Analyse-DVF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://naonedia.fr/wp-content/uploads/2018/10/logoNaonedia.png\" style=\"height:100px\">\n",
    "<h1>\n",
    "    <center>Expérience Logement</center>\n",
    "    <center>-</center>\n",
    "    <center>Analyse Demandes Valeurs Foncières</center>\n",
    "</h1>\n",
    "\n",
    "\n",
    "<h3> Introduction </h3>\n",
    "\n",
    "Analyse du jeu de données fournit par <a href=\"https://www.data.gouv.fr/fr/datasets/demandes-de-valeurs-foncieres-geolocalisees/\">l'Etat français</a>.\n",
    "<br>\n",
    "Ce jeu de données est dérivé du fichier de <a href=\"https://www.data.gouv.fr/fr/datasets/demandes-de-valeurs-foncieres/\">Demandes de valeurs foncières</a>. Il contient en plus le Géocodage latitude/longitude du bien en respectant la norme <a href=\"https://fr.wikipedia.org/wiki/WGS_84\">WGS-84</a>.\n",
    "\n",
    "Le but de ce notebook est de réaliser une première analyse des données en vue de la sélection d'un dataset.\n",
    "Pour cela nous allons regarder les différentes features disponibles.\n",
    "\n",
    "<h3>Informations</h3>\n",
    "\n",
    "Il est fortement conseillé d'utiliser Python3.x\n",
    "Si vous ne disposez pas de toutes les librairies requises , il suffit de l'installer en utilisant la commande suivante.\n",
    "<br>\n",
    "<code>pip3 install ....</code>\n",
    "\n",
    "<h3>Datasets</h3>\n",
    "\n",
    "Les datasets sont disponibles à l'addresse suivante: <a href=\"https://github.com/naonedia/expe_logement_dataset\"> github naonedia</a>\n",
    "\n",
    "<h3>Authors:</h3>\n",
    "<cite>Hector Basset - Ippon Technologies</cite>\n",
    "\n",
    "### Import des librairies requises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from os import path\n",
    "\n",
    "import plotly.offline as py\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "py.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fonctions pour l'affichage des graphiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def py_table(data, filename):\n",
    "    table = ff.create_table(data)\n",
    "    py.iplot(table, filename=filename)\n",
    "\n",
    "def py_pie(values, title, filename, labels=None):\n",
    "    if labels is None:\n",
    "        labels = values.index\n",
    "        values = values.values\n",
    "    pie = go.Pie(labels=labels, values=values, title=title)\n",
    "    py.iplot([pie], filename=filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fonctions pour le chargement des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_communes():\n",
    "    return pd.read_csv(\n",
    "        'data/code-postaux.csv',\n",
    "        delimiter=';',\n",
    "        header=0,\n",
    "        usecols=[\n",
    "            'Code_commune_INSEE'\n",
    "        ],\n",
    "        index_col=False,\n",
    "        squeeze=True\n",
    "    ).unique()\n",
    "\n",
    "def get_data_year(year, communes):\n",
    "    data_year = pd.read_csv(\n",
    "        'data/dvf/44_%i.csv.xz' % year,\n",
    "        header=0,\n",
    "        usecols=[\n",
    "            'date_mutation',\n",
    "            'nature_mutation',\n",
    "            'valeur_fonciere',\n",
    "            'code_postal',\n",
    "            'nom_commune',\n",
    "            'ancien_code_commune',\n",
    "            'code_commune',\n",
    "            'lot1_surface_carrez',\n",
    "            'lot2_surface_carrez',\n",
    "            'lot3_surface_carrez',\n",
    "            'lot4_surface_carrez',\n",
    "            'lot5_surface_carrez',\n",
    "            'nombre_lots',\n",
    "            'type_local',\n",
    "            'surface_reelle_bati',\n",
    "            'nombre_pieces_principales',\n",
    "            'surface_terrain',\n",
    "            'longitude',\n",
    "            'latitude'\n",
    "        ],\n",
    "        parse_dates=['date_mutation'],\n",
    "        index_col=False\n",
    "    )\n",
    "\n",
    "    # Suppression des observations qui ne sont pas dans Nantes Métropole\n",
    "    data_year.drop(data_year[~(data_year['code_commune'].isin(communes) | data_year['ancien_code_commune'].isin(communes))].index, inplace=True)\n",
    "\n",
    "    # Suppression des variables code commune INSEE\n",
    "    data_year.drop(['code_commune', 'ancien_code_commune'],axis=1, inplace=True)\n",
    "\n",
    "    return data_year\n",
    "\n",
    "def get_data():\n",
    "    communes = get_communes()\n",
    "\n",
    "    data = pd.concat([get_data_year(year, communes) for year in [2014, 2015, 2016, 2017, 2018]], ignore_index=True)\n",
    "\n",
    "    data['nature_mutation'].replace('nan', np.nan, inplace=True)\n",
    "    data['nature_mutation'] = data['nature_mutation'].astype('category')\n",
    "    data['valeur_fonciere'] = data['valeur_fonciere'].astype('float64')\n",
    "    data['code_postal'] = data['code_postal'].astype('str')\n",
    "    data['code_postal'] = data['code_postal'].apply(lambda n: n.split('.')[0])\n",
    "    data['code_postal'].replace('nan', np.nan, inplace=True)\n",
    "    data['code_postal'] = data['code_postal'].astype('category')\n",
    "    data['nom_commune'].replace('nan', np.nan, inplace=True)\n",
    "    data['nom_commune'] = data['nom_commune'].astype('category')\n",
    "    data['lot1_surface_carrez'] = data['lot1_surface_carrez'].astype('float64')\n",
    "    data['lot2_surface_carrez'] = data['lot2_surface_carrez'].astype('float64')\n",
    "    data['lot3_surface_carrez'] = data['lot3_surface_carrez'].astype('float64')\n",
    "    data['lot4_surface_carrez'] = data['lot4_surface_carrez'].astype('float64')\n",
    "    data['lot5_surface_carrez'] = data['lot5_surface_carrez'].astype('float64')\n",
    "    data['nombre_lots'] = data['nombre_lots'].astype('int64')\n",
    "    data['type_local'].replace('nan', np.nan, inplace=True)\n",
    "    data['type_local'] = data['type_local'].astype('category')\n",
    "    data['surface_reelle_bati'] = data['surface_reelle_bati'].astype('float64')\n",
    "    data['nombre_pieces_principales'] = data['nombre_pieces_principales'].astype('float64')\n",
    "    data['surface_terrain'] = data['surface_terrain'].astype('float64')\n",
    "    data['longitude'] = data['longitude'].astype('float64')\n",
    "    data['latitude'] = data['latitude'].astype('float64')\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chargement des données relatives à Nantes Métropole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151591 observations chargées\n"
     ]
    },
    {
     "data": {
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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></th>\n",
       "      <th>date_mutation</th>\n",
       "      <th>nature_mutation</th>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <th>code_postal</th>\n",
       "      <th>nom_commune</th>\n",
       "      <th>lot1_surface_carrez</th>\n",
       "      <th>lot2_surface_carrez</th>\n",
       "      <th>lot3_surface_carrez</th>\n",
       "      <th>lot4_surface_carrez</th>\n",
       "      <th>lot5_surface_carrez</th>\n",
       "      <th>nombre_lots</th>\n",
       "      <th>type_local</th>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <th>surface_terrain</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>Vente</td>\n",
       "      <td>29000.00</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Dépendance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-1.545448</td>\n",
       "      <td>47.221956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>Vente</td>\n",
       "      <td>29000.00</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Dépendance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-1.545448</td>\n",
       "      <td>47.221956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>Vente en l'état futur d'achèvement</td>\n",
       "      <td>285000.00</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Dépendance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>Vente en l'état futur d'achèvement</td>\n",
       "      <td>285000.00</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>65.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>Vente en l'état futur d'achèvement</td>\n",
       "      <td>251538.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.533561</td>\n",
       "      <td>47.216841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  date_mutation                     nature_mutation  valeur_fonciere  \\\n",
       "0    2014-01-08                               Vente         29000.00   \n",
       "1    2014-01-08                               Vente         29000.00   \n",
       "2    2014-01-09  Vente en l'état futur d'achèvement        285000.00   \n",
       "3    2014-01-09  Vente en l'état futur d'achèvement        285000.00   \n",
       "4    2014-01-06  Vente en l'état futur d'achèvement        251538.46   \n",
       "\n",
       "  code_postal nom_commune  lot1_surface_carrez  lot2_surface_carrez  \\\n",
       "0       44000      Nantes                  NaN                  NaN   \n",
       "1       44000      Nantes                  NaN                  NaN   \n",
       "2       44000      Nantes                  NaN                  NaN   \n",
       "3       44000      Nantes                  NaN                  NaN   \n",
       "4         NaN      Nantes                  NaN                  NaN   \n",
       "\n",
       "   lot3_surface_carrez  lot4_surface_carrez  lot5_surface_carrez  nombre_lots  \\\n",
       "0                  NaN                  NaN                  NaN            0   \n",
       "1                  NaN                  NaN                  NaN            0   \n",
       "2                  NaN                  NaN                  NaN            1   \n",
       "3                  NaN                  NaN                  NaN            1   \n",
       "4                  NaN                  NaN                  NaN            1   \n",
       "\n",
       "    type_local  surface_reelle_bati  nombre_pieces_principales  \\\n",
       "0   Dépendance                  NaN                        0.0   \n",
       "1   Dépendance                  NaN                        0.0   \n",
       "2   Dépendance                  NaN                        0.0   \n",
       "3  Appartement                 65.0                        3.0   \n",
       "4          NaN                  NaN                        NaN   \n",
       "\n",
       "   surface_terrain  longitude   latitude  \n",
       "0             26.0  -1.545448  47.221956  \n",
       "1             26.0  -1.545448  47.221956  \n",
       "2              NaN        NaN        NaN  \n",
       "3              NaN        NaN        NaN  \n",
       "4              NaN  -1.533561  47.216841  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = None\n",
    "if path.isfile('data/datasets/dvf_nantes.pkl.xz'):\n",
    "    df = pd.read_pickle('data/datasets/dvf_nantes.pkl.xz')\n",
    "else:\n",
    "    df = get_data()\n",
    "    df.to_pickle('data/datasets/dvf_nantes.pkl.xz')\n",
    "\n",
    "print(\"%i observations chargées\" % df.shape[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 variables\n"
     ]
    },
    {
     "data": {
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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>Colonnes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>date_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nature_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>valeur_fonciere</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>code_postal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nom_commune</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lot1_surface_carrez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lot2_surface_carrez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lot3_surface_carrez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lot4_surface_carrez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lot5_surface_carrez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>nombre_lots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>type_local</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>surface_reelle_bati</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>nombre_pieces_principales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>surface_terrain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>longitude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>latitude</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Colonnes\n",
       "0               date_mutation\n",
       "1             nature_mutation\n",
       "2             valeur_fonciere\n",
       "3                 code_postal\n",
       "4                 nom_commune\n",
       "5         lot1_surface_carrez\n",
       "6         lot2_surface_carrez\n",
       "7         lot3_surface_carrez\n",
       "8         lot4_surface_carrez\n",
       "9         lot5_surface_carrez\n",
       "10                nombre_lots\n",
       "11                 type_local\n",
       "12        surface_reelle_bati\n",
       "13  nombre_pieces_principales\n",
       "14            surface_terrain\n",
       "15                  longitude\n",
       "16                   latitude"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"%i variables\" % df.shape[1])\n",
    "pd.DataFrame(df.columns, columns=['Colonnes'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse des natures de mutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "data": [
        {
         "labels": [
          "Adjudication",
          "Echange",
          "Expropriation",
          "Vente",
          "Vente en l'état futur d'achèvement",
          "Vente terrain à bâtir"
         ],
         "title": {
          "text": "Natures de mutation dans le dataset"
         },
         "type": "pie",
         "values": [
          159,
          627,
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('9295f139-8d65-4a2a-8435-b4c3011da999');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "py_pie(df.groupby(['nature_mutation']).size(), 'Natures de mutation dans le dataset', 'natures')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ce dataset inclue des mutations autres qu'une simple vente (qui représente cependant la grande majorité des cas). Pour simplifier et se limiter aux cas d'utilisation les plus fréquents, nous n'allons garder que les ventes, et retirer la variable `nature_mutation`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['nature_mutation'] != 'Vente'].index, inplace=True)\n",
    "df.drop('nature_mutation', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113336 observations restantes\n"
     ]
    },
    {
     "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",
       "        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>date_mutation</th>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <th>code_postal</th>\n",
       "      <th>nom_commune</th>\n",
       "      <th>lot1_surface_carrez</th>\n",
       "      <th>lot2_surface_carrez</th>\n",
       "      <th>lot3_surface_carrez</th>\n",
       "      <th>lot4_surface_carrez</th>\n",
       "      <th>lot5_surface_carrez</th>\n",
       "      <th>nombre_lots</th>\n",
       "      <th>type_local</th>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <th>surface_terrain</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Dépendance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-1.545448</td>\n",
       "      <td>47.221956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Dépendance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-1.545448</td>\n",
       "      <td>47.221956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>192.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>Saint-Herblain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-1.629867</td>\n",
       "      <td>47.239388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>194400.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Dépendance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.603261</td>\n",
       "      <td>47.209692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>194400.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.603261</td>\n",
       "      <td>47.209692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_mutation  valeur_fonciere code_postal     nom_commune  \\\n",
       "0     2014-01-08          29000.0       44000          Nantes   \n",
       "1     2014-01-08          29000.0       44000          Nantes   \n",
       "8     2014-01-08            192.0       44800  Saint-Herblain   \n",
       "9     2014-01-02         194400.0       44100          Nantes   \n",
       "10    2014-01-02         194400.0       44100          Nantes   \n",
       "\n",
       "    lot1_surface_carrez  lot2_surface_carrez  lot3_surface_carrez  \\\n",
       "0                   NaN                  NaN                  NaN   \n",
       "1                   NaN                  NaN                  NaN   \n",
       "8                   NaN                  NaN                  NaN   \n",
       "9                   NaN                  NaN                  NaN   \n",
       "10                  NaN                  NaN                  NaN   \n",
       "\n",
       "    lot4_surface_carrez  lot5_surface_carrez  nombre_lots   type_local  \\\n",
       "0                   NaN                  NaN            0   Dépendance   \n",
       "1                   NaN                  NaN            0   Dépendance   \n",
       "8                   NaN                  NaN            0          NaN   \n",
       "9                   NaN                  NaN            1   Dépendance   \n",
       "10                  NaN                  NaN            1  Appartement   \n",
       "\n",
       "    surface_reelle_bati  nombre_pieces_principales  surface_terrain  \\\n",
       "0                   NaN                        0.0             26.0   \n",
       "1                   NaN                        0.0             26.0   \n",
       "8                   NaN                        NaN             24.0   \n",
       "9                   NaN                        0.0              NaN   \n",
       "10                 84.0                        4.0              NaN   \n",
       "\n",
       "    longitude   latitude  \n",
       "0   -1.545448  47.221956  \n",
       "1   -1.545448  47.221956  \n",
       "8   -1.629867  47.239388  \n",
       "9   -1.603261  47.209692  \n",
       "10  -1.603261  47.209692  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"%i observations restantes\" % df.shape[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse des types de locaux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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          "Appartement",
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          "text": "Types de locaux dans le dataset"
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   "source": [
    "Là aussi, pour simplifier et adresser le plus grand nombre de cas d'utilisation, nous allons nous limiter aux appartements et aux maisons."
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   "execution_count": 10,
   "metadata": {},
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    "df.drop(df[~df['type_local'].isin(['Appartement', 'Maison'])].index, inplace=True)\n",
    "df['type_local'].cat.remove_unused_categories(inplace=True)"
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   "metadata": {},
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     "text": [
      "57567 observations restantes\n"
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       "      <th>lot2_surface_carrez</th>\n",
       "      <th>lot3_surface_carrez</th>\n",
       "      <th>lot4_surface_carrez</th>\n",
       "      <th>lot5_surface_carrez</th>\n",
       "      <th>nombre_lots</th>\n",
       "      <th>type_local</th>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <th>surface_terrain</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>194400.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.603261</td>\n",
       "      <td>47.209692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>Saint-Herblain</td>\n",
       "      <td>45.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.644254</td>\n",
       "      <td>47.207462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>295000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.558665</td>\n",
       "      <td>47.216404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>208154.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>103.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>103.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.519753</td>\n",
       "      <td>47.272364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>26.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.551322</td>\n",
       "      <td>47.216626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_mutation  valeur_fonciere code_postal     nom_commune  \\\n",
       "10    2014-01-02         194400.0       44100          Nantes   \n",
       "12    2014-01-02         107000.0       44800  Saint-Herblain   \n",
       "13    2014-01-08         295000.0       44000          Nantes   \n",
       "14    2014-01-09         208154.0       44300          Nantes   \n",
       "17    2014-01-06          79000.0       44000          Nantes   \n",
       "\n",
       "    lot1_surface_carrez  lot2_surface_carrez  lot3_surface_carrez  \\\n",
       "10                  NaN                  NaN                  NaN   \n",
       "12                45.80                  NaN                  NaN   \n",
       "13                  NaN                 89.2                  NaN   \n",
       "14               103.64                  NaN                  NaN   \n",
       "17                26.45                  NaN                  NaN   \n",
       "\n",
       "    lot4_surface_carrez  lot5_surface_carrez  nombre_lots   type_local  \\\n",
       "10                  NaN                  NaN            1  Appartement   \n",
       "12                  NaN                  NaN            1  Appartement   \n",
       "13                  NaN                  NaN            2  Appartement   \n",
       "14                  NaN                  NaN            1  Appartement   \n",
       "17                  NaN                  NaN            1  Appartement   \n",
       "\n",
       "    surface_reelle_bati  nombre_pieces_principales  surface_terrain  \\\n",
       "10                 84.0                        4.0              NaN   \n",
       "12                 46.0                        2.0              NaN   \n",
       "13                105.0                        2.0              NaN   \n",
       "14                103.0                        5.0              NaN   \n",
       "17                 25.0                        1.0              NaN   \n",
       "\n",
       "    longitude   latitude  \n",
       "10  -1.603261  47.209692  \n",
       "12  -1.644254  47.207462  \n",
       "13  -1.558665  47.216404  \n",
       "14  -1.519753  47.272364  \n",
       "17  -1.551322  47.216626  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"%i observations restantes\" % df.shape[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse du nombre de lots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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   ],
   "source": [
    "py_pie(df.groupby(['nombre_lots']).size(), 'Nombres de lots de chaque bien dans le dataset', 'lots')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De nombreux biens sont vendus en lots, et la valeur foncière représente alors le total des lots. Là aussi dans un but de simplification nous n'allons garder que les biens contenant 0 ou 1 lot, et retirer les variables `nombre_lots` et `lot2_surface_carrez` et plus. Nous renommons du coup `lot1_surface_carrez` en `surface_carrez`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['nombre_lots'] > 1].index, inplace=True)\n",
    "df.drop('nombre_lots', axis=1, inplace=True)\n",
    "df.drop('lot2_surface_carrez', axis=1, inplace=True)\n",
    "df.drop('lot3_surface_carrez', axis=1, inplace=True)\n",
    "df.drop('lot4_surface_carrez', axis=1, inplace=True)\n",
    "df.drop('lot5_surface_carrez', axis=1, inplace=True)\n",
    "df.rename({'lot1_surface_carrez': 'surface_carrez'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42196 observations restantes\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_mutation</th>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <th>code_postal</th>\n",
       "      <th>nom_commune</th>\n",
       "      <th>surface_carrez</th>\n",
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       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>194400.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>-1.603261</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>Saint-Herblain</td>\n",
       "      <td>45.80</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.644254</td>\n",
       "      <td>47.207462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>208154.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>103.64</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>103.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.519753</td>\n",
       "      <td>47.272364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>26.45</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.551322</td>\n",
       "      <td>47.216626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>335000.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Maison</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>-1.506824</td>\n",
       "      <td>47.231122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_mutation  valeur_fonciere code_postal     nom_commune  surface_carrez  \\\n",
       "10    2014-01-02         194400.0       44100          Nantes             NaN   \n",
       "12    2014-01-02         107000.0       44800  Saint-Herblain           45.80   \n",
       "14    2014-01-09         208154.0       44300          Nantes          103.64   \n",
       "17    2014-01-06          79000.0       44000          Nantes           26.45   \n",
       "21    2014-01-02         335000.0       44300          Nantes             NaN   \n",
       "\n",
       "     type_local  surface_reelle_bati  nombre_pieces_principales  \\\n",
       "10  Appartement                 84.0                        4.0   \n",
       "12  Appartement                 46.0                        2.0   \n",
       "14  Appartement                103.0                        5.0   \n",
       "17  Appartement                 25.0                        1.0   \n",
       "21       Maison                118.0                        5.0   \n",
       "\n",
       "    surface_terrain  longitude   latitude  \n",
       "10              NaN  -1.603261  47.209692  \n",
       "12              NaN  -1.644254  47.207462  \n",
       "14              NaN  -1.519753  47.272364  \n",
       "17              NaN  -1.551322  47.216626  \n",
       "21            562.0  -1.506824  47.231122  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"%i observations restantes\" % df.shape[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Suppression des doublons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40275 observations restantes\n"
     ]
    },
    {
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       "      <td>2014-01-02</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>208154.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>103.64</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>103.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.519753</td>\n",
       "      <td>47.272364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>26.45</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.551322</td>\n",
       "      <td>47.216626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>335000.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Maison</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>-1.506824</td>\n",
       "      <td>47.231122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_mutation  valeur_fonciere code_postal     nom_commune  surface_carrez  \\\n",
       "10    2014-01-02         194400.0       44100          Nantes             NaN   \n",
       "12    2014-01-02         107000.0       44800  Saint-Herblain           45.80   \n",
       "14    2014-01-09         208154.0       44300          Nantes          103.64   \n",
       "17    2014-01-06          79000.0       44000          Nantes           26.45   \n",
       "21    2014-01-02         335000.0       44300          Nantes             NaN   \n",
       "\n",
       "     type_local  surface_reelle_bati  nombre_pieces_principales  \\\n",
       "10  Appartement                 84.0                        4.0   \n",
       "12  Appartement                 46.0                        2.0   \n",
       "14  Appartement                103.0                        5.0   \n",
       "17  Appartement                 25.0                        1.0   \n",
       "21       Maison                118.0                        5.0   \n",
       "\n",
       "    surface_terrain  longitude   latitude  \n",
       "10              NaN  -1.603261  47.209692  \n",
       "12              NaN  -1.644254  47.207462  \n",
       "14              NaN  -1.519753  47.272364  \n",
       "17              NaN  -1.551322  47.216626  \n",
       "21            562.0  -1.506824  47.231122  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"%i observations restantes\" % df.shape[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Données manquantes\n",
    "\n",
    "Observons le taux de remplissage des colonnes :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Taux de remplissage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>date_mutation</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <td>99.69 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code_postal</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nom_commune</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface_carrez</th>\n",
       "      <td>29.71 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_local</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface_terrain</th>\n",
       "      <td>58.87 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>96.77 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>96.77 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Taux de remplissage\n",
       "date_mutation                        100.00 %\n",
       "valeur_fonciere                       99.69 %\n",
       "code_postal                          100.00 %\n",
       "nom_commune                          100.00 %\n",
       "surface_carrez                        29.71 %\n",
       "type_local                           100.00 %\n",
       "surface_reelle_bati                  100.00 %\n",
       "nombre_pieces_principales            100.00 %\n",
       "surface_terrain                       58.87 %\n",
       "longitude                             96.77 %\n",
       "latitude                              96.77 %"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame((df.count() / df.shape[0] * 100).apply(lambda p: \"%.2f %%\" % p), columns=['Taux de remplissage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La valeur foncière n'est pas toujours remplie, or il s'agit de la variable que l'on cherche à prédire. On supprime donc les observations concernées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['valeur_fonciere'].isna()].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40150 observations restantes\n"
     ]
    },
    {
     "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",
       "        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>date_mutation</th>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <th>code_postal</th>\n",
       "      <th>nom_commune</th>\n",
       "      <th>surface_carrez</th>\n",
       "      <th>type_local</th>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <th>surface_terrain</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>194400.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.603261</td>\n",
       "      <td>47.209692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>Saint-Herblain</td>\n",
       "      <td>45.80</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.644254</td>\n",
       "      <td>47.207462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>208154.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>103.64</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>103.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.519753</td>\n",
       "      <td>47.272364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>26.45</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.551322</td>\n",
       "      <td>47.216626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>335000.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Maison</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>-1.506824</td>\n",
       "      <td>47.231122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_mutation  valeur_fonciere code_postal     nom_commune  surface_carrez  \\\n",
       "10    2014-01-02         194400.0       44100          Nantes             NaN   \n",
       "12    2014-01-02         107000.0       44800  Saint-Herblain           45.80   \n",
       "14    2014-01-09         208154.0       44300          Nantes          103.64   \n",
       "17    2014-01-06          79000.0       44000          Nantes           26.45   \n",
       "21    2014-01-02         335000.0       44300          Nantes             NaN   \n",
       "\n",
       "     type_local  surface_reelle_bati  nombre_pieces_principales  \\\n",
       "10  Appartement                 84.0                        4.0   \n",
       "12  Appartement                 46.0                        2.0   \n",
       "14  Appartement                103.0                        5.0   \n",
       "17  Appartement                 25.0                        1.0   \n",
       "21       Maison                118.0                        5.0   \n",
       "\n",
       "    surface_terrain  longitude   latitude  \n",
       "10              NaN  -1.603261  47.209692  \n",
       "12              NaN  -1.644254  47.207462  \n",
       "14              NaN  -1.519753  47.272364  \n",
       "17              NaN  -1.551322  47.216626  \n",
       "21            562.0  -1.506824  47.231122  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"%i observations restantes\" % df.shape[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Certains biens n'ont pas pu être géolocalisés, on les supprime donc également."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['longitude'].isna() | df['latitude'].isna()].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38865 observations restantes\n"
     ]
    },
    {
     "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",
       "        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>date_mutation</th>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <th>code_postal</th>\n",
       "      <th>nom_commune</th>\n",
       "      <th>surface_carrez</th>\n",
       "      <th>type_local</th>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <th>surface_terrain</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>194400.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.603261</td>\n",
       "      <td>47.209692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>Saint-Herblain</td>\n",
       "      <td>45.80</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.644254</td>\n",
       "      <td>47.207462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>208154.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>103.64</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>103.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.519753</td>\n",
       "      <td>47.272364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>26.45</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.551322</td>\n",
       "      <td>47.216626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>335000.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Maison</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>-1.506824</td>\n",
       "      <td>47.231122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_mutation  valeur_fonciere code_postal     nom_commune  surface_carrez  \\\n",
       "10    2014-01-02         194400.0       44100          Nantes             NaN   \n",
       "12    2014-01-02         107000.0       44800  Saint-Herblain           45.80   \n",
       "14    2014-01-09         208154.0       44300          Nantes          103.64   \n",
       "17    2014-01-06          79000.0       44000          Nantes           26.45   \n",
       "21    2014-01-02         335000.0       44300          Nantes             NaN   \n",
       "\n",
       "     type_local  surface_reelle_bati  nombre_pieces_principales  \\\n",
       "10  Appartement                 84.0                        4.0   \n",
       "12  Appartement                 46.0                        2.0   \n",
       "14  Appartement                103.0                        5.0   \n",
       "17  Appartement                 25.0                        1.0   \n",
       "21       Maison                118.0                        5.0   \n",
       "\n",
       "    surface_terrain  longitude   latitude  \n",
       "10              NaN  -1.603261  47.209692  \n",
       "12              NaN  -1.644254  47.207462  \n",
       "14              NaN  -1.519753  47.272364  \n",
       "17              NaN  -1.551322  47.216626  \n",
       "21            562.0  -1.506824  47.231122  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"%i observations restantes\" % df.shape[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Taux de remplissage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>date_mutation</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code_postal</th>\n",
       "      <td>99.99 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nom_commune</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface_carrez</th>\n",
       "      <td>30.31 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_local</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface_terrain</th>\n",
       "      <td>58.38 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Taux de remplissage\n",
       "date_mutation                        100.00 %\n",
       "valeur_fonciere                      100.00 %\n",
       "code_postal                           99.99 %\n",
       "nom_commune                          100.00 %\n",
       "surface_carrez                        30.31 %\n",
       "type_local                           100.00 %\n",
       "surface_reelle_bati                  100.00 %\n",
       "nombre_pieces_principales            100.00 %\n",
       "surface_terrain                       58.38 %\n",
       "longitude                            100.00 %\n",
       "latitude                             100.00 %"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame((df.count() / df.shape[0] * 100).apply(lambda p: \"%.2f %%\" % p), columns=['Taux de remplissage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Certains bien n'ont pas de code postal, on les supprime également."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['code_postal'].isna()].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38863 observations restantes\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_mutation</th>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <th>code_postal</th>\n",
       "      <th>nom_commune</th>\n",
       "      <th>surface_carrez</th>\n",
       "      <th>type_local</th>\n",
       "      <th>surface_reelle_bati</th>\n",
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       "      <th>surface_terrain</th>\n",
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       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>194400.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.603261</td>\n",
       "      <td>47.209692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>Saint-Herblain</td>\n",
       "      <td>45.80</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.644254</td>\n",
       "      <td>47.207462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>208154.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>103.64</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>103.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.519753</td>\n",
       "      <td>47.272364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>26.45</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.551322</td>\n",
       "      <td>47.216626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>335000.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Maison</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>-1.506824</td>\n",
       "      <td>47.231122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_mutation  valeur_fonciere code_postal     nom_commune  surface_carrez  \\\n",
       "10    2014-01-02         194400.0       44100          Nantes             NaN   \n",
       "12    2014-01-02         107000.0       44800  Saint-Herblain           45.80   \n",
       "14    2014-01-09         208154.0       44300          Nantes          103.64   \n",
       "17    2014-01-06          79000.0       44000          Nantes           26.45   \n",
       "21    2014-01-02         335000.0       44300          Nantes             NaN   \n",
       "\n",
       "     type_local  surface_reelle_bati  nombre_pieces_principales  \\\n",
       "10  Appartement                 84.0                        4.0   \n",
       "12  Appartement                 46.0                        2.0   \n",
       "14  Appartement                103.0                        5.0   \n",
       "17  Appartement                 25.0                        1.0   \n",
       "21       Maison                118.0                        5.0   \n",
       "\n",
       "    surface_terrain  longitude   latitude  \n",
       "10              NaN  -1.603261  47.209692  \n",
       "12              NaN  -1.644254  47.207462  \n",
       "14              NaN  -1.519753  47.272364  \n",
       "17              NaN  -1.551322  47.216626  \n",
       "21            562.0  -1.506824  47.231122  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"%i observations restantes\" % df.shape[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Taux de remplissage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>date_mutation</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code_postal</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nom_commune</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface_carrez</th>\n",
       "      <td>30.31 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_local</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface_terrain</th>\n",
       "      <td>58.38 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>100.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Taux de remplissage\n",
       "date_mutation                        100.00 %\n",
       "valeur_fonciere                      100.00 %\n",
       "code_postal                          100.00 %\n",
       "nom_commune                          100.00 %\n",
       "surface_carrez                        30.31 %\n",
       "type_local                           100.00 %\n",
       "surface_reelle_bati                  100.00 %\n",
       "nombre_pieces_principales            100.00 %\n",
       "surface_terrain                       58.38 %\n",
       "longitude                            100.00 %\n",
       "latitude                             100.00 %"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame((df.count() / df.shape[0] * 100).apply(lambda p: \"%.2f %%\" % p), columns=['Taux de remplissage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La surface Carrez n'est renseignée que dans moins de 30 % des cas, et la surface du terrain dans moins de 60 %.\n",
    "\n",
    "Proposition :\n",
    "* supprimer la surface Carrez, et n'utiliser que la surface batie ;\n",
    "* utiliser la surface batie à la place de celle du terrain là où cette dernière n'est pas remplie.\n",
    "\n",
    "Toutes les autres variables sont remplies à 100 %, hormis `adresse_suffixe` mais ça n'est pas grave car une adresse ne contient pas forcement un suffix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_mutation</th>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <th>code_postal</th>\n",
       "      <th>nom_commune</th>\n",
       "      <th>surface_carrez</th>\n",
       "      <th>type_local</th>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <th>surface_terrain</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>194400.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>84.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>Saint-Herblain</td>\n",
       "      <td>45.80</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.644254</td>\n",
       "      <td>47.207462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>208154.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>103.64</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>103.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.519753</td>\n",
       "      <td>47.272364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>26.45</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.551322</td>\n",
       "      <td>47.216626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>335000.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Maison</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5</td>\n",
       "      <td>562.0</td>\n",
       "      <td>-1.506824</td>\n",
       "      <td>47.231122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_mutation  valeur_fonciere code_postal     nom_commune  surface_carrez  \\\n",
       "10    2014-01-02         194400.0       44100          Nantes             NaN   \n",
       "12    2014-01-02         107000.0       44800  Saint-Herblain           45.80   \n",
       "14    2014-01-09         208154.0       44300          Nantes          103.64   \n",
       "17    2014-01-06          79000.0       44000          Nantes           26.45   \n",
       "21    2014-01-02         335000.0       44300          Nantes             NaN   \n",
       "\n",
       "     type_local  surface_reelle_bati  nombre_pieces_principales  \\\n",
       "10  Appartement                 84.0                          4   \n",
       "12  Appartement                 46.0                          2   \n",
       "14  Appartement                103.0                          5   \n",
       "17  Appartement                 25.0                          1   \n",
       "21       Maison                118.0                          5   \n",
       "\n",
       "    surface_terrain  longitude   latitude  \n",
       "10              NaN  -1.603261  47.209692  \n",
       "12              NaN  -1.644254  47.207462  \n",
       "14              NaN  -1.519753  47.272364  \n",
       "17              NaN  -1.551322  47.216626  \n",
       "21            562.0  -1.506824  47.231122  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nombre pieces principales est maintenant rempli à 100 %, on peut le convertir en int\n",
    "df['nombre_pieces_principales'] = df['nombre_pieces_principales'].astype('int64')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse des communes et codes postaux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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         "labels": [
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          "Sainte-Luce-sur-Loire",
          "Sautron",
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          "Vertou"
         ],
         "title": {
          "text": "Nombres de bien par commune dans le dataset"
         },
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     "metadata": {},
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    }
   ],
   "source": [
    "py_pie(df.groupby(['code_postal']).size(), 'Nombres de bien par code postal dans le dataset', 'codes postaux')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enregistrement du dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data/datasets/dvf_nantes_cleaned.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion de l'analyse\n",
    "\n",
    "En utilisant ce dataset, un fois nettoyé, nous disposons d'une base solide de plus de 40 000 observations contenant des données réelles (non estimées par le vendeur par exemple). De plus, le taux de remplissage des variables est élevé.\n",
    "\n",
    "Le seul bémol est le nombre un peu trop petit de variables. Cependant, il est tout à fait possible d'utiliser les informations de ce dataset pour l'enrichir et ajouter des variables qui pourraient être pertinentes. Je fais 2 propositions dans ce sens ci-dessous."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploitation des adresses\n",
    "\n",
    "Les adresses sont renseignées avec précision. Nous pourrions donc les utiliser afin d'enrichir le dataset de variables telles que : Distance de l'arrêt de tram le plus proche, de l'arrêt de bus, de la gare, d'une pharmacie, d'un supermarché, d'un groupe de commerces, etc. Le choix des variables est à arrêter en fonction des informations que l'on souhaite demander à l'utilisateur et de ce qu'il sera possible de faire pour enrichir le dataset via OpenStreetMap par exemple. Il peut également être intéressant de renseigner les distances à pied ou en voiture.\n",
    "\n",
    "Une fois le dataset enrichi, nous supprimerons les adresses des données car le but est de ne pas stocker des informations trop précises (pour ne pas inquiéter l'utilisateur).\n",
    "\n",
    "Pour éviter à l'utilisateur d'avoir à saisir toutes ces informations, on pourrais lui demander de saisir directement l'adresse du bien, en lui précisant bien qu'elle ne sera ni stockée ni utilisée pour la prédiction mais uniquement pour déduire les informations citées plus haut (et avec plus de précision que si c'est lui qui le fait du coup).\n",
    "\n",
    "Nantes Métropole fournit de nombreux datasets en open data que l'on pourrai utiliser :\n",
    "* [localisation des arrêts de tram et de bus](https://data.nantesmetropole.fr/explore/dataset/244400404_tan-arrets/table/) ;\n",
    "* [localisation des entreprises](https://data.nantesmetropole.fr/explore/dataset/244400404_base-sirene-entreprises-nantes-metropole/table/) ;\n",
    "* ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploitation de la date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les dates de vente sont elles aussi renseignées avec précision. Nous pourrions les utiliser afin d'enrichir le dataset de variables telles que : prix moyen du mètre carré à cette date, tension du marché à cette date, revenu moyen par habitant à cette date, densité de population, etc. À renseigner pour l'année courante ou le mois courant (ou précédent), au niveau national ou local en fonction de ce que l'on peut trouver comme historique sur le net.\n",
    "\n",
    "L'utilisateur n'aura bien entendu pas à rentrer ces données lorsqu'il voudra estimer un bien, mais on viendrait automatiquement enrichir sa saisie avec les derniers chiffres connus."
   ]
  }
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