Partie_1_P7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sommaire'></a>\n",
    "<h1 style='text-align:center;border:5px double;border-color:gray;'>\n",
    "    <br/>\n",
    "    PROJET 6<br/>\n",
    "    <br/>\n",
    "    \"Effectuez une prédiction de revenus\"\n",
    "    <br/>\n",
    "    <br/>\n",
    "    Partie 1: Préparation des données<br/>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Misson 1](#m1)  \n",
    "* [Analyse des données brutes](#analyse)  \n",
    "* [Correction des données](#correction)  \n",
    "* [Ajout de la population](#pop)  \n",
    "\n",
    "## [Misson 2](#m2)  \n",
    "* [Diversité des distributions de revenus](#div)  \n",
    "* [Représentation des indices de Gini](#gini)  \n",
    "* [Evolution des indices de Gini](#evol)  \n",
    "* [Calcul de l'indice de Gini à partir du fichier de la World Income Distribution](#calcul)  \n",
    "* [Classement par indice de Gini](#classement)  \n",
    "\n",
    "## [Mission 3](#m3)  \n",
    "* [Ajout du coefficient d'élasticité](#ajout)  \n",
    "* [Protocole du calcul de distribution condition conditionnelles](#proto)  \n",
    "* [Application du protocole sur nos données](#appli)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24.2\n"
     ]
    }
   ],
   "source": [
    "#! coding:utf-8\n",
    "import pandas as pd\n",
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.15.4\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(np.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.2\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "print(matplotlib.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1.0\n"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "import scipy.stats as st\n",
    "\n",
    "print(scipy.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9.0\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "print(sns.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functions1 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.makedirs('graph', exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='m1'></a>\n",
    "## Misson 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='analyse'></a>\n",
    "<h3 style='color:#8677b2'>Analyse des données brutes</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>7297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>916.66235</td>\n",
       "      <td>7297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>1010.91600</td>\n",
       "      <td>7297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>100</td>\n",
       "      <td>1086.90780</td>\n",
       "      <td>7297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2008</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>1132.69970</td>\n",
       "      <td>7297.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  year_survey  quantile  nb_quantiles      income  gdpppp\n",
       "0     ALB         2008         1           100   728.89795  7297.0\n",
       "1     ALB         2008         2           100   916.66235  7297.0\n",
       "2     ALB         2008         3           100  1010.91600  7297.0\n",
       "3     ALB         2008         4           100  1086.90780  7297.0\n",
       "4     ALB         2008         5           100  1132.69970  7297.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chargement du fichier de la World Income Distribution\n",
    "wid_brut = pd.read_csv(\"downloaded_files/data-projet7.csv\", decimal=',')\n",
    "wid_brut.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11594</th>\n",
       "      <td>COD</td>\n",
       "      <td>2008</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>810.6233</td>\n",
       "      <td>303.19305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11595</th>\n",
       "      <td>COD</td>\n",
       "      <td>2008</td>\n",
       "      <td>97</td>\n",
       "      <td>100</td>\n",
       "      <td>911.7834</td>\n",
       "      <td>303.19305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11596</th>\n",
       "      <td>COD</td>\n",
       "      <td>2008</td>\n",
       "      <td>98</td>\n",
       "      <td>100</td>\n",
       "      <td>1057.8074</td>\n",
       "      <td>303.19305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11597</th>\n",
       "      <td>COD</td>\n",
       "      <td>2008</td>\n",
       "      <td>99</td>\n",
       "      <td>100</td>\n",
       "      <td>1286.6029</td>\n",
       "      <td>303.19305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11598</th>\n",
       "      <td>COD</td>\n",
       "      <td>2008</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>2243.1226</td>\n",
       "      <td>303.19305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  year_survey  quantile  nb_quantiles     income     gdpppp\n",
       "11594     COD         2008        96           100   810.6233  303.19305\n",
       "11595     COD         2008        97           100   911.7834  303.19305\n",
       "11596     COD         2008        98           100  1057.8074  303.19305\n",
       "11597     COD         2008        99           100  1286.6029  303.19305\n",
       "11598     COD         2008       100           100  2243.1226  303.19305"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wid_brut.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11599, 6)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taille du DataFrame wid_brut\n",
    "wid_brut.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11599.000000</td>\n",
       "      <td>11599.000000</td>\n",
       "      <td>11599.0</td>\n",
       "      <td>11599.000000</td>\n",
       "      <td>1.139900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.982757</td>\n",
       "      <td>50.500819</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6069.224260</td>\n",
       "      <td>5.022128e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.909633</td>\n",
       "      <td>28.868424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9414.185972</td>\n",
       "      <td>4.000688e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.719418</td>\n",
       "      <td>3.031931e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>900.685515</td>\n",
       "      <td>2.576000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2403.244900</td>\n",
       "      <td>7.560000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7515.420900</td>\n",
       "      <td>1.877300e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>176928.550000</td>\n",
       "      <td>4.300332e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year_survey      quantile  nb_quantiles         income        gdpppp\n",
       "count  11599.000000  11599.000000       11599.0   11599.000000  1.139900e+04\n",
       "mean    2007.982757     50.500819         100.0    6069.224260  5.022128e+04\n",
       "std        0.909633     28.868424           0.0    9414.185972  4.000688e+05\n",
       "min     2004.000000      1.000000         100.0      16.719418  3.031931e+02\n",
       "25%     2008.000000     25.500000         100.0     900.685515  2.576000e+03\n",
       "50%     2008.000000     51.000000         100.0    2403.244900  7.560000e+03\n",
       "75%     2008.000000     75.500000         100.0    7515.420900  1.877300e+04\n",
       "max     2011.000000    100.000000         100.0  176928.550000  4.300332e+06"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description du fichier brut\n",
    "wid_brut.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country           0\n",
       "year_survey       0\n",
       "quantile          0\n",
       "nb_quantiles      0\n",
       "income            0\n",
       "gdpppp          200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comptage des valeurs manquantes\n",
    "wid_brut.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_survey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1500</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>7598</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             country  year_survey  quantile  nb_quantiles  income  gdpppp\n",
       "year_survey                                                              \n",
       "2004               1            1       100             1     100       1\n",
       "2006               5            1       100             1     500       5\n",
       "2007              15            1       100             1    1500      15\n",
       "2008              76            1       100             1    7598      75\n",
       "2009              12            1       100             1    1200      11\n",
       "2010               6            1       100             1     600       6\n",
       "2011               1            1       100             1     100       1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description par année\n",
    "wid_brut.groupby(by=['year_survey']).nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aland Islands</td>\n",
       "      <td>ALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pays ISO3\n",
       "0    Afghanistan  AFG\n",
       "1  Aland Islands  ALA\n",
       "2        Albania  ALB"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chargement du fichier de correspondance Code ISO et nom du pays en anglais\n",
    "code_pays = pd.read_csv(\"downloaded_files/country_code.csv\")\n",
    "code_pays = code_pays[['Pays', 'ISO3']]\n",
    "code_pays.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='color:#88cc8d;text-align:center'>\n",
    "    Le fichier de correspondance \"code pays/pays\" provient de trucsweb.com:<br/>\n",
    "    <a href=\"http://www.trucsweb.com/tutoriels/internet/iso_3166/\">Source</a><br/> \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>916.66235</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>1010.91600</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  year_survey  quantile  nb_quantiles      income  gdpppp     Pays  \\\n",
       "0     ALB         2008         1           100   728.89795  7297.0  Albania   \n",
       "1     ALB         2008         2           100   916.66235  7297.0  Albania   \n",
       "2     ALB         2008         3           100  1010.91600  7297.0  Albania   \n",
       "\n",
       "  ISO3  \n",
       "0  ALB  \n",
       "1  ALB  \n",
       "2  ALB  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Jointure de wid et de code_pays\n",
    "wid = pd.merge(wid_brut, code_pays, how='left', left_on='country', right_on='ISO3')\n",
    "\n",
    "wid.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11599, 8)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taille du DataFrame wid\n",
    "wid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country           0\n",
       "year_survey       0\n",
       "quantile          0\n",
       "nb_quantiles      0\n",
       "income            0\n",
       "gdpppp          200\n",
       "Pays            100\n",
       "ISO3            100\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification de valeurs nulles\n",
    "wid.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Pays</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALB</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARG</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARM</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  year_survey  quantile  nb_quantiles  income  gdpppp  Pays  \\\n",
       "country                                                                       \n",
       "ALB            1            1       100             1     100       1     1   \n",
       "ARG            1            1       100             1     100       1     1   \n",
       "ARM            1            1       100             1     100       1     1   \n",
       "\n",
       "         ISO3  \n",
       "country        \n",
       "ALB         1  \n",
       "ARG         1  \n",
       "ARM         1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrégation par 'country' avec 'nunique()'\n",
    "frt = wid.groupby(by='country').nunique()\n",
    "frt.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>nb_quantiles</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PSE</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XKX</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  year_survey  quantile  nb_quantiles  income  gdpppp  Pays  \\\n",
       "country                                                                       \n",
       "PSE            1            1       100             1     100       0     1   \n",
       "XKX            1            1       100             1     100       0     0   \n",
       "\n",
       "         ISO3  \n",
       "country        \n",
       "PSE         1  \n",
       "XKX         0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification des gdpppp non renseignés\n",
    "frt.loc[frt['gdpppp'] < 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
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       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LTU</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  year_survey  quantile  nb_quantiles  income  gdpppp  Pays  \\\n",
       "country                                                                       \n",
       "LTU            1            1        99             1      99       1     1   \n",
       "\n",
       "         ISO3  \n",
       "country        \n",
       "LTU         1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification de la quantité du nombre de quantiles\n",
    "frt.loc[frt['quantile'] < 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
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       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
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       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [country, year_survey, quantile, nb_quantiles, income, gdpppp, Pays, ISO3]\n",
       "Index: []"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification du nombre de quantile  par ligne\n",
    "frt.loc[frt['nb_quantiles'] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
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       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [country, year_survey, quantile, nb_quantiles, income, gdpppp, Pays, ISO3]\n",
       "Index: []"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification du nombre d'année renseignée par pays\n",
    "frt.loc[frt['year_survey'] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>XKX</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  year_survey  quantile  nb_quantiles  income  gdpppp  Pays  \\\n",
       "country                                                                       \n",
       "XKX            1            1       100             1     100       0     0   \n",
       "\n",
       "         ISO3  \n",
       "country        \n",
       "XKX         0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification des valeurs manquantes dans la correspondance entre entre pays et code pays ISO\n",
    "frt.loc[frt['Pays'] < 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='correction'></a>\n",
    "<h3 style='color:#8677b2'>Correction des données</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5800</th>\n",
       "      <td>XKX</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>437.8937</td>\n",
       "      <td>7530.632</td>\n",
       "      <td>Kosovo</td>\n",
       "      <td>XKX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  year_survey  quantile  nb_quantiles    income    gdpppp    Pays  \\\n",
       "5800     XKX         2008         1           100  437.8937  7530.632  Kosovo   \n",
       "\n",
       "     ISO3  \n",
       "5800  XKX  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Affectation du code ISO3-3166 Alpha-3, du nom du pays et du GDP PPP\n",
    "wid.loc[wid['country'] == 'XKX', 'ISO3'] = 'XKX'\n",
    "wid.loc[wid['country'] == 'XKX', 'Pays'] = 'Kosovo'\n",
    "wid.loc[wid['country'] == 'XKX', 'gdpppp'] = 7530.632\n",
    "\n",
    "wid.loc[wid['country'] == 'XKX'].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='color:#88cc8d;text-align:center'>\n",
    "    Les données manquantes sont remplacées par des valeurs issues de la Banque Mondiale<br/>\n",
    "    <a href=\"https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD?locations=XK\">Source</a><br/> \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11199</th>\n",
       "      <td>PSE</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>195.2899</td>\n",
       "      <td>3612.14</td>\n",
       "      <td>Palestinian Territory Occupied</td>\n",
       "      <td>PSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  year_survey  quantile  nb_quantiles    income   gdpppp  \\\n",
       "11199     PSE         2009         1           100  195.2899  3612.14   \n",
       "\n",
       "                                 Pays ISO3  \n",
       "11199  Palestinian Territory Occupied  PSE  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Affectation du GDP PPP\n",
    "wid.loc[wid['country'] == 'PSE', 'gdpppp'] = 3612.14\n",
    "\n",
    "wid.loc[wid['country'] == 'PSE'].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='color:#88cc8d;text-align:center'>\n",
    "    Les données manquantes sont remplacées par des valeurs issues du site theglobaleconomy.com<br/>\n",
    "    <a href=\"https://www.theglobaleconomy.com/Palestine/GDP_per_capita_PPP/\">Source</a><br/> \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NOR</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>50.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>22483.375020</td>\n",
       "      <td>49070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUX</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>50.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25217.562681</td>\n",
       "      <td>73127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FJI</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>50.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2098.730882</td>\n",
       "      <td>4300332.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year_survey  quantile  nb_quantiles        income     gdpppp\n",
       "country                                                              \n",
       "NOR           2008.0      50.5         100.0  22483.375020    49070.0\n",
       "LUX           2008.0      50.5         100.0  25217.562681    73127.0\n",
       "FJI           2008.0      50.5         100.0   2098.730882  4300332.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Affichage du GDP PPP maxi\n",
    "wid.groupby('country').mean().sort_values('gdpppp').tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3200</th>\n",
       "      <td>FJI</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>308.17334</td>\n",
       "      <td>7384.086</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>FJI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  year_survey  quantile  nb_quantiles     income    gdpppp  Pays  \\\n",
       "3200     FJI         2008         1           100  308.17334  7384.086  Fiji   \n",
       "\n",
       "     ISO3  \n",
       "3200  FJI  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Affectation d'une valeur de GDP PPP plus cohérente\n",
    "wid.loc[wid['country'] == 'FJI', 'gdpppp'] = 7384.086\n",
    "wid.loc[wid['country'] == 'FJI'].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='color:#88cc8d;text-align:center'>\n",
    "    Les données manquantes sont remplacées par des valeurs issues de la Banque Mondiale<br/>\n",
    "    <a href=\"https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD?locations=FJ\">Source</a><br/> \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6200</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>566.3453</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6201</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>1147.0587</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6202</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>1490.8408</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6203</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>100</td>\n",
       "      <td>1746.5715</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6204</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>1842.9542</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6205</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>100</td>\n",
       "      <td>1925.8641</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6206</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>100</td>\n",
       "      <td>2048.3857</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6207</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>2173.7112</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6208</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>100</td>\n",
       "      <td>2302.0857</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6209</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>2361.6480</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6210</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>100</td>\n",
       "      <td>2452.7578</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6211</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>2602.3857</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6212</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>2732.9660</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6213</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>14</td>\n",
       "      <td>100</td>\n",
       "      <td>2849.0960</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6214</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>15</td>\n",
       "      <td>100</td>\n",
       "      <td>3010.8962</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6215</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>16</td>\n",
       "      <td>100</td>\n",
       "      <td>3121.6091</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6216</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>17</td>\n",
       "      <td>100</td>\n",
       "      <td>3174.2260</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6217</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>18</td>\n",
       "      <td>100</td>\n",
       "      <td>3264.4023</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6218</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>19</td>\n",
       "      <td>100</td>\n",
       "      <td>3338.4468</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6219</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>20</td>\n",
       "      <td>100</td>\n",
       "      <td>3405.9260</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6220</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>21</td>\n",
       "      <td>100</td>\n",
       "      <td>3516.2354</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6221</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>22</td>\n",
       "      <td>100</td>\n",
       "      <td>3588.2507</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6222</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>23</td>\n",
       "      <td>100</td>\n",
       "      <td>3673.9355</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6223</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>24</td>\n",
       "      <td>100</td>\n",
       "      <td>3781.4660</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6224</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>25</td>\n",
       "      <td>100</td>\n",
       "      <td>3864.0430</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  year_survey  quantile  nb_quantiles     income   gdpppp  \\\n",
       "6200     LTU         2008         1           100   566.3453  17571.0   \n",
       "6201     LTU         2008         2           100  1147.0587  17571.0   \n",
       "6202     LTU         2008         3           100  1490.8408  17571.0   \n",
       "6203     LTU         2008         4           100  1746.5715  17571.0   \n",
       "6204     LTU         2008         5           100  1842.9542  17571.0   \n",
       "6205     LTU         2008         6           100  1925.8641  17571.0   \n",
       "6206     LTU         2008         7           100  2048.3857  17571.0   \n",
       "6207     LTU         2008         8           100  2173.7112  17571.0   \n",
       "6208     LTU         2008         9           100  2302.0857  17571.0   \n",
       "6209     LTU         2008        10           100  2361.6480  17571.0   \n",
       "6210     LTU         2008        11           100  2452.7578  17571.0   \n",
       "6211     LTU         2008        12           100  2602.3857  17571.0   \n",
       "6212     LTU         2008        13           100  2732.9660  17571.0   \n",
       "6213     LTU         2008        14           100  2849.0960  17571.0   \n",
       "6214     LTU         2008        15           100  3010.8962  17571.0   \n",
       "6215     LTU         2008        16           100  3121.6091  17571.0   \n",
       "6216     LTU         2008        17           100  3174.2260  17571.0   \n",
       "6217     LTU         2008        18           100  3264.4023  17571.0   \n",
       "6218     LTU         2008        19           100  3338.4468  17571.0   \n",
       "6219     LTU         2008        20           100  3405.9260  17571.0   \n",
       "6220     LTU         2008        21           100  3516.2354  17571.0   \n",
       "6221     LTU         2008        22           100  3588.2507  17571.0   \n",
       "6222     LTU         2008        23           100  3673.9355  17571.0   \n",
       "6223     LTU         2008        24           100  3781.4660  17571.0   \n",
       "6224     LTU         2008        25           100  3864.0430  17571.0   \n",
       "\n",
       "           Pays ISO3  \n",
       "6200  Lithuania  LTU  \n",
       "6201  Lithuania  LTU  \n",
       "6202  Lithuania  LTU  \n",
       "6203  Lithuania  LTU  \n",
       "6204  Lithuania  LTU  \n",
       "6205  Lithuania  LTU  \n",
       "6206  Lithuania  LTU  \n",
       "6207  Lithuania  LTU  \n",
       "6208  Lithuania  LTU  \n",
       "6209  Lithuania  LTU  \n",
       "6210  Lithuania  LTU  \n",
       "6211  Lithuania  LTU  \n",
       "6212  Lithuania  LTU  \n",
       "6213  Lithuania  LTU  \n",
       "6214  Lithuania  LTU  \n",
       "6215  Lithuania  LTU  \n",
       "6216  Lithuania  LTU  \n",
       "6217  Lithuania  LTU  \n",
       "6218  Lithuania  LTU  \n",
       "6219  Lithuania  LTU  \n",
       "6220  Lithuania  LTU  \n",
       "6221  Lithuania  LTU  \n",
       "6222  Lithuania  LTU  \n",
       "6223  Lithuania  LTU  \n",
       "6224  Lithuania  LTU  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recherche du quantile manquant dans les 25 premières lignes\n",
    "wid.loc[wid['country'] == 'LTU'] [ : 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6225</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>3930.2112</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6226</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>27</td>\n",
       "      <td>100</td>\n",
       "      <td>3990.3855</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6227</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>28</td>\n",
       "      <td>100</td>\n",
       "      <td>4048.9220</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6228</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "      <td>4109.7764</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6229</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>30</td>\n",
       "      <td>100</td>\n",
       "      <td>4190.6610</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6230</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>31</td>\n",
       "      <td>100</td>\n",
       "      <td>4254.9070</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6231</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>32</td>\n",
       "      <td>100</td>\n",
       "      <td>4317.0664</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6232</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>33</td>\n",
       "      <td>100</td>\n",
       "      <td>4360.1040</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6233</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>34</td>\n",
       "      <td>100</td>\n",
       "      <td>4425.0396</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6234</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>35</td>\n",
       "      <td>100</td>\n",
       "      <td>4503.2373</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6235</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>36</td>\n",
       "      <td>100</td>\n",
       "      <td>4591.1064</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6236</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>37</td>\n",
       "      <td>100</td>\n",
       "      <td>4701.1330</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6237</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>38</td>\n",
       "      <td>100</td>\n",
       "      <td>4756.4336</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6238</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>39</td>\n",
       "      <td>100</td>\n",
       "      <td>4802.3680</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6239</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>40</td>\n",
       "      <td>100</td>\n",
       "      <td>4868.4507</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6240</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>4895.8306</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6241</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>43</td>\n",
       "      <td>100</td>\n",
       "      <td>4950.6380</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6242</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>44</td>\n",
       "      <td>100</td>\n",
       "      <td>5006.7860</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6243</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>45</td>\n",
       "      <td>100</td>\n",
       "      <td>5028.5444</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6244</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>46</td>\n",
       "      <td>100</td>\n",
       "      <td>5088.5090</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6245</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>47</td>\n",
       "      <td>100</td>\n",
       "      <td>5165.5580</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6246</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>48</td>\n",
       "      <td>100</td>\n",
       "      <td>5239.3403</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6247</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>49</td>\n",
       "      <td>100</td>\n",
       "      <td>5327.5730</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6248</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>5444.4650</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6249</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>51</td>\n",
       "      <td>100</td>\n",
       "      <td>5504.8830</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  year_survey  quantile  nb_quantiles     income   gdpppp  \\\n",
       "6225     LTU         2008        26           100  3930.2112  17571.0   \n",
       "6226     LTU         2008        27           100  3990.3855  17571.0   \n",
       "6227     LTU         2008        28           100  4048.9220  17571.0   \n",
       "6228     LTU         2008        29           100  4109.7764  17571.0   \n",
       "6229     LTU         2008        30           100  4190.6610  17571.0   \n",
       "6230     LTU         2008        31           100  4254.9070  17571.0   \n",
       "6231     LTU         2008        32           100  4317.0664  17571.0   \n",
       "6232     LTU         2008        33           100  4360.1040  17571.0   \n",
       "6233     LTU         2008        34           100  4425.0396  17571.0   \n",
       "6234     LTU         2008        35           100  4503.2373  17571.0   \n",
       "6235     LTU         2008        36           100  4591.1064  17571.0   \n",
       "6236     LTU         2008        37           100  4701.1330  17571.0   \n",
       "6237     LTU         2008        38           100  4756.4336  17571.0   \n",
       "6238     LTU         2008        39           100  4802.3680  17571.0   \n",
       "6239     LTU         2008        40           100  4868.4507  17571.0   \n",
       "6240     LTU         2008        42           100  4895.8306  17571.0   \n",
       "6241     LTU         2008        43           100  4950.6380  17571.0   \n",
       "6242     LTU         2008        44           100  5006.7860  17571.0   \n",
       "6243     LTU         2008        45           100  5028.5444  17571.0   \n",
       "6244     LTU         2008        46           100  5088.5090  17571.0   \n",
       "6245     LTU         2008        47           100  5165.5580  17571.0   \n",
       "6246     LTU         2008        48           100  5239.3403  17571.0   \n",
       "6247     LTU         2008        49           100  5327.5730  17571.0   \n",
       "6248     LTU         2008        50           100  5444.4650  17571.0   \n",
       "6249     LTU         2008        51           100  5504.8830  17571.0   \n",
       "\n",
       "           Pays ISO3  \n",
       "6225  Lithuania  LTU  \n",
       "6226  Lithuania  LTU  \n",
       "6227  Lithuania  LTU  \n",
       "6228  Lithuania  LTU  \n",
       "6229  Lithuania  LTU  \n",
       "6230  Lithuania  LTU  \n",
       "6231  Lithuania  LTU  \n",
       "6232  Lithuania  LTU  \n",
       "6233  Lithuania  LTU  \n",
       "6234  Lithuania  LTU  \n",
       "6235  Lithuania  LTU  \n",
       "6236  Lithuania  LTU  \n",
       "6237  Lithuania  LTU  \n",
       "6238  Lithuania  LTU  \n",
       "6239  Lithuania  LTU  \n",
       "6240  Lithuania  LTU  \n",
       "6241  Lithuania  LTU  \n",
       "6242  Lithuania  LTU  \n",
       "6243  Lithuania  LTU  \n",
       "6244  Lithuania  LTU  \n",
       "6245  Lithuania  LTU  \n",
       "6246  Lithuania  LTU  \n",
       "6247  Lithuania  LTU  \n",
       "6248  Lithuania  LTU  \n",
       "6249  Lithuania  LTU  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recherche du quantile manquant dans les 25 lignes suivantes\n",
    "wid.loc[wid['country'] == 'LTU'] [25 : 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4882.14065"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcul du quantile manquant par la moyenne des quantiles autour\n",
    "inc_lit_qq = wid.loc[(wid['Pays'] == 'Lithuania') & (wid['quantile'] == 42) |\n",
    "        (wid['Pays'] == 'Lithuania') & (wid['quantile'] == 40),'income'].mean()\n",
    "inc_lit_qq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>quantile</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LTU</td>\n",
       "      <td>2008</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>4882.14065</td>\n",
       "      <td>17571.0</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  year_survey  quantile  nb_quantiles      income   gdpppp  \\\n",
       "0     LTU         2008        41           100  4882.14065  17571.0   \n",
       "\n",
       "        Pays ISO3  \n",
       "0  Lithuania  LTU  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Création de la ligne manquante (valeur moyenne entre q40 et q42)\n",
    "lit_inc = pd.DataFrame({'country' :  ['LTU'],\n",
    "                        'year_survey' : [2008],\n",
    "                        'quantile' : [41],\n",
    "                        'nb_quantiles' : [100],\n",
    "                        'income' : [inc_lit_qq],\n",
    "                        'gdpppp'  :  [17571.0],\n",
    "                        'Pays' : ['Lithuania'],\n",
    "                        'ISO3' :  ['LTU']})\n",
    "lit_inc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11600, 8)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concaténation avec wid\n",
    "wid1 = pd.concat([wid, lit_inc], ignore_index=True, sort=True)\n",
    "wid1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Pays</th>\n",
       "      <th>country</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>income</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>quantile</th>\n",
       "      <th>year_survey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11598</th>\n",
       "      <td>COD</td>\n",
       "      <td>Congo Democratic Republic of the</td>\n",
       "      <td>COD</td>\n",
       "      <td>303.19305</td>\n",
       "      <td>2243.12260</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11599</th>\n",
       "      <td>LTU</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "      <td>17571.00000</td>\n",
       "      <td>4882.14065</td>\n",
       "      <td>100</td>\n",
       "      <td>41</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ISO3                              Pays country       gdpppp      income  \\\n",
       "11598  COD  Congo Democratic Republic of the     COD    303.19305  2243.12260   \n",
       "11599  LTU                         Lithuania     LTU  17571.00000  4882.14065   \n",
       "\n",
       "       nb_quantiles  quantile  year_survey  \n",
       "11598           100       100         2008  \n",
       "11599           100        41         2008  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification\n",
    "wid1.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tri en place\n",
    "wid1.sort_values(by=['country', 'quantile', 'year_survey'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Pays</th>\n",
       "      <th>country</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>income</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>quantile</th>\n",
       "      <th>year_survey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11497</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>9602.0</td>\n",
       "      <td>46297.316</td>\n",
       "      <td>100</td>\n",
       "      <td>99</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11498</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>9602.0</td>\n",
       "      <td>82408.550</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ISO3          Pays country  gdpppp     income  nb_quantiles  quantile  \\\n",
       "11497  ZAF  South Africa     ZAF  9602.0  46297.316           100        99   \n",
       "11498  ZAF  South Africa     ZAF  9602.0  82408.550           100       100   \n",
       "\n",
       "       year_survey  \n",
       "11497         2008  \n",
       "11498         2008  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wid1.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11600, 8)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wid1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISO3            0\n",
       "Pays            0\n",
       "country         0\n",
       "gdpppp          0\n",
       "income          0\n",
       "nb_quantiles    0\n",
       "quantile        0\n",
       "year_survey     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification des valeurs nulles\n",
    "wid1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Pays</th>\n",
       "      <th>country</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>income</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>quantile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>916.66235</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>1010.91600</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ISO3     Pays country  gdpppp      income  nb_quantiles  quantile\n",
       "0  ALB  Albania     ALB  7297.0   728.89795           100         1\n",
       "1  ALB  Albania     ALB  7297.0   916.66235           100         2\n",
       "2  ALB  Albania     ALB  7297.0  1010.91600           100         3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour la suite de l'etude nous nous placerons en 2008\n",
    "# Nous prendnons comme approximation des valeurs de 2008, les valeurs des autres années\n",
    "wid2 = wid1.drop(columns='year_survey')\n",
    "wid2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11600, 7)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wid2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='pop'></a>\n",
    "<h3 style='color:#8677b2'>Ajout de la population</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Code</th>\n",
       "      <th>2008</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>101353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>27294031.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGO</td>\n",
       "      <td>21759420.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country Code        2008\n",
       "0          ABW    101353.0\n",
       "1          AFG  27294031.0\n",
       "2          AGO  21759420.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chargement du fichier des populations\n",
    "pop = pd.read_csv('downloaded_files/population.csv', skiprows=4)\n",
    "\n",
    "# Restriction sur les colonnes utiles\n",
    "pop = pop[['Country Code', '2008']]\n",
    "\n",
    "# Filtre des pays par le code ISO3\n",
    "pop_monde = pd.merge(code_pays, pop, how='left', left_on='ISO3', right_on='Country Code')\n",
    "pop.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='color:#88cc8d;text-align:center'>\n",
    "    Le fichier des tailles de populations provient de la banque mondiale:<br/>\n",
    "    <a href=\"https://donnees.banquemondiale.org/indicateur/sp.pop.totl\">Source</a><br/> \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Pays</th>\n",
       "      <th>country</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>income</th>\n",
       "      <th>nb_quantiles</th>\n",
       "      <th>quantile</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>2008</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>916.66235</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>1010.91600</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ISO3     Pays country  gdpppp      income  nb_quantiles  quantile  \\\n",
       "0  ALB  Albania     ALB  7297.0   728.89795           100         1   \n",
       "1  ALB  Albania     ALB  7297.0   916.66235           100         2   \n",
       "2  ALB  Albania     ALB  7297.0  1010.91600           100         3   \n",
       "\n",
       "  Country Code       2008  \n",
       "0          ALB  2947314.0  \n",
       "1          ALB  2947314.0  \n",
       "2          ALB  2947314.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Jointure de wid2 et de pop_monde\n",
    "wid_fin1 = pd.merge(wid2, pop_monde, how='left', on=['Pays', 'ISO3'])\n",
    "wid_fin1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2</td>\n",
       "      <td>916.66235</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>3</td>\n",
       "      <td>1010.91600</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pays  quantile      income  gdpppp ISO3  Population\n",
       "0  Albania         1   728.89795  7297.0  ALB   2947314.0\n",
       "1  Albania         2   916.66235  7297.0  ALB   2947314.0\n",
       "2  Albania         3  1010.91600  7297.0  ALB   2947314.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Restriction des colonnes utiles\n",
    "wid_fin1 = wid_fin1[['Pays', 'quantile', 'income', 'gdpppp', 'ISO3', '2008']]\n",
    "\n",
    "# Modification du nom de la colonne contenant les populations\n",
    "wid_fin1.rename(columns = {'2008' : 'Population'}, inplace = True)\n",
    "\n",
    "wid_fin1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pays            0\n",
       "quantile        0\n",
       "income          0\n",
       "gdpppp          0\n",
       "ISO3            0\n",
       "Population    200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification des valeurs manquantes\n",
    "wid_fin1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pays</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kosovo</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan Republic of China</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pays  quantile  income  gdpppp  ISO3  Population\n",
       "Pays                                                                      \n",
       "Kosovo                       1       100     100       1     1           0\n",
       "Taiwan Republic of China     1       100     100       1     1           0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identification des pays incomplets\n",
    "frt3 = wid_fin1.groupby(by='Pays').nunique()\n",
    "frt3.loc[frt3['Population'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11600, 6)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wid_fin1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Affectation manuelle des valeurs manquantes\n",
    "wid_fin1.loc[wid_fin1['Pays'] == 'Kosovo', 'Population'] = 1_747_400\n",
    "wid_fin1.loc[wid_fin1['Pays'] == 'Taiwan Republic of China', 'Population'] = 23_037_000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='color:#88cc8d;text-align:center'>\n",
    "    Les données manquantes pour le Kosovo sont remplacées par des valeurs issues de la banque mondiale<br/>\n",
    "    <a href=\"https://www.google.fr/publicdata/explore?ds=d5bncppjof8f9_&met_y=sp_pop_totl&hl=fr&dl=fr#!ctype=l&strail=false&bcs=d&nselm=h&met_y=sp_pop_totl&scale_y=lin&ind_y=false&rdim=region&idim=country:XKX&ifdim=region&hl=fr&dl=fr&ind=false\">Source</a><br/> \n",
    "</p>\n",
    "<p style='color:#88cc8d;text-align:center'>\n",
    "    Les données manquantes pour Taiwan sont remplacées par des valeurs issues du site countryeconomy.com<br/>\n",
    "    <a href=\"https://countryeconomy.com/demography/population/taiwan?year=2008\">Source</a><br/> \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pays          0\n",
       "quantile      0\n",
       "income        0\n",
       "gdpppp        0\n",
       "ISO3          0\n",
       "Population    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wid_fin1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11600, 6)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wid_fin1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6210658108.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcul de la population couverte par l'étude\n",
    "inter = wid_fin1.groupby(by='Pays').mean()\n",
    "pop_couv = inter['Population'].sum()\n",
    "\n",
    "pop_couv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6741214175.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcul de la population mondiale \n",
    "inter1 = pop_monde['2008'].sum()\n",
    "\n",
    "inter1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.12966606271637"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pourcentage de la population couverte par l'étude\n",
    "pourcentage_etude = 100 * pop_couv / inter1\n",
    "\n",
    "pourcentage_etude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='m2'></a>\n",
    "## Misson 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='div'></a>\n",
    "<h3 style='color:#8677b2'>Diversité des distributions de revenus</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Norway</td>\n",
       "      <td>50.5</td>\n",
       "      <td>22483.375020</td>\n",
       "      <td>49070.0</td>\n",
       "      <td>4768212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Canada</td>\n",
       "      <td>50.5</td>\n",
       "      <td>23739.640518</td>\n",
       "      <td>35895.0</td>\n",
       "      <td>33245773.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>50.5</td>\n",
       "      <td>25217.562681</td>\n",
       "      <td>73127.0</td>\n",
       "      <td>488650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>50.5</td>\n",
       "      <td>25503.581661</td>\n",
       "      <td>43261.0</td>\n",
       "      <td>304093966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>50.5</td>\n",
       "      <td>26888.511518</td>\n",
       "      <td>36527.0</td>\n",
       "      <td>317414.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pays  quantile        income   gdpppp   Population\n",
       "81                     Norway      50.5  22483.375020  49070.0    4768212.0\n",
       "16                     Canada      50.5  23739.640518  35895.0   33245773.0\n",
       "63                 Luxembourg      50.5  25217.562681  73127.0     488650.0\n",
       "111  United States of America      50.5  25503.581661  43261.0  304093966.0\n",
       "44                    Iceland      50.5  26888.511518  36527.0     317414.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description des données par pays\n",
    "wid_fin2 = wid_fin1.groupby(by='Pays').mean()\n",
    "wid_fin2.reset_index(inplace=True)\n",
    "wid_fin2.sort_values('income').tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Congo Democratic Republic of the</td>\n",
       "      <td>50.5</td>\n",
       "      <td>276.016044</td>\n",
       "      <td>303.19305</td>\n",
       "      <td>60373608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>50.5</td>\n",
       "      <td>345.237074</td>\n",
       "      <td>950.00000</td>\n",
       "      <td>19996469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>50.5</td>\n",
       "      <td>399.835204</td>\n",
       "      <td>1526.00000</td>\n",
       "      <td>19497986.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>50.5</td>\n",
       "      <td>519.320032</td>\n",
       "      <td>1429.00000</td>\n",
       "      <td>39148416.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>50.5</td>\n",
       "      <td>530.283828</td>\n",
       "      <td>4748.00000</td>\n",
       "      <td>1158897.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Pays  quantile      income      gdpppp  \\\n",
       "21  Congo Democratic Republic of the      50.5  276.016044   303.19305   \n",
       "65                        Madagascar      50.5  345.237074   950.00000   \n",
       "23                     Cote d'Ivoire      50.5  399.835204  1526.00000   \n",
       "55                             Kenya      50.5  519.320032  1429.00000   \n",
       "99                         Swaziland      50.5  530.283828  4748.00000   \n",
       "\n",
       "    Population  \n",
       "21  60373608.0  \n",
       "65  19996469.0  \n",
       "23  19497986.0  \n",
       "55  39148416.0  \n",
       "99   1158897.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wid_fin2.sort_values('income').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>116.0</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>1.160000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.5</td>\n",
       "      <td>6069.121925</td>\n",
       "      <td>12440.466006</td>\n",
       "      <td>5.354016e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6660.966925</td>\n",
       "      <td>13151.884845</td>\n",
       "      <td>1.682490e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>50.5</td>\n",
       "      <td>276.016044</td>\n",
       "      <td>303.193050</td>\n",
       "      <td>3.174140e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.5</td>\n",
       "      <td>1374.270126</td>\n",
       "      <td>2577.500000</td>\n",
       "      <td>4.698545e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.5</td>\n",
       "      <td>3287.174692</td>\n",
       "      <td>7517.816000</td>\n",
       "      <td>1.407229e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>50.5</td>\n",
       "      <td>7077.900152</td>\n",
       "      <td>17679.250000</td>\n",
       "      <td>3.945691e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.5</td>\n",
       "      <td>26888.511518</td>\n",
       "      <td>73127.000000</td>\n",
       "      <td>1.324655e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       quantile        income        gdpppp    Population\n",
       "count     116.0    116.000000    116.000000  1.160000e+02\n",
       "mean       50.5   6069.121925  12440.466006  5.354016e+07\n",
       "std         0.0   6660.966925  13151.884845  1.682490e+08\n",
       "min        50.5    276.016044    303.193050  3.174140e+05\n",
       "25%        50.5   1374.270126   2577.500000  4.698545e+06\n",
       "50%        50.5   3287.174692   7517.816000  1.407229e+07\n",
       "75%        50.5   7077.900152  17679.250000  3.945691e+07\n",
       "max        50.5  26888.511518  73127.000000  1.324655e+09"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wid_fin2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Congo Democratic Republic of the</td>\n",
       "      <td>50.5</td>\n",
       "      <td>276.016044</td>\n",
       "      <td>303.19305</td>\n",
       "      <td>60373608.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Pays  quantile      income     gdpppp  \\\n",
       "21  Congo Democratic Republic of the      50.5  276.016044  303.19305   \n",
       "\n",
       "    Population  \n",
       "21  60373608.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pays avec les revenus moyens les plus faibles\n",
    "wid_fin2[wid_fin2['income'] < 277]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>50.5</td>\n",
       "      <td>1377.773966</td>\n",
       "      <td>2578.0</td>\n",
       "      <td>86707801.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Pays  quantile       income  gdpppp  Population\n",
       "114  Viet Nam      50.5  1377.773966  2578.0  86707801.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pays avec les revenus moyens premier quartile\n",
    "wid_fin2[(wid_fin2['income'] > 1370) & (wid_fin2['income'] < 1378)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>50.5</td>\n",
       "      <td>3278.080965</td>\n",
       "      <td>4347.0</td>\n",
       "      <td>6047117.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pays  quantile       income  gdpppp  Population\n",
       "85  Paraguay      50.5  3278.080965  4347.0   6047117.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pays avec les revenus moyens médians\n",
    "wid_fin2[(wid_fin2['income'] > 3277) & (wid_fin2['income'] < 3296)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Chile</td>\n",
       "      <td>50.5</td>\n",
       "      <td>7051.609966</td>\n",
       "      <td>13390.0</td>\n",
       "      <td>16661942.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pays  quantile       income   gdpppp  Population\n",
       "18  Chile      50.5  7051.609966  13390.0  16661942.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pays avec les revenus moyens troisième quartile\n",
    "wid_fin2[(wid_fin2['income'] > 7051) & (wid_fin2['income'] < 7105)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>50.5</td>\n",
       "      <td>26888.511518</td>\n",
       "      <td>36527.0</td>\n",
       "      <td>317414.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pays  quantile        income   gdpppp  Population\n",
       "44  Iceland      50.5  26888.511518  36527.0    317414.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pays avec les revenus moyens les plus forts\n",
    "wid_fin2[wid_fin2['income'] > 26888]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>France</td>\n",
       "      <td>50.5</td>\n",
       "      <td>18309.407545</td>\n",
       "      <td>30357.0</td>\n",
       "      <td>64374989.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pays  quantile        income   gdpppp  Population\n",
       "35  France      50.5  18309.407545  30357.0  64374989.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# La france\n",
    "wid_fin2.loc[wid_fin2['Pays'] == 'France']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "incm_inter = wid_fin2[(wid_fin2['Pays'] == 'France') |\n",
    "                     (wid_fin2['Pays'] == 'Congo Democratic Republic of the') |\n",
    "                     (wid_fin2['Pays'] == 'Viet Nam') |\n",
    "                     (wid_fin2['Pays'] == 'Paraguay') |\n",
    "                     (wid_fin2['Pays'] == 'Chile') |\n",
    "                     (wid_fin2['Pays'] == 'Iceland')].sort_values('income')\n",
    "\n",
    "# Paramètres généraux\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "# Paramètres graphiques\n",
    "plt.barh(incm_inter.Pays, incm_inter.income)\n",
    "\n",
    "# Métadonnées\n",
    "plt.title('Diagramme bâton des pays choisis en fonction du revenu moyen')\n",
    "plt.xlabel('Revenus moyens')\n",
    "plt.tight_layout() # Permet de ne pas couper les labels\n",
    "\n",
    "# Sauvegarde\n",
    "plt.savefig('graph/diag_bat_incm.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Préparation des données par pays choisis\n",
    "incm_fra = wid_fin1[wid_fin1['Pays'] == 'France']\n",
    "incm_con = wid_fin1[wid_fin1['Pays'] == 'Congo Democratic Republic of the']\n",
    "incm_vie = wid_fin1[wid_fin1['Pays'] == 'Viet Nam']\n",
    "incm_par = wid_fin1[wid_fin1['Pays'] == 'Paraguay']\n",
    "incm_chi = wid_fin1[wid_fin1['Pays'] == 'Chile']\n",
    "incm_ice = wid_fin1[wid_fin1['Pays'] == 'Iceland']\n",
    "\n",
    "# Création des labels\n",
    "label = ['France',  'RD Congo (min)', 'Viet Nam (25%)', 'Paraguay (med)', 'Chile (75%)', 'Islande (max)']\n",
    "\n",
    "# Paramètres généraux\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.yscale('log')\n",
    "\n",
    "# Paramètres des courbes\n",
    "plt.plot(incm_fra['quantile'], incm_fra['income'], color='black')\n",
    "plt.text(40, 15000 , 'France', fontsize = '14', color='black', rotation = 10)\n",
    "\n",
    "plt.plot(incm_con['quantile'], incm_con['income'], color='darkgreen')\n",
    "plt.text(40, 192 , 'RD Congo', fontsize = '14', color='darkgreen', rotation = 12)\n",
    "\n",
    "plt.plot(incm_vie['quantile'], incm_vie['income'], color='darkgoldenrod')\n",
    "plt.text(40, 1090 , 'Viet Nam', fontsize = '14', color='darkgoldenrod', rotation = 11)\n",
    "\n",
    "plt.plot(incm_par['quantile'], incm_par['income'], color='darkviolet')\n",
    "plt.text(40, 2030 , 'Paraguay', fontsize = '14', color='darkviolet', rotation = 13)\n",
    "\n",
    "plt.plot(incm_chi['quantile'], incm_chi['income'], color='darkblue')\n",
    "plt.text(40, 3750 , 'Chile', fontsize = '14', color='darkblue', rotation = 11)\n",
    "\n",
    "plt.plot(incm_ice['quantile'], incm_ice['income'], color='darkcyan')\n",
    "plt.text(40, 23100 , 'Islande', fontsize = '14', color='darkcyan', rotation = 7)\n",
    "\n",
    "# Paramètre des lignes de référence\n",
    "plt.axvline(10, linestyle='--', color='r')\n",
    "plt.text(11, 30, '10%', color='r', fontsize=16)\n",
    "plt.axvline(90, linestyle='--', color='r')\n",
    "plt.text(91, 30, '90%', color='r', fontsize=16)\n",
    "\n",
    "# Métadonnées\n",
    "plt.title('Représentation des revenus par quantile\\n(min, 25%, med, 75%, max et France)', fontsize=22)\n",
    "plt.ylabel('Revenus (échelle logarithmique)', fontsize=20)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel('Quantile', fontsize=20)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Sauvegarde\n",
    "plt.savefig('graph/representation_revenus.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='gini'></a>\n",
    "<h3 style='color:#8677b2'>Représentation des indices de Gini</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculs des données des courbes de Lorenz\n",
    "income_fra = incm_fra['income'].values\n",
    "lorenz_fra = np.cumsum(np.sort(income_fra)) / income_fra.sum()\n",
    "lorenz_fra = np.append([0], lorenz_fra)\n",
    "\n",
    "income_con = incm_con['income'].values\n",
    "lorenz_con = np.cumsum(np.sort(income_con)) / income_con.sum()\n",
    "lorenz_con = np.append([0], lorenz_con)\n",
    "\n",
    "income_vie = incm_vie['income'].values\n",
    "lorenz_vie = np.cumsum(np.sort(income_vie)) / income_vie.sum()\n",
    "lorenz_vie = np.append([0], lorenz_vie)\n",
    "\n",
    "income_par = incm_par['income'].values\n",
    "lorenz_par = np.cumsum(np.sort(income_par)) / income_par.sum()\n",
    "lorenz_par = np.append([0], lorenz_par)\n",
    "\n",
    "income_chi = incm_chi['income'].values\n",
    "lorenz_chi = np.cumsum(np.sort(income_chi)) / income_chi.sum()\n",
    "lorenz_chi = np.append([0], lorenz_chi)\n",
    "\n",
    "income_ice = incm_ice['income'].values\n",
    "lorenz_ice = np.cumsum(np.sort(income_ice)) / income_ice.sum()\n",
    "lorenz_ice = np.append([0], lorenz_ice)\n",
    "\n",
    "# Paramètres généraux\n",
    "plt.figure(figsize=(15,15))\n",
    "\n",
    "# Paramètres des courbes de Lorenz\n",
    "plt.plot(np.linspace(0,1,len(lorenz_fra)),lorenz_fra, color='black')\n",
    "plt.plot(np.linspace(0,1,len(lorenz_con)),lorenz_con, color='darkgreen')\n",
    "plt.plot(np.linspace(0,1,len(lorenz_vie)),lorenz_vie, color='darkgoldenrod')\n",
    "plt.plot(np.linspace(0,1,len(lorenz_par)),lorenz_par, color='darkviolet')\n",
    "plt.plot(np.linspace(0,1,len(lorenz_chi)),lorenz_chi, color='darkblue')\n",
    "plt.plot(np.linspace(0,1,len(lorenz_ice)),lorenz_ice, color='darkcyan')\n",
    "\n",
    "# Paramètre de la médiatrice\n",
    "x = [0, 1]\n",
    "y = [0, 1]\n",
    "plt.plot(x, y, 'red', linestyle='--', label='Médiatrice')\n",
    "plt.text(0.5, 0.59, 'Médiatrice', fontsize=14, rotation = 45, color ='red')\n",
    "\n",
    "# Métadonnées\n",
    "plt.title('Indice de Gini des revenus', fontsize=22)\n",
    "plt.ylabel('proportion des revenus', fontsize=20)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel('proportion de population', fontsize=20)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.legend(label, prop={'size':20})\n",
    "plt.tight_layout()\n",
    "\n",
    "# Sauvegarde\n",
    "plt.savefig('graph/gini_revenus.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='evol'></a>\n",
    "<h3 style='color:#8677b2'>Evolution des indices de Gini</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kosovo</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yémen, Rép. du</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afrique du Sud</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambie</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1960  1961  1962  1963  1964  1965  1966  1967  1968  1969  \\\n",
       "Country                                                                      \n",
       "Kosovo           NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "Yémen, Rép. du   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "Afrique du Sud   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "Zambie           NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "Zimbabwe         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "                ...   2009   2010   2011  2012   2013   2014   2015   2016  \\\n",
       "Country         ...                                                          \n",
       "Kosovo          ...  0.318  0.333  0.278  0.29  0.263  0.273  0.265  0.267   \n",
       "Yémen, Rép. du  ...    NaN    NaN    NaN   NaN    NaN  0.367    NaN    NaN   \n",
       "Afrique du Sud  ...    NaN  0.634    NaN   NaN    NaN  0.630    NaN    NaN   \n",
       "Zambie          ...    NaN  0.556    NaN   NaN    NaN    NaN  0.571    NaN   \n",
       "Zimbabwe        ...    NaN    NaN  0.432   NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "                2017  2018  \n",
       "Country                     \n",
       "Kosovo          0.29   NaN  \n",
       "Yémen, Rép. du   NaN   NaN  \n",
       "Afrique du Sud   NaN   NaN  \n",
       "Zambie           NaN   NaN  \n",
       "Zimbabwe         NaN   NaN  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chargement du fichier\n",
    "gini_brut = pd.read_csv('downloaded_files/gini.csv', skiprows=3, skipinitialspace=True, index_col=1)\n",
    "\n",
    "# Mise en forme des données\n",
    "gini_brut.reset_index(inplace=True)\n",
    "gini_brut.rename(columns={'Country Name': 'Country'}, inplace =True)\n",
    "gini_brut.drop(columns=['Country Code',\n",
    "                      'Indicator Name',\n",
    "                      'Indicator Code',\n",
    "                       'Unnamed: 63'], inplace = True)\n",
    "gini_brut.set_index('Country', inplace=True)\n",
    "gini_brut = gini_brut.transform(lambda x: x / 100)\n",
    "\n",
    "gini_brut.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='color:#88cc8d;text-align:center'>\n",
    "    Les données ont été téléchargées sur le site de la banque mondiale<br/>\n",
    "    <a href=\"https://donnees.banquemondiale.org/indicateur/si.pov.gini?view=map\">Source</a><br/>\n",
    "    data > Dimension: Inequality > Inequality: Income inequality, Gini coefficient<br/>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chili\n",
      "Congo, République démocratique du\n",
      "Congo, République du\n",
      "Finlande\n",
      "Groenland\n",
      "Irlande\n",
      "Islande\n",
      "Nouvelle-Zélande\n",
      "Thaïlande\n"
     ]
    }
   ],
   "source": [
    "# Recherche de pays\n",
    "liste_pays = gini_brut.index\n",
    "for i in liste_pays:\n",
    "    if 'congo' in i.lower():\n",
    "        print(i)\n",
    "    if 'chil' in i.lower():\n",
    "        print(i)\n",
    "    if 'land' in i.lower():\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.314</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.323</td>\n",
       "      <td>0.327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          2003   2004   2005   2006   2007  2008   2009   2010   2011   2012  \\\n",
       "Country                                                                        \n",
       "France   0.314  0.306  0.298  0.297  0.324  0.33  0.327  0.337  0.333  0.331   \n",
       "\n",
       "          2013   2014   2015  \n",
       "Country                       \n",
       "France   0.325  0.323  0.327  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Données disponibles pour la France\n",
    "gini_fra = gini_brut.loc[gini_brut.index == 'France'].iloc[0:].dropna(axis=1)\n",
    "gini_fra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2004</th>\n",
       "      <th>2012</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Congo, République démocratique du</th>\n",
       "      <td>0.422</td>\n",
       "      <td>0.421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    2004   2012\n",
       "Country                                        \n",
       "Congo, République démocratique du  0.422  0.421"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Données disponibles pour la République démocratique du Congo\n",
    "gini_con = gini_brut.loc[gini_brut.index == 'Congo, République démocratique du'].iloc[0:].dropna(axis=1)\n",
    "gini_con"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1992</th>\n",
       "      <th>1998</th>\n",
       "      <th>2002</th>\n",
       "      <th>2004</th>\n",
       "      <th>2006</th>\n",
       "      <th>2008</th>\n",
       "      <th>2010</th>\n",
       "      <th>2012</th>\n",
       "      <th>2014</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>0.357</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1992   1998  2002   2004   2006   2008   2010   2012   2014   2016\n",
       "Country                                                                      \n",
       "Viet Nam  0.357  0.354  0.37  0.368  0.358  0.356  0.393  0.356  0.348  0.353"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Données disponibles pour le Viet Nam\n",
    "gini_vie = gini_brut.loc[gini_brut.index == 'Viet Nam'].iloc[0:].dropna(axis=1)\n",
    "gini_vie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1990</th>\n",
       "      <th>1995</th>\n",
       "      <th>1997</th>\n",
       "      <th>1999</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>...</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Paraguay</th>\n",
       "      <td>0.408</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           1990   1995   1997   1999   2001   2002   2003   2004   2005  2006  \\\n",
       "Country                                                                         \n",
       "Paraguay  0.408  0.582  0.549  0.546  0.546  0.573  0.549  0.523  0.514  0.53   \n",
       "\n",
       "          ...   2008   2009  2010   2011   2012   2013   2014   2015   2016  \\\n",
       "Country   ...                                                                 \n",
       "Paraguay  ...  0.507  0.491  0.51  0.523  0.476  0.479  0.507  0.476  0.479   \n",
       "\n",
       "           2017  \n",
       "Country          \n",
       "Paraguay  0.488  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Données disponibles pour le Paraguay\n",
    "gini_par = gini_brut.loc[gini_brut.index == 'Paraguay'].iloc[0:].dropna(axis=1)\n",
    "gini_par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1987</th>\n",
       "      <th>1990</th>\n",
       "      <th>1992</th>\n",
       "      <th>1994</th>\n",
       "      <th>1996</th>\n",
       "      <th>1998</th>\n",
       "      <th>2000</th>\n",
       "      <th>2003</th>\n",
       "      <th>2006</th>\n",
       "      <th>2009</th>\n",
       "      <th>2011</th>\n",
       "      <th>2013</th>\n",
       "      <th>2015</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chili</th>\n",
       "      <td>0.562</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1987   1990   1992   1994   1996   1998   2000   2003   2006  2009  \\\n",
       "Country                                                                        \n",
       "Chili    0.562  0.572  0.548  0.564  0.549  0.555  0.528  0.515  0.482  0.49   \n",
       "\n",
       "          2011   2013   2015   2017  \n",
       "Country                              \n",
       "Chili    0.476  0.473  0.477  0.466  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Données disponibles pour le Chili\n",
    "gini_chi = gini_brut.loc[gini_brut.index == 'Chili'].iloc[0:].dropna(axis=1)\n",
    "gini_chi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Islande</th>\n",
       "      <td>0.268</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          2003  2004  2005   2006   2007   2008   2009   2010   2011   2012  \\\n",
       "Country                                                                       \n",
       "Islande  0.268  0.28  0.29  0.302  0.295  0.318  0.287  0.262  0.268  0.268   \n",
       "\n",
       "          2013   2014  \n",
       "Country                \n",
       "Islande  0.254  0.278  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Données disponibles pour l'Islande\n",
    "gini_ice = gini_brut.loc[gini_brut.index == 'Islande'].iloc[0:].dropna(axis=1)\n",
    "gini_ice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Chili</th>\n",
       "      <th>Congo, République démocratique du</th>\n",
       "      <th>France</th>\n",
       "      <th>Islande</th>\n",
       "      <th>Paraguay</th>\n",
       "      <th>Viet Nam</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.323</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.476</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.488</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Chili  Congo, République démocratique du  France  Islande  Paraguay  \\\n",
       "year                                                                           \n",
       "2014       NaN                                NaN   0.323    0.278     0.507   \n",
       "2015     0.477                                NaN   0.327      NaN     0.476   \n",
       "2016       NaN                                NaN     NaN      NaN     0.479   \n",
       "2017     0.466                                NaN     NaN      NaN     0.488   \n",
       "2018       NaN                                NaN     NaN      NaN       NaN   \n",
       "\n",
       "Country  Viet Nam  \n",
       "year               \n",
       "2014        0.348  \n",
       "2015          NaN  \n",
       "2016        0.353  \n",
       "2017          NaN  \n",
       "2018          NaN  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Restriction des données sur les pays concernés\n",
    "gini = gini_brut.loc[(gini_brut.index == 'France') |\n",
    "                    (gini_brut.index == 'Congo, République démocratique du') |\n",
    "                    (gini_brut.index == 'Viet Nam') |\n",
    "                    (gini_brut.index == 'Paraguay') |\n",
    "                     (gini_brut.index == 'Chili' )|\n",
    "                    (gini_brut.index =='Islande')].T\n",
    "\n",
    "# Nommage de l'index\n",
    "gini.index.name = 'year'\n",
    "\n",
    "gini.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9B/gs8HVg9YjYsy03tBP7lnlwRGzdup1/MDOvbie3l1PDOdYY9gHklNxtMvOWzHx/Zv45Ih5PHViuCxwZbez3EDMPiuq+2/M/VBt/Qn3e7Rxt7PcIcv+5/Wgv4HFRPYj+m7oCeEpErDeCzG3bj/4H2CQinhQRnwUOB07tbVNDzn15256uyMxTM/PG9us1gHtk5pJhbsNTcqe291LgaRHxSqo4dnfgpKhhSqPKvI56n67V9hO/oYra2yz3jwyWu01E3H/Kjy4ENoyIe2Tm76neCdcDTx1h5n8C32/f/xwRvc+gYb+uU3NvBc7KzJ+0/dPVwJXUvmJUmWTmRzLz+oh4LPAtqjD3uqhhwCPJnbK/vYw6F7i8fe0VERsNqxC3jNf1YOAREXFYRFxAXSA5PVpPx2GZmtsKBr8FtouIJ0TEx6gC4InUBb+hXISa1tYbMvPMKcPyVwVuycybY8gFm2U8x1+mCpuvpY4t7gacHNVrbMGYt3NgRMS9qZ4Pd6YKNa/JzM9GxBnUifx1wKepE78tgIMy86cjyH11Zn4+qnJ/U/vwOwN4cGbeNNu8FeQekpnnR8SJVJfAbakryztQH0xvyMwrh5z5KuCq9v1qqlfCm4BfUScKb8zMG2aT2XLv1f72bai2HJaZl0TEe4DLM/PoqCLC9tR46IPaB9UwMw/JzEun/H434LjMHOpJ7opyI+I2mfn3iHgv8NHM/MQYMnegPpDeQ10dugZ422y3peXlAj9pB6tvBQ6lTjbPog58XjaEbXh6Zm9bOpS6Un55y9uZ2qbfOKK2viYzfxQRpwH3pk6IzqfeWy8B9srM740gd/p2vAnVJfJZmfnLGMKVzhU8x0cD9wN2og567g88FDg4M38ym8zl5B5KHayeQB3IvoE6CbuM6in2isycVS+QdlJ1KPBk4INUD4RDqHHPewEXZeY5bf90KHVF+x2zfZ6Xk3t4u2rd6wn4NGB3YO92kjIry8k8LDM/NmWZu1OF3u0y8/rZZq4g97WZ+dH2+12oz6PPUdvy94B3ZObfh5x5eGZ+pOXtnJkviYjDqc+cz1A9fG4Zwet6dGZmRBxGXb3ek5pr5CSqUPWOnMX8RMvJPAL4OHWl/NHAMe34Ym/qwsFbM/PHg2auILf3HC9qBeutqIsTm2XmUHqTLiN3H+o9+0mqrS8D/iMzPxURx1In2CfkLObsWU7mwdR+ah9qv78EuIW6en5RZp4+hP3E2i3vUcAbWzv+HFVweylwTWa+ta3fM6j98+unFJCGkXl8Zt7Qe03bMjsBx1G9FIbSE295bZ22zJrAV4BdZnuMuJzMXltXaccxDwPWz8xPR8RR1GfT8TnLOV2Wl9t+ty2wR9s/7UcNx/0mNZz9lkF7C6wk80BqOz6QGtJ3FLUtH5+znG9kJbmHALsBR2TmJyLiUdT76vWZ+fVhZ7Yi0Spt33Q/qvC36TA+X5eT+49tOGr+mBdQcxt+OCL2pfbFb8nMHw0jv+vmVQ+M+N9XeF4OfDdr2MbHaFfYqAOaLagJTz5EdUu/jHrhR5Hbu1p8U/t+IdXd9GmD5s0g9+O9XOrD93KqO/xpwOuprk8DXWFdQeYnqAPjn1InDfek5kv4CjWu8r6zKV4sI/cbmblT+9tHRk3KdzqwTUTcpx3A/YYqnAzUPXAlmUdHTRgKQGaeC1wZEUe2xw48A3Ifub0u/htQQ1kGHns3g8wHAmTmFzLz4ZnZK06tT3W1HcjKcoH7R/VQWJ86sX8mNQxs/cy8Mga4CjeDtt6HKtIckZmPzsxTgcOoq3/37ruRK8/9PHBMKwgeThVVr87Mj2fme6lC65NHlDt9O76U2q4GzltJZu85vn9mHkYVanbL6jJ9ArWvGrhXwkpyjwA2pHov3Ujt+y+i9l2LgVkfdGQNu9mEKsIcBxwJHEBdnfkV1ftjs7Z/upR2hXO2RaJl5B4O7B/tCl+7SvRVqsDc69EzqzlslpN5YPzvq4o7Uq/B9RFxhxjCkJnl5B4wpa3/LzP/MzP/Ro0RfjI1VGjYmQe2q2JXAze0QvLeVE+M72fmzSN6XV/c3j9HAw/IzC9m9SY6C3jKbIoXy8k8giqUbEoVgzan5oSA2oesCcz6wsSKtuEpJ1nXtMxNZ5u3ktyXAffPzPOpfUWvyPtxap6gWbV3Oc/xwdR8Ba8GzgU+k5l7UieAu7THzfaK4x2oguIB7d+Paj+/hip+PSiq58st1L7ikbMpXqwoc9qJ8xda/gHwj5Oz2Zqeu90ylnkEcHFWj7E1ovXWHmJmr61L2vdvZean27Kfps5FhlGIW97rCjVkZs2IOAd4JfWZ95PMvGnQ4sXKMrMmZd0xM7/U9sMfAx7G6Nv6KWo4dW8+iG9R5wF/G0VmZi5pxYtFwBVtme1nmbWi3H9sw5n5TWC9lgtD3BfPFfOqgEH7UG0HsTew9KBlLapb9MaZ+RVqI38WQPvg34A6kB5F7o9iSnfsqG7qFwDDmMBnebl3pNq7Wfsgupa6gkxmXgzcg7oSOczMNYGfRcSmmXkRVX19Yma+C/gOcNtYOixgNrm94RkXA2TmO6jx5XtQB5IXUifWZM0Yfk8G33mtLPMZEbH+lOWfQh3UHgG8LQafGGpGuVnjRe8HXJeZ34mIFwKHxWDjZVeWuWevrb2TxKw7zKzL0h3oIFaW+1yqeHEzddVgDeqq2EYR8eAcbJ6GFWVuRc0VcIfMfF/vAa2td6UOBga1vNyTpuReA5xGXUXoWZ86+RxF7rK24w9Rw2cWz+KAeWXP8T7tQ/9GINrvevvi2dy5aEW521Db0w1ZQ2b+tR1o/ZSaR2bQQudzImL7Ke+73wB3ihoLey410/1jqB41N9LmcaHaemEMOORsJbkfoZ7H6BU1s4ZznA0cFRF/pF77UWX2bju5JvDtiNiH+hx42CjbuoyH3ovabwxS6FxZ5sVUcWQ9aljdn4CHUMXPLaJ6APVtBrk/oN6zq2TmH6Y89L7ANwYs6q4o88NUUeZJVNflNwEvadvVY6neAgOd5Pa7DVPjvO9H9U4YeMjXDNp7MbBHVM+En7F0f7wFo2nrudR+6OmZeV1mnpOZ72nLbUKdAA5kSu4dsyY2PAXI1o6tI2KD9jn6deo9enxErEEVqn4RA0w2uZLMR0T1zJp6LHELtV98VUT8AdhykNe2j9zePndt4JdRPYkupHoAjiRzGbaiitoDFRz7yL0TtY/6NbX9vpAaCtb38JV+2pr/uxfCVtQ5x0Dzas0gd4OW+X1qyMj+UcXyZ1HDcn83qra2/fCtLC3q3tj7+Yja2stdnToufHF76E7UccxsC45zxrwYQhI1ruyVVJX8S5mZEbErNXnZZtR4649TVexDqA/h86jeATtRJ2AHZp9dm2aY+zFq1uxDgc9mdSM7HfhLZr5ohO39GPAv1NWE27Z2/z/qitjlVLf062d6gjJAWz/XKpNPbdmZmW8eUluPoq5ontMWex01JOgY6mDjK9SH0Y5UBfYAanKf2bR1euaxVHfPY7J1nY26kv19avs6IDN/MIK29nKPzRpy8Diqx8svqB3XS3PKkIBRtLWdnOxCbUP/077P+PntI/d11AfPm6hxwD/LpUNZng18MTNnXFCYxeu6a2vjFVQ321G19Xqqy/RlEfER6qRhB6ow9+LM/NVMM2fZ3mOADTJz737y+sz8A9XDZhFVUP4wVWC4inrvDGNfvLLteDG1v3otNQ77yJlekWoHJ3elbk93K7XfuQM1qe6BLffErJ4Hm1KFg50z89dRQ93uQhWm9syaK2im7ewndxPqwGfnrNs8P4Ca9f8XwMvbwd6oMz9P9ag5mxpmMKPM2eRSnwU7Ule1r6GGms3oOe4z8wFtucdRExz37spxN+DmfrbhWT7HO1H7j2uo/f8o2rop9T7qZb6BmhNiQ2rfNOMuy7Npa3v8R6lbPfd1S+0B37OPpXpcvLi198/UnEEzGi4zhNf1ROp47YWZ+cshtPUl2YZoRN2VKIBvZfWA6z32OOp1vSfwnJkeT/SZeWEunYB2ETWE773UXA0v7efYadDc9vP3U70630cNC5jtPnFlbb0j1evjdVRBoa/hkoO+rhGx7pTfrwHcJmd4m9xZtHV1auj6W6hCzVja2n5+ELVNbUzt/2d0UWQWbV2cdUHx/dRx6hEzbeds2trOOQ5vj72J2jctiOEjMA96YERdgT6Gmovh3cBTIuLlWfMBHEXNifDQzDycGtP4lKwuTdH+f2xm7jnAAfNMc4+guij/y5SHv5XqPjbK9h5BHZQ/Omts8EEsHYP2nMz8fR8n9P22dZdWvHgIVQ193YDFi+m5T42IF1FXuP5KnZCcRL2BF7fcm6mrYmdR4/ufm5l/mkVbl5d5BPX+6d02b0OqUPKMzNxhgOJFv7mPbQ9dj6qwH5uZu/RZvOg3c6f20O2pWf7fnpn79PP89pl7ODVGdLvM/HRmXho12/SirAn7+ileDPq6/lNbj7dn5r4jbusq1FVOqJ4Cp1Nj+582QPFioPY276YOpPsySGY7sHgaNYzvuMzcYwj74pm29X7UvBSvy8zD+yhe9HqmrAlclTVE5UVUUeZt1O3NHgk8OOruJj+milG9yQ7/DXhu1lCsfooX/eZeSt3KtNcz4XqqcPG4Pg7UB818evsTn6SuKu/VZ/Fi0Nyntc/3tah5np7Uxwl9v5k/oq6aPyMz/zhl3/SrPosXg7b1X9ufuENr6xNH2NYft8zeNvxq4EVZQ+z6KV4M2tapk9Ptlf0XLwZp70+pu1WcDzwHeH5mPiZnXryY7fv1CmpupCdmf8WL5eVeR13NBSAzL2gZm0RNptybkPsVwL6Z+YiZHk8MkLlpy7x92+/+kfqs26mfY6cBc+/YTuKhjpEjM/cewj5xZW29bVaRcwlVRH9S9ndCP+jreofMvDbqFrKLMvPPOfPixaBtvV3bD/99zG1ds/38OKpw8ficefFiNttwr2fJPtl/8WKQtq7dnuOLqWOY57b3zoIpXkBVf+ecWNol9laqmnlRZn68/e5zwHGtEnYd1T3sAe2F/W/gpVFdfn5LzVo7ttze32k76H520oPmnt/auygzv0z1DBhLW1vm9+hzbpEZ5L4V+FDWRJ33ycyft99dwNKhIr/NPiZFGkZm1sSOL5hAW8/OupXSqDO/ytKuaednZl/dz2eR+2XaMKVY2lVvbG3NzK9S3R/H0dapr+ufppw0jCP3H90es4YaXD6uzMz8LnXbvhkb0nv2UpaeBM4kc1WqYLs4Ij5NDdW7pf2tmyNif+qq2nHUlZQ9qNnBz6G24a+2ZW+irpaPI/dmqms4WUWwT40p82tt2eNn2s4h5X6rLXvO//3rI8m8iaXPb1/jyYfQ1gvbsjOevHkImRe0ZZfQx237hpD7jd7faieB48j9OzVfAFkT583omG2I79efUVdih9XWA4GrI2L7zPxie9ipVPH3s8A9I2KLrMks/zTGzK3a8dOMj8dnmXs+NQT1oZl59pgyP9cyt8jMz800cwi501/Xcbe1r2uPjgsAAAiRSURBVKGvw2xrzvAmCZPIHFLuRhGxZdYwk5/PNHc+mXM9MKLGql1JdT+G+lDZM5aON12NejGPpnbCd6bmJXgJ8C7qzbXQcpdEH+OxJpE5w9xVqQ/03kHx5e1x+1GTtH4b+pvoaliZ/RpiW2c8pnCWmb0x7H1PJDYHX9d9mPzrOu7nuO/Xdq4/x31mbk+dzNyJ6i1yNHXyumO0CefaCeyRwJuz5k/5L+A5EfGdtk599cqaVK5tta1zva2Tyu14W5dQJ0xHTHnoE6irvd8DHtTnSe6wMq8cc1u/23Jn3KtxiJl93elkjr6uC6mtfWcOKbf3HF/VT+58M6fmwIjq9vUB6ir/XlTXzR9HxAnUmOKNqIPVN1Lj5ndrP3sMNXnYO3OAW+kspNw50tY3UF21fhMRL6XGML4o6w4vnc60rbZ1PuQupLa23O2Ae+XSsacnUycaf6Xm7NgqqlfI+tRQlpdl3Yb2rsDts/UAmQu5ttW2zvW2Tip3jrT1ROCVmXlFRDwZ+H1mfmkuZNpW22pbB8+db+ZUD4ysbnwHZubbqKr1ke1XB1OTLL0qM59FjfX9LXBTZv4oM9+eNQZ3oPsAL6TcOdLWa9p3gFOyxpL3fVIyicxJ5drW0WYutNyF1NbmIiBj6R0eLgA2yszTqS6gB2RdWd2Q2if+sq3vrwc9KZlgrm21rXO9rZPKnQttvSUzr2i5H8/BT4YmkTmpXNtqW+d6W+edOTcHRi6duO8E4BMR8fjMPC8i/pB1i1SouQhuYMBb9iz03DnQ1r/Qbj2VmX+Za5mTyrWt87Otk8pdYG2d/vjHUnccAtgbeH5EfIq65eEpDMkkcm2rbR1W5kLLnUttjZrvaOAu2JPInFSubbWtw8qcZO58M+cKGD1Zt6N7N3WLzvOybmGzNXULz9WorsNDO7leiLm21bbO9dyF1NZJ5S6ktrYrJkuo4Sq9yRP/1NbhgdQdmYY+LnUSubbVtpo7dzL7zR3WydAkMieVa1tt61xv63wyp+bAmCrqLhe3RsS51D2G/0ZNHvnTrNmbzZ2DmZPKta221dy5kzmp3KiJiW8DnAZ8lJqM9HfUuNUZ3x1hLuTaVttq7tzJnFSubbWtcz13IbV1PpmzBQyAiLg98BlgM+CozDzR3LmfOalc2zp6tnV+5i6wtm5D3RL1q8B7M/Pdo86cVK5tHT3bOj9zbevo2db5lzmp3IXU1vlizg4haV5E3RLvsZn5N3PnTeakcm3r/MxdSG2dVO5CauuV1DCV48bc1knk2tb5mbuQ2jqpXNs6P3Nt6/zMXUhtnR+WLFkyZ7923333RebOv0zbOj8zbev8zV1IbfXLL7/88ssvv/zya3Jfc3oIiSRJkiRJWhgWTXoFJEmSJEmSVsYChiRJkiRJ6jwLGJIkSZIkqfPm+l1IJEmSVigijgIOA/bNzPdMen0kSdJgnMRTkiTNWxGxK/Ax4N8z802TXh9JkjQ4CxiSJGlWIuJQ4Jj2300z89JJrk9PRGwCfBM4JTNfMen1kSRJs+McGJIkaWARsQqwL9C7IvL8Ca7OdFsAh1q8kCRpfrAHhiRJGlhEPB74DHA6sAuwGNggM/8+yfWSJEnzj5N4SpKk2ej1uDgVuA44CHgqcM7UhSLiCOBwYEdgXeCVwAOBG4H/Ag7OzKumPeYLwPbAam35vYGNgN8CZwKHLatQEhGbAv8O7ASsD1wPnA8cuazhLRFxe+AlwNOBjaneJD8ATszMs/p4LiRJ0gg5hESSJA0kIu4C7Ar8JDO/Cry3/Wq/FTzsRcAHgCuAk4AfUoWDz0XE6st5zJnAAcCXgXcCf6UKGu9axjrtDHwbeCZwIfA2qnjxNOCbEbHltOXXBr4CvA64BXgP8D5gPeDMiDgGSZLUCfbAkCRJg9qb6h1xOkBm/jAivg3sGBH3y8zLlvGYnYGHZ+YPej+IiDOBPYEnA7mMx9wX2Dwzr2vLHwp8D3hORLw6M3/dfn4n4CzgL8CjMvOSKRmbA98ATgOmFjFOoObKeNXUu5RExG2pu5ccEhHnZuZ3Z/60SJKkUbAHhiRJ6lubvPN5wK3AGVN+dTrQ+92ynDi1eNGc2r5vvZzHvKpXvADIzBuAD1LHMQ+bstxzgLWBw6cWL9pjLm45W0TEZq0N6wDPAr41/RarmXkj8KrWlmcsZ70kSdIY2QNDkiQN4tFUz4jzps1dcSbwFuC5EXFYZt407XHfWsbf+mX7fqflZM30Mdu27w9pc25Md//2/QHAJcDDqUlHlyxn+dWmLC9JkibMAoYkSRpEb56L06f+MDN/FxGfBP6VGhJy7rTHXb+Mv3Vz+754WUGZOdPHrNO+r+xWrmtMW/7h7Wtly0uSpAmygCFJkvoSEesBT2n/PSsilnenjv34vwWMUfpD+/6QzPx+H8sfn5kHjWidJEnSkFjAkCRJ/doLuA1wEbC8yS13BR4TEffOzMvHtF5fp3p+bAfMpIDxTWoOj+1GuVKSJGk4LGBIkqR+9SbofFFmfnNZC0TE0cBr2rKHjmm93tuyDo+IC6evW0Qsou5O8gWAzPxtRHwQeHZEHAa8PjNvnvaY+wK3jrEII0mSlsMChiRJmrGI2AHYBPjB8ooXzbupYsLeEXH4ONatzb+xG/BR4OsRcT5wMdXLYiNqks91gNtOedj+wMbAUVQh4yvAb4C7U5N3Ppy6xasFDEmSJszbqEqSpH70Jsg8bUULZeYVwOeAuwFPGvE6Tc09H3gwcDJwL+AFVC+QBwKfB/aYtvwfge2BA4BrqSEoBwE7An8CXgZ8djxrL0mSVmSVJUuWTHodJEmSJEmSVsgeGJIkSZIkqfMsYEiSJEmSpM6zgCFJkiRJkjrPAoYkSZIkSeo8CxiSJEmSJKnzLGBIkiRJkqTOs4AhSZIkSZI6zwKGJEmSJEnqPAsYkiRJkiSp8/4/R2+czhHXOTQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Paramètre généraux\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "\n",
    "# Paramètres des courbes\n",
    "line, = ax.plot(gini['France'].interpolate(method='linear'),\n",
    "                ls = '--', lw = 1, color='Black', label='')\n",
    "ax.plot(gini['France'], color=line.get_color(), lw=1.5, marker = 'o')\n",
    "\n",
    "line, = ax.plot(gini['Congo, République démocratique du'].interpolate(method='linear'),\n",
    "                ls = '--', lw = 1, color='darkgreen', label='')\n",
    "ax.plot(gini['Congo, République démocratique du'], color=line.get_color(), lw=1.5, marker = 'o')\n",
    "\n",
    "line, = ax.plot(gini['Viet Nam'].interpolate(method='linear'),\n",
    "                ls = '--', lw = 1, color='darkgoldenrod', label='')\n",
    "ax.plot(gini['Viet Nam'], color=line.get_color(), lw=1.5, marker = 'o')\n",
    "\n",
    "line, = ax.plot(gini['Paraguay'].interpolate(method='linear'),\n",
    "                ls = '--', lw = 1, color='darkviolet', label='')\n",
    "ax.plot(gini['Paraguay'], color=line.get_color(), lw=1.5, marker = 'o')\n",
    "\n",
    "line, = ax.plot(gini['Chili'].interpolate(method='linear'),\n",
    "                ls = '--', lw = 1, color='darkblue', label='')\n",
    "ax.plot(gini['Chili'], color=line.get_color(), lw=1.5, marker = 'o')\n",
    "\n",
    "line, = ax.plot(gini['Islande'].interpolate(method='linear'),\n",
    "                ls = '--', lw = 1, color='darkcyan', label='')\n",
    "ax.plot(gini['Islande'], color=line.get_color(), lw=1.5, marker = 'o')\n",
    "\n",
    "\n",
    "# Métadonnées\n",
    "plt.title('Evolution des indices de Gini de 1986 à 2017\\n(min, 25%, med, 75%, max et France)', fontsize=22)\n",
    "plt.xlabel('Année', fontsize=20)\n",
    "plt.xticks(rotation=30, horizontalalignment='right')\n",
    "plt.ylabel('Indice de Gini', fontsize = 22)\n",
    "plt.ylim(0,0.7)\n",
    "plt.legend(fontsize=18)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Sauvegarde\n",
    "plt.savefig('graph/evolution_gini.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='calcul'></a>\n",
    "<h3 style='color:#8677b2'>Calcul de l'indice de Gini à partir du fichier de la World Income Distribution</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2</td>\n",
       "      <td>916.66235</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>3</td>\n",
       "      <td>1010.91600</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>4</td>\n",
       "      <td>1086.90780</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>5</td>\n",
       "      <td>1132.69970</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pays  quantile      income  gdpppp ISO3  Population     Gini\n",
       "0  Albania         1   728.89795  7297.0  ALB   2947314.0  0.32141\n",
       "1  Albania         2   916.66235  7297.0  ALB   2947314.0  0.32141\n",
       "2  Albania         3  1010.91600  7297.0  ALB   2947314.0  0.32141\n",
       "3  Albania         4  1086.90780  7297.0  ALB   2947314.0  0.32141\n",
       "4  Albania         5  1132.69970  7297.0  ALB   2947314.0  0.32141"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Liste des pays\n",
    "key = wid_fin1['Pays'].drop_duplicates().values\n",
    "\n",
    "# Création colonne vide\n",
    "wid_fin1['Gini'] = 0\n",
    "\n",
    "# Calcul de l'indice de Gini pour chaque pays et remplissage de la colonne Gini\n",
    "for i in key:\n",
    "    income1 = wid_fin1.loc[wid_fin1['Pays'] == i].income.values\n",
    "    lorenz = np.cumsum(np.sort(income1)) / income1.sum()\n",
    "    lorenz = np.append([0], lorenz)\n",
    "    wid_fin1.loc[wid_fin1['Pays'] == i, 'Gini'] = 2 * (0.5 - (lorenz[:-1].sum() / len(lorenz)))\n",
    "\n",
    "wid_fin1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pays</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>50.5</td>\n",
       "      <td>7051.609966</td>\n",
       "      <td>13390.00000</td>\n",
       "      <td>16661942.0</td>\n",
       "      <td>0.546115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Congo Democratic Republic of the</th>\n",
       "      <td>50.5</td>\n",
       "      <td>276.016044</td>\n",
       "      <td>303.19305</td>\n",
       "      <td>60373608.0</td>\n",
       "      <td>0.459403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>50.5</td>\n",
       "      <td>18309.407545</td>\n",
       "      <td>30357.00000</td>\n",
       "      <td>64374989.0</td>\n",
       "      <td>0.345640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>50.5</td>\n",
       "      <td>26888.511518</td>\n",
       "      <td>36527.00000</td>\n",
       "      <td>317414.0</td>\n",
       "      <td>0.302046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paraguay</th>\n",
       "      <td>50.5</td>\n",
       "      <td>3278.080965</td>\n",
       "      <td>4347.00000</td>\n",
       "      <td>6047117.0</td>\n",
       "      <td>0.539729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>50.5</td>\n",
       "      <td>1377.773966</td>\n",
       "      <td>2578.00000</td>\n",
       "      <td>86707801.0</td>\n",
       "      <td>0.393546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  quantile        income       gdpppp  \\\n",
       "Pays                                                                    \n",
       "Chile                                 50.5   7051.609966  13390.00000   \n",
       "Congo Democratic Republic of the      50.5    276.016044    303.19305   \n",
       "France                                50.5  18309.407545  30357.00000   \n",
       "Iceland                               50.5  26888.511518  36527.00000   \n",
       "Paraguay                              50.5   3278.080965   4347.00000   \n",
       "Viet Nam                              50.5   1377.773966   2578.00000   \n",
       "\n",
       "                                  Population      Gini  \n",
       "Pays                                                    \n",
       "Chile                             16661942.0  0.546115  \n",
       "Congo Democratic Republic of the  60373608.0  0.459403  \n",
       "France                            64374989.0  0.345640  \n",
       "Iceland                             317414.0  0.302046  \n",
       "Paraguay                           6047117.0  0.539729  \n",
       "Viet Nam                          86707801.0  0.393546  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sélection des pays suivis\n",
    "comp_gini = wid_fin1.loc[(wid_fin1['Pays'] == 'France') |\n",
    "             (wid_fin1['Pays'] == 'Congo Democratic Republic of the') |\n",
    "             (wid_fin1['Pays'] == 'Viet Nam') |\n",
    "             (wid_fin1['Pays'] == 'Paraguay') |\n",
    "             (wid_fin1['Pays'] == 'Chile') |\n",
    "             (wid_fin1['Pays'] == 'Iceland')].groupby('Pays').mean()\n",
    "\n",
    "comp_gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "      <th>gini2</th>\n",
       "      <th>Différence de Gini</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pays</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>50.5</td>\n",
       "      <td>7051.609966</td>\n",
       "      <td>13390.00000</td>\n",
       "      <td>16661942.0</td>\n",
       "      <td>0.546115</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.056115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Congo Democratic Republic of the</th>\n",
       "      <td>50.5</td>\n",
       "      <td>276.016044</td>\n",
       "      <td>303.19305</td>\n",
       "      <td>60373608.0</td>\n",
       "      <td>0.459403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>50.5</td>\n",
       "      <td>18309.407545</td>\n",
       "      <td>30357.00000</td>\n",
       "      <td>64374989.0</td>\n",
       "      <td>0.345640</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.015640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>50.5</td>\n",
       "      <td>26888.511518</td>\n",
       "      <td>36527.00000</td>\n",
       "      <td>317414.0</td>\n",
       "      <td>0.302046</td>\n",
       "      <td>0.318</td>\n",
       "      <td>-0.015954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paraguay</th>\n",
       "      <td>50.5</td>\n",
       "      <td>3278.080965</td>\n",
       "      <td>4347.00000</td>\n",
       "      <td>6047117.0</td>\n",
       "      <td>0.539729</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.032729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>50.5</td>\n",
       "      <td>1377.773966</td>\n",
       "      <td>2578.00000</td>\n",
       "      <td>86707801.0</td>\n",
       "      <td>0.393546</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.037546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  quantile        income       gdpppp  \\\n",
       "Pays                                                                    \n",
       "Chile                                 50.5   7051.609966  13390.00000   \n",
       "Congo Democratic Republic of the      50.5    276.016044    303.19305   \n",
       "France                                50.5  18309.407545  30357.00000   \n",
       "Iceland                               50.5  26888.511518  36527.00000   \n",
       "Paraguay                              50.5   3278.080965   4347.00000   \n",
       "Viet Nam                              50.5   1377.773966   2578.00000   \n",
       "\n",
       "                                  Population      Gini  gini2  \\\n",
       "Pays                                                            \n",
       "Chile                             16661942.0  0.546115  0.490   \n",
       "Congo Democratic Republic of the  60373608.0  0.459403    NaN   \n",
       "France                            64374989.0  0.345640  0.330   \n",
       "Iceland                             317414.0  0.302046  0.318   \n",
       "Paraguay                           6047117.0  0.539729  0.507   \n",
       "Viet Nam                          86707801.0  0.393546  0.356   \n",
       "\n",
       "                                  Différence de Gini  \n",
       "Pays                                                  \n",
       "Chile                                       0.056115  \n",
       "Congo Democratic Republic of the                 NaN  \n",
       "France                                      0.015640  \n",
       "Iceland                                    -0.015954  \n",
       "Paraguay                                    0.032729  \n",
       "Viet Nam                                    0.037546  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ajout des coefficients téléchargés sur le site de la banque mondiale\n",
    "comp_gini['gini2'] = gini_brut[(gini_brut.index == 'France') |\n",
    "          (gini_brut.index == 'Congo, République démocratique du') |\n",
    "         (gini_brut.index == 'Viet Nam') |\n",
    "         (gini_brut.index == 'Paraguay') |\n",
    "         (gini_brut.index == 'Chili') |\n",
    "         (gini_brut.index == 'Islande')]['2008'].values\n",
    "\n",
    "# La valeur de 2009 est choisie pour le Chili\n",
    "comp_gini.loc[comp_gini.index == 'Chile', 'gini2'] = gini_brut[gini_brut.index == 'Chili']['2009'].values\n",
    "\n",
    "comp_gini['Différence de Gini'] = comp_gini['Gini'] - comp_gini['gini2']\n",
    "comp_gini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='classement'></a>\n",
    "<h3 style='color:#8677b2'>Classement par indice de Gini</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>50.5</td>\n",
       "      <td>5617.904880</td>\n",
       "      <td>9602.0</td>\n",
       "      <td>50412129.0</td>\n",
       "      <td>0.682949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>50.5</td>\n",
       "      <td>3296.268419</td>\n",
       "      <td>3628.0</td>\n",
       "      <td>7872658.0</td>\n",
       "      <td>0.615512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>50.5</td>\n",
       "      <td>3547.005276</td>\n",
       "      <td>8185.0</td>\n",
       "      <td>44901544.0</td>\n",
       "      <td>0.583437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guatemala</td>\n",
       "      <td>50.5</td>\n",
       "      <td>2142.474753</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>14006366.0</td>\n",
       "      <td>0.582468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>50.5</td>\n",
       "      <td>811.299901</td>\n",
       "      <td>685.0</td>\n",
       "      <td>4345386.0</td>\n",
       "      <td>0.575972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Pays  quantile       income  gdpppp  Population  \\\n",
       "0              South Africa      50.5  5617.904880  9602.0  50412129.0   \n",
       "1                  Honduras      50.5  3296.268419  3628.0   7872658.0   \n",
       "2                  Colombia      50.5  3547.005276  8185.0  44901544.0   \n",
       "3                 Guatemala      50.5  2142.474753  4367.0  14006366.0   \n",
       "4  Central African Republic      50.5   811.299901   685.0   4345386.0   \n",
       "\n",
       "       Gini  \n",
       "0  0.682949  \n",
       "1  0.615512  \n",
       "2  0.583437  \n",
       "3  0.582468  \n",
       "4  0.575972  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Classement selon l'indice de Gini\n",
    "wid_fin3 = wid_fin1.groupby(by='Pays').mean()\n",
    "rank_gini = wid_fin3.sort_values(by='Gini', ascending=False)\n",
    "rank_gini.reset_index(inplace=True)\n",
    "\n",
    "# Les 5 pays avec le plus haut indice de Gini\n",
    "max_five_gini = rank_gini.head()\n",
    "\n",
    "max_five_gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6080675959510912"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Moyenne de l'indice de Gini des 5 plus forts indices\n",
    "max_five_gini['Gini'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>50.5</td>\n",
       "      <td>3349.388306</td>\n",
       "      <td>6721.0</td>\n",
       "      <td>46258200.0</td>\n",
       "      <td>0.272415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>50.5</td>\n",
       "      <td>16184.222707</td>\n",
       "      <td>34371.0</td>\n",
       "      <td>9219637.0</td>\n",
       "      <td>0.272166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>50.5</td>\n",
       "      <td>8235.293411</td>\n",
       "      <td>23223.0</td>\n",
       "      <td>10384603.0</td>\n",
       "      <td>0.270162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>50.5</td>\n",
       "      <td>6096.579894</td>\n",
       "      <td>20515.0</td>\n",
       "      <td>5379233.0</td>\n",
       "      <td>0.264573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>50.5</td>\n",
       "      <td>12106.007475</td>\n",
       "      <td>27197.0</td>\n",
       "      <td>2021316.0</td>\n",
       "      <td>0.248249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Pays  quantile        income   gdpppp  Population      Gini\n",
       "111         Ukraine      50.5   3349.388306   6721.0  46258200.0  0.272415\n",
       "112          Sweden      50.5  16184.222707  34371.0   9219637.0  0.272166\n",
       "113  Czech Republic      50.5   8235.293411  23223.0  10384603.0  0.270162\n",
       "114        Slovakia      50.5   6096.579894  20515.0   5379233.0  0.264573\n",
       "115        Slovenia      50.5  12106.007475  27197.0   2021316.0  0.248249"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Les 5 pays avec le plus faible indice de Gini\n",
    "min_five_gini = rank_gini.tail()\n",
    "\n",
    "min_five_gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26551310572405"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Moyenne de l'indice de Gini des 5 plus faible indices\n",
    "min_five_gini['Gini'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>France</td>\n",
       "      <td>50.5</td>\n",
       "      <td>18309.407545</td>\n",
       "      <td>30357.0</td>\n",
       "      <td>64374989.0</td>\n",
       "      <td>0.34564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pays  quantile        income   gdpppp  Population     Gini\n",
       "76  France      50.5  18309.407545  30357.0  64374989.0  0.34564"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Position de la France (77 ème)\n",
    "rank_gini.loc[rank_gini['Pays'] == 'France']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='m3'></a>\n",
    "## Misson 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='ajout'></a>\n",
    "<h3 style='color:#8677b2'>Ajout du coefficient d'élasticité</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>countryname</th>\n",
       "      <th>iso3</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>year</th>\n",
       "      <th>IGEincome</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Asia, Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Asia, Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Asia, Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Asia, Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Asia, Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   countryname iso3      region   incgroup4  year  IGEincome  \\\n",
       "0  Afghanistan  AFG  South Asia  Low income  1980        NaN   \n",
       "1  Afghanistan  AFG  South Asia  Low income  1980        NaN   \n",
       "2  Afghanistan  AFG  South Asia  Low income  1980        NaN   \n",
       "3  Afghanistan  AFG  South Asia  Low income  1980        NaN   \n",
       "4  Afghanistan  AFG  South Asia  Low income  1980        NaN   \n",
       "\n",
       "                    Group  \n",
       "0  South Asia, Low income  \n",
       "1  South Asia, Low income  \n",
       "2  South Asia, Low income  \n",
       "3  South Asia, Low income  \n",
       "4  South Asia, Low income  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chargement du fichier de la banque mondiale\n",
    "gdim = pd.read_csv('downloaded_files/gdim.csv')\n",
    "\n",
    "# Projection sur les colonnes utiles\n",
    "gdima = gdim[['countryname', 'iso3', 'region', 'incgroup4', 'year', 'IGEincome']]\n",
    "\n",
    "# Copie du dataframe initial\n",
    "gdimb = gdima.copy()\n",
    "\n",
    "# Création d'une colonne regroupant l'appelation 'region' et 'incgroup4'\n",
    "gdimb['Group'] = gdimb[['region', 'incgroup4']].apply(lambda x: ', '.join(x), axis=1)\n",
    "\n",
    "gdimb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>IGEincome</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East Asia &amp; Pacific, Lower middle income</th>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>0.527665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Asia &amp; Pacific, Upper middle income</th>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>0.469500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe &amp; Central Asia, Lower middle income</th>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>0.424817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe &amp; Central Asia, Upper middle income</th>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>0.477171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High income, High income</th>\n",
       "      <td>High income</td>\n",
       "      <td>High income</td>\n",
       "      <td>0.348050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America &amp; Caribbean, Lower middle income</th>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>0.940737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America &amp; Caribbean, Upper middle income</th>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>0.878852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle East &amp; North Africa, Lower middle income</th>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>0.916461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle East &amp; North Africa, Upper middle income</th>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>0.517398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Asia, Low income</th>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>0.436000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Asia, Lower middle income</th>\n",
       "      <td>South Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>0.523840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Asia, Upper middle income</th>\n",
       "      <td>South Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Saharan Africa, Low income</th>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>0.661654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Saharan Africa, Lower middle income</th>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>0.629976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Saharan Africa, Upper middle income</th>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>0.677000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     region  \\\n",
       "Group                                                                         \n",
       "East Asia & Pacific, Lower middle income                East Asia & Pacific   \n",
       "East Asia & Pacific, Upper middle income                East Asia & Pacific   \n",
       "Europe & Central Asia, Lower middle income            Europe & Central Asia   \n",
       "Europe & Central Asia, Upper middle income            Europe & Central Asia   \n",
       "High income, High income                                        High income   \n",
       "Latin America & Caribbean, Lower middle income    Latin America & Caribbean   \n",
       "Latin America & Caribbean, Upper middle income    Latin America & Caribbean   \n",
       "Middle East & North Africa, Lower middle income  Middle East & North Africa   \n",
       "Middle East & North Africa, Upper middle income  Middle East & North Africa   \n",
       "South Asia, Low income                                           South Asia   \n",
       "South Asia, Lower middle income                                  South Asia   \n",
       "South Asia, Upper middle income                                  South Asia   \n",
       "Sub-Saharan Africa, Low income                           Sub-Saharan Africa   \n",
       "Sub-Saharan Africa, Lower middle income                  Sub-Saharan Africa   \n",
       "Sub-Saharan Africa, Upper middle income                  Sub-Saharan Africa   \n",
       "\n",
       "                                                           incgroup4  \\\n",
       "Group                                                                  \n",
       "East Asia & Pacific, Lower middle income         Lower middle income   \n",
       "East Asia & Pacific, Upper middle income         Upper middle income   \n",
       "Europe & Central Asia, Lower middle income       Lower middle income   \n",
       "Europe & Central Asia, Upper middle income       Upper middle income   \n",
       "High income, High income                                 High income   \n",
       "Latin America & Caribbean, Lower middle income   Lower middle income   \n",
       "Latin America & Caribbean, Upper middle income   Upper middle income   \n",
       "Middle East & North Africa, Lower middle income  Lower middle income   \n",
       "Middle East & North Africa, Upper middle income  Upper middle income   \n",
       "South Asia, Low income                                    Low income   \n",
       "South Asia, Lower middle income                  Lower middle income   \n",
       "South Asia, Upper middle income                  Upper middle income   \n",
       "Sub-Saharan Africa, Low income                            Low income   \n",
       "Sub-Saharan Africa, Lower middle income          Lower middle income   \n",
       "Sub-Saharan Africa, Upper middle income          Upper middle income   \n",
       "\n",
       "                                                 IGEincome  \n",
       "Group                                                       \n",
       "East Asia & Pacific, Lower middle income          0.527665  \n",
       "East Asia & Pacific, Upper middle income          0.469500  \n",
       "Europe & Central Asia, Lower middle income        0.424817  \n",
       "Europe & Central Asia, Upper middle income        0.477171  \n",
       "High income, High income                          0.348050  \n",
       "Latin America & Caribbean, Lower middle income    0.940737  \n",
       "Latin America & Caribbean, Upper middle income    0.878852  \n",
       "Middle East & North Africa, Lower middle income   0.916461  \n",
       "Middle East & North Africa, Upper middle income   0.517398  \n",
       "South Asia, Low income                            0.436000  \n",
       "South Asia, Lower middle income                   0.523840  \n",
       "South Asia, Upper middle income                        NaN  \n",
       "Sub-Saharan Africa, Low income                    0.661654  \n",
       "Sub-Saharan Africa, Lower middle income           0.629976  \n",
       "Sub-Saharan Africa, Upper middle income           0.677000  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcul des 'IGEincome'  moyen par région et groupe de revenus\n",
    "frt6 = gdimb.groupby(['region', 'incgroup4', 'Group']).mean()[['IGEincome']]\n",
    "frt6.reset_index(inplace=True)\n",
    "frt6.set_index('Group', inplace=True)\n",
    "\n",
    "frt6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Affectation de la valeur moyenne du fichier elasticity.txt pour la zone 'Asie'  - 'Base case'\n",
    "gdimb.loc[(gdimb['region'] == 'South Asia') &\n",
    "          (gdimb['incgroup4'] == 'Upper middle income'), 'IGEincome'] = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>countryname</th>\n",
       "      <th>iso3</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>year</th>\n",
       "      <th>IGEincome</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      countryname  iso3  region  incgroup4  year  IGEincome  Group\n",
       "year                                                              \n",
       "1940          107   107       7          4     1          0     14\n",
       "1950          110   110       7          4     1          0     14\n",
       "1960          114   114       7          4     1         38     14\n",
       "1970          111   111       7          4     1         36     14\n",
       "1980          147   147       7          4     1          2     15"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification des années existantes pour l'IGEincome\n",
    "frt4 = gdimb.groupby(by='year').nunique()\n",
    "frt4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>1940</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>countryname</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.815874</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year         1940  1950  1960      1970  1980\n",
       "countryname                                  \n",
       "Afghanistan   NaN   NaN   NaN       NaN   NaN\n",
       "Albania       NaN   NaN   NaN  0.815874   NaN\n",
       "Angola        NaN   NaN   NaN       NaN   NaN"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Création d'un dataframe avec les 'IGEincome' en colonnes\n",
    "gdimc = pd.pivot_table(gdimb, values='IGEincome', index={'countryname'}, columns='year', dropna=False)\n",
    "gdimc.reset_index(inplace=True)\n",
    "gdimc.set_index('countryname', inplace=True)\n",
    "\n",
    "gdimc.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso3</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
       "      <th>1940</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>countryname</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>AFG</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>South Asia, Low income</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>AFG</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>South Asia, Low income</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>AFG</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>South Asia, Low income</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            iso3      region   incgroup4                   Group  1940  1950  \\\n",
       "countryname                                                                    \n",
       "Afghanistan  AFG  South Asia  Low income  South Asia, Low income   NaN   NaN   \n",
       "Afghanistan  AFG  South Asia  Low income  South Asia, Low income   NaN   NaN   \n",
       "Afghanistan  AFG  South Asia  Low income  South Asia, Low income   NaN   NaN   \n",
       "\n",
       "             1960  1970  1980  \n",
       "countryname                    \n",
       "Afghanistan   NaN   NaN   NaN  \n",
       "Afghanistan   NaN   NaN   NaN  \n",
       "Afghanistan   NaN   NaN   NaN  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Jointure et mise en forme du dataframe  \n",
    "gdimd = pd.merge(gdimb, gdimc, on='countryname')\n",
    "gdimd.drop(columns=['year', 'IGEincome'], inplace=True)\n",
    "gdimd.set_index('countryname', inplace=True)\n",
    "\n",
    "gdimd.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso3            0\n",
       "region          0\n",
       "incgroup4       0\n",
       "Group           0\n",
       "1940         6504\n",
       "1950         6504\n",
       "1960         4420\n",
       "1970         4442\n",
       "1980         6432\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification des valeurs nulles\n",
    "gdimd.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso3</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
       "      <th>1940</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>test_annee</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>countryname</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [iso3, region, incgroup4, Group, 1940, 1950, 1960, 1970, 1980, test_annee]\n",
       "Index: []"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification de l'existence d'un seul coefficient d'elasticité par pays\n",
    "frt2 = gdimd.groupby(by='countryname').nunique()\n",
    "frt2['test_annee'] = frt2[1940] + frt2[1950] + frt2[1960] + frt2[1970] + frt2[1980]\n",
    "frt2.loc[frt2['test_annee'] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nombre de pays sans coefficient d'élasticité\n",
    "len(frt2.loc[frt2['test_annee'] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso3</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
       "      <th>1940</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>countryname</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>AFG</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>South Asia, Low income</td>\n",
       "      <td>rien</td>\n",
       "      <td>rien</td>\n",
       "      <td>rien</td>\n",
       "      <td>rien</td>\n",
       "      <td>rien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>rien</td>\n",
       "      <td>rien</td>\n",
       "      <td>rien</td>\n",
       "      <td>0.815874</td>\n",
       "      <td>rien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Sub-Saharan Africa, Upper middle income</td>\n",
       "      <td>rien</td>\n",
       "      <td>rien</td>\n",
       "      <td>rien</td>\n",
       "      <td>rien</td>\n",
       "      <td>rien</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            iso3                 region            incgroup4  \\\n",
       "countryname                                                    \n",
       "Afghanistan  AFG             South Asia           Low income   \n",
       "Albania      ALB  Europe & Central Asia  Upper middle income   \n",
       "Angola       AGO     Sub-Saharan Africa  Upper middle income   \n",
       "\n",
       "                                                  Group  1940  1950  1960  \\\n",
       "countryname                                                                 \n",
       "Afghanistan                      South Asia, Low income  rien  rien  rien   \n",
       "Albania      Europe & Central Asia, Upper middle income  rien  rien  rien   \n",
       "Angola          Sub-Saharan Africa, Upper middle income  rien  rien  rien   \n",
       "\n",
       "                 1970  1980  \n",
       "countryname                  \n",
       "Afghanistan      rien  rien  \n",
       "Albania      0.815874  rien  \n",
       "Angola           rien  rien  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggrégation par pays selon la moyenne\n",
    "gdime = gdimd.groupby(by=['countryname', 'iso3', 'region', 'incgroup4', 'Group']).mean()\n",
    "gdime.reset_index(inplace=True)\n",
    "gdime.set_index('countryname', inplace = True)\n",
    "\n",
    "# Remplacement des 'NaN' par une string 'rien'\n",
    "gdime.fillna('rien', inplace=True)\n",
    "\n",
    "gdime.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Afghanistan', 'Albania', 'Angola', 'Argentina', 'Armenia', 'Australia',\n",
       "       'Austria', 'Azerbaijan', 'Bangladesh', 'Belarus',\n",
       "       ...\n",
       "       'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan', 'Vanuatu',\n",
       "       'Venezuela, RB', 'Vietnam', 'West Bank and Gaza', 'Yemen, Rep.',\n",
       "       'Zambia'],\n",
       "      dtype='object', name='countryname', length=150)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Création d'une clef pour itérer\n",
    "key1 = gdime.index\n",
    "key1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in key1:\n",
    "    if gdime.loc[i, 1980] != 'rien':\n",
    "        gdime.loc[i, [1940, 1950, 1960, 1970]] = gdime.loc[i, 1980] # Si il existe une valeur en 1980\n",
    "                                                                    #elle est propagée\n",
    "    elif gdime.loc[i, 1970] != 'rien':\n",
    "        gdime.loc[i, [1940, 1950, 1960, 1980]] = gdime.loc[i, 1970] # Sinon, si il existe une valeur en 1970\n",
    "                                                                    # Elle est propagée\n",
    "    \n",
    "    elif gdime.loc[i, 1960] != 'rien':\n",
    "        gdime.loc[i, [1940, 1950, 1970, 1980]] = gdime.loc[i, 1960] # Sinon, si il existe une valeur en 1960\n",
    "                                                                    #elle est propagée\n",
    "    \n",
    "    elif ((gdime.loc[i, 1980] == 'rien') and\n",
    "              (gdime.loc[i, 1970] == 'rien') and\n",
    "              (gdime.loc[i, 1960] == 'rien') and\n",
    "              (gdime.loc[i, 1950] == 'rien') and\n",
    "              (gdime.loc[i, 1940] == 'rien')): # Si il n'y a aucune valeur\n",
    "                                               # On choisi la valeur moyenne de la région\n",
    "                                               #du niveau de revenu\n",
    "            k = gdime.loc[i, 'Group']\n",
    "            gdime.loc[i, [1940, 1950, 1950, 1960, 1970, 1980]] = frt6.loc[k, 'IGEincome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso3         0\n",
       "region       0\n",
       "incgroup4    0\n",
       "Group        0\n",
       "1940         0\n",
       "1950         0\n",
       "1960         0\n",
       "1970         0\n",
       "1980         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdime.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso3          object\n",
       "region        object\n",
       "incgroup4     object\n",
       "Group         object\n",
       "1940         float64\n",
       "1950         float64\n",
       "1960         float64\n",
       "1970         float64\n",
       "1980         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assignation du type float pour les colonnes contenant les IGEincome\n",
    "gdime[[1940, 1950, 1960, 1970, 1980]] = gdime[[1940, 1950, 1960, 1970, 1980]].astype(float)\n",
    "\n",
    "gdime.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>countryname</th>\n",
       "      <th>iso3</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
       "      <th>1940</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>South Asia, Low income</td>\n",
       "      <td>0.436000</td>\n",
       "      <td>0.436000</td>\n",
       "      <td>0.436000</td>\n",
       "      <td>0.436000</td>\n",
       "      <td>0.436000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "      <td>0.815874</td>\n",
       "      <td>0.815874</td>\n",
       "      <td>0.815874</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Sub-Saharan Africa, Upper middle income</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>0.677000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   countryname iso3                 region            incgroup4  \\\n",
       "0  Afghanistan  AFG             South Asia           Low income   \n",
       "1      Albania  ALB  Europe & Central Asia  Upper middle income   \n",
       "2       Angola  AGO     Sub-Saharan Africa  Upper middle income   \n",
       "\n",
       "                                        Group      1940      1950      1960  \\\n",
       "0                      South Asia, Low income  0.436000  0.436000  0.436000   \n",
       "1  Europe & Central Asia, Upper middle income  0.815874  0.815874  0.815874   \n",
       "2     Sub-Saharan Africa, Upper middle income  0.677000  0.677000  0.677000   \n",
       "\n",
       "       1970      1980  \n",
       "0  0.436000  0.436000  \n",
       "1  0.815874  0.815874  \n",
       "2  0.677000  0.677000  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdime.reset_index(inplace=True)\n",
    "gdime.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pays</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
       "      <th>Coef Elas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>South Asia, Low income</td>\n",
       "      <td>0.436000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Sub-Saharan Africa, Upper middle income</td>\n",
       "      <td>0.677000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pays ISO3                 region            incgroup4  \\\n",
       "0  Afghanistan  AFG             South Asia           Low income   \n",
       "1      Albania  ALB  Europe & Central Asia  Upper middle income   \n",
       "2       Angola  AGO     Sub-Saharan Africa  Upper middle income   \n",
       "\n",
       "                                        Group  Coef Elas  \n",
       "0                      South Asia, Low income   0.436000  \n",
       "1  Europe & Central Asia, Upper middle income   0.815874  \n",
       "2     Sub-Saharan Africa, Upper middle income   0.677000  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mise en forme du dataframe\n",
    "gdimf = gdime.drop(columns=[1940,1950,1960,1970]).rename(columns={'countryname': 'pays',\n",
    "                                                                  'iso3': 'ISO3',\n",
    "                                                                  1980: 'Coef Elas'})\n",
    "gdimf.reset_index(inplace=True, drop=True)\n",
    "\n",
    "gdimf.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef Elas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East Asia &amp; Pacific, Lower middle income</th>\n",
       "      <td>0.527665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Asia &amp; Pacific, Upper middle income</th>\n",
       "      <td>0.469500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe &amp; Central Asia, Lower middle income</th>\n",
       "      <td>0.424817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe &amp; Central Asia, Upper middle income</th>\n",
       "      <td>0.477171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High income, High income</th>\n",
       "      <td>0.346502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America &amp; Caribbean, Lower middle income</th>\n",
       "      <td>0.940737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America &amp; Caribbean, Upper middle income</th>\n",
       "      <td>0.878852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle East &amp; North Africa, Lower middle income</th>\n",
       "      <td>0.916461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle East &amp; North Africa, Upper middle income</th>\n",
       "      <td>0.517398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Asia, Low income</th>\n",
       "      <td>0.436000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Asia, Lower middle income</th>\n",
       "      <td>0.526436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Asia, Upper middle income</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Saharan Africa, Low income</th>\n",
       "      <td>0.667098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Saharan Africa, Lower middle income</th>\n",
       "      <td>0.629976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Saharan Africa, Upper middle income</th>\n",
       "      <td>0.677000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Coef Elas\n",
       "Group                                                     \n",
       "East Asia & Pacific, Lower middle income          0.527665\n",
       "East Asia & Pacific, Upper middle income          0.469500\n",
       "Europe & Central Asia, Lower middle income        0.424817\n",
       "Europe & Central Asia, Upper middle income        0.477171\n",
       "High income, High income                          0.346502\n",
       "Latin America & Caribbean, Lower middle income    0.940737\n",
       "Latin America & Caribbean, Upper middle income    0.878852\n",
       "Middle East & North Africa, Lower middle income   0.916461\n",
       "Middle East & North Africa, Upper middle income   0.517398\n",
       "South Asia, Low income                            0.436000\n",
       "South Asia, Lower middle income                   0.526436\n",
       "South Asia, Upper middle income                   0.500000\n",
       "Sub-Saharan Africa, Low income                    0.667098\n",
       "Sub-Saharan Africa, Lower middle income           0.629976\n",
       "Sub-Saharan Africa, Upper middle income           0.677000"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification des valeurs moyennes par région et niveau de revenus\n",
    "gdimf.groupby(by='Group').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recherche des codes pays communs aux deux fichiers\n",
    "pays_gdim = gdimf.ISO3.unique()\n",
    "pays = wid_fin1.ISO3.unique()\n",
    "ctrl = np.intersect1d(pays, pays_gdim)\n",
    "\n",
    "len(ctrl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
       "      <th>Coef Elas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2</td>\n",
       "      <td>916.66235</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>3</td>\n",
       "      <td>1010.91600</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>4</td>\n",
       "      <td>1086.90780</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>5</td>\n",
       "      <td>1132.69970</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pays  quantile      income  gdpppp ISO3  Population     Gini  \\\n",
       "0  Albania         1   728.89795  7297.0  ALB   2947314.0  0.32141   \n",
       "1  Albania         2   916.66235  7297.0  ALB   2947314.0  0.32141   \n",
       "2  Albania         3  1010.91600  7297.0  ALB   2947314.0  0.32141   \n",
       "3  Albania         4  1086.90780  7297.0  ALB   2947314.0  0.32141   \n",
       "4  Albania         5  1132.69970  7297.0  ALB   2947314.0  0.32141   \n",
       "\n",
       "                  region            incgroup4  \\\n",
       "0  Europe & Central Asia  Upper middle income   \n",
       "1  Europe & Central Asia  Upper middle income   \n",
       "2  Europe & Central Asia  Upper middle income   \n",
       "3  Europe & Central Asia  Upper middle income   \n",
       "4  Europe & Central Asia  Upper middle income   \n",
       "\n",
       "                                        Group  Coef Elas  \n",
       "0  Europe & Central Asia, Upper middle income   0.815874  \n",
       "1  Europe & Central Asia, Upper middle income   0.815874  \n",
       "2  Europe & Central Asia, Upper middle income   0.815874  \n",
       "3  Europe & Central Asia, Upper middle income   0.815874  \n",
       "4  Europe & Central Asia, Upper middle income   0.815874  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Jointure de wid_fin1 et gdime\n",
    "ready_db = pd.merge(wid_fin1, gdimf, how='left', on='ISO3' )\n",
    "\n",
    "# Suppression des colonnes inutiles\n",
    "ready_db.drop(columns=['pays'], inplace=True)\n",
    "\n",
    "ready_db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pays            0\n",
       "quantile        0\n",
       "income          0\n",
       "gdpppp          0\n",
       "ISO3            0\n",
       "Population      0\n",
       "Gini            0\n",
       "region        200\n",
       "incgroup4     200\n",
       "Group         200\n",
       "Coef Elas     200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recherche des valleurs nulles ou manquantes\n",
    "ready_db.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pays</th>\n",
       "      <th>ISO3</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kosovo</th>\n",
       "      <th>XKX</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syrian Arab Republic (Syria)</th>\n",
       "      <th>SYR</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Pays  quantile  income  gdpppp  ISO3  \\\n",
       "Pays                         ISO3                                         \n",
       "Kosovo                       XKX      1       100     100       1     1   \n",
       "Syrian Arab Republic (Syria) SYR      1       100     100       1     1   \n",
       "\n",
       "                                   Population  Gini  region  incgroup4  Group  \\\n",
       "Pays                         ISO3                                               \n",
       "Kosovo                       XKX            1     1       0          0      0   \n",
       "Syrian Arab Republic (Syria) SYR            1     1       0          0      0   \n",
       "\n",
       "                                   Coef Elas  \n",
       "Pays                         ISO3             \n",
       "Kosovo                       XKX           0  \n",
       "Syrian Arab Republic (Syria) SYR           0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Affichage des lignes incomplètes\n",
    "ready_db.loc[ready_db['region'].isnull()].groupby(by=['Pays', 'ISO3']).nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='color:#88cc8d;text-align:center'>\n",
    "    Les données proviennent de la version anglaise du site de la FAO<br/>\n",
    "    <a href=\"http://www.fao.org/faostat/fr/#data\">Source</a><br/>\n",
    "English > Population > Annual population > REGIONS > Africa > (list)<br/>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KHM\n",
      "DNK\n",
      "ISR\n",
      "KAZ\n",
      "KEN\n",
      "KIR\n",
      "KOR\n",
      "KSV\n",
      "KGZ\n",
      "MKD\n",
      "PAK\n",
      "SRB\n",
      "SVK\n",
      "LKA\n",
      "TJK\n",
      "UKR\n"
     ]
    }
   ],
   "source": [
    "# Recherche de ces pays dans gdim\n",
    "for i in pays_gdim:\n",
    "    if 'k' in i.lower():\n",
    "        print(i)\n",
    "    elif 'sy' in i.lower():\n",
    "        print(i)\n",
    "    elif 'sr' in i.lower():\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pays</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
       "      <th>Coef Elas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>KSV</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Lower middle income</td>\n",
       "      <td>0.424817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pays ISO3                 region            incgroup4  \\\n",
       "71  Kosovo  KSV  Europe & Central Asia  Lower middle income   \n",
       "\n",
       "                                         Group  Coef Elas  \n",
       "71  Europe & Central Asia, Lower middle income   0.424817  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Affichage des valeurs retrouvées manuellement\n",
    "gdimf.loc[gdimf['ISO3'] == 'KSV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
       "      <th>Coef Elas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11300</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>1</td>\n",
       "      <td>437.8937</td>\n",
       "      <td>7530.632</td>\n",
       "      <td>XKX</td>\n",
       "      <td>1747400.0</td>\n",
       "      <td>0.331163</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Lower middle income</td>\n",
       "      <td>0.424817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Pays  quantile    income    gdpppp ISO3  Population      Gini  \\\n",
       "11300  Kosovo         1  437.8937  7530.632  XKX   1747400.0  0.331163   \n",
       "\n",
       "                      region            incgroup4  \\\n",
       "11300  Europe & Central Asia  Lower middle income   \n",
       "\n",
       "                                            Group  Coef Elas  \n",
       "11300  Europe & Central Asia, Lower middle income   0.424817  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saisie manuelle\n",
    "ready_db.loc[ready_db['Pays'] == 'Kosovo', ['region', 'incgroup4', 'Group', 'Coef Elas']] =\\\n",
    "['Europe & Central Asia', 'Lower middle income', 'Europe & Central Asia, Lower middle income', 0.424817]\n",
    "\n",
    "ready_db.loc[ready_db['Pays'] == 'Kosovo'].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "      <th>Coef Elas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pays</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Syrian Arab Republic (Syria)</th>\n",
       "      <td>50.5</td>\n",
       "      <td>685.817495</td>\n",
       "      <td>4512.0</td>\n",
       "      <td>20325443.0</td>\n",
       "      <td>0.373873</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              quantile      income  gdpppp  Population  \\\n",
       "Pays                                                                     \n",
       "Syrian Arab Republic (Syria)      50.5  685.817495  4512.0  20325443.0   \n",
       "\n",
       "                                  Gini  Coef Elas  \n",
       "Pays                                               \n",
       "Syrian Arab Republic (Syria)  0.373873        NaN  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Affichage des informations sur la Syrie\n",
    "ready_db.loc[ready_db['Pays'] == 'Syrian Arab Republic (Syria)'].groupby(['Pays']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "      <th>Coef Elas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>50.5</td>\n",
       "      <td>1969.577399</td>\n",
       "      <td>5011.00</td>\n",
       "      <td>80953881.0</td>\n",
       "      <td>0.347097</td>\n",
       "      <td>0.943205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>50.5</td>\n",
       "      <td>2337.600601</td>\n",
       "      <td>3973.00</td>\n",
       "      <td>31596855.0</td>\n",
       "      <td>0.422964</td>\n",
       "      <td>0.945745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Palestinian Territory Occupied</td>\n",
       "      <td>50.5</td>\n",
       "      <td>1114.098514</td>\n",
       "      <td>3612.14</td>\n",
       "      <td>3596688.0</td>\n",
       "      <td>0.371354</td>\n",
       "      <td>0.916461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>50.5</td>\n",
       "      <td>1042.635870</td>\n",
       "      <td>2224.00</td>\n",
       "      <td>22356391.0</td>\n",
       "      <td>0.390259</td>\n",
       "      <td>0.916461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Pays  quantile       income   gdpppp  Population  \\\n",
       "0                           Egypt      50.5  1969.577399  5011.00  80953881.0   \n",
       "1                         Morocco      50.5  2337.600601  3973.00  31596855.0   \n",
       "2  Palestinian Territory Occupied      50.5  1114.098514  3612.14   3596688.0   \n",
       "3                           Yemen      50.5  1042.635870  2224.00  22356391.0   \n",
       "\n",
       "       Gini  Coef Elas  \n",
       "0  0.347097   0.943205  \n",
       "1  0.422964   0.945745  \n",
       "2  0.371354   0.916461  \n",
       "3  0.390259   0.916461  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Restriction sur les pays du même Group\n",
    "inter = ready_db.loc[ready_db['Group'] == 'Middle East & North Africa, Lower middle income'].groupby('Pays').mean()\n",
    "inter.reset_index(inplace=True)\n",
    "\n",
    "# Restriction sur les pays de la même région\n",
    "inter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcul du Coef. d'Elas moyen des pays similaires\n",
    "coef_elas_moy_syr = inter['Coef Elas'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
       "      <th>Coef Elas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>Syrian Arab Republic (Syria)</td>\n",
       "      <td>1</td>\n",
       "      <td>143.18066</td>\n",
       "      <td>4512.0</td>\n",
       "      <td>SYR</td>\n",
       "      <td>20325443.0</td>\n",
       "      <td>0.373873</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Middle East &amp; North Africa, Upper middle income</td>\n",
       "      <td>0.930468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10001</th>\n",
       "      <td>Syrian Arab Republic (Syria)</td>\n",
       "      <td>2</td>\n",
       "      <td>179.06183</td>\n",
       "      <td>4512.0</td>\n",
       "      <td>SYR</td>\n",
       "      <td>20325443.0</td>\n",
       "      <td>0.373873</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Middle East &amp; North Africa, Upper middle income</td>\n",
       "      <td>0.930468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10002</th>\n",
       "      <td>Syrian Arab Republic (Syria)</td>\n",
       "      <td>3</td>\n",
       "      <td>195.83022</td>\n",
       "      <td>4512.0</td>\n",
       "      <td>SYR</td>\n",
       "      <td>20325443.0</td>\n",
       "      <td>0.373873</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Middle East &amp; North Africa, Upper middle income</td>\n",
       "      <td>0.930468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Pays  quantile     income  gdpppp ISO3  \\\n",
       "10000  Syrian Arab Republic (Syria)         1  143.18066  4512.0  SYR   \n",
       "10001  Syrian Arab Republic (Syria)         2  179.06183  4512.0  SYR   \n",
       "10002  Syrian Arab Republic (Syria)         3  195.83022  4512.0  SYR   \n",
       "\n",
       "       Population      Gini                      region            incgroup4  \\\n",
       "10000  20325443.0  0.373873  Middle East & North Africa  Upper middle income   \n",
       "10001  20325443.0  0.373873  Middle East & North Africa  Upper middle income   \n",
       "10002  20325443.0  0.373873  Middle East & North Africa  Upper middle income   \n",
       "\n",
       "                                                 Group  Coef Elas  \n",
       "10000  Middle East & North Africa, Upper middle income   0.930468  \n",
       "10001  Middle East & North Africa, Upper middle income   0.930468  \n",
       "10002  Middle East & North Africa, Upper middle income   0.930468  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Affectation manuelle\n",
    "ready_db.loc[ready_db['Pays'] == 'Syrian Arab Republic (Syria)',\n",
    "             ['region',\n",
    "              'incgroup4',\n",
    "              'Group',\n",
    "              'Coef Elas']] = ['Middle East & North Africa',\n",
    "                               'Upper middle income',\n",
    "                               'Middle East & North Africa, Upper middle income',\n",
    "                               coef_elas_moy_syr]\n",
    "\n",
    "ready_db.loc[ready_db['Pays'] == 'Syrian Arab Republic (Syria)'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pays          0\n",
       "quantile      0\n",
       "income        0\n",
       "gdpppp        0\n",
       "ISO3          0\n",
       "Population    0\n",
       "Gini          0\n",
       "region        0\n",
       "incgroup4     0\n",
       "Group         0\n",
       "Coef Elas     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérification des valeurs nulles\n",
    "ready_db.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
       "      <th>Coef Elas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2</td>\n",
       "      <td>916.66235</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>3</td>\n",
       "      <td>1010.91600</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pays  quantile      income  gdpppp ISO3  Population     Gini  \\\n",
       "0  Albania         1   728.89795  7297.0  ALB   2947314.0  0.32141   \n",
       "1  Albania         2   916.66235  7297.0  ALB   2947314.0  0.32141   \n",
       "2  Albania         3  1010.91600  7297.0  ALB   2947314.0  0.32141   \n",
       "\n",
       "                  region            incgroup4  \\\n",
       "0  Europe & Central Asia  Upper middle income   \n",
       "1  Europe & Central Asia  Upper middle income   \n",
       "2  Europe & Central Asia  Upper middle income   \n",
       "\n",
       "                                        Group  Coef Elas  \n",
       "0  Europe & Central Asia, Upper middle income   0.815874  \n",
       "1  Europe & Central Asia, Upper middle income   0.815874  \n",
       "2  Europe & Central Asia, Upper middle income   0.815874  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ready_db.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11600, 11)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ready_db.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='proto'></a>\n",
    "<h3 style='color:#8677b2'>Protocole du calcul de distribution condition conditionnelles</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Définition des paramètres\n",
    "nb_quantiles = 10\n",
    "n = 1000 * nb_quantiles\n",
    "pj = 0.9\n",
    "\n",
    "# Génération selon la loi Normale\n",
    "ln_y_parent = st.norm(0,1).rvs(size=n)\n",
    "residus = st.norm(0,1).rvs(size=n)\n",
    "\n",
    "# Calcul des revenus des parents et des enfants\n",
    "y_child = np.exp(pj * ln_y_parent + residus)\n",
    "y_parents = np.exp(ln_y_parent)\n",
    "\n",
    "# Conversion des tableaux en series\n",
    "y_child = pd.Series(y_child)\n",
    "y_parents = pd.Series(y_parents)\n",
    "\n",
    "# Détermination de la classe de revenus des enfants\n",
    "size_child = len(y_child)\n",
    "l_sorted_child = y_child.copy()\n",
    "l_sorted_child = l_sorted_child.sort_values()\n",
    "quantiles_child = np.round(np.arange(1,\n",
    "                                     nb_quantiles + 1,\n",
    "                                     nb_quantiles / size_child) - 0.5 + 1./size_child)\n",
    "q_dict_child = {a: int(b) for a, b in zip(l_sorted_child, quantiles_child)}\n",
    "c_i_child = pd.Series(q_dict_child[e] for e in  y_child)\n",
    "\n",
    "# Détermination de la classe de revenus des enfants\n",
    "size_parents = len(y_parents)\n",
    "l_sorted_parents = y_parents.copy()\n",
    "l_sorted_parents = l_sorted_parents.sort_values()\n",
    "quantiles_parents = np.round(np.arange(1,\n",
    "                                       nb_quantiles + 1,\n",
    "                                       nb_quantiles / size_parents) - 0.5 + 1./size_parents)\n",
    "q_dict_parents = {a: int(b) for a, b in zip(l_sorted_parents, quantiles_parents)}\n",
    "c_i_parent = pd.Series(q_dict_parents[e] for e in  y_parents)\n",
    "\n",
    "# Mise en forme\n",
    "sample = pd.concat([y_child, y_parents, c_i_child, c_i_parent], axis = 1)\n",
    "sample.columns = ['y_child', 'y_parents', 'c_i_child', 'c_i_parent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_child</th>\n",
       "      <th>y_parents</th>\n",
       "      <th>c_i_child</th>\n",
       "      <th>c_i_parent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.083373</td>\n",
       "      <td>0.197080</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.385278</td>\n",
       "      <td>1.079734</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.965344</td>\n",
       "      <td>2.025623</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.592264</td>\n",
       "      <td>2.717778</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.603061</td>\n",
       "      <td>1.482355</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    y_child  y_parents  c_i_child  c_i_parent\n",
       "0  0.083373   0.197080          1           1\n",
       "1  2.385278   1.079734          8           6\n",
       "2  1.965344   2.025623          7           8\n",
       "3  4.592264   2.717778          9           9\n",
       "4  2.603061   1.482355          8           7"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c_i_child</th>\n",
       "      <th>c_i_parent</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   c_i_child  c_i_parent  counts\n",
       "0          1           1     465\n",
       "1          1           2     221\n",
       "2          1           3     128"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On compte chaque combinaison c_i_child, c_i_parent\n",
    "counts = sample.groupby([\"c_i_child\",\"c_i_parent\"]).apply(len)\n",
    "counts = counts.reset_index()\n",
    "counts.columns = [\"c_i_child\",\"c_i_parent\",\"counts\"]\n",
    "\n",
    "counts.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 10000 [0.0465, 0.0221, 0.0128, 0.0078, 0.0046, 0.0028, 0.0018, 0.0012, 0.0004, 0.0002]\n"
     ]
    }
   ],
   "source": [
    "# On divise le nombre d'individus compté par le nombre d'individus total pour chaque quantile parent\n",
    "# Ces opération existe sous forme d'une fonction distribution()\n",
    "# Qui prend en arguments un dataframe et un le nombre de quantiles\n",
    "distrib = []\n",
    "total = counts[\"counts\"].sum()\n",
    "\n",
    "for q_p in range(1, nb_quantiles+1):\n",
    "    subset = counts[counts.c_i_parent == q_p]\n",
    "    if len(subset):\n",
    "        nb = subset[\"counts\"].values[0]\n",
    "        distrib += [nb / total]\n",
    "    else:\n",
    "        distrib += [0]\n",
    "print(len(distrib), total, distrib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.465, 0.221, 0.128, 0.078, 0.046, 0.028, 0.018, 0.012, 0.004,\n",
       "        0.   ],\n",
       "       [0.204, 0.203, 0.176, 0.13 , 0.093, 0.092, 0.052, 0.03 , 0.018,\n",
       "        0.002],\n",
       "       [0.123, 0.165, 0.158, 0.142, 0.127, 0.097, 0.086, 0.06 , 0.037,\n",
       "        0.005],\n",
       "       [0.096, 0.111, 0.158, 0.153, 0.158, 0.123, 0.087, 0.05 , 0.045,\n",
       "        0.019],\n",
       "       [0.043, 0.119, 0.127, 0.129, 0.137, 0.124, 0.113, 0.103, 0.08 ,\n",
       "        0.025],\n",
       "       [0.028, 0.081, 0.089, 0.124, 0.119, 0.145, 0.135, 0.126, 0.11 ,\n",
       "        0.043],\n",
       "       [0.02 , 0.052, 0.085, 0.093, 0.122, 0.119, 0.146, 0.153, 0.128,\n",
       "        0.082],\n",
       "       [0.017, 0.027, 0.05 , 0.072, 0.097, 0.118, 0.159, 0.17 , 0.163,\n",
       "        0.127],\n",
       "       [0.003, 0.018, 0.022, 0.054, 0.067, 0.11 , 0.13 , 0.148, 0.218,\n",
       "        0.23 ],\n",
       "       [0.001, 0.003, 0.007, 0.025, 0.034, 0.044, 0.074, 0.148, 0.197,\n",
       "        0.467]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour chaque quantile enfant, on crée la ligne de distribution du c_i_parent\n",
    "# Ces opérations et les précedentes seront réalisées par la fonction conditional_distributions()\n",
    "# Qui prend pour argument le dataframe avec les revenus et les quantiles enfant et parent\n",
    "mat = []\n",
    "for child_quantile in np.arange(nb_quantiles)+1:\n",
    "    subset = counts[counts.c_i_child == child_quantile]\n",
    "    mat += [distribution(subset, nb_quantiles)]\n",
    "cd = np.array(mat)\n",
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphique pour une faible mobilité\n",
    "plt.figure(figsize=(15,15))\n",
    "cumul = np.array([0] * nb_quantiles)\n",
    "for i, child_quantile in enumerate(cd):\n",
    "    plt.bar(np.arange(nb_quantiles) + 1, child_quantile, bottom=cumul, width=0.95, label = str(i+1) + 'e')\n",
    "    cumul = cumul + np.array(child_quantile)\n",
    "    \n",
    "plt.axis([.5, nb_quantiles*1.3 ,0 ,1])\n",
    "plt.title('p=' + str(pj), fontsize=22)\n",
    "plt.legend()\n",
    "plt.xlabel('quantile parents', fontsize=18)\n",
    "plt.ylabel('probabilité du quantile enfant', fontsize=18)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('graph/exemple_faible_mobi.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "P(c_i_parent = 1 | c_i_child = 1, pj = 0.9) = 0.465\n"
     ]
    }
   ],
   "source": [
    "# Sachant que la classe de revenus d'un enfant est 1 et Coef Elas est 0.9 (faible mobilité)\n",
    "# Alors la probabilité que la classe de revenus du parent soit 0 est:\n",
    "c_i_child = 0\n",
    "c_i_parent = 0\n",
    "p = proba_cond(c_i_parent, c_i_child, cd)\n",
    "print('\\nP(c_i_parent = {} | c_i_child = {}, pj = {}) = {}'.format(c_i_parent+1, c_i_child+1, pj, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "P(c_i_parent = 10 | c_i_child = 1, pj = 0.9) = 0.0\n"
     ]
    }
   ],
   "source": [
    "# Sachant que la classe de revenus d'un enfant est 1 et Coef Elas est 0.9 (faible mobilité)\n",
    "# Alors la probabilité que la classe de revenus du parent soit 10 est:\n",
    "c_i_child = 0\n",
    "c_i_parent = 9\n",
    "p = proba_cond(c_i_parent, c_i_child, cd)\n",
    "print('\\nP(c_i_parent = {} | c_i_child = {}, pj = {}) = {}'.format(c_i_parent+1, c_i_child+1, pj, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.131, 0.115, 0.094, 0.103, 0.106, 0.113, 0.092, 0.071, 0.1  ,\n",
       "        0.075],\n",
       "       [0.125, 0.089, 0.104, 0.116, 0.098, 0.092, 0.099, 0.11 , 0.083,\n",
       "        0.084],\n",
       "       [0.091, 0.122, 0.112, 0.096, 0.106, 0.083, 0.105, 0.102, 0.104,\n",
       "        0.079],\n",
       "       [0.1  , 0.086, 0.128, 0.109, 0.081, 0.105, 0.101, 0.101, 0.099,\n",
       "        0.09 ],\n",
       "       [0.103, 0.111, 0.1  , 0.093, 0.092, 0.109, 0.116, 0.091, 0.081,\n",
       "        0.104],\n",
       "       [0.094, 0.107, 0.107, 0.086, 0.096, 0.097, 0.103, 0.106, 0.097,\n",
       "        0.107],\n",
       "       [0.088, 0.09 , 0.09 , 0.112, 0.113, 0.103, 0.081, 0.118, 0.111,\n",
       "        0.094],\n",
       "       [0.106, 0.088, 0.096, 0.092, 0.107, 0.1  , 0.099, 0.099, 0.106,\n",
       "        0.107],\n",
       "       [0.093, 0.098, 0.09 , 0.099, 0.093, 0.098, 0.097, 0.112, 0.095,\n",
       "        0.125],\n",
       "       [0.069, 0.094, 0.079, 0.094, 0.108, 0.1  , 0.107, 0.09 , 0.124,\n",
       "        0.135]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Définition de nouveaux paramètres pour une forte mobilité\n",
    "pj1 = 0.1\n",
    "y_child1, y_parents1 = generate_incomes(n, pj1)\n",
    "sample1 = compute_quantiles(y_child1, y_parents1, nb_quantiles)\n",
    "cd1 = conditional_distributions(sample1, nb_quantiles)\n",
    "cd1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphique pour une forte mobilité\n",
    "plt.figure(figsize=(15,15))\n",
    "cumul1 = np.array([0] * nb_quantiles)\n",
    "for i, child_quantile in enumerate(cd1):\n",
    "    plt.bar(np.arange(nb_quantiles) + 1, child_quantile, bottom=cumul1, width=0.95, label = str(i+1) + 'e')\n",
    "    cumul1 = cumul1 + np.array(child_quantile)\n",
    "    \n",
    "plt.axis([.5, nb_quantiles*1.3 ,0 ,1])\n",
    "plt.title('p=' + str(pj1), fontsize=22)\n",
    "plt.legend()\n",
    "plt.xlabel('quantile parents', fontsize=18)\n",
    "plt.ylabel('probabilité du quantile enfant', fontsize=18)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('graph/exemple_forte_mobi.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "P(c_i_parent = 1 | c_i_child = 1, pj = 0.1) = 0.131\n"
     ]
    }
   ],
   "source": [
    "# Sachant que la classe de revenus d'un enfant est 1 et Coef Elas est 0.1 (forte mobilité)\n",
    "# Alors la probabilité que la classe de revenus du parent soit 1 est:\n",
    "c_i_child1 = 0\n",
    "c_i_parent1 = 0\n",
    "p1 = proba_cond(c_i_parent1, c_i_child1, cd1)\n",
    "print('\\nP(c_i_parent = {} | c_i_child = {}, pj = {}) = {}'.format(c_i_parent1+1, c_i_child1+1, pj1, p1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "P(c_i_parent = 10 | c_i_child = 1, pj = 0.1) = 0.075\n"
     ]
    }
   ],
   "source": [
    "# Sachant que la classe de revenus d'un enfant est 1 et Coef Elas est 0.1 (forte mobilité)\n",
    "# Alors la probabilité que la classe de revenus du parent soit 10 est:\n",
    "c_i_child1 = 0\n",
    "c_i_parent1 = 9\n",
    "p1 = proba_cond(c_i_parent1, c_i_child1, cd1)\n",
    "print('\\nP(c_i_parent = {} | c_i_child = {}, pj = {}) = {}'.format(c_i_parent1+1, c_i_child1+1, pj1, p1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='appli'></a>\n",
    "<h3 style='color:#8677b2'>Application du protocole sur nos données</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clonage des lignes \n",
    "ready_db1 = pd.concat([ready_db] * 500)\n",
    "ready_db1.sort_values(by=['Pays', 'quantile'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5800000, 11)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ready_db1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>quantile</th>\n",
       "      <th>income</th>\n",
       "      <th>gdpppp</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Population</th>\n",
       "      <th>Gini</th>\n",
       "      <th>region</th>\n",
       "      <th>incgroup4</th>\n",
       "      <th>Group</th>\n",
       "      <th>Coef Elas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia, Upper middle income</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pays  quantile     income  gdpppp ISO3  Population     Gini  \\\n",
       "0  Albania         1  728.89795  7297.0  ALB   2947314.0  0.32141   \n",
       "0  Albania         1  728.89795  7297.0  ALB   2947314.0  0.32141   \n",
       "0  Albania         1  728.89795  7297.0  ALB   2947314.0  0.32141   \n",
       "\n",
       "                  region            incgroup4  \\\n",
       "0  Europe & Central Asia  Upper middle income   \n",
       "0  Europe & Central Asia  Upper middle income   \n",
       "0  Europe & Central Asia  Upper middle income   \n",
       "\n",
       "                                        Group  Coef Elas  \n",
       "0  Europe & Central Asia, Upper middle income   0.815874  \n",
       "0  Europe & Central Asia, Upper middle income   0.815874  \n",
       "0  Europe & Central Asia, Upper middle income   0.815874  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ready_db1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>c_i_child</th>\n",
       "      <th>y_child</th>\n",
       "      <th>Gj</th>\n",
       "      <th>pj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>0.815874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pays  c_i_child    y_child       Gj        pj\n",
       "0  Albania          1  728.89795  0.32141  0.815874\n",
       "0  Albania          1  728.89795  0.32141  0.815874\n",
       "0  Albania          1  728.89795  0.32141  0.815874"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mise en forme \n",
    "ready_db1.rename(columns={'quantile': 'c_i_child', 'income': 'y_child', 'Gini': 'Gj', 'Coef Elas': 'pj'}, inplace=True)\n",
    "ready_db1 = ready_db1[['Pays', 'c_i_child', 'y_child', 'Gj', 'pj']]\n",
    "\n",
    "ready_db1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nombre de d'individus générés aléatoirement 1_000 * nb_quantiles\n",
    "n=100_000\n",
    "\n",
    "# Préparation des données\n",
    "nb_quantiles=100\n",
    "key3 = ready_db1['Pays'].drop_duplicates().values\n",
    "c_i_parent = []\n",
    "ready_db1['c_i_parent'] = 0\n",
    "\n",
    "# Pour chaque pays\n",
    "for pays in key3:\n",
    "    # Selection du pays\n",
    "    y_child, y_parent = generate_incomes(n, ready_db1.loc[ready_db1['Pays'] == pays, 'pj'].iloc[0]) \n",
    "    # Création des quantiles\n",
    "    sample = compute_quantiles(y_child, y_parent, nb_quantiles) \n",
    "    # Création de l'arrondi du nombre d'individus attendu en fonction des distributions conditionnelles\n",
    "    cd = np.round(conditional_distributions(sample, nb_quantiles) * 500)\n",
    "    # Typage en entier\n",
    "    cd = np.array(cd, int)\n",
    "    # Passage en dataframe\n",
    "    cdd = pd.DataFrame(cd)\n",
    "    # Création de la transposée\n",
    "    cdt = cdd.T\n",
    "    # Pour chaque c_i_child\n",
    "    for cic in np.arange(100):\n",
    "        # Création d'une liste d'index des valeurs supérieur à 5 grâce à la transposée\n",
    "        list_random = cdt.loc[cdt[cic] > 5, cic].index.tolist()\n",
    "\n",
    "        # Si la somme des individus par pays et c_i_child est inférieure à 500 individus\n",
    "        if cdd.loc[cdd.index == cic].sum().sum() < 500:        \n",
    "            # Calcul du nombre de valeurs manquantes\n",
    "            diff = 500 - cdd.loc[cdd.index == cic].sum().sum()\n",
    "            # Prélèvement d'index dans la liste des valeurs supérieures à 5 individus\n",
    "            al_ind = random.sample(list_random, diff)\n",
    "            # Ajout de 1 pour les individus prélévés\n",
    "            cdd.loc[cdd.index == cic, al_ind] = cdd.loc[cdd.index == cic, al_ind] + 1\n",
    "\n",
    "        # Si la somme des individus par pays et c_i_child est supérieure à 500 individus\n",
    "        elif cdd.loc[cdd.index == cic].sum().sum() > 500:\n",
    "            # Calcul du nombre de valeurs en trop\n",
    "            diff = cdd.loc[cdd.index == cic].sum().sum() - 500\n",
    "            # Prélèvement d'index dans la liste des valeurs supérieures à 5 individus\n",
    "            al_ind = random.sample(list_random, diff)\n",
    "            # Soustraction de 1 pour les individus prélévés\n",
    "            cdd.loc[cdd.index == cic, al_ind] = cdd.loc[cdd.index == cic, al_ind] - 1\n",
    "\n",
    "        # Pour chaque c_i_parent\n",
    "        for cip in range(0,100):\n",
    "            # On étend la liste c_i_parent de l'index + 1 de la valeur contenu en c_i_child, c_i_parent\n",
    "            c_i_parent.extend(np.repeat(cip + 1, cdd.iloc[cic, cip], axis=0))\n",
    "    # On affecte les valeurs de la liste c_i_parent à la colonne c_i_parent de notre dataframe principale\n",
    "    ready_db1.loc[(ready_db1['Pays'] == pays), 'c_i_parent'] = c_i_parent\n",
    "    # On reinitialise la liste c_i_parent\n",
    "    c_i_parent =[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>c_i_child</th>\n",
       "      <th>y_child</th>\n",
       "      <th>Gj</th>\n",
       "      <th>pj</th>\n",
       "      <th>c_i_parent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11499</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>100</td>\n",
       "      <td>9050.4375</td>\n",
       "      <td>0.390259</td>\n",
       "      <td>0.916461</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11499</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>100</td>\n",
       "      <td>9050.4375</td>\n",
       "      <td>0.390259</td>\n",
       "      <td>0.916461</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11499</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>100</td>\n",
       "      <td>9050.4375</td>\n",
       "      <td>0.390259</td>\n",
       "      <td>0.916461</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11499</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>100</td>\n",
       "      <td>9050.4375</td>\n",
       "      <td>0.390259</td>\n",
       "      <td>0.916461</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11499</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>100</td>\n",
       "      <td>9050.4375</td>\n",
       "      <td>0.390259</td>\n",
       "      <td>0.916461</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pays  c_i_child    y_child        Gj        pj  c_i_parent\n",
       "11499  Yemen        100  9050.4375  0.390259  0.916461         100\n",
       "11499  Yemen        100  9050.4375  0.390259  0.916461         100\n",
       "11499  Yemen        100  9050.4375  0.390259  0.916461         100\n",
       "11499  Yemen        100  9050.4375  0.390259  0.916461         100\n",
       "11499  Yemen        100  9050.4375  0.390259  0.916461         100"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ready_db1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppression de la variable c_i_child inutile pour la mission 4\n",
    "ready_db1.drop(columns='c_i_child', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>mj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2994.829902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>5847.884654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>1628.382785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pays           mj\n",
       "0    Albania  2994.829902\n",
       "1  Argentina  5847.884654\n",
       "2    Armenia  1628.382785"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Création d'un dataframe avec la variable revenu moyen du pays\n",
    "y_child_moy_df = ready_db1.groupby(by='Pays').mean()\n",
    "y_child_moy_df.reset_index(inplace=True)\n",
    "y_child_moy_df = y_child_moy_df[['Pays', 'y_child']]\n",
    "y_child_moy_df.rename(columns={'y_child': 'mj'}, inplace=True)\n",
    "\n",
    "y_child_moy_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>y_child</th>\n",
       "      <th>Gj</th>\n",
       "      <th>pj</th>\n",
       "      <th>c_i_parent</th>\n",
       "      <th>mj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>0.815874</td>\n",
       "      <td>1</td>\n",
       "      <td>2994.829902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>0.815874</td>\n",
       "      <td>1</td>\n",
       "      <td>2994.829902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>728.89795</td>\n",
       "      <td>0.32141</td>\n",
       "      <td>0.815874</td>\n",
       "      <td>1</td>\n",
       "      <td>2994.829902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pays    y_child       Gj        pj  c_i_parent           mj\n",
       "0  Albania  728.89795  0.32141  0.815874           1  2994.829902\n",
       "1  Albania  728.89795  0.32141  0.815874           1  2994.829902\n",
       "2  Albania  728.89795  0.32141  0.815874           1  2994.829902"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ajout de la colonne y_child_moy\n",
    "ready_db2 = pd.merge(ready_db1, y_child_moy_df, on='Pays')\n",
    "ready_db2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pays</th>\n",
       "      <th>y_child</th>\n",
       "      <th>Gj</th>\n",
       "      <th>pj</th>\n",
       "      <th>c_i_parent</th>\n",
       "      <th>mj</th>\n",
       "      <th>ln_y_child</th>\n",
       "      <th>ln_mj</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5799997</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>9050.4375</td>\n",
       "      <td>0.390259</td>\n",
       "      <td>0.916461</td>\n",
       "      <td>100</td>\n",
       "      <td>1042.63587</td>\n",
       "      <td>9.110568</td>\n",
       "      <td>6.949507</td>\n",
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       "    <tr>\n",
       "      <th>5799998</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>9050.4375</td>\n",
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       "      <th>5799999</th>\n",
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      ],
      "text/plain": [
       "          Pays    y_child        Gj        pj  c_i_parent          mj  \\\n",
       "5799997  Yemen  9050.4375  0.390259  0.916461         100  1042.63587   \n",
       "5799998  Yemen  9050.4375  0.390259  0.916461         100  1042.63587   \n",
       "5799999  Yemen  9050.4375  0.390259  0.916461         100  1042.63587   \n",
       "\n",
       "         ln_y_child     ln_mj  \n",
       "5799997    9.110568  6.949507  \n",
       "5799998    9.110568  6.949507  \n",
       "5799999    9.110568  6.949507  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ajout des colonnes 'logarithmes'\n",
    "ready_db2['ln_y_child'] = np.log(ready_db2['y_child'])\n",
    "ready_db2['ln_mj'] = np.log(ready_db2['mj'])\n",
    "\n",
    "ready_db2.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(116, 7)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ready_db2.groupby('Pays').count().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enregistrement du fichier\n",
    "ready_db2.to_csv('analyse.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Haut de page](#sommaire)"
   ]
  }
 ],
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