{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ciencia de Datos 2022\n", "#
**Aula 21 -- Data Imputation**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "\n", "In this tutorial we will explore some basic techniques of data imputation, that is, how to fill missing values in a dataset." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from sklearn.impute import KNNImputer" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_kg_hide-input": true }, "outputs": [], "source": [ "pd.set_option('max_rows', 30)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('hpi-data-2016.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryRegionAverage-Life-ExpectancyAverage-Wellbeing_(0-10)Happy-Life-YearsFootprint_(gha/capita)Inequality-of-OutcomesInequality-adjusted-Life-ExpectancyInequality-adjusted-WellbeingHappy-Planet-IndexGDP/capita($PPP)PopulationGINI-index
0AfghanistanMiddle East and North Africa59.73.812.40.80.4338.33.420.269129726803NaN
1AlbaniaPost-communist77.35.534.42.20.1769.75.136.84247290048929.0
2AlgeriaMiddle East and North Africa74.35.630.52.10.2460.55.233.3558437439427NaN
3ArgentinaAmericas75.96.540.23.10.1668.36.035.2143574209522442.5
4ArmeniaPost-communist74.44.324.02.20.2266.93.725.73566297833930.5
5AustraliaAsia Pacific82.17.253.19.30.0878.66.921.26764622728254NaN
6AustriaEurope81.07.454.46.10.0778.07.130.548324842999130.5
7BangladeshAsia Pacific70.84.723.30.70.2756.64.338.4859155257387NaN
8BelarusPost-communist70.95.734.05.10.1366.75.321.76722946400026.0
9BelgiumEurope80.46.949.57.40.0977.26.623.7447311112824627.6
\n", "
" ], "text/plain": [ " Country Region Average-Life-Expectancy \\\n", "0 Afghanistan Middle East and North Africa 59.7 \n", "1 Albania Post-communist 77.3 \n", "2 Algeria Middle East and North Africa 74.3 \n", "3 Argentina Americas 75.9 \n", "4 Armenia Post-communist 74.4 \n", "5 Australia Asia Pacific 82.1 \n", "6 Austria Europe 81.0 \n", "7 Bangladesh Asia Pacific 70.8 \n", "8 Belarus Post-communist 70.9 \n", "9 Belgium Europe 80.4 \n", "\n", " Average-Wellbeing_(0-10) Happy-Life-Years Footprint_(gha/capita) \\\n", "0 3.8 12.4 0.8 \n", "1 5.5 34.4 2.2 \n", "2 5.6 30.5 2.1 \n", "3 6.5 40.2 3.1 \n", "4 4.3 24.0 2.2 \n", "5 7.2 53.1 9.3 \n", "6 7.4 54.4 6.1 \n", "7 4.7 23.3 0.7 \n", "8 5.7 34.0 5.1 \n", "9 6.9 49.5 7.4 \n", "\n", " Inequality-of-Outcomes Inequality-adjusted-Life-Expectancy \\\n", "0 0.43 38.3 \n", "1 0.17 69.7 \n", "2 0.24 60.5 \n", "3 0.16 68.3 \n", "4 0.22 66.9 \n", "5 0.08 78.6 \n", "6 0.07 78.0 \n", "7 0.27 56.6 \n", "8 0.13 66.7 \n", "9 0.09 77.2 \n", "\n", " Inequality-adjusted-Wellbeing Happy-Planet-Index GDP/capita($PPP) \\\n", "0 3.4 20.2 691 \n", "1 5.1 36.8 4247 \n", "2 5.2 33.3 5584 \n", "3 6.0 35.2 14357 \n", "4 3.7 25.7 3566 \n", "5 6.9 21.2 67646 \n", "6 7.1 30.5 48324 \n", "7 4.3 38.4 859 \n", "8 5.3 21.7 6722 \n", "9 6.6 23.7 44731 \n", "\n", " Population GINI-index \n", "0 29726803 NaN \n", "1 2900489 29.0 \n", "2 37439427 NaN \n", "3 42095224 42.5 \n", "4 2978339 30.5 \n", "5 22728254 NaN \n", "6 8429991 30.5 \n", "7 155257387 NaN \n", "8 9464000 26.0 \n", "9 11128246 27.6 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head(10)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(140, 13)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Missing data\n", "\n", "Entries missing values are given the value `NaN`, short for \"Not a Number\". For technical reasons these `NaN` values are always of the `float64` dtype.\n", "\n", "Pandas provides some methods specific to missing data. To select `NaN` entries you can use `pd.isnull()` (or its companion `pd.notnull()`). This is meant to be used thusly:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Country 0\n", "Region 0\n", "Average-Life-Expectancy 0\n", "Average-Wellbeing_(0-10) 0\n", "Happy-Life-Years 0\n", "Footprint_(gha/capita) 0\n", "Inequality-of-Outcomes 0\n", "Inequality-adjusted-Life-Expectancy 0\n", "Inequality-adjusted-Wellbeing 0\n", "Happy-Planet-Index 0\n", "GDP/capita($PPP) 0\n", "Population 0\n", "GINI-index 75\n", "dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Replacing missing values is a common operation. Pandas provides a really handy method for this problem: `fillna()`. `fillna()` provides a few different strategies for mitigating such data. For example, we can simply replace each `NaN` with an `\"Unknown\"`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data['GINI-index'].fillna(\"Unknown\", inplace=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryRegionAverage-Life-ExpectancyAverage-Wellbeing_(0-10)Happy-Life-YearsFootprint_(gha/capita)Inequality-of-OutcomesInequality-adjusted-Life-ExpectancyInequality-adjusted-WellbeingHappy-Planet-IndexGDP/capita($PPP)PopulationGINI-index
0AfghanistanMiddle East and North Africa59.73.812.40.80.4338.33.420.269129726803Unknown
1AlbaniaPost-communist77.35.534.42.20.1769.75.136.84247290048929.0
2AlgeriaMiddle East and North Africa74.35.630.52.10.2460.55.233.3558437439427Unknown
3ArgentinaAmericas75.96.540.23.10.1668.36.035.2143574209522442.5
4ArmeniaPost-communist74.44.324.02.20.2266.93.725.73566297833930.5
5AustraliaAsia Pacific82.17.253.19.30.0878.66.921.26764622728254Unknown
6AustriaEurope81.07.454.46.10.0778.07.130.548324842999130.5
7BangladeshAsia Pacific70.84.723.30.70.2756.64.338.4859155257387Unknown
8BelarusPost-communist70.95.734.05.10.1366.75.321.76722946400026.0
9BelgiumEurope80.46.949.57.40.0977.26.623.7447311112824627.6
10BelizeAmericas69.86.134.22.50.1861.75.733.84674336707Unknown
11BeninSub Saharan Africa59.23.29.91.40.4437.32.813.480810049792Unknown
12BhutanAsia Pacific68.75.627.42.30.2754.55.228.6245274371138.7
13BoliviaAmericas67.56.025.63.00.3547.95.523.326451023876246.7
14Bosnia and HerzegovinaPost-communist76.24.828.63.10.1971.14.225.344953828419Unknown
\n", "
" ], "text/plain": [ " Country Region \\\n", "0 Afghanistan Middle East and North Africa \n", "1 Albania Post-communist \n", "2 Algeria Middle East and North Africa \n", "3 Argentina Americas \n", "4 Armenia Post-communist \n", "5 Australia Asia Pacific \n", "6 Austria Europe \n", "7 Bangladesh Asia Pacific \n", "8 Belarus Post-communist \n", "9 Belgium Europe \n", "10 Belize Americas \n", "11 Benin Sub Saharan Africa \n", "12 Bhutan Asia Pacific \n", "13 Bolivia Americas \n", "14 Bosnia and Herzegovina Post-communist \n", "\n", " Average-Life-Expectancy Average-Wellbeing_(0-10) Happy-Life-Years \\\n", "0 59.7 3.8 12.4 \n", "1 77.3 5.5 34.4 \n", "2 74.3 5.6 30.5 \n", "3 75.9 6.5 40.2 \n", "4 74.4 4.3 24.0 \n", "5 82.1 7.2 53.1 \n", "6 81.0 7.4 54.4 \n", "7 70.8 4.7 23.3 \n", "8 70.9 5.7 34.0 \n", "9 80.4 6.9 49.5 \n", "10 69.8 6.1 34.2 \n", "11 59.2 3.2 9.9 \n", "12 68.7 5.6 27.4 \n", "13 67.5 6.0 25.6 \n", "14 76.2 4.8 28.6 \n", "\n", " Footprint_(gha/capita) Inequality-of-Outcomes \\\n", "0 0.8 0.43 \n", "1 2.2 0.17 \n", "2 2.1 0.24 \n", "3 3.1 0.16 \n", "4 2.2 0.22 \n", "5 9.3 0.08 \n", "6 6.1 0.07 \n", "7 0.7 0.27 \n", "8 5.1 0.13 \n", "9 7.4 0.09 \n", "10 2.5 0.18 \n", "11 1.4 0.44 \n", "12 2.3 0.27 \n", "13 3.0 0.35 \n", "14 3.1 0.19 \n", "\n", " Inequality-adjusted-Life-Expectancy Inequality-adjusted-Wellbeing \\\n", "0 38.3 3.4 \n", "1 69.7 5.1 \n", "2 60.5 5.2 \n", "3 68.3 6.0 \n", "4 66.9 3.7 \n", "5 78.6 6.9 \n", "6 78.0 7.1 \n", "7 56.6 4.3 \n", "8 66.7 5.3 \n", "9 77.2 6.6 \n", "10 61.7 5.7 \n", "11 37.3 2.8 \n", "12 54.5 5.2 \n", "13 47.9 5.5 \n", "14 71.1 4.2 \n", "\n", " Happy-Planet-Index GDP/capita($PPP) Population GINI-index \n", "0 20.2 691 29726803 Unknown \n", "1 36.8 4247 2900489 29.0 \n", "2 33.3 5584 37439427 Unknown \n", "3 35.2 14357 42095224 42.5 \n", "4 25.7 3566 2978339 30.5 \n", "5 21.2 67646 22728254 Unknown \n", "6 30.5 48324 8429991 30.5 \n", "7 38.4 859 155257387 Unknown \n", "8 21.7 6722 9464000 26.0 \n", "9 23.7 44731 11128246 27.6 \n", "10 33.8 4674 336707 Unknown \n", "11 13.4 808 10049792 Unknown \n", "12 28.6 2452 743711 38.7 \n", "13 23.3 2645 10238762 46.7 \n", "14 25.3 4495 3828419 Unknown " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head(15)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fill with zeros" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "## reemplazar con 0\n", "data = pd.read_csv('hpi-data-2016.csv')\n", "data2 = data.copy()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "data2['GINI-index'] = data2['GINI-index'].fillna(0)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryRegionAverage-Life-ExpectancyAverage-Wellbeing_(0-10)Happy-Life-YearsFootprint_(gha/capita)Inequality-of-OutcomesInequality-adjusted-Life-ExpectancyInequality-adjusted-WellbeingHappy-Planet-IndexGDP/capita($PPP)PopulationGINI-index
0AfghanistanMiddle East and North Africa59.73.812.40.80.4338.33.420.2691297268030.0
1AlbaniaPost-communist77.35.534.42.20.1769.75.136.84247290048929.0
2AlgeriaMiddle East and North Africa74.35.630.52.10.2460.55.233.35584374394270.0
3ArgentinaAmericas75.96.540.23.10.1668.36.035.2143574209522442.5
4ArmeniaPost-communist74.44.324.02.20.2266.93.725.73566297833930.5
\n", "
" ], "text/plain": [ " Country Region Average-Life-Expectancy \\\n", "0 Afghanistan Middle East and North Africa 59.7 \n", "1 Albania Post-communist 77.3 \n", "2 Algeria Middle East and North Africa 74.3 \n", "3 Argentina Americas 75.9 \n", "4 Armenia Post-communist 74.4 \n", "\n", " Average-Wellbeing_(0-10) Happy-Life-Years Footprint_(gha/capita) \\\n", "0 3.8 12.4 0.8 \n", "1 5.5 34.4 2.2 \n", "2 5.6 30.5 2.1 \n", "3 6.5 40.2 3.1 \n", "4 4.3 24.0 2.2 \n", "\n", " Inequality-of-Outcomes Inequality-adjusted-Life-Expectancy \\\n", "0 0.43 38.3 \n", "1 0.17 69.7 \n", "2 0.24 60.5 \n", "3 0.16 68.3 \n", "4 0.22 66.9 \n", "\n", " Inequality-adjusted-Wellbeing Happy-Planet-Index GDP/capita($PPP) \\\n", "0 3.4 20.2 691 \n", "1 5.1 36.8 4247 \n", "2 5.2 33.3 5584 \n", "3 6.0 35.2 14357 \n", "4 3.7 25.7 3566 \n", "\n", " Population GINI-index \n", "0 29726803 0.0 \n", "1 2900489 29.0 \n", "2 37439427 0.0 \n", "3 42095224 42.5 \n", "4 2978339 30.5 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data2.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "36.52" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['GINI-index'].mean()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "16.955714285714286" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data2['GINI-index'].mean()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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quXLBXPXw+dVXsjC1wK3V+ltZ+OGzZdbI2ToRsQe4ArhxyepWjSesmrMkrRvTEkTEucBrgZsAMvOZzPwRQx7PVhf4UhExCrwSeKha9a6I+HpE3NySX/u2RcQjwCngvsx8COhk5hOw8MMIeMkWRgRWzQktG0/gY8B7gZ8uWde68WTlnNC+8YSFH9b3RsRMLExxAe0c05VyQrvG9BeAHwL/UF0+uzEidjLk8SyiwCNiBLgT+KPM/DHwCeBlwKXAE8DhrUu3IDP/LzMvZeETqa+OiL1bHGlFq+Rs1XhGxJXAqcyc2coc61kjZ6vGc4nLMvNVLMwg+gcR8dqtDrSKlXK2bUy3A68CPpGZrwSeBoY+pXbrC7y6VnsncFtm3gWQmSerIvop8PcszIzYCtWvUVMsXFc+WV3HP3s9/9TWJXu2pTlbOJ6XAW+uroVOAq+LiE/RvvFcMWcLxxOAzPx+9f0U8BkWcrVtTFfM2cIxPQ4cX/Ib7B0sFPpQx7PVBV790e0m4GhmfnTJ+ouWvOw3gEeHnW2piLgwIs6rlncArwceZ2FqgWurl10LfHZLAlZWy9m28czM92fmnswcZWGKhi9m5jto2XiulrNt4wkQETurGwGoftV/Iwu5WjWmq+Vs25hm5g+A70XEJdWqy4FvMOTx7Gc2wmG4DLgGmK2u28LCX3mviohLWbhWdgz4va0It8RFwK2x8I9cPA/4dGbeHREPAp+OiOuB7wK/tZUhWT3nJ1s2nqs5RLvGczUfaeF4doDPLJwTsR34x8z8fER8hXaN6Wo52/jf6LuB26o7UL4DvJPq/6thjWerbyOUJK2u1ZdQJEmrs8AlqVAWuCQVygKXpEJZ4JJUKAtckgplgUtSof4fpR50pzE+oW0AAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data['GINI-index'].hist()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data2['GINI-index'].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fill with statistical resumes" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36.52\n" ] } ], "source": [ "## reemplazar con la media\n", "\n", "data3 = data.copy()\n", "media = data3['GINI-index'].mean()\n", "print(media)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "data3['GINI-index'] = data3['GINI-index'].fillna(media)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryRegionAverage-Life-ExpectancyAverage-Wellbeing_(0-10)Happy-Life-YearsFootprint_(gha/capita)Inequality-of-OutcomesInequality-adjusted-Life-ExpectancyInequality-adjusted-WellbeingHappy-Planet-IndexGDP/capita($PPP)PopulationGINI-index
0AfghanistanMiddle East and North Africa59.73.812.40.80.4338.33.420.26912972680336.52
1AlbaniaPost-communist77.35.534.42.20.1769.75.136.84247290048929.00
2AlgeriaMiddle East and North Africa74.35.630.52.10.2460.55.233.355843743942736.52
3ArgentinaAmericas75.96.540.23.10.1668.36.035.2143574209522442.50
4ArmeniaPost-communist74.44.324.02.20.2266.93.725.73566297833930.50
\n", "
" ], "text/plain": [ " Country Region Average-Life-Expectancy \\\n", "0 Afghanistan Middle East and North Africa 59.7 \n", "1 Albania Post-communist 77.3 \n", "2 Algeria Middle East and North Africa 74.3 \n", "3 Argentina Americas 75.9 \n", "4 Armenia Post-communist 74.4 \n", "\n", " Average-Wellbeing_(0-10) Happy-Life-Years Footprint_(gha/capita) \\\n", "0 3.8 12.4 0.8 \n", "1 5.5 34.4 2.2 \n", "2 5.6 30.5 2.1 \n", "3 6.5 40.2 3.1 \n", "4 4.3 24.0 2.2 \n", "\n", " Inequality-of-Outcomes Inequality-adjusted-Life-Expectancy \\\n", "0 0.43 38.3 \n", "1 0.17 69.7 \n", "2 0.24 60.5 \n", "3 0.16 68.3 \n", "4 0.22 66.9 \n", "\n", " Inequality-adjusted-Wellbeing Happy-Planet-Index GDP/capita($PPP) \\\n", "0 3.4 20.2 691 \n", "1 5.1 36.8 4247 \n", "2 5.2 33.3 5584 \n", "3 6.0 35.2 14357 \n", "4 3.7 25.7 3566 \n", "\n", " Population GINI-index \n", "0 29726803 36.52 \n", "1 2900489 29.00 \n", "2 37439427 36.52 \n", "3 42095224 42.50 \n", "4 2978339 30.50 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data3.head()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "36.520000000000046" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data3['GINI-index'].mean()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "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": [ "data['GINI-index'].hist() # pandas histogram descarta los faltantes" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data3['GINI-index'].hist()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "35.2\n" ] } ], "source": [ "# reemplazar con la mediana\n", "\n", "mediana = data['GINI-index'].median()\n", "print(mediana)\n", "data3['GINI-index'] = data3['GINI-index'].fillna(mediana)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 Sub Saharan Africa\n", "dtype: object\n" ] } ], "source": [ "moda = data['Region'].mode()\n", "print(moda)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 26.1\n", "1 27.3\n", "2 27.4\n", "3 30.5\n", "4 35.2\n", "5 36.0\n", "6 38.7\n", "7 45.1\n", "dtype: float64\n" ] } ], "source": [ "moda = data['GINI-index'].mode()\n", "print(moda)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "36.0 2\n", "26.1 2\n", "30.5 2\n", "38.7 2\n", "27.4 2\n", " ..\n", "39.3 1\n", "51.9 1\n", "35.9 1\n", "33.1 1\n", "24.7 1\n", "Name: GINI-index, Length: 57, dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['GINI-index'].value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fill with group or cluster mean " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes it is more convenient to replace missing values with group means, where the groups correspond to some categorial variable values, or some clustering scheme." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Middle East and North Africa', 'Post-communist', 'Americas',\n", " 'Asia Pacific', 'Europe', 'Sub Saharan Africa'], dtype=object)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regions = data.Region.unique()\n", "regions" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Region\n", "Americas 48.726667\n", "Asia Pacific 37.557143\n", "Europe 31.394444\n", "Middle East and North Africa 34.850000\n", "Post-communist 30.957895\n", "Sub Saharan Africa 39.250000\n", "Name: GINI-index, dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.groupby('Region')['GINI-index'].mean()\n", "#means = data.groupby('Region')['GINI-index'].mean().values\n", "#means" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([48.72666667, 37.55714286, 31.39444444, 34.85 , 30.95789474,\n", " 39.25 ])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "means = data.groupby('Region')['GINI-index'].mean().values\n", "means" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryRegionAverage-Life-ExpectancyAverage-Wellbeing_(0-10)Happy-Life-YearsFootprint_(gha/capita)Inequality-of-OutcomesInequality-adjusted-Life-ExpectancyInequality-adjusted-WellbeingHappy-Planet-IndexGDP/capita($PPP)PopulationGINI-index
0AfghanistanMiddle East and North Africa59.73.812.40.80.4338.33.420.26912972680336.281429
1AlbaniaPost-communist77.35.534.42.20.1769.75.136.84247290048929.000000
2AlgeriaMiddle East and North Africa74.35.630.52.10.2460.55.233.355843743942736.281429
3ArgentinaAmericas75.96.540.23.10.1668.36.035.2143574209522442.500000
4ArmeniaPost-communist74.44.324.02.20.2266.93.725.73566297833930.500000
..........................................
135VenezuelaAmericas73.97.141.53.60.1965.56.533.6127722985423843.844000
136VietnamAsia Pacific75.55.532.81.70.1964.85.240.317558880920038.700000
137YemenMiddle East and North Africa63.34.115.21.00.3944.73.622.812892488279236.281429
138ZambiaSub Saharan Africa58.45.016.71.00.4138.74.525.216871478658136.841176
139ZimbabweSub Saharan Africa53.75.016.41.40.3736.94.622.18511456548236.841176
\n", "

140 rows × 13 columns

\n", "
" ], "text/plain": [ " Country Region Average-Life-Expectancy \\\n", "0 Afghanistan Middle East and North Africa 59.7 \n", "1 Albania Post-communist 77.3 \n", "2 Algeria Middle East and North Africa 74.3 \n", "3 Argentina Americas 75.9 \n", "4 Armenia Post-communist 74.4 \n", ".. ... ... ... \n", "135 Venezuela Americas 73.9 \n", "136 Vietnam Asia Pacific 75.5 \n", "137 Yemen Middle East and North Africa 63.3 \n", "138 Zambia Sub Saharan Africa 58.4 \n", "139 Zimbabwe Sub Saharan Africa 53.7 \n", "\n", " Average-Wellbeing_(0-10) Happy-Life-Years Footprint_(gha/capita) \\\n", "0 3.8 12.4 0.8 \n", "1 5.5 34.4 2.2 \n", "2 5.6 30.5 2.1 \n", "3 6.5 40.2 3.1 \n", "4 4.3 24.0 2.2 \n", ".. ... ... ... \n", "135 7.1 41.5 3.6 \n", "136 5.5 32.8 1.7 \n", "137 4.1 15.2 1.0 \n", "138 5.0 16.7 1.0 \n", "139 5.0 16.4 1.4 \n", "\n", " Inequality-of-Outcomes Inequality-adjusted-Life-Expectancy \\\n", "0 0.43 38.3 \n", "1 0.17 69.7 \n", "2 0.24 60.5 \n", "3 0.16 68.3 \n", "4 0.22 66.9 \n", ".. ... ... \n", "135 0.19 65.5 \n", "136 0.19 64.8 \n", "137 0.39 44.7 \n", "138 0.41 38.7 \n", "139 0.37 36.9 \n", "\n", " Inequality-adjusted-Wellbeing Happy-Planet-Index GDP/capita($PPP) \\\n", "0 3.4 20.2 691 \n", "1 5.1 36.8 4247 \n", "2 5.2 33.3 5584 \n", "3 6.0 35.2 14357 \n", "4 3.7 25.7 3566 \n", ".. ... ... ... \n", "135 6.5 33.6 12772 \n", "136 5.2 40.3 1755 \n", "137 3.6 22.8 1289 \n", "138 4.5 25.2 1687 \n", "139 4.6 22.1 851 \n", "\n", " Population GINI-index \n", "0 29726803 36.281429 \n", "1 2900489 29.000000 \n", "2 37439427 36.281429 \n", "3 42095224 42.500000 \n", "4 2978339 30.500000 \n", ".. ... ... \n", "135 29854238 43.844000 \n", "136 88809200 38.700000 \n", "137 24882792 36.281429 \n", "138 14786581 36.841176 \n", "139 14565482 36.841176 \n", "\n", "[140 rows x 13 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data4 = data.copy()\n", "data4['GINI-index'] = data4['GINI-index'].fillna(data3.groupby('Region')['GINI-index'].transform('mean'))\n", "data4" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data4['GINI-index'].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fill with linear regression estimations" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Average-Life-ExpectancyAverage-Wellbeing_(0-10)Happy-Life-YearsFootprint_(gha/capita)Inequality-of-OutcomesInequality-adjusted-Life-ExpectancyInequality-adjusted-WellbeingHappy-Planet-IndexGDP/capita($PPP)PopulationGINI-index
Average-Life-Expectancy1.0000000.6845970.8749940.621546-0.9336350.9825600.6704720.5405390.6207920.013189-0.329860
Average-Wellbeing_(0-10)0.6845971.0000000.9299160.669626-0.7569440.6961760.9944290.5096470.710701-0.023479-0.158217
Happy-Life-Years0.8749940.9299161.0000000.748900-0.9194830.8893900.9307790.4996040.796483-0.027091-0.357976
Footprint_(gha/capita)0.6215460.6696260.7489001.000000-0.7172090.6681540.681618-0.1306050.796346-0.056481-0.424435
Inequality-of-Outcomes-0.933635-0.756944-0.919483-0.7172091.000000-0.971618-0.757822-0.464021-0.6686700.0016810.579074
Inequality-adjusted-Life-Expectancy0.9825600.6961760.8893900.668154-0.9716181.0000000.6821410.4877410.642361-0.001109-0.470139
Inequality-adjusted-Wellbeing0.6704720.9944290.9307790.681618-0.7578220.6821411.0000000.4863680.730921-0.023779-0.211804
Happy-Planet-Index0.5405390.5096470.499604-0.130605-0.4640210.4877410.4863681.0000000.1140160.0662130.245163
GDP/capita($PPP)0.6207920.7107010.7964830.796346-0.6686700.6423610.7309210.1140161.000000-0.051025-0.401849
Population0.013189-0.023479-0.027091-0.0564810.001681-0.001109-0.0237790.066213-0.0510251.0000000.301755
GINI-index-0.329860-0.158217-0.357976-0.4244350.579074-0.470139-0.2118040.245163-0.4018490.3017551.000000
\n", "
" ], "text/plain": [ " Average-Life-Expectancy \\\n", "Average-Life-Expectancy 1.000000 \n", "Average-Wellbeing_(0-10) 0.684597 \n", "Happy-Life-Years 0.874994 \n", "Footprint_(gha/capita) 0.621546 \n", "Inequality-of-Outcomes -0.933635 \n", "Inequality-adjusted-Life-Expectancy 0.982560 \n", "Inequality-adjusted-Wellbeing 0.670472 \n", "Happy-Planet-Index 0.540539 \n", "GDP/capita($PPP) 0.620792 \n", "Population 0.013189 \n", "GINI-index -0.329860 \n", "\n", " Average-Wellbeing_(0-10) \\\n", "Average-Life-Expectancy 0.684597 \n", "Average-Wellbeing_(0-10) 1.000000 \n", "Happy-Life-Years 0.929916 \n", "Footprint_(gha/capita) 0.669626 \n", "Inequality-of-Outcomes -0.756944 \n", "Inequality-adjusted-Life-Expectancy 0.696176 \n", "Inequality-adjusted-Wellbeing 0.994429 \n", "Happy-Planet-Index 0.509647 \n", "GDP/capita($PPP) 0.710701 \n", "Population -0.023479 \n", "GINI-index -0.158217 \n", "\n", " Happy-Life-Years Footprint_(gha/capita) \\\n", "Average-Life-Expectancy 0.874994 0.621546 \n", "Average-Wellbeing_(0-10) 0.929916 0.669626 \n", "Happy-Life-Years 1.000000 0.748900 \n", "Footprint_(gha/capita) 0.748900 1.000000 \n", "Inequality-of-Outcomes -0.919483 -0.717209 \n", "Inequality-adjusted-Life-Expectancy 0.889390 0.668154 \n", "Inequality-adjusted-Wellbeing 0.930779 0.681618 \n", "Happy-Planet-Index 0.499604 -0.130605 \n", "GDP/capita($PPP) 0.796483 0.796346 \n", "Population -0.027091 -0.056481 \n", "GINI-index -0.357976 -0.424435 \n", "\n", " Inequality-of-Outcomes \\\n", "Average-Life-Expectancy -0.933635 \n", "Average-Wellbeing_(0-10) -0.756944 \n", "Happy-Life-Years -0.919483 \n", "Footprint_(gha/capita) -0.717209 \n", "Inequality-of-Outcomes 1.000000 \n", "Inequality-adjusted-Life-Expectancy -0.971618 \n", "Inequality-adjusted-Wellbeing -0.757822 \n", "Happy-Planet-Index -0.464021 \n", "GDP/capita($PPP) -0.668670 \n", "Population 0.001681 \n", "GINI-index 0.579074 \n", "\n", " Inequality-adjusted-Life-Expectancy \\\n", "Average-Life-Expectancy 0.982560 \n", "Average-Wellbeing_(0-10) 0.696176 \n", "Happy-Life-Years 0.889390 \n", "Footprint_(gha/capita) 0.668154 \n", "Inequality-of-Outcomes -0.971618 \n", "Inequality-adjusted-Life-Expectancy 1.000000 \n", "Inequality-adjusted-Wellbeing 0.682141 \n", "Happy-Planet-Index 0.487741 \n", "GDP/capita($PPP) 0.642361 \n", "Population -0.001109 \n", "GINI-index -0.470139 \n", "\n", " Inequality-adjusted-Wellbeing \\\n", "Average-Life-Expectancy 0.670472 \n", "Average-Wellbeing_(0-10) 0.994429 \n", "Happy-Life-Years 0.930779 \n", "Footprint_(gha/capita) 0.681618 \n", "Inequality-of-Outcomes -0.757822 \n", "Inequality-adjusted-Life-Expectancy 0.682141 \n", "Inequality-adjusted-Wellbeing 1.000000 \n", "Happy-Planet-Index 0.486368 \n", "GDP/capita($PPP) 0.730921 \n", "Population -0.023779 \n", "GINI-index -0.211804 \n", "\n", " Happy-Planet-Index GDP/capita($PPP) \\\n", "Average-Life-Expectancy 0.540539 0.620792 \n", "Average-Wellbeing_(0-10) 0.509647 0.710701 \n", "Happy-Life-Years 0.499604 0.796483 \n", "Footprint_(gha/capita) -0.130605 0.796346 \n", "Inequality-of-Outcomes -0.464021 -0.668670 \n", "Inequality-adjusted-Life-Expectancy 0.487741 0.642361 \n", "Inequality-adjusted-Wellbeing 0.486368 0.730921 \n", "Happy-Planet-Index 1.000000 0.114016 \n", "GDP/capita($PPP) 0.114016 1.000000 \n", "Population 0.066213 -0.051025 \n", "GINI-index 0.245163 -0.401849 \n", "\n", " Population GINI-index \n", "Average-Life-Expectancy 0.013189 -0.329860 \n", "Average-Wellbeing_(0-10) -0.023479 -0.158217 \n", "Happy-Life-Years -0.027091 -0.357976 \n", "Footprint_(gha/capita) -0.056481 -0.424435 \n", "Inequality-of-Outcomes 0.001681 0.579074 \n", "Inequality-adjusted-Life-Expectancy -0.001109 -0.470139 \n", "Inequality-adjusted-Wellbeing -0.023779 -0.211804 \n", "Happy-Planet-Index 0.066213 0.245163 \n", "GDP/capita($PPP) -0.051025 -0.401849 \n", "Population 1.000000 0.301755 \n", "GINI-index 0.301755 1.000000 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.corr()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "sns.pairplot(data[['Inequality-of-Outcomes', 'GINI-index']])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "datadropna = data.dropna()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryRegionAverage-Life-ExpectancyAverage-Wellbeing_(0-10)Happy-Life-YearsFootprint_(gha/capita)Inequality-of-OutcomesInequality-adjusted-Life-ExpectancyInequality-adjusted-WellbeingHappy-Planet-IndexGDP/capita($PPP)PopulationGINI-index
1AlbaniaPost-communist77.35.534.42.20.1769.75.136.84247290048929.0
3ArgentinaAmericas75.96.540.23.10.1668.36.035.2143574209522442.5
4ArmeniaPost-communist74.44.324.02.20.2266.93.725.73566297833930.5
6AustriaEurope81.07.454.46.10.0778.07.130.548324842999130.5
8BelarusPost-communist70.95.734.05.10.1366.75.321.76722946400026.0
..........................................
128UgandaSub Saharan Africa57.14.313.81.20.4136.83.919.46563540062042.4
129UkrainePost-communist70.35.028.32.80.1764.24.626.438554559330024.7
130United KingdomEurope80.46.949.14.90.0976.86.631.9412956370030032.6
132UruguayAmericas76.96.439.42.90.1869.65.836.115128339675341.3
136VietnamAsia Pacific75.55.532.81.70.1964.85.240.317558880920038.7
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65 rows × 13 columns

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" ], "text/plain": [ " Country Region Average-Life-Expectancy \\\n", "1 Albania Post-communist 77.3 \n", "3 Argentina Americas 75.9 \n", "4 Armenia Post-communist 74.4 \n", "6 Austria Europe 81.0 \n", "8 Belarus Post-communist 70.9 \n", ".. ... ... ... \n", "128 Uganda Sub Saharan Africa 57.1 \n", "129 Ukraine Post-communist 70.3 \n", "130 United Kingdom Europe 80.4 \n", "132 Uruguay Americas 76.9 \n", "136 Vietnam Asia Pacific 75.5 \n", "\n", " Average-Wellbeing_(0-10) Happy-Life-Years Footprint_(gha/capita) \\\n", "1 5.5 34.4 2.2 \n", "3 6.5 40.2 3.1 \n", "4 4.3 24.0 2.2 \n", "6 7.4 54.4 6.1 \n", "8 5.7 34.0 5.1 \n", ".. ... ... ... \n", "128 4.3 13.8 1.2 \n", "129 5.0 28.3 2.8 \n", "130 6.9 49.1 4.9 \n", "132 6.4 39.4 2.9 \n", "136 5.5 32.8 1.7 \n", "\n", " Inequality-of-Outcomes Inequality-adjusted-Life-Expectancy \\\n", "1 0.17 69.7 \n", "3 0.16 68.3 \n", "4 0.22 66.9 \n", "6 0.07 78.0 \n", "8 0.13 66.7 \n", ".. ... ... \n", "128 0.41 36.8 \n", "129 0.17 64.2 \n", "130 0.09 76.8 \n", "132 0.18 69.6 \n", "136 0.19 64.8 \n", "\n", " Inequality-adjusted-Wellbeing Happy-Planet-Index GDP/capita($PPP) \\\n", "1 5.1 36.8 4247 \n", "3 6.0 35.2 14357 \n", "4 3.7 25.7 3566 \n", "6 7.1 30.5 48324 \n", "8 5.3 21.7 6722 \n", ".. ... ... ... \n", "128 3.9 19.4 656 \n", "129 4.6 26.4 3855 \n", "130 6.6 31.9 41295 \n", "132 5.8 36.1 15128 \n", "136 5.2 40.3 1755 \n", "\n", " Population GINI-index \n", "1 2900489 29.0 \n", "3 42095224 42.5 \n", "4 2978339 30.5 \n", "6 8429991 30.5 \n", "8 9464000 26.0 \n", ".. ... ... \n", "128 35400620 42.4 \n", "129 45593300 24.7 \n", "130 63700300 32.6 \n", "132 3396753 41.3 \n", "136 88809200 38.7 \n", "\n", "[65 rows x 13 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datadropna" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "x = datadropna['Inequality-of-Outcomes'].values\n", "y = datadropna['GINI-index'].values" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.17, 0.16, 0.22, 0.07, 0.13, 0.09, 0.27, 0.35, 0.22, 0.19, 0.28,\n", " 0.24, 0.15, 0.12, 0.09, 0.07, 0.42, 0.3 , 0.22, 0.22, 0.12, 0.06,\n", " 0.09, 0.2 , 0.16, 0.42, 0.37, 0.31, 0.15, 0.05, 0.27, 0.08, 0.12,\n", " 0.18, 0.18, 0.14, 0.11, 0.07, 0.17, 0.19, 0.22, 0.16, 0.04, 0.07,\n", " 0.19, 0.22, 0.21, 0.26, 0.11, 0.16, 0.19, 0.16, 0.13, 0.1 , 0.1 ,\n", " 0.17, 0.06, 0.06, 0.15, 0.19, 0.41, 0.17, 0.09, 0.18, 0.19])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([29. , 42.5, 30.5, 30.5, 26. , 27.6, 38.7, 46.7, 52.7, 36. , 30.8,\n", " 53.5, 48.6, 34.3, 26.1, 29.1, 45.1, 45.7, 46.6, 41.8, 33.2, 27.1,\n", " 33.1, 41.4, 36.7, 33.7, 60.8, 57.4, 30.6, 26.9, 29.5, 32.5, 35.2,\n", " 27.4, 27.4, 35.5, 35.2, 34.8, 35.8, 48.1, 33.8, 32.2, 28. , 25.9,\n", " 51.9, 48.2, 45.1, 43. , 32.4, 36. , 27.3, 41.6, 26.1, 25.6, 35.9,\n", " 38.6, 27.3, 31.6, 39.3, 40.2, 42.4, 24.7, 32.6, 41.3, 38.7])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(65,)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.shape" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "65" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = x.shape[0]\n", "n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(65, 2)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# construct design matrix\n", "\n", "X = np.stack([np.ones(n), x]).T\n", "X.shape" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# compute linear regression coefficients\n", "\n", "c = np.linalg.inv(X.T @ X) @ (X.T) @ y" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([26.88757292, 54.87359863])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "def regresion(x, c):\n", " yhat = c @ np.hstack([np.array([1.]), x])\n", " return yhat" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "data4 = data.copy()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryRegionAverage-Life-ExpectancyAverage-Wellbeing_(0-10)Happy-Life-YearsFootprint_(gha/capita)Inequality-of-OutcomesInequality-adjusted-Life-ExpectancyInequality-adjusted-WellbeingHappy-Planet-IndexGDP/capita($PPP)PopulationGINI-index
0AfghanistanMiddle East and North Africa59.73.812.40.80.4338.33.420.269129726803NaN
1AlbaniaPost-communist77.35.534.42.20.1769.75.136.84247290048929.0
2AlgeriaMiddle East and North Africa74.35.630.52.10.2460.55.233.3558437439427NaN
3ArgentinaAmericas75.96.540.23.10.1668.36.035.2143574209522442.5
4ArmeniaPost-communist74.44.324.02.20.2266.93.725.73566297833930.5
..........................................
135VenezuelaAmericas73.97.141.53.60.1965.56.533.61277229854238NaN
136VietnamAsia Pacific75.55.532.81.70.1964.85.240.317558880920038.7
137YemenMiddle East and North Africa63.34.115.21.00.3944.73.622.8128924882792NaN
138ZambiaSub Saharan Africa58.45.016.71.00.4138.74.525.2168714786581NaN
139ZimbabweSub Saharan Africa53.75.016.41.40.3736.94.622.185114565482NaN
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140 rows × 13 columns

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" ], "text/plain": [ " Country Region Average-Life-Expectancy \\\n", "0 Afghanistan Middle East and North Africa 59.7 \n", "1 Albania Post-communist 77.3 \n", "2 Algeria Middle East and North Africa 74.3 \n", "3 Argentina Americas 75.9 \n", "4 Armenia Post-communist 74.4 \n", ".. ... ... ... \n", "135 Venezuela Americas 73.9 \n", "136 Vietnam Asia Pacific 75.5 \n", "137 Yemen Middle East and North Africa 63.3 \n", "138 Zambia Sub Saharan Africa 58.4 \n", "139 Zimbabwe Sub Saharan Africa 53.7 \n", "\n", " Average-Wellbeing_(0-10) Happy-Life-Years Footprint_(gha/capita) \\\n", "0 3.8 12.4 0.8 \n", "1 5.5 34.4 2.2 \n", "2 5.6 30.5 2.1 \n", "3 6.5 40.2 3.1 \n", "4 4.3 24.0 2.2 \n", ".. ... ... ... \n", "135 7.1 41.5 3.6 \n", "136 5.5 32.8 1.7 \n", "137 4.1 15.2 1.0 \n", "138 5.0 16.7 1.0 \n", "139 5.0 16.4 1.4 \n", "\n", " Inequality-of-Outcomes Inequality-adjusted-Life-Expectancy \\\n", "0 0.43 38.3 \n", "1 0.17 69.7 \n", "2 0.24 60.5 \n", "3 0.16 68.3 \n", "4 0.22 66.9 \n", ".. ... ... \n", "135 0.19 65.5 \n", "136 0.19 64.8 \n", "137 0.39 44.7 \n", "138 0.41 38.7 \n", "139 0.37 36.9 \n", "\n", " Inequality-adjusted-Wellbeing Happy-Planet-Index GDP/capita($PPP) \\\n", "0 3.4 20.2 691 \n", "1 5.1 36.8 4247 \n", "2 5.2 33.3 5584 \n", "3 6.0 35.2 14357 \n", "4 3.7 25.7 3566 \n", ".. ... ... ... \n", "135 6.5 33.6 12772 \n", "136 5.2 40.3 1755 \n", "137 3.6 22.8 1289 \n", "138 4.5 25.2 1687 \n", "139 4.6 22.1 851 \n", "\n", " Population GINI-index \n", "0 29726803 NaN \n", "1 2900489 29.0 \n", "2 37439427 NaN \n", "3 42095224 42.5 \n", "4 2978339 30.5 \n", ".. ... ... \n", "135 29854238 NaN \n", "136 88809200 38.7 \n", "137 24882792 NaN \n", "138 14786581 NaN \n", "139 14565482 NaN \n", "\n", "[140 rows x 13 columns]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data4" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data4['GINI-index'][i] = y\n" ] } ], "source": [ "for i in range(0, data4.shape[0]):\n", " if np.isnan(data4['GINI-index'][i]):\n", " y = regresion(data4['Inequality-of-Outcomes'][i], c)\n", " data4['GINI-index'][i] = y" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryRegionAverage-Life-ExpectancyAverage-Wellbeing_(0-10)Happy-Life-YearsFootprint_(gha/capita)Inequality-of-OutcomesInequality-adjusted-Life-ExpectancyInequality-adjusted-WellbeingHappy-Planet-IndexGDP/capita($PPP)PopulationGINI-index
0AfghanistanMiddle East and North Africa59.73.812.40.80.4338.33.420.26912972680350.483220
1AlbaniaPost-communist77.35.534.42.20.1769.75.136.84247290048929.000000
2AlgeriaMiddle East and North Africa74.35.630.52.10.2460.55.233.355843743942740.057237
3ArgentinaAmericas75.96.540.23.10.1668.36.035.2143574209522442.500000
4ArmeniaPost-communist74.44.324.02.20.2266.93.725.73566297833930.500000
..........................................
135VenezuelaAmericas73.97.141.53.60.1965.56.533.6127722985423837.313557
136VietnamAsia Pacific75.55.532.81.70.1964.85.240.317558880920038.700000
137YemenMiddle East and North Africa63.34.115.21.00.3944.73.622.812892488279248.288276
138ZambiaSub Saharan Africa58.45.016.71.00.4138.74.525.216871478658149.385748
139ZimbabweSub Saharan Africa53.75.016.41.40.3736.94.622.18511456548247.190804
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140 rows × 13 columns

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" ], "text/plain": [ " Country Region Average-Life-Expectancy \\\n", "0 Afghanistan Middle East and North Africa 59.7 \n", "1 Albania Post-communist 77.3 \n", "2 Algeria Middle East and North Africa 74.3 \n", "3 Argentina Americas 75.9 \n", "4 Armenia Post-communist 74.4 \n", ".. ... ... ... \n", "135 Venezuela Americas 73.9 \n", "136 Vietnam Asia Pacific 75.5 \n", "137 Yemen Middle East and North Africa 63.3 \n", "138 Zambia Sub Saharan Africa 58.4 \n", "139 Zimbabwe Sub Saharan Africa 53.7 \n", "\n", " Average-Wellbeing_(0-10) Happy-Life-Years Footprint_(gha/capita) \\\n", "0 3.8 12.4 0.8 \n", "1 5.5 34.4 2.2 \n", "2 5.6 30.5 2.1 \n", "3 6.5 40.2 3.1 \n", "4 4.3 24.0 2.2 \n", ".. ... ... ... \n", "135 7.1 41.5 3.6 \n", "136 5.5 32.8 1.7 \n", "137 4.1 15.2 1.0 \n", "138 5.0 16.7 1.0 \n", "139 5.0 16.4 1.4 \n", "\n", " Inequality-of-Outcomes Inequality-adjusted-Life-Expectancy \\\n", "0 0.43 38.3 \n", "1 0.17 69.7 \n", "2 0.24 60.5 \n", "3 0.16 68.3 \n", "4 0.22 66.9 \n", ".. ... ... \n", "135 0.19 65.5 \n", "136 0.19 64.8 \n", "137 0.39 44.7 \n", "138 0.41 38.7 \n", "139 0.37 36.9 \n", "\n", " Inequality-adjusted-Wellbeing Happy-Planet-Index GDP/capita($PPP) \\\n", "0 3.4 20.2 691 \n", "1 5.1 36.8 4247 \n", "2 5.2 33.3 5584 \n", "3 6.0 35.2 14357 \n", "4 3.7 25.7 3566 \n", ".. ... ... ... \n", "135 6.5 33.6 12772 \n", "136 5.2 40.3 1755 \n", "137 3.6 22.8 1289 \n", "138 4.5 25.2 1687 \n", "139 4.6 22.1 851 \n", "\n", " Population GINI-index \n", "0 29726803 50.483220 \n", "1 2900489 29.000000 \n", "2 37439427 40.057237 \n", "3 42095224 42.500000 \n", "4 2978339 30.500000 \n", ".. ... ... \n", "135 29854238 37.313557 \n", "136 88809200 38.700000 \n", "137 24882792 48.288276 \n", "138 14786581 49.385748 \n", "139 14565482 47.190804 \n", "\n", "[140 rows x 13 columns]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data4" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data['GINI-index'].hist()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data4['GINI-index'].hist()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## K nearest neighbour imputation" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "from sklearn.impute import KNNImputer" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# define imputer\n", "imputer = KNNImputer(n_neighbors=10, weights='uniform', metric='nan_euclidean')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KNNImputer(n_neighbors=10)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = data.iloc[:,2:].values\n", "\n", "# fit\n", "imputer.fit(X)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "# transform the dataset\n", "Xtrans = imputer.transform(X)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "data4 = data.copy()\n", "data4['GINI-index'] = imputer.transform(X)[:,-1]" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryRegionAverage-Life-ExpectancyAverage-Wellbeing_(0-10)Happy-Life-YearsFootprint_(gha/capita)Inequality-of-OutcomesInequality-adjusted-Life-ExpectancyInequality-adjusted-WellbeingHappy-Planet-IndexGDP/capita($PPP)PopulationGINI-index
0AfghanistanMiddle East and North Africa59.73.812.40.80.4338.33.420.26912972680335.98
1AlbaniaPost-communist77.35.534.42.20.1769.75.136.84247290048929.00
2AlgeriaMiddle East and North Africa74.35.630.52.10.2460.55.233.355843743942737.19
3ArgentinaAmericas75.96.540.23.10.1668.36.035.2143574209522442.50
4ArmeniaPost-communist74.44.324.02.20.2266.93.725.73566297833930.50
..........................................
135VenezuelaAmericas73.97.141.53.60.1965.56.533.6127722985423835.98
136VietnamAsia Pacific75.55.532.81.70.1964.85.240.317558880920038.70
137YemenMiddle East and North Africa63.34.115.21.00.3944.73.622.812892488279234.81
138ZambiaSub Saharan Africa58.45.016.71.00.4138.74.525.216871478658135.37
139ZimbabweSub Saharan Africa53.75.016.41.40.3736.94.622.18511456548235.37
\n", "

140 rows × 13 columns

\n", "
" ], "text/plain": [ " Country Region Average-Life-Expectancy \\\n", "0 Afghanistan Middle East and North Africa 59.7 \n", "1 Albania Post-communist 77.3 \n", "2 Algeria Middle East and North Africa 74.3 \n", "3 Argentina Americas 75.9 \n", "4 Armenia Post-communist 74.4 \n", ".. ... ... ... \n", "135 Venezuela Americas 73.9 \n", "136 Vietnam Asia Pacific 75.5 \n", "137 Yemen Middle East and North Africa 63.3 \n", "138 Zambia Sub Saharan Africa 58.4 \n", "139 Zimbabwe Sub Saharan Africa 53.7 \n", "\n", " Average-Wellbeing_(0-10) Happy-Life-Years Footprint_(gha/capita) \\\n", "0 3.8 12.4 0.8 \n", "1 5.5 34.4 2.2 \n", "2 5.6 30.5 2.1 \n", "3 6.5 40.2 3.1 \n", "4 4.3 24.0 2.2 \n", ".. ... ... ... \n", "135 7.1 41.5 3.6 \n", "136 5.5 32.8 1.7 \n", "137 4.1 15.2 1.0 \n", "138 5.0 16.7 1.0 \n", "139 5.0 16.4 1.4 \n", "\n", " Inequality-of-Outcomes Inequality-adjusted-Life-Expectancy \\\n", "0 0.43 38.3 \n", "1 0.17 69.7 \n", "2 0.24 60.5 \n", "3 0.16 68.3 \n", "4 0.22 66.9 \n", ".. ... ... \n", "135 0.19 65.5 \n", "136 0.19 64.8 \n", "137 0.39 44.7 \n", "138 0.41 38.7 \n", "139 0.37 36.9 \n", "\n", " Inequality-adjusted-Wellbeing Happy-Planet-Index GDP/capita($PPP) \\\n", "0 3.4 20.2 691 \n", "1 5.1 36.8 4247 \n", "2 5.2 33.3 5584 \n", "3 6.0 35.2 14357 \n", "4 3.7 25.7 3566 \n", ".. ... ... ... \n", "135 6.5 33.6 12772 \n", "136 5.2 40.3 1755 \n", "137 3.6 22.8 1289 \n", "138 4.5 25.2 1687 \n", "139 4.6 22.1 851 \n", "\n", " Population GINI-index \n", "0 29726803 35.98 \n", "1 2900489 29.00 \n", "2 37439427 37.19 \n", "3 42095224 42.50 \n", "4 2978339 30.50 \n", ".. ... ... \n", "135 29854238 35.98 \n", "136 88809200 38.70 \n", "137 24882792 34.81 \n", "138 14786581 35.37 \n", "139 14565482 35.37 \n", "\n", "[140 rows x 13 columns]" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data4" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 56, 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4lyX7I7EFoF6vR6PRKFTY5OQk/faxvpd7xl7ZX99zFalzVi/1lmHD6Aybp8rY2ze/omM7OTnJ5oeeH/h2iirjdz8MrrNcC1FnoV03ETGd/TwIbAVWz2myDzizbfoMYLrINs3M7Mj0HfSSlkg6cfY58Dbg0TnN7gbemx198ybgUEQ83Xe1ZmZ2xIr8H14Dtkqa7efrEXGfpA8DRMQNwDZgLbAH+A3wvmLlmpnZkeo76CPiKeANHebf0PY8gI/2uw0zMyvOZ8aamSXOQW9mljgHvZlZ4hz0ZmaJG/zZLxWzfEgnF/Vi+cZ72TA6M+8JT3s3XTLEiqqhl99RGeMyrO2YLTR/ojczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS9wxd2bsMFXpLNzUzDe2G0ZnKOut3e136DNn7WjgT/RmZokrcivBMyU9IGm3pMckfbxDm4akQ5J2ZY/PFCvXzMyOVJH/b2eADRHxSHbv2J2StkfET+e0+15EXFpgO2ZmVkDfn+gj4umIeCR7/mtgN7CsrMLMzKwcat3WtWAn0nLgQeCciHiubX4DuBPYB0wDn4yIx3L6GAfGAWq12qqJiYlCNTWbTUZGRl4xf2r/oUL9zhpdtrRrm162VVsMBw4Pfjtl6FZrVQyzzl5+P3ny3qNV4zrLNag6x8bGdkZEvdOywkEvaQT4LvDZiLhrzrLXAC9FRFPSWuALEbGiW5/1ej127NhRqK7JyUkajcYr5pd1JEwvR1v0sq0NozNsnsrfg1bWdsrQrdaqGGadRY66yXuPVo3rLNeg6pSUG/SFjrqRdDytT+y3zQ15gIh4LiKa2fNtwPGSTimyTTMzOzJFjroRcBOwOyI+n9Pm9Vk7JK3OtveLfrdpZmZHrsj/txcAVwFTknZl8z4N/A5ARNwAXA58RNIMcBi4Isr4UsDMzHrWd9BHxEOAurS5Hri+3230Y3Z/dbd7sZoNS953KO3v0W77+of53dKw+Kzj4fGZsWZmiXPQm5klzkFvZpY4B72ZWeIc9GZmiXPQm5klzkFvZpY4B72ZWeIc9GZmiav+pQgralhXjPR9Z48NVXk/lXU26tH0vu2l1jLGpZez9gd1NrA/0ZuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWuKL3jF0j6QlJeyRt7LBckr6YLf+JpPOKbM/MzI5ckXvGLgK+BFwMrATWSVo5p9nFwIrsMQ58ud/tmZlZf4p8ol8N7ImIpyLiBWACuGxOm8uAr0XLD4CTJJ1WYJtmZnaE1O+9uiVdDqyJiA9m01cB50fENW1t7gE2ZfeXRdL9wLURsaNDf+O0PvUDnA080Vdh/+8U4JmCfQzD0VInHD21us5yuc5yDarO342I13VaUOQSCJ1uDD73r0YvbVozI7YAWwrU8/INSzsiol5Wf4NytNQJR0+trrNcrrNcC1FnkV03+4Az26bPAKb7aGNmZgNUJOgfBlZIOkvSCcAVwN1z2twNvDc7+uZNwKGIeLrANs3M7Aj1vesmImYkXQN8G1gE3BwRj0n6cLb8BmAbsBbYA/wGeF/xkntW2m6gATta6oSjp1bXWS7XWa6h19n3l7FmZnZ08JmxZmaJc9CbmSUuiaCXdKakByTtlvSYpI9n86+TtF/SruyxdoHr/C1JP5L046zOv87mv1bSdklPZj9PrmidlRrPWZIWSfq37LyNyo3nrA51VnU890qaymrakc2r3Jjm1Fm5MZV0kqRvSno8y6g/HvZ4JrGPPjvb9rSIeETSicBO4B3Ae4BmRPzdQtY3S5KAJRHRlHQ88BDwceBdwC8jYlN2zaCTI+LaCta5hgqN5yxJfw7UgddExKWSPkeFxnNWhzqvo5rjuReoR8QzbfMqN6Y5dV5HxcZU0q3A9yLixuwIxVcDn2aI45nEJ/qIeDoiHsme/xrYDSxb2KpeKbsURDObPD57BK1LRdyazb+V1h+pBTNPnZUj6QzgEuDGttmVGk/IrfNoUrkxPRpIeg3wZuAmgIh4ISJ+xZDHM4mgbydpOfBG4IfZrGvUunLmzRX5d3ORpF3AQWB7RPwQqM2eX5D9PHUBSwRy64SKjSfwD8BfAC+1zavceNK5TqjeeELrj/p3JO1U69IkUM0x7VQnVGtMfw/4H+Afs912N0pawpDHM6mglzQC3Al8IiKeo3W1zN8HzgWeBjYvXHUtEfFiRJxL6yzh1ZLOWeCSOsqps1LjKelS4GBE7FzIOrqZp85KjWebCyLiPFpXn/2opDcvdEE5OtVZtTE9DjgP+HJEvBF4HnjFJd0HLZmgz/Yl3wncFhF3AUTEgSywXgK+SuuKm5WQ/fs2SWu/94Hse4bZ7xsOLlxlL9deZwXH8wLg7dm+2gngLZL+meqNZ8c6KzieAETEdPbzILCVVl1VG9OOdVZwTPcB+9r+I/4mreAf6ngmEfTZl4c3Absj4vNt89svifxO4NFh19ZO0usknZQ9Xwy8FXic1qUirs6aXQ18a0EKzOTVWbXxjIhPRcQZEbGc1iU4/jUi/oyKjWdenVUbTwBJS7IDGsh2MbyNVl2VGtO8Oqs2phHx38DPJZ2dzboQ+ClDHs8iV6+skguAq4CpbL8ytL7VXifpXFr78vYCH1qI4tqcBtyq1k1bXgXcERH3SPo+cIekDwD/Bbx7IYskv85/qth45tlEtcYzz+cqOJ41YGvrsxPHAV+PiPskPUy1xjSvziq+Rz8G3JYdcfMUrUvBvIohjmcSh1eamVm+JHbdmJlZPge9mVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZon7X+VrPH8a785cAAAAAElFTkSuQmCC\n", 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