{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import urllib.request\n",
    "import zipfile\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_name = \"indic-struct-distrib-revenu-2015-SUPRA.zip\"\n",
    "subarchive_name = \"indic-struct-distrib-revenu-2015-METROPOLE.zip\"\n",
    "file_name = \"FILO_DISP_METROPOLE.xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading file ...\n",
      "fully ownloaded !\n"
     ]
    }
   ],
   "source": [
    "print(\"downloading file ...\")\n",
    "urllib.request.urlretrieve(\n",
    "    \"https://www.insee.fr/fr/statistiques/fichier/3560118/indic-struct-distrib-revenu-2015-SUPRA.zip\", \n",
    "    archive_name\n",
    ")\n",
    "print(\"fully ownloaded !\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracting all from indic-struct-distrib-revenu-2015-SUPRA.zip ...\n",
      "done extracting !\n",
      "extracting all from indic-struct-distrib-revenu-2015-METROPOLE.zip ...\n",
      "done extracting !\n"
     ]
    }
   ],
   "source": [
    "def extract(path, member):\n",
    "    print(\"extracting all from %s ...\" % path)\n",
    "    with zipfile.ZipFile(path, 'r') as zip_ref:\n",
    "        zip_ref.extract(member, \"./\")\n",
    "    print(\"done extracting !\")\n",
    "\n",
    "    \n",
    "extract(archive_name, subarchive_name)\n",
    "extract(subarchive_name, file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Code géographique</th>\n",
       "      <th>Nbre de ménages fiscaux</th>\n",
       "      <th>Nbre de personnes dans les ménages fiscaux</th>\n",
       "      <th>Nbre d'unités de consommation dans les ménages fiscaux</th>\n",
       "      <th>1er quartile (€)</th>\n",
       "      <th>Médiane (€)</th>\n",
       "      <th>3e quartile (€)</th>\n",
       "      <th>Écart interquartile (€)</th>\n",
       "      <th>1er décile (€)</th>\n",
       "      <th>2e décile (€)</th>\n",
       "      <th>...</th>\n",
       "      <th>dont part des indemnités chômage (%)</th>\n",
       "      <th>dont part des salaires, traitements hors chômage (%)</th>\n",
       "      <th>dont part des revenus des activités non salariées (%)</th>\n",
       "      <th>Part des pensions, retraites et rentes (%)</th>\n",
       "      <th>Part des revenus du patrimoine et autres revenus (%)</th>\n",
       "      <th>Part de l'ensemble des prestations sociales (%)</th>\n",
       "      <th>dont part des prestations familiales (%)</th>\n",
       "      <th>dont part des minima sociaux (%)</th>\n",
       "      <th>dont part des prestations logement (%)</th>\n",
       "      <th>Part des impôts (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Libellé géographique</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>France métropolitaine</th>\n",
       "      <td>1</td>\n",
       "      <td>27071573</td>\n",
       "      <td>62770585.5</td>\n",
       "      <td>4.274229e+07</td>\n",
       "      <td>14976.666667</td>\n",
       "      <td>20565.555556</td>\n",
       "      <td>27670</td>\n",
       "      <td>12693.333333</td>\n",
       "      <td>10739</td>\n",
       "      <td>13743</td>\n",
       "      <td>...</td>\n",
       "      <td>3.2</td>\n",
       "      <td>64.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>28.3</td>\n",
       "      <td>10.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-17.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Code géographique  Nbre de ménages fiscaux  \\\n",
       "Libellé géographique                                                \n",
       "France métropolitaine                  1                 27071573   \n",
       "\n",
       "                       Nbre de personnes dans les ménages fiscaux  \\\n",
       "Libellé géographique                                                \n",
       "France métropolitaine                                  62770585.5   \n",
       "\n",
       "                       Nbre d'unités de consommation dans les ménages fiscaux  \\\n",
       "Libellé géographique                                                            \n",
       "France métropolitaine                                       4.274229e+07        \n",
       "\n",
       "                       1er quartile (€)   Médiane (€)  3e quartile (€)  \\\n",
       "Libellé géographique                                                     \n",
       "France métropolitaine      14976.666667  20565.555556            27670   \n",
       "\n",
       "                       Écart interquartile (€)  1er décile (€)  2e décile (€)  \\\n",
       "Libellé géographique                                                            \n",
       "France métropolitaine             12693.333333           10739          13743   \n",
       "\n",
       "                              ...           \\\n",
       "Libellé géographique          ...            \n",
       "France métropolitaine         ...            \n",
       "\n",
       "                       dont part des indemnités chômage (%)  \\\n",
       "Libellé géographique                                          \n",
       "France métropolitaine                                   3.2   \n",
       "\n",
       "                       dont part des salaires, traitements hors chômage (%)  \\\n",
       "Libellé géographique                                                          \n",
       "France métropolitaine                                               64.3      \n",
       "\n",
       "                       dont part des revenus des activités non salariées (%)  \\\n",
       "Libellé géographique                                                           \n",
       "France métropolitaine                                                5.6       \n",
       "\n",
       "                       Part des pensions, retraites et rentes (%)  \\\n",
       "Libellé géographique                                                \n",
       "France métropolitaine                                        28.3   \n",
       "\n",
       "                       Part des revenus du patrimoine et autres revenus (%)  \\\n",
       "Libellé géographique                                                          \n",
       "France métropolitaine                                               10.6      \n",
       "\n",
       "                       Part de l'ensemble des prestations sociales (%)  \\\n",
       "Libellé géographique                                                     \n",
       "France métropolitaine                                              5.3   \n",
       "\n",
       "                       dont part des prestations familiales (%)  \\\n",
       "Libellé géographique                                              \n",
       "France métropolitaine                                       2.1   \n",
       "\n",
       "                       dont part des minima sociaux (%)  \\\n",
       "Libellé géographique                                      \n",
       "France métropolitaine                               1.7   \n",
       "\n",
       "                       dont part des prestations logement (%)  \\\n",
       "Libellé géographique                                            \n",
       "France métropolitaine                                     1.4   \n",
       "\n",
       "                       Part des impôts (%)  \n",
       "Libellé géographique                        \n",
       "France métropolitaine                -17.4  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\n",
    "    \"FILO_DISP_METROPOLE.xls\", \n",
    "    sheet_name=\"ENSEMBLE\",\n",
    "    header=0,\n",
    "    index_col=1,\n",
    "    skiprows=[0,1,2,3,5]\n",
    ")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>France Métropolitaine - 2015</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>10739.000000</td>\n",
       "      <td>1er décile - 10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>13743.000000</td>\n",
       "      <td>2e décile - 20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>14976.666667</td>\n",
       "      <td>1er quartile - 25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>16153.448276</td>\n",
       "      <td>3e décile - 30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40</th>\n",
       "      <td>18391.000000</td>\n",
       "      <td>4e décile - 40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>20565.555556</td>\n",
       "      <td>Médiane - 50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.60</th>\n",
       "      <td>22917.500000</td>\n",
       "      <td>6e décile - 60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>25806.923077</td>\n",
       "      <td>7e décile - 70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>27670.000000</td>\n",
       "      <td>3e quartile - 80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.80</th>\n",
       "      <td>29978.000000</td>\n",
       "      <td>8e décile - 80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>37620.952381</td>\n",
       "      <td>9e décile - 90%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      France Métropolitaine - 2015               label\n",
       "0.10                  10739.000000    1er décile - 10%\n",
       "0.20                  13743.000000     2e décile - 20%\n",
       "0.25                  14976.666667  1er quartile - 25%\n",
       "0.30                  16153.448276     3e décile - 30%\n",
       "0.40                  18391.000000     4e décile - 40%\n",
       "0.50                  20565.555556       Médiane - 50%\n",
       "0.60                  22917.500000     6e décile - 60%\n",
       "0.70                  25806.923077     7e décile - 70%\n",
       "0.75                  27670.000000   3e quartile - 80%\n",
       "0.80                  29978.000000     8e décile - 80%\n",
       "0.90                  37620.952381     9e décile - 90%"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(\n",
    "    [\n",
    "        [df['1er décile (€)']['France métropolitaine']],\n",
    "        [df['2e décile (€)']['France métropolitaine']],\n",
    "        [df['1er quartile (€)']['France métropolitaine']],\n",
    "        [df['3e décile (€)']['France métropolitaine']],\n",
    "        [df['4e décile (€)']['France métropolitaine']],\n",
    "        [df['Médiane (€)']['France métropolitaine']],\n",
    "        [df['6e décile (€)']['France métropolitaine']],\n",
    "        [df['7e décile (€)']['France métropolitaine']],\n",
    "        [df['3e quartile (€)']['France métropolitaine']],\n",
    "        [df['8e décile (€)']['France métropolitaine']],\n",
    "        [df['9e décile (€)']['France métropolitaine']]\n",
    "    ], \n",
    "    index=[0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, .8, .9], \n",
    "    columns=[\"France Métropolitaine - 2015\"]\n",
    ")\n",
    "df2 = df2.assign(label=[\n",
    "    '1er décile - 10%', '2e décile - 20%', '1er quartile - 25%', \n",
    "    '3e décile - 30%', '4e décile - 40%', 'Médiane - 50%', \n",
    "    '6e décile - 60%', '7e décile - 70%', '3e quartile - 80%', \n",
    "    '8e décile - 80%', '9e décile - 90%'\n",
    "])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [8, 6]\n",
    "font = {'size': 14}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "def chart(df, ylabel):\n",
    "    plt.figure(figsize=(1,1), dpi=600)\n",
    "    ax = df.plot(kind=\"line\", marker=\"o\", grid=True, legend=True)\n",
    "    ax.set_ylabel(\"€\")\n",
    "    ax.set_title(ylabel)\n",
    "    ax.set_xticks(df.index.tolist())\n",
    "    ax.set_xlim([0,1])\n",
    "    ax.set_xticklabels(df.label, rotation=90)\n",
    "\n",
    "    \n",
    "chart(df2, \"Revenus disponibles annuels par unité de consommation\")\n",
    "df3 = df2.copy()\n",
    "df3['France Métropolitaine - 2015'] = df3['France Métropolitaine - 2015'] / 12\n",
    "chart(df3, \"Revenus disponibles mensuels par unité de consommation\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}