBmtkjStcCPU43KbaXYiPvbTpnkjfEZMOqMZFMAvnLup2scievJMnclA2xrZo/nlTWnEokrqSeOuHfOVSaLSWaVpNGEkfiSjgR8LEsG9cQR9865ymQxycwkqip7v6TXiAZnfiXdkFwhPuLeOVdO5tpkzOwl4EBJA4A6M2tMOyZXmI+4d86Vk5kkI+mbRcoBMLNfdmpALhEfce+cKyUzSQYYmHYAzjnnqiszScbMfph2DM4556orcw3/koZLuk3SCknLJd0iaXjacTnnnKtc5pIM0cqYc4D3EK1aeWcoc84518VkMcnUm9lvzaw53K4EEi0BKGmKpCWSlko6o8DzX5S0UtLCcPtStYN3zjm3RWbaZGJWSToOuC48PhZYXW4nSb2AXwOfJFoB8wlJc8xscd6mN5jZqdUMuKfzSTKdc8VkMcmcAFwInE806v8voaycPYGlYZwNYfXLw4H8JOOqKDdJZm4Os9yAzN12GuiJxjmXveoyM/unmR1mZvVmNtTMPmNm/0iw6zDg1djjhlCW7whJiyTdLGlEVYLuwYpNkrl6fVPKkTnnsiBzVzKS6oEvA6OIxWdm5a5mCn1tzp9z/k7gOjPbJOlk4CrgEwVimAHMABg5cmTi2HsinySzc3WF92bS2Z+T8Bmiu77MXckAdwDbAX8A7ordymkA4lcmw4HX4xuY2Woz2xQeXg5MKHQgM7vMzCaa2cT6+kR9DnosnySzc/l703U1mbuSIZrq//R27PcEMEbSLsBrwDHA5+MbSNrZzHIzOh8GPN+hSF3rJJn5bTI+SaZzDrKZZH4v6WAzm1vJTmbWLOlU4F6gFzDLzJ6TdDYw38zmAKdJOoxofZo3gC9WOfYexyfJdM6VkpkkI6mRqA1FwH9L2gS8Ex6bmQ0qd4yQmObmlf0gdv87wHeqGbfzSTKdc8VlJsmYmU+Q6Zxz3UwWG/6dc851E55knHPO1YwnGeecczWTySQjaV9Jx4f79aFbsnPOuS4mc0lG0pnA6WzpBdYHuCa9iJxzzrVX5pIMMJVooOR6ADN7HV+a2TnnuqQsJpkmMzPCvGOSBqQcj3POuXbKzDiZmBslXQoMlvRlomn+L085JleEryXjnCslc0nGzH4u6ZPAOmA34AdmNi/lsFwBvpZM91DNWZOrLcuxuWSyWF2Gmc0zs2+b2bc8wWSXryXjnCsnc0lG0mclvSBpraR1kholrUs7LhdpaTFWNm7itTUbaGreTP272s5Z5mvJOOfiMlddBvwUONTMfBr+jClUPfazI8fx03uW8NSrbwK+loxzrq3MXckAyz3BZFOh6rFv37yI0w4YA0QJ5tLjJtCrLkpIzjmXxSuZ+ZJuAG4HcqtYYma3lttR0hTg/4jWk/mNmZ2b93w/YDbRipirgc+Z2SvVC717K7bU8uihA3jwW/vx8qr1fO/2Z1n51ibvAOCcA7KZZAYBG4DJsTIDSiYZSb2AXwOfJFqK+QlJc8xscWyzE4E1ZvZvko4BzgM+V83gu7PcUsvxRDN8SH9619Vx9OWPtin/8uz53HbKJF9nxrkeLnNJxsyOb+euewJLzewlAEnXA4cD8SRzOHBWuH8zcKEkhcGfroxiSy33EgWvcLwDgHMuc0lG0nDgV8AkoiuYPwNfM7OGMrsOA16NPW4A9iq2TViueS2wA7AqL4YZwAyAkSNHtu8X6YaKLbW8en1TwSsc7wBQff7edF1NFhv+fwvMAd5DlBTuDGXlFKr8z79CSbINZnaZmU00s4n19fUJTt1z5JZaHjZkW+oH9qOuTq1XOMOH9AdovcLZYUDflKPtfvy96bqazF3JAPVmFk8qV0r6eoL9GoARscfDgdeLbNMgqTewHfBGR4J1xa9wvNHfOZfFK5lVko6T1CvcjiPqCVbOE8AYSbtI6gscQ3RFFDcHmB7uHwn80dtjqqPQFY5zzmUxyZwAHA38C1hGlAxOKLeTmTUDpwL3As8DN5rZc5LOlnRY2OwKYAdJS4FvAmfUIH7nnHOBsvZFXtIIM3s1r+zdZvavlOJZCfyjhqfYkbyOBxnWlWNdZWZT0gqmFhK8N7vS3ytNab9O3e69GZfFJNMM3AScYGYbQ9mTZrZHupHVhqT5ZjYx7TiS8Fi7Fn8NkvHXqbayWF32DPAw8GdJo0OZV/A751wXlMXeZWZmF0l6GrhT0ukU6GbsnHMu+7KYZARgZo9IOgC4AXh/uiHV1GVpB1ABj7Vr8dcgGX+daiiLbTI7m9my2OPewEfM7KEUw3LOOdcOWbyS2SDpfOCj4fGfgLNTjMc551w7ZbHhfxawjmiszNHhfpJpZZxzzmVMFpPMaDM708xeCrcfAu9LO6hCJL0i6RlJCyXND2XbS5oXlpCeJ2lIKJekCyQtlbRI0h6x40wP278gaXqsfEI4/tKwr0qdIy+2WZJWSHo2VpZabGXOUSjWsyS9Fl7bhZIOjj33nXCcJZI+FSufEsqWSjojVr6LpL+GmG4IM0IgqV94vDQ8P6rcObqKYq+Fi0gaIekBSc9Lek7S19KOqdsys0zdgEeBfWOPJwGPph1XkVhfAXbMK/spcEa4fwZwXrh/MHA3UceGvYG/hvLtgZfCzyHh/pDw3OPAPmGfu4GDSp0jL46PAXsAz2YhtmLnKBHrWcC3CvxeY4GngX7ALsCLRIvU9Qr33wf0DduMDfvcCBwT7l8CfCXcPwW4JNw/Brih1DnSfr9V8L4s+lr4rfU12hnYI9wfCPzdX6Pa3LJ4JXMy8OtwlfAKcCFwUrohVeRw4Kpw/yrgM7Hy2RZ5DBgsaWfgU8A8M3vDzNYA84Ap4blBZvaoRf8Js/OOVegcrSzqKJE/+WeasRU7R7FYizkcuN7MNpnZy8BSorWEWtcTMrMm4Hrg8HCF9Qmi9YMKxZSL9WbggLB9sXN0FQVfi5RjyhQzW2ZmT4b7jURTUQ1LN6ruKXNJxsyeNrMPAuOAcWb2ITNblHZcRRhwn6QFitb5ANjJQu+48HNoKC+03s2wMuUNBcpLnaOcNGMrdqxSTg1Va7NiVYKVxroD8KZFc9vln7fN+kJAbn2h9sSaJV09/k4Vqkk/BPw13Ui6p8wlmRwzW2dm69KOo4xJFk13cxAwU9LHSmxbbC2bSstroTNiq3Sfi4HRwHiiiVJ/UeY47Yk1C699LXT1+DuNpHcBtwBf7wKfN11SZpNMV2Bmr4efK4DbiKoplueqgcLPFWHzYuvdlCofXqCcEucoJ83Ykqz308rMlpvZZjNrAS5nS3VVpbGuIqqa651X3uZYaru+UEWxZlBXj79TSOpDlGB+Z2a3ph1Pd+VJpp0kDZA0MHcfmAw8S9s1a6YDd4T7c4BpoZfV3sDaUJ10LzBZ0pBQJTQZuDc81yhp79BOMC3vWIXOUU6asRU7R0G5RBVMJXptc8c5JvQM2wUYQ9QJoeB6QqHN6AGiJSMKxVRofaFi5+gqkqyt1KOF9+0VwPNm9su04+nW0u55UOgGfAT4PNGH1zRgWtoxFYjxfUS9dp4GngO+G8p3AO4HXgg/tw/lAn5N1OvnGWBi7FgnEDUuLwWOj5VPJPpwfZGoA4RKnSMvvuuIqpneIfpme2KasZU5R6FYrw7bLSL6gNw5tv13w3GWEHq1hfKDiXoJvZj7e8T+Vo+H3+EmoF8o3yY8Xhqef1+5c3SVW7HXwm+tr8++RFWIi4CF4XZw2nF1x1sWp5W5mqgufiGwORSbmZ2WXlTOOefaI4tJ5nmi/urZCsw551zFstgm8yzw7rSDcM4513FZnCBzR2CxpMeBTblCMzssvZCcc861RxaTzFlpB+Ccc646Mtcm45xzXYWkWcAhwAoz273MtucD+4eH2wJDzWxwjUNMXebaZMLYiyckvSWpSdJmSTUZiStpuKQ7wuy8L0r6v9wMvWkKMxB/q5377ifp92W2GZ83q/FhWZmpV9JcSRX940k6WdK0dp7PQo/G3OPeklaWew27A0lvVbh9u9+XtSbpZklpzNZ+JTCl3EaSzgJeM7PxZjYe+BVwq6SzJR0YtnlQ0sRaBSpplKTPV/F410saU267zCUZojEXxxKNs+gPfCmUVVUYjHUrcLuZjQF2Bd4FnFPtc2XQeKJxFACY2RwzOzfFeFqZ2cFm9maF+1xiZrPbecr1wO6S+ofHnwRea+exXIViMzF05BgfIJol+6UqhFSpR8ib3FXSaEn3hDkNH5ZUaPn4Y4HrzOwHZvaHTokURhGNP9xKO/8OFwP/VXartAfq5N+A+eHnoljZX2pwngOAh/LKBgGriS5lf8OWQVorgTOJBhT+jKgH3DPA58J++wEPEs3k+zfgd2ypipxAtLrnAqIR9DsXiee7RAP//kA0OPFboXw0cE/Y/2Hg/aH8SqJp6x8mGnR3SCyW34f7A4gWgXsCeIpoJt6+wD/D77QQ+BzwReDC2HEvAP5CNLX/kaG8DriIaODp74G5uefyfo9S8V5MNPr+JeDjIbbngStj+79C1PljAHAX0WDXZ2Ov9bnAYqJBdD8PZWfFXq/xwGPh+dvYsjTBg8B5RIMu/w58NJRvIJpy5eXYPqeXeg1D+ajw+z0Zbh8p917Ie522ihN4D1vec7lxYu8Nt/vDtvcDI0v9rcJz3w4xLwJ+WOQ99xbRl6qnQyw7hfJi54u/zgX/znnH3zPE9lT4uVso/yLRINg7iWZZKBovcHs4x3PAjCK/x0+AL4b7vcLrkvsf/Ubs//BpoqVEfkZYVoLYez88/j2wX7h/MTA/nDse0yvAD4A/E82m8DGgMfZaPAqMCdvuBfwx77U7HXib6L11JVv+xx4kDFImSkLPhN/jvLy/2XnhXH8Ir/GD4e9/WOw1+Fns9TwplD9GNAnsQuAbSf8OFP9frCP6v+ld8rO22h/eVfjwf4jog3A20dok3wCersF5TgPOL1D+FNHsz7nH7yX6sHgvcATRdPe9gJ2IPqx3JvpgWUs0R1RdeJPtC/Qh+ueqD8f6HDCrwDknhDfUtkSJbmnsDXl//hs29gFzTzjfGKKR8tvQNsn8BDgu3B9M9OE6gK3/sVofh+PeFI47lmjKeIimXZkbyt8NrKFwkikV7/VEifpwohVP/z0cbwEwPvYPvGN4rS+PHXc7onVtlrAlgQ8u8OG3CPh4uH828L+xf+BfhPsHA38I95uI1r25mWhdkacTvobbAtuE8jFs+XK0HwXeCwVep4Jxxp6fCdwY7t8JTA/3TyC6+i71t5oMXBZe6zqiD86PFYjBgEPD/Z8C3ytzvvjrXPDvnHf8QYQPIOBA4JbY+62BLbNBFI03tk1/og+4HQqc50/Av8f+l+bFnsu9R+Kvd9Ikkzt3L6L3z7jYe/S/Yvs8Avw99vffTNsvC8/nXjvgVKIvSb+O/Q3bJBmiLxv/BOqJOmf9EfhM7G+WW7vpNuA+os+ZDwILQ/mM2N+yH1Gi3IXY+7qSvwMF/hdj9+cBE0p91maxd9kXiH7BU4kSzAiiX7LaROGZaVvLJeWmHTnVzP4h6etEl7ibiSaC/BPwYaIPzMfNrCHst5Dom+6bwO7AvKh2jl5E06fk+yhwm5ltCPvPCT/fRTTFzk1hf4jeNDk3WjSB5AuSXgLyL8snA4fF6tG3AUaWeV0g+lBpIepKvlMo2xe4KZT/S9ID+TsliPdOMzNJzwDLzeyZhFzOpwAAB1pJREFUsN9zRK/Xwti2zwA/l3Qe0T/Gw+GS/m3gN5LuIvoniJ9/O6IPlT+FoquI/n45uUkQF4TzAbQQvee2I1rE7K68X6vYa/g6cKGk8UQfKrvG9in0Xvhz0jglTSKqJv5oKNoH+Gy4fzVRQsgp9LeaHG5PhcfvIkqED+X9bk1seQ0XEFUVljtfkr9zznbAVaHe3og+DHPmmVmumqlUvKdJmhrKR4Ty1Xnn2Znoyhyib/Tvk/Qror/lfQVe76uJZk4v52hFS3j0DucYS5SsAG6A1tdiAmDhb10HtFjU7tIqtMl8gehDfRPRFW4xHwYeNLOVYd/fEX3Y3070N7snbPcMsMnM3gn/U6NC+WRgnKTcfH3bEb1uTQXOleTv8DB5/4ux/VcQJcUFxX6ZzCWZ8GHen6ha6Yc1PNVz5CUvSYOI3sgvhqJLgFttS51poSnUczbF7m8mem0FPGdm++SdZwTRt8XcOaBwwqsjWgtlfIHnCu2T/1jAEWa2JO/8exU5Xk78d1Hez1LKxZs7bkveOVrIey+a2d8lTSC66vgfSfeZ2dmS9iSq6jyG6IvIJxLElX/+3N8HoBk4jKja6SdE3wLjir2GZwHLib5B1hElv/zz5J+rrDAx6BVEVR/FGubjf+dif6v/MbNLy5zuHQtfR8vEmf++Kvd3zvkR8ICZTVW0ZsuDsefW58W9VbyS9iO6AtrHzDZIepAoyefbmCs3szWSPki04N5M4GjgmwV+h5xm2rZNbxPOvQvRlceHwzGvzDt3Lv46oi+ZK3Kvh6S/SDrKzG4Kbb/jwrbPElVv9Se6wi2m1P9a/G/W+n9kZi2xdhUBXzWze9scNHo985X9O4R9t/pfDE9tQ/T6F5W5hn9JhxJ9o70nPB6f+2ZfZfcD2+Z6JUnqRbRmyZXhDT0TGGhtG8QfAj4nqZekeqJvF6Vm510C1EvaJ5yjj6QPmNmrFnqZmNkl4bhTJfVXNLPzof+/vbMJ0bKK4vjvb2Ig4SwSgiIYSMQWDdkmqKBFWFCrCBdiYuoiXRh9l5FEtAiqhbTQXPQFQR9kEOikEUxBX7NxchACibIMHGihU6KS6Wlx7sO8M70fz6jjPI3/3+aF9733vfc+z7n33HPux4GMqQP8Imllya/SgSpWSpon6QbyEshJAyG5BrS5CDqSlpfv/yJdQ9Pha+CBUt41pOk9iRr1rY2ka4GTEfEe8BpwS5k19kXEIPAoua7RWv44cExSZQGsIV0pXYsiZ78PkzO2qc+l0zPsA44WK2INaaXWolM9lVfPfwQ8ExGHWrJ8SypVgNW0WEUd2AesL88LSddJqhvYrmd503jPfUxsonjoPOrbBxwr/XEZGba7HT8CS0rexcC8iNgFbCVDLB8HxiXd0dKmisPAzUWur2cipMQicgAeL/LeyfLZSbpRl0n6XdIGUrlukFRdnltFJR0hZWwBaRl1Yhi4U9LiMi6torcct7IP2FTkCUlLlTfF9+r3bd9Du77YkmdpaWNHGmfJkL7LajGLiPihzIIuKsVtcz+wXdJWUuEOAs+VJE8CZ4oJDGlx7CRdCQfImdHTETGm9rtHiIi/i8n6ejHZ5wPbmPJSImK/pA9J5forKYgVq4Edkp4n3Q0flPIhlcpX5PrQxog43eK+gBT2bcBoGSQPk3v6h4BnS9tervO8yLgbd5GzsUNkRxhvk65bfafDTcCrks6RtzNvIjvIp0o3pkh36lTWAm9IWkgqj3U9yplPtukMMEauBQ60/N7pGW4HdpWBdojJM8I6tKvnbaSr5EVJlRV/L7l++Jakp0i3UNc2RcTnkm4EvivycAJ4kPpxh+qUV+c9v0K6yx4n1xWmW9+9wEZJo6Ssf9/hL/aQk54vyAigb0uqJtBbyue60qaT5GBa8Q25eF0tslchmQ9IGiH76s8lXbu6rypWzw5ScTxBhu6etK25WL5ExHpJ9wB7JK2Y+n8lzVFJW0i5EjAYEXXDeUBuWuoH9heZ/YMMOT4K/FOU3zvkumpruZ3ewxL+2xcpyvdUdAnZAQ08jClpOCJulTQSEcvLd6MRMdAr7+WE0nzfHREf90p7Ecu8KiJOSLqatOBuj4ixS1W+Me1QuteHSHk8WyN9P9l3uh6eNN2R9BjwZ0S82S1dEy2Zg8oDQ1coFwwfIc13M/vsVh6UXAC8ZAVjmkBEnJL0AmnF/Dbb9bmMOE5uouhKEy2ZheSZkbtJU3EfOaCd7prRGGNM42ickjHGGDN3aIy7TD12kIWv+jfGmP8djVEy5K6tI+SVKsPUO5dhjDGmwTTGXVb2g68g94QPkNsS34+IrnuwjTHGNJfGHMaMiLMRsTci1pKHrn4CvpS0eZarZowx5jxpkrsMSVcC95HWTD951ccn3fIYY4xpLk1yl71LXib5GXli9uAsV8kYY8wF0iQlc46JqzlaKyXyFphFl75WxhhjLoTGKBljjDFzj8Ys/BtjjJl7WMkYY4yZMaxkjDHGzBhWMsYYY2YMKxljjDEzhpWMMcaYGeNfViAFtudUhsQAAAAASUVORK5CYII=\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -4879,7 +4873,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -4952,7 +4946,7 @@ "4 1854 8.210500 14.991000" ] }, - "execution_count": 231, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -4963,7 +4957,7 @@ }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -4989,7 +4983,7 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -5056,7 +5050,7 @@ "4 801975 BCE 202.92" ] }, - "execution_count": 233, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -5067,7 +5061,7 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -5134,7 +5128,7 @@ "1744 1854 288.05" ] }, - "execution_count": 234, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -5146,7 +5140,7 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -5171,7 +5165,7 @@ }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 101, "metadata": {}, "outputs": [ { @@ -5193,7 +5187,7 @@ }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -5218,7 +5212,7 @@ }, { "cell_type": "code", - "execution_count": 265, + "execution_count": 103, "metadata": {}, "outputs": [ { @@ -5304,7 +5298,7 @@ "4 283.16 " ] }, - "execution_count": 265, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -5316,7 +5310,7 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -5343,7 +5337,7 @@ }, { "cell_type": "code", - "execution_count": 273, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -5352,7 +5346,7 @@ }, { "cell_type": "code", - "execution_count": 274, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -5361,7 +5355,7 @@ }, { "cell_type": "code", - "execution_count": 275, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -5465,7 +5459,7 @@ "4 283.16 -0.461 -0.431 -0.512 " ] }, - "execution_count": 275, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -5476,7 +5470,7 @@ }, { "cell_type": "code", - "execution_count": 299, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -5488,10 +5482,10 @@ "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 Year 267 non-null object \n", + " 0 Year 267 non-null int64 \n", " 1 Annual COâ‚‚ emissions (tonnes ) 267 non-null float64\n", - "dtypes: float64(1), object(1)\n", - "memory usage: 6.3+ KB\n" + "dtypes: float64(1), int64(1)\n", + "memory usage: 6.3 KB\n" ] } ], @@ -5501,7 +5495,7 @@ }, { "cell_type": "code", - "execution_count": 308, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -5618,7 +5612,7 @@ "4 279680448.0 " ] }, - "execution_count": 308, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -5630,29 +5624,29 @@ }, { "cell_type": "code", - "execution_count": 241, - "metadata": { - "collapsed": true - }, + "execution_count": 110, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 241, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -5667,7 +5661,7 @@ }, { "cell_type": "code", - "execution_count": 307, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -5676,7 +5670,7 @@ "0.9149133329199667" ] }, - "execution_count": 307, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -5688,7 +5682,7 @@ }, { "cell_type": "code", - "execution_count": 310, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -5697,7 +5691,7 @@ "0.8796467057646229" ] }, - "execution_count": 310, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -5709,7 +5703,7 @@ }, { "cell_type": "code", - "execution_count": 257, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -5718,10 +5712,8 @@ }, { "cell_type": "code", - "execution_count": 311, - "metadata": { - "collapsed": true - }, + "execution_count": 114, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -5739,12 +5731,14 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -5763,7 +5757,7 @@ }, { "cell_type": "code", - "execution_count": 312, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -5880,7 +5874,7 @@ "4 279680448.0 " ] }, - "execution_count": 312, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } @@ -5891,7 +5885,7 @@ }, { "cell_type": "code", - "execution_count": 322, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -5983,7 +5977,7 @@ "4 279680448.0 " ] }, - "execution_count": 322, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -5995,7 +5989,7 @@ }, { "cell_type": "code", - "execution_count": 324, + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ @@ -6004,7 +5998,7 @@ }, { "cell_type": "code", - "execution_count": 325, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -6020,10 +6014,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 325, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" }, @@ -6054,7 +6048,17 @@ }, { "cell_type": "code", - "execution_count": 317, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_a_corr = co_temp_global[\"LandAverageTemperature\"].corr(co_temp_global[\"Annual CO₂ emissions (tonnes )\"])\n", + "co_a_corr" + ] + }, + { + "cell_type": "code", + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -6063,7 +6067,7 @@ "Text(0, 0.5, 'Median (℃)')" ] }, - "execution_count": 317, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" }, @@ -6086,7 +6090,7 @@ }, { "cell_type": "code", - "execution_count": 318, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -6095,7 +6099,7 @@ "Text(0, 0.5, 'Median (℃)')" ] }, - "execution_count": 318, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" }, @@ -6125,7 +6129,7 @@ }, { "cell_type": "code", - "execution_count": 250, + "execution_count": 121, "metadata": {}, "outputs": [ { @@ -6241,7 +6245,7 @@ "4 13240000.0 14.754750 4.394831 18486041.0 237.737799 " ] }, - "execution_count": 250, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -6252,7 +6256,7 @@ }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ @@ -6261,7 +6265,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -6407,7 +6411,7 @@ "GDP_per_capita 0.304585 -0.048032 1.000000 " ] }, - "execution_count": 253, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" } @@ -6418,7 +6422,7 @@ }, { "cell_type": "code", - "execution_count": 255, + "execution_count": 124, "metadata": {}, "outputs": [], "source": [ @@ -6427,7 +6431,7 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -6439,7 +6443,7 @@ " dtype='object')" ] }, - "execution_count": 262, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } @@ -6450,7 +6454,7 @@ }, { "cell_type": "code", - "execution_count": 264, + "execution_count": 126, "metadata": {}, "outputs": [ { @@ -6459,7 +6463,7 @@ "0.10909684577014037" ] }, - "execution_count": 264, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -6471,9 +6475,19 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 127, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n" + ] + }, { "data": { "image/png": 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\n", diff --git a/your-project/Global climate change datasets.ipynb b/your-project/Global climate change datasets.ipynb new file mode 100644 index 00000000..ff28d65e --- /dev/null +++ b/your-project/Global climate change datasets.ipynb @@ -0,0 +1,5711 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [], + "source": [ + "#importing the needed libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import datetime\n", + "import re\n", + "import seaborn as sns\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Annual CO emissions globally " + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", + "0 Afghanistan AFG 1949 14656.0\n", + "1 Afghanistan AFG 1950 84272.0\n", + "2 Afghanistan AFG 1951 91600.0\n", + "3 Afghanistan AFG 1952 91600.0\n", + "4 Afghanistan AFG 1953 106256.0" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Annual CO emissions globally\n", + "data = pd.read_csv('data//annual-co-emissions-by-region.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
20353WorldOWID_WRL17519350528.0
20354WorldOWID_WRL17529354192.0
20355WorldOWID_WRL17539354192.0
20356WorldOWID_WRL17549357856.0
20357WorldOWID_WRL17559361520.0
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", + "20353 World OWID_WRL 1751 9350528.0\n", + "20354 World OWID_WRL 1752 9354192.0\n", + "20355 World OWID_WRL 1753 9354192.0\n", + "20356 World OWID_WRL 1754 9357856.0\n", + "20357 World OWID_WRL 1755 9361520.0" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "global_co = data[data.Entity == 'World']\n", + "global_co.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + } + ], + "source": [ + "#cleaning the dataset\n", + "global_co.drop(['Entity','Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [], + "source": [ + "global_co.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 267 entries, 1751 to 2017\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Annual COâ‚‚ emissions (tonnes ) 267 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 4.2 KB\n" + ] + } + ], + "source": [ + "global_co.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Total greenhouse gas emissions (1990-2016)" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
0AfghanistanAFG199015140000.0
1AfghanistanAFG199115060000.0
2AfghanistanAFG199213600000.0
3AfghanistanAFG199313430000.0
4AfghanistanAFG199413240000.0
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" + ], + "text/plain": [ + " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", + "0 Afghanistan AFG 1990 15140000.0\n", + "1 Afghanistan AFG 1991 15060000.0\n", + "2 Afghanistan AFG 1992 13600000.0\n", + "3 Afghanistan AFG 1993 13430000.0\n", + "4 Afghanistan AFG 1994 13240000.0" + ] + }, + "execution_count": 256, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Total greenhouse gas emissions\n", + "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", + "ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the dataset\n", + "ghg.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
5076World19903.496734e+10
5077World19913.512544e+10
5078World19923.498553e+10
5079World19933.507963e+10
5080World19943.528761e+10
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" + ], + "text/plain": [ + " Entity Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", + "5076 World 1990 3.496734e+10\n", + "5077 World 1991 3.512544e+10\n", + "5078 World 1992 3.498553e+10\n", + "5079 World 1993 3.507963e+10\n", + "5080 World 1994 3.528761e+10" + ] + }, + "execution_count": 258, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_ghg = ghg[ghg.Entity == 'World']\n", + "global_ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + } + ], + "source": [ + "global_ghg.drop(['Entity'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": {}, + "outputs": [], + "source": [ + "global_ghg = global_ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )'})" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [], + "source": [ + "global_ghg.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 27 entries, 1990 to 2016\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 GHG(tonnes COâ‚‚e ) 27 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 432.0 bytes\n" + ] + } + ], + "source": [ + "global_ghg.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global COâ‚‚ atmospheric concentration" + ] + }, + { + "cell_type": "code", + "execution_count": 380, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearCOâ‚‚ concentration (parts per million)
0WorldOWID_WRL803719 BCE207.29
1WorldOWID_WRL803182 BCE202.23
2WorldOWID_WRL802573 BCE204.86
3WorldOWID_WRL802060 BCE207.50
4WorldOWID_WRL801975 BCE202.92
...............
1866WorldOWID_WRL2014398.65
1867WorldOWID_WRL2015400.83
1868WorldOWID_WRL2016404.24
1869WorldOWID_WRL2017406.55
1870WorldOWID_WRL2018408.52
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1871 rows × 4 columns

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" + ], + "text/plain": [ + " Entity Code Year COâ‚‚ concentration (parts per million)\n", + "0 World OWID_WRL 803719 BCE 207.29\n", + "1 World OWID_WRL 803182 BCE 202.23\n", + "2 World OWID_WRL 802573 BCE 204.86\n", + "3 World OWID_WRL 802060 BCE 207.50\n", + "4 World OWID_WRL 801975 BCE 202.92\n", + "... ... ... ... ...\n", + "1866 World OWID_WRL 2014 398.65\n", + "1867 World OWID_WRL 2015 400.83\n", + "1868 World OWID_WRL 2016 404.24\n", + "1869 World OWID_WRL 2017 406.55\n", + "1870 World OWID_WRL 2018 408.52\n", + "\n", + "[1871 rows x 4 columns]" + ] + }, + "execution_count": 380, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Global COâ‚‚ atmospheric concentration dataset\n", + "co_concentration = pd.read_csv('data//global-co-concentration-ppm.csv')\n", + "co_concentration" + ] + }, + { + "cell_type": "code", + "execution_count": 381, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the data\n", + "co_concentration.drop(['Code', 'Entity'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 382, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration = co_concentration.rename(columns={'COâ‚‚ concentration (parts per million)':'COâ‚‚ concentration'})" + ] + }, + { + "cell_type": "code", + "execution_count": 392, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1'" + ] + }, + "execution_count": 392, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_concentration.Year[1648]" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'CO atmospheric concentration')" + ] + }, + "execution_count": 267, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting the Global COâ‚‚ atmospheric concentration increasement from 1864-2018\n", + "x = co_concentration.Year[1740:]\n", + "y = co_concentration['COâ‚‚ concentration'][1740:]\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('CO atmospheric concentration')" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 1871 entries, 803719 BCE to 2018\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 COâ‚‚ concentration 1871 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 29.2+ KB\n" + ] + } + ], + "source": [ + "co_concentration.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 396, + "metadata": {}, + "outputs": [], + "source": [ + "co_atm_conc = co_concentration.drop(co_concentration.index[:1648])" + ] + }, + { + "cell_type": "code", + "execution_count": 397, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
YearCOâ‚‚ concentration
16481276.7
164930277.9
165056277.4
1651104277.5
1652136278.1
\n", + "
" + ], + "text/plain": [ + " Year COâ‚‚ concentration\n", + "1648 1 276.7\n", + "1649 30 277.9\n", + "1650 56 277.4\n", + "1651 104 277.5\n", + "1652 136 278.1" + ] + }, + "execution_count": 397, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_atm_conc.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 398, + "metadata": {}, + "outputs": [], + "source": [ + "co_atm_conc.to_csv('co_atm_conc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global Temperature anomaly 1850-2018\n" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearMedian (℃)Upper (℃)Lower (℃)
0GlobalNaN1850-0.373-0.339-0.425
1GlobalNaN1851-0.218-0.184-0.274
2GlobalNaN1852-0.228-0.196-0.280
3GlobalNaN1853-0.269-0.239-0.321
4GlobalNaN1854-0.248-0.218-0.301
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Median (℃) Upper (℃) Lower (℃)\n", + "0 Global NaN 1850 -0.373 -0.339 -0.425\n", + "1 Global NaN 1851 -0.218 -0.184 -0.274\n", + "2 Global NaN 1852 -0.228 -0.196 -0.280\n", + "3 Global NaN 1853 -0.269 -0.239 -0.321\n", + "4 Global NaN 1854 -0.248 -0.218 -0.301" + ] + }, + "execution_count": 270, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Global Temperature anomaly (increase-decreage on previous average temperature)\n", + "temperature_a = pd.read_csv('data//temperature_anomaly.csv')\n", + "temperature_a.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 676 entries, 0 to 675\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Entity 676 non-null object \n", + " 1 Code 0 non-null float64\n", + " 2 Year 676 non-null int64 \n", + " 3 Median (℃) 676 non-null float64\n", + " 4 Upper (℃) 676 non-null float64\n", + " 5 Lower (℃) 676 non-null float64\n", + "dtypes: float64(4), int64(1), object(1)\n", + "memory usage: 31.8+ KB\n" + ] + } + ], + "source": [ + "temperature_a.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the dataset\n", + "temperature_a.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [], + "source": [ + "temperature_a.rename(columns={'Entity':'Country'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Global', 'Northern Hemisphere', 'Southern Hemisphere', 'Tropics'],\n", + " dtype=object)" + ] + }, + "execution_count": 274, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860,\n", + " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", + " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", + " 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893,\n", + " 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904,\n", + " 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915,\n", + " 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926,\n", + " 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937,\n", + " 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948,\n", + " 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", + " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", + " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", + " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", + " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", + " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", + " 2015, 2016, 2017, 2018], dtype=int64)" + ] + }, + "execution_count": 275, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.Year.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" + ] + }, + "execution_count": 276, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0.0, 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting the median temperature anomaly over time to see how it's actually ingreasing \n", + "x = temperature_a.Year\n", + "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='Temperature Anomaly', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('increse ordescrease in Temperature(℃)')" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearMedian (℃)Upper (℃)Lower (℃)
0Global1850-0.373-0.339-0.425
1Global1851-0.218-0.184-0.274
2Global1852-0.228-0.196-0.280
3Global1853-0.269-0.239-0.321
4Global1854-0.248-0.218-0.301
\n", + "
" + ], + "text/plain": [ + " Country Year Median (℃) Upper (℃) Lower (℃)\n", + "0 Global 1850 -0.373 -0.339 -0.425\n", + "1 Global 1851 -0.218 -0.184 -0.274\n", + "2 Global 1852 -0.228 -0.196 -0.280\n", + "3 Global 1853 -0.269 -0.239 -0.321\n", + "4 Global 1854 -0.248 -0.218 -0.301" + ] + }, + "execution_count": 277, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [], + "source": [ + "#keeping only the global data to compare it with the CO emissions and the CO concentration\n", + "global_a = temperature_a.where(temperature_a['Country']=='Global')\n", + "global_a.dropna(inplace=True)\n", + "global_a.drop(['Country'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [], + "source": [ + "global_a.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 360, + "metadata": {}, + "outputs": [], + "source": [ + "global_a.drop(['Upper (℃)','Lower (℃)'],axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ozone depletion crisis" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearOzone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)
0Natural emissionsNaN1961165000
1Natural emissionsNaN1962165000
2Natural emissionsNaN1963165000
3Natural emissionsNaN1964165000
4Natural emissionsNaN1965165000
\n", + "
" + ], + "text/plain": [ + " Entity Code Year \\\n", + "0 Natural emissions NaN 1961 \n", + "1 Natural emissions NaN 1962 \n", + "2 Natural emissions NaN 1963 \n", + "3 Natural emissions NaN 1964 \n", + "4 Natural emissions NaN 1965 \n", + "\n", + " Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents) \n", + "0 165000 \n", + "1 165000 \n", + "2 165000 \n", + "3 165000 \n", + "4 165000 " + ] + }, + "execution_count": 287, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# importing Ozone depleting emissions dataset\n", + "ozone = pd.read_csv('data//ozone-depleting-substance-emissions.csv')\n", + "ozone.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "metadata": {}, + "outputs": [], + "source": [ + "# cleaning the dataset\n", + "ozone.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": {}, + "outputs": [], + "source": [ + "ozone.rename(columns={'Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)':'Ozone-depleting emissions'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Natural emissions', 'Total emissions'], dtype=object)" + ] + }, + "execution_count": 290, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozone.Entity.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 291, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting natural and total emissions to see that we have nearly stopped using CFC\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(ozone.Year,ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Natural emissions'), label='Natural emissions')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Tonnes CFC11-equivalents')\n", + "plt.plot(ozone.Year, ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Total emissions'), label='Total emissions')\n", + "plt.legend(['Natural emissions', 'Total emissions'], loc='upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": 292, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + } + ], + "source": [ + "caused_ozone = ozone[ozone.Entity == 'Total emissions']\n", + "caused_ozone.drop(['Entity'],axis=1, inplace=True )" + ] + }, + { + "cell_type": "code", + "execution_count": 294, + "metadata": {}, + "outputs": [], + "source": [ + "ozone.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "metadata": {}, + "outputs": [], + "source": [ + "caused_ozone.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Antartica Ozone Hole area" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearMaximum ozone hole area (square kilometres)Mean ozone hole area (square kilometres)
0WorldOWID_WRL19791100000100000
1WorldOWID_WRL198033000001400000
2WorldOWID_WRL19813100000600000
3WorldOWID_WRL1982108000004800000
4WorldOWID_WRL1983122000007900000
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" + ], + "text/plain": [ + " Entity Code Year Maximum ozone hole area (square kilometres) \\\n", + "0 World OWID_WRL 1979 1100000 \n", + "1 World OWID_WRL 1980 3300000 \n", + "2 World OWID_WRL 1981 3100000 \n", + "3 World OWID_WRL 1982 10800000 \n", + "4 World OWID_WRL 1983 12200000 \n", + "\n", + " Mean ozone hole area (square kilometres) \n", + "0 100000 \n", + "1 1400000 \n", + "2 600000 \n", + "3 4800000 \n", + "4 7900000 " + ] + }, + "execution_count": 295, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Antartica Ozone Hole area dataset\n", + "oz_hole = pd.read_csv('data//antarctic-ozone-hole-area.csv')\n", + "oz_hole.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "metadata": {}, + "outputs": [], + "source": [ + "oz_hole.drop(['Entity', 'Code','Maximum ozone hole area (square kilometres)'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMean ozone hole area (square kilometres)
01979100000
119801400000
21981600000
319824800000
419837900000
\n", + "
" + ], + "text/plain": [ + " Year Mean ozone hole area (square kilometres)\n", + "0 1979 100000\n", + "1 1980 1400000\n", + "2 1981 600000\n", + "3 1982 4800000\n", + "4 1983 7900000" + ] + }, + "execution_count": 297, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oz_hole.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 298, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 298, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting natural and total emissionsMean ozone hole area (square kilometres) to see if it has reduced\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(oz_hole.Year,oz_hole['Mean ozone hole area (square kilometres)'], label='Mean ozone hole area (square kilometres)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Mean ozone hole area (square kilometres)')\n", + "plt.legend(['Mean ozone hole area (square kilometres)'], loc='upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": {}, + "outputs": [], + "source": [ + "oz_hole.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global average temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 300, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAverageTemperatureUncertaintyLandMaxTemperatureLandMaxTemperatureUncertaintyLandMinTemperatureLandMinTemperatureUncertaintyLandAndOceanAverageTemperatureLandAndOceanAverageTemperatureUncertainty
01750-01-013.0343.574NaNNaNNaNNaNNaNNaN
11750-02-013.0833.702NaNNaNNaNNaNNaNNaN
21750-03-015.6263.076NaNNaNNaNNaNNaNNaN
31750-04-018.4902.451NaNNaNNaNNaNNaNNaN
41750-05-0111.5732.072NaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " dt LandAverageTemperature LandAverageTemperatureUncertainty \\\n", + "0 1750-01-01 3.034 3.574 \n", + "1 1750-02-01 3.083 3.702 \n", + "2 1750-03-01 5.626 3.076 \n", + "3 1750-04-01 8.490 2.451 \n", + "4 1750-05-01 11.573 2.072 \n", + "\n", + " LandMaxTemperature LandMaxTemperatureUncertainty LandMinTemperature \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " LandMinTemperatureUncertainty LandAndOceanAverageTemperature \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " LandAndOceanAverageTemperatureUncertainty \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 300, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_temperature = pd.read_csv('data//GlobalTemperatures.csv')\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAndOceanAverageTemperature
12001850-01-010.74912.833
12011850-02-013.07113.588
12021850-03-014.95414.043
12031850-04-017.21714.667
12041850-05-0110.00415.507
\n", + "
" + ], + "text/plain": [ + " dt LandAverageTemperature LandAndOceanAverageTemperature\n", + "1200 1850-01-01 0.749 12.833\n", + "1201 1850-02-01 3.071 13.588\n", + "1202 1850-03-01 4.954 14.043\n", + "1203 1850-04-01 7.217 14.667\n", + "1204 1850-05-01 10.004 15.507" + ] + }, + "execution_count": 301, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_temperature.dropna(inplace=True)\n", + "global_temperature = global_temperature[['dt','LandAverageTemperature','LandAndOceanAverageTemperature']]\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 302, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAndOceanAverageTemperatureYear
12001850-01-010.74912.8331850
12011850-02-013.07113.5881850
12021850-03-014.95414.0431850
12031850-04-017.21714.6671850
12041850-05-0110.00415.5071850
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" + ], + "text/plain": [ + " dt LandAverageTemperature LandAndOceanAverageTemperature Year\n", + "1200 1850-01-01 0.749 12.833 1850\n", + "1201 1850-02-01 3.071 13.588 1850\n", + "1202 1850-03-01 4.954 14.043 1850\n", + "1203 1850-04-01 7.217 14.667 1850\n", + "1204 1850-05-01 10.004 15.507 1850" + ] + }, + "execution_count": 302, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#global_temperature['dt']= pd.to_datetime(global_temperature['dt']) \n", + "global_temperature['Year'] = pd.DatetimeIndex(global_temperature['dt']).year\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LandAverageTemperatureLandAndOceanAverageTemperature
Year
18507.90066714.867167
18518.17858314.991833
18528.10016715.006500
18538.04183314.955167
18548.21050014.991000
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" + ], + "text/plain": [ + " LandAverageTemperature LandAndOceanAverageTemperature\n", + "Year \n", + "1850 7.900667 14.867167\n", + "1851 8.178583 14.991833\n", + "1852 8.100167 15.006500\n", + "1853 8.041833 14.955167\n", + "1854 8.210500 14.991000" + ] + }, + "execution_count": 303, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_global_temp = global_temperature[['Year','LandAverageTemperature','LandAndOceanAverageTemperature']].groupby(['Year']).agg({'LandAverageTemperature':'mean',\n", + " 'LandAndOceanAverageTemperature':'mean'})\n", + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearLandAverageTemperatureLandAndOceanAverageTemperature
018507.90066714.867167
118518.17858314.991833
218528.10016715.006500
318538.04183314.955167
418548.21050014.991000
\n", + "
" + ], + "text/plain": [ + " Year LandAverageTemperature LandAndOceanAverageTemperature\n", + "0 1850 7.900667 14.867167\n", + "1 1851 8.178583 14.991833\n", + "2 1852 8.100167 15.006500\n", + "3 1853 8.041833 14.955167\n", + "4 1854 8.210500 14.991000" + ] + }, + "execution_count": 304, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_global_temp.reset_index(level=0, inplace=True)\n", + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "metadata": {}, + "outputs": [], + "source": [ + "annual_global_temp.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 306, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 166 entries, 1850 to 2015\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 LandAverageTemperature 166 non-null float64\n", + " 1 LandAndOceanAverageTemperature 166 non-null float64\n", + "dtypes: float64(2)\n", + "memory usage: 3.9 KB\n" + ] + } + ], + "source": [ + "annual_global_temp.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fossil fuel consumption per capita" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)
0AfricaNaN19650.9823451.0406860.032724
1AfricaNaN19661.0503401.0019700.035646
2AfricaNaN19671.0143901.0051970.034371
3AfricaNaN19681.0433631.0211390.034195
4AfricaNaN19691.0322301.0010830.039699
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" + ], + "text/plain": [ + " Entity Code Year Oil (megawatt-hours) Coal (megawatt-hours) \\\n", + "0 Africa NaN 1965 0.982345 1.040686 \n", + "1 Africa NaN 1966 1.050340 1.001970 \n", + "2 Africa NaN 1967 1.014390 1.005197 \n", + "3 Africa NaN 1968 1.043363 1.021139 \n", + "4 Africa NaN 1969 1.032230 1.001083 \n", + "\n", + " Natural gas (megawatt-hours) \n", + "0 0.032724 \n", + "1 0.035646 \n", + "2 0.034371 \n", + "3 0.034195 \n", + "4 0.039699 " + ] + }, + "execution_count": 312, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fossil_per_c = pd.read_csv('data//fossil-fuel-consumption-per-capita.csv')\n", + "fossil_per_c.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 313, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)
3302WorldOWID_WRL19655.3121354.8875542.043343
3303WorldOWID_WRL19665.6055414.8450912.178977
3304WorldOWID_WRL19675.8834724.6700632.288339
3305WorldOWID_WRL19686.2573574.6435392.440440
3306WorldOWID_WRL19696.6550134.6908202.622830
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" + ], + "text/plain": [ + " Entity Code Year Oil (megawatt-hours) Coal (megawatt-hours) \\\n", + "3302 World OWID_WRL 1965 5.312135 4.887554 \n", + "3303 World OWID_WRL 1966 5.605541 4.845091 \n", + "3304 World OWID_WRL 1967 5.883472 4.670063 \n", + "3305 World OWID_WRL 1968 6.257357 4.643539 \n", + "3306 World OWID_WRL 1969 6.655013 4.690820 \n", + "\n", + " Natural gas (megawatt-hours) \n", + "3302 2.043343 \n", + "3303 2.178977 \n", + "3304 2.288339 \n", + "3305 2.440440 \n", + "3306 2.622830 " + ] + }, + "execution_count": 313, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "fossil_per_c = fossil_per_c[fossil_per_c.Entity == 'World']\n", + "fossil_per_c.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "metadata": {}, + "outputs": [], + "source": [ + "fossil_per_c.drop(['Entity', 'Code',], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 315, + "metadata": {}, + "outputs": [], + "source": [ + "fossil_per_c.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 51 entries, 1965 to 2015\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Oil (megawatt-hours) 51 non-null float64\n", + " 1 Coal (megawatt-hours) 51 non-null float64\n", + " 2 Natural gas (megawatt-hours) 51 non-null float64\n", + "dtypes: float64(3)\n", + "memory usage: 1.6 KB\n" + ] + } + ], + "source": [ + "fossil_per_c.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global primary energy by source" + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearCoal (terawatt-hours)Solar (terawatt-hours)Crude oil (terawatt-hours)Natural gas (terawatt-hours)Traditional biofuels (terawatt-hours)Other renewables (terawatt-hours)Hydropower (terawatt-hours)Nuclear (terawatt-hours)Wind (terawatt-hours)
0WorldOWID_WRL180097.222300.00.00.05555.5555560.00.00.00.0
1WorldOWID_WRL1810127.777880.00.00.05833.3333330.00.00.00.0
2WorldOWID_WRL1820152.777900.00.00.06111.1111110.00.00.00.0
3WorldOWID_WRL1830263.889100.00.00.06388.8888890.00.00.00.0
4WorldOWID_WRL1840355.555840.00.00.06944.4444440.00.00.00.0
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" + ], + "text/plain": [ + " Entity Code Year Coal (terawatt-hours) Solar (terawatt-hours) \\\n", + "0 World OWID_WRL 1800 97.22230 0.0 \n", + "1 World OWID_WRL 1810 127.77788 0.0 \n", + "2 World OWID_WRL 1820 152.77790 0.0 \n", + "3 World OWID_WRL 1830 263.88910 0.0 \n", + "4 World OWID_WRL 1840 355.55584 0.0 \n", + "\n", + " Crude oil (terawatt-hours) Natural gas (terawatt-hours) \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Traditional biofuels (terawatt-hours) Other renewables (terawatt-hours) \\\n", + "0 5555.555556 0.0 \n", + "1 5833.333333 0.0 \n", + "2 6111.111111 0.0 \n", + "3 6388.888889 0.0 \n", + "4 6944.444444 0.0 \n", + "\n", + " Hydropower (terawatt-hours) Nuclear (terawatt-hours) \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Wind (terawatt-hours) \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 " + ] + }, + "execution_count": 317, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_energy = pd.read_csv('data//global-primary-energy.csv')\n", + "global_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 318, + "metadata": {}, + "outputs": [], + "source": [ + "global_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "metadata": {}, + "outputs": [], + "source": [ + "global_energy.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 320, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 71 entries, 1800 to 2018\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Coal (terawatt-hours) 71 non-null float64\n", + " 1 Solar (terawatt-hours) 71 non-null float64\n", + " 2 Crude oil (terawatt-hours) 71 non-null float64\n", + " 3 Natural gas (terawatt-hours) 71 non-null float64\n", + " 4 Traditional biofuels (terawatt-hours) 71 non-null float64\n", + " 5 Other renewables (terawatt-hours) 71 non-null float64\n", + " 6 Hydropower (terawatt-hours) 71 non-null float64\n", + " 7 Nuclear (terawatt-hours) 71 non-null float64\n", + " 8 Wind (terawatt-hours) 71 non-null float64\n", + "dtypes: float64(9)\n", + "memory usage: 5.5 KB\n" + ] + } + ], + "source": [ + "global_energy.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modern renewable energy consumtion" + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearHydropower (terawatt-hours)Solar (terawatt-hours)Wind (terawatt-hours)Other renewables (terawatt-hours)
0AfricaNaN196514.2788060.00.00.0
1AfricaNaN196615.6490490.00.00.0
2AfricaNaN196716.1583330.00.00.0
3AfricaNaN196818.6229830.00.00.0
4AfricaNaN196921.5828970.00.00.0
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" + ], + "text/plain": [ + " Entity Code Year Hydropower (terawatt-hours) Solar (terawatt-hours) \\\n", + "0 Africa NaN 1965 14.278806 0.0 \n", + "1 Africa NaN 1966 15.649049 0.0 \n", + "2 Africa NaN 1967 16.158333 0.0 \n", + "3 Africa NaN 1968 18.622983 0.0 \n", + "4 Africa NaN 1969 21.582897 0.0 \n", + "\n", + " Wind (terawatt-hours) Other renewables (terawatt-hours) \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 " + ] + }, + "execution_count": 321, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "renew_energy = pd.read_csv('data//modern-renewable-energy-consumption.csv')\n", + "renew_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearHydropower (terawatt-hours)Solar (terawatt-hours)Wind (terawatt-hours)Other renewables (terawatt-hours)
5037WorldOWID_WRL1965919.7702760.00.017.985232
5038WorldOWID_WRL1966986.2858720.00.019.806007
5039WorldOWID_WRL19671009.2445690.00.019.986571
5040WorldOWID_WRL19681059.0628600.00.022.120045
5041WorldOWID_WRL19691122.4425260.00.023.257964
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" + ], + "text/plain": [ + " Entity Code Year Hydropower (terawatt-hours) \\\n", + "5037 World OWID_WRL 1965 919.770276 \n", + "5038 World OWID_WRL 1966 986.285872 \n", + "5039 World OWID_WRL 1967 1009.244569 \n", + "5040 World OWID_WRL 1968 1059.062860 \n", + "5041 World OWID_WRL 1969 1122.442526 \n", + "\n", + " Solar (terawatt-hours) Wind (terawatt-hours) \\\n", + "5037 0.0 0.0 \n", + "5038 0.0 0.0 \n", + "5039 0.0 0.0 \n", + "5040 0.0 0.0 \n", + "5041 0.0 0.0 \n", + "\n", + " Other renewables (terawatt-hours) \n", + "5037 17.985232 \n", + "5038 19.806007 \n", + "5039 19.986571 \n", + "5040 22.120045 \n", + "5041 23.257964 " + ] + }, + "execution_count": 322, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "renew_energy = renew_energy[renew_energy.Entity == 'World']\n", + "renew_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 323, + "metadata": {}, + "outputs": [], + "source": [ + "renew_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 324, + "metadata": {}, + "outputs": [], + "source": [ + "renew_energy.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 325, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 54 entries, 1965 to 2018\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Hydropower (terawatt-hours) 54 non-null float64\n", + " 1 Solar (terawatt-hours) 54 non-null float64\n", + " 2 Wind (terawatt-hours) 54 non-null float64\n", + " 3 Other renewables (terawatt-hours) 54 non-null float64\n", + "dtypes: float64(4)\n", + "memory usage: 2.1 KB\n" + ] + } + ], + "source": [ + "renew_energy.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hubbert's peak vs actual oil production" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYear(barrels per year)(barrels per year).1
0United StatesUSA1910209557000.0NaN
1United StatesUSA1911220449000.0200158958.0
2United StatesUSA1912222935000.0213834803.7
3United StatesUSA1913248446000.0228406784.8
4United StatesUSA1914265763000.0243928136.8
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" + ], + "text/plain": [ + " Entity Code Year (barrels per year) (barrels per year).1\n", + "0 United States USA 1910 209557000.0 NaN\n", + "1 United States USA 1911 220449000.0 200158958.0\n", + "2 United States USA 1912 222935000.0 213834803.7\n", + "3 United States USA 1913 248446000.0 228406784.8\n", + "4 United States USA 1914 265763000.0 243928136.8" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hubbert_peak = pd.read_csv('data//hubberts-peak-vs-actual-oil-production-in-the-united-states.csv')\n", + "hubbert_peak.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Entity', 'Code', 'Year', ' (barrels per year)',\n", + " ' (barrels per year).1'],\n", + " dtype='object')" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hubbert_peak.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak.drop(['Entity', 'Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak.rename(columns={' (barrels per year)':'Actual Oil Prod',' (barrels per year).1':'Hubbert Predicted'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak = hubbert_peak.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Year Actual Predicted\n", + "102 2012 2.374136e+09 614194532.6\n", + "103 2013 2.725665e+09 577668428.7\n", + "104 2014 3.198694e+09 543068744.5\n", + "105 2015 3.436515e+09 510324290.7\n", + "106 2016 3.248117e+09 479362510.8" + ] + }, + "execution_count": 328, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hubbert_peak.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 329, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 329, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting predicted and actual emissions to see if it the prediction came true\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(hubbert_peak.Year,hubbert_peak['Actual'],label='Actual')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('(barrels per year)')\n", + "plt.plot(hubbert_peak.Year,hubbert_peak['Predicted'],label='Predicted')\n", + "plt.legend(['Actual', 'Predicted'], loc='upper left')" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 106 entries, 1911 to 2016\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Actual 106 non-null float64\n", + " 1 Predicted 106 non-null float64\n", + "dtypes: float64(2)\n", + "memory usage: 2.5 KB\n" + ] + } + ], + "source": [ + "hubbert_peak.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### share of global primary energy" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearCoal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
0WorldOWID_WRL18001.7199030.00.00.00.00.00.098.2800970.0
1WorldOWID_WRL18102.1435250.00.00.00.00.00.097.8564750.0
2WorldOWID_WRL18202.4390260.00.00.00.00.00.097.5609740.0
3WorldOWID_WRL18303.9666000.00.00.00.00.00.096.0334000.0
4WorldOWID_WRL18404.8706280.00.00.00.00.00.095.1293720.0
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" + ], + "text/plain": [ + " Entity Code Year Coal (%) Crude oil (%) Hydropower (%) \\\n", + "0 World OWID_WRL 1800 1.719903 0.0 0.0 \n", + "1 World OWID_WRL 1810 2.143525 0.0 0.0 \n", + "2 World OWID_WRL 1820 2.439026 0.0 0.0 \n", + "3 World OWID_WRL 1830 3.966600 0.0 0.0 \n", + "4 World OWID_WRL 1840 4.870628 0.0 0.0 \n", + "\n", + " Natural gas (%) Nuclear (%) Other renewables (%) Solar (%) \\\n", + "0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "\n", + " Traditional biofuels (%) Wind (%) \n", + "0 98.280097 0.0 \n", + "1 97.856475 0.0 \n", + "2 97.560974 0.0 \n", + "3 96.033400 0.0 \n", + "4 95.129372 0.0 " + ] + }, + "execution_count": 332, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "share_energy = pd.read_csv('data//global-primary-energy-share-inc-biomass.csv')\n", + "share_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "metadata": {}, + "outputs": [], + "source": [ + "share_energy.drop(['Entity', 'Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [], + "source": [ + "share_energy.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 70 entries, 1800 to 2017\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Coal (%) 70 non-null float64\n", + " 1 Crude oil (%) 70 non-null float64\n", + " 2 Hydropower (%) 70 non-null float64\n", + " 3 Natural gas (%) 70 non-null float64\n", + " 4 Nuclear (%) 70 non-null float64\n", + " 5 Other renewables (%) 70 non-null float64\n", + " 6 Solar (%) 70 non-null float64\n", + " 7 Traditional biofuels (%) 70 non-null float64\n", + " 8 Wind (%) 70 non-null float64\n", + "dtypes: float64(9)\n", + "memory usage: 5.5 KB\n" + ] + } + ], + "source": [ + "share_energy.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solar pannels price progression" + ] + }, + { + "cell_type": "code", + "execution_count": 445, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearSolar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))Cumulative Installed Capacity (megawatts-peak (MWp))Total population (Gapminder)
0AfghanistanAFG1800NaNNaN3280000.0
1AfghanistanAFG1820NaNNaN3280000.0
2AfghanistanAFG1870NaNNaN4207000.0
3AfghanistanAFG1913NaNNaN5730000.0
4AfghanistanAFG1950NaNNaN8151455.0
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" + ], + "text/plain": [ + " Entity Code Year \\\n", + "0 Afghanistan AFG 1800 \n", + "1 Afghanistan AFG 1820 \n", + "2 Afghanistan AFG 1870 \n", + "3 Afghanistan AFG 1913 \n", + "4 Afghanistan AFG 1950 \n", + "\n", + " Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp)) \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Cumulative Installed Capacity (megawatts-peak (MWp)) \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Total population (Gapminder) \n", + "0 3280000.0 \n", + "1 3280000.0 \n", + "2 4207000.0 \n", + "3 5730000.0 \n", + "4 8151455.0 " + ] + }, + "execution_count": 445, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solar_price = pd.read_csv('data//solar-pv-prices-vs-cumulative-capacity.csv')\n", + "solar_price.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 446, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearSolar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))Cumulative Installed Capacity (megawatts-peak (MWp))Total population (Gapminder)
19946WorldOWID_WRL197666.1048350.32NaN
19947WorldOWID_WRL197746.4405390.77NaN
19948WorldOWID_WRL197833.2826881.77NaN
19949WorldOWID_WRL197929.6924933.27NaN
19950WorldOWID_WRL198023.7553616.57NaN
\n", + "
" + ], + "text/plain": [ + " Entity Code Year \\\n", + "19946 World OWID_WRL 1976 \n", + "19947 World OWID_WRL 1977 \n", + "19948 World OWID_WRL 1978 \n", + "19949 World OWID_WRL 1979 \n", + "19950 World OWID_WRL 1980 \n", + "\n", + " Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp)) \\\n", + "19946 66.104835 \n", + "19947 46.440539 \n", + "19948 33.282688 \n", + "19949 29.692493 \n", + "19950 23.755361 \n", + "\n", + " Cumulative Installed Capacity (megawatts-peak (MWp)) \\\n", + "19946 0.32 \n", + "19947 0.77 \n", + "19948 1.77 \n", + "19949 3.27 \n", + "19950 6.57 \n", + "\n", + " Total population (Gapminder) \n", + "19946 NaN \n", + "19947 NaN \n", + "19948 NaN \n", + "19949 NaN \n", + "19950 NaN " + ] + }, + "execution_count": 446, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "solar_price = solar_price[solar_price.Entity == 'World']\n", + "solar_price.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 447, + "metadata": {}, + "outputs": [], + "source": [ + "solar_price.drop(['Entity', 'Code','Total population (Gapminder)', 'Year'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 448, + "metadata": {}, + "outputs": [], + "source": [ + "solar_price.rename(columns={'Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))':'Solar PV Module Cost($/Wp)','Cumulative Installed Capacity (megawatts-peak (MWp))':'Cumulative Installed Capacity(MWp)'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 449, + "metadata": {}, + "outputs": [], + "source": [ + "solar_price.set_index('Cumulative Installed Capacity(MWp)',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 428, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Solar PV Module Cost($/Wp)Cumulative Installed Capacity(MWp)
1994666.1048350.32
1994746.4405390.77
1994833.2826881.77
1994929.6924933.27
1995023.7553616.57
1995117.91880011.87
1995218.03281819.57
1995316.84394434.07
1995413.71777351.57
1995512.49436670.97
1995611.68843891.97
199579.801146116.87
199589.732873148.37
199599.789955186.27
1996010.009799228.97
199619.294041277.17
199628.779086331.27
199637.822745386.97
199647.645052447.97
199657.070296519.47
199665.880831602.07
199675.823569716.17
199684.892792850.97
199694.4797171026.47
199703.6500311278.47
199713.4387701631.37
199723.5146112136.27
199733.3207732811.57
199743.5367933861.37
199753.5801575269.07
199763.8859217251.47
199773.90805910324.47
199783.55885615816.27
199792.36926823729.27
199801.58915441131.57
199811.44128064710.87
199820.77472890772.67
199830.823302124572.67
199840.710000164472.67
199850.660000219868.00
199860.620000291064.00
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" + ], + "text/plain": [ + " Solar PV Module Cost($/Wp) Cumulative Installed Capacity(MWp)\n", + "19946 66.104835 0.32\n", + "19947 46.440539 0.77\n", + "19948 33.282688 1.77\n", + "19949 29.692493 3.27\n", + "19950 23.755361 6.57\n", + "19951 17.918800 11.87\n", + "19952 18.032818 19.57\n", + "19953 16.843944 34.07\n", + "19954 13.717773 51.57\n", + "19955 12.494366 70.97\n", + "19956 11.688438 91.97\n", + "19957 9.801146 116.87\n", + "19958 9.732873 148.37\n", + "19959 9.789955 186.27\n", + "19960 10.009799 228.97\n", + "19961 9.294041 277.17\n", + "19962 8.779086 331.27\n", + "19963 7.822745 386.97\n", + "19964 7.645052 447.97\n", + "19965 7.070296 519.47\n", + "19966 5.880831 602.07\n", + "19967 5.823569 716.17\n", + "19968 4.892792 850.97\n", + "19969 4.479717 1026.47\n", + "19970 3.650031 1278.47\n", + "19971 3.438770 1631.37\n", + "19972 3.514611 2136.27\n", + "19973 3.320773 2811.57\n", + "19974 3.536793 3861.37\n", + "19975 3.580157 5269.07\n", + "19976 3.885921 7251.47\n", + "19977 3.908059 10324.47\n", + "19978 3.558856 15816.27\n", + "19979 2.369268 23729.27\n", + "19980 1.589154 41131.57\n", + "19981 1.441280 64710.87\n", + "19982 0.774728 90772.67\n", + "19983 0.823302 124572.67\n", + "19984 0.710000 164472.67\n", + "19985 0.660000 219868.00\n", + "19986 0.620000 291064.00" + ] + }, + "execution_count": 428, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solar_price" + ] + }, + { + "cell_type": "code", + "execution_count": 441, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 441, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "solar_price['Solar PV Module Cost($/Wp)'] = solar_price['Solar PV Module Cost($/Wp)'].round()\n", + "x = solar_price.groupby('Solar PV Module Cost($/Wp)').mean()\n", + "x.loc[x['Cumulative Installed Capacity(MWp)']<10].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Merging datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 358, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"#concatenating the datasets on the index ('Year'), we'll have a lot of NaN's because the datasets have different timeframes,\\n#but I'll be good for plotting nonetheless\\n\\nglobal_indicators = pd.concat([global_co, global_ghg, co_concentration, global_a, caused_ozone, oz_hole, annual_global_temp, fossil_per_c,\\n global_energy, renew_energy, hubbert_peak, share_energy, solar_price], join='outer')\"" + ] + }, + "execution_count": 358, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''#concatenating the datasets on the index ('Year'), we'll have a lot of NaN's because the datasets have different timeframes,\n", + "#but I'll be good for plotting nonetheless\n", + "\n", + "global_indicators = pd.concat([global_co, global_ghg, co_concentration, global_a, caused_ozone, oz_hole, annual_global_temp,\n", + "fossil_per_c, global_energy, renew_energy, hubbert_peak, share_energy, solar_price], join='outer')'''" + ] + }, + { + "cell_type": "code", + "execution_count": 362, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1 and 2\n", + "global_indicators1 = pd.merge(global_co, global_ghg, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 365, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4\n", + "global_indicators2 = pd.merge(global_indicators1,global_a, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 366, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5\n", + "global_indicators3 = pd.merge(global_indicators2,caused_ozone, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 367, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6\n", + "global_indicators4 = pd.merge(global_indicators3,oz_hole, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 368, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7\n", + "global_indicators5 = pd.merge(global_indicators4,annual_global_temp, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 369, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8\n", + "global_indicators6 = pd.merge(global_indicators5,fossil_per_c, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 370, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8,9\n", + "global_indicators7 = pd.merge(global_indicators6,global_energy, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 371, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8,9,10\n", + "global_indicators8 = pd.merge(global_indicators7,renew_energy, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 372, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8,9,10,12\n", + "global_indicators = pd.merge(global_indicators8,share_energy, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 374, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Annual CO₂ emissions (tonnes )GHG(tonnes CO₂e )Median (℃)Ozone-depleting emissionsMean ozone hole area (square kilometres)LandAverageTemperatureLandAndOceanAverageTemperatureOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)...Other renewables (terawatt-hours)_yCoal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
Year
20143.550583e+104.944079e+100.579320000.020900000.09.57066715.9130006.7800186.1977514.896768...502.15891930.12992834.2842432.60799922.7981601.7045050.3383420.1325977.5263480.477878
20153.546275e+104.985498e+100.763NaN25600000.09.83100016.0585836.8380425.9617334.956876...536.46624429.23211234.7507802.59790623.1936921.7195140.3593070.1735807.4177810.555327
20163.567510e+104.935803e+100.797NaN20700000.0NaNNaNNaNNaNNaN...556.62335628.47941835.0213262.66686223.6166451.7264460.3680320.2168497.2704460.633975
20173.615326e+10NaN0.677NaN17400000.0NaNNaNNaNNaNNaN...584.97582728.25414134.9959602.64321823.8964861.7159090.3816330.2881717.0935080.730975
2018NaNNaN0.595NaNNaNNaNNaNNaNNaNNaN...625.805362NaNNaNNaNNaNNaNNaNNaNNaNNaN
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5 rows × 32 columns

\n", + "
" + ], + "text/plain": [ + " Annual CO₂ emissions (tonnes ) GHG(tonnes CO₂e ) Median (℃) \\\n", + "Year \n", + "2014 3.550583e+10 4.944079e+10 0.579 \n", + "2015 3.546275e+10 4.985498e+10 0.763 \n", + "2016 3.567510e+10 4.935803e+10 0.797 \n", + "2017 3.615326e+10 NaN 0.677 \n", + "2018 NaN NaN 0.595 \n", + "\n", + " Ozone-depleting emissions Mean ozone hole area (square kilometres) \\\n", + "Year \n", + "2014 320000.0 20900000.0 \n", + "2015 NaN 25600000.0 \n", + "2016 NaN 20700000.0 \n", + "2017 NaN 17400000.0 \n", + "2018 NaN NaN \n", + "\n", + " LandAverageTemperature LandAndOceanAverageTemperature \\\n", + "Year \n", + "2014 9.570667 15.913000 \n", + "2015 9.831000 16.058583 \n", + "2016 NaN NaN \n", + "2017 NaN NaN \n", + "2018 NaN NaN \n", + "\n", + " Oil (megawatt-hours) Coal (megawatt-hours) \\\n", + "Year \n", + "2014 6.780018 6.197751 \n", + "2015 6.838042 5.961733 \n", + "2016 NaN NaN \n", + "2017 NaN NaN \n", + "2018 NaN NaN \n", + "\n", + " Natural gas (megawatt-hours) ... Other renewables (terawatt-hours)_y \\\n", + "Year ... \n", + "2014 4.896768 ... 502.158919 \n", + "2015 4.956876 ... 536.466244 \n", + "2016 NaN ... 556.623356 \n", + "2017 NaN ... 584.975827 \n", + "2018 NaN ... 625.805362 \n", + "\n", + " Coal (%) Crude oil (%) Hydropower (%) Natural gas (%) Nuclear (%) \\\n", + "Year \n", + "2014 30.129928 34.284243 2.607999 22.798160 1.704505 \n", + "2015 29.232112 34.750780 2.597906 23.193692 1.719514 \n", + "2016 28.479418 35.021326 2.666862 23.616645 1.726446 \n", + "2017 28.254141 34.995960 2.643218 23.896486 1.715909 \n", + "2018 NaN NaN NaN NaN NaN \n", + "\n", + " Other renewables (%) Solar (%) Traditional biofuels (%) Wind (%) \n", + "Year \n", + "2014 0.338342 0.132597 7.526348 0.477878 \n", + "2015 0.359307 0.173580 7.417781 0.555327 \n", + "2016 0.368032 0.216849 7.270446 0.633975 \n", + "2017 0.381633 0.288171 7.093508 0.730975 \n", + "2018 NaN NaN NaN NaN \n", + "\n", + "[5 rows x 32 columns]" + ] + }, + "execution_count": 374, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_indicators.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 375, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 268 entries, 1751 to 2018\n", + "Data columns (total 32 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Annual CO₂ emissions (tonnes ) 267 non-null float64\n", + " 1 GHG(tonnes CO₂e ) 27 non-null float64\n", + " 2 Median (℃) 169 non-null float64\n", + " 3 Ozone-depleting emissions 54 non-null float64\n", + " 4 Mean ozone hole area (square kilometres) 38 non-null float64\n", + " 5 LandAverageTemperature 166 non-null float64\n", + " 6 LandAndOceanAverageTemperature 166 non-null float64\n", + " 7 Oil (megawatt-hours) 51 non-null float64\n", + " 8 Coal (megawatt-hours) 51 non-null float64\n", + " 9 Natural gas (megawatt-hours) 51 non-null float64\n", + " 10 Coal (terawatt-hours) 71 non-null float64\n", + " 11 Solar (terawatt-hours)_x 71 non-null float64\n", + " 12 Crude oil (terawatt-hours) 71 non-null float64\n", + " 13 Natural gas (terawatt-hours) 71 non-null float64\n", + " 14 Traditional biofuels (terawatt-hours) 71 non-null float64\n", + " 15 Other renewables (terawatt-hours)_x 71 non-null float64\n", + " 16 Hydropower (terawatt-hours)_x 71 non-null float64\n", + " 17 Nuclear (terawatt-hours) 71 non-null float64\n", + " 18 Wind (terawatt-hours)_x 71 non-null float64\n", + " 19 Hydropower (terawatt-hours)_y 54 non-null float64\n", + " 20 Solar (terawatt-hours)_y 54 non-null float64\n", + " 21 Wind (terawatt-hours)_y 54 non-null float64\n", + " 22 Other renewables (terawatt-hours)_y 54 non-null float64\n", + " 23 Coal (%) 70 non-null float64\n", + " 24 Crude oil (%) 70 non-null float64\n", + " 25 Hydropower (%) 70 non-null float64\n", + " 26 Natural gas (%) 70 non-null float64\n", + " 27 Nuclear (%) 70 non-null float64\n", + " 28 Other renewables (%) 70 non-null float64\n", + " 29 Solar (%) 70 non-null float64\n", + " 30 Traditional biofuels (%) 70 non-null float64\n", + " 31 Wind (%) 70 non-null float64\n", + "dtypes: float64(32)\n", + "memory usage: 69.1 KB\n" + ] + } + ], + "source": [ + "global_indicators.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "metadata": {}, + "outputs": [], + "source": [ + "global_indicators.to_csv('global_indicators')" + ] + }, + { + "cell_type": "code", + "execution_count": 444, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.890288126134981" + ] + }, + "execution_count": 444, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_a_corr = global_indicators[\"Median (℃)\"].corr(global_indicators[\"Annual CO₂ emissions (tonnes )\"])\n", + "co_a_corr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Inner merges" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearAnnual CO₂ emissions (tonnes )Median (℃)LandAverageTemperatureLandAndOceanAverageTemperatureCoal (terawatt-hours)Solar (terawatt-hours)Crude oil (terawatt-hours)Natural gas (terawatt-hours)Traditional biofuels (terawatt-hours)Other renewables (terawatt-hours)Hydropower (terawatt-hours)Nuclear (terawatt-hours)Wind (terawatt-hours)
018501.968960e+08-0.3737.90066714.867167569.444900.00.000000.000007222.2222220.00.0000000.00.0
118603.307090e+08-0.3437.96083314.8829171061.111960.00.000000.000006944.4444440.00.0000000.00.0
218705.325368e+08-0.2768.20133315.0043331641.667980.05.555560.000006944.4444440.00.0000000.00.0
318808.538915e+08-0.2278.11875015.0086672541.668700.033.333360.000006944.4444440.00.0000000.00.0
418901.298855e+09-0.4167.97166714.8407503855.558640.088.8889633.333366666.6666670.013.8888890.00.0
\n", + "
" + ], + "text/plain": [ + " Year Annual CO₂ emissions (tonnes ) Median (℃) LandAverageTemperature \\\n", + "0 1850 1.968960e+08 -0.373 7.900667 \n", + "1 1860 3.307090e+08 -0.343 7.960833 \n", + "2 1870 5.325368e+08 -0.276 8.201333 \n", + "3 1880 8.538915e+08 -0.227 8.118750 \n", + "4 1890 1.298855e+09 -0.416 7.971667 \n", + "\n", + " LandAndOceanAverageTemperature Coal (terawatt-hours) \\\n", + "0 14.867167 569.44490 \n", + "1 14.882917 1061.11196 \n", + "2 15.004333 1641.66798 \n", + "3 15.008667 2541.66870 \n", + "4 14.840750 3855.55864 \n", + "\n", + " Solar (terawatt-hours) Crude oil (terawatt-hours) \\\n", + "0 0.0 0.00000 \n", + "1 0.0 0.00000 \n", + "2 0.0 5.55556 \n", + "3 0.0 33.33336 \n", + "4 0.0 88.88896 \n", + "\n", + " Natural gas (terawatt-hours) Traditional biofuels (terawatt-hours) \\\n", + "0 0.00000 7222.222222 \n", + "1 0.00000 6944.444444 \n", + "2 0.00000 6944.444444 \n", + "3 0.00000 6944.444444 \n", + "4 33.33336 6666.666667 \n", + "\n", + " Other renewables (terawatt-hours) Hydropower (terawatt-hours) \\\n", + "0 0.0 0.000000 \n", + "1 0.0 0.000000 \n", + "2 0.0 0.000000 \n", + "3 0.0 0.000000 \n", + "4 0.0 13.888889 \n", + "\n", + " Nuclear (terawatt-hours) Wind (terawatt-hours) \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 " + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emissions_temp_merged = pd.merge(emissions_temp_merged,global_energy, on='Year')\n", + "emissions_temp_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearAnnual CO₂ emissions (tonnes )Median (℃)LandAverageTemperatureLandAndOceanAverageTemperatureCoal (terawatt-hours)Solar (terawatt-hours)Crude oil (terawatt-hours)Natural gas (terawatt-hours)Traditional biofuels (terawatt-hours)...Wind (terawatt-hours)Coal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
018501.968960e+08-0.3737.90066714.867167569.444900.00.000000.000007222.222222...0.07.3083830.0000000.000000.0000000.00.00.092.6916170.0
118603.307090e+08-0.3437.96083314.8829171061.111960.00.000000.000006944.444444...0.013.2546930.0000000.000000.0000000.00.00.086.7453070.0
218705.325368e+08-0.2768.20133315.0043331641.667980.05.555560.000006944.444444...0.019.1076750.0646620.000000.0000000.00.00.080.8276630.0
318808.538915e+08-0.2278.11875015.0086672541.668700.033.333360.000006944.444444...0.026.6997530.3501610.000000.0000000.00.00.072.9500860.0
418901.298855e+09-0.4167.97166714.8407503855.558640.088.8889633.333366666.666667...0.036.1741120.8339850.130310.3127440.00.00.062.5488480.0
\n", + "

5 rows × 23 columns

\n", + "
" + ], + "text/plain": [ + " Year Annual CO₂ emissions (tonnes ) Median (℃) LandAverageTemperature \\\n", + "0 1850 1.968960e+08 -0.373 7.900667 \n", + "1 1860 3.307090e+08 -0.343 7.960833 \n", + "2 1870 5.325368e+08 -0.276 8.201333 \n", + "3 1880 8.538915e+08 -0.227 8.118750 \n", + "4 1890 1.298855e+09 -0.416 7.971667 \n", + "\n", + " LandAndOceanAverageTemperature Coal (terawatt-hours) \\\n", + "0 14.867167 569.44490 \n", + "1 14.882917 1061.11196 \n", + "2 15.004333 1641.66798 \n", + "3 15.008667 2541.66870 \n", + "4 14.840750 3855.55864 \n", + "\n", + " Solar (terawatt-hours) Crude oil (terawatt-hours) \\\n", + "0 0.0 0.00000 \n", + "1 0.0 0.00000 \n", + "2 0.0 5.55556 \n", + "3 0.0 33.33336 \n", + "4 0.0 88.88896 \n", + "\n", + " Natural gas (terawatt-hours) Traditional biofuels (terawatt-hours) ... \\\n", + "0 0.00000 7222.222222 ... \n", + "1 0.00000 6944.444444 ... \n", + "2 0.00000 6944.444444 ... \n", + "3 0.00000 6944.444444 ... \n", + "4 33.33336 6666.666667 ... \n", + "\n", + " Wind (terawatt-hours) Coal (%) Crude oil (%) Hydropower (%) \\\n", + "0 0.0 7.308383 0.000000 0.00000 \n", + "1 0.0 13.254693 0.000000 0.00000 \n", + "2 0.0 19.107675 0.064662 0.00000 \n", + "3 0.0 26.699753 0.350161 0.00000 \n", + "4 0.0 36.174112 0.833985 0.13031 \n", + "\n", + " Natural gas (%) Nuclear (%) Other renewables (%) Solar (%) \\\n", + "0 0.000000 0.0 0.0 0.0 \n", + "1 0.000000 0.0 0.0 0.0 \n", + "2 0.000000 0.0 0.0 0.0 \n", + "3 0.000000 0.0 0.0 0.0 \n", + "4 0.312744 0.0 0.0 0.0 \n", + "\n", + " Traditional biofuels (%) Wind (%) \n", + "0 92.691617 0.0 \n", + "1 86.745307 0.0 \n", + "2 80.827663 0.0 \n", + "3 72.950086 0.0 \n", + "4 62.548848 0.0 \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emissions_temp_merged = pd.merge(emissions_temp_merged,share_energy, on='Year')\n", + "emissions_temp_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 63 entries, 0 to 62\n", + "Data columns (total 23 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Year 63 non-null int64 \n", + " 1 Annual CO₂ emissions (tonnes ) 63 non-null float64\n", + " 2 Median (℃) 63 non-null float64\n", + " 3 LandAverageTemperature 63 non-null float64\n", + " 4 LandAndOceanAverageTemperature 63 non-null float64\n", + " 5 Coal (terawatt-hours) 63 non-null float64\n", + " 6 Solar (terawatt-hours) 63 non-null float64\n", + " 7 Crude oil (terawatt-hours) 63 non-null float64\n", + " 8 Natural gas (terawatt-hours) 63 non-null float64\n", + " 9 Traditional biofuels (terawatt-hours) 63 non-null float64\n", + " 10 Other renewables (terawatt-hours) 63 non-null float64\n", + " 11 Hydropower (terawatt-hours) 63 non-null float64\n", + " 12 Nuclear (terawatt-hours) 63 non-null float64\n", + " 13 Wind (terawatt-hours) 63 non-null float64\n", + " 14 Coal (%) 63 non-null float64\n", + " 15 Crude oil (%) 63 non-null float64\n", + " 16 Hydropower (%) 63 non-null float64\n", + " 17 Natural gas (%) 63 non-null float64\n", + " 18 Nuclear (%) 63 non-null float64\n", + " 19 Other renewables (%) 63 non-null float64\n", + " 20 Solar (%) 63 non-null float64\n", + " 21 Traditional biofuels (%) 63 non-null float64\n", + " 22 Wind (%) 63 non-null float64\n", + "dtypes: float64(22), int64(1)\n", + "memory usage: 11.8 KB\n" + ] + } + ], + "source": [ + "emissions_temp_merged.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [], + "source": [ + "emissions_temp_merged[\"Year\"] = emissions_temp_merged[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [], + "source": [ + "emissions_temp_merged.to_csv('emissions_temp_merged')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_merged = pd.merge(co_annual, co_share, on=['Country','Year'])\n", + "co_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_merged.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### total emissions (1990-2016)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "total_emissions = pd.merge(co_merged,ghg, on=(['Country','Year']))\n", + "total_emissions.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "total_emissions.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging Temperature with Emissions (1990-2013)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "annual_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "annual_temp.rename(columns={'year':'Year'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_impact = pd.merge(total_emissions, annual_temp, on=(['Country','Year']))\n", + "co_impact.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging co_impact with demographic and economic indicators" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged = pd.merge(co_impact,gdp, on=(['Country','Year']))\n", + "indicators_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged_outer = pd.merge(co_impact,gdp, on=(['Country','Year']), how='outer')\n", + "indicators_merged_outer.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged_outer.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging ozone datasets 1979-2014" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ozone_merged = pd.merge(caused_ozone, oz_hole, on='Year')\n", + "ozone_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "oz_hole_emiss_corr = ozone_merged[\"Ozone-depleting emissions\"].corr(ozone_merged[\"Mean ozone hole area (square kilometres)\"])\n", + "oz_hole_emiss_corr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ozone_merged[\"Year\"] = ozone_merged[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ozone_merged.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ozone_merged = ozone_merged.set_index('Year')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.pairplot(ozone_merged)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging global co concentration, temp anomally and annual_global_temp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "annual_global_temp.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration_modern = co_concentration[1740:]\n", + "co_concentration_modern.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration_modern.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration_modern[\"Year\"] = co_concentration_modern[\"Year\"].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration_modern.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_global_impact = pd.merge(annual_global_temp,co_concentration_modern, on='Year')\n", + "co_global_impact.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_global_impact.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_impact_temp = pd.merge(co_global_impact,global_a, on='Year')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_impact_temp.drop(['Country'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_impact_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "global_co.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_temp_global = pd.merge(co_impact_temp,global_co, on='Year')\n", + "co_temp_global.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(17,6))\n", + "plt.plot(co_global_impact.Year,co_global_impact['LandAverageTemperature'], label='LandAverageTemperature')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Tonnes CFC11-equivalents')\n", + "plt.plot(co_global_impact.Year,co_global_impact['LandAndOceanAverageTemperature'], label='LandAndOceanAverageTemperature')\n", + "plt.legend(['LandAverageTemperature', 'LandAndOceanAverageTemperature'], loc='upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_conc_temp_corr = co_global_impact[\"LandAverageTemperature\"].corr(co_global_impact[\"CO₂ concentration\"])\n", + "co_conc_temp_corr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_temp_corr = co_temp_global[\"LandAverageTemperature\"].corr(co_temp_global[\"Annual CO₂ emissions (tonnes )\"])\n", + "co_temp_corr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_global_impact[\"Year\"] = co_global_impact[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "corr = co_temp_global.corr()\n", + "plt.figure(figsize=(12, 6))\n", + "sns.set(style=\"white\")\n", + "\n", + "mask = np.zeros_like(corr, dtype=np.bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "cmap = sns.diverging_palette(200, 10, as_cmap=True)\n", + "\n", + "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_temp_global.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_temp_global_no_colin = co_temp_global.drop(['LandAndOceanAverageTemperature','Upper (℃)','Lower (℃)'], axis=1)\n", + "co_temp_global_no_colin.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_temp_global_no_colin[\"Year\"] = co_temp_global_no_colin[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.pairplot(co_temp_global_no_colin)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(x=co_temp_global['CO₂ concentration'], y=co_temp_global['Median (℃)'])\n", + "plt.xlabel('CO₂ concentration')\n", + "plt.ylabel('Median (℃)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(x=co_temp_global['Annual CO₂ emissions (tonnes )'], y=co_temp_global['Median (℃)'])\n", + "plt.xlabel('Annual CO₂ emissions (tonnes )')\n", + "plt.ylabel('Median (℃)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking Correlations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged[\"Year\"] = indicators_merged[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged.corr()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ind_no_colin = indicators_merged.drop(['Share of global CO₂ emissions (%)','GDP','Annual CO₂'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp_ghg_corr = indicators_merged[\"GDP_per_capita\"].corr(indicators_merged[\"GHG(tonnes CO₂e )\"])\n", + "temp_ghg_corr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# here we are using seaborn to show the heatmap of the correlation between our final datasets' parameters\n", + "corr = ind_no_colin.corr()\n", + "plt.figure(figsize=(12, 6))\n", + "sns.set(style=\"white\")\n", + "\n", + "mask = np.zeros_like(corr, dtype=np.bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "cmap = sns.diverging_palette(200, 10, as_cmap=True)\n", + "\n", + "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 51968bbe497e4a0014e670691ab45922204df0a6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Wed, 27 May 2020 10:51:49 +0200 Subject: [PATCH 09/17] preparing the report --- ...ate change datasets and correlations.ipynb | 6169 +++++++++++++++++ 1 file changed, 6169 insertions(+) create mode 100644 your-project/Global climate change datasets and correlations.ipynb diff --git a/your-project/Global climate change datasets and correlations.ipynb b/your-project/Global climate change datasets and correlations.ipynb new file mode 100644 index 00000000..6fe52eb8 --- /dev/null +++ b/your-project/Global climate change datasets and correlations.ipynb @@ -0,0 +1,6169 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [], + "source": [ + "#importing the needed libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import datetime\n", + "import re\n", + "import seaborn as sns\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Global Indicators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Annual CO emissions globally " + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
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" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", + "0 Afghanistan AFG 1949 14656.0\n", + "1 Afghanistan AFG 1950 84272.0\n", + "2 Afghanistan AFG 1951 91600.0\n", + "3 Afghanistan AFG 1952 91600.0\n", + "4 Afghanistan AFG 1953 106256.0" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Annual CO emissions globally\n", + "data = pd.read_csv('data//annual-co-emissions-by-region.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
20353WorldOWID_WRL17519350528.0
20354WorldOWID_WRL17529354192.0
20355WorldOWID_WRL17539354192.0
20356WorldOWID_WRL17549357856.0
20357WorldOWID_WRL17559361520.0
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" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", + "20353 World OWID_WRL 1751 9350528.0\n", + "20354 World OWID_WRL 1752 9354192.0\n", + "20355 World OWID_WRL 1753 9354192.0\n", + "20356 World OWID_WRL 1754 9357856.0\n", + "20357 World OWID_WRL 1755 9361520.0" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "global_co = data[data.Entity == 'World']\n", + "global_co.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + } + ], + "source": [ + "#cleaning the dataset\n", + "global_co.drop(['Entity','Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [], + "source": [ + "global_co.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 267 entries, 1751 to 2017\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Annual COâ‚‚ emissions (tonnes ) 267 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 4.2 KB\n" + ] + } + ], + "source": [ + "global_co.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Total greenhouse gas emissions (1990-2016)" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
0AfghanistanAFG199015140000.0
1AfghanistanAFG199115060000.0
2AfghanistanAFG199213600000.0
3AfghanistanAFG199313430000.0
4AfghanistanAFG199413240000.0
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" + ], + "text/plain": [ + " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", + "0 Afghanistan AFG 1990 15140000.0\n", + "1 Afghanistan AFG 1991 15060000.0\n", + "2 Afghanistan AFG 1992 13600000.0\n", + "3 Afghanistan AFG 1993 13430000.0\n", + "4 Afghanistan AFG 1994 13240000.0" + ] + }, + "execution_count": 256, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Total greenhouse gas emissions\n", + "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", + "ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the dataset\n", + "ghg.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
5076World19903.496734e+10
5077World19913.512544e+10
5078World19923.498553e+10
5079World19933.507963e+10
5080World19943.528761e+10
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" + ], + "text/plain": [ + " Entity Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", + "5076 World 1990 3.496734e+10\n", + "5077 World 1991 3.512544e+10\n", + "5078 World 1992 3.498553e+10\n", + "5079 World 1993 3.507963e+10\n", + "5080 World 1994 3.528761e+10" + ] + }, + "execution_count": 258, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_ghg = ghg[ghg.Entity == 'World']\n", + "global_ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + } + ], + "source": [ + "global_ghg.drop(['Entity'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": {}, + "outputs": [], + "source": [ + "global_ghg = global_ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )'})" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [], + "source": [ + "global_ghg.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 27 entries, 1990 to 2016\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 GHG(tonnes COâ‚‚e ) 27 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 432.0 bytes\n" + ] + } + ], + "source": [ + "global_ghg.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global COâ‚‚ atmospheric concentration" + ] + }, + { + "cell_type": "code", + "execution_count": 380, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearCOâ‚‚ concentration (parts per million)
0WorldOWID_WRL803719 BCE207.29
1WorldOWID_WRL803182 BCE202.23
2WorldOWID_WRL802573 BCE204.86
3WorldOWID_WRL802060 BCE207.50
4WorldOWID_WRL801975 BCE202.92
...............
1866WorldOWID_WRL2014398.65
1867WorldOWID_WRL2015400.83
1868WorldOWID_WRL2016404.24
1869WorldOWID_WRL2017406.55
1870WorldOWID_WRL2018408.52
\n", + "

1871 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Entity Code Year COâ‚‚ concentration (parts per million)\n", + "0 World OWID_WRL 803719 BCE 207.29\n", + "1 World OWID_WRL 803182 BCE 202.23\n", + "2 World OWID_WRL 802573 BCE 204.86\n", + "3 World OWID_WRL 802060 BCE 207.50\n", + "4 World OWID_WRL 801975 BCE 202.92\n", + "... ... ... ... ...\n", + "1866 World OWID_WRL 2014 398.65\n", + "1867 World OWID_WRL 2015 400.83\n", + "1868 World OWID_WRL 2016 404.24\n", + "1869 World OWID_WRL 2017 406.55\n", + "1870 World OWID_WRL 2018 408.52\n", + "\n", + "[1871 rows x 4 columns]" + ] + }, + "execution_count": 380, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Global COâ‚‚ atmospheric concentration dataset\n", + "co_concentration = pd.read_csv('data//global-co-concentration-ppm.csv')\n", + "co_concentration" + ] + }, + { + "cell_type": "code", + "execution_count": 381, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the data\n", + "co_concentration.drop(['Code', 'Entity'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 382, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration = co_concentration.rename(columns={'COâ‚‚ concentration (parts per million)':'COâ‚‚ concentration'})" + ] + }, + { + "cell_type": "code", + "execution_count": 392, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1'" + ] + }, + "execution_count": 392, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_concentration.Year[1648]" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'CO atmospheric concentration')" + ] + }, + "execution_count": 267, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting the Global COâ‚‚ atmospheric concentration increasement from 1864-2018\n", + "x = co_concentration.Year[1740:]\n", + "y = co_concentration['COâ‚‚ concentration'][1740:]\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('CO atmospheric concentration')" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 1871 entries, 803719 BCE to 2018\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 COâ‚‚ concentration 1871 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 29.2+ KB\n" + ] + } + ], + "source": [ + "co_concentration.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 396, + "metadata": {}, + "outputs": [], + "source": [ + "co_atm_conc = co_concentration.drop(co_concentration.index[:1648])" + ] + }, + { + "cell_type": "code", + "execution_count": 397, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
YearCOâ‚‚ concentration
16481276.7
164930277.9
165056277.4
1651104277.5
1652136278.1
\n", + "
" + ], + "text/plain": [ + " Year COâ‚‚ concentration\n", + "1648 1 276.7\n", + "1649 30 277.9\n", + "1650 56 277.4\n", + "1651 104 277.5\n", + "1652 136 278.1" + ] + }, + "execution_count": 397, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_atm_conc.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 398, + "metadata": {}, + "outputs": [], + "source": [ + "co_atm_conc.to_csv('co_atm_conc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global Temperature anomaly 1850-2018\n" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearMedian (℃)Upper (℃)Lower (℃)
0GlobalNaN1850-0.373-0.339-0.425
1GlobalNaN1851-0.218-0.184-0.274
2GlobalNaN1852-0.228-0.196-0.280
3GlobalNaN1853-0.269-0.239-0.321
4GlobalNaN1854-0.248-0.218-0.301
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Median (℃) Upper (℃) Lower (℃)\n", + "0 Global NaN 1850 -0.373 -0.339 -0.425\n", + "1 Global NaN 1851 -0.218 -0.184 -0.274\n", + "2 Global NaN 1852 -0.228 -0.196 -0.280\n", + "3 Global NaN 1853 -0.269 -0.239 -0.321\n", + "4 Global NaN 1854 -0.248 -0.218 -0.301" + ] + }, + "execution_count": 270, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Global Temperature anomaly (increase-decreage on previous average temperature)\n", + "temperature_a = pd.read_csv('data//temperature_anomaly.csv')\n", + "temperature_a.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 676 entries, 0 to 675\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Entity 676 non-null object \n", + " 1 Code 0 non-null float64\n", + " 2 Year 676 non-null int64 \n", + " 3 Median (℃) 676 non-null float64\n", + " 4 Upper (℃) 676 non-null float64\n", + " 5 Lower (℃) 676 non-null float64\n", + "dtypes: float64(4), int64(1), object(1)\n", + "memory usage: 31.8+ KB\n" + ] + } + ], + "source": [ + "temperature_a.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the dataset\n", + "temperature_a.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [], + "source": [ + "temperature_a.rename(columns={'Entity':'Country'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Global', 'Northern Hemisphere', 'Southern Hemisphere', 'Tropics'],\n", + " dtype=object)" + ] + }, + "execution_count": 274, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 494, + "metadata": {}, + "outputs": [], + "source": [ + "#temperature_a.Year.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" + ] + }, + "execution_count": 276, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting the median temperature anomaly over time to see how it's actually ingreasing \n", + "x = temperature_a.Year\n", + "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='Temperature Anomaly', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('increse ordescrease in Temperature(℃)')" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryYearMedian (℃)Upper (℃)Lower (℃)
0Global1850-0.373-0.339-0.425
1Global1851-0.218-0.184-0.274
2Global1852-0.228-0.196-0.280
3Global1853-0.269-0.239-0.321
4Global1854-0.248-0.218-0.301
\n", + "
" + ], + "text/plain": [ + " Country Year Median (℃) Upper (℃) Lower (℃)\n", + "0 Global 1850 -0.373 -0.339 -0.425\n", + "1 Global 1851 -0.218 -0.184 -0.274\n", + "2 Global 1852 -0.228 -0.196 -0.280\n", + "3 Global 1853 -0.269 -0.239 -0.321\n", + "4 Global 1854 -0.248 -0.218 -0.301" + ] + }, + "execution_count": 277, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [], + "source": [ + "#keeping only the global data to compare it with the CO emissions and the CO concentration\n", + "global_a = temperature_a.where(temperature_a['Country']=='Global')\n", + "global_a.dropna(inplace=True)\n", + "global_a.drop(['Country'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [], + "source": [ + "global_a.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 360, + "metadata": {}, + "outputs": [], + "source": [ + "global_a.drop(['Upper (℃)','Lower (℃)'],axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ozone depletion crisis" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearOzone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)
0Natural emissionsNaN1961165000
1Natural emissionsNaN1962165000
2Natural emissionsNaN1963165000
3Natural emissionsNaN1964165000
4Natural emissionsNaN1965165000
\n", + "
" + ], + "text/plain": [ + " Entity Code Year \\\n", + "0 Natural emissions NaN 1961 \n", + "1 Natural emissions NaN 1962 \n", + "2 Natural emissions NaN 1963 \n", + "3 Natural emissions NaN 1964 \n", + "4 Natural emissions NaN 1965 \n", + "\n", + " Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents) \n", + "0 165000 \n", + "1 165000 \n", + "2 165000 \n", + "3 165000 \n", + "4 165000 " + ] + }, + "execution_count": 287, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# importing Ozone depleting emissions dataset\n", + "ozone = pd.read_csv('data//ozone-depleting-substance-emissions.csv')\n", + "ozone.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "metadata": {}, + "outputs": [], + "source": [ + "# cleaning the dataset\n", + "ozone.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": {}, + "outputs": [], + "source": [ + "ozone.rename(columns={'Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)':'Ozone-depleting emissions'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Natural emissions', 'Total emissions'], dtype=object)" + ] + }, + "execution_count": 290, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozone.Entity.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 291, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting natural and total emissions to see that we have nearly stopped using CFC\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(ozone.Year,ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Natural emissions'), label='Natural emissions')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Tonnes CFC11-equivalents')\n", + "plt.plot(ozone.Year, ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Total emissions'), label='Total emissions')\n", + "plt.legend(['Natural emissions', 'Total emissions'], loc='upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": 292, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + } + ], + "source": [ + "caused_ozone = ozone[ozone.Entity == 'Total emissions']\n", + "caused_ozone.drop(['Entity'],axis=1, inplace=True )" + ] + }, + { + "cell_type": "code", + "execution_count": 294, + "metadata": {}, + "outputs": [], + "source": [ + "ozone.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "metadata": {}, + "outputs": [], + "source": [ + "caused_ozone.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Antartica Ozone Hole area" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearMaximum ozone hole area (square kilometres)Mean ozone hole area (square kilometres)
0WorldOWID_WRL19791100000100000
1WorldOWID_WRL198033000001400000
2WorldOWID_WRL19813100000600000
3WorldOWID_WRL1982108000004800000
4WorldOWID_WRL1983122000007900000
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" + ], + "text/plain": [ + " Entity Code Year Maximum ozone hole area (square kilometres) \\\n", + "0 World OWID_WRL 1979 1100000 \n", + "1 World OWID_WRL 1980 3300000 \n", + "2 World OWID_WRL 1981 3100000 \n", + "3 World OWID_WRL 1982 10800000 \n", + "4 World OWID_WRL 1983 12200000 \n", + "\n", + " Mean ozone hole area (square kilometres) \n", + "0 100000 \n", + "1 1400000 \n", + "2 600000 \n", + "3 4800000 \n", + "4 7900000 " + ] + }, + "execution_count": 295, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Antartica Ozone Hole area dataset\n", + "oz_hole = pd.read_csv('data//antarctic-ozone-hole-area.csv')\n", + "oz_hole.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "metadata": {}, + "outputs": [], + "source": [ + "oz_hole.drop(['Entity', 'Code','Maximum ozone hole area (square kilometres)'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMean ozone hole area (square kilometres)
01979100000
119801400000
21981600000
319824800000
419837900000
\n", + "
" + ], + "text/plain": [ + " Year Mean ozone hole area (square kilometres)\n", + "0 1979 100000\n", + "1 1980 1400000\n", + "2 1981 600000\n", + "3 1982 4800000\n", + "4 1983 7900000" + ] + }, + "execution_count": 297, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oz_hole.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 298, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 298, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting natural and total emissionsMean ozone hole area (square kilometres) to see if it has reduced\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(oz_hole.Year,oz_hole['Mean ozone hole area (square kilometres)'], label='Mean ozone hole area (square kilometres)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Mean ozone hole area (square kilometres)')\n", + "plt.legend(['Mean ozone hole area (square kilometres)'], loc='upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": {}, + "outputs": [], + "source": [ + "oz_hole.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global average temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 300, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAverageTemperatureUncertaintyLandMaxTemperatureLandMaxTemperatureUncertaintyLandMinTemperatureLandMinTemperatureUncertaintyLandAndOceanAverageTemperatureLandAndOceanAverageTemperatureUncertainty
01750-01-013.0343.574NaNNaNNaNNaNNaNNaN
11750-02-013.0833.702NaNNaNNaNNaNNaNNaN
21750-03-015.6263.076NaNNaNNaNNaNNaNNaN
31750-04-018.4902.451NaNNaNNaNNaNNaNNaN
41750-05-0111.5732.072NaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " dt LandAverageTemperature LandAverageTemperatureUncertainty \\\n", + "0 1750-01-01 3.034 3.574 \n", + "1 1750-02-01 3.083 3.702 \n", + "2 1750-03-01 5.626 3.076 \n", + "3 1750-04-01 8.490 2.451 \n", + "4 1750-05-01 11.573 2.072 \n", + "\n", + " LandMaxTemperature LandMaxTemperatureUncertainty LandMinTemperature \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " LandMinTemperatureUncertainty LandAndOceanAverageTemperature \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " LandAndOceanAverageTemperatureUncertainty \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 300, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_temperature = pd.read_csv('data//GlobalTemperatures.csv')\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAndOceanAverageTemperature
12001850-01-010.74912.833
12011850-02-013.07113.588
12021850-03-014.95414.043
12031850-04-017.21714.667
12041850-05-0110.00415.507
\n", + "
" + ], + "text/plain": [ + " dt LandAverageTemperature LandAndOceanAverageTemperature\n", + "1200 1850-01-01 0.749 12.833\n", + "1201 1850-02-01 3.071 13.588\n", + "1202 1850-03-01 4.954 14.043\n", + "1203 1850-04-01 7.217 14.667\n", + "1204 1850-05-01 10.004 15.507" + ] + }, + "execution_count": 301, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_temperature.dropna(inplace=True)\n", + "global_temperature = global_temperature[['dt','LandAverageTemperature','LandAndOceanAverageTemperature']]\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 302, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAndOceanAverageTemperatureYear
12001850-01-010.74912.8331850
12011850-02-013.07113.5881850
12021850-03-014.95414.0431850
12031850-04-017.21714.6671850
12041850-05-0110.00415.5071850
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" + ], + "text/plain": [ + " dt LandAverageTemperature LandAndOceanAverageTemperature Year\n", + "1200 1850-01-01 0.749 12.833 1850\n", + "1201 1850-02-01 3.071 13.588 1850\n", + "1202 1850-03-01 4.954 14.043 1850\n", + "1203 1850-04-01 7.217 14.667 1850\n", + "1204 1850-05-01 10.004 15.507 1850" + ] + }, + "execution_count": 302, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#global_temperature['dt']= pd.to_datetime(global_temperature['dt']) \n", + "global_temperature['Year'] = pd.DatetimeIndex(global_temperature['dt']).year\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LandAverageTemperatureLandAndOceanAverageTemperature
Year
18507.90066714.867167
18518.17858314.991833
18528.10016715.006500
18538.04183314.955167
18548.21050014.991000
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" + ], + "text/plain": [ + " LandAverageTemperature LandAndOceanAverageTemperature\n", + "Year \n", + "1850 7.900667 14.867167\n", + "1851 8.178583 14.991833\n", + "1852 8.100167 15.006500\n", + "1853 8.041833 14.955167\n", + "1854 8.210500 14.991000" + ] + }, + "execution_count": 303, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_global_temp = global_temperature[['Year','LandAverageTemperature','LandAndOceanAverageTemperature']].groupby(['Year']).agg({'LandAverageTemperature':'mean',\n", + " 'LandAndOceanAverageTemperature':'mean'})\n", + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearLandAverageTemperatureLandAndOceanAverageTemperature
018507.90066714.867167
118518.17858314.991833
218528.10016715.006500
318538.04183314.955167
418548.21050014.991000
\n", + "
" + ], + "text/plain": [ + " Year LandAverageTemperature LandAndOceanAverageTemperature\n", + "0 1850 7.900667 14.867167\n", + "1 1851 8.178583 14.991833\n", + "2 1852 8.100167 15.006500\n", + "3 1853 8.041833 14.955167\n", + "4 1854 8.210500 14.991000" + ] + }, + "execution_count": 304, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_global_temp.reset_index(level=0, inplace=True)\n", + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "metadata": {}, + "outputs": [], + "source": [ + "annual_global_temp.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 306, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 166 entries, 1850 to 2015\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 LandAverageTemperature 166 non-null float64\n", + " 1 LandAndOceanAverageTemperature 166 non-null float64\n", + "dtypes: float64(2)\n", + "memory usage: 3.9 KB\n" + ] + } + ], + "source": [ + "annual_global_temp.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fossil fuel consumption per capita" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)
0AfricaNaN19650.9823451.0406860.032724
1AfricaNaN19661.0503401.0019700.035646
2AfricaNaN19671.0143901.0051970.034371
3AfricaNaN19681.0433631.0211390.034195
4AfricaNaN19691.0322301.0010830.039699
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" + ], + "text/plain": [ + " Entity Code Year Oil (megawatt-hours) Coal (megawatt-hours) \\\n", + "0 Africa NaN 1965 0.982345 1.040686 \n", + "1 Africa NaN 1966 1.050340 1.001970 \n", + "2 Africa NaN 1967 1.014390 1.005197 \n", + "3 Africa NaN 1968 1.043363 1.021139 \n", + "4 Africa NaN 1969 1.032230 1.001083 \n", + "\n", + " Natural gas (megawatt-hours) \n", + "0 0.032724 \n", + "1 0.035646 \n", + "2 0.034371 \n", + "3 0.034195 \n", + "4 0.039699 " + ] + }, + "execution_count": 312, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fossil_per_c = pd.read_csv('data//fossil-fuel-consumption-per-capita.csv')\n", + "fossil_per_c.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 313, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)
3302WorldOWID_WRL19655.3121354.8875542.043343
3303WorldOWID_WRL19665.6055414.8450912.178977
3304WorldOWID_WRL19675.8834724.6700632.288339
3305WorldOWID_WRL19686.2573574.6435392.440440
3306WorldOWID_WRL19696.6550134.6908202.622830
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" + ], + "text/plain": [ + " Entity Code Year Oil (megawatt-hours) Coal (megawatt-hours) \\\n", + "3302 World OWID_WRL 1965 5.312135 4.887554 \n", + "3303 World OWID_WRL 1966 5.605541 4.845091 \n", + "3304 World OWID_WRL 1967 5.883472 4.670063 \n", + "3305 World OWID_WRL 1968 6.257357 4.643539 \n", + "3306 World OWID_WRL 1969 6.655013 4.690820 \n", + "\n", + " Natural gas (megawatt-hours) \n", + "3302 2.043343 \n", + "3303 2.178977 \n", + "3304 2.288339 \n", + "3305 2.440440 \n", + "3306 2.622830 " + ] + }, + "execution_count": 313, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "fossil_per_c = fossil_per_c[fossil_per_c.Entity == 'World']\n", + "fossil_per_c.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "metadata": {}, + "outputs": [], + "source": [ + "fossil_per_c.drop(['Entity', 'Code',], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 315, + "metadata": {}, + "outputs": [], + "source": [ + "fossil_per_c.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 51 entries, 1965 to 2015\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Oil (megawatt-hours) 51 non-null float64\n", + " 1 Coal (megawatt-hours) 51 non-null float64\n", + " 2 Natural gas (megawatt-hours) 51 non-null float64\n", + "dtypes: float64(3)\n", + "memory usage: 1.6 KB\n" + ] + } + ], + "source": [ + "fossil_per_c.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global primary energy by source" + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearCoal (terawatt-hours)Solar (terawatt-hours)Crude oil (terawatt-hours)Natural gas (terawatt-hours)Traditional biofuels (terawatt-hours)Other renewables (terawatt-hours)Hydropower (terawatt-hours)Nuclear (terawatt-hours)Wind (terawatt-hours)
0WorldOWID_WRL180097.222300.00.00.05555.5555560.00.00.00.0
1WorldOWID_WRL1810127.777880.00.00.05833.3333330.00.00.00.0
2WorldOWID_WRL1820152.777900.00.00.06111.1111110.00.00.00.0
3WorldOWID_WRL1830263.889100.00.00.06388.8888890.00.00.00.0
4WorldOWID_WRL1840355.555840.00.00.06944.4444440.00.00.00.0
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" + ], + "text/plain": [ + " Entity Code Year Coal (terawatt-hours) Solar (terawatt-hours) \\\n", + "0 World OWID_WRL 1800 97.22230 0.0 \n", + "1 World OWID_WRL 1810 127.77788 0.0 \n", + "2 World OWID_WRL 1820 152.77790 0.0 \n", + "3 World OWID_WRL 1830 263.88910 0.0 \n", + "4 World OWID_WRL 1840 355.55584 0.0 \n", + "\n", + " Crude oil (terawatt-hours) Natural gas (terawatt-hours) \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Traditional biofuels (terawatt-hours) Other renewables (terawatt-hours) \\\n", + "0 5555.555556 0.0 \n", + "1 5833.333333 0.0 \n", + "2 6111.111111 0.0 \n", + "3 6388.888889 0.0 \n", + "4 6944.444444 0.0 \n", + "\n", + " Hydropower (terawatt-hours) Nuclear (terawatt-hours) \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Wind (terawatt-hours) \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 " + ] + }, + "execution_count": 317, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_energy = pd.read_csv('data//global-primary-energy.csv')\n", + "global_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 318, + "metadata": {}, + "outputs": [], + "source": [ + "global_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "metadata": {}, + "outputs": [], + "source": [ + "global_energy.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 320, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 71 entries, 1800 to 2018\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Coal (terawatt-hours) 71 non-null float64\n", + " 1 Solar (terawatt-hours) 71 non-null float64\n", + " 2 Crude oil (terawatt-hours) 71 non-null float64\n", + " 3 Natural gas (terawatt-hours) 71 non-null float64\n", + " 4 Traditional biofuels (terawatt-hours) 71 non-null float64\n", + " 5 Other renewables (terawatt-hours) 71 non-null float64\n", + " 6 Hydropower (terawatt-hours) 71 non-null float64\n", + " 7 Nuclear (terawatt-hours) 71 non-null float64\n", + " 8 Wind (terawatt-hours) 71 non-null float64\n", + "dtypes: float64(9)\n", + "memory usage: 5.5 KB\n" + ] + } + ], + "source": [ + "global_energy.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modern renewable energy consumtion" + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearHydropower (terawatt-hours)Solar (terawatt-hours)Wind (terawatt-hours)Other renewables (terawatt-hours)
0AfricaNaN196514.2788060.00.00.0
1AfricaNaN196615.6490490.00.00.0
2AfricaNaN196716.1583330.00.00.0
3AfricaNaN196818.6229830.00.00.0
4AfricaNaN196921.5828970.00.00.0
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" + ], + "text/plain": [ + " Entity Code Year Hydropower (terawatt-hours) Solar (terawatt-hours) \\\n", + "0 Africa NaN 1965 14.278806 0.0 \n", + "1 Africa NaN 1966 15.649049 0.0 \n", + "2 Africa NaN 1967 16.158333 0.0 \n", + "3 Africa NaN 1968 18.622983 0.0 \n", + "4 Africa NaN 1969 21.582897 0.0 \n", + "\n", + " Wind (terawatt-hours) Other renewables (terawatt-hours) \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 " + ] + }, + "execution_count": 321, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "renew_energy = pd.read_csv('data//modern-renewable-energy-consumption.csv')\n", + "renew_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EntityCodeYearHydropower (terawatt-hours)Solar (terawatt-hours)Wind (terawatt-hours)Other renewables (terawatt-hours)
5037WorldOWID_WRL1965919.7702760.00.017.985232
5038WorldOWID_WRL1966986.2858720.00.019.806007
5039WorldOWID_WRL19671009.2445690.00.019.986571
5040WorldOWID_WRL19681059.0628600.00.022.120045
5041WorldOWID_WRL19691122.4425260.00.023.257964
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" + ], + "text/plain": [ + " Entity Code Year Hydropower (terawatt-hours) \\\n", + "5037 World OWID_WRL 1965 919.770276 \n", + "5038 World OWID_WRL 1966 986.285872 \n", + "5039 World OWID_WRL 1967 1009.244569 \n", + "5040 World OWID_WRL 1968 1059.062860 \n", + "5041 World OWID_WRL 1969 1122.442526 \n", + "\n", + " Solar (terawatt-hours) Wind (terawatt-hours) \\\n", + "5037 0.0 0.0 \n", + "5038 0.0 0.0 \n", + "5039 0.0 0.0 \n", + "5040 0.0 0.0 \n", + "5041 0.0 0.0 \n", + "\n", + " Other renewables (terawatt-hours) \n", + "5037 17.985232 \n", + "5038 19.806007 \n", + "5039 19.986571 \n", + "5040 22.120045 \n", + "5041 23.257964 " + ] + }, + "execution_count": 322, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "renew_energy = renew_energy[renew_energy.Entity == 'World']\n", + "renew_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 323, + "metadata": {}, + "outputs": [], + "source": [ + "renew_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 324, + "metadata": {}, + "outputs": [], + "source": [ + "renew_energy.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 325, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 54 entries, 1965 to 2018\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Hydropower (terawatt-hours) 54 non-null float64\n", + " 1 Solar (terawatt-hours) 54 non-null float64\n", + " 2 Wind (terawatt-hours) 54 non-null float64\n", + " 3 Other renewables (terawatt-hours) 54 non-null float64\n", + "dtypes: float64(4)\n", + "memory usage: 2.1 KB\n" + ] + } + ], + "source": [ + "renew_energy.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hubbert's peak vs actual oil production" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYear(barrels per year)(barrels per year).1
0United StatesUSA1910209557000.0NaN
1United StatesUSA1911220449000.0200158958.0
2United StatesUSA1912222935000.0213834803.7
3United StatesUSA1913248446000.0228406784.8
4United StatesUSA1914265763000.0243928136.8
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" + ], + "text/plain": [ + " Entity Code Year (barrels per year) (barrels per year).1\n", + "0 United States USA 1910 209557000.0 NaN\n", + "1 United States USA 1911 220449000.0 200158958.0\n", + "2 United States USA 1912 222935000.0 213834803.7\n", + "3 United States USA 1913 248446000.0 228406784.8\n", + "4 United States USA 1914 265763000.0 243928136.8" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hubbert_peak = pd.read_csv('data//hubberts-peak-vs-actual-oil-production-in-the-united-states.csv')\n", + "hubbert_peak.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Entity', 'Code', 'Year', ' (barrels per year)',\n", + " ' (barrels per year).1'],\n", + " dtype='object')" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hubbert_peak.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak.drop(['Entity', 'Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak.rename(columns={' (barrels per year)':'Actual Oil Prod',' (barrels per year).1':'Hubbert Predicted'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak = hubbert_peak.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Year Actual Predicted\n", + "102 2012 2.374136e+09 614194532.6\n", + "103 2013 2.725665e+09 577668428.7\n", + "104 2014 3.198694e+09 543068744.5\n", + "105 2015 3.436515e+09 510324290.7\n", + "106 2016 3.248117e+09 479362510.8" + ] + }, + "execution_count": 328, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hubbert_peak.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 329, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 329, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting predicted and actual emissions to see if it the prediction came true\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(hubbert_peak.Year,hubbert_peak['Actual'],label='Actual')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('(barrels per year)')\n", + "plt.plot(hubbert_peak.Year,hubbert_peak['Predicted'],label='Predicted')\n", + "plt.legend(['Actual', 'Predicted'], loc='upper left')" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 106 entries, 1911 to 2016\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Actual 106 non-null float64\n", + " 1 Predicted 106 non-null float64\n", + "dtypes: float64(2)\n", + "memory usage: 2.5 KB\n" + ] + } + ], + "source": [ + "hubbert_peak.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### share of global primary energy" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearCoal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
0WorldOWID_WRL18001.7199030.00.00.00.00.00.098.2800970.0
1WorldOWID_WRL18102.1435250.00.00.00.00.00.097.8564750.0
2WorldOWID_WRL18202.4390260.00.00.00.00.00.097.5609740.0
3WorldOWID_WRL18303.9666000.00.00.00.00.00.096.0334000.0
4WorldOWID_WRL18404.8706280.00.00.00.00.00.095.1293720.0
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" + ], + "text/plain": [ + " Entity Code Year Coal (%) Crude oil (%) Hydropower (%) \\\n", + "0 World OWID_WRL 1800 1.719903 0.0 0.0 \n", + "1 World OWID_WRL 1810 2.143525 0.0 0.0 \n", + "2 World OWID_WRL 1820 2.439026 0.0 0.0 \n", + "3 World OWID_WRL 1830 3.966600 0.0 0.0 \n", + "4 World OWID_WRL 1840 4.870628 0.0 0.0 \n", + "\n", + " Natural gas (%) Nuclear (%) Other renewables (%) Solar (%) \\\n", + "0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "\n", + " Traditional biofuels (%) Wind (%) \n", + "0 98.280097 0.0 \n", + "1 97.856475 0.0 \n", + "2 97.560974 0.0 \n", + "3 96.033400 0.0 \n", + "4 95.129372 0.0 " + ] + }, + "execution_count": 332, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "share_energy = pd.read_csv('data//global-primary-energy-share-inc-biomass.csv')\n", + "share_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "metadata": {}, + "outputs": [], + "source": [ + "share_energy.drop(['Entity', 'Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [], + "source": [ + "share_energy.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 70 entries, 1800 to 2017\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Coal (%) 70 non-null float64\n", + " 1 Crude oil (%) 70 non-null float64\n", + " 2 Hydropower (%) 70 non-null float64\n", + " 3 Natural gas (%) 70 non-null float64\n", + " 4 Nuclear (%) 70 non-null float64\n", + " 5 Other renewables (%) 70 non-null float64\n", + " 6 Solar (%) 70 non-null float64\n", + " 7 Traditional biofuels (%) 70 non-null float64\n", + " 8 Wind (%) 70 non-null float64\n", + "dtypes: float64(9)\n", + "memory usage: 5.5 KB\n" + ] + } + ], + "source": [ + "share_energy.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solar pannels price progression" + ] + }, + { + "cell_type": "code", + "execution_count": 464, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearSolar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))Cumulative Installed Capacity (megawatts-peak (MWp))Total population (Gapminder)
0AfghanistanAFG1800NaNNaN3280000.0
1AfghanistanAFG1820NaNNaN3280000.0
2AfghanistanAFG1870NaNNaN4207000.0
3AfghanistanAFG1913NaNNaN5730000.0
4AfghanistanAFG1950NaNNaN8151455.0
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" + ], + "text/plain": [ + " Entity Code Year \\\n", + "0 Afghanistan AFG 1800 \n", + "1 Afghanistan AFG 1820 \n", + "2 Afghanistan AFG 1870 \n", + "3 Afghanistan AFG 1913 \n", + "4 Afghanistan AFG 1950 \n", + "\n", + " Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp)) \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Cumulative Installed Capacity (megawatts-peak (MWp)) \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Total population (Gapminder) \n", + "0 3280000.0 \n", + "1 3280000.0 \n", + "2 4207000.0 \n", + "3 5730000.0 \n", + "4 8151455.0 " + ] + }, + "execution_count": 464, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solar_price = pd.read_csv('data//solar-pv-prices-vs-cumulative-capacity.csv')\n", + "solar_price.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 465, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearSolar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))Cumulative Installed Capacity (megawatts-peak (MWp))Total population (Gapminder)
19946WorldOWID_WRL197666.1048350.32NaN
19947WorldOWID_WRL197746.4405390.77NaN
19948WorldOWID_WRL197833.2826881.77NaN
19949WorldOWID_WRL197929.6924933.27NaN
19950WorldOWID_WRL198023.7553616.57NaN
\n", + "
" + ], + "text/plain": [ + " Entity Code Year \\\n", + "19946 World OWID_WRL 1976 \n", + "19947 World OWID_WRL 1977 \n", + "19948 World OWID_WRL 1978 \n", + "19949 World OWID_WRL 1979 \n", + "19950 World OWID_WRL 1980 \n", + "\n", + " Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp)) \\\n", + "19946 66.104835 \n", + "19947 46.440539 \n", + "19948 33.282688 \n", + "19949 29.692493 \n", + "19950 23.755361 \n", + "\n", + " Cumulative Installed Capacity (megawatts-peak (MWp)) \\\n", + "19946 0.32 \n", + "19947 0.77 \n", + "19948 1.77 \n", + "19949 3.27 \n", + "19950 6.57 \n", + "\n", + " Total population (Gapminder) \n", + "19946 NaN \n", + "19947 NaN \n", + "19948 NaN \n", + "19949 NaN \n", + "19950 NaN " + ] + }, + "execution_count": 465, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "solar_price = solar_price[solar_price.Entity == 'World']\n", + "solar_price.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 466, + "metadata": {}, + "outputs": [], + "source": [ + "solar_price.drop(['Entity', 'Code','Total population (Gapminder)'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 467, + "metadata": {}, + "outputs": [], + "source": [ + "solar_price.rename(columns={'Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))':'Solar PV Module Cost($/Wp)','Cumulative Installed Capacity (megawatts-peak (MWp))':'Cumulative Installed Capacity(MWp)'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 468, + "metadata": {}, + "outputs": [], + "source": [ + "#solar_price.set_index('Cumulative Installed Capacity(MWp)',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 471, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Solar PV Module Cost($/Wp)')" + ] + }, + "execution_count": 471, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(17,6))\n", + "plt.plot(solar_price.Year,solar_price['Solar PV Module Cost($/Wp)'],label='Solar PV Module Cost($/Wp)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Solar PV Module Cost($/Wp)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 472, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Cumulative Installed Capacity(MWp)')" + ] + }, + "execution_count": 472, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(17,6))\n", + "plt.plot(solar_price.Year,solar_price['Cumulative Installed Capacity(MWp)'],label='Cumulative Installed Capacity(MWp)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Cumulative Installed Capacity(MWp)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Country Indicators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CO annual emissions (1949-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 475, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes)
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
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" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes)\n", + "0 Afghanistan AFG 1949 14656.0\n", + "1 Afghanistan AFG 1950 84272.0\n", + "2 Afghanistan AFG 1951 91600.0\n", + "3 Afghanistan AFG 1952 91600.0\n", + "4 Afghanistan AFG 1953 106256.0" + ] + }, + "execution_count": 475, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Annual CO per country (1949-2017)\n", + "co_annual = pd.read_csv('data//annual_co2_emissions_per_country.csv')\n", + "co_annual.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 477, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the dataset\n", + "co_annual.drop(['Code'], axis=1, inplace=True)\n", + "co_annual = co_annual.rename(columns={'Annual COâ‚‚ emissions (tonnes)':'Annual COâ‚‚', 'Entity':'Country'})" + ] + }, + { + "cell_type": "code", + "execution_count": 478, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 20586 entries, 0 to 20852\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 20586 non-null object \n", + " 1 Year 20586 non-null int64 \n", + " 2 Annual COâ‚‚ 20586 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 643.3+ KB\n" + ] + } + ], + "source": [ + "#keeping only Country indicators\n", + "co_annual = co_annual[co_annual.Country != 'World']\n", + "#co_annual.Year.unique()\n", + "co_annual.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Annual share of CO per country (1949-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 479, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearShare of global COâ‚‚ emissions (%)
0AfghanistanAFG19490.0003
1AfghanistanAFG19500.0015
2AfghanistanAFG19510.0015
3AfghanistanAFG19520.0015
4AfghanistanAFG19530.0016
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" + ], + "text/plain": [ + " Entity Code Year Share of global COâ‚‚ emissions (%)\n", + "0 Afghanistan AFG 1949 0.0003\n", + "1 Afghanistan AFG 1950 0.0015\n", + "2 Afghanistan AFG 1951 0.0015\n", + "3 Afghanistan AFG 1952 0.0015\n", + "4 Afghanistan AFG 1953 0.0016" + ] + }, + "execution_count": 479, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Annual share of CO per country (1949-2017)\n", + "co_share = pd.read_csv('data//annual_share_of_co2_emissions.csv')\n", + "co_share.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 480, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20586 entries, 0 to 20585\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 20586 non-null object \n", + " 1 Year 20586 non-null int64 \n", + " 2 Share of global COâ‚‚ emissions (%) 20586 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 482.6+ KB\n" + ] + } + ], + "source": [ + "#cleaning the data set\n", + "co_share.drop(['Code'], axis=1, inplace=True)\n", + "\n", + "co_share = co_share.rename(columns={'Share of global COâ‚‚ emissions (%)b':'Annual COâ‚‚', 'Entity':'Country'})\n", + "\n", + "co_share.Year.unique()\n", + "\n", + "co_share.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Total greenhouse gas emissions (1990-2016)" + ] + }, + { + "cell_type": "code", + "execution_count": 485, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
0AfghanistanAFG199015140000.0
1AfghanistanAFG199115060000.0
2AfghanistanAFG199213600000.0
3AfghanistanAFG199313430000.0
4AfghanistanAFG199413240000.0
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" + ], + "text/plain": [ + " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", + "0 Afghanistan AFG 1990 15140000.0\n", + "1 Afghanistan AFG 1991 15060000.0\n", + "2 Afghanistan AFG 1992 13600000.0\n", + "3 Afghanistan AFG 1993 13430000.0\n", + "4 Afghanistan AFG 1994 13240000.0" + ] + }, + "execution_count": 485, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Total greenhouse gas emissions\n", + "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", + "ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 486, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 5184 entries, 0 to 5183\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 5184 non-null object \n", + " 1 Year 5184 non-null int64 \n", + " 2 GHG(tonnes COâ‚‚e ) 5184 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 121.6+ KB\n" + ] + } + ], + "source": [ + "#cleaning the dataset\n", + "ghg.drop(['Code'], axis=1, inplace=True)\n", + "\n", + "ghg = ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )', 'Entity':'Country'})\n", + "\n", + "ghg.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Average temperatures by country" + ] + }, + { + "cell_type": "code", + "execution_count": 487, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dtAverageTemperatureAverageTemperatureUncertaintyCountry
01743-11-014.3842.294Ã…land
11743-12-01NaNNaNÃ…land
21744-01-01NaNNaNÃ…land
31744-02-01NaNNaNÃ…land
41744-03-01NaNNaNÃ…land
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" + ], + "text/plain": [ + " dt AverageTemperature AverageTemperatureUncertainty Country\n", + "0 1743-11-01 4.384 2.294 Åland\n", + "1 1743-12-01 NaN NaN Åland\n", + "2 1744-01-01 NaN NaN Åland\n", + "3 1744-02-01 NaN NaN Åland\n", + "4 1744-03-01 NaN NaN Åland" + ] + }, + "execution_count": 487, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Global temperatures by country data set\n", + "temperature = pd.read_csv('data//GlobalLandTemperaturesByCountry.csv')\n", + "temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 488, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the dataset\n", + "temperature['dt']= pd.to_datetime(temperature['dt']) \n", + "\n", + "temperature['Year'] = pd.DatetimeIndex(temperature['dt']).year\n", + "temperature.head()\n", + "\n", + "#keeping only the desired countries, and removing regions\n", + "non_wanted = ['Åland','Africa','Asia','Baker Island','Denmark','Federated States Of Micronesia','France', 'French Guiana', 'French Polynesia',\n", + " 'French Southern And Antarctic Lands','Gaza Strip','Guadeloupe', 'Guam','Guernsey','Heard Island And Mcdonald Islands','Isle Of Man','Jersey','Kingman Reef',\n", + " 'Montserrat','Netherlands (Europe)','Niue','North America','New Caledonia','Northern Mariana Islands','Oceania',\n", + " 'Palmyra Atoll','Saint Barthélemy','American Samoa','South America','South Georgia And The South Sandwich Isla',\n", + " 'Svalbard And Jan Mayen','Turks And Caicas Islands','United Kingdom','Western Sahara']\n", + "for region in non_wanted:\n", + " temperature = temperature[temperature.Country != region]" + ] + }, + { + "cell_type": "code", + "execution_count": 489, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearAverageTemperature
Country
Albania17438.620
Andorra17437.556
Austria17432.482
Belarus17430.767
Belgium17437.106
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" + ], + "text/plain": [ + " Year AverageTemperature\n", + "Country \n", + "Albania 1743 8.620\n", + "Andorra 1743 7.556\n", + "Austria 1743 2.482\n", + "Belarus 1743 0.767\n", + "Belgium 1743 7.106" + ] + }, + "execution_count": 489, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#grouping by year and agregating the mean so we have the annual mean temperature by country\n", + "annual_temp = temperature[['Year', 'Country','AverageTemperature']].groupby(['Year','Country']).agg({'AverageTemperature':'mean'})\n", + "\n", + "#reseting index to adapt it for the future merge\n", + "annual_temp.reset_index(level=0, inplace=True)\n", + "\n", + "annual_temp.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 491, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexCountryYearAverageTemperature
00Albania17438.620
11Andorra17437.556
22Austria17432.482
33Belarus17430.767
44Belgium17437.106
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" + ], + "text/plain": [ + " index Country Year AverageTemperature\n", + "0 0 Albania 1743 8.620\n", + "1 1 Andorra 1743 7.556\n", + "2 2 Austria 1743 2.482\n", + "3 3 Belarus 1743 0.767\n", + "4 4 Belgium 1743 7.106" + ] + }, + "execution_count": 491, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one more reset to have country as a column\n", + "annual_temp.reset_index(level=0, inplace=True)\n", + "annual_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 493, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 41931 entries, 0 to 41930\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 41931 non-null object \n", + " 1 Year 41931 non-null int64 \n", + " 2 AverageTemperature 39793 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 982.9+ KB\n" + ] + } + ], + "source": [ + "annual_temp.drop(['index'], axis=1, inplace=True)\n", + "annual_temp.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Merging datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging global indicators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### outer merges" + ] + }, + { + "cell_type": "code", + "execution_count": 362, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1 and 2\n", + "global_indicators1 = pd.merge(global_co, global_ghg, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 365, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4\n", + "global_indicators2 = pd.merge(global_indicators1,global_a, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 366, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5\n", + "global_indicators3 = pd.merge(global_indicators2,caused_ozone, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 367, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6\n", + "global_indicators4 = pd.merge(global_indicators3,oz_hole, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 368, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7\n", + "global_indicators5 = pd.merge(global_indicators4,annual_global_temp, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 369, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8\n", + "global_indicators6 = pd.merge(global_indicators5,fossil_per_c, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 370, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8,9\n", + "global_indicators7 = pd.merge(global_indicators6,global_energy, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 371, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8,9,10\n", + "global_indicators8 = pd.merge(global_indicators7,renew_energy, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 372, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8,9,10,12\n", + "global_indicators = pd.merge(global_indicators8,share_energy, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 374, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Annual CO₂ emissions (tonnes )GHG(tonnes CO₂e )Median (℃)Ozone-depleting emissionsMean ozone hole area (square kilometres)LandAverageTemperatureLandAndOceanAverageTemperatureOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)...Other renewables (terawatt-hours)_yCoal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
Year
20143.550583e+104.944079e+100.579320000.020900000.09.57066715.9130006.7800186.1977514.896768...502.15891930.12992834.2842432.60799922.7981601.7045050.3383420.1325977.5263480.477878
20153.546275e+104.985498e+100.763NaN25600000.09.83100016.0585836.8380425.9617334.956876...536.46624429.23211234.7507802.59790623.1936921.7195140.3593070.1735807.4177810.555327
20163.567510e+104.935803e+100.797NaN20700000.0NaNNaNNaNNaNNaN...556.62335628.47941835.0213262.66686223.6166451.7264460.3680320.2168497.2704460.633975
20173.615326e+10NaN0.677NaN17400000.0NaNNaNNaNNaNNaN...584.97582728.25414134.9959602.64321823.8964861.7159090.3816330.2881717.0935080.730975
2018NaNNaN0.595NaNNaNNaNNaNNaNNaNNaN...625.805362NaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

5 rows × 32 columns

\n", + "
" + ], + "text/plain": [ + " Annual CO₂ emissions (tonnes ) GHG(tonnes CO₂e ) Median (℃) \\\n", + "Year \n", + "2014 3.550583e+10 4.944079e+10 0.579 \n", + "2015 3.546275e+10 4.985498e+10 0.763 \n", + "2016 3.567510e+10 4.935803e+10 0.797 \n", + "2017 3.615326e+10 NaN 0.677 \n", + "2018 NaN NaN 0.595 \n", + "\n", + " Ozone-depleting emissions Mean ozone hole area (square kilometres) \\\n", + "Year \n", + "2014 320000.0 20900000.0 \n", + "2015 NaN 25600000.0 \n", + "2016 NaN 20700000.0 \n", + "2017 NaN 17400000.0 \n", + "2018 NaN NaN \n", + "\n", + " LandAverageTemperature LandAndOceanAverageTemperature \\\n", + "Year \n", + "2014 9.570667 15.913000 \n", + "2015 9.831000 16.058583 \n", + "2016 NaN NaN \n", + "2017 NaN NaN \n", + "2018 NaN NaN \n", + "\n", + " Oil (megawatt-hours) Coal (megawatt-hours) \\\n", + "Year \n", + "2014 6.780018 6.197751 \n", + "2015 6.838042 5.961733 \n", + "2016 NaN NaN \n", + "2017 NaN NaN \n", + "2018 NaN NaN \n", + "\n", + " Natural gas (megawatt-hours) ... Other renewables (terawatt-hours)_y \\\n", + "Year ... \n", + "2014 4.896768 ... 502.158919 \n", + "2015 4.956876 ... 536.466244 \n", + "2016 NaN ... 556.623356 \n", + "2017 NaN ... 584.975827 \n", + "2018 NaN ... 625.805362 \n", + "\n", + " Coal (%) Crude oil (%) Hydropower (%) Natural gas (%) Nuclear (%) \\\n", + "Year \n", + "2014 30.129928 34.284243 2.607999 22.798160 1.704505 \n", + "2015 29.232112 34.750780 2.597906 23.193692 1.719514 \n", + "2016 28.479418 35.021326 2.666862 23.616645 1.726446 \n", + "2017 28.254141 34.995960 2.643218 23.896486 1.715909 \n", + "2018 NaN NaN NaN NaN NaN \n", + "\n", + " Other renewables (%) Solar (%) Traditional biofuels (%) Wind (%) \n", + "Year \n", + "2014 0.338342 0.132597 7.526348 0.477878 \n", + "2015 0.359307 0.173580 7.417781 0.555327 \n", + "2016 0.368032 0.216849 7.270446 0.633975 \n", + "2017 0.381633 0.288171 7.093508 0.730975 \n", + "2018 NaN NaN NaN NaN \n", + "\n", + "[5 rows x 32 columns]" + ] + }, + "execution_count": 374, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_indicators.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 375, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 268 entries, 1751 to 2018\n", + "Data columns (total 32 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Annual CO₂ emissions (tonnes ) 267 non-null float64\n", + " 1 GHG(tonnes CO₂e ) 27 non-null float64\n", + " 2 Median (℃) 169 non-null float64\n", + " 3 Ozone-depleting emissions 54 non-null float64\n", + " 4 Mean ozone hole area (square kilometres) 38 non-null float64\n", + " 5 LandAverageTemperature 166 non-null float64\n", + " 6 LandAndOceanAverageTemperature 166 non-null float64\n", + " 7 Oil (megawatt-hours) 51 non-null float64\n", + " 8 Coal (megawatt-hours) 51 non-null float64\n", + " 9 Natural gas (megawatt-hours) 51 non-null float64\n", + " 10 Coal (terawatt-hours) 71 non-null float64\n", + " 11 Solar (terawatt-hours)_x 71 non-null float64\n", + " 12 Crude oil (terawatt-hours) 71 non-null float64\n", + " 13 Natural gas (terawatt-hours) 71 non-null float64\n", + " 14 Traditional biofuels (terawatt-hours) 71 non-null float64\n", + " 15 Other renewables (terawatt-hours)_x 71 non-null float64\n", + " 16 Hydropower (terawatt-hours)_x 71 non-null float64\n", + " 17 Nuclear (terawatt-hours) 71 non-null float64\n", + " 18 Wind (terawatt-hours)_x 71 non-null float64\n", + " 19 Hydropower (terawatt-hours)_y 54 non-null float64\n", + " 20 Solar (terawatt-hours)_y 54 non-null float64\n", + " 21 Wind (terawatt-hours)_y 54 non-null float64\n", + " 22 Other renewables (terawatt-hours)_y 54 non-null float64\n", + " 23 Coal (%) 70 non-null float64\n", + " 24 Crude oil (%) 70 non-null float64\n", + " 25 Hydropower (%) 70 non-null float64\n", + " 26 Natural gas (%) 70 non-null float64\n", + " 27 Nuclear (%) 70 non-null float64\n", + " 28 Other renewables (%) 70 non-null float64\n", + " 29 Solar (%) 70 non-null float64\n", + " 30 Traditional biofuels (%) 70 non-null float64\n", + " 31 Wind (%) 70 non-null float64\n", + "dtypes: float64(32)\n", + "memory usage: 69.1 KB\n" + ] + } + ], + "source": [ + "global_indicators.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "metadata": {}, + "outputs": [], + "source": [ + "global_indicators.to_csv('global_indicators')" + ] + }, + { + "cell_type": "code", + "execution_count": 444, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.890288126134981" + ] + }, + "execution_count": 444, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_a_corr = global_indicators[\"Median (℃)\"].corr(global_indicators[\"Annual CO₂ emissions (tonnes )\"])\n", + "co_a_corr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Inner merges" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearAnnual CO₂ emissions (tonnes )Median (℃)LandAverageTemperatureLandAndOceanAverageTemperatureCoal (terawatt-hours)Solar (terawatt-hours)Crude oil (terawatt-hours)Natural gas (terawatt-hours)Traditional biofuels (terawatt-hours)Other renewables (terawatt-hours)Hydropower (terawatt-hours)Nuclear (terawatt-hours)Wind (terawatt-hours)
018501.968960e+08-0.3737.90066714.867167569.444900.00.000000.000007222.2222220.00.0000000.00.0
118603.307090e+08-0.3437.96083314.8829171061.111960.00.000000.000006944.4444440.00.0000000.00.0
218705.325368e+08-0.2768.20133315.0043331641.667980.05.555560.000006944.4444440.00.0000000.00.0
318808.538915e+08-0.2278.11875015.0086672541.668700.033.333360.000006944.4444440.00.0000000.00.0
418901.298855e+09-0.4167.97166714.8407503855.558640.088.8889633.333366666.6666670.013.8888890.00.0
\n", + "
" + ], + "text/plain": [ + " Year Annual CO₂ emissions (tonnes ) Median (℃) LandAverageTemperature \\\n", + "0 1850 1.968960e+08 -0.373 7.900667 \n", + "1 1860 3.307090e+08 -0.343 7.960833 \n", + "2 1870 5.325368e+08 -0.276 8.201333 \n", + "3 1880 8.538915e+08 -0.227 8.118750 \n", + "4 1890 1.298855e+09 -0.416 7.971667 \n", + "\n", + " LandAndOceanAverageTemperature Coal (terawatt-hours) \\\n", + "0 14.867167 569.44490 \n", + "1 14.882917 1061.11196 \n", + "2 15.004333 1641.66798 \n", + "3 15.008667 2541.66870 \n", + "4 14.840750 3855.55864 \n", + "\n", + " Solar (terawatt-hours) Crude oil (terawatt-hours) \\\n", + "0 0.0 0.00000 \n", + "1 0.0 0.00000 \n", + "2 0.0 5.55556 \n", + "3 0.0 33.33336 \n", + "4 0.0 88.88896 \n", + "\n", + " Natural gas (terawatt-hours) Traditional biofuels (terawatt-hours) \\\n", + "0 0.00000 7222.222222 \n", + "1 0.00000 6944.444444 \n", + "2 0.00000 6944.444444 \n", + "3 0.00000 6944.444444 \n", + "4 33.33336 6666.666667 \n", + "\n", + " Other renewables (terawatt-hours) Hydropower (terawatt-hours) \\\n", + "0 0.0 0.000000 \n", + "1 0.0 0.000000 \n", + "2 0.0 0.000000 \n", + "3 0.0 0.000000 \n", + "4 0.0 13.888889 \n", + "\n", + " Nuclear (terawatt-hours) Wind (terawatt-hours) \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 " + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emissions_temp_merged = pd.merge(emissions_temp_merged,global_energy, on='Year')\n", + "emissions_temp_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearAnnual CO₂ emissions (tonnes )Median (℃)LandAverageTemperatureLandAndOceanAverageTemperatureCoal (terawatt-hours)Solar (terawatt-hours)Crude oil (terawatt-hours)Natural gas (terawatt-hours)Traditional biofuels (terawatt-hours)...Wind (terawatt-hours)Coal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
018501.968960e+08-0.3737.90066714.867167569.444900.00.000000.000007222.222222...0.07.3083830.0000000.000000.0000000.00.00.092.6916170.0
118603.307090e+08-0.3437.96083314.8829171061.111960.00.000000.000006944.444444...0.013.2546930.0000000.000000.0000000.00.00.086.7453070.0
218705.325368e+08-0.2768.20133315.0043331641.667980.05.555560.000006944.444444...0.019.1076750.0646620.000000.0000000.00.00.080.8276630.0
318808.538915e+08-0.2278.11875015.0086672541.668700.033.333360.000006944.444444...0.026.6997530.3501610.000000.0000000.00.00.072.9500860.0
418901.298855e+09-0.4167.97166714.8407503855.558640.088.8889633.333366666.666667...0.036.1741120.8339850.130310.3127440.00.00.062.5488480.0
\n", + "

5 rows × 23 columns

\n", + "
" + ], + "text/plain": [ + " Year Annual CO₂ emissions (tonnes ) Median (℃) LandAverageTemperature \\\n", + "0 1850 1.968960e+08 -0.373 7.900667 \n", + "1 1860 3.307090e+08 -0.343 7.960833 \n", + "2 1870 5.325368e+08 -0.276 8.201333 \n", + "3 1880 8.538915e+08 -0.227 8.118750 \n", + "4 1890 1.298855e+09 -0.416 7.971667 \n", + "\n", + " LandAndOceanAverageTemperature Coal (terawatt-hours) \\\n", + "0 14.867167 569.44490 \n", + "1 14.882917 1061.11196 \n", + "2 15.004333 1641.66798 \n", + "3 15.008667 2541.66870 \n", + "4 14.840750 3855.55864 \n", + "\n", + " Solar (terawatt-hours) Crude oil (terawatt-hours) \\\n", + "0 0.0 0.00000 \n", + "1 0.0 0.00000 \n", + "2 0.0 5.55556 \n", + "3 0.0 33.33336 \n", + "4 0.0 88.88896 \n", + "\n", + " Natural gas (terawatt-hours) Traditional biofuels (terawatt-hours) ... \\\n", + "0 0.00000 7222.222222 ... \n", + "1 0.00000 6944.444444 ... \n", + "2 0.00000 6944.444444 ... \n", + "3 0.00000 6944.444444 ... \n", + "4 33.33336 6666.666667 ... \n", + "\n", + " Wind (terawatt-hours) Coal (%) Crude oil (%) Hydropower (%) \\\n", + "0 0.0 7.308383 0.000000 0.00000 \n", + "1 0.0 13.254693 0.000000 0.00000 \n", + "2 0.0 19.107675 0.064662 0.00000 \n", + "3 0.0 26.699753 0.350161 0.00000 \n", + "4 0.0 36.174112 0.833985 0.13031 \n", + "\n", + " Natural gas (%) Nuclear (%) Other renewables (%) Solar (%) \\\n", + "0 0.000000 0.0 0.0 0.0 \n", + "1 0.000000 0.0 0.0 0.0 \n", + "2 0.000000 0.0 0.0 0.0 \n", + "3 0.000000 0.0 0.0 0.0 \n", + "4 0.312744 0.0 0.0 0.0 \n", + "\n", + " Traditional biofuels (%) Wind (%) \n", + "0 92.691617 0.0 \n", + "1 86.745307 0.0 \n", + "2 80.827663 0.0 \n", + "3 72.950086 0.0 \n", + "4 62.548848 0.0 \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emissions_temp_merged = pd.merge(emissions_temp_merged,share_energy, on='Year')\n", + "emissions_temp_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 63 entries, 0 to 62\n", + "Data columns (total 23 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Year 63 non-null int64 \n", + " 1 Annual CO₂ emissions (tonnes ) 63 non-null float64\n", + " 2 Median (℃) 63 non-null float64\n", + " 3 LandAverageTemperature 63 non-null float64\n", + " 4 LandAndOceanAverageTemperature 63 non-null float64\n", + " 5 Coal (terawatt-hours) 63 non-null float64\n", + " 6 Solar (terawatt-hours) 63 non-null float64\n", + " 7 Crude oil (terawatt-hours) 63 non-null float64\n", + " 8 Natural gas (terawatt-hours) 63 non-null float64\n", + " 9 Traditional biofuels (terawatt-hours) 63 non-null float64\n", + " 10 Other renewables (terawatt-hours) 63 non-null float64\n", + " 11 Hydropower (terawatt-hours) 63 non-null float64\n", + " 12 Nuclear (terawatt-hours) 63 non-null float64\n", + " 13 Wind (terawatt-hours) 63 non-null float64\n", + " 14 Coal (%) 63 non-null float64\n", + " 15 Crude oil (%) 63 non-null float64\n", + " 16 Hydropower (%) 63 non-null float64\n", + " 17 Natural gas (%) 63 non-null float64\n", + " 18 Nuclear (%) 63 non-null float64\n", + " 19 Other renewables (%) 63 non-null float64\n", + " 20 Solar (%) 63 non-null float64\n", + " 21 Traditional biofuels (%) 63 non-null float64\n", + " 22 Wind (%) 63 non-null float64\n", + "dtypes: float64(22), int64(1)\n", + "memory usage: 11.8 KB\n" + ] + } + ], + "source": [ + "emissions_temp_merged.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [], + "source": [ + "emissions_temp_merged[\"Year\"] = emissions_temp_merged[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [], + "source": [ + "emissions_temp_merged.to_csv('emissions_temp_merged')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging country indicators" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "total_emissions = pd.merge(co_merged,ghg, on=(['Country','Year']))\n", + "total_emissions.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "total_emissions.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging Temperature with Emissions (1990-2013)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "annual_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "annual_temp.rename(columns={'year':'Year'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_impact = pd.merge(total_emissions, annual_temp, on=(['Country','Year']))\n", + "co_impact.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging co_impact with demographic and economic indicators" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged = pd.merge(co_impact,gdp, on=(['Country','Year']))\n", + "indicators_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged_outer = pd.merge(co_impact,gdp, on=(['Country','Year']), how='outer')\n", + "indicators_merged_outer.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged_outer.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging ozone datasets 1979-2014" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ozone_merged = pd.merge(caused_ozone, oz_hole, on='Year')\n", + "ozone_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "oz_hole_emiss_corr = ozone_merged[\"Ozone-depleting emissions\"].corr(ozone_merged[\"Mean ozone hole area (square kilometres)\"])\n", + "oz_hole_emiss_corr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ozone_merged[\"Year\"] = ozone_merged[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ozone_merged.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ozone_merged = ozone_merged.set_index('Year')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.pairplot(ozone_merged)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging global co concentration, temp anomally and annual_global_temp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "annual_global_temp.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration_modern = co_concentration[1740:]\n", + "co_concentration_modern.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration_modern.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration_modern[\"Year\"] = co_concentration_modern[\"Year\"].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration_modern.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_global_impact = pd.merge(annual_global_temp,co_concentration_modern, on='Year')\n", + "co_global_impact.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_global_impact.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_impact_temp = pd.merge(co_global_impact,global_a, on='Year')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_impact_temp.drop(['Country'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_impact_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "global_co.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_temp_global = pd.merge(co_impact_temp,global_co, on='Year')\n", + "co_temp_global.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(17,6))\n", + "plt.plot(co_global_impact.Year,co_global_impact['LandAverageTemperature'], label='LandAverageTemperature')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Tonnes CFC11-equivalents')\n", + "plt.plot(co_global_impact.Year,co_global_impact['LandAndOceanAverageTemperature'], label='LandAndOceanAverageTemperature')\n", + "plt.legend(['LandAverageTemperature', 'LandAndOceanAverageTemperature'], loc='upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_conc_temp_corr = co_global_impact[\"LandAverageTemperature\"].corr(co_global_impact[\"CO₂ concentration\"])\n", + "co_conc_temp_corr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_temp_corr = co_temp_global[\"LandAverageTemperature\"].corr(co_temp_global[\"Annual CO₂ emissions (tonnes )\"])\n", + "co_temp_corr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_global_impact[\"Year\"] = co_global_impact[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "corr = co_temp_global.corr()\n", + "plt.figure(figsize=(12, 6))\n", + "sns.set(style=\"white\")\n", + "\n", + "mask = np.zeros_like(corr, dtype=np.bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "cmap = sns.diverging_palette(200, 10, as_cmap=True)\n", + "\n", + "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_temp_global.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_temp_global_no_colin = co_temp_global.drop(['LandAndOceanAverageTemperature','Upper (℃)','Lower (℃)'], axis=1)\n", + "co_temp_global_no_colin.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "co_temp_global_no_colin[\"Year\"] = co_temp_global_no_colin[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.pairplot(co_temp_global_no_colin)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(x=co_temp_global['CO₂ concentration'], y=co_temp_global['Median (℃)'])\n", + "plt.xlabel('CO₂ concentration')\n", + "plt.ylabel('Median (℃)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(x=co_temp_global['Annual CO₂ emissions (tonnes )'], y=co_temp_global['Median (℃)'])\n", + "plt.xlabel('Annual CO₂ emissions (tonnes )')\n", + "plt.ylabel('Median (℃)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking Correlations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged[\"Year\"] = indicators_merged[\"Year\"].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged.corr()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ind_no_colin = indicators_merged.drop(['Share of global CO₂ emissions (%)','GDP','Annual CO₂'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indicators_merged.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp_ghg_corr = indicators_merged[\"GDP_per_capita\"].corr(indicators_merged[\"GHG(tonnes CO₂e )\"])\n", + "temp_ghg_corr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# here we are using seaborn to show the heatmap of the correlation between our final datasets' parameters\n", + "corr = ind_no_colin.corr()\n", + "plt.figure(figsize=(12, 6))\n", + "sns.set(style=\"white\")\n", + "\n", + "mask = np.zeros_like(corr, dtype=np.bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "cmap = sns.diverging_palette(200, 10, as_cmap=True)\n", + "\n", + "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From efcb7f9736dfb46351b0b5b1518dffca3c44cc5e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Wed, 27 May 2020 13:19:08 +0200 Subject: [PATCH 10/17] datasets description done --- ...ate change datasets and correlations.ipynb | 1401 ++++++----------- 1 file changed, 488 insertions(+), 913 deletions(-) diff --git a/your-project/Global climate change datasets and correlations.ipynb b/your-project/Global climate change datasets and correlations.ipynb index 6fe52eb8..60d0e48f 100644 --- a/your-project/Global climate change datasets and correlations.ipynb +++ b/your-project/Global climate change datasets and correlations.ipynb @@ -16,6 +16,70 @@ "import os" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Datasets structure\n", + "\n", + "#### - Global Indicators datasets:\n", + "\n", + "1. Annual CO emissions (1751-2017) [global_co]\n", + "2. Annual greenhouse gas emissions (1990-2016) [global_ghg]\n", + "3. Global CO atmospheric concentration (800.000 BCE - 2018) [co_concentration]\n", + "4. Global Temperature Anomaly (1850-2018) [global_a]\n", + "5. Ozone-depleting emissions (1961-2014) [caused_ozone]\n", + "6. Antartica ozone hole area (1979-2018) [oz_hole]\n", + "7. Global average temperature (1850-2015) [annual_global_temp]\n", + "8. Fossil fuel consimpiton per capita (1965-2015) [fossil_per_c]\n", + "9. Primary energy consumption by source (1800-2018) [global_energy]\n", + "10. Modern renewable consumption (1965-2018) [renew_energy]\n", + "11. Hubbert's prediction on USA oil production (1991-2016) [hubbert_peak]\n", + "12. Share of global primary energy consumtion (1800-2017) [share_energy]\n", + "13. Solar PV price and capacity (1976-2018) [solar_price]\n", + "\n", + "#### - Country indicators datasets:\n", + "\n", + "14. CO annual emissions by country (1949-2017) [co_annual]\n", + "15. Annual share of CO emissions by country (1949-2017)[co_share]\n", + "16. Total greenhouse gas emissions by country (1990-2016) [ghg]\n", + "17. Average temperature by country (1743-2017)[annual_temp]\n", + "\n", + "#### - Merged datasets:\n", + "\n", + "18. Global indicators (1751-2018) [global_indicators]\n", + "19. Country indicators (1743-2017) [country_indicators]\n", + "20. Ozone datasets merged (1979-2014) [ozone_merged]" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -27,7 +91,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Annual CO emissions globally " + "### 1. Annual CO emissions globally (1751-2017)" ] }, { @@ -273,7 +337,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Total greenhouse gas emissions (1990-2016)" + "### 2. Total greenhouse gas emissions (1990-2016)" ] }, { @@ -530,7 +594,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Global CO₂ atmospheric concentration" + "### 3. Global CO₂ atmospheric concentration" ] }, { @@ -888,7 +952,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Global Temperature anomaly 1850-2018\n" + "### 4. Global Temperature anomaly 1850-2018\n" ] }, { @@ -1253,7 +1317,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Ozone depletion crisis" + "### 5. Ozone depletion substances emissions (CFC's) (1961-2014)" ] }, { @@ -1474,7 +1538,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Antartica Ozone Hole area" + "### 6. Antartica Ozone Hole area (1979-2017)" ] }, { @@ -1717,7 +1781,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Global average temperature" + "### 7. Global average temperature (1850-2015)" ] }, { @@ -2249,7 +2313,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Fossil fuel consumption per capita" + "### 8. Fossil fuel consumption per capita (1965-2015)" ] }, { @@ -2521,7 +2585,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Global primary energy by source" + "### 9. Global primary energy by source (1800-2018)" ] }, { @@ -2745,7 +2809,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Modern renewable energy consumtion" + "### 10. Modern renewable energy consumtion (1965-2018)" ] }, { @@ -3037,7 +3101,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Hubbert's peak vs actual oil production" + "### 11. Hubbert's peak vs actual oil production (1911-2016)" ] }, { @@ -3344,7 +3408,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### share of global primary energy" + "### 12. Share of global primary energy (1800-2017)" ] }, { @@ -3554,7 +3618,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Solar pannels price progression" + "### 13. Solar pannels price progression (1976-2016)" ] }, { @@ -3910,7 +3974,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### CO annual emissions (1949-2017)" + "### 14. CO annual emissions (1949-2017)" ] }, { @@ -4049,7 +4113,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Annual share of CO per country (1949-2017)" + "### 15. Annual share of CO per country (1949-2017)" ] }, { @@ -4181,12 +4245,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Total greenhouse gas emissions (1990-2016)" + "### 16. Total greenhouse gas emissions (1990-2016)" ] }, { "cell_type": "code", - "execution_count": 485, + "execution_count": 520, "metadata": {}, "outputs": [ { @@ -4265,7 +4329,7 @@ "4 Afghanistan AFG 1994 13240000.0" ] }, - "execution_count": 485, + "execution_count": 520, "metadata": {}, "output_type": "execute_result" } @@ -4278,7 +4342,7 @@ }, { "cell_type": "code", - "execution_count": 486, + "execution_count": 521, "metadata": {}, "outputs": [ { @@ -4286,15 +4350,15 @@ "output_type": "stream", "text": [ "\n", - "RangeIndex: 5184 entries, 0 to 5183\n", + "Int64Index: 5157 entries, 0 to 5183\n", "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 Country 5184 non-null object \n", - " 1 Year 5184 non-null int64 \n", - " 2 GHG(tonnes CO₂e ) 5184 non-null float64\n", + " 0 Country 5157 non-null object \n", + " 1 Year 5157 non-null int64 \n", + " 2 GHG(tonnes CO₂e ) 5157 non-null float64\n", "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 121.6+ KB\n" + "memory usage: 161.2+ KB\n" ] } ], @@ -4303,7 +4367,8 @@ "ghg.drop(['Code'], axis=1, inplace=True)\n", "\n", "ghg = ghg.rename(columns={'Total GHG emissions including LUCF (tonnes CO₂e)':'GHG(tonnes CO₂e )', 'Entity':'Country'})\n", - "\n", + "#keeping only Country indicators\n", + "ghg = ghg[ghg.Country != 'World']\n", "ghg.info()" ] }, @@ -4311,7 +4376,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Average temperatures by country" + "### 17. Average temperatures by country (1743-2013)" ] }, { @@ -4648,14 +4713,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Merging global indicators" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### outer merges" + "### 18. Merging global indicators" ] }, { @@ -4750,7 +4808,7 @@ }, { "cell_type": "code", - "execution_count": 374, + "execution_count": 504, "metadata": {}, "outputs": [ { @@ -5007,7 +5065,7 @@ "[5 rows x 32 columns]" ] }, - "execution_count": 374, + "execution_count": 504, "metadata": {}, "output_type": "execute_result" } @@ -5080,37 +5138,16 @@ "global_indicators.to_csv('global_indicators')" ] }, - { - "cell_type": "code", - "execution_count": 444, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.890288126134981" - ] - }, - "execution_count": 444, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_a_corr = global_indicators[\"Median (℃)\"].corr(global_indicators[\"Annual CO₂ emissions (tonnes )\"])\n", - "co_a_corr" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Inner merges" + "### 19. Merging country indicators" ] }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 523, "metadata": {}, "outputs": [ { @@ -5134,169 +5171,93 @@ " \n", " \n", " \n", + " Country\n", " Year\n", - " Annual CO₂ emissions (tonnes )\n", - " Median (℃)\n", - " LandAverageTemperature\n", - " LandAndOceanAverageTemperature\n", - " Coal (terawatt-hours)\n", - " Solar (terawatt-hours)\n", - " Crude oil (terawatt-hours)\n", - " Natural gas (terawatt-hours)\n", - " Traditional biofuels (terawatt-hours)\n", - " Other renewables (terawatt-hours)\n", - " Hydropower (terawatt-hours)\n", - " Nuclear (terawatt-hours)\n", - " Wind (terawatt-hours)\n", + " Annual CO₂\n", + " Share of global CO₂ emissions (%)\n", " \n", " \n", " \n", " \n", " 0\n", - " 1850\n", - " 1.968960e+08\n", - " -0.373\n", - " 7.900667\n", - " 14.867167\n", - " 569.44490\n", - " 0.0\n", - " 0.00000\n", - " 0.00000\n", - " 7222.222222\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", + " Afghanistan\n", + " 1949\n", + " 14656.0\n", + " 0.0003\n", " \n", " \n", " 1\n", - " 1860\n", - " 3.307090e+08\n", - " -0.343\n", - " 7.960833\n", - " 14.882917\n", - " 1061.11196\n", - " 0.0\n", - " 0.00000\n", - " 0.00000\n", - " 6944.444444\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", + " Afghanistan\n", + " 1950\n", + " 84272.0\n", + " 0.0015\n", " \n", " \n", " 2\n", - " 1870\n", - " 5.325368e+08\n", - " -0.276\n", - " 8.201333\n", - " 15.004333\n", - " 1641.66798\n", - " 0.0\n", - " 5.55556\n", - " 0.00000\n", - " 6944.444444\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", + " Afghanistan\n", + " 1951\n", + " 91600.0\n", + " 0.0015\n", " \n", " \n", " 3\n", - " 1880\n", - " 8.538915e+08\n", - " -0.227\n", - " 8.118750\n", - " 15.008667\n", - " 2541.66870\n", - " 0.0\n", - " 33.33336\n", - " 0.00000\n", - " 6944.444444\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", + " Afghanistan\n", + " 1952\n", + " 91600.0\n", + " 0.0015\n", " \n", " \n", " 4\n", - " 1890\n", - " 1.298855e+09\n", - " -0.416\n", - " 7.971667\n", - " 14.840750\n", - " 3855.55864\n", - " 0.0\n", - " 88.88896\n", - " 33.33336\n", - " 6666.666667\n", - " 0.0\n", - " 13.888889\n", - " 0.0\n", - " 0.0\n", + " Afghanistan\n", + " 1953\n", + " 106256.0\n", + " 0.0016\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Year Annual CO₂ emissions (tonnes ) Median (℃) LandAverageTemperature \\\n", - "0 1850 1.968960e+08 -0.373 7.900667 \n", - "1 1860 3.307090e+08 -0.343 7.960833 \n", - "2 1870 5.325368e+08 -0.276 8.201333 \n", - "3 1880 8.538915e+08 -0.227 8.118750 \n", - "4 1890 1.298855e+09 -0.416 7.971667 \n", - "\n", - " LandAndOceanAverageTemperature Coal (terawatt-hours) \\\n", - "0 14.867167 569.44490 \n", - "1 14.882917 1061.11196 \n", - "2 15.004333 1641.66798 \n", - "3 15.008667 2541.66870 \n", - "4 14.840750 3855.55864 \n", - "\n", - " Solar (terawatt-hours) Crude oil (terawatt-hours) \\\n", - "0 0.0 0.00000 \n", - "1 0.0 0.00000 \n", - "2 0.0 5.55556 \n", - "3 0.0 33.33336 \n", - "4 0.0 88.88896 \n", - "\n", - " Natural gas (terawatt-hours) Traditional biofuels (terawatt-hours) \\\n", - "0 0.00000 7222.222222 \n", - "1 0.00000 6944.444444 \n", - "2 0.00000 6944.444444 \n", - "3 0.00000 6944.444444 \n", - "4 33.33336 6666.666667 \n", - "\n", - " Other renewables (terawatt-hours) Hydropower (terawatt-hours) \\\n", - "0 0.0 0.000000 \n", - "1 0.0 0.000000 \n", - "2 0.0 0.000000 \n", - "3 0.0 0.000000 \n", - "4 0.0 13.888889 \n", - "\n", - " Nuclear (terawatt-hours) Wind (terawatt-hours) \n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 " + " Country Year Annual CO₂ Share of global CO₂ emissions (%)\n", + "0 Afghanistan 1949 14656.0 0.0003\n", + "1 Afghanistan 1950 84272.0 0.0015\n", + "2 Afghanistan 1951 91600.0 0.0015\n", + "3 Afghanistan 1952 91600.0 0.0015\n", + "4 Afghanistan 1953 106256.0 0.0016" ] }, - "execution_count": 224, + "execution_count": 523, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "emissions_temp_merged = pd.merge(emissions_temp_merged,global_energy, on='Year')\n", - "emissions_temp_merged.head()" + "#datasets 1 and 2\n", + "country_indicators1 = pd.merge(co_annual, co_share, on=['Year', 'Country'], how='outer')\n", + "country_indicators1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 524, + "metadata": {}, + "outputs": [], + "source": [ + "country_indicators2 = pd.merge(country_indicators1, ghg, on=['Year', 'Country'], how='outer')" ] }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 527, + "metadata": {}, + "outputs": [], + "source": [ + "country_indicators = pd.merge(country_indicators2, annual_temp, on=['Year', 'Country'], how='outer' )" + ] + }, + { + "cell_type": "code", + "execution_count": 528, "metadata": {}, "outputs": [ { @@ -5320,221 +5281,92 @@ " \n", " \n", " \n", + " Country\n", " Year\n", - " Annual CO₂ emissions (tonnes )\n", - " Median (℃)\n", - " LandAverageTemperature\n", - " LandAndOceanAverageTemperature\n", - " Coal (terawatt-hours)\n", - " Solar (terawatt-hours)\n", - " Crude oil (terawatt-hours)\n", - " Natural gas (terawatt-hours)\n", - " Traditional biofuels (terawatt-hours)\n", - " ...\n", - " Wind (terawatt-hours)\n", - " Coal (%)\n", - " Crude oil (%)\n", - " Hydropower (%)\n", - " Natural gas (%)\n", - " Nuclear (%)\n", - " Other renewables (%)\n", - " Solar (%)\n", - " Traditional biofuels (%)\n", - " Wind (%)\n", + " Annual CO₂\n", + " Share of global CO₂ emissions (%)\n", + " GHG(tonnes CO₂e )\n", + " AverageTemperature\n", " \n", " \n", " \n", " \n", " 0\n", - " 1850\n", - " 1.968960e+08\n", - " -0.373\n", - " 7.900667\n", - " 14.867167\n", - " 569.44490\n", - " 0.0\n", - " 0.00000\n", - " 0.00000\n", - " 7222.222222\n", - " ...\n", - " 0.0\n", - " 7.308383\n", - " 0.000000\n", - " 0.00000\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 92.691617\n", - " 0.0\n", + " Afghanistan\n", + " 1949\n", + " 14656.0\n", + " 0.0003\n", + " NaN\n", + " 13.350083\n", " \n", " \n", " 1\n", - " 1860\n", - " 3.307090e+08\n", - " -0.343\n", - " 7.960833\n", - " 14.882917\n", - " 1061.11196\n", - " 0.0\n", - " 0.00000\n", - " 0.00000\n", - " 6944.444444\n", - " ...\n", - " 0.0\n", - " 13.254693\n", - " 0.000000\n", - " 0.00000\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 86.745307\n", - " 0.0\n", + " Afghanistan\n", + " 1950\n", + " 84272.0\n", + " 0.0015\n", + " NaN\n", + " 13.043500\n", " \n", " \n", " 2\n", - " 1870\n", - " 5.325368e+08\n", - " -0.276\n", - " 8.201333\n", - " 15.004333\n", - " 1641.66798\n", - " 0.0\n", - " 5.55556\n", - " 0.00000\n", - " 6944.444444\n", - " ...\n", - " 0.0\n", - " 19.107675\n", - " 0.064662\n", - " 0.00000\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 80.827663\n", - " 0.0\n", + " Afghanistan\n", + " 1951\n", + " 91600.0\n", + " 0.0015\n", + " NaN\n", + " 13.967750\n", " \n", " \n", " 3\n", - " 1880\n", - " 8.538915e+08\n", - " -0.227\n", - " 8.118750\n", - " 15.008667\n", - " 2541.66870\n", - " 0.0\n", - " 33.33336\n", - " 0.00000\n", - " 6944.444444\n", - " ...\n", - " 0.0\n", - " 26.699753\n", - " 0.350161\n", - " 0.00000\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 72.950086\n", - " 0.0\n", + " Afghanistan\n", + " 1952\n", + " 91600.0\n", + " 0.0015\n", + " NaN\n", + " 14.175417\n", " \n", " \n", " 4\n", - " 1890\n", - " 1.298855e+09\n", - " -0.416\n", - " 7.971667\n", - " 14.840750\n", - " 3855.55864\n", - " 0.0\n", - " 88.88896\n", - " 33.33336\n", - " 6666.666667\n", - " ...\n", - " 0.0\n", - " 36.174112\n", - " 0.833985\n", - " 0.13031\n", - " 0.312744\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 62.548848\n", - " 0.0\n", + " Afghanistan\n", + " 1953\n", + " 106256.0\n", + " 0.0016\n", + " NaN\n", + " 14.650750\n", " \n", " \n", "\n", - "

5 rows × 23 columns

\n", "" ], "text/plain": [ - " Year Annual CO₂ emissions (tonnes ) Median (℃) LandAverageTemperature \\\n", - "0 1850 1.968960e+08 -0.373 7.900667 \n", - "1 1860 3.307090e+08 -0.343 7.960833 \n", - "2 1870 5.325368e+08 -0.276 8.201333 \n", - "3 1880 8.538915e+08 -0.227 8.118750 \n", - "4 1890 1.298855e+09 -0.416 7.971667 \n", - "\n", - " LandAndOceanAverageTemperature Coal (terawatt-hours) \\\n", - "0 14.867167 569.44490 \n", - "1 14.882917 1061.11196 \n", - "2 15.004333 1641.66798 \n", - "3 15.008667 2541.66870 \n", - "4 14.840750 3855.55864 \n", - "\n", - " Solar (terawatt-hours) Crude oil (terawatt-hours) \\\n", - "0 0.0 0.00000 \n", - "1 0.0 0.00000 \n", - "2 0.0 5.55556 \n", - "3 0.0 33.33336 \n", - "4 0.0 88.88896 \n", - "\n", - " Natural gas (terawatt-hours) Traditional biofuels (terawatt-hours) ... \\\n", - "0 0.00000 7222.222222 ... \n", - "1 0.00000 6944.444444 ... \n", - "2 0.00000 6944.444444 ... \n", - "3 0.00000 6944.444444 ... \n", - "4 33.33336 6666.666667 ... \n", - "\n", - " Wind (terawatt-hours) Coal (%) Crude oil (%) Hydropower (%) \\\n", - "0 0.0 7.308383 0.000000 0.00000 \n", - "1 0.0 13.254693 0.000000 0.00000 \n", - "2 0.0 19.107675 0.064662 0.00000 \n", - "3 0.0 26.699753 0.350161 0.00000 \n", - "4 0.0 36.174112 0.833985 0.13031 \n", - "\n", - " Natural gas (%) Nuclear (%) Other renewables (%) Solar (%) \\\n", - "0 0.000000 0.0 0.0 0.0 \n", - "1 0.000000 0.0 0.0 0.0 \n", - "2 0.000000 0.0 0.0 0.0 \n", - "3 0.000000 0.0 0.0 0.0 \n", - "4 0.312744 0.0 0.0 0.0 \n", - "\n", - " Traditional biofuels (%) Wind (%) \n", - "0 92.691617 0.0 \n", - "1 86.745307 0.0 \n", - "2 80.827663 0.0 \n", - "3 72.950086 0.0 \n", - "4 62.548848 0.0 \n", + " Country Year Annual CO₂ Share of global CO₂ emissions (%) \\\n", + "0 Afghanistan 1949 14656.0 0.0003 \n", + "1 Afghanistan 1950 84272.0 0.0015 \n", + "2 Afghanistan 1951 91600.0 0.0015 \n", + "3 Afghanistan 1952 91600.0 0.0015 \n", + "4 Afghanistan 1953 106256.0 0.0016 \n", "\n", - "[5 rows x 23 columns]" + " GHG(tonnes CO₂e ) AverageTemperature \n", + "0 NaN 13.350083 \n", + "1 NaN 13.043500 \n", + "2 NaN 13.967750 \n", + "3 NaN 14.175417 \n", + "4 NaN 14.650750 " ] }, - "execution_count": 225, + "execution_count": 528, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "emissions_temp_merged = pd.merge(emissions_temp_merged,share_energy, on='Year')\n", - "emissions_temp_merged.head()" + "country_indicators.head()" ] }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 529, "metadata": {}, "outputs": [ { @@ -5542,607 +5374,350 @@ "output_type": "stream", "text": [ "\n", - "Int64Index: 63 entries, 0 to 62\n", - "Data columns (total 23 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Year 63 non-null int64 \n", - " 1 Annual CO₂ emissions (tonnes ) 63 non-null float64\n", - " 2 Median (℃) 63 non-null float64\n", - " 3 LandAverageTemperature 63 non-null float64\n", - " 4 LandAndOceanAverageTemperature 63 non-null float64\n", - " 5 Coal (terawatt-hours) 63 non-null float64\n", - " 6 Solar (terawatt-hours) 63 non-null float64\n", - " 7 Crude oil (terawatt-hours) 63 non-null float64\n", - " 8 Natural gas (terawatt-hours) 63 non-null float64\n", - " 9 Traditional biofuels (terawatt-hours) 63 non-null float64\n", - " 10 Other renewables (terawatt-hours) 63 non-null float64\n", - " 11 Hydropower (terawatt-hours) 63 non-null float64\n", - " 12 Nuclear (terawatt-hours) 63 non-null float64\n", - " 13 Wind (terawatt-hours) 63 non-null float64\n", - " 14 Coal (%) 63 non-null float64\n", - " 15 Crude oil (%) 63 non-null float64\n", - " 16 Hydropower (%) 63 non-null float64\n", - " 17 Natural gas (%) 63 non-null float64\n", - " 18 Nuclear (%) 63 non-null float64\n", - " 19 Other renewables (%) 63 non-null float64\n", - " 20 Solar (%) 63 non-null float64\n", - " 21 Traditional biofuels (%) 63 non-null float64\n", - " 22 Wind (%) 63 non-null float64\n", - "dtypes: float64(22), int64(1)\n", - "memory usage: 11.8 KB\n" - ] - } - ], - "source": [ - "emissions_temp_merged.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 227, - "metadata": {}, - "outputs": [], - "source": [ - "emissions_temp_merged[\"Year\"] = emissions_temp_merged[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "metadata": {}, - "outputs": [], - "source": [ - "emissions_temp_merged.to_csv('emissions_temp_merged')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging country indicators" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ghg.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "total_emissions = pd.merge(co_merged,ghg, on=(['Country','Year']))\n", - "total_emissions.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "total_emissions.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging Temperature with Emissions (1990-2013)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "annual_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "annual_temp.rename(columns={'year':'Year'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_impact = pd.merge(total_emissions, annual_temp, on=(['Country','Year']))\n", - "co_impact.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging co_impact with demographic and economic indicators" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged = pd.merge(co_impact,gdp, on=(['Country','Year']))\n", - "indicators_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged_outer = pd.merge(co_impact,gdp, on=(['Country','Year']), how='outer')\n", - "indicators_merged_outer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged_outer.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging ozone datasets 1979-2014" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ozone_merged = pd.merge(caused_ozone, oz_hole, on='Year')\n", - "ozone_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "oz_hole_emiss_corr = ozone_merged[\"Ozone-depleting emissions\"].corr(ozone_merged[\"Mean ozone hole area (square kilometres)\"])\n", - "oz_hole_emiss_corr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ozone_merged[\"Year\"] = ozone_merged[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ozone_merged.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ozone_merged = ozone_merged.set_index('Year')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sns.pairplot(ozone_merged)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging global co concentration, temp anomally and annual_global_temp" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "annual_global_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "annual_global_temp.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration_modern = co_concentration[1740:]\n", - "co_concentration_modern.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration_modern.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration_modern[\"Year\"] = co_concentration_modern[\"Year\"].astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration_modern.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_global_impact = pd.merge(annual_global_temp,co_concentration_modern, on='Year')\n", - "co_global_impact.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_global_impact.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_impact_temp = pd.merge(co_global_impact,global_a, on='Year')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_impact_temp.drop(['Country'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_impact_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "global_co.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_temp_global = pd.merge(co_impact_temp,global_co, on='Year')\n", - "co_temp_global.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(17,6))\n", - "plt.plot(co_global_impact.Year,co_global_impact['LandAverageTemperature'], label='LandAverageTemperature')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Tonnes CFC11-equivalents')\n", - "plt.plot(co_global_impact.Year,co_global_impact['LandAndOceanAverageTemperature'], label='LandAndOceanAverageTemperature')\n", - "plt.legend(['LandAverageTemperature', 'LandAndOceanAverageTemperature'], loc='upper right')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_conc_temp_corr = co_global_impact[\"LandAverageTemperature\"].corr(co_global_impact[\"CO₂ concentration\"])\n", - "co_conc_temp_corr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_temp_corr = co_temp_global[\"LandAverageTemperature\"].corr(co_temp_global[\"Annual CO₂ emissions (tonnes )\"])\n", - "co_temp_corr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_global_impact[\"Year\"] = co_global_impact[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "corr = co_temp_global.corr()\n", - "plt.figure(figsize=(12, 6))\n", - "sns.set(style=\"white\")\n", - "\n", - "mask = np.zeros_like(corr, dtype=np.bool)\n", - "mask[np.triu_indices_from(mask)] = True\n", - "cmap = sns.diverging_palette(200, 10, as_cmap=True)\n", - "\n", - "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_temp_global.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_temp_global_no_colin = co_temp_global.drop(['LandAndOceanAverageTemperature','Upper (℃)','Lower (℃)'], axis=1)\n", - "co_temp_global_no_colin.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_temp_global_no_colin[\"Year\"] = co_temp_global_no_colin[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "Int64Index: 5 entries, 0 to 4\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 5 non-null object \n", + " 1 Year 5 non-null int64 \n", + " 2 Annual CO₂ 5 non-null float64\n", + " 3 Share of global CO₂ emissions (%) 5 non-null float64\n", + " 4 GHG(tonnes CO₂e ) 0 non-null float64\n", + " 5 AverageTemperature 5 non-null float64\n", + "dtypes: float64(4), int64(1), object(1)\n", + "memory usage: 280.0+ bytes\n" + ] + } + ], "source": [ - "sns.pairplot(co_temp_global_no_colin)" + "country_indicators.head().info()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "plt.scatter(x=co_temp_global['CO₂ concentration'], y=co_temp_global['Median (℃)'])\n", - "plt.xlabel('CO₂ concentration')\n", - "plt.ylabel('Median (℃)')" + "### 20. Merging ozone datasets 1979-2014" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.scatter(x=co_temp_global['Annual CO₂ emissions (tonnes )'], y=co_temp_global['Median (℃)'])\n", - "plt.xlabel('Annual CO₂ emissions (tonnes )')\n", - "plt.ylabel('Median (℃)')" - ] - }, - { - "cell_type": "markdown", + "execution_count": 530, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Ozone-depleting emissions Mean ozone hole area (square kilometres)\n", + "Year \n", + "2010 380000 19400000\n", + "2011 370000 24700000\n", + "2012 350000 17800000\n", + "2013 340000 21000000\n", + "2014 320000 20900000" + ] + }, + "execution_count": 540, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "indicators_merged.corr()" + "ozone_merged.tail()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 534, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 534, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "ind_no_colin = indicators_merged.drop(['Share of global CO₂ emissions (%)','GDP','Annual CO₂'], axis=1)" + "sns.pairplot(ozone_merged)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "indicators_merged.columns" + "### CO emissions and median temperature anomaly correlation" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 444, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.890288126134981" + ] + }, + "execution_count": 444, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "temp_ghg_corr = indicators_merged[\"GDP_per_capita\"].corr(indicators_merged[\"GHG(tonnes CO₂e )\"])\n", - "temp_ghg_corr" + "co_a_corr = global_indicators[\"Median (℃)\"].corr(global_indicators[\"Annual CO₂ emissions (tonnes )\"])\n", + "co_a_corr" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 538, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Median (℃)')" + ] + }, + "execution_count": 538, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# here we are using seaborn to show the heatmap of the correlation between our final datasets' parameters\n", - "corr = ind_no_colin.corr()\n", - "plt.figure(figsize=(12, 6))\n", - "sns.set(style=\"white\")\n", - "\n", - "mask = np.zeros_like(corr, dtype=np.bool)\n", - "mask[np.triu_indices_from(mask)] = True\n", - "cmap = sns.diverging_palette(200, 10, as_cmap=True)\n", - "\n", - "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)\n", - "plt.show()" + "plt.scatter(x=global_indicators['Annual CO₂ emissions (tonnes )'], y=global_indicators['Median (℃)'])\n", + "plt.xlabel('Annual CO₂ emissions (tonnes )')\n", + "plt.ylabel('Median (℃)')" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { From 9ca1dc7a9bf881b26a1314f17eec574f478b0632 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Wed, 27 May 2020 15:12:32 +0200 Subject: [PATCH 11/17] preparing report --- ...obal Warming report, can we solve it.ipynb | 5752 +++++++++++++++++ 1 file changed, 5752 insertions(+) create mode 100644 your-project/Global Warming report, can we solve it.ipynb diff --git a/your-project/Global Warming report, can we solve it.ipynb b/your-project/Global Warming report, can we solve it.ipynb new file mode 100644 index 00000000..eec2b6ed --- /dev/null +++ b/your-project/Global Warming report, can we solve it.ipynb @@ -0,0 +1,5752 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [], + "source": [ + "#importing the needed libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import datetime\n", + "import re\n", + "import seaborn as sns\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### About" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though nowadays the hottest topic is the current Covid crisis. I wanted to express through data, that Global Warming is not something we can leave out to chance. \n", + "In this project I gathered numerous Global and Country indicators to show that the Climate Change issue is one that we need to tackle quickly and unanimously." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Structure of the notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Libraries \n", + "- About\n", + "- Datasets structure\n", + "- Gathering and cleaning datasets\n", + "- Processing and merging datasets\n", + "- Strong correlation between emissions and temperature anomaly\n", + "- Plots and charts to explain the results\n", + "- Conclusions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Datasets structure\n", + "\n", + "#### - Global Indicators datasets:\n", + "\n", + "1. Annual CO emissions (1751-2017) [global_co]\n", + "2. Annual greenhouse gas emissions (1990-2016) [global_ghg]\n", + "3. Global CO atmospheric concentration (800.000 BCE - 2018) [co_concentration]\n", + "4. Global Temperature Anomaly (1850-2018) [global_a]\n", + "5. Ozone-depleting emissions (1961-2014) [caused_ozone]\n", + "6. Antartica ozone hole area (1979-2018) [oz_hole]\n", + "7. Global average temperature (1850-2015) [annual_global_temp]\n", + "8. Fossil fuel consimpiton per capita (1965-2015) [fossil_per_c]\n", + "9. Primary energy consumption by source (1800-2018) [global_energy]\n", + "10. Modern renewable consumption (1965-2018) [renew_energy]\n", + "11. Hubbert's prediction on USA oil production (1991-2016) [hubbert_peak]\n", + "12. Share of global primary energy consumtion (1800-2017) [share_energy]\n", + "13. Solar PV price and capacity (1976-2018) [solar_price]\n", + "\n", + "#### - Country indicators datasets:\n", + "\n", + "14. CO annual emissions by country (1949-2017) [co_annual]\n", + "15. Annual share of CO emissions by country (1949-2017)[co_share]\n", + "16. Total greenhouse gas emissions by country (1990-2016) [ghg]\n", + "17. Average temperature by country (1743-2017)[annual_temp]\n", + "\n", + "#### - Merged datasets:\n", + "\n", + "18. Global indicators (1751-2018) [global_indicators]\n", + "19. Country indicators (1743-2017) [country_indicators]\n", + "20. Ozone datasets merged (1979-2014) [ozone_merged]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Global Indicators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Annual CO emissions globally (1751-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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4AfghanistanAFG1953106256.0
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" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", + "0 Afghanistan AFG 1949 14656.0\n", + "1 Afghanistan AFG 1950 84272.0\n", + "2 Afghanistan AFG 1951 91600.0\n", + "3 Afghanistan AFG 1952 91600.0\n", + "4 Afghanistan AFG 1953 106256.0" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Annual CO emissions globally\n", + "data = pd.read_csv('data//annual-co-emissions-by-region.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
20353WorldOWID_WRL17519350528.0
20354WorldOWID_WRL17529354192.0
20355WorldOWID_WRL17539354192.0
20356WorldOWID_WRL17549357856.0
20357WorldOWID_WRL17559361520.0
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" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", + "20353 World OWID_WRL 1751 9350528.0\n", + "20354 World OWID_WRL 1752 9354192.0\n", + "20355 World OWID_WRL 1753 9354192.0\n", + "20356 World OWID_WRL 1754 9357856.0\n", + "20357 World OWID_WRL 1755 9361520.0" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "global_co = data[data.Entity == 'World']\n", + "global_co.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + } + ], + "source": [ + "#cleaning the dataset\n", + "global_co.drop(['Entity','Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [], + "source": [ + "global_co.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 267 entries, 1751 to 2017\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Annual COâ‚‚ emissions (tonnes ) 267 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 4.2 KB\n" + ] + } + ], + "source": [ + "global_co.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Total greenhouse gas emissions (1990-2016)" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
0AfghanistanAFG199015140000.0
1AfghanistanAFG199115060000.0
2AfghanistanAFG199213600000.0
3AfghanistanAFG199313430000.0
4AfghanistanAFG199413240000.0
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" + ], + "text/plain": [ + " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", + "0 Afghanistan AFG 1990 15140000.0\n", + "1 Afghanistan AFG 1991 15060000.0\n", + "2 Afghanistan AFG 1992 13600000.0\n", + "3 Afghanistan AFG 1993 13430000.0\n", + "4 Afghanistan AFG 1994 13240000.0" + ] + }, + "execution_count": 256, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Total greenhouse gas emissions\n", + "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", + "ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the dataset\n", + "ghg.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
5076World19903.496734e+10
5077World19913.512544e+10
5078World19923.498553e+10
5079World19933.507963e+10
5080World19943.528761e+10
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" + ], + "text/plain": [ + " Entity Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", + "5076 World 1990 3.496734e+10\n", + "5077 World 1991 3.512544e+10\n", + "5078 World 1992 3.498553e+10\n", + "5079 World 1993 3.507963e+10\n", + "5080 World 1994 3.528761e+10" + ] + }, + "execution_count": 258, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_ghg = ghg[ghg.Entity == 'World']\n", + "global_ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + } + ], + "source": [ + "global_ghg.drop(['Entity'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": {}, + "outputs": [], + "source": [ + "global_ghg = global_ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )'})" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [], + "source": [ + "global_ghg.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 27 entries, 1990 to 2016\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 GHG(tonnes COâ‚‚e ) 27 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 432.0 bytes\n" + ] + } + ], + "source": [ + "global_ghg.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Global COâ‚‚ atmospheric concentration" + ] + }, + { + "cell_type": "code", + "execution_count": 380, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearCOâ‚‚ concentration (parts per million)
0WorldOWID_WRL803719 BCE207.29
1WorldOWID_WRL803182 BCE202.23
2WorldOWID_WRL802573 BCE204.86
3WorldOWID_WRL802060 BCE207.50
4WorldOWID_WRL801975 BCE202.92
...............
1866WorldOWID_WRL2014398.65
1867WorldOWID_WRL2015400.83
1868WorldOWID_WRL2016404.24
1869WorldOWID_WRL2017406.55
1870WorldOWID_WRL2018408.52
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1871 rows × 4 columns

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" + ], + "text/plain": [ + " Entity Code Year COâ‚‚ concentration (parts per million)\n", + "0 World OWID_WRL 803719 BCE 207.29\n", + "1 World OWID_WRL 803182 BCE 202.23\n", + "2 World OWID_WRL 802573 BCE 204.86\n", + "3 World OWID_WRL 802060 BCE 207.50\n", + "4 World OWID_WRL 801975 BCE 202.92\n", + "... ... ... ... ...\n", + "1866 World OWID_WRL 2014 398.65\n", + "1867 World OWID_WRL 2015 400.83\n", + "1868 World OWID_WRL 2016 404.24\n", + "1869 World OWID_WRL 2017 406.55\n", + "1870 World OWID_WRL 2018 408.52\n", + "\n", + "[1871 rows x 4 columns]" + ] + }, + "execution_count": 380, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Global COâ‚‚ atmospheric concentration dataset\n", + "co_concentration = pd.read_csv('data//global-co-concentration-ppm.csv')\n", + "co_concentration" + ] + }, + { + "cell_type": "code", + "execution_count": 381, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the data\n", + "co_concentration.drop(['Code', 'Entity'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 382, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration = co_concentration.rename(columns={'COâ‚‚ concentration (parts per million)':'COâ‚‚ concentration'})" + ] + }, + { + "cell_type": "code", + "execution_count": 392, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1'" + ] + }, + "execution_count": 392, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_concentration.Year[1648]" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'CO atmospheric concentration')" + ] + }, + "execution_count": 267, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting the Global COâ‚‚ atmospheric concentration increasement from 1864-2018\n", + "x = co_concentration.Year[1740:]\n", + "y = co_concentration['COâ‚‚ concentration'][1740:]\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('CO atmospheric concentration')" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": {}, + "outputs": [], + "source": [ + "co_concentration.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 1871 entries, 803719 BCE to 2018\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 COâ‚‚ concentration 1871 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 29.2+ KB\n" + ] + } + ], + "source": [ + "co_concentration.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 396, + "metadata": {}, + "outputs": [], + "source": [ + "co_atm_conc = co_concentration.drop(co_concentration.index[:1648])" + ] + }, + { + "cell_type": "code", + "execution_count": 397, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearCOâ‚‚ concentration
16481276.7
164930277.9
165056277.4
1651104277.5
1652136278.1
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" + ], + "text/plain": [ + " Year COâ‚‚ concentration\n", + "1648 1 276.7\n", + "1649 30 277.9\n", + "1650 56 277.4\n", + "1651 104 277.5\n", + "1652 136 278.1" + ] + }, + "execution_count": 397, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_atm_conc.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 398, + "metadata": {}, + "outputs": [], + "source": [ + "co_atm_conc.to_csv('co_atm_conc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Global Temperature anomaly 1850-2018\n" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearMedian (℃)Upper (℃)Lower (℃)
0GlobalNaN1850-0.373-0.339-0.425
1GlobalNaN1851-0.218-0.184-0.274
2GlobalNaN1852-0.228-0.196-0.280
3GlobalNaN1853-0.269-0.239-0.321
4GlobalNaN1854-0.248-0.218-0.301
\n", + "
" + ], + "text/plain": [ + " Entity Code Year Median (℃) Upper (℃) Lower (℃)\n", + "0 Global NaN 1850 -0.373 -0.339 -0.425\n", + "1 Global NaN 1851 -0.218 -0.184 -0.274\n", + "2 Global NaN 1852 -0.228 -0.196 -0.280\n", + "3 Global NaN 1853 -0.269 -0.239 -0.321\n", + "4 Global NaN 1854 -0.248 -0.218 -0.301" + ] + }, + "execution_count": 270, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Global Temperature anomaly (increase-decreage on previous average temperature)\n", + "temperature_a = pd.read_csv('data//temperature_anomaly.csv')\n", + "temperature_a.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 676 entries, 0 to 675\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Entity 676 non-null object \n", + " 1 Code 0 non-null float64\n", + " 2 Year 676 non-null int64 \n", + " 3 Median (℃) 676 non-null float64\n", + " 4 Upper (℃) 676 non-null float64\n", + " 5 Lower (℃) 676 non-null float64\n", + "dtypes: float64(4), int64(1), object(1)\n", + "memory usage: 31.8+ KB\n" + ] + } + ], + "source": [ + "temperature_a.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the dataset\n", + "temperature_a.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [], + "source": [ + "temperature_a.rename(columns={'Entity':'Country'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Global', 'Northern Hemisphere', 'Southern Hemisphere', 'Tropics'],\n", + " dtype=object)" + ] + }, + "execution_count": 274, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 494, + "metadata": {}, + "outputs": [], + "source": [ + "#temperature_a.Year.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" + ] + }, + "execution_count": 276, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting the median temperature anomaly over time to see how it's actually ingreasing \n", + "x = temperature_a.Year\n", + "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='Temperature Anomaly', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('increse ordescrease in Temperature(℃)')" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryYearMedian (℃)Upper (℃)Lower (℃)
0Global1850-0.373-0.339-0.425
1Global1851-0.218-0.184-0.274
2Global1852-0.228-0.196-0.280
3Global1853-0.269-0.239-0.321
4Global1854-0.248-0.218-0.301
\n", + "
" + ], + "text/plain": [ + " Country Year Median (℃) Upper (℃) Lower (℃)\n", + "0 Global 1850 -0.373 -0.339 -0.425\n", + "1 Global 1851 -0.218 -0.184 -0.274\n", + "2 Global 1852 -0.228 -0.196 -0.280\n", + "3 Global 1853 -0.269 -0.239 -0.321\n", + "4 Global 1854 -0.248 -0.218 -0.301" + ] + }, + "execution_count": 277, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature_a.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [], + "source": [ + "#keeping only the global data to compare it with the CO emissions and the CO concentration\n", + "global_a = temperature_a.where(temperature_a['Country']=='Global')\n", + "global_a.dropna(inplace=True)\n", + "global_a.drop(['Country'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [], + "source": [ + "global_a.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 360, + "metadata": {}, + "outputs": [], + "source": [ + "global_a.drop(['Upper (℃)','Lower (℃)'],axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Ozone depletion substances emissions (CFC's) (1961-2014)" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearOzone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)
0Natural emissionsNaN1961165000
1Natural emissionsNaN1962165000
2Natural emissionsNaN1963165000
3Natural emissionsNaN1964165000
4Natural emissionsNaN1965165000
\n", + "
" + ], + "text/plain": [ + " Entity Code Year \\\n", + "0 Natural emissions NaN 1961 \n", + "1 Natural emissions NaN 1962 \n", + "2 Natural emissions NaN 1963 \n", + "3 Natural emissions NaN 1964 \n", + "4 Natural emissions NaN 1965 \n", + "\n", + " Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents) \n", + "0 165000 \n", + "1 165000 \n", + "2 165000 \n", + "3 165000 \n", + "4 165000 " + ] + }, + "execution_count": 287, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# importing Ozone depleting emissions dataset\n", + "ozone = pd.read_csv('data//ozone-depleting-substance-emissions.csv')\n", + "ozone.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "metadata": {}, + "outputs": [], + "source": [ + "# cleaning the dataset\n", + "ozone.drop(['Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": {}, + "outputs": [], + "source": [ + "ozone.rename(columns={'Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)':'Ozone-depleting emissions'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Natural emissions', 'Total emissions'], dtype=object)" + ] + }, + "execution_count": 290, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozone.Entity.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 291, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting natural and total emissions to see that we have nearly stopped using CFC\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(ozone.Year,ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Natural emissions'), label='Natural emissions')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Tonnes CFC11-equivalents')\n", + "plt.plot(ozone.Year, ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Total emissions'), label='Total emissions')\n", + "plt.legend(['Natural emissions', 'Total emissions'], loc='upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": 292, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + } + ], + "source": [ + "caused_ozone = ozone[ozone.Entity == 'Total emissions']\n", + "caused_ozone.drop(['Entity'],axis=1, inplace=True )" + ] + }, + { + "cell_type": "code", + "execution_count": 294, + "metadata": {}, + "outputs": [], + "source": [ + "ozone.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "metadata": {}, + "outputs": [], + "source": [ + "caused_ozone.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Antartica Ozone Hole area (1979-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearMaximum ozone hole area (square kilometres)Mean ozone hole area (square kilometres)
0WorldOWID_WRL19791100000100000
1WorldOWID_WRL198033000001400000
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3WorldOWID_WRL1982108000004800000
4WorldOWID_WRL1983122000007900000
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" + ], + "text/plain": [ + " Entity Code Year Maximum ozone hole area (square kilometres) \\\n", + "0 World OWID_WRL 1979 1100000 \n", + "1 World OWID_WRL 1980 3300000 \n", + "2 World OWID_WRL 1981 3100000 \n", + "3 World OWID_WRL 1982 10800000 \n", + "4 World OWID_WRL 1983 12200000 \n", + "\n", + " Mean ozone hole area (square kilometres) \n", + "0 100000 \n", + "1 1400000 \n", + "2 600000 \n", + "3 4800000 \n", + "4 7900000 " + ] + }, + "execution_count": 295, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Antartica Ozone Hole area dataset\n", + "oz_hole = pd.read_csv('data//antarctic-ozone-hole-area.csv')\n", + "oz_hole.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "metadata": {}, + "outputs": [], + "source": [ + "oz_hole.drop(['Entity', 'Code','Maximum ozone hole area (square kilometres)'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMean ozone hole area (square kilometres)
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\n", + "
" + ], + "text/plain": [ + " Year Mean ozone hole area (square kilometres)\n", + "0 1979 100000\n", + "1 1980 1400000\n", + "2 1981 600000\n", + "3 1982 4800000\n", + "4 1983 7900000" + ] + }, + "execution_count": 297, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oz_hole.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 298, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 298, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting natural and total emissionsMean ozone hole area (square kilometres) to see if it has reduced\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(oz_hole.Year,oz_hole['Mean ozone hole area (square kilometres)'], label='Mean ozone hole area (square kilometres)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Mean ozone hole area (square kilometres)')\n", + "plt.legend(['Mean ozone hole area (square kilometres)'], loc='upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": {}, + "outputs": [], + "source": [ + "oz_hole.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7. Global average temperature (1850-2015)" + ] + }, + { + "cell_type": "code", + "execution_count": 300, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAverageTemperatureUncertaintyLandMaxTemperatureLandMaxTemperatureUncertaintyLandMinTemperatureLandMinTemperatureUncertaintyLandAndOceanAverageTemperatureLandAndOceanAverageTemperatureUncertainty
01750-01-013.0343.574NaNNaNNaNNaNNaNNaN
11750-02-013.0833.702NaNNaNNaNNaNNaNNaN
21750-03-015.6263.076NaNNaNNaNNaNNaNNaN
31750-04-018.4902.451NaNNaNNaNNaNNaNNaN
41750-05-0111.5732.072NaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " dt LandAverageTemperature LandAverageTemperatureUncertainty \\\n", + "0 1750-01-01 3.034 3.574 \n", + "1 1750-02-01 3.083 3.702 \n", + "2 1750-03-01 5.626 3.076 \n", + "3 1750-04-01 8.490 2.451 \n", + "4 1750-05-01 11.573 2.072 \n", + "\n", + " LandMaxTemperature LandMaxTemperatureUncertainty LandMinTemperature \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " LandMinTemperatureUncertainty LandAndOceanAverageTemperature \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " LandAndOceanAverageTemperatureUncertainty \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 300, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_temperature = pd.read_csv('data//GlobalTemperatures.csv')\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAndOceanAverageTemperature
12001850-01-010.74912.833
12011850-02-013.07113.588
12021850-03-014.95414.043
12031850-04-017.21714.667
12041850-05-0110.00415.507
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" + ], + "text/plain": [ + " dt LandAverageTemperature LandAndOceanAverageTemperature\n", + "1200 1850-01-01 0.749 12.833\n", + "1201 1850-02-01 3.071 13.588\n", + "1202 1850-03-01 4.954 14.043\n", + "1203 1850-04-01 7.217 14.667\n", + "1204 1850-05-01 10.004 15.507" + ] + }, + "execution_count": 301, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_temperature.dropna(inplace=True)\n", + "global_temperature = global_temperature[['dt','LandAverageTemperature','LandAndOceanAverageTemperature']]\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 302, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtLandAverageTemperatureLandAndOceanAverageTemperatureYear
12001850-01-010.74912.8331850
12011850-02-013.07113.5881850
12021850-03-014.95414.0431850
12031850-04-017.21714.6671850
12041850-05-0110.00415.5071850
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" + ], + "text/plain": [ + " dt LandAverageTemperature LandAndOceanAverageTemperature Year\n", + "1200 1850-01-01 0.749 12.833 1850\n", + "1201 1850-02-01 3.071 13.588 1850\n", + "1202 1850-03-01 4.954 14.043 1850\n", + "1203 1850-04-01 7.217 14.667 1850\n", + "1204 1850-05-01 10.004 15.507 1850" + ] + }, + "execution_count": 302, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#global_temperature['dt']= pd.to_datetime(global_temperature['dt']) \n", + "global_temperature['Year'] = pd.DatetimeIndex(global_temperature['dt']).year\n", + "global_temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LandAverageTemperatureLandAndOceanAverageTemperature
Year
18507.90066714.867167
18518.17858314.991833
18528.10016715.006500
18538.04183314.955167
18548.21050014.991000
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" + ], + "text/plain": [ + " LandAverageTemperature LandAndOceanAverageTemperature\n", + "Year \n", + "1850 7.900667 14.867167\n", + "1851 8.178583 14.991833\n", + "1852 8.100167 15.006500\n", + "1853 8.041833 14.955167\n", + "1854 8.210500 14.991000" + ] + }, + "execution_count": 303, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_global_temp = global_temperature[['Year','LandAverageTemperature','LandAndOceanAverageTemperature']].groupby(['Year']).agg({'LandAverageTemperature':'mean',\n", + " 'LandAndOceanAverageTemperature':'mean'})\n", + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearLandAverageTemperatureLandAndOceanAverageTemperature
018507.90066714.867167
118518.17858314.991833
218528.10016715.006500
318538.04183314.955167
418548.21050014.991000
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" + ], + "text/plain": [ + " Year LandAverageTemperature LandAndOceanAverageTemperature\n", + "0 1850 7.900667 14.867167\n", + "1 1851 8.178583 14.991833\n", + "2 1852 8.100167 15.006500\n", + "3 1853 8.041833 14.955167\n", + "4 1854 8.210500 14.991000" + ] + }, + "execution_count": 304, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_global_temp.reset_index(level=0, inplace=True)\n", + "annual_global_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "metadata": {}, + "outputs": [], + "source": [ + "annual_global_temp.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 306, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 166 entries, 1850 to 2015\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 LandAverageTemperature 166 non-null float64\n", + " 1 LandAndOceanAverageTemperature 166 non-null float64\n", + "dtypes: float64(2)\n", + "memory usage: 3.9 KB\n" + ] + } + ], + "source": [ + "annual_global_temp.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8. Fossil fuel consumption per capita (1965-2015)" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)
0AfricaNaN19650.9823451.0406860.032724
1AfricaNaN19661.0503401.0019700.035646
2AfricaNaN19671.0143901.0051970.034371
3AfricaNaN19681.0433631.0211390.034195
4AfricaNaN19691.0322301.0010830.039699
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" + ], + "text/plain": [ + " Entity Code Year Oil (megawatt-hours) Coal (megawatt-hours) \\\n", + "0 Africa NaN 1965 0.982345 1.040686 \n", + "1 Africa NaN 1966 1.050340 1.001970 \n", + "2 Africa NaN 1967 1.014390 1.005197 \n", + "3 Africa NaN 1968 1.043363 1.021139 \n", + "4 Africa NaN 1969 1.032230 1.001083 \n", + "\n", + " Natural gas (megawatt-hours) \n", + "0 0.032724 \n", + "1 0.035646 \n", + "2 0.034371 \n", + "3 0.034195 \n", + "4 0.039699 " + ] + }, + "execution_count": 312, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fossil_per_c = pd.read_csv('data//fossil-fuel-consumption-per-capita.csv')\n", + "fossil_per_c.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 313, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)
3302WorldOWID_WRL19655.3121354.8875542.043343
3303WorldOWID_WRL19665.6055414.8450912.178977
3304WorldOWID_WRL19675.8834724.6700632.288339
3305WorldOWID_WRL19686.2573574.6435392.440440
3306WorldOWID_WRL19696.6550134.6908202.622830
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" + ], + "text/plain": [ + " Entity Code Year Oil (megawatt-hours) Coal (megawatt-hours) \\\n", + "3302 World OWID_WRL 1965 5.312135 4.887554 \n", + "3303 World OWID_WRL 1966 5.605541 4.845091 \n", + "3304 World OWID_WRL 1967 5.883472 4.670063 \n", + "3305 World OWID_WRL 1968 6.257357 4.643539 \n", + "3306 World OWID_WRL 1969 6.655013 4.690820 \n", + "\n", + " Natural gas (megawatt-hours) \n", + "3302 2.043343 \n", + "3303 2.178977 \n", + "3304 2.288339 \n", + "3305 2.440440 \n", + "3306 2.622830 " + ] + }, + "execution_count": 313, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "fossil_per_c = fossil_per_c[fossil_per_c.Entity == 'World']\n", + "fossil_per_c.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "metadata": {}, + "outputs": [], + "source": [ + "fossil_per_c.drop(['Entity', 'Code',], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 315, + "metadata": {}, + "outputs": [], + "source": [ + "fossil_per_c.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 51 entries, 1965 to 2015\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Oil (megawatt-hours) 51 non-null float64\n", + " 1 Coal (megawatt-hours) 51 non-null float64\n", + " 2 Natural gas (megawatt-hours) 51 non-null float64\n", + "dtypes: float64(3)\n", + "memory usage: 1.6 KB\n" + ] + } + ], + "source": [ + "fossil_per_c.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9. Global primary energy by source (1800-2018)" + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearCoal (terawatt-hours)Solar (terawatt-hours)Crude oil (terawatt-hours)Natural gas (terawatt-hours)Traditional biofuels (terawatt-hours)Other renewables (terawatt-hours)Hydropower (terawatt-hours)Nuclear (terawatt-hours)Wind (terawatt-hours)
0WorldOWID_WRL180097.222300.00.00.05555.5555560.00.00.00.0
1WorldOWID_WRL1810127.777880.00.00.05833.3333330.00.00.00.0
2WorldOWID_WRL1820152.777900.00.00.06111.1111110.00.00.00.0
3WorldOWID_WRL1830263.889100.00.00.06388.8888890.00.00.00.0
4WorldOWID_WRL1840355.555840.00.00.06944.4444440.00.00.00.0
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" + ], + "text/plain": [ + " Entity Code Year Coal (terawatt-hours) Solar (terawatt-hours) \\\n", + "0 World OWID_WRL 1800 97.22230 0.0 \n", + "1 World OWID_WRL 1810 127.77788 0.0 \n", + "2 World OWID_WRL 1820 152.77790 0.0 \n", + "3 World OWID_WRL 1830 263.88910 0.0 \n", + "4 World OWID_WRL 1840 355.55584 0.0 \n", + "\n", + " Crude oil (terawatt-hours) Natural gas (terawatt-hours) \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Traditional biofuels (terawatt-hours) Other renewables (terawatt-hours) \\\n", + "0 5555.555556 0.0 \n", + "1 5833.333333 0.0 \n", + "2 6111.111111 0.0 \n", + "3 6388.888889 0.0 \n", + "4 6944.444444 0.0 \n", + "\n", + " Hydropower (terawatt-hours) Nuclear (terawatt-hours) \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Wind (terawatt-hours) \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 " + ] + }, + "execution_count": 317, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_energy = pd.read_csv('data//global-primary-energy.csv')\n", + "global_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 318, + "metadata": {}, + "outputs": [], + "source": [ + "global_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "metadata": {}, + "outputs": [], + "source": [ + "global_energy.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 320, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 71 entries, 1800 to 2018\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Coal (terawatt-hours) 71 non-null float64\n", + " 1 Solar (terawatt-hours) 71 non-null float64\n", + " 2 Crude oil (terawatt-hours) 71 non-null float64\n", + " 3 Natural gas (terawatt-hours) 71 non-null float64\n", + " 4 Traditional biofuels (terawatt-hours) 71 non-null float64\n", + " 5 Other renewables (terawatt-hours) 71 non-null float64\n", + " 6 Hydropower (terawatt-hours) 71 non-null float64\n", + " 7 Nuclear (terawatt-hours) 71 non-null float64\n", + " 8 Wind (terawatt-hours) 71 non-null float64\n", + "dtypes: float64(9)\n", + "memory usage: 5.5 KB\n" + ] + } + ], + "source": [ + "global_energy.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10. Modern renewable energy consumtion (1965-2018)" + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearHydropower (terawatt-hours)Solar (terawatt-hours)Wind (terawatt-hours)Other renewables (terawatt-hours)
0AfricaNaN196514.2788060.00.00.0
1AfricaNaN196615.6490490.00.00.0
2AfricaNaN196716.1583330.00.00.0
3AfricaNaN196818.6229830.00.00.0
4AfricaNaN196921.5828970.00.00.0
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" + ], + "text/plain": [ + " Entity Code Year Hydropower (terawatt-hours) Solar (terawatt-hours) \\\n", + "0 Africa NaN 1965 14.278806 0.0 \n", + "1 Africa NaN 1966 15.649049 0.0 \n", + "2 Africa NaN 1967 16.158333 0.0 \n", + "3 Africa NaN 1968 18.622983 0.0 \n", + "4 Africa NaN 1969 21.582897 0.0 \n", + "\n", + " Wind (terawatt-hours) Other renewables (terawatt-hours) \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 " + ] + }, + "execution_count": 321, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "renew_energy = pd.read_csv('data//modern-renewable-energy-consumption.csv')\n", + "renew_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearHydropower (terawatt-hours)Solar (terawatt-hours)Wind (terawatt-hours)Other renewables (terawatt-hours)
5037WorldOWID_WRL1965919.7702760.00.017.985232
5038WorldOWID_WRL1966986.2858720.00.019.806007
5039WorldOWID_WRL19671009.2445690.00.019.986571
5040WorldOWID_WRL19681059.0628600.00.022.120045
5041WorldOWID_WRL19691122.4425260.00.023.257964
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" + ], + "text/plain": [ + " Entity Code Year Hydropower (terawatt-hours) \\\n", + "5037 World OWID_WRL 1965 919.770276 \n", + "5038 World OWID_WRL 1966 986.285872 \n", + "5039 World OWID_WRL 1967 1009.244569 \n", + "5040 World OWID_WRL 1968 1059.062860 \n", + "5041 World OWID_WRL 1969 1122.442526 \n", + "\n", + " Solar (terawatt-hours) Wind (terawatt-hours) \\\n", + "5037 0.0 0.0 \n", + "5038 0.0 0.0 \n", + "5039 0.0 0.0 \n", + "5040 0.0 0.0 \n", + "5041 0.0 0.0 \n", + "\n", + " Other renewables (terawatt-hours) \n", + "5037 17.985232 \n", + "5038 19.806007 \n", + "5039 19.986571 \n", + "5040 22.120045 \n", + "5041 23.257964 " + ] + }, + "execution_count": 322, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "renew_energy = renew_energy[renew_energy.Entity == 'World']\n", + "renew_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 323, + "metadata": {}, + "outputs": [], + "source": [ + "renew_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 324, + "metadata": {}, + "outputs": [], + "source": [ + "renew_energy.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 325, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 54 entries, 1965 to 2018\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Hydropower (terawatt-hours) 54 non-null float64\n", + " 1 Solar (terawatt-hours) 54 non-null float64\n", + " 2 Wind (terawatt-hours) 54 non-null float64\n", + " 3 Other renewables (terawatt-hours) 54 non-null float64\n", + "dtypes: float64(4)\n", + "memory usage: 2.1 KB\n" + ] + } + ], + "source": [ + "renew_energy.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 11. Hubbert's peak vs actual oil production (1911-2016)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYear(barrels per year)(barrels per year).1
0United StatesUSA1910209557000.0NaN
1United StatesUSA1911220449000.0200158958.0
2United StatesUSA1912222935000.0213834803.7
3United StatesUSA1913248446000.0228406784.8
4United StatesUSA1914265763000.0243928136.8
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" + ], + "text/plain": [ + " Entity Code Year (barrels per year) (barrels per year).1\n", + "0 United States USA 1910 209557000.0 NaN\n", + "1 United States USA 1911 220449000.0 200158958.0\n", + "2 United States USA 1912 222935000.0 213834803.7\n", + "3 United States USA 1913 248446000.0 228406784.8\n", + "4 United States USA 1914 265763000.0 243928136.8" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hubbert_peak = pd.read_csv('data//hubberts-peak-vs-actual-oil-production-in-the-united-states.csv')\n", + "hubbert_peak.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Entity', 'Code', 'Year', ' (barrels per year)',\n", + " ' (barrels per year).1'],\n", + " dtype='object')" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hubbert_peak.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak.drop(['Entity', 'Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak.rename(columns={' (barrels per year)':'Actual Oil Prod',' (barrels per year).1':'Hubbert Predicted'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak = hubbert_peak.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Year Actual Predicted\n", + "102 2012 2.374136e+09 614194532.6\n", + "103 2013 2.725665e+09 577668428.7\n", + "104 2014 3.198694e+09 543068744.5\n", + "105 2015 3.436515e+09 510324290.7\n", + "106 2016 3.248117e+09 479362510.8" + ] + }, + "execution_count": 328, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hubbert_peak.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 329, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 329, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting predicted and actual emissions to see if it the prediction came true\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(hubbert_peak.Year,hubbert_peak['Actual'],label='Actual')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('(barrels per year)')\n", + "plt.plot(hubbert_peak.Year,hubbert_peak['Predicted'],label='Predicted')\n", + "plt.legend(['Actual', 'Predicted'], loc='upper left')" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [], + "source": [ + "hubbert_peak.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 106 entries, 1911 to 2016\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Actual 106 non-null float64\n", + " 1 Predicted 106 non-null float64\n", + "dtypes: float64(2)\n", + "memory usage: 2.5 KB\n" + ] + } + ], + "source": [ + "hubbert_peak.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 12. Share of global primary energy (1800-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearCoal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
0WorldOWID_WRL18001.7199030.00.00.00.00.00.098.2800970.0
1WorldOWID_WRL18102.1435250.00.00.00.00.00.097.8564750.0
2WorldOWID_WRL18202.4390260.00.00.00.00.00.097.5609740.0
3WorldOWID_WRL18303.9666000.00.00.00.00.00.096.0334000.0
4WorldOWID_WRL18404.8706280.00.00.00.00.00.095.1293720.0
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" + ], + "text/plain": [ + " Entity Code Year Coal (%) Crude oil (%) Hydropower (%) \\\n", + "0 World OWID_WRL 1800 1.719903 0.0 0.0 \n", + "1 World OWID_WRL 1810 2.143525 0.0 0.0 \n", + "2 World OWID_WRL 1820 2.439026 0.0 0.0 \n", + "3 World OWID_WRL 1830 3.966600 0.0 0.0 \n", + "4 World OWID_WRL 1840 4.870628 0.0 0.0 \n", + "\n", + " Natural gas (%) Nuclear (%) Other renewables (%) Solar (%) \\\n", + "0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "\n", + " Traditional biofuels (%) Wind (%) \n", + "0 98.280097 0.0 \n", + "1 97.856475 0.0 \n", + "2 97.560974 0.0 \n", + "3 96.033400 0.0 \n", + "4 95.129372 0.0 " + ] + }, + "execution_count": 332, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "share_energy = pd.read_csv('data//global-primary-energy-share-inc-biomass.csv')\n", + "share_energy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "metadata": {}, + "outputs": [], + "source": [ + "share_energy.drop(['Entity', 'Code'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [], + "source": [ + "share_energy.set_index('Year',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 70 entries, 1800 to 2017\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Coal (%) 70 non-null float64\n", + " 1 Crude oil (%) 70 non-null float64\n", + " 2 Hydropower (%) 70 non-null float64\n", + " 3 Natural gas (%) 70 non-null float64\n", + " 4 Nuclear (%) 70 non-null float64\n", + " 5 Other renewables (%) 70 non-null float64\n", + " 6 Solar (%) 70 non-null float64\n", + " 7 Traditional biofuels (%) 70 non-null float64\n", + " 8 Wind (%) 70 non-null float64\n", + "dtypes: float64(9)\n", + "memory usage: 5.5 KB\n" + ] + } + ], + "source": [ + "share_energy.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 13. Solar pannels price progression (1976-2016)" + ] + }, + { + "cell_type": "code", + "execution_count": 464, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearSolar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))Cumulative Installed Capacity (megawatts-peak (MWp))Total population (Gapminder)
0AfghanistanAFG1800NaNNaN3280000.0
1AfghanistanAFG1820NaNNaN3280000.0
2AfghanistanAFG1870NaNNaN4207000.0
3AfghanistanAFG1913NaNNaN5730000.0
4AfghanistanAFG1950NaNNaN8151455.0
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" + ], + "text/plain": [ + " Entity Code Year \\\n", + "0 Afghanistan AFG 1800 \n", + "1 Afghanistan AFG 1820 \n", + "2 Afghanistan AFG 1870 \n", + "3 Afghanistan AFG 1913 \n", + "4 Afghanistan AFG 1950 \n", + "\n", + " Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp)) \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Cumulative Installed Capacity (megawatts-peak (MWp)) \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Total population (Gapminder) \n", + "0 3280000.0 \n", + "1 3280000.0 \n", + "2 4207000.0 \n", + "3 5730000.0 \n", + "4 8151455.0 " + ] + }, + "execution_count": 464, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solar_price = pd.read_csv('data//solar-pv-prices-vs-cumulative-capacity.csv')\n", + "solar_price.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 465, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearSolar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))Cumulative Installed Capacity (megawatts-peak (MWp))Total population (Gapminder)
19946WorldOWID_WRL197666.1048350.32NaN
19947WorldOWID_WRL197746.4405390.77NaN
19948WorldOWID_WRL197833.2826881.77NaN
19949WorldOWID_WRL197929.6924933.27NaN
19950WorldOWID_WRL198023.7553616.57NaN
\n", + "
" + ], + "text/plain": [ + " Entity Code Year \\\n", + "19946 World OWID_WRL 1976 \n", + "19947 World OWID_WRL 1977 \n", + "19948 World OWID_WRL 1978 \n", + "19949 World OWID_WRL 1979 \n", + "19950 World OWID_WRL 1980 \n", + "\n", + " Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp)) \\\n", + "19946 66.104835 \n", + "19947 46.440539 \n", + "19948 33.282688 \n", + "19949 29.692493 \n", + "19950 23.755361 \n", + "\n", + " Cumulative Installed Capacity (megawatts-peak (MWp)) \\\n", + "19946 0.32 \n", + "19947 0.77 \n", + "19948 1.77 \n", + "19949 3.27 \n", + "19950 6.57 \n", + "\n", + " Total population (Gapminder) \n", + "19946 NaN \n", + "19947 NaN \n", + "19948 NaN \n", + "19949 NaN \n", + "19950 NaN " + ] + }, + "execution_count": 465, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#keeping only the global data\n", + "solar_price = solar_price[solar_price.Entity == 'World']\n", + "solar_price.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 466, + "metadata": {}, + "outputs": [], + "source": [ + "solar_price.drop(['Entity', 'Code','Total population (Gapminder)'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 467, + "metadata": {}, + "outputs": [], + "source": [ + "solar_price.rename(columns={'Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))':'Solar PV Module Cost($/Wp)','Cumulative Installed Capacity (megawatts-peak (MWp))':'Cumulative Installed Capacity(MWp)'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 468, + "metadata": {}, + "outputs": [], + "source": [ + "#solar_price.set_index('Cumulative Installed Capacity(MWp)',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 471, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Solar PV Module Cost($/Wp)')" + ] + }, + "execution_count": 471, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(17,6))\n", + "plt.plot(solar_price.Year,solar_price['Solar PV Module Cost($/Wp)'],label='Solar PV Module Cost($/Wp)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Solar PV Module Cost($/Wp)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 472, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Cumulative Installed Capacity(MWp)')" + ] + }, + "execution_count": 472, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(17,6))\n", + "plt.plot(solar_price.Year,solar_price['Cumulative Installed Capacity(MWp)'],label='Cumulative Installed Capacity(MWp)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Cumulative Installed Capacity(MWp)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Country Indicators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 14. CO annual emissions (1949-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 475, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes)
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
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" + ], + "text/plain": [ + " Entity Code Year Annual COâ‚‚ emissions (tonnes)\n", + "0 Afghanistan AFG 1949 14656.0\n", + "1 Afghanistan AFG 1950 84272.0\n", + "2 Afghanistan AFG 1951 91600.0\n", + "3 Afghanistan AFG 1952 91600.0\n", + "4 Afghanistan AFG 1953 106256.0" + ] + }, + "execution_count": 475, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Annual CO per country (1949-2017)\n", + "co_annual = pd.read_csv('data//annual_co2_emissions_per_country.csv')\n", + "co_annual.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 477, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the dataset\n", + "co_annual.drop(['Code'], axis=1, inplace=True)\n", + "co_annual = co_annual.rename(columns={'Annual COâ‚‚ emissions (tonnes)':'Annual COâ‚‚', 'Entity':'Country'})" + ] + }, + { + "cell_type": "code", + "execution_count": 478, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 20586 entries, 0 to 20852\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 20586 non-null object \n", + " 1 Year 20586 non-null int64 \n", + " 2 Annual COâ‚‚ 20586 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 643.3+ KB\n" + ] + } + ], + "source": [ + "#keeping only Country indicators\n", + "co_annual = co_annual[co_annual.Country != 'World']\n", + "#co_annual.Year.unique()\n", + "co_annual.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 15. Annual share of CO per country (1949-2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 479, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearShare of global COâ‚‚ emissions (%)
0AfghanistanAFG19490.0003
1AfghanistanAFG19500.0015
2AfghanistanAFG19510.0015
3AfghanistanAFG19520.0015
4AfghanistanAFG19530.0016
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" + ], + "text/plain": [ + " Entity Code Year Share of global COâ‚‚ emissions (%)\n", + "0 Afghanistan AFG 1949 0.0003\n", + "1 Afghanistan AFG 1950 0.0015\n", + "2 Afghanistan AFG 1951 0.0015\n", + "3 Afghanistan AFG 1952 0.0015\n", + "4 Afghanistan AFG 1953 0.0016" + ] + }, + "execution_count": 479, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Annual share of CO per country (1949-2017)\n", + "co_share = pd.read_csv('data//annual_share_of_co2_emissions.csv')\n", + "co_share.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 480, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20586 entries, 0 to 20585\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 20586 non-null object \n", + " 1 Year 20586 non-null int64 \n", + " 2 Share of global COâ‚‚ emissions (%) 20586 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 482.6+ KB\n" + ] + } + ], + "source": [ + "#cleaning the data set\n", + "co_share.drop(['Code'], axis=1, inplace=True)\n", + "\n", + "co_share = co_share.rename(columns={'Share of global COâ‚‚ emissions (%)b':'Annual COâ‚‚', 'Entity':'Country'})\n", + "\n", + "co_share.Year.unique()\n", + "\n", + "co_share.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 16. Total greenhouse gas emissions (1990-2016)" + ] + }, + { + "cell_type": "code", + "execution_count": 520, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EntityCodeYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
0AfghanistanAFG199015140000.0
1AfghanistanAFG199115060000.0
2AfghanistanAFG199213600000.0
3AfghanistanAFG199313430000.0
4AfghanistanAFG199413240000.0
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" + ], + "text/plain": [ + " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", + "0 Afghanistan AFG 1990 15140000.0\n", + "1 Afghanistan AFG 1991 15060000.0\n", + "2 Afghanistan AFG 1992 13600000.0\n", + "3 Afghanistan AFG 1993 13430000.0\n", + "4 Afghanistan AFG 1994 13240000.0" + ] + }, + "execution_count": 520, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Total greenhouse gas emissions\n", + "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", + "ghg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 521, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 5157 entries, 0 to 5183\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 5157 non-null object \n", + " 1 Year 5157 non-null int64 \n", + " 2 GHG(tonnes COâ‚‚e ) 5157 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 161.2+ KB\n" + ] + } + ], + "source": [ + "#cleaning the dataset\n", + "ghg.drop(['Code'], axis=1, inplace=True)\n", + "\n", + "ghg = ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )', 'Entity':'Country'})\n", + "#keeping only Country indicators\n", + "ghg = ghg[ghg.Country != 'World']\n", + "ghg.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 17. Average temperatures by country (1743-2013)" + ] + }, + { + "cell_type": "code", + "execution_count": 487, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dtAverageTemperatureAverageTemperatureUncertaintyCountry
01743-11-014.3842.294Ã…land
11743-12-01NaNNaNÃ…land
21744-01-01NaNNaNÃ…land
31744-02-01NaNNaNÃ…land
41744-03-01NaNNaNÃ…land
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" + ], + "text/plain": [ + " dt AverageTemperature AverageTemperatureUncertainty Country\n", + "0 1743-11-01 4.384 2.294 Åland\n", + "1 1743-12-01 NaN NaN Åland\n", + "2 1744-01-01 NaN NaN Åland\n", + "3 1744-02-01 NaN NaN Åland\n", + "4 1744-03-01 NaN NaN Åland" + ] + }, + "execution_count": 487, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing Global temperatures by country data set\n", + "temperature = pd.read_csv('data//GlobalLandTemperaturesByCountry.csv')\n", + "temperature.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 488, + "metadata": {}, + "outputs": [], + "source": [ + "#cleaning the dataset\n", + "temperature['dt']= pd.to_datetime(temperature['dt']) \n", + "\n", + "temperature['Year'] = pd.DatetimeIndex(temperature['dt']).year\n", + "temperature.head()\n", + "\n", + "#keeping only the desired countries, and removing regions\n", + "non_wanted = ['Åland','Africa','Asia','Baker Island','Denmark','Federated States Of Micronesia','France', 'French Guiana', 'French Polynesia',\n", + " 'French Southern And Antarctic Lands','Gaza Strip','Guadeloupe', 'Guam','Guernsey','Heard Island And Mcdonald Islands','Isle Of Man','Jersey','Kingman Reef',\n", + " 'Montserrat','Netherlands (Europe)','Niue','North America','New Caledonia','Northern Mariana Islands','Oceania',\n", + " 'Palmyra Atoll','Saint Barthélemy','American Samoa','South America','South Georgia And The South Sandwich Isla',\n", + " 'Svalbard And Jan Mayen','Turks And Caicas Islands','United Kingdom','Western Sahara']\n", + "for region in non_wanted:\n", + " temperature = temperature[temperature.Country != region]" + ] + }, + { + "cell_type": "code", + "execution_count": 489, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearAverageTemperature
Country
Albania17438.620
Andorra17437.556
Austria17432.482
Belarus17430.767
Belgium17437.106
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" + ], + "text/plain": [ + " Year AverageTemperature\n", + "Country \n", + "Albania 1743 8.620\n", + "Andorra 1743 7.556\n", + "Austria 1743 2.482\n", + "Belarus 1743 0.767\n", + "Belgium 1743 7.106" + ] + }, + "execution_count": 489, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#grouping by year and agregating the mean so we have the annual mean temperature by country\n", + "annual_temp = temperature[['Year', 'Country','AverageTemperature']].groupby(['Year','Country']).agg({'AverageTemperature':'mean'})\n", + "\n", + "#reseting index to adapt it for the future merge\n", + "annual_temp.reset_index(level=0, inplace=True)\n", + "\n", + "annual_temp.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 491, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexCountryYearAverageTemperature
00Albania17438.620
11Andorra17437.556
22Austria17432.482
33Belarus17430.767
44Belgium17437.106
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" + ], + "text/plain": [ + " index Country Year AverageTemperature\n", + "0 0 Albania 1743 8.620\n", + "1 1 Andorra 1743 7.556\n", + "2 2 Austria 1743 2.482\n", + "3 3 Belarus 1743 0.767\n", + "4 4 Belgium 1743 7.106" + ] + }, + "execution_count": 491, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one more reset to have country as a column\n", + "annual_temp.reset_index(level=0, inplace=True)\n", + "annual_temp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 493, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 41931 entries, 0 to 41930\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 41931 non-null object \n", + " 1 Year 41931 non-null int64 \n", + " 2 AverageTemperature 39793 non-null float64\n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 982.9+ KB\n" + ] + } + ], + "source": [ + "annual_temp.drop(['index'], axis=1, inplace=True)\n", + "annual_temp.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Merging datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 18. Merging global indicators" + ] + }, + { + "cell_type": "code", + "execution_count": 362, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1 and 2\n", + "global_indicators1 = pd.merge(global_co, global_ghg, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 365, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4\n", + "global_indicators2 = pd.merge(global_indicators1,global_a, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 366, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5\n", + "global_indicators3 = pd.merge(global_indicators2,caused_ozone, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 367, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6\n", + "global_indicators4 = pd.merge(global_indicators3,oz_hole, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 368, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7\n", + "global_indicators5 = pd.merge(global_indicators4,annual_global_temp, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 369, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8\n", + "global_indicators6 = pd.merge(global_indicators5,fossil_per_c, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 370, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8,9\n", + "global_indicators7 = pd.merge(global_indicators6,global_energy, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 371, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8,9,10\n", + "global_indicators8 = pd.merge(global_indicators7,renew_energy, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 372, + "metadata": {}, + "outputs": [], + "source": [ + "#datasets 1,2,4,5,6,7,8,9,10,12\n", + "global_indicators = pd.merge(global_indicators8,share_energy, on='Year', how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 504, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Annual CO₂ emissions (tonnes )GHG(tonnes CO₂e )Median (℃)Ozone-depleting emissionsMean ozone hole area (square kilometres)LandAverageTemperatureLandAndOceanAverageTemperatureOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)...Other renewables (terawatt-hours)_yCoal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
Year
20143.550583e+104.944079e+100.579320000.020900000.09.57066715.9130006.7800186.1977514.896768...502.15891930.12992834.2842432.60799922.7981601.7045050.3383420.1325977.5263480.477878
20153.546275e+104.985498e+100.763NaN25600000.09.83100016.0585836.8380425.9617334.956876...536.46624429.23211234.7507802.59790623.1936921.7195140.3593070.1735807.4177810.555327
20163.567510e+104.935803e+100.797NaN20700000.0NaNNaNNaNNaNNaN...556.62335628.47941835.0213262.66686223.6166451.7264460.3680320.2168497.2704460.633975
20173.615326e+10NaN0.677NaN17400000.0NaNNaNNaNNaNNaN...584.97582728.25414134.9959602.64321823.8964861.7159090.3816330.2881717.0935080.730975
2018NaNNaN0.595NaNNaNNaNNaNNaNNaNNaN...625.805362NaNNaNNaNNaNNaNNaNNaNNaNNaN
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5 rows × 32 columns

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" + ], + "text/plain": [ + " Annual CO₂ emissions (tonnes ) GHG(tonnes CO₂e ) Median (℃) \\\n", + "Year \n", + "2014 3.550583e+10 4.944079e+10 0.579 \n", + "2015 3.546275e+10 4.985498e+10 0.763 \n", + "2016 3.567510e+10 4.935803e+10 0.797 \n", + "2017 3.615326e+10 NaN 0.677 \n", + "2018 NaN NaN 0.595 \n", + "\n", + " Ozone-depleting emissions Mean ozone hole area (square kilometres) \\\n", + "Year \n", + "2014 320000.0 20900000.0 \n", + "2015 NaN 25600000.0 \n", + "2016 NaN 20700000.0 \n", + "2017 NaN 17400000.0 \n", + "2018 NaN NaN \n", + "\n", + " LandAverageTemperature LandAndOceanAverageTemperature \\\n", + "Year \n", + "2014 9.570667 15.913000 \n", + "2015 9.831000 16.058583 \n", + "2016 NaN NaN \n", + "2017 NaN NaN \n", + "2018 NaN NaN \n", + "\n", + " Oil (megawatt-hours) Coal (megawatt-hours) \\\n", + "Year \n", + "2014 6.780018 6.197751 \n", + "2015 6.838042 5.961733 \n", + "2016 NaN NaN \n", + "2017 NaN NaN \n", + "2018 NaN NaN \n", + "\n", + " Natural gas (megawatt-hours) ... Other renewables (terawatt-hours)_y \\\n", + "Year ... \n", + "2014 4.896768 ... 502.158919 \n", + "2015 4.956876 ... 536.466244 \n", + "2016 NaN ... 556.623356 \n", + "2017 NaN ... 584.975827 \n", + "2018 NaN ... 625.805362 \n", + "\n", + " Coal (%) Crude oil (%) Hydropower (%) Natural gas (%) Nuclear (%) \\\n", + "Year \n", + "2014 30.129928 34.284243 2.607999 22.798160 1.704505 \n", + "2015 29.232112 34.750780 2.597906 23.193692 1.719514 \n", + "2016 28.479418 35.021326 2.666862 23.616645 1.726446 \n", + "2017 28.254141 34.995960 2.643218 23.896486 1.715909 \n", + "2018 NaN NaN NaN NaN NaN \n", + "\n", + " Other renewables (%) Solar (%) Traditional biofuels (%) Wind (%) \n", + "Year \n", + "2014 0.338342 0.132597 7.526348 0.477878 \n", + "2015 0.359307 0.173580 7.417781 0.555327 \n", + "2016 0.368032 0.216849 7.270446 0.633975 \n", + "2017 0.381633 0.288171 7.093508 0.730975 \n", + "2018 NaN NaN NaN NaN \n", + "\n", + "[5 rows x 32 columns]" + ] + }, + "execution_count": 504, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_indicators.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 375, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 268 entries, 1751 to 2018\n", + "Data columns (total 32 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Annual CO₂ emissions (tonnes ) 267 non-null float64\n", + " 1 GHG(tonnes CO₂e ) 27 non-null float64\n", + " 2 Median (℃) 169 non-null float64\n", + " 3 Ozone-depleting emissions 54 non-null float64\n", + " 4 Mean ozone hole area (square kilometres) 38 non-null float64\n", + " 5 LandAverageTemperature 166 non-null float64\n", + " 6 LandAndOceanAverageTemperature 166 non-null float64\n", + " 7 Oil (megawatt-hours) 51 non-null float64\n", + " 8 Coal (megawatt-hours) 51 non-null float64\n", + " 9 Natural gas (megawatt-hours) 51 non-null float64\n", + " 10 Coal (terawatt-hours) 71 non-null float64\n", + " 11 Solar (terawatt-hours)_x 71 non-null float64\n", + " 12 Crude oil (terawatt-hours) 71 non-null float64\n", + " 13 Natural gas (terawatt-hours) 71 non-null float64\n", + " 14 Traditional biofuels (terawatt-hours) 71 non-null float64\n", + " 15 Other renewables (terawatt-hours)_x 71 non-null float64\n", + " 16 Hydropower (terawatt-hours)_x 71 non-null float64\n", + " 17 Nuclear (terawatt-hours) 71 non-null float64\n", + " 18 Wind (terawatt-hours)_x 71 non-null float64\n", + " 19 Hydropower (terawatt-hours)_y 54 non-null float64\n", + " 20 Solar (terawatt-hours)_y 54 non-null float64\n", + " 21 Wind (terawatt-hours)_y 54 non-null float64\n", + " 22 Other renewables (terawatt-hours)_y 54 non-null float64\n", + " 23 Coal (%) 70 non-null float64\n", + " 24 Crude oil (%) 70 non-null float64\n", + " 25 Hydropower (%) 70 non-null float64\n", + " 26 Natural gas (%) 70 non-null float64\n", + " 27 Nuclear (%) 70 non-null float64\n", + " 28 Other renewables (%) 70 non-null float64\n", + " 29 Solar (%) 70 non-null float64\n", + " 30 Traditional biofuels (%) 70 non-null float64\n", + " 31 Wind (%) 70 non-null float64\n", + "dtypes: float64(32)\n", + "memory usage: 69.1 KB\n" + ] + } + ], + "source": [ + "global_indicators.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "metadata": {}, + "outputs": [], + "source": [ + "global_indicators.to_csv('global_indicators')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 19. Merging country indicators" + ] + }, + { + "cell_type": "code", + "execution_count": 523, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)
0Afghanistan194914656.00.0003
1Afghanistan195084272.00.0015
2Afghanistan195191600.00.0015
3Afghanistan195291600.00.0015
4Afghanistan1953106256.00.0016
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" + ], + "text/plain": [ + " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%)\n", + "0 Afghanistan 1949 14656.0 0.0003\n", + "1 Afghanistan 1950 84272.0 0.0015\n", + "2 Afghanistan 1951 91600.0 0.0015\n", + "3 Afghanistan 1952 91600.0 0.0015\n", + "4 Afghanistan 1953 106256.0 0.0016" + ] + }, + "execution_count": 523, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#datasets 1 and 2\n", + "country_indicators1 = pd.merge(co_annual, co_share, on=['Year', 'Country'], how='outer')\n", + "country_indicators1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 524, + "metadata": {}, + "outputs": [], + "source": [ + "country_indicators2 = pd.merge(country_indicators1, ghg, on=['Year', 'Country'], how='outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 527, + "metadata": {}, + "outputs": [], + "source": [ + "country_indicators = pd.merge(country_indicators2, annual_temp, on=['Year', 'Country'], how='outer' )" + ] + }, + { + "cell_type": "code", + "execution_count": 528, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperature
0Afghanistan194914656.00.0003NaN13.350083
1Afghanistan195084272.00.0015NaN13.043500
2Afghanistan195191600.00.0015NaN13.967750
3Afghanistan195291600.00.0015NaN14.175417
4Afghanistan1953106256.00.0016NaN14.650750
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" + ], + "text/plain": [ + " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", + "0 Afghanistan 1949 14656.0 0.0003 \n", + "1 Afghanistan 1950 84272.0 0.0015 \n", + "2 Afghanistan 1951 91600.0 0.0015 \n", + "3 Afghanistan 1952 91600.0 0.0015 \n", + "4 Afghanistan 1953 106256.0 0.0016 \n", + "\n", + " GHG(tonnes COâ‚‚e ) AverageTemperature \n", + "0 NaN 13.350083 \n", + "1 NaN 13.043500 \n", + "2 NaN 13.967750 \n", + "3 NaN 14.175417 \n", + "4 NaN 14.650750 " + ] + }, + "execution_count": 528, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_indicators.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 529, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 5 entries, 0 to 4\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 5 non-null object \n", + " 1 Year 5 non-null int64 \n", + " 2 Annual COâ‚‚ 5 non-null float64\n", + " 3 Share of global COâ‚‚ emissions (%) 5 non-null float64\n", + " 4 GHG(tonnes COâ‚‚e ) 0 non-null float64\n", + " 5 AverageTemperature 5 non-null float64\n", + "dtypes: float64(4), int64(1), object(1)\n", + "memory usage: 280.0+ bytes\n" + ] + } + ], + "source": [ + "country_indicators.head().info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 20. Merging ozone datasets 1979-2014" + ] + }, + { + "cell_type": "code", + "execution_count": 530, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Ozone-depleting emissionsMean ozone hole area (square kilometres)
Year
19791110000100000
198010800001400000
19811040000600000
198211000004800000
198310900007900000
\n", + "
" + ], + "text/plain": [ + " Ozone-depleting emissions Mean ozone hole area (square kilometres)\n", + "Year \n", + "1979 1110000 100000\n", + "1980 1080000 1400000\n", + "1981 1040000 600000\n", + "1982 1100000 4800000\n", + "1983 1090000 7900000" + ] + }, + "execution_count": 530, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozone_merged = pd.merge(caused_ozone, oz_hole, on='Year')\n", + "ozone_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 531, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.5597422292909034" + ] + }, + "execution_count": 531, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oz_hole_emiss_corr = ozone_merged[\"Ozone-depleting emissions\"].corr(ozone_merged[\"Mean ozone hole area (square kilometres)\"])\n", + "oz_hole_emiss_corr" + ] + }, + { + "cell_type": "code", + "execution_count": 532, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 35 entries, 1979 to 2014\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype\n", + "--- ------ -------------- -----\n", + " 0 Ozone-depleting emissions 35 non-null int64\n", + " 1 Mean ozone hole area (square kilometres) 35 non-null int64\n", + "dtypes: int64(2)\n", + "memory usage: 840.0 bytes\n" + ] + } + ], + "source": [ + "ozone_merged.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 540, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Ozone-depleting emissionsMean ozone hole area (square kilometres)
Year
201038000019400000
201137000024700000
201235000017800000
201334000021000000
201432000020900000
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" + ], + "text/plain": [ + " Ozone-depleting emissions Mean ozone hole area (square kilometres)\n", + "Year \n", + "2010 380000 19400000\n", + "2011 370000 24700000\n", + "2012 350000 17800000\n", + "2013 340000 21000000\n", + "2014 320000 20900000" + ] + }, + "execution_count": 540, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozone_merged.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 534, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 534, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(ozone_merged)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CO emissions and median temperature anomaly correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 444, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.890288126134981" + ] + }, + "execution_count": 444, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "co_a_corr = global_indicators[\"Median (℃)\"].corr(global_indicators[\"Annual CO₂ emissions (tonnes )\"])\n", + "co_a_corr" + ] + }, + { + "cell_type": "code", + "execution_count": 538, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Median (℃)')" + ] + }, + "execution_count": 538, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x=global_indicators['Annual CO₂ emissions (tonnes )'], y=global_indicators['Median (℃)'])\n", + "plt.xlabel('Annual CO₂ emissions (tonnes )')\n", + "plt.ylabel('Median (℃)')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From b645706bf65b342042a3d73af72a4aef28cb6c41 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Wed, 27 May 2020 22:46:22 +0200 Subject: [PATCH 12/17] report done --- ...obal Warming report, can we solve it.ipynb | 811 ++++++++++-------- 1 file changed, 464 insertions(+), 347 deletions(-) diff --git a/your-project/Global Warming report, can we solve it.ipynb b/your-project/Global Warming report, can we solve it.ipynb index eec2b6ed..5462bf26 100644 --- a/your-project/Global Warming report, can we solve it.ipynb +++ b/your-project/Global Warming report, can we solve it.ipynb @@ -1,8 +1,29 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Structure of the notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Libraries \n", + "- About\n", + "- Datasets structure\n", + "- Gathering and cleaning datasets\n", + "- Processing and merging datasets\n", + "- Strong correlation between emissions and temperature anomaly\n", + "- Data visualization to explain the results\n", + "- Conclusions" + ] + }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +34,8 @@ "import datetime\n", "import re\n", "import seaborn as sns\n", - "import os" + "import os\n", + "from IPython.display import Image" ] }, { @@ -27,29 +49,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Even though nowadays the hottest topic is the current Covid crisis. I wanted to express through data, that Global Warming is not something we can leave out to chance. \n", - "In this project I gathered numerous Global and Country indicators to show that the Climate Change issue is one that we need to tackle quickly and unanimously." + "Even though nowadays the hottest topic is the current Covid crisis. I wanted to express through data, that Global Warming is not something we can't leave out to chance. \n", + "In this project I gathered numerous Global and Country indicators to show that the Climate Change issue is one that we need to tackle quickly and unanimously.\n", + "If we don't reduce the CO emissions, mitigate deforestation and focus on renewable energies our future could be too hot for us the bear.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Structure of the notebook" + "\n", + "\n", + "_______________________________________________________________________________________________ source: Gavin Schmidt - TED2014" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "- Libraries \n", - "- About\n", - "- Datasets structure\n", - "- Gathering and cleaning datasets\n", - "- Processing and merging datasets\n", - "- Strong correlation between emissions and temperature anomaly\n", - "- Plots and charts to explain the results\n", - "- Conclusions" + "Here we can see three different scenarios where our planet could end up. The measure is the __temperature anomaly__, which is the difference in degrees celcious from the average temperature of the current year and the mean of the previous 30 years.\n", + "- Scenario 1 is if we have some mitigation, but the earth still warms up to fast acting like a threat multiplier.\n", + "- Scenario 2 is the right path to take, where we __unanimously__ make tighter climate laws and protocols and we manage to stabilize the temperature and look to revert the damage by the end of the century.\n", + "- Scenario 3 is if we keep on doing as we've been doing. In this case, in forty years, humanity will be at great risk." ] }, { @@ -88,6 +109,14 @@ "20. Ozone datasets merged (1979-2014) [ozone_merged]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gathering and cleaning the datasets\n", + "To reach the final datasets I went through a lot of steps to make sure all had the same structure to merge them, here is the last part of the process." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -787,46 +816,6 @@ "co_concentration.Year[1648]" ] }, - { - "cell_type": "code", - "execution_count": 267, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'CO atmospheric concentration')" - ] - }, - "execution_count": 267, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plotting the Global CO₂ atmospheric concentration increasement from 1864-2018\n", - "x = co_concentration.Year[1740:]\n", - "y = co_concentration['CO₂ concentration'][1740:]\n", - "plt.figure(figsize=(17,6))\n", - "\n", - "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", - " linewidth=2)\n", - "\n", - "plt.legend()\n", - "plt.xlabel('Year')\n", - "plt.ylabel('CO atmospheric concentration')" - ] - }, { "cell_type": "code", "execution_count": 268, @@ -1145,56 +1134,6 @@ "#temperature_a.Year.unique()" ] }, - { - "cell_type": "code", - "execution_count": 276, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" - ] - }, - "execution_count": 276, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0, flags=flags)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plotting the median temperature anomaly over time to see how it's actually ingreasing \n", - "x = temperature_a.Year\n", - "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", - "plt.figure(figsize=(17,6))\n", - "\n", - "plt.plot(x, y, 'dodgerblue', label='Temperature Anomaly', \n", - " linewidth=2)\n", - "\n", - "plt.legend()\n", - "plt.xlabel('Year')\n", - "plt.ylabel('increse ordescrease in Temperature(℃)')" - ] - }, { "cell_type": "code", "execution_count": 277, @@ -1466,42 +1405,6 @@ "ozone.Entity.unique()" ] }, - { - "cell_type": "code", - "execution_count": 291, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 291, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#ploting natural and total emissions to see that we have nearly stopped using CFC\n", - "plt.figure(figsize=(17,6))\n", - "plt.plot(ozone.Year,ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Natural emissions'), label='Natural emissions')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Tonnes CFC11-equivalents')\n", - "plt.plot(ozone.Year, ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Total emissions'), label='Total emissions')\n", - "plt.legend(['Natural emissions', 'Total emissions'], loc='upper right')" - ] - }, { "cell_type": "code", "execution_count": 292, @@ -1741,41 +1644,6 @@ "oz_hole.head()" ] }, - { - "cell_type": "code", - "execution_count": 298, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 298, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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samtr8fHxYdGiRTe9/pIlS1iwYAGfffYZ5ubmLF26tMn9rGfOnMmCBQsYM2YMarWaGTNm0Llz54aCdhYWFvz73/9uKHJmZ2fH22+/fdU1nJycGD9+PKNGjcLMzIzIyEiqqqoaEuTLbnStm51vY2PTcP7gwYNZtWoVU6dOZcCAAWzZsoVRo0ZhaWnJ2LFjCQ4OZs6cOcydO5cffvjhquJ5I0eOZPPmzYwcOZLOnTs3PDd9+nQWLFjAJ598gp2dHd27dycjI+Oa5HTWrFksWLCA0aNHo1armT179k0T2AULFjBlyhSGDRt2zXMWFhYsXbqU119/ncrKShwdHRuW6DfFjWKxt7entLSU2bNnM378+CadM2PGDObOncuyZcswNzdnwYIFQP0qh5MnT17zvW4JhfbP6zVagYyMDIYMGcL27dsb9p4IIYQQQghx2b6ELP6xMp7uwR7MfagP5mZK4k9eZOHnB3l+Wk/u6mGa7yFPnTpFaGioscMQt5HJkyfz4YcfNrltXnBwcMOAg2i6bdu2cfjwYV566aVrnrve7/XNcloZQhRCCCGEELeVY2dzeeerwwS3deHl6b0wN6t/y9szxBNfd1vW7r5wzb5iIW5Xc+bM4dNPPzV2GLc1jUbDmjVrmDlzpk6uJ8vdhRBCCCHEbeNsWiGLVhzE192W+Y/0wcrij7e7SqWCMf2DWP5jAieTC+jcztWIkQphGOHh4Q1V0ZtCZtGbT6lUNhRP1Mn1dHYlIYQQQgghjCg9p5QFnx7AwdaS1x6Lwu46VdyHRPhjZ23OL3suGCFCIYRonCTpQgghhLhKYlI+f3tnJ8lZxcYORYgmu1RYwfyPY1GpFCx8PApXR+vrHmdlacbwyLYcOJ7Nxfxr+0qbAlmKL8TtoyW/z5KkCyGEEKKBRqPl07XHSckuYfGKeMoqa40dkmih//6ayJaDqcYOwyCKy6qZ//F+KqvrWPhYFD5udjc9fnS/digUCtbtTTZQhE1nZWVFfn6+JOpC3Aa0Wi35+flX9V9vCtmTLoQQQogG+45lcSGjmNH9AtkYm8LSb44w96HeKJVNa00kTINWq2VDbDKOdpYM692mya2lWqOKqloWfHaA3MIKFj4eTaCPY6PnuDlZ07erD1sOpjJ1eDA2VuYGiLRp/Pz8yMjIaOhJLYRo3aysrJrdkUySdCGEEEIAUKfW8OWmUwR4O/DXcWH4uNnxyc/H+X7HWSYNDTZ2eKIZCkqqqKpRU1VQQXJWCe18G09cW6PaOjWL/htHUmYxcx/q3axCcOMGBLH790y2xqUxbkCQHqNsHnNzcwIDA40dhrjDJWcVczK5gFF95WfRGGS5uxBCCCEA2Howley8ch4YGYpKqWB0v0AGdvfj602nOXLmkrHDE82QlfvHXuvY41lGjER/1BotS746TML5PJ6e1J3enbyadX7HNs6EBrjw654k1BpZWi7ElT7+6TjLf0wgJbvE2KHckSRJF0IIIQRV1XV8u+UMnQJdiAj1BEChUDBrQlfaeNrzzleHuVRQYeQoRVNl5pYB4O1qS2xCtpGj0T2tVsuHa46x/3g2j47rwuAI/xZdZ9zAIHIKKjh44vZ7jYRoqaTMYhKT8gHYEGt6dRvuBJKkCyGEEIJf9yZRWFrNg6M6X7V/2crSjDkP9kat0bB4ZTw1tWojRimaKiuvHHMzJaP7BZKeU0p6TqmxQ9KplRtOseVgKpOGdmTsLSxVj+zshYezNWt3Szs2IS77dU8SlhYq+nT2YtfhdCqqpICooek1Sf/ggw8YNWoUo0aN4h//+Md1nx80aBDjxo1j3LhxfP311/oMRwghhBDXUVpRww87ztGnsxehgS7XPO/jbsezU3pwPr2IT34+boQIRXNl5Zbh7WZLdLgPAPuP3z4zxT/uPM+aHeeIiQpg2oiQW7qWSqVkTP92nEwu4Fx6oY4i1K3aOhkYE4ZTXFbNb79nMLinPxOHdqSyWs2uIxnGDuuOo7ckPTY2lr179/LTTz/x888/k5iYyNatW6865sSJE/zzn/9k7dq1rF27lmnTpukrHCGEEELcwPfbz1FRXcf9MaE3PCayizcThnRg84FUtt4hbb1as6y8Mnzd7XBzsia4jTP7b5N96dviUvnvukT6dfXh8b+E66Rq/bDebbG2VPHL7iQdRKhb6/YmMW3+JpKzio0dirhDbDqQQm2dhtH9Aung70R7P0c27EuWloAGprck3d3dnZdffhkLCwvMzc0JCgoiK+vq/0GcOHGCjz/+mDFjxrBw4UKqq6v1FY4QQgghriOvqJJ1e5MY1NOftt4ONz122ohQunZw46MfEzifUWSgCEVzqTVasvPK8XGzBSAqzJvzGcXktPKaAvuPZ/P+6qN07+jOc1N7otJRW0Bba3OG9W7LnqOZ5BdX6uSaunAxv5wV609SWV3H+6uPSnE7oXd1ag0b9qXQraM7bbwcUCgUjIwOJPViKSeTC4wd3h1Fb0l6hw4d6NatGwApKSls3LiRgQMHNjxfXl5OaGgos2fP5qeffqKkpIQPP/xQX+EIIYQQ4jq+2XwarRamDW982bBKqWD2fRE42lqw+It4SitqDBCh/qnVGvKLKzmfXkTcyYtsPpDCt5tPs2zNMd74z0Ge+9dvzFqyg0uFrSPJzS2soE6txcfdDoCocG+gdS95P34+jyVfHaKDvzOvPNgbczPdvoUd078dGq2W9ftMo0iWVqvlox8TUCrgodGdOJdexPp9pjfTL24vsQlZFJRUMaZ/u4bH+nf3xdbanA0m8rtxp9B7n/Rz587x+OOP8+KLLxIQENDwuK2tLZ9++mnD5w8//DBz5szh2Wef1XdIQgghhADSc0rZHp/G6P7t8HCxadI5jnaWvPJgb176YC/vfH2Yvz8SiVJHM5q6VqfWUFhSTUFJJQUl1RSWVlFQUkVBcRWFpdUUlFRRWFJFcVk115ukdLC1wMXBCltrc86lF5GYlI9Hz6a9TsZ0uf2a7/+SdB83OwK8Hdh/PIt7BppOP/CmOp9RxOv/OYiXqw3z/xqJtaXu3756udoS2cWbTftTmDi0I1YWen+LfFO7f8/kyOlLPHZPGKP7BZJwPo8vN5wisos3Hs6m/zMoWqdf9yTh7WpLRIhnw2NWFmYM7dWG9fuSKCypwtnByogR3jn0+hfo8OHDPPXUU8yZM4dRo0Zd9VxWVhaxsbGMHz8eqB8xNDMz7h9EIYQQ4k7y5cZTWFqYMXFIx2ad17GNM4/dG8aHa46xausZpjZhFl7fqmrqWL3tLBcyiusT8ZIqSsqvnelXKuoHGpwdrHBxsKK9nxPODpa4OFjhbG+Fq2P9f53sLRtma2vrNIx/ZR0Zl8oM/WW1yOX2a5eXuwNEh/vw7ZbTre5NdmZuGQs+3Y+djTkLH4vGwdZCb/caNyCI/cez2XkonZjoQL3dpzGlFTV8uvY4Hds4MbJvIAqFgif+ryszl+xg+Y8JvPpwH53sxRfiSmfTCjmdWsij47pcM/AaEx3A2t0X2BKXyqShwUaK8M6it6w4OzubmTNnsnTpUqKioq553srKiiVLltCnTx/8/Pz4+uuvGTZsmL7CEUIIIcQVzqQWsP94NlOHh+BoZ9ns80dEtuV0SgHfbjlDxzbODb3VjSHtYglvf3mItIultPdzxNPFhpAAF1zsLXFxtKpPyO2tcHawxMnOEpWqeUulzc2UeLvakHGpdbQxy8otw9rSDCf7P76v0WHefLP5NAdOZBs1AW2OvKJKXv04FoDXH4/Gzclar/frFOhCez9H1u5OYnhkgNFWiPznl0TKKmp5/fFuDfvuPV1suG9ECJ//ksi+hCz6dfU1SmzGVlldx474NEZEBTT791jc3K97k7C2VDG0d5trnvN1t6NbR3c27U9l/KAO8tobgN6S9M8//5zq6mreeuuthscmT57Mjh07eOqppwgLC2PhwoU88cQT1NbW0qNHDx566CF9hSOEEEKI/9FqtXyx/hROdpYtXv6sUCh4cnxXUrJKeOfrw/zr2YF4udo2fqIOabVatsWlsfyn49hYmvHaY1H0CPbQy738POxbzUx6Vl45vu62V822tvGyx9fdltiE1pGkl5TXMP+T/ZRV1PLmE30blu7rk0KhYNyAIN795ghHzlwyysDTsXO5bItPY/zgDgT6OF713Jh+7fjtSAYf/3Scbh3csbPR36oCU7VubxIrN5zCzcmaPl28jR3ObaOgpIq9RzMZERWAjZX5dY8ZGR3ImyviiD+VQ6S89nqnt2GQefPm8fvvvze0V1u7di1Tpkzh008/JSwsDIDhw4ezbt06Nm/ezOLFi7GwuPP+2AghhBCG9vuZXI5fyGPSsI63tL/X0lzFKw/2AmDxiniqaw3Xz7miqpZ3vz7Ce6uPEtLWmX8/f5feEnQAPw87snLLUas1eruHrmTmluHjdnVSq1AoiArzIeFCnskX/KusrmPhZwe4mF/Oqw/3ob2/k8Hu3berLy4OlqzdfcFg97ysulbNsjXH8Ha1ZfLd1y4pVqmUzJrQjZLyGlasP2nw+Izt8qAcQPypHCNHc3vZtD+FOrWWMf3a3fCY3p08cXO0kgJyBiJrFYQQQog7iEaj5Yv1J/F0sWF4ZMAtX8/L1Zbnp/YgKauYD9ccM0gv3fMZRTyz9Df2HM3gvhEhLHw8Ghc977P287CjTq0hx8QrvNfWqcktrGio7H6lqDBvNBotcYkXjRBZ09TWqXlzRRzn0guZfV8EYe3dDHp/czMlo/q24+jZXFKzSwx679XbzpKdV87M8V2xNFdd95ggPyfGDQhi84FUTlzIM2h8xnYyuYCsvHJsrcw4fCpH+nbrSG2dmo2xKUSEel7378ZlKpWSEVEB/H42l6zc1rGqqDWTJF0IIYS4g+w9lklSVjH3jQjRWRurXp28mDwsmB2H0tl0IFUn17werVbLr3uSmP3eHmpq1Sx6oi+ThgXrrF/2zfh52AOY/JL3i/kVaLTg637t1oMO/k64OVkTm2C6rdjW7DjP0bO5/G1iN6LCjLOkdkRUABbmKoPOpqdkl/DDjnMMjvCna0f3mx479e5gPFxs+OD7Y9TWGW71irFti0vD2lLFtBGh5BVXkWLgQZTb1Z6jWRSVVV/Vdu1G7u7TFpVSwcb9KXqP604nSboQQghxh6it0/DVxtMEeDswoLufTq89+e5geoR48MlPxzmbVqjTawOUVdTw5oo4Pvn5ON06uvPv5+6iS5DhZll9PepnmDJyTDtJb6jsfp0ZMYVCQXSYN7+fvURFVa2hQ2tUnVrDpv3J9AzxYGjvtkaLw8HWgsER/uw6kkFRabXe76fWaPlg9VFsrc15eEznRo+3sjRj5v91JTO3jO+3n9N7fKagoqqWvccy6d/Nj35dfQCIO2m6K0Jai/qBzwv4edjRvZHBIQBnByuiwrzZFpdGVU2dASK8c0mSLoQQQmcqqmp5c0Ucz/7rN77edJqzaYVorteAWhjFloOpZOeXM31UJ51XrlYpFTw/tScuDpYs/iKe4jLdJTenUwp46p+7OHQqh0fGdmb+I31aVJH+VtjbWOBkZ2nyFd4v90i/sv3alaLDfait03D41CVDhtUkB09cpKCkmpF9jV/Ybmz/dtTWaQwyY7gpNpkzafWtr5r6c90jxIO7evjx/fazpOeY9s+kLuw7lkVVjZphvdvg7GBFe38nDp2Ufem36nRKIeczihnTv12T2/qN7BtIWWUte49m6jm6O5sk6UIIIXSiuKyauR/tIy7xIiqlgtXbzvD8v3czfeFm/r3qd2ITskxy9u5OUVVdx6qtZ+jczpWeIfopsOZga8Er03tTXFbNO18dRn2LAzQajZbvt5/lpWV7USoUvD2rP/cMbG+0HtG+HnYmv9w9K68MB1uLG1b+DglwwcnOktjjWQaOrHEbYpPxcLamZ4jx2vld5u9pT88QDzbEJut1SXleUSVfbDhF947uDOzRvNUtj4ztgrWlGe+vPnrbD4Zui0/Dz8OO4LbOAPQK9eRMWqFOBwPvRL/uTcLWyoxBPf2bfE6Xdq74e9qzPjZFf4EJSdKFEELcuvziSl75cC9pF0uZ+1Bv3nlqAF++FsPz03oS3t6N/SeyWfxFPNPmb+TV5bH8svsCWXmmnYKzGZEAACAASURBVOzcbtbuuUBRaTUPjuqk1yS3vb8TT/wlnKPncvl606kWX6ewtIoFn+5n5YZTRId58+/n7qJjG2cdRtp8fq0gSc/MLbtpuzKVUkFkmDeHTuVQY8Bq/I1Jzykl4Xxeff9rI/Un/7NxA4IoKq3mtyP6mTHUarUs/zEBtUbLk+O7Nvv30snekofHdOFUSgGbD+qvFoSxZeaWcTK5gGG92zS8RhGhnmi1cOSM6a0IaS3yiirZl5DFsD5tm9XlQ6FQMCo6gPPpRXrZ2iTqSZIuhBDilmTllfHiB3vJK6ritcei6NXJC6ifVb2rhx+z74vg69dGsPjJvowbEER+SRWfrj3B44u3M+Ot7Xz+ywmOn8+jrhW0tmqtSspr+HHnefp09iIkwEXv9xvWpy1392nL99vPceBE84uUHTuXy9Pv7iIxKZ8nx3flxfsjsLW+fu9eQ/LzsKe0osakZ++ycsvxuU7RuCtFhXlTVaPmdxNKcDbEJmOmUjLMiHvR/6xbR3faetnzy54Leqkkvv94NgcTLzJteDBerjf/nt3IkF7+hLd3Y8W6RPKLK3UcoWnYFpeGUqm4ara3vZ8TTvaWxMuS9xbbEJsMWi2jWrC9ZFCEP1YWqvprCL2QJF0IIUSLpWSX8PIHe6msquPNJ/resJCXSqWkS5AbD47uzIcvDubTOUN57J4wPF1sWLc3mTkf7eO++Rt5e2U8Ow6lm3QS1Bp9v/0sVdV13D8y1GD3fPzeMNr7ObL02yNNbtejVmv4atMpXv04Fltrc959ZiAxUQFGW97+Z36Xi8eZ6Gx6ZXUdBSVV1/RI/7OwIDdsrc2JPW4aVd4rq+vYcSidfl19cLI3bK2Bm1EoFIwdEERyVgnHddzurLyylo9/SqCdjyPjBgS1+DoKhYKZE7pSV6fh059P6DBC06BWa9hxKI1eoZ44X9FmUalUEBHiyZEzl1DLAG+zVdeq2bQ/ld6dvVo0QGRjZc6gnv7s+T2TkvIaPUQoJEkXQgjRImdSC3hl2V4UCgVvzexLe3+nJp/r5WrLmP7teO2xKL55PYY5D/amb1dfEpPyWfrtEe5fsInZ7+1m9bazJGcVSz/cW3CpsIL1+5IZFOFPWy8Hg93XwlzFK9N7o1IqeHNFHFXVN68EnFdUydzlsXy39SyDI/xZ+sxAArwNF29TmHqSnp1XXzTuZsvdob4XeJ/OXsQlXjSJFSy/HcmgoqqOkdHGLxj3ZwN7+OFga8Ha35J0et0v1p+kqLSaWRO7olLd2ttxHzc7Jt8dzL6ELA62YOWKKTty5hIFJdUM7d3mmuciOnlSXlnLqZQCI0TWuu0+kkFpRU2T2q7dSEx0ADV1GrbHp+kwMnGZJOlCCCGa7djZXOYtj8XexoK3Z/WjzS0kf9aWZkSFefO3id1YMX84S58ZyJS7Q1BrtHy58RRPvbuLh9/YyodrjhF38iKlFTJq3xzfbj4DwNThIQa/t4eLDS/cF0FaTikffH/shoMt8Scv8tS7u7iQUcRzU3vwzOQeWDVjj6ShuDvbYG6mbGhzZmr+aL/W+MxYVJg3ZZW1HD+v2xni5tJqtazfl0ygjwMhAcatOXA9luYqYqIDiD91sckrQhqTmJTPxv0pjOkfRAd/3XzN997VngBvBz76MeG2KtC5NS4NJztLIkKvLSbYvaM7ZioFh07Jkvfm0Gq1/LIniQBvB8JuoY1loI8jnQJd2BibctsXLjQGSdKFEEI0y4ET2Sz47ACeLja8Natfi/dSXo9SqaC9vxNT7g7mn88MZOXfh/PUxG508Hdi15F0Xv/8IFNf3chfF21l8RdxfL/9LEdOX5Ll8TeQdrGEHYfSGNU3EA9nG6PE0CPYg2nDQ/jt9wzW77t6/2JtnYbPfznBws8P4u5kzb+eu6tZVYYNTaVU4OtuZ7Jt2C4nkd43aL92pe7BHlhZqIy+5P10SiEp2SWMjA40mW0NfzYqOhCVUskve259Nr22Ts2yNUfxcLZm2gjdDZyZqZTMmtCVgpIqvtzY8oKNpqS4rJq4xIsMivDH7DqrDWyszOnczpU42ZfeLCeS8knJLmF0v6a3XbuRkdGBZOeXc/Rsro6iE5eZ3jC1EEIIk7XjUDr//u53Ovg58fdHI7G/QZsnXXF2sGJYn7YM69OW2jo1J5MLOJdexPmMIpIyiolN+CPBcHOypr2fI0F+TrT3cyLI1/GqPYx3oi83nsLK0ozxgzsYNY4JQzpyJq2Qz9aeIMjXidBAFy7ml/OPLw9xLr2IUX0DeXhMZyzMVUaNsyl8PexIyiw2dhjXlZVXjpujFVYWjb+9szRX0TPUkwMnspnxl3CjVVTfEJuMjZVZs9uPGZKzgxUDuvuyLT6N+0aE3LC9XVP8sPM86Tll/P2vkc2qqN0UwW1dGNU3kPX7khnYw4+QtvovEqlPOw9noNZoGdrrxgN3EaFefP7LCXIKKvB0Mc5AZGvz654k7G3Muavnrf/ORYd747TWkg2xyfTQU2vPO5Uk6UIIIZpk3d4kPv7pOF07uDH3oT46f4PZGHMzFV07uNO1g3vDY2WVtSRlFnE+vZgLmUVcyCjiwImLDc+7OFgR5OfYkLQH+Tnh6mhlsjN2unQ6pYADJy5y34gQHO2MW4xLqVTw3NSePLf0N95aGc+Uu4P577pEFMAr03sRHe5j1Piaw8/Djv0JWdTWqTE3M61BhczcMnwa2Y9+pegwb/Ydy+J0SgGd27nqMbLrKyqtZu+xLEZENa8FlDHcMzCIHYfS2Xwglf9r4aBXek4p3209y4Buvtddvq0L98eEcuB4Nh+sPsq/nrvrujPQrYFWq2VrXCrBbZ1vup2qVydPPv/lBIdOXmRUv5bvr75T5BRUcPBENn8Z1AFLHQyKmpupGNanDT/sOMelggo8ZKBEZ5r0F7GmpobMzExUKhXe3t6Ymxu/DYoQQgjD0Gq1rN5+lq82nqZPZy9evD/CZGY87azNCW/vTnj7PxL3iqpakjKLuZBZzPmMIi5kFHP4VA6Xt8w52VnS7orEvb2fE+7O1rdV4q7Valmx/iRO9paMvYXK0bpkZ23OKw/24oX39rBszTGC2zgz+/6IVjf75edhj0ZbP2ttyEJ8TZGVW0bfrr5NPj4i1BMzlZLY41lGSdK3xqVSp9aYZMG4Pwv0cSS8vRvr9iYxbmBQs5NfjUbLsjXHsLRQ8dd7uugpyvol4E/8X1de/89Bftp1nglDOurtXvp0Lr2ItIulzBzf9abH+brb4e1mS/ypHEnSm2D9vmRQKHT6OzciMoAfdpxj04EUHhjZSWfXvdPdNEk/ffo0H3zwAbt378bS0hKVSkVNTQ2DBg3i8ccfp2PH1vmLL4QQomm0Wi3/+TWRn3+7wKCefjw9qfstVyLWNxsrc7oEuV3VDq6quo7krJL6pD2zPnFfc/ZcQ7EbexsLgvwc65N2fyeCfJ3wcrVptYn74dOXSEzKZ8a9YSY1Qxno48icB3uRnFXCPS1IdEzBlRXeTSlJLymvobSiFt8mFI27zMbKnO7B7uw/ns1fx3Yx6M+7WqNl0/4Uwtu74e9pb7D73opxA4J4/T8H2Z+QTf/uTR8MgfoCaIlJ+fxtYjec7fW7Dad3Zy/6hvvw7ZYz9A33adbqClOxLS4NC3MV/bs1/jr36uTJxtgUqqrrTLLgpKmoqq5jy8FUosK8cXe21tl1PVxs6NXJi60H05hyd7DJrTBqrW74k/zhhx9y6NAhxo8fz6JFi3B0dASgrKyMvXv3smjRInr16sWsWbMMFqwQQgjDUWu0LPv+KFvj0hjdN5BH7wlDaaR9q7fKytKM0EAXQgP/2KNZXasmJet/M+7pRVzILGbt7gvUqesTd1srM4L8nOo/fB0J8nPEx83O5F8DjUbLyg0n8XK14e7IAGOHc42eIZ70DNHPUl9DuNzezNSKx2Xl/a+yeyM90v8sOsyH+JM5nM8o0lml8aY4fDqHS4WVPDxGf7PKuhYR6omPmy1rd19oVpJeWFLFf9clEhbkxrDrtBLTh8fuDePo2UssW3OMN2ZEt6oBx6qaOn77PYO+4d7YWje+erdXqCe/7E4i4XwevTt7GSDC1mnn4XTKK2sZewtt125kZHQgBxMvEpuQbdL1JVqTGybpHTt25Mknn7zmcTs7O0aMGMGIESPYtm2bXoMTQghhHLV1Gt795jD7jmUxaWhHpo0IaVVv8prC0lxFcFsXgq8orlRbpyY1u/R/M+7FXMgoYt3eJGrr6ntJW1uqaOfr9L9Zdyfa+zni62FvtKJb17P7aCbJWSU8P60n5matb6ba1FlbmuHmZG1yvdKzcut7pDel/dqVenf2QqlUsP94tkGT9A37knFxsKRPl9aTVCmVCsb2b8fyn45zOqWAkICmFWb75Ofj1NSqmTmhq8H+jro4WPHg6M4sW3OM7fHp1+0zbqoOHM+moqqOYb3bNun4zu3csLZUEX8qR5L0G9Bqtfy6N4kgP0dCm/hz2xzdOrrj7WrLhthkSdJ15IZJ+tChQ695rKamhry8PHx8fG54jBBCiNatqqaOxV/Ec+T0JR4e05l772pv7JAMxtxMRXt/J9r7OzU8VqfWkJ5T2jDbfj6jiE37U6mprW/HZGmhop2PY0NhuiA/R9p42htlW0BtnYavN52inY8jA5qwTFS0jJ+HnQkm6WUolQo8XZqXpDvYWhAe5EZsQhb3x4QaJInMzivnyJlLTB4W3Oq2PAzu1YYvN53m590XeLkJyU7cyYvsPZbFfSNCGlZhGMrdfdqy83A6//n1BBGhnjjZG7eAZFNtjUvDy9WmyXUSzM2UdOvowaGTF9Fqw2+7AWVdOHo2l/ScMp6d0l0vr49SqSAmOoD//JpISnYJAd6msxWotWp048bWrVs5cOAAzz77LGPHjqW0tJRZs2Yxffp0Q8QnhBDCgMora1n4+QFOpRQwa0I3hkc2bSbjdmamUhLo40igjyPD/veYWq0h41IZFzKLOJ9RP+O+LT6Ndf/rA25hpiTAx+F/S+Wd6BLkapA36FsOpHAxv4IFj0aa/LL81szPw47t8WlotVqTSQgyc8vwdLZp0eqJqHBvPvohgbScUoPss9+0PwWFQtEq/75YW5oxvE9bfv7tfKPVrCur6/johwTaeNnzl0GGb4OoVCqYNaEbT727k89/OcHz03oaPIbmuphfTsL5PO4bEdKsv2G9Qj3ZfzyblOwSAn0c9Rhh6/Tr3iSc7CybtMe/pYb0asNXG0+xITaZJ//v5gX/ROMa/Uv+8ccfM3HiRLZs2UK3bt3YuXMna9euNURsQgghDKiotJo5H+3jbFohL94f0SrfQBuKSqWkrbcDgyPa8Ng9Ybw9qz+rFo3iwxcH8/zUHozsG4iFuYrfjmTwwfdHmfHWdp5+d1dDmxp9qKyuY9XWs3QJcqVHsPSr1Sc/D3sqq9UUlFQZO5QGWXnlzV7qfllkF28UCth/PFvHUV2rulbN1rhUIrt44eqou+JVhjS6XztQKPh1b9JNj/tq0ynyiyv524RuRtt64u9pz4QhHdl1JIMjpy8ZJYbm2B6fjkIBgyOatzz/cku7+JM5+girVcvKK+PQqRxGRAXotaibg60F/bv7sutwOhVVtXq7z52i0b8YWq2W4OBgYmNjGTBgAHZ2dmi1WkPEJoQQwkByCyt5edleMi6VMe/hPvRrRhsnUU+lVODvac9dPf15ZGwXFj/Zj29fH8nyl4fw13FdMDdTsmL9SR5ZtJUX39/Dr3uSKNRhkrd29wWKyqqZPqqTyczu3q4aKrznmMaSd61WS1Yze6RfycXBipC2LsQmZOk4smvtO5ZJaUVtq2i7diPuztb0Dfdhy8HUGyYjZ9MKWbcniZiogCbvXdeXCUM64Odhx7IfjlFVXWfUWG5GrdGyLT6N7h09ml193NnBivZ+jhw6JUn6n63fm4xSUb8cXd9GRgdSWa1m5+EMvd/rdtdokq5UKtmwYQN79+6lb9++/Pbbb/I/fyGEuI1k5Zbx0rI9FJZWsfCxqFZdedvUKJUKfN3tGDcgiHeeHsCnc4bywMhQKqvr+OTn4zy4cDPzlu9j84FUSitqWnyf4rJqftx5nsguXoS0NW5CcCf4ow2baVR4LyipoqpGja9by2bSAaLDvUnOKiE7r1yHkV1rw74UfN3tCG/v1vjBJmzcgHZUVNWxPT79mufq1Bo++P4oTvZWJtE32txMxawJ3bhUUME3W84YO5wbSjiXS15RZYuL3EWEenEmtYDismodR9Z6VVTVsjUujX5dfXFx0G/rP4CObZxp7+/EhthkmdS9RY0m6S+99BKrV6/m2Wefxd3dnY8++oh58+YZIjYhhBB6lpxVzEsf7KW6Rs2iJ/o2uVCPaBkvV1smDOnI+y8MYtnsQUwY2pFLhZV88P1RHliwiYWfH2DX4XQqmznb9f32c1TX1JlEQnAncHGwwtrSzGSKx2XlXa7s3vK6B1Fh9UWB9bnk/Xx6EWfSChnZN6DVT/gEt3UhpK0zv+5JQq25OhlZ+9sFkrNKmPGXsCa1EDOEzu1cGR7ZlrW/ned8RpGxw7mubXFp2NuYE9nCiv+9Onmi0cKRM6a/rN9QtsfX//9k7ADdt127kVHRAaRdLCUxKd9g97wdNZqkR0REsGLFCmJiYgBYtWoVPXr00HtgQggh9OtUcgGvfLgPM5WCt2b2o72fU+MnCZ1p4+XAfSNC+fjlISx9ZiBj+geRnFnMu98c4b6/b+KtlfHEJmRRU6u+6XUuFVSwfl8yQ3q1wd/T3kDR39kUCoVJVXjPyv1fj/RbSNI9XWwI8nMk9rj+lrxviE3G0kLV7P3GpmrcwCCy88uJP3mx4bHsvHK+2XKGqDDvhoEPU/Hg6M442lnywfdHUas1xg7nKqUVNew/Ud9ju6X7ptv7OeFkb8kh2ZcOgEajZd3eJILbONOxjeHaK/br5oudtTkbYlMMds/bUaNJelJSEiNHjmTUqFHk5OQQExPDhQsXDBGbEEIIPTmZnM+rn8TiaGvB27P6S3JnRAqFgvb+Tjw8pjOfz7ubt2b2Y1jvNiReyGfxF/Hc9/dNLP32CIdO5VB3nTfW32w5jUIBU+4OMUL0dy5fDzuTWe6elVuOuZkSN6dbK8QWHebDmdRC8osrdRTZH8oqavjt90zu6uGHnYnMLt+qqC7euDtbs3Z3/ftirVbLh2uOoVIqePzeMCNHdy07a3MevzecCxnFjRa9M7TdRzKordM0uTf69SiVCnqGeHD4zCWTG4QwhiNnLpGVV86Y/oabRQewsjBjaO82xCZk6bTuyp2m0ST9jTfeYO7cubi6uuLp6cl9993H/PnzDRGbEEIIPahTa3h/9VGc7Cx5a1a/m7YQEoalVCro3M6VGX8JZ8X8u1n4WBT9uvpw8EQ2r312gAcWbGbZmmMcv5CHWqMl9WIJOw+lM6pvYLMLLYlb4+dhR15xVbO3JuhDZm4Z3m62qG6x7V5UmDegnyXv2w+lU1OrbtUF4/5MpVIyum87TlzI50JGETsPZ3D0XC7TR3Uy2cr10eHe9O7kxVebTpOjp04TLbE1Po12vo6087219mm9Qr0or6zldGqhjiJrvX7dk4SLgyXR4YZf0RETFYBao2XLwVSD3/t20WiSXlRURN++fRs+nzZtGmVlprG8SwghRPNt2JdMxqUyHrsnDGd7/ReSES2jUinpHuzBU5O68+VrI5j3UG+6B7uz83A6cz7cx8Ovb+HtlfFYWZoxYUhHY4d7x/HzqF99kplr/PdEWXll+NxC0bjL/D3t8fe003mSrtFo2bAvmZC2zrechJmauyPbYmWh4uvNp/ls7QlC2joTExVg7LBuSKFQMOMv4SgV8OEPx0yiuFdSZjEXMooZ1sKCcVfqHuyOSqm4agvCnSg9p5QjZy4xMjrQKO3/fNzt6N7RnU37U2RVQws16btWXV3dUOAjNzcXjUZebCGEaI2Ky6r5ZvNpund0p1cnqeLeWpibqejTxZvZ90Xw1YIRvHhfBB3bOJGdV8HkYcE42FoYO8Q7zh8V3o2bpKs1WrLzKvC9hf3oV4oO8+HEhTydVshOOJ9LVl45I/vePrPol9lZmzO0dxviT+ZQWV3LrIndUN7iigZ9c3e25v6YThw5fYndv2caOxy2xadhplIysIffLV/Lxsqczu1cib/DW7Gt25uEmUrJ8MgAo8Uwsm8gecVVxEmNgBZpNEmfMmUKjzzyCPn5+bz77rtMmjSJKVOmGCI2IYQQOvbVptNU1qh59J6wVl9d+U5lZWlG/+6+zH2oD2veGs09A4OMHdIdycfNFqXC+G3YcgsrqFNrbqlo3JWiwrzRaOFgou5mIjfEpuBga0FfIyy7NYSx/YOwtFAxcUhH2no5GDucJhnZN5DgNs4s/zGBi/n6bbt3M7V1anYdTicqzBt7G90MNvbq5EnaxVIumdByfkMqq6xlx6F0Bvbwxcne0mhx9Ar1xM3Jmg2xyUaLoTVrNEmfMGECTz/9NGPGjKGuro7XX3+dqVOnGiI2IYQQOpSUWczmAymM7hsoheJuEyqlQgZbjMTcTIWnq63RZ9Kzcv/Xfk0Hy90B2vk64ulio7Ml73lFlRw8kc2w3m2wMG9Z1W5T5+1myxfzhzNleOsp3qhSKnh+Wk+0Wi1vrYynupEuEvpyMPEipRW1Le6Nfj0RofWrxO7U2fRtcalU1agZ08+wBeP+TKVSMiKqLUfP5prEtqDWptEkffr06fTq1YvZs2fz0ksvXbU/XQghROug1Wr55Ofj2FlbMOXuYGOHI8Rtwc/DjkxjJ+l59ffX1XJ3hUJBVJg3R89eoryy9pavt/lAKlpghAnv09YFU+mH3hzebrY8N60nFzKKWf5DglH2p2+NS8PNyZquHdx1dk1fdzu8XW3vyH3pao2WX/cm07mdK0Em0Fb17t5tMVMp2Cjt2Jqt0SS9tLSUioo7c7mIEELcLvYlZJGYlM/9I0Ox09GSQiHudH4e9mTmlqHWGK/4VmZuGdaWKp0ua40O86FOrb3lmcg6tYbNB1LoGeKJl6tuZvqFbvXu5MWkYR3ZFp9m8ErceUWV/H7mEkN6+d9yZ4IrKRQKenXy5Pj5PKpqjN99wZDiT17kUkGF0WfRL3N2sCI6zIdt8Wl33PfiVjWapFtbWzNo0CAeeOABZsyY0fAhhBCidaiuVfOfXxMJ8Hbg7j4t70ErhLian4cdtXUacguNN5mRlVeOj7udTrc9BLd1xsXBktiErFu6zoET2RSWVjMyOkA3gQm9mHJ3CN07urP8x+OcTTNc67Idh9LRamFoL90tdb8sItSTmjoNCefzdH5tU/brniTcnKyJ7OJl7FAajOwbSHllLXtMoEhha2LW2AHjx483RBxCCCH05Med58ktrOTZJ3vodLZCiDvdlRXejTVTnJVbRkd/Z51eU6lUENnFm23x6VTV1GFl0ejbxevasC8FDxcbeoRIJwlTplIqeOG+CJ5duou3Vsaz9JmBONrpt+CYVqtlW1wa4e3d9PK70yXIFSsLFYdO5tC7k+kkrPqUkl1Cwvk8po/qhEpl+LZrN9Ip0IW2Xvasj01maO82UkeliRr9DqampnLvvfde9ZGYmGiI2IQQQtyi3MJK1uw4R9+uPoQFuRk7HCFuK5d7pRurwnttnYZLBRV4u+s+yYkO96GmVs3vZy616Py0iyUcv5BHTFSADA62Ag62Frw8vRdFpdW88/VhvW/hSEzKJzu/XKcF465kbqaiW0d34k9eNIle8Ibw654kLMxVDI80rRVzCoWCkX0DuZBRzLn0ImOH02rccGj0vffeo6SkhA0bNlBW9kdRlNraWvbu3cu8efMMEqAQQoiWW7EuEbRaHh7d2dihCHHbcbC1wMHWwmgV3i/ml6PR6q5o3JW6tHPF3saC2IRsosKa3zptY2wKZiolw/SUhAnd6+DvzOP3hvPB90f5ZvNp7o8J1du9tsalYWNlRlSYt97u0auTFwdOXCQlu4RAH0e93ccUlJTXsOtwOoMi/HXWyk6X7urhx4p1iazfl0zHNrpd+XO7uuFMeteuXXFyckKpVOLk5NTw4eXlxTvvvGPIGIUQQrRAYlI+u49m8pdBHfBwsTF2OELclvw87IyWpGfl6ray+5VUKiWRXbyIO3mR2jpNs86trK5j+6F0+nXz0fuyaaFbwyPbMqx3G1ZvO0tcon6qo1dU1bIvIYsB3f1avJWiKS63Yjt0B7Ri23wghZo6jckUjPszGytzBvX0Z8/RTErKa4wdTqtww9+MgQMHMnDgQAYMGEB4eLghYxJCCHGL1Jr6lmtujlb836D2xg5HiNuWn4c9BxN101O8ubLydNsj/c+iwrzZGpfGsXO5DQlPU+w6kkFldR2jogP1EpfQrxl/CSc5q5h/fnOYfz47EB833Q4C7TmaRXWNWu+rLFwcrAjycyT+ZA4ThnTU672MSa3WsGFfMuHt3Wjr7WDscG5oZHQgG2JT2BaXxl/kfUmjGt2T7uXlxWOPPcbw4cPJz8/nkUce4dKllu1PEkIIYRjb4tJIyizmwdGdsbLU30yFEHc6Pw87istqjDI7lJlbhoOthd7aKnbr6I61pRn7jzd9EEKr1bJhXzLtfBwJbivLWlsjC3MVL0/vjVKpYPGKeJ23ztoWl0obL3s6+Ou/j3dEqCdnUguMPntbXaumoKSKyuo6NDre73/gxEXyiqsY2980Z9Eva+vtQOd2rmzcn6zz1+B21Og7t4ULFzJ06FC++uorHBwcCAkJYd68eXzyySeGiE8IIUQzlVfW8uXGk4QGuDCgu6+xwxHitna5wnvmpTIcAl0Meu+s3HK9zaJDffGtXp08OXAimyfHd21SAbhTKQWkZJcwa0JXqeLcinm62PDCtAgWfLafZWuO8dyUHjr5fqbnlHI6tZCHx3Q2yM9H705efLf1LEdO53BXT3+93+96SsprePqfu8grqgRAbhphJAAAIABJREFUoQArCxXWlmZYWZhhbWXW8G8bSzOsLOs/r/9QNfz76sf/eOyXPRfwdLEhohVUsR8ZHcCSrw7z+9lL9JSuDzfVaJKemZnJxIkT+eabbzA3N2f27NmMGTPGELEJIYRogVVbz1BSXsOCv4bJm2Qh9OzKCu+hhk7S88ro2sFdr/eIDvNh9++ZnEzKJ6x94x0iNuxLwcbKjIHd/fQal9C/HiEeTB0ewtebThPS1oVRfW99+8K2uDRUSgV39TTMz0d7Pyec7CyJP2W8JP2/vyZSWFLFw2M6o9Vqqaiuo6paTWV1HVXVdVRU11FZXUdhaRVZufX/rqqpo7Ja3eR7PDK2S6voohAV5oOT3Qk27EuRJL0RjSbpCoUCjeaPgiFlZWVXfS6EEMJ0ZFwq5dc9SQzt1Yb2BlhKKMSdzsPFBjOV0uDF4yqr68gvrtJL0bgr9QzxwMJMSezxrEaT9KLSavYlZBITHSjbbG4TE4d05ExqIZ+tPU6QryMhAS0fiKpTa9hxOJ1enTxxtrfSYZQ3plQq6BHiQVziRdRqjcH7hx87l8u2+DQmDOnAvXc1bx+2RqOlurY+mf/zR1XDv9VotVpiogP08wXomLmZkrsj2/L99rPkFFTgKUVtb6jRn9S7776bF154gdLSUlatWsX06dOJiYkxRGxCCCGa6fNfErG0UHH/SP21zhFC/EGlVODjbktmrmGT9OzLReP00CP9SlaWZvQI8WD/8exG95FujUulTq0lJipArzEJw1EqFTw/tQduTta8tTKeotLqFl/r8KkcikqrGdbbsH28e3XypKyyltOphQa9b3WtmmVrjuHtasukYcHNPl+pVGBtaYaLgxW+7na093MiLMiN3p28GNDdj+GRAdwzMIh772qPpblKD1+BfgyPbIsC2LAv2dihmLRGk/QZM2YwYMAAwsLCiI2NZdKkScycOdMQsQkhhGiGQ6dyOHQqh8nDgg02SyGEuNyGrdSg98zK01/7tT+LDvchv7iKc+k3TnLUGi0b96cQ3t4Nf097vcckDMfOxoJXpvemtLyGJV8dQq1u2YrarXFpONtb0jPEQ8cR3lz3/2fvvsOjKtP3gd9T0idlUiYJCUlIQgokdBUQEClGBAQRQRRZu2Dv69pQVtafrK5lXSxgW78q0qSsioDYKCoikJCEhPSezGQmbVJn5vz+CIkghJMyM2eSuT/XxbVMO3OzF4Z55n3f54nTQCGX4XCGbUbKdeXzPVko1xlxz8KR/aqItjWN2hOTR4Vj5/68zp9jdK5u7fmYP38+XnvtNbzxxhtYtGgRzzgSETmYNpMF67enISzIC3McdE4q0UAVrvFGeXVjj+eJ90XHyn1ogG1X0gHgomEhUMhlF+zyfiSzElpDE66ywrllcjzRYb6457qRSM3R4eOvM3v8ekNdMw5nVmLauMF233Lu5eGC4dEBdp2XXlBeh63f5WDauMEYGWfbvhH90S1zh0GpkOPtLakQBHZ6Px/R/0q++uorpKSkYMKECWf9IiIix/HlgTyUao24fV4yXJT2/QBE5OzCNSpYLAIqqo12e88yrREBvu52Ofut8nDByKFBOJha3uUH6i8P5sPfxx2XDHf8DtPUO9PGRWDWhChs+S4HB1PLevTa746UwGIRMP0i285G78q4xGAUVtSjytBo8/cyWwS8ufEYvDxccNvVSTZ/v/4owNcDN81KxNFsLfYf69nfJWch+knun//8Jx5//HH897//PesXERE5BkN9Mz7bnYWxCRqMS2S3VCJ76xjDZs8t72XaBrtsde8wcUQoyquNKCivO+excp0Rv5+swpXjI6G08yop2dcd85MQF+GH1zYc7fbfd0EQsPdwIRKj/CU7CtHxb6M9VtO/PpiPrCID7piXBB8vV5u/X3911aVDEBPui/U70tDY3CZ1HIcj+pM0LCwM06dPx9ChQ8/61R1vvvkmZs+ejdmzZ2PNmjXnPJ6ZmYkFCxYgJSUFTz31FEwmU8//BERETu7/vj6JllYzv7EnkkhHsWzPDu+lWiNCbTgj/c8uGR4KuQw4mHrulvevDxVALpfhivH2bQhG9ueiVOCJZRfDRSnHPz48jKYW8c/uWUUGFFc2YMbF0qyiA+1fpIUGeOFwhm2LdK2hCf/9KgOj44Jw2RiOIbwQhVyGu68dCUN9C/5v10mp4zgc0SJ9/vz5eOmll3Do0CEcPny485eYgwcPYv/+/fjiiy+wbds2pKenY8+ePWc957HHHsOzzz6Lb775BoIgYOPGjb3/kxAROaGckhrs+bUQcydHs1kTkUQ83V0Q4OtutyK9vrEV9Y2tdl1J9/N2w7DoABxKO3trakubGXt/LcSEpFAE+HrYLQ9JJ0jtgceXjkNpVT3+vfGY6Jnivb8Wwc1VgUkjB9kp4blkMhnGDQtG6iktmlttsygoCALe+SIVZgtw98KR7OHVDXERasyaEIUv9+chp6RG6jgORbRI/+WXX7B161b84x//wKpVqzp/iQkKCsITTzwBV1dXuLi4ICYmBmVlf/xgLy0tRXNzM0aNGgUAWLBgAXbt2tWHPwoRkXMRBAHvfpEGHy/XXo13ISLrsWeH9zKt/Tq7n2lCcigKK+rPGje3/1gp6hvbcNWlUXbNQtIaGReEpbMS8dOxUuz4Ka/L5zW3mPDj0VJMGjkInu4udkx4rnGJwWg1WZCWo7PJ9Q+lleOX9ArcmJKAEDs0dBwobrpqGHxUbli7+TjMImMenYlokZ6RkYEff/wRO3fuPOuXmKFDh3YW4AUFBfj6669x2WWXdT5eVVWFoKA/uh0GBQWhstJ+XReJiPq7n46VIrNAj5tmJULlIe2HHyJnF67xRklVg106FZdq2xvU2XO7OwBMSGpfCT2zadhXB/MRrlEhOSbQrllIegunDcX4pBB8sDMd6XnV533OwbQyNLWY7D4b/XySYwLg7qrAYRucS29oasM7X6QiepAv5k3hhJWeUHm44La5w3GquAbf/FwgdRyHIVqkBwYG9ums+KlTp3Drrbfi8ccfR1RUVOf9FovlrG0ggiBwWwgRUTc1t5rwwc50RIf5YoYDfPghcnbhGhUam00w1LfY/L3KdA2Qy2D31bogtQfiIvw6R7GdKjYgu6gGV00cws9wTkgmk+HB68cg2N8TL/33MPR1zec8Z8+vRQgN9MKwIf4SJDybi1KBUXFBOJxRafUv0/77ZQZq6ltw76KRdh8xNxBcNiYcI2ID8d8vM2A4z98jZyT6tyg4OBjz5s3Ds88+ixdeeKHzV3ccOXIEN998Mx555BFcc801Zz0WEhICrVbbeVun00Gj0fQwPhGRc9qyLwe62mbcOT8ZCjk/HBNJzZ4d3su0RgT7e0kybnFC8iCcKq5BlaERXx8sgJurAtPGDbZ7DnIMXh4uePLmi9HYYsJL/z0Mk9nS+ViZrgEncqsx8+IIh/kSZ1xiCHQ1TSissN5/p+l51fj6UAHmTo7B0MFqq13XmchkMqy4dgRa2ix4f2e61HEcguhP94iICMyfPx8ajQZ+fn6dv8SUl5fjnnvuwcsvv4zZs2ef83hYWBjc3Nxw5MgRAMD27dsxZcqUXvwRiIicS5W+EVu/O4XJo8IwPDpA6jhEhPbt7oB9OryX6RoQGiTNmdeJyaEA2puB/fB7CaaOCYcXj9s4tchQH9x33Shk5Ovx4f8yOu//9nAx5DI41Jc44xLbFwQPZ1RY5XptJjP+s/kYNGoP3HhlglWu6azCNd649vJYfP97CY6f0oq/YIBTij3h3nvvhdFoRHp6OkwmE0aMGAGVSrxRyXvvvYeWlhb8v//3/zrvu/7667Fv3z7cf//9SE5Oxssvv4ynn34aDQ0NGD58OJYtW9a3Pw0RkRP44H/pgEyGm+cMkzoKEZ0W4OsOd1eFzYt0QRBQpm3AsCHSfEE3KEiFqFAfbNybDbNFwFUTh0iSgxzLZWPCkVVkwPYfcxEfocbEkYPw7eEijEkIdqiu/wG+HogO88VvmZW4bnpcn6+3eV8OiisbsPL28fBwEy2rSMR1M+Lww9ESvLXlOP796OVwUSqkjiQZ0b9NqampuPvuuxEYGAiz2YzKykq8/fbbGDNmzAVf9/TTT+Ppp58+5/4lS5Z0/j4hIQGbN2/uRWwiIueUlqvD/uNluOGKeGjUnlLHIaLTZDJZe4f3SttudzfUt6CpxYxBdm4ad6aJyaH4tLwOiVH+iA7zlSwHOZZb5gxHTnEN3th4FLraJlTXNuOO+clSxzrHRcOCsWlvNuqMrfDxcu31dYor67FxbzamjArDuMRgKyZ0Xm4uCixfMALPrfsZW7/LcerJNaLb3V966SW8/PLL2LZtG3bu3InXX3/9rNVxIiKyD7NFwLptaQj088A1l8dKHYeI/iRc440SrW1X0jvGrw2y8/i1M00aFQaFXIar2cWazuCilOOvy8bB3U2J93emw8fLFRcPC5E61jkuSgyGRQB+z6rq9TUsFgH/2Xwcbq4K3D4/yYrpaGxCMC4dOQgb92ajXGeUOo5kRIt0o9GI8ePHd96eMGECmpqabBqKiBybyWyBhbMs7W73L4XIL6vDrXOHw92V2+qIHE24RgWtoQnNLb2fiiOmY/yavWekn2lwsDf+7/krMWlkmGQZyDEF+Hrg8ZvGQS6XYfpFEZI0NxQzdLAavipX/JbR+1Fse34tQnpeNW6dOxxqb3crpiMAuGNeEhQKGd7+ItUuYy0dkeh/OTKZDKWlpZ23S0pKoFA47/kAImdntgi488W9+OL7HKmjOJWGxlZ8/FUmhkcHYNLIQVLHIaLz6GgeV2rD1fQybQOUCjkC/aQ956vy7P02YRrYkmMCse5vM/CXqxKljnJecrkMYxOC8XtWJcxndKPvLkNdMz74XzqSYwIx8+IIGySkAF8P3HhlIn4/WYWDqeVSx5GE6FLMPffcg8WLF2PChAkAgAMHDmDlypU2D0ZEjimvtAZaQxN+z6rCtdOGSh1HEhaLgMKKOqTl6nAitxqZ+Xq4uMgRE+aL2HA/xIT7ISbc16rfrn+2JwsNTa24Y16Sw4yyIaKzhZ0ew1aqbUBMuPgknN4o0zUgNNCLoxfJoWn8HbtnykXDgrHvt2KcLDT0eErKu9vS0Npmxj3XjeS/xzY059Ih2He4GO9uS8Po+CB4ujvXFAnRIn3GjBmIjo7Gzz//DIvFguXLlyMmJsYe2YjIAaXl6AAA2UUGmC2CU3xQNFsE5JfV4kRuNU7k6pCRX436xjYAgEbtgdHxQTCbBeSW1uDnE3+MdfH3cT9dtPt2/q+/j3uP/1EvrqzHl/vzccUlkTb74E9EfTco0AsymW3HsJVqjQiTaPwa0UAxOk4DhVyG3zIre1Sk/5pRgf3Hy7D0ygRJj5w4A4VCjrsXjsBj//4Jn3xzEnfMc7wmhLbUZZG+e/fus24HBgYCAHJzc5Gbm4srrrjCtsmIyCGlni7Sm1vNKKqow5BBA6+zr9lsQW5pLU7k6pCWW43M/GoYm9vPmIYGeGF8UiiSYgKQFB14zmpBY3Mb8kprkVNSi9zSGuSW1OBwZgU6jlT5ebshJswXMeF+iA33RUyYH4LUHl0W7oLQ3izO3VWBm2Y55tZBImrn6qJAsL+nzYp0s0VARbURF7GTNFGfeHm4YNiQAPyWWYm/zO7eONPG5ja8tSUVESHeWHC5c+4ktLf4SH+kjI/C/37Kw/RxEU41TaLLIv3jjz/u8kUymYxFOpETMpktyMivxqi4IBzL1iKr0DAginST2YKc4po/tq8XVKOpxQygvTnTpFFhSIoJRHJMgOi8V093FyTFBCIpJrDzvqYWE/LLapFbUouckhrkldbiaPapzuZ73p6uZ622x4b7IdjfEzKZDIczK3E0W4vbrk6Cr8rNdv8nEJFVhGu8UVJlmzFsupomtJksknZ2JxooxiUG44P/paPK0Nitkaaf7DqJ6tom/PWmyQ7ZEG+g+stViTiUVoa1m49jzX2TIXeCHZxAL4t0InJOOSU1aGox44pLIpFbUousQgOunBAldaweazOZkV1UgxMdRXmhHi2t7UX54GBvTB07GMnRgRgeEwB/n76fK/dwU2LYkAAMG/LHlrqWNjMKyk6vuJfUILekFl98nwPz6cLdy8MFMWG+KNM2IFyjwpxJQ/qcg4hsL1yjQuopLSwWweofJks7x69xuztRX100rL1I/y2zEldNvPC/sdlFBuzcn4dZE6KQEOVvp4QEtDepvHVuEl797Hd880shZvXDz5290WWRvnr1ajz11FNYvnz5eR9/++23bRaKiBxTx3n0EbGBiI9UI6vIIHGi7quubcLunwuRlluNrEI9Wk3tHV2jQn0w8+KI9tXv6AC7rVa7uSgQH+mP+Mg//rFvM5lRUF7XueKeW1qLplYzHrh+NJQKfmtP1B+Ea1RoNVmgrWlCsJWbZ5WfLtJ5Fpao78I1KoQEeOJwxoWLdJPZgjc3HYPa2x3Lrure1niyrsvHhmPvr0X46MsMTEgKhZ/3wN9Z2GWR3tHNPSUlxW5hiMixpeboEBniDV+VGxIi1fgtsxINTW1QeTh+x82Pv87Evt+KMWSQL66cGIWk6EAMjw6Aj5fjjBFyUSowdLAaQwerpY5CRL3UMYatpKre6kV6qc4IDzcF1E7wAZXI1mQyGcYlBmP3L0VoaTPDzeX8I6a3/ZCL/LI6PHnzRfDqB593BiKZTIYV147A/a98hw/+l46HloyROpLNdVmkT5s2DQAwevRoREVFdd4vCALee+89mwcjIsfSZrIgs0DfORM0LqK9kDxVZMDoeI2U0bolI0+P8UmhePLmi6WOQkQDWPjpMWwlVQ0Ym2DdBm+l2gaEBqo49onISi5KDMH/9ucjLUeHcedpyFiuM+Kzb05ifFIIJiQPkiAhdRgc7I1rpsZi07enMOOiCCTHBoq/qB8T3T955513orKyEgBQVlaGm266CV9//bXNgxGRY8kuMqCl1YwRp38oxkWoIZOhX2x5N9Q1o7zaiESeIyMiG/PxcoW3p4tNOryXa43c6k5kRUkxAXB3VeDXjIpzHhMEAWs3H4dCIcfyBSMkSEd/tmhGHDT+nnhr63G0nT62OFCJFukPP/wwbr/9dnzyySdYsGABxo8fj40bN9ojGxE5kBO5OshkwPDo9iLdy8MF4RpvZBU6fpGeUaAHAAwbwiKdiGxLJpPZpMN7m8mCSr2RTeOIrMjVRYGRQ4PwW2YlhI5Zqad9d6QEx05p8ZfZw0Qnu5B9uLsqsWLBCBRXNmDbDzlSx7GpLre7d7jyyithNpvx17/+Fe+99x4uueQSe+QiIgeTmqNDVKjPWWe4EyLV+PlEBQRBcOjtlxn51XB1USA6zE/qKETkBMI1KhzOrLTqNSv1RlgEYFAgV9KJrOmiYcH4Jb0CRRX1iAz1AQDUNrRg/fYTSIhUO0038f5iXGIwJiSHYsPuLEweFYaQgIH5xWWXRfqfu7r7+vriueeeQ2RkJAB2dydyJm0mM04W6HHlxKiz7o+PVGPPr0UorzY69AfHjHw94iL8ONeUiOwiXKPCnl+L0NDYCpWndZpTlmmNAIAwrqQTWVXHWfTDmZWdRfp7O06gqaUN9143ymnmcvcnd85Pxt3ZVXjnizQ8e9slDr1Q1FtdFul/7urOLu9EzutkoQGtJgtGxJzdpKNjfFhWocFhi/TmFhPySmuxcNpQqaMQkZPo7PCubUBCpHWO2fwxI90xf9YS9VcBvh6IDvPF4YwKLJw2FEezqvDdkRIsnhHXWbSTYwn088ANKQl4b0c6fj5RPiCb+nVZpF9zzTX2zEFEDiwtRwe5DBj+pyJ9cLA3PNwUyCo04PKxgyVKd2FZRQZYLALPoxOR3XR2eK+0XpFepjPC29MV3lZamSeiP1yUGIxN32ajurYJa7ccx6BALyyaESd1LLqAuZOi8e3hYrz7RRpGxWng4SZ6irtf4d5PIhKVmqNDdJjvOfPQFXIZhg5WI6tQL1EycRn5eshksNoHZSIiMcH+nlAqZFZtHlembeBWdyIbGTcsGBYBeG7dz6iobsS9142Caxdz08kxKBRy3LNwJHS1zfj0m5NSx7E6FulEdEEtbWZkFRqQHBt03sfjI9XIL6tDc6vJzsm6JzO/GpEhPvD60xcMRES2olDIERqosuoYtlJtA7e6E9nI0MFq+Hi5oqC8DjMvHvgzuAeKhCh/pIyPxI6f8pBfVit1HKtikU5EF3QyXw+T2dI5H/3P4iPUMFsE5JY43g9Hs9mCk4V6bnUnIrsL16g6z5H3VXOLCdW1zRy/RmQjCrkME5JDofZ2wy1zh0sdh3rgL7OHQeXhgrWbj8NiEcRf0E+IFularRZ33nknUlJSoNPpcNttt6Gqqsoe2YjIAaTm6iCXy7osdOMi1QCA7CLHm5deUF6HphYzEocESB2FiJxMuEaFcp0RJrOlz9cqr+7o7M6VdCJbueuaZKz963T2fehnvD1dcevc4ThZaMCeX4ukjmM1okX6888/jxkzZsDNzQ2+vr5ISEjA008/bY9sROQA0nJ0GBruB0/3828XV3u7I9jfE1mFjlekZ+S3n5XnSjoR2Vu4RgWzRUDF6QK7LzrGrznqFA2igcBFqTin9w71D9PGDcbw6AB89GU6ahtapI5jFaJFemlpKRYtWgS5XA4XFxc89thjKC8vt0c2IpJYc4sJp4oNSIq58Ep0fIRjNo/LLNAj0M8DGrWn1FGIyMl0jmGzwrn0jm3zoYHc7k5E9GcymQwrrh2BxmYTPvhfutRxrEK0SJfJZLBY/tiq1dDQcNZtIhq4Mgr0MJkFjOiiaVyH+Eg1dLXN0NU02SmZOEEQkJ5XjWFRXEUnIvvr2JpurSI9wNd9wI0YIiKylsgQH1wzNRbfHi5GYXmd1HH6TPSn/RVXXIFHH30U9fX12LBhAzZt2oRZs2bZIxsRSSwtRweFXIZEke3i8afPpWcVGRDo52GPaKKqDE3Q1zVzqzsRScLLwwX+Pm5WGcNWrjNyqzsRkYjFM+OgVMjho+r/fQVEV9KXL1+OKVOmIDk5GQcPHsTixYtxzz332CMbEUksLUeHuAi16OpNdJgvlAo5sh3oXHpmfjUAYFg0m8YRkTTCNd5WW0lnZ3ciogtzd1XixisToPZ2lzpKn4mupD/++ONYs2YN5s+fb488ROQgGpvbcKqkBgunDRV9rotSgZhwX2Q5UIf3jHw9PN2ViAjxkToKETmpMI0KPx4thSAIkMlkvbpGQ2Mr6oytXEknInIioivpmZmZEISBM3OOiLonI18Pi0XAiJjzz0f/s/hINU4V11hl3JA1ZORXIyHSHwp57z4YExH1VbhGBWNTG2r60G24TNcxfo0r6UREzkJ0JV2j0WD27NkYOXIkvLz++AeCY9iIBrbUHB2UCjkSunmmOz5CjR0/5qGgvA6x4X42TndhDY2tKKqsx+RRYZLmICLndmaH995uv+zo7D6IM9KJiJyGaJE+evRojB492h5ZiMiBpOVoER+phpuLolvPj49sL+azCg2SF+knCw0QBGDYEJ5HJyLphGv+6PCe3M1dSX9WpjVCLgNCAriSTkTkLESL9Hvvvfec+xobG20ShogcQ0NTG/JKa7F4Zny3X6NRe8DP2w1ZhXrMvnSIDdOJy8ivhkIuw9AIab8sICLnFujrATdXRZ86vJdpG6Dx94SLUvSEIhERDRCiRfrevXvxxhtvoLGxEYIgwGKxoKamBkePHrVHPiKSQEZeNSwCerTyI5PJEB+hRrYDNI/LyNcjJtwX7q6cKUxE0pHLZQgLUvWpw3uproFb3YmInIzo17Jr1qzB8uXLERoaipUrV2Ly5Mm4/vrr7ZGNiCSSmqODi1LeOf+8u+Ij1SjVGlFnbLVRMnFtJjNOFRm41Z2IHEK4pvdFuiAIKNMaMSiQW92JiJyJaJHu4eGBq666CqNGjYKbmxuee+45fP/993aIRkRSScvRITHKH67dPI/eIeH0uXQpV9NzS2rRarIgMap7De+IiGwpXOMNraERza2mHr+2pr4FTS0mhHElnYjIqYgW6W5ubmhtbUVERAQyMzMhl8t7PeuTiBxffWMr8strkRzb8yZHsYP9IJe1N4+TSka+HgCQ2M2u9EREthSuUUEQgPLTo9R6orOzO2ekExE5FdEifdq0abjzzjsxZcoUfPjhh7jvvvugVvdsCywR9R8ncnUQengevYOHmxIRIT7IKtTbIFn3ZORXY1CgV6/HHRERWVNnh/fKnm9575iRPogz0omInIpoV6Xly5fj6quvRnBwMNauXYvDhw9jzpw59shGRBJIzdHBzVWBuIjefRkXH6nG/mOlsFgEyOX23XUjCAIy8vW4eHiwXd+XiKgrg4JUkMmAEm0vinRtA5QKOYLUnjZIRkREjkp0JT09PR0GgwHp6ekQBAHjxo1DRUWFPbIRkQQ6zqP3dtxPQqQaxmZT5zZNeyqpakB9YyubxhGRw3BzUUCj9uzVGLYynRGhgZ5Q2PkLTyIikpboSvp9993X+fu2tjbodDoMHz4cmzdvtmkwIrK/2oYWFFbU47Ix4b2+Rvzp5nFZhQYMDva2VrRuySxo32Y/jOfRiciBhPWyw3uptoHn0YmInJBokb5v376zbv/yyy/YuXOnzQIRkXTScnUA0KumcR3CglTwclciq8iAGRdHWCtat2TkV8PHy5WdkInIoYRrVEjPq+7RMSCLRUC5zohxCTy+Q0TkbHq8n/WSSy5Benq6LbIQkcRSc3TwcFMgNtyv19eQy2UYGqGWpHlcRr4eiVH+nEBBRA4lXOONllYzdLVN3X6NrqYJbSYLm8YRETkh0ZX0MwtyQRBw4sQJNDc32zQUEUnjRK4OiUMCoFT07jx6h/hINTbtzUZTiwkebqI/ZqzCUN+Mcp0RV46Pssv7ERF1V2eH96oGaLrZBK5z/Bp3BhEROZ0enUmXyWTw9/fHc88FuHAsAAAgAElEQVQ9Z8tMRCQBQ10ziisbMH1c37eoJ0T6wyIAOcU1fdo63xOZ+TyPTkSO6Y8ivR5j4jXdek1Z54x0rqQTETmbHp9JJ6KByRrn0Tt0jG/LKjLYrUjPyNfDVSlHTLivXd6PiKi7/FRu8PJw6VHzuDKdEe6uCvj7uNswGREROSLRIv3NN9+84OP33nuv1cIQkXRSc3TwdFciJqzvRa6PlysGBXrZ9Vx6ZkE1hkao4aJU2O09iYi6QyaTIVyjQmkPivRSbcPpGevssUFE5GxED54WFxdj06ZNqKurQ1NTE3bu3IlffvkFBoMBBoPBHhmJyA7ScnQYHh0ARR/Po3eIi1Qjq9AAQRCscr0LaW4xIbekllvdichhhWtUPZqVXqYzcqs7EZGTEl1JLy8vx9atWxEQEAAAWL58OZYvX45nnnnG5uGIyD6qa5tQpjNi1sQoq10zIUKN74+UQGtogsa/e42Seiu72ACzRcCwIQE2fR8iot4K13jj28PFMDa1wcvD5YLPbTNZUKlvxJRRYXZKR0REjkR0yUyr1XYW6ADg5eWFurq6br9BQ0MD5syZg5KSknMee/PNN3H55Zdj3rx5mDdvHj755JNuX5eIrCc15/R59BjrnR+Pj2xf1c4qtP2Om8x8PWQyICFSbfP3IiLqjY7mcR1d2y+kUm+ExSKwszsRkZMSXUmPj4/HE088gfnz50MQBGzatAljx47t1sWPHz+Op59+GgUFBed9/MSJE/jXv/6F0aNH9yg0EVlXWo4OKg8XDBlkvaZrUYN84KqUI6vIgMmjbbsalJGvR0SwN1SerjZ9HyKi3jqzw3tHc82ulOmMAMAZ6URETkp0Jf2FF16ASqXC6tWr8corryA6OhpPPvlkty6+ceNGrFy5EhrN+ceNnDhxAu+88w7mzp2LVatWoaWlpWfpicgqUnN0SIoJgFxuvQZFSoUcsYP9bN48zmwRkFmg51Z3InJoIQFeUMhl3erw3jF+LYwr6URETkl0JV2lUuHpp58GAFRWVqK4uBiurt1brVq9enWXjxmNRiQmJuKxxx5DZGQknnjiCaxduxYPPfRQN6MTkTVU6RtRqW/E1VOirX7t+Eh//G9/HtpMZpt1XS+qqENTi4lN44jIoSkVcoQGenWrSC/VGuHt6QJv7g4iInJKoivpn376KR555BHo9XosWLAATz31FF555ZU+v7GXlxfWrVuHmJgYKJVK3Hrrrfjhhx/6fF0i6pnO+ehWPI/eIT5CjTaTBfll3e9j0VMZedUAwJV0InJ43e3wXnZ6/BoRETkn0SJ98+bN+Nvf/oZdu3Zh2rRp+PLLL3HgwIE+v3FZWRk2b97ceVsQBCiVogv7RGRlqTk6eHu6IjLEx+rXjj/dyO2kDbe8Z+TrEeDrjiC1h83eg4jIGsI13ijXGWE2Wy74vDJtA7e6ExE5MdEiXSaTITAwEIcOHcKECROgVCphsVz4H5fucHd3xz//+U8UFxdDEAR88sknmDlzZp+vS0TdJwgC0nJ1SI617nn0DoF+Hgjwdbdph/eM0+fRZTLr5ycisqZwjQoms4BKfWOXz2luNUFX28wZ6URETky0SHd1dcW6devw66+/4tJLL8Wnn34KD4/er1jdcccdSEtLg7+/P1atWoUVK1bgyiuvhCAIuOWWW3p9XSLquUp9I7SGJoywwVb3DvGRamQX2aZIrzI0QlfTxPPoRNQv/NHhvetz6eWdnd25kk5E5KxE95evXr0a7733Hl566SX4+vriyJEjeOGFF3r0Jvv27ev8/bp16zp/n5KSgpSUlB5di4isp3M+eqwNi/QIfxxMLUdNfQv8vN2seu2M/PZt9IlRLNKJyPGFabwBtI9hu3h4yHmfU6ZtL9K53Z2IyHl1WaTr9Xr4+/sjOjr6rC7tZzaNq66uRkAAmzUR9VdpOTr4ebthcLC3zd6j41x6dpGhyw+lvZWRXw0PNyWiQq1/np6IyNpUHi7w83a74Ep6ma79sVBudyciclpdbnd/8skn8cEHH6C2tvacxxoaGrB+/Xo88cQTNg1HRLYjCAJSc3RIjgm06XnumHBfyOUymzSPy8zXIyFSDYVC9OQOEZFDaO/w3nWRXqptgL+POzzc2EyXiMhZdfkvwNq1a/H+++9jzpw5GDJkCCIjI2GxWFBUVIT8/HwsW7YMa9eutWdWogFJEARJmp6V6YzQ1zXbdKs7ALi7KjFkkI/Vm8c1NLWhsKIOE0ckWPW6RES2FK7xxv5jpV3+7C/TGjEoiKvoRETOrMsiXS6X4/bbb8fSpUvx888/Iy8vDzKZDDNnzsTEiRPh6upqz5xEA9LhjAq8uek4nr3tEsSE+9n1vTvOo4+wcZEOtM9L/+5IMcwWAQordZE/WaCHIIBN44ioXwnXqNDQ1IY6Yyt8Vef26SjTNWB8UqgEyYiIyFGI7qVyd3fH1KlTMXXqVDvEIXIeFouAj77MgL6uGa98egSvPjQVbi4Ku73/iRwd/H3c7DLmJz7SH18dLEBJZT0irXR+PLNAD7lchvgItVWuR0RkD2d2eP9zkd7Q1IbahlYMCmTTOCIiZ8aDnEQSOXSiHIUV9UgZH4niygZ89GWG3d5bEASk5uqQHBNkl632Caebx5204pb3jPxqRIf5wp3nNomoHwk/o8P7n5Vp28+qc7s7EZFzY5FOJAGLRcCG3VkIC/LCimtHYu7kaOz8KQ9Hs6rs8v4lVQ2oqW+x+Xn0DqGBXvD2dEGWlZrHtZksyC40cKs7EfU7QX4ecFXKz9s8rqNI5/g1IiLnxiKdSAK/pFegoLwOi2bEQyGX4S+zh2FwsDde23AU9Y2tNn9/e55HBwCZTIa4CDWyiqyzkp5XWoNWkwXDhnAEJBH1L3K5DGFddHgv1RohlwEhAZ4SJCMiIkchuk9Ur9djx44dMBqNEAQBFosFhYWFZ81LJ6LuEwQBG/ZkITTQC5eNDgMAuLko8MgNY/DoGz/iP5uO46/Lxtl0G3pajg6Bfh52/SAYH+mP37NOwtjUBi8Plz5dKyO/fUV+WBRX0omo/wnXeONU8blfWpbpGhCk9oSL0n79SYiIyPGIrqQ/+OCDOHjwILZs2YKKigps27YNcjkX4Il663BGJfJKa7F4RtxZ871jwv1wQ0oCDqSW4bsjxTZ7f4tFQFquDiNibTsf/c/iI9UQBJz3g2lPZeRXIzTAC2ofdyskIyKyr3CNCpX6RrS2mc+6v0zbwK3uREQkXqSXlZXh3XffxZQpU7B06VJ89tlnyMvLs0c2ogFHEAR8tvskQgI8MXVM+DmPL7h8KIZHB+DtrWmo1DfaJENRZT3qjK1IjrHPVvcOcae7sPd1y7sgCMgs0COR59GJqJ8K16ggCECZzth5nyAIKNNxRjoREXWjSA8MbP8gHxUVhezsbAQHB8NkMtk8GNFA9FtmJXJKarFo+tmr6B0UchkeWjIGAPDqZ7/DbBGsniE1RwvAfufRO6g8XDA4WIWsPnZ4L9MZUdvQyqZxRNRvna/De01DCxqbTRy/RkRE4kV6QEAA1q9fj6SkJGzZsgX79u1Dc3OzPbIRDSgdZ9E1/p64fNzgLp8X7O+Ju65JRnpeNb74PsfqOdJydAj294TG3/6NieIj/JFVaIAg9P7Lh4y8agBg0zgi6rc6VsvPbB5Xpm1fVed2dyIiEi3SV61aBVdXV4wbNw5JSUl444038Oijj9ojG9GA8ntWFbKLarBo+lAoz7OKfqZp4wZj4ohQfLIrE7klNVbLYLEIOJFbbfdV9A5xkWrUGVtRUd37rfyZBXp4e7oiXMMPskTUP7m7KqFRe6D0rCKdM9KJiKhdt1bSFy1ahKysLDzyyCP47LPPMHPmTHtkIxow2s+iZyFI7YFp4yJEny+TyXDPwlHw8XLFK5/+jpY/NRfqrYLyOjQ0tSHJzufROyREnj6X3od56Rn51UiM8rdr0zsiImsL13iftd29VNsApUKGIDXHrxEROTvRIv3YsWOYMWMG7rrrLlRVVWHq1Kn4/fff7ZGNaMA4lq1FVqEB100bChdl96Yj+Hi54oHrx6C4sh7//TLDKjnsPR/9zyKCveHuquh187ia+haUao08j05E/V746VnpHcd/ynRGhAZ6QSHnF5BERM5OtFpYs2YNPvzwQ/j5+SEkJARr1qzB6tWr7ZGNaEDoWEUP9HXHjIvFV9HPNCZegzmThmDHT3k4mlXV5yxpOTqEBnoh0M+jz9fqDYVCjqGD1b1uHpdZwPPoRDQwhGtUaG41o7q2vc9PmbaBTeOIiAhAN4r05uZmxMbGdt6+7LLLYDZbZ+stkTNIzdEhs0CPhdPj4KJU9Pj1N88ZjsHBKry24SjqG1t7ncNsEZCep5NsFb1DfKQaeaW1vdrCn5Gvh4tSjtjBvjZIRkRkP2Gn+2qUVNXDYhFQrjNiEJvGERERulGkK5VK1NbWdp7/5Ix0op75bHcW/H3cMbOHq+gd3FwUePiGsahtaMHazcd73Rk9r7QGxmaT3eej/1l8pBpmi4C8ktoevzYzX4+hg/169WUHEZEj+WMMWwN0tU1oNVkQxqZxRESEbhTpy5cvx9KlS1FRUYGHH34YS5YswYoVK+yRjajfS8vRIT2vGgunDYWrS+8Ly9hwP9x4ZQL2Hy/D97+X9DoLACRLvZIecbp5XFHPmsc1t5qQU1LDre5ENCCovd3g6a5ESVXDH53dud2diIgAKMWeMHnyZMTExODAgQOwWCy45557EBMTY49sRP3ehj1ZUHu74YrxkX2+1oLLh+K3zEq8vTUVw4cE9HjOeWqODuEaFfx93PucpS/UPu7QqD1wsofn0k8V18BsEdg0jogGBJlMdrp5XD1Kte2r6hy/RkREQDdW0hcuXIjIyEjccMMNWLp0KQt0om5Kz6tGao4O104bCrc+rKJ3UMhleGjJGAgC8OqG32G2dH/bu8lsQUZ+teSr6B3iI/2R3cMO7xn57U3jEqJYpBPRwNA+hq0BZboGuLsqJP8SlYiIHINoke7h4YGKigp7ZCEaUDbszoKftxtSrLCK3iEkwAt3zk/GidxqbP8hp9uvyympQVOLWfKmcR3iI9XQGppQXdvU7ddk5OsREeINb09XGyYjIrKfcI0K1bXNyC2pxaBAVWf/HyIicm6i292bmpowffp0hISEwNPzj+21O3futGkwov4sM1+PY6e0uHXucLi7iv5n1iPTLxqMXzMq8PHXmRgVp0F0mHin847z6EnRjlOkA0B2kQETksXHwZktArIK9Jg8OtzW0YiI7Cb8dIf3zPxqTBwxSOI0RETkKESrh6eeesoeOYgGlM92n4SvyhWzJkRZ/doymQz3LByJ+17W45VPj+DVBy8TbUqXlqNDRIg3/LzdrJ6nN2LCfKFUyJFVaMCEZPEPpkUVdTA2m5DIre5ENIB0dHi3COD4NSIi6iS63f3iiy9GXFwcBg8ejPDwcISGhqKtrc0e2Yj6pZOFehzN1mLB1Fi4u1l3Fb2Dr8oND1w/GkUV9fjoq4wLPrfNZEFGgR4jJB69diYXpQLRYT7dbh6Xkd/eCZ5N44hoIAkJ8IJc3r7FnePXiIiog2gF8frrr+Pdd98FACgUCrS1tSE2Npbb3Ym6sGF3Fny8XDFr4hCbvs/YhGDMvnQIdvyYh4sSgzEqTnPe550qNqCl1ewwTeM6xEf6Y/cvhTCbLVAoLvx9YWa+Hv4+7gjuYUd7IiJH5qKUIzTAE6VaI1fSiYiok+hK+vbt2/Hdd98hJSUFu3fvxosvvojY2Fh7ZCPqd7KLDDhysgrzL4uBh41W0c9085xhCNeo8NqGo6hvbD3vc9JydJDJgCQHWkkH2uelt7SaUVhRL/rcjIJqJA7xZ1MlIhpwOra8c0Y6ERF1EC3S/f39odFoEB0djZMnT2L+/PnIzs62RzaifmfDnix4e7pg9qW2XUXv4O6qxCM3jEVNfQve2pIKQTh3LFtqjg5RoT7w8XKsrugdzeOyCvUXfJ7W0AStoYlb3YloQEqKCcDgYJXD/YwmIiLpiBbpSqUSRUVFiI6Oxm+//QaTyYSWlhZ7ZCPqV3KKa3A4oxLzLouBp7uL3d43drAflqTE46djpfjh95KzHmszmXGyQO9wW90BINjfE34qN9Fz6ZkF7fPRhw0JsEcsIiK7mn9ZLNY+Pl3qGERE5EBEi/S77roLzzzzDKZOnYo9e/Zg6tSpGD9+vD2yEfUrG/ZkwcvDBXMnRdv9vRdePhSJUf54e2sqqgyNnfefLDSg1WRxqKZxHWQyGeIj1cgSKdIz8vVwd1VgSKiPnZIREREREUlHtEi//PLL8dFHH8HT0xPbtm3D+vXr8fe//90e2Yj6jbzSWvySXoF5U+y7it5BoZDj4RvGwCIIeO2zo7BY2re9p+XoIJcBwx2wSAeAuAg1SrUNXZ6nB4CM/GokRPqLNpcjIiIiIhoIevSp18PDAwkJCWzeRPQnG/ZkwctdibmT7b+K3iEkwAt3zEtGWq4O237IBdB+Hj06zBcqD/t/cdAdHefSs4vOv5pubGpDYXkdz6MTERERkdPg0hRRH+WX1eJQWjnmTo6RvBiecXEExieF4OOvM5FVqEdWoQHJsUGSZrqQoYP9IJMB2V1sec8qNMAiAIks0omIiIjISbBIJ+qjz/dkw9NdiXlTpFtF7yCTyXDvdaPg7emC59b9DJPZguQYx2245unugsgQH5zsYiU9I78acrkMcRFqOycjIiIiIpJGt4r01NRUbNiwAa2trTh69KitMxH1G4XldTiQWoa5k6Kh8nSM8Tm+Kjfcv3g0GpraIJfLMDzacYt0oH3Le3ahofMc/Zky8vWIHuQjyTl/IiIiIiIpiBbpW7duxd/+9jesX78e9fX1uPvuu7Fx40Z7ZCNyeJ/vzYaHmwJXT4mROspZxiUGY9GMOEwbO9jhC9z4CDUamtpQpms4636T2YKsIgMSOXqNiIiIiJyIaJH+8ccf4/PPP4dKpUJAQAC2bt2Kjz76yB7ZiBxaUUUd9h8vxZxJ0fDxcoxV9DPdNCsRD1w/WuoYouJON4/78yi2vNJatLaZ2TSOiIiIiJyKaJEul8uhUqk6b4eGhkKhUNg0FFF/sHHvKbi5KDDPwVbR+5vBGm94uiuR9adz6Rn51QCAxCgW6URERETkPESLdD8/P2RmZnaOXduxYwd8fX1tHozIkZVU1eOnYyWYfekQ+KrcpI7Tr8nlMsQNVp+zkp6Rr0ewvycCfD0kSkZEREREZH9KsSc8+eSTeOCBB1BUVIRJkybBzc0Na9eutUc2Iof1+d5suLgoMP+yWKmjDAjxkWps2ncKzS0muLspIQgCMvP1GB3vuOPjiIiIiIhsQbRIj4mJwfbt21FQUACz2YwhQ4bAxcWxG1ER2VKZtgE//l6Cq6fEwM+bq+jWEB+phsUiIKekBkkxgSjXGVHT0IJhbBpHRERERE6myyL9gw8+OO/9Bw4cAADccssttklE5OA+35sNpVKBBZdzFd1aOuagZxUakBQTiIx8PQCwaRwREREROZ0ui/Ts7Gx75iDqF8p1Rnz/ewnmToqG2ttd6jgDhq/KDaEBXp3N4zLyq6HycEG4xlviZERERERE9tVlkf7iiy+edbu0tBQmkwmRkZE2D0XkqDZ9mw2lXMZVdBuIj1QjNUcLQRCQka9H4hB/yOUyqWMREREREdmVaHf3wsJCzJ49G/Pnz8eCBQswY8YM5Obm2iMbkUOpqDZi32/FSJkQBX8frqJbW3ykGvq6FuSW1qJU28Dz6ERERETklESL9FWrVuH222/H4cOHceTIEaxYsQLPP/+8PbIROZTN+05BJpPhWq6i20R8ZPu59O0/tH8JyPnoREREROSMRIv06upqXHPNNZ23r732WhgMhgu84g8NDQ2YM2cOSkpKznksMzMTCxYsQEpKCp566imYTKYexCayryp9I/b+WoSU8ZGc220jUaG+cFXK8dOxUigVcgwd7Cd1JCIiIiIiuxMt0s1mM2pqajpv6/X6bl34+PHjWLJkCQoKCs77+GOPPYZnn30W33zzDQRBwMaNG7uXmEgCm06voi+cNlTqKAOWi1KOmHA/mC0Chg72g6uLQupIRERERER2J1qkL126FIsXL8Zrr72G119/HUuWLMGSJUtEL7xx40asXLkSGo3mnMdKS0vR3NyMUaNGAQAWLFiAXbt29SI+ke0dy67Cnl8KMfPiCAT6cRXdljq2vHP0GhERERE5qy67u3dYvHgxIiIisH//flgsFqxcuRITJ04UvfDq1au7fKyqqgpBQUGdt4OCglBZWdnNyET2k11kwOoPfkW4RoVlVyVKHWfAS4j0B5DLpnFERERE5LREi3QAiI6Ohq+vLwRBAACkp6dj+PDhvX5Ti8UCmeyP0UqCIJx1m8gRFFfW47l1P8NX5Ybn75wAlaer1JEGvPFJIXhs6ViMSwyWOgoRERERkSREi/RXX30VH3zwAQIDAzvvk8lk+Pbbb3v9piEhIdBqtZ23dTrdebfFE0mlytCIZ985CIVChlV3TWCzODtRKOSYMjpc6hhERERERJIRLdJ37tyJffv2nVWk91VYWBjc3Nxw5MgRjB07Ftu3b8eUKVOsdn2ivqhtaMGz7xxCU4sJL94zCYMCVVJHIiIiIiIiJyHaOE6tVlutQL/jjjuQlpYGAHj55Zfx4osv4sorr0RjYyOWLVtmlfcg6ovG5jY8t+4QtIZGPHPbeAwZ5Ct1JCIiIiIiciJdrqSnp6cDAIYNG4YXXngBc+fOhVL5x9O7eyZ93759nb9ft25d5+8TEhKwefPmHgcmspXWNjNWf/Ar8srq8NQtF2N4NJuXERERERGRfXVZpN93331n3T6z2O7rmXQiR2M2W/DyJ0eQmqPDQ0vG4OJhIVJHIiIiIiIiJ9RlkX5mUU40kAmCgP9sPo5DaeW4Y14Spo0bLHUkIiIiIiJyUqJn0okGuo++zMCeX4uweGYcrp4SI3UcIiIiIiJyYizSyalt/e4UtnyXg1kTo3BjSoLUcYiIiIiIyMmxSCenteeXQnzwvwxMHhWGu64ZAZlMJnUkIiIiIiJyct0q0lNTU7Fhwwa0trbi6NGjts5EZHOH0srx5qZjGB0XhIeWjIFCzgKdiIiIiIikJ1qkb926FX/729+wfv161NfX4+6778bGjRvtkY3IJlJztPjn//2GoRFqPHnzxXBRckMJERERERE5BtHq5OOPP8bnn38OlUqFgIAAbN26FR999JE9shFZXU5xDV54/1eEBnph5e3j4e7W5YADIiIiIiIiuxMt0uVyOVQqVeft0NBQKBQKm4YisoWSqnqsXHcI3p4uWHXnBHh7ukodiYiIiIiI6CyiRbqfnx8yMzM7m2rt2LEDvr6+Ng9GZE26miY8++4hyGTA3++aiABfD6kjERERERERnUN0r++TTz6JBx54AEVFRZg0aRLc3Nywdu1ae2Qjsoo6YyueffcgjE1t+MeKSzEoSCX+IiIiIiIiIgmIFukxMTHYvn07CgoKYDabMWTIELi4uNgjG1GfNbWY8Pz6Q6iobsTzd05ATLif1JGIiIiIiIi6JFqkNzU1YdeuXaitrYUgCDhw4AAA4JZbbrF5OKK+aDOZ8Y8Pf0VOSS2e/MtFSI4JlDoSERERERHRBYkW6Y8//jhKS0sRFxfXeS6dyNGZLQJe+fR3HMvW4sHrR+OSpFCpIxEREREREYkSLdKzsrLw1VdfQankqCrqHwRBwNtbU3HgeBluu3o4pl8UIXUkIiIiIiKibhHt7h4SEmKPHERW83+7TmLXoQJcN30o5l8WK3UcIiIiIiKibhNdHo+Li8OyZcswefJkuLu7d97PM+nkiLb9kIuNe7ORMj4SN81KlDoOERERERFRj4gW6UajEZGRkSgqKrJHHqJe++5IMd7bcQITR4RixbUj2UOBiIiIiIj6HdEi/cUXXwQAlJaWwmQyITIy0uahiHqqotqI/2w+jqSYADx641go5CzQiYiIiIio/xEt0gsLC3H33XejqqoKFosFarUa77zzDmJiYuyRj0iUIAh4c9MxyGXAQ0vGwEWpkDoSERERERFRr4g2jlu1ahVuv/12HD58GEeOHMGKFSvw/PPP2yMbUbfs/qUIx0/pcMuc4dCoPaWOQ0RERERE1GuiRXp1dTWuueaaztvXXnstDAaDTUMRdVd1bRPe33kCSTEBSBkfJXUcIiIiIiKiPhEt0s1mM2pqajpv6/V6mwYi6i5BEPCfzcdhMgu4b9EoyHkOnYiIiIiI+jnRM+lLly7F4sWLMWvWLMhkMnz11VdYtmyZPbIRXdAPR0txOKMSt109HIMCVVLHISIiIiIi6jPRIn3x4sWIiIjA/v37YbFYsHLlSkycONEe2Yi6VFPfgne/SEN8hBpzJ7OJIRERERERDQyiRfobb7yBO++8ExMmTOi875lnnsHf//53mwYjupB3vkhFU4sJ9y8exXFrREREREQ0YIieSX/33XexdOnSs86inzhxwqahiC7kUFoZ9h8vw/VXxCEixEfqOERERERERFYjWqTHxsbi2muvxfXXX4+8vDwA7Q27iKTQ0NiKt7akInqQL669fKjUcYiIiIiIiKxKdLu7TCbDkiVLEBgYiJtvvhmvvPIKlErRlxHZxPodJ1BrbMXK28dDqRD9jomIiIiIiKhfEa22O1bNZ86cCX9/fzz44IMwmUw2D0b0Z0dOVuLbw8W4bvpQxIT7SR2HiIiIiIjI6kSXIq+77rrO348dOxYffvghhg0bZtNQRH/W2NyGNzcdx+BgFa6fGS91HCIiIiIiIpsQXUm/8cYbcfz4cfz0009oa2vDpEmT8N5779kjG1GnD7/MQHVtE9bcOxmuLgqp4xAREREREdmE6Er6tm3bcP/996O2thZGoxEPP/wwNm7caI9sRACAtFwdvj5YgKsnxyAhyl/qOERERERERDYjupL+4YcfYiuul9sAABsnSURBVNOmTdBoNACAO+64A7fddhsWLVpk83BEza0m/PvzYwgJ8MTSWQlSxyEiIiIiIrIp0ZV0i8XSWaADQHBwMORydtUm+/hk10mUVxtx36JRcHflVAEiIiIiIhrYRKttPz8/7N27t/P23r174evra9NQRACQVajHjh9zceWEKIyIDZI6DhERERERkc2JLk0+88wzuPvuu/H3v/8dMpkMSqUS//nPf+yRjZxYm8mM1z8/Bn8fd9wyh9MEiIiIiIjIOYgW6UOHDsWuXbtQUFAAs9mM6OhoKJXcdky29fmebBRX1mPl7ePh6e4idRwiIiIiIiK76Fa1rVAoEBMTY+ssRACAvNJabN53CtPGDca4xGCp4xAREREREdkNO8CRQzGZLXj986Pw9nLF7fOSpI5DRERERERkVyzSyaF88X0O8kprsXzBCHh7ukodh4iIiIiIyK66td29tLQUtbW1EASh877hw4fbLBQ5p+LKenz6TRYuHTEIl44YJHUcIiIiIiIiuxMt0l9//XW8//77CAgI6LxPJpPh22+/tWkwci5mi4A3Pj8KDzcF7lqQLHUcIiIiIiIiSYgW6du3b8fu3bsRHMwGXmQ7X+7Pw8lCAx6+YQzU3u5SxyEiIiIiIpKE6Jn00NBQFuhkUxXVRvz360yMSwzG1DHhUschIiIiIiKSjOhK+oQJE7BmzRpMnz4d7u5/rHDyTDpZgyAI+PfGY1DIZbhn4UjIZDKpIxEREREREUlGtEjfunUrAGDXrl2d9/FMOlnLNz8XIjVHh3sWjkSgn4fUcYiIiIiIiCQlWqTv27ev1xffuXMn3nrrLZhMJvzlL3/BjTfeeNbjb775JrZs2QIfHx8AwKJFi855Dg1cWkMT3t+ZjhGxgUgZHyl1HCIiIiIiIsmJFul6vR47duyA0WiEIAiwWCwoLCzEK6+8csHXVVZW4tVXX8XWrVvh6uqK66+/HpdccgliY2M7n3PixAn861//wujRo/v+J6F+RRAErN1yHBZBwH2LRnGbOxEREREREbrROO7BBx/EwYMHsWXLFlRUVGDbtm2Qy0VfhoMHD2L8+PHw8/ODp6cnUlJSztoyD7QX6e+88w7mzp2LVatWoaWlpfd/EupXvv+9BL9lVmLZrESEBHhJHYeIiIiIiMghiFbbZWVlePfddzFlyhQsXboUn332GfLy8kQvXFVVhaCgoM7bGo0GlZWVnbeNRiMSExPx2GOP4YsvvkBdXR3Wrl3byz8G9SeG+mas25aGhEg1Zk+KljoOERERERGRwxAt0gMDAwEAUVFRyM7ORnBwMEwmk+iFLRbLWVuYBUE467aXlxfWrVuHmJgYKJVK3Hrrrfjhhx9682egfuadrWlobjXj/sWjoZBzmzsREREREVEH0SI9ICAA69evR1JSErZs2YJ9+/ahublZ9MIhISHQarWdt7VaLTQaTeftsrIybN68ufO2IAhQKkWPyFM/98uJchxILcOSK+IxONhb6jhEREREREQORbRIX7VqFVxdXTFu3DgkJSXhjTfewKOPPip64YkTJ+LQoUPQ6/VoamrC7t27MWXKlM7H3d3d8c9//hPFxcUQBAGffPIJZs6c2bc/DTk0QRDwyTcnMSjQC9dMjRV/ARERERERkZMRXboOCAjAokWLkJWVhUceeQT33nsvPDzE51kHBwfjoYcewrJly9DW1oaFCxdixIgRuOOOO3D//fcjOTkZq1atwooVK9DW1oYxY8bglltuscofihzT4cxK5JfV4YHFo6FUiDcfJCIiIiIicjaiRfqxY8dw7733QqlUYsOGDZg3bx7eeustjBkzRvTic+fOxdy5c8+6b926dZ2/T0lJQUpKSi9iU38jCAI27smGRu2BqWPDpY5DRERERETkkESXM9esWYMPP/wQfn5+CAkJwZo1a7B69Wp7ZKMBJPWUDllFBlw7bShX0YmIiIiIiLogWi01NzcjNvaP88OXXXYZzGazTUPRwPP53mz4+7hhxkURUkchIiIiIiJyWKJFulKpRG1tbef4tO7MSCc6U0Z+NdJydbhmaixcXRRSxyEiIiIiInJYomfSV6xYgaVLl0Kn0+Hhhx/GgQMHsGrVKntkowFi495seHu64srxUVJHISIiIiIicmiiRfrll1+O6OhoHDhwABaL5f+3d+dRVdf5H8dfl0UQTIECVFQ0cikdl8RlSiG3JBFwSa34SR6XPEVN489snHHGbcaTv46ONVmNp6b6Zc5JckOz1IE2TVM0t8oVFTc2g1KU9d7P749O9xfjUIgXvhd4Pv6R+/1evvf9OW/f6Ivvvd+vkpOTFRERURe1oQE4ef477Tuap4kP3Clfn1/86wYAAAAAjVqVqem7775zft2iRQuNGDGi0r6AgIDarQwNQkracfn7ein23g5WlwIAAAAAbq/KkN6/f3/n59ClH26hZbPZnH8eOXKkTgpE/ZWVc1m7DmdrwtBO8m/qbXU5AAAAAOD2qgzpo0aN0v79+zV48GCNHTu20hXegepYk35CPk08FTfwdqtLAQAAAIB6ocqQvnjxYhUXF2vbtm1atGiRrl27pvj4eMXFxal58+Z1WSPqoYuXivTZ/vOKj4pQi2Y+VpcDAAAAAPXCz17Jq2nTpkpISFBCQoJycnKUmpqqpKQktW/fXi+88EJd1Yh6aE36CXl6emj0fbwDAwAAAACq6xfvk/6jgoICFRQUqLCwUFeuXKnNmlDP5RVe08f7zmlY33YKau5rdTkAAAAAUG/87Jn07Oxsbdy4UampqfL09FR8fLxSUlIUGhpaV/WhHlr/8UkZI40d1NHqUgAAAACgXqkypE+cOFGnT5/WiBEjtGTJEt111111WRfqqcLLJdq2O0uDI9sqJMjP6nIAAAAAoF6pMqRnZGTIx8dH7733ntasWePc/uMt2L788ss6KRD1y4ZPM1Vhd+jBwZxFBwAAAIAbVWVIT09Pr8s60ABcvlqmD3ae1oCeYWod3MzqcgAAAACg3qkypIeFhdVlHWgANm0/pZIyu8YP6WR1KQAAAABQL1X76u7Az7laXK5NO06pf7eWCm/V3OpyAAAAAKBeIqTDJT7YeVpXi8s1fihn0QEAAACgpgjpuGklpRXa8Gmm7u4coo5tA60uBwAAAADqLUI6btrW3Vm6fLWMs+gAAAAAcJMI6bgp5RV2rfv4pLpF3Kqut99qdTkAAAAAUK8R0nFT0jLOqeByCVd0BwAAAAAXIKSjxirsDq356IQ6tQtQz07BVpcDAAAAAPUeIR019tn+88oruKbxQzrJZrNZXQ4AAAAA1HuEdNSI3WGUknZC7Vs1V5+7WlpdDgAAAAA0CIR01Miuwxd1Ib9I44d2kocHZ9EBAAAAwBUI6bhhxhit/tdxhQU30z3dW1tdDgAAAAA0GIR03LCMb3J1Jvuyxg3pKE/OogMAAACAyxDScUOMMUpJO66QID9F393G6nIAAAAAoEEhpOOGHDyRr2NnC/XgoDvk5clfHwAAAABwJVIWbsjqtOMKau6rIX3aWV0KAAAAADQ4hHRU29envtVXmd9q9H13qIm3p9XlAAAAAECDQ0hHtaWkH1dz/yaK6R9udSkAAAAA0CAR0lEtJ899py+P5mlUdIR8fbysLgcAAAAAGiTSVj317ffFevuDI6qwOzSsbzt1vyNYHrV4O7SU9OPy9/XSiHs61NprAAAAAEBjR0ivZxwOo61fnNFbm79Rhd2oiZeHPtt/QS1v9dOwvuEa2redgpr7uvQ1s7Iva9fhbE0Y1kn+Tb1demwAAAAAwP8jpNcj53KvaPl7B/TN6QL16Hibkh/sqVtb+Grn4Wxt+yJLKz88olVbj6rPnaEa3j9cd3cJlacLzq6/l35Cvk08FT8wwgWrAAAAAABUhZBeD5RXOLTu4xN691/H5dvEU09P6KUhfdrKZvshgN93dxvdd3cbXcwv0rbdWUrPOKfdX+fotha+Gto3XMP6tVNIoF+NXvtifpG2HzivhOg71Ny/iSuXBQAAAAD4N4R0N3csq0AvpRxQVs4VDewZpmmjuinwlv/8dvbWwc00aWRXJcbcqT3f5GjbF1lanXZMq9OOqVfnEA3vF66+XVvKy7P61wtc89EJeXp6aHQ0Z9EBAAAAoLYR0t1UcWmF3vnwiDbtOKVbm/vqT5P7qW/XltX6Xm8vD93bvbXu7d5auQXX9K89WUrbc1bP/W+GAm7x0ZDItrq/f7ha39bsZ4+TV3hNH+09p5hft1egiz/nDgAAAAC4HiHdDe07mqtX1hxUXmGxYu/toKQRd8rPt2YXbAsN8tN/xdyph4d11r5jedr2RZbWf5qptR+fVPc7btP9/cL161+1UhNvz+u+d93HJyVJYwbdcVPrAQAAAABUDyHdjXxfVKrXN36lT/adV5uQZvqfJwforg63uuTYnp4e6ntXS/W9q6W+/b5YaRlntW33WS1ZtU+3+HlrUGRbDe8XrnYtm0uSCi+XaNvuLA2ObFvjz7MDAAAAAG4MId0NGGP06Zfn9VrqV7pWUq6HhnXW+KEd5e11/dltV7i1RVNNGNpZ4wZ30sET+dq6O0sffH5aGz87pTvbB+n+fuHKPP+d7HaHHhzSsVZqAAAAAABcj5BusbyCa3p57UF9eTRPndsF6qnxPRXeqnmdvLaHh029OoeoV+cQfXelVB/tPadtu8/oxdX7JUnRvdr84ufWAQAAAACuQ0i3iN1htHnHKa388Igk6bFRv9KIezu45L7mNRFwi4/GDLpDo++L0NenvtXur3MUN/B2S2oBAAAAgMaKkG6BrOzLeinlgI6dLVTvLiF6YmwPhQS5x+e+bTabukXcpm4Rt1ldCgAAAAA0OoT0OlReYdfqtONak35C/k299Uxib0X1CpPNZs3ZcwAAAACAe6nVkL5p0ya9+uqrqqio0KOPPqrExMRK+48cOaI5c+bo6tWrioyM1IIFC+Tl1TB/b/D1qW+1/L0DOp9XpEG922hKfDe1aOZjdVkAAAAAADfiUVsHzs3N1bJly/TPf/5TGzZs0OrVq3Xy5MlKz5k1a5bmzp2rrVu3yhijlJSU2irHMiWlFXpl7UHNfnmHysrtWjDt1/rvR3oT0AEAAAAA16m1kL5z5071799fAQEB8vPz0/Dhw7Vlyxbn/gsXLqikpEQ9e/aUJI0ZM6bS/oZi5+GL2rrrjBKiIrR81mDd3SXE6pIAAAAAAG6q1t5bnpeXp+DgYOfjkJAQHTp0qMr9wcHBys3Nra1yLDOwZxvd3TlUAbdw5hwAAAAA8PNq7Uy6w+GodEE0Y0ylx7+0v6Hw9vIgoAMAAAAAqqXWQnrLli2Vn5/vfJyfn6+QkJAq91+6dKnSfgAAAAAAGptaC+n33HOPdu3apYKCAhUXF2vbtm2Kiopy7g8LC5OPj4/27dsnSUpNTa20HwAAAACAxqbWQnpoaKhmzJihpKQkjRo1SiNHjlT37t01bdo0HT58WJK0ZMkSPffcc4qJidG1a9eUlJRUW+UAAAAAAOD2avWm5HFxcYqLi6u07bXXXnN+3aVLF61Zs6Y2SwAAAAAAoN6otTPpAAAAAADgxhDSAQAAAABwE4R0AAAAAADcBCEdAAAAAAA3QUgHAAAAAMBNENIBAAAAAHAThHQAAAAAANxErd4nvbbY7XZJUk5OjsWVAAAAAABwY37Msj9m25+qlyE9Pz9fkpSYmGhxJQAAAAAA1Ex+fr7Cw8MrbbMZY4xF9dRYSUmJvvrqKwUHB8vT09PqcgAAAAAAqDa73a78/Hx169ZNvr6+lfbVy5AOAAAAAEBDxIXjAAAAAABwE4R0AAAAAADcBCEdAAAAAAA3QUgHAAAAAMBNENIBAAAAAHAThHQAAAAAANwEIR0AAAAAADdBSHdTRUVFGjlypM6fPy9JWrdunUaMGKG4uDj95S9/UUVFhSTp/PnzSkxMVEJCgiZOnKgLFy5IksrKyjRr1iw98MADGj16tDIzMy1bS2N1sz28cOGCevXqpYSEBCUkJGjKlCmWraUxq24fDx06pLFjxyouLk7Tp09Xfn6+JGbRHdxsD5lF6y1fvlyxsbGKjY3V888/L0nauXOn4uLidP/992vZsmXO5x45ckRjxozR8OHDNWfOHGd/L168qMTERMXExOjxxx/X1atXLVlLY+WKHq5fv14DBgxwzuJPvwd140b6+KNnn31W69atcz5mFq3lih4yi3XAwO0cOHDAjBw50nTt2tWcO3fOZGZmmoEDB5rc3FxjjDHz5s0zb7zxhjHGmGeeecasWrXKGGPM22+/bWbOnGmMMeb11183f/rTn4wxxuzZs8eMGzfOgpU0Xq7o4ZYtW5w9hDWq20eHw2Gio6PNrl27jDHGbN682UyfPt0YwyxazRU9ZBat9fnnn5sJEyaY0tJSU1ZWZpKSksymTZtMdHS0OXv2rCkvLzeTJ082n3zyiTHGmNjYWLN//35jjDG///3vnT9fH3vsMfP+++8bY4xZvny5ef75561ZUCPkqh4uXLjQbNq0ybJ1NHY32secnBwzffp00717d7N27VrncZhF67iqh8xi7eNMuhtKSUnRvHnzFBISIkk6duyYevbs6Xw8aNAgpaWlSZIcDoeKiookScXFxfL19ZUkffLJJ4qPj5ck9enTRwUFBbp48WJdL6XRckUPDx8+rOPHjyshIUFJSUk6duyYBStp3Krbx8LCQpWUlKh///7O7Tt27FBZWRmzaDFX9JBZtFZwcLBmz56tJk2ayNvbWxERETpz5ozCw8PVtm1beXl5KS4uTlu2bNGFCxdUUlKinj17SpLGjBmjLVu2qLy8XBkZGRo+fHil7agbruih9MO/i+vXr1dcXJyeeeYZff/991Yuq9G5kT5K0qZNmzRkyBA98MADzmMwi9ZyRQ8lZrEuENLd0KJFixQZGel83KVLFx08eFDZ2dmy2+3asmWLLl26JEl6+umn9dZbb2ngwIF64403NG3aNElSXl6egoODnccIDg5WTk5O3S6kEXNFD318fBQfH6/169drypQpSk5OVllZmSXraayq28fAwED5+flpx44dkqTNmzervLxchYWFzKLFXNFDZtFaHTt2dAa2M2fO6MMPP5TNZqs0VyEhIcrNzf2P85abm6vCwkI1a9ZMXl5elbajbriihz9+/cQTT2jjxo1q1aqVFi5cWLcLaeRupI+SNHXqVI0bN67SMZhFa7mihxKzWBcI6fVAhw4dNHPmTD3++ONKTExU586d5e3tLUn63e9+p4ULF2r79u1asGCBnnzySRljZIyRzWZzHsMYIw8P2m2VmvTwqaee0iOPPCIPDw9FR0fLz89Pp06dsngljVtVfbTZbPrb3/6mFStWaNSoUbpy5YoCAgLk7e3NLLqZmvSQWXQPJ06c0OTJk/Xss8+qbdu2182VzWaTw+H4j9v/fQ4lXfcYte9meihJL7/8snr37i2bzaapU6dq+/btdb4GVK+PVWEW3cPN9FBiFusC/1OsB0pLS9W9e3dt2LBB7777rkJDQ9W2bVsVFBTo1KlTGjp0qCRp+PDhys/PV2FhoUJDQ5WXl+c8xqVLl5xv70Tdq0kPV65cqcLCQucxjDHO3zzDGlX1UZK8vLy0cuVKbdiwQfHx8XI4HAoICGAW3UxNesgsWm/fvn2aNGmSZs6cqdGjR6tly5bOC/tJUn5+vkJCQq7b/uO8BQUF6cqVK7Lb7ZWej7pzsz28cuWK3nrrLed2Y4w8PT3rcglQ9ftYFWbRejfbQ2axbhDS64Fr165p0qRJKioqUllZmd555x2NGDFCgYGB8vHx0d69eyX9MHT+/v4KCgpSdHS0UlNTJUl79+6Vj4+PWrdubeUyGrWa9DAjI0Nr1qyRJO3Zs0cOh0O33367lcto9KrqoyT94Q9/0KFDhyRJb775pmJiYpxnXplF91GTHjKL1srOzlZycrKWLFmi2NhYSVKPHj10+vRpZWVlyW636/3331dUVJTCwsLk4+Ojffv2SZJSU1MVFRUlb29vRUZG6oMPPpAkbdiwQVFRUZatqbFxRQ/9/Pz0+uuv6+DBg5Kkd955R8OGDbNsTY3RjfSxKsyitVzRQ2axbnAqoB4IDAxUcnKyJkyYoIqKCo0cOVJxcXGSfriNwp///GeVlJTI399fL730kiRp4sSJmjt3rmJjY9WkSRPnLRZgjZr0cM6cOZo9e7ZSU1Pl4+OjpUuX8jZpi/1cH+fPn6958+apuLhYnTt31qJFiyQxi+6mJj1kFq31j3/8Q6WlpVq8eLFz20MPPaTFixfrqaeeUmlpqaKjoxUTEyNJWrJkif74xz+qqKhIXbt2VVJSkiRp3rx5mj17tl599VW1atVKf/3rXy1ZT2Pkih56enrqhRde0Pz581VSUqL27dvz87SO3Wgfq8IsWscVPWQW64bNGGOsLgIAAAAAAPB2dwAAAAAA3AYhHQAAAAAAN0FIBwAAAADATRDSAQAAAABwE4R0AAAAAADcBCEdAIBGYMGCBRo/frzsdrtzm91u10MPPaRly5ZZWBkAAPgpQjoAAI3A7NmzVVxcrBUrVji3rVixQp6envrNb35jYWUAAOCnuE86AACNxLFjx/Twww9r1apVMsboscce09q1axUaGqq0tDT9/e9/V0VFhZo2barZs2erR48eysvL09y5c1VYWKj8/HyFhYXpxRdfVFBQkKKiotS7d28dPXpUs2bN0uDBg61eIgAA9R4hHQCARmTlypVav369HA6HZsyYoejoaGVmZmrGjBlauXKlWrRooaNHj2rq1KlKT093BvopU6bI4XBo6tSpio6O1qOPPqqoqCglJiZq+vTpVi8LAIAGw8vqAgAAQN2ZOHGitm7dqoiICEVHR0uSPv/8c+Xm5iopKcn5PJvNprNnz2ry5MnKyMjQm2++qTNnzigzM1N9+vRxPq937951vgYAABoyQjoAAI1MmzZt1K5dO+djh8OhAQMGaOnSpc5t2dnZCg0N1eLFi3X06FGNHj1a/fr1U2lpqX76Jjx/f/86rR0AgIaOC8cBANDI9e/fX9u3b9fp06clSenp6Ro1apRKS0u1Y8cOTZo0SQkJCQoMDNSuXbvkcDgsrhgAgIaLM+kAADRyXbp00fz58/Xb3/5Wxhh5eXnplVdeUdOmTZWcnKxFixZp6dKl8vb2VmRkpLKysqwuGQCABosLxwEAAAAA4CZ4uzsAAAAAAG6CkA4AAAAAgJsgpAMAAAAA4CYI6QAAAAAAuAlCOgAAAAAAboKQDgAAAACAmyCkAwAAAADgJgjpAAAAAAC4if8DvXmiMaITcYYAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#ploting natural and total emissionsMean ozone hole area (square kilometres) to see if it has reduced\n", - "plt.figure(figsize=(17,6))\n", - "plt.plot(oz_hole.Year,oz_hole['Mean ozone hole area (square kilometres)'], label='Mean ozone hole area (square kilometres)')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Mean ozone hole area (square kilometres)')\n", - "plt.legend(['Mean ozone hole area (square kilometres)'], loc='upper right')" - ] - }, { "cell_type": "code", "execution_count": 299, @@ -1930,6 +1798,7 @@ } ], "source": [ + "#importing Global average temperature dataset\n", "global_temperature = pd.read_csv('data//GlobalTemperatures.csv')\n", "global_temperature.head()" ] @@ -2015,6 +1884,7 @@ } ], "source": [ + "#cleaning the dataset\n", "global_temperature.dropna(inplace=True)\n", "global_temperature = global_temperature[['dt','LandAverageTemperature','LandAndOceanAverageTemperature']]\n", "global_temperature.head()" @@ -2107,7 +1977,6 @@ } ], "source": [ - "#global_temperature['dt']= pd.to_datetime(global_temperature['dt']) \n", "global_temperature['Year'] = pd.DatetimeIndex(global_temperature['dt']).year\n", "global_temperature.head()" ] @@ -2193,6 +2062,7 @@ } ], "source": [ + "#grouping annualy to adapt to the other datasets\n", "annual_global_temp = global_temperature[['Year','LandAverageTemperature','LandAndOceanAverageTemperature']].groupby(['Year']).agg({'LandAverageTemperature':'mean',\n", " 'LandAndOceanAverageTemperature':'mean'})\n", "annual_global_temp.head()" @@ -2430,6 +2300,7 @@ } ], "source": [ + "#importing Fossil fuel consumption per capita dataset\n", "fossil_per_c = pd.read_csv('data//fossil-fuel-consumption-per-capita.csv')\n", "fossil_per_c.head()" ] @@ -2551,6 +2422,7 @@ "metadata": {}, "outputs": [], "source": [ + "#cleaning the dataset\n", "fossil_per_c.drop(['Entity', 'Code',], axis=1, inplace=True)" ] }, @@ -2598,7 +2470,7 @@ }, { "cell_type": "code", - "execution_count": 317, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -2753,12 +2625,13 @@ "4 0.0 " ] }, - "execution_count": 317, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#importing Global primary energy by source dataset\n", "global_energy = pd.read_csv('data//global-primary-energy.csv')\n", "global_energy.head()" ] @@ -2769,6 +2642,7 @@ "metadata": {}, "outputs": [], "source": [ + "#cleaning the dataset\n", "global_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" ] }, @@ -2822,7 +2696,7 @@ }, { "cell_type": "code", - "execution_count": 321, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -2926,19 +2800,20 @@ "4 0.0 0.0 " ] }, - "execution_count": 321, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#importing Modern renewable energy consumtion dataset\n", "renew_energy = pd.read_csv('data//modern-renewable-energy-consumption.csv')\n", "renew_energy.head()" ] }, { "cell_type": "code", - "execution_count": 322, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -3049,7 +2924,7 @@ "5041 23.257964 " ] }, - "execution_count": 322, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -3062,16 +2937,17 @@ }, { "cell_type": "code", - "execution_count": 323, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ + "#cleaning the dataset\n", "renew_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 324, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -3114,7 +2990,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -3199,44 +3075,24 @@ "4 United States USA 1914 265763000.0 243928136.8" ] }, - "execution_count": 133, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#importing Hubbert's peak dataset\n", "hubbert_peak = pd.read_csv('data//hubberts-peak-vs-actual-oil-production-in-the-united-states.csv')\n", "hubbert_peak.head()" ] }, { "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Entity', 'Code', 'Year', ' (barrels per year)',\n", - " ' (barrels per year).1'],\n", - " dtype='object')" - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hubbert_peak.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 156, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ + "#cleaning the dataset\n", "hubbert_peak.drop(['Entity', 'Code'], axis=1, inplace=True)" ] }, @@ -3342,42 +3198,6 @@ "hubbert_peak.tail()" ] }, - { - "cell_type": "code", - "execution_count": 329, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 329, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#ploting predicted and actual emissions to see if it the prediction came true\n", - "plt.figure(figsize=(17,6))\n", - "plt.plot(hubbert_peak.Year,hubbert_peak['Actual'],label='Actual')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('(barrels per year)')\n", - "plt.plot(hubbert_peak.Year,hubbert_peak['Predicted'],label='Predicted')\n", - "plt.legend(['Actual', 'Predicted'], loc='upper left')" - ] - }, { "cell_type": "code", "execution_count": 330, @@ -3421,7 +3241,7 @@ }, { "cell_type": "code", - "execution_count": 332, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -3562,22 +3382,24 @@ "4 95.129372 0.0 " ] }, - "execution_count": 332, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#importing Share of global primary energy dataset\n", "share_energy = pd.read_csv('data//global-primary-energy-share-inc-biomass.csv')\n", "share_energy.head()" ] }, { "cell_type": "code", - "execution_count": 333, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ + "#cleaning the dataset\n", "share_energy.drop(['Entity', 'Code'], axis=1, inplace=True)" ] }, @@ -3749,6 +3571,7 @@ } ], "source": [ + "#importing Share of global primary energy dataset\n", "solar_price = pd.read_csv('data//solar-pv-prices-vs-cumulative-capacity.csv')\n", "solar_price.head()" ] @@ -3884,6 +3707,7 @@ "metadata": {}, "outputs": [], "source": [ + "#cleaning the dataset\n", "solar_price.drop(['Entity', 'Code','Total population (Gapminder)'], axis=1, inplace=True)" ] }, @@ -3897,82 +3721,7 @@ ] }, { - "cell_type": "code", - "execution_count": 468, - "metadata": {}, - "outputs": [], - "source": [ - "#solar_price.set_index('Cumulative Installed Capacity(MWp)',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 471, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Solar PV Module Cost($/Wp)')" - ] - }, - "execution_count": 471, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(17,6))\n", - "plt.plot(solar_price.Year,solar_price['Solar PV Module Cost($/Wp)'],label='Solar PV Module Cost($/Wp)')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Solar PV Module Cost($/Wp)')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 472, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Cumulative Installed Capacity(MWp)')" - ] - }, - "execution_count": 472, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(17,6))\n", - "plt.plot(solar_price.Year,solar_price['Cumulative Installed Capacity(MWp)'],label='Cumulative Installed Capacity(MWp)')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Cumulative Installed Capacity(MWp)')" - ] - }, - { - "cell_type": "markdown", + "cell_type": "markdown", "metadata": {}, "source": [ "## Country Indicators" @@ -4717,6 +4466,13 @@ "## Merging datasets" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we merged the previous datasets into 3 so I can analyse the results using tableau." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -5623,26 +5379,75 @@ "ozone_merged.tail()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CO emissions and median temperature anomaly correlation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we have a closer look to the strong correlation between the median annual temperature anomaly and the mean annual CO₂ emissions in tonnes." + ] + }, { "cell_type": "code", - "execution_count": 534, + "execution_count": 444, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "0.890288126134981" ] }, - "execution_count": 534, + "execution_count": 444, "metadata": {}, "output_type": "execute_result" + } + ], + "source": [ + "co_a_corr = global_indicators[\"Median (℃)\"].corr(global_indicators[\"Annual CO₂ emissions (tonnes )\"])\n", + "co_a_corr" + ] + }, + { + "cell_type": "code", + "execution_count": 538, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Median (℃)')" + ] + }, + "execution_count": 538, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8322 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0.0, flags=flags)\n", + "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", + " font.set_text(s, 0, flags=flags)\n" + ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -5650,49 +5455,91 @@ } ], "source": [ - "sns.pairplot(ozone_merged)" + "plt.scatter(x=global_indicators['Annual CO₂ emissions (tonnes )'], y=global_indicators['Median (℃)'])\n", + "plt.xlabel('Annual CO₂ emissions (tonnes )')\n", + "plt.ylabel('Median (℃)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### CO emissions and median temperature anomaly correlation" + "From both the correlation and the scatter plots, we can see that the more CO₂ emissions the higher will be the mean temperature of earth." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we plot some charts that show aome of the progressions ans relationships between the indicators gathered." ] }, { "cell_type": "code", - "execution_count": 444, + "execution_count": 267, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.890288126134981" + "Text(0, 0.5, 'CO atmospheric concentration')" ] }, - "execution_count": 444, + "execution_count": 267, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "co_a_corr = global_indicators[\"Median (℃)\"].corr(global_indicators[\"Annual CO₂ emissions (tonnes )\"])\n", - "co_a_corr" + "#plotting the Global CO₂ atmospheric concentration increasement from 1864-2018\n", + "x = co_concentration.Year[1740:]\n", + "y = co_concentration['CO₂ concentration'][1740:]\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('CO atmospheric concentration')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The years go from 1800 to 2018 and we can see how the CO₂ concentration in the atmosphere only keeps incresing. Either we stop this progression or humanit won't see the next century." ] }, { "cell_type": "code", - "execution_count": 538, + "execution_count": 276, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0, 0.5, 'Median (℃)')" + "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" ] }, - "execution_count": 538, + "execution_count": 276, "metadata": {}, "output_type": "execute_result" }, @@ -5700,10 +5547,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8322 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8322 missing from current font.\n", - " font.set_text(s, 0, flags=flags)\n", "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", " font.set_text(s, 0.0, flags=flags)\n", "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", @@ -5712,9 +5555,9 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, "metadata": {}, @@ -5722,9 +5565,283 @@ } ], "source": [ - "plt.scatter(x=global_indicators['Annual CO₂ emissions (tonnes )'], y=global_indicators['Median (℃)'])\n", - "plt.xlabel('Annual CO₂ emissions (tonnes )')\n", - "plt.ylabel('Median (℃)')" + "#plotting the median temperature anomaly over time to see how it's actually ingreasing \n", + "x = temperature_a.Year\n", + "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", + "plt.figure(figsize=(17,6))\n", + "\n", + "plt.plot(x, y, 'dodgerblue', label='Temperature Anomaly', \n", + " linewidth=2)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Year')\n", + "plt.ylabel('increse ordescrease in Temperature(℃)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can see the consequence of the previous chart. We see how the temperatures were kind of stable until the 1960's and are in anincreasing trend since then." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Ozone depletion crisis (1970)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the 1970's scientists saw that there was a hole in the ozone layer on top of the Antartica and that it was growing due to the usage of Chlorofluorocarbons (CFC's). But humanity acted the right way and tackled the problem in the Montreal Protocol (and the succesive protocols) and managed to drastically reduce the emissions of that substance. This year we are starting to see the first signs of recovery, it took more than 30 years but if we keep on this path we will have solved that crisis.\n", + "Here are two plots that ilustrate that:" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 291, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting natural and total emissions to see that we have nearly stopped using CFC\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(ozone.Year,ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Natural emissions'), label='Natural emissions')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Tonnes CFC11-equivalents')\n", + "plt.plot(ozone.Year, ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Total emissions'), label='Total emissions')\n", + "plt.legend(['Natural emissions', 'Total emissions'], loc='upper right')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we see how the emissions have indeed been decreasing since 1990 and that we are approaching the level of natural emissions." + ] + }, + { + "cell_type": "code", + "execution_count": 298, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 298, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting natural and total emissions Mean ozone hole area (square kilometres) to see if it has reduced\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(oz_hole.Year,oz_hole['Mean ozone hole area (square kilometres)'], label='Mean ozone hole area (square kilometres)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Mean ozone hole area (square kilometres)')\n", + "plt.legend(['Mean ozone hole area (square kilometres)'], loc='upper right')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the area in kilometers squared of the Anartica's hole and we see it's reached a plateau. We took the right path there." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why not with global warming then? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How come are we not taking the same path regarding the COâ‚‚ emissions?" + ] + }, + { + "cell_type": "code", + "execution_count": 329, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 329, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ploting predicted and actual emissions to see if it the prediction came true\n", + "plt.figure(figsize=(17,6))\n", + "plt.plot(hubbert_peak.Year,hubbert_peak['Actual'],label='Actual')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('(barrels per year)')\n", + "plt.plot(hubbert_peak.Year,hubbert_peak['Predicted'],label='Predicted')\n", + "plt.legend(['Actual', 'Predicted'], loc='upper left')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can see that, despite Kyoto's Protocol (agreement to reduce the COâ‚‚ emissions), and unlike Hubbert had predicte, the USA is increasing the production of Oil in the 20th century. This shows the disregard that the country's government has towards the welbeing of earth in the future generations.\n", + "\n", + "Trump says that he need to make the american economy compatitive enough, therefore the country need to withdraw from the Paris agreement in 2016. But this excuse doesn't stand, because the productivity and cost effectivness of the renewable energies keep growing everyday. below there's we see the solarenergy example to ilustrate this." + ] + }, + { + "cell_type": "code", + "execution_count": 471, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Solar PV Module Cost($/Wp)')" + ] + }, + "execution_count": 471, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(17,6))\n", + "plt.plot(solar_price.Year,solar_price['Solar PV Module Cost($/Wp)'],label='Solar PV Module Cost($/Wp)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Solar PV Module Cost($/Wp)')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can see how the cost of the solarpanels has decreased dramatically through the years, but not only this, they also have increased their cumulative capacity, like the chart below shows." + ] + }, + { + "cell_type": "code", + "execution_count": 472, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Cumulative Installed Capacity(MWp)')" + ] + }, + "execution_count": 472, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(17,6))\n", + "plt.plot(solar_price.Year,solar_price['Cumulative Installed Capacity(MWp)'],label='Cumulative Installed Capacity(MWp)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Cumulative Installed Capacity(MWp)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Therefore, What is stopping __us__ from taking action?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we now that the competivity excuse isn't valid anymore, we can assume that the only reason why the second most contaminating country on Earth is disregarding it's health is that those making the decisions and policies have interests in the industry of fossil fuels.\n", + "\n", + "Therefore, they want to make the most profit they can before having to switch to renewable energies, but will it be too late by then, we don't know. \n", + "\n", + "Risk Management and decision making always have uncertainty, but it's not the moment to focus on hat uncertainty, it's the moment of taking __unanimous__ action and make sure that not even the USA and China can't build a bridge around the agreements and stick to the plan, otherwise our future is looking hot, in the worst sense of the word." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "..................................................................................................................................................................................................................Bootcamp's Final Project\n", + "..................................................................................................................................................................................................................Ironhack: Data March2020\n", + "..................................................................................................................................................................................................................by Andreu Carreño Mateu" ] } ], From d83a53863bf785add6df8afd1fd9ed41517de295 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Wed, 27 May 2020 23:09:32 +0200 Subject: [PATCH 13/17] repository organized, only readme left --- your-project/Data Gathering.ipynb | 6317 ---------------- your-project/Final Dataset notebook.ipynb | 6543 ----------------- ...obal Warming report, can we solve it.ipynb | 841 +-- ...ate change datasets and correlations.ipynb | 5744 --------------- .../Global climate change datasets.ipynb | 5711 -------------- 5 files changed, 391 insertions(+), 24765 deletions(-) delete mode 100644 your-project/Data Gathering.ipynb delete mode 100644 your-project/Final Dataset notebook.ipynb delete mode 100644 your-project/Global climate change datasets and correlations.ipynb delete mode 100644 your-project/Global climate change datasets.ipynb diff --git a/your-project/Data Gathering.ipynb b/your-project/Data Gathering.ipynb deleted file mode 100644 index 7a08fc66..00000000 --- a/your-project/Data Gathering.ipynb +++ /dev/null @@ -1,6317 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import datetime\n", - "import re" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CO annual emissions (1949-2017)" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes)
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
\n", - "
" - ], - "text/plain": [ - " Entity Code Year Annual COâ‚‚ emissions (tonnes)\n", - "0 Afghanistan AFG 1949 14656.0\n", - "1 Afghanistan AFG 1950 84272.0\n", - "2 Afghanistan AFG 1951 91600.0\n", - "3 Afghanistan AFG 1952 91600.0\n", - "4 Afghanistan AFG 1953 106256.0" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_annual = pd.read_csv('data//annual_co2_emissions_per_country.csv')\n", - "co_annual.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [], - "source": [ - "co_annual.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": {}, - "outputs": [], - "source": [ - "co_annual = co_annual.rename(columns={'Annual COâ‚‚ emissions (tonnes)':'Annual COâ‚‚', 'Entity':'Country'})" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [], - "source": [ - "co_annual = co_annual[co_annual.Country != 'World']" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 20586 entries, 0 to 20852\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 20586 non-null object \n", - " 1 Year 20586 non-null int64 \n", - " 2 Annual COâ‚‚ 20586 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 643.3+ KB\n" - ] - } - ], - "source": [ - "#co_annual.Year.unique()\n", - "co_annual.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Annual share of CO per country (1949-2017)" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearShare of global COâ‚‚ emissions (%)
0AfghanistanAFG19490.0003
1AfghanistanAFG19500.0015
2AfghanistanAFG19510.0015
3AfghanistanAFG19520.0015
4AfghanistanAFG19530.0016
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" - ], - "text/plain": [ - " Entity Code Year Share of global COâ‚‚ emissions (%)\n", - "0 Afghanistan AFG 1949 0.0003\n", - "1 Afghanistan AFG 1950 0.0015\n", - "2 Afghanistan AFG 1951 0.0015\n", - "3 Afghanistan AFG 1952 0.0015\n", - "4 Afghanistan AFG 1953 0.0016" - ] - }, - "execution_count": 144, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_share = pd.read_csv('data//annual_share_of_co2_emissions.csv')\n", - "co_share.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": {}, - "outputs": [], - "source": [ - "co_share.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [], - "source": [ - "co_share = co_share.rename(columns={'Share of global COâ‚‚ emissions (%)b':'Annual COâ‚‚', 'Entity':'Country'})" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", - " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", - " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", - " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", - " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", - " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", - " 2015, 2016, 2017, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758,\n", - " 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769,\n", - " 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780,\n", - " 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791,\n", - " 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802,\n", - " 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813,\n", - " 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824,\n", - " 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835,\n", - " 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846,\n", - " 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857,\n", - " 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868,\n", - " 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879,\n", - " 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890,\n", - " 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901,\n", - " 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912,\n", - " 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923,\n", - " 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934,\n", - " 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945,\n", - " 1946, 1947, 1948], dtype=int64)" - ] - }, - "execution_count": 152, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_share.Year.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 20586 entries, 0 to 20585\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 20586 non-null object \n", - " 1 Year 20586 non-null int64 \n", - " 2 Share of global COâ‚‚ emissions (%) 20586 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 482.6+ KB\n" - ] - } - ], - "source": [ - "co_share.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CO Emissions per capita (1800-2017)" - ] - }, - { - "cell_type": "code", - "execution_count": 250, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearPer capita COâ‚‚ emissions (tonnes per capita)
0AfghanistanAFG18000.0
1AfghanistanAFG18010.0
2AfghanistanAFG18020.0
3AfghanistanAFG18030.0
4AfghanistanAFG18040.0
\n", - "
" - ], - "text/plain": [ - " Entity Code Year Per capita COâ‚‚ emissions (tonnes per capita)\n", - "0 Afghanistan AFG 1800 0.0\n", - "1 Afghanistan AFG 1801 0.0\n", - "2 Afghanistan AFG 1802 0.0\n", - "3 Afghanistan AFG 1803 0.0\n", - "4 Afghanistan AFG 1804 0.0" - ] - }, - "execution_count": 250, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_per_c = pd.read_csv(\"data//co_emissions_per_capita.csv\")\n", - "co_per_c.head()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 251, - "metadata": {}, - "outputs": [], - "source": [ - "co_per_c.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 252, - "metadata": {}, - "outputs": [], - "source": [ - "co_per_c = co_per_c.rename(columns={'Per capita COâ‚‚ emissions (tonnes per capita)':'COâ‚‚', 'Entity':'Country'})" - ] - }, - { - "cell_type": "code", - "execution_count": 253, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CountryYearCOâ‚‚
0Afghanistan18000.0
1Afghanistan18010.0
2Afghanistan18020.0
3Afghanistan18030.0
4Afghanistan18040.0
\n", - "
" - ], - "text/plain": [ - " Country Year COâ‚‚\n", - "0 Afghanistan 1800 0.0\n", - "1 Afghanistan 1801 0.0\n", - "2 Afghanistan 1802 0.0\n", - "3 Afghanistan 1803 0.0\n", - "4 Afghanistan 1804 0.0" - ] - }, - "execution_count": 253, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_per_c.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 254, - "metadata": {}, - "outputs": [], - "source": [ - "co_per_c = co_per_c[co_per_c.Country != 'World']" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [], - "source": [ - "#co_per_c.Country.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 255, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 42723 entries, 0 to 42843\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 42723 non-null object \n", - " 1 Year 42723 non-null int64 \n", - " 2 COâ‚‚ 42723 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 1.3+ MB\n" - ] - } - ], - "source": [ - "#co_per_c.Year.unique()\n", - "co_per_c.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Total greenhouse gas emissions (1990-2016)" - ] - }, - { - "cell_type": "code", - "execution_count": 248, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
EntityCodeYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
0AfghanistanAFG199015140000.0
1AfghanistanAFG199115060000.0
2AfghanistanAFG199213600000.0
3AfghanistanAFG199313430000.0
4AfghanistanAFG199413240000.0
\n", - "
" - ], - "text/plain": [ - " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", - "0 Afghanistan AFG 1990 15140000.0\n", - "1 Afghanistan AFG 1991 15060000.0\n", - "2 Afghanistan AFG 1992 13600000.0\n", - "3 Afghanistan AFG 1993 13430000.0\n", - "4 Afghanistan AFG 1994 13240000.0" - ] - }, - "execution_count": 248, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", - "ghg.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 249, - "metadata": {}, - "outputs": [], - "source": [ - "ghg.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 256, - "metadata": {}, - "outputs": [], - "source": [ - "ghg = ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )', 'Entity':'Country'})" - ] - }, - { - "cell_type": "code", - "execution_count": 257, - "metadata": {}, - "outputs": [], - "source": [ - "ghg = ghg[ghg.Country != 'World']" - ] - }, - { - "cell_type": "code", - "execution_count": 259, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 5157 entries, 0 to 5183\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 5157 non-null object \n", - " 1 Year 5157 non-null int64 \n", - " 2 GHG(tonnes COâ‚‚e ) 5157 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 161.2+ KB\n" - ] - } - ], - "source": [ - "ghg.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Global COâ‚‚ atmospheric concentration" - ] - }, - { - "cell_type": "code", - "execution_count": 260, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
EntityCodeYearCOâ‚‚ concentration (parts per million)
0WorldOWID_WRL803719 BCE207.29
1WorldOWID_WRL803182 BCE202.23
2WorldOWID_WRL802573 BCE204.86
3WorldOWID_WRL802060 BCE207.50
4WorldOWID_WRL801975 BCE202.92
...............
1866WorldOWID_WRL2014398.65
1867WorldOWID_WRL2015400.83
1868WorldOWID_WRL2016404.24
1869WorldOWID_WRL2017406.55
1870WorldOWID_WRL2018408.52
\n", - "

1871 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " Entity Code Year COâ‚‚ concentration (parts per million)\n", - "0 World OWID_WRL 803719 BCE 207.29\n", - "1 World OWID_WRL 803182 BCE 202.23\n", - "2 World OWID_WRL 802573 BCE 204.86\n", - "3 World OWID_WRL 802060 BCE 207.50\n", - "4 World OWID_WRL 801975 BCE 202.92\n", - "... ... ... ... ...\n", - "1866 World OWID_WRL 2014 398.65\n", - "1867 World OWID_WRL 2015 400.83\n", - "1868 World OWID_WRL 2016 404.24\n", - "1869 World OWID_WRL 2017 406.55\n", - "1870 World OWID_WRL 2018 408.52\n", - "\n", - "[1871 rows x 4 columns]" - ] - }, - "execution_count": 260, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_concentration = pd.read_csv('data//global-co-concentration-ppm.csv')\n", - "co_concentration" - ] - }, - { - "cell_type": "code", - "execution_count": 261, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 262, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration = co_concentration.rename(columns={'COâ‚‚ concentration (parts per million)':'COâ‚‚ concentration', 'Entity':'Global'})" - ] - }, - { - "cell_type": "code", - "execution_count": 281, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1900'" - ] - }, - "execution_count": 281, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_concentration.Year[1768]" - ] - }, - { - "cell_type": "code", - "execution_count": 282, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'CO atmospheric concentration')" - ] - }, - "execution_count": 282, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "x = co_concentration.Year[1768:]\n", - "y = co_concentration['COâ‚‚ concentration'][1768:]\n", - "plt.figure(figsize=(17,6))\n", - "\n", - "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", - " linewidth=2)\n", - "\n", - "plt.legend()\n", - "plt.xlabel('Year')\n", - "plt.ylabel('CO atmospheric concentration')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CO emissions in imported goods as a share of domestic emissions (1990-2016)" - ] - }, - { - "cell_type": "code", - "execution_count": 284, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearCOâ‚‚ emissions embedded in trade as a share of territorial emissions (%)
0AfricaNaN1990-17.07
1AfricaNaN1991-16.34
2AfricaNaN1992-16.78
3AfricaNaN1993-18.40
4AfricaNaN1994-17.49
\n", - "
" - ], - "text/plain": [ - " Entity Code Year \\\n", - "0 Africa NaN 1990 \n", - "1 Africa NaN 1991 \n", - "2 Africa NaN 1992 \n", - "3 Africa NaN 1993 \n", - "4 Africa NaN 1994 \n", - "\n", - " COâ‚‚ emissions embedded in trade as a share of territorial emissions (%) \n", - "0 -17.07 \n", - "1 -16.34 \n", - "2 -16.78 \n", - "3 -18.40 \n", - "4 -17.49 " - ] - }, - "execution_count": 284, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_imported = pd.read_csv('data//co-emissions-in-imported-goods-as-a-share-of-domestic-emissions.csv')\n", - "co_imported.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 286, - "metadata": {}, - "outputs": [], - "source": [ - "#COâ‚‚ emissions embedded in trade as a share of territorial emissions (%)\n", - "co_imported = co_imported.rename(columns={'COâ‚‚ emissions embedded in trade as a share of territorial emissions (%)':'COâ‚‚ imported', 'Entity':'Country'})" - ] - }, - { - "cell_type": "code", - "execution_count": 287, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Africa', 'Albania', 'Argentina', 'Armenia', 'Asia', 'Australia',\n", - " 'Austria', 'Azerbaijan', 'Bahrain', 'Bangladesh', 'Belarus',\n", - " 'Belgium', 'Benin', 'Bolivia', 'Botswana', 'Brazil', 'Brunei',\n", - " 'Bulgaria', 'Burkina Faso', 'Cambodia', 'Cameroon', 'Canada',\n", - " 'Central America', 'Chile', 'China', 'Colombia', 'Costa Rica',\n", - " \"Cote d'Ivoire\", 'Croatia', 'Cyprus', 'Czech Republic', 'Denmark',\n", - " 'Dominican Republic', 'EU-28', 'Ecuador', 'Egypt', 'El Salvador',\n", - " 'Estonia', 'Ethiopia', 'Europe', 'Finland', 'France', 'Georgia',\n", - " 'Germany', 'Ghana', 'Greece', 'Guatemala', 'Guinea', 'Honduras',\n", - " 'Hong Kong', 'Hungary', 'India', 'Indonesia', 'Iran', 'Ireland',\n", - " 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'KP Annex B',\n", - " 'Kazakhstan', 'Kenya', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia',\n", - " 'Lithuania', 'Luxembourg', 'Madagascar', 'Malawi', 'Malaysia',\n", - " 'Malta', 'Mauritius', 'Mexico', 'Middle East', 'Mongolia',\n", - " 'Morocco', 'Mozambique', 'Namibia', 'Nepal', 'Netherlands',\n", - " 'New Zealand', 'Nicaragua', 'Nigeria', 'Non KP Annex B',\n", - " 'Non-OECD', 'North America', 'Norway', 'OECD', 'Oceania', 'Oman',\n", - " 'Pakistan', 'Panama', 'Paraguay', 'Peru', 'Philippines', 'Poland',\n", - " 'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda', 'Saudi Arabia',\n", - " 'Senegal', 'Singapore', 'Slovakia', 'Slovenia', 'South Africa',\n", - " 'South America', 'South Korea', 'Spain', 'Sri Lanka', 'Sweden',\n", - " 'Switzerland', 'Taiwan', 'Tanzania', 'Thailand', 'Togo',\n", - " 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Uganda', 'Ukraine',\n", - " 'United Arab Emirates', 'United Kingdom', 'United States',\n", - " 'Uruguay', 'Venezuela', 'Vietnam', 'Zambia', 'Zimbabwe'],\n", - " dtype=object)" - ] - }, - "execution_count": 287, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_imported.Country.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 296, - "metadata": {}, - "outputs": [], - "source": [ - "regions = ['Africa','Asia','Central America','EU-28','Europe','KP Annex B','Middle East','Non KP Annex B','Non-OECD','North America','OECD','Oceania','South America']\n", - "for region in regions:\n", - " co_imported = co_imported[co_imported.Country != region]" - ] - }, - { - "cell_type": "code", - "execution_count": 301, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 3213 entries, 27 to 3563\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 3213 non-null object \n", - " 1 Code 3213 non-null object \n", - " 2 Year 3213 non-null int64 \n", - " 3 COâ‚‚ imported 3213 non-null float64\n", - "dtypes: float64(1), int64(1), object(2)\n", - "memory usage: 125.5+ KB\n" - ] - } - ], - "source": [ - "co_imported.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Share of deaths by pollution (1990-2017)" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearShare of deaths from air pollution (%)
0AfghanistanAFG199013.56
1AfghanistanAFG199113.19
2AfghanistanAFG199213.05
3AfghanistanAFG199312.88
4AfghanistanAFG199412.80
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" - ], - "text/plain": [ - " Entity Code Year Share of deaths from air pollution (%)\n", - "0 Afghanistan AFG 1990 13.56\n", - "1 Afghanistan AFG 1991 13.19\n", - "2 Afghanistan AFG 1992 13.05\n", - "3 Afghanistan AFG 1993 12.88\n", - "4 Afghanistan AFG 1994 12.80" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "deaths = pd.read_csv('data//share_deaths_air_pollution.csv')\n", - "deaths.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Entity 0\n", - "Code 980\n", - "Year 0\n", - "Share of deaths from air pollution (%) 0\n", - "dtype: int64" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "deaths.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [], - "source": [ - "deaths.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [], - "source": [ - "deaths.rename(columns={'Share of deaths from air pollution (%)':'Pollution_deaths', 'Entity':'Country'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [], - "source": [ - "deaths = deaths[deaths.Country != 'World']" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryYearPollution_deaths
0Afghanistan199013.56
1Afghanistan199113.19
2Afghanistan199213.05
3Afghanistan199312.88
4Afghanistan199412.80
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" - ], - "text/plain": [ - " Country Year Pollution_deaths\n", - "0 Afghanistan 1990 13.56\n", - "1 Afghanistan 1991 13.19\n", - "2 Afghanistan 1992 13.05\n", - "3 Afghanistan 1993 12.88\n", - "4 Afghanistan 1994 12.80" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "deaths.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000,\n", - " 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,\n", - " 2012, 2013, 2014, 2015, 2016, 2017], dtype=int64)" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#deaths.info()\n", - "deaths.Year.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 6384 entries, 0 to 6411\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 6384 non-null object \n", - " 1 Year 6384 non-null int64 \n", - " 2 Pollution_deaths 6384 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 199.5+ KB\n" - ] - } - ], - "source": [ - "deaths.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Global temperatures by country (not convinced yet)" - ] - }, - { - "cell_type": "code", - "execution_count": 316, - "metadata": {}, - "outputs": [], - "source": [ - "temperature = pd.read_csv('data//GlobalLandTemperaturesByCountry.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtAverageTemperatureAverageTemperatureUncertaintyCountry
01743-11-014.3842.294Ã…land
11743-12-01NaNNaNÃ…land
21744-01-01NaNNaNÃ…land
31744-02-01NaNNaNÃ…land
41744-03-01NaNNaNÃ…land
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" - ], - "text/plain": [ - " dt AverageTemperature AverageTemperatureUncertainty Country\n", - "0 1743-11-01 4.384 2.294 Ã…land\n", - "1 1743-12-01 NaN NaN Ã…land\n", - "2 1744-01-01 NaN NaN Ã…land\n", - "3 1744-02-01 NaN NaN Ã…land\n", - "4 1744-03-01 NaN NaN Ã…land" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "metadata": {}, - "outputs": [], - "source": [ - "temperature['dt']= pd.to_datetime(temperature['dt']) " - ] - }, - { - "cell_type": "code", - "execution_count": 227, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtAverageTemperatureAverageTemperatureUncertaintyCountryyear
01743-11-014.3842.294Ã…land1743
11743-12-01NaNNaNÃ…land1743
21744-01-01NaNNaNÃ…land1744
31744-02-01NaNNaNÃ…land1744
41744-03-01NaNNaNÃ…land1744
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" - ], - "text/plain": [ - " dt AverageTemperature AverageTemperatureUncertainty Country year\n", - "0 1743-11-01 4.384 2.294 Åland 1743\n", - "1 1743-12-01 NaN NaN Åland 1743\n", - "2 1744-01-01 NaN NaN Åland 1744\n", - "3 1744-02-01 NaN NaN Åland 1744\n", - "4 1744-03-01 NaN NaN Åland 1744" - ] - }, - "execution_count": 227, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature['year'] = pd.DatetimeIndex(temperature['dt']).year\n", - "temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 303, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Åland', 'Afghanistan', 'Africa', 'Albania', 'Algeria',\n", - " 'American Samoa', 'Andorra', 'Angola', 'Anguilla', 'Antarctica',\n", - " 'Antigua And Barbuda', 'Argentina', 'Armenia', 'Aruba', 'Asia',\n", - " 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',\n", - " 'Baker Island', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium',\n", - " 'Belize', 'Benin', 'Bhutan', 'Bolivia',\n", - " 'Bonaire, Saint Eustatius And Saba', 'Bosnia And Herzegovina',\n", - " 'Botswana', 'Brazil', 'British Virgin Islands', 'Bulgaria',\n", - " 'Burkina Faso', 'Burma', 'Burundi', \"Côte D'Ivoire\", 'Cambodia',\n", - " 'Cameroon', 'Canada', 'Cape Verde', 'Cayman Islands',\n", - " 'Central African Republic', 'Chad', 'Chile', 'China',\n", - " 'Christmas Island', 'Colombia', 'Comoros',\n", - " 'Congo (Democratic Republic Of The)', 'Congo', 'Costa Rica',\n", - " 'Croatia', 'Cuba', 'Curaçao', 'Cyprus', 'Czech Republic',\n", - " 'Denmark (Europe)', 'Denmark', 'Djibouti', 'Dominica',\n", - " 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador',\n", - " 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Ethiopia', 'Europe',\n", - " 'Falkland Islands (Islas Malvinas)', 'Faroe Islands',\n", - " 'Federated States Of Micronesia', 'Fiji', 'Finland',\n", - " 'France (Europe)', 'France', 'French Guiana', 'French Polynesia',\n", - " 'French Southern And Antarctic Lands', 'Gabon', 'Gambia',\n", - " 'Gaza Strip', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland',\n", - " 'Grenada', 'Guadeloupe', 'Guam', 'Guatemala', 'Guernsey',\n", - " 'Guinea Bissau', 'Guinea', 'Guyana', 'Haiti',\n", - " 'Heard Island And Mcdonald Islands', 'Honduras', 'Hong Kong',\n", - " 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\n", - " 'Ireland', 'Isle Of Man', 'Israel', 'Italy', 'Jamaica', 'Japan',\n", - " 'Jersey', 'Jordan', 'Kazakhstan', 'Kenya', 'Kingman Reef',\n", - " 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon',\n", - " 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n", - " 'Luxembourg', 'Macau', 'Macedonia', 'Madagascar', 'Malawi',\n", - " 'Malaysia', 'Mali', 'Malta', 'Martinique', 'Mauritania',\n", - " 'Mauritius', 'Mayotte', 'Mexico', 'Moldova', 'Monaco', 'Mongolia',\n", - " 'Montenegro', 'Montserrat', 'Morocco', 'Mozambique', 'Namibia',\n", - " 'Nepal', 'Netherlands (Europe)', 'Netherlands', 'New Caledonia',\n", - " 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Niue',\n", - " 'North America', 'North Korea', 'Northern Mariana Islands',\n", - " 'Norway', 'Oceania', 'Oman', 'Pakistan', 'Palau', 'Palestina',\n", - " 'Palmyra Atoll', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n", - " 'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',\n", - " 'Reunion', 'Romania', 'Russia', 'Rwanda', 'Saint Barthélemy',\n", - " 'Saint Kitts And Nevis', 'Saint Lucia', 'Saint Martin',\n", - " 'Saint Pierre And Miquelon', 'Saint Vincent And The Grenadines',\n", - " 'Samoa', 'San Marino', 'Sao Tome And Principe', 'Saudi Arabia',\n", - " 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',\n", - " 'Sint Maarten', 'Slovakia', 'Slovenia', 'Solomon Islands',\n", - " 'Somalia', 'South Africa', 'South America',\n", - " 'South Georgia And The South Sandwich Isla', 'South Korea',\n", - " 'Spain', 'Sri Lanka', 'Sudan', 'Suriname',\n", - " 'Svalbard And Jan Mayen', 'Swaziland', 'Sweden', 'Switzerland',\n", - " 'Syria', 'Taiwan', 'Tajikistan', 'Tanzania', 'Thailand',\n", - " 'Timor Leste', 'Togo', 'Tonga', 'Trinidad And Tobago', 'Tunisia',\n", - " 'Turkey', 'Turkmenistan', 'Turks And Caicas Islands', 'Uganda',\n", - " 'Ukraine', 'United Arab Emirates', 'United Kingdom (Europe)',\n", - " 'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan',\n", - " 'Venezuela', 'Vietnam', 'Virgin Islands', 'Western Sahara',\n", - " 'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)" - ] - }, - "execution_count": 303, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature.Country.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 305, - "metadata": {}, - "outputs": [], - "source": [ - "non_wanted = ['Åland','Africa','Asia','Baker Island','Denmark','Federated States Of Micronesia','France', 'French Guiana', 'French Polynesia',\n", - " 'French Southern And Antarctic Lands','Gaza Strip','Guadeloupe', 'Guam','Guernsey','Heard Island And Mcdonald Islands','Isle Of Man','Jersey','Kingman Reef',\n", - " 'Montserrat','Netherlands (Europe)','Niue','North America','New Caledonia','Northern Mariana Islands','Oceania',\n", - " 'Palmyra Atoll','Saint Barthélemy','American Samoa','South America','South Georgia And The South Sandwich Isla',\n", - " 'Svalbard And Jan Mayen','Turks And Caicas Islands','United Kingdom','Western Sahara']\n", - "for region in non_wanted:\n", - " temperature = temperature[temperature.Country != region]" - ] - }, - { - "cell_type": "code", - "execution_count": 307, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AverageTemperature
yearCountry
1743Albania8.620
Andorra7.556
Austria2.482
Belarus0.767
Belgium7.106
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" - ], - "text/plain": [ - " AverageTemperature\n", - "year Country \n", - "1743 Albania 8.620\n", - " Andorra 7.556\n", - " Austria 2.482\n", - " Belarus 0.767\n", - " Belgium 7.106" - ] - }, - "execution_count": 307, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_temperature = temperature[['year', 'Country','AverageTemperature']].groupby(['year','Country']).agg({'AverageTemperature':'mean'})\n", - "annual_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 309, - "metadata": {}, - "outputs": [], - "source": [ - "annual_temperature.reset_index(level=0, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 310, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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yearAverageTemperature
Country
Albania17438.620
Andorra17437.556
Austria17432.482
Belarus17430.767
Belgium17437.106
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" - ], - "text/plain": [ - " year AverageTemperature\n", - "Country \n", - "Albania 1743 8.620\n", - "Andorra 1743 7.556\n", - "Austria 1743 2.482\n", - "Belarus 1743 0.767\n", - "Belgium 1743 7.106" - ] - }, - "execution_count": 310, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 311, - "metadata": {}, - "outputs": [], - "source": [ - "annual_temperature.reset_index(level=0, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 312, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryyearAverageTemperature
0Albania17438.620
1Andorra17437.556
2Austria17432.482
3Belarus17430.767
4Belgium17437.106
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" - ], - "text/plain": [ - " Country year AverageTemperature\n", - "0 Albania 1743 8.620\n", - "1 Andorra 1743 7.556\n", - "2 Austria 1743 2.482\n", - "3 Belarus 1743 0.767\n", - "4 Belgium 1743 7.106" - ] - }, - "execution_count": 312, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 313, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 41931 entries, 0 to 41930\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 41931 non-null object \n", - " 1 year 41931 non-null int64 \n", - " 2 AverageTemperature 39793 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 982.9+ KB\n" - ] - } - ], - "source": [ - "annual_temperature.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Global Temperature anomaly 1850-2018\n" - ] - }, - { - "cell_type": "code", - "execution_count": 430, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
EntityCodeYearMedian (℃)Upper (℃)Lower (℃)
0GlobalNaN1850-0.373-0.339-0.425
1GlobalNaN1851-0.218-0.184-0.274
2GlobalNaN1852-0.228-0.196-0.280
3GlobalNaN1853-0.269-0.239-0.321
4GlobalNaN1854-0.248-0.218-0.301
\n", - "
" - ], - "text/plain": [ - " Entity Code Year Median (℃) Upper (℃) Lower (℃)\n", - "0 Global NaN 1850 -0.373 -0.339 -0.425\n", - "1 Global NaN 1851 -0.218 -0.184 -0.274\n", - "2 Global NaN 1852 -0.228 -0.196 -0.280\n", - "3 Global NaN 1853 -0.269 -0.239 -0.321\n", - "4 Global NaN 1854 -0.248 -0.218 -0.301" - ] - }, - "execution_count": 430, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature_a = pd.read_csv('data//temperature_anomaly.csv')\n", - "temperature_a.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 431, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 676 entries, 0 to 675\n", - "Data columns (total 6 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Entity 676 non-null object \n", - " 1 Code 0 non-null float64\n", - " 2 Year 676 non-null int64 \n", - " 3 Median (℃) 676 non-null float64\n", - " 4 Upper (℃) 676 non-null float64\n", - " 5 Lower (℃) 676 non-null float64\n", - "dtypes: float64(4), int64(1), object(1)\n", - "memory usage: 31.8+ KB\n" - ] - } - ], - "source": [ - "temperature_a.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 432, - "metadata": {}, - "outputs": [], - "source": [ - "temperature_a.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 433, - "metadata": {}, - "outputs": [], - "source": [ - "temperature_a.rename(columns={'Entity':'Country'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 434, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Global', 'Northern Hemisphere', 'Southern Hemisphere', 'Tropics'],\n", - " dtype=object)" - ] - }, - "execution_count": 434, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature_a.Country.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 435, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860,\n", - " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", - " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", - " 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893,\n", - " 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904,\n", - " 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915,\n", - " 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926,\n", - " 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937,\n", - " 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948,\n", - " 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", - " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", - " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", - " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", - " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", - " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", - " 2015, 2016, 2017, 2018], dtype=int64)" - ] - }, - "execution_count": 435, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature_a.Year.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 436, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" - ] - }, - "execution_count": 436, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "x = temperature_a.Year\n", - "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", - "plt.figure(figsize=(17,6))\n", - "\n", - "plt.plot(x, y, 'dodgerblue', label='Temperature Anomaly', \n", - " linewidth=2)\n", - "\n", - "plt.legend()\n", - "plt.xlabel('Year')\n", - "plt.ylabel('increse ordescrease in Temperature(℃)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "temperature_a.Entity." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Women in parliment by country" - ] - }, - { - "cell_type": "code", - "execution_count": 314, - "metadata": {}, - "outputs": [], - "source": [ - "women_in_parliment = pd.read_csv('data//women_in_parliment.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Country NameCountry CodeIndicator NameIndicator Code196019611962196319641965...201120122013201420152016201720182019Unnamed: 64
0ArubaABWProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1AfghanistanAFGProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...27.727.727.727.727.727.727.727.727.87NaN
2AngolaAGOProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...38.234.134.136.836.836.838.230.530.00NaN
3AlbaniaALBProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...15.715.717.920.020.722.927.927.929.51NaN
4AndorraANDProportion of seats held by women in national ...SG.GEN.PARL.ZSNaNNaNNaNNaNNaNNaN...50.050.050.050.039.332.132.132.150.00NaN
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5 rows × 65 columns

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" - ], - "text/plain": [ - " Country Name 1997 1998 1999 2000 2001 2002 2003 2004 2005 ... \\\n", - "0 Aruba NaN NaN NaN NaN NaN NaN NaN NaN NaN ... \n", - "1 Afghanistan NaN NaN NaN NaN NaN NaN NaN NaN 27.3 ... \n", - "2 Angola 9.5 15.5 15.5 15.5 15.5 15.5 15.5 15.0 15.0 ... \n", - "3 Albania NaN NaN 5.2 5.2 5.7 5.7 5.7 6.4 7.1 ... \n", - "4 Andorra 7.1 7.1 7.1 7.1 14.3 14.3 14.3 14.3 28.6 ... \n", - "\n", - " 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 \n", - "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.87 \n", - "2 38.6 38.2 34.1 34.1 36.8 36.8 36.8 38.2 30.5 30.00 \n", - "3 16.4 15.7 15.7 17.9 20.0 20.7 22.9 27.9 27.9 29.51 \n", - "4 35.7 50.0 50.0 50.0 50.0 39.3 32.1 32.1 32.1 50.00 \n", - "\n", - "[5 rows x 24 columns]" - ] - }, - "execution_count": 172, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "women_in_parliment.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": {}, - "outputs": [], - "source": [ - "columns = list(women_in_parliment.columns)\n", - "columns.remove('Country Name')" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Country Name Year Women in Parliment\n", - "0 Aruba 1997 NaN\n", - "1 Afghanistan 1997 NaN\n", - "2 Angola 1997 9.5\n", - "3 Albania 1997 NaN\n", - "4 Andorra 1997 7.1" - ] - }, - "execution_count": 175, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "women_in_power = pd.melt(women_in_parliment, id_vars=['Country Name'], value_vars=columns, var_name=\"Year\", value_name=\"Women in Parliment\")\n", - "women_in_power.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 344, - "metadata": {}, - "outputs": [], - "source": [ - "women_in_power.rename(columns={'Country Name':'Country'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Country Name 299\n", - "Year 0\n", - "Women in Parliment 1128\n", - "dtype: int64" - ] - }, - "execution_count": 176, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "women_in_power.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 6072 entries, 0 to 6071\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country Name 5773 non-null object \n", - " 1 Year 6072 non-null object \n", - " 2 Women in Parliment 4944 non-null float64\n", - "dtypes: float64(1), object(2)\n", - "memory usage: 142.4+ KB\n" - ] - } - ], - "source": [ - "women_in_power.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 346, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Aruba', 'Afghanistan', 'Angola', 'Albania', 'Andorra',\n", - " 'Arab World', 'United Arab Emirates', 'Argentina', 'Armenia',\n", - " 'American Samoa', 'Antigua and Barbuda', 'Australia', 'Austria',\n", - " 'Azerbaijan', 'Burundi', 'Belgium', 'Benin', 'Burkina Faso',\n", - " 'Bangladesh', 'Bulgaria', 'Bahrain', nan, 'Bosnia and Herzegovina',\n", - " 'Belarus', 'Belize', 'Bermuda', 'Bolivia', 'Brazil', 'Barbados',\n", - " 'Brunei Darussalam', 'Bhutan', 'Botswana',\n", - " 'Central African Republic', 'Canada',\n", - " 'Central Europe and the Baltics', 'Switzerland', 'Channel Islands',\n", - " 'Chile', 'China', \"Cote d'Ivoire\", 'Cameroon', 'Colombia',\n", - " 'Comoros', 'Cabo Verde', 'Costa Rica', 'Caribbean small states',\n", - " 'Cuba', 'Curacao', 'Cayman Islands', 'Cyprus', 'Czech Republic',\n", - " 'Germany', 'Djibouti', 'Dominica', 'Denmark', 'Dominican Republic',\n", - " 'Algeria', 'East Asia & Pacific (excluding high income)',\n", - " 'Early-demographic dividend', 'East Asia & Pacific',\n", - " 'Europe & Central Asia (excluding high income)',\n", - " 'Europe & Central Asia', 'Ecuador', 'Euro area', 'Eritrea',\n", - " 'Spain', 'Estonia', 'Ethiopia', 'European Union',\n", - " 'Fragile and conflict affected situations', 'Finland', 'Fiji',\n", - " 'France', 'Faroe Islands', 'Gabon', 'United Kingdom', 'Georgia',\n", - " 'Ghana', 'Gibraltar', 'Guinea', 'Guinea-Bissau',\n", - " 'Equatorial Guinea', 'Greece', 'Grenada', 'Greenland', 'Guatemala',\n", - " 'Guam', 'Guyana', 'High income', 'Honduras',\n", - " 'Heavily indebted poor countries (HIPC)', 'Croatia', 'Haiti',\n", - " 'Hungary', 'IBRD only', 'IDA & IBRD total', 'IDA total',\n", - " 'IDA blend', 'Indonesia', 'IDA only', 'Isle of Man', 'India',\n", - " 'Not classified', 'Ireland', 'Iraq', 'Iceland', 'Israel', 'Italy',\n", - " 'Jamaica', 'Jordan', 'Japan', 'Kazakhstan', 'Kenya',\n", - " 'Kyrgyz Republic', 'Cambodia', 'Kiribati', 'St. Kitts and Nevis',\n", - " 'Kuwait', 'Latin America & Caribbean (excluding high income)',\n", - " 'Lao PDR', 'Lebanon', 'Liberia', 'Libya', 'St. Lucia',\n", - " 'Latin America & Caribbean',\n", - " 'Least developed countries: UN classification', 'Low income',\n", - " 'Liechtenstein', 'Sri Lanka', 'Lower middle income',\n", - " 'Low & middle income', 'Lesotho', 'Late-demographic dividend',\n", - " 'Lithuania', 'Luxembourg', 'Latvia', 'St. Martin (French part)',\n", - " 'Morocco', 'Monaco', 'Moldova', 'Madagascar', 'Maldives',\n", - " 'Middle East & North Africa', 'Mexico', 'Marshall Islands',\n", - " 'Middle income', 'North Macedonia', 'Mali', 'Malta', 'Myanmar',\n", - " 'Middle East & North Africa (excluding high income)', 'Montenegro',\n", - " 'Mongolia', 'Northern Mariana Islands', 'Mozambique', 'Mauritania',\n", - " 'Mauritius', 'Malawi', 'Malaysia', 'North America', 'Namibia',\n", - " 'New Caledonia', 'Niger', 'Nigeria', 'Nicaragua', 'Netherlands',\n", - " 'Norway', 'Nepal', 'Nauru', 'New Zealand', 'OECD members', 'Oman',\n", - " 'Other small states', 'Pakistan', 'Panama', 'Peru', 'Philippines',\n", - " 'Palau', 'Papua New Guinea', 'Poland', 'Pre-demographic dividend',\n", - " 'Puerto Rico', 'Portugal', 'Paraguay', 'West Bank and Gaza',\n", - " 'Pacific island small states', 'Post-demographic dividend',\n", - " 'French Polynesia', 'Qatar', 'Romania', 'Russian Federation',\n", - " 'Rwanda', 'South Asia', 'Saudi Arabia', 'Sudan', 'Senegal',\n", - " 'Singapore', 'Solomon Islands', 'Sierra Leone', 'El Salvador',\n", - " 'San Marino', 'Somalia', 'Serbia',\n", - " 'Sub-Saharan Africa (excluding high income)', 'South Sudan',\n", - " 'Sub-Saharan Africa', 'Small states', 'Sao Tome and Principe',\n", - " 'Suriname', 'Slovak Republic', 'Slovenia', 'Sweden', 'Eswatini',\n", - " 'Sint Maarten (Dutch part)', 'Seychelles', 'Syrian Arab Republic',\n", - " 'Turks and Caicos Islands', 'Chad',\n", - " 'East Asia & Pacific (IDA & IBRD countries)',\n", - " 'Europe & Central Asia (IDA & IBRD countries)', 'Togo', 'Thailand',\n", - " 'Tajikistan', 'Turkmenistan',\n", - " 'Latin America & the Caribbean (IDA & IBRD countries)',\n", - " 'Timor-Leste', 'Middle East & North Africa (IDA & IBRD countries)',\n", - " 'Tonga', 'South Asia (IDA & IBRD)',\n", - " 'Sub-Saharan Africa (IDA & IBRD countries)', 'Trinidad and Tobago',\n", - " 'Tunisia', 'Turkey', 'Tuvalu', 'Tanzania', 'Uganda', 'Ukraine',\n", - " 'Upper middle income', 'Uruguay', 'United States', 'Uzbekistan',\n", - " 'St. Vincent and the Grenadines', 'British Virgin Islands',\n", - " 'Virgin Islands (U.S.)', 'Vietnam', 'Vanuatu', 'World', 'Samoa',\n", - " 'Kosovo', 'South Africa', 'Zambia', 'Zimbabwe'], dtype=object)" - ] - }, - "execution_count": 346, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "women_in_power['Country'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 347, - "metadata": {}, - "outputs": [], - "source": [ - "Countries = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n", - " 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n", - " 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh',\n", - " 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',\n", - " 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n", - " 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia',\n", - " 'Cameroon', 'Canada', 'Cape Verde', 'Central African Republic',\n", - " 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'Congo',\n", - " 'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus',\n", - " 'Czech Republic', 'Democratic Republic of Congo', 'Denmark',\n", - " 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',\n", - " 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',\n", - " 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon', 'Gambia',\n", - " 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada', 'Guatemala',\n", - " 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Honduras',\n", - " 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\n", - " 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan',\n", - " 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos',\n", - " 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',\n", - " 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macedonia',\n", - " 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta',\n", - " 'Marshall Islands', 'Mauritania', 'Mauritius', 'Mexico',\n", - " 'Micronesia (country)', 'Moldova', 'Monaco', 'Mongolia',\n", - " 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\n", - " 'Nauru', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua',\n", - " 'Niger', 'Nigeria', 'North Korea', 'Norway', 'Oman', 'Pakistan',\n", - " 'Palau', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n", - " 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia',\n", - " 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia',\n", - " 'Saint Vincent and the Grenadines', 'Samoa', 'San Marino',\n", - " 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n", - " 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',\n", - " 'Solomon Islands', 'Somalia', 'South Africa', 'South Korea',\n", - " 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Swaziland', 'Sweden',\n", - " 'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand',\n", - " 'Timor', 'Togo', 'Tonga', 'Trinidad and Tobago', 'Tunisia',\n", - " 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine',\n", - " 'United Arab Emirates', 'United Kingdom', 'United States',\n", - " 'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam',\n", - " 'Yemen', 'Zambia', 'Zimbabwe']" - ] - }, - { - "cell_type": "code", - "execution_count": 353, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aruba\n", - "Arab World\n", - "American Samoa\n", - "nan\n", - "Bermuda\n", - "Brunei Darussalam\n", - "Central Europe and the Baltics\n", - "Channel Islands\n", - "Cabo Verde\n", - "Caribbean small states\n", - "Curacao\n", - "Cayman Islands\n", - "East Asia & Pacific (excluding high income)\n", - "Early-demographic dividend\n", - "East Asia & Pacific\n", - "Europe & Central Asia (excluding high income)\n", - "Europe & Central Asia\n", - "Euro area\n", - "European Union\n", - "Fragile and conflict affected situations\n", - "Faroe Islands\n", - "Gibraltar\n", - "Greenland\n", - "Guam\n", - "High income\n", - "Heavily indebted poor countries (HIPC)\n", - "IBRD only\n", - "IDA & IBRD total\n", - "IDA total\n", - "IDA blend\n", - "IDA only\n", - "Isle of Man\n", - "Not classified\n", - "Kyrgyz Republic\n", - "St. Kitts and Nevis\n", - "Latin America & Caribbean (excluding high income)\n", - "Lao PDR\n", - "St. Lucia\n", - "Latin America & Caribbean\n", - "Least developed countries: UN classification\n", - "Low income\n", - "Lower middle income\n", - "Low & middle income\n", - "Late-demographic dividend\n", - "St. Martin (French part)\n", - "Middle East & North Africa\n", - "Middle income\n", - "North Macedonia\n", - "Middle East & North Africa (excluding high income)\n", - "Northern Mariana Islands\n", - "North America\n", - "New Caledonia\n", - "OECD members\n", - "Other small states\n", - "Pre-demographic dividend\n", - "Puerto Rico\n", - "West Bank and Gaza\n", - "Pacific island small states\n", - "Post-demographic dividend\n", - "French Polynesia\n", - "Russian Federation\n", - "South Asia\n", - "Sub-Saharan Africa (excluding high income)\n", - "South Sudan\n", - "Sub-Saharan Africa\n", - "Small states\n", - "Slovak Republic\n", - "Eswatini\n", - "Sint Maarten (Dutch part)\n", - "Syrian Arab Republic\n", - "Turks and Caicos Islands\n", - "East Asia & Pacific (IDA & IBRD countries)\n", - "Europe & Central Asia (IDA & IBRD countries)\n", - "Latin America & the Caribbean (IDA & IBRD countries)\n", - "Timor-Leste\n", - "Middle East & North Africa (IDA & IBRD countries)\n", - "South Asia (IDA & IBRD)\n", - "Sub-Saharan Africa (IDA & IBRD countries)\n", - "Upper middle income\n", - "St. Vincent and the Grenadines\n", - "British Virgin Islands\n", - "Virgin Islands (U.S.)\n", - "World\n", - "Kosovo\n" - ] - } - ], - "source": [ - "for country in women_in_power['Country'].unique():\n", - " if country not in Countries:\n", - " print(country)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Proportion of women in ministerial positions (2005, 2008, 2010, 2012, 2014, 2015, 2016)" - ] - }, - { - "cell_type": "code", - "execution_count": 317, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearProportion of women in ministerial level positions (%) (%)
0AfghanistanAFG200510.0
1AfghanistanAFG20083.7
2AfghanistanAFG20107.4
3AfghanistanAFG201212.0
4AfghanistanAFG201411.1
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" - ], - "text/plain": [ - " Entity Code Year \\\n", - "0 Afghanistan AFG 2005 \n", - "1 Afghanistan AFG 2008 \n", - "2 Afghanistan AFG 2010 \n", - "3 Afghanistan AFG 2012 \n", - "4 Afghanistan AFG 2014 \n", - "\n", - " Proportion of women in ministerial level positions (%) (%) \n", - "0 10.0 \n", - "1 3.7 \n", - "2 7.4 \n", - "3 12.0 \n", - "4 11.1 " - ] - }, - "execution_count": 317, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "women_in_ministeries = pd.read_csv('data//proportion-of-women-in-ministerial-positions.csv')\n", - "women_in_ministeries.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 318, - "metadata": {}, - "outputs": [], - "source": [ - "women_in_ministeries.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 319, - "metadata": {}, - "outputs": [], - "source": [ - "women_in_ministeries.rename(columns={'Proportion of women in ministerial level positions (%) (%)':'Women in Ministeries(%)','Entity':'Country'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 320, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n", - " 'Antigua and Barbuda', 'Arab World', 'Argentina', 'Armenia',\n", - " 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',\n", - " 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin',\n", - " 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana',\n", - " 'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi',\n", - " 'Cambodia', 'Cameroon', 'Canada', 'Cape Verde',\n", - " 'Caribbean small states', 'Central African Republic',\n", - " 'Central Europe and the Baltics', 'Chad', 'Chile', 'China',\n", - " 'Colombia', 'Comoros', 'Congo', 'Costa Rica', \"Cote d'Ivoire\",\n", - " 'Croatia', 'Cuba', 'Cyprus', 'Czech Republic',\n", - " 'Democratic Republic of Congo', 'Denmark', 'Djibouti', 'Dominica',\n", - " 'Dominican Republic', 'Early-demographic dividend',\n", - " 'East Asia & Pacific', 'East Asia & Pacific (IDA & IBRD)',\n", - " 'East Asia & Pacific (excluding high income)', 'Ecuador', 'Egypt',\n", - " 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',\n", - " 'Ethiopia', 'Euro area', 'Europe & Central Asia',\n", - " 'Europe & Central Asia (IDA & IBRD)',\n", - " 'Europe & Central Asia (excluding high income)', 'European Union',\n", - " 'Fiji', 'Finland', 'Fragile and conflict affected situations',\n", - " 'France', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana',\n", - " 'Greece', 'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau',\n", - " 'Guyana', 'Haiti', 'Heavily indebted poor countries (HIPC)',\n", - " 'High income', 'Honduras', 'Hungary', 'IBRD only',\n", - " 'IDA & IBRD total', 'IDA blend', 'IDA only', 'IDA total',\n", - " 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland',\n", - " 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan',\n", - " 'Kenya', 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos',\n", - " 'Late-demographic dividend', 'Latin America & Caribbean',\n", - " 'Latin America & Caribbean (IDA & IBRD)',\n", - " 'Latin America & Caribbean (excluding high income)', 'Latvia',\n", - " 'Least developed countries: UN classification', 'Lebanon',\n", - " 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n", - " 'Low & middle income', 'Low income', 'Lower middle income',\n", - " 'Luxembourg', 'Macedonia', 'Madagascar', 'Malawi', 'Malaysia',\n", - " 'Maldives', 'Mali', 'Malta', 'Marshall Islands', 'Mauritania',\n", - " 'Mauritius', 'Mexico', 'Micronesia (country)',\n", - " 'Middle East & North Africa',\n", - " 'Middle East & North Africa (IDA & IBRD)',\n", - " 'Middle East & North Africa (excluding high income)',\n", - " 'Middle income', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro',\n", - " 'Morocco', 'Mozambique', 'Myanmar', 'Namibia', 'Nauru', 'Nepal',\n", - " 'Netherlands', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria',\n", - " 'North America', 'North Korea', 'Norway', 'OECD members', 'Oman',\n", - " 'Other small states', 'Pacific island small states', 'Pakistan',\n", - " 'Palau', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n", - " 'Philippines', 'Poland', 'Portugal', 'Post-demographic dividend',\n", - " 'Pre-demographic dividend', 'Qatar', 'Romania', 'Russia', 'Rwanda',\n", - " 'Saint Kitts and Nevis', 'Saint Lucia',\n", - " 'Saint Vincent and the Grenadines', 'Samoa', 'San Marino',\n", - " 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n", - " 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',\n", - " 'Small states', 'Solomon Islands', 'Somalia', 'South Africa',\n", - " 'South Asia', 'South Asia (IDA & IBRD)', 'South Korea',\n", - " 'South Sudan', 'Spain', 'Sri Lanka', 'Sub-Saharan Africa',\n", - " 'Sub-Saharan Africa (IDA & IBRD)',\n", - " 'Sub-Saharan Africa (excluding high income)', 'Sudan', 'Suriname',\n", - " 'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Tajikistan',\n", - " 'Tanzania', 'Thailand', 'Timor', 'Togo', 'Tonga',\n", - " 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Turkmenistan',\n", - " 'Tuvalu', 'Uganda', 'Ukraine', 'United Arab Emirates',\n", - " 'United Kingdom', 'United States', 'Upper middle income',\n", - " 'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam',\n", - " 'World', 'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)" - ] - }, - "execution_count": 320, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "women_in_ministeries.Country.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 323, - "metadata": {}, - "outputs": [], - "source": [ - "no_wanted = ['Arab World','Caribbean small states','Central Europe and the Baltics','Early-demographic dividend','East Asia & Pacific', \n", - " 'East Asia & Pacific (IDA & IBRD)','East Asia & Pacific (excluding high income)','Euro area', 'Europe & Central Asia',\n", - " 'Europe & Central Asia (IDA & IBRD)','Europe & Central Asia (excluding high income)', 'European Union','Fragile and conflict affected situations',\n", - " 'Heavily indebted poor countries (HIPC)','High income','IBRD only','IDA & IBRD total', 'IDA blend', 'IDA only', 'IDA total',\n", - " 'Late-demographic dividend', 'Latin America & Caribbean','Latin America & Caribbean (IDA & IBRD)','Latin America & Caribbean (excluding high income)',\n", - " 'Least developed countries: UN classification','Low & middle income', 'Low income', 'Lower middle income','Middle East & North Africa',\n", - " 'Middle East & North Africa (IDA & IBRD)','Middle East & North Africa (excluding high income)','Middle income','North America',\n", - " 'OECD members','Other small states', 'Pacific island small states','Post-demographic dividend','Pre-demographic dividend',\n", - " 'Small states','South Asia', 'South Asia (IDA & IBRD)','South Sudan','Sub-Saharan Africa','Sub-Saharan Africa (IDA & IBRD)',\n", - " 'Sub-Saharan Africa (excluding high income)','Upper middle income','World']\n", - "for region in no_wanted:\n", - " women_in_ministeries = women_in_ministeries[women_in_ministeries.Country != region]" - ] - }, - { - "cell_type": "code", - "execution_count": 327, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2005, 2008, 2010, 2012, 2014, 2015, 2016], dtype=int64)" - ] - }, - "execution_count": 327, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "women_in_ministeries.Year.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 342, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n", - " 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n", - " 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh',\n", - " 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',\n", - " 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n", - " 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia',\n", - " 'Cameroon', 'Canada', 'Cape Verde', 'Central African Republic',\n", - " 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'Congo',\n", - " 'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus',\n", - " 'Czech Republic', 'Democratic Republic of Congo', 'Denmark',\n", - " 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',\n", - " 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',\n", - " 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon', 'Gambia',\n", - " 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada', 'Guatemala',\n", - " 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Honduras',\n", - " 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\n", - " 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan',\n", - " 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos',\n", - " 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',\n", - " 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macedonia',\n", - " 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta',\n", - " 'Marshall Islands', 'Mauritania', 'Mauritius', 'Mexico',\n", - " 'Micronesia (country)', 'Moldova', 'Monaco', 'Mongolia',\n", - " 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\n", - " 'Nauru', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua',\n", - " 'Niger', 'Nigeria', 'North Korea', 'Norway', 'Oman', 'Pakistan',\n", - " 'Palau', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n", - " 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia',\n", - " 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia',\n", - " 'Saint Vincent and the Grenadines', 'Samoa', 'San Marino',\n", - " 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n", - " 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',\n", - " 'Solomon Islands', 'Somalia', 'South Africa', 'South Korea',\n", - " 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Swaziland', 'Sweden',\n", - " 'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand',\n", - " 'Timor', 'Togo', 'Tonga', 'Trinidad and Tobago', 'Tunisia',\n", - " 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine',\n", - " 'United Arab Emirates', 'United Kingdom', 'United States',\n", - " 'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam',\n", - " 'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)" - ] - }, - "execution_count": 342, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Share of top Female Managers (2008, 2014, 2013, 2010, 2017, 2009, 2007, 2016, 2015, 2011, 2012)" - ] - }, - { - "cell_type": "code", - "execution_count": 340, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearFirms with female top manager (% of firms) (% of firms)
0AfghanistanAFG20080.7
1AfghanistanAFG20144.7
2AlbaniaALB201311.7
3AngolaAGO201013.5
4Antigua and BarbudaATG201017.5
\n", - "
" - ], - "text/plain": [ - " Entity Code Year \\\n", - "0 Afghanistan AFG 2008 \n", - "1 Afghanistan AFG 2014 \n", - "2 Albania ALB 2013 \n", - "3 Angola AGO 2010 \n", - "4 Antigua and Barbuda ATG 2010 \n", - "\n", - " Firms with female top manager (% of firms) (% of firms) \n", - "0 0.7 \n", - "1 4.7 \n", - "2 11.7 \n", - "3 13.5 \n", - "4 17.5 " - ] - }, - "execution_count": 340, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "female_managers = pd.read_csv('data//share-firms-top-female-manager.csv')\n", - "female_managers.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 329, - "metadata": {}, - "outputs": [], - "source": [ - "female_managers.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 330, - "metadata": {}, - "outputs": [], - "source": [ - "female_managers.rename(columns={'Firms with female top manager (% of firms) (% of firms)':'Female top manager (%)','Entity':'Country'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 332, - "metadata": {}, - "outputs": [], - "source": [ - "for region in no_wanted:\n", - " female_managers = female_managers[female_managers.Country != region]" - ] - }, - { - "cell_type": "code", - "execution_count": 333, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Afghanistan', 'Albania', 'Angola', 'Antigua and Barbuda',\n", - " 'Argentina', 'Armenia', 'Azerbaijan', 'Bahamas', 'Bangladesh',\n", - " 'Barbados', 'Belarus', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\n", - " 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Bulgaria',\n", - " 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon', 'Cape Verde',\n", - " 'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',\n", - " 'Congo', 'Costa Rica', \"Cote d'Ivoire\", 'Croatia',\n", - " 'Czech Republic', 'Democratic Republic of Congo', 'Djibouti',\n", - " 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',\n", - " 'El Salvador', 'Eritrea', 'Estonia', 'Ethiopia', 'Gabon',\n", - " 'Georgia', 'Ghana', 'Grenada', 'Guatemala', 'Guinea', 'Guyana',\n", - " 'Honduras', 'Hungary', 'India', 'Indonesia', 'Iraq', 'Israel',\n", - " 'Jamaica', 'Jordan', 'Kazakhstan', 'Kenya', 'Kosovo', 'Kyrgyzstan',\n", - " 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Lithuania',\n", - " 'Macedonia', 'Madagascar', 'Malawi', 'Malaysia', 'Mali',\n", - " 'Mauritania', 'Mexico', 'Moldova', 'Mongolia', 'Montenegro',\n", - " 'Morocco', 'Myanmar', 'Namibia', 'Nepal', 'Nicaragua', 'Niger',\n", - " 'Nigeria', 'Pakistan', 'Palestine', 'Panama', 'Papua New Guinea',\n", - " 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Romania', 'Russia',\n", - " 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia',\n", - " 'Saint Vincent and the Grenadines', 'Senegal', 'Serbia',\n", - " 'Sierra Leone', 'Slovakia', 'Slovenia', 'Solomon Islands',\n", - " 'Sri Lanka', 'Sudan', 'Suriname', 'Swaziland', 'Sweden',\n", - " 'Syrian Arab Republic', 'Tajikistan', 'Tanzania', 'Thailand',\n", - " 'Timor', 'Togo', 'Trinidad and Tobago', 'Tunisia', 'Turkey',\n", - " 'Uganda', 'Ukraine', 'Uruguay', 'Uzbekistan', 'Venezuela',\n", - " 'Vietnam', 'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)" - ] - }, - "execution_count": 333, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "female_managers.Country.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 334, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2008, 2014, 2013, 2010, 2017, 2009, 2007, 2016, 2015, 2011, 2012],\n", - " dtype=int64)" - ] - }, - "execution_count": 334, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "female_managers.Year.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 337, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 339, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 False\n", - "1 False\n", - "2 False\n", - "3 False\n", - "4 False\n", - "Name: Country, dtype: bool" - ] - }, - "execution_count": 339, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### another set for women in parliment" - ] - }, - { - "cell_type": "code", - "execution_count": 355, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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LOCATIONINDICATORSUBJECTMEASUREFREQUENCYTIMEValueFlag Codes
0AUSWOMENPOLVOICEPOLREPRESPCA200225.3NaN
1AUSWOMENPOLVOICEPOLREPRESPCA201224.7NaN
2AUSWOMENPOLVOICEPOLREPRESPCA201426.0NaN
3AUSWOMENPOLVOICEPOLREPRESPCA201526.7NaN
4AUSWOMENPOLVOICEPOLREPRESPCA201728.7NaN
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" - ], - "text/plain": [ - " LOCATION INDICATOR SUBJECT MEASURE FREQUENCY TIME Value \\\n", - "0 AUS WOMENPOLVOICE POLREPRES PC A 2002 25.3 \n", - "1 AUS WOMENPOLVOICE POLREPRES PC A 2012 24.7 \n", - "2 AUS WOMENPOLVOICE POLREPRES PC A 2014 26.0 \n", - "3 AUS WOMENPOLVOICE POLREPRES PC A 2015 26.7 \n", - "4 AUS WOMENPOLVOICE POLREPRES PC A 2017 28.7 \n", - "\n", - " Flag Codes \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN " - ] - }, - "execution_count": 355, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "female_parlamentaries = pd.read_csv('data//DP_LIVE_23052020174320282.csv')\n", - "female_parlamentaries.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 382, - "metadata": {}, - "outputs": [], - "source": [ - "countries_dict = {'ABW':'Aruba','AFG':'Afghanistan','AGO':'Angola','AIA':'Anguilla','ALA':'Åland Islands','ALB':'Albania',\n", - " 'AND':'Andorra','ARE':'United Arab Emirates','ARG':'Argentina','ARM':'Armenia','ASM':'American Samoa',\n", - " 'ATA':'Antarctica','ATF':'French Southern Territories','ATG':'Antigua and Barbuda','AUS':'Australia',\n", - " 'AUT':'Austria','AZE':'Azerbaijan','BDI':'Burundi','BEL':'Belgium','BEN':'Benin','BES':'Bonaire','BFA':'Burkina Faso',\n", - " 'BGD':'Bangladesh','BGR':'Bulgaria','BHR':'Bahrain','BHS':'Bahamas','BIH':'Bosnia and Herzegovina',\n", - " 'BLM':'Saint Barthélemy','BLR':'Belarus','BLZ':'Belize','BMU':'Bermuda','BOL':'Bolivia','BRA':'Brazil',\n", - " 'BRB':'Barbados','BRN':'Brunei Darussalam','BTN':'Bhutan','BVT':'Bouvet Island','BWA':'Botswana',\n", - " 'CAF':'Central African Republic','CAN':'Canada','CCK':'Cocos Islands','CHE':'Switzerland','CHL':'Chile',\n", - " 'CHN':'China','CIV':\"Côte d'Ivoire\",'CMR':'Cameroon','COG':'Congo','COK':'Cook Islands','COL':'Colombia',\n", - " 'COM':'Comoros','CPV':'Cabo Verde','CRI':'Costa Rica','CUB':'Cuba','CUW':'Curaçao','CXR':'Christmas Island',\n", - " 'CYM':'Cayman Islands','CYP':'Cyprus','CZE':'Czechia','DEU':'Germany','DJI':'Djibouti','DMA':'Dominica',\n", - " 'DNK':'Denmark','DOM':'Dominican Republic','DZA':'Algeria','ECU':'Ecuador','EGY':'Egypt','ERI':'Eritrea',\n", - " 'ESH':'Western Sahara','ESP':'Spain','EST':'Estonia','ETH':'Ethiopia','FIN':'Finland','FJI':'Fiji',\n", - " 'FLK':'Falkland Islands (Malvinas)','FRA':'France','FRO':'Faroe Islands','FSM':'Micronesia','GAB':'Gabon',\n", - " 'GBR':'United Kingdom','GEO':'Georgia','GGY':'Guernsey','GHA':'Ghana','GIB':'Gibraltar','GIN':'Guinea',\n", - " 'GLP':'Guadeloupe','GMB':'Gambia','GNB':'Guinea Bissau','GNQ':'Equatorial Guinea','GRC':'Greece',\n", - " 'GRD':'Grenada','GRL':'Greenland','GTM':'Guatemala','GUF':'French Guiana','GUM':'Guam','GUY':'Guyana',\n", - " 'HKG':'Hong Kong','HMD':'Heard Island and McDonald Islands','HND':'Honduras','HRV':'Croatia','HTI':'Haiti',\n", - " 'HUN':'Hungary','IDN':'Indonesia','IMN':'Isle of Man','IND':'India','IRL':'Ireland','IRN':'Iran','IRQ':'Iraq',\n", - " 'ISL':'Iceland','ISR':'Israel','ITA':'Italy','JAM':'Jamaica','JEY':'Jersey','JOR':'Jordan','JPN':'Japan',\n", - " 'KAZ':'Kazakhstan','KEN':'Kenya','KGZ':'Kyrgyzstan','KHM':'Cambodia','KIR':'Kiribati',\n", - " 'KNA':'Saint Kitts and Nevis','KOR':'South Korea','KWT':'Kuwait','LBN':'Lebanon','LBR':'Liberia','LBY':'Libya',\n", - " 'LCA':'Saint Lucia','LIE':'Liechtenstein','LKA':'Sri Lanka','LSO':'Lesotho','LTU':'Lithuania',\n", - " 'LUX':'Luxembourg','LVA':'Latvia','MAC':'Macao','MAF':'Saint Martin','MAR':'Morocco','MCO':'Monaco',\n", - " 'MDA':'Moldova','MDG':'Madagascar','MDV':'Maldives','MEX':'Mexico','MHL':'Marshall Islands',\n", - " 'MKD':'North Macedonia','MLI':'Mali','MLT':'Malta','MMR':'Myanmar','MNE':'Montenegro','MNG':'Mongolia',\n", - " 'MNP':'Northern Mariana Islands','MOZ':'Mozambique','MRT':'Mauritania','MSR':'Montserrat','MTQ':'Martinique',\n", - " 'MUS':'Mauritius','MWI':'Malawi','MYS':'Malaysia','MYT':'Mayotte','NAM':'Namibia','NCL':'New Caledonia',\n", - " 'NER':'Niger','NFK':'Norfolk Island','NGA':'Nigeria','NIC':'Nicaragua','NIU':'Niue','NLD':'Netherlands',\n", - " 'NOR':'Norway','NPL':'Nepal','NRU':'Nauru','NZL':'New Zealand','OMN':'Oman','PAK':'Pakistan','PAN':'Panama',\n", - " 'PCN':'Pitcairn','PER':'Peru','PHL':'Philippines','PLW':'Palau','PNG':'Papua New Guinea','POL':'Poland',\n", - " 'PRI':'Puerto Rico','PRT':'Portugal','PRY':'Paraguay','PSE':'Palestine','PYF':'French Polynesia','QAT':'Qatar',\n", - " 'REU':'Réunion','ROU':'Romania','RUS':'Russia','RWA':'Rwanda','SAU':'Saudi Arabia','SDN':'Sudan',\n", - " 'SEN':'Senegal','SGP':'Singapore','SGS':'South Georgia and the South Sandwich Islands',\n", - " 'SHN':'Saint Helena, Ascension and Tristan da Cunha','SJM':'Svalbard and Jan Mayen','SLB':'Solomon Islands',\n", - " 'SLE':'Sierra Leone','SLV':'El Salvador','SMR':'San Marino','SOM':'Somalia','SPM':'Saint Pierre and Miquelon',\n", - " 'SRB':'Serbia','SSD':'South Sudan','STP':'Sao Tome and Principe','SUR':'Suriname','SVK':'Slovakia','SVN':'Slovenia',\n", - " 'SWE':'Sweden','SWZ':'Eswatini','SXM':'Sint Maarten','SYC':'Seychelles','SYR':'Syrian Arab Republic',\n", - " 'TCA':'Turks and Caicos Islands','TCD':'Chad','TGO':'Togo','THA':'Thailand','TJK':'Tajikistan',\n", - " 'TKL':'Tokelau','TKM':'Turkmenistan','TLS':'Timor Leste','TON':'Tonga','TTO':'Trinidad and Tobago',\n", - " 'TUN':'Tunisia','TUR':'Turkey','TUV':'Tuvalu','TZA':'Tanzania','UGA':'Uganda','UKR':'Ukraine','URY':'Uruguay',\n", - " 'USA':'United States','UZB':'Uzbekistan','VCT':'Saint Vincent and the Grenadines','VEN':'Venezuela',\n", - " 'VIR':'Virgin Islands','VNM':'Vietnam','VUT':'Vanuatu','WLF':'Wallis and Futuna','WSM':'Samoa',\n", - " 'YEM':'Yemen','ZAF':'South Africa','ZMB':'Zambia','ZWE':'Zimbabwe'}" - ] - }, - { - "cell_type": "code", - "execution_count": 406, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "2 columns passed, passed data had 241 columns", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36m_list_to_arrays\u001b[1;34m(data, columns, coerce_float, dtype)\u001b[0m\n\u001b[0;32m 496\u001b[0m result = _convert_object_array(\n\u001b[1;32m--> 497\u001b[1;33m \u001b[0mcontent\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 498\u001b[0m )\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36m_convert_object_array\u001b[1;34m(content, columns, coerce_float, dtype)\u001b[0m\n\u001b[0;32m 580\u001b[0m raise AssertionError(\n\u001b[1;32m--> 581\u001b[1;33m \u001b[1;34mf\"{len(columns)} columns passed, passed data had \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 582\u001b[0m \u001b[1;34mf\"{len(content)} columns\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mAssertionError\u001b[0m: 2 columns passed, passed data had 241 columns", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcountries_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcountries_dict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcountries_dict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Code'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Country'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m 472\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_named_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 473\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fields\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 474\u001b[1;33m \u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 475\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36mto_arrays\u001b[1;34m(data, columns, coerce_float, dtype)\u001b[0m\n\u001b[0;32m 483\u001b[0m \u001b[1;31m# last ditch effort\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 484\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 485\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_list_to_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 486\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 487\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36m_list_to_arrays\u001b[1;34m(data, columns, coerce_float, dtype)\u001b[0m\n\u001b[0;32m 498\u001b[0m )\n\u001b[0;32m 499\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mAssertionError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 500\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 501\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 502\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: 2 columns passed, passed data had 241 columns" - ] - } - ], - "source": [ - "countries_df = pd.DataFrame(data=[countries_dict.keys(),countries_dict.values()], columns=['Code','Country'])" - ] - }, - { - "cell_type": "code", - "execution_count": 401, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Code Country\n", - "0 ABW NaN\n", - "1 AFG NaN\n", - "2 AGO NaN\n", - "3 AIA NaN\n", - "4 ALA NaN\n", - ".. ... ...\n", - "236 NaN Samoa\n", - "237 NaN Yemen\n", - "238 NaN South Africa\n", - "239 NaN Zambia\n", - "240 NaN Zimbabwe\n", - "\n", - "[482 rows x 2 columns]" - ] - }, - "execution_count": 405, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "codes_df.append(countries_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 395, - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m female_parlamentaries['Country'] = (lambda countries_dict.values(): if countries_dict.key() in female_parlamentaries['LOCATION'])\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "source": [ - "female_parlamentaries['Country'] = (lambda countries_dict.values(): if countries_dict.key() in female_parlamentaries['LOCATION'])" - ] - }, - { - "cell_type": "code", - "execution_count": 394, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_values(['Aruba', 'Afghanistan', 'Angola', 'Anguilla', 'Åland Islands', 'Albania', 'Andorra', 'United Arab Emirates', 'Argentina', 'Armenia', 'American Samoa', 'Antarctica', 'French Southern Territories', 'Antigua and Barbuda', 'Australia', 'Austria', 'Azerbaijan', 'Burundi', 'Belgium', 'Benin', 'Bonaire', 'Burkina Faso', 'Bangladesh', 'Bulgaria', 'Bahrain', 'Bahamas', 'Bosnia and Herzegovina', 'Saint Barthélemy', 'Belarus', 'Belize', 'Bermuda', 'Bolivia', 'Brazil', 'Barbados', 'Brunei Darussalam', 'Bhutan', 'Bouvet Island', 'Botswana', 'Central African Republic', 'Canada', 'Cocos Islands', 'Switzerland', 'Chile', 'China', \"Côte d'Ivoire\", 'Cameroon', 'Congo', 'Cook Islands', 'Colombia', 'Comoros', 'Cabo Verde', 'Costa Rica', 'Cuba', 'Curaçao', 'Christmas Island', 'Cayman Islands', 'Cyprus', 'Czechia', 'Germany', 'Djibouti', 'Dominica', 'Denmark', 'Dominican Republic', 'Algeria', 'Ecuador', 'Egypt', 'Eritrea', 'Western Sahara', 'Spain', 'Estonia', 'Ethiopia', 'Finland', 'Fiji', 'Falkland Islands (Malvinas)', 'France', 'Faroe Islands', 'Micronesia', 'Gabon', 'United Kingdom', 'Georgia', 'Guernsey', 'Ghana', 'Gibraltar', 'Guinea', 'Guadeloupe', 'Gambia', 'Guinea Bissau', 'Equatorial Guinea', 'Greece', 'Grenada', 'Greenland', 'Guatemala', 'French Guiana', 'Guam', 'Guyana', 'Hong Kong', 'Heard Island and McDonald Islands', 'Honduras', 'Croatia', 'Haiti', 'Hungary', 'Indonesia', 'Isle of Man', 'India', 'Ireland', 'Iran', 'Iraq', 'Iceland', 'Israel', 'Italy', 'Jamaica', 'Jersey', 'Jordan', 'Japan', 'Kazakhstan', 'Kenya', 'Kyrgyzstan', 'Cambodia', 'Kiribati', 'Saint Kitts and Nevis', 'South Korea', 'Kuwait', 'Lebanon', 'Liberia', 'Libya', 'Saint Lucia', 'Liechtenstein', 'Sri Lanka', 'Lesotho', 'Lithuania', 'Luxembourg', 'Latvia', 'Macao', 'Saint Martin', 'Morocco', 'Monaco', 'Moldova', 'Madagascar', 'Maldives', 'Mexico', 'Marshall Islands', 'North Macedonia', 'Mali', 'Malta', 'Myanmar', 'Montenegro', 'Mongolia', 'Northern Mariana Islands', 'Mozambique', 'Mauritania', 'Montserrat', 'Martinique', 'Mauritius', 'Malawi', 'Malaysia', 'Mayotte', 'Namibia', 'New Caledonia', 'Niger', 'Norfolk Island', 'Nigeria', 'Nicaragua', 'Niue', 'Netherlands', 'Norway', 'Nepal', 'Nauru', 'New Zealand', 'Oman', 'Pakistan', 'Panama', 'Pitcairn', 'Peru', 'Philippines', 'Palau', 'Papua New Guinea', 'Poland', 'Puerto Rico', 'Portugal', 'Paraguay', 'Palestine', 'French Polynesia', 'Qatar', 'Réunion', 'Romania', 'Russia', 'Rwanda', 'Saudi Arabia', 'Sudan', 'Senegal', 'Singapore', 'South Georgia and the South Sandwich Islands', 'Saint Helena, Ascension and Tristan da Cunha', 'Svalbard and Jan Mayen', 'Solomon Islands', 'Sierra Leone', 'El Salvador', 'San Marino', 'Somalia', 'Saint Pierre and Miquelon', 'Serbia', 'South Sudan', 'Sao Tome and Principe', 'Suriname', 'Slovakia', 'Slovenia', 'Sweden', 'Eswatini', 'Sint Maarten', 'Seychelles', 'Syrian Arab Republic', 'Turks and Caicos Islands', 'Chad', 'Togo', 'Thailand', 'Tajikistan', 'Tokelau', 'Turkmenistan', 'Timor Leste', 'Tonga', 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Tuvalu', 'Tanzania', 'Uganda', 'Ukraine', 'Uruguay', 'United States', 'Uzbekistan', 'Saint Vincent and the Grenadines', 'Venezuela', 'Virgin Islands', 'Vietnam', 'Vanuatu', 'Wallis and Futuna', 'Samoa', 'Yemen', 'South Africa', 'Zambia', 'Zimbabwe'])" - ] - }, - "execution_count": 394, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "countries_dict.values()" - ] - }, - { - "cell_type": "code", - "execution_count": 376, - "metadata": {}, - "outputs": [], - "source": [ - "for code in female_parlamentaries['LOCATION']:\n", - " female_parlamentaries.rename({code : 'countries_dict.values'}, inplace=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 385, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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LOCATIONINDICATORSUBJECTMEASUREFREQUENCYTIMEValueFlag CodesCountry
0AUSWOMENPOLVOICEPOLREPRESPCA200225.3NaN<built-in method values of dict object at 0x00...
1AUSWOMENPOLVOICEPOLREPRESPCA201224.7NaN<built-in method values of dict object at 0x00...
2AUSWOMENPOLVOICEPOLREPRESPCA201426.0NaN<built-in method values of dict object at 0x00...
3AUSWOMENPOLVOICEPOLREPRESPCA201526.7NaN<built-in method values of dict object at 0x00...
4AUSWOMENPOLVOICEPOLREPRESPCA201728.7NaN<built-in method values of dict object at 0x00...
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" - ], - "text/plain": [ - " LOCATION INDICATOR SUBJECT MEASURE FREQUENCY TIME Value \\\n", - "0 AUS WOMENPOLVOICE POLREPRES PC A 2002 25.3 \n", - "1 AUS WOMENPOLVOICE POLREPRES PC A 2012 24.7 \n", - "2 AUS WOMENPOLVOICE POLREPRES PC A 2014 26.0 \n", - "3 AUS WOMENPOLVOICE POLREPRES PC A 2015 26.7 \n", - "4 AUS WOMENPOLVOICE POLREPRES PC A 2017 28.7 \n", - "\n", - " Flag Codes Country \n", - "0 NaN \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Unnamed: 0CountryYearGDPPopulationGDP_per_capita
00Canada1981808.68817124900000.032477.436602
11United States19816698.570801229465714.029192.033459
22Cuba198135.8826419705971.03696.965594
33Mexico1981589.54895069930971.08430.441359
44Antigua Barbuda19810.40892768264.05990.381182
\n", - "" - ], - "text/plain": [ - " Unnamed: 0 Country Year GDP Population GDP_per_capita\n", - "0 0 Canada 1981 808.688171 24900000.0 32477.436602\n", - "1 1 United States 1981 6698.570801 229465714.0 29192.033459\n", - "2 2 Cuba 1981 35.882641 9705971.0 3696.965594\n", - "3 3 Mexico 1981 589.548950 69930971.0 8430.441359\n", - "4 4 Antigua Barbuda 1981 0.408927 68264.0 5990.381182" - ] - }, - "execution_count": 236, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdp = pd.read_csv('data//GDP_P.csv')\n", - "gdp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 237, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n", - " 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n", - " 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,\n", - " 2014, 2015, 2016, 2017, 2018], dtype=int64)" - ] - }, - "execution_count": 237, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdp.Year.unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tertiary education (1970-2010)" - ] - }, - { - "cell_type": "code", - "execution_count": 239, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Unnamed: 0CountryYearTertiary_education
00Afghanistan19701.08
11Afghanistan19751.48
22Afghanistan19801.96
33Afghanistan19852.49
44Afghanistan19902.91
\n", - "
" - ], - "text/plain": [ - " Unnamed: 0 Country Year Tertiary_education\n", - "0 0 Afghanistan 1970 1.08\n", - "1 1 Afghanistan 1975 1.48\n", - "2 2 Afghanistan 1980 1.96\n", - "3 3 Afghanistan 1985 2.49\n", - "4 4 Afghanistan 1990 2.91" - ] - }, - "execution_count": 239, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "education = pd.read_csv('data//education_df.csv')\n", - "education.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 240, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010], dtype=int64)" - ] - }, - "execution_count": 240, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "education.Year.unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Merging datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CO merged " - ] - }, - { - "cell_type": "code", - "execution_count": 437, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)
0Afghanistan194914656.00.0003
1Afghanistan195084272.00.0015
2Afghanistan195191600.00.0015
3Afghanistan195291600.00.0015
4Afghanistan1953106256.00.0016
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" - ], - "text/plain": [ - " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%)\n", - "0 Afghanistan 1949 14656.0 0.0003\n", - "1 Afghanistan 1950 84272.0 0.0015\n", - "2 Afghanistan 1951 91600.0 0.0015\n", - "3 Afghanistan 1952 91600.0 0.0015\n", - "4 Afghanistan 1953 106256.0 0.0016" - ] - }, - "execution_count": 437, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_merged = pd.merge(co_annual, co_share, on=['Country','Year'])\n", - "co_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 438, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 20586 entries, 0 to 20585\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 20586 non-null object \n", - " 1 Year 20586 non-null int64 \n", - " 2 Annual COâ‚‚ 20586 non-null float64\n", - " 3 Share of global COâ‚‚ emissions (%) 20586 non-null float64\n", - "dtypes: float64(2), int64(1), object(1)\n", - "memory usage: 804.1+ KB\n" - ] - } - ], - "source": [ - "co_merged.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 411, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", - " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", - " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", - " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", - " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", - " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", - " 2015, 2016, 2017, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940,\n", - " 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1900, 1901, 1902,\n", - " 1903, 1904, 1905, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923,\n", - " 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1887, 1888,\n", - " 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899,\n", - " 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1860,\n", - " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", - " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", - " 1883, 1884, 1885, 1886, 1807, 1819, 1820, 1821, 1822, 1823, 1824,\n", - " 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835,\n", - " 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846,\n", - " 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857,\n", - " 1858, 1859, 1802, 1800, 1801, 1803, 1804, 1805, 1806, 1808, 1809,\n", - " 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818], dtype=int64)" - ] - }, - "execution_count": 411, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_merged.Year.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 416, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Country 0\n", - "Year 0\n", - "Annual COâ‚‚ 0\n", - "Share of global COâ‚‚ emissions (%) 0\n", - "COâ‚‚ 0\n", - "dtype: int64" - ] - }, - "execution_count": 416, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_merged.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 412, - "metadata": {}, - "outputs": [], - "source": [ - "years = [1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", - " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", - " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", - " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", - " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", - " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", - " 2015, 2016, 2017, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940,\n", - " 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1900, 1901, 1902,\n", - " 1903, 1904, 1905, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923,\n", - " 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1887, 1888,\n", - " 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899,\n", - " 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1860,\n", - " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", - " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", - " 1883, 1884, 1885, 1886, 1807, 1819, 1820, 1821, 1822, 1823, 1824,\n", - " 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835,\n", - " 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846,\n", - " 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857,\n", - " 1858, 1859, 1802, 1800, 1801, 1803, 1804, 1805, 1806, 1808, 1809,\n", - " 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818]" - ] - }, - { - "cell_type": "code", - "execution_count": 413, - "metadata": {}, - "outputs": [], - "source": [ - "years.sort()" - ] - }, - { - "cell_type": "code", - "execution_count": 414, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1800,\n", - " 1801,\n", - " 1802,\n", - " 1803,\n", - " 1804,\n", - " 1805,\n", - " 1806,\n", - " 1807,\n", - " 1808,\n", - " 1809,\n", - " 1810,\n", - " 1811,\n", - " 1812,\n", - " 1813,\n", - " 1814,\n", - " 1815,\n", - " 1816,\n", - " 1817,\n", - " 1818,\n", - " 1819,\n", - " 1820,\n", - " 1821,\n", - " 1822,\n", - " 1823,\n", - " 1824,\n", - " 1825,\n", - " 1826,\n", - " 1827,\n", - " 1828,\n", - " 1829,\n", - " 1830,\n", - " 1831,\n", - " 1832,\n", - " 1833,\n", - " 1834,\n", - " 1835,\n", - " 1836,\n", - " 1837,\n", - " 1838,\n", - " 1839,\n", - " 1840,\n", - " 1841,\n", - " 1842,\n", - " 1843,\n", - " 1844,\n", - " 1845,\n", - " 1846,\n", - " 1847,\n", - " 1848,\n", - " 1849,\n", - " 1850,\n", - " 1851,\n", - " 1852,\n", - " 1853,\n", - " 1854,\n", - " 1855,\n", - " 1856,\n", - " 1857,\n", - " 1858,\n", - " 1859,\n", - " 1860,\n", - " 1861,\n", - " 1862,\n", - " 1863,\n", - " 1864,\n", - " 1865,\n", - " 1866,\n", - " 1867,\n", - " 1868,\n", - " 1869,\n", - " 1870,\n", - " 1871,\n", - " 1872,\n", - " 1873,\n", - " 1874,\n", - " 1875,\n", - " 1876,\n", - " 1877,\n", - " 1878,\n", - " 1879,\n", - " 1880,\n", - " 1881,\n", - " 1882,\n", - " 1883,\n", - " 1884,\n", - " 1885,\n", - " 1886,\n", - " 1887,\n", - " 1888,\n", - " 1889,\n", - " 1890,\n", - " 1891,\n", - " 1892,\n", - " 1893,\n", - " 1894,\n", - " 1895,\n", - " 1896,\n", - " 1897,\n", - " 1898,\n", - " 1899,\n", - " 1900,\n", - " 1901,\n", - " 1902,\n", - " 1903,\n", - " 1904,\n", - " 1905,\n", - " 1906,\n", - " 1907,\n", - " 1908,\n", - " 1909,\n", - " 1910,\n", - " 1911,\n", - " 1912,\n", - " 1913,\n", - " 1914,\n", - " 1915,\n", - " 1916,\n", - " 1917,\n", - " 1918,\n", - " 1919,\n", - " 1920,\n", - " 1921,\n", - " 1922,\n", - " 1923,\n", - " 1924,\n", - " 1925,\n", - " 1926,\n", - " 1927,\n", - " 1928,\n", - " 1929,\n", - " 1930,\n", - " 1931,\n", - " 1932,\n", - " 1933,\n", - " 1934,\n", - " 1935,\n", - " 1936,\n", - " 1937,\n", - " 1938,\n", - " 1939,\n", - " 1940,\n", - " 1941,\n", - " 1942,\n", - " 1943,\n", - " 1944,\n", - " 1945,\n", - " 1946,\n", - " 1947,\n", - " 1948,\n", - " 1949,\n", - " 1950,\n", - " 1951,\n", - " 1952,\n", - " 1953,\n", - " 1954,\n", - " 1955,\n", - " 1956,\n", - " 1957,\n", - " 1958,\n", - " 1959,\n", - " 1960,\n", - " 1961,\n", - " 1962,\n", - " 1963,\n", - " 1964,\n", - " 1965,\n", - " 1966,\n", - " 1967,\n", - " 1968,\n", - " 1969,\n", - " 1970,\n", - " 1971,\n", - " 1972,\n", - " 1973,\n", - " 1974,\n", - " 1975,\n", - " 1976,\n", - " 1977,\n", - " 1978,\n", - " 1979,\n", - " 1980,\n", - " 1981,\n", - " 1982,\n", - " 1983,\n", - " 1984,\n", - " 1985,\n", - " 1986,\n", - " 1987,\n", - " 1988,\n", - " 1989,\n", - " 1990,\n", - " 1991,\n", - " 1992,\n", - " 1993,\n", - " 1994,\n", - " 1995,\n", - " 1996,\n", - " 1997,\n", - " 1998,\n", - " 1999,\n", - " 2000,\n", - " 2001,\n", - " 2002,\n", - " 2003,\n", - " 2004,\n", - " 2005,\n", - " 2006,\n", - " 2007,\n", - " 2008,\n", - " 2009,\n", - " 2010,\n", - " 2011,\n", - " 2012,\n", - " 2013,\n", - " 2014,\n", - " 2015,\n", - " 2016,\n", - " 2017]" - ] - }, - "execution_count": 414, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "years" - ] - }, - { - "cell_type": "code", - "execution_count": 427, - "metadata": {}, - "outputs": [], - "source": [ - "test = (co_merged['Year']== 1850).sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 428, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "17" - ] - }, - "execution_count": 428, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### global Temperature historical" - ] - }, - { - "cell_type": "code", - "execution_count": 442, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtLandAverageTemperatureLandAverageTemperatureUncertaintyLandMaxTemperatureLandMaxTemperatureUncertaintyLandMinTemperatureLandMinTemperatureUncertaintyLandAndOceanAverageTemperatureLandAndOceanAverageTemperatureUncertainty
31872015-08-0114.7550.07220.6990.1109.0050.17017.5890.057
31882015-09-0112.9990.07918.8450.0887.1990.22917.0490.058
31892015-10-0110.8010.10216.4500.0595.2320.11516.2900.062
31902015-11-017.4330.11912.8920.0932.1570.10615.2520.063
31912015-12-015.5180.10010.7250.1540.2870.09914.7740.062
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" - ], - "text/plain": [ - " dt LandAverageTemperature LandAverageTemperatureUncertainty \\\n", - "3187 2015-08-01 14.755 0.072 \n", - "3188 2015-09-01 12.999 0.079 \n", - "3189 2015-10-01 10.801 0.102 \n", - "3190 2015-11-01 7.433 0.119 \n", - "3191 2015-12-01 5.518 0.100 \n", - "\n", - " LandMaxTemperature LandMaxTemperatureUncertainty LandMinTemperature \\\n", - "3187 20.699 0.110 9.005 \n", - "3188 18.845 0.088 7.199 \n", - "3189 16.450 0.059 5.232 \n", - "3190 12.892 0.093 2.157 \n", - "3191 10.725 0.154 0.287 \n", - "\n", - " LandMinTemperatureUncertainty LandAndOceanAverageTemperature \\\n", - "3187 0.170 17.589 \n", - "3188 0.229 17.049 \n", - "3189 0.115 16.290 \n", - "3190 0.106 15.252 \n", - "3191 0.099 14.774 \n", - "\n", - " LandAndOceanAverageTemperatureUncertainty \n", - "3187 0.057 \n", - "3188 0.058 \n", - "3189 0.062 \n", - "3190 0.063 \n", - "3191 0.062 " - ] - }, - "execution_count": 442, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_temperature = pd.read_csv('data//GlobalTemperatures.csv')\n", - "global_temperature.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 443, - "metadata": {}, - "outputs": [], - "source": [ - "global_temperature.dropna(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 444, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 1992 entries, 1200 to 3191\n", - "Data columns (total 9 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 dt 1992 non-null object \n", - " 1 LandAverageTemperature 1992 non-null float64\n", - " 2 LandAverageTemperatureUncertainty 1992 non-null float64\n", - " 3 LandMaxTemperature 1992 non-null float64\n", - " 4 LandMaxTemperatureUncertainty 1992 non-null float64\n", - " 5 LandMinTemperature 1992 non-null float64\n", - " 6 LandMinTemperatureUncertainty 1992 non-null float64\n", - " 7 LandAndOceanAverageTemperature 1992 non-null float64\n", - " 8 LandAndOceanAverageTemperatureUncertainty 1992 non-null float64\n", - "dtypes: float64(8), object(1)\n", - "memory usage: 155.6+ KB\n" - ] - } - ], - "source": [ - "global_temperature.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 449, - "metadata": {}, - "outputs": [], - "source": [ - "global_temperature = global_temperature[['dt','LandAverageTemperature','LandAndOceanAverageTemperature']]" - ] - }, - { - "cell_type": "code", - "execution_count": 450, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtLandAverageTemperatureLandAndOceanAverageTemperature
12001850-01-010.74912.833
12011850-02-013.07113.588
12021850-03-014.95414.043
12031850-04-017.21714.667
12041850-05-0110.00415.507
............
31872015-08-0114.75517.589
31882015-09-0112.99917.049
31892015-10-0110.80116.290
31902015-11-017.43315.252
31912015-12-015.51814.774
\n", - "

1992 rows × 3 columns

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" - ], - "text/plain": [ - " dt LandAverageTemperature LandAndOceanAverageTemperature\n", - "1200 1850-01-01 0.749 12.833\n", - "1201 1850-02-01 3.071 13.588\n", - "1202 1850-03-01 4.954 14.043\n", - "1203 1850-04-01 7.217 14.667\n", - "1204 1850-05-01 10.004 15.507\n", - "... ... ... ...\n", - "3187 2015-08-01 14.755 17.589\n", - "3188 2015-09-01 12.999 17.049\n", - "3189 2015-10-01 10.801 16.290\n", - "3190 2015-11-01 7.433 15.252\n", - "3191 2015-12-01 5.518 14.774\n", - "\n", - "[1992 rows x 3 columns]" - ] - }, - "execution_count": 450, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_temperature" - ] - }, - { - "cell_type": "code", - "execution_count": 453, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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LandAverageTemperatureLandAndOceanAverageTemperature
Year
18431.91000013.210500
18447.99391714.880083
18458.17666714.989000
18468.02500015.003417
18477.97583314.932333
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" - ], - "text/plain": [ - " LandAverageTemperature LandAndOceanAverageTemperature\n", - "Year \n", - "1843 1.910000 13.210500\n", - "1844 7.993917 14.880083\n", - "1845 8.176667 14.989000\n", - "1846 8.025000 15.003417\n", - "1847 7.975833 14.932333" - ] - }, - "execution_count": 453, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_temperature['dt']= pd.to_datetime(temperature['dt']) \n", - "global_temperature['Year'] = pd.DatetimeIndex(global_temperature['dt']).year\n", - "annual_global_temp = global_temperature[['Year','LandAverageTemperature','LandAndOceanAverageTemperature']].groupby(['Year']).agg({'LandAverageTemperature':'mean',\n", - " 'LandAndOceanAverageTemperature':'mean'})\n", - "annual_global_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 454, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LandAverageTemperature 0\n", - "LandAndOceanAverageTemperature 0\n", - "dtype: int64" - ] - }, - "execution_count": 454, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/your-project/Final Dataset notebook.ipynb b/your-project/Final Dataset notebook.ipynb deleted file mode 100644 index 1f34d48e..00000000 --- a/your-project/Final Dataset notebook.ipynb +++ /dev/null @@ -1,6543 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "#importing the needed libraries\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import datetime\n", - "import re\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CO annual emissions (1949-2017)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes)
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
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" - ], - "text/plain": [ - " Entity Code Year Annual COâ‚‚ emissions (tonnes)\n", - "0 Afghanistan AFG 1949 14656.0\n", - "1 Afghanistan AFG 1950 84272.0\n", - "2 Afghanistan AFG 1951 91600.0\n", - "3 Afghanistan AFG 1952 91600.0\n", - "4 Afghanistan AFG 1953 106256.0" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing CO annual emissions dataset\n", - "co_annual = pd.read_csv('data//annual_co2_emissions_per_country.csv')\n", - "co_annual.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the data set\n", - "co_annual.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "co_annual = co_annual.rename(columns={'Annual COâ‚‚ emissions (tonnes)':'Annual COâ‚‚', 'Entity':'Country'})" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "co_annual = co_annual[co_annual.Country != 'World']" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 20586 entries, 0 to 20852\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 20586 non-null object \n", - " 1 Year 20586 non-null int64 \n", - " 2 Annual COâ‚‚ 20586 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 643.3+ KB\n" - ] - } - ], - "source": [ - "#co_annual.Year.unique()\n", - "co_annual.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Annual share of CO per country (1949-2017)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearShare of global COâ‚‚ emissions (%)
0AfghanistanAFG19490.0003
1AfghanistanAFG19500.0015
2AfghanistanAFG19510.0015
3AfghanistanAFG19520.0015
4AfghanistanAFG19530.0016
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" - ], - "text/plain": [ - " Entity Code Year Share of global COâ‚‚ emissions (%)\n", - "0 Afghanistan AFG 1949 0.0003\n", - "1 Afghanistan AFG 1950 0.0015\n", - "2 Afghanistan AFG 1951 0.0015\n", - "3 Afghanistan AFG 1952 0.0015\n", - "4 Afghanistan AFG 1953 0.0016" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Annual share of CO per country (1949-2017)\n", - "co_share = pd.read_csv('data//annual_share_of_co2_emissions.csv')\n", - "co_share.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the data set\n", - "co_share.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "co_share = co_share.rename(columns={'Share of global COâ‚‚ emissions (%)b':'Annual COâ‚‚', 'Entity':'Country'})" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", - " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", - " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", - " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", - " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", - " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", - " 2015, 2016, 2017, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758,\n", - " 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769,\n", - " 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780,\n", - " 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791,\n", - " 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802,\n", - " 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813,\n", - " 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824,\n", - " 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835,\n", - " 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846,\n", - " 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857,\n", - " 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868,\n", - " 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879,\n", - " 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890,\n", - " 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901,\n", - " 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912,\n", - " 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923,\n", - " 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934,\n", - " 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945,\n", - " 1946, 1947, 1948], dtype=int64)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_share.Year.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 20586 entries, 0 to 20585\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 20586 non-null object \n", - " 1 Year 20586 non-null int64 \n", - " 2 Share of global COâ‚‚ emissions (%) 20586 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 482.6+ KB\n" - ] - } - ], - "source": [ - "co_share.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CO Emissions per capita (1800-2017) (no using it for now)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearPer capita COâ‚‚ emissions (tonnes per capita)
0AfghanistanAFG18000.0
1AfghanistanAFG18010.0
2AfghanistanAFG18020.0
3AfghanistanAFG18030.0
4AfghanistanAFG18040.0
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" - ], - "text/plain": [ - " Entity Code Year Per capita COâ‚‚ emissions (tonnes per capita)\n", - "0 Afghanistan AFG 1800 0.0\n", - "1 Afghanistan AFG 1801 0.0\n", - "2 Afghanistan AFG 1802 0.0\n", - "3 Afghanistan AFG 1803 0.0\n", - "4 Afghanistan AFG 1804 0.0" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing CO Emissions per capita\n", - "co_per_c = pd.read_csv(\"data//co_emissions_per_capita.csv\")\n", - "co_per_c.head()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the data set\n", - "co_per_c.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "co_per_c = co_per_c.rename(columns={'Per capita COâ‚‚ emissions (tonnes per capita)':'COâ‚‚', 'Entity':'Country'})" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryYearCOâ‚‚
0Afghanistan18000.0
1Afghanistan18010.0
2Afghanistan18020.0
3Afghanistan18030.0
4Afghanistan18040.0
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" - ], - "text/plain": [ - " Country Year COâ‚‚\n", - "0 Afghanistan 1800 0.0\n", - "1 Afghanistan 1801 0.0\n", - "2 Afghanistan 1802 0.0\n", - "3 Afghanistan 1803 0.0\n", - "4 Afghanistan 1804 0.0" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_per_c.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "co_per_c = co_per_c[co_per_c.Country != 'World']" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "#co_per_c.Country.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 42723 entries, 0 to 42843\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 42723 non-null object \n", - " 1 Year 42723 non-null int64 \n", - " 2 COâ‚‚ 42723 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 1.3+ MB\n" - ] - } - ], - "source": [ - "#co_per_c.Year.unique()\n", - "co_per_c.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Annual CO emissions globally " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
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" - ], - "text/plain": [ - " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", - "0 Afghanistan AFG 1949 14656.0\n", - "1 Afghanistan AFG 1950 84272.0\n", - "2 Afghanistan AFG 1951 91600.0\n", - "3 Afghanistan AFG 1952 91600.0\n", - "4 Afghanistan AFG 1953 106256.0" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Annual CO emissions globally\n", - "data = pd.read_csv('data//annual-co-emissions-by-region.csv')\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
20353WorldOWID_WRL17519350528.0
20354WorldOWID_WRL17529354192.0
20355WorldOWID_WRL17539354192.0
20356WorldOWID_WRL17549357856.0
20357WorldOWID_WRL17559361520.0
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" - ], - "text/plain": [ - " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", - "20353 World OWID_WRL 1751 9350528.0\n", - "20354 World OWID_WRL 1752 9354192.0\n", - "20355 World OWID_WRL 1753 9354192.0\n", - "20356 World OWID_WRL 1754 9357856.0\n", - "20357 World OWID_WRL 1755 9361520.0" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#keeping only the global data\n", - "global_co = data[data.Entity == 'World']\n", - "global_co.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " errors=errors,\n" - ] - } - ], - "source": [ - "#cleaning the dataset\n", - "global_co.drop(['Entity','Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 267 entries, 20353 to 20619\n", - "Data columns (total 2 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Year 267 non-null int64 \n", - " 1 Annual COâ‚‚ emissions (tonnes ) 267 non-null float64\n", - "dtypes: float64(1), int64(1)\n", - "memory usage: 6.3 KB\n" - ] - } - ], - "source": [ - "global_co.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Total greenhouse gas emissions (1990-2016)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
0AfghanistanAFG199015140000.0
1AfghanistanAFG199115060000.0
2AfghanistanAFG199213600000.0
3AfghanistanAFG199313430000.0
4AfghanistanAFG199413240000.0
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" - ], - "text/plain": [ - " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", - "0 Afghanistan AFG 1990 15140000.0\n", - "1 Afghanistan AFG 1991 15060000.0\n", - "2 Afghanistan AFG 1992 13600000.0\n", - "3 Afghanistan AFG 1993 13430000.0\n", - "4 Afghanistan AFG 1994 13240000.0" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Total greenhouse gas emissions\n", - "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", - "ghg.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the dataset\n", - "ghg.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "ghg = ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )', 'Entity':'Country'})" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 5184 entries, 0 to 5183\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 5184 non-null object \n", - " 1 Year 5184 non-null int64 \n", - " 2 GHG(tonnes COâ‚‚e ) 5184 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 121.6+ KB\n" - ] - } - ], - "source": [ - "ghg.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Global COâ‚‚ atmospheric concentration" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearCOâ‚‚ concentration (parts per million)
0WorldOWID_WRL803719 BCE207.29
1WorldOWID_WRL803182 BCE202.23
2WorldOWID_WRL802573 BCE204.86
3WorldOWID_WRL802060 BCE207.50
4WorldOWID_WRL801975 BCE202.92
...............
1866WorldOWID_WRL2014398.65
1867WorldOWID_WRL2015400.83
1868WorldOWID_WRL2016404.24
1869WorldOWID_WRL2017406.55
1870WorldOWID_WRL2018408.52
\n", - "

1871 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " Entity Code Year COâ‚‚ concentration (parts per million)\n", - "0 World OWID_WRL 803719 BCE 207.29\n", - "1 World OWID_WRL 803182 BCE 202.23\n", - "2 World OWID_WRL 802573 BCE 204.86\n", - "3 World OWID_WRL 802060 BCE 207.50\n", - "4 World OWID_WRL 801975 BCE 202.92\n", - "... ... ... ... ...\n", - "1866 World OWID_WRL 2014 398.65\n", - "1867 World OWID_WRL 2015 400.83\n", - "1868 World OWID_WRL 2016 404.24\n", - "1869 World OWID_WRL 2017 406.55\n", - "1870 World OWID_WRL 2018 408.52\n", - "\n", - "[1871 rows x 4 columns]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Global COâ‚‚ atmospheric concentration dataset\n", - "co_concentration = pd.read_csv('data//global-co-concentration-ppm.csv')\n", - "co_concentration" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the data\n", - "co_concentration.drop(['Code', 'Entity'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration = co_concentration.rename(columns={'COâ‚‚ concentration (parts per million)':'COâ‚‚ concentration'})" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1846'" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_concentration.Year[1740]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'CO atmospheric concentration')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#plotting the Global COâ‚‚ atmospheric concentration increasement from 1864-2018\n", - "x = co_concentration.Year[1740:]\n", - "y = co_concentration['COâ‚‚ concentration'][1740:]\n", - "plt.figure(figsize=(17,6))\n", - "\n", - "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", - " linewidth=2)\n", - "\n", - "plt.legend()\n", - "plt.xlabel('Year')\n", - "plt.ylabel('CO atmospheric concentration')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearCOâ‚‚ concentration
0803719 BCE207.29
1803182 BCE202.23
2802573 BCE204.86
3802060 BCE207.50
4801975 BCE202.92
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" - ], - "text/plain": [ - " Year COâ‚‚ concentration\n", - "0 803719 BCE 207.29\n", - "1 803182 BCE 202.23\n", - "2 802573 BCE 204.86\n", - "3 802060 BCE 207.50\n", - "4 801975 BCE 202.92" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_concentration.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Global temperatures by country" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "#importing Global temperatures by country data set\n", - "temperature = pd.read_csv('data//GlobalLandTemperaturesByCountry.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtAverageTemperatureAverageTemperatureUncertaintyCountry
01743-11-014.3842.294Ã…land
11743-12-01NaNNaNÃ…land
21744-01-01NaNNaNÃ…land
31744-02-01NaNNaNÃ…land
41744-03-01NaNNaNÃ…land
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" - ], - "text/plain": [ - " dt AverageTemperature AverageTemperatureUncertainty Country\n", - "0 1743-11-01 4.384 2.294 Ã…land\n", - "1 1743-12-01 NaN NaN Ã…land\n", - "2 1744-01-01 NaN NaN Ã…land\n", - "3 1744-02-01 NaN NaN Ã…land\n", - "4 1744-03-01 NaN NaN Ã…land" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the dataset\n", - "temperature['dt']= pd.to_datetime(temperature['dt']) " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtAverageTemperatureAverageTemperatureUncertaintyCountryYear
01743-11-014.3842.294Ã…land1743
11743-12-01NaNNaNÃ…land1743
21744-01-01NaNNaNÃ…land1744
31744-02-01NaNNaNÃ…land1744
41744-03-01NaNNaNÃ…land1744
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" - ], - "text/plain": [ - " dt AverageTemperature AverageTemperatureUncertainty Country Year\n", - "0 1743-11-01 4.384 2.294 Åland 1743\n", - "1 1743-12-01 NaN NaN Åland 1743\n", - "2 1744-01-01 NaN NaN Åland 1744\n", - "3 1744-02-01 NaN NaN Åland 1744\n", - "4 1744-03-01 NaN NaN Åland 1744" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature['Year'] = pd.DatetimeIndex(temperature['dt']).year\n", - "temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "#temperature.Country.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "#keeping only the desired countries, and removing regions\n", - "non_wanted = ['Åland','Africa','Asia','Baker Island','Denmark','Federated States Of Micronesia','France', 'French Guiana', 'French Polynesia',\n", - " 'French Southern And Antarctic Lands','Gaza Strip','Guadeloupe', 'Guam','Guernsey','Heard Island And Mcdonald Islands','Isle Of Man','Jersey','Kingman Reef',\n", - " 'Montserrat','Netherlands (Europe)','Niue','North America','New Caledonia','Northern Mariana Islands','Oceania',\n", - " 'Palmyra Atoll','Saint Barthélemy','American Samoa','South America','South Georgia And The South Sandwich Isla',\n", - " 'Svalbard And Jan Mayen','Turks And Caicas Islands','United Kingdom','Western Sahara']\n", - "for region in non_wanted:\n", - " temperature = temperature[temperature.Country != region]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AverageTemperature
YearCountry
1743Albania8.620
Andorra7.556
Austria2.482
Belarus0.767
Belgium7.106
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" - ], - "text/plain": [ - " AverageTemperature\n", - "Year Country \n", - "1743 Albania 8.620\n", - " Andorra 7.556\n", - " Austria 2.482\n", - " Belarus 0.767\n", - " Belgium 7.106" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_temp = temperature[['Year', 'Country','AverageTemperature']].groupby(['Year','Country']).agg({'AverageTemperature':'mean'})\n", - "annual_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "#reseting index to adapt it for the future merge\n", - "annual_temp.reset_index(level=0, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearAverageTemperature
Country
Albania17438.620
Andorra17437.556
Austria17432.482
Belarus17430.767
Belgium17437.106
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" - ], - "text/plain": [ - " Year AverageTemperature\n", - "Country \n", - "Albania 1743 8.620\n", - "Andorra 1743 7.556\n", - "Austria 1743 2.482\n", - "Belarus 1743 0.767\n", - "Belgium 1743 7.106" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "annual_temp.reset_index(level=0, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryYearAverageTemperature
0Albania17438.620
1Andorra17437.556
2Austria17432.482
3Belarus17430.767
4Belgium17437.106
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" - ], - "text/plain": [ - " Country Year AverageTemperature\n", - "0 Albania 1743 8.620\n", - "1 Andorra 1743 7.556\n", - "2 Austria 1743 2.482\n", - "3 Belarus 1743 0.767\n", - "4 Belgium 1743 7.106" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 41931 entries, 0 to 41930\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 41931 non-null object \n", - " 1 Year 41931 non-null int64 \n", - " 2 AverageTemperature 39793 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 982.9+ KB\n" - ] - } - ], - "source": [ - "annual_temp.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Global Temperature anomaly 1850-2018\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearMedian (℃)Upper (℃)Lower (℃)
0GlobalNaN1850-0.373-0.339-0.425
1GlobalNaN1851-0.218-0.184-0.274
2GlobalNaN1852-0.228-0.196-0.280
3GlobalNaN1853-0.269-0.239-0.321
4GlobalNaN1854-0.248-0.218-0.301
\n", - "
" - ], - "text/plain": [ - " Entity Code Year Median (℃) Upper (℃) Lower (℃)\n", - "0 Global NaN 1850 -0.373 -0.339 -0.425\n", - "1 Global NaN 1851 -0.218 -0.184 -0.274\n", - "2 Global NaN 1852 -0.228 -0.196 -0.280\n", - "3 Global NaN 1853 -0.269 -0.239 -0.321\n", - "4 Global NaN 1854 -0.248 -0.218 -0.301" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Global Temperature anomaly (increase-decreage on previous average temperature)\n", - "temperature_a = pd.read_csv('data//temperature_anomaly.csv')\n", - "temperature_a.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 676 entries, 0 to 675\n", - "Data columns (total 6 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Entity 676 non-null object \n", - " 1 Code 0 non-null float64\n", - " 2 Year 676 non-null int64 \n", - " 3 Median (℃) 676 non-null float64\n", - " 4 Upper (℃) 676 non-null float64\n", - " 5 Lower (℃) 676 non-null float64\n", - "dtypes: float64(4), int64(1), object(1)\n", - "memory usage: 31.8+ KB\n" - ] - } - ], - "source": [ - "temperature_a.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the dataset\n", - "temperature_a.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "temperature_a.rename(columns={'Entity':'Country'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Global', 'Northern Hemisphere', 'Southern Hemisphere', 'Tropics'],\n", - " dtype=object)" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature_a.Country.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860,\n", - " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", - " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", - " 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893,\n", - " 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904,\n", - " 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915,\n", - " 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926,\n", - " 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937,\n", - " 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948,\n", - " 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", - " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", - " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", - " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", - " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", - " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", - " 2015, 2016, 2017, 2018], dtype=int64)" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature_a.Year.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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9wjFfNMYsM8YsNcY8nuk1ioiIiIiISOakovwegn31f1sPza3JnSuXZS2oN8bkA/cBZwHjgMuMMeM6HFMN/CtwgrV2PPDPGV+oiIiIiIiIZEyqMvXDe0NVL9jTCAu3Jr+uXJXNTP00YJW1do21tgl4AjivwzHXA/dZa3cBWGu3Z3iNIiIiIiIikiGhe9QnG9QDnFzlbt88gkvwsxnUVwAbQh5vDDwXajQw2hjzjjFmjjHmzHAnMsbcYIyZb4yZX1tbm6blioiIiIiISDrVHYSDzdCrOPE96kO17Ve/Nvlz5apsBvXhdhy0HR4XANXAycBlwEPGmN6HfZK1D1hra6y1Nf3790/5QkVERERERCT9UpmlBzcBvzgfVtTB3sbUnDPXFGTxvTcCQ0MeVwKbwxwzx1p7CPjEGLMCF+TPy8wSRUREREREJFNSHdSXFsLvL4Yx5dCtMDXnzDXZzNTPA6qNMSOMMUXApcDzHY55FjgFwBjTD1eOvyajqxQREREREZGMSNXk+1BTBh25AT1kMai31jYDNwMvA8uBJ621S40xdxtjzg0c9jJQZ4xZBrwB3GatrcvOikVERERERCSdVu10t6nK1HcF2Sy/x1r7EvBSh+e+G3LfAt8IfIiIiIiIiMgRqqEZXlnt7p8wNPqxEuQrqDfG9AQGAweBDYFgW0RERERERCQl3lgLe5pgfH8YXZ7t1XQeEYN6Y0wZ8BXgcqAHsAMoAcqNMW8D91tr/56RVYqIiIiIiEjOaWiGonzIC7e3WZye/cjdnn908ufqSqJl6p8BHgNOC+1jN8YYYBrwJWNMtbX2kTSvUURERERERHJM7X4463E4ZiA8fG7s46Opb4C/rnX7np83OhWr6zoiBvXW2tMjPG+B9wIfIiIiIiIi0gU99iHUHoC/rYdDLVCYn/i5XloFTS2ul35gj9StsSuIOP3eGDPIGHNdmOdPNsaMT++yREREREREJFc1tcDvPgzeX1uf3Pm80vsLVHoft2hb2n0TaAjz/CbgJ+lZjoiIiIiIiCTriSVw859hb2N6zv/iSpel93ycxMbjm/bAnE1QnA9njkx+bV1NtKD+OOCPHZ+01q4EKtK2IhERERERkU5m7W74wzLIhX3CrIUf/wP+9DH8NE1N079Z6G4rytztih2Jn+v5j93tGUdBWXFy6+qKogX1AP0jPF+W6oWIiIiIiIh0Vt99E775KszdnO2VuAsMXhb9NwuTy6KH88FWWLgNepfALdPccyuSeA9NvU9OtKD+BeAHxph24w6MMd8BtJWdiIiIiIhIwPJApnpDkr3lqTAvcGEhz0CLdRccUllB8OtAlv6y8TB5oLuf6IWD5bXwUZ27QHDS8NSsr6uJFtT/BGgB1hhj/mCMmW2MWQ6cDHw9E4sTERERERHJdXsaYft+d3/b/uyuBYLVAjfVQJ8S+MdGeGFlas69bZ/rp88z8KVJcFQfyDduUF5Dc/zne3aFuz2n2u13L/GLGNRba5uttbOA04EncZn7y621Z4TuWy8iIiIiItKVrdkVvL89B4L6+YGgfsZR8K3j3f0f/B32NyV/7sc+hOZWmDkSKnpCcQGM6AOtFlbtjO9crRaeCwT1549Jfm1dVbQt7WYYYx4D/gNYb619zFr7QeaWJiIiIiIikvtWhwT12c7Ub98Pn+yGboUwfgBcMh4mDYCt++AX85I7d2OzC+oBZk0OPj+m3N3GW4L/3ibYsg8qe8KnhiS3tq4sWvn9VdbaK4B/wm1vJyIiIiIiIh2sDslQZztT7/XTTxkEBXmQnwd3n+Kee3BB+6qCeL24EnYchHH9YFpIEO4F9fEOy/vjcnd73hhXzi+JiRbU/80Ycx/wAPB0htYjIiIiIiLSqazKoUy9V3ofGnRPGeQy9oda4a63EhuaZy08EhiQ90+TwYQE4aMTCOrX18MzH4EBLh4b/3okqCDSC9baB4wxvYBWa+3eDK5JRERERESk0wjNftfudwGwSXHmefNeeGqZ60Ef3x9+flb447wheVMr2j//7ePhz6vgrXXwjVfgu5+FPqX+339JLXy43Q3eO69D/3si5fc/e8/15l94tBu2J4mLuk+9tbZeAb2IiIiIiEh4za1uX3iAkgJobIH6xtScu6nFlbxf9Swc/wj8ZI7r33/+Y/hw2+HH72uCZbWu7H7KoPavlXeD/zgNivPh6Y/g9N/Cnz72n7Wfu8ndnnGU+zpDDe/lzrtpL+z18bV/XOey9AV5cOt0f+8vkUUblPeGMeYrxpghHZ4vMMZ81hjzsDFmVvqXKCIiIiIikps21Luy9ooy9wGp6auvO+AC75tectn1wny37dvMke71x5Yc/jkLtriJ8hP6u0F5HZ1dDS9fAdMrXG/8zX+G619wQ/RiWRS4iNDxYgG4vv1Rfd39j31MwP/JHLfOS8fDsF6xj5foomXqzwYKgWeMMRuNMYuNMR8Da4BZwC+ttb/OxCJFRERERERykTf5fmQfGNDd3d/mI0iO5Xcfwrp6GNoT7vwszL0W7vsc3BbYou65FS4zH8orva+JMkl+RB+YfRH8+6lQVgSvrnEXD5bWRl/Poq3udtLA8K/7LcH/cJtrAyjOh1umRT9W/Im2T/0Ba+3/WGuPA0bigvzjrbXDrLWzrLXzM7ZKERERERGRHOQNyRvZNxjUbz+Q3DkPtbigHlzJ/DVTgv3v1X3huAo4cCi4x7vHm3w/rUM/fUd5Bi6fCK99CU4YCnub4PEPIx+/uwHW1rtA3AveO/I7Af/ef7jbq4+BQT2iHyv+RO2pDzEFOMVau8MY09cYMyydixIREREREekMvO3sRvaBgSnK1L+82pXwj+zjgu6OLpvgbh8PKcFvaoEPtrj7NYP9vc+gHsGedi/LH87iQOn9+AGuDSCctgn4OyKfZ+4m10rQowi+UuNvjRJbzKDeGHMHcCdwR+CpUuDxdC5KRERERESkMwhXfp9spv7RRe726mPCT9E/axT0LoEl24MB95LtbkjfyD5uKJ5fkwa4DPzHdbDzYPhjvH76yRFK7yF2+b21cO+77v51U6BvHJP3JTo/mfqLgc8B+wGstZuAnulclIiIiIiISK6zFlalOFO/rNZlzXsUwUUR9m8vKQi+5mXr5/osve+ouACmBDL78yJk672gPlI/PcCQMrfmHQdhR5iLGn9b79bYu8QF9ZI6foL6RmutBSyAMSaO6z4iIiIiIiJHpp0H3fZ1PYpclr4tU5/E9Pv/W+xuLxrrzhuJV4L/3Aq3jdy8wJZz06IMyYvkuMDnvLfp8NeshYWBIXnRMvXGhJTgd8jWh2bpv1IDZcXxr1Ei8xPUP22MuQ/oFdjC7hXgkfQuS0REREREJLetCSm9NyYkU59gUF/f4PZvB7hqUvRjQwfmPbsC5nv99AkE9V52f26YoH7rPqg9AD2Loap39PNEKsH/y2r4cDv07wZXx/i6JH4FsQ6w1v7IGHMW0AQcA/zQWvvntK9MREREREQkh60KCeqhfabe2vD98NE8uQwamuEzw4L7vkdz2QSXXf/v99yE+kE93BZ48Tp2MBTkuW3t9ja2z6R7pffHDIz99YSbgN/SCj8OTLz/2jQoLYx/fRJd1Ey9MSbfGPOytfbP1tpbrbX/rIBeRERERESk/ZA8gO5FrmS+sQX2NMZ3rlYbLL2/+hh/n+MNzKsN9LBPHRL/hQSAboUwcYBbw/tb2r8WGtTHEq78/rkVbu5AZU+4dEL8a5PYogb11toWoMkYo8F4IiIiIiIiIdq2swvJqg8ITCCLtwT/zbWwvh4qy+DUKn+fEzowD1xQn6i2EvwOw/LiCepDy++tddvs/WSOe+7W46AownZ4khw/PfX7gEXGmF8ZY37ifaR7YSIiIiIiIrmsY6YeYEAPdxvvsDxvG7svTYJ8P1FawOUh2e9EhuR5vGF5oX31rTa4Zd4xg2Kfo183KC+FfU2weS88uRQ27HGtBBccnfjaJLqYPfXAa4EPERERERERwfW+b9gDeQaG9wo+72Xq4wnq1+2GN9e5/eIvGR/fOkb1ha/WuK3kxvSL73ND1QwBg8vMNzS7KoBPdsPeJter7w0BjGV0OfxjozvPf891z31jenwXKiQ+fgblPZyJhYiIiIiIiHQW63a7THZVL7fXu2dgIFMfT/n9Oxvc7RlHQZ/S+NfyrRPi/5yOepXA2H6wbIfbwm56JSwKbGXnp/TeMyYQ1N/ztruwMWGA6/2X9IkZ1BtjVhLYoz6UtXZ0WlYkIiIiIiKS49om33eYUp/IXvXeucb1T35dyZhW4YL69zYFgvo4+uk9Xl/9hj3u9l8+7aoZJH38lN+fGHK/BPgC0CvCsSIiIiIiIke8cP30kNhe9asCA/f8bGOXTtMq4DeLgn31iQT13gR8cIP7Th6euvVJeH7K77d1eOrHxpi307QeERERERGRnLcmQlCfSKY+0gWCTPMG7b2/BQ4cgmW17vHEBIP6245PbIs9iY+f8vtJIQ/zgBqUqRcRERERkS4s3HZ2EH+m/uAh2LQHCvLaD9zLhv7d3YWF1bvgD8ugscU97lXs/xw9i+Guk9x2dsdVpG+tEuSn/P6+kPvNwCfAJelZjoiIiIiISG6zNphdHxUlU29t7Ez1mt1ugNnwXlCYA/u4T6twX9vDH7jH8ZTee2ZNTu2aJDo/Gwtcaa39TODjFGvtNbi965NmjDnTGLPCGLPKGHN7lOMuNsZYY0xNKt5XREREREQkUdv2w/5D0Lf08Gn1PYqge6HbFm5PU+xzrc6RfnqPV4K/tt7dTkogqJfM8hPUPxPmuWeTfWNjTD6uCuAsYBxwmTFmXJjjyoCvAe8l+54iIiIiIiLJ8gbbHdU7/Ovx9NW3DcnLcj+9Z1qHkvnJg7KzDvEvYlBvjBltjDkP6GWMOTfk40rcFPxkTQNWWWvXWGubgCeA88Ic933gP4GGFLyniIiIiIhIUlZH2M7O09ZX76O+Oda5Mq2yJ1SUufsFeW7veslt0TL144GLgd64bey8j+OBG1Pw3hXAhpDHGwPPtTHGTAGGWmtfiHYiY8wNxpj5xpj5tbW1KViaiIiIiIhIeLGm1XfmTD0Es/Vj+0GJnylsklUR/4qstc8AzxhjTrTWpmMLu3AjI2zbi8bkAT8F/inWiay1DwAPANTU1NgYh4uIiIiIiCRsdYxA3G9Q39IKn+x294/KoaD+lCp45iM4YWi2VyJ++LnuMs8YcyMuc99Wdm+tvSHJ994IhH6bVAKbQx6XAROAN40bGTkIeN4Yc661dn6S7y0iIiIiIpKQtYFAfESMoD7Wtnab9rpt4wb1gLI4to1Lt3NHw5AeMGFAtlcifvgZlPd/QBVwDm5Y3UhS098+D6g2xowwxhQBlwLPey9aa+uttf2stVXW2ipgDqCAXkREREREsqalFbYEeuWHlIU/ZqDPTP1Kb6/7HMrSg9uGb2oFlBZmeyXih5+gfrS19l+Bfdbah4EzcRn0pFhrm4GbgZeB5cCT1tqlxpi7jTHnJnt+ERERERGRVNtxAFoslJdG7jf3m6lv2+s+R4bkSefkp/z+UOB2tzFmLLANGJ6KN7fWvgS81OG570Y49uRUvKeIiIiIiEiiNgey9IMjZOkBBvZwt7Ey9atyNFMvnYufoP5hY0wf4E5cVr0bEDbwFhEREREROZJt3utuB/eIfMyAbu52236w1pWzh7NaQb2kQNSg3hiTD+yw1u4C3gCGZWRVIiIiIiIiOWirl6mPEtT3KIJuhXDgEOxtgp4RhuB55ffVKr+XJETtqbfWtgD/nKG1iIiIiIiI5DQvUx9pSB64zHxotj6cugOwq8FdAPB68EUS4WdQ3svGmH82xgw2xvT0PtK+MhERERERkRyzxUemHmL31bcNyesTuTxfxA8/PfU3Bm6/CVjABG5Vii8iIiIiIl3KFh+D8iD2BPy2IXkqvZckxQzqrbVDM7EQERERERGRXLfFx6A8CAb1kTL1qwKZeg3Jk2TFLL83xpQaY243xvwy8HiUMeas9C9NREREREQkdzS3BjPvg/wG9fvCv+5Nvtce9ZIsPz31jwSO+0zg8WbgnrStSEREREREJAdt3w+tFvp3g6L86McO9IL6A+FfV6ZeUsVPUF9trb0HOARgrT2A66sXEf8jOKgAACAASURBVBERERHpMvwOyYOQnvowmfqDh2DTHijIg+G9Urc+6Zr8BPVNxpgS3HA8jDEjgKa0rkpERERERCTDDhyCppbIr7f108cYkgchmfowPfVrdrvgangvKIyR8ReJxU9QfzfwF6DSGPMo8Abwr2ldlYiIiIiISAbVN8JnfwM3vhD5mM2BrHu0Peo9odPvrW3/mvrpJZX8TL//izHmfeB4XNn9bdba7WlfmYiIiIiISIYs2AK1B+Dv691AvIIw6c+tgUx9rCF5AGVFUFoAB5thXxOUFQdfC92jXiRZfjL1AJ8GTgjcHpe+5YiIiIiIiGTeh4G05aFWWFcf/pi2TL2PoN6YyHvVa496SSU/W9r9HPg6sBJYBXwt8JyIiIiIiMgRYUlILfLKuvDHxNNTD5H76pWpl1SKWX4PnApMsNZ6g/IeARandVUiIiIiIiIZ1C6o3wlnhjlmSxyZeghm6v+4HMb1h94l0NIKawJB/VEK6iUF/JTffwxUhjweDCxJz3JEREREREQya+dB2LQ3+HjlzsOPOdTiMu6GYLAey4nD3O0flsOJv4afzYGP6qCxxfXlh/bZiyTKT1DfC1hujHnNGPMasBzobYx52hjzdHqXJyIiIiIiXd2LK+HfXnf7u6eD10/fo8jdhgvqt+1329AN6O5/G7rLJsBzl8Bnh8HeJvjpe3DeE+61kcrSS4r4Kb//YdpXISIiIiIiEsFP5rjhcgN7wNfTMLb7w23u9qyR8NRyt+VcSyvkh6RAt8SxnV2oyYPgtxfAe5vg3ndh3mb3fLWG5EmK+NnS7nUAY0y30OOttXvSuC4RERERERFaLWwITKP/3/dd9ttv+btfS2rd7aeHwt/Wu6z8xj0wvHfwmC1xbGcXznEV8NTFbsu8V9fADccmt2YRj5/p99caY7bgeuuXAEtRT72IiIiIiGRA7X7Xgw5w4JDrS081r/x+4oBgBn1VhxL8eLazi8QY+Oxw+P4pUNEz8fOIhPLTU387cIy1ttJaO8xaO9RaOyzdCxMREREREVkfqA8e1APyDTyxFD6OsOVcInYddFn50gLX515d7p7v2Fcf73Z2IpniJ6hfA6jUXkREREREMm5DIBKZOsSV3rdY+I93Und+byu7cf1dD72XqT8sqE+wp14k3fwMyrsdeMcYMwdo9J601n4jbasSEREREREh2E8/rCfMmgzPfASvfwLvboDjhyZ/fq/0fsIAdxsxqE+yp14kXfxk6v8XeAdYiOun9z5ERERERETSygvqh/aC/t3hyzXu8Q/fdkP0khXaTw/tg3obcv4tKeipF0kHP5n6Vmvt19K+EhERERERkQ688vuhgcFy10+B3y12ZfPPrYALjk7u/N7key+o71MK/Uphx0HYvNcNtGtqgdoDrqc/1ZP3RZLlJ1P/ujHmGmNMf2NMT+8j7SsTEREREZEub71Xft/L3ZYWwr982t2/911oaE783PUN7vzF+TAqZN/4UR1K8LcGsvQDurffu14kF/j5lrwa+B6wgGDpvba0ExERERGRtGpqcWXveQYGh5S9XzQWji6HTXtdf32iPgwZklcQEhl1DOq90ntNvpdcFLP83lqbgvETIiIiIiIi8dm0ByxQ0QMK84PP5+fBqSPgozpYWQdUJ3b+JR2G5Hk6bmvnDclTP73kopiZemNMqTHmdmPMLwOPRxljzkr/0kREREREpCtr66fvdfhrI/u429W7Ej9/xyF5Hm9Y3ipl6qUT8FN+/0jguM8EHm8G7knbikRERERERDh8SF6okV7gnURQvyRGUO9NwN8cyNQPVqZecpCfoL7aWnsPcAjAWnsAMGldlYiIiIiIdHkdh+SFOiqQqV+zK7Gt7fY0wtrAkLzqvu1f698NehW7Y7YfCMnUK6iXHOQnqG8yxpTg2lkwxowAmtK6KhERERER6fKiZep7Fbvgu6E5mEmPh5elP7pf+359AGNCsvV1IXvUq/xecpCfoP5u4C9ApTHmUeAN4F/TuioREREREenyvEx9uJ56CJbgJ9JXH6mf3hM6LM8blKeeeslFEYN6Y8wwAGvtX4AvANcDzwDTrLWvZ2Z5IiIiIiLSVXmZ+mFhMvUAo7xheTvjP3ekyfcdz710O9QddFve9SuN/31E0i3alnbPAscCWGtrgecysiIREREREeny9jTC7gYoLYB+3cIfk8wE/EhD8jxepv7vG9ztoO5uKz2RXBPt2zLtw/CMMWcaY1YYY1YZY24P8/o3jDHLjDGLjTGvG2OGp3tNIiIiIiKSfaHb2ZkIkYk3LC/eCfh7G2HNbijKh9Hl4Y/xeuq3ajs7yXHRMvUVxpj/ifSitfZrybyxMSYfuA84A9gIzDPGPG+tXRZy2AdAjbX2gDHmK8B/Apck874iIiIiIpL7Nnj99BFK7yHYU78mzqB+wVZ3O6bcBfbhDO4B3Qth/6HgY5FcFC2oPwi8n8b3ngasstauATDGPAGcB7QF9dbaN0KOnwNcmcb1iIiIiIhIBh1qcb3q4TLx66NMvvdUlLkt6bbvd+X6PYv9ve8fAhHH6SMiH+NNwF+4zT1Wpl5yVbSgvs5a+2ga37sC2BDyeCNwXJTjrwX+HO4FY8wNwA0Aw4YNS9X6RERERES6pOZWNxxuYPfUnrepBRZuhXc3wLsbYcEWmDIInrz48MB+Q5Q96j15xvXVL9vhsvWTB8VeQ30DvLza9Rp/YVz0Y9sF9crUS46KFtSney/6cJ0xNuyBxlwJ1AAnhXvdWvsA8ABATU1N2HOIiIiIiIg/3/kr/H4pvPqlYG95Mppa4NaX4fVP4GBz+9fmboZPdgf74z3R9qgPNbKvC+pX+wzqn1kBjS3w2WFQEePco0L67bVHveSqiIPyrLXT0/zeG4GhIY8rgc0dDzLGnA58BzjXWtuY5jWJiIiIiHR5C7a6bJs3IT5Z8zfDCytdQF/dF64+Bh44B2aOdK+/sfbwz1nvo6ceQibg+9zW7vdL3e0l42MfWx1yoUGZeslV0TL16TYPqDbGjAA2AZcCl4ceYIyZAvwKONNam6L/UkREREREJBJrYWMgS75lb2rO6W05d/FY+K8ZwecPHnKl8H/9BK6dEny+NWQNQ6OU30MwqPczAf/D7bCsFvqUwBlHxT6+OiRTr6BeclXWgnprbbMx5mbgZSAfeMRau9QYczcw31r7PHAv0AN4yrgmm/XW2nOztWYRERERkSPdzoNwIDDxfcu+1JzTC+o7lvKfNNz15L63CfY3Qfci93ztflci37cUehRFP7c3Ad/PXvVelv6Co6HYRyRU2ROG9HBVC+XdYh8vkg2+gvrA9nMDQ4+31q5P9s2ttS8BL3V47rsh909P9j1ERERERMQ/L0MOqQvqVwVK40d1COr7lMKUwW5g3jsbYEagHN+bfD8sRuk9wFG93e263W7AX0GEBuOGZnjuI3ffT+k9uEF8f74ieF8kF0XsqfcYY24BtgGvAi8GPl5I87pERERERCQLNqQhqPf63Uf2Ofy1U6rc7V/XHr6GWKX3AKWFUFkGh1qDE/PD+fMq2NMExwyEo/v5WHRA7xL3IZKrYgb1wNeBMdba8dbaiYGPSelemIiIiIiIZF67TH0Keur3N8HmfVCUHz5IP7XK3b6x1vXzQzA4jzUkz+OnBD+eAXkinYmfoH4DEOWal4iIiIiIHClCM/V1B13ZejLWBALtqt7hS+PH94cB3WHrPvhoR2ANPvaoD3VUjGF563bDPzZCSQF8frT/tYt0Bn566tcAbxpjXgTatpSz1v4kbasSEREREZGsCA3qAbbtg+G9Ez/fykCgHa70HsAYOHk4PLnMleCP7R/sqfedqY+xrd2Ty9zt2dXQs9jfOUU6Cz+Z+vW4fvoioCzkQ0REREREjjBe+b0X/G5Osq9+dYQheaFOqXK33n71cZffe0F9mEx9Syv8Ybm7r9J7ORLFzNRba7+XiYWIiIiIZFJzq/tl38+2ViJdRege9TWDXeY82WF5q2Nk6gE+M8yV5r+/xW1nt2WfmzY/xGcq0eupX7XTfQ0mZFL9X9e60v4RvWHakIS+BJGcFjFTb4z5WeD2T8aY5zt+ZG6JIiIiIql35TNw6m+T7xcW6Uxq98OOA5Ff337A7Q/fpwSqy91zyQ7Li7SdXaiyYpg6BFotzF7q9oUf0gMK8/29x4BuUFYE9Y2w82Dw+VYL//UPd//Kie2DfZEjRbRr078N3P44EwsRERERyZQDh9zQLICltfCpwdldj0gm7GmEsx6H/Dx4Z1b4oXVelr6yJwzu4e4nk6lvboW1u939aJl6cCX4/9gIj33oHvvZzs5jjDv/wm2uMqC8m3v+2Y9g+Q53geBK7d8lR6iImXpr7fuB27fCfWRuiSIiIiKptSak73bJ9uytQyST/m8R1B5wpeif7A5/zMaQAXVtQX0Smfr19W7/+Ioy6FYY/dhTqtzt1n3BNcSj47Z2Dc3w40CW/pufdpPvRY5EfgbliYiIiBxRViuoly7mwCF46IPgY2/ruI68AXWhmfpkBuX56af3VPd17+uJJ1Mf+h7ee/5uMWzaC2PK4YKj4zuXSGeioF5ERES6nNBtr5bUZm8dIpny2IewqyH4eHmEoH5jICs/tCcMDgyp25pEUO/104+M0k/vMSaYrQcYFm+mPmRbu/pG+Pk89/jbJ7iWA5Ejle9vb2NM93QuRERERCRTVoVk6j+ug0YNy5MjWEMzPLDA3b9orLv1k6nv1w0K89zguUQHSrZtZ+cjUw/tg/q4M/Uh5fe/mg+7G2B6BZxaFfXTRDq9mEG9MeZ4Y8wyYHng8THGmPvTvjIRERGRNPECjcI8N8jr47rsrkcknZ5aBtv3w7j+8JUa91ykoD50UF6egYGBEvxEs/XeBTQ/mXqA4ytd77sBhscZ1A/vBfkGNuyBhxe6524/URPv5cjnJ1P/U2AmUAdgrV0EfDadixIRERFJl5bW4JCwk4a7W5Xgy5HqUAv8cr67f/NUt1d7cb7rNa9vbH9sS6t7HoJD6oZ4ffUJDMuzNv5MfWkh/Ops+NlMVykQj6J8GNbLbWPX0AxnjYIpg+I7h0hn5Kv83lq7ocNTLWlYi4iIiEjabdrr9uEe1AOOq3TPaVieHKme+ch9z4/q64LcgjwYHdh/fkWHbP32/W5Sfb9SF1yD+3cCiWXqaw/AniboWRxfgH5yFZyf4GA7r68+38Btxyd2DpHOxk9Qv8EYczxgjTFFxph/IVCKLyIiItLZtA3u6gMT+rv7CurlSNTSCvcHsvRfnerK6QHG9nO3HYflbfC2swspex8SGJaXSKbe+7c2qm/mSuDHBf5NXzre38R9kSOBn90avwz8N1ABbAReAb6azkWJiIiIpEvoFlvjB7j7y3e4MuXC/Oyty6+31sELH8P3T9G+2xLdiytdq8mwXnDu6ODzXlDfsa8+tJ/e42XqtySQqY9nO7tUuf5Y11t/zujYx4ocKWL+KLDW7gCuyMBaRERERNJudcjgrl7FLuBZX++eP7pfdtcWi7VwxxtuvTNGwhlHZXtFkqtaLfwisKXbTTWu7N5zdKxMfUhQ72XqEwnqV8XZT58KPYvh4nGZez+RXOBn+v1/GmN6GmMKjTGvG2N2GGOuzMTiRERERFKtY6DRmUrwl2x3AT0kt3e4HPkeXQQr6mBwD7iwQ3+6F9SvqHPBv2dDmEz9YC9Tn0D5vXcBbZTPyfcikhg/PfUzrLV7gHNw5fejgdvSuioRERGRNOkYaEwIlOB3hgn4L6wM3ldQL5Gs2w3/8Y67f/fJUNyhNrdvKQzsDgcOBS8SQfhM/eBkyu9D5leISPr4CeoDsy/5HDDbWrszjesRERERSZudB91H90IX1EBIUJ/jmXpr2wf12/Znby2Su1otfOs1t6XbeWNcm0Y44Urww/XUl3eDwjzY1QAHD/lfx/4m2LzPbTM3NM795kUkPn6C+j8ZYz4CaoDXjTH9gYb0LktEREQk9UIHd3nTuMcHyu+X1rYvRc41i7YFgy5w24+JdPTbxTBnk9uW7nsnRT6u47C85tZgiX1FWfC4PJPYtnZrAv/Wqnq37+cXkdSL+U/MWns78Gmgxlp7CNgPnJfuhYmIiIikWls5cEiPb79ursT4wCE3KTxXeVn64yrcrcrvpaP19cGy+x+eCn1KIx/bMVO/ZR+0WFfB0rFc3yvB3xzH99xKr81Fpfciaef3ulkFcJEx5irgYmBG+pYkIiIikh6RttjK9RJ8a+HFj939a6e4W5Xf5x5r3Uc2tFq47TV3cerzo+HMUdGP75ip3xDorQ/tp/cMDmTu47mQFO4Cmoikh5/p93cCPw98nAL8J3BumtclIiIiknKrIgzuyvUJ+B9sdVnSwT3g9BGux3l3g+ubltzQ3ArnPgE3vpid93/sQ5izEcpjlN17jurjvo/W1bv+93D99J4hXqY+jgn4kf6tiUjq+cnUXwycBmy11s4CjgGK07oqERERkTSItMWWl6lfmqMT8L3S+89VQ34eDAgM+VNffe5YtRMWb4fX1mR+NsO2/XDP2+7+909xw+1iKcoP/jtYURcy+T7MULtB3l71cQT12s5OJHP8BPUHrbWtQLMxpiewHTgqvcsSERERSa2GZhe45BkY3iFwCS2/z1b5dCStFl4MBPXnVLtbL6jfpr76nOH1prdY2NOY2fd+Z70ruz9xKJxd7f/zQkvw2zL1ZYcfNyTObe2aW2FtYD6FMvUi6ecnqJ9vjOkNPAi8DywA5qZ1VSIiIiIptm63C5CH9zp8ENjA7m5aeH0jbIwjG5kJ729xvcwVZTBlkHvOm0aezr76v62DuZvSd36AxdvgrMfh1TXpfZ9MCN0abseB1Jxz4x74zl+hNsbfs9c2Mr0yvvOHDsuLlqmPd6/69fVwqNV9z3YrjH28iCTHz/T7m6y1u621/wucAVwdKMMXERER6TRWRRiSB257u/E5OizvxZDSe28bvoFepj5NQf2aXXDVs3DZ0zAvTYH9gUNwy59hWS08vyI975FJy0NaN3YeTM05f7sYfvchPPhB9OOWBN7bqzjxK1ymPtqgPL9BvTd8T1l6kczwMyjPGGOuNMZ811q7FthtjJmW/qWJiIiIpE7b5PsIPb65OAE/XOk9BIP6dG1r98hCsLgy6q+8FF8vNcC8zfCt16J/3n+8A2sDE9frUhQEZ1M6MvW1gfMs3Br5mFYbnAXhDXz0y8vUL9vhvpfyTDArH6pvqevB390ABw/FPu8ba93t1Ir41iMiifFTfn8/bp/6ywKP9wL3pW1FIhLT/86Hmgfj/yVLRKQrWx1jGncuTsCfv9kNw6vsCccMDD6fzvL7+gb4wzJ3f3x/F1h++cX4Ju3/+F34/VK4/Onww/zeXg+PLgo+rktREByP/U3w93WpmaFQuz8YgEPqMvXen8vibe4CSzjrdsO+Jnehp3/3+M7fv5ublr+vyV3EGdwDCvMPPy7PBL/nYmXrW1rh9U/c/RmawiWSEX6C+uOstV8FGgCstbuAorSuSkSienKZ++Vh7uZsr0REpPOIVn4PuTks74WQLL1Xeg8hg/LSENTPXgoHm93Qtd9d4AanLdwG/+8Nf38uBw65OQAAa3bDFc+0D3L3NMJtr7r7V050t9nI1P/PXLjyWfi/xcmf66Md7R+n6uvZ1eBuDzbDyrrwx3hZ+vFxZunBfU952XoIX3rvGexzW7sFW9zXP7QnjCmPf00iEj8/Qf0hY0w+7gIexpj+QIRrhSKSbvWNwRLSbGQ2REQ6o1YbO1M/tCf0LIYdB9M7gM4va+Evq9z9jhPNB3qZ+hSX3ze3wqML3f1rpriy61+dAyUF7oLy7z6MfY65m9yQtOq+7uPjOhfY1wcC1Lvfgs37XOXB//use27nwcxvA+eVtP92cfIXcZYFgvrCwG/WqQrqQ8+zcFv4Y7zKknj76T2hQX24Peo9foflvRIYejhjZPsLUSKSPn6C+v8BngEGGGN+CLwN3JPWVYlIRB+G/FBPVc+eiEhn0tLqypH/dz7c9JIroY5l6z6X7SwvhT6l4Y8xBiYFAqMFW1K33kQt2+EuLgzsDhM7BGyhg/JSWVXw8moXcB/VG06pcs9NGAA/Os3dv+ut2IPz/r7e3c44Ch6/0J1rWS186Vl4+iN4ajkU58NPZriLBb2K3TZwXtCfKasCF3lW7gxWFiTK66f/1BB3m6qL7qEVDpH66hMdkucZG2emPlpQby28strdV+m9SOb4mX7/GPAt4N+BLcD51tqn0r0wEQkv9If6jiNgsJCIiB8Nza4H+/o/weRfweefgH9/xw2Ru+01aIzR7+1l6UdFGJLnmRYY7JUL7U1/DfQln1J1eMazrMhtFXbgkOuHTpWHA1PWZ012fdSe84+G66YEB+dFG5b2zgZ3e+Iw1ybw+IUuWFy0DW592b327ROCfxf9urnbTP5M293Q/v2eWJrc+bzJ9ycOdbepyNQ3NLu/X8+iMJl6a0My9QmU30McmXpvAn6U8vuVO93ww94lUDMksfWISPyiBvXGmDxjzBJr7UfW2vustb+w1i5P1ZsbY840xqwwxqwyxtwe5vViY8zvA6+/Z4ypStV7i3RWi5SpF5Eu6JEP4LtvutLePU0wrBdcOt5lgbfsg6eWRf/8WP30nragPs37s/vhTRA/perw14wJmYCfolaBhVtdxrpnMVw09vDX//VEGBcYnPfXteHPUbvfZa1LCuBTg91zg8tg9kUwJJDpnV7pLhp4ygOVE5lsKVsZuMjTP3BB4YWPYW9jYudqbHbfXwY4PoVBvZel710C+QZW1LUP8sH1t+9qgD4lMKQssfep7uvOD+H3qPcM8bGt3auB0vvTRkCBn3pgEUmJqP/crLWtwCJjzLBUv3GgT/8+4CxgHHCZMWZch8OuBXZZa0cBPwV+lOp1iHQm1sIHIZl69dSLSFdgLfzxI3f/G9Ph7Vnw93+CH50O3/y0e/7++dDUEvkcq30G9VMGub7oZbVuoFu27Dro/r8vzHMZ73DaSvBT1FfvZekvmwDdw4xELsiDLwZ+U3v2o/Dn8LL0x1VAcUHw+aE94ckvwG2fhvvOal8FUB4IrDM5LM8rvT9xGEyvcK0Zz61I7Fyrd7kKhqrewUx3Kn4+e38eg3u4bHqrhQ877MwQWnqfaP96SYG7mFVWBKOjVLK0Tb+Pkqn3gnqV3otklp9raIOBpcaY140xz3sfKXjvacAqa+0aa20T8ARwXodjzgMeDdz/A3CaMRq5IV3X1n0uQ+L9I1D5vYh0Bct3uCCsTwncVNO+7/dz1S7TuGkv/DFKLaEXxEXao95TUuAGuFncdnLZ8tY6F8RNq4AeEfYc8oblhdsyLl5b9sJLq1zG9upJkY87u9oF5G+sDd8D/3YgqD9h6OGvDe0JN08Lltt7vEx9JqvPvO+HUX3g0gnu/uwES/C9fvqx/aBvibu/q8HNfkjGrsDP+L6l7mITHN5Xn2zpveeBc+D1qyLPm4BgpUWkTP22/e5CVHE+fHZ4cusRkfj4Ceq/B5wD3A38V8hHsiqADSGPNwaeC3uMtbYZqAe0OYZ0Wd7k2ymBksYdB3Jn2yURkXTxMqhnVx++h3aegVumufu/mAeHImTr/WbqwWWZIbsl+NFK7z0DU7it3W8Xu2zzmaOgIkpf9YDuLmA/1OouAoSy1u0/D5GrC8LxgvxMVp+tCpmxcNYoN6xvyfbDM+F+LAtky4/u574/exW7CzK7kxz8VxcS1B8z0N2PGNQnOCTP07M4+P0USd9SF7DXNx7eBgDwWiBLf+IwN+9BRDLHT1C/HnjPWvuWtfYtYC7gY85sTOEy7h3DEz/HYIy5wRgz3xgzv7a2NgVLE8lNiwI/zI+vdNmkjkN0REQ6k8Zm+LfX4fTfwrrd4Y9ptfB8IKg/f0z4Y86pdsH6xj1uunpHv13sstndCqHCR9/x1EBQ/16WMvUtrS5TD3DqiMjHtfXUJ1l+39AMjy1x96+dEvv48wJ/Dx3L1dfsdlnc8tL2E9Vjaeupz2T5feAiz6i+7ufphYEZAk8sif9cXqZ+XCBb3i9F7QShmfrJgUx9x2F5yU6+j4cxwRL8cHvVe1Pvz1DpvUjG+Qnqn6L9vvQtgeeStREILc6qBDr++Gw7xhhTAPQCdnY8kbX2AWttjbW2pn//JOuPRHLYB16mfhD0y0K5oohIquxugKuedcHkyp3w43+EP27+ZrfFWkVZcLuwjvLzXFk3uGx9c8hvLY8ugjvecPdvP8EdG8unBrsKgMXbok95T5dF21z59rBebhBgJG171SeZqV++w/19jOoLxw6KffzMkS5jO2dj+wsKXpb+hKHte+Zj8XrqM/Xz7OAh2LTHzQgYHhgMd+l4d/vcivgullvbvvweXBAOyQf13ueXl7qLVj2KXJuJ126xbb+7X1bkvlcywdvWruOFpH1N8O5Gl407PcqFKBFJDz9BfUGg5x2AwP0I3V1xmQdUG2NGGGOKgEuBjr36zwNXB+5fDPzVWhUbS9fU0hrco37SwOxsASQikgrrdsOFT8KcTa6cuzAP/vQxfFx3+LFeNvjc0dEDxXNHw4jesL4++Dm/Xugm5gPcdRJcfYy/9fUsdlnX5lZYEGFv8HTyJsufUhV9+NmgFJXf1wY+f1hPf8PWeha7CgKL+3vzhAb18UhVZtuv1bvc2qt6B9s5ju7nLpjvbYKXVvo/1/YDbkp9z6JgFYhXebAzya/H+/w+Je5i1KRANt7L1i8NlN6P7x/fRZRkeBPwH/qgfbvEW+vcoMpjB0P/GGX8IpJ6foL6WmPMud4DY8x5wI5k3zjQI38z8DKwHHjSWrvUGHN3yPs9DJQbY1YB3wAO2/ZOpKtYvQv2H3K/NAzonvnMhohIKry/Bc5/0v2fdnQ5PHcJXDLeBVn/M7f9sU0t8EIgwDovQum9pyAPbp7q7v98Ljy4AO56yz2+++T2W6j5cVygKiAbffVeP/2pVdGPaJGzYgAAIABJREFUa8vUJ1l+7/0c6TjALpqOJfjNrfCPje7+Z+LcM6lvhre0Cx2SF8rL1s+OowR/eUg/vXdBpF+Kfj57Qb33894rwfd2wfFK78dnoPTec+UkVzHwxlqY+Ri8Huij90rvNfVeJDv8BPVfBv7NGLPeGLMB+DZwYyre3Fr7krV2tLV2pLX2h4HnvmutfT5wv8Fa+wVr7Shr7TRr7ZpUvK9IZ+QNx/GG5WRjsJCISDJeWwOX/dEFKycNhz98wWX+vjoVivLdXuGh2fq/r3Nl4aPLXdAUy/lHuzLkT3bDD/7unvv+yf4z9KG8/erfy3BQv22/G35WUuD2c49mQCAjun2/mz2QqNrAz5H+cQT1p1S5su8Pt7sLNIu3uSz3iN7RB+2F09ZOlqFMfWg/fajPj4buhTB/S/iqkXDaSu9Duj/7pjhT752vra/eC+pTNCQvHp8aDC9f4b43aw/ANX+Cb70WrC45Y2Tm1iIiQTGDemvtamvtdNxe8uOstcdba1fF+jwRSS2v3O6YwA/1TP8SJCKSrDvfgsYWuGICPHIulBW754eUuSypBX72XvD4ZwNZ4PNG+ysLD83WA9xzKlyVQEAPMDWQqV+wxVUMZMqba92tNxA1mpIC6F3iJtHvSuJngVf2Hk+mvqTATcoHN8jw7wmW3gP0KnFb6e1pzMyfdejk+1Ddi4IVCKFtBdF4k+9DBwOmqpKubfp9YJu8yYGL+ou2uYs4S1O0nV28KnvC7Avhjs+42Qq/X+r+7kb28be7hIikXsyg3hjzdWNMT2A/8FNjzAJjzIz0L01EQnnb2Xk/1FV+LyKdya6Dbjp9aQF8/xQXgIf66lQXILy4Ej7aAfub4NVAfd65MUrvQ100Fr51PDz0ebhiYuLrLe8G1X3dRYjF22Ifnyptpfc+h421TcBPoq8+kfJ7CO5G8NyKYD99vKX34PrBU5Xd9iNSUA/B6oh4M/XjQoP6FH0tuwJb4nl/NgN7uEF1e5tcG8vGve7iSjYC6TwD1x8LL1zmevohdouMiKRPjGvAAFxjrf1vY8xMYAAwC/g18EpaVyYibRqa3S+5eQYmBsrsVH4vIp3JUi+j2T/8BPpBPeCyCfCbRfDTOW7v8IPNbvBWPJO9C/LcBYJUOK7CTeafuwlqOkzef32NG8JnjNsqr1uhu2DRp9RVC4xLIHt6qCWY8T65yt/nDOwOK+pcX/34BDO2XlBfHmdQ/+lKV7L/yW73kWfcc4no182Vc+84ENw2LR2aW2FtYPvEcMHwiMBuA2sjbLEYqqEZ1uxyX/fo8uDzqdiiryWk+qJPafD5Ywa6bQMf+9A9HtvP344O6TK6HJ69xFUsTMxgG4CItOfnvwGv4O1zwK+ttYsIv3+8iKTJ0lr3i0h1X1ceCMFfGpSpF5HOYEnIpO5Ibqpx2fq/rHZb00Hkvekzweurn9thw92t++DWV1ymdMMeF1R/sNVt6fXiSrj2edjbGP/7zdvstgar7gtDffalD0zBBPwdCfTUgwsmPz86+HjSAFdKn4hM7VW/rt61K1SUuQsxHVV5QX197DkFK3dCi3UXAkpDzpWKr6W+0bWj9CpuX9UyJdCC503oz2Q/fSRF+a7fP5sXF0S6Oj///N43xryCC+pfNsaU0X7fehFJs0UdhuRB8Jcv9dSLSGfgZeqjBfUDe8CVgZL5lTtdn/XZ1elfWyTTAtn5+Ztd5hRcoPfNV1zQdfJw+NvV8JfL4ekvwmMXuP+nN++De96O//280vtTqvx/Tiom4NcmWH4P7UuuT0yg9N5TnqFt7aKV3oPbrq9fqcvCd9yLvaPlYfrpIeRrSeKie+ge9aG8uTqNgdkDme6nF5Hc5Ceovxa3ldxUa+0B3B71s9K6KhFpp62fflDwuVT80iAikileUB8rs/jlmuCAuBOHJRZopsrgMpcx39sU7J3+9UJ4e4Prc773DBje27UUfGqwW++Pz3CZy8eXBPvM/Yq3nx6Sz9Q3NrshZ/nGDd2L1zEDgyXrJw1PbA0QHP6a7p9pkbazC+Vl6z+JUYLfNvm+Q1Dfp8SVtO5ucFV2idgZpvQeXDVE6J70uZCpF5Hs8xPUW9zk+68FHncHEiyuEpFEhMvU9ylxP9h3Nbg+TBGRXLW/yfUeF+TB6AgZUs+A7nDDse7+lUkMukuV47wS/E1utsmP3nGPf3RacEu5UKPL4evT3P1vvebK6f3Y3eCqE0oKoGaw//W1ZeoTDOpD90LPS6C50hj41dnws5nBdoVEZOpCdaxMPcCIQMAfq69+WZjt7MCVofcpdb9AJ7orwf9n777D4yqv/IF/j7pc5CLJcu8FGxt3OqYbU4INhCQEQgmEQHryS7LJbtomuxvS26ZBCm0JGEIN1fRe3HDvvfduS5b0/v4499W9Gk25d+ZO03w/z6NnxjOj0ZWnaM57znvOnhiZ+o5luj0DAEqLWu/lJ6LC5Seo/wOA0wBc4/z7IIDfp+2IiKiVfcd0b195MTDC88e7uMgdc7PnWHaOjYjIj6W7NMAZ3h0o99Gi92unAu/fAkzNgZnXNlB9fQPw5ee07Pma0fGP7bZJ2jRs80Hgxz7L8G3PgZE1QGmx/+PrmWKmPtnO914jaoArTkj++wFPn5gsl98DbuXBmr2xb2OMLvIAbTP1QOrd/CNn1HvZqr0RNVoVQkTkJ6g/xRjzeQDHAMAYsxdagk9EGWDn04/u0faDHsfaEVE+8LOf3kvELSvPNpupf2UdsGw3MLAL8N2z4n9PSZGW4ZcWAfcv9FeGH/T/yEp1T/3OJJvkhS3ZiS7bD+v4uURN7QANxFc7gbqfoD5epn7bIV1071KuY+Yi1aS4SLE7TlB/ujNh4OTeba8josLkZ6TdcREphi6yQ0RqwUZ5RBkz3ym9H1fX9rqaDtp1mfvqiSiXtQSsebj/d0AXLbPfcVj3nf96mjuFJJ4TaoAvngz88l3g314Enr8O6BTn+/z2HIhU00H3b+86oluxgmT5Ac84uyjBYyYl0yivvhG49AFdmOheqeP0Tu8LnN5PA3OJ2E6w9RBw+LjeNlqwbA3ysad+iWdEY+TPAdz7T/bv8944Qf30Edr/IHLMIhEVLj+Z+t8CeAxADxH5bwBvAviftB4VEbWwmfqxPdtel6lyRSKiVCSbhc4FIsAUp6v7l09xR4r58blJOq9+00HgV+/Gv22y/0clRRrYGyRXtRVG+X0YqpMIgt/cqAG9QMvVn14J/McrwLn3Ah95ENgfsTXNT5M8wG2Ut2F/7EZ3i+wiTIzHK9Vu/rG63wP6nDxnYPxFIiIqLAmDemPM/wH4JoAfA9gKYIYx5uF0HxgRKfshJNqevVqW3xNRjmtoApbH2XucD747BbhvBvClk4N9X2kx8KNz9Pxzq2Lf7uhx3b9dLMk1PuvplH9vS2Jffa4F9buOapm8H886/6dfPRV49Xrgf84DLhumJfELd2iVhJef/fSAzpzv1Unn2W85GP02iapPUp1VvzdG93siomhiBvUi0t1+AdgB4B8AHgCw3bmMiDLA/mGPtt+RY+2IKChj/AdNYVi5R4OjgV2AzuWZ+7lh6loBTBkQvcw6kfE9gaoyzdbHChCX7tI94cO6u+P8gmgZa5fEvvpc2VPfoVR/92ONwJHjiW/f2AzMWqPnLx6qHeuvHQP8/hLgoau0k/+9C9wyecB/UA+42fpYzfIWO40NY2bqbaO8JP8+x8vUExFFipepnwNgtnO6E8AKACud83PSf2hE1NgMHGjQ0sKqKB+Ga5ipJ6KA/vcDYMTvgXnbMvPzbPCTj/vpw1BcBExwRtTN3hL9Nqn2HEhlVr0NHrOdqRfxzKr3kd1+d5M2qhvSzR3xZo2sBa4/SRdKvvequ4i1ykeTPGtwnH31+47pIk15MTA4Ril/SyPbNHS/JyKKFDOoN8YMMsYMBvA8gI8YY2qMMdUALgPwaKYOkKiQ7XP2A3ap0A+GkVq66zKoJyIf9h0D/jBbx7Il2uMdlnzeTx+WyU5Dsw9iBPWLEmR9E0mlA36ulN8DwSa6PLdaT6cNjV5B8bXTNMv9wRbg8eV6md899QAwMM6sevucHlmjPQ2iqU5hpJ0xDOqJKBg/jfImG2Oesf8wxjwL4Oz0HRIRWS1/1CuiX59qJoCICsu9C9zS5tfWu8FJOiXb1b09meyMxcvFTH1OBfU+M/XNBnjeCeovHhL9Nl3KgW+doef/+w1terf7qJb59+6c+FjidcD3s1CVyva4I8d14a2iRI+XiCgRP0H9LhH5jogMFJEBIvIfAHan+8CIyM3Ud42xUp/sXF8iKjzHGoG75+t5Ww7+p9np/ZnNxt3TXMiZ+rF1OrN+6S7gQH3r64436WhSABiVZCPBlkx9wKC+sVkXjwW5kRH2m6mfu1VHDPbtHH+x6KOjtKfBziPAV5/Xy4Z089cbIW5Qbysr4vzsVBrlcT89EQXlJ6i/BkAtdKzdY875a9J5UESk9jpBfbcYmfoaz8icTDa+IqLwGQO8tDZ922lmLtb3ipN6AP87TcuGn16pGcx0Wb9P54LXdcyNTHC2VJQAY+p07Nzcra2vW7VHJwQMSKGRYLKZ+j1H9Zi6VcYuI8+kGp9j7WzX+4tilN5bRaLTBwTAbOf/3c9+egDo30W/f9MBfXy8/GTqu1bo9++v14WbINj5noiCivsWLiLFAL5tjPmyMWa8MWaCMeYrxpg9GTo+ooLW8oc9RlBfUaJzahuatKEeEeWvV9YBn34S+P6r4d93YzNw51w9f9skoE8VcPlwoMkAf5kX/s+zuJ/eFWtffRj/Ry0j7QLuqc+l0nvA32x3Y9zxgBcPTXyfY+q0K77lZz89AJQVA306a7WJd+Hr6HFgtTN+cEScyooicbfO7Tnm72dazNQTUVBxg3pjTBOAiRk6FiKK0JKpj/OHvdpnZoOIcts7m/T0rY3hV948sxLYeEDHyk1z9iDf5vx1f2hx+t4/Ut0r3p7YoD5yX30YQX23Ci3vP1CvQadfLUF9jgSPfsrvF+3UzvO1HYCJvfzd79dP08w5AAyv9n88g6M0y7PjB4f6GD+Y7L56+7c/F7ZEEFF+8FNsNU9EnhSRT4nIlfYr7UdGRAnL7wGOtSNqL+yIub3Hou/jTZYxwB+dQbS3TnQnaYyoAc4bqHvt7/kwvJ/nZbu6M1MPTHIC0HnbgPpG9/IwFj5E3BL8HQFK8HflyDg7y89Iu5bS+yGaDfejWyXw50uBW8YD5w70fzwts+o9r8cgizDdk+yAvztBk1wiokh+gvru0MZ45wH4iPN1WToPiohUovJ7gEE9UXtwvAlYuMP995ytsW8b1BsbtFldbQfgqpGtr7t9kp7e4+mKHxZjWH7v1a1S56nXN2m2GQi3kaBtlrctSFDv/N2ozZGg3ma24wXBQUrvvU7tC3x3ClBa7P97bLO8dXvdy4I8p5P9+7zHuT0z9UTkV8Kg3hhzU5SvT2fi4IgKXUv3+zhBfSoddokoNyzbrRlzK9bos2T80elwf9O4tuXCk3trJ/x9x4AHF/m7v/nbgP0+9ghvP6zvS1XlQL+qYMfcXkXuq9+wHzjYoEF1j46p3bfN1AfZV2+DzepcCeqdv2exguAVu3U/e9cK4JQ+6T+eQdEy9bb6xEdlRbKZ+j0svyeigBIG9SLSV0QeE5EdIrJdRP4pIn0zcXBEhc7Pvjpm6ony33yn9N4GEWFl6hdsB97eBHQsBa47qe31Iu7e+r/MS9yle/FOYPpDwI1PJt73b4OfUbX+RogVgsh99WFWMtjnzvub/X9PrjXK8wbBzVGeX885s+kvHBQs456slky9E9S3Gj/o4zFrWaQIGtTb8nsG9UTkk5/y+78DeBJAbwB9ADzlXEZEabbHR/m937m+RPmuqVm7uLdHdj/9tWOA8mJg5R5/2fBEbGf7T44BusQYl3bhYJ3dvfkg8MyqBMfpLDbM3Qq8tj7+bRex9L6NSZ6gvtkEy/omMn2Enj65vHXVRzy5FtSXFWtlR5OJ/vx/NsnS+2T1qdIGhFsPuV3v65t03F2s15NXso1s2f2eiILyE9TXGmP+boxpdL7uhs6qJ6I0aym/j/OHvZZBPRWI77wCjPszsDPgLO58YDP1p/QBxjgB3txtqd/vwu16etUJsW9TJMDHTtTzibK8qzx7i3/9Xvxsvc1Cj2bn+xb9qrRMfu8xDRDDXPgYUQOc1EPHmz6XYHHG2plje+oBt1leZHZ70wHtP9CxFDijf2aOpaRIA3hAs/VBKyv89AiIhnPqiSgoP0H9LhG5TkSKna/roI3ziCiNjHGD+riZeu6ppwLQbICnVuj+YxsItRf7nQCvvBg4oQaYGGP0WVDGaIYR0IxjPKOcedsr98S/3UrPX/9524A3N0S/XbNxO9+PZhqghUjrEnzbJC+s/yO7ODNzib/b51qmHog9Bs5WhpzVP/EouTDZEvy1+zyVFX6D+jjl9zsPAx/GWLhjpp6IgvIT1H8awMcAbAOwFcBHncuIKI0ONGgJYqcyLUmMpSbJObhE+WTlbg3ogfaXqZ/vZNNH99DXup29neq++gP1wNFGzWx2Lot/Wzu7e/nu+Nl3G/RfM1pPY2Xr/zxHy/lrKt1Z36RsCf7TKzVTXlXmZoNTdflwXRx6e6NmtuNpNm4GOZeCx1gL1TaoP2dgRg+nZaxdSpn6KH+fP/Mv7U+xbFfry4836Wu3SIAuHGlHRD756X6/wRhzuTGm1hjTwxgzwxiTYCcdEaXKlt/F63wPsFEeFQZvgBtkDnc+sPvUx/fUUxvUz9+WWg8Bm6Xv2Slxo7q6jrqXed8xYEeM95L99drRvqIE+Pcz9b1p9lZtxOf1wWbgZ2/r+Z9eqCXM5LKZ+jecKocwGwl2qdD57QbAP5fGv+3+Y/r8qioHyjOY+U4k2t+0403AWxv1/JQMld5bLR3w93qCep9bSmpiLFCs2K2VLgbAqxGfqPd6KvSK2GCSiHzin1qiHLXXR+k9oB/ISoo0s1/vszkSUb7xBvU729kClt07b4P6mg7AwC6aZV+6K/b3JWKD+l6dEt9WBBjhZOtXxthgt8rJ0g/upu87t4zXf//2Pfc2e44CX3hOq4xumwicPyi5Y2/PTqjRCiwr7EaCHxulpzOXRO8gb9nXUU0OZemB6M3l5m4DDjUAQ7sn3koStkFOpckbG93xg3U+xw/av88HI/4+P77MPf9uxKIYO98TUTIY1BPlqL0+Ot8DupLPffXU3rXXTL0xbpM8G9QD4eyr3xYgqAfcoH55jKDeBvvDuuvpDWM1aHl3swYmzQb42gv6cyf2Ar5+WvLH3p6VFAETPI91GJ3vvU7vB/TprOX3kQGjVy7upwc8e+o9f89eW6enZ2c4Sw+4mXr7egqyCCPSdlZ9swGeWO7e5oMtrStycnFLBBHlPgb1RDmqpfO9jz11sRoL5StjgIeXuJlBKmx7jmqTKqs9ZerX7tOy9toOQO/O7uW2BH9uCvvqbRDSs3P821nDEgX1zuvRBvXebP1v3gPunAO8sk7fs343LTNzxPOV3VcPhJ+pLy4Crhqp5+M1zMvVoD5a+f1rzlaFswdm/HDQs5P2KbCCLsJELrrP3gJsOgj07qQVOYcagIU73NvvYed7IkpCwqBeROpE5K8i8qzz71EicnP6D42osAUpwbPjiNpLsDN7C/D1WcD3X832kVAusIGtLXltT5l6m6Wf0Kv1vupJITTLC1J+D7iZ+hUJyu9tUA8AN47TRm9vbwJ+4uyj/+WFmS+Rzjd2X315MTAkDY0Er3ZK8J9dpU3XosnVoD4yCN55WCcplBfryMdMKxI3Ww8EX4SJ/H1s6f30EVpVAbSuqGDneyJKhp9M/d0Angdg15VXAPhKug6IiJTfPfVA+yu/tyPLEnVvpsJgA9uLhujpjsPxO7TnE7ufflxd68uHVWvH+s0Hga0Hk7vvbZ5GeX4Md4L1FTE64LcE9dXuZV3KgZvG6flmA9w6ATh/cHLHW0gm9gLOHqCLIumoaOjfBTi1L3CsUbvsR2PHrOVcUB9RefaG0yDv1L6ZHWXnNdAT1AcdP9jd8/e5oQn4l/N4zDhBfycAeMcT1O/lnnoiSoKfoL7GGDMTQDMAGGMaATSl9aiIyC2/9/GHvb2NtbOZwlyqPNh1JH7TKUofm6k/e4B+qD/aCBw+nt1jCkvLfvperS8vEs3eA8ln64Nm6qs7aNO0w8d1McHrcIOWDJcWAQMixq/dPB7oVwWc2Q/45unJHWuhKS8B7p2hUwTSxTbMe2hx9Ottpr4214L6iNnuLfvpB2TlcAC4Yxk7lwH9Ao4f9P59fnWdbrcZWaMNE21Q/8EW7fAPuIvzDOqJKAg/Qf1hEamGTt6AiJwKYH8qP1REuovILBFZ6Zy2KT4TkXEi8o6ILBaRBSLy8VR+JlG+CZSpb2fl93ZP7+HjGkxk2+KdwKS7gKsebj8LJ/nieJM7x31CL6BHFkrwjQFufxq49V/hVggcc7rbFwlwUpR9uqnOq9/mBOZ+M/VA63n1Xqv36umgbm1H1HWpAN64Ebj/Cu6jzyUXD9Uu+/O2uf0QvHK1/L6rM8rtQL2+RuzovylZDOptpn5UbfAxc95GeY85pfczRuhpXUfdfnHkOLDA2Vdv//YzqCeiIPwE9V8D8CSAISLyFoB7AXwxxZ/7LQAvGWOGAXjJ+XekIwCuN8acCGAagF+LSNcotyNql1r21PsI6mPNws1HxrTe05sLCxULtuuq5tytwIyZbOCXSUt36Qf7wV31Q242+kd8sAV4ZhXw/GpgY4hbQhbu0K7XI6qBjmVtr08lqD/UoGMuy4v9LQxaI2r0NHKsXWSTvEgi4c1ap3B0KAUuG6bnH4sys94G9dU5FtR7J7q8sUH/rvXpDAxNQ+8Bv6YN1e0/n58c/Hvtosm6fcBLawEBcPkI9/rTbAm+s83ALhxzTz0RBZEwqDfGzAVwNoDTAXwWwInGmAUp/tzpAO5xzt8DYEaUn7vCGLPSOb8FwA4AIfeIJcpdyZTf7/IZ6BzP4Q002w7pTF9rZw40RdviZDxLioAN+4ErZ7beA0npYwNaG+BmI1PvLV/2dqlO1Ty7n75n9OvH9dQAZ/FO4GjA7Qbe/fRBgu1YY+0ix9lRfvjIcD2dtbbtdblafg+4Ae0/ncWIswdkd9GoSzlw52XJbQGwGfdZa4D6JuCUvq0nXdig/t3Nesru90SUDD/d768GUGmMWQwNvh8SkQkp/tw6Y8xWAHBO4w4IEZGTAZQBWJ3izyXKG8mU3/spDX9lHTDmT8CMh1KbgZ0ukcHEjhzI1NtGZf9xJjB1sO6J/NRj7gfOZGw+ALy5IZzja8/sfno7t70lU5+hoP5gfetGYwu2h3ff0ebTe3Uq0723jc3AhwF/btAZ9ZYN2tsE9Qky9ZSbTu6j+8BX7NYFScuY3C2/B9y/aS85ixHZLL1PlV2gOO7Mor9iROvrbUf/2Vu0kd6eY62/j4jIDz/l9981xhwUkTMBXATNrP8x0TeJyIsisijK1/QgBygivQDcB+AmY0xzjNvcKiKzRWT2zp07g9w9Uc6yHXD9BPU20NmVoPx+yU7g889oo7F523SP+O1Pt/6wl21tgvocyNTbpmGDuwF/uhS4eZx+QPvaC8A9HyZ3n198Drj2MQb2idhM/QQn8M10pv6pFfp66VCq/w4zqJ/n/G6xgnrArVB4Z5O+fh9bBvzkLeCWp4C/zI39fUE731t2T/2qPUCT5y8ug/r8VFbsZpdfXONefrBBs8YdSt3ndi6xAW1DE1AswBn9sns8qfAG52XFwMXDWl9f21FfV0cbdaEvyN9+IiLLT1BvC3UvBfBHY8wT0Kx5XMaYC4wxo6N8PQFguxOs26A9akGjiFQBeBrAd4wx78b5WXcaYyYZYybV1rJCn/Lf0eP6gau82N8HrpaROXE6tG87BNz0pDafu3w48KWTtZP4M6uA8+8D/uuN2POMM8mW+dqZ5Lmwp952Ee/dGSguAr53NvCds/Syv84Lfn97j7oZ6GQXBQrBtkO6oNK5zB2j1iPDzwtbev+lk/V00Y5wmuVtPwRsOaS/29A4gbIN6n/9HnDxA8BXngf+MFtLef/nTQ16ogna+d6qKgd6d9L3H7vYd6xRewkUS+vRXpQfLnBGDL7oKcHfncNZeqD1cU3spc/LfOXtWXD+IC3lj2S74L+wRheMO5XphAQiIr/8BPWbReTPAD4G4BkRKff5ffE8CeAG5/wNAJ6IvIGIlAF4DMC9xpiHU/x5eaPZtJ/5y5Q8u6eua4W/fYRlxfpBockA+4+1vf5wA/DpJzVImtwb+PmFwP87DXj1euCqkRoY3DUX+MKz4f4eybCZepuZyfaeemPcPfXefZDXjtHTrYeCv2bf2uiME4F+0N4cYvO19mSOJ5NtO07bqpRMZOqX79LO+53LgBvH6s8+0ACsD6Gyxe6nP6kufjftM/sDVWV6m8FdgWlDgK+coosbTUabb0XTkqnvHP36eIZF7Ktfs1f/Ng3sykAjH507UBdk3tvsLtzmcuk90Dq7nc+l94C+f5Q6n5pnjIh+G7uv/llnqw873xNRUH6C848BeB7ANGPMPgDdAXwjxZ97B4ALRWQlgAudf0NEJonIXzw/dwqAG0VkvvM1LsWfm9PW7QNG/gE4917g7/O1ezEVpiD76a2aGF3Bm5q11HvxTmBQV+Cuy9wP5r06A7+cCvzrE7ow8Pp6t/QvG5oNsMIp8z2zv55mu/x+7zHNWlaVafbE6lCqCykNTcGnDtgRTeXF+jv/36Lwjrc9iWySB2S2/N5m6aePACpLNQAHwinBf9tptDixV/zb1XQAPvgMsPRzwCs3AH++DPjqqcAYpxNNtFFlQPKZesBtlmdfi/ZnxKsooNzVtQKY1Ft7M7y2Xi+zfydqcjR49Ga3z8nzoF5Eu91P6qULLNGc6uyr3+QsIPuZekNE5OWn+/0RaHn8mc5Zifl8AAAgAElEQVRFjQBWxv6OxIwxu40x5xtjhjmne5zLZxtjbnHO32+MKTXGjPN8zU/l5+a6NzdomePafcAPXgNO+Svwn6/FzsRQ9qWri3yQzvdWdZSxdk3NwA9f12ZDXSuAv18evaPumDrN4Bu4AWc2bNyvr4G6ju7e3WyX39ssfa8oGU+bubeN9Pwwxt1H/29n6OlDi4D6xuSPsb2aGyWor81Q+X19I/CoM1P64yfqqQ2kF6TYAd8Y4GWnFPqcgYlvX1GiX142wI41XjGUoN7J1DOoz3/nD9JTu68+1zP19riqK4ET47ZSzg+/nAr882OxK12qO7ivO4CZeiIKzk/3++8D+DcA33YuKgVwfzoPqlCtcYL3i4YAJ/fWTP3f5gPn3APc8WZ2j43aemYlMPR/9TRsNlMfZLXefgj6zXvAJx8FzrobGP574O4PNQt/12XAoDhzfm0zpdfXJ3XIobCZweHV2ZlHHk284MhetuWQ//tbt0+zMV0rtKT7hGptcPgcZ3u0cqxR968LWo98q6nUUvTdR9M7mvHFtfo6HFnjBvMnOaepjrVbtVf3qHevBMbVJXcfdmZ3rKA+2UZ5QNuxdhxnl//svvpX1mnGPteD+km9gCHdgFvGx9+e0p7YEnyAne+JKDg/5fdXALgcwGGgZWZ8Erv0KJG1e/X0ihOAh68Gnr4GuHqUNub64xx3/BHlhn85wfwDaSid3pNE91ubNX5nk+7Z3rBfP7z16Qz85iIdbRSPDepf25C9vg7Ld+np8OrWY/qaos69yIxo++mtlqA+QKbeVkKc2U9f29eP1X/ftyD5Y2yPFu7QhlEjqoHOnsZSxUXRq1LCZkvvP36i29ditBOAL9oRuyGlH3ZM1zkD9PdJxjBPl/pIxxr1PaSkKLmgbWh3XUxZs1e3l7Dzff4b0k17Muyv120tuR7Ud6sEXr4e+NzkbB9J5pzqCeo5o56IgvLT8qbBGGNExACAiHRM8zEVrDVOUD/EycCM7qENzWoqNaj/4evAP6/21ziN2rpzrq5i3Tw+nP9Du8jy7iadZd05xO68yZTf3zoB6FKh+7z7VQH9uwB9q9qW7cYyolrL3rcfBpbtAkZmYZCEzQyOqNbqgu6VGpzsOeqWXWdavPJ7e9nWAJn6Nzfqqe0ZMGOEdjH/YAuwdGd2/t9zUbT99FZtR63g2HE4uUx0IpsPaMVKWXHrxlZ1Hd3XyNp97nt1UK84Qf15g5I/RvuzV+/VRS/v4sD2Q+7xJpPlrCzV94/1+7UEf90+DfKT/X0pN5w/GFgzVycn2PGnuRrUF6JT++jrzICZeiIKzk+OYKbT/b6riHwGwIsA7krvYRWehiYtxxTohymvz0/WwH7OVjc7TMFs2A/89xvAj94A7ngr9Uz0jsPu7PLjzcDrIe9DT6ZRXl0nHbt1w1gNFoZ29x/QA7rQYbsMv5alEnybEbTlv7lQgm+D+j4h7KlvbAbedoL6s5ygvmOZTiAAgPsXJn+c7Y3dTz8hSlDfwz4v0tQs75Gl+sH6oiFtM2a2Wd7CJJvl7a/XBZxiSa2rd1W5Bu31Te57kbXN+X9JZcHDvgZfWK1d9vt10WCf8tcFziLSS2vcTH0tg/qc0a1St/sA3FNPRMH5aZT3cwCPAPgngBEAvmeM+V26D6zQbDygH5z6RMmsdi4Hvn66nr/jTS2tjGb+Nv1jTW15g9Q/zdGZz6mI3Aphy2nDsjeJ8vsw2C7Dr2YhqD/epFlHwC3zzYWg3s+eer+Z+g+3AwcbdApB3yr38uuc8XiPLtOqD3JfY9GCelu1sSMNz4vjTcDMJXr+46PaXp9qs7w31ut7/aTe0edVB2Eb19k975ZdZEqmSZ5ly/vtQvJQZunznn3OrdkHLNmplzFTn1s+PV63SdhKLiIiv+IG9SJSLCIvGmNmGWO+YYz5ujFmVqYOrpBElt5H+tgoXcHddBD467y2189cDFw5E/j0U8AfPkjfceaaTQd0/nqiZnW2+du0IVqO+uv3gN+n8P9kA46Lhujpy2vD3ffdkqnP8Gr9mf31/2f2Fp1tn0nr9mvFSt8qzV4Dnk7nWRxrtzXOnvqgmXrb9T7yA9vwat1PeeS423G9kB09riXupUXAgC5tr0/nWLsfvq7vK/2qgDOifLC2QX2ymfqXQii9t+zi18q9rS9PpUmeZTP1LQtt1bFvS/mhpMidtnDUSQ5UM6jPKVeP0tGV0f7eEBHFEzeoN8Y0ATgiIlE+VlGYbFA/qGv064uLgO+cped//4H7YdYY/fc3XtTsDwD85O3ogX97c7gBuOUp/ZD8kzgl9Q1N2jgOAL5/NvCLqbrN4acp/D/Nc4L6q0YCA7toEG7LhcOQrUx91wpgbJ1uKXhnU2Z/9grPfnorkzPJo2lqjl/KbC/bdtjfoo5dXDorSrB4/Ul6eu+C7DUqzBUbD+hpn87RG8nZCo6wnxf3LdD//7Ji4DfTou9Ht0H94p3BF/Kamt0qmPMGpnSoAGKPtUtlnJ01IiKIZ5O89uHCwe75smKgqix7x0JEROHxs6f+GICFIvJXEfmt/Ur3gRWatc44u3gjx87sr3viDh8HfvGOdl/+wWsanAqAH54D3HG+3vaHrwP3t+Nu2s0G+OoLwFKnW/q6/cCSXdFvO3er/p8Nr9bV7ytPaP3/dPeHwYKopma39HZcT20+BOgIrLAks6c+LNkqwbdB/XBP8JDt8vtdR3QffHVl9P4EFSW699E7IiqWg/W6GFQsrUcXWVMH6yLGqj2657qQ2aC+X4zl5B5pmFX/1kbg+6/q+TvOj96gD9Dqkd6d9D3FjiH168Pt2vSxb1U4QXLCoD6FbN/gbprZtRjUtw9nD3Af15pKNt4lImov/AT1TwP4LoDXAczxfFGIWsrvY2Tqrf84S/8gP7QYuOFxdwb57y/RBmnXjAb+8xzntq9oWX579PN3gOdXa7MoG4Q+HaME3+6nn+LJjn5itC6CAPpB/tNPajM9P1bvBQ416Af7uo5u86Ewg/p9WSq/B9zmXZmeV2873w+vcS/LdlC/xUdw5HdW/bubtZpmbE993kYqLdbtIQAwt8DHV7YE9VXRr+8RcqZ+3T7g9qf18bltotu4MJYxSTbLe3mdnp43MJxgyjur3rswGUb5fVkxMNDz92gog/p2oaocOLm3nud+eiKi9sNPUP8IgPuNMfcYY+4BcD+Ah9N7WIWnpfw+QTOiwd20TNdAO653KgPumQ5cOsy9zY1jNfgHgG++CDzezvboPr5MtxwUC/CHi4HPTNDLn1kZPeNuM87nRHSavmEs8NMLgM5l+mH7gvuAX78buxGhZUvvx/XU08m9tYRx1R5gfcDMXTQNTdpMrUiiB3/pNrZOmymt36/BTqZEK7/P9p76lhn1cYIjv/vq7Xz6KXEaII2IM3u8kGx0FthiBvUhPi8O1AM3P6Vd6c8fBHzz9MTf07KvPmCzPLuf/vwQ9tMDGpR1rdD3C+8CRxjl94D7fOzdSf/WUPtwgVNd1oMDiomI2g0/Qf1LALz5wkroWDsKycF6zUSWF/trjvLlU3SvaV1HYOZHgdP7tb3NrROA/3eaBv9fe8FdNMh387fpQgUAfO9s4KwB2mCse6VuYVgWUYK/47B2+a0oASb3aXt/Hz8RePl6LcmvbwJ+9R5w4f3AK+viHwPgBvWlxW7zoTCy9S0z6iuSmzGdquIiN1ufqRL8Y426gFAkrZtFZntP/ZY4TfIsvx3wYzXJ84pVTl1oEpXf13rK71PpP9DUDHzxWf3/HlEN/HZa9D38kexYuwVRMvXHGoHlUbYCbTuk70WVJfqeFQYRN1tvx0Eeb9LFjiJJfVyZDerZJK99+cSJOnHj9knZPhIiIgqLn6C+whjT8nHVOc+irRC17Kfv6i+I61oBvPgp4K2bgBNrY9/uSycDlwzVktIwS8PD9vRK4NpH3WA2lh2Hgc/8S4Pv68YANziNxUqK3LLlp1e1/h6bHT21T+yZ7T06Ar+6SBdIhldrGf5NT0T/wA64Qf34nu5lNvM2K4SRgvuyuJ/estnkTJXgr9mrz9OBXVo/Tj2yXH7fMhosTlBvA/4tcTL1Ww7qto1OZcC4uti3a+lmvqewm+UlKr/vUKr/l/VNmmlP1nOrdeGqWwXwl4/4z0bHapZ3sB64YiYw9f908dFb9WMXCs/oF/u9KBlDPc8ZQN8nDTSgLy1O7b6nDtaqHW8lGOW/jmXAf5+nI+6IiKh98BPUHxaRCfYfIjIRwNH0HVLh8Vt679Wh1N8HtqlOsGuzhGF4dJk75igM9y8A3tyYOIB8Yrl+YJ3cG/jB2a33pNoPnZEl+HY/vc2kx3NKH+CZa4CrR+qH4vuiNBo8chxYtltL/+0HewA4d6Be9sEWLeNNxV5Ppj5bznYy9W9vBOoTbEcIQ0uTvIiMYFW57u092KBjzjLN7pOPV37vJ1NvF5dO6xv/dVvdQatODjXoSLd0+ft84K656bv/VG1KUH4PePbVp7Dg88BCPf3yKUD/ADNeulcCfTvrWDD7XtjQBHz2aXf+90OLgasednt1vBziKDuvYRHVHXZaQ6ql9wAwshZYcJtWNBEREVHu8hPUfwXAwyLyhoi8AeAhAF9I72EVFpupH5ygSV4yznRK89/bnHivuB/ztgFffR74zFPhZRJtFnZbgiBms5MJnTqkbWBkS/BX73UbrjU1uwsFUyL208dSWgx8brKef2qFZt68Fu7QzvsjaoDKUvfyLhVa3t/YDLy2zt/PisWOs+uehSZ5Vl0n4IRqDVoy0Yk92n56QBdustksL6xMvZ/Se6ulnHp34tsmY+9RnZrxX2+kvgCVDvuPAQcadOEy3mugNsWtGev36WJiebFuvwlqjKcEv9kAX5+lHfRrOwB3XaaLBIt2AJf9A3hulbuwc+7A5I43lpYtG87iQhhN8oiIiCi/JAzqjTEfADgBwO0APgdgpDGG3e9DZDP1gwNk6v2q7QiMrNGAfk4Ic9RtNmj13vCCvZagPsGe5G1xmj+VFAEX2RJ8pwv+oh2a9e5bFWzBZHA3Ldc/2qiBvZdtkjc+Sgl1WF3wcyFTDwBnD9TT10Os8ohleYxMPZDdffV+MvU9E2TqjQHe2aTnz4zS/yLSsIggLWzvbXbPbzqQnp+Rig2e0vt4HeJTfV78Y5GeXjZcF+WCOsmp1FmwA/jxm1pJ1LEUuHu6Ljz+6xp9T9hfrxn8o43AqBp/fVOCaAnqndeQXYhiUE9ERFQ4/GTqAWAygJMAjAdwjYhcn75DKjxrfMyoT4XNDoZRgu8NAuyH4lTUN7p7yP0G9T1jdOy9dKiePu2U4L/m/L5n9w8+Purjo/U08neMbJLnZYP6V9Zps6pk2Ux9NvfUA24JfqqVB37EC+qzlalv8DQcq0sQ1As0uGxsbnv9jiN67FXlrZsAxtKyRzpNmXpvUL/R5xjHTErU+d5K5XnR0AQ8vETPf3J08O8H3O03jywB7pyrC4t/vhQY7VzepRy46yPAN05ze6WcG3LpPaCLBJUlwK6j+l4aVud7IiIiyh8Jg3oRuQ/AzwGcCQ3uJwNgz9SQGAOstZn6NJTfA8BZtulZCEH9Rk9Q//RKLZVNxS7PB/JEQb39sNozRqbrtH4aCK/eq+XcNhi1GecgLhmqQdiCHdoMy4oX1A/qpkHbgXpgdgpVEXtzoFEeAEzqpaXJy3ant0z7yHF9XpUWabPISC3BW4Yz9dudhmN1HTVgi6WsWEeLNZvoWePFztizUbX+FpfSnal/d5N7fmMOZuoTdb63UsnUz1qjQfDwamBir+DfD7jl94edXg8/u1CncXgVCfCFk4H7r9B96TeOTe5nxVMkrZvlsfyeiIio8PjJ1E8CcIYx5nPGmC86X19K94EVih2H9UNhtwqgW5r2UJ/cWwOPxTuAPSm2OLSZ+qpy7Tz9+PLU7m+nz6C+sdkN6upiZOq9JfgPLtZS+ZIi4PQkxkdVlLj7bB90svXbD+nCQucy90N0pDC64LcE9VncUw8A5SW6dQMAFsaYBBAGm5Ee3C16E7mWvdMZztRv8bGf3oq3r942Tjuxxt/PjexmHqb9x4ClnnFruVh+n6jzvZXKrHrbIO+To4NX8VhdK9zKi2+fEX9f/hn9gJ9ekL654N5RiC2Z+pDL/ImIiCh3+QnqFwGIkpekMKS79B7Qhm6TemnW8a2Nqd2XLY29baKePrgotYZ53qB++2HNdkaz64iOPKup1AWKWC5xuuDf86HefmIvoHN5csf2Cafj8+PLtPO63U8/ti726MFpzhaAf61oPeoqiFwpvwfcedwLd6TvZ9ggM7JJnpVK8JaKlhn1PjKe8TrgL3F+v1Fxxk969eyko9X2HAV2h7yQ8f4WfR8odp6/OZmpD1h+HzRTn2qDPK8/XqJN8T47MbX7SZV3Vn283iNERETUPvkJ6msALBGR50XkSfuV7gMrFGvSXHpv2RL8N1IowW9o0g71RaJlpN0qNGCJNc/dD2+g1tjcuhzfa1uC0nvr9L6aQWtyFgfO9tn1PpqRtTpT/EAD8Myq+KX31oSeOmt9+2EdB5eMXMnUA7qAAQAfpjFTbxcMRveIfn229tT76XxvxcvU2/L7E30G9SJukBZ2Cb4tvbevi5wM6oNm6gM+L2yfjI8k2SDPa0SNNsVLNtsfFm8fBjsKMV1VAURERJR7/AT1PwAwA8D/APiF54tC0DKjPt1BvfMh/s0NyWfWtx7UTHrPjkDHMuCqkXr5PxYnf1yRH8i3xyjB99v8qbQYmDrY/bffUXaxfMLTMG+eE9iOjxPUiwBXONm/R5cl9zP35cieesDN1KeycJPIfOe+Yy2W1GYrU++j871lA//ITP2hBmDdfq0uGRJjy0Y0w9LULM82yfvoKD3ddCC80ZRhMMbdEpBoT30ymfpWDfLGBD++XDXMqXKZvVUXR6srdQsRERERFQY/I+1ei/aViYMrBC0z6tNYfg9olrB7pc56tz8zqMgGVjbgfXI5cLghufuM/EAeayyYzdTH2k/vdalTgl9T6T87Gsvlw3VM1QdbgLlO87t4mXrADeqfW61N4ILKlUZ5gO4Z7lCqz5tYVRSpONYILNul1R+jYzxWLcFbDmfq7WJTZKbebi0Y1j3+tpFIkbPHw3CgXps+lhbprPTOZfr8TLXPRph2HNFeHd0qdAtCPN0qtWfG/nqdouGHbZA3olqratqLAV30cbXvN2ySR0REVFhiBvUi8qZzelBEDni+DopIDhZt5qd0zqj3KhJt1gQkX4JvM2h9nbLYYd11r/7h423nuftlM/U2gI3VLC/IPtEpA3SM1M+nxt777lfHMi3TBTTL17dKO53HM6ArMKGXfsB+bnWwn9ds3Ex9tufUA0BxkRtspyNbv2SnZhaHddf/62hsUL/rSPSeCxv2A+uSXKiKJ1Cm3rlN5PPXTk4IuriUjkz9B1v0/++kOl2oseXtudQsz+9+ekBf2zUBt2a0NMgbk/2S+TCVFAEDPdVe3E9PRERUWGIG9caYM53TzsaYKs9XZ2OMj49clMjxJg1IBK0/kKXLmU5Qn+y8epup7+vJXF7jZOsfTLIE32bq7cznWEF9kI7OdozUuQOTO6ZI13jmWMcrvfeyDbgeWxrsZx2o18Crc1n0TvDZkM5meXavvv0Z0ZSX6MzvxmZ3wcNqaAKunAnMeEiz/mGymfreKeypt53v/TbJs2w5dZiZelt6f2ofPbUVN7m0r97vODurR4ASfNsgr6LEraZpT4Z5tncwU09ERFRY/OyppzTZeEAbuvXpnJn9j7ZZ3jubNEAKKtpe10uHaQA6b5uWUQdlM2x25vO2GB/Og5Tfh21snTvabVyc4NPrsmFaDvvmRrdxlR+2830uZOmtk9LYLM/eZ6L/11j76udt0+fQ3mPhZrWPHtf7LCsGqhNUZgDalKxItJqgocm9PNlMfZ/O2p192yFd6AnDe06TvFOcEY92cS4ng3qfy8ZBmuW9vE5PLxqii0TtjXfMJjP1REREhYVBfRa1NMlLc+m91adKu+wfbHA7uQexMaL8HtBxedNH6HnbVdovY9wgzWbqt0bpHg54yu+zMHtZBPjRObq/3jYYS6RbJXDeIM26P7nc/8/Kpf301lhPs7ywm6p96BkTGE+spmjerSRLklhUisWW3vfs5G8LR0mRLjgZuM/V403ACmehYaTPGfVWcZHbWG91CNn6Qw1aaVEsOuYRcBfn8rX8HnAXe/xk6pc7j4Xfapt8M5SZeiIiooLFoD6LWmbUZ6D03jrTydYnU4K/KUYWzZanP7bMf8MqQBcX6pu0Ed0QZ2EjWlbbGM9IuyyNaZrcB/jdxcEy6Ml0wW+ZUZ8D4+ysAV2AqnLNhsbaHpGM/fX6Gigv1tFg8cTKyHqfx7bUPQxbA8yotyI74K/eq1n7AV2Azklkhltmj4dQgTB7i1YFjenhNqCzr+OczNQHLL/3MxnBBvXDq4MfVz5gUE9ERFS4GNRn0doMNcnzSnZe/bFGDbiLpe0HxtE9NBO5vx54ZZ3/+7TZtdoOQJ1zn1sPtc0G7z2mwX9VWexmarnovIFa5rtkJ7DcZxY5FzP1IsBJTiVFmCX4C537GlWbuDN8tA74+4+1Pp6lYWbqA3S+t3pHdMBfnOR+eivMffUt++n7upfZihubHc8FQcvvWzL1CcrvjXGrJka006B+SDftzwJkp6KJiIiIsodBfRZlqvO916l9NTCftw04GGCv7hZP07CSKM+aZLLSNuvao6MG7B1KtWP8wYjxeC1Z+jz7oFpeonvrAf//Ly1BfQ5l6gFPCX6IzfJsQJ6o9B6Ivqf+rY26vcFWeSzdGd72gK0BOt9bPSM64Kca1IeZqX/XCepP6eNeZgPnzQejTxXItMZmrZAQaE8BP3rE6LUQactB3YJQXZl4ekW+qijRyR8DuvhfFCEiIqL2gUF9FmWj/L6qXOesNxn3g74f0fbTe80YoXuPX17btkN5LPaDeG1HzQbb0vrIEu+tWS69T8UVI/X08eVAk4/mhLmYqQfcRoZhjrULEtT3iLJ32pbezzgBqKkEDjQAm2L0ZAgqqUx9RAf8JUk2ybPCytQfOa6PW5EAk3u7l3csA7pXahWMn/L1dNtyUN+X6jrpgpgfsXotRGrvpffWPdOBl69PXPlCRERE7QuD+iw51KAfRMuL/WelwnKaU4I7e4v/74m1n96q6wSc0Q843gw8vdLffdpMvf1gHpnptLYdan19PpnUC+jfRX+HdzYlvv0+u6c+x4L6ZJvlHWqIfV1LUO+jcVmPKPPI39iop2f1B0Y6gXNY++pbMvUBXpu9IraQJNv53hrYRatiNu7XbvzJmrtVs+An1rbd22874G/IgX31QUvvAf/d79t76b0lEr2SioiIiNo3/vnPEruffkBX7XSdSbb79Zyt/r/H7ruNlakHPCX4Pmez7/TsqQfcoH1rjKA+H8c0iQBXONMB7luQOCDek6Pl97066eO0vx7Y4GMP9rFG4JangPF3Rm/KuP2QPq6dy/xVqtRGBPUb9utXVbnu9x8VclC/OYlGed5M/eaDOoque2XyYxhLi4GBXbWjfiod8O1ikrf03sqlDvhBO98DrZ8X8bYQLC+QoJ6IiIgKE4P6LMlG6b01wQnqF2xvPVM7HlvWHK8r9bQhQGUJMHurv8DPNrey+6V7xcjUb83jTD2gY/DKi4HnVgN/nhv/trlafi/ijh1MtK/+UANwwxPArDX6/PrNe21vY7P0J9X5GxkXObrMNno8va8uio1yuueH0SzPGE/3+yCZek/3e2/pvfj4/WIZ5nQ0X7Un+fuwTfJO69v2ulzqgJ9Mpr68RJtRNja7kyOiKZTyeyIiIipMDOqzxGbqh2SwSZ7VtUJ/bn2T/8xmSxYtTpDTsQy4aIief8xHY7jITH1djKB+e54H9f27AL+6SM//+M342xNs+X2Q0XmZYkvw43XA33cMuPZR4N1NbgPE97doY0av+c59jPOxnx7Q/4/SIs1+H2t0g/opA/TUZuoXh5CpP9AAHD6ujRurAoyiq+2gx7jnqJa8e48rWXZM2cokg/pjjfp4CXQsYyRbeZMTmfqA4+ysRCX4Tc3uogiDeiIiImqPGNRnye2TgFnXAdeNyc7PD1qCv8nnB+4rncZwjy5NXGru7X4PJM7U52P5vXXpMODbZ+j5rz7fNsgF9P/Llt93z7HyeyBxs7ydh4GPP6IBe98q4J9XA9edpNf9eU7r2y7wZOr9KBK3a/mOw8Dbnv30gE6QKC/W5+n+AFMdorFZ+l6dgmXZi8RdmHppnZ7aCoJkpZqpX7lbqyWGdteMdqR+OTTWLplMPZC4Wd76/bqA2btTsEUaIiIionzBoD5Lyks0a9QnS6OHggT1R44Du45qR+UeCfYHn9FPP2Sv2x89cPXydr8H2mejPK/PTgSuGa0Bxi1PulsUmg3w3Cpg+kMamAhyM6i3mfpFO9p28t9yELj6EWDZbq0CeeSjWqFw41jNXj+3CljvbDlpNm62f5yPJnmWDd5eWquBe/8u+gVoczCbhV2WYgn+liRK7y278GQbs53YI7VjSTVTv9KpCLKLA5HsIl1OlN8nsacecBuNxuo7wNJ7IiIiau+yEtSLSHcRmSUiK53TmEXoIlIlIptF5H8zeYztnd1XP9dHUG+z9H06J97/XFIETHcaw8UrwW9sBnYf1QC22glgW4J6T8btUIPOrS8vzs2S9CBEgB+dA0zpr4skNz0JPLgIuPB+4LNPa6DbvRL4z3N05nSuqemgz4HDx1sHUEt2AlfMBNbu08z0zI+6+8vrOunIOQPgrnl62bp9Wkbfo2OwhRq7+GOfV2f1a319GM3yjAFeXa/nk6kM8X5PRQkwOMWeGUO66Wtk/X7//S+8VjkBbaygvo+nD0Cjj5GL6XKsUSt3SouCL96d4vQKiNaQEfB0vk+xaoKIiIgoV2UrU/8tAC8ZY4YBeMn5d9NSk80AABqvSURBVCw/AvBaRo6qgAztrqWoWw+5mclYNiWYUR/JdsF/akXsQGT3UQ30qivdEUw1HfT8nqP6IR9o3fk+lYZjuaK0GPj9JdqFe9Ue4N9e0tPenYAfnA28fRNww9hsH2VstlneQqdZ3strgY8+rI/T5N7Ag1e5ZfLWrRP09OEl+th+GHA/vWWrROz3n9m/9fUjnaAt2aC+oQn4+izgng81kL5kWPD78Gb3T6hOfbJFRYlm0xubdTEkKJvhHxojqK8o0f/Xxua2FTKZZCsFencO/n9mt2C8syn6+w073xMREVF7l62gfjqAe5zz9wCYEe1GIjIRQB2AFzJ0XAWjSIAJTulzohL8oHtdT6zVUte9x4DX1ke/zY6I0nt7THURXc7bS+m9V1U58PfLgQFdNIP68wuB124EbhoHVJZm++ji8zbLu28BcPNTmrmfPgL4vyuALlGqKYZXA+cO1IWa+xa07nwfRK1nsaBIdKuHl50HvySJ8vv9x4DrHwceWaoTHP7yET3moHp5gvpk59NHSmVfvf2eWJl6IDc64NvS+/4Bm+QB+t4wrLs+D6Nt+WH5PREREbV32Qrq64wxWwHAOW2z81REigD8AsA3Et2ZiNwqIrNFZPbOnSENqi4Afkvwg2bqRYArE8ysj+x8b9VFzKpvj0E9oL0UXrsBePFTwNWjtF9BPrCB+MzFwHde0f3xXzoZ+M1F2iciFputv+dD4H1nxFrQTL33uXJSj7YLCCc4mfqVu4HjAUrVN+zX7QPvbNKf8fBHgQsGBzs2yzvXPtXO95YNyBcFfGurb9TeFkUCDIozZSMXOuAn2yTPmuJk61+PWESsb9RJI4L4CxtERERE+SxtQb2IvCgii6J8Tfd5F58D8IwxZmOiGxpj7jTGTDLGTKqtDemTdAHw2yxvY8CgHgBmjNAP0i+tdUvpvSI731uRHfDzfUZ9PPm4ncCW3x9t1K0SP78Q+H+nJf5dTuur37v7qDt2LnCm3vNciSy9B7QCol+VNiJcE6NpWqRlu4AZD2mPgBOqgSc+7nb5T4Y3Ux9WUG/ny7+yNtj3rduniy79q+L3aMiFDvipBvVnOaMN34jYV79mL9BkgIFdc7NPBREREVEY0hbUG2MuMMaMjvL1BIDtItILAJzTHVHu4jQAXxCRdQB+DuB6EbkjXcdbiMb11Cze4p3RA29rUxIfuHt1BoZVa4AVrRt5rEx9zwLJ1OerLhXAqX20aeG9M7TKwA8RN1sPAIO6Ri/Vj8e7AHRWlKAe8DTL81mC/9O3daFhSn/gkatTn0ZhG88J3MqBVJ3aF+hQqr/T5gDZdLuffliCsvNcKr8POqPeOqWPVrss2A7sPepeztJ7IiIiKgTZKr9/EsANzvkbADwReQNjzLXGmP7GmIEAvg7gXmNMvIZ6FFCnMs1ONjbHnj0OJJepB9o2VfOymfraiEy9Dd63RwT1+Tyjvr154ErgvZvb7mlP5JJhQF8n6A2apQeAns5zpUOpu3Uk0qgAzfJ2HQFeXacVB7+6COgcwgzz7pXAl08G/v0sPc4wVJS4ixgvBcjW2/30Q+OU3gO5UX6/IcVMfYdSYFIvbb759ib38hVskkdEREQFIFtB/R0ALhSRlQAudP4NEZkkIn/J0jEVpAkJSvAP1gP7julIuciseiKjnaxp3KA+4j57RWTq23P5fb4qLkqulLmkyCnVB3DJ0ODf36cK+N4U4FdTY/cgsJn6pT4y9U8s19Lscwa07difiq+d1roqIQwXOnv8XwwQ1PvN1PfP8qz6+kZgU5Iz6r2mOCX43n317HxPREREhSAruwyNMbsBnB/l8tkAboly+d0A7k77gRWgib2B+xfGDuq9TfKC7gGPl6mP1v0ecLOx25ipb5euHAlcNjz5xoA3j49//UjPrHpj4j9n/7nUPaZcd+5AXQx5ZxNwuAHoWJb4e/xm6nt10m042w7pSLhMNW00BnhmFfDjN4EDDbrA170y+fs7qz9wx1u6r94+9iy/JyIiokKQrUw95YhJnky9MW2v3+TMsE8mgzaqVgORFbvb7tmPuafeKc/edkgzeLuPAsUSbiaVsiudQWPfzkBVmT5v7MJRNMt2aS+JqnLggkHpO56w1HQAxvfSoDuyGVw0jc3AGmeu/ZAEXd9LizWwNwC2HEz5UH2ZuxW46mHgc89ohcDwauBPl6bWPHJULVBdCWw+qL/74Qa979Ii7eFARERE1F4xqC9w/aqAmkpgz1FgfZTu17aBVdD99IBmE4d01wDD7m21YnW/986pt6X3PTpqyTdRIiL+muXZLP3lw+OP4ssldvHhxTWJb7thvy4A9OmsvTMSyVQH/GYDfGOWjhCcs1Xfe358HvDsJ4FJvVO77yJxpyK8vt7dfjCkmy5cEBEREbVXDJUKnIiW4APRS/BbOt8n2ZU6Wgn+4Qbg8HHdp985IuAoK9YP+k0GWOR8D/fTUxDeEvxoGpuBx5bp+XwovbdsUP/yOqCpOf5tW0rvfc5mt0H9pjRn6j/YAsxcoq/9z08GXr0B+OQY7bcQBjuv/o0Nnv30IU0hICIiIspVDOrJbZa3pe11qc6PjtYsb5en8320cts6J4ift01PuZ+egrAd8GM1y3tzg1aKDOoKTOiZueNK1fBqrZjZfRSYH2daBeBmqf0G9X0zlKmf5ywcXj0K+Obp4Uwc8LJTAt7Z5C4KskkeERERtXcM6gkTncBm7ra21yUzo95rjDO6zBvU74jR+d6yQfx853iYqacgEmXqvQ3yUtnDnWkibhf8lxKU4NtM/TC/mfoMdcC37zHj07SYUtdJg/gjx3W6AcCgnoiIiNo/BvWEMXXaTGrZLh1h57UpyRn11olOs7zlu7TxHeA2MIvcT2/ZIH4hy+8pCcO6azn3mr3aK8LrYD3w/Go9f8UJmT+2VLXsq08w2i5opr5lT30ag3pj3Oqb8b3S93Psvvr9znsZO98TERFRe8egnlBRAozuod2vX13vdsHff0xHTXUsBbpVJHffncqAwd2A483ACifQiDWj3rJBfH2TnrL8noKoKAHG1enz+fIH3TJsAHh6pT6vTu2b2kz0bDm5j/ahWL5bm+FF02yA1Xv1vN9MvV2025TGoH7rIV3QqypPbzd6u68eADqUJr8gSURERJQvGNQTAHdf/ReeBU75K/CV54G/zNPLkplR7zXaaZZng6udPjP1sf5NlMhvpmmTxo0HgCtnAg8t1ssfdRrkXZWHWXpAG0lOGaDnX4qRrd9yUMvPazsAXX0uxtV11GqdnUeAo8fDOdZI8zyl90Vp3PZwSh93bOLw7un9WURERES5gEE9AQBuGQ9MH6Gd57cf1u7gv31fr0s1o2mD+oVOc69EmfrIzDyDegqqbxXwyNXANaM1M//NF4HPPwO8t1kz+RcPzfYRJi/RaLugne8BHRnZ39lX/97m5I8tnnlp3k9vVZYCk52JHiy9JyIiokLAoJ4AAL07A7+dBsz+DPDCtcAPzgamDtaAfkaKWc3IsXY2U18bI1NfFxHE18W4HVE8FSXAHecDP7tAR6j9a6VePm1I+F3XM+ncgZp9fm9z2x4YQPD99NZHnfF+v3nf3YITJtv4clwGJg7Y3+Xcgen/WURERETZVpLtA6DcIqJznUfUADeNC+c+T3S6kS/bDTQ0BcvUV1cC5XyWUgo+diIwqha4/RndM37tmGwfUWq6VQKTegHvbwFe3wBcOqz19SsDdr63bhgL3DUPmLsVeG09cM7AUA4XAHC8CVjgVOqMqwvvfmO5ciRw/iCgS5K9QIiIiIjyCTP1lHa2MVZDkwYctvt9rKC+U5l+ASy9p3CM7gHMug549XptNpfvzndG29leAV7JlN8DQMcy4LMT9fyv3g03W79st26DGNRVFyUygQE9ERERFQoG9ZQRtgR/wXZgl5Opr4kR1ANuMM+gnsJSUQIMSGPX9Uy6eiRQVaYZ9VfXuZcbk3ymHgCuP0n7aszfDryyLuHNfZu3VU/TvZ+eiIiIqBAxqKeMONEJ6l9fDzQZ7codr6y+p7OPnuPsiNqq7gB84WQ9/19vAI3Nen7nEeBAPdClPHYlTDwdSoHPTtLzQbL1r68HTv8b8Nyq6NfPt6X3DOqJiIiIQsegnjLCZurf2KCniQKOXszUE8V141hgQBfNzD+wUC/zNslLdgzlp8bo63PBjthj87y2HwK+9Byw+SDw07ejLwTYTP0EBvVEREREoWNQTxlhx9odbNDTREH9ZcOBEdXa7IqI2iovAb59pp7/5bvA/np3P30ypfdWZSlwm91b/178bH1TM/CV54G9x/Tfq/cCb25ofZt9x4A1+3QCwQk1yR8XEREREUXHoJ4yoku5ZhWtWOPsrHMGAi9cp13LiSi6aUOAU/toUP2795MfZxfpupN04W3RDmDWmti3+/Mc4O1Nug/fTsv42/zWt7Gj7Mb0AEqLUzsuIiIiImqLQT1ljM3WA8nt9yWi1kSA70wBBMDd84G3nCx5Kpl6QJsKfn6ynv/Vu5qRjzR3K/Dzd/T8L6YCX5ys2fiX1wFr97q3s0H9+F6pHRMRERERRcegnjJmjDeoT5CpJyJ/xvQArhoJHG/WMncg9Uw9AFwzGqjrCCzZBVxwn47Pa2jS6w7U6z76JgPcMl4ra6o7AJeP0OvvXeDez1wb1HM/PREREVFaMKinjPEG9T0Y1BOF5punA5XONIkOpUDvzqnfZ0UJ8KuLgL5VuljwzReBKXcDf50H/PvLwMYDWn3zzdPd77lxrJ7OXAIcatD9+PMZ1BMRERGlFYN6yhhv+X0Plt8ThaauE3C7M4puWHegKMnO95HO6Ae8er0G98O6A1sPAT98HXhqhS4e/G5a69GUo3sAJ/fWgP6RpcDafdrAr0dHjqckIiIiSpc4k8KJwtW1AhjcVbN+fULIJBKR67MTgfomYEr/cO+3tBi48gRgxgjgxTXA7z8AFu4A/uc8YHC3tre/aRzw/hbgnvlAp1K9bHzP5EfsEREREVF8DOopo347TYP6QVGCASJKXkVJ61L4sBUJMHUIcOFg4Fijjr6LZuoQoHcnfZ3/YbZextJ7IiIiovRh+T1l1Jg6YPqIbB8FESVLJHZADwAlRcD1zt761U4XfAb1REREROnDoJ6IiEL1iRN1vB2gGf6T6rJ7PERERETtGYN6IiIKVbdK4IoT9PwJ1dpUj4iIiIjSg0E9ERGF7vZJwJBuwCfHZPtIiIiIiNo3NsojIqLQDewKvHx9to+CiIiIqP1jpp6IiIiIiIgoTzGoJyIiIiIiIspTDOqJiIiIiIiI8hSDeiIiIiIiIqI8xaCeiIiIiIiIKE8xqCciIiIiIiLKUwzqiYiIiIiIiPJUVoJ6EekuIrNEZKVz2i3G7fqLyAsislRElojIwMweKREREREREVHuylam/lsAXjLGDAPwkvPvaO4F8DNjzEgAJwPYkaHjIyIiIiIiIsp52QrqpwO4xzl/D4AZkTcQkVEASowxswDAGHPIGHMkc4dIRERERERElNuyFdTXGWO2AoBz2iPKbYYD2Ccij4rIPBH5mYgUR7szEblVRGaLyOydO3em8bCJiIiIiIiIckdJuu5YRF4E0DPKVf/h8y5KAJwFYDyADQAeAnAjgL9G3tAYcyeAOwFg0qRJJonDJSIiIiIiIso7aQvqjTEXxLpORLaLSC9jzFYR6YXoe+U3AZhnjFnjfM/jAE5FlKCeiIiIiIiIqBClLahP4EkANwC4wzl9IsptPgDQTURqjTE7AZwHYHaiO54zZ84uEVkf5sGmWQ2AXdk+CMoaPv6FjY8/8TlQ2Pj4FzY+/sTnQGGL9/gPCHJHYkzmq9VFpBrATAD9oaX1Vxtj9ojIJAC3GWNucW53IYBfABAAcwDcaoxpyPgBp5GIzDbGTMr2cVB28PEvbHz8ic+BwsbHv7Dx8Sc+BwpbmI9/VjL1xpjdAM6PcvlsALd4/j0LwEkZPDQiIiIiIiKivJGt7vdERERERERElCIG9dl3Z7YPgLKKj39h4+NPfA4UNj7+hY2PP/E5UNhCe/yzsqeeiIiIiIiIiFLHTD0RERERERFRnmJQHzIR+ZuI7BCRRZ7LxonIuyIyX0Rmi8jJzuXniMh+5/L5IvI9z/dME5HlIrJKRL6Vjd+Fggv4+H/D89gvEpEmEenuXLdORBba78nW70PBxXgOjBWRd5zH9CkRqfJc923ndb5cRC7yXM73gDwU5PEXkQtFZI5z+RwROc/zPa86j799j+iRjd+Hggn4+A8UkaOex/hPnu+Z6Nx+lYj8VkQkG78PBRfwOXCt5/GfLyLNIjLOuY7vAXlIRPqJyCsislREFovIl53Lu4vILBFZ6Zx2cy4X5zW+SkQWiMgEz33d4Nx+pYjckK3fifxL4vG/1nncF4jI2yIy1nNfwWIBYwy/QvwCMAXABACLPJe9AOBi5/wlAF51zp8D4F9R7qMYwGoAgwGUAfgQwKhs/278Cvfxj/i+jwB42fPvdQBqsv378Cu058AHAM52zn8awI+c86Oc13c5gEHO676Y7wH5+xXw8R8PoLdzfjSAzZ7veRXApGz/PvxK6+M/0Hu7iPt5H8Bp0JG+z9q/IfzK/a8gz4GI7xsDYI3n33wPyMMvAL0ATHDOdwawwvlb/1MA33Iu/xaAnzjnL3Fe4wLgVADvOZd3B7DGOe3mnO+W7d+PX6E//qfbxxXAxfbxd/4dKBZgpj5kxpjXAeyJvBiAzcx1AbAlwd2cDGCVMWaNMaYBwIMApod6oJQWKTz+1wD4RxoPjTIkxnNgBIDXnfOzAFzlnJ8O4EFjTL0xZi2AVdDXP98D8lSQx98YM88YY98PFgOoEJHyjBwopUXA139UItILQJUx5h2jn+zuBTAj7GOl9EjhOcDPAe2AMWarMWauc/4ggKUA+kD/ht/j3OweuK/p6QDuNepdAF2d94CLAMwyxuwxxuyFPm+mZfBXoSQEffyNMW87jy8AvAugb7I/m0F9ZnwFwM9EZCOAnwP4tue600TkQxF5VkROdC7rA2Cj5zabnMsoP8V7/CEiHaBv1P/0XGwAvOCU5N6asSOldFkE4HLn/NUA+jnnY73W+R7QvsR6/L2uAjDPGFPvuezvTtndd1l+ndfiPf6DRGSeiLwmImc5l/WBvuYtvv7zn5/3gI+jbVDP94A8JiIDoRVZ7wGoM8ZsBTTwA2C3U/BzQDvl8/H3uhlatWEFigUY1GfG7QC+aozpB+CrAP7qXD4XwABjzFgAvwPwuHN5tDdujinIX7Eef+sjAN4yxnhX9s8wxkyAluJ8XkSmZOZQKU0+DX0c50DLsRqcy2O91vke0L7EevwBAM6C7k8AfNZz8bXGmDEAznK+PpWhY6XwxXr8twLob4wZD+BrAB5w9lrz9d/+JHoPOAXAEWPMIs/FfA/IYyLSCZqs+Yox5kC8m0a5jJ8D8lyAx9/e/lxoUP9vnosDxQIM6jPjBgCPOucfhpbWwhhzwBhzyDn/DIBSEamBrsZ5V3H7InHJPuWuqI+/xycQsTpvS3KNMTsAPBbleyiPGGOWGWOmGmMmQh/r1c5VsV7rfA9oR+I8/hCRvtDX+PXGmNWe79nsnB4E8AD4HpC3Yj3+zrab3c75Oc7lw6Gvf28JJl//eS7ee4Aj2ucAvgfkKREphQZ0/2eMsZ//tjtl9XaLzQ7ncn4OaGcCPv4QkZMA/AXAdPs3AQgeCzCoz4wtAM52zp8HYCUAiEhPW04l2hG9CMBuaEOVYSIySETKoG/2T2b8qCksUR9/ABCRLs51T3gu6ygine15AFOhpXuUp2zXYhEpAvAdALbL9ZMAPiEi5SIyCMAwaIMsvge0I7EefxHpCuBpAN82xrzluX2Js8BrPxxcBr4H5K04j3+tiBQ75wdDX/9rnNLMgyJyqvMZ4Xp4/kZQ/onzN8BedjW0d4q9jO8Becp5zf4VwFJjzC89Vz0JTfLAOX3Cc/n1Thf8UwHsd94DngcwVUS6OZ3SpzqXUQ4L+viLSH9o4u9TxpgVnvsJHAuUhPVLkBKRf0C72teIyCYA3wfwGQC/EZESAMcA2H0RHwVwu4g0AjgK4BNOU5xGEfkC9MVbDOBvxpjFmf1NKBkBH38AuALAC8aYw57L6gA85qz3lAB4wBjzXAYOn0IQ4znQSUQ+79zkUQB/BwBjzGIRmQlgCYBGAJ83xjQ598P3gDwU5PEH8AUAQwF8V0S+61w2FcBhAM87H+aLAbwI4K7M/AaUioCP/xQAP3Q+AzQBuM2zDet2AHcDqITusfTus6QcFvA5AOjzYJMxZo3nsnLwPSBfnQHdKrFQROY7l/07gDsAzBSRmwFsgC7kAMAz0A74qwAcAXATABhj9ojIj6CL/ADww4htmpSbgj7+3wNQDeAPzuf+RmPMJCQRC4jTMp+IiIiIiIiI8gzL74mIiIiIiIjyFIN6IiIiIiIiojzFoJ6IiIiIiIgoTzGoJyIiIiIiIspTDOqJiIiIiIiI8hSDeiIiogLnzEh+U0Qu9lz2MRHhOE0iIqIcx5F2REREBBEZDeBhAOOhs7HnA5hmjFmdwn2WGGMaQzpEIiIiioJBPREREQEAROSnAA4D6AjgoDHmRyJyA4DPAygD8DaALxhjmkXkTgATAFQCeMgY80PnPjYB+DOAaQB+bYx5OAu/ChERUcEoyfYBEBERUc74TwBzATQAmORk768AcLoxptEJ5D8B4AEA3zLG7BGREgCviMgjxpglzv0cNsackY1fgIiIqNAwqCciIiIAgDHmsIg8BOCQMaZeRC4AMBnAbBEBNCu/0bn5NSJyM/SzRG8AowDYoP6hzB45ERFR4WJQT0RERF7NzhcACIC/GWO+672BiAwD8GUAJxtj9onI/QAqPDc5nJEjJSIiIna/JyIiopheBPAxEakBABGpFpH+AKoAHARwQER6Abgoi8dIRERU0JipJyIioqiMMQtF5D8BvCgiRQCOA7gNwGxoqf0iAGsAvJW9oyQiIips7H5PRERERERElKdYfk9ERERERESUpxjUExEREREREeUpBvVEREREREREeYpBPREREREREVGeYlBPRERERERElKcY1BMRERERERH9//brgAQAAABA0P/X7Qj0hVNSDwAAAFNSDwAAAFMBTtLbGesPrb8AAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#plotting the median temperature anomaly over time to see how it's actually ingreasing \n", - "x = temperature_a.Year\n", - "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", - "plt.figure(figsize=(17,6))\n", - "\n", - "plt.plot(x, y, 'dodgerblue', label='Temperature Anomaly', \n", - " linewidth=2)\n", - "\n", - "plt.legend()\n", - "plt.xlabel('Year')\n", - "plt.ylabel('increse ordescrease in Temperature(℃)')" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CountryYearMedian (℃)Upper (℃)Lower (℃)
0Global1850-0.373-0.339-0.425
1Global1851-0.218-0.184-0.274
2Global1852-0.228-0.196-0.280
3Global1853-0.269-0.239-0.321
4Global1854-0.248-0.218-0.301
\n", - "
" - ], - "text/plain": [ - " Country Year Median (℃) Upper (℃) Lower (℃)\n", - "0 Global 1850 -0.373 -0.339 -0.425\n", - "1 Global 1851 -0.218 -0.184 -0.274\n", - "2 Global 1852 -0.228 -0.196 -0.280\n", - "3 Global 1853 -0.269 -0.239 -0.321\n", - "4 Global 1854 -0.248 -0.218 -0.301" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature_a.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryYearMedian (℃)Upper (℃)Lower (℃)
0Global1850.0-0.373-0.339-0.425
1Global1851.0-0.218-0.184-0.274
2Global1852.0-0.228-0.196-0.280
3Global1853.0-0.269-0.239-0.321
4Global1854.0-0.248-0.218-0.301
..................
164Global2014.00.5790.6160.545
165Global2015.00.7630.8020.730
166Global2016.00.7970.8350.762
167Global2017.00.6770.7160.636
168Global2018.00.5950.6340.558
\n", - "

169 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Country Year Median (℃) Upper (℃) Lower (℃)\n", - "0 Global 1850.0 -0.373 -0.339 -0.425\n", - "1 Global 1851.0 -0.218 -0.184 -0.274\n", - "2 Global 1852.0 -0.228 -0.196 -0.280\n", - "3 Global 1853.0 -0.269 -0.239 -0.321\n", - "4 Global 1854.0 -0.248 -0.218 -0.301\n", - ".. ... ... ... ... ...\n", - "164 Global 2014.0 0.579 0.616 0.545\n", - "165 Global 2015.0 0.763 0.802 0.730\n", - "166 Global 2016.0 0.797 0.835 0.762\n", - "167 Global 2017.0 0.677 0.716 0.636\n", - "168 Global 2018.0 0.595 0.634 0.558\n", - "\n", - "[169 rows x 5 columns]" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#keeping only the global data to compare it with the CO emissions and the CO concentration\n", - "global_a = temperature_a.where(temperature_a['Country']=='Global')\n", - "global_a.dropna(inplace=True)\n", - "global_a" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ozone depletion crisis" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearOzone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)
0Natural emissionsNaN1961165000
1Natural emissionsNaN1962165000
2Natural emissionsNaN1963165000
3Natural emissionsNaN1964165000
4Natural emissionsNaN1965165000
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" - ], - "text/plain": [ - " Entity Code Year \\\n", - "0 Natural emissions NaN 1961 \n", - "1 Natural emissions NaN 1962 \n", - "2 Natural emissions NaN 1963 \n", - "3 Natural emissions NaN 1964 \n", - "4 Natural emissions NaN 1965 \n", - "\n", - " Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents) \n", - "0 165000 \n", - "1 165000 \n", - "2 165000 \n", - "3 165000 \n", - "4 165000 " - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# importing Ozone depleting emissions dataset\n", - "ozone = pd.read_csv('data//ozone-depleting-substance-emissions.csv')\n", - "ozone.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# cleaning the dataset\n", - "ozone.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "ozone.rename(columns={'Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)':'Ozone-depleting emissions'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Natural emissions', 'Total emissions'], dtype=object)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ozone.Entity.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#ploting natural and total emissions to see that we have nearly stopped using CFC\n", - "plt.figure(figsize=(17,6))\n", - "plt.plot(ozone.Year,ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Natural emissions'), label='Natural emissions')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Tonnes CFC11-equivalents')\n", - "plt.plot(ozone.Year, ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Total emissions'), label='Total emissions')\n", - "plt.legend(['Natural emissions', 'Total emissions'], loc='upper right')" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityYearOzone-depleting emissions
0Natural emissions1961165000
1Natural emissions1962165000
2Natural emissions1963165000
3Natural emissions1964165000
4Natural emissions1965165000
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" - ], - "text/plain": [ - " Entity Year Ozone-depleting emissions\n", - "0 Natural emissions 1961 165000\n", - "1 Natural emissions 1962 165000\n", - "2 Natural emissions 1963 165000\n", - "3 Natural emissions 1964 165000\n", - "4 Natural emissions 1965 165000" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ozone.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " errors=errors,\n" - ] - } - ], - "source": [ - "caused_ozone = ozone[ozone.Entity == 'Total emissions']\n", - "caused_ozone.drop(['Entity'],axis=1, inplace=True )" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearOzone-depleting emissions
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" - ], - "text/plain": [ - " Year Ozone-depleting emissions\n", - "54 1961 380000\n", - "55 1962 400000\n", - "56 1963 440000\n", - "57 1964 480000\n", - "58 1965 510000" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "caused_ozone.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Antartica Ozone Hole area" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearMaximum ozone hole area (square kilometres)Mean ozone hole area (square kilometres)
0WorldOWID_WRL19791100000100000
1WorldOWID_WRL198033000001400000
2WorldOWID_WRL19813100000600000
3WorldOWID_WRL1982108000004800000
4WorldOWID_WRL1983122000007900000
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" - ], - "text/plain": [ - " Entity Code Year Maximum ozone hole area (square kilometres) \\\n", - "0 World OWID_WRL 1979 1100000 \n", - "1 World OWID_WRL 1980 3300000 \n", - "2 World OWID_WRL 1981 3100000 \n", - "3 World OWID_WRL 1982 10800000 \n", - "4 World OWID_WRL 1983 12200000 \n", - "\n", - " Mean ozone hole area (square kilometres) \n", - "0 100000 \n", - "1 1400000 \n", - "2 600000 \n", - "3 4800000 \n", - "4 7900000 " - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Antartica Ozone Hole area dataset\n", - "oz_hole = pd.read_csv('data//antarctic-ozone-hole-area.csv')\n", - "oz_hole.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "oz_hole.drop(['Entity', 'Code','Maximum ozone hole area (square kilometres)'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMean ozone hole area (square kilometres)
01979100000
119801400000
21981600000
319824800000
419837900000
\n", - "
" - ], - "text/plain": [ - " Year Mean ozone hole area (square kilometres)\n", - "0 1979 100000\n", - "1 1980 1400000\n", - "2 1981 600000\n", - "3 1982 4800000\n", - "4 1983 7900000" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oz_hole.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#ploting natural and total emissionsMean ozone hole area (square kilometres) to see if it has reduced\n", - "plt.figure(figsize=(17,6))\n", - "plt.plot(oz_hole.Year,oz_hole['Mean ozone hole area (square kilometres)'], label='Mean ozone hole area (square kilometres)')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Mean ozone hole area (square kilometres)')\n", - "plt.legend(['Mean ozone hole area (square kilometres)'], loc='upper right')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### GDP per capita (1981-2018)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Unnamed: 0CountryYearGDPPopulationGDP_per_capita
00Canada1981808.68817124900000.032477.436602
11United States19816698.570801229465714.029192.033459
22Cuba198135.8826419705971.03696.965594
33Mexico1981589.54895069930971.08430.441359
44Antigua Barbuda19810.40892768264.05990.381182
\n", - "
" - ], - "text/plain": [ - " Unnamed: 0 Country Year GDP Population GDP_per_capita\n", - "0 0 Canada 1981 808.688171 24900000.0 32477.436602\n", - "1 1 United States 1981 6698.570801 229465714.0 29192.033459\n", - "2 2 Cuba 1981 35.882641 9705971.0 3696.965594\n", - "3 3 Mexico 1981 589.548950 69930971.0 8430.441359\n", - "4 4 Antigua Barbuda 1981 0.408927 68264.0 5990.381182" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdp = pd.read_csv('data//GDP_P.csv')\n", - "gdp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "gdp.drop(['Unnamed: 0'],axis=1, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tertiary education (1970-2010)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Unnamed: 0CountryYearTertiary_education
00Afghanistan19701.08
11Afghanistan19751.48
22Afghanistan19801.96
33Afghanistan19852.49
44Afghanistan19902.91
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" - ], - "text/plain": [ - " Unnamed: 0 Country Year Tertiary_education\n", - "0 0 Afghanistan 1970 1.08\n", - "1 1 Afghanistan 1975 1.48\n", - "2 2 Afghanistan 1980 1.96\n", - "3 3 Afghanistan 1985 2.49\n", - "4 4 Afghanistan 1990 2.91" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "education = pd.read_csv('data//education_df.csv')\n", - "education.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "education.drop(['Unnamed: 0'],axis=1, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Global average temperature" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtLandAverageTemperatureLandAverageTemperatureUncertaintyLandMaxTemperatureLandMaxTemperatureUncertaintyLandMinTemperatureLandMinTemperatureUncertaintyLandAndOceanAverageTemperatureLandAndOceanAverageTemperatureUncertainty
01750-01-013.0343.574NaNNaNNaNNaNNaNNaN
11750-02-013.0833.702NaNNaNNaNNaNNaNNaN
21750-03-015.6263.076NaNNaNNaNNaNNaNNaN
31750-04-018.4902.451NaNNaNNaNNaNNaNNaN
41750-05-0111.5732.072NaNNaNNaNNaNNaNNaN
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" - ], - "text/plain": [ - " dt LandAverageTemperature LandAverageTemperatureUncertainty \\\n", - "0 1750-01-01 3.034 3.574 \n", - "1 1750-02-01 3.083 3.702 \n", - "2 1750-03-01 5.626 3.076 \n", - "3 1750-04-01 8.490 2.451 \n", - "4 1750-05-01 11.573 2.072 \n", - "\n", - " LandMaxTemperature LandMaxTemperatureUncertainty LandMinTemperature \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " LandMinTemperatureUncertainty LandAndOceanAverageTemperature \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " LandAndOceanAverageTemperatureUncertainty \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN " - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_temperature = pd.read_csv('data//GlobalTemperatures.csv')\n", - "global_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtLandAverageTemperatureLandAndOceanAverageTemperature
12001850-01-010.74912.833
12011850-02-013.07113.588
12021850-03-014.95414.043
12031850-04-017.21714.667
12041850-05-0110.00415.507
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" - ], - "text/plain": [ - " dt LandAverageTemperature LandAndOceanAverageTemperature\n", - "1200 1850-01-01 0.749 12.833\n", - "1201 1850-02-01 3.071 13.588\n", - "1202 1850-03-01 4.954 14.043\n", - "1203 1850-04-01 7.217 14.667\n", - "1204 1850-05-01 10.004 15.507" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_temperature.dropna(inplace=True)\n", - "global_temperature = global_temperature[['dt','LandAverageTemperature','LandAndOceanAverageTemperature']]\n", - "global_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtLandAverageTemperatureLandAndOceanAverageTemperatureYear
12001850-01-010.74912.8331850
12011850-02-013.07113.5881850
12021850-03-014.95414.0431850
12031850-04-017.21714.6671850
12041850-05-0110.00415.5071850
\n", - "
" - ], - "text/plain": [ - " dt LandAverageTemperature LandAndOceanAverageTemperature Year\n", - "1200 1850-01-01 0.749 12.833 1850\n", - "1201 1850-02-01 3.071 13.588 1850\n", - "1202 1850-03-01 4.954 14.043 1850\n", - "1203 1850-04-01 7.217 14.667 1850\n", - "1204 1850-05-01 10.004 15.507 1850" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#global_temperature['dt']= pd.to_datetime(global_temperature['dt']) \n", - "global_temperature['Year'] = pd.DatetimeIndex(global_temperature['dt']).year\n", - "global_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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LandAverageTemperatureLandAndOceanAverageTemperature
Year
18507.90066714.867167
18518.17858314.991833
18528.10016715.006500
18538.04183314.955167
18548.21050014.991000
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" - ], - "text/plain": [ - " LandAverageTemperature LandAndOceanAverageTemperature\n", - "Year \n", - "1850 7.900667 14.867167\n", - "1851 8.178583 14.991833\n", - "1852 8.100167 15.006500\n", - "1853 8.041833 14.955167\n", - "1854 8.210500 14.991000" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_global_temp = global_temperature[['Year','LandAverageTemperature','LandAndOceanAverageTemperature']].groupby(['Year']).agg({'LandAverageTemperature':'mean',\n", - " 'LandAndOceanAverageTemperature':'mean'})\n", - "annual_global_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearLandAverageTemperatureLandAndOceanAverageTemperature
018507.90066714.867167
118518.17858314.991833
218528.10016715.006500
318538.04183314.955167
418548.21050014.991000
\n", - "
" - ], - "text/plain": [ - " Year LandAverageTemperature LandAndOceanAverageTemperature\n", - "0 1850 7.900667 14.867167\n", - "1 1851 8.178583 14.991833\n", - "2 1852 8.100167 15.006500\n", - "3 1853 8.041833 14.955167\n", - "4 1854 8.210500 14.991000" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_global_temp.reset_index(level=0, inplace=True)\n", - "annual_global_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearLandAverageTemperatureLandAndOceanAverageTemperature
018507.90066714.867167
118518.17858314.991833
218528.10016715.006500
318538.04183314.955167
418548.21050014.991000
\n", - "
" - ], - "text/plain": [ - " Year LandAverageTemperature LandAndOceanAverageTemperature\n", - "0 1850 7.900667 14.867167\n", - "1 1851 8.178583 14.991833\n", - "2 1852 8.100167 15.006500\n", - "3 1853 8.041833 14.955167\n", - "4 1854 8.210500 14.991000" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_global_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 166 entries, 0 to 165\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Year 166 non-null int64 \n", - " 1 LandAverageTemperature 166 non-null float64\n", - " 2 LandAndOceanAverageTemperature 166 non-null float64\n", - "dtypes: float64(2), int64(1)\n", - "memory usage: 4.0 KB\n" - ] - } - ], - "source": [ - "annual_global_temp.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Merging datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CO merged " - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)
0Afghanistan194914656.00.0003
1Afghanistan195084272.00.0015
2Afghanistan195191600.00.0015
3Afghanistan195291600.00.0015
4Afghanistan1953106256.00.0016
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" - ], - "text/plain": [ - " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%)\n", - "0 Afghanistan 1949 14656.0 0.0003\n", - "1 Afghanistan 1950 84272.0 0.0015\n", - "2 Afghanistan 1951 91600.0 0.0015\n", - "3 Afghanistan 1952 91600.0 0.0015\n", - "4 Afghanistan 1953 106256.0 0.0016" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_merged = pd.merge(co_annual, co_share, on=['Country','Year'])\n", - "co_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 20586 entries, 0 to 20585\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 20586 non-null object \n", - " 1 Year 20586 non-null int64 \n", - " 2 Annual COâ‚‚ 20586 non-null float64\n", - " 3 Share of global COâ‚‚ emissions (%) 20586 non-null float64\n", - "dtypes: float64(2), int64(1), object(1)\n", - "memory usage: 804.1+ KB\n" - ] - } - ], - "source": [ - "co_merged.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### total emissions (1990-2016)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CountryYearGHG(tonnes COâ‚‚e )
0Afghanistan199015140000.0
1Afghanistan199115060000.0
2Afghanistan199213600000.0
3Afghanistan199313430000.0
4Afghanistan199413240000.0
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" - ], - "text/plain": [ - " Country Year GHG(tonnes COâ‚‚e )\n", - "0 Afghanistan 1990 15140000.0\n", - "1 Afghanistan 1991 15060000.0\n", - "2 Afghanistan 1992 13600000.0\n", - "3 Afghanistan 1993 13430000.0\n", - "4 Afghanistan 1994 13240000.0" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ghg.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )
0Afghanistan19902602407.590.011715140000.0
1Afghanistan19912426486.160.010815060000.0
2Afghanistan19921381521.840.006213600000.0
3Afghanistan19931333837.110.006013430000.0
4Afghanistan19941282488.380.005713240000.0
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" - ], - "text/plain": [ - " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", - "0 Afghanistan 1990 2602407.59 0.0117 \n", - "1 Afghanistan 1991 2426486.16 0.0108 \n", - "2 Afghanistan 1992 1381521.84 0.0062 \n", - "3 Afghanistan 1993 1333837.11 0.0060 \n", - "4 Afghanistan 1994 1282488.38 0.0057 \n", - "\n", - " GHG(tonnes COâ‚‚e ) \n", - "0 15140000.0 \n", - "1 15060000.0 \n", - "2 13600000.0 \n", - "3 13430000.0 \n", - "4 13240000.0 " - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "total_emissions = pd.merge(co_merged,ghg, on=(['Country','Year']))\n", - "total_emissions.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 5074 entries, 0 to 5073\n", - "Data columns (total 5 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 5074 non-null object \n", - " 1 Year 5074 non-null int64 \n", - " 2 Annual COâ‚‚ 5074 non-null float64\n", - " 3 Share of global COâ‚‚ emissions (%) 5074 non-null float64\n", - " 4 GHG(tonnes COâ‚‚e ) 5074 non-null float64\n", - "dtypes: float64(3), int64(1), object(1)\n", - "memory usage: 237.8+ KB\n" - ] - } - ], - "source": [ - "total_emissions.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging Temperature with Emissions (1990-2013)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CountryYearAverageTemperature
0Albania17438.620
1Andorra17437.556
2Austria17432.482
3Belarus17430.767
4Belgium17437.106
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" - ], - "text/plain": [ - " Country Year AverageTemperature\n", - "0 Albania 1743 8.620\n", - "1 Andorra 1743 7.556\n", - "2 Austria 1743 2.482\n", - "3 Belarus 1743 0.767\n", - "4 Belgium 1743 7.106" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [], - "source": [ - "annual_temp.rename(columns={'year':'Year'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperature
0Afghanistan19902602407.590.011715140000.014.993333
1Afghanistan19912426486.160.010815060000.014.370750
2Afghanistan19921381521.840.006213600000.014.056083
3Afghanistan19931333837.110.006013430000.014.439250
4Afghanistan19941282488.380.005713240000.014.754750
\n", - "
" - ], - "text/plain": [ - " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", - "0 Afghanistan 1990 2602407.59 0.0117 \n", - "1 Afghanistan 1991 2426486.16 0.0108 \n", - "2 Afghanistan 1992 1381521.84 0.0062 \n", - "3 Afghanistan 1993 1333837.11 0.0060 \n", - "4 Afghanistan 1994 1282488.38 0.0057 \n", - "\n", - " GHG(tonnes COâ‚‚e ) AverageTemperature \n", - "0 15140000.0 14.993333 \n", - "1 15060000.0 14.370750 \n", - "2 13600000.0 14.056083 \n", - "3 13430000.0 14.439250 \n", - "4 13240000.0 14.754750 " - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_impact = pd.merge(total_emissions, annual_temp, on=(['Country','Year']))\n", - "co_impact.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging co_impact with demographic and economic indicators" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperatureGDPPopulationGDP_per_capita
0Afghanistan19902602407.590.011715140000.014.9933334.59266513568282.0338.485353
1Afghanistan19912426486.160.010815060000.014.3707504.62940613671918.0338.606915
2Afghanistan19921381521.840.006213600000.014.0560834.67570015023317.0311.229518
3Afghanistan19931333837.110.006013430000.014.4392504.53075317003437.0266.461018
4Afghanistan19941282488.380.005713240000.014.7547504.39483118486041.0237.737799
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" - ], - "text/plain": [ - " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", - "0 Afghanistan 1990 2602407.59 0.0117 \n", - "1 Afghanistan 1991 2426486.16 0.0108 \n", - "2 Afghanistan 1992 1381521.84 0.0062 \n", - "3 Afghanistan 1993 1333837.11 0.0060 \n", - "4 Afghanistan 1994 1282488.38 0.0057 \n", - "\n", - " GHG(tonnes COâ‚‚e ) AverageTemperature GDP Population GDP_per_capita \n", - "0 15140000.0 14.993333 4.592665 13568282.0 338.485353 \n", - "1 15060000.0 14.370750 4.629406 13671918.0 338.606915 \n", - "2 13600000.0 14.056083 4.675700 15023317.0 311.229518 \n", - "3 13430000.0 14.439250 4.530753 17003437.0 266.461018 \n", - "4 13240000.0 14.754750 4.394831 18486041.0 237.737799 " - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "indicators_merged = pd.merge(co_impact,gdp, on=(['Country','Year']))\n", - "indicators_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 3663 entries, 0 to 3662\n", - "Data columns (total 9 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 3663 non-null object \n", - " 1 Year 3663 non-null int64 \n", - " 2 Annual COâ‚‚ 3663 non-null float64\n", - " 3 Share of global COâ‚‚ emissions (%) 3663 non-null float64\n", - " 4 GHG(tonnes COâ‚‚e ) 3663 non-null float64\n", - " 5 AverageTemperature 3663 non-null float64\n", - " 6 GDP 3663 non-null float64\n", - " 7 Population 3663 non-null float64\n", - " 8 GDP_per_capita 3663 non-null float64\n", - "dtypes: float64(7), int64(1), object(1)\n", - "memory usage: 286.2+ KB\n" - ] - } - ], - "source": [ - "indicators_merged.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperatureGDPPopulationGDP_per_capita
0Afghanistan19902602407.590.011715140000.014.9933334.59266513568282.0338.485353
1Afghanistan19912426486.160.010815060000.014.3707504.62940613671918.0338.606915
2Afghanistan19921381521.840.006213600000.014.0560834.67570015023317.0311.229518
3Afghanistan19931333837.110.006013430000.014.4392504.53075317003437.0266.461018
4Afghanistan19941282488.380.005713240000.014.7547504.39483118486041.0237.737799
\n", - "
" - ], - "text/plain": [ - " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", - "0 Afghanistan 1990 2602407.59 0.0117 \n", - "1 Afghanistan 1991 2426486.16 0.0108 \n", - "2 Afghanistan 1992 1381521.84 0.0062 \n", - "3 Afghanistan 1993 1333837.11 0.0060 \n", - "4 Afghanistan 1994 1282488.38 0.0057 \n", - "\n", - " GHG(tonnes COâ‚‚e ) AverageTemperature GDP Population GDP_per_capita \n", - "0 15140000.0 14.993333 4.592665 13568282.0 338.485353 \n", - "1 15060000.0 14.370750 4.629406 13671918.0 338.606915 \n", - "2 13600000.0 14.056083 4.675700 15023317.0 311.229518 \n", - "3 13430000.0 14.439250 4.530753 17003437.0 266.461018 \n", - "4 13240000.0 14.754750 4.394831 18486041.0 237.737799 " - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "indicators_merged_outer = pd.merge(co_impact,gdp, on=(['Country','Year']), how='outer')\n", - "indicators_merged_outer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 7131 entries, 0 to 7130\n", - "Data columns (total 9 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 7131 non-null object \n", - " 1 Year 7131 non-null int64 \n", - " 2 Annual COâ‚‚ 4030 non-null float64\n", - " 3 Share of global COâ‚‚ emissions (%) 4030 non-null float64\n", - " 4 GHG(tonnes COâ‚‚e ) 4030 non-null float64\n", - " 5 AverageTemperature 4030 non-null float64\n", - " 6 GDP 6764 non-null float64\n", - " 7 Population 6764 non-null float64\n", - " 8 GDP_per_capita 6764 non-null float64\n", - "dtypes: float64(7), int64(1), object(1)\n", - "memory usage: 557.1+ KB\n" - ] - } - ], - "source": [ - "indicators_merged_outer.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged_outer.to_csv('country_indicators')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging ozone datasets 1979-2014" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearOzone-depleting emissionsMean ozone hole area (square kilometres)
019791110000100000
1198010800001400000
219811040000600000
3198211000004800000
4198310900007900000
\n", - "
" - ], - "text/plain": [ - " Year Ozone-depleting emissions Mean ozone hole area (square kilometres)\n", - "0 1979 1110000 100000\n", - "1 1980 1080000 1400000\n", - "2 1981 1040000 600000\n", - "3 1982 1100000 4800000\n", - "4 1983 1090000 7900000" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ozone_merged = pd.merge(caused_ozone, oz_hole, on='Year')\n", - "ozone_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.5597422292909034" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oz_hole_emiss_corr = ozone_merged[\"Ozone-depleting emissions\"].corr(ozone_merged[\"Mean ozone hole area (square kilometres)\"])\n", - "oz_hole_emiss_corr" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "ozone_merged[\"Year\"] = ozone_merged[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 35 entries, 0 to 34\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Year 35 non-null object\n", - " 1 Ozone-depleting emissions 35 non-null int64 \n", - " 2 Mean ozone hole area (square kilometres) 35 non-null int64 \n", - "dtypes: int64(2), object(1)\n", - "memory usage: 1.1+ KB\n" - ] - } - ], - "source": [ - "ozone_merged.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [], - "source": [ - "ozone_merged = ozone_merged.set_index('Year')" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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YearLandAverageTemperatureLandAndOceanAverageTemperature
018507.90066714.867167
118518.17858314.991833
218528.10016715.006500
318538.04183314.955167
418548.21050014.991000
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" - ], - "text/plain": [ - " Year LandAverageTemperature LandAndOceanAverageTemperature\n", - "0 1850 7.900667 14.867167\n", - "1 1851 8.178583 14.991833\n", - "2 1852 8.100167 15.006500\n", - "3 1853 8.041833 14.955167\n", - "4 1854 8.210500 14.991000" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_global_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 166 entries, 0 to 165\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Year 166 non-null int64 \n", - " 1 LandAverageTemperature 166 non-null float64\n", - " 2 LandAndOceanAverageTemperature 166 non-null float64\n", - "dtypes: float64(2), int64(1)\n", - "memory usage: 4.0 KB\n" - ] - } - ], - "source": [ - "annual_global_temp.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearCOâ‚‚ concentration
0803719 BCE207.29
1803182 BCE202.23
2802573 BCE204.86
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YearCOâ‚‚ concentration
17401846283.62
17411847286.84
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YearLandAverageTemperatureLandAndOceanAverageTemperatureCOâ‚‚ concentration
018507.90066714.867167284.00
118518.17858314.991833287.13
218548.21050014.991000288.05
318558.11075015.021083285.57
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" - ], - "text/plain": [ - " Year LandAverageTemperature LandAndOceanAverageTemperature \\\n", - "0 1850 7.900667 14.867167 \n", - "1 1851 8.178583 14.991833 \n", - "2 1854 8.210500 14.991000 \n", - "3 1855 8.110750 15.021083 \n", - "4 1857 7.758167 14.758250 \n", - "\n", - " COâ‚‚ concentration \n", - "0 284.00 \n", - "1 287.13 \n", - "2 288.05 \n", - "3 285.57 \n", - "4 283.16 " - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_global_impact = pd.merge(annual_global_temp,co_concentration_modern, on='Year')\n", - "co_global_impact.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 126 entries, 0 to 125\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Year 126 non-null int64 \n", - " 1 LandAverageTemperature 126 non-null float64\n", - " 2 LandAndOceanAverageTemperature 126 non-null float64\n", - " 3 COâ‚‚ concentration 126 non-null float64\n", - "dtypes: float64(3), int64(1)\n", - "memory usage: 4.9 KB\n" - ] - } - ], - "source": [ - "co_global_impact.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "co_impact_temp = pd.merge(co_global_impact,global_a, on='Year')" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "co_impact_temp.drop(['Country'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearLandAverageTemperatureLandAndOceanAverageTemperatureCO₂ concentrationMedian (℃)Upper (℃)Lower (℃)
018507.90066714.867167284.00-0.373-0.339-0.425
118518.17858314.991833287.13-0.218-0.184-0.274
218548.21050014.991000288.05-0.248-0.218-0.301
318558.11075015.021083285.57-0.272-0.241-0.324
418577.75816714.758250283.16-0.461-0.431-0.512
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YearLandAverageTemperatureLandAndOceanAverageTemperatureCO₂ concentrationMedian (℃)Upper (℃)Lower (℃)Annual CO₂ emissions (tonnes )
018507.90066714.867167284.00-0.373-0.339-0.425196896032.0
118518.17858314.991833287.13-0.218-0.184-0.274198742688.0
218548.21050014.991000288.05-0.248-0.218-0.301255010736.0
318558.11075015.021083285.57-0.272-0.241-0.324260034080.0
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" - ], - "text/plain": [ - " Year LandAverageTemperature LandAndOceanAverageTemperature \\\n", - "0 1850 7.900667 14.867167 \n", - "1 1851 8.178583 14.991833 \n", - "2 1854 8.210500 14.991000 \n", - "3 1855 8.110750 15.021083 \n", - "4 1857 7.758167 14.758250 \n", - "\n", - " CO₂ concentration Median (℃) Upper (℃) Lower (℃) \\\n", - "0 284.00 -0.373 -0.339 -0.425 \n", - "1 287.13 -0.218 -0.184 -0.274 \n", - "2 288.05 -0.248 -0.218 -0.301 \n", - "3 285.57 -0.272 -0.241 -0.324 \n", - "4 283.16 -0.461 -0.431 -0.512 \n", - "\n", - " Annual CO₂ emissions (tonnes ) \n", - "0 196896032.0 \n", - "1 198742688.0 \n", - "2 255010736.0 \n", - "3 260034080.0 \n", - "4 279680448.0 " - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_temp_global = pd.merge(co_impact_temp,global_co, on='Year')\n", - "co_temp_global.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(17,6))\n", - "plt.plot(co_global_impact.Year,co_global_impact['LandAverageTemperature'], label='LandAverageTemperature')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Tonnes CFC11-equivalents')\n", - "plt.plot(co_global_impact.Year,co_global_impact['LandAndOceanAverageTemperature'], label='LandAndOceanAverageTemperature')\n", - "plt.legend(['LandAverageTemperature', 'LandAndOceanAverageTemperature'], loc='upper right')" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9149133329199667" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_conc_temp_corr = co_global_impact[\"LandAverageTemperature\"].corr(co_global_impact[\"COâ‚‚ concentration\"])\n", - "co_conc_temp_corr" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8796467057646229" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_temp_corr = co_temp_global[\"LandAverageTemperature\"].corr(co_temp_global[\"Annual COâ‚‚ emissions (tonnes )\"])\n", - "co_temp_corr" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [], - "source": [ - "co_global_impact[\"Year\"] = co_global_impact[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8322 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8322 missing from current font.\n", - " font.set_text(s, 0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0, flags=flags)\n" - ] - }, - { - "data": { - "image/png": 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YearLandAverageTemperatureLandAndOceanAverageTemperatureCO₂ concentrationMedian (℃)Upper (℃)Lower (℃)Annual CO₂ emissions (tonnes )
018507.90066714.867167284.00-0.373-0.339-0.425196896032.0
118518.17858314.991833287.13-0.218-0.184-0.274198742688.0
218548.21050014.991000288.05-0.248-0.218-0.301255010736.0
318558.11075015.021083285.57-0.272-0.241-0.324260034080.0
418577.75816714.758250283.16-0.461-0.431-0.512279680448.0
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" - ], - "text/plain": [ - " Year LandAverageTemperature LandAndOceanAverageTemperature \\\n", - "0 1850 7.900667 14.867167 \n", - "1 1851 8.178583 14.991833 \n", - "2 1854 8.210500 14.991000 \n", - "3 1855 8.110750 15.021083 \n", - "4 1857 7.758167 14.758250 \n", - "\n", - " CO₂ concentration Median (℃) Upper (℃) Lower (℃) \\\n", - "0 284.00 -0.373 -0.339 -0.425 \n", - "1 287.13 -0.218 -0.184 -0.274 \n", - "2 288.05 -0.248 -0.218 -0.301 \n", - "3 285.57 -0.272 -0.241 -0.324 \n", - "4 283.16 -0.461 -0.431 -0.512 \n", - "\n", - " Annual CO₂ emissions (tonnes ) \n", - "0 196896032.0 \n", - "1 198742688.0 \n", - "2 255010736.0 \n", - "3 260034080.0 \n", - "4 279680448.0 " - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_temp_global.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearLandAverageTemperatureCO₂ concentrationMedian (℃)Annual CO₂ emissions (tonnes )
018507.900667284.00-0.373196896032.0
118518.178583287.13-0.218198742688.0
218548.210500288.05-0.248255010736.0
318558.110750285.57-0.272260034080.0
418577.758167283.16-0.461279680448.0
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" - ], - "text/plain": [ - " Year LandAverageTemperature CO₂ concentration Median (℃) \\\n", - "0 1850 7.900667 284.00 -0.373 \n", - "1 1851 8.178583 287.13 -0.218 \n", - "2 1854 8.210500 288.05 -0.248 \n", - "3 1855 8.110750 285.57 -0.272 \n", - "4 1857 7.758167 283.16 -0.461 \n", - "\n", - " Annual CO₂ emissions (tonnes ) \n", - "0 196896032.0 \n", - "1 198742688.0 \n", - "2 255010736.0 \n", - "3 260034080.0 \n", - "4 279680448.0 " - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_temp_global_no_colin = co_temp_global.drop(['LandAndOceanAverageTemperature','Upper (℃)','Lower (℃)'], axis=1)\n", - "co_temp_global_no_colin.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [], - "source": [ - "co_temp_global_no_colin[\"Year\"] = co_temp_global_no_colin[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.pairplot(co_temp_global_no_colin)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_a_corr = co_temp_global[\"LandAverageTemperature\"].corr(co_temp_global[\"Annual CO₂ emissions (tonnes )\"])\n", - "co_a_corr" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Median (℃)')" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(x=co_temp_global['CO₂ concentration'], y=co_temp_global['Median (℃)'])\n", - "plt.xlabel('CO₂ concentration')\n", - "plt.ylabel('Median (℃)')" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Median (℃)')" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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jhBDuLoQzXb58GU888QT27NmDpk2burs4LsFBRXIGe/4eVZ1eC9xrrVReS5s8l9xzU7ZFUVJSgpycHNy8eROVY8pzzz3n3JKSTfhyVM2kdPC39+8RU33UbrKB4pVXXsHvv/+O1q1bM2usCnBQseZxR/B35O8RFyWqvWQDxY8//ohdu3bBx4f5A9WAg4o1jzuCP/8ekT1k04yHhYUpUQ6yEdcPqHnc8dDm3yOyh2wzoXXr1khJScHjjz8Of39/43aOUbiHs6dUkvu5Y0bRY21DsOvQbxa3E1UlGyiKiorQvHlzXLx4UYnykAwOKtY87gj+R87mS263P4Mb1XSygSI9PV2JcpAdOKhYs7gj+FtqwVjbTrWbbKA4ceIEMjIyUFxcDCEE9Ho9Ll++jH379ilQPKLawVLwd+WUWS8vDfR681eovLw4s5HMyQ5mp6WloXPnzrhz5w4SEhIQGBiI6OhoJcpGVGu5OgmfpSBhbTvVbrKBQqPRYOzYsfjb3/6Gv/71r3j77bdx4MABJcpGVGtZmzLrDMESA+VS26l2kw0U9erVAwA8+OCDOHfuHPz9/eHlJfs1IqoGV0+ZtSW1OJGB7BhFhw4dMHHiRPzjH//AuHHj8Ouvv/LlOyIXc/WUWc6eI3vIPvFnzZqFkydPokWLFpg5cyYOHjyIJUuWKFE2Io/h7IFnS1NmAaD0bjn2HbvklAc6Z8+RrST7kH755RcAwA8//ABfX198//33CA4OxsCBA1FSwil0RAauGHju06UZxg99BPUDfE223y7WcWU5Upxki+KNN95ARkYGXnrpJbN9Go0Ge/bscWnByP2cWUuuyanRHcnVZMv96NOlGdZln8HtYp3kuWvyfSX1kAwUGRkZAIC9e/cqVhhSD3szmlp7YNX01Oj2Djzbcz+snbum31dSD8lAsXLlSqtfHD9+vNMLQ+phTy1Z7oHlrtToStW27R14tud+WDs3U86TUiQDRWFhIQDg/PnzuHDhAvr16wcfHx/s2bMHbdq0UayAtZEzHnDVPYc9tWS5B5Y7sqMqWdu2N1eTPffD2rnf2nDcrvMTOUoyULz66qsAgJSUFGzZsgWNGjUCAKSmpuLFF19UpnS1kLUHHGDbdEZnPCTlasmVA5HUu7yGB5Y7sqMqWdu2d6qpPffD2rnXZZ/hOtakCNnpsVqt1hgkAKBBgwa4ceOGSwtVm0k94N759CRKy/6z3drD3xkPSWs1WUvrJ1siAIyan4vH2oZgz9HLimZHVboVY89UU6mpr9rCEoyan2sWZKTOzZTzpBTZV6zbtGmDGTNm4Ouvv8ahQ4fw8ssv45FHHqnWRXfu3Im4uDhER0cjMzPTbP+ZM2cwePBgxMTEYNasWSgvL6/W9TyJ1IOscpAwkErp4IyHpGF6ZnDDutDgXmqH8UMfkRxzkKItLMGeo5fxRNemFs/lKmpemKfyva3Knqm11n5HRM4k26KYP38+li9fjgULFgAAIiMjLU6ZtVVeXh6WLl2KLVu2oE6dOkhKSkK3bt3QqlUr4zFTp07F/Pnz0alTJ8ycORObNm1CcnKyw9f0JFLdElIsPfyd1dUjVZO1t1Z+V1eBI2fz8a805ZJJqr22bbi3o+bnmv2u7Gn98aU5UoJsiyIwMBCTJ0/Gm2++ie3bt2P8+PEmK93Z6+DBg4iIiEBQUBACAgIQExODnJwc4/7ff/8dpaWl6NSpEwBg8ODBJvtrupTYdvDxtj3Vs6WHf3Xz+Ow7dgmj5uciccp2jJqfa1a7lQo4hpqtJUoPsHpKbZtrV5MnkG1RfPvttxg/fjx8fHywceNGDBw4EKtWrcKjjz7q0AXz8/MRHBxs/BwSEoJTp05J7g8ODkZeXp5D1/JUwo5Mz5Ye/tXJ42PLQLi12rqaBlg9obYt1foLDPDFqPm5fJGOVEG2RbFo0SKsXbsWQUFBCAsLw6JFi4zdUI7Q6/XQaP5T7xRCmHyW21/Trcs+gwob1wSI695c8uHRp0sz/CstGjuWDMS/0qJtfsjYkt7aWm2dWUntY+l+eXtpUHK33GVrURDZS7ZFUVpaajJ+0Lt3byxdutThC4aFheHo0aPGz1qtFiEhISb7tVqt8fP169dN9td01roc6gf44naxzrg62ZGz+WjnpARxctevul2qtu5pWUndnQLD0v0qvVtuMW1HxrbTHnNfqWaRDRQ+Pj64efOmsVZ//vz5al2wR48eWLFiBQoKClC3bl3k5uZi3rx5xv1NmjSBn58fjh07hi5dumD79u2IjIys1jU9ibXB7OLScvh4a1Beca/FoS0swZINx3Hmwg2kPt3Jpde3p+vIE7p8APWkFql6vxKnbLd43O1inTGAMF0HKUm26yk1NRX//d//jWvXrmHy5MkYNmwYUlNTHb5gaGgoJk2ahJSUFAwaNAgDBgxAx44dMWbMGJw+fRoAsHjxYqSnp6N///4oLi5GSkqKw9fzNJa6Igwq9MIYJCrbdeg3JEgMPDvj+jW168jVq8g5ytagrIayUu0g26KIiorCX//6Vxw4cAB6vR5///vf0bJly2pdNCEhAQkJCSbbVq9ebfxz27Zt8emnn1brGp7KUDtcIpGewRpn1DId6Tpyd/eNo9Q640jqhTxL3F1Wqh0kA8Uff/xh/PMDDzyAuLg4k31BQUGuLVkt1qdLM2RsO23WT20LZ6SpsKfrSC3dN45wR2oRW9g6bgG4v6xUO0gGioiICIuzjwz/f+YMm7xqpWQt05MzmNrzUp6rWk1S560arC2lTampXYKkPpKBYtCgQThx4gT69u2LIUOGmMx8Ite740BrwkDJWqaru29c2a1lazebq1pN9pzX02aTUc0iGSgWLlyIkpIS5ObmYsGCBSguLkZiYiISEhLQoEEDJcvoFu7ud7c2+0lz/38svZindC3Tld03lh6kSzYcx5INxxHspN+JLd1srmo12XteT5lNRjWP1VlPdevWxcCBA/HBBx9g2bJluHPnDlJSUjBx4kSlyucWrlgD2V7WZj8JWA4S7khT4cpZUtaSDyr5O3FVq0mtg+lEVcnOejIoKChAQUEBCgsL8ac//cmVZXI7pfrdrbVa7J39FNywrqJJ9wxc2SUi98BUaizEVa0mtQ6mE1VlNVBcvXoVO3bswPbt2+Ht7Y3ExERs2rQJoaGhSpXPLZSo6dnSP21tcRpXls1eruoSsSWTrhI/t6sy0ao9wy2RgWTX04gRIzB06FDcuHEDixcvxs6dOzFmzJgaHyQAZdYysPVlL2tdUK4qm1rY8rMr8XO7KhOtp2S4JZJsURw5cgR+fn745JNPTF5+M0yPPX7c/hfCPIUSNT17cioB/+naCQzwRcndcpM3tGtqLbTyz26pZVF5xT1XTzxwVauJA9TkCSQDxZ49e5Qsh6ooMRXR3nWTq86pry3TJCv/7JZ+bgAe+8IfkaeQDBRNmjRRshyq4+qaXnVaLbW1Fmrp5x41P9djX/gj8hQ2z3oi5zI8xCqn6qjjK5uj0SE1uQXCKaZErsdAobDKD23DeIPB7WKd07tNPCkXkyMBjVNMiVzPNVVYsqjqi3y3i3VmacOdnTparam0q3L0JcfalBadyF3YolCQtTeNK9MWljhtvWRP6Zpx9CVHNeRAqslde0QAA4Wi7Hk4G7pTqttV5CldM9UJaO4c3Pekrj0iR7HrSUGOPpyr01XkKV0zSrzk6Aqe0rVHVB1sUdjIGd0LlqbEentpEODvgzvFOqspK+Rq1tbWNQDUn57aU9NZeErXHlF1MFDYwFndC1IP7crbvLw00OvNU8Naq1nLlc8T3rvwlIBWlad07RFVBwOFDZyZTVZu5TJhIUjI1aw9eZW5ytQS0OxpPXpqS4jIHgwUNnBl94LUTCgvLw2EXthUs3a0fJytY87e1qOntoSI7KF4oLhy5QqmTp2KGzduoEWLFli8eDHq1atnckx+fj5mzJiB69evw8vLC6+88gq6d++udFGNXNm9IPUwF3qBHUsG2nQOR8rH2TqWOdI6U0tLiMhVFJ/1NGfOHCQnJyMnJwfh4eF49913zY5ZtGgR+vbti+3bt2PJkiV4+eWXUVEh//6Bq8jNHNp37BJGzc9F4pTtGDU/165V16Qe5oEBvk4rnyWcrWMZB6eJzCkaKHQ6HY4cOYKYmBgAwODBg5GTk2N23JNPPokBAwYAAJo3b467d++iuLhYyaKasLZuQHWXTU2JbQcfb43Z9uLScpvP4ci6BnwgWuboNN3qVBaI1E7RrqfCwkIEBgbCx+feZYODg5GXl2d2nCGQAMCaNWvQrl071K9f36Vlk+uvl+pecMZActU0HgBQoRd2ncPe7g/O1rHMkcFpduNRTeeyQJGdnY309HSTbc2bN4dGY1p7rvq5srVr1+Ljjz/GRx995JIyGkj9Qz9z4QaOnM23OkhZnZq54bpSXFm7V8NsHTUOpjsyOF1TZp0RSXFZoIiNjUVsbKzJNp1Oh27duqGiogLe3t7QarUICQmx+P1Fixbhyy+/RGZmJsLCwlxVTADS/9B3HfrN+Fmqllidmrlc7idX1u7dPVtHzbVwe1tn7Majmk7RridfX1907doVu3btQkJCArZt24bIyEiz49auXYvDhw8jKysLDRo0cHm5bP0HbamWWJ2aubXrKlG7d+dsnZpUC7elsqDG1hORrRSfHjt79mxMnz4dq1atwp///Ge89dZbAICsrCzk5+djwoQJeOeddxAYGIgRI0YYv5eRkYHQ0FCXlMla6oyq5Na0tvYQqLoWBTQAzIcn4OWlkR2M9nQ1qRYuV1lQc+uJyBaKB4omTZpg/fr1ZtuHDRtm/PORI0eULBIeaxti0s1kjS1rWltS9WFhWNWuKj9f7xofJICaNZguV1moSa0nqp34ZjaAI2fzbTquOt1BtqxFURtaEgZqGEx3JmuVhZrUeqLaiYECtv2DDXbRAkKVCb1wa5BQsh/d3YPpSqpJrSeqnRgoYH2MwlldQYEBvpLdTZXL4S7u6EevLakvalrriWofLlyEe2MUUur4Vv8W7Tt2CSV3y2WPK71r+9vYzsaUHq7jyJvzRGrCFgWsj1HcLtY5VLOu3I2jkVhjwlnXcgb2o7tWbWk9Uc3EQAH5h6G1GSqW+vUByK4x4ci1XIn96EQkhV1PsO1hqC0sMUv4JpUQMGPbadkZTta4oxbvKWtrE5HyGCgAmx+GVbPDSvXryw1ay3FHLZ796EQkhV1PuPeQXPnJt7ir09t0vKF7yN6av2HVOsNaE5YCijtr8exHJyJLGCjuGz+0E97eeAIVNo4nGMYkLPXr1w/wRZlObzYd0lINnTmAiEjtGCju69OlGc5cuGFXKg+p+fFjB3UAYNvLZKzFE5HaMVBUYm8qD7m3i60FALYkiMhTMFBU4kgqD0daBMwmSkSehIGiEqkxBy8vDSYldXbaQ5zZRInIkzBQVCI15uDsaaJyb0GzW4qI1ISBohKlMppaewua3VJEpDYMFFVUDRbrss/gzIUbOHI232nBw1o2UXZLEZHaMFDcZ+juqVrT1xaWmEyZdUYN31rL5a0Nxy1+h8n5iMhdGChgPgtJjlwNX26Mwdp+JucjIrVhrifYtkxpVVI1fKlEgXKJBA37mZyPiNRG8UBx5coVDB8+HP3790dqaiqKiookj71z5w769euHw4cPu7RMjnTrSNXw5RYAktvP5HxEpDaKdz3NmTMHycnJiI+PxzvvvIN3330XU6dOtXjsvHnzcOvWLZeXydpSqJZYquFLjXEYGIKRLQsEMa0HEamJoi0KnU6HI0eOICYmBgAwePBg5OTkWDx2165dqFevHtq0aePyclnq7jEIblgXj7T6E7y8NMZtGg3w1objxrUpKncnSTG0QKRaIhyDICK1UjRQFBYWIjAwED4+9xoywcHByMvLMzvuypUr+PDDD/HKK68oUi5L3T1Tkh/FziUDkRLbDmd/+8NkKdPSsgq7Fiqq3ALhGAQReRqXdT1lZ2cjPT3dZFvz5s2h0WhMtlX9rNfrMWvWLLz66qvw9/d3VfHMSHX3yA1039VVWN1vKTeU4bx885qIPIHLAkVsbCxiY2NNtul0OnTr1g0VFRXw9vaGVqtFSEiIyTHnz5/H+fPnMWvWLADAxYsXkZaWhlmgIKkAABPXSURBVHnz5iEiIsJVxZVkz9hFVcEN6+JfadFm2y291Fd5OxGRmig6mO3r64uuXbti165dSEhIwLZt2xAZGWlyTKtWrfDll18aP48YMQLjx49Ht27dlCyqkZeXxqTbyRKphYqkupOYpoOIPIni02Nnz56NTZs2IS4uDkePHsXEiRMBAFlZWVi2bJnSxZElFyQAYOygDnZNaZWbIktEpCaKT49t0qQJ1q9fb7Z92LBhFo+3dKySgmWmzsZ1b27TQkWV2TJFlohILfhmtgxrU2frB/iiXYs/2X1OTpElIk/CXE+VWMvBZOllutvFOofGFqxljyUiUhu2KO6zloOpT5dm+FdaNIIt1PgdGVtgmg4i8iRsUdxnyzoQzhxbYJoOIvIUbFHcZ0sQCAzwtXiM1HYiopqALYr7XL0OBNfBJiJPxRbFfVKzmx5r+583x+8U6yx+V2q7gdwaFEREasZAcV+fLs3wRNemZtv3HL1sfKA7Oq2VL9gRkSdjoKjkyNl8s22VH+iOZn7lC3ZE5Mk4RlGJ3APd0cyvXAebiDwZA0UlgQG+uG1hvKHyA11uWqulQevH2oZg16HfTI7jC3ZE5CkYKO7bd+wSikvLzbb7eGtsfqBbygr79sYTqLLkBgDgia5NOeuJiDwCxyjuW5d9BhUWMsXW9fOx+YFuadC6Qi9QXmF+XkvjIUREasRAcZ/U+ITc1FdbzmGJtrCE02OJyCMwUNznjIyu9g5O810KIvIEDBT3OTr1Ve4c3l4a+HhbGKQA36UgIs/Awez7HJ36ass5AGDJhuMWv8N3KYhI7RgoKnFGRlepc1hazwLguxREpH7selKIM7q2iIjcgS0KhTija4uIyB0YKBTExYqIyBMp3vV05coVDB8+HP3790dqaiqKiorMjikrK8P8+fMxaNAgxMfHY//+/UoXk4iI7lM8UMyZMwfJycnIyclBeHg43n33XbNj3n//fRQWFmLr1q14++23MWPGDAhh/nYzERG5nqKBQqfT4ciRI4iJiQEADB48GDk5OWbHZWdnY8yYMdBoNHjooYfwwQcfMFAQEbmJooGisLAQgYGB8PG5NzQSHByMvLw8s+N+++03HDlyBMnJyXjmmWdw/fp1eHlxghYRkTu4bDA7Ozsb6enpJtuaN28OTZVUqlU/A0BFRQWuXbuGzMxM/Pjjj3j++eeRnZ2N+vXru6q4REQkwWWBIjY2FrGxsSbbdDodunXrhoqKCnh7e0Or1SIkJMTsu40bN0Z8fDw0Gg3atm2LsLAwXLhwAR07dnRVcYmISIKi02N9fX3RtWtX7Nq1CwkJCdi2bRsiIyPNjouKisKuXbvw8MMP49KlS7h69SpatGhh0zUqKu6l+b527ZpTy05EVFMZnpeG52dVGqHwKPHvv/+O6dOn48aNG/jzn/+Mt956Cw888ACysrKQn5+Pf/zjH7hz5w7mzp2L77//HgDw8ssvIyoqyqbzHz16FMOHD3flj0BEVCNlZmaia9euZtsVDxSuVlpaiu+++w7BwcHw9vaW/wIRUS1XUVEBrVaL8PBw+Pv7m+2vcYGCiIici3NOiYjIKgYKIiKyioGCiIisYqAgIiKrGCiIiMgqBgoiIrKKgYKIiKyqdYFi586diIuLQ3R0NDIzM832nzlzBoMHD0ZMTAxmzZqF8vJyN5TyHrmyrly5ElFRURg4cCAGDhxo8Rgl3blzBwMGDMDly5fN9qnpvgLWy6qm+7py5UrEx8cjPj4eixYtMtuvpvsqV1Y13ddly5YhLi4O8fHx+OCDD8z2q+m+AvLldfm9FbXItWvXRFRUlCgsLBRFRUUiISFBnDt3zuSY+Ph4ceLECSGEEDNmzBCZmZnuKKpNZR03bpw4fvy4W8pX1bfffisGDBgg2rdvLy5dumS2Xy33VQj5sqrlvh44cEA888wz4u7du6KsrEykpKSI3Nxck2PUcl9tKata7uvhw4dFUlKS0Ol0oqSkRERFRYlffvnF5Bi13FchbCuvq+9trWpRHDx4EBEREQgKCkJAQABiYmJMFk76/fffUVpaik6dOgGQXlhJDWUFgO+++w7vvfceEhISMHfuXNy9e9ctZQWATZs2Yfbs2RazAavpvgLWywqo574GBwdj+vTpqFOnDnx9fdGyZUtcuXLFuF9N91WurIB67uvf/vY3rFu3Dj4+Prhx4wYqKioQEBBg3K+m+wrIlxdw/b2tVYEiPz8fwcHBxs8hISEmCydV3S+1sJIS5MpaVFSEdu3aYerUqdi6dStu3bplcVlZpSxYsMBiMjFAXfcVsF5WNd3Xhx56yPiw+vXXX5GdnY3evXsb96vpvsqVVU33FbiXyXr58uWIj49H9+7dERoaatynpvtqYK28StzbWhUo9Hq9yUJJQgiTz3L7lSRXlnr16mH16tVo2bIlfHx8MGrUKHz55ZfuKKosNd1XOWq8r+fOncOoUaPwyiuv4L/+67+M29V4X6XKqsb7OmHCBBw6dAhXr17Fpk2bjNvVeF8B6fIqcW9rVaAICwuDVqs1fq66cFLV/devX5fsnnA1ubJeuXIFn376qfGzEMK4xKzaqOm+ylHbfT127BhGjhyJKVOm4KmnnjLZp7b7aq2sarqvv/zyC86cOQMAqFu3LqKjo/Hjjz8a96vtvsqVV4l7W6sCRY8ePXDo0CEUFBSgpKQEubm5JgsnNWnSBH5+fjh27BgAYPv27RYXVlJDWf39/fHmm2/i0qVLEEIgMzMTTz75pFvKKkdN91WOmu7r1atX8fe//x2LFy9GfHy82X413Ve5sqrpvl6+fBlpaWkoKytDWVkZ9uzZgy5duhj3q+m+AvLlVeTeumyYXKV27Ngh4uPjRXR0tMjIyBBCCPH888+LU6dOCSGEOHPmjBgyZIiIiYkRkydPFnfv3lVtWXNycoz7p0+f7tayGkRFRRlnEqn1vhpIlVUt93XevHmiU6dOIjEx0fjfhg0bVHlfbSmrWu6rEEIsX75cxMbGigEDBojly5cLIdT991WuvK6+t1yPgoiIrKpVXU9ERGQ/BgoiIrKKgYKIiKxioCAiIqsYKIiIPIi1hJaWvPLKK9iyZYvx85UrVzB8+HD0798fqampKCoqkj0HAwVJ0ul06NWrF55//nm3XH/69OlYs2aNxX15eXmYPn06EhISkJiYiKFDh2L37t0mxxw6dAgjRoxAdHQ0EhMT8dxzz+Ho0aMmx6xatQpLly41+e+tt95CQUGBS36mrKwsZGRkOPTdMWPG4Oeff3ZyiczduXMHo0ePRmlpKU6dOoV//vOfLr+mI+7cuYPnn38epaWl7i6KYk6ePIlhw4bh119/lT02Ly8PL7zwAj7//HOT7XPmzEFycjJycnIQHh5uW7oPp062pRrl3//+txg9erTo1q2b+PnnnxW//rRp08T7779vtv3GjRuiT58+YuvWrUKv1wsh7s17j4iIEPv37xdCCLF7927Rr18/k4yaJ06cEH369BH79u0zbvvhhx/Mzn/z5k2LWWVri9mzZ4vs7GwhhBCbN28WY8eOdXOJpG3dulUsXLjQ3cVQzMyZM8WRI0dM3gHaunWrGDRokEhMTBQzZswQpaWlQgghVq9eLTZt2iSmTZsmNm/eLIQQoqysTHTu3FnodDohhBBXrlwRffv2lb2uOnM+kCpkZWUhLi4ODz74ID788EPMnTsXhw8fxtKlS9GsWTOcO3cO5eXlmDNnDrp06YLp06cjMDAQP/74I65du4Y2bdrgjTfeQL169dCmTRscOnQIjRo1AgDj56CgILz++us4efIkioqKIITA/PnzTd48rWrDhg149NFHMWjQIOO2tm3bYvny5WjQoAEAYNGiRUhLS0Pnzp2Nx3Tq1AkzZ87EokWLTBL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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(x=co_temp_global['Annual CO₂ emissions (tonnes )'], y=co_temp_global['Median (℃)'])\n", - "plt.xlabel('Annual CO₂ emissions (tonnes )')\n", - "plt.ylabel('Median (℃)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Checking Correlations" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperatureGDPPopulationGDP_per_capita
0Afghanistan19902602407.590.011715140000.014.9933334.59266513568282.0338.485353
1Afghanistan19912426486.160.010815060000.014.3707504.62940613671918.0338.606915
2Afghanistan19921381521.840.006213600000.014.0560834.67570015023317.0311.229518
3Afghanistan19931333837.110.006013430000.014.4392504.53075317003437.0266.461018
4Afghanistan19941282488.380.005713240000.014.7547504.39483118486041.0237.737799
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" - ], - "text/plain": [ - " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", - "0 Afghanistan 1990 2602407.59 0.0117 \n", - "1 Afghanistan 1991 2426486.16 0.0108 \n", - "2 Afghanistan 1992 1381521.84 0.0062 \n", - "3 Afghanistan 1993 1333837.11 0.0060 \n", - "4 Afghanistan 1994 1282488.38 0.0057 \n", - "\n", - " GHG(tonnes COâ‚‚e ) AverageTemperature GDP Population GDP_per_capita \n", - "0 15140000.0 14.993333 4.592665 13568282.0 338.485353 \n", - "1 15060000.0 14.370750 4.629406 13671918.0 338.606915 \n", - "2 13600000.0 14.056083 4.675700 15023317.0 311.229518 \n", - "3 13430000.0 14.439250 4.530753 17003437.0 266.461018 \n", - "4 13240000.0 14.754750 4.394831 18486041.0 237.737799 " - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "indicators_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged[\"Year\"] = indicators_merged[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Annual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperatureGDPPopulationGDP_per_capita
Annual COâ‚‚1.0000000.9857080.971729-0.2249290.8386000.6760940.150583
Share of global COâ‚‚ emissions (%)0.9857081.0000000.957867-0.2345630.8368880.6665550.154708
GHG(tonnes COâ‚‚e )0.9717290.9578671.000000-0.1936020.7916030.7248910.109097
AverageTemperature-0.224929-0.234563-0.1936021.000000-0.208351-0.068548-0.386509
GDP0.8386000.8368880.791603-0.2083511.0000000.3766200.304585
Population0.6760940.6665550.724891-0.0685480.3766201.000000-0.048032
GDP_per_capita0.1505830.1547080.109097-0.3865090.304585-0.0480321.000000
\n", - "
" - ], - "text/plain": [ - " Annual COâ‚‚ \\\n", - "Annual COâ‚‚ 1.000000 \n", - "Share of global COâ‚‚ emissions (%) 0.985708 \n", - "GHG(tonnes COâ‚‚e ) 0.971729 \n", - "AverageTemperature -0.224929 \n", - "GDP 0.838600 \n", - "Population 0.676094 \n", - "GDP_per_capita 0.150583 \n", - "\n", - " Share of global COâ‚‚ emissions (%) \\\n", - "Annual COâ‚‚ 0.985708 \n", - "Share of global COâ‚‚ emissions (%) 1.000000 \n", - "GHG(tonnes COâ‚‚e ) 0.957867 \n", - "AverageTemperature -0.234563 \n", - "GDP 0.836888 \n", - "Population 0.666555 \n", - "GDP_per_capita 0.154708 \n", - "\n", - " GHG(tonnes COâ‚‚e ) AverageTemperature \\\n", - "Annual COâ‚‚ 0.971729 -0.224929 \n", - "Share of global COâ‚‚ emissions (%) 0.957867 -0.234563 \n", - "GHG(tonnes COâ‚‚e ) 1.000000 -0.193602 \n", - "AverageTemperature -0.193602 1.000000 \n", - "GDP 0.791603 -0.208351 \n", - "Population 0.724891 -0.068548 \n", - "GDP_per_capita 0.109097 -0.386509 \n", - "\n", - " GDP Population GDP_per_capita \n", - "Annual COâ‚‚ 0.838600 0.676094 0.150583 \n", - "Share of global COâ‚‚ emissions (%) 0.836888 0.666555 0.154708 \n", - "GHG(tonnes COâ‚‚e ) 0.791603 0.724891 0.109097 \n", - "AverageTemperature -0.208351 -0.068548 -0.386509 \n", - "GDP 1.000000 0.376620 0.304585 \n", - "Population 0.376620 1.000000 -0.048032 \n", - "GDP_per_capita 0.304585 -0.048032 1.000000 " - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "indicators_merged.corr()" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [], - "source": [ - "ind_no_colin = indicators_merged.drop(['Share of global COâ‚‚ emissions (%)','GDP','Annual COâ‚‚'], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Country', 'Year', 'Annual COâ‚‚', 'Share of global COâ‚‚ emissions (%)',\n", - " 'GHG(tonnes COâ‚‚e )', 'AverageTemperature', 'GDP', 'Population',\n", - " 'GDP_per_capita'],\n", - " dtype='object')" - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "indicators_merged.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.10909684577014037" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_ghg_corr = indicators_merged[\"GDP_per_capita\"].corr(indicators_merged[\"GHG(tonnes COâ‚‚e )\"])\n", - "temp_ghg_corr" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8322 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# here we are using seaborn to show the heatmap of the correlation between our final datasets' parameters\n", - "corr = ind_no_colin.corr()\n", - "plt.figure(figsize=(12, 6))\n", - "sns.set(style=\"white\")\n", - "\n", - "mask = np.zeros_like(corr, dtype=np.bool)\n", - "mask[np.triu_indices_from(mask)] = True\n", - "cmap = sns.diverging_palette(200, 10, as_cmap=True)\n", - "\n", - "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/your-project/Global Warming report, can we solve it.ipynb b/your-project/Global Warming report, can we solve it.ipynb index 5462bf26..4a6cde33 100644 --- a/your-project/Global Warming report, can we solve it.ipynb +++ b/your-project/Global Warming report, can we solve it.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 251, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -212,20 +212,20 @@ "4 Afghanistan AFG 1953 106256.0" ] }, - "execution_count": 251, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Annual CO emissions globally\n", - "data = pd.read_csv('data//annual-co-emissions-by-region.csv')\n", + "data = pd.read_csv('data//1annual-co-emissions-by-region.csv')\n", "data.head()" ] }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -304,7 +304,7 @@ "20357 World OWID_WRL 1755 9361520.0" ] }, - "execution_count": 252, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -339,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 254, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -348,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 255, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -379,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -458,20 +458,20 @@ "4 Afghanistan AFG 1994 13240000.0" ] }, - "execution_count": 256, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Total greenhouse gas emissions\n", - "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", + "ghg = pd.read_csv('data//2total-ghg-emissions.csv')\n", "ghg.head()" ] }, { "cell_type": "code", - "execution_count": 257, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -481,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 258, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -554,7 +554,7 @@ "5080 World 1994 3.528761e+10" ] }, - "execution_count": 258, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -566,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": 259, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -587,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 260, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -596,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": 261, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -605,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -636,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 380, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -766,20 +766,20 @@ "[1871 rows x 4 columns]" ] }, - "execution_count": 380, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Global CO₂ atmospheric concentration dataset\n", - "co_concentration = pd.read_csv('data//global-co-concentration-ppm.csv')\n", + "co_concentration = pd.read_csv('data//3global-co-concentration-ppm.csv')\n", "co_concentration" ] }, { "cell_type": "code", - "execution_count": 381, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -789,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": 382, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -798,7 +798,7 @@ }, { "cell_type": "code", - "execution_count": 392, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -807,7 +807,7 @@ "'1'" ] }, - "execution_count": 392, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -818,16 +818,16 @@ }, { "cell_type": "code", - "execution_count": 268, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "co_concentration.set_index('Year',inplace=True)" + "#co_concentration.set_index('Year',inplace=True)" ] }, { "cell_type": "code", - "execution_count": 269, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -835,13 +835,14 @@ "output_type": "stream", "text": [ "\n", - "Index: 1871 entries, 803719 BCE to 2018\n", - "Data columns (total 1 columns):\n", + "RangeIndex: 1871 entries, 0 to 1870\n", + "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 CO₂ concentration 1871 non-null float64\n", - "dtypes: float64(1)\n", - "memory usage: 29.2+ KB\n" + " 0 Year 1871 non-null object \n", + " 1 CO₂ concentration 1871 non-null float64\n", + "dtypes: float64(1), object(1)\n", + "memory usage: 29.4+ KB\n" ] } ], @@ -851,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 396, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -860,7 +861,7 @@ }, { "cell_type": "code", - "execution_count": 397, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -927,7 +928,7 @@ "1652 136 278.1" ] }, - "execution_count": 397, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -938,11 +939,11 @@ }, { "cell_type": "code", - "execution_count": 398, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "co_atm_conc.to_csv('co_atm_conc')" + "#co_atm_conc.to_csv('co_atm_conc')" ] }, { @@ -954,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": 270, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1045,20 +1046,20 @@ "4 Global NaN 1854 -0.248 -0.218 -0.301" ] }, - "execution_count": 270, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Global Temperature anomaly (increase-decreage on previous average temperature)\n", - "temperature_a = pd.read_csv('data//temperature_anomaly.csv')\n", + "temperature_a = pd.read_csv('data//4temperature_anomaly.csv')\n", "temperature_a.head()" ] }, { "cell_type": "code", - "execution_count": 271, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1087,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 272, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1097,7 +1098,7 @@ }, { "cell_type": "code", - "execution_count": 273, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1106,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 274, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1116,7 +1117,7 @@ " dtype=object)" ] }, - "execution_count": 274, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1127,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": 494, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1136,7 +1137,7 @@ }, { "cell_type": "code", - "execution_count": 277, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1221,7 +1222,7 @@ "4 Global 1854 -0.248 -0.218 -0.301" ] }, - "execution_count": 277, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1232,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 243, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1244,16 +1245,16 @@ }, { "cell_type": "code", - "execution_count": 278, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ - "global_a.set_index('Year',inplace=True)" + "#global_a.set_index('Year',inplace=True)" ] }, { "cell_type": "code", - "execution_count": 360, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1269,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 287, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1355,20 +1356,20 @@ "4 165000 " ] }, - "execution_count": 287, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing Ozone depleting emissions dataset\n", - "ozone = pd.read_csv('data//ozone-depleting-substance-emissions.csv')\n", + "ozone = pd.read_csv('data//5ozone-depleting-substance-emissions.csv')\n", "ozone.head()" ] }, { "cell_type": "code", - "execution_count": 288, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1378,7 +1379,7 @@ }, { "cell_type": "code", - "execution_count": 289, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1387,7 +1388,7 @@ }, { "cell_type": "code", - "execution_count": 290, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1396,7 +1397,7 @@ "array(['Natural emissions', 'Total emissions'], dtype=object)" ] }, - "execution_count": 290, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1407,7 +1408,7 @@ }, { "cell_type": "code", - "execution_count": 292, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1429,16 +1430,16 @@ }, { "cell_type": "code", - "execution_count": 294, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ - "ozone.set_index('Year',inplace=True)" + "#ozone.set_index('Year',inplace=True)" ] }, { "cell_type": "code", - "execution_count": 293, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1454,7 +1455,7 @@ }, { "cell_type": "code", - "execution_count": 295, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1546,20 +1547,20 @@ "4 7900000 " ] }, - "execution_count": 295, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Antartica Ozone Hole area dataset\n", - "oz_hole = pd.read_csv('data//antarctic-ozone-hole-area.csv')\n", + "oz_hole = pd.read_csv('data//6antarctic-ozone-hole-area.csv')\n", "oz_hole.head()" ] }, { "cell_type": "code", - "execution_count": 296, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1568,7 +1569,7 @@ }, { "cell_type": "code", - "execution_count": 297, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1635,7 +1636,7 @@ "4 1983 7900000" ] }, - "execution_count": 297, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1646,11 +1647,11 @@ }, { "cell_type": "code", - "execution_count": 299, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "oz_hole.set_index('Year',inplace=True)" + "#oz_hole.set_index('Year',inplace=True)" ] }, { @@ -1662,7 +1663,7 @@ }, { "cell_type": "code", - "execution_count": 300, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1792,20 +1793,20 @@ "4 NaN " ] }, - "execution_count": 300, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Global average temperature dataset\n", - "global_temperature = pd.read_csv('data//GlobalTemperatures.csv')\n", + "global_temperature = pd.read_csv('data//7GlobalTemperatures.csv')\n", "global_temperature.head()" ] }, { "cell_type": "code", - "execution_count": 301, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1878,7 +1879,7 @@ "1204 1850-05-01 10.004 15.507" ] }, - "execution_count": 301, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1892,7 +1893,7 @@ }, { "cell_type": "code", - "execution_count": 302, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1971,7 +1972,7 @@ "1204 1850-05-01 10.004 15.507 1850" ] }, - "execution_count": 302, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1983,7 +1984,7 @@ }, { "cell_type": "code", - "execution_count": 303, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -2056,7 +2057,7 @@ "1854 8.210500 14.991000" ] }, - "execution_count": 303, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -2070,7 +2071,7 @@ }, { "cell_type": "code", - "execution_count": 304, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -2143,7 +2144,7 @@ "4 1854 8.210500 14.991000" ] }, - "execution_count": 304, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -2155,7 +2156,7 @@ }, { "cell_type": "code", - "execution_count": 305, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -2164,7 +2165,7 @@ }, { "cell_type": "code", - "execution_count": 306, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -2196,7 +2197,7 @@ }, { "cell_type": "code", - "execution_count": 312, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -2294,20 +2295,20 @@ "4 0.039699 " ] }, - "execution_count": 312, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Fossil fuel consumption per capita dataset\n", - "fossil_per_c = pd.read_csv('data//fossil-fuel-consumption-per-capita.csv')\n", + "fossil_per_c = pd.read_csv('data//8fossil-fuel-consumption-per-capita.csv')\n", "fossil_per_c.head()" ] }, { "cell_type": "code", - "execution_count": 313, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -2405,7 +2406,7 @@ "3306 2.622830 " ] }, - "execution_count": 313, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -2418,7 +2419,7 @@ }, { "cell_type": "code", - "execution_count": 314, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -2428,7 +2429,7 @@ }, { "cell_type": "code", - "execution_count": 315, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -2437,7 +2438,7 @@ }, { "cell_type": "code", - "execution_count": 316, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -2470,7 +2471,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -2625,20 +2626,20 @@ "4 0.0 " ] }, - "execution_count": 10, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Global primary energy by source dataset\n", - "global_energy = pd.read_csv('data//global-primary-energy.csv')\n", + "global_energy = pd.read_csv('data//9global-primary-energy.csv')\n", "global_energy.head()" ] }, { "cell_type": "code", - "execution_count": 318, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -2648,7 +2649,7 @@ }, { "cell_type": "code", - "execution_count": 319, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -2657,7 +2658,7 @@ }, { "cell_type": "code", - "execution_count": 320, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -2696,7 +2697,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -2800,20 +2801,20 @@ "4 0.0 0.0 " ] }, - "execution_count": 12, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Modern renewable energy consumtion dataset\n", - "renew_energy = pd.read_csv('data//modern-renewable-energy-consumption.csv')\n", + "renew_energy = pd.read_csv('data//10modern-renewable-energy-consumption.csv')\n", "renew_energy.head()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -2924,7 +2925,7 @@ "5041 23.257964 " ] }, - "execution_count": 13, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -2937,7 +2938,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -2947,7 +2948,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -2956,7 +2957,7 @@ }, { "cell_type": "code", - "execution_count": 325, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -2990,7 +2991,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -3075,20 +3076,20 @@ "4 United States USA 1914 265763000.0 243928136.8" ] }, - "execution_count": 16, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Hubbert's peak dataset\n", - "hubbert_peak = pd.read_csv('data//hubberts-peak-vs-actual-oil-production-in-the-united-states.csv')\n", + "hubbert_peak = pd.read_csv('data//11hubberts-peak-vs-actual-oil-production-in-the-united-states.csv')\n", "hubbert_peak.head()" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -3098,16 +3099,16 @@ }, { "cell_type": "code", - "execution_count": 326, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ - "hubbert_peak.rename(columns={' (barrels per year)':'Actual Oil Prod',' (barrels per year).1':'Hubbert Predicted'}, inplace=True)" + "hubbert_peak.rename(columns={' (barrels per year)':'Actual',' (barrels per year).1':'Predicted'}, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 327, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -3116,7 +3117,7 @@ }, { "cell_type": "code", - "execution_count": 328, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -3189,7 +3190,7 @@ "106 2016 3.248117e+09 479362510.8" ] }, - "execution_count": 328, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -3200,16 +3201,16 @@ }, { "cell_type": "code", - "execution_count": 330, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ - "hubbert_peak.set_index('Year',inplace=True)" + "#hubbert_peak.set_index('Year',inplace=True)" ] }, { "cell_type": "code", - "execution_count": 331, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -3217,14 +3218,15 @@ "output_type": "stream", "text": [ "\n", - "Int64Index: 106 entries, 1911 to 2016\n", - "Data columns (total 2 columns):\n", + "Int64Index: 106 entries, 1 to 106\n", + "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 Actual 106 non-null float64\n", - " 1 Predicted 106 non-null float64\n", - "dtypes: float64(2)\n", - "memory usage: 2.5 KB\n" + " 0 Year 106 non-null int64 \n", + " 1 Actual 106 non-null float64\n", + " 2 Predicted 106 non-null float64\n", + "dtypes: float64(2), int64(1)\n", + "memory usage: 3.3 KB\n" ] } ], @@ -3241,7 +3243,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -3382,20 +3384,20 @@ "4 95.129372 0.0 " ] }, - "execution_count": 18, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Share of global primary energy dataset\n", - "share_energy = pd.read_csv('data//global-primary-energy-share-inc-biomass.csv')\n", + "share_energy = pd.read_csv('data//12global-primary-energy-share-inc-biomass.csv')\n", "share_energy.head()" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -3405,7 +3407,7 @@ }, { "cell_type": "code", - "execution_count": 334, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -3414,7 +3416,7 @@ }, { "cell_type": "code", - "execution_count": 335, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -3453,7 +3455,7 @@ }, { "cell_type": "code", - "execution_count": 464, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -3565,20 +3567,20 @@ "4 8151455.0 " ] }, - "execution_count": 464, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Share of global primary energy dataset\n", - "solar_price = pd.read_csv('data//solar-pv-prices-vs-cumulative-capacity.csv')\n", + "solar_price = pd.read_csv('data//13solar-pv-prices-vs-cumulative-capacity.csv')\n", "solar_price.head()" ] }, { "cell_type": "code", - "execution_count": 465, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -3690,7 +3692,7 @@ "19950 NaN " ] }, - "execution_count": 465, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -3703,7 +3705,7 @@ }, { "cell_type": "code", - "execution_count": 466, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -3713,7 +3715,7 @@ }, { "cell_type": "code", - "execution_count": 467, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -3736,7 +3738,7 @@ }, { "cell_type": "code", - "execution_count": 475, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -3815,20 +3817,20 @@ "4 Afghanistan AFG 1953 106256.0" ] }, - "execution_count": 475, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Annual CO per country (1949-2017)\n", - "co_annual = pd.read_csv('data//annual_co2_emissions_per_country.csv')\n", + "co_annual = pd.read_csv('data//14annual_co2_emissions_per_country.csv')\n", "co_annual.head()" ] }, { "cell_type": "code", - "execution_count": 477, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -3839,7 +3841,7 @@ }, { "cell_type": "code", - "execution_count": 478, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -3875,7 +3877,7 @@ }, { "cell_type": "code", - "execution_count": 479, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -3954,20 +3956,20 @@ "4 Afghanistan AFG 1953 0.0016" ] }, - "execution_count": 479, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Annual share of CO per country (1949-2017)\n", - "co_share = pd.read_csv('data//annual_share_of_co2_emissions.csv')\n", + "co_share = pd.read_csv('data//15annual_share_of_co2_emissions.csv')\n", "co_share.head()" ] }, { "cell_type": "code", - "execution_count": 480, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -4007,7 +4009,7 @@ }, { "cell_type": "code", - "execution_count": 520, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -4086,20 +4088,20 @@ "4 Afghanistan AFG 1994 13240000.0" ] }, - "execution_count": 520, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Total greenhouse gas emissions\n", - "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", + "ghg = pd.read_csv('data//2total-ghg-emissions.csv')\n", "ghg.head()" ] }, { "cell_type": "code", - "execution_count": 521, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -4138,7 +4140,7 @@ }, { "cell_type": "code", - "execution_count": 487, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -4217,20 +4219,20 @@ "4 1744-03-01 NaN NaN Åland" ] }, - "execution_count": 487, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing Global temperatures by country data set\n", - "temperature = pd.read_csv('data//GlobalLandTemperaturesByCountry.csv')\n", + "temperature = pd.read_csv('data//17GlobalLandTemperaturesByCountry.csv')\n", "temperature.head()" ] }, { "cell_type": "code", - "execution_count": 488, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ @@ -4252,7 +4254,7 @@ }, { "cell_type": "code", - "execution_count": 489, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -4325,7 +4327,7 @@ "Belgium 1743 7.106" ] }, - "execution_count": 489, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -4342,7 +4344,7 @@ }, { "cell_type": "code", - "execution_count": 491, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -4366,7 +4368,6 @@ " \n", " \n", " \n", - " index\n", " Country\n", " Year\n", " AverageTemperature\n", @@ -4375,35 +4376,30 @@ " \n", " \n", " 0\n", - " 0\n", " Albania\n", " 1743\n", " 8.620\n", " \n", " \n", " 1\n", - " 1\n", " Andorra\n", " 1743\n", " 7.556\n", " \n", " \n", " 2\n", - " 2\n", " Austria\n", " 1743\n", " 2.482\n", " \n", " \n", " 3\n", - " 3\n", " Belarus\n", " 1743\n", " 0.767\n", " \n", " \n", " 4\n", - " 4\n", " Belgium\n", " 1743\n", " 7.106\n", @@ -4413,15 +4409,15 @@ "" ], "text/plain": [ - " index Country Year AverageTemperature\n", - "0 0 Albania 1743 8.620\n", - "1 1 Andorra 1743 7.556\n", - "2 2 Austria 1743 2.482\n", - "3 3 Belarus 1743 0.767\n", - "4 4 Belgium 1743 7.106" + " Country Year AverageTemperature\n", + "0 Albania 1743 8.620\n", + "1 Andorra 1743 7.556\n", + "2 Austria 1743 2.482\n", + "3 Belarus 1743 0.767\n", + "4 Belgium 1743 7.106" ] }, - "execution_count": 491, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -4432,33 +4428,6 @@ "annual_temp.head()" ] }, - { - "cell_type": "code", - "execution_count": 493, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 41931 entries, 0 to 41930\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 41931 non-null object \n", - " 1 Year 41931 non-null int64 \n", - " 2 AverageTemperature 39793 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 982.9+ KB\n" - ] - } - ], - "source": [ - "annual_temp.drop(['index'], axis=1, inplace=True)\n", - "annual_temp.info()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -4482,7 +4451,7 @@ }, { "cell_type": "code", - "execution_count": 362, + "execution_count": 91, "metadata": {}, "outputs": [], "source": [ @@ -4492,7 +4461,7 @@ }, { "cell_type": "code", - "execution_count": 365, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -4502,7 +4471,7 @@ }, { "cell_type": "code", - "execution_count": 366, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -4512,7 +4481,7 @@ }, { "cell_type": "code", - "execution_count": 367, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -4522,7 +4491,7 @@ }, { "cell_type": "code", - "execution_count": 368, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -4532,7 +4501,7 @@ }, { "cell_type": "code", - "execution_count": 369, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -4542,7 +4511,7 @@ }, { "cell_type": "code", - "execution_count": 370, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -4552,7 +4521,7 @@ }, { "cell_type": "code", - "execution_count": 371, + "execution_count": 98, "metadata": {}, "outputs": [], "source": [ @@ -4562,7 +4531,7 @@ }, { "cell_type": "code", - "execution_count": 372, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ @@ -4572,7 +4541,7 @@ }, { "cell_type": "code", - "execution_count": 504, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -4596,6 +4565,7 @@ " \n", " \n", " \n", + " Year\n", " Annual CO₂ emissions (tonnes )\n", " GHG(tonnes CO₂e )\n", " Median (℃)\n", @@ -4605,7 +4575,6 @@ " LandAndOceanAverageTemperature\n", " Oil (megawatt-hours)\n", " Coal (megawatt-hours)\n", - " Natural gas (megawatt-hours)\n", " ...\n", " Other renewables (terawatt-hours)_y\n", " Coal (%)\n", @@ -4618,34 +4587,11 @@ " Traditional biofuels (%)\n", " Wind (%)\n", " \n", - " \n", - " Year\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " 2014\n", + " 263\n", + " 2014\n", " 3.550583e+10\n", " 4.944079e+10\n", " 0.579\n", @@ -4655,7 +4601,6 @@ " 15.913000\n", " 6.780018\n", " 6.197751\n", - " 4.896768\n", " ...\n", " 502.158919\n", " 30.129928\n", @@ -4669,7 +4614,8 @@ " 0.477878\n", " \n", " \n", - " 2015\n", + " 264\n", + " 2015\n", " 3.546275e+10\n", " 4.985498e+10\n", " 0.763\n", @@ -4679,7 +4625,6 @@ " 16.058583\n", " 6.838042\n", " 5.961733\n", - " 4.956876\n", " ...\n", " 536.466244\n", " 29.232112\n", @@ -4693,7 +4638,8 @@ " 0.555327\n", " \n", " \n", - " 2016\n", + " 265\n", + " 2016\n", " 3.567510e+10\n", " 4.935803e+10\n", " 0.797\n", @@ -4703,7 +4649,6 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", " ...\n", " 556.623356\n", " 28.479418\n", @@ -4717,7 +4662,8 @@ " 0.633975\n", " \n", " \n", - " 2017\n", + " 266\n", + " 2017\n", " 3.615326e+10\n", " NaN\n", " 0.677\n", @@ -4727,7 +4673,6 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", " ...\n", " 584.975827\n", " 28.254141\n", @@ -4741,7 +4686,8 @@ " 0.730975\n", " \n", " \n", - " 2018\n", + " 267\n", + " 2018\n", " NaN\n", " NaN\n", " 0.595\n", @@ -4751,7 +4697,6 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", " ...\n", " 625.805362\n", " NaN\n", @@ -4766,70 +4711,63 @@ " \n", " \n", "\n", - "

5 rows × 32 columns

\n", + "

5 rows × 33 columns

\n", "" ], "text/plain": [ - " Annual CO₂ emissions (tonnes ) GHG(tonnes CO₂e ) Median (℃) \\\n", - "Year \n", - "2014 3.550583e+10 4.944079e+10 0.579 \n", - "2015 3.546275e+10 4.985498e+10 0.763 \n", - "2016 3.567510e+10 4.935803e+10 0.797 \n", - "2017 3.615326e+10 NaN 0.677 \n", - "2018 NaN NaN 0.595 \n", + " Year Annual CO₂ emissions (tonnes ) GHG(tonnes CO₂e ) Median (℃) \\\n", + "263 2014 3.550583e+10 4.944079e+10 0.579 \n", + "264 2015 3.546275e+10 4.985498e+10 0.763 \n", + "265 2016 3.567510e+10 4.935803e+10 0.797 \n", + "266 2017 3.615326e+10 NaN 0.677 \n", + "267 2018 NaN NaN 0.595 \n", "\n", - " Ozone-depleting emissions Mean ozone hole area (square kilometres) \\\n", - "Year \n", - "2014 320000.0 20900000.0 \n", - "2015 NaN 25600000.0 \n", - "2016 NaN 20700000.0 \n", - "2017 NaN 17400000.0 \n", - "2018 NaN NaN \n", + " Ozone-depleting emissions Mean ozone hole area (square kilometres) \\\n", + "263 320000.0 20900000.0 \n", + "264 NaN 25600000.0 \n", + "265 NaN 20700000.0 \n", + "266 NaN 17400000.0 \n", + "267 NaN NaN \n", "\n", - " LandAverageTemperature LandAndOceanAverageTemperature \\\n", - "Year \n", - "2014 9.570667 15.913000 \n", - "2015 9.831000 16.058583 \n", - "2016 NaN NaN \n", - "2017 NaN NaN \n", - "2018 NaN NaN \n", + " LandAverageTemperature LandAndOceanAverageTemperature \\\n", + "263 9.570667 15.913000 \n", + "264 9.831000 16.058583 \n", + "265 NaN NaN \n", + "266 NaN NaN \n", + "267 NaN NaN \n", "\n", - " Oil (megawatt-hours) Coal (megawatt-hours) \\\n", - "Year \n", - "2014 6.780018 6.197751 \n", - "2015 6.838042 5.961733 \n", - "2016 NaN NaN \n", - "2017 NaN NaN \n", - "2018 NaN NaN \n", + " Oil (megawatt-hours) Coal (megawatt-hours) ... \\\n", + "263 6.780018 6.197751 ... \n", + "264 6.838042 5.961733 ... \n", + "265 NaN NaN ... \n", + "266 NaN NaN ... \n", + "267 NaN NaN ... \n", "\n", - " Natural gas (megawatt-hours) ... Other renewables (terawatt-hours)_y \\\n", - "Year ... \n", - "2014 4.896768 ... 502.158919 \n", - "2015 4.956876 ... 536.466244 \n", - "2016 NaN ... 556.623356 \n", - "2017 NaN ... 584.975827 \n", - "2018 NaN ... 625.805362 \n", + " Other renewables (terawatt-hours)_y Coal (%) Crude oil (%) \\\n", + "263 502.158919 30.129928 34.284243 \n", + "264 536.466244 29.232112 34.750780 \n", + "265 556.623356 28.479418 35.021326 \n", + "266 584.975827 28.254141 34.995960 \n", + "267 625.805362 NaN NaN \n", "\n", - " Coal (%) Crude oil (%) Hydropower (%) Natural gas (%) Nuclear (%) \\\n", - "Year \n", - "2014 30.129928 34.284243 2.607999 22.798160 1.704505 \n", - "2015 29.232112 34.750780 2.597906 23.193692 1.719514 \n", - "2016 28.479418 35.021326 2.666862 23.616645 1.726446 \n", - "2017 28.254141 34.995960 2.643218 23.896486 1.715909 \n", - "2018 NaN NaN NaN NaN NaN \n", + " Hydropower (%) Natural gas (%) Nuclear (%) Other renewables (%) \\\n", + "263 2.607999 22.798160 1.704505 0.338342 \n", + "264 2.597906 23.193692 1.719514 0.359307 \n", + "265 2.666862 23.616645 1.726446 0.368032 \n", + "266 2.643218 23.896486 1.715909 0.381633 \n", + "267 NaN NaN NaN NaN \n", "\n", - " Other renewables (%) Solar (%) Traditional biofuels (%) Wind (%) \n", - "Year \n", - "2014 0.338342 0.132597 7.526348 0.477878 \n", - "2015 0.359307 0.173580 7.417781 0.555327 \n", - "2016 0.368032 0.216849 7.270446 0.633975 \n", - "2017 0.381633 0.288171 7.093508 0.730975 \n", - "2018 NaN NaN NaN NaN \n", + " Solar (%) Traditional biofuels (%) Wind (%) \n", + "263 0.132597 7.526348 0.477878 \n", + "264 0.173580 7.417781 0.555327 \n", + "265 0.216849 7.270446 0.633975 \n", + "266 0.288171 7.093508 0.730975 \n", + "267 NaN NaN NaN \n", "\n", - "[5 rows x 32 columns]" + "[5 rows x 33 columns]" ] }, - "execution_count": 504, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } @@ -4840,7 +4778,7 @@ }, { "cell_type": "code", - "execution_count": 375, + "execution_count": 101, "metadata": {}, "outputs": [ { @@ -4848,44 +4786,45 @@ "output_type": "stream", "text": [ "\n", - "Int64Index: 268 entries, 1751 to 2018\n", - "Data columns (total 32 columns):\n", + "Int64Index: 268 entries, 0 to 267\n", + "Data columns (total 33 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 Annual CO₂ emissions (tonnes ) 267 non-null float64\n", - " 1 GHG(tonnes CO₂e ) 27 non-null float64\n", - " 2 Median (℃) 169 non-null float64\n", - " 3 Ozone-depleting emissions 54 non-null float64\n", - " 4 Mean ozone hole area (square kilometres) 38 non-null float64\n", - " 5 LandAverageTemperature 166 non-null float64\n", - " 6 LandAndOceanAverageTemperature 166 non-null float64\n", - " 7 Oil (megawatt-hours) 51 non-null float64\n", - " 8 Coal (megawatt-hours) 51 non-null float64\n", - " 9 Natural gas (megawatt-hours) 51 non-null float64\n", - " 10 Coal (terawatt-hours) 71 non-null float64\n", - " 11 Solar (terawatt-hours)_x 71 non-null float64\n", - " 12 Crude oil (terawatt-hours) 71 non-null float64\n", - " 13 Natural gas (terawatt-hours) 71 non-null float64\n", - " 14 Traditional biofuels (terawatt-hours) 71 non-null float64\n", - " 15 Other renewables (terawatt-hours)_x 71 non-null float64\n", - " 16 Hydropower (terawatt-hours)_x 71 non-null float64\n", - " 17 Nuclear (terawatt-hours) 71 non-null float64\n", - " 18 Wind (terawatt-hours)_x 71 non-null float64\n", - " 19 Hydropower (terawatt-hours)_y 54 non-null float64\n", - " 20 Solar (terawatt-hours)_y 54 non-null float64\n", - " 21 Wind (terawatt-hours)_y 54 non-null float64\n", - " 22 Other renewables (terawatt-hours)_y 54 non-null float64\n", - " 23 Coal (%) 70 non-null float64\n", - " 24 Crude oil (%) 70 non-null float64\n", - " 25 Hydropower (%) 70 non-null float64\n", - " 26 Natural gas (%) 70 non-null float64\n", - " 27 Nuclear (%) 70 non-null float64\n", - " 28 Other renewables (%) 70 non-null float64\n", - " 29 Solar (%) 70 non-null float64\n", - " 30 Traditional biofuels (%) 70 non-null float64\n", - " 31 Wind (%) 70 non-null float64\n", - "dtypes: float64(32)\n", - "memory usage: 69.1 KB\n" + " 0 Year 268 non-null int64 \n", + " 1 Annual CO₂ emissions (tonnes ) 267 non-null float64\n", + " 2 GHG(tonnes CO₂e ) 27 non-null float64\n", + " 3 Median (℃) 169 non-null float64\n", + " 4 Ozone-depleting emissions 54 non-null float64\n", + " 5 Mean ozone hole area (square kilometres) 38 non-null float64\n", + " 6 LandAverageTemperature 166 non-null float64\n", + " 7 LandAndOceanAverageTemperature 166 non-null float64\n", + " 8 Oil (megawatt-hours) 51 non-null float64\n", + " 9 Coal (megawatt-hours) 51 non-null float64\n", + " 10 Natural gas (megawatt-hours) 51 non-null float64\n", + " 11 Coal (terawatt-hours) 71 non-null float64\n", + " 12 Solar (terawatt-hours)_x 71 non-null float64\n", + " 13 Crude oil (terawatt-hours) 71 non-null float64\n", + " 14 Natural gas (terawatt-hours) 71 non-null float64\n", + " 15 Traditional biofuels (terawatt-hours) 71 non-null float64\n", + " 16 Other renewables (terawatt-hours)_x 71 non-null float64\n", + " 17 Hydropower (terawatt-hours)_x 71 non-null float64\n", + " 18 Nuclear (terawatt-hours) 71 non-null float64\n", + " 19 Wind (terawatt-hours)_x 71 non-null float64\n", + " 20 Hydropower (terawatt-hours)_y 54 non-null float64\n", + " 21 Solar (terawatt-hours)_y 54 non-null float64\n", + " 22 Wind (terawatt-hours)_y 54 non-null float64\n", + " 23 Other renewables (terawatt-hours)_y 54 non-null float64\n", + " 24 Coal (%) 70 non-null float64\n", + " 25 Crude oil (%) 70 non-null float64\n", + " 26 Hydropower (%) 70 non-null float64\n", + " 27 Natural gas (%) 70 non-null float64\n", + " 28 Nuclear (%) 70 non-null float64\n", + " 29 Other renewables (%) 70 non-null float64\n", + " 30 Solar (%) 70 non-null float64\n", + " 31 Traditional biofuels (%) 70 non-null float64\n", + " 32 Wind (%) 70 non-null float64\n", + "dtypes: float64(32), int64(1)\n", + "memory usage: 71.2 KB\n" ] } ], @@ -4895,11 +4834,11 @@ }, { "cell_type": "code", - "execution_count": 376, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ - "global_indicators.to_csv('global_indicators')" + "#global_indicators.to_csv('global_indicators')" ] }, { @@ -4911,7 +4850,7 @@ }, { "cell_type": "code", - "execution_count": 523, + "execution_count": 103, "metadata": {}, "outputs": [ { @@ -4990,7 +4929,7 @@ "4 Afghanistan 1953 106256.0 0.0016" ] }, - "execution_count": 523, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -5003,7 +4942,7 @@ }, { "cell_type": "code", - "execution_count": 524, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -5012,7 +4951,7 @@ }, { "cell_type": "code", - "execution_count": 527, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -5021,7 +4960,7 @@ }, { "cell_type": "code", - "execution_count": 528, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -5119,7 +5058,7 @@ "4 NaN 14.650750 " ] }, - "execution_count": 528, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -5130,7 +5069,7 @@ }, { "cell_type": "code", - "execution_count": 529, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -5166,7 +5105,7 @@ }, { "cell_type": "code", - "execution_count": 530, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -5190,38 +5129,39 @@ " \n", " \n", " \n", + " Year\n", " Ozone-depleting emissions\n", " Mean ozone hole area (square kilometres)\n", " \n", - " \n", - " Year\n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " 1979\n", + " 0\n", + " 1979\n", " 1110000\n", " 100000\n", " \n", " \n", - " 1980\n", + " 1\n", + " 1980\n", " 1080000\n", " 1400000\n", " \n", " \n", - " 1981\n", + " 2\n", + " 1981\n", " 1040000\n", " 600000\n", " \n", " \n", - " 1982\n", + " 3\n", + " 1982\n", " 1100000\n", " 4800000\n", " \n", " \n", - " 1983\n", + " 4\n", + " 1983\n", " 1090000\n", " 7900000\n", " \n", @@ -5230,16 +5170,15 @@ "" ], "text/plain": [ - " Ozone-depleting emissions Mean ozone hole area (square kilometres)\n", - "Year \n", - "1979 1110000 100000\n", - "1980 1080000 1400000\n", - "1981 1040000 600000\n", - "1982 1100000 4800000\n", - "1983 1090000 7900000" + " Year Ozone-depleting emissions Mean ozone hole area (square kilometres)\n", + "0 1979 1110000 100000\n", + "1 1980 1080000 1400000\n", + "2 1981 1040000 600000\n", + "3 1982 1100000 4800000\n", + "4 1983 1090000 7900000" ] }, - "execution_count": 530, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -5251,7 +5190,7 @@ }, { "cell_type": "code", - "execution_count": 531, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -5260,7 +5199,7 @@ "-0.5597422292909034" ] }, - "execution_count": 531, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -5272,7 +5211,7 @@ }, { "cell_type": "code", - "execution_count": 532, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -5280,14 +5219,15 @@ "output_type": "stream", "text": [ "\n", - "Int64Index: 35 entries, 1979 to 2014\n", - "Data columns (total 2 columns):\n", + "Int64Index: 35 entries, 0 to 34\n", + "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype\n", "--- ------ -------------- -----\n", - " 0 Ozone-depleting emissions 35 non-null int64\n", - " 1 Mean ozone hole area (square kilometres) 35 non-null int64\n", - "dtypes: int64(2)\n", - "memory usage: 840.0 bytes\n" + " 0 Year 35 non-null int64\n", + " 1 Ozone-depleting emissions 35 non-null int64\n", + " 2 Mean ozone hole area (square kilometres) 35 non-null int64\n", + "dtypes: int64(3)\n", + "memory usage: 1.1 KB\n" ] } ], @@ -5297,7 +5237,7 @@ }, { "cell_type": "code", - "execution_count": 540, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -5321,38 +5261,39 @@ " \n", " \n", " \n", + " Year\n", " Ozone-depleting emissions\n", " Mean ozone hole area (square kilometres)\n", " \n", - " \n", - " Year\n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " 2010\n", + " 30\n", + " 2010\n", " 380000\n", " 19400000\n", " \n", " \n", - " 2011\n", + " 31\n", + " 2011\n", " 370000\n", " 24700000\n", " \n", " \n", - " 2012\n", + " 32\n", + " 2012\n", " 350000\n", " 17800000\n", " \n", " \n", - " 2013\n", + " 33\n", + " 2013\n", " 340000\n", " 21000000\n", " \n", " \n", - " 2014\n", + " 34\n", + " 2014\n", " 320000\n", " 20900000\n", " \n", @@ -5361,16 +5302,15 @@ "" ], "text/plain": [ - " Ozone-depleting emissions Mean ozone hole area (square kilometres)\n", - "Year \n", - "2010 380000 19400000\n", - "2011 370000 24700000\n", - "2012 350000 17800000\n", - "2013 340000 21000000\n", - "2014 320000 20900000" + " Year Ozone-depleting emissions Mean ozone hole area (square kilometres)\n", + "30 2010 380000 19400000\n", + "31 2011 370000 24700000\n", + "32 2012 350000 17800000\n", + "33 2013 340000 21000000\n", + "34 2014 320000 20900000" ] }, - "execution_count": 540, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -5379,6 +5319,15 @@ "ozone_merged.tail()" ] }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "ozone_merged.to_csv('ozone_merged')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -5395,7 +5344,7 @@ }, { "cell_type": "code", - "execution_count": 444, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -5404,7 +5353,7 @@ "0.890288126134981" ] }, - "execution_count": 444, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -5416,7 +5365,7 @@ }, { "cell_type": "code", - "execution_count": 538, + "execution_count": 114, "metadata": {}, "outputs": [ { @@ -5425,32 +5374,20 @@ "Text(0, 0.5, 'Median (℃)')" ] }, - "execution_count": 538, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8322 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8322 missing from current font.\n", - " font.set_text(s, 0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0, flags=flags)\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -5483,7 +5420,7 @@ }, { "cell_type": "code", - "execution_count": 267, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -5492,18 +5429,20 @@ "Text(0, 0.5, 'CO atmospheric concentration')" ] }, - "execution_count": 267, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -5530,7 +5469,7 @@ }, { "cell_type": "code", - "execution_count": 276, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -5539,28 +5478,20 @@ "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" ] }, - "execution_count": 276, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0, flags=flags)\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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5IIbM778ql7F9WnbDp0WXq97/tddeIygoiHnz5pGcnMydd97JTTfdxLRp03j77beZNm0aO3fuxNPTk6VLl1JcXMzIkSPZunUrjRpdvFB+7rnnGDZsGBEREcTHxzNy5EjWrFkDwLFjx/joo4/YvXs39957L2vXruW6666jV69eHD16tGSMgoIC3nvvPbZs2YLVamXq1KkkJiayaNEi7r//fnr06MHy5cvZv38/tWrVKvV8APbv38+aNWuwWCzcfvvtjBgxgjVr1tCjRw/uueceYmJi+O67767dUmDt2rUcPXqUqKgocnJySEpK4rHHHuPll18mMzMTp9OJzWYjMTGx5Gq/w+EgKSmJkJAQioqKyM7Oxs/Pj+DgYBISEqhbty4ASUlJOBwOQkJCSEhIKDnmf2//fZFCONcc1a9fv6JOXURERP6LUewkYdVrAAQNGI/FaquQ49r9Q3BEPXbucYXr3iKo/6OXvBAhIiIVY+fOncydOxeAwMBAunbtyq5du877vNahQwcCAgL46KOPOH78OKdOnSInJ+eSY+7YsYOTJ08yb9484Nwt66dOnQKgY8eOuLi4ULt2bUJCQmjQoAFw7rNnenp6yRiurq40b96cO+64g8jISMaNG0dQUBBdunRh5syZxMTE0LVrV7p163beRehLnY+rqytt2rTBy8sLgNDQUNLS0ujUqRPjx4/n4MGDdOnShREjRpTFt/WKVFgp8OKLL5b8/65du/jXv/7F/PnzAQgLCyM6Opr+/fuzcuVKwsPDAYiIiGDlypU8+OCDREdHExYWht1uJyIiglWrVhEWFsaePXtwc3Ojdu3ahIeH89lnn5U88eDEiRPcfPPNuLm5MWPGDP7yl7+Qm5vLzp07mTBhQkWduoiIiPyXtG0ryI89giPqMex+jtJ3KEOeN7TBP3woqVs+wa3WDdRo16dCjy8iYgafFl3+1NX88vS/s7oNw8DpdJ63bePGjSxYsIDRo0czePBgEhMTMQzjkmM6nU4+/PBDfHx8AIiPjy+5/dxut5e8z2b741L6rbfeKlmjbuzYscybN4++ffvStm1bvvrqK9577z22bt3K9OnTL+t83Nz+/ww1i8WCYRi0a9eOL774gpiYGNauXcuqVatYuHDhH+Yqa5ViDv3MmTNZunQpffr0Yc+ePTz22GMATJgwgf3799O3b18+/vjjklUgR40aRUFBAX379mXWrFnMmTMHgF69etGoUSMGDBjAww8/zKxZs3B3d6dFixYMGDCAO+64g+HDhzN+/HiCg3UvoYiISEXLO/0zqVuX4t08HO/mt5mSwa/zEDwbhZH8n/fJ/e1HUzKIiMg5HTp0YPny5QCkpKTw1Vdfccstt2Cz2Uo+TG/bto2+ffsyePBgvL292b1790VvEf9d+/btWbJkCQA//fQTAwYMIC8v74pyJSQk0LdvX2688UYee+wxOnTowM8//8yjjz7K4cOHGTFiBI8++ig//nj+vyOXOp9LefHFF4mOjmbw4MFMmzbtgvEqQrnPFPjqqwvvXWnfvj3t27cveV2nTh0WL158wfv8/Px48803L9ju5ubG7NmzL9husViYPHkykydPvuBrY8eOZezYsn3UkYiIiFy+4vxcElbOx8U3kJo97zMth8VixTFgPKcXTSZhxSvUufdlXHwCTMsjIlKdjR8/nqeffpr+/fvjdDp55JFHuPHGG0lJSSElJYUpU6YwZswYJk2axOrVq7Hb7bRt25bY2FjatGlz0TGffvpppk+fXrKOwCuvvIKnp+cV5XI4HAwZMoTBgwfj6elJgwYNGDRoEK1atWLatGn84x//KJmRfjnnc/DgwYseZ/To0UycOJFly5Zhs9l46aWXrihnWbAYfzTvQgCIjY0lMjKSTZs2nffkBBEREbl8CWv+RdbBzdQe9Szu1zU1Ow4Fib9xetFUXB3XU3vUM1hs9tJ3EhGpIg4fPkzTpub/XSsV739/9qV9nq0Utw+IiIjItS3rx21kHfgav06DK0UhAOAaVJeg/o+Qf/onkje+b3YcERERU6gUEBERkXJVlJFE0rq3cKvdCP/Od5od5zzeTTtSo0MUGXvXl9vjukRERCozlQIiIiJSboyiQhJWzscoduIY+BgWW4U9+OiyBXQdiXu9m0la9zb5Z4+bHUdERKRCqRQQERGRcmEYxSSs+Sd5pw4T1Och7P4hZke6KIvVRvDAx7F61SD+szk4czLMjiQiUia0fFz1czU/c5UCIiIiUi5SNn1A9o/bCOg2Cu9mnc2O84dsXjUIGTIJZ1YaCStfxSh2lr6TiEgl5u7uTnJysoqBasQwDJKTk3F3d7+i/SrfHD4RERGp8tJ2rSF91xp8w/pQo0OU2XEui1vtGwjsNY6kL14nJeZjAruNMjuSiMhVCw0NJTY2lsTERLOjSAVyd3e/4ifmqRQQERGRMpX14zZS/vM+nk3aE9jjHiwWi9mRLptvq0jyz/xC+o6VuNW+Ae8bbzU7kojIVbHb7dSvX9/sGFIF6PYBERERKTO5J38gYfVruF/XFEfUBCxWm9mRrljN28fiVqcxiWv+RUHiKbPjiIiIlCuVAiIiIlImChJ+I37ZS9j9Qwi+czJWu5vZka6KxcVO8OCJWO1uxC+fQ3FettmRREREyo1KAREREfnTijKSOfvJ81jsboQMewqbh4/Zkf4UF99AHIOfoDA1joQ1/8Qwis2OJCIiUi5UCoiIiMif4szL5uwnz1Ocn0PIsGnYazjMjlQmPOo2I7D7GHJ+3k3a9s/NjiMiIlIuVAqIiIjIVTOKColfPpvC5NME3zEJt+B6ZkcqU77t+uLd7DZSY5aQ++sBs+OIiIiUOZUCIiIiclUMo5iENf8k7+QPBPV7BM/6Lc2OVOYsFgs1+z6Ei38wSV++i1HsNDuSiIhImVIpICIiIlclZdNisn/cRkDXu/G5OcLsOOXGancjoNvdFCbFknnga7PjiIiIlCmVAiIiInLF0r9dS/qu1fiG9abGrQPNjlPuvJp0wK1OY1I3f0pxQZ7ZcURERMqMSgERERG5IlmHt5O88X08m7QnsMdfsFgsZkcqdxaLhcDI0TizUkj/dq3ZcURERMqMSgERERG5bLknfyBh1T9wC22CI2oCFqvN7EgVxv26png2voW0HStxZqebHUdERKRMqBQQERGRy1KQ+Bvxy2dj9wsm5M4pWO1uZkeqcAFdR2IU5pP6zTKzo4iIiJQJlQIiIiJSqqKMZM4ueR6LzU7IsGnYPH3MjmQK15qh+LTuTsZ3X1KYcsbsOCIiIn+aSgERERH5Q8V52cR9+jzF+TmEDJuG3c9hdiRT+d92FxabnZSvPzY7ioiIyJ+mUkBEREQuyXAWEbd8DgVJpwkeMgm3kPpmRzKdi7c/fh2iyD6yg7zTP5sdR0RE5E9RKSAiIiKXlLp1GXknDxHU9yE8G7Q0O06lUaNDf2xefqRs+gDDMMyOIyIictVUCoiIiMhF5Z06TNr2FXi36IpPi65mx6lUrK4e+IcPJe/UYXKO7jE7joiIyFVTKSAiIiIXKM7LJmHVP3CpEUTN2+81O06l5NMqEntgbVK+WoxR7DQ7joiIyFVRKSAiIiIXSNqwkKKMZBwDH8Pq5mF2nErJYrUR0HUUhcmnydy/yew4IiIiV0WlgIiIiJwn89AWsg5twf+2u3Cv09jsOJWaZ+N2uF/XlNQtn1JckGt2HBERkSumUkBERERKFKYlkLT+HdxCb8Sv02Cz41R6FouFgMjROLPTSN+1xuw4IiIiV0ylgIiIiABgFDtJWPUPABxRE7BYbSYnqhrc6zTG68ZbSduxiqKsNLPjiIiIXBGVAiIiIgJA2vbPyY89Qs1e47D7OcyOU6UEdB2B4SwkbetSs6OIiIhcEZUCIiIiQt7pn0nd8inezW7Dp3m42XGqHHtAbXzb3E7Gvo0UJJ82O46IiMhlUykgIiJSzRXn5557/KBvIIG9xpkdp8ry73wnFrsrKV9/ZHYUERGRy6ZSQEREpJpL+vI9itISCBowHpu7l9lxqiybVw38bh1Ezk+7yDt1xOw4IiIil0WlgIiISDWWdXgHWQe+wq/jIDzq3mR2nCqvxi39sHn7k7zpAwzDMDuOiIhIqVQKiIiIVFNFGckkRb+JW60b8L/tLrPjXBOsru74hw8j//RP5Py0y+w4IiIipVIpICIiUg0ZRjEJq1/DcBbhGDgBi83F7EjXDJ+WXbHXDCXl6w8xnEVmxxEREflDKgVERESqofSdq8k7eYjA28diD6htdpxrisVqI6DbKApTzpKx7z9mxxEREflD5V4KZGVl0a9fP2JjYwH49NNP6devH/3792fq1KkUFBQAcPjwYQYPHkzPnj156qmnKCo616yfOXOGkSNH0qtXLx566CGys7MByMjI4P7776d3796MHDmSxMREAAoKCpg0aRK9e/dm0KBBHDt2DADDMJg9eza9evWiT58+7N27t7xPXUREpFLKP3uclJgleN3YAZ+W3cyOc03yvKEt7nWbkbr1U4rzc82OIyIicknlWgp8//33DB8+nBMnTgDw66+/8u677/LJJ5+wevVqiouL+fjjjwGYNGkSM2bMYMOGDRiGwdKlSwF45plnGDFiBOvXr6d58+a8/vrrAMyfP5+wsDDWrVvHnXfeyaxZswBYvHgxHh4erFu3jieffJKpU6cCsGHDBo4dO0Z0dDQLFixg6tSpJcWDiIhIdVFcmE/CqlexeflSs8+DWCwWsyNdkywWCwHdRlGck0HazlVmxxEREbmkci0Fli5dysyZM3E4HAC4uroyc+ZMvL29sVgsNG7cmDNnznD69Gny8vJo1aoVAIMHD2b9+vUUFhaye/duevbsed52gJiYGPr37w9Av3792LJlC4WFhcTExDBgwAAA2rVrR0pKCmfOnGHz5s306dMHq9VK/fr1qVWrFvv27SvP0xcREal0kv/zPoXJZ3EMGI/Nw8fsONc09zqN8LqpE+m7VlOUmWp2HBERkYsq11Jg1qxZhIWFlbyuU6cOnTp1AiAlJYWPPvqIyMhIEhISCAoKKnlfUFAQ8fHxpKam4u3tjYuLy3nbgfP2cXFxwdvbm5SUlIuOFRcXR0JCQkk58d/bRUREqovsn3eT+d2X1OgwAI96N5sdp1oI6DICw+kkdeunZkcRERG5KFMWGoyPj2fMmDEMGTKE9u3bU1xcfN70RcMwsFgsJX/+t0tNczQMA6vVesE+v2+/2DGsVq2zKCIi1UNRViqJX7yOa3B9AiKGmx2n2rD7h+DbtieZ+zdRkBRrdhwREZELVPin4mPHjjFs2DAGDRrEI488AkBISEjJQoEASUlJOBwOAgICyMzMxOl0ApCYmFhytd/hcJCUlARAUVER2dnZ+Pn5ERwcTEJCwgVjhYSEXHS7iIjItc4wiklc8y+MgjwcAx/D4mI3O1K14t/5Diyu7iT/530MwzA7joiIyHkqtBTIysri3nvvZcKECYwdO7Zke506dXBzcyt5IsCqVasIDw/HbrcTFhZGdHQ0ACtXriQ8PByAiIgIVq5cCUB0dDRhYWHY7XYiIiJYtercgj579uzBzc2N2rVrEx4ezpo1a3A6nZw8eZITJ05w882aOikiIte+jD3ryD2+n8Du9+BaM9TsONWOzdOXgPCh5B7bR/aP35gdR0RE5DwuFXmw5cuXk5SUxKJFi1i0aBEA3bp1Y8KECcydO5dp06aRlZVFs2bNGD16NAAzZ85kypQpvPHGG9SqVYt58+YBMGHCBKZMmULfvn3x8fFh7ty5AIwaNYoZM2bQt29fXF1dmTNnDgC9evXiwIEDJYsQzpo1C3d394o8fRERkQpVXFRA9g/fkLJpMZ6NwvBpc7vZkaot37DeZP3wDUlfvodH/ZbYPH3NjiQiIgKAxdA8tlLFxsYSGRnJpk2bCA3VFRYREancCtMTyPzuSzL2/Yfi3ExcHfWoNWIGNq8aZker1goSfiP23Ul439QRR9QEs+OIiEg1Udrn2QqdKSAiIiLlwzAM8k4cJH3POnKO7gHAs3E7aoT1xv365pdcqFcqjqujLn6dBpO2dSneN3XGs1FbsyOJiIioFBAREanKivNzyTwYQ8aedRQmn8bq6YvfrQPxbXM7LjVVUKQAACAASURBVDWCSh9AKpR/x8FkH9lB4vq3ua7ufKxuHmZHEhGRak6lgIiISBVUkBRLxt71ZB6IwSjIxa12I4IGPIpX045YXVzNjieXYHGxE9T3Yc68/yQpX39IzV7jzI4kIiLVnEoBERGRKsIodpJzdC8Ze9eR++sBsLngfVNnfNv2wr1OI7PjyWVyr9MY33Z9yNj9Bd7NOuN+XVOzI4mISDWmUkBERKSSc+ZkkLl/ExnfbaAoPRGbTyD+XUbg26q7Fg+sogK6DCfn590kfvE6de57RbM7RETENCoFREREKinDKCZ18yek71yN4SzE/frmBHa/B8/G7bBYbWbHkz/B6upBzT4PELfkOdK2LiOg60izI4mISDWlUkBERKQSKi7II2HVP8j5+Vu8m4fj13EQrkF1zY4lZcizQSu8W3QhbecqvG7qhFtwPbMjiYhINWQ1O4CIiIicrygjmTMfTCPn6B4Cb7+XoAHjVQhcowK734PNw5vEta9jFDvNjiMiItWQSgEREZFKJP/sMU4vmkJhahwhd02hRrs+WCwWs2NJObF5+BDY8z4K4o6RvmuN2XFERKQaUikgIiJSSWQf2cmZD6ZhsdmoM2YWnje0NTuSVACvG2/Fs3E7Urd8SmHKWbPjiIhINaNSQERExGSGYZC6bQXxn72Ma3A9at/zEq6O682OJRXEYrFQs9f9WGwuJEa/gWEYZkcSEZFq5IpKgaSkJPbt21deWURERKodo6iQxLX/IjXmI7yadabW3c/g4u1ndiypYC4+AQREjibv5A9k7v+P2XFERKQaKbUU+PTTT3niiSdITU0lKiqKv//977z66qsVkU1EROSa5szJ4OzHz5B1IAb/8KE4oh7T8+qrMZ9W3XG/vjnJmz6gKCPZ7DgiIlJNXFYpMHnyZNavX0+XLl2Ijo5m69atFZFNRETkmlWQFMvp96eSf+YXHAMfx/+2u7SgYDVnsVgI6vMgOItI2vCObiMQEZEKUWopYLFYcDgc7Nixg06dOmG32ykuLq6IbCIiUoUVZSST8vWHZOzbSHFBntlxKpWcX7/nzPtTMQpyqTXqWbybdTY7klQS9oBa+IcPJefn3WQf2WF2HBERqQZcSnuD3W5n0aJF7Nq1i2effZalS5fi7u5eEdlERKQKKi7II23nKtJ3rsIoLAAMUr5ajE/Lbvi27YXdP8TsiKbK+O5Lkta/g71mKCFDp2Kv4TA7klQyNdr3J+vH7SRvWIhHvZuxefiYHUlERK5hpZYCzz//PAsXLmTWrFn4+fmxfft2Zs2aVRHZRESkCjGMYrIObibl649xZqXg1bQjAV1HUpSZQsaedaTvjiZ911o8GramRrs+eDRoicVSuR+CYxQ7yf31AFZXd1z8Q7B5+V31FH+j2Enypg/I+HYtHg3bEDzocaxunmWcWK4FFquNoH4Pc/q9v5P8n/dx9H/U7EgiInINK7UU2Lt3Ly+99FLJ6/nz5/Pee+/RsGHDcg0mIiJVR+7JQyT/598UxB3HrXYjgoc8gXvojQDY/UPwqHsTRRnJZOzbSOa+jcR98jwu/iHUCOuNd4uu2Ny9TD6DCxUX5JKw8h/kHN1dss1id8fuH4I9IAQX/xDs/rWwB5z70+bjf8mSozg/l4SVr5Lzy158b+lHYORoLFZbRZ2KVEFuwfXw6xBF2vYVeDe7Dc8GrcyOJCIi16hLlgJLly4lLy+Pd999l/z8/JLtRUVFfPjhh4wdO7ZCAoqISOVVmHKG5E2Lyfn5W2y+NXEMfAyvmzpd9MOxi28gARHD8O88hOwjO0nfs47kjYtIifkY7+YR1AjrhavjehPO4kJFGcnELX2RgoSTBESOwTXoOgpTzlKYGkdRahwFib+R/fMeKC4q2cfi4vp/RUFISVFg9w/B6u5F4tp/UZAYS81e9+PbtqeJZyZVid9td5L9006Sot8k9P5Xsbp6mB1JRESuQZcsBQzD4ODBg+Tl5XHw4MH/v4OLCxMnTqyQcCIiUjk5czNJ/WY5GXvWYXGx499lBDVu6YfV7lbqvhabHe9mt+Hd7Dbyzx4nY+86sg7GkLnvS9zrNsM3rDdejdthsZU6ma1c5J35hfhlL1FckEfI0CfxbNj63Bd+//P/GMVOijKSKUw9S1FKHIWpcRSmnqUw9Sy5x/djFBWUvNfq5knIsGl4NmhZkaciVZzVxZWgvg9z5oNppMQsoebtuiAjIiJlz2KU8rybDRs20LNn9b6qERsbS2RkJJs2bSI0NNTsOCIipjGcRWTsXU/q1mUU5+fg07Ib/hHDcPH2/1PjOnMyyfx+Exl7N1CUnoDNJwDfNj3xbd0Dm1eNMkpfuqzDO0hc/Ro2Lz9C7pqKq6PuVY1jGMU4M1PPFQZpCbjXvanaL7AoVy9p/Ttk7N1A7XtewL1OY7PjiIhIFVPa59lSL8O0a9eON954g/T09POelzt16tSyTSoiIpWWYRjkHN1DyqYPKEw5g0f9FgREjsEtuF6ZjG/z9MHv1oHUaN+fnF++I2PvOlI3LyH1m2X4NI84Vzz4BJTJsS7GMAzStq8gNeZj3EKbEHLH5D9VRlgsVlx8A3HxDYTKcUeEVGEBXUeSfXQPiWsXEHrfXCw2u9mRRETkGlJqKTBp0iRsNhtNmza96hWXRUSk6sqPO07yf/5N3slD2APrEDL0STwatimXfxMsVhtejdvh1bgdBcmnydizjox9G8n6cdu50qDDgMu6ReFKGEWFJEa/QdbBzXg3D6dm34ewuriW6TFE/gyrmyc1e95H/LKXSN+9Dr8OA8yOJCIi15BSS4HTp0+zfv36isgiIiJlxJmXTe6JAxQmxv6pcQpTzpB1aCtWTx8Ce47Dt3X3CrvX3zWwDjV73keNW/qR8tWHpG75hIx9XxLQdSTezcPL5HGGzux04j97mbxTh/EPH4Zf5ztUgEul5NW4HZ43tCV166d4N7sNF58/d8uOiIjI70r9za5OnTrk5ubi4aEVb0VEKivDKKYg7gQ5x/eRe2wfebE/gVH8p8e1uLhSo8MA/DoNMe2xgXb/EIKHTCTv1GGSNy4icfU/Sf82msAeY/Co2+yqxy1IPEXc0hdwZqXhGPQ3vG/qVIapRcpeYI+/cOrtx0j56gMcURPMjiMiIteIUkuB4OBgBg4cSIcOHXB3dy/ZrjUFRETM5cxOJ/fXA+eKgOP7cWanA+Aa0hC/joPwbNgat1o3gPVPXFG3WMrkinxZcL+uKbX/8hJZP3xDylcfcnbxDDybtCew2yjsAbWuaKyc4/uJX/EKVhdXat39LO51GpVTapGyYw+ohV/7AaRtX4Fvm9txv66p2ZFEROQaUGop4HA46N27d0VkERGRP2AUO8k/c5ScY/vIPbaf/LPHAAOrhw+eDVrh0bA1HvVb4uLtZ3bUcmOxWPFpHo5Xk/ak71pD2vbPOXV0LzXa9cav0x3YPLxLHSNj73qSNryLa9B1hNw1FZcaQRWQXKRs+HUaQubBzSRteJc6Y2djsdrMjiQiIlVcqaXAY489RkFBAadOnaJBgwYUFBTg5la2izyJiMjFFWUkk3N8P7nH95H76wGK87LBYsWtTiP8w4fi0bA1biH1q90HA6vdDf/Od+DTMpLUzUtI37WWzANf43/bUHzb3H7RdQ+MYifJG98nY080nje0xTHwcaxuujVOqharqzuB3ceQ8Pk8MvdtxLdtL7MjiYhIFVdqKXDgwAEeeeQRbDYbn3zyCQMHDuTNN9+kVatWFZFPROSaZxQVUpgWT2FqHEWpcRSmnKXw//4sSosHwOYdgFeT9udmA9RrcVlXxKsDFx9/gvo9jG+7PiT/532Sv3yXjL3rCIgcg+cNbUsWDSzOzyH+83nkHttHjfb9Ceg2qtoVKXLt8GraEffvviQlZgleTTti8/Q1O5KIiFRhpZYCs2fPZuHChUyePJmQkBBmzZrF888/z/Llyysin4jINaG4MJ+i1HgKU3//wB9H0f/9f1F6EmCUvNfi5ondvxZutW/At83teDZsjT2orlbF/wNuwfWoNWImOb/sJWXTv4lf+iIe9W4moPs9WN08iVv6AoXJZ6jZ+wF829xudlyRP8VisVCz573EvvMEKTEfE9TnQbMjiYhIFVZqKZCbm0uTJk1KXkdGRvLPf/6zXEOJiFR1xfk5pG75lPy4XylMPYszM+W8r1s9fLD7h+B+XVNcWoRg9w/BHlALu38IVg8fFQBXwWKx4NUoDM8Grcj47ktSt37K6YUTsbh5YLFYqDVsGh71W5gdU6RMuAbVxbddHzK+/QLf1j1wq9XQ7EgiIlJFlVoK2Gw2MjMzS35BPXHiRHlnEhGp0grTEohb+iKFSbG41WmER70W533od/EP0fT/cmSxuVCjXR+8m4eTtm05eaePEtT3IVxrhpodTaRMBdx2F9k/bCVpw0Jqj5lVaZ4UIiIiVUuppcADDzzA3XffTUJCApMmTWLLli08/fTTFRBNRKTqyYv9ifjlszGKCgkZPg3P+i3NjlRt2Ty8Cex+j9kxRMqN1d2LgK53k7h2AVkHN+PToqvZkUREpAoqtRTo3r07DRo0YNu2bTidTu67777zbicQEZFzsn7YSuKaBdh8A6l197O6Mi0i5c67RRcy9m0k5avFeDW+Bau7l9mRRESkirnkPLMjR46U/FdQUEC7du3o0KEDhmFw5MiRiswoIlKpGYZB6palJKycj1udRtS55yUVAiJSISwWKzV73oczO4OUrUvNjiMiIlXQJWcKPPDAA5fcyWKxEBMTUx55RESqlOLCfBK/eJ3sH77Bu0VXgvo8gMVmNzuWiFQjbrUa4tO6Oxm7o/FtFYlrUF2zI4mISBVyyVJg8+bNZXKArKwshg0bxptvvkloaCjbt2/nxRdfJD8/n969e/P4448DcPjwYZ566imys7MJCwvjmWeewcXFhTNnzjBp0iSSk5OpX78+c+fOxcvLi4yMDCZOnMipU6cICAhg/vz5BAUFUVBQwFNPPcWhQ4dwd3dn7ty5NGzYEMMwmDNnDl9//TVWq5XnnnuOtm3blsk5ikj1VJSVRvzy2eSf/pmArndT49aBemqAiJgioMsIsg/vIGnDu9Qa+bT+LhIRkctW6jK1aWlpLF68mLfeeos333yTBQsWMHny5Msa/Pvvv2f48OElTyzIy8vjySef5PXXXyc6OppDhw6VlA+TJk1ixowZbNiwAcMwWLr03BS4Z555hhEjRrB+/XqaN2/O66+/DsD8+fMJCwtj3bp13HnnncyaNQuAxYsX4+Hhwbp163jyySeZOnUqABs2bODYsWNER0ezYMECpk6dSlFR0ZV9t0RE/k9BwknOLJpMQfwJgodMwq/jIP0SLiKmsXn64h8xnLyTh8g+ssPsOCIiUoWUWgpMmDCBmJgYPvnkE06ePMmyZctwOp2XNfjSpUuZOXMmDocDgAMHDnD99ddz3XXX4eLiQv/+/Vm/fj2nT58mLy+PVq1aATB48GDWr19PYWEhu3fvpmfPnudtB4iJiaF///4A9OvXjy1btlBYWEhMTAwDBgwAoF27dqSkpHDmzBk2b95Mnz59sFqt1K9fn1q1arFv374r/HaJiEDOL3s5/e8nMYqLqT36ebxu7GB2JBERfNv0wDW4Pskb36e4IM/sOCIiUkWUWgqcPn2ahQsXEhERwZgxY1iyZEnJlf/SzJo1i7CwsJLXCQkJBAUFlbx2OBzEx8dfsD0oKIj4+HhSU1Px9vbGxcXlvO3/O5aLiwve3t6kpKRcdKy4uDgSEhJKyon/3i4icrkMwyD927XELX0Je0Bt6vzlJdxqNTQ7logIABar7dyig5nJpG37zOw4IiJSRZRaCtSsWROLxUK9evU4evQotWrVorCw8KoOVlxcfN70WsMwsFgsl9z++5//7VLTcw3DwGq1XrDP79svdgyrtdTTFxEBwHAWkbT+bZI3LsKzcTtqj3oOF99As2OJiJzH/bob8W4eTtqu1RSmnDU7joiIVAGlfir29/dn0aJFNG/enM8//5zNmzeTm5t7VQcLCQkhMTGx5HViYiIOh+OC7UlJSTgcDgICAsjMzCy5XeH398O5WQZJSUkAFBUVkZ2djZ+fH8HBwSQkJFwwVkhIyEW3i4iUxpmXTdynL5D53ZfUuHUgwUMmYnV1NzuWiMhFBXQbjcXmQvLGRWZHERGRKqDUUuDZZ58FICwsjMaNGzN37lyeeOKJqzpYy5Yt+fXXXzl58iROp5O1a9cSHh5OnTp1cHNzY+/evQCsWrWK8PBw7HY7YWFhREdHA7By5UrCw8MBiIiIYOXKlQBER0cTFhaG3W4nIiKCVatWAbBnzx7c3NyoXbs24eHhrFmzBqfTycmTJzlx4gQ333zzVZ2HiFQfhalxnHl/KrknfyCo3yMEdhuFxaJZRiJSebn4+ON/213k/LKX7KN7zI4jIiKV3CUfSfi7mJgYhg4dCsCUKVP+1MHc3Nx46aWXePTRR8nPzyciIoJevXoBMHfuXKZNm0ZWVhbNmjVj9OjRAMycOZMpU6bwxhtvUKtWLebNmwecWwBxypQp9O3bFx8fH+bOnQvAqFGjmDFjBn379sXV1ZU5c+YA0KtXLw4cOFCyCOGsWbNwd9eVPhG5tNzffiR++RzAoNaIGXhc38zsSCIil6VGuz5k7t9E8sZFeNRvgdXF1exIIiJSSVkMwzD+6A3jx4/n22+/5fbbb+eOO+6gRYsWFZWt0oiNjSUyMpJNmzYRGhpqdhwRqQBZh3eQsHI+dn8HIXc9iT2gltmRRESuSM7x74lb8iz+EcPx73yH2XFERMQkpX2eLXUO7Guvvca6deu44YYbmDlzJlFRUXz00UflElZEpDLI+uEbEj6fh1vtG6g95kUVAiJSJXk2aIlnk/akbfuMovTE0ncQEZFq6bJujPX392fkyJE8+uijuLm5sWDBgvLOJSJiisxDW0hY9Q/cr7uRWsOnYfPwNjuSiMhVC+xxDwDJm/5tbhAREam0Sl1T4MiRI6xYsYLo6GgaN27MPffcQ48ePSoim4hIhco88DWJaxbgfn0zQu6aqicMiEiVZ6/hwK/jIFK3fEruiYN41NMiyyIicr5SZwqMGzcOT09PlixZwnvvvUefPn2w2+0VkU1EpMJk7N9E4poFeNS/mZChT6oQEJFrRo0OUbj4OUjasBDDWWR2HBERqWQu6+kDNputIrKIiJgiY99GkqLfxKNBS4LvmIzV7mZ2JBGRMmO1uxHY/S/EL59N5v5N+LbtaXYkERGpRC5ZCtx99918+OGHdOjQAYvFUrLdMAwsFgvffvtthQQUESlPGXs3kLT+bTwatib4jr/rsV0ick3ybNwOtzpNSN32GT4tu2Fx0axPERE555KlwNy5cwH47LPPKiyMiEhFSt8dTfKX7+J5Q1uCh0zSL8kics2yWCz4Rwwl7uNnydj/H2qE9TY7koiIVBKXLAVCQkIAyMnJqbAwIiIVJf3btSRvXIRn43YED34Ci02FgIhc2zzqtcD9uqakbVuBT8tuulVKRESAy1hT4IEHHij5/8LCQlJTU2natCkrVqwo12AiIuUlbedqUjb9G68bO+AY+DgWW6l/FYqIVHnnZgsM4+yHM8nct5Eat/QzO5KIiFQCpf4mvHnz5vNeb9++nfXr15dbIBGR8pS2/XNSvv4Qr6YdcURNUCEgItWKx/XNcb++OWnbP8endQ/NFhARkdIfSfi/OnbsyMGDB8sji4hIuUr9Zvm5QqBZZxwDH1MhICLVkn/4UJzZaWTs3WB2FBERqQRK/Y34yJEj570+ePAgubm55RZIRKQ8pG5ZSurWT/FuHk5Q/79isepRqyJSPXnUvQmP+i1J2/E5vm16YHX1MDuSiIiY6IrWFLBYLAQEBDBz5sxyDSUiUlYMwyB1yyekfbMc7xZdCOr7sAoBEan2/MOHcubfT5KxZz1+HQeZHUdEREx0xWsKiIhUFYZhkBrzMWnbV+DTMpKafR/EYrniu6ZERK457qFN8GjYmrSdK/Ft2xOrm6fZkURExCSllgJvvvnmH379wQcfLLMwIiJlxTAMUr7+kPQdK/Fp3YOave9XISAi8l/8w4dxZtFk0ndH49/5DrPjiIiISUotBY4dO8aOHTvo2bMndrudTZs2ERISQoMGDSoin4jIFTMMg5RN/yZ91xp82/YisOe9KgRERP6He+0b8GwURvqu1dQI643V3cvsSCIiYoJSS4GEhAQ+//xzgoKCAHjooYd45JFHeOaZZ8o9nIjIlTIMg+SN75GxOxrfsD4E3j4Wi8VidiwRkUrJP3wop9+dRPq3X+AffpfZcURExASlXjpLSEgoKQQAfH19SU9PL9dQIiJXwzCKSd6wkIzd0dS4pZ8KARGRUriFNMCzSXvSvl2DMzfL7DgiImKCUkuBRo0a8dRTT7Fnzx52797NpEmTaN26dUVkExG5bIZRTNK6d8jYu54aHaII6H6PCgERkcvgf9tdGPk5pO9aY3YUERExQamlwKxZs7Db7UyfPp0XXniB0NBQnnrqqYrIJiJyWQyjmKTot8jc9yV+HQcT0G2UCgERkcvkFlwPr6a3kr57Lc6cTLPjiIhIBSt1TQEfHx+efPJJfvvtNxo2bEhBQQFubm4VkU1EpFRGsZPEL94g68DX+HW+A//wYSoERESukP9td5F9eCfpu1YR0PVus+OIiEgFKnWmwIEDB4iMjOS+++4jPj6eiIgI9u/fXxHZRET+kFHsJHHtArIOfI3/bUMJiBiuQkBE5Cq4BtXFq1kn0nevw5mttaNERKqTUkuB2bNns3DhQvz8/AgJCWHWrFk8//zzFZFNROSSjGIniav/SdbBzfhHDNeq2SIif5J/5zsxigpI27nS7CgiIlKBSi0FcnNzadKkScnryMhIioqKyjWUiMgfMZxFJKycT9YPWwnoOhL/zneYHUlEpMpzrRmKd7POZOxZT1FWqtlxRESkgpRaCthsNjIzM0um5J44caK8M4mIXNK5QuBVsg9vJyByNH4dB5sdSUTkmuF/250YziLSd2i2gIhIdVFqKfDAAw9w9913ExcXx6RJkxg6dCgPPPBARWQTETmP4SwkfsUrZB/ZSWCPv+DXIcrsSCIi1xR7QG28b44g47svKcpMMTuOiIhUgFKfPtC9e3caNGjAtm3bcDqd3HfffefdTiAiUhGMokLiV8wl5+geAm+/lxrt+pgdSUTkmuTf+Q6yDm4mbfvn1Ox5r9lxRESknJU6UwCgQYMGjBo1ipycHBUCIlLhiosKiFs+h5yje6jZa5wKARGRcmT3D8GnZTcy9n1JUUay2XFERKScXVYp8LuNGzeWVw4RkYsqLswnftkcco99R80+D+LbtpfZkURErnl+nYaAAWnbPjM7ioiIlLMrKgUMwyivHCIiFzhXCMwm9/h+avZ9GN/WPcyOJCJSLdj9HPi06kbG/k0UpieYHUdERMrRFZUCERER5ZVDROQ8xQV5xC19kdxfDxDU/6/4too0O5KISLXi32kIWCDtG80WEBG5ll1RKfD444+XVw4RkRLFBbnEffoCeSd/IChqPD4tupgdSUSk2nHxrYlv6x5kHviawtQ4s+OIiEg5uaJSQESkvBXn5xL3ySzyTh3GETUBn+bhZkcSEam2/DoOxmKxkqrZAiIi16xLPpJw8ODBf7iGwOeff14ugUSk+irOz+HskufJP3MUx6DH8W7a0exIIiLVmotPAD5te5KxOxr/ToOwB9Q2O5KIiJSxS5YCEyZMYOLEibz44ot4eXlVZCYRqYaK87I5u+Q58uOOEzz4Cbxu7GB2JBERAfxuHUjmd1+S+s1yHAPGmx1HRETK2CVLgYiICIYPH87evXuZPHlyRWYSkWrGmZtF3JLnyI8/QfDgiXg1ucXsSCIi8n9cvP3xDetF+q61+HUcjGvNULMjiYhIGfrDNQUefvhhWrduXVFZRKQacv4/9u48Pqr6UP/458yWTPaFrEDYxIVNkODVWhMRK4QAKopaqKLe1uXqrdcfoGEplAoVkQrXIm29bbWC1dKKoWISqiCg4kIQpCgoDYKQPZkh+zLL+f0RiFKMgEImIc/79coryZkzM8+x3w6ZZ875fhtqKP7zfJrKDpBw0wwVAiIiHVDUZddj2B2U/m2xJh0UETnHfGMpEBwczLXXXnvGn3Tt2rVkZmaSmZnJ448/DsCePXuYOHEio0ePZvbs2Xi9XgCKioqYMmUKY8aM4b777qOurg6A6upq7r77bjIyMpgyZQrl5eUANDc3M2PGDDIyMrjhhhsoKCgAwDRNHn/8ccaMGcPYsWPZvn37GT8uETk9vvpqil+Yj6f8EIk3PUJo/9RARxIRka9hDY0k8eZZ+OqrKHw2i4YvPgl0JBEROUPaffWBhoYGFi5cyMqVK1m7di35+fls3bqVGTNmMHfuXNavX49pmqxevRqA+fPnM3nyZPLy8hg0aBArVqwAYNmyZaSmppKbm8ukSZNYuHAhACtXrsTpdJKbm8usWbOYOXMmAOvXr6egoICcnByefvppZs6c2Vo8iEj789VVUfzCPDyVhSTcnEXIeZcEOpKIiHwDZ6+BdL/jMazOcIpfmE/Nrk2BjiQiImdAm6XA3r17v/Hr2/L5fPj9fhoaGvB6vXi9Xmw2G42NjQwdOhRoWfkgLy8Pj8fDtm3bGD169HHbATZt2sT48eMBGDduHFu2bMHj8bBp0yYmTJgAwIgRI3C5XBQVFbF582bGjh2LxWKhT58+JCUlsWPHjm99HCLy7Xlrj1D0wjw8rhISb55JSN+hgY4kIiKnwB6TTPIdjxGcchHlr/4a15svYJr+QMcSEZHvoM2JBmfMmMGBAweIjY09YWlCwzDYtGnTt3rCsLAwHnzwQTIyMnA6nYwYMQK73U5cXFzrPnFxcZSWluJ2uwkLC8Nmsx23HaCsrKz1PjabjbCwMFwu13Hbj92npKSEsrIy4uPjxfRk7QAAIABJREFUT9guIu3LW+Om+IV5eKsrSLx1Ns5egwIdSUREToPVGUbSrXOoyPs/jmxdg8dVRNyEn2KxBwU6moiIfAttlgKrVq3i5ptvZunSpQwYMOCMPeHevXt5+eWXefPNNwkPD2f69Om88847GIbRuo9pmhiG0fr9q/7996/ex2KxnHCfY9v9fv/XbheR9uOtrqT4hZ/jrXGReOscnCln7rVFRETaj2G10W3svdi7dcf1xvN4q35GwqSZ2MKjAx1NREROU5vviiMjI3nkkUf41a9+dUaf8O233+byyy8nNjYWh8PBxIkTef/991snCgSoqKggPj6emJgYampq8Pl8AJSXl7d+2h8fH09FRQUAXq+Xuro6oqKiSEhIoKys7ITHSkxM/NrtItI+vNWVFK2ai7fWRdIPf6ZCQESkkzMMg6j/mEDCpEdoriik8NlHaCr5PNCxRETkNH3jR+VXX301f/jDH87oE1544YVs3bqV+vp6TNNk48aNXHrppQQFBbWuCLB27VrS0tKw2+2kpqaSk5MDQHZ2NmlpaQCkp6eTnZ0NQE5ODqmpqdjtdtLT01m7di0A+fn5BAUFkZycTFpaGq+++io+n4+DBw9y4MABBg8efEaPTUS+nreqnKKVP8NXX03SD+cS3PPCQEcSEZEzJPT8ESRPXQiGQdHzc6j7bFugI4mIyGkwzH+fMODf+Hw+rFYrAJWVlcTGxn7nJ33mmWdYs2YNdrudwYMHM2/ePD7//HPmzJlDbW0tAwcO5LHHHsPhcFBYWEhWVhaVlZUkJSXx5JNPEhkZyZEjR8jKyuLQoUOEh4ezZMkSevToQVNTE3PnzmX37t04HA4WLFjAwIEDMU2TxYsXs2XLFgBmzpzJ97///VPKe/jwYUaNGsWGDRvo0aPHdz5+ka7Ec6SM4lXz8DfWkvjDuQR37x/oSCIichZ4a9yU/nURTcUFxIy6jcj/mNDmZZ8iItJ+TvZ+ts1SoLa2lvvvv5+bb76ZzMxMAH7yk5/g8Xh4+umnCQ0NPbvJOxCVAiLfjsdd0lIINDeSNHkuQUn9Ah1JRETOIr+nifJXf03dnncJv3gU3TJ+gmG1BzqWiEiXdrL3s21ePrBkyRJ69erFqFGjWrf9+te/pmfPnmd8ngEROfd4XMUUrZqH39NI0pR5KgRERLoAiz2I+Bv+H1FX3ETNRxsofvFRfA01gY4lIiLfoM1SID8/n7lz5xIcHNy6LTg4mDlz5vDee++1SzgR6ZwaD++laOVcTG8zSVPmE5TYN9CRRESknRiGhZirfkjchJ/SePhTip6bSXNlUaBjiYhIG9osBex2OzbbiSsWBgUFERSkdWhF5ESmz4tr84sUPf8zDJuN5CnzCUroHehYIiISAOGD00meMh9fYx1Fz82k4cA/Ax1JRES+RpulgMPhwO12n7Dd5XLh9/vPaigR6XyaK4so+tNsjrz9N8IGX0WPH/8KR3xKoGOJiEgABfe8kO53LsIaFkXxi49SvfONQEcSEZF/02YpMGnSJB588EGKir483auwsJCHHnqIiRMntks4Een4TNOk+sN/UPiH6XjcJcRPnE78+PuxBIUEOpqIiHQA9qgEuk/9Jc7eg6l47TdUbnge0+8LdCwRETnqxOsDjrrpppsoKSkhMzOT8PBw/H4/DQ0N/Od//idTp05tz4wi0kH56qoof20F9fvycfYZQty4B7BFfPdlS0VE5NxiCQ4l8ZZZVP7jj1S9txaPq4j46x7E4nAGOpqISJfXZikA8MADD3DnnXdSUFCAYRicf/75mk9ARACo37ed8teext9YT+wP7iRixFgMo82Tj0REpIszLFa6jfkJ9tjuVL7+LEXP/4zEm2eqTBYRCbA2/4JfsWIFAKGhoQwZMoTBgwerEBAR/J4mKnKfoWT1L7GGRtH9rsVEXjpOhYCIiJySyBFjSbxlFp4jpRQ++whNRf8KdCQRkS6tzb/iX3/99dafb7nllnYJIyIdW1NxAYV/mE71h+uJvGwC3e98XJMJiojIaQvpN4zuUxdiWO0UrfwZtXveDXQkEZEuq81SwDTN1p+bmpraJYyIdEym34f7nTUUPjcTf3MjSVN+TuyoqRg2e6CjiYhIJ+WIS6H7nYtwJPahbM0S3O+sOe7vTxERaR/fOKfAMYZhnO0cItJBeY6UUf73p2g8tIfQi75Ht4x7sDrDAh1LRETOAdbQSJKm/JyK136De9MLeCoLiRt7r0pnEZF21GYpoCJApGszTZPa3ZupyPs9GAZxE35K2KA0vTaIiMgZZbE5iJvwU+wxybi3vIT3SCkJNz2MNSQi0NFERLqENkuBTz/9lEsvvRSA2tra1p9N08QwDD744IP2SSgi7c7XWEdFzm+p27OV4J4XtfyxFhUf6FgiInKOMgyD6CsnYY9NpvzV5RQ+m0XiLbNwdOsR6GgiIue8NkuBvLy89swhIh2Ex1VMyV9+iedIKdFXTSHq8uswLNZAxxIRkS4gbMAV2CLjKf3rIoqem0n8xOmE9L040LFERM5pbU40mJKSQvfu3enWrRspKSmkpKTQ3NxMcnIyKSmabVzkXNRw8GMKn8vC11BD0pSfE33FRBUCIiLSroK796f7nYuwRcZR8tICqrfrgyoRkbOpzVKgtLSUcePGsXHjxtZtTz31FNdddx3l5eXtEk5E2k/NRxsp/vMvsIZE0v3ORThTBgQ6koiIdFG2yDiSb19ISL9hVOT9HxX/+COm3xfoWCIi56Q2S4HFixczYcIEMjMzW7c99dRTZGRk8MQTT7RLOBE5+0zTT+XGlZSvexpnrwEk3/EY9ujEQMcSEZEuzhLkJGHSI0ReOo7qba9RsnoR/qb6QMcSETnntFkKfPbZZ9x3330nzDT+X//1X+zevfusBxORs8/f3Ejpy0uoejeb8EuuJfGW2ViDQwMdS0REBADDYiX2B3fSLeMeGvbvpPBPs/EcKQt0LBGRc0qbpYDN9vVzEFosFoKDg89aIBFpH97qSopW/oz6z7YRe+1ddBtzN4a1zblHRUREAibikmtJ+uHP8NVUUvRcFo2HPw10JBGRc0abpUBYWBiFhYUnbD906JDWKRfp5JqK91P4bBYeVxGJk7KIHJGp/1+LiEiH5uwzhOSpv8RwOCleNY/aj98KdCQRkXNCm6XA1KlTuffee9m2bRter5fm5ma2bdvG/fffz9SpU9szo4icQXV736do5RwMi4XuU39JSP/hgY4kIiJyShzdetD9jkUEde9PWfYy3FtWY5pmoGOJiHRqbZ4rfM0111BbW8vDDz9MSUkJ0LJM4X333ceECRPaLaCInBmmaVL1bjauN1cRlNyfhElZ2MKiAh1LRETktFhDwkmaPJfynN/hfusvNLsKicv8Lyz2oEBHExHplL7xAuLrr7+e66+/nsrKSgzDICYmpr1yicgZZPo8lOc8Q+2ujYQOuIK4cffrjycREem0DKuduHH344jtjuvNVXiPlJFw0yMqu0VEvoU2Lx/4qtjYWBUCIp2Ur76G4j//gtpdG4m68mbir39IhYCIiHR6hmEQ9b0bSLhxBs2lByh69hGayw4GOpaISKdzSqWAiHROzZWFFD6XRVPhPuKv+x9i0m7RhIIiInJOCb3wMpJvX4Dp91P4p1nU/2t7oCOJiHQqKgVEzlENn++i6LmZmM0NJP1oPmGDrgx0JBERkbMiKKkf3e9chD0mmZLVi6j6YJ0mIBQROUWnVArU19cDsHfvXtatW4fX6z2roUTk2/M11OB++28Uv7QAa3gsyXcsIrjHBYGOJSIiclbZImJJvu1RQs4fQeXrz1KR9wymT3+zioiczDdONAiwfPlyCgoKePjhh7nrrrvo06cP77//Po8++mh75BORU9RU8jnV+bnUfvwWpreZkPMvJX7Cf2MJCgl0NBERkXZhcQSTcON0XG++QNW72XjdpcRPnIY1ODTQ0UREOqyTlgIbN25k1apVrF69mrFjxzJnzhwmTpzYHtlE5CRMn4e6ve9TlZ9L0+G9GPYgwgZfRWTqGBzxvQIdT0REpN0ZhoXYq2/DEdud8pzfUfTcTBJvmYU9OjHQ0UREOqSTlgIAISEhvPvuu0yaNAkAj8dzVkOJyDfz1rio3vE6NR/+A1/dEWzRicRccwfhQ0ZidYYFOp6IiEjAhV98NbaoBEpfXkzhs1kk3PQwzpQBgY4lItLhnLQUiIyMZMGCBezatYulS5eydOlS4uLi2iObiHyFaZo0Hd5LVX4udXvfA78fZ79hRKZm4Ow3FMPQvKEiIiJf5ew1kO53LKJk9S8pfmE+cZn3Ej5kZKBjiYh0KCctBR5//HH+8pe/8Nvf/paQkBA8Hg+LFi1qj2wiAvg9TdR+/BbV+Xk0l36OJTiUyBFjibhkNPaYpEDHExER6dDsMUkkT32MsjVLKH91OfUFO4i5+kfYI+MDHU1EpEM4aSkQHx/PzTffzL59+/D5fEydOpX4eL2IipxtHncJ1R+up2bnRvyNtTjiU+g29l7CBl6JxREc6HgiIiKdhtUZRuKtc3C//Teq3ltL/acfEPkf44n63g2akFdEuryTlgKbN29m7ty5WK1WXnzxRcaMGcOTTz7JyJE69UrkbPDWuKhY/3vqP/0ADIPQCy8jIjWD4J4XYRhGoOOJiIh0SobVRkz6rUQMuwbXmy9wZOsaaj7aQHTarYQPHYVhsQY6oohIQJz0IuTly5ezevVqIiIiSEhIYNWqVSxbtqw9sol0OXV73+Pw/z1Ew/6dRF1xIykP/JaEidNwpgxQISAiInIG2CK6EX/dgyTf+Tj2mGQqcn/H4d9Pp37/zkBHExEJiJOeKeDz+UhISGj9feDAgWc1kEhX5G9qoOIff6R210aCks4j7roHccQmBzqWiIjIOSs4+TySbnuUuk/fw7VhJSUvPoqz3zBiR03FEdcz0PFERNrNSUuB4OBgSkpKWj+l/PDDD3E4HGc9mEhX0Xh4L2Vrn8JbVU7UFTcRfeUkDOsprRYqIiIi34FhGIRdeDmh56VSlZ/Lkbf/yuH/+39EDPsB0Wm3YA2NDHREEZGz7qTvPKZNm8Ydd9xBeXk5kydPpqCggP/93//9Tk+6ceNGli9fTkNDA1dccQVz5sxh69atPPbYYzQ1NZGRkcFDDz0EwJ49e5g9ezZ1dXWkpqYyf/58bDYbRUVFzJgxg8rKSvr06cOSJUsIDQ2lurqa6dOnc+jQIWJiYli2bBlxcXE0Nzcze/Zsdu/eTXBwMEuWLKFfv37f6ThEvgvT58X99l858s4abJHdSL7tUYJ7XhjoWCIiIl2OYbMTddkEwodchfut1VRvX0/Nx28RfcWNRIwYi8WmD8RE5Nx10jkFhg8fzksvvcQTTzzBXXfdRU5ODpdddtm3fsJDhw4xb948VqxYwd///nc++eQTNm/ezKxZs1ixYgU5OTns3r2bzZs3AzBjxgzmzp3L+vXrMU2T1atXAzB//nwmT55MXl4egwYNYsWKFQAsW7aM1NRUcnNzmTRpEgsXLgRg5cqVOJ1OcnNzmTVrFjNnzvzWxyDyXXlcRRQ9P4cjb/+NsMHp9Pjxr1QIiIiIBJg1JIJuo39Mj7uX4ux5Ea6NKzn8uwep3bMV0zQDHU9E5Kw4aSkAUFBQALScYrVz5042bNjwrZ/w9ddfZ+zYsSQmJmK321m6dClOp5NevXrRs2dPbDYb48ePJy8vj8LCQhobGxk6dCgAEydOJC8vD4/Hw7Zt2xg9evRx2wE2bdrE+PHjARg3bhxbtmzB4/GwadMmJkyYAMCIESNwuVwUFRV96+MQ+TZM06R6x+sc/v10PK5i4idOJ378A1oOSUREpANxdOtB4i2zSJw8F4vDSdmaX1H0/GwaC/cFOpqIyBl30ssH5s6dy8aNG+nZs2frvAKGYTBq1Khv9YQHDx7Ebrdz7733UlxczFVXXUX//v2Ji4tr3Sc+Pp7S0lLKysqO2x4XF0dpaSlut5uwsDBsNttx24Hj7mOz2QgLC8Plcn3tY5WUlJCcrMncpH346qoof+031O/bhrPPEOLGPYAtIjbQsURERKQNIX0uxvmfT1Cz603cm16k6LksHIl9scckYY9ObPmKScIWnYg1NEorBYlIp3TSUuCdd94hLy+PsLCwM/KEPp+P/Px8Vq5cSUhICPfddx/BwcHHvYiapolhGPj9/q/dfuz7V7X1ImyaJhaL5YT7HNsu0h7q/7Wd8nUr8DfWEfuDO4kYMRbD0PgTERHp6AyLlYih1xB20RVUbXuNxkN7aSouoG7Pu2D6v9zPHny0JEjEFp2IPToJe0zLd2t4tP7dF5EO66SlQFJS0hkrBAC6devG5ZdfTkxMDADXXHMNeXl5WK3W1n3Ky8uJj48nMTGR8vLy1u0VFRXEx8cTExNDTU0NPp8Pq9Xauj+0nGVQUVFBYmIiXq+Xuro6oqKiSEhIoKysjJSUlOMeS+Rs8nuacG14nurteTjie5E0eR6O+JRAxxIREZHTZAlyEv39m1p/N31evNUVeFzFLV/uErzuEprLv6Dus3zwe1v3NWwObNEJ2KMTCUrsS/jFo3S2oIh0GCctBYYPH8706dO5+uqrCQoKat3+bS8fGDlyJI888gjV1dWEhoby1ltvMWbMGJ555hkOHjxIjx49WLduHTfeeCPdu3cnKCiI7du3M3z4cNauXUtaWhp2u53U1FRycnIYP3482dnZpKWlAZCenk52djb33nsvOTk5pKamYrfbSU9PZ+3ataSmppKfn09QUJAuHZCzqqm4gLK1y/BUFhF52QRi0idj2OyBjiUiIiJngGG1tV5CQL9hx91m+n14qyvxuIvxukrwuEvwuFvKg/rP8nG/9VdCL/wPIlIzCO45QJcdiEhAGeZJplKdPHnyiXcyDF544YVv/aR/+9vfeO655/B4PK1LEr7//vutSxKmp6czc+ZMDMNg7969zJkzh9raWgYOHMhjjz2Gw+GgsLCQrKwsKisrSUpK4sknnyQyMpIjR46QlZXFoUOHCA8PZ8mSJfTo0YOmpibmzp3L7t27cTgcLFiwgIEDB55S3sOHDzNq1Cg2bNhAjx49vvVxS9dg+n0ceXct7i0vYQ2NJH7CT3H2HhzoWCIiItIBeNwlVH+4npqdG/E31uKITyFieAZhg9KwOIIDHU9EzkEnez970lJAVArIqTFNk4aCD6nc8DyeisOEXvQ9umXcjdUZHuhoIiIi0sH4PU3UfvwW1dtyaS47gCUohPCLryZi+BjsMUmBjici55CTvZ896eUDBw4c4Pe//z1VVVXHrc+6fPnyM5tUpBNrLjtI5Rt/ouHzj7DHJJFw08OEnH+pTgcUERGRr2WxBxEx9BrCLx5F0+FPqcrPoSo/l6oP1uHsN4zI1Ayc/YZpgkIROetOWgo88sgjXHTRRa3X7IvIl7y1btybX6Lmo41YgkJaVhYYPhrDqrkDRERE5OQMwyC454UE97wQb42bmh2vU/3hekr+8kts0YlEDB9D+JCRWJ1nbuJvEZGvOmkpUF9fz89//vN2iCLSefg9TVR98BpHtr6M6fUQMWIs0d+/SZcKiIiIyLdmC48mOu1moq64gbpPP6A6PxfXG8/h3vwiYYPSiBg+hqCE3oGOKSLnmJOWAj179qSiooJu3bq1Rx6RDs00Teo+eRvXxlV4qysIOX8EMVffjiNWK1mIiIjImWFY7YQNuIKwAVfQVPI51dvzqP3nZmp2vE5wygAiUjMIPf9SDOtJ/5QXETmpk76SWCwWMjMzGTx4MMHBX86IqjkFpKtpPLyXytefo6loH46EPiSNf0CrCoiIiMhZFZTYh7jM+4i5+kfUfPQm1dtzKVvzK6xhMURcci3hw67BFhYd6Jgi0omdtBRIT08nPT29PbKIdEieI6W4Nq6ibs9WrGHRxI27n7DB6RgWa6CjiYiISBdhdYYTddkEIi/NpKFgJ1X5ubi3vIT77b8RdtHlRKRmENT9fE1yLCKn7aSlwKRJkygpKeGDDz7A6/Vy6aWXalk+6RL8jXW4t66h6oN1GIaFqCtvJuqy67SGsIiIiASMYbES0n84If2H01xZRPX2PGp2vUntx2/hSOxLZGoGoQOuwGIPCnRUEekkTloKvPPOO0ybNo2hQ4fi8/n45S9/yRNPPMHIkSPbI59IuzP9Pmp2vIFry0v466sJG3IVMemTsUXEBjqaiIiISCtHbDLdrr2LmKt+SO0/t1C1PZfydU9TueF5woeOIuKS0dij4gMdU0Q6uJOWAsuWLeP555/n/PPPB+DTTz8lKytLpYCcc3x1VVTvfIPq7evx1VQSnDKQ2GumEpTUL9DRRERERNpkcTiJGD6a8EuupfGLj6nOz6Xqvb9T9d7fCek/nIjUDJy9h+jSAhH5WictBTweT2shAHDBBRfg8/nOaiiR9tRYuI/q7bnUfvIO+Lw4+wwhIuNuQs4brn88RUREpNMwDANnr0E4ew3CW11B9fb1VO98g/rPtmGP7U7E8DGED7kKS1BIoKOKSAdy0lLA4XDwySefMGDAAAA+/vhjHA7HWQ8mcjb5vc3UfbKV6vxcmor/heEIJmLYD4gYPgZHN82ZISIiIp2bLaIbMSOnEHXlJOr2bKU6P4/Kf/wB16YXiBwxjqjvXY/F4Qx0TBHpAE5aCkyfPp2f/OQn9O3bF8Mw2LdvH0uXLm2PbCJn3Fdbc399NfbY7sSO/jHhg9PVmouIiMg5x2JzED74KsIHX0Vj4T6q3v87R975GzU73yD6qh8SPmSkVlQS6eLaLAWam5txOBxceumlvPrqq+zcuRO/38+wYcOIjdWEa9J5mKZJ48HdVOXnUv/ZNgBC+qcSmZpBcO/BukRAREREuoTg7v0JnjiNxsLxVL7+HBWv/YbqbbnEXjMVZ58hgY4nIgHSZilwyy238MorrwAQExPD1Vdf3W6hRM4Ef3MDtf/cTFV+Lp6Kw1ic4URdfh3hl1yLPVIz8YqIiEjXFNz9fJKnLqRuz1ZcG1dR/Of5hPRPJWbU7Thiuwc6noi0szZLAdM02zOHyBnTsmZvLjW7NmE21eNI7Efc+Ada1uy1aT4MEREREcMwCBtwBSHnj6B6Ww7ut//G4WceIuKS0URfeTPWkPBARxSRdtJmKdDU1MQnn3zSZjkwcODAsxZK5HSZfh/1//qQ6u25NOz/CCw2wgZ8j4jUDIKS++sSAREREZGvYbE5iLr8esKHjMS95S9Ub8+jdvdmor5/E5HDMzBs9kBHFJGzrM1S4NChQ/z3f//315YChmGwYcOGsxpM5FT4Gmqo+Wgj1dvz8B4pwxoeQ3T6Dwkfeg22sKhAxxMRERHpFKyhkXTLuJuI1DFUbnge1xt/onr7emKvvo2QC/5DH7CInMPaLAXOO+88srOz2zOLyClrKtlPdX4utR+/jeltJjhlIDFX307o+SMwrCddVENEREREvoYjLoWkW+dQX7CDyg1/ovTlJwhOGUDsNXcQlNQv0PFE5CzQuyfpNEyfh7q971OVn0PT4U8x7EGEDb6KyNQxOOJ7BTqeiIiIyDkjpN8wnH2GULNzA67NL1L4x4cJG3wVMVdNxhahlchEziVtlgKpqantmUOkTd4aF9U7Xqfmw3/gqzuCLTqR2B/cSdiQkViDQwMdT0REROScZFisRFxyLWEDv8+RrWuoen8ddXu2EnrR5dhjkrHHJGGPTsQWnai/yUQ6sTZLgTlz5rRnDpHjmKZJ0+G9VOXnUrf3PfD7CTnvEiJSM3D2vRjDsAQ6ooiIiEiXYAkKIWbkjwgfdi3uzS/ScOCf1P5z8/H7hERgj048+pWELebLny3OMM1JINKB6fIB6VBMv4+aXZuo3pZDc9kBLMGhRI7IJGL4aOzRiYGOJyIiItJl2aPiib/uQQD8nia87lI87mI87hI8rhK87mIaD+2hdvdbwJeTlVuCQ1vPKLBHJ2GPOVocRCdiDY1UYSASYCoFpEMwTZOGgg+p3PA8norDOOJ70W3sfYQNuhKLPSjQ8URERETkKyz2IBzxKTjiU064zfR68BwpxeMuwesuweNqKQ6aiguo2/MumP7WfQ1HcEtREJ2IPeYrxUF0ItbwaJ0dKtIOVApIwDWXHaTyjT/R8PlH2GOSSLjpEULOH6HWWERERKQTMmx2HN164OjW44TbTJ8Xb1UZHndpa1ngdZfQXH6Qus+2gd/7lcdxHC0JElvPLrBHJ2KPTcYaHqu/FUXOEJUCEjDeWjfuzS9R89FGLEEhxP7gTiKGj8aw2gMdTURERETOAsNqOzpJYTL0G3bcbabfh7e6Eo+7GK+rpOWyBHcxHncxDft3YnqbW/e1RSW0rJDQdyjO3oOwOJztfSgi5wyVAtLu/J4mqj54jSNbX8b0eogYMZbo79+E1Rke6GgiIiIiEiCGxYo9Kh57VDz0ufi420zTj6/GjcddTHPZFzR8/lHLPFTb88BiIzjlIkL6DiWk3zDscSk6i0DkNKgUkHZjmiZ1n7yNa+MqvNUVhJx/KTFX34YjNjnQ0URERESkAzMMC7aIWGwRsTh7DSJyxFhMr4fGw3upL9hBw/4duDauxLVxJdawGEL6DcXZbxjO3kOwOsMCHV+kQ1MpIO2i8fBeKl9/jqaifTgS+pA04b9x9hoU6FgiIiIi0kkZNjvO3oNx9h4Mo27HW11J/f6dNOzfQd2n71Pz0UYwLAR1709I32E4+w0jKKmvJi8U+TcqBeSs8hwpxbVxFXV7tmINiyZu3P2EDU7HsFgDHU1EREREziG2iFgiho4iYugoTL+PpqJ9LWcRFOzEveUvuLe8hCUkgpA+F+PsNxRnn6HYwqICHVsk4FQKyFnhb6zDvXUNVR+swzAsRF15M1GXXYfFERzoaCIiIiJyjjMsVoJ7XEhwjwsh/Yf46qpo+HwX9ft3UF+wg9qP3wLAkdiXkL4tlxoE97hAH1xJl6RSQM4o0++jZscbuLa8hL//0vgPAAAcLklEQVS+mrAhVxGTPhlbRGygo4mIiIhIF2UNjSRs0JWEDboS0/TTXHKA+v07aCjYwZF3szmydQ2WoBCcfYbg7DuMkH5DsUV0C3RskXahUkDOmPqCHVS+8RyeisMEpwwg9po7CErqF+hYIiIiIiKtDMNCUFJfgpL6En3Fjfga62g4sIuGgp3UF+ygbu97ANjjeh6di2Aozp4DMGxaNlvOTSoF5DtrLvuCyg1/omH/TmzRiSTcOIOQC/5DS8GIiIiISIdnDQ4l7MLLCbvwckzTxFNxiPqClgkLq/JzqHr/7xj2IJy9BuE8tuxhTFKgY4ucMSoF5Fvz1h7BveUv1Ox8A0uQk5hr7iAydQyGVS2qiIiIiHQ+hmHgiEvBEZdC1GUT8Dc30njw49a5COr/tZ1KwBad2DoXgbPXIM2bJZ2aSgE5bX5vM9UfrMP9zhpMbzMRqWOI/v7NWEPCAx1NREREROSMsTiCCek/nJD+wwHwuIpbVjTYv5OaXW9SvT0PrDacKQNa5yKwd+upM2alU1EpIKfMNE3qPnkH15ur8FaVE9J/BDGjbsMR2z3Q0UREREREzjp7TBKRMUlEjhiL6fXQeGjP0bMIduLa8CdcG/6ENTyWkH5H5yLoPQRrcGigY4t8I5UCckoaCz+j8vVnaSr8DEd8b5Km3I+z9+BAxxIRERERCQjDZm9ZraDPEGJHTcVbXXn0LIId1O7ZSs3ON8CwENzjgta5CByJfTAMS6CjixwnoKXA448/jtvtZtGiRezZs4fZs2dTV1dHamoq8+fPx2azUVRUxIwZM6isrKRPnz4sWbKE0NBQqqurmT59OocOHSImJoZly5YRFxdHc3Mzs2fPZvfu3QQHB7NkyRL69euHaZosXryYN998E4vFwqOPPsrw4cMDefidgudIGa43V1H3yTtYQ6OIG3c/YYPTtYariIiIiMhX2CJiiRh2DRHDrsH0eWkq2tcyD0HBTtybX8S9+UWsoZE4+w7F2Xsw9tju2KOTsDjDdLmBBFTASoF3332XV155hauuugqAGTNmsGDBAoYOHcqsWbNYvXo1kydPZv78+UyePJnMzEyefvppVqxYwYwZM1i2bBmpqak888wzZGdns3DhQpYtW8bKlStxOp3k5uaybds2Zs6cyerVq1m/fj0FBQXk5ORw8OBB7rnnHnJycrDZdLLE1/E31XNk6xqq3l8HhkHU9ycRdfl1WBzOQEcTEREREenQDKuN4J4XEdzzImKumoyvror6/Ttp2N+y7GHtPze37msJDsUenYgtOhF7dBL2mJbvtuhErKGRKgzkrAvIO+IjR46wdOlS7r33Xvbu3UthYSGNjY0MHToUgIkTJ/LUU08xadIktm3bxtNPP926/Uc/+hEzZsxg06ZNvPDCCwCMGzeOX/ziF3g8HjZt2sSDDz4IwIgRI3C5XBQVFbF582bGjh2LxWKhT58+JCUlsWPHDkaMGBGI/wQdlun3UbNzA67NL+KvryZscDoxV03GFtEt0NFERERERDola2gk4YPTCR+cjmn68VQW4XGX4HWX4HEV43GX0FRcQN2ed8H0t97PcAS3FAXRidhjvlIcRCdiDY/WpQhyRgSkFJg7dy4PPfQQxcXFAJSVlREXF9d6e1xcHKWlpbjdbsLCwlo/zT+2/d/vY7PZCAsLw+Vyfe1jlZSUUFZWRnx8/AnbBbw1bhr272hpLz//CH9DLcE9LyL2ltkEJZ8X6HgiIiIiIucMw7Dg6NYDR7ceJ9xm+rx4q8pbiwKPuxiPq4TmsoPUfbYN/N4vH8fmOFoSJLaeXWCPTsQWk4gtPFaX+8opa/dS4K9//StJSUlcfvnlrFmzBgC/33/caTGmaWIYRuv3r2rr9BnTNLFYLCfc59j2r3sOi6VrNmumz0Pj4U9bJkIp2Elz2QEArKFRhPRPJfTCywk5b7hOVRIRERERaUeG1YY9Jgl7TNIJt5l+H97qCjyuErzuo6WBqwSPq4iGgh2YPs+XO1tt2KMSjpYER880iE7EHpOELTJOhYEcp91LgZycHMrLy7nuuuuoqqqivr4ewzAoLy9v3aeiooL4+HhiYmKoqanB5/NhtVopLy9v/bQ/Pj6eiooKEhMT8Xq91NXVERUVRUJCAmVlZaSkpBz3WImJiZSVlZ3wHF2F50hZ6zVMDQf+idncABYrwT0uJGbkFJx9h+FI6KVTkEREREREOiDDYm15ox+VAFx83G2m6cdX4/rKGQYtlyV43SU0HNyN6Wn6cmeLFVtk3FfmLzg6h0FMIvaoeAyrvX0PTAKu3UuBZ599tvXnNWvW8MEHH/DYY48xbtw4tm/fzvDhw1m7di1paWnY7XZSU1PJyclh/PjxZGdnk5aWBkB6ejrZ2dnce++95OTkkJqait1uJz09nbVr15Kamkp+fj5BQUEkJyeTlpbGyy+/zLhx4zh8+DAHDhxg8OBzd0k9v6eJxi8+abkkoGAHnspCAGwR3QgbeCUh/VpmPbUEhQQ4qYiIiIiIfBeGYcEW0Q1bRLcTlg03TRNf7RE87uKvzGFQjMddSuPhvS0fFrY+kAVbZLfjLkVo/Tk6AYvN0c5HJu2hw0y9v2TJEubMmUNtbS0DBw7k9ttvB2DevHlkZWXxm9/8hqSkJJ588kkAHnzwQbKyssjMzCQ8PJwlS5YAcNtttzF37lwyMzNxOBwsXrwYgDFjxrBr1y4mTJgAwMKFCwkODg7AkZ5d9fu2U5WfS+MXH2N6mzGsdoJ7DSTikmtx9h2KPba7LgsQEREREekiDMPAFh6NLTwaUgYcd5tpmvjrq4+bv8B79EyD2j3v4G+o/eojYY2IPe5ShC9XTUjE4jj33lt1FYZpmmagQ3R0hw8fZtSoUWzYsIEePU6cEKQjKV3zK5pLD+DsN4yQfsMIThmAxR4U6FgiIiIiItLJ+Bpq8LhLW+YwcJW0lgdedwm+uqrj9rWGRR8tCY6/LMEenYAlODRARyBw8vezHeZMATkzEiZOC3QEERERERE5B1id4Vid4fA1K5L5m+q/Mn/B0ckPXcU07N9B7S73cftaQiJOmMPAHpuMI6G3Jj3sAFQKiIiIiIiIyGmxBIUQlNiXoMS+J9zmb27E4/7yUoRjEyA2HPyY2n9u/vIxgsNw9r2YkL5DcfYd1nKJg7Q7lQIiIiIiIiJyxlgcwQQl9CYoofcJt/k9TXiPlNFcdoD6/R/RULCDuk/eAcAR3xtnv6Etl0H3uEArIbQTlQIiIiIiIiLSLiz2IBxxPXHE9SRs4JWYpklz2UEaCnZQv38HVe+/StW72RiOYJy9BxPSdxjOfsOwR3Wd5eTbm0oBERERERERCQjDMFrPKoj63g34m+ppOLCb+v07WoqCz7YBYI9NxtlXk6mfDSoFREREREREpEOwBIUQesGlhF5wKaZp4nEVtZQDBTup2fE61dtew7A5CE65CEdCn69MYJiENTwaw7AE+hA6HZUCIiIiIiIi0uEYhoEjtjuO2O5EXjoOv6eJxi8+ob5gBw0HdtFw4GPwe7/c3+bAFn10hYOYY0siJmKLScQWHquVDtqgUkBEREREREQ6PIs9iJB+LZcQAJh+H97qii+XRDy6POKxswtMn+fLO1tt2KMSjpYELWVBUGJfgpLP6/JlgUoBERERERER6XQMi7XljX5UAnDxcbeZph9fjat1OcRjSyN63SU0HNyN6WkCji6L2GcIIf2G4ew7FFt4TACOJLBUCoiIiIiIiMg5xTAs2CK6YYvohrP34ONuM00TX62bxkN7qC/YScP+HdTt2QqAI74Xzn7DCOk7lOCeF3aJZRFVCoiIiIiIiEiXYRgGtvAYwgZcQdiAK75cFnH/TuoLdlD1/rovl0XsNZiQfkOPLouYEOjoZ4VKAREREREREemyjlsW8fLr8Tc10HBw99FVD3ZQv+/osogxyTj7DSWk7zCCew08Z5ZFVCkgIiIiIiIicpQlyEno+SMIPX/E0WURi2ko+PDosohvUL0tB8NqJ+zikcRl3BPouN+ZSgERERERERGRr9GyLGIyjtjk45dF3L8TiyM40PHOCJUCIiIiIiIiIqfg35dFPBdYAh1ARERERERERAJDpYCIiIiIiIhIF6VSQERERERERKSLUikgIiIiIiIi0kWpFBARERERERHpolQKiIiIiIiIiHRRKgVEREREREREuiiVAiIiIiIiIiJdlEoBERERERERkS5KpYCIiIiIiIhIF6VSQERERERERKSLUikgIiIiIiIi0kWpFBARERERERHpolQKiIiIiIiIiHRRKgVEREREREREuiiVAiIiIiIiIiJdlEoBERERERERkS5KpYCIiIiIiIhIF6VSQERERERERKSLUikgIiIiIiIi0kWpFBARERERERHpolQKiIiIiIiIiHRRASkFli9fTmZmJpmZmSxevBiArVu3Mn78eK699lqWLl3auu+ePXuYOHEio0ePZvbs2Xi9XgCKioqYMmUKY8aM4b777qOurg6A6upq7r77bjIyMpgyZQrl5eUANDc3M2PGDDIyMrjhhhsoKCho56MWERERERER6VjavRTYunUrb7/9Nq+88grZ2dl8/PHHrFu3jlmzZrFixQpycnLYvXs3mzdvBmDGjBnMnTuX9evXY5omq1evBmD+/PlMnjyZvLw8Bg0axIoVKwBYtmwZqamp5ObmMmnSJBYuXAjAypUrcTqd5ObmMmvWLGbOnNnehy4iIiIiIiLSobR7KRAXF0dWVhYOhwO73U6/fv04cOAAvXr1omfPnthsNsaPH09eXh6FhYU0NjYydOhQACZOnEheXh4ej4dt27YxevTo47YDbNq0ifHjxwMwbtw4tmzZgsfjYdOmTUyYMAGAESNG4HK5KCoqau/DFxEREREREekw2r0U6N+/f+ub/AMHDpCbm4thGMTFxbXuEx8fT2lpKWVlZcdtj4uLo7S0FLfbTVhYGDab7bjtwHH3sdlshIWF4XK5vvaxSkpKzvrxioiIiIiIiHRUAZtocN++fdx11108/PDD9OzZE8MwWm8zTRPDMPD7/V+7/dj3r/r33796H4vFcsJ9jm0XERERERER6aoC8q54+/bt3HHHHUybNo0bbriBxMTE1gkBAcrLy4mPjz9he0VFBfHx8cTExFBTU4PP5ztuf2g5y6CiogIAr9dLXV0dUVFRJCQkUFZWdsJjiYiIiIiIiHRV7V4KFBcXc//997NkyRIyMzMBuPjii/n88885ePAgPp+PdevWkZaWRvfu3QkKCmL79u0ArF27lrS0NOx2O6mpqeTk5ACQnZ1NWloaAOnp6WRnZwOQk5NDamoqdrud9PR01q5dC0B+fj5BQUEkJye39+GLiIiIiIiIdBiGaZpmez7hggULePnll0lJSWndduutt9K7d28ee+wxmpqaSE9PZ+bMmRiGwd69e5kzZw61tbUMHDiQxx57DIfDQWFhIVlZWVRWVpKUlMSTTz5JZGQkR44cISsri0OHDhEeHs6SJUvo0aMHTU1NzJ07l927d+NwOFiwYAEDBw48pcyHDx9m1KhRbNiwgR49epyt/zQiIiIiIiIiZ9TJ3s+2eynQGakUEBERERERkc7oZO9nNdOeiIiIiIiISBdlC3QAObM25n/B6x98EegYIiIiIiIi57QfXJrC1akpJ9+xg9OZAiIiIiIiIiJdlM4UOMdcnXputFUiIiIiIiJy9ulMAREREREREZEuSqWAiIiIiIiISBelUkBERERERESki1IpICIiIiIiItJFqRQQERERERER6aJUCoiIiIiIiIh0USoFRERERERERLoolQIiIiIiIiIiXZRKAREREREREZEuSqWAiIiIiIiISBelUkBERERERESki1IpICIiIiIiItJF2QIdoDPw+XwAlJSUBDiJiIiIiIiIyKk79j722Pvaf6dS4BSUl5cDMGXKlAAnERERERERETl95eXl9OrV64TthmmaZgDydCqNjY3s3r2buLg4rFZroOOIiIiIiIiInBKfz0d5eTmDBg0iODj4hNtVCoiIiIiIiIh0UZpoUERERERERKSLUikgIiIiIiIi0kWpFBARERERERHpolQKiIiIiIiIiHRRKgVEREREREREuiiVAiIiIiIiIiJdlEoBERERERERkS5KpUA7qa2tZdy4cRw+fBiANWvWMHbsWMaPH8+CBQvwer0AlJWVcffdd3P99ddz6623tu5fXV3N3XffTUZGBlOmTKG8vDxgxyKd26mMxcrKSq677rrWr6uvvpphw4YBGoty5pzq6+Lhw4eZMmUK1113HbfddhuFhYUANDc3M2PGDDIyMrjhhhsoKCgI2LFI53aqY3HXrl3ceOONjB8/nnvuuaf19U9jUb6r5cuXk5mZSWZmJosXLwZg69atjB8/nmuvvZalS5e27rtnzx4mTpzI6NGjmT17duv4LCoqYsqUKYwZM4b77ruPurq6gByLdG6nMxaPefjhh1mzZk3r7xqLnZApZ93OnTvNcePGmQMHDjQPHTpkFhQUmFdeeaVZWlpqmqZpzps3z/zjH/9omqZpTp061fzzn/9smqZp/vnPfzYffPBB0zRNc/78+ebvfvc70zRN85VXXmndLnI6TmcsHuPz+cwf/ehH5t///nfTNDUW5cw4nbE4ffp084UXXjBN0zSff/55c9q0aaZpmubvf/9782c/+5lpmqb5wQcfmJMmTQrAkUhnd6pj0e/3m+np6ea7775rmqZpvvbaa+Y999xjmqbGonw377zzjnnLLbeYTU1NZnNzs3n77bebr776qpmenm5+8cUXpsfjMe+66y5z06ZNpmmaZmZmprljxw7TNE1z5syZra+Pd999t7lu3TrTNE1z+fLl5uLFiwNzQNJpne5YLCkpMe+55x5zyJAh5ssvv9z6OBqLnY/OFGgHq1evZt68ecTHxwPw6aefMnTo0NbfR44cyRtvvIHL5WLv3r3ceuutANx44438z//8DwCbNm1i/PjxAIwbN44tW7bg8XgCcDTSmZ3qWPyql19+GafT2Tr+NBblTDidsej3+6mtrQWgoaGB4OBgoGUsTpgwAYARI0bgcrkoKipq70ORTu5Ux6Lb7aaxsZHLLrusdfvbb79Nc3OzxqJ8J3FxcWRlZeFwOLDb7fTr148DBw7Qq1cvevbsic1mY/z48eTl5VFYWEhjYyNDhw4FYOLEieTl5eHxeNi2bRujR48+brvI6TidsQjw6quvMmrUKDIyMlofQ2Oxc1Ip0A4WLlxIampq6+8XXnghH330EcXFxfh8PvLy8qioqODQoUMkJyezaNEibrzxRn76059it9uBlssK4uLiALDZbISFheFyuQJyPNJ5nepYPMbn8/Hb3/6WadOmtW7TWJQz4XTG4oMPPshzzz3HlVdeyR//+Ed+8pOfAMePRWj5Y6akpKR9D0Q6vVMdi9HR0YSEhPD2228D8Nprr+HxeHC73RqL8p3079+/9U3+gQMHyM3NxTCM48ZUfHw8paWlXzvWSktLcbvdhIWFYbPZjtsucjpOZywC/PjHP2bSpEnHPYbGYuekUiAA+vTpw7Rp07jvvvuYMmUKF1xwAXa7Ha/XyyeffMJll13Gyy+/zKhRo8jKyvraxzBNE4tF//PJd9PWWDzmrbfeonfv3lxwwQVtPobGopwJ3zQWH3nkEX7xi1/w1ltvMX/+fB544AFM08Q0TQzDaH0MjUU5E9oai4Zh8NRTT/G73/2O66+/npqaGqKiorDb7RqLckbs27ePu+66i4cffpiePXueMKYMw8Dv93/t9n8fg8AJv4ucqlMZi23RWOyc9C9WADQ1NTFkyBCys7N56aWXSEhIoGfPnsTFxREaGsrIkSOBllOzd+3aBbS0csc+NfN6vdTV1REVFRWwY5BzQ1tj8Zg33niDsWPHHncfjUU5G9oaiy6Xi/3793PNNdcAMHr0aMrLy3G73SQkJFBWVtb6GBUVFa2nfIt8W9/0umiz2Vi5ciXZ2dlMmDABv99PVFSUxqJ8Z9u3b+eOO+5g2rRp3HDDDSQmJh43kW95eTnx8fEnbD821mJiYqipqcHn8x23v8jpOtWx2BaNxc5JpUAA1NfXc8cdd1BbW0tzczOrVq1i7NixpKSkkJiYyObNmwF48803GThwIADp6elkZ2cDkJOTQ2pq6nGf6Ip8G22NxWN27tx53Gm1oLEoZ0dbYzE6OpqgoCDy8/OBlj9WQkNDiYmJIT09nbVr1wKQn59PUFAQycnJgTwMOQd80+virFmzWsv6Z599ljFjxmCxWDQW5TspLi7m/vvvZ8mSJWRmZgJw8cUX8/nnn3Pw4EF8Ph/r1q0jLS2N7t27ExQUxPbt2wFYu3YtaWlp2O12UlNTycnJASA7O5u0tLSAHZN0TqczFtuisdg52QIdoCuKjo7m/vvv55ZbbsHr9TJu3LjWidt+/etfM2/ePJ544gnCwsJYtGgR0HJNbVZWFpmZmYSHh7NkyZJAHoKcI75pLAIcOnSIxMTE4+6jsShnwzeNxeXLl/Poo4/S2NhIaGgov/71rwG47f+3d/cujURRGIff4AcEC9EmiGJjoWBhEUULyYCVYKE2EpFE0aCFKKYQppAkFoEURrFRU4hFsBRrwVQqFv4DsQh+NKIWNkIYMDPbhWVhF5bdzbCZ39PN5RbnwCmGl3tnIhElEglNTEyoubm5+usk4E/8ahZTqZSSyaTK5bJ6e3uVTqclMYv4M8fHx7Isq/rOJ0nhcFiZTEZra2uyLEuGYWh8fFyStLOzo62tLX1+fqq/v1/RaFSSlEwmZZqmDg8P1dHRod3dXVf6wf/rd2fxZ5jF/4/PcRzH7SIAAAAAAEDtcX0AAAAAAACPIhQAAAAAAMCjCAUAAAAAAPAoQgEAAAAAADyKUAAAAAAAAI8iFAAAAH/V9va2ZmZmVKlUqmuVSkXhcFh7e3suVgYAAH5EKAAAAP4q0zRVLpeVy+Wqa7lcTg0NDVpfX3exMgAA8COf4ziO20UAAID6cn9/r9nZWZ2enspxHC0vL+vs7EyBQECXl5c6OjrS19eX/H6/TNPUwMCA3t7elEgk9PHxoff3d3V2dmp/f1/t7e0KhUIKBoMqFova3NzU2NiY2y0CAFAXCAUAAMA/kc/ndX5+Ltu2FY/HZRiGSqWS4vG48vm8WltbVSwWFYvFVCgUqgHC0tKSbNtWLBaTYRian59XKBTS3NycVlZW3G4LAIC60uh2AQAAoD5FIhFdXFyop6dHhmFIkm5ubvT6+qpoNFrd5/P59Pz8rMXFRd3d3enk5ESPj48qlUoaGhqq7gsGgzXvAQCAekcoAAAA/pmuri51d3dXn23b1ujoqLLZbHXt5eVFgUBAmUxGxWJR09PTGh4elmVZ+v5AY0tLS01rBwDAC/jQIAAAqJmRkRFdXV3p4eFBklQoFDQ1NSXLsnR9fa2FhQVNTk6qra1Nt7e3sm3b5YoBAKhvnBQAAAA109fXp1QqpY2NDTmOo8bGRh0cHMjv92t1dVXpdFrZbFZNTU0aHBzU09OT2yUDAFDX+NAgAAAAAAAexfUBAAAAAAA8ilAAAAAAAACPIhQAAAAAAMCjCAUAAAAAAPAoQgEAAAAAADyKUAAAAAAAAI8iFAAAAAAAwKMIBQAAAAAA8KhvHaLc/KQ4bvUAAAAASUVORK5CYII=\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -5645,27 +5578,29 @@ }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 298, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -5701,27 +5636,29 @@ }, { "cell_type": "code", - "execution_count": 329, + "execution_count": 119, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 329, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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cYX96LiuS0hnZvsEp9/2o7+fJiLgwZsQnczAzzwkJy4exLMvZGUolLi7Oio+P/8t127Zto0WLFk5KVPXo+ykiIhXBGwt3MemnnTw4MIpbe4Y7O85ZHTtRyMWTFtMqpBaf3tyxwr2ZICJSGbz40w7eXJTI8om9CfbzOuUxBzJy6TVpMWM6NeKxoa3KOeGFM8astSwr7kzHaERdREREKpwFWw/z4vydXNYmhHE9mjo7zjmp4+PBff0iWZGUzo9bDjs7johIpVNis5ixNpkekUGnLdIBGvh7M6xtKNPX7OdIdn45Jiw/KtRFRESkQkk8ks09X2ygVUgtnhseU6lGpq/p2JDm9Xx56vut5BeVODuOiEilsnTXUQ5m5jPyb5vIncodvSIoKrHx7tLd5ZCs/KlQFxERkQojM6+IWz5ai6e7C5OvjcPT3dXZkUrFzdWFRy5tyYGMPN7/ZY+z41QblmWx/VAW7y3bzez1Kc6OIyLn6ctfD+Dv40HfFvXOemzjQB+Gxobwyar9pOcUlEO68lVl2rNZllWp3nGvqCrbngUiIlJ1lNgs7pq+nuRjuXx2SydCa59+2mNF1jUikAGt6vHmokSGtwujvp+nsyNVSUey8vklMY1lu9L4JTGNo9l/faF+edtQJyUTkfORnlPAgm2Hua5zYzzczm08eULvCL7ZmMrU5Xu4f0CUgxOWrypRqHt6epKenk5AQICK9QtgWRbp6el4euoFhYiIlL/nf9zOkp1HeWpYazo09nd2nAvy30Et6fvyEp6dt41Xrmrr7DhVxvr9x/hu00GW7Upjx2H77vr+Ph50jQike7NAOjUJ4IGvN/LAjE0E+3nS8RStnUSkYpq1PoWiEotRHc4+7f03EXV9GdQ6mA9X7GNc93D8vN0dmLB8VYlCPSwsjOTkZI4ePersKJWep6cnYWFhzo4hIiLVzDcbUpi8ZDfXdGzINR1P3aa0MmkY4M0t3Zvw5qIkru3ciPaNKvcbDxXBtOV7eGLuVtxcXejQuA7D2kXRLSKQlsG1/tLCafKYOK54eznjPl7LzPFdCA+q6cTUInIuLMvii18P0KZBbSLr+ZbqvhN6R/Dd5oN8vGovE3o3c1DC8lcl2rOJiIhI5ZWQksnwt1cQE+bHpzd3OucpjxXdiYJier+4mHq1PJk9vusp+wHL2ZXYLJ6cu5VpK/bSt0U9XrmqDTVrnHmsaX96LsPeWo5PDTdmje9CQM0a5ZRWRM7Huv3HuOKtFTxzRTRXX9Sw1Peft/kg3SODzvq7oaJQezYRERGp0NJyChj3UTwBPh68dU37KlOkA/jUcGPiwCg2JWcyY12ys+NUSicKihn3UTzTVuzlpm5NmHxt+3N6Id4wwJv3ro/jcFY+N38Urx34RSq4L389gJe7K0Nigs/r/gOjgytNkX6uqs5fQxEREal0nvl+O2knCplyXRxBvlVv1PPyNqG0a1ib53/YQXZ+kbPjVCoHM/MY8c5KFu04wpOXt+bhIS1xLcWshLYN6/DKqDZsOHCc+77cgM1WuWaRilQXuYXFzNmYyuCYYHw9q84a8wulQl1EREScYn96LrM3pDCmYyNah/o5O45DGGN4bGgr0k8U8PL8Xc6OU2kkpGRy+ZvL2Zd+gvfHduDaTue3b8HA6GAeGtiC7zcf4rkft5dxShEpC/M2H+JEYck59U6vTlSoi4iIiFO8tTgRVxfDrT2bOjuKQ8WE1ebqixoybcUeNidnOjtOhffztsOMnLwSV2OYcXsXejWve0Hnu7l7E8Z0asjkJbv5bPX+MkopImVl1e50/H086NC4jrOjVCgq1EVERKTcpRzP4+t1yYyKa0C9WlW/Leh/LokioGYNJs7cRHGJrdwff396Lr0mLeaZedvILSwu98c/m6ISG0lHc3h7cRK3fBRPeFBNZt/RlRbBtS743MYYHru0Fb2aB/HwNwks3nGkDBKLSFlJSM2idaif2mz/TdVacS8iIiKVwjuLkwC47eJwJycpH35e7jx2aSvu+Gwd01bs5ebu5TuL4Pkft3MgI5fJS3Yzd+NBnry8Fb2j6pVrBoDMvCJ2H80h6egJko7mkHQkh6SjOexLz6X45Bry/i3tO7t7e5Tdy1Q3VxfeGN2OEe+sZMJn6/lmQle1bROpAAqKS9h1OJtezYOcHaXCUaEuIiIi5epwVj5fxB9geLswQmt7OTtOuRkUXZ/eUXV58aedDGhVnwb+3uXyuBsPHGfupoPc2TuCbhGB/Hd2AjdOi+eSVvV5dGhLgv3O7d8gv6gEm2WVqoC2LIvth7JZsPUwC7YdZuOfpv67uxoaBfgQUbcmA1rVJzyoJhF1axId6ueQVnY+Ndx4f2wcg1/7hfGfrGP2HV3x8nAt88cRkXO381AOxTaLViFVc5+SC6FCXURERMrV5CW7KbFZjL84wtlRypUxhicua0X/l5fyyDcJTB3bweFTPS3L4pl52wjw8WBcj6b4errz/V3deXfZbl77eRfLXjzKff2bc33nRri5/nVFpM1mse1QFst2pbFs11F+3XOMIpuNJoE+tArxo3VILVqF+NEqpBZ1fDx+v19RiY01ezKYf7I4Tz6WB0DbhrW5r18kLYNrEV63Jg3qeP3jMR0t2M+Ll0e1YewHa3jkmwReGBFbro8vIn+VkGp/86516IUvc6lqVKiLiIhIuUnLKeCzNfu4vE0oDQPKZ0S5Igmr4819/SL533fb+G7zQYbEhDj08RbvOMqq3Rk8PrTV722PPNxcuKNXBJfGhPDwNwk8OXcrM9cl89SwaIL9PH8vzH/ZlUb6iUIAour7cl3nRvh6urMlNZN1+44xZ2Pq748TWtuLliG1qOHmwpKdR8nOL6aGmwvdmwUyoVcEvVvUpa5vxdiLoGdkEHf2iuC1hYl0aOKvnaZFnGhLaia+nm40LKcZRpWJCnUREREpN+8u201hsY07elWPtemnMrZLY77ZkMpj326le0QQft6O6RtcYrN4dt52GgV4c/VFDf9xe8MAb6bd0IHvNx/i8TlbuPzN5b/fFljTg+7NAuneLIhuzQJPueHfsROFbD2YRUJKJltSs0hIzeREQTEDW9enb4t6dG8WVGGnlt/dN5L4fcd4eHYC0aF+ZbJpnYiUXkJKFq1CamkjuVNQoS4iIiLl4tiJQj5euY8hMSE0rcYbebm5uvDMFdEMfeMXnv1hO89cEe2Qx5m5Lpkdh7N5Y3RbPNxOPcXcGMPgmGB6RAbywfK9eJwcBW9Rv9ZZ14nX8fGga0QgXSMCHRHfoVxdDK9e1ZZBry1j/Kfr+HZC199nHIhI+SgusbHtYBZjOjVydpQKSe3ZREREpFxMXb6H3MISJvSuXmvTT6V1qB83dm3C9DX7+XVvRpmfP7+ohJfm7yQ2zI/B0cFnPd7X0527+jTjtp7htApxzGZuFU2Qbw1ev7ot+9JPMHHmZizLcnYkkWol6egJCoptWp9+GirURURExOEy84qYtnwvA1vXJ7Ker7PjVAj39osktLYXD87cTEFxSZme+4PlezmYmc+Dg1poSukZdGoawL8HNOe7TQf5aOU+Z8cRqVa2/LaRnHZ8PyUV6iIiIuJw05bvJbugWKPpf+JTw43/Xd6axCM5TF6yu8zOe+xEIW8tTqR3VF06NQ0os/NWVbf1CKd3VF3+991WNhw47uw4ItVGQkoWnu4u1Xop1JmoUBcRERGHys4vYuryPfRtUU+9cv+mV1RdBscE88bCRJKO5pTJOd9clMiJgmL+c0lUmZyvqnNxMbw4Ipa6vp7c8ek6jucWOjuSSLWQkJpJi+BauFaDpTbnQ4W6iIiIONTHq/aRmVfEXX00mn4qj17akhruLtz+yVoOZuZd0LkOZOTy0cp9DG8XRvP6WmJwrur4ePDG6LYcyc7nX19uxGbTenURR7LZLLalZmna+xmoUBcRERGHyS0s5r1le+gZGURMWG1nx6mQ6vp68vY17Uk9ns+wN1ew7WDWeZ/rxZ92YAzc1z+yDBNWD20b1uGhQS34efsRbvzwV9JzCpwdSaTK2p+RS3ZBsTaSOwOHFerGGE9jzBpjzEZjzBZjzOOnOKaGMeYLY0yiMWa1Maaxo/KIiIhI+fts9X4yThRqNP0sujUL5MtbO2NhMeKdlfyyK63U50hIyWT2hlRu7NaEYD8vB6Ss+sZ2acyTl7dmRVI6g15bxqrd6c6OJFIlJZzcSE7LoU7PkSPqBUBvy7JigTbAJcaYTn875ibgmGVZEcDLwHMOzCMiIiLlKLewmHeWJNE1IoD2jfydHafCaxlSi1njuxJa24uxH6xhxtrkUt3/uR+2U9vbndt6hjsoYdVnjOHaTo2YNb4L3h5ujH53Fa/9vIsSTYUXKVMJKVm4uxqa1dNGcqfjsELdsvttVxT3k5e//5a7DPjw5OczgD5GPURERESqhI9W7iMtp5D7+mka9rkKqe3FV7d3pmNTf/791UZe+3nXGft722wWv+7N4IEZG1m2K40JvSLw83Ivx8RVU6sQP+bc2Y2hsSG8NH8n101dzZHsfGfHEqkytqRmElnPlxpurs6OUmG5OfLkxhhXYC0QAbxpWdbqvx0SChwAsCyr2BiTCQQApZ/vJSIiIhVGTkExk5ck0TMyyHGj6ZYFOUfg+D7IOwb5WVDw2yX7T19ng60EXN3Bxe3kR3dwdTv50R08fKBmfahZF3xPfqxZHzy8HZP9DGp5uvPB2IuYOHMTL83fSerxPJ68vDXurn+MryQeyWH2+hRmb0gh+VgeXu6uXNWhAdd2blTueauqmjXceHlUG7qEB/LItwkMenUZr4xqS7dmgc6OJlKpWZbFltQs+rWo5+woFZpDC3XLskqANsaY2sAsY0xry7IS/nTIqUbP//G2sTFmHDAOoGHDhg7JKiIiImXng1/2cCy3iH9d6KZmthJIT4Jje+DYXsg4+fG3S/Fpdkk3rlDDFzxrQY1aYFzAVgwlRWArgpJi+9e/fV6YA1bJP8/j4Qu+9aBWCARFQd0WENQC6kaBV50Le25n4OHmwosjYgmr7cVrCxM5mJnP40NbsXD7EWZvSGFTciYuBro1C+Jf/SPp37I+PjUc+rKuWjLGMLJDA9o0rM0dn67j2qmruePiCO7p2ww3V+3JLHI+Dmbmk3GikFbaSO6MyuU3umVZx40xi4FLgD8X6slAAyDZGOMG+AEZp7j/FGAKQFxcnBYJiYiIVGCZuUVMWbabfi3rlW6nd8uyF+Qp6+yX1HVwcCMU5f5xjLsP1GkM/k0hvLf98zqNwDvAXpD/Vpy7e0NpVtPZSiA3A3IOQc5hyD5s//jb5fgB2PCZvaD/jW+IvWCv29J+adARAsJL97hnYIzhvv7NCantxX9nJ3DxpMUARIf68fCQllwaG0xdX88yeSw5s8h6vnw7oRuPfpvAG4sScXd14e6+zZwdS6RS2pJq72yhjeTOzGGFujEmCCg6WaR7AX3552Zx3wLXAyuBK4GF1pkWYomIiEiF994vu8nOLz772vSifNi/Evb+Yi/KU9fbp7ADuHlC/Whoey2EtIWACHtR7hNYZoXwX7i4Qs0g+4XoUx9jWZB5AI5s+9NlK6x5F0pOtvKqWQ8adYFGXaFxNwhsDi4XNvJ61UUNaRLoQ/y+Y/RvWY9m9dQf3Rm8PFx5/spYMk4UMW3FHm7p0QRvD81iECmthBT7jKAWwfpddiaO/O0SDHx4cp26C/ClZVlzjTFPAPGWZX0LvA98bIxJxD6SfpUD84iIiIiDZZwoZOovexgcHUyL4L9Na7QsyNgNiQsg8WfYu8w+Wm5c7SPSLS6FkHYQ2s7+tWsF2xTNGKjd0H6JHPDH9bYSSNtlf9Nh33LYuxy2zLLf5h0ADTvbi/aIfhB4fm3qOjYNoGPTgDJ4EnKhbuvZlCvfOcxX8clc36Wxs+OIVDpbUjMJD6qpN7rOwmHfHcuyNgFtT3H9I3/6PB8Y4agMIiIiUr4mL00it6iEe36bFlx4AvYsPVmcL7CvKwfwD4e2YyCir72I9fBxWuYL5uJ6cgp8FMTdcHIK/1570b5vhX3GwPa5wET7OveowRA1xD5TQM1uKp24xv60a1ib937ZzTUdG2qtukgpJaRk0ampWnaejd7GEBERkTJxJDufD1fsZaICUPUAACAASURBVHhsXZod/wWWfQU75tlHzd19oEkP6DwBIvrY15hXVcaAfxP7pe0Y+3XH99u/F9vmwC+vwLIXoVaYvWhvMQQadrHvQi+Vwq09w7n147XMSzjEpbEhzo4jUmmk5RRwKCuf1qFan342+osgIiIiF85m4/s5X/EYMxmxZx1sPw5e/hB7FbS8HBp2Arcazk7pPLUbQsdb7ZfcDHvRvn0urPsQ1ky27yDfYqh9TX5YnEbaK7h+LerRNNCHKUt3MyQmGKN/L5Fzoo3kzp0KdRERETl/BzfCpi8p2fw1Y3MOUuDuhWvkpRA9AsJ7Vbx15hWBtz+0vcZ+KTxhXxKwbQ5s/speuAdF2Qv2mFEnN7eTisbFxXBLj6Y8OHMzK3en0yVcvdVFzkVCSiYALUPUmu1sVKiLiIhI6RTlw9bZ9t3OU+LBxZ2dNTvyTvEI7r/zbsLqqWg5Zx4+0PIy+yU/C7bMhHUfw0//hQWPQvOB9qI9vI+mxlcww9qG8uJPO5m8ZLcKdZFztCU1k4b+3vh56U3cs9FvfBERETk3x/dD/FRY9xHkpttbpl3yLCkNhzL0zY2MjGugIv1CeNaC9mPtlyPbYP0nsPFz+2i7b7B9vXuHm8G3vrOTCuDp7srYLo2Y9NNOth3M+meXAxH5hy2pWbQO1c/KudA2lSIiInJ6Npu9ldr0q+HVWFj+qr3d2LWz4Y5fodPtvLoiDWMME3qfX+sxOYW6LWDAU3DfNhj1CdSPgaWT4JVomH0HHN7q7IQCjOnUCG8PV95dutvZUUQqvMy8Ival52p9+jnSiLqIiIj8U3EhbPrcXpinJ4J3IHS7F9rfALUb/H7Y3rQTfL0uhes6NyLYz8uJgasoNw97f/kWl0J6Eqx62z7SvuET+3T4LndC04u1+ZyT1Pb2YFSHBny8ch//HtCckNr6GRA5na0nN5LTju/nRiPqIiIi8ofCXFj1DrzWBr69076G+op34b6t0OeRvxTpNpvFCz/uwN3VcPvF4U4MXU0EhMPgSfZ/i97/B4c2w8eXwzvdYMN0+5srUu5u6tYEC5j6yx5nRxGp0Lak2jeSa6WN5M6JCnURERGxb2S27CV4NQZ++I+9ndg1X8O4JRAz8h+t1faln+CqKav4bvNBxvUIp66vp5OCV0Pe/tDjfrg3AS57E2wlMPs2+9KENe9CcYGzE1YrYXW8GRITzPQ1+8nMK3J2HJEKa0tqFvVreRJYsxq36iwFFeoiIiLVWW4GLHoaXmkNPz9uXwt9wzy48Qdo1vcfU6ptNosPV+zlkleWse1QFpNGxHJv32ZOCl/NudWwbzA3fiWM+RrqNIbv/w2vt4e106BERWN5GdejKScKS/hs9X5nRxGpsBJSMrWRXClojbqIiEh1lJ8FK16HlW9C0QmIGgLd/wWh7U57lwMZudw/YyOrdmfQMzKIZ4dHa116RWAMRPS1r1nfvQgWPgVz7oZfXoae/4HokWrt5mCtQvzo3iyQqcv3cGO3xtRwc3V2JJEKJa+whKSjOQyKDnZ2lEpDI+oiIiLVSXHBH2vQlz4Pkf3h9pVw1aenLdJtNouPV+1jwCtLSUjJ4rnh0Uy7oYOK9IrGGAjvDTcvgNFfgqcfzL4d3uoIm76yT5EXhxnXoylHswv4Zn2qs6OIVDjbDmVhs7Q+vTT09qqIiEh1YLNBwtew8Ek4vg+a9IC+j59xBB0g+Vgu//l6E8sT0+neLJBnh8cQqp2tKzZjIHIANOsP2+fComdg5s2wbJL93zxygHaJd4BuEYG0DK7F5KVJXNk+DBcXfY9FfrMlxb6RnHZ8P3cq1EVERKq6pIUw/1E4tAnqRdvXM4f3OW2xZrNZrN6TwYy1yXy/+SAuBp4eFs3VFzXAqMCrPIyxt3VrPhi2zoZFT8H0UdC0Fwx4Guq1dHbCKsUYw609m3L35xtYuP0IfVvWc3YkkQojISULfx8Pgv208ei5UqEuIiJSVR3aDD89bF+3XLuhvc1a6yvB5dQr3/amnWDmumS+XpdCyvE8atZwY2hsCBN6R9DA37ucw0uZcXGB1lfYi/Zf34PFz8I7XaH9WOj1X/AJdHbCKmNQdDAv/LiDR7/dQlSwL2F19HMjArDlYCatQmrpzd5SMJZlOTtDqcTFxVnx8fHOjiEiIlJx5R2zbygW/z541ra38upw0z9arAFk5Rfx3aaDfL02mfh9x3Ax0DUikCvbh9G/ZX28PLQpVpWTmwFLnrMX7e7e0OPf0PG2U/7/kNLbnJzJ6PdWUcfbgy9v7Ux9jSBKNVdYbKPVoz9wU7emTBwY5ew4FYIxZq1lWXFnPEaFuoiISBVhs8H6j+1t1vKOQYeboddD4FXnlId/vHIv//tuGwXFNiLq1mR4uzCGtQ1VYVFdHN0JP/0f7PrR3tqt35P2UXeNeF2w9fuPce37a6jrW4PPx3Wibi39TEn1lZCSyZDXf+GN0W0ZEhPi7DgVwrkU6tr1XUREpCpIWQvv9YE5d0FgJNy6FAa9cMoivajExv/N3szD32yhc3gA39zRlfn39uD2i8NVpFcnQZFwzZcwZia4ecGX18JHl0FaorOTVXptG9Zh2g0dOJSVz+j3VpOWU+DsSCJOsyX15EZyIdpIrjRUqIuIiFRmJ9Lg2zvh3T6QlQLDpsAN86B+9CkPP55byNgP1vDJqv3c2rMp71/fgdgGtbVusDqL6AO3/QKDJkHqBni7i30de7GKywsR19ifqWM7kHwslzHvrSbjRKGzI4k4xZbULGrWcKOh9jopFRXqIiIilZHNZl9j/Ho72PAZdL4DJsRD7KjTTl1OOprDsLdW8OueY0waEcuDA1vgqhZSAuDqBhfdAhN+hRZDYPEz9oJ99xJnJ6vUOjUN4P3rO7An7QRj3lvN8VwV61L9rN13jNahtdSysJRUqIuIiFQ2R3fCtEHw3b+gfgzcthwGPAWetU57l2W7jjLszeVk5RXx2S0dubJ9WDkGlkrDtx5cOdXews9WDB8NhZnjIOeos5NVWl0jApl8bXsSj+Rw3dQ1ZOUX/eMYm81i28EsPlyxlzs+Xcc1760i8Ui2E9KKlK1jJwrZejCLLuHqLlFa2kxORESksigpguWvwJLn7bt1D3ga2ow+6+ZfH63cy+NzttKsbk3evS5Ordbk3BTlwbIX4ZdXwMMH+j0Oba87bXs/ObOftx3mtk/W0jrUj2ljL2JfxglW785g9Z4Mft2bQWaevYAP8fMkv9iGzbJ4//oOtG906s0gRSqD7zcfZPyn6/j69s60b+Tv7DgVhnZ9FxERqSpS1tnXoh9OgJaXw8Dn7aOfZ1BUYuPxOVv4ZNV++raoxytXtaFmDbdyCixVxtEdMPde2LccGnaGy96EgHBnp6qUfkg4xB2frcOyLGwnX4I3CfThosb+XNTEfmng783+9Fyu/2ANBzPzeOPqdvRteeafdZGK6r+zNjN7fQobHu2Pu6ve5PuNCnUREZHKrjAXFj0Fq96CmvVg8IsQNfisd1u7L4NHv91CQkoWt/ZsygMDorQeXc6fZcGGT+HHh6C4EPo+ChfdqtH187Bk51GW7DhK24a1uaiJP/VO07otPaeAG6f9yuaUTJ4eFs1VFzUs56QiF673pMU0DvRh6tgOzo5SoZxLoa631UVERCqq3Uvs7daO7YX2Y6HfE+B55vY2R7LzeXbedmauS6F+LU/evqYdA6ODyyWuVGHGQNsxEN4b5twDP0yErd/CZW9odL2UekYG0TMy6KzHBdSswWe3dOKOz9YxceZmDmcVcFefCHVokErjYGYeu9NOMLqj3mQ6HyrURUREKprCXFjwKKyZAv7hMPY7aNztjHcpKrHx4Yq9vLJgFwXFJYy/OJw7ekXgo6nuUpZqhcDoL2DjdJg3Ed7uqtF1B/Kp4ca718Xx4MzNvLxgJ4ez83nystaaHSOVwvLEdMC+oaKUnv56i4iIVCTJa2HWOEhPhE7joc8j4O51xrssT0zjsW+3sOtIDhc3D+KRIS1pGlSznAJLtWOMfRPDphfDnLvto+vb5thH1/2bOjtdlePu6sILV8ZQr1YN3lyUxNHsAl6/ui2e7q7OjiZyRisS0wjw8aB5PV9nR6mU9NaniIhIRVBSBAufgvf7QVE+XD8HLnnmjEV6yvG8k62cVpNfXMJ718XxwdgOKtKlfNQKgdFfwmVvwaEE++j66slgszk7WZVjjOH+AVE8PrQVC7Yd5pr3Vp+yzZtIRWFZFsuT0ugcHqD+6edJI+oiIiJ/Y1kWj327hVW7M37vfGaMwWAfTDQGDIauEYFMHBh14Q94ZLt9FP3gRogdDQOfPeNa9CNZ+by5KJHpaw7g4gL39YtkXI+mGmGT8mcMtL3mj9H1eQ/Azh/h8rfAt76z01U513dpTJBvDe6avp7/zNjEW9e005p1qZCSjp7gcFaBpr1fABXqIiIif/PJ6v18uHIfXSMCqFnDDcsCC3sB/9vnGScKeWdJEl0jAuje7OwbQ52SzQar34YFj0ONmjDqE2hx6WkPP5pdwDtLkvhk1T5KbBYj4sKY0LsZobXPPDVexOH8QuGaryB+Kvz4X3i7Cwx9/Zw6FEjpDIoOZn9GLs/O285na/ZzTcdGzo4k8g8rktIA6BquQv18qVAXERH5kz1pJ3j6u230iAziwxs6nHa0qqC4hD4vLuGZ77fT9c7A0k/ty0yGWbfB3mUQORCGvgY1657y0IwThUxemsRHK/ZRUFzCFe3CuKt3MxoGeJf26Yk4jjHQ4SZo3B2+vgk+Hw3tb4ABT4GHj7PTVSnjujdleWIaT8zZSlwjf5rX1xpgqViWJ6YRVsdLf6cugNaoi4iInFRcYuPeLzbg4WbfvOlMU0pruLly/4DmbD2YxTcbU0r3QNvmwNtdsVLXU3Lp6xSN/JRCz0AKikvIL7Jf8gpLSMspYNKPO+j+3EKmLN3NgFb1WHBfTyaNiNWLH6m4giLh5p+h692wdhpM7gGp652dqkpxcTG8NLINvp7u3Dl9HXmFJc6OJPK7EpvFyqR0jaZfII2oi4iInPT24iQ2HDjO61e3pV4tz7Mef2lMCO8t28OkH3cysHXw2deIF+XZpwXHv8+JgGhGpd9MwlcB8NUPZ7zb4Jhg7unTjGbaOVcqCzcP6PcERPSFmbfCe32h9/9Bl7vARXsplIUg3xq8NDKW66au4Ym5W3nmimhnRxIBYEtqJln5xXSJCHB2lEpNhbqIiAiwOTmTV3/exdDYEC6NDTmn+7i4GB4cFMXod1fz0cq9jOsRfvqDj2yDGTfCka0UdBjPoE0XY/N251/dGwC/bVJn/vgcg4uBHpFBtAiudaFPT8Q5mvSA25fD3HthwWOwawFcMcW+pl0uWI/IIG7t2ZTJS3bTLSKQwTHBzo4k8nv/9M7hKtQvhAp1ERGp9vKLSrj3yw0E1qzBk5e1LtV9u4QH0qt5EG8sTGRkXANqe3v89QDLsk///eFBqFET65oZ3L3Kn9Scw3x9e0diwmqX3RMRqYi8/WHENNg4Hb77N7zTzV6sN+vn7GRVwr/7N2f17gwmztxETJgfDfy1LEaca0VSGpH1alLX9+wz0+T0tEZdRESqved/2EHikRxeGBGDn7d7qe8/cWALcgqKeWNh4l9vyDsGX10Pc++Bhp3gtuV8kh7JD1sO8cCAKBXpUn0YA21Gw61L7P3XP70S5j8CJeoFfqHcXV14/eq2YMHdn6+nqER97MV5CopL+HVvBl20Pv2COaxQN8Y0MMYsMsZsM8ZsMcbcfYpjLjbGZBpjNpy8POKoPCIiIqeyIjGNqcv3cH3nRufdZq15fV+ubB/GRyv3cSAj137lgV/hne6w/Tv7Wt0xM9l+wosn526lZ2QQN3VrUobPQqSSCGwGNy+w7wa//FWYNhiOH3B2qkqvgb83T18Rzbr9x3l5/k5nx5FqbN2+4+QX2dQ/vQw4ckS9GPiXZVktgE7AHcaYlqc4bpllWW1OXp5wYB4REZG/yMwr4t9fbaRpkA8TB7a4oHPd2y8SFxeY9ON2WPkWfHAJGBe48Sfoejd5xRYTPluPn5c7L46MLX07N5Gqwt0LLn0Fhr8Ph7fC5O6wY56zU1V6l8aGcFWHBry9JInliWnOjiPV1IqkNFwMdGzq7+wolZ7DCnXLsg5alrXu5OfZwDZAO4eIiEiF8fi3WzicXcDLI9vg5XFhO1EH+3lxe6cgBmz9D/z4IEReArcuhbD2ADwxdwtJR3N4eWQbAmvWKIv4IpVb9JX2qfB+DWD6VfaOCMWFzk5VqT16aSvCg2pyzxcbSMspcHYcqYaWJ6YRE1abWp6lX0Ymf1Uua9SNMY2BtsDqU9zc2Riz0RgzzxjT6jT3H2eMiTfGxB89etSBSUVEpLqYt/kgM9encGfvCGIblMFa8YObmJB4M/1d4/mk1i1YIz8GL/t5525KZfqaA9zeM5xuzTQdUOR3AeFw03zocAusfMM+E+X4fmenqrS8PFx5Y3RbsvKKGP72CjYlH3d2JKlGsvOL2JicSVe1ZSsTDi/UjTE1ga+BeyzLyvrbzeuARpZlxQKvA7NPdQ7LsqZYlhVnWVZcUND5rR8UERH5zfHcQh6atZnYMD/u6BVxYSezLFj3EbzfD9fiAn7q8D7/d6QXi3fZp54eyMjlwa8307Zhbe7tF1kG6UWqGHdPGDwJRnwIabtgcg9IXODsVJVWVP1afHpzR4qKbQx/ewXv/7IHy7KcHUuqgV/3ZlBis+iqjeTKhEMLdWOMO/Yi/VPLsmb+/XbLsrIsy8o5+fn3gLsxRv+yIiLiUG8uSiQzr4jnrozB3fUC/hQW5sLs8fDtndCwM9y2jL4DLqdRgDfPfr+d/KIS7py+Hgy8dlXbC3sskaqu1eUwbjH4hsAnV8KS58GmHczPR1xjf76/uzu9mtflyblbufnDeDJOaFmBONbyxHRquLnQrlEdZ0epEhy567sB3ge2WZb10mmOqX/yOIwxF53Mk+6oTCIiIsnHcvlwxT6Gtwsjqn6t8z9R2i54r4+9N3TPiTDma/AJxMPNhQcGRLHjcDYjJ69kw4HjPHtFjHobi5yLgHC4eT7EjIRFT9nXrucdc3aqSqm2tweTr23P40NbsWxXGgNfXcqq3XqZLY6zPDGNuMZ18HS/sD1fxM6Rb+13Ba4Fev+p/dogY8xtxpjbTh5zJZBgjNkIvAZcZWlujoiIONBL83diDBc2DX3bXJjSC3IO2wv0Xg+Cyx8vTAZF16dNg9psSs7k6osaMjgmuAySi1QTHj4wbDIMmgRJC2FyTzi40dmpKiVjDNd3acysO7rg4+HG6HdX8cqCnZTY9HJbylZaTgHbD2Wrf3oZcnPUiS3L+gU4Y+8Zy7LeAN5wVAYREZE/25qaxaz1KYzr0ZSQ2l6lP4GtBBY9DcsmQUg7GPUx+IX94zBjDM8Nj2H6mv3855KoMkguUs0YAxfdAsFt4Mvr4P3+MPhFaDvG2ckqpVYhfsy5sxsPf5PAKwt2sTIpnZdGtSH0fH4PipzCyiT7bI0u4dpIrqxosZyIiFQbz/2wnVqe7ozveR4byOVmwGcj7UV622vhhnmnLNJ/07y+L48NbXXBbd9EqrUGHextDhtcBN/cAXPuhqJ8Z6eqlHxquPHSyDa8OCKWzSmZ9Hx+EeM/XcvyxDRsGmGXC7QiKQ3fGm5Eh/o5O0qV4bARdRERkYpkRWIaS3Ye5b+DWuDnXcr+roc2wxdjIDMFhrwCcTc4JqSI/FPNIBgzCxb9D355GQ5uglGfgF+os5NVSsPbh3FRE38+XrWPr+IP8P3mQzQO8GZ0x4Zc2b4B/j4ezo4oldDyxHQ6Ng3ATZumlhl9J0VEpMqz2Syembed0NpeXNu5UenuvOkreK8fFBfYR9FVpIuUP1c36PsYjPoU0nbClIth30onh6q8Gvh789CgFqx8sA+vjGpDXV9Pnv5+O52e/pm7P1/Pmj0Zaukm5+xARi77M3LVP72MaURdRESqvO82H2RzSiYvjog9991oS4pg/iOw6i1o2AVGTAPfeg7NKSJn0WIIBPwMn18NHw6Bgc9Dh5ucnarS8nR35fK2oVzeNpSdh7P5bPV+vl6XzDcbUmka6EP/VvXp17IebRvUxsXljFtPSTW2IikNgK4R2kiuLJnK9m5ZXFycFR8f7+wYIiJSSRQW2+j70hK8PVz57q7uuJ7Li80TafDVWNi7DDreBv3/B66lnC4vIo6Tdxy+vhkS50O762HQC+BWw9mpqoS8whLmbEzlm40prN6dQbHNIrCmB32i6tGvZT26NQtU+y35i7umr2fl7nTWPNSHk5235SyMMWsty4o70zEaURcRkSrts9X72J+Ry7QbOpxbkX5oM0wfbW+9NmwyxF7l+JAiUjpetWH0F/Ze68tehCPbYORHUEutEC+Ul4crIzs0YGSHBmTmFbF4xxHmbz3M95sP8kX8ATzdXejeLIjeUXVpGuhDaB0v6tfy1Nrkaspms1iRlEbXiEAV6WVMhbqIiFQ6m5KPs2ZPBpfGhlCvludpj8vOL+K1hYl0CQ+gZ2TQ2U+8ZTbMvh08a8ONP0BouzJMLSJlysUV+jwC9aNh9nj7uvVRn9h3ipcy4eflzmVtQrmsTSiFxTZW70ln/tbDLNh6mPlbD/9+nKuLoX4tT0LreBFW24vQOl408PdmUHQwNWuo3KjKElIzScspPLe/sVIq+skREZFKw2azmLJsN5N+3EGxzeK5H7ZzWZtQxvVoSmQ9338cP2XpbjJOFDJxYNSZ3+m32WDx07D0BQi7yN4f3be+A5+JiJSZVsMgMBKmXw3TBtn7rbe7ztmpqhwPN/tIevdmQTw+tBV703NJPpZLyrE8ko/lkXI8j5Rjeazek8GhjfmU2CwmL0liynVxhAfVdHZ8cZCF249gDCrUHUCFuoiIVApHswu478sNLNuVxqDo+oy/OIKv4g/wRfwBZqxNpndUXcb1aErHJv4YYziSlc97y/YwJCaYmLDapz9xfhbMuhV2fA9tx8Dgl7TWVaSyqdcKxi2GGTfCt3fC4S3Q/yn7bvFS5owxNAn0oUmgzylvLy6xsXJ3Ovd8voHL3ljOSyNj6d9Kb35WRYu2H6FNg9oE1NTfzbKmzeRERKTCW7rzKPd9uZHs/CIevbQVV1/U4PcR8mMnCvl41T4+XLGX9BOFxIb5cWvPcJbtOsqMtcksuK8njQJO/WKSjN32Ubi0XXDJM3DRONAaO5HKq6QY5j9s79bQtBeM+AC86jg7VbWVejyP2z5Zy6bkTO7qHcE9fSO1e3wVcjS7gA5PLeBf/SK5s08zZ8epVM5lM7kzFurGmDDgKqA7EALkAQnAd8A8y7JsZRf33KhQFxGpPopKbEz6aQeTl+wmsl5NXr+6Hc3r/3OKO0B+UQkz1ibz3rLd7E3PBWBsl8Y8NrTVqU+etMi+s7sx9tZrTS92xFMQEWdY9zHMvRdqN4SrP4egSGcnqrbyi0p4eHYCX61NplfzIF65qi1+XuqiURV8FX+A+2dsYu6d3Wgd6ufsOJXKBRXqxpgPgFBgLhAPHAE8gUigF9AemGhZ1tKyDH02KtRFRKqH/em53Pn5ejYeOM7ojg15eHBLvDzO3hKoxGYxf+shFu84yn8uiaKOj8dfD7AsWPMu/DDRvq716ung38RBz0JEnGbfSvhiDJQUwZVToVlfZyeqtizL4pPV+3lizhZCansx5dq4077pKpXH+E/XsnbfMVY9qLZspXWhhXpry7ISznByD6ChZVmJFxazdFSoi4hUbQXFJXy9NoVnvt8GBp4bHsOg6DJquVRSBPMegPip0HwQXDEFaujFokiVdXy/vd3ikS3Q/3/QabyWtzjR2n0Z3PbJOnLyi3lhRAxDYkKcHUnOU2GxjXZPzmdITDDPDo9xdpxK51wK9dM2PLQsK8EY42qM+eQ0txeWd5EuIiJVV2ZeEW8tTqT7c4t4aNZmmtf35fu7upddkZ6bAZ9cYS/Su95jb+OkIl2kaqvd0N5qsfkg+PEh+GYCFBc4O1W11b6RP9/d2Y2WIbWY8Nl63lu229mR5DzF780gp6CYXlF1nR2lyjrjVpiWZZUYY4KMMR6WZRWWVygREak+Uo/nMfWXPUxfs58ThSV0bxbIiyNj6RYRWHZT6Y7uhOmjIDMZLn8H2lxdNucVkYqvRk0Y+TEseRaWPAfpu2DUp1BT7aScoW4tT6bf0okJn63juR+20zk8gFYhWt9c2SzcfgQPVxe6RQQ6O0qVdS49K/YCy40x3wInfrvSsqyXHBVKRESqvm0Hs3h36W6+3ZiKBQyJCeaW7k3LfkOaxJ/hqxvA1R2unwsNO5bt+UWk4nNxgV4PQVAUzB4P7/WGq7+Aei2dnaxa8nBz4bnhMfR/ZSn/+nLj/7N33+FRFusbx7+TCiQhIRB670iH0KVIFbAhRVBRiiAqYu8e6/H4s3dUutLBLkW60kvovfcWIARIKCk7vz9ePEelZFM2m3J/rmsvSPbd2XtPCXnemXmGnwc3I9Av5R4kknXM3x5No/LhBAXqCERPuebS9784gtNQzgcI+ctDREQkTZ79bj0dP1nEb5uP0btJGf54phWf9KybsUW6tbDiaxjfDcJKwcAFKtJFcrsad0LfGZCUACPbw47Z3k6UaxUICuD/7qzJtmPn+HTeTm/HkVTYfyqePSfiaa1l7x6V4i0Qa+3rmRFERERyh30n45kSdYgekSV5sVM1wvIFpPyi1LqiadxwZ/mriEiJejBgPkzs6WyJ6fAfaDRITea8oE21IvSILMmXv++mbbUi1C2tM++zg/nbogFUqHtYijPql/eov2eMmWGMmf/nIzPCiYhIzjN941EAHmtb2TNF+oXTMK7rX5rGjVeRLiJ/F1rif03mfnveOXM9OdHbqXKlf91yA8VC8/LUlPVcSEj2dhxxw/xt0ZSPCKJMwSBvR8nR3Fn6Ph7YBpQDXsfZs77Kg5lERCQH9Hte0wAAIABJREFUm77hKPVKh1EiLG/GDx6zB0a0g/1L4Y4vod3rzt5UEZF/Cghymszd+ASsHu3c4Ltw2tupcp2QPP68260We07G896s7d6OIymIv5TEij0xtK6i2XRPc+e3l4LW2pFAorX2D2ttP6Cxh3OJiEgOtOdEHFuOns24I9f+at8SGN4Gzp+E+36GOndn/HuISM7i4wNtX3Nu7O1fCiPawqnd3k6V6zSrWIj7m5Rh1JK9LNt9yttx5DqW7DpJQrJLy94zgTuF+p/rgI4aYzobY+oCJT2YSUREcqgZl5e9Z3ihvm4ifHs75CsID8yDss0ydnwRydnq3A33/wLnY2B4a9i7yNuJcp3nOlalbMF8PPPdeuIuJXk7jlzDgu3RBAf6EVk23NtRcjx3CvV/G2NCgaeAp4ERwBMeTSUiIjnS9I3HqF+mAMUzatm7ywXz3oCfBkGZJvDAHChYIWPGFpHcpUxTp8lccBEY2wXWjvN2olwlX4AfH/SozZHYC7w1fau348hVWGtZsO0EzSsVIsBP28o8LcX/hK2106y1Z6y1m6y1N1lr61trf8mMcCIiknPsPhHH1qNn6ZxRs+kJ5+G7PrDoA6h3P9z7A+RVx2ARSYfwctB/trMq5+dHYO7rzg1ByRT1y4QzoEV5Jq48wO/bo70dR/5hy9GzHDt7kZu07D1TuNP1vbIxZp4xZtPlr2sZY172fDQREclJZmzIwGXv547BmM6w5Rdo/xbc+gn4+qd/XBGRvGFwz3dQvw8s/tC5IZh4wdupco0n2lamcpFgnvt+A2fOqxN/VrLg8rFsrapEeDlJ7uDOmoXhwAtc3qturd0A9PRkKBERyXmmbzxKZJkCFA3Nk76Bjm1ymsad2A49J0DTwTr/WEQylq8/3PIxtP+3c0NwTGc4d9zbqXKFPP6+fNC9DqfiErhn5HLe+W0bP609zNajZ0lI0uoGb5q/LZpaJUMpHJLOf8fFLX5uXJPPWrvS/P2XIHV4EBERt+2KjmPbsXO8eusN6Rto5xyY2tc5F73fTChWO2MCioj8kzHQ9FEoUA5+GAAj2sDdk6FIdW8ny/FqlgzlP11qMmrJXkYs2kNisgXAz8dQrlAQVYqGUKVICJWLhlCuUBClw/ORx9/Xy6lztpj4BNYejGVI60rejpJruFOonzTGVAAsgDGmG3DUo6lERCRHmbHxKMZAxxrpWPa+cjjMfNb5JfnuKZC/eMYFFBG5lmq3QN+ZMLEnjOwA3cdApbbeTpXj9WhQih4NSpGQ5GLfqXi2HTvH9mNn2X4sjvWHYpm24X/liDFQPDQvZQrmo2yhIMoVDHL+LBRE+UJB+Pho1VV6/bEjGmvRsWyZyJ1C/RFgGFDVGHMY2Avc49FUIiKSo0zfcJQGZcLTtuzdlQyzX4blQ6FyR+g6wplRFxHJLMXrOEc/TrgLJnSHju9CwwHeTpUrBPj5ULlICJWLhEDt/92gjbuUxO7oOPadimffyfPsOxXP3pPxzNh4lNi/7G0PyeNHvdIFqF/GedQuFUZwoDslkPzV/G0nKBQcSM0Sod6Okmu487/S/dbatsaYIMDHWnvO06FERCTn2Hn8HNuPn+P129KwXPRSnLPkdPsMaPQQdHgLfLS8UUS8ILQE9PsNvu8PM56GmL3Q/k39TPKS4EA/apcKo3apsCueiz2fwN6T8eyKjmPNgVjW7D/NR3N3YC34GKhWLP9/C/dWlQsTmk/NSK8nKdnFH9ujaV+9qFYnZCJ3CvVdxpjvgFHWWh1qKCIiqTL9v8vei6buhWePOLNXxzdBp/c1eyUi3hcY7DSxnPUiLP8CTu91VvkEBHk7mfxFWL4A6pYOoG7pAnSPLAXAmQuJrD1wmjX7T7P6wGm+W32Ib5ftJ8DPh3Y3FKF7/ZI0rxSBrwrRK6w5EMvZi0la9p7J3CnUa+F0eR9pjPEBRgGTrLVnPZpMRERyhBkbj9KgbDiF86di2fvRDU6Rfumssx+9UjvPBRQRSQ0fX+j4DoSXh9+eh9EdoddkyJ8BR0+Kx4Tm9adVlcK0quIUm0nJLjYdOctPaw/z07rDTN9wlCL5A+lStyTd6pekYmFtsfrT/G3R+PkYbqxUyNtRchVjrXX/YmNaABOBMOA74E1r7S4PZbuqyMhIGxUVlZlvKSIiabTj+Dnaf7SQN26vzn1Nyrr5ollOZ/e8YU6H5aI1PZpRRCTN/vbzagoUreHtRJIGl5KSmb81mu9WH+L3HSdIdlnqlg6jW/2S3F6nRK7f097+oz8oGBTIxIGNvR0lxzDGrLbWRl7vmhTPUTfG+BpjbjPG/Ah8AnwAlAd+BWZkSFIREcmRpm9wlr3f7O6y9xXDnM7KhSo6jZtUpItIVla5g7Nv3bpgVAfYOdfbiSQNAv186VizGCP7NGDZ8615sVNV4i4m8dKPm7j544VsPHTG2xG9IinZxWfzdrLjeBxtqmnZe2ZLsVAHdgK3A+9Za+taaz+01h631n4H/ObZeCIikp1N33iURuXCKRySwrJ3VzLMfB5mPgOVb3aOQtIyUhHJDorVggHzIbwcTOgBq0Z4O5GkQ+H8eRjYogKzn2jBpIGNcbksXb9cyvgV+0nNSuTsblf0Obp+uZQP5uzg1trFubtRaW9HynXcKdRrWWv7W2uX/vMJa+0QD2QSEZEcYMfxc+yKjqNzzRQK7ktxMOkeWPElNH4Y7hqnxkwikr3kLw59f3P6aUx/Cma95NyAlGzLGEPj8gWZNqQ5jSsU5KUfN/HUlPWcT0jydjSPSnZZhi/cQ6dPF3Mg5jxf3F2Pz3rVJV9A7l7+7w0pFurW2ri0DGyMKWWMWWCM2WqM2WyMeewq1xhjzKfGmF3GmA3GmHppeS8REck40Wcvsmz3qXSPM23DUXwMdLjesvezR2FMJ9g5Czq+Bze/raOORCR7+rMjfKNBsOxzmHIfJMR7O5WkU3hQAKP7NODxtpX4cd1hunyxlN0n0lQeZXn7TsZz19fLeGvGVlpWjmD2Ey3pXEur27zFnRn1tEoCnrLWVgMaA48YY274xzUdgUqXHwOBLz2YR0REUmCt5cFxq7l7xHJ2Hj+XrnGmbzhCo3IFr73s/dgmGNEGTu6CXpOg0cA0v5+ISJbwZ0f4m9+B7TNgTGc4d9zbqSSdfH0Mj7etzJi+DYk+d5HbP1/CjI1HvR0rw7hclm+X7aPjJ4vYfvwcH/aozbDe9YkICfR2tFztuoW6McbHGNMjLQNba49aa9dc/vs5YCtQ4h+X3Q58ax3LgTBjjG7biIh4ya8bjrL2QCwAH8/dmeZxth8/x+4T8de+E79zrtN4ybqcRkyVO6T5vUREspzGg5zZ9RPbnRuSx7d4O5FkgJaVI5g+pDkVCwfz8Pg1vPHrFhKTXd6OlS47jp+j96gVvPLzZhqUC2f2Ey24s15JjNF58t523ULdWusCBqf3TYwxZYG6wIp/PFUCOPiXrw9xZTGPMWagMSbKGBN14sSJ9MYREZGruJiYzDszt1G9eH4eblWB6RuPsvlI2jrdTr+87P2q3d5XjXAaLhUo53R2L1YrnclFRLKgKh2dxpjJic6NyV3zvJ1IMkDxsLxMebAJfZqWZdSSvdw7YgUXErJfP4JNh88waOxq2n+0kHUHYvlPl5p807cBxULzejuaXObO0vc5xpinL+85D//z4e4bGGOCge+Bx621Z//59FVeckU7RWvtMGttpLU2MiIiwt23FhGRVBi1ZC+HYy/wUudqDGxRgfx5/Phozo5Uj2OtZfrGozSpUJBCwX9ZNudKdhosTX8KKraBfjMh9Ip7syIiOUfxOjBgHoSVhvHdYfUYbyeSDBDg58Nrt1Xnwx61WbUvhofGryYhKXvMrK85cJp+Y1Zxy2eLWbL7JENaV2Txc625u1FpzaJnMe607+t3+c9H/vI9i3OW+nUZY/xxivTx1tofrnLJIaDUX74uCRxxI5OIiGSgE+cuMXTBbtrdUISmFQoBMLBFed6fvYN1B2OpUyrM7bFmbT7GnhPxDGj+l38mEuLhh4GwbRo0GAA3/x/4qoOsiOQCoSWdLT5T+8Kvj0HMHmjzGvh4slWUZIY765XkUpKLF37YyNNT1/PxXXXw8cl6xa61lhV7Y/hs/k6W7DpFgXz+PNOhCr2blCF/Hn9vx5NrSPG3JGttubQMbJxbMiOBrdbaD69x2S/AYGPMJKARcMZam3M6M4iIZBMfzd3BxcRkXuhY9b/f69OsHKOW7OOD2dsZ27+RW+NEn73ICz9spFbJULrVL+l889xxmHgXHFnnFOiNBoHu2otIbhIY4jTNnPksLPkEYvbCncPAX8uMs7teDUsTez6Rd37bRmhef964vXqWmpneevQsr/y8iVX7ThMREsjLnavRq2FpggJ1szyrS/G/IWNMPuBJoLS1dqAxphJQxVo7LYWXNgN6AxuNMesuf+9FoDSAtfYrYAbQCdgFnAf6pulTiIhImm0/do5JKw9wf9OylI8I/u/3gwP9eKhlBd6asZUVe07RqHzB645jreXZ7zdwITGZj+6qg7+vj9NAaUIPOH/KaaxUtZOnP46ISNbk6wedP4Dw8jD7ZacjfK9JEFzY28kknR5qVYHY8wl8vXAPBfL582T7Kt6OBEDs+QT6j1lFQrLljdur0yOyFHn8dQRqduHOrZTRwGqg6eWvDwFTgesW6tbaxVx9D/pfr7H8fUm9iIhksrdmbCUkjz+Ptal0xXP3Ni7D8EV7+GD2DiY/2Pi6swTjlu/n9+0neOP26lSICHYaJ03tA/75oO8MKF7Xg59CRCQbMAaaDoYCZeH7B2B4G7hnKhSumuJLJWt7vmNVTp9P4NP5uwjLF0C/G9O0KDnDWGt5euoGTsRd4vuHmlKrpPtb2CRrcGdzTAVr7btAIoC19gIpFOAiIpI9LNgezcIdJxjSphJh+QKueD5vgC+DW1dk5b4YFu86ec1xdp+I460ZW2lZOYLejcs4DZPGd4fQUk4jJRXpIiL/U+0W5wZm8iUY2Q52L/B2IkknYwz/6VKTDtWL8Ma0LXy/+pBX84xeso+5W4/zQsdqKtKzKXcK9QRjTF4ud2M3xlQALnk0lYiIeFxSsou3pm+lbMF8TnF9DXc1KEWJsLy8P3sHzkKov0tMdvHE5HXk8fflva41MHNecRomVbjJaaAUWtKTH0NEJHsqUc85ojK0JIzvBqu/8XYiSSc/Xx8+6VmXphUK8uz3G5iz5bhXcmw4FMvbM7fStloR+jYr65UMkn7uFOqvAr8BpYwx44F5wLMeTSUiIh43cdVBdkXH8UKnagT4Xfufg0A/X4a0qcj6g7HM2xp9xfOfzdvJhkNneOfWihT+bSAs/RQi+0OvyZAnvyc/gohI9hZWCvrNgnIt4dchMOdVcGWPY77k6vL4+zLsvkhqFM/PIxPWsHzPqUx9/7MXExk8YS0RwYG8371WlmpsJ6mTYqFurZ0D3An0ASYCkdba3z0bS0REPOnsxUQ+mrODRuXCaX9DkRSvv7NeScoWzMcHc3bgcv1vVn31/tN8vmAXfWrlpcOq/rB1GnR422mYpOPXRERSlic/3D0F6veFJR/Dd30g8YK3U0k6BAf6MbpvQ0qH56PP6JU8990GVu8/fdVVaRnJWssLP2zkcOwFPru77lW3tEn24e4Bji2BNsBNQHPPxRERkczwxYJdnD6fwL9uucGtu+3+vj483rYyW4+eZeamYwDEX0riySnraBoSzSvHHoUT25zO7k0e1vFrIiKp4esHt3wE7f8NW36BMbdA3JUrmCT7CA8KYPwDjbitdnF+3XCErl8upf1HCxmxaA+n4jyzi3jiyoNM33CUp9pXpn6ZcI+8h2Qek9KdHWPMUKAizmw6wF3AbmutV7q1R0ZG2qioKG+8tYhIjnAw5jxtPviDW2sX54Metd1+XbLLcvPHC3FZy+wnWvLSjxs5unoao4K+wDcwGO6epKZxIiLptfVX+H4ABEXAPVOgcDVvJ5J0iruUxPQNR5i06iBrD8Ti72tof0NRejQoxY0VC+Hrk/6b21uPnuWOL5bQsFw43/RtiE8GjCmeY4xZba2NvO41bhTqm4Eal49SwxjjA2y01lbPsKSpoEJdRCTtrLUMHLuaRTtP8PvTN1E0NE+qXj9z41EeGr+GLnVLkG/DN7zp/w0+RW6AuydDaAkPpRYRyWUOr4GJvSDxPHQfDRXbejuRZJDtx84xedVBflx7iNPnEymSP5BioXnJ6+9LHn8f8vj7ktffl8DLX+f196VK0RAaly9IkfxX/zc7/lISt32+mLMXk5j5WHMKBQdm8qeS1HKnUHdnA+F2oDSw//LXpYAN6cwmIiJeMG7FAeZsOc6LnaqmukgH6FC9KDWLBXHDxncY4D8DV8X20H0UBIZ4IK2ISC5Vop5ztOWEnjC+B3R6Fxo84O1UkgGqFA3hlVtv4LmOVZiz5TgzNx3j7IVELiYmcyIukYuJLi4mJl9+uLiQmEzy5d4w5QoF0bh8OI3LF/xb4f7Kz5vZczKe8Q80UpGeg7gzo/4H0ABYeflbDYBlwHkAa+1tngz4T5pRFxFJm81HztBl6FKaVijIqPsbpG1Z3KU4YsbeT/ihuZyu0ZcCXd5X0zgREU+5dA6+6w87Z0HjR6D9m+Dj6+1UkomSXZYtR86yfM8plu85xcq9MZy7lAQ4hXvFwsHM2XKcx9pU4ol2lb2cVtyVUUvfW17veWvtH2nIlmYq1EVEUi/uUhK3fraY8wlJzHysBeFBaegEe+YwTLwLjm+Gm/8PGj2Y8UFFROTvXMkw6yVY8SVU7ghdR0BgsLdTiZdcrXCvXSqMb/o1zJC97pI5MqRQz2pUqIuIpI61lscnr+PX9UeYOKAxjcoXTP0gR9Y6+yUvxUG3UVC5fcYHFRGRa1s5HGY+C0WqQy/1BRGHy2UxBp2Xns24U6i7ezybiIhkU1OiDvLzuiM80bZy2or0rb/C6E7g4wf9Z6lIFxHxhoYDnPPWY/bBiDZwZJ23E0kW4ONjVKTnUCrURURysO3HzvHqL5tpVrEgD99UMXUvthYWfwyTe0PhG+CBec5MjoiIeEelds4NUx8/GN3RuZEqIjlSqgp1Y0wBY0wtT4UREZGMcz4hicET1hAc6M9Hd9VJ3d61pAT4ZTDMfRWq3wF9pkFIEc+FFRER9xSpDgPmOzdQJ98Liz9ybqyKSI6SYqFujPndGJPfGBMOrAdGG2M+9Hw0ERFJj1d/3syuE3F8fFcdCoek4ii28zEw7k5YOw5aPANdR4F/Xs8FFRGR1Aku7NxArdEV5r4GPz0MSZe8nUpEMpA7Z+qEWmvPGmMeAEZba181xugcdRGRLOyHNYeYuvoQj7auyI2VCrn/wpO7YEIPOHMQunwNtXt6LqSIiKSdf17oOhIKVYbf34bT++CucRCUhl4kIpLluLP03c8YUwzoAUzzcB4REUmn3SfiePmnTTQsF85jbSq5/8I9v8OI1nAxFu77RUW6iEhWZwy0et4p2A+vdn6Gn9ju7VQikgHcmVF/A5gFLLbWrjLGlAd2ejaWiIi4y1rL/lPnWXcwlnUHY5mz5TiBfj582rMufr5utiJZNRJmPAMRVaDXJChQxrOhRUQk49TsBmFlYFIvGNEOeoyBCq29nUpE0kHnqIuIZDNnziey7lAsaw+cZt3BWNYfjOX0+UQA8vr7UrNkKE+3r0LDcuEpD5acBLNfghVfQaUO0HUE5Mnv4U8gIiIeEXsAJvSEE9ug4zvOkW4ikuW4c476NWfUjTGfAdes4q21Q9KRTUREUiEx2cX8bdFMWXWQBdujcVlnxWOlwsG0u6EIdUoVoE6pMCoXCXZ/Fv3iGZjaF3bPgyaDod0b4OPr2Q8iIiKeE1baOb7tu/4w42mnYL/5/8DX39vJRCSVrrf0XdPWIiJetudEHFOiDvHd6kOcjLtE4ZBAHmxZgeYVC1GzZCghedL4y1fMHmfWJWY33Pop1L8/Y4OLiIh3BIZAr4lON/iln8LJndB9DORzY5WViGQZ1yzUrbXf/PVrY0yQtTbe85FERHK3CwnJzNh4lMlRB1m5NwZfH0PrqoXp2aAULStHuD9jfi37ljhn72Kh909QrnmG5BYRkSzCxxfavwkRVWHa4zCiDfSaDBGVvZ1MRNyUYjM5Y0wTYCQQDJQ2xtQGHrTWPuzpcCIiuc2y3acYODaKcxeTKFswH8/dXJWu9UpQOH8qzkG/nqjRznLI8PJO07iCFTJmXBERyXrq3uP8nJ90D4xoC91HQ8U23k4lIm5wp+v7x0AH4BcAa+16Y0wLj6YSEcmFkl2W137ZTFg+f4bfF0mjcuEYYzJo8ESY9SKsHAYV20K3UZAnNGPGFhGRrKt0Yxi4ACb2gvHdoMPb0OhBp9GJiGRZbq2ftNYe/Me3kj2QRUQkV/tp7WG2Hz/Hsx2q0rh8wYwr0s/HwLg7nSK9yWC4e4qKdBGR3CSsNPSbBVU6wW/POcvhkxO9nUpErsOdQv2gMaYpYI0xAcaYp4GtHs4lIpKrXEpK5sM5O6hRIj+daxbLuIGjt8Hw1nBgOdzxJXR4S53dRURyo8Bg6DEWbnwSVo+Bb++A+JPeTiUi1+BOoT4IeAQoARwC6lz+WkREMsj45Qc4HHuBZztUxccng2bSd8xy9iQmxEOf6VDn7owZV0REsicfH2j7Ktw5HA5HwbCb4NhGb6cSkau4bqFujPEFeltr77HWFrHWFrbW3mutPZVJ+UREcry4S0l8vmAXTSsUpHmlQukf0FpY/DFMuAvCyzl7E0s1TP+4IiKSM9TqAX1ngisJRraHLT97O5GI/MN1C3VrbTJweyZlERHJlYYv3ENMfALP3Vw1/fvSEy/Aj4Ng7qtww+3Q7zcILZkxQUVEJOcoUc+5kVukOky5D+a/BS6Xt1OJyGXudH1fYoz5HJgM/PccdWvtGo+lEhHJJU7GXWLEoj10rFGU2qXC0jfYmUPO+ehH1sJNL0GLZ9TVV0REri2kqLM1atqTsPBdiN4CXb6CwBBvJxPJ9dwp1Jte/vONv3zPAq0zPo6ISO7y+fxdXExy8XSHKukbaP8ymNLbmVHvOQGqds6YgCIikrP5BcLtn0PRGjDrJWcpfM8JztYpEfGa6xbqxhgf4Etr7ZRMyiMikmscjDnP+BX76V6/JBUigtM+UNQomPGsc/zO/dOgcNWMCykiIjmfMdD4IYioClP7wPCboPs3UL6lt5OJ5Fop7VF3AYMzKYuISK7y0Zwd+BjDY20rpW2ApAT49TGY9oTzy9SA+SrSRUQk7Src5OxbDy4CY7vAsi+cBqUikuncOZ5tjjHmaWNMKWNM+J8PjycTEcnBth07y4/rDtOnaVmKheZN/QDnjsM3tzpn4TZ7HO6eAnnTucddREQkvDw8MBeqdIRZL8IPAyHhvLdTieQ67uxR73f5z7+enW6B8hkfR0Qkd3jvt+0EB/rxUKsKqX/x4dUw6V64cBq6jYIaXTM+oIiI5F6BIdBjLCz+wOkGf2Ir3DUeCpTxdjKRXCPFGXVrbbmrPFIs0o0xo4wx0caYTdd4vpUx5owxZt3lxytp+QAiItnNqn0xzNsWzaCWFQjLF5C6F6+bAKM6go8f9J+tIl1ERDzDx8c5PeTuKXD6AAxrBXt+93YqkVzDnaXvGGNqGGN6GGPu+/PhxsvGADencM0ia22dy483UrhWRCTbs9byzsxtFA4JpF+zVHTUTUqA6U/BTw9B6UbOHsJitTwXVEREBKBy+8v71gs7+9aXfq596yKZIMVC3RjzKvDZ5cdNwLvAbSm9zlq7EIhJb0ARkcx0JPYC1oO/gMzbGk3U/tMMaVOJvAG+7r3o7FEY0xlWjYCmQ+DeHyGokMcyioiI/E3BCs6+9aq3wOyX4PsHtG9dxMPcmVHvBrQBjllr+wK1gcAMev8mxpj1xpiZxpjqGTSmiEiaLNgWTdP/m89Hc3Z4ZPyTcZd4+adNlI8I4q4Gpdx70f6lMKwlHN8M3cdA+zfB1532IiIiIhkoMAR6fAut/wWbvnfOW4/Z6+1UIjmWO4X6hcvHtCUZY/ID0WRMI7k1QBlrbW2c2fqfrnWhMWagMSbKGBN14sSJDHhrEZErjV+xH4BP5+9ixKI9GTp2ssvy+KR1xJxP4LNedfH3TeHHr7Ww4muns3tAMAyYB9W7ZGgmERGRVDEGWjwN90yFMwecG8k7Zns7lUiO5E6hHmWMCQOGA6txCuyV6X1ja+1Za23c5b/PAPyNMVddy2mtHWatjbTWRkZERKT3rUVErhB99iILtp9gYIvydKxRlH9P38p3qw9l2PifzNvJ4l0nefP26lQvHnr9ixPOw4+DYOazUOny3sDC1TIsi4iISLpUagcD/4Cw0jChOyz4D7iSvZ1KJEdJcf2ktfbhy3/9yhjzG5DfWrshvW9sjCkKHLfWWmNMQ5ybBqfSO66ISFp8v+YwyS5LzwalKFEgL+fGRPHc9xsIyeNHh+pF0zX279uj+Wz+TrrVL0mPyBSWvJ/eB5PvhWOb4KaXoflTTuddERGRrCS8HPSf4zQ6/eMd5+jQO4dDvnBvJxPJEdzt+n6nMeZD4FHArUN/jTETgWVAFWPMIWNMf2PMIGPMoMuXdAM2GWPWA58CPa0nOziJiFyDtZapUQdpULYA5SOCCfTz5eve9alZIpRHJ6xl6a6TaR77SOwFnpi8jipFQnjz9hoYY6598faZ8HULiD3gHIfT8hkV6SIiknX554Xbv4BbPoa9C+HrlnBkrbdTieQI7nR9HwoMAjYCm4AHjTFfpPQ6a20va20xa62/tbaktXaktfYra+1Xl5//3Fpb3Vpb21rb2Fq7NL0fRkQkLaL2n2bPyfi/zXYHBfoxuk8DyhbKx4Bvo1h/MDbV4yYkuXhkwhoSky1D76l37S7vyUkw93WY2BMKlIUHFzrH4YiIiGR1xkBkX+j3G1gXjOwAa8Z6O5VItufOVE1vFbjRAAAgAElEQVRLoIO1drS1djTQCWjl0VQiIployqqDBAX40qlmsb99v0BQAGP7N6JAUAB9Rq9kV/S5VI379sytrD0Qyztda1E+IvjqF8VFw9g7YPGHUL8P9JvtFOsiIiLZSYn6zo3mMk3gl8Hwy6OQeNHbqUSyLXcK9e1A6b98XQpI9x51EZGsIO5SEtM3HuXW2sUJCryybUeR/HkY178Rvj4+3DtiJYdOu3du7PQNRxm9ZB99m5Wlc61iV79o/zL4qjkcioI7voRbPwH/POn5OCIiIt4TVBDu/QGaPw1rvoWR7SAmY09REcktrlmoG2N+Ncb8AhQEthpjfjfGLAC2Amq9LiI5wvQNRzifkEz36zR5K1soiLH9GxKfkETvkStZuvsk5y4mXvP63SfiePa79dQtHcYLHa/Srd1aWPoZjOkMAUHwwFyoc3dGfBwRERHv8vGFNv+CXpOdnitft4Qtv3g7lUi2Y67Vv80Y0/J6L7TW/uGRRCmIjIy0UVFR3nhrEcmBun65lDMXEpnzRIvrN3oDovbFcN+olZxPcI6gKR8RRK0SodQsGUbtkqHcUDw/BsMdXywh+txFpg9pTvGwvH8f5OIZ+PkR2PorVLvVacKTJ4Xj2kRERLKj0/thah84sgYaPwxtXwe/AG+nEvE6Y8xqa23k9a653vFsC1Pqwm6MMerULiLZ1a7oc6zef5oXO1VNsUgHiCwbztLnW7PuYCwbD51hw+EzLN8Tw0/rjgDgY6BgcCAn4y4xpm/DK4v0o+thyv3ODEP7t6DJI04THhERkZyoQBnoNwvm/AuWD4VDq6DbaAhL4ahSEbluob7AGPM98LO19sCf3zTGBAA3AvcDC4AxHk0oIuIhU6MO4edj6FK3pNuvCcsXQKsqhWlVpfB/vxd99iIbD59hw6EzbDp8hhsrFaJl5b/sELIWVo2AWS9CUAT0me402xEREcnp/AKg4ztQugn8PBi+bg5dhul0E5EUXK9QvxnoB0w0xpQDYoE8gC8wG/jIWrvO8xFFRDJeYrKL79cconXVwkSEBKZrrML589Amfx7aVCty5ZMXzzidb7f8DJXawx1fOc12REREcpPqd0DRms7Ksgnd4cYn4aaXwPd65YhI7nXN/2dYay8CQ4Ghxhh/oBBwwVqb+sOERUSymAXbojkZl/C3s9Mz3JG1zt682IPQ7g1o8ij4uHPYhoiISA5UsAI8MAdmPuscS3pgOXQdAaElvJ1MJMtx6zdGa22itfaoinQRySmmRB0iIiSQVlU8cIiFtbBiGIxsD8lJ0HcmNHtMRbqIiIh/XrjtM2f5+9H18FUz2D7T26lEshz91igiuU702Yss2B5N13ol8fPN4B+DF2JhSm+Y+QyUvwkGLYLSjTL2PURERLK72nfBgwshtCRM7Am/vQBJl7ydSiTLUKEuIrnOD2sPk+yy9Ih0v4mcWw6ucprkbJ8J7d6EXpMgX3jGvoeIiEhOUagiPDAPGj7odIUf2Q5O7fZ2KpEsQYW6iOQq1lqmrDpIg7IFKB8RnDGDupJh4fswqoPzdd+Z0GyIlrqLiIikxC8QOr0LPSc4565/3QI2TPV2KhGv02+RIpKrrN5/mj0n4zOuidzZI/Dt7TD/Taej7aDFUKphxowtIiKSW1Tt7PwbWqQG/PAA/PwIJMR7O5WI16hQF5FsIynZhbU2XWNMXnWQoABfOtUslv5A22fCl83g8Bq4fSh0HQl5QtM/roiISG4UVgr6TIfmT8Pa8TCsldNwTiQXUqEuItnCpaRkOny8kC5Dl7L3ZNrusMddSmL6xqPcWrs4QYHpOLc18SLMeMZpfhNa0mmGU/ceMCbtY4qIiIhzrnqbf8F9P8GlczC8DSz9DFwubycTyVQq1EUkW5i86iC7T8Sz4/g5On2yiAkrDqRqdv10fAKv/bKZ8wnJdE/PsvfobTC8NawcBk0GwwNznWY4IiIiknHKt4KHlkLlDjD7ZRjXBc4e9XYqkUyjQl1EsryLicl8Pn8XDcuFM++pltQvU4AXf9zIgG+jOBl3/aNcLiYmM2zhblq8t4Af1hyib7Oy1CsdlvoQ1sLK4c4yvPhouOd76PCW0wRHREREMl6+cLhrHNz6KRxcCV82ha3TvJ1KJFOoUBeRLG/c8v1En7vEU+0qUyw0L9/2a8grt9zAwp0nufnjhczbevyK11hr+WX9Edp++Af/mbGNyDIF+O3xFrx6a3VMapeonzsG47vBjKeh7I0waAlUaptBn05ERESuyRiof7+zzSysNEy+B359TI3mJMcz6W3MlNkiIyNtVFSUt2OISCaJv5REi3cXUK1YfsY90Ohvz20/do7HJ69j69Gz3N2oNC93rka+AD9W7o3hrRlbWX8wlmrF8vNSp2rcWKlQ2gJs/RV+GQKJF6D9m9DgAe1FFxER8YakBFjwFiz5BApWhK4joHgdb6cSSTVjzGprbeT1rklHNyUREc/7Ztk+TsUn8GT7ylc8V6VoCD890pQPZ+9g2KI9LNt9ioqFg5mz5ThF8gfyXrda3FmvJL4+aSisL52Dmc/DunFQrA7cORwirswgIiIimcQvANq9DhXbwA8Pwoi20Op5aPa404ROJAfRjLqIZFlnLybS/J0F1C9TgFF9Glz32mW7T/HUlHXEXkjkoZYVeKB5efIG+KbtjQ+sgB8HQuwBuPFJaPmc88uBiIiIZA3nY2D6U7D5ByjZELp8BQUreDuViFs0oy4i2dqoxXs5cyGRJ9ulPJPdpEJB5j3VioRkF6F5/dP2hsmJ8Mc7sOgD59i1PjOgTJO0jSUiIiKeky8cuo+Gqp1h+pPw1Y3Q/t8Q2U9b1CRHUDM5EcmSYs8nMHLRXjpUL0KNEqFuvSZvgG/ai/Tjm51j1xa+B7V6Og3jVKSLiIhkbTW7wcPLoXRjp2Af11XHuEmOoEJdRLKkYQv3EJeQxBNuzKanS3KSM4P+dUs4e8Q5BqbLl5Anv2ffV0RERDJG/uJw7w/Q6X3YvxSGNoZN33s7lUi6qFAXkSznVNwlxizdxy21ilO1qAcL5pM7YVQHmPcGVO0Ej6yAard67v1ERETEM4yBhgPgoSVOR/jv+jmP8zHeTiaSJirURSTL+eqP3VxMTObxtpU88wYuFyz7wtnPFrMbuo6E7t9AUBqPcBMREZGsoWAF6DcLWr8MW36GLxrB1mneTiWSairURSRLiT57kW+X7adL3ZJUiAjO+DeI2Qvf3AKzXoTyNzn72mp2U+MZERGRnMLXD1o8AwN/h5AiMPke+K4/xJ/ydjIRt6lQF5Es5YsFu0h2WR5rk8Gz6S4XrBwOXzaDY5vgji+h10QIKZqx7yMiIiJZQ9GaMGAB3PSSM7s+tJHzp0g2oEJdRLKMw7EXmLjyIN0jS1K6YL6MG/jkLhjTCWY87XSFfXgp1Llbs+giIiI5na8/tHzWmV3PXxym3AdT+0D8SS8HE7k+FeoikmV8Pn8XAINbZ9BsenISLP4IvmwK0VucWfR7v3fOSBcREZHco2gNeGCes3d96zRn7/rmH72dSuSaVKiLSJawal8MU6MO0qthKUqE5U3/gMc2wog2MPc1qNweHlmlWXQREZHczNff2bv+4ELnpv3UPjDpHp27LlmSCnUR8bpd0XE88E0UpcPzpf/c9KRLMP/fMKwVnD3sdHO/a5zTTEZERESkyA3O7HqbV2HXXGd2PWq0089GJItQoS4iXhV97iJ9Rq/E39fwTb+GhOULSPtgB1fB1y1g4XtQoxs8shKq35FxYUVERCRn8PWD5k/CQ0uhWC2Y9jiM6Qwnd3o7mQigQl1EvCj+UhL9x0RxKi6BUX0aUCo8jQ3kLp6BaU/CyHZw6RzcPRXu/BryhWdsYBEREclZClaA+3+F2z6H6M1OX5uF70FSgreTSS6nQl1EvCIp2cXgCWvYfOQMn99dl1olw1I/iLWw+Sf4vCGsHg2NBsEjK5w96SIiIiLuMAbq9Xb62VTtfHkLXUs4FOXtZJKLqVAXkUxnreVfP29mwfYTvHlHDdpUS8P+8dgDMOEumHo/BBd29pp1/D8IDMn4wCIiIpLzhRSB7mOg1yRntd6ItjDjWefvIpnMY4W6MWaUMSbaGLPpGs8bY8ynxphdxpgNxph6nsoiIlnL0N93M3HlAR5uVYF7GpVJ3YuTk2DpZ07jl32LocN/YMACKKEfISIiIpIBqnSEh5dDwwGwchh83gA2fues5BPJJJ6cUR8D3Hyd5zsClS4/BgJfejCLiGQRP649xHuztnNHneI806FK6l58eDUMbwWzX4ZyLZ1l7k0ecRrCiIiIiGSUPPmh03swYD6EFIPv+8PYO+DUbm8nk1zCY4W6tXYhEHOdS24HvrWO5UCYMaaYp/KIZGcfzt5O75EriL+U5O0o6bJ010me/W4DTcoX5N1utTHunml+PgZ+fQyGt4H4k9BjLPSaCGGlPBtYREREcrcS9ZxivdP7cHgNDG0MC/4DiRe9nUxyOG/uUS8BHPzL14cuf+8KxpiBxpgoY0zUiRMnMiWcSFYxeslePp2/i0U7T/LYpHUku7LnsqtNh8/w4NjVlCsUxFe96xPg58aPH5cLVo+Bz+rBmrHQ+GHnyLUbbnMav4iIiIh4mo+vswx+8Cqodhv88Q582QR2zfN2MsnBvFmoX+237KtWINbaYdbaSGttZEREhIdjiWQdszcf441pW2h3QxFeueUG5m49zru/bfN2rFRbsecUvYYtJ39ef8b0bUhoXv+UX3R4DYxs68ykR1SDQYvg5v84S9FEREREMltIUeg2Enr/BMYHxt0JU+6H2IMpv1Yklby5sfMQ8Nd1qyWBI17KIpLlrD8Yy5BJa6lVIpRPe9Ylj78Pe07G8fXCPVQoHEyPyOyx7HvBtmgGjVtNyQJ5GfdAI4qF5r3+C87HwPw3IWo0BEVAl2FQq4dm0EVERCRrqHATPLQUlnwCiz6AHbOg+VPQ9FHwz+PtdJJDeHNG/Rfgvsvd3xsDZ6y1R72YRyTLOBhznv7frKJQcCAj7m9A3gBfjDG8emt1bqxYiJd+3MjyPae8HTNFv6w/woBvo6hcJIQpDza5fpHucsHqb+Cz+s6fjR+CR6Og9l0q0kVERCRr8QuEls86y+Ert4cF/4YvGsK26eoOLxnCk8ezTQSWAVWMMYeMMf2NMYOMMYMuXzID2APsAoYDD3sqi0h2cuZ8In3HrCIhycWYvg2ICAn873P+vj58cU89SoXnY9C41ew/Fe/FpNc3fsV+Hpu0lnplCjBhQCMKBgde++L9y5xu7r8OgYgq8OBCuPltyBOaaXlFREREUi2sNPT4Fu77GfzzwqS7nSXxJ3Z4O5lkc8Zmszs+kZGRNioqytsxRDwiIcnFfaNWsHr/acb2b0Tj8gWvet2+k/HcMXQJBYMC+OHhZu7t+c5EQ3/fxbu/bad11cIMvaceefx9r35h7EGY8wps/gHyl4C2r0PNbppBFxERkewnORFWjYAFb0NiPDQaBC2fU38duYIxZrW1NvJ613hz6buI/IW1lue/38DyPTG81632NYt0gLKFgvjq3vociDnP4AlrSEp2ZWLSa7PW8n8zt/Hub9u5rXZxvu5d/+pFekI8zH8LPo+E7TOh5fPO0rFa3VWki4iISPbk6395695qqN0Lln3xvy19rmRvp5NsRoW6SBbx0dyd/LD2ME+1q8wdda96UuHfNC5fkLfuqMminSd5Y9qWTEh4bS6X5UJCMi//tImv/tjNPY1K89FddfD3/cePGGthwxT4LBIWvgtVOzsF+k0vQECQd8KLiIiIZKTgCLj9cxgwD8LLOVv7vmoOuxd4O5lkI97s+i6Sa8zfdpy9J8/z51YTa8FiL/8J0WcvMWrJXnpElmRw64puj9ujQSl2nYhj2MI9VIgI5v6mZT2S31rLe7O2M3PTMRKSXFxKcpGY7CIhyUVCsutvZ7s/3KoCz3SogvnnzPjBlTDrJTi0EorVge6joXRjj+QVERER8boS9aHfLNjyk7PVb+wdUPlmaPcmRFT2djrJ4rRHXcTDZm48ykPj16R4XZuqhfmqd/0rZ6FTkOyyPDh2NXO3HqdTzaI80bYylYqEpDXuVU1aeYDnf9hI0woFKRaalwA/Q4CvDwF+zsP/8t8rRATToXrRv7/41G6Y9zps+RmCi0CbV53lYD5a0CMiIiK5ROJFWPk1LHzf2QLYoL+z9S/o2lsdJedyZ4+6CnURDzp25iI3f7KQ0uH5GNO3Ib4+BmPAAMaYy3+CwZDH3+fKWWg3XUhI5svfdzFqyT7iE5K4rXZxHmtTifIRwen+DJuPnKHL0KU0LBvON/2cz+CW+FPO8vZVI8E3AJoNgSaDITD9mURERESypfiT8PvbEDUaAoKh5TPQcKBz3JvkGirURbzI5bLcN2olq/efZvqQGzOkaE7J6fgEvl64h2+W7uNSUjJ31ivJkNaVKF0wX5rGO3sxkVs/W8zFxGSmD2lOoesdsfanxAuw4itY9KFzx7jefdDqBQgpkqYMIiIiIjlO9DaY8y/YORtCS0Prl6Bmd/C5xkk5kqOoUBfxopGL9/LmtC38p0tN7m5UOlPf+2TcJb76fTdjl+8n2WXpHlmSwa0rUSIsr9tjWGt5aNwa5mw9zqSBjWlQNvz6L3C5YMNkmP9vOHsIKneEdq8756KLiIiIyJV2L4C5r8HRdVC4OrR9DSq10yk4OZwKdREv2XbsLLd9voQWlSIYfl/9NC9pT6/jZy8ydMEuJq48CMBjbSsxqGUFt5av/3mj4aVO1RjQovy1L7QWdvzmFOjHN0HxutD+31D2xoz6GCIiIiI5l8sFW36EeW/C6b1Qphm0fR1KNfB2MvEQFeoiXnAxMZk7vljCybgEZj3enILuLBf3sCOxF3hr+lambzxK4/LhfHRXHYqFXnt2ffX+GO76ejmtqxbm697XudGwdxHMe8Pp5B5eHm56CarfqUZxIiIiIqmVnAirx8Af70J8NFS7FVq/og7xOZAKdREveHPaFkYu3svoPg24qWphb8f5L2stU1cf4rVfNuPv68M7XWtyc41iV1wXE59A508X4e/rw6+P3khoXv8rBzu82rnru2cBhBSHVs9BnXvA9yrXioiIiIj7LsXB8qGw5BOn90+dXtDiWShQxtvJJIO4U6hr2kskAy3eeZKRi/dyX5MyWapIB6fLfI/IUkwf0pwyBfMxaNwaXvhhI+cTkv57jctleXzyOk7FJzD0nnpXFunR22DyvTC8NRxdD+3fgiFroX4fFekiIiIiGSEwGFo+C4+th0YPwoap8Fl9mPYknD3i7XSSSTSjLpJBTscncPMnCwnJ48+vg28kb0DW7dqZkOTio7k7+OqP3ZQrFMSnPetSo0Qon87byYdzdlzZAO/UbmcZ1sYp4B8ETQdD44chT37vfQgRERGR3ODMYVj0PqwZC8YHIvvBjU/oRJ1sTEvfRTKJtZaHx69h7tbj/PhwM2qUCPV2JLcs3XWSJ6asIyY+gZ4NSjNuxX7uqFOCD3vUdvaln9oNC99zurn7BkKD/nDjkxBU0NvRRURERHKX0/th4buwbiL4BkCjgdD0Mf1elg2pUBfJJFOjDvLMdxt4vmNVBrWs4O04qXI6PoHnvt/A7C3HqVQ4mJ8HNyPfuf2w8P3LBbo/NHgAmg7RnVsRERERbzu1G37/P9g4FQKCoNEgZ6WjCvZsQ4W6SCY4GHOemz9eSI0SoUwY0Nito8+yGmst87dFUzPfKQqv/RzWT3IK9Mj+0OwxFegiIiIiWU30Vqdg3/Iz+OdzVj42fRSCs1afJLmSO4W6X2aFEcmpXv91Mxb4oEftbFmkA5hTu2iz/aP/FeiNBqlAFxEREcnKCleDHt84BfvC92HZ57ByuNPkt9ljkP/K030k+1ChLpIOc7YcZ+7WaF7sVJWSBfJ5O07qHVkHiz+ELb+AXx5oOBBufBxCino7mYiIiIi4o3A16DYSWr0Aiz6AlcMgaiTUuw+aPQ5hpbydUNJAS98lx9t69CxnLyRSvUQowYEZd2/qfEIS7T5cSHCgH9OG3Ii/bzY67XD/UucH+a65EJgfGg6ARg9BcIS3k4mIiIhIesTshcUfwboJzte1ezoFe6GK3s0l/6Wl75LrnYq7RLcvlxKfkIwxUL5QEDVLhFKzZBg1S4RSvXh+gtJYvH82fxeHYy8wdVCT7FGkW+sU5os+gAPLIF8haPOK0yguT/boUi8iIiIiKQgvB7d9Ci2egSWfwNqxsHYcVLsFmj0BJet7O6G4QYW65GhDf9/NhcRk3u9em8OnL7Dx8BmW7TnFT+uOAGAMVIgI5pZaxXisTSXnSDI37Dx+juEL99CtfkkalA335EdIv+REp8nIkk/g2AbIXxI6vgt1e0NANlyuLyIiIiIpCysFnd+Hls/Ciq9h1XDY+iuUbe7MsFds4/wyLFmSCnXJsY7EXmDs8v10rVeSbvVL/u256LMX2Xj4DBsPn2HVvhg+nruT0/EJvHZb9RSLdWst//p5E0GBfrzQsaonP0L6XDoHa76F5V/CmYNQsBLc/gXU7AF+Ad5OJyIiIiKZIbgwtPmX04do9Tew7AsY3xWK1HSazlXvAr4qC7Ma/TciOdan83aChcfaVrriucL589Amfx7aVCuCtZa3Z25j2MI9+Pr48K9bql23WP9p3WGW74nhP11qUjA40JMfIW3OHoEVX0HUGLh0Bso0g07vQ6X24JMNluiLiIiISMYLDIGmg53mwRunOqstf3gA5r/hnPhTtzfkye/tlHKZCnXJkfaciGPq6kP0blwmxW7sxhhe6FiVxGQXo5bsxc/X+fpqxfqZC4m8NX0rtUuF0bNBFuugeWyTcyzHxqlgXXDD7dDkUe1DEhEREZH/8QuAuvdA7V6wcxYs+RRmvQgL3oZ6vaHRg1CgrLdT5noq1CVH+nDODgL9fHjkJve6WxpjeOWWG0h2WYYt3IOfj+GZDlWuKNbfn7WdmPgExvRtiE9WODPdlQw7Zjkz6Hv/AP8gaDAAGg/SD1gRERERuTYfH6jS0XkcWQvLhjpHu634Cqp2hsaPQOnG2sfuJSrUJcfZfOQM0zYcZfBNFYkIcX9pujGG126tTpLLMvT33fj5+vBku8r/fX7DoVjGrdjP/U3KUqOEl7ukX4iFdeOdH6an90H+EtDmVajfB/Jl8eZ2IiIiIpK1FK8LXYdDu9dh5XBYPdppPFe8rlOw33C7ehxlMhXqkuN8MHsHoXn9GdCifKpf6+Nj+PftNUhOtnw6byd+PoYhbSqR7LK8/NMmCgUH8mT7yikP5CkndsDKr2HdREiMh9JNoO1rUPVWNQERERERkfTJXxzavuoc7bZ+otOU+IcHYPZLUL+vMymUv5i3U+YK+s1ecpSofTHM3xbNczdXJTSvf5rG8PExvH1nTRJdLj6cswM/X0NIoB8bDp3hk551yJ8nbeOmmSsZds52Zs93zwffAKjZ/f/bu/Moq8oz3+Pfh2KeKeZBoBQEkUGxKIzaJg7BIbZoO8ThOnQ0Jr3i7dwMfTvJSjod06u7TbrT3l7pvj1EW2M6HRNjorbTVZNI1DgwKLOAIFACBVhAMUNVvfePvYmlC5CSOnUOVd/PWmfVOe/ZZ9e713p4qd/Z7353thDIsFNaty+SJElq+zp3h2m3ZOF8xTPZrd2euxN++3cw/pLs79BRZzgtvoAM6mozUkp858k3GNirCzefMfqo9tWhQ/DdK6fQ0Jjts3PHDpw5pj+XThnWMp09EttrsturzbkX6qqh5xA45+vZN5k9B7ZePyRJktQ+degAJ87IHrUrYfY9MPd+WPxLGDQBpt0Kkz8JXXoWu6dtjkFdbcZzyzbxylu1fHvmyXTrXHbU+yvrEPz9VVNoTPDM4hrumDnxA++xftRSglWzYPbdsPQxaKyHio/ChX8N4y6GslY+my9JkiQBlB8PM/4KPvY1WPjzbLbnY1+EZ/4SplwDU2+CIROL3cs2I1JKxe5Ds1RWVqbZs2cXuxsqMY2NiT/8/vPU7dnPs1/8GJ07ttz9wlNKbN9bX9gp77tqs+uAZt8D76yArn3h1P+RTTcacGQr10uSJEmtJiWofjVbfG7xw9CwF4ZXZrM/J/4RdO5R7B6WrIiYk1KqPNw2nlFXm/DEwg0sWlfH966e0qIhHbLV4AsS0hsb4a1Z2fShJY9mg9uIKrjsX+Dky6BTt5b/nZIkSVJLiIDjqrLHRXfC6z+BuffBI7fDk1+FyVfDaTfB0CnF7ukxyTPqOubVNzQy465ZdOwQPPH5sykrhfubH862anjtxzDvR7B1NXTtA5Ouhqk3wtDJxe6dJEmS9OGkBGtfztZYWvQLqN+T3eJt6o0w8Yrs7155Rl3tw0Nz32blpp386w2nlW5Ir98HbzwO8+6HFc8CCSrOhnO/ASdd4tlzSZIkHfsiYOTp2ePCv4H5P8tC+39/ITvLPv4SOPX6bA2mDke/plRbZlDXMa125z7uemYZpxzXlxkTBhe7O++VEqybC68/AAsfhF3vQK9hcPaX4ZTrobyi2D2UJEmSCqNbP5h+G1R9GtbNy2aULvhZ9ndx7+Ew5Vo45Trof0Kxe1qSnPquY9a6rbu54e6Xqd6ymx9/ejqnjSovdpcy26ph/gPZdTqbl0FZFxh3URbOx5znt4eSJElqn/bvgWVPwLz/hDefhdQIIz+ShfYJM6Fb32L3sFUcydR3g7qOSSs37eCGu1+hbvd+fnBTJdOP71/cDu3dni0I9/p/warfAikfdK6BCZe1m0FHkiRJOiJ162H+T7Iz7QdObp04I7sv+9gZ0LFLsXtYMEUP6hFxIfB/gDLgBymlv33f+zcD3wXezpu+n1L6weH2aVDXwre3cdM9rwBw36eqmDi8SItS1O+FFc9k95F84wnYvwv6VWTfCE6+2qntkiRJ0gc5cLno/Hxa/M5N2aJzE2ZmCy6POhM6tOxdnYqtqEE9IsqAZTadvkAAABIDSURBVMDHgWrgVeDalNLiJtvcDFSmlG4/0v0a1Nu3V1bVcsu9r9K7Wyfuv6WK4wf2bN0ONNRnt1Rb+PPsDPqebdC9f3bWfPIns9tTRIkuaCdJkiSVsoZ6WPWbLLQveRT278yuZ594RfYYOqVN/K1d7FXfq4AVKaWVeWd+AswEFh/2U9Ih/GppDX/yo7mM6NeN+2+ZzrC+rbRSemMjVL8CCx6Exb/MvuXr3CtbrX3ilXD8R6GsAPdZlyRJktqTso4w5vzsse972azV+T+Fl/4ZXvxHKD8eTr48ewye2CZC+6EUMqgPB9Y2eV0NTD/IdldExNlkZ9+/kFJa+/4NIuI24DaAkSNHFqCrKnUPv/Y2X/rp65w0tDf3/vE0+vcs8DUrjQ3ZPSAXPwyLH4Ht66BjVzjxgiycj50BnboWtg+SJElSe9W5B0y6Mnvsqs3OsC/6BTx/F/z276H/mDy0/xEMOqnNhfZCTn2/CrggpXRr/voGoCql9D+bbNMf2JFS2hsRnwWuTimde7j9OvW9/bn/d2/xF48sYnpFOf9+YyW9uhbo7HVDPax5MQvnSx6FHTXZohZjzs+ukRl/MXTpVZjfLUmSJOmD7dwMSx7JQvtbz2crxw8YB+d+HSZcWuzeHZFiT32vBo5r8noEsK7pBimld5q8/HfgzgL2R8eIlBKL1tXx1KINPLVoA8tqdnD+SYP5/nWn0rVTC9/arH4vvPXbLJgv+W/YtRk6dstWnJwwMztzbjiXJEmSSkOPAVD5qeyxY2Me2n/Z5i5FLWRQfxUYGxEVZKu6XwNc13SDiBiaUlqfv7wUWFLA/qiENTQm5qze8vtwXr1lNx0CqirKuWPmyVxXNZKOZS202uPurbD8aXjjMVj+DOzbDp17ZtPaJ8zMzqB37tEyv0uSJElSYfQcBNNuzR5tTMGCekqpPiJuB54iuz3bPSmlRRFxBzA7pfQI8KcRcSlQD9QCNxeqPypNKzZu5+7nV/H04ho279hH57IOnDV2AH967ljOO2lQy12Lvq0alj6ehfO3nofGeugxCCZeDuM+kS0I16mVFqeTJEmSpMMo6H3UC8Fr1NuGxsbED3/3Fn/zxFI6dgjOGT+IC04ewsfGDWyZa9AbG+DtObD8/8Gyp2DD/Ky9/1gY/4nsMbyyzd2TUZIkSVJpK/Y16tJB1dTt4c8enM+sZZs4d/wg7rxiMgN7tcCZ81218OavsnC+4hnY9Q5EBzhuOpz/lzD+Ehgw9uh/jyRJkiQVkEFdrerJhev56kML2L2/gb+6bCLXTx9JfNhbKTQ2Qs0CWPFsFs7Xvpyt+titHMZ+PFsI7oRzoXt5yx6EJEmSJBWQQV2tYsfeeu54dBE/nV3NpOF9uOuaUzhhYM/m72h7TXbW/M1fwcpfw85NWfuQSXDWF7MF4YafBh1aeHV4SZIkSWolBnUV3JzVW/jCA69RvWUXt58zhs+fP5ZOR7qC+/49sPal7Kz5m7+CmoVZe/cBcMI5cMJ52c9eQwp3AJIkSZLUigzqKoh1W3fzyqpanl+xmYfmVjOsbzce+MxHmDb6A6ahN9TDunmw6jewahaseRka9kKHTjDydDjvm9l09iGTXQhOkiRJUptkUNdRSymxpnYXL6+s5eVVtby86h2qt+wGoFfXjnxy2ki+dvH4g6/m3tgIGxdloXzlc7D6xey+5gCDJ0HVp6HibBh1JnT5EFPlJUmSJOkYY1BXs+1vaGTJ+jrmrN7C3DVbeXVVLRvq9gBQ3qMzVaPLueWsCqoqyhk/pDdlHZosFtfYkN0qbfWL8NYLsOZF2L0le6//GJh8VRbMR58NPfoX4egkSZIkqbgM6vpAm3fsZW4eyueu2cL86q3s2d8IwJDeXZlWUU5VRTmnV5QzZlDP967i3rAf1s6D1S9kwXzty7C3LnuvX0V2P/NRZ2bhvM+IIhydJEmSJJUWg7oOam3tLh6cU82jr69j5eadAHQqCyYM68N1VaOYOqovU0f2Y1jfbu/94K5aqH4V1ryUhfK350J9Ng2eAeNg4hUw+iwYdQb0HtbKRyVJkiRJpc+grt/bta+exxds4ME5a3lpZS0RcMYJ/fnktOOYOqofk4b3oWunJrc9SwneeTML5AeC+aal2XtRBkMnw2k3Z4vAjToTeg4synFJkiRJ0rHEoN7OpZR4ZVUtD86p5vEF69m5r4HR/bvz5RkncvnUEQxvesZ89xZYPQeq52Rnzd+e/e715V36wHFVMOlKOO50GD4VOvcozkFJkiRJ0jHMoN7ONDQmVmzcwetrtzJv7VZeWLGZNbW76NG5jEsmD+PKyhFUjupHNOyDmkWwfO67wfyd5fleAgaOh/GXwIhKGFGVvfZ2aZIkSZJ01AzqbVxN3R7mrdnK69VbeW3NVha8vY0de+sB6N21I1NH9eML547mwsFb6bZxHiy4B56al4X0xv3ZTnoMhOGVMOWaLJgPmwpdexfxqCRJkiSp7TKotxG1O/exrGY7yzfuYHnNdpbVbGfFxh1s3rEPyBaCO2lob64+ZQBn9dnEpA6rGbDjDWLDfHhyAdRnt1ejSx8Ydgp85HMw7NTs0XckNF3JXZIkSZJUMAb1Y8ze+gaW1+xg8bo6Fq+vY+mGuvcEcoCeXToydnBPPjG2O9O7b2VS2WqG7V5OWc0CWPAGNGZn1OnSG4ZMgmm3vhvK+1U4hV2SJEmSisigXsK27d7P4nV1LFq3jcXr61i8Lgvl9Y0JgO6dyzhxcC/OH1dOZa8tTCirZuT+lfTYupSoWQxL1r67s56DYchkGHdh9nPoZOg72lAuSZIkSSXGoF5C6hsaeb16G88t28SsZZuYX72VPJMzsFcXTh7ai5kVjUzttoExVFO+cwWxcTEsXQoNe7MNowwGnAjHTYfKT8HgiVko7zWkeAcmSZIkSTpiBvUiW7d1N7OWbWLW8k08v3wzdXvqiUicN7Se7566jSldNzB831t027ocNiyFNdvf/XDPITDoJJh+WxbIB5+chfSOXYp3QJIkSZKko2JQL5AXV2zm0fnr2VffyL6GRvbVNzR5nj127toFW1ZxQqzj1G4bubXPJo7vu44+O1cRtbugNt9Z9wFZID/l2uw2aINOyn52Ly/qMUqSJEmSWp5BvUBW1+7i6cU1dC2DoWVbqYj1jGI9I9M6RjS8zdCGagbWb6CsS2P2gQYgjcjOiI/7g+zngBOzQN5zYFGPRZIkSZLUegzqBXJtt1e4tv9dUPsm7N317hudukP/E6D/dCg/AQaOgwFjof9Y6NKzeB2WJEmSJJUEg3qhdOoOvYdCxdl5MB+TPXoNdaV1SZIkSdIhGdQLZfzF2UOSJEmSpGbw1K4kSZIkSSXEoC5JkiRJUgkxqEuSJEmSVEIM6pIkSZIklRCDuiRJkiRJJcSgLkmSJElSCTGoS5IkSZJUQgzqkiRJkiSVEIO6JEmSJEklxKAuSZIkSVIJMahLkiRJklRCDOqSJEmSJJUQg7okSZIkSSUkUkrF7kOzRMQmYHWx+6EWNQDYXOxOSDnrUaXGmlQpsR5VSqxHlZojrclRKaWBh9vgmAvqansiYnZKqbLY/ZDAelTpsSZVSqxHlRLrUaWmJWvSqe+SJEmSJJUQg7okSZIkSSXEoK5S8G/F7oDUhPWoUmNNqpRYjyol1qNKTYvVpNeoS5IkSZJUQjyjLkmSJElSCTGoqyAi4p6I2BgRC5u0TYmI30XEgoh4NCJ65+0fj4g5efuciDi3yWdOy9tXRMQ/RkQU43h0bGtOPTZ5f2RE7IiILzdpuzAi3sjr8SuteQxqO5pbjxExOX9vUf5+17zd8VEtopn/Z3eKiPvy9iUR8dUmn3GM1FGJiOMi4td5bS2KiM/n7eUR8XRELM9/9svbIx//VkTE/IiY2mRfN+XbL4+Im4p1TDq2fYiavD6vxfkR8WJETGmyr2aNkQZ1Fcq9wIXva/sB8JWU0iTgF8Cf5e2bgT/M228C7m/ymf8L3AaMzR/v36d0JO7lyOvxgH8AnjjwIiLKgH8CLgImANdGxIRCdVht2r0cYT1GREfgR8BnU0onAx8D9uefcXxUS7mXIx8jrwK65O2nAZ+JiNGOkWoh9cCXUkonAacDn8vr6CvAsymlscCz+WvI6u3AGHgb2bhIRJQD3wSmA1XANw8EKamZmluTq4CPppQmA98mv2b9w4yRBnUVREppFlD7vuZxwKz8+dPAFfm281JK6/L2RUDXiOgSEUOB3iml36VsMYUfApcVvvdqa5pTjwARcRmwkqweD6gCVqSUVqaU9gE/AWYWrNNqs5pZjzOA+Sml1/PPvpNSanB8VEtqZk0moEf+JVI3YB9Qh2OkWkBKaX1KaW7+fDuwBBhOVkv35Zvdx7vj3UzghynzEtA3Hx8vAJ5OKdWmlLaQ1bBfZqrZmluTKaUX85oDeAkYkT9v9hhpUFdrWghcmj+/CjjuINtcAcxLKe0l+0dQ3eS96rxNagkHrceI6AH8OfCt920/HFjb5LX1qJZ0qPHxRCBFxFMRMTci/nfe7vioQjtUTT4I7ATWA2uAv0sp1eIYqRYWEaOBU4GXgcEppfWQBSdgUL7ZoerOelSLO8KabOoW3p2d2eyaNKirNX2KbLrIHKAX2bfwvxcRJwN3Ap850HSQfXibArWUQ9Xjt4B/SCnteN/21qMK6VD12BE4C7g+/3l5RJyH9ajCO1RNVgENwDCgAvhSRByPNakWFBE9gZ8D/yulVHe4TQ/Slg7TLn0ozajJA9ufQxbU//xA00E2O2xNdmxuJ6UPK6W0lGwaJxFxIvCJA+9FxAiya+BuTCm9mTdX8+50EfLn65BawGHqcTpwZUR8B+gLNEbEHmAO750FYj2qxRymHquB51JKm/P3Hgemkl237viogjlMTV4HPJlS2g9sjIgXgEqyM0WOkTpqEdGJLBD9Z0rpoby5JiKGppTW51PbN+bt1Ry87qrJ1vRo2v6bQvZbbVcza5KImEy2zsdFKaV38uZD1eoheUZdrSYiBuU/OwBfB/4lf90XeAz4akrphQPb59NItkfE6flqxjcCD7d6x9UmHaoeU0p/kFIanVIaDdwF/HVK6fvAq8DYiKiIiM7ANcAjRem82pxD1SPwFDA5Irrn1wR/FFjs+KhCO0xNrgHOzVfb7kG2uNJSHCPVAvLx7G5gSUrpe03eeoRswWHynw83ab8xr8fTgW35+PgUMCMi+uWLyM3I26RmaW5NRsRI4CHghpTSsibbN3uM9Iy6CiIi/ovsm8wBEVFNtvJmz4j4XL7JQ8B/5M9vB8YA34iIb+RtM1JKG4E/IVuNthvZNR6/X4VbOlLNrMeDSinVR8TtZP/RlwH3pJQWHe4z0sE0px5TSlsi4ntk/8En4PGU0mP5do6PahHNHCP/KX++kGwq53+klObn+3GM1NE6E7gBWBARr+VtXwP+FvhpRNxC9mXRVfl7jwMXAyuAXcAfA6SUaiPi22RjJ8Ad+VoKUnM1tyb/AugP/HOW8alPKVV+mL8jI1ssVpIkSZIklQKnvkuSJEmSVEIM6pIkSZIklRCDuiRJkiRJJcSgLkmSJElSCTGoS5IkSZJUQgzqkiS1cfk9hp+PiIuatF0dEU8Ws1+SJOngvD2bJEntQERMBH4GnEp2D9fXgAtTSm8exT47ppTqW6iLkiQpZ1CXJKmdiIjvADuBHsD2lNK3I+Im4HNAZ+BF4PaUUmNE/BswFegGPJBSuiPfRzXwr8CFwF0ppZ8V4VAkSWrTOha7A5IkqdV8C5gL7AMq87PslwNnpJTq83B+DfBj4CsppdqI6Aj8OiIeTCktzvezM6V0ZjEOQJKk9sCgLklSO5FS2hkRDwA7Ukp7I+J8YBowOyIgO3u+Nt/82oi4hexvhWHABOBAUH+gdXsuSVL7YlCXJKl9acwfAAHck1L6RtMNImIs8HmgKqW0NSJ+BHRtssnOVumpJEntlKu+S5LUfj0DXB0RAwAion9EjAR6A9uBuogYClxQxD5KktTueEZdkqR2KqW0ICK+BTwTER2A/cBngdlk09wXAiuBF4rXS0mS2h9XfZckSZIkqYQ49V2SJEmSpBJiUJckSZIkqYQY1CVJkiRJKiEGdUmSJEmSSohBXZIkSZKkEmJQlyRJkiSphBjUJUmSJEkqIQZ1SZIkSZJKyP8Hy/jEt0dJhXMAAAAASUVORK5CYII=\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -5746,7 +5683,7 @@ }, { "cell_type": "code", - "execution_count": 471, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -5755,18 +5692,20 @@ "Text(0, 0.5, 'Solar PV Module Cost($/Wp)')" ] }, - "execution_count": 471, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -5786,7 +5725,7 @@ }, { "cell_type": "code", - "execution_count": 472, + "execution_count": 121, "metadata": {}, "outputs": [ { @@ -5795,18 +5734,20 @@ "Text(0, 0.5, 'Cumulative Installed Capacity(MWp)')" ] }, - "execution_count": 472, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], diff --git a/your-project/Global climate change datasets and correlations.ipynb b/your-project/Global climate change datasets and correlations.ipynb deleted file mode 100644 index 60d0e48f..00000000 --- a/your-project/Global climate change datasets and correlations.ipynb +++ /dev/null @@ -1,5744 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 170, - "metadata": {}, - "outputs": [], - "source": [ - "#importing the needed libraries\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import datetime\n", - "import re\n", - "import seaborn as sns\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Introduction" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Datasets structure\n", - "\n", - "#### - Global Indicators datasets:\n", - "\n", - "1. Annual CO emissions (1751-2017) [global_co]\n", - "2. Annual greenhouse gas emissions (1990-2016) [global_ghg]\n", - "3. Global CO atmospheric concentration (800.000 BCE - 2018) [co_concentration]\n", - "4. Global Temperature Anomaly (1850-2018) [global_a]\n", - "5. Ozone-depleting emissions (1961-2014) [caused_ozone]\n", - "6. Antartica ozone hole area (1979-2018) [oz_hole]\n", - "7. Global average temperature (1850-2015) [annual_global_temp]\n", - "8. Fossil fuel consimpiton per capita (1965-2015) [fossil_per_c]\n", - "9. Primary energy consumption by source (1800-2018) [global_energy]\n", - "10. Modern renewable consumption (1965-2018) [renew_energy]\n", - "11. Hubbert's prediction on USA oil production (1991-2016) [hubbert_peak]\n", - "12. Share of global primary energy consumtion (1800-2017) [share_energy]\n", - "13. Solar PV price and capacity (1976-2018) [solar_price]\n", - "\n", - "#### - Country indicators datasets:\n", - "\n", - "14. CO annual emissions by country (1949-2017) [co_annual]\n", - "15. Annual share of CO emissions by country (1949-2017)[co_share]\n", - "16. Total greenhouse gas emissions by country (1990-2016) [ghg]\n", - "17. Average temperature by country (1743-2017)[annual_temp]\n", - "\n", - "#### - Merged datasets:\n", - "\n", - "18. Global indicators (1751-2018) [global_indicators]\n", - "19. Country indicators (1743-2017) [country_indicators]\n", - "20. Ozone datasets merged (1979-2014) [ozone_merged]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Global Indicators" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Annual CO emissions globally (1751-2017)" - ] - }, - { - "cell_type": "code", - "execution_count": 251, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
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4AfghanistanAFG1953106256.0
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" - ], - "text/plain": [ - " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", - "0 Afghanistan AFG 1949 14656.0\n", - "1 Afghanistan AFG 1950 84272.0\n", - "2 Afghanistan AFG 1951 91600.0\n", - "3 Afghanistan AFG 1952 91600.0\n", - "4 Afghanistan AFG 1953 106256.0" - ] - }, - "execution_count": 251, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Annual CO emissions globally\n", - "data = pd.read_csv('data//annual-co-emissions-by-region.csv')\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 252, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
20353WorldOWID_WRL17519350528.0
20354WorldOWID_WRL17529354192.0
20355WorldOWID_WRL17539354192.0
20356WorldOWID_WRL17549357856.0
20357WorldOWID_WRL17559361520.0
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" - ], - "text/plain": [ - " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", - "20353 World OWID_WRL 1751 9350528.0\n", - "20354 World OWID_WRL 1752 9354192.0\n", - "20355 World OWID_WRL 1753 9354192.0\n", - "20356 World OWID_WRL 1754 9357856.0\n", - "20357 World OWID_WRL 1755 9361520.0" - ] - }, - "execution_count": 252, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#keeping only the global data\n", - "global_co = data[data.Entity == 'World']\n", - "global_co.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 253, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " errors=errors,\n" - ] - } - ], - "source": [ - "#cleaning the dataset\n", - "global_co.drop(['Entity','Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 254, - "metadata": {}, - "outputs": [], - "source": [ - "global_co.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 255, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 267 entries, 1751 to 2017\n", - "Data columns (total 1 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Annual COâ‚‚ emissions (tonnes ) 267 non-null float64\n", - "dtypes: float64(1)\n", - "memory usage: 4.2 KB\n" - ] - } - ], - "source": [ - "global_co.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Total greenhouse gas emissions (1990-2016)" - ] - }, - { - "cell_type": "code", - "execution_count": 256, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
0AfghanistanAFG199015140000.0
1AfghanistanAFG199115060000.0
2AfghanistanAFG199213600000.0
3AfghanistanAFG199313430000.0
4AfghanistanAFG199413240000.0
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" - ], - "text/plain": [ - " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", - "0 Afghanistan AFG 1990 15140000.0\n", - "1 Afghanistan AFG 1991 15060000.0\n", - "2 Afghanistan AFG 1992 13600000.0\n", - "3 Afghanistan AFG 1993 13430000.0\n", - "4 Afghanistan AFG 1994 13240000.0" - ] - }, - "execution_count": 256, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Total greenhouse gas emissions\n", - "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", - "ghg.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 257, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the dataset\n", - "ghg.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 258, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
5076World19903.496734e+10
5077World19913.512544e+10
5078World19923.498553e+10
5079World19933.507963e+10
5080World19943.528761e+10
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" - ], - "text/plain": [ - " Entity Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", - "5076 World 1990 3.496734e+10\n", - "5077 World 1991 3.512544e+10\n", - "5078 World 1992 3.498553e+10\n", - "5079 World 1993 3.507963e+10\n", - "5080 World 1994 3.528761e+10" - ] - }, - "execution_count": 258, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_ghg = ghg[ghg.Entity == 'World']\n", - "global_ghg.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 259, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " errors=errors,\n" - ] - } - ], - "source": [ - "global_ghg.drop(['Entity'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 260, - "metadata": {}, - "outputs": [], - "source": [ - "global_ghg = global_ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )'})" - ] - }, - { - "cell_type": "code", - "execution_count": 261, - "metadata": {}, - "outputs": [], - "source": [ - "global_ghg.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 262, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 27 entries, 1990 to 2016\n", - "Data columns (total 1 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 GHG(tonnes COâ‚‚e ) 27 non-null float64\n", - "dtypes: float64(1)\n", - "memory usage: 432.0 bytes\n" - ] - } - ], - "source": [ - "global_ghg.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Global COâ‚‚ atmospheric concentration" - ] - }, - { - "cell_type": "code", - "execution_count": 380, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearCOâ‚‚ concentration (parts per million)
0WorldOWID_WRL803719 BCE207.29
1WorldOWID_WRL803182 BCE202.23
2WorldOWID_WRL802573 BCE204.86
3WorldOWID_WRL802060 BCE207.50
4WorldOWID_WRL801975 BCE202.92
...............
1866WorldOWID_WRL2014398.65
1867WorldOWID_WRL2015400.83
1868WorldOWID_WRL2016404.24
1869WorldOWID_WRL2017406.55
1870WorldOWID_WRL2018408.52
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1871 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " Entity Code Year COâ‚‚ concentration (parts per million)\n", - "0 World OWID_WRL 803719 BCE 207.29\n", - "1 World OWID_WRL 803182 BCE 202.23\n", - "2 World OWID_WRL 802573 BCE 204.86\n", - "3 World OWID_WRL 802060 BCE 207.50\n", - "4 World OWID_WRL 801975 BCE 202.92\n", - "... ... ... ... ...\n", - "1866 World OWID_WRL 2014 398.65\n", - "1867 World OWID_WRL 2015 400.83\n", - "1868 World OWID_WRL 2016 404.24\n", - "1869 World OWID_WRL 2017 406.55\n", - "1870 World OWID_WRL 2018 408.52\n", - "\n", - "[1871 rows x 4 columns]" - ] - }, - "execution_count": 380, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Global COâ‚‚ atmospheric concentration dataset\n", - "co_concentration = pd.read_csv('data//global-co-concentration-ppm.csv')\n", - "co_concentration" - ] - }, - { - "cell_type": "code", - "execution_count": 381, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the data\n", - "co_concentration.drop(['Code', 'Entity'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 382, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration = co_concentration.rename(columns={'COâ‚‚ concentration (parts per million)':'COâ‚‚ concentration'})" - ] - }, - { - "cell_type": "code", - "execution_count": 392, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1'" - ] - }, - "execution_count": 392, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_concentration.Year[1648]" - ] - }, - { - "cell_type": "code", - "execution_count": 267, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'CO atmospheric concentration')" - ] - }, - "execution_count": 267, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plotting the Global COâ‚‚ atmospheric concentration increasement from 1864-2018\n", - "x = co_concentration.Year[1740:]\n", - "y = co_concentration['COâ‚‚ concentration'][1740:]\n", - "plt.figure(figsize=(17,6))\n", - "\n", - "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", - " linewidth=2)\n", - "\n", - "plt.legend()\n", - "plt.xlabel('Year')\n", - "plt.ylabel('CO atmospheric concentration')" - ] - }, - { - "cell_type": "code", - "execution_count": 268, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 269, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Index: 1871 entries, 803719 BCE to 2018\n", - "Data columns (total 1 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 COâ‚‚ concentration 1871 non-null float64\n", - "dtypes: float64(1)\n", - "memory usage: 29.2+ KB\n" - ] - } - ], - "source": [ - "co_concentration.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 396, - "metadata": {}, - "outputs": [], - "source": [ - "co_atm_conc = co_concentration.drop(co_concentration.index[:1648])" - ] - }, - { - "cell_type": "code", - "execution_count": 397, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearCOâ‚‚ concentration
16481276.7
164930277.9
165056277.4
1651104277.5
1652136278.1
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" - ], - "text/plain": [ - " Year COâ‚‚ concentration\n", - "1648 1 276.7\n", - "1649 30 277.9\n", - "1650 56 277.4\n", - "1651 104 277.5\n", - "1652 136 278.1" - ] - }, - "execution_count": 397, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_atm_conc.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 398, - "metadata": {}, - "outputs": [], - "source": [ - "co_atm_conc.to_csv('co_atm_conc')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. Global Temperature anomaly 1850-2018\n" - ] - }, - { - "cell_type": "code", - "execution_count": 270, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
EntityCodeYearMedian (℃)Upper (℃)Lower (℃)
0GlobalNaN1850-0.373-0.339-0.425
1GlobalNaN1851-0.218-0.184-0.274
2GlobalNaN1852-0.228-0.196-0.280
3GlobalNaN1853-0.269-0.239-0.321
4GlobalNaN1854-0.248-0.218-0.301
\n", - "
" - ], - "text/plain": [ - " Entity Code Year Median (℃) Upper (℃) Lower (℃)\n", - "0 Global NaN 1850 -0.373 -0.339 -0.425\n", - "1 Global NaN 1851 -0.218 -0.184 -0.274\n", - "2 Global NaN 1852 -0.228 -0.196 -0.280\n", - "3 Global NaN 1853 -0.269 -0.239 -0.321\n", - "4 Global NaN 1854 -0.248 -0.218 -0.301" - ] - }, - "execution_count": 270, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Global Temperature anomaly (increase-decreage on previous average temperature)\n", - "temperature_a = pd.read_csv('data//temperature_anomaly.csv')\n", - "temperature_a.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 271, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 676 entries, 0 to 675\n", - "Data columns (total 6 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Entity 676 non-null object \n", - " 1 Code 0 non-null float64\n", - " 2 Year 676 non-null int64 \n", - " 3 Median (℃) 676 non-null float64\n", - " 4 Upper (℃) 676 non-null float64\n", - " 5 Lower (℃) 676 non-null float64\n", - "dtypes: float64(4), int64(1), object(1)\n", - "memory usage: 31.8+ KB\n" - ] - } - ], - "source": [ - "temperature_a.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 272, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the dataset\n", - "temperature_a.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 273, - "metadata": {}, - "outputs": [], - "source": [ - "temperature_a.rename(columns={'Entity':'Country'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 274, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Global', 'Northern Hemisphere', 'Southern Hemisphere', 'Tropics'],\n", - " dtype=object)" - ] - }, - "execution_count": 274, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature_a.Country.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 494, - "metadata": {}, - "outputs": [], - "source": [ - "#temperature_a.Year.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 276, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" - ] - }, - "execution_count": 276, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0, flags=flags)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plotting the median temperature anomaly over time to see how it's actually ingreasing \n", - "x = temperature_a.Year\n", - "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", - "plt.figure(figsize=(17,6))\n", - "\n", - "plt.plot(x, y, 'dodgerblue', label='Temperature Anomaly', \n", - " linewidth=2)\n", - "\n", - "plt.legend()\n", - "plt.xlabel('Year')\n", - "plt.ylabel('increse ordescrease in Temperature(℃)')" - ] - }, - { - "cell_type": "code", - "execution_count": 277, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryYearMedian (℃)Upper (℃)Lower (℃)
0Global1850-0.373-0.339-0.425
1Global1851-0.218-0.184-0.274
2Global1852-0.228-0.196-0.280
3Global1853-0.269-0.239-0.321
4Global1854-0.248-0.218-0.301
\n", - "
" - ], - "text/plain": [ - " Country Year Median (℃) Upper (℃) Lower (℃)\n", - "0 Global 1850 -0.373 -0.339 -0.425\n", - "1 Global 1851 -0.218 -0.184 -0.274\n", - "2 Global 1852 -0.228 -0.196 -0.280\n", - "3 Global 1853 -0.269 -0.239 -0.321\n", - "4 Global 1854 -0.248 -0.218 -0.301" - ] - }, - "execution_count": 277, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature_a.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 243, - "metadata": {}, - "outputs": [], - "source": [ - "#keeping only the global data to compare it with the CO emissions and the CO concentration\n", - "global_a = temperature_a.where(temperature_a['Country']=='Global')\n", - "global_a.dropna(inplace=True)\n", - "global_a.drop(['Country'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 278, - "metadata": {}, - "outputs": [], - "source": [ - "global_a.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 360, - "metadata": {}, - "outputs": [], - "source": [ - "global_a.drop(['Upper (℃)','Lower (℃)'],axis=1, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5. Ozone depletion substances emissions (CFC's) (1961-2014)" - ] - }, - { - "cell_type": "code", - "execution_count": 287, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearOzone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)
0Natural emissionsNaN1961165000
1Natural emissionsNaN1962165000
2Natural emissionsNaN1963165000
3Natural emissionsNaN1964165000
4Natural emissionsNaN1965165000
\n", - "
" - ], - "text/plain": [ - " Entity Code Year \\\n", - "0 Natural emissions NaN 1961 \n", - "1 Natural emissions NaN 1962 \n", - "2 Natural emissions NaN 1963 \n", - "3 Natural emissions NaN 1964 \n", - "4 Natural emissions NaN 1965 \n", - "\n", - " Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents) \n", - "0 165000 \n", - "1 165000 \n", - "2 165000 \n", - "3 165000 \n", - "4 165000 " - ] - }, - "execution_count": 287, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# importing Ozone depleting emissions dataset\n", - "ozone = pd.read_csv('data//ozone-depleting-substance-emissions.csv')\n", - "ozone.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 288, - "metadata": {}, - "outputs": [], - "source": [ - "# cleaning the dataset\n", - "ozone.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 289, - "metadata": {}, - "outputs": [], - "source": [ - "ozone.rename(columns={'Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)':'Ozone-depleting emissions'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 290, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Natural emissions', 'Total emissions'], dtype=object)" - ] - }, - "execution_count": 290, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ozone.Entity.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 291, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 291, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#ploting natural and total emissions to see that we have nearly stopped using CFC\n", - "plt.figure(figsize=(17,6))\n", - "plt.plot(ozone.Year,ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Natural emissions'), label='Natural emissions')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Tonnes CFC11-equivalents')\n", - "plt.plot(ozone.Year, ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Total emissions'), label='Total emissions')\n", - "plt.legend(['Natural emissions', 'Total emissions'], loc='upper right')" - ] - }, - { - "cell_type": "code", - "execution_count": 292, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " errors=errors,\n" - ] - } - ], - "source": [ - "caused_ozone = ozone[ozone.Entity == 'Total emissions']\n", - "caused_ozone.drop(['Entity'],axis=1, inplace=True )" - ] - }, - { - "cell_type": "code", - "execution_count": 294, - "metadata": {}, - "outputs": [], - "source": [ - "ozone.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 293, - "metadata": {}, - "outputs": [], - "source": [ - "caused_ozone.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6. Antartica Ozone Hole area (1979-2017)" - ] - }, - { - "cell_type": "code", - "execution_count": 295, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearMaximum ozone hole area (square kilometres)Mean ozone hole area (square kilometres)
0WorldOWID_WRL19791100000100000
1WorldOWID_WRL198033000001400000
2WorldOWID_WRL19813100000600000
3WorldOWID_WRL1982108000004800000
4WorldOWID_WRL1983122000007900000
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" - ], - "text/plain": [ - " Entity Code Year Maximum ozone hole area (square kilometres) \\\n", - "0 World OWID_WRL 1979 1100000 \n", - "1 World OWID_WRL 1980 3300000 \n", - "2 World OWID_WRL 1981 3100000 \n", - "3 World OWID_WRL 1982 10800000 \n", - "4 World OWID_WRL 1983 12200000 \n", - "\n", - " Mean ozone hole area (square kilometres) \n", - "0 100000 \n", - "1 1400000 \n", - "2 600000 \n", - "3 4800000 \n", - "4 7900000 " - ] - }, - "execution_count": 295, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Antartica Ozone Hole area dataset\n", - "oz_hole = pd.read_csv('data//antarctic-ozone-hole-area.csv')\n", - "oz_hole.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 296, - "metadata": {}, - "outputs": [], - "source": [ - "oz_hole.drop(['Entity', 'Code','Maximum ozone hole area (square kilometres)'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 297, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMean ozone hole area (square kilometres)
01979100000
119801400000
21981600000
319824800000
419837900000
\n", - "
" - ], - "text/plain": [ - " Year Mean ozone hole area (square kilometres)\n", - "0 1979 100000\n", - "1 1980 1400000\n", - "2 1981 600000\n", - "3 1982 4800000\n", - "4 1983 7900000" - ] - }, - "execution_count": 297, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oz_hole.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 298, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 298, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#ploting natural and total emissionsMean ozone hole area (square kilometres) to see if it has reduced\n", - "plt.figure(figsize=(17,6))\n", - "plt.plot(oz_hole.Year,oz_hole['Mean ozone hole area (square kilometres)'], label='Mean ozone hole area (square kilometres)')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Mean ozone hole area (square kilometres)')\n", - "plt.legend(['Mean ozone hole area (square kilometres)'], loc='upper right')" - ] - }, - { - "cell_type": "code", - "execution_count": 299, - "metadata": {}, - "outputs": [], - "source": [ - "oz_hole.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7. Global average temperature (1850-2015)" - ] - }, - { - "cell_type": "code", - "execution_count": 300, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtLandAverageTemperatureLandAverageTemperatureUncertaintyLandMaxTemperatureLandMaxTemperatureUncertaintyLandMinTemperatureLandMinTemperatureUncertaintyLandAndOceanAverageTemperatureLandAndOceanAverageTemperatureUncertainty
01750-01-013.0343.574NaNNaNNaNNaNNaNNaN
11750-02-013.0833.702NaNNaNNaNNaNNaNNaN
21750-03-015.6263.076NaNNaNNaNNaNNaNNaN
31750-04-018.4902.451NaNNaNNaNNaNNaNNaN
41750-05-0111.5732.072NaNNaNNaNNaNNaNNaN
\n", - "
" - ], - "text/plain": [ - " dt LandAverageTemperature LandAverageTemperatureUncertainty \\\n", - "0 1750-01-01 3.034 3.574 \n", - "1 1750-02-01 3.083 3.702 \n", - "2 1750-03-01 5.626 3.076 \n", - "3 1750-04-01 8.490 2.451 \n", - "4 1750-05-01 11.573 2.072 \n", - "\n", - " LandMaxTemperature LandMaxTemperatureUncertainty LandMinTemperature \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " LandMinTemperatureUncertainty LandAndOceanAverageTemperature \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " LandAndOceanAverageTemperatureUncertainty \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN " - ] - }, - "execution_count": 300, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_temperature = pd.read_csv('data//GlobalTemperatures.csv')\n", - "global_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 301, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtLandAverageTemperatureLandAndOceanAverageTemperature
12001850-01-010.74912.833
12011850-02-013.07113.588
12021850-03-014.95414.043
12031850-04-017.21714.667
12041850-05-0110.00415.507
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" - ], - "text/plain": [ - " dt LandAverageTemperature LandAndOceanAverageTemperature\n", - "1200 1850-01-01 0.749 12.833\n", - "1201 1850-02-01 3.071 13.588\n", - "1202 1850-03-01 4.954 14.043\n", - "1203 1850-04-01 7.217 14.667\n", - "1204 1850-05-01 10.004 15.507" - ] - }, - "execution_count": 301, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_temperature.dropna(inplace=True)\n", - "global_temperature = global_temperature[['dt','LandAverageTemperature','LandAndOceanAverageTemperature']]\n", - "global_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 302, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtLandAverageTemperatureLandAndOceanAverageTemperatureYear
12001850-01-010.74912.8331850
12011850-02-013.07113.5881850
12021850-03-014.95414.0431850
12031850-04-017.21714.6671850
12041850-05-0110.00415.5071850
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" - ], - "text/plain": [ - " dt LandAverageTemperature LandAndOceanAverageTemperature Year\n", - "1200 1850-01-01 0.749 12.833 1850\n", - "1201 1850-02-01 3.071 13.588 1850\n", - "1202 1850-03-01 4.954 14.043 1850\n", - "1203 1850-04-01 7.217 14.667 1850\n", - "1204 1850-05-01 10.004 15.507 1850" - ] - }, - "execution_count": 302, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#global_temperature['dt']= pd.to_datetime(global_temperature['dt']) \n", - "global_temperature['Year'] = pd.DatetimeIndex(global_temperature['dt']).year\n", - "global_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 303, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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LandAverageTemperatureLandAndOceanAverageTemperature
Year
18507.90066714.867167
18518.17858314.991833
18528.10016715.006500
18538.04183314.955167
18548.21050014.991000
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" - ], - "text/plain": [ - " LandAverageTemperature LandAndOceanAverageTemperature\n", - "Year \n", - "1850 7.900667 14.867167\n", - "1851 8.178583 14.991833\n", - "1852 8.100167 15.006500\n", - "1853 8.041833 14.955167\n", - "1854 8.210500 14.991000" - ] - }, - "execution_count": 303, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_global_temp = global_temperature[['Year','LandAverageTemperature','LandAndOceanAverageTemperature']].groupby(['Year']).agg({'LandAverageTemperature':'mean',\n", - " 'LandAndOceanAverageTemperature':'mean'})\n", - "annual_global_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 304, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearLandAverageTemperatureLandAndOceanAverageTemperature
018507.90066714.867167
118518.17858314.991833
218528.10016715.006500
318538.04183314.955167
418548.21050014.991000
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" - ], - "text/plain": [ - " Year LandAverageTemperature LandAndOceanAverageTemperature\n", - "0 1850 7.900667 14.867167\n", - "1 1851 8.178583 14.991833\n", - "2 1852 8.100167 15.006500\n", - "3 1853 8.041833 14.955167\n", - "4 1854 8.210500 14.991000" - ] - }, - "execution_count": 304, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_global_temp.reset_index(level=0, inplace=True)\n", - "annual_global_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 305, - "metadata": {}, - "outputs": [], - "source": [ - "annual_global_temp.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 306, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 166 entries, 1850 to 2015\n", - "Data columns (total 2 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 LandAverageTemperature 166 non-null float64\n", - " 1 LandAndOceanAverageTemperature 166 non-null float64\n", - "dtypes: float64(2)\n", - "memory usage: 3.9 KB\n" - ] - } - ], - "source": [ - "annual_global_temp.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 8. Fossil fuel consumption per capita (1965-2015)" - ] - }, - { - "cell_type": "code", - "execution_count": 312, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)
0AfricaNaN19650.9823451.0406860.032724
1AfricaNaN19661.0503401.0019700.035646
2AfricaNaN19671.0143901.0051970.034371
3AfricaNaN19681.0433631.0211390.034195
4AfricaNaN19691.0322301.0010830.039699
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" - ], - "text/plain": [ - " Entity Code Year Oil (megawatt-hours) Coal (megawatt-hours) \\\n", - "0 Africa NaN 1965 0.982345 1.040686 \n", - "1 Africa NaN 1966 1.050340 1.001970 \n", - "2 Africa NaN 1967 1.014390 1.005197 \n", - "3 Africa NaN 1968 1.043363 1.021139 \n", - "4 Africa NaN 1969 1.032230 1.001083 \n", - "\n", - " Natural gas (megawatt-hours) \n", - "0 0.032724 \n", - "1 0.035646 \n", - "2 0.034371 \n", - "3 0.034195 \n", - "4 0.039699 " - ] - }, - "execution_count": 312, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fossil_per_c = pd.read_csv('data//fossil-fuel-consumption-per-capita.csv')\n", - "fossil_per_c.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 313, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)
3302WorldOWID_WRL19655.3121354.8875542.043343
3303WorldOWID_WRL19665.6055414.8450912.178977
3304WorldOWID_WRL19675.8834724.6700632.288339
3305WorldOWID_WRL19686.2573574.6435392.440440
3306WorldOWID_WRL19696.6550134.6908202.622830
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" - ], - "text/plain": [ - " Entity Code Year Oil (megawatt-hours) Coal (megawatt-hours) \\\n", - "3302 World OWID_WRL 1965 5.312135 4.887554 \n", - "3303 World OWID_WRL 1966 5.605541 4.845091 \n", - "3304 World OWID_WRL 1967 5.883472 4.670063 \n", - "3305 World OWID_WRL 1968 6.257357 4.643539 \n", - "3306 World OWID_WRL 1969 6.655013 4.690820 \n", - "\n", - " Natural gas (megawatt-hours) \n", - "3302 2.043343 \n", - "3303 2.178977 \n", - "3304 2.288339 \n", - "3305 2.440440 \n", - "3306 2.622830 " - ] - }, - "execution_count": 313, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#keeping only the global data\n", - "fossil_per_c = fossil_per_c[fossil_per_c.Entity == 'World']\n", - "fossil_per_c.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 314, - "metadata": {}, - "outputs": [], - "source": [ - "fossil_per_c.drop(['Entity', 'Code',], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 315, - "metadata": {}, - "outputs": [], - "source": [ - "fossil_per_c.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 316, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 51 entries, 1965 to 2015\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Oil (megawatt-hours) 51 non-null float64\n", - " 1 Coal (megawatt-hours) 51 non-null float64\n", - " 2 Natural gas (megawatt-hours) 51 non-null float64\n", - "dtypes: float64(3)\n", - "memory usage: 1.6 KB\n" - ] - } - ], - "source": [ - "fossil_per_c.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 9. Global primary energy by source (1800-2018)" - ] - }, - { - "cell_type": "code", - "execution_count": 317, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearCoal (terawatt-hours)Solar (terawatt-hours)Crude oil (terawatt-hours)Natural gas (terawatt-hours)Traditional biofuels (terawatt-hours)Other renewables (terawatt-hours)Hydropower (terawatt-hours)Nuclear (terawatt-hours)Wind (terawatt-hours)
0WorldOWID_WRL180097.222300.00.00.05555.5555560.00.00.00.0
1WorldOWID_WRL1810127.777880.00.00.05833.3333330.00.00.00.0
2WorldOWID_WRL1820152.777900.00.00.06111.1111110.00.00.00.0
3WorldOWID_WRL1830263.889100.00.00.06388.8888890.00.00.00.0
4WorldOWID_WRL1840355.555840.00.00.06944.4444440.00.00.00.0
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" - ], - "text/plain": [ - " Entity Code Year Coal (terawatt-hours) Solar (terawatt-hours) \\\n", - "0 World OWID_WRL 1800 97.22230 0.0 \n", - "1 World OWID_WRL 1810 127.77788 0.0 \n", - "2 World OWID_WRL 1820 152.77790 0.0 \n", - "3 World OWID_WRL 1830 263.88910 0.0 \n", - "4 World OWID_WRL 1840 355.55584 0.0 \n", - "\n", - " Crude oil (terawatt-hours) Natural gas (terawatt-hours) \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "\n", - " Traditional biofuels (terawatt-hours) Other renewables (terawatt-hours) \\\n", - "0 5555.555556 0.0 \n", - "1 5833.333333 0.0 \n", - "2 6111.111111 0.0 \n", - "3 6388.888889 0.0 \n", - "4 6944.444444 0.0 \n", - "\n", - " Hydropower (terawatt-hours) Nuclear (terawatt-hours) \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "\n", - " Wind (terawatt-hours) \n", - "0 0.0 \n", - "1 0.0 \n", - "2 0.0 \n", - "3 0.0 \n", - "4 0.0 " - ] - }, - "execution_count": 317, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_energy = pd.read_csv('data//global-primary-energy.csv')\n", - "global_energy.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 318, - "metadata": {}, - "outputs": [], - "source": [ - "global_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 319, - "metadata": {}, - "outputs": [], - "source": [ - "global_energy.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 320, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 71 entries, 1800 to 2018\n", - "Data columns (total 9 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Coal (terawatt-hours) 71 non-null float64\n", - " 1 Solar (terawatt-hours) 71 non-null float64\n", - " 2 Crude oil (terawatt-hours) 71 non-null float64\n", - " 3 Natural gas (terawatt-hours) 71 non-null float64\n", - " 4 Traditional biofuels (terawatt-hours) 71 non-null float64\n", - " 5 Other renewables (terawatt-hours) 71 non-null float64\n", - " 6 Hydropower (terawatt-hours) 71 non-null float64\n", - " 7 Nuclear (terawatt-hours) 71 non-null float64\n", - " 8 Wind (terawatt-hours) 71 non-null float64\n", - "dtypes: float64(9)\n", - "memory usage: 5.5 KB\n" - ] - } - ], - "source": [ - "global_energy.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10. Modern renewable energy consumtion (1965-2018)" - ] - }, - { - "cell_type": "code", - "execution_count": 321, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearHydropower (terawatt-hours)Solar (terawatt-hours)Wind (terawatt-hours)Other renewables (terawatt-hours)
0AfricaNaN196514.2788060.00.00.0
1AfricaNaN196615.6490490.00.00.0
2AfricaNaN196716.1583330.00.00.0
3AfricaNaN196818.6229830.00.00.0
4AfricaNaN196921.5828970.00.00.0
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" - ], - "text/plain": [ - " Entity Code Year Hydropower (terawatt-hours) Solar (terawatt-hours) \\\n", - "0 Africa NaN 1965 14.278806 0.0 \n", - "1 Africa NaN 1966 15.649049 0.0 \n", - "2 Africa NaN 1967 16.158333 0.0 \n", - "3 Africa NaN 1968 18.622983 0.0 \n", - "4 Africa NaN 1969 21.582897 0.0 \n", - "\n", - " Wind (terawatt-hours) Other renewables (terawatt-hours) \n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 " - ] - }, - "execution_count": 321, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "renew_energy = pd.read_csv('data//modern-renewable-energy-consumption.csv')\n", - "renew_energy.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 322, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearHydropower (terawatt-hours)Solar (terawatt-hours)Wind (terawatt-hours)Other renewables (terawatt-hours)
5037WorldOWID_WRL1965919.7702760.00.017.985232
5038WorldOWID_WRL1966986.2858720.00.019.806007
5039WorldOWID_WRL19671009.2445690.00.019.986571
5040WorldOWID_WRL19681059.0628600.00.022.120045
5041WorldOWID_WRL19691122.4425260.00.023.257964
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" - ], - "text/plain": [ - " Entity Code Year Hydropower (terawatt-hours) \\\n", - "5037 World OWID_WRL 1965 919.770276 \n", - "5038 World OWID_WRL 1966 986.285872 \n", - "5039 World OWID_WRL 1967 1009.244569 \n", - "5040 World OWID_WRL 1968 1059.062860 \n", - "5041 World OWID_WRL 1969 1122.442526 \n", - "\n", - " Solar (terawatt-hours) Wind (terawatt-hours) \\\n", - "5037 0.0 0.0 \n", - "5038 0.0 0.0 \n", - "5039 0.0 0.0 \n", - "5040 0.0 0.0 \n", - "5041 0.0 0.0 \n", - "\n", - " Other renewables (terawatt-hours) \n", - "5037 17.985232 \n", - "5038 19.806007 \n", - "5039 19.986571 \n", - "5040 22.120045 \n", - "5041 23.257964 " - ] - }, - "execution_count": 322, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#keeping only the global data\n", - "renew_energy = renew_energy[renew_energy.Entity == 'World']\n", - "renew_energy.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 323, - "metadata": {}, - "outputs": [], - "source": [ - "renew_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 324, - "metadata": {}, - "outputs": [], - "source": [ - "renew_energy.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 325, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 54 entries, 1965 to 2018\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Hydropower (terawatt-hours) 54 non-null float64\n", - " 1 Solar (terawatt-hours) 54 non-null float64\n", - " 2 Wind (terawatt-hours) 54 non-null float64\n", - " 3 Other renewables (terawatt-hours) 54 non-null float64\n", - "dtypes: float64(4)\n", - "memory usage: 2.1 KB\n" - ] - } - ], - "source": [ - "renew_energy.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 11. Hubbert's peak vs actual oil production (1911-2016)" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYear(barrels per year)(barrels per year).1
0United StatesUSA1910209557000.0NaN
1United StatesUSA1911220449000.0200158958.0
2United StatesUSA1912222935000.0213834803.7
3United StatesUSA1913248446000.0228406784.8
4United StatesUSA1914265763000.0243928136.8
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" - ], - "text/plain": [ - " Entity Code Year (barrels per year) (barrels per year).1\n", - "0 United States USA 1910 209557000.0 NaN\n", - "1 United States USA 1911 220449000.0 200158958.0\n", - "2 United States USA 1912 222935000.0 213834803.7\n", - "3 United States USA 1913 248446000.0 228406784.8\n", - "4 United States USA 1914 265763000.0 243928136.8" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hubbert_peak = pd.read_csv('data//hubberts-peak-vs-actual-oil-production-in-the-united-states.csv')\n", - "hubbert_peak.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Entity', 'Code', 'Year', ' (barrels per year)',\n", - " ' (barrels per year).1'],\n", - " dtype='object')" - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hubbert_peak.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [], - "source": [ - "hubbert_peak.drop(['Entity', 'Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 326, - "metadata": {}, - "outputs": [], - "source": [ - "hubbert_peak.rename(columns={' (barrels per year)':'Actual Oil Prod',' (barrels per year).1':'Hubbert Predicted'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 327, - "metadata": {}, - "outputs": [], - "source": [ - "hubbert_peak = hubbert_peak.dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": 328, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearActualPredicted
10220122.374136e+09614194532.6
10320132.725665e+09577668428.7
10420143.198694e+09543068744.5
10520153.436515e+09510324290.7
10620163.248117e+09479362510.8
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" - ], - "text/plain": [ - " Year Actual Predicted\n", - "102 2012 2.374136e+09 614194532.6\n", - "103 2013 2.725665e+09 577668428.7\n", - "104 2014 3.198694e+09 543068744.5\n", - "105 2015 3.436515e+09 510324290.7\n", - "106 2016 3.248117e+09 479362510.8" - ] - }, - "execution_count": 328, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hubbert_peak.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 329, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 329, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#ploting predicted and actual emissions to see if it the prediction came true\n", - "plt.figure(figsize=(17,6))\n", - "plt.plot(hubbert_peak.Year,hubbert_peak['Actual'],label='Actual')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('(barrels per year)')\n", - "plt.plot(hubbert_peak.Year,hubbert_peak['Predicted'],label='Predicted')\n", - "plt.legend(['Actual', 'Predicted'], loc='upper left')" - ] - }, - { - "cell_type": "code", - "execution_count": 330, - "metadata": {}, - "outputs": [], - "source": [ - "hubbert_peak.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 331, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 106 entries, 1911 to 2016\n", - "Data columns (total 2 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Actual 106 non-null float64\n", - " 1 Predicted 106 non-null float64\n", - "dtypes: float64(2)\n", - "memory usage: 2.5 KB\n" - ] - } - ], - "source": [ - "hubbert_peak.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 12. Share of global primary energy (1800-2017)" - ] - }, - { - "cell_type": "code", - "execution_count": 332, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearCoal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
0WorldOWID_WRL18001.7199030.00.00.00.00.00.098.2800970.0
1WorldOWID_WRL18102.1435250.00.00.00.00.00.097.8564750.0
2WorldOWID_WRL18202.4390260.00.00.00.00.00.097.5609740.0
3WorldOWID_WRL18303.9666000.00.00.00.00.00.096.0334000.0
4WorldOWID_WRL18404.8706280.00.00.00.00.00.095.1293720.0
\n", - "
" - ], - "text/plain": [ - " Entity Code Year Coal (%) Crude oil (%) Hydropower (%) \\\n", - "0 World OWID_WRL 1800 1.719903 0.0 0.0 \n", - "1 World OWID_WRL 1810 2.143525 0.0 0.0 \n", - "2 World OWID_WRL 1820 2.439026 0.0 0.0 \n", - "3 World OWID_WRL 1830 3.966600 0.0 0.0 \n", - "4 World OWID_WRL 1840 4.870628 0.0 0.0 \n", - "\n", - " Natural gas (%) Nuclear (%) Other renewables (%) Solar (%) \\\n", - "0 0.0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 0.0 \n", - "\n", - " Traditional biofuels (%) Wind (%) \n", - "0 98.280097 0.0 \n", - "1 97.856475 0.0 \n", - "2 97.560974 0.0 \n", - "3 96.033400 0.0 \n", - "4 95.129372 0.0 " - ] - }, - "execution_count": 332, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "share_energy = pd.read_csv('data//global-primary-energy-share-inc-biomass.csv')\n", - "share_energy.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 333, - "metadata": {}, - "outputs": [], - "source": [ - "share_energy.drop(['Entity', 'Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 334, - "metadata": {}, - "outputs": [], - "source": [ - "share_energy.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 335, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 70 entries, 1800 to 2017\n", - "Data columns (total 9 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Coal (%) 70 non-null float64\n", - " 1 Crude oil (%) 70 non-null float64\n", - " 2 Hydropower (%) 70 non-null float64\n", - " 3 Natural gas (%) 70 non-null float64\n", - " 4 Nuclear (%) 70 non-null float64\n", - " 5 Other renewables (%) 70 non-null float64\n", - " 6 Solar (%) 70 non-null float64\n", - " 7 Traditional biofuels (%) 70 non-null float64\n", - " 8 Wind (%) 70 non-null float64\n", - "dtypes: float64(9)\n", - "memory usage: 5.5 KB\n" - ] - } - ], - "source": [ - "share_energy.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 13. Solar pannels price progression (1976-2016)" - ] - }, - { - "cell_type": "code", - "execution_count": 464, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearSolar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))Cumulative Installed Capacity (megawatts-peak (MWp))Total population (Gapminder)
0AfghanistanAFG1800NaNNaN3280000.0
1AfghanistanAFG1820NaNNaN3280000.0
2AfghanistanAFG1870NaNNaN4207000.0
3AfghanistanAFG1913NaNNaN5730000.0
4AfghanistanAFG1950NaNNaN8151455.0
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" - ], - "text/plain": [ - " Entity Code Year \\\n", - "0 Afghanistan AFG 1800 \n", - "1 Afghanistan AFG 1820 \n", - "2 Afghanistan AFG 1870 \n", - "3 Afghanistan AFG 1913 \n", - "4 Afghanistan AFG 1950 \n", - "\n", - " Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp)) \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "\n", - " Cumulative Installed Capacity (megawatts-peak (MWp)) \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "\n", - " Total population (Gapminder) \n", - "0 3280000.0 \n", - "1 3280000.0 \n", - "2 4207000.0 \n", - "3 5730000.0 \n", - "4 8151455.0 " - ] - }, - "execution_count": 464, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solar_price = pd.read_csv('data//solar-pv-prices-vs-cumulative-capacity.csv')\n", - "solar_price.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 465, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearSolar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))Cumulative Installed Capacity (megawatts-peak (MWp))Total population (Gapminder)
19946WorldOWID_WRL197666.1048350.32NaN
19947WorldOWID_WRL197746.4405390.77NaN
19948WorldOWID_WRL197833.2826881.77NaN
19949WorldOWID_WRL197929.6924933.27NaN
19950WorldOWID_WRL198023.7553616.57NaN
\n", - "
" - ], - "text/plain": [ - " Entity Code Year \\\n", - "19946 World OWID_WRL 1976 \n", - "19947 World OWID_WRL 1977 \n", - "19948 World OWID_WRL 1978 \n", - "19949 World OWID_WRL 1979 \n", - "19950 World OWID_WRL 1980 \n", - "\n", - " Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp)) \\\n", - "19946 66.104835 \n", - "19947 46.440539 \n", - "19948 33.282688 \n", - "19949 29.692493 \n", - "19950 23.755361 \n", - "\n", - " Cumulative Installed Capacity (megawatts-peak (MWp)) \\\n", - "19946 0.32 \n", - "19947 0.77 \n", - "19948 1.77 \n", - "19949 3.27 \n", - "19950 6.57 \n", - "\n", - " Total population (Gapminder) \n", - "19946 NaN \n", - "19947 NaN \n", - "19948 NaN \n", - "19949 NaN \n", - "19950 NaN " - ] - }, - "execution_count": 465, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#keeping only the global data\n", - "solar_price = solar_price[solar_price.Entity == 'World']\n", - "solar_price.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 466, - "metadata": {}, - "outputs": [], - "source": [ - "solar_price.drop(['Entity', 'Code','Total population (Gapminder)'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 467, - "metadata": {}, - "outputs": [], - "source": [ - "solar_price.rename(columns={'Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))':'Solar PV Module Cost($/Wp)','Cumulative Installed Capacity (megawatts-peak (MWp))':'Cumulative Installed Capacity(MWp)'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 468, - "metadata": {}, - "outputs": [], - "source": [ - "#solar_price.set_index('Cumulative Installed Capacity(MWp)',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 471, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Solar PV Module Cost($/Wp)')" - ] - }, - "execution_count": 471, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(17,6))\n", - "plt.plot(solar_price.Year,solar_price['Solar PV Module Cost($/Wp)'],label='Solar PV Module Cost($/Wp)')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Solar PV Module Cost($/Wp)')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 472, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Cumulative Installed Capacity(MWp)')" - ] - }, - "execution_count": 472, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(17,6))\n", - "plt.plot(solar_price.Year,solar_price['Cumulative Installed Capacity(MWp)'],label='Cumulative Installed Capacity(MWp)')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Cumulative Installed Capacity(MWp)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Country Indicators" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 14. CO annual emissions (1949-2017)" - ] - }, - { - "cell_type": "code", - "execution_count": 475, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes)
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
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" - ], - "text/plain": [ - " Entity Code Year Annual COâ‚‚ emissions (tonnes)\n", - "0 Afghanistan AFG 1949 14656.0\n", - "1 Afghanistan AFG 1950 84272.0\n", - "2 Afghanistan AFG 1951 91600.0\n", - "3 Afghanistan AFG 1952 91600.0\n", - "4 Afghanistan AFG 1953 106256.0" - ] - }, - "execution_count": 475, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Annual CO per country (1949-2017)\n", - "co_annual = pd.read_csv('data//annual_co2_emissions_per_country.csv')\n", - "co_annual.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 477, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the dataset\n", - "co_annual.drop(['Code'], axis=1, inplace=True)\n", - "co_annual = co_annual.rename(columns={'Annual COâ‚‚ emissions (tonnes)':'Annual COâ‚‚', 'Entity':'Country'})" - ] - }, - { - "cell_type": "code", - "execution_count": 478, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 20586 entries, 0 to 20852\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 20586 non-null object \n", - " 1 Year 20586 non-null int64 \n", - " 2 Annual COâ‚‚ 20586 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 643.3+ KB\n" - ] - } - ], - "source": [ - "#keeping only Country indicators\n", - "co_annual = co_annual[co_annual.Country != 'World']\n", - "#co_annual.Year.unique()\n", - "co_annual.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 15. Annual share of CO per country (1949-2017)" - ] - }, - { - "cell_type": "code", - "execution_count": 479, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearShare of global COâ‚‚ emissions (%)
0AfghanistanAFG19490.0003
1AfghanistanAFG19500.0015
2AfghanistanAFG19510.0015
3AfghanistanAFG19520.0015
4AfghanistanAFG19530.0016
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" - ], - "text/plain": [ - " Entity Code Year Share of global COâ‚‚ emissions (%)\n", - "0 Afghanistan AFG 1949 0.0003\n", - "1 Afghanistan AFG 1950 0.0015\n", - "2 Afghanistan AFG 1951 0.0015\n", - "3 Afghanistan AFG 1952 0.0015\n", - "4 Afghanistan AFG 1953 0.0016" - ] - }, - "execution_count": 479, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Annual share of CO per country (1949-2017)\n", - "co_share = pd.read_csv('data//annual_share_of_co2_emissions.csv')\n", - "co_share.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 480, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 20586 entries, 0 to 20585\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 20586 non-null object \n", - " 1 Year 20586 non-null int64 \n", - " 2 Share of global COâ‚‚ emissions (%) 20586 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 482.6+ KB\n" - ] - } - ], - "source": [ - "#cleaning the data set\n", - "co_share.drop(['Code'], axis=1, inplace=True)\n", - "\n", - "co_share = co_share.rename(columns={'Share of global COâ‚‚ emissions (%)b':'Annual COâ‚‚', 'Entity':'Country'})\n", - "\n", - "co_share.Year.unique()\n", - "\n", - "co_share.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 16. Total greenhouse gas emissions (1990-2016)" - ] - }, - { - "cell_type": "code", - "execution_count": 520, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
0AfghanistanAFG199015140000.0
1AfghanistanAFG199115060000.0
2AfghanistanAFG199213600000.0
3AfghanistanAFG199313430000.0
4AfghanistanAFG199413240000.0
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" - ], - "text/plain": [ - " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", - "0 Afghanistan AFG 1990 15140000.0\n", - "1 Afghanistan AFG 1991 15060000.0\n", - "2 Afghanistan AFG 1992 13600000.0\n", - "3 Afghanistan AFG 1993 13430000.0\n", - "4 Afghanistan AFG 1994 13240000.0" - ] - }, - "execution_count": 520, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Total greenhouse gas emissions\n", - "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", - "ghg.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 521, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 5157 entries, 0 to 5183\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 5157 non-null object \n", - " 1 Year 5157 non-null int64 \n", - " 2 GHG(tonnes COâ‚‚e ) 5157 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 161.2+ KB\n" - ] - } - ], - "source": [ - "#cleaning the dataset\n", - "ghg.drop(['Code'], axis=1, inplace=True)\n", - "\n", - "ghg = ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )', 'Entity':'Country'})\n", - "#keeping only Country indicators\n", - "ghg = ghg[ghg.Country != 'World']\n", - "ghg.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 17. Average temperatures by country (1743-2013)" - ] - }, - { - "cell_type": "code", - "execution_count": 487, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtAverageTemperatureAverageTemperatureUncertaintyCountry
01743-11-014.3842.294Ã…land
11743-12-01NaNNaNÃ…land
21744-01-01NaNNaNÃ…land
31744-02-01NaNNaNÃ…land
41744-03-01NaNNaNÃ…land
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" - ], - "text/plain": [ - " dt AverageTemperature AverageTemperatureUncertainty Country\n", - "0 1743-11-01 4.384 2.294 Åland\n", - "1 1743-12-01 NaN NaN Åland\n", - "2 1744-01-01 NaN NaN Åland\n", - "3 1744-02-01 NaN NaN Åland\n", - "4 1744-03-01 NaN NaN Åland" - ] - }, - "execution_count": 487, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Global temperatures by country data set\n", - "temperature = pd.read_csv('data//GlobalLandTemperaturesByCountry.csv')\n", - "temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 488, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the dataset\n", - "temperature['dt']= pd.to_datetime(temperature['dt']) \n", - "\n", - "temperature['Year'] = pd.DatetimeIndex(temperature['dt']).year\n", - "temperature.head()\n", - "\n", - "#keeping only the desired countries, and removing regions\n", - "non_wanted = ['Åland','Africa','Asia','Baker Island','Denmark','Federated States Of Micronesia','France', 'French Guiana', 'French Polynesia',\n", - " 'French Southern And Antarctic Lands','Gaza Strip','Guadeloupe', 'Guam','Guernsey','Heard Island And Mcdonald Islands','Isle Of Man','Jersey','Kingman Reef',\n", - " 'Montserrat','Netherlands (Europe)','Niue','North America','New Caledonia','Northern Mariana Islands','Oceania',\n", - " 'Palmyra Atoll','Saint Barthélemy','American Samoa','South America','South Georgia And The South Sandwich Isla',\n", - " 'Svalbard And Jan Mayen','Turks And Caicas Islands','United Kingdom','Western Sahara']\n", - "for region in non_wanted:\n", - " temperature = temperature[temperature.Country != region]" - ] - }, - { - "cell_type": "code", - "execution_count": 489, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearAverageTemperature
Country
Albania17438.620
Andorra17437.556
Austria17432.482
Belarus17430.767
Belgium17437.106
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" - ], - "text/plain": [ - " Year AverageTemperature\n", - "Country \n", - "Albania 1743 8.620\n", - "Andorra 1743 7.556\n", - "Austria 1743 2.482\n", - "Belarus 1743 0.767\n", - "Belgium 1743 7.106" - ] - }, - "execution_count": 489, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#grouping by year and agregating the mean so we have the annual mean temperature by country\n", - "annual_temp = temperature[['Year', 'Country','AverageTemperature']].groupby(['Year','Country']).agg({'AverageTemperature':'mean'})\n", - "\n", - "#reseting index to adapt it for the future merge\n", - "annual_temp.reset_index(level=0, inplace=True)\n", - "\n", - "annual_temp.head()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 491, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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indexCountryYearAverageTemperature
00Albania17438.620
11Andorra17437.556
22Austria17432.482
33Belarus17430.767
44Belgium17437.106
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" - ], - "text/plain": [ - " index Country Year AverageTemperature\n", - "0 0 Albania 1743 8.620\n", - "1 1 Andorra 1743 7.556\n", - "2 2 Austria 1743 2.482\n", - "3 3 Belarus 1743 0.767\n", - "4 4 Belgium 1743 7.106" - ] - }, - "execution_count": 491, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#one more reset to have country as a column\n", - "annual_temp.reset_index(level=0, inplace=True)\n", - "annual_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 493, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 41931 entries, 0 to 41930\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 41931 non-null object \n", - " 1 Year 41931 non-null int64 \n", - " 2 AverageTemperature 39793 non-null float64\n", - "dtypes: float64(1), int64(1), object(1)\n", - "memory usage: 982.9+ KB\n" - ] - } - ], - "source": [ - "annual_temp.drop(['index'], axis=1, inplace=True)\n", - "annual_temp.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Merging datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 18. Merging global indicators" - ] - }, - { - "cell_type": "code", - "execution_count": 362, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1 and 2\n", - "global_indicators1 = pd.merge(global_co, global_ghg, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 365, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4\n", - "global_indicators2 = pd.merge(global_indicators1,global_a, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 366, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5\n", - "global_indicators3 = pd.merge(global_indicators2,caused_ozone, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 367, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6\n", - "global_indicators4 = pd.merge(global_indicators3,oz_hole, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 368, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6,7\n", - "global_indicators5 = pd.merge(global_indicators4,annual_global_temp, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 369, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6,7,8\n", - "global_indicators6 = pd.merge(global_indicators5,fossil_per_c, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 370, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6,7,8,9\n", - "global_indicators7 = pd.merge(global_indicators6,global_energy, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 371, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6,7,8,9,10\n", - "global_indicators8 = pd.merge(global_indicators7,renew_energy, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 372, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6,7,8,9,10,12\n", - "global_indicators = pd.merge(global_indicators8,share_energy, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 504, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Annual CO₂ emissions (tonnes )GHG(tonnes CO₂e )Median (℃)Ozone-depleting emissionsMean ozone hole area (square kilometres)LandAverageTemperatureLandAndOceanAverageTemperatureOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)...Other renewables (terawatt-hours)_yCoal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
Year
20143.550583e+104.944079e+100.579320000.020900000.09.57066715.9130006.7800186.1977514.896768...502.15891930.12992834.2842432.60799922.7981601.7045050.3383420.1325977.5263480.477878
20153.546275e+104.985498e+100.763NaN25600000.09.83100016.0585836.8380425.9617334.956876...536.46624429.23211234.7507802.59790623.1936921.7195140.3593070.1735807.4177810.555327
20163.567510e+104.935803e+100.797NaN20700000.0NaNNaNNaNNaNNaN...556.62335628.47941835.0213262.66686223.6166451.7264460.3680320.2168497.2704460.633975
20173.615326e+10NaN0.677NaN17400000.0NaNNaNNaNNaNNaN...584.97582728.25414134.9959602.64321823.8964861.7159090.3816330.2881717.0935080.730975
2018NaNNaN0.595NaNNaNNaNNaNNaNNaNNaN...625.805362NaNNaNNaNNaNNaNNaNNaNNaNNaN
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5 rows × 32 columns

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" - ], - "text/plain": [ - " Annual CO₂ emissions (tonnes ) GHG(tonnes CO₂e ) Median (℃) \\\n", - "Year \n", - "2014 3.550583e+10 4.944079e+10 0.579 \n", - "2015 3.546275e+10 4.985498e+10 0.763 \n", - "2016 3.567510e+10 4.935803e+10 0.797 \n", - "2017 3.615326e+10 NaN 0.677 \n", - "2018 NaN NaN 0.595 \n", - "\n", - " Ozone-depleting emissions Mean ozone hole area (square kilometres) \\\n", - "Year \n", - "2014 320000.0 20900000.0 \n", - "2015 NaN 25600000.0 \n", - "2016 NaN 20700000.0 \n", - "2017 NaN 17400000.0 \n", - "2018 NaN NaN \n", - "\n", - " LandAverageTemperature LandAndOceanAverageTemperature \\\n", - "Year \n", - "2014 9.570667 15.913000 \n", - "2015 9.831000 16.058583 \n", - "2016 NaN NaN \n", - "2017 NaN NaN \n", - "2018 NaN NaN \n", - "\n", - " Oil (megawatt-hours) Coal (megawatt-hours) \\\n", - "Year \n", - "2014 6.780018 6.197751 \n", - "2015 6.838042 5.961733 \n", - "2016 NaN NaN \n", - "2017 NaN NaN \n", - "2018 NaN NaN \n", - "\n", - " Natural gas (megawatt-hours) ... Other renewables (terawatt-hours)_y \\\n", - "Year ... \n", - "2014 4.896768 ... 502.158919 \n", - "2015 4.956876 ... 536.466244 \n", - "2016 NaN ... 556.623356 \n", - "2017 NaN ... 584.975827 \n", - "2018 NaN ... 625.805362 \n", - "\n", - " Coal (%) Crude oil (%) Hydropower (%) Natural gas (%) Nuclear (%) \\\n", - "Year \n", - "2014 30.129928 34.284243 2.607999 22.798160 1.704505 \n", - "2015 29.232112 34.750780 2.597906 23.193692 1.719514 \n", - "2016 28.479418 35.021326 2.666862 23.616645 1.726446 \n", - "2017 28.254141 34.995960 2.643218 23.896486 1.715909 \n", - "2018 NaN NaN NaN NaN NaN \n", - "\n", - " Other renewables (%) Solar (%) Traditional biofuels (%) Wind (%) \n", - "Year \n", - "2014 0.338342 0.132597 7.526348 0.477878 \n", - "2015 0.359307 0.173580 7.417781 0.555327 \n", - "2016 0.368032 0.216849 7.270446 0.633975 \n", - "2017 0.381633 0.288171 7.093508 0.730975 \n", - "2018 NaN NaN NaN NaN \n", - "\n", - "[5 rows x 32 columns]" - ] - }, - "execution_count": 504, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_indicators.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 375, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 268 entries, 1751 to 2018\n", - "Data columns (total 32 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Annual CO₂ emissions (tonnes ) 267 non-null float64\n", - " 1 GHG(tonnes CO₂e ) 27 non-null float64\n", - " 2 Median (℃) 169 non-null float64\n", - " 3 Ozone-depleting emissions 54 non-null float64\n", - " 4 Mean ozone hole area (square kilometres) 38 non-null float64\n", - " 5 LandAverageTemperature 166 non-null float64\n", - " 6 LandAndOceanAverageTemperature 166 non-null float64\n", - " 7 Oil (megawatt-hours) 51 non-null float64\n", - " 8 Coal (megawatt-hours) 51 non-null float64\n", - " 9 Natural gas (megawatt-hours) 51 non-null float64\n", - " 10 Coal (terawatt-hours) 71 non-null float64\n", - " 11 Solar (terawatt-hours)_x 71 non-null float64\n", - " 12 Crude oil (terawatt-hours) 71 non-null float64\n", - " 13 Natural gas (terawatt-hours) 71 non-null float64\n", - " 14 Traditional biofuels (terawatt-hours) 71 non-null float64\n", - " 15 Other renewables (terawatt-hours)_x 71 non-null float64\n", - " 16 Hydropower (terawatt-hours)_x 71 non-null float64\n", - " 17 Nuclear (terawatt-hours) 71 non-null float64\n", - " 18 Wind (terawatt-hours)_x 71 non-null float64\n", - " 19 Hydropower (terawatt-hours)_y 54 non-null float64\n", - " 20 Solar (terawatt-hours)_y 54 non-null float64\n", - " 21 Wind (terawatt-hours)_y 54 non-null float64\n", - " 22 Other renewables (terawatt-hours)_y 54 non-null float64\n", - " 23 Coal (%) 70 non-null float64\n", - " 24 Crude oil (%) 70 non-null float64\n", - " 25 Hydropower (%) 70 non-null float64\n", - " 26 Natural gas (%) 70 non-null float64\n", - " 27 Nuclear (%) 70 non-null float64\n", - " 28 Other renewables (%) 70 non-null float64\n", - " 29 Solar (%) 70 non-null float64\n", - " 30 Traditional biofuels (%) 70 non-null float64\n", - " 31 Wind (%) 70 non-null float64\n", - "dtypes: float64(32)\n", - "memory usage: 69.1 KB\n" - ] - } - ], - "source": [ - "global_indicators.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 376, - "metadata": {}, - "outputs": [], - "source": [ - "global_indicators.to_csv('global_indicators')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 19. Merging country indicators" - ] - }, - { - "cell_type": "code", - "execution_count": 523, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)
0Afghanistan194914656.00.0003
1Afghanistan195084272.00.0015
2Afghanistan195191600.00.0015
3Afghanistan195291600.00.0015
4Afghanistan1953106256.00.0016
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" - ], - "text/plain": [ - " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%)\n", - "0 Afghanistan 1949 14656.0 0.0003\n", - "1 Afghanistan 1950 84272.0 0.0015\n", - "2 Afghanistan 1951 91600.0 0.0015\n", - "3 Afghanistan 1952 91600.0 0.0015\n", - "4 Afghanistan 1953 106256.0 0.0016" - ] - }, - "execution_count": 523, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#datasets 1 and 2\n", - "country_indicators1 = pd.merge(co_annual, co_share, on=['Year', 'Country'], how='outer')\n", - "country_indicators1.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 524, - "metadata": {}, - "outputs": [], - "source": [ - "country_indicators2 = pd.merge(country_indicators1, ghg, on=['Year', 'Country'], how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 527, - "metadata": {}, - "outputs": [], - "source": [ - "country_indicators = pd.merge(country_indicators2, annual_temp, on=['Year', 'Country'], how='outer' )" - ] - }, - { - "cell_type": "code", - "execution_count": 528, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryYearAnnual COâ‚‚Share of global COâ‚‚ emissions (%)GHG(tonnes COâ‚‚e )AverageTemperature
0Afghanistan194914656.00.0003NaN13.350083
1Afghanistan195084272.00.0015NaN13.043500
2Afghanistan195191600.00.0015NaN13.967750
3Afghanistan195291600.00.0015NaN14.175417
4Afghanistan1953106256.00.0016NaN14.650750
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" - ], - "text/plain": [ - " Country Year Annual COâ‚‚ Share of global COâ‚‚ emissions (%) \\\n", - "0 Afghanistan 1949 14656.0 0.0003 \n", - "1 Afghanistan 1950 84272.0 0.0015 \n", - "2 Afghanistan 1951 91600.0 0.0015 \n", - "3 Afghanistan 1952 91600.0 0.0015 \n", - "4 Afghanistan 1953 106256.0 0.0016 \n", - "\n", - " GHG(tonnes COâ‚‚e ) AverageTemperature \n", - "0 NaN 13.350083 \n", - "1 NaN 13.043500 \n", - "2 NaN 13.967750 \n", - "3 NaN 14.175417 \n", - "4 NaN 14.650750 " - ] - }, - "execution_count": 528, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "country_indicators.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 529, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 5 entries, 0 to 4\n", - "Data columns (total 6 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Country 5 non-null object \n", - " 1 Year 5 non-null int64 \n", - " 2 Annual COâ‚‚ 5 non-null float64\n", - " 3 Share of global COâ‚‚ emissions (%) 5 non-null float64\n", - " 4 GHG(tonnes COâ‚‚e ) 0 non-null float64\n", - " 5 AverageTemperature 5 non-null float64\n", - "dtypes: float64(4), int64(1), object(1)\n", - "memory usage: 280.0+ bytes\n" - ] - } - ], - "source": [ - "country_indicators.head().info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 20. Merging ozone datasets 1979-2014" - ] - }, - { - "cell_type": "code", - "execution_count": 530, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Ozone-depleting emissionsMean ozone hole area (square kilometres)
Year
19791110000100000
198010800001400000
19811040000600000
198211000004800000
198310900007900000
\n", - "
" - ], - "text/plain": [ - " Ozone-depleting emissions Mean ozone hole area (square kilometres)\n", - "Year \n", - "1979 1110000 100000\n", - "1980 1080000 1400000\n", - "1981 1040000 600000\n", - "1982 1100000 4800000\n", - "1983 1090000 7900000" - ] - }, - "execution_count": 530, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ozone_merged = pd.merge(caused_ozone, oz_hole, on='Year')\n", - "ozone_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 531, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.5597422292909034" - ] - }, - "execution_count": 531, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oz_hole_emiss_corr = ozone_merged[\"Ozone-depleting emissions\"].corr(ozone_merged[\"Mean ozone hole area (square kilometres)\"])\n", - "oz_hole_emiss_corr" - ] - }, - { - "cell_type": "code", - "execution_count": 532, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 35 entries, 1979 to 2014\n", - "Data columns (total 2 columns):\n", - " # Column Non-Null Count Dtype\n", - "--- ------ -------------- -----\n", - " 0 Ozone-depleting emissions 35 non-null int64\n", - " 1 Mean ozone hole area (square kilometres) 35 non-null int64\n", - "dtypes: int64(2)\n", - "memory usage: 840.0 bytes\n" - ] - } - ], - "source": [ - "ozone_merged.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 540, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Ozone-depleting emissionsMean ozone hole area (square kilometres)
Year
201038000019400000
201137000024700000
201235000017800000
201334000021000000
201432000020900000
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" - ], - "text/plain": [ - " Ozone-depleting emissions Mean ozone hole area (square kilometres)\n", - "Year \n", - "2010 380000 19400000\n", - "2011 370000 24700000\n", - "2012 350000 17800000\n", - "2013 340000 21000000\n", - "2014 320000 20900000" - ] - }, - "execution_count": 540, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ozone_merged.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 534, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 534, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.pairplot(ozone_merged)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CO emissions and median temperature anomaly correlation" - ] - }, - { - "cell_type": "code", - "execution_count": 444, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.890288126134981" - ] - }, - "execution_count": 444, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_a_corr = global_indicators[\"Median (℃)\"].corr(global_indicators[\"Annual CO₂ emissions (tonnes )\"])\n", - "co_a_corr" - ] - }, - { - "cell_type": "code", - "execution_count": 538, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Median (℃)')" - ] - }, - "execution_count": 538, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8322 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8322 missing from current font.\n", - " font.set_text(s, 0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0.0, flags=flags)\n", - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0, flags=flags)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(x=global_indicators['Annual CO₂ emissions (tonnes )'], y=global_indicators['Median (℃)'])\n", - "plt.xlabel('Annual CO₂ emissions (tonnes )')\n", - "plt.ylabel('Median (℃)')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/your-project/Global climate change datasets.ipynb b/your-project/Global climate change datasets.ipynb deleted file mode 100644 index ff28d65e..00000000 --- a/your-project/Global climate change datasets.ipynb +++ /dev/null @@ -1,5711 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 170, - "metadata": {}, - "outputs": [], - "source": [ - "#importing the needed libraries\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import datetime\n", - "import re\n", - "import seaborn as sns\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Annual CO emissions globally " - ] - }, - { - "cell_type": "code", - "execution_count": 251, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
0AfghanistanAFG194914656.0
1AfghanistanAFG195084272.0
2AfghanistanAFG195191600.0
3AfghanistanAFG195291600.0
4AfghanistanAFG1953106256.0
\n", - "
" - ], - "text/plain": [ - " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", - "0 Afghanistan AFG 1949 14656.0\n", - "1 Afghanistan AFG 1950 84272.0\n", - "2 Afghanistan AFG 1951 91600.0\n", - "3 Afghanistan AFG 1952 91600.0\n", - "4 Afghanistan AFG 1953 106256.0" - ] - }, - "execution_count": 251, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Annual CO emissions globally\n", - "data = pd.read_csv('data//annual-co-emissions-by-region.csv')\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 252, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearAnnual COâ‚‚ emissions (tonnes )
20353WorldOWID_WRL17519350528.0
20354WorldOWID_WRL17529354192.0
20355WorldOWID_WRL17539354192.0
20356WorldOWID_WRL17549357856.0
20357WorldOWID_WRL17559361520.0
\n", - "
" - ], - "text/plain": [ - " Entity Code Year Annual COâ‚‚ emissions (tonnes )\n", - "20353 World OWID_WRL 1751 9350528.0\n", - "20354 World OWID_WRL 1752 9354192.0\n", - "20355 World OWID_WRL 1753 9354192.0\n", - "20356 World OWID_WRL 1754 9357856.0\n", - "20357 World OWID_WRL 1755 9361520.0" - ] - }, - "execution_count": 252, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#keeping only the global data\n", - "global_co = data[data.Entity == 'World']\n", - "global_co.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 253, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " errors=errors,\n" - ] - } - ], - "source": [ - "#cleaning the dataset\n", - "global_co.drop(['Entity','Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 254, - "metadata": {}, - "outputs": [], - "source": [ - "global_co.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 255, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 267 entries, 1751 to 2017\n", - "Data columns (total 1 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Annual COâ‚‚ emissions (tonnes ) 267 non-null float64\n", - "dtypes: float64(1)\n", - "memory usage: 4.2 KB\n" - ] - } - ], - "source": [ - "global_co.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Total greenhouse gas emissions (1990-2016)" - ] - }, - { - "cell_type": "code", - "execution_count": 256, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
0AfghanistanAFG199015140000.0
1AfghanistanAFG199115060000.0
2AfghanistanAFG199213600000.0
3AfghanistanAFG199313430000.0
4AfghanistanAFG199413240000.0
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" - ], - "text/plain": [ - " Entity Code Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", - "0 Afghanistan AFG 1990 15140000.0\n", - "1 Afghanistan AFG 1991 15060000.0\n", - "2 Afghanistan AFG 1992 13600000.0\n", - "3 Afghanistan AFG 1993 13430000.0\n", - "4 Afghanistan AFG 1994 13240000.0" - ] - }, - "execution_count": 256, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Total greenhouse gas emissions\n", - "ghg = pd.read_csv('data//total-ghg-emissions.csv')\n", - "ghg.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 257, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the dataset\n", - "ghg.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 258, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityYearTotal GHG emissions including LUCF (tonnes COâ‚‚e)
5076World19903.496734e+10
5077World19913.512544e+10
5078World19923.498553e+10
5079World19933.507963e+10
5080World19943.528761e+10
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" - ], - "text/plain": [ - " Entity Year Total GHG emissions including LUCF (tonnes COâ‚‚e)\n", - "5076 World 1990 3.496734e+10\n", - "5077 World 1991 3.512544e+10\n", - "5078 World 1992 3.498553e+10\n", - "5079 World 1993 3.507963e+10\n", - "5080 World 1994 3.528761e+10" - ] - }, - "execution_count": 258, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_ghg = ghg[ghg.Entity == 'World']\n", - "global_ghg.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 259, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " errors=errors,\n" - ] - } - ], - "source": [ - "global_ghg.drop(['Entity'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 260, - "metadata": {}, - "outputs": [], - "source": [ - "global_ghg = global_ghg.rename(columns={'Total GHG emissions including LUCF (tonnes COâ‚‚e)':'GHG(tonnes COâ‚‚e )'})" - ] - }, - { - "cell_type": "code", - "execution_count": 261, - "metadata": {}, - "outputs": [], - "source": [ - "global_ghg.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 262, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 27 entries, 1990 to 2016\n", - "Data columns (total 1 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 GHG(tonnes COâ‚‚e ) 27 non-null float64\n", - "dtypes: float64(1)\n", - "memory usage: 432.0 bytes\n" - ] - } - ], - "source": [ - "global_ghg.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Global COâ‚‚ atmospheric concentration" - ] - }, - { - "cell_type": "code", - "execution_count": 380, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearCOâ‚‚ concentration (parts per million)
0WorldOWID_WRL803719 BCE207.29
1WorldOWID_WRL803182 BCE202.23
2WorldOWID_WRL802573 BCE204.86
3WorldOWID_WRL802060 BCE207.50
4WorldOWID_WRL801975 BCE202.92
...............
1866WorldOWID_WRL2014398.65
1867WorldOWID_WRL2015400.83
1868WorldOWID_WRL2016404.24
1869WorldOWID_WRL2017406.55
1870WorldOWID_WRL2018408.52
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1871 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " Entity Code Year COâ‚‚ concentration (parts per million)\n", - "0 World OWID_WRL 803719 BCE 207.29\n", - "1 World OWID_WRL 803182 BCE 202.23\n", - "2 World OWID_WRL 802573 BCE 204.86\n", - "3 World OWID_WRL 802060 BCE 207.50\n", - "4 World OWID_WRL 801975 BCE 202.92\n", - "... ... ... ... ...\n", - "1866 World OWID_WRL 2014 398.65\n", - "1867 World OWID_WRL 2015 400.83\n", - "1868 World OWID_WRL 2016 404.24\n", - "1869 World OWID_WRL 2017 406.55\n", - "1870 World OWID_WRL 2018 408.52\n", - "\n", - "[1871 rows x 4 columns]" - ] - }, - "execution_count": 380, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Global COâ‚‚ atmospheric concentration dataset\n", - "co_concentration = pd.read_csv('data//global-co-concentration-ppm.csv')\n", - "co_concentration" - ] - }, - { - "cell_type": "code", - "execution_count": 381, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the data\n", - "co_concentration.drop(['Code', 'Entity'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 382, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration = co_concentration.rename(columns={'COâ‚‚ concentration (parts per million)':'COâ‚‚ concentration'})" - ] - }, - { - "cell_type": "code", - "execution_count": 392, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1'" - ] - }, - "execution_count": 392, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_concentration.Year[1648]" - ] - }, - { - "cell_type": "code", - "execution_count": 267, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'CO atmospheric concentration')" - ] - }, - "execution_count": 267, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plotting the Global COâ‚‚ atmospheric concentration increasement from 1864-2018\n", - "x = co_concentration.Year[1740:]\n", - "y = co_concentration['COâ‚‚ concentration'][1740:]\n", - "plt.figure(figsize=(17,6))\n", - "\n", - "plt.plot(x, y, 'dodgerblue', label='CO atmospheric concentration', \n", - " linewidth=2)\n", - "\n", - "plt.legend()\n", - "plt.xlabel('Year')\n", - "plt.ylabel('CO atmospheric concentration')" - ] - }, - { - "cell_type": "code", - "execution_count": 268, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 269, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Index: 1871 entries, 803719 BCE to 2018\n", - "Data columns (total 1 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 COâ‚‚ concentration 1871 non-null float64\n", - "dtypes: float64(1)\n", - "memory usage: 29.2+ KB\n" - ] - } - ], - "source": [ - "co_concentration.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 396, - "metadata": {}, - "outputs": [], - "source": [ - "co_atm_conc = co_concentration.drop(co_concentration.index[:1648])" - ] - }, - { - "cell_type": "code", - "execution_count": 397, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
YearCOâ‚‚ concentration
16481276.7
164930277.9
165056277.4
1651104277.5
1652136278.1
\n", - "
" - ], - "text/plain": [ - " Year COâ‚‚ concentration\n", - "1648 1 276.7\n", - "1649 30 277.9\n", - "1650 56 277.4\n", - "1651 104 277.5\n", - "1652 136 278.1" - ] - }, - "execution_count": 397, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_atm_conc.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 398, - "metadata": {}, - "outputs": [], - "source": [ - "co_atm_conc.to_csv('co_atm_conc')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Global Temperature anomaly 1850-2018\n" - ] - }, - { - "cell_type": "code", - "execution_count": 270, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
EntityCodeYearMedian (℃)Upper (℃)Lower (℃)
0GlobalNaN1850-0.373-0.339-0.425
1GlobalNaN1851-0.218-0.184-0.274
2GlobalNaN1852-0.228-0.196-0.280
3GlobalNaN1853-0.269-0.239-0.321
4GlobalNaN1854-0.248-0.218-0.301
\n", - "
" - ], - "text/plain": [ - " Entity Code Year Median (℃) Upper (℃) Lower (℃)\n", - "0 Global NaN 1850 -0.373 -0.339 -0.425\n", - "1 Global NaN 1851 -0.218 -0.184 -0.274\n", - "2 Global NaN 1852 -0.228 -0.196 -0.280\n", - "3 Global NaN 1853 -0.269 -0.239 -0.321\n", - "4 Global NaN 1854 -0.248 -0.218 -0.301" - ] - }, - "execution_count": 270, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Global Temperature anomaly (increase-decreage on previous average temperature)\n", - "temperature_a = pd.read_csv('data//temperature_anomaly.csv')\n", - "temperature_a.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 271, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 676 entries, 0 to 675\n", - "Data columns (total 6 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Entity 676 non-null object \n", - " 1 Code 0 non-null float64\n", - " 2 Year 676 non-null int64 \n", - " 3 Median (℃) 676 non-null float64\n", - " 4 Upper (℃) 676 non-null float64\n", - " 5 Lower (℃) 676 non-null float64\n", - "dtypes: float64(4), int64(1), object(1)\n", - "memory usage: 31.8+ KB\n" - ] - } - ], - "source": [ - "temperature_a.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 272, - "metadata": {}, - "outputs": [], - "source": [ - "#cleaning the dataset\n", - "temperature_a.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 273, - "metadata": {}, - "outputs": [], - "source": [ - "temperature_a.rename(columns={'Entity':'Country'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 274, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Global', 'Northern Hemisphere', 'Southern Hemisphere', 'Tropics'],\n", - " dtype=object)" - ] - }, - "execution_count": 274, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature_a.Country.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 275, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860,\n", - " 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871,\n", - " 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n", - " 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893,\n", - " 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904,\n", - " 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915,\n", - " 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926,\n", - " 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937,\n", - " 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948,\n", - " 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,\n", - " 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n", - " 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n", - " 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n", - " 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", - " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n", - " 2015, 2016, 2017, 2018], dtype=int64)" - ] - }, - "execution_count": 275, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature_a.Year.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 276, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'increse ordescrease in Temperature(℃)')" - ] - }, - "execution_count": 276, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8451 missing from current font.\n", - " font.set_text(s, 0.0, 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plotting the median temperature anomaly over time to see how it's actually ingreasing \n", - "x = temperature_a.Year\n", - "y = temperature_a['Median (℃)'].where(temperature_a['Country']=='Global')\n", - "plt.figure(figsize=(17,6))\n", - "\n", - "plt.plot(x, y, 'dodgerblue', label='Temperature Anomaly', \n", - " linewidth=2)\n", - "\n", - "plt.legend()\n", - "plt.xlabel('Year')\n", - "plt.ylabel('increse ordescrease in Temperature(℃)')" - ] - }, - { - "cell_type": "code", - "execution_count": 277, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CountryYearMedian (℃)Upper (℃)Lower (℃)
0Global1850-0.373-0.339-0.425
1Global1851-0.218-0.184-0.274
2Global1852-0.228-0.196-0.280
3Global1853-0.269-0.239-0.321
4Global1854-0.248-0.218-0.301
\n", - "
" - ], - "text/plain": [ - " Country Year Median (℃) Upper (℃) Lower (℃)\n", - "0 Global 1850 -0.373 -0.339 -0.425\n", - "1 Global 1851 -0.218 -0.184 -0.274\n", - "2 Global 1852 -0.228 -0.196 -0.280\n", - "3 Global 1853 -0.269 -0.239 -0.321\n", - "4 Global 1854 -0.248 -0.218 -0.301" - ] - }, - "execution_count": 277, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temperature_a.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 243, - "metadata": {}, - "outputs": [], - "source": [ - "#keeping only the global data to compare it with the CO emissions and the CO concentration\n", - "global_a = temperature_a.where(temperature_a['Country']=='Global')\n", - "global_a.dropna(inplace=True)\n", - "global_a.drop(['Country'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 278, - "metadata": {}, - "outputs": [], - "source": [ - "global_a.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 360, - "metadata": {}, - "outputs": [], - "source": [ - "global_a.drop(['Upper (℃)','Lower (℃)'],axis=1, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ozone depletion crisis" - ] - }, - { - "cell_type": "code", - "execution_count": 287, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
EntityCodeYearOzone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)
0Natural emissionsNaN1961165000
1Natural emissionsNaN1962165000
2Natural emissionsNaN1963165000
3Natural emissionsNaN1964165000
4Natural emissionsNaN1965165000
\n", - "
" - ], - "text/plain": [ - " Entity Code Year \\\n", - "0 Natural emissions NaN 1961 \n", - "1 Natural emissions NaN 1962 \n", - "2 Natural emissions NaN 1963 \n", - "3 Natural emissions NaN 1964 \n", - "4 Natural emissions NaN 1965 \n", - "\n", - " Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents) \n", - "0 165000 \n", - "1 165000 \n", - "2 165000 \n", - "3 165000 \n", - "4 165000 " - ] - }, - "execution_count": 287, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# importing Ozone depleting emissions dataset\n", - "ozone = pd.read_csv('data//ozone-depleting-substance-emissions.csv')\n", - "ozone.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 288, - "metadata": {}, - "outputs": [], - "source": [ - "# cleaning the dataset\n", - "ozone.drop(['Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 289, - "metadata": {}, - "outputs": [], - "source": [ - "ozone.rename(columns={'Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents)':'Ozone-depleting emissions'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 290, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Natural emissions', 'Total emissions'], dtype=object)" - ] - }, - "execution_count": 290, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ozone.Entity.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 291, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 291, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#ploting natural and total emissions to see that we have nearly stopped using CFC\n", - "plt.figure(figsize=(17,6))\n", - "plt.plot(ozone.Year,ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Natural emissions'), label='Natural emissions')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Tonnes CFC11-equivalents')\n", - "plt.plot(ozone.Year, ozone['Ozone-depleting emissions'].where(ozone['Entity']=='Total emissions'), label='Total emissions')\n", - "plt.legend(['Natural emissions', 'Total emissions'], loc='upper right')" - ] - }, - { - "cell_type": "code", - "execution_count": 292, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Andreu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " errors=errors,\n" - ] - } - ], - "source": [ - "caused_ozone = ozone[ozone.Entity == 'Total emissions']\n", - "caused_ozone.drop(['Entity'],axis=1, inplace=True )" - ] - }, - { - "cell_type": "code", - "execution_count": 294, - "metadata": {}, - "outputs": [], - "source": [ - "ozone.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 293, - "metadata": {}, - "outputs": [], - "source": [ - "caused_ozone.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Antartica Ozone Hole area" - ] - }, - { - "cell_type": "code", - "execution_count": 295, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearMaximum ozone hole area (square kilometres)Mean ozone hole area (square kilometres)
0WorldOWID_WRL19791100000100000
1WorldOWID_WRL198033000001400000
2WorldOWID_WRL19813100000600000
3WorldOWID_WRL1982108000004800000
4WorldOWID_WRL1983122000007900000
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" - ], - "text/plain": [ - " Entity Code Year Maximum ozone hole area (square kilometres) \\\n", - "0 World OWID_WRL 1979 1100000 \n", - "1 World OWID_WRL 1980 3300000 \n", - "2 World OWID_WRL 1981 3100000 \n", - "3 World OWID_WRL 1982 10800000 \n", - "4 World OWID_WRL 1983 12200000 \n", - "\n", - " Mean ozone hole area (square kilometres) \n", - "0 100000 \n", - "1 1400000 \n", - "2 600000 \n", - "3 4800000 \n", - "4 7900000 " - ] - }, - "execution_count": 295, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#importing Antartica Ozone Hole area dataset\n", - "oz_hole = pd.read_csv('data//antarctic-ozone-hole-area.csv')\n", - "oz_hole.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 296, - "metadata": {}, - "outputs": [], - "source": [ - "oz_hole.drop(['Entity', 'Code','Maximum ozone hole area (square kilometres)'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 297, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMean ozone hole area (square kilometres)
01979100000
119801400000
21981600000
319824800000
419837900000
\n", - "
" - ], - "text/plain": [ - " Year Mean ozone hole area (square kilometres)\n", - "0 1979 100000\n", - "1 1980 1400000\n", - "2 1981 600000\n", - "3 1982 4800000\n", - "4 1983 7900000" - ] - }, - "execution_count": 297, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oz_hole.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 298, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 298, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#ploting natural and total emissionsMean ozone hole area (square kilometres) to see if it has reduced\n", - "plt.figure(figsize=(17,6))\n", - "plt.plot(oz_hole.Year,oz_hole['Mean ozone hole area (square kilometres)'], label='Mean ozone hole area (square kilometres)')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Mean ozone hole area (square kilometres)')\n", - "plt.legend(['Mean ozone hole area (square kilometres)'], loc='upper right')" - ] - }, - { - "cell_type": "code", - "execution_count": 299, - "metadata": {}, - "outputs": [], - "source": [ - "oz_hole.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Global average temperature" - ] - }, - { - "cell_type": "code", - "execution_count": 300, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtLandAverageTemperatureLandAverageTemperatureUncertaintyLandMaxTemperatureLandMaxTemperatureUncertaintyLandMinTemperatureLandMinTemperatureUncertaintyLandAndOceanAverageTemperatureLandAndOceanAverageTemperatureUncertainty
01750-01-013.0343.574NaNNaNNaNNaNNaNNaN
11750-02-013.0833.702NaNNaNNaNNaNNaNNaN
21750-03-015.6263.076NaNNaNNaNNaNNaNNaN
31750-04-018.4902.451NaNNaNNaNNaNNaNNaN
41750-05-0111.5732.072NaNNaNNaNNaNNaNNaN
\n", - "
" - ], - "text/plain": [ - " dt LandAverageTemperature LandAverageTemperatureUncertainty \\\n", - "0 1750-01-01 3.034 3.574 \n", - "1 1750-02-01 3.083 3.702 \n", - "2 1750-03-01 5.626 3.076 \n", - "3 1750-04-01 8.490 2.451 \n", - "4 1750-05-01 11.573 2.072 \n", - "\n", - " LandMaxTemperature LandMaxTemperatureUncertainty LandMinTemperature \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " LandMinTemperatureUncertainty LandAndOceanAverageTemperature \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " LandAndOceanAverageTemperatureUncertainty \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN " - ] - }, - "execution_count": 300, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_temperature = pd.read_csv('data//GlobalTemperatures.csv')\n", - "global_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 301, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtLandAverageTemperatureLandAndOceanAverageTemperature
12001850-01-010.74912.833
12011850-02-013.07113.588
12021850-03-014.95414.043
12031850-04-017.21714.667
12041850-05-0110.00415.507
\n", - "
" - ], - "text/plain": [ - " dt LandAverageTemperature LandAndOceanAverageTemperature\n", - "1200 1850-01-01 0.749 12.833\n", - "1201 1850-02-01 3.071 13.588\n", - "1202 1850-03-01 4.954 14.043\n", - "1203 1850-04-01 7.217 14.667\n", - "1204 1850-05-01 10.004 15.507" - ] - }, - "execution_count": 301, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_temperature.dropna(inplace=True)\n", - "global_temperature = global_temperature[['dt','LandAverageTemperature','LandAndOceanAverageTemperature']]\n", - "global_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 302, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dtLandAverageTemperatureLandAndOceanAverageTemperatureYear
12001850-01-010.74912.8331850
12011850-02-013.07113.5881850
12021850-03-014.95414.0431850
12031850-04-017.21714.6671850
12041850-05-0110.00415.5071850
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" - ], - "text/plain": [ - " dt LandAverageTemperature LandAndOceanAverageTemperature Year\n", - "1200 1850-01-01 0.749 12.833 1850\n", - "1201 1850-02-01 3.071 13.588 1850\n", - "1202 1850-03-01 4.954 14.043 1850\n", - "1203 1850-04-01 7.217 14.667 1850\n", - "1204 1850-05-01 10.004 15.507 1850" - ] - }, - "execution_count": 302, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#global_temperature['dt']= pd.to_datetime(global_temperature['dt']) \n", - "global_temperature['Year'] = pd.DatetimeIndex(global_temperature['dt']).year\n", - "global_temperature.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 303, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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LandAverageTemperatureLandAndOceanAverageTemperature
Year
18507.90066714.867167
18518.17858314.991833
18528.10016715.006500
18538.04183314.955167
18548.21050014.991000
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" - ], - "text/plain": [ - " LandAverageTemperature LandAndOceanAverageTemperature\n", - "Year \n", - "1850 7.900667 14.867167\n", - "1851 8.178583 14.991833\n", - "1852 8.100167 15.006500\n", - "1853 8.041833 14.955167\n", - "1854 8.210500 14.991000" - ] - }, - "execution_count": 303, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_global_temp = global_temperature[['Year','LandAverageTemperature','LandAndOceanAverageTemperature']].groupby(['Year']).agg({'LandAverageTemperature':'mean',\n", - " 'LandAndOceanAverageTemperature':'mean'})\n", - "annual_global_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 304, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearLandAverageTemperatureLandAndOceanAverageTemperature
018507.90066714.867167
118518.17858314.991833
218528.10016715.006500
318538.04183314.955167
418548.21050014.991000
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" - ], - "text/plain": [ - " Year LandAverageTemperature LandAndOceanAverageTemperature\n", - "0 1850 7.900667 14.867167\n", - "1 1851 8.178583 14.991833\n", - "2 1852 8.100167 15.006500\n", - "3 1853 8.041833 14.955167\n", - "4 1854 8.210500 14.991000" - ] - }, - "execution_count": 304, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "annual_global_temp.reset_index(level=0, inplace=True)\n", - "annual_global_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 305, - "metadata": {}, - "outputs": [], - "source": [ - "annual_global_temp.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 306, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 166 entries, 1850 to 2015\n", - "Data columns (total 2 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 LandAverageTemperature 166 non-null float64\n", - " 1 LandAndOceanAverageTemperature 166 non-null float64\n", - "dtypes: float64(2)\n", - "memory usage: 3.9 KB\n" - ] - } - ], - "source": [ - "annual_global_temp.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fossil fuel consumption per capita" - ] - }, - { - "cell_type": "code", - "execution_count": 312, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)
0AfricaNaN19650.9823451.0406860.032724
1AfricaNaN19661.0503401.0019700.035646
2AfricaNaN19671.0143901.0051970.034371
3AfricaNaN19681.0433631.0211390.034195
4AfricaNaN19691.0322301.0010830.039699
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" - ], - "text/plain": [ - " Entity Code Year Oil (megawatt-hours) Coal (megawatt-hours) \\\n", - "0 Africa NaN 1965 0.982345 1.040686 \n", - "1 Africa NaN 1966 1.050340 1.001970 \n", - "2 Africa NaN 1967 1.014390 1.005197 \n", - "3 Africa NaN 1968 1.043363 1.021139 \n", - "4 Africa NaN 1969 1.032230 1.001083 \n", - "\n", - " Natural gas (megawatt-hours) \n", - "0 0.032724 \n", - "1 0.035646 \n", - "2 0.034371 \n", - "3 0.034195 \n", - "4 0.039699 " - ] - }, - "execution_count": 312, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fossil_per_c = pd.read_csv('data//fossil-fuel-consumption-per-capita.csv')\n", - "fossil_per_c.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 313, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)
3302WorldOWID_WRL19655.3121354.8875542.043343
3303WorldOWID_WRL19665.6055414.8450912.178977
3304WorldOWID_WRL19675.8834724.6700632.288339
3305WorldOWID_WRL19686.2573574.6435392.440440
3306WorldOWID_WRL19696.6550134.6908202.622830
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" - ], - "text/plain": [ - " Entity Code Year Oil (megawatt-hours) Coal (megawatt-hours) \\\n", - "3302 World OWID_WRL 1965 5.312135 4.887554 \n", - "3303 World OWID_WRL 1966 5.605541 4.845091 \n", - "3304 World OWID_WRL 1967 5.883472 4.670063 \n", - "3305 World OWID_WRL 1968 6.257357 4.643539 \n", - "3306 World OWID_WRL 1969 6.655013 4.690820 \n", - "\n", - " Natural gas (megawatt-hours) \n", - "3302 2.043343 \n", - "3303 2.178977 \n", - "3304 2.288339 \n", - "3305 2.440440 \n", - "3306 2.622830 " - ] - }, - "execution_count": 313, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#keeping only the global data\n", - "fossil_per_c = fossil_per_c[fossil_per_c.Entity == 'World']\n", - "fossil_per_c.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 314, - "metadata": {}, - "outputs": [], - "source": [ - "fossil_per_c.drop(['Entity', 'Code',], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 315, - "metadata": {}, - "outputs": [], - "source": [ - "fossil_per_c.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 316, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 51 entries, 1965 to 2015\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Oil (megawatt-hours) 51 non-null float64\n", - " 1 Coal (megawatt-hours) 51 non-null float64\n", - " 2 Natural gas (megawatt-hours) 51 non-null float64\n", - "dtypes: float64(3)\n", - "memory usage: 1.6 KB\n" - ] - } - ], - "source": [ - "fossil_per_c.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Global primary energy by source" - ] - }, - { - "cell_type": "code", - "execution_count": 317, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearCoal (terawatt-hours)Solar (terawatt-hours)Crude oil (terawatt-hours)Natural gas (terawatt-hours)Traditional biofuels (terawatt-hours)Other renewables (terawatt-hours)Hydropower (terawatt-hours)Nuclear (terawatt-hours)Wind (terawatt-hours)
0WorldOWID_WRL180097.222300.00.00.05555.5555560.00.00.00.0
1WorldOWID_WRL1810127.777880.00.00.05833.3333330.00.00.00.0
2WorldOWID_WRL1820152.777900.00.00.06111.1111110.00.00.00.0
3WorldOWID_WRL1830263.889100.00.00.06388.8888890.00.00.00.0
4WorldOWID_WRL1840355.555840.00.00.06944.4444440.00.00.00.0
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" - ], - "text/plain": [ - " Entity Code Year Coal (terawatt-hours) Solar (terawatt-hours) \\\n", - "0 World OWID_WRL 1800 97.22230 0.0 \n", - "1 World OWID_WRL 1810 127.77788 0.0 \n", - "2 World OWID_WRL 1820 152.77790 0.0 \n", - "3 World OWID_WRL 1830 263.88910 0.0 \n", - "4 World OWID_WRL 1840 355.55584 0.0 \n", - "\n", - " Crude oil (terawatt-hours) Natural gas (terawatt-hours) \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "\n", - " Traditional biofuels (terawatt-hours) Other renewables (terawatt-hours) \\\n", - "0 5555.555556 0.0 \n", - "1 5833.333333 0.0 \n", - "2 6111.111111 0.0 \n", - "3 6388.888889 0.0 \n", - "4 6944.444444 0.0 \n", - "\n", - " Hydropower (terawatt-hours) Nuclear (terawatt-hours) \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "\n", - " Wind (terawatt-hours) \n", - "0 0.0 \n", - "1 0.0 \n", - "2 0.0 \n", - "3 0.0 \n", - "4 0.0 " - ] - }, - "execution_count": 317, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_energy = pd.read_csv('data//global-primary-energy.csv')\n", - "global_energy.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 318, - "metadata": {}, - "outputs": [], - "source": [ - "global_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 319, - "metadata": {}, - "outputs": [], - "source": [ - "global_energy.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 320, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 71 entries, 1800 to 2018\n", - "Data columns (total 9 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Coal (terawatt-hours) 71 non-null float64\n", - " 1 Solar (terawatt-hours) 71 non-null float64\n", - " 2 Crude oil (terawatt-hours) 71 non-null float64\n", - " 3 Natural gas (terawatt-hours) 71 non-null float64\n", - " 4 Traditional biofuels (terawatt-hours) 71 non-null float64\n", - " 5 Other renewables (terawatt-hours) 71 non-null float64\n", - " 6 Hydropower (terawatt-hours) 71 non-null float64\n", - " 7 Nuclear (terawatt-hours) 71 non-null float64\n", - " 8 Wind (terawatt-hours) 71 non-null float64\n", - "dtypes: float64(9)\n", - "memory usage: 5.5 KB\n" - ] - } - ], - "source": [ - "global_energy.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Modern renewable energy consumtion" - ] - }, - { - "cell_type": "code", - "execution_count": 321, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearHydropower (terawatt-hours)Solar (terawatt-hours)Wind (terawatt-hours)Other renewables (terawatt-hours)
0AfricaNaN196514.2788060.00.00.0
1AfricaNaN196615.6490490.00.00.0
2AfricaNaN196716.1583330.00.00.0
3AfricaNaN196818.6229830.00.00.0
4AfricaNaN196921.5828970.00.00.0
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" - ], - "text/plain": [ - " Entity Code Year Hydropower (terawatt-hours) Solar (terawatt-hours) \\\n", - "0 Africa NaN 1965 14.278806 0.0 \n", - "1 Africa NaN 1966 15.649049 0.0 \n", - "2 Africa NaN 1967 16.158333 0.0 \n", - "3 Africa NaN 1968 18.622983 0.0 \n", - "4 Africa NaN 1969 21.582897 0.0 \n", - "\n", - " Wind (terawatt-hours) Other renewables (terawatt-hours) \n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 " - ] - }, - "execution_count": 321, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "renew_energy = pd.read_csv('data//modern-renewable-energy-consumption.csv')\n", - "renew_energy.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 322, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearHydropower (terawatt-hours)Solar (terawatt-hours)Wind (terawatt-hours)Other renewables (terawatt-hours)
5037WorldOWID_WRL1965919.7702760.00.017.985232
5038WorldOWID_WRL1966986.2858720.00.019.806007
5039WorldOWID_WRL19671009.2445690.00.019.986571
5040WorldOWID_WRL19681059.0628600.00.022.120045
5041WorldOWID_WRL19691122.4425260.00.023.257964
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" - ], - "text/plain": [ - " Entity Code Year Hydropower (terawatt-hours) \\\n", - "5037 World OWID_WRL 1965 919.770276 \n", - "5038 World OWID_WRL 1966 986.285872 \n", - "5039 World OWID_WRL 1967 1009.244569 \n", - "5040 World OWID_WRL 1968 1059.062860 \n", - "5041 World OWID_WRL 1969 1122.442526 \n", - "\n", - " Solar (terawatt-hours) Wind (terawatt-hours) \\\n", - "5037 0.0 0.0 \n", - "5038 0.0 0.0 \n", - "5039 0.0 0.0 \n", - "5040 0.0 0.0 \n", - "5041 0.0 0.0 \n", - "\n", - " Other renewables (terawatt-hours) \n", - "5037 17.985232 \n", - "5038 19.806007 \n", - "5039 19.986571 \n", - "5040 22.120045 \n", - "5041 23.257964 " - ] - }, - "execution_count": 322, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#keeping only the global data\n", - "renew_energy = renew_energy[renew_energy.Entity == 'World']\n", - "renew_energy.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 323, - "metadata": {}, - "outputs": [], - "source": [ - "renew_energy.drop(['Entity', 'Code',], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 324, - "metadata": {}, - "outputs": [], - "source": [ - "renew_energy.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 325, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 54 entries, 1965 to 2018\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Hydropower (terawatt-hours) 54 non-null float64\n", - " 1 Solar (terawatt-hours) 54 non-null float64\n", - " 2 Wind (terawatt-hours) 54 non-null float64\n", - " 3 Other renewables (terawatt-hours) 54 non-null float64\n", - "dtypes: float64(4)\n", - "memory usage: 2.1 KB\n" - ] - } - ], - "source": [ - "renew_energy.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hubbert's peak vs actual oil production" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYear(barrels per year)(barrels per year).1
0United StatesUSA1910209557000.0NaN
1United StatesUSA1911220449000.0200158958.0
2United StatesUSA1912222935000.0213834803.7
3United StatesUSA1913248446000.0228406784.8
4United StatesUSA1914265763000.0243928136.8
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" - ], - "text/plain": [ - " Entity Code Year (barrels per year) (barrels per year).1\n", - "0 United States USA 1910 209557000.0 NaN\n", - "1 United States USA 1911 220449000.0 200158958.0\n", - "2 United States USA 1912 222935000.0 213834803.7\n", - "3 United States USA 1913 248446000.0 228406784.8\n", - "4 United States USA 1914 265763000.0 243928136.8" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hubbert_peak = pd.read_csv('data//hubberts-peak-vs-actual-oil-production-in-the-united-states.csv')\n", - "hubbert_peak.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Entity', 'Code', 'Year', ' (barrels per year)',\n", - " ' (barrels per year).1'],\n", - " dtype='object')" - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hubbert_peak.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [], - "source": [ - "hubbert_peak.drop(['Entity', 'Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 326, - "metadata": {}, - "outputs": [], - "source": [ - "hubbert_peak.rename(columns={' (barrels per year)':'Actual Oil Prod',' (barrels per year).1':'Hubbert Predicted'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 327, - "metadata": {}, - "outputs": [], - "source": [ - "hubbert_peak = hubbert_peak.dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": 328, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearActualPredicted
10220122.374136e+09614194532.6
10320132.725665e+09577668428.7
10420143.198694e+09543068744.5
10520153.436515e+09510324290.7
10620163.248117e+09479362510.8
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" - ], - "text/plain": [ - " Year Actual Predicted\n", - "102 2012 2.374136e+09 614194532.6\n", - "103 2013 2.725665e+09 577668428.7\n", - "104 2014 3.198694e+09 543068744.5\n", - "105 2015 3.436515e+09 510324290.7\n", - "106 2016 3.248117e+09 479362510.8" - ] - }, - "execution_count": 328, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hubbert_peak.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 329, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 329, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#ploting predicted and actual emissions to see if it the prediction came true\n", - "plt.figure(figsize=(17,6))\n", - "plt.plot(hubbert_peak.Year,hubbert_peak['Actual'],label='Actual')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('(barrels per year)')\n", - "plt.plot(hubbert_peak.Year,hubbert_peak['Predicted'],label='Predicted')\n", - "plt.legend(['Actual', 'Predicted'], loc='upper left')" - ] - }, - { - "cell_type": "code", - "execution_count": 330, - "metadata": {}, - "outputs": [], - "source": [ - "hubbert_peak.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 331, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 106 entries, 1911 to 2016\n", - "Data columns (total 2 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Actual 106 non-null float64\n", - " 1 Predicted 106 non-null float64\n", - "dtypes: float64(2)\n", - "memory usage: 2.5 KB\n" - ] - } - ], - "source": [ - "hubbert_peak.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### share of global primary energy" - ] - }, - { - "cell_type": "code", - "execution_count": 332, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearCoal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
0WorldOWID_WRL18001.7199030.00.00.00.00.00.098.2800970.0
1WorldOWID_WRL18102.1435250.00.00.00.00.00.097.8564750.0
2WorldOWID_WRL18202.4390260.00.00.00.00.00.097.5609740.0
3WorldOWID_WRL18303.9666000.00.00.00.00.00.096.0334000.0
4WorldOWID_WRL18404.8706280.00.00.00.00.00.095.1293720.0
\n", - "
" - ], - "text/plain": [ - " Entity Code Year Coal (%) Crude oil (%) Hydropower (%) \\\n", - "0 World OWID_WRL 1800 1.719903 0.0 0.0 \n", - "1 World OWID_WRL 1810 2.143525 0.0 0.0 \n", - "2 World OWID_WRL 1820 2.439026 0.0 0.0 \n", - "3 World OWID_WRL 1830 3.966600 0.0 0.0 \n", - "4 World OWID_WRL 1840 4.870628 0.0 0.0 \n", - "\n", - " Natural gas (%) Nuclear (%) Other renewables (%) Solar (%) \\\n", - "0 0.0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 0.0 \n", - "\n", - " Traditional biofuels (%) Wind (%) \n", - "0 98.280097 0.0 \n", - "1 97.856475 0.0 \n", - "2 97.560974 0.0 \n", - "3 96.033400 0.0 \n", - "4 95.129372 0.0 " - ] - }, - "execution_count": 332, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "share_energy = pd.read_csv('data//global-primary-energy-share-inc-biomass.csv')\n", - "share_energy.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 333, - "metadata": {}, - "outputs": [], - "source": [ - "share_energy.drop(['Entity', 'Code'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 334, - "metadata": {}, - "outputs": [], - "source": [ - "share_energy.set_index('Year',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 335, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 70 entries, 1800 to 2017\n", - "Data columns (total 9 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Coal (%) 70 non-null float64\n", - " 1 Crude oil (%) 70 non-null float64\n", - " 2 Hydropower (%) 70 non-null float64\n", - " 3 Natural gas (%) 70 non-null float64\n", - " 4 Nuclear (%) 70 non-null float64\n", - " 5 Other renewables (%) 70 non-null float64\n", - " 6 Solar (%) 70 non-null float64\n", - " 7 Traditional biofuels (%) 70 non-null float64\n", - " 8 Wind (%) 70 non-null float64\n", - "dtypes: float64(9)\n", - "memory usage: 5.5 KB\n" - ] - } - ], - "source": [ - "share_energy.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solar pannels price progression" - ] - }, - { - "cell_type": "code", - "execution_count": 445, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearSolar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))Cumulative Installed Capacity (megawatts-peak (MWp))Total population (Gapminder)
0AfghanistanAFG1800NaNNaN3280000.0
1AfghanistanAFG1820NaNNaN3280000.0
2AfghanistanAFG1870NaNNaN4207000.0
3AfghanistanAFG1913NaNNaN5730000.0
4AfghanistanAFG1950NaNNaN8151455.0
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" - ], - "text/plain": [ - " Entity Code Year \\\n", - "0 Afghanistan AFG 1800 \n", - "1 Afghanistan AFG 1820 \n", - "2 Afghanistan AFG 1870 \n", - "3 Afghanistan AFG 1913 \n", - "4 Afghanistan AFG 1950 \n", - "\n", - " Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp)) \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "\n", - " Cumulative Installed Capacity (megawatts-peak (MWp)) \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "\n", - " Total population (Gapminder) \n", - "0 3280000.0 \n", - "1 3280000.0 \n", - "2 4207000.0 \n", - "3 5730000.0 \n", - "4 8151455.0 " - ] - }, - "execution_count": 445, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solar_price = pd.read_csv('data//solar-pv-prices-vs-cumulative-capacity.csv')\n", - "solar_price.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 446, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EntityCodeYearSolar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))Cumulative Installed Capacity (megawatts-peak (MWp))Total population (Gapminder)
19946WorldOWID_WRL197666.1048350.32NaN
19947WorldOWID_WRL197746.4405390.77NaN
19948WorldOWID_WRL197833.2826881.77NaN
19949WorldOWID_WRL197929.6924933.27NaN
19950WorldOWID_WRL198023.7553616.57NaN
\n", - "
" - ], - "text/plain": [ - " Entity Code Year \\\n", - "19946 World OWID_WRL 1976 \n", - "19947 World OWID_WRL 1977 \n", - "19948 World OWID_WRL 1978 \n", - "19949 World OWID_WRL 1979 \n", - "19950 World OWID_WRL 1980 \n", - "\n", - " Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp)) \\\n", - "19946 66.104835 \n", - "19947 46.440539 \n", - "19948 33.282688 \n", - "19949 29.692493 \n", - "19950 23.755361 \n", - "\n", - " Cumulative Installed Capacity (megawatts-peak (MWp)) \\\n", - "19946 0.32 \n", - "19947 0.77 \n", - "19948 1.77 \n", - "19949 3.27 \n", - "19950 6.57 \n", - "\n", - " Total population (Gapminder) \n", - "19946 NaN \n", - "19947 NaN \n", - "19948 NaN \n", - "19949 NaN \n", - "19950 NaN " - ] - }, - "execution_count": 446, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#keeping only the global data\n", - "solar_price = solar_price[solar_price.Entity == 'World']\n", - "solar_price.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 447, - "metadata": {}, - "outputs": [], - "source": [ - "solar_price.drop(['Entity', 'Code','Total population (Gapminder)', 'Year'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 448, - "metadata": {}, - "outputs": [], - "source": [ - "solar_price.rename(columns={'Solar PV Module Cost (2016 $ per watt-peak (2016 $/Wp))':'Solar PV Module Cost($/Wp)','Cumulative Installed Capacity (megawatts-peak (MWp))':'Cumulative Installed Capacity(MWp)'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 449, - "metadata": {}, - "outputs": [], - "source": [ - "solar_price.set_index('Cumulative Installed Capacity(MWp)',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 428, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Solar PV Module Cost($/Wp)Cumulative Installed Capacity(MWp)
1994666.1048350.32
1994746.4405390.77
1994833.2826881.77
1994929.6924933.27
1995023.7553616.57
1995117.91880011.87
1995218.03281819.57
1995316.84394434.07
1995413.71777351.57
1995512.49436670.97
1995611.68843891.97
199579.801146116.87
199589.732873148.37
199599.789955186.27
1996010.009799228.97
199619.294041277.17
199628.779086331.27
199637.822745386.97
199647.645052447.97
199657.070296519.47
199665.880831602.07
199675.823569716.17
199684.892792850.97
199694.4797171026.47
199703.6500311278.47
199713.4387701631.37
199723.5146112136.27
199733.3207732811.57
199743.5367933861.37
199753.5801575269.07
199763.8859217251.47
199773.90805910324.47
199783.55885615816.27
199792.36926823729.27
199801.58915441131.57
199811.44128064710.87
199820.77472890772.67
199830.823302124572.67
199840.710000164472.67
199850.660000219868.00
199860.620000291064.00
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" - ], - "text/plain": [ - " Solar PV Module Cost($/Wp) Cumulative Installed Capacity(MWp)\n", - "19946 66.104835 0.32\n", - "19947 46.440539 0.77\n", - "19948 33.282688 1.77\n", - "19949 29.692493 3.27\n", - "19950 23.755361 6.57\n", - "19951 17.918800 11.87\n", - "19952 18.032818 19.57\n", - "19953 16.843944 34.07\n", - "19954 13.717773 51.57\n", - "19955 12.494366 70.97\n", - "19956 11.688438 91.97\n", - "19957 9.801146 116.87\n", - "19958 9.732873 148.37\n", - "19959 9.789955 186.27\n", - "19960 10.009799 228.97\n", - "19961 9.294041 277.17\n", - "19962 8.779086 331.27\n", - "19963 7.822745 386.97\n", - "19964 7.645052 447.97\n", - "19965 7.070296 519.47\n", - "19966 5.880831 602.07\n", - "19967 5.823569 716.17\n", - "19968 4.892792 850.97\n", - "19969 4.479717 1026.47\n", - "19970 3.650031 1278.47\n", - "19971 3.438770 1631.37\n", - "19972 3.514611 2136.27\n", - "19973 3.320773 2811.57\n", - "19974 3.536793 3861.37\n", - "19975 3.580157 5269.07\n", - "19976 3.885921 7251.47\n", - "19977 3.908059 10324.47\n", - "19978 3.558856 15816.27\n", - "19979 2.369268 23729.27\n", - "19980 1.589154 41131.57\n", - "19981 1.441280 64710.87\n", - "19982 0.774728 90772.67\n", - "19983 0.823302 124572.67\n", - "19984 0.710000 164472.67\n", - "19985 0.660000 219868.00\n", - "19986 0.620000 291064.00" - ] - }, - "execution_count": 428, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solar_price" - ] - }, - { - "cell_type": "code", - "execution_count": 441, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 441, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "solar_price['Solar PV Module Cost($/Wp)'] = solar_price['Solar PV Module Cost($/Wp)'].round()\n", - "x = solar_price.groupby('Solar PV Module Cost($/Wp)').mean()\n", - "x.loc[x['Cumulative Installed Capacity(MWp)']<10].plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Merging datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 358, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"#concatenating the datasets on the index ('Year'), we'll have a lot of NaN's because the datasets have different timeframes,\\n#but I'll be good for plotting nonetheless\\n\\nglobal_indicators = pd.concat([global_co, global_ghg, co_concentration, global_a, caused_ozone, oz_hole, annual_global_temp, fossil_per_c,\\n global_energy, renew_energy, hubbert_peak, share_energy, solar_price], join='outer')\"" - ] - }, - "execution_count": 358, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "'''#concatenating the datasets on the index ('Year'), we'll have a lot of NaN's because the datasets have different timeframes,\n", - "#but I'll be good for plotting nonetheless\n", - "\n", - "global_indicators = pd.concat([global_co, global_ghg, co_concentration, global_a, caused_ozone, oz_hole, annual_global_temp,\n", - "fossil_per_c, global_energy, renew_energy, hubbert_peak, share_energy, solar_price], join='outer')'''" - ] - }, - { - "cell_type": "code", - "execution_count": 362, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1 and 2\n", - "global_indicators1 = pd.merge(global_co, global_ghg, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 365, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4\n", - "global_indicators2 = pd.merge(global_indicators1,global_a, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 366, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5\n", - "global_indicators3 = pd.merge(global_indicators2,caused_ozone, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 367, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6\n", - "global_indicators4 = pd.merge(global_indicators3,oz_hole, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 368, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6,7\n", - "global_indicators5 = pd.merge(global_indicators4,annual_global_temp, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 369, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6,7,8\n", - "global_indicators6 = pd.merge(global_indicators5,fossil_per_c, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 370, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6,7,8,9\n", - "global_indicators7 = pd.merge(global_indicators6,global_energy, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 371, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6,7,8,9,10\n", - "global_indicators8 = pd.merge(global_indicators7,renew_energy, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 372, - "metadata": {}, - "outputs": [], - "source": [ - "#datasets 1,2,4,5,6,7,8,9,10,12\n", - "global_indicators = pd.merge(global_indicators8,share_energy, on='Year', how='outer')" - ] - }, - { - "cell_type": "code", - "execution_count": 374, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Annual CO₂ emissions (tonnes )GHG(tonnes CO₂e )Median (℃)Ozone-depleting emissionsMean ozone hole area (square kilometres)LandAverageTemperatureLandAndOceanAverageTemperatureOil (megawatt-hours)Coal (megawatt-hours)Natural gas (megawatt-hours)...Other renewables (terawatt-hours)_yCoal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
Year
20143.550583e+104.944079e+100.579320000.020900000.09.57066715.9130006.7800186.1977514.896768...502.15891930.12992834.2842432.60799922.7981601.7045050.3383420.1325977.5263480.477878
20153.546275e+104.985498e+100.763NaN25600000.09.83100016.0585836.8380425.9617334.956876...536.46624429.23211234.7507802.59790623.1936921.7195140.3593070.1735807.4177810.555327
20163.567510e+104.935803e+100.797NaN20700000.0NaNNaNNaNNaNNaN...556.62335628.47941835.0213262.66686223.6166451.7264460.3680320.2168497.2704460.633975
20173.615326e+10NaN0.677NaN17400000.0NaNNaNNaNNaNNaN...584.97582728.25414134.9959602.64321823.8964861.7159090.3816330.2881717.0935080.730975
2018NaNNaN0.595NaNNaNNaNNaNNaNNaNNaN...625.805362NaNNaNNaNNaNNaNNaNNaNNaNNaN
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5 rows × 32 columns

\n", - "
" - ], - "text/plain": [ - " Annual CO₂ emissions (tonnes ) GHG(tonnes CO₂e ) Median (℃) \\\n", - "Year \n", - "2014 3.550583e+10 4.944079e+10 0.579 \n", - "2015 3.546275e+10 4.985498e+10 0.763 \n", - "2016 3.567510e+10 4.935803e+10 0.797 \n", - "2017 3.615326e+10 NaN 0.677 \n", - "2018 NaN NaN 0.595 \n", - "\n", - " Ozone-depleting emissions Mean ozone hole area (square kilometres) \\\n", - "Year \n", - "2014 320000.0 20900000.0 \n", - "2015 NaN 25600000.0 \n", - "2016 NaN 20700000.0 \n", - "2017 NaN 17400000.0 \n", - "2018 NaN NaN \n", - "\n", - " LandAverageTemperature LandAndOceanAverageTemperature \\\n", - "Year \n", - "2014 9.570667 15.913000 \n", - "2015 9.831000 16.058583 \n", - "2016 NaN NaN \n", - "2017 NaN NaN \n", - "2018 NaN NaN \n", - "\n", - " Oil (megawatt-hours) Coal (megawatt-hours) \\\n", - "Year \n", - "2014 6.780018 6.197751 \n", - "2015 6.838042 5.961733 \n", - "2016 NaN NaN \n", - "2017 NaN NaN \n", - "2018 NaN NaN \n", - "\n", - " Natural gas (megawatt-hours) ... Other renewables (terawatt-hours)_y \\\n", - "Year ... \n", - "2014 4.896768 ... 502.158919 \n", - "2015 4.956876 ... 536.466244 \n", - "2016 NaN ... 556.623356 \n", - "2017 NaN ... 584.975827 \n", - "2018 NaN ... 625.805362 \n", - "\n", - " Coal (%) Crude oil (%) Hydropower (%) Natural gas (%) Nuclear (%) \\\n", - "Year \n", - "2014 30.129928 34.284243 2.607999 22.798160 1.704505 \n", - "2015 29.232112 34.750780 2.597906 23.193692 1.719514 \n", - "2016 28.479418 35.021326 2.666862 23.616645 1.726446 \n", - "2017 28.254141 34.995960 2.643218 23.896486 1.715909 \n", - "2018 NaN NaN NaN NaN NaN \n", - "\n", - " Other renewables (%) Solar (%) Traditional biofuels (%) Wind (%) \n", - "Year \n", - "2014 0.338342 0.132597 7.526348 0.477878 \n", - "2015 0.359307 0.173580 7.417781 0.555327 \n", - "2016 0.368032 0.216849 7.270446 0.633975 \n", - "2017 0.381633 0.288171 7.093508 0.730975 \n", - "2018 NaN NaN NaN NaN \n", - "\n", - "[5 rows x 32 columns]" - ] - }, - "execution_count": 374, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_indicators.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 375, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 268 entries, 1751 to 2018\n", - "Data columns (total 32 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Annual CO₂ emissions (tonnes ) 267 non-null float64\n", - " 1 GHG(tonnes CO₂e ) 27 non-null float64\n", - " 2 Median (℃) 169 non-null float64\n", - " 3 Ozone-depleting emissions 54 non-null float64\n", - " 4 Mean ozone hole area (square kilometres) 38 non-null float64\n", - " 5 LandAverageTemperature 166 non-null float64\n", - " 6 LandAndOceanAverageTemperature 166 non-null float64\n", - " 7 Oil (megawatt-hours) 51 non-null float64\n", - " 8 Coal (megawatt-hours) 51 non-null float64\n", - " 9 Natural gas (megawatt-hours) 51 non-null float64\n", - " 10 Coal (terawatt-hours) 71 non-null float64\n", - " 11 Solar (terawatt-hours)_x 71 non-null float64\n", - " 12 Crude oil (terawatt-hours) 71 non-null float64\n", - " 13 Natural gas (terawatt-hours) 71 non-null float64\n", - " 14 Traditional biofuels (terawatt-hours) 71 non-null float64\n", - " 15 Other renewables (terawatt-hours)_x 71 non-null float64\n", - " 16 Hydropower (terawatt-hours)_x 71 non-null float64\n", - " 17 Nuclear (terawatt-hours) 71 non-null float64\n", - " 18 Wind (terawatt-hours)_x 71 non-null float64\n", - " 19 Hydropower (terawatt-hours)_y 54 non-null float64\n", - " 20 Solar (terawatt-hours)_y 54 non-null float64\n", - " 21 Wind (terawatt-hours)_y 54 non-null float64\n", - " 22 Other renewables (terawatt-hours)_y 54 non-null float64\n", - " 23 Coal (%) 70 non-null float64\n", - " 24 Crude oil (%) 70 non-null float64\n", - " 25 Hydropower (%) 70 non-null float64\n", - " 26 Natural gas (%) 70 non-null float64\n", - " 27 Nuclear (%) 70 non-null float64\n", - " 28 Other renewables (%) 70 non-null float64\n", - " 29 Solar (%) 70 non-null float64\n", - " 30 Traditional biofuels (%) 70 non-null float64\n", - " 31 Wind (%) 70 non-null float64\n", - "dtypes: float64(32)\n", - "memory usage: 69.1 KB\n" - ] - } - ], - "source": [ - "global_indicators.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 376, - "metadata": {}, - "outputs": [], - "source": [ - "global_indicators.to_csv('global_indicators')" - ] - }, - { - "cell_type": "code", - "execution_count": 444, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.890288126134981" - ] - }, - "execution_count": 444, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co_a_corr = global_indicators[\"Median (℃)\"].corr(global_indicators[\"Annual CO₂ emissions (tonnes )\"])\n", - "co_a_corr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Inner merges" - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearAnnual CO₂ emissions (tonnes )Median (℃)LandAverageTemperatureLandAndOceanAverageTemperatureCoal (terawatt-hours)Solar (terawatt-hours)Crude oil (terawatt-hours)Natural gas (terawatt-hours)Traditional biofuels (terawatt-hours)Other renewables (terawatt-hours)Hydropower (terawatt-hours)Nuclear (terawatt-hours)Wind (terawatt-hours)
018501.968960e+08-0.3737.90066714.867167569.444900.00.000000.000007222.2222220.00.0000000.00.0
118603.307090e+08-0.3437.96083314.8829171061.111960.00.000000.000006944.4444440.00.0000000.00.0
218705.325368e+08-0.2768.20133315.0043331641.667980.05.555560.000006944.4444440.00.0000000.00.0
318808.538915e+08-0.2278.11875015.0086672541.668700.033.333360.000006944.4444440.00.0000000.00.0
418901.298855e+09-0.4167.97166714.8407503855.558640.088.8889633.333366666.6666670.013.8888890.00.0
\n", - "
" - ], - "text/plain": [ - " Year Annual CO₂ emissions (tonnes ) Median (℃) LandAverageTemperature \\\n", - "0 1850 1.968960e+08 -0.373 7.900667 \n", - "1 1860 3.307090e+08 -0.343 7.960833 \n", - "2 1870 5.325368e+08 -0.276 8.201333 \n", - "3 1880 8.538915e+08 -0.227 8.118750 \n", - "4 1890 1.298855e+09 -0.416 7.971667 \n", - "\n", - " LandAndOceanAverageTemperature Coal (terawatt-hours) \\\n", - "0 14.867167 569.44490 \n", - "1 14.882917 1061.11196 \n", - "2 15.004333 1641.66798 \n", - "3 15.008667 2541.66870 \n", - "4 14.840750 3855.55864 \n", - "\n", - " Solar (terawatt-hours) Crude oil (terawatt-hours) \\\n", - "0 0.0 0.00000 \n", - "1 0.0 0.00000 \n", - "2 0.0 5.55556 \n", - "3 0.0 33.33336 \n", - "4 0.0 88.88896 \n", - "\n", - " Natural gas (terawatt-hours) Traditional biofuels (terawatt-hours) \\\n", - "0 0.00000 7222.222222 \n", - "1 0.00000 6944.444444 \n", - "2 0.00000 6944.444444 \n", - "3 0.00000 6944.444444 \n", - "4 33.33336 6666.666667 \n", - "\n", - " Other renewables (terawatt-hours) Hydropower (terawatt-hours) \\\n", - "0 0.0 0.000000 \n", - "1 0.0 0.000000 \n", - "2 0.0 0.000000 \n", - "3 0.0 0.000000 \n", - "4 0.0 13.888889 \n", - "\n", - " Nuclear (terawatt-hours) Wind (terawatt-hours) \n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 " - ] - }, - "execution_count": 224, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emissions_temp_merged = pd.merge(emissions_temp_merged,global_energy, on='Year')\n", - "emissions_temp_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 225, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearAnnual CO₂ emissions (tonnes )Median (℃)LandAverageTemperatureLandAndOceanAverageTemperatureCoal (terawatt-hours)Solar (terawatt-hours)Crude oil (terawatt-hours)Natural gas (terawatt-hours)Traditional biofuels (terawatt-hours)...Wind (terawatt-hours)Coal (%)Crude oil (%)Hydropower (%)Natural gas (%)Nuclear (%)Other renewables (%)Solar (%)Traditional biofuels (%)Wind (%)
018501.968960e+08-0.3737.90066714.867167569.444900.00.000000.000007222.222222...0.07.3083830.0000000.000000.0000000.00.00.092.6916170.0
118603.307090e+08-0.3437.96083314.8829171061.111960.00.000000.000006944.444444...0.013.2546930.0000000.000000.0000000.00.00.086.7453070.0
218705.325368e+08-0.2768.20133315.0043331641.667980.05.555560.000006944.444444...0.019.1076750.0646620.000000.0000000.00.00.080.8276630.0
318808.538915e+08-0.2278.11875015.0086672541.668700.033.333360.000006944.444444...0.026.6997530.3501610.000000.0000000.00.00.072.9500860.0
418901.298855e+09-0.4167.97166714.8407503855.558640.088.8889633.333366666.666667...0.036.1741120.8339850.130310.3127440.00.00.062.5488480.0
\n", - "

5 rows × 23 columns

\n", - "
" - ], - "text/plain": [ - " Year Annual CO₂ emissions (tonnes ) Median (℃) LandAverageTemperature \\\n", - "0 1850 1.968960e+08 -0.373 7.900667 \n", - "1 1860 3.307090e+08 -0.343 7.960833 \n", - "2 1870 5.325368e+08 -0.276 8.201333 \n", - "3 1880 8.538915e+08 -0.227 8.118750 \n", - "4 1890 1.298855e+09 -0.416 7.971667 \n", - "\n", - " LandAndOceanAverageTemperature Coal (terawatt-hours) \\\n", - "0 14.867167 569.44490 \n", - "1 14.882917 1061.11196 \n", - "2 15.004333 1641.66798 \n", - "3 15.008667 2541.66870 \n", - "4 14.840750 3855.55864 \n", - "\n", - " Solar (terawatt-hours) Crude oil (terawatt-hours) \\\n", - "0 0.0 0.00000 \n", - "1 0.0 0.00000 \n", - "2 0.0 5.55556 \n", - "3 0.0 33.33336 \n", - "4 0.0 88.88896 \n", - "\n", - " Natural gas (terawatt-hours) Traditional biofuels (terawatt-hours) ... \\\n", - "0 0.00000 7222.222222 ... \n", - "1 0.00000 6944.444444 ... \n", - "2 0.00000 6944.444444 ... \n", - "3 0.00000 6944.444444 ... \n", - "4 33.33336 6666.666667 ... \n", - "\n", - " Wind (terawatt-hours) Coal (%) Crude oil (%) Hydropower (%) \\\n", - "0 0.0 7.308383 0.000000 0.00000 \n", - "1 0.0 13.254693 0.000000 0.00000 \n", - "2 0.0 19.107675 0.064662 0.00000 \n", - "3 0.0 26.699753 0.350161 0.00000 \n", - "4 0.0 36.174112 0.833985 0.13031 \n", - "\n", - " Natural gas (%) Nuclear (%) Other renewables (%) Solar (%) \\\n", - "0 0.000000 0.0 0.0 0.0 \n", - "1 0.000000 0.0 0.0 0.0 \n", - "2 0.000000 0.0 0.0 0.0 \n", - "3 0.000000 0.0 0.0 0.0 \n", - "4 0.312744 0.0 0.0 0.0 \n", - "\n", - " Traditional biofuels (%) Wind (%) \n", - "0 92.691617 0.0 \n", - "1 86.745307 0.0 \n", - "2 80.827663 0.0 \n", - "3 72.950086 0.0 \n", - "4 62.548848 0.0 \n", - "\n", - "[5 rows x 23 columns]" - ] - }, - "execution_count": 225, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emissions_temp_merged = pd.merge(emissions_temp_merged,share_energy, on='Year')\n", - "emissions_temp_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 226, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 63 entries, 0 to 62\n", - "Data columns (total 23 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Year 63 non-null int64 \n", - " 1 Annual CO₂ emissions (tonnes ) 63 non-null float64\n", - " 2 Median (℃) 63 non-null float64\n", - " 3 LandAverageTemperature 63 non-null float64\n", - " 4 LandAndOceanAverageTemperature 63 non-null float64\n", - " 5 Coal (terawatt-hours) 63 non-null float64\n", - " 6 Solar (terawatt-hours) 63 non-null float64\n", - " 7 Crude oil (terawatt-hours) 63 non-null float64\n", - " 8 Natural gas (terawatt-hours) 63 non-null float64\n", - " 9 Traditional biofuels (terawatt-hours) 63 non-null float64\n", - " 10 Other renewables (terawatt-hours) 63 non-null float64\n", - " 11 Hydropower (terawatt-hours) 63 non-null float64\n", - " 12 Nuclear (terawatt-hours) 63 non-null float64\n", - " 13 Wind (terawatt-hours) 63 non-null float64\n", - " 14 Coal (%) 63 non-null float64\n", - " 15 Crude oil (%) 63 non-null float64\n", - " 16 Hydropower (%) 63 non-null float64\n", - " 17 Natural gas (%) 63 non-null float64\n", - " 18 Nuclear (%) 63 non-null float64\n", - " 19 Other renewables (%) 63 non-null float64\n", - " 20 Solar (%) 63 non-null float64\n", - " 21 Traditional biofuels (%) 63 non-null float64\n", - " 22 Wind (%) 63 non-null float64\n", - "dtypes: float64(22), int64(1)\n", - "memory usage: 11.8 KB\n" - ] - } - ], - "source": [ - "emissions_temp_merged.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 227, - "metadata": {}, - "outputs": [], - "source": [ - "emissions_temp_merged[\"Year\"] = emissions_temp_merged[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "metadata": {}, - "outputs": [], - "source": [ - "emissions_temp_merged.to_csv('emissions_temp_merged')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_merged = pd.merge(co_annual, co_share, on=['Country','Year'])\n", - "co_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_merged.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### total emissions (1990-2016)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ghg.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "total_emissions = pd.merge(co_merged,ghg, on=(['Country','Year']))\n", - "total_emissions.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "total_emissions.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging Temperature with Emissions (1990-2013)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "annual_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "annual_temp.rename(columns={'year':'Year'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_impact = pd.merge(total_emissions, annual_temp, on=(['Country','Year']))\n", - "co_impact.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging co_impact with demographic and economic indicators" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged = pd.merge(co_impact,gdp, on=(['Country','Year']))\n", - "indicators_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged_outer = pd.merge(co_impact,gdp, on=(['Country','Year']), how='outer')\n", - "indicators_merged_outer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged_outer.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging ozone datasets 1979-2014" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ozone_merged = pd.merge(caused_ozone, oz_hole, on='Year')\n", - "ozone_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "oz_hole_emiss_corr = ozone_merged[\"Ozone-depleting emissions\"].corr(ozone_merged[\"Mean ozone hole area (square kilometres)\"])\n", - "oz_hole_emiss_corr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ozone_merged[\"Year\"] = ozone_merged[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ozone_merged.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ozone_merged = ozone_merged.set_index('Year')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sns.pairplot(ozone_merged)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging global co concentration, temp anomally and annual_global_temp" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "annual_global_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "annual_global_temp.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration_modern = co_concentration[1740:]\n", - "co_concentration_modern.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration_modern.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration_modern[\"Year\"] = co_concentration_modern[\"Year\"].astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_concentration_modern.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_global_impact = pd.merge(annual_global_temp,co_concentration_modern, on='Year')\n", - "co_global_impact.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_global_impact.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_impact_temp = pd.merge(co_global_impact,global_a, on='Year')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_impact_temp.drop(['Country'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_impact_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "global_co.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_temp_global = pd.merge(co_impact_temp,global_co, on='Year')\n", - "co_temp_global.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(17,6))\n", - "plt.plot(co_global_impact.Year,co_global_impact['LandAverageTemperature'], label='LandAverageTemperature')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Tonnes CFC11-equivalents')\n", - "plt.plot(co_global_impact.Year,co_global_impact['LandAndOceanAverageTemperature'], label='LandAndOceanAverageTemperature')\n", - "plt.legend(['LandAverageTemperature', 'LandAndOceanAverageTemperature'], loc='upper right')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_conc_temp_corr = co_global_impact[\"LandAverageTemperature\"].corr(co_global_impact[\"CO₂ concentration\"])\n", - "co_conc_temp_corr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_temp_corr = co_temp_global[\"LandAverageTemperature\"].corr(co_temp_global[\"Annual CO₂ emissions (tonnes )\"])\n", - "co_temp_corr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_global_impact[\"Year\"] = co_global_impact[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "corr = co_temp_global.corr()\n", - "plt.figure(figsize=(12, 6))\n", - "sns.set(style=\"white\")\n", - "\n", - "mask = np.zeros_like(corr, dtype=np.bool)\n", - "mask[np.triu_indices_from(mask)] = True\n", - "cmap = sns.diverging_palette(200, 10, as_cmap=True)\n", - "\n", - "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_temp_global.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_temp_global_no_colin = co_temp_global.drop(['LandAndOceanAverageTemperature','Upper (℃)','Lower (℃)'], axis=1)\n", - "co_temp_global_no_colin.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "co_temp_global_no_colin[\"Year\"] = co_temp_global_no_colin[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sns.pairplot(co_temp_global_no_colin)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.scatter(x=co_temp_global['CO₂ concentration'], y=co_temp_global['Median (℃)'])\n", - "plt.xlabel('CO₂ concentration')\n", - "plt.ylabel('Median (℃)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.scatter(x=co_temp_global['Annual CO₂ emissions (tonnes )'], y=co_temp_global['Median (℃)'])\n", - "plt.xlabel('Annual CO₂ emissions (tonnes )')\n", - "plt.ylabel('Median (℃)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Checking Correlations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged[\"Year\"] = indicators_merged[\"Year\"].astype(str)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged.corr()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ind_no_colin = indicators_merged.drop(['Share of global CO₂ emissions (%)','GDP','Annual CO₂'], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indicators_merged.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "temp_ghg_corr = indicators_merged[\"GDP_per_capita\"].corr(indicators_merged[\"GHG(tonnes CO₂e )\"])\n", - "temp_ghg_corr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# here we are using seaborn to show the heatmap of the correlation between our final datasets' parameters\n", - "corr = ind_no_colin.corr()\n", - "plt.figure(figsize=(12, 6))\n", - "sns.set(style=\"white\")\n", - "\n", - "mask = np.zeros_like(corr, dtype=np.bool)\n", - "mask[np.triu_indices_from(mask)] = True\n", - "cmap = sns.diverging_palette(200, 10, as_cmap=True)\n", - "\n", - "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 811883ed11d96d56e1ff1be9fc241b6f76305e0d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Wed, 27 May 2020 23:12:51 +0200 Subject: [PATCH 14/17] pushing datasets and images used --- .../10modern-renewable-energy-consumption.csv | 5092 + ...al-oil-production-in-the-united-states.csv | 122 + ...lobal-primary-energy-share-inc-biomass.csv | 71 + ...solar-pv-prices-vs-cumulative-capacity.csv | 20123 + .../14annual_co2_emissions_per_country.csv | 20854 + .../data/15annual_share_of_co2_emissions.csv | 20587 + .../17GlobalLandTemperaturesByCountry.csv | 577463 +++++++++++++++ your-project/data/18global_indicators.csv | 269 + your-project/data/19country_indicators.csv | 7132 + .../data/1annual-co-emissions-by-region.csv | 20854 + your-project/data/2total-ghg-emissions.csv | 5185 + .../data/3global-co-concentration-ppm.csv | 1872 + your-project/data/4temperature_anomaly.csv | 677 + .../5ozone-depleting-substance-emissions.csv | 109 + .../data/6antarctic-ozone-hole-area.csv | 39 + your-project/data/7GlobalTemperatures.csv | 3193 + .../8fossil-fuel-consumption-per-capita.csv | 3354 + your-project/data/9global-primary-energy.csv | 72 + your-project/images/2020-05-23 (3).png | Bin 0 -> 516383 bytes your-project/images/2020-05-24 (33).png | Bin 0 -> 330754 bytes your-project/images/2020-05-24 (34).png | Bin 0 -> 425980 bytes your-project/images/CFC_emissions.png | Bin 0 -> 31988 bytes your-project/images/Inkedfuture_worlds_LI.jpg | Bin 0 -> 1046006 bytes your-project/images/future_worlds.png | Bin 0 -> 2443829 bytes your-project/images/hubbert.png | Bin 0 -> 42481 bytes your-project/images/ozone_hole.png | Bin 0 -> 39929 bytes your-project/images/projection ghg.png | Bin 0 -> 205793 bytes your-project/images/trump_blown.jpg | Bin 0 -> 93867 bytes your-project/images/trump_tweet.png | Bin 0 -> 54316 bytes 29 files changed, 687068 insertions(+) create mode 100644 your-project/data/10modern-renewable-energy-consumption.csv create mode 100644 your-project/data/11hubberts-peak-vs-actual-oil-production-in-the-united-states.csv create mode 100644 your-project/data/12global-primary-energy-share-inc-biomass.csv create mode 100644 your-project/data/13solar-pv-prices-vs-cumulative-capacity.csv create mode 100644 your-project/data/14annual_co2_emissions_per_country.csv create mode 100644 your-project/data/15annual_share_of_co2_emissions.csv create mode 100644 your-project/data/17GlobalLandTemperaturesByCountry.csv create mode 100644 your-project/data/18global_indicators.csv create mode 100644 your-project/data/19country_indicators.csv create mode 100644 your-project/data/1annual-co-emissions-by-region.csv create mode 100644 your-project/data/2total-ghg-emissions.csv create mode 100644 your-project/data/3global-co-concentration-ppm.csv create mode 100644 your-project/data/4temperature_anomaly.csv create mode 100644 your-project/data/5ozone-depleting-substance-emissions.csv create mode 100644 your-project/data/6antarctic-ozone-hole-area.csv create mode 100644 your-project/data/7GlobalTemperatures.csv create mode 100644 your-project/data/8fossil-fuel-consumption-per-capita.csv create mode 100644 your-project/data/9global-primary-energy.csv create mode 100644 your-project/images/2020-05-23 (3).png create mode 100644 your-project/images/2020-05-24 (33).png create mode 100644 your-project/images/2020-05-24 (34).png create mode 100644 your-project/images/CFC_emissions.png create mode 100644 your-project/images/Inkedfuture_worlds_LI.jpg create mode 100644 your-project/images/future_worlds.png create mode 100644 your-project/images/hubbert.png create mode 100644 your-project/images/ozone_hole.png create mode 100644 your-project/images/projection ghg.png create mode 100644 your-project/images/trump_blown.jpg create mode 100644 your-project/images/trump_tweet.png diff --git a/your-project/data/10modern-renewable-energy-consumption.csv b/your-project/data/10modern-renewable-energy-consumption.csv new file mode 100644 index 00000000..4fafd190 --- /dev/null +++ b/your-project/data/10modern-renewable-energy-consumption.csv @@ -0,0 +1,5092 @@ +Entity,Code,Year,Hydropower (terawatt-hours),Solar (terawatt-hours),Wind (terawatt-hours),Other renewables (terawatt-hours) +Africa,,1965,14.278805574733,0,0,0 +Africa,,1966,15.649048937511,0,0,0 +Africa,,1967,16.158332959104,0,0,0 +Africa,,1968,18.622982764834,0,0,0 +Africa,,1969,21.582896820817,0,0,0 +Africa,,1970,27.076287015552,0,0,0 +Africa,,1971,25.836605304094,0,0,0.164 +Africa,,1972,29.836878415376,0,0,0.165 +Africa,,1973,29.803741951533,0,0,0.17 +Africa,,1974,35.089185489357,0,0,0.175 +Africa,,1975,36.879048371158,0,0,0.172 +Africa,,1976,40.861179239816,0,0,0.185 +Africa,,1977,45.083351122531,0,0,0.189 +Africa,,1978,46.085079340575,0,0,0.195 +Africa,,1979,47.28020678388,0,0,0.201 +Africa,,1980,46.0301705558753,0,0,0.207 +Africa,,1981,48.3774464618705,0,0,0.218 +Africa,,1982,48.6327147347722,0,0,0.236 +Africa,,1983,45.5089146618705,0,0,0.23 +Africa,,1984,45.1259603194245,0,0,0.234 +Africa,,1985,49.4467506519585,0,0,0.269 +Africa,,1986,51.1419867013589,0,0,0.659 +Africa,,1987,49.0583647084245,0,0,0.658 +Africa,,1988,53.5950697571399,0,0,0.626 +Africa,,1989,56.2775699671271,0,0,0.617 +Africa,,1990,57.254659123238,0,0,0.732 +Africa,,1991,60.651488412762,0,0,0.738 +Africa,,1992,57.8602916846068,0,0,0.762250000088655 +Africa,,1993,56.6825072679302,0,0.002,0.839700000043806 +Africa,,1994,57.5960485671106,0,0.0054,0.72900000004172 +Africa,,1995,59.7688768746669,0,0.0063,0.912700000022946 +Africa,,1996,62.316458674204,0,0.0071,1.12009308793189 +Africa,,1997,63.8524975622244,0,0.0072,1.22864040821181 +Africa,,1998,66.6548878792363,0,0.0076,1.53054928559653 +Africa,,1999,70.6842613226394,0,0.0198,1.48812479896766 +Africa,,2000,75.2458166108861,0.0184,0.176,2.22442343908982 +Africa,,2001,80.8641146713105,0.0247,0.41608,2.29135263157895 +Africa,,2002,85.1814681338189,0.0308010101010101,0.444564,2.2404 +Africa,,2003,82.8726014711286,0.0339010101010101,0.541187,2.5774 +Africa,,2004,87.40546921543,0.0391010101010101,0.769317505050505,2.93901390728477 +Africa,,2005,89.065734167962,0.0442010101010101,0.782410111111111,2.96888293460925 +Africa,,2006,92.24055302449,0.0519010101010101,0.852500606060606,3.73185862745098 +Africa,,2007,95.34056187249,0.0636252931010101,1.01629736363636,3.10485 +Africa,,2008,97.1572092505416,0.0849172021010101,1.31218413131313,3.232422 +Africa,,2009,99.7611368819747,0.10786992710101,1.58988193939394,3.52372219 +Africa,,2010,107.426832620947,0.22386475010101,2.28414643434343,3.788542064 +Africa,,2011,110.452821026865,0.46053030110101,2.53747593939394,3.891561611 +Africa,,2012,110.95230022523,0.553427126280338,2.96169624040404,4.050949862 +Africa,,2013,117.672846409979,0.82428314110101,3.5597563020202,4.38662584849727 +Africa,,2014,123.757794762619,1.82625588410101,5.15716761867677,5.50354968074189 +Africa,,2015,120.270851995,3.568277276,8.88708223954545,7.06791974251621 +Africa,,2016,116.68881497412,4.91132132668317,11.3047001777414,7.17597491283842 +Africa,,2017,124.578162878692,6.61057145845309,12.7959088357325,7.49182538832269 +Africa,,2018,132.840769336531,9.02908899919936,14.6859395177227,8.1610228862498 +Algeria,DZA,1965,0.3933266,0,0,0 +Algeria,DZA,1966,0.3623908,0,0,0 +Algeria,DZA,1967,0.4065848,0,0,0 +Algeria,DZA,1968,0.5568444,0,0,0 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Pacific,,1967,154.022797489096,0,0,1.424 +Asia Pacific,,1968,164.446862089096,0,0,1.577 +Asia Pacific,,1969,175.383321289096,0,0,1.608 +Asia Pacific,,1970,179.118672089096,0,0,1.776704 +Asia Pacific,,1971,194.691911889096,0,0,1.754611 +Asia Pacific,,1972,203.609022489096,0,0,1.785591 +Asia Pacific,,1973,197.179032289096,0,0,1.834998 +Asia Pacific,,1974,215.163364177984,0,0,2.49626910526317 +Asia Pacific,,1975,229.481713056586,0,0,2.65433436842106 +Asia Pacific,,1976,232.71207419194,0,0,2.58706457894738 +Asia Pacific,,1977,225.823647313152,0,0,2.62244347368422 +Asia Pacific,,1978,235.116742452546,0,0,2.91747136842107 +Asia Pacific,,1979,255.172753743461,0,0,3.71292042105264 +Asia Pacific,,1980,271.622590605311,0,0,5.17343315789475 +Asia Pacific,,1981,288.896901745949,0,0,7.07196542105264 +Asia Pacific,,1982,279.799084984179,0,0,15.9518942631579 +Asia Pacific,,1983,300.647127486434,0,0,18.0678064210526 +Asia Pacific,,1984,297.164135342223,0,0,19.7421374210526 +Asia Pacific,,1985,320.013024228781,0,0,20.7914868152094 +Asia Pacific,,1986,328.125890448795,0,0,21.6898897111064 +Asia Pacific,,1987,326.845727441079,0,0,22.0097497368421 +Asia Pacific,,1988,361.755022214787,0,0,23.8267043684211 +Asia Pacific,,1989,387.941551172478,0,0,25.9293627894737 +Asia Pacific,,1990,401.67060703388,0.004,0.0323232323232319,21.9477381784416 +Asia Pacific,,1991,413.566225731453,0.00945,0.122000909090909,22.402523742374 +Asia Pacific,,1992,403.959635449544,0.0276,0.220474040404039,22.5691151550498 +Asia Pacific,,1993,443.311060952425,0.0431,0.311836666666664,22.5184310758654 +Asia Pacific,,1994,446.114536140525,0.0534,0.583588080808076,25.9677874042327 +Asia Pacific,,1995,486.727774786274,0.070660101010101,1.12160131313131,30.3678505144696 +Asia Pacific,,1996,476.744037951501,0.098160606060606,0.991205066484847,30.8433230755399 +Asia Pacific,,1997,479.226719968151,0.147015451951017,1.21395004149495,34.877811142193 +Asia Pacific,,1998,510.29949280204,0.202918856060606,1.48955016659596,37.5350822266725 +Asia Pacific,,1999,502.866957093112,0.286266606060606,2.05324900639393,41.2156530259867 +Asia Pacific,,2000,524.433428701542,0.428429426248789,2.56397697970706,44.068178788671 +Asia Pacific,,2001,573.088154956524,0.608465426289193,3.51743241797979,44.6399478654384 +Asia Pacific,,2002,580.682280013488,0.829254425131313,4.06703623152968,46.5968834820202 +Asia Pacific,,2003,595.313399650878,1.11654260816162,5.34084763795393,48.7379275307541 +Asia Pacific,,2004,683.719661545877,1.46405501346949,9.18665430653978,52.3251176558058 +Asia Pacific,,2005,727.418805559214,1.85281352746949,12.0191786582367,60.4524994170618 +Asia Pacific,,2006,800.649044885362,2.29127578,18.5697520267721,64.751267922208 +Asia Pacific,,2007,852.30221559978,2.7485809757153,24.6031247820448,71.33836429985 +Asia Pacific,,2008,996.676667319962,3.33060255465225,36.0894879310322,79.1869605354272 +Asia Pacific,,2009,965.844415387686,4.38349798767104,54.8425886174204,88.3478739476447 +Asia Pacific,,2010,1094.87910762506,6.73874849678111,76.7212708975274,98.0538860449395 +Asia Pacific,,2011,1115.87312618593,12.1543992395848,109.802787179035,104.820493060851 +Asia Pacific,,2012,1279.09174674525,17.5830281029571,140.728531900333,114.31239261731 +Asia Pacific,,2013,1365.03665034598,32.3389070851243,191.519400161439,126.101182001051 +Asia Pacific,,2014,1510.90077779239,62.8747490847576,210.682543378679,144.272212574627 +Asia Pacific,,2015,1565.08835586467,99.1937257806647,243.162155359375,161.548391185001 +Asia Pacific,,2016,1626.95073071228,141.75522940233,307.2584778413,172.628595157678 +Asia Pacific,,2017,1649.44719434668,227.196637891226,376.740026528658,192.645559814682 +Asia Pacific,,2018,1718.50832483692,314.20855289155,460.469456010208,221.301517275185 +Australia,AUS,1965,7.689756,0,0,0.296 +Australia,AUS,1966,7.3450428,0,0,0.284 +Australia,AUS,1967,7.6057874,0,0,0.29 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America,,1975,2.4885656,0,0,0.338 +Central America,,1976,2.6564626,0,0,0.724 +Central America,,1977,2.9382402,0,0,0.699 +Central America,,1978,3.9608372,0,0,0.752 +Central America,,1979,4.6588148,0,0,0.642 +Central America,,1980,5.2532118,0,0,0.682105263157895 +Central America,,1981,5.4819118,0,0,1.1091052631579 +Central America,,1982,5.3605924,0,0,1.1981052631579 +Central America,,1983,6.16467,0,0,1.0261052631579 +Central America,,1984,6.9864058,0,0,1.2371052631579 +Central America,,1985,7.97932,0,0,0.93699 +Central America,,1986,9.32351,0,0,0.86075 +Central America,,1987,9.683975,0,0,0.910516 +Central America,,1988,10.314301,0,0,0.883962 +Central America,,1989,11.134195,0,0,1.171867 +Central America,,1990,11.642524,0,0,1.22075563157895 +Central America,,1991,11.327269,0,0,1.33506391578948 +Central America,,1992,10.786794,0,0,1.82165317368422 +Central America,,1993,12.02355,0,0,1.72201002105263 +Central America,,1994,11.615682,0,0,1.69305722105263 +Central America,,1995,11.462188,0,0,1.75083889473684 +Central America,,1996,13.225811715,0,0.022590363,1.73942529548791 +Central America,,1997,12.765683653,0,0.07571247,1.8508323641579 +Central America,,1998,12.849074204,0,0.06420843131,1.61432092968421 +Central America,,1999,14.995346087,0,0.09965608567,2.41370143136842 +Central America,,2000,15.365460538,0,0.1749790818,2.80561786169474 +Central America,,2001,13.711556255,0.0001,0.1794242027,3.0532751956 +Central America,,2002,14.461674842,0.0001,0.2543143672,3.32633306673684 +Central America,,2003,15.091870756,0.0002,0.2302238435,3.546236776 +Central America,,2004,16.03553485,0.0002,0.2554340283,3.720174545 +Central America,,2005,17.1180505655556,0.000677777777777778,0.2023131143,3.99598306011111 +Central America,,2006,18.0028641033333,0.000677777777777778,0.2721626218,4.29515444444444 +Central America,,2007,17.9442239311111,0.000777777777777778,0.2403966951,4.75568846588889 +Central America,,2008,20.1023906733333,0.000977777777777777,0.1980772051,4.91883754966667 +Central America,,2009,18.8338778334444,0.00107777777777778,0.4213865622,5.29447689633333 +Central America,,2010,21.2351890522222,0.00117777777777778,0.5218727958,5.81644914422222 +Central America,,2011,20.9176944354444,0.00165555555555556,0.74175713177,5.88440416030889 +Central America,,2012,22.3139406587607,0.003896889376,1.12925645,6.60712428583833 +Central America,,2013,21.8301300988364,0.005043458756,1.34897902023,7.17959568308809 +Central America,,2014,21.4874589041046,0.015794872017,2.09395367073,7.6070034366976 +Central America,,2015,22.4256404936742,0.599812979866679,3.13533065138963,7.74769390608407 +Central America,,2016,22.8130361457894,1.18204195860885,3.29947169781621,8.52186082360971 +Central America,,2017,27.1658196743194,1.3962696732467,3.20957467794138,8.18839718209286 +Central America,,2018,26.9075739417851,1.88550331359999,4.43554990182949,8.864061226099 +Chile,CHL,1965,3.955363,0,0,0 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Republic,CZE,1987,2.4660252,0,0,0 +Czech Republic,CZE,1988,2.1036344,0,0,0 +Czech Republic,CZE,1989,1.6395974,0,0,0 +Czech Republic,CZE,1990,1.161,0,0,0 +Czech Republic,CZE,1991,1.089,0,0,0 +Czech Republic,CZE,1992,1.402,0,0,0 +Czech Republic,CZE,1993,1.369,0,0,0.221 +Czech Republic,CZE,1994,1.46,0,0,0.309 +Czech Republic,CZE,1995,2.002,0,0,0.405 +Czech Republic,CZE,1996,1.969,0,0,0.292 +Czech Republic,CZE,1997,1.699,0,0,0.494 +Czech Republic,CZE,1998,1.396,0,0,0.587 +Czech Republic,CZE,1999,1.681,0,0,0.683 +Czech Republic,CZE,2000,1.758,0,0,0.517 +Czech Republic,CZE,2001,2.054,0,0.0002,0.514 +Czech Republic,CZE,2002,2.492,0,0.0016,0.494 +Czech Republic,CZE,2003,1.383,0,0.0039,0.49 +Czech Republic,CZE,2004,2.0191,0.0001,0.0099,0.580831 +Czech Republic,CZE,2005,2.37991,0.000068,0.02128,0.648312 +Czech Republic,CZE,2006,2.55073,0.00017,0.049375,0.912375 +Czech Republic,CZE,2007,2.079338,0.001754,0.125098,1.187319 +Czech Republic,CZE,2008,2.024335,0.012937,0.244661,1.456526 +Czech Republic,CZE,2009,2.42962,0.088807,0.288067,1.86202 +Czech Republic,CZE,2010,2.7894,0.615702,0.335493,2.146246 +Czech Republic,CZE,2011,2.1341,2.118,0.3968,2.705329 +Czech Republic,CZE,2012,2.2315,2.1731,0.4173,3.361419 +Czech Republic,CZE,2013,2.8564,2.0702,0.4783,3.995469 +Czech Republic,CZE,2014,1.9092,2.1229,0.4765,4.661073 +Czech Republic,CZE,2015,1.7948,2.2638,0.5726,4.791685 +Czech Republic,CZE,2016,2.0005,2.131,0.497,4.76655 +Czech Republic,CZE,2017,1.8695,2.193368,0.591038,4.964567 +Czech Republic,CZE,2018,1.6272,2.3386,0.6093,4.7285 +Denmark,DNK,1965,0.022097,0,0,0 +Denmark,DNK,1966,0.022097,0,0,0 +Denmark,DNK,1967,0.022097,0,0,0 +Denmark,DNK,1968,0.022097,0,0,0 +Denmark,DNK,1969,0.022097,0,0,0 +Denmark,DNK,1970,0.022097,0,0,0 +Denmark,DNK,1971,0.022097,0,0,0 +Denmark,DNK,1972,0.022097,0,0,0 +Denmark,DNK,1973,0.022097,0,0,0 +Denmark,DNK,1974,0.022097,0,0,0 +Denmark,DNK,1975,0.021,0,0,0 +Denmark,DNK,1976,0.019,0,0,0 +Denmark,DNK,1977,0.026,0,0,0 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Africa,,1976,17.115110299246,0,0,0.146 +Eastern Africa,,1977,19.410797423891,0,0,0.149 +Eastern Africa,,1978,18.516830891895,0,0,0.155 +Eastern Africa,,1979,19.8973036,0,0,0.161 +Eastern Africa,,1980,18.2546333558753,0,0,0.167 +Eastern Africa,,1981,19.0310418618705,0,0,0.177 +Eastern Africa,,1982,19.6677253347722,0,0,0.195 +Eastern Africa,,1983,19.4449134618705,0,0,0.189 +Eastern Africa,,1984,18.9151803194245,0,0,0.192 +Eastern Africa,,1985,19.6916850519584,0,0,0.227 +Eastern Africa,,1986,18.8453733013589,0,0,0.617 +Eastern Africa,,1987,16.4269603084245,0,0,0.615 +Eastern Africa,,1988,18.6184563571399,0,0,0.583 +Eastern Africa,,1989,18.0262147671271,0,0,0.574 +Eastern Africa,,1990,21.3455682141471,0,0,0.685 +Eastern Africa,,1991,21.7425570996307,0,0,0.686 +Eastern Africa,,1992,19.6329896492222,0,0,0.677 +Eastern Africa,,1993,20.5325324824716,0,0,0.662 +Eastern Africa,,1994,20.6723213840232,0,0,0.546 +Eastern Africa,,1995,20.8618979157914,0,0,0.69 +Eastern Africa,,1996,20.7356768111942,0,0,0.834093087911035 +Eastern Africa,,1997,21.813017528,0,0,0.832093087911035 +Eastern Africa,,1998,27.86504585,0,0,1.04650196529575 +Eastern Africa,,1999,29.365874407,0,0,1.01218617582207 +Eastern Africa,,2000,30.854148745,0.0053,0.0001,1.50776315789474 +Eastern Africa,,2001,34.958432699,0.0087,0,1.60365263157895 +Eastern Africa,,2002,37.998413104,0.0119,0.0003,1.4967 +Eastern Africa,,2003,38.378885663,0.012,0.0004,1.9048 +Eastern Africa,,2004,38.951771183,0.0132,0.0009,2.1902 +Eastern Africa,,2005,40.263620978966,0.0143,0.0013,2.1613 +Eastern Africa,,2006,42.559585214,0.0182,0.0048,2.30119 +Eastern Africa,,2007,46.386645062,0.022824283,0.011,2.21755 +Eastern Africa,,2008,45.787681399,0.031416192,0.01607,2.331822 +Eastern Africa,,2009,48.020242975,0.045168917,0.0267,2.69072219 +Eastern Africa,,2010,53.97224481,0.112431865,0.03821,2.946542064 +Eastern Africa,,2011,55.613467824,0.194606791,0.03413,2.951661611 +Eastern Africa,,2012,58.061104493,0.267353864,0.1540242,3.101649862 +Eastern Africa,,2013,61.757797601,0.327192381,0.3147081,3.371167925 +Eastern Africa,,2014,65.9884917904,0.397598607,0.4413495,4.564418028 +Eastern Africa,,2015,65.266757995,0.447760352,0.7779207,6.176652605 +Eastern Africa,,2016,61.78509997412,0.49364847678,0.8900376,6.2065395183 +Eastern Africa,,2017,63.4159751944052,0.594366287906549,0.791781194824936,6.47787581765676 +Eastern Africa,,2018,67.0555287575366,0.69012472594067,0.863199466304525,7.0999104088702 +Ecuador,ECU,1965,0.3447132,0,0,0 +Ecuador,ECU,1966,0.3933266,0,0,0 +Ecuador,ECU,1967,0.4375206,0,0,0 +Ecuador,ECU,1968,0.4551982,0,0,0 +Ecuador,ECU,1969,0.508231,0,0,0 +Ecuador,ECU,1970,0.5568444,0,0,0 +Ecuador,ECU,1971,0.6098772,0,0,0 +Ecuador,ECU,1972,1.0739142,0,0,0 +Ecuador,ECU,1973,0.5789414,0,0,0 +Ecuador,ECU,1974,0.5789414,0,0,0 +Ecuador,ECU,1975,0.6054578,0,0,0 +Ecuador,ECU,1976,0.6319742,0,0,0 +Ecuador,ECU,1977,0.950171,0,0,0 +Ecuador,ECU,1978,1.082753,0,0,0 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(other),,1967,11.186913382,0,0,0 +Europe (other),,1968,12.345931448,0,0,0 +Europe (other),,1969,15.346839314,0,0,0 +Europe (other),,1970,15.39558798,0,0,0 +Europe (other),,1971,16.518398398,0,0,0 +Europe (other),,1972,19.058997416,0,0,0 +Europe (other),,1973,17.710943834,0,0,0 +Europe (other),,1974,22.151304252,0,0,0 +Europe (other),,1975,21.09676987,0,0,0 +Europe (other),,1976,22.633584488,0,0,0 +Europe (other),,1977,26.725812306,0,0,0 +Europe (other),,1978,27.772073524,0,0,0 +Europe (other),,1979,29.440050742,0,0,0 +Europe (other),,1980,31.29806216,0,0,0 +Europe (other),,1981,28.584606114,0,0,0 +Europe (other),,1982,26.843418068,0,0,0 +Europe (other),,1983,25.230392622,0,0,0 +Europe (other),,1984,29.380264776,0,0,0 +Europe (other),,1985,33.01269853,0,0,0 +Europe (other),,1986,37.545652884,0,0,0 +Europe (other),,1987,38.675288438,0,0,0 +Europe (other),,1988,38.687674792,0,0,0 +Europe (other),,1989,35.642399546,0,0,0 +Europe (other),,1990,23.2851958292929,0,0,0 +Europe (other),,1991,28.5061330852525,0,0,0 +Europe (other),,1992,24.7017143612121,0,0,0 +Europe (other),,1993,22.6137813852525,0,0.000427272727272722,0 +Europe (other),,1994,25.4887799937374,0,0.000611111111111104,0 +Europe (other),,1995,25.6016639983838,0,0.000575757575757571,0 +Europe (other),,1996,31.9275101076768,0,0.000585858585858577,0.0142855 +Europe (other),,1997,28.9564540189899,0,0.000555555555555549,0.012857 +Europe (other),,1998,29.2217555854545,0,0.000551515151515144,0.0142855 +Europe (other),,1999,31.4153478963636,0,0.00056767676767676,0.011802 +Europe (other),,2000,28.0087437989899,0,0.000528282828282821,0.011311 +Europe (other),,2001,27.1858290422222,0.0001,0.000509090909090904,0.0111205 +Europe (other),,2002,27.1556733274747,0.0003,0.000558585858585854,0.010448 +Europe (other),,2003,27.0006395852525,0.0004,0.00302323232323229,0.0110195 +Europe (other),,2004,31.2736985040404,0.0005,0.00758484848484838,0.045295 +Europe (other),,2005,31.9782163975758,0.0006,0.0114464646464645,0.054092 +Europe (other),,2006,29.8674393432476,0.0006,0.0161393939393938,0.0558085 +Europe (other),,2007,25.5110240707071,0.001,0.0169282828282826,0.058165 +Europe (other),,2008,27.4853724848485,0.0012,0.0141101010101009,0.0841045 +Europe (other),,2009,31.8741963939394,0.003110101010101,0.0161666666666665,0.0781644896030246 +Europe (other),,2010,40.4745215059392,0.00587925,0.015731313131313,0.0771539949494949 +Europe (other),,2011,26.8722813636364,0.015201,0.0156828282828281,0.080921 +Europe (other),,2012,27.5453840764646,0.0325272465753425,0.0119060606060604,0.090928288 +Europe (other),,2013,35.9596605609091,0.052813,0.0238757575757573,0.107249937 +Europe (other),,2014,32.5220165206061,0.10400625,0.0370264646464642,0.105182682 +Europe (other),,2015,32.2450569825252,0.2375925,0.0585746262626255,0.14049829 +Europe (other),,2016,35.635168394551,0.315703980822,0.0896171512384107,0.154252051219178 +Europe (other),,2017,28.7271561887895,0.3847307897235,0.293734581884417,0.197188829901639 +Europe (other),,2018,38.9320359067664,0.441810564595928,0.531134614898358,0.23331661969487 +Finland,FIN,1965,9.35353535353526,0,0,0 +Finland,FIN,1966,10.3808080808079,0,0,0 +Finland,FIN,1967,11.6292929292928,0,0,0 +Finland,FIN,1968,10.4888888888887,0,0,0 +Finland,FIN,1969,8.74545454545444,0,0,0 +Finland,FIN,1970,9.52424242424232,0,0,0 +Finland,FIN,1971,10.680808080808,0,0,0 +Finland,FIN,1972,10.3797979797978,0,0,0 +Finland,FIN,1973,10.5797979797978,0,0,0 +Finland,FIN,1974,12.7030303030302,0,0,0 +Finland,FIN,1975,12.2090909090908,0,0,0 +Finland,FIN,1976,9.48181818181806,0,0,0 +Finland,FIN,1977,12.181818181818,0,0,0 +Finland,FIN,1978,9.7989898989898,0,0,0 +Finland,FIN,1979,10.870707070707,0,0,0 +Finland,FIN,1980,10.2171717171716,0,0,0 +Finland,FIN,1981,13.6545454545453,0,0,0 +Finland,FIN,1982,13.0888888888887,0,0,0 +Finland,FIN,1983,13.5808080808079,0,0,0 +Finland,FIN,1984,13.2474747474746,0,0,0 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Africa,,1979,6.299,0,0,0 +Middle Africa,,1980,6.639,0,0,0 +Middle Africa,,1981,7.012,0,0,0 +Middle Africa,,1982,7.3,0,0,0 +Middle Africa,,1983,7.709,0,0,0 +Middle Africa,,1984,8.065,0,0,0 +Middle Africa,,1985,9.145,0,0,0 +Middle Africa,,1986,9.438,0,0,0 +Middle Africa,,1987,9.454,0,0,0 +Middle Africa,,1988,9.604,0,0,0 +Middle Africa,,1989,11.446,0,0,0 +Middle Africa,,1990,10.288,0,0,0.003 +Middle Africa,,1991,9.976,0,0,0.008 +Middle Africa,,1992,10.821,0,0,0.008 +Middle Africa,,1993,10.213,0,0,0.008 +Middle Africa,,1994,9.732,0,0,0.009 +Middle Africa,,1995,10.831,0,0,0.009 +Middle Africa,,1996,10.867,0,0,0.007 +Middle Africa,,1997,10.138,0,0,0.007 +Middle Africa,,1998,9.925,0,0,0.007 +Middle Africa,,1999,10.329,0,0,0.007 +Middle Africa,,2000,11.3598,0,0,0.007 +Middle Africa,,2001,11.6228,0,0,0.007 +Middle Africa,,2002,11.7011,0.0004,0,0.007 +Middle Africa,,2003,12.1149,0.0005,0,0.008 +Middle Africa,,2004,13.9772,0.0008,0,0.008 +Middle Africa,,2005,14.4562,0.0012,0,0.008 +Middle Africa,,2006,15.644,0.0016,0,0.651 +Middle Africa,,2007,15.122,0.0045,0,0.084 +Middle Africa,,2008,16.192,0.0078,0,0.086 +Middle Africa,,2009,16.12,0.0102,0,0.08 +Middle Africa,,2010,16.8208,0.0135,0,0.068 +Middle Africa,,2011,17.124,0.0166,0,0.071 +Middle Africa,,2012,16.9244,0.0199,0,0.074 +Middle Africa,,2013,19.3679,0.0234,0,0.078 +Middle Africa,,2014,20.4437,0.0284,0,0.083 +Middle Africa,,2015,20.6856,0.0327,0,0.014 +Middle Africa,,2016,21.5926,0.0372,0.0089,0.014 +Middle Africa,,2017,24.8529684889854,0.0444145144953655,0.00887568306010929,0.0139617486338798 +Middle Africa,,2018,28.3317038787561,0.0512828364638223,0.00887568306010929,0.0139617486338798 +Middle East,,1965,2.345857,0,0,0 +Middle East,,1966,2.5461166,0,0,0 +Middle East,,1967,2.8287956,0,0,0 +Middle East,,1968,3.0811522,0,0,0 +Middle East,,1969,3.3959282,0,0,0 +Middle East,,1970,3.57964,0,0,0 +Middle East,,1971,3.7537712,0,0,0 +Middle East,,1972,4.5680378,0,0,0 +Middle East,,1973,3.7520978,0,0,0 +Middle East,,1974,4.8225214,0,0,0 +Middle East,,1975,5.4434914,0,0,0 +Middle East,,1976,6.3300762,0,0,0 +Middle East,,1977,7.1023044,0,0,0 +Middle East,,1978,9.8246192,0,0,0 +Middle East,,1979,9.631227,0,0,0 +Middle East,,1980,9.6909674,0,0,0 +Middle East,,1981,10.1877134,0,0,0 +Middle East,,1982,10.5892326,0,0,0 +Middle East,,1983,10.2042312,0,0,0 +Middle East,,1984,10.3466464,0,0,0 +Middle East,,1985,10.438,0,0,0 +Middle East,,1986,11.076,0,0,0 +Middle East,,1987,13.918097,0,0,0 +Middle East,,1988,14.294097,0,0,0 +Middle East,,1989,12.6202582,0,0,0 +Middle East,,1990,15.2755222,0,0.001,0 +Middle East,,1991,11.0154422,0,0.001,0 +Middle East,,1992,17.8253242,0,0.001,0 +Middle East,,1993,20.046829,0,0.001,0 +Middle East,,1994,16.5490192,0,0.001,0 +Middle East,,1995,18.716804,0,0.001,0 +Middle East,,1996,18.697472,0,0.001,0 +Middle East,,1997,16.0883132,0,0.003,0 +Middle East,,1998,17.2088182,0,0.003,0 +Middle East,,1999,11.8003082,0,0.038,0 +Middle East,,2000,10.7283516,0,0.0395,0.003 +Middle East,,2001,11.6202,0,0.0467,0.005 +Middle East,,2002,16.8240728,0,0.0443,0.005 +Middle East,,2003,18.5816746,0,0.0426,0.006 +Middle East,,2004,23.1329558,0,0.0614,0.006 +Middle East,,2005,23.9192712,0,0.086,0.005 +Middle East,,2006,28.25476,0.0001,0.1394,0.006 +Middle East,,2007,26.70531,0.0001,0.155,0.01 +Middle East,,2008,13.690469,0.0035,0.2101,0.016 +Middle East,,2009,12.116322,0.0448,0.2405,0.0507 +Middle East,,2010,17.390324,0.0979385,0.2222,0.0802 +Middle East,,2011,18.32824,0.227483,0.2258,0.2178 +Middle East,,2012,21.407368,0.429933602739726,0.2166,0.2194 +Middle East,,2013,23.360935,0.766215148813953,0.2281,0.1832 +Middle East,,2014,19.943495,1.4992021527907,0.2158,0.233 +Middle East,,2015,16.767386,2.3880045596999,0.3818,0.2093 +Middle East,,2016,20.1705491232877,3.45573848925747,0.746328082146955,0.2607 +Middle East,,2017,20.7880662716628,4.40988930954985,0.873228209816983,0.261310382513661 +Middle East,,2018,15.1905000716628,6.1211535859565,1.06038595384135,0.261310382513661 +Morocco,MAR,1965,1.213,0,0,0 +Morocco,MAR,1966,1.054,0,0,0 +Morocco,MAR,1967,0.932,0,0,0 +Morocco,MAR,1968,1.058,0,0,0 +Morocco,MAR,1969,1.368,0,0,0 +Morocco,MAR,1970,1.346,0,0,0 +Morocco,MAR,1971,1.52,0,0,0 +Morocco,MAR,1972,1.596,0,0,0 +Morocco,MAR,1973,1.192,0,0,0 +Morocco,MAR,1974,1.337,0,0,0 +Morocco,MAR,1975,1.016,0,0,0 +Morocco,MAR,1976,0.998,0,0,0 +Morocco,MAR,1977,1.365,0,0,0 +Morocco,MAR,1978,1.416,0,0,0 +Morocco,MAR,1979,1.582,0,0,0 +Morocco,MAR,1980,1.515,0,0,0 +Morocco,MAR,1981,1.024,0,0,0 +Morocco,MAR,1982,0.572,0,0,0 +Morocco,MAR,1983,0.481,0,0,0 +Morocco,MAR,1984,0.366,0,0,0 +Morocco,MAR,1985,0.486,0,0,0 +Morocco,MAR,1986,0.643,0,0,0 +Morocco,MAR,1987,0.825,0,0,0 +Morocco,MAR,1988,0.936,0,0,0 +Morocco,MAR,1989,1.157,0,0,0 +Morocco,MAR,1990,1.22,0,0,0 +Morocco,MAR,1991,1.226,0,0,0 +Morocco,MAR,1992,0.964,0,0,0 +Morocco,MAR,1993,0.443,0,0,0 +Morocco,MAR,1994,0.84,0,0,0 +Morocco,MAR,1995,0.611,0,0,0 +Morocco,MAR,1996,1.957,0,0,0 +Morocco,MAR,1997,2.083,0,0,0 +Morocco,MAR,1998,1.777,0,0,0 +Morocco,MAR,1999,0.825,0,0,0 +Morocco,MAR,2000,0.718,0,0.064,0 +Morocco,MAR,2001,0.8623,0,0.2058,0 +Morocco,MAR,2002,0.842,0.0001010101010101,0.1939,0 +Morocco,MAR,2003,1.44171171171171,0.0001010101010101,0.2028,0 +Morocco,MAR,2004,1.5913,0.0001010101010101,0.1989,0 +Morocco,MAR,2005,0.9647,0.0001010101010101,0.2063,0 +Morocco,MAR,2006,0.9825,0.0001010101010101,0.1832,0 +Morocco,MAR,2007,0.902,0.0001010101010101,0.2789,0 +Morocco,MAR,2008,0.9158,0.0001010101010101,0.2982,0 +Morocco,MAR,2009,2.5683,0.0001010101010101,0.3911,0 +Morocco,MAR,2010,3.4678,0.0001010101010101,0.6588,0 +Morocco,MAR,2011,2.0054,0.0001010101010101,0.6922,0 +Morocco,MAR,2012,1.631,0.000101282828282827,0.728,0 +Morocco,MAR,2013,2.785,0.0001010101010101,1.2149,0 +Morocco,MAR,2014,1.6374,0.0001010101010101,1.9238,0 +Morocco,MAR,2015,1.885,0.0057,2.5194,0 +Morocco,MAR,2016,1.2557,0.4015,3.0001,0 +Morocco,MAR,2017,1.184712,0.4153,3.0349,0 +Morocco,MAR,2018,1.6934,0.9502,3.8407,0 +Netherlands,NLD,1965,0,0,0,0 +Netherlands,NLD,1966,0,0,0,0 +Netherlands,NLD,1967,0,0,0,0 +Netherlands,NLD,1968,0,0,0,0 +Netherlands,NLD,1969,0,0,0,0 +Netherlands,NLD,1970,0,0,0,0 +Netherlands,NLD,1971,0,0,0,0 +Netherlands,NLD,1972,0,0,0,0 +Netherlands,NLD,1973,0,0,0,0 +Netherlands,NLD,1974,0,0,0,0 +Netherlands,NLD,1975,0,0,0,0.803 +Netherlands,NLD,1976,0,0,0,0.872 +Netherlands,NLD,1977,0,0,0,0.867 +Netherlands,NLD,1978,0,0,0,1.211 +Netherlands,NLD,1979,0,0,0,1.106 +Netherlands,NLD,1980,0,0,0,1.024 +Netherlands,NLD,1981,0,0,0,1.085 +Netherlands,NLD,1982,0,0,0,0.265 +Netherlands,NLD,1983,0,0,0,0.231 +Netherlands,NLD,1984,0,0,0,0.017 +Netherlands,NLD,1985,0,0,0,0.441 +Netherlands,NLD,1986,0,0,0.001,0.553 +Netherlands,NLD,1987,0,0,0.002,0.702 +Netherlands,NLD,1988,0,0,0.016,0.835 +Netherlands,NLD,1989,0.0353552,0,0.023,0.9 +Netherlands,NLD,1990,0.085,0,0.056,0.67 +Netherlands,NLD,1991,0.104,0,0.088,0.75 +Netherlands,NLD,1992,0.12,0.001,0.147,0.752 +Netherlands,NLD,1993,0.092,0.001,0.174,0.863 +Netherlands,NLD,1994,0.1,0.00001,0.238,0.939 +Netherlands,NLD,1995,0.088,0.001,0.317,1.017 +Netherlands,NLD,1996,0.08,0.002,0.437,1.316 +Netherlands,NLD,1997,0.092,0.002,0.475,1.463 +Netherlands,NLD,1998,0.11231,0.0035,0.64,1.590281 +Netherlands,NLD,1999,0.090249,0.00527,0.645,1.788338 +Netherlands,NLD,2000,0.142393,0.00771,0.829,2.013194 +Netherlands,NLD,2001,0.117324,0.01306,0.825,2.358034 +Netherlands,NLD,2002,0.109636,0.01704,0.946,2.906752 +Netherlands,NLD,2003,0.072023,0.03074,1.318,2.547534 +Netherlands,NLD,2004,0.095732,0.03314,1.867,3.324906 +Netherlands,NLD,2005,0.088,0.034199,2.067,5.276719 +Netherlands,NLD,2006,0.105199,0.035223,2.734,5.194 +Netherlands,NLD,2007,0.107,0.035712,3.437,4.026282 +Netherlands,NLD,2008,0.1,0.038889,4.258333,5.082893 +Netherlands,NLD,2009,0.097222,0.044444,4.583333,6.111 +Netherlands,NLD,2010,0.104167,0.059444,3.993056,7.058056 +Netherlands,NLD,2011,0.057,0.100344,5.100171,7.083559 +Netherlands,NLD,2012,0.104388,0.253753,4.981829,7.203605 +Netherlands,NLD,2013,0.114312,0.5159,5.627148,5.954 +Netherlands,NLD,2014,0.111925,0.784762,5.797334,5.013182 +Netherlands,NLD,2015,0.093,1.121509,7.549873,4.93 +Netherlands,NLD,2016,0.100079,1.559424,8.170458,4.90458 +Netherlands,NLD,2017,0.060757,2.204346,10.568801,4.599349 +Netherlands,NLD,2018,0.072348,3.2012,10.549225,4.87586029586284 +New Zealand,NZL,1965,10.0187798,0,0,1.348 +New Zealand,NZL,1966,10.805433,0,0,1.359 +New Zealand,NZL,1967,11.2208566,0,0,1.134 +New Zealand,NZL,1968,11.7732816,0,0,1.295 +New Zealand,NZL,1969,11.6362802,0,0,1.327 +New Zealand,NZL,1970,12.6704198,0,0,1.273 +New Zealand,NZL,1971,14.3939858,0,0,1.256 +New Zealand,NZL,1972,15.2646076,0,0,1.257 +New Zealand,NZL,1973,15.0966704,0,0,1.243 +New Zealand,NZL,1974,15.1888888888887,0,0,1.75972210526317 +New Zealand,NZL,1975,16.6636363636362,0,0,1.78682736842106 +New Zealand,NZL,1976,15.4989898989897,0,0,1.73551157894738 +New Zealand,NZL,1977,14.7202020202018,0,0,1.65866947368422 +New Zealand,NZL,1978,15.6595959595958,0,0,1.68182736842106 +New Zealand,NZL,1979,18.4434343434341,0,0,1.55824842105264 +New Zealand,NZL,1980,19.3646464646462,0,0,1.65214315789475 +New Zealand,NZL,1981,19.6797979797977,0,0,1.61924842105264 +New Zealand,NZL,1982,18.3040404040402,0,0,1.64898526315791 +New Zealand,NZL,1983,19.7515151515149,0,0,1.66704842105264 +New Zealand,NZL,1984,20.3767676767674,0,0,1.8190957894737 +New Zealand,NZL,1985,19.7078654338315,0,0,1.69087181520938 +New Zealand,NZL,1986,22.0976928083911,0,0,1.76369871110644 +New Zealand,NZL,1987,21.9285596592604,0,0,1.75856473684211 +New Zealand,NZL,1988,22.9626571450895,0,0,1.76794736842107 +New Zealand,NZL,1989,22.5588572947006,0,0,2.2609157894737 +New Zealand,NZL,1990,23.1853026420414,0,0,2.60853684210528 +New Zealand,NZL,1991,22.8944638963522,0,0,2.78516842105265 +New Zealand,NZL,1992,21.0926701815138,0,0.000690909090909082,2.76110526315792 +New Zealand,NZL,1993,23.4928928484246,0,0.000985858585858576,2.88321052631581 +New Zealand,NZL,1994,25.8370802915549,0,0.001010101010101,2.73663157894739 +New Zealand,NZL,1995,27.53475928785,0,0.001010101010101,2.68163040942456 +New Zealand,NZL,1996,26.1829292675521,0,0.00832626262626252,2.62549661689126 +New Zealand,NZL,1997,23.2581656565654,0,0.0135935555555554,2.71738421052633 +New Zealand,NZL,1998,25.319326262626,0,0.0220696969696967,3.08545715789476 +New Zealand,NZL,1999,22.919527272727,0,0.0389929292929289,3.30906526315792 +New Zealand,NZL,2000,24.4349767676765,0,0.120208080808079,3.47937473684213 +New Zealand,NZL,2001,21.6811686868684,0,0.138987878787877,3.30495789473687 +New Zealand,NZL,2002,24.8730255401557,0,0.155483159595958,3.17587347406103 +New Zealand,NZL,2003,23.6227967168684,0,0.146540942424241,3.10910617513066 +New Zealand,NZL,2004,27.2400868065653,0,0.361536570202016,3.21402959587097 +New Zealand,NZL,2005,23.3275542252522,0,0.614385961616156,3.62873350125475 +New Zealand,NZL,2006,23.5732190040401,0,0.622216759393934,3.88891107002378 +New Zealand,NZL,2007,23.6404992767674,0.00340589450317706,0.929880219191908,4.08255884085004 +New Zealand,NZL,2008,22.3473378003775,0.00345561559081468,1.05845803046211,4.72969953542719 +New Zealand,NZL,2009,24.217443176057,0.00358986252743626,1.47646107895937,5.4187741163289 +New Zealand,NZL,2010,24.737841105657,0.00382852374809684,1.63716505106639,6.43001766057102 +New Zealand,NZL,2011,25.1208709936728,0.00412685027392257,1.9572194915745,6.67921105585088 +New Zealand,NZL,2012,22.9067473071834,0.00482294550084926,2.0792032887733,6.7533354633099 +New Zealand,NZL,2013,23.052219932199,0.0068501732236971,2.02186971987907,6.98987355313071 +New Zealand,NZL,2014,24.3305823174688,0.0182482957575755,2.21114863711885,7.84714239684728 +New Zealand,NZL,2015,24.5390388087797,0.0358135849696966,2.35367545311503,8.410724454611 +New Zealand,NZL,2016,25.924437384956,0.055813170424242,2.30644913929989,8.42291548651259 +New Zealand,NZL,2017,25.1855751535071,0.0754208610909081,2.14119352000131,8.42823467097971 +New Zealand,NZL,2018,26.3203773202054,0.098244292363635,2.04600387409782,8.32090759883475 +North America,,1965,324.957921658584,0,0,13.3322320956527 +North America,,1966,338.922617670704,0,0,14.0620068952729 +North America,,1967,371.41699633131,0,0,14.0735707426482 +North America,,1968,377.119420850502,0,0,15.5460450961761 +North America,,1969,417.805078622219,0,0,16.2270603765373 +North America,,1970,423.820461056563,0,0,16.2359025339551 +North America,,1971,443.739977189896,0,0,16.5844139735814 +North America,,1972,465.466573678784,0,0,18.6729784402925 +North America,,1973,473.315952810098,0,0,20.1506917718544 +North America,,1974,522.001200515148,0,0,20.8672648058215 +North America,,1975,516.428001612117,0,0,20.2723019617015 +North America,,1976,509.904163226259,0,0,23.8507535418538 +North America,,1977,443.623194595957,0,0,25.1971218624594 +North America,,1978,519.540247090905,0,0,26.4087866573099 +North America,,1979,516.570557092926,0,0,28.1200372422996 +North America,,1980,521.436364311108,0,0,28.8711163181234 +North America,,1981,516.188184016158,0,0,30.2396032732909 +North America,,1982,596.337982060602,0,0,29.8400489453562 +North America,,1983,625.496691313127,0.003,0.00269494949494946,33.2425991641745 +North America,,1984,637.444629573733,0.0053010101010101,0.00655555555555549,39.3563261351536 +North America,,1985,619.310951082824,0.0107373737373737,0.00682020202020197,39.7108944238886 +North America,,1986,628.964999086864,0.0141737373737374,0.00523131313131308,43.9606722006726 +North America,,1987,590.962880193936,0.0106030303030303,0.00457676767676763,49.3954331757932 +North America,,1988,557.715097606057,0.00918585858585858,0.00187979797979797,49.512061259651 +North America,,1989,590.528116666659,0.253132323232323,2.13437676767674,57.0062678947372 +North America,,1990,612.445181818174,0.37179494949495,2.81968686868683,66.5373152631583 +North America,,1991,617.227269949487,0.480252872967454,2.98367777777774,70.3682756332728 +North America,,1992,593.414026767669,0.413897109488221,2.97668989898987,75.6459350862224 +North America,,1993,628.119773989891,0.481214524029938,3.09618888888885,78.2414843539421 +North America,,1994,607.468221464639,0.508925425360281,3.54592828282824,78.3063827684286 +North America,,1995,672.878216161608,0.523313678478503,3.26221515151512,76.8173887569623 +North America,,1996,733.477832323223,0.553235954427102,3.33473636363633,78.5390299545865 +North America,,1997,731.014839646456,0.54564309553425,3.39524747474744,79.871549429127 +North America,,1998,677.552143434335,0.541764609224187,3.13325858585855,80.7429655439502 +North America,,1999,694.318284646456,0.538605227511368,4.71233131313127,81.4808700112985 +North America,,2000,662.59630828282,0.541933706754834,5.9327585858585,81.8585095153268 +North America,,2001,570.196359292922,0.601930002861358,7.15238484848477,82.7500820357954 +North America,,2002,632.30380818181,0.632334439763792,10.8848686868686,87.906521269244 +North America,,2003,625.962537323224,0.64144379203756,12.0094707070706,87.6991465860534 +North America,,2004,626.129398661608,0.7213955956735,15.2516070707069,89.0117332833158 +North America,,2005,656.092939494941,0.7763743178249,19.5614535353533,89.8898349294742 +North America,,2006,668.877774722214,0.857116339605119,29.3507141414138,89.5072826394744 +North America,,2007,638.005196388881,1.13335786704506,38.0231120606057,91.1101202094217 +North America,,2008,667.76550108585,1.68748361633131,59.9279522323228,89.4256507275794 +North America,,2009,666.942490404032,2.21226039424442,81.8040443434335,89.6480397893162 +North America,,2010,645.817477424234,3.29823642097211,105.484390404039,92.8051380740533 +North America,,2011,728.089484823223,5.35150412797013,133.226425429291,93.3557623567375 +North America,,2012,686.20785169191,9.9870137555406,157.24285472222,94.7107510034741 +North America,,2013,686.360042146456,17.6444954805979,191.682239848483,99.0227713043164 +North America,,2014,677.144727474739,31.5569616161616,212.454380505048,100.353372183737 +North America,,2015,659.537925404037,42.5672626262626,228.356483611109,98.7131282544742 +North America,,2016,681.630305580804,58.7037922727273,268.842544217169,99.7284800000006 +North America,,2017,725.156678055551,82.5352707575758,296.557264494946,99.8788060198498 +North America,,2018,708.352331221244,102.907232717845,322.528364402709,99.7459563095254 +Norway,NOR,1965,49.364698,0,0,0 +Norway,NOR,1966,48.6929492,0,0,0 +Norway,NOR,1967,53.3244804,0,0,0 +Norway,NOR,1968,60.2275832,0,0,0 +Norway,NOR,1969,57.3947478,0,0,0 +Norway,NOR,1970,57.8543654,0,0,0 +Norway,NOR,1971,63.9398792,0,0,0 +Norway,NOR,1972,68.1383092,0,0,0 +Norway,NOR,1973,73.6537204,0,0,0 +Norway,NOR,1974,76.654493,0,0,0 +Norway,NOR,1975,77.4323074,0,0,0 +Norway,NOR,1976,82.0505804,0,0,0 +Norway,NOR,1977,72.2085766,0,0,0 +Norway,NOR,1978,80.87502,0,0,0 +Norway,NOR,1979,88.9934578,0,0,0 +Norway,NOR,1980,83.9818582,0,0,0 +Norway,NOR,1981,93.2846952,0,0,0 +Norway,NOR,1982,92.9002074,0,0,0 +Norway,NOR,1983,106.0656,0,0,0 +Norway,NOR,1984,106.3572804,0,0,0 +Norway,NOR,1985,102.383,0,0,0.005 +Norway,NOR,1986,96.201,0,0,0.026 +Norway,NOR,1987,103.273,0,0,0.021 +Norway,NOR,1988,108.857,0,0,0.027 +Norway,NOR,1989,118.4,0,0,0.029 +Norway,NOR,1990,121.145,0,0,0.213 +Norway,NOR,1991,110.134,0,0,0.209 +Norway,NOR,1992,116.671,0,0,0.227 +Norway,NOR,1993,119.233,0,0,0.243 +Norway,NOR,1994,111.643,0,0,0.27 +Norway,NOR,1995,121.53,0,0,0.289 +Norway,NOR,1996,103.862,0,0,0.303 +Norway,NOR,1997,109.775,0,0,0.241 +Norway,NOR,1998,115.703,0,0,0.27 +Norway,NOR,1999,121.452,0,0.014276,0.273 +Norway,NOR,2000,141.818,0.0053,0.030199,0.256 +Norway,NOR,2001,120.463,0.0053,0.028758,0.255 +Norway,NOR,2002,129.372,0.0053,0.038903,0.25 +Norway,NOR,2003,105.48,0.0062,0.219796,0.33 +Norway,NOR,2004,108.781,0.0062,0.259519,0.359 +Norway,NOR,2005,135.688,0.0062,0.50703,0.333 +Norway,NOR,2006,119.369,0.0071,0.636,0.388 +Norway,NOR,2007,133.656,0.0071,0.892,0.386 +Norway,NOR,2008,139.044,0.0074,0.913,0.397 +Norway,NOR,2009,125.283,0.0077,0.977,0.227 +Norway,NOR,2010,116.75,0.0081,0.879,0.344 +Norway,NOR,2011,120.291,0.0085,1.283,0.339 +Norway,NOR,2012,141.738,0.0089,1.548,0.326 +Norway,NOR,2013,128.154,0.0098,1.881,0.396 +Norway,NOR,2014,135.439,0.0116,2.217,0.258 +Norway,NOR,2015,137.302,0.0134,2.515,0.256 +Norway,NOR,2016,142.418,0.0238,2.115742,0.233 +Norway,NOR,2017,141.955,0.0407535741898285,2.854,0.21 +Norway,NOR,2018,138.496217519582,0.0644885468674241,3.875881,0.178266653038577 +Oman,OMN,1965,0,0,0,0 +Oman,OMN,1966,0,0,0,0 +Oman,OMN,1967,0,0,0,0 +Oman,OMN,1968,0,0,0,0 +Oman,OMN,1969,0,0,0,0 +Oman,OMN,1970,0,0,0,0 +Oman,OMN,1971,0,0,0,0 +Oman,OMN,1972,0,0,0,0 +Oman,OMN,1973,0,0,0,0 +Oman,OMN,1974,0,0,0,0 +Oman,OMN,1975,0,0,0,0 +Oman,OMN,1976,0,0,0,0 +Oman,OMN,1977,0,0,0,0 +Oman,OMN,1978,0,0,0,0 +Oman,OMN,1979,0,0,0,0 +Oman,OMN,1980,0,0,0,0 +Oman,OMN,1981,0,0,0,0 +Oman,OMN,1982,0,0,0,0 +Oman,OMN,1983,0,0,0,0 +Oman,OMN,1984,0,0,0,0 +Oman,OMN,1985,0,0,0,0 +Oman,OMN,1986,0,0,0,0 +Oman,OMN,1987,0,0,0,0 +Oman,OMN,1988,0,0,0,0 +Oman,OMN,1989,0,0,0,0 +Oman,OMN,1990,0,0,0,0 +Oman,OMN,1991,0,0,0,0 +Oman,OMN,1992,0,0,0,0 +Oman,OMN,1993,0,0,0,0 +Oman,OMN,1994,0,0,0,0 +Oman,OMN,1995,0,0,0,0 +Oman,OMN,1996,0,0,0,0 +Oman,OMN,1997,0,0,0,0 +Oman,OMN,1998,0,0,0,0 +Oman,OMN,1999,0,0,0,0 +Oman,OMN,2000,0,0,0,0 +Oman,OMN,2001,0,0,0,0 +Oman,OMN,2002,0,0,0,0 +Oman,OMN,2003,0,0,0,0 +Oman,OMN,2004,0,0,0,0 +Oman,OMN,2005,0,0,0,0 +Oman,OMN,2006,0,0,0,0 +Oman,OMN,2007,0,0,0,0 +Oman,OMN,2008,0,0,0,0 +Oman,OMN,2009,0,0,0,0 +Oman,OMN,2010,0,0,0,0 +Oman,OMN,2011,0,0,0,0 +Oman,OMN,2012,0,0,0,0 +Oman,OMN,2013,0,0.0012,0,0 +Oman,OMN,2014,0,0.0012,0,0 +Oman,OMN,2015,0,0.0037,0,0 +Oman,OMN,2016,0,0.0037,0,0 +Oman,OMN,2017,0,0.00870887721433953,0,0 +Oman,OMN,2018,0,0.0137186744953366,0,0 +Other Asia & Pacific,,1965,9.97822768909577,0,0,0 +Other Asia & Pacific,,1966,9.99190528909577,0,0,0 +Other Asia & Pacific,,1967,10.2043246890958,0,0,0 +Other Asia & Pacific,,1968,10.1640992890958,0,0,0 +Other Asia & Pacific,,1969,10.2660350890958,0,0,0 +Other Asia & Pacific,,1970,10.3224872890958,0,0,0 +Other Asia & Pacific,,1971,10.5651006890958,0,0,0 +Other Asia & Pacific,,1972,10.9674558890958,0,0,0 +Other Asia & Pacific,,1973,11.0903588890958,0,0,0 +Other Asia & Pacific,,1974,11.4599080890958,0,0,0 +Other Asia & Pacific,,1975,11.83158949295,0,0,0 +Other Asia & Pacific,,1976,12.25394469295,0,0,0 +Other Asia & Pacific,,1977,12.40097749295,0,0,0 +Other Asia & Pacific,,1978,12.81717149295,0,0,0 +Other Asia & Pacific,,1979,13.7132376000266,0,0,0 +Other Asia & Pacific,,1980,13.7722341406648,0,0,0 +Other Asia & Pacific,,1981,13.9453749661514,0,0,0 +Other Asia & Pacific,,1982,14.418417180139,0,0,0 +Other Asia & Pacific,,1983,14.5129383349186,0,0,0 +Other Asia & Pacific,,1984,15.1002808654557,0,0,0.00105263157894738 +Other Asia & Pacific,,1985,16.7167965949495,0,0,0 +Other Asia & Pacific,,1986,17.787765840404,0,0,0 +Other Asia & Pacific,,1987,18.7982121818182,0,0,0 +Other Asia & Pacific,,1988,20.801240269697,0,0,0 +Other Asia & Pacific,,1989,21.2570918777778,0,0,0 +Other Asia & Pacific,,1990,22.2878907918384,0,0,0 +Other Asia & Pacific,,1991,21.555556035101,0,0,0 +Other Asia & Pacific,,1992,20.5811274680303,0,0,0 +Other Asia & Pacific,,1993,20.237063304,0,0,0 +Other Asia & Pacific,,1994,21.3752204489697,0,0,0 +Other Asia & Pacific,,1995,21.8268530984242,0,0,0 +Other Asia & Pacific,,1996,20.8291278839495,0,0.001640218,0 +Other Asia & Pacific,,1997,18.5461079115858,0.000002,0.004917092,0 +Other Asia & Pacific,,1998,18.7070664484141,0.000012,0.005854207,0 +Other Asia & Pacific,,1999,19.7394497044646,0.000009,0.005816067,0 +Other Asia & Pacific,,2000,21.546123393505,0.002711,0.007402049,0 +Other Asia & Pacific,,2001,22.6322025715152,0.005114,0.006933847,0.0201170212765957 +Other Asia & Pacific,,2002,22.9967289012323,0.00621,0.008262989,0.0251414267834794 +Other Asia & Pacific,,2003,24.2339513109091,0.007709,0.004774632,0.0330724260588895 +Other Asia & Pacific,,2004,25.3683412812121,0.009406,0.011509401,0.0390972267966538 +Other Asia & Pacific,,2005,27.1901044024646,0.011602,0.017618918,0.0449026743956262 +Other Asia & Pacific,,2006,29.2226591105151,0.014804,0.028396607,0.0467894736842106 +Other Asia & Pacific,,2007,32.4584661715253,0.0172030202020202,0.04084338,0.038653 +Other Asia & Pacific,,2008,34.4041527125353,0.0261020202020202,0.043745987010101,0.035532 +Other Asia & Pacific,,2009,34.1588011865455,0.0295020202020202,0.0571583431010101,0.045486 +Other Asia & Pacific,,2010,41.1195142605353,0.0335751512020202,0.0719395411010101,0.05362 +Other Asia & Pacific,,2011,46.9926574505556,0.0440142462020202,0.0689735691010101,0.069007 +Other Asia & Pacific,,2012,47.7786846884444,0.0949958807575758,0.070883383,0.075447 +Other Asia & Pacific,,2013,53.3948533848889,0.11254359220202,0.123002016,0.083663772 +Other Asia & Pacific,,2014,53.187472532,0.164955692,0.237676002,0.101097984 +Other Asia & Pacific,,2015,49.932362749,0.222921288,0.27818362,0.128339453 +Other Asia & Pacific,,2016,58.062597449,0.32192900596009,0.248753351,0.131519562 +Other Asia & Pacific,,2017,60.0706839374015,0.477047579246616,0.314518371535519,0.150633142076503 +Other Asia & Pacific,,2018,62.9119824426977,0.585723529390912,0.497064956234973,0.163131004876503 +Other CIS,,1985,22.3886804,0,0,0 +Other CIS,,1986,20.6430174,0,0,0 +Other CIS,,1987,21.1203126,0,0,0 +Other CIS,,1988,29.1194266,0,0,0 +Other CIS,,1989,25.9153616,0,0,0 +Other CIS,,1990,27.719366,0,0,0 +Other CIS,,1991,28.3543444,0,0,0 +Other CIS,,1992,28.4919666,0,0,0 +Other CIS,,1993,30.8512374,0,0,0 +Other CIS,,1994,32.215423,0,0,0 +Other CIS,,1995,27.9600196,0,0,0 +Other CIS,,1996,29.089887,0,0,0 +Other CIS,,1997,26.3992722,0,0,0 +Other CIS,,1998,25.9535318,0,0,0 +Other CIS,,1999,29.3156574,0,0,0 +Other CIS,,2000,29.378529,0,0,0 +Other CIS,,2001,27.9748486,0,0,0 +Other CIS,,2002,28.021275,0,0,0 +Other CIS,,2003,31.76973,0,0,0 +Other CIS,,2004,32.7865464,0,0,0 +Other CIS,,2005,33.1295794,0,0,0 +Other CIS,,2006,32.5522,0,0.0026,0 +Other CIS,,2007,33.3336,0,0.0029,0 +Other CIS,,2008,28.767,0,0.0019,0 +Other CIS,,2009,28.4007,0.000007375,0.0043,0 +Other CIS,,2010,30.6456,0.000080625,0.0041,0 +Other CIS,,2011,33.3671,0.000166,0.003,0 +Other CIS,,2012,33.6939,0.00112382054794521,0.0042,0 +Other CIS,,2013,32.6511,0.00420775,0.0033,0.0035 +Other CIS,,2014,31.8752,0.00812075,0.0055,0.013 +Other CIS,,2015,30.4237,0.01044875,0.0057,0.015 +Other CIS,,2016,30.6726,0.0107677054794521,0.005,0.014 +Other CIS,,2017,33.8418,0.01014375,0.0081,0.022 +Other CIS,,2018,35.2030572933918,0.01484375,0.0079,0.022 +Other Caribbean,,1965,0.4180328,0,0,0 +Other Caribbean,,1966,0.4704522,0,0,0 +Other Caribbean,,1967,0.3838716,0,0,0 +Other Caribbean,,1968,0.3928716,0,0,0 +Other Caribbean,,1969,0.4227104,0,0,0 +Other Caribbean,,1970,0.4941298,0,0,0 +Other Caribbean,,1971,0.9971298,0,0,0.97 +Other Caribbean,,1972,0.9385492,0,0,0.831 +Other Caribbean,,1973,0.8289686,0,0,0.978 +Other Caribbean,,1974,0.7999686,0,0,0.984 +Other Caribbean,,1975,0.736388,0,0,1.012 +Other Caribbean,,1976,1.1130656,0,0,1.103 +Other Caribbean,,1977,0.7703238,0,0,1.193 +Other Caribbean,,1978,1.0930014,0,0,1.339 +Other Caribbean,,1979,1.320679,0,0,1.289 +Other Caribbean,,1980,1.0019372,0,0,1.292 +Other Caribbean,,1981,1.7819372,0,0,1.308 +Other Caribbean,,1982,1.1949372,0,0,1.406 +Other Caribbean,,1983,1.3671954,0,0,1.515 +Other Caribbean,,1984,1.6517118,0,0,1.289 +Other Caribbean,,1985,1.622,0,0,1.209 +Other Caribbean,,1986,2.055,0,0,1.266 +Other Caribbean,,1987,2.233,0,0,1.304 +Other Caribbean,,1988,2.465,0,0,1.422 +Other Caribbean,,1989,1.305,0,0,1.389 +Other Caribbean,,1990,1.256,0,0,1.60384210526316 +Other Caribbean,,1991,1.292,0,0,1.36784210526316 +Other Caribbean,,1992,1.315,0,0,1.47 +Other Caribbean,,1993,1.705,0,0.0017171717171717,1.064 +Other Caribbean,,1994,0.937,0,0.00777777777777769,1.131 +Other Caribbean,,1995,1.1052,0,0.00777777777777769,0.951 +Other Caribbean,,1996,1.46845084242424,0,0.00736562222222213,1.22217109473684 +Other Caribbean,,1997,1.19006159232323,0,0.00802152797979788,1.179 +Other Caribbean,,1998,1.77197321649892,0,0.00912294888888878,1.040475684 +Other Caribbean,,1999,2.43996462136604,0.0001010101010101,0.0114348346464645,1.21712006505263 +Other Caribbean,,2000,1.55069450436364,0.0001,0.0395497139393935,1.33550642199743 +Other Caribbean,,2001,1.3735680788505,0.0003,0.0480778226262621,1.26892886091316 +Other Caribbean,,2002,1.72774208558643,0.0003,0.0609193712121205,1.18301222491928 +Other Caribbean,,2003,2.19837014397035,0.0003,0.0646916103030295,0.965124540110934 +Other Caribbean,,2004,2.29019515376735,0.0005,0.0953164636363629,1.0528671531118 +Other Caribbean,,2005,2.60375812934917,0.00510313818181818,0.106660361212121,0.75854745662436 +Other Caribbean,,2006,2.51698645784344,0.00442826909090908,0.122777255757575,0.756636889042735 +Other Caribbean,,2007,2.34706565052643,0.00631111111111109,0.128433333333332,0.795227809701924 +Other Caribbean,,2008,2.08485090915609,0.00988888888888885,0.138950505050504,0.881007294291037 +Other Caribbean,,2009,2.19965549016536,0.0185989898989898,0.19320707070707,0.895638754625561 +Other Caribbean,,2010,2.06738808732391,0.0437689393939392,0.281660606060605,0.765436084594868 +Other Caribbean,,2011,2.14422618832718,0.0812229839969693,0.350442081474899,0.812442477976429 +Other Caribbean,,2012,2.38126740823877,0.198541163744197,0.669264373410626,0.961568051825326 +Other Caribbean,,2013,2.45470283280147,0.242372199088863,1.00776456855707,1.10666830474866 +Other Caribbean,,2014,1.78803664351578,0.308081809422171,1.06069470791245,1.04727791015064 +Other Caribbean,,2015,1.37193948539375,0.386912563261214,1.14655713486566,1.14675622227321 +Other Caribbean,,2016,1.9252034300918,0.4795239461023,1.15924917524094,1.18145821316874 +Other Caribbean,,2017,2.71817558414136,0.62261301905248,1.31526880705816,1.24278255538343 +Other Caribbean,,2018,2.25567869874926,0.697743205794847,1.42815626922188,1.39699068988323 +Other Middle East,,1965,0.556,0,0,0 +Other Middle East,,1966,0.606,0,0,0 +Other Middle East,,1967,0.734,0,0,0 +Other Middle East,,1968,0.814,0,0,0 +Other Middle East,,1969,0.952,0,0,0 +Other Middle East,,1970,0.928,0,0,0 +Other Middle East,,1971,0.89,0,0,0 +Other Middle East,,1972,0.869,0,0,0 +Other Middle East,,1973,0.495,0,0,0 +Other Middle East,,1974,1.15,0,0,0 +Other Middle East,,1975,1.55,0,0,0 +Other Middle East,,1976,2.03,0,0,0 +Other Middle East,,1977,2.568,0,0,0 +Other Middle East,,1978,2.895,0,0,0 +Other Middle East,,1979,3.201,0,0,0 +Other Middle East,,1980,3.411,0,0,0 +Other Middle East,,1981,3.508,0,0,0 +Other Middle East,,1982,3.609,0,0,0 +Other Middle East,,1983,3.362,0,0,0 +Other Middle East,,1984,3.91,0,0,0 +Other Middle East,,1985,3.927,0,0,0 +Other Middle East,,1986,3.42,0,0,0 +Other Middle East,,1987,3.492097,0,0,0 +Other Middle East,,1988,3.438097,0,0,0 +Other Middle East,,1989,3.2852582,0,0,0 +Other Middle East,,1990,3.2412582,0,0.001,0 +Other Middle East,,1991,3.2572582,0,0.001,0 +Other Middle East,,1992,3.3732582,0,0.001,0 +Other Middle East,,1993,3.326097,0,0.001,0 +Other Middle East,,1994,3.3822582,0,0.001,0 +Other Middle East,,1995,3.249097,0,0.001,0 +Other Middle East,,1996,3.370097,0,0.001,0 +Other Middle East,,1997,3.5142582,0,0.003,0 +Other Middle East,,1998,3.4982582,0,0.003,0 +Other Middle East,,1999,3.0652582,0,0.003,0 +Other Middle East,,2000,3.7197746,0,0.003,0.003 +Other Middle East,,2001,3.787194,0,0.003,0.005 +Other Middle East,,2002,4.2310328,0,0.003,0.005 +Other Middle East,,2003,4.2067746,0,0.003,0.006 +Other Middle East,,2004,5.4220328,0,0.0041,0.006 +Other Middle East,,2005,4.5494522,0,0.0041,0.005 +Other Middle East,,2006,4.74,0,0.0041,0.006 +Other Middle East,,2007,4.166,0,0.0041,0.01 +Other Middle East,,2008,3.304433,0.0027,0.0053,0.009 +Other Middle East,,2009,2.539112,0.003,0.0053,0.007 +Other Middle East,,2010,3.492252,0.0050385,0.0053,0.009 +Other Middle East,,2011,4.215731,0.007483,0.0053,0.008 +Other Middle East,,2012,4.475459,0.011433602739726,0.0063,0.006 +Other Middle East,,2013,4.100514,0.02204,0.0093,0.006 +Other Middle East,,2014,2.591593,0.1443525,0.0107,0.0035 +Other Middle East,,2015,0.944749,0.637445,0.1314,0.0041 +Other Middle East,,2016,1.352748,1.23725991780822,0.399,0.0041 +Other Middle East,,2017,1.35556627166276,1.96250154592749,0.457477322404372,0.0041 +Other Middle East,,2018,1.28854127166276,3.02003980180305,0.574940540795176,0.0041 +Other Northern Africa,,1965,0.22097,0,0, +Other Northern Africa,,1966,0.1900342,0,0, +Other Northern Africa,,1967,0.1723566,0,0, +Other Northern Africa,,1968,0.1944536,0,0, +Other Northern Africa,,1969,0.2519058,0,0, +Other Northern Africa,,1970,0.243067,0,0, +Other Northern Africa,,1971,0.2784222,0,0, +Other Northern Africa,,1972,0.2916804,0,0, +Other Northern Africa,,1973,0.2253894,0,0, +Other Northern Africa,,1974,0.2386476,0,0, +Other Northern Africa,,1975,0.1856148,0,0, +Other Northern Africa,,1976,0.1856148,0,0, +Other Northern Africa,,1977,0.2474864,0,0, +Other Northern Africa,,1978,0.2563252,0,0, +Other Northern Africa,,1979,0.287261,0,0, +Other Northern Africa,,1980,0.2695834,0,0, +Other Northern Africa,,1981,0.1856148,0,0, +Other Northern Africa,,1982,0.1060656,0,0, +Other Northern Africa,,1983,0.088388,0,0, +Other Northern Africa,,1984,0.0751298,0,0, +Other Northern Africa,,1985,0.1060656,0,0, +Other Northern Africa,,1986,0.0486134,0,0, +Other Northern Africa,,1987,0.1149044,0,0, +Other Northern Africa,,1988,0.0486134,0,0, +Other Northern Africa,,1989,0.0353552,0,0, +Other Northern Africa,,1990,0.044,0,0, +Other Northern Africa,,1991,0.1048,0,0, +Other Northern Africa,,1992,0.0651,0,0, +Other Northern Africa,,1993,0.064,0,0, +Other Northern Africa,,1994,0.04,0,0, +Other Northern Africa,,1995,0.039,0,0, +Other Northern Africa,,1996,0.067,0,0, +Other Northern Africa,,1997,0.044,0,0, +Other Northern Africa,,1998,0.073,0,0, +Other Northern Africa,,1999,0.09,0,0, +Other Northern Africa,,2000,0.064,0.0012,0.0235, +Other Northern Africa,,2001,0.054,0.0014,0.0245, +Other Northern Africa,,2002,0.064,0.0016,0.031, +Other Northern Africa,,2003,0.166,0.002,0.0386, +Other Northern Africa,,2004,0.153,0.0028,0.045, +Other Northern Africa,,2005,0.145,0.0036,0.044, +Other Northern Africa,,2006,0.092,0.0043,0.038, +Other Northern Africa,,2007,0.049,0.0052,0.043, +Other Northern Africa,,2008,0.038,0.0061,0.0394, +Other Northern Africa,,2009,0.079,0.0069,0.0975, +Other Northern Africa,,2010,0.05,0.008221875,0.1386, +Other Northern Africa,,2011,0.054,0.01124375,0.109, +Other Northern Africa,,2012,0.11,0.0156449657534247,0.196, +Other Northern Africa,,2013,0.06,0.02124375,0.358, +Other Northern Africa,,2014,0.056,0.03214375,0.507, +Other Northern Africa,,2015,0.069,0.04694375,0.449, +Other Northern Africa,,2016,0.04466,0.0782449657534247,0.47434, +Other Northern Africa,,2017,0.01746,0.100492439985613,0.44861, +Other Northern Africa,,2018,0.0165,0.111623598045101,0.453, +Other South America,,1965,1.10048894867905,0,0,0 +Other South America,,1966,2.33607326496663,0,0,0 +Other South America,,1967,2.43066177766396,0,0,0 +Other South America,,1968,2.57100107974135,0,0,0 +Other South America,,1969,2.68319549192343,0,0,0 +Other South America,,1970,3.11762120849327,0,0,0 +Other South America,,1971,3.72717442817099,0,0,0.056 +Other South America,,1972,3.36012014917787,0,0,0.059 +Other South America,,1973,3.86317442817098,0,0,0.061 +Other South America,,1974,3.84585882792158,0,0,0.066 +Other South America,,1975,3.76358959243867,0,0,0.063 +Other South America,,1976,3.60778084710047,0,0,0.072 +Other South America,,1977,3.96821499734167,0,0,0.074 +Other South America,,1978,4.03056273309041,0,0,0.056 +Other South America,,1979,3.95354586574771,0,0,0.079 +Other South America,,1980,5.99746740633376,0,0,0.084 +Other South America,,1981,5.1070861988,0,0,0.089 +Other South America,,1982,5.4128332026,0,0,0.091 +Other South America,,1983,6.7740187104,0,0,0.075 +Other South America,,1984,7.2401114559,0,0,0.08 +Other South America,,1985,10.0591510652,0,0,0.095 +Other South America,,1986,18.78567625,0,0,0.097 +Other South America,,1987,24.739092,0,0,0.11 +Other South America,,1988,25.563161,0,0,0.107 +Other South America,,1989,29.5900613496,0,0,0.095 +Other South America,,1990,34.6073581407,0,0,0.111 +Other South America,,1991,36.8541906012,0,0,0.111894736842105 +Other South America,,1992,36.6129110016,0,0,0.123842105263158 +Other South America,,1993,40.5532181489,0,0,0.133578947368421 +Other South America,,1994,45.5723561644,0,0,0.119842105263158 +Other South America,,1995,50.8122705,0,0,0.121631578947369 +Other South America,,1996,53.61104131,0,0,0.119578947368421 +Other South America,,1997,60.52004806,0,0,0.115789473684211 +Other South America,,1998,63.13979735116,0,0,0.128789473684211 +Other South America,,1999,60.724686018,0,0,0.134789473684211 +Other South America,,2000,63.72841068,0.0001,0.001,0.167419816748349 +Other South America,,2001,58.0956703081493,0.0001,0.001,0.180426501035197 +Other South America,,2002,61.361456511,0.0001,0.001,0.205 +Other South America,,2003,63.636543398,0.0001,0.001,0.194 +Other South America,,2004,60.19795317,0.0002,0.001,0.1953 +Other South America,,2005,60.8790066454545,0.0002,0.001,0.1705 +Other South America,,2006,60.8248129644101,0.0003,0.001,0.1886 +Other South America,,2007,65.49246921403,0.0004,0.0049,0.300054 +Other South America,,2008,63.8637455760448,0.0004,0.012491,1.058418 +Other South America,,2009,63.7382438999034,0.0005,0.047298,1.138909 +Other South America,,2010,66.0744502895111,0.00924433838383827,0.075214,1.30424842105263 +Other South America,,2011,68.2823472552444,0.0351097777777774,0.117507,1.360793 +Other South America,,2012,69.6588345902182,0.0539002984640922,0.119156,1.52828626618552 +Other South America,,2013,72.7951754983333,0.0467888433333328,0.150691,1.59188866826156 +Other South America,,2014,68.4718338613798,0.0714818103848477,0.747425513,2.18300220868813 +Other South America,,2015,67.663392,0.130221619727272,2.085918872,2.69236693040613 +Other South America,,2016,74.5548929861656,0.27125622674763,3.038559444,2.65967588303097 +Other South America,,2017,70.7856281483508,0.35387122021636,3.84413384616394,2.76286347886403 +Other South America,,2018,69.4668430675882,0.675171296552089,4.80031359016393,2.64731557832343 +Other Southern Africa,,1965,0.352455,0,0,0 +Other Southern Africa,,1966,0.3612938,0,0,0 +Other Southern Africa,,1967,0.4169072,0,0,0 +Other Southern Africa,,1968,0.4504236,0,0,0 +Other Southern Africa,,1969,0.5176176,0,0,0 +Other Southern Africa,,1970,0.583134,0,0,0 +Other Southern Africa,,1971,0.627328,0,0,0 +Other Southern Africa,,1972,0.77791,0,0,0 +Other Southern Africa,,1973,0.8025876,0,0,0 +Other Southern Africa,,1974,0.967686,0,0,0 +Other Southern Africa,,1975,0.9792024,0,0,0 +Other Southern Africa,,1976,1.050977,0,0,0 +Other Southern Africa,,1977,1.0697516,0,0,0 +Other Southern Africa,,1978,1.0767516,0,0,0 +Other Southern Africa,,1979,1.108365,0,0,0 +Other Southern Africa,,1980,1.1832366,0,0,0 +Other Southern Africa,,1981,1.2392694,0,0,0 +Other Southern Africa,,1982,1.2352052,0,0,0 +Other Southern Africa,,1983,1.2238828,0,0,0 +Other Southern Africa,,1984,1.2967216,0,0,0 +Other Southern Africa,,1985,1.387,0,0,0 +Other Southern Africa,,1986,1.353,0,0,0 +Other Southern Africa,,1987,1.385,0,0,0 +Other Southern Africa,,1988,1.393,0,0,0 +Other Southern Africa,,1989,1.4,0,0,0 +Other Southern Africa,,1990,1.342,0,0,0 +Other Southern Africa,,1991,1.526,0,0,0 +Other Southern Africa,,1992,1.353,0,0,0.032250000088655 +Other Southern Africa,,1993,1.171,0,0,0.124700000043806 +Other Southern Africa,,1994,0.876,0,0,0.12900000004172 +Other Southern Africa,,1995,1.344,0,0,0.167700000022946 +Other Southern Africa,,1996,1.064,0,0,0.17200000002086 +Other Southern Africa,,1997,0.81,0,0,0.195547320300773 +Other Southern Africa,,1998,1.192,0,0,0.197047320300773 +Other Southern Africa,,1999,1.7442,0,0,0.221938623145586 +Other Southern Africa,,2000,1.9599,0,0,0.1298 +Other Southern Africa,,2001,1.8131,0,0,0.1446 +Other Southern Africa,,2002,2.0046,0,0,0.1554 +Other Southern Africa,,2003,1.9708,0,0,0.1472 +Other Southern Africa,,2004,1.8744,0.0001,0,0.156 +Other Southern Africa,,2005,2.2727,0.0002,0,0.1592 +Other Southern Africa,,2006,2.14988,0.0002,0,0.1506 +Other Southern Africa,,2007,2.22238,0.0003,0,0.1548 +Other Southern Africa,,2008,2.0584,0.0058,0,0.1425 +Other Southern Africa,,2009,2.1598,0.0072,0,0.1249 +Other Southern Africa,,2010,2.0357,0.0086,0,0.1777 +Other Southern Africa,,2011,2.2269,0.0147,0,0.2589 +Other Southern Africa,,2012,2.3647,0.0209,0,0.2847 +Other Southern Africa,,2013,2.0271,0.0263,0,0.2759 +Other Southern Africa,,2014,2.3022,0.0317,0,0.227205175785504 +Other Southern Africa,,2015,2.2662,0.0376,0,0.222533905376971 +Other Southern Africa,,2016,1.99,0.05557160901107,0,0.22314358730951 +Other Southern Africa,,2017,2.21641284153005,0.0715100695778232,0,0.222533905376971 +Other Southern Africa,,2018,1.85295284153005,0.128029165595811,0,0.222533905376971 +Pakistan,PAK,1965,2.1964418,0,0,0 +Pakistan,PAK,1966,2.1964418,0,0,0 +Pakistan,PAK,1967,2.6030266,0,0,0 +Pakistan,PAK,1968,3.1289352,0,0,0 +Pakistan,PAK,1969,3.3012918,0,0,0 +Pakistan,PAK,1970,2.9212234,0,0,0 +Pakistan,PAK,1971,3.8139422,0,0,0 +Pakistan,PAK,1972,4.0128152,0,0,0 +Pakistan,PAK,1973,4.2337852,0,0,0 +Pakistan,PAK,1974,4.2558822,0,0,0 +Pakistan,PAK,1975,4.8966952,0,0,0 +Pakistan,PAK,1976,5.3165382,0,0,0 +Pakistan,PAK,1977,6.3153226,0,0,0 +Pakistan,PAK,1978,7.8400156,0,0,0 +Pakistan,PAK,1979,8.4764092,0,0,0 +Pakistan,PAK,1980,8.8874134,0,0,0 +Pakistan,PAK,1981,9.28074,0,0,0 +Pakistan,PAK,1982,10.4430422,0,0,0 +Pakistan,PAK,1983,12.0958978,0,0,0 +Pakistan,PAK,1984,12.5378378,0,0,0 +Pakistan,PAK,1985,13.0239718,0,0,0 +Pakistan,PAK,1986,12.4538692,0,0,0 +Pakistan,PAK,1987,16.373877,0,0,0 +Pakistan,PAK,1988,17.1649496,0,0,0 +Pakistan,PAK,1989,17.4433718,0,0,0 +Pakistan,PAK,1990,17.0854004,0,0,0 +Pakistan,PAK,1991,18.2830578,0,0,0 +Pakistan,PAK,1992,19.9447522,0,0,0 +Pakistan,PAK,1993,21.9334822,0,0,0 +Pakistan,PAK,1994,21.5268974,0,0,0 +Pakistan,PAK,1995,22.7466518,0,0,0 +Pakistan,PAK,1996,24.7972534,0,0,0 +Pakistan,PAK,1997,18.4465756,0,0,0 +Pakistan,PAK,1998,24.107827,0,0,0 +Pakistan,PAK,1999,21.5048004,0,0,0 +Pakistan,PAK,2000,17.5582762,0,0,0.9267 +Pakistan,PAK,2001,18.2918966,0,0,0.8721 +Pakistan,PAK,2002,20.417628,0,0,0.9608 +Pakistan,PAK,2003,24.5975,0,0,1.0411 +Pakistan,PAK,2004,26.216,0,0,1.0764 +Pakistan,PAK,2005,30.699,0,0,0.9449 +Pakistan,PAK,2006,30.247,0,0,0.8933 +Pakistan,PAK,2007,31.622,0.0002,0,1.1041 +Pakistan,PAK,2008,26.964,0.0016,0.0105,1.2968 +Pakistan,PAK,2009,28.18,0.0053,0.0105,1.0193 +Pakistan,PAK,2010,29.475,0.0134,0.0105,1.0059 +Pakistan,PAK,2011,30.5695,0.0268,0.0105,1.1246 +Pakistan,PAK,2012,29.4765,0.0657,0.0984,1.1863 +Pakistan,PAK,2013,31.151,0.1444,0.2633,1.3886 +Pakistan,PAK,2014,32.444,0.2375,0.4593,1.3099 +Pakistan,PAK,2015,32.898,0.3305,0.5399,1.363 +Pakistan,PAK,2016,34.587,0.5904,0.7863,1.5061 +Pakistan,PAK,2017,30.479,1.05917491385381,1.20352418046919,1.62192713302928 +Pakistan,PAK,2018,35.9970072709644,2.12332225977397,1.72268087031436,1.66765852943038 +Peru,PER,1965,2.629543,0,0,0 +Peru,PER,1966,2.8239966,0,0,0 +Peru,PER,1967,3.1642904,0,0,0 +Peru,PER,1968,3.3940992,0,0,0 +Peru,PER,1969,3.6990378,0,0,0 +Peru,PER,1970,3.8272004,0,0,0 +Peru,PER,1971,4.2779792,0,0,0.227 +Peru,PER,1972,4.441497,0,0,0.227 +Peru,PER,1973,4.7685326,0,0,0.23 +Peru,PER,1974,5.2237308,0,0,0.266 +Peru,PER,1975,5.4667978,0,0,0.266 +Peru,PER,1976,5.8026722,0,0,0.245 +Peru,PER,1977,6.0236422,0,0,0.242 +Peru,PER,1978,6.2004182,0,0,0.215 +Peru,PER,1979,6.6998104,0,0,0.163 +Peru,PER,1980,7.0135878,0,0,0.085 +Peru,PER,1981,8.0035334,0,0,0.076 +Peru,PER,1982,8.39686,0,0,0.151 +Peru,PER,1983,8.1184378,0,0,0.145 +Peru,PER,1984,8.5692166,0,0,0.142 +Peru,PER,1985,9.382,0,0,0.169 +Peru,PER,1986,10.06,0,0,0.154 +Peru,PER,1987,10.878,0,0,0.136 +Peru,PER,1988,10.423,0,0,0.136 +Peru,PER,1989,10.507,0,0,0.145 +Peru,PER,1990,10.469,0,0,0.136 +Peru,PER,1991,11.492,0,0,0.133 +Peru,PER,1992,9.689,0,0,0.133 +Peru,PER,1993,11.799,0,0,0.109 +Peru,PER,1994,12.747,0,0,0.119 +Peru,PER,1995,12.9376,0,0,0.142 +Peru,PER,1996,13.3236,0,0.0004,0.137 +Peru,PER,1997,13.2145,0,0.0006,0.154 +Peru,PER,1998,13.8083,0,0.0005,0.126 +Peru,PER,1999,14.5406,0,0.0006,0.139 +Peru,PER,2000,16.1761,0,0.0008,0.158 +Peru,PER,2001,17.6148,0,0.0012,0.163 +Peru,PER,2002,18.0401,0,0.0012,0.186 +Peru,PER,2003,18.5337,0,0.0012,0.196 +Peru,PER,2004,17.5253,0,0.0012,0.153 +Peru,PER,2005,17.977,0,0.0012,0.348 +Peru,PER,2006,19.5943,0,0.0012,0.348 +Peru,PER,2007,19.5488,0,0.0012,0.417 +Peru,PER,2008,19.0596,0,0.0012,0.46 +Peru,PER,2009,19.9038,0,0.0012,0.46 +Peru,PER,2010,20.0521,0,0.0012,0.673 +Peru,PER,2011,21.5573,0,0.0012,0.675 +Peru,PER,2012,22.0319,0.0597,0.0012,0.668 +Peru,PER,2013,22.3196,0.1969,0.0012,0.96 +Peru,PER,2014,22.2107,0.1993,0.2575,1.291 +Peru,PER,2015,23.6870628274576,0.230255343,0.59437413148867,0.907 +Peru,PER,2016,23.826156,0.241818780861103,1.0541146262971,0.643 +Peru,PER,2017,29.0745134976158,0.287200343,1.073432268,0.577350339049708 +Peru,PER,2018,30.7686880671429,0.74519271519,1.4936338640475,0.674414967768594 +Philippines,PHL,1965,0.7601368,0,0,0 +Philippines,PHL,1966,0.795492,0,0,0 +Philippines,PHL,1967,1.0739142,0,0,0 +Philippines,PHL,1968,0.7026846,0,0,0 +Philippines,PHL,1969,0.7999114,0,0,0 +Philippines,PHL,1970,1.5423706,0,0,0 +Philippines,PHL,1971,1.7058884,0,0,0 +Philippines,PHL,1972,1.4318856,0,0,0 +Philippines,PHL,1973,1.8473092,0,0,0 +Philippines,PHL,1974,2.2229582,0,0,0 +Philippines,PHL,1975,2.2229582,0,0,0 +Philippines,PHL,1976,2.6339624,0,0,0 +Philippines,PHL,1977,1.9268584,0,0,0.001 +Philippines,PHL,1978,2.8018996,0,0,0.003 +Philippines,PHL,1979,2.7753832,0,0,0.63694 +Philippines,PHL,1980,3.4382932,0,0,2.04485 +Philippines,PHL,1981,3.690199,0,0,3.91219 +Philippines,PHL,1982,3.8183616,0,0,3.90786 +Philippines,PHL,1983,2.960998,0,0,4.93498 +Philippines,PHL,1984,5.214892,0,0,5.35846 +Philippines,PHL,1985,5.553,0,0,5.94218 +Philippines,PHL,1986,6.017,0,0,5.5733 +Philippines,PHL,1987,5.247,0,0,4.89597 +Philippines,PHL,1988,6.264,0,0,5.22291 +Philippines,PHL,1989,6.485,0,0,5.70166 +Philippines,PHL,1990,6.062,0,0,5.89576 +Philippines,PHL,1991,5.145,0,0,5.758 +Philippines,PHL,1992,4.44,0,0,5.7 +Philippines,PHL,1993,5.03,0,0,5.667 +Philippines,PHL,1994,5.862,0,0,6.32 +Philippines,PHL,1995,6.232,0,0,6.135 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+Slovenia,SVN,2018,4.71248788591198,0.253621,0.006159,0.288275670203735 +South & Central America,,1965,39.7930724145406,0,0,0 +South & Central America,,1966,44.3486322332899,0,0,0 +South & Central America,,1967,46.9556299166329,0,0,0 +South & Central America,,1968,49.9115885701269,0,0,0 +South & Central America,,1969,53.9498737707901,0,0,0 +South & Central America,,1970,65.884596431438,0,0,0.562232491253644 +South & Central America,,1971,72.7803598350451,0,0,2.14425986370262 +South & Central America,,1972,82.5895197431398,0,0,2.13092333942744 +South & Central America,,1973,92.5914633973303,0,0,2.26584280940233 +South & Central America,,1974,106.317131536979,0,0,2.37821947142857 +South & Central America,,1975,115.285595592439,0,0,2.86410021650943 +South & Central America,,1976,128.7125010471,0,0,3.35254051654519 +South & Central America,,1977,142.955395197342,0,0,3.67370057031671 +South & Central America,,1978,157.90912433309,0,0,4.07408073708791 +South & Central America,,1979,179.175710065748,0,0,4.37241316339286 +South & Central America,,1980,200.675677606334,0,0,4.39286509360215 +South & Central America,,1981,203.6502961988,0,0,4.87145063815789 +South & Central America,,1982,219.6879074026,0,0,5.50649220359568 +South & Central America,,1983,236.7603897104,0,0,6.34087499721105 +South & Central America,,1984,261.6288892559,0,0,6.16617694246974 +South & Central America,,1985,283.5115186652,0,0,6.05943720287795 +South & Central America,,1986,305.98564345,0,0,6.25076134467477 +South & Central America,,1987,326.4309152,0,0,6.70437013803319 +South & Central America,,1988,339.5146364,0,0,6.63786184144537 +South & Central America,,1989,347.1482867496,0,0,6.94440624640606 +South & Central America,,1990,360.9930032407,0,0,8.30542352057227 +South & Central America,,1991,385.4264438012,0,0,8.37401372470741 +South & Central America,,1992,392.79107040665,0,0,10.6927215113981 +South & Central America,,1993,423.063092996375,0,0.0017171717171717,10.2869644044233 +South & Central America,,1994,448.442967543188,0,0.00777777777777769,10.8324130126873 +South & Central America,,1995,465.694070012121,0,0.00777777777777769,11.0638034199407 +South & Central America,,1996,485.694592965893,0,0.0419739852222221,12.5732824578787 +South & Central America,,1997,513.913338001282,0,0.102925997979798,13.3682048860548 +South & Central America,,1998,526.607967050446,0.000017,0.111372380198889,12.5897003025361 +South & Central America,,1999,527.285051370376,0.0001270101010101,0.148471920316465,14.8560683939211 +South & Central America,,2000,555.651625248888,0.00217101369863014,0.252086795739393,14.887921410545 +South & Central America,,2001,521.39916789705,0.00381175,0.320963025326262,16.4733634078022 +South & Central America,,2002,547.363317973761,0.00619825,0.45883873841212,18.2747408942876 +South & Central America,,2003,570.648342016434,0.00857,0.443069453803029,20.0119041744679 +South & Central America,,2004,589.034864276435,0.0111217260273973,0.543386491936363,21.0209330632097 +South & Central America,,2005,624.152097561074,0.017905555889596,0.53227314051212,22.355898979053 +South & Central America,,2006,652.684198566938,0.00821092186868686,0.773761270347575,24.0660094625005 +South & Central America,,2007,675.737596411807,0.0105159508888889,1.16135797473595,29.2920766414479 +South & Central America,,2008,680.299249163882,0.0145370536666666,1.6703935558605,32.5696571454236 +South & Central America,,2009,697.527986014208,0.0237607302767676,2.07784961220307,36.4989477238297 +South & Central America,,2010,700.875058378779,0.0607369455555552,3.4491877679706,46.5722967440538 +South & Central America,,2011,744.100769228145,0.126640437330302,4.3244780178249,48.7674126095536 +South & Central America,,2012,730.32111515162,0.335077368384289,7.80437935207362,54.9286366310847 +South & Central America,,2013,709.805447610155,0.530739656562566,10.1972555446908,61.9563278510107 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Africa,ZAF,1979,1.1578828,0,0,0 +South Africa,ZAF,1980,1.0032038,0,0,0 +South Africa,ZAF,1981,1.653,0,0,0 +South Africa,ZAF,1982,1.016,0,0,0 +South Africa,ZAF,1983,0.595,0,0,0 +South Africa,ZAF,1984,0.56,0,0,0 +South Africa,ZAF,1985,0.624,0,0,0 +South Africa,ZAF,1986,1.623,0,0,0 +South Africa,ZAF,1987,1.617,0,0,0 +South Africa,ZAF,1988,3.162,0,0,0 +South Africa,ZAF,1989,2.759,0,0,0 +South Africa,ZAF,1990,1.01,0,0,0 +South Africa,ZAF,1991,1.98,0,0,0 +South Africa,ZAF,1992,0.752000015182507,0,0,0 +South Africa,ZAF,1993,0.146000037983867,0,0,0 +South Africa,ZAF,1994,1.07399991036014,0,0,0 +South Africa,ZAF,1995,0.528999958875573,0,0,0 +South Africa,ZAF,1996,1.31899986300979,0,0,0.06 +South Africa,ZAF,1997,2.09200003422436,0,0,0.146 +South Africa,ZAF,1998,1.59500002923634,0,0,0.231 +South Africa,ZAF,1999,0.72599991563943,0,0,0.197 +South Africa,ZAF,2000,1.1009998658861,0.0093,0,0.4775 +South Africa,ZAF,2001,1.72099997231045,0.0108,0,0.4231 +South Africa,ZAF,2002,2.08400002981888,0.0124,0,0.4603 +South Africa,ZAF,2003,0.827412096416907,0.0139,0.006,0.4084 +South Africa,ZAF,2004,0.949939032429949,0.0155,0.012,0.3819 +South Africa,ZAF,2005,1.33194018899589,0.017,0.012,0.4253 +South Africa,ZAF,2006,2.89761981049,0.0186,0.012,0.4055 +South Africa,ZAF,2007,2.86561981049,0.0201,0.012,0.4305 +South Africa,ZAF,2008,1.25961985154161,0.0217,0.021,0.4561 +South Africa,ZAF,2009,1.45161990697473,0.0233,0.03,0.4491 +South Africa,ZAF,2010,1.14289981094658,0.0276,0.034,0.3963 +South Africa,ZAF,2011,3.06382420286512,0.0803,0.037,0.412 +South Africa,ZAF,2012,1.01850983222985,0.086,0.037,0.4226 +South Africa,ZAF,2013,1.10391580897875,0.243,0.037,0.4777 +South Africa,ZAF,2014,1.79505997221915,1.123,1.066,0.4321 +South Africa,ZAF,2015,0.801,2.749,3.071,0.4179 +South Africa,ZAF,2016,0.7,3.274,4.209,0.4373 +South Africa,ZAF,2017,0.871083139684999,4.2762318389753,5.86696022727273,0.483268097970506 +South Africa,ZAF,2018,0.914864856414944,4.93523481933365,6.89476518066635,0.530431004684181 +South Korea,KOR,1965,0.710427,0,0,0 +South Korea,KOR,1966,0.985322,0,0,0 +South Korea,KOR,1967,0.953171,0,0,0 +South Korea,KOR,1968,0.929483,0,0,0 +South Korea,KOR,1969,1.428512,0,0,0 +South Korea,KOR,1970,1.220684,0,0,0 +South Korea,KOR,1971,1.319976,0,0,0 +South Korea,KOR,1972,1.368076,0,0,0 +South Korea,KOR,1973,1.284465,0,0,0 +South Korea,KOR,1974,1.905858,0,0,0 +South Korea,KOR,1975,1.682809,0,0,0 +South Korea,KOR,1976,1.788661,0,0,0 +South Korea,KOR,1977,1.392729,0,0,0 +South Korea,KOR,1978,1.807524,0,0,0 +South Korea,KOR,1979,2.296168,0,0,0 +South Korea,KOR,1980,1.920375,0,0,0 +South Korea,KOR,1981,2.597707,0,0,0 +South Korea,KOR,1982,1.715362,0,0,0 +South Korea,KOR,1983,2.294191,0,0,0 +South Korea,KOR,1984,1.998143,0,0,0 +South Korea,KOR,1985,3.184966,0,0,0 +South Korea,KOR,1986,3.10988,0,0,0 +South Korea,KOR,1987,3.965371,0,0,0 +South Korea,KOR,1988,1.987232,0,0,0 +South Korea,KOR,1989,2.963969,0,0,0 +South Korea,KOR,1990,4.683946,0.001,0,0 +South Korea,KOR,1991,3.486046,0.001,0,0 +South Korea,KOR,1992,3.097448,0.002,0,0 +South Korea,KOR,1993,4.227549,0.002,0.001,0 +South Korea,KOR,1994,2.346092,0.002,0.001,0 +South Korea,KOR,1995,2.759923,0.002,0,0.25 +South Korea,KOR,1996,2.424357,0.003,0,0.403 +South Korea,KOR,1997,2.813992,0.003,0.001,0.078 +South Korea,KOR,1998,4.279418,0.004,0.002,0.058 +South Korea,KOR,1999,4.15926,0.004572,0.005839,0.087 +South Korea,KOR,2000,4.009553,0.005284,0.016685,0.079 +South Korea,KOR,2001,2.329938791,0.006184,0.01259,0.088 +South Korea,KOR,2002,3.2327778,0.007044,0.014881,0.179 +South Korea,KOR,2003,4.885576514,0.007752,0.024865,0.188 +South Korea,KOR,2004,4.3110792,0.009872,0.047442,0.244 +South Korea,KOR,2005,3.673299595,0.014399,0.129888,0.234 +South Korea,KOR,2006,3.467537745,0.031022,0.238911,0.28 +South Korea,KOR,2007,3.631649214,0.071279,0.375641,0.307299 +South Korea,KOR,2008,3.070111801,0.284315,0.436034,0.416359 +South Korea,KOR,2009,2.813171173,0.566191,0.685353,0.455542 +South Korea,KOR,2010,3.681969249,0.772801,0.81695,0.417752 +South Korea,KOR,2011,4.597667427,0.917198,0.862884,0.7247195 +South Korea,KOR,2012,4.011737,1.103227,0.91276,1.766586 +South Korea,KOR,2013,4.289267,1.605182,1.148179,2.542375 +South Korea,KOR,2014,2.751419,2.5563,1.145557,5.414046 +South Korea,KOR,2015,2.145719654,3.979159,1.342439,6.4760755 +South Korea,KOR,2016,2.846275,5.122441,1.683142,7.092781 +South Korea,KOR,2017,2.808647,7.056219,2.169014,8.3480395 +South Korea,KOR,2018,2.92039075633473,9.34987691208115,2.40369046984238,10.1747942023222 +Spain,ESP,1965,20.0331402,0,0,0 +Spain,ESP,1966,27.7426708,0,0,0 +Spain,ESP,1967,23.0595262,0,0,0 +Spain,ESP,1968,24.819125,0,0,0 +Spain,ESP,1969,31.1438998,0,0,0 +Spain,ESP,1970,28.2695166,0,0,0.047 +Spain,ESP,1971,32.3868566,0,0,0.052 +Spain,ESP,1972,34.7904624,0,0,0.061 +Spain,ESP,1973,28.5206848,0,0,0.056 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Lanka,LKA,1991,3.1166,0,0,0 +Sri Lanka,LKA,1992,2.8999,0,0,0 +Sri Lanka,LKA,1993,3.796,0,0,0 +Sri Lanka,LKA,1994,4.0891,0,0,0 +Sri Lanka,LKA,1995,4.5141,0,0,0 +Sri Lanka,LKA,1996,3.2519,0,0,0 +Sri Lanka,LKA,1997,3.44727,0,0,0 +Sri Lanka,LKA,1998,3.91495,0,0,0 +Sri Lanka,LKA,1999,4.17749360092,0,0.0035,0 +Sri Lanka,LKA,2000,3.20420758836,0.000432,0.0034,0 +Sri Lanka,LKA,2001,3.11784367314,0.000903096,0.0035,0 +Sri Lanka,LKA,2002,2.7004116791,0.001501112,0.00360574656,0 +Sri Lanka,LKA,2003,3.3187416791,0.002403992,0.00340574656,0 +Sri Lanka,LKA,2004,2.9692226771,0.003539072,0.00270574656,0.00012 +Sri Lanka,LKA,2005,3.45953171538,0.004615336,0.00240574656,0.00228 +Sri Lanka,LKA,2006,4.6433768815,0.00538678,0.00230574656,0.00178 +Sri Lanka,LKA,2007,3.9564732127,0.00597646,0.00230574656,0.0011 +Sri Lanka,LKA,2008,4.13919291078,0.006375132,0.00324174656,0.00572 +Sri Lanka,LKA,2009,3.89111107146,0.006787804,0.00350144736,0.02301 +Sri Lanka,LKA,2010,5.64455925662,0.007195476,0.05316144736,0.03251 +Sri Lanka,LKA,2011,4.58389166476,0.008714148,0.09162044736,0.031626 +Sri Lanka,LKA,2012,3.30216352196,0.01000782,0.14680774656,0.022173 +Sri Lanka,LKA,2013,6.92924252196,0.01438312,0.23458174656,0.026389 +Sri Lanka,LKA,2014,4.56264852196,0.02808402,0.27245374656,0.041393 +Sri Lanka,LKA,2015,5.97989,0.04872,0.34321,0.05731 +Sri Lanka,LKA,2016,4.23154,0.08297,0.34487,0.07196 +Sri Lanka,LKA,2017,4.01494932701422,0.148725691829501,0.370274308170499,0.0721359195636819 +Sri Lanka,LKA,2018,6.36937016113744,0.205337810632247,0.305662189367753,0.113393639658395 +Sweden,SWE,1965,46.4523134,0,0,0 +Sweden,SWE,1966,45.5330782,0,0,0 +Sweden,SWE,1967,49.3116652,0,0,0 +Sweden,SWE,1968,48.7857566,0,0,0 +Sweden,SWE,1969,41.8561374,0,0,0 +Sweden,SWE,1970,41.54236,0,0,0.13 +Sweden,SWE,1971,52.0340156,0,0,0.16 +Sweden,SWE,1972,53.7752592,0,0,0.226 +Sweden,SWE,1973,59.904967,0,0,0.399 +Sweden,SWE,1974,57.3019404,0,0,0.357 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Kingdom,GBR,2004,4.84446036126594,0.004,1.939,7.3639 +United Kingdom,GBR,2005,4.92290011991847,0.008,2.912,9.1023 +United Kingdom,GBR,2006,4.5934068375,0.011,4.236,9.2769 +United Kingdom,GBR,2007,5.0773044,0.014,5.2738175,9.32450116541498 +United Kingdom,GBR,2008,5.14118626512,0.017,7.09724817,9.64927751572395 +United Kingdom,GBR,2009,5.22789253064297,0.0200002063914214,9.28097151759362,10.7149476650373 +United Kingdom,GBR,2010,3.59137579131275,0.0402770998790807,10.2856445487459,12.2629684145089 +United Kingdom,GBR,2011,5.69175016719455,0.243663429678101,15.9629686138003,13.3137936964753 +United Kingdom,GBR,2012,5.30906833099297,1.35376358665009,19.8471199734079,14.737996831243 +United Kingdom,GBR,2013,4.70150890828536,2.01026208187311,28.3971517268193,18.1048904689818 +United Kingdom,GBR,2014,5.88784889445528,4.05406244535365,31.9592445769616,22.6212461720444 +United Kingdom,GBR,2015,6.29726853387506,7.53341379738692,40.2752167513138,29.2589764578175 +United 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Africa,,1983,5.9170148,0,0,0.041 +Western Africa,,1984,5.7628578,0,0,0.042 +Western Africa,,1985,8.213,0,0,0.042 +Western Africa,,1986,9.969,0,0,0.042 +Western Africa,,1987,9.767,0,0,0.043 +Western Africa,,1988,10.821,0,0,0.043 +Western Africa,,1989,11.741,0,0,0.043 +Western Africa,,1990,11.9170909090909,0,0,0.044 +Western Africa,,1991,13.8871313131313,0,0,0.044 +Western Africa,,1992,14.273202020202,0,0,0.045 +Western Africa,,1993,13.6674747474747,0,0.002,0.045 +Western Africa,,1994,13.4677272727273,0,0.0054,0.045 +Western Africa,,1995,14.167979,0,0.0063,0.046 +Western Africa,,1996,14.687782,0,0.0071,0.047 +Western Africa,,1997,15.02648,0,0.0072,0.048 +Western Africa,,1998,11.908342,0,0.0076,0.049 +Western Africa,,1999,13.960687,0,0.0078,0.05 +Western Africa,,2000,14.956968,0.0024,0.0079,0.102360281195079 +Western Africa,,2001,15.349982,0.0029,0.00678,0.113 +Western Africa,,2002,16.435855,0.0034,0.006864,0.121 +Western Africa,,2003,14.768892,0.0044,0.007387,0.109 +Western Africa,,2004,16.825359,0.0056,0.008467,0.202913907284768 +Western Africa,,2005,16.432273,0.0068,0.008699,0.21508293460925 +Western Africa,,2006,14.912868,0.0079,0.00944,0.22356862745098 +Western Africa,,2007,13.349417,0.0094,0.008961,0.218 +Western Africa,,2008,15.526708,0.0105,0.007701,0.216 +Western Africa,,2009,15.247674,0.0128,0.007388,0.179 +Western Africa,,2010,16.809888,0.01911,0.005393,0.2 +Western Africa,,2011,16.997229,0.03126,0.019352,0.198 +Western Africa,,2012,17.4260859,0.058704,0.064368,0.168 +Western Africa,,2013,17.235633,0.095346,0.092228,0.183857923497268 +Western Africa,,2014,17.754943,0.111812517,0.091841351,0.196826476956387 +Western Africa,,2015,15.468794,0.125173174,0.179616085,0.236833232139243 +Western Africa,,2016,15.746255,0.221656275138679,0.177467492643836,0.294991807228916 +Western Africa,,2017,18.5561512140862,0.34148444958894,0.176432376377049,0.294185818684574 +Western Africa,,2018,19.3754390022931,0.52385478278206,0.176432376377049,0.294185818684574 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States,USA,1912,222935000,213834803.7 +United States,USA,1913,248446000,228406784.8 +United States,USA,1914,265763000,243928136.8 +United States,USA,1915,281104000,260454464.7 +United States,USA,1916,300767000,278043727.6 +United States,USA,1917,335316000,296756200.5 +United States,USA,1918,355928000,316654409.7 +United States,USA,1919,378367000,337803035.9 +United States,USA,1920,442929000,360268782.5 +United States,USA,1921,472183000,384120202.3 +United States,USA,1922,557531000,409427478 +United States,USA,1923,732407000,436262150.2 +United States,USA,1924,713940000,464696786.4 +United States,USA,1925,620373000,494804585.2 +United States,USA,1926,770874000,526658907.5 +United States,USA,1927,901129000,560332728.8 +United States,USA,1928,901474000,595898005 +United States,USA,1929,1007323000,633424944.4 +United States,USA,1930,898011000,672981179.4 +United States,USA,1931,851081000,714630832.9 +United States,USA,1932,785159000,758433471.8 +United States,USA,1933,905656000,804442945.5 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States,USA,1955,2484428000,2308031664 +United States,USA,1956,2617283000,2382889415 +United States,USA,1957,2616901000,2455590677 +United States,USA,1958,2448987000,2525650982 +United States,USA,1959,2574590000,2592580053 +United States,USA,1960,2574933000,2655888067 +United States,USA,1961,2621758000,2715092410 +United States,USA,1962,2676189000,2769724784 +United States,USA,1963,2752723000,2819338505 +United States,USA,1964,2786822000,2863515829 +United States,USA,1965,2848514000,2901875093 +United States,USA,1966,3027763000,2934077498 +United States,USA,1967,3215742000,2959833309 +United States,USA,1968,3329042000,2978907304 +United States,USA,1969,3371751000,2991123285 +United States,USA,1970,3517450000,2996367512 +United States,USA,1971,3453914000,2994590945 +United States,USA,1972,3455368000,2985810210 +United States,USA,1973,3360903000,2970107276 +United States,USA,1974,3202585000,2947627830 +United States,USA,1975,3056779000,2918578425 +United States,USA,1976,2976180000,2883222482 +United States,USA,1977,3009265000,2841875283 +United States,USA,1978,3178216000,2794898125 +United States,USA,1979,3121310000,2742691795 +United States,USA,1980,3146365000,2685689583 +United States,USA,1981,3128624000,2624350025 +United States,USA,1982,3156715000,2559149563 +United States,USA,1983,3170999000,2490575327 +United States,USA,1984,3249696000,2419118185 +United States,USA,1985,3274553000,2345266226 +United States,USA,1986,3168252000,2269498775 +United States,USA,1987,3047378000,2192281051 +United States,USA,1988,2979123000,2114059515 +United States,USA,1989,2778773000,2035257957 +United States,USA,1990,2684687000,1956274325 +United States,USA,1991,2707039000,1877478289 +United States,USA,1992,2624632000,1799209507 +United States,USA,1993,2499033000,1721776552 +United States,USA,1994,2431476000,1645456442 +United States,USA,1995,2394268000,1570494698 +United States,USA,1996,2366017000,1497105882 +United States,USA,1997,2354831000,1425474514 +United States,USA,1998,2281919000,1355756331 +United States,USA,1999,2146732000,1288079787 +United States,USA,2000,2130707000,1222547756 +United States,USA,2001,2117511000,1159239363 +United States,USA,2002,2096588000,1098211904 +United States,USA,2003,2061995000,1039502794 +United States,USA,2004,1991394000,983131522.8 +United States,USA,2005,1892095000,929101569.6 +United States,USA,2006,1856340000,877402259.2 +United States,USA,2007,1852990000,828010537.2 +United States,USA,2008,1829985000,780892648.5 +United States,USA,2009,1953858000,736005707.9 +United States,USA,2010,1998452000,693299156.8 +United States,USA,2011,2060744000,652716101.4 +United States,USA,2012,2374136000,614194532.6 +United States,USA,2013,2725665000,577668428.7 +United States,USA,2014,3198694000,543068744.5 +United States,USA,2015,3436515000,510324290.7 +United States,USA,2016,3248117000,479362510.8 +United States,USA,2017,,450110160.6 +United States,USA,2018,,422493898.1 +United States,USA,2019,,396440789.9 +United States,USA,2020,,371878742.7 +United States,USA,2021,,348736865.3 +United States,USA,2022,,326945768.7 +United States,USA,2023,,306437811.3 +United States,USA,2024,,287147293.6 +United States,USA,2025,,269010610.6 +United States,USA,2026,,251966364.7 +United States,USA,2027,,235955445.8 +United States,USA,2028,,220921082.5 +United States,USA,2029,,206808867.5 +United States,USA,2030,,193566763.2 \ No newline at end of file diff --git a/your-project/data/12global-primary-energy-share-inc-biomass.csv b/your-project/data/12global-primary-energy-share-inc-biomass.csv new file mode 100644 index 00000000..21957f09 --- /dev/null +++ b/your-project/data/12global-primary-energy-share-inc-biomass.csv @@ -0,0 +1,71 @@ +Entity,Code,Year,Coal (%),Crude oil (%),Hydropower (%),Natural gas (%),Nuclear (%),Other renewables (%),Solar (%),Traditional biofuels (%),Wind (%) +World,OWID_WRL,1800,1.719903072,0,0,0,0,0,0,98.28009693,0 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+Niue,NIU,1972,,,4727 +Niue,NIU,1973,,,4455 +Niue,NIU,1974,,,4192 +Niue,NIU,1975,,,3973 +Niue,NIU,1976,,,3808 +Niue,NIU,1977,,,3689 +Niue,NIU,1978,,,3599 +Niue,NIU,1979,,,3510 +Niue,NIU,1980,,,3403 +Niue,NIU,1981,,,3273 +Niue,NIU,1982,,,3130 +Niue,NIU,1983,,,2981 +Niue,NIU,1984,,,2840 +Niue,NIU,1985,,,2716 +Niue,NIU,1986,,,2611 +Niue,NIU,1987,,,2522 +Niue,NIU,1988,,,2448 +Niue,NIU,1989,,,2385 +Niue,NIU,1990,,,2333 +Niue,NIU,1991,,,2292 +Niue,NIU,1992,,,2260 +Niue,NIU,1993,,,2233 +Niue,NIU,1994,,,2204 +Niue,NIU,1995,,,2168 +Niue,NIU,1996,,,2123 +Niue,NIU,1997,,,2069 +Niue,NIU,1998,,,2011 +Niue,NIU,1999,,,1953 +Niue,NIU,2000,,,1900 +Niue,NIU,2001,,,1852 +Niue,NIU,2002,,,1809 +Niue,NIU,2003,,,1768 +Niue,NIU,2004,,,1728 +Niue,NIU,2005,,,1686 +Niue,NIU,2006,,,1643 +Niue,NIU,2007,,,1598 +Niue,NIU,2008,,,1555 +Niue,NIU,2009,,,1511 +Niue,NIU,2010,,,1468 +Niue,NIU,2011,,,1426 +Niue,NIU,2012,,,1385 +Niue,NIU,2013,,,1346 +Norfolk Island,NFK,1792,,,1156 +Norfolk Island,NFK,1800,,,1121 +Norfolk 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Korea,PRK,1964,,,12011509 +North Korea,PRK,1965,,,12323345 +North Korea,PRK,1966,,,12671954 +North Korea,PRK,1967,,,13048499 +North Korea,PRK,1968,,,13444411 +North Korea,PRK,1969,,,13847248 +North Korea,PRK,1970,,,14246772 +North Korea,PRK,1971,,,14642731 +North Korea,PRK,1972,,,15034521 +North Korea,PRK,1973,,,15411302 +North Korea,PRK,1974,,,15759909 +North Korea,PRK,1975,,,16072089 +North Korea,PRK,1976,,,16341363 +North Korea,PRK,1977,,,16572788 +North Korea,PRK,1978,,,16784068 +North Korea,PRK,1979,,,17000252 +North Korea,PRK,1980,,,17239340 +North Korea,PRK,1981,,,17508435 +North Korea,PRK,1982,,,17802273 +North Korea,PRK,1983,,,18111491 +North Korea,PRK,1984,,,18421195 +North Korea,PRK,1985,,,18720888 +North Korea,PRK,1986,,,19006458 +North Korea,PRK,1987,,,19282372 +North Korea,PRK,1988,,,19556657 +North Korea,PRK,1989,,,19841107 +North Korea,PRK,1990,,,20143212 +North Korea,PRK,1991,,,20466180 +North Korea,PRK,1992,,,20804249 +North Korea,PRK,1993,,,21145052 +North Korea,PRK,1994,,,21471407 +North Korea,PRK,1995,,,21770898 +North Korea,PRK,1996,,,22038893 +North Korea,PRK,1997,,,22278723 +North Korea,PRK,1998,,,22495715 +North Korea,PRK,1999,,,22698674 +North Korea,PRK,2000,,,22894095 +North Korea,PRK,2001,,,23083419 +North Korea,PRK,2002,,,23264576 +North Korea,PRK,2003,,,23436550 +North Korea,PRK,2004,,,23597383 +North Korea,PRK,2005,,,23745941 +North Korea,PRK,2006,,,23882465 +North Korea,PRK,2007,,,24008606 +North Korea,PRK,2008,,,24126329 +North Korea,PRK,2009,,,24238179 +North Korea,PRK,2010,,,24346229 +North Korea,PRK,2011,,,24451285 +North Korea,PRK,2012,,,24553672 +North Korea,PRK,2013,,,24654358 +Northern Mariana Islands,MNP,1800,,,3360 +Northern Mariana Islands,MNP,1820,,,3360 +Northern Mariana Islands,MNP,1950,,,6999 +Northern Mariana Islands,MNP,1951,,,7089 +Northern Mariana Islands,MNP,1952,,,7222 +Northern Mariana Islands,MNP,1953,,,7417 +Northern Mariana Islands,MNP,1954,,,7685 +Northern Mariana Islands,MNP,1955,,,8026 +Northern Mariana Islands,MNP,1956,,,8427 +Northern Mariana Islands,MNP,1957,,,8864 +Northern Mariana Islands,MNP,1958,,,9307 +Northern Mariana Islands,MNP,1959,,,9721 +Northern Mariana Islands,MNP,1960,,,10070 +Northern Mariana Islands,MNP,1961,,,10340 +Northern Mariana Islands,MNP,1962,,,10541 +Northern Mariana Islands,MNP,1963,,,10709 +Northern Mariana Islands,MNP,1964,,,10901 +Northern Mariana Islands,MNP,1965,,,11157 +Northern Mariana Islands,MNP,1966,,,11489 +Northern Mariana Islands,MNP,1967,,,11882 +Northern Mariana Islands,MNP,1968,,,12318 +Northern Mariana Islands,MNP,1969,,,12766 +Northern Mariana Islands,MNP,1970,,,13203 +Northern Mariana Islands,MNP,1971,,,13658 +Northern Mariana Islands,MNP,1972,,,14142 +Northern Mariana Islands,MNP,1973,,,14605 +Northern Mariana Islands,MNP,1974,,,14978 +Northern Mariana Islands,MNP,1975,,,15232 +Northern Mariana Islands,MNP,1976,,,15316 +Northern Mariana Islands,MNP,1977,,,15293 +Northern Mariana Islands,MNP,1978,,,15390 +Northern Mariana 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Islands,MNP,2002,,,69393 +Northern Mariana Islands,MNP,2003,,,69105 +Northern Mariana Islands,MNP,2004,,,68404 +Northern Mariana Islands,MNP,2005,,,67381 +Northern Mariana Islands,MNP,2006,,,66006 +Northern Mariana Islands,MNP,2007,,,64349 +Northern Mariana Islands,MNP,2008,,,62707 +Northern Mariana Islands,MNP,2009,,,61473 +Northern Mariana Islands,MNP,2010,,,60917 +Northern Mariana Islands,MNP,2011,,,61174 +Northern Mariana Islands,MNP,2012,,,62152 +Northern Mariana Islands,MNP,2013,,,63597 +Norway,NOR,1700,,,500000 +Norway,NOR,1800,,,868570 +Norway,NOR,1820,,,970000 +Norway,NOR,1821,,,984000 +Norway,NOR,1822,,,998000 +Norway,NOR,1823,,,1013000 +Norway,NOR,1824,,,1028000 +Norway,NOR,1825,,,1044000 +Norway,NOR,1826,,,1062000 +Norway,NOR,1827,,,1079000 +Norway,NOR,1828,,,1093000 +Norway,NOR,1829,,,1108000 +Norway,NOR,1830,,,1124000 +Norway,NOR,1831,,,1137000 +Norway,NOR,1832,,,1150000 +Norway,NOR,1833,,,1163000 +Norway,NOR,1834,,,1174000 +Norway,NOR,1835,,,1188000 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+Somalia,SOM,2013,,,10052842 +South Africa,ZAF,1700,,,1000000 +South Africa,ZAF,1800,,,1440820 +South Africa,ZAF,1820,,,1550000 +South Africa,ZAF,1870,,,2547000 +South Africa,ZAF,1913,,,6153000 +South Africa,ZAF,1950,,,13683162 +South Africa,ZAF,1951,,,13993912 +South Africa,ZAF,1952,,,14322458 +South Africa,ZAF,1953,,,14665211 +South Africa,ZAF,1954,,,15019719 +South Africa,ZAF,1955,,,15384665 +South Africa,ZAF,1956,,,15759932 +South Africa,ZAF,1957,,,16146536 +South Africa,ZAF,1958,,,16546450 +South Africa,ZAF,1959,,,16962304 +South Africa,ZAF,1960,,,17396477 +South Africa,ZAF,1961,,,17850139 +South Africa,ZAF,1962,,,18322409 +South Africa,ZAF,1963,,,18809986 +South Africa,ZAF,1964,,,19308185 +South Africa,ZAF,1965,,,19813932 +South Africa,ZAF,1966,,,20325179 +South Africa,ZAF,1967,,,20843695 +South Africa,ZAF,1968,,,21374801 +South Africa,ZAF,1969,,,21926000 +South Africa,ZAF,1970,,,22502306 +South Africa,ZAF,1971,,,23106584 +South Africa,ZAF,1972,,,23736249 +South Africa,ZAF,1973,,,24384286 +South Africa,ZAF,1974,,,25040691 +South Africa,ZAF,1975,,,25698624 +South Africa,ZAF,1976,,,26353940 +South Africa,ZAF,1977,,,27009603 +South Africa,ZAF,1978,,,27674353 +South Africa,ZAF,1979,,,28360792 +South Africa,ZAF,1980,,,29077186 +South Africa,ZAF,1981,,,29828924 +South Africa,ZAF,1982,,,30611185 +South Africa,ZAF,1983,,,31409842 +South Africa,ZAF,1984,,,32204995 +South Africa,ZAF,1985,,,32983394 +South Africa,ZAF,1986,,,33734360 +South Africa,ZAF,1987,,,34464223 +South Africa,ZAF,1988,,,35196881 +South Africa,ZAF,1989,,,35966236 +South Africa,ZAF,1990,,,36793907 +South Africa,ZAF,1991,,,37690924 +South Africa,ZAF,1992,,,38641823 +South Africa,ZAF,1993,,,39609488 +South Africa,ZAF,1994,,,40542036 +South Africa,ZAF,1995,,,41402400 +South Africa,ZAF,1996,,,42175740 +South Africa,ZAF,1997,,,42873355 +South Africa,ZAF,1998,,,43516225 +South Africa,ZAF,1999,,,44137342 +South Africa,ZAF,2000,,,44760380 +South Africa,ZAF,2001,,,45389577 +South Africa,ZAF,2002,,,46015405 +South Africa,ZAF,2003,,,46631364 +South Africa,ZAF,2004,,,47226670 +South Africa,ZAF,2005,,,47792787 +South Africa,ZAF,2006,,,48330914 +South Africa,ZAF,2007,,,48842462 +South Africa,ZAF,2008,,,49319363 +South Africa,ZAF,2009,,,49751503 +South Africa,ZAF,2010,,,50132817 +South Africa,ZAF,2011,,,50459978 +South Africa,ZAF,2012,,,50738255 +South Africa,ZAF,2013,,,50981076 +South Korea,KOR,1800,,,9395000 +South Korea,KOR,1820,,,9395000 +South Korea,KOR,1850,,,9545000 +South Korea,KOR,1870,,,9753000 +South Korea,KOR,1890,,,9848000 +South Korea,KOR,1900,,,9896000 +South Korea,KOR,1910,,,10096000 +South Korea,KOR,1911,,,10258000 +South Korea,KOR,1912,,,10422000 +South Korea,KOR,1913,,,10589000 +South Korea,KOR,1914,,,10764000 +South Korea,KOR,1915,,,10911000 +South Korea,KOR,1916,,,11086000 +South Korea,KOR,1917,,,11263000 +South Korea,KOR,1918,,,11443000 +South Korea,KOR,1919,,,11627000 +South Korea,KOR,1920,,,11804000 +South Korea,KOR,1921,,,12040000 +South Korea,KOR,1922,,,12281000 +South Korea,KOR,1923,,,12526000 +South Korea,KOR,1924,,,12777000 +South Korea,KOR,1925,,,13005000 +South Korea,KOR,1926,,,13179000 +South Korea,KOR,1927,,,13356000 +South Korea,KOR,1928,,,13535000 +South Korea,KOR,1929,,,13716000 +South Korea,KOR,1930,,,13900000 +South Korea,KOR,1931,,,14117000 +South Korea,KOR,1932,,,14338000 +South Korea,KOR,1933,,,14562000 +South Korea,KOR,1934,,,14789000 +South Korea,KOR,1935,,,15020000 +South Korea,KOR,1936,,,15139000 +South Korea,KOR,1937,,,15260000 +South Korea,KOR,1938,,,15381000 +South Korea,KOR,1939,,,15504000 +South Korea,KOR,1940,,,15627000 +South Korea,KOR,1941,,,15859000 +South Korea,KOR,1942,,,16094000 +South Korea,KOR,1943,,,16332000 +South Korea,KOR,1944,,,16574000 +South Korea,KOR,1945,,,16881440 +South Korea,KOR,1946,,,18249518 +South Korea,KOR,1947,,,18736637 +South Korea,KOR,1948,,,18869487 +South Korea,KOR,1949,,,19040026 +South Korea,KOR,1950,,,19211386 +South Korea,KOR,1951,,,19304677 +South Korea,KOR,1952,,,19566709 +South Korea,KOR,1953,,,19978822 +South Korea,KOR,1954,,,20520266 +South Korea,KOR,1955,,,21168216 +South Korea,KOR,1956,,,21897498 +South Korea,KOR,1957,,,22680855 +South Korea,KOR,1958,,,23489746 +South Korea,KOR,1959,,,24295670 +South Korea,KOR,1960,,,25074152 +South Korea,KOR,1961,,,25808981 +South Korea,KOR,1962,,,26495936 +South Korea,KOR,1963,,,27144368 +South Korea,KOR,1964,,,27772698 +South Korea,KOR,1965,,,28395140 +South Korea,KOR,1966,,,29009251 +South Korea,KOR,1967,,,29610399 +South Korea,KOR,1968,,,30208614 +South Korea,KOR,1969,,,30816726 +South Korea,KOR,1970,,,31443029 +South Korea,KOR,1971,,,32094421 +South Korea,KOR,1972,,,32766592 +South Korea,KOR,1973,,,33442949 +South Korea,KOR,1974,,,34099932 +South Korea,KOR,1975,,,34721507 +South Korea,KOR,1976,,,35299344 +South Korea,KOR,1977,,,35840876 +South Korea,KOR,1978,,,36364813 +South Korea,KOR,1979,,,36898192 +South Korea,KOR,1980,,,37459530 +South Korea,KOR,1981,,,38054603 +South Korea,KOR,1982,,,38674216 +South Korea,KOR,1983,,,39303579 +South Korea,KOR,1984,,,39920981 +South Korea,KOR,1985,,,40509932 +South Korea,KOR,1986,,,41066824 +South Korea,KOR,1987,,,41594331 +South Korea,KOR,1988,,,42089874 +South Korea,KOR,1989,,,42552039 +South Korea,KOR,1990,,,42980365 +South Korea,KOR,1991,,,43372546 +South Korea,KOR,1992,,,43729283 +South Korea,KOR,1993,,,44057249 +South Korea,KOR,1994,,,44365820 +South Korea,KOR,1995,,,44662143 +South Korea,KOR,1996,,,44949627 +South Korea,KOR,1997,,,45227776 +South Korea,KOR,1998,,,45495222 +South Korea,KOR,1999,,,45749004 +South Korea,KOR,2000,,,45987624 +South Korea,KOR,2001,,,46210554 +South Korea,KOR,2002,,,46420867 +South Korea,KOR,2003,,,46624837 +South Korea,KOR,2004,,,46830699 +South Korea,KOR,2005,,,47044126 +South Korea,KOR,2006,,,47267733 +South Korea,KOR,2007,,,47499360 +South Korea,KOR,2008,,,47733951 +South Korea,KOR,2009,,,47963923 +South Korea,KOR,2010,,,48183584 +South Korea,KOR,2011,,,48391343 +South Korea,KOR,2012,,,48588326 +South Korea,KOR,2013,,,48774767 +South Sudan,SSD,1700,,,900216 +South Sudan,SSD,1800,,,1027378 +South Sudan,SSD,1820,,,1054890 +South Sudan,SSD,1950,,,1880222 +South Sudan,SSD,1951,,,1919582 +South Sudan,SSD,1952,,,1961925 +South Sudan,SSD,1953,,,2006745 +South Sudan,SSD,1954,,,2053619 +South Sudan,SSD,1955,,,2102215 +South Sudan,SSD,1956,,,2152290 +South Sudan,SSD,1957,,,2203691 +South Sudan,SSD,1958,,,2256344 +South Sudan,SSD,1959,,,2310256 +South Sudan,SSD,1960,,,2365478 +South Sudan,SSD,1961,,,2422089 +South Sudan,SSD,1962,,,2480164 +South Sudan,SSD,1963,,,2539771 +South Sudan,SSD,1964,,,2600994 +South Sudan,SSD,1965,,,2664019 +South Sudan,SSD,1966,,,2728851 +South Sudan,SSD,1967,,,2795865 +South Sudan,SSD,1968,,,2866074 +South Sudan,SSD,1969,,,2940782 +South Sudan,SSD,1970,,,3020949 +South Sudan,SSD,1971,,,3107019 +South Sudan,SSD,1972,,,3198874 +South Sudan,SSD,1973,,,3296208 +South Sudan,SSD,1974,,,3398449 +South Sudan,SSD,1975,,,3505168 +South Sudan,SSD,1976,,,3615772 +South Sudan,SSD,1977,,,3730284 +South Sudan,SSD,1978,,,3849541 +South Sudan,SSD,1979,,,3974741 +South Sudan,SSD,1980,,,4106390 +South Sudan,SSD,1981,,,4245451 +South Sudan,SSD,1982,,,4390699 +South Sudan,SSD,1983,,,4537851 +South Sudan,SSD,1984,,,4681151 +South Sudan,SSD,1985,,,4816725 +South Sudan,SSD,1986,,,4942648 +South Sudan,SSD,1987,,,5060777 +South Sudan,SSD,1988,,,5175686 +South Sudan,SSD,1989,,,5294006 +South Sudan,SSD,1990,,,5420563 +South Sudan,SSD,1991,,,5556767 +South Sudan,SSD,1992,,,5701228 +South Sudan,SSD,1993,,,5852564 +South Sudan,SSD,1994,,,6008344 +South Sudan,SSD,1995,,,6166715 +South Sudan,SSD,1996,,,6327927 +South Sudan,SSD,1997,,,6492553 +South Sudan,SSD,1998,,,6659415 +South Sudan,SSD,1999,,,6827051 +South Sudan,SSD,2000,,,6994624 +South Sudan,SSD,2001,,,7161267 +South Sudan,SSD,2002,,,7327782 +South Sudan,SSD,2003,,,7497047 +South Sudan,SSD,2004,,,7673043 +South Sudan,SSD,2005,,,7858543 +South Sudan,SSD,2006,,,8054628 +South Sudan,SSD,2007,,,8260226 +South Sudan,SSD,2008,,,8473315 +South Sudan,SSD,2009,,,8940854 +South Sudan,SSD,2010,,,9415421 +South Sudan,SSD,2011,,,9897118 +South Sudan,SSD,2012,,,10386101 +South Sudan,SSD,2013,,,10881979 +Spain,ESP,1700,,,8770000 +Spain,ESP,1800,,,11549294 +Spain,ESP,1820,,,12203000 +Spain,ESP,1821,,,12284000 +Spain,ESP,1822,,,12366000 +Spain,ESP,1823,,,12449000 +Spain,ESP,1824,,,12532000 +Spain,ESP,1825,,,12615000 +Spain,ESP,1826,,,12699000 +Spain,ESP,1827,,,12784000 +Spain,ESP,1828,,,12869000 +Spain,ESP,1829,,,12955000 +Spain,ESP,1830,,,13041000 +Spain,ESP,1831,,,13128000 +Spain,ESP,1832,,,13216000 +Spain,ESP,1833,,,13304000 +Spain,ESP,1834,,,13392000 +Spain,ESP,1835,,,13482000 +Spain,ESP,1836,,,13571000 +Spain,ESP,1837,,,13662000 +Spain,ESP,1838,,,13753000 +Spain,ESP,1839,,,13845000 +Spain,ESP,1840,,,13937000 +Spain,ESP,1841,,,14030000 +Spain,ESP,1842,,,14123000 +Spain,ESP,1843,,,14217000 +Spain,ESP,1844,,,14312000 +Spain,ESP,1845,,,14407000 +Spain,ESP,1846,,,14503000 +Spain,ESP,1847,,,14600000 +Spain,ESP,1848,,,14697000 +Spain,ESP,1849,,,14795000 +Spain,ESP,1850,,,14894000 +Spain,ESP,1851,,,14974000 +Spain,ESP,1852,,,15055000 +Spain,ESP,1853,,,15136000 +Spain,ESP,1854,,,15217000 +Spain,ESP,1855,,,15299000 +Spain,ESP,1856,,,15381000 +Spain,ESP,1857,,,15455000 +Spain,ESP,1858,,,15526000 +Spain,ESP,1859,,,15584000 +Spain,ESP,1860,,,15642000 +Spain,ESP,1861,,,15699000 +Spain,ESP,1862,,,15754000 +Spain,ESP,1863,,,15809000 +Spain,ESP,1864,,,15864000 +Spain,ESP,1865,,,15920000 +Spain,ESP,1866,,,15976000 +Spain,ESP,1867,,,16032000 +Spain,ESP,1868,,,16088000 +Spain,ESP,1869,,,16144000 +Spain,ESP,1870,,,16201000 +Spain,ESP,1871,,,16258000 +Spain,ESP,1872,,,16315000 +Spain,ESP,1873,,,16372000 +Spain,ESP,1874,,,16429000 +Spain,ESP,1875,,,16487000 +Spain,ESP,1876,,,16545000 +Spain,ESP,1877,,,16603000 +Spain,ESP,1878,,,16677000 +Spain,ESP,1879,,,16768000 +Spain,ESP,1880,,,16859000 +Spain,ESP,1881,,,16951000 +Spain,ESP,1882,,,17043000 +Spain,ESP,1883,,,17136000 +Spain,ESP,1884,,,17230000 +Spain,ESP,1885,,,17323000 +Spain,ESP,1886,,,17418000 +Spain,ESP,1887,,,17513000 +Spain,ESP,1888,,,17600000 +Spain,ESP,1889,,,17678000 +Spain,ESP,1890,,,17757000 +Spain,ESP,1891,,,17836000 +Spain,ESP,1892,,,17916000 +Spain,ESP,1893,,,17996000 +Spain,ESP,1894,,,18076000 +Spain,ESP,1895,,,18157000 +Spain,ESP,1896,,,18238000 +Spain,ESP,1897,,,18320000 +Spain,ESP,1898,,,18402000 +Spain,ESP,1899,,,18484000 +Spain,ESP,1900,,,18566000 +Spain,ESP,1901,,,18659000 +Spain,ESP,1902,,,18788000 +Spain,ESP,1903,,,18919000 +Spain,ESP,1904,,,19050000 +Spain,ESP,1905,,,19133000 +Spain,ESP,1906,,,19316000 +Spain,ESP,1907,,,19450000 +Spain,ESP,1908,,,19585000 +Spain,ESP,1909,,,19721000 +Spain,ESP,1910,,,19858000 +Spain,ESP,1911,,,19994000 +Spain,ESP,1912,,,20128000 +Spain,ESP,1913,,,20263000 +Spain,ESP,1914,,,20398000 +Spain,ESP,1915,,,20535000 +Spain,ESP,1916,,,20673000 +Spain,ESP,1917,,,20811000 +Spain,ESP,1918,,,20950000 +Spain,ESP,1919,,,21091000 +Spain,ESP,1920,,,21232000 +Spain,ESP,1921,,,21411000 +Spain,ESP,1922,,,21628000 +Spain,ESP,1923,,,21847000 +Spain,ESP,1924,,,22069000 +Spain,ESP,1925,,,22292000 +Spain,ESP,1926,,,22518000 +Spain,ESP,1927,,,22747000 +Spain,ESP,1928,,,22977000 +Spain,ESP,1929,,,23210000 +Spain,ESP,1930,,,23445000 +Spain,ESP,1931,,,23675000 +Spain,ESP,1932,,,23897000 +Spain,ESP,1933,,,24122000 +Spain,ESP,1934,,,24349000 +Spain,ESP,1935,,,24579000 +Spain,ESP,1936,,,24810000 +Spain,ESP,1937,,,25043000 +Spain,ESP,1938,,,25279000 +Spain,ESP,1939,,,25517000 +Spain,ESP,1940,,,25757000 +Spain,ESP,1941,,,25979000 +Spain,ESP,1942,,,26182000 +Spain,ESP,1943,,,26387000 +Spain,ESP,1944,,,26594000 +Spain,ESP,1945,,,26802000 +Spain,ESP,1946,,,27012000 +Spain,ESP,1947,,,27223000 +Spain,ESP,1948,,,27437000 +Spain,ESP,1949,,,27651000 +Spain,ESP,1950,,,28069737 +Spain,ESP,1951,,,28236442 +Spain,ESP,1952,,,28427994 +Spain,ESP,1953,,,28637153 +Spain,ESP,1954,,,28858741 +Spain,ESP,1955,,,29089637 +Spain,ESP,1956,,,29328890 +Spain,ESP,1957,,,29577608 +Spain,ESP,1958,,,29838623 +Spain,ESP,1959,,,30115936 +Spain,ESP,1960,,,30413044 +Spain,ESP,1961,,,30731163 +Spain,ESP,1962,,,31067664 +Spain,ESP,1963,,,31415410 +Spain,ESP,1964,,,31764645 +Spain,ESP,1965,,,32108561 +Spain,ESP,1966,,,32443113 +Spain,ESP,1967,,,32770565 +Spain,ESP,1968,,,33098047 +Spain,ESP,1969,,,33436107 +Spain,ESP,1970,,,33791761 +Spain,ESP,1971,,,34165661 +Spain,ESP,1972,,,34553119 +Spain,ESP,1973,,,34949102 +Spain,ESP,1974,,,35346395 +Spain,ESP,1975,,,35738587 +Spain,ESP,1976,,,36124485 +Spain,ESP,1977,,,36501995 +Spain,ESP,1978,,,36862658 +Spain,ESP,1979,,,37196233 +Spain,ESP,1980,,,37495321 +Spain,ESP,1981,,,37757496 +Spain,ESP,1982,,,37984198 +Spain,ESP,1983,,,38177151 +Spain,ESP,1984,,,38339878 +Spain,ESP,1985,,,38476148 +Spain,ESP,1986,,,38586351 +Spain,ESP,1987,,,38673057 +Spain,ESP,1988,,,38745231 +Spain,ESP,1989,,,38814280 +Spain,ESP,1990,,,38889158 +Spain,ESP,1991,,,38979222 +Spain,ESP,1992,,,39086335 +Spain,ESP,1993,,,39203352 +Spain,ESP,1994,,,39318577 +Spain,ESP,1995,,,39426834 +Spain,ESP,1996,,,39519508 +Spain,ESP,1997,,,39608652 +Spain,ESP,1998,,,39734033 +Spain,ESP,1999,,,39948910 +Spain,ESP,2000,,,40288457 +Spain,ESP,2001,,,40767010 +Spain,ESP,2002,,,41364726 +Spain,ESP,2003,,,42039800 +Spain,ESP,2004,,,42732092 +Spain,ESP,2005,,,43395441 +Spain,ESP,2006,,,44017887 +Spain,ESP,2007,,,44604384 +Spain,ESP,2008,,,45146258 +Spain,ESP,2009,,,45638113 +Spain,ESP,2010,,,46076989 +Spain,ESP,2011,,,46454895 +Spain,ESP,2012,,,46771596 +Spain,ESP,2013,,,47042602 +Sri Lanka,LKA,1800,,,1213000 +Sri Lanka,LKA,1820,,,1213000 +Sri Lanka,LKA,1850,,,2217000 +Sri Lanka,LKA,1870,,,2786000 +Sri Lanka,LKA,1871,,,2820000 +Sri Lanka,LKA,1872,,,2842000 +Sri Lanka,LKA,1873,,,2863000 +Sri Lanka,LKA,1874,,,2885000 +Sri Lanka,LKA,1875,,,2908000 +Sri Lanka,LKA,1876,,,2930000 +Sri Lanka,LKA,1877,,,2952000 +Sri Lanka,LKA,1878,,,2975000 +Sri Lanka,LKA,1879,,,2998000 +Sri Lanka,LKA,1880,,,3021000 +Sri Lanka,LKA,1881,,,3044000 +Sri Lanka,LKA,1882,,,3073000 +Sri 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+Zimbabwe,ZWE,2013,,,13327925 \ No newline at end of file diff --git a/your-project/data/14annual_co2_emissions_per_country.csv b/your-project/data/14annual_co2_emissions_per_country.csv new file mode 100644 index 00000000..b3601102 --- /dev/null +++ b/your-project/data/14annual_co2_emissions_per_country.csv @@ -0,0 +1,20854 @@ +Entity,Code,Year,Annual CO₂ emissions (tonnes) +Afghanistan,AFG,1949,14656 +Afghanistan,AFG,1950,84272 +Afghanistan,AFG,1951,91600 +Afghanistan,AFG,1952,91600 +Afghanistan,AFG,1953,106256 +Afghanistan,AFG,1954,106256 +Afghanistan,AFG,1955,153888 +Afghanistan,AFG,1956,183200 +Afghanistan,AFG,1957,293120 +Afghanistan,AFG,1958,329760 +Afghanistan,AFG,1959,384571.42 +Afghanistan,AFG,1960,413883.42 +Afghanistan,AFG,1961,490797.7 +Afghanistan,AFG,1962,688594.27 +Afghanistan,AFG,1963,706735.98 +Afghanistan,AFG,1964,838550.83 +Afghanistan,AFG,1965,1006916.53 +Afghanistan,AFG,1966,1091158.82 +Afghanistan,AFG,1967,1281865.11 +Afghanistan,AFG,1968,1223389.69 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Sudan,SSD,1969,348385.33 +South Sudan,SSD,1970,404261.33 +South Sudan,SSD,1971,414948 +South Sudan,SSD,1972,447924 +South Sudan,SSD,1973,451893.33 +South Sudan,SSD,1974,473266.67 +South Sudan,SSD,1975,352965.33 +South Sudan,SSD,1976,312661.33 +South Sudan,SSD,1977,316936 +South Sudan,SSD,1978,286402.67 +South Sudan,SSD,1979,297394.67 +South Sudan,SSD,1980,312050.67 +South Sudan,SSD,1981,303196 +South Sudan,SSD,1982,318768 +South Sudan,SSD,1983,325485.33 +South Sudan,SSD,1984,291898.67 +South Sudan,SSD,1985,339225.33 +South Sudan,SSD,1986,353881.33 +South Sudan,SSD,1987,279074.67 +South Sudan,SSD,1988,403650.67 +South Sudan,SSD,1989,312966.67 +South Sudan,SSD,1990,434794.67 +South Sudan,SSD,1991,407314.67 +South Sudan,SSD,1992,364568 +South Sudan,SSD,1993,251594.67 +South Sudan,SSD,1994,345332 +South Sudan,SSD,1995,357850.67 +South Sudan,SSD,1996,369148 +South Sudan,SSD,1997,451282.67 +South Sudan,SSD,1998,391132 +South Sudan,SSD,1999,424108 +South Sudan,SSD,2000,460748 +South Sudan,SSD,2001,530364 +South Sudan,SSD,2002,676008 +South Sudan,SSD,2003,755394.67 +South Sudan,SSD,2004,954472 +South Sudan,SSD,2005,914473.33 +South Sudan,SSD,2006,1000272 +South Sudan,SSD,2007,1176144 +South Sudan,SSD,2008,1241790.67 +South Sudan,SSD,2009,1295834.67 +South Sudan,SSD,2010,1327284 +South Sudan,SSD,2011,1303773.33 +South Sudan,SSD,2015,1631139.61 +South Sudan,SSD,2016,1761869.59 +South Sudan,SSD,2017,1840678.99 +Spain,ESP,1830,3664 +Spain,ESP,1831,3664 +Spain,ESP,1832,3664 +Spain,ESP,1833,3664 +Spain,ESP,1834,3664 +Spain,ESP,1835,3664 +Spain,ESP,1836,21984 +Spain,ESP,1837,36640 +Spain,ESP,1838,25648 +Spain,ESP,1839,47632 +Spain,ESP,1840,36640 +Spain,ESP,1841,102592 +Spain,ESP,1842,142896 +Spain,ESP,1843,172208 +Spain,ESP,1844,201520 +Spain,ESP,1845,274800 +Spain,ESP,1846,282128 +Spain,ESP,1847,260144 +Spain,ESP,1848,293120 +Spain,ESP,1849,326096 +Spain,ESP,1850,494640 +Spain,ESP,1851,509296 +Spain,ESP,1852,534944 +Spain,ESP,1853,652192 +Spain,ESP,1854,549600 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Lanka,LKA,1975,2892985.06 +Sri Lanka,LKA,1976,2798767.31 +Sri Lanka,LKA,1977,2911030.74 +Sri Lanka,LKA,1978,3435544.34 +Sri Lanka,LKA,1979,3809015.72 +Sri Lanka,LKA,1980,3388599.13 +Sri Lanka,LKA,1981,4005592.2 +Sri Lanka,LKA,1982,4466888.73 +Sri Lanka,LKA,1983,4854897.93 +Sri Lanka,LKA,1984,3852101.33 +Sri Lanka,LKA,1985,3916565.79 +Sri Lanka,LKA,1986,3660301.47 +Sri Lanka,LKA,1987,4026917.15 +Sri Lanka,LKA,1988,3456637.88 +Sri Lanka,LKA,1989,3447011 +Sri Lanka,LKA,1990,3829432.12 +Sri Lanka,LKA,1991,4137030.16 +Sri Lanka,LKA,1992,5105983.75 +Sri Lanka,LKA,1993,4975607.38 +Sri Lanka,LKA,1994,5435782 +Sri Lanka,LKA,1995,5815497.85 +Sri Lanka,LKA,1996,6970167.52 +Sri Lanka,LKA,1997,7519559.47 +Sri Lanka,LKA,1998,7736311.65 +Sri Lanka,LKA,1999,8517572.13 +Sri Lanka,LKA,2000,10133816.82 +Sri Lanka,LKA,2001,10327266.07 +Sri Lanka,LKA,2002,10946683.17 +Sri Lanka,LKA,2003,10952128.15 +Sri Lanka,LKA,2004,12146168.79 +Sri Lanka,LKA,2005,11935194.05 +Sri Lanka,LKA,2006,11793656.59 +Sri Lanka,LKA,2007,12151310.26 +Sri Lanka,LKA,2008,11987816.3 +Sri Lanka,LKA,2009,12943484.25 +Sri Lanka,LKA,2010,13050301.14 +Sri Lanka,LKA,2011,14886602.36 +Sri Lanka,LKA,2012,15775749.77 +Sri Lanka,LKA,2013,15253298.29 +Sri Lanka,LKA,2014,18158931.55 +Sri Lanka,LKA,2015,20713437.7 +Sri Lanka,LKA,2016,21950727.06 +Sri Lanka,LKA,2017,23138426.06 +Statistical differences,,1751,0 +Statistical differences,,1752,0 +Statistical differences,,1753,0 +Statistical differences,,1754,0 +Statistical differences,,1755,0 +Statistical differences,,1756,0 +Statistical differences,,1757,0 +Statistical differences,,1758,0 +Statistical differences,,1759,0 +Statistical differences,,1760,0 +Statistical differences,,1761,0 +Statistical differences,,1762,0 +Statistical differences,,1763,0 +Statistical differences,,1764,0 +Statistical differences,,1765,0 +Statistical differences,,1766,0 +Statistical differences,,1767,0 +Statistical differences,,1768,0 +Statistical differences,,1769,0 +Statistical differences,,1770,0 +Statistical differences,,1771,0 +Statistical differences,,1772,0 +Statistical differences,,1773,0 +Statistical differences,,1774,0 +Statistical differences,,1775,0 +Statistical differences,,1776,0 +Statistical differences,,1777,0 +Statistical differences,,1778,0 +Statistical differences,,1779,0 +Statistical differences,,1780,0 +Statistical differences,,1781,0 +Statistical differences,,1782,0 +Statistical differences,,1783,0 +Statistical differences,,1784,0 +Statistical differences,,1785,0 +Statistical differences,,1786,0 +Statistical differences,,1787,0 +Statistical differences,,1788,0 +Statistical differences,,1789,0 +Statistical differences,,1790,0 +Statistical differences,,1791,0 +Statistical differences,,1792,0 +Statistical differences,,1793,0 +Statistical differences,,1794,0 +Statistical differences,,1795,0 +Statistical differences,,1796,0 +Statistical differences,,1797,0 +Statistical differences,,1798,0 +Statistical differences,,1799,0 +Statistical differences,,1800,0 +Statistical differences,,1801,0 +Statistical differences,,1802,0 +Statistical differences,,1803,0 +Statistical differences,,1804,0 +Statistical differences,,1805,0 +Statistical differences,,1806,0 +Statistical differences,,1807,0 +Statistical differences,,1808,0 +Statistical differences,,1809,0 +Statistical differences,,1810,0 +Statistical differences,,1811,0 +Statistical differences,,1812,0 +Statistical differences,,1813,0 +Statistical differences,,1814,0 +Statistical differences,,1815,0 +Statistical differences,,1816,0 +Statistical differences,,1817,0 +Statistical differences,,1818,0 +Statistical differences,,1819,0 +Statistical differences,,1820,0 +Statistical differences,,1821,0 +Statistical differences,,1822,0 +Statistical differences,,1823,0 +Statistical differences,,1824,0 +Statistical differences,,1825,0 +Statistical differences,,1826,0 +Statistical differences,,1827,0 +Statistical differences,,1828,0 +Statistical differences,,1829,0 +Statistical differences,,1830,0 +Statistical differences,,1831,0 +Statistical differences,,1832,0 +Statistical differences,,1833,0 +Statistical differences,,1834,0 +Statistical differences,,1835,0 +Statistical differences,,1836,0 +Statistical differences,,1837,0 +Statistical differences,,1838,0 +Statistical differences,,1839,0 +Statistical differences,,1840,0 +Statistical differences,,1841,0 +Statistical differences,,1842,0 +Statistical differences,,1843,0 +Statistical differences,,1844,0 +Statistical differences,,1845,0 +Statistical differences,,1846,0 +Statistical differences,,1847,0 +Statistical differences,,1848,0 +Statistical differences,,1849,0 +Statistical differences,,1850,0 +Statistical differences,,1851,0 +Statistical differences,,1852,0 +Statistical differences,,1853,0 +Statistical differences,,1854,0 +Statistical differences,,1855,0 +Statistical differences,,1856,0 +Statistical differences,,1857,0 +Statistical differences,,1858,0 +Statistical differences,,1859,0 +Statistical differences,,1860,0 +Statistical differences,,1861,0 +Statistical differences,,1862,0 +Statistical differences,,1863,0 +Statistical differences,,1864,0 +Statistical differences,,1865,0 +Statistical differences,,1866,0 +Statistical differences,,1867,0 +Statistical differences,,1868,0 +Statistical differences,,1869,0 +Statistical differences,,1870,0 +Statistical differences,,1871,0 +Statistical differences,,1872,0 +Statistical differences,,1873,0 +Statistical differences,,1874,0 +Statistical differences,,1875,0 +Statistical differences,,1876,0 +Statistical differences,,1877,0 +Statistical differences,,1878,0 +Statistical differences,,1879,0 +Statistical differences,,1880,0 +Statistical differences,,1881,0 +Statistical differences,,1882,0 +Statistical differences,,1883,0 +Statistical differences,,1884,0 +Statistical differences,,1885,0 +Statistical differences,,1886,0 +Statistical differences,,1887,0 +Statistical differences,,1888,0 +Statistical differences,,1889,0 +Statistical differences,,1890,0 +Statistical differences,,1891,0 +Statistical differences,,1892,0 +Statistical differences,,1893,0 +Statistical differences,,1894,0 +Statistical differences,,1895,0 +Statistical differences,,1896,0 +Statistical differences,,1897,0 +Statistical differences,,1898,0 +Statistical differences,,1899,0 +Statistical differences,,1900,0 +Statistical differences,,1901,0 +Statistical differences,,1902,0 +Statistical differences,,1903,0 +Statistical differences,,1904,0 +Statistical differences,,1905,0 +Statistical differences,,1906,0 +Statistical differences,,1907,0 +Statistical differences,,1908,0 +Statistical differences,,1909,0 +Statistical differences,,1910,0 +Statistical differences,,1911,0 +Statistical differences,,1912,0 +Statistical differences,,1913,0 +Statistical differences,,1914,0 +Statistical differences,,1915,0 +Statistical differences,,1916,0 +Statistical differences,,1917,0 +Statistical differences,,1918,0 +Statistical differences,,1919,0 +Statistical differences,,1920,0 +Statistical differences,,1921,0 +Statistical differences,,1922,0 +Statistical differences,,1923,0 +Statistical differences,,1924,0 +Statistical differences,,1925,0 +Statistical differences,,1926,0 +Statistical differences,,1927,0 +Statistical differences,,1928,0 +Statistical differences,,1929,0 +Statistical differences,,1930,0 +Statistical differences,,1931,0 +Statistical differences,,1932,0 +Statistical differences,,1933,0 +Statistical differences,,1934,0 +Statistical differences,,1935,0 +Statistical differences,,1936,0 +Statistical differences,,1937,0 +Statistical differences,,1938,0 +Statistical differences,,1939,0 +Statistical differences,,1940,0 +Statistical differences,,1941,0 +Statistical differences,,1942,0 +Statistical differences,,1943,0 +Statistical differences,,1944,0 +Statistical differences,,1945,0 +Statistical differences,,1946,0 +Statistical differences,,1947,0 +Statistical differences,,1948,0 +Statistical differences,,1949,0 +Statistical differences,,1950,0 +Statistical differences,,1951,0 +Statistical differences,,1952,0 +Statistical differences,,1953,0 +Statistical differences,,1954,0 +Statistical differences,,1955,0 +Statistical differences,,1956,0 +Statistical differences,,1957,0 +Statistical differences,,1958,0 +Statistical differences,,1959,132394976 +Statistical differences,,1960,66494272 +Statistical differences,,1961,86235904 +Statistical differences,,1962,139957472 +Statistical differences,,1963,131559584 +Statistical differences,,1964,188703328 +Statistical differences,,1965,184764528 +Statistical differences,,1966,239570640 +Statistical differences,,1967,247158784 +Statistical differences,,1968,214537389.7 +Statistical differences,,1969,141804128 +Statistical differences,,1970,6151856 +Statistical differences,,1971,-29377952 +Statistical differences,,1972,-132765040 +Statistical differences,,1973,-121974560 +Statistical differences,,1974,-17847344 +Statistical differences,,1975,-93736112 +Statistical differences,,1976,-13007200 +Statistical differences,,1977,50475264 +Statistical differences,,1978,-418377504 +Statistical differences,,1979,110597840 +Statistical differences,,1980,-9108704 +Statistical differences,,1981,-85799888 +Statistical differences,,1982,-110436624 +Statistical differences,,1983,-357884864 +Statistical differences,,1984,-241585840 +Statistical differences,,1985,-360728128 +Statistical differences,,1986,-48027712 +Statistical differences,,1987,-272304816 +Statistical differences,,1988,-315239568 +Statistical differences,,1989,-187820304 +Statistical differences,,1990,-581499017.7 +Statistical differences,,1991,-311707026.9 +Statistical differences,,1992,-320640450.1 +Statistical differences,,1993,-603904195.4 +Statistical differences,,1994,-382923420.4 +Statistical differences,,1995,-415290520.4 +Statistical differences,,1996,-601406772.8 +Statistical differences,,1997,-335278003 +Statistical differences,,1998,-197686138.5 +Statistical differences,,1999,-565757974.1 +Statistical differences,,2000,-625522256.7 +Statistical differences,,2001,-271153713.6 +Statistical differences,,2002,-470695645.7 +Statistical differences,,2003,-364016570.9 +Statistical differences,,2004,-325533728.8 +Statistical differences,,2005,-212372213.8 +Statistical differences,,2006,-165209053 +Statistical differences,,2007,-488640473.9 +Statistical differences,,2008,-204836709.7 +Statistical differences,,2009,-103698554.3 +Statistical differences,,2010,-172010026.3 +Statistical differences,,2011,-60312512.43 +Statistical differences,,2012,-1774597.04 +Statistical differences,,2013,161034286.1 +Statistical differences,,2014,155499749.8 +Statistical differences,,2015,143390525.2 +Statistical differences,,2016,227584343.4 +Statistical differences,,2017,256677450.8 +Sudan,SDN,1950,549600 +Sudan,SDN,1951,721808 +Sudan,SDN,1952,758448 +Sudan,SDN,1953,831728 +Sudan,SDN,1954,817072 +Sudan,SDN,1955,926992 +Sudan,SDN,1956,897680 +Sudan,SDN,1957,1154160 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a/your-project/data/15annual_share_of_co2_emissions.csv b/your-project/data/15annual_share_of_co2_emissions.csv new file mode 100644 index 00000000..41b2edbc --- /dev/null +++ b/your-project/data/15annual_share_of_co2_emissions.csv @@ -0,0 +1,20587 @@ +Entity,Code,Year,Share of global COâ‚‚ emissions (%) +Afghanistan,AFG,1949,0.0003 +Afghanistan,AFG,1950,0.0015 +Afghanistan,AFG,1951,0.0015 +Afghanistan,AFG,1952,0.0015 +Afghanistan,AFG,1953,0.0016 +Afghanistan,AFG,1954,0.0016 +Afghanistan,AFG,1955,0.0021 +Afghanistan,AFG,1956,0.0024 +Afghanistan,AFG,1957,0.0037 +Afghanistan,AFG,1958,0.004 +Afghanistan,AFG,1959,0.0043 +Afghanistan,AFG,1960,0.0044 +Afghanistan,AFG,1961,0.0052 +Afghanistan,AFG,1962,0.007 +Afghanistan,AFG,1963,0.0068 +Afghanistan,AFG,1964,0.0076 +Afghanistan,AFG,1965,0.0088 +Afghanistan,AFG,1966,0.0091 +Afghanistan,AFG,1967,0.0103 +Afghanistan,AFG,1968,0.0094 +Afghanistan,AFG,1969,0.0068 +Afghanistan,AFG,1970,0.0113 +Afghanistan,AFG,1971,0.0123 +Afghanistan,AFG,1972,0.0096 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Africa,ZAF,1929,0.7171 +South Africa,ZAF,1930,0.7243 +South Africa,ZAF,1931,0.7217 +South Africa,ZAF,1932,0.7339 +South Africa,ZAF,1933,0.7527 +South Africa,ZAF,1934,0.7885 +South Africa,ZAF,1935,0.8361 +South Africa,ZAF,1936,0.8347 +South Africa,ZAF,1937,0.8141 +South Africa,ZAF,1938,0.9075 +South Africa,ZAF,1939,0.8918 +South Africa,ZAF,1940,0.8467 +South Africa,ZAF,1941,0.8823 +South Africa,ZAF,1942,0.9609 +South Africa,ZAF,1943,0.9349 +South Africa,ZAF,1944,1.0533 +South Africa,ZAF,1945,1.2893 +South Africa,ZAF,1946,1.1848 +South Africa,ZAF,1947,1.0663 +South Africa,ZAF,1948,1.0372 +South Africa,ZAF,1949,1.1553 +South Africa,ZAF,1950,1.0549 +South Africa,ZAF,1951,1.0463 +South Africa,ZAF,1952,1.1234 +South Africa,ZAF,1953,1.0947 +South Africa,ZAF,1954,1.1149 +South Africa,ZAF,1955,1.1428 +South Africa,ZAF,1956,1.1297 +South Africa,ZAF,1957,1.1257 +South Africa,ZAF,1958,1.1717 +South Africa,ZAF,1959,1.051 +South Africa,ZAF,1960,1.0397 +South Africa,ZAF,1961,1.0804 +South Africa,ZAF,1962,1.0741 +South Africa,ZAF,1963,1.0572 +South Africa,ZAF,1964,1.0898 +South Africa,ZAF,1965,1.1176 +South Africa,ZAF,1966,1.0646 +South Africa,ZAF,1967,1.076 +South Africa,ZAF,1968,1.056 +South Africa,ZAF,1969,1.0338 +South Africa,ZAF,1970,1.0078 +South Africa,ZAF,1971,1.0928 +South Africa,ZAF,1972,1.0705 +South Africa,ZAF,1973,1.0259 +South Africa,ZAF,1974,1.0429 +South Africa,ZAF,1975,1.0991 +South Africa,ZAF,1976,1.0832 +South Africa,ZAF,1977,1.0877 +South Africa,ZAF,1978,1.087 +South Africa,ZAF,1979,1.1154 +South Africa,ZAF,1980,1.1763 +South Africa,ZAF,1981,1.3674 +South Africa,ZAF,1982,1.5048 +South Africa,ZAF,1983,1.5726 +South Africa,ZAF,1984,1.6411 +South Africa,ZAF,1985,1.6353 +South Africa,ZAF,1986,1.6194 +South Africa,ZAF,1987,1.5706 +South Africa,ZAF,1988,1.5791 +South Africa,ZAF,1989,1.5366 +South Africa,ZAF,1990,1.4094 +South Africa,ZAF,1991,1.4522 +South Africa,ZAF,1992,1.3563 +South Africa,ZAF,1993,1.446 +South Africa,ZAF,1994,1.5017 +South Africa,ZAF,1995,1.5706 +South Africa,ZAF,1996,1.5452 +South Africa,ZAF,1997,1.6104 +South Africa,ZAF,1998,1.5709 +South Africa,ZAF,1999,1.5642 +South Africa,ZAF,2000,1.5381 +South Africa,ZAF,2001,1.4764 +South Africa,ZAF,2002,1.3943 +South Africa,ZAF,2003,1.5009 +South Africa,ZAF,2004,1.5913 +South Africa,ZAF,2005,1.4187 +South Africa,ZAF,2006,1.4716 +South Africa,ZAF,2007,1.5025 +South Africa,ZAF,2008,1.5502 +South Africa,ZAF,2009,1.5905 +South Africa,ZAF,2010,1.4289 +South Africa,ZAF,2011,1.3649 +South Africa,ZAF,2012,1.3376 +South Africa,ZAF,2013,1.3196 +South Africa,ZAF,2014,1.3744 +South Africa,ZAF,2015,1.3003 +South Africa,ZAF,2016,1.312 +South Africa,ZAF,2017,1.2622 +South Korea,KOR,1945,0.0001 +South Korea,KOR,1946,0.0103 +South Korea,KOR,1947,0.0171 +South Korea,KOR,1948,0.0303 +South Korea,KOR,1949,0.0415 +South Korea,KOR,1950,0.0377 +South Korea,KOR,1951,0.047 +South Korea,KOR,1952,0.0549 +South Korea,KOR,1953,0.0724 +South Korea,KOR,1954,0.0778 +South Korea,KOR,1955,0.0888 +South Korea,KOR,1956,0.0978 +South Korea,KOR,1957,0.1031 +South Korea,KOR,1958,0.1097 +South Korea,KOR,1959,0.1248 +South Korea,KOR,1960,0.1333 +South Korea,KOR,1961,0.1529 +South Korea,KOR,1962,0.1755 +South Korea,KOR,1963,0.2031 +South Korea,KOR,1964,0.2023 +South Korea,KOR,1965,0.2178 +South Korea,KOR,1966,0.2489 +South Korea,KOR,1967,0.2825 +South Korea,KOR,1968,0.2847 +South Korea,KOR,1969,0.3067 +South Korea,KOR,1970,0.3618 +South Korea,KOR,1971,0.38 +South Korea,KOR,1972,0.3762 +South Korea,KOR,1973,0.4323 +South Korea,KOR,1974,0.4469 +South Korea,KOR,1975,0.4861 +South Korea,KOR,1976,0.524 +South Korea,KOR,1977,0.5758 +South Korea,KOR,1978,0.6109 +South Korea,KOR,1979,0.6799 +South Korea,KOR,1980,0.6957 +South Korea,KOR,1981,0.744 +South Korea,KOR,1982,0.7624 +South Korea,KOR,1983,0.8142 +South Korea,KOR,1984,0.8537 +South Korea,KOR,1985,0.9018 +South Korea,KOR,1986,0.8956 +South Korea,KOR,1987,0.9226 +South Korea,KOR,1988,1.0254 +South Korea,KOR,1989,1.0664 +South Korea,KOR,1990,1.1155 +South Korea,KOR,1991,1.1683 +South Korea,KOR,1992,1.2714 +South Korea,KOR,1993,1.4528 +South Korea,KOR,1994,1.5278 +South Korea,KOR,1995,1.6255 +South Korea,KOR,1996,1.707 +South Korea,KOR,1997,1.7888 +South Korea,KOR,1998,1.5157 +South Korea,KOR,1999,1.6645 +South Korea,KOR,2000,1.8138 +South Korea,KOR,2001,1.7844 +South Korea,KOR,2002,1.8186 +South Korea,KOR,2003,1.7211 +South Korea,KOR,2004,1.6979 +South Korea,KOR,2005,1.5701 +South Korea,KOR,2006,1.5373 +South Korea,KOR,2007,1.5976 +South Korea,KOR,2008,1.5867 +South Korea,KOR,2009,1.6076 +South Korea,KOR,2010,1.7116 +South Korea,KOR,2011,1.7127 +South Korea,KOR,2012,1.6714 +South Korea,KOR,2013,1.6801 +South Korea,KOR,2014,1.652 +South Korea,KOR,2015,1.6653 +South Korea,KOR,2016,1.668 +South Korea,KOR,2017,1.7041 +South Sudan,SSD,1959,0.0013 +South Sudan,SSD,1960,0.0012 +South Sudan,SSD,1961,0.0012 +South Sudan,SSD,1962,0.0013 +South Sudan,SSD,1963,0.0013 +South Sudan,SSD,1964,0.0014 +South Sudan,SSD,1965,0.0018 +South Sudan,SSD,1966,0.0018 +South Sudan,SSD,1967,0.0021 +South Sudan,SSD,1968,0.002 +South Sudan,SSD,1969,0.0025 +South Sudan,SSD,1970,0.0027 +South Sudan,SSD,1971,0.0027 +South Sudan,SSD,1972,0.0028 +South Sudan,SSD,1973,0.0027 +South Sudan,SSD,1974,0.0028 +South Sudan,SSD,1975,0.0021 +South Sudan,SSD,1976,0.0018 +South Sudan,SSD,1977,0.0017 +South Sudan,SSD,1978,0.0015 +South Sudan,SSD,1979,0.0015 +South Sudan,SSD,1980,0.0016 +South Sudan,SSD,1981,0.0016 +South Sudan,SSD,1982,0.0017 +South Sudan,SSD,1983,0.0018 +South Sudan,SSD,1984,0.0015 +South Sudan,SSD,1985,0.0017 +South Sudan,SSD,1986,0.0017 +South Sudan,SSD,1987,0.0013 +South Sudan,SSD,1988,0.0019 +South Sudan,SSD,1989,0.0014 +South Sudan,SSD,1990,0.002 +South Sudan,SSD,1991,0.0018 +South Sudan,SSD,1992,0.0016 +South Sudan,SSD,1993,0.0011 +South Sudan,SSD,1994,0.0015 +South Sudan,SSD,1995,0.0016 +South Sudan,SSD,1996,0.0016 +South Sudan,SSD,1997,0.0019 +South Sudan,SSD,1998,0.0016 +South Sudan,SSD,1999,0.0018 +South Sudan,SSD,2000,0.0019 +South Sudan,SSD,2001,0.0021 +South Sudan,SSD,2002,0.0027 +South Sudan,SSD,2003,0.0028 +South Sudan,SSD,2004,0.0034 +South Sudan,SSD,2005,0.0031 +South Sudan,SSD,2006,0.0033 +South Sudan,SSD,2007,0.0038 +South Sudan,SSD,2008,0.0039 +South Sudan,SSD,2009,0.0041 +South Sudan,SSD,2010,0.004 +South Sudan,SSD,2011,0.0038 +South Sudan,SSD,2015,0.0046 +South Sudan,SSD,2016,0.0049 +South Sudan,SSD,2017,0.0051 +Spain,ESP,1830,0.0041 +Spain,ESP,1831,0.0043 +Spain,ESP,1832,0.0043 +Spain,ESP,1833,0.0042 +Spain,ESP,1834,0.0041 +Spain,ESP,1835,0.0041 +Spain,ESP,1836,0.021 +Spain,ESP,1837,0.035 +Spain,ESP,1838,0.0237 +Spain,ESP,1839,0.0427 +Spain,ESP,1840,0.0308 +Spain,ESP,1841,0.0837 +Spain,ESP,1842,0.1103 +Spain,ESP,1843,0.1296 +Spain,ESP,1844,0.1425 +Spain,ESP,1845,0.177 +Spain,ESP,1846,0.1788 +Spain,ESP,1847,0.1509 +Spain,ESP,1848,0.1686 +Spain,ESP,1849,0.1777 +Spain,ESP,1850,0.2512 +Spain,ESP,1851,0.2563 +Spain,ESP,1852,0.2578 +Spain,ESP,1853,0.3004 +Spain,ESP,1854,0.2155 +Spain,ESP,1855,0.2128 +Spain,ESP,1856,0.2724 +Spain,ESP,1857,0.3039 +Spain,ESP,1858,0.4052 +Spain,ESP,1859,0.5417 +Spain,ESP,1860,0.5839 +Spain,ESP,1861,0.5763 +Spain,ESP,1862,0.6177 +Spain,ESP,1863,0.6438 +Spain,ESP,1864,0.5732 +Spain,ESP,1865,0.533 +Spain,ESP,1866,0.5134 +Spain,ESP,1867,0.5352 +Spain,ESP,1868,0.542 +Spain,ESP,1869,0.5062 +Spain,ESP,1870,0.5773 +Spain,ESP,1871,0.519 +Spain,ESP,1872,0.5206 +Spain,ESP,1873,0.4713 +Spain,ESP,1874,0.5005 +Spain,ESP,1875,0.4869 +Spain,ESP,1876,0.5206 +Spain,ESP,1877,0.5409 +Spain,ESP,1878,0.533 +Spain,ESP,1879,0.526 +Spain,ESP,1880,0.5171 +Spain,ESP,1881,0.5588 +Spain,ESP,1882,0.5774 +Spain,ESP,1883,0.5955 +Spain,ESP,1884,0.5855 +Spain,ESP,1885,0.5703 +Spain,ESP,1886,0.594 +Spain,ESP,1887,0.5702 +Spain,ESP,1888,0.5395 +Spain,ESP,1889,0.5723 +Spain,ESP,1890,0.565 +Spain,ESP,1891,0.5841 +Spain,ESP,1892,0.6055 +Spain,ESP,1893,0.5996 +Spain,ESP,1894,0.6233 +Spain,ESP,1895,0.579 +Spain,ESP,1896,0.6063 +Spain,ESP,1897,0.5939 +Spain,ESP,1898,0.5529 +Spain,ESP,1899,0.5738 +Spain,ESP,1900,0.5744 +Spain,ESP,1901,0.585 +Spain,ESP,1902,0.5962 +Spain,ESP,1903,0.5394 +Spain,ESP,1904,0.5695 +Spain,ESP,1905,0.5588 +Spain,ESP,1906,0.5434 +Spain,ESP,1907,0.4886 +Spain,ESP,1908,0.5326 +Spain,ESP,1909,0.5256 +Spain,ESP,1910,0.4922 +Spain,ESP,1911,0.478 +Spain,ESP,1912,0.4953 +Spain,ESP,1913,0.4989 +Spain,ESP,1914,0.5461 +Spain,ESP,1915,0.49 +Spain,ESP,1916,0.5298 +Spain,ESP,1917,0.4492 +Spain,ESP,1918,0.4808 +Spain,ESP,1919,0.528 +Spain,ESP,1920,0.4007 +Spain,ESP,1921,0.4824 +Spain,ESP,1922,0.463 +Spain,ESP,1923,0.4701 +Spain,ESP,1924,0.5006 +Spain,ESP,1925,0.515 +Spain,ESP,1926,0.5046 +Spain,ESP,1927,0.5392 +Spain,ESP,1928,0.5391 +Spain,ESP,1929,0.5588 +Spain,ESP,1930,0.5962 +Spain,ESP,1931,0.6361 +Spain,ESP,1932,0.6627 +Spain,ESP,1933,0.556 +Spain,ESP,1934,0.5531 +Spain,ESP,1935,0.5839 +Spain,ESP,1936,0.1908 +Spain,ESP,1937,0.116 +Spain,ESP,1938,0.3101 +Spain,ESP,1939,0.3491 +Spain,ESP,1940,0.441 +Spain,ESP,1941,0.4282 +Spain,ESP,1942,0.4576 +Spain,ESP,1943,0.4573 +Spain,ESP,1944,0.4979 +Spain,ESP,1945,0.6525 +Spain,ESP,1946,0.5939 +Spain,ESP,1947,0.5954 +Spain,ESP,1948,0.5944 +Spain,ESP,1949,0.6819 +Spain,ESP,1950,0.5711 +Spain,ESP,1951,0.532 +Spain,ESP,1952,0.5961 +Spain,ESP,1953,0.6004 +Spain,ESP,1954,0.5995 +Spain,ESP,1955,0.5551 +Spain,ESP,1956,0.5482 +Spain,ESP,1957,0.6307 +Spain,ESP,1958,0.6804 +Spain,ESP,1959,0.5794 +Spain,ESP,1960,0.5193 +Spain,ESP,1961,0.5671 +Spain,ESP,1962,0.6096 +Spain,ESP,1963,0.566 +Spain,ESP,1964,0.5864 +Spain,ESP,1965,0.6194 +Spain,ESP,1966,0.6444 +Spain,ESP,1967,0.695 +Spain,ESP,1968,0.7427 +Spain,ESP,1969,0.6986 +Spain,ESP,1970,0.7865 +Spain,ESP,1971,0.8336 +Spain,ESP,1972,0.9031 +Spain,ESP,1973,0.9173 +Spain,ESP,1974,1.0203 +Spain,ESP,1975,1.0761 +Spain,ESP,1976,1.1109 +Spain,ESP,1977,1.0614 +Spain,ESP,1978,1.0749 +Spain,ESP,1979,1.0347 +Spain,ESP,1980,1.1033 +Spain,ESP,1981,1.097 +Spain,ESP,1982,1.12 +Spain,ESP,1983,1.0967 +Spain,ESP,1984,1.0286 +Spain,ESP,1985,1.0101 +Spain,ESP,1986,0.9272 +Spain,ESP,1987,0.9062 +Spain,ESP,1988,0.9107 +Spain,ESP,1989,1.0151 +Spain,ESP,1990,1.0454 +Spain,ESP,1991,1.0766 +Spain,ESP,1992,1.1278 +Spain,ESP,1993,1.0916 +Spain,ESP,1994,1.1282 +Spain,ESP,1995,1.1641 +Spain,ESP,1996,1.086 +Spain,ESP,1997,1.1209 +Spain,ESP,1998,1.1534 +Spain,ESP,1999,1.2544 +Spain,ESP,2000,1.2701 +Spain,ESP,2001,1.2487 +Spain,ESP,2002,1.3101 +Spain,ESP,2003,1.258 +Spain,ESP,2004,1.2569 +Spain,ESP,2005,1.2612 +Spain,ESP,2006,1.1896 +Spain,ESP,2007,1.1921 +Spain,ESP,2008,1.0556 +Spain,ESP,2009,0.943 +Spain,ESP,2010,0.8585 +Spain,ESP,2011,0.8283 +Spain,ESP,2012,0.7998 +Spain,ESP,2013,0.7177 +Spain,ESP,2014,0.7187 +Spain,ESP,2015,0.7662 +Spain,ESP,2016,0.7316 +Spain,ESP,2017,0.7784 +Sri Lanka,LKA,1950,0.0281 +Sri Lanka,LKA,1951,0.0248 +Sri Lanka,LKA,1952,0.0307 +Sri Lanka,LKA,1953,0.0319 +Sri Lanka,LKA,1954,0.0269 +Sri Lanka,LKA,1955,0.0229 +Sri Lanka,LKA,1956,0.0184 +Sri Lanka,LKA,1957,0.0291 +Sri Lanka,LKA,1958,0.0237 +Sri Lanka,LKA,1959,0.0229 +Sri Lanka,LKA,1960,0.024 +Sri Lanka,LKA,1961,0.0247 +Sri Lanka,LKA,1962,0.026 +Sri Lanka,LKA,1963,0.0244 +Sri Lanka,LKA,1964,0.0207 +Sri Lanka,LKA,1965,0.0232 +Sri Lanka,LKA,1966,0.0221 +Sri Lanka,LKA,1967,0.0237 +Sri Lanka,LKA,1968,0.0248 +Sri Lanka,LKA,1969,0.0312 +Sri Lanka,LKA,1970,0.0242 +Sri Lanka,LKA,1971,0.0207 +Sri Lanka,LKA,1972,0.0221 +Sri Lanka,LKA,1973,0.0218 +Sri Lanka,LKA,1974,0.0173 +Sri Lanka,LKA,1975,0.0172 +Sri Lanka,LKA,1976,0.0157 +Sri Lanka,LKA,1977,0.0159 +Sri Lanka,LKA,1978,0.0185 +Sri Lanka,LKA,1979,0.0194 +Sri Lanka,LKA,1980,0.0175 +Sri Lanka,LKA,1981,0.0213 +Sri Lanka,LKA,1982,0.024 +Sri Lanka,LKA,1983,0.0262 +Sri Lanka,LKA,1984,0.02 +Sri Lanka,LKA,1985,0.0198 +Sri Lanka,LKA,1986,0.0179 +Sri Lanka,LKA,1987,0.0193 +Sri Lanka,LKA,1988,0.0159 +Sri Lanka,LKA,1989,0.0156 +Sri Lanka,LKA,1990,0.0173 +Sri Lanka,LKA,1991,0.0184 +Sri Lanka,LKA,1992,0.023 +Sri Lanka,LKA,1993,0.0225 +Sri Lanka,LKA,1994,0.0241 +Sri Lanka,LKA,1995,0.0253 +Sri Lanka,LKA,1996,0.0296 +Sri Lanka,LKA,1997,0.0314 +Sri Lanka,LKA,1998,0.0323 +Sri Lanka,LKA,1999,0.0356 +Sri Lanka,LKA,2000,0.0413 +Sri Lanka,LKA,2001,0.0411 +Sri Lanka,LKA,2002,0.0429 +Sri Lanka,LKA,2003,0.0408 +Sri Lanka,LKA,2004,0.0431 +Sri Lanka,LKA,2005,0.0408 +Sri Lanka,LKA,2006,0.0389 +Sri Lanka,LKA,2007,0.0393 +Sri Lanka,LKA,2008,0.0376 +Sri Lanka,LKA,2009,0.0411 +Sri Lanka,LKA,2010,0.0395 +Sri Lanka,LKA,2011,0.0433 +Sri Lanka,LKA,2012,0.0452 +Sri Lanka,LKA,2013,0.0433 +Sri Lanka,LKA,2014,0.0511 +Sri Lanka,LKA,2015,0.0584 +Sri Lanka,LKA,2016,0.0615 +Sri Lanka,LKA,2017,0.064 +Statistical differences,,1751,0 +Statistical differences,,1752,0 +Statistical differences,,1753,0 +Statistical differences,,1754,0 +Statistical differences,,1755,0 +Statistical differences,,1756,0 +Statistical differences,,1757,0 +Statistical differences,,1758,0 +Statistical differences,,1759,0 +Statistical differences,,1760,0 +Statistical differences,,1761,0 +Statistical differences,,1762,0 +Statistical differences,,1763,0 +Statistical differences,,1764,0 +Statistical differences,,1765,0 +Statistical differences,,1766,0 +Statistical differences,,1767,0 +Statistical differences,,1768,0 +Statistical differences,,1769,0 +Statistical differences,,1770,0 +Statistical differences,,1771,0 +Statistical differences,,1772,0 +Statistical differences,,1773,0 +Statistical differences,,1774,0 +Statistical differences,,1775,0 +Statistical differences,,1776,0 +Statistical differences,,1777,0 +Statistical differences,,1778,0 +Statistical differences,,1779,0 +Statistical differences,,1780,0 +Statistical differences,,1781,0 +Statistical differences,,1782,0 +Statistical differences,,1783,0 +Statistical differences,,1784,0 +Statistical differences,,1785,0 +Statistical differences,,1786,0 +Statistical differences,,1787,0 +Statistical differences,,1788,0 +Statistical differences,,1789,0 +Statistical differences,,1790,0 +Statistical differences,,1791,0 +Statistical differences,,1792,0 +Statistical differences,,1793,0 +Statistical differences,,1794,0 +Statistical differences,,1795,0 +Statistical differences,,1796,0 +Statistical differences,,1797,0 +Statistical differences,,1798,0 +Statistical differences,,1799,0 +Statistical differences,,1800,0 +Statistical differences,,1801,0 +Statistical differences,,1802,0 +Statistical differences,,1803,0 +Statistical differences,,1804,0 +Statistical differences,,1805,0 +Statistical differences,,1806,0 +Statistical differences,,1807,0 +Statistical differences,,1808,0 +Statistical differences,,1809,0 +Statistical differences,,1810,0 +Statistical differences,,1811,0 +Statistical differences,,1812,0 +Statistical differences,,1813,0 +Statistical differences,,1814,0 +Statistical differences,,1815,0 +Statistical differences,,1816,0 +Statistical differences,,1817,0 +Statistical differences,,1818,0 +Statistical differences,,1819,0 +Statistical differences,,1820,0 +Statistical differences,,1821,0 +Statistical differences,,1822,0 +Statistical differences,,1823,0 +Statistical differences,,1824,0 +Statistical differences,,1825,0 +Statistical differences,,1826,0 +Statistical differences,,1827,0 +Statistical differences,,1828,0 +Statistical differences,,1829,0 +Statistical differences,,1830,0 +Statistical differences,,1831,0 +Statistical differences,,1832,0 +Statistical differences,,1833,0 +Statistical differences,,1834,0 +Statistical differences,,1835,0 +Statistical differences,,1836,0 +Statistical differences,,1837,0 +Statistical differences,,1838,0 +Statistical differences,,1839,0 +Statistical differences,,1840,0 +Statistical differences,,1841,0 +Statistical differences,,1842,0 +Statistical differences,,1843,0 +Statistical differences,,1844,0 +Statistical differences,,1845,0 +Statistical differences,,1846,0 +Statistical differences,,1847,0 +Statistical differences,,1848,0 +Statistical differences,,1849,0 +Statistical differences,,1850,0 +Statistical differences,,1851,0 +Statistical differences,,1852,0 +Statistical differences,,1853,0 +Statistical differences,,1854,0 +Statistical differences,,1855,0 +Statistical differences,,1856,0 +Statistical differences,,1857,0 +Statistical differences,,1858,0 +Statistical differences,,1859,0 +Statistical differences,,1860,0 +Statistical differences,,1861,0 +Statistical differences,,1862,0 +Statistical differences,,1863,0 +Statistical differences,,1864,0 +Statistical differences,,1865,0 +Statistical differences,,1866,0 +Statistical differences,,1867,0 +Statistical differences,,1868,0 +Statistical differences,,1869,0 +Statistical differences,,1870,0 +Statistical differences,,1871,0 +Statistical differences,,1872,0 +Statistical differences,,1873,0 +Statistical differences,,1874,0 +Statistical differences,,1875,0 +Statistical differences,,1876,0 +Statistical differences,,1877,0 +Statistical differences,,1878,0 +Statistical differences,,1879,0 +Statistical differences,,1880,0 +Statistical differences,,1881,0 +Statistical differences,,1882,0 +Statistical differences,,1883,0 +Statistical differences,,1884,0 +Statistical differences,,1885,0 +Statistical differences,,1886,0 +Statistical differences,,1887,0 +Statistical differences,,1888,0 +Statistical differences,,1889,0 +Statistical differences,,1890,0 +Statistical differences,,1891,0 +Statistical differences,,1892,0 +Statistical differences,,1893,0 +Statistical differences,,1894,0 +Statistical differences,,1895,0 +Statistical differences,,1896,0 +Statistical differences,,1897,0 +Statistical differences,,1898,0 +Statistical differences,,1899,0 +Statistical differences,,1900,0 +Statistical differences,,1901,0 +Statistical differences,,1902,0 +Statistical differences,,1903,0 +Statistical differences,,1904,0 +Statistical differences,,1905,0 +Statistical differences,,1906,0 +Statistical differences,,1907,0 +Statistical differences,,1908,0 +Statistical differences,,1909,0 +Statistical differences,,1910,0 +Statistical differences,,1911,0 +Statistical differences,,1912,0 +Statistical differences,,1913,0 +Statistical differences,,1914,0 +Statistical differences,,1915,0 +Statistical differences,,1916,0 +Statistical differences,,1917,0 +Statistical differences,,1918,0 +Statistical differences,,1919,0 +Statistical differences,,1920,0 +Statistical differences,,1921,0 +Statistical differences,,1922,0 +Statistical differences,,1923,0 +Statistical differences,,1924,0 +Statistical differences,,1925,0 +Statistical differences,,1926,0 +Statistical differences,,1927,0 +Statistical differences,,1928,0 +Statistical differences,,1929,0 +Statistical differences,,1930,0 +Statistical differences,,1931,0 +Statistical differences,,1932,0 +Statistical differences,,1933,0 +Statistical differences,,1934,0 +Statistical differences,,1935,0 +Statistical differences,,1936,0 +Statistical differences,,1937,0 +Statistical differences,,1938,0 +Statistical differences,,1939,0 +Statistical differences,,1940,0 +Statistical differences,,1941,0 +Statistical differences,,1942,0 +Statistical differences,,1943,0 +Statistical differences,,1944,0 +Statistical differences,,1945,0 +Statistical differences,,1946,0 +Statistical differences,,1947,0 +Statistical differences,,1948,0 +Statistical differences,,1949,0 +Statistical differences,,1950,0 +Statistical differences,,1951,0 +Statistical differences,,1952,0 +Statistical differences,,1953,0 +Statistical differences,,1954,0 +Statistical differences,,1955,0 +Statistical differences,,1956,0 +Statistical differences,,1957,0 +Statistical differences,,1958,0 +Statistical differences,,1959,1.4728 +Statistical differences,,1960,0.7066 +Statistical differences,,1961,0.9123 +Statistical differences,,1962,1.4226 +Statistical differences,,1963,1.2675 +Statistical differences,,1964,1.7203 +Statistical differences,,1965,1.6114 +Statistical differences,,1966,1.9889 +Statistical differences,,1967,1.9882 +Statistical differences,,1968,1.6423 +Statistical differences,,1969,1.0241 +Statistical differences,,1970,0.0414 +Statistical differences,,1971,-0.1906 +Statistical differences,,1972,-0.8285 +Statistical differences,,1973,-0.722 +Statistical differences,,1974,-0.1055 +Statistical differences,,1975,-0.5571 +Statistical differences,,1976,-0.0731 +Statistical differences,,1977,0.275 +Statistical differences,,1978,-2.254 +Statistical differences,,1979,0.5644 +Statistical differences,,1980,-0.047 +Statistical differences,,1981,-0.4567 +Statistical differences,,1982,-0.593 +Statistical differences,,1983,-1.9294 +Statistical differences,,1984,-1.2572 +Statistical differences,,1985,-1.8225 +Statistical differences,,1986,-0.2355 +Statistical differences,,1987,-1.3021 +Statistical differences,,1988,-1.454 +Statistical differences,,1989,-0.8478 +Statistical differences,,1990,-2.6215 +Statistical differences,,1991,-1.3897 +Statistical differences,,1992,-1.4446 +Statistical differences,,1993,-2.726 +Statistical differences,,1994,-1.6993 +Statistical differences,,1995,-1.805 +Statistical differences,,1996,-2.5567 +Statistical differences,,1997,-1.4019 +Statistical differences,,1998,-0.8242 +Statistical differences,,1999,-2.3642 +Statistical differences,,2000,-2.5471 +Statistical differences,,2001,-1.0787 +Statistical differences,,2002,-1.8463 +Statistical differences,,2003,-1.3549 +Statistical differences,,2004,-1.1549 +Statistical differences,,2005,-0.7259 +Statistical differences,,2006,-0.5455 +Statistical differences,,2007,-1.5824 +Statistical differences,,2008,-0.6426 +Statistical differences,,2009,-0.329 +Statistical differences,,2010,-0.5202 +Statistical differences,,2011,-0.1755 +Statistical differences,,2012,-0.0051 +Statistical differences,,2013,0.4574 +Statistical differences,,2014,0.438 +Statistical differences,,2015,0.4043 +Statistical differences,,2016,0.6379 +Statistical differences,,2017,0.71 +Sudan,SDN,1950,0.0095 +Sudan,SDN,1951,0.0116 +Sudan,SDN,1952,0.012 +Sudan,SDN,1953,0.0128 +Sudan,SDN,1954,0.0123 +Sudan,SDN,1955,0.0128 +Sudan,SDN,1956,0.0116 +Sudan,SDN,1957,0.0145 +Sudan,SDN,1958,0.0157 +Sudan,SDN,1959,0.0152 +Sudan,SDN,1960,0.0144 +Sudan,SDN,1961,0.0147 +Sudan,SDN,1962,0.0151 +Sudan,SDN,1963,0.0162 +Sudan,SDN,1964,0.0169 +Sudan,SDN,1965,0.0212 +Sudan,SDN,1966,0.0219 +Sudan,SDN,1967,0.0256 +Sudan,SDN,1968,0.0238 +Sudan,SDN,1969,0.0302 +Sudan,SDN,1970,0.0327 +Sudan,SDN,1971,0.0323 +Sudan,SDN,1972,0.0335 +Sudan,SDN,1973,0.0321 +Sudan,SDN,1974,0.0336 +Sudan,SDN,1975,0.0252 +Sudan,SDN,1976,0.0211 +Sudan,SDN,1977,0.0207 +Sudan,SDN,1978,0.0185 +Sudan,SDN,1979,0.0182 +Sudan,SDN,1980,0.0193 +Sudan,SDN,1981,0.0194 +Sudan,SDN,1982,0.0205 +Sudan,SDN,1983,0.0211 +Sudan,SDN,1984,0.0182 +Sudan,SDN,1985,0.0206 +Sudan,SDN,1986,0.0208 +Sudan,SDN,1987,0.016 +Sudan,SDN,1988,0.0223 +Sudan,SDN,1989,0.017 +Sudan,SDN,1990,0.0235 +Sudan,SDN,1991,0.0218 +Sudan,SDN,1992,0.0197 +Sudan,SDN,1993,0.0136 +Sudan,SDN,1994,0.0184 +Sudan,SDN,1995,0.0187 +Sudan,SDN,1996,0.0188 +Sudan,SDN,1997,0.0226 +Sudan,SDN,1998,0.0196 +Sudan,SDN,1999,0.0213 +Sudan,SDN,2000,0.0225 +Sudan,SDN,2001,0.0253 +Sudan,SDN,2002,0.0318 +Sudan,SDN,2003,0.0337 +Sudan,SDN,2004,0.0406 +Sudan,SDN,2005,0.0375 +Sudan,SDN,2006,0.0396 +Sudan,SDN,2007,0.0457 +Sudan,SDN,2008,0.0467 +Sudan,SDN,2009,0.0493 +Sudan,SDN,2010,0.0482 +Sudan,SDN,2011,0.0455 +Sudan,SDN,2015,0.046 +Sudan,SDN,2016,0.049 +Sudan,SDN,2017,0.0504 +Suriname,SUR,1950,0.0037 +Suriname,SUR,1951,0.0038 +Suriname,SUR,1952,0.0042 +Suriname,SUR,1953,0.0048 +Suriname,SUR,1954,0.0051 +Suriname,SUR,1955,0.0043 +Suriname,SUR,1956,0.0044 +Suriname,SUR,1957,0.0047 +Suriname,SUR,1958,0.0039 +Suriname,SUR,1959,0.0041 +Suriname,SUR,1960,0.0046 +Suriname,SUR,1961,0.0045 +Suriname,SUR,1962,0.005 +Suriname,SUR,1963,0.0051 +Suriname,SUR,1964,0.0055 +Suriname,SUR,1965,0.0069 +Suriname,SUR,1966,0.0091 +Suriname,SUR,1967,0.0107 +Suriname,SUR,1968,0.0111 +Suriname,SUR,1969,0.011 +Suriname,SUR,1970,0.0108 +Suriname,SUR,1971,0.0111 +Suriname,SUR,1972,0.0109 +Suriname,SUR,1973,0.0125 +Suriname,SUR,1974,0.0095 +Suriname,SUR,1975,0.012 +Suriname,SUR,1976,0.0112 +Suriname,SUR,1977,0.0103 +Suriname,SUR,1978,0.0129 +Suriname,SUR,1979,0.0117 +Suriname,SUR,1980,0.0122 +Suriname,SUR,1981,0.0108 +Suriname,SUR,1982,0.01 +Suriname,SUR,1983,0.0074 +Suriname,SUR,1984,0.0081 +Suriname,SUR,1985,0.0081 +Suriname,SUR,1986,0.0086 +Suriname,SUR,1987,0.0084 +Suriname,SUR,1988,0.0086 +Suriname,SUR,1989,0.0084 +Suriname,SUR,1990,0.008 +Suriname,SUR,1991,0.0091 +Suriname,SUR,1992,0.0093 +Suriname,SUR,1993,0.0092 +Suriname,SUR,1994,0.0091 +Suriname,SUR,1995,0.009 +Suriname,SUR,1996,0.0089 +Suriname,SUR,1997,0.0088 +Suriname,SUR,1998,0.0088 +Suriname,SUR,1999,0.0089 +Suriname,SUR,2000,0.0089 +Suriname,SUR,2001,0.0094 +Suriname,SUR,2002,0.0061 +Suriname,SUR,2003,0.0057 +Suriname,SUR,2004,0.0055 +Suriname,SUR,2005,0.0054 +Suriname,SUR,2006,0.0057 +Suriname,SUR,2007,0.0057 +Suriname,SUR,2008,0.0061 +Suriname,SUR,2009,0.0063 +Suriname,SUR,2010,0.0072 +Suriname,SUR,2011,0.0057 +Suriname,SUR,2012,0.0064 +Suriname,SUR,2013,0.0054 +Suriname,SUR,2014,0.0056 +Suriname,SUR,2015,0.0056 +Suriname,SUR,2016,0.0057 +Suriname,SUR,2017,0.0057 +Swaziland,SWZ,1950,0.0001 +Swaziland,SWZ,1951,0.0001 +Swaziland,SWZ,1952,0.0001 +Swaziland,SWZ,1953,0.0001 +Swaziland,SWZ,1954,0.0001 +Swaziland,SWZ,1955,0.0001 +Swaziland,SWZ,1956,0 +Swaziland,SWZ,1957,0 +Swaziland,SWZ,1958,0 +Swaziland,SWZ,1959,0 +Swaziland,SWZ,1960,0.0004 +Swaziland,SWZ,1961,0 +Swaziland,SWZ,1964,0.0001 +Swaziland,SWZ,1965,0.0007 +Swaziland,SWZ,1966,0.0017 +Swaziland,SWZ,1967,0.0018 +Swaziland,SWZ,1968,0.0021 +Swaziland,SWZ,1969,0.0022 +Swaziland,SWZ,1970,0.0025 +Swaziland,SWZ,1971,0.0026 +Swaziland,SWZ,1972,0.0024 +Swaziland,SWZ,1973,0.0022 +Swaziland,SWZ,1974,0.0018 +Swaziland,SWZ,1975,0.002 +Swaziland,SWZ,1976,0.0019 +Swaziland,SWZ,1977,0.0019 +Swaziland,SWZ,1978,0.0024 +Swaziland,SWZ,1979,0.0023 +Swaziland,SWZ,1980,0.0024 +Swaziland,SWZ,1981,0.0023 +Swaziland,SWZ,1982,0.0023 +Swaziland,SWZ,1983,0.0015 +Swaziland,SWZ,1984,0.0017 +Swaziland,SWZ,1985,0.0022 +Swaziland,SWZ,1986,0.0022 +Swaziland,SWZ,1987,0.0021 +Swaziland,SWZ,1988,0.002 +Swaziland,SWZ,1989,0.002 +Swaziland,SWZ,1990,0.0019 +Swaziland,SWZ,1991,0.0015 +Swaziland,SWZ,1992,0.0012 +Swaziland,SWZ,1993,0.0006 +Swaziland,SWZ,1994,0.0021 +Swaziland,SWZ,1995,0.002 +Swaziland,SWZ,1996,0.0014 +Swaziland,SWZ,1997,0.005 +Swaziland,SWZ,1998,0.0051 +Swaziland,SWZ,1999,0.0052 +Swaziland,SWZ,2000,0.0048 +Swaziland,SWZ,2001,0.0045 +Swaziland,SWZ,2002,0.0044 +Swaziland,SWZ,2003,0.0039 +Swaziland,SWZ,2004,0.0037 +Swaziland,SWZ,2005,0.0035 +Swaziland,SWZ,2006,0.0034 +Swaziland,SWZ,2007,0.0034 +Swaziland,SWZ,2008,0.0034 +Swaziland,SWZ,2009,0.0033 +Swaziland,SWZ,2010,0.0031 +Swaziland,SWZ,2011,0.0031 +Swaziland,SWZ,2012,0.0035 +Swaziland,SWZ,2013,0.0031 +Swaziland,SWZ,2014,0.0034 +Swaziland,SWZ,2015,0.0035 +Swaziland,SWZ,2016,0.0035 +Swaziland,SWZ,2017,0.0037 +Sweden,SWE,1834,0.0373 +Sweden,SWE,1839,0.0394 +Sweden,SWE,1840,0.0339 +Sweden,SWE,1841,0.0329 +Sweden,SWE,1842,0.0622 +Sweden,SWE,1843,0.0441 +Sweden,SWE,1844,0.07 +Sweden,SWE,1845,0.0567 +Sweden,SWE,1846,0.0511 +Sweden,SWE,1847,0.0468 +Sweden,SWE,1848,0.0801 +Sweden,SWE,1849,0.1058 +Sweden,SWE,1850,0.1023 +Sweden,SWE,1851,0.1088 +Sweden,SWE,1852,0.1042 +Sweden,SWE,1853,0.1046 +Sweden,SWE,1854,0.1322 +Sweden,SWE,1855,0.1212 +Sweden,SWE,1856,0.1719 +Sweden,SWE,1857,0.2188 +Sweden,SWE,1858,0.1806 +Sweden,SWE,1859,0.2508 +Sweden,SWE,1860,0.2271 +Sweden,SWE,1861,0.2781 +Sweden,SWE,1862,0.2783 +Sweden,SWE,1863,0.2608 +Sweden,SWE,1864,0.2547 +Sweden,SWE,1865,0.25 +Sweden,SWE,1866,0.2497 +Sweden,SWE,1867,0.217 +Sweden,SWE,1868,0.2494 +Sweden,SWE,1869,0.2088 +Sweden,SWE,1870,0.2518 +Sweden,SWE,1871,0.253 +Sweden,SWE,1872,0.2732 +Sweden,SWE,1873,0.261 +Sweden,SWE,1874,0.3005 +Sweden,SWE,1875,0.3367 +Sweden,SWE,1876,0.3543 +Sweden,SWE,1877,0.3679 +Sweden,SWE,1878,0.3094 +Sweden,SWE,1879,0.2931 +Sweden,SWE,1880,0.3313 +Sweden,SWE,1881,0.3118 +Sweden,SWE,1882,0.3377 +Sweden,SWE,1883,0.3245 +Sweden,SWE,1884,0.3542 +Sweden,SWE,1885,0.3791 +Sweden,SWE,1886,0.3658 +Sweden,SWE,1887,0.3531 +Sweden,SWE,1888,0.3561 +Sweden,SWE,1889,0.4158 +Sweden,SWE,1890,0.3808 +Sweden,SWE,1891,0.3884 +Sweden,SWE,1892,0.3831 +Sweden,SWE,1893,0.3899 +Sweden,SWE,1894,0.4204 +Sweden,SWE,1895,0.4007 +Sweden,SWE,1896,0.4012 +Sweden,SWE,1897,0.4266 +Sweden,SWE,1898,0.4306 +Sweden,SWE,1899,0.4933 +Sweden,SWE,1900,0.4695 +Sweden,SWE,1901,0.4223 +Sweden,SWE,1902,0.4314 +Sweden,SWE,1903,0.4344 +Sweden,SWE,1904,0.4526 +Sweden,SWE,1905,0.4185 +Sweden,SWE,1906,0.4427 +Sweden,SWE,1907,0.4428 +Sweden,SWE,1908,0.4916 +Sweden,SWE,1909,0.4353 +Sweden,SWE,1910,0.4292 +Sweden,SWE,1911,0.4106 +Sweden,SWE,1912,0.4344 +Sweden,SWE,1913,0.4514 +Sweden,SWE,1914,0.4686 +Sweden,SWE,1915,0.4783 +Sweden,SWE,1916,0.4673 +Sweden,SWE,1917,0.1917 +Sweden,SWE,1918,0.2299 +Sweden,SWE,1919,0.2546 +Sweden,SWE,1920,0.2966 +Sweden,SWE,1921,0.1954 +Sweden,SWE,1922,0.3081 +Sweden,SWE,1923,0.3304 +Sweden,SWE,1924,0.3931 +Sweden,SWE,1925,0.3486 +Sweden,SWE,1926,0.3442 +Sweden,SWE,1927,0.4432 +Sweden,SWE,1928,0.4071 +Sweden,SWE,1929,0.4515 +Sweden,SWE,1930,0.4774 +Sweden,SWE,1931,0.5347 +Sweden,SWE,1932,0.5862 +Sweden,SWE,1933,0.5799 +Sweden,SWE,1934,0.5936 +Sweden,SWE,1935,0.5945 +Sweden,SWE,1936,0.5931 +Sweden,SWE,1937,0.64 +Sweden,SWE,1938,0.6129 +Sweden,SWE,1939,0.6508 +Sweden,SWE,1940,0.3768 +Sweden,SWE,1941,0.2994 +Sweden,SWE,1942,0.2584 +Sweden,SWE,1943,0.3037 +Sweden,SWE,1944,0.2402 +Sweden,SWE,1945,0.1051 +Sweden,SWE,1946,0.3674 +Sweden,SWE,1947,0.4793 +Sweden,SWE,1948,0.5596 +Sweden,SWE,1949,0.5117 +Sweden,SWE,1950,0.4954 +Sweden,SWE,1951,0.5371 +Sweden,SWE,1952,0.5472 +Sweden,SWE,1953,0.502 +Sweden,SWE,1954,0.5205 +Sweden,SWE,1955,0.5388 +Sweden,SWE,1956,0.5616 +Sweden,SWE,1957,0.4878 +Sweden,SWE,1958,0.5122 +Sweden,SWE,1959,0.4715 +Sweden,SWE,1960,0.5225 +Sweden,SWE,1961,0.5163 +Sweden,SWE,1962,0.5212 +Sweden,SWE,1963,0.5329 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+1828-11-01,27.663,1.449,"Bonaire, Saint Eustatius And Saba" +1828-12-01,26.471,2.044,"Bonaire, Saint Eustatius And Saba" +1829-01-01,25.292,1.133,"Bonaire, Saint Eustatius And Saba" +1829-02-01,24.761999999999997,2.738,"Bonaire, Saint Eustatius And Saba" +1829-03-01,25.587,1.159,"Bonaire, Saint Eustatius And Saba" +1829-04-01,26.398000000000003,1.193,"Bonaire, Saint Eustatius And Saba" +1829-05-01,27.857,2.929,"Bonaire, Saint Eustatius And Saba" +1829-06-01,,,"Bonaire, Saint Eustatius And Saba" +1829-07-01,28.505000000000006,1.262,"Bonaire, Saint Eustatius And Saba" +1829-08-01,28.099,1.163,"Bonaire, Saint Eustatius And Saba" +1829-09-01,27.388,1.327,"Bonaire, Saint Eustatius And Saba" +1829-10-01,,,"Bonaire, Saint Eustatius And Saba" +1829-11-01,26.688000000000002,2.547,"Bonaire, Saint Eustatius And Saba" +1829-12-01,25.973000000000003,3.902,"Bonaire, Saint Eustatius And Saba" +1830-01-01,,,"Bonaire, Saint Eustatius And Saba" +1830-02-01,,,"Bonaire, Saint Eustatius And Saba" +1830-03-01,,,"Bonaire, Saint Eustatius And Saba" +1830-04-01,,,"Bonaire, Saint Eustatius And Saba" +1830-05-01,,,"Bonaire, Saint Eustatius And Saba" +1830-06-01,,,"Bonaire, Saint Eustatius And Saba" +1830-07-01,,,"Bonaire, Saint Eustatius And Saba" +1830-08-01,,,"Bonaire, Saint Eustatius And Saba" +1830-09-01,,,"Bonaire, Saint Eustatius And Saba" +1830-10-01,,,"Bonaire, Saint Eustatius And Saba" +1830-11-01,,,"Bonaire, Saint Eustatius And Saba" +1830-12-01,,,"Bonaire, Saint Eustatius And Saba" +1831-01-01,,,"Bonaire, Saint Eustatius And Saba" +1831-02-01,,,"Bonaire, Saint Eustatius And Saba" +1831-03-01,,,"Bonaire, Saint Eustatius And Saba" +1831-04-01,,,"Bonaire, Saint Eustatius And Saba" +1831-05-01,,,"Bonaire, Saint Eustatius And Saba" +1831-06-01,,,"Bonaire, Saint Eustatius And Saba" +1831-07-01,,,"Bonaire, Saint Eustatius And Saba" +1831-08-01,,,"Bonaire, Saint Eustatius And Saba" +1831-09-01,,,"Bonaire, Saint Eustatius And Saba" +1831-10-01,,,"Bonaire, Saint Eustatius And Saba" +1831-11-01,,,"Bonaire, Saint Eustatius And Saba" +1831-12-01,,,"Bonaire, Saint Eustatius And Saba" +1832-01-01,,,"Bonaire, Saint Eustatius And Saba" +1832-02-01,,,"Bonaire, Saint Eustatius And Saba" +1832-03-01,,,"Bonaire, Saint Eustatius And Saba" +1832-04-01,,,"Bonaire, Saint Eustatius And Saba" +1832-05-01,,,"Bonaire, Saint Eustatius And Saba" +1832-06-01,,,"Bonaire, Saint Eustatius And Saba" +1832-07-01,,,"Bonaire, Saint Eustatius And Saba" +1832-08-01,,,"Bonaire, Saint Eustatius And Saba" +1832-09-01,,,"Bonaire, Saint Eustatius And Saba" +1832-10-01,,,"Bonaire, Saint Eustatius And Saba" +1832-11-01,,,"Bonaire, Saint Eustatius And Saba" +1832-12-01,,,"Bonaire, Saint Eustatius And Saba" +1833-01-01,,,"Bonaire, Saint Eustatius And Saba" +1833-02-01,,,"Bonaire, Saint Eustatius And Saba" +1833-03-01,,,"Bonaire, Saint Eustatius And Saba" +1833-04-01,,,"Bonaire, Saint Eustatius And Saba" +1833-05-01,,,"Bonaire, Saint Eustatius And Saba" +1833-06-01,,,"Bonaire, Saint Eustatius And Saba" +1833-07-01,,,"Bonaire, Saint Eustatius And Saba" +1833-08-01,,,"Bonaire, Saint Eustatius And Saba" +1833-09-01,,,"Bonaire, Saint Eustatius And Saba" +1833-10-01,,,"Bonaire, Saint Eustatius And Saba" +1833-11-01,,,"Bonaire, Saint Eustatius And Saba" +1833-12-01,,,"Bonaire, Saint Eustatius And Saba" +1834-01-01,,,"Bonaire, Saint Eustatius And Saba" +1834-02-01,,,"Bonaire, Saint Eustatius And Saba" +1834-03-01,,,"Bonaire, Saint Eustatius And Saba" +1834-04-01,,,"Bonaire, Saint Eustatius And Saba" +1834-05-01,,,"Bonaire, Saint Eustatius And Saba" +1834-06-01,,,"Bonaire, Saint Eustatius And Saba" +1834-07-01,,,"Bonaire, Saint Eustatius And Saba" +1834-08-01,,,"Bonaire, Saint Eustatius And Saba" +1834-09-01,,,"Bonaire, Saint Eustatius And Saba" +1834-10-01,,,"Bonaire, Saint Eustatius And Saba" +1834-11-01,,,"Bonaire, Saint Eustatius And Saba" +1834-12-01,,,"Bonaire, Saint Eustatius And Saba" +1835-01-01,,,"Bonaire, Saint Eustatius And Saba" +1835-02-01,,,"Bonaire, Saint Eustatius And Saba" +1835-03-01,,,"Bonaire, Saint Eustatius And Saba" +1835-04-01,,,"Bonaire, Saint Eustatius And Saba" +1835-05-01,,,"Bonaire, Saint Eustatius And Saba" +1835-06-01,,,"Bonaire, Saint Eustatius And Saba" +1835-07-01,,,"Bonaire, Saint Eustatius And Saba" +1835-08-01,,,"Bonaire, Saint Eustatius And Saba" +1835-09-01,,,"Bonaire, Saint Eustatius And Saba" +1835-10-01,,,"Bonaire, Saint Eustatius And Saba" +1835-11-01,,,"Bonaire, Saint Eustatius And Saba" +1835-12-01,,,"Bonaire, Saint Eustatius And Saba" +1836-01-01,,,"Bonaire, Saint Eustatius And Saba" +1836-02-01,,,"Bonaire, Saint Eustatius And Saba" +1836-03-01,,,"Bonaire, Saint Eustatius And Saba" +1836-04-01,,,"Bonaire, Saint Eustatius And Saba" +1836-05-01,,,"Bonaire, Saint Eustatius And Saba" +1836-06-01,,,"Bonaire, Saint Eustatius And Saba" +1836-07-01,,,"Bonaire, Saint Eustatius And Saba" +1836-08-01,,,"Bonaire, Saint Eustatius And Saba" +1836-09-01,,,"Bonaire, Saint Eustatius And Saba" +1836-10-01,,,"Bonaire, Saint Eustatius And Saba" +1836-11-01,,,"Bonaire, Saint Eustatius And Saba" +1836-12-01,,,"Bonaire, Saint Eustatius And Saba" +1837-01-01,,,"Bonaire, Saint Eustatius And Saba" +1837-02-01,,,"Bonaire, Saint Eustatius And Saba" +1837-03-01,,,"Bonaire, Saint Eustatius And Saba" +1837-04-01,,,"Bonaire, Saint Eustatius And Saba" +1837-05-01,,,"Bonaire, Saint Eustatius And Saba" +1837-06-01,,,"Bonaire, Saint Eustatius And Saba" +1837-07-01,,,"Bonaire, Saint Eustatius And Saba" +1837-08-01,,,"Bonaire, Saint Eustatius And Saba" +1837-09-01,,,"Bonaire, Saint Eustatius And Saba" +1837-10-01,,,"Bonaire, Saint Eustatius And Saba" +1837-11-01,,,"Bonaire, Saint Eustatius And Saba" +1837-12-01,,,"Bonaire, Saint Eustatius And Saba" +1838-01-01,,,"Bonaire, Saint Eustatius And Saba" +1838-02-01,,,"Bonaire, Saint Eustatius And Saba" +1838-03-01,,,"Bonaire, Saint Eustatius And Saba" +1838-04-01,,,"Bonaire, Saint Eustatius And Saba" +1838-05-01,,,"Bonaire, Saint Eustatius And Saba" +1838-06-01,,,"Bonaire, Saint Eustatius And Saba" +1838-07-01,,,"Bonaire, Saint Eustatius And Saba" +1838-08-01,,,"Bonaire, Saint Eustatius And Saba" +1838-09-01,,,"Bonaire, Saint Eustatius And Saba" +1838-10-01,,,"Bonaire, Saint Eustatius And Saba" +1838-11-01,,,"Bonaire, Saint Eustatius And Saba" +1838-12-01,,,"Bonaire, Saint Eustatius And Saba" +1839-01-01,,,"Bonaire, Saint Eustatius And Saba" +1839-02-01,,,"Bonaire, Saint Eustatius And Saba" +1839-03-01,,,"Bonaire, Saint Eustatius And Saba" +1839-04-01,,,"Bonaire, Saint Eustatius And Saba" +1839-05-01,,,"Bonaire, Saint Eustatius And Saba" +1839-06-01,,,"Bonaire, Saint Eustatius And Saba" +1839-07-01,,,"Bonaire, Saint Eustatius And Saba" +1839-08-01,,,"Bonaire, Saint Eustatius And Saba" +1839-09-01,,,"Bonaire, Saint Eustatius And Saba" +1839-10-01,,,"Bonaire, Saint Eustatius And Saba" +1839-11-01,,,"Bonaire, Saint Eustatius And Saba" +1839-12-01,,,"Bonaire, Saint Eustatius And Saba" +1840-01-01,,,"Bonaire, Saint Eustatius And Saba" +1840-02-01,,,"Bonaire, Saint Eustatius And Saba" +1840-03-01,,,"Bonaire, Saint Eustatius And Saba" +1840-04-01,,,"Bonaire, Saint Eustatius And Saba" +1840-05-01,,,"Bonaire, Saint Eustatius And Saba" +1840-06-01,,,"Bonaire, Saint Eustatius And Saba" +1840-07-01,,,"Bonaire, Saint Eustatius And Saba" +1840-08-01,,,"Bonaire, Saint Eustatius And Saba" +1840-09-01,,,"Bonaire, Saint Eustatius And Saba" +1840-10-01,,,"Bonaire, Saint Eustatius And Saba" +1840-11-01,,,"Bonaire, Saint Eustatius And Saba" +1840-12-01,,,"Bonaire, Saint Eustatius And Saba" +1841-01-01,,,"Bonaire, Saint Eustatius And Saba" +1841-02-01,,,"Bonaire, Saint Eustatius And Saba" +1841-03-01,,,"Bonaire, Saint Eustatius And Saba" +1841-04-01,,,"Bonaire, Saint Eustatius And Saba" +1841-05-01,,,"Bonaire, Saint Eustatius And Saba" +1841-06-01,,,"Bonaire, Saint Eustatius And Saba" +1841-07-01,,,"Bonaire, Saint Eustatius And Saba" +1841-08-01,,,"Bonaire, Saint Eustatius And Saba" +1841-09-01,,,"Bonaire, Saint Eustatius And Saba" +1841-10-01,,,"Bonaire, Saint Eustatius And Saba" +1841-11-01,,,"Bonaire, Saint Eustatius And Saba" +1841-12-01,,,"Bonaire, Saint Eustatius And Saba" +1842-01-01,,,"Bonaire, Saint Eustatius And Saba" +1842-02-01,,,"Bonaire, Saint Eustatius And Saba" +1842-03-01,,,"Bonaire, Saint Eustatius And Saba" +1842-04-01,,,"Bonaire, Saint Eustatius And Saba" +1842-05-01,,,"Bonaire, Saint Eustatius And Saba" +1842-06-01,,,"Bonaire, Saint Eustatius And Saba" +1842-07-01,,,"Bonaire, Saint Eustatius And Saba" +1842-08-01,,,"Bonaire, Saint Eustatius And Saba" +1842-09-01,,,"Bonaire, Saint Eustatius And Saba" +1842-10-01,,,"Bonaire, Saint Eustatius And Saba" +1842-11-01,,,"Bonaire, Saint Eustatius And Saba" +1842-12-01,,,"Bonaire, Saint Eustatius And Saba" +1843-01-01,,,"Bonaire, Saint Eustatius And Saba" +1843-02-01,,,"Bonaire, Saint Eustatius And Saba" +1843-03-01,,,"Bonaire, Saint Eustatius And Saba" +1843-04-01,,,"Bonaire, Saint Eustatius And Saba" +1843-05-01,,,"Bonaire, Saint Eustatius And Saba" +1843-06-01,,,"Bonaire, Saint Eustatius And Saba" +1843-07-01,,,"Bonaire, Saint Eustatius And Saba" +1843-08-01,,,"Bonaire, Saint Eustatius And Saba" +1843-09-01,,,"Bonaire, Saint Eustatius And Saba" +1843-10-01,,,"Bonaire, Saint Eustatius And Saba" +1843-11-01,,,"Bonaire, Saint Eustatius And Saba" +1843-12-01,,,"Bonaire, Saint Eustatius And Saba" +1844-01-01,,,"Bonaire, Saint Eustatius And Saba" +1844-02-01,,,"Bonaire, Saint Eustatius And Saba" +1844-03-01,,,"Bonaire, Saint Eustatius And Saba" +1844-04-01,,,"Bonaire, Saint Eustatius And Saba" +1844-05-01,,,"Bonaire, Saint Eustatius And Saba" +1844-06-01,,,"Bonaire, Saint Eustatius And Saba" +1844-07-01,,,"Bonaire, Saint Eustatius And Saba" +1844-08-01,,,"Bonaire, Saint Eustatius And Saba" +1844-09-01,,,"Bonaire, Saint Eustatius And Saba" +1844-10-01,,,"Bonaire, Saint Eustatius And Saba" +1844-11-01,,,"Bonaire, Saint Eustatius And Saba" +1844-12-01,,,"Bonaire, Saint Eustatius And Saba" +1845-01-01,25.585,1.335,"Bonaire, Saint Eustatius And Saba" +1845-02-01,24.16,2.028,"Bonaire, Saint Eustatius And Saba" +1845-03-01,25.357,0.8390000000000001,"Bonaire, Saint Eustatius And Saba" +1845-04-01,26.101999999999997,0.912,"Bonaire, Saint Eustatius And Saba" +1845-05-01,26.789,1.094,"Bonaire, Saint Eustatius And Saba" +1845-06-01,27.345,1.0490000000000002,"Bonaire, Saint Eustatius And Saba" +1845-07-01,28.033,0.968,"Bonaire, Saint Eustatius And Saba" +1845-08-01,27.854,1.048,"Bonaire, Saint Eustatius And Saba" +1845-09-01,27.549,0.949,"Bonaire, Saint Eustatius And Saba" +1845-10-01,,,"Bonaire, Saint Eustatius And Saba" +1845-11-01,,,"Bonaire, Saint Eustatius And Saba" +1845-12-01,,,"Bonaire, Saint Eustatius And Saba" +1846-01-01,26.411,1.378,"Bonaire, Saint Eustatius And Saba" +1846-02-01,25.936,1.134,"Bonaire, Saint Eustatius And Saba" +1846-03-01,26.633000000000003,1.207,"Bonaire, Saint Eustatius And Saba" +1846-04-01,25.688000000000002,1.172,"Bonaire, Saint Eustatius And Saba" +1846-05-01,27.412,1.493,"Bonaire, Saint Eustatius And Saba" +1846-06-01,28.413,1.374,"Bonaire, Saint Eustatius And Saba" +1846-07-01,28.738000000000003,1.536,"Bonaire, Saint Eustatius And Saba" +1846-08-01,28.531,1.074,"Bonaire, Saint Eustatius And Saba" +1846-09-01,27.946,1.131,"Bonaire, Saint Eustatius And Saba" +1846-10-01,27.051,0.85,"Bonaire, Saint Eustatius And Saba" +1846-11-01,26.45,1.398,"Bonaire, Saint Eustatius And Saba" +1846-12-01,26.124000000000002,1.044,"Bonaire, Saint Eustatius And Saba" +1847-01-01,26.025,4.006,"Bonaire, Saint Eustatius And Saba" +1847-02-01,25.426,1.072,"Bonaire, Saint Eustatius And Saba" +1847-03-01,24.669,1.737,"Bonaire, Saint Eustatius And Saba" +1847-04-01,25.897,0.907,"Bonaire, Saint Eustatius And Saba" +1847-05-01,26.47,0.8740000000000001,"Bonaire, Saint Eustatius And Saba" +1847-06-01,27.689,1.306,"Bonaire, Saint Eustatius And Saba" +1847-07-01,28.178,1.102,"Bonaire, Saint Eustatius And Saba" +1847-08-01,28.12,0.914,"Bonaire, Saint Eustatius And Saba" +1847-09-01,27.92,0.785,"Bonaire, Saint Eustatius And Saba" +1847-10-01,27.381,0.867,"Bonaire, Saint Eustatius And Saba" +1847-11-01,27.111,0.763,"Bonaire, Saint Eustatius And Saba" +1847-12-01,24.467,1.683,"Bonaire, Saint Eustatius And Saba" +1848-01-01,24.164,1.3,"Bonaire, Saint Eustatius And Saba" +1848-02-01,25.666,1.128,"Bonaire, Saint Eustatius And Saba" +1848-03-01,25.274,0.8909999999999999,"Bonaire, Saint Eustatius And Saba" +1848-04-01,26.006,0.978,"Bonaire, Saint Eustatius And Saba" +1848-05-01,26.613000000000003,1.137,"Bonaire, Saint Eustatius And Saba" +1848-06-01,27.564,1.445,"Bonaire, Saint Eustatius And Saba" +1848-07-01,28.388,1.538,"Bonaire, Saint Eustatius And Saba" +1848-08-01,27.733,1.199,"Bonaire, Saint Eustatius And Saba" +1848-09-01,27.649,1.101,"Bonaire, Saint Eustatius And Saba" +1848-10-01,27.418000000000003,0.871,"Bonaire, Saint Eustatius And Saba" +1848-11-01,26.596999999999998,0.7340000000000001,"Bonaire, Saint Eustatius And Saba" +1848-12-01,25.8,1.358,"Bonaire, Saint Eustatius And Saba" +1849-01-01,25.618000000000002,1.117,"Bonaire, Saint Eustatius And Saba" +1849-02-01,25.197,1.039,"Bonaire, Saint Eustatius And Saba" +1849-03-01,26.052,1.456,"Bonaire, Saint Eustatius And Saba" +1849-04-01,25.415,1.214,"Bonaire, Saint Eustatius And Saba" +1849-05-01,26.68,0.723,"Bonaire, Saint Eustatius And Saba" +1849-06-01,27.559,1.186,"Bonaire, Saint Eustatius And Saba" +1849-07-01,28.195999999999998,1.109,"Bonaire, Saint Eustatius And Saba" +1849-08-01,28.025,1.024,"Bonaire, Saint Eustatius And Saba" +1849-09-01,27.662,0.954,"Bonaire, Saint Eustatius And Saba" +1849-10-01,27.783,1.139,"Bonaire, Saint Eustatius And Saba" +1849-11-01,26.803,1.25,"Bonaire, Saint Eustatius And Saba" +1849-12-01,25.476999999999997,1.533,"Bonaire, Saint Eustatius And Saba" +1850-01-01,24.644000000000002,1.212,"Bonaire, Saint Eustatius And Saba" +1850-02-01,25.471,1.043,"Bonaire, Saint Eustatius And Saba" +1850-03-01,25.875,2.148,"Bonaire, Saint Eustatius And Saba" +1850-04-01,25.858,1.611,"Bonaire, Saint Eustatius And Saba" +1850-05-01,26.607,0.929,"Bonaire, Saint Eustatius And Saba" +1850-06-01,27.677,0.8959999999999999,"Bonaire, Saint Eustatius And Saba" +1850-07-01,28.232000000000006,0.823,"Bonaire, Saint Eustatius And Saba" +1850-08-01,28.305999999999997,0.946,"Bonaire, Saint Eustatius And Saba" +1850-09-01,28.241,0.83,"Bonaire, Saint Eustatius And Saba" +1850-10-01,27.191,0.858,"Bonaire, Saint Eustatius And Saba" +1850-11-01,26.517,1.354,"Bonaire, Saint Eustatius And Saba" +1850-12-01,25.645,1.092,"Bonaire, Saint Eustatius And Saba" +1851-01-01,25.597,0.841,"Bonaire, Saint Eustatius And Saba" +1851-02-01,25.194000000000003,0.7829999999999999,"Bonaire, Saint Eustatius And Saba" +1851-03-01,25.033,0.929,"Bonaire, Saint Eustatius And Saba" +1851-04-01,25.843000000000004,1.192,"Bonaire, Saint Eustatius And Saba" +1851-05-01,27.16,0.964,"Bonaire, Saint Eustatius And Saba" +1851-06-01,27.717,1.144,"Bonaire, Saint Eustatius And Saba" +1851-07-01,28.529,1.279,"Bonaire, Saint Eustatius And Saba" +1851-08-01,28.203000000000003,1.142,"Bonaire, Saint Eustatius And Saba" +1851-09-01,27.923000000000002,0.884,"Bonaire, Saint Eustatius And Saba" +1851-10-01,27.726,1.704,"Bonaire, Saint Eustatius And Saba" +1851-11-01,26.665,0.851,"Bonaire, Saint Eustatius And Saba" +1851-12-01,25.884,2.514,"Bonaire, Saint Eustatius And Saba" +1852-01-01,24.914,1.948,"Bonaire, Saint Eustatius And Saba" +1852-02-01,24.651,1.102,"Bonaire, Saint Eustatius And Saba" +1852-03-01,24.41,0.885,"Bonaire, Saint Eustatius And Saba" +1852-04-01,25.937,1.442,"Bonaire, Saint Eustatius And Saba" +1852-05-01,27.436,0.957,"Bonaire, Saint Eustatius And Saba" +1852-06-01,27.885,0.67,"Bonaire, Saint Eustatius And Saba" +1852-07-01,28.002,1.054,"Bonaire, Saint Eustatius And Saba" +1852-08-01,28.141,0.841,"Bonaire, Saint Eustatius And Saba" +1852-09-01,28.13,1.18,"Bonaire, Saint Eustatius And Saba" +1852-10-01,27.066,1.274,"Bonaire, Saint Eustatius And Saba" +1852-11-01,26.455,1.209,"Bonaire, Saint Eustatius And Saba" +1852-12-01,25.539,1.16,"Bonaire, Saint Eustatius And Saba" +1853-01-01,25.273000000000003,1.088,"Bonaire, Saint Eustatius And Saba" +1853-02-01,24.077,1.268,"Bonaire, Saint Eustatius And Saba" +1853-03-01,25.615,1.359,"Bonaire, Saint Eustatius And Saba" +1853-04-01,26.339000000000002,1.096,"Bonaire, Saint Eustatius And Saba" +1853-05-01,27.333000000000002,0.7509999999999999,"Bonaire, Saint Eustatius And Saba" +1853-06-01,28.096,1.186,"Bonaire, Saint Eustatius And Saba" +1853-07-01,28.296000000000006,0.738,"Bonaire, Saint Eustatius And Saba" +1853-08-01,27.933000000000003,0.753,"Bonaire, Saint Eustatius And Saba" +1853-09-01,27.795,0.5479999999999999,"Bonaire, Saint Eustatius And Saba" +1853-10-01,27.595,0.795,"Bonaire, Saint Eustatius And Saba" +1853-11-01,26.854,1.763,"Bonaire, Saint Eustatius And Saba" +1853-12-01,26.085,1.139,"Bonaire, Saint Eustatius And Saba" +1854-01-01,25.104,1.735,"Bonaire, Saint Eustatius And Saba" +1854-02-01,24.526,1.077,"Bonaire, Saint Eustatius And Saba" +1854-03-01,25.456999999999997,0.85,"Bonaire, Saint Eustatius And Saba" +1854-04-01,25.845,0.8759999999999999,"Bonaire, Saint Eustatius And Saba" +1854-05-01,27.194000000000003,0.813,"Bonaire, Saint Eustatius And Saba" +1854-06-01,27.701,1.055,"Bonaire, Saint Eustatius And Saba" +1854-07-01,28.284,1.03,"Bonaire, Saint Eustatius And Saba" +1854-08-01,27.926,1.042,"Bonaire, Saint Eustatius And Saba" +1854-09-01,27.619,0.741,"Bonaire, Saint Eustatius And Saba" +1854-10-01,27.865,1.449,"Bonaire, Saint Eustatius And Saba" +1854-11-01,26.589,0.994,"Bonaire, Saint Eustatius And Saba" +1854-12-01,26.272,2.673,"Bonaire, Saint Eustatius And Saba" +1855-01-01,25.151,1.982,"Bonaire, Saint Eustatius And Saba" +1855-02-01,25.264,1.253,"Bonaire, Saint Eustatius And Saba" +1855-03-01,25.719,0.8079999999999999,"Bonaire, Saint Eustatius And Saba" +1855-04-01,26.21,1.221,"Bonaire, Saint Eustatius And Saba" +1855-05-01,27.384,0.6890000000000001,"Bonaire, Saint Eustatius And Saba" +1855-06-01,28.108,0.83,"Bonaire, Saint Eustatius And Saba" +1855-07-01,27.76,0.6409999999999999,"Bonaire, Saint Eustatius And Saba" +1855-08-01,28.483,0.966,"Bonaire, Saint Eustatius And Saba" +1855-09-01,27.976999999999997,0.5539999999999999,"Bonaire, Saint Eustatius And Saba" +1855-10-01,27.506,1.115,"Bonaire, Saint Eustatius And Saba" +1855-11-01,26.83,0.85,"Bonaire, Saint Eustatius And Saba" +1855-12-01,25.993000000000002,2.096,"Bonaire, Saint Eustatius And Saba" +1856-01-01,26.45,1.063,"Bonaire, Saint Eustatius And Saba" +1856-02-01,25.557,1.505,"Bonaire, Saint Eustatius And Saba" +1856-03-01,26.339000000000002,1.079,"Bonaire, Saint Eustatius And Saba" +1856-04-01,26.288,0.608,"Bonaire, Saint Eustatius And Saba" +1856-05-01,27.689,1.587,"Bonaire, Saint Eustatius And Saba" +1856-06-01,28.145,1.847,"Bonaire, Saint Eustatius And Saba" +1856-07-01,28.355999999999998,0.8059999999999999,"Bonaire, Saint Eustatius And Saba" +1856-08-01,28.025,1.36,"Bonaire, Saint Eustatius And Saba" +1856-09-01,27.873,1.235,"Bonaire, Saint Eustatius And Saba" +1856-10-01,27.628,0.8809999999999999,"Bonaire, Saint Eustatius And Saba" +1856-11-01,27.034000000000002,0.8809999999999999,"Bonaire, Saint Eustatius And Saba" +1856-12-01,25.96,1.48,"Bonaire, Saint Eustatius And Saba" +1857-01-01,24.897,1.14,"Bonaire, Saint Eustatius And Saba" +1857-02-01,24.176,1.092,"Bonaire, Saint Eustatius And Saba" +1857-03-01,25.458000000000002,0.835,"Bonaire, Saint Eustatius And Saba" +1857-04-01,25.831999999999997,1.005,"Bonaire, Saint Eustatius And Saba" +1857-05-01,26.649,0.999,"Bonaire, Saint Eustatius And Saba" +1857-06-01,27.551,0.657,"Bonaire, Saint Eustatius And Saba" +1857-07-01,28.055,0.716,"Bonaire, Saint Eustatius And Saba" +1857-08-01,27.714000000000002,0.764,"Bonaire, Saint Eustatius And Saba" +1857-09-01,27.287,0.563,"Bonaire, Saint Eustatius And Saba" +1857-10-01,27.062,1.124,"Bonaire, Saint Eustatius And Saba" +1857-11-01,26.206999999999997,0.91,"Bonaire, Saint Eustatius And Saba" +1857-12-01,25.063000000000002,1.508,"Bonaire, Saint Eustatius And Saba" +1858-01-01,24.806,1.724,"Bonaire, Saint Eustatius And Saba" +1858-02-01,24.565,0.989,"Bonaire, Saint Eustatius And Saba" +1858-03-01,24.85,1.136,"Bonaire, Saint Eustatius And Saba" +1858-04-01,25.865,1.0,"Bonaire, Saint Eustatius And Saba" +1858-05-01,26.865,1.013,"Bonaire, Saint Eustatius And Saba" +1858-06-01,27.258000000000003,1.046,"Bonaire, Saint Eustatius And Saba" +1858-07-01,27.901,1.005,"Bonaire, Saint Eustatius And Saba" +1858-08-01,27.984,0.672,"Bonaire, Saint Eustatius And Saba" +1858-09-01,27.984,1.19,"Bonaire, Saint Eustatius And Saba" +1858-10-01,27.165,0.911,"Bonaire, Saint Eustatius And Saba" +1858-11-01,26.897,0.913,"Bonaire, Saint Eustatius And Saba" +1858-12-01,24.811999999999998,1.034,"Bonaire, Saint Eustatius And Saba" +1859-01-01,24.952,0.846,"Bonaire, Saint Eustatius And Saba" +1859-02-01,24.31,0.954,"Bonaire, Saint Eustatius And Saba" +1859-03-01,25.023000000000003,1.182,"Bonaire, Saint Eustatius And Saba" +1859-04-01,26.144000000000002,1.513,"Bonaire, Saint Eustatius And Saba" +1859-05-01,27.421999999999997,1.008,"Bonaire, Saint Eustatius And Saba" +1859-06-01,27.498,1.018,"Bonaire, Saint Eustatius And Saba" +1859-07-01,27.753,0.7609999999999999,"Bonaire, Saint Eustatius And Saba" +1859-08-01,27.968000000000004,0.617,"Bonaire, Saint Eustatius And Saba" +1859-09-01,27.837,0.77,"Bonaire, Saint Eustatius And Saba" +1859-10-01,27.546,0.943,"Bonaire, Saint Eustatius And Saba" +1859-11-01,26.406,1.668,"Bonaire, Saint Eustatius And Saba" +1859-12-01,25.129,1.035,"Bonaire, Saint Eustatius And Saba" +1860-01-01,25.151,0.907,"Bonaire, Saint Eustatius And Saba" +1860-02-01,24.493000000000002,0.755,"Bonaire, Saint Eustatius And Saba" +1860-03-01,25.072,1.102,"Bonaire, Saint Eustatius And Saba" +1860-04-01,26.1,0.8009999999999999,"Bonaire, Saint Eustatius And Saba" +1860-05-01,27.212,1.028,"Bonaire, Saint Eustatius And Saba" +1860-06-01,27.725,0.828,"Bonaire, Saint Eustatius And Saba" +1860-07-01,28.159,0.583,"Bonaire, Saint Eustatius And Saba" +1860-08-01,28.074,0.706,"Bonaire, Saint Eustatius And Saba" +1860-09-01,27.975,0.7070000000000001,"Bonaire, Saint Eustatius And Saba" +1860-10-01,27.484,0.7709999999999999,"Bonaire, Saint Eustatius And Saba" +1860-11-01,26.651999999999997,1.208,"Bonaire, Saint Eustatius And Saba" +1860-12-01,25.313000000000002,0.916,"Bonaire, Saint Eustatius And Saba" +1861-01-01,24.084,1.446,"Bonaire, Saint Eustatius And Saba" +1861-02-01,25.041,0.6779999999999999,"Bonaire, Saint Eustatius And Saba" +1861-03-01,25.542,0.78,"Bonaire, Saint Eustatius And Saba" +1861-04-01,26.449,0.62,"Bonaire, Saint Eustatius And Saba" +1861-05-01,27.229,1.141,"Bonaire, Saint Eustatius And Saba" +1861-06-01,27.346,0.989,"Bonaire, Saint Eustatius And Saba" +1861-07-01,28.174,0.93,"Bonaire, Saint Eustatius And Saba" +1861-08-01,28.366,0.794,"Bonaire, Saint Eustatius And Saba" +1861-09-01,28.147,0.971,"Bonaire, Saint Eustatius And Saba" 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And Saba" +1870-07-01,28.139000000000006,0.732,"Bonaire, Saint Eustatius And Saba" +1870-08-01,28.029,1.096,"Bonaire, Saint Eustatius And Saba" +1870-09-01,28.339000000000002,0.7859999999999999,"Bonaire, Saint Eustatius And Saba" +1870-10-01,27.357,1.064,"Bonaire, Saint Eustatius And Saba" +1870-11-01,26.766,1.465,"Bonaire, Saint Eustatius And Saba" +1870-12-01,25.605999999999998,1.071,"Bonaire, Saint Eustatius And Saba" +1871-01-01,24.794,1.442,"Bonaire, Saint Eustatius And Saba" +1871-02-01,24.521,1.814,"Bonaire, Saint Eustatius And Saba" +1871-03-01,25.096,1.271,"Bonaire, Saint Eustatius And Saba" +1871-04-01,26.176,0.636,"Bonaire, Saint Eustatius And Saba" +1871-05-01,27.666999999999998,0.669,"Bonaire, Saint Eustatius And Saba" +1871-06-01,28.337,0.7290000000000001,"Bonaire, Saint Eustatius And Saba" +1871-07-01,28.324,0.8390000000000001,"Bonaire, Saint Eustatius And Saba" +1871-08-01,28.616,0.601,"Bonaire, Saint Eustatius And Saba" +1871-09-01,28.534000000000002,0.524,"Bonaire, Saint Eustatius And Saba" +1871-10-01,27.772,0.931,"Bonaire, Saint Eustatius And Saba" +1871-11-01,26.787,1.4680000000000002,"Bonaire, Saint Eustatius And Saba" +1871-12-01,25.754,0.941,"Bonaire, Saint Eustatius And Saba" +1872-01-01,25.136,1.675,"Bonaire, Saint Eustatius And Saba" +1872-02-01,25.921999999999997,2.065,"Bonaire, Saint Eustatius And Saba" +1872-03-01,26.674,1.375,"Bonaire, Saint Eustatius And Saba" +1872-04-01,26.171999999999997,1.854,"Bonaire, Saint Eustatius And Saba" +1872-05-01,27.403000000000002,1.388,"Bonaire, Saint Eustatius And Saba" +1872-06-01,27.816,2.044,"Bonaire, Saint Eustatius And Saba" +1872-07-01,28.322000000000006,1.5119999999999998,"Bonaire, Saint Eustatius And Saba" +1872-08-01,28.221,1.956,"Bonaire, Saint Eustatius And Saba" +1872-09-01,28.757,0.984,"Bonaire, Saint Eustatius And Saba" +1872-10-01,28.45,0.52,"Bonaire, Saint Eustatius And Saba" +1872-11-01,27.316999999999997,0.977,"Bonaire, Saint Eustatius And Saba" 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Saint Eustatius And Saba" +1874-03-01,25.118000000000002,1.252,"Bonaire, Saint Eustatius And Saba" +1874-04-01,26.248,1.106,"Bonaire, Saint Eustatius And Saba" +1874-05-01,27.221999999999998,0.545,"Bonaire, Saint Eustatius And Saba" +1874-06-01,27.499000000000002,0.74,"Bonaire, Saint Eustatius And Saba" +1874-07-01,27.913,0.8029999999999999,"Bonaire, Saint Eustatius And Saba" +1874-08-01,28.199,0.878,"Bonaire, Saint Eustatius And Saba" +1874-09-01,27.988000000000003,0.565,"Bonaire, Saint Eustatius And Saba" +1874-10-01,27.939,0.706,"Bonaire, Saint Eustatius And Saba" +1874-11-01,26.851,0.624,"Bonaire, Saint Eustatius And Saba" +1874-12-01,25.529,0.438,"Bonaire, Saint Eustatius And Saba" +1875-01-01,25.131,1.452,"Bonaire, Saint Eustatius And Saba" +1875-02-01,25.19,0.957,"Bonaire, Saint Eustatius And Saba" +1875-03-01,25.026999999999997,1.085,"Bonaire, Saint Eustatius And Saba" +1875-04-01,26.13,0.626,"Bonaire, Saint Eustatius And Saba" +1875-05-01,27.101,0.551,"Bonaire, Saint Eustatius And Saba" +1875-06-01,27.852,0.753,"Bonaire, Saint Eustatius And Saba" +1875-07-01,27.953000000000007,0.5329999999999999,"Bonaire, Saint Eustatius And Saba" +1875-08-01,28.46,0.435,"Bonaire, Saint Eustatius And Saba" +1875-09-01,28.265,0.643,"Bonaire, Saint Eustatius And Saba" +1875-10-01,28.048000000000002,0.769,"Bonaire, Saint Eustatius And Saba" +1875-11-01,26.639,0.784,"Bonaire, Saint Eustatius And Saba" +1875-12-01,25.535,0.568,"Bonaire, Saint Eustatius And Saba" +1876-01-01,25.12,1.04,"Bonaire, Saint Eustatius And Saba" +1876-02-01,25.177,0.58,"Bonaire, Saint Eustatius And Saba" +1876-03-01,25.285,1.504,"Bonaire, Saint Eustatius And Saba" +1876-04-01,26.152,0.856,"Bonaire, Saint Eustatius And Saba" +1876-05-01,27.169,0.977,"Bonaire, Saint Eustatius And Saba" +1876-06-01,27.733,0.946,"Bonaire, Saint Eustatius And Saba" +1876-07-01,28.084,0.612,"Bonaire, Saint Eustatius And Saba" +1876-08-01,28.174,0.754,"Bonaire, Saint Eustatius And Saba" +1876-09-01,28.536,0.901,"Bonaire, Saint Eustatius And Saba" +1876-10-01,28.26,0.585,"Bonaire, Saint Eustatius And Saba" +1876-11-01,27.968000000000004,0.863,"Bonaire, Saint Eustatius And Saba" +1876-12-01,26.866,0.948,"Bonaire, Saint Eustatius And Saba" +1877-01-01,25.879,1.41,"Bonaire, Saint Eustatius And Saba" +1877-02-01,25.613000000000003,1.3090000000000002,"Bonaire, Saint Eustatius And Saba" +1877-03-01,26.309,0.581,"Bonaire, Saint Eustatius And Saba" +1877-04-01,26.536,0.999,"Bonaire, Saint Eustatius And Saba" +1877-05-01,27.63,1.3030000000000002,"Bonaire, Saint Eustatius And Saba" +1877-06-01,27.596,1.774,"Bonaire, Saint Eustatius And Saba" +1877-07-01,28.293000000000006,1.104,"Bonaire, Saint Eustatius And Saba" +1877-08-01,29.011999999999997,0.818,"Bonaire, Saint Eustatius And Saba" +1877-09-01,29.18,0.557,"Bonaire, Saint Eustatius And Saba" +1877-10-01,28.574,0.78,"Bonaire, Saint Eustatius And Saba" +1877-11-01,27.943,0.93,"Bonaire, Saint Eustatius And Saba" +1877-12-01,26.677,2.318,"Bonaire, Saint Eustatius And Saba" +1878-01-01,26.281,1.872,"Bonaire, Saint Eustatius And Saba" +1878-02-01,26.851,1.55,"Bonaire, Saint Eustatius And Saba" +1878-03-01,27.301,0.81,"Bonaire, Saint Eustatius And Saba" +1878-04-01,27.851999999999997,0.94,"Bonaire, Saint Eustatius And Saba" +1878-05-01,28.011999999999997,0.449,"Bonaire, Saint Eustatius And Saba" +1878-06-01,28.272,0.778,"Bonaire, Saint Eustatius And Saba" +1878-07-01,28.200000000000006,0.872,"Bonaire, Saint Eustatius And Saba" +1878-08-01,28.142,0.7390000000000001,"Bonaire, Saint Eustatius And Saba" +1878-09-01,28.693,0.644,"Bonaire, Saint Eustatius And Saba" +1878-10-01,28.41,0.496,"Bonaire, Saint Eustatius And Saba" +1878-11-01,27.691999999999997,1.036,"Bonaire, Saint Eustatius And Saba" +1878-12-01,26.798,1.338,"Bonaire, Saint Eustatius And Saba" +1879-01-01,25.897,0.6859999999999999,"Bonaire, Saint Eustatius And Saba" +1879-02-01,26.04,0.991,"Bonaire, Saint Eustatius And Saba" +1879-03-01,25.685,0.7070000000000001,"Bonaire, Saint Eustatius And Saba" +1879-04-01,26.638,0.7140000000000001,"Bonaire, Saint Eustatius And Saba" +1879-05-01,27.511999999999997,0.925,"Bonaire, Saint Eustatius And Saba" +1879-06-01,27.487,0.815,"Bonaire, Saint Eustatius And Saba" +1879-07-01,27.610000000000007,1.385,"Bonaire, Saint Eustatius And Saba" +1879-08-01,28.017,0.77,"Bonaire, Saint Eustatius And Saba" +1879-09-01,28.078000000000003,0.596,"Bonaire, Saint Eustatius And Saba" +1879-10-01,27.456,0.8490000000000001,"Bonaire, Saint Eustatius And Saba" +1879-11-01,27.054,1.08,"Bonaire, Saint Eustatius And Saba" +1879-12-01,25.549,1.102,"Bonaire, Saint Eustatius And Saba" +1880-01-01,24.624000000000002,1.027,"Bonaire, Saint Eustatius And Saba" +1880-02-01,24.492,0.54,"Bonaire, Saint Eustatius And Saba" +1880-03-01,25.294,0.826,"Bonaire, Saint Eustatius And Saba" +1880-04-01,25.557,0.818,"Bonaire, Saint Eustatius And Saba" +1880-05-01,26.752,1.014,"Bonaire, Saint Eustatius And Saba" +1880-06-01,27.418000000000003,0.99,"Bonaire, Saint Eustatius And Saba" +1880-07-01,27.589,0.7759999999999999,"Bonaire, Saint Eustatius And Saba" +1880-08-01,27.513,0.486,"Bonaire, Saint Eustatius And Saba" +1880-09-01,27.746,0.8290000000000001,"Bonaire, Saint Eustatius And Saba" +1880-10-01,27.659000000000002,0.6609999999999999,"Bonaire, Saint Eustatius And Saba" +1880-11-01,26.94,1.5590000000000002,"Bonaire, Saint Eustatius And Saba" +1880-12-01,25.737,0.6890000000000001,"Bonaire, Saint Eustatius And Saba" +1881-01-01,25.392,0.759,"Bonaire, Saint Eustatius And Saba" +1881-02-01,25.007,1.025,"Bonaire, Saint Eustatius And Saba" +1881-03-01,26.014,0.68,"Bonaire, Saint Eustatius And Saba" +1881-04-01,26.326,0.82,"Bonaire, Saint Eustatius And Saba" +1881-05-01,27.318,0.946,"Bonaire, Saint Eustatius And Saba" +1881-06-01,27.836,1.027,"Bonaire, Saint Eustatius And Saba" +1881-07-01,27.964,0.5710000000000001,"Bonaire, Saint Eustatius And Saba" +1881-08-01,27.948,0.6409999999999999,"Bonaire, Saint Eustatius And Saba" +1881-09-01,27.463,0.35200000000000004,"Bonaire, Saint Eustatius And Saba" +1881-10-01,27.132,0.536,"Bonaire, Saint Eustatius And Saba" +1881-11-01,26.406,1.056,"Bonaire, Saint Eustatius And Saba" +1881-12-01,25.226999999999997,1.037,"Bonaire, Saint Eustatius And Saba" +1882-01-01,24.446,0.917,"Bonaire, Saint Eustatius And Saba" +1882-02-01,24.341,1.217,"Bonaire, Saint Eustatius And Saba" +1882-03-01,24.408,0.956,"Bonaire, Saint Eustatius And Saba" +1882-04-01,25.552,0.467,"Bonaire, Saint Eustatius And Saba" +1882-05-01,27.02,0.483,"Bonaire, Saint Eustatius And Saba" +1882-06-01,27.548000000000002,0.868,"Bonaire, Saint Eustatius And Saba" +1882-07-01,27.683000000000003,0.524,"Bonaire, Saint Eustatius And Saba" +1882-08-01,27.778000000000002,0.705,"Bonaire, Saint Eustatius And Saba" +1882-09-01,28.085,0.547,"Bonaire, Saint Eustatius And Saba" +1882-10-01,27.156999999999996,0.7090000000000001,"Bonaire, Saint Eustatius And Saba" +1882-11-01,26.541,0.8809999999999999,"Bonaire, Saint Eustatius And Saba" +1882-12-01,26.265,0.5489999999999999,"Bonaire, Saint Eustatius And Saba" +1883-01-01,25.389,0.828,"Bonaire, Saint Eustatius And Saba" +1883-02-01,24.888,0.8,"Bonaire, Saint Eustatius And Saba" +1883-03-01,25.689,0.851,"Bonaire, Saint Eustatius And Saba" +1883-04-01,26.051,1.057,"Bonaire, Saint Eustatius And Saba" +1883-05-01,27.078000000000003,0.7440000000000001,"Bonaire, Saint Eustatius And Saba" +1883-06-01,27.428,0.812,"Bonaire, Saint Eustatius And Saba" +1883-07-01,27.677,0.716,"Bonaire, Saint Eustatius And Saba" +1883-08-01,28.034000000000002,1.52,"Bonaire, Saint Eustatius And Saba" +1883-09-01,27.876,1.21,"Bonaire, Saint Eustatius And Saba" +1883-10-01,26.969,0.968,"Bonaire, Saint Eustatius And Saba" +1883-11-01,26.179,0.843,"Bonaire, Saint Eustatius And Saba" +1883-12-01,25.36,0.649,"Bonaire, Saint Eustatius And Saba" +1884-01-01,25.261,0.987,"Bonaire, Saint Eustatius And Saba" +1884-02-01,24.303,1.204,"Bonaire, Saint Eustatius And Saba" +1884-03-01,24.463,0.7709999999999999,"Bonaire, Saint Eustatius And Saba" +1884-04-01,25.18,0.672,"Bonaire, Saint Eustatius And Saba" +1884-05-01,26.834,0.677,"Bonaire, Saint Eustatius And Saba" +1884-06-01,27.7,0.522,"Bonaire, Saint Eustatius And Saba" +1884-07-01,28.084,0.447,"Bonaire, Saint Eustatius And Saba" +1884-08-01,27.544,0.8809999999999999,"Bonaire, Saint Eustatius And Saba" +1884-09-01,27.781,1.109,"Bonaire, Saint Eustatius And Saba" +1884-10-01,26.947,0.486,"Bonaire, Saint Eustatius And Saba" +1884-11-01,27.464,1.284,"Bonaire, Saint Eustatius And Saba" +1884-12-01,25.465999999999998,1.04,"Bonaire, Saint Eustatius And Saba" +1885-01-01,24.812,1.404,"Bonaire, Saint Eustatius And Saba" +1885-02-01,25.472,0.993,"Bonaire, Saint Eustatius And Saba" +1885-03-01,25.613000000000003,0.782,"Bonaire, Saint Eustatius And Saba" +1885-04-01,25.717,0.594,"Bonaire, Saint Eustatius And Saba" +1885-05-01,27.313000000000002,1.015,"Bonaire, Saint Eustatius And Saba" 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Saba" +1888-01-01,24.568,1.027,"Bonaire, Saint Eustatius And Saba" +1888-02-01,24.811,1.139,"Bonaire, Saint Eustatius And Saba" +1888-03-01,25.193,0.871,"Bonaire, Saint Eustatius And Saba" +1888-04-01,25.906,0.7170000000000001,"Bonaire, Saint Eustatius And Saba" +1888-05-01,27.105999999999998,0.5529999999999999,"Bonaire, Saint Eustatius And Saba" +1888-06-01,27.92,0.508,"Bonaire, Saint Eustatius And Saba" +1888-07-01,28.340999999999998,0.99,"Bonaire, Saint Eustatius And Saba" +1888-08-01,28.4,0.471,"Bonaire, Saint Eustatius And Saba" +1888-09-01,28.857,0.508,"Bonaire, Saint Eustatius And Saba" +1888-10-01,27.915,0.458,"Bonaire, Saint Eustatius And Saba" +1888-11-01,26.892,0.5489999999999999,"Bonaire, Saint Eustatius And Saba" +1888-12-01,26.345,0.782,"Bonaire, Saint Eustatius And Saba" +1889-01-01,25.473000000000003,0.86,"Bonaire, Saint Eustatius And Saba" +1889-02-01,26.295,0.464,"Bonaire, Saint Eustatius And Saba" +1889-03-01,26.527,0.745,"Bonaire, Saint Eustatius And Saba" 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Eustatius And Saba" +1890-08-01,27.756999999999998,0.914,"Bonaire, Saint Eustatius And Saba" +1890-09-01,27.662,1.216,"Bonaire, Saint Eustatius And Saba" +1890-10-01,27.286,0.599,"Bonaire, Saint Eustatius And Saba" +1890-11-01,26.46,0.852,"Bonaire, Saint Eustatius And Saba" +1890-12-01,25.459,0.8140000000000001,"Bonaire, Saint Eustatius And Saba" +1891-01-01,25.015,0.755,"Bonaire, Saint Eustatius And Saba" +1891-02-01,24.867,0.809,"Bonaire, Saint Eustatius And Saba" +1891-03-01,25.44,0.508,"Bonaire, Saint Eustatius And Saba" +1891-04-01,26.505,0.371,"Bonaire, Saint Eustatius And Saba" +1891-05-01,27.766,0.68,"Bonaire, Saint Eustatius And Saba" +1891-06-01,27.929000000000002,0.425,"Bonaire, Saint Eustatius And Saba" +1891-07-01,28.523000000000003,0.348,"Bonaire, Saint Eustatius And Saba" +1891-08-01,28.671999999999997,0.693,"Bonaire, Saint Eustatius And Saba" +1891-09-01,28.785,0.37,"Bonaire, Saint Eustatius And Saba" +1891-10-01,28.086,0.362,"Bonaire, Saint Eustatius And Saba" +1891-11-01,27.541999999999998,0.779,"Bonaire, Saint Eustatius And Saba" +1891-12-01,25.683000000000003,0.507,"Bonaire, Saint Eustatius And Saba" +1892-01-01,25.771,0.6629999999999999,"Bonaire, Saint Eustatius And Saba" +1892-02-01,25.046999999999997,0.8,"Bonaire, Saint Eustatius And Saba" +1892-03-01,26.054000000000002,0.7959999999999999,"Bonaire, Saint Eustatius And Saba" +1892-04-01,25.934,0.34,"Bonaire, Saint Eustatius And Saba" +1892-05-01,27.046,0.5,"Bonaire, Saint Eustatius And Saba" +1892-06-01,27.28,0.28300000000000003,"Bonaire, Saint Eustatius And Saba" +1892-07-01,27.848000000000006,0.35600000000000004,"Bonaire, Saint Eustatius And Saba" +1892-08-01,27.761999999999997,0.417,"Bonaire, Saint Eustatius And Saba" +1892-09-01,28.378,0.477,"Bonaire, Saint Eustatius And Saba" +1892-10-01,27.646,0.841,"Bonaire, Saint Eustatius And Saba" +1892-11-01,26.653,0.624,"Bonaire, Saint Eustatius And Saba" +1892-12-01,25.6,1.113,"Bonaire, Saint Eustatius And Saba" +1893-01-01,24.505,0.598,"Bonaire, Saint Eustatius And Saba" +1893-02-01,25.267,0.938,"Bonaire, Saint Eustatius And Saba" +1893-03-01,25.034000000000002,0.552,"Bonaire, Saint Eustatius And Saba" +1893-04-01,26.267,0.498,"Bonaire, Saint Eustatius And Saba" +1893-05-01,26.863000000000003,0.457,"Bonaire, Saint Eustatius And Saba" +1893-06-01,27.393,0.471,"Bonaire, Saint Eustatius And Saba" +1893-07-01,27.634,0.53,"Bonaire, Saint Eustatius And Saba" +1893-08-01,28.5,0.987,"Bonaire, Saint Eustatius And Saba" +1893-09-01,27.88,0.453,"Bonaire, Saint Eustatius And Saba" +1893-10-01,27.271,0.5489999999999999,"Bonaire, Saint Eustatius And Saba" +1893-11-01,26.725,0.514,"Bonaire, Saint Eustatius And Saba" +1893-12-01,25.241999999999997,0.519,"Bonaire, Saint Eustatius And Saba" +1894-01-01,24.725,0.525,"Bonaire, Saint Eustatius And Saba" +1894-02-01,24.524,0.6729999999999999,"Bonaire, Saint Eustatius And Saba" +1894-03-01,24.494,0.521,"Bonaire, Saint Eustatius And Saba" +1894-04-01,25.806,0.7959999999999999,"Bonaire, Saint Eustatius And Saba" +1894-05-01,27.4,0.616,"Bonaire, Saint Eustatius And Saba" +1894-06-01,27.646,0.269,"Bonaire, Saint Eustatius And Saba" +1894-07-01,27.6,0.861,"Bonaire, Saint Eustatius And Saba" +1894-08-01,27.988000000000003,0.752,"Bonaire, Saint Eustatius And Saba" +1894-09-01,28.228,0.274,"Bonaire, Saint Eustatius And Saba" +1894-10-01,27.366999999999997,0.465,"Bonaire, Saint Eustatius And Saba" +1894-11-01,26.615,0.364,"Bonaire, Saint Eustatius And Saba" +1894-12-01,25.526999999999997,0.892,"Bonaire, Saint Eustatius And Saba" +1895-01-01,24.73,0.528,"Bonaire, Saint Eustatius And Saba" +1895-02-01,24.982,0.675,"Bonaire, Saint Eustatius And Saba" +1895-03-01,25.411,0.34,"Bonaire, Saint Eustatius And Saba" +1895-04-01,26.468000000000004,0.446,"Bonaire, Saint Eustatius And Saba" +1895-05-01,27.419,0.258,"Bonaire, Saint Eustatius And Saba" +1895-06-01,27.655,0.911,"Bonaire, Saint Eustatius And Saba" +1895-07-01,28.022,0.341,"Bonaire, Saint Eustatius And Saba" +1895-08-01,28.291999999999998,0.329,"Bonaire, Saint Eustatius And Saba" +1895-09-01,28.499000000000002,0.318,"Bonaire, Saint Eustatius And Saba" +1895-10-01,27.482,1.109,"Bonaire, Saint Eustatius And Saba" +1895-11-01,26.648000000000003,0.499,"Bonaire, Saint Eustatius And Saba" +1895-12-01,25.392,0.578,"Bonaire, Saint Eustatius And Saba" +1896-01-01,24.864,0.57,"Bonaire, Saint Eustatius And Saba" +1896-02-01,25.327,0.941,"Bonaire, Saint Eustatius And Saba" +1896-03-01,25.627,0.578,"Bonaire, Saint Eustatius And Saba" +1896-04-01,25.715,1.094,"Bonaire, Saint Eustatius And Saba" +1896-05-01,27.018,0.872,"Bonaire, Saint Eustatius And Saba" +1896-06-01,27.662,0.7809999999999999,"Bonaire, Saint Eustatius And Saba" +1896-07-01,28.339,0.508,"Bonaire, Saint Eustatius And Saba" +1896-08-01,28.256999999999998,0.439,"Bonaire, Saint Eustatius And Saba" +1896-09-01,28.534000000000002,0.3670000000000001,"Bonaire, Saint Eustatius And Saba" +1896-10-01,28.476,0.512,"Bonaire, Saint Eustatius And Saba" +1896-11-01,26.945,0.555,"Bonaire, Saint Eustatius And Saba" +1896-12-01,25.94,0.307,"Bonaire, Saint Eustatius And Saba" +1897-01-01,25.289,0.6920000000000001,"Bonaire, Saint Eustatius And Saba" +1897-02-01,25.505,0.539,"Bonaire, Saint Eustatius And Saba" +1897-03-01,25.772,0.5770000000000001,"Bonaire, Saint Eustatius And Saba" +1897-04-01,27.218000000000004,0.5379999999999999,"Bonaire, Saint Eustatius And Saba" +1897-05-01,27.491,0.438,"Bonaire, Saint Eustatius And Saba" +1897-06-01,27.743000000000002,0.8909999999999999,"Bonaire, Saint Eustatius And Saba" +1897-07-01,27.865,0.888,"Bonaire, Saint Eustatius And Saba" +1897-08-01,28.451999999999998,0.588,"Bonaire, Saint Eustatius And Saba" +1897-09-01,28.331999999999997,0.314,"Bonaire, Saint Eustatius And Saba" +1897-10-01,28.316,0.6729999999999999,"Bonaire, Saint Eustatius And Saba" +1897-11-01,26.576,0.855,"Bonaire, Saint Eustatius And Saba" +1897-12-01,25.871,0.5,"Bonaire, Saint Eustatius And Saba" +1898-01-01,25.069000000000003,0.863,"Bonaire, Saint Eustatius And Saba" +1898-02-01,24.648000000000003,0.65,"Bonaire, Saint Eustatius And Saba" +1898-03-01,24.724,1.152,"Bonaire, Saint Eustatius And Saba" +1898-04-01,26.177,1.226,"Bonaire, Saint Eustatius And Saba" +1898-05-01,27.774,0.579,"Bonaire, Saint Eustatius And Saba" +1898-06-01,27.94,0.611,"Bonaire, Saint Eustatius And Saba" +1898-07-01,27.747,0.457,"Bonaire, Saint Eustatius And Saba" +1898-08-01,27.92,0.423,"Bonaire, Saint Eustatius And Saba" +1898-09-01,28.022,0.311,"Bonaire, Saint Eustatius And Saba" +1898-10-01,27.679000000000002,0.428,"Bonaire, Saint Eustatius And Saba" +1898-11-01,26.668000000000003,0.611,"Bonaire, Saint Eustatius And Saba" +1898-12-01,25.653000000000002,0.83,"Bonaire, Saint Eustatius And Saba" +1899-01-01,24.996,0.544,"Bonaire, Saint Eustatius And Saba" +1899-02-01,25.292,0.547,"Bonaire, Saint Eustatius And Saba" +1899-03-01,25.421999999999997,0.465,"Bonaire, Saint Eustatius And Saba" +1899-04-01,26.44,0.625,"Bonaire, Saint Eustatius And Saba" +1899-05-01,27.618000000000002,0.449,"Bonaire, Saint Eustatius And Saba" +1899-06-01,27.904,0.317,"Bonaire, Saint Eustatius And Saba" +1899-07-01,28.168000000000006,0.314,"Bonaire, Saint Eustatius And Saba" +1899-08-01,28.362,0.395,"Bonaire, Saint Eustatius And Saba" +1899-09-01,28.409000000000002,0.908,"Bonaire, Saint Eustatius And Saba" +1899-10-01,27.464000000000002,0.947,"Bonaire, Saint Eustatius And Saba" +1899-11-01,27.5,0.387,"Bonaire, Saint Eustatius And Saba" +1899-12-01,25.7,0.617,"Bonaire, Saint Eustatius And Saba" +1900-01-01,25.388,0.8809999999999999,"Bonaire, Saint Eustatius And Saba" +1900-02-01,26.038,0.531,"Bonaire, Saint Eustatius And Saba" +1900-03-01,26.441999999999997,0.354,"Bonaire, Saint Eustatius And Saba" +1900-04-01,26.862,0.405,"Bonaire, Saint Eustatius And Saba" +1900-05-01,28.426,1.098,"Bonaire, Saint Eustatius And Saba" +1900-06-01,28.187,0.455,"Bonaire, Saint Eustatius And Saba" +1900-07-01,28.541,0.649,"Bonaire, Saint Eustatius And Saba" +1900-08-01,28.419,0.273,"Bonaire, Saint Eustatius And Saba" +1900-09-01,28.208000000000002,0.818,"Bonaire, Saint Eustatius And Saba" +1900-10-01,27.536,0.847,"Bonaire, Saint Eustatius And Saba" +1900-11-01,26.898000000000003,0.644,"Bonaire, Saint Eustatius And Saba" +1900-12-01,25.743000000000002,1.129,"Bonaire, Saint Eustatius And Saba" +1901-01-01,25.314,0.54,"Bonaire, Saint Eustatius And Saba" +1901-02-01,25.415,1.135,"Bonaire, Saint Eustatius And Saba" +1901-03-01,26.1,0.345,"Bonaire, Saint Eustatius And Saba" +1901-04-01,26.799,0.603,"Bonaire, Saint Eustatius And Saba" +1901-05-01,27.683000000000003,0.6759999999999999,"Bonaire, Saint Eustatius And Saba" +1901-06-01,28.154,0.657,"Bonaire, Saint Eustatius And Saba" +1901-07-01,28.116999999999997,0.679,"Bonaire, Saint Eustatius And Saba" +1901-08-01,28.447,0.503,"Bonaire, Saint Eustatius And Saba" +1901-09-01,28.709,0.6679999999999999,"Bonaire, Saint Eustatius And Saba" +1901-10-01,27.752,0.454,"Bonaire, Saint Eustatius And Saba" +1901-11-01,26.811999999999998,0.204,"Bonaire, Saint Eustatius And Saba" +1901-12-01,25.83,0.458,"Bonaire, Saint Eustatius And Saba" +1902-01-01,25.342,0.642,"Bonaire, Saint Eustatius And Saba" +1902-02-01,25.661,0.449,"Bonaire, Saint Eustatius And Saba" +1902-03-01,25.66,0.314,"Bonaire, Saint Eustatius And Saba" +1902-04-01,26.855,0.464,"Bonaire, Saint Eustatius And Saba" +1902-05-01,28.017,0.8079999999999999,"Bonaire, Saint Eustatius And Saba" +1902-06-01,28.104,0.551,"Bonaire, Saint Eustatius And Saba" +1902-07-01,28.439,0.542,"Bonaire, Saint Eustatius And Saba" +1902-08-01,28.677,0.621,"Bonaire, Saint Eustatius And Saba" +1902-09-01,28.557,0.489,"Bonaire, Saint Eustatius And Saba" +1902-10-01,27.726999999999997,0.574,"Bonaire, Saint Eustatius And Saba" +1902-11-01,27.171999999999997,1.304,"Bonaire, Saint Eustatius And Saba" +1902-12-01,26.123,1.034,"Bonaire, Saint Eustatius And Saba" +1903-01-01,25.503,0.51,"Bonaire, Saint Eustatius And Saba" +1903-02-01,26.081999999999997,0.7190000000000001,"Bonaire, Saint Eustatius And Saba" +1903-03-01,25.967,0.368,"Bonaire, Saint Eustatius And Saba" +1903-04-01,26.918000000000003,0.574,"Bonaire, Saint Eustatius And Saba" +1903-05-01,28.191999999999997,0.438,"Bonaire, Saint Eustatius And Saba" +1903-06-01,28.489,0.408,"Bonaire, Saint Eustatius And Saba" +1903-07-01,28.131,0.599,"Bonaire, Saint Eustatius And Saba" +1903-08-01,28.171999999999997,0.421,"Bonaire, Saint Eustatius And Saba" +1903-09-01,28.256,0.471,"Bonaire, Saint Eustatius And Saba" +1903-10-01,27.791,0.5539999999999999,"Bonaire, Saint Eustatius And Saba" +1903-11-01,26.848000000000003,0.6,"Bonaire, Saint Eustatius And Saba" +1903-12-01,25.65,0.598,"Bonaire, Saint Eustatius And Saba" +1904-01-01,25.047,0.767,"Bonaire, Saint Eustatius And Saba" +1904-02-01,24.674,0.515,"Bonaire, Saint Eustatius And Saba" +1904-03-01,25.053,0.503,"Bonaire, Saint Eustatius And Saba" +1904-04-01,25.831999999999997,0.443,"Bonaire, Saint Eustatius And Saba" +1904-05-01,27.273000000000003,0.552,"Bonaire, Saint Eustatius And Saba" +1904-06-01,27.61,0.45,"Bonaire, Saint Eustatius And Saba" +1904-07-01,27.599,0.536,"Bonaire, Saint Eustatius And Saba" +1904-08-01,28.046,0.606,"Bonaire, Saint Eustatius And Saba" +1904-09-01,28.029,0.617,"Bonaire, Saint Eustatius And Saba" +1904-10-01,27.469,0.4,"Bonaire, Saint Eustatius And Saba" +1904-11-01,27.206999999999997,0.271,"Bonaire, Saint Eustatius And Saba" +1904-12-01,25.803,0.298,"Bonaire, Saint Eustatius And Saba" +1905-01-01,25.098000000000003,0.509,"Bonaire, Saint Eustatius And Saba" +1905-02-01,25.07,0.342,"Bonaire, Saint Eustatius And Saba" +1905-03-01,25.271,0.333,"Bonaire, Saint Eustatius And Saba" +1905-04-01,26.851999999999997,0.638,"Bonaire, Saint Eustatius And Saba" +1905-05-01,27.305999999999997,0.297,"Bonaire, Saint Eustatius And Saba" +1905-06-01,28.083000000000002,0.386,"Bonaire, Saint Eustatius And Saba" +1905-07-01,28.329,0.285,"Bonaire, Saint Eustatius And Saba" +1905-08-01,28.601,0.362,"Bonaire, Saint Eustatius And Saba" +1905-09-01,28.388,0.569,"Bonaire, Saint Eustatius And Saba" +1905-10-01,28.013,0.615,"Bonaire, Saint Eustatius And Saba" +1905-11-01,27.336,0.208,"Bonaire, Saint Eustatius And Saba" +1905-12-01,25.855,0.7070000000000001,"Bonaire, Saint Eustatius And Saba" +1906-01-01,25.224,0.524,"Bonaire, Saint Eustatius And Saba" +1906-02-01,25.236,0.471,"Bonaire, Saint Eustatius And Saba" +1906-03-01,25.757,0.396,"Bonaire, Saint Eustatius And Saba" +1906-04-01,26.573,0.343,"Bonaire, Saint Eustatius And Saba" +1906-05-01,27.741999999999997,0.468,"Bonaire, Saint Eustatius And Saba" +1906-06-01,27.922,1.1909999999999998,"Bonaire, Saint Eustatius And Saba" +1906-07-01,27.971999999999998,0.35100000000000003,"Bonaire, Saint Eustatius And Saba" +1906-08-01,28.308000000000003,0.348,"Bonaire, Saint Eustatius And Saba" +1906-09-01,28.526999999999997,0.525,"Bonaire, Saint Eustatius And Saba" +1906-10-01,27.585,0.56,"Bonaire, Saint Eustatius And Saba" +1906-11-01,27.32,0.4370000000000001,"Bonaire, Saint Eustatius And Saba" +1906-12-01,25.131,0.522,"Bonaire, Saint Eustatius And Saba" +1907-01-01,24.34,0.474,"Bonaire, Saint Eustatius And Saba" +1907-02-01,24.583000000000002,0.628,"Bonaire, Saint Eustatius And Saba" +1907-03-01,24.998,0.415,"Bonaire, Saint Eustatius And Saba" +1907-04-01,26.294,0.349,"Bonaire, Saint Eustatius And Saba" +1907-05-01,27.195,0.341,"Bonaire, Saint Eustatius And Saba" +1907-06-01,27.932,0.35100000000000003,"Bonaire, Saint Eustatius And Saba" +1907-07-01,27.799000000000003,0.329,"Bonaire, Saint Eustatius And Saba" +1907-08-01,28.261999999999997,0.499,"Bonaire, Saint Eustatius And Saba" +1907-09-01,28.364,0.384,"Bonaire, Saint Eustatius And Saba" +1907-10-01,27.736,0.7120000000000001,"Bonaire, Saint Eustatius And Saba" +1907-11-01,26.941,1.031,"Bonaire, Saint Eustatius And Saba" +1907-12-01,26.026999999999997,0.211,"Bonaire, Saint Eustatius And Saba" +1908-01-01,25.34,0.642,"Bonaire, Saint Eustatius And Saba" +1908-02-01,25.09,0.235,"Bonaire, Saint Eustatius And Saba" +1908-03-01,25.334,0.32,"Bonaire, Saint Eustatius And Saba" +1908-04-01,26.06,0.366,"Bonaire, Saint Eustatius And Saba" +1908-05-01,27.45,0.785,"Bonaire, Saint Eustatius And Saba" +1908-06-01,27.935,0.568,"Bonaire, Saint Eustatius And Saba" +1908-07-01,28.024,0.61,"Bonaire, Saint Eustatius And Saba" +1908-08-01,28.495,0.674,"Bonaire, Saint Eustatius And Saba" +1908-09-01,28.256,0.504,"Bonaire, Saint Eustatius And Saba" +1908-10-01,27.511999999999997,0.417,"Bonaire, Saint Eustatius And Saba" +1908-11-01,26.747,0.391,"Bonaire, Saint Eustatius And Saba" +1908-12-01,25.649,0.606,"Bonaire, Saint Eustatius And Saba" +1909-01-01,25.047,0.768,"Bonaire, Saint Eustatius And Saba" +1909-02-01,24.678,0.398,"Bonaire, Saint Eustatius And Saba" +1909-03-01,26.109,1.206,"Bonaire, Saint Eustatius And Saba" +1909-04-01,25.889,0.484,"Bonaire, Saint Eustatius And Saba" +1909-05-01,27.508000000000003,0.691,"Bonaire, Saint Eustatius And Saba" +1909-06-01,27.627,0.5670000000000001,"Bonaire, Saint Eustatius And Saba" +1909-07-01,27.913,0.37,"Bonaire, Saint Eustatius And Saba" +1909-08-01,28.101999999999997,0.5710000000000001,"Bonaire, Saint Eustatius And Saba" +1909-09-01,28.516,0.623,"Bonaire, Saint Eustatius And Saba" +1909-10-01,27.795,0.447,"Bonaire, Saint Eustatius And Saba" +1909-11-01,26.743,0.276,"Bonaire, Saint Eustatius And Saba" +1909-12-01,25.146,0.5660000000000001,"Bonaire, Saint Eustatius And Saba" +1910-01-01,24.577,0.537,"Bonaire, Saint Eustatius And Saba" +1910-02-01,24.641,0.307,"Bonaire, Saint Eustatius And Saba" +1910-03-01,24.873,0.496,"Bonaire, Saint Eustatius And Saba" +1910-04-01,26.089000000000002,0.907,"Bonaire, Saint Eustatius And Saba" +1910-05-01,27.706999999999997,1.649,"Bonaire, Saint Eustatius And Saba" +1910-06-01,28.091,0.531,"Bonaire, Saint Eustatius And Saba" +1910-07-01,28.271,1.243,"Bonaire, Saint Eustatius And Saba" +1910-08-01,28.561,1.144,"Bonaire, Saint Eustatius And Saba" +1910-09-01,28.173000000000002,0.4970000000000001,"Bonaire, Saint Eustatius And Saba" +1910-10-01,27.439,0.4270000000000001,"Bonaire, Saint Eustatius And Saba" +1910-11-01,27.188000000000002,0.452,"Bonaire, Saint Eustatius And Saba" +1910-12-01,25.878,0.509,"Bonaire, Saint Eustatius And Saba" +1911-01-01,24.543000000000003,0.313,"Bonaire, Saint Eustatius And Saba" +1911-02-01,24.898000000000003,0.345,"Bonaire, Saint Eustatius And Saba" +1911-03-01,25.21,0.248,"Bonaire, Saint Eustatius And Saba" +1911-04-01,26.499000000000002,0.28800000000000003,"Bonaire, Saint Eustatius And Saba" +1911-05-01,27.28,0.359,"Bonaire, Saint Eustatius And Saba" +1911-06-01,27.841,0.297,"Bonaire, Saint Eustatius And Saba" +1911-07-01,27.973000000000006,0.691,"Bonaire, Saint Eustatius And Saba" +1911-08-01,28.085,0.526,"Bonaire, Saint Eustatius And Saba" +1911-09-01,28.386,0.601,"Bonaire, Saint Eustatius And Saba" +1911-10-01,27.984,0.35100000000000003,"Bonaire, Saint Eustatius And Saba" +1911-11-01,27.134,0.366,"Bonaire, Saint Eustatius And Saba" +1911-12-01,25.913,0.6779999999999999,"Bonaire, Saint Eustatius And Saba" +1912-01-01,25.43,0.311,"Bonaire, Saint Eustatius And Saba" +1912-02-01,25.565,0.364,"Bonaire, Saint Eustatius And Saba" +1912-03-01,25.687,0.594,"Bonaire, Saint Eustatius And Saba" +1912-04-01,26.236,0.628,"Bonaire, Saint Eustatius And Saba" +1912-05-01,28.054000000000002,0.388,"Bonaire, Saint Eustatius And Saba" +1912-06-01,27.544,1.046,"Bonaire, Saint Eustatius And Saba" +1912-07-01,27.895,0.672,"Bonaire, Saint Eustatius And Saba" +1912-08-01,28.081999999999997,0.632,"Bonaire, Saint Eustatius And Saba" +1912-09-01,28.134,0.674,"Bonaire, Saint Eustatius And Saba" +1912-10-01,27.612,0.773,"Bonaire, Saint Eustatius And Saba" +1912-11-01,26.936999999999998,0.7,"Bonaire, Saint Eustatius And Saba" +1912-12-01,25.79,0.395,"Bonaire, Saint Eustatius And Saba" +1913-01-01,25.061,0.404,"Bonaire, Saint Eustatius And Saba" +1913-02-01,24.96,0.471,"Bonaire, Saint Eustatius And Saba" +1913-03-01,25.079,0.6,"Bonaire, Saint Eustatius And Saba" +1913-04-01,25.652,0.398,"Bonaire, Saint Eustatius And Saba" +1913-05-01,26.941,0.293,"Bonaire, Saint Eustatius And Saba" +1913-06-01,27.668000000000003,0.199,"Bonaire, Saint Eustatius And Saba" +1913-07-01,27.92,0.406,"Bonaire, Saint Eustatius And Saba" +1913-08-01,28.065,0.414,"Bonaire, Saint Eustatius And Saba" +1913-09-01,27.996,0.6579999999999999,"Bonaire, Saint Eustatius And Saba" +1913-10-01,27.263,0.395,"Bonaire, Saint Eustatius And Saba" +1913-11-01,26.643,0.552,"Bonaire, Saint Eustatius And Saba" +1913-12-01,25.3,0.409,"Bonaire, Saint Eustatius And Saba" +1914-01-01,25.123,0.422,"Bonaire, Saint Eustatius And Saba" +1914-02-01,25.291,0.467,"Bonaire, Saint Eustatius And Saba" +1914-03-01,25.761999999999997,0.201,"Bonaire, Saint Eustatius And Saba" +1914-04-01,26.458000000000002,0.293,"Bonaire, Saint Eustatius And Saba" +1914-05-01,27.475,0.493,"Bonaire, Saint Eustatius And Saba" +1914-06-01,27.609,0.539,"Bonaire, Saint Eustatius And Saba" +1914-07-01,28.104000000000006,0.664,"Bonaire, Saint Eustatius And Saba" +1914-08-01,28.239,0.348,"Bonaire, Saint Eustatius And Saba" +1914-09-01,28.506,0.4970000000000001,"Bonaire, Saint Eustatius And Saba" +1914-10-01,27.799,0.336,"Bonaire, Saint Eustatius And Saba" +1914-11-01,27.14,0.362,"Bonaire, Saint Eustatius And Saba" +1914-12-01,26.673,0.747,"Bonaire, Saint Eustatius And Saba" +1915-01-01,25.636999999999997,0.527,"Bonaire, Saint Eustatius And Saba" +1915-02-01,25.611,0.544,"Bonaire, Saint Eustatius And Saba" +1915-03-01,26.558000000000003,0.295,"Bonaire, Saint Eustatius And Saba" +1915-04-01,26.649,0.28300000000000003,"Bonaire, Saint Eustatius And Saba" +1915-05-01,28.263,0.4270000000000001,"Bonaire, Saint Eustatius And Saba" +1915-06-01,28.604,0.316,"Bonaire, Saint Eustatius And Saba" +1915-07-01,28.566999999999997,0.486,"Bonaire, Saint Eustatius And Saba" +1915-08-01,28.878,0.453,"Bonaire, Saint Eustatius And Saba" +1915-09-01,28.733,0.476,"Bonaire, Saint Eustatius And Saba" +1915-10-01,28.328000000000003,0.413,"Bonaire, Saint Eustatius And Saba" +1915-11-01,27.283,0.522,"Bonaire, Saint Eustatius And Saba" +1915-12-01,26.241,0.293,"Bonaire, Saint Eustatius And Saba" +1916-01-01,25.063000000000002,0.496,"Bonaire, Saint Eustatius And Saba" +1916-02-01,24.903000000000002,0.454,"Bonaire, Saint Eustatius And Saba" +1916-03-01,25.423000000000002,0.466,"Bonaire, Saint Eustatius And Saba" +1916-04-01,26.505,0.389,"Bonaire, Saint Eustatius And Saba" +1916-05-01,28.018,0.406,"Bonaire, Saint Eustatius And Saba" +1916-06-01,28.224,0.362,"Bonaire, Saint Eustatius And Saba" +1916-07-01,28.26,0.474,"Bonaire, Saint Eustatius And Saba" +1916-08-01,28.348000000000003,0.327,"Bonaire, Saint Eustatius And Saba" +1916-09-01,28.167,0.6559999999999999,"Bonaire, Saint Eustatius And Saba" +1916-10-01,27.502,0.337,"Bonaire, Saint Eustatius And Saba" +1916-11-01,26.509,0.211,"Bonaire, Saint Eustatius And Saba" +1916-12-01,24.861,0.508,"Bonaire, Saint Eustatius And Saba" +1917-01-01,24.303,0.634,"Bonaire, Saint Eustatius And Saba" +1917-02-01,24.299,0.81,"Bonaire, Saint Eustatius And Saba" +1917-03-01,24.699,0.26,"Bonaire, Saint Eustatius And Saba" +1917-04-01,25.779,0.6629999999999999,"Bonaire, Saint Eustatius And Saba" +1917-05-01,27.046,0.7859999999999999,"Bonaire, Saint Eustatius And Saba" +1917-06-01,27.519000000000002,0.775,"Bonaire, Saint Eustatius And Saba" +1917-07-01,27.524,1.23,"Bonaire, Saint Eustatius And Saba" +1917-08-01,28.033,0.313,"Bonaire, Saint Eustatius And Saba" +1917-09-01,27.734,0.91,"Bonaire, Saint Eustatius And Saba" +1917-10-01,27.581,0.329,"Bonaire, Saint Eustatius And Saba" +1917-11-01,26.79,0.504,"Bonaire, Saint Eustatius And Saba" +1917-12-01,25.358,0.36,"Bonaire, Saint Eustatius And Saba" +1918-01-01,24.645,0.377,"Bonaire, Saint Eustatius And Saba" +1918-02-01,24.958000000000002,0.892,"Bonaire, Saint Eustatius And Saba" +1918-03-01,25.276999999999997,1.215,"Bonaire, Saint Eustatius And Saba" +1918-04-01,25.663,0.411,"Bonaire, Saint Eustatius And Saba" +1918-05-01,26.76,0.399,"Bonaire, Saint Eustatius And Saba" +1918-06-01,27.44,0.54,"Bonaire, Saint Eustatius And Saba" +1918-07-01,27.93,0.765,"Bonaire, Saint Eustatius And Saba" +1918-08-01,28.239,0.7509999999999999,"Bonaire, Saint Eustatius And Saba" +1918-09-01,28.518,0.466,"Bonaire, Saint Eustatius And Saba" +1918-10-01,27.598000000000003,0.408,"Bonaire, Saint Eustatius And Saba" +1918-11-01,26.886999999999997,0.552,"Bonaire, Saint Eustatius And Saba" +1918-12-01,25.506,0.277,"Bonaire, Saint Eustatius And Saba" +1919-01-01,25.018,0.34700000000000003,"Bonaire, Saint Eustatius And Saba" +1919-02-01,25.581999999999997,0.361,"Bonaire, Saint Eustatius And Saba" +1919-03-01,25.643,0.585,"Bonaire, Saint Eustatius And Saba" +1919-04-01,26.545,0.746,"Bonaire, Saint Eustatius And Saba" +1919-05-01,27.834,0.905,"Bonaire, Saint Eustatius And Saba" +1919-06-01,28.156999999999996,0.3720000000000001,"Bonaire, Saint Eustatius And Saba" +1919-07-01,28.171000000000006,0.292,"Bonaire, Saint Eustatius And Saba" +1919-08-01,28.62,0.185,"Bonaire, Saint Eustatius And Saba" +1919-09-01,28.386999999999997,0.332,"Bonaire, Saint Eustatius And Saba" +1919-10-01,28.06,0.278,"Bonaire, Saint Eustatius And Saba" +1919-11-01,27.108,0.3720000000000001,"Bonaire, Saint Eustatius And Saba" +1919-12-01,26.171999999999997,0.4320000000000001,"Bonaire, Saint Eustatius And Saba" +1920-01-01,25.21,0.45,"Bonaire, Saint Eustatius And Saba" +1920-02-01,25.436,0.339,"Bonaire, Saint Eustatius And Saba" +1920-03-01,25.303,0.851,"Bonaire, Saint Eustatius And Saba" +1920-04-01,26.463,0.623,"Bonaire, Saint Eustatius And Saba" +1920-05-01,27.55,0.506,"Bonaire, Saint Eustatius And Saba" +1920-06-01,27.865,0.486,"Bonaire, Saint Eustatius And Saba" +1920-07-01,28.059,0.424,"Bonaire, Saint Eustatius And Saba" +1920-08-01,28.236,0.312,"Bonaire, Saint Eustatius And Saba" +1920-09-01,28.198,0.36,"Bonaire, Saint Eustatius And Saba" +1920-10-01,27.681,0.574,"Bonaire, Saint Eustatius And Saba" +1920-11-01,26.86,0.708,"Bonaire, Saint Eustatius And Saba" +1920-12-01,25.561999999999998,0.667,"Bonaire, Saint Eustatius And Saba" +1921-01-01,25.202,0.5479999999999999,"Bonaire, Saint Eustatius And Saba" +1921-02-01,25.139,0.35100000000000003,"Bonaire, Saint Eustatius And Saba" +1921-03-01,25.01,0.212,"Bonaire, Saint Eustatius And Saba" +1921-04-01,25.527,0.506,"Bonaire, Saint Eustatius And Saba" +1921-05-01,27.105,0.461,"Bonaire, Saint Eustatius And Saba" +1921-06-01,27.68,0.328,"Bonaire, Saint Eustatius And Saba" +1921-07-01,27.794,0.48,"Bonaire, Saint Eustatius And Saba" +1921-08-01,27.883000000000003,0.6859999999999999,"Bonaire, Saint Eustatius And Saba" +1921-09-01,28.307,0.551,"Bonaire, Saint Eustatius And Saba" +1921-10-01,27.441999999999997,0.313,"Bonaire, Saint Eustatius And Saba" +1921-11-01,27.096999999999998,0.421,"Bonaire, Saint Eustatius And Saba" +1921-12-01,25.819000000000003,0.379,"Bonaire, Saint Eustatius And Saba" +1922-01-01,25.315,0.418,"Bonaire, Saint Eustatius And Saba" +1922-02-01,25.163,0.476,"Bonaire, Saint Eustatius And Saba" +1922-03-01,25.215,0.445,"Bonaire, Saint Eustatius And Saba" +1922-04-01,26.107,0.322,"Bonaire, Saint Eustatius And Saba" +1922-05-01,27.221,0.446,"Bonaire, Saint Eustatius And Saba" +1922-06-01,27.526,0.3720000000000001,"Bonaire, Saint Eustatius And Saba" +1922-07-01,28.253,0.404,"Bonaire, Saint Eustatius And Saba" +1922-08-01,28.419,0.581,"Bonaire, Saint Eustatius And Saba" +1922-09-01,28.448,0.362,"Bonaire, Saint Eustatius And Saba" +1922-10-01,27.816,0.42,"Bonaire, Saint Eustatius And Saba" +1922-11-01,26.848000000000003,0.399,"Bonaire, Saint Eustatius And Saba" +1922-12-01,25.463,0.378,"Bonaire, Saint Eustatius And Saba" +1923-01-01,24.546,0.436,"Bonaire, Saint Eustatius And Saba" +1923-02-01,24.68,0.256,"Bonaire, Saint Eustatius And Saba" +1923-03-01,25.066999999999997,0.279,"Bonaire, Saint Eustatius And Saba" +1923-04-01,25.746,0.397,"Bonaire, Saint Eustatius And Saba" +1923-05-01,27.568,0.22,"Bonaire, Saint Eustatius And Saba" +1923-06-01,27.98,0.67,"Bonaire, Saint Eustatius And Saba" +1923-07-01,28.279,0.447,"Bonaire, Saint Eustatius And Saba" +1923-08-01,28.604,0.415,"Bonaire, Saint Eustatius And Saba" +1923-09-01,28.697,0.425,"Bonaire, Saint Eustatius And Saba" +1923-10-01,27.89,0.185,"Bonaire, Saint Eustatius And Saba" +1923-11-01,27.15,0.687,"Bonaire, Saint Eustatius And Saba" +1923-12-01,25.693,0.531,"Bonaire, Saint Eustatius And Saba" +1924-01-01,25.181,0.34600000000000003,"Bonaire, Saint Eustatius And Saba" +1924-02-01,25.179000000000002,0.445,"Bonaire, Saint Eustatius And Saba" +1924-03-01,26.329,0.4320000000000001,"Bonaire, Saint Eustatius And Saba" +1924-04-01,26.873,0.46,"Bonaire, Saint Eustatius And Saba" +1924-05-01,28.087,0.4370000000000001,"Bonaire, Saint Eustatius And Saba" +1924-06-01,28.089000000000002,0.541,"Bonaire, Saint Eustatius And Saba" +1924-07-01,28.340999999999998,0.792,"Bonaire, Saint Eustatius And Saba" +1924-08-01,28.506999999999998,0.765,"Bonaire, Saint Eustatius And Saba" +1924-09-01,28.3,0.642,"Bonaire, Saint Eustatius And Saba" +1924-10-01,27.697,0.377,"Bonaire, Saint Eustatius And Saba" +1924-11-01,27.267,0.264,"Bonaire, Saint Eustatius And Saba" +1924-12-01,25.73,0.34600000000000003,"Bonaire, Saint Eustatius And Saba" +1925-01-01,24.652,0.578,"Bonaire, Saint Eustatius And Saba" +1925-02-01,24.608,0.353,"Bonaire, Saint Eustatius And Saba" +1925-03-01,25.134,0.451,"Bonaire, Saint Eustatius And Saba" +1925-04-01,26.536,0.232,"Bonaire, Saint Eustatius And Saba" +1925-05-01,27.88,0.325,"Bonaire, Saint Eustatius And Saba" +1925-06-01,27.96,0.276,"Bonaire, Saint Eustatius And Saba" +1925-07-01,28.18,0.25,"Bonaire, Saint Eustatius And Saba" +1925-08-01,28.340999999999998,0.224,"Bonaire, Saint Eustatius And Saba" +1925-09-01,28.206,0.273,"Bonaire, Saint Eustatius And Saba" +1925-10-01,27.953000000000003,0.555,"Bonaire, Saint Eustatius And Saba" +1925-11-01,27.18,0.338,"Bonaire, Saint Eustatius And Saba" +1925-12-01,26.389,0.402,"Bonaire, Saint Eustatius And Saba" +1926-01-01,25.722,0.401,"Bonaire, Saint Eustatius And Saba" +1926-02-01,25.975,0.326,"Bonaire, Saint Eustatius And Saba" +1926-03-01,26.215,0.597,"Bonaire, Saint Eustatius And Saba" +1926-04-01,27.024,0.489,"Bonaire, Saint Eustatius And Saba" +1926-05-01,28.425,0.434,"Bonaire, Saint Eustatius And Saba" +1926-06-01,28.737,0.442,"Bonaire, Saint Eustatius And Saba" +1926-07-01,28.976000000000006,0.777,"Bonaire, Saint Eustatius And Saba" +1926-08-01,28.75,0.333,"Bonaire, Saint Eustatius And Saba" +1926-09-01,28.734,0.191,"Bonaire, Saint Eustatius And Saba" +1926-10-01,28.25,0.237,"Bonaire, Saint Eustatius And Saba" +1926-11-01,27.480999999999998,0.366,"Bonaire, Saint Eustatius And Saba" +1926-12-01,25.894000000000002,0.34,"Bonaire, Saint Eustatius And Saba" +1927-01-01,25.362,0.237,"Bonaire, Saint Eustatius And Saba" +1927-02-01,24.717,0.19,"Bonaire, Saint Eustatius And Saba" +1927-03-01,25.7,0.13,"Bonaire, Saint Eustatius And Saba" +1927-04-01,26.152,0.38,"Bonaire, Saint Eustatius And Saba" +1927-05-01,27.703000000000003,0.416,"Bonaire, Saint Eustatius And Saba" +1927-06-01,28.034000000000002,0.338,"Bonaire, Saint Eustatius And Saba" +1927-07-01,28.41,0.486,"Bonaire, Saint Eustatius And Saba" +1927-08-01,28.759,0.488,"Bonaire, Saint Eustatius And Saba" +1927-09-01,28.741999999999997,0.534,"Bonaire, Saint Eustatius And Saba" +1927-10-01,28.299,0.293,"Bonaire, Saint Eustatius And Saba" +1927-11-01,27.694000000000003,0.398,"Bonaire, Saint Eustatius And Saba" +1927-12-01,26.0,0.342,"Bonaire, Saint Eustatius And Saba" +1928-01-01,25.198,0.16,"Bonaire, Saint Eustatius And Saba" +1928-02-01,25.519000000000002,0.59,"Bonaire, Saint Eustatius And Saba" +1928-03-01,25.697,0.241,"Bonaire, Saint Eustatius And Saba" +1928-04-01,27.153000000000002,1.412,"Bonaire, Saint Eustatius And Saba" +1928-05-01,27.949,0.304,"Bonaire, Saint Eustatius And Saba" +1928-06-01,28.427,0.461,"Bonaire, Saint Eustatius And Saba" +1928-07-01,28.35,0.268,"Bonaire, Saint Eustatius And Saba" +1928-08-01,28.491999999999997,0.223,"Bonaire, Saint Eustatius And Saba" +1928-09-01,28.713,0.5,"Bonaire, Saint Eustatius And Saba" +1928-10-01,28.141,0.343,"Bonaire, Saint Eustatius And Saba" +1928-11-01,27.453000000000003,0.29100000000000004,"Bonaire, Saint Eustatius And Saba" +1928-12-01,25.892,0.5489999999999999,"Bonaire, Saint Eustatius And Saba" +1929-01-01,25.003,0.3670000000000001,"Bonaire, Saint Eustatius And Saba" +1929-02-01,24.845,0.5670000000000001,"Bonaire, Saint Eustatius And Saba" +1929-03-01,25.268,0.498,"Bonaire, Saint Eustatius And Saba" +1929-04-01,26.296,0.404,"Bonaire, Saint Eustatius And Saba" +1929-05-01,27.14,0.207,"Bonaire, Saint Eustatius And Saba" +1929-06-01,28.089000000000002,0.49,"Bonaire, Saint Eustatius And Saba" +1929-07-01,28.406999999999996,0.383,"Bonaire, Saint Eustatius And Saba" +1929-08-01,28.711,0.385,"Bonaire, Saint Eustatius And Saba" +1929-09-01,28.604,0.324,"Bonaire, Saint Eustatius And Saba" +1929-10-01,28.456,0.199,"Bonaire, Saint Eustatius And Saba" +1929-11-01,27.45,0.441,"Bonaire, Saint Eustatius And Saba" +1929-12-01,25.878,0.344,"Bonaire, Saint Eustatius And Saba" +1930-01-01,25.274,0.601,"Bonaire, Saint Eustatius And Saba" +1930-02-01,25.124000000000002,0.39,"Bonaire, Saint Eustatius And Saba" +1930-03-01,25.675,1.025,"Bonaire, Saint Eustatius And Saba" +1930-04-01,25.955,1.3259999999999998,"Bonaire, Saint Eustatius And Saba" +1930-05-01,27.439,0.44,"Bonaire, Saint Eustatius And Saba" +1930-06-01,27.724,0.344,"Bonaire, Saint Eustatius And Saba" +1930-07-01,28.114,0.507,"Bonaire, Saint Eustatius And Saba" +1930-08-01,28.449,0.187,"Bonaire, Saint Eustatius And Saba" +1930-09-01,28.408,0.434,"Bonaire, Saint Eustatius And Saba" +1930-10-01,27.792,0.6629999999999999,"Bonaire, Saint Eustatius And Saba" +1930-11-01,27.480999999999998,0.627,"Bonaire, Saint Eustatius And Saba" +1930-12-01,26.454,0.421,"Bonaire, Saint Eustatius And Saba" +1931-01-01,26.029,0.294,"Bonaire, Saint Eustatius And Saba" +1931-02-01,25.805,0.257,"Bonaire, Saint Eustatius And Saba" +1931-03-01,26.956,0.431,"Bonaire, Saint Eustatius And Saba" +1931-04-01,27.316,0.37,"Bonaire, Saint Eustatius And Saba" +1931-05-01,28.364,0.353,"Bonaire, Saint Eustatius And Saba" +1931-06-01,28.583000000000002,0.46,"Bonaire, Saint Eustatius And Saba" +1931-07-01,28.445,0.667,"Bonaire, Saint Eustatius And Saba" +1931-08-01,28.805999999999997,0.473,"Bonaire, Saint Eustatius And Saba" +1931-09-01,28.684,0.648,"Bonaire, Saint Eustatius And Saba" +1931-10-01,28.349,0.635,"Bonaire, Saint Eustatius And Saba" +1931-11-01,26.587,1.26,"Bonaire, Saint Eustatius And Saba" +1931-12-01,26.051,0.381,"Bonaire, Saint Eustatius And Saba" +1932-01-01,25.245,0.454,"Bonaire, Saint Eustatius And Saba" +1932-02-01,25.367,0.325,"Bonaire, Saint Eustatius And Saba" +1932-03-01,26.079,0.301,"Bonaire, Saint Eustatius And Saba" +1932-04-01,27.295,0.596,"Bonaire, Saint Eustatius And Saba" +1932-05-01,27.834,0.547,"Bonaire, Saint Eustatius And Saba" +1932-06-01,28.428,0.637,"Bonaire, Saint Eustatius And Saba" +1932-07-01,28.335,0.377,"Bonaire, Saint Eustatius And Saba" +1932-08-01,28.573,0.388,"Bonaire, Saint Eustatius And Saba" +1932-09-01,29.186,0.86,"Bonaire, Saint Eustatius And Saba" +1932-10-01,28.151,0.258,"Bonaire, Saint Eustatius And Saba" +1932-11-01,27.537,0.313,"Bonaire, Saint Eustatius And Saba" +1932-12-01,26.031,0.355,"Bonaire, Saint Eustatius And Saba" +1933-01-01,25.249000000000002,0.384,"Bonaire, Saint Eustatius And Saba" +1933-02-01,24.86,0.514,"Bonaire, Saint Eustatius And Saba" +1933-03-01,25.811,0.215,"Bonaire, Saint Eustatius And Saba" +1933-04-01,26.535,0.462,"Bonaire, Saint Eustatius And Saba" +1933-05-01,27.715999999999998,0.393,"Bonaire, Saint Eustatius And Saba" +1933-06-01,27.903000000000002,0.325,"Bonaire, Saint Eustatius And Saba" +1933-07-01,28.278,0.31,"Bonaire, Saint Eustatius And Saba" +1933-08-01,28.243,0.179,"Bonaire, Saint Eustatius And Saba" +1933-09-01,28.244,0.29,"Bonaire, Saint Eustatius And Saba" +1933-10-01,27.645,0.226,"Bonaire, Saint Eustatius And Saba" +1933-11-01,27.049,0.261,"Bonaire, Saint Eustatius And Saba" +1933-12-01,25.695999999999998,0.382,"Bonaire, Saint Eustatius And Saba" +1934-01-01,25.162,0.258,"Bonaire, Saint Eustatius And Saba" +1934-02-01,25.057,0.638,"Bonaire, Saint Eustatius And Saba" +1934-03-01,25.134,0.307,"Bonaire, Saint Eustatius And Saba" +1934-04-01,27.079,1.091,"Bonaire, Saint Eustatius And Saba" +1934-05-01,28.114,0.609,"Bonaire, Saint Eustatius And Saba" +1934-06-01,28.825,1.26,"Bonaire, Saint Eustatius And Saba" +1934-07-01,28.41,0.846,"Bonaire, Saint Eustatius And Saba" +1934-08-01,28.65,0.512,"Bonaire, Saint Eustatius And Saba" +1934-09-01,28.738000000000003,0.279,"Bonaire, Saint Eustatius And Saba" +1934-10-01,28.046,0.257,"Bonaire, Saint Eustatius And Saba" +1934-11-01,27.274,0.274,"Bonaire, Saint Eustatius And Saba" +1934-12-01,26.201999999999998,0.191,"Bonaire, Saint Eustatius And Saba" +1935-01-01,25.303,0.34,"Bonaire, Saint Eustatius And Saba" +1935-02-01,25.383000000000003,0.693,"Bonaire, Saint Eustatius And Saba" +1935-03-01,25.35,0.716,"Bonaire, Saint Eustatius And Saba" +1935-04-01,26.371,0.414,"Bonaire, Saint Eustatius And Saba" +1935-05-01,27.01,0.628,"Bonaire, Saint Eustatius And Saba" +1935-06-01,27.919,0.504,"Bonaire, Saint Eustatius And Saba" +1935-07-01,28.201999999999998,0.49,"Bonaire, Saint Eustatius And Saba" +1935-08-01,27.943,0.44,"Bonaire, Saint Eustatius And Saba" +1935-09-01,28.435,0.446,"Bonaire, Saint Eustatius And Saba" +1935-10-01,27.851999999999997,0.429,"Bonaire, Saint Eustatius And Saba" +1935-11-01,27.11,0.284,"Bonaire, Saint Eustatius And Saba" +1935-12-01,26.068,0.385,"Bonaire, Saint Eustatius And Saba" +1936-01-01,25.495,0.585,"Bonaire, Saint Eustatius And Saba" +1936-02-01,25.099,0.502,"Bonaire, Saint Eustatius And Saba" +1936-03-01,26.174,0.7829999999999999,"Bonaire, Saint Eustatius And Saba" +1936-04-01,26.834,0.403,"Bonaire, Saint Eustatius And Saba" +1936-05-01,27.875999999999998,0.428,"Bonaire, Saint Eustatius And Saba" +1936-06-01,27.897,0.6659999999999999,"Bonaire, Saint Eustatius And Saba" +1936-07-01,28.052,0.595,"Bonaire, Saint Eustatius And Saba" +1936-08-01,28.56,0.433,"Bonaire, Saint Eustatius And Saba" +1936-09-01,28.439,0.508,"Bonaire, Saint Eustatius And Saba" +1936-10-01,27.756,0.392,"Bonaire, Saint Eustatius And Saba" +1936-11-01,27.127,0.34700000000000003,"Bonaire, Saint Eustatius And Saba" +1936-12-01,25.645,0.34700000000000003,"Bonaire, Saint Eustatius And Saba" +1937-01-01,25.146,0.343,"Bonaire, Saint Eustatius And Saba" +1937-02-01,25.023000000000003,0.445,"Bonaire, Saint Eustatius And Saba" +1937-03-01,26.087,0.379,"Bonaire, Saint Eustatius And Saba" +1937-04-01,26.691999999999997,0.213,"Bonaire, Saint Eustatius And Saba" +1937-05-01,27.783,0.37,"Bonaire, Saint Eustatius And Saba" +1937-06-01,28.268,0.322,"Bonaire, Saint Eustatius And Saba" +1937-07-01,28.354000000000006,0.168,"Bonaire, Saint Eustatius And Saba" +1937-08-01,28.68,0.517,"Bonaire, Saint Eustatius And Saba" +1937-09-01,28.659000000000002,0.202,"Bonaire, Saint Eustatius And Saba" +1937-10-01,28.531999999999996,0.727,"Bonaire, Saint Eustatius And Saba" +1937-11-01,27.826,0.269,"Bonaire, Saint Eustatius And Saba" +1937-12-01,26.435,0.318,"Bonaire, Saint Eustatius And Saba" +1938-01-01,24.932,0.747,"Bonaire, Saint Eustatius And Saba" +1938-02-01,25.2,0.25,"Bonaire, Saint Eustatius And Saba" +1938-03-01,24.86,0.359,"Bonaire, Saint Eustatius And Saba" +1938-04-01,25.882,0.629,"Bonaire, Saint Eustatius And Saba" +1938-05-01,27.401,0.517,"Bonaire, Saint Eustatius And Saba" +1938-06-01,27.946,0.502,"Bonaire, Saint Eustatius And Saba" +1938-07-01,28.211,0.388,"Bonaire, Saint Eustatius And Saba" +1938-08-01,28.211,0.4270000000000001,"Bonaire, Saint Eustatius And Saba" +1938-09-01,28.331999999999997,0.278,"Bonaire, Saint Eustatius And Saba" +1938-10-01,27.714000000000002,0.399,"Bonaire, Saint Eustatius And Saba" +1938-11-01,27.153,0.498,"Bonaire, Saint Eustatius And Saba" +1938-12-01,25.54,0.737,"Bonaire, Saint Eustatius And Saba" +1939-01-01,25.03,0.4370000000000001,"Bonaire, Saint Eustatius And Saba" +1939-02-01,23.912,0.519,"Bonaire, Saint Eustatius And Saba" +1939-03-01,24.562,0.32,"Bonaire, Saint Eustatius And Saba" +1939-04-01,26.035,0.469,"Bonaire, Saint Eustatius And Saba" +1939-05-01,27.829,0.378,"Bonaire, Saint Eustatius And Saba" +1939-06-01,28.175,0.479,"Bonaire, Saint Eustatius And Saba" +1939-07-01,28.449,0.609,"Bonaire, Saint Eustatius And Saba" +1939-08-01,28.995,0.325,"Bonaire, Saint Eustatius And Saba" +1939-09-01,29.173000000000002,0.451,"Bonaire, Saint Eustatius And Saba" +1939-10-01,28.251,0.376,"Bonaire, Saint Eustatius And Saba" +1939-11-01,27.338,0.35700000000000004,"Bonaire, Saint Eustatius And Saba" +1939-12-01,26.326999999999998,0.583,"Bonaire, Saint Eustatius And Saba" +1940-01-01,25.416,0.439,"Bonaire, Saint Eustatius And Saba" +1940-02-01,26.04,0.29600000000000004,"Bonaire, Saint Eustatius And Saba" +1940-03-01,26.124000000000002,0.43,"Bonaire, Saint Eustatius And Saba" +1940-04-01,27.524,0.34600000000000003,"Bonaire, Saint Eustatius And Saba" +1940-05-01,28.730999999999998,0.398,"Bonaire, Saint Eustatius And Saba" +1940-06-01,28.917,0.399,"Bonaire, Saint Eustatius And Saba" +1940-07-01,28.823,0.48,"Bonaire, Saint Eustatius And Saba" +1940-08-01,29.11,0.273,"Bonaire, Saint Eustatius And Saba" +1940-09-01,29.184,0.322,"Bonaire, Saint Eustatius And Saba" +1940-10-01,28.464000000000002,0.287,"Bonaire, Saint Eustatius And Saba" +1940-11-01,27.704,0.42,"Bonaire, Saint Eustatius And Saba" +1940-12-01,26.676,0.499,"Bonaire, Saint Eustatius And Saba" +1941-01-01,25.826,0.442,"Bonaire, Saint Eustatius And Saba" +1941-02-01,26.862,0.338,"Bonaire, Saint Eustatius And Saba" +1941-03-01,26.741,0.5720000000000001,"Bonaire, Saint Eustatius And Saba" +1941-04-01,27.588,0.601,"Bonaire, Saint Eustatius And Saba" +1941-05-01,28.730999999999998,0.465,"Bonaire, Saint Eustatius And Saba" +1941-06-01,28.771,0.523,"Bonaire, Saint Eustatius And Saba" +1941-07-01,28.471,0.35700000000000004,"Bonaire, Saint Eustatius And Saba" +1941-08-01,28.863000000000003,0.153,"Bonaire, Saint Eustatius And Saba" +1941-09-01,29.058000000000003,0.395,"Bonaire, Saint Eustatius And Saba" +1941-10-01,28.561,0.457,"Bonaire, Saint Eustatius And Saba" +1941-11-01,27.765,0.179,"Bonaire, Saint Eustatius And Saba" +1941-12-01,26.787,0.384,"Bonaire, Saint Eustatius And Saba" +1942-01-01,26.483,0.29600000000000004,"Bonaire, Saint Eustatius And Saba" +1942-02-01,26.689,0.339,"Bonaire, Saint Eustatius And Saba" +1942-03-01,27.076,0.471,"Bonaire, Saint Eustatius And Saba" +1942-04-01,27.511999999999997,0.5820000000000001,"Bonaire, Saint Eustatius And Saba" +1942-05-01,28.305999999999997,0.254,"Bonaire, Saint Eustatius And Saba" +1942-06-01,28.659000000000002,0.54,"Bonaire, Saint Eustatius And Saba" +1942-07-01,28.846,0.415,"Bonaire, Saint Eustatius And Saba" +1942-08-01,28.866,0.309,"Bonaire, Saint Eustatius And Saba" +1942-09-01,28.951,0.28600000000000003,"Bonaire, Saint Eustatius And Saba" +1942-10-01,28.417,0.35600000000000004,"Bonaire, Saint Eustatius And Saba" +1942-11-01,27.779,0.5720000000000001,"Bonaire, Saint Eustatius And Saba" +1942-12-01,26.323,0.428,"Bonaire, Saint Eustatius And Saba" +1943-01-01,25.495,0.301,"Bonaire, Saint Eustatius And Saba" +1943-02-01,25.595,0.426,"Bonaire, Saint Eustatius And Saba" +1943-03-01,25.688000000000002,0.328,"Bonaire, Saint Eustatius And Saba" +1943-04-01,26.851999999999997,0.6579999999999999,"Bonaire, Saint Eustatius And Saba" +1943-05-01,27.281,0.132,"Bonaire, Saint Eustatius And Saba" +1943-06-01,27.917,0.348,"Bonaire, Saint Eustatius And Saba" +1943-07-01,28.269,0.574,"Bonaire, Saint Eustatius And Saba" +1943-08-01,28.691,0.307,"Bonaire, Saint Eustatius And Saba" +1943-09-01,29.104,0.34,"Bonaire, Saint Eustatius And Saba" +1943-10-01,28.314,0.203,"Bonaire, Saint Eustatius And Saba" +1943-11-01,27.287,0.221,"Bonaire, Saint Eustatius And Saba" +1943-12-01,26.16,0.247,"Bonaire, Saint Eustatius And Saba" +1944-01-01,25.661,0.254,"Bonaire, Saint Eustatius And Saba" +1944-02-01,25.531,0.166,"Bonaire, Saint Eustatius And Saba" +1944-03-01,25.809,0.244,"Bonaire, Saint Eustatius And Saba" +1944-04-01,26.827,0.514,"Bonaire, Saint Eustatius And Saba" +1944-05-01,27.726,0.677,"Bonaire, Saint Eustatius And Saba" +1944-06-01,28.312,0.404,"Bonaire, Saint Eustatius And Saba" +1944-07-01,28.734,0.329,"Bonaire, Saint Eustatius And Saba" +1944-08-01,29.023000000000003,0.386,"Bonaire, Saint Eustatius And Saba" +1944-09-01,28.713,0.225,"Bonaire, Saint Eustatius And Saba" +1944-10-01,28.456999999999997,0.373,"Bonaire, Saint Eustatius And Saba" +1944-11-01,27.28,0.484,"Bonaire, Saint Eustatius And Saba" +1944-12-01,26.145,0.479,"Bonaire, Saint Eustatius And Saba" +1945-01-01,25.037,0.3,"Bonaire, Saint Eustatius And Saba" +1945-02-01,25.199,0.409,"Bonaire, Saint Eustatius And Saba" +1945-03-01,25.738000000000003,0.266,"Bonaire, Saint Eustatius And Saba" +1945-04-01,26.312,0.534,"Bonaire, Saint Eustatius And Saba" +1945-05-01,27.316,0.645,"Bonaire, Saint Eustatius And Saba" +1945-06-01,27.906999999999996,0.31,"Bonaire, Saint Eustatius And Saba" +1945-07-01,28.215999999999998,0.27,"Bonaire, Saint Eustatius And Saba" +1945-08-01,28.719,0.299,"Bonaire, Saint Eustatius And Saba" +1945-09-01,28.639,0.532,"Bonaire, Saint Eustatius And Saba" +1945-10-01,27.861,0.239,"Bonaire, Saint Eustatius And Saba" +1945-11-01,27.281999999999996,0.4,"Bonaire, Saint Eustatius And Saba" +1945-12-01,26.36,0.4920000000000001,"Bonaire, Saint Eustatius And Saba" +1946-01-01,25.02,0.4970000000000001,"Bonaire, Saint Eustatius And Saba" +1946-02-01,24.969,0.238,"Bonaire, Saint Eustatius And Saba" +1946-03-01,25.629,0.306,"Bonaire, Saint Eustatius And Saba" +1946-04-01,26.156,0.433,"Bonaire, Saint Eustatius And Saba" +1946-05-01,27.553,0.47,"Bonaire, Saint Eustatius And Saba" +1946-06-01,27.804000000000002,0.46,"Bonaire, Saint Eustatius And Saba" +1946-07-01,28.005000000000006,0.416,"Bonaire, Saint Eustatius And Saba" +1946-08-01,27.712,0.541,"Bonaire, Saint Eustatius And Saba" +1946-09-01,28.335,0.5539999999999999,"Bonaire, Saint Eustatius And Saba" +1946-10-01,27.976999999999997,0.688,"Bonaire, Saint Eustatius And Saba" +1946-11-01,27.178,0.508,"Bonaire, Saint Eustatius And Saba" +1946-12-01,26.174,0.956,"Bonaire, Saint Eustatius And Saba" +1947-01-01,25.549,0.308,"Bonaire, Saint Eustatius And Saba" +1947-02-01,26.061,0.469,"Bonaire, Saint Eustatius And Saba" +1947-03-01,26.404,0.464,"Bonaire, Saint Eustatius And Saba" +1947-04-01,26.835,0.206,"Bonaire, Saint Eustatius And Saba" +1947-05-01,28.01,0.537,"Bonaire, Saint Eustatius And Saba" +1947-06-01,28.444000000000003,0.158,"Bonaire, Saint Eustatius And Saba" +1947-07-01,28.112,0.151,"Bonaire, Saint Eustatius And Saba" +1947-08-01,28.546,0.353,"Bonaire, Saint Eustatius And Saba" +1947-09-01,28.764,0.285,"Bonaire, Saint Eustatius And Saba" +1947-10-01,28.351,0.618,"Bonaire, Saint Eustatius And Saba" +1947-11-01,27.684,0.4320000000000001,"Bonaire, Saint Eustatius And Saba" +1947-12-01,26.395,0.287,"Bonaire, Saint Eustatius And Saba" +1948-01-01,26.189,0.123,"Bonaire, Saint Eustatius And Saba" +1948-02-01,25.691999999999997,0.187,"Bonaire, Saint Eustatius And Saba" +1948-03-01,25.888,0.225,"Bonaire, Saint Eustatius And Saba" +1948-04-01,26.136,0.253,"Bonaire, Saint Eustatius And Saba" +1948-05-01,28.415,0.209,"Bonaire, Saint Eustatius And Saba" +1948-06-01,28.497,0.241,"Bonaire, Saint Eustatius And Saba" +1948-07-01,28.326,0.455,"Bonaire, Saint Eustatius And Saba" +1948-08-01,28.522,0.5539999999999999,"Bonaire, Saint Eustatius And Saba" +1948-09-01,28.772,0.298,"Bonaire, Saint Eustatius And Saba" +1948-10-01,28.284000000000002,0.377,"Bonaire, Saint Eustatius And Saba" +1948-11-01,27.659000000000002,0.141,"Bonaire, Saint Eustatius And Saba" +1948-12-01,26.156999999999996,0.28300000000000003,"Bonaire, Saint Eustatius And Saba" +1949-01-01,24.903000000000002,0.557,"Bonaire, Saint Eustatius And Saba" +1949-02-01,25.09,0.419,"Bonaire, Saint Eustatius And Saba" +1949-03-01,25.916,0.314,"Bonaire, Saint Eustatius And Saba" +1949-04-01,27.018,0.35200000000000004,"Bonaire, Saint Eustatius And Saba" +1949-05-01,28.058000000000003,0.621,"Bonaire, Saint Eustatius And Saba" +1949-06-01,28.028000000000002,0.303,"Bonaire, Saint Eustatius And Saba" +1949-07-01,28.112,0.179,"Bonaire, Saint Eustatius And Saba" +1949-08-01,28.314,0.223,"Bonaire, Saint Eustatius And Saba" +1949-09-01,28.651,0.424,"Bonaire, Saint Eustatius And Saba" +1949-10-01,27.987,0.495,"Bonaire, Saint Eustatius And Saba" +1949-11-01,27.218000000000004,0.276,"Bonaire, Saint Eustatius And Saba" +1949-12-01,25.688000000000002,0.297,"Bonaire, Saint Eustatius And Saba" +1950-01-01,24.573,0.237,"Bonaire, Saint Eustatius And Saba" +1950-02-01,25.333000000000002,0.636,"Bonaire, Saint Eustatius And Saba" +1950-03-01,25.199,0.23,"Bonaire, Saint Eustatius And Saba" +1950-04-01,26.072,0.276,"Bonaire, Saint Eustatius And Saba" +1950-05-01,26.944000000000003,0.287,"Bonaire, Saint Eustatius And Saba" +1950-06-01,27.48,0.595,"Bonaire, Saint Eustatius And Saba" +1950-07-01,27.909,0.199,"Bonaire, Saint Eustatius And Saba" +1950-08-01,28.046999999999997,0.244,"Bonaire, Saint Eustatius And Saba" +1950-09-01,28.699,0.188,"Bonaire, Saint Eustatius And Saba" +1950-10-01,27.593000000000004,0.302,"Bonaire, Saint Eustatius And Saba" +1950-11-01,27.194000000000003,0.246,"Bonaire, Saint Eustatius And Saba" +1950-12-01,26.069000000000003,0.185,"Bonaire, Saint Eustatius And Saba" +1951-01-01,25.301,0.416,"Bonaire, Saint Eustatius And Saba" +1951-02-01,24.998,0.454,"Bonaire, Saint Eustatius And Saba" +1951-03-01,25.273000000000003,0.446,"Bonaire, Saint Eustatius And Saba" +1951-04-01,26.809,0.515,"Bonaire, Saint Eustatius And Saba" +1951-05-01,27.945999999999998,0.273,"Bonaire, Saint Eustatius And Saba" +1951-06-01,28.249000000000002,0.407,"Bonaire, Saint Eustatius And Saba" +1951-07-01,28.384,0.177,"Bonaire, Saint Eustatius And Saba" +1951-08-01,29.083,0.361,"Bonaire, Saint Eustatius And Saba" +1951-09-01,29.288,0.306,"Bonaire, Saint Eustatius And Saba" +1951-10-01,28.724,0.259,"Bonaire, Saint Eustatius And Saba" +1951-11-01,28.043000000000003,0.307,"Bonaire, Saint Eustatius And Saba" +1951-12-01,26.781999999999996,0.305,"Bonaire, Saint Eustatius And Saba" +1952-01-01,25.814,0.364,"Bonaire, Saint Eustatius And Saba" +1952-02-01,26.111,0.404,"Bonaire, Saint Eustatius And Saba" +1952-03-01,26.548000000000002,0.684,"Bonaire, Saint Eustatius And Saba" +1952-04-01,27.175,0.479,"Bonaire, Saint Eustatius And Saba" +1952-05-01,28.534000000000002,0.302,"Bonaire, Saint Eustatius And Saba" +1952-06-01,28.594,0.319,"Bonaire, Saint Eustatius And Saba" +1952-07-01,28.465999999999998,0.19,"Bonaire, Saint Eustatius And Saba" +1952-08-01,28.994,0.238,"Bonaire, Saint Eustatius And Saba" +1952-09-01,29.186,0.392,"Bonaire, Saint Eustatius And Saba" +1952-10-01,28.934,0.33,"Bonaire, Saint Eustatius And Saba" +1952-11-01,27.741999999999997,0.165,"Bonaire, Saint Eustatius And Saba" +1952-12-01,26.463,0.423,"Bonaire, Saint Eustatius And Saba" +1953-01-01,25.931,0.396,"Bonaire, Saint Eustatius And Saba" +1953-02-01,25.628,0.315,"Bonaire, Saint Eustatius And Saba" +1953-03-01,25.915,0.363,"Bonaire, Saint Eustatius And Saba" +1953-04-01,27.324,0.514,"Bonaire, Saint Eustatius And Saba" +1953-05-01,27.77,0.5589999999999999,"Bonaire, Saint Eustatius And Saba" +1953-06-01,28.686,0.171,"Bonaire, Saint Eustatius And Saba" +1953-07-01,28.642000000000003,0.149,"Bonaire, Saint Eustatius And Saba" +1953-08-01,28.983,0.234,"Bonaire, Saint Eustatius And Saba" +1953-09-01,29.065,0.28300000000000003,"Bonaire, Saint Eustatius And Saba" +1953-10-01,28.563000000000002,0.209,"Bonaire, Saint Eustatius And Saba" +1953-11-01,28.34,0.207,"Bonaire, Saint Eustatius And Saba" +1953-12-01,26.783,0.287,"Bonaire, Saint Eustatius And Saba" +1954-01-01,25.906,0.359,"Bonaire, Saint Eustatius And Saba" +1954-02-01,25.859,0.415,"Bonaire, Saint Eustatius And Saba" +1954-03-01,26.522,0.655,"Bonaire, Saint Eustatius And Saba" +1954-04-01,26.466,0.545,"Bonaire, Saint Eustatius And Saba" +1954-05-01,27.844,0.298,"Bonaire, Saint Eustatius And Saba" +1954-06-01,28.209,0.333,"Bonaire, Saint Eustatius And Saba" +1954-07-01,28.331999999999997,0.241,"Bonaire, Saint Eustatius And Saba" +1954-08-01,28.761,0.311,"Bonaire, Saint Eustatius And Saba" +1954-09-01,28.769000000000002,0.471,"Bonaire, Saint Eustatius And Saba" +1954-10-01,27.405,0.412,"Bonaire, Saint Eustatius And Saba" +1954-11-01,27.156999999999996,0.423,"Bonaire, Saint Eustatius And Saba" +1954-12-01,25.829,0.434,"Bonaire, Saint Eustatius And Saba" +1955-01-01,25.518,0.36,"Bonaire, Saint Eustatius And Saba" +1955-02-01,25.563000000000002,0.331,"Bonaire, Saint Eustatius And Saba" +1955-03-01,25.342,0.477,"Bonaire, Saint Eustatius And Saba" +1955-04-01,25.958000000000002,0.583,"Bonaire, Saint Eustatius And Saba" +1955-05-01,27.525,0.634,"Bonaire, Saint Eustatius And Saba" +1955-06-01,27.926,0.633,"Bonaire, Saint Eustatius And Saba" +1955-07-01,28.264000000000006,0.569,"Bonaire, Saint Eustatius And Saba" +1955-08-01,28.764,1.055,"Bonaire, Saint Eustatius And Saba" +1955-09-01,28.394000000000002,0.67,"Bonaire, Saint Eustatius And Saba" +1955-10-01,27.997,0.231,"Bonaire, Saint Eustatius And Saba" +1955-11-01,27.58,0.498,"Bonaire, Saint Eustatius And Saba" +1955-12-01,26.2,0.334,"Bonaire, Saint Eustatius And Saba" +1956-01-01,25.465,0.244,"Bonaire, Saint Eustatius And Saba" +1956-02-01,25.593000000000004,0.247,"Bonaire, Saint Eustatius And Saba" +1956-03-01,25.847,0.187,"Bonaire, Saint Eustatius And Saba" +1956-04-01,26.803,0.252,"Bonaire, Saint Eustatius And Saba" +1956-05-01,27.601,0.313,"Bonaire, Saint Eustatius And Saba" +1956-06-01,27.975,0.253,"Bonaire, Saint Eustatius And Saba" +1956-07-01,28.135,0.188,"Bonaire, Saint Eustatius And Saba" +1956-08-01,28.484,0.317,"Bonaire, Saint Eustatius And Saba" +1956-09-01,28.435,0.342,"Bonaire, Saint Eustatius And Saba" +1956-10-01,27.97,0.183,"Bonaire, Saint Eustatius And Saba" +1956-11-01,27.428,0.375,"Bonaire, Saint Eustatius And Saba" +1956-12-01,25.814,0.343,"Bonaire, Saint Eustatius And Saba" +1957-01-01,25.194000000000003,0.464,"Bonaire, Saint Eustatius And Saba" +1957-02-01,25.338,0.556,"Bonaire, Saint Eustatius And Saba" +1957-03-01,25.986,0.29,"Bonaire, Saint Eustatius And Saba" +1957-04-01,26.366,0.338,"Bonaire, Saint Eustatius And Saba" +1957-05-01,27.48,0.5579999999999999,"Bonaire, Saint Eustatius And Saba" +1957-06-01,28.365,0.439,"Bonaire, Saint Eustatius And Saba" +1957-07-01,28.497,0.429,"Bonaire, Saint Eustatius And Saba" +1957-08-01,28.979,0.6970000000000001,"Bonaire, Saint Eustatius And Saba" +1957-09-01,29.131,0.595,"Bonaire, Saint Eustatius And Saba" +1957-10-01,28.658,0.368,"Bonaire, Saint Eustatius And Saba" +1957-11-01,27.612,0.6990000000000001,"Bonaire, Saint Eustatius And Saba" +1957-12-01,26.574,0.785,"Bonaire, Saint Eustatius And Saba" +1958-01-01,26.293000000000003,0.826,"Bonaire, Saint Eustatius And Saba" +1958-02-01,26.397,0.732,"Bonaire, Saint Eustatius And Saba" +1958-03-01,27.568,0.723,"Bonaire, Saint Eustatius And Saba" +1958-04-01,27.844,0.342,"Bonaire, Saint Eustatius And Saba" +1958-05-01,28.789,0.406,"Bonaire, Saint Eustatius And Saba" +1958-06-01,29.139,0.35,"Bonaire, Saint Eustatius And Saba" +1958-07-01,28.791,0.326,"Bonaire, Saint Eustatius And Saba" +1958-08-01,29.145,0.337,"Bonaire, Saint Eustatius And Saba" +1958-09-01,29.406,0.364,"Bonaire, Saint Eustatius And Saba" +1958-10-01,28.728,0.193,"Bonaire, Saint Eustatius And Saba" +1958-11-01,27.68,0.21,"Bonaire, Saint Eustatius And Saba" +1958-12-01,26.646,0.14400000000000002,"Bonaire, Saint Eustatius And Saba" +1959-01-01,26.007,0.28300000000000003,"Bonaire, Saint Eustatius And Saba" +1959-02-01,25.783,0.365,"Bonaire, Saint Eustatius And Saba" +1959-03-01,26.337,0.345,"Bonaire, Saint Eustatius And Saba" +1959-04-01,27.234,0.489,"Bonaire, Saint Eustatius And Saba" +1959-05-01,27.653000000000002,0.599,"Bonaire, Saint Eustatius And Saba" +1959-06-01,28.165,0.37,"Bonaire, Saint Eustatius And Saba" +1959-07-01,28.546999999999997,0.578,"Bonaire, Saint Eustatius And Saba" +1959-08-01,28.486,0.267,"Bonaire, Saint Eustatius And Saba" +1959-09-01,28.866,0.256,"Bonaire, Saint Eustatius And Saba" +1959-10-01,28.206,0.32299999999999995,"Bonaire, Saint Eustatius And Saba" +1959-11-01,27.601,0.333,"Bonaire, Saint Eustatius And Saba" +1959-12-01,27.247,0.726,"Bonaire, Saint Eustatius And Saba" +1960-01-01,26.245,0.271,"Bonaire, Saint Eustatius And Saba" +1960-02-01,26.575,0.5379999999999999,"Bonaire, Saint Eustatius And Saba" +1960-03-01,26.701,0.289,"Bonaire, Saint Eustatius And Saba" +1960-04-01,27.289,0.342,"Bonaire, Saint Eustatius And Saba" +1960-05-01,28.348000000000003,0.809,"Bonaire, Saint Eustatius And Saba" +1960-06-01,28.606,0.362,"Bonaire, Saint Eustatius And Saba" +1960-07-01,28.581999999999997,0.18,"Bonaire, Saint Eustatius And Saba" +1960-08-01,28.451999999999998,0.5670000000000001,"Bonaire, Saint Eustatius And Saba" +1960-09-01,28.836,0.335,"Bonaire, Saint Eustatius And Saba" +1960-10-01,28.28,0.29,"Bonaire, Saint Eustatius And Saba" +1960-11-01,27.831,0.257,"Bonaire, Saint Eustatius And Saba" +1960-12-01,26.174,0.17800000000000002,"Bonaire, Saint Eustatius And Saba" +1961-01-01,26.244,0.226,"Bonaire, Saint Eustatius And Saba" +1961-02-01,25.987,0.2,"Bonaire, Saint Eustatius And Saba" +1961-03-01,26.169,0.267,"Bonaire, Saint Eustatius And Saba" +1961-04-01,26.93,0.341,"Bonaire, Saint Eustatius And Saba" +1961-05-01,27.923,0.546,"Bonaire, Saint Eustatius And Saba" +1961-06-01,28.236,0.557,"Bonaire, Saint Eustatius And Saba" +1961-07-01,28.281999999999996,0.44,"Bonaire, Saint Eustatius And Saba" +1961-08-01,28.537,0.433,"Bonaire, Saint Eustatius And Saba" +1961-09-01,28.648000000000003,0.17800000000000002,"Bonaire, Saint Eustatius And Saba" +1961-10-01,28.251,0.328,"Bonaire, Saint Eustatius And Saba" +1961-11-01,27.074,0.154,"Bonaire, Saint Eustatius And Saba" +1961-12-01,26.359,0.133,"Bonaire, Saint Eustatius And Saba" +1962-01-01,25.783,0.211,"Bonaire, Saint Eustatius And Saba" +1962-02-01,25.458000000000002,0.254,"Bonaire, Saint Eustatius And Saba" +1962-03-01,26.263,0.171,"Bonaire, Saint Eustatius And Saba" +1962-04-01,26.779,0.417,"Bonaire, Saint Eustatius And Saba" +1962-05-01,28.044,0.396,"Bonaire, Saint Eustatius And Saba" +1962-06-01,28.036,0.5710000000000001,"Bonaire, Saint Eustatius And Saba" +1962-07-01,28.695999999999998,0.16399999999999998,"Bonaire, Saint Eustatius And Saba" +1962-08-01,28.684,0.28800000000000003,"Bonaire, Saint Eustatius And Saba" +1962-09-01,29.089000000000002,0.408,"Bonaire, Saint Eustatius And Saba" +1962-10-01,28.518,0.498,"Bonaire, Saint Eustatius And Saba" +1962-11-01,27.802,0.14,"Bonaire, Saint Eustatius And Saba" +1962-12-01,26.438000000000002,0.664,"Bonaire, Saint Eustatius And Saba" +1963-01-01,25.763,0.37,"Bonaire, Saint Eustatius And Saba" +1963-02-01,26.017,0.155,"Bonaire, Saint Eustatius And Saba" +1963-03-01,26.208000000000002,0.29,"Bonaire, Saint Eustatius And Saba" +1963-04-01,26.763,0.314,"Bonaire, Saint Eustatius And Saba" +1963-05-01,27.415,0.404,"Bonaire, Saint Eustatius And Saba" +1963-06-01,28.327,0.273,"Bonaire, Saint Eustatius And Saba" +1963-07-01,28.343000000000004,0.344,"Bonaire, Saint Eustatius And Saba" +1963-08-01,29.214000000000002,0.093,"Bonaire, Saint Eustatius And Saba" +1963-09-01,29.143,0.353,"Bonaire, Saint Eustatius And Saba" +1963-10-01,28.905,0.104,"Bonaire, Saint Eustatius And Saba" +1963-11-01,27.698,0.381,"Bonaire, Saint Eustatius And Saba" +1963-12-01,27.072,0.122,"Bonaire, Saint Eustatius And Saba" +1964-01-01,26.068,0.507,"Bonaire, Saint Eustatius And Saba" +1964-02-01,26.313000000000002,0.235,"Bonaire, Saint Eustatius And Saba" +1964-03-01,26.451,0.312,"Bonaire, Saint Eustatius And Saba" +1964-04-01,26.875,0.23,"Bonaire, Saint Eustatius And Saba" +1964-05-01,28.0,0.428,"Bonaire, Saint Eustatius And Saba" +1964-06-01,28.108,0.395,"Bonaire, Saint Eustatius And Saba" +1964-07-01,28.269,0.198,"Bonaire, Saint Eustatius And Saba" +1964-08-01,28.826,0.231,"Bonaire, Saint Eustatius And Saba" +1964-09-01,28.928,0.271,"Bonaire, Saint Eustatius And Saba" +1964-10-01,28.083000000000002,0.453,"Bonaire, Saint Eustatius And Saba" +1964-11-01,27.531,0.131,"Bonaire, Saint Eustatius And Saba" +1964-12-01,25.548,0.319,"Bonaire, Saint Eustatius And Saba" +1965-01-01,25.31,0.319,"Bonaire, Saint Eustatius And Saba" +1965-02-01,25.235,0.436,"Bonaire, Saint Eustatius And Saba" +1965-03-01,25.966,0.624,"Bonaire, Saint Eustatius And Saba" +1965-04-01,26.219,0.31,"Bonaire, Saint Eustatius And Saba" +1965-05-01,27.518,0.34,"Bonaire, Saint Eustatius And Saba" +1965-06-01,27.724,0.317,"Bonaire, Saint Eustatius And Saba" +1965-07-01,28.438,0.298,"Bonaire, Saint Eustatius And Saba" +1965-08-01,28.385,0.326,"Bonaire, Saint Eustatius And Saba" +1965-09-01,28.968000000000004,0.305,"Bonaire, Saint Eustatius And Saba" +1965-10-01,28.331,0.4,"Bonaire, Saint Eustatius And Saba" +1965-11-01,27.750999999999998,0.414,"Bonaire, Saint Eustatius And Saba" +1965-12-01,26.92,0.304,"Bonaire, Saint Eustatius And Saba" +1966-01-01,26.547,0.373,"Bonaire, Saint Eustatius And Saba" +1966-02-01,26.146,0.203,"Bonaire, Saint Eustatius And Saba" +1966-03-01,26.546999999999997,0.281,"Bonaire, Saint Eustatius And Saba" +1966-04-01,26.999000000000002,0.257,"Bonaire, Saint Eustatius And Saba" +1966-05-01,27.897,0.199,"Bonaire, Saint Eustatius And Saba" +1966-06-01,28.099,0.2,"Bonaire, Saint Eustatius And Saba" +1966-07-01,28.641,0.363,"Bonaire, Saint Eustatius And Saba" +1966-08-01,28.886999999999997,0.284,"Bonaire, Saint Eustatius And Saba" +1966-09-01,28.759,0.4,"Bonaire, Saint Eustatius And Saba" +1966-10-01,28.077,0.456,"Bonaire, Saint Eustatius And Saba" +1966-11-01,27.358,0.556,"Bonaire, Saint Eustatius And Saba" +1966-12-01,26.221,0.5670000000000001,"Bonaire, Saint Eustatius And Saba" +1967-01-01,25.82,0.845,"Bonaire, Saint Eustatius And Saba" +1967-02-01,25.867,0.482,"Bonaire, Saint Eustatius And Saba" +1967-03-01,25.893,0.6629999999999999,"Bonaire, Saint Eustatius And Saba" +1967-04-01,26.568,0.713,"Bonaire, Saint Eustatius And Saba" +1967-05-01,27.546,0.4920000000000001,"Bonaire, Saint Eustatius And Saba" +1967-06-01,28.167,0.303,"Bonaire, Saint Eustatius And Saba" +1967-07-01,27.916999999999998,0.41,"Bonaire, Saint Eustatius And Saba" +1967-08-01,28.283,0.501,"Bonaire, Saint Eustatius And Saba" +1967-09-01,28.539,0.334,"Bonaire, Saint Eustatius And Saba" +1967-10-01,28.158,0.237,"Bonaire, Saint Eustatius And Saba" +1967-11-01,27.285999999999998,0.207,"Bonaire, Saint Eustatius And Saba" +1967-12-01,26.185,0.396,"Bonaire, Saint Eustatius And Saba" +1968-01-01,25.422,0.2,"Bonaire, Saint Eustatius And Saba" +1968-02-01,25.585,0.219,"Bonaire, Saint Eustatius And Saba" +1968-03-01,26.026,0.42,"Bonaire, Saint Eustatius And Saba" +1968-04-01,26.503,0.272,"Bonaire, Saint Eustatius And Saba" +1968-05-01,27.794,0.332,"Bonaire, Saint Eustatius And Saba" +1968-06-01,28.19,0.58,"Bonaire, Saint Eustatius And Saba" +1968-07-01,28.409,0.47,"Bonaire, Saint Eustatius And Saba" +1968-08-01,28.695999999999998,0.341,"Bonaire, Saint Eustatius And Saba" +1968-09-01,28.816,0.14,"Bonaire, Saint Eustatius And Saba" +1968-10-01,28.371,0.28800000000000003,"Bonaire, Saint Eustatius And Saba" +1968-11-01,27.746,0.157,"Bonaire, Saint Eustatius And Saba" +1968-12-01,26.563000000000002,0.129,"Bonaire, Saint Eustatius And Saba" +1969-01-01,25.689,0.378,"Bonaire, Saint Eustatius And Saba" +1969-02-01,26.109,0.2,"Bonaire, Saint Eustatius And Saba" +1969-03-01,27.027,0.6609999999999999,"Bonaire, Saint Eustatius And Saba" +1969-04-01,27.765,0.228,"Bonaire, Saint Eustatius And Saba" +1969-05-01,28.754,0.226,"Bonaire, Saint Eustatius And Saba" +1969-06-01,29.096,0.487,"Bonaire, Saint Eustatius And Saba" +1969-07-01,28.843000000000004,0.324,"Bonaire, Saint Eustatius And Saba" +1969-08-01,28.893,0.258,"Bonaire, Saint Eustatius And Saba" +1969-09-01,29.314,0.256,"Bonaire, Saint Eustatius And Saba" +1969-10-01,28.136999999999997,0.285,"Bonaire, Saint Eustatius And Saba" +1969-11-01,27.814,0.204,"Bonaire, Saint Eustatius And Saba" +1969-12-01,26.939,0.163,"Bonaire, Saint Eustatius And Saba" +1970-01-01,26.441999999999997,0.181,"Bonaire, Saint Eustatius And Saba" +1970-02-01,26.319000000000003,0.226,"Bonaire, Saint Eustatius And Saba" +1970-03-01,26.486,0.309,"Bonaire, Saint Eustatius And Saba" +1970-04-01,27.251,0.217,"Bonaire, Saint Eustatius And Saba" +1970-05-01,28.301,0.418,"Bonaire, Saint Eustatius And Saba" +1970-06-01,28.507,0.373,"Bonaire, Saint Eustatius And Saba" +1970-07-01,28.444000000000006,0.189,"Bonaire, Saint Eustatius And Saba" +1970-08-01,28.691999999999997,0.275,"Bonaire, Saint Eustatius And Saba" +1970-09-01,28.604,0.383,"Bonaire, Saint Eustatius And Saba" +1970-10-01,28.301,0.259,"Bonaire, Saint Eustatius And Saba" +1970-11-01,27.226999999999997,0.205,"Bonaire, Saint Eustatius And Saba" +1970-12-01,26.506999999999998,0.255,"Bonaire, Saint Eustatius And Saba" +1971-01-01,25.675,0.3670000000000001,"Bonaire, Saint Eustatius And Saba" +1971-02-01,25.804000000000002,0.332,"Bonaire, Saint Eustatius And Saba" +1971-03-01,26.068,0.294,"Bonaire, Saint Eustatius And Saba" +1971-04-01,26.481,0.214,"Bonaire, Saint Eustatius And Saba" +1971-05-01,27.392,0.139,"Bonaire, Saint Eustatius And Saba" +1971-06-01,27.988000000000003,0.245,"Bonaire, Saint Eustatius And Saba" +1971-07-01,28.130000000000006,0.242,"Bonaire, Saint Eustatius And Saba" +1971-08-01,28.305,0.255,"Bonaire, Saint Eustatius And Saba" +1971-09-01,28.374000000000002,0.371,"Bonaire, Saint Eustatius And Saba" +1971-10-01,27.575,0.264,"Bonaire, Saint Eustatius And Saba" +1971-11-01,27.188000000000002,0.14,"Bonaire, Saint Eustatius And Saba" +1971-12-01,26.025,0.355,"Bonaire, Saint Eustatius And Saba" +1972-01-01,25.369,0.304,"Bonaire, Saint Eustatius And Saba" +1972-02-01,25.658,0.305,"Bonaire, Saint Eustatius And Saba" +1972-03-01,26.241999999999997,0.419,"Bonaire, Saint Eustatius And Saba" +1972-04-01,26.564,0.275,"Bonaire, Saint Eustatius And Saba" +1972-05-01,27.678,0.157,"Bonaire, Saint Eustatius And Saba" +1972-06-01,28.679000000000002,0.16899999999999998,"Bonaire, Saint Eustatius And Saba" +1972-07-01,28.513,0.276,"Bonaire, Saint Eustatius And Saba" +1972-08-01,28.706999999999997,0.54,"Bonaire, Saint Eustatius And Saba" +1972-09-01,29.144000000000002,0.281,"Bonaire, Saint Eustatius And Saba" +1972-10-01,28.641,0.42,"Bonaire, Saint Eustatius And Saba" +1972-11-01,27.932,0.14800000000000002,"Bonaire, Saint Eustatius And Saba" +1972-12-01,26.998,0.28300000000000003,"Bonaire, Saint Eustatius And Saba" +1973-01-01,26.788,0.294,"Bonaire, Saint Eustatius And Saba" +1973-02-01,26.399,0.266,"Bonaire, Saint Eustatius And Saba" +1973-03-01,26.976,0.4,"Bonaire, Saint Eustatius And Saba" +1973-04-01,27.231,0.163,"Bonaire, Saint Eustatius And Saba" +1973-05-01,28.217,0.269,"Bonaire, Saint Eustatius And Saba" +1973-06-01,28.685,0.255,"Bonaire, Saint Eustatius And Saba" +1973-07-01,28.753,0.34700000000000003,"Bonaire, Saint Eustatius And Saba" +1973-08-01,28.816,0.199,"Bonaire, Saint Eustatius And Saba" +1973-09-01,28.608,0.172,"Bonaire, Saint Eustatius And Saba" +1973-10-01,28.213,0.54,"Bonaire, Saint Eustatius And Saba" +1973-11-01,27.055999999999997,0.182,"Bonaire, Saint Eustatius And Saba" +1973-12-01,25.564,0.21600000000000005,"Bonaire, Saint Eustatius And Saba" +1974-01-01,25.077,0.213,"Bonaire, Saint Eustatius And Saba" +1974-02-01,25.545,0.274,"Bonaire, Saint Eustatius And Saba" +1974-03-01,25.803,0.303,"Bonaire, Saint Eustatius And Saba" +1974-04-01,26.231,0.3720000000000001,"Bonaire, Saint Eustatius And Saba" +1974-05-01,27.19,0.211,"Bonaire, Saint Eustatius And Saba" +1974-06-01,28.16,0.32,"Bonaire, Saint Eustatius And Saba" +1974-07-01,28.265,0.266,"Bonaire, Saint Eustatius And Saba" +1974-08-01,28.31,0.477,"Bonaire, Saint Eustatius And Saba" +1974-09-01,28.266,0.194,"Bonaire, Saint Eustatius And Saba" +1974-10-01,27.776,0.253,"Bonaire, Saint Eustatius And Saba" +1974-11-01,27.604,0.414,"Bonaire, Saint Eustatius And Saba" +1974-12-01,25.99,0.191,"Bonaire, Saint Eustatius And Saba" +1975-01-01,25.656999999999996,0.109,"Bonaire, Saint Eustatius And Saba" +1975-02-01,25.608,0.227,"Bonaire, Saint Eustatius And Saba" +1975-03-01,26.461,0.129,"Bonaire, Saint Eustatius And Saba" +1975-04-01,26.566,0.336,"Bonaire, Saint Eustatius And Saba" +1975-05-01,27.561999999999998,0.603,"Bonaire, Saint Eustatius And Saba" +1975-06-01,28.36,0.494,"Bonaire, Saint Eustatius And Saba" +1975-07-01,28.255000000000006,0.345,"Bonaire, Saint Eustatius And Saba" +1975-08-01,28.768,0.654,"Bonaire, Saint Eustatius And Saba" +1975-09-01,28.517,0.396,"Bonaire, Saint Eustatius And Saba" +1975-10-01,27.584,0.386,"Bonaire, Saint Eustatius And Saba" +1975-11-01,27.373,0.6,"Bonaire, Saint Eustatius And Saba" +1975-12-01,25.364,0.513,"Bonaire, Saint Eustatius And Saba" +1976-01-01,24.85,0.213,"Bonaire, Saint Eustatius And Saba" +1976-02-01,25.008000000000003,0.331,"Bonaire, Saint Eustatius And Saba" +1976-03-01,25.196,0.266,"Bonaire, Saint Eustatius And Saba" +1976-04-01,26.449,0.182,"Bonaire, Saint Eustatius And Saba" +1976-05-01,27.584,0.364,"Bonaire, Saint Eustatius And Saba" +1976-06-01,27.847,0.279,"Bonaire, Saint Eustatius And Saba" +1976-07-01,27.936,0.421,"Bonaire, Saint Eustatius And Saba" +1976-08-01,28.483,0.447,"Bonaire, Saint Eustatius And Saba" +1976-09-01,28.701,0.327,"Bonaire, Saint Eustatius And Saba" +1976-10-01,28.101999999999997,0.448,"Bonaire, Saint Eustatius And Saba" +1976-11-01,27.754,0.239,"Bonaire, Saint Eustatius And Saba" +1976-12-01,26.453000000000003,0.295,"Bonaire, Saint Eustatius And Saba" +1977-01-01,26.197,0.2,"Bonaire, Saint Eustatius And Saba" +1977-02-01,26.33,0.383,"Bonaire, Saint Eustatius And Saba" +1977-03-01,26.412,0.394,"Bonaire, Saint Eustatius And Saba" +1977-04-01,26.925,0.517,"Bonaire, Saint Eustatius And Saba" +1977-05-01,28.101999999999997,0.468,"Bonaire, Saint Eustatius And Saba" +1977-06-01,28.162,0.364,"Bonaire, Saint Eustatius And Saba" +1977-07-01,28.686999999999998,0.28800000000000003,"Bonaire, Saint Eustatius And Saba" +1977-08-01,28.951999999999998,0.378,"Bonaire, Saint Eustatius And Saba" +1977-09-01,29.118000000000002,0.453,"Bonaire, Saint Eustatius And Saba" +1977-10-01,28.581,0.233,"Bonaire, Saint Eustatius And Saba" +1977-11-01,28.069000000000003,0.28300000000000003,"Bonaire, Saint Eustatius And Saba" +1977-12-01,26.79,0.14,"Bonaire, Saint Eustatius And Saba" +1978-01-01,26.033,0.116,"Bonaire, Saint Eustatius And Saba" +1978-02-01,26.686,0.377,"Bonaire, Saint Eustatius And Saba" +1978-03-01,26.97,0.325,"Bonaire, Saint Eustatius And Saba" +1978-04-01,27.358,0.384,"Bonaire, Saint Eustatius And Saba" +1978-05-01,28.180999999999997,0.285,"Bonaire, Saint Eustatius And Saba" +1978-06-01,28.224,0.374,"Bonaire, Saint Eustatius And Saba" +1978-07-01,28.340999999999998,0.276,"Bonaire, Saint Eustatius And Saba" +1978-08-01,28.413,0.355,"Bonaire, Saint Eustatius And Saba" +1978-09-01,28.861,0.212,"Bonaire, Saint Eustatius And Saba" +1978-10-01,28.338,0.201,"Bonaire, Saint Eustatius And Saba" +1978-11-01,27.983,0.146,"Bonaire, Saint Eustatius And Saba" +1978-12-01,26.353,0.295,"Bonaire, Saint Eustatius And Saba" +1979-01-01,25.59,0.218,"Bonaire, Saint Eustatius And Saba" +1979-02-01,26.302,0.314,"Bonaire, Saint Eustatius And Saba" +1979-03-01,26.861,0.465,"Bonaire, Saint Eustatius And Saba" +1979-04-01,27.107,0.499,"Bonaire, Saint Eustatius And Saba" +1979-05-01,28.175,0.483,"Bonaire, Saint Eustatius And Saba" +1979-06-01,28.545,0.298,"Bonaire, Saint Eustatius And Saba" +1979-07-01,28.531999999999996,0.314,"Bonaire, Saint Eustatius And Saba" +1979-08-01,28.904,0.396,"Bonaire, Saint Eustatius And Saba" +1979-09-01,28.879,0.285,"Bonaire, Saint Eustatius And Saba" +1979-10-01,28.686,0.287,"Bonaire, Saint Eustatius And Saba" +1979-11-01,28.226999999999997,0.394,"Bonaire, Saint Eustatius And Saba" +1979-12-01,26.73,0.476,"Bonaire, Saint Eustatius And Saba" +1980-01-01,26.3,0.41,"Bonaire, Saint Eustatius And Saba" +1980-02-01,26.182,0.35,"Bonaire, Saint Eustatius And Saba" +1980-03-01,26.635,0.239,"Bonaire, Saint Eustatius And Saba" +1980-04-01,27.569000000000003,0.305,"Bonaire, Saint Eustatius And Saba" +1980-05-01,28.535,0.243,"Bonaire, Saint Eustatius And Saba" +1980-06-01,28.848000000000003,0.206,"Bonaire, Saint Eustatius And Saba" +1980-07-01,28.863000000000003,0.203,"Bonaire, Saint Eustatius And Saba" +1980-08-01,29.016,0.259,"Bonaire, Saint Eustatius And Saba" +1980-09-01,29.145,0.16699999999999998,"Bonaire, Saint Eustatius And Saba" +1980-10-01,28.607,0.353,"Bonaire, Saint Eustatius And Saba" +1980-11-01,27.713,0.306,"Bonaire, Saint Eustatius And Saba" +1980-12-01,26.768,0.112,"Bonaire, Saint Eustatius And Saba" +1981-01-01,26.553,0.31,"Bonaire, Saint Eustatius And Saba" +1981-02-01,26.738000000000003,0.233,"Bonaire, Saint Eustatius And Saba" +1981-03-01,27.062,0.241,"Bonaire, Saint Eustatius And Saba" +1981-04-01,26.773000000000003,0.395,"Bonaire, Saint Eustatius And Saba" +1981-05-01,28.121,0.453,"Bonaire, Saint Eustatius And Saba" +1981-06-01,28.256,0.568,"Bonaire, Saint Eustatius And Saba" +1981-07-01,28.541,0.339,"Bonaire, Saint Eustatius And Saba" +1981-08-01,28.814,0.368,"Bonaire, Saint Eustatius And Saba" +1981-09-01,28.746,0.375,"Bonaire, Saint Eustatius And Saba" +1981-10-01,28.423000000000002,0.406,"Bonaire, Saint Eustatius And Saba" +1981-11-01,28.281,0.255,"Bonaire, Saint Eustatius And Saba" +1981-12-01,27.338,0.184,"Bonaire, Saint Eustatius And Saba" +1982-01-01,26.107,0.254,"Bonaire, Saint Eustatius And Saba" +1982-02-01,26.075,0.191,"Bonaire, Saint Eustatius And Saba" +1982-03-01,26.155,0.225,"Bonaire, Saint Eustatius And Saba" +1982-04-01,26.704,0.14400000000000002,"Bonaire, Saint Eustatius And Saba" +1982-05-01,27.884,0.337,"Bonaire, Saint Eustatius And Saba" +1982-06-01,28.239,0.34600000000000003,"Bonaire, Saint Eustatius And Saba" +1982-07-01,28.345,0.344,"Bonaire, Saint Eustatius And Saba" +1982-08-01,28.768,0.203,"Bonaire, Saint Eustatius And Saba" +1982-09-01,28.896,0.279,"Bonaire, Saint Eustatius And Saba" +1982-10-01,28.245,0.251,"Bonaire, Saint Eustatius And Saba" +1982-11-01,27.608,0.329,"Bonaire, Saint Eustatius And Saba" +1982-12-01,26.504,0.306,"Bonaire, Saint Eustatius And Saba" +1983-01-01,26.351999999999997,0.311,"Bonaire, Saint Eustatius And Saba" +1983-02-01,26.369,0.28,"Bonaire, Saint Eustatius And Saba" +1983-03-01,27.3,0.48,"Bonaire, Saint Eustatius And Saba" +1983-04-01,27.347,0.341,"Bonaire, Saint Eustatius And Saba" +1983-05-01,28.081,0.446,"Bonaire, Saint Eustatius And Saba" +1983-06-01,28.842,0.361,"Bonaire, Saint Eustatius And Saba" +1983-07-01,28.878,0.289,"Bonaire, Saint Eustatius And Saba" +1983-08-01,29.051,0.209,"Bonaire, Saint Eustatius And Saba" +1983-09-01,29.226999999999997,0.384,"Bonaire, Saint Eustatius And Saba" +1983-10-01,28.316,0.272,"Bonaire, Saint Eustatius And Saba" +1983-11-01,28.037,0.405,"Bonaire, Saint Eustatius And Saba" +1983-12-01,26.819000000000003,0.334,"Bonaire, Saint Eustatius And Saba" +1984-01-01,26.191999999999997,0.257,"Bonaire, Saint Eustatius And Saba" +1984-02-01,26.371,0.168,"Bonaire, Saint Eustatius And Saba" +1984-03-01,26.415,0.337,"Bonaire, Saint Eustatius And Saba" +1984-04-01,27.009,0.119,"Bonaire, Saint Eustatius And Saba" +1984-05-01,27.843000000000004,0.165,"Bonaire, Saint Eustatius And Saba" +1984-06-01,28.401,0.33,"Bonaire, Saint Eustatius And Saba" +1984-07-01,28.24,0.259,"Bonaire, Saint Eustatius And Saba" +1984-08-01,28.621,0.249,"Bonaire, Saint Eustatius And Saba" +1984-09-01,28.546999999999997,0.217,"Bonaire, Saint Eustatius And Saba" +1984-10-01,28.136999999999997,0.245,"Bonaire, Saint Eustatius And Saba" +1984-11-01,27.090999999999998,0.254,"Bonaire, Saint Eustatius And Saba" +1984-12-01,25.96,0.29100000000000004,"Bonaire, Saint Eustatius And Saba" +1985-01-01,25.247,0.146,"Bonaire, Saint Eustatius And Saba" +1985-02-01,25.595,0.237,"Bonaire, Saint Eustatius And Saba" +1985-03-01,25.85,0.285,"Bonaire, Saint Eustatius And Saba" +1985-04-01,26.54,0.438,"Bonaire, Saint Eustatius And Saba" +1985-05-01,27.930999999999997,0.396,"Bonaire, Saint Eustatius And Saba" +1985-06-01,28.263,0.415,"Bonaire, Saint Eustatius And Saba" +1985-07-01,28.373,0.252,"Bonaire, Saint Eustatius And Saba" +1985-08-01,28.563000000000002,0.254,"Bonaire, Saint Eustatius And Saba" +1985-09-01,28.647,0.318,"Bonaire, Saint Eustatius And Saba" +1985-10-01,27.823,0.35700000000000004,"Bonaire, Saint Eustatius And Saba" +1985-11-01,27.054,0.34,"Bonaire, Saint Eustatius And Saba" +1985-12-01,25.888,0.45,"Bonaire, Saint Eustatius And Saba" +1986-01-01,25.665,0.438,"Bonaire, Saint Eustatius And Saba" +1986-02-01,25.648000000000003,0.236,"Bonaire, Saint Eustatius And Saba" +1986-03-01,26.342,0.229,"Bonaire, Saint Eustatius And Saba" +1986-04-01,27.225,0.473,"Bonaire, Saint Eustatius And Saba" +1986-05-01,27.815,0.314,"Bonaire, Saint Eustatius And Saba" +1986-06-01,28.328000000000003,0.234,"Bonaire, Saint Eustatius And Saba" +1986-07-01,28.404,0.243,"Bonaire, Saint Eustatius And Saba" +1986-08-01,28.745,0.247,"Bonaire, Saint Eustatius And Saba" +1986-09-01,28.43,0.266,"Bonaire, Saint Eustatius And Saba" +1986-10-01,28.022,0.326,"Bonaire, Saint Eustatius And Saba" +1986-11-01,27.405,0.325,"Bonaire, Saint Eustatius And Saba" +1986-12-01,26.32,0.331,"Bonaire, Saint Eustatius And Saba" +1987-01-01,26.006,0.338,"Bonaire, Saint Eustatius And Saba" +1987-02-01,26.441999999999997,0.333,"Bonaire, Saint Eustatius And Saba" +1987-03-01,27.007,0.249,"Bonaire, Saint Eustatius And Saba" +1987-04-01,28.154,0.436,"Bonaire, Saint Eustatius And Saba" +1987-05-01,28.65,0.26,"Bonaire, Saint Eustatius And Saba" +1987-06-01,29.109,0.373,"Bonaire, Saint Eustatius And Saba" +1987-07-01,29.01,0.385,"Bonaire, Saint Eustatius And Saba" +1987-08-01,29.379,0.469,"Bonaire, Saint Eustatius And Saba" +1987-09-01,29.603,0.17600000000000002,"Bonaire, Saint Eustatius And Saba" +1987-10-01,29.042,0.173,"Bonaire, Saint Eustatius And Saba" +1987-11-01,28.473000000000003,0.217,"Bonaire, Saint Eustatius And Saba" +1987-12-01,27.639,0.29600000000000004,"Bonaire, Saint Eustatius And Saba" +1988-01-01,26.174,0.306,"Bonaire, Saint Eustatius And Saba" +1988-02-01,26.195,0.21600000000000005,"Bonaire, Saint Eustatius And Saba" +1988-03-01,26.49,0.214,"Bonaire, Saint Eustatius And Saba" +1988-04-01,27.335,0.234,"Bonaire, Saint Eustatius And Saba" +1988-05-01,28.523000000000003,0.179,"Bonaire, Saint Eustatius And Saba" +1988-06-01,28.97,0.409,"Bonaire, Saint Eustatius And Saba" +1988-07-01,28.939,0.276,"Bonaire, Saint Eustatius And Saba" +1988-08-01,28.480999999999998,0.304,"Bonaire, Saint Eustatius And Saba" +1988-09-01,28.682,0.28800000000000003,"Bonaire, Saint Eustatius And Saba" +1988-10-01,27.758000000000003,0.457,"Bonaire, Saint Eustatius And Saba" +1988-11-01,27.570999999999998,0.334,"Bonaire, Saint Eustatius And Saba" +1988-12-01,25.863000000000003,0.282,"Bonaire, Saint Eustatius And Saba" +1989-01-01,25.338,0.462,"Bonaire, Saint Eustatius And Saba" +1989-02-01,25.439,0.442,"Bonaire, Saint Eustatius And Saba" +1989-03-01,25.904,0.338,"Bonaire, Saint Eustatius And Saba" +1989-04-01,26.697,0.371,"Bonaire, Saint Eustatius And Saba" +1989-05-01,27.518,0.445,"Bonaire, Saint Eustatius And Saba" +1989-06-01,28.036,0.381,"Bonaire, Saint Eustatius And Saba" +1989-07-01,28.6,0.306,"Bonaire, Saint Eustatius And Saba" +1989-08-01,29.1,0.7340000000000001,"Bonaire, Saint Eustatius And Saba" +1989-09-01,29.025,0.394,"Bonaire, Saint Eustatius And Saba" +1989-10-01,28.385,0.245,"Bonaire, Saint Eustatius And Saba" +1989-11-01,27.831,0.172,"Bonaire, Saint Eustatius And Saba" +1989-12-01,26.718000000000004,0.263,"Bonaire, Saint Eustatius And Saba" +1990-01-01,25.995,0.246,"Bonaire, Saint Eustatius And Saba" +1990-02-01,25.734,0.51,"Bonaire, Saint Eustatius And Saba" +1990-03-01,26.318,0.293,"Bonaire, Saint Eustatius And Saba" +1990-04-01,27.151,0.16699999999999998,"Bonaire, Saint Eustatius And Saba" +1990-05-01,28.025,0.076,"Bonaire, Saint Eustatius And Saba" +1990-06-01,28.679000000000002,0.262,"Bonaire, Saint Eustatius And Saba" +1990-07-01,28.686999999999998,0.409,"Bonaire, Saint Eustatius And Saba" +1990-08-01,28.94,0.227,"Bonaire, Saint Eustatius And Saba" +1990-09-01,29.416,0.278,"Bonaire, Saint Eustatius And Saba" +1990-10-01,28.315,0.485,"Bonaire, Saint Eustatius And Saba" +1990-11-01,28.059,0.541,"Bonaire, Saint Eustatius And Saba" +1990-12-01,26.502,0.333,"Bonaire, Saint Eustatius And Saba" +1991-01-01,25.882,0.269,"Bonaire, Saint Eustatius And Saba" +1991-02-01,26.354,0.28300000000000003,"Bonaire, Saint Eustatius And Saba" +1991-03-01,26.534000000000002,0.502,"Bonaire, Saint Eustatius And Saba" +1991-04-01,26.938000000000002,0.383,"Bonaire, Saint Eustatius And Saba" +1991-05-01,28.017,0.268,"Bonaire, Saint Eustatius And Saba" +1991-06-01,28.497,0.244,"Bonaire, Saint Eustatius And Saba" +1991-07-01,28.586,0.277,"Bonaire, Saint Eustatius And Saba" +1991-08-01,28.934,0.327,"Bonaire, Saint Eustatius And Saba" +1991-09-01,28.885,0.202,"Bonaire, Saint Eustatius And Saba" +1991-10-01,28.436,0.16,"Bonaire, Saint Eustatius And Saba" +1991-11-01,27.485,0.141,"Bonaire, Saint Eustatius And Saba" +1991-12-01,25.741999999999997,0.312,"Bonaire, Saint Eustatius And Saba" +1992-01-01,25.878,0.254,"Bonaire, Saint Eustatius And Saba" +1992-02-01,26.258000000000003,0.242,"Bonaire, Saint Eustatius And Saba" +1992-03-01,26.802,0.317,"Bonaire, Saint Eustatius And Saba" +1992-04-01,27.561,0.261,"Bonaire, Saint Eustatius And Saba" +1992-05-01,28.48,0.319,"Bonaire, Saint Eustatius And Saba" +1992-06-01,28.683000000000003,0.39,"Bonaire, Saint Eustatius And Saba" +1992-07-01,28.215,0.271,"Bonaire, Saint Eustatius And Saba" +1992-08-01,28.86,0.374,"Bonaire, Saint Eustatius And Saba" +1992-09-01,28.724,0.468,"Bonaire, Saint Eustatius And Saba" +1992-10-01,28.668000000000003,0.208,"Bonaire, Saint Eustatius And Saba" +1992-11-01,27.712,0.25,"Bonaire, Saint Eustatius And Saba" +1992-12-01,26.631,0.532,"Bonaire, Saint Eustatius And Saba" +1993-01-01,25.981,0.3670000000000001,"Bonaire, Saint Eustatius And Saba" +1993-02-01,26.129,0.483,"Bonaire, Saint Eustatius And Saba" +1993-03-01,26.703000000000003,0.379,"Bonaire, Saint Eustatius And Saba" +1993-04-01,27.535,0.282,"Bonaire, Saint Eustatius And Saba" +1993-05-01,28.565,0.277,"Bonaire, Saint Eustatius And Saba" +1993-06-01,28.789,0.218,"Bonaire, Saint Eustatius And Saba" +1993-07-01,28.843000000000004,0.236,"Bonaire, Saint Eustatius And Saba" +1993-08-01,28.724,0.268,"Bonaire, Saint Eustatius And Saba" +1993-09-01,28.744,0.475,"Bonaire, Saint Eustatius And Saba" +1993-10-01,28.723000000000003,0.299,"Bonaire, Saint Eustatius And Saba" +1993-11-01,27.7,0.282,"Bonaire, Saint Eustatius And Saba" +1993-12-01,26.72,0.35100000000000003,"Bonaire, Saint Eustatius And Saba" +1994-01-01,26.04,0.314,"Bonaire, Saint Eustatius And Saba" +1994-02-01,25.975,0.313,"Bonaire, Saint Eustatius And Saba" +1994-03-01,26.624000000000002,0.303,"Bonaire, Saint Eustatius And Saba" +1994-04-01,27.117,0.263,"Bonaire, Saint Eustatius And Saba" +1994-05-01,28.235,0.316,"Bonaire, Saint Eustatius And Saba" +1994-06-01,28.682,0.222,"Bonaire, Saint Eustatius And Saba" +1994-07-01,28.316000000000006,0.327,"Bonaire, Saint Eustatius And Saba" +1994-08-01,28.750999999999998,0.388,"Bonaire, Saint Eustatius And Saba" +1994-09-01,28.88,0.255,"Bonaire, Saint Eustatius And Saba" +1994-10-01,28.605,0.173,"Bonaire, Saint Eustatius And Saba" +1994-11-01,28.156,0.225,"Bonaire, Saint Eustatius And Saba" +1994-12-01,26.953000000000003,0.241,"Bonaire, Saint Eustatius And Saba" +1995-01-01,26.642,0.268,"Bonaire, Saint Eustatius And Saba" +1995-02-01,26.553,0.32299999999999995,"Bonaire, Saint Eustatius And Saba" +1995-03-01,26.728,0.262,"Bonaire, Saint Eustatius And Saba" +1995-04-01,27.537,0.331,"Bonaire, Saint Eustatius And Saba" +1995-05-01,28.733,0.266,"Bonaire, Saint Eustatius And Saba" +1995-06-01,28.944000000000003,0.243,"Bonaire, Saint Eustatius And Saba" +1995-07-01,28.928,0.315,"Bonaire, Saint Eustatius And Saba" +1995-08-01,29.169,0.353,"Bonaire, Saint Eustatius And Saba" +1995-09-01,29.264,0.443,"Bonaire, Saint Eustatius And Saba" +1995-10-01,28.283,0.268,"Bonaire, Saint Eustatius And Saba" +1995-11-01,27.987,0.215,"Bonaire, Saint Eustatius And Saba" +1995-12-01,27.118000000000002,0.21600000000000005,"Bonaire, Saint Eustatius And Saba" +1996-01-01,26.355,0.299,"Bonaire, Saint Eustatius And Saba" +1996-02-01,26.702,0.32,"Bonaire, Saint Eustatius And Saba" +1996-03-01,26.815,0.446,"Bonaire, Saint Eustatius And Saba" +1996-04-01,27.275,0.207,"Bonaire, Saint Eustatius And Saba" +1996-05-01,28.066,0.159,"Bonaire, Saint Eustatius And Saba" +1996-06-01,28.443,0.25,"Bonaire, Saint Eustatius And Saba" +1996-07-01,28.438,0.306,"Bonaire, Saint Eustatius And Saba" +1996-08-01,29.008000000000003,0.34700000000000003,"Bonaire, Saint Eustatius And Saba" +1996-09-01,29.311999999999998,0.203,"Bonaire, Saint Eustatius And Saba" +1996-10-01,28.38,0.238,"Bonaire, Saint Eustatius And Saba" +1996-11-01,27.521,0.168,"Bonaire, Saint Eustatius And Saba" +1996-12-01,26.535,0.242,"Bonaire, Saint Eustatius And Saba" +1997-01-01,25.97,0.35200000000000004,"Bonaire, Saint Eustatius And Saba" +1997-02-01,26.075,0.426,"Bonaire, Saint Eustatius And Saba" +1997-03-01,25.836,0.391,"Bonaire, Saint Eustatius And Saba" +1997-04-01,26.982,0.205,"Bonaire, Saint Eustatius And Saba" +1997-05-01,28.441999999999997,0.295,"Bonaire, Saint Eustatius And Saba" +1997-06-01,28.939,0.387,"Bonaire, Saint Eustatius And Saba" +1997-07-01,28.913,0.5489999999999999,"Bonaire, Saint Eustatius And Saba" +1997-08-01,29.081,0.341,"Bonaire, Saint Eustatius And Saba" +1997-09-01,29.441,0.325,"Bonaire, Saint Eustatius And Saba" +1997-10-01,29.015,0.317,"Bonaire, Saint Eustatius And Saba" +1997-11-01,28.314,0.231,"Bonaire, Saint Eustatius And Saba" +1997-12-01,27.428,0.309,"Bonaire, Saint Eustatius And Saba" +1998-01-01,27.124000000000002,0.374,"Bonaire, Saint Eustatius And Saba" +1998-02-01,27.238000000000003,0.375,"Bonaire, Saint Eustatius And Saba" +1998-03-01,27.302,0.428,"Bonaire, Saint Eustatius And Saba" +1998-04-01,27.911,0.254,"Bonaire, Saint Eustatius And Saba" +1998-05-01,28.803,0.314,"Bonaire, Saint Eustatius And Saba" +1998-06-01,29.081,0.28600000000000003,"Bonaire, Saint Eustatius And Saba" +1998-07-01,28.926,0.33,"Bonaire, Saint Eustatius And Saba" +1998-08-01,29.761,0.278,"Bonaire, Saint Eustatius And Saba" +1998-09-01,29.572,0.244,"Bonaire, Saint Eustatius And Saba" +1998-10-01,28.66,0.239,"Bonaire, Saint Eustatius And Saba" +1998-11-01,28.061999999999998,0.278,"Bonaire, Saint Eustatius And Saba" +1998-12-01,26.938000000000002,0.315,"Bonaire, Saint Eustatius And Saba" +1999-01-01,25.894000000000002,0.31,"Bonaire, Saint Eustatius And Saba" +1999-02-01,25.717,0.329,"Bonaire, Saint Eustatius And Saba" +1999-03-01,26.665,0.315,"Bonaire, Saint Eustatius And Saba" +1999-04-01,26.909000000000002,0.41,"Bonaire, Saint Eustatius And Saba" +1999-05-01,28.31,0.452,"Bonaire, Saint Eustatius And Saba" +1999-06-01,28.645,0.338,"Bonaire, Saint Eustatius And Saba" +1999-07-01,28.529,0.35200000000000004,"Bonaire, Saint Eustatius And Saba" +1999-08-01,29.066,0.235,"Bonaire, Saint Eustatius And Saba" +1999-09-01,29.005,0.39,"Bonaire, Saint Eustatius And Saba" +1999-10-01,28.0,0.373,"Bonaire, Saint Eustatius And Saba" +1999-11-01,27.656999999999996,0.395,"Bonaire, Saint Eustatius And Saba" +1999-12-01,26.022,0.342,"Bonaire, Saint Eustatius And Saba" +2000-01-01,25.225,0.354,"Bonaire, Saint Eustatius And Saba" +2000-02-01,25.615,0.408,"Bonaire, Saint Eustatius And Saba" +2000-03-01,25.814,0.302,"Bonaire, Saint Eustatius And Saba" +2000-04-01,26.984,0.334,"Bonaire, Saint Eustatius And Saba" +2000-05-01,27.984,0.265,"Bonaire, Saint Eustatius And Saba" +2000-06-01,28.228,0.3,"Bonaire, Saint Eustatius And Saba" +2000-07-01,28.519,0.244,"Bonaire, Saint Eustatius And Saba" +2000-08-01,29.1,0.46,"Bonaire, Saint Eustatius And Saba" +2000-09-01,28.868000000000002,0.311,"Bonaire, Saint Eustatius And Saba" +2000-10-01,28.589000000000002,0.334,"Bonaire, Saint Eustatius And Saba" +2000-11-01,27.346,0.263,"Bonaire, Saint Eustatius And Saba" +2000-12-01,26.674,0.35200000000000004,"Bonaire, Saint Eustatius And Saba" +2001-01-01,26.163,0.4270000000000001,"Bonaire, Saint Eustatius And Saba" +2001-02-01,26.09,0.435,"Bonaire, Saint Eustatius And Saba" +2001-03-01,26.717,0.304,"Bonaire, Saint Eustatius And Saba" +2001-04-01,27.065,0.154,"Bonaire, Saint Eustatius And Saba" +2001-05-01,28.539,0.162,"Bonaire, Saint Eustatius And Saba" +2001-06-01,28.595,0.325,"Bonaire, Saint Eustatius And Saba" +2001-07-01,28.814,0.428,"Bonaire, Saint Eustatius And Saba" +2001-08-01,29.232,0.565,"Bonaire, Saint Eustatius And Saba" +2001-09-01,29.078000000000003,0.302,"Bonaire, Saint Eustatius And Saba" +2001-10-01,28.565,0.439,"Bonaire, Saint Eustatius And Saba" +2001-11-01,28.248,0.401,"Bonaire, Saint Eustatius And Saba" +2001-12-01,27.468000000000004,0.428,"Bonaire, Saint Eustatius And Saba" +2002-01-01,26.779,0.396,"Bonaire, Saint Eustatius And Saba" +2002-02-01,26.478,0.337,"Bonaire, Saint Eustatius And Saba" +2002-03-01,26.933000000000003,0.364,"Bonaire, Saint Eustatius And Saba" +2002-04-01,27.205,0.234,"Bonaire, Saint Eustatius And Saba" +2002-05-01,28.308000000000003,0.237,"Bonaire, Saint Eustatius And Saba" +2002-06-01,28.718000000000004,0.422,"Bonaire, Saint Eustatius And Saba" +2002-07-01,28.935,0.358,"Bonaire, Saint Eustatius And Saba" +2002-08-01,29.403000000000002,0.279,"Bonaire, Saint Eustatius And Saba" +2002-09-01,29.53,0.3,"Bonaire, Saint Eustatius And Saba" +2002-10-01,28.899,0.345,"Bonaire, Saint Eustatius And Saba" +2002-11-01,28.398000000000003,0.262,"Bonaire, Saint Eustatius And Saba" +2002-12-01,27.309,0.256,"Bonaire, Saint Eustatius And Saba" +2003-01-01,26.967,0.386,"Bonaire, Saint Eustatius And Saba" +2003-02-01,26.924,0.293,"Bonaire, Saint Eustatius And Saba" +2003-03-01,27.153000000000002,0.218,"Bonaire, Saint Eustatius And Saba" +2003-04-01,27.581,0.317,"Bonaire, Saint Eustatius And Saba" +2003-05-01,28.502,0.234,"Bonaire, Saint Eustatius And Saba" +2003-06-01,28.615,0.302,"Bonaire, Saint Eustatius And Saba" +2003-07-01,28.544,0.37,"Bonaire, Saint Eustatius And Saba" +2003-08-01,29.195999999999998,0.393,"Bonaire, Saint Eustatius And Saba" +2003-09-01,29.365,0.363,"Bonaire, Saint Eustatius And Saba" +2003-10-01,29.052,0.375,"Bonaire, Saint Eustatius And Saba" +2003-11-01,28.226999999999997,0.252,"Bonaire, Saint Eustatius And Saba" +2003-12-01,27.214000000000002,0.471,"Bonaire, Saint Eustatius And Saba" +2004-01-01,26.086,0.242,"Bonaire, Saint Eustatius And Saba" +2004-02-01,26.308000000000003,0.363,"Bonaire, Saint Eustatius And Saba" +2004-03-01,26.691,0.403,"Bonaire, Saint Eustatius And Saba" +2004-04-01,27.43,0.449,"Bonaire, Saint Eustatius And Saba" +2004-05-01,27.921999999999997,0.364,"Bonaire, Saint Eustatius And Saba" +2004-06-01,28.479,0.328,"Bonaire, Saint Eustatius And Saba" +2004-07-01,28.573,0.223,"Bonaire, Saint Eustatius And Saba" +2004-08-01,29.026999999999997,0.213,"Bonaire, Saint Eustatius And Saba" +2004-09-01,29.116,0.234,"Bonaire, Saint Eustatius And Saba" +2004-10-01,28.651,0.318,"Bonaire, Saint Eustatius And Saba" +2004-11-01,27.247,0.336,"Bonaire, Saint Eustatius And Saba" +2004-12-01,26.765,0.261,"Bonaire, Saint Eustatius And Saba" +2005-01-01,26.034000000000002,0.238,"Bonaire, Saint Eustatius And Saba" +2005-02-01,25.876,0.398,"Bonaire, Saint Eustatius And Saba" +2005-03-01,27.535,0.29100000000000004,"Bonaire, Saint Eustatius And Saba" +2005-04-01,27.953000000000003,0.292,"Bonaire, Saint Eustatius And Saba" +2005-05-01,28.877,0.316,"Bonaire, Saint Eustatius And Saba" +2005-06-01,29.321,0.378,"Bonaire, Saint Eustatius And Saba" +2005-07-01,29.205,0.28600000000000003,"Bonaire, Saint Eustatius And Saba" +2005-08-01,29.468000000000004,0.363,"Bonaire, Saint Eustatius And Saba" +2005-09-01,29.69,0.247,"Bonaire, Saint Eustatius And Saba" +2005-10-01,28.843000000000004,0.464,"Bonaire, Saint Eustatius And Saba" +2005-11-01,27.736,0.105,"Bonaire, Saint Eustatius And Saba" +2005-12-01,26.486,0.534,"Bonaire, Saint Eustatius And Saba" +2006-01-01,26.016,0.605,"Bonaire, Saint Eustatius And Saba" +2006-02-01,26.098000000000003,0.484,"Bonaire, Saint Eustatius And Saba" +2006-03-01,26.791,0.423,"Bonaire, Saint Eustatius And Saba" +2006-04-01,27.178,0.38,"Bonaire, Saint Eustatius And Saba" +2006-05-01,28.684,0.336,"Bonaire, Saint Eustatius And Saba" +2006-06-01,29.124000000000002,0.272,"Bonaire, Saint Eustatius And Saba" +2006-07-01,28.941000000000006,0.353,"Bonaire, Saint Eustatius And Saba" +2006-08-01,29.198,0.241,"Bonaire, Saint Eustatius And Saba" +2006-09-01,29.706999999999997,0.265,"Bonaire, Saint Eustatius And Saba" +2006-10-01,29.041,0.295,"Bonaire, Saint Eustatius And Saba" +2006-11-01,28.737,0.335,"Bonaire, Saint Eustatius And Saba" +2006-12-01,27.447,0.439,"Bonaire, Saint Eustatius And Saba" +2007-01-01,26.658,0.368,"Bonaire, Saint Eustatius And Saba" +2007-02-01,26.77,0.547,"Bonaire, Saint Eustatius And Saba" +2007-03-01,27.155,0.375,"Bonaire, Saint Eustatius And Saba" +2007-04-01,27.744,0.276,"Bonaire, Saint Eustatius And Saba" +2007-05-01,28.886999999999997,0.305,"Bonaire, Saint Eustatius And Saba" +2007-06-01,29.259,0.338,"Bonaire, Saint Eustatius And Saba" +2007-07-01,29.378,0.248,"Bonaire, Saint Eustatius And Saba" +2007-08-01,29.135,0.292,"Bonaire, Saint Eustatius And Saba" +2007-09-01,29.176,0.376,"Bonaire, Saint Eustatius And Saba" +2007-10-01,28.507,0.194,"Bonaire, Saint Eustatius And Saba" +2007-11-01,28.204,0.318,"Bonaire, Saint Eustatius And Saba" +2007-12-01,26.756,0.453,"Bonaire, Saint Eustatius And Saba" +2008-01-01,26.248,0.362,"Bonaire, Saint Eustatius And Saba" +2008-02-01,26.16,0.363,"Bonaire, Saint Eustatius And Saba" +2008-03-01,26.372,0.303,"Bonaire, Saint Eustatius And Saba" +2008-04-01,27.093000000000004,0.147,"Bonaire, Saint Eustatius And Saba" +2008-05-01,28.18,0.121,"Bonaire, Saint Eustatius And Saba" +2008-06-01,28.814,0.242,"Bonaire, Saint Eustatius And Saba" +2008-07-01,28.971,0.232,"Bonaire, Saint Eustatius And Saba" +2008-08-01,29.476,0.262,"Bonaire, Saint Eustatius And Saba" +2008-09-01,29.486,0.439,"Bonaire, Saint Eustatius And Saba" +2008-10-01,28.161,0.3670000000000001,"Bonaire, Saint Eustatius And Saba" +2008-11-01,27.78,0.218,"Bonaire, Saint Eustatius And Saba" +2008-12-01,26.623,0.423,"Bonaire, Saint Eustatius And Saba" +2009-01-01,26.669,0.327,"Bonaire, Saint Eustatius And Saba" +2009-02-01,25.997,0.364,"Bonaire, Saint Eustatius And Saba" +2009-03-01,26.156999999999996,0.245,"Bonaire, Saint Eustatius And Saba" +2009-04-01,26.945,0.448,"Bonaire, Saint Eustatius And Saba" +2009-05-01,28.289,0.3,"Bonaire, Saint Eustatius And Saba" +2009-06-01,29.009,0.343,"Bonaire, Saint Eustatius And Saba" +2009-07-01,29.296000000000006,0.636,"Bonaire, Saint Eustatius And Saba" +2009-08-01,29.496,0.389,"Bonaire, Saint Eustatius And Saba" +2009-09-01,29.929000000000002,0.284,"Bonaire, Saint Eustatius And Saba" +2009-10-01,29.301,0.378,"Bonaire, Saint Eustatius And Saba" +2009-11-01,28.759,0.406,"Bonaire, Saint Eustatius And Saba" +2009-12-01,27.823,0.263,"Bonaire, Saint Eustatius And Saba" +2010-01-01,27.129,0.35,"Bonaire, Saint Eustatius And Saba" +2010-02-01,27.451,0.265,"Bonaire, Saint Eustatius And Saba" +2010-03-01,28.293000000000003,0.539,"Bonaire, Saint Eustatius And Saba" +2010-04-01,28.097,0.251,"Bonaire, Saint Eustatius And Saba" +2010-05-01,28.987,0.175,"Bonaire, Saint Eustatius And Saba" +2010-06-01,29.247,0.344,"Bonaire, Saint Eustatius And Saba" +2010-07-01,29.311999999999998,0.284,"Bonaire, Saint Eustatius And Saba" +2010-08-01,29.741,0.382,"Bonaire, Saint Eustatius And Saba" +2010-09-01,29.119,0.333,"Bonaire, Saint Eustatius And Saba" +2010-10-01,28.834,0.25,"Bonaire, Saint Eustatius And Saba" +2010-11-01,27.386999999999997,0.26,"Bonaire, Saint Eustatius And Saba" +2010-12-01,26.85,0.434,"Bonaire, Saint Eustatius And Saba" +2011-01-01,26.623,0.469,"Bonaire, Saint Eustatius And Saba" +2011-02-01,26.423000000000002,0.34700000000000003,"Bonaire, Saint Eustatius And Saba" +2011-03-01,26.211,0.28300000000000003,"Bonaire, Saint Eustatius And Saba" +2011-04-01,26.878,0.238,"Bonaire, Saint Eustatius And Saba" +2011-05-01,28.002,0.366,"Bonaire, Saint Eustatius And Saba" +2011-06-01,29.181,0.49,"Bonaire, Saint Eustatius And Saba" +2011-07-01,28.916999999999998,0.424,"Bonaire, Saint Eustatius And Saba" +2011-08-01,29.384,0.382,"Bonaire, Saint Eustatius And Saba" +2011-09-01,29.22,0.392,"Bonaire, Saint Eustatius And Saba" +2011-10-01,28.535,0.397,"Bonaire, Saint Eustatius And Saba" +2011-11-01,28.048,0.344,"Bonaire, Saint Eustatius And Saba" +2011-12-01,26.796,0.53,"Bonaire, Saint Eustatius And Saba" +2012-01-01,26.188000000000002,0.514,"Bonaire, Saint Eustatius And Saba" 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+1862-04-01,25.934,1.787,Cameroon +1862-05-01,,,Cameroon +1862-06-01,,,Cameroon +1862-07-01,,,Cameroon +1862-08-01,,,Cameroon +1862-09-01,,,Cameroon +1862-10-01,,,Cameroon +1862-11-01,,,Cameroon +1862-12-01,,,Cameroon +1863-01-01,,,Cameroon +1863-02-01,,,Cameroon +1863-03-01,,,Cameroon +1863-04-01,,,Cameroon +1863-05-01,,,Cameroon +1863-06-01,,,Cameroon +1863-07-01,,,Cameroon +1863-08-01,,,Cameroon +1863-09-01,,,Cameroon +1863-10-01,,,Cameroon +1863-11-01,,,Cameroon +1863-12-01,,,Cameroon +1864-01-01,,,Cameroon +1864-02-01,,,Cameroon +1864-03-01,,,Cameroon +1864-04-01,,,Cameroon +1864-05-01,,,Cameroon +1864-06-01,,,Cameroon +1864-07-01,,,Cameroon +1864-08-01,,,Cameroon +1864-09-01,,,Cameroon +1864-10-01,,,Cameroon +1864-11-01,,,Cameroon +1864-12-01,,,Cameroon +1865-01-01,,,Cameroon +1865-02-01,,,Cameroon +1865-03-01,,,Cameroon +1865-04-01,,,Cameroon +1865-05-01,,,Cameroon +1865-06-01,,,Cameroon +1865-07-01,,,Cameroon +1865-08-01,,,Cameroon +1865-09-01,,,Cameroon +1865-10-01,,,Cameroon 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+1869-06-01,,,Cameroon +1869-07-01,,,Cameroon +1869-08-01,,,Cameroon +1869-09-01,,,Cameroon +1869-10-01,,,Cameroon +1869-11-01,,,Cameroon +1869-12-01,,,Cameroon +1870-01-01,,,Cameroon +1870-02-01,,,Cameroon +1870-03-01,,,Cameroon +1870-04-01,,,Cameroon +1870-05-01,,,Cameroon +1870-06-01,,,Cameroon +1870-07-01,,,Cameroon +1870-08-01,,,Cameroon +1870-09-01,,,Cameroon +1870-10-01,,,Cameroon +1870-11-01,,,Cameroon +1870-12-01,,,Cameroon +1871-01-01,,,Cameroon +1871-02-01,,,Cameroon +1871-03-01,,,Cameroon +1871-04-01,,,Cameroon +1871-05-01,,,Cameroon +1871-06-01,,,Cameroon +1871-07-01,,,Cameroon +1871-08-01,,,Cameroon +1871-09-01,,,Cameroon +1871-10-01,,,Cameroon +1871-11-01,,,Cameroon +1871-12-01,,,Cameroon +1872-01-01,,,Cameroon +1872-02-01,,,Cameroon +1872-03-01,,,Cameroon +1872-04-01,,,Cameroon +1872-05-01,,,Cameroon +1872-06-01,,,Cameroon +1872-07-01,,,Cameroon +1872-08-01,,,Cameroon +1872-09-01,,,Cameroon +1872-10-01,,,Cameroon +1872-11-01,,,Cameroon +1872-12-01,,,Cameroon 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+1875-05-01,24.885,1.046,Cameroon +1875-06-01,24.136999999999997,1.082,Cameroon +1875-07-01,23.526,1.057,Cameroon +1875-08-01,23.089000000000002,1.069,Cameroon +1875-09-01,22.945,0.85,Cameroon +1875-10-01,22.967,0.89,Cameroon +1875-11-01,22.658,0.8290000000000001,Cameroon +1875-12-01,22.565,0.838,Cameroon +1876-01-01,22.75,1.389,Cameroon +1876-02-01,23.876,1.273,Cameroon +1876-03-01,24.723000000000003,1.3,Cameroon +1876-04-01,24.641,2.027,Cameroon +1876-05-01,24.416999999999998,0.778,Cameroon +1876-06-01,23.73,0.8690000000000001,Cameroon +1876-07-01,,,Cameroon +1876-08-01,,,Cameroon +1876-09-01,,,Cameroon +1876-10-01,23.840999999999998,0.991,Cameroon +1876-11-01,23.546999999999997,1.376,Cameroon +1876-12-01,22.880000000000006,1.5719999999999998,Cameroon +1877-01-01,23.574,1.038,Cameroon +1877-02-01,24.668000000000003,1.127,Cameroon +1877-03-01,25.9,0.966,Cameroon +1877-04-01,25.415,1.039,Cameroon +1877-05-01,24.741999999999997,0.8340000000000001,Cameroon 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(Europe) +1937-03-01,5.4190000000000005,0.309,France (Europe) +1937-04-01,9.682,0.28800000000000003,France (Europe) +1937-05-01,14.425999999999998,0.311,France (Europe) +1937-06-01,17.009,0.317,France (Europe) +1937-07-01,18.904,0.272,France (Europe) +1937-08-01,19.215999999999998,0.307,France (Europe) +1937-09-01,15.357999999999999,0.29600000000000004,France (Europe) +1937-10-01,11.350999999999999,0.207,France (Europe) +1937-11-01,6.457999999999999,0.35600000000000004,France (Europe) +1937-12-01,2.4850000000000003,0.34600000000000003,France (Europe) +1938-01-01,3.827,0.431,France (Europe) +1938-02-01,3.4539999999999997,0.246,France (Europe) +1938-03-01,8.817,0.298,France (Europe) +1938-04-01,7.706,0.406,France (Europe) +1938-05-01,11.552999999999999,0.201,France (Europe) +1938-06-01,17.270000000000003,0.18,France (Europe) +1938-07-01,17.904,0.287,France (Europe) +1938-08-01,18.246,0.325,France (Europe) +1938-09-01,15.474,0.208,France (Europe) +1938-10-01,11.114,0.261,France (Europe) 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+1750-09-01,15.104,2.216,Hungary +1750-10-01,7.403,2.272,Hungary +1750-11-01,,,Hungary +1750-12-01,-1.145,2.41,Hungary +1751-01-01,-2.403,2.423,Hungary +1751-02-01,-2.715,2.7060000000000004,Hungary +1751-03-01,6.223,2.414,Hungary +1751-04-01,9.309,2.199,Hungary +1751-05-01,,,Hungary +1751-06-01,19.658,2.397,Hungary +1751-07-01,21.155,2.207,Hungary +1751-08-01,20.605,2.168,Hungary +1751-09-01,14.735,2.149,Hungary +1751-10-01,,,Hungary +1751-11-01,,,Hungary +1751-12-01,,,Hungary +1752-01-01,-3.342,2.7880000000000003,Hungary +1752-02-01,,,Hungary +1752-03-01,5.8290000000000015,2.507,Hungary +1752-04-01,10.114999999999998,2.572,Hungary +1752-05-01,,,Hungary +1752-06-01,,,Hungary +1752-07-01,,,Hungary +1752-08-01,,,Hungary +1752-09-01,,,Hungary +1752-10-01,9.546,2.45,Hungary +1752-11-01,5.605,2.294,Hungary +1752-12-01,0.963,2.327,Hungary +1753-01-01,-3.8489999999999998,6.857,Hungary +1753-02-01,-1.537,3.449,Hungary +1753-03-01,5.902,3.047,Hungary +1753-04-01,10.736,6.227,Hungary +1753-05-01,15.9,2.261,Hungary +1753-06-01,20.404,2.608,Hungary +1753-07-01,21.373,2.187,Hungary +1753-08-01,19.407,6.182,Hungary +1753-09-01,16.142,2.015,Hungary +1753-10-01,10.442,2.169,Hungary +1753-11-01,3.215,2.968,Hungary +1753-12-01,-2.71,3.579,Hungary +1754-01-01,-3.529,3.017,Hungary +1754-02-01,-3.3589999999999995,4.896,Hungary +1754-03-01,2.202,3.755,Hungary +1754-04-01,11.185,6.944,Hungary +1754-05-01,16.331,3.525,Hungary +1754-06-01,19.849,1.932,Hungary +1754-07-01,20.099,2.846,Hungary +1754-08-01,19.71,4.413,Hungary +1754-09-01,15.245,3.4160000000000004,Hungary +1754-10-01,11.11,2.401,Hungary +1754-11-01,5.052,2.081,Hungary +1754-12-01,0.12199999999999997,2.64,Hungary +1755-01-01,-6.853999999999999,5.417000000000002,Hungary +1755-02-01,-4.792999999999998,4.738,Hungary +1755-03-01,3.99,4.503,Hungary +1755-04-01,13.024000000000001,4.359,Hungary +1755-05-01,16.309,3.062,Hungary +1755-06-01,21.588,3.936,Hungary +1755-07-01,20.893,2.272,Hungary +1755-08-01,18.902,3.225,Hungary +1755-09-01,15.119000000000002,1.904,Hungary +1755-10-01,9.437000000000001,6.902,Hungary +1755-11-01,4.159,6.769,Hungary +1755-12-01,-0.18600000000000003,7.454,Hungary +1756-01-01,-0.15399999999999991,4.742,Hungary +1756-02-01,2.414,4.3,Hungary +1756-03-01,5.233,6.832000000000002,Hungary +1756-04-01,10.524000000000001,2.631,Hungary +1756-05-01,16.164,5.324,Hungary +1756-06-01,21.335,3.692,Hungary +1756-07-01,21.193,2.596,Hungary +1756-08-01,19.151,8.413,Hungary +1756-09-01,16.840999999999998,3.342,Hungary +1756-10-01,9.299,3.154,Hungary +1756-11-01,2.18,4.043,Hungary +1756-12-01,-2.2059999999999995,2.72,Hungary +1757-01-01,-4.475,2.97,Hungary +1757-02-01,0.935,4.546,Hungary +1757-03-01,4.955,4.17,Hungary +1757-04-01,11.037999999999998,5.947,Hungary +1757-05-01,16.320999999999998,3.879,Hungary +1757-06-01,21.94,4.197,Hungary +1757-07-01,24.248,6.3039999999999985,Hungary +1757-08-01,20.739,4.258,Hungary +1757-09-01,15.380999999999998,7.501,Hungary +1757-10-01,6.334,9.95,Hungary +1757-11-01,3.5580000000000003,4.417,Hungary +1757-12-01,-2.881,9.761,Hungary +1758-01-01,-6.465,2.224,Hungary +1758-02-01,-1.29,3.281,Hungary +1758-03-01,4.334,4.522,Hungary +1758-04-01,9.198,7.841,Hungary +1758-05-01,15.562999999999999,5.433,Hungary +1758-06-01,19.787000000000006,5.15,Hungary +1758-07-01,19.433,7.582999999999998,Hungary +1758-08-01,19.048,6.355,Hungary +1758-09-01,13.654000000000002,3.269,Hungary +1758-10-01,6.926,10.907,Hungary +1758-11-01,4.2989999999999995,7.859,Hungary +1758-12-01,-0.229,5.73,Hungary +1759-01-01,0.2200000000000002,6.121,Hungary +1759-02-01,1.3030000000000002,7.631,Hungary +1759-03-01,4.984,3.545,Hungary +1759-04-01,9.394,5.8870000000000005,Hungary +1759-05-01,14.53,3.347,Hungary +1759-06-01,19.791,5.649,Hungary +1759-07-01,21.518,4.584,Hungary +1759-08-01,20.607,6.57,Hungary +1759-09-01,16.261,6.034,Hungary +1759-10-01,10.354000000000001,4.582,Hungary +1759-11-01,1.33,5.879,Hungary +1759-12-01,-3.859,7.928,Hungary +1760-01-01,-4.1099999999999985,4.3,Hungary +1760-02-01,-1.411,2.842,Hungary +1760-03-01,2.59,8.943,Hungary +1760-04-01,10.628,5.942,Hungary +1760-05-01,15.537,3.999,Hungary +1760-06-01,19.046000000000006,2.9130000000000003,Hungary +1760-07-01,20.537,4.038,Hungary +1760-08-01,20.074,7.2879999999999985,Hungary +1760-09-01,16.598,5.135,Hungary +1760-10-01,9.776,4.9110000000000005,Hungary +1760-11-01,3.8560000000000003,3.21,Hungary +1760-12-01,0.8440000000000001,3.26,Hungary +1761-01-01,-3.398,6.209,Hungary +1761-02-01,1.039,3.892,Hungary +1761-03-01,6.654,2.134,Hungary +1761-04-01,9.265,4.332,Hungary +1761-05-01,17.379,5.008,Hungary +1761-06-01,21.377,2.545,Hungary +1761-07-01,22.448,6.263,Hungary +1761-08-01,21.16,3.53,Hungary +1761-09-01,17.335,4.165,Hungary +1761-10-01,8.506,4.772,Hungary +1761-11-01,3.164,2.6010000000000004,Hungary +1761-12-01,-4.025,2.242,Hungary +1762-01-01,0.2690000000000001,2.639,Hungary +1762-02-01,0.708,8.087,Hungary +1762-03-01,2.2840000000000003,4.731,Hungary +1762-04-01,12.088,3.823,Hungary +1762-05-01,16.023,6.283,Hungary +1762-06-01,20.088,6.292000000000002,Hungary +1762-07-01,21.674,3.096,Hungary +1762-08-01,18.859,2.9760000000000004,Hungary +1762-09-01,15.199000000000002,7.697999999999999,Hungary +1762-10-01,7.879,4.0680000000000005,Hungary +1762-11-01,4.102,2.492,Hungary +1762-12-01,-3.0650000000000004,5.797999999999999,Hungary +1763-01-01,-5.095,6.127000000000002,Hungary +1763-02-01,1.4500000000000002,4.462,Hungary +1763-03-01,2.745,6.9689999999999985,Hungary +1763-04-01,9.608,2.39,Hungary +1763-05-01,14.602,3.133,Hungary +1763-06-01,18.311,5.712999999999999,Hungary +1763-07-01,21.833000000000002,2.635,Hungary +1763-08-01,20.821,3.385,Hungary +1763-09-01,14.472999999999999,3.084,Hungary +1763-10-01,7.34,5.9529999999999985,Hungary +1763-11-01,2.992,3.529,Hungary +1763-12-01,1.223,2.585,Hungary +1764-01-01,-0.6609999999999998,5.202000000000001,Hungary +1764-02-01,3.237,3.334,Hungary +1764-03-01,4.015,3.595,Hungary +1764-04-01,9.414,4.626,Hungary +1764-05-01,16.89,7.15,Hungary +1764-06-01,18.562,3.2260000000000004,Hungary +1764-07-01,20.886999999999997,5.227,Hungary +1764-08-01,18.613,2.134,Hungary +1764-09-01,14.558,5.055,Hungary +1764-10-01,9.209,3.149,Hungary +1764-11-01,3.842,3.628,Hungary +1764-12-01,1.042,4.125,Hungary +1765-01-01,0.8160000000000003,9.559,Hungary +1765-02-01,-1.4219999999999997,4.363,Hungary +1765-03-01,6.263999999999999,3.229,Hungary +1765-04-01,11.007,2.227,Hungary +1765-05-01,14.342,2.8080000000000003,Hungary +1765-06-01,18.636000000000006,3.31,Hungary +1765-07-01,18.656,4.159,Hungary +1765-08-01,19.883,2.974,Hungary +1765-09-01,15.257,2.227,Hungary +1765-10-01,10.022,1.888,Hungary +1765-11-01,4.648,3.859,Hungary +1765-12-01,-1.181,2.74,Hungary +1766-01-01,-5.462999999999999,6.877000000000002,Hungary +1766-02-01,-1.0259999999999998,4.433,Hungary +1766-03-01,5.226,3.051,Hungary +1766-04-01,11.268999999999998,3.537,Hungary +1766-05-01,16.482,2.888,Hungary +1766-06-01,19.766,6.7620000000000005,Hungary +1766-07-01,19.651,4.093,Hungary +1766-08-01,19.798,3.4530000000000003,Hungary +1766-09-01,16.207,3.66,Hungary +1766-10-01,9.155,2.485,Hungary +1766-11-01,5.857,5.118,Hungary +1766-12-01,-1.409,3.831,Hungary +1767-01-01,-8.434999999999999,6.529,Hungary +1767-02-01,0.8270000000000001,7.68,Hungary +1767-03-01,5.061,8.017999999999999,Hungary +1767-04-01,8.001,6.143,Hungary +1767-05-01,14.292000000000002,2.747,Hungary +1767-06-01,18.71,3.638,Hungary +1767-07-01,20.801,4.093,Hungary +1767-08-01,20.13,2.477,Hungary +1767-09-01,15.761,4.072,Hungary +1767-10-01,10.149,3.011,Hungary +1767-11-01,6.574,7.687,Hungary +1767-12-01,-2.763,5.2570000000000014,Hungary +1768-01-01,-7.073,11.536,Hungary +1768-02-01,-3.051,7.758999999999999,Hungary +1768-03-01,2.509,5.854,Hungary +1768-04-01,9.778,3.867,Hungary +1768-05-01,14.386,2.795,Hungary +1768-06-01,18.107000000000006,4.83,Hungary +1768-07-01,20.818,4.725,Hungary +1768-08-01,19.658,2.049,Hungary +1768-09-01,14.668,4.671,Hungary +1768-10-01,8.668,5.565,Hungary +1768-11-01,3.913,5.084,Hungary +1768-12-01,-0.98,6.709,Hungary +1769-01-01,-0.4149999999999998,5.62,Hungary +1769-02-01,-0.6709999999999999,3.761,Hungary +1769-03-01,4.118,5.739,Hungary +1769-04-01,10.709000000000001,6.2429999999999986,Hungary +1769-05-01,15.314,8.344,Hungary +1769-06-01,19.741,4.56,Hungary +1769-07-01,20.616999999999997,5.669,Hungary +1769-08-01,19.339000000000002,5.17,Hungary +1769-09-01,16.01,2.007,Hungary +1769-10-01,5.7410000000000005,3.032,Hungary +1769-11-01,3.77,1.976,Hungary +1769-12-01,-1.23,4.178,Hungary +1770-01-01,-2.931,6.862,Hungary +1770-02-01,-0.027000000000000024,3.56,Hungary +1770-03-01,1.4909999999999999,4.187,Hungary +1770-04-01,9.95,11.685,Hungary +1770-05-01,15.024,2.239,Hungary +1770-06-01,18.258000000000006,4.121,Hungary +1770-07-01,19.221,7.942,Hungary +1770-08-01,19.035,4.619,Hungary +1770-09-01,16.324,2.958,Hungary +1770-10-01,10.43,5.007,Hungary +1770-11-01,4.856,6.444,Hungary +1770-12-01,1.642,9.337,Hungary +1771-01-01,-0.8379999999999999,7.495,Hungary +1771-02-01,-3.642,4.284,Hungary +1771-03-01,1.398,7.771,Hungary +1771-04-01,6.901999999999999,7.112,Hungary +1771-05-01,16.782,10.505999999999998,Hungary +1771-06-01,18.863000000000003,3.085,Hungary +1771-07-01,20.144000000000002,2.047,Hungary +1771-08-01,19.533,2.721,Hungary +1771-09-01,16.092,2.262,Hungary +1771-10-01,10.351,2.291,Hungary +1771-11-01,1.5299999999999998,4.783,Hungary +1771-12-01,0.214,3.661,Hungary +1772-01-01,-0.623,7.961,Hungary +1772-02-01,1.327,9.753,Hungary +1772-03-01,5.315,4.444,Hungary +1772-04-01,10.640999999999998,4.433,Hungary +1772-05-01,13.818,2.092,Hungary +1772-06-01,19.955,4.794,Hungary +1772-07-01,21.585,3.289,Hungary +1772-08-01,20.315,4.589,Hungary +1772-09-01,16.52,3.52,Hungary +1772-10-01,10.859000000000002,2.605,Hungary +1772-11-01,7.051,5.608,Hungary +1772-12-01,1.494,3.488,Hungary +1773-01-01,-1.64,2.966,Hungary +1773-02-01,-1.161,3.305,Hungary +1773-03-01,3.449,2.383,Hungary +1773-04-01,10.463,3.643,Hungary +1773-05-01,17.547,3.661,Hungary +1773-06-01,18.148,2.085,Hungary +1773-07-01,19.727,2.4530000000000003,Hungary +1773-08-01,19.209,2.366,Hungary +1773-09-01,16.964000000000002,2.548,Hungary +1773-10-01,11.414000000000001,2.472,Hungary +1773-11-01,3.803,6.617000000000001,Hungary +1773-12-01,2.6910000000000003,5.698,Hungary +1774-01-01,-2.857,8.22,Hungary +1774-02-01,0.738,7.385,Hungary +1774-03-01,5.662999999999999,3.0380000000000003,Hungary +1774-04-01,12.565,5.489,Hungary +1774-05-01,17.225,2.295,Hungary +1774-06-01,20.546000000000006,6.657,Hungary +1774-07-01,21.673000000000002,7.039,Hungary +1774-08-01,20.795,2.948,Hungary +1774-09-01,15.232,3.15,Hungary +1774-10-01,9.31,4.715,Hungary +1774-11-01,-0.06500000000000039,6.807,Hungary +1774-12-01,-2.9560000000000004,5.555,Hungary +1775-01-01,-2.317,5.063,Hungary +1775-02-01,2.892,5.1389999999999985,Hungary +1775-03-01,6.4830000000000005,2.721,Hungary +1775-04-01,8.620999999999999,1.8,Hungary +1775-05-01,14.013,3.753,Hungary +1775-06-01,20.268,5.162000000000001,Hungary +1775-07-01,21.295,2.102,Hungary +1775-08-01,22.183000000000003,1.609,Hungary +1775-09-01,17.104,3.507,Hungary +1775-10-01,11.445,3.1010000000000004,Hungary +1775-11-01,5.313,7.039,Hungary +1775-12-01,-1.216,2.048,Hungary +1776-01-01,-6.412999999999999,6.0470000000000015,Hungary +1776-02-01,1.865,3.956,Hungary +1776-03-01,5.6,3.542,Hungary +1776-04-01,9.616,1.774,Hungary +1776-05-01,14.012,3.958,Hungary +1776-06-01,19.048,3.802,Hungary +1776-07-01,20.616,2.84,Hungary +1776-08-01,20.616,4.686,Hungary +1776-09-01,15.058,3.212,Hungary +1776-10-01,8.54,3.512,Hungary +1776-11-01,3.318,6.141,Hungary +1776-12-01,-1.507,3.812,Hungary +1777-01-01,-2.903,4.008,Hungary +1777-02-01,-0.42,3.953,Hungary +1777-03-01,5.334,2.261,Hungary +1777-04-01,8.128,1.963,Hungary +1777-05-01,15.865,3.78,Hungary +1777-06-01,18.713,3.024,Hungary 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+1964-07-01,20.569000000000003,0.244,Hungary +1964-08-01,18.721,0.328,Hungary +1964-09-01,15.485,0.253,Hungary +1964-10-01,10.413,0.16899999999999998,Hungary +1964-11-01,6.101,0.247,Hungary +1964-12-01,0.033999999999999975,0.269,Hungary +1965-01-01,-0.5379999999999999,0.457,Hungary +1965-02-01,-3.743,0.425,Hungary +1965-03-01,4.9430000000000005,0.445,Hungary +1965-04-01,8.764,0.172,Hungary +1965-05-01,13.843,0.384,Hungary +1965-06-01,18.547,0.418,Hungary +1965-07-01,19.786,0.245,Hungary +1965-08-01,17.897000000000002,0.215,Hungary +1965-09-01,16.479,0.243,Hungary +1965-10-01,9.05,0.318,Hungary +1965-11-01,2.635,0.401,Hungary +1965-12-01,1.409,0.419,Hungary +1966-01-01,-4.324999999999998,0.381,Hungary +1966-02-01,5.621,0.434,Hungary +1966-03-01,4.875,0.29600000000000004,Hungary +1966-04-01,12.189,0.236,Hungary +1966-05-01,15.45,0.254,Hungary +1966-06-01,18.512,0.247,Hungary +1966-07-01,19.624000000000002,0.23,Hungary +1966-08-01,19.243,0.173,Hungary +1966-09-01,15.774,0.224,Hungary 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+1984-01-01,-0.502,0.185,Japan +1984-02-01,-0.975,0.202,Japan +1984-03-01,2.215,0.219,Japan +1984-04-01,8.857999999999997,0.157,Japan +1984-05-01,14.3,0.12,Japan +1984-06-01,20.019000000000002,0.22,Japan +1984-07-01,23.935,0.298,Japan +1984-08-01,25.569000000000003,0.239,Japan +1984-09-01,20.33,0.166,Japan +1984-10-01,13.883,0.189,Japan +1984-11-01,8.989999999999998,0.166,Japan +1984-12-01,3.297,0.151,Japan +1985-01-01,-0.9269999999999998,0.106,Japan +1985-02-01,2.2569999999999997,0.136,Japan +1985-03-01,4.989,0.125,Japan +1985-04-01,10.864999999999998,0.245,Japan +1985-05-01,15.79,0.181,Japan +1985-06-01,18.155,0.332,Japan +1985-07-01,23.295,0.212,Japan +1985-08-01,26.008000000000003,0.21600000000000005,Japan +1985-09-01,20.965999999999998,0.249,Japan +1985-10-01,14.764,0.146,Japan +1985-11-01,8.921999999999997,0.172,Japan +1985-12-01,2.292,0.261,Japan +1986-01-01,-0.4119999999999999,0.238,Japan +1986-02-01,-0.355,0.156,Japan +1986-03-01,4.274,0.189,Japan +1986-04-01,10.463,0.159,Japan +1986-05-01,14.803,0.17,Japan +1986-06-01,18.554000000000002,0.211,Japan +1986-07-01,21.515,0.127,Japan +1986-08-01,24.569000000000003,0.213,Japan +1986-09-01,20.927,0.224,Japan +1986-10-01,13.187,0.099,Japan +1986-11-01,8.315999999999999,0.17800000000000002,Japan +1986-12-01,4.456,0.08199999999999999,Japan +1987-01-01,1.242,0.213,Japan +1987-02-01,2.0980000000000003,0.185,Japan +1987-03-01,5.075,0.12,Japan +1987-04-01,10.249,0.091,Japan +1987-05-01,15.470999999999998,0.133,Japan +1987-06-01,19.457,0.181,Japan +1987-07-01,23.432,0.184,Japan +1987-08-01,23.993000000000002,0.223,Japan +1987-09-01,20.456,0.192,Japan +1987-10-01,15.669,0.168,Japan +1987-11-01,9.251,0.154,Japan +1987-12-01,4.133,0.122,Japan +1988-01-01,2.52,0.181,Japan +1988-02-01,0.458,0.162,Japan +1988-03-01,4.427,0.098,Japan +1988-04-01,10.207999999999998,0.13699999999999998,Japan +1988-05-01,14.794,0.136,Japan +1988-06-01,19.164,0.219,Japan +1988-07-01,21.187,0.196,Japan +1988-08-01,24.609,0.119,Japan +1988-09-01,20.724,0.18,Japan +1988-10-01,14.18,0.17800000000000002,Japan +1988-11-01,7.424999999999999,0.134,Japan +1988-12-01,3.9160000000000004,0.13699999999999998,Japan +1989-01-01,3.5180000000000002,0.246,Japan +1989-02-01,3.5789999999999997,0.133,Japan +1989-03-01,6.112,0.227,Japan +1989-04-01,11.151,0.179,Japan +1989-05-01,14.775,0.154,Japan +1989-06-01,17.953,0.262,Japan +1989-07-01,22.715999999999998,0.205,Japan +1989-08-01,24.655,0.261,Japan +1989-09-01,21.188000000000002,0.161,Japan +1989-10-01,14.485,0.202,Japan +1989-11-01,10.308,0.099,Japan +1989-12-01,4.501,0.081,Japan +1990-01-01,0.7869999999999999,0.199,Japan +1990-02-01,4.543,0.182,Japan +1990-03-01,6.7010000000000005,0.26,Japan +1990-04-01,10.890999999999998,0.101,Japan +1990-05-01,15.677,0.149,Japan +1990-06-01,20.377,0.28300000000000003,Japan +1990-07-01,23.708000000000002,0.179,Japan +1990-08-01,25.64,0.197,Japan +1990-09-01,21.750999999999998,0.24,Japan +1990-10-01,15.78,0.228,Japan +1990-11-01,11.483999999999998,0.22,Japan +1990-12-01,5.747999999999999,0.195,Japan +1991-01-01,2.316,0.138,Japan +1991-02-01,1.6969999999999998,0.197,Japan +1991-03-01,5.656000000000001,0.162,Japan +1991-04-01,11.309000000000001,0.11,Japan +1991-05-01,15.499,0.182,Japan +1991-06-01,20.462,0.139,Japan +1991-07-01,22.883000000000003,0.263,Japan +1991-08-01,23.281999999999996,0.165,Japan +1991-09-01,21.1,0.163,Japan +1991-10-01,15.206,0.119,Japan +1991-11-01,9.097000000000001,0.103,Japan +1991-12-01,4.891,0.174,Japan +1992-01-01,2.676,0.249,Japan +1992-02-01,2.367,0.213,Japan +1992-03-01,6.0,0.182,Japan +1992-04-01,10.839,0.244,Japan +1992-05-01,14.249,0.13699999999999998,Japan +1992-06-01,18.109,0.242,Japan +1992-07-01,22.843000000000004,0.245,Japan +1992-08-01,23.78,0.184,Japan +1992-09-01,20.059,0.273,Japan +1992-10-01,14.665999999999999,0.124,Japan +1992-11-01,9.387,0.21600000000000005,Japan +1992-12-01,4.836,0.34600000000000003,Japan +1993-01-01,2.798,0.196,Japan +1993-02-01,3.185,0.161,Japan +1993-03-01,5.086,0.168,Japan +1993-04-01,9.636000000000001,0.19,Japan +1993-05-01,14.63,0.287,Japan +1993-06-01,18.187,0.168,Japan +1993-07-01,20.766,0.138,Japan +1993-08-01,22.171,0.204,Japan +1993-09-01,19.637,0.16,Japan +1993-10-01,13.98,0.13,Japan +1993-11-01,10.305,0.161,Japan +1993-12-01,4.281000000000001,0.133,Japan +1994-01-01,1.296,0.235,Japan +1994-02-01,2.5069999999999997,0.187,Japan +1994-03-01,4.268,0.121,Japan +1994-04-01,11.411,0.17,Japan +1994-05-01,16.314,0.16399999999999998,Japan +1994-06-01,19.189,0.193,Japan +1994-07-01,24.888,0.168,Japan +1994-08-01,26.340999999999998,0.203,Japan +1994-09-01,21.901999999999997,0.179,Japan +1994-10-01,16.092,0.099,Japan +1994-11-01,10.114,0.16899999999999998,Japan +1994-12-01,4.435,0.175,Japan +1995-01-01,1.464,0.139,Japan +1995-02-01,2.174,0.202,Japan +1995-03-01,5.4350000000000005,0.16899999999999998,Japan +1995-04-01,10.678999999999998,0.187,Japan +1995-05-01,15.526,0.16699999999999998,Japan +1995-06-01,17.907999999999994,0.171,Japan +1995-07-01,23.441,0.159,Japan +1995-08-01,25.213,0.175,Japan +1995-09-01,20.12,0.247,Japan +1995-10-01,15.897,0.102,Japan +1995-11-01,8.537999999999998,0.117,Japan +1995-12-01,3.31,0.117,Japan +1996-01-01,1.545,0.185,Japan +1996-02-01,1.3090000000000002,0.136,Japan +1996-03-01,4.92,0.157,Japan +1996-04-01,8.684,0.184,Japan +1996-05-01,14.42,0.149,Japan +1996-06-01,18.97,0.192,Japan +1996-07-01,23.01,0.18600000000000005,Japan +1996-08-01,23.773000000000003,0.14400000000000002,Japan +1996-09-01,20.075,0.17600000000000002,Japan +1996-10-01,14.765999999999998,0.139,Japan +1996-11-01,9.263,0.115,Japan +1996-12-01,4.259,0.114,Japan +1997-01-01,1.896,0.182,Japan +1997-02-01,2.506,0.197,Japan +1997-03-01,5.979,0.22,Japan +1997-04-01,10.93,0.095,Japan +1997-05-01,15.409,0.168,Japan +1997-06-01,19.195,0.22,Japan +1997-07-01,23.307,0.196,Japan +1997-08-01,23.822,0.227,Japan +1997-09-01,20.049,0.16699999999999998,Japan +1997-10-01,14.203,0.18600000000000005,Japan +1997-11-01,10.720999999999998,0.095,Japan +1997-12-01,5.0120000000000005,0.128,Japan +1998-01-01,0.929,0.194,Japan +1998-02-01,3.016,0.185,Japan +1998-03-01,6.275,0.166,Japan +1998-04-01,12.973999999999998,0.17,Japan +1998-05-01,16.985,0.113,Japan +1998-06-01,18.784000000000002,0.17800000000000002,Japan +1998-07-01,22.974,0.166,Japan +1998-08-01,24.285999999999998,0.104,Japan +1998-09-01,21.971,0.16399999999999998,Japan +1998-10-01,16.727999999999998,0.13699999999999998,Japan +1998-11-01,9.246,0.114,Japan +1998-12-01,4.769,0.142,Japan +1999-01-01,2.016,0.138,Japan +1999-02-01,1.8630000000000002,0.17800000000000002,Japan +1999-03-01,5.899,0.126,Japan +1999-04-01,10.678,0.21,Japan +1999-05-01,15.902999999999999,0.14400000000000002,Japan +1999-06-01,19.809,0.17600000000000002,Japan +1999-07-01,23.062,0.183,Japan +1999-08-01,25.546999999999997,0.13,Japan +1999-09-01,22.698,0.17600000000000002,Japan +1999-10-01,15.700999999999999,0.165,Japan 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+1869-10-01,27.107000000000006,1.754,Kuwait +1869-11-01,20.159000000000002,1.599,Kuwait +1869-12-01,13.839,1.757,Kuwait +1870-01-01,13.616,1.7819999999999998,Kuwait +1870-02-01,14.845,1.854,Kuwait +1870-03-01,20.042,1.855,Kuwait +1870-04-01,22.645,2.069,Kuwait +1870-05-01,31.308000000000003,1.722,Kuwait +1870-06-01,33.694,1.733,Kuwait +1870-07-01,35.43700000000001,1.72,Kuwait +1870-08-01,35.561,1.705,Kuwait +1870-09-01,32.777,1.607,Kuwait +1870-10-01,25.761999999999997,1.714,Kuwait +1870-11-01,19.517,2.048,Kuwait +1870-12-01,14.386,1.766,Kuwait +1871-01-01,13.002,1.828,Kuwait +1871-02-01,13.706,2.158,Kuwait +1871-03-01,18.222,1.903,Kuwait +1871-04-01,24.739,1.696,Kuwait +1871-05-01,30.938,1.689,Kuwait +1871-06-01,33.417,1.702,Kuwait +1871-07-01,35.155,1.992,Kuwait +1871-08-01,34.866,1.902,Kuwait +1871-09-01,31.772,1.658,Kuwait +1871-10-01,26.148000000000003,2.005,Kuwait +1871-11-01,18.939,1.873,Kuwait +1871-12-01,15.187,1.591,Kuwait +1872-01-01,12.449000000000002,2.267,Kuwait +1872-02-01,13.909,1.915,Kuwait +1872-03-01,19.179000000000002,1.755,Kuwait +1872-04-01,24.378,1.855,Kuwait +1872-05-01,29.680999999999997,1.631,Kuwait +1872-06-01,33.853,1.693,Kuwait +1872-07-01,35.933,1.908,Kuwait +1872-08-01,35.613,1.781,Kuwait +1872-09-01,32.981,1.66,Kuwait +1872-10-01,26.539,1.611,Kuwait +1872-11-01,19.756,1.754,Kuwait +1872-12-01,13.833,2.165,Kuwait +1873-01-01,12.981,1.95,Kuwait +1873-02-01,15.378,1.803,Kuwait +1873-03-01,19.057000000000002,1.628,Kuwait +1873-04-01,25.15,1.75,Kuwait +1873-05-01,31.818,1.859,Kuwait +1873-06-01,34.042,1.644,Kuwait +1873-07-01,35.400999999999996,1.6369999999999998,Kuwait +1873-08-01,35.181000000000004,1.598,Kuwait +1873-09-01,32.393,1.75,Kuwait +1873-10-01,26.918000000000006,1.705,Kuwait +1873-11-01,19.761,1.558,Kuwait +1873-12-01,13.815,1.7480000000000002,Kuwait +1874-01-01,12.593,1.884,Kuwait +1874-02-01,14.163,1.715,Kuwait +1874-03-01,16.162,1.82,Kuwait +1874-04-01,24.259,1.651,Kuwait +1874-05-01,31.645,1.69,Kuwait +1874-06-01,33.766,1.743,Kuwait +1874-07-01,35.32100000000001,1.595,Kuwait +1874-08-01,34.804,1.998,Kuwait +1874-09-01,31.753,1.631,Kuwait +1874-10-01,26.451,1.589,Kuwait +1874-11-01,21.478,1.692,Kuwait +1874-12-01,15.71,2.132,Kuwait +1875-01-01,11.216,1.768,Kuwait +1875-02-01,15.482000000000001,1.68,Kuwait +1875-03-01,18.222,1.713,Kuwait +1875-04-01,23.839000000000002,1.597,Kuwait +1875-05-01,29.961,1.747,Kuwait +1875-06-01,34.15,1.69,Kuwait +1875-07-01,35.41900000000001,1.536,Kuwait +1875-08-01,34.944,1.629,Kuwait +1875-09-01,31.714000000000002,1.845,Kuwait +1875-10-01,26.173,1.629,Kuwait +1875-11-01,18.989,1.584,Kuwait +1875-12-01,13.725999999999999,1.555,Kuwait +1876-01-01,11.425,1.699,Kuwait +1876-02-01,13.326,1.676,Kuwait +1876-03-01,19.255,2.382,Kuwait +1876-04-01,24.644000000000002,1.713,Kuwait +1876-05-01,31.426,2.014,Kuwait +1876-06-01,34.065,1.5830000000000002,Kuwait +1876-07-01,35.692,1.617,Kuwait +1876-08-01,34.946999999999996,1.681,Kuwait +1876-09-01,32.244,1.533,Kuwait 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+1890-05-01,29.897,1.08,Kuwait +1890-06-01,34.731,1.937,Kuwait +1890-07-01,35.079,1.803,Kuwait +1890-08-01,35.058,2.517,Kuwait +1890-09-01,32.601,1.5,Kuwait +1890-10-01,26.51,1.165,Kuwait +1890-11-01,19.483,1.16,Kuwait +1890-12-01,14.142,1.3630000000000002,Kuwait +1891-01-01,13.267999999999999,1.244,Kuwait +1891-02-01,14.671,1.391,Kuwait +1891-03-01,18.947,1.03,Kuwait +1891-04-01,24.653000000000002,1.297,Kuwait +1891-05-01,29.648000000000003,1.757,Kuwait +1891-06-01,34.279,1.134,Kuwait +1891-07-01,35.453,0.807,Kuwait +1891-08-01,35.329,0.946,Kuwait +1891-09-01,32.466,1.101,Kuwait +1891-10-01,27.616,1.305,Kuwait +1891-11-01,20.037,0.823,Kuwait +1891-12-01,14.987,1.005,Kuwait +1892-01-01,12.635,0.757,Kuwait +1892-02-01,14.966,1.529,Kuwait +1892-03-01,20.714000000000002,3.439,Kuwait +1892-04-01,24.394000000000002,1.3119999999999998,Kuwait +1892-05-01,30.166999999999998,1.3230000000000002,Kuwait +1892-06-01,34.760999999999996,0.763,Kuwait +1892-07-01,35.661,0.934,Kuwait 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+1894-10-01,26.037000000000006,1.121,Kuwait +1894-11-01,19.826,0.906,Kuwait +1894-12-01,13.752,1.158,Kuwait +1895-01-01,11.427999999999999,1.812,Kuwait +1895-02-01,16.534000000000002,1.071,Kuwait +1895-03-01,17.685,1.081,Kuwait +1895-04-01,24.449,0.971,Kuwait +1895-05-01,28.967,0.895,Kuwait +1895-06-01,34.708,1.177,Kuwait +1895-07-01,35.154,0.944,Kuwait +1895-08-01,35.199,1.619,Kuwait +1895-09-01,31.373,0.7120000000000001,Kuwait +1895-10-01,25.99,1.338,Kuwait +1895-11-01,18.542,0.7559999999999999,Kuwait +1895-12-01,15.635000000000002,0.879,Kuwait +1896-01-01,13.877,1.279,Kuwait +1896-02-01,12.557,2.202,Kuwait +1896-03-01,18.177,0.775,Kuwait +1896-04-01,23.561,1.366,Kuwait +1896-05-01,29.005000000000006,1.286,Kuwait +1896-06-01,33.653,0.823,Kuwait +1896-07-01,35.73600000000001,1.241,Kuwait +1896-08-01,35.031,1.311,Kuwait +1896-09-01,33.036,1.654,Kuwait +1896-10-01,25.589,1.854,Kuwait +1896-11-01,18.379,0.823,Kuwait +1896-12-01,15.213,0.747,Kuwait +1897-01-01,12.259,0.848,Kuwait +1897-02-01,13.257,0.605,Kuwait +1897-03-01,17.700999999999997,0.528,Kuwait +1897-04-01,24.434,0.899,Kuwait +1897-05-01,30.405,0.855,Kuwait +1897-06-01,34.063,1.0659999999999998,Kuwait +1897-07-01,35.80900000000001,0.8029999999999999,Kuwait +1897-08-01,34.718,0.655,Kuwait +1897-09-01,32.611,0.88,Kuwait +1897-10-01,27.108,0.942,Kuwait +1897-11-01,18.591,1.132,Kuwait +1897-12-01,13.595,1.073,Kuwait +1898-01-01,9.837,0.61,Kuwait +1898-02-01,13.815,1.567,Kuwait +1898-03-01,17.343999999999998,0.8079999999999999,Kuwait +1898-04-01,24.071,1.358,Kuwait +1898-05-01,29.691000000000006,0.924,Kuwait +1898-06-01,33.951,0.835,Kuwait +1898-07-01,36.247,1.136,Kuwait +1898-08-01,35.75,0.932,Kuwait +1898-09-01,32.736,0.901,Kuwait +1898-10-01,27.273000000000003,0.912,Kuwait +1898-11-01,19.41,1.406,Kuwait +1898-12-01,12.789,1.056,Kuwait +1899-01-01,11.585,0.645,Kuwait +1899-02-01,14.621,1.021,Kuwait +1899-03-01,18.735,1.089,Kuwait +1899-04-01,25.39,0.8590000000000001,Kuwait +1899-05-01,30.497,0.7090000000000001,Kuwait +1899-06-01,34.462,0.966,Kuwait +1899-07-01,36.601000000000006,0.988,Kuwait +1899-08-01,35.976,1.012,Kuwait +1899-09-01,32.744,0.978,Kuwait +1899-10-01,27.541,0.898,Kuwait +1899-11-01,19.625,1.052,Kuwait +1899-12-01,13.055,1.041,Kuwait +1900-01-01,11.672,1.253,Kuwait +1900-02-01,14.947000000000001,0.807,Kuwait +1900-03-01,20.209,0.635,Kuwait +1900-04-01,25.767,0.698,Kuwait +1900-05-01,29.956,0.883,Kuwait +1900-06-01,34.306999999999995,1.704,Kuwait +1900-07-01,35.88800000000001,2.189,Kuwait +1900-08-01,35.313,1.057,Kuwait +1900-09-01,32.299,0.865,Kuwait +1900-10-01,27.61,0.95,Kuwait +1900-11-01,18.662,0.5579999999999999,Kuwait +1900-12-01,14.534,0.946,Kuwait +1901-01-01,11.627,0.6729999999999999,Kuwait +1901-02-01,15.826,0.963,Kuwait +1901-03-01,20.919,0.779,Kuwait +1901-04-01,26.358,1.6769999999999998,Kuwait +1901-05-01,31.116999999999997,0.987,Kuwait +1901-06-01,35.537,1.6840000000000002,Kuwait +1901-07-01,36.899,1.3219999999999998,Kuwait +1901-08-01,35.88,0.922,Kuwait +1901-09-01,33.33,0.679,Kuwait +1901-10-01,26.660999999999998,1.01,Kuwait +1901-11-01,20.273,0.6579999999999999,Kuwait +1901-12-01,15.067,0.901,Kuwait +1902-01-01,13.038,0.698,Kuwait +1902-02-01,16.494999999999994,0.5770000000000001,Kuwait +1902-03-01,20.426,0.805,Kuwait +1902-04-01,25.148000000000003,0.871,Kuwait +1902-05-01,31.636999999999997,0.982,Kuwait +1902-06-01,35.056999999999995,0.4920000000000001,Kuwait +1902-07-01,35.779,0.557,Kuwait +1902-08-01,36.275999999999996,0.746,Kuwait +1902-09-01,32.151,0.399,Kuwait +1902-10-01,25.366,0.732,Kuwait +1902-11-01,20.209,1.216,Kuwait +1902-12-01,14.115,1.166,Kuwait +1903-01-01,10.661,0.635,Kuwait +1903-02-01,13.649000000000001,0.69,Kuwait +1903-03-01,17.586,0.662,Kuwait +1903-04-01,23.968000000000004,0.451,Kuwait +1903-05-01,30.78,0.402,Kuwait +1903-06-01,34.163000000000004,1.406,Kuwait +1903-07-01,35.56200000000001,0.831,Kuwait +1903-08-01,37.045,1.139,Kuwait +1903-09-01,31.988000000000003,0.942,Kuwait 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+1917-01-01,13.109000000000002,1.56,Kuwait +1917-02-01,15.425999999999998,0.7120000000000001,Kuwait +1917-03-01,18.716,1.067,Kuwait +1917-04-01,26.793000000000003,1.455,Kuwait +1917-05-01,30.398000000000003,0.684,Kuwait +1917-06-01,33.608000000000004,0.8859999999999999,Kuwait +1917-07-01,37.14,0.836,Kuwait +1917-08-01,35.604,0.922,Kuwait +1917-09-01,32.379,0.6559999999999999,Kuwait +1917-10-01,25.566000000000006,0.47,Kuwait +1917-11-01,20.63,0.987,Kuwait +1917-12-01,12.66,0.669,Kuwait +1918-01-01,12.255999999999998,1.103,Kuwait +1918-02-01,14.186,0.7090000000000001,Kuwait +1918-03-01,17.456999999999994,0.635,Kuwait +1918-04-01,22.45,0.895,Kuwait +1918-05-01,29.651,0.445,Kuwait +1918-06-01,32.913000000000004,0.838,Kuwait +1918-07-01,35.575,0.74,Kuwait +1918-08-01,34.653,0.515,Kuwait +1918-09-01,32.523,1.123,Kuwait +1918-10-01,27.646,0.547,Kuwait +1918-11-01,20.462,0.552,Kuwait +1918-12-01,14.285,0.392,Kuwait +1919-01-01,14.283,0.934,Kuwait +1919-02-01,15.104000000000001,0.998,Kuwait +1919-03-01,19.237000000000002,0.757,Kuwait +1919-04-01,25.122,0.922,Kuwait +1919-05-01,29.570999999999998,0.9,Kuwait +1919-06-01,33.397,1.527,Kuwait +1919-07-01,35.762,0.8290000000000001,Kuwait +1919-08-01,35.028,0.897,Kuwait +1919-09-01,32.121,0.626,Kuwait +1919-10-01,27.648000000000003,0.5710000000000001,Kuwait +1919-11-01,20.94,1.005,Kuwait +1919-12-01,14.298,0.58,Kuwait +1920-01-01,12.969000000000001,2.068,Kuwait +1920-02-01,11.259,1.033,Kuwait +1920-03-01,19.147000000000002,1.3259999999999998,Kuwait +1920-04-01,24.165,1.161,Kuwait +1920-05-01,30.090999999999998,1.022,Kuwait +1920-06-01,34.184,0.975,Kuwait +1920-07-01,34.265,2.965,Kuwait +1920-08-01,35.783,0.982,Kuwait +1920-09-01,32.459,0.589,Kuwait +1920-10-01,28.735,2.342,Kuwait +1920-11-01,19.092,0.7140000000000001,Kuwait +1920-12-01,11.095,0.611,Kuwait +1921-01-01,11.716,0.7709999999999999,Kuwait +1921-02-01,13.902000000000001,1.062,Kuwait +1921-03-01,18.798,1.633,Kuwait +1921-04-01,25.618000000000002,1.406,Kuwait +1921-05-01,29.315,1.15,Kuwait +1921-06-01,33.54,1.989,Kuwait +1921-07-01,37.641999999999996,1.992,Kuwait +1921-08-01,35.429,0.753,Kuwait +1921-09-01,32.315,0.642,Kuwait +1921-10-01,26.151999999999997,0.648,Kuwait +1921-11-01,19.174,1.494,Kuwait +1921-12-01,13.3,0.7659999999999999,Kuwait +1922-01-01,13.414000000000001,0.732,Kuwait +1922-02-01,14.204999999999998,1.392,Kuwait +1922-03-01,18.455,0.8590000000000001,Kuwait +1922-04-01,24.515,0.6829999999999999,Kuwait +1922-05-01,30.397,0.7759999999999999,Kuwait +1922-06-01,34.962,0.596,Kuwait +1922-07-01,36.564,0.619,Kuwait +1922-08-01,35.731,0.665,Kuwait +1922-09-01,31.354,0.799,Kuwait +1922-10-01,27.177,0.7759999999999999,Kuwait +1922-11-01,22.298000000000002,1.569,Kuwait +1922-12-01,14.167,0.63,Kuwait +1923-01-01,12.194,0.97,Kuwait +1923-02-01,14.206,0.513,Kuwait +1923-03-01,19.39,0.8290000000000001,Kuwait +1923-04-01,22.585,0.737,Kuwait +1923-05-01,30.49,0.501,Kuwait +1923-06-01,34.295,0.7240000000000001,Kuwait +1923-07-01,35.982,0.655,Kuwait +1923-08-01,34.269,0.914,Kuwait +1923-09-01,32.245,0.774,Kuwait +1923-10-01,26.975,0.475,Kuwait +1923-11-01,19.137,0.525,Kuwait +1923-12-01,15.622,1.185,Kuwait +1924-01-01,14.085999999999999,1.078,Kuwait +1924-02-01,14.352,0.619,Kuwait +1924-03-01,21.105,0.8190000000000001,Kuwait +1924-04-01,24.45,0.703,Kuwait +1924-05-01,29.829,2.073,Kuwait +1924-06-01,34.613,0.6990000000000001,Kuwait +1924-07-01,36.615,0.636,Kuwait +1924-08-01,35.524,0.68,Kuwait +1924-09-01,32.841,1.2009999999999998,Kuwait +1924-10-01,27.764000000000006,0.7170000000000001,Kuwait +1924-11-01,21.226,1.111,Kuwait +1924-12-01,13.383,0.7140000000000001,Kuwait +1925-01-01,9.062,0.994,Kuwait +1925-02-01,12.011,0.532,Kuwait +1925-03-01,21.779,1.7919999999999998,Kuwait +1925-04-01,24.918000000000003,0.818,Kuwait +1925-05-01,30.838,0.991,Kuwait +1925-06-01,33.449,1.745,Kuwait +1925-07-01,35.336999999999996,0.7390000000000001,Kuwait +1925-08-01,35.753,0.684,Kuwait +1925-09-01,32.795,0.5539999999999999,Kuwait +1925-10-01,27.029,0.496,Kuwait +1925-11-01,19.584,0.45,Kuwait +1925-12-01,15.549,0.6829999999999999,Kuwait +1926-01-01,13.696,0.654,Kuwait +1926-02-01,15.254000000000001,0.52,Kuwait +1926-03-01,19.296,0.5329999999999999,Kuwait +1926-04-01,23.24,0.723,Kuwait +1926-05-01,30.861,0.787,Kuwait +1926-06-01,34.361,0.596,Kuwait +1926-07-01,35.35800000000001,0.87,Kuwait +1926-08-01,35.408,0.782,Kuwait +1926-09-01,30.687,1.126,Kuwait +1926-10-01,26.64,0.6659999999999999,Kuwait +1926-11-01,20.246,0.456,Kuwait +1926-12-01,14.079,0.8690000000000001,Kuwait +1927-01-01,12.414000000000001,0.7759999999999999,Kuwait +1927-02-01,13.356,0.677,Kuwait +1927-03-01,18.174,0.655,Kuwait +1927-04-01,23.268,0.485,Kuwait +1927-05-01,31.609,0.938,Kuwait +1927-06-01,35.486999999999995,1.08,Kuwait +1927-07-01,36.154,0.5539999999999999,Kuwait +1927-08-01,35.615,1.041,Kuwait +1927-09-01,33.149,0.5429999999999999,Kuwait +1927-10-01,27.908,0.762,Kuwait 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+1944-05-01,23.166999999999998,0.248,Mexico +1944-06-01,24.834,0.185,Mexico +1944-07-01,25.511999999999997,0.162,Mexico +1944-08-01,25.065,0.168,Mexico +1944-09-01,23.42,0.218,Mexico +1944-10-01,20.71,0.243,Mexico +1944-11-01,17.849999999999994,0.253,Mexico +1944-12-01,14.899000000000001,0.244,Mexico +1945-01-01,15.593,0.255,Mexico +1945-02-01,17.205,0.381,Mexico +1945-03-01,19.457,0.25,Mexico +1945-04-01,20.985,0.276,Mexico +1945-05-01,23.84,0.192,Mexico +1945-06-01,25.543000000000003,0.212,Mexico +1945-07-01,25.322,0.21600000000000005,Mexico +1945-08-01,25.206999999999997,0.151,Mexico +1945-09-01,24.355999999999998,0.228,Mexico +1945-10-01,21.134,0.325,Mexico +1945-11-01,18.898,0.365,Mexico +1945-12-01,15.911,0.239,Mexico +1946-01-01,14.758,0.14400000000000002,Mexico +1946-02-01,16.735,0.314,Mexico +1946-03-01,19.396,0.265,Mexico +1946-04-01,22.241,0.261,Mexico +1946-05-01,24.009,0.161,Mexico +1946-06-01,25.169,0.3,Mexico +1946-07-01,25.298000000000002,0.257,Mexico +1946-08-01,25.256999999999998,0.27,Mexico +1946-09-01,24.576,0.22,Mexico +1946-10-01,21.555,0.208,Mexico +1946-11-01,18.174,0.204,Mexico +1946-12-01,16.317,0.248,Mexico +1947-01-01,14.644,0.28600000000000003,Mexico +1947-02-01,16.177,0.359,Mexico +1947-03-01,18.692999999999998,0.237,Mexico +1947-04-01,21.289,0.153,Mexico +1947-05-01,23.663,0.18600000000000005,Mexico +1947-06-01,25.357,0.209,Mexico +1947-07-01,25.646,0.207,Mexico +1947-08-01,24.285999999999998,0.16699999999999998,Mexico +1947-09-01,23.698,0.211,Mexico +1947-10-01,22.29,0.195,Mexico +1947-11-01,18.027,0.205,Mexico +1947-12-01,15.134,0.34700000000000003,Mexico +1948-01-01,14.452,0.16,Mexico +1948-02-01,17.108999999999998,0.244,Mexico +1948-03-01,18.908,0.233,Mexico +1948-04-01,22.134,0.139,Mexico +1948-05-01,24.022,0.197,Mexico +1948-06-01,25.564,0.16,Mexico +1948-07-01,25.149,0.201,Mexico +1948-08-01,25.412000000000006,0.269,Mexico +1948-09-01,23.666999999999998,0.208,Mexico +1948-10-01,20.971999999999998,0.253,Mexico +1948-11-01,17.987000000000002,0.181,Mexico +1948-12-01,17.25,0.14300000000000002,Mexico +1949-01-01,14.34,0.201,Mexico +1949-02-01,16.997999999999998,0.279,Mexico +1949-03-01,19.485000000000007,0.328,Mexico +1949-04-01,20.662,0.183,Mexico +1949-05-01,23.928,0.107,Mexico +1949-06-01,25.293000000000003,0.121,Mexico +1949-07-01,25.183000000000003,0.187,Mexico +1949-08-01,25.072000000000006,0.153,Mexico +1949-09-01,24.410999999999998,0.177,Mexico +1949-10-01,21.509,0.17,Mexico +1949-11-01,17.863999999999994,0.227,Mexico +1949-12-01,16.248,0.192,Mexico +1950-01-01,17.041,0.131,Mexico +1950-02-01,17.555999999999994,0.146,Mexico +1950-03-01,19.407,0.258,Mexico +1950-04-01,21.627,0.187,Mexico +1950-05-01,23.997,0.205,Mexico +1950-06-01,25.1,0.193,Mexico +1950-07-01,24.63,0.091,Mexico +1950-08-01,25.046,0.155,Mexico +1950-09-01,24.327,0.207,Mexico +1950-10-01,22.090999999999998,0.156,Mexico +1950-11-01,18.338,0.214,Mexico +1950-12-01,15.921,0.188,Mexico +1951-01-01,15.052,0.181,Mexico +1951-02-01,16.169999999999998,0.16699999999999998,Mexico +1951-03-01,18.656,0.117,Mexico +1951-04-01,21.188000000000002,0.182,Mexico +1951-05-01,23.553,0.23,Mexico +1951-06-01,25.598000000000003,0.453,Mexico +1951-07-01,25.599,0.208,Mexico +1951-08-01,25.519,0.133,Mexico +1951-09-01,24.465999999999998,0.308,Mexico +1951-10-01,22.061999999999998,0.172,Mexico +1951-11-01,18.564,0.318,Mexico +1951-12-01,16.983999999999998,0.274,Mexico +1952-01-01,17.102,0.147,Mexico +1952-02-01,16.808,0.162,Mexico +1952-03-01,18.541,0.159,Mexico +1952-04-01,20.634,0.208,Mexico +1952-05-01,23.576,0.229,Mexico +1952-06-01,24.875,0.17600000000000002,Mexico +1952-07-01,25.054000000000002,0.2,Mexico +1952-08-01,25.549,0.256,Mexico +1952-09-01,24.340999999999998,0.194,Mexico +1952-10-01,20.788,0.173,Mexico +1952-11-01,18.127,0.102,Mexico +1952-12-01,15.253,0.14400000000000002,Mexico +1953-01-01,15.85,0.16899999999999998,Mexico +1953-02-01,16.767,0.124,Mexico +1953-03-01,20.243,0.11,Mexico +1953-04-01,21.816,0.212,Mexico +1953-05-01,23.616,0.256,Mexico +1953-06-01,26.005,0.181,Mexico +1953-07-01,25.701,0.243,Mexico +1953-08-01,25.471,0.199,Mexico +1953-09-01,23.739,0.224,Mexico +1953-10-01,21.410999999999998,0.136,Mexico +1953-11-01,17.791999999999994,0.149,Mexico +1953-12-01,14.447000000000001,0.13699999999999998,Mexico +1954-01-01,16.291,0.246,Mexico +1954-02-01,17.72,0.264,Mexico +1954-03-01,19.182,0.32299999999999995,Mexico +1954-04-01,22.193,0.26,Mexico +1954-05-01,23.482,0.118,Mexico +1954-06-01,25.089000000000002,0.19,Mexico +1954-07-01,24.884,0.17800000000000002,Mexico +1954-08-01,24.748,0.152,Mexico +1954-09-01,24.346,0.279,Mexico +1954-10-01,21.629,0.259,Mexico +1954-11-01,17.982,0.125,Mexico +1954-12-01,15.636,0.269,Mexico +1955-01-01,14.339,0.149,Mexico +1955-02-01,15.698,0.119,Mexico +1955-03-01,19.406,0.168,Mexico +1955-04-01,21.734,0.166,Mexico +1955-05-01,23.820999999999998,0.285,Mexico +1955-06-01,25.288,0.194,Mexico +1955-07-01,24.444000000000003,0.177,Mexico +1955-08-01,24.55,0.232,Mexico +1955-09-01,23.623,0.155,Mexico +1955-10-01,20.808000000000003,0.14,Mexico +1955-11-01,18.14,0.224,Mexico +1955-12-01,15.752,0.2,Mexico +1956-01-01,15.092,0.192,Mexico +1956-02-01,16.391,0.212,Mexico +1956-03-01,19.198,0.245,Mexico +1956-04-01,21.039,0.303,Mexico +1956-05-01,23.750999999999998,0.29,Mexico +1956-06-01,24.868000000000002,0.181,Mexico +1956-07-01,24.665,0.17600000000000002,Mexico +1956-08-01,24.883000000000006,0.179,Mexico +1956-09-01,23.84,0.224,Mexico +1956-10-01,21.656,0.17,Mexico +1956-11-01,17.56,0.26,Mexico +1956-12-01,15.742,0.343,Mexico +1957-01-01,16.755,0.235,Mexico +1957-02-01,18.678,0.249,Mexico +1957-03-01,18.314,0.265,Mexico +1957-04-01,20.969,0.405,Mexico +1957-05-01,22.944000000000003,0.23,Mexico +1957-06-01,25.514,0.4270000000000001,Mexico +1957-07-01,25.908,0.397,Mexico +1957-08-01,25.427,0.745,Mexico +1957-09-01,24.121,0.495,Mexico +1957-10-01,20.752,0.453,Mexico +1957-11-01,17.756999999999994,0.315,Mexico +1957-12-01,16.118,0.244,Mexico +1958-01-01,13.677999999999999,0.225,Mexico +1958-02-01,15.954999999999998,0.285,Mexico +1958-03-01,17.783,0.157,Mexico +1958-04-01,21.582,0.279,Mexico +1958-05-01,23.923,0.276,Mexico +1958-06-01,25.625,0.272,Mexico +1958-07-01,25.428,0.409,Mexico +1958-08-01,25.336,0.224,Mexico +1958-09-01,24.098000000000003,0.135,Mexico +1958-10-01,21.178,0.225,Mexico +1958-11-01,18.591,0.152,Mexico +1958-12-01,15.764000000000001,0.181,Mexico +1959-01-01,15.325999999999999,0.29,Mexico +1959-02-01,17.064,0.146,Mexico +1959-03-01,18.378,0.378,Mexico +1959-04-01,20.905,0.293,Mexico +1959-05-01,23.461,0.106,Mexico +1959-06-01,25.327,0.163,Mexico +1959-07-01,24.984,0.294,Mexico +1959-08-01,24.776,0.243,Mexico +1959-09-01,24.281999999999996,0.305,Mexico +1959-10-01,21.570999999999998,0.231,Mexico +1959-11-01,17.558999999999994,0.159,Mexico +1959-12-01,15.382,0.185,Mexico +1960-01-01,14.978,0.233,Mexico +1960-02-01,14.635,0.326,Mexico +1960-03-01,18.622,0.184,Mexico +1960-04-01,21.023000000000003,0.206,Mexico +1960-05-01,23.526,0.31,Mexico +1960-06-01,26.039,0.226,Mexico +1960-07-01,25.4,0.223,Mexico +1960-08-01,25.074,0.276,Mexico +1960-09-01,23.818,0.177,Mexico +1960-10-01,22.090999999999998,0.17800000000000002,Mexico +1960-11-01,18.999000000000002,0.272,Mexico +1960-12-01,14.754000000000001,0.14800000000000002,Mexico +1961-01-01,14.645999999999999,0.294,Mexico +1961-02-01,16.067999999999998,0.136,Mexico +1961-03-01,18.996,0.18,Mexico +1961-04-01,21.506,0.285,Mexico +1961-05-01,24.381,0.244,Mexico +1961-06-01,25.027,0.232,Mexico +1961-07-01,24.651999999999997,0.237,Mexico +1961-08-01,24.658,0.149,Mexico +1961-09-01,23.891,0.125,Mexico +1961-10-01,21.294,0.212,Mexico +1961-11-01,17.665999999999997,0.12,Mexico +1961-12-01,15.902000000000001,0.12,Mexico +1962-01-01,14.177,0.177,Mexico +1962-02-01,17.945,0.199,Mexico +1962-03-01,17.948,0.21600000000000005,Mexico +1962-04-01,21.269000000000002,0.226,Mexico +1962-05-01,23.675,0.147,Mexico +1962-06-01,25.079,0.508,Mexico +1962-07-01,25.436999999999998,0.354,Mexico +1962-08-01,25.781999999999996,0.211,Mexico +1962-09-01,24.283,0.28,Mexico +1962-10-01,22.254,0.297,Mexico +1962-11-01,18.337,0.192,Mexico +1962-12-01,15.734000000000002,0.275,Mexico +1963-01-01,14.823,0.168,Mexico +1963-02-01,16.128999999999998,0.211,Mexico +1963-03-01,19.474,0.2,Mexico +1963-04-01,22.418000000000003,0.225,Mexico +1963-05-01,24.085,0.292,Mexico +1963-06-01,25.211,0.278,Mexico +1963-07-01,25.151,0.315,Mexico +1963-08-01,24.928,0.205,Mexico +1963-09-01,24.093000000000004,0.256,Mexico +1963-10-01,21.401,0.25,Mexico +1963-11-01,18.189,0.244,Mexico +1963-12-01,14.651,0.198,Mexico +1964-01-01,14.037,0.234,Mexico +1964-02-01,15.316999999999998,0.163,Mexico +1964-03-01,18.480999999999998,0.136,Mexico +1964-04-01,21.868000000000002,0.18600000000000005,Mexico +1964-05-01,23.836,0.25,Mexico +1964-06-01,24.499000000000002,0.277,Mexico +1964-07-01,25.337,0.253,Mexico +1964-08-01,25.19,0.331,Mexico +1964-09-01,23.943,0.23,Mexico +1964-10-01,20.633000000000003,0.292,Mexico +1964-11-01,18.317,0.168,Mexico +1964-12-01,15.578,0.125,Mexico +1965-01-01,15.595999999999998,0.192,Mexico +1965-02-01,15.342,0.147,Mexico +1965-03-01,17.845000000000006,0.345,Mexico +1965-04-01,21.725,0.153,Mexico +1965-05-01,23.553,0.28,Mexico +1965-06-01,24.8,0.456,Mexico +1965-07-01,25.105,0.263,Mexico +1965-08-01,24.607,0.127,Mexico +1965-09-01,23.935,0.222,Mexico +1965-10-01,20.656,0.353,Mexico +1965-11-01,19.641,0.324,Mexico +1965-12-01,16.009,0.279,Mexico +1966-01-01,13.607999999999999,0.222,Mexico +1966-02-01,14.886,0.16399999999999998,Mexico +1966-03-01,18.183,0.231,Mexico +1966-04-01,21.24,0.13699999999999998,Mexico +1966-05-01,23.725,0.227,Mexico +1966-06-01,24.963,0.229,Mexico +1966-07-01,25.472,0.184,Mexico +1966-08-01,24.874,0.248,Mexico +1966-09-01,23.938000000000002,0.333,Mexico +1966-10-01,20.796,0.237,Mexico +1966-11-01,18.172,0.236,Mexico +1966-12-01,14.819,0.165,Mexico +1967-01-01,14.289000000000001,0.172,Mexico +1967-02-01,16.119,0.28800000000000003,Mexico +1967-03-01,19.613000000000003,0.205,Mexico +1967-04-01,21.846,0.141,Mexico +1967-05-01,23.769,0.219,Mexico +1967-06-01,25.031,0.193,Mexico +1967-07-01,25.241999999999997,0.35600000000000004,Mexico +1967-08-01,24.522,0.272,Mexico +1967-09-01,23.115,0.275,Mexico +1967-10-01,20.738000000000003,0.153,Mexico +1967-11-01,18.55,0.154,Mexico +1967-12-01,15.366,0.18,Mexico +1968-01-01,14.892999999999999,0.19,Mexico +1968-02-01,15.858,0.215,Mexico +1968-03-01,16.987000000000002,0.209,Mexico +1968-04-01,20.755,0.219,Mexico +1968-05-01,23.438000000000002,0.195,Mexico +1968-06-01,24.848000000000003,0.321,Mexico +1968-07-01,24.456999999999997,0.325,Mexico +1968-08-01,24.380000000000006,0.274,Mexico +1968-09-01,23.349,0.236,Mexico +1968-10-01,21.69,0.196,Mexico +1968-11-01,17.720999999999997,0.16899999999999998,Mexico +1968-12-01,15.417,0.15,Mexico +1969-01-01,16.11,0.18600000000000005,Mexico +1969-02-01,16.695,0.14,Mexico +1969-03-01,17.330000000000002,0.13,Mexico +1969-04-01,21.769000000000002,0.251,Mexico +1969-05-01,23.586,0.215,Mexico +1969-06-01,25.538,0.301,Mexico +1969-07-01,25.886,0.226,Mexico +1969-08-01,25.396,0.269,Mexico +1969-09-01,24.065,0.154,Mexico +1969-10-01,21.559,0.147,Mexico +1969-11-01,17.926,0.157,Mexico +1969-12-01,15.646,0.27,Mexico +1970-01-01,14.482999999999999,0.252,Mexico +1970-02-01,16.02,0.165,Mexico +1970-03-01,17.919,0.309,Mexico +1970-04-01,21.513,0.198,Mexico +1970-05-01,23.004,0.146,Mexico +1970-06-01,24.781999999999996,0.269,Mexico +1970-07-01,24.995,0.265,Mexico +1970-08-01,25.146,0.24,Mexico +1970-09-01,23.4,0.243,Mexico +1970-10-01,20.638,0.16899999999999998,Mexico +1970-11-01,17.255,0.21600000000000005,Mexico +1970-12-01,16.726,0.207,Mexico +1971-01-01,15.925,0.179,Mexico +1971-02-01,16.453,0.275,Mexico +1971-03-01,19.329,0.202,Mexico +1971-04-01,20.351,0.228,Mexico +1971-05-01,23.346999999999998,0.209,Mexico +1971-06-01,24.394000000000002,0.16399999999999998,Mexico +1971-07-01,24.801,0.285,Mexico +1971-08-01,23.804,0.226,Mexico +1971-09-01,23.627,0.248,Mexico +1971-10-01,20.97,0.26,Mexico +1971-11-01,18.409000000000002,0.207,Mexico +1971-12-01,16.274,0.17,Mexico +1972-01-01,15.91,0.25,Mexico +1972-02-01,16.412,0.162,Mexico +1972-03-01,19.97,0.2,Mexico +1972-04-01,22.314,0.272,Mexico +1972-05-01,23.445999999999998,0.2,Mexico +1972-06-01,24.521,0.254,Mexico +1972-07-01,24.821,0.226,Mexico +1972-08-01,24.326999999999998,0.257,Mexico +1972-09-01,23.981,0.21,Mexico +1972-10-01,21.983,0.258,Mexico +1972-11-01,18.13,0.191,Mexico +1972-12-01,15.885,0.205,Mexico +1973-01-01,14.235,0.229,Mexico +1973-02-01,15.922,0.261,Mexico +1973-03-01,18.965,0.301,Mexico +1973-04-01,20.33,0.184,Mexico +1973-05-01,23.594,0.285,Mexico +1973-06-01,24.67,0.225,Mexico +1973-07-01,24.871,0.27,Mexico +1973-08-01,24.085,0.305,Mexico +1973-09-01,23.971,0.17800000000000002,Mexico +1973-10-01,21.31,0.236,Mexico +1973-11-01,19.043,0.141,Mexico +1973-12-01,14.958,0.257,Mexico +1974-01-01,16.101,0.269,Mexico +1974-02-01,16.127,0.265,Mexico +1974-03-01,19.688,0.197,Mexico +1974-04-01,21.421,0.19,Mexico +1974-05-01,23.851,0.211,Mexico +1974-06-01,25.042,0.224,Mexico +1974-07-01,24.279,0.248,Mexico +1974-08-01,24.660999999999998,0.272,Mexico +1974-09-01,23.068,0.222,Mexico +1974-10-01,20.58,0.092,Mexico +1974-11-01,17.7,0.362,Mexico +1974-12-01,15.06,0.202,Mexico +1975-01-01,15.032,0.162,Mexico +1975-02-01,16.413999999999998,0.19,Mexico +1975-03-01,19.317,0.155,Mexico +1975-04-01,21.117,0.132,Mexico +1975-05-01,23.236,0.211,Mexico +1975-06-01,24.819000000000003,0.27,Mexico +1975-07-01,24.156999999999996,0.408,Mexico +1975-08-01,24.36,0.471,Mexico +1975-09-01,22.809,0.275,Mexico +1975-10-01,20.8,0.239,Mexico +1975-11-01,18.111,0.146,Mexico +1975-12-01,15.22,0.179,Mexico +1976-01-01,14.442,0.179,Mexico +1976-02-01,16.919999999999998,0.191,Mexico +1976-03-01,19.230999999999998,0.134,Mexico +1976-04-01,20.745,0.152,Mexico +1976-05-01,22.803,0.34,Mexico +1976-06-01,24.831999999999997,0.248,Mexico +1976-07-01,23.85,0.436,Mexico +1976-08-01,24.361,0.281,Mexico +1976-09-01,23.358,0.299,Mexico +1976-10-01,20.003,0.266,Mexico +1976-11-01,16.383,0.187,Mexico +1976-12-01,14.82,0.189,Mexico +1977-01-01,14.690999999999999,0.13699999999999998,Mexico +1977-02-01,16.275,0.253,Mexico +1977-03-01,18.441000000000006,0.16899999999999998,Mexico +1977-04-01,20.247,0.12,Mexico +1977-05-01,23.160999999999998,0.242,Mexico +1977-06-01,24.83,0.213,Mexico +1977-07-01,25.015,0.253,Mexico +1977-08-01,25.392,0.28300000000000003,Mexico +1977-09-01,24.726999999999997,0.227,Mexico +1977-10-01,21.877,0.222,Mexico +1977-11-01,18.656,0.131,Mexico +1977-12-01,16.783,0.243,Mexico +1978-01-01,15.015999999999998,0.212,Mexico +1978-02-01,15.161,0.222,Mexico +1978-03-01,18.76,0.175,Mexico +1978-04-01,21.565,0.282,Mexico +1978-05-01,24.055999999999997,0.218,Mexico +1978-06-01,25.514,0.252,Mexico +1978-07-01,25.523000000000003,0.268,Mexico +1978-08-01,24.730999999999998,0.253,Mexico +1978-09-01,23.276999999999997,0.189,Mexico +1978-10-01,20.958,0.161,Mexico +1978-11-01,18.725,0.229,Mexico +1978-12-01,15.78,0.246,Mexico +1979-01-01,14.258,0.231,Mexico +1979-02-01,16.035,0.139,Mexico +1979-03-01,18.727,0.183,Mexico +1979-04-01,21.527,0.217,Mexico +1979-05-01,22.953000000000003,0.28800000000000003,Mexico +1979-06-01,24.751,0.108,Mexico +1979-07-01,25.835,0.229,Mexico +1979-08-01,24.473000000000006,0.319,Mexico +1979-09-01,23.421999999999997,0.138,Mexico +1979-10-01,21.921,0.14,Mexico +1979-11-01,17.217,0.118,Mexico +1979-12-01,15.709000000000001,0.149,Mexico +1980-01-01,15.664000000000001,0.165,Mexico +1980-02-01,16.715999999999998,0.101,Mexico +1980-03-01,19.272,0.136,Mexico +1980-04-01,20.693,0.196,Mexico +1980-05-01,23.947,0.211,Mexico +1980-06-01,26.521,0.21600000000000005,Mexico +1980-07-01,26.346999999999998,0.295,Mexico +1980-08-01,25.09,0.218,Mexico +1980-09-01,24.273000000000003,0.17600000000000002,Mexico +1980-10-01,20.892,0.16899999999999998,Mexico +1980-11-01,17.18,0.131,Mexico +1980-12-01,15.833,0.16,Mexico +1981-01-01,14.321,0.253,Mexico +1981-02-01,16.665,0.189,Mexico +1981-03-01,18.187,0.108,Mexico +1981-04-01,21.348000000000003,0.181,Mexico +1981-05-01,23.378,0.201,Mexico +1981-06-01,24.918000000000003,0.336,Mexico +1981-07-01,24.988000000000003,0.32,Mexico +1981-08-01,24.974,0.214,Mexico +1981-09-01,23.756,0.229,Mexico +1981-10-01,21.406,0.267,Mexico +1981-11-01,18.61,0.184,Mexico +1981-12-01,16.494,0.244,Mexico +1982-01-01,15.864,0.201,Mexico +1982-02-01,16.957,0.205,Mexico +1982-03-01,19.8,0.203,Mexico +1982-04-01,22.34,0.17600000000000002,Mexico +1982-05-01,23.239,0.265,Mexico +1982-06-01,25.599,0.265,Mexico +1982-07-01,25.255,0.234,Mexico +1982-08-01,25.340999999999998,0.153,Mexico +1982-09-01,24.305999999999997,0.161,Mexico +1982-10-01,21.155,0.172,Mexico +1982-11-01,17.793999999999997,0.219,Mexico +1982-12-01,14.963,0.303,Mexico +1983-01-01,14.617,0.26,Mexico +1983-02-01,15.728,0.265,Mexico +1983-03-01,17.948,0.191,Mexico +1983-04-01,20.021,0.179,Mexico +1983-05-01,23.971999999999998,0.156,Mexico +1983-06-01,25.445,0.289,Mexico +1983-07-01,25.264,0.19,Mexico +1983-08-01,24.865,0.215,Mexico +1983-09-01,24.248,0.159,Mexico +1983-10-01,21.642,0.172,Mexico +1983-11-01,18.601,0.227,Mexico +1983-12-01,15.754000000000001,0.272,Mexico +1984-01-01,14.520999999999999,0.224,Mexico +1984-02-01,15.945,0.197,Mexico +1984-03-01,19.105,0.225,Mexico +1984-04-01,21.666,0.156,Mexico +1984-05-01,23.788,0.188,Mexico +1984-06-01,24.515,0.152,Mexico +1984-07-01,24.065,0.26,Mexico +1984-08-01,24.268,0.194,Mexico +1984-09-01,23.092,0.14400000000000002,Mexico +1984-10-01,21.085,0.257,Mexico +1984-11-01,17.656,0.185,Mexico +1984-12-01,16.432000000000002,0.274,Mexico +1985-01-01,13.758,0.172,Mexico +1985-02-01,15.696,0.154,Mexico +1985-03-01,19.313,0.142,Mexico +1985-04-01,21.037,0.317,Mexico +1985-05-01,23.885,0.208,Mexico +1985-06-01,24.783,0.35,Mexico +1985-07-01,24.611,0.21600000000000005,Mexico +1985-08-01,25.006,0.255,Mexico +1985-09-01,23.686,0.204,Mexico +1985-10-01,21.362,0.166,Mexico +1985-11-01,18.564,0.14,Mexico +1985-12-01,15.546,0.345,Mexico +1986-01-01,15.335,0.261,Mexico +1986-02-01,17.381999999999998,0.225,Mexico +1986-03-01,18.76,0.232,Mexico +1986-04-01,22.455,0.185,Mexico +1986-05-01,23.682,0.185,Mexico +1986-06-01,24.565,0.213,Mexico +1986-07-01,24.483,0.221,Mexico +1986-08-01,25.068,0.135,Mexico +1986-09-01,23.785999999999998,0.181,Mexico +1986-10-01,20.779,0.168,Mexico +1986-11-01,18.593,0.159,Mexico +1986-12-01,15.32,0.162,Mexico +1987-01-01,14.297,0.255,Mexico +1987-02-01,16.035999999999998,0.197,Mexico +1987-03-01,17.57,0.171,Mexico +1987-04-01,20.065,0.251,Mexico +1987-05-01,22.809,0.196,Mexico +1987-06-01,24.956999999999997,0.16399999999999998,Mexico +1987-07-01,25.054000000000002,0.21,Mexico +1987-08-01,24.94,0.239,Mexico +1987-09-01,24.186,0.227,Mexico +1987-10-01,21.495,0.16,Mexico +1987-11-01,18.002,0.165,Mexico +1987-12-01,16.051,0.223,Mexico +1988-01-01,14.493,0.191,Mexico +1988-02-01,16.813,0.153,Mexico +1988-03-01,18.649,0.203,Mexico +1988-04-01,21.332,0.182,Mexico +1988-05-01,23.703000000000003,0.258,Mexico +1988-06-01,24.811,0.253,Mexico +1988-07-01,24.8,0.13,Mexico +1988-08-01,24.623,0.14300000000000002,Mexico +1988-09-01,23.57,0.289,Mexico +1988-10-01,21.509,0.105,Mexico +1988-11-01,18.911,0.153,Mexico +1988-12-01,15.619000000000002,0.215,Mexico +1989-01-01,15.785,0.277,Mexico +1989-02-01,16.871,0.236,Mexico +1989-03-01,18.912000000000006,0.318,Mexico +1989-04-01,21.969,0.29,Mexico +1989-05-01,24.435,0.18,Mexico +1989-06-01,25.598000000000003,0.25,Mexico +1989-07-01,25.511,0.207,Mexico +1989-08-01,24.642,0.374,Mexico +1989-09-01,23.599,0.214,Mexico +1989-10-01,21.209,0.242,Mexico +1989-11-01,18.783,0.205,Mexico +1989-12-01,14.267999999999999,0.17800000000000002,Mexico +1990-01-01,15.812999999999999,0.256,Mexico +1990-02-01,16.541999999999998,0.16699999999999998,Mexico +1990-03-01,19.098000000000006,0.202,Mexico +1990-04-01,21.734,0.146,Mexico +1990-05-01,24.229,0.14400000000000002,Mexico +1990-06-01,26.144000000000002,0.258,Mexico +1990-07-01,24.485,0.156,Mexico +1990-08-01,24.32,0.224,Mexico +1990-09-01,23.491999999999997,0.194,Mexico +1990-10-01,21.158,0.185,Mexico +1990-11-01,18.73,0.333,Mexico +1990-12-01,15.785,0.258,Mexico +1991-01-01,15.434000000000001,0.184,Mexico +1991-02-01,17.141,0.14,Mexico +1991-03-01,19.514000000000006,0.221,Mexico +1991-04-01,22.058000000000003,0.211,Mexico +1991-05-01,24.116,0.212,Mexico +1991-06-01,25.074,0.161,Mexico +1991-07-01,24.313000000000002,0.228,Mexico +1991-08-01,24.92,0.172,Mexico +1991-09-01,22.943,0.174,Mexico +1991-10-01,21.425,0.22,Mexico +1991-11-01,17.34,0.146,Mexico +1991-12-01,15.798,0.138,Mexico +1992-01-01,14.186,0.227,Mexico +1992-02-01,15.995,0.187,Mexico +1992-03-01,18.903,0.173,Mexico +1992-04-01,20.981,0.301,Mexico +1992-05-01,22.791999999999998,0.121,Mexico +1992-06-01,25.652,0.263,Mexico +1992-07-01,25.298000000000002,0.268,Mexico +1992-08-01,24.717,0.135,Mexico +1992-09-01,24.069000000000003,0.28300000000000003,Mexico +1992-10-01,21.479,0.17600000000000002,Mexico +1992-11-01,17.378999999999998,0.282,Mexico +1992-12-01,16.352999999999998,0.403,Mexico +1993-01-01,16.084,0.236,Mexico +1993-02-01,16.881,0.227,Mexico +1993-03-01,18.854000000000006,0.196,Mexico +1993-04-01,21.325,0.163,Mexico +1993-05-01,23.406999999999996,0.28800000000000003,Mexico +1993-06-01,25.217,0.17600000000000002,Mexico +1993-07-01,25.042,0.128,Mexico +1993-08-01,25.158,0.13,Mexico +1993-09-01,23.441999999999997,0.189,Mexico +1993-10-01,21.386999999999997,0.12,Mexico +1993-11-01,18.136,0.222,Mexico +1993-12-01,16.333,0.236,Mexico +1994-01-01,15.577,0.224,Mexico +1994-02-01,17.108999999999998,0.163,Mexico +1994-03-01,19.285999999999998,0.239,Mexico +1994-04-01,21.451,0.14400000000000002,Mexico +1994-05-01,24.033,0.227,Mexico +1994-06-01,25.685,0.208,Mexico +1994-07-01,25.834,0.264,Mexico +1994-08-01,25.191000000000006,0.225,Mexico +1994-09-01,23.898000000000003,0.221,Mexico +1994-10-01,21.734,0.149,Mexico +1994-11-01,19.392,0.247,Mexico +1994-12-01,16.719,0.263,Mexico +1995-01-01,15.600999999999999,0.238,Mexico +1995-02-01,18.112000000000002,0.16,Mexico +1995-03-01,19.319000000000006,0.202,Mexico +1995-04-01,21.296,0.22,Mexico +1995-05-01,24.439,0.27,Mexico +1995-06-01,25.42,0.2,Mexico +1995-07-01,25.645,0.177,Mexico +1995-08-01,25.351999999999997,0.146,Mexico +1995-09-01,24.281999999999996,0.196,Mexico +1995-10-01,21.656999999999996,0.21,Mexico +1995-11-01,19.293,0.111,Mexico +1995-12-01,16.032,0.177,Mexico +1996-01-01,15.287,0.245,Mexico +1996-02-01,17.75,0.187,Mexico +1996-03-01,18.298,0.207,Mexico +1996-04-01,21.277,0.187,Mexico +1996-05-01,25.105,0.149,Mexico +1996-06-01,25.747,0.234,Mexico +1996-07-01,25.778000000000002,0.324,Mexico +1996-08-01,24.72,0.235,Mexico +1996-09-01,23.976999999999997,0.188,Mexico +1996-10-01,21.259,0.17,Mexico +1996-11-01,18.459,0.217,Mexico +1996-12-01,15.929,0.21,Mexico +1997-01-01,14.743,0.189,Mexico +1997-02-01,16.561999999999998,0.131,Mexico +1997-03-01,19.703000000000007,0.292,Mexico +1997-04-01,19.879,0.163,Mexico +1997-05-01,23.644,0.179,Mexico +1997-06-01,25.444000000000003,0.16899999999999998,Mexico +1997-07-01,25.706999999999997,0.265,Mexico +1997-08-01,25.779,0.234,Mexico +1997-09-01,24.785999999999998,0.205,Mexico +1997-10-01,21.144,0.192,Mexico +1997-11-01,18.704,0.147,Mexico +1997-12-01,14.688,0.199,Mexico +1998-01-01,16.047,0.256,Mexico +1998-02-01,16.311999999999998,0.161,Mexico +1998-03-01,18.765,0.174,Mexico +1998-04-01,21.346,0.132,Mexico +1998-05-01,24.893,0.171,Mexico +1998-06-01,26.926,0.16899999999999998,Mexico +1998-07-01,26.265,0.253,Mexico +1998-08-01,25.648000000000003,0.175,Mexico +1998-09-01,24.636999999999997,0.187,Mexico +1998-10-01,22.14,0.101,Mexico +1998-11-01,19.407,0.103,Mexico +1998-12-01,15.995,0.257,Mexico +1999-01-01,15.772,0.269,Mexico +1999-02-01,17.9,0.26,Mexico +1999-03-01,19.477999999999998,0.108,Mexico +1999-04-01,22.11,0.219,Mexico +1999-05-01,24.425,0.183,Mexico +1999-06-01,25.277,0.262,Mexico +1999-07-01,24.632,0.285,Mexico +1999-08-01,25.281999999999996,0.2,Mexico +1999-09-01,24.143,0.309,Mexico +1999-10-01,21.241,0.282,Mexico +1999-11-01,18.445,0.212,Mexico +1999-12-01,14.837,0.156,Mexico +2000-01-01,16.099,0.214,Mexico +2000-02-01,18.049,0.213,Mexico +2000-03-01,19.951,0.159,Mexico +2000-04-01,22.275,0.211,Mexico +2000-05-01,24.8,0.202,Mexico +2000-06-01,24.765,0.242,Mexico +2000-07-01,25.811999999999998,0.258,Mexico +2000-08-01,24.857,0.359,Mexico +2000-09-01,24.482,0.276,Mexico +2000-10-01,20.929,0.207,Mexico +2000-11-01,17.58,0.197,Mexico +2000-12-01,15.186,0.232,Mexico +2001-01-01,14.828,0.194,Mexico +2001-02-01,17.349,0.28600000000000003,Mexico +2001-03-01,18.424000000000003,0.155,Mexico +2001-04-01,21.982,0.245,Mexico +2001-05-01,24.108,0.285,Mexico +2001-06-01,25.566999999999997,0.337,Mexico +2001-07-01,25.536,0.28600000000000003,Mexico +2001-08-01,25.489,0.182,Mexico +2001-09-01,24.278000000000002,0.321,Mexico +2001-10-01,21.665,0.21600000000000005,Mexico +2001-11-01,18.745,0.208,Mexico +2001-12-01,15.625,0.255,Mexico +2002-01-01,15.748,0.327,Mexico +2002-02-01,16.151999999999994,0.208,Mexico +2002-03-01,19.208,0.172,Mexico +2002-04-01,22.881,0.237,Mexico +2002-05-01,24.824,0.203,Mexico +2002-06-01,25.705,0.109,Mexico +2002-07-01,25.123,0.328,Mexico +2002-08-01,25.568,0.225,Mexico +2002-09-01,24.142,0.25,Mexico +2002-10-01,21.871,0.261,Mexico +2002-11-01,18.011,0.231,Mexico +2002-12-01,15.467,0.301,Mexico +2003-01-01,15.880999999999998,0.275,Mexico +2003-02-01,17.294999999999998,0.249,Mexico +2003-03-01,19.288,0.151,Mexico +2003-04-01,21.84,0.236,Mexico +2003-05-01,25.305,0.187,Mexico +2003-06-01,25.519000000000002,0.303,Mexico +2003-07-01,25.307,0.375,Mexico +2003-08-01,25.298,0.366,Mexico +2003-09-01,24.013,0.26,Mexico +2003-10-01,21.886999999999997,0.337,Mexico +2003-11-01,19.116,0.168,Mexico +2003-12-01,15.204,0.315,Mexico +2004-01-01,15.149,0.29100000000000004,Mexico +2004-02-01,15.954999999999998,0.213,Mexico +2004-03-01,20.268,0.146,Mexico +2004-04-01,21.085,0.181,Mexico +2004-05-01,24.006,0.219,Mexico +2004-06-01,25.076,0.28600000000000003,Mexico +2004-07-01,25.417,0.246,Mexico +2004-08-01,25.232,0.227,Mexico +2004-09-01,23.601,0.134,Mexico +2004-10-01,22.045,0.228,Mexico +2004-11-01,17.935,0.166,Mexico +2004-12-01,15.329,0.165,Mexico +2005-01-01,16.441,0.161,Mexico +2005-02-01,16.869999999999994,0.235,Mexico +2005-03-01,18.947000000000006,0.136,Mexico +2005-04-01,21.911,0.21600000000000005,Mexico +2005-05-01,24.073,0.124,Mexico +2005-06-01,26.095,0.22,Mexico +2005-07-01,26.002,0.187,Mexico +2005-08-01,25.074,0.207,Mexico +2005-09-01,24.994,0.212,Mexico +2005-10-01,21.671999999999997,0.175,Mexico +2005-11-01,18.756,0.232,Mexico +2005-12-01,16.127,0.226,Mexico +2006-01-01,16.098,0.295,Mexico +2006-02-01,17.518,0.246,Mexico +2006-03-01,19.793000000000006,0.328,Mexico +2006-04-01,22.982,0.267,Mexico +2006-05-01,24.538,0.306,Mexico +2006-06-01,25.751,0.245,Mexico +2006-07-01,25.613000000000003,0.234,Mexico +2006-08-01,25.066999999999997,0.218,Mexico +2006-09-01,23.840999999999998,0.207,Mexico +2006-10-01,21.805999999999997,0.177,Mexico +2006-11-01,18.755,0.136,Mexico +2006-12-01,15.255,0.181,Mexico +2007-01-01,14.467,0.177,Mexico +2007-02-01,16.865,0.25,Mexico +2007-03-01,19.776000000000003,0.215,Mexico +2007-04-01,21.353,0.21,Mexico +2007-05-01,23.885,0.16,Mexico +2007-06-01,25.431,0.262,Mexico +2007-07-01,25.114,0.177,Mexico +2007-08-01,25.194000000000006,0.166,Mexico +2007-09-01,24.086,0.253,Mexico +2007-10-01,21.726999999999997,0.156,Mexico +2007-11-01,18.816,0.182,Mexico 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Islands +1991-02-01,26.014,0.813,Northern Mariana Islands +1991-03-01,26.818,0.563,Northern Mariana Islands +1991-04-01,27.285,0.257,Northern Mariana Islands +1991-05-01,28.127,0.55,Northern Mariana Islands +1991-06-01,28.13,0.472,Northern Mariana Islands +1991-07-01,28.103,0.529,Northern Mariana Islands +1991-08-01,28.006,0.303,Northern Mariana Islands +1991-09-01,27.438000000000002,0.267,Northern Mariana Islands +1991-10-01,27.115,0.165,Northern Mariana Islands +1991-11-01,26.891,0.929,Northern Mariana Islands +1991-12-01,26.683000000000003,0.319,Northern Mariana Islands +1992-01-01,26.177,0.506,Northern Mariana Islands +1992-02-01,26.521,0.949,Northern Mariana Islands +1992-03-01,26.764,0.328,Northern Mariana Islands +1992-04-01,26.991999999999997,0.256,Northern Mariana Islands +1992-05-01,27.299,0.765,Northern Mariana Islands +1992-06-01,28.073,0.305,Northern Mariana Islands +1992-07-01,27.82,0.35600000000000004,Northern Mariana Islands +1992-08-01,27.123,0.763,Northern Mariana Islands +1992-09-01,27.423000000000002,0.297,Northern Mariana Islands +1992-10-01,27.455,0.382,Northern Mariana Islands +1992-11-01,27.251,0.311,Northern Mariana Islands +1992-12-01,26.487,0.49,Northern Mariana Islands +1993-01-01,26.142,0.299,Northern Mariana Islands +1993-02-01,25.948,0.29600000000000004,Northern Mariana Islands +1993-03-01,26.661,0.424,Northern Mariana Islands +1993-04-01,27.514000000000006,0.122,Northern Mariana Islands +1993-05-01,28.024,0.285,Northern Mariana Islands +1993-06-01,28.367,0.264,Northern Mariana Islands +1993-07-01,28.084,0.32299999999999995,Northern Mariana Islands +1993-08-01,27.165,0.302,Northern Mariana Islands +1993-09-01,27.268,0.279,Northern Mariana Islands +1993-10-01,27.228,0.317,Northern Mariana Islands +1993-11-01,27.519000000000002,0.238,Northern Mariana Islands +1993-12-01,26.993,0.161,Northern Mariana Islands +1994-01-01,26.483,0.28300000000000003,Northern Mariana Islands +1994-02-01,26.414,0.504,Northern Mariana Islands 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Mariana Islands +2000-05-01,27.95,0.407,Northern Mariana Islands +2000-06-01,28.189,0.458,Northern Mariana Islands +2000-07-01,27.673000000000002,0.222,Northern Mariana Islands +2000-08-01,27.42,0.211,Northern Mariana Islands +2000-09-01,27.198,0.52,Northern Mariana Islands +2000-10-01,27.47,0.353,Northern Mariana Islands +2000-11-01,27.959,0.3,Northern Mariana Islands +2000-12-01,27.307,0.247,Northern Mariana Islands +2001-01-01,26.985,0.212,Northern Mariana Islands +2001-02-01,26.872,0.261,Northern Mariana Islands +2001-03-01,27.191,0.213,Northern Mariana Islands +2001-04-01,28.000999999999998,0.244,Northern Mariana Islands +2001-05-01,28.511,0.207,Northern Mariana Islands +2001-06-01,28.449,0.307,Northern Mariana Islands +2001-07-01,27.684,0.371,Northern Mariana Islands +2001-08-01,27.703000000000003,0.424,Northern Mariana Islands +2001-09-01,27.55,0.511,Northern Mariana Islands +2001-10-01,27.801,0.359,Northern Mariana Islands +2001-11-01,27.425,0.389,Northern Mariana Islands 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+1998-02-01,22.393,0.405,Oman +1998-03-01,25.433000000000003,0.231,Oman +1998-04-01,29.247,0.532,Oman +1998-05-01,32.9,0.993,Oman +1998-06-01,34.364000000000004,1.086,Oman +1998-07-01,33.655,1.176,Oman +1998-08-01,32.736999999999995,1.479,Oman +1998-09-01,31.061,1.212,Oman +1998-10-01,28.518,0.431,Oman +1998-11-01,24.908,0.249,Oman +1998-12-01,22.758000000000003,0.568,Oman +1999-01-01,20.72,0.42,Oman +1999-02-01,23.180999999999997,0.34600000000000003,Oman +1999-03-01,24.578000000000007,0.262,Oman +1999-04-01,29.438,0.604,Oman +1999-05-01,32.565,0.598,Oman +1999-06-01,34.076,0.654,Oman +1999-07-01,33.618,0.7829999999999999,Oman +1999-08-01,32.705,1.073,Oman +1999-09-01,30.761,0.825,Oman +1999-10-01,28.558000000000003,0.3670000000000001,Oman +1999-11-01,25.581000000000003,0.484,Oman +1999-12-01,21.573,0.529,Oman +2000-01-01,20.875,0.353,Oman +2000-02-01,21.778000000000002,0.599,Oman +2000-03-01,24.444000000000006,0.684,Oman +2000-04-01,30.01,0.445,Oman +2000-05-01,33.153,0.5670000000000001,Oman +2000-06-01,33.841,0.461,Oman +2000-07-01,33.262,0.8490000000000001,Oman +2000-08-01,32.421,0.659,Oman +2000-09-01,30.356,0.593,Oman +2000-10-01,28.511,0.28300000000000003,Oman +2000-11-01,25.151999999999997,0.403,Oman +2000-12-01,21.266,0.409,Oman +2001-01-01,19.205,0.58,Oman +2001-02-01,21.259,0.501,Oman +2001-03-01,25.573,0.344,Oman +2001-04-01,29.068,0.41,Oman +2001-05-01,33.66,0.406,Oman +2001-06-01,34.361999999999995,0.409,Oman +2001-07-01,33.736000000000004,0.63,Oman +2001-08-01,32.301,0.612,Oman +2001-09-01,30.964000000000002,0.501,Oman +2001-10-01,28.822,0.34700000000000003,Oman +2001-11-01,24.866,0.318,Oman +2001-12-01,23.184,0.327,Oman +2002-01-01,20.613000000000003,0.505,Oman +2002-02-01,21.193,0.7609999999999999,Oman +2002-03-01,25.750999999999998,0.279,Oman +2002-04-01,28.886,0.381,Oman +2002-05-01,33.227,0.464,Oman +2002-06-01,33.928000000000004,0.422,Oman +2002-07-01,33.455000000000005,0.731,Oman +2002-08-01,32.277,0.999,Oman 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+1817-03-01,16.125,2.191,Pakistan +1817-04-01,22.029,2.033,Pakistan +1817-05-01,25.517,2.208,Pakistan +1817-06-01,27.613000000000003,1.935,Pakistan +1817-07-01,27.813,1.902,Pakistan +1817-08-01,28.003,2.017,Pakistan +1817-09-01,22.609,2.379,Pakistan +1817-10-01,18.668,2.519,Pakistan +1817-11-01,14.515,2.235,Pakistan +1817-12-01,7.657,2.35,Pakistan +1818-01-01,8.059,2.805,Pakistan +1818-02-01,9.495,1.98,Pakistan +1818-03-01,17.019000000000002,1.946,Pakistan +1818-04-01,23.666999999999998,3.932,Pakistan +1818-05-01,27.479,2.622,Pakistan +1818-06-01,30.219,3.151,Pakistan +1818-07-01,28.588,2.484,Pakistan +1818-08-01,27.587000000000003,1.764,Pakistan +1818-09-01,24.183000000000003,2.106,Pakistan +1818-10-01,19.055,2.246,Pakistan +1818-11-01,14.883,3.621,Pakistan +1818-12-01,9.487,1.973,Pakistan +1819-01-01,7.141,2.346,Pakistan +1819-02-01,10.155,2.587,Pakistan +1819-03-01,14.240000000000002,4.517,Pakistan +1819-04-01,20.799,1.822,Pakistan +1819-05-01,25.853,1.905,Pakistan +1819-06-01,28.793000000000006,1.913,Pakistan +1819-07-01,28.37,1.753,Pakistan +1819-08-01,26.91,1.845,Pakistan +1819-09-01,24.746,1.778,Pakistan +1819-10-01,21.125,1.875,Pakistan +1819-11-01,14.885,1.975,Pakistan +1819-12-01,9.006,1.984,Pakistan +1820-01-01,,,Pakistan +1820-02-01,10.013,2.2,Pakistan +1820-03-01,15.722,2.182,Pakistan +1820-04-01,22.828000000000003,1.7380000000000002,Pakistan +1820-05-01,27.212,1.891,Pakistan +1820-06-01,29.355,1.786,Pakistan +1820-07-01,28.598000000000006,2.548,Pakistan +1820-08-01,26.381999999999998,2.17,Pakistan +1820-09-01,24.854,1.962,Pakistan +1820-10-01,19.775,1.736,Pakistan +1820-11-01,14.107999999999999,1.704,Pakistan +1820-12-01,8.661,1.901,Pakistan +1821-01-01,9.564,2.642,Pakistan +1821-02-01,12.081,3.19,Pakistan +1821-03-01,16.686,1.945,Pakistan +1821-04-01,21.53,1.83,Pakistan +1821-05-01,27.223000000000003,1.947,Pakistan +1821-06-01,28.980999999999998,2.371,Pakistan +1821-07-01,28.455,1.986,Pakistan +1821-08-01,27.537000000000006,1.86,Pakistan +1821-09-01,23.806,1.777,Pakistan +1821-10-01,20.554000000000002,1.715,Pakistan +1821-11-01,15.582,2.286,Pakistan +1821-12-01,9.937000000000001,1.915,Pakistan +1822-01-01,9.482000000000001,2.48,Pakistan +1822-02-01,12.809,2.105,Pakistan +1822-03-01,18.939,1.851,Pakistan +1822-04-01,22.121,1.818,Pakistan +1822-05-01,26.594,1.801,Pakistan +1822-06-01,29.287000000000006,1.834,Pakistan +1822-07-01,29.528,1.774,Pakistan +1822-08-01,27.497,2.227,Pakistan +1822-09-01,24.345,1.998,Pakistan +1822-10-01,19.833,1.742,Pakistan +1822-11-01,14.220999999999998,1.88,Pakistan +1822-12-01,8.406,2.182,Pakistan +1823-01-01,8.546,1.898,Pakistan +1823-02-01,10.664,2.301,Pakistan +1823-03-01,16.416,1.852,Pakistan +1823-04-01,21.916999999999998,2.196,Pakistan +1823-05-01,26.624000000000002,1.827,Pakistan +1823-06-01,30.200000000000006,2.22,Pakistan +1823-07-01,29.503,1.784,Pakistan +1823-08-01,27.404,1.884,Pakistan +1823-09-01,23.735,1.823,Pakistan +1823-10-01,19.753,1.897,Pakistan +1823-11-01,14.116,2.661,Pakistan +1823-12-01,,,Pakistan +1824-01-01,,,Pakistan +1824-02-01,,,Pakistan +1824-03-01,,,Pakistan +1824-04-01,,,Pakistan +1824-05-01,,,Pakistan +1824-06-01,,,Pakistan +1824-07-01,,,Pakistan +1824-08-01,,,Pakistan +1824-09-01,,,Pakistan +1824-10-01,,,Pakistan +1824-11-01,,,Pakistan +1824-12-01,,,Pakistan +1825-01-01,,,Pakistan +1825-02-01,,,Pakistan +1825-03-01,,,Pakistan +1825-04-01,,,Pakistan +1825-05-01,,,Pakistan +1825-06-01,,,Pakistan +1825-07-01,,,Pakistan +1825-08-01,,,Pakistan +1825-09-01,,,Pakistan +1825-10-01,,,Pakistan +1825-11-01,,,Pakistan +1825-12-01,,,Pakistan +1826-01-01,,,Pakistan +1826-02-01,,,Pakistan +1826-03-01,,,Pakistan +1826-04-01,,,Pakistan +1826-05-01,,,Pakistan +1826-06-01,,,Pakistan +1826-07-01,,,Pakistan +1826-08-01,,,Pakistan +1826-09-01,,,Pakistan +1826-10-01,,,Pakistan +1826-11-01,,,Pakistan +1826-12-01,,,Pakistan +1827-01-01,,,Pakistan +1827-02-01,,,Pakistan +1827-03-01,,,Pakistan +1827-04-01,,,Pakistan +1827-05-01,,,Pakistan +1827-06-01,,,Pakistan +1827-07-01,,,Pakistan +1827-08-01,,,Pakistan +1827-09-01,,,Pakistan +1827-10-01,,,Pakistan +1827-11-01,,,Pakistan +1827-12-01,,,Pakistan +1828-01-01,,,Pakistan +1828-02-01,,,Pakistan +1828-03-01,,,Pakistan +1828-04-01,,,Pakistan +1828-05-01,,,Pakistan +1828-06-01,,,Pakistan +1828-07-01,,,Pakistan +1828-08-01,,,Pakistan +1828-09-01,,,Pakistan +1828-10-01,,,Pakistan +1828-11-01,,,Pakistan +1828-12-01,,,Pakistan +1829-01-01,,,Pakistan +1829-02-01,,,Pakistan +1829-03-01,,,Pakistan +1829-04-01,,,Pakistan +1829-05-01,,,Pakistan +1829-06-01,,,Pakistan +1829-07-01,,,Pakistan +1829-08-01,,,Pakistan +1829-09-01,,,Pakistan +1829-10-01,,,Pakistan +1829-11-01,,,Pakistan +1829-12-01,,,Pakistan +1830-01-01,,,Pakistan +1830-02-01,,,Pakistan +1830-03-01,,,Pakistan +1830-04-01,,,Pakistan +1830-05-01,,,Pakistan +1830-06-01,,,Pakistan +1830-07-01,,,Pakistan +1830-08-01,,,Pakistan +1830-09-01,,,Pakistan +1830-10-01,,,Pakistan +1830-11-01,,,Pakistan +1830-12-01,,,Pakistan +1831-01-01,,,Pakistan +1831-02-01,,,Pakistan +1831-03-01,,,Pakistan +1831-04-01,,,Pakistan +1831-05-01,,,Pakistan +1831-06-01,,,Pakistan +1831-07-01,,,Pakistan +1831-08-01,,,Pakistan +1831-09-01,,,Pakistan +1831-10-01,,,Pakistan +1831-11-01,,,Pakistan +1831-12-01,,,Pakistan +1832-01-01,,,Pakistan +1832-02-01,,,Pakistan +1832-03-01,,,Pakistan +1832-04-01,,,Pakistan +1832-05-01,,,Pakistan +1832-06-01,,,Pakistan +1832-07-01,,,Pakistan +1832-08-01,,,Pakistan +1832-09-01,,,Pakistan +1832-10-01,,,Pakistan +1832-11-01,,,Pakistan +1832-12-01,,,Pakistan +1833-01-01,9.317,2.363,Pakistan +1833-02-01,11.175999999999998,1.807,Pakistan +1833-03-01,15.682,2.293,Pakistan +1833-04-01,21.601999999999997,1.896,Pakistan +1833-05-01,26.406,1.857,Pakistan +1833-06-01,29.463,1.942,Pakistan +1833-07-01,29.188,1.84,Pakistan +1833-08-01,27.430000000000003,1.951,Pakistan +1833-09-01,24.691999999999997,1.798,Pakistan +1833-10-01,20.122,1.781,Pakistan +1833-11-01,16.53,1.86,Pakistan +1833-12-01,10.694,2.262,Pakistan +1834-01-01,7.247999999999998,1.981,Pakistan +1834-02-01,11.17,2.206,Pakistan +1834-03-01,16.915000000000006,2.121,Pakistan +1834-04-01,21.639,2.076,Pakistan +1834-05-01,27.202,1.769,Pakistan +1834-06-01,28.915000000000006,2.257,Pakistan +1834-07-01,29.346,1.919,Pakistan +1834-08-01,27.599,1.774,Pakistan +1834-09-01,25.19,2.266,Pakistan +1834-10-01,20.578000000000003,1.715,Pakistan +1834-11-01,15.965,2.08,Pakistan +1834-12-01,10.876,1.847,Pakistan +1835-01-01,9.607999999999999,2.006,Pakistan +1835-02-01,11.342,2.07,Pakistan +1835-03-01,15.462000000000002,1.834,Pakistan +1835-04-01,21.369,2.165,Pakistan +1835-05-01,25.706999999999997,1.756,Pakistan +1835-06-01,28.199,1.963,Pakistan +1835-07-01,28.397,1.96,Pakistan +1835-08-01,26.458,1.725,Pakistan +1835-09-01,23.511999999999997,1.823,Pakistan +1835-10-01,19.807000000000002,1.6880000000000002,Pakistan +1835-11-01,13.757,1.689,Pakistan +1835-12-01,8.76,1.808,Pakistan +1836-01-01,8.543,1.6880000000000002,Pakistan +1836-02-01,10.81,1.88,Pakistan +1836-03-01,16.522000000000002,1.736,Pakistan +1836-04-01,21.263,1.731,Pakistan +1836-05-01,,,Pakistan +1836-06-01,29.076,1.7719999999999998,Pakistan +1836-07-01,29.061999999999998,1.915,Pakistan +1836-08-01,26.980999999999998,1.888,Pakistan +1836-09-01,24.517,2.136,Pakistan +1836-10-01,20.879,3.302,Pakistan +1836-11-01,14.142000000000001,1.942,Pakistan +1836-12-01,10.290999999999999,2.188,Pakistan +1837-01-01,,,Pakistan +1837-02-01,,,Pakistan +1837-03-01,,,Pakistan +1837-04-01,,,Pakistan +1837-05-01,,,Pakistan +1837-06-01,,,Pakistan +1837-07-01,29.034,1.781,Pakistan +1837-08-01,27.49,1.709,Pakistan +1837-09-01,24.85,1.725,Pakistan +1837-10-01,19.737000000000002,2.184,Pakistan +1837-11-01,14.347999999999999,2.133,Pakistan +1837-12-01,10.261,2.821,Pakistan +1838-01-01,,,Pakistan +1838-02-01,,,Pakistan +1838-03-01,,,Pakistan +1838-04-01,21.151999999999997,1.926,Pakistan +1838-05-01,,,Pakistan +1838-06-01,29.016,1.894,Pakistan +1838-07-01,29.835,2.435,Pakistan +1838-08-01,27.893,2.524,Pakistan +1838-09-01,24.677,1.905,Pakistan +1838-10-01,20.891,2.634,Pakistan +1838-11-01,15.032,2.232,Pakistan +1838-12-01,,,Pakistan +1839-01-01,,,Pakistan +1839-02-01,,,Pakistan +1839-03-01,,,Pakistan +1839-04-01,,,Pakistan +1839-05-01,,,Pakistan +1839-06-01,,,Pakistan +1839-07-01,,,Pakistan +1839-08-01,,,Pakistan +1839-09-01,,,Pakistan +1839-10-01,,,Pakistan +1839-11-01,,,Pakistan +1839-12-01,,,Pakistan +1840-01-01,9.312000000000001,1.974,Pakistan +1840-02-01,10.749,2.075,Pakistan +1840-03-01,15.172,1.798,Pakistan +1840-04-01,21.881,2.042,Pakistan +1840-05-01,26.786,1.887,Pakistan +1840-06-01,29.552,1.908,Pakistan +1840-07-01,30.184,1.7280000000000002,Pakistan +1840-08-01,27.625,1.736,Pakistan +1840-09-01,24.526,1.751,Pakistan +1840-10-01,,,Pakistan +1840-11-01,14.258,2.468,Pakistan +1840-12-01,9.715,1.852,Pakistan +1841-01-01,7.757000000000001,2.057,Pakistan +1841-02-01,,,Pakistan +1841-03-01,14.514,2.5540000000000003,Pakistan +1841-04-01,20.627,1.831,Pakistan +1841-05-01,26.526,1.715,Pakistan +1841-06-01,29.125999999999998,1.716,Pakistan +1841-07-01,29.776,1.836,Pakistan +1841-08-01,27.759,1.856,Pakistan +1841-09-01,24.582,1.676,Pakistan +1841-10-01,,,Pakistan +1841-11-01,14.649,1.703,Pakistan +1841-12-01,9.632,1.842,Pakistan +1842-01-01,,,Pakistan +1842-02-01,,,Pakistan +1842-03-01,17.295,2.099,Pakistan +1842-04-01,22.498,1.822,Pakistan +1842-05-01,26.441999999999997,1.696,Pakistan +1842-06-01,,,Pakistan +1842-07-01,28.815,1.729,Pakistan +1842-08-01,27.660999999999998,1.843,Pakistan +1842-09-01,24.428,1.702,Pakistan +1842-10-01,20.34,1.653,Pakistan +1842-11-01,14.978,1.854,Pakistan +1842-12-01,11.15,1.582,Pakistan +1843-01-01,9.198,1.982,Pakistan +1843-02-01,12.298,1.695,Pakistan +1843-03-01,16.150000000000002,1.915,Pakistan +1843-04-01,21.930999999999997,1.65,Pakistan +1843-05-01,26.17,1.535,Pakistan +1843-06-01,29.103,1.611,Pakistan +1843-07-01,29.281999999999996,1.664,Pakistan +1843-08-01,27.689,1.828,Pakistan +1843-09-01,24.81,1.52,Pakistan +1843-10-01,20.656,1.668,Pakistan +1843-11-01,14.748,1.476,Pakistan +1843-12-01,10.425999999999998,2.5340000000000003,Pakistan +1844-01-01,8.878,1.608,Pakistan +1844-02-01,10.132,1.989,Pakistan +1844-03-01,16.088,1.871,Pakistan +1844-04-01,21.139,1.651,Pakistan +1844-05-01,25.935,1.508,Pakistan +1844-06-01,28.915000000000006,1.483,Pakistan +1844-07-01,29.038,1.644,Pakistan +1844-08-01,27.561,2.381,Pakistan +1844-09-01,24.297,1.503,Pakistan +1844-10-01,20.901999999999997,1.509,Pakistan +1844-11-01,14.4,1.429,Pakistan +1844-12-01,9.491,1.644,Pakistan +1845-01-01,9.018,1.862,Pakistan +1845-02-01,10.203,1.963,Pakistan +1845-03-01,16.448,1.648,Pakistan +1845-04-01,21.986,1.76,Pakistan +1845-05-01,26.741,1.7,Pakistan +1845-06-01,28.994,1.682,Pakistan +1845-07-01,29.365,1.736,Pakistan +1845-08-01,27.898000000000003,1.751,Pakistan +1845-09-01,24.917,1.682,Pakistan +1845-10-01,20.205,1.668,Pakistan +1845-11-01,14.692,1.732,Pakistan +1845-12-01,8.746,1.992,Pakistan +1846-01-01,9.83,2.245,Pakistan +1846-02-01,12.335999999999999,1.81,Pakistan +1846-03-01,17.637,1.946,Pakistan +1846-04-01,21.230999999999998,1.778,Pakistan +1846-05-01,26.886999999999997,1.884,Pakistan +1846-06-01,29.796999999999997,1.958,Pakistan +1846-07-01,30.171000000000006,2.049,Pakistan +1846-08-01,,,Pakistan +1846-09-01,,,Pakistan +1846-10-01,20.167,1.755,Pakistan +1846-11-01,,,Pakistan +1846-12-01,,,Pakistan +1847-01-01,,,Pakistan +1847-02-01,,,Pakistan +1847-03-01,,,Pakistan +1847-04-01,,,Pakistan +1847-05-01,,,Pakistan +1847-06-01,,,Pakistan +1847-07-01,,,Pakistan +1847-08-01,,,Pakistan +1847-09-01,,,Pakistan +1847-10-01,,,Pakistan +1847-11-01,,,Pakistan +1847-12-01,,,Pakistan +1848-01-01,7.3969999999999985,1.828,Pakistan +1848-02-01,11.179,1.696,Pakistan +1848-03-01,15.655,1.349,Pakistan +1848-04-01,21.604,1.433,Pakistan +1848-05-01,26.247,1.6130000000000002,Pakistan +1848-06-01,28.605,2.016,Pakistan +1848-07-01,29.27,1.781,Pakistan +1848-08-01,27.691999999999997,1.646,Pakistan +1848-09-01,24.63,1.629,Pakistan +1848-10-01,20.588,1.306,Pakistan +1848-11-01,14.738,1.29,Pakistan +1848-12-01,9.781,1.577,Pakistan +1849-01-01,8.597999999999999,1.648,Pakistan +1849-02-01,11.009,1.803,Pakistan +1849-03-01,16.514000000000006,1.769,Pakistan +1849-04-01,21.421999999999997,1.6469999999999998,Pakistan +1849-05-01,26.328000000000003,1.328,Pakistan +1849-06-01,28.614,1.732,Pakistan +1849-07-01,29.316000000000006,1.495,Pakistan +1849-08-01,27.885,1.415,Pakistan +1849-09-01,24.51,1.454,Pakistan +1849-10-01,20.820999999999998,1.505,Pakistan +1849-11-01,15.065999999999999,1.52,Pakistan +1849-12-01,9.347999999999999,1.507,Pakistan +1850-01-01,7.534,1.5530000000000002,Pakistan +1850-02-01,11.255,1.597,Pakistan +1850-03-01,16.535,2.419,Pakistan +1850-04-01,21.346,1.6030000000000002,Pakistan +1850-05-01,25.776999999999997,1.286,Pakistan +1850-06-01,28.945,1.379,Pakistan +1850-07-01,29.813,1.367,Pakistan +1850-08-01,28.098000000000006,1.471,Pakistan +1850-09-01,25.225,1.289,Pakistan +1850-10-01,20.655,1.554,Pakistan +1850-11-01,14.978,2.019,Pakistan +1850-12-01,9.67,1.361,Pakistan +1851-01-01,8.072000000000001,1.457,Pakistan +1851-02-01,11.084000000000001,1.304,Pakistan +1851-03-01,16.173000000000002,1.571,Pakistan +1851-04-01,21.114,1.462,Pakistan +1851-05-01,27.195,1.574,Pakistan +1851-06-01,29.592,1.529,Pakistan +1851-07-01,29.303,1.5590000000000002,Pakistan +1851-08-01,27.947000000000006,1.509,Pakistan +1851-09-01,25.204,1.375,Pakistan +1851-10-01,20.941,1.802,Pakistan +1851-11-01,14.662,1.537,Pakistan +1851-12-01,10.294,2.551,Pakistan +1852-01-01,8.859,2.269,Pakistan +1852-02-01,11.072,1.694,Pakistan +1852-03-01,15.338,1.878,Pakistan +1852-04-01,21.588,1.869,Pakistan +1852-05-01,26.858,1.654,Pakistan +1852-06-01,29.215999999999998,1.626,Pakistan +1852-07-01,29.533,1.739,Pakistan +1852-08-01,27.705,1.591,Pakistan +1852-09-01,25.133000000000003,1.57,Pakistan +1852-10-01,21.121,1.6369999999999998,Pakistan +1852-11-01,14.252,1.588,Pakistan +1852-12-01,10.492,1.802,Pakistan +1853-01-01,7.8870000000000005,1.696,Pakistan +1853-02-01,10.265,1.757,Pakistan +1853-03-01,16.150000000000002,1.5530000000000002,Pakistan +1853-04-01,21.369,1.629,Pakistan +1853-05-01,27.038,1.565,Pakistan +1853-06-01,29.674,1.524,Pakistan +1853-07-01,29.873,1.551,Pakistan +1853-08-01,28.307,1.547,Pakistan +1853-09-01,25.476,1.543,Pakistan +1853-10-01,20.61,1.52,Pakistan +1853-11-01,14.415,1.734,Pakistan +1853-12-01,10.182,1.5490000000000002,Pakistan +1854-01-01,8.464,1.554,Pakistan +1854-02-01,10.702,1.6769999999999998,Pakistan +1854-03-01,16.246000000000002,1.531,Pakistan +1854-04-01,21.346999999999998,1.611,Pakistan +1854-05-01,26.87,1.675,Pakistan +1854-06-01,28.985000000000007,1.6840000000000002,Pakistan +1854-07-01,29.596,1.755,Pakistan +1854-08-01,28.054,1.781,Pakistan +1854-09-01,25.148000000000003,1.6469999999999998,Pakistan +1854-10-01,21.761,1.7480000000000002,Pakistan +1854-11-01,15.421,1.67,Pakistan +1854-12-01,10.675999999999998,1.982,Pakistan +1855-01-01,9.391,1.944,Pakistan +1855-02-01,12.050999999999998,1.967,Pakistan +1855-03-01,,,Pakistan +1855-04-01,23.02,1.836,Pakistan +1855-05-01,27.48,1.737,Pakistan +1855-06-01,29.474,1.746,Pakistan +1855-07-01,29.289,1.676,Pakistan +1855-08-01,28.081999999999997,1.658,Pakistan +1855-09-01,24.897,1.635,Pakistan +1855-10-01,20.552,1.768,Pakistan +1855-11-01,14.602,1.685,Pakistan +1855-12-01,9.573,2.014,Pakistan +1856-01-01,8.77,1.685,Pakistan +1856-02-01,10.902999999999999,1.784,Pakistan +1856-03-01,15.995,1.759,Pakistan +1856-04-01,21.654,1.6669999999999998,Pakistan +1856-05-01,,,Pakistan +1856-06-01,29.515,1.825,Pakistan +1856-07-01,29.363000000000003,1.724,Pakistan +1856-08-01,27.339,1.843,Pakistan +1856-09-01,24.679000000000002,1.7990000000000002,Pakistan +1856-10-01,20.219,1.6130000000000002,Pakistan +1856-11-01,14.703,1.634,Pakistan +1856-12-01,10.202,1.725,Pakistan +1857-01-01,8.382,1.824,Pakistan +1857-02-01,10.24,1.807,Pakistan +1857-03-01,15.595999999999998,1.661,Pakistan +1857-04-01,20.676,1.711,Pakistan +1857-05-01,26.041,1.665,Pakistan +1857-06-01,28.385,1.666,Pakistan +1857-07-01,29.022,1.7,Pakistan +1857-08-01,27.735000000000007,1.805,Pakistan +1857-09-01,25.265,1.779,Pakistan +1857-10-01,19.973,1.804,Pakistan +1857-11-01,14.615,1.788,Pakistan +1857-12-01,10.147,1.681,Pakistan +1858-01-01,,,Pakistan +1858-02-01,,,Pakistan +1858-03-01,,,Pakistan +1858-04-01,,,Pakistan +1858-05-01,,,Pakistan +1858-06-01,,,Pakistan +1858-07-01,,,Pakistan +1858-08-01,,,Pakistan +1858-09-01,,,Pakistan +1858-10-01,,,Pakistan +1858-11-01,,,Pakistan +1858-12-01,,,Pakistan +1859-01-01,,,Pakistan +1859-02-01,,,Pakistan +1859-03-01,,,Pakistan +1859-04-01,,,Pakistan +1859-05-01,,,Pakistan +1859-06-01,,,Pakistan +1859-07-01,,,Pakistan 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+1866-10-01,,,Pakistan +1866-11-01,,,Pakistan +1866-12-01,,,Pakistan +1867-01-01,,,Pakistan +1867-02-01,,,Pakistan +1867-03-01,,,Pakistan +1867-04-01,,,Pakistan +1867-05-01,,,Pakistan +1867-06-01,,,Pakistan +1867-07-01,,,Pakistan +1867-08-01,,,Pakistan +1867-09-01,,,Pakistan +1867-10-01,,,Pakistan +1867-11-01,,,Pakistan +1867-12-01,,,Pakistan +1868-01-01,,,Pakistan +1868-02-01,,,Pakistan +1868-03-01,,,Pakistan +1868-04-01,,,Pakistan +1868-05-01,,,Pakistan +1868-06-01,,,Pakistan +1868-07-01,,,Pakistan +1868-08-01,,,Pakistan +1868-09-01,,,Pakistan +1868-10-01,,,Pakistan +1868-11-01,,,Pakistan +1868-12-01,,,Pakistan +1869-01-01,,,Pakistan +1869-02-01,,,Pakistan +1869-03-01,,,Pakistan +1869-04-01,,,Pakistan +1869-05-01,,,Pakistan +1869-06-01,,,Pakistan +1869-07-01,,,Pakistan +1869-08-01,,,Pakistan +1869-09-01,,,Pakistan +1869-10-01,,,Pakistan +1869-11-01,,,Pakistan +1869-12-01,,,Pakistan +1870-01-01,8.01,1.853,Pakistan +1870-02-01,10.67,1.811,Pakistan +1870-03-01,16.771,1.919,Pakistan +1870-04-01,22.311,1.676,Pakistan +1870-05-01,27.421999999999997,1.6840000000000002,Pakistan +1870-06-01,30.038,1.722,Pakistan +1870-07-01,29.586,1.721,Pakistan +1870-08-01,28.055000000000003,1.648,Pakistan +1870-09-01,26.135,1.6230000000000002,Pakistan +1870-10-01,20.918000000000003,1.679,Pakistan +1870-11-01,14.772,1.92,Pakistan +1870-12-01,9.224,1.6980000000000002,Pakistan +1871-01-01,7.999,1.729,Pakistan +1871-02-01,10.44,1.922,Pakistan +1871-03-01,16.053,1.949,Pakistan +1871-04-01,22.589,1.662,Pakistan +1871-05-01,27.546,1.724,Pakistan +1871-06-01,29.424,1.682,Pakistan +1871-07-01,29.351999999999997,1.654,Pakistan +1871-08-01,28.353,1.651,Pakistan +1871-09-01,25.471,1.638,Pakistan +1871-10-01,21.467,1.633,Pakistan +1871-11-01,14.561,1.773,Pakistan +1871-12-01,10.006,1.715,Pakistan +1872-01-01,8.119,2.083,Pakistan +1872-02-01,10.712,1.756,Pakistan +1872-03-01,16.463,1.7009999999999998,Pakistan +1872-04-01,22.232,1.806,Pakistan +1872-05-01,27.235,1.664,Pakistan +1872-06-01,29.238000000000003,1.682,Pakistan +1872-07-01,29.756999999999998,1.76,Pakistan +1872-08-01,27.869,1.7480000000000002,Pakistan +1872-09-01,25.773000000000003,1.6569999999999998,Pakistan +1872-10-01,21.393,1.658,Pakistan +1872-11-01,14.857999999999999,1.702,Pakistan +1872-12-01,10.222999999999999,2.218,Pakistan +1873-01-01,8.297,1.761,Pakistan +1873-02-01,11.43,1.751,Pakistan +1873-03-01,16.331,1.643,Pakistan +1873-04-01,21.865,1.7,Pakistan +1873-05-01,27.131,1.6840000000000002,Pakistan +1873-06-01,29.585,1.651,Pakistan +1873-07-01,29.717,1.6880000000000002,Pakistan +1873-08-01,27.984,1.625,Pakistan +1873-09-01,25.433000000000003,1.665,Pakistan +1873-10-01,21.316,1.661,Pakistan +1873-11-01,15.905999999999999,1.765,Pakistan +1873-12-01,10.738,1.833,Pakistan +1874-01-01,9.049,1.84,Pakistan +1874-02-01,11.665,1.747,Pakistan +1874-03-01,15.464000000000002,1.77,Pakistan +1874-04-01,22.51,1.703,Pakistan +1874-05-01,,,Pakistan +1874-06-01,29.557,1.759,Pakistan +1874-07-01,29.339,1.63,Pakistan +1874-08-01,28.406,1.732,Pakistan +1874-09-01,25.933000000000003,1.679,Pakistan +1874-10-01,21.061999999999998,1.6669999999999998,Pakistan +1874-11-01,15.436,1.676,Pakistan +1874-12-01,9.955,1.72,Pakistan +1875-01-01,8.193,1.524,Pakistan +1875-02-01,10.494000000000002,1.85,Pakistan +1875-03-01,19.186,1.317,Pakistan +1875-04-01,24.208,1.298,Pakistan +1875-05-01,27.549,1.419,Pakistan +1875-06-01,30.888,1.3090000000000002,Pakistan +1875-07-01,29.815,1.508,Pakistan +1875-08-01,28.018,0.871,Pakistan +1875-09-01,25.564,0.955,Pakistan +1875-10-01,19.155,1.904,Pakistan +1875-11-01,14.735999999999999,1.59,Pakistan +1875-12-01,10.457,1.506,Pakistan +1876-01-01,8.993,0.974,Pakistan +1876-02-01,10.83,1.307,Pakistan +1876-03-01,15.912,1.37,Pakistan +1876-04-01,21.855,1.157,Pakistan +1876-05-01,27.61,0.83,Pakistan +1876-06-01,29.449,0.71,Pakistan +1876-07-01,29.133000000000006,0.767,Pakistan +1876-08-01,27.72,0.789,Pakistan +1876-09-01,24.876,0.823,Pakistan +1876-10-01,19.97,0.8240000000000001,Pakistan +1876-11-01,14.776,1.784,Pakistan +1876-12-01,10.897,1.195,Pakistan +1877-01-01,8.959,1.477,Pakistan +1877-02-01,10.005,1.444,Pakistan +1877-03-01,17.083000000000002,0.61,Pakistan +1877-04-01,20.495,1.208,Pakistan +1877-05-01,25.731,0.8690000000000001,Pakistan +1877-06-01,29.206,1.886,Pakistan +1877-07-01,29.843000000000004,0.969,Pakistan +1877-08-01,29.305,1.038,Pakistan +1877-09-01,26.193,0.782,Pakistan +1877-10-01,20.552,0.919,Pakistan +1877-11-01,16.394000000000002,1.124,Pakistan +1877-12-01,9.717,2.003,Pakistan +1878-01-01,7.5920000000000005,1.384,Pakistan +1878-02-01,11.397,0.873,Pakistan +1878-03-01,18.133000000000006,0.738,Pakistan +1878-04-01,21.476999999999997,0.775,Pakistan +1878-05-01,26.104,0.878,Pakistan +1878-06-01,29.848000000000006,1.0659999999999998,Pakistan +1878-07-01,29.996,1.016,Pakistan +1878-08-01,27.643,1.216,Pakistan +1878-09-01,27.036,0.851,Pakistan +1878-10-01,22.011999999999997,0.644,Pakistan +1878-11-01,14.142999999999999,1.101,Pakistan +1878-12-01,8.892999999999999,1.225,Pakistan +1879-01-01,9.469,0.57,Pakistan +1879-02-01,12.417,0.906,Pakistan +1879-03-01,15.629,0.8690000000000001,Pakistan +1879-04-01,22.680999999999997,1.158,Pakistan +1879-05-01,27.305,1.211,Pakistan +1879-06-01,29.15,1.156,Pakistan +1879-07-01,29.315,1.043,Pakistan +1879-08-01,28.142000000000003,0.7509999999999999,Pakistan +1879-09-01,25.159000000000002,0.598,Pakistan +1879-10-01,20.678,0.8490000000000001,Pakistan +1879-11-01,13.2,0.647,Pakistan +1879-12-01,9.537,0.943,Pakistan +1880-01-01,10.532,1.013,Pakistan +1880-02-01,9.29,0.5720000000000001,Pakistan +1880-03-01,20.498,1.257,Pakistan +1880-04-01,23.805,0.5720000000000001,Pakistan +1880-05-01,27.549,1.219,Pakistan +1880-06-01,29.875,0.931,Pakistan +1880-07-01,28.475,0.765,Pakistan +1880-08-01,27.932,0.955,Pakistan +1880-09-01,25.308000000000003,0.897,Pakistan +1880-10-01,21.197,0.8109999999999999,Pakistan +1880-11-01,14.797,1.757,Pakistan +1880-12-01,10.696,0.816,Pakistan +1881-01-01,9.825,0.99,Pakistan +1881-02-01,13.425999999999998,0.779,Pakistan +1881-03-01,16.492,0.738,Pakistan +1881-04-01,21.874,1.36,Pakistan +1881-05-01,27.807,0.7190000000000001,Pakistan +1881-06-01,29.357000000000006,0.7859999999999999,Pakistan +1881-07-01,29.500999999999998,0.917,Pakistan +1881-08-01,27.950000000000006,0.747,Pakistan +1881-09-01,25.891,0.568,Pakistan +1881-10-01,21.572,0.436,Pakistan +1881-11-01,14.697,0.573,Pakistan +1881-12-01,10.405999999999999,1.031,Pakistan +1882-01-01,9.994,0.974,Pakistan +1882-02-01,9.845,0.759,Pakistan +1882-03-01,16.406000000000002,0.479,Pakistan +1882-04-01,21.695,0.606,Pakistan +1882-05-01,26.789,1.133,Pakistan +1882-06-01,29.951999999999998,0.6,Pakistan +1882-07-01,27.845,0.965,Pakistan +1882-08-01,27.636999999999997,0.754,Pakistan +1882-09-01,24.906999999999996,0.461,Pakistan +1882-10-01,21.237,1.013,Pakistan +1882-11-01,14.218,1.06,Pakistan +1882-12-01,11.417,0.431,Pakistan 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+1970-03-01,15.974,0.31,Pakistan +1970-04-01,23.684,0.244,Pakistan +1970-05-01,28.425,0.207,Pakistan +1970-06-01,30.343000000000004,0.305,Pakistan +1970-07-01,29.615,0.311,Pakistan +1970-08-01,29.113000000000003,0.322,Pakistan +1970-09-01,26.273000000000003,0.231,Pakistan +1970-10-01,22.326999999999998,0.316,Pakistan +1970-11-01,15.731,0.243,Pakistan +1970-12-01,11.033,0.269,Pakistan +1971-01-01,8.269,0.316,Pakistan +1971-02-01,11.934,0.41,Pakistan +1971-03-01,17.614,0.269,Pakistan +1971-04-01,24.351999999999997,0.302,Pakistan +1971-05-01,27.678,0.404,Pakistan +1971-06-01,30.28,0.371,Pakistan +1971-07-01,29.014000000000006,0.244,Pakistan +1971-08-01,28.252,0.269,Pakistan +1971-09-01,25.351999999999997,0.321,Pakistan +1971-10-01,20.857,0.254,Pakistan +1971-11-01,16.208,0.218,Pakistan +1971-12-01,11.785,0.138,Pakistan +1972-01-01,9.347000000000001,0.412,Pakistan +1972-02-01,7.789,0.425,Pakistan +1972-03-01,16.816000000000006,0.17800000000000002,Pakistan +1972-04-01,21.308000000000003,0.294,Pakistan +1972-05-01,26.516,0.187,Pakistan +1972-06-01,30.006,0.23,Pakistan +1972-07-01,29.205,0.382,Pakistan +1972-08-01,27.66,0.249,Pakistan +1972-09-01,25.226999999999997,0.222,Pakistan +1972-10-01,20.68,0.295,Pakistan +1972-11-01,16.342,0.222,Pakistan +1972-12-01,10.306,0.272,Pakistan +1973-01-01,7.374,0.45,Pakistan +1973-02-01,12.587,0.454,Pakistan +1973-03-01,16.482000000000006,0.319,Pakistan +1973-04-01,23.406,0.152,Pakistan +1973-05-01,27.98,0.34700000000000003,Pakistan +1973-06-01,30.903,0.227,Pakistan +1973-07-01,29.278,0.338,Pakistan +1973-08-01,28.232000000000006,0.417,Pakistan +1973-09-01,26.324,0.202,Pakistan +1973-10-01,21.119,0.408,Pakistan +1973-11-01,15.462,0.253,Pakistan +1973-12-01,9.728,0.362,Pakistan +1974-01-01,8.61,0.441,Pakistan +1974-02-01,9.02,0.28300000000000003,Pakistan +1974-03-01,18.533,0.386,Pakistan +1974-04-01,23.625,0.244,Pakistan +1974-05-01,26.996,0.191,Pakistan +1974-06-01,29.164,0.194,Pakistan 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+1994-03-01,18.715999999999998,0.312,Pakistan +1994-04-01,21.393,0.25,Pakistan +1994-05-01,28.06,0.215,Pakistan +1994-06-01,31.06,0.316,Pakistan +1994-07-01,29.518,0.406,Pakistan +1994-08-01,28.796000000000006,0.284,Pakistan +1994-09-01,24.81,0.292,Pakistan +1994-10-01,21.269000000000002,0.265,Pakistan +1994-11-01,17.775,0.267,Pakistan +1994-12-01,11.163,0.243,Pakistan +1995-01-01,9.423,0.18,Pakistan +1995-02-01,12.356,0.293,Pakistan +1995-03-01,15.981000000000002,0.34,Pakistan +1995-04-01,21.291,0.278,Pakistan +1995-05-01,27.726999999999997,0.3,Pakistan +1995-06-01,30.971999999999998,0.411,Pakistan +1995-07-01,29.971,0.24,Pakistan +1995-08-01,28.895000000000003,0.33,Pakistan +1995-09-01,26.476999999999997,0.18,Pakistan +1995-10-01,22.476999999999997,0.203,Pakistan +1995-11-01,16.382,0.284,Pakistan +1995-12-01,10.935,0.246,Pakistan +1996-01-01,9.004,0.226,Pakistan +1996-02-01,12.725,0.271,Pakistan +1996-03-01,17.867,0.311,Pakistan +1996-04-01,22.959,0.293,Pakistan +1996-05-01,26.544,0.303,Pakistan +1996-06-01,29.698,0.383,Pakistan +1996-07-01,29.795,0.28800000000000003,Pakistan +1996-08-01,28.206999999999997,0.238,Pakistan +1996-09-01,27.046,0.154,Pakistan +1996-10-01,21.435,0.278,Pakistan +1996-11-01,15.190999999999999,0.246,Pakistan +1996-12-01,10.735,0.27,Pakistan +1997-01-01,9.479,0.5529999999999999,Pakistan +1997-02-01,12.440999999999999,0.376,Pakistan +1997-03-01,16.909000000000002,0.423,Pakistan +1997-04-01,21.281,0.482,Pakistan +1997-05-01,25.731,0.364,Pakistan +1997-06-01,29.309,0.25,Pakistan +1997-07-01,30.706000000000003,0.364,Pakistan +1997-08-01,28.724,0.358,Pakistan +1997-09-01,27.056,0.378,Pakistan +1997-10-01,21.003,0.267,Pakistan +1997-11-01,15.865,0.303,Pakistan +1997-12-01,10.360999999999999,0.334,Pakistan +1998-01-01,9.834,0.366,Pakistan +1998-02-01,12.104,0.626,Pakistan +1998-03-01,16.602,0.447,Pakistan +1998-04-01,23.991999999999997,0.293,Pakistan +1998-05-01,28.148000000000003,0.315,Pakistan +1998-06-01,30.002,0.361,Pakistan +1998-07-01,30.432,0.218,Pakistan +1998-08-01,29.638,0.354,Pakistan +1998-09-01,27.135,0.263,Pakistan +1998-10-01,22.798,0.292,Pakistan +1998-11-01,16.820999999999998,0.304,Pakistan +1998-12-01,12.447000000000001,0.371,Pakistan +1999-01-01,9.815,0.295,Pakistan +1999-02-01,13.306,0.265,Pakistan +1999-03-01,17.765,0.271,Pakistan +1999-04-01,24.165,0.198,Pakistan +1999-05-01,28.187,0.193,Pakistan +1999-06-01,30.146,0.5329999999999999,Pakistan +1999-07-01,30.221,0.35200000000000004,Pakistan +1999-08-01,28.816999999999997,0.362,Pakistan +1999-09-01,27.467,0.28600000000000003,Pakistan +1999-10-01,23.051,0.338,Pakistan +1999-11-01,17.561,0.284,Pakistan +1999-12-01,11.855,0.353,Pakistan +2000-01-01,10.295,0.374,Pakistan +2000-02-01,11.355,0.3720000000000001,Pakistan +2000-03-01,17.115000000000002,0.19,Pakistan +2000-04-01,25.131,0.261,Pakistan +2000-05-01,30.058000000000003,0.374,Pakistan +2000-06-01,30.449,0.3720000000000001,Pakistan +2000-07-01,29.796999999999997,0.273,Pakistan +2000-08-01,28.971,0.299,Pakistan +2000-09-01,26.78,0.363,Pakistan +2000-10-01,23.038,0.239,Pakistan +2000-11-01,16.811,0.251,Pakistan +2000-12-01,12.07,0.304,Pakistan +2001-01-01,9.099,0.433,Pakistan +2001-02-01,12.673,0.403,Pakistan +2001-03-01,18.320999999999998,0.28300000000000003,Pakistan +2001-04-01,23.361,0.711,Pakistan +2001-05-01,29.743000000000002,0.364,Pakistan +2001-06-01,30.539,0.391,Pakistan +2001-07-01,29.909,0.28,Pakistan +2001-08-01,28.988000000000003,0.207,Pakistan +2001-09-01,26.732,0.28600000000000003,Pakistan +2001-10-01,23.087,0.406,Pakistan +2001-11-01,17.434,0.274,Pakistan +2001-12-01,13.279000000000002,0.225,Pakistan +2002-01-01,9.93,0.34600000000000003,Pakistan +2002-02-01,12.092,0.25,Pakistan +2002-03-01,18.71,0.228,Pakistan +2002-04-01,24.336,0.248,Pakistan +2002-05-01,29.596999999999998,0.257,Pakistan +2002-06-01,31.158,0.233,Pakistan +2002-07-01,30.585,0.29,Pakistan +2002-08-01,29.511000000000006,0.366,Pakistan +2002-09-01,26.058000000000003,0.191,Pakistan +2002-10-01,23.195,0.386,Pakistan +2002-11-01,17.133,0.241,Pakistan +2002-12-01,12.001,0.445,Pakistan +2003-01-01,10.353,0.411,Pakistan +2003-02-01,12.81,0.511,Pakistan +2003-03-01,17.817,0.154,Pakistan +2003-04-01,23.715,0.332,Pakistan +2003-05-01,27.156999999999996,0.292,Pakistan +2003-06-01,31.019,0.406,Pakistan +2003-07-01,29.693,0.555,Pakistan +2003-08-01,28.864,0.279,Pakistan +2003-09-01,26.625,0.33,Pakistan +2003-10-01,22.12,0.414,Pakistan +2003-11-01,15.627,0.249,Pakistan +2003-12-01,11.385,0.272,Pakistan +2004-01-01,10.504000000000001,0.16699999999999998,Pakistan +2004-02-01,13.917,0.318,Pakistan +2004-03-01,20.748,0.226,Pakistan +2004-04-01,25.05,0.276,Pakistan +2004-05-01,28.515,0.392,Pakistan +2004-06-01,30.254,0.4,Pakistan +2004-07-01,30.284,0.214,Pakistan +2004-08-01,29.151999999999997,0.35200000000000004,Pakistan +2004-09-01,27.004,0.313,Pakistan +2004-10-01,20.736,0.446,Pakistan +2004-11-01,17.504,0.21,Pakistan 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+1897-04-01,27.045,0.731,Palmyra Atoll +1897-05-01,27.158,0.706,Palmyra Atoll +1897-06-01,27.265,0.631,Palmyra Atoll +1897-07-01,27.331000000000003,0.812,Palmyra Atoll +1897-08-01,27.654,0.55,Palmyra Atoll +1897-09-01,27.565,0.7609999999999999,Palmyra Atoll +1897-10-01,27.256,0.765,Palmyra Atoll +1897-11-01,26.959,1.321,Palmyra Atoll +1897-12-01,26.48,0.604,Palmyra Atoll +1898-01-01,26.661,0.7340000000000001,Palmyra Atoll +1898-02-01,25.724,0.623,Palmyra Atoll +1898-03-01,25.891,0.936,Palmyra Atoll +1898-04-01,26.503,0.639,Palmyra Atoll +1898-05-01,26.997,1.0,Palmyra Atoll +1898-06-01,27.041999999999998,0.7709999999999999,Palmyra Atoll +1898-07-01,27.467,0.743,Palmyra Atoll +1898-08-01,27.388,0.6679999999999999,Palmyra Atoll +1898-09-01,27.129,0.588,Palmyra Atoll +1898-10-01,26.651999999999997,0.5670000000000001,Palmyra Atoll +1898-11-01,26.478,0.667,Palmyra Atoll +1898-12-01,25.994,0.943,Palmyra Atoll +1899-01-01,25.944000000000003,0.812,Palmyra Atoll +1899-02-01,25.96,0.602,Palmyra Atoll +1899-03-01,26.335,0.635,Palmyra Atoll +1899-04-01,26.491999999999997,0.904,Palmyra Atoll +1899-05-01,26.926,0.579,Palmyra Atoll +1899-06-01,26.872,0.69,Palmyra Atoll +1899-07-01,27.354000000000006,0.674,Palmyra Atoll +1899-08-01,27.562,0.679,Palmyra Atoll +1899-09-01,27.575,0.7340000000000001,Palmyra Atoll +1899-10-01,27.123,0.7559999999999999,Palmyra Atoll +1899-11-01,27.245,0.754,Palmyra Atoll +1899-12-01,26.236,0.705,Palmyra Atoll +1900-01-01,25.714000000000002,0.775,Palmyra Atoll +1900-02-01,26.439,0.794,Palmyra Atoll +1900-03-01,27.164,0.715,Palmyra Atoll +1900-04-01,27.014,0.7070000000000001,Palmyra Atoll +1900-05-01,27.273000000000003,0.6829999999999999,Palmyra Atoll +1900-06-01,27.283,0.721,Palmyra Atoll +1900-07-01,27.424000000000003,0.775,Palmyra Atoll +1900-08-01,27.719,0.687,Palmyra Atoll +1900-09-01,27.546999999999997,0.7140000000000001,Palmyra Atoll +1900-10-01,27.386000000000006,0.762,Palmyra Atoll +1900-11-01,26.755,0.612,Palmyra Atoll +1900-12-01,26.631,0.7120000000000001,Palmyra Atoll +1901-01-01,26.537,0.741,Palmyra Atoll +1901-02-01,26.378,0.731,Palmyra Atoll +1901-03-01,27.358,0.722,Palmyra Atoll +1901-04-01,27.254,0.662,Palmyra Atoll +1901-05-01,27.062,0.6559999999999999,Palmyra Atoll +1901-06-01,27.145,0.6459999999999999,Palmyra Atoll +1901-07-01,27.366000000000003,0.696,Palmyra Atoll +1901-08-01,27.389,0.705,Palmyra Atoll +1901-09-01,27.177,0.8220000000000001,Palmyra Atoll +1901-10-01,26.693,0.725,Palmyra Atoll +1901-11-01,26.813000000000002,0.6890000000000001,Palmyra Atoll +1901-12-01,26.216,0.9,Palmyra Atoll +1902-01-01,26.283,0.8220000000000001,Palmyra Atoll +1902-02-01,26.535999999999998,1.105,Palmyra Atoll +1902-03-01,26.264,0.716,Palmyra Atoll +1902-04-01,26.271,0.8590000000000001,Palmyra Atoll +1902-05-01,26.813000000000002,0.617,Palmyra Atoll +1902-06-01,27.17,0.6809999999999999,Palmyra Atoll +1902-07-01,27.590999999999998,0.752,Palmyra Atoll +1902-08-01,27.582,0.72,Palmyra Atoll +1902-09-01,27.403000000000002,0.765,Palmyra Atoll +1902-10-01,26.72,0.616,Palmyra Atoll +1902-11-01,26.155,0.682,Palmyra Atoll +1902-12-01,26.136999999999997,0.944,Palmyra Atoll +1903-01-01,25.715,1.416,Palmyra Atoll +1903-02-01,26.316,0.958,Palmyra Atoll +1903-03-01,26.478,0.622,Palmyra Atoll +1903-04-01,26.471999999999998,0.682,Palmyra Atoll +1903-05-01,26.651,0.644,Palmyra Atoll +1903-06-01,26.769,0.608,Palmyra Atoll +1903-07-01,27.112,0.573,Palmyra Atoll +1903-08-01,27.281,0.6809999999999999,Palmyra Atoll +1903-09-01,26.796,0.674,Palmyra Atoll +1903-10-01,26.289,0.993,Palmyra Atoll +1903-11-01,26.467,0.713,Palmyra Atoll +1903-12-01,26.086,0.696,Palmyra Atoll +1904-01-01,25.783,0.733,Palmyra Atoll +1904-02-01,25.835,0.727,Palmyra Atoll +1904-03-01,26.378,0.615,Palmyra Atoll +1904-04-01,26.331999999999997,0.551,Palmyra Atoll +1904-05-01,26.584,0.61,Palmyra Atoll +1904-06-01,26.874,0.5720000000000001,Palmyra Atoll +1904-07-01,26.997,0.591,Palmyra Atoll +1904-08-01,27.336,0.76,Palmyra Atoll +1904-09-01,27.098000000000003,0.8320000000000001,Palmyra Atoll +1904-10-01,26.928,0.696,Palmyra Atoll +1904-11-01,26.966,0.754,Palmyra Atoll +1904-12-01,26.478,0.5710000000000001,Palmyra Atoll +1905-01-01,25.63,1.045,Palmyra Atoll +1905-02-01,25.958000000000002,0.685,Palmyra Atoll +1905-03-01,26.017,1.029,Palmyra Atoll +1905-04-01,26.749,0.64,Palmyra Atoll +1905-05-01,27.035,0.562,Palmyra Atoll +1905-06-01,27.026,0.581,Palmyra Atoll +1905-07-01,27.443,0.601,Palmyra Atoll +1905-08-01,27.572,0.537,Palmyra Atoll +1905-09-01,27.487,0.652,Palmyra Atoll +1905-10-01,27.234,0.654,Palmyra Atoll +1905-11-01,27.205,0.634,Palmyra Atoll +1905-12-01,26.738000000000003,0.88,Palmyra Atoll +1906-01-01,26.704,1.026,Palmyra Atoll +1906-02-01,26.045,0.633,Palmyra Atoll +1906-03-01,26.683000000000003,0.59,Palmyra Atoll +1906-04-01,27.24,0.601,Palmyra Atoll +1906-05-01,27.268,0.6890000000000001,Palmyra Atoll +1906-06-01,27.559,0.6459999999999999,Palmyra Atoll +1906-07-01,27.476999999999997,0.552,Palmyra Atoll +1906-08-01,27.35,0.736,Palmyra Atoll +1906-09-01,27.443,0.57,Palmyra Atoll +1906-10-01,27.109,0.6629999999999999,Palmyra Atoll +1906-11-01,26.495,0.586,Palmyra Atoll +1906-12-01,26.604,0.887,Palmyra Atoll +1907-01-01,26.256,0.601,Palmyra Atoll +1907-02-01,25.953000000000003,0.565,Palmyra Atoll +1907-03-01,26.271,0.55,Palmyra Atoll +1907-04-01,26.454,0.6609999999999999,Palmyra Atoll +1907-05-01,26.715,0.637,Palmyra Atoll +1907-06-01,26.925,0.56,Palmyra Atoll +1907-07-01,27.101000000000006,0.536,Palmyra Atoll +1907-08-01,27.267,0.5379999999999999,Palmyra Atoll +1907-09-01,27.089000000000002,0.561,Palmyra Atoll +1907-10-01,26.987,0.565,Palmyra Atoll +1907-11-01,26.34,0.544,Palmyra Atoll +1907-12-01,26.395,0.589,Palmyra Atoll +1908-01-01,25.796999999999997,0.861,Palmyra Atoll +1908-02-01,26.131,0.607,Palmyra Atoll +1908-03-01,26.099,0.65,Palmyra Atoll +1908-04-01,26.339,0.513,Palmyra Atoll +1908-05-01,27.035,0.5720000000000001,Palmyra Atoll +1908-06-01,26.865,0.608,Palmyra Atoll +1908-07-01,27.346999999999998,0.716,Palmyra Atoll +1908-08-01,27.366,0.8340000000000001,Palmyra Atoll +1908-09-01,27.08,0.615,Palmyra Atoll +1908-10-01,26.569000000000006,0.584,Palmyra Atoll +1908-11-01,26.564,0.843,Palmyra Atoll +1908-12-01,26.076,0.6459999999999999,Palmyra Atoll +1909-01-01,25.74,0.77,Palmyra Atoll +1909-02-01,26.135,0.5429999999999999,Palmyra Atoll +1909-03-01,26.331999999999997,0.728,Palmyra Atoll +1909-04-01,26.534000000000002,0.698,Palmyra Atoll +1909-05-01,26.379,0.581,Palmyra Atoll +1909-06-01,26.767,0.532,Palmyra Atoll +1909-07-01,27.173,0.578,Palmyra Atoll +1909-08-01,27.103,0.536,Palmyra Atoll +1909-09-01,27.119,0.586,Palmyra Atoll +1909-10-01,26.631,0.8170000000000001,Palmyra Atoll +1909-11-01,26.436,1.159,Palmyra Atoll +1909-12-01,25.923000000000002,0.95,Palmyra Atoll +1910-01-01,26.045,0.79,Palmyra Atoll +1910-02-01,25.901999999999997,0.6920000000000001,Palmyra Atoll +1910-03-01,26.255,0.444,Palmyra Atoll +1910-04-01,26.351999999999997,0.513,Palmyra Atoll +1910-05-01,26.388,0.39,Palmyra Atoll +1910-06-01,26.331,0.8859999999999999,Palmyra Atoll +1910-07-01,26.502,0.8859999999999999,Palmyra Atoll +1910-08-01,26.778000000000002,0.8859999999999999,Palmyra Atoll +1910-09-01,26.674,0.669,Palmyra Atoll +1910-10-01,26.201999999999998,0.85,Palmyra Atoll +1910-11-01,26.509,0.512,Palmyra Atoll +1910-12-01,26.031999999999996,0.507,Palmyra Atoll +1911-01-01,25.844,0.455,Palmyra Atoll +1911-02-01,26.203000000000003,0.511,Palmyra Atoll +1911-03-01,26.319000000000003,0.96,Palmyra Atoll +1911-04-01,26.677,0.411,Palmyra Atoll +1911-05-01,26.519000000000002,0.624,Palmyra Atoll +1911-06-01,26.698,0.413,Palmyra Atoll +1911-07-01,26.85,0.5479999999999999,Palmyra Atoll +1911-08-01,27.448,0.423,Palmyra Atoll +1911-09-01,27.250999999999998,0.608,Palmyra Atoll +1911-10-01,26.730999999999998,0.63,Palmyra Atoll +1911-11-01,27.384,0.903,Palmyra Atoll +1911-12-01,26.515,0.6579999999999999,Palmyra Atoll +1912-01-01,26.471,0.745,Palmyra Atoll +1912-02-01,26.616,0.913,Palmyra Atoll +1912-03-01,26.799,0.708,Palmyra Atoll +1912-04-01,27.115,0.644,Palmyra Atoll +1912-05-01,26.534000000000002,1.239,Palmyra Atoll +1912-06-01,26.953000000000003,0.662,Palmyra Atoll +1912-07-01,27.34,0.7440000000000001,Palmyra Atoll +1912-08-01,26.974,0.5660000000000001,Palmyra Atoll +1912-09-01,26.744,0.451,Palmyra Atoll +1912-10-01,26.695999999999998,0.4370000000000001,Palmyra Atoll +1912-11-01,27.289,1.341,Palmyra Atoll +1912-12-01,26.519000000000002,0.384,Palmyra Atoll +1913-01-01,26.621,1.094,Palmyra Atoll +1913-02-01,26.146,0.578,Palmyra Atoll +1913-03-01,26.144,0.6609999999999999,Palmyra Atoll +1913-04-01,26.65,0.5379999999999999,Palmyra Atoll +1913-05-01,26.622,0.5539999999999999,Palmyra Atoll +1913-06-01,26.741,0.784,Palmyra Atoll +1913-07-01,27.028,1.004,Palmyra Atoll +1913-08-01,27.391,0.97,Palmyra Atoll +1913-09-01,27.179,0.647,Palmyra Atoll +1913-10-01,27.237,0.42,Palmyra Atoll +1913-11-01,27.399,1.142,Palmyra Atoll +1913-12-01,26.702,0.6629999999999999,Palmyra Atoll +1914-01-01,26.180999999999997,1.159,Palmyra Atoll +1914-02-01,26.083000000000002,1.015,Palmyra Atoll +1914-03-01,26.315,0.675,Palmyra Atoll +1914-04-01,26.980999999999998,0.557,Palmyra Atoll +1914-05-01,27.007,0.659,Palmyra Atoll +1914-06-01,27.288,0.594,Palmyra Atoll +1914-07-01,27.538,0.396,Palmyra Atoll +1914-08-01,28.008000000000003,0.87,Palmyra Atoll +1914-09-01,27.798,0.865,Palmyra Atoll +1914-10-01,27.358,0.404,Palmyra Atoll +1914-11-01,27.542,1.544,Palmyra Atoll +1914-12-01,26.775,0.926,Palmyra Atoll +1915-01-01,26.184,0.46,Palmyra Atoll +1915-02-01,26.936999999999998,1.265,Palmyra Atoll +1915-03-01,26.535999999999998,0.908,Palmyra Atoll +1915-04-01,27.07,0.711,Palmyra Atoll +1915-05-01,27.077,1.21,Palmyra Atoll +1915-06-01,27.47,0.7490000000000001,Palmyra Atoll +1915-07-01,27.674000000000003,0.596,Palmyra Atoll +1915-08-01,27.919,0.866,Palmyra Atoll +1915-09-01,27.788,0.836,Palmyra Atoll +1915-10-01,26.695999999999998,0.472,Palmyra Atoll +1915-11-01,26.774,0.453,Palmyra Atoll +1915-12-01,26.774,0.552,Palmyra Atoll +1916-01-01,26.254,0.6920000000000001,Palmyra Atoll +1916-02-01,26.673000000000002,0.429,Palmyra Atoll +1916-03-01,27.235,1.173,Palmyra Atoll +1916-04-01,26.916999999999998,0.63,Palmyra Atoll +1916-05-01,26.989,0.6940000000000001,Palmyra Atoll +1916-06-01,26.73,0.512,Palmyra Atoll +1916-07-01,26.921999999999997,0.5529999999999999,Palmyra Atoll +1916-08-01,26.859,0.787,Palmyra Atoll +1916-09-01,26.749000000000002,0.988,Palmyra Atoll +1916-10-01,26.412000000000006,1.128,Palmyra Atoll +1916-11-01,26.13,0.89,Palmyra Atoll +1916-12-01,25.313000000000002,0.921,Palmyra Atoll +1917-01-01,25.664,0.612,Palmyra Atoll +1917-02-01,25.803,0.499,Palmyra Atoll +1917-03-01,26.093000000000004,0.647,Palmyra Atoll +1917-04-01,26.296,0.777,Palmyra Atoll +1917-05-01,26.04,0.642,Palmyra Atoll +1917-06-01,26.3,0.473,Palmyra Atoll +1917-07-01,26.471,1.048,Palmyra Atoll +1917-08-01,26.933000000000003,0.6970000000000001,Palmyra Atoll +1917-09-01,26.881999999999998,0.613,Palmyra Atoll +1917-10-01,26.238000000000003,0.513,Palmyra Atoll +1917-11-01,26.618000000000002,0.613,Palmyra Atoll +1917-12-01,26.083000000000002,1.224,Palmyra Atoll +1918-01-01,26.006,0.5710000000000001,Palmyra Atoll +1918-02-01,26.316,0.5,Palmyra Atoll +1918-03-01,26.831999999999997,1.351,Palmyra Atoll +1918-04-01,25.964,0.928,Palmyra Atoll +1918-05-01,26.433000000000003,0.4,Palmyra Atoll +1918-06-01,26.561999999999998,0.821,Palmyra Atoll +1918-07-01,26.85,0.735,Palmyra Atoll +1918-08-01,27.04,0.598,Palmyra Atoll +1918-09-01,27.199,0.763,Palmyra Atoll +1918-10-01,27.143,0.502,Palmyra Atoll +1918-11-01,27.351999999999997,0.565,Palmyra Atoll +1918-12-01,26.433000000000003,1.051,Palmyra Atoll +1919-01-01,25.983,0.946,Palmyra Atoll +1919-02-01,26.955,0.848,Palmyra Atoll +1919-03-01,26.831,0.45,Palmyra Atoll +1919-04-01,26.956999999999997,0.42,Palmyra Atoll +1919-05-01,26.962,0.433,Palmyra Atoll +1919-06-01,27.069000000000006,0.434,Palmyra Atoll +1919-07-01,27.183000000000003,0.426,Palmyra Atoll +1919-08-01,27.877,0.628,Palmyra Atoll +1919-09-01,27.248,0.394,Palmyra Atoll +1919-10-01,26.304,1.419,Palmyra Atoll +1919-11-01,26.881,0.672,Palmyra Atoll +1919-12-01,26.644000000000002,0.735,Palmyra Atoll +1920-01-01,26.961,0.476,Palmyra Atoll +1920-02-01,26.265,0.59,Palmyra Atoll +1920-03-01,26.429,0.8490000000000001,Palmyra Atoll +1920-04-01,26.717,0.522,Palmyra Atoll +1920-05-01,26.904,0.48,Palmyra Atoll +1920-06-01,26.974,0.695,Palmyra Atoll +1920-07-01,27.285999999999998,0.4370000000000001,Palmyra Atoll +1920-08-01,27.314,0.399,Palmyra Atoll +1920-09-01,26.158,1.715,Palmyra Atoll +1920-10-01,26.738000000000003,0.68,Palmyra Atoll +1920-11-01,26.478,0.706,Palmyra Atoll +1920-12-01,25.988000000000003,0.789,Palmyra Atoll +1921-01-01,26.634,0.6659999999999999,Palmyra Atoll +1921-02-01,26.568,0.701,Palmyra Atoll +1921-03-01,26.616999999999997,0.6809999999999999,Palmyra Atoll +1921-04-01,27.061,0.703,Palmyra Atoll +1921-05-01,27.024,0.682,Palmyra Atoll +1921-06-01,27.191999999999997,0.74,Palmyra Atoll +1921-07-01,27.738000000000003,0.667,Palmyra Atoll +1921-08-01,27.632,0.621,Palmyra Atoll +1921-09-01,27.052,0.544,Palmyra Atoll +1921-10-01,26.535999999999998,1.138,Palmyra Atoll +1921-11-01,26.772,0.682,Palmyra Atoll +1921-12-01,26.732,0.672,Palmyra Atoll +1922-01-01,26.11,0.7,Palmyra Atoll +1922-02-01,25.735,0.452,Palmyra Atoll +1922-03-01,26.65,0.679,Palmyra Atoll +1922-04-01,26.750999999999998,1.222,Palmyra Atoll +1922-05-01,27.124000000000002,0.446,Palmyra Atoll +1922-06-01,26.837,0.725,Palmyra Atoll +1922-07-01,27.233,0.624,Palmyra Atoll +1922-08-01,27.101999999999997,0.5329999999999999,Palmyra Atoll +1922-09-01,27.076999999999998,0.409,Palmyra Atoll +1922-10-01,26.576,0.799,Palmyra Atoll +1922-11-01,26.768,0.562,Palmyra Atoll +1922-12-01,26.281999999999996,0.451,Palmyra Atoll 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Atoll +2013-01-01,27.395,0.627,Palmyra Atoll +2013-02-01,27.43,0.628,Palmyra Atoll +2013-03-01,27.668000000000003,0.63,Palmyra Atoll +2013-04-01,27.665,0.64,Palmyra Atoll +2013-05-01,28.021,0.629,Palmyra Atoll +2013-06-01,28.183000000000003,0.634,Palmyra Atoll +2013-07-01,28.200000000000006,0.675,Palmyra Atoll +2013-08-01,28.476999999999997,0.649,Palmyra Atoll +2013-09-01,,,Palmyra Atoll +1825-01-01,26.673000000000002,1.639,Panama +1825-02-01,26.405,1.235,Panama +1825-03-01,26.838,1.567,Panama +1825-04-01,27.76,1.808,Panama +1825-05-01,26.658,1.028,Panama +1825-06-01,26.969,0.938,Panama +1825-07-01,26.281999999999996,1.825,Panama +1825-08-01,25.729000000000006,1.838,Panama +1825-09-01,24.978,1.46,Panama +1825-10-01,24.405,1.261,Panama +1825-11-01,26.226999999999997,1.484,Panama +1825-12-01,25.456,1.176,Panama +1826-01-01,,,Panama +1826-02-01,25.246,1.391,Panama +1826-03-01,26.921,1.608,Panama +1826-04-01,27.042,1.986,Panama +1826-05-01,27.486,1.154,Panama +1826-06-01,,,Panama 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+1919-06-01,22.382,1.265,Reunion +1919-07-01,20.784,0.52,Reunion +1919-08-01,21.019,0.912,Reunion +1919-09-01,21.256999999999998,0.713,Reunion +1919-10-01,21.386,0.47,Reunion +1919-11-01,23.272,0.754,Reunion +1919-12-01,24.593000000000004,1.124,Reunion +1920-01-01,25.316,0.466,Reunion +1920-02-01,25.101,0.675,Reunion +1920-03-01,25.314,0.499,Reunion +1920-04-01,23.886999999999997,1.082,Reunion +1920-05-01,23.071,0.722,Reunion +1920-06-01,21.158,0.7709999999999999,Reunion +1920-07-01,20.612,0.456,Reunion +1920-08-01,20.758000000000006,0.491,Reunion +1920-09-01,20.73,0.884,Reunion +1920-10-01,21.665,0.5579999999999999,Reunion +1920-11-01,22.375,0.617,Reunion +1920-12-01,23.619,1.013,Reunion +1921-01-01,24.823,1.34,Reunion +1921-02-01,25.369,0.589,Reunion +1921-03-01,25.071,0.579,Reunion +1921-04-01,23.545,1.393,Reunion +1921-05-01,22.625,1.726,Reunion +1921-06-01,21.443,1.037,Reunion +1921-07-01,21.337,0.539,Reunion +1921-08-01,20.684,0.401,Reunion +1921-09-01,20.901,0.426,Reunion +1921-10-01,21.713,0.456,Reunion +1921-11-01,22.773000000000003,0.447,Reunion +1921-12-01,24.264000000000006,1.072,Reunion +1922-01-01,24.844,0.994,Reunion +1922-02-01,25.474,1.113,Reunion +1922-03-01,25.524,0.862,Reunion +1922-04-01,23.408,0.759,Reunion +1922-05-01,22.745,0.716,Reunion +1922-06-01,21.456,0.453,Reunion +1922-07-01,20.815,0.42,Reunion +1922-08-01,20.770000000000003,1.258,Reunion +1922-09-01,20.4,0.657,Reunion +1922-10-01,21.291,0.902,Reunion +1922-11-01,22.566999999999997,0.6679999999999999,Reunion +1922-12-01,24.039,0.757,Reunion +1923-01-01,25.248,0.608,Reunion +1923-02-01,25.566000000000006,0.654,Reunion +1923-03-01,25.159000000000002,0.545,Reunion +1923-04-01,24.625,1.035,Reunion +1923-05-01,23.608,1.117,Reunion +1923-06-01,21.423000000000002,0.636,Reunion +1923-07-01,21.200000000000006,0.433,Reunion +1923-08-01,20.442999999999998,0.7859999999999999,Reunion +1923-09-01,20.697,0.395,Reunion +1923-10-01,21.33,0.441,Reunion +1923-11-01,23.397,0.7929999999999999,Reunion +1923-12-01,25.039,1.352,Reunion +1924-01-01,24.761999999999997,0.878,Reunion +1924-02-01,25.555,0.416,Reunion +1924-03-01,25.193,1.1840000000000002,Reunion +1924-04-01,24.225,0.633,Reunion +1924-05-01,23.435,0.892,Reunion +1924-06-01,22.036,0.54,Reunion +1924-07-01,21.647,0.458,Reunion +1924-08-01,21.051,0.613,Reunion +1924-09-01,20.451,0.441,Reunion +1924-10-01,20.463,1.294,Reunion +1924-11-01,22.484,0.921,Reunion +1924-12-01,23.814,0.512,Reunion +1925-01-01,25.164,0.564,Reunion +1925-02-01,25.503,0.506,Reunion +1925-03-01,25.134,0.5720000000000001,Reunion +1925-04-01,24.506999999999998,0.451,Reunion +1925-05-01,24.042,0.71,Reunion +1925-06-01,22.208000000000002,0.423,Reunion +1925-07-01,21.774,0.522,Reunion +1925-08-01,20.491000000000003,0.494,Reunion +1925-09-01,21.633000000000003,0.486,Reunion +1925-10-01,21.721999999999998,0.487,Reunion +1925-11-01,22.84,0.417,Reunion +1925-12-01,24.103,0.72,Reunion +1926-01-01,25.909000000000002,0.9,Reunion +1926-02-01,26.095,0.483,Reunion +1926-03-01,26.07,0.893,Reunion +1926-04-01,25.031999999999996,0.516,Reunion +1926-05-01,23.228,0.5,Reunion +1926-06-01,22.618000000000002,0.574,Reunion +1926-07-01,21.834,0.893,Reunion +1926-08-01,21.369000000000003,0.695,Reunion +1926-09-01,21.418000000000003,0.795,Reunion +1926-10-01,21.480999999999998,0.489,Reunion +1926-11-01,22.895,1.555,Reunion +1926-12-01,24.188,0.396,Reunion +1927-01-01,25.141,0.441,Reunion +1927-02-01,25.559,0.701,Reunion +1927-03-01,25.444000000000003,0.983,Reunion +1927-04-01,24.372,0.489,Reunion +1927-05-01,22.766,1.034,Reunion +1927-06-01,21.554000000000002,1.306,Reunion +1927-07-01,20.689,0.792,Reunion +1927-08-01,20.903,0.531,Reunion +1927-09-01,20.596,0.831,Reunion +1927-10-01,21.517,1.022,Reunion +1927-11-01,22.891,0.775,Reunion +1927-12-01,24.273000000000003,0.531,Reunion +1928-01-01,25.849,0.919,Reunion +1928-02-01,26.051,0.444,Reunion +1928-03-01,25.294,0.461,Reunion +1928-04-01,24.811999999999998,0.5720000000000001,Reunion +1928-05-01,23.813000000000002,0.561,Reunion +1928-06-01,21.822,0.583,Reunion +1928-07-01,21.459,0.46,Reunion +1928-08-01,20.734,0.516,Reunion +1928-09-01,20.694000000000003,0.523,Reunion +1928-10-01,21.212,0.897,Reunion +1928-11-01,22.461,1.58,Reunion +1928-12-01,23.715999999999998,1.231,Reunion +1929-01-01,25.055,0.843,Reunion +1929-02-01,25.083,1.415,Reunion +1929-03-01,25.542,1.161,Reunion +1929-04-01,24.294,0.909,Reunion +1929-05-01,23.014,0.5820000000000001,Reunion +1929-06-01,21.866,0.464,Reunion +1929-07-01,21.133000000000006,0.5379999999999999,Reunion +1929-08-01,20.656999999999996,0.557,Reunion +1929-09-01,20.659000000000002,0.643,Reunion +1929-10-01,21.668000000000003,0.4320000000000001,Reunion +1929-11-01,23.143,0.473,Reunion +1929-12-01,23.943,0.8370000000000001,Reunion +1930-01-01,24.794,0.705,Reunion +1930-02-01,25.558000000000003,1.021,Reunion +1930-03-01,25.331999999999997,0.962,Reunion +1930-04-01,24.436999999999998,0.415,Reunion +1930-05-01,23.299,1.165,Reunion +1930-06-01,21.714000000000002,0.414,Reunion +1930-07-01,21.355,0.602,Reunion +1930-08-01,20.314,0.434,Reunion +1930-09-01,21.388,0.579,Reunion +1930-10-01,21.159000000000002,0.419,Reunion +1930-11-01,22.788,1.0490000000000002,Reunion +1930-12-01,24.41,0.419,Reunion +1931-01-01,25.436999999999998,0.5770000000000001,Reunion +1931-02-01,25.388,0.597,Reunion +1931-03-01,25.516,0.516,Reunion +1931-04-01,24.765,0.527,Reunion +1931-05-01,23.54,0.474,Reunion +1931-06-01,23.235,1.501,Reunion +1931-07-01,21.644,1.051,Reunion +1931-08-01,21.156999999999996,0.546,Reunion +1931-09-01,21.326,0.487,Reunion +1931-10-01,21.844,0.664,Reunion +1931-11-01,23.138,0.616,Reunion +1931-12-01,24.305999999999997,0.5820000000000001,Reunion +1932-01-01,25.663,1.124,Reunion +1932-02-01,25.526999999999997,0.435,Reunion +1932-03-01,25.625,0.706,Reunion +1932-04-01,24.384,0.53,Reunion +1932-05-01,22.673000000000002,0.877,Reunion +1932-06-01,21.916,0.736,Reunion +1932-07-01,21.358,0.501,Reunion +1932-08-01,21.138,0.624,Reunion +1932-09-01,20.776999999999997,0.986,Reunion +1932-10-01,21.254,0.473,Reunion +1932-11-01,22.758000000000003,0.607,Reunion +1932-12-01,23.633000000000006,1.09,Reunion +1933-01-01,25.544,0.476,Reunion +1933-02-01,25.624,0.443,Reunion +1933-03-01,25.36,0.67,Reunion +1933-04-01,24.744,0.403,Reunion +1933-05-01,24.386999999999997,1.6230000000000002,Reunion +1933-06-01,23.147,1.976,Reunion +1933-07-01,20.714,0.922,Reunion +1933-08-01,20.435,0.8079999999999999,Reunion +1933-09-01,20.253,0.525,Reunion +1933-10-01,21.252,0.56,Reunion +1933-11-01,22.645,0.807,Reunion +1933-12-01,23.929,0.478,Reunion +1934-01-01,25.224,0.438,Reunion +1934-02-01,25.627,1.005,Reunion +1934-03-01,25.476,0.476,Reunion +1934-04-01,24.572,0.833,Reunion +1934-05-01,23.646,0.381,Reunion +1934-06-01,21.865,0.5710000000000001,Reunion +1934-07-01,21.24,0.375,Reunion +1934-08-01,20.407,0.654,Reunion +1934-09-01,20.364,0.526,Reunion +1934-10-01,20.912,0.738,Reunion +1934-11-01,22.638,0.384,Reunion +1934-12-01,24.359,0.726,Reunion +1935-01-01,25.186,0.767,Reunion +1935-02-01,25.796999999999997,0.532,Reunion +1935-03-01,25.467,0.7020000000000001,Reunion +1935-04-01,24.4,0.562,Reunion +1935-05-01,23.299,0.561,Reunion +1935-06-01,22.699,0.639,Reunion +1935-07-01,20.857000000000006,0.56,Reunion +1935-08-01,20.929,0.93,Reunion +1935-09-01,21.21,0.408,Reunion +1935-10-01,21.746,0.418,Reunion +1935-11-01,22.79,0.7340000000000001,Reunion +1935-12-01,24.368,0.75,Reunion +1936-01-01,25.412,0.43,Reunion +1936-02-01,25.956,0.405,Reunion +1936-03-01,25.718000000000004,0.615,Reunion +1936-04-01,24.802,0.419,Reunion +1936-05-01,23.481,0.8340000000000001,Reunion +1936-06-01,22.351,0.7909999999999999,Reunion +1936-07-01,21.302,0.5760000000000001,Reunion +1936-08-01,20.485000000000007,1.419,Reunion +1936-09-01,20.908,0.65,Reunion +1936-10-01,22.034000000000002,0.698,Reunion +1936-11-01,23.546999999999997,0.471,Reunion +1936-12-01,24.143,0.985,Reunion +1937-01-01,25.072,1.217,Reunion +1937-02-01,26.368,0.49,Reunion +1937-03-01,25.678,0.4920000000000001,Reunion +1937-04-01,24.656999999999996,0.732,Reunion +1937-05-01,23.822,0.481,Reunion +1937-06-01,22.014,1.008,Reunion +1937-07-01,21.441000000000006,0.6809999999999999,Reunion +1937-08-01,20.816999999999997,0.851,Reunion +1937-09-01,21.629,0.4370000000000001,Reunion +1937-10-01,21.57,0.511,Reunion +1937-11-01,23.289,0.483,Reunion +1937-12-01,24.315,0.434,Reunion +1938-01-01,25.255,0.402,Reunion +1938-02-01,26.01,0.404,Reunion +1938-03-01,25.78,0.484,Reunion +1938-04-01,24.458000000000002,0.907,Reunion +1938-05-01,23.743000000000002,0.575,Reunion +1938-06-01,22.11,0.703,Reunion +1938-07-01,21.818,0.5820000000000001,Reunion +1938-08-01,20.718000000000004,0.85,Reunion +1938-09-01,20.921,0.564,Reunion +1938-10-01,21.534000000000002,0.879,Reunion +1938-11-01,22.337,0.726,Reunion +1938-12-01,23.834,0.703,Reunion +1939-01-01,25.506,0.8340000000000001,Reunion +1939-02-01,26.215,1.122,Reunion +1939-03-01,25.61,0.725,Reunion +1939-04-01,24.793000000000003,0.525,Reunion +1939-05-01,23.813000000000002,0.642,Reunion +1939-06-01,22.271,1.44,Reunion +1939-07-01,21.795,0.615,Reunion +1939-08-01,20.730999999999998,0.645,Reunion +1939-09-01,21.307,0.606,Reunion +1939-10-01,21.445,0.55,Reunion +1939-11-01,22.865,0.627,Reunion +1939-12-01,24.668000000000006,0.872,Reunion +1940-01-01,25.171999999999997,0.37,Reunion +1940-02-01,25.480999999999998,0.353,Reunion +1940-03-01,25.132,0.7959999999999999,Reunion +1940-04-01,24.761,0.8420000000000001,Reunion +1940-05-01,24.203000000000003,0.513,Reunion +1940-06-01,22.566,0.429,Reunion +1940-07-01,21.908,0.4970000000000001,Reunion +1940-08-01,21.320999999999998,0.632,Reunion +1940-09-01,21.62,0.436,Reunion +1940-10-01,21.73,0.625,Reunion +1940-11-01,23.377,0.424,Reunion +1940-12-01,24.795,0.411,Reunion +1941-01-01,25.248,1.187,Reunion +1941-02-01,25.978,0.924,Reunion +1941-03-01,26.213,0.4270000000000001,Reunion +1941-04-01,25.441999999999997,0.732,Reunion +1941-05-01,23.527,0.612,Reunion +1941-06-01,22.792,0.588,Reunion +1941-07-01,21.713,0.487,Reunion +1941-08-01,21.204,0.523,Reunion +1941-09-01,21.622,0.889,Reunion +1941-10-01,22.155,0.871,Reunion +1941-11-01,23.346,1.072,Reunion +1941-12-01,24.250999999999998,0.464,Reunion +1942-01-01,25.566,0.393,Reunion +1942-02-01,26.388,0.945,Reunion +1942-03-01,26.266,0.7020000000000001,Reunion +1942-04-01,24.633000000000003,0.411,Reunion +1942-05-01,23.403000000000002,0.565,Reunion +1942-06-01,21.825,0.451,Reunion +1942-07-01,21.07,0.58,Reunion +1942-08-01,20.862,0.431,Reunion +1942-09-01,20.95,0.561,Reunion +1942-10-01,20.956999999999997,1.0590000000000002,Reunion +1942-11-01,22.861,0.976,Reunion +1942-12-01,24.16,0.561,Reunion +1943-01-01,25.592,0.4320000000000001,Reunion +1943-02-01,25.543000000000003,0.609,Reunion +1943-03-01,25.345,0.506,Reunion +1943-04-01,24.163,0.818,Reunion +1943-05-01,22.756,1.125,Reunion +1943-06-01,21.685,0.405,Reunion 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+1958-08-01,21.183000000000003,0.443,Reunion +1958-09-01,21.462,0.329,Reunion +1958-10-01,21.451999999999998,0.266,Reunion +1958-11-01,22.863000000000003,0.248,Reunion +1958-12-01,24.656,0.261,Reunion +1959-01-01,25.538,0.41,Reunion +1959-02-01,26.090999999999998,0.24,Reunion +1959-03-01,26.01,0.366,Reunion +1959-04-01,24.22,0.551,Reunion +1959-05-01,22.786,0.589,Reunion +1959-06-01,21.66,0.529,Reunion +1959-07-01,20.759,0.5870000000000001,Reunion +1959-08-01,20.588,0.336,Reunion +1959-09-01,20.787,0.4970000000000001,Reunion +1959-10-01,21.401999999999997,0.647,Reunion +1959-11-01,23.333000000000002,0.326,Reunion +1959-12-01,24.357000000000006,0.295,Reunion +1960-01-01,25.491,0.484,Reunion +1960-02-01,26.035,0.268,Reunion +1960-03-01,25.366999999999997,0.364,Reunion +1960-04-01,23.853,0.458,Reunion +1960-05-01,23.372,0.563,Reunion +1960-06-01,22.483,0.331,Reunion +1960-07-01,20.903,0.275,Reunion +1960-08-01,21.218000000000004,0.364,Reunion +1960-09-01,21.147,0.25,Reunion 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+1969-05-01,24.368000000000002,0.262,Reunion +1969-06-01,22.372,0.256,Reunion +1969-07-01,21.788,0.287,Reunion +1969-08-01,20.677000000000003,0.3720000000000001,Reunion +1969-09-01,21.031999999999996,0.299,Reunion +1969-10-01,22.31,0.568,Reunion +1969-11-01,24.166,0.421,Reunion +1969-12-01,25.462,0.469,Reunion +1970-01-01,26.395,0.345,Reunion +1970-02-01,26.791999999999998,0.47,Reunion +1970-03-01,26.431,0.4970000000000001,Reunion +1970-04-01,24.451,0.557,Reunion +1970-05-01,23.346,0.35700000000000004,Reunion +1970-06-01,22.215,0.433,Reunion +1970-07-01,21.645,0.32299999999999995,Reunion +1970-08-01,20.855999999999998,0.245,Reunion +1970-09-01,21.164,0.309,Reunion +1970-10-01,21.509,0.3,Reunion +1970-11-01,23.055999999999997,0.393,Reunion +1970-12-01,24.245,0.421,Reunion +1971-01-01,25.658,0.385,Reunion +1971-02-01,25.677,0.335,Reunion +1971-03-01,25.293000000000003,0.27,Reunion +1971-04-01,24.754,0.363,Reunion +1971-05-01,23.381,0.472,Reunion +1971-06-01,21.719,0.23,Reunion 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+1830-09-01,,,Saint Barthélemy +1830-10-01,,,Saint Barthélemy +1830-11-01,,,Saint Barthélemy +1830-12-01,,,Saint Barthélemy +1831-01-01,,,Saint Barthélemy +1831-02-01,,,Saint Barthélemy +1831-03-01,,,Saint Barthélemy +1831-04-01,,,Saint Barthélemy +1831-05-01,,,Saint Barthélemy +1831-06-01,,,Saint Barthélemy +1831-07-01,,,Saint Barthélemy +1831-08-01,,,Saint Barthélemy +1831-09-01,,,Saint Barthélemy +1831-10-01,,,Saint Barthélemy +1831-11-01,,,Saint Barthélemy +1831-12-01,,,Saint Barthélemy +1832-01-01,,,Saint Barthélemy +1832-02-01,,,Saint Barthélemy +1832-03-01,,,Saint Barthélemy +1832-04-01,,,Saint Barthélemy +1832-05-01,,,Saint Barthélemy +1832-06-01,,,Saint Barthélemy +1832-07-01,,,Saint Barthélemy +1832-08-01,,,Saint Barthélemy +1832-09-01,,,Saint Barthélemy +1832-10-01,,,Saint Barthélemy +1832-11-01,,,Saint Barthélemy +1832-12-01,,,Saint Barthélemy +1833-01-01,,,Saint Barthélemy +1833-02-01,,,Saint Barthélemy +1833-03-01,,,Saint Barthélemy +1833-04-01,,,Saint Barthélemy +1833-05-01,,,Saint Barthélemy +1833-06-01,,,Saint Barthélemy +1833-07-01,,,Saint Barthélemy +1833-08-01,,,Saint Barthélemy +1833-09-01,,,Saint Barthélemy +1833-10-01,,,Saint Barthélemy +1833-11-01,,,Saint Barthélemy +1833-12-01,,,Saint Barthélemy +1834-01-01,,,Saint Barthélemy +1834-02-01,,,Saint Barthélemy +1834-03-01,,,Saint Barthélemy +1834-04-01,,,Saint Barthélemy +1834-05-01,,,Saint Barthélemy +1834-06-01,,,Saint Barthélemy +1834-07-01,,,Saint Barthélemy +1834-08-01,,,Saint Barthélemy +1834-09-01,,,Saint Barthélemy +1834-10-01,,,Saint Barthélemy +1834-11-01,,,Saint Barthélemy +1834-12-01,,,Saint Barthélemy +1835-01-01,,,Saint Barthélemy +1835-02-01,,,Saint Barthélemy +1835-03-01,,,Saint Barthélemy +1835-04-01,,,Saint Barthélemy +1835-05-01,,,Saint Barthélemy +1835-06-01,,,Saint Barthélemy +1835-07-01,,,Saint Barthélemy +1835-08-01,,,Saint Barthélemy +1835-09-01,,,Saint Barthélemy +1835-10-01,,,Saint Barthélemy +1835-11-01,,,Saint Barthélemy +1835-12-01,,,Saint Barthélemy 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Barthélemy +1856-11-01,26.298000000000002,0.926,Saint Barthélemy +1856-12-01,24.951,1.7380000000000002,Saint Barthélemy +1857-01-01,23.599,1.46,Saint Barthélemy +1857-02-01,23.79,1.213,Saint Barthélemy +1857-03-01,24.839,0.816,Saint Barthélemy +1857-04-01,24.863000000000003,0.969,Saint Barthélemy +1857-05-01,26.123,1.058,Saint Barthélemy +1857-06-01,27.446,0.835,Saint Barthélemy +1857-07-01,27.777,0.684,Saint Barthélemy +1857-08-01,27.638,0.816,Saint Barthélemy +1857-09-01,26.916,0.481,Saint Barthélemy +1857-10-01,26.348000000000003,1.129,Saint Barthélemy +1857-11-01,25.999000000000002,0.821,Saint Barthélemy +1857-12-01,25.03,0.914,Saint Barthélemy +1858-01-01,23.741999999999997,1.626,Saint Barthélemy +1858-02-01,24.464000000000002,0.6629999999999999,Saint Barthélemy +1858-03-01,24.340999999999998,1.048,Saint Barthélemy +1858-04-01,25.171,0.957,Saint Barthélemy +1858-05-01,26.144,1.248,Saint Barthélemy +1858-06-01,26.573,1.137,Saint Barthélemy +1858-07-01,27.76,1.052,Saint Barthélemy 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Barthélemy +1930-08-01,28.291,0.461,Saint Barthélemy +1930-09-01,28.212,0.264,Saint Barthélemy +1930-10-01,27.87,0.423,Saint Barthélemy +1930-11-01,27.113000000000003,0.608,Saint Barthélemy +1930-12-01,26.381,0.632,Saint Barthélemy +1931-01-01,25.314,0.5479999999999999,Saint Barthélemy +1931-02-01,25.186,0.34600000000000003,Saint Barthélemy +1931-03-01,26.171000000000006,0.303,Saint Barthélemy +1931-04-01,26.594,0.451,Saint Barthélemy +1931-05-01,27.467,0.443,Saint Barthélemy +1931-06-01,28.166,0.419,Saint Barthélemy +1931-07-01,28.183000000000003,0.35200000000000004,Saint Barthélemy +1931-08-01,28.297,0.303,Saint Barthélemy +1931-09-01,27.912,0.259,Saint Barthélemy +1931-10-01,27.737,0.35,Saint Barthélemy +1931-11-01,26.83,0.35600000000000004,Saint Barthélemy +1931-12-01,25.793000000000003,0.303,Saint Barthélemy +1932-01-01,25.0,0.6920000000000001,Saint Barthélemy +1932-02-01,24.697,0.421,Saint Barthélemy +1932-03-01,25.753,0.473,Saint Barthélemy +1932-04-01,26.153,0.511,Saint Barthélemy +1932-05-01,27.087,0.32,Saint Barthélemy +1932-06-01,27.715,0.562,Saint Barthélemy +1932-07-01,27.814,0.38,Saint Barthélemy +1932-08-01,28.123,0.455,Saint Barthélemy +1932-09-01,28.513,0.482,Saint Barthélemy +1932-10-01,27.609,0.588,Saint Barthélemy +1932-11-01,26.894000000000002,0.335,Saint Barthélemy +1932-12-01,25.505000000000006,0.35200000000000004,Saint Barthélemy +1933-01-01,24.695999999999998,0.36,Saint Barthélemy +1933-02-01,24.441999999999997,0.292,Saint Barthélemy +1933-03-01,25.495,0.274,Saint Barthélemy +1933-04-01,25.629,0.249,Saint Barthélemy +1933-05-01,26.97,0.217,Saint Barthélemy +1933-06-01,27.465999999999998,0.423,Saint Barthélemy +1933-07-01,27.902,0.486,Saint Barthélemy +1933-08-01,27.945,0.657,Saint Barthélemy +1933-09-01,27.331999999999997,0.86,Saint Barthélemy +1933-10-01,27.393,0.293,Saint Barthélemy +1933-11-01,26.644000000000002,0.66,Saint Barthélemy +1933-12-01,25.32,0.486,Saint Barthélemy +1934-01-01,24.638,0.434,Saint Barthélemy 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Barthélemy +1939-06-01,27.542,0.266,Saint Barthélemy +1939-07-01,28.005,0.329,Saint Barthélemy +1939-08-01,28.365,0.345,Saint Barthélemy +1939-09-01,28.548000000000002,0.28800000000000003,Saint Barthélemy +1939-10-01,27.752,0.299,Saint Barthélemy +1939-11-01,26.956999999999997,0.257,Saint Barthélemy +1939-12-01,25.964,0.34600000000000003,Saint Barthélemy +1940-01-01,25.166,0.444,Saint Barthélemy +1940-02-01,25.171999999999997,0.298,Saint Barthélemy +1940-03-01,25.49,0.248,Saint Barthélemy +1940-04-01,26.095,0.448,Saint Barthélemy +1940-05-01,26.761999999999997,0.302,Saint Barthélemy +1940-06-01,27.619,0.27,Saint Barthélemy +1940-07-01,28.177,0.278,Saint Barthélemy +1940-08-01,28.576,0.308,Saint Barthélemy +1940-09-01,28.197,0.315,Saint Barthélemy +1940-10-01,27.844,0.329,Saint Barthélemy +1940-11-01,27.013,0.3720000000000001,Saint Barthélemy +1940-12-01,25.833,0.333,Saint Barthélemy +1941-01-01,25.169,0.34700000000000003,Saint Barthélemy +1941-02-01,25.853,0.248,Saint Barthélemy 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Barthélemy +1943-01-01,25.084000000000003,0.268,Saint Barthélemy +1943-02-01,25.068,0.318,Saint Barthélemy +1943-03-01,25.124,0.317,Saint Barthélemy +1943-04-01,25.754,0.368,Saint Barthélemy +1943-05-01,26.186,0.328,Saint Barthélemy +1943-06-01,27.058000000000003,0.375,Saint Barthélemy +1943-07-01,27.833000000000002,0.398,Saint Barthélemy +1943-08-01,27.982,0.337,Saint Barthélemy +1943-09-01,28.043000000000003,0.404,Saint Barthélemy +1943-10-01,27.615,0.319,Saint Barthélemy +1943-11-01,26.976999999999997,0.301,Saint Barthélemy +1943-12-01,25.778,0.302,Saint Barthélemy +1944-01-01,25.066000000000006,0.294,Saint Barthélemy +1944-02-01,24.808000000000003,0.35600000000000004,Saint Barthélemy +1944-03-01,25.416,0.369,Saint Barthélemy +1944-04-01,26.059,0.315,Saint Barthélemy +1944-05-01,26.561999999999998,0.446,Saint Barthélemy +1944-06-01,27.624000000000002,0.265,Saint Barthélemy +1944-07-01,27.996,0.274,Saint Barthélemy +1944-08-01,28.037,0.409,Saint Barthélemy 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Barthélemy +1946-07-01,28.054000000000002,0.342,Saint Barthélemy +1946-08-01,28.198,0.333,Saint Barthélemy +1946-09-01,27.824,0.31,Saint Barthélemy +1946-10-01,27.706,0.487,Saint Barthélemy +1946-11-01,26.978,0.334,Saint Barthélemy +1946-12-01,25.813,0.243,Saint Barthélemy +1947-01-01,25.162000000000006,0.416,Saint Barthélemy +1947-02-01,25.525,0.46,Saint Barthélemy +1947-03-01,26.287000000000006,0.328,Saint Barthélemy +1947-04-01,26.146,0.472,Saint Barthélemy +1947-05-01,26.645,0.608,Saint Barthélemy +1947-06-01,27.76,0.462,Saint Barthélemy +1947-07-01,28.286,0.342,Saint Barthélemy +1947-08-01,28.484,0.333,Saint Barthélemy +1947-09-01,28.043000000000003,0.28,Saint Barthélemy +1947-10-01,27.732,0.28600000000000003,Saint Barthélemy +1947-11-01,26.929000000000002,0.28,Saint Barthélemy +1947-12-01,25.933000000000003,0.24,Saint Barthélemy +1948-01-01,25.433000000000003,0.231,Saint Barthélemy +1948-02-01,25.171999999999997,0.21600000000000005,Saint Barthélemy +1948-03-01,25.112,0.34,Saint Barthélemy +1948-04-01,25.651999999999997,0.363,Saint Barthélemy +1948-05-01,27.41,0.254,Saint Barthélemy +1948-06-01,28.225,0.189,Saint Barthélemy +1948-07-01,28.392,0.362,Saint Barthélemy +1948-08-01,28.502,0.28300000000000003,Saint Barthélemy +1948-09-01,28.051,0.236,Saint Barthélemy +1948-10-01,27.426,0.253,Saint Barthélemy +1948-11-01,27.052,0.369,Saint Barthélemy +1948-12-01,25.741,0.353,Saint Barthélemy +1949-01-01,24.354000000000006,0.568,Saint Barthélemy +1949-02-01,24.613000000000003,0.325,Saint Barthélemy +1949-03-01,25.104000000000006,0.28600000000000003,Saint Barthélemy +1949-04-01,25.820999999999998,0.273,Saint Barthélemy +1949-05-01,26.851999999999997,0.28600000000000003,Saint Barthélemy +1949-06-01,27.444000000000003,0.259,Saint Barthélemy +1949-07-01,27.724,0.502,Saint Barthélemy +1949-08-01,28.412,0.279,Saint Barthélemy +1949-09-01,27.749000000000002,0.246,Saint Barthélemy +1949-10-01,27.186,0.407,Saint Barthélemy +1949-11-01,26.678,0.353,Saint Barthélemy 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Barthélemy +1955-03-01,24.936999999999998,0.26,Saint Barthélemy +1955-04-01,25.558000000000003,0.393,Saint Barthélemy +1955-05-01,27.278,0.345,Saint Barthélemy +1955-06-01,27.929000000000002,0.455,Saint Barthélemy +1955-07-01,28.354,0.551,Saint Barthélemy +1955-08-01,28.54,0.435,Saint Barthélemy +1955-09-01,28.109,0.292,Saint Barthélemy +1955-10-01,27.759,0.225,Saint Barthélemy +1955-11-01,27.155,0.28300000000000003,Saint Barthélemy +1955-12-01,25.713,0.229,Saint Barthélemy +1956-01-01,24.987,0.211,Saint Barthélemy +1956-02-01,25.026,0.32299999999999995,Saint Barthélemy +1956-03-01,25.802000000000003,0.46,Saint Barthélemy +1956-04-01,26.105999999999998,0.715,Saint Barthélemy +1956-05-01,27.037,0.669,Saint Barthélemy +1956-06-01,27.215999999999998,0.35100000000000003,Saint Barthélemy +1956-07-01,27.862,0.247,Saint Barthélemy +1956-08-01,27.797,0.253,Saint Barthélemy +1956-09-01,28.03,0.319,Saint Barthélemy +1956-10-01,27.49,0.37,Saint Barthélemy +1956-11-01,26.737,0.718,Saint Barthélemy 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Barthélemy +1958-10-01,27.671,0.284,Saint Barthélemy +1958-11-01,27.161,0.311,Saint Barthélemy +1958-12-01,26.084,0.233,Saint Barthélemy +1959-01-01,25.304,0.473,Saint Barthélemy +1959-02-01,25.34,0.277,Saint Barthélemy +1959-03-01,26.351000000000006,0.308,Saint Barthélemy +1959-04-01,26.663,0.269,Saint Barthélemy +1959-05-01,26.535999999999998,0.307,Saint Barthélemy +1959-06-01,28.078000000000003,0.398,Saint Barthélemy +1959-07-01,28.331,0.431,Saint Barthélemy +1959-08-01,28.472,0.285,Saint Barthélemy +1959-09-01,28.59,0.265,Saint Barthélemy +1959-10-01,27.937,0.267,Saint Barthélemy +1959-11-01,27.139,0.411,Saint Barthélemy +1959-12-01,26.342,0.231,Saint Barthélemy +1960-01-01,25.599,0.35700000000000004,Saint Barthélemy +1960-02-01,25.908,0.565,Saint Barthélemy +1960-03-01,26.031,0.42,Saint Barthélemy +1960-04-01,26.483,0.379,Saint Barthélemy +1960-05-01,27.603,0.414,Saint Barthélemy +1960-06-01,28.428,0.277,Saint Barthélemy +1960-07-01,28.333000000000002,0.333,Saint Barthélemy 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+1967-12-01,25.186999999999998,0.509,Saint Barthélemy +1968-01-01,24.424000000000003,0.269,Saint Barthélemy +1968-02-01,24.734,0.408,Saint Barthélemy +1968-03-01,25.074,0.42,Saint Barthélemy +1968-04-01,25.228,0.29100000000000004,Saint Barthélemy +1968-05-01,27.044,0.221,Saint Barthélemy +1968-06-01,27.543000000000003,0.35200000000000004,Saint Barthélemy +1968-07-01,27.975,0.221,Saint Barthélemy +1968-08-01,28.27,0.265,Saint Barthélemy +1968-09-01,28.391,0.24,Saint Barthélemy +1968-10-01,27.875,0.268,Saint Barthélemy +1968-11-01,27.168000000000003,0.341,Saint Barthélemy +1968-12-01,25.576999999999998,0.363,Saint Barthélemy +1969-01-01,24.744,0.229,Saint Barthélemy +1969-02-01,24.842,0.472,Saint Barthélemy +1969-03-01,26.115,0.28300000000000003,Saint Barthélemy +1969-04-01,27.221999999999998,0.242,Saint Barthélemy +1969-05-01,27.553,0.353,Saint Barthélemy +1969-06-01,28.441999999999997,0.294,Saint Barthélemy +1969-07-01,28.323,0.468,Saint Barthélemy +1969-08-01,28.254,0.281,Saint Barthélemy +1969-09-01,28.285,0.243,Saint Barthélemy +1969-10-01,27.988000000000003,0.239,Saint Barthélemy +1969-11-01,27.088,0.284,Saint Barthélemy +1969-12-01,25.735,0.386,Saint Barthélemy +1970-01-01,25.693,0.429,Saint Barthélemy +1970-02-01,25.549,0.272,Saint Barthélemy +1970-03-01,25.835,0.482,Saint Barthélemy +1970-04-01,26.578000000000003,0.295,Saint Barthélemy +1970-05-01,27.151,0.32,Saint Barthélemy +1970-06-01,27.803,0.451,Saint Barthélemy +1970-07-01,27.966,0.338,Saint Barthélemy +1970-08-01,28.2,0.417,Saint Barthélemy +1970-09-01,28.118000000000002,0.321,Saint Barthélemy +1970-10-01,27.634,0.314,Saint Barthélemy +1970-11-01,26.872,0.509,Saint Barthélemy +1970-12-01,25.595,0.251,Saint Barthélemy +1971-01-01,25.046999999999997,0.328,Saint Barthélemy +1971-02-01,24.904,0.514,Saint Barthélemy +1971-03-01,25.666999999999998,0.243,Saint Barthélemy +1971-04-01,25.825,0.317,Saint Barthélemy +1971-05-01,26.58,0.202,Saint Barthélemy +1971-06-01,27.645,0.273,Saint Barthélemy +1971-07-01,27.944000000000003,0.285,Saint Barthélemy +1971-08-01,27.9,0.276,Saint Barthélemy +1971-09-01,28.16,0.358,Saint Barthélemy +1971-10-01,27.436,0.254,Saint Barthélemy +1971-11-01,26.479,0.758,Saint Barthélemy +1971-12-01,25.054,0.488,Saint Barthélemy +1972-01-01,24.724,0.438,Saint Barthélemy +1972-02-01,24.824,0.238,Saint Barthélemy +1972-03-01,25.31,0.212,Saint Barthélemy +1972-04-01,26.119,0.249,Saint Barthélemy +1972-05-01,26.703000000000003,0.397,Saint Barthélemy +1972-06-01,27.744,0.393,Saint Barthélemy +1972-07-01,28.088,0.5329999999999999,Saint Barthélemy +1972-08-01,28.028000000000002,0.338,Saint Barthélemy +1972-09-01,28.194000000000003,0.28300000000000003,Saint Barthélemy +1972-10-01,27.654,0.361,Saint Barthélemy +1972-11-01,27.127,0.331,Saint Barthélemy +1972-12-01,25.534,1.025,Saint Barthélemy +1973-01-01,25.604000000000006,0.664,Saint Barthélemy +1973-02-01,25.268,0.523,Saint Barthélemy +1973-03-01,25.874,0.609,Saint Barthélemy +1973-04-01,26.385,0.228,Saint Barthélemy +1973-05-01,27.698,0.336,Saint Barthélemy +1973-06-01,28.214000000000002,0.433,Saint Barthélemy +1973-07-01,28.388,0.412,Saint Barthélemy +1973-08-01,28.491,0.365,Saint Barthélemy +1973-09-01,28.206999999999997,0.484,Saint Barthélemy +1973-10-01,27.96,0.383,Saint Barthélemy +1973-11-01,26.974,0.322,Saint Barthélemy +1973-12-01,25.085,0.202,Saint Barthélemy +1974-01-01,24.715,0.241,Saint Barthélemy +1974-02-01,24.982,0.253,Saint Barthélemy +1974-03-01,25.343000000000004,0.308,Saint Barthélemy +1974-04-01,25.485,0.376,Saint Barthélemy +1974-05-01,26.758000000000003,0.181,Saint Barthélemy +1974-06-01,27.65,0.234,Saint Barthélemy +1974-07-01,28.167,0.254,Saint Barthélemy +1974-08-01,28.278000000000002,0.379,Saint Barthélemy +1974-09-01,27.525,0.308,Saint Barthélemy +1974-10-01,27.161,0.319,Saint Barthélemy +1974-11-01,26.445,0.618,Saint Barthélemy +1974-12-01,25.393,0.3,Saint Barthélemy +1975-01-01,24.537000000000006,0.257,Saint Barthélemy +1975-02-01,24.818,0.344,Saint Barthélemy +1975-03-01,25.429,0.455,Saint Barthélemy +1975-04-01,25.772,0.234,Saint Barthélemy +1975-05-01,26.68,0.281,Saint Barthélemy +1975-06-01,27.819000000000003,0.251,Saint Barthélemy +1975-07-01,28.2,0.317,Saint Barthélemy +1975-08-01,28.414,0.5,Saint Barthélemy +1975-09-01,27.975,0.267,Saint Barthélemy +1975-10-01,27.244,0.201,Saint Barthélemy +1975-11-01,26.276,0.405,Saint Barthélemy +1975-12-01,24.713,0.462,Saint Barthélemy +1976-01-01,24.033,0.387,Saint Barthélemy +1976-02-01,24.195,0.318,Saint Barthélemy +1976-03-01,24.398000000000003,0.451,Saint Barthélemy +1976-04-01,25.463,0.207,Saint Barthélemy +1976-05-01,26.750999999999998,0.418,Saint Barthélemy +1976-06-01,27.371,0.371,Saint Barthélemy +1976-07-01,28.105,0.491,Saint Barthélemy +1976-08-01,28.231,0.28300000000000003,Saint Barthélemy +1976-09-01,28.214000000000002,0.333,Saint Barthélemy +1976-10-01,27.77,0.266,Saint Barthélemy +1976-11-01,27.214000000000002,0.348,Saint Barthélemy +1976-12-01,25.709,0.333,Saint Barthélemy +1977-01-01,25.458,0.442,Saint Barthélemy +1977-02-01,25.820999999999998,0.434,Saint Barthélemy +1977-03-01,25.873,0.253,Saint Barthélemy +1977-04-01,25.936,0.193,Saint Barthélemy +1977-05-01,27.398000000000003,0.355,Saint Barthélemy +1977-06-01,28.243000000000002,0.234,Saint Barthélemy +1977-07-01,28.591,0.34,Saint Barthélemy +1977-08-01,28.31,0.316,Saint Barthélemy +1977-09-01,28.109,0.25,Saint Barthélemy +1977-10-01,27.816999999999997,0.377,Saint Barthélemy +1977-11-01,27.004,0.246,Saint Barthélemy +1977-12-01,26.02,0.245,Saint Barthélemy +1978-01-01,25.114,0.472,Saint Barthélemy +1978-02-01,25.561,0.28800000000000003,Saint Barthélemy +1978-03-01,26.123,0.253,Saint Barthélemy +1978-04-01,26.473000000000006,0.4920000000000001,Saint Barthélemy +1978-05-01,27.48,0.341,Saint Barthélemy +1978-06-01,28.031,0.29600000000000004,Saint Barthélemy +1978-07-01,28.164,0.258,Saint Barthélemy +1978-08-01,28.147,0.425,Saint Barthélemy +1978-09-01,28.51,0.258,Saint Barthélemy +1978-10-01,27.81,0.228,Saint Barthélemy +1978-11-01,26.984,0.233,Saint Barthélemy +1978-12-01,26.09,0.29100000000000004,Saint Barthélemy +1979-01-01,24.979000000000006,0.249,Saint Barthélemy +1979-02-01,25.289,0.246,Saint Barthélemy +1979-03-01,25.408,0.318,Saint Barthélemy +1979-04-01,26.046,0.5920000000000001,Saint Barthélemy +1979-05-01,27.074,0.252,Saint Barthélemy +1979-06-01,28.058000000000003,0.422,Saint Barthélemy +1979-07-01,27.972,0.277,Saint Barthélemy +1979-08-01,28.486,0.255,Saint Barthélemy +1979-09-01,27.683000000000003,0.426,Saint Barthélemy +1979-10-01,27.736,0.417,Saint Barthélemy +1979-11-01,27.1,0.185,Saint Barthélemy +1979-12-01,25.845,0.257,Saint Barthélemy +1980-01-01,25.188,0.238,Saint Barthélemy +1980-02-01,25.480999999999998,0.242,Saint Barthélemy +1980-03-01,25.877,0.232,Saint Barthélemy +1980-04-01,26.486,0.247,Saint Barthélemy +1980-05-01,27.975,0.213,Saint Barthélemy +1980-06-01,28.48,0.382,Saint Barthélemy +1980-07-01,28.417,0.28,Saint Barthélemy +1980-08-01,28.671,0.231,Saint Barthélemy +1980-09-01,28.699,0.226,Saint Barthélemy +1980-10-01,28.37,0.287,Saint Barthélemy +1980-11-01,27.428,0.412,Saint Barthélemy +1980-12-01,25.953000000000003,0.306,Saint Barthélemy +1981-01-01,25.712000000000003,0.28,Saint Barthélemy +1981-02-01,25.511999999999997,0.338,Saint Barthélemy +1981-03-01,26.360000000000007,0.259,Saint Barthélemy +1981-04-01,25.979000000000006,0.266,Saint Barthélemy +1981-05-01,27.366999999999997,0.257,Saint Barthélemy +1981-06-01,28.099,0.417,Saint Barthélemy +1981-07-01,28.244,0.359,Saint Barthélemy +1981-08-01,28.631,0.218,Saint Barthélemy +1981-09-01,28.66,0.328,Saint Barthélemy +1981-10-01,27.841,0.341,Saint Barthélemy +1981-11-01,27.612,0.223,Saint Barthélemy +1981-12-01,26.182,0.233,Saint Barthélemy +1982-01-01,25.315,0.489,Saint Barthélemy +1982-02-01,25.179000000000002,0.312,Saint Barthélemy +1982-03-01,25.621,0.29,Saint Barthélemy +1982-04-01,26.065,0.55,Saint Barthélemy +1982-05-01,27.235,0.294,Saint Barthélemy +1982-06-01,28.12,0.262,Saint Barthélemy +1982-07-01,28.198,0.24,Saint Barthélemy +1982-08-01,28.406999999999996,0.154,Saint Barthélemy +1982-09-01,28.324,0.23,Saint Barthélemy +1982-10-01,27.747,0.241,Saint Barthélemy +1982-11-01,26.635,0.474,Saint Barthélemy +1982-12-01,25.128,0.325,Saint Barthélemy +1983-01-01,25.362,0.386,Saint Barthélemy +1983-02-01,25.396,0.393,Saint Barthélemy +1983-03-01,26.594,0.359,Saint Barthélemy +1983-04-01,26.548,0.308,Saint Barthélemy +1983-05-01,27.32,0.394,Saint Barthélemy +1983-06-01,28.288,0.44,Saint Barthélemy +1983-07-01,28.601,0.488,Saint Barthélemy +1983-08-01,28.51,0.232,Saint Barthélemy +1983-09-01,28.824,0.228,Saint Barthélemy +1983-10-01,28.152,0.487,Saint Barthélemy +1983-11-01,27.327,0.48,Saint Barthélemy +1983-12-01,26.241999999999997,0.318,Saint Barthélemy +1984-01-01,25.395000000000003,0.219,Saint Barthélemy +1984-02-01,25.462,0.322,Saint Barthélemy +1984-03-01,25.775,0.258,Saint Barthélemy +1984-04-01,26.636000000000006,0.326,Saint Barthélemy +1984-05-01,27.11,0.326,Saint Barthélemy +1984-06-01,27.877,0.622,Saint Barthélemy +1984-07-01,28.031,0.369,Saint Barthélemy +1984-08-01,28.49,0.293,Saint Barthélemy +1984-09-01,28.066,0.392,Saint Barthélemy +1984-10-01,27.835,0.369,Saint Barthélemy +1984-11-01,26.244,0.32,Saint Barthélemy +1984-12-01,25.391,0.29600000000000004,Saint Barthélemy +1985-01-01,24.927000000000003,0.333,Saint Barthélemy +1985-02-01,25.279,0.223,Saint Barthélemy +1985-03-01,25.384,0.343,Saint Barthélemy +1985-04-01,25.765,0.503,Saint Barthélemy +1985-05-01,27.160999999999998,0.234,Saint Barthélemy +1985-06-01,28.146,0.34,Saint Barthélemy +1985-07-01,28.515,0.28800000000000003,Saint Barthélemy +1985-08-01,28.619,0.37,Saint Barthélemy +1985-09-01,28.308000000000003,0.353,Saint Barthélemy +1985-10-01,27.307,0.425,Saint Barthélemy +1985-11-01,26.826,0.275,Saint Barthélemy +1985-12-01,25.866999999999997,0.36,Saint Barthélemy +1986-01-01,25.215999999999998,0.276,Saint Barthélemy +1986-02-01,24.906999999999996,0.28300000000000003,Saint Barthélemy +1986-03-01,25.62,0.488,Saint Barthélemy +1986-04-01,26.389,0.226,Saint Barthélemy +1986-05-01,26.912,0.184,Saint Barthélemy +1986-06-01,27.848000000000003,0.219,Saint Barthélemy +1986-07-01,28.22,0.194,Saint Barthélemy +1986-08-01,28.469,0.273,Saint Barthélemy +1986-09-01,28.374000000000002,0.29,Saint Barthélemy +1986-10-01,28.026,0.265,Saint Barthélemy +1986-11-01,26.776,0.218,Saint Barthélemy +1986-12-01,25.855999999999998,0.268,Saint Barthélemy +1987-01-01,25.419,0.37,Saint Barthélemy +1987-02-01,25.447,0.295,Saint Barthélemy +1987-03-01,25.759,0.204,Saint Barthélemy +1987-04-01,27.302,0.246,Saint Barthélemy +1987-05-01,27.323,0.396,Saint Barthélemy +1987-06-01,28.22,0.6409999999999999,Saint Barthélemy +1987-07-01,28.614,0.294,Saint Barthélemy +1987-08-01,28.983,0.35700000000000004,Saint Barthélemy +1987-09-01,28.962,0.28800000000000003,Saint Barthélemy +1987-10-01,28.527,0.644,Saint Barthélemy +1987-11-01,27.43,0.242,Saint Barthélemy +1987-12-01,26.569000000000006,0.35200000000000004,Saint Barthélemy +1988-01-01,25.551,0.28,Saint Barthélemy +1988-02-01,25.468000000000004,0.232,Saint Barthélemy +1988-03-01,25.783,0.204,Saint Barthélemy +1988-04-01,26.53,0.328,Saint Barthélemy +1988-05-01,27.682,0.209,Saint Barthélemy +1988-06-01,28.51,0.222,Saint Barthélemy +1988-07-01,28.617,0.3,Saint Barthélemy +1988-08-01,28.28,0.207,Saint Barthélemy +1988-09-01,28.506,0.42,Saint Barthélemy +1988-10-01,28.046,0.331,Saint Barthélemy +1988-11-01,27.168000000000003,0.218,Saint Barthélemy +1988-12-01,25.514,0.217,Saint Barthélemy +1989-01-01,24.976000000000006,0.246,Saint Barthélemy +1989-02-01,24.888,0.285,Saint Barthélemy +1989-03-01,25.349,0.29600000000000004,Saint Barthélemy +1989-04-01,25.829,0.32299999999999995,Saint Barthélemy +1989-05-01,26.943,0.248,Saint Barthélemy +1989-06-01,27.781999999999996,0.321,Saint Barthélemy +1989-07-01,28.588,0.257,Saint Barthélemy +1989-08-01,28.268,0.366,Saint Barthélemy +1989-09-01,28.051,0.275,Saint Barthélemy +1989-10-01,27.892,0.308,Saint Barthélemy +1989-11-01,27.353,0.211,Saint Barthélemy +1989-12-01,26.24,0.337,Saint Barthélemy +1990-01-01,25.566999999999997,0.32,Saint Barthélemy +1990-02-01,25.041,0.165,Saint Barthélemy +1990-03-01,25.616999999999997,0.261,Saint Barthélemy +1990-04-01,26.219,0.184,Saint Barthélemy +1990-05-01,27.288,0.261,Saint Barthélemy +1990-06-01,28.06,0.342,Saint Barthélemy +1990-07-01,28.45,0.253,Saint Barthélemy +1990-08-01,28.808000000000003,0.267,Saint Barthélemy +1990-09-01,28.751,0.326,Saint Barthélemy +1990-10-01,27.91,0.248,Saint Barthélemy +1990-11-01,27.662,0.229,Saint Barthélemy +1990-12-01,25.811999999999998,0.231,Saint Barthélemy +1991-01-01,25.453000000000007,0.237,Saint Barthélemy +1991-02-01,25.728,0.384,Saint Barthélemy +1991-03-01,25.997,0.293,Saint Barthélemy +1991-04-01,26.274,0.44,Saint Barthélemy +1991-05-01,27.37,0.232,Saint Barthélemy +1991-06-01,28.309,0.236,Saint Barthélemy +1991-07-01,28.6,0.402,Saint Barthélemy +1991-08-01,28.827,0.231,Saint Barthélemy +1991-09-01,28.526,0.218,Saint Barthélemy +1991-10-01,27.919,0.403,Saint Barthélemy +1991-11-01,26.892,0.214,Saint Barthélemy +1991-12-01,25.273000000000003,0.175,Saint Barthélemy +1992-01-01,25.215,0.302,Saint Barthélemy +1992-02-01,25.561999999999998,0.307,Saint Barthélemy +1992-03-01,25.991999999999997,0.253,Saint Barthélemy +1992-04-01,26.595,0.373,Saint Barthélemy +1992-05-01,27.221,0.175,Saint Barthélemy +1992-06-01,28.077,0.293,Saint Barthélemy +1992-07-01,28.428,0.25,Saint Barthélemy +1992-08-01,28.501,0.35700000000000004,Saint Barthélemy +1992-09-01,28.295,0.398,Saint Barthélemy +1992-10-01,28.002,0.353,Saint Barthélemy +1992-11-01,26.914,0.271,Saint Barthélemy +1992-12-01,25.802,0.407,Saint Barthélemy +1993-01-01,25.116999999999997,0.251,Saint Barthélemy +1993-02-01,25.163,0.28,Saint Barthélemy +1993-03-01,25.851999999999997,0.281,Saint Barthélemy +1993-04-01,26.526999999999997,0.28300000000000003,Saint Barthélemy +1993-05-01,27.338,0.8109999999999999,Saint Barthélemy +1993-06-01,28.285,0.35,Saint Barthélemy +1993-07-01,28.372,0.203,Saint Barthélemy +1993-08-01,28.87,0.184,Saint Barthélemy +1993-09-01,28.565,0.24,Saint Barthélemy +1993-10-01,28.196,0.29,Saint Barthélemy +1993-11-01,27.464000000000002,0.233,Saint Barthélemy +1993-12-01,26.212,0.21,Saint Barthélemy +1994-01-01,25.56,0.282,Saint Barthélemy +1994-02-01,25.465,0.28300000000000003,Saint Barthélemy +1994-03-01,25.923,0.225,Saint Barthélemy +1994-04-01,26.436,0.42,Saint Barthélemy +1994-05-01,27.587,0.33,Saint Barthélemy +1994-06-01,28.184,0.359,Saint Barthélemy +1994-07-01,28.487,0.245,Saint Barthélemy +1994-08-01,28.741,0.19,Saint Barthélemy +1994-09-01,28.148000000000003,0.268,Saint Barthélemy +1994-10-01,28.01,0.386,Saint Barthélemy +1994-11-01,27.389,0.277,Saint Barthélemy +1994-12-01,26.101000000000006,0.179,Saint Barthélemy +1995-01-01,25.67,0.205,Saint Barthélemy +1995-02-01,26.019,0.394,Saint Barthélemy +1995-03-01,25.826,0.3670000000000001,Saint Barthélemy +1995-04-01,26.92,0.451,Saint Barthélemy +1995-05-01,28.144,0.16699999999999998,Saint Barthélemy +1995-06-01,28.772,0.218,Saint Barthélemy +1995-07-01,29.347,0.174,Saint Barthélemy +1995-08-01,29.195,0.317,Saint Barthélemy +1995-09-01,28.535,0.4,Saint Barthélemy +1995-10-01,28.225,0.245,Saint Barthélemy +1995-11-01,27.564,0.246,Saint Barthélemy +1995-12-01,26.386999999999997,0.274,Saint Barthélemy +1996-01-01,25.625999999999998,0.239,Saint Barthélemy +1996-02-01,25.77,0.222,Saint Barthélemy +1996-03-01,26.165000000000006,0.249,Saint Barthélemy +1996-04-01,26.393,0.263,Saint Barthélemy +1996-05-01,27.143,0.29100000000000004,Saint Barthélemy +1996-06-01,27.859,0.29100000000000004,Saint Barthélemy +1996-07-01,28.112,0.317,Saint Barthélemy +1996-08-01,28.496,0.284,Saint Barthélemy +1996-09-01,28.373,0.28300000000000003,Saint Barthélemy +1996-10-01,28.051,0.243,Saint Barthélemy +1996-11-01,27.178,0.293,Saint Barthélemy +1996-12-01,25.516,0.192,Saint Barthélemy +1997-01-01,25.357000000000006,0.177,Saint Barthélemy +1997-02-01,25.406,0.235,Saint Barthélemy +1997-03-01,25.541999999999998,0.223,Saint Barthélemy +1997-04-01,26.031999999999996,0.255,Saint Barthélemy +1997-05-01,27.548,0.335,Saint Barthélemy +1997-06-01,28.593000000000004,0.24,Saint Barthélemy +1997-07-01,28.815,0.223,Saint Barthélemy +1997-08-01,29.046,0.218,Saint Barthélemy +1997-09-01,28.914,0.29600000000000004,Saint Barthélemy +1997-10-01,28.225,0.364,Saint Barthélemy +1997-11-01,28.026999999999997,0.208,Saint Barthélemy +1997-12-01,27.218000000000004,0.149,Saint Barthélemy +1998-01-01,26.1,0.267,Saint Barthélemy +1998-02-01,26.416999999999998,0.266,Saint Barthélemy +1998-03-01,26.658,0.29100000000000004,Saint Barthélemy +1998-04-01,26.831,0.262,Saint Barthélemy 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Barthélemy +2000-02-01,24.982,0.226,Saint Barthélemy +2000-03-01,24.976000000000006,0.168,Saint Barthélemy +2000-04-01,26.179,0.305,Saint Barthélemy +2000-05-01,27.249,0.265,Saint Barthélemy +2000-06-01,28.124000000000002,0.278,Saint Barthélemy +2000-07-01,28.572,0.343,Saint Barthélemy +2000-08-01,28.875,0.273,Saint Barthélemy +2000-09-01,28.699,0.191,Saint Barthélemy +2000-10-01,28.372,0.223,Saint Barthélemy +2000-11-01,27.354,0.247,Saint Barthélemy +2000-12-01,26.303,0.321,Saint Barthélemy +2001-01-01,25.584000000000003,0.305,Saint Barthélemy +2001-02-01,25.318,0.205,Saint Barthélemy +2001-03-01,26.068,0.299,Saint Barthélemy +2001-04-01,26.289,0.262,Saint Barthélemy +2001-05-01,27.680999999999997,0.267,Saint Barthélemy +2001-06-01,28.623,0.21,Saint Barthélemy +2001-07-01,28.851999999999997,0.172,Saint Barthélemy +2001-08-01,28.967,0.188,Saint Barthélemy +2001-09-01,29.145,0.297,Saint Barthélemy +2001-10-01,28.427,0.258,Saint Barthélemy +2001-11-01,27.467,0.217,Saint Barthélemy 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Barthélemy +2005-06-01,28.951,0.249,Saint Barthélemy +2005-07-01,29.296,0.251,Saint Barthélemy +2005-08-01,29.377,0.262,Saint Barthélemy +2005-09-01,29.636,0.246,Saint Barthélemy +2005-10-01,28.384,0.242,Saint Barthélemy +2005-11-01,27.671,0.267,Saint Barthélemy +2005-12-01,26.419,0.315,Saint Barthélemy +2006-01-01,25.708,0.312,Saint Barthélemy +2006-02-01,25.513,0.211,Saint Barthélemy +2006-03-01,26.303,0.362,Saint Barthélemy +2006-04-01,26.998,0.4270000000000001,Saint Barthélemy +2006-05-01,28.044,0.383,Saint Barthélemy +2006-06-01,28.811,0.232,Saint Barthélemy +2006-07-01,28.778000000000002,0.403,Saint Barthélemy +2006-08-01,29.003,0.268,Saint Barthélemy +2006-09-01,29.386999999999997,0.289,Saint Barthélemy +2006-10-01,28.524,0.24,Saint Barthélemy +2006-11-01,27.841,0.203,Saint Barthélemy +2006-12-01,26.725,0.303,Saint Barthélemy +2007-01-01,25.844,0.383,Saint Barthélemy +2007-02-01,26.241999999999997,0.226,Saint Barthélemy +2007-03-01,26.654,0.329,Saint Barthélemy 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Marino +1773-09-01,20.543000000000003,2.263,San Marino +1773-10-01,16.544999999999998,1.885,San Marino +1773-11-01,10.845,6.972,San Marino +1773-12-01,10.025,5.684,San Marino +1774-01-01,5.5939999999999985,8.776,San Marino +1774-02-01,6.65,7.854,San Marino +1774-03-01,9.51,2.81,San Marino +1774-04-01,13.277000000000001,5.631,San Marino +1774-05-01,16.364,2.301,San Marino +1774-06-01,20.882,7.309,San Marino +1774-07-01,22.998,7.485,San Marino +1774-08-01,24.003,2.883,San Marino +1774-09-01,19.297,3.07,San Marino +1774-10-01,14.012,4.828,San Marino +1774-11-01,8.282,7.967,San Marino +1774-12-01,4.308,5.4529999999999985,San Marino +1775-01-01,5.489,5.66,San Marino +1775-02-01,8.611,5.397,San Marino +1775-03-01,9.953,2.447,San Marino +1775-04-01,11.008,1.466,San Marino +1775-05-01,14.773,3.528,San Marino +1775-06-01,20.463,5.518,San Marino +1775-07-01,22.919,1.7619999999999998,San Marino +1775-08-01,23.252,1.625,San Marino +1775-09-01,19.855999999999998,3.107,San Marino 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Marino +1844-02-01,5.521,1.033,San Marino +1844-03-01,7.050000000000002,2.435,San Marino +1844-04-01,12.195,0.97,San Marino +1844-05-01,15.1,1.015,San Marino +1844-06-01,20.413,1.001,San Marino +1844-07-01,21.579,1.087,San Marino +1844-08-01,20.906999999999996,2.756,San Marino +1844-09-01,19.921,0.34700000000000003,San Marino +1844-10-01,16.261,0.85,San Marino +1844-11-01,11.679,0.6629999999999999,San Marino +1844-12-01,5.973000000000001,1.505,San Marino +1845-01-01,8.201,1.8,San Marino +1845-02-01,2.9930000000000003,3.2110000000000003,San Marino +1845-03-01,6.3260000000000005,2.047,San Marino +1845-04-01,11.188,1.129,San Marino +1845-05-01,13.833,1.442,San Marino +1845-06-01,20.081,1.3,San Marino +1845-07-01,22.612,0.994,San Marino +1845-08-01,20.418000000000006,1.048,San Marino +1845-09-01,18.784,0.978,San Marino +1845-10-01,15.548,0.764,San Marino +1845-11-01,12.144,1.082,San Marino +1845-12-01,8.511000000000001,2.476,San Marino +1846-01-01,7.164,2.043,San Marino 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+1848-04-01,12.982000000000001,1.216,San Marino +1848-05-01,15.95,1.808,San Marino +1848-06-01,21.328000000000003,2.024,San Marino +1848-07-01,22.304,2.344,San Marino +1848-08-01,22.938,1.467,San Marino +1848-09-01,18.594,1.3090000000000002,San Marino +1848-10-01,15.957,0.895,San Marino +1848-11-01,9.521,0.956,San Marino +1848-12-01,6.495,1.461,San Marino +1849-01-01,4.56,1.35,San Marino +1849-02-01,7.138,1.288,San Marino +1849-03-01,7.399,1.83,San Marino +1849-04-01,10.332999999999998,1.519,San Marino +1849-05-01,16.302,0.5720000000000001,San Marino +1849-06-01,22.096,1.499,San Marino +1849-07-01,22.083,1.041,San Marino +1849-08-01,21.655,1.203,San Marino +1849-09-01,19.018,0.953,San Marino +1849-10-01,15.919,1.245,San Marino +1849-11-01,10.290999999999999,1.907,San Marino +1849-12-01,5.96,0.807,San Marino +1850-01-01,2.875,1.6769999999999998,San Marino +1850-02-01,6.541,1.317,San Marino +1850-03-01,6.1,2.467,San Marino +1850-04-01,11.344000000000001,2.027,San Marino 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Marino +1852-05-01,15.744000000000002,1.742,San Marino +1852-06-01,19.59,0.937,San Marino +1852-07-01,22.355,1.315,San Marino +1852-08-01,21.861,0.872,San Marino +1852-09-01,18.949,0.828,San Marino +1852-10-01,14.584000000000001,0.726,San Marino +1852-11-01,13.882,1.188,San Marino +1852-12-01,9.744,1.651,San Marino +1853-01-01,7.571000000000001,0.922,San Marino +1853-02-01,5.599,1.091,San Marino +1853-03-01,6.247000000000001,1.525,San Marino +1853-04-01,9.602,0.977,San Marino +1853-05-01,15.299000000000001,0.696,San Marino +1853-06-01,18.513,1.055,San Marino +1853-07-01,23.703000000000007,1.151,San Marino +1853-08-01,22.838,1.35,San Marino +1853-09-01,18.992,0.62,San Marino +1853-10-01,15.52,0.941,San Marino +1853-11-01,11.298,1.947,San Marino +1853-12-01,5.547999999999999,1.51,San Marino +1854-01-01,6.528,2.425,San Marino +1854-02-01,4.208,1.669,San Marino +1854-03-01,7.309,1.007,San Marino +1854-04-01,11.02,1.0659999999999998,San Marino +1854-05-01,15.619000000000002,1.147,San Marino 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Marino +1856-05-01,14.813,2.338,San Marino +1856-06-01,20.315,1.858,San Marino +1856-07-01,21.296,1.126,San Marino +1856-08-01,23.110000000000007,1.661,San Marino +1856-09-01,17.962,1.381,San Marino +1856-10-01,15.603,0.838,San Marino +1856-11-01,7.323000000000001,1.038,San Marino +1856-12-01,6.412000000000001,1.976,San Marino +1857-01-01,5.143,1.3969999999999998,San Marino +1857-02-01,4.716,1.993,San Marino +1857-03-01,7.2890000000000015,1.3630000000000002,San Marino +1857-04-01,11.836,0.985,San Marino +1857-05-01,15.318,0.879,San Marino +1857-06-01,18.715999999999998,0.883,San Marino +1857-07-01,22.410999999999998,1.023,San Marino +1857-08-01,22.218000000000004,0.905,San Marino +1857-09-01,19.442999999999998,0.684,San Marino +1857-10-01,17.188,1.426,San Marino +1857-11-01,10.244000000000002,1.043,San Marino +1857-12-01,6.7860000000000005,1.096,San Marino +1858-01-01,1.7080000000000002,2.258,San Marino +1858-02-01,1.6719999999999997,1.266,San Marino 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Marino +1860-04-01,10.521999999999998,1.182,San Marino +1860-05-01,16.261,0.618,San Marino +1860-06-01,19.804000000000002,0.8740000000000001,San Marino +1860-07-01,20.215,0.7659999999999999,San Marino +1860-08-01,21.468000000000004,0.965,San Marino +1860-09-01,19.352,0.964,San Marino +1860-10-01,14.648,0.752,San Marino +1860-11-01,9.538,0.758,San Marino +1860-12-01,7.021,1.075,San Marino +1861-01-01,3.958,1.087,San Marino +1861-02-01,8.074,1.491,San Marino +1861-03-01,8.487,0.462,San Marino +1861-04-01,10.664000000000001,0.825,San Marino +1861-05-01,14.5,0.769,San Marino +1861-06-01,20.295,1.907,San Marino +1861-07-01,22.188,1.207,San Marino +1861-08-01,24.555,0.715,San Marino +1861-09-01,20.07,0.809,San Marino +1861-10-01,15.885,0.621,San Marino +1861-11-01,11.588,1.7380000000000002,San Marino +1861-12-01,5.329,1.839,San Marino +1862-01-01,4.57,1.362,San Marino +1862-02-01,5.917000000000001,1.376,San Marino +1862-03-01,10.432,0.687,San Marino +1862-04-01,13.692,0.732,San Marino 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Marino +1866-06-01,20.94,0.6409999999999999,San Marino +1866-07-01,22.64,0.8420000000000001,San Marino +1866-08-01,20.721999999999998,0.5660000000000001,San Marino +1866-09-01,19.504,0.778,San Marino +1866-10-01,14.004000000000001,0.731,San Marino +1866-11-01,10.321000000000002,0.445,San Marino +1866-12-01,7.763999999999999,1.158,San Marino +1867-01-01,6.862,1.599,San Marino +1867-02-01,8.427,0.825,San Marino +1867-03-01,8.992,1.014,San Marino +1867-04-01,12.742,0.935,San Marino +1867-05-01,16.641,0.508,San Marino +1867-06-01,20.113,0.532,San Marino +1867-07-01,21.889000000000006,0.877,San Marino +1867-08-01,22.543000000000006,0.752,San Marino +1867-09-01,21.064,1.032,San Marino +1867-10-01,14.076,0.931,San Marino +1867-11-01,9.291,1.939,San Marino +1867-12-01,5.269,1.703,San Marino +1868-01-01,4.41,0.66,San Marino +1868-02-01,6.273,1.52,San Marino +1868-03-01,7.749,0.637,San Marino +1868-04-01,11.077,1.138,San Marino +1868-05-01,19.261,0.923,San Marino 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Marino +1878-11-01,10.728000000000002,0.985,San Marino +1878-12-01,5.419,1.538,San Marino +1879-01-01,6.225,0.784,San Marino +1879-02-01,8.59,0.947,San Marino +1879-03-01,8.585,0.481,San Marino +1879-04-01,10.888,1.015,San Marino +1879-05-01,12.664000000000001,0.978,San Marino +1879-06-01,20.298,0.649,San Marino +1879-07-01,20.551,0.935,San Marino +1879-08-01,24.065,0.949,San Marino +1879-09-01,20.425,0.713,San Marino +1879-10-01,14.223,0.672,San Marino +1879-11-01,8.582,0.782,San Marino +1879-12-01,3.545,1.247,San Marino +1880-01-01,2.0460000000000003,1.209,San Marino +1880-02-01,6.007000000000001,0.735,San Marino +1880-03-01,8.304,0.622,San Marino +1880-04-01,12.986,0.482,San Marino +1880-05-01,15.192,0.581,San Marino +1880-06-01,18.438,0.57,San Marino +1880-07-01,23.846999999999998,0.604,San Marino +1880-08-01,20.951999999999998,0.628,San Marino +1880-09-01,19.52,0.911,San Marino +1880-10-01,16.217,0.616,San Marino +1880-11-01,12.415999999999999,1.714,San Marino 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+1875-05-01,29.74,0.7709999999999999,Senegal +1875-06-01,29.039,1.518,Senegal +1875-07-01,27.691999999999997,1.928,Senegal +1875-08-01,26.404,1.716,Senegal +1875-09-01,27.619,1.5719999999999998,Senegal +1875-10-01,27.638,0.669,Senegal +1875-11-01,25.686,0.882,Senegal +1875-12-01,24.129,0.8140000000000001,Senegal +1876-01-01,23.566,0.758,Senegal +1876-02-01,25.972,1.054,Senegal +1876-03-01,29.18,1.415,Senegal +1876-04-01,30.555999999999997,1.486,Senegal +1876-05-01,30.396,0.804,Senegal +1876-06-01,30.295,1.68,Senegal +1876-07-01,27.66,1.846,Senegal +1876-08-01,26.5,1.604,Senegal +1876-09-01,27.063000000000002,1.021,Senegal +1876-10-01,27.831,2.077,Senegal +1876-11-01,26.887,2.928,Senegal +1876-12-01,24.483,3.26,Senegal +1877-01-01,24.206,1.914,Senegal +1877-02-01,25.857,1.22,Senegal +1877-03-01,28.272,1.087,Senegal +1877-04-01,29.459,1.669,Senegal +1877-05-01,30.712,0.941,Senegal +1877-06-01,29.566999999999997,1.247,Senegal +1877-07-01,28.504,0.7440000000000001,Senegal +1877-08-01,27.533,1.23,Senegal +1877-09-01,27.952,0.616,Senegal +1877-10-01,28.231,0.757,Senegal +1877-11-01,27.041,0.726,Senegal +1877-12-01,23.57,2.478,Senegal +1878-01-01,23.148000000000003,2.0380000000000003,Senegal +1878-02-01,26.838,1.182,Senegal +1878-03-01,29.343000000000004,1.236,Senegal +1878-04-01,30.678,1.14,Senegal +1878-05-01,31.134,0.604,Senegal +1878-06-01,31.361,2.481,Senegal +1878-07-01,27.915000000000006,1.734,Senegal +1878-08-01,27.129,0.938,Senegal +1878-09-01,27.566999999999997,0.799,Senegal +1878-10-01,27.568,1.607,Senegal +1878-11-01,27.448,0.907,Senegal +1878-12-01,23.763,2.487,Senegal +1879-01-01,23.663,0.8859999999999999,Senegal +1879-02-01,26.571,1.036,Senegal +1879-03-01,27.996,0.7979999999999999,Senegal +1879-04-01,29.814,1.181,Senegal +1879-05-01,30.566,1.203,Senegal +1879-06-01,30.428,0.995,Senegal +1879-07-01,27.318,2.251,Senegal +1879-08-01,26.072,1.418,Senegal +1879-09-01,27.539,0.765,Senegal +1879-10-01,28.066,0.833,Senegal +1879-11-01,26.742,0.777,Senegal +1879-12-01,23.849,1.401,Senegal +1880-01-01,23.854,1.062,Senegal +1880-02-01,25.736,0.918,Senegal +1880-03-01,27.745,1.443,Senegal +1880-04-01,29.05,0.715,Senegal +1880-05-01,30.87,1.471,Senegal +1880-06-01,30.062,1.4340000000000002,Senegal +1880-07-01,28.37,1.194,Senegal +1880-08-01,27.449,1.777,Senegal +1880-09-01,27.264,0.794,Senegal +1880-10-01,28.822,2.397,Senegal +1880-11-01,27.001,2.3280000000000003,Senegal +1880-12-01,24.362,1.599,Senegal +1881-01-01,25.25,1.242,Senegal +1881-02-01,26.208000000000002,0.919,Senegal +1881-03-01,28.829,0.856,Senegal +1881-04-01,30.116999999999997,0.978,Senegal +1881-05-01,31.437,1.04,Senegal +1881-06-01,30.749000000000002,1.924,Senegal +1881-07-01,28.663,1.406,Senegal +1881-08-01,,,Senegal +1881-09-01,28.253,1.135,Senegal +1881-10-01,28.063000000000002,1.002,Senegal +1881-11-01,26.668000000000003,1.198,Senegal +1881-12-01,24.092,1.057,Senegal +1882-01-01,24.259,1.212,Senegal +1882-02-01,26.481,0.833,Senegal 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+1884-06-01,30.813000000000002,2.502,Senegal +1884-07-01,29.101000000000006,2.571,Senegal +1884-08-01,28.164,1.901,Senegal +1884-09-01,27.312,0.995,Senegal +1884-10-01,27.546,1.05,Senegal +1884-11-01,26.056,1.063,Senegal +1884-12-01,23.975,1.115,Senegal +1885-01-01,23.339000000000002,0.967,Senegal +1885-02-01,25.412,0.957,Senegal +1885-03-01,27.53,0.907,Senegal +1885-04-01,28.51,0.93,Senegal +1885-05-01,30.134,1.093,Senegal +1885-06-01,29.372,0.8859999999999999,Senegal +1885-07-01,27.772,0.96,Senegal +1885-08-01,26.909000000000002,1.09,Senegal +1885-09-01,27.156,0.836,Senegal +1885-10-01,28.053,0.88,Senegal +1885-11-01,26.477,0.995,Senegal +1885-12-01,24.706999999999997,1.053,Senegal +1886-01-01,23.328000000000003,0.904,Senegal +1886-02-01,25.627,1.144,Senegal +1886-03-01,28.991999999999997,1.755,Senegal +1886-04-01,29.322,0.915,Senegal +1886-05-01,30.38,1.197,Senegal +1886-06-01,30.033,1.192,Senegal +1886-07-01,27.968000000000004,0.794,Senegal +1886-08-01,26.777,0.738,Senegal +1886-09-01,27.22,0.807,Senegal +1886-10-01,27.5,0.995,Senegal +1886-11-01,25.857,0.894,Senegal +1886-12-01,23.537,1.093,Senegal +1887-01-01,23.528000000000002,1.4980000000000002,Senegal +1887-02-01,25.518,1.26,Senegal +1887-03-01,27.917,0.777,Senegal +1887-04-01,29.674,0.804,Senegal +1887-05-01,30.761,0.872,Senegal +1887-06-01,29.77,0.924,Senegal +1887-07-01,28.384,0.845,Senegal +1887-08-01,26.311,1.016,Senegal +1887-09-01,27.355,0.6609999999999999,Senegal +1887-10-01,28.188000000000002,0.752,Senegal +1887-11-01,26.456,0.91,Senegal +1887-12-01,24.502,0.8190000000000001,Senegal +1888-01-01,23.607,1.239,Senegal +1888-02-01,25.52,1.222,Senegal +1888-03-01,28.24,1.16,Senegal +1888-04-01,29.641,0.963,Senegal +1888-05-01,30.15,0.911,Senegal +1888-06-01,30.17,1.266,Senegal +1888-07-01,28.213,0.775,Senegal +1888-08-01,26.707,0.88,Senegal +1888-09-01,27.751,1.057,Senegal +1888-10-01,28.411,1.053,Senegal +1888-11-01,26.729,0.953,Senegal +1888-12-01,24.014,0.947,Senegal 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Georgia And The South Sandwich Isla +1886-04-01,,,South Georgia And The South Sandwich Isla +1886-05-01,,,South Georgia And The South Sandwich Isla +1886-06-01,,,South Georgia And The South Sandwich Isla +1886-07-01,,,South Georgia And The South Sandwich Isla +1886-08-01,,,South Georgia And The South Sandwich Isla +1886-09-01,,,South Georgia And The South Sandwich Isla +1886-10-01,,,South Georgia And The South Sandwich Isla +1886-11-01,,,South Georgia And The South Sandwich Isla +1886-12-01,,,South Georgia And The South Sandwich Isla +1887-01-01,,,South Georgia And The South Sandwich Isla +1887-02-01,,,South Georgia And The South Sandwich Isla +1887-03-01,,,South Georgia And The South Sandwich Isla +1887-04-01,,,South Georgia And The South Sandwich Isla +1887-05-01,,,South Georgia And The South Sandwich Isla +1887-06-01,,,South Georgia And The South Sandwich Isla +1887-07-01,,,South Georgia And The South Sandwich Isla +1887-08-01,,,South Georgia And The South Sandwich Isla +1887-09-01,,,South Georgia And The South Sandwich Isla +1887-10-01,,,South Georgia And The South Sandwich Isla +1887-11-01,,,South Georgia And The South Sandwich Isla +1887-12-01,,,South Georgia And The South Sandwich Isla +1888-01-01,,,South Georgia And The South Sandwich Isla +1888-02-01,,,South Georgia And The South Sandwich Isla +1888-03-01,,,South Georgia And The South Sandwich Isla +1888-04-01,,,South Georgia And The South Sandwich Isla +1888-05-01,,,South Georgia And The South Sandwich Isla +1888-06-01,,,South Georgia And The South Sandwich Isla +1888-07-01,,,South Georgia And The South Sandwich Isla +1888-08-01,,,South Georgia And The South Sandwich Isla +1888-09-01,,,South Georgia And The South Sandwich Isla +1888-10-01,,,South Georgia And The South Sandwich Isla +1888-11-01,,,South Georgia And The South Sandwich Isla +1888-12-01,,,South Georgia And The South Sandwich Isla +1889-01-01,,,South Georgia And The South Sandwich Isla +1889-02-01,,,South Georgia And The South Sandwich Isla +1889-03-01,,,South Georgia And The South Sandwich Isla +1889-04-01,,,South Georgia And The South Sandwich Isla +1889-05-01,,,South Georgia And The South Sandwich Isla +1889-06-01,,,South Georgia And The South Sandwich Isla +1889-07-01,,,South Georgia And The South Sandwich Isla +1889-08-01,,,South Georgia And The South Sandwich Isla +1889-09-01,,,South Georgia And The South Sandwich Isla +1889-10-01,,,South Georgia And The South Sandwich Isla +1889-11-01,,,South Georgia And The South Sandwich Isla +1889-12-01,,,South Georgia And The South Sandwich Isla +1890-01-01,,,South Georgia And The South Sandwich Isla +1890-02-01,,,South Georgia And The South Sandwich Isla +1890-03-01,,,South Georgia And The South Sandwich Isla +1890-04-01,,,South Georgia And The South Sandwich Isla +1890-05-01,,,South Georgia And The South Sandwich Isla +1890-06-01,,,South Georgia And The South Sandwich Isla +1890-07-01,,,South Georgia And The South Sandwich Isla +1890-08-01,,,South Georgia And The South Sandwich Isla +1890-09-01,,,South Georgia And The South Sandwich Isla +1890-10-01,,,South Georgia And The South Sandwich Isla +1890-11-01,,,South Georgia And The South Sandwich Isla +1890-12-01,,,South Georgia And The South Sandwich Isla +1891-01-01,,,South Georgia And The South Sandwich Isla +1891-02-01,,,South Georgia And The South Sandwich Isla +1891-03-01,,,South Georgia And The South Sandwich Isla +1891-04-01,,,South Georgia And The South Sandwich Isla +1891-05-01,,,South Georgia And The South Sandwich Isla +1891-06-01,,,South Georgia And The South Sandwich Isla +1891-07-01,,,South Georgia And The South Sandwich Isla +1891-08-01,,,South Georgia And The South Sandwich Isla +1891-09-01,,,South Georgia And The South Sandwich Isla +1891-10-01,,,South Georgia And The South Sandwich Isla +1891-11-01,,,South Georgia And The South Sandwich Isla +1891-12-01,,,South Georgia And The South Sandwich Isla +1892-01-01,,,South Georgia And The South Sandwich Isla +1892-02-01,,,South Georgia And The South Sandwich Isla +1892-03-01,,,South Georgia And The South Sandwich Isla +1892-04-01,,,South Georgia And The South Sandwich Isla +1892-05-01,,,South Georgia And The South Sandwich Isla +1892-06-01,,,South Georgia And The South Sandwich Isla +1892-07-01,,,South Georgia And The South Sandwich Isla +1892-08-01,,,South Georgia And The South Sandwich Isla +1892-09-01,,,South Georgia And The South Sandwich Isla +1892-10-01,,,South Georgia And The South Sandwich Isla +1892-11-01,,,South Georgia And The South Sandwich Isla +1892-12-01,,,South Georgia And The South Sandwich Isla +1893-01-01,,,South Georgia And The South Sandwich Isla +1893-02-01,,,South Georgia And The South Sandwich Isla +1893-03-01,,,South Georgia And The South Sandwich Isla +1893-04-01,,,South Georgia And The South Sandwich Isla +1893-05-01,,,South Georgia And The South Sandwich Isla +1893-06-01,,,South Georgia And The South Sandwich Isla +1893-07-01,,,South Georgia And The South Sandwich Isla 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South Sandwich Isla +1895-02-01,4.126,1.765,South Georgia And The South Sandwich Isla +1895-03-01,3.772,1.822,South Georgia And The South Sandwich Isla +1895-04-01,1.6709999999999998,1.693,South Georgia And The South Sandwich Isla +1895-05-01,-0.292,1.689,South Georgia And The South Sandwich Isla +1895-06-01,-1.4469999999999998,1.675,South Georgia And The South Sandwich Isla +1895-07-01,-2.2569999999999997,1.7280000000000002,South Georgia And The South Sandwich Isla +1895-08-01,-2.197,1.7519999999999998,South Georgia And The South Sandwich Isla +1895-09-01,-1.2570000000000001,1.737,South Georgia And The South Sandwich Isla +1895-10-01,-0.12099999999999975,1.836,South Georgia And The South Sandwich Isla +1895-11-01,1.7930000000000001,1.703,South Georgia And The South Sandwich Isla +1895-12-01,3.009,1.7,South Georgia And The South Sandwich Isla +1896-01-01,4.307,1.795,South Georgia And The South Sandwich Isla +1896-02-01,4.8839999999999995,1.834,South Georgia And The South Sandwich Isla 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Georgia And The South Sandwich Isla +1897-05-01,0.016999999999999987,1.679,South Georgia And The South Sandwich Isla +1897-06-01,-1.622,1.676,South Georgia And The South Sandwich Isla +1897-07-01,-1.4409999999999998,1.897,South Georgia And The South Sandwich Isla +1897-08-01,-1.351,1.879,South Georgia And The South Sandwich Isla +1897-09-01,-0.54,1.828,South Georgia And The South Sandwich Isla +1897-10-01,0.5830000000000001,1.705,South Georgia And The South Sandwich Isla +1897-11-01,2.41,2.3,South Georgia And The South Sandwich Isla +1897-12-01,3.4730000000000003,2.465,South Georgia And The South Sandwich Isla +1898-01-01,4.8119999999999985,2.185,South Georgia And The South Sandwich Isla +1898-02-01,4.5360000000000005,2.042,South Georgia And The South Sandwich Isla +1898-03-01,3.1050000000000004,2.023,South Georgia And The South Sandwich Isla +1898-04-01,1.42,1.681,South Georgia And The South Sandwich Isla +1898-05-01,-0.574,1.682,South Georgia And The South Sandwich Isla 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Georgia And The South Sandwich Isla +1899-08-01,-1.964,1.6780000000000002,South Georgia And The South Sandwich Isla +1899-09-01,-0.45,1.717,South Georgia And The South Sandwich Isla +1899-10-01,0.6970000000000001,1.6769999999999998,South Georgia And The South Sandwich Isla +1899-11-01,2.191,1.6669999999999998,South Georgia And The South Sandwich Isla +1899-12-01,2.4799999999999995,1.69,South Georgia And The South Sandwich Isla +1900-01-01,3.564,1.823,South Georgia And The South Sandwich Isla +1900-02-01,4.7250000000000005,1.777,South Georgia And The South Sandwich Isla +1900-03-01,4.072,1.724,South Georgia And The South Sandwich Isla +1900-04-01,1.828,1.696,South Georgia And The South Sandwich Isla +1900-05-01,0.266,1.845,South Georgia And The South Sandwich Isla +1900-06-01,-1.4429999999999998,1.71,South Georgia And The South Sandwich Isla +1900-07-01,-2.164,1.726,South Georgia And The South Sandwich Isla +1900-08-01,-1.2819999999999998,1.913,South Georgia And The South Sandwich Isla 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South Sandwich Isla +1902-12-01,,,South Georgia And The South Sandwich Isla +1903-01-01,3.885,1.822,South Georgia And The South Sandwich Isla +1903-02-01,4.792,1.85,South Georgia And The South Sandwich Isla +1903-03-01,3.3140000000000005,1.839,South Georgia And The South Sandwich Isla +1903-04-01,0.018999999999999684,1.55,South Georgia And The South Sandwich Isla +1903-05-01,-1.137,1.308,South Georgia And The South Sandwich Isla +1903-06-01,-2.888,1.4380000000000002,South Georgia And The South Sandwich Isla +1903-07-01,-0.986,1.355,South Georgia And The South Sandwich Isla +1903-08-01,-0.8670000000000001,1.33,South Georgia And The South Sandwich Isla +1903-09-01,-2.792,1.429,South Georgia And The South Sandwich Isla +1903-10-01,0.925,1.297,South Georgia And The South Sandwich Isla +1903-11-01,2.082,1.291,South Georgia And The South Sandwich Isla +1903-12-01,3.104,1.318,South Georgia And The South Sandwich Isla +1904-01-01,3.852,1.524,South Georgia And The South Sandwich Isla 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Georgia And The South Sandwich Isla +1908-08-01,-0.19800000000000015,0.696,South Georgia And The South Sandwich Isla +1908-09-01,1.34,0.71,South Georgia And The South Sandwich Isla +1908-10-01,0.4690000000000001,1.716,South Georgia And The South Sandwich Isla +1908-11-01,2.738,1.265,South Georgia And The South Sandwich Isla +1908-12-01,2.861,1.4469999999999998,South Georgia And The South Sandwich Isla +1909-01-01,4.7780000000000005,1.168,South Georgia And The South Sandwich Isla +1909-02-01,3.932,1.416,South Georgia And The South Sandwich Isla +1909-03-01,3.828,0.67,South Georgia And The South Sandwich Isla +1909-04-01,1.6189999999999998,0.758,South Georgia And The South Sandwich Isla +1909-05-01,-0.0020000000000000018,2.5780000000000003,South Georgia And The South Sandwich Isla +1909-06-01,-1.6629999999999998,0.758,South Georgia And The South Sandwich Isla +1909-07-01,-2.149,1.028,South Georgia And The South Sandwich Isla +1909-08-01,-0.28200000000000003,0.7490000000000001,South Georgia And The South Sandwich Isla +1909-09-01,-1.33,0.989,South Georgia And The South Sandwich Isla +1909-10-01,0.912,1.384,South Georgia And The South Sandwich Isla +1909-11-01,2.015,0.6609999999999999,South Georgia And The South Sandwich Isla +1909-12-01,2.946,0.685,South Georgia And The South Sandwich Isla +1910-01-01,4.978999999999999,0.696,South Georgia And The South Sandwich Isla +1910-02-01,4.953,0.65,South Georgia And The South Sandwich Isla +1910-03-01,4.2010000000000005,0.679,South Georgia And The South Sandwich Isla +1910-04-01,2.154,0.687,South Georgia And The South Sandwich Isla +1910-05-01,0.135,0.894,South Georgia And The South Sandwich Isla +1910-06-01,-0.8899999999999999,0.703,South Georgia And The South Sandwich Isla +1910-07-01,-1.173,0.653,South Georgia And The South Sandwich Isla +1910-08-01,-1.8719999999999999,0.7979999999999999,South Georgia And The South Sandwich Isla +1910-09-01,-0.349,0.977,South Georgia And The South Sandwich Isla +1910-10-01,0.1910000000000001,1.245,South Georgia And The South Sandwich Isla +1910-11-01,2.927,0.901,South Georgia And The South Sandwich Isla +1910-12-01,2.446,1.258,South Georgia And The South Sandwich Isla +1911-01-01,3.482,1.285,South Georgia And The South Sandwich Isla +1911-02-01,4.739,0.845,South Georgia And The South Sandwich Isla +1911-03-01,4.037000000000001,1.523,South Georgia And The South Sandwich Isla +1911-04-01,0.5259999999999998,0.868,South Georgia And The South Sandwich Isla +1911-05-01,0.19399999999999995,0.75,South Georgia And The South Sandwich Isla +1911-06-01,-1.7979999999999998,0.964,South Georgia And The South Sandwich Isla +1911-07-01,-1.8530000000000002,0.931,South Georgia And The South Sandwich Isla +1911-08-01,-1.6669999999999998,0.6509999999999999,South Georgia And The South Sandwich Isla +1911-09-01,-0.64,0.664,South Georgia And The South Sandwich Isla +1911-10-01,0.932,0.546,South Georgia And The South Sandwich Isla +1911-11-01,-0.278,0.698,South Georgia And The South Sandwich Isla +1911-12-01,2.6289999999999996,1.3259999999999998,South Georgia And The South Sandwich Isla +1912-01-01,4.499,0.92,South Georgia And The South Sandwich Isla +1912-02-01,5.021,0.922,South Georgia And The South Sandwich Isla +1912-03-01,3.4060000000000006,1.356,South Georgia And The South Sandwich Isla +1912-04-01,0.44099999999999984,0.6609999999999999,South Georgia And The South Sandwich Isla +1912-05-01,0.188,0.743,South Georgia And The South Sandwich Isla +1912-06-01,-1.685,0.759,South Georgia And The South Sandwich Isla +1912-07-01,-4.199,0.767,South Georgia And The South Sandwich Isla +1912-08-01,-3.427,0.512,South Georgia And The South Sandwich Isla +1912-09-01,-0.15399999999999994,0.586,South Georgia And The South Sandwich Isla +1912-10-01,-0.09299999999999996,0.552,South Georgia And The South Sandwich Isla +1912-11-01,0.8459999999999999,1.804,South Georgia And The South Sandwich Isla +1912-12-01,2.12,1.193,South Georgia And The South Sandwich Isla +1913-01-01,3.394,1.293,South Georgia And The South Sandwich Isla +1913-02-01,4.0440000000000005,1.13,South Georgia And The South Sandwich Isla +1913-03-01,2.3720000000000003,1.027,South Georgia And The South Sandwich Isla +1913-04-01,1.2519999999999998,1.964,South Georgia And The South Sandwich Isla +1913-05-01,1.112,0.68,South Georgia And The South Sandwich Isla +1913-06-01,-3.1860000000000004,6.5779999999999985,South Georgia And The South Sandwich Isla +1913-07-01,-3.697,7.7029999999999985,South Georgia And The South Sandwich Isla +1913-08-01,-2.2430000000000003,6.689,South Georgia And The South Sandwich Isla +1913-09-01,0.3070000000000001,1.081,South Georgia And The South Sandwich Isla +1913-10-01,0.7630000000000001,1.68,South Georgia And The South Sandwich Isla +1913-11-01,0.6930000000000001,0.698,South Georgia And The South Sandwich Isla +1913-12-01,3.071,1.596,South Georgia And The South Sandwich Isla +1914-01-01,4.302,0.8109999999999999,South Georgia And The South Sandwich Isla +1914-02-01,5.4510000000000005,0.5539999999999999,South Georgia And The South Sandwich Isla +1914-03-01,4.621,0.72,South Georgia And The South Sandwich Isla +1914-04-01,3.32,0.8390000000000001,South Georgia And The South Sandwich Isla +1914-05-01,1.891,0.805,South Georgia And The South Sandwich Isla +1914-06-01,-0.9469999999999998,1.161,South Georgia And The South Sandwich Isla +1914-07-01,-3.232,1.875,South Georgia And The South Sandwich Isla +1914-08-01,-1.769,1.845,South Georgia And The South Sandwich Isla +1914-09-01,-0.5539999999999999,1.053,South Georgia And The South Sandwich Isla +1914-10-01,-0.4069999999999998,0.728,South Georgia And The South Sandwich Isla +1914-11-01,0.9309999999999998,1.07,South Georgia And The South Sandwich Isla +1914-12-01,3.978,0.8809999999999999,South Georgia And The South Sandwich Isla +1915-01-01,4.2780000000000005,0.7020000000000001,South Georgia And The South Sandwich Isla +1915-02-01,4.221,1.029,South Georgia And The South Sandwich Isla +1915-03-01,4.638,1.203,South Georgia And The South Sandwich Isla +1915-04-01,1.8980000000000001,0.593,South Georgia And The South Sandwich Isla +1915-05-01,-1.122,0.585,South Georgia And The South Sandwich Isla +1915-06-01,-2.622,0.594,South Georgia And The South Sandwich Isla +1915-07-01,-2.6719999999999997,0.782,South Georgia And The South Sandwich Isla +1915-08-01,-3.823,0.875,South Georgia And The South Sandwich Isla +1915-09-01,0.11900000000000005,1.431,South Georgia And The South Sandwich Isla +1915-10-01,0.6060000000000001,0.962,South Georgia And The South Sandwich Isla +1915-11-01,3.5610000000000004,2.763,South Georgia And The South Sandwich Isla +1915-12-01,2.824,0.691,South Georgia And The South Sandwich Isla +1916-01-01,4.774,0.784,South Georgia And The South Sandwich Isla +1916-02-01,4.651,0.8590000000000001,South Georgia And The South Sandwich Isla +1916-03-01,2.4040000000000004,1.2,South Georgia And The South Sandwich Isla +1916-04-01,1.858,0.642,South Georgia And The South Sandwich Isla +1916-05-01,-1.1190000000000002,0.821,South Georgia And The South Sandwich Isla +1916-06-01,-1.881,0.589,South Georgia And The South Sandwich Isla +1916-07-01,-3.869,1.156,South Georgia And The South Sandwich Isla +1916-08-01,-2.283,1.261,South Georgia And The South Sandwich Isla +1916-09-01,-0.637,0.912,South Georgia And The South Sandwich Isla +1916-10-01,0.0,0.5720000000000001,South Georgia And The South Sandwich Isla +1916-11-01,2.212,0.609,South Georgia And The South Sandwich Isla +1916-12-01,3.315,0.627,South Georgia And The South Sandwich Isla +1917-01-01,4.606,0.755,South Georgia And The South Sandwich Isla +1917-02-01,5.205,0.657,South Georgia And The South Sandwich Isla +1917-03-01,4.87,0.563,South Georgia And The South Sandwich Isla +1917-04-01,2.69,1.015,South Georgia And The South Sandwich Isla +1917-05-01,1.3430000000000002,0.733,South Georgia And The South Sandwich Isla +1917-06-01,-0.8549999999999999,0.787,South Georgia And The South Sandwich Isla +1917-07-01,-1.4409999999999998,0.8109999999999999,South Georgia And The South Sandwich Isla +1917-08-01,-2.16,0.83,South Georgia And The South Sandwich Isla +1917-09-01,-2.693,0.7170000000000001,South Georgia And The South Sandwich Isla +1917-10-01,1.06,0.691,South Georgia And The South Sandwich Isla +1917-11-01,4.354,0.662,South Georgia And The South Sandwich Isla +1917-12-01,3.295,0.579,South Georgia And The South Sandwich Isla +1918-01-01,5.7620000000000005,0.615,South Georgia And The South Sandwich Isla +1918-02-01,6.001,0.862,South Georgia And The South Sandwich Isla +1918-03-01,4.428000000000001,0.7440000000000001,South Georgia And The South Sandwich Isla +1918-04-01,2.912,0.782,South Georgia And The South Sandwich Isla +1918-05-01,-1.615,0.62,South Georgia And The South Sandwich Isla +1918-06-01,-1.6340000000000001,0.713,South Georgia And The South Sandwich Isla +1918-07-01,-1.6580000000000001,0.6459999999999999,South Georgia And The South Sandwich Isla +1918-08-01,-5.472,1.619,South Georgia And The South Sandwich Isla +1918-09-01,-0.575,1.958,South Georgia And The South Sandwich Isla +1918-10-01,3.3789999999999996,0.696,South Georgia And The South Sandwich Isla +1918-11-01,1.835,0.887,South Georgia And The South Sandwich Isla +1918-12-01,4.251,0.7340000000000001,South Georgia And The South Sandwich Isla +1919-01-01,4.505,1.279,South Georgia And The South Sandwich Isla +1919-02-01,5.367000000000001,1.406,South Georgia And The South Sandwich Isla +1919-03-01,4.194,0.87,South Georgia And The South Sandwich Isla +1919-04-01,0.413,2.064,South Georgia And The South Sandwich Isla +1919-05-01,-2.23,2.278,South Georgia And The South Sandwich Isla +1919-06-01,-0.975,0.7140000000000001,South Georgia And The South Sandwich Isla +1919-07-01,0.16199999999999992,1.406,South Georgia And The South Sandwich Isla +1919-08-01,-0.7170000000000001,0.7609999999999999,South Georgia And The South Sandwich Isla +1919-09-01,0.022000000000000075,1.044,South Georgia And The South Sandwich Isla +1919-10-01,0.6160000000000001,0.6679999999999999,South Georgia And The South Sandwich Isla +1919-11-01,0.6539999999999999,1.119,South Georgia And The South Sandwich Isla +1919-12-01,3.194,0.978,South Georgia And The South Sandwich Isla +1920-01-01,4.38,1.257,South Georgia And The South Sandwich Isla +1920-02-01,4.053999999999999,1.234,South Georgia And The South Sandwich Isla +1920-03-01,2.782,2.093,South Georgia And The South Sandwich Isla +1920-04-01,2.234,1.34,South Georgia And The South Sandwich Isla +1920-05-01,-0.44799999999999995,0.7490000000000001,South Georgia And The South Sandwich Isla +1920-06-01,-2.95,1.124,South Georgia And The South Sandwich Isla +1920-07-01,-1.642,0.691,South Georgia And The South Sandwich Isla +1920-08-01,-2.4770000000000003,0.787,South Georgia And The South Sandwich Isla +1920-09-01,-0.342,0.91,South Georgia And The South Sandwich Isla +1920-10-01,-2.127,0.622,South Georgia And The South Sandwich Isla +1920-11-01,1.0190000000000001,0.6829999999999999,South Georgia And The South Sandwich Isla +1920-12-01,2.81,0.665,South Georgia And The South Sandwich Isla +1921-01-01,3.662,0.787,South Georgia And The South Sandwich Isla +1921-02-01,3.679,0.745,South Georgia And The South Sandwich Isla +1921-03-01,3.506,0.634,South Georgia And The South Sandwich Isla +1921-04-01,1.612,0.986,South Georgia And The South Sandwich Isla +1921-05-01,-2.765,0.758,South Georgia And The South Sandwich Isla +1921-06-01,-1.489,0.835,South Georgia And The South Sandwich Isla +1921-07-01,-0.5720000000000001,0.867,South Georgia And The South Sandwich Isla +1921-08-01,-2.9539999999999997,0.74,South Georgia And The South Sandwich Isla +1921-09-01,-0.396,0.643,South Georgia And The South Sandwich Isla +1921-10-01,0.8710000000000001,0.921,South Georgia And The South Sandwich Isla +1921-11-01,1.0459999999999998,0.921,South Georgia And The South Sandwich Isla +1921-12-01,3.0260000000000002,1.264,South Georgia And The South Sandwich Isla +1922-01-01,4.166,0.674,South Georgia And The South Sandwich Isla +1922-02-01,4.253,0.746,South Georgia And The South Sandwich Isla +1922-03-01,3.702,0.5770000000000001,South Georgia And The South Sandwich Isla +1922-04-01,3.063,0.69,South Georgia And The South Sandwich Isla +1922-05-01,-2.2630000000000003,0.885,South Georgia And The South Sandwich Isla +1922-06-01,-1.838,0.66,South Georgia And The South Sandwich Isla +1922-07-01,0.276,0.741,South Georgia And The South Sandwich Isla +1922-08-01,-0.5090000000000001,0.6779999999999999,South Georgia And The South Sandwich Isla +1922-09-01,-0.688,0.581,South Georgia And The South Sandwich Isla +1922-10-01,0.2790000000000001,1.014,South Georgia And The South Sandwich Isla +1922-11-01,3.924,0.608,South Georgia And The South Sandwich Isla +1922-12-01,3.19,0.616,South Georgia And The South Sandwich Isla +1923-01-01,4.584,1.249,South Georgia And The South Sandwich Isla +1923-02-01,5.292999999999999,0.517,South Georgia And The South Sandwich Isla +1923-03-01,2.9819999999999998,1.3119999999999998,South Georgia And The South Sandwich Isla +1923-04-01,1.4219999999999997,0.6940000000000001,South Georgia And The South Sandwich Isla +1923-05-01,0.645,0.5660000000000001,South Georgia And The South Sandwich Isla +1923-06-01,-1.114,0.8690000000000001,South Georgia And The South Sandwich Isla +1923-07-01,-2.508,0.583,South Georgia And The South Sandwich Isla +1923-08-01,-3.642,1.342,South Georgia And The South Sandwich Isla +1923-09-01,-1.4509999999999998,0.998,South Georgia And The South Sandwich Isla +1923-10-01,-0.6159999999999999,0.995,South Georgia And The South Sandwich Isla +1923-11-01,1.176,0.82,South Georgia And The South Sandwich Isla +1923-12-01,2.0269999999999997,1.265,South Georgia And The South Sandwich Isla +1924-01-01,3.777,1.24,South Georgia And The South Sandwich Isla +1924-02-01,2.741,0.648,South Georgia And The South Sandwich Isla +1924-03-01,4.4030000000000005,0.606,South Georgia And The South Sandwich Isla +1924-04-01,-0.955,1.169,South Georgia And The South Sandwich Isla +1924-05-01,-1.3190000000000002,0.569,South Georgia And The South Sandwich Isla +1924-06-01,-1.5419999999999998,1.533,South Georgia And The South Sandwich Isla +1924-07-01,-3.289,2.167,South Georgia And The South Sandwich Isla +1924-08-01,-1.7120000000000002,1.78,South Georgia And The South Sandwich Isla +1924-09-01,-0.595,1.155,South Georgia And The South Sandwich Isla +1924-10-01,1.3030000000000002,0.525,South Georgia And The South Sandwich Isla +1924-11-01,1.731,0.515,South Georgia And The South Sandwich Isla +1924-12-01,2.843,0.836,South Georgia And The South Sandwich Isla +1925-01-01,4.0680000000000005,1.296,South Georgia And The South Sandwich Isla +1925-02-01,3.878,0.931,South Georgia And The South Sandwich Isla +1925-03-01,3.325,0.72,South Georgia And The South Sandwich Isla +1925-04-01,2.26,0.745,South Georgia And The South Sandwich Isla +1925-05-01,0.020000000000000004,1.311,South Georgia And The South Sandwich Isla +1925-06-01,-1.6709999999999998,0.58,South Georgia And The South Sandwich Isla +1925-07-01,-1.15,0.625,South Georgia And The South Sandwich Isla +1925-08-01,-2.2960000000000003,0.7979999999999999,South Georgia And The South Sandwich Isla +1925-09-01,-1.86,0.76,South Georgia And The South Sandwich Isla +1925-10-01,-0.4049999999999998,0.8909999999999999,South Georgia And The South Sandwich Isla +1925-11-01,1.2959999999999998,0.7759999999999999,South Georgia And The South Sandwich Isla +1925-12-01,2.858,1.359,South Georgia And The South Sandwich Isla +1926-01-01,5.194,1.304,South Georgia And The South Sandwich Isla +1926-02-01,4.641,0.764,South Georgia And The South Sandwich Isla +1926-03-01,2.575,0.973,South Georgia And The South Sandwich Isla +1926-04-01,-0.016000000000000014,1.142,South Georgia And The South Sandwich Isla +1926-05-01,-1.273,1.635,South Georgia And The South Sandwich Isla +1926-06-01,-3.978,1.138,South Georgia And The South Sandwich Isla +1926-07-01,-2.3409999999999997,0.608,South Georgia And The South Sandwich Isla +1926-08-01,-1.0140000000000002,0.618,South Georgia And The South Sandwich Isla +1926-09-01,-2.041,0.7340000000000001,South Georgia And The South Sandwich Isla +1926-10-01,-1.923,0.649,South Georgia And The South Sandwich Isla +1926-11-01,2.589,0.8029999999999999,South Georgia And The South Sandwich Isla +1926-12-01,3.653,0.687,South Georgia And The South Sandwich Isla +1927-01-01,3.654,1.534,South Georgia And The South Sandwich Isla +1927-02-01,3.28,0.8190000000000001,South Georgia And The South Sandwich Isla +1927-03-01,3.4360000000000004,0.815,South Georgia And The South Sandwich Isla +1927-04-01,1.067,0.7090000000000001,South Georgia And The South Sandwich Isla +1927-05-01,0.285,0.516,South Georgia And The South Sandwich Isla +1927-06-01,-1.537,0.523,South Georgia And The South Sandwich Isla +1927-07-01,-1.095,1.16,South Georgia And The South Sandwich Isla +1927-08-01,-3.984,0.8420000000000001,South Georgia And The South Sandwich Isla +1927-09-01,-3.1500000000000004,0.8540000000000001,South Georgia And The South Sandwich Isla +1927-10-01,-0.4390000000000001,0.605,South Georgia And The South Sandwich Isla +1927-11-01,0.2369999999999997,0.8959999999999999,South Georgia And The South Sandwich Isla +1927-12-01,2.607,0.8440000000000001,South Georgia And The South Sandwich Isla +1928-01-01,4.601,0.611,South Georgia And The South Sandwich Isla +1928-02-01,4.507,0.63,South Georgia And The South Sandwich Isla +1928-03-01,3.1100000000000003,1.796,South Georgia And The South Sandwich Isla +1928-04-01,0.4909999999999999,2.03,South Georgia And The South Sandwich Isla +1928-05-01,-2.053,1.61,South Georgia And The South Sandwich Isla +1928-06-01,-4.4529999999999985,0.5479999999999999,South Georgia And The South Sandwich Isla +1928-07-01,-2.905,1.081,South Georgia And The South Sandwich Isla +1928-08-01,-1.205,0.979,South Georgia And The South Sandwich Isla +1928-09-01,-0.24600000000000002,0.746,South Georgia And The South Sandwich Isla +1928-10-01,-0.3639999999999999,0.7170000000000001,South Georgia And The South Sandwich Isla +1928-11-01,1.369,0.7040000000000001,South Georgia And The South Sandwich Isla +1928-12-01,2.7260000000000004,1.262,South Georgia And The South Sandwich Isla +1929-01-01,3.257,1.275,South Georgia And The South Sandwich Isla +1929-02-01,4.89,0.8079999999999999,South Georgia And The South Sandwich Isla +1929-03-01,2.9610000000000003,0.7440000000000001,South Georgia And The South Sandwich Isla +1929-04-01,1.3979999999999997,0.541,South Georgia And The South Sandwich Isla +1929-05-01,-0.11599999999999999,0.5529999999999999,South Georgia And The South Sandwich Isla +1929-06-01,-1.575,1.283,South Georgia And The South Sandwich Isla +1929-07-01,-1.294,0.894,South Georgia And The South Sandwich Isla +1929-08-01,-2.8120000000000003,0.991,South Georgia And The South Sandwich Isla +1929-09-01,-0.6459999999999999,0.7440000000000001,South Georgia And The South Sandwich Isla +1929-10-01,1.131,0.664,South Georgia And The South Sandwich Isla +1929-11-01,3.375,0.588,South Georgia And The South Sandwich Isla +1929-12-01,2.064,0.5429999999999999,South Georgia And The South Sandwich Isla +1930-01-01,4.6930000000000005,0.649,South Georgia And The South Sandwich Isla +1930-02-01,4.2780000000000005,0.598,South Georgia And The South Sandwich Isla +1930-03-01,3.853,0.574,South Georgia And The South Sandwich Isla +1930-04-01,0.6099999999999999,0.855,South Georgia And The South Sandwich Isla +1930-05-01,0.461,0.862,South Georgia And The South Sandwich Isla +1930-06-01,-3.6839999999999997,0.7879999999999999,South Georgia And The South Sandwich Isla +1930-07-01,-3.23,1.043,South Georgia And The South Sandwich Isla +1930-08-01,-3.465,1.394,South Georgia And The South Sandwich Isla +1930-09-01,-1.479,0.604,South Georgia And The South Sandwich Isla +1930-10-01,-1.705,0.545,South Georgia And The South Sandwich Isla +1930-11-01,2.101,0.627,South Georgia And The South Sandwich Isla +1930-12-01,2.905,0.75,South Georgia And The South Sandwich Isla +1931-01-01,3.54,1.2819999999999998,South Georgia And The South Sandwich Isla +1931-02-01,3.215,1.711,South Georgia And The South Sandwich Isla +1931-03-01,3.957,1.079,South Georgia And The South Sandwich Isla +1931-04-01,1.617,1.462,South Georgia And The South Sandwich Isla +1931-05-01,0.333,2.541,South Georgia And The South Sandwich Isla +1931-06-01,-0.06599999999999984,1.624,South Georgia And The South Sandwich Isla +1931-07-01,0.78,1.515,South Georgia And The South Sandwich Isla +1931-08-01,0.113,0.5760000000000001,South Georgia And The South Sandwich Isla +1931-09-01,-0.713,4.423,South Georgia And The South Sandwich Isla +1931-10-01,0.996,1.508,South Georgia And The South Sandwich Isla +1931-11-01,1.095,0.706,South Georgia And The South Sandwich Isla +1931-12-01,2.226,0.7340000000000001,South Georgia And The South Sandwich Isla +1932-01-01,4.7269999999999985,1.069,South Georgia And The South Sandwich Isla +1932-02-01,3.9739999999999998,1.097,South Georgia And The South Sandwich Isla +1932-03-01,4.408,1.6030000000000002,South Georgia And The South Sandwich Isla +1932-04-01,0.379,3.812,South Georgia And The South Sandwich Isla +1932-05-01,-0.025,1.368,South Georgia And The South Sandwich Isla +1932-06-01,-3.911,4.645,South Georgia And The South Sandwich Isla +1932-07-01,-3.0589999999999997,3.714,South Georgia And The South Sandwich Isla +1932-08-01,-0.7640000000000001,1.112,South Georgia And The South Sandwich Isla +1932-09-01,0.9420000000000001,0.818,South Georgia And The South Sandwich Isla +1932-10-01,1.766,0.701,South Georgia And The South Sandwich Isla +1932-11-01,3.803,2.029,South Georgia And The South Sandwich Isla +1932-12-01,4.196000000000001,2.23,South Georgia And The South Sandwich Isla +1933-01-01,3.861,1.269,South Georgia And The South Sandwich Isla +1933-02-01,4.757,0.88,South Georgia And The South Sandwich Isla +1933-03-01,4.8580000000000005,1.4280000000000002,South Georgia And The South Sandwich Isla +1933-04-01,2.752,1.7009999999999998,South Georgia And The South Sandwich Isla +1933-05-01,1.2959999999999998,1.515,South Georgia And The South Sandwich Isla +1933-06-01,-0.95,3.14,South Georgia And The South Sandwich Isla +1933-07-01,-0.055999999999999835,0.773,South Georgia And The South Sandwich Isla +1933-08-01,-1.351,3.208,South Georgia And The South Sandwich Isla +1933-09-01,0.3650000000000001,1.214,South Georgia And The South Sandwich Isla +1933-10-01,-0.2809999999999999,1.2,South Georgia And The South Sandwich Isla +1933-11-01,1.527,1.456,South Georgia And The South Sandwich Isla +1933-12-01,1.808,1.289,South Georgia And The South Sandwich Isla +1934-01-01,4.697,1.052,South Georgia And The South Sandwich Isla +1934-02-01,3.796,0.805,South Georgia And The South Sandwich Isla +1934-03-01,3.5660000000000003,1.001,South Georgia And The South Sandwich Isla +1934-04-01,2.057,0.8809999999999999,South Georgia And The South Sandwich Isla +1934-05-01,-0.688,1.327,South Georgia And The South Sandwich Isla +1934-06-01,-3.62,0.603,South Georgia And The South Sandwich Isla +1934-07-01,-1.3239999999999998,1.061,South Georgia And The South Sandwich Isla +1934-08-01,-0.30400000000000005,1.781,South Georgia And The South Sandwich Isla +1934-09-01,-0.569,0.527,South Georgia And The South Sandwich Isla +1934-10-01,2.785,1.827,South Georgia And The South Sandwich Isla +1934-11-01,2.715,0.647,South Georgia And The South Sandwich Isla +1934-12-01,3.071,1.152,South Georgia And The South Sandwich Isla +1935-01-01,3.813,1.288,South Georgia And The South Sandwich Isla +1935-02-01,4.61,1.1,South Georgia And The South Sandwich Isla +1935-03-01,4.617000000000001,0.784,South Georgia And The South Sandwich Isla +1935-04-01,0.738,1.21,South Georgia And The South Sandwich Isla +1935-05-01,-0.19399999999999998,2.7430000000000003,South Georgia And The South Sandwich Isla +1935-06-01,-1.291,2.414,South Georgia And The South Sandwich Isla +1935-07-01,-3.36,4.145,South Georgia And The South Sandwich Isla +1935-08-01,-3.1290000000000004,2.932,South Georgia And The South Sandwich Isla +1935-09-01,-3.974,0.878,South Georgia And The South Sandwich Isla +1935-10-01,0.623,0.6890000000000001,South Georgia And The South Sandwich Isla +1935-11-01,1.346,0.748,South Georgia And The South Sandwich Isla +1935-12-01,3.005,1.295,South Georgia And The South Sandwich Isla +1936-01-01,4.1389999999999985,1.301,South Georgia And The South Sandwich Isla +1936-02-01,5.132000000000001,1.087,South Georgia And The South Sandwich Isla +1936-03-01,4.15,0.735,South Georgia And The South Sandwich Isla +1936-04-01,3.682,1.64,South Georgia And The South Sandwich Isla +1936-05-01,-0.966,0.6579999999999999,South Georgia And The South Sandwich Isla +1936-06-01,-1.869,2.062,South Georgia And The South Sandwich Isla +1936-07-01,-1.185,1.5730000000000002,South Georgia And The South Sandwich Isla +1936-08-01,-2.1919999999999997,1.6869999999999998,South Georgia And The South Sandwich Isla +1936-09-01,0.268,1.001,South Georgia And The South Sandwich Isla +1936-10-01,1.558,0.8320000000000001,South Georgia And The South Sandwich Isla +1936-11-01,3.286,1.736,South Georgia And The South Sandwich Isla +1936-12-01,4.51,0.7090000000000001,South Georgia And The South Sandwich Isla +1937-01-01,4.502999999999999,0.6970000000000001,South Georgia And The South Sandwich Isla +1937-02-01,3.9810000000000003,2.587,South Georgia And The South Sandwich Isla +1937-03-01,5.016,1.268,South Georgia And The South Sandwich Isla +1937-04-01,2.31,0.7509999999999999,South Georgia And The South Sandwich Isla +1937-05-01,1.523,1.638,South Georgia And The South Sandwich Isla +1937-06-01,-0.6329999999999998,1.806,South Georgia And The South Sandwich Isla +1937-07-01,-1.598,0.8190000000000001,South Georgia And The South Sandwich Isla +1937-08-01,-3.3560000000000003,0.741,South Georgia And The South Sandwich Isla +1937-09-01,1.507,0.7509999999999999,South Georgia And The South Sandwich Isla +1937-10-01,0.4800000000000001,1.581,South Georgia And The South Sandwich Isla +1937-11-01,2.594,0.782,South Georgia And The South Sandwich Isla +1937-12-01,2.773,0.784,South Georgia And The South Sandwich Isla +1938-01-01,4.348999999999998,0.813,South Georgia And The South Sandwich Isla +1938-02-01,4.5889999999999995,1.258,South Georgia And The South Sandwich Isla +1938-03-01,4.085,0.716,South Georgia And The South Sandwich Isla +1938-04-01,2.7960000000000003,0.773,South Georgia And The South Sandwich Isla +1938-05-01,0.15699999999999995,2.2230000000000003,South Georgia And The South Sandwich Isla +1938-06-01,-1.176,1.236,South Georgia And The South Sandwich Isla +1938-07-01,-1.681,1.394,South Georgia And The South Sandwich Isla +1938-08-01,-2.9730000000000003,0.8009999999999999,South Georgia And The South Sandwich Isla +1938-09-01,-1.461,0.6729999999999999,South Georgia And The South Sandwich Isla +1938-10-01,2.0570000000000004,0.816,South Georgia And The South Sandwich Isla +1938-11-01,1.866,0.711,South Georgia And The South Sandwich Isla +1938-12-01,2.496,1.3,South Georgia And The South Sandwich Isla +1939-01-01,4.629,1.275,South Georgia And The South Sandwich Isla +1939-02-01,5.287999999999999,0.736,South Georgia And The South Sandwich Isla +1939-03-01,2.841,1.886,South Georgia And The South Sandwich Isla +1939-04-01,2.247,0.716,South Georgia And The South Sandwich Isla +1939-05-01,-1.006,2.593,South Georgia And The South Sandwich Isla +1939-06-01,-2.677,4.282,South Georgia And The South Sandwich Isla +1939-07-01,-2.397,0.902,South Georgia And The South Sandwich Isla +1939-08-01,-5.252000000000001,5.831,South Georgia And The South Sandwich Isla +1939-09-01,-1.4780000000000002,0.78,South Georgia And The South Sandwich Isla +1939-10-01,0.8710000000000001,1.775,South Georgia And The South Sandwich Isla +1939-11-01,0.7040000000000001,2.366,South Georgia And The South Sandwich Isla +1939-12-01,3.156,1.307,South Georgia And The South Sandwich Isla +1940-01-01,4.3359999999999985,1.342,South Georgia And The South Sandwich Isla +1940-02-01,5.227,0.71,South Georgia And The South Sandwich Isla +1940-03-01,5.0710000000000015,0.918,South Georgia And The South Sandwich Isla +1940-04-01,2.963,1.021,South Georgia And The South Sandwich Isla +1940-05-01,2.169,3.373,South Georgia And The South Sandwich Isla +1940-06-01,-0.5419999999999999,0.7979999999999999,South Georgia And The South Sandwich Isla +1940-07-01,-0.3230000000000002,2.627,South Georgia And The South Sandwich Isla +1940-08-01,-1.598,0.682,South Georgia And The South Sandwich Isla +1940-09-01,0.4190000000000001,0.669,South Georgia And The South Sandwich Isla +1940-10-01,-0.19799999999999995,1.843,South Georgia And The South Sandwich Isla +1940-11-01,2.392,0.995,South Georgia And The South Sandwich Isla +1940-12-01,3.7710000000000004,0.923,South Georgia And The South Sandwich Isla +1941-01-01,5.0059999999999985,0.7759999999999999,South Georgia And The South Sandwich Isla +1941-02-01,5.205,0.797,South Georgia And The South Sandwich Isla +1941-03-01,2.6500000000000004,1.511,South Georgia And The South Sandwich Isla +1941-04-01,1.852,1.119,South Georgia And The South Sandwich Isla +1941-05-01,-0.737,4.864,South Georgia And The South Sandwich Isla +1941-06-01,-0.3979999999999999,1.02,South Georgia And The South Sandwich Isla +1941-07-01,-1.186,3.841,South Georgia And The South Sandwich Isla +1941-08-01,-2.33,1.147,South Georgia And The South Sandwich Isla +1941-09-01,-1.482,1.203,South Georgia And The South Sandwich Isla +1941-10-01,0.17700000000000016,1.617,South Georgia And The South Sandwich Isla +1941-11-01,1.926,0.634,South Georgia And The South Sandwich Isla +1941-12-01,2.614,1.147,South Georgia And The South Sandwich Isla +1942-01-01,4.42,0.943,South Georgia And The South Sandwich Isla +1942-02-01,5.742000000000001,1.37,South Georgia And The South Sandwich Isla +1942-03-01,3.3320000000000003,0.667,South Georgia And The South Sandwich Isla +1942-04-01,1.015,2.9360000000000004,South Georgia And The South Sandwich Isla +1942-05-01,-0.010999999999999996,2.403,South Georgia And The South Sandwich Isla +1942-06-01,-1.861,1.165,South Georgia And The South Sandwich Isla +1942-07-01,-0.05000000000000005,1.924,South Georgia And The South Sandwich Isla +1942-08-01,0.2530000000000001,0.82,South Georgia And The South Sandwich Isla +1942-09-01,-2.595,0.8059999999999999,South Georgia And The South Sandwich Isla +1942-10-01,-1.475,0.6679999999999999,South Georgia And The South Sandwich Isla +1942-11-01,0.3260000000000001,1.235,South Georgia And The South Sandwich Isla +1942-12-01,3.528,0.659,South Georgia And The South Sandwich Isla +1943-01-01,4.114999999999998,1.273,South Georgia And The South Sandwich Isla +1943-02-01,4.675,0.684,South Georgia And The South Sandwich Isla +1943-03-01,4.618,1.213,South Georgia And The South Sandwich Isla +1943-04-01,3.428,0.944,South Georgia And The South Sandwich Isla +1943-05-01,-0.6849999999999999,2.085,South Georgia And The South Sandwich Isla +1943-06-01,0.841,1.838,South Georgia And The South Sandwich Isla +1943-07-01,0.812,1.804,South Georgia And The South Sandwich Isla +1943-08-01,-0.9229999999999999,3.872,South Georgia And The South Sandwich Isla +1943-09-01,0.2940000000000001,2.301,South Georgia And The South Sandwich Isla +1943-10-01,1.7469999999999999,1.422,South Georgia And The South Sandwich Isla +1943-11-01,2.371,1.349,South Georgia And The South Sandwich Isla +1943-12-01,3.652,0.73,South Georgia And The South Sandwich Isla +1944-01-01,4.214,1.4340000000000002,South Georgia And The South Sandwich Isla +1944-02-01,4.613,1.126,South Georgia And The South Sandwich Isla +1944-03-01,5.436,0.728,South Georgia And The South Sandwich Isla +1944-04-01,1.5389999999999997,3.39,South Georgia And The South Sandwich Isla +1944-05-01,1.41,1.3259999999999998,South Georgia And The South Sandwich Isla +1944-06-01,-0.7379999999999999,0.6990000000000001,South Georgia And The South Sandwich Isla +1944-07-01,-2.295,3.3360000000000003,South Georgia And The South Sandwich Isla +1944-08-01,-1.5670000000000002,1.021,South Georgia And The South Sandwich Isla +1944-09-01,-0.903,1.63,South Georgia And The South Sandwich Isla +1944-10-01,0.07299999999999995,0.937,South Georgia And The South Sandwich Isla +1944-11-01,0.7359999999999998,1.147,South Georgia And The South Sandwich Isla +1944-12-01,2.6430000000000002,1.325,South Georgia And The South Sandwich Isla +1945-01-01,3.49,1.38,South Georgia And The South Sandwich Isla +1945-02-01,4.0150000000000015,0.804,South Georgia And The South Sandwich Isla +1945-03-01,3.3830000000000005,0.75,South Georgia And The South Sandwich Isla +1945-04-01,0.5309999999999997,2.59,South Georgia And The South Sandwich Isla +1945-05-01,-2.052,1.9,South Georgia And The South Sandwich Isla +1945-06-01,-4.013,1.537,South Georgia And The South Sandwich Isla +1945-07-01,-1.9680000000000002,3.026,South Georgia And The South Sandwich Isla +1945-08-01,-2.688,3.295,South Georgia And The South Sandwich Isla +1945-09-01,-2.154,1.166,South Georgia And The South Sandwich Isla +1945-10-01,1.834,0.8340000000000001,South Georgia And The South Sandwich Isla +1945-11-01,0.504,1.395,South Georgia And The South Sandwich Isla +1945-12-01,2.982,1.275,South Georgia And The South Sandwich Isla +1946-01-01,4.909,0.721,South Georgia And The South Sandwich Isla +1946-02-01,5.321000000000001,1.33,South Georgia And The South Sandwich Isla +1946-03-01,3.4790000000000005,1.857,South Georgia And The South Sandwich Isla +1946-04-01,1.295,2.8560000000000003,South Georgia And The South Sandwich Isla +1946-05-01,0.495,0.982,South Georgia And The South Sandwich Isla +1946-06-01,-2.567,1.995,South Georgia And The South Sandwich Isla +1946-07-01,-2.342,2.528,South Georgia And The South Sandwich Isla +1946-08-01,-1.864,4.5360000000000005,South Georgia And The South Sandwich Isla +1946-09-01,-1.484,3.717,South Georgia And The South Sandwich Isla +1946-10-01,1.072,1.32,South Georgia And The South Sandwich Isla +1946-11-01,1.008,1.114,South Georgia And The South Sandwich Isla +1946-12-01,3.034,1.3090000000000002,South Georgia And The South Sandwich Isla +1947-01-01,3.758,1.3030000000000002,South Georgia And The South Sandwich Isla +1947-02-01,4.382,0.765,South Georgia And The South Sandwich Isla +1947-03-01,3.7770000000000006,1.032,South Georgia And The South Sandwich Isla +1947-04-01,2.995,0.754,South Georgia And The South Sandwich Isla +1947-05-01,-0.386,0.7390000000000001,South Georgia And The South Sandwich Isla +1947-06-01,-1.604,1.236,South Georgia And The South Sandwich Isla +1947-07-01,-1.82,1.078,South Georgia And The South Sandwich Isla +1947-08-01,-2.045,1.6769999999999998,South Georgia And The South Sandwich Isla +1947-09-01,1.2000000000000002,0.7759999999999999,South Georgia And The South Sandwich Isla +1947-10-01,1.903,1.131,South Georgia And The South Sandwich Isla +1947-11-01,4.191,1.941,South Georgia And The South Sandwich Isla +1947-12-01,3.128,0.907,South Georgia And The South Sandwich Isla +1948-01-01,4.307,1.286,South Georgia And The South Sandwich Isla +1948-02-01,4.41,0.725,South Georgia And The South Sandwich Isla +1948-03-01,4.242,1.3030000000000002,South Georgia And The South Sandwich Isla +1948-04-01,1.7049999999999998,1.2819999999999998,South Georgia And The South Sandwich Isla +1948-05-01,-0.465,1.646,South Georgia And The South Sandwich Isla +1948-06-01,-0.43,2.674,South Georgia And The South Sandwich Isla +1948-07-01,-2.487,1.793,South Georgia And The South Sandwich Isla +1948-08-01,-3.7889999999999997,0.652,South Georgia And The South Sandwich Isla +1948-09-01,-1.449,1.579,South Georgia And The South Sandwich Isla +1948-10-01,0.7470000000000001,1.406,South Georgia And The South Sandwich Isla +1948-11-01,1.4379999999999995,0.873,South Georgia And The South Sandwich Isla +1948-12-01,3.297,1.254,South Georgia And The South Sandwich Isla +1949-01-01,4.096,1.2990000000000002,South Georgia And The South Sandwich Isla +1949-02-01,4.0280000000000005,0.825,South Georgia And The South Sandwich Isla +1949-03-01,3.931000000000001,0.894,South Georgia And The South Sandwich Isla +1949-04-01,0.977,2.15,South Georgia And The South Sandwich Isla +1949-05-01,0.242,0.779,South Georgia And The South Sandwich Isla +1949-06-01,-0.239,5.271,South Georgia And The South Sandwich Isla +1949-07-01,-0.6609999999999998,7.805,South Georgia And The South Sandwich Isla +1949-08-01,-2.008,1.37,South Georgia And The South Sandwich Isla +1949-09-01,0.8320000000000001,3.524,South Georgia And The South Sandwich Isla +1949-10-01,1.2919999999999998,0.785,South Georgia And The South Sandwich Isla +1949-11-01,0.7169999999999999,2.375,South Georgia And The South Sandwich Isla +1949-12-01,2.7030000000000003,0.805,South Georgia And The South Sandwich Isla +1950-01-01,3.128,1.362,South Georgia And The South Sandwich Isla +1950-02-01,4.801,1.3259999999999998,South Georgia And The South Sandwich Isla +1950-03-01,3.63,1.356,South Georgia And The South Sandwich Isla +1950-04-01,2.007,1.072,South Georgia And The South Sandwich Isla +1950-05-01,-2.399,1.625,South Georgia And The South Sandwich Isla +1950-06-01,-0.8170000000000001,2.066,South Georgia And The South Sandwich Isla +1950-07-01,-2.126,1.5,South Georgia And The South Sandwich Isla +1950-08-01,-3.012,1.117,South Georgia And The South Sandwich Isla +1950-09-01,-1.247,1.228,South Georgia And The South Sandwich Isla +1950-10-01,-0.7289999999999999,1.308,South Georgia And The South Sandwich Isla +1950-11-01,1.7790000000000001,0.883,South Georgia And The South Sandwich Isla +1950-12-01,2.765,0.992,South Georgia And The South Sandwich Isla +1951-01-01,3.744,0.813,South Georgia And The South Sandwich Isla +1951-02-01,5.19,1.38,South Georgia And The South Sandwich Isla +1951-03-01,3.899000000000001,0.835,South Georgia And The South Sandwich Isla +1951-04-01,4.090999999999998,1.29,South Georgia And The South Sandwich Isla +1951-05-01,0.5720000000000001,0.696,South Georgia And The South Sandwich Isla +1951-06-01,-0.17899999999999994,1.673,South Georgia And The South Sandwich Isla +1951-07-01,-1.7919999999999998,0.7559999999999999,South Georgia And The South Sandwich Isla +1951-08-01,-2.406,2.834,South Georgia And The South Sandwich Isla +1951-09-01,-0.453,0.953,South Georgia And The South Sandwich Isla +1951-10-01,-0.5669999999999997,1.27,South Georgia And The South Sandwich Isla +1951-11-01,0.3580000000000001,1.1840000000000002,South Georgia And The South Sandwich Isla +1951-12-01,2.778,1.044,South Georgia And The South Sandwich Isla +1952-01-01,4.281000000000001,1.357,South Georgia And The South Sandwich Isla +1952-02-01,4.681,1.253,South Georgia And The South Sandwich Isla +1952-03-01,4.7650000000000015,2.025,South Georgia And The South Sandwich Isla +1952-04-01,0.474,0.845,South Georgia And The South Sandwich Isla +1952-05-01,-0.6689999999999999,4.475,South Georgia And The South Sandwich Isla +1952-06-01,-2.6060000000000003,2.207,South Georgia And The South Sandwich Isla +1952-07-01,-1.403,0.774,South Georgia And The South Sandwich Isla +1952-08-01,-1.229,2.079,South Georgia And The South Sandwich Isla +1952-09-01,-0.41600000000000004,2.647,South Georgia And The South Sandwich Isla +1952-10-01,0.7670000000000001,0.716,South Georgia And The South Sandwich Isla +1952-11-01,2.946,1.78,South Georgia And The South Sandwich Isla +1952-12-01,3.2680000000000002,0.778,South Georgia And The South Sandwich Isla +1953-01-01,4.331,1.247,South Georgia And The South Sandwich Isla +1953-02-01,4.511,0.662,South Georgia And The South Sandwich Isla +1953-03-01,3.855,0.8170000000000001,South Georgia And The South Sandwich Isla +1953-04-01,1.5779999999999998,0.677,South Georgia And The South Sandwich Isla +1953-05-01,-1.5380000000000005,2.209,South Georgia And The South Sandwich Isla +1953-06-01,0.562,1.263,South Georgia And The South Sandwich Isla +1953-07-01,-1.406,1.246,South Georgia And The South Sandwich Isla +1953-08-01,-0.6820000000000002,2.234,South Georgia And The South Sandwich Isla +1953-09-01,-0.6759999999999999,2.7960000000000003,South Georgia And The South Sandwich Isla +1953-10-01,1.5619999999999998,0.66,South Georgia And The South Sandwich Isla +1953-11-01,2.151,1.4380000000000002,South Georgia And The South Sandwich Isla +1953-12-01,3.919,0.6609999999999999,South Georgia And The South Sandwich Isla +1954-01-01,3.714,0.648,South Georgia And The South Sandwich Isla +1954-02-01,5.4510000000000005,0.8740000000000001,South Georgia And The South Sandwich Isla +1954-03-01,3.3770000000000007,1.64,South Georgia And The South Sandwich Isla +1954-04-01,4.385,2.199,South Georgia And The South Sandwich Isla +1954-05-01,0.946,1.265,South Georgia And The South Sandwich Isla +1954-06-01,-1.936,3.297,South Georgia And The South Sandwich Isla +1954-07-01,-1.268,0.821,South Georgia And The South Sandwich Isla +1954-08-01,-4.2860000000000005,0.74,South Georgia And The South Sandwich Isla +1954-09-01,-2.3440000000000003,1.59,South Georgia And The South Sandwich Isla +1954-10-01,1.391,1.04,South Georgia And The South Sandwich Isla +1954-11-01,3.7560000000000002,2.238,South Georgia And The South Sandwich Isla +1954-12-01,2.7139999999999995,1.334,South Georgia And The South Sandwich Isla +1955-01-01,4.792999999999998,1.422,South Georgia And The South Sandwich Isla +1955-02-01,5.962999999999999,0.6940000000000001,South Georgia And The South Sandwich Isla +1955-03-01,4.025,1.526,South Georgia And The South Sandwich Isla +1955-04-01,2.658,1.031,South Georgia And The South Sandwich Isla +1955-05-01,1.4859999999999998,0.88,South Georgia And The South Sandwich Isla +1955-06-01,-1.0539999999999998,0.8440000000000001,South Georgia And The South Sandwich Isla +1955-07-01,-0.8029999999999999,0.76,South Georgia And The South Sandwich Isla +1955-08-01,-1.379,1.679,South Georgia And The South Sandwich Isla +1955-09-01,-0.442,2.375,South Georgia And The South Sandwich Isla +1955-10-01,1.877,1.17,South Georgia And The South Sandwich Isla +1955-11-01,2.153,0.895,South Georgia And The South Sandwich Isla +1955-12-01,2.746,0.685,South Georgia And The South Sandwich Isla +1956-01-01,3.246,1.461,South Georgia And The South Sandwich Isla +1956-02-01,4.58,0.696,South Georgia And The South Sandwich Isla +1956-03-01,4.2730000000000015,0.679,South Georgia And The South Sandwich Isla +1956-04-01,1.5559999999999998,2.563,South Georgia And The South Sandwich Isla +1956-05-01,1.6169999999999998,2.174,South Georgia And The South Sandwich Isla +1956-06-01,1.306,2.987,South Georgia And The South Sandwich Isla +1956-07-01,-1.062,2.935,South Georgia And The South Sandwich Isla +1956-08-01,0.1509999999999998,1.043,South Georgia And The South Sandwich Isla +1956-09-01,-0.394,0.6809999999999999,South Georgia And The South Sandwich Isla +1956-10-01,1.914,2.283,South Georgia And The South Sandwich Isla +1956-11-01,3.245,1.445,South Georgia And The South Sandwich Isla +1956-12-01,2.56,0.735,South Georgia And The South Sandwich Isla +1957-01-01,4.1869999999999985,1.334,South Georgia And The South Sandwich Isla +1957-02-01,4.83,0.745,South Georgia And The South Sandwich Isla +1957-03-01,6.141,2.9360000000000004,South Georgia And The South Sandwich Isla +1957-04-01,3.139,1.3219999999999998,South Georgia And The South Sandwich Isla +1957-05-01,2.039,2.005,South Georgia And The South Sandwich Isla +1957-06-01,-1.392,1.841,South Georgia And The South Sandwich Isla +1957-07-01,-0.8099999999999999,0.89,South Georgia And The South Sandwich Isla +1957-08-01,-3.3630000000000004,2.485,South Georgia And The South Sandwich Isla +1957-09-01,0.07300000000000001,1.199,South Georgia And The South Sandwich Isla +1957-10-01,0.5070000000000001,1.93,South Georgia And The South Sandwich Isla +1957-11-01,3.0580000000000003,1.916,South Georgia And The South Sandwich Isla +1957-12-01,3.077,1.266,South Georgia And The South Sandwich Isla +1958-01-01,4.642,0.841,South Georgia And The South Sandwich Isla +1958-02-01,3.892,1.301,South Georgia And The South Sandwich Isla +1958-03-01,3.1980000000000004,1.118,South Georgia And The South Sandwich Isla +1958-04-01,2.02,0.696,South Georgia And The South Sandwich Isla +1958-05-01,-1.57,4.557,South Georgia And The South Sandwich Isla +1958-06-01,-1.707,2.094,South Georgia And The South Sandwich Isla +1958-07-01,-2.65,2.134,South Georgia And The South Sandwich Isla +1958-08-01,-0.3790000000000002,1.604,South Georgia And The South Sandwich Isla +1958-09-01,0.7670000000000001,0.76,South Georgia And The South Sandwich Isla +1958-10-01,1.584,1.393,South Georgia And The South Sandwich Isla +1958-11-01,3.168,0.6829999999999999,South Georgia And The South Sandwich Isla +1958-12-01,2.6630000000000003,1.589,South Georgia And The South Sandwich Isla +1959-01-01,4.096,0.685,South Georgia And The South Sandwich Isla +1959-02-01,3.5669999999999997,3.108,South Georgia And The South Sandwich Isla +1959-03-01,4.207000000000002,0.794,South Georgia And The South Sandwich Isla +1959-04-01,1.1159999999999997,0.743,South Georgia And The South Sandwich Isla +1959-05-01,-0.478,0.764,South Georgia And The South Sandwich Isla +1959-06-01,-2.256,0.902,South Georgia And The South Sandwich Isla +1959-07-01,-3.548,2.382,South Georgia And The South Sandwich Isla +1959-08-01,-3.5680000000000005,0.727,South Georgia And The South Sandwich Isla +1959-09-01,-0.9329999999999999,0.669,South Georgia And The South Sandwich Isla +1959-10-01,0.1180000000000001,0.7709999999999999,South Georgia And The South Sandwich Isla +1959-11-01,2.01,0.643,South Georgia And The South Sandwich Isla +1959-12-01,3.414,1.023,South Georgia And The South Sandwich Isla +1960-01-01,4.444,0.7040000000000001,South Georgia And The South Sandwich Isla +1960-02-01,4.9670000000000005,1.595,South Georgia And The South Sandwich Isla +1960-03-01,5.622999999999999,2.168,South Georgia And The South Sandwich Isla +1960-04-01,1.761,0.826,South Georgia And The South Sandwich Isla +1960-05-01,0.348,1.671,South Georgia And The South Sandwich Isla +1960-06-01,-1.7730000000000001,2.702,South Georgia And The South Sandwich Isla +1960-07-01,-2.314,0.7490000000000001,South Georgia And The South Sandwich Isla +1960-08-01,0.234,1.968,South Georgia And The South Sandwich Isla +1960-09-01,-1.3880000000000001,3.959,South Georgia And The South Sandwich Isla +1960-10-01,1.038,0.6579999999999999,South Georgia And The South Sandwich Isla +1960-11-01,3.083,1.801,South Georgia And The South Sandwich Isla +1960-12-01,3.642,1.489,South Georgia And The South Sandwich Isla +1961-01-01,4.324,1.4069999999999998,South Georgia And The South Sandwich Isla +1961-02-01,4.939,0.6829999999999999,South Georgia And The South Sandwich Isla +1961-03-01,3.8,0.72,South Georgia And The South Sandwich Isla +1961-04-01,1.5549999999999995,1.696,South Georgia And The South Sandwich Isla +1961-05-01,0.76,2.006,South Georgia And The South Sandwich Isla +1961-06-01,-1.231,1.426,South Georgia And The South Sandwich Isla +1961-07-01,-2.461,0.723,South Georgia And The South Sandwich Isla +1961-08-01,-2.951,2.057,South Georgia And The South Sandwich Isla +1961-09-01,-2.039,0.792,South Georgia And The South Sandwich Isla +1961-10-01,1.7600000000000002,0.958,South Georgia And The South Sandwich Isla +1961-11-01,2.588,0.81,South Georgia And The South Sandwich Isla +1961-12-01,3.142,0.64,South Georgia And The South Sandwich Isla +1962-01-01,4.6309999999999985,0.818,South Georgia And The South Sandwich Isla +1962-02-01,4.61,0.635,South Georgia And The South Sandwich Isla +1962-03-01,4.631,1.47,South Georgia And The South Sandwich Isla +1962-04-01,2.261,1.304,South Georgia And The South Sandwich Isla +1962-05-01,-0.38799999999999996,1.167,South Georgia And The South Sandwich Isla +1962-06-01,1.0590000000000002,1.418,South Georgia And The South Sandwich Isla +1962-07-01,-1.19,0.987,South Georgia And The South Sandwich Isla +1962-08-01,-1.5510000000000002,2.946,South Georgia And The South Sandwich Isla +1962-09-01,0.7650000000000001,1.614,South Georgia And The South Sandwich Isla +1962-10-01,1.4620000000000002,2.083,South Georgia And The South Sandwich Isla +1962-11-01,2.176,0.838,South Georgia And The South Sandwich Isla +1962-12-01,3.7230000000000003,0.799,South Georgia And The South Sandwich Isla +1963-01-01,5.159,0.83,South Georgia And The South Sandwich Isla +1963-02-01,6.0600000000000005,0.948,South Georgia And The South Sandwich Isla +1963-03-01,4.0840000000000005,1.02,South Georgia And The South Sandwich Isla +1963-04-01,1.7630000000000001,0.977,South Georgia And The South Sandwich Isla +1963-05-01,0.016,1.6669999999999998,South Georgia And The South Sandwich Isla +1963-06-01,-1.168,2.19,South Georgia And The South Sandwich Isla +1963-07-01,-1.337,2.133,South Georgia And The South Sandwich Isla +1963-08-01,-2.1590000000000003,1.093,South Georgia And The South Sandwich Isla +1963-09-01,-1.5419999999999998,1.554,South Georgia And The South Sandwich Isla +1963-10-01,-1.3049999999999995,1.024,South Georgia And The South Sandwich Isla +1963-11-01,1.875,1.115,South Georgia And The South Sandwich Isla +1963-12-01,4.6,1.7519999999999998,South Georgia And The South Sandwich Isla +1964-01-01,4.2010000000000005,1.236,South Georgia And The South Sandwich Isla +1964-02-01,4.638999999999999,0.684,South Georgia And The South Sandwich Isla +1964-03-01,3.1919999999999997,1.4680000000000002,South Georgia And The South Sandwich Isla +1964-04-01,0.5769999999999997,0.846,South Georgia And The South Sandwich Isla +1964-05-01,-2.23,5.936,South Georgia And The South Sandwich Isla +1964-06-01,-1.465,1.846,South Georgia And The South Sandwich Isla +1964-07-01,-3.628,2.863,South Georgia And The South Sandwich Isla +1964-08-01,-2.7769999999999997,2.193,South Georgia And The South Sandwich Isla +1964-09-01,-0.112,0.672,South Georgia And The South Sandwich Isla +1964-10-01,2.9139999999999997,1.8,South Georgia And The South Sandwich Isla +1964-11-01,1.565,1.244,South Georgia And The South Sandwich Isla +1964-12-01,3.123,1.291,South Georgia And The South Sandwich Isla +1965-01-01,4.3519999999999985,0.923,South Georgia And The South Sandwich Isla +1965-02-01,4.235,1.723,South Georgia And The South Sandwich Isla +1965-03-01,4.45,0.665,South Georgia And The South Sandwich Isla +1965-04-01,1.1449999999999998,3.013,South Georgia And The South Sandwich Isla +1965-05-01,0.414,2.892,South Georgia And The South Sandwich Isla +1965-06-01,0.1080000000000001,2.9530000000000003,South Georgia And The South Sandwich Isla +1965-07-01,-0.772,0.894,South Georgia And The South Sandwich Isla +1965-08-01,-1.9769999999999999,1.431,South Georgia And The South Sandwich Isla +1965-09-01,0.15200000000000008,0.946,South Georgia And The South Sandwich Isla +1965-10-01,1.3530000000000002,2.472,South Georgia And The South Sandwich Isla +1965-11-01,3.999,3.079,South Georgia And The South Sandwich Isla +1965-12-01,2.883,0.733,South Georgia And The South Sandwich Isla +1966-01-01,5.166,2.404,South Georgia And The South Sandwich Isla +1966-02-01,3.696,0.647,South Georgia And The South Sandwich Isla +1966-03-01,3.8169999999999997,0.882,South Georgia And The South Sandwich Isla +1966-04-01,2.553,1.829,South Georgia And The South Sandwich Isla +1966-05-01,-0.312,3.502,South Georgia And The South Sandwich Isla +1966-06-01,-2.237,0.8959999999999999,South Georgia And The South Sandwich Isla +1966-07-01,-2.108,1.036,South Georgia And The South Sandwich Isla +1966-08-01,-0.04600000000000004,0.784,South Georgia And The South Sandwich Isla +1966-09-01,-1.166,2.024,South Georgia And The South Sandwich Isla +1966-10-01,-0.18799999999999994,1.218,South Georgia And The South Sandwich Isla +1966-11-01,0.36499999999999977,1.503,South Georgia And The South Sandwich Isla +1966-12-01,2.998,1.287,South Georgia And The South Sandwich Isla +1967-01-01,3.787,1.332,South Georgia And The South Sandwich Isla +1967-02-01,4.352,0.695,South Georgia And The South Sandwich Isla +1967-03-01,4.234,0.6890000000000001,South Georgia And The South Sandwich Isla +1967-04-01,2.336,1.616,South Georgia And The South Sandwich Isla +1967-05-01,0.06599999999999999,2.437,South Georgia And The South Sandwich Isla +1967-06-01,-1.0119999999999998,1.629,South Georgia And The South Sandwich Isla +1967-07-01,-0.4239999999999999,2.003,South Georgia And The South Sandwich Isla +1967-08-01,-1.615,0.795,South Georgia And The South Sandwich Isla +1967-09-01,-0.8079999999999999,2.0380000000000003,South Georgia And The South Sandwich Isla +1967-10-01,0.22100000000000009,0.667,South Georgia And The South Sandwich Isla +1967-11-01,2.971,0.818,South Georgia And The South Sandwich Isla +1967-12-01,2.9210000000000003,1.128,South Georgia And The South Sandwich Isla +1968-01-01,4.823,1.142,South Georgia And The South Sandwich Isla +1968-02-01,5.078,0.715,South Georgia And The South Sandwich Isla +1968-03-01,3.84,0.6759999999999999,South Georgia And The South Sandwich Isla +1968-04-01,1.585,0.893,South Georgia And The South Sandwich Isla +1968-05-01,-0.9840000000000001,0.8340000000000001,South Georgia And The South Sandwich Isla +1968-06-01,-0.8479999999999999,2.567,South Georgia And The South Sandwich Isla +1968-07-01,0.6420000000000001,2.772,South Georgia And The South Sandwich Isla +1968-08-01,0.28400000000000003,0.812,South Georgia And The South Sandwich Isla +1968-09-01,-0.292,1.094,South Georgia And The South Sandwich Isla +1968-10-01,1.853,0.893,South Georgia And The South Sandwich Isla +1968-11-01,2.653,0.6559999999999999,South Georgia And The South Sandwich Isla +1968-12-01,3.905,1.2990000000000002,South Georgia And The South Sandwich Isla +1969-01-01,3.97,0.63,South Georgia And The South Sandwich Isla +1969-02-01,5.577999999999999,1.5419999999999998,South Georgia And The South Sandwich Isla +1969-03-01,3.6960000000000006,0.68,South Georgia And The South Sandwich Isla +1969-04-01,2.056,1.119,South Georgia And The South Sandwich Isla +1969-05-01,-0.757,2.087,South Georgia And The South Sandwich Isla +1969-06-01,0.004000000000000226,3.384,South Georgia And The South Sandwich Isla +1969-07-01,-1.0659999999999998,0.662,South Georgia And The South Sandwich Isla +1969-08-01,-1.6569999999999998,2.178,South Georgia And The South Sandwich Isla +1969-09-01,-0.404,1.589,South Georgia And The South Sandwich Isla +1969-10-01,0.8260000000000001,0.789,South Georgia And The South Sandwich Isla +1969-11-01,3.054,1.405,South Georgia And The South Sandwich Isla +1969-12-01,4.032,1.052,South Georgia And The South Sandwich Isla +1970-01-01,3.849,0.6859999999999999,South Georgia And The South Sandwich Isla +1970-02-01,4.195,1.432,South Georgia And The South Sandwich Isla +1970-03-01,2.4670000000000005,0.693,South Georgia And The South Sandwich Isla +1970-04-01,1.3249999999999995,0.945,South Georgia And The South Sandwich Isla +1970-05-01,0.198,1.734,South Georgia And The South Sandwich Isla +1970-06-01,-0.4749999999999999,1.952,South Georgia And The South Sandwich Isla +1970-07-01,-1.3279999999999998,2.919,South Georgia And The South Sandwich Isla +1970-08-01,-0.6870000000000002,1.335,South Georgia And The South Sandwich Isla +1970-09-01,0.3680000000000001,2.0780000000000003,South Georgia And The South Sandwich Isla +1970-10-01,1.272,1.24,South Georgia And The South Sandwich Isla +1970-11-01,3.1910000000000003,1.035,South Georgia And The South Sandwich Isla +1970-12-01,3.8160000000000003,0.79,South Georgia And The South Sandwich Isla +1971-01-01,4.141,1.65,South Georgia And The South Sandwich Isla +1971-02-01,4.864,0.701,South Georgia And The South Sandwich Isla +1971-03-01,4.2040000000000015,0.6559999999999999,South Georgia And The South Sandwich Isla +1971-04-01,2.009,0.664,South Georgia And The South Sandwich Isla +1971-05-01,-1.003,1.6230000000000002,South Georgia And The South Sandwich Isla +1971-06-01,-0.95,2.508,South Georgia And The South Sandwich Isla +1971-07-01,-2.46,1.36,South Georgia And The South Sandwich Isla +1971-08-01,-0.341,0.733,South Georgia And The South Sandwich Isla +1971-09-01,0.483,1.943,South Georgia And The South Sandwich Isla +1971-10-01,1.0750000000000002,0.6579999999999999,South Georgia And The South Sandwich Isla +1971-11-01,3.028,1.231,South Georgia And The South Sandwich Isla +1971-12-01,2.445,0.68,South Georgia And The South Sandwich Isla +1972-01-01,3.5260000000000002,1.395,South Georgia And The South Sandwich Isla +1972-02-01,4.078,1.058,South Georgia And The South Sandwich Isla +1972-03-01,4.0870000000000015,1.334,South Georgia And The South Sandwich Isla +1972-04-01,2.507,0.969,South Georgia And The South Sandwich Isla +1972-05-01,-0.161,0.667,South Georgia And The South Sandwich Isla +1972-06-01,-2.904,1.962,South Georgia And The South Sandwich Isla +1972-07-01,-1.354,1.015,South Georgia And The South Sandwich Isla +1972-08-01,-3.415,2.083,South Georgia And The South Sandwich Isla +1972-09-01,-0.397,0.706,South Georgia And The South Sandwich Isla +1972-10-01,0.026000000000000027,0.695,South Georgia And The South Sandwich Isla +1972-11-01,0.865,1.2990000000000002,South Georgia And The South Sandwich Isla +1972-12-01,4.299,0.6990000000000001,South Georgia And The South Sandwich Isla +1973-01-01,3.699,1.3259999999999998,South Georgia And The South Sandwich Isla +1973-02-01,3.673,3.016,South Georgia And The South Sandwich Isla +1973-03-01,5.729000000000001,1.589,South Georgia And The South Sandwich Isla +1973-04-01,1.946,1.3259999999999998,South Georgia And The South Sandwich Isla +1973-05-01,-0.458,4.885,South Georgia And The South Sandwich Isla +1973-06-01,-2.17,2.459,South Georgia And The South Sandwich Isla +1973-07-01,-1.893,2.188,South Georgia And The South Sandwich Isla +1973-08-01,-0.4820000000000002,2.16,South Georgia And The South Sandwich Isla +1973-09-01,-0.298,1.814,South Georgia And The South Sandwich Isla +1973-10-01,1.408,0.913,South Georgia And The South Sandwich Isla +1973-11-01,2.362,0.657,South Georgia And The South Sandwich Isla +1973-12-01,3.777,0.74,South Georgia And The South Sandwich Isla +1974-01-01,4.9609999999999985,0.821,South Georgia And The South Sandwich Isla +1974-02-01,6.268000000000002,2.256,South Georgia And The South Sandwich Isla +1974-03-01,5.532,0.684,South Georgia And The South Sandwich Isla +1974-04-01,1.848,0.8029999999999999,South Georgia And The South Sandwich Isla +1974-05-01,-0.578,0.878,South Georgia And The South Sandwich Isla +1974-06-01,-2.046,0.967,South Georgia And The South Sandwich Isla +1974-07-01,-1.569,0.7340000000000001,South Georgia And The South Sandwich Isla +1974-08-01,-2.4490000000000003,2.367,South Georgia And The South Sandwich Isla +1974-09-01,-0.46,1.211,South Georgia And The South Sandwich Isla +1974-10-01,1.129,0.66,South Georgia And The South Sandwich Isla +1974-11-01,2.08,1.091,South Georgia And The South Sandwich Isla +1974-12-01,2.9760000000000004,1.4069999999999998,South Georgia And The South Sandwich Isla +1975-01-01,4.396,0.649,South Georgia And The South Sandwich Isla +1975-02-01,5.022,1.19,South Georgia And The South Sandwich Isla +1975-03-01,3.873,0.691,South Georgia And The South Sandwich Isla +1975-04-01,3.4130000000000003,1.224,South Georgia And The South Sandwich Isla +1975-05-01,0.439,1.147,South Georgia And The South Sandwich Isla +1975-06-01,-1.6269999999999998,5.7410000000000005,South Georgia And The South Sandwich Isla +1975-07-01,-2.5669999999999997,1.115,South Georgia And The South Sandwich Isla +1975-08-01,-1.776,3.463,South Georgia And The South Sandwich Isla +1975-09-01,-0.443,2.258,South Georgia And The South Sandwich Isla +1975-10-01,1.514,0.7909999999999999,South Georgia And The South Sandwich Isla +1975-11-01,2.171,0.644,South Georgia And The South Sandwich Isla +1975-12-01,3.352,1.085,South Georgia And The South Sandwich Isla +1976-01-01,4.926,1.252,South Georgia And The South Sandwich Isla +1976-02-01,5.676,1.226,South Georgia And The South Sandwich Isla +1976-03-01,4.184,0.752,South Georgia And The South Sandwich Isla +1976-04-01,2.0540000000000003,1.625,South Georgia And The South Sandwich Isla +1976-05-01,0.4879999999999999,1.3130000000000002,South Georgia And The South Sandwich Isla +1976-06-01,-2.474,1.824,South Georgia And The South Sandwich Isla +1976-07-01,-1.5930000000000002,1.1,South Georgia And The South Sandwich Isla +1976-08-01,-1.528,0.691,South Georgia And The South Sandwich Isla +1976-09-01,-1.172,1.933,South Georgia And The South Sandwich Isla +1976-10-01,2.279,2.025,South Georgia And The South Sandwich Isla +1976-11-01,2.6689999999999996,0.87,South Georgia And The South Sandwich Isla +1976-12-01,2.892,1.278,South Georgia And The South Sandwich Isla +1977-01-01,4.683,0.866,South Georgia And The South Sandwich Isla +1977-02-01,5.4220000000000015,0.6970000000000001,South Georgia And The South Sandwich Isla +1977-03-01,3.742,1.865,South Georgia And The South Sandwich Isla +1977-04-01,1.992,2.274,South Georgia And The South Sandwich Isla +1977-05-01,0.9890000000000001,2.488,South Georgia And The South Sandwich Isla +1977-06-01,-0.05400000000000005,4.462,South Georgia And The South Sandwich Isla +1977-07-01,-2.291,3.959,South Georgia And The South Sandwich Isla +1977-08-01,-2.188,1.942,South Georgia And The South Sandwich Isla +1977-09-01,0.07800000000000001,0.772,South Georgia And The South Sandwich Isla +1977-10-01,0.9309999999999999,0.76,South Georgia And The South Sandwich Isla +1977-11-01,3.214,0.6729999999999999,South Georgia And The South Sandwich Isla +1977-12-01,3.198,1.175,South Georgia And The South Sandwich Isla +1978-01-01,4.922,0.708,South Georgia And The South Sandwich Isla +1978-02-01,5.815,1.014,South Georgia And The South Sandwich Isla +1978-03-01,3.806000000000001,0.78,South Georgia And The South Sandwich Isla +1978-04-01,1.245,1.71,South Georgia And The South Sandwich Isla +1978-05-01,-0.971,5.2879999999999985,South Georgia And The South Sandwich Isla +1978-06-01,-0.5569999999999999,1.9,South Georgia And The South Sandwich Isla +1978-07-01,-2.559,0.93,South Georgia And The South Sandwich Isla +1978-08-01,0.5099999999999998,2.185,South Georgia And The South Sandwich Isla +1978-09-01,2.051,3.184,South Georgia And The South Sandwich Isla +1978-10-01,2.26,0.703,South Georgia And The South Sandwich Isla +1978-11-01,1.475,1.132,South Georgia And The South Sandwich Isla +1978-12-01,3.298,0.6609999999999999,South Georgia And The South Sandwich Isla +1979-01-01,4.9369999999999985,0.975,South Georgia And The South Sandwich Isla +1979-02-01,5.004,0.905,South Georgia And The South Sandwich Isla +1979-03-01,5.3900000000000015,0.981,South Georgia And The South Sandwich Isla +1979-04-01,2.964,0.8190000000000001,South Georgia And The South Sandwich Isla +1979-05-01,-0.7679999999999999,1.57,South Georgia And The South Sandwich Isla +1979-06-01,-2.003,2.355,South Georgia And The South Sandwich Isla +1979-07-01,-0.733,3.559,South Georgia And The South Sandwich Isla +1979-08-01,-0.7540000000000001,0.914,South Georgia And The South Sandwich Isla +1979-09-01,0.3509999999999999,1.187,South Georgia And The South Sandwich Isla +1979-10-01,0.55,0.764,South Georgia And The South Sandwich Isla +1979-11-01,1.251,1.109,South Georgia And The South Sandwich Isla +1979-12-01,2.855,1.975,South Georgia And The South Sandwich Isla +1980-01-01,4.569,1.065,South Georgia And The South Sandwich Isla +1980-02-01,5.267,0.935,South Georgia And The South Sandwich Isla +1980-03-01,3.5780000000000003,1.599,South Georgia And The South Sandwich Isla +1980-04-01,2.189,2.216,South Georgia And The South Sandwich Isla +1980-05-01,0.09000000000000001,4.458,South Georgia And The South Sandwich Isla +1980-06-01,-0.813,3.156,South Georgia And The South Sandwich Isla +1980-07-01,-3.8539999999999996,2.591,South Georgia And The South Sandwich Isla +1980-08-01,-3.5340000000000003,1.625,South Georgia And The South Sandwich Isla +1980-09-01,-1.209,0.7390000000000001,South Georgia And The South Sandwich Isla +1980-10-01,1.4700000000000002,0.988,South Georgia And The South Sandwich Isla +1980-11-01,2.52,0.937,South Georgia And The South Sandwich Isla +1980-12-01,3.384,1.24,South Georgia And The South Sandwich Isla +1981-01-01,4.909,0.659,South Georgia And The South Sandwich Isla +1981-02-01,6.2940000000000005,1.052,South Georgia And The South Sandwich Isla +1981-03-01,3.1380000000000003,2.096,South Georgia And The South Sandwich Isla +1981-04-01,2.857,0.805,South Georgia And The South Sandwich Isla +1981-05-01,0.78,2.1790000000000003,South Georgia And The South Sandwich Isla +1981-06-01,-0.92,0.838,South Georgia And The South Sandwich Isla +1981-07-01,-1.0490000000000002,1.211,South Georgia And The South Sandwich Isla +1981-08-01,-0.6779999999999999,3.762,South Georgia And The South Sandwich Isla +1981-09-01,-0.204,1.513,South Georgia And The South Sandwich Isla +1981-10-01,-0.4530000000000001,1.937,South Georgia And The South Sandwich Isla +1981-11-01,3.852,1.724,South Georgia And The South Sandwich Isla +1981-12-01,4.18,0.7659999999999999,South Georgia And The South Sandwich Isla +1982-01-01,5.795,1.444,South Georgia And The South Sandwich Isla +1982-02-01,5.676,0.675,South Georgia And The South Sandwich Isla +1982-03-01,5.096,1.382,South Georgia And The South Sandwich Isla +1982-04-01,3.345,1.321,South Georgia And The South Sandwich Isla +1982-05-01,2.713,1.342,South Georgia And The South Sandwich Isla +1982-06-01,0.9490000000000004,1.634,South Georgia And The South Sandwich Isla +1982-07-01,-3.325,1.611,South Georgia And The South Sandwich Isla +1982-08-01,-2.117,1.421,South Georgia And The South Sandwich Isla +1982-09-01,-0.918,1.499,South Georgia And The South Sandwich Isla +1982-10-01,1.304,1.381,South Georgia And The South Sandwich Isla +1982-11-01,1.1740000000000002,1.381,South Georgia And The South Sandwich Isla +1982-12-01,4.026,1.383,South Georgia And The South Sandwich Isla +1983-01-01,4.758,1.374,South Georgia And The South Sandwich Isla +1983-02-01,5.175,1.298,South Georgia And The South Sandwich Isla +1983-03-01,4.026000000000002,1.277,South Georgia And The South Sandwich Isla +1983-04-01,1.4899999999999998,1.513,South Georgia And The South Sandwich Isla +1983-05-01,-0.823,1.308,South Georgia And The South Sandwich Isla +1983-06-01,-1.5030000000000001,1.406,South Georgia And The South Sandwich Isla +1983-07-01,0.6809999999999998,1.469,South Georgia And The South Sandwich Isla +1983-08-01,0.8439999999999999,1.4680000000000002,South Georgia And The South Sandwich Isla +1983-09-01,-1.679,1.538,South Georgia And The South Sandwich Isla +1983-10-01,0.6200000000000001,1.34,South Georgia And The South Sandwich Isla +1983-11-01,3.958,1.371,South Georgia And The South Sandwich Isla +1983-12-01,3.764,1.34,South Georgia And The South Sandwich Isla +1984-01-01,4.7410000000000005,1.31,South Georgia And The South Sandwich Isla +1984-02-01,5.072,1.351,South Georgia And The South Sandwich Isla +1984-03-01,3.841,1.347,South Georgia And The South Sandwich Isla +1984-04-01,2.6060000000000003,1.188,South Georgia And The South Sandwich Isla +1984-05-01,0.75,0.6970000000000001,South Georgia And The South Sandwich Isla +1984-06-01,-1.912,2.314,South Georgia And The South Sandwich Isla +1984-07-01,-2.519,1.05,South Georgia And The South Sandwich Isla +1984-08-01,-0.40299999999999997,1.03,South Georgia And The South Sandwich Isla +1984-09-01,0.526,1.495,South Georgia And The South Sandwich Isla +1984-10-01,0.42,1.43,South Georgia And The South Sandwich Isla +1984-11-01,1.721,2.721,South Georgia And The South Sandwich Isla +1984-12-01,3.482,1.2990000000000002,South Georgia And The South Sandwich Isla +1985-01-01,4.6160000000000005,1.795,South Georgia And The South Sandwich Isla +1985-02-01,5.93,1.602,South Georgia And The South Sandwich Isla +1985-03-01,3.5420000000000003,1.008,South Georgia And The South Sandwich Isla +1985-04-01,2.557,0.914,South Georgia And The South Sandwich Isla +1985-05-01,-0.33399999999999996,0.821,South Georgia And The South Sandwich Isla +1985-06-01,-2.35,0.772,South Georgia And The South Sandwich Isla +1985-07-01,0.242,1.944,South Georgia And The South Sandwich Isla +1985-08-01,0.5509999999999997,0.6970000000000001,South Georgia And The South Sandwich Isla +1985-09-01,1.442,0.947,South Georgia And The South Sandwich Isla +1985-10-01,0.97,0.957,South Georgia And The South Sandwich Isla +1985-11-01,2.124,0.675,South Georgia And The South Sandwich Isla +1985-12-01,3.567,0.718,South Georgia And The South Sandwich Isla +1986-01-01,4.209,1.6469999999999998,South Georgia And The South Sandwich Isla +1986-02-01,4.428999999999999,2.242,South Georgia And The South Sandwich Isla +1986-03-01,4.489000000000001,0.6890000000000001,South Georgia And The South Sandwich Isla +1986-04-01,3.119,0.715,South Georgia And The South Sandwich Isla +1986-05-01,0.141,0.6920000000000001,South Georgia And The South Sandwich Isla +1986-06-01,1.164,1.389,South Georgia And The South Sandwich Isla +1986-07-01,-0.995,1.3630000000000002,South Georgia And The South Sandwich Isla +1986-08-01,-0.19800000000000015,1.955,South Georgia And The South Sandwich Isla +1986-09-01,0.11300000000000004,1.283,South Georgia And The South Sandwich Isla +1986-10-01,1.05,1.208,South Georgia And The South Sandwich Isla +1986-11-01,1.923,0.966,South Georgia And The South Sandwich Isla +1986-12-01,2.9810000000000003,0.92,South Georgia And The South Sandwich Isla +1987-01-01,4.125,0.924,South Georgia And The South Sandwich Isla +1987-02-01,5.218,1.18,South Georgia And The South Sandwich Isla +1987-03-01,4.4590000000000005,0.726,South Georgia And The South Sandwich Isla +1987-04-01,1.8980000000000001,0.66,South Georgia And The South Sandwich Isla +1987-05-01,-0.046,1.295,South Georgia And The South Sandwich Isla +1987-06-01,-1.483,0.919,South Georgia And The South Sandwich Isla +1987-07-01,-2.787,0.857,South Georgia And The South Sandwich Isla +1987-08-01,-1.7109999999999999,2.311,South Georgia And The South Sandwich Isla +1987-09-01,-1.959,1.639,South Georgia And The South Sandwich Isla +1987-10-01,-0.5269999999999997,1.247,South Georgia And The South Sandwich Isla +1987-11-01,3.005,0.8640000000000001,South Georgia And The South Sandwich Isla +1987-12-01,4.1530000000000005,0.974,South Georgia And The South Sandwich Isla +1988-01-01,3.931,1.655,South Georgia And The South Sandwich Isla +1988-02-01,5.461,0.727,South Georgia And The South Sandwich Isla +1988-03-01,5.7440000000000015,0.785,South Georgia And The South Sandwich Isla +1988-04-01,0.7229999999999999,2.57,South Georgia And The South Sandwich Isla +1988-05-01,0.486,1.074,South Georgia And The South Sandwich Isla +1988-06-01,-0.5829999999999999,1.266,South Georgia And The South Sandwich Isla +1988-07-01,-0.1609999999999998,2.442,South Georgia And The South Sandwich Isla +1988-08-01,-2.3120000000000003,3.293,South Georgia And The South Sandwich Isla +1988-09-01,-0.978,1.28,South Georgia And The South Sandwich Isla +1988-10-01,-0.3180000000000001,0.602,South Georgia And The South Sandwich Isla +1988-11-01,2.245,0.568,South Georgia And The South Sandwich Isla +1988-12-01,3.709,0.568,South Georgia And The South Sandwich Isla +1989-01-01,4.645,0.597,South Georgia And The South Sandwich Isla +1989-02-01,4.456,0.631,South Georgia And The South Sandwich Isla +1989-03-01,5.195,0.818,South Georgia And The South Sandwich Isla +1989-04-01,1.493,0.599,South Georgia And The South Sandwich Isla +1989-05-01,2.845,1.144,South Georgia And The South Sandwich Isla +1989-06-01,1.439,1.025,South Georgia And The South Sandwich Isla +1989-07-01,0.4530000000000001,3.976,South Georgia And The South Sandwich Isla +1989-08-01,0.05499999999999994,0.7,South Georgia And The South Sandwich Isla +1989-09-01,0.496,0.64,South Georgia And The South Sandwich Isla +1989-10-01,2.051,1.007,South Georgia And The South Sandwich Isla +1989-11-01,2.5380000000000003,0.861,South Georgia And The South Sandwich Isla +1989-12-01,4.041,1.4469999999999998,South Georgia And The South Sandwich Isla +1990-01-01,5.303,1.257,South Georgia And The South Sandwich Isla +1990-02-01,6.0329999999999995,0.799,South Georgia And The South Sandwich Isla +1990-03-01,5.412000000000001,0.912,South Georgia And The South Sandwich Isla +1990-04-01,1.5549999999999995,0.594,South Georgia And The South Sandwich Isla +1990-05-01,-0.982,0.667,South Georgia And The South Sandwich Isla +1990-06-01,1.0490000000000004,0.87,South Georgia And The South Sandwich Isla +1990-07-01,0.222,1.429,South Georgia And The South Sandwich Isla +1990-08-01,-0.6050000000000001,0.804,South Georgia And The South Sandwich Isla +1990-09-01,-0.201,1.3090000000000002,South Georgia And The South Sandwich Isla +1990-10-01,1.7009999999999998,1.202,South Georgia And The South Sandwich Isla +1990-11-01,3.375,0.7440000000000001,South Georgia And The South Sandwich Isla +1990-12-01,3.122,1.3219999999999998,South Georgia And The South Sandwich Isla +1991-01-01,5.533,1.308,South Georgia And The South Sandwich Isla +1991-02-01,5.482,1.09,South Georgia And The South Sandwich Isla +1991-03-01,3.625,0.7,South Georgia And The South Sandwich Isla +1991-04-01,2.6060000000000003,0.6659999999999999,South Georgia And The South Sandwich Isla +1991-05-01,0.5120000000000001,4.9289999999999985,South Georgia And The South Sandwich Isla +1991-06-01,-2.076,1.298,South Georgia And The South Sandwich Isla +1991-07-01,-0.912,1.4880000000000002,South Georgia And The South Sandwich Isla +1991-08-01,-0.4590000000000001,0.748,South Georgia And The South Sandwich Isla +1991-09-01,-1.0190000000000001,0.948,South Georgia And The South Sandwich Isla +1991-10-01,-0.436,0.6559999999999999,South Georgia And The South Sandwich Isla +1991-11-01,2.565,0.667,South Georgia And The South Sandwich Isla +1991-12-01,3.074,1.285,South Georgia And The South Sandwich Isla +1992-01-01,5.305,0.74,South Georgia And The South Sandwich Isla +1992-02-01,5.063,0.8540000000000001,South Georgia And The South Sandwich Isla +1992-03-01,3.673000000000001,1.115,South Georgia And The South Sandwich Isla +1992-04-01,1.8030000000000002,1.02,South Georgia And The South Sandwich Isla +1992-05-01,-0.482,1.38,South Georgia And The South Sandwich Isla +1992-06-01,-1.588,1.969,South Georgia And The South Sandwich Isla +1992-07-01,-1.942,1.564,South Georgia And The South Sandwich Isla +1992-08-01,0.5069999999999997,2.071,South Georgia And The South Sandwich Isla +1992-09-01,0.6740000000000002,0.7340000000000001,South Georgia And The South Sandwich Isla +1992-10-01,0.7220000000000002,1.443,South Georgia And The South Sandwich Isla +1992-11-01,2.944,0.84,South Georgia And The South Sandwich Isla +1992-12-01,4.441,0.7,South Georgia And The South Sandwich Isla +1993-01-01,4.997999999999998,1.231,South Georgia And The South Sandwich Isla +1993-02-01,5.1610000000000005,0.675,South Georgia And The South Sandwich Isla +1993-03-01,4.666,0.705,South Georgia And The South Sandwich Isla +1993-04-01,3.8560000000000003,2.513,South Georgia And The South Sandwich Isla +1993-05-01,2.121,1.392,South Georgia And The South Sandwich Isla +1993-06-01,0.06500000000000017,2.3040000000000003,South Georgia And The South Sandwich Isla +1993-07-01,-0.562,0.856,South Georgia And The South Sandwich Isla +1993-08-01,-0.6240000000000001,0.7809999999999999,South Georgia And The South Sandwich Isla +1993-09-01,-0.8049999999999999,3.216,South Georgia And The South Sandwich Isla +1993-10-01,1.411,0.889,South Georgia And The South Sandwich Isla +1993-11-01,3.147,0.7020000000000001,South Georgia And The South Sandwich Isla +1993-12-01,3.672,0.662,South Georgia And The South Sandwich Isla +1994-01-01,5.3119999999999985,1.446,South Georgia And The South Sandwich Isla +1994-02-01,5.777,0.7659999999999999,South Georgia And The South Sandwich Isla +1994-03-01,3.6640000000000006,1.497,South Georgia And The South Sandwich Isla +1994-04-01,1.4009999999999998,1.994,South Georgia And The South Sandwich Isla +1994-05-01,-0.7040000000000001,0.835,South Georgia And The South Sandwich Isla +1994-06-01,-1.432,0.693,South Georgia And The South Sandwich Isla +1994-07-01,-3.292,1.775,South Georgia And The South Sandwich Isla +1994-08-01,-0.6409999999999999,0.968,South Georgia And The South Sandwich Isla +1994-09-01,0.6640000000000001,2.822,South Georgia And The South Sandwich Isla +1994-10-01,-0.5579999999999999,0.69,South Georgia And The South Sandwich Isla +1994-11-01,2.662,1.325,South Georgia And The South Sandwich Isla +1994-12-01,3.426,0.703,South Georgia And The South Sandwich Isla +1995-01-01,4.223999999999998,1.28,South Georgia And The South Sandwich Isla +1995-02-01,5.5200000000000005,1.175,South Georgia And The South Sandwich Isla +1995-03-01,4.227,0.9,South Georgia And The South Sandwich Isla +1995-04-01,3.2510000000000003,0.884,South Georgia And The South Sandwich Isla +1995-05-01,0.28600000000000003,1.185,South Georgia And The South Sandwich Isla +1995-06-01,-0.3029999999999998,0.883,South Georgia And The South Sandwich Isla +1995-07-01,-1.369,1.6780000000000002,South Georgia And The South Sandwich Isla +1995-08-01,-2.115,1.972,South Georgia And The South Sandwich Isla +1995-09-01,-0.893,0.642,South Georgia And The South Sandwich Isla +1995-10-01,0.684,1.221,South Georgia And The South Sandwich Isla +1995-11-01,2.707,1.061,South Georgia And The South Sandwich Isla +1995-12-01,3.3789999999999996,0.946,South Georgia And The South Sandwich Isla +1996-01-01,4.895,0.946,South Georgia And The South Sandwich Isla +1996-02-01,5.187,0.665,South Georgia And The South Sandwich Isla +1996-03-01,5.327999999999999,0.868,South Georgia And The South Sandwich Isla +1996-04-01,1.5299999999999998,1.139,South Georgia And The South Sandwich Isla +1996-05-01,1.857,1.147,South Georgia And The South Sandwich Isla +1996-06-01,-1.1840000000000002,1.07,South Georgia And The South Sandwich Isla +1996-07-01,-0.921,0.687,South Georgia And The South Sandwich Isla +1996-08-01,-0.638,0.633,South Georgia And The South Sandwich Isla +1996-09-01,-0.5549999999999999,0.601,South Georgia And The South Sandwich Isla +1996-10-01,1.882,0.703,South Georgia And The South Sandwich Isla +1996-11-01,3.749,0.782,South Georgia And The South Sandwich Isla +1996-12-01,3.86,1.232,South Georgia And The South Sandwich Isla +1997-01-01,5.1469999999999985,1.125,South Georgia And The South Sandwich Isla +1997-02-01,4.73,1.3869999999999998,South Georgia And The South Sandwich Isla +1997-03-01,5.2330000000000005,1.54,South Georgia And The South Sandwich Isla +1997-04-01,3.448,1.048,South Georgia And The South Sandwich Isla +1997-05-01,0.77,0.628,South Georgia And The South Sandwich Isla +1997-06-01,0.1100000000000001,1.568,South Georgia And The South Sandwich Isla +1997-07-01,-0.913,1.271,South Georgia And The South Sandwich Isla +1997-08-01,-0.010000000000000007,1.113,South Georgia And The South Sandwich Isla +1997-09-01,-2.275,2.682,South Georgia And The South Sandwich Isla +1997-10-01,0.19700000000000006,2.794,South Georgia And The South Sandwich Isla +1997-11-01,1.338,0.7440000000000001,South Georgia And The South Sandwich Isla +1997-12-01,2.426,1.634,South Georgia And The South Sandwich Isla +1998-01-01,5.7109999999999985,2.25,South Georgia And The South Sandwich Isla +1998-02-01,4.979,1.108,South Georgia And The South Sandwich Isla +1998-03-01,4.993,0.972,South Georgia And The South Sandwich Isla +1998-04-01,3.3930000000000002,1.4469999999999998,South Georgia And The South Sandwich Isla +1998-05-01,-0.157,0.871,South Georgia And The South Sandwich Isla +1998-06-01,-1.127,3.323,South Georgia And The South Sandwich Isla +1998-07-01,0.5999999999999999,1.72,South Georgia And The South Sandwich Isla +1998-08-01,-0.2890000000000001,0.925,South Georgia And The South Sandwich Isla +1998-09-01,0.7330000000000001,1.671,South Georgia And The South Sandwich Isla +1998-10-01,1.7380000000000002,1.625,South Georgia And The South Sandwich Isla +1998-11-01,4.379,2.756,South Georgia And The South Sandwich Isla +1998-12-01,3.841,1.156,South Georgia And The South Sandwich Isla +1999-01-01,5.233,1.329,South Georgia And The South Sandwich Isla +1999-02-01,5.916,1.331,South Georgia And The South Sandwich Isla +1999-03-01,4.5280000000000005,1.314,South Georgia And The South Sandwich Isla +1999-04-01,3.666,1.278,South Georgia And The South Sandwich Isla +1999-05-01,1.4519999999999995,1.826,South Georgia And The South Sandwich Isla +1999-06-01,0.31500000000000017,3.201,South Georgia And The South Sandwich Isla +1999-07-01,0.040999999999999925,3.826,South Georgia And The South Sandwich Isla +1999-08-01,-1.0470000000000002,1.47,South Georgia And The South Sandwich Isla +1999-09-01,-0.37799999999999995,2.264,South Georgia And The South Sandwich Isla +1999-10-01,1.807,1.616,South Georgia And The South Sandwich Isla +1999-11-01,4.513,3.534,South Georgia And The South Sandwich Isla +1999-12-01,5.167000000000001,2.067,South Georgia And The South Sandwich Isla +2000-01-01,5.936,2.155,South Georgia And The South Sandwich Isla +2000-02-01,6.473000000000001,1.517,South Georgia And The South Sandwich Isla +2000-03-01,5.223000000000001,0.608,South Georgia And The South Sandwich Isla +2000-04-01,3.22,0.633,South Georgia And The South Sandwich Isla +2000-05-01,1.1440000000000001,1.548,South Georgia And The South Sandwich Isla +2000-06-01,0.111,1.4280000000000002,South Georgia And The South Sandwich Isla +2000-07-01,-1.228,2.867,South Georgia And The South Sandwich Isla +2000-08-01,-1.898,1.731,South Georgia And The South Sandwich Isla +2000-09-01,-1.013,0.989,South Georgia And The South Sandwich Isla +2000-10-01,1.257,1.497,South Georgia And The South Sandwich Isla +2000-11-01,1.891,0.799,South Georgia And The South Sandwich Isla +2000-12-01,3.227,0.86,South Georgia And The South Sandwich Isla +2001-01-01,4.808,1.101,South Georgia And The South Sandwich Isla +2001-02-01,5.723,2.298,South Georgia And The South Sandwich Isla +2001-03-01,6.101000000000001,3.41,South Georgia And The South Sandwich Isla +2001-04-01,0.9489999999999998,2.326,South Georgia And The South Sandwich Isla +2001-05-01,0.846,0.7240000000000001,South Georgia And The South Sandwich Isla +2001-06-01,-1.122,2.026,South Georgia And The South Sandwich Isla +2001-07-01,-2.298,2.249,South Georgia And The South Sandwich Isla +2001-08-01,1.742,0.742,South Georgia And The South Sandwich Isla +2001-09-01,0.565,1.404,South Georgia And The South Sandwich Isla +2001-10-01,3.464,1.077,South Georgia And The South Sandwich Isla +2001-11-01,4.045,0.727,South Georgia And The South Sandwich Isla +2001-12-01,5.132999999999999,1.4580000000000002,South Georgia And The South Sandwich Isla +2002-01-01,5.895,0.878,South Georgia And The South Sandwich Isla +2002-02-01,5.7700000000000005,0.723,South Georgia And The South Sandwich Isla +2002-03-01,3.1770000000000005,3.028,South Georgia And The South Sandwich Isla +2002-04-01,2.886,0.722,South Georgia And The South Sandwich Isla +2002-05-01,0.325,1.445,South Georgia And The South Sandwich Isla +2002-06-01,-0.20999999999999985,0.669,South Georgia And The South Sandwich Isla +2002-07-01,-1.248,0.927,South Georgia And The South Sandwich Isla +2002-08-01,-1.584,0.952,South Georgia And The South Sandwich Isla +2002-09-01,-0.637,1.235,South Georgia And The South Sandwich Isla +2002-10-01,-0.7409999999999999,1.229,South Georgia And The South Sandwich Isla +2002-11-01,2.688,1.294,South Georgia And The South Sandwich Isla +2002-12-01,3.71,1.271,South Georgia And The South Sandwich Isla +2003-01-01,3.9410000000000003,1.5630000000000002,South Georgia And The South Sandwich Isla +2003-02-01,5.9030000000000005,2.012,South Georgia And The South Sandwich Isla +2003-03-01,3.907000000000001,0.862,South Georgia And The South Sandwich Isla +2003-04-01,2.365,1.166,South Georgia And The South Sandwich Isla +2003-05-01,0.7640000000000001,0.845,South Georgia And The South Sandwich Isla +2003-06-01,-2.0540000000000003,2.091,South Georgia And The South Sandwich Isla +2003-07-01,-0.0030000000000001137,0.919,South Georgia And The South Sandwich Isla +2003-08-01,0.6080000000000001,1.823,South Georgia And The South Sandwich Isla +2003-09-01,0.451,1.981,South Georgia And The South Sandwich Isla +2003-10-01,1.9769999999999999,0.63,South Georgia And The South Sandwich Isla +2003-11-01,2.155,0.5579999999999999,South Georgia And The South Sandwich Isla +2003-12-01,3.36,0.698,South Georgia And The South Sandwich Isla +2004-01-01,5.986000000000001,3.043,South Georgia And The South Sandwich Isla +2004-02-01,4.871,0.763,South Georgia And The South Sandwich Isla +2004-03-01,4.9830000000000005,1.6740000000000002,South Georgia And The South Sandwich Isla +2004-04-01,2.234,0.457,South Georgia And The South Sandwich Isla +2004-05-01,0.263,1.098,South Georgia And The South Sandwich Isla +2004-06-01,0.2510000000000001,0.5579999999999999,South Georgia And The South Sandwich Isla +2004-07-01,0.4440000000000002,0.609,South Georgia And The South Sandwich Isla +2004-08-01,0.3280000000000001,0.628,South Georgia And The South Sandwich Isla +2004-09-01,0.42100000000000004,0.92,South Georgia And The South Sandwich Isla +2004-10-01,1.7169999999999999,0.647,South Georgia And The South Sandwich Isla +2004-11-01,2.985,1.266,South Georgia And The South Sandwich Isla +2004-12-01,3.832,0.735,South Georgia And The South Sandwich Isla +2005-01-01,5.1629999999999985,1.216,South Georgia And The South Sandwich Isla +2005-02-01,6.5,2.873,South Georgia And The South Sandwich Isla +2005-03-01,4.6880000000000015,1.038,South Georgia And The South Sandwich Isla +2005-04-01,2.766,0.863,South Georgia And The South Sandwich Isla +2005-05-01,1.669,0.838,South Georgia And The South Sandwich Isla +2005-06-01,-0.33299999999999985,0.885,South Georgia And The South Sandwich Isla +2005-07-01,-1.93,1.958,South Georgia And The South Sandwich Isla +2005-08-01,-0.6629999999999999,2.661,South Georgia And The South Sandwich Isla +2005-09-01,0.645,1.906,South Georgia And The South Sandwich Isla +2005-10-01,0.4830000000000001,2.426,South Georgia And The South Sandwich Isla +2005-11-01,2.8169999999999997,1.3130000000000002,South Georgia And The South Sandwich Isla +2005-12-01,2.56,1.081,South Georgia And The South Sandwich Isla +2006-01-01,5.478999999999999,1.22,South Georgia And The South Sandwich Isla +2006-02-01,4.827,1.103,South Georgia And The South Sandwich Isla +2006-03-01,6.082999999999999,1.121,South Georgia And The South Sandwich Isla +2006-04-01,4.846,0.88,South Georgia And The South Sandwich Isla +2006-05-01,3.262,0.969,South Georgia And The South Sandwich Isla +2006-06-01,-0.1519999999999999,1.4980000000000002,South Georgia And The South Sandwich Isla +2006-07-01,-0.735,1.901,South Georgia And The South Sandwich Isla +2006-08-01,-2.878,5.141,South Georgia And The South Sandwich Isla +2006-09-01,-1.551,1.863,South Georgia And The South Sandwich Isla +2006-10-01,1.4660000000000002,0.685,South Georgia And The South Sandwich Isla +2006-11-01,2.497,0.802,South Georgia And The South Sandwich Isla +2006-12-01,4.728,1.137,South Georgia And The South Sandwich Isla +2007-01-01,4.577,1.3119999999999998,South Georgia And The South Sandwich Isla +2007-02-01,4.595,0.906,South Georgia And The South Sandwich Isla +2007-03-01,3.2110000000000003,1.429,South Georgia And The South Sandwich Isla +2007-04-01,2.9730000000000003,2.391,South Georgia And The South Sandwich Isla +2007-05-01,-0.18600000000000003,2.547,South Georgia And The South Sandwich Isla +2007-06-01,-0.2659999999999998,2.1630000000000003,South Georgia And The South Sandwich Isla +2007-07-01,-2.6870000000000003,1.934,South Georgia And The South Sandwich Isla +2007-08-01,0.3839999999999999,0.98,South Georgia And The South Sandwich Isla +2007-09-01,-0.369,0.632,South Georgia And The South Sandwich Isla +2007-10-01,1.054,1.388,South Georgia And The South Sandwich Isla +2007-11-01,1.0299999999999998,1.342,South Georgia And The South Sandwich Isla +2007-12-01,3.104,1.594,South Georgia And The South Sandwich Isla +2008-01-01,5.612,1.987,South Georgia And The South Sandwich Isla +2008-02-01,4.905,1.133,South Georgia And The South Sandwich Isla +2008-03-01,4.2970000000000015,0.611,South Georgia And The South Sandwich Isla +2008-04-01,1.721,0.816,South Georgia And The South Sandwich Isla +2008-05-01,0.24799999999999994,1.433,South Georgia And The South Sandwich Isla +2008-06-01,-0.982,1.207,South Georgia And The South Sandwich Isla +2008-07-01,-0.5679999999999998,1.378,South Georgia And The South Sandwich Isla +2008-08-01,-0.91,0.6729999999999999,South Georgia And The South Sandwich Isla +2008-09-01,0.5229999999999999,0.498,South Georgia And The South Sandwich Isla +2008-10-01,1.6270000000000002,0.479,South Georgia And The South Sandwich Isla +2008-11-01,4.147,1.481,South Georgia And The South Sandwich Isla +2008-12-01,4.2010000000000005,1.08,South Georgia And The South Sandwich Isla +2009-01-01,5.906000000000001,1.229,South Georgia And The South Sandwich Isla +2009-02-01,5.303,0.909,South Georgia And The South Sandwich Isla +2009-03-01,4.944000000000001,0.685,South Georgia And The South Sandwich Isla +2009-04-01,2.017,1.521,South Georgia And The South Sandwich Isla +2009-05-01,1.055,1.476,South Georgia And The South Sandwich Isla +2009-06-01,0.4900000000000002,1.656,South Georgia And The South Sandwich Isla +2009-07-01,-0.133,0.86,South Georgia And The South Sandwich Isla +2009-08-01,-1.209,1.6,South Georgia And The South Sandwich Isla +2009-09-01,0.581,1.094,South Georgia And The South Sandwich Isla +2009-10-01,2.23,0.687,South Georgia And The South Sandwich Isla +2009-11-01,1.8419999999999999,1.496,South Georgia And The South Sandwich Isla +2009-12-01,4.109,0.6579999999999999,South Georgia And The South Sandwich Isla +2010-01-01,4.191,2.414,South Georgia And The South Sandwich Isla +2010-02-01,4.484,0.878,South Georgia And The South Sandwich Isla +2010-03-01,4.0569999999999995,0.7609999999999999,South Georgia And The South Sandwich Isla +2010-04-01,3.391,1.517,South Georgia And The South Sandwich Isla +2010-05-01,2.555,1.7990000000000002,South Georgia And The South Sandwich Isla +2010-06-01,1.122,0.853,South Georgia And The South Sandwich Isla +2010-07-01,-0.018000000000000016,0.852,South Georgia And The South Sandwich Isla +2010-08-01,-0.05600000000000005,0.62,South Georgia And The South Sandwich Isla +2010-09-01,0.8050000000000002,0.784,South Georgia And The South Sandwich Isla +2010-10-01,2.319,1.0490000000000002,South Georgia And The South Sandwich Isla +2010-11-01,3.7960000000000003,0.8340000000000001,South Georgia And The South Sandwich Isla +2010-12-01,4.327,0.5710000000000001,South Georgia And The South Sandwich Isla +2011-01-01,5.608,0.6629999999999999,South Georgia And The South Sandwich Isla +2011-02-01,6.157,0.754,South Georgia And The South Sandwich Isla +2011-03-01,5.659000000000002,0.913,South Georgia And The South Sandwich Isla +2011-04-01,3.347,1.09,South Georgia And The South Sandwich Isla +2011-05-01,1.336,1.294,South Georgia And The South Sandwich Isla +2011-06-01,0.4690000000000001,2.263,South Georgia And The South Sandwich Isla +2011-07-01,-1.385,0.8140000000000001,South Georgia And The South Sandwich Isla +2011-08-01,-2.4320000000000004,1.7080000000000002,South Georgia And The South Sandwich Isla +2011-09-01,-0.364,0.7909999999999999,South Georgia And The South Sandwich Isla +2011-10-01,1.878,0.792,South Georgia And The South Sandwich Isla +2011-11-01,4.774,2.338,South Georgia And The South Sandwich Isla +2011-12-01,5.063,1.336,South Georgia And The South Sandwich Isla +2012-01-01,5.2799999999999985,0.7020000000000001,South Georgia And The South Sandwich Isla 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And The South Sandwich Isla +2013-04-01,2.954,0.928,South Georgia And The South Sandwich Isla +2013-05-01,1.1740000000000002,1.016,South Georgia And The South Sandwich Isla +2013-06-01,0.6970000000000001,1.7180000000000002,South Georgia And The South Sandwich Isla +2013-07-01,-0.2919999999999998,1.121,South Georgia And The South Sandwich Isla +2013-08-01,-0.5940000000000001,1.711,South Georgia And The South Sandwich Isla +2013-09-01,,,South Georgia And The South Sandwich Isla +1841-01-01,-3.677,2.67,South Korea +1841-02-01,-1.42,1.998,South Korea +1841-03-01,2.849,2.006,South Korea +1841-04-01,9.675,2.254,South Korea +1841-05-01,15.345,1.813,South Korea +1841-06-01,19.36,3.199,South Korea +1841-07-01,24.153,2.382,South Korea +1841-08-01,23.983,2.242,South Korea +1841-09-01,18.977999999999998,1.893,South Korea +1841-10-01,12.543,2.101,South Korea +1841-11-01,6.114,2.164,South Korea +1841-12-01,0.4609999999999999,2.029,South Korea +1842-01-01,-2.016,2.139,South Korea 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Korea +1844-03-01,5.2029999999999985,1.896,South Korea +1844-04-01,10.031,1.902,South Korea +1844-05-01,14.833,1.826,South Korea +1844-06-01,19.687,1.964,South Korea +1844-07-01,23.639000000000006,2.217,South Korea +1844-08-01,24.105,2.042,South Korea +1844-09-01,19.313,1.767,South Korea +1844-10-01,13.581,1.76,South Korea +1844-11-01,7.0120000000000005,2.1590000000000003,South Korea +1844-12-01,-0.16000000000000014,1.866,South Korea +1845-01-01,-0.07400000000000008,1.994,South Korea +1845-02-01,0.26,3.831,South Korea +1845-03-01,5.103,1.157,South Korea +1845-04-01,10.525,1.26,South Korea +1845-05-01,16.067999999999998,1.035,South Korea +1845-06-01,21.197,1.324,South Korea +1845-07-01,24.735000000000007,1.7009999999999998,South Korea +1845-08-01,23.792,1.003,South Korea +1845-09-01,18.926,1.797,South Korea +1845-10-01,12.338,1.135,South Korea +1845-11-01,5.751,1.6269999999999998,South Korea +1845-12-01,-1.915,1.365,South Korea +1846-01-01,-2.201,2.89,South Korea 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Sahara +1984-10-01,24.055,0.391,Western Sahara +1984-11-01,20.1,0.6709999999999999,Western Sahara +1984-12-01,17.132,0.24,Western Sahara +1985-01-01,15.826999999999998,0.214,Western Sahara +1985-02-01,19.764,0.418,Western Sahara +1985-03-01,19.921,1.004,Western Sahara +1985-04-01,21.631999999999998,0.338,Western Sahara +1985-05-01,21.892,0.841,Western Sahara +1985-06-01,26.206,0.4,Western Sahara +1985-07-01,28.279,1.335,Western Sahara +1985-08-01,28.929,0.417,Western Sahara +1985-09-01,27.901999999999997,0.657,Western Sahara +1985-10-01,25.484,1.195,Western Sahara +1985-11-01,21.041999999999998,0.6759999999999999,Western Sahara +1985-12-01,17.393,0.489,Western Sahara +1986-01-01,16.064999999999998,0.609,Western Sahara +1986-02-01,18.271,0.281,Western Sahara +1986-03-01,19.952,0.382,Western Sahara +1986-04-01,21.053,0.7170000000000001,Western Sahara +1986-05-01,25.121,2.031,Western Sahara +1986-06-01,24.829,0.505,Western Sahara +1986-07-01,27.131,0.333,Western Sahara 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Sahara +1988-07-01,28.412,0.996,Western Sahara +1988-08-01,28.808000000000003,0.532,Western Sahara +1988-09-01,27.456999999999997,0.311,Western Sahara +1988-10-01,24.402,0.459,Western Sahara +1988-11-01,21.000999999999998,0.281,Western Sahara +1988-12-01,16.612000000000002,0.765,Western Sahara +1989-01-01,16.139,0.7,Western Sahara +1989-02-01,18.576,0.147,Western Sahara +1989-03-01,21.428,0.489,Western Sahara +1989-04-01,20.631999999999998,0.485,Western Sahara +1989-05-01,23.923000000000002,0.52,Western Sahara +1989-06-01,26.234,0.619,Western Sahara +1989-07-01,28.886999999999997,0.6729999999999999,Western Sahara +1989-08-01,29.619,0.616,Western Sahara +1989-09-01,27.711,1.011,Western Sahara +1989-10-01,25.557,0.383,Western Sahara +1989-11-01,20.787,0.207,Western Sahara +1989-12-01,19.552,1.166,Western Sahara +1990-01-01,16.145,0.368,Western Sahara +1990-02-01,21.03,0.456,Western Sahara +1990-03-01,22.858,0.765,Western Sahara +1990-04-01,21.593000000000004,0.5429999999999999,Western Sahara +1990-05-01,23.606,0.324,Western Sahara +1990-06-01,26.105,0.642,Western Sahara +1990-07-01,28.276999999999997,0.319,Western Sahara +1990-08-01,29.47,1.042,Western Sahara +1990-09-01,27.91,0.8059999999999999,Western Sahara +1990-10-01,25.111,0.494,Western Sahara +1990-11-01,20.926,0.6559999999999999,Western Sahara +1990-12-01,17.792,0.47,Western Sahara +1991-01-01,17.195,0.447,Western Sahara +1991-02-01,17.622,0.474,Western Sahara +1991-03-01,19.812,0.248,Western Sahara +1991-04-01,21.430999999999997,0.193,Western Sahara +1991-05-01,22.969,0.787,Western Sahara +1991-06-01,25.397,0.755,Western Sahara +1991-07-01,28.465,0.768,Western Sahara +1991-08-01,28.418000000000003,0.623,Western Sahara +1991-09-01,27.741,0.19,Western Sahara +1991-10-01,23.445,0.416,Western Sahara +1991-11-01,20.987,0.336,Western Sahara +1991-12-01,18.202,0.412,Western Sahara +1992-01-01,17.462999999999994,0.449,Western Sahara +1992-02-01,19.063,0.376,Western Sahara +1992-03-01,20.136,0.298,Western Sahara 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Sahara +1994-03-01,20.067,0.326,Western Sahara +1994-04-01,21.858,0.329,Western Sahara +1994-05-01,24.514,0.805,Western Sahara +1994-06-01,26.651999999999997,0.777,Western Sahara +1994-07-01,28.388,0.36,Western Sahara +1994-08-01,28.429,0.55,Western Sahara +1994-09-01,26.999,0.802,Western Sahara +1994-10-01,23.726,0.348,Western Sahara +1994-11-01,21.651999999999997,0.412,Western Sahara +1994-12-01,18.62,0.325,Western Sahara +1995-01-01,16.476,0.324,Western Sahara +1995-02-01,19.825,0.736,Western Sahara +1995-03-01,20.254,0.527,Western Sahara +1995-04-01,21.704,0.4270000000000001,Western Sahara +1995-05-01,24.8,0.8440000000000001,Western Sahara +1995-06-01,26.311999999999998,0.601,Western Sahara +1995-07-01,28.33,0.574,Western Sahara +1995-08-01,29.017,0.5770000000000001,Western Sahara +1995-09-01,26.98,0.455,Western Sahara +1995-10-01,25.498,0.465,Western Sahara +1995-11-01,22.576999999999998,0.467,Western Sahara +1995-12-01,18.68,0.28300000000000003,Western Sahara 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+0,Afghanistan,1990,2602407.59,0.0117,15140000.0,14.993333333333332,4.59266471862793,13568282.0,338.48535272394315 +1,Afghanistan,1991,2426486.16,0.0108,15060000.0,14.370750000000001,4.629405975341797,13671918.0,338.60691494359435 +2,Afghanistan,1992,1381521.84,0.0062,13600000.0,14.056083333333333,4.675699710845947,15023317.0,311.22951814475766 +3,Afghanistan,1993,1333837.11,0.006,13430000.0,14.439249999999996,4.530753135681152,17003437.0,266.46101818597924 +4,Afghanistan,1994,1282488.38,0.0057,13240000.0,14.75475,4.3948307037353525,18486041.0,237.73779922566175 +5,Afghanistan,1995,1231139.65,0.0054,13450000.0,14.859166666666667,5.546276569366455,19445013.0,285.228740621899 +6,Afghanistan,1996,1165134.92,0.005,14400000.0,14.426000000000002,5.8790526390075675,20110508.0,292.3373511503324 +7,Afghanistan,1997,1084474.19,0.0045,15300000.0,14.904000000000002,6.231795787811278,20769357.0,300.04760319788807 +8,Afghanistan,1998,1029461.47,0.0043,16050000.0,15.1325,5.982523918151856,21451549.0,278.8854044130732 +9,Afghanistan,1999,809568.74,0.0034,16890000.0,15.38875,6.233789920806885,22205667.0,280.7296858413163 +10,Afghanistan,2000,768195.25,0.0031,15050000.0,15.497833333333334,6.12158203125,22461349.0,272.53848516622935 +11,Afghanistan,2001,812074.37,0.0032,13470000.0,15.778083333333333,5.723679065704347,22507460.0,254.30142120454045 +12,Afghanistan,2002,1064074.85,0.0042,16100000.000000002,15.537666666666668,7.36065149307251,23600088.0,311.8908494354983 +13,Afghanistan,2003,1187070.59,0.0044,16780000.0,14.916000000000002,8.010765075683594,25005388.0,320.3615587042118 +14,Afghanistan,2004,941455.62,0.0033,16350000.000000002,15.77091666666667,8.124046325683594,25697635.0,316.1398442184892 +15,Afghanistan,2005,1320250.12,0.0045,17090000.0,14.979999999999999,9.03635311126709,26334702.0,343.13481547150565 +16,Afghanistan,2006,1643246.0,0.0054,20740000.0,15.595583333333332,9.520467758178713,27154386.0,350.6051566836647 +17,Afghanistan,2007,2268343.1,0.0073,24780000.0,15.106166666666669,10.836797714233398,27387083.0,395.6901037702117 +18,Afghanistan,2008,4198329.64,0.0132,31390000.0,15.216500000000002,11.262140274047852,27705724.0,406.4914627045245 +19,Afghanistan,2009,6760313.49,0.0214,37720000.0,15.257749999999996,13.671172142028807,28483631.0,479.96591944435767 +20,Afghanistan,2010,8452224.14,0.0256,44750000.0,15.828666666666669,15.634685516357422,29120727.0,536.8920053526624 +21,Afghanistan,2011,12226017.38,0.0356,58620000.0,15.518,15.70134449005127,29757566.0,527.6420957967889 +22,Afghanistan,2012,10742056.72,0.0308,67050000.0,14.481583333333333,17.703624725341797,30419928.0,581.9745768412666 +23,Afghanistan,2013,10000414.29,0.0284,76290000.0,16.533625,18.69515991210937,31108077.0,600.9744643524373 +24,Albania,1990,5510656.0,0.0248,11430000.0,13.46866666666667,6.1786208152771,3244925.0,1904.0874027218194 +25,Albania,1991,4283216.0,0.0191,9030000.0,12.338833333333334,4.350428581237793,3181048.0,1367.6085935320034 +26,Albania,1992,2513504.0,0.0113,6950000.0,13.084666666666665,4.037198066711427,3072011.0,1314.1873732585675 +27,Albania,1993,2333968.0,0.0105,6950000.0,13.122583333333337,4.424768924713135,3066285.0,1443.039027589782 +28,Albania,1994,1923600.0,0.0085,7730000.0,14.09966666666667,4.792025089263916,3111162.0,1540.2685842987012 +29,Albania,1995,2084816.0,0.0091,7480000.0,12.782083333333334,5.429364204406738,3158154.0,1719.1575218962528 +30,Albania,1996,2015200.0,0.0086,7280000.0,12.7365,5.923436641693115,3203599.0,1848.994409629019 +31,Albania,1997,1542544.0,0.0065,6510000.0,12.900916666666667,5.276597023010254,3225414.0,1635.9441060931258 +32,Albania,1998,1751392.0,0.0073,6810000.0,13.311250000000003,5.742520332336426,3212771.0,1787.4041854637092 +33,Albania,1999,2982496.0,0.0125,7960000.0,13.65375,6.482731342315674,3187329.0,2033.907181315664 +34,Albania,2000,3019136.0,0.0123,8400000.0,13.746583333333335,6.933281421661378,3158352.0,2195.2212488226064 +35,Albania,2001,3220656.0,0.0128,9030000.0,13.690416666666666,7.5080504417419425,3128002.0,2400.2703456525737 +36,Albania,2002,3748272.0,0.0147,9720000.0,13.559916666666668,7.848916053771973,3098028.0,2533.520050100249 +37,Albania,2003,4290544.0,0.016,10090000.0,13.62658333333333,8.282960891723633,3069665.0,2698.327306635621 +38,Albania,2004,4162304.0,0.0148,10150000.0,13.258166666666666,8.739352226257324,3045640.0,2869.463306975652 +39,Albania,2005,4250240.0,0.0145,9920000.0,12.747083333333334,9.222639083862305,3024533.0,3049.277056610824 +40,Albania,2006,3894832.0,0.0129,8750000.0,12.976916666666668,9.76677417755127,3005779.0,3249.3320957899 +41,Albania,2007,3924144.0,0.0127,9060000.0,13.890500000000001,10.35082721710205,2991575.0,3459.992551449337 +42,Albania,2008,4371152.0,0.0137,8530000.0,13.955166666666665,11.127140045166016,2984121.0,3728.783130833507 +43,Albania,2009,4374816.0,0.0139,8640000.0,13.84425,11.499898910522461,2982540.0,3855.740043896297 +44,Albania,2010,4594656.0,0.0139,8850000.0,13.775416666666667,11.926544189453125,2986952.0,3992.881100684954 +45,Albania,2011,5235856.0,0.0152,9290000.0,13.443250000000004,12.230670928955078,2994667.0,4084.1505679780353 +46,Albania,2012,4906096.0,0.013999999999999999,8950000.0,13.76825,12.404346466064453,3002859.0,4130.845459631789 +47,Albania,2013,5059984.0,0.0144,9010000.0,14.993875000000003,12.528390884399414,3011405.0,4160.314167107849 +48,Algeria,1990,76736640.21,0.3459,96030000.0,24.014416666666666,91.98975372314452,25190449.0,3651.7711027359833 +49,Algeria,1991,78805351.89,0.3513,94850000.0,23.011083333333335,90.88587188720705,25825978.0,3519.1647684051704 +50,Algeria,1992,79881770.87,0.3599,94540000.0,22.767,92.52182006835938,26493629.0,3492.2290211114287 +51,Algeria,1993,81942440.4,0.3699,102030000.0,23.450833333333335,90.578857421875,27154754.0,3335.653765151951 +52,Algeria,1994,86146675.45,0.3823,99770000.0,23.642083333333332,89.76365661621094,27758508.0,3233.7349189016554 +53,Algeria,1995,94981961.04,0.4128,103960000.0,23.7225,93.17467498779295,28321697.0,3289.869070620767 +54,Algeria,1996,96786119.98,0.4115,105310000.0,23.734333333333336,96.99483489990234,28837598.0,3363.4852285513634 +55,Algeria,1997,87004345.51,0.3638,109400000.0,23.955166666666667,98.06178283691406,29318183.0,3344.742845657047 +56,Algeria,1998,106620830.2,0.4445,114700000.0,23.92058333333333,103.06293487548828,29768857.0,3462.1058805008292 +57,Algeria,1999,91667911.41,0.3831,120680000.0,24.299416666666662,106.36095428466795,30204849.0,3521.3205099839424 +58,Algeria,2000,87442682.76,0.3561,127650000.0,24.032249999999994,110.42359924316406,30638018.0,3604.1365091946896 +59,Algeria,2001,83780673.62,0.3333,125230000.0,24.724416666666666,113.74557495117188,31077277.0,3660.0882037114093 +60,Algeria,2002,89406721.23,0.3507,132440000.0,24.263000000000005,120.12593841552734,31523711.0,3810.653460676865 +61,Algeria,2003,91048193.45,0.3389,138300000.0,24.393666666666665,128.77725219726562,31975415.0,4027.383294236076 +62,Algeria,2004,87853102.49,0.3117,142190000.0,23.916000000000007,134.3167724609375,32450849.0,4139.083463145679 +63,Algeria,2005,106473190.0,0.3639,147710000.0,24.222583333333333,142.25192260742188,32917567.0,4321.459195554212 +64,Algeria,2006,100201161.5,0.3308,145930000.0,24.200833333333335,144.6481475830078,33420968.0,4328.065769459694 +65,Algeria,2007,108399973.2,0.35100000000000003,153160000.0,24.06533333333334,149.5269317626953,34002400.0,4397.5405195720095 +66,Algeria,2008,109031837.0,0.3421,157230000.0,23.95025,153.05596923828125,34619419.0,4421.101614625053 +67,Algeria,2009,120185864.1,0.3813,166450000.0,24.15433333333333,155.55421447753906,35268128.0,4410.617271138946 +68,Algeria,2010,117814076.6,0.3563,167180000.0,25.215666666666664,161.207275390625,35949869.0,4484.224278831865 +69,Algeria,2011,119806887.5,0.3487,179590000.0,24.144166666666667,165.8691864013672,36654293.0,4525.232184982185 +70,Algeria,2012,128109951.1,0.3669,189600000.0,23.95483333333333,171.46688842773438,37367226.0,4588.6972832217825 +71,Algeria,2013,132434630.3,0.3762,193450000.0,25.1215,176.21246337890625,38087812.0,4626.479026385297 +72,Andorra,1990,406704.0,0.0018,410000.0,12.455583333333335,,, +73,Andorra,1991,406704.0,0.0018,410000.0,11.565416666666666,,, +74,Andorra,1992,406704.0,0.0018,410000.0,11.498416666666664,,, +75,Andorra,1993,410368.0,0.0019,420000.0,11.432166666666667,,, +76,Andorra,1994,406704.0,0.0018,420000.0,12.632249999999999,,, +77,Andorra,1995,425024.0,0.0018,440000.0,12.376416666666664,,, +78,Andorra,1996,454336.0,0.0019,470000.0,11.741416666666666,,, +79,Andorra,1997,465328.0,0.0019,490000.0,12.896166666666668,,, +80,Andorra,1998,490976.0,0.002,520000.0,12.118749999999999,,, +81,Andorra,1999,512960.0,0.0021,550000.0,12.178666666666667,,, +82,Andorra,2000,523952.0,0.0021,570000.0,12.311916666666667,,, +83,Andorra,2001,523952.0,0.0021,560000.0,12.216083333333335,,, +84,Andorra,2002,531280.0,0.0021,580000.0,12.319583333333334,,, +85,Andorra,2003,534944.0,0.002,580000.0,12.900666666666668,,, +86,Andorra,2004,560592.0,0.002,610000.0,12.043,,, +87,Andorra,2005,575248.0,0.002,630000.0,11.643416666666667,,, +88,Andorra,2006,545936.0,0.0018,600000.0,12.834333333333333,,, +89,Andorra,2007,538608.0,0.0017,600000.0,12.15525,,, +90,Andorra,2008,538608.0,0.0017,600000.0,11.978583333333333,,, +91,Andorra,2009,516624.0,0.0016,580000.0,12.566666666666665,,, +92,Andorra,2010,516624.0,0.0016,590000.0,11.480833333333335,,, +93,Andorra,2011,490976.0,0.0014,580000.0,12.994416666666668,,, +94,Andorra,2012,487312.0,0.0014,580000.0,12.339916666666667,,, +95,Andorra,2013,476320.0,0.0014,570000.0,12.307875000000001,,, +96,Angola,1990,5088684.98,0.0229,150590000.0,22.46641666666667,31.822139739990234,11125154.0,2860.3774599425983 +97,Angola,1991,5062876.68,0.0226,151270000.0,22.095416666666665,32.13761138916016,11514904.0,2790.957822067831 +98,Angola,1992,5165043.98,0.0233,151620000.0,22.498583333333332,30.261329650878906,11932712.0,2535.9976550912243 +99,Angola,1993,5748383.38,0.0259,152370000.0,22.291,23.00362968444824,12392463.0,1856.2597027280408 +100,Angola,1994,3865595.55,0.0172,152560000.0,22.189416666666663,23.311731338500977,12873711.0,1810.801200873701 +101,Angola,1995,10948303.24,0.0476,156890000.0,22.936166666666665,26.808490753173828,13360649.0,2006.5260866574545 +102,Angola,1996,10424825.05,0.0443,161390000.0,22.479249999999997,30.439531326293945,13828835.0,2201.163823727302 +103,Angola,1997,7347849.05,0.0307,155980000.0,22.317166666666665,32.65378570556641,14282706.0,2286.2464371643864 +104,Angola,1998,7269605.77,0.0303,170050000.0,23.120166666666666,34.185623168945305,14736965.0,2319.719370232969 +105,Angola,1999,9129777.79,0.0382,161940000.0,22.382083333333338,34.93137741088867,15197666.0,2298.469871024187 +106,Angola,2000,9515060.92,0.0387,161840000.0,22.360666666666663,35.9984016418457,15657215.0,2299.157394328793 +107,Angola,2001,9671518.88,0.0385,143580000.0,22.502916666666668,37.51249694824219,16131419.0,2325.430698207156 +108,Angola,2002,12597545.05,0.0494,164500000.0,22.9255,42.63883590698242,16685168.0,2555.4933523583595 +109,Angola,2003,8988367.85,0.0335,181040000.0,23.06658333333333,43.913673400878906,17346091.0,2531.6178383290453 +110,Angola,2004,18701931.74,0.0663,193160000.0,22.572833333333335,48.72347640991211,18075828.0,2695.5045384317723 +111,Angola,2005,19006429.59,0.065,191800000.0,22.96608333333333,56.04608535766602,18789081.0,2982.9072192336607 +112,Angola,2006,22102407.09,0.073,165860000.0,21.947083333333335,62.51810836791992,19490185.0,3207.671367302051 +113,Angola,2007,24976104.38,0.0809,196180000.0,22.42708333333334,71.27690887451172,20225887.0,3524.043661200704 +114,Angola,2008,25486004.45,0.08,173970000.0,22.287333333333333,79.23577880859375,20995867.0,3773.875058771984 +115,Angola,2009,27565503.37,0.0874,180680000.0,22.3165,79.91619110107422,21803771.0,3665.246305378745 +116,Angola,2010,28864472.37,0.0873,186780000.0,22.6815,83.79949188232422,22640745.0,3701.269188903643 +117,Angola,2011,30142144.46,0.0877,220880000.0,22.029666666666667,86.70899200439453,23498752.0,3689.9402999952736 +118,Angola,2012,33185815.75,0.095,185500000.0,22.12333333333333,94.11580657958984,24406974.0,3856.103037582203 +119,Angola,2013,32373114.67,0.0919,202250000.0,22.507875000000002,98.77886199951172,25352999.0,3896.141123166996 +120,Argentina,1990,111890017.8,0.5044,329740000.0,15.155083333333332,205.10211181640625,33035578.0,6208.521970355907 +121,Argentina,1991,116845003.3,0.5209,334380000.0,15.097583333333338,223.83432006835938,33504421.0,6680.7398363445645 +122,Argentina,1992,121016713.6,0.5452,338740000.0,14.739166666666664,241.60069274902344,33966873.0,7112.8329283953635 +123,Argentina,1993,117564672.1,0.5307,338810000.0,15.098666666666668,261.42880249023443,34407178.0,7598.089052529515 +124,Argentina,1994,122010040.7,0.5415,348840000.0,15.578666666666669,276.68630981445307,34846915.0,7940.051789791238 +125,Argentina,1995,127598460.1,0.5546,351330000.0,15.149833333333333,268.81399536132807,35273867.0,7620.769091217818 +126,Argentina,1996,134643650.1,0.5724,351400000.0,15.211583333333332,283.6705017089844,35682983.0,7949.741805750499 +127,Argentina,1997,137558052.2,0.5752,356310000.0,15.620666666666667,306.6791687011719,36102182.0,8494.754380806453 +128,Argentina,1998,139354451.3,0.581,359590000.0,15.261750000000001,318.4868469238281,36517941.0,8721.380181972147 +129,Argentina,1999,146510308.4,0.6122,367050000.0,15.064916666666663,307.70458984375,36923439.0,8333.584253724308 +130,Argentina,2000,141716804.3,0.5771,366340000.0,14.7505,305.2768249511719,37335675.0,8176.544952010962 +131,Argentina,2001,133310609.8,0.5303,383270000.0,15.370416666666666,291.8176574707031,37739190.0,7732.483327562227 +132,Argentina,2002,124382213.6,0.4879,386170000.0,15.183750000000003,260.025634765625,38131971.0,6819.097674379984 +133,Argentina,2003,134620660.1,0.5011,408490000.0,15.518333333333336,283.0041809082031,38529402.0,7345.1485415787965 +134,Argentina,2004,157033639.3,0.5571,436710000.0,15.442166666666667,308.5582580566406,38945354.0,7922.85154364345 +135,Argentina,2005,161424779.4,0.5518,428510000.0,15.112833333333334,335.87078857421875,39367281.0,8531.724316297556 +136,Argentina,2006,174617506.0,0.5765,462740000.0,15.561083333333329,362.8988037109375,39761782.0,9126.82443938095 +137,Argentina,2007,174232910.8,0.5642,465580000.0,14.628083333333336,395.5874633789063,40134011.0,9856.664049324803 +138,Argentina,2008,188041184.3,0.5899,482380000.0,15.475999999999999,411.6373901367188,40531409.0,10156.009877098493 +139,Argentina,2009,178867384.3,0.5674,469510000.0,15.534416666666667,387.2745056152344,40943942.0,9458.652164347888 +140,Argentina,2010,186686451.5,0.5646,451700000.0,15.078833333333336,426.4875793457031,41358092.0,10312.070956892863 +141,Argentina,2011,190247901.5,0.5537,460120000.0,15.339666666666668,452.09368896484375,41791721.0,10817.781085513176 +142,Argentina,2012,191044680.3,0.5471,478030000.0,15.688999999999998,447.4533081054688,42216144.0,10599.104174589436 +143,Argentina,2013,188428149.5,0.5352,466520000.0,14.457124999999998,458.2160034179688,42635450.0,10747.30074194054 +144,Armenia,1990,6642549.18,0.0299,24390000.0,9.258083333333333,6.357865810394287,3529364.0,1801.4196921582154 +145,Armenia,1991,6440640.87,0.0287,25080000.0,9.372583333333333,5.613995552062988,3589331.0,1564.0785294148095 +146,Armenia,1992,5719931.63,0.0258,14850000.0,7.614666666666667,3.2673451900482178,3508952.0,931.145592771921 +147,Armenia,1993,2508264.53,0.0113,8680000.0,7.769666666666667,2.9798188209533687,3328557.0,895.2284190877216 +148,Armenia,1994,2683496.48,0.0119,6230000.0,9.02125,3.1407291889190674,3198061.0,982.0729463631454 +149,Armenia,1995,3360091.62,0.0146,6800000.0,9.892166666666668,3.357439517974853,3130255.0,1072.5770002683016 +150,Armenia,1996,2497343.84,0.0106,5680000.0,9.819166666666666,3.5543665885925293,3120068.0,1139.1952318322965 +151,Armenia,1997,3246443.17,0.0136,6410000.0,9.246333333333334,3.672410249710083,3112661.0,1179.8298143325223 +152,Armenia,1998,3411071.87,0.0142,6340000.0,10.448416666666667,3.940496206283569,3106213.0,1268.5853179687192 +153,Armenia,1999,3033700.26,0.0127,5750000.0,10.331000000000001,4.070532321929932,3104064.0,1311.3557974094385 +154,Armenia,2000,3491410.0,0.0142,6130000.0,9.918166666666668,4.3106937408447275,3100042.0,1390.5275286092015 +155,Armenia,2001,3532700.0,0.0141,6300000.0,10.443416666666668,4.722651481628418,3099474.0,1523.6944983659866 +156,Armenia,2002,3076608.0,0.0121,5920000.0,9.604999999999999,5.3453946113586435,3096321.0,1726.369653326849 +157,Armenia,2003,3455562.0,0.0129,6370000.0,9.423083333333333,6.095930099487305,3088904.0,1973.4928956961128 +158,Armenia,2004,3691194.0,0.0131,6780000.0,9.720166666666666,6.734042644500732,3086691.0,2181.6380857367103 +159,Armenia,2005,4379830.0,0.015,7430000.0,9.735416666666667,7.667765140533447,3084080.0,2486.240674863637 +160,Armenia,2006,4405250.0,0.0145,7950000.0,10.053500000000001,8.679757118225098,3081101.0,2817.095940128252 +161,Armenia,2007,5107762.0,0.0165,8570000.0,9.53525,9.873154640197754,3078606.0,3207.0211778310554 +162,Armenia,2008,5582114.0,0.0175,9140000.0,9.706750000000001,10.554402351379396,3076465.0,3430.6915083966164 +163,Armenia,2009,4362330.0,0.0138,8050000.000000001,9.842083333333333,9.060956001281738,3074268.0,2947.3539721591414 +164,Armenia,2010,4260176.0,0.0129,7830000.0,11.509500000000003,9.260296821594238,3071972.0,3014.447013707885 +165,Armenia,2011,4968876.0,0.0145,8260000.0,9.136000000000001,9.695530891418457,3069558.0,3158.6081420903133 +166,Armenia,2012,5748252.0,0.0165,9180000.0,10.35075,10.393609046936037,3067015.0,3388.8354138913683 +167,Armenia,2013,5549428.33,0.0158,9130000.0,11.34375,10.73659896850586,3064111.0,3503.9849954867364 +168,Australia,1990,278424375.7,1.2552,511200000.0,22.35575,637.947998046875,16956242.0,37623.19493003668 +169,Australia,1991,279872363.9,1.2478,511080000.0,22.48741666666667,635.4756469726562,17202125.0,36941.69452742939 +170,Australia,1992,284912124.3,1.2837,513320000.00000006,21.932583333333337,638.288330078125,17418927.0,36643.37821027236 +171,Australia,1993,289234856.5,1.3056,513000000.0,22.160166666666665,664.0692749023438,17607798.0,37714.498706899285 +172,Australia,1994,294014610.4,1.3048,516030000.0,22.03025,690.489501953125,17781089.0,38832.80163285415 +173,Australia,1995,305409799.3,1.3274,524110000.0,21.955166666666667,716.9308471679688,17975634.0,39883.480447363836 +174,Australia,1996,312361210.0,1.3279,517130000.0,22.325333333333333,744.7379760742188,18195852.0,40928.99722828141 +175,Australia,1997,320794696.4,1.3414,528170000.0,22.086000000000002,774.2774658203125,18415263.0,42045.419922610534 +176,Australia,1998,334684106.5,1.3954,554160000.0,22.664833333333334,809.714111328125,18620837.0,43484.3026297972 +177,Australia,1999,344063336.2,1.4378,589370000.0,22.0645,850.3461303710938,18830251.0,45158.512776653566 +178,Australia,2000,350194581.8,1.4259,608160000.0,21.709666666666667,883.8768920898438,19053186.0,46389.97866760152 +179,Australia,2001,357668742.8,1.4228,700990000.0,21.79275,900.963134765625,19294257.0,46695.922769434714 +180,Australia,2002,362209093.8,1.4208,688480000.0,22.46083333333333,937.0288696289062,19534938.0,47966.82076128966 +181,Australia,2003,369725835.4,1.3761,620390000.0,22.44033333333333,965.0061645507812,19766540.0,48820.18626177273 +182,Australia,2004,383204944.7,1.3595,664400000.0,22.286916666666666,1003.6171264648438,19995075.0,50193.21640278137 +183,Australia,2005,386515211.6,1.3212,622460000.0,22.896833333333333,1035.6357421875,20232346.0,51187.13085410362 +184,Australia,2006,392681873.2,1.2965,637390000.0,22.264416666666666,1064.988037109375,20489472.0,51977.32948459458 +185,Australia,2007,400415729.4,1.2967,628770000.0,22.48675,1105.216064453125,20749625.0,53264.38740233257 +186,Australia,2008,405048537.2,1.2707,597420000.0,22.225916666666667,1145.642333984375,21007310.0,54535.413338708044 +187,Australia,2009,408672631.7,1.2964,617920000.0,22.690583333333333,1167.6700439453125,21262641.0,54916.51032180399 +188,Australia,2010,406752772.7,1.2301,579140000.0,22.078166666666664,1191.6416015625,21515754.0,55384.608020825115 +189,Australia,2011,404246571.2,1.1766,617790000.0,21.767,1220.8499755859375,21766711.0,56087.93977123772 +190,Australia,2012,407146057.1,1.166,617860000.0,22.0245,1268.36474609375,22015576.0,57612.15359951291 +191,Australia,2013,398163798.2,1.1309,529260000.0,22.142874999999997,1301.843017578125,22262501.0,58476.943699098534 +192,Austria,1990,62292228.93,0.2808,64220000.0,7.344,260.19461059570307,7722953.0,33691.07782938769 +193,Austria,1991,65899677.65,0.2938,68450000.0,6.357916666666667,269.1495361328125,7818423.0,34425.041486347385 +194,Austria,1992,60431459.18,0.2723,62720000.0,7.615583333333334,274.78424072265625,7914969.0,34717.033095474704 +195,Austria,1993,60787739.13,0.2744,62630000.0,6.858666666666665,276.23184204101557,7988599.0,34578.258595908446 +196,Austria,1994,61190346.75,0.2715,62710000.0,8.164916666666667,282.86724853515625,8027539.0,35237.10673160931 +197,Austria,1995,64205950.22,0.2791,65120000.00000001,6.846499999999999,290.4140930175781,8047432.0,36087.79707831991 +198,Austria,1996,67673887.44,0.2877,68920000.0,5.863250000000001,297.2374572753906,8060851.0,36874.20314249583 +199,Austria,1997,67453011.31,0.282,67910000.0,7.025083333333335,303.46044921875,8069875.0,37604.1077735095 +200,Austria,1998,67054364.04,0.2796,67880000.0,7.222750000000001,314.3286437988281,8078621.0,38908.70035849288 +201,Austria,1999,65621004.96,0.2742,65980000.00000001,7.234249999999999,325.5072326660156,8094155.0,40215.09751987893 +202,Austria,2000,66261743.06,0.2698,66220000.0,8.084916666666668,336.49542236328125,8113392.0,41474.074266753196 +203,Austria,2001,70390857.43,0.28,75320000.0,7.1889166666666675,340.7593994140625,8141540.0,41854.41567738567 +204,Austria,2002,72146538.21,0.28300000000000003,77000000.0,7.91525,346.38720703125,8178095.0,42355.48829296431 +205,Austria,2003,77764249.22,0.2894,82930000.0,7.509333333333333,349.64837646484375,8215680.0,42558.66543789969 +206,Austria,2004,78052545.98,0.2769,82530000.0,6.906083333333332,359.2117004394531,8263235.0,43471.07403328759 +207,Austria,2005,79366679.88,0.2713,82420000.0,6.609500000000001,367.2726135253906,8314652.0,44171.736054063425 +208,Austria,2006,76688464.51,0.2532,82820000.0,7.277583333333335,379.9583740234375,8352180.0,45492.11990443663 +209,Austria,2007,74032117.13,0.2397,80370000.0,7.976500000000001,394.1210021972656,8379589.0,47033.452618889256 +210,Austria,2008,73806271.05,0.2315,80160000.0,7.801916666666668,399.8768310546875,8406796.0,47565.901569954534 +211,Austria,2009,67483059.72,0.2141,72870000.0,7.616,384.8231506347656,8428464.0,45657.56591411741 +212,Austria,2010,72383144.13,0.2189,78380000.0,6.6490833333333335,391.8927307128906,8447901.0,46389.36118130298 +213,Austria,2011,70115670.43,0.2041,75850000.0,7.855583333333332,403.3469543457031,8475092.0,47592.044351341916 +214,Austria,2012,67661399.55,0.1938,73200000.0,7.611333333333334,406.0915222167969,8513010.0,47702.46037732798 +215,Austria,2013,68001230.43,0.1931,73790000.0,8.055125,406.1950988769531,8562634.0,47438.101275489884 +216,Azerbaijan,1990,63052952.43,0.2843,75560000.0,11.992000000000003,22.32567596435547,7497290.0,2977.8327854938875 +217,Azerbaijan,1991,60800253.65,0.2711,84610000.0,11.956000000000001,22.169397354125977,7601153.0,2916.583491231656 +218,Azerbaijan,1992,56175802.12,0.2531,60830000.0,10.721333333333334,17.159112930297848,7718036.0,2223.24862572523 +219,Azerbaijan,1993,48408341.95,0.2185,69050000.0,10.526083333333334,13.195358276367188,7839115.0,1683.2714249462072 +220,Azerbaijan,1994,41946559.46,0.1861,62500000.0,11.472916666666668,10.59587287902832,7952142.0,1332.45518993855 +221,Azerbaijan,1995,33311115.77,0.1448,60460000.0,12.731583333333333,9.345559120178224,8050585.0,1160.8546608946085 +222,Azerbaijan,1996,31241610.46,0.1328,55820000.0,12.202250000000001,9.46705150604248,8138330.0,1163.2670960802134 +223,Azerbaijan,1997,29803859.9,0.1246,55910000.0,11.894416666666666,10.016140937805176,8223513.0,1217.9880955748688 +224,Azerbaijan,1998,31665041.51,0.132,56760000.0,12.95875,11.017754554748537,8308674.0,1326.054500964719 +225,Azerbaijan,1999,28570228.23,0.1194,57060000.0,12.851750000000003,11.833067893981935,8388447.0,1410.6386908067648 +226,Azerbaijan,2000,29506320.0,0.1201,59130000.0,12.632,13.146538734436035,8463076.0,1553.3995836071936 +227,Azerbaijan,2001,28792679.66,0.1145,59660000.0,13.09725,14.448046684265135,8532310.0,1693.3335385452633 +228,Azerbaijan,2002,29641849.5,0.1163,60600000.0,12.432250000000002,15.97953987121582,8597653.0,1858.593254602834 +229,Azerbaijan,2003,30626721.45,0.114,58280000.0,11.984749999999998,17.769248962402344,8662912.0,2051.186594346375 +230,Azerbaijan,2004,32078155.38,0.1138,59580000.0,12.6195,19.581710815429688,8737809.0,2241.0321415162184 +231,Azerbaijan,2005,34286420.0,0.1172,63560000.0,12.687249999999999,24.751283645629886,8825439.0,2804.5385216112068 +232,Azerbaijan,2006,39108540.0,0.1291,60100000.0,12.71225,33.290477752685554,8919169.0,3732.464061695159 +233,Azerbaijan,2007,30458270.0,0.0986,58760000.0,12.51483333333333,41.6294059753418,9015038.0,4617.773765938846 +234,Azerbaijan,2008,35450600.0,0.1112,66319999.99999999,12.365499999999999,46.1139030456543,9111089.0,5061.294324493406 +235,Azerbaijan,2009,31856630.0,0.1011,67230000.0,12.611666666666665,50.45352172851562,9206777.0,5480.0416832639285 +236,Azerbaijan,2010,30632888.0,0.0926,71510000.0,13.962666666666665,52.90270614624024,9301673.0,5687.439898848329 +237,Azerbaijan,2011,33396618.0,0.0972,74010000.0,11.808666666666666,52.937583923339844,9397279.0,5633.288521426239 +238,Azerbaijan,2012,35496946.0,0.1017,75300000.0,13.08675,54.102210998535156,9493600.0,5698.808776284565 +239,Azerbaijan,2013,35579960.99,0.1011,74980000.0,14.173875,57.23834228515625,9590159.0,5968.445599823344 +240,Bahamas,1990,1949248.0,0.0088,810000.0,25.992333333333335,7.606466293334961,245126.0,31030.84247829672 +241,Bahamas,1991,1780704.0,0.0079,640000.0,25.92675,7.288380146026611,249308.0,29234.4415182289 +242,Bahamas,1992,1791696.0,0.0081,650000.0,25.465499999999995,7.0095577239990225,253558.0,27644.790241282168 +243,Bahamas,1993,1700096.0,0.0077,560000.0,25.54983333333333,7.03113317489624,257656.0,27288.839285311584 +244,Bahamas,1994,1685440.0,0.0075,550000.0,26.0245,7.252537727355957,261513.0,27732.99119873948 +245,Bahamas,1995,1681776.0,0.0073,550000.0,25.76866666666667,7.570108413696289,265181.0,28546.948739526168 +246,Bahamas,1996,1667120.0,0.0071,540000.0,25.38541666666667,7.889834403991699,268881.0,29343.220249819435 +247,Bahamas,1997,1363008.0,0.0057,240000.0,26.190833333333334,8.052541732788086,272561.0,29543.998344547035 +248,Bahamas,1998,1678112.0,0.006999999999999999,580000.0,26.2295,8.432338714599611,275992.0,30552.83745398276 +249,Bahamas,1999,1674448.0,0.006999999999999999,570000.0,25.622666666666664,9.034754753112793,279305.0,32347.27181079033 +250,Bahamas,2000,1667120.0,0.0068,590000.0,25.44916666666667,9.409628868103027,282591.0,33297.694788945955 +251,Bahamas,2001,1568192.0,0.0062,890000.0,25.580666666666673,9.656696319580078,285685.0,33801.90181346615 +252,Bahamas,2002,1575520.0,0.0062,950000.0,26.116250000000004,9.917867660522461,288579.0,34367.94659529093 +253,Bahamas,2003,1516896.0,0.0056,830000.0,26.180333333333333,9.792433738708496,291347.0,33610.89607481284 +254,Bahamas,2004,1722080.0,0.0061,1250000.0,25.84975,9.878884315490724,294064.0,33594.33427924099 +255,Bahamas,2005,1740400.0,0.0059,1190000.0,25.801666666666673,10.21430778503418,296708.0,34425.45460531627 +256,Bahamas,2006,1663456.0,0.0055,6940000.0,25.715750000000003,10.471386909484863,299288.0,34987.660412328136 +257,Bahamas,2007,1689104.0,0.0055,6950000.0,26.034750000000003,10.622859954833984,301972.0,35178.2945267574 +258,Bahamas,2008,1494912.0,0.0047,6860000.0,25.899749999999997,10.375988006591797,304727.0,34050.11044834162 +259,Bahamas,2009,1641472.0,0.0052,6950000.0,25.769166666666667,9.942763328552246,307552.0,32328.722715352997 +260,Bahamas,2010,1652464.0,0.005,6930000.0,25.22916666666667,10.095760345458984,310426.0,32522.276953151424 +261,Bahamas,2011,1864976.0,0.0054,2770000.0,26.003249999999994,10.157636642456056,313312.0,32420.19661696984 +262,Bahamas,2012,1967568.0,0.0056,2710000.0,25.816416666666665,10.471170425415039,316182.0,33117.5412433821 +263,Bahamas,2013,2799296.0,0.008,3750000.0,26.01166666666667,10.428194046020508,319031.0,32687.086979072592 +264,Bahrain,1990,12405381.15,0.0559,14080000.0,26.344583333333336,8.894396781921387,505672.0,17589.260987203932 +265,Bahrain,1991,11855329.08,0.0529,13770000.0,25.811916666666672,9.893237113952637,522518.0,18933.772834529405 +266,Bahrain,1992,10873843.51,0.049,15800000.0,25.177666666666667,10.555094718933104,538020.0,19618.405856535268 +267,Bahrain,1993,14609781.84,0.0659,16690000.000000002,26.165916666666664,11.913536071777346,552642.0,21557.420666140726 +268,Bahrain,1994,14755641.13,0.0655,17140000.0,26.733750000000004,11.88375186920166,567501.0,20940.49502855794 +269,Bahrain,1995,14787087.46,0.0643,18000000.0,26.257416666666668,12.35078239440918,582068.0,21218.79641967808 +270,Bahrain,1996,15589624.52,0.0663,19350000.0,26.827499999999997,12.858400344848633,596466.0,21557.64175132972 +271,Bahrain,1997,17287730.73,0.0723,20280000.0,26.190916666666666,13.256110191345215,610845.0,21701.26659192629 +272,Bahrain,1998,18365542.27,0.0766,22150000.0,27.61241666666667,13.891077995300293,625214.0,22218.117309113833 +273,Bahrain,1999,17988113.31,0.0752,22860000.0,27.74241666666667,14.488394737243652,639964.0,22639.39024264436 +274,Bahrain,2000,18617914.65,0.0758,23980000.0,27.07075,15.256278038024902,654861.0,23296.971476427672 +275,Bahrain,2001,13905337.45,0.0553,24890000.0,27.431,15.636317253112793,672086.0,23265.351834605684 +276,Bahrain,2002,15677178.19,0.0615,26020000.0,27.266416666666668,16.20143699645996,714831.0,22664.709555769066 +277,Bahrain,2003,16437202.57,0.0612,27060000.0,27.370666666666665,17.177404403686527,781004.0,21994.003108417528 +278,Bahrain,2004,17439106.33,0.0619,27380000.0,27.15025,18.376548767089844,848123.0,21667.3156689417 +279,Bahrain,2005,19132508.94,0.0654,30490000.0,26.983166666666666,19.620445251464844,915966.0,21420.495140065068 +280,Bahrain,2006,18756920.19,0.0619,32930000.0,27.22125,20.889606475830078,984393.0,21220.799493525534 +281,Bahrain,2007,26770051.89,0.0867,34060000.0,27.216083333333334,22.621868133544922,1053582.0,21471.38821045246 +282,Bahrain,2008,29682446.87,0.0931,36410000.0,27.033083333333334,24.034631729125977,1107292.0,21705.775648271618 +283,Bahrain,2009,28097529.42,0.0891,37030000.0,27.310333333333336,24.645055770874023,1144365.0,21536.00972668163 +284,Bahrain,2010,29074829.99,0.0879,38960000.0,28.069666666666667,25.713274002075195,1180080.0,21789.43292156057 +285,Bahrain,2011,28438830.2,0.0828,39680000.0,27.10383333333333,26.22337913513184,1214705.0,21588.269691103465 +286,Bahrain,2012,26448224.54,0.0757,40880000.0,27.52933333333333,27.20210075378418,1248348.0,21790.47889994151 +287,Bahrain,2013,31083889.86,0.0883,43760000.0,27.925375000000003,28.67550468444824,1281332.0,22379.44942017232 +288,Bangladesh,1990,15489962.47,0.0698,116290000.0,25.28175,42.13683319091797,106098971.0,397.1464830786903 +289,Bangladesh,1991,15902559.19,0.0709,116540000.0,25.135166666666667,43.60540008544922,108307424.0,402.60767429432366 +290,Bangladesh,1992,17708789.25,0.0798,118340000.0,25.122249999999998,45.97870254516602,110604795.0,415.70261529046735 +291,Bangladesh,1993,17367915.32,0.0784,119610000.0,25.00316666666667,48.14501953125,112886613.0,426.49007045016054 +292,Bangladesh,1994,18927975.9,0.084,122080000.0,25.26166666666666,50.017921447753906,115142278.0,434.40100644659736 +293,Bangladesh,1995,22771386.67,0.099,127090000.0,25.415833333333335,52.5794792175293,117374090.0,447.96495732175043 +294,Bangladesh,1996,23949642.08,0.1018,127080000.0,25.440250000000006,54.95761108398438,119605451.0,459.4908561816666 +295,Bangladesh,1997,24948031.93,0.1043,129320000.0,24.85925,57.425148010253906,121880765.0,471.158414621486 +296,Bangladesh,1998,23911114.73,0.0997,129470000.0,25.52416666666667,60.39806365966797,124225950.0,486.19522458607054 +297,Bangladesh,1999,25017429.09,0.1045,133780000.0,25.71483333333333,63.21874618530274,126541747.0,499.58806231197934 +298,Bangladesh,2000,27504891.51,0.11199999999999999,136200000.0,25.150166666666667,66.56510162353516,128734672.0,517.072056730258 +299,Bangladesh,2001,31943866.38,0.1271,140360000.0,25.337,69.94480133056639,130845545.0,534.5600519342589 +300,Bangladesh,2002,33185370.29,0.1302,143710000.0,25.38033333333334,72.6258773803711,132901054.0,546.4657743073361 +301,Bangladesh,2003,35140640.25,0.1308,145520000.0,25.280833333333334,76.06803131103516,134884606.0,563.948945449232 +302,Bangladesh,2004,37161862.21,0.1318,148320000.0,25.38633333333333,80.05364227294922,136788684.0,585.2358538148465 +303,Bangladesh,2005,38915897.88,0.133,151840000.0,25.532,85.28589630126953,138605601.0,615.3134915613514 +304,Bangladesh,2006,42955696.65,0.1418,147640000.0,25.76566666666667,90.9760971069336,140353878.0,648.1908330807473 +305,Bangladesh,2007,43766440.57,0.1417,149920000.0,25.34375,97.39773559570312,141950868.0,686.1369498332558 +306,Bangladesh,2008,48307613.72,0.1515,160830000.0,25.3895,103.2550277709961,143398838.0,720.0548429199691 +307,Bangladesh,2009,52292608.83,0.1659,164160000.0,25.904916666666665,108.4643783569336,144954330.0,748.2658735129444 +308,Bangladesh,2010,58281848.69,0.1763,174160000.0,25.94033333333333,114.5077896118164,146615872.0,781.0054126460225 +309,Bangladesh,2011,61597468.63,0.1793,187090000.0,25.205083333333334,121.91001892089844,148248955.0,822.3330742594336 +310,Bangladesh,2012,65602526.31,0.1879,190020000.0,25.533583333333336,129.86029052734378,149867054.0,866.503257796365 +311,Bangladesh,2013,67706514.62,0.1923,194460000.0,25.9675,137.66958618164062,151466689.0,908.9099860210227 +312,Barbados,1990,1060309.5,0.0048,3180000.0,26.94316666666667,3.63541579246521,262264.0,13861.665316113571 +313,Barbados,1991,1192105.13,0.0053,3350000.0,26.893166666666662,3.4931585788726807,263336.0,13265.024830910625 +314,Barbados,1992,966324.24,0.0044,3160000.0,26.904666666666667,3.249171257019043,264604.0,12279.373165254656 +315,Barbados,1993,1109835.97,0.005,3340000.0,27.05683333333334,3.2888708114624023,265699.0,12378.182874088357 +316,Barbados,1994,741884.31,0.0033,3010000.0,26.96008333333333,3.4133460521698,266630.0,12801.80794422908 +317,Barbados,1995,822958.69,0.0036,3100000.0,27.326000000000004,3.482407093048096,267667.0,13010.222003639208 +318,Barbados,1996,842329.84,0.0036,3140000.0,27.01825,3.620528697967529,268810.0,13468.727718342061 +319,Barbados,1997,888898.62,0.0037,3190000.0,27.239333333333338,3.792147159576416,270062.0,14041.765074599223 +320,Barbados,1998,1121214.01,0.0047,3440000.0,27.625083333333336,3.933990955352783,271294.0,14500.8402520984 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+329,Barbados,2007,1334369.73,0.0043,3770000.0,27.36641666666667,4.699037075042725,282359.0,16642.065863113003 +330,Barbados,2008,1589006.38,0.005,4040000.0,27.003833333333336,4.704826831817627,283498.0,16595.62618366841 +331,Barbados,2009,1589309.13,0.005,4040000.0,27.320333333333334,4.51790714263916,284589.0,15875.199472358947 +332,Barbados,2010,1452631.84,0.0044,3950000.0,27.84466666666667,4.529900074005127,285653.0,15858.051811131432 +333,Barbados,2011,1504161.64,0.0044,3930000.0,27.39525,4.559674739837647,286705.0,15903.71545608778 +334,Barbados,2012,1449892.49,0.0042,3930000.0,27.346666666666668,4.5724945068359375,287733.0,15891.449735817363 +335,Barbados,2013,1429527.0,0.0041,3930000.0,27.251125000000002,4.572908401489258,288725.0,15838.283492905906 +336,Belarus,1990,104134684.0,0.4695,126040000.0,7.757416666666667,32.39566421508789,10200432.0,3175.9110021112724 +337,Belarus,1991,97040059.43,0.4326,120890000.0,6.895416666666666,32.006919860839844,10205137.0,3136.353765837719 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+363,Belgium,1993,121557284.5,0.5487,127920000.0,9.925833333333333,337.88201904296875,10085426.0,33502.007653714245 +364,Belgium,1994,124938189.0,0.5544,133380000.0,10.996749999999999,348.7853698730469,10122914.0,34455.03635346965 +365,Belgium,1995,126082964.2,0.5479999999999999,133460000.0,10.696750000000002,357.1030578613281,10155459.0,35163.65512000276 +366,Belgium,1996,129583624.6,0.5509,138900000.0,8.893166666666668,362.7931518554688,10178934.0,35641.5663816534 +367,Belgium,1997,124046824.6,0.5187,135570000.0,10.472416666666666,376.25439453125,10199787.0,36888.456056116665 +368,Belgium,1998,130285112.6,0.5432,138180000.0,10.369166666666667,383.68646240234375,10217030.0,37553.62002483537 +369,Belgium,1999,124745701.4,0.5213,134260000.0,10.93425,397.3583984375,10235655.0,38821.00348609835 +370,Belgium,2000,126794789.3,0.5163,134840000.0,11.03425,411.7970275878906,10263618.0,40122.01424369951 +371,Belgium,2001,126129005.9,0.5017,135090000.0,10.543333333333335,415.1389465332031,10292429.0,40334.399832459676 +372,Belgium,2002,126444626.4,0.496,127830000.0,11.035750000000002,422.5304870605469,10326359.0,40917.66391818712 +373,Belgium,2003,127953184.7,0.4763,131300000.0,10.87025,425.8032836914063,10368851.0,41065.61890911598 +374,Belgium,2004,129040882.7,0.4578,129950000.0,10.452583333333331,441.2798767089844,10413348.0,42376.368936194616 +375,Belgium,2005,125638201.6,0.4295,125900000.0,10.727833333333335,450.5214538574219,10469152.0,43033.232668455086 +376,Belgium,2006,124030888.5,0.4095,123680000.0,11.09525,461.8121643066406,10535515.0,43833.84811341834 +377,Belgium,2007,120611401.8,0.3906,119450000.0,11.1455,477.7399291992188,10610155.0,45026.668243698485 +378,Belgium,2008,120659010.0,0.3785,121290000.0,10.504916666666666,481.4814758300781,10692801.0,45028.564155461056 +379,Belgium,2009,107642367.0,0.3415,114360000.0,10.628499999999999,470.6328430175781,10777618.0,43667.612177159936 +380,Belgium,2010,113582031.4,0.3435,121560000.0,9.361749999999999,483.5480041503906,10865722.0,44502.15127447496 +381,Belgium,2011,104158641.9,0.3032,109550000.0,11.305333333333335,492.2436828613281,10962548.0,44902.30581989954 +382,Belgium,2012,101394975.2,0.2904,108860000.0,10.351833333333333,493.3993835449219,11058133.0,44618.687760847315 +383,Belgium,2013,101970442.0,0.2896,109670000.0,10.060125,494.3894348144531,11148086.0,44347.47227590935 +384,Belize,1990,311440.0,0.0014,13210000.0,25.550333333333338,0.5294730067253113,190865.0,2774.070713464025 +385,Belize,1991,359072.0,0.0016,13370000.0,25.86333333333333,0.5901719927787781,195745.0,3015.004177776077 +386,Belize,1992,355408.0,0.0016,13520000.0,25.647916666666664,0.6613434553146362,200824.0,3293.1495006305827 +387,Belize,1993,377392.0,0.0017,13640000.0,25.603083333333334,0.7028473615646362,206093.0,3410.3407760799073 +388,Belize,1994,373728.0,0.0017,13760000.0,25.908916666666666,0.7039603590965271,211546.0,3327.6940197239705 +389,Belize,1995,377392.0,0.0016,12670000.0,26.00475,0.708495557308197,217167.0,3262.445755147868 +390,Belize,1996,307776.0,0.0013,9750000.0,25.4505,0.718614399433136,222972.0,3222.890764011337 +391,Belize,1997,388384.0,0.0016,8530000.0,26.08825,0.7440869212150574,228954.0,3249.940692082503 +392,Belize,1998,370064.0,0.0015,7790000.0,26.22816666666667,0.7719206213951111,235126.0,3283.00835039558 +393,Belize,1999,348080.0,0.0015,6610000.0,25.366666666666664,0.8396875262260437,241480.0,3477.254953727197 +394,Belize,2000,395712.0,0.0016,5580000.0,25.284999999999997,0.9490112662315368,248004.0,3826.596612278579 +395,Belize,2001,447008.0,0.0018,7130000.0,25.52391666666667,0.9966097474098206,254639.0,3913.814252372263 +396,Belize,2002,432352.0,0.0017,9570000.0,25.787333333333336,1.0476349592208862,261298.0,4009.349322309724 +397,Belize,2003,432352.0,0.0016,10170000.0,25.865333333333336,1.145357966423035,267958.0,4274.393622967162 +398,Belize,2004,392048.0,0.0014,6880000.0,25.744083333333332,1.1985950469970703,274620.0,4364.558469874992 +399,Belize,2005,421360.0,0.0014,9280000.0,25.919,1.2294950485229492,281289.0,4370.931847754264 +400,Belize,2006,443344.0,0.0015,7520000.0,25.7085,1.2858256101608276,287953.0,4465.400986136028 +401,Belize,2007,476320.0,0.0015,9800000.0,25.85875,1.3000415563583374,294610.0,4412.754340851761 +402,Belize,2008,436016.0,0.0014,8359999.999999999,25.524666666666672,1.3420178890228271,301270.0,4454.535430088716 +403,Belize,2009,586240.0,0.0019,7130000.0,25.907749999999997,1.3510202169418335,307899.0,4387.868154628088 +404,Belize,2010,538608.0,0.0016,9290000.0,25.50233333333333,1.396731734275818,314522.0,4440.807747234909 +405,Belize,2011,600896.0,0.0017,7190000.0,25.84275,1.426937460899353,321115.0,4443.6960618449875 +406,Belize,2012,476320.0,0.0014,3310000.0,25.734416666666664,1.4688582420349119,327719.0,4482.066166547903 +407,Belize,2013,512960.0,0.0015,7640000.0,26.21333333333333,1.4813719987869265,334297.0,4431.305093335945 +408,Benin,1990,680683.55,0.0031,19990000.0,27.793833333333335,3.0336263179779053,4705442.0,644.7059209268556 +409,Benin,1991,792129.31,0.0035,20080000.0,27.380916666666664,3.1618213653564453,4855063.0,651.2420879721736 +410,Benin,1992,864943.39,0.0039,20190000.0,27.230583333333332,3.2553389072418213,5010143.0,649.7496992085497 +411,Benin,1993,1086683.45,0.0049,20310000.0,27.597916666666663,3.445326089859009,5250360.0,656.2075914525877 +412,Benin,1994,1222254.77,0.0054,20590000.0,27.335583333333332,3.5149354934692383,5488151.0,640.4589621293652 +413,Benin,1995,1273558.66,0.0055,20440000.0,27.616166666666672,3.727420330047608,5646826.0,660.0912317906746 +414,Benin,1996,1231100.41,0.0052,21920000.0,27.703916666666668,3.888604640960693,5820226.0,668.1191831658588 +415,Benin,1997,1198151.91,0.005,22250000.0,27.542,4.1116042137146005,6006230.0,684.5565710461636 +416,Benin,1998,1194364.25,0.005,23320000.0,28.088583333333336,4.274465560913087,6202318.0,689.1722676768727 +417,Benin,1999,1542320.58,0.0064,23070000.0,27.680416666666662,4.50278377532959,6406766.0,702.8169555950052 +418,Benin,2000,1573997.31,0.0064,23310000.0,27.63766666666666,4.766646385192871,6619120.0,720.1329459494419 +419,Benin,2001,1789772.5,0.0071,18460000.0,27.67583333333334,5.020728588104248,6839006.0,734.1313325509947 +420,Benin,2002,2053179.7,0.0081,19700000.0,27.819500000000005,5.2539143562316895,7064487.0,743.7078384080386 +421,Benin,2003,2327578.89,0.0087,19550000.0,27.964583333333334,5.434861660003662,7293710.0,745.1436456897328 +422,Benin,2004,2481066.09,0.0088,19450000.0,27.872,5.675605773925781,7526831.0,754.049848326046 +423,Benin,2005,2367081.29,0.0081,20920000.0,28.153333333333332,5.7727484703063965,7777709.0,742.2170809304381 +424,Benin,2006,3711342.37,0.0123,21400000.0,28.144333333333336,6.000599384307861,8031708.0,747.1137377389543 +425,Benin,2007,4319630.78,0.013999999999999999,22490000.0,28.0705,6.3598265647888175,8278158.0,768.2659070760451 +426,Benin,2008,4254813.41,0.0133,22040000.0,27.690916666666666,6.670815944671631,8532547.0,781.8082859281794 +427,Benin,2009,4507933.71,0.0143,22850000.0,28.15608333333333,6.826199054718018,8791832.0,776.425101698715 +428,Benin,2010,4938944.44,0.0149,23480000.0,28.325583333333338,6.970245838165282,9056010.0,769.6817735586957 +429,Benin,2011,5121284.06,0.0149,23680000.0,28.06116666666667,7.176756858825684,9325032.0,769.6227593455641 +430,Benin,2012,5305521.07,0.0152,23620000.0,27.751833333333337,7.52242374420166,9598787.0,783.6848285311113 +431,Benin,2013,5652638.86,0.0161,24070000.0,28.160624999999996,8.063264846801758,9877292.0,816.343674642985 +432,Bhutan,1990,128240.0,0.0006,-2930000.0,12.205583333333331,0.4240306317806244,614744.0,689.7678249492868 +433,Bhutan,1991,176118.57,0.0008,-2870000.0,11.994666666666667,0.4223011136054993,625045.0,675.6331361829938 +434,Bhutan,1992,205377.84,0.0009,-2880000.0,11.826833333333335,0.4417307376861572,601655.0,734.1927478142079 +435,Bhutan,1993,173027.29,0.0008,-2910000.0,12.08775,0.4505051374435425,576693.0,781.1871089878713 +436,Bhutan,1994,201608.38,0.0009,-2880000.0,12.299666666666665,0.4728118777275085,574720.0,822.6821369145123 +437,Bhutan,1995,236141.34,0.001,-2790000.0,12.428416666666665,0.5062591433525085,566313.0,893.9564222479593 +438,Bhutan,1996,285310.52,0.0012,-2790000.0,12.374666666666668,0.5344333648681641,565330.0,945.3476108965808 +439,Bhutan,1997,376838.02,0.0016,-2690000.0,11.578833333333334,0.5631529688835144,574838.0,979.6724796960438 +440,Bhutan,1998,367162.83,0.0015,-2680000.0,12.589833333333331,0.5964580178260803,585274.0,1019.1090289780176 +441,Bhutan,1999,370760.92,0.0015,-2620000.0,12.814916666666663,0.6440790295600891,595628.0,1081.344445795176 +442,Bhutan,2000,381687.01,0.0016,-2670000.0,11.998416666666666,0.6887332201004028,605872.0,1136.7635739898903 +443,Bhutan,2001,369013.18,0.0015,-3020000.0,12.425749999999999,0.7452353239059448,615980.0,1209.836884161734 +444,Bhutan,2002,401709.84,0.0016,-3000000.0,12.180333333333337,0.8251830339431763,625943.0,1318.3037975393547 +445,Bhutan,2003,361126.5,0.0013,-3030000.0,12.232583333333336,0.8884278535842896,635757.0,1397.4330657535654 +446,Bhutan,2004,290479.13,0.001,-3070000.0,12.208083333333335,0.9408131837844848,645415.0,1457.6871993747973 +447,Bhutan,2005,378123.09,0.0013,-2890000.0,12.445916666666667,1.0078232288360596,654897.0,1538.903413568942 +448,Bhutan,2006,373005.72,0.0012,-1450000.0,12.854,1.0768526792526243,664205.0,1621.2655418923746 +449,Bhutan,2007,372026.62,0.0012,-1330000.0,12.6995,1.2698873281478882,673353.0,1885.9161957366912 +450,Bhutan,2008,400823.87,0.0013,-1470000.0,12.456333333333333,1.3304400444030762,682321.0,1949.87409797306 +451,Bhutan,2009,367800.95,0.0012,-1460000.0,13.114749999999999,1.4190105199813845,691141.0,2053.1418624873713 +452,Bhutan,2010,464157.57,0.0014,-1260000.0,12.947250000000002,1.585472583770752,699847.0,2265.4559979120463 +453,Bhutan,2011,667463.58,0.0019,-1990000.0,12.400250000000002,1.710580825805664,708427.0,2414.6183386653306 +454,Bhutan,2012,753807.04,0.0022,-1770000.0,12.27975,1.7973365783691406,716896.0,2507.109229747607 +455,Bhutan,2013,853144.38,0.0024,-1820000.0,13.331374999999998,1.8358445167541504,725296.0,2531.1659195061748 +456,Bolivia,1990,5726770.92,0.0258,110130000.0,21.326083333333333,9.312430381774902,6573900.0,1416.5762153021647 +457,Bolivia,1991,5974016.84,0.0266,110850000.0,21.75408333333333,9.802871704101562,6731484.0,1456.2720054153826 +458,Bolivia,1992,6621986.56,0.0298,112650000.0,21.205916666666667,9.964276313781738,6893451.0,1445.4699560179276 +459,Bolivia,1993,7791825.61,0.0352,114350000.0,21.52491666666667,10.389680862426758,7054343.0,1472.8063070404653 +460,Bolivia,1994,8592436.51,0.0381,116410000.0,21.984750000000002,10.874594688415527,7214813.0,1507.2593965242795 +461,Bolivia,1995,9798968.84,0.0426,116030000.0,21.730583333333332,11.38333797454834,7374871.0,1543.5304528782046 +462,Bolivia,1996,9774539.03,0.0416,112440000.0,21.05366666666667,11.879804611206055,7534395.0,1576.7430047410649 +463,Bolivia,1997,10883062.76,0.0455,113360000.0,21.611333333333334,12.468355178833008,7695149.0,1620.2876875851275 +464,Bolivia,1998,10923850.35,0.0455,114500000.0,21.467333333333332,13.09543228149414,7858896.0,1666.3195799377088 +465,Bolivia,1999,10376044.38,0.0434,120000000.0,21.03175,13.15133571624756,8025555.0,1638.682398444414 +466,Bolivia,2000,11004693.64,0.0448,110950000.0,21.077166666666667,13.481146812438965,8195101.0,1645.0250963885574 +467,Bolivia,2001,8560603.66,0.0341,109950000.0,21.234083333333334,13.708142280578613,8367448.0,1638.27038788632 +468,Bolivia,2002,10328100.87,0.0405,118540000.0,21.60575,14.048867225646973,8542488.0,1644.5872941989471 +469,Bolivia,2003,11011167.44,0.040999999999999995,114410000.0,21.26275,14.429780006408693,8719064.0,1654.9689285924146 +470,Bolivia,2004,11018326.24,0.0391,133000000.0,21.571,15.031977653503418,8895965.0,1689.7523375489245 +471,Bolivia,2005,12070483.44,0.0413,128550000.0,21.371750000000002,15.696606636047365,9072944.0,1730.045576832323 +472,Bolivia,2006,14920842.64,0.0493,133330000.0,21.470000000000002,16.449573516845707,9249710.0,1778.3880269592996 +473,Bolivia,2007,12176849.87,0.0394,142360000.0,21.12933333333333,17.200395584106445,9425936.0,1824.7944378262748 +474,Bolivia,2008,13040638.6,0.0409,135580000.0,21.189000000000004,18.257963180541992,9601257.0,1901.6221709867773 +475,Bolivia,2009,13746585.64,0.0436,133100000.0,21.37833333333333,18.87088203430176,9775246.0,1930.4764334628264 +476,Bolivia,2010,14941383.54,0.0452,169750000.0,21.446416666666664,19.649633407592773,9947418.0,1975.3501267959962 +477,Bolivia,2011,15851237.6,0.0461,142710000.0,21.55541666666667,20.672216415405273,10118683.0,2042.975001332216 +478,Bolivia,2012,18485711.54,0.0529,138620000.0,21.60716666666667,21.73110580444336,10290003.0,2111.8658375943487 +479,Bolivia,2013,18571625.04,0.0527,137590000.0,20.640874999999998,23.20795249938965,10461053.0,2218.510172865929 +480,Botswana,1990,2700368.0,0.0122,61180000.0,22.944833333333335,5.3187522888183585,1257071.0,4231.067528260822 +481,Botswana,1991,2634416.0,0.0117,60590000.0,22.059666666666672,5.715462684631348,1294763.0,4414.292565227263 +482,Botswana,1992,2777312.0,0.0125,60890000.0,23.290833333333335,5.8821868896484375,1335381.0,4404.87537987169 +483,Botswana,1993,3195008.0,0.0144,60530000.0,22.68075,5.994895935058594,1378761.0,4348.031265069576 +484,Botswana,1994,3030128.0,0.0134,60870000.0,22.379166666666666,6.212385654449463,1421785.0,4369.426920701416 +485,Botswana,1995,3044784.0,0.0132,61790000.0,23.213833333333337,6.649142265319824,1464188.0,4541.180685349029 +486,Botswana,1996,2755328.0,0.0117,75640000.0,22.19966666666667,7.036774158477782,1505674.0,4673.504462770681 +487,Botswana,1997,2799296.0,0.0117,86610000.0,22.33683333333333,7.622647762298584,1545888.0,4930.918515635404 +488,Botswana,1998,3341568.0,0.0139,95980000.0,23.070583333333335,7.656466960906982,1584400.0,4832.4078268789335 +489,Botswana,1999,3158368.0,0.0132,80520000.0,22.977999999999998,8.396636009216309,1620722.0,5180.799673982527 +490,Botswana,2000,3777584.0,0.0154,68300000.0,21.872333333333334,8.563534736633299,1654761.0,5175.088569668551 +491,Botswana,2001,3847200.0,0.0153,55530000.0,22.3505,8.584993362426758,1687937.0,5086.086366035437 +492,Botswana,2002,3975440.0,0.0156,148320000.0,22.98566666666667,9.106061935424805,1719736.0,5295.034781748364 +493,Botswana,2003,3825216.0,0.0142,89410000.0,23.395166666666668,9.527298927307127,1750323.0,5443.1661626494815 +494,Botswana,2004,3894832.0,0.0138,30240000.0,22.346083333333336,9.785090446472168,1781586.0,5492.348080009704 +495,Botswana,2005,4092688.0,0.013999999999999999,79240000.0,23.36883333333334,10.230962753295898,1813417.0,5641.814736100907 +496,Botswana,2006,4132992.0,0.0136,41570000.0,21.804000000000002,11.08666706085205,1845723.0,6006.679800193231 +497,Botswana,2007,4228256.0,0.0137,83790000.0,22.400499999999997,12.004283905029295,1878406.0,6390.675873602031 +498,Botswana,2008,4503056.0,0.0141,46180000.0,22.311166666666665,12.754003524780273,1911356.0,6672.7514522570755 +499,Botswana,2009,3759264.0,0.0119,28280000.0,22.139750000000003,11.77802848815918,1944488.0,6057.13611406148 +500,Botswana,2010,4653585.98,0.0141,37590000.0,22.730166666666662,12.78665542602539,1977723.0,6465.341924033542 +501,Botswana,2011,4138367.31,0.012,47860000.0,21.955083333333334,13.56003189086914,2011047.0,6742.772242950632 +502,Botswana,2012,4185619.24,0.012,34180000.0,22.40008333333333,14.16429042816162,2044548.0,6927.834625629539 +503,Botswana,2013,5184760.08,0.0147,66340000.0,21.82575,15.771005630493164,2078313.0,7588.368850357558 +504,Brazil,1990,206921120.0,0.9328,1496210000.0,25.27491666666667,1189.58154296875,149409483.0,7961.887820525756 +505,Brazil,1991,217227472.0,0.9685,1523470000.0,25.29216666666667,1207.5672607421875,151944196.0,7947.439208156312 +506,Brazil,1992,218383504.0,0.9839,1533440000.0,25.251333333333335,1201.928955078125,154460821.0,7781.448701985891 +507,Brazil,1993,228333216.0,1.0307,1543620000.0,25.354916666666668,1258.0009765625,156963840.0,8014.590981989864 +508,Brazil,1994,239470720.0,1.0627,1559950000.0,25.454666666666668,1325.1097412109375,159448367.0,8310.588349963706 +509,Brazil,1995,255583376.0,1.1109,1579960000.0,25.55441666666667,1383.63623046875,161911449.0,8545.635524939005 +510,Brazil,1996,281194784.0,1.1954,1575860000.0,25.337833333333332,1414.1805419921875,164369473.0,8603.669015792171 +511,Brazil,1997,296585256.0,1.2401,1606560000.0,25.559416666666667,1462.189697265625,166840211.0,8764.012515337954 +512,Brazil,1998,308305720.0,1.2854,1641470000.0,25.95875,1467.1334228515625,169322084.0,8664.749382907208 +513,Brazil,1999,316282440.0,1.3217,1650530000.0,25.286583333333336,1473.9986572265625,171814744.0,8578.999816375259 +514,Brazil,2000,324226040.0,1.3202,1646960000.0,25.1965,1538.6768798828125,174315386.0,8826.971130837599 +515,Brazil,2001,332987400.0,1.3246,1899730000.0,25.50441666666667,1560.0628662109375,176789957.0,8824.386252952918 +516,Brazil,2002,327434832.0,1.2844,1945010000.0,25.80033333333334,1607.69873046875,179204829.0,8971.291339859765 +517,Brazil,2003,317508448.0,1.1818,1958830000.0,25.621,1626.0399169921875,181553173.0,8956.273746822302 +518,Brazil,2004,333675144.0,1.1838,2005240000.0,25.4965,1719.69921875,183827544.0,9354.959443672924 +519,Brazil,2005,342086472.0,1.1693,2015480000.0,25.806,1774.7662353515625,186020004.0,9540.727863609563 +520,Brazil,2006,341946368.0,1.129,1420950000.0,25.516999999999996,1845.082275390625,188131059.0,9807.430443426276 +521,Brazil,2007,356918752.0,1.1558,1466130000.0,25.668000000000003,1957.076416015625,190167417.0,10291.334061794743 +522,Brazil,2008,380344928.0,1.1932,1454360000.0,25.41033333333333,2056.773681640625,192130270.0,10705.099626626376 +523,Brazil,2009,360090776.0,1.1423,1433190000.0,25.600583333333333,2054.18603515625,194019058.0,10587.547719957747 +524,Brazil,2010,411240192.0,1.2437,1540260000.0,25.812416666666667,2208.829833984375,195834188.0,11279.081842361331 +525,Brazil,2011,430194902.3,1.2521,1307810000.0,25.428916666666662,2296.617919921875,197595498.0,11622.82513097477 +526,Brazil,2012,460063092.5,1.3175,1353140000.0,25.717083333333335,2340.739990234375,199321413.0,11743.545036149102 +527,Brazil,2013,493593511.3,1.4019,1368380000.0,25.348125,2411.074951171875,201009622.0,11994.82356706225 +528,Bulgaria,1990,78672941.86,0.3547,89990000.0,11.597666666666667,36.29263305664063,8894028.0,4080.56204192753 +529,Bulgaria,1991,60058049.46,0.2678,68960000.0,10.180666666666665,33.227588653564446,8772368.0,3787.755900523605 +530,Bulgaria,1992,56839995.02,0.2561,63910000.0,11.021749999999999,30.81114959716797,8658506.0,3558.4833685127633 +531,Bulgaria,1993,57483412.74,0.2595,62340000.0,10.713416666666667,30.355079650878906,8441872.0,3595.7758718538853 +532,Bulgaria,1994,55936140.24,0.2482,58480000.0,12.200416666666664,30.906938552856445,8363157.0,3695.6066414700153 +533,Bulgaria,1995,57536986.81,0.2501,59670000.0,10.809666666666667,31.790935516357425,8284000.0,3837.631037706111 +534,Bulgaria,1996,57952542.89,0.2464,60250000.0,10.4285,32.01496124267578,8204086.0,3902.3190691413747 +535,Bulgaria,1997,55181619.81,0.2307,57140000.0,10.225333333333333,31.66637420654297,8121167.0,3899.23938352 +536,Bulgaria,1998,52526259.83,0.21899999999999997,53350000.0,11.098916666666666,33.676868438720696,8037611.0,4189.910215699752 +537,Bulgaria,1999,46096271.91,0.1926,46680000.0,11.7885,30.845272064208984,7968514.0,3870.893878608858 +538,Bulgaria,2000,45213926.4,0.1841,46380000.0,11.906833333333333,32.31528854370117,7908664.0,4086.061633633844 +539,Bulgaria,2001,48923315.34,0.1946,46640000.0,11.623833333333332,33.533645629882805,7846982.0,4273.444953726516 +540,Bulgaria,2002,46166363.77,0.1811,43610000.0,11.680333333333332,35.52471160888672,7784233.0,4563.675266257667 +541,Bulgaria,2003,50414461.15,0.1876,48420000.0,10.953000000000001,37.356441497802734,7722734.0,4837.204220396913 +542,Bulgaria,2004,49508639.44,0.1756,47140000.0,11.241666666666667,39.76045989990234,7665631.0,5186.847618924306 +543,Bulgaria,2005,50591294.73,0.1729,47220000.0,10.781583333333332,42.59280014038086,7611110.0,5596.135141967579 +544,Bulgaria,2006,51825570.51,0.1711,51140000.0,11.117583333333334,45.52078247070313,7558417.0,6022.528589081963 +545,Bulgaria,2007,55637034.18,0.1802,55520000.0,12.314000000000002,48.86402130126953,7509498.0,6506.962422956838 +546,Bulgaria,2008,53972792.56,0.1693,53010000.0,11.98775,51.80655288696289,7465355.0,6939.596695262701 +547,Bulgaria,2009,45707145.13,0.145,45610000.0,11.894,49.94869613647461,7427946.0,6724.429086651225 +548,Bulgaria,2010,47792803.35,0.1445,47270000.0,11.766333333333334,50.61006164550781,7391079.0,6847.452401132205 +549,Bulgaria,2011,53116467.41,0.1546,53100000.0,11.047833333333331,51.57919692993164,7350504.0,7017.096641255027 +550,Bulgaria,2012,48269491.35,0.1382,47340000.0,12.027583333333332,51.59516906738281,7308613.0,7059.502133630937 +551,Bulgaria,2013,42586002.72,0.121,42310000.0,13.411874999999998,51.85000610351562,7267961.0,7134.051228881886 +552,Burkina Faso,1990,582576.0,0.0026,22860000.0,28.80633333333334,3.0076711177825928,8361039.0,359.72456506692436 +553,Burkina Faso,1991,626544.0,0.0028,23340000.0,28.459416666666666,3.2804665565490723,8642245.0,379.58499863739945 +554,Burkina Faso,1992,630208.0,0.0028,23640000.0,28.132000000000005,3.288100481033325,8934969.0,368.00356901443365 +555,Burkina Faso,1993,626544.0,0.0028,23880000.0,28.621083333333335,3.401914358139038,9237704.0,368.26405762070726 +556,Burkina Faso,1994,644864.0,0.0029,24370000.0,28.039333333333342,3.4466495513916016,9570647.0,360.12712112269963 +557,Burkina Faso,1995,623870.11,0.0027,24720000.0,28.469583333333333,3.6436731815338135,9902779.0,367.94451148852386 +558,Burkina Faso,1996,704459.79,0.003,26180000.0,28.73908333333333,4.045014381408691,10220180.0,395.78699997541054 +559,Burkina Faso,1997,802691.84,0.0034,26720000.0,28.569166666666664,4.3005313873291025,10542567.0,407.920707293499 +560,Burkina Faso,1998,857628.93,0.0036,27510000.0,28.99291666666667,4.614802360534668,10872709.0,424.43905750946413 +561,Burkina Faso,1999,914172.96,0.0038,27420000.0,28.451166666666662,4.956490516662598,11225306.0,441.54613840037837 +562,Burkina Faso,2000,1030896.77,0.0042,28770000.0,28.591416666666664,5.046710968017578,11587750.0,435.5212157681671 +563,Burkina Faso,2001,991685.87,0.0039,25190000.0,28.732166666666668,5.380470275878906,12039362.0,446.9065948742887 +564,Burkina Faso,2002,1001076.2,0.0039,26520000.0,28.978166666666667,5.614682197570801,12584957.0,446.1423426056045 +565,Burkina Faso,2003,1074309.05,0.004,29100000.0,28.8835,6.052763938903809,13065394.0,463.2668512640192 +566,Burkina Faso,2004,1099909.89,0.0039,30020000.0,28.989166666666662,6.323835372924805,13478093.0,469.19362946410934 +567,Burkina Faso,2005,1121846.74,0.0038,30740000.0,29.238333333333326,6.871597290039063,13903886.0,494.2213486243387 +568,Burkina Faso,2006,1356233.82,0.0045,32500000.0,28.920249999999996,7.301289081573486,14343529.0,509.03017531971983 +569,Burkina Faso,2007,1641867.12,0.0053,33500000.0,28.983249999999998,7.714177131652832,14797172.0,521.3278004508451 +570,Burkina Faso,2008,1862771.29,0.0058,34020000.0,28.48958333333334,8.276887893676758,15264735.0,542.2228354227411 +571,Burkina Faso,2009,1866828.6,0.0059,35020000.0,29.082999999999995,8.522050857543945,15746232.0,541.2120726751609 +572,Burkina Faso,2010,1893940.54,0.0057,36190000.0,29.288750000000004,8.979971885681152,16241811.0,552.8922781875218 +573,Burkina Faso,2011,2141238.46,0.0062,34310000.0,29.060666666666663,9.57506275177002,16751455.0,571.5958853586162 +574,Burkina Faso,2012,2550341.84,0.0073,34920000.0,28.63,10.192912101745604,17275115.0,590.0343992931803 +575,Burkina Faso,2013,2992411.8,0.0085,35940000.0,29.3855,10.783346176147461,17812961.0,605.3651706837207 +576,Burundi,1990,208848.0,0.0009,12450000.0,20.409333333333336,1.7839057445526123,5508757.0,323.8309013362928 +577,Burundi,1991,238160.0,0.0011,12540000.0,20.238333333333333,1.8730446100234983,5643309.0,331.90537856840706 +578,Burundi,1992,216176.0,0.001,12580000.0,20.568249999999995,1.8919622898101809,5820913.0,325.0284430999365 +579,Burundi,1993,227168.0,0.001,12610000.0,20.483333333333334,1.7739038467407229,5647937.0,314.079963487681 +580,Burundi,1994,234496.0,0.001,12550000.0,20.538999999999998,1.7059632539749146,5539656.0,307.95472750923784 +581,Burundi,1995,238160.0,0.001,12630000.0,20.824333333333332,1.5708509683609009,5799090.0,270.87887381656446 +582,Burundi,1996,241824.0,0.001,12010000.0,20.395750000000003,1.4451829195022583,5974357.0,241.8976501575414 +583,Burundi,1997,249152.0,0.001,12180000.0,20.784250000000004,1.4222044944763184,6153366.0,231.12626397914872 +584,Burundi,1998,249152.0,0.001,12420000.0,20.8135,1.4897592067718506,6336587.0,235.1043561418553 +585,Burundi,1999,252816.0,0.0011,12100000.0,20.313999999999997,1.474712610244751,6524134.0,226.03959548420545 +586,Burundi,2000,271136.0,0.0011,12240000.0,20.595833333333335,1.4620764255523682,6715979.0,217.70116100011157 +587,Burundi,2001,205184.0,0.0008,9200000.0,20.510333333333335,1.492133855819702,6913694.0,215.82295308697525 +588,Burundi,2002,212512.0,0.0008,10170000.0,20.74983333333333,1.5584819316864014,7119264.0,218.9105407084779 +589,Burundi,2003,161216.0,0.0006,10380000.0,20.93133333333333,1.5394103527069092,7333389.0,209.918000082487 +590,Burundi,2004,197856.0,0.0007,9590000.0,20.960416666666667,1.613820195198059,7556450.0,213.5685666150188 +591,Burundi,2005,153888.0,0.0005,8860000.0,21.442166666666665,1.6283446550369265,7788872.0,209.06039475766534 +592,Burundi,2006,186864.0,0.0006,3140000.0,21.066333333333333,1.7165000438690186,8031787.0,213.7133422324345 +593,Burundi,2007,186864.0,0.0006,2690000.0,20.580916666666667,1.7757527828216553,8286261.0,214.3008508688847 +594,Burundi,2008,190528.0,0.0006,3440000.0,20.567916666666665,1.8620848655700684,8552574.0,217.72215774690383 +595,Burundi,2009,190528.0,0.0006,3810000.0,20.76425,1.9330813884735107,8831095.0,218.8948696026383 +596,Burundi,2010,212512.0,0.0006,3920000.0,21.06875,2.0321357250213623,9120944.0,222.79883803928212 +597,Burundi,2011,237564.4,0.0007,7410000.0,20.545916666666667,2.114083766937256,9422208.0,224.37243658145263 +598,Burundi,2012,273600.65,0.0008,7240000.0,20.75425,2.208090782165528,9735984.0,226.79687868894683 +599,Burundi,2013,278226.9,0.0008,7870000.0,20.86275,2.316821575164795,10060714.0,230.28401117105557 +600,Cambodia,1990,1260416.0,0.0057,47370000.0,27.25933333333333,4.307020187377931,9367516.0,459.7825279805159 +601,Cambodia,1991,1304384.0,0.0058,47180000.0,27.20141666666667,4.633978843688965,9706809.0,477.3946663305073 +602,Cambodia,1992,1348352.0,0.0061,47830000.0,27.070499999999996,4.9591708183288565,10142764.0,488.9368241564979 +603,Cambodia,1993,1384992.0,0.0063,48760000.0,26.90825,5.084968566894531,10624234.0,478.61978255510286 +604,Cambodia,1994,1467051.33,0.0065,47680000.0,27.123083333333337,3.3149602413177486,10974244.0,302.0672987877569 +605,Cambodia,1995,1540285.2,0.0067,49880000.0,27.080416666666668,3.643256425857544,11234143.0,324.3021230776165 +606,Cambodia,1996,1597246.18,0.0068,46960000.0,26.83883333333334,3.8581175804138175,11484578.0,335.9389940504404 +607,Cambodia,1997,1525052.74,0.0064,48250000.0,27.219333333333335,4.012697696685791,11721710.0,342.3304020220421 +608,Cambodia,1998,1935354.83,0.0081,51810000.0,27.956833333333336,4.2005577087402335,11944465.0,351.6739936648678 +609,Cambodia,1999,1894288.0,0.0079,51320000.0,26.834749999999996,4.734254360198975,12154664.0,389.50104751550316 +610,Cambodia,2000,1974896.0,0.008,49700000.0,27.05716666666667,5.241387844085693,12351152.0,424.3642895889949 +611,Cambodia,2001,2249696.0,0.0089,52650000.0,27.245833333333334,5.631693363189697,12539614.0,449.11217866751696 +612,Cambodia,2002,2205728.0,0.0087,54210000.0,27.55541666666667,6.002199172973633,12729204.0,471.5298123098375 +613,Cambodia,2003,2377936.0,0.0089,56570000.0,27.299416666666673,6.512740135192871,12920705.0,504.0545492829432 +614,Cambodia,2004,2443888.0,0.0087,58510000.0,27.17941666666667,7.186192035675049,13109935.0,548.1485633357487 +615,Cambodia,2005,2773648.0,0.0095,59060000.0,27.39,8.138368606567383,13297399.0,612.0271044410551 +616,Cambodia,2006,2997152.0,0.0099,51610000.0,27.413916666666665,9.014959335327148,13498711.0,667.8385317921948 +617,Cambodia,2007,3452469.31,0.0112,53290000.0,27.241833333333336,9.93561840057373,13718693.0,724.2394301391342 +618,Cambodia,2008,3804986.41,0.0119,53290000.0,26.897083333333338,10.600467681884766,13957013.0,759.5083333292564 +619,Cambodia,2009,4540697.38,0.0144,54200000.0,27.252750000000002,10.609658241271973,14206230.0,746.8313719594835 +620,Cambodia,2010,4916927.86,0.0149,54680000.0,27.875249999999998,11.242321014404297,14453680.0,777.8172074104517 +621,Cambodia,2011,5094280.27,0.0148,57230000.0,27.003500000000003,12.037104606628418,14701717.0,818.755020697815 +622,Cambodia,2012,5333521.58,0.0153,56290000.0,27.806250000000002,12.917420387268065,14952665.0,863.8875001391435 +623,Cambodia,2013,5475786.54,0.0156,58650000.0,28.259624999999996,13.867711067199707,15205539.0,912.0170660967498 +624,Cameroon,1990,1678198.38,0.0076,136700000.0,24.889666666666667,15.811250686645508,11983123.0,1319.459934329766 +625,Cameroon,1991,1056703.2,0.0047,136400000.0,24.733833333333333,15.209063529968262,12341588.0,1232.3425097295633 +626,Cameroon,1992,3729914.49,0.0168,136410000.0,24.297333333333338,14.737582206726074,12702616.0,1160.2005607920505 +627,Cameroon,1993,3839449.76,0.0173,140540000.0,24.66225,13.56858730316162,13071446.0,1038.0326172912794 +628,Cameroon,1994,3771070.99,0.0167,197040000.0,24.631666666666664,13.856775283813475,13447059.0,1030.4688396037734 +629,Cameroon,1995,4174456.55,0.0181,197080000.0,24.817999999999998,14.337576866149902,13827639.0,1036.8781587478456 +630,Cameroon,1996,4431492.84,0.0188,191900000.0,24.70075,14.962851524353027,14214250.0,1052.6655661996256 +631,Cameroon,1997,3156406.84,0.0132,192840000.0,24.817333333333334,15.745606422424315,14606562.0,1077.9816922301304 +632,Cameroon,1998,3137096.48,0.0131,196640000.0,25.32308333333333,16.454519271850586,15005259.0,1096.5834892853622 +633,Cameroon,1999,2998091.99,0.0125,195880000.0,24.656166666666667,17.160749435424805,15408304.0,1113.733830499762 +634,Cameroon,2000,3345847.83,0.0136,197560000.0,24.613000000000003,17.77053451538086,15817991.0,1123.4381480796685 +635,Cameroon,2001,3324991.5,0.0132,196880000.0,24.65191666666667,18.54676818847656,16242956.0,1141.8345397522814 +636,Cameroon,2002,3324049.35,0.013000000000000001,195310000.0,24.95025,19.332632064819336,16694951.0,1157.9927407285793 +637,Cameroon,2003,3698490.75,0.0138,203040000.0,25.01233333333333,20.215591430664066,17164362.0,1177.7653856673533 +638,Cameroon,2004,3850062.25,0.0137,199660000.0,25.17083333333333,21.586402893066406,17641434.0,1223.619513757578 +639,Cameroon,2005,3591269.14,0.0123,206730000.0,25.267833333333332,22.02259063720703,18131040.0,1214.6347168836994 +640,Cameroon,2006,3752445.88,0.0124,197700000.0,25.293416666666662,22.78405952453613,18628001.0,1223.1081329948465 +641,Cameroon,2007,5701937.83,0.0185,202370000.0,25.209833333333336,23.900978088378906,19152718.0,1247.9157312491575 +642,Cameroon,2008,5426279.0,0.017,204680000.0,25.121083333333335,24.73483657836914,19704727.0,1255.2742587283315 +643,Cameroon,2009,6594907.04,0.0209,202260000.0,25.40733333333333,25.27867126464844,20262861.0,1247.5371204810829 +644,Cameroon,2010,6662026.91,0.0201,201390000.0,25.46475,26.143835067749023,20822264.0,1255.5712033883071 +645,Cameroon,2011,5630572.59,0.0164,200930000.0,24.87658333333333,27.22338676452637,21381571.0,1273.2173311552442 +646,Cameroon,2012,5975020.83,0.0171,199180000.0,24.877,28.4602165222168,21950850.0,1296.5428000381214 +647,Cameroon,2013,6615013.1,0.0188,209020000.0,25.307625,29.99828338623047,22534532.0,1331.2139513805064 +648,Canada,1990,463879790.7,2.0913,662910000.0,-5.021916666666667,1014.0775756835938,27790600.0,36489.94896416752 +649,Canada,1991,454627504.2,2.0269,657880000.0,-4.426666666666668,992.5213623046876,28117900.0,35298.559362708016 +650,Canada,1992,468864767.5,2.1125,673690000.0,-4.928916666666666,1001.2850952148438,28544892.0,35077.55766652905 +651,Canada,1993,467879575.2,2.112,672180000.0,-4.4014999999999995,1027.87158203125,28953336.0,35500.972393345284 +652,Canada,1994,483058456.9,2.1437,692690000.0,-4.433666666666666,1074.0587158203123,29330810.0,36618.78808734953 +653,Canada,1995,495664515.6,2.1544,708890000.0,-4.314333333333334,1102.81884765625,29690529.0,37143.792475245216 +654,Canada,1996,510556786.4,2.1705,703230000.0,-4.941166666666667,1120.5858154296877,30026324.0,37320.1133588543 +655,Canada,1997,525253071.6,2.1963,700960000.0,-4.2315000000000005,1168.544921875,30305602.0,38558.71009838379 +656,Canada,1998,534236497.5,2.2274,809410000.0,-2.553916666666666,1213.905029296875,30551660.0,39732.86653808255 +657,Canada,1999,550043150.2,2.2985,744270000.0,-3.1863333333333337,1276.58154296875,30820257.0,41420.21083629348 +658,Canada,2000,572530740.0,2.3313,731300000.0,-4.025916666666666,1342.7427978515625,31099561.0,43175.61903370798 +659,Canada,2001,564635320.1,2.2461,903030000.0,-3.1000833333333326,1366.520263671875,31376736.0,43552.02095182479 +660,Canada,2002,570885097.8,2.2393,981570000.0,-4.362833333333334,1407.6527099609375,31640962.0,44488.30316729743 +661,Canada,2003,587596290.5,2.1871,1001570000.0,-3.8595000000000006,1433.0224609375,31889309.0,44937.39456497788 +662,Canada,2004,585832799.3,2.0783,961050000.0,-4.9230833333333335,1477.2449951171875,32134763.0,45970.30932256098 +663,Canada,2005,576801628.7,1.9716,971700000.0,-3.30875,1524.5372314453125,32386378.0,47073.409426806305 +664,Canada,2006,570487642.1,1.8836,882120000.0,-2.4601666666666664,1564.5321044921875,32656679.0,47908.48770912032 +665,Canada,2007,594140241.8,1.9240000000000002,894320000.0,-4.060916666666667,1596.804443359375,32935961.0,48482.09661650301 +666,Canada,2008,574805870.6,1.8033,877950000.0,-4.29525,1612.7781982421875,33212696.0,48559.087110609384 +667,Canada,2009,540518686.7,1.7147,800900000.0,-4.135083333333335,1565.2080078125,33487208.0,46740.47498413424 +668,Canada,2010,555459693.7,1.6798,884340000.0,-1.8879166666666662,1613.471435546875,33759742.0,47792.765582950095 +669,Canada,2011,560775900.5,1.6322,756740000.0,-3.557083333333333,1664.1541748046875,34030589.0,48901.715301039534 +670,Canada,2012,563675200.3,1.6142,791740000.0,-3.176833333333334,1693.2015380859375,34300083.0,49364.356875927595 +671,Canada,2013,568892395.7,1.6158,857340000.0,-1.6406666666666676,1735.1082763671875,34568211.0,50193.7539193795 +672,Cape Verde,1990,95264.0,0.0004,-170000.0,24.745333333333335,,, +673,Cape Verde,1991,98928.0,0.0004,-150000.0,24.63141666666667,,, +674,Cape Verde,1992,106256.0,0.0005,-120000.0,24.682249999999996,,, +675,Cape Verde,1993,109920.0,0.0005,-110000.0,24.44966666666667,,, +676,Cape Verde,1994,113584.0,0.0005,-130000.0,24.458833333333335,,, +677,Cape Verde,1995,120912.0,0.0005,-90000.0,25.167333333333332,,, +678,Cape Verde,1996,142896.0,0.0006,-100000.0,25.230666666666668,,, +679,Cape Verde,1997,146560.0,0.0006,-90000.0,25.306166666666666,,, +680,Cape Verde,1998,157552.0,0.0007,-60000.0,25.373666666666665,,, +681,Cape Verde,1999,183200.0,0.0008,-30000.0,24.627,,, +682,Cape Verde,2000,216176.0,0.0009,0.0,24.71591666666667,,, +683,Cape Verde,2001,234496.0,0.0009,420000.0,25.28558333333334,,, +684,Cape Verde,2002,274800.0,0.0011,460000.0,25.104666666666663,,, +685,Cape Verde,2003,307776.0,0.0011,500000.0,24.825666666666674,,, +686,Cape Verde,2004,329760.0,0.0012,530000.0,24.892916666666665,,, +687,Cape Verde,2005,439680.0,0.0015,640000.0,25.133,,, +688,Cape Verde,2006,472656.0,0.0016,600000.0,25.068416666666668,,, +689,Cape Verde,2007,505632.0,0.0016,640000.0,24.79366666666667,,, +690,Cape Verde,2008,468992.0,0.0015,600000.0,24.96975,,, +691,Cape Verde,2009,523952.0,0.0017,670000.0,24.599083333333336,,, +692,Cape Verde,2010,556928.0,0.0017,710000.0,25.83241666666667,,, +693,Cape Verde,2011,615552.0,0.0018,640000.0,25.308833333333336,,, +694,Cape Verde,2012,505632.0,0.0014,540000.0,25.1035,,, +695,Cape Verde,2013,498304.0,0.0014,540000.0,24.5305,,, +696,Central African Republic,1990,197856.0,0.0009,52320000.0,25.71591666666667,,, +697,Central African Republic,1991,205184.0,0.0009,53090000.0,25.699833333333334,,, +698,Central African Republic,1992,216176.0,0.001,53580000.0,25.092083333333335,,, +699,Central African Republic,1993,223504.0,0.001,54070000.0,25.74875,,, +700,Central African Republic,1994,234496.0,0.001,57020000.0,25.682750000000002,,, +701,Central African Republic,1995,234496.0,0.001,57580000.0,25.631833333333333,,, +702,Central African Republic,1996,234496.0,0.001,58780000.0,25.717250000000007,,, +703,Central African Republic,1997,245488.0,0.001,62470000.0,26.042666666666666,,, +704,Central African Republic,1998,249152.0,0.001,68000000.0,26.204333333333338,,, +705,Central African Republic,1999,263808.0,0.0011,64660000.0,25.46941666666667,,, +706,Central African Republic,2000,267472.0,0.0011,66260000.00000001,25.613333333333333,,, +707,Central African Republic,2001,245488.0,0.001,60880000.0,25.710166666666666,,, +708,Central African Republic,2002,245488.0,0.001,57130000.0,25.819416666666672,,, +709,Central African Republic,2003,234496.0,0.0009,64800000.0,26.01483333333334,,, +710,Central African Republic,2004,234496.0,0.0008,59650000.0,26.14,,, +711,Central African Republic,2005,234496.0,0.0008,61910000.0,26.332166666666666,,, +712,Central African Republic,2006,249152.0,0.0008,60350000.0,26.205083333333334,,, +713,Central African Republic,2007,252816.0,0.0008,61960000.0,26.06783333333333,,, +714,Central African Republic,2008,252816.0,0.0008,64730000.00000001,25.968666666666667,,, +715,Central African Republic,2009,252816.0,0.0008,58070000.0,26.51091666666667,,, +716,Central African Republic,2010,263808.0,0.0008,62100000.0,26.329833333333337,,, +717,Central African Republic,2011,278464.0,0.0008,66459999.99999999,25.63875,,, +718,Central African Republic,2012,293120.0,0.0008,63840000.0,25.639499999999998,,, +719,Central African Republic,2013,296784.0,0.0008,67210000.0,26.210875,,, +720,Chad,1990,142896.0,0.0006,25000000.0,27.619250000000005,3.0804998874664307,5841411.0,527.355443310945 +721,Chad,1991,65952.0,0.0003,25190000.0,27.5535,3.343424797058105,6028221.0,554.6287697577951 +722,Chad,1992,84272.0,0.0004,25450000.0,26.919666666666668,3.610947132110596,6263272.0,576.5272739409363 +723,Chad,1993,91600.0,0.0004,25770000.0,27.612416666666665,3.043673038482666,6379099.0,477.13212139875327 +724,Chad,1994,102592.0,0.0005,25990000.0,27.261083333333335,3.3522067070007324,6496685.0,515.9872622731027 +725,Chad,1995,109920.0,0.0005,26400000.0,27.461833333333328,3.3936583995819087,6769686.0,501.3021873661362 +726,Chad,1996,124576.0,0.0005,29000000.0,27.7835,3.4688100814819336,7051000.0,491.9600172290361 +727,Chad,1997,142896.0,0.0006,30660000.0,27.797416666666667,3.6649155616760254,7261713.0,504.6902241490438 +728,Chad,1998,150224.0,0.0006,34440000.0,27.89583333333334,3.9196851253509517,7479317.0,524.069928490924 +729,Chad,1999,161216.0,0.0007,35390000.0,27.779833333333332,3.8929195404052734,7708743.0,505.0005611038367 +730,Chad,2000,175872.0,0.0007,34720000.0,27.447833333333335,3.8586740493774414,7942675.0,485.81542734374017 +731,Chad,2001,172208.0,0.0007,30420000.0,27.635250000000003,4.308523654937743,8179408.0,526.7525051859186 +732,Chad,2002,168544.0,0.0007,34250000.0,27.772833333333335,4.6743693351745605,8436193.0,554.0851584564933 +733,Chad,2003,381056.0,0.0014,36170000.0,27.878083333333336,5.362514019012451,8745740.0,613.1572650241662 +734,Chad,2004,377392.0,0.0013,37440000.0,28.017416666666673,7.165894031524657,9097655.0,787.6638575022528 +735,Chad,2005,399376.0,0.0014,39530000.0,28.211249999999996,8.407924652099611,9400658.0,894.3974615499902 +736,Chad,2006,406704.0,0.0013,56930000.0,28.048000000000002,8.462430953979492,9648955.0,877.0308239575678 +737,Chad,2007,461664.0,0.0015,57570000.0,27.860166666666668,8.739279747009277,9885661.0,884.0359533883751 +738,Chad,2008,509296.0,0.0016,57150000.0,27.702499999999997,9.006062507629395,10111337.0,890.6895801840442 +739,Chad,2009,490976.0,0.0016,55400000.0,28.324250000000003,9.385910987854004,10329208.0,908.6767337683591 +740,Chad,2010,516624.0,0.0016,56890000.0,28.6965,10.65771198272705,10543464.0,1010.8359058016464 +741,Chad,2011,538608.0,0.0016,57230000.0,27.78483333333334,10.666543960571287,10758945.0,991.4117007356473 +742,Chad,2012,595686.03,0.0017,57970000.0,27.701166666666666,11.614007949829102,10975648.0,1058.161481657311 +743,Chad,2013,680574.12,0.0019,57210000.0,28.356000000000005,12.276006698608398,11193452.0,1096.713212207315 +744,Chile,1990,32946676.07,0.1485,35560000.0,9.83125,78.7610855102539,13007851.0,6054.888352446064 +745,Chile,1991,30942204.83,0.138,34640000.0,9.725416666666666,84.90791320800781,13250594.0,6407.857127613132 +746,Chile,1992,32057897.2,0.1444,36990000.0,9.73775,94.38933563232422,13489083.0,6997.461253098096 +747,Chile,1993,34163019.64,0.1542,38440000.0,9.944583333333332,100.6084442138672,13722358.0,7331.71691147157 +748,Chile,1994,37642356.15,0.16699999999999998,41930000.0,10.054333333333334,105.66925048828124,13952144.0,7573.692651701505 +749,Chile,1995,41090115.77,0.1786,45510000.0,9.784916666666668,115.10900115966795,14172533.0,8121.977995018108 +750,Chile,1996,47628510.91,0.2025,51630000.0,9.758000000000003,122.93976593017578,14383328.0,8547.37971143923 +751,Chile,1997,55450281.02,0.2319,58930000.0,10.336999999999998,132.07159423828122,14591015.0,9051.570040760103 +752,Chile,1998,56807225.79,0.2368,60170000.0,10.292416666666668,137.78314208984378,14794081.0,9313.396492140591 +753,Chile,1999,60907276.77,0.2545,62940000.0,9.829583333333334,137.21534729003906,14990469.0,9153.505957021029 +754,Chile,2000,58113694.29,0.2366,59240000.0,9.564083333333333,144.52471923828125,15174571.0,9524.138721172496 +755,Chile,2001,52576557.22,0.2092,62760000.0,9.819666666666668,149.29843139648438,15347732.0,9727.719469983209 +756,Chile,2002,54490336.19,0.2137,63710000.0,9.742416666666665,153.9370880126953,15514805.0,9921.947972449241 +757,Chile,2003,54884195.52,0.2043,64629999.99999999,10.078416666666667,160.2347412109375,15675163.0,10222.205741078258 +758,Chile,2004,59047507.78,0.2095,70000000.0,10.205833333333334,171.78692626953125,15828678.0,10852.891585104659 +759,Chile,2005,60947142.8,0.2083,70880000.0,9.891833333333333,181.65235900878903,15979150.0,11368.086475738013 +760,Chile,2006,63971578.71,0.2112,71460000.0,10.174000000000001,193.12765502929688,16130386.0,11972.909701559334 +761,Chile,2007,70706929.91,0.22899999999999998,79860000.0,9.489916666666668,202.60118103027344,16283043.0,12442.464288172268 +762,Chile,2008,70818677.29,0.2222,84530000.0,10.1625,209.75205993652344,16441033.0,12757.839482259018 +763,Chile,2009,65919858.48,0.2091,81790000.0,9.994416666666666,206.47103881835938,16601954.0,12436.550469803697 +764,Chile,2010,71329785.86,0.2157,86460000.0,9.712833333333334,218.53756713867188,16759904.0,13039.309004316008 +765,Chile,2011,78083781.19,0.2273,-4720000.0,10.032250000000001,231.8922119140625,16915020.0,13709.24846166676 +766,Chile,2012,79703889.85,0.2283,-2620000.0,10.272583333333335,244.2257080078125,17067369.0,14309.511208658609 +767,Chile,2013,81675970.15,0.23199999999999998,280000.0,9.877375,254.10464477539062,17216945.0,14758.985683894014 +768,China,1990,2420302108.0,10.9112,2901780000.0,7.635999999999998,829.5680541992188,1153163985.0,719.3842896500264 +769,China,1991,2538923968.0,11.3196,3051320000.0,7.097166666666667,906.6687622070312,1168317741.0,776.0463873731686 +770,China,1992,2657112406.0,11.9716,3182670000.0,6.858416666666667,1035.562255859375,1182230625.0,875.9392913369801 +771,China,1993,2835795473.0,12.8009,3405730000.0,6.919416666666668,1179.169677734375,1195448924.0,986.3823155144477 +772,China,1994,3010242208.0,13.3588,3556720000.0,7.4959999999999996,1333.0767822265625,1208483036.0,1103.0992926793244 +773,China,1995,3265056964.0,14.1913,3946200000.0,7.2115833333333335,1479.038330078125,1221055333.0,1211.2787112147396 +774,China,1996,3408346591.0,14.4894,3980480000.0,6.962666666666667,1625.8826904296875,1232555700.0,1319.1149823327964 +775,China,1997,3414549286.0,14.2777,3989450000.0,7.367333333333332,1775.9642333984375,1242796398.0,1429.0065824590827 +776,China,1998,3265902621.0,13.6164,4128970000.000001,8.051833333333333,1915.157470703125,1252170542.0,1529.4701531982894 +777,China,1999,3258134887.0,13.615,4079240000.0,7.84025,2062.001953125,1260658633.0,1635.6544897630506 +778,China,2000,3349294776.0,13.6379,4276120000.0,7.238083333333333,2237.096923828125,1268301605.0,1763.8524740557473 +779,China,2001,3426144062.0,13.6293,4452290000.0,7.596416666666667,2423.6689453125,1275373334.0,1900.3603734688872 +780,China,2002,3782439297.0,14.8365,4762980000.0,7.7956666666666665,2644.965576171875,1282138369.0,2062.9330188712847 +781,China,2003,4452310296.0,16.5718,5385870000.0,7.522000000000001,2910.40380859375,1288741095.0,2258.3308780059892 +782,China,2004,5125894416.0,18.1849,6132260000.0,7.71275,3204.68115234375,1295385090.0,2473.9215983594117 +783,China,2005,5771168440.0,19.7272,6856610000.0,7.4135,3569.87939453125,1302284553.0,2741.2437522256705 +784,China,2006,6377748016.0,21.0578,7483960000.0,7.948,4023.949462890625,1309395209.0,3073.135929650881 +785,China,2007,6861750652.0,22.2206,8102460000.0,8.12675,4596.61328125,1316489520.0,3491.5684564279704 +786,China,2008,7375189907.0,23.1373,8396809999.999998,7.566083333333335,5040.3837890625,1323480266.0,3808.4313899868152 +787,China,2009,7758811768.0,24.6131,8989400000.0,7.8095,5514.17041015625,1330233426.0,4145.265261253667 +788,China,2010,8500542695.0,25.7073,9788640000.0,7.548416666666665,6100.66552734375,1336680939.0,4564.040190404593 +789,China,2011,9388199234.0,27.3251,10781300000.0,7.357083333333333,6682.4521484375,1342946472.0,4975.96314355372 +790,China,2012,9633899303.0,27.589000000000002,11106420000.0,7.077083333333332,7207.44287109375,1349160868.0,5342.167151481413 +791,China,2013,9796527160.0,27.8248,11570950000.0,9.295,7766.56982421875,1355286257.0,5730.57520808222 +792,Colombia,1990,56916708.07,0.2566,273460000.0,25.219166666666666,147.72787475585938,33147250.0,4456.715858958417 +793,Colombia,1991,56655254.46,0.2526,275190000.0,25.34183333333333,150.684814453125,33832213.0,4453.885841080659 +794,Colombia,1992,61500346.84,0.2771,278940000.0,25.533583333333336,156.7799072265625,34520324.0,4541.669632838976 +795,Colombia,1993,63336717.79,0.2859,282870000.0,25.206416666666666,165.22314453125,35205604.0,4693.092171668181 +796,Colombia,1994,66709452.15,0.29600000000000004,284930000.0,25.16241666666667,174.83030700683594,35887620.0,4871.604943622227 +797,Colombia,1995,58620256.85,0.2548,287910000.0,25.492833333333333,183.92575073242188,36531834.0,5034.670603518615 +798,Colombia,1996,59458523.67,0.2528,285040000.0,25.14991666666667,187.70700073242188,37097908.0,5059.773201562251 +799,Colombia,1997,63771230.47,0.2667,289590000.0,25.580000000000002,194.1459045410156,37619943.0,5160.717668844305 +800,Colombia,1998,64613723.26,0.2694,291570000.0,25.879166666666663,195.2521209716797,38132591.0,5120.34760427582 +801,Colombia,1999,55027452.96,0.2299,282010000.0,24.87066666666667,187.043701171875,38563857.0,4850.233242278515 +802,Colombia,2000,56215715.58,0.2289,286060000.0,25.03016666666667,192.51446533203125,38910346.0,4947.642082957353 +803,Colombia,2001,55251474.9,0.2198,309040000.0,25.411916666666666,195.7446746826172,39312450.0,4979.203145126218 +804,Colombia,2002,54853226.55,0.2152,308210000.0,25.57075,200.6460723876953,39804949.0,5040.731804170766 +805,Colombia,2003,56728246.39,0.2111,317930000.0,25.65533333333333,208.5079345703125,40351016.0,5167.352776701149 +806,Colombia,2004,54551411.78,0.1935,319580000.0,25.55391666666667,219.62771606445312,40922152.0,5366.963987242243 +807,Colombia,2005,60250755.21,0.20600000000000002,316150000.0,25.670833333333334,229.96463012695312,41487778.0,5542.9488204201525 +808,Colombia,2006,62197352.72,0.2054,310830000.0,25.476583333333338,245.5548553466797,42046246.0,5840.1136535870455 +809,Colombia,2007,61571506.14,0.1994,318810000.0,25.494,262.3720703125,42597321.0,6159.356132102767 +810,Colombia,2008,67226517.13,0.2109,317540000.0,25.153916666666664,270.91766357421875,43141109.0,6279.802950225938 +811,Colombia,2009,71851614.45,0.2279,321640000.0,25.61833333333334,274.183349609375,43677372.0,6277.469020099813 +812,Colombia,2010,75300420.0,0.2277,325720000.0,25.68766666666667,286.1036071777344,44205293.0,6472.157240937966 +813,Colombia,2011,75743096.52,0.2205,183900000.0,25.246916666666667,307.1680603027344,44725543.0,6867.844182523047 +814,Colombia,2012,79350288.0,0.2272,182280000.0,25.381333333333334,319.1570129394531,45239079.0,7054.896341710518 +815,Colombia,2013,89000052.05,0.2528,192090000.0,25.579500000000003,333.7325134277344,45745783.0,7295.372196989925 +816,Comoros,1990,62288.0,0.0003,330000.0,26.15325,,, +817,Comoros,1991,65952.0,0.0003,340000.0,26.19683333333333,,, +818,Comoros,1992,65952.0,0.0003,340000.0,26.21783333333333,,, +819,Comoros,1993,69616.0,0.0003,350000.0,26.17816666666667,,, +820,Comoros,1994,69616.0,0.0003,350000.0,26.4525,,, +821,Comoros,1995,73280.0,0.0003,360000.0,26.385083333333338,,, +822,Comoros,1996,76944.0,0.0003,360000.0,25.953333333333333,,, +823,Comoros,1997,80608.0,0.0003,370000.0,26.30516666666667,,, +824,Comoros,1998,87936.0,0.0004,390000.0,26.64725,,, +825,Comoros,1999,91600.0,0.0004,390000.0,26.288083333333333,,, +826,Comoros,2000,102592.0,0.0004,380000.0,25.995499999999996,,, +827,Comoros,2001,102592.0,0.0004,420000.0,26.41425,,, +828,Comoros,2002,102592.0,0.0004,430000.0,26.657750000000004,,, +829,Comoros,2003,131904.0,0.0005,450000.0,26.6005,,, +830,Comoros,2004,142896.0,0.0005,460000.0,26.39191666666667,,, +831,Comoros,2005,139232.0,0.0005,530000.0,26.68216666666667,,, +832,Comoros,2006,161216.0,0.0005,500000.0,26.465833333333332,,, +833,Comoros,2007,102592.0,0.0003,420000.0,26.604583333333334,,, +834,Comoros,2008,106256.0,0.0003,450000.0,26.188250000000007,,, +835,Comoros,2009,131904.0,0.0004,470000.0,26.59325,,, +836,Comoros,2010,161216.0,0.0005,520000.0,26.78683333333333,,, +837,Comoros,2011,135568.0,0.0004,500000.0,26.454000000000004,,, +838,Comoros,2012,142896.0,0.0004,520000.0,26.383249999999993,,, +839,Comoros,2013,175872.0,0.0005,550000.0,26.542875,,, +840,Congo,1990,1179389.14,0.0053,38040000.0,24.766499999999997,,, +841,Congo,1991,994833.87,0.0044,40320000.0,24.8215,,, +842,Congo,1992,1268260.28,0.0057,40200000.0,24.549833333333336,,, +843,Congo,1993,1308491.0,0.0059,40180000.0,24.679833333333335,,, +844,Congo,1994,2139137.29,0.0095,39970000.0,24.842916666666667,,, +845,Congo,1995,1555837.86,0.0068,40100000.0,25.09691666666667,,, +846,Congo,1996,1710040.63,0.0073,20120000.0,24.668000000000003,,, +847,Congo,1997,2339263.1,0.0098,20950000.0,25.0605,,, +848,Congo,1998,776768.0,0.0032,20660000.0,25.540499999999998,,, +849,Congo,1999,820736.0,0.0034,20610000.0,24.8125,,, +850,Congo,2000,1045829.88,0.0043,19770000.0,24.786,,, +851,Congo,2001,765776.0,0.003,26530000.0,24.732916666666668,,, +852,Congo,2002,575248.0,0.0023,22970000.0,25.047666666666668,,, +853,Congo,2003,916000.0,0.0034,35800000.0,25.056749999999997,,, +854,Congo,2004,948976.0,0.0034,40170000.0,25.12816666666666,,, +855,Congo,2005,971447.59,0.0033,51250000.0,25.26891666666667,,, +856,Congo,2006,1106634.37,0.0037,47720000.0,25.282666666666668,,, +857,Congo,2007,1201342.93,0.0039,43360000.0,25.22975,,, +858,Congo,2008,1296344.72,0.0041,37900000.0,25.29241666666667,,, +859,Congo,2009,1731577.1,0.0055,35640000.0,25.34925,,, +860,Congo,2010,1969443.82,0.006,43120000.0,25.559416666666674,,, +861,Congo,2011,2248504.81,0.0065,51940000.0,24.77508333333334,,, +862,Congo,2012,2950785.3,0.0085,49220000.0,24.873500000000003,,, +863,Congo,2013,3057817.33,0.0087,50940000.0,25.139375,,, +864,Costa Rica,1990,2911985.11,0.0131,15280000.0,26.29025,15.23719310760498,3023272.0,5039.967660073252 +865,Costa Rica,1991,3287264.73,0.0147,15590000.0,26.28991666666667,15.582415580749512,3093015.0,5037.9372815034885 +866,Costa Rica,1992,3741219.45,0.0169,16700000.0,26.361750000000004,17.01546287536621,3168359.0,5370.433992917536 +867,Costa Rica,1993,3890999.09,0.0176,16700000.0,26.319333333333333,18.221437454223636,3256528.0,5595.357219168278 +868,Costa Rica,1994,5200333.82,0.0231,17050000.0,26.425833333333333,19.039081573486328,3351198.0,5681.276240164361 +869,Costa Rica,1995,4801885.39,0.0209,16629999.999999998,26.292833333333334,19.82377815246582,3444736.0,5754.803315106244 +870,Costa Rica,1996,4686736.51,0.0199,16550000.0,25.962499999999995,20.06916046142578,3536004.0,5675.661130877053 +871,Costa Rica,1997,4916664.62,0.0206,16620000.000000002,26.806250000000002,21.185998916625977,3625320.0,5843.897619141476 +872,Costa Rica,1998,5235459.46,0.0218,17410000.0,26.97408333333333,22.660903930664066,3713735.0,6101.917323304991 +873,Costa Rica,1999,5438996.29,0.0227,17220000.0,25.86441666666666,23.55450439453125,3800169.0,6198.278127770436 +874,Costa Rica,2000,5394476.45,0.022000000000000002,17040000.0,26.072166666666664,24.457386016845703,3882581.0,6299.259697826189 +875,Costa Rica,2001,5665918.37,0.0225,3560000.0,26.114333333333335,25.31106376647949,3956208.0,6397.809156262636 +876,Costa Rica,2002,6226436.24,0.0244,3540000.0,26.306749999999997,26.14424133300781,4020945.0,6502.014161598284 +877,Costa Rica,2003,6528540.71,0.0243,4960000.0,26.577500000000004,27.256805419921875,4083816.0,6674.347086137543 +878,Costa Rica,2004,6798004.42,0.0241,3910000.0,26.528499999999998,28.43881607055664,4146696.0,6858.1868722849795 +879,Costa Rica,2005,6687160.05,0.0229,4460000.0,26.40425,29.53964614868164,4208691.0,7018.725334951329 +880,Costa Rica,2006,6965280.63,0.023,4830000.0,26.514916666666664,31.67764663696289,4269766.0,7419.05917958101 +881,Costa Rica,2007,7891665.56,0.0256,6240000.0,26.38291666666667,34.26504135131836,4331130.0,7911.339846949493 +882,Costa Rica,2008,7910460.14,0.0248,6380000.0,26.056083333333337,35.85823059082031,4393241.0,8162.13601548841 +883,Costa Rica,2009,7679255.64,0.0244,5970000.0,26.522916666666664,35.51021194458008,4455046.0,7970.784576540866 +884,Costa Rica,2010,7428009.93,0.0225,6190000.0,26.22675,37.26862716674805,4516220.0,8252.172650302255 +885,Costa Rica,2011,7584437.06,0.0221,2930000.0,26.11341666666667,38.87382507324219,4576562.0,8494.110879136388 +886,Costa Rica,2012,7606994.24,0.0218,2970000.0,26.282,40.73857116699219,4636348.0,8786.78027770827 +887,Costa Rica,2013,7431224.07,0.0211,3620000.0,26.457555555555555,41.662940979003906,4695942.0,8872.115749939821 +888,Croatia,1990,23441965.06,0.1057,17070000.0,12.434416666666666,60.34286117553711,4508347.0,13384.697578854755 +889,Croatia,1991,17304185.2,0.0771,10560000.0,11.333249999999998,47.61051559448242,4540641.0,10485.417277975164 +890,Croatia,1992,16632547.36,0.0749,11590000.0,12.54908333333333,42.04008483886719,4490682.0,9361.625881963406 +891,Croatia,1993,17103715.58,0.0772,12500000.0,11.93158333333333,38.6768798828125,4478370.0,8636.37436898079 +892,Croatia,1994,16361217.01,0.0726,12600000.0,13.110000000000001,38.8821907043457,4499274.0,8641.881046663462 +893,Croatia,1995,17040096.64,0.0741,13900000.0,11.881749999999998,39.56144714355469,4480936.0,8828.835569969016 +894,Croatia,1996,17629209.16,0.0749,13250000.0,11.125750000000002,41.885162353515625,4445463.0,9422.002242177165 +895,Croatia,1997,18692941.57,0.0782,14730000.0,11.827416666666666,44.66863250732422,4424536.0,10095.664835210791 +896,Croatia,1998,19398242.43,0.0809,15030000.0,12.100916666666668,45.50030899047852,4399628.0,10341.853672737447 +897,Croatia,1999,20241737.06,0.0846,14670000.0,12.316500000000003,45.07465362548828,4387816.0,10272.685460258195 +898,Croatia,2000,19815735.27,0.0807,13760000.0,13.226416666666665,46.77513885498047,4390263.0,10654.290837469298 +899,Croatia,2001,20966698.0,0.0834,14550000.0,12.411,48.39000701904297,4418543.0,10951.575444449216 +900,Croatia,2002,22053970.71,0.0865,15540000.0,12.898916666666665,50.93210983276367,4442298.0,11465.261860587398 +901,Croatia,2003,23386284.52,0.087,16600000.000000002,12.567,53.77472686767578,4440867.0,12109.060430694228 +902,Croatia,2004,23044755.76,0.0818,16350000.000000002,11.919416666666665,55.87976837158203,4441435.0,12581.467109522493 +903,Croatia,2005,23489533.94,0.0803,16219999.999999998,11.376249999999999,58.17618179321289,4441957.0,13096.970950689729 +904,Croatia,2006,23725639.0,0.0783,19020000.0,12.241666666666669,61.008094787597656,4440594.0,13738.723870634798 +905,Croatia,2007,25056225.05,0.0811,20590000.0,13.0665,64.23155212402344,4437353.0,14475.195487945955 +906,Croatia,2008,23785297.02,0.0746,19730000.0,12.977083333333333,65.53960418701172,4434262.0,14780.273287192258 +907,Croatia,2009,21995506.09,0.0698,17180000.0,12.875083333333334,60.76082992553711,4428935.0,13719.061111878389 +908,Croatia,2010,21245083.26,0.0642,16540000.0,11.960833333333333,59.86587142944336,4417475.0,13552.056645355857 +909,Croatia,2011,20801673.51,0.0605,22130000.0,12.755166666666668,59.66221237182617,4403676.0,13548.274753144004 +910,Croatia,2012,19242973.03,0.0551,20100000.0,12.973500000000001,58.28959655761719,4389754.0,13278.55651082434 +911,Croatia,2013,18588178.03,0.0528,19000000.0,13.589,58.00296020507813,4375160.0,13257.334635779749 +912,Cuba,1990,33116643.11,0.1493,19780000.0,26.133416666666665,44.71926879882813,10596559.0,4220.168905663445 +913,Cuba,1991,29551230.4,0.1318,10970000.0,26.165833333333335,39.93757247924805,10696589.0,3733.67364860406 +914,Cuba,1992,31132986.72,0.1403,4960000.0,25.859583333333333,35.31256103515625,10780309.0,3275.653882941226 +915,Cuba,1993,29284109.99,0.1322,1170000.0,25.874416666666665,30.058696746826172,10849789.0,2770.440673715053 +916,Cuba,1994,32146757.99,0.1427,2480000.0,26.26466666666666,30.27419662475586,10892869.0,2779.267484512653 +917,Cuba,1995,25586197.88,0.1112,4250000.0,26.127499999999998,31.01809501647949,10920889.0,2840.253665839795 +918,Cuba,1996,26872488.26,0.1142,7330000.0,25.80133333333333,33.44952774047852,10962788.0,3051.1880500177977 +919,Cuba,1997,24509102.62,0.1025,9780000.0,26.63825,34.380569458007805,11013894.0,3121.563495890537 +920,Cuba,1998,24357424.91,0.1016,9300000.0,26.407083333333333,34.43528366088867,11064251.0,3112.3013804448824 +921,Cuba,1999,25182951.92,0.1052,9600000.0,25.7975,36.566402435302734,11107797.0,3291.9581115231704 +922,Cuba,2000,25944239.19,0.1056,9880000.0,25.784999999999997,38.72921752929688,11146699.0,3474.50106343563 +923,Cuba,2001,25332188.33,0.1008,10990000.0,25.85641666666667,39.9627571105957,11178711.0,3574.8984932695457 +924,Cuba,2002,25965596.34,0.1018,9610000.0,26.313000000000002,40.53215789794922,11210056.0,3615.696290718728 +925,Cuba,2003,25355110.16,0.0944,8850000.0,26.361583333333332,42.069435119628906,11240950.0,3742.5159901635457 +926,Cuba,2004,24865950.73,0.0882,8400000.0,26.26891666666667,44.497066497802734,11260196.0,3951.7133181165527 +927,Cuba,2005,25847646.65,0.0884,8760000.0,26.14408333333333,49.48147583007813,11267709.0,4391.440693940368 +928,Cuba,2006,27228168.52,0.0899,79940000.0,26.154500000000002,55.45184326171875,11266419.0,4921.869430004223 +929,Cuba,2007,26597438.62,0.0861,80960000.0,26.279750000000003,59.47883224487305,11261457.0,5281.628500190786 +930,Cuba,2008,30246046.66,0.0949,80450000.0,26.054750000000002,61.92747116088867,11258324.0,5500.594152458987 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Republic,1996,134648713.8,0.5724,138120000.0,6.850083333333334,144.9069061279297,10311817.0,14052.50947800273 +967,Czech Republic,1997,130849714.1,0.5471,135630000.0,8.139333333333335,144.04519653320312,10300707.0,13984.010663850851 +968,Czech Republic,1998,125125662.6,0.5217,129060000.0,8.696,143.5717010498047,10290949.0,13951.259601986629 +969,Czech Republic,1999,116441581.7,0.4866,120920000.0,8.897250000000001,145.6288604736328,10280436.0,14165.630764457152 +970,Czech Republic,2000,126896914.3,0.5167,131520000.0,9.659,151.84246826171875,10268899.0,14786.635671625434 +971,Czech Republic,2001,126666372.5,0.5039,131520000.0,8.378833333333334,156.2592010498047,10250327.0,15244.313771629402 +972,Czech Republic,2002,123598029.5,0.4848,125870000.0,9.203666666666667,158.84136962890625,10235948.0,15517.993021155076 +973,Czech Republic,2003,127048374.1,0.4729,130250000.0,8.871333333333334,164.5644073486328,10238458.0,16073.163297503666 +974,Czech Republic,2004,127759325.4,0.4532,130770000.0,8.445916666666667,172.63885498046875,10247111.0,16847.56366750285 +975,Czech Republic,2005,125294530.7,0.4283,127750000.0,8.327916666666667,183.9181671142578,10266923.0,17913.659926567852 +976,Czech Republic,2006,126380290.9,0.4173,134240000.0,8.865333333333334,196.5230407714844,10300228.0,19079.48452903027 +977,Czech Republic,2007,128180733.9,0.4151,137190000.0,9.601333333333331,207.53353881835938,10365253.0,20022.042763293797 +978,Czech Republic,2008,122933867.9,0.3857,132200000.0,9.41475,213.1001739501953,10455429.0,20381.772373969092 +979,Czech Republic,2009,115255283.6,0.3656,124470000.0,8.965666666666667,202.86587524414062,10518271.0,19286.998333104428 +980,Czech Republic,2010,117495552.1,0.3553,127690000.0,7.75225,207.4778594970703,10550885.0,19664.498238495664 +981,Czech Republic,2011,115023302.1,0.3348,119390000.0,9.172166666666664,211.1664886474609,10573220.0,19971.823971076068 +982,Czech Republic,2012,110914080.3,0.3176,116050000.0,8.927583333333333,209.47747802734372,10591707.0,19777.499323512606 +983,Czech Republic,2013,106401191.6,0.3022,110740000.0,9.281,208.46429443359372,10609762.0,19648.34785489003 +984,Djibouti,1990,322432.0,0.0015,990000.0,29.302999999999997,0.6343588829040527,499189.0,1270.778969296304 +985,Djibouti,1991,311440.0,0.0014,980000.0,29.28941666666667,0.6143977046012878,551981.0,1113.0776323846069 +986,Djibouti,1992,311440.0,0.0014,990000.0,28.999,0.6504188179969788,547085.0,1188.8807369914707 +987,Djibouti,1993,311440.0,0.0014,1010000.0,28.74083333333333,0.6818933486938477,534434.0,1275.9168553906518 +988,Djibouti,1994,304112.0,0.0013,1020000.0,29.06233333333333,0.6653710603713989,546892.0,1216.6406902485298 +989,Djibouti,1995,304112.0,0.0013,1070000.0,29.25975,0.702642023563385,553027.0,1270.5383707547462 +990,Djibouti,1996,311440.0,0.0013,1120000.0,29.165250000000004,0.6668708920478821,568430.0,1173.180324838383 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+1038,Dominican Republic,1996,17197656.66,0.0731,12510000.0,25.87075,,, +1039,Dominican Republic,1997,17856936.59,0.0747,14210000.0,26.697249999999997,,, +1040,Dominican Republic,1998,18248509.78,0.0761,16110000.0,26.676333333333332,,, +1041,Dominican Republic,1999,18383926.74,0.0768,16750000.0,26.17283333333333,,, +1042,Dominican Republic,2000,19593674.0,0.0798,17530000.0,25.880416666666665,,, +1043,Dominican Republic,2001,19593788.75,0.0779,19640000.0,26.340666666666667,,, +1044,Dominican Republic,2002,21074255.13,0.0827,22050000.0,26.581249999999997,,, +1045,Dominican Republic,2003,21259925.78,0.0791,19710000.0,26.626333333333335,,, +1046,Dominican Republic,2004,18069690.41,0.0641,19020000.0,26.358750000000004,,, +1047,Dominican Republic,2005,18380202.01,0.0628,20300000.0,26.52116666666667,,, +1048,Dominican Republic,2006,19420402.05,0.0641,21810000.0,26.58566666666667,,, +1049,Dominican Republic,2007,20756432.18,0.0672,23160000.0,26.626,,, +1050,Dominican Republic,2008,20269016.28,0.0636,23410000.0,26.199250000000003,,, +1051,Dominican Republic,2009,19813658.54,0.0629,22870000.0,26.423083333333327,,, +1052,Dominican Republic,2010,20574429.09,0.0622,24280000.0,26.455583333333333,,, +1053,Dominican Republic,2011,20780658.03,0.0605,24580000.0,26.25075,,, +1054,Dominican Republic,2012,21651295.88,0.062,25410000.0,26.340083333333336,,, +1055,Dominican Republic,2013,20957795.31,0.0595,25250000.0,26.290333333333333,,, +1056,Ecuador,1990,16710583.14,0.0753,76170000.0,22.434749999999998,38.02086639404297,10318036.0,3684.893752458605 +1057,Ecuador,1991,16377625.57,0.073,77030000.0,22.529500000000002,39.65247344970703,10429924.0,3801.798886521803 +1058,Ecuador,1992,22157328.98,0.0998,79280000.0,22.611500000000003,40.49084854125977,10553806.0,3836.6110331438495 +1059,Ecuador,1993,24087388.71,0.1087,79020000.0,22.421583333333334,41.28982162475586,10753682.0,3839.598532368342 +1060,Ecuador,1994,13538501.37,0.0601,80670000.0,22.217083333333335,43.04804229736328,11012554.0,3908.99715882104 +1061,Ecuador,1995,22697911.22,0.0987,83800000.0,22.574166666666667,44.017723083496094,11265611.0,3907.264602292418 +1062,Ecuador,1996,24025485.48,0.1021,87120000.0,22.0325,44.77999877929688,11526481.0,3884.9670406168957 +1063,Ecuador,1997,18205056.4,0.0761,89140000.0,23.082916666666666,46.71802139282226,11792472.0,3961.681774001436 +1064,Ecuador,1998,22314743.35,0.09300000000000001,88100000.0,23.121,48.24407958984375,12050626.0,4003.4500771863427 +1065,Ecuador,1999,21365020.48,0.0893,84090000.0,21.90216666666667,45.95760726928711,12268309.0,3746.0425287044127 +1066,Ecuador,2000,20562617.58,0.0837,84480000.0,22.026,46.45936965942383,12445838.0,3732.92418392589 +1067,Ecuador,2001,22939469.75,0.0913,79560000.0,22.236083333333337,48.32500839233398,12629117.0,3826.475627103145 +1068,Ecuador,2002,24753138.01,0.0971,80240000.0,22.76366666666667,50.30477905273438,12827279.0,3921.70304027334 +1069,Ecuador,2003,26747852.5,0.0996,80340000.0,22.668333333333337,51.67451477050781,13074181.0,3952.409315008551 +1070,Ecuador,2004,28785844.78,0.1021,84290000.0,22.64791666666667,55.917518615722656,13368398.0,4182.8137235084305 +1071,Ecuador,2005,29970092.72,0.1024,85790000.0,22.5295,58.87628936767578,13662318.0,4309.392400885105 +1072,Ecuador,2006,28524600.62,0.0942,87940000.0,22.603166666666667,61.46892166137695,13917057.0,4416.804620501084 +1073,Ecuador,2007,31493981.71,0.102,88400000.0,22.359750000000002,62.81512832641602,14134956.0,4443.956410364208 +1074,Ecuador,2008,33324993.46,0.1045,89280000.0,22.22425,66.80836486816406,14354469.0,4654.185736035522 +1075,Ecuador,2009,35503217.19,0.1126,92530000.0,22.67716666666667,67.18682861328125,14573101.0,4610.33163863211 +1076,Ecuador,2010,35884097.21,0.1085,96210000.0,22.639916666666668,69.55536651611328,14790608.0,4702.671216498557 +1077,Ecuador,2011,37973460.94,0.1105,96710000.0,22.33666666666667,75.028076171875,15007343.0,4999.424359920006 +1078,Ecuador,2012,37460526.2,0.1073,98180000.0,22.445916666666665,79.2611312866211,15223680.0,5206.437030114998 +1079,Ecuador,2013,40252210.7,0.1143,100380000.0,22.264125,83.18179321289062,15439429.0,5387.621084490276 +1080,Egypt,1990,75621975.85,0.3409,129330000.0,23.09141666666667,85.69135284423828,55146417.0,1553.8879496783677 +1081,Egypt,1991,78320710.96,0.3492,134970000.0,23.088499999999996,86.65572357177734,56165811.0,1542.855378190432 +1082,Egypt,1992,80826148.13,0.3642,140040000.0,22.554916666666667,90.5317153930664,56995165.0,1588.4104448696023 +1083,Egypt,1993,92702607.18,0.4185,142330000.0,23.39225,93.15785217285156,57822207.0,1611.1085516478392 +1084,Egypt,1994,84834941.9,0.3765,138260000.0,23.235,96.85916900634766,58664429.0,1651.071537853844 +1085,Egypt,1995,95166896.06,0.4136,147200000.0,23.16916666666667,101.35581970214844,59523990.0,1702.7726081895455 +1086,Egypt,1996,101408464.5,0.4311,154630000.0,23.426333333333332,106.41218566894531,60410338.0,1761.489658755846 +1087,Egypt,1997,107511990.2,0.4496,164080000.0,22.939666666666668,112.25672149658205,61642311.0,1821.0985226783926 +1088,Egypt,1998,121465258.8,0.5064,170850000.0,23.682416666666665,118.51558685302734,62900550.0,1884.1740947102585 +1089,Egypt,1999,124497417.5,0.5202,179720000.0,23.779333333333337,125.68984985351562,64187293.0,1958.17340129168 +1090,Egypt,2000,140347602.6,0.5715,180360000.0,22.881416666666667,133.69630432128906,65504309.0,2041.0306796960344 +1091,Egypt,2001,124292785.7,0.4944,192780000.0,23.616333333333333,138.42279052734378,66852387.0,2070.5736435012227 +1092,Egypt,2002,125781450.0,0.4934,197200000.0,23.716250000000002,141.73138427734375,68265356.0,2076.1831854702955 +1093,Egypt,2003,146462043.1,0.5451,204500000.0,23.496416666666665,146.2575225830078,69734297.0,2097.3542270456646 +1094,Egypt,2004,149143733.5,0.5291,215210000.0,23.511083333333335,152.2424774169922,71210963.0,2137.907858611492 +1095,Egypt,2005,165041693.0,0.5642,234140000.0,23.52025,159.0503692626953,72688283.0,2188.115645305521 +1096,Egypt,2006,176019518.2,0.5812,243290000.0,23.505666666666666,169.93553161621094,74207202.0,2290.014001824391 +1097,Egypt,2007,185905188.6,0.602,259480000.00000003,23.584333333333333,181.9802703857422,75814404.0,2400.3389960797185 +1098,Egypt,2008,195285376.0,0.6126,271340000.0,23.907916666666665,195.0032958984375,77516306.0,2515.6422688464736 +1099,Egypt,2009,202709705.6,0.6431,274870000.0,23.735000000000003,204.11697387695312,79317792.0,2573.4071603626226 +1100,Egypt,2010,198807663.8,0.6012,277420000.0,25.151916666666665,214.62335205078125,81238995.0,2641.876011031171 +1101,Egypt,2011,213271004.6,0.6207,283900000.0,23.331416666666666,218.41053771972656,83298127.0,2622.0341991570417 +1102,Egypt,2012,211992734.9,0.6071,294690000.0,23.770916666666665,223.2727813720703,85458032.0,2612.6599939964717 +1103,Egypt,2013,208450844.5,0.5921,292440000.0,24.50525,228.1523284912109,87679255.0,2602.124396371877 +1104,El Salvador,1990,2467169.72,0.0111,8039999.999999999,25.566499999999994,11.342595100402832,5109818.0,2219.764989751657 +1105,El Salvador,1991,3123868.16,0.0139,8910000.0,25.64075,11.512093544006348,5196237.0,2215.467374564776 +1106,El Salvador,1992,3287506.07,0.0148,9330000.0,25.78191666666667,12.32001781463623,5286006.0,2330.6855525014976 +1107,El Salvador,1993,3733447.09,0.0169,9890000.0,25.663083333333336,13.036948204040527,5334151.0,2444.0530843691017 +1108,El Salvador,1994,4532236.02,0.0201,10530000.0,25.913166666666665,13.64835262298584,5395198.0,2529.722286927345 +1109,El Salvador,1995,4961569.8,0.0216,10790000.0,25.734083333333334,14.294596672058105,5479829.0,2608.584441605405 +1110,El Salvador,1996,4498967.81,0.0191,10700000.0,25.354416666666665,14.411480903625488,5564105.0,2590.0806874826208 +1111,El Salvador,1997,5398272.49,0.0226,11530000.0,26.035750000000004,14.863271713256836,5636808.0,2636.824194341343 +1112,El Salvador,1998,5731667.18,0.0239,11580000.0,26.131083333333333,15.257768630981445,5711571.0,2671.3786156175674 +1113,El Salvador,1999,5620134.14,0.0235,11800000.0,25.27958333333333,15.587414741516113,5788365.0,2692.8873250937204 +1114,El Salvador,2000,5660887.26,0.0231,11660000.0,25.40716666666667,15.762938499450685,5849822.0,2694.601391196293 +1115,El Salvador,2001,5854442.96,0.0233,12370000.0,25.49291666666667,15.901229858398438,5883698.0,2702.5911014464773 +1116,El Salvador,2002,5930844.34,0.0233,12560000.0,25.71041666666667,16.152606964111328,5901830.0,2736.881096898984 +1117,El Salvador,2003,6434281.78,0.0239,12930000.0,25.724083333333336,16.40494728088379,5919845.0,2771.178515802997 +1118,El Salvador,2004,6253663.02,0.0222,13020000.0,25.74258333333334,16.551080703735348,5935121.0,2788.667780106817 +1119,El Salvador,2005,6349269.26,0.0217,13540000.0,25.622833333333332,16.999195098876953,5956221.0,2854.02356609618 +1120,El Salvador,2006,6720562.95,0.0222,13840000.0,25.758499999999998,17.73745346069336,5970893.0,2970.653378094928 +1121,El Salvador,2007,6845867.67,0.0222,14450000.0,25.475166666666663,18.067472457885746,5981752.0,3020.4315487980352 +1122,El Salvador,2008,6408648.63,0.0201,13800000.0,25.248250000000002,18.45180892944336,6006449.0,3071.999600669773 +1123,El Salvador,2009,6312649.33,0.02,13500000.0,25.787583333333334,18.067384719848636,6030596.0,2995.9534214940995 +1124,El Salvador,2010,6317045.02,0.0191,13220000.0,25.45866666666667,18.44792366027832,6052064.0,3048.2036641182776 +1125,El Salvador,2011,6500473.85,0.0189,13020000.0,25.549666666666667,19.151123046875,6071774.0,3154.1231684306763 +1126,El Salvador,2012,6505744.53,0.0186,13200000.0,25.560333333333332,19.69005012512207,6090646.0,3232.8344358089557 +1127,El Salvador,2013,6093602.43,0.0173,12430000.0,25.91077777777778,20.15772819519043,6108590.0,3299.898699239993 +1128,Equatorial Guinea,1990,65952.0,0.0003,5530000.0,25.285000000000007,0.21679344773292544,371100.0,584.1914517190122 +1129,Equatorial Guinea,1991,142896.0,0.0006,5660000.0,25.235083333333336,0.2145773023366928,381377.0,562.6382879321322 +1130,Equatorial Guinea,1992,157552.0,0.0007,5720000.0,24.889166666666668,0.2891328632831573,392024.0,737.5386794766579 +1131,Equatorial Guinea,1993,98928.0,0.0004,6650000.0,25.189333333333337,0.3210335075855255,403044.0,796.5222347573108 +1132,Equatorial Guinea,1994,32976.0,0.0001,6630000.0,25.2865,0.3745460510253906,414443.0,903.733567765388 +1133,Equatorial Guinea,1995,128240.0,0.0006,6780000.0,25.614583333333332,0.4400401711463928,426231.0,1032.398326603163 +1134,Equatorial Guinea,1996,113584.0,0.0005,8760000.0,25.238416666666666,0.7330188751220703,438420.0,1671.9558303044348 +1135,Equatorial Guinea,1997,289456.0,0.0012,10500000.0,25.32525,1.8323489427566528,451026.0,4062.623757292601 +1136,Equatorial Guinea,1998,164880.0,0.0007,11890000.0,26.003500000000003,2.2679805755615234,464060.0,4887.257198555194 +1137,Equatorial Guinea,1999,465328.0,0.0019,13780000.0,25.322999999999997,2.8500354290008545,477538.0,5968.18562920826 +1138,Equatorial Guinea,2000,454336.0,0.0019,15380000.0,25.220583333333337,3.369134426116944,491483.0,6855.037562066121 +1139,Equatorial Guinea,2001,3092416.0,0.0123,17630000.0,25.207250000000002,5.504487991333008,505919.0,10880.176453805861 +1140,Equatorial Guinea,2002,4975712.0,0.0195,20050000.0,25.526500000000002,6.575817108154298,520764.0,12627.249787147915 +1141,Equatorial Guinea,2003,6012624.0,0.0224,23920000.0,25.627416666666665,7.493488788604736,535927.0,13982.293835923057 +1142,Equatorial Guinea,2004,5213872.0,0.0185,26590000.0,25.59416666666667,10.34091854095459,551415.0,18753.422632599024 +1143,Equatorial Guinea,2005,4708240.0,0.0161,27710000.0,25.62391666666667,12.072888374328613,567232.0,21283.863347499107 +1144,Equatorial Guinea,2006,4752208.0,0.0157,28090000.0,25.785416666666666,13.003094673156738,583355.0,22290.19151829802 +1145,Equatorial Guinea,2007,4792512.0,0.0155,27680000.0,25.773666666666667,14.990242004394533,599763.0,24993.609149604974 +1146,Equatorial Guinea,2008,4499392.0,0.0141,27100000.0,25.76875,17.658370971679688,616459.0,28644.842514554395 +1147,Equatorial Guinea,2009,4616640.0,0.0146,27420000.0,25.832833333333337,17.895586013793945,633441.0,28251.385707262314 +1148,Equatorial Guinea,2010,4675264.0,0.0141,26970000.0,25.9025,16.298553466796875,650702.0,25047.646183347944 +1149,Equatorial Guinea,2011,6122544.0,0.0178,26370000.0,25.545666666666662,17.361858367919922,668225.0,25982.05449948733 +1150,Equatorial Guinea,2012,5111280.0,0.0146,26170000.0,25.536833333333334,18.805126190185547,685991.0,27413.08004067917 +1151,Equatorial Guinea,2013,5158912.0,0.0147,25380000.0,25.5905,18.027873992919922,704001.0,25607.739183495367 +1152,Eritrea,1994,860720.64,0.0038,7280000.0,26.890583333333336,1.6220920085906982,3185537.0,509.20520106678975 +1153,Eritrea,1995,929640.69,0.004,7920000.0,27.13666666666667,1.6684576272964478,3361437.0,496.3524907045551 +1154,Eritrea,1996,1007225.68,0.0043,8450000.0,27.183916666666672,1.822937488555908,3518610.0,518.0845528648837 +1155,Eritrea,1997,859282.94,0.0036,8880000.0,27.146,1.9671078920364382,3613539.0,544.3715681597564 +1156,Eritrea,1998,585810.72,0.0024,8890000.0,27.577833333333334,2.001976490020752,3717993.0,538.4562289441513 +1157,Eritrea,1999,615095.24,0.0026,9010000.0,27.514083333333332,2.002246379852295,3805989.0,526.0778157404803 +1158,Eritrea,2000,604075.76,0.0025,9110000.0,27.51725,1.939336180686951,3884349.0,499.26929343551535 +1159,Eritrea,2001,625989.03,0.0025,8119999.999999999,27.41058333333334,2.10913348197937,3993336.0,528.163290536877 +1160,Eritrea,2002,600270.3,0.0024,7940000.0,27.639416666666666,2.1725220680236816,4147327.0,523.8366948214311 +1161,Eritrea,2003,721111.57,0.0027,7920000.0,27.729750000000006,2.1148302555084233,4342028.0,487.06048314483985 +1162,Eritrea,2004,765008.84,0.0027,7830000.0,27.5775,2.1455318927764893,4550848.0,471.4576036766091 +1163,Eritrea,2005,761274.11,0.0026,7670000.0,27.69,2.200767517089844,4708626.0,467.3905969787882 +1164,Eritrea,2006,555926.73,0.0018,7470000.0,27.492,2.179437160491944,4844793.0,449.8514509272003 +1165,Eritrea,2007,574008.69,0.0019,7910000.0,27.485416666666666,2.210533857345581,4967933.0,444.9604810180775 +1166,Eritrea,2008,409016.31,0.0013,7920000.0,27.59433333333334,1.994276762008667,5105526.0,390.61142025496827 +1167,Eritrea,2009,507965.24,0.0016,8170000.0,28.092583333333334,2.071584939956665,5243580.0,395.0707226659391 +1168,Eritrea,2010,507987.54,0.0015,8100000.0,28.05416666666667,2.1170394420623784,5364704.0,394.62371867345865 +1169,Eritrea,2011,571418.1,0.0017,8289999.999999999,27.58575,2.3007941246032715,5481467.0,419.7405775868525 +1170,Eritrea,2012,629674.27,0.0018,8170000.0,27.880083333333335,2.4624688625335693,5582115.0,441.13545896735724 +1171,Eritrea,2013,638775.25,0.0018,8140000.000000001,28.503500000000003,2.576963186264038,5681550.0,453.5669291415262 +1172,Estonia,1990,37068977.48,0.1671,35160000.0,6.698583333333332,15.302617073059082,1569322.0,9751.100840400555 +1173,Estonia,1991,34037705.0,0.1518,31430000.0,6.2580833333333326,14.078432083129885,1559547.0,9027.257327371271 +1174,Estonia,1992,24514590.79,0.1105,23790000.0,6.401833333333333,11.098217010498047,1528715.0,7259.833919663278 +1175,Estonia,1993,19066027.57,0.0861,17950000.0,5.146250000000001,10.461124420166016,1491426.0,7014.175976659932 +1176,Estonia,1994,19900974.39,0.0883,17110000.0,5.252250000000002,10.25275993347168,1467535.0,6986.381880821704 +1177,Estonia,1995,18203993.21,0.0791,15320000.0,6.165833333333333,10.51343059539795,1446509.0,7268.140464662128 +1178,Estonia,1996,18925430.33,0.0805,16160000.0,4.5785,11.073149681091307,1427827.0,7755.246035473 +1179,Estonia,1997,18482320.57,0.0773,15740000.0,5.778333333333333,12.37962818145752,1413351.0,8759.0613948393 +1180,Estonia,1998,16909588.86,0.0705,15210000.0,5.361416666666666,12.889328956604006,1400972.0,9200.275920292486 +1181,Estonia,1999,15727068.82,0.0657,13980000.0,6.456833333333332,12.778938293457033,1389756.0,9195.094889647558 +1182,Estonia,2000,15362207.36,0.0626,13550000.0,7.008750000000002,14.129426956176758,1379671.0,10241.156736770405 +1183,Estonia,2001,15707868.32,0.0625,21370000.0,5.973,15.023637771606445,1369399.0,10970.97177054054 +1184,Estonia,2002,15233321.73,0.0598,21330000.0,6.211583333333333,15.936532974243164,1359260.0,11724.418414610276 +1185,Estonia,2003,17087248.52,0.0636,23010000.0,5.662166666666665,17.118412017822266,1349483.0,12685.163146050945 +1186,Estonia,2004,17286590.62,0.0613,23080000.0,5.940833333333334,18.195972442626953,1340354.0,13575.497549622676 +1187,Estonia,2005,17136321.46,0.0586,23190000.0,6.0014166666666675,19.901611328125,1331702.0,14944.493083381267 +1188,Estonia,2006,16439189.16,0.0543,16600000.000000002,6.531833333333334,21.94588279724121,1322944.0,16588.67102253853 +1189,Estonia,2007,20071647.7,0.065,20350000.0,6.830833333333332,23.64629173278809,1316118.0,17966.69579231352 +1190,Estonia,2008,17872650.45,0.0561,18900000.0,7.267833333333333,22.364791870117188,1311467.0,17053.263155014338 +1191,Estonia,2009,14609067.15,0.0463,15470000.0,6.061833333333333,19.07170867919922,1307409.0,14587.408132573066 +1192,Estonia,2010,19014857.92,0.0575,19500000.0,5.042999999999999,19.502552032470703,1302817.0,14969.525292094517 +1193,Estonia,2011,19097197.34,0.0556,21770000.0,6.865333333333333,20.984220504760746,1297236.0,16176.100959856758 +1194,Estonia,2012,17938059.04,0.0514,20630000.0,5.486,21.8880672454834,1288868.0,16982.3963706783 +1195,Estonia,2013,19695596.98,0.0559,23040000.0,6.961749999999999,22.31193733215332,1279745.0,17434.674354776398 +1196,Ethiopia,1990,2985775.69,0.0135,102730000.0,23.37958333333334,8.914345741271973,47554116.0,187.45687000620453 +1197,Ethiopia,1991,2957140.07,0.0132,104460000.0,23.41241666666667,8.278085708618164,49447575.0,167.41135856749625 +1198,Ethiopia,1992,2937331.98,0.0132,107010000.0,23.239500000000003,7.5601701736450195,51205192.0,147.6446016186214 +1199,Ethiopia,1993,2994817.01,0.0135,102610000.0,22.98875,8.553791046142576,52525039.0,162.8516838634347 +1200,Ethiopia,1994,2196878.77,0.0097,105030000.0,23.20866666666667,8.826654434204102,54030146.0,163.3653633696289 +1201,Ethiopia,1995,2505652.77,0.0109,107110000.0,23.396500000000003,9.367508888244627,55725462.0,168.10105384580982 +1202,Ethiopia,1996,2794653.04,0.0119,104080000.0,23.358583333333332,10.531532287597656,57424029.0,183.39939692489457 +1203,Ethiopia,1997,2979329.44,0.0125,106800000.0,23.423833333333334,10.861580848693848,59076269.0,183.85691975053211 +1204,Ethiopia,1998,3147406.27,0.0131,116950000.0,23.689416666666663,10.48597240447998,60764879.0,172.56633399171218 +1205,Ethiopia,1999,3094952.99,0.0129,117930000.0,23.446833333333334,11.027273178100586,62538426.0,176.32796159757183 +1206,Ethiopia,2000,3463787.2,0.0141,111270000.0,23.762583333333335,11.696983337402346,64365225.0,181.72830651026769 +1207,Ethiopia,2001,4263382.28,0.017,122340000.0,23.6615,12.667985916137695,66230452.0,191.27131906238083 +1208,Ethiopia,2002,4430488.1,0.0174,141710000.0,23.82325,12.859870910644533,68167888.0,188.64998297504144 +1209,Ethiopia,2003,4871122.29,0.0181,136700000.0,23.857166666666668,12.58192253112793,70174826.0,179.29396121520742 +1210,Ethiopia,2004,5158619.71,0.0183,138170000.0,23.778666666666666,14.289617538452148,72238990.0,197.81031737088446 +1211,Ethiopia,2005,4955126.88,0.0169,132560000.0,23.89691666666667,15.978473663330078,74354300.0,214.89643051350197 +1212,Ethiopia,2006,5316940.93,0.0176,144580000.0,23.77808333333333,17.709697723388672,76535275.0,231.39261893798215 +1213,Ethiopia,2007,5828354.59,0.0189,144730000.0,23.724000000000004,19.738550186157227,78799447.0,250.49097344753227 +1214,Ethiopia,2008,6365064.81,0.02,157420000.0,23.60258333333333,21.86804962158203,81135648.0,269.5245574618697 +1215,Ethiopia,2009,6433046.26,0.0204,160680000.0,24.273583333333335,23.79299545288086,83548430.0,284.78088041727244 +1216,Ethiopia,2010,6395734.24,0.0193,164860000.0,23.9275,26.77914428710937,86042927.0,311.23004784704005 +1217,Ethiopia,2011,7478954.84,0.0218,150380000.0,24.074166666666667,29.77259635925293,88588758.0,336.0764619733458 +1218,Ethiopia,2012,8149538.09,0.0233,166220000.0,23.976166666666668,32.347274780273445,91195675.0,354.70185159848245 +1219,Ethiopia,2013,10080186.66,0.0286,177130000.0,24.6315,35.770347595214844,93877025.0,381.03409854769933 +1220,Fiji,1990,806056.77,0.0036,220000.0,25.400500000000005,2.132210731506348,739619.0,2882.850131630404 +1221,Fiji,1991,663124.51,0.003,90000.0,25.348,2.0746407508850098,746167.0,2780.3973519131905 +1222,Fiji,1992,740032.26,0.0033,170000.0,25.296333333333333,2.2011940479278564,752740.0,2924.2421658578746 +1223,Fiji,1993,743660.01,0.0034,250000.0,25.007166666666667,2.248080253601074,759328.0,2960.6181434124305 +1224,Fiji,1994,738596.81,0.0033,300000.0,25.148749999999996,2.362732410430908,765923.0,3084.817155811887 +1225,Fiji,1995,753894.74,0.0033,370000.0,25.715999999999998,2.4218008518218994,772517.0,3134.9482947584315 +1226,Fiji,1996,813167.26,0.0035,410000.0,25.86108333333333,2.5380470752716064,778890.0,3258.5436650510424 +1227,Fiji,1997,782436.28,0.0033,400000.0,25.323416666666663,2.4822099208831787,785608.0,3159.6036711479246 +1228,Fiji,1998,761104.1,0.0032,350000.0,25.865333333333336,2.51447868347168,792693.0,3172.0712602125664 +1229,Fiji,1999,775022.6,0.0032,320000.0,25.686666666666667,2.735752820968628,799078.0,3423.636767585427 +1230,Fiji,2000,833603.76,0.0034,360000.0,25.837166666666672,2.6892452239990234,804933.0,3340.955363985603 +1231,Fiji,2001,1045950.69,0.0042,250000.0,25.751416666666668,2.743030071258545,810728.0,3383.415980771041 +1232,Fiji,2002,879452.34,0.0034,70000.0,25.713333333333335,2.830806970596313,816669.0,3466.284346040212 +1233,Fiji,2003,1036074.55,0.0039,210000.0,25.44666666666667,2.8591148853302,823178.0,3473.2644523179683 +1234,Fiji,2004,1339983.39,0.0048,530000.0,25.507749999999998,3.010648250579834,829822.0,3628.065115868022 +1235,Fiji,2005,1081006.03,0.0037,260000.0,25.643416666666667,3.0317225456237797,836558.0,3624.0434561904603 +1236,Fiji,2006,1201372.86,0.004,3040000.0,25.631500000000003,3.0878846645355225,843945.0,3658.869552560324 +1237,Fiji,2007,1141139.52,0.0037,2990000.0,26.055333333333333,3.061617612838745,852114.0,3592.9671532667517 +1238,Fiji,2008,877758.23,0.0028,2740000.0,25.730166666666673,3.09323787689209,860535.0,3594.5520831716203 +1239,Fiji,2009,778559.6,0.0025,2680000.0,25.22741666666667,3.050364255905152,868498.0,3512.2294534992043 +1240,Fiji,2010,1201245.43,0.0036,3030000.0,25.918499999999998,3.1404924392700195,875983.0,3585.1066051167877 +1241,Fiji,2011,1067900.73,0.0031,-690000.0,25.722833333333337,3.225447177886963,883125.0,3652.3110294544517 +1242,Fiji,2012,1041074.88,0.003,-660000.0,25.478250000000003,3.2709684371948238,890057.0,3675.010069236941 +1243,Fiji,2013,1129175.61,0.0032,-510000.0,25.941374999999997,3.4258234500885014,896758.0,3820.231824069037 +1244,Finland,1990,56966986.68,0.2568,45100000.0,2.943250000000001,167.1197967529297,4986431.0,33514.91211909473 +1245,Finland,1991,55225267.32,0.2462,45070000.0,2.3282499999999997,157.23562622070312,5013786.0,31360.657638898654 +1246,Finland,1992,54280474.65,0.2446,41930000.0,2.6495833333333336,152.00811767578125,5041039.0,30154.124511986764 +1247,Finland,1993,56348524.02,0.2544,43180000.0,1.7829166666666667,150.8916473388672,5064846.0,29791.951687942183 +1248,Finland,1994,61711568.31,0.2739,49810000.0,1.7739166666666673,156.83566284179688,5086044.0,30836.473857048204 +1249,Finland,1995,58141560.0,0.2527,45770000.0,2.3540833333333335,163.43394470214844,5104654.0,32016.654743328043 +1250,Finland,1996,64058350.01,0.2723,52280000.0,1.6930833333333337,169.41371154785156,5120310.0,33086.612245713935 +1251,Finland,1997,62718271.1,0.2623,50300000.0,2.2275833333333335,180.00514221191406,5134406.0,35058.610910768264 +1252,Finland,1998,59375064.98,0.2476,46570000.0,1.2267500000000002,189.77674865722656,5146983.0,36871.45433688562 +1253,Finland,1999,58898261.66,0.2461,45650000.0,2.3225000000000007,198.210693359375,5158097.0,38427.09692341478 +1254,Finland,2000,57041531.95,0.2323,44090000.0,3.590666666666668,209.3795623779297,5168595.0,40509.95722782103 +1255,Finland,2001,62539274.13,0.2488,58680000.0,2.09525,214.78321838378903,5180309.0,41461.46849228281 +1256,Finland,2002,65061819.8,0.2552,61060000.0,2.230166666666667,218.3922882080078,5192708.0,42057.4945111506 +1257,Finland,2003,72658369.77,0.2704,69280000.0,2.499833333333333,222.7469787597656,5205082.0,42794.134417049645 +1258,Finland,2004,68961595.52,0.2447,65300000.0,2.6147499999999995,231.49215698242188,5220193.0,44345.51691526001 +1259,Finland,2005,57045015.54,0.195,53060000.0,3.23025,237.9275360107422,5238011.0,45423.260090660784 +1260,Finland,2006,68379818.2,0.2258,60380000.0,2.8921666666666668,247.5759735107422,5257972.0,47085.82957663947 +1261,Finland,2007,66755662.84,0.2162,58390000.0,3.092333333333334,260.41229248046875,5280151.0,49319.09948796327 +1262,Finland,2008,58608815.58,0.1839,50290000.0,3.2009166666666666,262.2890014648437,5304716.0,49444.49457140471 +1263,Finland,2009,55818226.93,0.1771,46690000.0,2.43925,240.6002349853516,5330210.0,45138.978574080866 +1264,Finland,2010,63991753.14,0.1935,54750000.0,1.1437500000000005,247.79981994628903,5354669.0,46277.336647006385 +1265,Finland,2011,56544141.87,0.1646,72700000.0,3.541166666666667,254.17030334472656,5379680.0,47246.36100004584 +1266,Finland,2012,51111890.38,0.1464,66870000.00000001,1.9072499999999994,250.54534912109372,5405388.0,46351.0388377474 +1267,Finland,2013,51901569.29,0.1474,67440000.0,4.064125,248.6461181640625,5430353.0,45788.20532736315 +1268,Gabon,1990,4485398.85,0.0202,8730000.0,24.570833333333336,10.585821151733398,938084.0,11284.513062511885 +1269,Gabon,1991,4521965.57,0.0202,8940000.0,24.56683333333333,11.232879638671877,960425.0,11695.738489389463 +1270,Gabon,1992,4917604.28,0.0222,9110000.0,24.240250000000003,10.885767936706543,986367.0,11036.224789258504 +1271,Gabon,1993,4974836.38,0.0225,9160000.0,24.544500000000003,11.315380096435547,1013159.0,11168.414924444778 +1272,Gabon,1994,4374521.83,0.0194,8880000.0,24.64708333333333,11.735491752624512,1040811.0,11275.334092956851 +1273,Gabon,1995,4606266.07,0.02,8700000.0,24.936916666666672,12.31919765472412,1069065.0,11523.338295355401 +1274,Gabon,1996,4753701.68,0.0202,7930000.0,24.464750000000006,12.765774726867676,1097708.0,11629.481361953884 +1275,Gabon,1997,4774215.91,0.02,7770000.0,24.730416666666667,13.498321533203125,1126940.0,11977.852887645415 +1276,Gabon,1998,4957292.25,0.0207,7590000.0,25.394833333333334,13.96777057647705,1157171.0,12070.619274486702 +1277,Gabon,1999,4787020.96,0.02,7210000.0,24.736833333333333,12.72008228302002,1195647.0,10638.6603094559 +1278,Gabon,2000,4669870.17,0.019,6820000.0,24.57241666666667,12.48056697845459,1236429.0,10094.042584292822 +1279,Gabon,2001,4772971.67,0.019,6800000.0,24.513583333333333,12.74705696105957,1269154.0,10043.7432817921 +1280,Gabon,2002,4543984.75,0.0178,6650000.0,24.83391666666667,12.715312957763672,1300423.0,9777.828412573195 +1281,Gabon,2003,4613188.16,0.0172,6870000.0,24.912000000000003,13.00106716156006,1332623.0,9755.997879040102 +1282,Gabon,2004,4656743.56,0.0165,7380000.0,24.853250000000003,13.090715408325195,1364412.0,9594.400671003477 +1283,Gabon,2005,4857850.97,0.0166,7500000.0,24.96541666666667,13.441049575805664,1395690.0,9630.397563789711 +1284,Gabon,2006,4142417.93,0.0137,7210000.0,25.157750000000004,13.06381607055664,1426392.0,9158.643676182033 +1285,Gabon,2007,4089338.21,0.0132,7330000.0,25.06358333333333,13.848705291748047,1456451.0,9508.528121954016 +1286,Gabon,2008,4143717.59,0.013000000000000001,8270000.0,25.127916666666668,13.390530586242676,1485832.0,9012.1430863265 +1287,Gabon,2009,4218272.37,0.0134,7080000.0,25.134666666666664,13.40798282623291,1514993.0,8850.194572669914 +1288,Gabon,2010,4782305.7,0.0145,7980000.0,25.221,14.358593940734865,1545255.0,9292.05467106391 +1289,Gabon,2011,4941974.5,0.0144,-85070000.0,24.730916666666662,15.376869201660154,1576665.0,9752.781473337807 +1290,Gabon,2012,5077264.15,0.0145,-85260000.0,24.831666666666667,16.184320449829098,1608321.0,10062.867083019559 +1291,Gabon,2013,5257712.2,0.0149,-84880000.0,24.838500000000003,17.096906661987305,1640286.0,10423.125395197729 +1292,Gambia,1990,175872.0,0.0008,3950000.0,28.13566666666667,0.4724599123001098,951025.0,496.7902129808468 +1293,Gambia,1991,179536.0,0.0008,4110000.0000000005,28.04525,0.4871394038200378,991493.0,491.3190550211024 +1294,Gambia,1992,179536.0,0.0008,4230000.0,28.034333333333333,0.5035983324050903,1033734.0,487.16433086760253 +1295,Gambia,1993,186864.0,0.0008,4350000.0,28.24066666666667,0.5187672376632689,1076885.0,481.7294675506382 +1296,Gambia,1994,186864.0,0.0008,3890000.0,27.904083333333332,0.5195679068565369,1119592.0,464.068970532602 +1297,Gambia,1995,197856.0,0.0009,3420000.0,28.19008333333333,0.5241497159004211,1161456.0,451.2867606697294 +1298,Gambia,1996,197856.0,0.0008,3310000.0,28.298750000000002,0.5358044505119324,1202416.0,445.6065542307591 +1299,Gambia,1997,197856.0,0.0008,2980000.0,28.35016666666667,0.5620588660240173,1242460.0,452.3758237883049 +1300,Gambia,1998,212512.0,0.0009,2710000.0,28.82608333333334,0.5817309021949768,1281565.0,453.92227643153234 +1301,Gambia,1999,230832.0,0.001,2650000.0,27.757499999999997,0.6189616918563843,1319628.0,469.0425573391776 +1302,Gambia,2000,245488.0,0.001,2330000.0,28.063750000000002,0.6530045866966248,1356581.0,481.3605576789184 +1303,Gambia,2001,252816.0,0.001,2040000.0,28.48975,0.6908788681030273,1393296.0,495.85936376981437 +1304,Gambia,2002,289456.0,0.0011,1740000.0,28.547916666666666,0.6684253215789795,1430711.0,467.1980026567067 +1305,Gambia,2003,289456.0,0.0011,1540000.0,28.228583333333336,0.7143461108207703,1468917.0,486.308015238962 +1306,Gambia,2004,293120.0,0.001,1440000.0,28.3725,0.7647075057029724,1508001.0,507.10013169949656 +1307,Gambia,2005,293120.0,0.001,1290000.0,28.605,0.7575064897537231,1547905.0,489.37531034121804 +1308,Gambia,2006,307776.0,0.001,1430000.0,28.177333333333333,0.7660216093063354,1588501.0,482.2292269921992 +1309,Gambia,2007,351744.0,0.0011,1470000.0,28.36491666666667,0.7938360571861267,1629648.0,487.1211802709093 +1310,Gambia,2008,366400.0,0.0011,1640000.0,28.346083333333336,0.8393597602844238,1671296.0,502.22088743371836 +1311,Gambia,2009,381056.0,0.0012,1970000.0,27.959,0.8934959173202515,1713278.0,521.5125142097496 +1312,Gambia,2010,432352.0,0.0013,2160000.0,28.802916666666672,0.9518080949783324,1755456.0,542.199915565148 +1313,Gambia,2011,447008.0,0.0013,1920000.0,28.2825,0.9109268188476562,1797860.0,506.67283261636396 +1314,Gambia,2012,454336.0,0.0013,2020000.0,28.301666666666666,0.9619365334510804,1840454.0,522.6626329433283 +1315,Gambia,2013,432352.0,0.0012,2090000.0,28.724375000000006,1.0080028772354126,1883051.0,535.3030147539354 +1316,Georgia,1990,17153177.81,0.0773,47970000.0,8.84425,16.879283905029293,5431164.0,3107.8575246538862 +1317,Georgia,1991,16538977.75,0.0737,37820000.0,8.857083333333334,13.317754745483398,5430453.0,2452.420589126432 +1318,Georgia,1992,15276955.81,0.0688,29840000.0,7.390916666666667,7.338082313537598,5371193.0,1366.1922618564627 +1319,Georgia,1993,9916290.59,0.0448,23570000.0,7.397833333333332,5.188024520874023,5268584.0,984.7094628981947 +1320,Georgia,1994,6069035.33,0.0269,16200000.0,8.718333333333332,4.648470401763916,5145842.0,903.344953413633 +1321,Georgia,1995,2291405.2,0.01,12720000.0,9.427583333333335,4.769330024719238,5041416.0,946.0298504862996 +1322,Georgia,1996,4055119.19,0.0172,11090000.0,9.117916666666668,5.303494930267334,4965838.0,1067.9959616619258 +1323,Georgia,1997,4425143.65,0.0185,10420000.0,8.550666666666666,5.861371994018555,4916657.0,1192.145800290432 +1324,Georgia,1998,4938636.22,0.0206,9900000.0,9.85875,6.0433616638183585,4882649.0,1237.7219136207332 +1325,Georgia,1999,4309734.12,0.018000000000000002,9630000.0,9.769166666666669,6.216761112213135,4852143.0,1281.2402916016974 +1326,Georgia,2000,4499409.28,0.0183,9950000.0,9.311166666666667,6.331046581268311,4818805.0,1313.8208707902293 +1327,Georgia,2001,3733750.46,0.0149,8140000.000000001,9.861749999999999,6.635281562805176,4793253.0,1384.2961268276838 +1328,Georgia,2002,3351383.75,0.0131,7460000.0,9.206,6.998486518859862,4774895.0,1465.6838566837312 +1329,Georgia,2003,3735506.98,0.0139,8010000.0,8.804583333333335,7.772386074066162,4750669.0,1636.0613787376392 +1330,Georgia,2004,4276237.8,0.0152,8119999.999999999,9.218583333333333,8.222871780395508,4742862.0,1733.736250473977 +1331,Georgia,2005,5016999.16,0.0171,8790000.0,9.4135,9.01130485534668,4790009.0,1881.2709653252593 +1332,Georgia,2006,6096128.86,0.0201,9100000.0,9.40375,9.860236167907717,4830793.0,2041.1216477103687 +1333,Georgia,2007,6364776.34,0.0206,9920000.0,9.525916666666667,11.100534439086914,4824432.0,2300.8997616894408 +1334,Georgia,2008,5110379.99,0.016,9030000.0,9.308583333333333,11.368955612182615,4823790.0,2356.851275072633 +1335,Georgia,2009,6134928.02,0.0195,9750000.0,9.552083333333334,10.953977584838867,4855888.0,2255.813475277615 +1336,Georgia,2010,6209072.14,0.0188,9490000.0,11.102416666666668,11.638537406921387,4902939.0,2373.787927388325 +1337,Georgia,2011,7785419.38,0.0227,14420000.0,8.523749999999998,12.479016304016113,4938341.0,2526.9652913835057 +1338,Georgia,2012,8247406.29,0.0236,15250000.0,9.787333333333333,13.271493911743164,4950270.0,2680.9636467795017 +1339,Georgia,2013,7664332.55,0.0218,15540000.0,10.686625,13.7210054397583,4942157.0,2776.3192144155482 +1340,Germany,1990,1012901808.0,4.5664,1107230000.0,9.6895,2568.63427734375,79380394.0,32358.547846761132 +1341,Germany,1991,1014972286.0,4.5252,1077610000.0,8.523083333333334,2699.8466796875,79984244.0,33754.7314904608 +1342,Germany,1992,966705828.7,4.3555,1029960000.0,9.505833333333333,2751.78662109375,80597764.0,34142.22038583788 +1343,Germany,1993,956994179.8,4.3199,1022250000.0,8.6335,2725.474853515625,81132272.0,33592.980774846605 +1344,Germany,1994,940407989.4,4.1733,1009380000.0,9.847000000000001,2792.44970703125,81414164.0,34299.31070754777 +1345,Germany,1995,940086746.0,4.086,1002760000.0,9.033499999999998,2840.972412109375,81653702.0,34792.940705975285 +1346,Germany,1996,960174299.5,4.0819,1030960000.0,7.374416666666668,2864.208740234375,81890667.0,34976.009417952046 +1347,Germany,1997,932347358.9,3.8985,992920000.0,9.048666666666666,2917.173828125,82011073.0,35570.48726974954 +1348,Germany,1998,924404529.9,3.8541,966780000.0,9.17175,2974.9228515625,82023672.0,36269.076707057204 +1349,Germany,1999,896882683.4,3.7479,932270000.0,9.620750000000001,3034.03857421875,82074778.0,36966.759437579596 +1350,Germany,2000,900960091.2,3.6686,922880000.0,10.020416666666666,3123.907958984375,82183670.0,38011.29785253415 +1351,Germany,2001,917476828.5,3.6498,970630000.0,9.090416666666666,3176.873046875,82320660.0,38591.442863492586 +1352,Germany,2002,900955099.1,3.534,952160000.0,9.637333333333334,3176.873046875,82458415.0,38526.97201219548 +1353,Germany,2003,901969864.6,3.3572,951080000.0,9.494250000000001,3154.3203125,82504552.0,38232.07612229687 +1354,Germany,2004,887996509.2,3.1503,931260000.0,9.053416666666664,3191.224853515625,82487209.0,38687.51158153048 +1355,Germany,2005,867223505.9,2.9644,906560000.0,9.128416666666665,3213.777587890625,82439417.0,38983.50697810763 +1356,Germany,2006,878747131.4,2.9014,918190000.0,9.706000000000001,3332.6923828125,82346364.0,40471.63980200147 +1357,Germany,2007,852096162.2,2.7594,888290000.0,9.9975,3441.35595703125,82236860.0,41846.879331619064 +1358,Germany,2008,854420962.4,2.6805,892660000.0,9.642166666666666,3478.602294921875,82065368.0,42388.188583055824 +1359,Germany,2009,789963521.6,2.506,834120000.0,9.359166666666665,3283.14453125,81837700.0,40117.75173605808 +1360,Germany,2010,833684918.0,2.5212,864720000.0,8.008999999999999,3417.094482421875,81644454.0,41853.35702559631 +1361,Germany,2011,810802325.3,2.3599,812510000.0,9.819583333333334,3542.16015625,81471834.0,43477.11328371472 +1362,Germany,2012,815197408.6,2.3345,826220000.0,9.227166666666667,3559.58740234375,81305856.0,43780.20941497437 +1363,Germany,2013,832642995.5,2.3649,844050000.0,9.23525,3577.0146484375,81147265.0,44080.53245463664 +1364,Ghana,1990,3754831.38,0.0169,34350000.0,27.24475,12.047718048095705,15548811.0,774.8321108344362 +1365,Ghana,1991,3865156.48,0.0172,34680000.0,26.96941666666667,12.684057235717773,15892340.0,798.1239537864011 +1366,Ghana,1992,3888096.22,0.0175,35400000.0,26.833250000000003,13.176124572753904,16233043.0,811.6854352418031 +1367,Ghana,1993,4435232.77,0.02,35860000.0,27.088916666666666,13.815167427062988,16637953.0,830.3405729697031 +1368,Ghana,1994,4828571.67,0.0214,36310000.0,26.96608333333334,14.27106761932373,17022080.0,838.3856508325499 +1369,Ghana,1995,5154176.34,0.0224,36910000.0,27.212249999999997,14.857953071594238,17321469.0,857.7767319616044 +1370,Ghana,1996,5468617.23,0.0232,36940000.0,27.13083333333333,15.541784286499025,17617186.0,882.1944825069693 +1371,Ghana,1997,6204170.8,0.0259,38320000.0,27.072916666666668,16.193973541259766,17927437.0,903.3066768696364 +1372,Ghana,1998,6245892.71,0.026000000000000002,41940000.0,27.6635,16.95515251159668,18261420.0,928.4684603714653 +1373,Ghana,1999,6369458.53,0.0266,40970000.0,27.119916666666665,17.7011775970459,18611539.0,951.0861835254943 +1374,Ghana,2000,6100546.08,0.0248,41760000.0,27.245916666666663,18.356121063232425,18981004.0,967.0785098213153 +1375,Ghana,2001,6732552.29,0.0268,59880000.0,27.27875,19.09036445617676,19366666.0,985.7331383820404 +1376,Ghana,2002,7231478.89,0.0284,61100000.0,27.401083333333332,19.949432373046875,19778785.0,1008.627798575437 +1377,Ghana,2003,7437285.5,0.0277,61180000.0,27.5335,20.98680305480957,20211370.0,1038.3661797695836 +1378,Ghana,2004,7152116.1,0.0254,60030000.0,27.415833333333335,22.16206359863281,20650251.0,1073.2103739868737 +1379,Ghana,2005,6803420.8,0.0233,63070000.0,27.60116666666667,23.46962738037109,21106982.0,1111.936674810785 +1380,Ghana,2006,9160029.48,0.0302,19080000.0,27.58158333333333,24.971662521362305,21583020.0,1157.0050216032002 +1381,Ghana,2007,9619301.33,0.0312,19820000.0,27.62766666666667,26.0571346282959,22068569.0,1180.7351273340785 +1382,Ghana,2008,8925880.6,0.027999999999999997,19220000.0,27.367833333333333,28.44131088256836,22554470.0,1261.005507226211 +1383,Ghana,2009,7497775.37,0.0238,21010000.0,27.557583333333337,29.81914710998535,23051965.0,1293.562050349519 +1384,Ghana,2010,9710739.42,0.0294,23160000.0,27.935,32.17477035522461,23571204.0,1365.0032622527306 +1385,Ghana,2011,9533531.44,0.0277,42290000.0,27.59975,36.69440078735352,24107863.0,1522.0926378814047 +1386,Ghana,2012,11519780.08,0.033,44700000.0,27.2795,40.10433578491211,24652402.0,1626.7922202839347 +1387,Ghana,2013,14281120.0,0.0406,47720000.0,27.717500000000005,43.0369758605957,25199609.0,1707.8430010797272 +1388,Greece,1990,83375363.77,0.3759,94720000.0,15.499333333333333,197.6539459228516,10129603.0,19512.506652319105 +1389,Greece,1991,83350936.29,0.3716,94780000.0,14.412833333333333,203.78121948242188,10250880.0,19879.3878654732 +1390,Greece,1992,84915798.12,0.3826,96850000.0,14.785333333333334,205.2076873779297,10325429.0,19874.010791990306 +1391,Greece,1993,84229453.79,0.3802,97210000.0,15.040583333333332,201.9243621826172,10382995.0,19447.602756489552 +1392,Greece,1994,86391992.45,0.3834,99840000.0,16.044666666666664,205.9628448486328,10429826.0,19747.486185160982 +1393,Greece,1995,86945641.44,0.3779,104280000.0,15.102250000000003,210.28749084472656,10457554.0,20108.66889568312 +1394,Greece,1996,89098551.49,0.3788,105290000.0,14.84675,216.3061828613281,10479420.0,20641.045292709718 +1395,Greece,1997,93804204.26,0.3922,106410000.0,14.763250000000001,226.0057830810547,10502372.0,21519.4989361503 +1396,Greece,1998,98624771.25,0.4112,110620000.0,15.506083333333336,234.80848693847656,10520068.0,22320.054104068204 +1397,Greece,1999,97941650.36,0.4093,111500000.0,15.992166666666668,242.0232086181641,10536749.0,22969.43854486465 +1398,Greece,2000,102982297.8,0.4193,117200000.0,15.673333333333332,251.50994873046875,10559110.0,23819.237485968868 +1399,Greece,2001,105368972.3,0.4192,117940000.0,15.931583333333334,261.9013671875,10581515.0,24750.83834285544 +1400,Greece,2002,105011397.7,0.4119,118130000.0,15.622333333333335,272.17544555664057,10603863.0,25667.57469015213 +1401,Greece,2003,109083182.1,0.406,121380000.0,15.472583333333333,287.94671630859375,10625945.0,27098.457248611183 +1402,Greece,2004,109530021.0,0.3886,121210000.0,15.455666666666668,302.5196838378906,10647529.0,28412.19627933304 +1403,Greece,2005,113925112.1,0.3894,123280000.0,15.175333333333334,304.33221435546875,10668354.0,28526.63253914041 +1404,Greece,2006,112464909.9,0.3713,121590000.0,15.21266666666667,321.5343933105469,10688058.0,30083.518756218094 +1405,Greece,2007,114582632.9,0.3711,125140000.0,16.083250000000003,332.0606384277344,10706290.0,31015.472066209157 +1406,Greece,2008,111112528.2,0.3486,121420000.0,16.054916666666667,330.9476623535156,10722816.0,30863.875902889282 +1407,Greece,2009,104340534.0,0.331,114190000.0,15.956166666666666,316.7144775390625,10737428.0,29496.307452684432 +1408,Greece,2010,97342975.8,0.2944,107440000.0,16.371250000000003,299.361572265625,10749943.0,27847.735775494344 +1409,Greece,2011,94531703.27,0.2751,105110000.0,15.261333333333331,272.02236938476557,10760136.0,25280.56981666083 +1410,Greece,2012,91417801.13,0.2618,99910000.0,16.0395,252.1634063720703,10767827.0,23418.226014596105 +1411,Greece,2013,81722579.66,0.2321,92290000.0,17.1535,243.98971557617188,10772967.0,22648.33036025933 +1412,Grenada,1990,106256.0,0.0005,1840000.0,27.205666666666662,0.4594407975673676,94204.0,4877.083749812828 +1413,Grenada,1991,109920.0,0.0005,1850000.0,27.14166666666667,0.4658065438270569,94060.0,4952.227767670177 +1414,Grenada,1992,117248.0,0.0005,1870000.0,27.157083333333336,0.4618418216705322,94648.0,4879.572961610728 +1415,Grenada,1993,120912.0,0.0005,1890000.0,27.34841666666666,0.4527952075004578,95758.0,4728.536597469222 +1416,Grenada,1994,142896.0,0.0006,1920000.0,27.251000000000005,0.4604635238647461,96750.0,4759.31290816275 +1417,Grenada,1995,150224.0,0.0007,1940000.0,27.635333333333335,0.4702689945697784,97743.0,4811.280547658435 +1418,Grenada,1996,153888.0,0.0007,1960000.0,27.286250000000006,0.4911406338214874,98654.0,4978.415815085931 +1419,Grenada,1997,168544.0,0.0007,1990000.0,27.452333333333332,0.5157737135887146,99568.0,5180.115233696716 +1420,Grenada,1998,175872.0,0.0007,2010000.0,27.945999999999998,0.5763952136039734,100385.0,5741.846028828742 +1421,Grenada,1999,194192.0,0.0008,2040000.0,27.463916666666666,0.6161570549011229,100985.0,6101.471059079299 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+1543,Honduras,2001,5608876.33,0.0223,42830000.0,25.154749999999996,10.857538223266602,6526981.0,1663.485495555541 +1544,Honduras,2002,5990210.85,0.0235,43320000.0,25.43258333333333,11.265167236328125,6693687.0,1682.9539887849735 +1545,Honduras,2003,6660647.54,0.0248,48800000.0,25.548750000000002,11.777398109436035,6862050.0,1716.3089906713062 +1546,Honduras,2004,7243355.39,0.0257,45790000.0,25.463916666666666,12.511401176452635,7029439.0,1779.8577064901815 +1547,Honduras,2005,7426530.27,0.0254,49280000.0,25.62641666666666,13.26841640472412,7193065.0,1844.6123321176885 +1548,Honduras,2006,6848710.82,0.0226,45700000.0,25.589916666666667,14.139784812927244,7355157.0,1922.4314060090421 +1549,Honduras,2007,8598354.49,0.0278,47480000.0,25.383416666666665,15.01480197906494,7516214.0,1997.654933596215 +1550,Honduras,2008,8484700.67,0.0266,47360000.0,25.107833333333335,15.650168418884276,7675849.0,2038.884352582272 +1551,Honduras,2009,7681003.92,0.0244,47670000.0,25.51858333333333,15.269614219665527,7833696.0,1949.22220873334 +1552,Honduras,2010,7800705.96,0.0236,47330000.0,25.24566666666667,15.83934497833252,7989415.0,1982.5412722123608 +1553,Honduras,2011,8773494.7,0.0255,50260000.0,25.474249999999998,16.446891784667972,8143564.0,2019.6184108908544 +1554,Honduras,2012,8787120.66,0.0252,48380000.0,25.260583333333326,17.125932693481445,8296693.0,2064.1878268222586 +1555,Honduras,2013,8871001.53,0.0252,50230000.0,25.732333333333333,17.604013442993164,8448465.0,2083.693717496985 +1556,Hungary,1990,73455459.18,0.3312,90790000.0,10.920333333333334,96.29882049560548,10371878.0,9284.607907613788 +1557,Hungary,1991,69777271.55,0.3111,85780000.0,9.680166666666667,91.8265380859375,10397808.0,8831.336189890937 +1558,Hungary,1992,62408845.03,0.2812,75740000.0,10.996333333333332,89.01280212402344,10383053.0,8572.892975122388 +1559,Hungary,1993,63539702.92,0.2868,76370000.0,10.120916666666668,88.49999237060547,10330031.0,8567.253319046718 +1560,Hungary,1994,61882727.41,0.2746,75980000.0,11.545416666666668,91.10822296142578,10296552.0,8848.420613174758 +1561,Hungary,1995,61612876.49,0.2678,74240000.0,10.30275,92.46530151367188,10280807.0,8993.973091185533 +1562,Hungary,1996,63176983.12,0.2686,76450000.0,9.357000000000001,92.4783935546875,10259301.0,9014.102769251776 +1563,Hungary,1997,61755899.9,0.2582,74820000.0,9.871916666666666,95.56732177734376,10229552.0,9342.278310657568 +1564,Hungary,1998,61348289.98,0.2558,74680000.0,10.361833333333333,99.61540985107422,10200404.0,9765.829848609352 +1565,Hungary,1999,61828203.67,0.2584,75450000.0,10.655,102.79004669189452,10171610.0,10105.582763386969 +1566,Hungary,2000,58551660.59,0.2384,72660000.0,11.700333333333333,107.11666107177734,10147425.0,10556.043633904892 +1567,Hungary,2001,60171658.83,0.2394,76650000.0,10.523749999999998,111.2304916381836,10129929.0,10980.382156497208 +1568,Hungary,2002,59162844.39,0.2321,74680000.0,11.432083333333333,116.2672348022461,10112777.0,11497.063052240359 +1569,Hungary,2003,61936782.98,0.2305,77620000.0,10.597916666666668,120.7419204711914,10091987.0,11964.137535174332 +1570,Hungary,2004,60406241.13,0.2143,76420000.0,10.3115,126.78495025634766,10072608.0,12587.102591141009 +1571,Hungary,2005,60499964.51,0.2068,75390000.0,9.942583333333332,132.34854125976562,10057624.0,13159.02655137691 +1572,Hungary,2006,59839431.29,0.1976,72570000.0,10.629083333333334,137.44551086425778,10046269.0,13681.249313974951 +1573,Hungary,2007,58522308.45,0.1895,70430000.0,11.707250000000002,138.04307556152344,10034228.0,13757.219345775622 +1574,Hungary,2008,57438167.2,0.1802,69010000.0,11.531083333333335,139.22406005859378,10020481.0,13893.949807259129 +1575,Hungary,2009,51781050.5,0.1643,63050000.0,11.381083333333335,130.03530883789062,10007168.0,12994.216629309174 +1576,Hungary,2010,52120536.86,0.1576,62940000.0,10.347833333333334,130.92263793945312,9992339.0,13102.301467099258 +1577,Hungary,2011,50358001.09,0.1466,63480000.0,10.877,133.0928955078125,9976062.0,13341.225777046346 +1578,Hungary,2012,46808494.95,0.134,59710000.0,11.293666666666667,130.92161560058594,9958453.0,13146.782497300126 +1579,Hungary,2013,43767699.67,0.1243,57160000.0,12.3015,133.66264343261722,9939470.0,13447.663047689382 +1580,Iceland,1990,2237420.56,0.0101,3380000.0,1.7747500000000003,7.92061185836792,254719.0,31095.488983420633 +1581,Iceland,1991,2117704.28,0.0094,3290000.0,2.5400833333333335,7.902907371520996,256831.0,30770.846866308966 +1582,Iceland,1992,2263016.39,0.0102,3060000.0,1.7690833333333336,7.636271476745605,259012.0,29482.30767974304 +1583,Iceland,1993,2421023.49,0.0109,2970000.0,1.7032499999999995,7.736570358276367,261270.0,29611.399541762803 +1584,Iceland,1994,2361911.91,0.0105,3140000.0,1.5843333333333334,8.015771865844728,266075.0,30125.986529530117 +1585,Iceland,1995,2465117.75,0.0107,3070000.0,1.0682500000000001,8.025116920471191,267527.0,29997.40930998064 +1586,Iceland,1996,2520135.12,0.0107,3270000.0,2.3195000000000006,8.409124374389647,268999.0,31260.8016178114 +1587,Iceland,1997,2615622.63,0.0109,3270000.0,2.223666666666667,8.838323593139648,271190.0,32590.89049426472 +1588,Iceland,1998,2635456.44,0.011000000000000001,3420000.0,1.60775,9.458650588989258,273998.0,34520.87456473864 +1589,Iceland,1999,2841273.75,0.0119,3440000.0,1.84225,9.827016830444336,277278.0,35441.026083729455 +1590,Iceland,2000,2933983.78,0.0119,3470000.0,2.10325,10.279353141784668,281038.0,36576.3816344575 +1591,Iceland,2001,2862684.17,0.0114,3280000.0,2.364916666666667,10.68307876586914,284805.0,37510.15173844961 +1592,Iceland,2002,2987901.53,0.0117,3330000.0,2.6658333333333335,10.74188995361328,287448.0,37369.854560175336 +1593,Iceland,2003,2979691.74,0.0111,3320000.0,3.340833333333333,10.995044708251953,289516.0,37977.330124248576 +1594,Iceland,2004,3101239.5,0.011000000000000001,3410000.0,2.9484166666666667,11.882816314697266,292078.0,40683.70885413234 +1595,Iceland,2005,2968905.33,0.0101,3310000.0,2.2003333333333335,12.642189979553224,296699.0,42609.47957206873 +1596,Iceland,2006,3147041.63,0.0104,4030000.0000000005,2.6824999999999997,13.27647304534912,303734.0,43710.855700544285 +1597,Iceland,2007,3493947.41,0.0113,3780000.0,2.5614166666666667,14.528215408325195,311492.0,46640.73365712505 +1598,Iceland,2008,3794949.86,0.0119,3670000.0,2.3129166666666667,14.769227027893065,317363.0,46537.331156729255 +1599,Iceland,2009,3731083.46,0.0118,3370000.0,2.464,13.808450698852539,318453.0,43361.031922615075 +1600,Iceland,2010,3620931.2,0.011000000000000001,3180000.0,2.6764166666666664,13.683690071105955,317924.0,43040.75839227601 +1601,Iceland,2011,3486406.56,0.0101,2950000.0,2.3154166666666662,13.94116497039795,318852.0,43722.99678345423 +1602,Iceland,2012,3488686.34,0.01,2950000.0,2.463416666666667,14.121903419494627,320533.0,44057.56480454315 +1603,Iceland,2013,3481781.92,0.0099,3040000.0,2.9202500000000002,14.705681800842285,323746.0,45423.51658659037 +1604,India,1990,616579098.4,2.7797,1183050000.0,24.17816666666667,505.7489013671875,838158831.0,603.404608603578 +1605,India,1991,656740892.9,2.928,1238060000.0,24.311750000000004,511.09381103515625,853724391.0,598.6637097676131 +1606,India,1992,698756949.7,3.1482,1269780000.0,24.222916666666663,539.114013671875,870113186.0,619.5906720483547 +1607,India,1993,722894259.4,3.2632,1301280000.0,24.408666666666672,564.7261352539062,886743707.0,636.853840400478 +1608,India,1994,764241966.7,3.3915,1347490000.0,24.29641666666667,602.330810546875,903579294.0,666.6053710465782 +1609,India,1995,811460787.1,3.5269999999999997,1413990000.0,24.429749999999995,647.9542846679688,920584971.0,703.850600519926 +1610,India,1996,878826964.5,3.736,1459040000.0,24.436916666666672,696.8717651367188,937725479.0,743.1511468365666 +1611,India,1997,915049313.0,3.8262,1515750000.0,24.04733333333333,725.0938110351562,954926046.0,759.3193358505966 +1612,India,1998,933549258.1,3.8922,1548000000.0,24.755416666666665,769.9366455078125,972112809.0,792.0239692138574 +1613,India,1999,995233071.6,4.1588,1627120000.0,24.659833333333335,838.0433349609375,989250189.0,847.1500377554513 +1614,India,2000,1029637759.0,4.1926,1664020000.0,24.498,870.2324829101562,1006300297.0,864.7840863254324 +1615,India,2001,1035640714.0,4.1198,1569360000.0,24.581916666666668,912.2122802734376,1023295084.0,891.4459714862047 +1616,India,2002,1047595651.0,4.1092,1591500000.0,24.913,946.9125366210938,1040284824.0,910.2435359771181 +1617,India,2003,1091873009.0,4.064,1634280000.0,24.649916666666666,1021.343505859375,1057251124.0,966.0368125173778 +1618,India,2004,1145545234.0,4.064,1728850000.0,24.634583333333335,1102.2640380859377,1074159021.0,1026.1646707205166 +1619,India,2005,1210145667.0,4.1366,1803320000.0,24.49541666666667,1189.60107421875,1090973030.0,1090.4037418952053 +1620,India,2006,1287149211.0,4.2499,1936950000.0,24.732083333333332,1285.4915771484375,1107624346.0,1160.58443622224 +1621,India,2007,1390253491.0,4.5021,2078820000.0,24.649916666666666,1383.9708251953125,1124134801.0,1231.1431191029487 +1622,India,2008,1547991039.0,4.8563,2170720000.0,24.406000000000006,1426.6898193359375,1140566211.0,1250.8610246178312 +1623,India,2009,1720120560.0,5.4567,2355660000.0,25.146666666666658,1538.8544921875,1156897766.0,1330.155989070775 +1624,India,2010,1700027394.0,5.1412,2457760000.0,25.05083333333333,1669.6199951171875,1173108018.0,1423.2448926260665 +1625,India,2011,1817171600.0,5.289,2800430000.0,24.41558333333333,1757.130126953125,1189172906.0,1477.6069300666736 +1626,India,2012,1983758813.0,5.681,2943690000.0,24.640833333333337,1853.0059814453125,1205073612.0,1537.6703655222952 +1627,India,2013,1997878578.0,5.6745,2994460000.0,25.413249999999998,1971.3408203125,1220800359.0,1614.793775066788 +1628,Indonesia,1990,150345427.3,0.6778,1512130000.0,26.237833333333338,309.8211669921875,181599235.0,1706.0708818084365 +1629,Indonesia,1991,180695289.5,0.8056,1529730000.0,26.075166666666664,331.2359313964844,184751080.0,1792.8768340433212 +1630,Indonesia,1992,203659373.7,0.9176,1550510000.0,26.17016666666667,352.75799560546875,187915917.0,1877.2119000726727 +1631,Indonesia,1993,219856328.5,0.9924,1566950000.0,26.138833333333334,375.6745910644531,191081420.0,1966.0445848918912 +1632,Indonesia,1994,222967641.4,0.9895,1583950000.0,26.08425,404.0003356933594,194301847.0,2079.2408406357526 +1633,Indonesia,1995,226669943.6,0.9852,1615020000.0,26.229666666666663,437.2091979980469,197603427.0,2212.55878319381 +1634,Indonesia,1996,255207090.1,1.0849,1403980000.0,26.215000000000003,471.3910217285156,200984463.0,2345.410260535988 +1635,Indonesia,1997,280896520.2,1.1745,2390380000.0,26.371916666666664,493.54583740234375,204258115.0,2416.2850881216823 +1636,Indonesia,1998,216104916.5,0.9009999999999999,1598720000.0,26.75475,428.7594299316406,207471653.0,2066.5928271735543 +1637,Indonesia,1999,244101581.6,1.02,1484750000.0,26.133499999999998,432.1514587402344,210746403.0,2050.575727929432 +1638,Indonesia,2000,265983184.0,1.0831,1406680000.0,26.190250000000002,453.4136352539063,214090575.0,2117.8589260826 +1639,Indonesia,2001,297770560.0,1.1845,1663980000.0,26.32975,469.93359375,217197016.0,2163.6282229126027 +1640,Indonesia,2002,307578168.0,1.2065,2199080000.0,26.529,491.078125,220276027.0,2229.3761681111127 +1641,Indonesia,2003,316854168.0,1.1794,1822690000.0,26.445500000000006,514.553466796875,223347627.0,2303.8232987220185 +1642,Indonesia,2004,340076520.0,1.2065,2205430000.0,26.43483333333333,540.4400024414061,226403092.0,2387.0698834864247 +1643,Indonesia,2005,343579936.0,1.1744,1912930000.0,26.45375,571.2049560546875,229244745.0,2491.6817877534663 +1644,Indonesia,2006,346699720.0,1.1447,2681880000.0,26.381750000000007,602.6266479492188,232199251.0,2595.2997064112783 +1645,Indonesia,2007,377122624.0,1.2212,2057190000.0,26.29308333333334,640.8634643554688,235098987.0,2725.930351862676 +1646,Indonesia,2008,417792048.0,1.3107,2043340000.0,26.065250000000002,679.403076171875,237938809.0,2855.3689035733337 +1647,Indonesia,2009,447301088.0,1.419,2475020000.0,26.455000000000002,710.8517456054688,240714694.0,2953.0882963275553 +1648,Indonesia,2010,427789520.0,1.2937,2049350000.0,26.537000000000003,755.0941162109375,243422739.0,3101.9867712972273 +1649,Indonesia,2011,601325416.0,1.7502,2276710000.0,26.202250000000003,801.6818237304688,246065161.0,3258.0062145834163 +1650,Indonesia,2012,633145032.0,1.8132,2319980000.0,26.322083333333335,850.0236206054688,248645008.0,3418.6233113735743 +1651,Indonesia,2013,486130031.7,1.3807,2294460000.0,26.467,897.2616577148438,251160124.0,3572.468604589651 +1652,Iran,1990,209238343.5,0.9433,260770000.0,18.42641666666667,235.119140625,57596486.0,4082.1785659805705 +1653,Iran,1991,225954206.0,1.0074,301180000.0,18.02925,264.23483276367193,59938948.0,4408.399572906616 +1654,Iran,1992,226957012.8,1.0226,322100000.0,16.949749999999998,271.72518920898443,61387591.0,4426.386257916268 +1655,Iran,1993,235576603.6,1.0634,320430000.0,17.741083333333332,274.55111694335943,61732529.0,4447.430250968001 +1656,Iran,1994,263520377.9,1.1694,352910000.0,18.386,270.40103149414057,61928011.0,4366.376816044369 +1657,Iran,1995,271147525.0,1.1785,370190000.0,18.40791666666667,276.5733642578125,62693318.0,4411.528582006339 +1658,Iran,1996,274007761.9,1.1649,384420000.0,18.206416666666666,290.8813171386719,63434648.0,4585.527409857652 +1659,Iran,1997,267099278.9,1.1169,401820000.0,18.303250000000002,292.28350830078125,64228293.0,4550.6971250315055 +1660,Iran,1998,306006254.7,1.2758,409930000.0,19.16991666666667,298.6488952636719,65019303.0,4593.23433940336 +1661,Iran,1999,379627035.1,1.5864,447820000.0,19.15791666666667,301.2037048339844,65763053.0,4580.1356703129995 +1662,Iran,2000,369263500.2,1.5036,466630000.0,18.892083333333336,318.84844970703125,66482211.0,4795.996476516571 +1663,Iran,2001,394931363.0,1.5711,480140000.0,19.47383333333334,321.33111572265625,67250500.0,4778.122329538906 +1664,Iran,2002,397798324.6,1.5603,504890000.0,19.142416666666666,344.6775817871094,67780932.0,5085.170292245456 +1665,Iran,2003,413993478.0,1.5409,534120000.0,18.915166666666668,374.7837524414063,68916744.0,5438.210378038264 +1666,Iran,2004,442089693.9,1.5684,571070000.0,19.149916666666666,391.1786804199219,69600640.0,5620.331658156044 +1667,Iran,2005,463529961.4,1.5845,609880000.0,18.806666666666665,403.6565246582031,70459627.0,5728.9052162907865 +1668,Iran,2006,503790553.5,1.6634,658380000.0,19.211916666666667,423.8385314941406,71306699.0,5943.880973849885 +1669,Iran,2007,513313832.8,1.6623,696800000.0,18.74041666666667,458.4058532714844,72203143.0,6348.835164578421 +1670,Iran,2008,534910104.2,1.6781,710220000.0,18.90491666666667,459.5557861328125,73119823.0,6284.968525331529 +1671,Iran,2009,546884971.4,1.7349,734520000.0,19.1095,464.1852722167969,74081610.0,6265.863717281481 +1672,Iran,2010,564034508.7,1.7058,740760000.0,20.09016666666667,491.0984802246094,75034532.0,6544.966259329896 +1673,Iran,2011,579040046.4,1.6853,817820000.0,18.915416666666665,504.091552734375,75847232.0,6646.143035705971 +1674,Iran,2012,600680295.7,1.7202,825980000.0,18.959666666666664,466.5641784667969,76680536.0,6084.519003190026 +1675,Iran,2013,608962881.9,1.7296,846810000.0,20.541000000000004,465.6587219238281,77665975.0,5995.659256499748 +1676,Iraq,1990,46843774.39,0.2112,77770000.0,22.5415,71.26209259033203,18139958.0,3928.4596243459896 +1677,Iraq,1991,45185332.67,0.2015,57450000.0,22.28033333333333,25.620784759521484,17489877.0,1464.892220769848 +1678,Iraq,1992,58889341.66,0.2653,79130000.0,20.68583333333333,33.97116470336914,17896515.0,1898.1999961092504 +1679,Iraq,1993,63129769.45,0.285,102100000.0,21.90308333333333,44.26097106933594,18458363.0,2397.881711901317 +1680,Iraq,1994,71089157.24,0.3155,113160000.0,22.642416666666666,45.96702575683594,19044950.0,2413.6070589230185 +1681,Iraq,1995,74084185.61,0.322,109090000.0,22.430999999999997,46.9415397644043,19657981.0,2387.912561539473 +1682,Iraq,1996,69284354.51,0.2945,110930000.0,23.01375,52.1148681640625,20298494.0,2567.4253550072485 +1683,Iraq,1997,68078825.31,0.2847,129460000.0,21.921166666666668,63.18298721313477,20965466.0,3013.6695846939333 +1684,Iraq,1998,72101476.4,0.3006,95900000.0,23.455416666666665,85.20674133300781,21657963.0,3934.1992288475053 +1685,Iraq,1999,71681328.47,0.2995,79300000.0,23.493333333333336,100.18801879882812,22379177.0,4476.841074130122 +1686,Iraq,2000,71715411.94,0.292,89670000.0,22.988833333333336,101.59712982177734,23128328.0,4392.757220572856 +1687,Iraq,2001,84555922.42,0.3364,101240000.0,23.517083333333336,103.93926239013672,23908878.0,4347.308242157441 +1688,Iraq,2002,86320877.25,0.3386,93790000.0,22.89558333333333,96.76726531982422,24779946.0,3905.063607476151 +1689,Iraq,2003,90786641.5,0.3379,85800000.0,22.95941666666667,64.73648834228516,25700722.0,2518.858744212912 +1690,Iraq,2004,113635003.1,0.4031,90070000.0,22.778916666666664,99.79632568359376,26611119.0,3750.1739661377537 +1691,Iraq,2005,112980382.2,0.3862,104210000.0,22.696083333333334,104.18798065185548,27537849.0,3783.446581171081 +1692,Iraq,2006,98181050.58,0.3242,99700000.0,22.90433333333333,114.7716064453125,27836375.0,4123.080194361245 +1693,Iraq,2007,61491756.09,0.1991,93940000.0,23.015000000000004,116.35270690917969,27681323.0,4203.29284511364 +1694,Iraq,2008,92167049.56,0.2891,103300000.0,23.118166666666667,125.92633056640624,28335381.0,4444.137545438554 +1695,Iraq,2009,103489093.1,0.3283,126530000.0,22.998083333333337,130.1817626953125,29496529.0,4413.460400554673 +1696,Iraq,2010,110985346.7,0.3356,140200000.0,24.650333333333336,138.5167236328125,30526292.0,4537.620344875575 +1697,Iraq,2011,132830074.4,0.3866,151650000.0,22.593249999999998,148.9698486328125,31639690.0,4708.322004191966 +1698,Iraq,2012,151092578.6,0.4327,173610000.0,23.428916666666666,169.73092651367188,33102634.0,5127.414528815801 +1699,Iraq,2013,163570399.8,0.4646,184760000.0,24.240500000000004,182.6304626464844,34699942.0,5263.134521852641 +1700,Ireland,1990,32877935.7,0.1482,54340000.0,10.312666666666667,83.3329849243164,3508200.0,23753.772568358818 +1701,Ireland,1991,33606745.97,0.1498,54890000.0,9.825166666666666,84.94100952148438,3530771.0,24057.35447625586 +1702,Ireland,1992,33417982.65,0.1506,55100000.0,9.789,87.78082275390625,3557761.0,24673.05216789612 +1703,Ireland,1993,33649184.97,0.1519,55790000.0,9.641583333333333,90.14441680908205,3578349.0,25191.622395993803 +1704,Ireland,1994,34772191.86,0.1543,57310000.0,9.884833333333331,95.33296966552734,3595542.0,26514.21389752292 +1705,Ireland,1995,35794378.76,0.1556,58070000.0,10.518916666666668,104.51775360107422,3613890.0,28921.12200456412 +1706,Ireland,1996,37410959.7,0.159,59660000.0,9.591166666666668,112.67982482910156,3636179.0,30988.52527037353 +1707,Ireland,1997,38752357.22,0.162,61150000.0,10.6835,124.27452087402344,3681226.0,33759.00335215047 +1708,Ireland,1998,40650102.4,0.1695,64540000.00000001,10.4595,134.83810424804688,3728010.0,36168.92236019937 +1709,Ireland,1999,42377781.15,0.1771,66090000.0,10.458166666666669,149.15501403808594,3774856.0,39512.769238902336 +1710,Ireland,2000,45193952.6,0.184,69070000.0,10.13075,163.4140625,3822196.0,42753.97245457847 +1711,Ireland,2001,47557354.23,0.1892,71840000.0,10.023083333333334,172.89801025390625,3872700.0,44645.340525707186 +1712,Ireland,2002,46034585.94,0.1806,71090000.0,10.420666666666667,183.8066253662109,3933126.0,46732.961355982734 +1713,Ireland,2003,45639123.35,0.1699,71400000.0,10.522416666666667,189.5404815673828,4002170.0,47359.42790220876 +1714,Ireland,2004,46129741.06,0.1637,70620000.0,10.439083333333334,202.20411682128903,4091162.0,49424.61746107561 +1715,Ireland,2005,48104566.61,0.1644,71820000.0,10.575750000000001,214.3492584228516,4199185.0,51045.44296639742 +1716,Ireland,2006,47550194.52,0.157,67370000.0,10.654166666666667,226.18492126464844,4309024.0,52490.98665142 +1717,Ireland,2007,47622946.16,0.1542,65769999.99999999,10.842666666666666,237.96624755859372,4419984.0,53838.7124384599 +1718,Ireland,2008,47300105.44,0.1484,64680000.00000001,10.15875,228.60006713867188,4517706.0,50600.91717758347 +1719,Ireland,2009,42108507.15,0.1336,59260000.0,10.15725,218.0232696533203,4579996.0,47603.375560441615 +1720,Ireland,2010,41679517.5,0.126,58340000.0,9.258166666666666,222.134033203125,4622917.0,48050.621112843895 +1721,Ireland,2011,38009303.68,0.1106,60600000.0,10.345416666666667,230.39964294433597,4670976.0,49325.80320351377 +1722,Ireland,2012,38194753.69,0.1094,62000000.0,10.035833333333333,230.8204498291016,4722028.0,48881.635142591615 +1723,Ireland,2013,37182847.4,0.1056,61080000.0,9.97475,233.9063415527344,4775982.0,48975.54922793562 +1724,Israel,1990,35823058.99,0.1615,44070000.0,20.129833333333334,98.51910400390624,4459090.0,22093.993169885838 +1725,Israel,1991,36148642.25,0.1612,45140000.0,20.021916666666666,106.13079833984376,4630756.0,22918.676419108182 +1726,Israel,1992,42083020.26,0.1896,48000000.0,19.028666666666666,114.3657684326172,4804016.0,23806.28383265526 +1727,Israel,1993,44234274.63,0.1997,51960000.0,19.975416666666668,119.0739288330078,4979463.0,23913.00604764165 +1728,Israel,1994,47474499.67,0.2107,55390000.0,20.500333333333334,127.9185562133789,5156685.0,24806.354511353496 +1729,Israel,1995,49865162.03,0.2167,59190000.0,20.043333333333333,136.36492919921878,5334866.0,25561.078609887998 +1730,Israel,1996,51935752.0,0.2208,62310000.0,20.52808333333333,143.61187744140625,5496620.0,26127.30686156333 +1731,Israel,1997,54863876.45,0.2294,65239999.99999999,19.82316666666667,148.8700714111328,5642331.0,26384.498075552958 +1732,Israel,1998,56009298.46,0.2335,66640000.0,20.96641666666667,155.0728759765625,5791103.0,26777.778944108315 +1733,Israel,1999,54754513.71,0.2288,68840000.0,20.892166666666665,160.68006896972656,5942902.0,27037.30752580584 +1734,Israel,2000,59519380.4,0.2424,73680000.0,20.230416666666667,173.80609130859375,6097408.0,28504.914105894462 +1735,Israel,2001,63022354.14,0.2507,73410000.0,20.929750000000002,173.85006713867188,6234319.0,27885.975539376774 +1736,Israel,2002,59299733.73,0.2326,75490000.0,20.728333333333335,174.13140869140625,6352659.0,27410.79108628469 +1737,Israel,2003,62414677.8,0.2323,76270000.0,20.614,175.46798706054688,6475178.0,27098.558072155993 +1738,Israel,2004,58679118.7,0.2082,77390000.0,20.4905,183.4863739013672,6600852.0,27797.377353918437 +1739,Israel,2005,56524006.09,0.1932,76030000.0,20.43775,191.07069396972656,6725548.0,28409.68408369497 +1740,Israel,2006,62149082.51,0.2052,80410000.0,20.409166666666668,201.0270538330078,6850164.0,29346.31256025517 +1741,Israel,2007,62799906.46,0.2034,83650000.0,20.701583333333332,212.63345336914062,6975352.0,30483.544539277817 +1742,Israel,2008,67980275.98,0.2133,84310000.0,20.930083333333336,218.9877777099609,7101746.0,30835.765980642074 +1743,Israel,2009,63860946.43,0.2026,83920000.0,20.831416666666666,222.0133209228516,7232112.0,30698.269180960084 +1744,Israel,2010,68286901.35,0.2065,89280000.0,22.311166666666665,233.7329406738281,7368099.0,31722.285581915785 +1745,Israel,2011,68594737.31,0.1997,89500000.0,20.331249999999997,245.3452606201172,7507033.0,32682.05436423647 +1746,Israel,2012,74784472.67,0.2142,96690000.0,21.006083333333333,250.5372467041016,7647578.0,32760.338855530674 +1747,Israel,2013,65997069.72,0.1874,88910000.0,21.222375,260.92095947265625,7787179.0,33506.480263604615 +1748,Italy,1990,439944101.6,1.9834,441120000.0,13.716916666666664,1749.1806640625,56713409.0,30842.453220586685 +1749,Italy,1991,440094422.4,1.9621,438900000.0,12.853500000000002,1776.0908203125,56714708.0,31316.229650913483 +1750,Italy,1992,439210414.2,1.9789,434880000.0,13.58175,1790.9083251953125,56908936.0,31469.7207692534 +1751,Italy,1993,431600973.5,1.9483,431020000.0,13.409666666666666,1775.6353759765625,57086549.0,31104.268992973506 +1752,Italy,1994,425490635.3,1.8882,425970000.0,14.284833333333333,1813.8297119140625,57198073.0,31711.37796747213 +1753,Italy,1995,451978906.6,1.9645,449430000.0,13.234083333333333,1866.1920166015625,57294513.0,32571.91516055931 +1754,Italy,1996,444561573.8,1.8899,446930000.0,13.006999999999998,1890.1981201171875,57406046.0,32926.81262383387 +1755,Italy,1997,449465620.7,1.8794,450450000.0,13.838833333333332,1924.89013671875,57492895.0,33480.487227486985 +1756,Italy,1998,460240327.6,1.9189,461390000.0,13.662333333333336,1955.9979248046875,57567021.0,33977.75133795246 +1757,Italy,1999,464601452.9,1.9415,467880000.0,13.762999999999998,1986.508544921875,57655529.0,34454.779608767014 +1758,Italy,2000,470767746.9,1.9169,472220000.0,14.1255,2060.210205078125,57784373.0,35653.41454995324 +1759,Italy,2001,476201869.1,1.8944,469300000.0,13.954250000000002,2096.720947265625,57931053.0,36193.38573503273 +1760,Italy,2002,477709338.5,1.8738,477710000.0,13.934,2101.93212890625,58076703.0,36192.345989514084 +1761,Italy,2003,491480723.9,1.8293,495790000.0,14.296916666666668,2105.11279296875,58336021.0,36085.985243469906 +1762,Italy,2004,495527786.0,1.758,506070000.0,13.559833333333332,2138.414306640625,58716002.0,36419.617034562834 +1763,Italy,2005,495234014.9,1.6928,507320000.0,13.028583333333332,2158.721923828125,59037808.0,36565.075787165486 +1764,Italy,2006,489749969.4,1.617,495920000.0,13.787333333333335,2202.03857421875,59272880.0,37150.861814353375 +1765,Italy,2007,480491585.7,1.556,488700000.0,14.073333333333336,2234.49365234375,59626720.0,37474.70349440234 +1766,Italy,2008,468062059.9,1.4684,475470000.0,13.881250000000001,2211.0224609375,60091306.0,36794.381885085015 +1767,Italy,2009,415932393.3,1.3195,429390000.0,14.052916666666667,2089.81298828125,60461585.0,34564.31035146118 +1768,Italy,2010,424873161.2,1.2849,437790000.0,13.324916666666669,2125.058349609375,60748965.0,34980.980327967314 +1769,Italy,2011,413431505.5,1.2033,427740000.0,14.173166666666665,2137.311767578125,61016804.0,35028.24840806353 +1770,Italy,2012,391990364.4,1.1226,412410000.0,14.079,2077.060546875,61261254.0,33904.962945665466 +1771,Italy,2013,363399902.1,1.0322,381690000.0,14.165,2041.1656494140625,61482297.0,33199.24187956189 +1772,Jamaica,1990,7527131.68,0.0339,9450000.0,26.740250000000003,10.365013122558594,2347015.0,4416.253463466826 +1773,Jamaica,1991,7750598.13,0.0346,9470000.0,26.687833333333334,10.866497039794922,2357719.0,4608.902519678944 +1774,Jamaica,1992,7710769.34,0.0347,10280000.0,26.720416666666665,11.07899570465088,2377845.0,4659.25899486757 +1775,Jamaica,1993,8000275.38,0.0361,10480000.0,26.522999999999996,12.122316360473633,2406849.0,5036.591975846276 +1776,Jamaica,1994,8190958.86,0.0363,10550000.0,26.911,12.29001522064209,2436435.0,5044.261480664204 +1777,Jamaica,1995,9173406.4,0.0399,10810000.0,26.89391666666667,12.57882595062256,2469066.0,5094.568533454577 +1778,Jamaica,1996,9687712.52,0.0412,11150000.0,26.474833333333333,12.564520835876465,2501252.0,5023.29266938176 +1779,Jamaica,1997,10068095.32,0.0421,11300000.0,27.199166666666667,12.42122745513916,2531182.0,4907.283417446537 +1780,Jamaica,1998,9694035.0,0.0404,11840000.0,27.235583333333334,12.131247520446776,2560432.0,4737.969030400643 +1781,Jamaica,1999,9958188.24,0.0416,12040000.0,26.535666666666668,12.258342742919922,2588866.0,4735.024038679453 +1782,Jamaica,2000,10313989.68,0.042,12220000.0,26.46941666666667,12.36605739593506,2615632.0,4727.751226447397 +1783,Jamaica,2001,10576526.96,0.0421,12320000.0,26.70308333333334,12.53237247467041,2640514.0,4746.186717688454 +1784,Jamaica,2002,10202488.8,0.04,12470000.0,27.00075,12.782679557800293,2664984.0,4796.531445517231 +1785,Jamaica,2003,10666973.6,0.0397,12860000.0,27.06583333333333,13.251333236694336,2689150.0,4927.703265602267 +1786,Jamaica,2004,10574223.28,0.0375,12790000.0,26.903833333333335,13.42674446105957,2713001.0,4949.0377854853605 +1787,Jamaica,2005,10350586.88,0.0354,12730000.0,26.834999999999997,13.546751976013185,2736513.0,4950.370042463962 +1788,Jamaica,2006,11423418.48,0.0377,14090000.0,27.034166666666664,13.939489364624025,2759614.0,5051.246067248544 +1789,Jamaica,2007,9617054.96,0.0311,14030000.0,27.013916666666663,14.1390962600708,2782221.0,5081.945776439326 +1790,Jamaica,2008,10138434.84,0.0318,11580000.0,26.621499999999997,14.024324417114258,2804332.0,5000.950107588637 +1791,Jamaica,2009,7754252.16,0.0246,9730000.0,26.860166666666668,13.416138648986815,2825928.0,4747.516089931101 +1792,Jamaica,2010,7259598.88,0.022000000000000002,9010000.0,26.52125,13.220551490783693,2847232.0,4643.299699772864 +1793,Jamaica,2011,7797605.24,0.0227,9360000.0,26.640583333333336,13.44870662689209,2868380.0,4688.60702797122 +1794,Jamaica,2012,7418189.08,0.0212,9200000.0,26.885083333333338,13.366008758544922,2889187.0,4626.217949390234 +1795,Jamaica,2013,8038040.04,0.0228,9640000.0,27.12533333333333,13.433348655700685,2909714.0,4616.7247556635075 +1796,Japan,1990,1155263415.0,5.2082,1092000000.0,13.590583333333333,4703.5078125,123537399.0,38073.553843399284 +1797,Japan,1991,1167040517.0,5.2032,1105540000.0,12.783166666666666,4864.25,123962538.0,39239.677393504164 +1798,Japan,1992,1176942570.0,5.3027,1120550000.0,12.484250000000001,4905.501953125,124378689.0,39440.051929836634 +1799,Japan,1993,1170196733.0,5.2823,1111290000.0,12.055166666666667,4880.095703125,124738157.0,39122.71770317241 +1800,Japan,1994,1225173421.0,5.437,1165800000.0,13.229749999999997,4928.55810546875,125046546.0,39413.78841018728 +1801,Japan,1995,1237852824.0,5.3802,1182520000.0,12.475416666666668,5063.7060546875,125327055.0,40403.93396850744 +1802,Japan,1996,1249875688.0,5.3134,1194500000.0,12.082833333333333,5220.68115234375,125616709.0,41560.403818123836 +1803,Japan,1997,1243367132.0,5.199,1180480000.0,12.752416666666667,5276.85791015625,125924562.0,41904.91375428608 +1804,Japan,1998,1203710533.0,5.0186,1130420000.0,13.244750000000002,5217.3134765625,126236616.0,41329.636692435575 +1805,Japan,1999,1239840120.0,5.181,1159370000.0,13.080083333333333,5204.16845703125,126512858.0,41135.490410241546 +1806,Japan,2000,1262734462.0,5.1417,1174850000.0,12.833833333333336,5348.82568359375,126775612.0,42191.28268608753 +1807,Japan,2001,1248087424.0,4.9649,1189350000.0,12.450000000000001,5370.5595703125,127108598.0,42251.741068786716 +1808,Japan,2002,1277236546.0,5.0099,1225230000.0,12.837333333333333,5376.896484375,127371380.0,42214.322278482025 +1809,Japan,2003,1285385412.0,4.7843,1231650000.0,12.507,5459.0673828125,127552251.0,42798.67536647785 +1810,Japan,2004,1280036122.0,4.5411,1219100000.0,13.435666666666668,5579.4228515625,127693749.0,43693.78215657605 +1811,Japan,2005,1286856314.0,4.3988,1224800000.0,12.555916666666667,5672.1904296875,127715356.0,44412.751977041036 +1812,Japan,2006,1262639452.0,4.1689,1103330000.0,12.757083333333334,5752.73583984375,127727574.0,45039.10674639252 +1813,Japan,2007,1300051973.0,4.21,1144400000.0,13.192166666666665,5847.89697265625,127779038.0,45765.69885160858 +1814,Japan,2008,1229245450.0,3.8564,1065320000.0,12.874916666666666,5783.9482421875,127787736.0,45262.15443857227 +1815,Japan,2009,1160203633.0,3.6805,1002880000.0,12.933833333333332,5470.66552734375,127704286.0,42838.54284533371 +1816,Japan,2010,1211572868.0,3.6639999999999997,1055530000.0,13.215583333333333,5699.9814453125,127579145.0,44678.00317451963 +1817,Japan,2011,1261398590.0,3.6714,1261050000.0,12.737749999999998,5693.40234375,127469543.0,44664.80548808432 +1818,Japan,2012,1302082927.0,3.7288,1308390000.0,12.565666666666667,5778.52392578125,127368088.0,45368.69491030791 +1819,Japan,2013,1314069572.0,3.7323,1335180000.0,12.964749999999999,5894.10986328125,127253075.0,46318.0152093083 +1820,Jordan,1990,10247023.89,0.0462,18700000.0,19.921583333333334,8.689223289489746,3320836.0,2616.577057551094 +1821,Jordan,1991,9675813.98,0.0431,19130000.0,19.791749999999997,8.847705841064453,3696745.0,2393.377374166856 +1822,Jordan,1992,11983035.39,0.054000000000000006,21860000.0,18.604916666666664,10.499115943908693,3938474.0,2665.78272292992 +1823,Jordan,1993,11774021.43,0.0531,22660000.0,19.679916666666667,10.985321044921877,4058337.0,2706.852842659906 +1824,Jordan,1994,13249482.48,0.0588,23730000.0,20.265583333333336,11.53309726715088,4159452.0,2772.744406510973 +1825,Jordan,1995,13207729.04,0.0574,23340000.0,19.847916666666666,12.246574401855467,4248928.0,2882.2739292959227 +1826,Jordan,1996,13826423.7,0.0588,22600000.0,20.422,12.502202987670898,4353079.0,2872.0367784896393 +1827,Jordan,1997,14073593.99,0.0588,22090000.0,19.484583333333333,12.915762901306152,4458246.0,2897.05029765207 +1828,Jordan,1998,14251858.06,0.0594,21300000.0,20.773583333333335,13.30245304107666,4565026.0,2913.992831821037 +1829,Jordan,1999,14265372.84,0.0596,20360000.0,20.62116666666667,13.755996704101562,4674039.0,2943.0641687203643 +1830,Jordan,2000,15200037.16,0.0619,19850000.0,20.045250000000003,14.339973449707033,4785043.0,2996.832724325994 +1831,Jordan,2001,15610452.11,0.0621,20080000.0,20.730833333333333,15.095492362976074,4897811.0,3082.0896034934945 +1832,Jordan,2002,16420674.03,0.0644,21770000.0,20.322833333333335,15.968687057495115,5012128.0,3186.009427032813 +1833,Jordan,2003,16982038.16,0.0632,21480000.0,20.33141666666667,16.63315200805664,5127792.0,3243.725956134071 +1834,Jordan,2004,18669486.92,0.0662,23440000.0,20.25525,18.058164596557614,5253826.0,3437.145538614644 +1835,Jordan,2005,20436098.57,0.0699,24880000.0,20.137416666666667,19.52928733825684,5390895.0,3622.642870665601 +1836,Jordan,2006,20527441.92,0.0678,25720000.0,20.173333333333332,21.1098575592041,5868448.0,3597.178940531483 +1837,Jordan,2007,21436988.38,0.0694,27280000.0,20.43975,22.83574104309082,6355001.0,3593.3497167177193 +1838,Jordan,2008,20717915.44,0.065,26950000.0,20.572750000000003,24.487314224243164,6512446.0,3760.0794270298998 +1839,Jordan,2009,21336259.41,0.0677,27600000.0,20.498500000000003,25.82838249206543,6655519.0,3880.746564177103 +1840,Jordan,2010,20614344.74,0.0623,27440000.0,22.227999999999998,26.42537879943848,6800252.0,3885.9411091586717 +1841,Jordan,2011,21243769.85,0.0618,28590000.0,20.114500000000003,27.10894775390625,6962866.0,3893.360543475381 +1842,Jordan,2012,23813089.61,0.0682,32110000.0,20.820249999999998,27.827651977539066,7077408.0,3931.898793674049 +1843,Jordan,2013,23739282.99,0.0674,32380000.000000004,21.319125,28.61483192443848,7845504.0,3647.2904639954895 +1844,Kazakhstan,1990,266516854.4,1.2015,309080000.0,6.555750000000002,96.29095458984376,16775025.0,5740.137769681044 +1845,Kazakhstan,1991,257097203.1,1.1463,319110000.0,6.471,85.69894409179688,16902211.0,5070.280100739298 +1846,Kazakhstan,1992,232939189.3,1.0495,320000000.0,5.809666666666668,81.15689849853516,16964630.0,4783.888507944775 +1847,Kazakhstan,1993,205244532.3,0.9265,278340000.0,4.702249999999999,73.69046020507812,16902393.0,4359.764928260639 +1848,Kazakhstan,1994,171065998.9,0.7592,253370000.0,5.536500000000001,64.40546417236328,16663072.0,3865.161488371609 +1849,Kazakhstan,1995,162683135.1,0.7071,221710000.0,7.335333333333335,59.12421417236328,16389860.0,3607.3654181526426 +1850,Kazakhstan,1996,149002665.6,0.6334,193940000.0,5.100833333333333,59.41983413696289,16236550.0,3659.634228759367 +1851,Kazakhstan,1997,140769296.7,0.5886,166120000.0,7.065333333333334,60.42997360229492,16052382.0,3764.548688306503 +1852,Kazakhstan,1998,135093762.4,0.5632,165420000.0,6.248916666666667,59.28180313110352,15840791.0,3742.3511951583423 +1853,Kazakhstan,1999,107323002.3,0.4485,149250000.0,6.9755,60.88241577148438,15709484.0,3875.519767007266 +1854,Kazakhstan,2000,133538744.0,0.5438,148460000.0,6.761416666666668,66.84889221191406,15687251.0,4261.351604045481 +1855,Kazakhstan,2001,128218684.6,0.5101,148420000.0,6.813083333333334,75.87348937988281,15736939.0,4821.362615682936 +1856,Kazakhstan,2002,146782200.8,0.5757,164970000.0,7.301583333333334,83.30909729003906,15802133.0,5272.015954430903 +1857,Kazakhstan,2003,164319000.3,0.6116,176440000.0,6.131416666666669,91.05684661865234,15882506.0,5733.153610560723 +1858,Kazakhstan,2004,171060079.4,0.6069,193920000.0,7.393000000000001,99.79830169677734,15988619.0,6241.833750418177 +1859,Kazakhstan,2005,184318978.9,0.63,206010000.0,6.998833333333333,109.47874450683594,16122665.0,6790.362790942809 +1860,Kazakhstan,2006,202969922.2,0.6702,227440000.0,6.869833333333332,121.1929702758789,16283721.0,7442.584546608167 +1861,Kazakhstan,2007,213216397.6,0.6905,235740000.0,7.155166666666666,131.97914123535156,16459828.0,8018.2576169903805 +1862,Kazakhstan,2008,213380401.3,0.6694,282100000.0,7.028916666666667,136.33445739746094,16649642.0,8188.431763125052 +1863,Kazakhstan,2009,209346692.6,0.6641,251270000.0,6.382083333333333,137.97047424316406,16863280.0,8181.710452721181 +1864,Kazakhstan,2010,236962075.2,0.7166,277170000.0,6.710000000000001,148.0423126220703,17084817.0,8665.13891381279 +1865,Kazakhstan,2011,227079619.8,0.6609,294110000.0,5.69625,158.99745178222656,17304513.0,9188.2072487233 +1866,Kazakhstan,2012,233860798.9,0.6697,294070000.0,6.075416666666668,166.62933349609375,17522010.0,9509.715694494738 +1867,Kazakhstan,2013,239836711.7,0.6812,307530000.0,8.937249999999999,176.62709045410156,17736896.0,9958.173654178361 +1868,Kenya,1990,5686089.74,0.0256,97270000.0,24.132416666666668,21.765132904052734,23360942.0,931.6890091184137 +1869,Kenya,1991,4710350.49,0.021,96040000.0,24.371583333333334,22.07819175720215,24132867.0,914.8598779085032 +1870,Kenya,1992,5402083.89,0.0243,96010000.0,24.408916666666666,21.90167808532715,25039576.0,874.6824660819797 +1871,Kenya,1993,6215136.71,0.0281,95860000.0,24.281583333333334,21.97903251647949,25844500.0,850.4336518980631 +1872,Kenya,1994,6420445.18,0.0285,96130000.0,24.52491666666667,22.55769348144531,26487165.0,851.6462022811921 +1873,Kenya,1995,7405455.33,0.0322,96420000.0,24.475333333333328,23.55163383483887,27163001.0,867.0482998118973 +1874,Kenya,1996,9154971.96,0.0389,95760000.0,24.453,24.528284072875977,27836550.0,881.1538812416043 +1875,Kenya,1997,8119741.25,0.034,96010000.0,24.56475,24.64476776123047,28513632.0,864.3152777320851 +1876,Kenya,1998,9896018.67,0.0413,97350000.0,24.590999999999998,25.45563316345215,29188217.0,872.1201834100433 +1877,Kenya,1999,10029324.54,0.0419,99710000.0,24.57058333333333,26.042484283447266,29874295.0,871.7355265939253 +1878,Kenya,2000,10301569.77,0.0419,98340000.0,24.71666666666667,26.198659896850586,30605901.0,856.0002823262934 +1879,Kenya,2001,9235654.06,0.0367,-8230000.0,24.45833333333334,27.188945770263672,31423090.0,865.253728079055 +1880,Kenya,2002,7819596.94,0.0307,-7250000.0,24.661749999999998,27.337631225585934,32313484.0,846.0131140791237 +1881,Kenya,2003,6585571.86,0.0245,-6030000.0,24.858333333333334,28.139301300048828,33244826.0,846.4264875397099 +1882,Kenya,2004,7437994.0,0.0264,-3730000.0,24.867083333333337,29.57561492919922,34223442.0,864.1917119031807 +1883,Kenya,2005,8338598.93,0.0285,-3120000.0,25.13366666666667,31.322547912597656,35246309.0,888.675972074059 +1884,Kenya,2006,9336550.69,0.0308,15100000.0,24.820083333333333,33.349899291992195,36316148.0,918.3214941185997 +1885,Kenya,2007,9540426.2,0.0309,31140000.0,24.788750000000004,35.63460922241211,37408875.0,952.570993445061 +1886,Kenya,2008,9911711.8,0.0311,33600000.0,24.649749999999997,35.717384338378906,38540806.0,926.7420182748359 +1887,Kenya,2009,11964080.2,0.038,33320000.0,25.228083333333334,36.898536682128906,39707699.0,929.2539636237525 +1888,Kenya,2010,11745964.63,0.0355,34040000.0,24.961249999999996,40.00011444091797,40843138.0,979.3594811671416 +1889,Kenya,2011,12928061.15,0.0376,36770000.0,24.96575,42.44342422485352,41943504.0,1011.9188951131388 +1890,Kenya,2012,11967156.15,0.0343,38640000.0,24.925416666666667,44.38020706176758,43013341.0,1031.7777236083002 +1891,Kenya,2013,12770474.04,0.0363,36840000.0,24.935250000000003,46.989173889160156,44037656.0,1067.0225928727941 +1892,Kiribati,1990,21984.0,0.0001,30000.0,27.075666666666667,,, +1893,Kiribati,1991,21984.0,0.0001,30000.0,27.363083333333336,,, +1894,Kiribati,1992,25648.0,0.0001,30000.0,27.287333333333336,,, +1895,Kiribati,1993,29312.0,0.0001,40000.0,27.138583333333333,,, +1896,Kiribati,1994,29312.0,0.0001,40000.0,27.247083333333332,,, +1897,Kiribati,1995,29312.0,0.0001,40000.0,27.46591666666667,,, +1898,Kiribati,1996,32976.0,0.0001,40000.0,27.353499999999997,,, +1899,Kiribati,1997,25648.0,0.0001,40000.0,27.496333333333336,,, +1900,Kiribati,1998,29312.0,0.0001,40000.0,27.465999999999998,,, +1901,Kiribati,1999,29312.0,0.0001,40000.0,26.93541666666667,,, +1902,Kiribati,2000,29312.0,0.0001,40000.0,27.07925,,, +1903,Kiribati,2001,25648.0,0.0001,40000.0,27.392250000000004,,, +1904,Kiribati,2002,40304.0,0.0002,50000.0,27.657916666666665,,, +1905,Kiribati,2003,40304.0,0.0002,50000.0,27.767083333333336,,, +1906,Kiribati,2004,43968.0,0.0002,60000.0,27.683666666666667,,, +1907,Kiribati,2005,62288.0,0.0002,80000.0,27.814083333333333,,, +1908,Kiribati,2006,69616.0,0.0002,90000.0,27.756,,, +1909,Kiribati,2007,51296.0,0.0002,70000.0,27.295416666666668,,, +1910,Kiribati,2008,54960.0,0.0002,70000.0,26.961000000000002,,, +1911,Kiribati,2009,40304.0,0.0001,60000.0,27.66616666666667,,, +1912,Kiribati,2010,62288.0,0.0002,80000.0,27.53166666666667,,, +1913,Kiribati,2011,62288.0,0.0002,80000.0,27.160666666666668,,, +1914,Kiribati,2012,62288.0,0.0002,80000.0,27.3385,,, +1915,Kiribati,2013,62288.0,0.0002,80000.0,27.551375,,, +1916,Kuwait,1990,50886231.43,0.2294,32280000.0,25.75858333333333,40.718841552734375,2131160.0,19106.421644894974 +1917,Kuwait,1991,10174217.5,0.0454,38990000.0,25.348583333333337,23.74103736877441,940940.0,25231.191541197542 +1918,Kuwait,1992,21021851.62,0.0947,26550000.0,24.30033333333333,43.84392547607422,1402721.0,31256.340695030744 +1919,Kuwait,1993,33202014.36,0.1499,33130000.000000004,25.361833333333333,58.7466812133789,1488038.0,39479.28830673605 +1920,Kuwait,1994,38781722.62,0.1721,38080000.0,25.842333333333332,63.7026481628418,1575330.0,40437.65316653768 +1921,Kuwait,1995,54588978.35,0.2373,38520000.0,25.491749999999996,66.79750823974611,1664348.0,40134.339837429485 +1922,Kuwait,1996,49913596.82,0.2122,37960000.0,26.327916666666663,67.20172119140625,1740735.0,38605.37140426674 +1923,Kuwait,1997,54670648.61,0.2286,39540000.0,25.315833333333334,68.86383819580078,1801731.0,38220.932090195914 +1924,Kuwait,1998,51560591.31,0.215,45870000.0,26.67075000000001,71.38566589355469,1859847.0,38382.54753942377 +1925,Kuwait,1999,53822208.72,0.2249,50300000.0,26.836833333333335,70.10857391357422,1916332.0,36584.774409431266 +1926,Kuwait,2000,53384077.5,0.2174,54090000.0,26.376083333333337,73.39987182617188,1972259.0,37216.14241647364 +1927,Kuwait,2001,57484681.08,0.2287,58020000.0,26.654750000000003,73.93497467041016,2027734.0,36461.87057592868 +1928,Kuwait,2002,57979850.46,0.2274,61060000.0,26.386750000000003,76.15302276611328,2083305.0,36553.94806142801 +1929,Kuwait,2003,59622933.97,0.2219,63460000.0,26.49658333333333,89.34272766113281,2139818.0,41752.48907202987 +1930,Kuwait,2004,63107931.9,0.2239,66580000.0,26.155416666666667,98.95796966552734,2197285.0,45036.47440615457 +1931,Kuwait,2005,71166438.54,0.2433,77590000.0,25.987916666666667,108.92892456054688,2256657.0,48270.04040071082 +1932,Kuwait,2006,73389091.29,0.2423,78240000.0,26.314666666666668,117.1206512451172,2316880.0,50551.02173833655 +1933,Kuwait,2007,74874777.99,0.2425,77970000.0,26.330916666666667,124.13811492919922,2375798.0,52251.123592662014 +1934,Kuwait,2008,82298263.72,0.2582,82870000.0,26.266583333333333,127.21654510498048,2433192.0,52283.80871915594 +1935,Kuwait,2009,86954927.18,0.2758,90030000.0,26.453166666666664,118.21456909179688,2489004.0,47494.728450334704 +1936,Kuwait,2010,89272224.68,0.27,89040000.0,27.63866666666667,115.4163589477539,2543172.0,45382.836452962634 +1937,Kuwait,2011,90630115.95,0.2638,91890000.0,26.087499999999995,126.5291519165039,2595628.0,48747.028432619736 +1938,Kuwait,2012,101890324.4,0.2918,92220000.0,26.67558333333334,134.9134521484375,2646314.0,50981.649248138165 +1939,Kuwait,2013,97814671.95,0.2778,103600000.0,27.273375,136.46365356445312,2695316.0,50629.927460992745 +1940,Kyrgyzstan,1992,11166355.81,0.0503,20310000.0,3.7207499999999993,3.818717241287232,4592059.0,831.5915020445581 +1941,Kyrgyzstan,1993,8585856.98,0.0388,15460000.0,3.1161666666666665,3.2283694744110107,4582706.0,704.4679441384656 +1942,Kyrgyzstan,1994,6204460.34,0.0275,10310000.0,3.7527500000000003,2.579946279525757,4575291.0,563.8868171501565 +1943,Kyrgyzstan,1995,4511047.89,0.0196,7630000.0,4.050166666666668,2.440014600753784,4620023.0,528.1390592111304 +1944,Kyrgyzstan,1996,5664198.74,0.0241,8510000.0,3.414583333333333,2.612877368927002,4685470.0,557.6553406439486 +1945,Kyrgyzstan,1997,5573045.63,0.0233,8430000.0,4.873916666666665,2.871948719024658,4750460.0,604.5622358728751 +1946,Kyrgyzstan,1998,5913136.99,0.0247,8620000.0,4.393166666666666,2.9328866004943848,4816674.0,608.9028654408384 +1947,Kyrgyzstan,1999,4646844.57,0.0194,7280000.0,4.600333333333333,3.040107011795044,4883113.0,622.5756012189446 +1948,Kyrgyzstan,2000,4602483.09,0.0187,7310000.0,4.73075,3.2050838470458984,4937128.0,649.1798160886043 +1949,Kyrgyzstan,2001,3862299.78,0.0154,6700000.0,4.781000000000001,3.3756449222564697,4976833.0,678.2716884927563 +1950,Kyrgyzstan,2002,4919282.29,0.0193,7580000.0,4.7090000000000005,3.3750593662261963,5013962.0,673.1322188373579 +1951,Kyrgyzstan,2003,5381618.8,0.02,8289999.999999999,4.042333333333333,3.6123359203338623,5059349.0,713.9922389884276 +1952,Kyrgyzstan,2004,5806821.5,0.0206,8410000.0,5.02925,3.866169691085816,5113537.0,756.0656530080481 +1953,Kyrgyzstan,2005,5513620.26,0.0188,8090000.0,4.518083333333334,3.8593826293945312,5164248.0,747.3271286341268 +1954,Kyrgyzstan,2006,5418969.47,0.0179,25080000.0,4.917333333333332,3.979135274887085,5212766.0,763.3443118081811 +1955,Kyrgyzstan,2007,6518274.64,0.0211,26350000.0,5.05325,4.31906795501709,5255561.0,821.8091189536359 +1956,Kyrgyzstan,2008,7548271.14,0.0237,27470000.0,4.577583333333332,4.681939601898193,5298689.0,883.6033973494564 +1957,Kyrgyzstan,2009,6737377.45,0.0214,27090000.0,4.349166666666666,4.8170742988586435,5358180.0,899.0131535070942 +1958,Kyrgyzstan,2010,6312857.93,0.0191,26740000.0,4.595750000000001,4.794358253479004,5410468.0,886.1263486779709 +1959,Kyrgyzstan,2011,7553506.65,0.022000000000000002,12990000.0,4.201583333333334,5.079923152923584,5450776.0,931.9632934693306 +1960,Kyrgyzstan,2012,9999825.81,0.0286,15600000.0,3.5769166666666674,5.075445175170898,5496737.0,923.35601560906 +1961,Kyrgyzstan,2013,9658455.71,0.0274,15260000.0,6.263125,5.629454135894775,5548042.0,1014.674030206472 +1962,Laos,1990,212512.0,0.001,22390000.0,24.057333333333336,1.9674211740493772,4210452.0,467.2707761659265 +1963,Laos,1991,234496.0,0.001,22240000.0,24.259583333333335,2.051952600479126,4330626.0,473.8235535645715 +1964,Laos,1992,255805.11,0.0012,22470000.0,23.71183333333333,2.166038274765014,4454183.0,486.29305862938605 +1965,Laos,1993,255801.82,0.0012,22410000.0,23.770333333333337,2.294106721878052,4581218.0,500.7634916910857 +1966,Laos,1994,274118.52,0.0012,22710000.0,24.06091666666667,2.481283187866211,4711829.0,526.6072236208512 +1967,Laos,1995,347395.23,0.0015,22740000.0,23.945833333333336,2.6557486057281494,4846116.0,548.015896798209 +1968,Laos,1996,468751.26,0.002,18760000.0,23.701500000000006,2.839747428894043,4947629.0,573.9612709227072 +1969,Laos,1997,600619.01,0.0025,18470000.0,24.11791666666667,3.034897565841675,5054901.0,600.3871422687953 +1970,Laos,1998,677526.76,0.0028,20180000.0,24.86883333333334,3.1553103923797607,5167653.0,610.5886738873064 +1971,Laos,1999,919314.51,0.0038,19020000.0,23.937083333333334,3.3858492374420166,5285813.0,640.554109167694 +1972,Laos,2000,928867.76,0.0038,18400000.0,23.835166666666666,3.5821871757507324,5397406.0,663.6868109885994 +1973,Laos,2001,862750.69,0.0034,25250000.0,24.155083333333337,3.7882137298583984,5498518.0,688.9517738886003 +1974,Laos,2002,1126516.76,0.0044,27950000.0,24.22041666666667,4.012428283691406,5578027.0,719.3275119843283 +1975,Laos,2003,1074062.41,0.004,28460000.0,24.331916666666668,4.255862236022948,5654002.0,752.7167899875078 +1976,Laos,2004,1366750.71,0.0048,39980000.0,23.86375,4.526436805725098,5746043.0,787.7485089695808 +1977,Laos,2005,1377311.01,0.0047,31980000.0,24.31775,4.848156929016113,5836357.0,830.6820383016518 +1978,Laos,2006,1507026.86,0.005,28370000.0,24.405500000000004,5.2660322189331055,5930788.0,887.9144253568169 +1979,Laos,2007,863623.91,0.0028,39250000.0,24.21075,5.666083335876465,6035414.0,938.8060762486988 +1980,Laos,2008,899105.7,0.0028,27620000.0,23.631583333333335,6.10944938659668,6145183.0,994.1851018263704 +1981,Laos,2009,1140114.74,0.0036,30260000.0,24.3655,6.567766189575195,6256865.0,1049.68961126302 +1982,Laos,2010,1498023.85,0.0045,40680000.0,24.934166666666666,7.127792835235597,6368162.0,1119.2857272217 +1983,Laos,2011,1466874.34,0.0043,28960000.0,23.610666666666663,7.7007713317871085,6477211.0,1188.9023426575282 +1984,Laos,2012,1514656.62,0.0043,33200000.000000004,24.907666666666668,8.318842887878418,6586266.0,1263.0590516505738 +1985,Laos,2013,1401545.6,0.004,35200000.0,25.385250000000003,8.98653793334961,6695166.0,1342.2427365280575 +1986,Latvia,1990,19805894.32,0.0893,12310000.0,7.325666666666668,26.33777236938477,2663390.0,9888.815520590213 +1987,Latvia,1991,18038633.66,0.0804,10560000.0,6.689916666666666,23.002944946289066,2650832.0,8677.632134472899 +1988,Latvia,1992,14267237.24,0.0643,8300000.000000001,6.920083333333332,15.750566482543945,2613118.0,6027.4991341929235 +1989,Latvia,1993,11987761.82,0.0541,5110000.0,5.639916666666668,13.859047889709473,2560457.0,5412.724326051745 +1990,Latvia,1994,10420264.02,0.0462,2130000.0,5.9385,14.154548645019533,2516781.0,5624.068460871062 +1991,Latvia,1995,9167583.93,0.0398,400000.0,6.566333333333333,12.778679847717285,2480257.0,5152.1595736721165 +1992,Latvia,1996,9243541.57,0.0393,400000.0,4.967,13.080130577087402,2452526.0,5333.330034865035 +1993,Latvia,1997,8706818.52,0.0364,-210000.0,6.232083333333333,14.253958702087402,2428216.0,5870.136224325761 +1994,Latvia,1998,8328911.44,0.0347,-670000.0,5.997750000000001,15.183568954467773,2405422.0,6312.226692226052 +1995,Latvia,1999,7737970.81,0.0323,-1380000.0,7.108916666666668,15.581219673156738,2385906.0,6530.52537407456 +1996,Latvia,2000,7099045.69,0.0289,-2220000.0,7.497083333333333,16.423673629760742,2368427.0,6934.422563904543 +1997,Latvia,2001,7516325.91,0.0299,7000000.0,6.53925,17.484859466552734,2350481.0,7438.843141702798 +1998,Latvia,2002,7542934.27,0.0296,7340000.0,6.917583333333333,18.72681427001953,2330425.0,8035.793587015043 +1999,Latvia,2003,7740669.66,0.0288,7560000.0,6.2772499999999996,20.305614471435547,2308159.0,8797.320492841069 +2000,Latvia,2004,7750956.32,0.0275,7360000.0,6.334083333333332,21.998199462890625,2283318.0,9634.312637526014 +2001,Latvia,2005,7835154.95,0.0268,7460000.0,6.374500000000001,24.351354598999023,2258990.0,10779.753163581523 +2002,Latvia,2006,8333306.34,0.0275,-3580000.0,6.970583333333335,27.24658203125,2238543.0,12171.569646529017 +2003,Latvia,2007,8655065.49,0.027999999999999997,-3130000.0,7.278916666666667,29.965591430664066,2220509.0,13494.920052413236 +2004,Latvia,2008,8218723.54,0.0258,-3540000.0,7.681083333333333,28.90251922607422,2197506.0,13152.41879934536 +2005,Latvia,2009,7480704.55,0.0237,-4270000.0,6.559333333333332,24.740068435668945,2161153.0,11447.624687224341 +2006,Latvia,2010,8646955.88,0.0262,-3080000.0,5.711083333333334,23.7651424407959,2115339.0,11234.673232420857 +2007,Latvia,2011,7882679.59,0.0229,-1950000.0,7.2452499999999995,25.28160095214844,2076493.0,12175.143837300891 +2008,Latvia,2012,7583855.02,0.0217,-2100000.0,6.043333333333336,26.30162048339844,2051103.0,12823.159287173015 +2009,Latvia,2013,7423592.75,0.0211,-2120000.0,7.322375000000001,26.940710067749023,2029430.0,13275.013214424258 +2010,Lebanon,1990,7867853.3,0.0355,7200000.0,18.184583333333336,8.42707633972168,3451187.0,2441.790705551939 +2011,Lebanon,1991,8114867.24,0.0362,8670000.0,18.114833333333333,12.594043731689453,3518504.0,3579.374567057321 +2012,Lebanon,1992,9726936.51,0.0438,8770000.0,16.92125,14.66429615020752,3561928.0,4116.954680220239 +2013,Lebanon,1993,10369613.25,0.0468,12120000.0,17.92625,16.243005752563473,3600767.0,4510.984951973698 +2014,Lebanon,1994,11128700.0,0.0494,13090000.0,18.664250000000003,17.559541702270508,3637528.0,4827.328257616301 +2015,Lebanon,1995,12280178.95,0.0534,15340000.0,18.104833333333335,18.691930770874023,3672123.0,5090.224584218455 +2016,Lebanon,1996,12785601.67,0.0544,16360000.0,18.50016666666667,20.801490783691406,3704663.0,5614.9481838675765 +2017,Lebanon,1997,15132366.65,0.0633,18530000.0,17.731583333333337,21.020938873291016,3735317.0,5627.618452005818 +2018,Lebanon,1998,16315522.61,0.068,18930000.0,19.00166666666667,21.7915096282959,3764271.0,5789.038469413042 +2019,Lebanon,1999,16458232.09,0.0688,19930000.0,18.9815,21.676977157592773,3791412.0,5717.388972127738 +2020,Lebanon,2000,15466264.0,0.063,18730000.0,18.377333333333333,21.967853546142575,3834478.0,5729.033664071766 +2021,Lebanon,2001,16406232.53,0.0653,20040000.0,19.06275,22.81134605407715,3894693.0,5857.033161298504 +2022,Lebanon,2002,16244385.02,0.0637,19960000.0,18.677083333333336,23.592212677001953,3955261.0,5964.767603706039 +2023,Lebanon,2003,18527469.06,0.069,20690000.0,18.554,24.353557586669922,4016050.0,6064.0573664844605 +2024,Lebanon,2004,17207687.52,0.061,21190000.0,18.47425,25.886314392089844,4077336.0,6348.830312755645 +2025,Lebanon,2005,16653479.63,0.0569,20650000.0,18.39275,26.59777069091797,4138961.0,6426.195050138904 +2026,Lebanon,2006,14792032.0,0.0488,19220000.0,18.314333333333337,27.05031394958496,4223074.0,6405.361106526894 +2027,Lebanon,2007,13752814.44,0.0445,17950000.0,18.755166666666664,29.57706642150879,4334429.0,6823.7515071786365 +2028,Lebanon,2008,17426058.34,0.0547,21940000.0,18.957166666666666,32.31260299682617,4424106.0,7303.7587699811365 +2029,Lebanon,2009,20970509.96,0.0665,25850000.0,18.85225,35.561466217041016,4475008.0,7946.682154990788 +2030,Lebanon,2010,20015172.16,0.0605,24920000.0,20.268833333333333,38.41962814331055,4492280.0,8552.36720402792 +2031,Lebanon,2011,20264128.0,0.059000000000000004,25660000.0,18.261666666666667,38.77220153808594,4511987.0,8593.154532157549 +2032,Lebanon,2012,22368832.51,0.0641,28270000.0,18.989083333333333,39.8576774597168,4612387.0,8641.44258920962 +2033,Lebanon,2013,22196360.0,0.063,28240000.0,19.17825,40.91496276855469,5149085.0,7946.064741319028 +2034,Lesotho,1990,1472928.0,0.0066,3340000.0,14.14725,1.0823838710784912,1703257.0,635.4788919572861 +2035,Lesotho,1991,1527888.0,0.0068,3400000.0,14.353,1.1577893495559692,1732498.0,668.2774523006486 +2036,Lesotho,1992,1586512.0,0.0071,3620000.0,14.890083333333337,1.238329291343689,1761713.0,702.9120471630106 +2037,Lesotho,1993,1634144.0,0.0074,3640000.0,14.63275,1.281791925430298,1790788.0,715.76977589212 +2038,Lesotho,1994,1674448.0,0.0074,3650000.0,13.902750000000003,1.3584413528442385,1819497.0,746.6026890092363 +2039,Lesotho,1995,1707424.0,0.0074,3620000.0,14.246,1.4031468629837036,1847573.0,759.4540854319173 +2040,Lesotho,1996,1736736.0,0.0074,3660000.0,13.655666666666667,1.4816814661026,1869054.0,792.7440652343912 +2041,Lesotho,1997,1769712.0,0.0074,3680000.0,13.953249999999999,1.5365570783615112,1883539.0,815.7819287848625 +2042,Lesotho,1998,1802688.0,0.0075,3700000.0,14.542333333333332,1.560184121131897,1896603.0,822.6202959353627 +2043,Lesotho,1999,1824672.0,0.0076,4210000.0,14.982166666666666,1.5676109790802002,1907672.0,821.7403091727508 +2044,Lesotho,2000,1850320.0,0.0075,4170000.0,14.053916666666666,1.6283644437789917,1916324.0,849.7333664761238 +2045,Lesotho,2001,1875968.0,0.0075,4190000.0000000005,14.333416666666666,1.6863601207733154,1922344.0,877.2415971196182 +2046,Lesotho,2002,1905280.0,0.0075,4000000.0,14.387333333333336,1.6985665559768677,1925636.0,882.0808065370961 +2047,Lesotho,2003,1930928.0,0.0072,4090000.0,14.684833333333332,1.7760173082351685,1926345.0,921.962217689546 +2048,Lesotho,2004,1982224.0,0.006999999999999999,4060000.0,14.562916666666668,1.806074261665344,1924895.0,938.271574119806 +2049,Lesotho,2005,2011536.0,0.0069,4260000.0,14.576500000000001,1.868674874305725,1922116.0,972.1967218969743 +2050,Lesotho,2006,2037184.0,0.0067,4340000.0,13.903500000000001,1.9477217197418213,1919198.0,1014.8623121438336 +2051,Lesotho,2007,2073824.0,0.0067,4540000.0,14.525333333333334,2.0418543815612797,1916857.0,1065.2095495706144 +2052,Lesotho,2008,2125120.0,0.0067,4620000.0,14.427750000000003,2.1794657707214355,1915469.0,1137.8235673463969 +2053,Lesotho,2009,2216720.0,0.006999999999999999,4830000.0,14.257166666666665,2.226418256759644,1916134.0,1161.9324414470198 +2054,Lesotho,2010,2275344.0,0.0069,4990000.0,14.906833333333337,2.361607074737549,1919552.0,1230.2907526014135 +2055,Lesotho,2011,2330304.0,0.0068,5020000.0,14.026916666666667,2.5245895385742188,1924886.0,1311.5527561498284 +2056,Lesotho,2012,2403584.0,0.0069,5200000.0,14.3865,2.676016330718994,1930493.0,1386.1828717944036 +2057,Lesotho,2013,2432896.0,0.0069,5230000.0,13.773625000000001,2.725355625152588,1936181.0,1407.5934146407735 +2058,Liberia,1990,468137.0,0.0021,19620000.0,25.881583333333335,0.8735315799713135,2178140.0,401.04473540328604 +2059,Liberia,1991,278464.0,0.0012,19240000.0,25.844916666666666,0.7492698431015015,1907431.0,392.81622407389915 +2060,Liberia,1992,281473.27,0.0013,19090000.0,25.655916666666666,0.4863834083080292,2008546.0,242.15696743217686 +2061,Liberia,1993,314444.69,0.0014,18930000.0,25.832583333333332,0.3259965777397156,2084336.0,156.40308363896972 +2062,Liberia,1994,311440.0,0.0014,18770000.0,25.720916666666668,0.2550520598888397,2069284.0,123.25618904357242 +2063,Liberia,1995,325427.53,0.0014,18630000.0,25.948750000000004,0.24417100846767426,2074910.0,117.67787926593165 +2064,Liberia,1996,335741.89,0.0014,17800000.0,25.99625,0.2737675011157989,2129757.0,128.54400812665432 +2065,Liberia,1997,347402.37,0.0015,17950000.0,25.99066666666667,0.5647270679473877,2317085.0,243.72306926478214 +2066,Liberia,1998,373045.79,0.0016,18140000.0,26.460000000000004,0.7324509620666504,2583863.0,283.4712839135242 +2067,Liberia,1999,390674.41,0.0016,18260000.0,25.889666666666667,0.90018230676651,2740658.0,328.4548114965494 +2068,Liberia,2000,414446.27,0.0017,18410000.0,25.859750000000002,1.1315292119979858,2846680.0,397.4908356394066 +2069,Liberia,2001,484647.55,0.0019,18550000.0,26.153500000000005,1.1645728349685671,2917070.0,399.22690746830455 +2070,Liberia,2002,482307.35,0.0019,18620000.0,26.23608333333333,1.2083959579467771,2978030.0,405.7702433980777 +2071,Liberia,2003,518107.78,0.0019,19250000.0,26.386916666666668,0.8441234230995178,2983448.0,282.935523964057 +2072,Liberia,2004,603735.57,0.0021,19230000.0,26.283500000000004,0.8662381768226624,3005087.0,288.2572706955447 +2073,Liberia,2005,710465.7,0.0024,19170000.0,26.290916666666664,0.9119859933853148,3138399.0,290.5895628265606 +2074,Liberia,2006,731127.56,0.0024,20120000.0,26.18316666666667,0.9853453636169434,3310671.0,297.627086357099 +2075,Liberia,2007,649686.4,0.0021,19780000.0,26.289833333333334,1.0793007612228394,3469165.0,311.1125476080957 +2076,Liberia,2008,553388.67,0.0017,19810000.0,26.297416666666663,1.1564242839813232,3602820.0,320.9775353698834 +2077,Liberia,2009,508299.4,0.0016,24760000.0,26.494,1.2177211046218872,3708025.0,328.40153575606615 +2078,Liberia,2010,783136.57,0.0024,20420000.0,26.532750000000004,1.2920000553131104,3817364.0,338.45346037556567 +2079,Liberia,2011,880980.89,0.0026,7630000.0,26.240666666666666,1.3979538679122925,3984084.0,350.8846369484912 +2080,Liberia,2012,1011423.5,0.0029,8560000.0,25.995583333333332,1.5097037553787231,4140690.0,364.60197584912737 +2081,Liberia,2013,935449.96,0.0027,8860000.0,26.314,1.6411087512969968,4243924.0,386.69607450486785 +2082,Libya,1990,36512995.08,0.1646,105270000.0,23.101000000000003,47.25260925292969,4098435.0,11529.427513899742 +2083,Libya,1991,42619147.02,0.19,102740000.0,22.61816666666667,54.75254440307617,4205118.0,13020.453743052198 +2084,Libya,1992,37024972.81,0.1668,99760000.0,22.397833333333338,53.21399688720703,4310510.0,12345.174210756277 +2085,Libya,1993,38708979.23,0.1747,100470000.0,23.002750000000002,51.13705444335938,4414050.0,11585.06461035996 +2086,Libya,1994,43753007.57,0.1942,105660000.0,22.816083333333335,52.12348937988281,4517088.0,11539.179528909512 +2087,Libya,1995,45691077.46,0.1986,107610000.0,22.949499999999997,45.50797653198242,4583908.0,9927.76829988351 +2088,Libya,1996,43796803.51,0.1862,109840000.0,23.12325,46.67844009399414,4613639.0,10117.488623187497 +2089,Libya,1997,44706531.91,0.1869,109550000.0,23.046499999999998,46.32601928710938,4685156.0,9887.828556212296 +2090,Libya,1998,45158921.08,0.1883,111070000.0,23.276333333333337,46.13423156738281,4798780.0,9613.74173589596 +2091,Libya,1999,44303340.39,0.1851,109470000.0,23.693583333333336,46.32245635986328,4912162.0,9430.15648911076 +2092,Libya,2000,46792991.69,0.1905,111360000.0,22.835083333333333,48.026756286621094,5024753.0,9558.033257877769 +2093,Libya,2001,47773798.04,0.19,115410000.0,23.413166666666665,47.18010711669922,5135952.0,9186.243780451849 +2094,Libya,2002,47472203.44,0.1862,120130000.0,23.40941666666666,46.72771835327149,5245124.0,8908.791928135824 +2095,Libya,2003,48780984.53,0.1816,123410000.0,23.369,52.80979537963867,5353270.0,9864.960179411588 +2096,Libya,2004,49955833.89,0.1772,127310000.0,23.27841666666666,55.165977478027344,5461632.0,10100.63978642782 +2097,Libya,2005,51696284.96,0.1767,133500000.0,23.256666666666664,61.71458053588867,5570030.0,11079.757296798882 +2098,Libya,2006,52666318.08,0.1739,134570000.0,23.315083333333334,65.72636413574219,5678213.0,11575.184681473234 +2099,Libya,2007,49339744.9,0.1598,132360000.0,23.325833333333332,69.90151214599611,5786146.0,12080.841400475563 +2100,Libya,2008,55341474.53,0.1736,138240000.0,23.47433333333333,71.76603698730469,5894108.0,12175.894467373977 +2101,Libya,2009,57195609.6,0.1814,141430000.0,23.379833333333334,71.19898223876953,6002252.0,11862.044818972869 +2102,Libya,2010,61121644.38,0.1848,145730000.0,24.51591666666667,74.77409362792969,6110364.0,12237.256835751466 +2103,Libya,2011,39264614.42,0.1143,115140000.0,22.988333333333333,28.35738754272461,5767159.0,4917.0462514948185 +2104,Libya,2012,52408744.05,0.1501,117760000.0,23.270666666666667,63.27654647827149,5613380.0,11272.450195474292 +2105,Libya,2013,56002250.67,0.1591,109050000.0,24.314,54.670936584472656,6002347.0,9108.25991640814 +2106,Liechtenstein,2007,200764.39,0.0007,100000.0,6.279250000000001,,, +2107,Liechtenstein,2008,219503.55,0.0007,100000.0,5.9422500000000005,,, +2108,Liechtenstein,2009,205352.4,0.0007,90000.0,5.8935,,, +2109,Liechtenstein,2010,190805.68,0.0006,90000.0,4.847833333333333,,, +2110,Liechtenstein,2011,176779.77,0.0005,80000.0,6.563083333333334,,, +2111,Liechtenstein,2012,185317.96,0.0005,80000.0,5.876083333333335,,, +2112,Liechtenstein,2013,192539.86,0.0005,90000.0,6.002375,,, +2113,Lithuania,1990,35787206.01,0.1613,45820000.0,7.978416666666665,34.00757598876953,3683732.0,9231.826850804979 +2114,Lithuania,1991,37883283.4,0.1689,47580000.0,7.091583333333333,32.077503204345696,3665048.0,8752.27369582764 +2115,Lithuania,1992,21187061.98,0.0955,32760000.0,7.468250000000001,25.258146286010746,3617607.0,6982.003928566795 +2116,Lithuania,1993,16332141.32,0.0737,26900000.0,6.241749999999999,21.15981101989746,3554527.0,5952.918917171669 +2117,Lithuania,1994,15748218.61,0.0699,24820000.0,6.809416666666667,19.093303680419922,3522133.0,5420.949089775974 +2118,Lithuania,1995,15004147.33,0.0652,23220000.0,7.0073333333333325,19.32068634033203,3509712.0,5504.920728633014 +2119,Lithuania,1996,15658819.88,0.0666,24020000.0,5.405583333333333,20.315692901611328,3500538.0,5803.591591238642 +2120,Lithuania,1997,15070195.51,0.063,23860000.0,6.726749999999999,22.000518798828125,3495369.0,6294.190627320928 +2121,Lithuania,1998,15888040.21,0.0662,24710000.0,6.6531666666666665,23.64333724975586,3493125.0,6768.534549939054 +2122,Lithuania,1999,13407518.49,0.055999999999999994,22000000.0,7.678250000000001,23.37507057189941,3493176.0,6691.6383749056495 +2123,Lithuania,2000,11788024.43,0.048,20260000.0,8.107666666666665,24.27072525024414,3489408.0,6955.542387202683 +2124,Lithuania,2001,12496126.26,0.0497,21140000.0,7.120416666666666,25.854251861572266,3475394.0,7439.229008731748 +2125,Lithuania,2002,12602371.8,0.0494,20860000.0,7.706083333333332,27.60219383239746,3453904.0,7991.5926535298795 +2126,Lithuania,2003,12591879.79,0.0469,21260000.0,6.830333333333333,30.51106834411621,3424737.0,8909.025231460462 +2127,Lithuania,2004,13167324.7,0.0467,22230000.0,6.835333333333332,32.509567260742195,3384474.0,9605.500665906191 +2128,Lithuania,2005,14089324.04,0.0482,22950000.0,6.920999999999999,35.021713256835945,3326634.0,10527.67249322767 +2129,Lithuania,2006,14402957.44,0.0476,21320000.0,7.422000000000001,37.615577697753906,3271019.0,11499.65123949262 +2130,Lithuania,2007,15763269.16,0.051,22100000.0,7.854,41.7859992980957,3230567.0,12934.57133007788 +2131,Lithuania,2008,15079845.47,0.0473,21780000.0,8.175333333333333,42.8841667175293,3195991.0,13418.112478267085 +2132,Lithuania,2009,12828004.95,0.0407,18260000.0,7.0960833333333335,36.5312385559082,3158759.0,11565.060378429695 +2133,Lithuania,2010,13712631.72,0.0415,19280000.0,6.3258333333333345,37.13028335571289,3088774.0,12021.04244457927 +2134,Lithuania,2011,14066638.52,0.0409,18270000.0,7.634999999999999,39.37411880493164,3015012.0,13059.357244658277 +2135,Lithuania,2012,14133749.57,0.0405,18720000.0,6.723583333333333,40.88094711303711,2973160.0,13749.99902899175 +2136,Lithuania,2013,13106309.08,0.0372,17890000.0,7.803999999999999,42.31119918823242,2943364.0,14375.116087657669 +2137,Luxembourg,1990,11812041.31,0.0533,11980000.0,10.590916666666667,25.17227935791016,382966.0,65729.80201352117 +2138,Luxembourg,1991,12424027.28,0.0554,12470000.0,9.453083333333334,27.34821891784668,387152.0,70639.4876375343 +2139,Luxembourg,1992,12180194.44,0.0549,12180000.0,10.223833333333332,27.845861434936523,392468.0,70950.65440987934 +2140,Luxembourg,1993,12320629.27,0.0556,12390000.0,9.65275,29.015567779541016,398000.0,72903.4366320126 +2141,Luxembourg,1994,11508950.02,0.0511,11540000.0,10.822166666666668,30.124231338500977,403671.0,74625.70097554932 +2142,Luxembourg,1995,9118080.14,0.0396,9390000.0,10.342666666666666,30.5556697845459,409619.0,74595.34295173295 +2143,Luxembourg,1996,9165479.98,0.039,9490000.0,8.652583333333332,30.97991371154785,415481.0,74563.97214685594 +2144,Luxembourg,1997,8518761.77,0.0356,8990000.0,10.167,32.748779296875,420928.0,77801.38003856954 +2145,Luxembourg,1998,7639373.0,0.0319,8289999.999999999,9.995416666666667,34.728328704833984,426408.0,81443.89576376144 +2146,Luxembourg,1999,8090458.55,0.0338,8750000.0,10.542333333333334,37.67363739013672,432439.0,87118.96334543535 +2147,Luxembourg,2000,8672869.96,0.0353,8700000.0,10.79275,40.77786636352539,438499.0,92994.20606096114 +2148,Luxembourg,2001,9166124.34,0.0365,9970000.0,10.206583333333333,41.81035232543945,443831.0,94203.31686033524 +2149,Luxembourg,2002,9942247.96,0.039,10770000.0,10.714,43.40728378295898,448481.0,96787.34167770536 +2150,Luxembourg,2003,10411633.5,0.0388,11100000.0,10.700583333333332,44.11451721191406,453937.0,97182.02572584756 +2151,Luxembourg,2004,11785231.56,0.0418,12560000.0,9.983083333333333,45.70801162719727,460400.0,99278.91317810006 +2152,Luxembourg,2005,12048396.33,0.0412,12830000.0,10.308083333333334,47.1580696105957,467463.0,100880.86032604869 +2153,Luxembourg,2006,11879257.02,0.0392,12620000.0,10.730333333333334,49.60014343261719,474942.0,104434.10654904637 +2154,Luxembourg,2007,11268834.53,0.0365,12030000.0,10.761666666666668,53.74401473999024,482297.0,111433.44192476883 +2155,Luxembourg,2008,11132259.84,0.0349,11920000.0,10.128666666666666,53.05631637573242,490953.0,108068.01542251992 +2156,Luxembourg,2009,10588452.58,0.0336,11400000.0,10.328083333333334,50.74380111694336,500087.0,101469.94646320213 +2157,Luxembourg,2010,11154289.32,0.0337,11910000.0,9.134666666666666,53.21247100830078,509257.0,104490.40662828546 +2158,Luxembourg,2011,11050369.81,0.0322,11820000.0,11.032000000000002,54.56365966796875,520556.0,104818.04007247779 +2159,Luxembourg,2012,10788081.33,0.0309,11570000.0,10.10925,54.37131118774414,533061.0,101998.29135454318 +2160,Luxembourg,2013,10233338.48,0.0291,11070000.0,9.90025,56.35824203491211,545474.0,103319.75865928002 +2161,Macedonia,1990,14342211.45,0.0647,13260000.0,11.297750000000002,7.693258762359619,1861361.0,4133.136324635371 +2162,Macedonia,1991,10144664.25,0.0452,12600000.0,9.984416666666668,7.2185258865356445,1867710.0,3864.907232137561 +2163,Macedonia,1992,10768496.0,0.0485,11960000.0,10.843333333333334,6.744615077972412,1889358.0,3569.7920023481056 +2164,Macedonia,1993,10332480.0,0.0466,11950000.0,10.915166666666664,6.240841865539551,1917563.0,3254.5694016517577 +2165,Macedonia,1994,10460720.0,0.0464,11590000.0,12.036583333333333,6.131117343902588,1935034.0,3168.4804214823034 +2166,Macedonia,1995,10830784.0,0.0471,11170000.0,10.571250000000001,6.062771797180176,1954048.0,3102.6729114024706 +2167,Macedonia,1996,11732128.0,0.0499,12740000.0,10.506916666666667,6.134622097015381,1971102.0,3112.280387831467 +2168,Macedonia,1997,10625600.0,0.0444,11550000.0,10.460166666666668,6.222959518432617,1984860.0,3135.213324079591 +2169,Macedonia,1998,12615152.0,0.0526,12350000.0,10.997583333333333,6.433216571807861,1995474.0,3223.9039806120563 +2170,Macedonia,1999,11713808.0,0.0489,11420000.0,11.406833333333333,6.712356090545653,2004985.0,3347.8335700993543 +2171,Macedonia,2000,12054560.0,0.0491,11090000.0,11.582,7.017709732055664,2014512.0,3483.5780238865113 +2172,Macedonia,2001,11988608.0,0.0477,13150000.0,11.470500000000001,6.802458763122559,2023912.0,3361.044730760309 +2173,Macedonia,2002,10926048.0,0.0429,12480000.0,11.281083333333333,6.904065132141112,2030445.0,3400.27192666687 +2174,Macedonia,2003,11299776.0,0.0421,13380000.0,11.207666666666666,7.057514667510986,2035145.0,3467.819082920866 +2175,Macedonia,2004,11182528.0,0.0397,13160000.0,11.058583333333333,7.3874144554138175,2040085.0,3621.130715344615 +2176,Macedonia,2005,11270464.0,0.0385,13560000.0,10.550000000000002,7.736376285552978,2045262.0,3782.584473555456 +2177,Macedonia,2006,10929712.0,0.0361,12360000.0,10.752583333333334,8.133795738220215,2050554.0,3966.6332796991524 +2178,Macedonia,2007,9486096.0,0.0307,12830000.0,11.905749999999998,8.660335540771483,2055915.0,4212.399608335697 +2179,Macedonia,2008,9394496.0,0.0295,12590000.0,11.924083333333334,9.13422966003418,2061315.0,4431.263373154603 +2180,Macedonia,2009,8691008.0,0.0276,11910000.0,11.643833333333333,9.101472854614258,2066718.0,4403.829092606858 +2181,Macedonia,2010,8595744.0,0.026000000000000002,11730000.0,11.741,9.407169342041016,2072086.0,4539.951209573838 +2182,Macedonia,2011,9390832.0,0.0273,12660000.0,11.023833333333334,9.62728500366211,2077328.0,4634.455898953901 +2183,Macedonia,2012,8958480.0,0.0257,12170000.0,11.537916666666666,9.583392143249512,2082370.0,4602.15626581708 +2184,Macedonia,2013,7840960.0,0.0223,11290000.0,13.260125000000002,9.863680839538574,2087171.0,4725.861388232481 +2185,Madagascar,1990,943811.42,0.0043,71760000.0,23.124166666666667,5.711154460906982,11632868.0,490.94982087882215 +2186,Madagascar,1991,1028046.78,0.0046,71880000.0,23.326999999999998,5.350807189941406,11988712.0,446.32043792038763 +2187,Madagascar,1992,998698.14,0.0045,72170000.0,23.341333333333328,5.414275646209717,12356680.0,438.1658864848581 +2188,Madagascar,1993,1024309.5,0.0046,72620000.0,23.455833333333334,5.52778959274292,12736607.0,434.00802056174933 +2189,Madagascar,1994,1264424.54,0.0056,72610000.0,23.44458333333333,5.5238308906555185,13128410.0,420.75399006090737 +2190,Madagascar,1995,1260737.64,0.0055,72820000.0,23.47975,5.618410587310791,13532361.0,415.1833214699777 +2191,Madagascar,1996,1358969.68,0.0058,70930000.0,23.262916666666666,5.739145278930664,13948484.0,411.45297789571 +2192,Madagascar,1997,1645411.84,0.0069,69720000.0,23.452916666666667,5.951120376586914,14376599.0,413.9449376439389 +2193,Madagascar,1998,1732642.72,0.0072,71840000.0,23.753416666666666,6.185138702392577,14816581.0,417.44709541240167 +2194,Madagascar,1999,1912151.24,0.008,70990000.0,23.41925,6.473311424255371,15263710.0,424.09816645202056 +2195,Madagascar,2000,1867464.39,0.0076,68340000.0,23.320333333333334,6.781445503234862,15712415.0,431.5979117936271 +2196,Madagascar,2001,1735477.87,0.0069,60190000.0,23.586916666666667,7.189540386199951,16168611.0,444.66035989114664 +2197,Madagascar,2002,1231908.2,0.0048,50710000.0,23.617500000000003,6.278353691101074,16638898.0,377.32989835631383 +2198,Madagascar,2003,1680474.06,0.0063,55050000.0,23.49525,6.892683506011963,17123055.0,402.53818643997596 +2199,Madagascar,2004,1789365.88,0.0063,55360000.0,23.453000000000003,7.255032062530518,17621089.0,411.72438675785116 +2200,Madagascar,2005,1725393.7,0.0059,61050000.0,23.62183333333334,7.588973999023437,18131781.0,418.54542579261454 +2201,Madagascar,2006,1666225.1,0.0055,65720000.0,23.66116666666667,7.970138549804687,18653420.0,427.2749206207058 +2202,Madagascar,2007,1783442.89,0.0058,69660000.0,23.64975,8.467521667480469,19185672.0,441.34610804773837 +2203,Madagascar,2008,1868569.13,0.0059,64340000.0,23.332250000000002,9.07112979888916,19728709.0,459.7933802403979 +2204,Madagascar,2009,1753895.27,0.0056,72070000.0,23.618666666666666,8.707027435302733,20282443.0,429.2888896718573 +2205,Madagascar,2010,1938343.65,0.0059,71760000.0,23.846166666666665,8.729936599731445,20846615.0,418.76998254783547 +2206,Madagascar,2011,2314630.18,0.0067,59600000.0,23.77683333333334,8.856904029846191,21420952.0,413.4692066835401 +2207,Madagascar,2012,2677597.21,0.0077,55630000.0,23.325333333333333,9.12504768371582,22005222.0,414.6764656005661 +2208,Madagascar,2013,3089535.57,0.0088,60220000.0,22.9595,9.330836296081543,22599098.0,412.8853415336108 +2209,Malawi,1990,739063.56,0.0033,18080000.0,22.28025,3.1199524402618413,9359819.0,333.33469806006303 +2210,Malawi,1991,784685.57,0.0035,17360000.0,21.889499999999998,3.392331600189209,9691444.0,350.03365857443015 +2211,Malawi,1992,785267.02,0.0035,16480000.0,22.390333333333334,3.1435725688934326,10014226.0,313.9106875452414 +2212,Malawi,1993,879143.69,0.004,15740000.0,21.94016666666667,3.4482424259185787,10141863.0,340.0008879945015 +2213,Malawi,1994,904713.83,0.004,14470000.0,21.980166666666666,3.0951361656188965,9915102.0,312.16382500340353 +2214,Malawi,1995,906963.02,0.0039,14910000.0,22.301666666666673,3.612915754318237,9808275.0,368.3538394180666 +2215,Malawi,1996,904259.37,0.0038,15490000.0,21.963333333333335,3.8772611618041988,10016046.0,387.10496754948997 +2216,Malawi,1997,947368.81,0.004,15550000.0,22.03475,4.024303436279297,10280296.0,391.45793431232886 +2217,Malawi,1998,922040.17,0.0038,15880000.0,22.377916666666668,4.181059837341309,10556999.0,396.0462473607612 +2218,Malawi,1999,924478.17,0.0039,15700000.0,21.689249999999998,4.308259963989259,10839431.0,397.4618191664542 +2219,Malawi,2000,853849.16,0.0035,16350000.000000002,21.71441666666667,4.3761610984802255,11129225.0,393.2134626157908 +2220,Malawi,2001,847124.96,0.0034,17870000.0,21.99875,4.1584486961364755,11435630.0,363.6396679620165 +2221,Malawi,2002,839323.75,0.0033,17180000.0,22.224,4.229142665863037,11761078.0,359.5880127538511 +2222,Malawi,2003,900011.76,0.0033,18100000.0,22.233500000000003,4.470442295074463,12107029.0,369.2435439837852 +2223,Malawi,2004,918839.57,0.0033,17760000.0,21.865916666666664,4.712762832641602,12473490.0,377.82231217098035 +2224,Malawi,2005,858438.75,0.0029,18750000.0,22.81116666666667,4.866809844970703,12863421.0,378.3449087898704 +2225,Malawi,2006,892119.83,0.0029,17300000.0,22.102166666666665,5.095550060272217,13278889.0,383.7331617330498 +2226,Malawi,2007,866257.52,0.0028,17660000.0,22.098333333333333,5.584722995758057,13719274.0,407.07132139485344 +2227,Malawi,2008,1028481.65,0.0032,18320000.0,21.8225,6.011381149291992,14184967.0,423.7853460844845 +2228,Malawi,2009,918673.29,0.0029,18030000.0,22.014833333333332,6.5120158195495605,14673498.0,443.7943712909874 +2229,Malawi,2010,1121620.67,0.0034,19460000.0,22.60066666666667,6.95965576171875,15182821.0,458.39016094036486 +2230,Malawi,2011,1155954.26,0.0034,17140000.0,22.31325,7.2974815368652335,15709663.0,464.52183836567565 +2231,Malawi,2012,1075923.09,0.0031,18080000.0,21.887416666666667,7.4350972175598145,16250624.0,457.52687512552217 +2232,Malawi,2013,1188484.55,0.0034,18770000.0,21.269374999999997,7.8217220306396475,16806366.0,465.40233805687967 +2233,Malaysia,1990,56011876.77,0.2525,210930000.0,26.41891666666667,81.80101776123047,17882218.0,4574.433538458734 +2234,Malaysia,1991,67854799.76,0.3025,218940000.0,26.332083333333333,89.60930633544922,18376069.0,4876.413248962508 +2235,Malaysia,1992,74468556.74,0.3355,226180000.0,26.28658333333333,97.57119750976562,18861911.0,5172.922166251638 +2236,Malaysia,1993,90836527.93,0.41,229650000.0,26.215000000000003,107.22581481933594,19338220.0,5544.761349252203 +2237,Malaysia,1994,93013975.13,0.4128,236410000.0,26.27716666666667,117.10350036621094,19835020.0,5903.876092195063 +2238,Malaysia,1995,120035199.6,0.5217,244070000.0,26.334499999999995,128.61370849609378,20339029.0,6323.493048566564 +2239,Malaysia,1996,124116895.0,0.5276,252060000.0,26.238916666666668,141.47854614257812,20856695.0,6783.363622212346 +2240,Malaysia,1997,123527677.8,0.5165,262280000.0,26.55641666666666,151.83865356445312,21407153.0,7092.893369074025 +2241,Malaysia,1998,113111049.1,0.4716,292570000.0,27.055000000000003,140.66421508789062,21977233.0,6400.451553109103 +2242,Malaysia,1999,106887187.3,0.4467,269370000.0,26.248916666666663,149.29763793945312,22553411.0,6619.736497483823 +2243,Malaysia,2000,124539386.6,0.5071,279730000.0,26.371416666666665,162.52371215820312,23151284.0,7020.073364319798 +2244,Malaysia,2001,134167156.4,0.5337,306670000.0,26.500083333333333,163.3650665283203,23737357.0,6882.192761743455 +2245,Malaysia,2002,132216452.8,0.5186,325450000.0,26.745499999999993,172.1720428466797,24282607.0,7090.344247085154 +2246,Malaysia,2003,156393493.8,0.5821,334500000.0,26.576750000000004,182.1382141113281,24880396.0,7320.551252935367 +2247,Malaysia,2004,162088015.1,0.575,360050000.0,26.575999999999997,194.4934539794922,25475465.0,7634.539898662976 +2248,Malaysia,2005,172486570.6,0.5896,374480000.0,26.649583333333336,204.86410522460938,25968120.0,7889.061866034559 +2249,Malaysia,2006,165580554.0,0.5467,202840000.0,26.577666666666673,216.30545043945312,26432158.0,8183.4200007223435 +2250,Malaysia,2007,182455740.7,0.5909,229030000.0,26.496333333333336,229.9300842285156,26895559.0,8548.998153506147 +2251,Malaysia,2008,201580315.1,0.6324,240290000.0,26.25408333333333,241.03976440429688,27357758.0,8810.654893734234 +2252,Malaysia,2009,196444095.1,0.6232,243130000.0,26.642,237.3915557861328,27817866.0,8533.7802614382 +2253,Malaysia,2010,215992304.0,0.6532,252040000.0,26.80666666666666,255.01751708984372,28274729.0,9019.273609654887 +2254,Malaysia,2011,217679248.4,0.6336,128440000.0,26.440583333333336,268.5179443359375,28728607.0,9346.709512784157 +2255,Malaysia,2012,215895218.2,0.6183,128730000.0,26.62275,283.21514892578125,29179952.0,9705.812707497986 +2256,Malaysia,2013,233828491.9,0.6641,147090000.0,26.9735,296.5085144042969,29628392.0,10007.5803777774 +2257,Mali,1990,419423.28,0.0019,23220000.0,29.321083333333334,4.0739150047302255,8531420.0,477.5189833263659 +2258,Mali,1991,434065.54,0.0019,23180000.0,28.855833333333333,4.552404880523682,8729066.0,521.5225638715164 +2259,Mali,1992,441379.8,0.002,23230000.0,28.56616666666667,4.40587854385376,8897685.0,495.17133320113714 +2260,Mali,1993,452358.06,0.002,23450000.0,28.9665,4.545639991760254,9072598.0,501.02958290009695 +2261,Mali,1994,460336.21,0.002,23710000.0,28.376250000000002,4.717462539672852,9243615.0,510.3482284444832 +2262,Mali,1995,467655.05,0.002,24060000.0,28.903999999999996,4.760910987854004,9476561.0,502.38804856044334 +2263,Mali,1996,485965.89,0.0021,25740000.0,29.252583333333337,5.09683084487915,9767669.0,521.806261542969 +2264,Mali,1997,521241.47,0.0022,26720000.0,29.133916666666668,5.342958450317383,10036744.0,532.339815613249 +2265,Mali,1998,762112.0,0.0032,31170000.0,29.49308333333333,5.747509956359862,10309101.0,557.518056750037 +2266,Mali,1999,795088.0,0.0033,32440000.0,28.792583333333337,6.075171947479247,10572728.0,574.6077972950072 +2267,Mali,2000,820736.0,0.0033,31070000.0,29.150333333333332,6.0714759826660165,10843590.0,559.913827677551 +2268,Mali,2001,828064.0,0.0033,30370000.0,29.31083333333334,7.005040645599365,11132068.0,629.266785434599 +2269,Mali,2002,846384.0,0.0033,30900000.0,29.426500000000004,7.222638607025146,11437860.0,631.4676527799035 +2270,Mali,2003,842720.0,0.0031,30580000.0,29.429000000000002,7.881274223327638,11761326.0,670.1008222480727 +2271,Mali,2004,875696.0,0.0031,33880000.0,29.426000000000002,8.00422191619873,12102276.0,661.3815381667655 +2272,Mali,2005,897680.0,0.0031,33440000.0,29.52625,8.527278900146483,12459990.0,684.3728526384439 +2273,Mali,2006,941648.0,0.0031,34110000.0,29.03791666666667,8.924837112426758,12834556.0,695.3756025862334 +2274,Mali,2007,1007600.0,0.0033,34140000.0,29.202166666666674,9.236637115478516,13225656.0,698.3878240503544 +2275,Mali,2008,1069888.0,0.0034,36170000.0,28.949916666666663,9.677536964416504,13636273.0,709.690761135136 +2276,Mali,2009,806080.0,0.0026,35020000.0,29.440916666666666,10.130355834960938,14067464.0,720.1266578653365 +2277,Mali,2010,963632.0,0.0029,38010000.0,30.12708333333333,10.678757667541504,14514077.0,735.7517579341425 +2278,Mali,2011,1044240.0,0.003,38170000.0,29.436666666666667,11.024776458740234,14993806.0,735.2887224724818 +2279,Mali,2012,992944.0,0.0028,38810000.0,28.60183333333333,10.932589530944824,15408711.0,709.5070788818626 +2280,Mali,2013,1025920.0,0.0029,39910000.0,29.941999999999997,11.18443202972412,15814633.0,707.2204602992759 +2281,Malta,1990,1943321.14,0.0088,2510000.0,19.56175,4.259137630462647,359107.0,11860.358139670478 +2282,Malta,1991,2110575.34,0.0094,2420000.0,18.590083333333336,4.525610446929932,362579.0,12481.722457533204 +2283,Malta,1992,2319452.75,0.0105,2440000.0,18.832083333333333,4.737903594970703,365897.0,12948.735832681608 +2284,Malta,1993,2333913.05,0.0105,3040000.0,19.05125,4.950197219848633,370008.0,13378.622137490627 +2285,Malta,1994,2502530.26,0.0111,2750000.0,19.837666666666667,5.22993803024292,373606.0,13998.538648316462 +2286,Malta,1995,2498301.34,0.0109,2640000.0,19.072750000000003,5.561646461486816,376893.0,14756.566085034256 +2287,Malta,1996,2469968.16,0.0105,2550000.0,18.962416666666666,5.771728515625,379962.0,15190.278279472683 +2288,Malta,1997,2515047.44,0.0105,2760000.0,19.39666666666667,6.0751471519470215,382953.0,15863.94975870935 +2289,Malta,1998,2516050.62,0.0105,2660000.0,19.311083333333332,6.386534690856934,385608.0,16562.246350845766 +2290,Malta,1999,2570286.61,0.0107,2670000.0,19.842,6.687977313995361,387799.0,17245.989066488983 +2291,Malta,2000,2570868.55,0.0105,2380000.0,19.609166666666663,7.140766620635986,389947.0,18312.14657539611 +2292,Malta,2001,2468113.67,0.0098,2790000.0,19.90966666666667,7.184207439422607,391873.0,18332.999312079693 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+2310,Mauritania,1995,1014928.0,0.0044,8250000.0,28.26,2.4128878116607666,2234790.0,1079.6933097341434 +2311,Mauritania,1996,1062560.0,0.0045,8730000.0,28.538500000000003,2.5532896518707275,2270682.0,1124.45937029964 +2312,Mauritania,1997,1077216.0,0.0045,9020000.0,28.599999999999994,2.450016736984253,2315223.0,1058.2206279845411 +2313,Mauritania,1998,1069888.0,0.0045,9360000.0,28.969916666666666,2.518073558807373,2374103.0,1060.6420862141924 +2314,Mauritania,1999,1095536.0,0.0046,9830000.0,27.872916666666665,2.7122881412506104,2436909.0,1113.0034569409897 +2315,Mauritania,2000,1113856.0,0.0045,10250000.0,28.355583333333332,2.700614213943481,2500867.0,1079.871186250001 +2316,Mauritania,2001,1172480.0,0.0047,10560000.0,28.878416666666666,2.754863977432251,2565791.0,1073.689937111889 +2317,Mauritania,2002,1249424.0,0.0049,10490000.0,28.65025,2.773184061050415,2631845.0,1053.7034137840242 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+2326,Mauritania,2011,2110464.0,0.0061,9630000.0,28.63291666666667,4.54191255569458,3281634.0,1384.039949517399 +2327,Mauritania,2012,2330304.0,0.0067,10090000.0,28.026833333333332,4.80511999130249,3359185.0,1430.4422028862627 +2328,Mauritania,2013,2322976.0,0.0066,10310000.0,29.11,5.097764015197754,3437610.0,1482.9384413001342 +2329,Mauritius,1990,1461936.0,0.0066,2290000.0,23.840666666666664,3.9257853031158447,1061598.0,3697.99613706492 +2330,Mauritius,1991,1520560.0,0.0068,2400000.0,24.138,4.099911689758302,1071723.0,3825.5329873095016 +2331,Mauritius,1992,1707424.0,0.0077,2430000.0,23.809083333333334,4.366926670074463,1084809.0,4025.5258483977027 +2332,Mauritius,1993,1773376.0,0.008,2640000.0,23.911083333333334,4.58885669708252,1098641.0,4176.848212548521 +2333,Mauritius,1994,1623152.0,0.0072,2670000.0,24.165833333333335,4.778658866882324,1111297.0,4300.073577884512 +2334,Mauritius,1995,1828336.0,0.0079,2790000.0,24.023416666666666,4.983555316925049,1123115.0,4437.2618270836465 +2335,Mauritius,1996,1949248.0,0.0083,2900000.0,23.891166666666663,5.262028217315674,1135344.0,4634.743493879982 +2336,Mauritius,1997,1996880.0,0.0083,3000000.0,24.09741666666667,5.561305046081543,1148350.0,4842.865891132096 +2337,Mauritius,1998,2169088.0,0.009000000000000001,3310000.0,24.405916666666666,5.898910999298097,1160659.0,5082.380784793892 +2338,Mauritius,1999,2425568.0,0.0101,3840000.0,24.1075,6.052918434143066,1173129.0,5159.635840681687 +2339,Mauritius,2000,2689376.0,0.011000000000000001,4240000.0,24.002583333333334,6.599291801452638,1185932.0,5564.646034892925 +2340,Mauritius,2001,2861584.0,0.0114,4620000.0,24.4905,6.769129276275635,1197878.0,5650.9337981627805 +2341,Mauritius,2002,2883568.0,0.0113,4590000.0,24.395749999999996,6.912182331085205,1209590.0,5714.483693718702 +2342,Mauritius,2003,3059440.0,0.0114,4740000.0,24.517083333333336,7.164950847625732,1221004.0,5868.081388452235 +2343,Mauritius,2004,3088752.0,0.011000000000000001,4850000.0,24.25458333333333,7.576632499694824,1232059.0,6149.56954147068 +2344,Mauritius,2005,3293936.0,0.0113,5050000.0,24.15591666666667,7.670687675476074,1242821.0,6171.997154438229 +2345,Mauritius,2006,3627360.0,0.012,5250000.0,24.31975,8.325620651245117,1253425.0,6642.2966282347315 +2346,Mauritius,2007,3685984.0,0.0119,5400000.0,24.284333333333336,8.802438735961914,1263899.0,6964.511195880299 +2347,Mauritius,2008,3766592.0,0.0118,5530000.0,24.189000000000004,9.276640892028807,1274189.0,7280.427701093644 +2348,Mauritius,2009,3707968.0,0.0118,5470000.0,24.584833333333332,9.584157943725586,1284264.0,7462.763064078404 +2349,Mauritius,2010,3913152.0,0.0118,5810000.0,24.669083333333337,10.003670692443848,1294104.0,7730.190689808429 +2350,Mauritius,2011,3916816.0,0.0114,5770000.0,24.522333333333332,10.411565780639648,1303717.0,7986.062757975578 +2351,Mauritius,2012,3964448.0,0.0114,5950000.0,24.374,10.77556610107422,1313095.0,8206.234964777277 +2352,Mauritius,2013,4067040.0,0.0116,6090000.0,24.312375000000003,11.137661933898926,1322238.0,8423.341284926712 +2353,Mexico,1990,318414806.0,1.4355,415480000.0,20.96083333333333,653.9837646484375,84634242.0,7727.176958097379 +2354,Mexico,1991,331374329.3,1.4774,433270000.0,20.83966666666667,681.5475463867188,86192159.0,7907.303335872103 +2355,Mexico,1992,334207995.5,1.5058,436360000.0,20.650333333333332,705.681884765625,87804312.0,8036.984388256752 +2356,Mexico,1993,339472907.3,1.5324,456020000.0,20.938833333333335,719.3802490234375,89435486.0,8043.566163697454 +2357,Mexico,1994,353238154.5,1.5676,481890000.0,21.32575,754.9253540039062,91026615.0,8293.457402583917 +2358,Mexico,1995,333009773.4,1.4474,462600000.0,21.37066666666667,707.4312744140625,92583839.0,7640.980132764449 +2359,Mexico,1996,347421995.2,1.4769,487160000.0,21.132166666666667,755.3473510742188,94125591.0,8024.8882694847425 +2360,Mexico,1997,370406386.2,1.5488,515250000.0,20.898583333333335,807.0648803710938,95658972.0,8436.896858677237 +2361,Mexico,1998,390179344.5,1.6268,551200000.0,21.53175,848.7411499023438,97132683.0,8737.956408579219 +2362,Mexico,1999,392324454.4,1.6394,529750000.0,21.1285,872.1116943359375,98468947.0,8856.717989844428 +2363,Mexico,2000,397899666.1,1.6202,556170000.0,21.232083333333332,915.2154541015624,99775434.0,9172.753426475323 +2364,Mexico,2001,412627401.3,1.6414,550630000.0,21.133,911.514404296875,101125485.0,9013.696243799226 +2365,Mexico,2002,413972575.8,1.6238,557000000.0,21.225,911.1511840820312,102470489.0,8891.839913851014 +2366,Mexico,2003,439786050.0,1.6369,584260000.0,21.32941666666667,924.3299560546876,103843015.0,8901.224180121191 +2367,Mexico,2004,440799635.4,1.5638,583840000.0,20.924833333333336,960.5691528320312,105230503.0,9128.238727814798 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+2376,Mexico,2013,492559728.7,1.399,667840000.0,22.219222222222225,1151.876953125,118818228.0,9694.446487831816 +2377,Moldova,1990,0.0,0.0,37060000.0,10.875583333333333,10.132777214050293,4374000.0,2316.592870153245 +2378,Moldova,1991,0.0,0.0,31010000.0,9.18325,8.361279487609862,4380536.0,1908.7343392703228 +2379,Moldova,1992,21080649.8,0.095,25800000.0,9.753333333333334,5.932488441467285,4382308.0,1353.736077306133 +2380,Moldova,1993,15970388.5,0.0721,22450000.0,8.870166666666666,5.8631324768066415,4380766.0,1338.3806569003325 +2381,Moldova,1994,12390324.1,0.055,19390000.0,10.664166666666667,4.0503015518188485,4371945.0,926.4301247657158 +2382,Moldova,1995,11405405.8,0.0496,17330000.0,9.477916666666667,3.9945981502532963,4355074.0,917.2285362437691 +2383,Moldova,1996,11687090.0,0.0497,15540000.0,8.573333333333332,3.759833335876465,4333973.0,867.5257865880717 +2384,Moldova,1997,7301343.3,0.0305,13830000.0,8.690666666666669,3.8217599391937256,4304362.0,887.8806985085654 +2385,Moldova,1998,6443857.2,0.0269,12350000.0,9.532000000000002,3.571733236312866,4266196.0,837.2173327978524 +2386,Moldova,1999,4684556.2,0.0196,10980000.0,10.629166666666668,3.451427936553955,4224863.0,816.932510368728 +2387,Moldova,2000,3572952.0,0.0145,10140000.0,10.69,3.524174213409424,4180215.0,843.0605156455887 +2388,Moldova,2001,3786588.7,0.0151,10730000.0,10.000833333333334,3.739148855209351,4135094.0,904.247607239243 +2389,Moldova,2002,4051735.7,0.0159,10940000.0,10.541083333333335,4.0308027267456055,4088890.0,985.7938772492304 +2390,Moldova,2003,4404267.6,0.0164,11540000.0,9.428416666666667,4.296835422515868,4040951.0,1063.3228224039017 +2391,Moldova,2004,4613424.5,0.0164,11500000.0,9.901833333333332,4.614801406860352,3994611.0,1155.2567714003571 +2392,Moldova,2005,4937753.7,0.0169,11770000.0,9.842666666666666,4.960911273956299,3948261.0,1256.4800741279005 +2393,Moldova,2006,5029747.3,0.0166,11450000.0,9.591833333333334,5.199034690856934,3901080.0,1332.7167581431129 +2394,Moldova,2007,4978553.6,0.0161,11730000.0,11.358916666666666,5.355005741119385,3855442.0,1388.9472960867745 +2395,Moldova,2008,5190380.0,0.0163,11480000.0,10.744416666666666,5.772696495056152,3811763.0,1514.4426594875265 +2396,Moldova,2009,4539511.9,0.0144,10860000.0,10.731916666666669,5.4263343811035165,3770698.0,1439.0795500205838 +2397,Moldova,2010,4830905.3,0.0146,11570000.0,10.225666666666667,5.811604022979736,3731799.0,1557.3196795914614 +2398,Moldova,2011,4963294.4,0.0144,11540000.0,9.8705,6.2067928314208975,3694121.0,1680.1812478315946 +2399,Moldova,2012,4765681.5,0.0136,11320000.0,10.587583333333335,6.163345336914063,3656843.0,1685.4279324854972 +2400,Moldova,2013,4899358.4,0.0139,10470000.0,11.9605,6.74269962310791,3619925.0,1862.6627963584635 +2401,Mongolia,1990,9889986.0,0.0446,52100000.0,0.6622499999999997,3.8467652797698975,2121375.0,1813.3358221766061 +2402,Mongolia,1991,12121010.0,0.054000000000000006,54400000.0,0.12483333333333224,3.512345314025879,2169556.0,1618.92355579938 +2403,Mongolia,1992,10993888.0,0.0495,52230000.0,0.2616666666666667,3.1872260570526123,2210189.0,1442.0604106945661 +2404,Mongolia,1993,9262624.0,0.0418,51010000.0,-0.21258333333333232,3.0862295627593994,2242277.0,1376.3819379850925 +2405,Mongolia,1994,7927060.0,0.0352,49270000.0,0.6934999999999999,3.1521015167236333,2272058.0,1387.3332092418561 +2406,Mongolia,1995,7899350.0,0.0343,50150000.0,0.7484999999999994,3.353092908859253,2306417.0,1453.8103512327789 +2407,Mongolia,1996,8023326.0,0.0341,49490000.0,-0.082666666666666,3.4280378818511963,2340705.0,1464.5322165122034 +2408,Mongolia,1997,7688408.0,0.0321,56550000.0,1.2594999999999994,3.561618566513061,2372034.0,1501.5040115415975 +2409,Mongolia,1998,7687468.0,0.0321,50790000.0,1.5969166666666668,3.6805744171142583,2402673.0,1531.8665574192817 +2410,Mongolia,1999,7535224.0,0.0315,50740000.0,1.099166666666666,3.79358172416687,2432721.0,1559.3986010590077 +2411,Mongolia,2000,7486846.0,0.0305,54730000.0,0.17499999999999982,3.837058544158936,2460815.0,1559.2633107970066 +2412,Mongolia,2001,7869924.0,0.0313,46950000.0,0.7503333333333334,3.950355768203736,2489156.0,1587.0261920923135 +2413,Mongolia,2002,8262520.0,0.0324,48190000.0,1.0959166666666673,4.1373252868652335,2518612.0,1642.7005377824112 +2414,Mongolia,2003,8007876.0,0.0298,43790000.0,0.04641666666666685,4.427129745483398,2546571.0,1738.4670388076352 +2415,Mongolia,2004,8570716.0,0.0304,44670000.0,1.0906666666666665,4.89753007888794,2574146.0,1902.5844217414005 +2416,Mongolia,2005,8573730.0,0.0293,46540000.0,-0.1398333333333325,5.2527809143066415,2602416.0,2018.4247692554306 +2417,Mongolia,2006,9405752.0,0.0311,57510000.0,0.9519166666666671,5.702221393585205,2632747.0,2165.882780831278 +2418,Mongolia,2007,12078744.0,0.0391,59730000.0,2.075583333333334,6.286585807800293,2666190.0,2357.891150968345 +2419,Mongolia,2008,12016684.0,0.0377,59180000.0,1.1155833333333336,6.846115112304688,2702400.0,2533.346326341285 +2420,Mongolia,2009,13074014.0,0.0415,55360000.0,0.5644999999999998,6.759265422821045,2741412.0,2465.614589423642 +2421,Mongolia,2010,13819950.0,0.0418,51170000.0,-0.31866666666666604,7.1895036697387695,2783419.0,2582.9757107136115 +2422,Mongolia,2011,21426490.0,0.0624,56180000.0,-0.09200000000000023,8.432624816894531,2826886.0,2983.008447066677 +2423,Mongolia,2012,26189690.0,0.075,60470000.0,-0.5962499999999994,9.471508979797363,2869964.0,3300.2187413491465 +2424,Mongolia,2013,38689342.0,0.1099,61500000.0,2.8077500000000004,10.574836730957033,2912192.0,3631.229235901009 +2425,Montenegro,1990,2550905.65,0.0115,1360000.0,11.242583333333334,,, +2426,Montenegro,1991,1804329.93,0.008,1350000.0,10.069666666666668,,, +2427,Montenegro,1992,1915284.64,0.0086,1230000.0,10.94225,,, +2428,Montenegro,1993,1667801.43,0.0075,1240000.0,10.783333333333331,,, +2429,Montenegro,1994,1622424.35,0.0072,1250000.0,11.772499999999999,,, +2430,Montenegro,1995,1713798.0,0.0074,1320000.0,10.383583333333332,,, +2431,Montenegro,1996,2006038.81,0.0085,1340000.0,10.323583333333332,,, +2432,Montenegro,1997,2155334.06,0.009000000000000001,1360000.0,10.572166666666666,,, +2433,Montenegro,1998,2269163.95,0.0095,1370000.0,10.930833333333334,,, +2434,Montenegro,1999,1564812.49,0.0065,1350000.0,11.248666666666667,,, +2435,Montenegro,2000,1738422.42,0.0071,1300000.0,11.561333333333335,,, +2436,Montenegro,2001,1867584.5,0.0074,1240000.0,11.235250000000002,,, +2437,Montenegro,2002,1996591.7,0.0078,1240000.0,11.417166666666667,,, +2438,Montenegro,2003,2135200.88,0.0079,1250000.0,11.27825,,, +2439,Montenegro,2004,2333590.12,0.0083,1100000.0,10.82125,,, +2440,Montenegro,2005,2208919.29,0.0076,2870000.0,10.12575,,, +2441,Montenegro,2006,2381600.0,0.0079,-12660000.0,10.560833333333331,,, +2442,Montenegro,2007,2249696.0,0.0073,-12820000.0,11.595083333333337,,, +2443,Montenegro,2008,2748000.0,0.0086,-12390000.0,11.627666666666665,,, +2444,Montenegro,2009,1824672.0,0.0058,-13510000.0,11.462666666666665,,, +2445,Montenegro,2010,2579456.0,0.0078,-12700000.0,11.244416666666666,,, +2446,Montenegro,2011,2568464.0,0.0075,4120000.0,11.23466666666667,,, +2447,Montenegro,2012,2333968.0,0.0067,3850000.0,11.531833333333331,,, +2448,Montenegro,2013,2246032.0,0.0064,3720000.0,12.870375,,, +2449,Morocco,1990,23502045.27,0.106,30920000.0,18.697666666666667,43.18559646606445,23999967.0,1799.402326930885 +2450,Morocco,1991,24926595.0,0.1111,32270000.000000004,17.61725,46.30191040039063,24433716.0,1895.0007604406399 +2451,Morocco,1992,25986027.45,0.1171,33370000.0,17.68575,45.33061599731445,24861078.0,1823.3568149102164 +2452,Morocco,1993,28162076.56,0.1271,33040000.0,17.502666666666666,44.99490356445313,25292640.0,1778.9722055290838 +2453,Morocco,1994,29583416.0,0.1313,36200000.0,18.460416666666664,49.75896453857422,25723174.0,1934.4022062974896 +2454,Morocco,1995,30281869.15,0.1316,36850000.0,18.788833333333333,47.06927108764648,26147598.0,1800.1374767826278 +2455,Morocco,1996,31065620.65,0.1321,37500000.0,18.24833333333334,52.8930892944336,26563574.0,1991.1887344087656 +2456,Morocco,1997,31683748.07,0.1325,39490000.0,18.79925,52.06758499145508,26968723.0,1930.6655710563336 +2457,Morocco,1998,31816451.79,0.1327,40330000.0,18.712666666666667,55.8365249633789,27361050.0,2040.730343439996 +2458,Morocco,1999,32887129.21,0.1374,42570000.0,18.368333333333336,56.44029998779297,27741989.0,2034.4720051540994 +2459,Morocco,2000,33576916.0,0.1367,43720000.0,18.56508333333333,57.51993179321289,28113036.0,2046.0234815340787 +2460,Morocco,2001,37425861.44,0.1489,46840000.0,19.233916666666666,61.73037338256836,28477475.0,2167.691250104455 +2461,Morocco,2002,38073956.63,0.1493,48590000.0,18.71258333333333,63.65725708007813,28838895.0,2207.340367239387 +2462,Morocco,2003,37495410.19,0.1396,48930000.0,18.856000000000005,67.45196533203125,29197060.0,2310.231418232906 +2463,Morocco,2004,43362160.0,0.1538,53460000.0,18.486,70.68765258789062,29551507.0,2392.0151546887478 +2464,Morocco,2005,45525104.0,0.1556,56320000.0,18.580750000000002,73.01443481445312,29900959.0,2441.876021917997 +2465,Morocco,2006,47390796.0,0.1565,69130000.0,18.793166666666668,78.54501342773438,30247772.0,2596.7206254971234 +2466,Morocco,2007,50230420.78,0.1627,70350000.0,18.419666666666668,81.31890869140625,30594445.0,2657.9631920568013 +2467,Morocco,2008,52861024.0,0.1658,73380000.0,18.38216666666667,86.13565063476562,30940455.0,2783.9167405510243 +2468,Morocco,2009,52590348.3,0.1668,74000000.0,19.010750000000005,89.7910385131836,31285174.0,2870.0827591108678 +2469,Morocco,2010,56199984.0,0.17,77760000.0,19.435000000000006,93.21720886230469,31627428.0,2947.3534446843 +2470,Morocco,2011,57697424.79,0.1679,80750000.0,18.878833333333333,98.10710144042969,31968361.0,3068.881180377989 +2471,Morocco,2012,62483316.0,0.1789,82940000.0,18.66875,101.06008911132812,32309239.0,3127.9006327362927 +2472,Morocco,2013,58830435.24,0.1671,83770000.0,19.30525,105.64358520507812,32649130.0,3235.724357894931 +2473,Mozambique,1990,1015154.71,0.0046,60290000.0,24.200666666666667,2.29021954536438,12989180.0,176.31748465756732 +2474,Mozambique,1991,1015104.33,0.0045,60250000.0,24.132750000000005,2.402440071105957,13207757.0,181.8961441451381 +2475,Mozambique,1992,1001317.07,0.0045,60610000.0,24.743,2.2767605781555176,13406479.0,169.82539398715485 +2476,Mozambique,1993,1075238.06,0.0049,61020000.0,24.247500000000002,2.476367473602295,13880649.0,178.4043003754576 +2477,Mozambique,1994,1057248.86,0.0047,60990000.0,24.042250000000006,2.6288836002349854,14940225.0,175.96010771156293 +2478,Mozambique,1995,1108508.22,0.0048,61160000.0,24.476499999999998,2.687699794769287,15888208.0,169.1631803139339 +2479,Mozambique,1996,1024422.73,0.0044,58460000.0,24.09016666666666,3.4092214107513428,16383412.0,208.08982956366737 +2480,Mozambique,1997,1104519.01,0.0046,56180000.0,24.15225,3.778850317001343,16811392.0,224.7791448204493 +2481,Mozambique,1998,1112391.82,0.0046,58940000.0,24.623416666666667,4.226998805999757,17219552.0,245.4767003229675 +2482,Mozambique,1999,1167219.0,0.0049,57650000.0,24.081499999999995,4.557676315307617,17605244.0,258.88174655844693 +2483,Mozambique,2000,1322771.19,0.0054,55300000.0,23.92025,4.634177207946777,17996793.0,257.5001672768464 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+2492,Mozambique,2009,2439919.27,0.0077,60310000.0,24.081333333333333,9.515878677368164,21921697.0,434.0849468619224 +2493,Mozambique,2010,2633894.8,0.008,63050000.0,24.605749999999997,10.15227508544922,22417453.0,452.8737089556614 +2494,Mozambique,2011,3107350.73,0.009000000000000001,69020000.0,24.30216666666667,10.874874114990234,22948858.0,473.87430411527384 +2495,Mozambique,2012,2980773.65,0.0085,70540000.0,24.009583333333328,11.657668113708494,23515934.0,495.7348542357916 +2496,Mozambique,2013,3873776.12,0.011000000000000001,73230000.0,23.488,12.490221977233887,24096669.0,518.3381145847953 +2497,Namibia,1991,1055232.0,0.0047,17890000.0,20.78075,5.433386325836182,1499367.0,3623.7867885822357 +2498,Namibia,1992,1150496.0,0.0052,17840000.0,21.336833333333335,5.8240113258361825,1538535.0,3785.426607673002 +2499,Namibia,1993,1388656.0,0.0063,17800000.0,21.24833333333333,5.73201847076416,1579302.0,3629.463187385415 +2500,Namibia,1994,1571856.0,0.006999999999999999,18070000.0,20.951083333333333,5.831175804138184,1620363.0,3598.6848651432947 +2501,Namibia,1995,1619488.0,0.006999999999999999,18150000.0,21.600250000000003,6.058534145355225,1661362.0,3646.7272908344025 +2502,Namibia,1996,1722080.0,0.0073,18250000.0,20.828666666666667,6.2518815994262695,1702444.0,3672.29794309021 +2503,Namibia,1997,1769712.0,0.0074,18060000.0,20.818166666666663,6.51571798324585,1743326.0,3737.521257209409 +2504,Namibia,1998,1832000.0,0.0076,19050000.0,21.676666666666662,6.7301878929138175,1783962.0,3772.607204028908 +2505,Namibia,1999,1663456.0,0.006999999999999999,19070000.0,21.46591666666667,6.956946849822997,1825962.0,3810.017322278885 +2506,Namibia,2000,1641472.0,0.0067,20380000.0,21.017666666666667,7.1998958587646475,1877985.0,3833.8409831626177 +2507,Namibia,2001,2015200.0,0.008,19820000.0,21.237166666666667,7.284707069396973,1929718.0,3775.011203397063 +2508,Namibia,2002,1758720.0,0.0069,18090000.0,21.547583333333336,7.633546829223633,1964738.0,3885.274692719148 +2509,Namibia,2003,1872304.0,0.006999999999999999,18290000.0,22.040833333333335,7.957193374633789,1991740.0,3995.096435595906 +2510,Namibia,2004,1960240.0,0.006999999999999999,18950000.0,21.643833333333333,8.933505058288574,2017940.0,4427.041962738523 +2511,Namibia,2005,2308320.0,0.0079,20490000.0,21.473916666666668,9.159457206726074,2043307.0,4482.663254579989 +2512,Namibia,2006,2330304.0,0.0077,22200000.0,20.381833333333333,9.807321548461914,2068919.0,4740.31199310457 +2513,Namibia,2007,2359616.0,0.0076,21050000.0,21.18058333333333,10.334371566772461,2095803.0,4930.984241730956 +2514,Namibia,2008,3323248.0,0.0104,19210000.0,21.021083333333333,10.6082124710083,2123842.0,4994.821870463199 +2515,Namibia,2009,3066768.0,0.0097,22480000.0,20.976250000000004,10.639610290527344,2152941.0,4941.895895209085 +2516,Namibia,2010,3096080.0,0.0094,21570000.0,21.39225,11.2821626663208,2183015.0,5168.156273008111 +2517,Namibia,2011,2764816.0,0.008,25990000.0,20.350416666666668,11.856575965881348,2213988.0,5355.302723357736 +2518,Namibia,2012,3364120.0,0.0096,27410000.0,20.795,12.45671844482422,2249438.0,5537.702503836166 +2519,Namibia,2013,2601155.39,0.0074,20930000.0,21.265874999999998,13.156128883361815,2292905.0,5737.755765442447 +2520,Nepal,1990,623847.59,0.0028,57890000.0,15.272333333333334,6.813836097717285,18649113.0,365.37051910818946 +2521,Nepal,1991,947879.39,0.0042,58250000.0,15.135000000000003,7.247751235961914,19149602.0,378.4805154677322 +2522,Nepal,1992,1308435.68,0.0059,58100000.0,15.075583333333334,7.5453734397888175,19705216.0,382.9124958482474 +2523,Nepal,1993,1440507.32,0.0065,59090000.0,15.276250000000003,7.835858821868896,20278488.0,386.41238054182816 +2524,Nepal,1994,1667128.52,0.0074,59930000.0,15.458250000000001,8.479653358459473,20843641.0,406.8220786598403 +2525,Nepal,1995,2003390.04,0.0087,61240000.0,15.453333333333333,8.773765563964844,21358915.0,410.7776806061938 +2526,Nepal,1996,2455293.17,0.0104,59750000.0,15.459833333333334,9.241256713867188,21804106.0,423.8310304429445 +2527,Nepal,1997,2759543.75,0.0115,60650000.0,14.691916666666666,9.707812309265137,22241375.0,436.4753667102478 +2528,Nepal,1998,2223298.98,0.0093,61900000.0,15.577750000000002,10.000638008117676,22671580.0,441.1090011422969 +2529,Nepal,1999,3189771.8,0.0133,63930000.0,15.944583333333334,10.441923141479492,23086887.0,452.28805171868737 +2530,Nepal,2000,3038016.77,0.0124,64269999.99999999,15.279333333333335,11.089322090148926,23486982.0,472.1475960661496 +2531,Nepal,2001,3236780.08,0.0129,60970000.0,15.59025,11.621597290039062,23887333.0,486.5171549305677 +2532,Nepal,2002,2595084.9,0.0102,59420000.0,15.648666666666669,11.635560989379885,24272768.0,479.3668768794677 +2533,Nepal,2003,2810026.37,0.0105,63110000.0,15.47025,12.09458827972412,24644751.0,490.75717095799104 +2534,Nepal,2004,2638062.52,0.0094,62300000.0,15.551833333333335,12.660929679870605,24988596.0,506.66830901066254 +2535,Nepal,2005,3051599.33,0.0104,63970000.0,15.616166666666667,13.10142707824707,25254661.0,518.7726367915637 +2536,Nepal,2006,2529398.86,0.0084,36410000.0,16.056000000000004,13.54223918914795,25484399.0,531.3933120081798 +2537,Nepal,2007,2578228.82,0.0083,36470000.0,15.657083333333334,14.00424098968506,25732200.0,544.2302247644997 +2538,Nepal,2008,3384285.11,0.0106,39490000.0,15.409083333333333,14.859148979187012,25998331.0,571.542418595525 +2539,Nepal,2009,4191054.81,0.0133,42970000.0,16.077,15.532726287841795,26297989.0,590.6431205763223 +2540,Nepal,2010,4894005.28,0.0148,43660000.0,16.114,16.280847549438473,26663726.0,610.5991169215614 +2541,Nepal,2011,5276459.56,0.0154,43780000.0,15.280666666666669,16.837949752807614,27080967.0,621.7632388388353 +2542,Nepal,2012,5521717.36,0.0158,48140000.0,15.419833333333331,17.643003463745114,27505393.0,641.4379705007348 +2543,Nepal,2013,6284744.02,0.0179,48590000.0,15.951250000000002,18.371463775634766,27917117.0,658.0716689203532 +2544,Netherlands,1990,162440836.2,0.7323,204710000.0,11.000333333333332,530.52880859375,14965871.0,35449.24372218296 +2545,Netherlands,1991,170819825.4,0.7616,210020000.0,9.75125,543.4691162109375,15081173.0,36036.26297576041 +2546,Netherlands,1992,170654887.0,0.7689,209430000.0,10.75216666666667,552.7411499023439,15189788.0,36388.996996030735 +2547,Netherlands,1993,170796225.4,0.7709999999999999,214900000.0,9.8475,559.692138671875,15291077.0,36602.53222659692 +2548,Netherlands,1994,171398399.4,0.7606,214690000.0,10.842,576.26513671875,15398925.0,37422.42635240772 +2549,Netherlands,1995,173032706.8,0.7521,220090000.0,10.594666666666667,594.2217407226562,15476372.0,38395.41597492334 +2550,Netherlands,1996,182473197.5,0.7757,230990000.0,8.739333333333335,615.416015625,15545307.0,39588.54049167379 +2551,Netherlands,1997,175292382.2,0.733,222230000.0,10.409416666666667,641.8780517578125,15622141.0,41087.713377943044 +2552,Netherlands,1998,176402161.0,0.7355,222380000.0,10.51858333333333,670.9256591796875,15717093.0,42687.643267090636 +2553,Netherlands,1999,170991277.1,0.7145,209890000.0,11.086166666666669,704.8187866210938,15819914.0,44552.630729920136 +2554,Netherlands,2000,171935801.2,0.7001,207820000.0,11.103499999999999,734.6944580078125,15930181.0,46119.6553892145 +2555,Netherlands,2001,177079434.6,0.7044,209280000.0,10.579,750.3013305664062,16046805.0,46757.054165387206 +2556,Netherlands,2002,176374898.4,0.6918,208760000.0,11.015499999999998,751.078857421875,16145467.0,46519.48794183996 +2557,Netherlands,2003,179824198.7,0.6693,208680000.0,10.699833333333332,753.2113647460938,16216609.0,46446.90913779161 +2558,Netherlands,2004,181453315.9,0.6437,210300000.0,10.627500000000001,768.5068969726562,16266338.0,47245.231039257655 +2559,Netherlands,2005,177530881.5,0.6068,204320000.0,10.823666666666668,785.1094360351562,16299097.0,48168.891567131366 +2560,Netherlands,2006,172736702.3,0.5703,197750000.0,11.291333333333334,812.7345581054688,16320437.0,49798.578194043985 +2561,Netherlands,2007,172600460.5,0.5589,197480000.0,11.388416666666666,842.7933349609375,16351022.0,51543.77108421342 +2562,Netherlands,2008,175519219.7,0.5506,195440000.0,10.794583333333334,857.1128540039062,16410055.0,52230.95559423209 +2563,Netherlands,2009,170180412.3,0.5399,190150000.0,10.707166666666666,824.8204345703125,16490410.0,50018.18842407876 +2564,Netherlands,2010,182179635.6,0.5509,200660000.0,9.264583333333334,846.5548706054688,16573840.0,51077.775012035156 +2565,Netherlands,2011,169118699.9,0.4922,188090000.0,11.076,859.6865234375,16653734.0,51621.24742940533 +2566,Netherlands,2012,164948743.6,0.4724,185570000.0,10.438166666666667,850.8286743164062,16730632.0,50854.544784465186 +2567,Netherlands,2013,164757276.7,0.46799999999999997,185210000.0,9.8565,849.7211303710938,16805037.0,50563.47869814828 +2568,New Zealand,1990,25453569.53,0.1147,47700000.0,11.26933333333333,82.68023681640625,3413677.0,24220.28704426525 +2569,New Zealand,1991,26118072.09,0.1164,47130000.0,10.53433333333333,81.32217407226562,3455200.0,23536.169851894432 +2570,New Zealand,1992,28112568.12,0.1267,47960000.0,9.936166666666667,82.35211944580078,3493554.0,23572.590962040602 +2571,New Zealand,1993,27706969.09,0.1251,47020000.0,10.238249999999999,87.73204040527344,3536637.0,24806.628558507262 +2572,New Zealand,1994,27888757.31,0.1238,48740000.0,10.542250000000001,92.30848693847656,3586304.0,25739.16961263645 +2573,New Zealand,1995,28153435.53,0.1224,50130000.0,10.821083333333334,96.53363800048828,3642182.0,26504.342177433275 +2574,New Zealand,1996,29375228.4,0.1249,50590000.0,10.7435,99.7751922607422,3697014.0,26988.048262933862 +2575,New Zealand,1997,31356089.21,0.1311,52940000.0,10.542,102.77682495117188,3738420.0,27492.048766904703 +2576,New Zealand,1998,29934433.0,0.1248,51380000.0,11.632999999999997,103.87254333496094,3764312.0,27594.031348878878 +2577,New Zealand,1999,31528566.54,0.1318,53650000.0,11.612249999999998,109.26741027832031,3783756.0,28878.02762078747 +2578,New Zealand,2000,32359158.05,0.1318,53860000.0,11.115583333333333,111.7690200805664,3802400.0,29394.33517793141 +2579,New Zealand,2001,34582667.69,0.1376,63550000.0,11.191083333333331,116.01361846923828,3837878.0,30228.584251307177 +2580,New Zealand,2002,34768718.05,0.1364,64819999.99999999,10.997583333333333,121.88459014892578,3898456.0,31264.836681220917 +2581,New Zealand,2003,36493091.67,0.1358,67310000.0,10.930250000000001,127.41848754882812,3960281.0,32174.1026833268 +2582,New Zealand,2004,36064917.99,0.1279,66160000.0,10.475416666666668,131.5845947265625,4008166.0,32829.12801679433 +2583,New Zealand,2005,37647573.63,0.1287,67620000.0,11.375416666666666,135.95176696777344,4048224.0,33583.06431851929 +2584,New Zealand,2006,37520080.7,0.1239,66430000.00000001,10.703833333333334,139.44969177246094,4089964.0,34095.57926975908 +2585,New Zealand,2007,36642046.21,0.1187,65129999.99999999,11.085166666666666,144.69357299804688,4132341.0,35014.915999925186 +2586,New Zealand,2008,37655730.94,0.1181,65099999.99999999,11.171666666666667,142.45729064941406,4173460.0,34134.097523257464 +2587,New Zealand,2009,34806699.32,0.1104,62320000.0,10.653583333333334,145.18687438964844,4213418.0,34458.217625132005 +2588,New Zealand,2010,35010698.68,0.1059,62170000.0,11.335749999999999,146.58380126953125,4252277.0,34471.83738724717 +2589,New Zealand,2011,34328816.19,0.0999,62410000.0,11.16325,150.535400390625,4290347.0,35086.99888158813 +2590,New Zealand,2012,35969262.78,0.10300000000000001,64459999.99999999,10.656916666666666,154.29791259765625,4327944.0,35651.55015814813 +2591,New Zealand,2013,35214465.08,0.1,64000000.0,11.0885,157.41165161132812,4365113.0,36061.30050042877 +2592,Nicaragua,1990,2043946.22,0.0092,39400000.0,26.55475,4.700947284698486,3643561.0,1290.2068291702776 +2593,Nicaragua,1991,1987890.28,0.0089,38060000.0,26.610249999999997,4.692049503326416,3838878.0,1222.2450162069272 +2594,Nicaragua,1992,2373649.38,0.0107,38970000.0,26.731250000000003,4.710176944732666,4006508.0,1175.6314837590905 +2595,Nicaragua,1993,2283404.38,0.0103,39670000.0,26.611250000000002,4.69166898727417,4156381.0,1128.7870354700808 +2596,Nicaragua,1994,2517878.28,0.0112,39980000.0,26.86691666666667,4.848264217376709,4289361.0,1130.2998785545701 +2597,Nicaragua,1995,2754848.65,0.012,39350000.0,26.92175,5.134889125823975,4401893.0,1166.5183878444964 +2598,Nicaragua,1996,2847363.34,0.0121,38940000.0,26.40975,5.460659980773926,4502776.0,1212.7318749087065 +2599,Nicaragua,1997,3113601.56,0.013000000000000001,39780000.0,27.026833333333332,5.677265644073486,4598209.0,1234.6688991460558 +2600,Nicaragua,1998,3391860.87,0.0141,40770000.0,27.39625,5.8879876136779785,4688840.0,1255.7450486000755 +2601,Nicaragua,1999,3598419.93,0.015,41300000.0,26.460000000000004,6.30226469039917,4777854.0,1319.0576125597747 +2602,Nicaragua,2000,3721060.93,0.0152,41820000.0,26.381333333333334,6.560757637023926,4866172.0,1348.2379243939438 +2603,Nicaragua,2001,3922036.8,0.0156,41980000.0,26.409999999999997,6.755011558532715,4952226.0,1364.035397118935 +2604,Nicaragua,2002,3990829.14,0.0157,42400000.0,26.664916666666667,6.805940628051758,5035653.0,1351.5507577769472 +2605,Nicaragua,2003,4335264.84,0.0161,44600000.0,26.93225,6.977499961853027,5116137.0,1363.8219543090865 +2606,Nicaragua,2004,4378262.48,0.0155,42900000.0,26.778583333333334,7.348156929016112,5193390.0,1414.9056645112562 +2607,Nicaragua,2005,4269732.95,0.0146,44690000.0,26.838499999999996,7.662834167480469,5267146.0,1454.8361043116079 +2608,Nicaragua,2006,4414162.09,0.0146,43810000.0,26.936083333333332,7.980999946594237,5338287.0,1495.0488699079385 +2609,Nicaragua,2007,4550435.12,0.0147,43950000.0,26.823083333333333,8.386143684387207,5407784.0,1550.7541877388608 +2610,Nicaragua,2008,4365255.31,0.0137,44150000.0,26.388500000000008,8.674267768859862,5475502.0,1584.195890871716 +2611,Nicaragua,2009,4437519.13,0.0141,44010000.0,26.781666666666666,8.388653755187987,5541165.0,1513.8790769067493 +2612,Nicaragua,2010,4468989.51,0.0135,44850000.0,26.618916666666664,8.758601188659668,5604452.0,1562.7935057093305 +2613,Nicaragua,2011,4801650.53,0.013999999999999999,16970000.0,26.614,9.31185531616211,5666301.0,1643.3746312033386 +2614,Nicaragua,2012,4548839.73,0.013000000000000001,16980000.0,26.531916666666664,9.916766166687012,5727707.0,1731.3675728676435 +2615,Nicaragua,2013,4476012.01,0.0127,18040000.0,26.871555555555556,10.405374526977539,5788531.0,1797.5846595582782 +2616,Niger,1990,730863.28,0.0033,20370000.0,28.048083333333334,3.0649907588958736,7825471.0,391.66853457074654 +2617,Niger,1991,719857.54,0.0032,20900000.0,27.859166666666667,3.141744375228882,8057147.0,389.9326120311423 +2618,Niger,1992,697205.07,0.0031,21470000.0,27.18508333333334,2.93701434135437,8298180.0,353.93475935137224 +2619,Niger,1993,777794.75,0.0035,22200000.0,27.75333333333333,2.9795846939086914,8554483.0,348.306811049679 +2620,Niger,1994,770448.43,0.0034,22770000.0,27.40058333333333,3.0989041328430176,8822062.0,351.26755319142137 +2621,Niger,1995,807070.11,0.0035,23480000.0,27.895416666666666,3.1796538829803467,9110284.0,349.01808582260963 +2622,Niger,1996,898651.79,0.0038,24500000.0,28.128,3.2883691787719727,9414292.0,349.2954306889963 +2623,Niger,1997,905961.47,0.0038,25090000.0,28.131,3.378929615020752,9716696.0,347.7447081827765 +2624,Niger,1998,953575.15,0.004,26080000.0,28.327666666666673,3.731090545654297,10035645.0,371.7838310994756 +2625,Niger,1999,949892.83,0.004,26720000.0,28.26875,3.7098696231842037,10372564.0,357.6617722661633 +2626,Niger,2000,692703.13,0.0028,27290000.0,27.975833333333338,3.657578706741333,10725320.0,341.02280461014993 +2627,Niger,2001,651637.03,0.0026,22510000.0,28.118000000000006,3.917425632476807,11099193.0,352.94688834375677 +2628,Niger,2002,694819.35,0.0027,23430000.0,28.149666666666665,4.034914016723634,11490858.0,351.14123042192614 +2629,Niger,2003,753360.83,0.0028,24230000.0,28.30225,4.2487640380859375,11901375.0,356.99774505768767 +2630,Niger,2004,808238.31,0.0029,25200000.0,28.371583333333337,4.2530126571655265,12331681.0,344.88506937258 +2631,Niger,2005,708477.48,0.0024,26060000.0,28.636416666666673,4.444398403167725,12782094.0,347.70503198988564 +2632,Niger,2006,686275.64,0.0023,27040000.0,28.291083333333333,4.702173233032227,13253323.0,354.79201955858366 +2633,Niger,2007,716904.69,0.0023,28310000.0,28.411916666666666,4.850130558013916,13740434.0,352.98234087903745 +2634,Niger,2008,803056.41,0.0025,29520000.0,27.901416666666666,5.315145969390869,14238664.0,373.28965480124185 +2635,Niger,2009,962301.24,0.0031,31020000.0,28.850416666666664,5.277266025543213,14748785.0,357.8102213533666 +2636,Niger,2010,1169165.03,0.0035,32430000.0,29.29991666666667,5.718668460845947,15270018.0,374.50305958028 +2637,Niger,2011,1317848.81,0.0038,32740000.000000004,28.487416666666665,5.849225997924805,15801752.0,370.1631311467744 +2638,Niger,2012,1856448.65,0.0053,35230000.0,28.07275,6.542348861694336,16344687.0,400.27373186738515 +2639,Niger,2013,1935047.69,0.0055,37040000.0,29.231374999999996,6.887026786804199,16899327.0,407.5326068786171 +2640,Nigeria,1990,38857203.74,0.1752,386460000.0,27.36616666666667,144.24806213378906,96684078.0,1491.9526060308403 +2641,Nigeria,1991,41929119.59,0.1869,396630000.0,27.04333333333334,144.76498413085938,99209508.0,1459.1845786681995 +2642,Nigeria,1992,46265115.45,0.2084,408760000.0,26.66366666666667,151.46932983398438,101782546.0,1488.1660538731696 +2643,Nigeria,1993,44813375.12,0.2023,408700000.0,27.103333333333335,148.38674926757812,104399690.0,1421.3332364069101 +2644,Nigeria,1994,34933726.37,0.155,407340000.0,26.818833333333334,145.69363403320312,107057692.0,1360.888987156599 +2645,Nigeria,1995,35575296.68,0.1546,401940000.0,27.165166666666664,145.5877685546875,109752901.0,1326.5049691459865 +2646,Nigeria,1996,39400611.74,0.1675,413830000.0,27.194666666666667,151.69651794433594,112481808.0,1348.631575555186 +2647,Nigeria,1997,42061123.91,0.1759,404550000.0,27.14491666666667,156.15199279785156,115242321.0,1354.9882668351636 +2648,Nigeria,1998,37587755.82,0.1567,396360000.0,27.611916666666673,160.1826934814453,118033550.0,1357.0946013353432 +2649,Nigeria,1999,40019194.32,0.1672,387400000.0,27.329166666666666,161.1183624267578,120914811.0,1332.4948457038718 +2650,Nigeria,2000,75759957.08,0.3085,384810000.0,27.0335,169.199951171875,123945463.0,1365.1161331486169 +2651,Nigeria,2001,85435089.3,0.3399,350290000.0,27.09466666666667,179.21267700195312,127128734.0,1409.6944991362311 +2652,Nigeria,2002,93396068.29,0.3663,354200000.0,27.464250000000003,206.68446350097656,130470532.0,1584.146706023829 +2653,Nigeria,2003,101305634.9,0.3771,361860000.0,27.429000000000002,221.86997985839844,133977275.0,1656.0269632174445 +2654,Nigeria,2004,103987657.1,0.3689,364380000.0,27.5695,242.3941802978516,137574107.0,1761.917162928425 +2655,Nigeria,2005,105705834.4,0.3613,382160000.0,27.72458333333334,258.00079345703125,141189271.0,1827.3399361700167 +2656,Nigeria,2006,98461634.31,0.3251,401690000.0,27.795083333333334,273.6341552734375,144858470.0,1888.975876063288 +2657,Nigeria,2007,94456005.02,0.3059,399660000.0,27.690083333333337,291.66973876953125,148604497.0,1962.7248478862064 +2658,Nigeria,2008,94933189.95,0.2978,403260000.0,27.325666666666667,311.3996887207031,152429000.0,2042.9163001837128 +2659,Nigeria,2009,75540718.78,0.2396,383360000.0,27.90291666666667,336.4266662597656,156342001.0,2151.863632983472 +2660,Nigeria,2010,90315357.11,0.2731,404250000.0,28.004250000000003,363.3598327636719,160341173.0,2266.166736622738 +2661,Nigeria,2011,94225411.72,0.2743,430360000.0,27.622250000000008,382.6466979980469,164423884.0,2327.19656469158 +2662,Nigeria,2012,96566816.42,0.2765,432320000.0,27.40791666666667,398.8328857421875,168587778.0,2365.72834919378 +2663,Nigeria,2013,95874314.66,0.2723,455670000.0,28.005875,425.4403381347656,172831537.0,2461.5897394626863 +2664,North Korea,1990,122399984.2,0.5518,175490000.0,8.179083333333333,,, +2665,North Korea,1991,117226573.1,0.5226,168020000.0,7.537000000000002,,, +2666,North Korea,1992,102962593.6,0.4639,153890000.0,7.602333333333333,,, +2667,North Korea,1993,94868526.31,0.4282,144320000.0,7.274083333333333,,, +2668,North Korea,1994,86719499.02,0.3848,132840000.0,8.275416666666667,,, +2669,North Korea,1995,81259847.72,0.3532,123770000.0,7.512416666666668,,, +2670,North Korea,1996,71608580.43,0.3044,111720000.0,7.266083333333332,,, +2671,North Korea,1997,65132606.58,0.2723,104170000.0,7.870333333333334,,, +2672,North Korea,1998,58768831.61,0.245,95480000.0,8.751333333333331,,, +2673,North Korea,1999,64099385.07,0.2679,99600000.0,8.280666666666667,,, +2674,North Korea,2000,69166025.84,0.2816,102960000.0,7.525000000000001,,, +2675,North Korea,2001,71664953.5,0.2851,105880000.0,7.727083333333333,,, +2676,North Korea,2002,68642901.97,0.2692,101830000.0,7.925750000000001,,, +2677,North Korea,2003,70136492.37,0.2611,103160000.0,8.054333333333332,,, +2678,North Korea,2004,71721986.95,0.2544,105420000.0,8.443999999999999,,, +2679,North Korea,2005,74918706.34,0.2561,107670000.0,7.308500000000001,,, +2680,North Korea,2006,76172850.95,0.2515,111910000.0,7.940333333333335,,, +2681,North Korea,2007,63479217.21,0.2056,99430000.0,8.511166666666666,,, +2682,North Korea,2008,70328125.79,0.2206,107720000.0,8.299916666666666,,, +2683,North Korea,2009,69873800.56,0.2217,91460000.0,7.789749999999999,,, +2684,North Korea,2010,65687223.7,0.1987,88010000.0,7.361916666666665,,, +2685,North Korea,2011,47238069.61,0.1375,74220000.0,7.373666666666666,,, +2686,North Korea,2012,48229298.69,0.1381,75520000.0,7.1139166666666656,,, +2687,North Korea,2013,35156660.06,0.0999,64900000.00000001,8.4915,,, +2688,Norway,1990,35704388.09,0.161,30630000.0,2.0665,255.62789916992188,4242006.0,60261.088543939324 +2689,Norway,1991,34069334.14,0.1519,28080000.0,1.1335000000000002,263.51318359375,4261733.0,61832.400949038805 +2690,Norway,1992,34831892.59,0.1569,28620000.0,1.2961666666666665,272.93222045898443,4286357.0,63674.63570089574 +2691,Norway,1993,36531562.52,0.1649,30400000.0,0.4189999999999999,280.697998046875,4311964.0,65097.48180802878 +2692,Norway,1994,38445957.49,0.1706,31960000.0,0.4645000000000002,294.8885498046875,4336638.0,67999.34645333263 +2693,Norway,1995,38477220.08,0.1672,31290000.0,0.5059999999999998,307.1380920410156,4359101.0,70459.04466104723 +2694,Norway,1996,41593833.8,0.1768,32200000.000000004,0.25566666666666604,322.5809631347656,4381277.0,73627.15553816059 +2695,Norway,1997,41698909.6,0.1744,33760000.0,1.0437500000000002,339.62835693359375,4405672.0,77088.8883542837 +2696,Norway,1998,41956663.25,0.1749,35400000.0,0.4654166666666671,348.5415344238281,4432718.0,78629.3047344379 +2697,Norway,1999,42709488.72,0.1785,37290000.0,1.2797499999999997,355.5587463378906,4463377.0,79661.37441177176 +2698,Norway,2000,42215884.96,0.1719,31940000.0,1.7969166666666663,366.9554138183594,4492400.0,81683.60204308596 +2699,Norway,2001,43559843.15,0.1733,25600000.0,0.7025000000000002,374.6076354980469,4515195.0,82965.99271970466 +2700,Norway,2002,42667047.64,0.1674,25060000.0,1.37075,379.993408203125,4539602.0,83706.3267227226 +2701,Norway,2003,43921782.2,0.1635,27050000.0,1.2833333333333334,383.4887390136719,4566303.0,83982.32421582007 +2702,Norway,2004,44337010.57,0.1573,27530000.0,1.5249166666666667,398.67120361328125,4593413.0,86791.93523710611 +2703,Norway,2005,43560708.06,0.1489,26270000.0,1.807333333333334,409.1352233886719,4624875.0,88464.06084243832 +2704,Norway,2006,43923347.59,0.145,25300000.0,2.2566666666666664,418.93438720703125,4662366.0,89854.46170614475 +2705,Norway,2007,45851890.6,0.1485,25830000.0,1.7515833333333326,431.4397888183594,4710952.0,91582.29351909325 +2706,Norway,2008,44903022.1,0.1409,24450000.0,1.6424999999999994,433.5141296386719,4770096.0,90881.63626867716 +2707,Norway,2009,43205403.59,0.1371,23800000.0,1.3854999999999995,426.1832275390625,4830658.0,88224.67405870225 +2708,Norway,2010,45823277.24,0.1386,26530000.0,-0.21550000000000002,429.1309814453125,4891300.0,87733.52308083996 +2709,Norway,2011,44982556.87,0.1309,25840000.0,2.1189166666666672,433.3018493652344,4955373.0,87440.81411535204 +2710,Norway,2012,44560819.66,0.1276,24990000.0,1.141666666666666,445.0946960449219,5021106.0,88644.7519819183 +2711,Norway,2013,44302653.91,0.1258,24150000.0,1.7353750000000001,449.74322509765625,5085582.0,88434.95692285686 +2712,Oman,1990,11295699.04,0.0509,14820000.0,27.4445,27.00691032409668,1794377.0,15050.85627161777 +2713,Oman,1991,11677963.28,0.0521,19150000.0,27.010833333333338,28.64733123779297,1866066.0,15351.724557327 +2714,Oman,1992,11984610.63,0.054000000000000006,18170000.0,26.738916666666668,31.05768585205078,1939491.0,16013.317850946863 +2715,Oman,1993,13356636.72,0.0603,17490000.0,27.368583333333333,32.93448638916016,2014566.0,16348.179403980885 +2716,Oman,1994,15181692.37,0.0674,17370000.0,27.75608333333334,34.210952758789055,2076785.0,16473.03536899056 +2717,Oman,1995,15770038.25,0.0685,19330000.0,27.323500000000006,35.920413970947266,2138569.0,16796.471832775682 +2718,Oman,1996,14966952.65,0.0636,19660000.0,27.173583333333337,37.0145263671875,2199657.0,16827.40825828186 +2719,Oman,1997,15343389.44,0.0642,21290000.0,27.346500000000002,39.247806549072266,2259725.0,17368.399495103284 +2720,Oman,1998,16406064.46,0.0684,21530000.0,28.186750000000004,40.28486251831055,2318607.0,17374.597125908163 +2721,Oman,1999,20557944.82,0.0859,24860000.0,28.112833333333338,40.23458480834961,2376176.0,16932.493556180016 +2722,Oman,2000,21739685.05,0.0885,27070000.0,27.922416666666674,42.407806396484375,2432376.0,17434.724893061095 +2723,Oman,2001,20105962.43,0.08,29850000.0,28.083333333333332,44.3087158203125,2487301.0,17813.974191427777 +2724,Oman,2002,25234394.79,0.099,31520000.0,27.99416666666666,43.82107162475586,2541078.0,17245.071432185814 +2725,Oman,2003,32027485.08,0.1192,32250000.0,28.269000000000005,42.6515007019043,2593790.0,16443.698488275568 +2726,Oman,2004,27591208.25,0.0979,32080000.0,28.12516666666666,43.20260620117188,2645614.0,16329.897785985358 +2727,Oman,2005,29466596.18,0.1007,32500000.0,28.07375,44.27854537963867,2696889.0,16418.378872708025 +2728,Oman,2006,39046377.3,0.1289,42370000.0,28.044833333333333,46.65721893310547,2748203.0,16977.355360250123 +2729,Oman,2007,43131323.37,0.1397,46110000.0,28.363,48.73472213745117,2800283.0,17403.498909735612 +2730,Oman,2008,42110702.55,0.1321,44890000.0,27.751916666666663,52.73081970214844,2853884.0,18476.861604097587 +2731,Oman,2009,40514606.36,0.1285,48170000.0,28.342500000000005,55.9539680480957,2909634.0,19230.586406433147 +2732,Oman,2010,46750085.53,0.1414,52260000.0,28.45866666666667,58.64239501953125,2967717.0,19760.10348005933 +2733,Oman,2011,53263109.21,0.155,58500000.0,28.150666666666666,57.9921989440918,3027959.0,19152.240484131984 +2734,Oman,2012,58342965.5,0.1671,65030000.0,28.211583333333326,63.40442276000977,3090150.0,20518.23463586226 +2735,Oman,2013,60657229.1,0.1723,68000000.0,28.194375,66.17684936523439,3154134.0,20980.98855826492 +2736,Pakistan,1990,67828480.38,0.3058,180120000.0,21.376416666666668,78.57093048095703,118816188.0,661.2813607600089 +2737,Pakistan,1991,67477841.41,0.3008,181000000.0,20.901333333333334,82.5478515625,122248178.0,675.2481134115552 +2738,Pakistan,1992,72015021.78,0.3245,190290000.0,20.7085,88.90890502929688,124961767.0,711.4888590627634 +2739,Pakistan,1993,77176571.49,0.3484,200100000.0,21.44483333333333,90.4717025756836,127562732.0,709.2330272111418 +2740,Pakistan,1994,84019317.22,0.3729,206770000.0,21.163083333333336,93.8530044555664,130746253.0,717.8255766577603 +2741,Pakistan,1995,83615082.6,0.3634,217150000.0,21.07391666666667,98.51055908203124,134185366.0,734.1378722477922 +2742,Pakistan,1996,93537422.58,0.3976,224210000.0,20.93383333333333,103.2849578857422,137911171.0,748.9237973749217 +2743,Pakistan,1997,93787563.89,0.3922,231170000.0,20.738749999999996,104.33267974853516,141445497.0,737.6175414657079 +2744,Pakistan,1998,96742080.71,0.4033,234990000.0,21.66275,106.993408203125,144885289.0,738.4697849008328 +2745,Pakistan,1999,99390472.18,0.4153,247550000.0,21.863,110.90950775146484,148379201.0,747.4734127424292 +2746,Pakistan,2000,105418374.2,0.4293,249660000.0,21.822500000000005,115.63435363769531,152429036.0,758.6110669734558 +2747,Pakistan,2001,107126191.9,0.4262,251870000.0,21.93041666666667,117.9267807006836,156795444.0,752.1059138726224 +2748,Pakistan,2002,112903645.0,0.4429,258290000.00000003,22.025499999999997,121.729248046875,160269228.0,759.5297585564897 +2749,Pakistan,2003,117490717.5,0.4373,266230000.00000003,21.43208333333334,127.6286392211914,163165640.0,782.2029149102189 +2750,Pakistan,2004,129959474.6,0.4611,283060000.0,22.143083333333326,137.03305053710938,166223613.0,824.3897967559482 +2751,Pakistan,2005,134756236.6,0.4606,291300000.0,21.27241666666667,147.53977966308594,169278640.0,871.5794246875207 +2752,Pakistan,2006,143726699.4,0.4746,312190000.0,21.636916666666668,156.65411376953125,172381868.0,908.7621313485896 +2753,Pakistan,2007,155844165.9,0.5047,328950000.0,21.690916666666666,164.22491455078125,175495078.0,935.7807433823372 +2754,Pakistan,2008,155356244.9,0.4874,331940000.0,21.53516666666667,167.01904296875,178479391.0,935.78895598512 +2755,Pakistan,2009,155219048.8,0.4924,342300000.0,21.911416666666664,171.7484588623047,181457277.0,946.4952946599363 +2756,Pakistan,2010,157764740.5,0.4771,336910000.0,22.08791666666667,174.50791931152344,184404791.0,946.330723649818 +2757,Pakistan,2011,158341969.4,0.4609,352210000.0,21.768249999999995,179.30409240722656,187342721.0,957.091321456928 +2758,Pakistan,2012,159727596.4,0.4574,356030000.0,21.198,185.59234619140625,190291129.0,975.3073995972047 +2759,Pakistan,2013,160595697.1,0.4561,366540000.0,22.320875,193.7518310546875,193238868.0,1002.65455423123 +2760,Palau,1990,234496.0,0.0011,0.0,27.67691666666667,,, +2761,Palau,1991,234496.0,0.001,-130000.0,27.59958333333334,,, +2762,Palau,1992,234496.0,0.0011,100000.0,27.539083333333334,,, +2763,Palau,1993,234496.0,0.0011,110000.0,27.484583333333333,,, +2764,Palau,1994,234496.0,0.001,110000.0,27.6165,,, +2765,Palau,1995,234496.0,0.001,110000.0,27.634083333333333,,, +2766,Palau,1996,234496.0,0.001,110000.0,27.701583333333335,,, +2767,Palau,1997,238160.0,0.001,110000.0,27.798083333333334,,, +2768,Palau,1998,238160.0,0.001,110000.0,28.006416666666667,,, +2769,Palau,1999,241824.0,0.001,120000.0,27.66633333333333,,, +2770,Palau,2000,249152.0,0.001,120000.0,27.690749999999998,,, +2771,Palau,2001,252816.0,0.001,130000.0,27.758583333333334,,, +2772,Palau,2002,249152.0,0.001,130000.0,27.801833333333335,,, +2773,Palau,2003,252816.0,0.0009,130000.0,27.629583333333333,,, +2774,Palau,2004,252816.0,0.0009,130000.0,27.88891666666667,,, +2775,Palau,2005,260144.0,0.0009,140000.0,27.71875,,, +2776,Palau,2006,263808.0,0.0009,280000.0,27.81441666666667,,, +2777,Palau,2007,260144.0,0.0008,280000.0,27.842749999999995,,, +2778,Palau,2008,252816.0,0.0008,270000.0,27.625749999999996,,, +2779,Palau,2009,245488.0,0.0008,270000.0,27.716916666666666,,, +2780,Palau,2010,252816.0,0.0008,270000.0,27.767750000000003,,, +2781,Palau,2011,252816.0,0.0007,270000.0,27.553416666666667,,, +2782,Palau,2012,252816.0,0.0007,280000.0,27.72125,,, +2783,Palau,2013,256480.0,0.0007,280000.0,27.820375000000006,,, +2784,Panama,1990,2746211.02,0.0124,16890000.0,26.92533333333333,10.0359525680542,2393441.0,4193.106313485146 +2785,Panama,1991,3222366.46,0.0144,17360000.0,27.068083333333337,10.981239318847656,2440754.0,4499.1176164610015 +2786,Panama,1992,4225963.34,0.019,18000000.0,27.108500000000003,11.881885528564453,2489234.0,4773.309993582144 +2787,Panama,1993,3899604.72,0.0176,18250000.0,26.936999999999998,12.530130386352539,2538364.0,4936.301644032353 +2788,Panama,1994,4291964.57,0.019,18620000.0,27.006000000000004,12.88725757598877,2587713.0,4980.172676022716 +2789,Panama,1995,2939611.43,0.0128,18830000.0,26.95808333333333,13.113000869750975,2638173.0,4970.485585953225 +2790,Panama,1996,4575024.12,0.0194,18560000.0,26.668333333333333,13.64797306060791,2689792.0,5073.9882714380565 +2791,Panama,1997,5593297.02,0.0234,18470000.0,27.41291666666667,14.529767036437988,2743684.0,5295.714461445993 +2792,Panama,1998,5889705.74,0.0246,19550000.0,27.547416666666667,15.59646987915039,2797225.0,5575.693724727325 +2793,Panama,1999,5610305.77,0.0234,18670000.0,26.425250000000002,16.207416534423828,2847789.0,5691.228013881586 +2794,Panama,2000,5717008.83,0.0233,18820000.0,26.711833333333335,16.64750862121582,2899513.0,5741.484387625033 +2795,Panama,2001,6939908.47,0.0276,20650000.0,26.665833333333335,16.743108749389652,2952285.0,5671.237278714504 +2796,Panama,2002,5807264.59,0.0228,20150000.0,27.206999999999997,17.116338729858402,3003421.0,5698.947543437433 +2797,Panama,2003,6075664.84,0.0226,20730000.0,27.248416666666667,17.836164474487305,3053605.0,5841.0188857063395 +2798,Panama,2004,5711740.96,0.0203,20550000.0,27.105916666666662,19.177814483642575,3104177.0,6178.067321432566 +2799,Panama,2005,6741087.17,0.023,22020000.0,27.084999999999997,20.55694580078125,3155032.0,6515.606117713307 +2800,Panama,2006,7268135.04,0.024,21920000.0,27.13175,22.33562660217285,3206481.0,6965.775441105951 +2801,Panama,2007,7090050.2,0.023,21690000.0,27.059416666666667,25.012325286865234,3258329.0,7676.4271768950375 +2802,Panama,2008,7181697.03,0.0225,21800000.0,26.80066666666667,27.47745513916016,3309679.0,8302.151096574671 +2803,Panama,2009,8419354.57,0.0267,23500000.0,27.269750000000002,27.81899833679199,3360474.0,8278.295959674733 +2804,Panama,2010,8999435.59,0.0272,24280000.0,27.029166666666665,29.44028663635254,3410676.0,8631.8039697563 +2805,Panama,2011,9900845.13,0.0288,25360000.0,26.70925,32.77104949951172,3460462.0,9470.137079821052 +2806,Panama,2012,9851868.12,0.0282,26320000.0,27.0825,35.97567749023437,3510045.0,10249.34936453361 +2807,Panama,2013,10450032.68,0.0297,26610000.0,27.217499999999998,38.45923614501953,3559408.0,10804.952999212095 +2808,Papua New Guinea,1990,2139776.0,0.0096,63120000.0,24.93675,6.430563926696777,3682825.0,1746.095436708716 +2809,Papua New Guinea,1991,2062832.0,0.0092,64040000.00000001,24.72616666666666,7.044483184814452,3783089.0,1862.098191402437 +2810,Papua New Guinea,1992,2051840.0,0.0092,65069999.99999999,24.831083333333336,8.020079612731934,3886602.0,2063.519653602796 +2811,Papua New Guinea,1993,1993216.0,0.009000000000000001,66040000.00000001,24.673750000000002,9.479917526245117,3993245.0,2373.9884545639247 +2812,Papua New Guinea,1994,2180080.0,0.0097,67260000.0,24.69483333333333,10.043224334716797,4103096.0,2447.7185848726904 +2813,Papua New Guinea,1995,2059168.0,0.009000000000000001,68170000.0,24.984000000000005,9.71054744720459,4215889.0,2303.3214221732574 +2814,Papua New Guinea,1996,2187408.0,0.0093,54400000.0,25.042333333333332,10.461532592773438,4331067.0,2415.463116311393 +2815,Papua New Guinea,1997,2575792.0,0.0108,171220000.0,24.767500000000002,10.053072929382324,4448371.0,2259.9448043749776 +2816,Papua New Guinea,1998,2861584.0,0.0119,57010000.0,25.419749999999997,9.674160957336426,4567798.0,2117.9047228744407 +2817,Papua New Guinea,1999,2443888.0,0.0102,56390000.0,24.91983333333333,9.853671073913574,4689319.0,2101.301078880233 +2818,Papua New Guinea,2000,2663728.0,0.0108,56230000.0,24.942000000000004,9.607836723327637,4812791.0,1996.3128927326443 +2819,Papua New Guinea,2001,3206000.0,0.0128,53140000.0,25.054666666666662,9.596183776855467,4937454.0,1943.5489985031693 +2820,Papua New Guinea,2002,3484464.0,0.0137,75990000.0,25.096333333333334,9.58093547821045,5062602.0,1892.4923346157666 +2821,Papua New Guinea,2003,3942464.0,0.0147,64560000.0,25.066833333333335,9.78827667236328,5188113.0,1886.6737621873847 +2822,Papua New Guinea,2004,4484736.0,0.0159,103640000.0,24.908916666666666,10.054633140563963,5313886.0,1892.1431774343607 +2823,Papua New Guinea,2005,4382144.0,0.015,60850000.0,25.03375,10.69257926940918,5439769.0,1965.631126874906 +2824,Papua New Guinea,2006,4305200.0,0.0142,72850000.0,24.739,10.93791675567627,5565559.0,1965.2862822362085 +2825,Papua New Guinea,2007,6115216.0,0.0198,58680000.0,24.882666666666665,12.151782989501953,5691088.0,2135.230203697773 +2826,Papua New Guinea,2008,4792512.0,0.015,63430000.0,24.77591666666667,12.116023063659668,5816233.0,2083.139218057404 +2827,Papua New Guinea,2009,5089296.0,0.0161,58840000.0,24.78316666666667,12.94058609008789,5940775.0,2178.265645490342 +2828,Papua New Guinea,2010,4736381.57,0.0143,66910000.0,25.342,14.250779151916506,6064515.0,2349.8629572054 +2829,Papua New Guinea,2011,5299953.04,0.0154,58450000.0,24.829833333333337,14.40857982635498,6187591.0,2328.625118621282 +2830,Papua New Guinea,2012,5050581.49,0.0145,60400000.0,24.84358333333334,15.078351974487305,6310129.0,2389.5473411854664 +2831,Papua New Guinea,2013,6158141.98,0.0175,64250000.0,25.069125000000003,15.655841827392578,6431902.0,2434.092097079927 +2832,Paraguay,1990,2146875.8,0.0097,107600000.0,23.599416666666666,14.995479583740234,4200491.0,3569.9349394488013 +2833,Paraguay,1991,2117447.17,0.0094,104660000.0,24.11566666666667,15.519369125366213,4322340.0,3590.501701709308 +2834,Paraguay,1992,2513042.55,0.0113,103970000.0,23.090749999999996,15.782644271850586,4445889.0,3549.941141546851 +2835,Paraguay,1993,2842050.65,0.0128,104220000.0,23.73425,16.56173324584961,4570988.0,3623.2283361605 +2836,Paraguay,1994,3398104.83,0.0151,104270000.0,24.470916666666668,17.442472457885742,4697579.0,3713.0769823957703 +2837,Paraguay,1995,3930552.4,0.0171,106210000.0,23.96375,18.632539749145508,4825564.0,3861.2149272386628 +2838,Paraguay,1996,3718874.99,0.0158,102280000.0,23.414833333333334,18.9257755279541,4954835.0,3819.658077000365 +2839,Paraguay,1997,4158787.5,0.0174,103520000.0,24.407416666666666,19.72870445251465,5081156.0,3882.7196906598906 +2840,Paraguay,1998,4463803.28,0.0186,103810000.0,23.41725,19.742128372192386,5200184.0,3796.428813325141 +2841,Paraguay,1999,4463540.87,0.0187,107000000.0,23.697583333333338,19.472434997558594,5311544.0,3666.0592470962483 +2842,Paraguay,2000,3653883.07,0.0149,102440000.0,23.626083333333337,19.02181625366211,5418241.0,3510.6995524307813 +2843,Paraguay,2001,3782779.71,0.015,110010000.0,24.28825,18.8631649017334,5523401.0,3415.1358740264195 +2844,Paraguay,2002,3868410.93,0.0152,112460000.0,24.759,18.859127044677734,5626892.0,3351.606365410556 +2845,Paraguay,2003,4033861.21,0.015,112250000.0,24.263833333333334,19.673982620239254,5728549.0,3434.3745022062762 +2846,Paraguay,2004,4053265.19,0.0144,109480000.0,23.8045,20.472238540649414,5828163.0,3512.6400103513597 +2847,Paraguay,2005,3788705.8,0.013000000000000001,111390000.0,23.820083333333333,20.909011840820312,5925541.0,3528.624954383121 +2848,Paraguay,2006,3937135.6,0.013000000000000001,166320000.0,24.376583333333333,21.914133071899414,6020467.0,3639.939073148215 +2849,Paraguay,2007,4082760.21,0.0132,170400000.0,23.726666666666663,23.10223388671875,6112995.0,3779.200520648021 +2850,Paraguay,2008,4372845.07,0.0137,166960000.0,23.81833333333333,24.57133483886719,6203197.0,3961.076012718472 +2851,Paraguay,2009,4497153.51,0.0143,169050000.0,24.188583333333337,24.50794792175293,6290878.0,3895.7913222530988 +2852,Paraguay,2010,5026004.75,0.0152,171920000.0,23.615416666666672,27.23904800415039,6375830.0,4272.235615465028 +2853,Paraguay,2011,5247878.99,0.0153,187710000.0,23.991500000000002,28.396467208862305,6459058.0,4396.379039925374 +2854,Paraguay,2012,5193484.05,0.0149,189600000.0,24.46575,28.243549346923828,6541591.0,4317.53519089222 +2855,Paraguay,2013,5327312.04,0.0151,193560000.0,22.817749999999997,30.620948791503906,6623252.0,4623.24984637515 +2856,Peru,1990,20952537.36,0.0945,88520000.0,20.233083333333333,58.49349975585938,21564996.0,2712.428036428079 +2857,Peru,1991,20174638.56,0.0899,87200000.0,20.50425,59.79162216186523,22025564.0,2714.646588022229 +2858,Peru,1992,20310903.71,0.0915,88140000.0,20.836583333333333,59.46844100952149,22491912.0,2643.992249726101 +2859,Peru,1993,23478442.11,0.106,89240000.0,20.5025,62.58671569824219,22963040.0,2725.5413785910832 +2860,Peru,1994,23119347.97,0.1026,91750000.0,20.530583333333336,70.29011535644531,23423635.0,3000.820127040287 +2861,Peru,1995,23682242.37,0.1029,96560000.0,20.686,75.49959564208984,23863296.0,3163.837704652779 +2862,Peru,1996,24184027.4,0.1028,99310000.0,20.115000000000002,77.61280822753906,24286416.0,3195.7291774767864 +2863,Peru,1997,27245605.59,0.1139,101220000.0,20.93658333333333,82.6396484375,24692146.0,3346.798955323689 +2864,Peru,1998,27572017.58,0.115,101490000.0,21.181833333333334,82.31608581542969,25078825.0,3282.294358504822 +2865,Peru,1999,29146761.41,0.1218,105410000.0,20.06775,83.54663848876953,25444838.0,3283.4415565455574 +2866,Peru,2000,30078268.09,0.1225,105930000.0,20.231916666666667,85.79769897460938,25796957.0,3325.8844822127417 +2867,Peru,2001,26928671.66,0.1071,103930000.0,20.225750000000005,86.32783508300781,26142281.0,3302.2304015096393 +2868,Peru,2002,26930571.41,0.1056,106050000.0,20.535999999999998,91.03575134277344,26479981.0,3437.9084842535744 +2869,Peru,2003,26104601.06,0.0972,106410000.0,20.485583333333334,94.8274154663086,26809346.0,3537.1028993511663 +2870,Peru,2004,31555544.64,0.1119,110400000.0,20.443083333333337,99.5291519165039,27129674.0,3668.6453333904383 +2871,Peru,2005,36733047.12,0.1256,115470000.0,20.45058333333333,105.78461456298828,27442387.0,3854.78911010869 +2872,Peru,2006,34410900.79,0.1136,137580000.0,20.541000000000004,113.74903106689452,27749145.0,4099.190481973213 +2873,Peru,2007,42401238.33,0.1373,144130000.0,20.191666666666666,123.43861389160156,28049728.0,4400.706270363889 +2874,Peru,2008,40332196.61,0.1265,146260000.0,20.226416666666665,134.70433044433594,28347879.0,4751.831008038941 +2875,Peru,2009,50844467.5,0.1613,149920000.0,20.52308333333333,136.18045043945312,28647373.0,4753.68022189864 +2876,Peru,2010,56677887.51,0.1714,155800000.0,20.584500000000002,147.52761840820312,28947973.0,5096.302197331852 +2877,Peru,2011,48691282.41,0.1417,158340000.0,20.284083333333335,156.8619842529297,29248943.0,5362.996681723838 +2878,Peru,2012,53937593.35,0.1545,159390000.0,20.460000000000004,166.4928741455078,29549517.0,5634.368715587055 +2879,Peru,2013,55984066.6,0.159,160560000.0,19.97625,176.23690795898438,29849303.0,5904.2218828018995 +2880,Philippines,1990,41349483.26,0.1864,93440000.0,26.932333333333332,94.52008056640624,61480927.0,1537.3886696016516 +2881,Philippines,1991,43471186.42,0.1938,93410000.0,26.793416666666673,93.9734344482422,62864738.0,1494.8512860777719 +2882,Philippines,1992,48293804.82,0.2176,95560000.0,26.901500000000002,94.29069519042969,64168154.0,1469.4313193181415 +2883,Philippines,1993,48929190.41,0.2209,101090000.0,26.846333333333337,96.2861785888672,65504005.0,1469.928114912473 +2884,Philippines,1994,54123344.79,0.2402,108910000.0,26.831333333333333,100.51084899902344,66926461.0,1501.8103078694605 +2885,Philippines,1995,59948737.07,0.2606,121100000.0,26.82641666666667,105.21344757080078,68471642.0,1536.5988677590176 +2886,Philippines,1996,61253681.58,0.2604,129820000.0,26.88791666666667,111.36408996582031,70096910.0,1588.7161069699125 +2887,Philippines,1997,70062609.86,0.293,138350000.0,26.97633333333334,117.13872528076172,71696558.0,1633.8123969739484 +2888,Philippines,1998,68252959.29,0.2846,132220000.0,27.779416666666666,116.46316528320312,73249205.0,1589.9580791791955 +2889,Philippines,1999,68176638.31,0.2849,137860000.0,26.925749999999997,120.05245971679688,74819070.0,1604.5703283507382 +2890,Philippines,2000,72346435.42,0.2946,139740000.0,27.009749999999997,125.3482437133789,76451531.0,1639.5779400857114 +2891,Philippines,2001,70304563.22,0.2797,153270000.0,27.104250000000004,128.97579956054688,78179792.0,1649.7332144417428 +2892,Philippines,2002,70155141.92,0.2752,155070000.0,27.083166666666667,133.67814636230472,79872190.0,1673.6506957215606 +2893,Philippines,2003,70214684.86,0.2613,158170000.0,27.014833333333332,140.32243347167972,81536343.0,1720.9802194793024 +2894,Philippines,2004,72770752.46,0.2582,161910000.0,27.089833333333335,149.720703125,83255520.0,1798.3276439207873 +2895,Philippines,2005,73271293.81,0.2505,163380000.0,27.144333333333336,156.8738555908203,84964745.0,1846.3405685595872 +2896,Philippines,2006,66521913.38,0.2196,174340000.0,27.178083333333337,165.09869384765625,86656261.0,1905.2136792245885 +2897,Philippines,2007,70764075.09,0.2292,180030000.0,27.162000000000003,176.02272033691406,88350267.0,1992.3281084924631 +2898,Philippines,2008,77299991.63,0.2425,183140000.0,26.87575,183.33251953125,90020447.0,2036.565309781788 +2899,Philippines,2009,75767071.76,0.2404,185250000.0,27.008749999999996,185.43777465820312,91586193.0,2024.735045578356 +2900,Philippines,2010,82932929.78,0.2508,191370000.0,27.3565,199.5908966064453,93136651.0,2142.9898376574147 +2901,Philippines,2011,83534839.91,0.2431,103170000.0,26.78591666666667,206.8954162597656,94678277.0,2185.246952263037 +2902,Philippines,2012,88927136.0,0.2547,108250000.0,27.14891666666667,220.72393798828125,96190033.0,2294.6653733685816 +2903,Philippines,2013,95722937.07,0.2719,119610000.0,27.416750000000004,236.31593322753903,97760753.0,2417.2883900304973 +2904,Poland,1990,376038705.7,1.6953,426630000.0,9.231666666666667,226.66232299804688,38119408.0,5946.113407586154 +2905,Poland,1991,373378200.5,1.6647,422360000.0,7.944666666666666,210.7606506347656,38253222.0,5509.618265221308 +2906,Poland,1992,363718927.6,1.6387,411550000.0,8.8135,216.0612335205078,38370697.0,5630.891550406494 +2907,Poland,1993,364565344.4,1.6457,411100000.0,7.831083333333333,224.1382751464844,38467745.0,5826.654906506331 +2908,Poland,1994,359611076.8,1.5959,406380000.0,8.921,236.00148010253903,38548295.0,6122.228754930382 +2909,Poland,1995,361303851.0,1.5704,405910000.0,8.262250000000002,252.4079742431641,38600640.0,6538.958272276421 +2910,Poland,1996,375304644.6,1.5955,420690000.0,6.7685,267.6952514648437,38629840.0,6929.753047510519 +2911,Poland,1997,366570115.9,1.5328,410460000.0,7.980666666666668,284.9871520996094,38654956.0,7372.590259826175 +2912,Poland,1998,337341530.6,1.4065,380740000.0,8.315166666666666,298.1383056640625,38668755.0,7710.057012801744 +2913,Poland,1999,327652449.7,1.3692,369990000.0,9.034999999999998,311.9785461425781,38665538.0,8068.64619710136 +2914,Poland,2000,317097260.4,1.2912,356570000.0,9.622166666666667,326.2033996582031,38654162.0,8439.023969998447 +2915,Poland,2001,313545668.9,1.2473,345710000.0,8.333833333333333,330.2744445800781,38643639.0,8546.67037387649 +2916,Poland,2002,305730543.5,1.1992,338060000.0,9.171166666666666,337.017333984375,38625974.0,8725.147849588855 +2917,Poland,2003,318415559.2,1.1852,350130000.0,8.385083333333334,349.02349853515625,38599320.0,9042.2188405173 +2918,Poland,2004,322538948.6,1.1443,354020000.0,8.3905,366.9481201171875,38574205.0,9512.78503645603 +2919,Poland,2005,321668846.1,1.0995,355740000.0,8.4185,379.7680358886719,38556604.0,9849.623579106497 +2920,Poland,2006,334622822.1,1.1048,286660000.0,8.799916666666665,403.2361755371094,38542453.0,10462.130563851486 +2921,Poland,2007,334365693.2,1.0828,286630000.0,9.418083333333334,431.6030578613281,38537963.0,11199.425819712584 +2922,Poland,2008,327448211.3,1.0273,280380000.0,9.469166666666666,449.9449462890625,38556061.0,11669.888848061071 +2923,Poland,2009,314144580.2,0.9966,264550000.0,8.652916666666666,462.6345520019531,38587546.0,11989.21931967255 +2924,Poland,2010,332130710.9,1.0044,282520000.0,7.621416666666665,479.3214721679688,38616395.0,12412.382672384843 +2925,Poland,2011,331694639.5,0.9654,360530000.0,8.951666666666666,503.3701477050781,38631804.0,13029.941540008798 +2926,Poland,2012,324216621.4,0.9285,353510000.0,8.53775,511.4638671875,38632334.0,13239.269136249959 +2927,Poland,2013,319911258.7,0.9086,348120000.0,9.257875,518.5828857421875,38619322.0,13428.068098714617 +2928,Portugal,1990,45579085.94,0.2055,57960000.0,15.742750000000001,166.5925750732422,9922689.0,16789.0553733209 +2929,Portugal,1991,47206212.67,0.2105,59670000.0,15.232833333333332,173.86968994140625,9919009.0,17528.93761276013 +2930,Portugal,1992,51458520.14,0.2318,63650000.0,15.111333333333334,175.76394653320312,9927680.0,17704.433113597854 +2931,Portugal,1993,50070049.5,0.226,62610000.0,14.520166666666666,172.172607421875,9967832.0,17272.823962309456 +2932,Portugal,1994,50820363.17,0.2255,64510000.00000001,15.312833333333336,173.83380126953125,10027688.0,17335.381921488908 +2933,Portugal,1995,54790936.81,0.2381,68260000.0,16.34741666666667,181.2787170410156,10065543.0,18009.82987614435 +2934,Portugal,1996,52098076.4,0.2215,66640000.0,15.296333333333335,187.61746215820312,10099918.0,18576.13716846049 +2935,Portugal,1997,55108776.84,0.2304,68910000.0,16.267583333333334,195.92181396484372,10156415.0,19290.449825538217 +2936,Portugal,1998,59597616.69,0.2485,74080000.0,15.645000000000001,205.3099365234375,10224828.0,20079.549164390588 +2937,Portugal,1999,67223504.65,0.2809,80820000.0,15.27908333333333,213.29283142089844,10283381.0,20741.508208331332 +2938,Portugal,2000,66044345.51,0.2689,80340000.0,15.392999999999999,221.37127685546875,10335597.0,21418.33479531649 +2939,Portugal,2001,65704175.79,0.2614,83940000.0,15.40775,225.67320251464844,10386753.0,21727.021188878607 +2940,Portugal,2002,69556728.46,0.2728,88770000.0,15.463166666666666,227.40817260742188,10433867.0,21795.195645815867 +2941,Portugal,2003,64437188.19,0.2398,84380000.0,15.81583333333333,225.2837219238281,10479955.0,21496.630655745004 +2942,Portugal,2004,67220525.69,0.2385,85630000.0,15.463833333333335,229.36492919921875,10524145.0,21794.162775144086 +2943,Portugal,2005,69497372.12,0.2376,89700000.0,15.536916666666665,231.1236572265625,10566212.0,21873.84251106853 +2944,Portugal,2006,64770176.83,0.2139,77730000.0,16.002583333333334,234.71311950683597,10605870.0,22130.491841483625 +2945,Portugal,2007,62270128.78,0.2017,76270000.0,15.241083333333334,240.56219482421875,10642836.0,22603.20414823819 +2946,Portugal,2008,59923525.13,0.188,73990000.0,15.269999999999998,241.04156494140625,10676910.0,22575.966730206237 +2947,Portugal,2009,57077123.09,0.1811,73270000.0,15.983499999999998,233.86309814453125,10707924.0,21840.190324896896 +2948,Portugal,2010,52881799.72,0.1599,68180000.0,15.68425,238.3034210205078,10735765.0,22197.15325554423 +2949,Portugal,2011,51701800.43,0.1505,68840000.0,16.066416666666665,233.94998168945312,10760305.0,21741.947062788007 +2950,Portugal,2012,49877899.43,0.1428,67190000.0,15.392166666666666,224.52587890625,10781459.0,20825.185061340027 +2951,Portugal,2013,48081942.95,0.1366,65530000.0,15.88925,221.98838806152344,10799270.0,20555.869800599805 +2952,Qatar,1990,11743657.28,0.0529,14040000.0,27.320333333333338,,, +2953,Qatar,1991,17645271.08,0.0787,16210000.0,26.719750000000005,,, +2954,Qatar,1992,26732745.72,0.1204,16469999.999999998,26.14741666666667,,, +2955,Qatar,1993,30647889.04,0.1383,17370000.0,27.049750000000007,,, +2956,Qatar,1994,30154773.94,0.1338,18270000.0,27.529583333333335,,, +2957,Qatar,1995,31717146.54,0.1379,19070000.0,27.103083333333334,,, +2958,Qatar,1996,32203763.72,0.1369,20020000.0,27.608083333333337,,, +2959,Qatar,1997,37393526.97,0.1564,21710000.0,27.157916666666665,,, +2960,Qatar,1998,32271043.34,0.1345,23110000.0,28.42483333333333,,, +2961,Qatar,1999,31271074.57,0.1307,25410000.0,28.567000000000004,,, +2962,Qatar,2000,34566150.38,0.1407,24270000.0,27.993,,, +2963,Qatar,2001,41214534.38,0.16399999999999998,26190000.0,28.30566666666667,,, +2964,Qatar,2002,40701935.77,0.1597,30540000.0,28.1855,,, +2965,Qatar,2003,41299356.96,0.1537,32070000.0,28.32508333333333,,, +2966,Qatar,2004,42710329.98,0.1515,35590000.0,28.140416666666667,,, +2967,Qatar,2005,50690079.09,0.1733,44840000.0,28.013083333333338,,, +2968,Qatar,2006,63196167.43,0.2087,49080000.0,28.071416666666668,,, +2969,Qatar,2007,62899427.93,0.2037,56380000.0,28.170416666666664,,, +2970,Qatar,2008,64257096.11,0.2016,60640000.0,27.86533333333333,,, +2971,Qatar,2009,68596891.43,0.2176,63380000.0,28.122500000000006,,, +2972,Qatar,2010,71919911.52,0.2175,69340000.0,28.889583333333334,,, +2973,Qatar,2011,79647573.21,0.2318,73310000.0,28.1615,,, +2974,Qatar,2012,93219420.62,0.267,80910000.0,28.484499999999997,,, +2975,Qatar,2013,84152512.38,0.239,83140000.0,28.57375,,, +2976,Romania,1990,171231788.3,0.7719,246680000.0,10.226,123.95468139648438,22865945.0,5420.929744932229 +2977,Romania,1991,140966758.5,0.6285,204540000.0,8.728166666666665,107.94195556640624,22825559.0,4728.995051836682 +2978,Romania,1992,130286047.4,0.5870000000000001,183490000.0,9.562583333333334,98.47843933105469,22797027.0,4319.7930735027285 +2979,Romania,1993,121627189.6,0.5489999999999999,171210000.0,8.862916666666669,99.9840087890625,22765757.0,4391.859615696614 +2980,Romania,1994,119670747.5,0.5311,167740000.0,10.609,103.91532135009766,22733020.0,4571.1181950351365 +2981,Romania,1995,124836930.8,0.5426,173320000.0,9.138749999999998,111.33301544189452,22687407.0,4907.26046576828 +2982,Romania,1996,128277631.7,0.5453,176440000.0,8.649416666666665,115.6413803100586,22622471.0,5111.792620269403 +2983,Romania,1997,119265891.7,0.4987,163570000.0,8.523666666666667,110.0719223022461,22557739.0,4879.563607959383 +2984,Romania,1998,104755659.1,0.4368,146130000.0,9.178416666666669,107.77444458007812,22511376.0,4787.554727000168 +2985,Romania,1999,88682298.25,0.3706,130520000.0,10.091000000000001,107.34170532226562,22476289.0,4775.775276882479 +2986,Romania,2000,93538381.76,0.3809,135080000.0,10.472249999999999,109.9126434326172,22447353.0,4896.4634463857365 +2987,Romania,2001,98877063.45,0.3933,140350000.0,9.638583333333335,116.0589599609375,22415499.0,5177.621071961748 +2988,Romania,2002,99151878.22,0.3889,138020000.0,10.392666666666665,122.07567596435548,22365752.0,5458.15208736802 +2989,Romania,2003,103576693.0,0.3855,143630000.0,9.340166666666667,128.81947326660156,22304678.0,5775.446445207664 +2990,Romania,2004,102874430.1,0.365,140160000.0,9.558166666666667,139.58697509765622,22247630.0,6274.240226831184 +2991,Romania,2005,100966617.1,0.3451,138760000.0,9.123166666666664,145.41041564941406,22197164.0,6550.855579992744 +2992,Romania,2006,104130559.8,0.3438,135940000.0,9.469,157.12405395507812,22150478.0,7093.483669069269 +2993,Romania,2007,108559692.9,0.3516,133840000.0,10.868333333333332,167.90866088867188,22106151.0,7595.562922223406 +2994,Romania,2008,105365397.6,0.3306,130360000.0,10.460999999999999,181.77769470214844,22060808.0,8239.847547839065 +2995,Romania,2009,87205422.53,0.2766,113370000.0,10.470666666666668,171.03396606445312,22011818.0,7770.097229790521 +2996,Romania,2010,83551258.13,0.2527,110230000.0,9.851666666666665,166.22518920898438,21959278.0,7569.701936875355 +2997,Romania,2011,89343217.71,0.26,-42820000.0,9.569,169.5615997314453,21904551.0,7740.930171608873 +2998,Romania,2012,86320925.39,0.2472,-44780000.0,10.301083333333331,173.08367919921875,21848504.0,7921.992242545245 +2999,Romania,2013,76829198.18,0.2182,-56920000.0,11.6455,179.16671752929688,21790479.0,8222.24777754068 +3000,Russia,1990,2571210651.0,11.5915,3559400000.0,-4.022916666666667,1413.8896484375,147972865.0,9555.060304046288 +3001,Russia,1991,2437038082.0,10.8654,3463010000.0,-4.236999999999999,1342.531494140625,148298981.0,9052.870660929391 +3002,Russia,1992,1993217628.0,8.9804,3223070000.0,-4.903250000000001,1147.447265625,148444313.0,7729.8162687108115 +3003,Russia,1993,1890935002.0,8.5357,2965810000.0,-4.62175,1047.9803466796877,148488770.0,7057.640430853374 +3004,Russia,1994,1669637144.0,7.4095,2682660000.0,-4.898416666666667,916.2518310546876,148621859.0,6164.9870161743 +3005,Russia,1995,1640263218.0,7.1293,2602080000.0,-3.1301666666666663,878.2866821289062,148758039.0,5904.129202247054 +3006,Russia,1996,1606949015.0,6.8314,2508250000.0,-4.810333333333333,846.6683959960938,148596840.0,5697.755053176728 +3007,Russia,1997,1502137425.0,6.2811,2317880000.0,-4.2965,858.521728515625,148373548.0,5786.218231538313 +3008,Russia,1998,1484852223.0,6.1908,2370700000.0,-5.281083333333334,813.0201416015625,148154792.0,5487.639857113515 +3009,Russia,1999,1512984706.0,6.3224,2352950000.0,-4.829750000000001,865.053466796875,147698905.0,5856.871225936814 +3010,Russia,2000,1499616209.0,6.1063,2418990000.0,-4.545666666666667,951.5587768554688,147053966.0,6470.81342134947 +3011,Russia,2001,1538244400.0,6.1192,2329980000.0,-4.490666666666667,1000.0120239257812,146334163.0,6833.756406737238 +3012,Russia,2002,1528961747.0,5.9973,2381150000.0,-4.122916666666667,1047.44921875,145529792.0,7197.489973393214 +3013,Russia,2003,1562472351.0,5.8156,2552060000.0,-3.8328333333333338,1123.86962890625,144737284.0,7764.893729153091 +3014,Russia,2004,1570864198.0,5.5729,2443690000.0,-4.493583333333333,1204.517822265625,144000047.0,8364.704368920275 +3015,Russia,2005,1588662864.0,5.4304,2492300000.0,-3.4980833333333337,1281.320068359375,143319518.0,8940.304057953746 +3016,Russia,2006,1648658734.0,5.4435,2379410000.0,-4.762999999999999,1385.7916259765625,142735754.0,9708.791155273979 +3017,Russia,2007,1647728940.0,5.3359,2352000000.0,-2.9750833333333353,1504.0699462890625,142441276.0,10559.228255502729 +3018,Russia,2008,1679794450.0,5.2698,2616540000.0,-3.305416666666666,1583.0029296875,142393151.0,11117.128306876924 +3019,Russia,2009,1572439009.0,4.9882,2281070000.0,-4.608500000000002,1459.197998046875,142461491.0,10242.753938654727 +3020,Russia,2010,1657560679.0,5.0128,2401660000.0,-4.5205833333333345,1524.916259765625,142526896.0,10699.14733683406 +3021,Russia,2011,1712424769.0,4.9842,2478200000.0,-3.401583333333333,1605.50634765625,142525588.0,11264.688468826032 +3022,Russia,2012,1726099500.0,4.9431,2541380000.0,-3.90175,1664.2021484375,142517670.0,11677.163599696094 +3023,Russia,2013,1665988574.0,4.7319,2413770000.0,-2.2631250000000014,1693.9139404296875,142500482.0,11887.075163926023 +3024,Rwanda,1990,537107.42,0.0024,13420000.0,19.545833333333334,2.605442523956299,7140915.0,364.8611591030418 +3025,Rwanda,1991,482110.78,0.0021,13400000.0,19.38075,2.5399317741394043,7312394.0,347.3461323527431 +3026,Rwanda,1992,482074.14,0.0022,13330000.0,19.691999999999997,2.689095020294189,7496290.0,358.7234512397718 +3027,Rwanda,1993,489365.5,0.0022,13250000.0,19.693500000000004,2.471044540405273,7823959.0,315.83045621855547 +3028,Rwanda,1994,471992.11,0.0021,13130000.0,19.712416666666666,1.229392409324646,6128862.0,200.59064950795857 +3029,Rwanda,1995,460995.53,0.002,12880000.0,19.9635,1.6624345779418943,5384523.0,308.74314733949404 +3030,Rwanda,1996,475945.68,0.002,12380000.0,19.477416666666667,1.8743234872817995,6479294.0,289.2789688632433 +3031,Rwanda,1997,492882.94,0.0021,12630000.0,19.95575,2.1339125633239746,7563089.0,282.14828138661 +3032,Rwanda,1998,481854.3,0.002,12750000.0,19.951833333333337,2.322948932647705,7896686.0,294.16757012342964 +3033,Rwanda,1999,503114.86,0.0021,12840000.0,19.508833333333335,2.42197060585022,7986197.0,303.2695794819762 +3034,Rwanda,2000,520702.27,0.0021,12730000.0,19.753083333333333,2.6247117519378658,8047103.0,326.1685294618282 +3035,Rwanda,2001,522842.06,0.0021,-5750000.0,19.66125,2.8474061489105225,8163136.0,348.8127784359494 +3036,Rwanda,2002,521270.7,0.002,-5710000.0,19.94725,3.2230379581451416,8329249.0,386.95420897431956 +3037,Rwanda,2003,510113.66,0.0019,-3780000.0,20.094333333333335,3.2940211296081543,8477210.0,388.5737323492227 +3038,Rwanda,2004,517276.63,0.0018,-5120000.0,20.08491666666667,3.5393500328063965,8660954.0,408.65590936130087 +3039,Rwanda,2005,517111.59,0.0018,-4540000.0,20.432583333333337,3.8712658882141113,8842495.0,437.80244017261083 +3040,Rwanda,2006,516730.37,0.0017,4050000.0,20.162833333333335,4.228471279144287,9035491.0,467.98467057786763 +3041,Rwanda,2007,545486.93,0.0018,3170000.0,19.76325,4.55328369140625,9266906.0,491.3488591992031 +3042,Rwanda,2008,530568.72,0.0017,4730000.0,19.730499999999996,5.061760425567627,9501799.0,532.7160073126812 +3043,Rwanda,2009,564426.02,0.0018,3920000.0,20.022666666666666,5.379884243011475,9705671.0,554.3031741969694 +3044,Rwanda,2010,579130.34,0.0018,4440000.0,20.267083333333336,5.773083686828612,9923431.0,581.7628687929217 +3045,Rwanda,2011,652109.05,0.0019,5410000.0,19.77375,6.222455978393555,10200636.0,610.0066680541837 +3046,Rwanda,2012,724525.71,0.0021,5280000.0,19.959416666666666,6.771296977996826,10505592.0,644.5421617360379 +3047,Rwanda,2013,790384.82,0.0022,6420000.0,20.103874999999995,7.090465545654298,10831947.0,654.588279065093 +3048,Saint Lucia,1990,161216.0,0.0007,840000.0,27.142749999999996,,, +3049,Saint Lucia,1991,168544.0,0.0008,860000.0,27.087166666666665,,, +3050,Saint Lucia,1992,201520.0,0.0009,910000.0,27.103000000000005,,, +3051,Saint Lucia,1993,223504.0,0.001,940000.0,27.247416666666666,,, +3052,Saint Lucia,1994,260144.0,0.0012,1010000.0,27.140249999999998,,, +3053,Saint Lucia,1995,311440.0,0.0014,990000.0,27.49275,,, +3054,Saint Lucia,1996,326096.0,0.0014,930000.0,27.17508333333333,,, +3055,Saint Lucia,1997,307776.0,0.0013,830000.0,27.44208333333333,,, +3056,Saint Lucia,1998,307776.0,0.0013,700000.0,27.812,,, +3057,Saint Lucia,1999,318768.0,0.0013,630000.0,27.328583333333338,,, +3058,Saint Lucia,2000,329760.0,0.0013,550000.0,27.07841666666667,,, +3059,Saint Lucia,2001,362736.0,0.0014,610000.0,27.42125,,, +3060,Saint Lucia,2002,326096.0,0.0013,570000.0,27.462833333333336,,, +3061,Saint Lucia,2003,359072.0,0.0013,600000.0,27.60766666666667,,, +3062,Saint Lucia,2004,355408.0,0.0013,600000.0,27.377916666666675,,, +3063,Saint Lucia,2005,366400.0,0.0013,620000.0,27.858666666666668,,, +3064,Saint Lucia,2006,366400.0,0.0012,860000.0,27.69216666666666,,, +3065,Saint Lucia,2007,384720.0,0.0012,880000.0,27.583250000000003,,, +3066,Saint Lucia,2008,395712.0,0.0012,900000.0,27.20866666666667,,, +3067,Saint Lucia,2009,384720.0,0.0012,890000.0,27.495,,, +3068,Saint Lucia,2010,403040.0,0.0012,920000.0,28.005916666666664,,, +3069,Saint Lucia,2011,406704.0,0.0012,740000.0,27.540166666666668,,, +3070,Saint Lucia,2012,406704.0,0.0012,750000.0,27.57891666666667,,, +3071,Saint Lucia,2013,406704.0,0.0012,760000.0,27.465500000000002,,, +3072,Samoa,1990,102592.0,0.0005,-1710000.0,26.858583333333332,0.38822007179260254,162834.0,2384.146258107045 +3073,Samoa,1991,106256.0,0.0005,-1700000.0,26.849083333333336,0.3792909979820252,163692.0,2317.1016175624045 +3074,Samoa,1992,109920.0,0.0005,-1700000.0,26.759166666666662,0.3785324394702912,164799.0,2296.934080123612 +3075,Samoa,1993,109920.0,0.0005,-1700000.0,26.522916666666664,0.3940522074699402,166119.0,2372.1079916803033 +3076,Samoa,1994,113584.0,0.0005,-1660000.0,26.748916666666663,0.3840350210666657,167483.0,2292.979114696212 +3077,Samoa,1995,117248.0,0.0005,-1660000.0,27.037333333333333,0.4096641540527344,168887.0,2425.670146623093 +3078,Samoa,1996,124576.0,0.0005,-1650000.0,26.845833333333335,0.4390738010406494,170330.0,2577.7831329809746 +3079,Samoa,1997,128240.0,0.0005,-1640000.0,26.706916666666668,0.4418988823890686,171806.0,2572.080616445692 +3080,Samoa,1998,131904.0,0.0005,-1630000.0,27.121916666666664,0.4515980780124664,173305.0,2605.7994749861027 +3081,Samoa,1999,135568.0,0.0006,-1630000.0,26.780333333333328,0.4614674746990204,174755.0,2640.6539137593795 +3082,Samoa,2000,142896.0,0.0006,-1600000.0,26.838333333333328,0.4933954477310181,176209.0,2800.0581566833594 +3083,Samoa,2001,146560.0,0.0006,370000.0,26.953833333333332,0.527635931968689,177786.0,2967.8148558867906 +3084,Samoa,2002,146560.0,0.0006,350000.0,27.236666666666665,0.5505564212799072,179429.0,3068.3803692820397 +3085,Samoa,2003,153888.0,0.0006,360000.0,27.022666666666662,0.575416624546051,181088.0,3177.5524857861983 +3086,Samoa,2004,150224.0,0.0005,360000.0,27.05775,0.6020296812057495,182772.0,3293.8835336142824 +3087,Samoa,2005,161216.0,0.0006,370000.0,27.12433333333333,0.6270529627799988,184491.0,3398.8268413093256 +3088,Samoa,2006,164880.0,0.0005,-60000.0,26.834999999999997,0.639398455619812,186252.0,3432.974978093185 +3089,Samoa,2007,172208.0,0.0006,-60000.0,27.149249999999995,0.6798253059387207,187956.0,3616.9385704032898 +3090,Samoa,2008,179536.0,0.0006,-50000.0,26.671166666666668,0.6866853833198547,189503.0,3623.6122030778133 +3091,Samoa,2009,186864.0,0.0006,-40000.0,26.728333333333328,0.653667688369751,190897.0,3424.190471142821 +3092,Samoa,2010,186864.0,0.0006,-30000.0,27.16166666666666,0.6567997336387634,192001.0,3420.814129294969 +3093,Samoa,2011,201520.0,0.0006,550000.0,26.71558333333334,0.6947532296180725,193161.0,3596.757262687978 +3094,Samoa,2012,197856.0,0.0006,540000.0,26.926666666666666,0.6975468993186951,194320.0,3589.6814497668543 +3095,Samoa,2013,197856.0,0.0006,550000.0,27.176500000000004,0.6840572953224182,195476.0,3499.4438975752428 +3096,Saudi Arabia,1990,185485266.4,0.8362,190620000.0,26.018750000000008,293.9273681640625,16060761.0,18300.96146527941 +3097,Saudi Arabia,1991,267386176.1,1.1921,212820000.0,25.77625,338.0396423339844,16305774.0,20731.284656219592 +3098,Saudi Arabia,1992,285074114.5,1.2844,227560000.0,24.77125,351.5191345214844,17060750.0,20603.967265301017 +3099,Saudi Arabia,1993,313327384.8,1.4144,235950000.0,25.741,346.7253112792969,17636361.0,19659.68553712962 +3100,Saudi Arabia,1994,307473247.7,1.3645,250080000.0,26.297583333333336,348.66253662109375,18202417.0,19154.738440564994 +3101,Saudi Arabia,1995,234711948.7,1.0202,255120000.0,25.989166666666666,349.4020080566406,18755041.0,18629.765088577547 +3102,Saudi Arabia,1996,258263647.3,1.0979,269680000.0,26.432999999999996,358.6172180175781,19293360.0,18587.59791024364 +3103,Saudi Arabia,1997,215796742.0,0.9023,253560000.0,25.86766666666667,362.5755615234375,19818379.0,18294.91511507765 +3104,Saudi Arabia,1998,207230515.8,0.8640000000000001,271900000.0,26.853749999999994,373.0661926269531,20329087.0,18351.350093929606 +3105,Saudi Arabia,1999,225976480.8,0.9443,278620000.0,26.940583333333333,359.0266418457031,20824550.0,17240.547423387452 +3106,Saudi Arabia,2000,296353320.6,1.2067,291530000.0,26.642916666666675,379.2234191894531,21311904.0,17793.971819198 +3107,Saudi Arabia,2001,296574890.6,1.1798,297910000.0,26.76966666666667,374.6319580078125,21796085.0,17188.038953225434 +3108,Saudi Arabia,2002,325681176.8,1.2775,318210000.0,26.62733333333333,364.0704345703125,22274039.0,16345.056887541255 +3109,Saudi Arabia,2003,326517114.2,1.2153,331490000.0,26.816666666666666,404.9994812011719,22742890.0,17807.740405954206 +3110,Saudi Arabia,2004,394584793.8,1.3999,349910000.0,26.519000000000002,437.2311401367188,23200040.0,18846.137340139016 +3111,Saudi Arabia,2005,395854759.6,1.3531,367360000.0,26.495500000000003,461.6017150878906,23642207.0,19524.47650457889 +3112,Saudi Arabia,2006,431291967.7,1.4240000000000002,390250000.0,26.558583333333335,474.4730224609375,24073195.0,19709.599098122933 +3113,Saudi Arabia,2007,386507147.4,1.2516,412810000.0,26.682583333333337,483.2371520996094,24498521.0,19725.156147165348 +3114,Saudi Arabia,2008,432336409.8,1.3563,450540000.0,26.477750000000004,513.4383544921875,24917413.0,20605.604381650195 +3115,Saudi Arabia,2009,465844308.7,1.4778,470820000.0,26.827750000000005,502.8653259277344,25328871.0,19853.444155791007 +3116,Saudi Arabia,2010,517715947.4,1.5657,515510000.0,27.530083333333334,528.2072143554689,25731776.0,20527.429368088266 +3117,Saudi Arabia,2011,497659010.0,1.4485,534470000.0,26.63525000000001,581.0112915039062,26131703.0,22233.96200025334 +3118,Saudi Arabia,2012,563179493.4,1.6128,568120000.0,26.988916666666665,612.4524536132812,26534504.0,23081.360541477665 +3119,Saudi Arabia,2013,540805232.0,1.536,585750000.0,27.740749999999995,628.9840698242188,26939583.0,23347.95122197024 +3120,Senegal,1990,3139035.39,0.0142,21120000.0,28.508,8.057238578796388,7347606.0,1096.580107697172 +3121,Senegal,1991,3377965.66,0.0151,21520000.0,28.490333333333336,8.263168334960938,7553972.0,1093.8838977641085 +3122,Senegal,1992,3423447.96,0.0154,22070000.0,28.459833333333336,8.365849494934082,7759247.0,1078.1780106928006 +3123,Senegal,1993,3537975.01,0.016,22470000.0,28.646500000000003,8.474671363830566,7961004.0,1064.522937537849 +3124,Senegal,1994,3836754.0,0.017,22830000.0,28.241083333333332,8.473201751708983,8166145.0,1037.6011877953408 +3125,Senegal,1995,3428955.57,0.0149,23240000.0,28.556916666666666,8.92765998840332,8378056.0,1065.6004195249257 +3126,Senegal,1996,3663244.19,0.0156,24640000.0,28.775666666666666,9.107295989990234,8594914.0,1059.6145569333485 +3127,Senegal,1997,3182354.58,0.0133,25230000.0,28.82133333333333,9.391810417175293,8809850.0,1066.0579257507552 +3128,Senegal,1998,3333059.03,0.0139,26470000.0,29.254250000000003,9.945802688598633,9037144.0,1100.5471074267084 +3129,Senegal,1999,3603572.13,0.0151,28760000.0,28.12733333333333,10.57707405090332,9252822.0,1143.1187210672938 +3130,Senegal,2000,3809306.06,0.0155,28740000.0,28.52608333333333,10.915433883666992,9469281.0,1152.7204529749401 +3131,Senegal,2001,4180223.21,0.0166,28110000.0,28.936333333333334,11.415460586547852,9716196.0,1174.8899040887866 +3132,Senegal,2002,4345856.24,0.017,27460000.0,28.97816666666667,11.490209579467773,9972439.0,1152.196526794275 +3133,Senegal,2003,4804898.17,0.0179,25630000.0,28.704166666666666,12.258127212524414,10240144.0,1197.0659018588424 +3134,Senegal,2004,4988506.8,0.0177,27650000.0,28.852083333333336,12.977773666381836,10519120.0,1233.7318774176772 +3135,Senegal,2005,5539577.5,0.0189,28640000.0,28.955083333333334,13.707463264465332,10804122.0,1268.7253313564333 +3136,Senegal,2006,4421762.37,0.0146,29040000.0,28.630750000000003,14.044881820678713,11096111.0,1265.7481365028439 +3137,Senegal,2007,4826645.02,0.0156,28440000.0,28.816833333333335,14.738486289978027,11394298.0,1293.4966498136198 +3138,Senegal,2008,4739033.55,0.0149,30060000.0,28.79608333333334,15.33633518218994,11698343.0,1310.983545463656 +3139,Senegal,2009,4203728.2,0.0133,29780000.0,28.473749999999995,15.657252311706545,12008068.0,1303.894374324541 +3140,Senegal,2010,7280072.95,0.022000000000000002,31390000.0,29.302333333333337,16.215080261230472,12323252.0,1315.8117890659435 +3141,Senegal,2011,7819427.33,0.0228,30980000.0,28.77491666666667,16.45155906677246,12643799.0,1301.1563270479435 +3142,Senegal,2012,7362867.01,0.0211,30100000.0,28.63558333333333,17.293449401855472,12969606.0,1333.3827875615855 +3143,Senegal,2013,7849940.62,0.0223,31330000.0,29.3015,17.7814884185791,13300410.0,1336.9128033330628 +3144,Serbia,1990,57607543.09,0.2597,77360000.0,11.196083333333332,50.21550750732422,7786397.0,6449.1326999283765 +3145,Serbia,1991,40718952.67,0.1815,67060000.0,9.747083333333334,44.358341217041016,7751781.0,5722.341900144112 +3146,Serbia,1992,43293823.23,0.1951,61800000.0,10.975583333333335,31.875961303710934,7712022.0,4133.2819465129805 +3147,Serbia,1993,37740784.05,0.1704,56010000.0,10.414416666666666,22.17628669738769,7706118.0,2877.750729665403 +3148,Serbia,1994,36698427.69,0.1629,52350000.0,11.682250000000003,22.751739501953125,7700799.0,2954.464790205941 +3149,Serbia,1995,38775202.1,0.1685,58880000.0,10.276166666666667,24.04564476013184,7691299.0,3126.343776276522 +3150,Serbia,1996,45392750.37,0.193,66440000.0,9.824916666666665,24.62903022766113,7707798.0,3195.339346939441 +3151,Serbia,1997,48792684.32,0.204,71220000.0,9.984,26.39858436584473,7693798.0,3431.151216323164 +3152,Serbia,1998,51379641.86,0.2142,70940000.0,10.568333333333333,27.04018974304199,7675298.0,3523.0149686750915 +3153,Serbia,1999,35440108.3,0.1481,53600000.0,10.917250000000003,23.755727767944336,7677153.0,3094.340801589383 +3154,Serbia,2000,39380545.58,0.1604,55560000.0,11.791000000000002,25.59898376464844,7604335.0,3366.3671793323724 +3155,Serbia,2001,42277557.56,0.1682,57650000.0,10.913166666666669,26.87706756591797,7548376.0,3560.6423906172618 +3156,Serbia,2002,45173016.78,0.1772,60950000.0,11.413416666666668,28.78958320617676,7544319.0,3816.061225165155 +3157,Serbia,2003,48298465.12,0.1798,63910000.0,10.795499999999999,30.06076431274414,7535230.0,3989.362542715238 +3158,Serbia,2004,52737826.27,0.1871,69300000.0,10.6165,32.780216217041016,7522560.0,4357.587871288632 +3159,Serbia,2005,49879228.71,0.1705,61680000.0,9.922583333333334,34.596378326416016,7502197.0,4611.4995815780385 +3160,Serbia,2006,53759585.19,0.1775,4080000.0,10.574166666666665,36.29310607910156,7476102.0,4854.549346584833 +3161,Serbia,2007,52185841.74,0.16899999999999998,3560000.0,11.745916666666664,38.430320739746094,7447093.0,5160.445926987362 +3162,Serbia,2008,51789739.64,0.1625,1610000.0,11.666333333333334,40.49275207519531,7413882.0,5461.747580443729 +3163,Serbia,2009,45926000.32,0.1457,-440000.0,11.41216666666667,39.23101043701172,7379339.0,5316.331237392906 +3164,Serbia,2010,45696360.0,0.1382,-500000.0,10.9925,39.46030807495117,7344847.0,5372.516006793766 +3165,Serbia,2011,48957080.0,0.1425,67390000.0,10.823,40.013328552246094,7310555.0,5473.363999346984 +3166,Serbia,2012,43724476.0,0.1252,62370000.0,11.451750000000002,39.60708618164063,7276604.0,5443.072919955604 +3167,Serbia,2013,44545828.0,0.1265,63050000.0,12.843625,40.625675201416016,7243007.0,5608.951530961659 +3168,Seychelles,1990,150224.0,0.0007,220000.0,27.22008333333334,0.5242623686790466,70800.0,7404.835715805743 +3169,Seychelles,1991,172208.0,0.0008,240000.0,27.329666666666668,0.5387342572212219,71649.0,7519.075733383886 +3170,Seychelles,1992,172208.0,0.0008,240000.0,26.95316666666667,0.5773155689239502,72501.0,7962.863531867839 +3171,Seychelles,1993,183200.0,0.0008,250000.0,26.964583333333337,0.6130897998809814,73318.0,8362.063884461953 +3172,Seychelles,1994,201520.0,0.0009,270000.0,27.301833333333335,0.608193039894104,74133.0,8204.079693174484 +3173,Seychelles,1995,197856.0,0.0009,270000.0,27.297749999999997,0.6031715869903564,74931.0,8049.693544599118 +3174,Seychelles,1996,234496.0,0.001,310000.0,26.91175,0.6328327059745789,75714.0,8358.199355133514 +3175,Seychelles,1997,340752.0,0.0014,420000.0,27.367916666666662,0.7085293531417847,76405.0,9273.337519033894 +3176,Seychelles,1998,432352.0,0.0018,520000.0,27.654416666666666,0.768069863319397,77174.0,9952.443352934886 +3177,Seychelles,1999,509296.0,0.0021,600000.0,27.13683333333334,0.7824481725692749,78137.0,10013.798489438741 +3178,Seychelles,2000,571584.0,0.0023,660000.0,27.17925,0.7942970991134644,79158.0,10034.325009644816 +3179,Seychelles,2001,633872.0,0.0025,720000.0,27.467833333333342,0.7762573361396791,80173.0,9682.278773897431 +3180,Seychelles,2002,538608.0,0.0021,630000.0,27.53108333333334,0.7856720089912415,81125.0,9684.708893574623 +3181,Seychelles,2003,549600.0,0.002,640000.0,27.68541666666667,0.7394184470176697,82049.0,9011.91296685724 +3182,Seychelles,2004,736464.0,0.0026,830000.0,27.25775,0.7183416485786438,82970.0,8657.848000224705 +3183,Seychelles,2005,688832.0,0.0024,790000.0,27.468416666666666,0.7830337882041931,83886.0,9334.499060679891 +3184,Seychelles,2006,732800.0,0.0024,830000.0,27.470166666666668,0.8566869497299194,84798.0,10102.678715652719 +3185,Seychelles,2007,641200.0,0.0021,750000.0,27.71258333333334,0.9459660649299622,85702.0,11037.852849758025 +3186,Seychelles,2008,692496.0,0.0022,800000.0,27.376833333333334,0.9256571531295776,86595.0,10689.498852469284 +3187,Seychelles,2009,523952.0,0.0017,630000.0,27.741249999999997,0.9154263734817504,87476.0,10464.886065683737 +3188,Seychelles,2010,443344.0,0.0013,560000.0,27.744833333333332,0.9699397087097168,88340.0,10979.620881930234 +3189,Seychelles,2011,340752.0,0.001,460000.0,27.73166666666667,1.0464415550231934,89188.0,11732.985996133935 +3190,Seychelles,2012,439680.0,0.0013,560000.0,27.77258333333334,1.0596354007720947,90024.0,11770.587851818345 +3191,Seychelles,2013,403040.0,0.0011,530000.0,27.451625000000007,1.1234049797058103,90846.0,12366.036806307493 +3192,Sierra Leone,1990,487312.0,0.0022,11030000.0,26.55141666666667,1.7938110828399658,4228201.0,424.2492452085334 +3193,Sierra Leone,1991,545936.0,0.0024,11060000.0,26.62158333333333,1.836000680923462,4334898.0,423.53953447658097 +3194,Sierra Leone,1992,447008.0,0.002,10980000.0,26.397166666666667,1.4869235754013062,4246282.0,350.1707082575548 +3195,Sierra Leone,1993,447008.0,0.002,11060000.0,26.608833333333337,1.5073620080947876,4099669.0,367.6789536166914 +3196,Sierra Leone,1994,461664.0,0.002,11040000.0,26.35825,1.4780079126358032,3977955.0,371.5496813402372 +3197,Sierra Leone,1995,348080.0,0.0015,10820000.0,26.57816666666667,1.3597710132598877,3881436.0,350.32678968811734 +3198,Sierra Leone,1996,392048.0,0.0017,9010000.0,26.7045,1.383618950843811,3810500.0,363.1069284460861 +3199,Sierra Leone,1997,362736.0,0.0015,9030000.0,26.6695,1.3023024797439575,3765211.0,345.8776891239183 +3200,Sierra Leone,1998,362736.0,0.0015,9120000.0,27.07575,1.3255488872528076,3745727.0,353.88294108268104 +3201,Sierra Leone,1999,344416.0,0.0014,8890000.0,26.521500000000003,1.2993123531341553,3735565.0,347.82217767169226 +3202,Sierra Leone,2000,388384.0,0.0016,8860000.0,26.54683333333333,1.3857520818710327,3809058.0,363.80440567485 +3203,Sierra Leone,2001,512960.0,0.002,9440000.0,26.82675,1.2978198528289795,4043412.0,320.9714599523817 +3204,Sierra Leone,2002,534944.0,0.0021,9600000.0,26.99266666666667,1.6406689882278442,4286828.0,382.72330688981316 +3205,Sierra Leone,2003,567920.0,0.0021,11860000.0,26.992416666666667,1.7934664487838743,4465683.0,401.6107835652183 +3206,Sierra Leone,2004,553264.0,0.002,13050000.0,26.978916666666667,1.9117983579635618,4602202.0,415.40948397388087 +3207,Sierra Leone,2005,461664.0,0.0016,11090000.0,27.028583333333334,1.9979265928268437,4708054.0,424.36356779825445 +3208,Sierra Leone,2006,615552.0,0.002,15100000.0,26.71775,2.082317352294922,4817621.0,432.2293829869394 +3209,Sierra Leone,2007,516624.0,0.0017,12410000.0,26.83741666666667,2.2501134872436523,4918325.0,457.4958928585753 +3210,Sierra Leone,2008,534944.0,0.0017,11120000.0,26.978749999999994,2.371581077575684,5022995.0,472.14482148114496 +3211,Sierra Leone,2009,534944.0,0.0017,12950000.0,26.985000000000003,2.447188138961792,5132138.0,476.83599680324113 +3212,Sierra Leone,2010,575248.0,0.0017,11190000.0,27.177166666666675,2.5780260562896733,5245695.0,491.4555757225064 +3213,Sierra Leone,2011,743792.0,0.0022,11780000.0,26.92625,2.740829944610596,5363669.0,510.9990837634828 +3214,Sierra Leone,2012,861040.0,0.0025,14010000.0,26.676416666666668,3.1569361686706543,5485998.0,575.4533940170329 +3215,Sierra Leone,2013,1033248.0,0.0029,13000000.0,26.948,3.810920000076294,5612685.0,678.9834099145585 +3216,Singapore,1990,44290565.9,0.1997,31620000.0,27.13,67.58036804199219,3047100.0,22178.585554130874 +3217,Singapore,1991,45049407.64,0.2009,34800000.0,26.994083333333336,72.09951782226562,3152032.0,22873.980283913883 +3218,Singapore,1992,48212663.64,0.2172,37360000.0,26.99741666666667,77.20964050292969,3256010.0,23712.96172399031 +3219,Singapore,1993,50871112.83,0.2296,44300000.0,26.928833333333333,86.11761474609375,3360250.0,25628.33561374712 +3220,Singapore,1994,61344561.04,0.2722,47850000.0,27.08775,95.52635955810548,3464148.0,27575.715459647072 +3221,Singapore,1995,41841788.29,0.1819,49200000.0,27.0725,102.239990234375,3566684.0,28665.278514826376 +3222,Singapore,1996,49349249.79,0.2098,50600000.0,27.043333333333337,109.94064331054688,3668466.0,29969.105154728677 +3223,Singapore,1997,58071754.09,0.2428,50760000.0,27.38891666666667,119.05594635009766,3769760.0,31581.83713289378 +3224,Singapore,1998,48356410.64,0.2016,49750000.0,27.77591666666667,116.40667724609376,3868483.0,30091.0401431501 +3225,Singapore,1999,49870518.41,0.2084,50640000.0,27.016750000000005,123.5019073486328,3965084.0,31147.36216146564 +3226,Singapore,2000,48856301.08,0.1989,55260000.0,27.096500000000002,134.49053955078122,4063333.0,33098.576845850745 +3227,Singapore,2001,49442096.38,0.1967,55120000.0,27.189333333333334,133.20980834960938,4169481.0,31948.774523642 +3228,Singapore,2002,47172700.62,0.185,54180000.0,27.40608333333333,138.8201904296875,4281211.0,32425.449348253922 +3229,Singapore,2003,31092573.18,0.1157,51120000.0,27.311833333333336,144.97731018066406,4390624.0,33019.750764507284 +3230,Singapore,2004,28450960.0,0.1009,52490000.0,27.328666666666663,158.8214569091797,4498226.0,35307.57612204894 +3231,Singapore,2005,30334256.0,0.1037,50800000.0,27.493583333333333,170.71585083007812,4605741.0,37065.88165293665 +3232,Singapore,2006,30773936.0,0.1016,51400000.0,27.32841666666667,185.84161376953125,4713561.0,39427.0093819792 +3233,Singapore,2007,19910176.0,0.0645,52660000.0,27.129999999999995,202.77462768554688,4821918.0,42052.691000872874 +3234,Singapore,2008,36105056.0,0.1133,52820000.0,27.06766666666667,206.39947509765625,4930613.0,41860.814283671476 +3235,Singapore,2009,55886992.0,0.1773,53890000.0,27.473333333333333,205.15408325195312,5035897.0,40738.33981353334 +3236,Singapore,2010,55597536.0,0.1681,58160000.0,27.6035,236.4203338623047,5140105.0,45995.23431180972 +3237,Singapore,2011,45184448.0,0.1315,61310000.0,27.282166666666665,251.4346160888672,5246787.0,47921.63586760186 +3238,Singapore,2012,36343216.0,0.1041,61100000.0,27.303583333333336,261.70144653320307,5353494.0,48884.23271478461 +3239,Singapore,2013,55630512.0,0.158,62280000.0,27.602874999999997,275.07720947265625,5460302.0,50377.65483899174 +3240,Slovakia,1990,61995726.89,0.2795,57050000.0,8.71675,51.10056686401367,5262616.0,9710.107456826352 +3241,Slovakia,1991,53617069.55,0.239,48990000.0,7.639166666666668,43.65327453613281,5281610.0,8265.145388647175 +3242,Slovakia,1992,49155950.67,0.2215,46490000.0,8.727166666666665,40.7191162109375,5302888.0,7678.667965632595 +3243,Slovakia,1993,46658972.1,0.2106,43810000.0,7.982166666666667,41.49332046508789,5323577.0,7794.255716614579 +3244,Slovakia,1994,43827873.4,0.1945,41110000.0,9.297583333333334,44.06819915771485,5345264.0,8244.344742881707 +3245,Slovakia,1995,44866516.53,0.195,41660000.0,8.153,46.64332580566406,5361594.0,8699.525888320537 +3246,Slovakia,1996,45165534.99,0.192,41750000.0,7.126166666666665,49.795082092285156,5373190.0,9267.322036310861 +3247,Slovakia,1997,45204312.28,0.18899999999999997,41570000.0,7.773083333333335,52.81642150878906,5383010.0,9811.689279564605 +3248,Slovakia,1998,44175151.48,0.1842,40360000.0,8.333583333333333,54.93531799316406,5390236.0,10191.635021762324 +3249,Slovakia,1999,43340782.07,0.1811,38850000.0,8.675583333333332,54.82265853881836,5395739.0,10160.36145165998 +3250,Slovakia,2000,41533153.32,0.1691,36310000.0,9.482,55.48611068725586,5400320.0,10274.59681782855 +3251,Slovakia,2001,43836558.63,0.1744,37650000.0,8.30775,57.32629013061523,5402649.0,10610.774479447995 +3252,Slovakia,2002,42051490.73,0.1649,37060000.0,9.136416666666667,59.91904067993164,5403359.0,11089.220738420609 +3253,Slovakia,2003,42426775.16,0.1579,37340000.0,8.487083333333333,63.1658821105957,5403962.0,11688.809453248508 +3254,Slovakia,2004,42991163.76,0.1525,36690000.0,8.145583333333333,66.4876708984375,5405587.0,12299.805904231585 +3255,Slovakia,2005,42907487.33,0.1467,37620000.0,7.936916666666666,70.97622680664062,5407676.0,13125.08863449671 +3256,Slovakia,2006,42603719.01,0.1407,39920000.0,8.528333333333334,76.97576904296875,5408945.0,14231.198328503755 +3257,Slovakia,2007,40949068.73,0.1326,38920000.0,9.478916666666665,85.288818359375,5411226.0,15761.459299496084 +3258,Slovakia,2008,41484590.49,0.1301,38770000.0,9.397416666666667,90.09039306640624,5415211.0,16636.543445196552 +3259,Slovakia,2009,37639425.69,0.1194,35140000.0,9.001666666666667,85.2052001953125,5420248.0,15719.797358960792 +3260,Slovakia,2010,38626518.73,0.1168,36770000.0,8.073,89.50100708007812,5426044.0,16494.707208433643 +3261,Slovakia,2011,37932845.18,0.1104,36610000.0,8.846,92.02413177490234,5433281.0,16937.119905063322 +3262,Slovakia,2012,36109053.13,0.1034,34300000.0,8.873166666666666,93.54911041259766,5438951.0,17199.8443105293 +3263,Slovakia,2013,35633934.53,0.1012,34910000.0,9.685624999999998,94.94359588623048,5441540.0,17447.927587820814 +3264,Slovenia,1990,15074429.75,0.068,10980000.0,10.726333333333331,28.87559509277344,1991210.0,14501.531778553459 +3265,Slovenia,1991,13995034.23,0.0624,9940000.0,9.758000000000001,26.3056697845459,1999480.0,13156.255518707812 +3266,Slovenia,1992,13996011.39,0.0631,9670000.0,10.925333333333334,24.885173797607425,1999210.0,12447.503662750498 +3267,Slovenia,1993,14292670.05,0.0645,10460000.0,10.35,25.592809677124023,1999926.0,12796.878323059966 +3268,Slovenia,1994,14633781.17,0.0649,10480000.0,11.502000000000002,26.95587921142578,2001036.0,13470.961647579443 +3269,Slovenia,1995,15244453.58,0.0663,11410000.0,10.289583333333335,29.97307777404785,2002827.0,14965.38531488134 +3270,Slovenia,1996,15905820.13,0.0676,12230000.0,9.444999999999999,31.02701568603516,2007429.0,15456.096173780072 +3271,Slovenia,1997,16232995.89,0.0679,12690000.0,10.382916666666667,32.61385726928711,2011612.0,16212.79713448076 +3272,Slovenia,1998,15948223.64,0.0665,12360000.0,10.597333333333331,33.6854248046875,2010807.0,16752.191933232527 +3273,Slovenia,1999,15323442.1,0.064,11620000.0,10.686916666666667,35.46211242675781,2009653.0,17645.88833333805 +3274,Slovenia,2000,15430093.6,0.0628,11420000.0,11.590583333333335,36.93571853637695,2010557.0,18370.888533066685 +3275,Slovenia,2001,16363297.02,0.0651,8770000.0,10.894666666666668,38.025089263916016,2011179.0,18906.864711652222 +3276,Slovenia,2002,16508204.0,0.0648,8840000.0,11.33725,39.483848571777344,2011497.0,19629.086482245482 +3277,Slovenia,2003,16270134.59,0.0606,8830000.0,11.009666666666666,40.60601043701172,2011604.0,20185.88670385012 +3278,Slovenia,2004,16630663.55,0.059000000000000004,8980000.0,10.24125,42.37310028076172,2011473.0,21065.70671381705 +3279,Slovenia,2005,16928806.56,0.0579,9170000.0,9.795083333333334,44.06930160522461,2011070.0,21913.360353058128 +3280,Slovenia,2006,17124006.01,0.0565,12080000.0,10.723416666666667,46.56185913085938,2010347.0,23161.10558568216 +3281,Slovenia,2007,17259960.28,0.0559,12050000.0,11.462583333333333,49.79401779174805,2009245.0,24782.45201145109 +3282,Slovenia,2008,18197391.32,0.0571,13000000.0,11.247916666666667,51.43728637695313,2007711.0,25619.86579590047 +3283,Slovenia,2009,16310010.1,0.0517,11000000.0,11.237416666666666,47.42657852172852,2005692.0,23645.992765453775 +3284,Slovenia,2010,16352627.37,0.0495,11260000.0,10.194,48.01360321044922,2003136.0,23969.217871601933 +3285,Slovenia,2011,16340572.4,0.0476,12860000.0,11.208083333333333,48.32538604736328,2000092.0,24161.581590928457 +3286,Slovenia,2012,15800755.06,0.0452,12310000.0,11.278250000000005,47.0353012084961,1996617.0,23557.49811230501 +3287,Slovenia,2013,15167076.94,0.0431,11600000.0,11.74475,46.50283432006836,1992690.0,23336.712845484424 +3288,Solomon Islands,1990,146560.0,0.0007,2080000.0,27.264916666666675,0.4049919545650482,321133.0,1261.1346531345212 +3289,Solomon Islands,1991,146560.0,0.0007,2090000.0,26.993416666666665,0.4293083846569061,331470.0,1295.1651270308207 +3290,Solomon Islands,1992,142896.0,0.0006,2090000.0,27.00191666666667,0.4837895631790161,342040.0,1414.4239363203603 +3291,Solomon Islands,1993,142896.0,0.0006,2100000.0,26.780999999999995,0.5030272603034973,352836.0,1425.6687534817802 +3292,Solomon Islands,1994,139232.0,0.0006,2100000.0,26.79308333333334,0.5492746829986572,363851.0,1509.6143283889758 +3293,Solomon Islands,1995,142896.0,0.0006,2110000.0,27.2005,0.5944447517395021,375074.0,1584.8732563160922 +3294,Solomon Islands,1996,142896.0,0.0006,2110000.0,27.124916666666667,0.6039866805076599,386494.0,1562.7323593837418 +3295,Solomon Islands,1997,142896.0,0.0006,2120000.0,26.95041666666667,0.595291256904602,398091.0,1495.3647706293334 +3296,Solomon Islands,1998,142896.0,0.0006,2130000.0,27.374333333333336,0.6059104800224304,409841.0,1478.403771273324 +3297,Solomon Islands,1999,150224.0,0.0006,2150000.0,26.994250000000005,0.6029863357543945,421716.0,1429.8398347570273 +3298,Solomon Islands,2000,150224.0,0.0006,2160000.0,27.264750000000003,0.5169553756713867,433730.0,1191.8829125755349 +3299,Solomon Islands,2001,157552.0,0.0006,2170000.0,27.448333333333338,0.4757097363471985,445906.0,1066.838608018727 +3300,Solomon Islands,2002,157552.0,0.0006,2180000.0,27.37541666666667,0.4623898565769196,458223.0,1009.0935124970148 +3301,Solomon Islands,2003,161216.0,0.0006,2190000.0,27.379750000000005,0.4924452304840088,470654.0,1046.2998943682808 +3302,Solomon Islands,2004,161216.0,0.0006,2200000.0,27.178583333333336,0.5165669918060303,483173.0,1069.1139442933074 +3303,Solomon Islands,2005,161216.0,0.0006,2210000.0,27.383333333333336,0.5445711016654968,495751.0,1098.4770613987603 +3304,Solomon Islands,2006,164880.0,0.0005,2150000.0,27.207416666666663,0.5824005603790283,508382.0,1145.5963436530567 +3305,Solomon Islands,2007,179536.0,0.0006,2170000.0,27.321416666666675,0.6250323057174683,521065.0,1199.528476711098 +3306,Solomon Islands,2008,186864.0,0.0006,2190000.0,27.183916666666665,0.6693751811981201,533776.0,1254.0376135272475 +3307,Solomon Islands,2009,194192.0,0.0006,2210000.0,27.387833333333337,0.6377279758453369,546494.0,1166.944149149555 +3308,Solomon Islands,2010,197856.0,0.0006,2220000.0,27.586250000000003,0.6811512112617493,559198.0,1218.0859217338925 +3309,Solomon Islands,2011,197856.0,0.0006,2600000.0,27.23575,0.7710334062576294,571890.0,1348.2197734837632 +3310,Solomon Islands,2012,197856.0,0.0006,2620000.0,27.179999999999996,0.8061569929122925,584578.0,1379.0409370730551 +3311,Solomon Islands,2013,201520.0,0.0006,2640000.0,27.210250000000002,0.8304855823516846,597248.0,1390.520491239292 +3312,Somalia,1990,729572.14,0.0033,40920000.0,27.142666666666667,,, +3313,Somalia,1991,706501.85,0.0031,38050000.0,27.256083333333333,,, +3314,Somalia,1992,679539.8,0.0031,36120000.0,27.25825,,, +3315,Somalia,1993,639222.06,0.0029,38120000.0,26.909750000000003,,, +3316,Somalia,1994,628216.32,0.0028,40080000.0,27.265000000000004,,, +3317,Somalia,1995,587898.58,0.0026,40760000.0,27.16325,,, +3318,Somalia,1996,567920.0,0.0024,41240000.0,27.126000000000005,,, +3319,Somalia,1997,538608.0,0.0023,41660000.0,27.222333333333335,,, +3320,Somalia,1998,512960.0,0.0021,41530000.0,27.549916666666665,,, +3321,Somalia,1999,490976.0,0.0021,41630000.0,27.4995,,, +3322,Somalia,2000,479984.0,0.002,41830000.0,27.710250000000002,,, +3323,Somalia,2001,501968.0,0.002,44240000.0,27.477833333333333,,, +3324,Somalia,2002,586240.0,0.0023,44700000.0,27.516000000000002,,, +3325,Somalia,2003,593568.0,0.0022,44970000.0,27.787166666666668,,, +3326,Somalia,2004,593568.0,0.0021,45300000.0,27.683666666666667,,, +3327,Somalia,2005,593568.0,0.002,45780000.0,27.83241666666667,,, +3328,Somalia,2006,593568.0,0.002,42350000.0,27.653166666666667,,, +3329,Somalia,2007,608224.0,0.002,41270000.0,27.87275,,, +3330,Somalia,2008,600896.0,0.0019,41330000.0,27.451333333333334,,, +3331,Somalia,2009,597232.0,0.0019,41410000.0,27.94508333333334,,, +3332,Somalia,2010,611888.0,0.0019,41650000.0,27.820999999999998,,, +3333,Somalia,2011,604560.0,0.0018,39610000.0,27.92441666666667,,, +3334,Somalia,2012,608224.0,0.0017,39950000.0,27.9385,,, +3335,Somalia,2013,608224.0,0.0017,40100000.0,27.975625,,, +3336,South Africa,1990,312629945.2,1.4094,315930000.0,17.72833333333333,223.00299072265625,38503254.0,5791.795953730461 +3337,South Africa,1991,325727173.0,1.4522,311920000.0,17.752000000000006,220.7323303222656,39327903.0,5612.613780151604 +3338,South Africa,1992,301029332.8,1.3563,308890000.0,18.25616666666667,216.01515197753903,40107298.0,5385.931308001328 +3339,South Africa,1993,320338088.4,1.446,317250000.0,18.20275,218.67974853515625,40934648.0,5342.1675577900705 +3340,South Africa,1994,338388667.8,1.5017,323240000.0,17.61475,225.6775054931641,41618862.0,5422.481409827209 +3341,South Africa,1995,361364140.0,1.5706,333660000.0,17.87075,232.6734771728516,42147059.0,5520.51513660423 +3342,South Africa,1996,363486466.9,1.5452,342300000.0,17.258666666666667,242.67843627929688,42672525.0,5686.994999224838 +3343,South Africa,1997,385120690.8,1.6104,355600000.0,17.61525,248.9880828857422,43199215.0,5763.717763985808 +3344,South Africa,1998,376776275.9,1.5709,363580000.0,18.095333333333333,250.23301696777344,43787349.0,5714.733197658836 +3345,South Africa,1999,374317561.2,1.5642,342280000.0,18.5455,256.23858642578125,44351539.0,5777.445207161386 +3346,South Africa,2000,377730906.0,1.5381,345420000.0,17.585583333333332,267.0006103515625,44913298.0,5944.800810476276 +3347,South Africa,2001,371140576.0,1.4764,380990000.0,17.867916666666666,274.20962524414057,45483334.0,6028.7934311090885 +3348,South Africa,2002,355454058.0,1.3943,396800000.0,17.949416666666668,284.3564453125,46104939.0,6167.591834629691 +3349,South Africa,2003,403235268.0,1.5009,417930000.0,18.261666666666667,292.7423400878906,46770284.0,6259.152501359424 +3350,South Africa,2004,448539068.0,1.5913,445750000.0,18.082833333333337,306.075439453125,47438933.0,6451.988274127602 +3351,South Africa,2005,415050850.0,1.4187,447080000.0,18.21791666666667,322.22723388671875,48104397.0,6698.498556934801 +3352,South Africa,2006,445708664.0,1.4716,447830000.0,17.5975,340.2841796875,48737071.0,6982.040009903343 +3353,South Africa,2007,463975000.0,1.5025,468370000.0,17.986250000000002,358.5250244140625,49371903.0,7261.721801852817 +3354,South Africa,2008,494127156.0,1.5502,502330000.0,17.956083333333332,369.9657287597656,50024791.0,7395.647665169969 +3355,South Africa,2009,501376938.0,1.5905,482210000.0,17.886,364.2752990722656,50680856.0,7187.631145619672 +3356,South Africa,2010,472482580.0,1.4289,490010000.0,18.302666666666667,375.3483276367188,51122965.0,7342.068826342892 +3357,South Africa,2011,468927239.3,1.3649,476140000.0,17.552416666666666,387.6753845214844,51453823.0,7534.433049250479 +3358,South Africa,2012,467080646.4,1.3376,496320000.0,17.84691666666667,396.2560119628906,51913821.0,7632.957935477156 +3359,South Africa,2013,464619057.0,1.3196,505110000.0,17.325375,406.103759765625,52425155.0,7746.35305829091 +3360,South Korea,1990,247438196.7,1.1155,252240000.0,13.229500000000002,,, +3361,South Korea,1991,262038316.3,1.1683,277550000.0,12.407916666666665,,, +3362,South Korea,1992,282194043.0,1.2714,302120000.0,12.60375,,, +3363,South Korea,1993,321837776.4,1.4528,333000000.0,11.971916666666665,,, +3364,South Korea,1994,344263753.4,1.5278,360570000.0,13.480416666666668,,, +3365,South Korea,1995,373992430.8,1.6255,392980000.0,12.32725,,, +3366,South Korea,1996,401529873.6,1.7069999999999999,420920000.0,12.165,,, +3367,South Korea,1997,427786068.0,1.7888,446720000.0,12.818833333333332,,, +3368,South Korea,1998,363548260.1,1.5157,381940000.0,13.64925,,, +3369,South Korea,1999,398325257.6,1.6645,419830000.0,13.033416666666666,,, +3370,South Korea,2000,445440883.6,1.8138,477350000.0,12.575333333333333,,, +3371,South Korea,2001,448550576.0,1.7844,485320000.0,12.842583333333337,,, +3372,South Korea,2002,463628014.2,1.8186,478120000.0,12.817166666666665,,, +3373,South Korea,2003,462407318.0,1.7211,486050000.0,12.646583333333334,,, +3374,South Korea,2004,478605780.5,1.6979,510140000.0,13.365083333333333,,, +3375,South Korea,2005,459345663.5,1.5701,505580000.0,12.356666666666667,,, +3376,South Korea,2006,465605909.8,1.5373,508670000.0,12.903333333333336,,, +3377,South Korea,2007,493353218.0,1.5976,517490000.0,13.292916666666665,,, +3378,South Korea,2008,505782554.4,1.5867,531530000.0,13.027916666666664,,, +3379,South Korea,2009,506764540.7,1.6076,545250000.0,12.949333333333334,,, +3380,South Korea,2010,565960924.7,1.7116,598000000.0,12.63975,,, +3381,South Korea,2011,588435086.6,1.7127,631360000.0,12.3635,,, +3382,South Korea,2012,583631976.4,1.6714,636170000.0,12.391666666666666,,, +3383,South Korea,2013,591539733.6,1.6801,635550000.0,13.7605,,, +3384,Spain,1990,231895048.4,1.0454,228770000.0,14.762083333333335,873.1439208984375,39350769.0,22188.738443674058 +3385,Spain,1991,241464996.6,1.0766,234980000.0,14.007166666666665,895.3742065429688,39461418.0,22689.86397151184 +3386,Spain,1992,250326924.4,1.1278,244780000.0,13.955249999999998,903.6941528320312,39552425.0,22848.008758806347 +3387,Spain,1993,241817825.8,1.0916,230050000.0,13.50066666666667,894.3726196289062,39637696.0,22563.68835436112 +3388,Spain,1994,254234048.2,1.1282,243010000.0,14.650999999999998,915.687255859375,39707884.0,23060.5905834563 +3389,Spain,1995,267832186.9,1.1641,256860000.0,15.072166666666666,940.937255859375,39764708.0,23662.62203809908 +3390,Spain,1996,255447995.9,1.0859999999999999,250190000.0,14.151250000000003,966.1052856445312,39820561.0,24261.46848218766 +3391,Spain,1997,268058721.8,1.1209,271800000.0,15.105916666666666,1001.7508544921876,39907236.0,25101.985376591543 +3392,Spain,1998,276635970.8,1.1534,281330000.0,14.552000000000001,1044.8861083984377,40046103.0,26092.079631279918 +3393,Spain,1999,300176561.0,1.2544,305960000.0,14.3665,1091.746826171875,40251435.0,27123.177749361606 +3394,Spain,2000,311926058.9,1.2701,323430000.0,14.46875,1149.490478515625,40589004.0,28320.243544670986 +3395,Spain,2001,313891993.7,1.2487,321270000.0,14.616083333333334,1195.4825439453125,41046894.0,29124.799161303472 +3396,Spain,2002,334007115.6,1.3101,337910000.0,14.647916666666667,1229.910888671875,41639614.0,29537.038663996136 +3397,Spain,2003,337979232.9,1.258,347290000.0,14.996916666666666,1269.1151123046875,42321028.0,29987.813913799248 +3398,Spain,2004,354285655.6,1.2569,363330000.0,14.396416666666669,1309.3048095703125,43000157.0,30448.8378861108 +3399,Spain,2005,368964132.6,1.2612,378350000.0,14.303583333333334,1358.0501708984375,43704367.0,31073.557727044477 +3400,Spain,2006,360279239.6,1.1896,385900000.0,15.105916666666666,1414.7366943359375,44431750.0,31840.670113959888 +3401,Spain,2007,368133603.0,1.1921,401660000.0,14.241333333333335,1468.05810546875,45211636.0,32470.802548900243 +3402,Spain,2008,336490118.5,1.0556,368400000.0,14.229500000000002,1484.4664306640625,45910365.0,32334.01500214739 +3403,Spain,2009,297248141.0,0.943,331710000.0,14.968083333333333,1431.415283203125,46295240.0,30919.275571378934 +3404,Spain,2010,283877489.7,0.8585,316470000.0,14.246000000000002,1431.6165771484375,46505963.0,30783.505701160033 +3405,Spain,2011,284594749.3,0.8283,316310000.0,15.321083333333334,1417.318115234375,46754784.0,30313.862967143104 +3406,Spain,2012,279271283.7,0.7998,309740000.0,14.58,1375.8226318359375,47042984.0,29246.074862851758 +3407,Spain,2013,252682710.7,0.7177,283470000.0,14.858875000000001,1352.3551025390625,47370542.0,28548.44055909393 +3408,Sri Lanka,1990,3829432.12,0.0173,28170000.0,27.528750000000002,20.61184883117676,16861677.0,1222.4079983964086 +3409,Sri Lanka,1991,4137030.16,0.0184,28080000.0,27.56,21.55999183654785,17003368.0,1267.9836039864485 +3410,Sri Lanka,1992,5105983.75,0.023,29930000.0,27.269833333333334,22.50863075256348,17202861.0,1308.423683279396 +3411,Sri Lanka,1993,4975607.38,0.0225,30380000.0,27.367416666666667,24.06174087524414,17448705.0,1378.9986635251237 +3412,Sri Lanka,1994,5435782.0,0.0241,31320000.0,27.36525,25.40917778015137,17708824.0,1434.8314591726346 +3413,Sri Lanka,1995,5815497.85,0.0253,31520000.0,27.587500000000002,26.80670547485352,17941066.0,1494.1534396480965 +3414,Sri Lanka,1996,6970167.52,0.0296,31180000.0,27.39958333333333,27.82535171508789,18150801.0,1533.0095743481454 +3415,Sri Lanka,1997,7519559.47,0.0314,29080000.0,27.657666666666668,29.607675552368164,18367446.0,1611.9647528768105 +3416,Sri Lanka,1998,7736311.65,0.0323,26490000.0,28.153250000000003,30.99876976013184,18569803.0,1669.3106415901038 +3417,Sri Lanka,1999,8517572.13,0.0356,25730000.0,27.361250000000002,32.331886291503906,18792874.0,1720.4333031501144 +3418,Sri Lanka,2000,10133816.82,0.0413,24810000.0,27.469750000000005,34.27180862426758,19041167.0,1799.8796305009867 +3419,Sri Lanka,2001,10327266.07,0.0411,25890000.0,27.663083333333336,33.74216842651367,19263057.0,1751.6517978695526 +3420,Sri Lanka,2002,10946683.17,0.0429,26930000.0,27.79591666666667,35.079933166503906,19478732.0,1800.9351515542135 +3421,Sri Lanka,2003,10952128.15,0.0408,28100000.0,27.797416666666667,37.16377639770508,19708717.0,1885.6517345956652 +3422,Sri Lanka,2004,12146168.79,0.0431,28320000.0,27.60633333333333,39.1873664855957,19933590.0,1965.8960822207996 +3423,Sri Lanka,2005,11935194.05,0.0408,30340000.0,27.723833333333335,41.63334274291992,20102989.0,2071.0026127418128 +3424,Sri Lanka,2006,11793656.59,0.0389,26230000.0,27.572,44.82590866088867,20311049.0,2206.9716173147267 +3425,Sri Lanka,2007,12151310.26,0.0393,27150000.0,27.5565,47.87265014648438,20507595.0,2334.386364977677 +3426,Sri Lanka,2008,11987816.3,0.0376,27450000.0,27.441166666666664,50.72111511230469,20697509.0,2450.590315593277 +3427,Sri Lanka,2009,12943484.25,0.0411,26640000.0,27.74541666666667,52.516090393066406,20879197.0,2515.23515933426 +3428,Sri Lanka,2010,13050301.14,0.0395,27860000.0,27.883499999999998,56.72576141357422,21083826.0,2690.487078273849 +3429,Sri Lanka,2011,14886602.36,0.0433,31410000.0,27.66916666666667,61.49341201782226,21283913.0,2889.1967382981816 +3430,Sri Lanka,2012,15775749.77,0.0452,32890000.0,27.949833333333334,67.11672210693361,21481334.0,3124.420583327534 +3431,Sri Lanka,2013,15253298.29,0.0433,30540000.0,28.175124999999998,69.39582061767578,21675648.0,3201.5569092871306 +3432,Sudan,1990,5217536.0,0.0235,109260000.0,27.644083333333338,19.81883811950684,21129427.0,937.9732881306642 +3433,Sudan,1991,4887776.0,0.0218,110050000.0,27.515249999999998,21.307411193847656,21939378.0,971.1948622175003 +3434,Sudan,1992,4374816.0,0.0197,113530000.0,26.879666666666665,22.708984375,22780376.0,996.8660910162323 +3435,Sudan,1993,3019136.0,0.0136,115370000.0,27.680249999999997,23.74650001525879,23565247.0,1007.6915389539006 +3436,Sudan,1994,4143984.0,0.0184,119720000.0,27.25733333333333,23.98544311523437,24036332.0,997.8828348366287 +3437,Sudan,1995,4294208.0,0.0187,120630000.0,27.498833333333337,25.42387390136719,24516834.0,1036.9966163399072 +3438,Sudan,1996,4429776.0,0.0188,110480000.0,27.75066666666667,26.92874336242676,25006941.0,1076.8507576527156 +3439,Sudan,1997,5415392.0,0.0226,115630000.0,27.694,29.77423477172852,25506846.0,1167.303663170606 +3440,Sudan,1998,4693584.0,0.0196,121930000.0,27.802999999999997,31.057058334350586,26016745.0,1193.7334333849444 +3441,Sudan,1999,5089296.0,0.0213,122440000.0,27.719499999999996,32.02109909057617,26536837.0,1206.666005092324 +3442,Sudan,2000,5528976.0,0.0225,125770000.0,27.45658333333333,34.053115844726555,27067325.0,1258.089443442474 +3443,Sudan,2001,6364368.0,0.0253,150720000.0,27.72583333333333,36.266693115234375,27608419.0,1313.6099214965684 +3444,Sudan,2002,8112096.0,0.0318,178510000.0,27.905416666666664,38.597652435302734,28160329.0,1370.6392576344804 +3445,Sudan,2003,9064736.0,0.0337,152670000.0,27.85375,41.58302307128906,28723273.0,1447.7118631741253 +3446,Sudan,2004,11453664.0,0.0406,173120000.0,27.893333333333334,43.19781875610352,29297470.0,1474.4556016647007 +3447,Sudan,2005,10973680.0,0.0375,167240000.0,28.03283333333334,46.43322372436523,29883145.0,1553.8265374800824 +3448,Sudan,2006,12003264.0,0.0396,142360000.0,27.72941666666667,51.106407165527344,30480529.0,1676.6902951562076 +3449,Sudan,2007,14113728.0,0.0457,147020000.0,27.647500000000004,56.99483871459961,31089855.0,1833.2294799895208 +3450,Sudan,2008,14901488.0,0.0467,150870000.0,27.841499999999996,61.44155502319336,31732535.0,1936.2321674960208 +3451,Sudan,2009,15550016.0,0.0493,185070000.0,28.347916666666666,63.43339538574219,32426590.0,1956.2154202998893 +3452,Sudan,2010,15927408.0,0.0482,155100000.0,28.647666666666666,65.63410949707031,32996691.0,1989.1118626734515 +3453,Sudan,2011,15645280.0,0.0455,172260000.0,27.80758333333333,64.34260559082031,33557327.0,1917.393646723421 +3454,Suriname,1990,1767344.06,0.008,6970000.0,26.677083333333332,2.461841106414795,416080.0,5916.749438605064 +3455,Suriname,1991,2049453.5,0.0091,7320000.0,26.676416666666668,2.528310775756836,417519.0,6055.558611121496 +3456,Suriname,1992,2064090.95,0.0093,7430000.0,26.80558333333333,2.538424015045166,419565.0,6050.13291157548 +3457,Suriname,1993,2038424.4,0.0092,7350000.0,26.589583333333334,2.368349552154541,421004.0,5625.479929298868 +3458,Suriname,1994,2041720.96,0.0091,7380000.0,26.566083333333335,2.448873519897461,423065.0,5788.409629483557 +3459,Suriname,1995,2060019.76,0.009000000000000001,7430000.0,27.045333333333332,2.448873519897461,427744.0,5725.091456332435 +3460,Suriname,1996,2081982.55,0.0089,6370000.0,26.821583333333333,2.47336220741272,433180.0,5709.7793236361795 +3461,Suriname,1997,2103945.35,0.0088,6510000.0,27.04716666666667,2.614343881607056,439683.0,5945.974444331611 +3462,Suriname,1998,2103924.14,0.0088,7020000.0,27.400166666666667,2.674473762512207,447429.0,5977.426055334382 +3463,Suriname,1999,2122222.94,0.0089,6160000.0,26.7225,2.6370313167572017,455740.0,5786.262598756313 +3464,Suriname,2000,2191817.73,0.0089,5330000.0,26.58608333333333,2.692409038543701,464036.0,5802.155519278032 +3465,Suriname,2001,2363353.38,0.0094,5440000.0,26.804083333333335,2.805490016937256,471851.0,5945.711711826944 +3466,Suriname,2002,1560629.81,0.0061,6500000.0,26.90391666666667,2.8840436935424805,480008.0,6008.324222809788 +3467,Suriname,2003,1538338.24,0.0057,9970000.0,27.19558333333333,3.0657384395599365,488131.0,6280.56492941431 +3468,Suriname,2004,1556350.67,0.0055,8620000.0,27.150333333333336,3.3263263702392583,495897.0,6707.696094631058 +3469,Suriname,2005,1589019.1,0.0054,6120000.0,27.48391666666667,3.4760112762451167,503639.0,6901.791315297499 +3470,Suriname,2006,1739055.57,0.0057,6410000.0,27.045666666666673,3.6776199340820312,511895.0,7184.324781609572 +3471,Suriname,2007,1756870.75,0.0057,5630000.0,26.90858333333333,3.8651397228240967,520925.0,7419.76238964169 +3472,Suriname,2008,1935706.62,0.0061,6870000.0,26.919500000000003,4.025517463684082,529824.0,7597.839025193426 +3473,Suriname,2009,1995348.42,0.0063,7700000.0,27.233083333333337,4.1469812393188485,538125.0,7706.353057967661 +3474,Suriname,2010,2395354.87,0.0072,6930000.0,27.412583333333334,4.361091136932373,545859.0,7989.40960382145 +3475,Suriname,2011,1959774.25,0.0057,7200000.0,27.060499999999994,4.616284370422363,553159.0,8345.311873118513 +3476,Suriname,2012,2244350.25,0.0064,7420000.0,27.18941666666667,4.740478515625,560157.0,8462.767609125656 +3477,Suriname,2013,1902155.61,0.0054,7830000.0,26.95725,4.879537582397461,566846.0,8608.224424971617 +3478,Swaziland,1990,425024.0,0.0019,2380000.0,19.2115,2.34295392036438,881603.0,2657.606564819289 +3479,Swaziland,1991,326096.0,0.0015,2330000.0,19.412416666666665,2.3841986656188965,922469.0,2584.584051733876 +3480,Swaziland,1992,263808.0,0.0012,2320000.0,20.269833333333334,2.46111798286438,959023.0,2566.276286245877 +3481,Swaziland,1993,131904.0,0.0006,2020000.0,19.725,2.5375630855560303,988716.0,2566.5237394317787 +3482,Swaziland,1994,483648.0,0.0021,2380000.0,18.97041666666667,2.5984818935394287,994916.0,2611.7600817952757 +3483,Swaziland,1995,454336.0,0.002,2400000.0,19.539333333333335,2.7238757610321045,1003635.0,2714.010333469941 +3484,Swaziland,1996,340752.0,0.0014,2420000.0,19.111,2.828528642654419,1032455.0,2739.6144555011297 +3485,Swaziland,1997,1201792.0,0.005,3270000.0,19.1525,2.9162921905517583,1061056.0,2748.480938378142 +3486,Swaziland,1998,1212784.0,0.0051,3370000.0,19.865416666666672,2.9922337532043457,1089403.0,2746.6729513360488 +3487,Swaziland,1999,1238432.0,0.0052,3320000.0,19.788833333333336,3.0805306434631348,1117139.0,2757.5177694656927 +3488,Swaziland,2000,1187136.0,0.0048,3280000.0,19.0235,3.1347534656524663,1143927.0,2740.343977939559 +3489,Swaziland,2001,1143168.0,0.0045,3110000.0,19.545999999999996,3.1678175926208496,1169520.0,2708.6476440085244 +3490,Swaziland,2002,1124848.0,0.0044,3150000.0,19.59616666666667,3.306571245193481,1193707.0,2770.002391871273 +3491,Swaziland,2003,1040576.0,0.0039,3170000.0,19.99716666666667,3.434873342514038,1216439.0,2823.711951453413 +3492,Swaziland,2004,1029584.0,0.0037,3170000.0,19.469916666666666,3.55935001373291,1237945.0,2875.2085219722285 +3493,Swaziland,2005,1018592.0,0.0035,3170000.0,19.975749999999998,3.772867441177368,1258977.0,2996.772332756967 +3494,Swaziland,2006,1014928.0,0.0034,3060000.0,19.26683333333333,3.99894380569458,1279797.0,3124.6704013953618 +3495,Swaziland,2007,1062560.0,0.0034,3380000.0,19.615000000000002,4.17631196975708,1299910.0,3212.7700915887103 +3496,Swaziland,2008,1095536.0,0.0034,3290000.0,19.420666666666673,4.210627555847168,1319101.0,3192.0433354588977 +3497,Swaziland,2009,1044240.0,0.0033,3050000.0,19.293000000000003,4.276525974273682,1337186.0,3198.153416408549 +3498,Swaziland,2010,1036912.0,0.0031,3130000.0,19.75633333333333,4.438766479492188,1354051.0,3278.1383267633105 +3499,Swaziland,2011,1047904.0,0.0031,3140000.0,19.358999999999998,4.538516044616699,1370424.0,3311.7604804182497 +3500,Swaziland,2012,1205456.0,0.0035,3340000.0,19.57175,4.7526397705078125,1386914.0,3426.7732321598983 +3501,Swaziland,2013,1088208.0,0.0031,3230000.0,18.95825,5.05780839920044,1403362.0,3604.0653795673816 +3502,Sweden,1990,57506342.82,0.2593,48040000.0,4.497916666666666,321.0674743652344,8600815.0,37329.88959362972 +3503,Sweden,1991,57713493.44,0.2573,48660000.0,3.459,317.38812255859375,8668112.0,36615.600093606736 +3504,Sweden,1992,57447091.7,0.2588,51260000.0,3.8722499999999997,313.71087646484375,8718867.0,35980.69295756476 +3505,Sweden,1993,57395714.04,0.2591,51160000.0,2.945,307.2308044433594,8769284.0,35034.879066906644 +3506,Sweden,1994,59845492.34,0.2656,53010000.0,2.8799166666666665,319.7891845703125,8831493.0,36210.09319379096 +3507,Sweden,1995,59276126.28,0.2576,52490000.0,3.0660000000000007,332.65850830078125,8877890.0,37470.447178415285 +3508,Sweden,1996,63194022.88,0.2686,58060000.0,2.39175,337.7077941894531,8892234.0,37977.83483761821 +3509,Sweden,1997,58132710.66,0.2431,51840000.0,3.730583333333333,347.5029296875,8897619.0,39055.7215011679 +3510,Sweden,1998,58717075.01,0.2448,52600000.0,2.8729166666666663,362.191162109375,8902684.0,40683.367185601004 +3511,Sweden,1999,55856974.06,0.2334,51230000.0,3.5439166666666666,378.5989685058594,8909790.0,42492.46822942621 +3512,Sweden,2000,54724980.5,0.2228,46960000.0,4.421000000000001,396.5267333984375,8924354.0,44431.98167603363 +3513,Sweden,2001,55682290.71,0.2215,14740000.0,3.1396666666666655,402.72607421875,8948312.0,45005.81497591388 +3514,Sweden,2002,56641312.84,0.2222,16000000.0,3.7953333333333332,411.076904296875,8977439.0,45789.99693530361 +3515,Sweden,2003,57224353.61,0.213,17030000.0,3.733,420.8841247558594,9010865.0,46708.51519314288 +3516,Sweden,2004,56443340.56,0.2002,15600000.0,3.6477500000000007,439.0685729980469,9046268.0,48535.87943647557 +3517,Sweden,2005,53843310.57,0.184,12270000.0,4.000166666666667,451.4429626464844,9082561.0,49704.3689160452 +3518,Sweden,2006,53672750.5,0.1772,56730000.0,4.354166666666667,472.607177734375,9133723.0,51743.10385090231 +3519,Sweden,2007,52816477.68,0.171,54580000.0,4.080583333333333,488.69921875,9201391.0,53111.45007858051 +3520,Sweden,2008,50723095.64,0.1591,52480000.0,4.217999999999999,485.9769287109375,9273134.0,52406.97791177584 +3521,Sweden,2009,47153238.82,0.1496,49440000.0,3.5024166666666656,460.78070068359375,9352378.0,49268.82774451521 +3522,Sweden,2010,52927274.75,0.1601,55230000.0,1.6585,488.9078979492188,9432309.0,51833.32076474793 +3523,Sweden,2011,48969956.41,0.1425,51180000.0,4.51875,502.0068359375,9503420.0,52823.80826455108 +3524,Sweden,2012,46403566.28,0.1329,47690000.0,2.9728333333333343,500.5082702636719,9573256.0,52281.92688711885 +3525,Sweden,2013,44768997.2,0.1272,45920000.0,4.199750000000001,506.706298828125,9647386.0,52522.65212857918 +3526,Switzerland,1990,44161939.66,0.1991,50770000.0,8.255583333333334,432.09552001953125,6843505.0,63139.505270987785 +3527,Switzerland,1991,46140163.72,0.2057,53000000.0,7.405000000000001,428.1383361816406,6925077.0,61824.34306241514 +3528,Switzerland,1992,46015492.76,0.2073,52940000.0,8.059666666666667,427.9510803222656,7000610.0,61130.54152741913 +3529,Switzerland,1993,43604679.31,0.1968,50430000.0,7.617333333333335,427.41192626953125,7064021.0,60505.472204786936 +3530,Switzerland,1994,42678569.6,0.1894,49620000.0,8.821083333333332,432.8390197753906,7120988.0,60783.562586454376 +3531,Switzerland,1995,43414060.79,0.1887,50580000.0,7.751916666666666,434.92041015625,7164212.0,60707.361836340126 +3532,Switzerland,1996,44098465.42,0.1875,51110000.0,6.974583333333334,437.2691040039063,7188778.0,60826.625054203396 +3533,Switzerland,1997,43035015.67,0.1799,49630000.0,8.212416666666668,447.5018310546875,7202163.0,62134.365891842135 +3534,Switzerland,1998,44612347.11,0.18600000000000003,51170000.0,7.957999999999999,460.88165283203125,7217045.0,63860.160610337225 +3535,Switzerland,1999,44441709.5,0.1857,51310000.0,7.902833333333334,468.69232177734375,7242498.0,64714.180352876014 +3536,Switzerland,2000,43611917.32,0.1776,50550000.0,8.562666666666667,487.1439208984375,7277250.0,66940.66040034869 +3537,Switzerland,2001,45075797.17,0.1793,52460000.0,7.979749999999999,493.5373229980469,7312215.0,67494.91405792184 +3538,Switzerland,2002,43458527.07,0.1705,50800000.0,8.484833333333334,494.3398742675781,7348652.0,67269.4630617395 +3539,Switzerland,2003,44644101.19,0.1662,51990000.0,8.669749999999999,494.5355224609375,7384538.0,66969.05377979469 +3540,Switzerland,2004,45228284.21,0.1605,52390000.0,7.8460833333333335,508.2649230957031,7417342.0,68523.86246929197 +3541,Switzerland,2005,45779837.77,0.1565,53110000.0,7.573916666666668,524.0986328125,7448224.0,70365.58417315323 +3542,Switzerland,2006,45365628.28,0.1498,50960000.0,8.337750000000002,545.0029907226561,7478651.0,72874.50513771217 +3543,Switzerland,2007,43359827.5,0.1404,49090000.0,8.552750000000001,567.4129028320312,7529531.0,75358.33278753102 +3544,Switzerland,2008,44706687.82,0.1403,50630000.0,8.109666666666667,579.638916015625,7610995.0,76158.09969861037 +3545,Switzerland,2009,43528067.64,0.1381,49150000.0,8.300666666666666,566.7587280273438,7693227.0,73669.83036212811 +3546,Switzerland,2010,45045102.6,0.1362,50770000.0,7.2265000000000015,583.7767944335938,7769856.0,75133.5410120334 +3547,Switzerland,2011,40979689.24,0.1193,48420000.0,8.847333333333333,593.6589965820312,7850100.0,75624.38651508023 +3548,Switzerland,2012,42250900.62,0.121,49760000.0,8.159249999999998,599.63134765625,7925517.0,75658.32584249707 +3549,Switzerland,2013,43183909.83,0.1227,50790000.0,8.1775,610.73681640625,7996026.0,76380.04383755756 +3550,Syria,1990,37045258.64,0.16699999999999998,56950000.0,18.543166666666668,22.585073471069336,12533628.0,1801.9581777175242 +3551,Syria,1991,42200739.27,0.1881,61360000.0,18.479499999999998,24.36958122253418,12967853.0,1879.2302181813889 +3552,Syria,1992,42561058.26,0.1918,62510000.0,17.028583333333334,27.65224647521973,13347357.0,2071.739481847959 +3553,Syria,1993,45221697.26,0.2041,63010000.0,18.045666666666666,29.08435249328613,13721787.0,2119.5746948474084 +3554,Syria,1994,46189815.79,0.205,66390000.0,19.05183333333333,31.310157775878906,14098874.0,2220.7559111372234 +3555,Syria,1995,41200150.12,0.1791,70520000.0,18.482249999999997,33.11049652099609,14486815.0,2285.5608027710778 +3556,Syria,1996,42322276.84,0.1799,73730000.0,18.90341666666667,34.56735992431641,14882746.0,2322.6466355279067 +3557,Syria,1997,40430768.82,0.1691,74200000.0,17.933666666666667,35.189571380615234,15284321.0,2302.3313486163524 +3558,Syria,1998,49746529.32,0.2074,77460000.0,19.415333333333333,37.41962432861328,15690604.0,2384.8428224058985 +3559,Syria,1999,51771423.31,0.2163,76500000.0,19.281,36.08985900878906,16100671.0,2241.51273004641 +3560,Syria,2000,50481494.92,0.2056,76450000.0,18.773333333333337,37.07975387573242,16514089.0,2245.3405619730174 +3561,Syria,2001,48140692.14,0.1915,76970000.0,19.39625,39.00825119018555,16929187.0,2304.2010930699475 +3562,Syria,2002,38438581.18,0.1508,77580000.0,18.856583333333333,41.30973815917969,17345363.0,2381.6012475022685 +3563,Syria,2003,53581372.06,0.1994,77900000.0,18.839333333333336,41.55759811401367,17762908.0,2339.5717702311845 +3564,Syria,2004,50384626.15,0.1787,80270000.0,18.77325,44.425071716308594,18186448.0,2442.7569207746665 +3565,Syria,2005,49880827.75,0.1705,91420000.0,18.74191666666667,47.17942428588867,18614058.0,2534.612510925273 +3566,Syria,2006,52840385.88,0.1745,94680000.0,18.744,49.53839874267578,19376129.0,2556.6716005387752 +3567,Syria,2007,65718480.22,0.2128,97960000.0,19.0415,52.36208724975586,20543421.0,2548.8494467282667 +3568,Syria,2008,66877395.9,0.2098,98980000.0,19.161166666666663,54.69659805297852,21180801.0,2582.3668355591712 +3569,Syria,2009,61562217.17,0.1953,93910000.0,19.070083333333333,57.953392028808594,21389802.0,2709.393571235891 +3570,Syria,2010,60716402.04,0.1836,93100000.0,20.709333333333337,60.9482307434082,21765977.0,2800.1605782919005 +3571,Syria,2011,56139349.21,0.1634,86740000.0,18.607,62.7893180847168,22023924.0,2850.959623939712 +3572,Syria,2012,43790055.95,0.1254,71310000.0,19.357583333333334,46.14076232910156,21772036.0,2119.267225587059 +3573,Syria,2013,35808975.93,0.1017,57300000.0,20.021124999999998,34.11293411254883,20136511.0,1694.0836529500482 +3574,Tajikistan,1990,8197125.4,0.037000000000000005,17860000.0,5.199333333333333,6.7814979553222665,5272333.0,1286.2423438205185 +3575,Tajikistan,1991,7903715.12,0.0352,17040000.0,4.489333333333334,6.30001163482666,5407166.0,1165.1226603412324 +3576,Tajikistan,1992,7294624.73,0.0329,14010000.0,4.4305,4.473008155822754,5500029.0,813.2699219990938 +3577,Tajikistan,1993,5161394.44,0.0233,11140000.0,4.142666666666668,3.739434957504273,5546629.0,674.1815537877643 +3578,Tajikistan,1994,2347446.17,0.0104,8660000.0,4.72025,2.9429354667663574,5604488.0,525.1033576602105 +3579,Tajikistan,1995,2442887.96,0.0106,8140000.000000001,4.808666666666667,2.577531337738037,5678225.0,453.9325824070088 +3580,Tajikistan,1996,2822485.18,0.012,7450000.0,4.275333333333332,2.1470835208892822,5764362.0,372.47548313053255 +3581,Tajikistan,1997,2148921.13,0.009000000000000001,7720000.0,5.452416666666667,2.183168888092041,5864687.0,372.25667594741896 +3582,Tajikistan,1998,2494409.02,0.0104,7690000.0,5.116416666666667,2.299157857894897,5983489.0,384.2503692903752 +3583,Tajikistan,1999,2515545.58,0.0105,7140000.0,5.425,2.384216547012329,6110862.0,390.1604302326463 +3584,Tajikistan,2000,2232073.97,0.0091,6840000.0,5.3925833333333335,2.582686424255371,6229697.0,414.57657158211236 +3585,Tajikistan,2001,2285489.34,0.0091,6740000.0,5.718166666666666,2.8301296234130864,6339104.0,446.4557804088852 +3586,Tajikistan,2002,1872934.22,0.0073,6730000.0,5.29125,3.1368558406829834,6452011.0,486.1826554051106 +3587,Tajikistan,2003,2062191.54,0.0077,6960000.0,4.890166666666668,3.4796676635742188,6567848.0,529.8033181605632 +3588,Tajikistan,2004,2547275.25,0.009000000000000001,7660000.0,5.8380833333333335,3.840522289276123,6688433.0,574.2035973562303 +3589,Tajikistan,2005,2421168.61,0.0083,7730000.0,5.184500000000001,4.09569787979126,6814791.0,601.0012456422007 +3590,Tajikistan,2006,2634138.72,0.0087,8270000.0,5.531333333333333,4.384381294250488,6944062.0,631.3856780441315 +3591,Tajikistan,2007,3204293.47,0.0104,9000000.0,5.441166666666667,4.724615573883057,7076598.0,667.6393902667718 +3592,Tajikistan,2008,2885313.02,0.0091,9310000.0,5.174666666666666,5.098357677459717,7211884.0,706.9383918903461 +3593,Tajikistan,2009,2434691.13,0.0077,8990000.0,5.096916666666668,5.296827793121338,7349145.0,720.7406838647678 +3594,Tajikistan,2010,2517762.83,0.0076,9030000.0,5.5640833333333335,5.642216682434082,7487489.0,753.5525838414029 +3595,Tajikistan,2011,2320034.51,0.0068,9320000.0,5.351583333333333,6.059776782989503,7627200.0,794.4955924834147 +3596,Tajikistan,2012,2906292.25,0.0083,10130000.0,4.337583333333333,6.51342248916626,7768385.0,838.4525855974258 +3597,Tajikistan,2013,3437059.44,0.0098,10530000.0,6.562624999999999,6.995420932769775,7910041.0,884.3722722511521 +3598,Tanzania,1990,2217225.25,0.01,289590000.0,22.71433333333334,12.637092590332033,24804769.0,509.4622163315462 +3599,Tanzania,1991,2348794.9,0.0105,289720000.0,22.691166666666664,12.898931503295898,25503557.0,505.7699011669588 +3600,Tanzania,1992,2275180.55,0.0103,289740000.0,22.802999999999997,12.974302291870115,26240313.0,494.441598004952 +3601,Tanzania,1993,2545982.2,0.0115,290230000.0,22.689999999999998,13.130746841430664,27114589.0,484.2687027795503 +3602,Tanzania,1994,2375036.62,0.0105,290430000.0,22.862416666666665,13.336592674255373,28159908.0,473.6021394052627 +3603,Tanzania,1995,3496269.72,0.0152,294350000.0,23.006416666666667,13.812697410583494,29064552.0,475.2420546713913 +3604,Tanzania,1996,3392183.78,0.0144,296550000.0,22.756666666666664,14.440396308898926,29674398.0,486.6281131937007 +3605,Tanzania,1997,2822982.84,0.0118,281670000.0,22.948749999999993,14.949460983276367,30376572.0,492.1378548993732 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+3614,Tanzania,2006,5878992.64,0.0194,308540000.0,23.045416666666668,25.34530258178711,39618057.0,639.7411811938963 +3615,Tanzania,2007,5710289.37,0.0185,319930000.0,22.9255,27.06080818176269,40787957.0,663.4509343471823 +3616,Tanzania,2008,5937408.29,0.0186,323920000.0,22.70641666666667,28.59959983825684,41967410.0,681.4716428356393 +3617,Tanzania,2009,5693926.63,0.0181,314790000.0,23.150000000000002,30.106542587280273,43094715.0,698.6133354700287 +3618,Tanzania,2010,6840606.6,0.0207,316030000.0,23.26766666666667,32.01425170898437,44288197.0,722.8619333720985 +3619,Tanzania,2011,7807018.47,0.0227,311670000.0,23.069416666666665,34.47043228149414,45590856.0,756.0821468562499 +3620,Tanzania,2012,9238081.98,0.0265,309610000.0,23.06866666666667,36.02165603637695,46912768.0,767.8433307618292 +3621,Tanzania,2013,10912008.98,0.031,309430000.0,22.772750000000002,38.46449661254883,48261942.0,796.994381464153 +3622,Thailand,1990,89390102.02,0.40299999999999997,174510000.0,26.590666666666667,141.6105499267578,56096722.0,2524.3997309995725 +3623,Thailand,1991,98365594.92,0.4386,186490000.0,26.653333333333332,153.7299346923828,56848084.0,2704.22367607645 +3624,Thailand,1992,108076713.8,0.4869,198430000.0,26.29116666666667,166.15652465820312,57599368.0,2884.693537925679 +3625,Thailand,1993,122865944.9,0.5546,214550000.0,26.297083333333337,179.8661651611328,58344466.0,3082.831628986592 +3626,Thailand,1994,136844214.0,0.6073,229290000.0,26.476999999999993,194.2499084472656,59082163.0,3287.7927716909358 +3627,Thailand,1995,158457068.5,0.6887,248470000.0,26.529916666666676,210.02349853515625,59817770.0,3511.055302381821 +3628,Thailand,1996,177264411.7,0.7536,267810000.0,26.185499999999994,221.89480590820312,60530767.0,3665.8185069454535 +3629,Thailand,1997,183045934.3,0.7654,275080000.0,26.635166666666667,215.78472900390625,61200359.0,3525.873581949188 +3630,Thailand,1998,162551647.5,0.6777,256820000.0,27.37966666666667,199.3123016357422,61844958.0,3222.7736598308006 +3631,Thailand,1999,174131016.7,0.7277,266620000.0,26.22225,208.42544555664062,62411983.0,3339.5100674279274 +3632,Thailand,2000,179263989.8,0.7299,267950000.0,26.302166666666665,217.71221923828125,62901817.0,3461.1435666203606 +3633,Thailand,2001,192377279.3,0.7653,272250000.0,26.679833333333335,225.2107696533203,63394597.0,3552.523090466532 +3634,Thailand,2002,205776032.5,0.8071,284790000.0,26.786083333333334,239.05870056152344,63884702.0,3742.033586718827 +3635,Thailand,2003,221916442.2,0.826,297240000.0,26.649,256.24542236328125,64334844.0,3982.9959386126943 +3636,Thailand,2004,240241704.5,0.8523,323230000.0,26.5,272.3614501953125,64735887.0,4207.2714659075045 +3637,Thailand,2005,244303940.0,0.8351,327490000.0,26.83316666666667,283.7674865722656,65112909.0,4358.083380551553 +3638,Thailand,2006,248619994.9,0.8209,324740000.0,26.744833333333332,297.8648376464844,65470877.0,4549.577633525275 +3639,Thailand,2007,249166110.2,0.8069,341270000.0,26.636916666666668,314.0540771484375,65813156.0,4771.904224566249 +3640,Thailand,2008,249591018.8,0.7829999999999999,343700000.0,26.221999999999998,319.4736022949219,66140640.0,4830.216373698861 +3641,Thailand,2009,264750299.2,0.8399,343260000.0,26.648083333333336,317.2668762207031,66441491.0,4775.131795592954 +3642,Thailand,2010,279428513.3,0.845,363470000.0,27.389666666666667,341.10504150390625,66720446.0,5112.451459091059 +3643,Thailand,2011,275835966.0,0.8028,377260000.0,26.25166666666667,343.97015380859375,66987118.0,5134.87016725505 +3644,Thailand,2012,293664430.5,0.841,400910000.0,27.288999999999998,368.8831787109375,67245400.0,5485.626953084337 +3645,Thailand,2013,296902994.6,0.8433,411830000.0,27.864874999999998,378.7964782714844,67497151.0,5612.036547609015 +3646,Togo,1990,972673.97,0.0044,10900000.0,27.516833333333327,2.1053335666656494,3720792.0,565.8294166042201 +3647,Togo,1991,1048466.69,0.0047,10680000.0,27.056833333333334,2.090596914291382,3846054.0,543.5693087750151 +3648,Togo,1992,1030669.72,0.0046,10410000.0,26.975499999999997,2.007376909255981,3972812.0,505.27860599897036 +3649,Togo,1993,1068922.81,0.0048,10330000.0,27.23675,1.7043466567993164,3952577.0,431.19884996530527 +3650,Togo,1994,1004920.42,0.0045,10590000.0,27.081666666666667,1.9596989154815676,3981991.0,492.14046829376747 +3651,Togo,1995,1174741.0,0.0051,10640000.0,27.311333333333337,2.1134536266326904,4202905.0,502.85543609305716 +3652,Togo,1996,1265171.0,0.0054,11080000.0,27.30583333333333,2.3002028465271,4402067.0,522.5279048517662 +3653,Togo,1997,878986.0,0.0037,10950000.0,27.225416666666664,2.6309115886688232,4572447.0,575.3837253157495 +3654,Togo,1998,1289967.0,0.0054,11860000.0,27.805583333333335,2.570403814315796,4715170.0,545.134918638309 +3655,Togo,1999,1850434.0,0.0077,12350000.0,27.280333333333335,2.634194850921631,4852448.0,542.8589550926937 +3656,Togo,2000,1331372.0,0.0054,11890000.0,27.44241666666667,2.613556385040283,4991834.0,523.5663655963486 +3657,Togo,2001,1158913.0,0.0046,11710000.0,27.410500000000003,2.571038722991944,5134565.0,500.7315562256868 +3658,Togo,2002,1322471.0,0.0052,12240000.0,27.497250000000005,2.547330141067505,5277933.0,482.63783209591804 +3659,Togo,2003,1804731.0,0.0067,12680000.0,27.666750000000004,2.673534870147705,5424964.0,492.82075791612715 +3660,Togo,2004,1743929.0,0.0062,12440000.0,27.534916666666664,2.7301888465881348,5578206.0,489.4385124156646 +3661,Togo,2005,1721591.0,0.0059,13480000.0,27.76666666666667,2.762416362762451,5714516.0,483.4033823271212 +3662,Togo,2006,1500506.0,0.005,12660000.0,27.7685,2.8743607997894287,5865402.0,490.05350354322326 +3663,Togo,2007,1516752.0,0.0049,12490000.0,27.863833333333332,2.9401967525482178,6042377.0,486.596045322597 +3664,Togo,2008,1522778.0,0.0048,12720000.0,27.46491666666667,3.0596349239349365,6220348.0,491.87520118407133 +3665,Togo,2009,2740838.0,0.0087,14290000.0,27.710333333333335,3.2290749549865723,6405008.0,504.14846554236505 +3666,Togo,2010,2599362.0,0.0079,14020000.0,28.059416666666667,3.4260246753692627,6587239.0,520.1002537435279 +3667,Togo,2011,2507584.0,0.0073,14610000.0,27.772000000000006,3.645228624343872,6771993.0,538.2800343036197 +3668,Togo,2012,2292573.0,0.0066,14050000.0,27.419916666666666,3.8837544918060303,6961049.0,557.9266130443889 +3669,Togo,2013,2236120.0,0.0064,14310000.0,27.718000000000004,4.121142387390137,7154237.0,576.0421953298635 +3670,Tonga,1990,76944.0,0.0003,250000.0,23.639750000000003,0.24501173198223114,96239.0,2545.8673924524483 +3671,Tonga,1991,95264.0,0.0004,270000.0,23.58791666666667,0.2607291042804718,96474.0,2702.5841602967826 +3672,Tonga,1992,69616.0,0.0003,240000.0,23.576166666666666,0.2613860964775085,96711.0,2702.754562330123 +3673,Tonga,1993,84272.0,0.0004,260000.0,23.192333333333337,0.27115684747695923,96947.0,2796.9596529749165 +3674,Tonga,1994,91600.0,0.0004,260000.0,23.22775,0.28458598256111145,97178.0,2928.502156466602 +3675,Tonga,1995,95264.0,0.0004,260000.0,23.890083333333333,0.29514944553375244,97397.0,3030.375119703404 +3676,Tonga,1996,76944.0,0.0003,230000.0,23.993666666666666,0.2951796054840088,97598.0,3024.4431800242714 +3677,Tonga,1997,98928.0,0.0004,250000.0,23.386416666666666,0.29479119181633,98046.0,3006.6620955095564 +3678,Tonga,1998,87936.0,0.0004,230000.0,24.13383333333333,0.30280601978302,98749.0,3066.4211261179357 +3679,Tonga,1999,109920.0,0.0005,250000.0,23.869500000000002,0.31456005573272705,99442.0,3163.251500701183 +3680,Tonga,2000,95264.0,0.0004,230000.0,24.028666666666666,0.3251602053642273,100125.0,3247.5426253605724 +3681,Tonga,2001,87936.0,0.0003,230000.0,23.995083333333337,0.33687180280685425,100796.0,3342.114794305868 +3682,Tonga,2002,102592.0,0.0004,240000.0,23.89316666666667,0.34838902950286865,101452.0,3434.028205485044 +3683,Tonga,2003,117248.0,0.0004,280000.0,23.9195,0.35573509335517883,102089.0,3484.558506354052 +3684,Tonga,2004,109920.0,0.0004,260000.0,23.89158333333333,0.3544849753379822,102703.0,3451.5542422128096 +3685,Tonga,2005,113584.0,0.0004,260000.0,23.792083333333327,0.3600935339927673,103296.0,3486.0356063426207 +3686,Tonga,2006,128240.0,0.0004,-230000.0,23.816,0.3571687638759613,103871.0,3438.5801992467705 +3687,Tonga,2007,113584.0,0.0004,-250000.0,24.178583333333332,0.34228992462158203,104407.0,3278.41930734129 +3688,Tonga,2008,120912.0,0.0004,-240000.0,23.848583333333334,0.3528992533683777,104881.0,3364.758663326796 +3689,Tonga,2009,131904.0,0.0004,-200000.0,23.136916666666664,0.3609828054904938,105289.0,3428.494956647834 +3690,Tonga,2010,117248.0,0.0004,-220000.0,24.11441666666667,0.3739264309406281,105632.0,3539.8972938184265 +3691,Tonga,2011,102592.0,0.0003,250000.0,24.236666666666668,0.3843408226966858,105916.0,3628.7324171672444 +3692,Tonga,2012,106256.0,0.0003,270000.0,23.84166666666667,0.3877626955509186,106146.0,3653.106999330343 +3693,Tonga,2013,113584.0,0.0003,280000.0,24.285875,0.3756621778011322,106322.0,3533.2497300759223 +3694,Tunisia,1990,13167427.19,0.0594,19200000.0,20.929,18.33831214904785,8238186.0,2226.013366176468 +3695,Tunisia,1991,15362853.13,0.0685,20200000.0,19.924750000000003,19.054340362548828,8398112.0,2268.883811331503 +3696,Tunisia,1992,14891799.83,0.0671,21000000.0,20.03316666666667,20.54166984558105,8557637.0,2400.390416838323 +3697,Tunisia,1993,16353918.83,0.0738,22550000.0,20.567666666666668,20.991498947143555,8715627.0,2408.4898249022767 +3698,Tunisia,1994,15776113.11,0.07,22550000.0,21.266000000000005,21.658693313598636,8867534.0,2442.4708508136123 +3699,Tunisia,1995,15552481.03,0.0676,23180000.0,20.683166666666665,22.16803550720215,8998898.0,2463.4166880435973 +3700,Tunisia,1996,16580845.08,0.0705,24740000.0,20.5275,23.752182006835934,9122126.0,2603.7989397247893 +3701,Tunisia,1997,16766148.66,0.0701,25380000.0,21.084000000000003,25.04452514648437,9239267.0,2710.6614785008787 +3702,Tunisia,1998,17808536.66,0.0742,26790000.0,20.761333333333337,26.242595672607425,9347424.0,2807.468204353137 +3703,Tunisia,1999,18120233.04,0.0757,28240000.0,21.65691666666667,27.83148765563965,9447881.0,2945.791511942165 +3704,Tunisia,2000,19670323.94,0.0801,29770000.0,21.140500000000003,29.14231300354004,9545892.0,3052.8643110083417 +3705,Tunisia,2001,20545273.45,0.0817,30790000.0,21.70125,30.24860763549805,9644933.0,3136.2174973634387 +3706,Tunisia,2002,20558628.35,0.0806,31060000.0,21.32841666666667,30.648664474487305,9742979.0,3145.7180062163025 +3707,Tunisia,2003,20925446.97,0.0779,31370000.0,21.47758333333334,32.089889526367195,9842039.0,3260.492010483517 +3708,Tunisia,2004,21926580.69,0.0778,32250000.0,20.945083333333333,34.09095001220703,9938757.0,3430.101974744632 +3709,Tunisia,2005,22268684.67,0.0761,32910000.0,20.84575,35.279544830322266,10029805.0,3517.4706617249553 +3710,Tunisia,2006,22602492.56,0.0746,33760000.0,21.310000000000002,37.12967300415039,10120017.0,3668.933856944153 +3711,Tunisia,2007,23684349.69,0.0767,34490000.0,21.154666666666667,39.62093353271485,10212137.0,3879.7886801474406 +3712,Tunisia,2008,24333417.13,0.0763,34620000.0,21.20458333333333,41.29998016357422,10307653.0,4006.7297728759613 +3713,Tunisia,2009,24321607.28,0.0772,35090000.0,21.0925,42.55692672729492,10411019.0,4087.681208467194 +3714,Tunisia,2010,27102531.27,0.08199999999999999,37770000.0,21.609750000000002,44.05093383789063,10525963.0,4184.97897416993 +3715,Tunisia,2011,25524332.18,0.0743,35910000.0,20.994916666666665,43.206398010253906,10647404.0,4057.927924051149 +3716,Tunisia,2012,26306406.51,0.0753,37030000.0,21.479166666666668,44.93364715576172,10772543.0,4171.127203276118 +3717,Tunisia,2013,26865500.82,0.0763,36930000.0,21.887125,46.22572708129883,10901933.0,4240.140448606576 +3718,Turkey,1990,146507199.6,0.6605,184840000.0,12.032499999999999,365.2875671386719,56544219.0,6460.2106740332165 +3719,Turkey,1991,153538017.5,0.6845,187290000.0,11.751083333333334,367.9186706542969,57546504.0,6393.414805081763 +3720,Turkey,1992,159065847.3,0.7167,194720000.0,10.516666666666666,386.4456787109375,58501681.0,6605.71922216966 +3721,Turkey,1993,167301865.5,0.7552,201230000.0,11.277416666666667,416.0136413574219,59436968.0,6999.240630131434 +3722,Turkey,1994,163783172.1,0.7268,196410000.0,12.744833333333338,396.5935363769531,60364174.0,6570.015128128037 +3723,Turkey,1995,178310252.7,0.775,211000000.0,12.188833333333335,427.8382263183594,61295574.0,6979.920382479157 +3724,Turkey,1996,195750702.8,0.8322,228750000.0,12.405916666666668,459.4112548828125,62229966.0,7382.476392206489 +3725,Turkey,1997,208023000.1,0.8698,237830000.0,11.568000000000003,494.223876953125,63165562.0,7824.261532781501 +3726,Turkey,1998,208042303.4,0.8674,239060000.0,12.883166666666668,505.63128662109375,64101036.0,7888.036109449054 +3727,Turkey,1999,203949027.5,0.8523,239000000.0,13.087250000000003,488.4939880371094,65035346.0,7511.207644487805 +3728,Turkey,2000,226029842.5,0.9204,262940000.0,12.201916666666667,520.9307250976561,65969922.0,7896.488419338381 +3729,Turkey,2001,209529144.9,0.8335,232390000.0,13.28325,489.8711242675781,66895864.0,7322.891057473719 +3730,Turkey,2002,217652912.6,0.8537,242670000.0,12.339,521.3707275390625,67800721.0,7689.751965013212 +3731,Turkey,2003,232963018.8,0.8671,254510000.0,12.331333333333333,550.6110229492189,68691464.0,8015.7124464434 +3732,Turkey,2004,241909220.8,0.8582,260870000.0,12.310166666666667,603.7133178710938,69579052.0,8676.653396644348 +3733,Turkey,2005,260897954.9,0.8918,273390000.0,12.432916666666666,658.1072387695312,70464462.0,9339.562384930026 +3734,Turkey,2006,281541718.9,0.9296,277830000.0,12.450083333333334,704.8970336914062,71341435.0,9880.611928977967 +3735,Turkey,2007,312862498.2,1.0132,307700000.0,12.815333333333333,740.3565063476562,72204657.0,10253.583869910999 +3736,Turkey,2008,310396695.7,0.9738,306300000.0,12.657833333333334,746.6141357421875,73049044.0,10220.72425399828 +3737,Turkey,2009,316771031.9,1.0049,309800000.0,12.814833333333334,711.4900512695312,73875971.0,9630.872415464173 +3738,Turkey,2010,319528399.8,0.9663,319510000.0,14.281666666666668,771.8768310546875,74686698.0,10334.863526229095 +3739,Turkey,2011,344745495.1,1.0034,332180000.0,11.914500000000002,857.6593017578125,75481903.0,11362.449377539044 +3740,Turkey,2012,354134764.7,1.0142,346170000.0,12.854416666666667,898.740478515625,76381757.0,11766.428448557748 +3741,Turkey,2013,346780965.1,0.985,337450000.0,14.026625,975.0553588867188,77388641.0,12599.463516702905 +3742,Turkmenistan,1990,36940966.51,0.1665,80170000.0,15.926416666666663,13.679877281188965,3658462.0,3739.242687552574 +3743,Turkmenistan,1991,35618528.73,0.1588,70940000.0,15.521083333333332,13.049882888793945,3745234.0,3484.397206901877 +3744,Turkmenistan,1992,32900415.23,0.1482,56940000.0,15.036833333333334,11.09683895111084,3830233.0,2897.1707337675907 +3745,Turkmenistan,1993,28191680.9,0.1273,54020000.0,14.735416666666671,11.263291358947756,3917964.0,2874.781738409989 +3746,Turkmenistan,1994,33442539.76,0.1484,58180000.0,15.356416666666666,9.314757347106934,4003901.0,2326.4204951888005 +3747,Turkmenistan,1995,33932206.47,0.1475,60150000.0,16.52091666666667,8.644091606140138,4078729.0,2119.3101101201223 +3748,Turkmenistan,1996,30603723.9,0.1301,57590000.0,15.327,9.223244667053224,4142668.0,2226.402083645907 +3749,Turkmenistan,1997,30555890.87,0.1278,58720000.0,16.334999999999997,8.171795845031737,4200612.0,1945.3822074097152 +3750,Turkmenistan,1998,32024953.84,0.1335,60250000.0,16.162583333333334,8.751993179321289,4262264.0,2053.3672197032583 +3751,Turkmenistan,1999,37605024.81,0.1571,67320000.0,16.3785,10.196072578430176,4324693.0,2357.6407801502155 +3752,Turkmenistan,2000,37465591.78,0.1526,70370000.0,16.398166666666665,10.753702163696287,4385485.0,2452.112403461941 +3753,Turkmenistan,2001,37999761.26,0.1512,73330000.0,16.57383333333333,11.220826148986816,4444584.0,2524.6066108744517 +3754,Turkmenistan,2002,39889610.73,0.1565,77140000.0,16.379583333333336,11.249683380126953,4502617.0,2498.476637059504 +3755,Turkmenistan,2003,44520132.21,0.1657,84570000.0,15.642416666666664,11.617416381835938,4556130.0,2549.843042633976 +3756,Turkmenistan,2004,46447085.16,0.1648,88890000.0,16.663833333333333,12.198333740234375,4608471.0,2646.9372900978165 +3757,Turkmenistan,2005,48231551.32,0.1649,89310000.0,16.57916666666667,13.788269996643065,4664155.0,2956.22036502712 +3758,Turkmenistan,2006,49372149.44,0.163,89940000.0,16.51,15.301299095153807,4719514.0,3242.134485702089 +3759,Turkmenistan,2007,55890131.33,0.18100000000000002,94590000.0,16.302083333333336,16.993515014648438,4774232.0,3559.4238014927714 +3760,Turkmenistan,2008,56665733.56,0.1778,94630000.0,15.858083333333331,19.49156188964844,4829332.0,4036.0782587837075 +3761,Turkmenistan,2009,50170747.96,0.1592,86390000.0,16.355416666666667,20.68054962158203,4884887.0,4233.57789475622 +3762,Turkmenistan,2010,57108890.86,0.1727,92370000.0,17.043833333333335,22.58315849304199,4940916.0,4570.642061723371 +3763,Turkmenistan,2011,62182264.98,0.18100000000000002,101670000.0,15.786166666666666,25.90288352966309,4997503.0,5183.165178622821 +3764,Turkmenistan,2012,64532904.04,0.1848,109000000.0,15.855833333333335,28.77810287475586,5054828.0,5693.191316253661 +3765,Turkmenistan,2013,66374122.36,0.1885,114450000.0,18.6135,31.71347045898437,5113040.0,6202.468679882099 +3766,Uganda,1990,755865.71,0.0034,40470000.0,23.267083333333332,4.9090118408203125,16548955.0,296.6357598301713 +3767,Uganda,1991,775880.94,0.0035,41020000.0,23.176000000000002,5.181663036346436,17125765.0,302.56534737843447 +3768,Uganda,1992,783176.87,0.0035,41340000.0,23.295416666666668,5.358790874481201,17735302.0,302.15391170002067 +3769,Uganda,1993,775816.81,0.0035,41780000.0,23.390916666666673,5.80497932434082,18404136.0,315.417106477632 +3770,Uganda,1994,703832.54,0.0031,41540000.0,23.41558333333333,6.1767096519470215,19090249.0,323.5531213839601 +3771,Uganda,1995,929962.33,0.004,42310000.0,23.571583333333333,6.888466835021973,19640269.0,350.7317967499311 +3772,Uganda,1996,1036012.21,0.0044,42510000.0,23.3005,7.513396263122559,20197441.0,371.9974358693538 +3773,Uganda,1997,1080100.32,0.0045,43490000.0,23.813666666666666,7.8965797424316415,20813102.0,379.40426863961176 +3774,Uganda,1998,1234062.63,0.0051,45940000.0,23.744249999999997,8.283927917480469,21461307.0,385.9936357781224 +3775,Uganda,1999,1279432.72,0.0053,45310000.0,23.28408333333333,8.951111793518066,22190623.0,403.3736138691584 +3776,Uganda,2000,1394391.33,0.0057,45460000.0,23.560916666666667,9.232346534729004,22916079.0,402.87636182127864 +3777,Uganda,2001,1468081.46,0.0058,44150000.0,23.405833333333334,9.710920333862305,23637605.0,410.8250532937793 +3778,Uganda,2002,1507868.45,0.0059,47390000.0,23.830499999999997,10.558944702148438,24391047.0,432.9024786081728 +3779,Uganda,2003,1547359.05,0.0058,50820000.0,23.843833333333333,11.242452621459961,25200119.0,446.12696556948646 +3780,Uganda,2004,1680607.97,0.006,51580000.0,23.811916666666665,12.007752418518065,26046980.0,461.0036333777685 +3781,Uganda,2005,2104560.9,0.0072,54780000.0,24.029833333333332,12.768150329589846,26916576.0,474.36012402134077 +3782,Uganda,2006,2468619.13,0.0082,56910000.0,23.770083333333332,14.145163536071776,27810686.0,508.6233232819851 +3783,Uganda,2007,2815316.72,0.0091,57820000.0,23.4905,15.33511447906494,28710199.0,534.1347330634992 +3784,Uganda,2008,3052256.33,0.0096,66190000.0,23.49625,16.670610427856445,29598626.0,563.2224424152812 +3785,Uganda,2009,3246679.36,0.0103,67590000.0,24.04716666666667,17.80446434020996,30520924.0,583.3527300880526 +3786,Uganda,2010,3765155.98,0.0114,69810000.0,23.92233333333333,18.80820846557617,31507228.0,596.9490069255273 +3787,Uganda,2011,4067846.32,0.0118,71500000.0,23.72675,20.574613571166992,32560222.0,631.8941428337617 +3788,Uganda,2012,3860678.11,0.0111,73750000.0,23.805666666666667,21.364154815673828,33640833.0,635.0661654446496 +3789,Uganda,2013,4645220.43,0.0132,75470000.0,24.024624999999997,22.13046646118164,34758809.0,636.6865579652525 +3790,Ukraine,1990,705832231.0,3.182,874780000.0,9.400833333333333,205.77163696289062,51622275.0,3986.1016772873854 +3791,Ukraine,1991,632538757.8,2.8201,829180000.0,8.300166666666668,188.4649200439453,51730360.0,3643.216866148724 +3792,Ukraine,1992,589062246.2,2.654,739380000.0,8.37975,170.1857147216797,51869138.0,3281.059244163257 +3793,Ukraine,1993,510201372.9,2.3031,650270000.0,7.458416666666666,145.97491455078125,51887206.0,2813.312294186379 +3794,Ukraine,1994,419267635.3,1.8606,555010000.0,8.80725,112.4969711303711,51637842.0,2178.576152163196 +3795,Ukraine,1995,389865789.2,1.6945,524370000.0,8.709083333333334,98.7723388671875,51247738.0,1927.3502152853562 +3796,Ukraine,1996,351409867.3,1.4939,460140000.0,7.552583333333332,88.89510345458984,50813392.0,1749.4424197186013 +3797,Ukraine,1997,340243797.4,1.4227,435580000.0,7.604583333333333,86.2282485961914,50371404.0,1711.849218977327 +3798,Ukraine,1998,321067218.8,1.3386,407750000.0,8.455583333333335,84.58991241455078,49941236.0,1693.7889245382469 +3799,Ukraine,1999,292311281.4,1.2215,404090000.0,9.50725,84.42073059082031,49491190.0,1705.772897980839 +3800,Ukraine,2000,279537842.8,1.1382,385470000.0,9.316333333333334,89.40155792236328,49013834.0,1824.006624790121 +3801,Ukraine,2001,301300733.2,1.1986,408800000.0,8.938666666666668,97.62979888916016,48520343.0,2012.141564810458 +3802,Ukraine,2002,294199978.3,1.1540000000000001,417040000.0,9.377166666666666,102.75467681884766,48074073.0,2137.423987745903 +3803,Ukraine,2003,305183445.0,1.1359,442500000.0,8.219666666666667,112.53276824951172,47692440.0,2359.5514980888315 +3804,Ukraine,2004,306255428.8,1.0865,407320000.0,8.850499999999998,126.15908813476562,47342447.0,2664.819757516244 +3805,Ukraine,2005,313533699.8,1.0717,388140000.0,8.917333333333334,129.94052124023438,47002642.0,2764.536539036133 +3806,Ukraine,2006,332764711.4,1.0987,395810000.0,8.454666666666666,139.60797119140622,46685257.0,2990.408110881905 +3807,Ukraine,2007,336601096.6,1.09,405230000.0,10.055416666666668,150.2090606689453,46406827.0,3236.787998217273 +3808,Ukraine,2008,325732775.8,1.0219,391780000.0,9.55975,153.669921875,46155640.0,3329.3855718391083 +3809,Ukraine,2009,277169902.1,0.8793,331880000.0,9.4285,130.990478515625,45950761.0,2850.6704930441742 +3810,Ukraine,2010,293518810.8,0.8877,348010000.0,9.453,136.01315307617188,45768171.0,2971.7847601157555 +3811,Ukraine,2011,307071163.9,0.8938,386440000.0,8.790833333333333,143.44700622558594,45603516.0,3145.5251438416713 +3812,Ukraine,2012,303705068.4,0.8697,377120000.0,9.290666666666665,143.78936767578125,45491526.0,3160.7945549195524 +3813,Ukraine,2013,296074061.2,0.8409,360890000.0,10.913499999999999,143.7509307861328,45388586.0,3167.1163051021863 +3814,United Arab Emirates,1990,51702598.13,0.2331,68570000.0,28.26008333333333,125.7606430053711,1826250.0,68862.7750884989 +3815,United Arab Emirates,1991,56672348.65,0.2527,75130000.0,27.697083333333335,126.84229278564452,1936688.0,65494.43833268164 +3816,United Arab Emirates,1992,57757684.96,0.2602,73690000.0,27.28408333333333,131.08509826660156,2056289.0,63748.382774309226 +3817,United Arab Emirates,1993,65565378.9,0.29600000000000004,76580000.0,28.070416666666674,132.73832702636722,2184199.0,60772.084881628085 +3818,United Arab Emirates,1994,72604565.1,0.3222,83220000.0,28.39741666666667,141.89215087890625,2319182.0,61181.98178448533 +3819,United Arab Emirates,1995,70012743.45,0.3043,89690000.0,27.986333333333334,151.38172912597656,2457980.0,61587.86040813048 +3820,United Arab Emirates,1996,40429014.45,0.1719,94890000.0,28.319250000000007,160.15945434570312,2599579.0,61609.76617587045 +3821,United Arab Emirates,1997,41073714.6,0.1717,102780000.0,28.12066666666667,173.27716064453125,2746332.0,63094.03256581188 +3822,United Arab Emirates,1998,80682450.25,0.3364,107260000.0,29.29258333333333,173.78311157226562,2898504.0,59956.139985408205 +3823,United Arab Emirates,1999,77542664.08,0.324,111480000.0,29.298416666666668,178.82667541503906,3056080.0,58515.050461715364 +3824,United Arab Emirates,2000,111788067.0,0.4552,112230000.0,28.877750000000002,198.2342071533203,3219297.0,61576.86201469461 +3825,United Arab Emirates,2001,100602944.1,0.4002,127130000.0,29.009583333333328,201.00767517089844,3387327.0,59341.08964705753 +3826,United Arab Emirates,2002,83745587.73,0.3285,131490000.0,29.058000000000007,205.89910888671875,3558098.0,57867.745319751935 +3827,United Arab Emirates,2003,105677097.9,0.3933,139190000.0,29.228333333333335,224.01934814453125,3731700.0,60031.446296468435 +3828,United Arab Emirates,2004,111867460.8,0.3969,147890000.0,29.104,245.45001220703125,3907949.0,62807.885212174275 +3829,United Arab Emirates,2005,114554436.6,0.3916,156550000.0,28.992916666666673,257.366943359375,4086602.0,62978.22576296273 +3830,United Arab Emirates,2006,121883794.7,0.4024,162620000.0,28.984083333333334,282.6849670410156,4265914.0,66265.97888307537 +3831,United Arab Emirates,2007,133336992.3,0.4318,174010000.0,29.15608333333333,291.6867370605469,4444011.0,65635.91698142666 +3832,United Arab Emirates,2008,154155409.4,0.4836,199620000.0,28.685000000000006,300.9969177246094,4621399.0,65131.12538532366 +3833,United Arab Emirates,2009,165185136.2,0.524,201820000.0,29.127666666666673,285.21588134765625,4798491.0,59438.66130991103 +3834,United Arab Emirates,2010,158164662.1,0.4783,208350000.0,29.671333333333333,289.787353515625,4975593.0,58241.772089402206 +3835,United Arab Emirates,2011,162704863.6,0.4736,214470000.0,29.205416666666668,309.8703918457031,5148664.0,60184.62106785433 +3836,United Arab Emirates,2012,173923739.0,0.4981,226340000.0,29.42583333333333,323.7669372558594,5314317.0,60923.527380067724 +3837,United Arab Emirates,2013,168210994.8,0.4778,237450000.0,29.370625000000004,340.12799072265625,5473972.0,62135.50064243228 +3838,United States,1990,5121264461.0,23.0877,5669590000.0,9.521583333333334,9064.4130859375,250181000.0,36231.42079509435 +3839,United States,1991,5070839400.0,22.608,5627950000.0,9.493666666666666,9057.6982421875,253530000.0,35726.337089052584 +3840,United States,1992,5174059076.0,23.3117,5709170000.0,9.056333333333333,9379.7353515625,256922000.00000003,36508.10499514444 +3841,United States,1993,5284687882.0,23.8553,5819340000.0,8.867250000000002,9637.2890625,260282000.0,37026.33705942017 +3842,United States,1994,5377987137.0,23.8663,5906320000.0,9.270333333333335,10026.408203125,263455000.0,38057.38438490445 +3843,United States,1995,5439213314.0,23.6411,5970680000.0,9.331000000000001,10299.0244140625,266588000.00000003,38632.73821050647 +3844,United States,1996,5626186717.0,23.9178,6069300000.0,8.754333333333333,10689.962890625,269714000.0,39634.43829621377 +3845,United States,1997,5703890708.0,23.8504,6302580000.0,9.172916666666667,11169.625,272958000.0,40920.6727774969 +3846,United States,1998,5752324190.0,23.983,6345670000.0,10.141166666666667,11666.6630859375,276154000.0,42246.94585607125 +3847,United States,1999,5832250178.0,24.3716,6365600000.0,9.567,12213.2685546875,279328000.0,43723.75327460011 +3848,United States,2000,6000606067.0,24.4337,6495120000.0,9.649750000000001,12713.0576171875,282398000.0,45018.22823528318 +3849,United States,2001,5902454993.0,23.4802,6439610000.0,9.785583333333333,12837.134765625,285225000.0,45007.04624638443 +3850,United States,2002,5943651279.0,23.3137,6321650000.0,9.816166666666666,13066.4228515625,287955000.0,45376.61388606727 +3851,United States,2003,5991319569.0,22.3001,6353160000.0,9.75508333333333,13433.16796875,290626000.0,46221.494184106035 +3852,United States,2004,6106620735.0,21.6642,6500270000.0,9.667499999999999,13941.712890625,293262000.0,47540.12756724362 +3853,United States,2005,6132005704.0,20.9606,6477600000.0,9.892666666666669,14408.0927734375,295993000.0,48677.14024803796 +3854,United States,2006,6052236286.0,19.9831,6388860000.0,9.9745,14792.302734375,298818000.0,49502.716484197736 +3855,United States,2007,6130983563.0,19.8541,6601130000.0,9.837499999999999,15055.39453125,301696000.0,49902.53278548605 +3856,United States,2008,5932775281.0,18.6122,6267350000.0,8.951416666666669,15011.490234375,304543000.0,49291.85774874156 +3857,United States,2009,5495394958.0,17.4329,5865860000.0,9.137083333333335,14594.841796875,307240000.0,47503.06534590223 +3858,United States,2010,5701075808.0,17.2412,6111810000.0,9.505583333333334,14992.0517578125,309794000.0,48393.61562138873 +3859,United States,2011,5570706560.0,16.214000000000002,6235850000.0,9.549916666666665,15224.5537109375,312098000.0,48781.324170412816 +3860,United States,2012,5366730281.0,15.369000000000002,5936090000.0,10.261083333333334,15567.037109375,314374000.0,49517.57177557622 +3861,United States,2013,5519612557.0,15.6772,6056310000.0,11.297111111111112,15853.7939453125,316598000.0,50075.470929419964 +3862,Uruguay,1990,3967093.14,0.0179,3230000.0,17.70358333333333,21.388317108154297,3085115.0,6932.745491871226 +3863,Uruguay,1991,4527504.9,0.0202,4820000.0,17.973916666666664,22.14520835876465,3098710.0,7146.589502975318 +3864,Uruguay,1992,5142876.66,0.0232,6430000.0,17.528833333333328,23.90167617797852,3111431.0,7681.891765550486 +3865,Uruguay,1993,4439208.42,0.02,6950000.0,17.646416666666664,24.53687286376953,3123830.0,7854.740131111338 +3866,Uruguay,1994,4029883.55,0.0179,6860000.0,18.131916666666665,26.323486328125,3137031.0,8391.210137268328 +3867,Uruguay,1995,4558502.52,0.0198,7090000.0,17.664749999999998,25.94242858886719,3150038.0,8235.59226551146 +3868,Uruguay,1996,5404437.18,0.023,8350000.0,17.874166666666667,27.389484405517575,3164141.0,8656.214879652196 +3869,Uruguay,1997,5513135.86,0.0231,8060000.000000001,18.365500000000004,29.73064994812012,3180923.0,9346.548139681508 +3870,Uruguay,1998,5640085.63,0.0235,7550000.0,17.5215,31.074146270751953,3195923.0,9723.058493822271 +3871,Uruguay,1999,6681027.8,0.0279,8620000.0,17.607750000000003,30.4715518951416,3207949.0,9498.764442683349 +3872,Uruguay,2000,5267153.59,0.0214,6720000.0,17.756750000000004,29.883472442626953,3219793.0,9281.17815108827 +3873,Uruguay,2001,5030575.82,0.02,18040000.0,18.57425,28.73471260070801,3231191.0,8892.916760633465 +3874,Uruguay,2002,4557734.0,0.0179,18370000.0,17.993916666666667,26.512943267822266,3241594.0,8178.983323581629 +3875,Uruguay,2003,4527831.12,0.0169,19340000.0,17.60466666666667,26.72644805908203,3250653.0,8221.870516195371 +3876,Uruguay,2004,5563795.56,0.0197,21220000.0,18.060833333333335,28.06388092041016,3258465.0,8612.607752549178 +3877,Uruguay,2005,5725804.9,0.0196,21570000.0,18.194416666666665,30.15748405456543,3264911.0,9236.84720795312 +3878,Uruguay,2006,6596086.62,0.0218,18080000.0,18.240416666666665,31.393510818481445,3271307.0,9596.62630822526 +3879,Uruguay,2007,5946511.04,0.0193,17010000.0,17.516166666666667,33.447120666503906,3278878.0,10200.78230007457 +3880,Uruguay,2008,8200664.45,0.0257,19640000.0,18.2805,35.84733581542969,3286372.0,10907.875254362469 +3881,Uruguay,2009,7947333.75,0.0252,19420000.0,17.87133333333333,37.36851501464844,3293765.0,11345.22803376939 +3882,Uruguay,2010,6296711.38,0.019,16690000.000000002,17.92008333333333,40.28453063964844,3301076.0,12203.454461408472 +3883,Uruguay,2011,7653810.49,0.0223,27130000.0,17.824583333333333,42.36407470703125,3308535.0,12804.48135112104 +3884,Uruguay,2012,8593082.95,0.0246,27840000.0,18.508999999999997,43.86299133300781,3316328.0,13226.373064729367 +3885,Uruguay,2013,7489843.7,0.0213,27010000.0,16.754375,45.89715576171875,3324460.0,13805.898029069009 +3886,Uzbekistan,1990,124154260.4,0.5597,193420000.0,13.463083333333335,20.324092864990234,20530144.0,989.9634832074356 +3887,Uzbekistan,1991,119875250.4,0.5345,199130000.0,13.031083333333333,20.22410011291504,21058973.0,960.3554794868221 +3888,Uzbekistan,1992,111270202.0,0.5013,189450000.0,12.863333333333337,17.958999633789062,21609643.0,831.0641519523973 +3889,Uzbekistan,1993,115866394.0,0.523,221560000.0,11.956000000000001,17.545944213867188,22127596.0,792.9439878542247 +3890,Uzbekistan,1994,108284090.0,0.4805,199500000.0,12.63916666666667,16.633554458618164,22603815.0,735.873765495699 +3891,Uzbekistan,1995,103202290.0,0.4486,189440000.0,13.971333333333334,16.483854293823242,23067326.0,714.5975347911259 +3892,Uzbekistan,1996,105872056.0,0.4501,191820000.0,12.45225,16.76407814025879,23523857.0,712.6415595987847 +3893,Uzbekistan,1997,107003306.0,0.4474,186310000.0,13.847249999999997,17.63581085205078,23952217.0,736.2913776228222 +3894,Uzbekistan,1998,118945966.0,0.4959,197910000.0,13.245,18.39414978027344,24342332.0,755.6445200186013 +3895,Uzbekistan,1999,122204526.0,0.5107,204650000.0,13.908500000000002,19.185100555419922,24701545.0,776.6761372788593 +3896,Uzbekistan,2000,121455636.0,0.4946,217740000.0,13.938833333333335,19.914133071899414,25041821.0,795.2350219219047 +3897,Uzbekistan,2001,123040688.0,0.4895,222420000.0,13.998,20.750526428222656,25372377.0,817.8392756903563 +3898,Uzbekistan,2002,127943388.0,0.5019,227790000.0,13.871916666666664,21.580549240112305,25696313.0,839.8305718066364 +3899,Uzbekistan,2003,126437996.0,0.4706,225150000.0,13.157666666666666,22.48693084716797,25992144.0,865.1433620546258 +3900,Uzbekistan,2004,125719050.0,0.446,222070000.0,14.442000000000002,24.218425750732425,26268097.0,921.971079623028 +3901,Uzbekistan,2005,116894940.0,0.3996,220970000.0,14.043999999999999,25.913715362548828,26539888.0,976.4063571989765 +3902,Uzbekistan,2006,119698362.0,0.3952,240320000.0,13.786250000000003,27.80541610717773,26810463.0,1037.1106275627442 +3903,Uzbekistan,2007,119417270.0,0.3867,242510000.0,13.928333333333333,30.56403350830078,27079266.0,1128.68766488356 +3904,Uzbekistan,2008,123335630.0,0.3869,246330000.0,13.309,33.314796447753906,27345026.0,1218.3128459177146 +3905,Uzbekistan,2009,106533902.0,0.33799999999999997,220040000.0,13.700333333333335,36.01329803466797,27606007.0,1304.5457111804676 +3906,Uzbekistan,2010,103581488.0,0.3133,215040000.0,14.325916666666666,39.07442474365234,27865738.0,1402.2390056079746 +3907,Uzbekistan,2011,113045364.0,0.32899999999999996,210090000.0,13.141083333333333,42.317604064941406,28128600.0,1504.4333548396082 +3908,Uzbekistan,2012,115121942.0,0.3297,213650000.0,13.144166666666669,45.78764724731445,28394180.0,1612.5715638667664 +3909,Uzbekistan,2013,102654856.6,0.2916,203070000.0,16.18825,49.450660705566406,28661637.0,1725.325762292168 +3910,Venezuela,1990,121821980.8,0.5492,303150000.0,25.59475,175.37109375,19839480.0,8839.500518662788 +3911,Venezuela,1991,115052504.1,0.513,304970000.0,25.506500000000003,192.4344940185547,20290936.0,9483.766250041628 +3912,Venezuela,1992,105578089.7,0.4757,308980000.0,25.593333333333334,204.0969390869141,20741454.0,9840.049742265614 +3913,Venezuela,1993,123967868.7,0.5596,315260000.0,25.468,204.65899658203125,21190097.0,9658.237835439417 +3914,Venezuela,1994,129530843.3,0.5748,318020000.0,25.5065,199.8505096435547,21636158.0,9236.876049969442 +3915,Venezuela,1995,132874713.9,0.5775,326540000.0,25.921583333333334,207.74794006347656,22079084.0,9409.264445186069 +3916,Venezuela,1996,122339598.1,0.5201,331000000.0,25.598499999999998,207.3369445800781,22519111.0,9207.154961848988 +3917,Venezuela,1997,133483526.7,0.5582,337570000.0,25.75266666666667,220.54623413085938,22956507.0,9607.13379134114 +3918,Venezuela,1998,166802153.5,0.6954,339200000.0,26.158999999999992,221.19476318359372,23390849.0,9456.465782135305 +3919,Venezuela,1999,172562478.6,0.7211,325600000.0,25.34483333333333,207.9884185791016,23821700.0,8731.04852210806 +3920,Venezuela,2000,151871027.6,0.6184,336290000.0,25.388500000000004,215.65684509277344,24248583.0,8893.585455808838 +3921,Venezuela,2001,171974192.9,0.6841,370460000.0,25.77133333333333,222.97674560546875,24673364.0,9037.14408807282 +3922,Venezuela,2002,192746410.0,0.7559999999999999,342080000.0,25.772166666666664,203.230712890625,25100956.0,8096.532773119278 +3923,Venezuela,2003,191456697.7,0.7126,363150000.0,26.008333333333336,187.4695587158203,25531016.0,7342.816232453118 +3924,Venezuela,2004,151267984.7,0.5366,345140000.0,25.817833333333336,221.7513732910156,25958905.0,8542.400894452814 +3925,Venezuela,2005,164537975.5,0.5624,366870000.0,26.019416666666668,244.6314849853516,26383980.0,9271.970528531008 +3926,Venezuela,2006,168543215.0,0.5565,249270000.0,25.84991666666667,268.7818603515625,26808481.0,10026.001113288086 +3927,Venezuela,2007,159988917.9,0.5181,259240000.0,25.85408333333333,292.3099060058594,27231194.0,10734.377126682708 +3928,Venezuela,2008,178212809.2,0.5591,282550000.0,25.64825,307.7375793457031,27650239.0,11129.653503020469 +3929,Venezuela,2009,178845105.8,0.5673,278240000.0,26.08491666666667,297.8829040527344,28067042.0,10613.263202183343 +3930,Venezuela,2010,188179332.3,0.5691,307750000.0,26.15025,293.44805908203125,28480787.0,10303.369042506842 +3931,Venezuela,2011,175855839.3,0.5118,319150000.0,25.677333333333333,305.70367431640625,28890744.0,10581.37077800441 +3932,Venezuela,2012,197729759.1,0.5662,346310000.0,25.68858333333333,322.9024353027344,29299421.0,11020.778714457681 +3933,Venezuela,2013,182868919.2,0.5194,367760000.0,25.912875,327.23931884765625,29706761.0,11015.651246787094 +3934,Vietnam,1990,21163212.03,0.0954,25640000.0,24.274083333333333,29.45847320556641,67258111.0,437.99138524075414 +3935,Vietnam,1991,21160391.45,0.0943,28510000.0,24.2765,31.214445114135746,68638265.0,454.7673970799778 +3936,Vietnam,1992,21092910.5,0.095,31010000.0,23.851249999999997,33.91326141357422,69979722.0,484.6155492525994 +3937,Vietnam,1993,22600694.84,0.102,37670000.0,23.935666666666673,36.6509895324707,71285149.0,514.1462148374089 +3938,Vietnam,1994,25773128.71,0.1144,43290000.0,24.15941666666667,39.89056396484375,72559537.0,549.7632098292434 +3939,Vietnam,1995,28523488.01,0.124,49500000.0,23.919,43.6963119506836,73783390.0,592.2242384184786 +3940,Vietnam,1996,34022947.71,0.1446,56030000.0,23.749666666666666,47.77755355834961,74946552.0,637.4883471403675 +3941,Vietnam,1997,44309259.15,0.1853,63800000.0,24.22266666666667,51.67242050170898,76052857.0,679.4277367082867 +3942,Vietnam,1998,46554720.31,0.1941,74980000.0,24.89983333333333,54.65105438232422,77107502.0,708.764425831409 +3943,Vietnam,1999,46658003.76,0.195,78260000.0,24.066416666666665,57.25987243652344,78141672.0,732.7699928985835 +3944,Vietnam,2000,52332095.73,0.2131,86210000.0,23.981916666666667,61.14628219604492,79177981.0,772.2637205922807 +3945,Vietnam,2001,59529184.78,0.2368,118010000.0,24.18491666666667,64.93300628662111,80209475.0,809.5428412493798 +3946,Vietnam,2002,68661255.31,0.2693,135420000.0,24.39375,69.03730773925781,81257964.0,849.6066642681056 +3947,Vietnam,2003,76276942.8,0.2839,141590000.0,24.49775,73.80023193359375,82296588.0,896.7593156303607 +3948,Vietnam,2004,87800153.09,0.3115,159720000.0,24.05691666666667,79.36212158203125,83352452.0,952.1270181953523 +3949,Vietnam,2005,94859056.17,0.3243,169510000.0,24.298,85.3517837524414,84424930.0,1010.9784367300205 +3950,Vietnam,2006,99134033.04,0.3273,184610000.0,24.523083333333336,91.30758666992188,85470535.0,1068.293145350288 +3951,Vietnam,2007,100577241.9,0.3257,200300000.0,24.302166666666665,97.81736755371094,86518851.0,1130.5902288705956 +3952,Vietnam,2008,113240815.2,0.3553,209210000.0,23.783999999999995,103.35556030273438,87558363.0,1180.4190572034152 +3953,Vietnam,2009,122836528.3,0.3897,231320000.0,24.46558333333333,108.9345932006836,88576758.0,1229.8326972035213 +3954,Vietnam,2010,136113090.3,0.4116,250600000.0,24.833333333333332,115.93172454833984,89571130.0,1294.2978898261065 +3955,Vietnam,2011,145047830.5,0.4222,235370000.0,23.692583333333335,123.16622161865234,90549390.0,1360.2103958806606 +3956,Vietnam,2012,135375322.8,0.3877,236850000.0,24.704333333333334,129.62919616699222,91519289.0,1416.4139339739866 +3957,Vietnam,2013,140438875.8,0.3989,248860000.0,25.232125000000003,136.65754699707028,92477857.0,1477.7326316836075 +3958,Yemen,1990,9521369.11,0.04,12380000.0,26.640666666666664,11.72646713256836,12416080.0,944.4580844009025 +3959,Yemen,1991,9128575.86,0.04,13970000.0,26.594416666666664,12.464471817016602,12882420.0,967.5567026239327 +3960,Yemen,1992,9874232.47,0.04,14800000.0,26.266500000000004,13.487504959106445,13367997.0,1008.9398553206172 +3961,Yemen,1993,8626008.67,0.04,13930000.0,26.402000000000005,14.027270317077635,13885756.0,1010.1913296674403 +3962,Yemen,1994,8982616.86,0.04,14340000.0,26.994250000000005,14.970176696777346,14394750.0,1039.9747614079677 +3963,Yemen,1995,10352159.38,0.04,15780000.0,26.577666666666673,15.818891525268555,14831575.0,1066.5685556165517 +3964,Yemen,1996,10526872.95,0.04,16510000.000000002,26.546166666666668,16.552091598510742,15283816.0,1082.9816060668843 +3965,Yemen,1997,13221285.32,0.06,17860000.0,26.94,17.417951583862305,15741184.0,1106.5210586358883 +3966,Yemen,1998,12063546.75,0.05,19280000.0,27.368666666666666,18.464195251464844,16224216.0,1138.0639441354112 +3967,Yemen,1999,13724659.2,0.06,21070000.0,27.353583333333333,19.16131591796875,16728304.0,1145.4428325769757 +3968,Yemen,2000,14474213.9,0.06,22200000.0,27.43616666666667,20.345853805541992,17235921.0,1180.4332246325564 +3969,Yemen,2001,16016670.49,0.06,23820000.0,27.06891666666667,21.11973762512207,17762475.0,1189.0087178235055 +3970,Yemen,2002,15553532.39,0.06,24060000.0,27.12516666666667,21.95084953308105,18309989.0,1198.8455882240594 +3971,Yemen,2003,17082499.37,0.06,26940000.0,27.35775,22.77343559265137,18855729.0,1207.772746025962 +3972,Yemen,2004,18646238.4,0.07,28190000.0,27.334083333333336,23.67815399169922,19411357.0,1219.8093101733805 +3973,Yemen,2005,19794843.14,0.07,29290000.0,27.294666666666668,25.002178192138672,20002597.0,1249.946604040399 +3974,Yemen,2006,21085054.93,0.07,29680000.0,27.175,25.794849395751953,20614589.0,1251.2909859979238 +3975,Yemen,2007,20719015.15,0.07,31410000.0,27.310750000000002,26.65599250793457,21242156.0,1254.8628542194385 +3976,Yemen,2008,22024975.12,0.07,33100000.0,26.807500000000005,27.628286361694336,21885623.0,1262.3943289937113 +3977,Yemen,2009,24247558.66,0.08,37000000.0,27.34241666666667,28.696460723876953,22544016.0,1272.9081066956728 +3978,Yemen,2010,23151863.57,0.07,35620000.0,27.302750000000003,30.9067497253418,23209521.0,1331.6409987669197 +3979,Yemen,2011,19513013.74,0.06,32390000.0,27.288250000000005,26.97698974609375,23898794.0,1128.8013004377435 +3980,Yemen,2012,18238251.18,0.05,31450000.0,27.445000000000004,27.622547149658203,24617071.0,1122.0891043316324 +3981,Yemen,2013,24980412.88,0.07,38300000.0,28.12975,28.95492553710937,25338458.0,1142.7264254639874 +3982,Zambia,1990,2405799.25,0.01,485470000.0,22.219333333333335,8.387543678283691,7603379.0,1103.133709142171 +3983,Zambia,1991,2382068.39,0.01,484930000.0,21.62891666666667,8.384513854980469,7817776.0,1072.4934885548612 +3984,Zambia,1992,2424107.58,0.01,484270000.0,22.414666666666662,8.239383697509767,8035054.0,1025.429785227301 +3985,Zambia,1993,2467201.01,0.01,483030000.0,21.711583333333333,8.799437522888184,8252521.0,1066.2726605467813 +3986,Zambia,1994,2393012.08,0.01,481410000.0,21.84658333333333,8.040447235107424,8471277.0,949.1422881234342 +3987,Zambia,1995,2141012.15,0.01,478950000.0,22.45141666666667,8.273432731628418,8690098.0,952.052868866199 +3988,Zambia,1996,1837006.52,0.01,444440000.0,22.018083333333337,8.787919998168945,8915420.0,985.6989348980693 +3989,Zambia,1997,2353691.09,0.01,405510000.0,21.949750000000005,9.123091697692873,9169242.0,994.9668356111521 +3990,Zambia,1998,2279237.77,0.01,411840000.0,22.300666666666668,9.087899208068848,9428653.0,963.8597589781753 +3991,Zambia,1999,1778193.48,0.01,371410000.0,21.451750000000004,9.510503768920898,9692594.0,981.2134676146446 +3992,Zambia,2000,1785056.59,0.01,412830000.0,21.55875,9.881158828735352,9983878.0,989.7114957469784 +3993,Zambia,2001,1884888.31,0.01,402050000.0,21.80241666666667,10.406527519226074,10284605.0,1011.8548567714633 +3994,Zambia,2002,1960060.55,0.01,453110000.0,22.29241666666667,10.875447273254396,10558408.0,1030.02718527778 +3995,Zambia,2003,2075580.56,0.01,474630000.0,22.156750000000002,11.630743980407715,10819896.0,1074.9404597241703 +3996,Zambia,2004,2104298.08,0.01,488470000.0,21.768083333333333,12.448663711547852,11088247.0,1122.6899717825418 +3997,Zambia,2005,2242067.41,0.01,565590000.0,22.8135,13.34939956665039,11372462.0,1173.8354954846534 +3998,Zambia,2006,2231684.13,0.01,482320000.0,21.78425,14.404495239257812,11678306.0,1233.4404698128144 +3999,Zambia,2007,1867607.01,0.01,452180000.0,21.8385,15.607621192932127,12000691.0,1300.5602088189862 +4000,Zambia,2008,2105555.91,0.01,470580000.0,21.535833333333333,16.820941925048828,12337728.0,1363.3743526400349 +4001,Zambia,2009,2406280.83,0.01,412120000.0,21.670250000000006,18.37189102172852,12678115.0,1449.1027271584549 +4002,Zambia,2010,2562578.27,0.01,459880000.0,22.2675,20.263866424560547,13041876.0,1553.7539556855584 +4003,Zambia,2011,2792593.48,0.01,431870000.0,21.77158333333333,21.3914737701416,13424596.0,1593.4538194029528 +4004,Zambia,2012,3480661.95,0.01,454490000.0,21.69775,23.01671600341797,13817479.0,1665.7681190192484 +4005,Zambia,2013,3756144.7,0.01,467690000.0,21.195999999999998,24.18121910095215,14222233.0,1700.2406795720578 +4006,Zimbabwe,1990,15569294.66,0.07,71050000.0,22.13025,,, +4007,Zimbabwe,1991,15865346.05,0.07,71350000.0,21.789666666666665,,, +4008,Zimbabwe,1992,16917418.67,0.08,72200000.0,22.840333333333334,,, +4009,Zimbabwe,1993,16259101.52,0.07,68700000.0,21.85225,,, +4010,Zimbabwe,1994,17684463.69,0.08,68380000.0,21.60875,,, +4011,Zimbabwe,1995,15029425.61,0.07,68280000.0,22.496333333333336,,, +4012,Zimbabwe,1996,14890560.82,0.06,69660000.0,21.55316666666667,,, +4013,Zimbabwe,1997,14294211.06,0.06,68810000.0,21.566666666666666,,, +4014,Zimbabwe,1998,14139636.04,0.06,70080000.0,22.142166666666668,,, +4015,Zimbabwe,1999,15727748.13,0.07,72400000.0,21.486333333333334,,, +4016,Zimbabwe,2000,13818181.23,0.06,69590000.0,21.149000000000004,,, +4017,Zimbabwe,2001,12509195.38,0.05,71440000.0,21.69366666666667,,, +4018,Zimbabwe,2002,11897006.1,0.05,67450000.0,22.08108333333334,,, +4019,Zimbabwe,2003,10608340.13,0.04,64680000.00000001,21.891250000000003,,, +4020,Zimbabwe,2004,9428548.18,0.03,64870000.00000001,21.330916666666667,,, +4021,Zimbabwe,2005,10699642.16,0.04,65050000.0,22.50991666666667,,, +4022,Zimbabwe,2006,10364869.23,0.03,63960000.0,21.618750000000006,,, +4023,Zimbabwe,2007,9834014.17,0.03,64450000.0,21.604250000000004,,, +4024,Zimbabwe,2008,7718874.78,0.02,62880000.0,21.545583333333337,,, +4025,Zimbabwe,2009,5519508.33,0.02,63540000.0,21.37725,,, +4026,Zimbabwe,2010,7681010.98,0.02,66540000.00000001,21.986250000000002,,, +4027,Zimbabwe,2011,9439850.98,0.03,67420000.0,21.602416666666667,,, +4028,Zimbabwe,2012,7614600.24,0.02,67630000.0,21.521333333333335,,, +4029,Zimbabwe,2013,11536239.29,0.03,67550000.0,20.710750000000004,,, +4030,Canada,1981,,,,,808.6881713867188,24900000.0,32477.436601876256 +4031,United States,1981,,,,,6698.57080078125,229465714.0,29192.03345899968 +4032,Cuba,1981,,,,,35.88264083862305,9705971.0,3696.9655935117717 +4033,Mexico,1981,,,,,589.5489501953125,69930971.0,8430.441359026925 +4034,Antigua Barbuda,1981,,,,,0.40892738103866577,68264.0,5990.38118244852 +4035,Bahamas,1981,,,,,5.104179859161377,213447.0,23913.101890218077 +4036,Barbados,1981,,,,,3.2537198066711426,252361.0,12893.116633200625 +4037,Belize,1981,,,,,0.32853081822395325,149207.0,2201.8458800455287 +4038,Costa Rica,1981,,,,,11.735068321228027,2357285.0,4978.213631880756 +4039,Dominica,1981,,,,,0.2181016355752945,73519.0,2966.602314711769 +4040,Dominican Rep,1981,,,,,15.866055488586426,5831714.0,2720.6504791878383 +4041,El Salvador,1981,,,,,10.558793067932127,4519721.0,2336.1603665208827 +4042,Grenada,1981,,,,,0.2984691560268402,92354.0,3231.7945733464726 +4043,Guatemala,1981,,,,,18.34723663330078,6828684.0,2686.78952391131 +4044,Haiti,1981,,,,,6.973071575164795,5595388.0,1246.2177020011472 +4045,Honduras,1981,,,,,5.776612758636475,3543061.0,1630.401722870838 +4046,Jamaica,1981,,,,,8.173055648803711,2258368.0,3619.009678140901 +4047,Nicaragua,1981,,,,,5.6783080101013175,2898649.0,1958.9498452904504 +4048,Panama,1981,,,,,9.56881046295166,1998895.0,4787.050076643175 +4049,Puerto Rico,1981,,,,,48.95873641967773,3238968.0,15115.535695220742 +4050,St Kitts Nevis,1981,,,,,0.2312019765377045,43484.0,5316.94362380886 +4051,St Lucia,1981,,,,,0.4470874965190888,123995.0,3605.6897174812593 +4052,St Vincent Grenadines,1981,,,,,0.2440682649612427,99199.0,2460.3903765284194 +4053,Trinidad and Tobago,1981,,,,,10.639623641967773,1107842.0,9603.917925090196 +4054,Argentina,1981,,,,,210.56954956054688,28848055.0,7299.2633146514345 +4055,Brazil,1981,,,,,963.4417114257812,123961522.0,7772.102954865149 +4056,Bolivia,1981,,,,,9.228102684020996,5545224.0,1664.1532756875101 +4057,Chile,1981,,,,,62.915245056152344,11175845.0,5629.573876172437 +4058,Colombia,1981,,,,,108.04165649414062,27214894.0,3969.945886768496 +4059,Ecuador,1981,,,,,31.06316566467285,8141078.0,3815.6084077161354 +4060,Guyana,1981,,,,,1.5934430360794067,761931.0,2091.3219649540533 +4061,Paraguay,1981,,,,,10.959839820861816,3250841.0,3371.385995458349 +4062,Peru,1981,,,,,68.3043212890625,17754798.0,3847.090870257296 +4063,Suriname,1981,,,,,2.6348061561584477,361170.0,7295.196600377792 +4064,Uruguay,1981,,,,,21.777746200561523,2948127.0,7386.976951997496 +4065,Venezuela,1981,,,,,161.10147094726562,15166255.0,10622.363328802372 +4066,Austria,1981,,,,,208.03228759765625,7564629.0,27500.659661915506 +4067,Belgium,1981,,,,,269.67010498046875,9852400.0,27371.00655479566 +4068,Denmark,1981,,,,,185.125,5121572.0,36146.128571462046 +4069,Finland,1981,,,,,124.23362731933594,4799964.0,25882.19980802688 +4070,France,1981,,,,,1505.784912109375,55454695.0,27153.425189866703 +4071,Germany,1981,,,,,2051.343505859375,78401830.0,26164.485010864857 +4072,Greece,1981,,,,,181.71969604492188,9729350.0,18677.47547831272 +4073,Ireland,1981,,,,,60.326698303222656,3443400.0,17519.515102289206 +4074,Italy,1981,,,,,1391.465576171875,56502489.0,24626.62443369309 +4075,Luxembourg,1981,,,,,15.440834999084473,365400.0,42257.34810915291 +4076,Netherlands,1981,,,,,422.2324523925781,14257723.0,29614.297626106083 +4077,Portugal,1981,,,,,122.9316864013672,9850079.0,12480.27415834606 +4078,Spain,1981,,,,,653.00146484375,37750800.0,17297.685475374034 +4079,Sweden,1981,,,,,259.58401489257807,8320485.0,31198.183145883697 +4080,UK,1981,,,,,1221.824462890625,56382597.0,21670.240959114122 +4081,Bulgaria,1981,,,,,29.96777153015137,8869441.0,3378.766658479533 +4082,Croatia,1981,,,,,37.54268264770508,4390829.0,8550.249314583893 +4083,Cyprus,1981,,,,,6.630361557006836,638047.0,10391.650704425905 +4084,Czech Republic,1981,,,,,114.94984436035156,10297904.0,11162.450568615865 +4085,Estonia,1981,,,,,13.106183052062988,1493318.0,8776.551981602704 +4086,Hungary,1981,,,,,86.56082916259766,10711848.0,8080.8492766698755 +4087,Latvia,1981,,,,,19.75013732910156,2536976.0,7784.912955069958 +4088,Lithuania,1981,,,,,26.10877799987793,3462341.0,7540.787576924956 +4089,Malta,1981,,,,,3.000856876373291,363652.0,8252.00157395887 +4090,Poland,1981,,,,,210.0561370849609,35901961.0,5850.82628452972 +4091,Romania,1981,,,,,116.3373794555664,22256551.0,5227.107266330997 +4092,Slovakia,1981,,,,,42.01586151123047,5013692.0,8380.223897126203 +4093,Slovenia,1981,,,,,18.85478973388672,1839256.0,10251.313429934016 +4094,Iceland,1981,,,,,6.302258968353272,230803.0,27305.79311513833 +4095,Norway,1981,,,,,201.47833251953125,4099702.0,49144.62868753174 +4096,Switzerland,1981,,,,,352.4323425292969,6426096.0,54843.92740620384 +4097,Albania,1981,,,,,5.7557311058044425,2724272.0,2112.759337468668 +4098,Bosnia Herzegovina,1981,,,,,2.274451494216919,4132963.0,550.3198296759297 +4099,Macedonia,1981,,,,,6.367001533508301,1808103.0,3521.3710355595344 +4100,Serbia,1981,,,,,31.261722564697266,7619061.0,4103.093880557888 +4101,Russia,1981,,,,,1073.1678466796877,139899972.0,7670.965414343954 +4102,Ukraine,1981,,,,,167.92446899414062,50235677.0,3342.733272891707 +4103,Armenia,1981,,,,,5.135944366455078,3198217.0,1605.8773893250766 +4104,Azerbaijan,1981,,,,,21.334428787231445,6321218.0,3375.050312650417 +4105,Belarus,1981,,,,,24.62818145751953,9712761.0,2535.651959058761 +4106,Georgia,1981,,,,,18.4719352722168,5073148.0,3641.1189407872193 +4107,Kazakhstan,1981,,,,,74.3788070678711,15203036.0,4892.365384642324 +4108,Kyrgyzstan,1981,,,,,3.1672911643981934,3707318.0,854.3349031289447 +4109,Moldova,1981,,,,,7.800861835479736,4026204.0,1937.5227473520306 +4110,Tajikistan,1981,,,,,6.469012260437013,4072967.0,1588.280057372675 +4111,Turkmenistan,1981,,,,,6.064900875091553,2946676.0,2058.2177596354513 +4112,Uzbekistan,1981,,,,,13.124384880065918,16400495.0,800.2432170532608 +4113,China,1981,,,,,359.0184631347656,1000228550.0,358.9364282140973 +4114,Hong Kong,1981,,,,,59.34343719482422,5183400.0,11448.74738488718 +4115,Japan,1981,,,,,3146.37841796875,117648092.0,26743.981687087202 +4116,Korea,1981,,,,,151.180419921875,38723000.0,3904.150502850373 +4117,Taiwan,1981,,,,,75.81920623779297,18177430.0,4171.063029140696 +4118,Macau,1981,,,,,3.555728912353516,260929.0,13627.189436028635 +4119,Mongolia,1981,,,,,2.4972941875457764,1694243.0,1473.9881985912152 +4120,Cambodia,1981,,,,,2.73525333404541,7079196.0,386.3790936210002 +4121,Indonesia,1981,,,,,195.92779541015625,153464781.0,1276.6955006449084 +4122,Malaysia,1981,,,,,48.9497299194336,13875214.0,3527.854051075075 +4123,Myanmar,1981,,,,,6.2761478424072275,33981811.0,184.69138806072542 +4124,Philippines,1981,,,,,82.7100830078125,49486886.0,1671.3535583510445 +4125,Thailand,1981,,,,,70.44275665283203,48014098.0,1467.12652298148 +4126,Vietnam,1981,,,,,23.22453308105469,54902677.0,423.0127627666441 +4127,Brunei,1981,,,,,9.235101699829102,192831.0,47892.20457202992 +4128,Laos,1981,,,,,0.27060747146606445,3336978.0,81.09357372630699 +4129,Singapore,1981,,,,,35.55937194824219,2535368.0,14025.329635872264 +4130,Bangladesh,1981,,,,,30.49237823486328,85726506.0,355.6937014890504 +4131,India,1981,,,,,312.2223815917969,699153765.0,446.5718375868245 +4132,Pakistan,1981,,,,,46.10425186157226,90975079.0,506.7789153727722 +4133,Afghanistan,1981,,,,,8.482517242431639,13676225.0,620.2382047993243 +4134,Bhutan,1981,,,,,0.18813617527484894,461945.0,407.26964308488886 +4135,Nepal,1981,,,,,4.649407863616943,14945796.0,311.08465976766604 +4136,Sri Lanka,1981,,,,,14.451722145080565,15315491.0,943.6016217227752 +4137,Australia,1981,,,,,473.0933532714844,14923260.0,31701.743001963667 +4138,New Zealand,1981,,,,,73.28593444824219,3185450.0,23006.462022082334 +4139,Fiji,1981,,,,,1.977842926979065,646772.0,3058.021879393457 +4140,Maldives,1981,,,,,0.4177696406841278,157246.0,2656.790256566957 +4141,Papua New Guinea,1981,,,,,5.65851354598999,2917159.0,1939.734360036594 +4142,Samoa,1981,,,,,0.3582264184951782,160919.0,2226.1287883666832 +4143,Solomon Islands,1981,,,,,0.3499540984630585,238737.0,1465.856144891904 +4144,Tonga,1981,,,,,0.20850636065006256,92329.0,2258.2976166758285 +4145,Iran,1981,,,,,177.07107543945312,41450841.0,4271.8331200916555 +4146,Iraq,1981,,,,,45.35108947753906,13703103.0,3309.5489012626604 +4147,Saudi Arabia,1981,,,,,362.6190185546875,10654927.0,34032.98948502298 +4148,Turkey,1981,,,,,229.9370574951172,46248621.0,4971.760293028352 +4149,Bahrain,1981,,,,,7.2188658714294425,363520.0,19858.23578188117 +4150,Israel,1981,,,,,71.71562194824219,3783423.0,18955.22175242953 +4151,Jordan,1981,,,,,7.473848819732666,2282741.0,3274.067806962185 +4152,Kuwait,1981,,,,,42.49433898925781,1432188.0,29670.922385369668 +4153,Lebanon,1981,,,,,10.633990287780762,2954746.0,3598.9524269702915 +4154,Oman,1981,,,,,13.386470794677734,1249511.0,10713.367705188455 +4155,Syria,1981,,,,,19.921350479125977,9035810.0,2204.711086125757 +4156,United Arab Emirates,1981,,,,,124.02149963378906,1069722.0,115938.06580942436 +4157,Yemen,1981,,,,,8.650056838989258,9390138.0,921.1852732078332 +4158,Egypt,1981,,,,,50.24959945678711,44196104.0,1136.968983890234 +4159,Morocco,1981,,,,,26.740163803100586,19846221.0,1347.3680356124514 +4160,Algeria,1981,,,,,72.11661529541016,19407036.0,3716.003582175566 +4161,Libya,1981,,,,,58.543418884277344,3197888.0,18306.90095596761 +4162,Tunisia,1981,,,,,13.633350372314453,6605707.0,2063.8745212759895 +4163,Nigeria,1981,,,,,131.39579772949222,77157630.0,1702.952744005903 +4164,South Africa,1981,,,,,202.2542724609375,30168679.0,6704.1143054668555 +4165,Benin,1981,,,,,2.4262311458587646,3564850.0,680.5983830620544 +4166,Burkina Faso,1981,,,,,2.218540668487549,6474370.0,342.6651038614644 +4167,Cabo Verde,1981,,,,,0.2094305455684662,300202.0,697.632079627938 +4168,Cote d'Ivoire,1981,,,,,17.122631072998047,8922474.0,1919.045219184505 +4169,Gambia,1981,,,,,0.34218162298202515,671374.0,509.6736289788183 +4170,Ghana,1981,,,,,9.394332885742188,11362599.0,826.7767687429774 +4171,Guinea,1981,,,,,2.551382541656494,4540361.0,561.9338509991813 +4172,Guinea Bissau,1981,,,,,0.4514690637588501,807056.0,559.402400525924 +4173,Liberia,1981,,,,,1.753079533576965,1913336.0,916.2423816710527 +4174,Mali,1981,,,,,3.2198033332824707,7007670.0,459.4684586007148 +4175,Niger,1981,,,,,3.117303371429444,6238998.0,499.6480799367853 +4176,Senegal,1981,,,,,6.557535171508789,5754459.0,1139.5571975591083 +4177,Sierra Leone,1981,,,,,1.6895303726196291,3402148.0,496.60695907986036 +4178,Togo,1981,,,,,1.8330546617507932,2719567.0,674.0244538012092 +4179,Angola,1981,,,,,25.354507446289066,8336368.0,3041.4333251949847 +4180,Botswana,1981,,,,,2.0652525424957275,934438.0,2210.1547052835267 +4181,Burundi,1981,,,,,1.2937270402908323,4474730.0,289.1184586088619 +4182,Cameroon,1981,,,,,13.34592056274414,9115820.0,1464.0395008615944 +4183,Central Afr Rep,1981,,,,,1.224480152130127,2412768.0,507.5001625229309 +4184,Chad,1981,,,,,1.8716517686843872,4514940.0,414.54632147589723 +4185,Dem Rep Congo,1981,,,,,22.78350067138672,29806317.0,764.3849681725764 +4186,Djibouti,1981,,,,,0.592702329158783,344829.0,1718.8297073586702 +4187,Equatorial Guinea,1981,,,,,0.1743331253528595,271778.0,641.4541476972363 +4188,Eritrea,1981,,,,,0.7906546592712402,2639252.0,299.5752809020284 +4189,Ethiopia,1981,,,,,7.360516548156737,37720093.0,195.13516438458245 +4190,Gabon,1981,,,,,9.302760124206543,730836.0,12728.929779330168 +4191,Kenya,1981,,,,,15.16161060333252,16987469.0,892.5173375346567 +4192,Lesotho,1981,,,,,0.7302600145339966,1395176.0,523.4178444396955 +4193,Madagascar,1981,,,,,4.893617153167725,8938764.0,547.4601581569582 +4194,Malawi,1981,,,,,2.3766653537750244,6458082.0,368.01411839846946 +4195,Mauritania,1981,,,,,1.812895894050598,1577646.0,1149.1144997360616 +4196,Mauritius,1981,,,,,2.3167004585266118,979050.0,2366.273896661673 +4197,Mozambique,1981,,,,,2.369248628616333,12367521.0,191.5702127060333 +4198,Namibia,1981,,,,,4.508330821990967,1074086.0,4197.364849733604 +4199,Rep Congo,1981,,,,,4.865082740783691,1722676.0,2824.142636679034 +4200,Rwanda,1981,,,,,2.24662446975708,5307835.0,423.2656949127243 +4201,Sao Tome Principe,1981,,,,,0.10655679553747176,96443.0,1104.868114196694 +4202,Seychelles,1981,,,,,0.35538408160209656,64695.0,5493.2233032243075 +4203,Sudan,1981,,,,,16.65865707397461,15238102.0,1093.2238853614847 +4204,Swaziland,1981,,,,,1.108418583869934,631315.0,1755.7298398896494 +4205,Tanzania,1981,,,,,9.630474090576172,19187724.0,501.90809970876023 +4206,Uganda,1981,,,,,3.4926507472991943,12295706.0,284.0545103550129 +4207,Zambia,1981,,,,,8.029079437255861,5711497.0,1405.7749548421125 +4208,Canada,1982,,,,,782.7938232421875,25201900.0,31060.90506041955 +4209,United States,1982,,,,,6570.568359375,231664458.0,28362.43598219542 +4210,Cuba,1982,,,,,39.08273696899414,9791907.0,3991.330490474853 +4211,Mexico,1982,,,,,586.478515625,71567658.0,8194.74231817115 +4212,Antigua Barbuda,1982,,,,,0.4085838198661804,68008.0,6007.878776999478 +4213,Bahamas,1982,,,,,5.446103572845459,217129.0,25082.340787483292 +4214,Barbados,1982,,,,,3.09326696395874,253475.0,12203.440039288846 +4215,Belize,1982,,,,,0.3281769752502441,153296.0,2140.805860885112 +4216,Costa Rica,1982,,,,,10.880101203918457,2424367.0,4487.811129221961 +4217,Dominica,1982,,,,,0.22710418701171875,73813.0,3076.7505319079132 +4218,Dominican Rep,1982,,,,,16.135574340820312,5968735.0,2703.349091695361 +4219,El Salvador,1982,,,,,9.892977714538574,4479917.0,2208.2948667438645 +4220,Grenada,1982,,,,,0.31077879667282104,92414.0,3362.8973604953912 +4221,Guatemala,1982,,,,,17.699562072753906,6982721.0,2534.765755749644 +4222,Haiti,1982,,,,,6.724569320678711,5709417.0,1177.8031488466706 +4223,Honduras,1982,,,,,5.65676736831665,3670043.0,1541.3354471096525 +4224,Jamaica,1982,,,,,8.342174530029299,2298309.0,3629.701023678408 +4225,Nicaragua,1982,,,,,5.63195276260376,2976076.0,1892.40891785148 +4226,Panama,1982,,,,,10.080594062805176,2039504.0,4942.669424921538 +4227,Puerto Rico,1982,,,,,48.19662475585938,3279001.0,14698.569703351533 +4228,St Kitts Nevis,1982,,,,,0.23247167468070984,43375.0,5359.577514252675 +4229,St Lucia,1982,,,,,0.4564454555511474,125576.0,3634.8144195638292 +4230,St Vincent Grenadines,1982,,,,,0.2549937963485718,100476.0,2537.857760545521 +4231,Trinidad and Tobago,1982,,,,,11.044913291931152,1127317.0,9797.522162737856 +4232,Argentina,1982,,,,,209.0204620361328,29329880.0,7126.536557126479 +4233,Brazil,1982,,,,,969.031982421875,126890221.0,7636.7743296930275 +4234,Bolivia,1982,,,,,8.864631652832031,5642224.0,1571.1236655673422 +4235,Chile,1982,,,,,55.98556900024414,11368918.0,4924.441270510011 +4236,Colombia,1982,,,,,109.06634521484376,27826039.0,3919.5785363070813 +4237,Ecuador,1982,,,,,31.2541561126709,8365850.0,3735.9211691186074 +4238,Guyana,1982,,,,,1.383265733718872,762859.0,1813.2652740793148 +4239,Paraguay,1982,,,,,10.806663513183594,3338368.0,3237.109723428812 +4240,Peru,1982,,,,,68.15219116210939,18161256.0,3752.61442061658 +4241,Suriname,1982,,,,,2.524144172668457,370214.0,6818.067854452984 +4242,Uruguay,1982,,,,,19.652700424194336,2968351.0,6620.746813363492 +4243,Venezuela,1982,,,,,157.76504516601562,15666415.0,10070.271033035677 +4244,Austria,1982,,,,,212.2163391113281,7574613.0,28016.789651343 +4245,Belgium,1982,,,,,271.27459716796875,9856303.0,27522.95634255245 +4246,Denmark,1982,,,,,191.9461975097656,5117810.0,37505.53410731653 +4247,Finland,1982,,,,,128.06915283203122,4826933.0,26532.20022569844 +4248,France,1982,,,,,1543.5579833984375,55756472.0,27683.92489751571 +4249,Germany,1982,,,,,2043.2440185546875,78335266.0,26083.322657699122 +4250,Greece,1982,,,,,179.6614532470703,9786480.0,18358.128075372384 +4251,Ireland,1982,,,,,61.70424270629883,3480000.0,17731.10422594794 +4252,Italy,1982,,,,,1397.220458984375,56532372.0,24715.40481238564 +4253,Luxembourg,1982,,,,,15.615516662597654,365718.0,42698.24472024253 +4254,Netherlands,1982,,,,,416.9935607910156,14322414.0,29114.75403455141 +4255,Portugal,1982,,,,,125.5567398071289,9859650.0,12734.401303000503 +4256,Spain,1982,,,,,661.140869140625,37983310.0,17406.088862203556 +4257,Sweden,1982,,,,,262.82534790039057,8325260.0,31569.626402105234 +4258,UK,1982,,,,,1246.3900146484375,56330706.0,22126.298481833997 +4259,Bulgaria,1982,,,,,30.667388916015625,8892098.0,3448.836136985403 +4260,Croatia,1982,,,,,38.0274543762207,4413368.0,8616.425001545464 +4261,Cyprus,1982,,,,,7.029622554779053,645323.0,10893.184583191754 +4262,Czech Republic,1982,,,,,118.5049819946289,10303704.0,11501.202091464285 +4263,Estonia,1982,,,,,13.853242874145508,1504043.0,9210.669425106536 +4264,Hungary,1982,,,,,89.01446533203125,10705535.0,8314.807744968492 +4265,Latvia,1982,,,,,20.21432304382324,2552229.0,7920.262266365299 +4266,Lithuania,1982,,,,,26.9162540435791,3491974.0,7708.033921094228 +4267,Malta,1982,,,,,3.0694100856781006,360222.0,8520.884581391754 +4268,Poland,1982,,,,,200.04283142089844,36227381.0,5521.868429321414 +4269,Romania,1982,,,,,120.92597198486328,22356726.0,5408.930269345488 +4270,Slovakia,1982,,,,,43.3153190612793,5048043.0,8580.616104355551 +4271,Slovenia,1982,,,,,19.437923431396484,1861252.0,10443.466780100967 +4272,Iceland,1982,,,,,6.4380455017089835,233997.0,27513.367700051647 +4273,Norway,1982,,,,,201.95248413085938,4114787.0,49079.69334278041 +4274,Switzerland,1982,,,,,347.8175354003906,6469427.0,53763.26765884994 +4275,Albania,1982,,,,,5.92544412612915,2780097.0,2131.3803533218993 +4276,Bosnia Herzegovina,1982,,,,,2.3447957038879395,4158688.0,563.8306369431753 +4277,Macedonia,1982,,,,,6.496941089630128,1823791.0,3562.3276404095245 +4278,Serbia,1982,,,,,32.06330108642578,7645035.0,4194.0031780659965 +4279,Russia,1982,,,,,1155.1934814453125,140821820.0,8203.227890715461 +4280,Ukraine,1982,,,,,173.11793518066406,50397455.0,3435.0531228345567 +4281,Armenia,1982,,,,,5.294787883758545,3264747.0,1621.8064933541696 +4282,Azerbaijan,1982,,,,,21.994260787963867,6446514.0,3411.806875462284 +4283,Belarus,1982,,,,,25.38987922668457,9778781.0,2596.425794450716 +4284,Georgia,1982,,,,,18.841657638549805,5101213.0,3693.56418533196 +4285,Kazakhstan,1982,,,,,76.67916870117188,15404564.0,4977.691591996494 +4286,Kyrgyzstan,1982,,,,,3.2652492523193364,3788867.0,861.800969081083 +4287,Moldova,1982,,,,,8.715973854064941,4055086.0,2149.3930964879514 +4288,Tajikistan,1982,,,,,6.601032257080077,4185256.0,1577.2111089692194 +4289,Turkmenistan,1982,,,,,6.252474308013916,3018436.0,2071.428484159981 +4290,Uzbekistan,1982,,,,,13.39222240447998,16830106.0,795.7301281691263 +4291,China,1982,,,,,391.0947265625,1015874386.0,384.98335222569534 +4292,Hong Kong,1982,,,,,61.09392166137695,5264500.0,11604.885869764828 +4293,Japan,1982,,,,,3250.60107421875,118454974.0,27441.65959817568 +4294,Korea,1982,,,,,163.67552185058594,39326000.0,4162.018050414126 +4295,Taiwan,1982,,,,,79.45852661132812,18501390.0,4294.732807174387 +4296,Macau,1982,,,,,4.132402420043944,270811.0,15259.359553503902 +4297,Mongolia,1982,,,,,2.705621719360352,1733592.0,1560.7027024584513 +4298,Cambodia,1982,,,,,2.873165369033813,7297127.0,393.7392577974611 +4299,Indonesia,1982,,,,,200.32920837402344,156620140.0,1279.0769333626151 +4300,Malaysia,1982,,,,,51.85888671875,14308292.0,3624.3939331647694 +4301,Myanmar,1982,,,,,6.62785530090332,34642188.0,191.32322995600973 +4302,Philippines,1982,,,,,85.7036361694336,50724769.0,1689.5815961120215 +4303,Thailand,1982,,,,,74.21310424804688,48985360.0,1515.0057945485526 +4304,Vietnam,1982,,,,,22.945837020874023,56142181.0,408.709398391096 +4305,Brunei,1982,,,,,9.600502967834473,198862.0,48277.2121764564 +4306,Laos,1982,,,,,1.392524003982544,3411156.0,408.2264205983379 +4307,Singapore,1982,,,,,38.1098518371582,2651869.0,14370.940584605878 +4308,Bangladesh,1982,,,,,31.143184661865234,87942605.0,354.1307954417001 +4309,India,1982,,,,,323.07440185546875,713710936.0,452.66842016761353 +4310,Pakistan,1982,,,,,49.1183090209961,94096117.0,522.001444767334 +4311,Afghanistan,1982,,,,,8.038421630859375,12582762.0,638.8439700964999 +4312,Bhutan,1982,,,,,0.1945590227842331,478235.0,406.8272351129321 +4313,Nepal,1982,,,,,4.825126647949219,15298113.0,315.4066549220299 +4314,Sri Lanka,1982,,,,,15.05023956298828,15514549.0,970.072643619114 +4315,Australia,1982,,,,,488.8384704589844,15161775.0,32241.50671402157 +4316,New Zealand,1982,,,,,73.96758270263672,3216901.0,22993.428365571934 +4317,Fiji,1982,,,,,1.8595978021621704,658906.0,2822.250521564791 +4318,Maldives,1982,,,,,0.4489738643169403,161899.0,2773.172560157508 +4319,Papua New Guinea,1982,,,,,5.678484916687013,2990998.0,1898.5251466858256 +4320,Samoa,1982,,,,,0.3545947074890137,160736.0,2206.069004386159 +4321,Solomon Islands,1982,,,,,0.34423330426216125,246927.0,1394.0691146053744 +4322,Tonga,1982,,,,,0.21839219331741333,92877.0,2351.4130873888407 +4323,Iran,1982,,,,,225.2528533935547,43218264.0,5211.982910594344 +4324,Iraq,1982,,,,,46.9084358215332,14173318.0,3309.629814383139 +4325,Saudi Arabia,1982,,,,,287.448486328125,11288809.0,25463.13666287781 +4326,Turkey,1982,,,,,238.1302490234375,47456570.0,5017.856305743072 +4327,Bahrain,1982,,,,,6.6733927726745605,377803.0,17663.683911124474 +4328,Israel,1982,,,,,73.20909881591797,3839652.0,19066.597393700777 +4329,Jordan,1982,,,,,8.02579402923584,2378796.0,3373.889156210049 +4330,Kuwait,1982,,,,,37.26123046875,1497494.0,24882.39049288344 +4331,Lebanon,1982,,,,,6.721869945526122,3009269.0,2233.721859204386 +4332,Oman,1982,,,,,14.935263633728027,1313598.0,11369.736885811355 +4333,Syria,1982,,,,,20.3441104888916,9379757.0,2168.9379041367065 +4334,United Arab Emirates,1982,,,,,115.68810272216795,1137549.0,101699.4456697408 +4335,Yemen,1982,,,,,9.342061996459961,9657618.0,967.3256900883802 +4336,Egypt,1982,,,,,55.22790908813477,45681811.0,1208.9693442349464 +4337,Morocco,1982,,,,,29.13537979125977,20198730.0,1442.4362220426615 +4338,Algeria,1982,,,,,76.73208618164062,20033753.0,3830.1403726820745 +4339,Libya,1982,,,,,59.432106018066406,3336396.0,17813.26497755854 +4340,Tunisia,1982,,,,,13.566038131713867,6734098.0,2014.5293596430981 +4341,Nigeria,1982,,,,,122.45642852783205,79519450.0,1539.9556778603478 +4342,South Africa,1982,,,,,201.47885131835938,31140029.0,6470.091961647157 +4343,Benin,1982,,,,,2.480458974838257,3675130.0,674.9309479768763 +4344,Burkina Faso,1982,,,,,2.430682182312012,6637227.0,366.2195345001778 +4345,Cabo Verde,1982,,,,,0.215347021818161,304089.0,708.1710348554567 +4346,Cote d'Ivoire,1982,,,,,17.15701675415039,9259826.0,1852.8444005481733 +4347,Gambia,1982,,,,,0.3395653367042541,690836.0,491.5281437334681 +4348,Ghana,1982,,,,,8.743902206420897,11638087.0,751.3178245205504 +4349,Guinea,1982,,,,,2.597307682037353,4636011.0,560.2462293634234 +4350,Guinea Bissau,1982,,,,,0.4704311192035675,825767.0,569.689899455376 +4351,Liberia,1982,,,,,1.7838411331176758,1971759.0,904.6953167794217 +4352,Mali,1982,,,,,2.982232093811035,7151973.0,416.9803344910607 +4353,Niger,1982,,,,,3.1679003238677983,6389183.0,495.8224430052792 +4354,Senegal,1982,,,,,7.07184648513794,5902850.0,1198.0393344126887 +4355,Sierra Leone,1982,,,,,1.770319581031799,3472250.0,509.8479605534738 +4356,Togo,1982,,,,,1.7673872709274292,2816600.0,627.4896225688523 +4357,Angola,1982,,,,,25.354507446289066,8547737.0,2966.224562862552 +4358,Botswana,1982,,,,,2.316501379013061,970466.0,2386.9990077066705 +4359,Burundi,1982,,,,,1.2800962924957275,4581943.0,279.37848473796544 +4360,Cameroon,1982,,,,,14.349027633666992,9365932.0,1532.04482305306 +4361,Central Afr Rep,1982,,,,,1.318958401679993,2477573.0,532.359047212733 +4362,Chad,1982,,,,,1.9717216491699217,4732876.0,416.60116368354505 +4363,Dem Rep Congo,1982,,,,,22.67922401428223,30647148.0,740.0109143690051 +4364,Djibouti,1982,,,,,0.6027166843414307,356156.0,1692.2828320776027 +4365,Equatorial Guinea,1982,,,,,0.17817138135433194,285483.0,624.1050477763367 +4366,Eritrea,1982,,,,,0.8067903518676758,2711307.0,297.56510489873546 +4367,Ethiopia,1982,,,,,7.4279398918151855,39478541.0,188.15132737086682 +4368,Gabon,1982,,,,,9.014604568481444,754241.0,11951.888810713612 +4369,Kenya,1982,,,,,15.390016555786133,17660336.0,871.4452859665939 +4370,Lesotho,1982,,,,,0.7628092169761658,1432822.0,532.3824012865281 +4371,Madagascar,1982,,,,,4.805033206939697,9195873.0,522.5206140776082 +4372,Malawi,1982,,,,,2.436164140701294,6666199.0,365.45025744075366 +4373,Mauritania,1982,,,,,1.7704755067825315,1611888.0,1098.386182403822 +4374,Mauritius,1982,,,,,2.4440512657165527,992040.0,2463.6620153588087 +4375,Mozambique,1982,,,,,2.205770492553711,12613458.0,174.8743677232454 +4376,Namibia,1982,,,,,4.4903836250305185,1097970.0,4089.7143137157823 +4377,Rep Congo,1982,,,,,6.013130187988281,1771522.0,3394.329953558737 +4378,Rwanda,1982,,,,,2.2873072624206543,5486568.0,416.89217420082184 +4379,Sao Tome Principe,1982,,,,,0.1098523661494255,98238.0,1118.2268180279068 +4380,Seychelles,1982,,,,,0.3514120578765869,65282.0,5382.985476495618 +4381,Sudan,1982,,,,,17.651288986206055,15706422.0,1123.8262276542712 +4382,Swaziland,1982,,,,,1.1214154958724976,649807.0,1725.7670290909414 +4383,Tanzania,1982,,,,,9.687509536743164,19734083.0,490.9024420715755 +4384,Uganda,1982,,,,,3.764833927154541,12617664.0,298.3780458216783 +4385,Zambia,1982,,,,,7.803238868713379,5912513.0,1319.7837989892587 +4386,Canada,1983,,,,,803.13525390625,25456300.0,31549.567451131938 +4387,United States,1983,,,,,6874.947265625,233791994.0,29406.256168143213 +4388,Cuba,1983,,,,,41.19768905639648,9886945.0,4166.877539664323 +4389,Mexico,1983,,,,,566.0313720703125,73257049.0,7726.647193641564 +4390,Antigua Barbuda,1983,,,,,0.430500328540802,65620.0,6560.50485432493 +4391,Bahamas,1983,,,,,5.642003059387207,220857.0,25545.955343897665 +4392,Barbados,1983,,,,,3.1089811325073238,254847.0,12199.402514086194 +4393,Belize,1983,,,,,0.3211435675621033,156846.0,2047.5088147743854 +4394,Costa Rica,1983,,,,,11.191597938537598,2494353.0,4486.773900301039 +4395,Dominica,1983,,,,,0.23290969431400302,74286.0,3135.310749185621 +4396,Dominican Rep,1983,,,,,16.882320404052734,6105818.0,2764.956375059449 +4397,El Salvador,1983,,,,,10.044836044311523,4526713.0,2219.0132319657832 +4398,Grenada,1983,,,,,0.3220680952072144,92526.0,3480.8388475370643 +4399,Guatemala,1983,,,,,17.244314193725586,7188649.0,2398.825452978103 +4400,Haiti,1983,,,,,6.7856407165527335,5827392.0,1164.4386917085267 +4401,Honduras,1983,,,,,5.708000659942628,3796140.0,1503.6328112089193 +4402,Jamaica,1983,,,,,8.501433372497559,2306386.0,3686.0410063612762 +4403,Nicaragua,1983,,,,,5.891766548156737,3043908.0,1935.5928458273831 +4404,Panama,1983,,,,,9.627838134765623,2080789.0,4627.013183348059 +4405,Puerto Rico,1983,,,,,47.06787109375,3316031.0,14194.038322847406 +4406,St Kitts Nevis,1983,,,,,0.2352294623851776,43230.0,5441.347730399667 +4407,St Lucia,1983,,,,,0.4787628054618835,127291.0,3761.167760971974 +4408,St Vincent Grenadines,1983,,,,,0.2605217695236206,101805.0,2559.0272533138905 +4409,Trinidad and Tobago,1983,,,,,9.905806541442873,1148146.0,8627.654097512748 +4410,Argentina,1983,,,,,218.1109619140625,29793552.0,7320.743827861225 +4411,Brazil,1983,,,,,935.989990234375,129821262.0,7209.835860587883 +4412,Bolivia,1983,,,,,8.506312370300293,5737434.0,1482.5987314712977 +4413,Chile,1983,,,,,53.17654037475586,11567646.0,4597.006199425177 +4414,Colombia,1983,,,,,110.78299713134766,28454991.0,3893.2712061426287 +4415,Ecuador,1983,,,,,31.14887046813965,8593494.0,3624.703812924016 +4416,Guyana,1983,,,,,1.2893089056015017,762158.0,1691.655674547143 +4417,Paraguay,1983,,,,,10.47785758972168,3431924.0,3053.05641666939 +4418,Peru,1983,,,,,61.05885314941406,18566804.0,3288.6033131719423 +4419,Suriname,1983,,,,,2.4257025718688965,379144.0,6397.83979667065 +4420,Uruguay,1983,,,,,17.633502960205078,2988825.0,5899.811116477237 +4421,Venezuela,1983,,,,,151.8254852294922,16178563.0,9384.36159190975 +4422,Austria,1983,,,,,218.52572631835938,7552896.0,28932.706913792983 +4423,Belgium,1983,,,,,272.1205444335937,9855520.0,27610.97785135576 +4424,Denmark,1983,,,,,196.9292449951172,5114297.0,38505.633324603004 +4425,Finland,1983,,,,,132.06626892089844,4855787.0,27197.7063493309 +4426,France,1983,,,,,1562.9495849609375,55992281.0,27913.66161633846 +4427,Germany,1983,,,,,2075.3720703125,78121655.0,26565.89994557207 +4428,Greece,1983,,,,,177.72357177734375,9840526.0,18060.373172871427 +4429,Ireland,1983,,,,,61.55349731445313,3504000.0,17566.637361430687 +4430,Italy,1983,,,,,1413.556884765625,56623590.0,24964.09861624148 +4431,Luxembourg,1983,,,,,16.082277297973633,365885.0,43954.45918245797 +4432,Netherlands,1983,,,,,425.6251525878906,14374759.0,29609.202671703268 +4433,Portugal,1983,,,,,125.33939361572266,9872243.0,12696.141455971318 +4434,Spain,1983,,,,,672.8438110351562,38184166.0,17621.016288143004 +4435,Sweden,1983,,,,,267.82070922851557,8329028.0,32155.097717106437 +4436,UK,1983,,,,,1299.011474609375,56364615.0,23046.577619830718 +4437,Bulgaria,1983,,,,,31.71937370300293,8910284.0,3559.861133831753 +4438,Croatia,1983,,,,,39.38390731811523,4430941.0,8888.384503001786 +4439,Cyprus,1983,,,,,7.4220290184021,656496.0,11305.520549100222 +4440,Czech Republic,1983,,,,,122.17005157470705,10306888.0,11853.2433431611 +4441,Estonia,1983,,,,,14.365793228149414,1514671.0,9484.431423160157 +4442,Hungary,1983,,,,,89.66427612304688,10689463.0,8388.099207887886 +4443,Latvia,1983,,,,,21.26940155029297,2569826.0,8276.59209234126 +4444,Lithuania,1983,,,,,27.74871635437012,3523380.0,7875.595693444965 +4445,Malta,1983,,,,,3.0506131649017334,355773.0,8574.605619037233 +4446,Poland,1983,,,,,211.17286682128903,36571418.0,5774.259746266581 +4447,Romania,1983,,,,,128.23785400390622,22407281.0,5723.043951825581 +4448,Slovakia,1983,,,,,44.65496826171875,5081376.0,8787.967720105491 +4449,Slovenia,1983,,,,,20.039093017578125,1878555.0,10667.291092130987 +4450,Iceland,1983,,,,,6.299552917480469,237041.0,26575.794556555484 +4451,Norway,1983,,,,,209.97560119628903,4128432.0,50860.85981222147 +4452,Switzerland,1983,,,,,350.0403747558594,6503035.0,53827.23217018814 +4453,Albania,1983,,,,,5.990917205810548,2837315.0,2111.474124589813 +4454,Bosnia Herzegovina,1983,,,,,2.4173150062561035,4181892.0,578.0433847301899 +4455,Macedonia,1983,,,,,6.62952995300293,1831444.0,3619.837654333373 +4456,Serbia,1983,,,,,32.88544464111328,7670692.0,4287.154880043845 +4457,Russia,1983,,,,,1208.9952392578125,141861725.0,8522.349768817576 +4458,Ukraine,1983,,,,,178.4722137451172,50573066.0,3528.997307482153 +4459,Armenia,1983,,,,,5.458543300628662,3330619.0,1638.8975444590515 +4460,Azerbaijan,1983,,,,,22.67449760437012,6575300.0,3448.435448476893 +4461,Belarus,1983,,,,,26.17513465881348,9845174.0,2658.676693658586 +4462,Georgia,1983,,,,,19.652204513549805,5130925.0,3830.1484651500077 +4463,Kazakhstan,1983,,,,,79.05070495605469,15603171.0,5066.32305420832 +4464,Kyrgyzstan,1983,,,,,3.366236686706543,3877527.0,868.1401023659006 +4465,Moldova,1983,,,,,9.095742225646973,4082711.0,2227.8682536302404 +4466,Tajikistan,1983,,,,,6.735745906829834,4304082.0,1564.9669097451754 +4467,Turkmenistan,1983,,,,,6.445849895477295,3091431.0,2085.0699548129314 +4468,Uzbekistan,1983,,,,,13.665539741516113,17271094.0,791.2376449063454 +4469,China,1983,,,,,433.4706726074219,1031901049.0,420.069998986329 +4470,Hong Kong,1983,,,,,64.74551391601561,5345100.0,12113.059421903357 +4471,Japan,1983,,,,,3365.121337890625,119269949.0,28214.32696253291 +4472,Korea,1983,,,,,185.3495330810547,39910000.0,4644.187749462658 +4473,Taiwan,1983,,,,,86.6415786743164,18803390.0,4607.763742299469 +4474,Macau,1983,,,,,4.546550273895264,285451.0,15927.60324502371 +4475,Mongolia,1983,,,,,2.863468647003174,1774315.0,1613.8445805864087 +4476,Cambodia,1983,,,,,3.018030405044556,7530652.0,400.7661494707969 +4477,Indonesia,1983,,,,,208.72894287109372,159782359.0,1306.332840355009 +4478,Malaysia,1983,,,,,55.10122299194336,14752622.0,3735.0121891514173 +4479,Myanmar,1983,,,,,6.9188904762268075,35325994.0,195.85833809026883 +4480,Philippines,1983,,,,,87.31024932861328,51959692.0,1680.3457828159046 +4481,Thailand,1983,,,,,78.35731506347656,49935955.0,1569.1562334890073 +4482,Vietnam,1983,,,,,22.69343376159668,57436347.0,395.105798800134 +4483,Brunei,1983,,,,,9.648646354675293,204951.0,47077.82033108057 +4484,Laos,1983,,,,,1.4432016611099243,3494963.0,412.93760795462623 +4485,Singapore,1983,,,,,41.36461639404297,2689263.0,15381.394974773002 +4486,Bangladesh,1983,,,,,32.35186767578125,90163247.0,358.81435897912206 +4487,India,1983,,,,,346.6229248046875,728521552.0,475.7895272323906 +4488,Pakistan,1983,,,,,52.44773483276367,96881426.0,541.360062482603 +4489,Afghanistan,1983,,,,,8.542438507080076,12439282.0,686.7308343906086 +4490,Bhutan,1983,,,,,0.21656502783298487,494890.0,437.60235170034736 +4491,Nepal,1983,,,,,4.68146276473999,15664172.0,298.8643615979185 +4492,Sri Lanka,1983,,,,,15.77475643157959,15637137.0,1008.8008074355037 +4493,Australia,1983,,,,,477.9855651855469,15348066.0,31143.048589023976 +4494,New Zealand,1983,,,,,76.55066680908203,3258452.0,23492.955185186718 +4495,Fiji,1983,,,,,1.7859554290771484,671914.0,2658.011931701302 +4496,Maldives,1983,,,,,0.4687977433204651,166848.0,2809.729474254801 +4497,Papua New Guinea,1983,,,,,5.861511707305907,3067596.0,1910.7834627851607 +4498,Samoa,1983,,,,,0.356123149394989,160658.0,2216.653695396364 +4499,Solomon Islands,1983,,,,,0.35764753818511963,255347.0,1400.6334054644058 +4500,Tonga,1983,,,,,0.22272160649299624,93421.0,2384.063609820021 +4501,Iran,1983,,,,,244.54019165039062,44989345.0,5435.513489925017 +4502,Iraq,1983,,,,,40.77594757080078,14652218.0,2782.9197989547233 +4503,Saudi Arabia,1983,,,,,241.3090362548828,11952263.0,20189.4014760956 +4504,Turkey,1983,,,,,249.9678955078125,48651590.0,5137.918318965783 +4505,Bahrain,1983,,,,,7.0989131927490225,392638.0,18080.046232787005 +4506,Israel,1983,,,,,75.66827392578125,3907195.0,19366.393007203696 +4507,Jordan,1983,,,,,8.185539245605469,2474092.0,3308.5023700030024 +4508,Kuwait,1983,,,,,41.141685485839844,1565822.0,26274.816349393383 +4509,Lebanon,1983,,,,,8.248394012451174,3064601.0,2691.50666349426 +4510,Oman,1983,,,,,17.424474716186527,1376751.0,12656.228116911863 +4511,Syria,1983,,,,,20.634862899780273,9734673.0,2119.728407906488 +4512,United Arab Emirates,1983,,,,,110.19774627685548,1209100.0,91140.30789583613 +4513,Yemen,1983,,,,,9.454166412353516,9936258.0,951.4815750912984 +4514,Egypt,1983,,,,,58.04144668579102,47093025.0,1232.4849950877228 +4515,Morocco,1983,,,,,29.53936195373535,20679401.0,1428.4437906946798 +4516,Algeria,1983,,,,,80.87561798095703,20680982.0,3910.627550517525 +4517,Libya,1983,,,,,56.63344955444336,3475276.0,16296.10124618688 +4518,Tunisia,1983,,,,,14.20118808746338,6859890.0,2070.1772313351057 +4519,Nigeria,1983,,,,,109.07918548583984,81173873.0,1343.7720962980275 +4520,South Africa,1983,,,,,197.75845336914062,32143960.0,6152.274124567745 +4521,Benin,1983,,,,,2.372612953186035,3789035.0,626.1786848593467 +4522,Burkina Faso,1983,,,,,2.439091682434082,6807281.0,358.3063020953714 +4523,Cabo Verde,1983,,,,,0.23584966361522675,308196.0,765.2586782931212 +4524,Cote d'Ivoire,1983,,,,,16.48785400390625,9606456.0,1716.330559772121 +4525,Gambia,1983,,,,,0.3765209913253784,710862.0,529.668193440328 +4526,Ghana,1983,,,,,8.344853401184082,12437182.0,670.9601420308942 +4527,Guinea,1983,,,,,2.631072998046875,4734400.0,555.7352564309891 +4528,Guinea Bissau,1983,,,,,0.4544445276260376,845006.0,537.8003560046171 +4529,Liberia,1983,,,,,1.7224842309951782,2032607.0,847.4261040108482 +4530,Mali,1983,,,,,3.0365350246429443,7301933.0,415.85358625489226 +4531,Niger,1983,,,,,3.0173952579498287,6544130.0,461.0842477074614 +4532,Senegal,1983,,,,,6.695172309875487,6055544.0,1105.6268949371831 +4533,Sierra Leone,1983,,,,,1.73309326171875,3545635.0,488.7962979039721 +4534,Togo,1983,,,,,1.6717008352279663,2917500.0,572.9908604037588 +4535,Angola,1983,,,,,26.419397354125977,8852515.0,2984.3945312858527 +4536,Botswana,1983,,,,,2.621045589447021,1006371.0,2604.4526217935745 +4537,Burundi,1983,,,,,1.3276561498641968,4700988.0,282.42066345717046 +4538,Cameroon,1983,,,,,15.3343505859375,9586213.0,1599.625481505314 +4539,Central Afr Rep,1983,,,,,1.2117730379104614,2543280.0,476.4607270573674 +4540,Chad,1983,,,,,2.280862331390381,4971162.0,458.8187492965188 +4541,Dem Rep Congo,1983,,,,,22.99938774108887,31445744.0,731.3990644040373 +4542,Djibouti,1983,,,,,0.6101857423782349,370117.0,1648.6293317470822 +4543,Equatorial Guinea,1983,,,,,0.1870877742767334,299577.0,624.5064683761883 +4544,Eritrea,1983,,,,,0.8232555389404297,2785812.0,295.51726352691054 +4545,Ethiopia,1983,,,,,8.039630889892576,40673915.0,197.6606109810816 +4546,Gabon,1983,,,,,9.52016544342041,779018.0,12220.72589262432 +4547,Kenya,1983,,,,,15.591479301452635,18348477.0,849.74242284265 +4548,Lesotho,1983,,,,,0.7779598236083984,1471479.0,528.6924404686704 +4549,Madagascar,1983,,,,,4.848380088806152,9463017.0,512.3503517753537 +4550,Malawi,1983,,,,,2.5267577171325684,6880285.0,367.2460831393712 +4551,Mauritania,1983,,,,,1.8366087675094604,1647586.0,1114.7271022632267 +4552,Mauritius,1983,,,,,2.453431367874145,1002175.0,2448.106735723946 +4553,Mozambique,1983,,,,,1.8594646453857424,12862126.0,144.56899624414675 +4554,Namibia,1983,,,,,4.408504009246826,1131950.0,3894.6101941312127 +4555,Rep Congo,1983,,,,,6.3651366233825675,1821668.0,3494.1255066140307 +4556,Rwanda,1983,,,,,2.4240963459014893,5674439.0,427.19577140603485 +4557,Sao Tome Principe,1983,,,,,0.10559672117233276,100133.0,1054.5646407511283 +4558,Seychelles,1983,,,,,0.3454055488109589,65846.0,5245.657273197444 +4559,Sudan,1983,,,,,18.01534843444824,16237967.0,1109.4583721255403 +4560,Swaziland,1983,,,,,1.1348989009857178,672409.0,1687.810396627228 +4561,Tanzania,1983,,,,,9.457298278808594,20301757.0,465.8364435555304 +4562,Uganda,1983,,,,,3.981107234954834,12974328.0,306.8449660710624 +4563,Zambia,1983,,,,,7.649773597717285,6132709.0,1247.3726696827264 +4564,Canada,1984,,,,,850.7238159179688,25701800.0,33099.775732359936 +4565,United States,1984,,,,,7374.005859375,235824902.0,31268.987273341474 +4566,Cuba,1984,,,,,44.46454620361328,9983215.0,4453.93054277738 +4567,Mexico,1984,,,,,585.337646484375,74943413.0,7810.394843965472 +4568,Antigua Barbuda,1984,,,,,0.474260687828064,64470.0,7356.300416132525 +4569,Bahamas,1984,,,,,6.441539764404298,224617.0,28677.881747170948 +4570,Barbados,1984,,,,,3.219396114349365,256114.0,12570.16841855332 +4571,Belize,1984,,,,,0.3274767100811005,160810.0,2036.4200614458084 +4572,Costa Rica,1984,,,,,12.08962345123291,2567601.0,4708.528876267345 +4573,Dominica,1984,,,,,0.24268409609794614,73993.0,3279.8250658568536 +4574,Dominican Rep,1984,,,,,17.09383773803711,6241548.0,2738.717660752927 +4575,El Salvador,1984,,,,,10.17913055419922,4593745.0,2215.8675664842567 +4576,Grenada,1984,,,,,0.333560973405838,92738.0,3596.810082229917 +4577,Guatemala,1984,,,,,17.329967498779293,7380338.0,2348.1265355027504 +4578,Haiti,1984,,,,,6.80605936050415,5949797.0,1143.9145504467047 +4579,Honduras,1984,,,,,6.069162368774414,3932993.0,1543.1409028122894 +4580,Jamaica,1984,,,,,8.370312690734862,2313059.0,3618.7199248851252 +4581,Nicaragua,1984,,,,,5.799496173858643,3114238.0,1862.2520738166586 +4582,Panama,1984,,,,,9.888701438903807,2123462.0,4656.877042727306 +4583,Puerto Rico,1984,,,,,49.85837936401367,3350037.0,14882.933938942666 +4584,St Kitts Nevis,1984,,,,,0.2560861110687256,43088.0,5943.327865501429 +4585,St Lucia,1984,,,,,0.5131276249885559,129118.0,3974.098305337412 +4586,St Vincent Grenadines,1984,,,,,0.2776840329170227,102894.0,2698.7388275023104 +4587,Trinidad and Tobago,1984,,,,,9.336133003234863,1168881.0,7987.23993566057 +4588,Argentina,1984,,,,,221.53692626953125,30230644.0,7328.223846952492 +4589,Brazil,1984,,,,,985.3086547851562,132793071.0,7419.880023598191 +4590,Bolivia,1984,,,,,8.489242553710938,5834293.0,1455.0593454444158 +4591,Chile,1984,,,,,55.35873413085938,11761672.0,4706.706166509266 +4592,Colombia,1984,,,,,114.49498748779295,29095455.0,3935.1502661770705 +4593,Ecuador,1984,,,,,31.96661376953125,8825829.0,3621.938944152583 +4594,Guyana,1984,,,,,1.2244729995727541,762072.0,1606.7681263355091 +4595,Paraguay,1984,,,,,10.772976875305176,3530545.0,3051.363705973207 +4596,Peru,1984,,,,,63.26203155517578,18970733.0,3334.7173013913475 +4597,Suriname,1984,,,,,2.379614353179932,387024.0,6148.492995731353 +4598,Uruguay,1984,,,,,17.43202018737793,3008270.0,5794.6993412751945 +4599,Venezuela,1984,,,,,154.0150604248047,16689995.0,9227.987211787942 +4600,Austria,1984,,,,,218.6377716064453,7554132.0,28942.805289402582 +4601,Belgium,1984,,,,,278.83206176757807,9855300.0,28292.60010020782 +4602,Denmark,1984,,,,,205.1336212158203,5111619.0,40130.85114829965 +4603,Finland,1984,,,,,136.31105041503906,4881788.0,27922.361727924086 +4604,France,1984,,,,,1586.7762451171875,56312924.0,28177.834365645576 +4605,Germany,1984,,,,,2133.958740234375,77855422.0,27409.2501898503 +4606,Greece,1984,,,,,181.2968597412109,9887169.0,18336.579433527528 +4607,Ireland,1984,,,,,64.2337646484375,3529000.0,18201.69018091173 +4608,Italy,1984,,,,,1459.1561279296875,56687144.0,25740.512309628568 +4609,Luxembourg,1984,,,,,17.077278137207028,366272.0,46624.579922044366 +4610,Netherlands,1984,,,,,438.6564331054688,14433533.0,30391.480249878437 +4611,Portugal,1984,,,,,122.98303985595705,9885387.0,12440.892790131235 +4612,Spain,1984,,,,,684.8519897460938,38362861.0,17851.952953824108 +4613,Sweden,1984,,,,,279.14828491210943,8342621.0,33460.501790997034 +4614,UK,1984,,,,,1328.580078125,56435179.0,23541.700436973897 +4615,Bulgaria,1984,,,,,32.796703338623054,8928271.0,3673.35437495379 +4616,Croatia,1984,,,,,41.40658950805664,4441854.0,9321.915918005554 +4617,Cyprus,1984,,,,,8.072042465209961,667970.0,12084.438620312229 +4618,Czech Republic,1984,,,,,125.94849395751952,10309224.0,12217.068322263589 +4619,Estonia,1984,,,,,14.76806640625,1526128.0,9676.820297019649 +4620,Hungary,1984,,,,,92.03951263427734,10668095.0,8627.549026726641 +4621,Latvia,1984,,,,,22.493206024169922,2587996.0,8691.360428752565 +4622,Lithuania,1984,,,,,28.60692405700684,3555054.0,8046.832497342329 +4623,Malta,1984,,,,,3.0793614387512207,351379.0,8763.64677101142 +4624,Poland,1984,,,,,223.0846099853516,36904134.0,6044.976153223148 +4625,Romania,1984,,,,,135.89509582519528,22453788.0,6052.212474135558 +4626,Slovakia,1984,,,,,46.06805419921875,5114080.0,9008.082431095869 +4627,Slovenia,1984,,,,,20.658864974975586,1896413.0,10893.65289890735 +4628,Iceland,1984,,,,,6.55966854095459,239498.0,27389.241417275254 +4629,Norway,1984,,,,,222.6842498779297,4140095.0,53787.23190601416 +4630,Switzerland,1984,,,,,360.5711975097656,6532311.0,55198.10638375387 +4631,Albania,1984,,,,,5.915935039520264,2895998.0,2042.7966592243035 +4632,Bosnia Herzegovina,1984,,,,,2.492076873779297,4204488.0,592.7182748004742 +4633,Macedonia,1984,,,,,6.764827251434326,1837295.0,3681.949415545313 +4634,Serbia,1984,,,,,33.72864532470703,7695799.0,4382.734700413437 +4635,Russia,1984,,,,,1257.946044921875,142922263.0,8801.610179667216 +4636,Ukraine,1984,,,,,183.9920196533203,50769375.0,3624.07493992826 +4637,Armenia,1984,,,,,5.627364635467528,3397776.0,1656.1905892170435 +4638,Azerbaijan,1984,,,,,23.37576675415039,6712656.0,3482.342422157547 +4639,Belarus,1984,,,,,26.984676361083984,9914262.0,2721.8038378533856 +4640,Georgia,1984,,,,,20.71871566772461,5161724.0,4013.9138915069093 +4641,Kazakhstan,1984,,,,,81.49555206298828,15802751.0,5157.048419163744 +4642,Kyrgyzstan,1984,,,,,3.470345497131348,3969460.0,874.2613597646399 +4643,Moldova,1984,,,,,9.40110969543457,4113243.0,2285.57118931086 +4644,Tajikistan,1984,,,,,6.873210906982423,4430622.0,1551.2970655096328 +4645,Turkmenistan,1984,,,,,6.645205974578857,3164611.0,2099.8492309414514 +4646,Uzbekistan,1984,,,,,13.944424629211426,17729265.0,786.5201760598325 +4647,China,1984,,,,,499.0945739746094,1046459302.0,476.93643987944535 +4648,Hong Kong,1984,,,,,71.20306396484375,5397900.0,13190.882373671937 +4649,Japan,1984,,,,,3516.61865234375,120034697.0,29296.684544001066 +4650,Korea,1984,,,,,204.70542907714844,40406000.0,5066.213658296996 +4651,Taiwan,1984,,,,,95.34905242919922,19083348.0,4996.45305578451 +4652,Macau,1984,,,,,4.9311790466308585,298982.0,16493.230517659456 +4653,Mongolia,1984,,,,,3.03337025642395,1816339.0,1670.0463164772382 +4654,Cambodia,1984,,,,,3.170198678970337,7690581.0,412.2183589211708 +4655,Indonesia,1984,,,,,223.2888946533203,162939884.0,1370.3759274391055 +4656,Malaysia,1984,,,,,59.3780632019043,15197923.0,3906.985395432277 +4657,Myanmar,1984,,,,,7.260180473327638,36027290.0,201.51891727986302 +4658,Philippines,1984,,,,,80.91592407226562,53237123.0,1519.915418274305 +4659,Thailand,1984,,,,,82.8647689819336,50861767.0,1629.215300009801 +4660,Vietnam,1984,,,,,22.46649932861328,58762040.0,382.33014593457415 +4661,Brunei,1984,,,,,9.70652675628662,211223.0,45953.92905264399 +4662,Laos,1984,,,,,1.5168657302856443,3576706.0,424.09572670654086 +4663,Singapore,1984,,,,,45.00493240356445,2743297.0,16405.41742420323 +4664,Bangladesh,1984,,,,,33.90582656860352,92379840.0,367.02625344018264 +4665,India,1984,,,,,359.8664855957031,744016969.0,483.6804812118514 +4666,Pakistan,1984,,,,,55.10432052612305,99354344.0,554.6241694889862 +4667,Afghanistan,1984,,,,,9.674222946166992,12768664.0,757.6534981394287 +4668,Bhutan,1984,,,,,0.2262717187404633,511887.0,442.0345090624753 +4669,Nepal,1984,,,,,5.134681701660156,16044345.0,320.0306214844019 +4670,Sri Lanka,1984,,,,,16.579133987426758,15714198.0,1055.0416882507627 +4671,Australia,1984,,,,,499.88275146484375,15510476.0,32228.717639925668 +4672,New Zealand,1984,,,,,80.21974182128906,3298090.0,24323.09058312207 +4673,Fiji,1984,,,,,1.9358510971069336,685572.0,2823.702101466999 +4674,Maldives,1984,,,,,0.5502223968505859,172102.0,3197.0714858083343 +4675,Papua New Guinea,1984,,,,,5.840719223022461,3146895.0,1856.0260901690272 +4676,Samoa,1984,,,,,0.3607084453105927,160664.0,2245.1105743078265 +4677,Solomon Islands,1984,,,,,0.3582393527030945,264010.0,1356.9158467599502 +4678,Tonga,1984,,,,,0.22659339010715485,93792.0,2415.9138317463626 +4679,Iran,1984,,,,,223.05685424804688,46792113.0,4766.97545691187 +4680,Iraq,1984,,,,,40.170326232910156,15160546.0,2649.662237290805 +4681,Saudi Arabia,1984,,,,,230.0605316162109,12606940.0,18248.721070792035 +4682,Turkey,1984,,,,,266.74578857421875,49830462.0,5353.066736050305 +4683,Bahrain,1984,,,,,7.454117774963379,407712.0,18282.802014567584 +4684,Israel,1984,,,,,76.37442016601562,3977784.0,19200.24319219335 +4685,Jordan,1984,,,,,8.892822265625,2570135.0,3460.060372558251 +4686,Kuwait,1984,,,,,43.29747009277344,1637314.0,26444.206849006016 +4687,Lebanon,1984,,,,,11.917217254638672,3120549.0,3818.948926819823 +4688,Oman,1984,,,,,20.33638000488281,1438115.0,14140.997072475298 +4689,Syria,1984,,,,,19.79466438293457,10100847.0,1959.7034172416006 +4690,United Arab Emirates,1984,,,,,114.62433624267578,1284385.0,89244.53045050807 +4691,Yemen,1984,,,,,9.681066513061523,10229272.0,946.4081620922315 +4692,Egypt,1984,,,,,63.69802856445313,48550155.0,1312.0046385939058 +4693,Morocco,1984,,,,,31.435312271118164,21160790.0,1485.5453067261747 +4694,Algeria,1984,,,,,85.40464782714844,21340546.0,4001.989819152164 +4695,Libya,1984,,,,,51.92947769165039,3613956.0,14369.150507546408 +4696,Tunisia,1984,,,,,15.017592430114744,7184530.0,2090.267899238328 +4697,Nigeria,1984,,,,,107.86227416992188,82731816.0,1303.7580870933846 +4698,South Africa,1984,,,,,207.84239196777344,33181594.0,6263.7856387421725 +4699,Benin,1984,,,,,2.560757637023926,3907047.0,655.4202283780886 +4700,Burkina Faso,1984,,,,,2.3957078456878658,6985039.0,342.97701783595863 +4701,Cabo Verde,1984,,,,,0.2447725087404251,312486.0,783.3071201283421 +4702,Cote d'Ivoire,1984,,,,,16.042472839355472,9963431.0,1610.135388036056 +4703,Gambia,1984,,,,,0.3898319602012634,741062.0,526.045000554965 +4704,Ghana,1984,,,,,9.06648063659668,13131315.0,690.4472733002506 +4705,Guinea,1984,,,,,2.667908191680908,4953578.0,538.5820495167146 +4706,Guinea Bissau,1984,,,,,0.4958408772945404,864775.0,573.3755916793853 +4707,Liberia,1984,,,,,1.7123411893844604,2096264.0,816.8537881604897 +4708,Mali,1984,,,,,3.0278198719024663,7459820.0,405.883770909012 +4709,Niger,1984,,,,,2.5097076892852783,6704046.0,374.3571701753357 +4710,Senegal,1984,,,,,6.945957660675049,6213697.0,1117.8462130797573 +4711,Sierra Leone,1984,,,,,1.80402946472168,3622544.0,498.0007046765146 +4712,Togo,1984,,,,,1.7645729780197144,3021789.0,583.9497655262212 +4713,Angola,1984,,,,,28.00456047058105,9171983.0,3053.2721735944183 +4714,Botswana,1984,,,,,2.84502387046814,1042064.0,2730.1815152122517 +4715,Burundi,1984,,,,,1.329721212387085,4811264.0,276.3766886180191 +4716,Cameroon,1984,,,,,16.480527877807614,9887686.0,1666.7729818491018 +4717,Central Afr Rep,1984,,,,,1.3266700506210327,2629271.0,504.57714348236937 +4718,Chad,1984,,,,,2.327594995498657,5025174.0,463.1869454666957 +4719,Dem Rep Congo,1984,,,,,24.273799896240234,32341528.0,750.5458584467696 +4720,Djibouti,1984,,,,,0.6136072874069214,374612.0,1637.9808639523596 +4721,Equatorial Guinea,1984,,,,,0.18897993862628934,314637.0,600.6284659028956 +4722,Eritrea,1984,,,,,0.8400569558143616,2862243.0,293.49602944766104 +4723,Ethiopia,1984,,,,,7.810661315917969,40072730.0,194.9121339104665 +4724,Gabon,1984,,,,,10.235025405883787,805260.0,12710.212112713645 +4725,Kenya,1984,,,,,15.86514377593994,19050801.0,832.7809301005212 +4726,Lesotho,1984,,,,,0.8210191130638123,1511106.0,543.3233095916581 +4727,Madagascar,1984,,,,,4.931029319763184,9740535.0,506.2380372087553 +4728,Malawi,1984,,,,,2.6621997356414795,7098349.0,375.04492039507767 +4729,Mauritania,1984,,,,,1.7773438692092896,1684770.0,1054.9474819763466 +4730,Mauritius,1984,,,,,2.5687031745910645,1012042.0,2538.138905886381 +4731,Mozambique,1984,,,,,1.738599419593811,13083602.0,132.8838510674515 +4732,Namibia,1984,,,,,4.398060321807861,1166056.0,3771.7402267196962 +4733,Rep Congo,1984,,,,,6.809176921844482,1873114.0,3635.2175691626258 +4734,Rwanda,1984,,,,,2.321295976638794,5856599.0,396.355628349968 +4735,Sao Tome Principe,1984,,,,,0.09922875463962556,102116.0,971.7258278783496 +4736,Seychelles,1984,,,,,0.3609872460365296,66499.0,5428.461270643612 +4737,Sudan,1984,,,,,17.11297607421875,16860414.0,1014.9795891262663 +4738,Swaziland,1984,,,,,1.2048286199569702,696337.0,1730.2378301841927 +4739,Tanzania,1984,,,,,9.777196884155273,20886728.0,468.10572168868543 +4740,Uganda,1984,,,,,3.967385292053223,13384354.0,296.4196323597853 +4741,Zambia,1984,,,,,7.624006271362305,6339559.0,1202.6082999404698 +4742,Canada,1985,,,,,890.997802734375,25941600.0,34346.293317851436 +4743,United States,1985,,,,,7686.57080078125,237923795.0,32306.860273396567 +4744,Cuba,1985,,,,,45.17449951171875,10084188.0,4479.7359501547135 +4745,Mexico,1985,,,,,598.1430053710938,76600787.0,7808.575196115071 +4746,Antigua Barbuda,1985,,,,,0.5105121731758118,64398.0,7927.453852228513 +4747,Bahamas,1985,,,,,6.752613067626952,228198.0,29591.026510429325 +4748,Barbados,1985,,,,,3.2541332244873047,257247.0,12649.839354734184 +4749,Belize,1985,,,,,0.3307924568653107,165558.0,1998.045741464083 +4750,Costa Rica,1985,,,,,12.176862716674805,2644306.0,4604.937067296601 +4751,Dominica,1985,,,,,0.2461324781179428,73109.0,3366.6508653919873 +4752,Dominican Rep,1985,,,,,16.731016159057614,6378551.0,2623.01205384383 +4753,El Salvador,1985,,,,,10.241894721984863,4671054.0,2192.630340386744 +4754,Grenada,1985,,,,,0.3535069525241852,93059.0,3798.740073761648 +4755,Guatemala,1985,,,,,17.224369049072266,7580844.0,2272.0912142595553 +4756,Haiti,1985,,,,,6.8471431732177725,6076064.0,1126.9043863293366 +4757,Honduras,1985,,,,,6.410627841949463,4077312.0,1572.2681614626165 +4758,Jamaica,1985,,,,,8.123921394348145,2318176.0,3504.445475385883 +4759,Nicaragua,1985,,,,,5.562768936157227,3180138.0,1749.2224979410412 +4760,Panama,1985,,,,,10.377424240112305,2167617.0,4787.4805558880125 +4761,Puerto Rico,1985,,,,,51.40271377563477,3382106.0,15198.433690616072 +4762,St Kitts Nevis,1985,,,,,0.2779797911643982,42931.0,6475.036480967091 +4763,St Lucia,1985,,,,,0.5557326078414917,131006.0,4242.0393557660855 +4764,St Vincent Grenadines,1985,,,,,0.294839084148407,103853.0,2839.004016719854 +4765,Trinidad and Tobago,1985,,,,,8.951361656188965,1189032.0,7528.276493979107 +4766,Argentina,1985,,,,,210.0413055419922,30671759.0,6848.035860675359 +4767,Brazil,1985,,,,,1063.599853515625,135733183.0,7835.960448342429 +4768,Bolivia,1985,,,,,8.346929550170897,5934935.0,1406.4062285721575 +4769,Chile,1985,,,,,57.578575134277344,11952148.0,4817.424879132801 +4770,Colombia,1985,,,,,118.0524673461914,29747620.0,3968.467640308415 +4771,Ecuador,1985,,,,,33.224498748779304,9061664.0,3666.4898134359532 +4772,Guyana,1985,,,,,1.2538563013076782,762243.0,1644.9561377509247 +4773,Paraguay,1985,,,,,11.260252952575685,3633464.0,3099.0407370420307 +4774,Peru,1985,,,,,64.56594848632811,19379177.0,3331.717775544757 +4775,Suriname,1985,,,,,2.427206516265869,394467.0,6153.129453834843 +4776,Uruguay,1985,,,,,17.687665939331055,3018871.0,5859.033373513163 +4777,Venezuela,1985,,,,,154.31277465820312,17197072.0,8973.200476116115 +4778,Austria,1985,,,,,224.100830078125,7559776.0,29643.845277707303 +4779,Belgium,1985,,,,,283.4377746582031,9858200.0,28751.47335803728 +4780,Denmark,1985,,,,,213.34652709960938,5113691.0,41720.65287081472 +4781,Finland,1985,,,,,141.1319580078125,4901783.0,28791.963660531786 +4782,France,1985,,,,,1612.533447265625,56560972.0,28509.648795738958 +4783,Germany,1985,,,,,2183.635986328125,77684907.0,28108.883316654097 +4784,Greece,1985,,,,,185.8466033935547,9923253.0,18728.39515364112 +4785,Ireland,1985,,,,,66.21577453613281,3540000.0,18705.021055404748 +4786,Italy,1985,,,,,1499.9844970703125,56718115.0,26446.303743879933 +4787,Luxembourg,1985,,,,,17.554025650024414,367169.0,47809.116918978485 +4788,Netherlands,1985,,,,,449.97454833984375,14504372.0,31023.373389750606 +4789,Portugal,1985,,,,,126.43571472167969,9897192.0,12774.907743699394 +4790,Spain,1985,,,,,700.7503662109375,38534853.0,18184.845968166468 +4791,Sweden,1985,,,,,285.1789245605469,8356337.0,34127.2646807503 +4792,UK,1985,,,,,1384.318359375,56584077.0,24464.80410690449 +4793,Bulgaria,1985,,,,,33.67678451538086,8943573.0,3765.473208009915 +4794,Croatia,1985,,,,,43.44182968139648,4457874.0,9744.965802397397 +4795,Cyprus,1985,,,,,8.46616268157959,679224.0,12464.463389956169 +4796,Czech Republic,1985,,,,,129.84381103515622,10310162.0,12593.770207990547 +4797,Estonia,1985,,,,,14.841897010803224,1538224.0,9648.722819825476 +4798,Hungary,1985,,,,,91.8135223388672,10648713.0,8622.029942854802 +4799,Latvia,1985,,,,,22.408750534057614,2606405.0,8597.570421349565 +4800,Lithuania,1985,,,,,29.491683959960934,3587032.0,8221.751007507304 +4801,Malta,1985,,,,,3.1589715480804443,347039.0,9102.641340254106 +4802,Poland,1985,,,,,231.1994781494141,37225792.0,6210.733626551561 +4803,Romania,1985,,,,,135.76187133789062,22521195.0,6028.182400529396 +4804,Slovakia,1985,,,,,47.67970275878906,5144632.0,9267.854874515624 +4805,Slovenia,1985,,,,,21.29779815673828,1914220.0,11126.097395669402 +4806,Iceland,1985,,,,,6.775668144226074,241403.0,28067.87050793103 +4807,Norway,1985,,,,,235.0506134033203,4152419.0,56605.7070356629 +4808,Switzerland,1985,,,,,373.817138671875,6567071.0,56922.962866074544 +4809,Albania,1985,,,,,6.0212769508361825,2956697.0,2036.4876586394146 +4810,Bosnia Herzegovina,1985,,,,,2.5691521167755127,4226764.0,607.829563414355 +4811,Macedonia,1985,,,,,6.902885913848878,1844740.0,3741.928897215259 +4812,Serbia,1985,,,,,34.593494415283196,7719700.0,4481.196732422659 +4813,Russia,1985,,,,,1275.5858154296875,143937997.0,8862.050619126563 +4814,Ukraine,1985,,,,,189.68240356445312,50944248.0,3723.333075099138 +4815,Armenia,1985,,,,,5.8014068603515625,3464297.0,1674.6274526553475 +4816,Azerbaijan,1985,,,,,24.098730087280273,6845030.0,3520.617161251342 +4817,Belarus,1985,,,,,27.819255828857425,9981744.0,2787.0135548314424 +4818,Georgia,1985,,,,,21.742563247680664,5192957.0,4186.933041748789 +4819,Kazakhstan,1985,,,,,84.01605987548828,15998854.0,5251.379872301372 +4820,Kyrgyzstan,1985,,,,,3.5776767730712886,4061575.0,880.8594629106416 +4821,Moldova,1985,,,,,8.903709411621094,4148172.0,2146.417605543139 +4822,Tajikistan,1985,,,,,6.264791488647461,4568802.0,1371.2109845529442 +4823,Turkmenistan,1985,,,,,6.850728511810303,3240472.0,2114.114398090865 +4824,Uzbekistan,1985,,,,,14.229007720947266,18215415.0,781.1519924716107 +4825,China,1985,,,,,566.1898193359375,1061875848.0,533.1977560298909 +4826,Hong Kong,1985,,,,,71.74213409423828,5456200.0,13148.736133983044 +4827,Japan,1985,,,,,3700.65673828125,120754335.0,30646.160556316674 +4828,Korea,1985,,,,,220.5693817138672,40806000.0,5405.317397291261 +4829,Taiwan,1985,,,,,99.93534088134766,19337363.0,5167.992185974254 +4830,Macau,1985,,,,,4.967307090759277,305843.0,16241.362695105912 +4831,Mongolia,1985,,,,,3.206662893295288,1859256.0,1724.7021890989124 +4832,Cambodia,1985,,,,,3.330043077468872,7841242.0,424.6831149285881 +4833,Indonesia,1985,,,,,228.7865753173828,166069318.0,1377.6571017012475 +4834,Malaysia,1985,,,,,58.76929092407226,15649109.0,3755.4400652505046 +4835,Myanmar,1985,,,,,7.46716833114624,36733456.0,203.27976575757646 +4836,Philippines,1985,,,,,75.00370788574219,54588816.0,1373.975722165181 +4837,Thailand,1985,,,,,86.71569061279297,51758689.0,1675.3842164123007 +4838,Vietnam,1985,,,,,23.321542739868164,60093068.0,388.0903990434997 +4839,Brunei,1985,,,,,9.5618257522583,217836.0,43894.60765097734 +4840,Laos,1985,,,,,1.5937702655792236,3657169.0,435.79344175213765 +4841,Singapore,1985,,,,,44.69573211669922,2749897.0,16253.602268266492 +4842,Bangladesh,1985,,,,,35.03896331787109,94584305.0,370.4521941338057 +4843,India,1985,,,,,378.7749633789063,759612212.0,498.6425407532893 +4844,Pakistan,1985,,,,,59.28790283203125,102078649.0,580.8061079651559 +4845,Afghanistan,1985,,,,,7.698607444763184,13119943.0,586.7866533233554 +4846,Bhutan,1985,,,,,0.2358017712831497,529200.0,445.581578388416 +4847,Nepal,1985,,,,,5.450202941894531,16438782.0,331.54542361438524 +4848,Sri Lanka,1985,,,,,17.407991409301758,15847023.0,1098.5023123460953 +4849,Australia,1985,,,,,526.1226196289061,15695238.0,33521.162255004114 +4850,New Zealand,1985,,,,,81.5150146484375,3323811.0,24524.563715697885 +4851,Fiji,1985,,,,,1.8465410470962524,698942.0,2641.9088380670387 +4852,Maldives,1985,,,,,0.6262139081954956,177297.0,3532.005099891682 +4853,Papua New Guinea,1985,,,,,6.074358940124513,3228928.0,1881.230841977434 +4854,Samoa,1985,,,,,0.3748463690280914,160737.0,2332.047811195253 +4855,Solomon Islands,1985,,,,,0.3469950556755066,272932.0,1271.3608359426764 +4856,Tonga,1985,,,,,0.2414544224739075,94380.0,2558.3219164431816 +4857,Iran,1985,,,,,227.8575134277344,48618977.0,4686.596211757693 +4858,Iraq,1985,,,,,40.75436401367188,15693620.0,2596.874654392797 +4859,Saudi Arabia,1985,,,,,207.5272521972656,13330067.0,15568.35777324042 +4860,Turkey,1985,,,,,278.05938720703125,50988566.0,5453.367470797889 +4861,Bahrain,1985,,,,,7.099430561065674,423224.0,16774.64076013098 +4862,Israel,1985,,,,,79.45761108398438,4049057.0,19623.73241077722 +4863,Jordan,1985,,,,,9.200193405151367,2668563.0,3447.6208375636497 +4864,Kuwait,1985,,,,,41.45398330688477,1732824.0,23922.789219727314 +4865,Lebanon,1985,,,,,14.813089370727539,3176885.0,4662.771668073456 +4866,Oman,1985,,,,,23.18493843078613,1497225.0,15485.273376270188 +4867,Syria,1985,,,,,21.0051212310791,10478546.0,2004.5835778245473 +4868,United Arab Emirates,1985,,,,,110.50418853759766,1363482.0,81045.57928714692 +4869,Yemen,1985,,,,,10.00054168701172,10539928.0,948.8244784036208 +4870,Egypt,1985,,,,,67.38710021972656,50052381.0,1346.331560525094 +4871,Morocco,1985,,,,,33.323837280273445,21643785.0,1539.649247129069 +4872,Algeria,1985,,,,,88.56462097167969,22008450.0,4024.1189621113567 +4873,Libya,1985,,,,,52.24468612670898,3660030.0,14274.387403029206 +4874,Tunisia,1985,,,,,15.865872383117676,7368354.0,2153.245132239531 +4875,Nigeria,1985,,,,,114.24020385742188,84897264.0,1345.6288044503046 +4876,South Africa,1985,,,,,205.32440185546875,34254092.0,5994.156898261053 +4877,Benin,1985,,,,,2.753591060638428,4030484.0,683.1911652889399 +4878,Burkina Faso,1985,,,,,2.599759101867676,7170932.0,362.54131288201813 +4879,Cabo Verde,1985,,,,,0.2659259736537933,316887.0,839.1823383533983 +4880,Cote d'Ivoire,1985,,,,,16.764579772949222,10331684.0,1622.637681616009 +4881,Gambia,1985,,,,,0.3866654932498932,772519.0,500.52554467902166 +4882,Ghana,1985,,,,,9.528111457824707,13572481.0,702.0169310109703 +4883,Guinea,1985,,,,,2.8013017177581787,5226741.0,535.9557165274075 +4884,Guinea Bissau,1985,,,,,0.5165000557899475,885063.0,583.5743396684162 +4885,Liberia,1985,,,,,1.6777551174163818,2163053.0,775.6421675365243 +4886,Mali,1985,,,,,3.6420624256134033,7625898.0,477.591285067464 +4887,Niger,1985,,,,,2.703521013259888,6869209.0,393.57093564337436 +4888,Senegal,1985,,,,,7.174026012420653,6378411.0,1124.7356139986364 +4889,Sierra Leone,1985,,,,,1.7082113027572632,3703092.0,461.2932389358037 +4890,Togo,1985,,,,,1.8626047372817995,3129427.0,595.1903454791562 +4891,Angola,1985,,,,,28.98472023010254,9472346.0,3059.9304786905523 +4892,Botswana,1985,,,,,3.0477395057678223,1077420.0,2828.738565988957 +4893,Burundi,1985,,,,,1.4864046573638916,4914042.0,302.48106494895484 +4894,Cameroon,1985,,,,,17.80937957763672,10226702.0,1741.4587398397564 +4895,Central Afr Rep,1985,,,,,1.378771185874939,2714154.0,507.99298266603114 +4896,Chad,1985,,,,,2.8348424434661865,5066042.0,559.5773669989682 +4897,Dem Rep Congo,1985,,,,,24.387365341186523,33347628.0,731.3073463931685 +4898,Djibouti,1985,,,,,0.61237633228302,382218.0,1602.1650793081958 +4899,Equatorial Guinea,1985,,,,,0.21336813271045685,325194.0,656.1256748601046 +4900,Eritrea,1985,,,,,0.8572006821632385,2941211.0,291.4448103734273 +4901,Ethiopia,1985,,,,,6.940213203430176,40683742.0,170.58935246001155 +4902,Gabon,1985,,,,,9.99624729156494,833036.0,11999.778270764938 +4903,Kenya,1985,,,,,16.547433853149414,19762320.0,837.3224324446428 +4904,Lesotho,1985,,,,,0.8424497842788696,1551635.0,542.943272276579 +4905,Madagascar,1985,,,,,4.988025665283203,10028632.0,497.3784724858986 +4906,Malawi,1985,,,,,2.783888339996338,7320634.0,380.2796779618183 +4907,Mauritania,1985,,,,,1.8305710554122925,1723466.0,1062.1451513475129 +4908,Mauritius,1985,,,,,2.7473430633544917,1020544.0,2692.0378380104057 +4909,Mozambique,1985,,,,,1.7559853792190552,13293047.0,132.0980343497661 +4910,Namibia,1985,,,,,4.418475151062012,1199645.0,3683.1522250849303 +4911,Rep Congo,1985,,,,,6.728442668914795,1926206.0,3493.1064844127754 +4912,Rwanda,1985,,,,,2.423496723175049,6025284.0,402.22116055858095 +4913,Sao Tome Principe,1985,,,,,0.10845690965652466,104186.0,1040.9931243787519 +4914,Seychelles,1985,,,,,0.4011497497558594,67175.0,5971.711942774237 +4915,Sudan,1985,,,,,16.038103103637695,17506721.0,916.1111954453204 +4916,Swaziland,1985,,,,,1.2505207061767578,721571.0,1733.0528890112796 +4917,Tanzania,1985,,,,,10.226048469543457,21484003.0,475.9843158439076 +4918,Uganda,1985,,,,,3.8362085819244394,13810985.0,277.7650241401637 +4919,Zambia,1985,,,,,7.747157573699951,6535429.0,1185.4091864053532 +4920,Canada,1986,,,,,910.2595825195312,26203800.0,34737.693865757305 +4921,United States,1986,,,,,7956.49365234375,240132887.0,33133.710887104564 +4922,Cuba,1986,,,,,45.21755599975586,10182674.0,4440.636712886601 +4923,Mexico,1986,,,,,579.7264404296875,78248289.0,7408.806605722553 +4924,Antigua Barbuda,1986,,,,,0.5691895484924316,64402.0,8838.072551977139 +4925,Bahamas,1986,,,,,6.873957633972168,231427.0,29702.487756278082 +4926,Barbados,1986,,,,,3.419962167739868,258265.0,13242.065969991552 +4927,Belize,1986,,,,,0.34580862522125244,171241.0,2019.426569695648 +4928,Costa Rica,1986,,,,,12.851003646850586,2723378.0,4718.773393502696 +4929,Dominica,1986,,,,,0.2635422945022583,72379.0,3641.1430732983085 +4930,Dominican Rep,1986,,,,,17.320281982421875,6516820.0,2657.781246439502 +4931,El Salvador,1986,,,,,10.261292457580566,4758322.0,2156.4939189866864 +4932,Grenada,1986,,,,,0.3789392113685608,93554.0,4050.4864716480406 +4933,Guatemala,1986,,,,,17.249008178710938,7819999.0,2205.7558036402484 +4934,Haiti,1986,,,,,6.813077926635742,6204938.0,1098.0090254948143 +4935,Honduras,1986,,,,,6.752364158630371,4226919.0,1597.4671288071454 +4936,Jamaica,1986,,,,,8.276861190795897,2322553.0,3563.6909860812207 +4937,Nicaragua,1986,,,,,5.506178855895996,3248094.0,1695.2030501260112 +4938,Panama,1986,,,,,10.747668266296387,2212156.0,4858.4585654431185 +4939,Puerto Rico,1986,,,,,53.961219787597656,3413303.0,15809.091600598498 +4940,St Kitts Nevis,1986,,,,,0.30816105008125305,42729.0,7211.988347053595 +4941,St Lucia,1986,,,,,0.6346067190170288,132714.0,4781.761675610929 +4942,St Vincent Grenadines,1986,,,,,0.3113522231578827,104763.0,2971.967423211274 +4943,Trinidad and Tobago,1986,,,,,8.6575927734375,1208327.0,7164.941918402469 +4944,Argentina,1986,,,,,222.9659576416016,31144993.0,7158.9663751602575 +4945,Brazil,1986,,,,,1148.5633544921875,138567223.0,8288.853089681876 +4946,Bolivia,1986,,,,,8.132089614868164,6041350.0,1346.0715924202643 +4947,Chile,1986,,,,,60.67497253417969,12144931.0,4995.9092014750595 +4948,Colombia,1986,,,,,124.92794036865234,30410385.0,4108.0683578538155 +4949,Ecuador,1986,,,,,34.375656127929695,9301083.0,3695.8767197249695 +4950,Guyana,1986,,,,,1.242564082145691,763375.0,1627.7243584682376 +4951,Paraguay,1986,,,,,11.819305419921875,3739904.0,3160.3232114840052 +4952,Peru,1986,,,,,70.65190887451172,19798248.0,3568.5939924841687 +4953,Suriname,1986,,,,,2.446624279022217,400143.0,6114.374808561482 +4954,Uruguay,1986,,,,,19.24591636657715,3032257.0,6347.059753370888 +4955,Venezuela,1986,,,,,164.3590850830078,17706633.0,9282.34549634636 +4956,Austria,1986,,,,,229.25827026367188,7568242.0,30292.143177196485 +4957,Belgium,1986,,,,,288.60418701171875,9861800.0,29264.85905328832 +4958,Denmark,1986,,,,,223.80958557128903,5120534.0,43708.25104789638 +4959,Finland,1986,,,,,144.97940063476562,4917386.0,29483.022206262765 +4960,France,1986,,,,,1650.4481201171875,56799537.0,29057.4220722466 +4961,Germany,1986,,,,,2233.583251953125,77713485.0,28741.257092679927 +4962,Greece,1986,,,,,186.80865478515625,9951370.0,18772.15446568224 +4963,Ireland,1986,,,,,65.93215179443361,3540500.0,18622.27137252749 +4964,Italy,1986,,,,,1542.8836669921875,56717458.0,27202.97632154437 +4965,Luxembourg,1986,,,,,19.30660820007324,369019.0,52318.73751777887 +4966,Netherlands,1986,,,,,462.5152587890625,14585132.0,31711.420835208246 +4967,Portugal,1986,,,,,131.67135620117188,9907411.0,13290.188143115478 +4968,Spain,1986,,,,,723.5480346679688,38707556.0,18692.68198353749 +4969,Sweden,1986,,,,,292.8540344238281,8384069.0,34929.821596629045 +4970,UK,1986,,,,,1427.568359375,56750920.0,25154.981793687217 +4971,Bulgaria,1986,,,,,35.09376907348633,8958770.0,3917.253046287194 +4972,Croatia,1986,,,,,43.737693786621094,4471752.0,9780.88538600108 +4973,Cyprus,1986,,,,,8.779745101928711,690425.0,12716.435676472769 +4974,Czech Republic,1986,,,,,133.85964965820312,10308836.0,12984.94317478745 +4975,Estonia,1986,,,,,15.005521774291992,1550072.0,9680.532113535366 +4976,Hungary,1986,,,,,93.2573013305664,10630563.0,8772.564663843901 +4977,Latvia,1986,,,,,23.463830947875977,2626695.0,8932.83420719801 +4978,Lithuania,1986,,,,,30.40379905700684,3619593.0,8399.783914104939 +4979,Malta,1986,,,,,3.281703472137451,346625.0,9467.59025499445 +4980,Poland,1986,,,,,240.96849060058597,37504275.0,6425.09395530472 +4981,Romania,1986,,,,,138.93418884277344,22599678.0,6147.618069725305 +4982,Slovakia,1986,,,,,49.66635513305664,5172084.0,9602.774265278103 +4983,Slovenia,1986,,,,,21.956493377685547,1932129.0,11363.885836652495 +4984,Iceland,1986,,,,,7.2005391120910645,242999.0,29631.97014016957 +4985,Norway,1986,,,,,244.5521240234375,4166596.0,58693.50520747332 +4986,Switzerland,1986,,,,,380.7650146484375,6607178.0,57628.99299041701 +4987,Albania,1986,,,,,6.360711097717285,3015438.0,2109.382152018143 +4988,Bosnia Herzegovina,1986,,,,,2.6486098766326904,4247288.0,623.6002542405155 +4989,Macedonia,1986,,,,,7.043760776519775,1850679.0,3806.0413375413973 +4990,Serbia,1986,,,,,35.480506896972656,7739838.0,4584.140765862626 +4991,Russia,1986,,,,,1318.955810546875,144966550.0,9098.345863558698 +4992,Ukraine,1986,,,,,195.54879760742188,51095072.0,3827.1557305452448 +4993,Armenia,1986,,,,,5.980830669403076,3528533.0,1694.990714102171 +4994,Azerbaijan,1986,,,,,24.844053268432614,6970134.0,3564.3580551582827 +4995,Belarus,1986,,,,,28.679645538330078,10044184.0,2855.348482099699 +4996,Georgia,1986,,,,,19.965049743652344,5218973.0,3825.474809632536 +4997,Kazakhstan,1986,,,,,86.61449432373047,16193001.0,5348.884639958366 +4998,Kyrgyzstan,1986,,,,,3.787774085998535,4156285.0,911.3364665797786 +4999,Moldova,1986,,,,,9.607867240905762,4183283.0,2296.728966437547 +5000,Tajikistan,1986,,,,,6.484058856964111,4715514.0,1375.0481616562079 +5001,Turkmenistan,1986,,,,,7.062605381011963,3323673.0,2124.9399026354167 +5002,Uzbekistan,1986,,,,,14.519388198852539,18717630.0,775.706550394069 +5003,China,1986,,,,,616.8054809570312,1078563527.0,571.8768208977563 +5004,Hong Kong,1986,,,,,79.674072265625,5524600.0,14421.6906682158 +5005,Japan,1986,,,,,3823.760009765625,121491913.0,31473.370657729498 +5006,Korea,1986,,,,,245.3262634277344,41214000.0,5952.498263399194 +5007,Taiwan,1986,,,,,111.4478988647461,19555581.0,5699.032867637433 +5008,Macau,1986,,,,,5.2997708320617685,312196.0,16975.780702064625 +5009,Mongolia,1986,,,,,3.5072872638702397,1902828.0,1843.197211660875 +5010,Cambodia,1986,,,,,3.4979426860809326,8101420.0,431.7690832077503 +5011,Indonesia,1986,,,,,242.22789001464844,169155944.0,1431.9797713679422 +5012,Malaysia,1986,,,,,59.49837875366211,16111169.0,3692.989549899334 +5013,Myanmar,1986,,,,,7.388214588165282,37443565.0,197.3160031146949 +5014,Philippines,1986,,,,,77.56642150878906,55972240.0,1385.8016314656884 +5015,Thailand,1986,,,,,91.5143814086914,52637818.0,1738.567153537622 +5016,Vietnam,1986,,,,,23.97204971313477,61439826.0,390.1711849433748 +5017,Brunei,1986,,,,,9.30217742919922,224731.0,41392.497827176565 +5018,Laos,1986,,,,,1.6716279983520508,3761769.0,444.37284648580254 +5019,Singapore,1986,,,,,45.28912353515625,2750069.0,16468.358988504016 +5020,Bangladesh,1986,,,,,36.50127410888672,96806446.0,377.05416960443637 +5021,India,1986,,,,,396.8674011230469,775063241.0,512.0451856431763 +5022,Pakistan,1986,,,,,62.54971694946289,105239621.0,594.3552091418392 +5023,Afghanistan,1986,,,,,8.489184379577638,13125920.0,646.7496662769266 +5024,Bhutan,1986,,,,,0.2634626626968384,546811.0,481.81668382098815 +5025,Nepal,1986,,,,,5.699040412902832,16847975.0,338.26263470255816 +5026,Sri Lanka,1986,,,,,18.16620445251465,16069843.0,1130.453138373203 +5027,Australia,1986,,,,,547.4576416015625,15900415.0,34430.399558851925 +5028,New Zealand,1986,,,,,83.72064971923828,3340633.0,25061.313146112807 +5029,Fiji,1986,,,,,1.9896461963653564,712989.0,2790.570676918377 +5030,Maldives,1986,,,,,0.6800320148468018,184612.0,3683.574279281963 +5031,Papua New Guinea,1986,,,,,6.3594322204589835,3313754.0,1919.1020879820844 +5032,Samoa,1986,,,,,0.39548006653785706,160859.0,2458.551069805588 +5033,Solomon Islands,1986,,,,,0.3462059795856476,282121.0,1227.1542337707851 +5034,Tonga,1986,,,,,0.24657002091407776,94892.0,2598.4279066104386 +5035,Iran,1986,,,,,204.6018218994141,50464396.0,4054.3796838351946 +5036,Iraq,1986,,,,,42.6484603881836,16247340.0,2624.9503234488598 +5037,Saudi Arabia,1986,,,,,242.8333282470703,13998295.0,17347.350391391974 +5038,Turkey,1986,,,,,297.5570068359375,52120785.0,5708.989356855187 +5039,Bahrain,1986,,,,,7.1834163665771475,439410.0,16347.867291543545 +5040,Israel,1986,,,,,82.78700256347656,4120412.0,20091.923468691133 +5041,Jordan,1986,,,,,9.84550952911377,2768259.0,3556.5709455342762 +5042,Kuwait,1986,,,,,45.00461196899414,1809382.0,24872.919023729726 +5043,Lebanon,1986,,,,,13.809762001037598,3232383.0,4272.316121275727 +5044,Oman,1986,,,,,23.64913749694824,1554393.0,15214.387543528723 +5045,Syria,1986,,,,,19.966270446777344,10867866.0,1837.1840844170645 +5046,United Arab Emirates,1986,,,,,93.97482299804688,1446574.0,64963.716338083555 +5047,Yemen,1986,,,,,10.300559043884276,10869580.0,947.6501432331587 +5048,Egypt,1986,,,,,70.58429718017578,51593433.0,1368.0868489634288 +5049,Morocco,1986,,,,,36.41332244873047,22127436.0,1645.6186992804078 +5050,Algeria,1986,,,,,88.91887664794922,22642538.0,3927.071984949268 +5051,Libya,1986,,,,,46.31334686279297,3680901.0,12582.068048772018 +5052,Tunisia,1986,,,,,15.636295318603516,7556541.0,2069.2397908783287 +5053,Nigeria,1986,,,,,114.30982971191406,87100425.0,1312.391181924934 +5054,South Africa,1986,,,,,205.3610382080078,35103314.0,5850.189478064886 +5055,Benin,1986,,,,,2.813375473022461,4157854.0,676.6412368068867 +5056,Burkina Faso,1986,,,,,2.806579351425171,7371099.0,380.7545321837586 +5057,Cabo Verde,1986,,,,,0.27356192469596863,321341.0,851.3134791264376 +5058,Cote d'Ivoire,1986,,,,,17.310997009277347,10712062.0,1616.0284555184 +5059,Gambia,1986,,,,,0.4024842381477356,805303.0,499.7922994794948 +5060,Ghana,1986,,,,,10.023492813110352,14142708.0,708.7392890463658 +5061,Guinea,1986,,,,,2.888535499572754,5394334.0,535.4758343796943 +5062,Guinea Bissau,1986,,,,,0.5123998522758484,905930.0,565.6064511340263 +5063,Liberia,1986,,,,,1.6579679250717163,2232988.0,742.4885064638576 +5064,Mali,1986,,,,,3.7401294708251953,7796017.0,479.74875770860876 +5065,Niger,1986,,,,,2.8754656314849854,7039813.0,408.457672311038 +5066,Senegal,1986,,,,,7.397377490997314,6549785.0,1129.407681473104 +5067,Sierra Leone,1986,,,,,1.729325532913208,3787392.0,456.60061934788047 +5068,Togo,1986,,,,,1.8921550512313845,3240720.0,583.8687239969464 +5069,Angola,1986,,,,,29.82527732849121,9801022.0,3043.078296170666 +5070,Botswana,1986,,,,,3.2968287467956543,1112309.0,2963.950437149798 +5071,Burundi,1986,,,,,1.5347154140472412,5008734.0,306.40784957780573 +5072,Cameroon,1986,,,,,19.01537132263184,10581537.0,1797.0330135056784 +5073,Central Afr Rep,1986,,,,,1.4281034469604492,2775360.0,514.5651183848039 +5074,Chad,1986,,,,,2.719133853912353,5214200.0,521.4862977853463 +5075,Dem Rep Congo,1986,,,,,25.53776741027832,34385929.0,742.6807462517102 +5076,Djibouti,1986,,,,,0.6050950288772583,396506.0,1526.0677742007895 +5077,Equatorial Guinea,1986,,,,,0.20839546620845795,333651.0,624.5911632467996 +5078,Eritrea,1986,,,,,0.8746945858001709,2877999.0,303.9245621003242 +5079,Ethiopia,1986,,,,,7.610750198364258,41642553.0,182.76377527487946 +5080,Gabon,1986,,,,,9.91591739654541,859448.0,11537.541999685158 +5081,Kenya,1986,,,,,17.73513412475586,20478459.0,866.0385102587973 +5082,Lesotho,1986,,,,,0.8819172978401184,1586595.0,555.855336642381 +5083,Madagascar,1986,,,,,5.085746765136719,10327391.0,492.4522335928521 +5084,Malawi,1986,,,,,2.777908563613892,7594475.0,365.78019726365443 +5085,Mauritania,1986,,,,,1.93516206741333,1763680.0,1097.229694396563 +5086,Mauritius,1986,,,,,3.0149900913238525,1028233.0,2932.205143507213 +5087,Mozambique,1986,,,,,1.7155977487564087,13432713.0,127.71788906354277 +5088,Namibia,1986,,,,,4.629115581512451,1234462.0,3749.9052879006817 +5089,Rep Congo,1986,,,,,6.266768932342528,1981276.0,3162.9964388316057 +5090,Rwanda,1986,,,,,2.5561139583587646,6218629.0,411.04139808931586 +5091,Sao Tome Principe,1986,,,,,0.10213157534599304,106345.0,960.3796637923084 +5092,Seychelles,1986,,,,,0.400440514087677,67838.0,5902.893866087989 +5093,Sudan,1986,,,,,16.906375885009766,18177803.0,930.0560626061227 +5094,Swaziland,1986,,,,,1.403890609741211,749420.0,1873.302833846456 +5095,Tanzania,1986,,,,,10.419476509094238,22105671.0,471.348574268306 +5096,Uganda,1986,,,,,3.8511731624603267,14271109.0,269.8580161121555 +5097,Zambia,1986,,,,,7.803238868713379,6757156.0,1154.8111170902935 +5098,Canada,1987,,,,,947.5479736328124,26549700.0,35689.59248627339 +5099,United States,1987,,,,,8231.9267578125,242288918.0,33975.66354154299 +5100,Cuba,1987,,,,,44.13135147094727,10278541.0,4293.542388063371 +5101,Mexico,1987,,,,,589.7118530273438,79903511.0,7380.299634484693 +5102,Antigua Barbuda,1987,,,,,0.6069061756134033,64418.0,9421.375634347594 +5103,Bahamas,1987,,,,,7.079112529754639,234482.0,30190.43052240529 +5104,Barbados,1987,,,,,3.5076322555541988,259124.0,13536.500885885518 +5105,Belize,1987,,,,,0.38293221592903137,176353.0,2171.396097197277 +5106,Costa Rica,1987,,,,,13.46329402923584,2799340.0,4809.452952923131 +5107,Dominica,1987,,,,,0.2801653742790222,71815.0,3901.2096954539056 +5108,Dominican Rep,1987,,,,,19.07263946533203,6656963.0,2865.0661668589764 +5109,El Salvador,1987,,,,,10.51921844482422,4850135.0,2168.8506494817607 +5110,Grenada,1987,,,,,0.4134129285812378,94064.0,4395.017526165566 +5111,Guatemala,1987,,,,,17.860309600830078,8159885.0,2188.7942784524635 +5112,Haiti,1987,,,,,6.7590065002441415,6335271.0,1066.8851419685348 +5113,Honduras,1987,,,,,7.200610160827638,4373262.0,1646.507838045751 +5114,Jamaica,1987,,,,,8.926624298095703,2325869.0,3837.973805960569 +5115,Nicaragua,1987,,,,,5.467285633087158,3322384.0,1645.5911276622926 +5116,Panama,1987,,,,,10.55323600769043,2256735.0,4676.329302151306 +5117,Puerto Rico,1987,,,,,57.0742416381836,3444468.0,16569.827804521217 +5118,St Kitts Nevis,1987,,,,,0.32834312319755554,42477.0,7729.903787874745 +5119,St Lucia,1987,,,,,0.6609755158424377,134180.0,4926.03603996451 +5120,St Vincent Grenadines,1987,,,,,0.31271618604660034,105495.0,2964.274951861229 +5121,Trinidad and Tobago,1987,,,,,8.262683868408201,1224989.0,6745.10862416577 +5122,Argentina,1987,,,,,228.99688720703125,31624620.0,7241.095298758728 +5123,Brazil,1987,,,,,1189.9073486328125,141352474.0,8418.015723112228 +5124,Bolivia,1987,,,,,8.332422256469728,6156369.0,1353.4637472948302 +5125,Chile,1987,,,,,64.59485626220703,12344445.0,5232.706392406222 +5126,Colombia,1987,,,,,131.63523864746094,31085303.0,4234.6455058669035 +5127,Ecuador,1987,,,,,34.286590576171875,9545158.0,3592.040129264689 +5128,Guyana,1987,,,,,1.253739595413208,764960.0,1638.9609854282685 +5129,Paraguay,1987,,,,,12.715481758117676,3849890.0,3302.816900773184 +5130,Peru,1987,,,,,77.52362060546875,20227047.0,3832.6712053157707 +5131,Suriname,1987,,,,,2.294933557510376,403912.0,5681.766220142942 +5132,Uruguay,1987,,,,,20.78426742553711,3044980.0,6825.748420527264 +5133,Venezuela,1987,,,,,170.245849609375,18226524.0,9340.554985107143 +5134,Austria,1987,,,,,232.36968994140625,7578903.0,30660.06913420138 +5135,Belgium,1987,,,,,295.2613220214844,9870200.0,29914.42139181419 +5136,Denmark,1987,,,,,224.3785552978516,5127024.0,43763.89798406474 +5137,Finland,1987,,,,,150.1393585205078,4931729.0,30443.55408022375 +5138,France,1987,,,,,1692.9854736328125,57066371.0,29666.955230652613 +5139,Germany,1987,,,,,2264.901611328125,77718298.0,29142.44997140989 +5140,Greece,1987,,,,,182.5889129638672,9974490.0,18305.58885355213 +5141,Ireland,1987,,,,,69.00662231445311,3539900.0,19493.94681048988 +5142,Italy,1987,,,,,1592.1318359375,56710054.0,28074.948331692653 +5143,Luxembourg,1987,,,,,20.069406509399414,371660.0,53999.37176290001 +5144,Netherlands,1987,,,,,471.44757080078125,14678769.0,32117.65038340621 +5145,Portugal,1987,,,,,140.07382202148438,9915289.0,14127.053888341969 +5146,Spain,1987,,,,,763.6842041015625,38880702.0,19641.7287965007 +5147,Sweden,1987,,,,,302.6759033203125,8421403.0,35941.26813789965 +5148,UK,1987,,,,,1503.3231201171875,56927038.0,26407.89285606582 +5149,Bulgaria,1987,,,,,37.218528747558594,8971958.0,4148.317317976588 +5150,Croatia,1987,,,,,45.014949798583984,4484310.0,10038.322461779846 +5151,Cyprus,1987,,,,,9.400056838989258,701618.0,13397.684835607493 +5152,Czech Republic,1987,,,,,137.99964904785156,10311597.0,13382.95601038826 +5153,Estonia,1987,,,,,15.531519889831545,1562224.0,9941.928871808104 +5154,Hungary,1987,,,,,96.99210357666016,10612740.0,9139.214149848216 +5155,Latvia,1987,,,,,24.012470245361328,2648965.0,9064.849949078727 +5156,Lithuania,1987,,,,,31.3441219329834,3653339.0,8579.582111866268 +5157,Malta,1987,,,,,3.416598081588745,349047.0,9788.361113514069 +5158,Poland,1987,,,,,245.68630981445312,37740710.0,6509.84864392994 +5159,Romania,1987,,,,,140.09390258789062,22686371.0,6175.245154365615 +5160,Slovakia,1987,,,,,50.88808822631836,5199318.0,9787.454475051989 +5161,Slovenia,1987,,,,,22.63556289672852,1945870.0,11632.618261614864 +5162,Iceland,1987,,,,,7.8158931732177725,244676.0,31943.84889902472 +5163,Norway,1987,,,,,248.8400421142578,4186147.0,59443.694192835996 +5164,Switzerland,1987,,,,,386.8019409179688,6654625.0,58125.27992455905 +5165,Albania,1987,,,,,6.310598850250244,3075321.0,2052.0130582304237 +5166,Bosnia Herzegovina,1987,,,,,2.7305238246917725,4266650.0,639.9690212911237 +5167,Macedonia,1987,,,,,7.187511920928955,1856378.0,3871.7933098371973 +5168,Serbia,1987,,,,,36.3902702331543,7755900.0,4691.946806064325 +5169,Russia,1987,,,,,1346.6539306640625,145958907.0,9226.253870646362 +5170,Ukraine,1987,,,,,206.22776794433597,51218101.0,4026.462596579595 +5171,Armenia,1987,,,,,6.165805339813232,3591617.0,1716.7212817550517 +5172,Azerbaijan,1987,,,,,25.612422943115234,7098945.0,3607.9196194808146 +5173,Belarus,1987,,,,,29.56664848327637,10097446.0,2928.131379289017 +5174,Georgia,1987,,,,,20.221010208129886,5239970.0,3858.993507239523 +5175,Kazakhstan,1987,,,,,89.29329681396484,16394711.0,5446.4697068441665 +5176,Kyrgyzstan,1987,,,,,3.912769556045532,4253647.0,919.8623101647908 +5177,Moldova,1987,,,,,9.723373413085938,4216682.0,2305.9299736347057 +5178,Tajikistan,1987,,,,,6.406250476837157,4864637.0,1316.902058023478 +5179,Turkmenistan,1987,,,,,9.505675315856934,3410704.0,2787.0126859020697 +5180,Uzbekistan,1987,,,,,17.779403686523438,19219173.0,925.0868227536864 +5181,China,1987,,,,,688.9034423828125,1097953369.0,627.4432611015674 +5182,Hong Kong,1987,,,,,90.34917449951172,5584510.0,16178.532136125054 +5183,Japan,1987,,,,,4004.649169921875,122091325.0,32800.43991595533 +5184,Korea,1987,,,,,275.9117431640625,41622000.0,6628.988111192699 +5185,Taiwan,1987,,,,,125.6017837524414,19757799.0,6357.073667590273 +5186,Macau,1987,,,,,6.057309150695801,322983.0,18754.266170961942 +5187,Mongolia,1987,,,,,3.6285920143127437,1947193.0,1863.4989003723533 +5188,Cambodia,1987,,,,,3.6743109226226807,8403953.0,437.21221699153733 +5189,Indonesia,1987,,,,,254.1598663330078,172231273.0,1475.6894140415939 +5190,Malaysia,1987,,,,,62.58749771118164,16578904.0,3775.1287848208567 +5191,Myanmar,1987,,,,,7.092269420623778,38155635.0,185.87737880980828 +5192,Philippines,1987,,,,,80.91080474853516,57337128.0,1411.14156866272 +5193,Thailand,1987,,,,,100.2255859375,53512779.0,1872.9280708351928 +5194,Vietnam,1987,,,,,24.83107948303223,62826491.0,395.2326333653144 +5195,Brunei,1987,,,,,9.489069938659668,231818.0,40933.27497717894 +5196,Laos,1987,,,,,1.6477940082550049,3869313.0,425.8621642278629 +5197,Singapore,1987,,,,,50.16082000732422,2794552.0,17949.50317880083 +5198,Bangladesh,1987,,,,,37.87825012207031,99076293.0,382.31396205013766 +5199,India,1987,,,,,412.6046142578125,790640434.0,521.8612614717507 +5200,Pakistan,1987,,,,,66.58563995361328,108584077.0,613.2173500320242 +5201,Afghanistan,1987,,,,,7.430811405181885,13056450.0,569.1295417346894 +5202,Bhutan,1987,,,,,0.3390666246414185,564715.0,600.4207868418911 +5203,Nepal,1987,,,,,5.7956743240356445,17272792.0,335.5377824289 +5204,Sri Lanka,1987,,,,,18.47968292236328,16287539.0,1134.5902485552472 +5205,Australia,1987,,,,,561.1732177734375,16136716.0,34776.17241162561 +5206,New Zealand,1987,,,,,80.11135864257812,3352493.0,23896.055455620084 +5207,Fiji,1987,,,,,1.8580307960510252,719671.0,2581.778056988576 +5208,Maldives,1987,,,,,0.7403112649917603,192207.0,3851.635294197195 +5209,Papua New Guinea,1987,,,,,6.5356202125549325,3401467.0,1921.4122061319226 +5210,Samoa,1987,,,,,0.3973906338214874,161096.0,2466.7939230116667 +5211,Solomon Islands,1987,,,,,0.37540170550346375,291544.0,1287.6331034199427 +5212,Tonga,1987,,,,,0.2539317309856415,95347.0,2663.2377629672824 +5213,Iran,1987,,,,,205.9286956787109,52208991.0,3944.314795869373 +5214,Iraq,1987,,,,,46.61701965332031,16543189.0,2817.8980276003804 +5215,Saudi Arabia,1987,,,,,226.7274169921875,14619745.0,15508.301751650764 +5216,Turkey,1987,,,,,325.7818603515625,53239145.0,6119.216609349428 +5217,Bahrain,1987,,,,,7.930420875549316,455777.0,17399.782954272192 +5218,Israel,1987,,,,,88.69774627685547,4191248.0,21162.609866286955 +5219,Jordan,1987,,,,,10.130597114562988,2867272.0,3533.1831491965145 +5220,Kuwait,1987,,,,,48.66913986206055,1887220.0,25788.800384724917 +5221,Lebanon,1987,,,,,16.120000839233402,3285734.0,4906.057775593946 +5222,Oman,1987,,,,,22.83542251586914,1611672.0,14168.77783808935 +5223,Syria,1987,,,,,20.34713554382324,11268554.0,1805.6563019375196 +5224,United Arab Emirates,1987,,,,,97.1530303955078,1530444.0,63480.290945312474 +5225,Yemen,1987,,,,,10.630176544189453,11219340.0,947.4867990621063 +5226,Egypt,1987,,,,,73.28961944580078,52432011.0,1397.8029461010142 +5227,Morocco,1987,,,,,36.29497909545898,22610591.0,1605.2202746694672 +5228,Algeria,1987,,,,,88.29644775390625,23254956.0,3796.8873281852802 +5229,Libya,1987,,,,,39.50436019897461,3775716.0,10462.746721144971 +5230,Tunisia,1987,,,,,16.684141159057614,7742353.0,2154.9186867425988 +5231,Nigeria,1987,,,,,117.96788024902344,89418367.0,1319.2801904895382 +5232,South Africa,1987,,,,,209.6751251220703,35953740.0,5831.802897892411 +5233,Benin,1987,,,,,2.771174907684326,4287908.0,646.2766709743602 +5234,Burkina Faso,1987,,,,,2.7999463081359863,7591809.0,368.8114793372682 +5235,Cabo Verde,1987,,,,,0.2853498756885529,325869.0,875.6582420805687 +5236,Cote d'Ivoire,1987,,,,,17.250585556030273,11105274.0,1553.368746780158 +5237,Gambia,1987,,,,,0.41236254572868347,839453.0,491.2276753179552 +5238,Ghana,1987,,,,,10.504109382629396,14497722.0,724.5351637056771 +5239,Guinea,1987,,,,,2.9838552474975586,5528408.0,539.7313742939302 +5240,Guinea Bissau,1987,,,,,0.5272594690322876,927409.0,568.5296013218414 +5241,Liberia,1987,,,,,1.6877318620681765,2306075.0,731.8633878205072 +5242,Mali,1987,,,,,3.7349245548248287,7965610.0,468.88117229249605 +5243,Niger,1987,,,,,2.8776094913482666,7215959.0,398.78406894333335 +5244,Senegal,1987,,,,,7.848201274871826,6727841.0,1166.5259739152316 +5245,Sierra Leone,1987,,,,,1.8543776273727417,3875583.0,478.4770774804053 +5246,Togo,1987,,,,,1.9018081426620483,3355920.0,566.7024668830152 +5247,Angola,1987,,,,,31.04296875,10104878.0,3072.077540174161 +5248,Botswana,1987,,,,,3.6885557174682617,1147348.0,3214.8534860114473 +5249,Burundi,1987,,,,,1.6191723346710205,5130840.0,315.57646207463506 +5250,Cameroon,1987,,,,,18.607177734375,10936402.0,1701.3984795342199 +5251,Central Afr Rep,1987,,,,,1.3575695753097534,2840877.0,477.869888527294 +5252,Chad,1987,,,,,2.6541788578033447,5391517.0,492.2879512024806 +5253,Dem Rep Congo,1987,,,,,26.221065521240234,35504145.0,738.5353321771369 +5254,Djibouti,1987,,,,,0.6080889105796814,409458.0,1485.1069232489813 +5255,Equatorial Guinea,1987,,,,,0.2176414132118225,342469.0,635.506901973091 +5256,Eritrea,1987,,,,,0.8925450444221497,2812612.0,317.3367120748079 +5257,Ethiopia,1987,,,,,8.665549278259277,43058596.0,201.2501586967508 +5258,Gabon,1987,,,,,8.215729713439941,880916.0,9326.3486114907 +5259,Kenya,1987,,,,,18.788087844848636,21197835.0,886.3210721683904 +5260,Lesotho,1987,,,,,0.8883529305458069,1615692.0,549.8281420876051 +5261,Madagascar,1987,,,,,5.145661354064941,10636882.0,483.7565514090447 +5262,Malawi,1987,,,,,2.8230562210083012,8013773.0,352.2755412473376 +5263,Mauritania,1987,,,,,1.9719376564025881,1805394.0,1092.247817596928 +5264,Mauritius,1987,,,,,3.2830538749694824,1036272.0,3168.1391323605017 +5265,Mozambique,1987,,,,,1.9677906036376955,13229589.0,148.741627849338 +5266,Namibia,1987,,,,,4.793623924255371,1270353.0,3773.4581838712324 +5267,Rep Congo,1987,,,,,6.278636932373048,2038301.0,3080.3286327058895 +5268,Rwanda,1987,,,,,2.55549955368042,6448300.0,396.30593391753166 +5269,Sao Tome Principe,1987,,,,,0.099139004945755,108595.0,912.9242133224826 +5270,Seychelles,1987,,,,,0.418076366186142,68460.0,6106.870671722787 +5271,Sudan,1987,,,,,19.31061172485352,18874610.0,1023.0999064273919 +5272,Swaziland,1987,,,,,1.608951210975647,777590.0,2069.1511091650445 +5273,Tanzania,1987,,,,,10.933218002319336,22788161.0,479.77623127725565 +5274,Uganda,1987,,,,,4.003752708435059,14811287.0,270.31767789220874 +5275,Zambia,1987,,,,,8.012026786804201,6985531.0,1146.9459926244974 +5276,Canada,1988,,,,,989.5186767578124,26894800.0,36792.19316588384 +5277,United States,1988,,,,,8577.9951171875,244498982.0,35083.970685765475 +5278,Cuba,1988,,,,,45.76665496826172,10381668.0,4408.410572199161 +5279,Mexico,1988,,,,,597.2794189453125,81540513.0,7324.940657968543 +5280,Antigua Barbuda,1988,,,,,0.6385458111763,64427.0,9911.15233017679 +5281,Bahamas,1988,,,,,7.242619037628174,237708.0,30468.55401428716 +5282,Barbados,1988,,,,,3.628798961639404,259953.0,13959.44252091495 +5283,Belize,1988,,,,,0.420412540435791,181396.0,2317.650556990182 +5284,Costa Rica,1988,,,,,13.925374031066895,2874123.0,4845.086320615678 +5285,Dominica,1988,,,,,0.30206483602523804,71338.0,4234.276767294262 +5286,Dominican Rep,1988,,,,,19.483837127685547,6798773.0,2865.787271862959 +5287,El Salvador,1988,,,,,10.716712951660156,4938989.0,2169.819157657601 +5288,Grenada,1988,,,,,0.4256780445575714,94286.0,4514.753458175884 +5289,Guatemala,1988,,,,,18.555503845214844,8495815.0,2184.0757885164453 +5290,Haiti,1988,,,,,6.772553443908691,6458500.0,1048.626375150374 +5291,Honduras,1988,,,,,7.326335430145264,4514984.0,1622.6714048477832 +5292,Jamaica,1988,,,,,9.281394958496094,2329431.0,3984.4043281368254 +5293,Nicaragua,1988,,,,,4.786618232727051,3403564.0,1406.3547013445468 +5294,Panama,1988,,,,,9.141229629516602,2301676.0,3971.5536111583915 +5295,Puerto Rico,1988,,,,,60.43498611450195,3474992.0,17391.402948410232 +5296,St Kitts Nevis,1988,,,,,0.3586362898349762,42191.0,8500.303141309194 +5297,St Lucia,1988,,,,,0.7511067986488342,135495.0,5543.42816080914 +5298,St Vincent Grenadines,1988,,,,,0.3567285239696503,106138.0,3360.987808039065 +5299,Trinidad and Tobago,1988,,,,,7.938982963562013,1238380.0,6410.780990941402 +5300,Argentina,1988,,,,,226.50099182128903,32099320.0,7056.255142516697 +5301,Brazil,1988,,,,,1188.6856689453125,144096245.0,8249.248056015009 +5302,Bolivia,1988,,,,,8.574854850769043,6283164.0,1364.7351638074456 +5303,Chile,1988,,,,,69.33942413330078,12554615.0,5523.0227397097215 +5304,Colombia,1988,,,,,136.98512268066406,31770867.0,4311.658308873474 +5305,Ecuador,1988,,,,,36.30622863769531,9794122.0,3706.940615779068 +5306,Guyana,1988,,,,,1.2074323892593384,766550.0,1575.1515090461658 +5307,Paraguay,1988,,,,,13.467649459838867,3963437.0,3397.972381001355 +5308,Peru,1988,,,,,70.20439910888672,20664916.0,3397.274835711247 +5309,Suriname,1988,,,,,2.473938465118408,407914.0,6064.853045294862 +5310,Uruguay,1988,,,,,21.09206962585449,3057630.0,6898.1759159396315 +5311,Venezuela,1988,,,,,180.15647888183597,18757806.0,9604.347058597148 +5312,Austria,1988,,,,,240.0283050537109,7599791.0,31583.54026495083 +5313,Belgium,1988,,,,,309.2071228027344,9884000.0,31283.60206421837 +5314,Denmark,1988,,,,,224.34799194335938,5129516.0,43736.67845920734 +5315,Finland,1988,,,,,157.96141052246094,4946633.0,31933.117035862764 +5316,France,1988,,,,,1773.130615234375,57335273.0,30925.65051943461 +5317,Germany,1988,,,,,2348.86669921875,78030572.0,30101.87723881801 +5318,Greece,1988,,,,,190.4180603027344,9982723.0,19074.761495709576 +5319,Ireland,1988,,,,,72.60698699951172,3529600.0,20570.882536126395 +5320,Italy,1988,,,,,1658.911865234375,56711103.0,29251.976729043254 +5321,Luxembourg,1988,,,,,21.76793670654297,375624.0,57951.40008770197 +5322,Netherlands,1988,,,,,487.6710815429688,14774965.0,33006.581169090336 +5323,Portugal,1988,,,,,150.56410217285156,9920611.0,15176.89809356012 +5324,Spain,1988,,,,,802.5887451171875,39053881.0,20550.80633643523 +5325,Sweden,1988,,,,,310.4215087890625,8469339.0,36652.3891402933 +5326,UK,1988,,,,,1589.728515625,57095731.0,27843.2115288094 +5327,Bulgaria,1988,,,,,41.2919807434082,8982025.0,4597.179449334442 +5328,Croatia,1988,,,,,44.18175506591797,4493676.0,9831.985008691763 +5329,Cyprus,1988,,,,,10.212858200073242,713026.0,14323.261984939178 +5330,Czech Republic,1988,,,,,142.26766967773438,10313782.0,13793.938021739685 +5331,Estonia,1988,,,,,15.975533485412598,1569187.0,10180.770988679229 +5332,Hungary,1988,,,,,99.05845642089844,10442500.0,9486.08632232688 +5333,Latvia,1988,,,,,25.23627281188965,2664068.0,9472.833580783092 +5334,Lithuania,1988,,,,,32.31351852416992,3673950.0,8795.30710112275 +5335,Malta,1988,,,,,3.704078912734986,351951.0,10524.416503248991 +5336,Poland,1988,,,,,255.83990478515625,37866840.0,6756.304586946158 +5337,Romania,1988,,,,,139.39765930175778,22768897.0,6122.284241601946 +5338,Slovakia,1988,,,,,51.87609481811523,5223222.0,9931.818869294706 +5339,Slovenia,1988,,,,,23.335634231567386,1961775.0,11895.163426777986 +5340,Iceland,1988,,,,,7.808879852294923,249885.0,31249.89436058556 +5341,Norway,1988,,,,,248.2046203613281,4208729.0,58973.771027150506 +5342,Switzerland,1988,,,,,399.479736328125,6709508.0,59539.34868668835 +5343,Albania,1988,,,,,6.220985889434815,3136843.0,1983.1996339742905 +5344,Bosnia Herzegovina,1988,,,,,2.8149738311767583,4285510.0,656.8585375315325 +5345,Macedonia,1988,,,,,7.334195137023926,1858920.0,3945.406546287052 +5346,Serbia,1988,,,,,37.32334899902344,7768886.0,4804.2086084186885 +5347,Russia,1988,,,,,1422.0665283203125,146865701.0,9682.768125147972 +5348,Ukraine,1988,,,,,211.5205383300781,51422963.0,4113.347928435748 +5349,Armenia,1988,,,,,6.3565006256103525,3655035.0,1739.1080046047034 +5350,Azerbaijan,1988,,,,,26.44517517089844,7233154.0,3656.105645047573 +5351,Belarus,1988,,,,,30.48108291625977,10149612.0,3003.177157536639 +5352,Georgia,1988,,,,,21.34440040588379,5320248.0,4011.9183176956767 +5353,Kazakhstan,1988,,,,,92.05492401123048,16530068.0,5568.938011097745 +5354,Kyrgyzstan,1988,,,,,4.4292564392089835,4333005.0,1022.2135536905644 +5355,Moldova,1988,,,,,9.887937545776367,4289659.0,2305.063769818619 +5356,Tajikistan,1988,,,,,7.296719074249268,5005734.0,1457.6721564208701 +5357,Turkmenistan,1988,,,,,10.553545951843262,3492186.0,3022.0457764401044 +5358,Uzbekistan,1988,,,,,19.40419006347656,19624686.0,988.7643584960576 +5359,China,1988,,,,,766.2984008789062,1117284539.0,685.8578760650928 +5360,Hong Kong,1988,,,,,98.03939056396484,5628407.0,17418.674691429536 +5361,Japan,1988,,,,,4276.36572265625,122613000.0,34876.935746260584 +5362,Korea,1988,,,,,308.75823974609375,42031000.0,7345.964639101942 +5363,Taiwan,1988,,,,,135.675048828125,19976096.0,6791.8700845312815 +5364,Macau,1988,,,,,6.5304951667785645,332136.0,19662.11180594264 +5365,Mongolia,1988,,,,,3.8139395713806152,1992349.0,1914.2929132298684 +5366,Cambodia,1988,,,,,4.114380359649658,8716411.0,472.0268880907128 +5367,Indonesia,1988,,,,,268.8515625,175334751.0,1533.3615325349854 +5368,Malaysia,1988,,,,,68.80726623535156,17055114.0,4034.4067026084704 +5369,Myanmar,1988,,,,,6.287124156951903,38819808.0,161.95660104634996 +5370,Philippines,1988,,,,,86.37434387207031,58672114.0,1472.153259588879 +5371,Thailand,1988,,,,,113.5436782836914,54382553.0,2087.8695835352087 +5372,Vietnam,1988,,,,,26.106157302856445,64211347.0,406.566105876216 +5373,Brunei,1988,,,,,9.593199729919434,238878.0,40159.410786759065 +5374,Laos,1988,,,,,1.6146700382232666,3979884.0,405.70781415319306 +5375,Singapore,1988,,,,,55.736289978027344,2869266.0,19425.27809482541 +5376,Bangladesh,1988,,,,,38.79348373413086,101386934.0,382.62803897522787 +5377,India,1988,,,,,452.3293151855469,806379398.0,560.9385809054944 +5378,Pakistan,1988,,,,,71.6629867553711,112021093.0,639.7276158997225 +5379,Afghanistan,1988,,,,,6.168144226074219,13168732.0,468.3931775720106 +5380,Bhutan,1988,,,,,0.3559768795967102,582886.0,610.7144100162128 +5381,Nepal,1988,,,,,6.2417564392089835,17713762.0,352.36763592109816 +5382,Sri Lanka,1988,,,,,18.936628341674805,16480228.0,1149.051356672663 +5383,Australia,1988,,,,,593.0537719726562,16400734.0,36160.19697488273 +5384,New Zealand,1988,,,,,81.71681213378906,3361313.0,24310.979707569357 +5385,Fiji,1988,,,,,1.8757889270782468,726371.0,2582.4116423676705 +5386,Maldives,1988,,,,,0.8048490285873413,200075.0,4022.736616705442 +5387,Papua New Guinea,1988,,,,,6.725756645202638,3492134.0,1925.9732430664567 +5388,Samoa,1988,,,,,0.39165905117988586,161516.0,2424.893206740421 +5389,Solomon Islands,1988,,,,,0.3801361322402954,301178.0,1262.164342150806 +5390,Tonga,1988,,,,,0.2491530478000641,95703.0,2603.3985120640327 +5391,Iran,1988,,,,,194.8163604736328,53835124.0,3618.7593897551496 +5392,Iraq,1988,,,,,46.6081428527832,17040559.0,2735.129924598319 +5393,Saudi Arabia,1988,,,,,256.4498291015625,15233131.0,16835.004510993997 +5394,Turkey,1988,,,,,333.3424072265625,54356391.0,6132.533839977758 +5395,Bahrain,1988,,,,,8.485549926757812,471975.0,17978.81228191708 +5396,Israel,1988,,,,,91.27886962890624,4263054.0,21411.614684896384 +5397,Jordan,1988,,,,,9.94296646118164,2966869.0,3351.333160035593 +5398,Kuwait,1988,,,,,43.778053283691406,1966721.0,22259.41212998255 +5399,Lebanon,1988,,,,,11.572726249694824,3336549.0,3468.47184012428 +5400,Oman,1988,,,,,24.19733238220215,1670760.0,14482.829599824121 +5401,Syria,1988,,,,,23.046443939208984,11676074.0,1973.8179065333932 +5402,United Arab Emirates,1988,,,,,94.60868072509766,1622747.0,58301.55946989744 +5403,Yemen,1988,,,,,11.161685943603516,11591069.0,962.9556983573746 +5404,Egypt,1988,,,,,77.29219818115234,53277807.0,1450.7391075828693 +5405,Morocco,1988,,,,,40.60509490966797,23086484.0,1758.8254196554126 +5406,Algeria,1988,,,,,87.4134750366211,23917674.0,3654.764883768425 +5407,Libya,1988,,,,,42.49839401245117,3883238.0,10944.061119213186 +5408,Tunisia,1988,,,,,16.69618034362793,7917820.0,2108.683999336677 +5409,Nigeria,1988,,,,,126.61968231201172,91787745.0,1379.4835281334315 +5410,South Africa,1988,,,,,218.48175048828125,36806859.0,5935.897721896923 +5411,Benin,1988,,,,,2.865540027618408,4422114.0,648.002296552827 +5412,Burkina Faso,1988,,,,,2.962219715118408,7833491.0,378.1480970768216 +5413,Cabo Verde,1988,,,,,0.3024619221687317,330500.0,915.1646661686284 +5414,Cote d'Ivoire,1988,,,,,17.446636199951172,11511744.0,1515.5510928623125 +5415,Gambia,1988,,,,,0.4308232963085175,875080.0,492.324468972571 +5416,Ghana,1988,,,,,11.095297813415527,14850715.0,747.1221293665341 +5417,Guinea,1988,,,,,3.1720802783966064,5666825.0,559.7632322149716 +5418,Guinea Bissau,1988,,,,,0.5515133738517761,949542.0,580.8204101048465 +5419,Liberia,1988,,,,,1.7354538440704346,2382311.0,728.4749321438028 +5420,Mali,1988,,,,,4.010924816131592,8138605.0,492.8270650967324 +5421,Niger,1988,,,,,3.076139211654663,7399129.0,415.7434221858631 +5422,Senegal,1988,,,,,7.801705837249756,6912574.0,1128.6252902681053 +5423,Sierra Leone,1988,,,,,1.7230877876281738,3967779.0,434.27010113924536 +5424,Togo,1988,,,,,2.0281331539154053,3475139.0,583.6120954918365 +5425,Angola,1988,,,,,32.945560455322266,10424712.0,3160.33291426394 +5426,Botswana,1988,,,,,4.4059786796569815,1183187.0,3723.8227597640794 +5427,Burundi,1988,,,,,1.7006332874298096,5260596.0,323.27768325676584 +5428,Cameroon,1988,,,,,17.151420593261722,11282275.0,1520.2094075230145 +5429,Central Afr Rep,1988,,,,,1.3807862997055054,2917175.0,473.3299509647194 +5430,Chad,1988,,,,,3.065108299255371,5556341.0,551.6415027903023 +5431,Dem Rep Congo,1988,,,,,26.34440612792969,36669267.0,718.4328535372601 +5432,Djibouti,1988,,,,,0.6153806447982788,417145.0,1475.2199949616531 +5433,Equatorial Guinea,1988,,,,,0.2234203666448593,351649.0,635.3504962188413 +5434,Eritrea,1988,,,,,0.9107605814933776,2890943.0,315.03927316912774 +5435,Ethiopia,1988,,,,,8.709195137023926,44596107.0,195.29047988480085 +5436,Gabon,1988,,,,,9.271069526672363,899743.0,10304.130764754342 +5437,Kenya,1988,,,,,19.95354652404785,21918734.0,910.3421084469501 +5438,Lesotho,1988,,,,,0.9647682905197144,1644841.0,586.5419761057235 +5439,Madagascar,1988,,,,,5.320789337158203,10957343.0,485.59120008912765 +5440,Malawi,1988,,,,,2.912752866744995,8500739.0,342.6470177175179 +5441,Mauritania,1988,,,,,2.0058627128601074,1848504.0,1085.1276020285093 +5442,Mauritius,1988,,,,,3.5060176849365234,1044942.0,3355.22706995845 +5443,Mozambique,1988,,,,,2.129149436950684,12903657.0,165.00356735696582 +5444,Namibia,1988,,,,,4.832603454589844,1323006.0,3652.7449267727006 +5445,Rep Congo,1988,,,,,6.389533996582031,2097281.0,3046.5798319738897 +5446,Rwanda,1988,,,,,2.6704671382904053,6692714.0,399.0110944962545 +5447,Sao Tome Principe,1988,,,,,0.10112053155899048,110939.0,911.4966924074536 +5448,Seychelles,1988,,,,,0.4406388998031616,69195.0,6368.0742799792115 +5449,Sudan,1988,,,,,19.246675491333008,19598127.0,982.067086887079 +5450,Swaziland,1988,,,,,1.7146563529968262,814990.0,2103.898640470222 +5451,Tanzania,1988,,,,,11.37710952758789,23477486.0,484.59658447194425 +5452,Uganda,1988,,,,,4.3347458839416495,15397260.0,281.5270953365502 +5453,Zambia,1988,,,,,8.515242576599121,7194863.0,1183.5169865776625 +5454,Canada,1989,,,,,1012.5148315429688,27379300.0,36981.034268332965 +5455,United States,1989,,,,,8893.7099609375,246819230.0,36033.29433017638 +5456,Cuba,1989,,,,,46.07790756225586,10488855.0,4393.035041694814 +5457,Mexico,1989,,,,,621.8008422851562,83105466.0,7482.069137126989 +5458,Antigua Barbuda,1989,,,,,0.6720840334892273,64385.0,10438.518808561425 +5459,Bahamas,1989,,,,,7.729854106903076,241244.0,32041.642929577836 +5460,Barbados,1989,,,,,3.759477376937866,261092.0,14399.052352955534 +5461,Belize,1989,,,,,0.4751049280166626,186433.0,2548.3950159932124 +5462,Costa Rica,1989,,,,,14.71438217163086,2949892.0,4988.108775382577 +5463,Dominica,1989,,,,,0.3014874756336212,70509.0,4275.872238063527 +5464,Dominican Rep,1989,,,,,20.341232299804688,6941869.0,2930.2241658269104 +5465,El Salvador,1989,,,,,10.819828033447266,5024777.0,2153.2951678148634 +5466,Grenada,1989,,,,,0.4417164325714111,94303.0,4684.012518916801 +5467,Guatemala,1989,,,,,19.287073135375977,8730575.0,2209.1412232729203 +5468,Haiti,1989,,,,,6.575240135192871,6586139.0,998.345181477778 +5469,Honduras,1989,,,,,7.526186943054199,4656018.0,1616.4428365728393 +5470,Jamaica,1989,,,,,9.94717788696289,2336663.0,4257.001496134826 +5471,Nicaragua,1989,,,,,4.703416347503662,3492060.0,1346.8887554920768 +5472,Panama,1989,,,,,9.284038543701172,2347202.0,3955.364107435649 +5473,Puerto Rico,1989,,,,,63.05481338500977,3505600.0,17986.881956016023 +5474,St Kitts Nevis,1989,,,,,0.3782869577407837,41916.0,9024.882091344203 +5475,St Lucia,1989,,,,,0.8198733925819397,136499.0,6006.44248369541 +5476,St Vincent Grenadines,1989,,,,,0.3617408275604248,106749.0,3388.704602014303 +5477,Trinidad and Tobago,1989,,,,,7.873325824737549,1248162.0,6307.93584866191 +5478,Argentina,1989,,,,,210.29042053222656,32571624.0,6456.246103425072 +5479,Brazil,1989,,,,,1227.6680908203125,146797954.0,8362.978211673935 +5480,Bolivia,1989,,,,,8.899852752685547,6423135.0,1385.5932893650133 +5481,Chile,1989,,,,,76.2202377319336,12774248.0,5966.710348189074 +5482,Colombia,1989,,,,,141.66192626953125,32460845.0,4364.086217396104 +5483,Ecuador,1989,,,,,36.67139053344727,10048223.0,3649.539877194929 +5484,Guyana,1989,,,,,1.1480668783187866,768586.0,1493.7389938390586 +5485,Paraguay,1989,,,,,14.401658058166506,4080563.0,3529.3311384155822 +5486,Peru,1989,,,,,61.56080627441406,21111159.0,2916.0315771585097 +5487,Suriname,1989,,,,,2.5778439044952397,412929.0,6242.826017294109 +5488,Uruguay,1989,,,,,21.32490539550781,3071177.0,6943.56118045551 +5489,Venezuela,1989,,,,,164.71728515625,19295964.0,8536.359476844484 +5490,Austria,1989,,,,,249.3583831787109,7627861.0,32690.472883382507 +5491,Belgium,1989,,,,,319.93402099609375,9937697.0,32193.9802547908 +5492,Denmark,1989,,,,,225.79551696777344,5132593.0,43992.484299412296 +5493,Finland,1989,,,,,165.99801635742188,4962441.0,33450.87958877937 +5494,France,1989,,,,,1850.319091796875,57905275.0,31954.24064209824 +5495,Germany,1989,,,,,2440.3916015625,78644914.0,31030.507599798504 +5496,Greece,1989,,,,,197.6539459228516,10030765.0,19704.77285858572 +5497,Ireland,1989,,,,,76.82830047607422,3513200.0,21868.467629532683 +5498,Italy,1989,,,,,1715.1220703125,56711329.0,30243.023758312916 +5499,Luxembourg,1989,,,,,23.900774002075195,377621.0,63293.021315221325 +5500,Netherlands,1989,,,,,509.2267150878906,14862677.0,34262.11274643798 +5501,Portugal,1989,,,,,160.26141357421875,9923147.0,16150.2609579621 +5502,Spain,1989,,,,,841.3299560546875,39214523.0,21454.55029644725 +5503,Sweden,1989,,,,,318.66259765625,8526276.0,37374.18277994402 +5504,UK,1989,,,,,1630.574951171875,57251270.0,28481.026729570807 +5505,Bulgaria,1989,,,,,39.93352508544922,8990055.0,4441.966716048925 +5506,Croatia,1989,,,,,45.13134002685547,4501397.0,10026.074133620175 +5507,Cyprus,1989,,,,,11.024518013000488,726864.0,15167.236254650783 +5508,Czech Republic,1989,,,,,146.66757202148438,10314343.0,14219.768726082151 +5509,Estonia,1989,,,,,16.46554183959961,1570926.0,10481.424229785242 +5510,Hungary,1989,,,,,99.81330871582031,10397959.0,9599.317396406384 +5511,Latvia,1989,,,,,26.671085357666016,2667309.0,9999.248440156733 +5512,Lithuania,1989,,,,,33.312911987304695,3681250.0,9049.347908266129 +5513,Malta,1989,,,,,4.0070390701293945,355323.0,11277.173360940313 +5514,Poland,1989,,,,,256.25015258789057,37970155.0,6748.725481576007 +5515,Romania,1989,,,,,131.329833984375,22852123.0,5746.942373116713 +5516,Slovakia,1989,,,,,52.50291061401367,5244662.0,10010.732934555872 +5517,Slovenia,1989,,,,,24.057357788085934,1976217.0,12173.43934805031 +5518,Iceland,1989,,,,,7.829061031341553,252746.0,30976.00370071753 +5519,Norway,1989,,,,,250.78170776367188,4226142.0,59340.57770980527 +5520,Switzerland,1989,,,,,416.7803955078125,6769778.0,61564.854195781976 +5521,Albania,1989,,,,,6.832915782928468,3195800.0,2138.092428477523 +5522,Bosnia Herzegovina,1989,,,,,2.9020347595214844,4301804.0,674.6087826227052 +5523,Macedonia,1989,,,,,7.483872413635254,1863007.0,4017.0930187783797 +5524,Serbia,1989,,,,,54.58205795288086,7779554.0,7016.090890670705 +5525,Russia,1989,,,,,1457.6181640625,147351694.0,9892.103202169495 +5526,Ukraine,1989,,,,,219.7129364013672,51501478.0,4266.148175424542 +5527,Armenia,1989,,,,,6.5530943870544425,3472148.0,1887.3315270704024 +5528,Azerbaijan,1989,,,,,25.28388977050781,7393439.0,3419.773906365875 +5529,Belarus,1989,,,,,31.423797607421875,10184084.0,3085.5791848753283 +5530,Georgia,1989,,,,,19.80862617492676,5398106.0,3669.551167562615 +5531,Kazakhstan,1989,,,,,94.9020004272461,16625996.0,5708.0490352124525 +5532,Kyrgyzstan,1989,,,,,4.552821636199951,4402443.0,1034.157997321022 +5533,Moldova,1989,,,,,10.385526657104492,4359496.0,2382.276909327246 +5534,Tajikistan,1989,,,,,6.822432518005371,5148306.0,1325.1800724365198 +5535,Turkmenistan,1989,,,,,10.104458808898926,3572311.0,2828.5495884593824 +5536,Uzbekistan,1989,,,,,20.004030227661133,20033271.0,998.540389518074 +5537,China,1989,,,,,798.3746337890625,1135336837.0,703.205082201576 +5538,Hong Kong,1989,,,,,100.27178955078124,5660721.0,17713.607427531097 +5539,Japan,1989,,,,,4484.11279296875,123107500.0,36424.36726412891 +5540,Korea,1989,,,,,330.46307373046875,42449000.0,7784.94366723524 +5541,Taiwan,1989,,,,,147.54661560058594,20207541.0,7301.5621049877345 +5542,Macau,1989,,,,,6.858641147613525,341237.0,20099.34780698906 +5543,Mongolia,1989,,,,,3.97329330444336,2069928.0,1919.5321308003754 +5544,Cambodia,1989,,,,,4.257534980773927,9038142.0,471.06307698793916 +5545,Indonesia,1989,,,,,288.8987121582031,178463522.0,1618.8109980156232 +5546,Malaysia,1989,,,,,75.04093170166016,17471980.0,4294.930036644969 +5547,Myanmar,1989,,,,,6.519452095031737,39454402.0,165.24017003303555 +5548,Philippines,1989,,,,,91.73413848876952,60047504.0,1527.6927828468865 +5549,Thailand,1989,,,,,127.38522338867188,55244020.0,2305.864478882454 +5550,Vietnam,1989,,,,,28.028749465942386,65868481.0,425.52597297548704 +5551,Brunei,1989,,,,,9.490152359008787,245853.0,38600.92152224618 +5552,Laos,1989,,,,,1.843801975250244,4093568.0,450.4144001639265 +5553,Singapore,1989,,,,,61.41201400756836,2957752.0,20763.070740064868 +5554,Bangladesh,1989,,,,,39.89389419555664,103731720.0,384.58722361449935 +5555,India,1989,,,,,479.2308654785156,822218830.0,582.8507545594833 +5556,Pakistan,1989,,,,,75.2173080444336,115419215.0,651.6879190733848 +5557,Afghanistan,1989,,,,,4.715261459350586,13502720.0,349.2082676194564 +5558,Bhutan,1989,,,,,0.3824352920055389,599611.0,637.8056640147345 +5559,Nepal,1989,,,,,6.512002468109131,18172823.0,358.33741780840165 +5560,Sri Lanka,1989,,,,,19.372039794921875,16687488.0,1160.8721333565531 +5561,Australia,1989,,,,,615.9906616210938,16680597.0,36928.57405649773 +5562,New Zealand,1989,,,,,82.14657592773438,3377543.0,24321.400475947867 +5563,Fiji,1989,,,,,2.015321969985962,733034.0,2749.2885322999505 +5564,Maldives,1989,,,,,0.879665732383728,208203.0,4225.038699652397 +5565,Papua New Guinea,1989,,,,,6.630279064178468,3585878.0,1848.9973903681241 +5566,Samoa,1989,,,,,0.4061790704727173,162102.0,2505.700549485616 +5567,Solomon Islands,1989,,,,,0.3963121473789215,311037.0,1274.163997784577 +5568,Tonga,1989,,,,,0.2501245141029358,95992.0,2605.680828641301 +5569,Iran,1989,,,,,206.5460205078125,55380525.0,3729.578592976728 +5570,Iraq,1989,,,,,45.1546516418457,17572536.0,2569.6149742897496 +5571,Saudi Arabia,1989,,,,,255.15985107421875,15829361.0,16119.403118939465 +5572,Turkey,1989,,,,,334.3099060058594,55469243.0,6026.9419217756285 +5573,Bahrain,1989,,,,,8.516437530517576,488626.0,17429.358099072866 +5574,Israel,1989,,,,,91.8034896850586,4336493.0,21169.98452091554 +5575,Jordan,1989,,,,,8.605427742004395,3072474.0,2800.81385294209 +5576,Kuwait,1989,,,,,55.11445999145508,2048192.0,26908.8347144482 +5577,Lebanon,1989,,,,,6.659974575042725,3384331.0,1967.885107881801 +5578,Oman,1989,,,,,27.042179107666016,1731785.0,15615.205760337463 +5579,Syria,1989,,,,,20.981895446777344,12085350.0,1736.1429703547967 +5580,United Arab Emirates,1989,,,,,106.2813949584961,1722063.0,61717.48359873948 +5581,Yemen,1989,,,,,11.496536254882812,11986392.0,959.13234398498 +5582,Egypt,1989,,,,,81.09564208984375,54124464.0,1498.317694006979 +5583,Morocco,1989,,,,,41.76079559326172,23549802.0,1773.2971000461796 +5584,Algeria,1989,,,,,91.25967407226562,24561722.0,3715.524264636886 +5585,Libya,1989,,,,,45.55785369873047,3990977.0,11415.213292066197 +5586,Tunisia,1989,,,,,16.987812042236328,8080407.0,2102.346087546869 +5587,Nigeria,1989,,,,,129.04998779296878,94209548.0,1369.8185643876434 +5588,South Africa,1989,,,,,223.71392822265625,37658393.0,5940.612713417066 +5589,Benin,1989,,,,,2.783752918243408,4561161.0,610.3167413391915 +5590,Burkina Faso,1989,,,,,3.0259153842926025,8091541.0,373.96033515650515 +5591,Cabo Verde,1989,,,,,0.3196960389614105,335166.0,953.8438832143194 +5592,Cote d'Ivoire,1989,,,,,17.96096420288086,11930647.0,1505.4476260072784 +5593,Gambia,1989,,,,,0.4562234580516815,912247.0,500.1095734507009 +5594,Ghana,1989,,,,,11.659590721130373,15201278.0,767.0138471995822 +5595,Guinea,1989,,,,,3.2990736961364746,5809689.0,567.8571944447413 +5596,Guinea Bissau,1989,,,,,0.585155725479126,972389.0,601.771230936514 +5597,Liberia,1989,,,,,1.7838410139083862,2461630.0,724.6584636636644 +5598,Mali,1989,,,,,4.1784772872924805,8327162.0,501.7888792475132 +5599,Niger,1989,,,,,3.104868173599243,7602298.0,408.4117951702555 +5600,Senegal,1989,,,,,8.11204719543457,7127287.0,1138.1676078758396 +5601,Sierra Leone,1989,,,,,1.735666275024414,4064153.0,427.0671588949565 +5602,Togo,1989,,,,,2.110475778579712,3597137.0,586.7098691486345 +5603,Angola,1989,,,,,32.95927047729492,10761767.0,3062.626284075368 +5604,Botswana,1989,,,,,4.981372833251953,1219773.0,4083.852350602901 +5605,Burundi,1989,,,,,1.7235833406448364,5381778.0,320.2628091766024 +5606,Cameroon,1989,,,,,16.839414596557614,11628238.0,1448.1484294144666 +5607,Central Afr Rep,1989,,,,,1.4080880880355835,2999869.0,469.3831924112631 +5608,Chad,1989,,,,,3.2148308753967285,5714913.0,562.5336510628822 +5609,Dem Rep Congo,1989,,,,,26.010873794555664,37872107.0,686.8082041106312 +5610,Djibouti,1989,,,,,0.5993807315826416,442446.0,1354.6980458239912 +5611,Equatorial Guinea,1989,,,,,0.2206741273403168,361192.0,610.9607282008371 +5612,Eritrea,1989,,,,,0.929347574710846,2973468.0,312.54668781061235 +5613,Ethiopia,1989,,,,,8.677750587463379,46093687.0,188.26332090690377 +5614,Gabon,1989,,,,,10.063310623168944,918835.0,10952.249993925943 +5615,Kenya,1989,,,,,20.88943672180176,22638926.0,922.7220726726064 +5616,Lesotho,1989,,,,,1.0206789970397951,1674022.0,609.7165969382689 +5617,Madagascar,1989,,,,,5.537818908691406,11289164.0,490.5428700204379 +5618,Malawi,1989,,,,,2.951920509338379,8956112.0,329.5984361672095 +5619,Mauritania,1989,,,,,2.1016945838928223,1885828.0,1114.4678008242654 +5620,Mauritius,1989,,,,,3.6625664234161377,1053403.0,3476.890063362396 +5621,Mozambique,1989,,,,,2.267544269561768,12856244.0,176.37688500325348 +5622,Namibia,1989,,,,,4.922425746917725,1397505.0,3522.295624643722 +5623,Rep Congo,1989,,,,,6.555654525756836,2158239.0,3037.5016510019677 +5624,Rwanda,1989,,,,,2.6694915294647217,6934160.0,384.9769156559297 +5625,Sao Tome Principe,1989,,,,,0.10429097712039948,113383.0,919.811410179652 +5626,Seychelles,1989,,,,,0.4899852573871613,70014.0,6998.3897133025 +5627,Sudan,1989,,,,,20.96567153930664,20349378.0,1030.285620489562 +5628,Swaziland,1989,,,,,1.9360374212265012,849298.0,2279.5737435228875 +5629,Tanzania,1989,,,,,11.805394172668455,24137855.0,489.08215633362846 +5630,Uganda,1989,,,,,4.610519886016847,15985653.0,288.4161119984805 +5631,Zambia,1989,,,,,8.428089141845701,7396712.0,1139.4372447981889 +5632,Antigua Barbuda,1990,,,,,0.6923248171806335,64162.0,10790.262416705113 +5633,Dominican Rep,1990,,,,,19.23175811767578,7083623.0,2714.9607083374967 +5634,Puerto Rico,1990,,,,,61.26371383666992,3536910.0,17321.253251190992 +5635,St Kitts Nevis,1990,,,,,0.3967379927635193,41659.0,9523.46414372691 +5636,St Lucia,1990,,,,,0.9013824462890624,137561.0,6552.601727881176 +5637,St Vincent Grenadines,1990,,,,,0.3767527341842651,107385.0,3508.4298010361326 +5638,Trinidad and Tobago,1990,,,,,7.992088317871094,1255189.0,6367.23897187682 +5639,Denmark,1990,,,,,229.1265411376953,5140954.0,44568.87595914986 +5640,France,1990,,,,,1904.2376708984373,58255473.0,32687.70422477623 +5641,UK,1990,,,,,1642.5074462890625,57410719.0,28609.76965449714 +5642,Bosnia Herzegovina,1990,,,,,2.9917879104614262,4314951.0,693.3538551101567 +5643,Kyrgyzstan,1990,,,,,4.812459945678711,4483248.0,1073.4315714140084 +5644,Hong Kong,1990,,,,,104.11271667480469,5687959.0,18304.055404549275 +5645,Korea,1990,,,,,362.88555908203125,42869000.0,8464.987731974883 +5646,Taiwan,1990,,,,,155.88299560546875,20278248.0,7687.2023463500755 +5647,Macau,1990,,,,,7.405845165252685,351756.0,21053.927055267533 +5648,Myanmar,1990,,,,,6.7031006813049325,40124887.0,167.05593915578956 +5649,Brunei,1990,,,,,9.59354019165039,252887.0,37936.07497281549 +5650,Maldives,1990,,,,,0.8440561294555664,216606.0,3896.7347601431466 +5651,Cabo Verde,1990,,,,,0.3219088912010193,339756.0,947.4708061109128 +5652,Cote d'Ivoire,1990,,,,,17.764129638671875,12490877.0,1422.168326425108 +5653,Guinea Bissau,1990,,,,,0.6208502054214478,995989.0,623.3504641330857 +5654,Central Afr Rep,1990,,,,,1.377848982810974,3084970.0,446.632862819079 +5655,Dem Rep Congo,1990,,,,,24.302398681640625,39151065.0,620.734038311362 +5656,Eritrea,1990,,,,,0.9483136534690856,3060332.0,309.8728025158988 +5657,Namibia,1990,,,,,5.023210525512695,1458647.0,3443.7465168150316 +5658,Rep Congo,1990,,,,,6.621211051940918,2220993.0,2981.1940208460437 +5659,Sao Tome Principe,1990,,,,,0.1020459607243538,115933.0,880.2149579874048 +5660,Antigua Barbuda,1991,,,,,0.7073958516120911,64025.0,11048.74426571013 +5661,Dominican Rep,1991,,,,,19.413331985473636,7224795.0,2687.0426061187386 +5662,Puerto Rico,1991,,,,,62.67546844482422,3562110.0,17595.04014329266 +5663,St Kitts Nevis,1991,,,,,0.38934195041656494,41408.0,9402.578014310398 +5664,St Lucia,1991,,,,,0.9061548113822936,139171.0,6511.089317331152 +5665,St Vincent Grenadines,1991,,,,,0.3810316920280457,107962.0,3529.313017802983 +5666,Trinidad and Tobago,1991,,,,,8.206439971923826,1260387.0,6511.047774948352 +5667,Denmark,1991,,,,,232.3197784423828,5154352.0,45072.54809962199 +5668,France,1991,,,,,1924.024658203125,58604822.0,32830.48378174623 +5669,UK,1991,,,,,1624.6610107421875,57573614.0,28218.84710489405 +5670,Bosnia Herzegovina,1991,,,,,2.7121148109436035,4328421.0,626.5829527542733 +5671,Kyrgyzstan,1991,,,,,4.434630393981934,4547919.0,975.0900123731168 +5672,Hong Kong,1991,,,,,110.0491943359375,5752000.0,19132.335593869524 +5673,Korea,1991,,,,,400.4585571289063,43339909.0,9239.949191607817 +5674,Taiwan,1991,,,,,168.91481018066406,20492092.0,8242.926597277821 +5675,Macau,1991,,,,,7.676891803741455,363858.0,21098.592867935997 +5676,Myanmar,1991,,,,,6.659489154815674,40781018.0,163.29874734406272 +5677,Brunei,1991,,,,,9.895346641540527,259254.0,38168.53989346559 +5678,Maldives,1991,,,,,0.9022796154022216,227218.0,3970.98652132411 +5679,Cabo Verde,1991,,,,,0.32642897963523865,346826.0,941.1894714791816 +5680,Cote d'Ivoire,1991,,,,,17.771398544311523,13039524.0,1362.887061238702 +5681,Guinea Bissau,1991,,,,,0.6525135636329651,1020216.0,639.583738769991 +5682,Central Afr Rep,1991,,,,,1.370235800743103,3174488.0,431.6399371309965 +5683,Dem Rep Congo,1991,,,,,22.25588035583496,40514176.0,549.3356289866283 +5684,Eritrea,1991,,,,,1.059682846069336,3001349.0,353.0688520626345 +5685,Rep Congo,1991,,,,,6.779813289642334,2286231.0,2965.4979263435466 +5686,Sao Tome Principe,1991,,,,,0.10327153652906418,118598.0,870.7696295811412 +5687,Antigua Barbuda,1992,,,,,0.7155898213386536,65181.0,10978.503265348083 +5688,Dominican Rep,1992,,,,,21.59169006347656,7362934.0,2932.484531774502 +5689,Puerto Rico,1992,,,,,65.5360107421875,3585176.0,18279.71925009748 +5690,St Kitts Nevis,1992,,,,,0.4059623777866364,41543.0,9772.10066164303 +5691,St Lucia,1992,,,,,0.9763326048851012,140992.0,6924.737608411126 +5692,St Vincent Grenadines,1992,,,,,0.4053304791450501,108224.0,3745.2919790901283 +5693,Trinidad and Tobago,1992,,,,,8.071263313293457,1263500.0,6388.020034264707 +5694,Denmark,1992,,,,,236.86631774902344,5171393.0,45803.19417785952 +5695,France,1992,,,,,1954.802734375,58954114.0,33158.03769648714 +5696,UK,1992,,,,,1630.73681640625,57735940.0,28244.74350649266 +5697,Bosnia Herzegovina,1992,,,,,2.537538290023804,4129131.0,614.5453583390315 +5698,Hong Kong,1992,,,,,116.91069793701172,5828755.0,20057.5762640584 +5699,Korea,1992,,,,,425.18890380859375,43837115.0,9699.290288801938 +5700,Taiwan,1992,,,,,182.9178466796875,20687201.0,8842.078088750986 +5701,Macau,1992,,,,,8.697898864746094,375327.0,23174.18907977868 +5702,Myanmar,1992,,,,,7.302858829498291,41327503.0,176.70699411716916 +5703,Brunei,1992,,,,,10.36622428894043,265759.0,39006.10812405386 +5704,Maldives,1992,,,,,0.9605764746665956,237848.0,4038.6148913028296 +5705,Cabo Verde,1992,,,,,0.36213263869285583,356492.0,1015.8226234890428 +5706,Cote d'Ivoire,1992,,,,,17.727937698364258,13422141.0,1320.7980528862167 +5707,Guinea Bissau,1992,,,,,0.6596912145614624,1051003.0,627.6777654882644 +5708,Central Afr Rep,1992,,,,,1.2822108268737793,3266519.0,392.5312624459797 +5709,Dem Rep Congo,1992,,,,,19.91901206970215,41846148.0,476.00586963708463 +5710,Eritrea,1992,,,,,1.1794334650039673,2933383.0,402.07278251901215 +5711,Rep Congo,1992,,,,,6.956898212432861,2352825.0,2956.8277336533147 +5712,Sao Tome Principe,1992,,,,,0.10399443656206132,120905.0,860.1334648034517 +5713,Antigua Barbuda,1993,,,,,0.7533735632896423,66331.0,11357.78992159989 +5714,Dominican Rep,1993,,,,,23.181812286376953,7494284.0,3093.265785814489 +5715,Puerto Rico,1993,,,,,68.57984161376953,3615497.0,18968.30272954715 +5716,St Kitts Nevis,1993,,,,,0.4335764944553375,42063.0,10307.788185705671 +5717,St Lucia,1993,,,,,0.9823339581489564,142793.0,6879.4265695724325 +5718,St Vincent Grenadines,1993,,,,,0.4225611686706543,108536.0,3893.2812032012816 +5719,Trinidad and Tobago,1993,,,,,7.953949451446532,1265120.0,6287.110670487015 +5720,Denmark,1993,,,,,236.8915557861328,5188386.0,45658.04390539424 +5721,France,1993,,,,,1942.82666015625,59268430.0,32780.12696061377 +5722,UK,1993,,,,,1671.9443359375,57854243.0,28899.251796233857 +5723,Bosnia Herzegovina,1993,,,,,2.482185840606689,3816556.0,650.3732267014265 +5724,Hong Kong,1993,,,,,124.1605224609375,5932791.0,20927.84365081081 +5725,Korea,1993,,,,,454.3005065917969,44307038.0,10253.461461174562 +5726,Taiwan,1993,,,,,195.3562469482422,20886065.0,9353.425211893298 +5727,Macau,1993,,,,,9.148966789245604,384404.0,23800.39434877266 +5728,Myanmar,1993,,,,,7.743908882141112,41924597.0,184.71039523984246 +5729,Brunei,1993,,,,,10.397796630859377,273107.0,38072.24505728296 +5730,Maldives,1993,,,,,1.0126324892044067,246910.0,4101.221048983058 +5731,Cabo Verde,1993,,,,,0.3936682641506195,366241.0,1074.8885683214592 +5732,Cote d'Ivoire,1993,,,,,17.693815231323242,13860566.0,1276.5579148299746 +5733,Guinea Bissau,1993,,,,,0.6735447645187378,1084455.0,621.0905611747263 +5734,Central Afr Rep,1993,,,,,1.2865097522735596,3367026.0,382.0908280107012 +5735,Dem Rep Congo,1993,,,,,17.236108779907227,43290020.0,398.1543270228849 +5736,Eritrea,1993,,,,,1.338123440742493,3053544.0,438.2197999251009 +5737,Rep Congo,1993,,,,,6.888742446899414,2424881.0,2840.8579418534005 +5738,Sao Tome Principe,1993,,,,,0.1051383763551712,122854.0,855.7993745028343 +5739,Antigua Barbuda,1994,,,,,0.8036702275276184,67485.0,11908.872009003755 +5740,Dominican Rep,1994,,,,,23.78462028503418,7624133.0,3119.6491830656914 +5741,Puerto Rico,1994,,,,,71.45056915283203,3649237.0,19579.5913372664 +5742,St Kitts Nevis,1994,,,,,0.4565610289573669,42609.0,10715.131285816777 +5743,St Lucia,1994,,,,,0.9993708729743958,144635.0,6909.606063362226 +5744,St Vincent Grenadines,1994,,,,,0.4172886908054352,108691.0,3839.2202740377334 +5745,Trinidad and Tobago,1994,,,,,8.237579345703125,1265247.0,6510.6491820989295 +5746,Denmark,1994,,,,,249.523681640625,5205603.0,47933.67485776863 +5747,France,1994,,,,,1988.3934326171875,59548134.0,33391.36424689962 +5748,UK,1994,,,,,1736.8165283203125,58005687.0,29942.176675199316 +5749,Bosnia Herzegovina,1994,,,,,2.7585020065307617,3634965.0,758.8799359913402 +5750,Hong Kong,1994,,,,,131.65499877929688,6064948.0,21707.523094888344 +5751,Korea,1994,,,,,496.1240539550781,44718897.0,11094.281997945478 +5752,Taiwan,1994,,,,,209.98843383789062,21093246.0,9955.245097785833 +5753,Macau,1994,,,,,9.538089752197266,392941.0,24273.592606007685 +5754,Myanmar,1994,,,,,8.322995185852049,42669847.0,195.0556604023457 +5755,Brunei,1994,,,,,10.724848747253418,280501.0,38234.61858336839 +5756,Maldives,1994,,,,,1.0886240005493164,253376.0,4296.47638509297 +5757,Cabo Verde,1994,,,,,0.4691842794418335,375918.0,1248.1027230455404 +5758,Cote d'Ivoire,1994,,,,,17.83734703063965,14379919.0,1240.4344579854485 +5759,Guinea Bissau,1994,,,,,0.6950981616973877,1116514.0,622.5610800199438 +5760,Central Afr Rep,1994,,,,,1.349548101425171,3465853.0,389.3841145095221 +5761,Dem Rep Congo,1994,,,,,16.563901901245114,44820303.0,369.56247041090097 +5762,Rep Congo,1994,,,,,6.51033878326416,2498635.0,2605.558148054502 +5763,Sao Tome Principe,1994,,,,,0.1074514165520668,124921.0,860.1549503451524 +5764,Antigua Barbuda,1995,,,,,0.7686342597007751,68710.0,11186.643279009973 +5765,Dominican Rep,1995,,,,,25.13687896728516,7758971.0,3239.7181233549077 +5766,Puerto Rico,1995,,,,,74.70085144042969,3683103.0,20282.04246268152 +5767,St Kitts Nevis,1995,,,,,0.4811381697654724,43132.0,11155.016455658733 +5768,St Lucia,1995,,,,,1.0164282321929932,146496.0,6938.266110972267 +5769,St Vincent Grenadines,1995,,,,,0.4497012197971344,108792.0,4133.587210430312 +5770,Trinidad and Tobago,1995,,,,,8.6212158203125,1264315.0,6818.882810306372 +5771,Denmark,1995,,,,,257.078369140625,5232612.0,49130.027057352054 +5772,France,1995,,,,,2029.85302734375,59823035.0,33930.960328972775 +5773,UK,1995,,,,,1779.7113037109375,58186804.0,30586.166989184312 +5774,Bosnia Herzegovina,1995,,,,,3.33227014541626,3546649.0,939.5545331427666 +5775,Hong Kong,1995,,,,,134.78021240234378,6222544.0,21659.985433987087 +5776,Korea,1995,,,,,543.6061401367189,45105079.0,12051.993970273696 +5777,Taiwan,1995,,,,,223.6376800537109,21290414.0,10504.148958949832 +5778,Macau,1995,,,,,9.852401733398438,401295.0,24551.518791408907 +5779,Myanmar,1995,,,,,8.901281356811523,43417520.0,205.01588660088197 +5780,Brunei,1995,,,,,11.20518398284912,287990.0,38908.23980988618 +5781,Maldives,1995,,,,,1.1689473390579224,260584.0,4485.875337925285 +5782,Cabo Verde,1995,,,,,0.5358630418777466,385425.0,1390.3172909846185 +5783,Cote d'Ivoire,1995,,,,,19.1083927154541,14845795.0,1287.1249209256966 +5784,Guinea Bissau,1995,,,,,0.7256824970245361,1143328.0,634.7106840946221 +5785,Central Afr Rep,1995,,,,,1.4467161893844604,3544408.0,408.1686389897722 +5786,Dem Rep Congo,1995,,,,,16.67984962463379,46704580.0,357.1352022571189 +5787,Rep Congo,1995,,,,,6.769796848297119,2569044.0,2635.142429750957 +5788,Sao Tome Principe,1995,,,,,0.10960043966770172,127140.0,862.045301775222 +5789,Antigua Barbuda,1996,,,,,0.819401204586029,70055.0,11696.54135445049 +5790,Dominican Rep,1996,,,,,26.639516830444336,7898976.0,3372.52788594931 +5791,Puerto Rico,1996,,,,,76.42921447753906,3724655.0,20519.810419364763 +5792,St Kitts Nevis,1996,,,,,0.5092512369155884,43582.0,11684.898281758256 +5793,St Lucia,1996,,,,,1.0463457107543943,148172.0,7061.696614437239 +5794,St Vincent Grenadines,1996,,,,,0.4554894268512726,108768.0,4187.7153836723355 +5795,Trinidad and Tobago,1996,,,,,9.236294746398926,1262458.0,7316.120414618883 +5796,Denmark,1996,,,,,264.5338134765625,5262075.0,50271.76797680809 +5797,France,1996,,,,,2058.027587890625,60094948.0,34246.26622342073 +5798,UK,1996,,,,,1824.8919677734373,58344567.0,31277.838907835878 +5799,Bosnia Herzegovina,1996,,,,,6.296580314636231,3559086.0,1769.1565516079777 +5800,Hong Kong,1996,,,,,140.51998901367188,6388452.0,21995.937202576133 +5801,Korea,1996,,,,,584.8903198242188,45467969.0,12863.788127950442 +5802,Taiwan,1996,,,,,237.45849609375,21457706.0,11066.35052664763 +5803,Macau,1996,,,,,9.811162948608398,410395.0,23906.63372752689 +5804,Myanmar,1996,,,,,9.47476577758789,44112664.0,214.7856175176337 +5805,Brunei,1996,,,,,11.527704238891602,295573.0,39001.208631680165 +5806,Maldives,1996,,,,,1.2608107328414917,270531.0,4660.50372357139 +5807,Cabo Verde,1996,,,,,0.5966648459434509,394798.0,1511.3167897087899 +5808,Cote d'Ivoire,1996,,,,,20.585344314575195,15281453.0,1347.0803014985024 +5809,Guinea Bissau,1996,,,,,0.8098617196083069,1166256.0,694.4116211263281 +5810,Central Afr Rep,1996,,,,,1.3888461589813232,3621608.0,383.4888146318771 +5811,Dem Rep Congo,1996,,,,,16.509185791015625,47684709.0,346.21550885453917 +5812,Rep Congo,1996,,,,,7.060254096984862,2638692.0,2675.664343161257 +5813,Sao Tome Principe,1996,,,,,0.11124444752931596,129534.0,858.8050050899063 +5814,Antigua Barbuda,1997,,,,,0.8642317056655884,71453.0,12095.107352603647 +5815,Dominican Rep,1997,,,,,29.00650978088379,8039937.0,3607.803118467693 +5816,Puerto Rico,1997,,,,,80.15902709960938,3759430.0,21322.1225291093 +5817,St Kitts Nevis,1997,,,,,0.5440006256103516,44066.0,12345.132882729353 +5818,St Lucia,1997,,,,,1.0405033826828003,149715.0,6949.894016516718 +5819,St Vincent Grenadines,1997,,,,,0.4714581072330475,108607.0,4340.95506949872 +5820,Trinidad and Tobago,1997,,,,,9.931127548217773,1260150.0,7880.9090570311255 +5821,Denmark,1997,,,,,273.15994262695307,5283663.0,51698.971457292624 +5822,France,1997,,,,,2106.130615234375,60365735.0,34889.50503517227 +5823,UK,1997,,,,,1898.5855712890625,58494073.0,32457.742706497163 +5824,Bosnia Herzegovina,1997,,,,,8.461947441101074,3590693.0,2356.633508100268 +5825,Hong Kong,1997,,,,,147.68609619140625,6492649.0,22746.662601259708 +5826,Korea,1997,,,,,619.5286254882812,45807825.0,13524.515199931046 +5827,Taiwan,1997,,,,,251.96722412109372,21638532.0,11644.376990134717 +5828,Macau,1997,,,,,9.783759117126463,416985.0,23463.09607570168 +5829,Myanmar,1997,,,,,10.01024055480957,44766116.0,223.6119960643798 +5830,Brunei,1997,,,,,11.358111381530762,303110.0,37471.9124460782 +5831,Maldives,1997,,,,,1.3666929006576538,275714.0,4956.9223929784275 +5832,Cabo Verde,1997,,,,,0.6630045771598816,404001.0,1641.0963763948148 +5833,Cote d'Ivoire,1997,,,,,21.355966567993164,15686232.0,1361.4465582297369 +5834,Guinea Bissau,1997,,,,,0.8625026941299438,1194113.0,722.2957074664992 +5835,Central Afr Rep,1997,,,,,1.462454080581665,3707380.0,394.4710497930249 +5836,Dem Rep Congo,1997,,,,,15.581856727600098,48288928.0,322.67969849320525 +5837,Rep Congo,1997,,,,,7.016140460968018,2695515.0,2602.894237638454 +5838,Sao Tome Principe,1997,,,,,0.11235279589891434,132090.0,850.5776054123274 +5839,Antigua Barbuda,1998,,,,,0.9051212072372437,72781.0,12436.2293350908 +5840,Dominican Rep,1998,,,,,30.954145431518555,8182207.0,3783.104660089699 +5841,Puerto Rico,1998,,,,,84.59239959716797,3781101.0,22372.425279612467 +5842,St Kitts Nevis,1998,,,,,0.5413553714752197,44607.0,12136.10804302508 +5843,St Lucia,1998,,,,,1.105703830718994,151022.0,7321.475220292369 +5844,St Vincent Grenadines,1998,,,,,0.4907383024692536,108404.0,4526.939065617999 +5845,Trinidad and Tobago,1998,,,,,10.737988471984863,1257636.0,8538.232423360068 +5846,Denmark,1998,,,,,279.21905517578125,5302767.0,52655.35053223746 +5847,France,1998,,,,,2181.02880859375,60643715.0,35964.630606712504 +5848,UK,1998,,,,,1958.15966796875,58680368.0,33369.92821804986 +5849,Bosnia Herzegovina,1998,,,,,9.782011032104492,3622227.0,2700.551630834979 +5850,Hong Kong,1998,,,,,138.99819946289062,6541288.0,21249.362428758774 +5851,Korea,1998,,,,,585.6328735351562,46151512.0,12689.354002858157 +5852,Taiwan,1998,,,,,262.57504272460943,21834438.0,12025.729387887583 +5853,Macau,1998,,,,,9.33647918701172,421609.0,22144.87638312209 +5854,Myanmar,1998,,,,,10.597461700439453,45404941.0,233.39886512438045 +5855,Brunei,1998,,,,,11.29467487335205,310525.0,36372.8359177266 +5856,Maldives,1998,,,,,1.4691221714019775,281184.0,5224.771578048459 +5857,Cabo Verde,1998,,,,,0.7459947466850281,412972.0,1806.405147770377 +5858,Cote d'Ivoire,1998,,,,,22.408960342407227,16045358.0,1396.6008326150918 +5859,Guinea Bissau,1998,,,,,0.6201395988464355,1222239.0,507.3799795673641 +5860,Central Afr Rep,1998,,,,,1.53119158744812,3797957.0,403.16190716433067 +5861,Dem Rep Congo,1998,,,,,15.32878303527832,49348728.0,310.6216442960459 +5862,Rep Congo,1998,,,,,7.278372764587402,2756174.0,2640.752276375658 +5863,Sao Tome Principe,1998,,,,,0.11516159772872925,134801.0,854.3081856123415 +5864,Antigua Barbuda,1999,,,,,0.9386821389198304,74016.0,12682.151682336662 +5865,Dominican Rep,1999,,,,,32.792835235595696,8325197.0,3938.986096736895 +5866,Puerto Rico,1999,,,,,89.15167236328125,3800081.0,23460.466333028493 +5867,St Kitts Nevis,1999,,,,,0.5587837100028992,45131.0,12381.372227579695 +5868,St Lucia,1999,,,,,1.1356593370437622,152114.0,7465.843624148745 +5869,St Vincent Grenadines,1999,,,,,0.5040417909622192,108118.0,4661.959997060797 +5870,Trinidad and Tobago,1999,,,,,11.599692344665527,1254627.0,9245.530619590943 +5871,Denmark,1999,,,,,287.4504699707031,5320134.0,54030.68230437488 +5872,France,1999,,,,,2255.33837890625,60935252.0,37012.046473628274 +5873,UK,1999,,,,,2021.148193359375,58909682.0,34309.27013592392 +5874,Bosnia Herzegovina,1999,,,,,10.721083641052246,3717232.0,2884.157792963217 +5875,Hong Kong,1999,,,,,142.4824676513672,6596171.0,21600.784402249 +5876,Korea,1999,,,,,651.85986328125,46485422.0,14022.887934226992 +5877,Taiwan,1999,,,,,280.22006225585943,22007811.0,12732.754850351059 +5878,Macau,1999,,,,,9.116276741027832,427773.0,21311.014816334442 +5879,Myanmar,1999,,,,,11.757368087768555,46030100.0,255.42781979114872 +5880,Brunei,1999,,,,,11.639406204223633,317901.0,36613.30478426816 +5881,Maldives,1999,,,,,1.5598500967025757,291480.0,5351.482423159653 +5882,Cabo Verde,1999,,,,,0.8297151327133179,421678.0,1967.650986566332 +5883,Cote d'Ivoire,1999,,,,,22.77143096923828,16456878.0,1383.7029702254754 +5884,Guinea Bissau,1999,,,,,0.626499354839325,1250409.0,501.03554504112253 +5885,Central Afr Rep,1999,,,,,1.586314082145691,3889215.0,407.87513216566606 +5886,Dem Rep Congo,1999,,,,,14.674222946166992,50889947.0,288.3520972455914 +5887,Rep Congo,1999,,,,,7.090430736541747,2828666.0,2506.6341294948747 +5888,Sao Tome Principe,1999,,,,,0.11804064363241196,137666.0,857.4422416022253 +5889,Antigua Barbuda,2000,,,,,1.0014963150024414,75351.0,13291.08193656941 +5890,Dominican Rep,2000,,,,,34.32155990600586,8468914.0,4052.651840130371 +5891,Puerto Rico,2000,,,,,92.06867980957031,3810605.0,24161.171207608848 +5892,St Kitts Nevis,2000,,,,,0.6169373989105225,45658.0,13512.142426530345 +5893,St Lucia,2000,,,,,1.1360313892364502,153302.0,7410.414666713091 +5894,St Vincent Grenadines,2000,,,,,0.5123039484024048,107861.0,4749.668076528169 +5895,Trinidad and Tobago,2000,,,,,12.400228500366213,1251775.0,9906.116115409088 +5896,Denmark,2000,,,,,298.220947265625,5337416.0,55873.65632838531 +5897,France,2000,,,,,2342.736572265625,61255363.0,38245.41162649914 +5898,UK,2000,,,,,2095.205810546875,59139969.0,35427.915265678865 +5899,Bosnia Herzegovina,2000,,,,,11.310744285583494,3805512.0,2972.200399206072 +5900,Hong Kong,2000,,,,,153.4015655517578,6655870.0,23047.560356761445 +5901,Korea,2000,,,,,710.0346069335938,46840607.0,15158.527021940468 +5902,Taiwan,2000,,,,,298.2101745605469,22184552.0,13442.244610598711 +5903,Macau,2000,,,,,9.640217781066896,431739.0,22328.809259916048 +5904,Myanmar,2000,,,,,13.373528480529785,46641496.0,286.730264409396 +5905,Brunei,2000,,,,,11.971061706542967,325297.0,36800.40611054811 +5906,Maldives,2000,,,,,1.6198389530181885,299981.0,5399.805164387706 +5907,Cabo Verde,2000,,,,,0.9482389092445374,430132.0,2204.5300262350565 +5908,Cote d'Ivoire,2000,,,,,22.30042839050293,16884578.0,1320.7572253510232 +5909,Guinea Bissau,2000,,,,,0.6604993939399719,1278859.0,516.4755410408592 +5910,Central Afr Rep,2000,,,,,1.5468238592147827,3979862.0,388.6626871019103 +5911,Dem Rep Congo,2000,,,,,13.6600980758667,52445195.0,260.46424416701467 +5912,Rep Congo,2000,,,,,7.6276001930236825,2939099.0,2595.217171324845 +5913,Sao Tome Principe,2000,,,,,0.11856990307569505,140687.0,842.792177498241 +5914,Antigua Barbuda,2001,,,,,0.9518814086914062,76734.0,12404.9496793 +5915,Dominican Rep,2001,,,,,35.16570281982422,8613550.0,4082.6027386877904 +5916,Puerto Rico,2001,,,,,97.88140869140624,3818807.0,25631.41019994104 +5917,St Kitts Nevis,2001,,,,,0.6509754061698914,46188.0,14094.037545897017 +5918,St Lucia,2001,,,,,1.0990312099456787,154400.0,7118.077784622272 +5919,St Vincent Grenadines,2001,,,,,0.5210605859756471,107605.0,4842.345485578244 +5920,Trinidad and Tobago,2001,,,,,12.917135238647461,1249053.0,10341.542943852231 +5921,Denmark,2001,,,,,300.6757507324219,5355826.0,56139.940082523564 +5922,France,2001,,,,,2388.52392578125,61605930.0,38771.00671609453 +5923,UK,2001,,,,,2148.510498046875,59374727.0,36185.60634471422 +5924,Bosnia Herzegovina,2001,,,,,11.808417320251465,3822357.0,3089.302574367456 +5925,Hong Kong,2001,,,,,154.26194763183594,6714968.0,22972.849257336144 +5926,Korea,2001,,,,,742.1658935546875,47180521.0,15730.345443931987 +5927,Taiwan,2001,,,,,294.4527587890625,22341406.0,13179.687920673501 +5928,Macau,2001,,,,,9.918665885925293,434778.0,22813.17335726576 +5929,Myanmar,2001,,,,,14.890621185302734,47208843.0,315.42016789741564 +5930,Brunei,2001,,,,,12.299552917480467,332654.0,36974.0117884663 +5931,Maldives,2001,,,,,1.5559585094451904,305913.0,5086.277828811429 +5932,Cabo Verde,2001,,,,,0.9694003462791444,438454.0,2210.951083304393 +5933,Cote d'Ivoire,2001,,,,,22.32749366760254,17310968.0,1289.7888591557987 +5934,Guinea Bissau,2001,,,,,0.6749570965766907,1306455.0,516.6324875917584 +5935,Central Afr Rep,2001,,,,,1.6158854961395264,4053927.0,398.5975810959414 +5936,Dem Rep Congo,2001,,,,,13.373211860656738,54057719.0,247.387646168658 +5937,Rep Congo,2001,,,,,7.917647361755371,3050784.0,2595.282839347319 +5938,Sao Tome Principe,2001,,,,,0.12220346182584765,143853.0,849.5023518859364 +5939,Antigua Barbuda,2002,,,,,0.9616296291351318,77935.0,12338.867378393941 +5940,Dominican Rep,2002,,,,,36.746437072753906,8756901.0,4196.283259654746 +5941,Puerto Rico,2002,,,,,98.77845001220705,3823766.0,25832.7653973091 +5942,St Kitts Nevis,2002,,,,,0.6670525670051575,46685.0,14288.37029035359 +5943,St Lucia,2002,,,,,1.1028872728347778,155297.0,7101.793806929803 +5944,St Vincent Grenadines,2002,,,,,0.5540002584457397,107262.0,5164.925681469111 +5945,Trinidad and Tobago,2002,,,,,13.942330360412598,1245844.0,11191.072365731665 +5946,Denmark,2002,,,,,302.07794189453125,5374693.0,56203.75747871204 +5947,France,2002,,,,,2415.238525390625,61963618.0,38978.332824119876 +5948,UK,2002,,,,,2201.318359375,59601520.0,36933.9298624431 +5949,Bosnia Herzegovina,2002,,,,,12.4342622756958,3826637.0,3249.396866150565 +5950,Hong Kong,2002,,,,,156.81752014160156,6760298.0,23196.835426722544 +5951,Korea,2002,,,,,797.326904296875,47437578.0,16807.917644886402 +5952,Taiwan,2002,,,,,310.8537902832031,22461928.0,13839.141069422141 +5953,Macau,2002,,,,,10.806087493896484,438977.0,24616.523175238075 +5954,Myanmar,2002,,,,,16.68129539489746,47739006.0,349.42695277102035 +5955,Brunei,2002,,,,,12.775803565979006,339989.0,37577.1085710979 +5956,Maldives,2002,,,,,1.6690516471862793,311855.0,5352.011823399591 +5957,Cabo Verde,2002,,,,,1.0203025341033936,446704.0,2284.068497491389 +5958,Cote d'Ivoire,2002,,,,,21.95515251159668,17687648.0,1241.2703210509774 +5959,Guinea Bissau,2002,,,,,0.6683075428009033,1333120.0,501.3108668393718 +5960,Central Afr Rep,2002,,,,,1.6743247509002686,4128418.0,405.5608591233418 +5961,Dem Rep Congo,2002,,,,,13.767422676086426,55701642.0,247.16367743856503 +5962,Rep Congo,2002,,,,,8.280424118041992,3117384.0,2656.2092183837453 +5963,Sao Tome Principe,2002,,,,,0.12510809302330014,147133.0,850.3061381423621 +5964,Antigua Barbuda,2003,,,,,1.01988422870636,79066.0,12899.150440219055 +5965,Dominican Rep,2003,,,,,36.25193786621094,8896576.0,4074.819106385528 +5966,Puerto Rico,2003,,,,,98.83122253417969,3826193.0,25830.171801103526 +5967,St Kitts Nevis,2003,,,,,0.6440775990486145,47139.0,13663.3700131232 +5968,St Lucia,2003,,,,,1.1507139205932615,156008.0,7375.99302980143 +5969,St Vincent Grenadines,2003,,,,,0.596601665019989,106882.0,5581.872205048456 +5970,Trinidad and Tobago,2003,,,,,15.95580005645752,1242426.0,12842.45504879769 +5971,Denmark,2003,,,,,303.2562561035156,5394138.0,56219.59543925565 +5972,France,2003,,,,,2435.0322265625,62311901.0,39078.124523315375 +5973,UK,2003,,,,,2274.531982421875,59823619.0,38020.63500073232 +5974,Bosnia Herzegovina,2003,,,,,12.931632995605467,3842733.0,3365.2176707581475 +5975,Hong Kong,2003,,,,,161.61036682128906,6796891.0,23777.10144554165 +5976,Korea,2003,,,,,820.7141723632812,47656117.0,17221.59135129035 +5977,Taiwan,2003,,,,,323.6609802246094,22561453.0,14345.750702519443 +5978,Macau,2003,,,,,12.065361976623535,444177.0,27163.41002938814 +5979,Myanmar,2003,,,,,18.990652084350586,48261784.0,393.49254234676835 +5980,Brunei,2003,,,,,13.146807670593262,347260.0,37858.6870661558 +5981,Maldives,2003,,,,,1.8985470533370967,318490.0,5961.088427696623 +5982,Cabo Verde,2003,,,,,1.0629129409790041,454825.0,2336.9712328456085 +5983,Cote d'Ivoire,2003,,,,,21.65666389465332,18068004.0,1198.6196092636087 +5984,Guinea Bissau,2003,,,,,0.672106921672821,1359917.0,494.2264282840946 +5985,Central Afr Rep,2003,,,,,1.5839532613754272,4214915.0,375.79720145612123 +5986,Dem Rep Congo,2003,,,,,14.535345077514648,57303278.0,253.65643266541662 +5987,Rep Congo,2003,,,,,8.347765922546388,3208947.0,2601.403489227584 +5988,Sao Tome Principe,2003,,,,,0.1333320289850235,150508.0,885.8800129230573 +5989,Antigua Barbuda,2004,,,,,1.0783895254135132,80193.0,13447.427149670337 +5990,Dominican Rep,2004,,,,,37.18365859985352,9032393.0,4116.700701558659 +5991,Puerto Rico,2004,,,,,107.4776382446289,3827008.0,28083.985778087972 +5992,St Kitts Nevis,2004,,,,,0.6702136397361755,47529.0,14101.151712347737 +5993,St Lucia,2004,,,,,1.2341115474700928,156657.0,7877.793826449458 +5994,St Vincent Grenadines,2004,,,,,0.6213981509208679,106479.0,5835.875157738784 +5995,Trinidad and Tobago,2004,,,,,17.224220275878903,1239328.0,13898.032059211853 +5996,Denmark,2004,,,,,311.34771728515625,5413392.0,57514.349096676575 +5997,France,2004,,,,,2502.882568359375,62678427.0,39932.121595192155 +5998,UK,2004,,,,,2328.312255859375,60126953.0,38723.27034199413 +5999,Bosnia Herzegovina,2004,,,,,13.720458030700685,3876893.0,3539.0344873332033 +6000,Hong Kong,2004,,,,,175.67050170898438,6847993.0,25652.8448129232 +6001,Korea,2004,,,,,860.9278564453125,47849421.0,17992.440419400533 +6002,Taiwan,2004,,,,,344.7313232421875,22646408.0,15222.340039188002 +6003,Macau,2004,,,,,15.293513298034668,455227.0,33595.35637832261 +6004,Myanmar,2004,,,,,21.566669464111328,48771141.0,442.2014540137851 +6005,Brunei,2004,,,,,13.213109016418455,354305.0,37293.0357077051 +6006,Maldives,2004,,,,,2.0131006240844727,326599.0,6163.829724170842 +6007,Cabo Verde,2004,,,,,1.1712989807128906,462795.0,2530.9240175734194 +6008,Cote d'Ivoire,2004,,,,,21.92342567443848,18504380.0,1184.769534263697 +6009,Guinea Bissau,2004,,,,,0.6906664967536926,1386926.0,497.98366802099935 +6010,Central Afr Rep,2004,,,,,1.6789094209671018,4288752.0,391.4680589987722 +6011,Dem Rep Congo,2004,,,,,15.514791488647461,58942550.0,263.21887140355244 +6012,Rep Congo,2004,,,,,8.637986183166504,3332121.0,2592.3386885309696 +6013,Sao Tome Principe,2004,,,,,0.13844162225723267,153970.0,899.1467315531122 +6014,Antigua Barbuda,2005,,,,,1.1475403308868408,81277.0,14118.88149029665 +6015,Dominican Rep,2005,,,,,40.68942260742188,9164472.0,4439.909097591424 +6016,Puerto Rico,2005,,,,,105.34212493896484,3821525.0,27565.4679582012 +6017,St Kitts Nevis,2005,,,,,0.7351923584938049,47904.0,15347.20187236567 +6018,St Lucia,2005,,,,,1.2293678522109983,157371.0,7811.908497823605 +6019,St Vincent Grenadines,2005,,,,,0.6368585228919983,106070.0,6004.13427823134 +6020,Trinidad and Tobago,2005,,,,,18.293672561645508,1236690.0,14792.448036003774 +6021,Denmark,2005,,,,,318.62286376953125,5432335.0,58653.021908540475 +6022,France,2005,,,,,2543.121826171875,63059742.0,40328.76991745185 +6023,UK,2005,,,,,2400.398681640625,60487347.0,39684.310863239305 +6024,Bosnia Herzegovina,2005,,,,,14.922268867492676,3893097.0,3833.00720929704 +6025,Hong Kong,2005,,,,,188.64947509765625,6904226.0,27323.768818931512 +6026,Korea,2005,,,,,894.7079467773438,47988429.0,18644.243319099773 +6027,Taiwan,2005,,,,,363.4157409667969,22727551.0,15990.096819793602 +6028,Macau,2005,,,,,16.52608871459961,474027.0,34863.1801872037 +6029,Myanmar,2005,,,,,24.49304008483887,49279344.0,497.0244750993209 +6030,Brunei,2005,,,,,13.264311790466307,361110.0,36732.05336453244 +6031,Maldives,2005,,,,,1.7487995624542236,336248.0,5200.921826908186 +6032,Cabo Verde,2005,,,,,1.2522644996643066,470597.0,2661.0125004288307 +6033,Cote d'Ivoire,2005,,,,,22.30078125,18920502.0,1178.6569537108476 +6034,Guinea Bissau,2005,,,,,0.7201310396194458,1414159.0,509.22918824505996 +6035,Central Afr Rep,2005,,,,,1.6941574811935425,4363038.0,388.29766809125715 +6036,Dem Rep Congo,2005,,,,,16.466646194458008,60698054.0,271.28787678198063 +6037,Rep Congo,2005,,,,,9.307928085327148,3472384.0,2680.5583959974324 +6038,Sao Tome Principe,2005,,,,,0.14826235175132751,157514.0,941.2645971236051 +6039,Antigua Barbuda,2006,,,,,1.293605089187622,82343.0,15709.958213662632 +6040,Dominican Rep,2006,,,,,44.42241287231445,9295039.0,4779.152930107603 +6041,Puerto Rico,2006,,,,,103.857421875,3805408.0,27292.059583361362 +6042,St Kitts Nevis,2006,,,,,0.7566431164741516,48280.0,15671.978385960056 +6043,St Lucia,2006,,,,,1.3064454793930054,158128.0,8261.949050092364 +6044,St Vincent Grenadines,2006,,,,,0.6858224868774414,105685.0,6489.3077246292405 +6045,Trinidad and Tobago,2006,,,,,20.709909439086914,1234559.0,16775.147594474558 +6046,Denmark,2006,,,,,331.0905456542969,5450661.0,60743.191633876486 +6047,France,2006,,,,,2603.51953125,63456871.0,41028.17378515244 +6048,UK,2006,,,,,2459.3525390625,60846809.0,40418.75949587594 +6049,Bosnia Herzegovina,2006,,,,,15.730648040771484,3894410.0,4039.2891454088 +6050,Hong Kong,2006,,,,,201.9166717529297,6955186.0,29031.09589778472 +6051,Korea,2006,,,,,941.0193481445312,48163516.0,19538.01188734916 +6052,Taiwan,2006,,,,,383.8397216796875,22817266.0,16822.33628164248 +6053,Macau,2006,,,,,18.72644805908203,499427.0,37495.866381036736 +6054,Myanmar,2006,,,,,27.69577598571777,49767531.0,556.5029132290645 +6055,Brunei,2006,,,,,13.847639083862305,367819.0,37647.971104979086 +6056,Maldives,2006,,,,,2.205437183380127,348119.0,6335.2967904082425 +6057,Cabo Verde,2006,,,,,1.3522422313690186,478309.0,2827.1310624910225 +6058,Cote d'Ivoire,2006,,,,,22.638826370239254,19326799.0,1171.3696805269853 +6059,Guinea Bissau,2006,,,,,0.7367614507675171,1442747.0,510.66573055949317 +6060,Central Afr Rep,2006,,,,,1.7749872207641602,4448948.0,398.96785054897487 +6061,Dem Rep Congo,2006,,,,,17.34283447265625,62522207.0,277.3867927064099 +6062,Rep Congo,2006,,,,,9.888370513916016,3628152.0,2725.456517234122 +6063,Sao Tome Principe,2006,,,,,0.16177742183208466,161114.0,1004.1177168469819 +6064,Antigua Barbuda,2007,,,,,1.4133855104446411,83425.0,16941.989936405647 +6065,Dominican Rep,2007,,,,,47.716758728027344,9426233.0,5062.123833351811 +6066,Puerto Rico,2007,,,,,102.6497573852539,3783221.0,27132.9000830916 +6067,St Kitts Nevis,2007,,,,,0.7562560439109802,48657.0,15542.594979365358 +6068,St Lucia,2007,,,,,1.324356198310852,158875.0,8335.837597550602 +6069,St Vincent Grenadines,2007,,,,,0.7087464928627014,105307.0,6730.288517028322 +6070,Trinidad and Tobago,2007,,,,,21.69449806213379,1232811.0,17597.58637952921 +6071,Denmark,2007,,,,,334.1009826660156,5468120.0,61099.789811857765 +6072,France,2007,,,,,2665.00146484375,63852860.0,41736.602946896186 +6073,UK,2007,,,,,2517.312255859375,61249260.0,41099.472154592164 +6074,Bosnia Herzegovina,2007,,,,,16.651769638061527,3891845.0,4278.631250232608 +6075,Hong Kong,2007,,,,,214.9702453613281,7003726.0,30693.69723506147 +6076,Korea,2007,,,,,992.4310302734376,48453272.0,20482.22935849281 +6077,Taiwan,2007,,,,,408.8660583496094,22909368.0,17847.11207876225 +6078,Macau,2007,,,,,21.431978225708008,525531.0,40781.56802492718 +6079,Myanmar,2007,,,,,31.01689720153809,50256010.0,617.1778699012931 +6080,Brunei,2007,,,,,13.86904525756836,374577.0,37025.88588612851 +6081,Maldives,2007,,,,,2.3755617141723637,364968.0,6508.958906458549 +6082,Cabo Verde,2007,,,,,1.5573866367340088,485994.0,3204.5388147467024 +6083,Cote d'Ivoire,2007,,,,,23.038410186767575,19746949.0,1166.682011827122 +6084,Guinea Bissau,2007,,,,,0.7608016729354858,1472780.0,516.5752338675742 +6085,Central Afr Rep,2007,,,,,1.856770396232605,4543731.0,408.6444369687829 +6086,Dem Rep Congo,2007,,,,,18.42840385437012,64355014.0,286.3553701405475 +6087,Rep Congo,2007,,,,,9.731914520263672,3782813.0,2572.6660345789423 +6088,Sao Tome Principe,2007,,,,,0.16704101860523224,164749.0,1013.912185234704 +6089,Antigua Barbuda,2008,,,,,1.4129595756530762,84522.0,16717.062725125717 +6090,Dominican Rep,2008,,,,,49.24822998046875,9558183.0,5152.467783936418 +6091,Puerto Rico,2008,,,,,100.75675964355469,3761122.0,26789.01658695322 +6092,St Kitts Nevis,2008,,,,,0.8053208589553833,49064.0,16413.68129291096 +6093,St Lucia,2008,,,,,1.3946981430053709,159585.0,8739.531553751109 +6094,St Vincent Grenadines,2008,,,,,0.7199764251708984,104938.0,6860.9695741380465 +6095,Trinidad and Tobago,2008,,,,,22.43003273010254,1231323.0,18216.205439273483 +6096,Denmark,2008,,,,,332.3902893066406,5484723.0,60602.93096053176 +6097,France,2008,,,,,2670.2060546875,64220250.0,41578.87978772272 +6098,UK,2008,,,,,2505.4169921875,61642600.0,40644.245897926114 +6099,Bosnia Herzegovina,2008,,,,,17.555416107177734,3888949.0,4514.180079805041 +6100,Hong Kong,2008,,,,,219.5446929931641,7036409.0,31201.241001363633 +6101,Korea,2008,,,,,1020.509033203125,48753802.0,20931.88615737343 +6102,Taiwan,2008,,,,,411.7281188964844,22987017.0,17911.333118885515 +6103,Macau,2008,,,,,22.159292221069336,545674.0,40609.03070527336 +6104,Myanmar,2008,,,,,34.19777297973633,50617389.0,675.6131372113313 +6105,Brunei,2008,,,,,13.600025177001953,381371.0,35660.87924095422 +6106,Maldives,2008,,,,,2.600891590118408,385925.0,6739.3705774915015 +6107,Cabo Verde,2008,,,,,1.6609609127044678,493619.0,3364.8642226179863 +6108,Cote d'Ivoire,2008,,,,,23.62424087524414,20179602.0,1170.6990492302148 +6109,Guinea Bissau,2008,,,,,0.7851744890213013,1503182.0,522.3415987028193 +6110,Central Afr Rep,2008,,,,,1.8949109315872192,4641320.0,408.26983090741834 +6111,Dem Rep Congo,2008,,,,,19.57573890686035,66187146.0,295.76345393198176 +6112,Rep Congo,2008,,,,,10.274199485778807,3910032.0,2627.650997684625 +6113,Sao Tome Principe,2008,,,,,0.18071529269218445,168415.0,1073.0356125771718 +6114,Antigua Barbuda,2009,,,,,1.2418850660324097,85632.0,14502.581582030196 +6115,Dominican Rep,2009,,,,,49.71419525146485,9690787.0,5130.047255343126 +6116,Puerto Rico,2009,,,,,98.78955841064452,3740697.0,26409.398679081612 +6117,St Kitts Nevis,2009,,,,,0.7781937718391418,49480.0,15727.440821324612 +6118,St Lucia,2009,,,,,1.3797523975372314,160267.0,8609.08607222467 +6119,St Vincent Grenadines,2009,,,,,0.7048637270927429,104574.0,6740.334376544293 +6120,Trinidad and Tobago,2009,,,,,21.445222854614254,1229953.0,17435.80677848199 +6121,Denmark,2009,,,,,316.08148193359375,5500510.0,57464.03186860741 +6122,France,2009,,,,,2591.666259765625,64579931.0,40131.14011790482 +6123,UK,2009,,,,,2400.496337890625,61996848.0,38719.65132631622 +6124,Bosnia Herzegovina,2009,,,,,17.028268814086914,3886938.0,4380.895402521704 +6125,Hong Kong,2009,,,,,214.14596557617188,7038760.0,30423.819760323106 +6126,Korea,2009,,,,,1027.729248046875,49000481.0,20973.86040040862 +6127,Taiwan,2009,,,,,405.2640075683594,23065096.0,17570.445298313927 +6128,Macau,2009,,,,,22.452014923095703,559846.0,40103.91236714328 +6129,Myanmar,2009,,,,,37.805641174316406,51017774.0,741.0288260384784 +6130,Brunei,2009,,,,,13.360048294067385,388190.0,34416.260836362046 +6131,Maldives,2009,,,,,2.412877082824707,396334.0,6087.989127414521 +6132,Cabo Verde,2009,,,,,1.639859676361084,501182.0,3271.9843816439616 +6133,Cote d'Ivoire,2009,,,,,24.392370223999023,20617068.0,1183.1153791605589 +6134,Guinea Bissau,2009,,,,,0.8116268515586853,1533964.0,529.104236839121 +6135,Central Afr Rep,2009,,,,,1.9272993803024288,4741916.0,406.43895427553525 +6136,Dem Rep Congo,2009,,,,,20.1346378326416,68015710.0,296.02922372848275 +6137,Rep Congo,2009,,,,,11.041573524475098,4063165.0,2717.4809599105865 +6138,Sao Tome Principe,2009,,,,,0.18509994447231293,172103.0,1075.5184074206313 +6139,Antigua Barbuda,2010,,,,,1.152469038963318,86754.0,13284.333160007815 +6140,Dominican Rep,2010,,,,,53.86018371582031,9823821.0,5482.61045430493 +6141,Puerto Rico,2010,,,,,98.38129425048828,3721525.0,26435.7472408457 +6142,St Kitts Nevis,2010,,,,,0.7667850852012634,49898.0,15367.050487018789 +6143,St Lucia,2010,,,,,1.3820546865463257,160922.0,8588.351415880525 +6144,St Vincent Grenadines,2010,,,,,0.6812258958816528,104217.0,6536.610110458494 +6145,Trinidad and Tobago,2010,,,,,22.157917022705078,1228691.0,18033.75870963902 +6146,Denmark,2010,,,,,321.9953308105469,5515575.0,58379.28607815992 +6147,France,2010,,,,,2642.609619140625,64940833.0,40692.573486709436 +6148,UK,2010,,,,,2452.89990234375,62348447.0,39341.79631360746 +6149,Bosnia Herzegovina,2010,,,,,17.176780700683597,3884899.0,4421.422719273678 +6150,Hong Kong,2010,,,,,228.63868713378903,7029211.0,32526.934692071278 +6151,Korea,2010,,,,,1094.498779296875,49258140.0,22219.65302175184 +6152,Taiwan,2010,,,,,448.34356689453125,23126580.0,19386.505349884468 +6153,Macau,2010,,,,,28.12434387207031,567957.0,49518.438670656964 +6154,Myanmar,2010,,,,,41.44800567626953,51414732.0,806.1503787721684 +6155,Brunei,2010,,,,,13.707271575927734,395027.0,34699.58148665214 +6156,Maldives,2010,,,,,2.588175773620605,395650.0,6541.579106838381 +6157,Cabo Verde,2010,,,,,1.663913011550903,508659.0,3271.175800587237 +6158,Cote d'Ivoire,2010,,,,,24.884521484375,21058798.0,1181.6686538507565 +6159,Guinea Bissau,2010,,,,,0.8490506410598755,1565126.0,542.4806955221978 +6160,Central Afr Rep,2010,,,,,1.986017227172852,4844927.0,409.9168526528576 +6161,Dem Rep Congo,2010,,,,,21.565805435180664,69851289.0,308.738832796352 +6162,Rep Congo,2010,,,,,12.007887840270994,4237641.0,2833.6255573020453 +6163,Sao Tome Principe,2010,,,,,0.197449803352356,175808.0,1123.0990816820392 +6164,Antigua Barbuda,2011,,,,,1.1285077333450315,87884.0,12840.878127361428 +6165,Dominican Rep,2011,,,,,55.5478515625,9956648.0,5578.971111813936 +6166,Puerto Rico,2011,,,,,98.02859497070312,3679460.0,26642.114595811105 +6167,St Kitts Nevis,2011,,,,,0.7804637551307678,50314.0,15511.860617934726 +6168,St Lucia,2011,,,,,1.4306516647338867,161557.0,8855.398804965967 +6169,St Vincent Grenadines,2011,,,,,0.6783692240715027,103869.0,6531.00755828498 +6170,Trinidad and Tobago,2011,,,,,22.09269523620605,1227505.0,17998.049080212346 +6171,Denmark,2011,,,,,326.2996826171875,5529888.0,59006.56263150131 +6172,France,2011,,,,,2700.553955078125,65296094.0,41358.58348706318 +6173,UK,2011,,,,,2493.244384765625,62698362.0,39765.70208908528 +6174,Bosnia Herzegovina,2011,,,,,17.341499328613278,3882359.0,4466.742856241085 +6175,Hong Kong,2011,,,,,239.6469268798828,7030206.0,34088.17990253527 +6176,Korea,2011,,,,,1134.7947998046875,49559043.0,22897.83521051218 +6177,Taiwan,2011,,,,,465.380615234375,23174265.0,20081.785343974232 +6178,Macau,2011,,,,,34.21954345703125,573003.0,59719.65846083048 +6179,Myanmar,2011,,,,,43.76556396484375,51955263.0,842.3701746027876 +6180,Brunei,2011,,,,,14.22065258026123,401890.0,35384.439971786385 +6181,Maldives,2011,,,,,2.809897899627685,394999.0,7113.6835780032 +6182,Cabo Verde,2011,,,,,1.7299517393112185,516100.0,3351.9700432304166 +6183,Cote d'Ivoire,2011,,,,,23.79277420043945,21504162.0,1106.4264769043061 +6184,Guinea Bissau,2011,,,,,0.9176945686340332,1596677.0,574.7527951076098 +6185,Central Afr Rep,2011,,,,,2.0515701770782466,4950027.0,414.45636096090936 +6186,Dem Rep Congo,2011,,,,,23.048383712768555,71712867.0,321.39816293732275 +6187,Rep Congo,2011,,,,,12.4186372756958,4379194.0,2835.8271580788155 +6188,Sao Tome Principe,2011,,,,,0.20613403618335724,179506.0,1148.3406470165746 +6189,Antigua Barbuda,2012,,,,,1.1680800914764404,89018.0,13121.841554252405 +6190,Dominican Rep,2012,,,,,57.05729293823242,10088598.0,5655.621617417249 +6191,Puerto Rico,2012,,,,,98.05728912353516,3636204.0,26966.938357566065 +6192,St Kitts Nevis,2012,,,,,0.7753464579582214,50726.0,15284.991088558558 +6193,St Lucia,2012,,,,,1.4216482639312744,162178.0,8765.97481736903 +6194,St Vincent Grenadines,2012,,,,,0.6877471208572388,103537.0,6642.525095929366 +6195,Trinidad and Tobago,2012,,,,,22.378570556640625,1226383.0,18247.619672354092 +6196,Denmark,2012,,,,,327.0386657714844,5543453.0,58995.479130333464 +6197,France,2012,,,,,2709.01025390625,65630692.0,41276.576116342796 +6198,UK,2012,,,,,2529.3232421875,63047162.0,40117.95554235257 +6199,Bosnia Herzegovina,2012,,,,,17.198999404907227,3879296.0,4433.536240830095 +6200,Hong Kong,2012,,,,,243.72164916992188,7050155.0,34569.68664801297 +6201,Korea,2012,,,,,1160.8087158203125,49823397.0,23298.465895858375 +6202,Taiwan,2012,,,,,474.9674682617188,23234936.0,20441.95293938915 +6203,Macau,2012,,,,,37.380462646484375,578025.0,64669.283588918086 +6204,Myanmar,2012,,,,,46.97474670410156,52523267.0,894.3607164440393 +6205,Brunei,2012,,,,,14.350464820861815,408786.0,35105.078992093215 +6206,Maldives,2012,,,,,2.88063383102417,394451.0,7302.893974217761 +6207,Cabo Verde,2012,,,,,1.7486684322357178,523568.0,3339.9070077539445 +6208,Cote d'Ivoire,2012,,,,,26.34014892578125,21952093.0,1199.8923713461513 +6209,Guinea Bissau,2012,,,,,0.9019773602485656,1628603.0,553.8350108949606 +6210,Central Afr Rep,2012,,,,,2.135946989059448,5057208.0,422.3569584362455 +6211,Dem Rep Congo,2012,,,,,24.681798934936523,73599190.0,335.354219726284 +6212,Rep Congo,2012,,,,,12.890542030334473,4489969.0,2870.964594707552 +6213,Sao Tome Principe,2012,,,,,0.2126107215881348,183176.0,1160.690928877881 +6214,Sudan,2012,,,,,64.67819213867189,34206710.0,1890.804235153625 +6215,Antigua Barbuda,2013,,,,,1.1668667793273926,90156.0,12942.752332927288 +6216,Dominican Rep,2013,,,,,59.83895492553711,10219630.0,5855.295634532476 +6217,Puerto Rico,2013,,,,,97.75642395019531,3589467.0,27234.2450704228 +6218,St Kitts Nevis,2013,,,,,0.8177266716957092,51134.0,15991.838535919529 +6219,St Lucia,2013,,,,,1.4028370380401611,162781.0,8617.940902440463 +6220,St Vincent Grenadines,2013,,,,,0.7003546953201294,103220.0,6785.067770975872 +6221,Trinidad and Tobago,2013,,,,,22.59807014465332,1225225.0,18444.016523212733 +6222,Denmark,2013,,,,,330.091064453125,5556452.0,59406.80571939162 +6223,France,2013,,,,,2724.623046875,65951611.0,41312.45629276592 +6224,UK,2013,,,,,2581.082275390625,63395574.0,40713.919167142885 +6225,Bosnia Herzegovina,2013,,,,,17.603073120117188,3875723.0,4541.881120017398 +6226,Hong Kong,2013,,,,,251.28065490722656,7082316.0,35480.011751413884 +6227,Korea,2013,,,,,1194.4281005859375,50063069.0,23858.46741808692 +6228,Taiwan,2013,,,,,485.416748046875,23299716.0,20833.590763375614 +6229,Macau,2013,,,,,41.56714630126953,583003.0,71298.34031946582 +6230,Myanmar,2013,,,,,50.932838439941406,53044727.0,960.1866447524823 +6231,Brunei,2013,,,,,14.045370101928713,415717.0,33785.89305207319 +6232,Maldives,2013,,,,,3.0903747081756587,393988.0,7843.829528248725 +6233,Cabo Verde,2013,,,,,1.762706637382507,531046.0,3319.3106385934693 +6234,Cote d'Ivoire,2013,,,,,28.6816349029541,22400835.0,1280.3824010557687 +6235,Guinea Bissau,2013,,,,,0.9313448667526244,1660870.0,560.7572337104194 +6236,Central Afr Rep,2013,,,,,1.352055549621582,5166510.0,261.6961061957844 +6237,Dem Rep Congo,2013,,,,,26.775299072265625,75507308.0,354.60539888755704 +6238,Rep Congo,2013,,,,,13.334067344665527,4574099.0,2915.1243435407778 +6239,Sao Tome Principe,2013,,,,,0.22284749150276184,186817.0,1192.8651648552425 +6240,Sudan,2013,,,,,67.52061462402344,34847910.0,1937.5800334660944 +6241,Canada,2014,,,,,1784.6583251953125,34834841.0,51231.99285437566 +6242,United States,2014,,,,,16242.525390625,318953000.0,50924.510478424716 +6243,Cuba,2014,,,,,70.7294921875,11237982.0,6293.789417664132 +6244,Mexico,2014,,,,,1184.179443359375,120286655.0,9844.645221528315 +6245,Antigua Barbuda,2014,,,,,1.2212209701538086,91295.0,13376.646806000424 +6246,Bahamas,2014,,,,,10.412761688232422,321834.0,32354.448840807436 +6247,Barbados,2014,,,,,4.573735237121582,289680.0,15788.923077608333 +6248,Belize,2014,,,,,1.5360697507858276,340844.0,4506.66507489006 +6249,Costa Rica,2014,,,,,43.12753677368164,4755234.0,9069.487805159879 +6250,Dominica,2014,,,,,0.5058064460754395,73449.0,6886.498741649845 +6251,Dominican Rep,2014,,,,,64.40827178955078,10349741.0,6223.17715868936 +6252,El Salvador,2014,,,,,20.55670166015625,6125512.0,3355.9156622591304 +6253,Grenada,2014,,,,,0.8437183499336243,110152.0,7659.5826669840235 +6254,Guatemala,2014,,,,,47.89601516723633,14647083.0,3270.0036701667027 +6255,Haiti,2014,,,,,7.80417013168335,10047305.0,776.7426321469638 +6256,Honduras,2014,,,,,18.142358779907227,8598561.0,2109.9296475197684 +6257,Jamaica,2014,,,,,13.525786399841307,2930050.0,4616.230576215869 +6258,Nicaragua,2014,,,,,10.9033203125,5848641.0,1864.2485173051311 +6259,Panama,2014,,,,,40.40781784057617,3608431.0,11198.168356434187 +6260,Puerto Rico,2014,,,,,96.59276580810548,3534090.0,27331.722114633598 +6261,St Kitts Nevis,2014,,,,,0.8672447800636292,51538.0,16827.28821575593 +6262,St Lucia,2014,,,,,1.4528191089630127,163362.0,8893.250015077023 +6263,St Vincent Grenadines,2014,,,,,0.7088546752929688,102918.0,6887.567532336119 +6264,Trinidad and Tobago,2014,,,,,22.54108619689941,1223916.0,18417.18401989958 +6265,Argentina,2014,,,,,446.7027893066406,43057443.0,10374.577731117024 +6266,Brazil,2014,,,,,2423.225830078125,202656788.0,11957.289237595756 +6267,Bolivia,2014,,,,,24.47524070739746,10631486.0,2302.146727879571 +6268,Chile,2014,,,,,258.59402465820307,17363894.0,14892.628615344182 +6269,Colombia,2014,,,,,349.5124206542969,46245297.0,7557.793836945125 +6270,Ecuador,2014,,,,,86.33344268798828,15654411.0,5514.95950170136 +6271,Guyana,2014,,,,,2.7451000213623047,735554.0,3732.016984969567 +6272,Paraguay,2014,,,,,32.10940170288086,6703860.0,4789.688582828529 +6273,Peru,2014,,,,,180.42433166503903,30147935.0,5984.633165257888 +6274,Suriname,2014,,,,,4.89200496673584,573311.0,8532.899188635558 +6275,Uruguay,2014,,,,,47.3836669921875,3332972.0,14216.641181560331 +6276,Venezuela,2014,,,,,314.4953308105469,30111977.0,10444.194043139276 +6277,Austria,2014,,,,,408.88116455078125,8615828.0,47456.97854585552 +6278,Belgium,2014,,,,,500.5924377441406,11236729.0,44549.6583342128 +6279,Denmark,2014,,,,,335.43646240234375,5569077.0,60231.96705708032 +6280,Finland,2014,,,,,247.0753631591797,5454355.0,45298.731593227734 +6281,France,2014,,,,,2750.675537109375,66259012.0,41513.98359379966 +6282,Germany,2014,,,,,3654.924560546875,80996685.0,45124.37219556424 +6283,Greece,2014,,,,,245.79470825195312,10775557.0,22810.394697179287 +6284,Ireland,2014,,,,,254.4913482666016,4832765.0,52659.57443960167 +6285,Italy,2014,,,,,2043.48583984375,61680122.0,33130.3793439927 +6286,Luxembourg,2014,,,,,58.77983474731445,557989.0,105342.28228032176 +6287,Netherlands,2014,,,,,861.8159790039062,16877351.0,51063.46244762619 +6288,Portugal,2014,,,,,223.97116088867188,10813834.0,20711.540503458058 +6289,Spain,2014,,,,,1371.017578125,47737941.0,28719.66300609823 +6290,Sweden,2014,,,,,519.8903198242189,9723809.0,53465.70668183823 +6291,UK,2014,,,,,2657.1611328125,63742977.0,41685.551222568414 +6292,Bulgaria,2014,,,,,52.80276107788086,7228183.0,7305.122335430752 +6293,Croatia,2014,,,,,57.95219802856445,4356748.0,13301.709905774778 +6294,Cyprus,2014,,,,,23.17043113708496,1172458.0,19762.26963958194 +6295,Czech Republic,2014,,,,,214.1243438720703,10627448.0,20148.23726938681 +6296,Estonia,2014,,,,,22.956432342529297,1272468.0,18040.87202391675 +6297,Hungary,2014,,,,,139.30941772460938,9919128.0,14044.522635922169 +6298,Latvia,2014,,,,,27.441335678100586,2007730.0,13667.841631145913 +6299,Lithuania,2014,,,,,43.8079948425293,2914462.0,15031.245850015988 +6300,Malta,2014,,,,,10.287996292114258,412655.0,24931.228973632347 +6301,Poland,2014,,,,,535.7918090820311,38593885.0,13882.816127011603 +6302,Romania,2014,,,,,185.27780151367188,21729871.0,8526.410557783425 +6303,Slovakia,2014,,,,,97.55486297607422,5443583.0,17921.075691520495 +6304,Slovenia,2014,,,,,47.88824462890625,1988292.0,24085.11658695315 +6305,Iceland,2014,,,,,15.011602401733398,327872.0,45784.947789788086 +6306,Norway,2014,,,,,458.6261901855469,5147792.0,89091.82620151453 +6307,Switzerland,2014,,,,,625.695068359375,8061516.0,77615.06252166156 +6308,Albania,2014,,,,,12.750144004821776,3020209.0,4221.609830585161 +6309,Bosnia Herzegovina,2014,,,,,17.80544090270996,3871643.0,4598.936653691975 +6310,Macedonia,2014,,,,,10.221696853637695,2091719.0,4886.74475569505 +6311,Serbia,2014,,,,,39.88169860839844,7209764.0,5531.6233108876295 +6312,Russia,2014,,,,,1706.42529296875,142470272.0,11977.413035111986 +6313,Ukraine,2014,,,,,134.33148193359378,44943869.0,2988.872229348874 +6314,Armenia,2014,,,,,11.123116493225098,3060631.0,3634.255973106558 +6315,Azerbaijan,2014,,,,,58.38311004638672,9686210.0,6027.44624021023 +6316,Belarus,2014,,,,,64.56880187988281,9608058.0,6720.2760307944445 +6317,Georgia,2014,,,,,14.35537338256836,4935880.0,2908.37163435261 +6318,Kazakhstan,2014,,,,,184.0454406738281,17948816.0,10253.904250499205 +6319,Kyrgyzstan,2014,,,,,5.855985164642334,5604212.0,1044.9257031394127 +6320,Moldova,2014,,,,,7.0663490295410165,3583288.0,1972.0293288010944 +6321,Tajikistan,2014,,,,,7.464531898498535,8051512.0,927.0969103068512 +6322,Turkmenistan,2014,,,,,34.97995758056641,5171943.0,6763.40740425144 +6323,Uzbekistan,2014,,,,,53.30411148071289,28929716.0,1842.5383602352988 +6324,China,2014,,,,,8333.3486328125,1361344333.0,6121.411336431148 +6325,Hong Kong,2014,,,,,258.22207641601557,7112688.0,36304.42898887392 +6326,Japan,2014,,,,,5916.1962890625,127103388.0,46546.33037053662 +6327,Korea,2014,,,,,1234.33935546875,50345320.0,24517.459725526627 +6328,Taiwan,2014,,,,,504.9305114746094,23359928.0,21615.24262722939 +6329,Macau,2014,,,,,41.06787490844727,587914.0,69853.54134864497 +6330,Mongolia,2014,,,,,11.408686637878418,2953190.0,3863.173936617156 +6331,Cambodia,2014,,,,,14.858222007751465,15458332.0,961.1788650775172 +6332,Indonesia,2014,,,,,942.1846313476562,253609643.0,3715.097818057558 +6333,Malaysia,2014,,,,,314.3189392089844,30073353.0,10451.74241824596 +6334,Myanmar,2014,,,,,55.00283813476562,53552091.0,1027.090391946892 +6335,Philippines,2014,,,,,250.83827209472656,99377560.0,2524.093689709493 +6336,Thailand,2014,,,,,382.5254211425781,67741401.0,5646.848389548042 +6337,Vietnam,2014,,,,,144.83465576171875,93421835.0,1550.3298106028292 +6338,Brunei,2014,,,,,13.715339660644533,422675.0,32448.902018440964 +6339,Laos,2014,,,,,9.670589447021484,6803699.0,1421.3723221767284 +6340,Singapore,2014,,,,,285.7608947753906,5567301.0,51328.4434909107 +6341,Bangladesh,2014,,,,,146.01382446289062,153044873.0,954.0589083496487 +6342,India,2014,,,,,2117.421630859375,1236344631.0,1712.646763505357 +6343,Pakistan,2014,,,,,202.8091583251953,196174380.0,1033.820819646252 +6344,Afghanistan,2014,,,,,19.204517364501953,31822848.0,603.4820442375852 +6345,Bhutan,2014,,,,,1.9413220882415767,733643.0,2646.140000302023 +6346,Nepal,2014,,,,,19.47172737121582,28311268.0,687.7730581059039 +6347,Sri Lanka,2014,,,,,72.83833312988281,21866445.0,3331.0550997147825 +6348,Australia,2014,,,,,1335.1485595703125,22507617.0,59319.85423291646 +6349,New Zealand,2014,,,,,162.9918975830078,4401916.0,37027.48929852542 +6350,Fiji,2014,,,,,3.6177899837493896,903207.0,4005.493739252895 +6351,Maldives,2014,,,,,3.3168869018554688,393595.0,8427.157107827765 +6352,Papua New Guinea,2014,,,,,18.070199966430664,6552730.0,2757.65977942486 +6353,Samoa,2014,,,,,0.6936119198799133,196628.0,3527.533819597989 +6354,Solomon Islands,2014,,,,,0.8491722941398621,609883.0,1392.3527859275666 +6355,Tonga,2014,,,,,0.3834481835365296,106440.0,3602.4819948941145 +6356,Iran,2014,,,,,487.0949401855469,78783743.0,6182.683401898625 +6357,Iraq,2014,,,,,183.90887451171875,35974993.0,5112.13093249855 +6358,Saudi Arabia,2014,,,,,651.9575805664062,27345986.0,23841.07051639704 +6359,Turkey,2014,,,,,1025.433349609375,78401967.0,13079.178863068257 +6360,Bahrain,2014,,,,,29.92284393310547,1314089.0,22770.78944660937 +6361,Israel,2014,,,,,271.09136962890625,7921139.0,34223.78645658235 +6362,Jordan,2014,,,,,29.50084114074707,8998611.0,3278.3772007421003 +6363,Kuwait,2014,,,,,137.14717102050778,2742711.0,50004.23705614912 +6364,Lebanon,2014,,,,,41.720603942871094,5882562.0,7092.250611701346 +6365,Oman,2014,,,,,67.99739837646484,3219775.0,21118.68014891253 +6366,Syria,2014,,,,,28.951662063598636,17951639.0,1612.7587048513305 +6367,United Arab Emirates,2014,,,,,355.08917236328125,5628805.0,63084.290957544494 +6368,Yemen,2014,,,,,28.90029144287109,26052966.0,1109.2898767407555 +6369,Egypt,2014,,,,,234.8050537109375,89959189.0,2610.128618555437 +6370,Morocco,2014,,,,,108.46373748779295,32987206.0,3288.0546927130767 +6371,Algeria,2014,,,,,182.88937377929688,38813722.0,4711.977217214491 +6372,Libya,2014,,,,,41.5499153137207,6244174.0,6654.1892192178975 +6373,Tunisia,2014,,,,,47.59927749633789,11032836.0,4314.328382687632 +6374,Nigeria,2014,,,,,452.284423828125,177155754.0,2553.03264848019 +6375,South Africa,2014,,,,,413.60443115234375,53006857.0,7802.847679732147 +6376,Benin,2014,,,,,8.575429916381836,10160556.0,843.9921906224263 +6377,Burkina Faso,2014,,,,,11.249923706054688,18365123.0,612.5700168768097 +6378,Cabo Verde,2014,,,,,1.7734805345535278,538535.0,3293.1574262648246 +6379,Cote d'Ivoire,2014,,,,,31.203920364379886,22848945.0,1365.6613189090301 +6380,Gambia,2014,,,,,0.998525321483612,1925527.0,518.5724850825836 +6381,Ghana,2014,,,,,44.2839469909668,25758108.0,1719.2235932455442 +6382,Guinea,2014,,,,,8.263428688049316,11474383.0,720.1632269072172 +6383,Guinea Bissau,2014,,,,,0.9403282403945924,1693398.0,555.2907470037121 +6384,Liberia,2014,,,,,1.652615308761597,4348279.0,380.06192996392286 +6385,Mali,2014,,,,,11.97219181060791,16319381.0,733.6180098134795 +6386,Niger,2014,,,,,7.4055542945861825,17466172.0,423.9941238747782 +6387,Senegal,2014,,,,,18.95746612548828,13635927.0,1390.2586986193369 +6388,Sierra Leone,2014,,,,,3.984575033187866,5743725.0,693.7266378853212 +6389,Togo,2014,,,,,4.365138530731201,7351374.0,593.7853972238661 +6390,Angola,2014,,,,,103.54259490966795,26313499.0,3934.961097711405 +6391,Botswana,2014,,,,,16.425384521484375,2112305.0,7776.047740020676 +6392,Burundi,2014,,,,,2.415069818496704,10395931.0,232.3091427306226 +6393,Cameroon,2014,,,,,31.76340103149414,23130708.0,1373.2135234033535 +6394,Central Afr Rep,2014,,,,,1.366117238998413,5277959.0,258.83437878134583 +6395,Chad,2014,,,,,13.12304973602295,11412107.0,1149.9234747819091 +6396,Dem Rep Congo,2014,,,,,29.310997009277344,77433744.0,378.5300244461554 +6397,Djibouti,2014,,,,,1.413583517074585,810179.0,1744.7792612183048 +6398,Equatorial Guinea,2014,,,,,18.10270118713379,722254.0,25064.175743067928 +6399,Eritrea,2014,,,,,2.650872707366944,5766838.0,459.67525138853273 +6400,Ethiopia,2014,,,,,39.43948745727539,96633458.0,408.1349076556424 +6401,Gabon,2014,,,,,17.834630966186523,1672597.0,10662.838069293754 +6402,Kenya,2014,,,,,49.50645065307617,45010056.0,1099.897557405309 +6403,Lesotho,2014,,,,,2.810433387756348,1942008.0,1447.1790990337563 +6404,Madagascar,2014,,,,,9.64023208618164,23201926.0,415.49275203194946 +6405,Malawi,2014,,,,,8.267560958862305,17377468.0,475.7632676326784 +6406,Mauritania,2014,,,,,5.382196426391602,3516806.0,1530.421759514628 +6407,Mauritius,2014,,,,,11.5547456741333,1331155.0,8680.240598678065 +6408,Mozambique,2014,,,,,13.420002937316895,24692144.0,543.4928184979358 +6409,Namibia,2014,,,,,13.991764068603516,2340492.0,5978.129414073415 +6410,Rep Congo,2014,,,,,14.238106727600098,4662446.0,3053.7848004245197 +6411,Rwanda,2014,,,,,7.631083011627197,11149288.0,684.4457701359222 +6412,Sao Tome Principe,2014,,,,,0.2374438494443893,190428.0,1246.8956741886138 +6413,Seychelles,2014,,,,,1.1740119457244873,91650.0,12809.732086464674 +6414,Sudan,2014,,,,,69.32977294921875,35482233.0,1953.9292509921445 +6415,Swaziland,2014,,,,,5.155475616455078,1419623.0,3631.5807904317403 +6416,Tanzania,2014,,,,,41.05410385131836,49639138.0,827.0511033313745 +6417,Uganda,2014,,,,,23.26051521301269,35918915.0,647.5840156366833 +6418,Zambia,2014,,,,,25.31672668457031,14638505.0,1729.4612178340826 +6419,Canada,2015,,,,,1802.520751953125,35099836.0,51354.107522129874 +6420,United States,2015,,,,,16710.45703125,321323000.0,52005.169350622265 +6421,Cuba,2015,,,,,73.86856842041016,11214038.0,6587.151605907718 +6422,Mexico,2015,,,,,1223.115234375,121736809.0,10047.209586173729 +6423,Antigua Barbuda,2015,,,,,1.2703841924667358,92436.0,13743.392103365959 +6424,Bahamas,2015,,,,,10.521571159362793,324597.0,32414.25878662709 +6425,Barbados,2015,,,,,4.615089416503906,290604.0,15881.025094299825 +6426,Belize,2015,,,,,1.5887868404388428,347369.0,4573.772675278574 +6427,Costa Rica,2015,,,,,44.69381332397461,4814144.0,9283.85468402578 +6428,Dominica,2015,,,,,0.4929058253765106,73607.0,6696.453127780111 +6429,Dominican Rep,2015,,,,,68.93834686279297,10478756.0,6578.867459342784 +6430,El Salvador,2015,,,,,21.04546356201172,6141350.0,3426.846468937891 +6431,Grenada,2015,,,,,0.8980803489685059,110694.0,8113.180018506026 +6432,Guatemala,2015,,,,,49.87893295288086,14918999.0,3343.3163279172322 +6433,Haiti,2015,,,,,7.898695468902588,10254052.0,770.2999232793619 +6434,Honduras,2015,,,,,18.839040756225586,8746673.0,2153.8521854224555 +6435,Jamaica,2015,,,,,13.648174285888672,2950210.0,4626.17043732096 +6436,Nicaragua,2015,,,,,11.423256874084473,5907881.0,1933.5624522708688 +6437,Panama,2015,,,,,42.72426986694336,3657024.0,11682.797232652387 +6438,Puerto Rico,2015,,,,,95.5790786743164,3472857.0,27521.743243190376 +6439,St Kitts Nevis,2015,,,,,0.8858615159988403,51936.0,17056.791358572864 +6440,St Lucia,2015,,,,,1.439237117767334,163922.0,8780.01194328604 +6441,St Vincent Grenadines,2015,,,,,0.718364953994751,102627.0,6999.76569513628 +6442,Trinidad and Tobago,2015,,,,,22.88306999206543,1222363.0,18720.355567098668 +6443,Argentina,2015,,,,,458.9029235839844,43474726.0,10555.62543589083 +6444,Brazil,2015,,,,,2337.303955078125,204259812.0,11442.798914737692 +6445,Bolivia,2015,,,,,25.66404914855957,10800882.0,2376.1067983669823 +6446,Chile,2015,,,,,264.55526733398443,17508260.0,15110.311780495857 +6447,Colombia,2015,,,,,359.84381103515625,46736728.0,7699.379619282638 +6448,Ecuador,2015,,,,,86.41880798339844,15868396.0,5445.96996340389 +6449,Guyana,2015,,,,,2.831911325454712,735222.0,3851.777184924705 +6450,Paraguay,2015,,,,,33.098487854003906,6783272.0,4879.428077482948 +6451,Peru,2015,,,,,186.29058837890625,30444999.0,6118.922466671988 +6452,Suriname,2015,,,,,4.725133895874023,579633.0,8151.940789903307 +6453,Uruguay,2015,,,,,47.55934143066406,3341893.0,14231.257981827683 +6454,Venezuela,2015,,,,,294.9294128417969,30514414.0,9665.249112822448 +6455,Austria,2015,,,,,413.5545959472656,8665550.0,47723.98704609236 +6456,Belgium,2015,,,,,509.1860046386719,11323973.0,44965.31426193544 +6457,Denmark,2015,,,,,343.2944641113281,5581503.0,61505.73852801442 +6458,Finland,2015,,,,,248.3123779296875,5476922.0,45337.943087319385 +6459,France,2015,,,,,2781.2880859375,66553766.0,41790.093229848186 +6460,Germany,2015,,,,,3718.482421875,80854408.0,45989.854033375654 +6461,Greece,2015,,,,,244.71853637695312,10775643.0,22710.34186794729 +6462,Ireland,2015,,,,,318.4125671386719,4892305.0,65084.36557791713 +6463,Italy,2015,,,,,2062.367431640625,61855120.0,33341.903332183734 +6464,Luxembourg,2015,,,,,61.08163452148438,570252.0,107113.40691744066 +6465,Netherlands,2015,,,,,878.700439453125,16947904.0,51847.145195838086 +6466,Portugal,2015,,,,,228.05206298828125,10825309.0,21066.563826333382 +6467,Spain,2015,,,,,1420.98681640625,48146134.0,29514.03775028437 +6468,Sweden,2015,,,,,543.0595703125,9801616.0,55405.1056797675 +6469,UK,2015,,,,,2719.581298828125,64088222.0,42434.96252444209 +6470,Bulgaria,2015,,,,,54.635643005371094,7186893.0,7602.122781759947 +6471,Croatia,2015,,,,,59.3433952331543,4335250.0,13688.575107122842 +6472,Cyprus,2015,,,,,23.625104904174805,1189197.0,19866.43500124437 +6473,Czech Republic,2015,,,,,225.4927215576172,10644842.0,21183.28497103265 +6474,Estonia,2015,,,,,23.39266967773437,1265420.0,18486.091319668078 +6475,Hungary,2015,,,,,144.23599243164062,9897541.0,14572.911840591581 +6476,Latvia,2015,,,,,28.25681114196777,1986705.0,14222.952648716227 +6477,Lithuania,2015,,,,,44.69319152832031,2884433.0,15494.619402953826 +6478,Malta,2015,,,,,11.277871131896973,413965.0,27243.537815750053 +6479,Poland,2015,,,,,556.3605346679689,38562189.0,14427.618065664497 +6480,Romania,2015,,,,,192.45083618164062,21666350.0,8882.476106111118 +6481,Slovakia,2015,,,,,101.62764739990234,5445027.0,18664.305502966723 +6482,Slovenia,2015,,,,,48.96999740600586,1983412.0,24689.775702680967 +6483,Iceland,2015,,,,,15.682299613952635,331918.0,47247.51177686247 +6484,Norway,2015,,,,,467.661376953125,5207689.0,89802.09397164943 +6485,Switzerland,2015,,,,,634.0379028320312,8121830.0,78065.8919026908 +6486,Albania,2015,,,,,13.033196449279785,3029278.0,4302.410161523566 +6487,Bosnia Herzegovina,2015,,,,,18.3532829284668,3867055.0,4746.062036476543 +6488,Macedonia,2015,,,,,10.615805625915527,2096015.0,5064.756514583878 +6489,Serbia,2015,,,,,40.18389129638672,7176794.0,5599.142360277684 +6490,Russia,2015,,,,,1658.163330078125,142423773.0,11642.46175445812 +6491,Ukraine,2015,,,,,121.20330810546876,44429471.0,2727.993500203249 +6492,Armenia,2015,,,,,11.479055404663086,3056382.0,3755.765936542973 +6493,Azerbaijan,2015,,,,,59.02532577514648,9780780.0,6034.82807865492 +6494,Belarus,2015,,,,,62.096092224121094,9589689.0,6475.297814571577 +6495,Georgia,2015,,,,,14.768853187561035,4931226.0,2994.965793001788 +6496,Kazakhstan,2015,,,,,186.2539825439453,18157122.0,10257.902245958656 +6497,Kyrgyzstan,2015,,,,,6.0829529762268075,5664939.0,1073.7896694433614 +6498,Moldova,2015,,,,,7.038083553314209,3546847.0,1984.3211599807403 +6499,Tajikistan,2015,,,,,7.913022518157959,8191958.0,965.9500839918808 +6500,Turkmenistan,2015,,,,,37.2536506652832,5231422.0,7121.132775234572 +6501,Uzbekistan,2015,,,,,57.568443298339844,29199942.0,1971.525946809752 +6502,China,2015,,,,,8908.3671875,1367485388.0,6514.4149002782615 +6503,Hong Kong,2015,,,,,264.38803100585943,7141106.0,37023.40099780894 +6504,Japan,2015,,,,,5996.29150390625,126919659.0,47244.78107765992 +6505,Korea,2015,,,,,1268.7803955078125,50645073.0,25052.39543257865 +6506,Taiwan,2015,,,,,509.02044677734375,23415126.0,21738.958260457097 +6507,Macau,2015,,,,,32.19947052001953,592731.0,54323.91847232477 +6508,Mongolia,2015,,,,,11.680193901062012,2992908.0,3902.623769612034 +6509,Cambodia,2015,,,,,15.903658866882324,15708756.0,1012.4072757182253 +6510,Indonesia,2015,,,,,988.1285400390624,255993674.0,3859.972493066616 +6511,Malaysia,2015,,,,,330.3225402832031,30513848.0,10825.332166667513 +6512,Myanmar,2015,,,,,58.84910202026367,54089696.0,1087.9909922263876 +6513,Philippines,2015,,,,,266.05551147460943,100998376.0,2634.255341636477 +6514,Thailand,2015,,,,,394.0783386230469,67976405.0,5797.281256975077 +6515,Vietnam,2015,,,,,154.50857543945312,94348835.0,1637.6309833550476 +6516,Brunei,2015,,,,,13.63759994506836,429646.0,31741.480067470333 +6517,Laos,2015,,,,,10.373602867126463,6911544.0,1500.9096183322376 +6518,Singapore,2015,,,,,292.16497802734375,5674472.0,51487.60590013375 +6519,Bangladesh,2015,,,,,155.58160400390625,154596024.0,1006.3751963239771 +6520,India,2015,,,,,2286.736328125,1251695584.0,1826.9109177627329 +6521,Pakistan,2015,,,,,212.40435791015625,199085847.0,1066.8983311011368 +6522,Afghanistan,2015,,,,,19.48323631286621,32564342.0,598.2997080937859 +6523,Bhutan,2015,,,,,2.0703144073486333,741919.0,2790.4857637405603 +6524,Nepal,2015,,,,,20.11875343322754,28678486.0,701.5277387107373 +6525,Sri Lanka,2015,,,,,76.48585510253906,22053488.0,3468.1976430458108 +6526,Australia,2015,,,,,1366.5396728515625,22751014.0,60065.000744650875 +6527,New Zealand,2015,,,,,170.2194366455078,4438393.0,38351.59181386322 +6528,Fiji,2015,,,,,3.7568726539611816,909389.0,4131.205297140366 +6529,Maldives,2015,,,,,3.412564754486084,393253.0,8677.784414832395 +6530,Papua New Guinea,2015,,,,,19.02773666381836,6672429.0,2851.695636449389 +6531,Samoa,2015,,,,,0.703676700592041,197773.0,3558.0018536000416 +6532,Solomon Islands,2015,,,,,0.8707603216171265,622469.0,1398.8814248052938 +6533,Tonga,2015,,,,,0.39768117666244507,106501.0,3734.0604939150335 +6534,Iran,2015,,,,,480.66217041015625,79927977.0,6013.691181126182 +6535,Iraq,2015,,,,,188.46551513671875,37056169.0,5085.941699389345 +6536,Saudi Arabia,2015,,,,,678.7296142578125,27752316.0,24456.683696517885 +6537,Turkey,2015,,,,,1087.840087890625,79414269.0,13698.295049352217 +6538,Bahrain,2015,,,,,30.779226303100586,1346613.0,22856.771992473397 +6539,Israel,2015,,,,,278.05123901367193,8049314.0,34543.470289974015 +6540,Jordan,2015,,,,,30.206411361694336,9677747.0,3121.2234998181225 +6541,Kuwait,2015,,,,,137.96047973632812,2788534.0,49474.196741487875 +6542,Lebanon,2015,,,,,41.82212448120117,6184701.0,6762.190198232893 +6543,Oman,2015,,,,,71.22132873535156,3286936.0,21667.999844034555 +6544,Syria,2015,,,,,27.129045486450195,17064854.0,1589.7613590160336 +6545,United Arab Emirates,2015,,,,,373.0733947753906,5779760.0,64548.250234506384 +6546,Yemen,2015,,,,,24.08016586303711,26737317.0,900.6201281541117 +6547,Egypt,2015,,,,,245.07077026367188,92296970.0,2655.2417729820586 +6548,Morocco,2015,,,,,113.38406372070312,33322699.0,3402.607445474423 +6549,Algeria,2015,,,,,189.77235412597656,39542166.0,4799.240237016266 +6550,Libya,2015,,,,,37.86774444580078,6411776.0,5905.968088373765 +6551,Tunisia,2015,,,,,48.14839172363281,11161625.0,4313.74389693551 +6552,Nigeria,2015,,,,,464.28216552734375,181562056.0,2557.1541529984866 +6553,South Africa,2015,,,,,418.896728515625,53675563.0,7804.2353932202805 +6554,Benin,2015,,,,,8.755154609680176,10448647.0,837.9223271376835 +6555,Burkina Faso,2015,,,,,11.688057899475098,18931686.0,617.3807182030747 +6556,Cabo Verde,2015,,,,,1.7913371324539185,545993.0,3280.8793014817384 +6557,Cote d'Ivoire,2015,,,,,33.963241577148445,23295302.0,1457.9438196228766 +6558,Gambia,2015,,,,,1.0571211576461792,1967709.0,537.2344984172859 +6559,Ghana,2015,,,,,45.24854278564453,26327649.0,1718.6700865559444 +6560,Guinea,2015,,,,,8.578675270080566,11780162.0,728.2306703490635 +6561,Guinea Bissau,2015,,,,,0.9980087876319884,1726170.0,578.1636731214123 +6562,Liberia,2015,,,,,1.652615308761597,4459961.0,370.54478923954633 +6563,Mali,2015,,,,,12.686042785644533,16834405.0,753.5783287644873 +6564,Niger,2015,,,,,7.726738452911378,18045729.0,428.17546760850604 +6565,Senegal,2015,,,,,20.16449546813965,13975834.0,1442.8116038112396 +6566,Sierra Leone,2015,,,,,3.163801431655884,5879098.0,538.1440199935234 +6567,Togo,2015,,,,,4.615822792053223,7552318.0,611.1796129417779 +6568,Angola,2015,,,,,104.51959228515624,27292149.0,3829.657836220821 +6569,Botswana,2015,,,,,16.146492004394528,2146408.0,7522.5642116478 +6570,Burundi,2015,,,,,2.3208820819854736,10742276.0,216.051242956844 +6571,Cameroon,2015,,,,,33.55849456787109,23739218.0,1413.6310036780105 +6572,Central Afr Rep,2015,,,,,1.4316892623901367,5391539.0,265.54370883529486 +6573,Chad,2015,,,,,13.486252784729006,11631456.0,1159.463852567469 +6574,Dem Rep Congo,2015,,,,,31.33819961547852,79375136.0,394.8112871955081 +6575,Djibouti,2015,,,,,1.5504720211029053,828324.0,1871.8182994853528 +6576,Equatorial Guinea,2015,,,,,16.453536987304688,740743.0,22212.207185629413 +6577,Eritrea,2015,,,,,2.719435930252075,5823083.0,467.0096459645303 +6578,Ethiopia,2015,,,,,43.53822326660156,99465819.0,437.72045215453926 +6579,Gabon,2015,,,,,18.526418685913086,1705336.0,10863.793813015784 +6580,Kenya,2015,,,,,52.33746719360352,45925301.0,1139.621647631738 +6581,Lesotho,2015,,,,,2.888188362121582,1947701.0,1482.870503286481 +6582,Madagascar,2015,,,,,9.940681457519533,23812681.0,417.4532660778319 +6583,Malawi,2015,,,,,8.499052047729492,17964697.0,473.09743369061516 +6584,Mauritania,2015,,,,,5.4575467109680185,3596702.0,1517.3752818465412 +6585,Mauritius,2015,,,,,11.965292930603027,1339827.0,8930.476046984444 +6586,Mozambique,2015,,,,,14.304916381835938,25303113.0,565.342153032156 +6587,Namibia,2015,,,,,14.843355178833008,2388360.0,6214.873460798625 +6588,Rep Congo,2015,,,,,14.61491584777832,4755097.0,3073.5263334855877 +6589,Rwanda,2015,,,,,8.307806015014647,11377040.0,730.225613605529 +6590,Sao Tome Principe,2015,,,,,0.2465141713619232,194006.0,1270.6523064334258 +6591,Seychelles,2015,,,,,1.231977462768555,92430.0,13328.76190380347 +6592,Sudan,2015,,,,,72.73112487792969,36108853.0,2014.2186426672065 +6593,Swaziland,2015,,,,,5.175640106201172,1435613.0,3605.177792483888 +6594,Tanzania,2015,,,,,43.58329772949219,51045882.0,853.8063409207463 +6595,Uganda,2015,,,,,24.46723747253418,37101745.0,659.4632536160814 +6596,Zambia,2015,,,,,26.055944442749023,15066266.0,1729.4228339489707 +6597,Canada,2016,,,,,1828.01025390625,35362905.0,51692.875738185256 +6598,United States,2016,,,,,16972.345703125,323128000.0,52525.147010240515 +6599,Cuba,2016,,,,,74.24807739257812,11179995.0,6641.154794128094 +6600,Mexico,2016,,,,,1258.559326171875,123166749.0,10218.336818907796 +6601,Antigua Barbuda,2016,,,,,1.3413952589035034,93581.0,14334.05561923364 +6602,Bahamas,2016,,,,,10.34380340576172,327316.0,31601.88749025932 +6603,Barbados,2016,,,,,4.707722187042236,291495.0,16150.267370082633 +6604,Belize,2016,,,,,1.579444408416748,353858.0,4463.497811033658 +6605,Costa Rica,2016,,,,,46.59150695800781,4872543.0,9562.051470455532 +6606,Dominica,2016,,,,,0.5053371191024779,73757.0,6851.378433267053 +6607,Dominican Rep,2016,,,,,73.49668884277344,10606865.0,6929.162277711032 +6608,El Salvador,2016,,,,,21.58855628967285,6156670.0,3506.5313375043406 +6609,Grenada,2016,,,,,0.9316291213035583,111219.0,8376.528482575444 +6610,Guatemala,2016,,,,,51.42142868041992,15189958.0,3385.225204731963 +6611,Haiti,2016,,,,,8.013440132141112,10485800.0,764.2182887467923 +6612,Honduras,2016,,,,,19.5459098815918,8893259.0,2197.8343239066576 +6613,Jamaica,2016,,,,,13.836607933044435,2970340.0,4658.257281336289 +6614,Nicaragua,2016,,,,,11.955438613891602,5966798.0,2003.660692701781 +6615,Panama,2016,,,,,44.846153259277344,3705246.0,12103.421273318247 +6616,Puerto Rico,2016,,,,,94.3108139038086,3411489.0,27645.05877164153 +6617,St Kitts Nevis,2016,,,,,0.906391441822052,52329.0,17321.015915114986 +6618,St Lucia,2016,,,,,1.487669587135315,164464.0,9045.563692572934 +6619,St Vincent Grenadines,2016,,,,,0.7325559258460999,102350.0,7157.3612686477745 +6620,Trinidad and Tobago,2016,,,,,21.51991271972656,1220479.0,17632.349855857054 +6621,Argentina,2016,,,,,450.5392456054688,43886748.0,10265.951936230698 +6622,Brazil,2016,,,,,2260.04541015625,205823665.0,10980.493473169132 +6623,Bolivia,2016,,,,,26.758344650268555,10969649.0,2439.307278680344 +6624,Chile,2016,,,,,267.9041748046875,17650114.0,15178.608750328045 +6625,Colombia,2016,,,,,366.9061584472656,47220856.0,7770.002272878442 +6626,Ecuador,2016,,,,,85.35897827148438,16080778.0,5308.137347054003 +6627,Guyana,2016,,,,,2.9258213043212886,735909.0,3975.792257359659 +6628,Paraguay,2016,,,,,34.525997161865234,6862812.0,5030.881971102404 +6629,Peru,2016,,,,,193.65151977539062,30741062.0,6299.441436844005 +6630,Suriname,2016,,,,,4.46235990524292,585824.0,7617.236414422968 +6631,Uruguay,2016,,,,,48.36299514770508,3351016.0,14432.337878334534 +6632,Venezuela,2016,,,,,244.6724548339844,30912302.0,7915.051258039093 +6633,Austria,2016,,,,,421.9893493652344,8711770.0,48438.99108507621 +6634,Belgium,2016,,,,,516.7661743164061,11409077.0,45294.30157377378 +6635,Denmark,2016,,,,,351.5324401855469,5593785.0,62843.39497952582 +6636,Finland,2016,,,,,255.1954040527344,5498211.0,46414.26166670111 +6637,France,2016,,,,,2813.858154296875,66836154.0,42100.838930631384 +6638,Germany,2016,,,,,3801.859619140625,80722792.0,47097.72203048459 +6639,Greece,2016,,,,,244.2512359619141,10773253.0,22672.00407916848 +6640,Ireland,2016,,,,,334.2936706542969,4952473.0,67500.35197653715 +6641,Italy,2016,,,,,2085.9921875,62007540.0,33640.94410937767 +6642,Luxembourg,2016,,,,,62.55457305908203,582291.0,107428.37010890091 +6643,Netherlands,2016,,,,,897.9590454101562,17016967.0,52768.45429682953 +6644,Portugal,2016,,,,,232.4437561035156,10833816.0,21455.39079706685 +6645,Spain,2016,,,,,1466.0689697265625,48563476.0,30188.715686796437 +6646,Sweden,2016,,,,,557.6351318359375,9880604.0,56437.35259868097 +6647,UK,2016,,,,,2768.240966796875,64430428.0,42964.807975462696 +6648,Bulgaria,2016,,,,,56.78651809692383,7144653.0,7948.1142186924735 +6649,Croatia,2016,,,,,61.441856384277344,4313707.0,14243.400486930925 +6650,Cyprus,2016,,,,,24.76198959350586,1205575.0,20539.5679186329 +6651,Czech Republic,2016,,,,,231.0185089111328,10660932.0,21669.63534812273 +6652,Estonia,2016,,,,,24.20886421203613,1258545.0,19235.59682970107 +6653,Hungary,2016,,,,,147.52574157714844,9874784.0,14939.642383787681 +6654,Latvia,2016,,,,,28.84013938903809,1965686.0,14671.793658314751 +6655,Lithuania,2016,,,,,45.74490356445313,2854235.0,16027.0277550563 +6656,Malta,2016,,,,,11.867769241333008,415196.0,28583.534623004576 +6657,Poland,2016,,,,,573.399658203125,38523261.0,14884.50466857219 +6658,Romania,2016,,,,,201.69004821777344,21599736.0,9337.616358726487 +6659,Slovakia,2016,,,,,104.8039321899414,5445802.0,19244.903173112318 +6660,Slovenia,2016,,,,,50.51174545288086,1978029.0,25536.402880281767 +6661,Iceland,2016,,,,,16.835180282592773,335878.0,50122.90260925923 +6662,Norway,2016,,,,,473.21417236328125,5265158.0,89876.53786710318 +6663,Switzerland,2016,,,,,644.1902465820312,8179294.0,78758.66139327321 +6664,Albania,2016,,,,,13.469808578491213,3038594.0,4432.908305121122 +6665,Bosnia Herzegovina,2016,,,,,18.93032455444336,3861912.0,4901.801116763759 +6666,Macedonia,2016,,,,,10.918190002441406,2100025.0,5199.076202636353 +6667,Serbia,2016,,,,,41.30796432495117,7143921.0,5782.2537966126965 +6668,Russia,2016,,,,,1654.4339599609375,142355415.0,11621.854777782339 +6669,Ukraine,2016,,,,,124.161865234375,44209733.0,2808.473537588997 +6670,Armenia,2016,,,,,11.502013206481935,3051250.0,3769.606950096496 +6671,Azerbaijan,2016,,,,,57.19553756713867,9872765.0,5793.264355744179 +6672,Belarus,2016,,,,,60.52727127075195,9570376.0,6324.440259270059 +6673,Georgia,2016,,,,,15.189614295959473,4928052.0,3082.2755717592822 +6674,Kazakhstan,2016,,,,,188.3027801513672,18360353.0,10255.945523017295 +6675,Kyrgyzstan,2016,,,,,6.346701145172119,5727553.0,1108.099941663066 +6676,Moldova,2016,,,,,7.35479736328125,3510485.0,2095.0943710858332 +6677,Tajikistan,2016,,,,,8.456881523132324,8330946.0,1015.1165933775496 +6678,Turkmenistan,2016,,,,,39.563377380371094,5291317.0,7477.03783016045 +6679,Uzbekistan,2016,,,,,62.05878067016602,29473614.0,2105.5707885081893 +6680,China,2016,,,,,9505.2275390625,1373541278.0,6920.234354298379 +6681,Hong Kong,2016,,,,,270.10653686523443,7167403.0,37685.41225674549 +6682,Japan,2016,,,,,6052.54833984375,126702133.0,47769.90092063999 +6683,Korea,2016,,,,,1305.9468994140625,50924172.0,25644.931436765677 +6684,Taiwan,2016,,,,,516.7066650390625,23464787.0,22020.51376128249 +6685,Macau,2016,,,,,31.921550750732425,597425.0,53431.896473586516 +6686,Mongolia,2016,,,,,11.816664695739744,3031330.0,3898.1782569828247 +6687,Cambodia,2016,,,,,16.995141983032227,15957223.0,1065.0438351981559 +6688,Indonesia,2016,,,,,1037.8616943359377,258316051.0,4017.797927454139 +6689,Malaysia,2016,,,,,344.2734069824219,30949962.0,11123.548616389959 +6690,Myanmar,2016,,,,,62.29911422729492,54616716.0,1140.6602005747638 +6691,Philippines,2016,,,,,284.3487243652344,102624209.0,2770.776283062356 +6692,Thailand,2016,,,,,407.0146789550781,68200824.0,5967.885064776902 +6693,Vietnam,2016,,,,,164.1048126220703,95261021.0,1722.6858467333698 +6694,Brunei,2016,,,,,13.301362037658693,436620.0,30464.39017374076 +6695,Laos,2016,,,,,11.102149963378906,7019073.0,1581.7117108454215 +6696,Singapore,2016,,,,,299.168212890625,5781728.0,51743.736974590465 +6697,Bangladesh,2016,,,,,166.64889526367188,156186882.0,1066.983943399753 +6698,India,2016,,,,,2473.5517578125,1266883598.0,1952.4696362929 +6699,Pakistan,2016,,,,,224.1433868408203,201995540.0,1109.6452270224395 +6700,Afghanistan,2016,,,,,19.92361831665039,33332025.0,597.7320104809231 +6701,Bhutan,2016,,,,,2.2363972663879395,750125.0,2981.3661274960027 +6702,Nepal,2016,,,,,20.23719024658203,29033914.0,697.0190187441498 +6703,Sri Lanka,2016,,,,,79.90387725830078,22235000.0,3593.608151936172 +6704,Australia,2016,,,,,1405.176025390625,22992654.0,61114.12912100642 +6705,New Zealand,2016,,,,,176.12852478027344,4474549.0,39362.29657564895 +6706,Fiji,2016,,,,,3.7711489200592037,915303.0,4120.109865322417 +6707,Maldives,2016,,,,,3.6613130569458012,392960.0,9317.266533351489 +6708,Papua New Guinea,2016,,,,,19.330730438232425,6791317.0,2846.3890638932653 +6709,Samoa,2016,,,,,0.7541261911392212,198926.0,3790.9885642863237 +6710,Solomon Islands,2016,,,,,0.9008590579032898,635027.0,1418.6153626590522 +6711,Tonga,2016,,,,,0.4111207127571106,106513.0,3859.8172312967495 +6712,Iran,2016,,,,,545.0527954101561,80987449.0,6730.089688467115 +6713,Iraq,2016,,,,,213.0115509033203,38146025.0,5584.108721769052 +6714,Saudi Arabia,2016,,,,,690.0687255859375,28160273.0,24505.043881710153 +6715,Turkey,2016,,,,,1122.4749755859377,80274604.0,13982.94005394206 +6716,Bahrain,2016,,,,,31.847551345825195,1378904.0,23096.278889484107 +6717,Israel,2016,,,,,289.19525146484375,8174527.0,35377.612853299484 +6718,Jordan,2016,,,,,30.81166839599609,10041098.0,3068.555689427201 +6719,Kuwait,2016,,,,,142.8539581298828,2832776.0,50428.96371964562 +6720,Lebanon,2016,,,,,42.550140380859375,6237738.0,6821.405512841254 +6721,Oman,2016,,,,,74.76827239990234,3355262.0,22283.884954409623 +6722,Syria,2016,,,,,25.965614318847656,17185170.0,1510.9314786439504 +6723,United Arab Emirates,2016,,,,,384.2225036621094,5927482.0,64820.52643299622 +6724,Yemen,2016,,,,,20.80009651184082,27392779.0,759.3277232602367 +6725,Egypt,2016,,,,,255.7231292724609,94666993.0,2701.291349482929 +6726,Morocco,2016,,,,,114.6607437133789,33655786.0,3406.8657232779797 +6727,Algeria,2016,,,,,196.03485107421875,40263711.0,4868.772554875003 +6728,Libya,2016,,,,,36.809165954589844,6541948.0,5626.636890814456 +6729,Tunisia,2016,,,,,48.68206405639648,11285452.0,4313.7008651843535 +6730,Nigeria,2016,,,,,456.7752990722656,186053386.0,2455.076518049855 +6731,South Africa,2016,,,,,421.2649230957031,54300704.0,7758.001131913559 +6732,Benin,2016,,,,,9.10228443145752,10741458.0,847.3974791371451 +6733,Burkina Faso,2016,,,,,12.38164234161377,19512533.0,634.5481820127491 +6734,Cabo Verde,2016,,,,,1.8756338357925413,553432.0,3389.0953826170903 +6735,Cote d'Ivoire,2016,,,,,36.67070770263672,23740424.0,1544.6526019348566 +6736,Gambia,2016,,,,,1.061408758163452,2009648.0,528.1565518754786 +6737,Ghana,2016,,,,,46.8086166381836,26908262.0,1739.5629876869634 +6738,Guinea,2016,,,,,9.47549819946289,12093349.0,783.5297070698025 +6739,Guinea Bissau,2016,,,,,1.0605120658874512,1759159.0,602.8517410236658 +6740,Liberia,2016,,,,,1.6261804103851318,4575528.0,355.4082524213887 +6741,Mali,2016,,,,,13.421833038330078,17352160.0,773.4963853681662 +6742,Niger,2016,,,,,8.107352256774902,18638600.0,434.9764605053439 +6743,Senegal,2016,,,,,21.42043113708496,14320055.0,1495.8344180301654 +6744,Sierra Leone,2016,,,,,3.355384826660156,6018888.0,557.4758704033297 +6745,Togo,2016,,,,,4.851229667663574,7756937.0,625.405320123597 +6746,Angola,2016,,,,,101.82288360595705,28289713.0,3599.2900884486544 +6747,Botswana,2016,,,,,16.843290328979492,2180597.0,7724.16468012177 +6748,Burundi,2016,,,,,2.3069562911987305,11099298.0,207.84704502921988 +6749,Cameroon,2016,,,,,35.118457794189446,24360803.0,1441.5968880085543 +6750,Central Afr Rep,2016,,,,,1.4965630769729614,5507257.0,271.74382400766143 +6751,Chad,2016,,,,,12.642617225646973,11852462.0,1066.6659151193203 +6752,Dem Rep Congo,2016,,,,,32.09012222290039,81331050.0,394.5617599047399 +6753,Djibouti,2016,,,,,1.6857082843780518,846687.0,1990.9462226041642 +6754,Equatorial Guinea,2016,,,,,15.04940700531006,759451.0,19816.16589524546 +6755,Eritrea,2016,,,,,2.7697560787200928,5869869.0,471.8599475933948 +6756,Ethiopia,2016,,,,,46.83047866821289,102374044.0,457.4448447910575 +6757,Gabon,2016,,,,,18.91388702392578,1738541.0,10879.172262216296 +6758,Kenya,2016,,,,,55.40925598144531,46790758.0,1184.192313820719 +6759,Lesotho,2016,,,,,2.9800035953521733,1953070.0,1525.804807483691 +6760,Madagascar,2016,,,,,10.356201171875,24430325.0,423.9076300407383 +6761,Malawi,2016,,,,,8.710171699523926,18570321.0,469.0372180170674 +6762,Mauritania,2016,,,,,5.566697597503662,3677293.0,1513.8031148194236 +6763,Mauritius,2016,,,,,12.424515724182127,1348242.0,9215.34540845199 +6764,Mozambique,2016,,,,,14.843249320983887,25930150.0,572.4320654135779 +6765,Namibia,2016,,,,,14.938597679138185,2436469.0,6131.248819146964 +6766,Rep Congo,2016,,,,,14.205695152282715,4852412.0,2927.553380109256 +6767,Rwanda,2016,,,,,8.80472183227539,11604797.0,758.7139897643526 +6768,Sao Tome Principe,2016,,,,,0.2568165361881256,197541.0,1300.0670047642038 +6769,Seychelles,2016,,,,,1.2874537706375122,93186.0,13815.957017551049 +6770,Sudan,2016,,,,,76.14948272705078,36729501.0,2073.2512191508067 +6771,Swaziland,2016,,,,,5.342423915863037,1451428.0,3680.8053281754496 +6772,Tanzania,2016,,,,,46.576210021972656,52482726.0,887.4579041868492 +6773,Uganda,2016,,,,,25.63701820373535,38319241.0,669.0377349524056 +6774,Zambia,2016,,,,,27.034914016723636,15510711.0,1742.9835432253 +6775,Canada,2017,,,,,1883.716064453125,35623680.0,52878.199682153136 +6776,United States,2017,,,,,17348.623046875,325511000.0,53296.57998308814 +6777,Cuba,2017,,,,,75.5722885131836,11147407.0,6779.360304435247 +6778,Mexico,2017,,,,,1284.25244140625,124574795.0,10309.087335092543 +6779,Antigua Barbuda,2017,,,,,1.3820744752883911,94731.0,14589.46358940992 +6780,Bahamas,2017,,,,,10.492464065551758,329988.0,31796.501889619496 +6781,Barbados,2017,,,,,4.7547993659973145,292336.0,16264.84376196334 +6782,Belize,2017,,,,,1.6021456718444824,378844.0,4229.037999399443 +6783,Costa Rica,2017,,,,,48.11992645263672,4930258.0,9760.123395699924 +6784,Dominica,2017,,,,,0.4571771621704102,73897.0,6186.68095011178 +6785,Dominican Rep,2017,,,,,76.84211730957031,10196160.0,7536.378137413528 +6786,El Salvador,2017,,,,,22.08997344970703,6172011.0,3579.0560726004915 +6787,Grenada,2017,,,,,0.9787622690200806,111724.0,8760.537297448 +6788,Guatemala,2017,,,,,52.84083557128906,16296181.0,3242.528760038261 +6789,Haiti,2017,,,,,8.107460021972656,10646714.0,761.4988081743021 +6790,Honduras,2017,,,,,20.481239318847656,9038741.0,2265.939395635704 +6791,Jamaica,2017,,,,,13.972007751464846,2813340.0,4966.3416975782675 +6792,Nicaragua,2017,,,,,12.536603927612305,6025951.0,2080.4357565490172 +6793,Panama,2017,,,,,47.23250961303711,3753142.0,12584.791519488766 +6794,Puerto Rico,2017,,,,,92.06681060791016,3351827.0,27467.649913885816 +6795,St Kitts Nevis,2017,,,,,0.9170013070106506,52715.0,17395.45304013375 +6796,St Lucia,2017,,,,,1.5444636344909668,164994.0,9360.72605361993 +6797,St Vincent Grenadines,2017,,,,,0.7388554811477661,102089.0,7237.366230913871 +6798,Trinidad and Tobago,2017,,,,,21.017202377319336,1218208.0,17252.556523450294 +6799,Argentina,2017,,,,,463.3997497558594,44293293.0,10462.074918562941 +6800,Brazil,2017,,,,,2284.08935546875,207353391.0,11015.442498689354 +6801,Bolivia,2017,,,,,27.88091278076172,11138234.0,2503.171757817417 +6802,Chile,2017,,,,,271.89572143554693,17789267.0,15284.25659334625 +6803,Colombia,2017,,,,,373.4708251953125,47698524.0,7829.819329321646 +6804,Ecuador,2017,,,,,87.38060760498047,16290913.0,5363.763688688317 +6805,Guyana,2017,,,,,3.011391878128052,737718.0,4082.036602235613 +6806,Paraguay,2017,,,,,36.32498550415039,6943739.0,5231.329331956515 +6807,Peru,2017,,,,,198.54747009277344,31036656.0,6397.192728906537 +6808,Suriname,2017,,,,,4.537643432617188,591919.0,7665.987124280835 +6809,Uruguay,2017,,,,,49.64784622192383,3360148.0,14775.493883580075 +6810,Venezuela,2017,,,,,206.32882690429688,31304016.0,6591.129614305616 +6811,Austria,2017,,,,,432.7537841796875,8754413.0,49432.64433374202 +6812,Belgium,2017,,,,,525.514404296875,11491346.0,45731.31853282244 +6813,Denmark,2017,,,,,359.4853820800781,5773437.0,62265.4031004544 +6814,Finland,2017,,,,,261.96453857421875,5518371.0,47471.35315371488 +6815,France,2017,,,,,2874.670166015625,67106161.0,42837.64893085785 +6816,Germany,2017,,,,,3883.86962890625,80594017.0,48190.54532182271 +6817,Greece,2017,,,,,247.92745971679688,10768477.0,23023.44702196948 +6818,Ireland,2017,,,,,358.4356384277344,5011102.0,71528.30623438403 +6819,Italy,2017,,,,,2118.779052734375,62137802.0,34098.068881393236 +6820,Luxembourg,2017,,,,,63.52145767211914,594130.0,106915.0820058222 +6821,Netherlands,2017,,,,,923.719970703125,17084719.0,54067.02742392924 +6822,Portugal,2017,,,,,238.94065856933597,10383790.0,23010.929397583724 +6823,Spain,2017,,,,,1509.745849609375,48958159.0,30837.47184221888 +6824,Sweden,2017,,,,,569.3717651367188,9960487.0,57163.04485279874 +6825,UK,2017,,,,,2818.7041015625,64769452.0,43519.03581896138 +6826,Bulgaria,2017,,,,,58.9507942199707,7101510.0,8301.163304701493 +6827,Croatia,2017,,,,,63.23679733276367,4292095.0,14733.317257135193 +6828,Cyprus,2017,,,,,25.809772491455078,1221549.0,21128.72466962445 +6829,Czech Republic,2017,,,,,241.0738220214844,10674723.0,22583.613834427775 +6830,Estonia,2017,,,,,25.384611129760746,1251581.0,20282.03618444251 +6831,Hungary,2017,,,,,153.62896728515625,9850845.0,15595.51158150963 +6832,Latvia,2017,,,,,30.17730712890625,1944643.0,15518.173324824274 +6833,Lithuania,2017,,,,,47.63871765136719,2823859.0,16870.076604875518 +6834,Malta,2017,,,,,12.629975318908693,444457.0,28416.641697416602 +6835,Poland,2017,,,,,600.955810546875,38476269.0,15618.869141050942 +6836,Romania,2017,,,,,216.3327178955078,21529967.0,10047.981861537819 +6837,Slovakia,2017,,,,,108.14543914794922,5445829.0,19858.397894599562 +6838,Slovenia,2017,,,,,53.0377426147461,2101416.0,25239.049581209096 +6839,Iceland,2017,,,,,17.512773513793945,339747.0,51546.51406427119 +6840,Norway,2017,,,,,482.5859069824219,5320045.0,90710.86935964297 +6841,Switzerland,2017,,,,,654.6047973632812,8236303.0,79477.98877278813 +6842,Albania,2017,,,,,13.986449241638185,3047987.0,4588.749637593003 +6843,Bosnia Herzegovina,2017,,,,,19.53348159790039,3856181.0,5065.4991552264755 +6844,Macedonia,2017,,,,,10.944451332092283,2114760.0,5175.268745433186 +6845,Serbia,2017,,,,,42.07936477661133,7111024.0,5917.4831608796885 +6846,Russia,2017,,,,,1681.6806640625,142257519.0,11821.383332732663 +6847,Ukraine,2017,,,,,127.29634857177734,44033874.0,2890.8732529819513 +6848,Armenia,2017,,,,,12.364665031433105,3045191.0,4060.390639350079 +6849,Azerbaijan,2017,,,,,57.25273513793945,9961396.0,5747.461012285773 +6850,Belarus,2017,,,,,61.99318313598633,9549747.0,6491.604765653617 +6851,Georgia,2017,,,,,15.923763275146484,4926330.0,3232.3785201451146 +6852,Kazakhstan,2017,,,,,196.023193359375,18556698.0,10563.473811955932 +6853,Kyrgyzstan,2017,,,,,6.637610912322997,5789122.0,1146.5660789879705 +6854,Moldova,2017,,,,,7.685763359069824,3474121.0,2212.2900610168226 +6855,Tajikistan,2017,,,,,9.101083755493164,8468555.0,1074.691462178986 +6856,Turkmenistan,2017,,,,,42.1349983215332,5351277.0,7873.821205953868 +6857,Uzbekistan,2017,,,,,65.347900390625,29748859.0,2196.6523284346804 +6858,China,2017,,,,,10161.0888671875,1379302771.0,7366.8298801579795 +6859,Hong Kong,2017,,,,,280.34722900390625,7191503.0,38983.12063610434 +6860,Japan,2017,,,,,6157.533203125,126451398.0,48694.86063827464 +6861,Korea,2017,,,,,1345.9449462890625,51181299.0,26297.59253060503 +6862,Taiwan,2017,,,,,532.6212768554689,23508428.0,22656.609657416007 +6863,Macau,2017,,,,,34.82516860961914,601969.0,57852.09638639056 +6864,Mongolia,2017,,,,,12.443145751953125,3068243.0,4055.462931701669 +6865,Cambodia,2017,,,,,18.20177459716797,16204486.0,1123.2552885150426 +6866,Indonesia,2017,,,,,1090.454345703125,260580739.0,4184.708163342514 +6867,Malaysia,2017,,,,,364.57525634765625,31381992.0,11617.339534968216 +6868,Myanmar,2017,,,,,66.50968170166016,55123814.0,1206.550796751113 +6869,Philippines,2017,,,,,303.3560485839844,104256076.0,2909.720567115766 +6870,Thailand,2017,,,,,422.940673828125,68414135.0,6182.065648102181 +6871,Vietnam,2017,,,,,175.28402709960938,96160163.0,1822.834130383175 +6872,Brunei,2017,,,,,13.478104591369627,443593.0,30383.94337009292 +6873,Laos,2017,,,,,11.867393493652346,7126706.0,1665.2003735880703 +6874,Singapore,2017,,,,,309.9937438964844,5888926.0,52640.115344713864 +6875,Bangladesh,2017,,,,,178.78790283203125,157826578.0,1132.8123887475485 +6876,India,2017,,,,,2650.853271484375,1281935911.0,2067.8516365272303 +6877,Pakistan,2017,,,,,236.92095947265625,204924861.0,1156.135757840803 +6878,Afghanistan,2017,,,,,20.45464324951172,34124811.0,599.4067849785812 +6879,Bhutan,2017,,,,,2.3399224281311035,758288.0,3085.796462730656 +6880,Nepal,2017,,,,,21.83708953857422,29384297.0,743.1550783254817 +6881,Sri Lanka,2017,,,,,82.54773712158203,22409381.0,3683.624153723034 +6882,Australia,2017,,,,,1432.683349609375,23232413.0,61667.436336009305 +6883,New Zealand,2017,,,,,181.0943756103516,4510327.0,40151.05237610301 +6884,Fiji,2017,,,,,3.91445255279541,920938.0,4250.5060631610495 +6885,Maldives,2017,,,,,3.914371728897095,392709.0,9967.614006547074 +6886,Papua New Guinea,2017,,,,,19.82283973693848,6909701.0,2868.841898793953 +6887,Samoa,2017,,,,,0.7745237946510315,200108.0,3870.528887655824 +6888,Solomon Islands,2017,,,,,0.9300326704978944,647581.0,1436.164233505761 +6889,Tonga,2017,,,,,0.4222209751605988,106479.0,3965.298088454989 +6890,Iran,2017,,,,,565.5205688476562,82021564.0,6894.7791442706 +6891,Iraq,2017,,,,,208.595458984375,39192111.0,5322.383858944852 +6892,Saudi Arabia,2017,,,,,684.1575927734375,32548604.0,21019.567929040444 +6893,Turkey,2017,,,,,1206.0009765625,80845215.0,14917.407005009509 +6894,Bahrain,2017,,,,,33.05887985229492,1410942.0,23430.360604684614 +6895,Israel,2017,,,,,299.15692138671875,8299706.0,36044.279325884396 +6896,Jordan,2017,,,,,31.41866683959961,10248069.0,3065.813358555608 +6897,Kuwait,2017,,,,,138.75914001464844,2875422.0,48256.96541747556 +6898,Lebanon,2017,,,,,43.200775146484375,6229794.0,6934.543123975588 +6899,Oman,2017,,,,,74.07438659667969,3424386.0,21631.435999528 +6900,Syria,2017,,,,,25.320425033569336,18028549.0,1404.462723737187 +6901,United Arab Emirates,2017,,,,,387.2593994140625,9566312.0,40481.57737423393 +6902,Yemen,2017,,,,,19.564088821411133,28036829.0,697.7996271051599 +6903,Egypt,2017,,,,,266.4154663085937,97041072.0,2745.388739198942 +6904,Morocco,2017,,,,,119.3475112915039,33986655.0,3511.599223033391 +6905,Algeria,2017,,,,,199.17140197753903,40969443.0,4861.462284892159 +6906,Libya,2017,,,,,46.62832641601562,6653210.0,7008.39540853447 +6907,Tunisia,2017,,,,,49.63385772705078,11403800.0,4352.396370249459 +6908,Nigeria,2017,,,,,460.4564208984375,198346055.0,2321.480106566463 +6909,South Africa,2017,,,,,426.8119201660156,54841552.0,7782.6375184643875 +6910,Benin,2017,,,,,9.633659362792967,11038805.0,872.7085370919197 +6911,Burkina Faso,2017,,,,,13.161945343017578,19199732.0,685.5275554376267 +6912,Cabo Verde,2017,,,,,1.950907826423645,560899.0,3478.1802542412183 +6913,Cote d'Ivoire,2017,,,,,39.4951171875,25659816.0,1539.1816210802135 +6914,Gambia,2017,,,,,1.109785079956055,2051363.0,540.9988773103807 +6915,Ghana,2017,,,,,50.62045288085938,27499924.0,1840.7488282825573 +6916,Guinea,2017,,,,,10.480310440063477,11534306.0,908.6208082275152 +6917,Guinea Bissau,2017,,,,,1.1232856512069702,1788610.0,628.0215649062512 +6918,Liberia,2017,,,,,1.6663247346878052,4689021.0,355.36730048507036 +6919,Mali,2017,,,,,14.146612167358398,17885245.0,790.9655231090431 +6920,Niger,2017,,,,,8.504079818725586,19245344.0,441.8772571030991 +6921,Senegal,2017,,,,,22.95280456542969,14668522.0,1564.766004743333 +6922,Sierra Leone,2017,,,,,3.4966859817504883,6163195.0,567.3495616722314 +6923,Togo,2017,,,,,5.06468391418457,7965055.0,635.8630184204088 +6924,Angola,2017,,,,,101.67298889160156,29310273.0,3468.8516511463936 +6925,Botswana,2017,,,,,17.240806579589847,2214858.0,7784.158884944246 +6926,Burundi,2017,,,,,2.318491220474243,11466756.0,202.19242656547704 +6927,Cameroon,2017,,,,,36.36484146118164,24994885.0,1454.8913292132227 +6928,Central Afr Rep,2017,,,,,1.560914397239685,5625118.0,277.49007171755056 +6929,Chad,2017,,,,,12.269183158874512,15327016.0,800.4939225531253 +6930,Dem Rep Congo,2017,,,,,33.27772903442383,83301151.0,399.487025508493 +6931,Djibouti,2017,,,,,1.7546582221984863,865267.0,2027.8806682775214 +6932,Equatorial Guinea,2017,,,,,14.309503555297852,778358.0,18384.21851551324 +6933,Eritrea,2017,,,,,2.9085850715637207,5918919.0,491.4047770485997 +6934,Ethiopia,2017,,,,,51.62864303588867,105350020.0,490.0677098674369 +6935,Gabon,2017,,,,,19.007570266723636,2060381.0,9225.269630579796 +6936,Kenya,2017,,,,,58.10987091064453,47615739.0,1220.392083185867 +6937,Lesotho,2017,,,,,2.911871910095215,1958042.0,1487.1345507886015 +6938,Madagascar,2017,,,,,10.78813648223877,25054161.0,430.59260624368017 +6939,Malawi,2017,,,,,9.058581352233887,19196246.0,471.8933770818464 +6940,Mauritania,2017,,,,,5.761532306671143,3758571.0,1532.9050074273289 +6941,Mauritius,2017,,,,,12.89894199371338,1356388.0,9509.77301016625 +6942,Mozambique,2017,,,,,15.397934913635256,26573706.0,579.4425103384245 +6943,Namibia,2017,,,,,14.797003746032715,2484780.0,5955.055878602016 +6944,Rep Congo,2017,,,,,13.765317916870115,4954674.0,2778.2489658996974 +6945,Rwanda,2017,,,,,9.338096618652344,11901484.0,784.6161553174666 +6946,Sao Tome Principe,2017,,,,,0.26676806807518005,201025.0,1327.0392641471462 +6947,Seychelles,2017,,,,,1.355493426322937,93920.0,14432.425748753589 +6948,Sudan,2017,,,,,79.41102600097656,41824518.0,1898.6716356414806 +6949,Swaziland,2017,,,,,5.442490100860597,1078215.0,5047.685388220898 +6950,Tanzania,2017,,,,,49.73672103881836,53950935.0,921.8880273125637 +6951,Uganda,2017,,,,,26.62738037109375,39570125.0,672.9162561678476 +6952,Zambia,2017,,,,,27.95495796203613,15972000.0,1750.2478062882626 +6953,Canada,2018,,,,,1919.1033935546875,35881659.0,53484.243678774365 +6954,United States,2018,,,,,17844.271484375,327892000.0,54421.1858916198 +6955,Cuba,2018,,,,,76.4035873413086,11116396.0,6873.05376142669 +6956,Mexico,2018,,,,,1309.8631591796875,125959205.0,10399.106275557133 +6957,Antigua Barbuda,2018,,,,,1.4502197504043581,95882.0,15125.046936905343 +6958,Bahamas,2018,,,,,10.721199035644533,332634.0,32231.218202722906 +6959,Barbados,2018,,,,,4.731025218963623,293131.0,16139.627739691887 +6960,Belize,2018,,,,,1.6510053873062134,385854.0,4278.834448538083 +6961,Costa Rica,2018,,,,,49.40186309814453,4987142.0,9905.846494474095 +6962,Dominica,2018,,,,,0.4596127271652222,74027.0,6208.717456674216 +6963,Dominican Rep,2018,,,,,82.18321990966797,10298756.0,7979.917177343357 +6964,El Salvador,2018,,,,,22.65053558349609,6187271.0,3660.828107172951 +6965,Grenada,2018,,,,,1.0260069370269775,112207.0,9143.876380501908 +6966,Guatemala,2018,,,,,54.50267791748047,16581273.0,3287.0020243608838 +6967,Haiti,2018,,,,,8.227766036987305,10788440.0,762.646502829631 +6968,Honduras,2018,,,,,21.24838256835937,9182766.0,2313.9414168192216 +6969,Jamaica,2018,,,,,14.231366157531738,2812090.0,5060.7790495794015 +6970,Nicaragua,2018,,,,,12.058242797851562,6085213.0,1981.5646219535065 +6971,Panama,2018,,,,,48.96925354003906,3800644.0,12884.46209117167 +6972,Puerto Rico,2018,,,,,87.55036163330078,3294626.0,26573.687463554517 +6973,St Kitts Nevis,2018,,,,,0.9445246458053588,53094.0,17789.668245100365 +6974,St Lucia,2018,,,,,1.5536938905715942,165510.0,9387.311283738713 +6975,St Vincent Grenadines,2018,,,,,0.757747232913971,101844.0,7440.273682435597 +6976,Trinidad and Tobago,2018,,,,,21.16432380676269,1215527.0,17411.644337610513 +6977,Argentina,2018,,,,,451.7471618652344,44694198.0,10107.512430701503 +6978,Brazil,2018,,,,,2309.61572265625,208846892.0,11058.894391668755 +6979,Bolivia,2018,,,,,29.05849838256836,11306341.0,2570.106313135997 +6980,Chile,2018,,,,,282.83856201171875,17925262.0,15778.768645708988 +6981,Colombia,2018,,,,,383.3981323242188,48168996.0,7959.437899104618 +6982,Ecuador,2018,,,,,88.58395385742188,16498502.0,5369.21193556978 +6983,Guyana,2018,,,,,3.1150636672973637,740685.0,4205.652426196511 +6984,Paraguay,2018,,,,,37.646427154541016,7025763.0,5358.340034319549 +6985,Peru,2018,,,,,206.44271850585938,31331228.0,6589.040126542738 +6986,Suriname,2018,,,,,4.62827205657959,597927.0,7740.530293128742 +6987,Uruguay,2018,,,,,50.45218276977539,3369299.0,14974.088903886355 +6988,Venezuela,2018,,,,,163.1122589111328,31689176.0,5147.254662321697 +6989,Austria,2018,,,,,444.5639953613281,8793370.0,50556.725733288615 +6990,Belgium,2018,,,,,533.0703735351561,11570762.0,46070.46394482544 +6991,Denmark,2018,,,,,364.5939331054688,5809502.0,62758.20769240957 +6992,Finland,2018,,,,,268.066162109375,5537364.0,48410.42815848389 +6993,France,2018,,,,,2924.2548828125,67364357.0,43409.527130385875 +6994,Germany,2018,,,,,3939.226806640625,80457737.0,48960.19890095374 +6995,Greece,2018,,,,,252.72329711914062,10761523.0,23483.971285397114 +6996,Ireland,2018,,,,,382.2845764160156,5068050.0,75430.30878069783 +6997,Italy,2018,,,,,2136.9638671875,62246674.0,34330.5710950516 +6998,Luxembourg,2018,,,,,65.17471313476561,605764.0,107590.93167432467 +6999,Netherlands,2018,,,,,948.4144287109376,17151228.0,55297.173398367595 +7000,Portugal,2018,,,,,244.06500244140625,10355493.0,23568.651192309844 +7001,Spain,2018,,,,,1548.7236328125,49331076.0,31394.48312079185 +7002,Sweden,2018,,,,,582.8028564453125,10040995.0,58042.34106732575 +7003,UK,2018,,,,,2858.098388671875,65105246.0,43899.6634567954 +7004,Bulgaria,2018,,,,,60.76715850830078,7057504.0,8610.290338949972 +7005,Croatia,2018,,,,,64.89981842041016,4270480.0,15197.312344375845 +7006,Cyprus,2018,,,,,26.810155868530273,1237088.0,21671.987658541893 +7007,Czech Republic,2018,,,,,248.04803466796875,10686269.0,23211.84640476192 +7008,Estonia,2018,,,,,26.36591720581055,1244288.0,21189.561585268482 +7009,Hungary,2018,,,,,161.21888732910156,9825704.0,16407.87136770063 +7010,Latvia,2018,,,,,31.61667823791504,1923559.0,16436.552368768018 +7011,Lithuania,2018,,,,,49.30306625366211,2793284.0,17650.574110495785 +7012,Malta,2018,,,,,13.457584381103516,449043.0,29969.478159337785 +7013,Poland,2018,,,,,631.9017944335938,38420687.0,16446.915549261103 +7014,Romania,2018,,,,,225.1929931640625,21457116.0,10495.026133244677 +7015,Slovakia,2018,,,,,112.58918762207031,5445040.0,20677.384853384054 +7016,Slovenia,2018,,,,,55.41766357421875,2102126.0,26362.674537215542 +7017,Iceland,2018,,,,,18.320371627807614,343518.0,53331.6205491637 +7018,Norway,2018,,,,,489.5642395019531,5372191.0,91129.3435959282 +7019,Switzerland,2018,,,,,671.2345581054688,8292809.0,80941.76027754512 +7020,Albania,2018,,,,,14.546524047851562,3057220.0,4758.088736777714 +7021,Bosnia Herzegovina,2018,,,,,20.13240623474121,3849891.0,5229.3444761789915 +7022,Macedonia,2018,,,,,11.236076354980467,2118945.0,5302.674847615426 +7023,Serbia,2018,,,,,43.8895263671875,7078110.0,6200.740927618743 +7024,Russia,2018,,,,,1719.599853515625,142122776.0,12099.396746342927 +7025,Ukraine,2018,,,,,131.54231262207028,43952299.0,2992.8425956073493 +7026,Armenia,2018,,,,,13.007627487182615,3038217.0,4281.3358911435935 +7027,Azerbaijan,2018,,,,,58.0616455078125,10046516.0,5779.281644284695 +7028,Belarus,2018,,,,,63.88391876220703,9527543.0,6705.182937742398 +7029,Georgia,2018,,,,,16.67494773864746,4926087.0,3385.0290785866064 +7030,Kazakhstan,2018,,,,,204.0601348876953,18744548.0,10886.372660876927 +7031,Kyrgyzstan,2018,,,,,6.869927406311035,5849296.0,1174.4879052643319 +7032,Moldova,2018,,,,,7.993193626403809,3437720.0,2325.1438821090164 +7033,Tajikistan,2018,,,,,9.765462875366213,8604882.0,1134.874699660752 +7034,Turkmenistan,2018,,,,,44.747371673583984,5411012.0,8269.686275614244 +7035,Uzbekistan,2018,,,,,68.70219421386719,30023709.0,2288.2647248501903 +7036,China,2018,,,,,10831.720703125,1384688986.0,7822.493579886826 +7037,Hong Kong,2018,,,,,288.817626953125,7213338.0,40039.38633585796 +7038,Japan,2018,,,,,6206.0517578125,126168156.0,49188.73315238514 +7039,Korea,2018,,,,,1381.8590087890625,51418097.0,26874.95433347256 +7040,Taiwan,2018,,,,,546.6292114257811,23545963.0,23215.41112698517 +7041,Macau,2018,,,,,36.46480941772461,606340.0,60139.21136280736 +7042,Mongolia,2018,,,,,13.307799339294435,3103428.0,4288.096691559925 +7043,Cambodia,2018,,,,,19.570629119873047,16449519.0,1189.7386859684498 +7044,Indonesia,2018,,,,,1146.8447265625,262787403.0,4364.154116483658 +7045,Malaysia,2018,,,,,381.7964477539063,31809660.0,12002.531550287122 +7046,Myanmar,2018,,,,,70.63331604003906,55622506.0,1269.8693589972204 +7047,Philippines,2018,,,,,322.29681396484375,105893381.0,3043.597351612031 +7048,Thailand,2018,,,,,440.4048767089844,68615858.0,6418.412442047789 +7049,Vietnam,2018,,,,,187.686767578125,97040334.0,1934.110898444816 +7050,Brunei,2018,,,,,13.485159873962402,450565.0,29929.44386262227 +7051,Laos,2018,,,,,12.638876914978027,7234171.0,1747.107846217352 +7052,Singapore,2018,,,,,319.72589111328125,5995991.0,53323.27735536648 +7053,Bangladesh,2018,,,,,192.8472595214844,159453001.0,1209.43010361708 +7054,India,2018,,,,,2835.94482421875,1296834042.0,2186.8216999031783 +7055,Pakistan,2018,,,,,249.7857818603516,207862518.0,1201.6874627697505 +7056,Afghanistan,2018,,,,,20.66546058654785,34940837.0,591.4414868352425 +7057,Bhutan,2018,,,,,2.393521785736084,766397.0,3123.0834485731075 +7058,Nepal,2018,,,,,23.21059226989746,29717587.0,781.0389272149673 +7059,Sri Lanka,2018,,,,,85.1968002319336,22576592.0,3773.6785176404655 +7060,Australia,2018,,,,,1473.2991943359375,23470145.0,62773.33158086317 +7061,New Zealand,2018,,,,,186.13363647460938,4545627.0,40947.846463119255 +7062,Fiji,2018,,,,,4.110175132751465,926276.0,4437.311484645467 +7063,Maldives,2018,,,,,4.151318073272705,392473.0,10577.334168905136 +7064,Papua New Guinea,2018,,,,,19.907594680786133,7027332.0,2832.88091138801 +7065,Samoa,2018,,,,,0.7801320552825928,201316.0,3875.1617123457286 +7066,Solomon Islands,2018,,,,,0.9615191221237184,660121.0,1456.5801150451482 +7067,Tonga,2018,,,,,0.4234876334667206,106398.0,3980.2217472764573 +7068,Iran,2018,,,,,553.259765625,83024745.0,6663.793615084273 +7069,Iraq,2018,,,,,209.9147186279297,40194216.0,5222.510587790285 +7070,Saudi Arabia,2018,,,,,699.3114624023438,33091113.0,21132.90847613206 +7071,Turkey,2018,,,,,1236.9544677734375,81257239.0,15222.698715783803 +7072,Bahrain,2018,,,,,33.646568298339844,1442659.0,23322.606588486844 +7073,Israel,2018,,,,,309.0555114746094,8424904.0,36683.56475926721 +7074,Jordan,2018,,,,,32.02827835083008,10458413.0,3062.4415339908724 +7075,Kuwait,2018,,,,,140.47982788085938,2916467.0,48167.809846934455 +7076,Lebanon,2018,,,,,43.28717041015625,6100075.0,7096.170196293692 +7077,Oman,2018,,,,,75.6485595703125,3494116.0,21650.271362001862 +7078,Syria,2018,,,,,25.88325309753418,19454263.0,1330.4669057642625 +7079,United Arab Emirates,2018,,,,,392.77337646484375,9701315.0,40486.612017529966 +7080,Yemen,2018,,,,,19.035568237304688,28667230.0,664.0184014048336 +7081,Egypt,2018,,,,,280.5731201171875,99413317.0,2822.2890914824575 +7082,Morocco,2018,,,,,122.86833953857422,34314130.0,3580.692255306319 +7083,Algeria,2018,,,,,203.35400390625,41657488.0,4881.571445360556 +7084,Libya,2018,,,,,50.28316879272461,6754507.0,7444.387694427529 +7085,Tunisia,2018,,,,,50.87734985351562,11516189.0,4417.898130494005 +7086,Nigeria,2018,,,,,469.3766784667969,203452505.0,2307.0577502439537 +7087,South Africa,2018,,,,,429.4670104980469,55380210.0,7754.882303589077 +7088,Benin,2018,,,,,10.294336318969727,11340504.0,907.7494544307491 +7089,Burkina Faso,2018,,,,,14.01876163482666,19742715.0,710.0726336183578 +7090,Cabo Verde,2018,,,,,2.0586087703704834,568373.0,3621.9327279277577 +7091,Cote d'Ivoire,2018,,,,,42.43081665039063,26260582.0,1615.7607112588225 +7092,Gambia,2018,,,,,1.1830307245254517,2092731.0,565.3047259898437 +7093,Ghana,2018,,,,,53.791053771972656,28102471.0,1914.1040576813568 +7094,Guinea,2018,,,,,11.387842178344727,11855411.0,960.5607244105437 +7095,Guinea Bissau,2018,,,,,1.1659704446792605,1833247.0,636.0138293853803 +7096,Liberia,2018,,,,,1.6866964101791382,4809768.0,350.6814487058706 +7097,Mali,2018,,,,,14.83979606628418,18429893.0,805.2025080278099 +7098,Niger,2018,,,,,8.943970680236816,19866231.0,450.2097393429492 +7099,Senegal,2018,,,,,24.505859375,15020945.0,1631.445916019265 +7100,Sierra Leone,2018,,,,,3.627041101455689,6312212.0,574.6069842799462 +7101,Togo,2018,,,,,5.312049865722656,8176449.0,649.6768787676235 +7102,Angola,2018,,,,,99.5038070678711,30355880.0,3277.9088291253984 +7103,Botswana,2018,,,,,18.008636474609375,2249104.0,8007.027009248739 +7104,Burundi,2018,,,,,2.355586528778076,11844520.0,198.8756428101836 +7105,Cameroon,2018,,,,,37.76848602294922,25640965.0,1472.9744384795667 +7106,Central Afr Rep,2018,,,,,1.6286675930023191,5745062.0,283.4899941901966 +7107,Chad,2018,,,,,12.593253135681152,15833116.0,795.3742734962058 +7108,Dem Rep Congo,2018,,,,,35.19384002685547,85281024.0,412.68078613661424 +7109,Djibouti,2018,,,,,1.8591545820236208,884017.0,2103.0755992516215 +7110,Equatorial Guinea,2018,,,,,13.887412071228027,797457.0,17414.621818139443 +7111,Eritrea,2018,,,,,3.00195050239563,5970646.0,502.784874935749 +7112,Ethiopia,2018,,,,,55.14464569091797,108386391.0,508.7783178509742 +7113,Gabon,2018,,,,,19.23967170715332,2119036.0,9079.445421009044 +7114,Kenya,2018,,,,,61.78228759765625,48397527.0,1276.5587712292872 +7115,Lesotho,2018,,,,,2.9547731876373287,1962461.0,1505.6468320324984 +7116,Madagascar,2018,,,,,11.34779453277588,25683610.0,441.83019960106384 +7117,Malawi,2018,,,,,9.375632286071776,19842560.0,472.5011433036754 +7118,Mauritania,2018,,,,,5.968947410583496,3840429.0,1554.2397504506646 +7119,Mauritius,2018,,,,,13.38572597503662,1364283.0,9811.54641305112 +7120,Mozambique,2018,,,,,15.900656700134276,27233789.0,583.8576740142283 +7121,Namibia,2018,,,,,14.785954475402832,2533224.0,5836.812881688644 +7122,Rep Congo,2018,,,,,13.906489372253418,5062021.0,2747.2207982253367 +7123,Rwanda,2018,,,,,10.147867202758787,12187400.0,832.652346091766 +7124,Sao Tome Principe,2018,,,,,0.2738579511642456,204454.0,1339.459982021607 +7125,Seychelles,2018,,,,,1.4046618938446045,94633.0,14843.256515640469 +7126,Sudan,2018,,,,,77.56803131103516,43120843.0,1798.852385864422 +7127,Swaziland,2018,,,,,5.47645378112793,1087200.0,5037.209143789487 +7128,Tanzania,2018,,,,,52.32303237915039,55451343.0,943.5845833192964 +7129,Uganda,2018,,,,,28.249027252197266,40853749.0,691.4671956347818 +7130,Zambia,2018,,,,,29.01581954956055,16445079.0,1764.4074284812218 diff --git a/your-project/data/1annual-co-emissions-by-region.csv b/your-project/data/1annual-co-emissions-by-region.csv new file mode 100644 index 00000000..a7a2ac84 --- /dev/null +++ b/your-project/data/1annual-co-emissions-by-region.csv @@ -0,0 +1,20854 @@ +Entity,Code,Year,Annual COâ‚‚ emissions (tonnes ) +Afghanistan,AFG,1949,14656 +Afghanistan,AFG,1950,84272 +Afghanistan,AFG,1951,91600 +Afghanistan,AFG,1952,91600 +Afghanistan,AFG,1953,106256 +Afghanistan,AFG,1954,106256 +Afghanistan,AFG,1955,153888 +Afghanistan,AFG,1956,183200 +Afghanistan,AFG,1957,293120 +Afghanistan,AFG,1958,329760 +Afghanistan,AFG,1959,384571.42 +Afghanistan,AFG,1960,413883.42 +Afghanistan,AFG,1961,490797.7 +Afghanistan,AFG,1962,688594.27 +Afghanistan,AFG,1963,706735.98 +Afghanistan,AFG,1964,838550.83 +Afghanistan,AFG,1965,1006916.53 +Afghanistan,AFG,1966,1091158.82 +Afghanistan,AFG,1967,1281865.11 +Afghanistan,AFG,1968,1223389.69 +Afghanistan,AFG,1969,941231.98 +Afghanistan,AFG,1970,1670397.69 +Afghanistan,AFG,1971,1893931.41 +Afghanistan,AFG,1972,1531165.69 +Afghanistan,AFG,1973,1637243.4 +Afghanistan,AFG,1974,1915677.68 +Afghanistan,AFG,1975,2124525.68 +Afghanistan,AFG,1976,1984225.38 +Afghanistan,AFG,1977,2386745.67 +Afghanistan,AFG,1978,2155419.66 +Afghanistan,AFG,1979,2234903.94 +Afghanistan,AFG,1980,1757021.97 +Afghanistan,AFG,1981,1979372.53 +Afghanistan,AFG,1982,2095539.37 +Afghanistan,AFG,1983,2520091.42 +Afghanistan,AFG,1984,2822415.06 +Afghanistan,AFG,1985,3501893.65 +Afghanistan,AFG,1986,3134071.92 +Afghanistan,AFG,1987,3114159.9 +Afghanistan,AFG,1988,2857083.9 +Afghanistan,AFG,1989,2764887.89 +Afghanistan,AFG,1990,2602407.59 +Afghanistan,AFG,1991,2426486.16 +Afghanistan,AFG,1992,1381521.84 +Afghanistan,AFG,1993,1333837.11 +Afghanistan,AFG,1994,1282488.38 +Afghanistan,AFG,1995,1231139.65 +Afghanistan,AFG,1996,1165134.92 +Afghanistan,AFG,1997,1084474.19 +Afghanistan,AFG,1998,1029461.47 +Afghanistan,AFG,1999,809568.74 +Afghanistan,AFG,2000,768195.25 +Afghanistan,AFG,2001,812074.37 +Afghanistan,AFG,2002,1064074.85 +Afghanistan,AFG,2003,1187070.59 +Afghanistan,AFG,2004,941455.62 +Afghanistan,AFG,2005,1320250.12 +Afghanistan,AFG,2006,1643246 +Afghanistan,AFG,2007,2268343.1 +Afghanistan,AFG,2008,4198329.64 +Afghanistan,AFG,2009,6760313.49 +Afghanistan,AFG,2010,8452224.14 +Afghanistan,AFG,2011,12226017.38 +Afghanistan,AFG,2012,10742056.72 +Afghanistan,AFG,2013,10000414.29 +Afghanistan,AFG,2014,9789185.57 +Afghanistan,AFG,2015,10107428.68 +Afghanistan,AFG,2016,12257907.69 +Afghanistan,AFG,2017,13014739.66 +Africa,,1751,0 +Africa,,1752,0 +Africa,,1753,0 +Africa,,1754,0 +Africa,,1755,0 +Africa,,1756,0 +Africa,,1757,0 +Africa,,1758,0 +Africa,,1759,0 +Africa,,1760,0 +Africa,,1761,0 +Africa,,1762,0 +Africa,,1763,0 +Africa,,1764,0 +Africa,,1765,0 +Africa,,1766,0 +Africa,,1767,0 +Africa,,1768,0 +Africa,,1769,0 +Africa,,1770,0 +Africa,,1771,0 +Africa,,1772,0 +Africa,,1773,0 +Africa,,1774,0 +Africa,,1775,0 +Africa,,1776,0 +Africa,,1777,0 +Africa,,1778,0 +Africa,,1779,0 +Africa,,1780,0 +Africa,,1781,0 +Africa,,1782,0 +Africa,,1783,0 +Africa,,1784,0 +Africa,,1785,0 +Africa,,1786,0 +Africa,,1787,0 +Africa,,1788,0 +Africa,,1789,0 +Africa,,1790,0 +Africa,,1791,0 +Africa,,1792,0 +Africa,,1793,0 +Africa,,1794,0 +Africa,,1795,0 +Africa,,1796,0 +Africa,,1797,0 +Africa,,1798,0 +Africa,,1799,0 +Africa,,1800,0 +Africa,,1801,0 +Africa,,1802,0 +Africa,,1803,0 +Africa,,1804,0 +Africa,,1805,0 +Africa,,1806,0 +Africa,,1807,0 +Africa,,1808,0 +Africa,,1809,0 +Africa,,1810,0 +Africa,,1811,0 +Africa,,1812,0 +Africa,,1813,0 +Africa,,1814,0 +Africa,,1815,0 +Africa,,1816,0 +Africa,,1817,0 +Africa,,1818,0 +Africa,,1819,0 +Africa,,1820,0 +Africa,,1821,0 +Africa,,1822,0 +Africa,,1823,0 +Africa,,1824,0 +Africa,,1825,0 +Africa,,1826,0 +Africa,,1827,0 +Africa,,1828,0 +Africa,,1829,0 +Africa,,1830,0 +Africa,,1831,0 +Africa,,1832,0 +Africa,,1833,0 +Africa,,1834,0 +Africa,,1835,0 +Africa,,1836,0 +Africa,,1837,0 +Africa,,1838,0 +Africa,,1839,0 +Africa,,1840,0 +Africa,,1841,0 +Africa,,1842,0 +Africa,,1843,0 +Africa,,1844,0 +Africa,,1845,0 +Africa,,1846,0 +Africa,,1847,0 +Africa,,1848,0 +Africa,,1849,0 +Africa,,1850,0 +Africa,,1851,0 +Africa,,1852,0 +Africa,,1853,0 +Africa,,1854,0 +Africa,,1855,0 +Africa,,1856,0 +Africa,,1857,0 +Africa,,1858,0 +Africa,,1859,0 +Africa,,1860,0 +Africa,,1861,0 +Africa,,1862,0 +Africa,,1863,0 +Africa,,1864,0 +Africa,,1865,0 +Africa,,1866,0 +Africa,,1867,0 +Africa,,1868,0 +Africa,,1869,0 +Africa,,1870,0 +Africa,,1871,0 +Africa,,1872,0 +Africa,,1873,0 +Africa,,1874,0 +Africa,,1875,0 +Africa,,1876,0 +Africa,,1877,0 +Africa,,1878,0 +Africa,,1879,0 +Africa,,1880,0 +Africa,,1881,0 +Africa,,1882,0 +Africa,,1883,0 +Africa,,1884,21984 +Africa,,1885,36640 +Africa,,1886,47632 +Africa,,1887,47632 +Africa,,1888,80608 +Africa,,1889,131904 +Africa,,1890,296784 +Africa,,1891,296784 +Africa,,1892,479984 +Africa,,1893,1799024 +Africa,,1894,2432896 +Africa,,1895,3337904 +Africa,,1896,4257568 +Africa,,1897,4777856 +Africa,,1898,6049264 +Africa,,1899,5517984 +Africa,,1900,2330304 +Africa,,1901,3909488 +Africa,,1902,5825760 +Africa,,1903,7895920 +Africa,,1904,8914512 +Africa,,1905,10259200 +Africa,,1906,11717472 +Africa,,1907,12930256 +Africa,,1908,13402912 +Africa,,1909,15385136 +Africa,,1910,17411328 +Africa,,1911,16865392 +Africa,,1912,17953600 +Africa,,1913,19606064 +Africa,,1914,19551104 +Africa,,1915,19606064 +Africa,,1916,23251744 +Africa,,1917,25343888 +Africa,,1918,24728336 +Africa,,1919,25446480 +Africa,,1920,28051584 +Africa,,1921,27985632 +Africa,,1922,23973552 +Africa,,1923,29048192 +Africa,,1924,30631040 +Africa,,1925,32180912 +Africa,,1926,34437936 +Africa,,1927,33837040 +Africa,,1928,34771360 +Africa,,1929,35808272 +Africa,,1930,33701472 +Africa,,1931,29469552 +Africa,,1932,26629952 +Africa,,1933,28502256 +Africa,,1934,32539984 +Africa,,1935,35914528 +Africa,,1936,39025264 +Africa,,1937,41846544 +Africa,,1938,44022960 +Africa,,1939,46858896 +Africa,,1940,49650864 +Africa,,1941,54033008 +Africa,,1942,58829184 +Africa,,1943,60126240 +Africa,,1944,66648160 +Africa,,1945,67531184 +Africa,,1946,68168720 +Africa,,1947,67952544 +Africa,,1948,71744784 +Africa,,1949,77354368 +Africa,,1950,94941568 +Africa,,1951,101203344 +Africa,,1952,110425632 +Africa,,1953,112078096 +Africa,,1954,117640048 +Africa,,1955,129529728 +Africa,,1956,135260224 +Africa,,1957,141752832 +Africa,,1958,148769392 +Africa,,1959,147841143.3 +Africa,,1960,156567067.8 +Africa,,1961,161994254.1 +Africa,,1962,166334786.6 +Africa,,1963,176334743 +Africa,,1964,193639229.6 +Africa,,1965,213634280.9 +Africa,,1966,221081974.5 +Africa,,1967,237045012.2 +Africa,,1968,257282104.4 +Africa,,1969,277915809.1 +Africa,,1970,302623753.9 +Africa,,1971,331868172.1 +Africa,,1972,351877790.8 +Africa,,1973,378012574.7 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differences,,1860,0 +Statistical differences,,1861,0 +Statistical differences,,1862,0 +Statistical differences,,1863,0 +Statistical differences,,1864,0 +Statistical differences,,1865,0 +Statistical differences,,1866,0 +Statistical differences,,1867,0 +Statistical differences,,1868,0 +Statistical differences,,1869,0 +Statistical differences,,1870,0 +Statistical differences,,1871,0 +Statistical differences,,1872,0 +Statistical differences,,1873,0 +Statistical differences,,1874,0 +Statistical differences,,1875,0 +Statistical differences,,1876,0 +Statistical differences,,1877,0 +Statistical differences,,1878,0 +Statistical differences,,1879,0 +Statistical differences,,1880,0 +Statistical differences,,1881,0 +Statistical differences,,1882,0 +Statistical differences,,1883,0 +Statistical differences,,1884,0 +Statistical differences,,1885,0 +Statistical differences,,1886,0 +Statistical differences,,1887,0 +Statistical differences,,1888,0 +Statistical differences,,1889,0 +Statistical differences,,1890,0 +Statistical differences,,1891,0 +Statistical differences,,1892,0 +Statistical differences,,1893,0 +Statistical differences,,1894,0 +Statistical differences,,1895,0 +Statistical differences,,1896,0 +Statistical differences,,1897,0 +Statistical differences,,1898,0 +Statistical differences,,1899,0 +Statistical differences,,1900,0 +Statistical differences,,1901,0 +Statistical differences,,1902,0 +Statistical differences,,1903,0 +Statistical differences,,1904,0 +Statistical differences,,1905,0 +Statistical differences,,1906,0 +Statistical differences,,1907,0 +Statistical differences,,1908,0 +Statistical differences,,1909,0 +Statistical differences,,1910,0 +Statistical differences,,1911,0 +Statistical differences,,1912,0 +Statistical differences,,1913,0 +Statistical differences,,1914,0 +Statistical differences,,1915,0 +Statistical differences,,1916,0 +Statistical differences,,1917,0 +Statistical differences,,1918,0 +Statistical differences,,1919,0 +Statistical 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differences,,1999,-565757974.1 +Statistical differences,,2000,-625522256.7 +Statistical differences,,2001,-271153713.6 +Statistical differences,,2002,-470695645.7 +Statistical differences,,2003,-364016570.9 +Statistical differences,,2004,-325533728.8 +Statistical differences,,2005,-212372213.8 +Statistical differences,,2006,-165209053 +Statistical differences,,2007,-488640473.9 +Statistical differences,,2008,-204836709.7 +Statistical differences,,2009,-103698554.3 +Statistical differences,,2010,-172010026.3 +Statistical differences,,2011,-60312512.43 +Statistical differences,,2012,-1774597.04 +Statistical differences,,2013,161034286.1 +Statistical differences,,2014,155499749.8 +Statistical differences,,2015,143390525.2 +Statistical differences,,2016,227584343.4 +Statistical differences,,2017,256677450.8 +Sudan,SDN,1950,549600 +Sudan,SDN,1951,721808 +Sudan,SDN,1952,758448 +Sudan,SDN,1953,831728 +Sudan,SDN,1954,817072 +Sudan,SDN,1955,926992 +Sudan,SDN,1956,897680 +Sudan,SDN,1957,1154160 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+Global,,2007,0.491,0.531,0.457 +Global,,2008,0.395,0.432,0.355 +Global,,2009,0.506,0.546,0.465 +Global,,2010,0.56,0.603,0.523 +Global,,2011,0.425,0.466,0.393 +Global,,2012,0.47,0.509,0.437 +Global,,2013,0.514,0.552,0.478 +Global,,2014,0.579,0.616,0.545 +Global,,2015,0.763,0.802,0.73 +Global,,2016,0.797,0.835,0.762 +Global,,2017,0.677,0.716,0.636 +Global,,2018,0.595,0.634,0.558 +Northern Hemisphere,,1850,-0.336,-0.284,-0.406 +Northern Hemisphere,,1851,-0.159,-0.108,-0.242 +Northern Hemisphere,,1852,-0.107,-0.058,-0.18 +Northern Hemisphere,,1853,-0.177,-0.132,-0.253 +Northern Hemisphere,,1854,-0.071,-0.026,-0.145 +Northern Hemisphere,,1855,-0.19,-0.145,-0.258 +Northern Hemisphere,,1856,-0.378,-0.335,-0.454 +Northern Hemisphere,,1857,-0.405,-0.361,-0.479 +Northern Hemisphere,,1858,-0.4,-0.357,-0.48 +Northern Hemisphere,,1859,-0.215,-0.158,-0.303 +Northern Hemisphere,,1860,-0.363,-0.301,-0.454 +Northern Hemisphere,,1861,-0.295,-0.208,-0.419 +Northern Hemisphere,,1862,-0.668,-0.573,-0.777 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+Northern Hemisphere,,1884,-0.474,-0.406,-0.556 +Northern Hemisphere,,1885,-0.367,-0.306,-0.456 +Northern Hemisphere,,1886,-0.338,-0.28,-0.423 +Northern Hemisphere,,1887,-0.356,-0.292,-0.45 +Northern Hemisphere,,1888,-0.266,-0.193,-0.355 +Northern Hemisphere,,1889,-0.138,-0.054,-0.244 +Northern Hemisphere,,1890,-0.363,-0.273,-0.47 +Northern Hemisphere,,1891,-0.289,-0.19,-0.409 +Northern Hemisphere,,1892,-0.466,-0.37,-0.576 +Northern Hemisphere,,1893,-0.543,-0.449,-0.65 +Northern Hemisphere,,1894,-0.375,-0.279,-0.49 +Northern Hemisphere,,1895,-0.368,-0.269,-0.476 +Northern Hemisphere,,1896,-0.152,-0.061,-0.258 +Northern Hemisphere,,1897,-0.144,-0.048,-0.242 +Northern Hemisphere,,1898,-0.288,-0.203,-0.389 +Northern Hemisphere,,1899,-0.22,-0.131,-0.323 +Northern Hemisphere,,1900,-0.096,-0.004,-0.196 +Northern Hemisphere,,1901,-0.124,-0.043,-0.22 +Northern Hemisphere,,1902,-0.367,-0.294,-0.454 +Northern Hemisphere,,1903,-0.4,-0.329,-0.492 +Northern Hemisphere,,1904,-0.485,-0.414,-0.573 +Northern Hemisphere,,1905,-0.336,-0.265,-0.42 +Northern Hemisphere,,1906,-0.199,-0.131,-0.277 +Northern Hemisphere,,1907,-0.477,-0.411,-0.552 +Northern Hemisphere,,1908,-0.458,-0.4,-0.532 +Northern Hemisphere,,1909,-0.448,-0.393,-0.518 +Northern Hemisphere,,1910,-0.417,-0.36,-0.486 +Northern Hemisphere,,1911,-0.421,-0.365,-0.486 +Northern Hemisphere,,1912,-0.466,-0.409,-0.528 +Northern Hemisphere,,1913,-0.447,-0.4,-0.505 +Northern Hemisphere,,1914,-0.237,-0.187,-0.289 +Northern Hemisphere,,1915,-0.125,-0.078,-0.185 +Northern Hemisphere,,1916,-0.385,-0.339,-0.441 +Northern Hemisphere,,1917,-0.493,-0.448,-0.551 +Northern Hemisphere,,1918,-0.353,-0.303,-0.414 +Northern Hemisphere,,1919,-0.36,-0.315,-0.413 +Northern Hemisphere,,1920,-0.278,-0.237,-0.328 +Northern Hemisphere,,1921,-0.101,-0.062,-0.154 +Northern Hemisphere,,1922,-0.245,-0.207,-0.294 +Northern Hemisphere,,1923,-0.231,-0.194,-0.279 +Northern Hemisphere,,1924,-0.222,-0.189,-0.272 +Northern Hemisphere,,1925,-0.117,-0.079,-0.165 +Northern Hemisphere,,1926,-0.008,0.025,-0.057 +Northern Hemisphere,,1927,-0.113,-0.081,-0.156 +Northern Hemisphere,,1928,-0.142,-0.111,-0.187 +Northern Hemisphere,,1929,-0.318,-0.293,-0.361 +Northern Hemisphere,,1930,-0.011,0.014,-0.052 +Northern Hemisphere,,1931,0.035,0.062,-0.002 +Northern Hemisphere,,1932,-0.068,-0.042,-0.106 +Northern Hemisphere,,1933,-0.239,-0.216,-0.279 +Northern Hemisphere,,1934,-0.034,-0.011,-0.073 +Northern Hemisphere,,1935,-0.097,-0.073,-0.134 +Northern Hemisphere,,1936,-0.049,-0.024,-0.081 +Northern Hemisphere,,1937,0.073,0.097,0.045 +Northern Hemisphere,,1938,0.107,0.128,0.078 +Northern Hemisphere,,1939,0.041,0.062,0.015 +Northern Hemisphere,,1940,0.008,0.034,-0.017 +Northern Hemisphere,,1941,0.039,0.077,0.003 +Northern Hemisphere,,1942,0.022,0.063,-0.016 +Northern Hemisphere,,1943,0.1,0.141,0.062 +Northern Hemisphere,,1944,0.186,0.222,0.143 +Northern Hemisphere,,1945,0.024,0.051,-0.015 +Northern Hemisphere,,1946,0.027,0.051,-0.01 +Northern Hemisphere,,1947,0.019,0.046,-0.034 +Northern Hemisphere,,1948,0.08,0.109,0.033 +Northern Hemisphere,,1949,-0.027,-0.002,-0.062 +Northern Hemisphere,,1950,-0.17,-0.141,-0.204 +Northern Hemisphere,,1951,0.051,0.08,0.023 +Northern Hemisphere,,1952,0.039,0.072,0.009 +Northern Hemisphere,,1953,0.199,0.227,0.169 +Northern Hemisphere,,1954,-0.058,-0.03,-0.085 +Northern Hemisphere,,1955,-0.088,-0.066,-0.118 +Northern Hemisphere,,1956,-0.262,-0.237,-0.292 +Northern Hemisphere,,1957,0.009,0.036,-0.014 +Northern Hemisphere,,1958,0.116,0.138,0.097 +Northern Hemisphere,,1959,0.073,0.094,0.052 +Northern Hemisphere,,1960,0.015,0.034,-0.006 +Northern Hemisphere,,1961,0.065,0.083,0.043 +Northern Hemisphere,,1962,0.089,0.108,0.069 +Northern Hemisphere,,1963,0.148,0.17,0.128 +Northern Hemisphere,,1964,-0.169,-0.145,-0.189 +Northern Hemisphere,,1965,-0.134,-0.108,-0.153 +Northern Hemisphere,,1966,0.003,0.027,-0.014 +Northern Hemisphere,,1967,0.026,0.045,0.011 +Northern Hemisphere,,1968,-0.063,-0.051,-0.079 +Northern Hemisphere,,1969,-0.054,-0.042,-0.072 +Northern Hemisphere,,1970,-0.056,-0.04,-0.071 +Northern Hemisphere,,1971,-0.193,-0.174,-0.208 +Northern Hemisphere,,1972,-0.222,-0.202,-0.233 +Northern Hemisphere,,1973,0.024,0.04,0.014 +Northern Hemisphere,,1974,-0.261,-0.247,-0.274 +Northern Hemisphere,,1975,-0.123,-0.112,-0.134 +Northern Hemisphere,,1976,-0.293,-0.281,-0.304 +Northern Hemisphere,,1977,0.034,0.048,0.022 +Northern Hemisphere,,1978,-0.056,-0.042,-0.069 +Northern Hemisphere,,1979,0.006,0.02,-0.009 +Northern Hemisphere,,1980,0.068,0.079,0.057 +Northern Hemisphere,,1981,0.236,0.246,0.222 +Northern Hemisphere,,1982,-0.029,-0.015,-0.049 +Northern Hemisphere,,1983,0.168,0.183,0.149 +Northern Hemisphere,,1984,-0.085,-0.067,-0.1 +Northern Hemisphere,,1985,-0.131,-0.114,-0.148 +Northern Hemisphere,,1986,0.026,0.044,0.008 +Northern Hemisphere,,1987,0.153,0.174,0.136 +Northern Hemisphere,,1988,0.233,0.253,0.211 +Northern Hemisphere,,1989,0.164,0.189,0.14 +Northern Hemisphere,,1990,0.412,0.443,0.387 +Northern Hemisphere,,1991,0.316,0.344,0.288 +Northern Hemisphere,,1992,0.074,0.103,0.047 +Northern Hemisphere,,1993,0.13,0.16,0.101 +Northern Hemisphere,,1994,0.28,0.308,0.248 +Northern Hemisphere,,1995,0.47,0.497,0.437 +Northern Hemisphere,,1996,0.175,0.198,0.143 +Northern Hemisphere,,1997,0.456,0.48,0.426 +Northern Hemisphere,,1998,0.614,0.645,0.579 +Northern Hemisphere,,1999,0.408,0.436,0.374 +Northern Hemisphere,,2000,0.404,0.435,0.373 +Northern Hemisphere,,2001,0.558,0.588,0.529 +Northern Hemisphere,,2002,0.593,0.627,0.562 +Northern Hemisphere,,2003,0.642,0.679,0.608 +Northern Hemisphere,,2004,0.604,0.636,0.571 +Northern Hemisphere,,2005,0.724,0.758,0.69 +Northern Hemisphere,,2006,0.676,0.707,0.641 +Northern Hemisphere,,2007,0.713,0.742,0.677 +Northern Hemisphere,,2008,0.569,0.597,0.535 +Northern Hemisphere,,2009,0.594,0.624,0.561 +Northern Hemisphere,,2010,0.74,0.77,0.705 +Northern Hemisphere,,2011,0.575,0.606,0.535 +Northern Hemisphere,,2012,0.627,0.657,0.592 +Northern Hemisphere,,2013,0.674,0.707,0.641 +Northern Hemisphere,,2014,0.779,0.809,0.744 +Northern Hemisphere,,2015,1.033,1.061,0.997 +Northern Hemisphere,,2016,1.064,1.096,1.027 +Northern Hemisphere,,2017,0.922,0.954,0.885 +Northern Hemisphere,,2018,0.785,0.752,0.82 +Southern Hemisphere,,1850,-0.408,-0.382,-0.451 +Southern Hemisphere,,1851,-0.277,-0.249,-0.322 +Southern Hemisphere,,1852,-0.349,-0.323,-0.39 +Southern Hemisphere,,1853,-0.361,-0.341,-0.403 +Southern Hemisphere,,1854,-0.425,-0.403,-0.463 +Southern Hemisphere,,1855,-0.354,-0.332,-0.393 +Southern Hemisphere,,1856,-0.336,-0.311,-0.374 +Southern Hemisphere,,1857,-0.516,-0.491,-0.547 +Southern Hemisphere,,1858,-0.528,-0.503,-0.565 +Southern Hemisphere,,1859,-0.355,-0.326,-0.386 +Southern Hemisphere,,1860,-0.33,-0.303,-0.362 +Southern Hemisphere,,1861,-0.516,-0.481,-0.554 +Southern Hemisphere,,1862,-0.38,-0.347,-0.416 +Southern Hemisphere,,1863,-0.361,-0.33,-0.402 +Southern Hemisphere,,1864,-0.472,-0.435,-0.511 +Southern Hemisphere,,1865,-0.312,-0.273,-0.352 +Southern Hemisphere,,1866,-0.298,-0.262,-0.336 +Southern Hemisphere,,1867,-0.299,-0.266,-0.34 +Southern Hemisphere,,1868,-0.35,-0.322,-0.381 +Southern Hemisphere,,1869,-0.397,-0.373,-0.427 +Southern Hemisphere,,1870,-0.349,-0.328,-0.379 +Southern Hemisphere,,1871,-0.266,-0.243,-0.296 +Southern Hemisphere,,1872,-0.271,-0.249,-0.302 +Southern Hemisphere,,1873,-0.312,-0.285,-0.34 +Southern Hemisphere,,1874,-0.466,-0.438,-0.499 +Southern Hemisphere,,1875,-0.285,-0.258,-0.321 +Southern Hemisphere,,1876,-0.421,-0.396,-0.453 +Southern Hemisphere,,1877,-0.154,-0.127,-0.193 +Southern Hemisphere,,1878,-0.117,-0.09,-0.147 +Southern Hemisphere,,1879,-0.237,-0.212,-0.263 +Southern Hemisphere,,1880,-0.214,-0.189,-0.244 +Southern Hemisphere,,1881,-0.16,-0.138,-0.19 +Southern Hemisphere,,1882,-0.184,-0.162,-0.21 +Southern Hemisphere,,1883,-0.228,-0.207,-0.255 +Southern Hemisphere,,1884,-0.344,-0.322,-0.367 +Southern Hemisphere,,1885,-0.408,-0.388,-0.436 +Southern Hemisphere,,1886,-0.398,-0.377,-0.422 +Southern Hemisphere,,1887,-0.481,-0.462,-0.507 +Southern Hemisphere,,1888,-0.35,-0.328,-0.375 +Southern Hemisphere,,1889,-0.207,-0.183,-0.234 +Southern Hemisphere,,1890,-0.476,-0.452,-0.503 +Southern Hemisphere,,1891,-0.378,-0.352,-0.41 +Southern Hemisphere,,1892,-0.45,-0.422,-0.484 +Southern Hemisphere,,1893,-0.405,-0.375,-0.439 +Southern Hemisphere,,1894,-0.442,-0.416,-0.473 +Southern Hemisphere,,1895,-0.414,-0.392,-0.441 +Southern Hemisphere,,1896,-0.221,-0.197,-0.249 +Southern Hemisphere,,1897,-0.273,-0.25,-0.301 +Southern Hemisphere,,1898,-0.538,-0.511,-0.569 +Southern Hemisphere,,1899,-0.36,-0.331,-0.393 +Southern Hemisphere,,1900,-0.314,-0.286,-0.347 +Southern Hemisphere,,1901,-0.394,-0.362,-0.433 +Southern Hemisphere,,1902,-0.439,-0.408,-0.472 +Southern Hemisphere,,1903,-0.561,-0.529,-0.59 +Southern Hemisphere,,1904,-0.556,-0.524,-0.589 +Southern Hemisphere,,1905,-0.421,-0.384,-0.455 +Southern Hemisphere,,1906,-0.367,-0.337,-0.402 +Southern Hemisphere,,1907,-0.457,-0.424,-0.494 +Southern Hemisphere,,1908,-0.566,-0.534,-0.599 +Southern Hemisphere,,1909,-0.597,-0.566,-0.631 +Southern Hemisphere,,1910,-0.567,-0.525,-0.606 +Southern Hemisphere,,1911,-0.668,-0.63,-0.704 +Southern Hemisphere,,1912,-0.412,-0.373,-0.448 +Southern Hemisphere,,1913,-0.4,-0.365,-0.438 +Southern Hemisphere,,1914,-0.253,-0.211,-0.295 +Southern Hemisphere,,1915,-0.16,-0.101,-0.217 +Southern Hemisphere,,1916,-0.379,-0.328,-0.434 +Southern Hemisphere,,1917,-0.443,-0.386,-0.5 +Southern Hemisphere,,1918,-0.314,-0.237,-0.382 +Southern Hemisphere,,1919,-0.193,-0.118,-0.255 +Southern Hemisphere,,1920,-0.219,-0.157,-0.275 +Southern Hemisphere,,1921,-0.277,-0.236,-0.322 +Southern Hemisphere,,1922,-0.359,-0.32,-0.399 +Southern Hemisphere,,1923,-0.325,-0.284,-0.367 +Southern Hemisphere,,1924,-0.368,-0.33,-0.407 +Southern Hemisphere,,1925,-0.319,-0.276,-0.361 +Southern Hemisphere,,1926,-0.209,-0.171,-0.25 +Southern Hemisphere,,1927,-0.309,-0.275,-0.344 +Southern Hemisphere,,1928,-0.273,-0.237,-0.314 +Southern Hemisphere,,1929,-0.384,-0.343,-0.427 +Southern Hemisphere,,1930,-0.263,-0.218,-0.308 +Southern Hemisphere,,1931,-0.209,-0.168,-0.257 +Southern Hemisphere,,1932,-0.205,-0.169,-0.253 +Southern Hemisphere,,1933,-0.307,-0.269,-0.355 +Southern Hemisphere,,1934,-0.228,-0.193,-0.276 +Southern Hemisphere,,1935,-0.259,-0.224,-0.307 +Southern Hemisphere,,1936,-0.246,-0.214,-0.289 +Southern Hemisphere,,1937,-0.127,-0.096,-0.173 +Southern Hemisphere,,1938,-0.12,-0.092,-0.166 +Southern Hemisphere,,1939,-0.145,-0.114,-0.187 +Southern Hemisphere,,1940,0.018,0.056,-0.029 +Southern Hemisphere,,1941,0.002,0.036,-0.049 +Southern Hemisphere,,1942,-0.076,-0.028,-0.154 +Southern Hemisphere,,1943,-0.111,-0.066,-0.179 +Southern Hemisphere,,1944,0.102,0.145,0.035 +Southern Hemisphere,,1945,0.027,0.068,-0.034 +Southern Hemisphere,,1946,-0.168,-0.123,-0.218 +Southern Hemisphere,,1947,-0.095,-0.045,-0.163 +Southern Hemisphere,,1948,-0.159,-0.105,-0.234 +Southern Hemisphere,,1949,-0.127,-0.076,-0.19 +Southern Hemisphere,,1950,-0.183,-0.129,-0.245 +Southern Hemisphere,,1951,-0.156,-0.11,-0.211 +Southern Hemisphere,,1952,0.019,0.064,-0.033 +Southern Hemisphere,,1953,-0.003,0.038,-0.061 +Southern Hemisphere,,1954,-0.2,-0.161,-0.25 +Southern Hemisphere,,1955,-0.29,-0.25,-0.333 +Southern Hemisphere,,1956,-0.269,-0.234,-0.308 +Southern Hemisphere,,1957,-0.023,0.014,-0.06 +Southern Hemisphere,,1958,-0.024,0.015,-0.059 +Southern Hemisphere,,1959,-0.038,-0.008,-0.072 +Southern Hemisphere,,1960,-0.113,-0.08,-0.147 +Southern Hemisphere,,1961,0.015,0.039,-0.029 +Southern Hemisphere,,1962,-0.058,-0.036,-0.093 +Southern Hemisphere,,1963,-0.051,-0.023,-0.085 +Southern Hemisphere,,1964,-0.275,-0.249,-0.303 +Southern Hemisphere,,1965,-0.147,-0.119,-0.174 +Southern Hemisphere,,1966,-0.14,-0.113,-0.165 +Southern Hemisphere,,1967,-0.174,-0.15,-0.199 +Southern Hemisphere,,1968,-0.162,-0.14,-0.186 +Southern Hemisphere,,1969,0.118,0.142,0.094 +Southern Hemisphere,,1970,0.001,0.028,-0.023 +Southern Hemisphere,,1971,-0.18,-0.153,-0.2 +Southern Hemisphere,,1972,0.09,0.121,0.071 +Southern Hemisphere,,1973,0.099,0.123,0.086 +Southern Hemisphere,,1974,-0.166,-0.148,-0.18 +Southern Hemisphere,,1975,-0.175,-0.157,-0.192 +Southern Hemisphere,,1976,-0.19,-0.171,-0.205 +Southern Hemisphere,,1977,0.059,0.081,0.044 +Southern Hemisphere,,1978,-0.068,-0.046,-0.085 +Southern Hemisphere,,1979,0.107,0.128,0.087 +Southern Hemisphere,,1980,0.116,0.134,0.095 +Southern Hemisphere,,1981,0.043,0.061,0.023 +Southern Hemisphere,,1982,0.052,0.074,0.027 +Southern Hemisphere,,1983,0.22,0.24,0.197 +Southern Hemisphere,,1984,0.056,0.078,0.033 +Southern Hemisphere,,1985,0.071,0.089,0.05 +Southern Hemisphere,,1986,0.064,0.089,0.038 +Southern Hemisphere,,1987,0.231,0.262,0.206 +Southern Hemisphere,,1988,0.164,0.192,0.14 +Southern Hemisphere,,1989,0.072,0.107,0.042 +Southern Hemisphere,,1990,0.181,0.22,0.148 +Southern Hemisphere,,1991,0.193,0.233,0.153 +Southern Hemisphere,,1992,0.136,0.176,0.094 +Southern Hemisphere,,1993,0.167,0.212,0.127 +Southern Hemisphere,,1994,0.138,0.186,0.097 +Southern Hemisphere,,1995,0.179,0.225,0.14 +Southern Hemisphere,,1996,0.187,0.235,0.149 +Southern Hemisphere,,1997,0.323,0.371,0.285 +Southern Hemisphere,,1998,0.465,0.511,0.417 +Southern Hemisphere,,1999,0.204,0.254,0.157 +Southern Hemisphere,,2000,0.184,0.239,0.143 +Southern Hemisphere,,2001,0.323,0.378,0.283 +Southern Hemisphere,,2002,0.398,0.451,0.36 +Southern Hemisphere,,2003,0.369,0.422,0.334 +Southern Hemisphere,,2004,0.289,0.337,0.25 +Southern Hemisphere,,2005,0.367,0.418,0.324 +Southern Hemisphere,,2006,0.336,0.389,0.293 +Southern Hemisphere,,2007,0.27,0.325,0.226 +Southern Hemisphere,,2008,0.22,0.273,0.175 +Southern Hemisphere,,2009,0.413,0.468,0.367 +Southern Hemisphere,,2010,0.379,0.435,0.336 +Southern Hemisphere,,2011,0.275,0.332,0.234 +Southern Hemisphere,,2012,0.312,0.366,0.275 +Southern Hemisphere,,2013,0.352,0.406,0.31 +Southern Hemisphere,,2014,0.381,0.432,0.339 +Southern Hemisphere,,2015,0.497,0.548,0.455 +Southern Hemisphere,,2016,0.531,0.584,0.488 +Southern Hemisphere,,2017,0.434,0.486,0.39 +Southern Hemisphere,,2018,0.406,0.462,0.36 +Tropics,,1850,-0.308,-0.278,-0.363 +Tropics,,1851,-0.229,-0.198,-0.28 +Tropics,,1852,-0.133,-0.104,-0.184 +Tropics,,1853,-0.141,-0.115,-0.195 +Tropics,,1854,-0.2,-0.172,-0.255 +Tropics,,1855,-0.247,-0.217,-0.299 +Tropics,,1856,-0.296,-0.27,-0.351 +Tropics,,1857,-0.528,-0.501,-0.58 +Tropics,,1858,-0.478,-0.453,-0.53 +Tropics,,1859,-0.358,-0.332,-0.414 +Tropics,,1860,-0.296,-0.268,-0.35 +Tropics,,1861,-0.446,-0.413,-0.493 +Tropics,,1862,-0.386,-0.353,-0.433 +Tropics,,1863,-0.395,-0.361,-0.448 +Tropics,,1864,-0.503,-0.471,-0.552 +Tropics,,1865,-0.197,-0.166,-0.245 +Tropics,,1866,-0.258,-0.218,-0.306 +Tropics,,1867,-0.15,-0.113,-0.204 +Tropics,,1868,-0.159,-0.125,-0.216 +Tropics,,1869,-0.158,-0.132,-0.207 +Tropics,,1870,-0.235,-0.211,-0.276 +Tropics,,1871,-0.17,-0.142,-0.213 +Tropics,,1872,-0.323,-0.295,-0.36 +Tropics,,1873,-0.302,-0.271,-0.337 +Tropics,,1874,-0.482,-0.442,-0.522 +Tropics,,1875,-0.237,-0.201,-0.289 +Tropics,,1876,-0.331,-0.291,-0.386 +Tropics,,1877,0.092,0.138,0.035 +Tropics,,1878,0.215,0.256,0.156 +Tropics,,1879,-0.215,-0.181,-0.27 +Tropics,,1880,-0.215,-0.179,-0.273 +Tropics,,1881,-0.052,-0.017,-0.106 +Tropics,,1882,-0.24,-0.208,-0.296 +Tropics,,1883,-0.312,-0.282,-0.361 +Tropics,,1884,-0.379,-0.349,-0.424 +Tropics,,1885,-0.288,-0.262,-0.34 +Tropics,,1886,-0.337,-0.311,-0.385 +Tropics,,1887,-0.444,-0.415,-0.493 +Tropics,,1888,-0.079,-0.049,-0.127 +Tropics,,1889,-0.053,-0.016,-0.113 +Tropics,,1890,-0.513,-0.468,-0.58 +Tropics,,1891,-0.235,-0.185,-0.309 +Tropics,,1892,-0.458,-0.402,-0.538 +Tropics,,1893,-0.498,-0.443,-0.573 +Tropics,,1894,-0.485,-0.426,-0.564 +Tropics,,1895,-0.264,-0.207,-0.335 +Tropics,,1896,-0.016,0.034,-0.083 +Tropics,,1897,0.006,0.055,-0.057 +Tropics,,1898,-0.345,-0.296,-0.412 +Tropics,,1899,-0.207,-0.147,-0.279 +Tropics,,1900,0.023,0.083,-0.043 +Tropics,,1901,-0.127,-0.07,-0.202 +Tropics,,1902,-0.177,-0.127,-0.242 +Tropics,,1903,-0.359,-0.307,-0.425 +Tropics,,1904,-0.494,-0.435,-0.556 +Tropics,,1905,-0.262,-0.199,-0.325 +Tropics,,1906,-0.241,-0.184,-0.297 +Tropics,,1907,-0.366,-0.322,-0.423 +Tropics,,1908,-0.479,-0.424,-0.533 +Tropics,,1909,-0.453,-0.404,-0.504 +Tropics,,1910,-0.501,-0.455,-0.559 +Tropics,,1911,-0.479,-0.423,-0.535 +Tropics,,1912,-0.237,-0.19,-0.293 +Tropics,,1913,-0.361,-0.312,-0.412 +Tropics,,1914,-0.198,-0.146,-0.255 +Tropics,,1915,-0.027,0.038,-0.091 +Tropics,,1916,-0.386,-0.323,-0.449 +Tropics,,1917,-0.539,-0.47,-0.607 +Tropics,,1918,-0.334,-0.262,-0.411 +Tropics,,1919,-0.204,-0.131,-0.267 +Tropics,,1920,-0.272,-0.209,-0.334 +Tropics,,1921,-0.243,-0.193,-0.301 +Tropics,,1922,-0.296,-0.241,-0.35 +Tropics,,1923,-0.251,-0.195,-0.311 +Tropics,,1924,-0.224,-0.171,-0.283 +Tropics,,1925,-0.212,-0.158,-0.28 +Tropics,,1926,0.044,0.092,-0.018 +Tropics,,1927,-0.113,-0.061,-0.169 +Tropics,,1928,-0.129,-0.08,-0.194 +Tropics,,1929,-0.244,-0.195,-0.31 +Tropics,,1930,-0.076,-0.025,-0.145 +Tropics,,1931,0.068,0.121,0 +Tropics,,1932,-0.11,-0.061,-0.175 +Tropics,,1933,-0.204,-0.154,-0.267 +Tropics,,1934,-0.189,-0.143,-0.252 +Tropics,,1935,-0.133,-0.088,-0.192 +Tropics,,1936,-0.095,-0.051,-0.153 +Tropics,,1937,0.02,0.063,-0.038 +Tropics,,1938,-0.099,-0.061,-0.154 +Tropics,,1939,-0.107,-0.075,-0.154 +Tropics,,1940,0.13,0.171,0.077 +Tropics,,1941,0.241,0.276,0.182 +Tropics,,1942,0.004,0.052,-0.066 +Tropics,,1943,-0.119,-0.074,-0.181 +Tropics,,1944,0.075,0.12,0.014 +Tropics,,1945,0.05,0.092,-0.002 +Tropics,,1946,-0.005,0.029,-0.062 +Tropics,,1947,0.014,0.062,-0.057 +Tropics,,1948,-0.056,-0.005,-0.131 +Tropics,,1949,-0.113,-0.066,-0.166 +Tropics,,1950,-0.223,-0.175,-0.274 +Tropics,,1951,-0.049,-0.01,-0.092 +Tropics,,1952,0.071,0.111,0.03 +Tropics,,1953,0.072,0.113,0.031 +Tropics,,1954,-0.159,-0.118,-0.2 +Tropics,,1955,-0.284,-0.249,-0.321 +Tropics,,1956,-0.317,-0.284,-0.352 +Tropics,,1957,0.014,0.048,-0.021 +Tropics,,1958,0.145,0.18,0.114 +Tropics,,1959,0.043,0.073,0.014 +Tropics,,1960,-0.021,0.008,-0.05 +Tropics,,1961,-0.032,-0.007,-0.068 +Tropics,,1962,-0.077,-0.056,-0.106 +Tropics,,1963,0.076,0.106,0.041 +Tropics,,1964,-0.145,-0.112,-0.172 +Tropics,,1965,-0.073,-0.04,-0.097 +Tropics,,1966,0.043,0.075,0.018 +Tropics,,1967,-0.147,-0.121,-0.17 +Tropics,,1968,-0.115,-0.096,-0.135 +Tropics,,1969,0.246,0.267,0.223 +Tropics,,1970,0.008,0.029,-0.017 +Tropics,,1971,-0.29,-0.263,-0.311 +Tropics,,1972,0.043,0.075,0.024 +Tropics,,1973,0.064,0.087,0.049 +Tropics,,1974,-0.296,-0.277,-0.313 +Tropics,,1975,-0.332,-0.316,-0.35 +Tropics,,1976,-0.25,-0.23,-0.266 +Tropics,,1977,0.021,0.039,0.007 +Tropics,,1978,-0.062,-0.042,-0.077 +Tropics,,1979,0.084,0.104,0.062 +Tropics,,1980,0.136,0.152,0.118 +Tropics,,1981,0.018,0.034,-0.001 +Tropics,,1982,0.065,0.086,0.037 +Tropics,,1983,0.222,0.244,0.198 +Tropics,,1984,0.003,0.024,-0.022 +Tropics,,1985,-0.063,-0.042,-0.086 +Tropics,,1986,0.048,0.071,0.023 +Tropics,,1987,0.358,0.389,0.332 +Tropics,,1988,0.205,0.231,0.175 +Tropics,,1989,-0.014,0.022,-0.046 +Tropics,,1990,0.206,0.244,0.17 +Tropics,,1991,0.206,0.243,0.166 +Tropics,,1992,0.109,0.15,0.068 +Tropics,,1993,0.162,0.204,0.121 +Tropics,,1994,0.159,0.201,0.113 +Tropics,,1995,0.285,0.33,0.239 +Tropics,,1996,0.193,0.233,0.15 +Tropics,,1997,0.368,0.409,0.321 +Tropics,,1998,0.564,0.606,0.515 +Tropics,,1999,0.158,0.202,0.106 +Tropics,,2000,0.17,0.22,0.128 +Tropics,,2001,0.35,0.401,0.305 +Tropics,,2002,0.469,0.513,0.422 +Tropics,,2003,0.507,0.552,0.46 +Tropics,,2004,0.43,0.473,0.383 +Tropics,,2005,0.487,0.532,0.438 +Tropics,,2006,0.449,0.495,0.399 +Tropics,,2007,0.365,0.408,0.32 +Tropics,,2008,0.274,0.318,0.226 +Tropics,,2009,0.541,0.584,0.492 +Tropics,,2010,0.535,0.579,0.485 +Tropics,,2011,0.243,0.287,0.197 +Tropics,,2012,0.346,0.391,0.3 +Tropics,,2013,0.404,0.448,0.356 +Tropics,,2014,0.516,0.562,0.472 +Tropics,,2015,0.737,0.781,0.69 +Tropics,,2016,0.763,0.806,0.713 +Tropics,,2017,0.563,0.607,0.514 +Tropics,,2018,0.472,0.517,0.424 \ No newline at end of file diff --git a/your-project/data/5ozone-depleting-substance-emissions.csv b/your-project/data/5ozone-depleting-substance-emissions.csv new file mode 100644 index 00000000..a8b0af6f --- /dev/null +++ b/your-project/data/5ozone-depleting-substance-emissions.csv @@ -0,0 +1,109 @@ +Entity,Code,Year,Ozone-depleting substance emissions (Scientific Assessment 2014) (tonnes CFC11-equivalents) +Natural emissions,,1961,165000 +Natural emissions,,1962,165000 +Natural emissions,,1963,165000 +Natural emissions,,1964,165000 +Natural emissions,,1965,165000 +Natural emissions,,1966,165000 +Natural emissions,,1967,165000 +Natural emissions,,1968,165000 +Natural emissions,,1969,165000 +Natural emissions,,1970,165000 +Natural emissions,,1971,165000 +Natural emissions,,1972,165000 +Natural emissions,,1973,165000 +Natural emissions,,1974,165000 +Natural emissions,,1975,165000 +Natural emissions,,1976,165000 +Natural emissions,,1977,165000 +Natural emissions,,1978,165000 +Natural emissions,,1979,165000 +Natural emissions,,1980,165000 +Natural emissions,,1981,165000 +Natural emissions,,1982,165000 +Natural emissions,,1983,165000 +Natural emissions,,1984,165000 +Natural emissions,,1985,165000 +Natural emissions,,1986,165000 +Natural emissions,,1987,165000 +Natural emissions,,1988,165000 +Natural emissions,,1989,165000 +Natural emissions,,1990,165000 +Natural emissions,,1991,165000 +Natural emissions,,1992,165000 +Natural emissions,,1993,165000 +Natural emissions,,1994,165000 +Natural emissions,,1995,165000 +Natural emissions,,1996,165000 +Natural emissions,,1997,165000 +Natural emissions,,1998,165000 +Natural emissions,,1999,165000 +Natural emissions,,2000,165000 +Natural emissions,,2001,165000 +Natural emissions,,2002,165000 +Natural emissions,,2003,165000 +Natural emissions,,2004,165000 +Natural emissions,,2005,165000 +Natural emissions,,2006,165000 +Natural emissions,,2007,165000 +Natural emissions,,2008,165000 +Natural emissions,,2009,165000 +Natural emissions,,2010,165000 +Natural emissions,,2011,165000 +Natural emissions,,2012,165000 +Natural emissions,,2013,165000 +Natural emissions,,2014,165000 +Total emissions,,1961,380000 +Total emissions,,1962,400000 +Total emissions,,1963,440000 +Total emissions,,1964,480000 +Total emissions,,1965,510000 +Total emissions,,1966,540000 +Total emissions,,1967,580000 +Total emissions,,1968,630000 +Total emissions,,1969,660000 +Total emissions,,1970,720000 +Total emissions,,1971,770000 +Total emissions,,1972,840000 +Total emissions,,1973,910000 +Total emissions,,1974,980000 +Total emissions,,1975,1040000 +Total emissions,,1976,1050000 +Total emissions,,1977,1070000 +Total emissions,,1978,1070000 +Total emissions,,1979,1110000 +Total emissions,,1980,1080000 +Total emissions,,1981,1040000 +Total emissions,,1982,1100000 +Total emissions,,1983,1090000 +Total emissions,,1984,1150000 +Total emissions,,1985,1180000 +Total emissions,,1986,1280000 +Total emissions,,1987,1360000 +Total emissions,,1988,1460000 +Total emissions,,1989,1410000 +Total emissions,,1990,1320000 +Total emissions,,1991,1190000 +Total emissions,,1992,1080000 +Total emissions,,1993,960000 +Total emissions,,1994,820000 +Total emissions,,1995,760000 +Total emissions,,1996,700000 +Total emissions,,1997,640000 +Total emissions,,1998,600000 +Total emissions,,1999,590000 +Total emissions,,2000,560000 +Total emissions,,2001,530000 +Total emissions,,2002,490000 +Total emissions,,2003,480000 +Total emissions,,2004,470000 +Total emissions,,2005,450000 +Total emissions,,2006,430000 +Total emissions,,2007,410000 +Total emissions,,2008,400000 +Total emissions,,2009,390000 +Total emissions,,2010,380000 +Total emissions,,2011,370000 +Total emissions,,2012,350000 +Total emissions,,2013,340000 +Total emissions,,2014,320000 \ No newline at end of file diff --git a/your-project/data/6antarctic-ozone-hole-area.csv b/your-project/data/6antarctic-ozone-hole-area.csv new file mode 100644 index 00000000..277f9f0f --- /dev/null +++ b/your-project/data/6antarctic-ozone-hole-area.csv @@ -0,0 +1,39 @@ +Entity,Code,Year,Maximum ozone hole area (square kilometres),Mean ozone hole area (square kilometres) +World,OWID_WRL,1979,1100000,100000 +World,OWID_WRL,1980,3300000,1400000 +World,OWID_WRL,1981,3100000,600000 +World,OWID_WRL,1982,10800000,4800000 +World,OWID_WRL,1983,12200000,7900000 +World,OWID_WRL,1984,14700000,10100000 +World,OWID_WRL,1985,18800000,14200000 +World,OWID_WRL,1986,14400000,11300000 +World,OWID_WRL,1987,22400000,19300000 +World,OWID_WRL,1988,13800000,10000000 +World,OWID_WRL,1989,21700000,18700000 +World,OWID_WRL,1990,21100000,19200000 +World,OWID_WRL,1991,22600000,18800000 +World,OWID_WRL,1992,24900000,22300000 +World,OWID_WRL,1993,25800000,24200000 +World,OWID_WRL,1994,25200000,23600000 +World,OWID_WRL,1996,26900000,22800000 +World,OWID_WRL,1997,25100000,22100000 +World,OWID_WRL,1998,27900000,25900000 +World,OWID_WRL,1999,25800000,23300000 +World,OWID_WRL,2000,29900000,24800000 +World,OWID_WRL,2001,26500000,25000000 +World,OWID_WRL,2002,21900000,12000000 +World,OWID_WRL,2003,28400000,25800000 +World,OWID_WRL,2004,22800000,19500000 +World,OWID_WRL,2005,27300000,24400000 +World,OWID_WRL,2006,29600000,26600000 +World,OWID_WRL,2007,25200000,22000000 +World,OWID_WRL,2008,27000000,25200000 +World,OWID_WRL,2009,24400000,22000000 +World,OWID_WRL,2010,22600000,19400000 +World,OWID_WRL,2011,26100000,24700000 +World,OWID_WRL,2012,21100000,17800000 +World,OWID_WRL,2013,24000000,21000000 +World,OWID_WRL,2014,24100000,20900000 +World,OWID_WRL,2015,28200000,25600000 +World,OWID_WRL,2016,23000000,20700000 +World,OWID_WRL,2017,19600000,17400000 \ No newline at end of file diff --git a/your-project/data/7GlobalTemperatures.csv b/your-project/data/7GlobalTemperatures.csv new file mode 100644 index 00000000..36717cff --- /dev/null +++ b/your-project/data/7GlobalTemperatures.csv @@ -0,0 +1,3193 @@ +dt,LandAverageTemperature,LandAverageTemperatureUncertainty,LandMaxTemperature,LandMaxTemperatureUncertainty,LandMinTemperature,LandMinTemperatureUncertainty,LandAndOceanAverageTemperature,LandAndOceanAverageTemperatureUncertainty +1750-01-01,3.0340000000000003,3.574,,,,,, +1750-02-01,3.083,3.702,,,,,, +1750-03-01,5.626,3.076,,,,,, +1750-04-01,8.49,2.451,,,,,, +1750-05-01,11.573,2.072,,,,,, +1750-06-01,12.937000000000001,1.724,,,,,, +1750-07-01,15.868,1.911,,,,,, +1750-08-01,14.75,2.231,,,,,, +1750-09-01,11.412999999999998,2.637,,,,,, +1750-10-01,6.366999999999999,2.668,,,,,, +1750-11-01,,,,,,,, +1750-12-01,2.772,2.97,,,,,, +1751-01-01,2.495,3.469,,,,,, +1751-02-01,0.963,3.827,,,,,, +1751-03-01,5.8,3.051,,,,,, +1751-04-01,7.67,2.3680000000000003,,,,,, +1751-05-01,,,,,,,, +1751-06-01,13.827,1.801,,,,,, +1751-07-01,,,,,,,, +1751-08-01,14.405,2.296,,,,,, +1751-09-01,10.673,2.656,,,,,, +1751-10-01,,,,,,,, +1751-11-01,,,,,,,, +1751-12-01,,,,,,,, +1752-01-01,0.3480000000000003,3.789,,,,,, +1752-02-01,,,,,,,, +1752-03-01,5.806,3.192,,,,,, +1752-04-01,8.264999999999999,2.398,,,,,, +1752-05-01,,,,,,,, +1752-06-01,,,,,,,, +1752-07-01,,,,,,,, +1752-08-01,,,,,,,, +1752-09-01,,,,,,,, +1752-10-01,7.838999999999999,2.754,,,,,, +1752-11-01,7.335,2.747,,,,,, +1752-12-01,5.086,2.982,,,,,, +1753-01-01,2.039,3.478,,,,,, +1753-02-01,0.7149999999999999,6.341,,,,,, +1753-03-01,5.4239999999999995,2.929,,,,,, +1753-04-01,8.272,3.177,,,,,, +1753-05-01,12.186,3.111,,,,,, +1753-06-01,14.585,2.59,,,,,, +1753-07-01,15.092,1.929,,,,,, +1753-08-01,14.148,2.4530000000000003,,,,,, +1753-09-01,13.322000000000001,2.5380000000000003,,,,,, +1753-10-01,8.962,2.585,,,,,, +1753-11-01,5.353,2.807,,,,,, +1753-12-01,0.5589999999999997,4.174,,,,,, +1754-01-01,1.574,5.02,,,,,, +1754-02-01,-1.2489999999999997,7.492000000000001,,,,,, +1754-03-01,3.677,3.3360000000000003,,,,,, +1754-04-01,11.61,3.293,,,,,, +1754-05-01,11.936,3.937,,,,,, +1754-06-01,14.388,3.366,,,,,, +1754-07-01,14.681,2.401,,,,,, +1754-08-01,13.556,1.892,,,,,, +1754-09-01,11.129000000000001,2.318,,,,,, +1754-10-01,9.582,2.529,,,,,, +1754-11-01,6.1610000000000005,3.048,,,,,, +1754-12-01,4.587,3.299,,,,,, +1755-01-01,1.067,4.6930000000000005,,,,,, +1755-02-01,-0.1080000000000001,5.857,,,,,, +1755-03-01,3.6069999999999998,3.909,,,,,, +1755-04-01,9.263,3.372,,,,,, +1755-05-01,12.880999999999998,4.151,,,,,, +1755-06-01,15.38,5.322,,,,,, +1755-07-01,14.397,1.936,,,,,, +1755-08-01,13.515,2.381,,,,,, +1755-09-01,11.773,3.4160000000000004,,,,,, +1755-10-01,9.97,3.931,,,,,, +1755-11-01,5.957999999999999,4.021,,,,,, +1755-12-01,2.5639999999999996,3.215,,,,,, +1756-01-01,3.887,3.513,,,,,, +1756-02-01,5.666,4.265,,,,,, +1756-03-01,4.192,5.574,,,,,, +1756-04-01,10.042,3.752,,,,,, +1756-05-01,14.163999999999998,2.608,,,,,, +1756-06-01,14.547,3.213,,,,,, +1756-07-01,13.905999999999999,2.192,,,,,, +1756-08-01,13.437000000000001,2.175,,,,,, +1756-09-01,12.652000000000001,2.954,,,,,, +1756-10-01,7.3119999999999985,2.201,,,,,, +1756-11-01,4.225,2.86,,,,,, +1756-12-01,2.165,3.841,,,,,, +1757-01-01,-0.27600000000000025,4.022,,,,,, +1757-02-01,3.704,4.261,,,,,, +1757-03-01,4.529,3.638,,,,,, +1757-04-01,8.245999999999999,4.3039999999999985,,,,,, +1757-05-01,13.039000000000001,4.98,,,,,, +1757-06-01,17.115,4.59,,,,,, +1757-07-01,17.61,4.322,,,,,, +1757-08-01,17.91,4.035,,,,,, +1757-09-01,14.852,3.189,,,,,, +1757-10-01,6.481999999999998,3.173,,,,,, +1757-11-01,4.032,2.643,,,,,, +1757-12-01,1.0210000000000004,5.155,,,,,, +1758-01-01,-1.5029999999999997,7.349,,,,,, +1758-02-01,0.8059999999999999,3.753,,,,,, +1758-03-01,3.765,3.3680000000000003,,,,,, +1758-04-01,7.209999999999999,2.986,,,,,, +1758-05-01,9.715,3.091,,,,,, +1758-06-01,13.139000000000001,2.055,,,,,, +1758-07-01,13.847000000000001,3.608,,,,,, +1758-08-01,11.818,2.979,,,,,, +1758-09-01,10.028,2.347,,,,,, 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Africa,ZAF,1990,5.1377724570000005,20.84362981,0.07288321284 +South Africa,ZAF,1991,5.059468359,19.99988974,0.07877341640999999 +South Africa,ZAF,1992,5.104615993,19.6056905,0.2006121619 +South Africa,ZAF,1993,5.195489681,19.22397824,0.2676033007 +South Africa,ZAF,1994,5.299714413,19.3995959,0.27025247350000003 +South Africa,ZAF,1995,5.536022775,19.713697189999998,0.2546246929 +South Africa,ZAF,1996,5.603546471,19.63954285,0.2573356793 +South Africa,ZAF,1997,5.594542971,19.723899969999998,0.23639059739999999 +South Africa,ZAF,1998,5.586805923,18.82326616,0.2648231794 +South Africa,ZAF,1999,5.579183121,19.36802252,0.29255366880000006 +South Africa,ZAF,2000,5.592872593,18.98091738,0.2689637558 +South Africa,ZAF,2001,5.632961755,18.779103770000003,0.2731041623 +South Africa,ZAF,2002,5.7007625850000005,17.619935,0.2323159754 +South Africa,ZAF,2003,5.829304251,19.328243699999998,0.2271562941 +South Africa,ZAF,2004,5.970750543,20.91526074,0.46454516030000004 +South Africa,ZAF,2005,5.914435846000001,19.05474951,0.6736917726 +South Africa,ZAF,2006,5.950083633,19.19448877,0.736785739 +South Africa,ZAF,2007,6.007257997,19.503556680000003,0.7335876552 +South Africa,ZAF,2008,5.635405355,21.53256687,0.7764706926 +South Africa,ZAF,2009,5.507412315,21.407730739999998,0.6904740533 +South Africa,ZAF,2010,5.775918054,20.92746439,0.7948160309 +South Africa,ZAF,2011,5.727474209,20.14131873,0.8273110611 +South Africa,ZAF,2012,5.819860895,19.385255580000003,0.8716523243000001 +South Africa,ZAF,2013,5.89703523,19.167151030000003,0.8931908458000001 +South Africa,ZAF,2014,5.76207465,19.15731676,0.9520559378000001 +South Africa,ZAF,2015,5.861755362,17.53199646,0.9591318320000001 +South Korea,KOR,1965,0.5177906124999999,1.9923070029999999,0 +South Korea,KOR,1966,0.7575220391,2.2041650610000003,0 +South Korea,KOR,1967,1.284897818,2.0565285540000002,0 +South Korea,KOR,1968,1.859724355,1.900365843,0 +South Korea,KOR,1969,2.472486006,1.943641556,0 +South Korea,KOR,1970,3.02581421,2.032495011,0 +South Korea,KOR,1971,3.3481140770000004,2.013256631,0 +South Korea,KOR,1972,3.426712832,2.023203982,0 +South Korea,KOR,1973,4.1387217110000005,2.510193609,0 +South Korea,KOR,1974,4.1953657,2.542291327,0 +South Korea,KOR,1975,4.664948140000001,2.64075372,0 +South Korea,KOR,1976,5.159732423,2.8977191870000003,0 +South Korea,KOR,1977,6.0519278420000004,3.1171381400000002,0 +South Korea,KOR,1978,6.812441821,3.11010979,0 +South Korea,KOR,1979,7.553697925000001,3.67129497,0 +South Korea,KOR,1980,7.373123884,4.034235466,0 +South Korea,KOR,1981,7.191251515,4.592134639,0 +South Korea,KOR,1982,7.030027786,4.586155863,0 +South Korea,KOR,1983,7.229542073,4.824668299,0 +South Korea,KOR,1984,7.148194381000001,5.7332732250000005,0 +South Korea,KOR,1985,7.432802410000001,6.276031389,0 +South Korea,KOR,1986,7.989803757,6.574848739,0.01724661364 +South Korea,KOR,1987,8.30402789,6.5942930429999995,0.5282352598 +South Korea,KOR,1988,9.848648498000001,6.952409761,0.6758995611 +South Korea,KOR,1989,11.20943663,6.703111193,0.6477877023 +South Korea,KOR,1990,13.419314870000001,6.607103056,0.7371722487 +South Korea,KOR,1991,16.05696616,6.578020785,0.8450216389 +South Korea,KOR,1992,19.17672015,6.263528925,1.093399247 +South Korea,KOR,1993,20.801281080000003,6.788540189000001,1.350967234 +South Korea,KOR,1994,22.57122442,6.927529261,1.778829487 +South Korea,KOR,1995,24.35057777,7.219550286,2.1288173940000004 +South Korea,KOR,1996,25.78140488,8.193573707,2.784345977 +South Korea,KOR,1997,28.02923749,8.749193877,3.351063589 +South Korea,KOR,1998,23.423401610000003,8.999714374,3.106375555 +South Korea,KOR,1999,24.9059816,9.451625004,3.7504026250000004 +South Korea,KOR,2000,25.46674913,10.561814140000001,4.1800574820000005 +South Korea,KOR,2001,25.23228594,11.14206384,4.560151232 +South Korea,KOR,2002,25.51997503,11.89412343,5.036523504 +South Korea,KOR,2003,25.63104597,12.31625132,5.2468343850000005 +South Korea,KOR,2004,25.10153677,12.739549929999999,6.11879684 +South Korea,KOR,2005,25.009108100000002,13.07730827,6.523005319 +South Korea,KOR,2006,24.904790679999998,13.03139257,6.851008874000001 +South Korea,KOR,2007,25.51782205,14.1406782,7.3950195370000005 +South Korea,KOR,2008,24.36085789,15.60937919,7.585860558 +South Korea,KOR,2009,24.43446273,16.15790436,7.1875400670000005 +South Korea,KOR,2010,24.63343523,17.81270686,9.084536824 +South Korea,KOR,2011,24.73312525,19.554525440000003,9.738826996 +South Korea,KOR,2012,25.32473072,18.853490150000003,10.51577172 +South Korea,KOR,2013,25.1042703,18.989153750000003,10.95807349 +South Korea,KOR,2014,24.913776549999998,19.530150300000003,9.924271775 +South Korea,KOR,2015,26.15428772,19.647737499999998,9.023036872999999 +Spain,ESP,1965,5.125475632,3.498987517,0 +Spain,ESP,1966,5.996077177,3.202713921,0 +Spain,ESP,1967,7.184352839000001,3.2213027480000003,0 +Spain,ESP,1968,7.553779671,3.227644817,0 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Emirates,ARE,1984,62.67161834,0,64.93855794 +United Arab Emirates,ARE,1985,74.19177716,0,76.14815261999999 +United Arab Emirates,ARE,1986,84.77109368,0,87.37797663 +United Arab Emirates,ARE,1987,87.27429299,0,94.02553184 +United Arab Emirates,ARE,1988,98.47771747,0,89.63502107000001 +United Arab Emirates,ARE,1989,97.39555353,0,102.93887450000001 +United Arab Emirates,ARE,1990,96.23237396,0,95.15076008999999 +United Arab Emirates,ARE,1991,112.4907235,0,108.175283 +United Arab Emirates,ARE,1992,106.9788401,0,94.05376301 +United Arab Emirates,ARE,1993,104.64025860000001,0,93.12830224000001 +United Arab Emirates,ARE,1994,104.1647135,0,102.07712420000001 +United Arab Emirates,ARE,1995,99.00236849,0,105.9599848 +United Arab Emirates,ARE,1996,91.21630714999999,0,110.5316372 +United Arab Emirates,ARE,1997,88.36182829,0,112.422917 +United Arab Emirates,ARE,1998,83.62272188,0,112.0774767 +United Arab Emirates,ARE,1999,77.44739743,0,110.0586715 +United Arab Emirates,ARE,2000,72.49477563,0,104.2743679 +United Arab Emirates,ARE,2001,67.81420624,0.06464273645,119.14516160000001 +United Arab Emirates,ARE,2002,66.40632271,0.31263961440000004,108.7517108 +United Arab Emirates,ARE,2003,68.89859035,0.3447415934,105.9586224 +United Arab Emirates,ARE,2004,68.2477444,0.3576370712,102.9562559 +United Arab Emirates,ARE,2005,63.41495759,0.3693208302,96.10904929 +United Arab Emirates,ARE,2006,59.622798949999996,0.6247903141000001,86.57885339 +United Arab Emirates,ARE,2007,55.24766023,0.2608620106,85.14720058 +United Arab Emirates,ARE,2008,50.8855242,0.5790069214,90.27094017 +United Arab Emirates,ARE,2009,43.89015277,0.8357257305000001,80.64158858 +United Arab Emirates,ARE,2010,43.22852378,1.006889092,76.93485783999999 +United Arab Emirates,ARE,2011,44.56760267,1.796402671,76.29744814 +United Arab Emirates,ARE,2012,45.7413471,2.262740463,77.11081560000001 +United Arab Emirates,ARE,2013,45.899241370000006,1.8716044010000001,77.77008327 +United Arab Emirates,ARE,2014,49.5256766,1.87502299,76.07684651000001 +United Arab Emirates,ARE,2015,51.911985200000004,1.605895792,84.34953653 +United Kingdom,GBR,1965,16.05854763,25.14270703,0.15827031779999998 +United Kingdom,GBR,1966,17.08938273,23.78539953,0.15348852640000002 +United Kingdom,GBR,1967,18.240724200000002,22.098439080000002,0.2568281094 +United Kingdom,GBR,1968,19.19184891,22.00861442,0.5771335078 +United Kingdom,GBR,1969,20.5447172,21.36131925,1.119785491 +United Kingdom,GBR,1970,21.717929390000002,20.072983280000003,2.125950179 +United Kingdom,GBR,1971,21.77930953,17.73369349,3.417122277 +United Kingdom,GBR,1972,23.02915062,15.493690619999999,4.836711929 +United Kingdom,GBR,1973,23.56769074,16.74756607,5.22249091 +United Kingdom,GBR,1974,21.85692384,14.72178666,6.2370833370000005 +United Kingdom,GBR,1975,19.07493935,14.79406415,6.528381235 +United Kingdom,GBR,1976,18.93079174,15.045468849999999,6.920220942 +United Kingdom,GBR,1977,19.06912514,15.090964560000002,7.35334405 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s08_)i(!x0F`~MXh{=sqg?-}D~__lWbu|iE_QDDDJjVvMMhPNaC1rKf!=l}o! literal 0 HcmV?d00001 From 1634e27003c2d5e05a2ea0c340a497841d586055 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Wed, 27 May 2020 23:56:50 +0200 Subject: [PATCH 15/17] all done --- your-project/README.md | 73 ++++++++++++++++++++++-------------------- 1 file changed, 38 insertions(+), 35 deletions(-) diff --git a/your-project/README.md b/your-project/README.md index 9563cd70..538e7590 100644 --- a/your-project/README.md +++ b/your-project/README.md @@ -1,9 +1,9 @@ Ironhack Logo -# Title of My Project -*[Your Name]* +# Global Warming, Can we solve it? +*[Andreu Carreño Mateu]* -*[Your Cohort, Campus & Date]* +*[Data Analytics, Barcelona 29/05/2020]* ## Content - [Project Description](#project-description) @@ -11,7 +11,6 @@ - [Dataset](#dataset) - [Cleaning](#cleaning) - [Analysis](#analysis) -- [Model Training and Evaluation](#model-training-and-evaluation) - [Conclusion](#conclusion) - [Future Work](#future-work) - [Workflow](#workflow) @@ -19,54 +18,58 @@ - [Links](#links) ## Project Description -Write a short description of your project: 3-5 sentences about what your project is about, why you chose this topic (if relevant), and what you are trying to show. +Even though nowadays the hottest topic is the current Covid crisis. I wanted to express through data, that Global Warming is not something we can't leave out to chance. +I enjoyed the Machine Learning module, but for my Final project I choose to do a statistical analysis of the current Climate Change. +My goal was to see how much could I show using python and tableau about a topic that is always in my mind since I heard about it in school when Iwas a kid. +I gathered numerous Global and Country indicators to show that the Climate Change issue is one that we need to tackle quickly and unanimously. ## Hypotheses / Questions -* What data/business/research/personal question you would like to answer? -* What is the context for the question and the possible scientific or business application? -* What are the hypotheses you would like to test in order to answer your question? -Frame your hypothesis with statistical/data languages (i.e. define Null and Alternative Hypothesis). You can use formulas if you want but that is not required. +- The first interest I wanted wasn't a question per se. I wanted to convince as many people as I coyld that we must take global warming seriously, because before solving a problem you have to ackowledge it. +- The second thing I wanted to do was answer if this crisis can be solved somehow. + +- And finnaly, if it can be solvet, why are we not taking the needed action unanimously? + ## Dataset -* Where did you get your data? If you downloaded a dataset (either public or private), describe where you downloaded it and include the command to load the dataset. -* Did you build your own datset? If so, did you use an API or a web scraper? PRovide the relevant scripts in your repo. -* For all types of datasets, provide a description of the size, complexity, and data types included in your dataset, as well as a schema of the tables if necessary. -* If the question cannot be answered with the available data, why not? What data would you need to answer it better? +- for the datasets I used csv from the indicators I thought I would need. They are mainly from _Kaggle_ and _Our World in Data_. +- It wasn't easy to find the porper data and hadto change directions a few times, but finnaly I found what I needed to tell my story. +- Regarding the good results, I wish that there was even more data on the topic and more accurate data because I'm sure that the governments of certain countries don't give away the full information for obvious reasons. ## Cleaning -Describe your full process of data wrangling and cleaning. Document why you chose to fill missing values, extract outliers, or create the variables you did as well as your reasoning behind the process. +- To clean the data I had to use few methods and functions from pandas in order to adapt them all so I could merge them and analyse the results in Tableau. +- The main problem was to know exactly what I wanted to get out of each indicator to processit in the way the fit me the best. ## Analysis -* Overview the general steps you went through to analyze your data in order to test your hypothesis. -* Document each step of your data exploration and analysis. -* Include charts to demonstrate the effect of your work. -* If you used Machine Learning in your final project, describe your feature selection process. +- the analysis is mainly based on correlations and timeseries progresion from certain indicators. +- It's not a very complicated analysis, but I think it's power lays on it's simplicity. +- In just a few charts I'm able to show that the global warming is a real threat that we need to adress right now, even if it's through this Covid crisis. -## Model Training and Evaluation -*Include this section only if you chose to include ML in your project.* -* Describe how you trained your model, the results you obtained, and how you evaluated those results. ## Conclusion -* Summarize your results. What do they mean? -* What can you say about your hypotheses? -* Interpret your findings in terms of the questions you try to answer. + - If we don't reduce the CO emissions, mitigate deforestation and focus on renewable energies our future could be too hot for us the bear. + - We already solved together a big crisis with the ozone depletion, how come we can't do the same with the global warming? + - Descarted the excuse of the economic competitiveness, we can only assume that those making the desicions have interests in the highly contaminating fossil fuel industry. ## Future Work -Address any questions you were unable to answer, or any next steps or future extensions to your project. +- I would love to do this analysis again after this Crisis to see in what ways it has affected our planet's enviroment and the prespective in which we look at this issue. +- I'm waiting to see if the USA does indeed withdraw from the Paris agreement this next November, because It could be the worst news for our future. ## Workflow -Outline the workflow you used in your project. What were the steps? -How did you test the accuracy of your analysis and/or machine learning algorithm? +- I researched many topics, most of them about machine learning, but at the end I decided I felt more like doing a statistical analysis and test my visualization and storytelling skills (I'll be practising my ML skills everyday from now on). +- I Researched for data, found messy datasets, serached again and so on until I found those datasets that I could clean and adapt for the analysis. +- Here I started coding. I cleaned and merged the data sets. +- I plotted some charts using matplotlib and them I moved on to tableau, where I performed my analysis. +- I used my results to build the story I wanted to tell and then I made the presentation. ## Organization -How did you organize your work? Did you use any tools like a trello or kanban board? - -What does your repository look like? Explain your folder and file structure. +In the repository you will find: +- The main Jupyter Notebook with the name of the project +- A folder with all the images I used for the presentation and the notebook. +- A folder with all the datasets used in the project. +- This Readme file ## Links -Include links to your repository, slides and trello/kanban board. Feel free to include any other links associated with your project. - -[Repository](https://github.com/) -[Slides](https://slides.com/) -[Trello](https://trello.com/en) +[Repository](https://github.com/andreuCM11/Project-Week-8-Final-Project) +[Slides](https://docs.google.com/presentation/d/1SYobI8pTTekKPECIG84-9INWj7PrG2xXutOTHXiTiII/edit#slide=id.g8079223f8a_0_37228) +[Trello](https://trello.com/b/Abpk4fft/ironhack-final-project) From f3b3939d7a1c9268a46664cf66c3c2f8d8512d0e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Thu, 28 May 2020 11:32:16 +0200 Subject: [PATCH 16/17] kikoff deleted --- .gitignore | 220 ++++++++++++++++++++++++++++++++++++++++++++++++++++ Kick-Off.md | 104 ------------------------- 2 files changed, 220 insertions(+), 104 deletions(-) create mode 100644 .gitignore delete mode 100644 Kick-Off.md diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..c550d4bf --- /dev/null +++ b/.gitignore @@ -0,0 +1,220 @@ +# Created by https://www.gitignore.io/api/text,python,images,microsoftoffice,jupyternotebooks +# Edit at https://www.gitignore.io/?templates=text,python,images,microsoftoffice,jupyternotebooks + +### Images ### +# JPEG + +*.jpeg +*.jpe +*.jif +*.jfif +*.jfi + +# JPEG 2000 +*.jp2 +*.j2k +*.jpf +*.jpx +*.jpm +*.mj2 + +# JPEG XR +*.jxr +*.hdp +*.wdp + +# Graphics Interchange Format +*.gif + +# RAW +*.raw + +# Web P +*.webp + + + +# Animated Portable Network Graphics +*.apng + +# Multiple-image Network Graphics +*.mng + +# Tagged Image File Format +*.tiff +*.tif + +# Scalable Vector Graphics +*.svg +*.svgz + +# Portable Document Format +*.pdf + +# X BitMap +*.xbm + +# BMP +*.bmp +*.dib + +# ICO +*.ico + +# 3D Images +*.3dm +*.max + +### JupyterNotebooks ### +# gitignore template for Jupyter Notebooks +# website: http://jupyter.org/ + +.ipynb_checkpoints +*/.ipynb_checkpoints/* + +# IPython +profile_default/ +ipython_config.py + +# Remove previous ipynb_checkpoints +# git rm -r .ipynb_checkpoints/ + +### MicrosoftOffice ### +*.tmp + +# Word temporary +~$*.doc* + +# Word Auto Backup File +Backup of *.doc* + +# Excel temporary +~$*.xls* + +# Excel Backup File +*.xlk + +# PowerPoint temporary +~$*.ppt* + +# Visio autosave temporary files +*.~vsd* + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# Mr Developer +.mr.developer.cfg +.project +.pydevproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +### Text ### +*.doc +*.docx +*.log +*.msg +*.pages +*.rtf +*.txt +*.wpd +*.wps + + +# End of https://www.gitignore.io/api/text,python,images,microsoftoffice,jupyternotebooks \ No newline at end of file diff --git a/Kick-Off.md b/Kick-Off.md deleted file mode 100644 index 5694243e..00000000 --- a/Kick-Off.md +++ /dev/null @@ -1,104 +0,0 @@ -Ironhack Logo - -# Welcome to your Final Project! - -## Content -- [Project Description](#project-description) -- [Project Goals](#project-goals) -- [Requirements](#requirements) -- [Deliverables](#deliverables) -- [Schedule](#schedule) -- [Presentation](#presentation) -- [Tips & Tricks](#tips-&-tricks) - - - -## Project Description -In this project, you will pick a topic of your choosing and perform an end-to-end analysis using what you have learned. You will apply the statistical or machine learning techniques we have learned over the last few weeks and present your results to all of us as well as to the jury for the HackShow. - - - -## Project Goals -* Ask interesting and thoughful questions and find the data to answer them. -* Focus on improving in areas that are hard for you or learning more about something with which you feel comfortable. -* Apply the statistical and/or machine learning techniques we have learned. -* Create useful and clear graphs. -* Present your insights in a thoughtful, clear and accurate way. - - - -## Requirements -* You must plan your project. That is why creating a Kanban or Trello Board is mandatory. -* You **CAN'T CODE** until you project is planned. -* Create a *.gitignore* file and include it in your repository. -* You may be on team (3 people max) or work on your own. -* You can include ML or statistics, but it is not required to do so, as long as you have a rigorous analysis. -* You may use the data from your last project or from past projects. - - - -## Deliverables -* A well-commented notebook with your analysis (Jupyter or Zeppelin). -* A 5 minute presentation in the auditorium (+2 minutes of questions) -* A 5 minute presentation for the jury (+5 minutes of questions) -* Repository with your workflow + documentation + code. Even if you are working alone, you need to keep good practices! -* The database where you have kept your data. - - - -## Schedule -*Wednesday* -* Choose your topic and to work as a team or individually. - -*Thursday* -* Create your repository and README overview (template provided). -* Choose the dataset you would like to use. -* Teacher's validation - -**NO CODE UNTIL HERE** - -*Monday morning* -* Database schema validation. - -*Monday* -* Data in the database. - -*Tuesday evening* -* Analysis validation. - -*Wednesday* -* First rehearsal. - -*Thursday morning* -* General rehearsal. - -*Thursday evening* -* Presentation! - -*Friday morning* -* Jury presentation: they will select their two prefered projects. -* We will vote for the third project to be presented at Friday's HackShow. - -*Friday evening. HACKSHOW!* -* Auditorium presentation at 18:00. -* Vote for the winner of the HackShow. - - - -## Presentation -* 5 minute presentation in the auditorium (+2 minutes of questions) -* 5 minute presentation for the jury (+5 minutes of questions) - - - -## Tips & Tricks -* Organize yourself (don't get lost!). Respect deadlines. -* Ask for help vs Google is your friend. -* Define a simple approach first. You never know how the data can betray you 😉. -* Document yourself. Learn about the problem and what research has been done before you. -* Before making a graph, think what you want to represent. -* Don't force yourself to use tecniques if they are not helpful for your objective. -* If using machine learning, remember: - * This is an iterative process. Try your best to improve your model performance by: - * Try different models and select one that is the simplest yet produce the best result. - * Try different hyperparameters and see if they improve the result. From 331a10bd0c7cb442bffaeed1e6e1eed4260347bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreu=20Carre=C3=B1o=20Mateu?= Date: Thu, 28 May 2020 11:35:51 +0200 Subject: [PATCH 17/17] readme outside folders --- your-project/README.md => README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename your-project/README.md => README.md (100%) diff --git a/your-project/README.md b/README.md similarity index 100% rename from your-project/README.md rename to README.md

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