iPython Notebook revenus disponibles France Métropolitaine - deciles

revenus disponibles france metropolitaine - deciles.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<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": 50,
     "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": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Libellé géographique</th>\n",
       "      <th>France métropolitaine</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centile</th>\n",
       "      <th></th>\n",
       "      <th></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": [
       "Libellé géographique  France métropolitaine               label\n",
       "centile                                                        \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": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[[\n",
    "    '1er décile (€)', '2e décile (€)', '1er quartile (€)', \n",
    "    '3e décile (€)', '4e décile (€)', 'Médiane (€)', \n",
    "    '6e décile (€)', '7e décile (€)', '3e quartile (€)', \n",
    "    '8e décile (€)', '9e décile (€)'\n",
    "]].transpose()\n",
    "print()\n",
    "df2 = df2.assign(centile=[0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, .8, .9])\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",
    "df3 = df2.set_index('centile')\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
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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=False)\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(df3, \"Revenus disponibles annuels par unité de consommation\")\n",
    "df4 = df3.copy()\n",
    "df4['France métropolitaine'] = df4['France métropolitaine'] / 12\n",
    "chart(df4, \"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
}