geopandas_vector_data_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Starting by importing Geopandas module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "link where our shapefile is located"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "link_shp = r\"data\\delim parcelaire FR\\2024-07-22_delim-parcellaire-aoc-shp.shp\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the geodatafile with geopandas <i>gpd</i>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>type_prod</th>\n",
       "      <th>categorie</th>\n",
       "      <th>type_denom</th>\n",
       "      <th>signe</th>\n",
       "      <th>id_app</th>\n",
       "      <th>app</th>\n",
       "      <th>id_denom</th>\n",
       "      <th>denom</th>\n",
       "      <th>insee</th>\n",
       "      <th>cvi</th>\n",
       "      <th>nomcom</th>\n",
       "      <th>insee2011</th>\n",
       "      <th>nomcom2011</th>\n",
       "      <th>id_aire</th>\n",
       "      <th>crinao</th>\n",
       "      <th>grp_name1</th>\n",
       "      <th>grp_name2</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Angers</td>\n",
       "      <td>Vins</td>\n",
       "      <td>Vin mousseux, Vin primeur, Vin tranquille</td>\n",
       "      <td>appellation</td>\n",
       "      <td>AOC</td>\n",
       "      <td>76</td>\n",
       "      <td>Anjou</td>\n",
       "      <td>173</td>\n",
       "      <td>Anjou</td>\n",
       "      <td>49002</td>\n",
       "      <td>1B200, 1B200M 1, 1R200S, 1R203S, 1R203S01, 1S2...</td>\n",
       "      <td>ALLONNES</td>\n",
       "      <td>49002</td>\n",
       "      <td>ALLONNES</td>\n",
       "      <td>133</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>MULTIPOLYGON (((476099.07 6692210.88, 476039.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angers</td>\n",
       "      <td>Vins</td>\n",
       "      <td>Vin mousseux, Vin primeur, Vin tranquille</td>\n",
       "      <td>appellation</td>\n",
       "      <td>AOC</td>\n",
       "      <td>76</td>\n",
       "      <td>Anjou</td>\n",
       "      <td>173</td>\n",
       "      <td>Anjou</td>\n",
       "      <td>49003</td>\n",
       "      <td>1B200, 1B200M 1, 1R200S, 1R203S, 1R203S01, 1S2...</td>\n",
       "      <td>TUFFALUN</td>\n",
       "      <td>49003,49181,49230</td>\n",
       "      <td>AMBILLOU-CHATEAU,LOUERRE,NOYANT-LA-PLAINE</td>\n",
       "      <td>133</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>MULTIPOLYGON (((445501.95 6686844.11, 445500.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angers</td>\n",
       "      <td>Vins</td>\n",
       "      <td>Vin mousseux, Vin primeur, Vin tranquille</td>\n",
       "      <td>appellation</td>\n",
       "      <td>AOC</td>\n",
       "      <td>76</td>\n",
       "      <td>Anjou</td>\n",
       "      <td>173</td>\n",
       "      <td>Anjou</td>\n",
       "      <td>49007</td>\n",
       "      <td>1B200, 1B200M 1, 1R200S, 1R203S, 1R203S01, 1S2...</td>\n",
       "      <td>ANGERS</td>\n",
       "      <td>49007</td>\n",
       "      <td>ANGERS</td>\n",
       "      <td>133</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>MULTIPOLYGON (((430316.54 6711699.87, 430309.2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dt type_prod                                  categorie   type_denom  \\\n",
       "0  Angers      Vins  Vin mousseux, Vin primeur, Vin tranquille  appellation   \n",
       "1  Angers      Vins  Vin mousseux, Vin primeur, Vin tranquille  appellation   \n",
       "2  Angers      Vins  Vin mousseux, Vin primeur, Vin tranquille  appellation   \n",
       "\n",
       "  signe  id_app    app  id_denom  denom  insee  \\\n",
       "0   AOC      76  Anjou       173  Anjou  49002   \n",
       "1   AOC      76  Anjou       173  Anjou  49003   \n",
       "2   AOC      76  Anjou       173  Anjou  49007   \n",
       "\n",
       "                                                 cvi    nomcom  \\\n",
       "0  1B200, 1B200M 1, 1R200S, 1R203S, 1R203S01, 1S2...  ALLONNES   \n",
       "1  1B200, 1B200M 1, 1R200S, 1R203S, 1R203S01, 1S2...  TUFFALUN   \n",
       "2  1B200, 1B200M 1, 1R200S, 1R203S, 1R203S01, 1S2...    ANGERS   \n",
       "\n",
       "           insee2011                                 nomcom2011  id_aire  \\\n",
       "0              49002                                   ALLONNES      133   \n",
       "1  49003,49181,49230  AMBILLOU-CHATEAU,LOUERRE,NOYANT-LA-PLAINE      133   \n",
       "2              49007                                     ANGERS      133   \n",
       "\n",
       "  crinao grp_name1 grp_name2  \\\n",
       "0   None      None      None   \n",
       "1   None      None      None   \n",
       "2   None      None      None   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((476099.07 6692210.88, 476039.9...  \n",
       "1  MULTIPOLYGON (((445501.95 6686844.11, 445500.5...  \n",
       "2  MULTIPOLYGON (((430316.54 6711699.87, 430309.2...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = gpd.read_file(link_shp)\n",
    "gdf.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dt              object\n",
       "type_prod       object\n",
       "categorie       object\n",
       "type_denom      object\n",
       "signe           object\n",
       "id_app           int64\n",
       "app             object\n",
       "id_denom         int64\n",
       "denom           object\n",
       "insee           object\n",
       "cvi             object\n",
       "nomcom          object\n",
       "insee2011       object\n",
       "nomcom2011      object\n",
       "id_aire          int64\n",
       "crinao          object\n",
       "grp_name1       object\n",
       "grp_name2       object\n",
       "geometry      geometry\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11764"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gdf) # number of records' rows '"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11764, 19)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.shape # (rows, columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 11764 entries, 0 to 11763\n",
      "Data columns (total 19 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   dt          11764 non-null  object  \n",
      " 1   type_prod   11764 non-null  object  \n",
      " 2   categorie   11764 non-null  object  \n",
      " 3   type_denom  11764 non-null  object  \n",
      " 4   signe       11764 non-null  object  \n",
      " 5   id_app      11764 non-null  int64   \n",
      " 6   app         11764 non-null  object  \n",
      " 7   id_denom    11764 non-null  int64   \n",
      " 8   denom       11764 non-null  object  \n",
      " 9   insee       11764 non-null  object  \n",
      " 10  cvi         11764 non-null  object  \n",
      " 11  nomcom      11764 non-null  object  \n",
      " 12  insee2011   11764 non-null  object  \n",
      " 13  nomcom2011  11764 non-null  object  \n",
      " 14  id_aire     11764 non-null  int64   \n",
      " 15  crinao      0 non-null      object  \n",
      " 16  grp_name1   0 non-null      object  \n",
      " 17  grp_name2   0 non-null      object  \n",
      " 18  geometry    11764 non-null  geometry\n",
      "dtypes: geometry(1), int64(3), object(15)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# showing all infos about the geodataframe \n",
    "gdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: IGNF:LAMB93>\n",
       "Name: RGF93 Lambert 93\n",
       "Axis Info [cartesian]:\n",
       "- [east]: Easting (metre)\n",
       "- [north]: Northing (metre)\n",
       "Area of Use:\n",
       "- undefined\n",
       "Coordinate Operation:\n",
       "- name: unnamed\n",
       "- method: Lambert Conic Conformal (2SP)\n",
       "Datum: Reseau Geodesique Francais 1993 v1\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#showing the metadata #coordinates reference system\n",
    "gdf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        MultiPolygon\n",
       "1        MultiPolygon\n",
       "2        MultiPolygon\n",
       "3        MultiPolygon\n",
       "4        MultiPolygon\n",
       "             ...     \n",
       "11759    MultiPolygon\n",
       "11760    MultiPolygon\n",
       "11761    MultiPolygon\n",
       "11762    MultiPolygon\n",
       "11763    MultiPolygon\n",
       "Length: 11764, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.geom_type #showing the geometric type of data contained in the geodataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now, let's plot a part of our the geodata :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_1000 = gdf.head(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#making the legend aside the plot:\n",
    "fig,axes = plt.subplots(1,1,figsize=(8,10))\n",
    "gdf_1000.plot(ax=axes, column=\"categorie\", legend=True, legend_kwds={\"loc\": \"lower right\", \"bbox_to_anchor\": (1.8, 0)})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dt              14\n",
       "type_prod        3\n",
       "categorie       28\n",
       "type_denom       2\n",
       "signe            1\n",
       "id_app         355\n",
       "app            355\n",
       "id_denom      1255\n",
       "denom         1261\n",
       "insee         3647\n",
       "cvi           1254\n",
       "nomcom        3788\n",
       "insee2011     3772\n",
       "nomcom2011    3714\n",
       "id_aire       1261\n",
       "crinao           0\n",
       "grp_name1        0\n",
       "grp_name2        0\n",
       "geometry      8426\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the unique values od each column with pandas\n",
    "gdf.nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gdf['categorie'].unique())#testing the number of unique values on categroy column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transform our geodataframe to another form like geojson :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_1000.to_file(r\"data\\Output_gesjson_from_shp\\output.geojson\", driver=\"GeoJSON\", encoding = \"utf-8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading data from CSV :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "DataSourceError",
     "evalue": "data\\charging_station.csv: No such file or directory",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mDataSourceError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[15], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Reading data from CSV :\u001b[39;00m\n\u001b[0;32m      2\u001b[0m link_to_csv \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mcharging_station.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 3\u001b[0m gdf_1  \u001b[38;5;241m=\u001b[39m \u001b[43mgpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlink_to_csv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m;\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m gdf_1\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m3\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Proprietaire\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\geopandas\\io\\file.py:294\u001b[0m, in \u001b[0;36m_read_file\u001b[1;34m(filename, bbox, mask, columns, rows, engine, **kwargs)\u001b[0m\n\u001b[0;32m    291\u001b[0m             from_bytes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m engine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyogrio\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 294\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read_file_pyogrio\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    295\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    296\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    298\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m engine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiona\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    299\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mapi\u001b[38;5;241m.\u001b[39mtypes\u001b[38;5;241m.\u001b[39mis_file_like(filename):\n",
      "File \u001b[1;32mc:\\Users\\Proprietaire\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\geopandas\\io\\file.py:547\u001b[0m, in \u001b[0;36m_read_file_pyogrio\u001b[1;34m(path_or_bytes, bbox, mask, rows, **kwargs)\u001b[0m\n\u001b[0;32m    538\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    539\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minclude_fields\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore_fields\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m keywords are deprecated, and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    540\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwill be removed in a future release. You can use the \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m keyword \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    543\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[0;32m    544\u001b[0m     )\n\u001b[0;32m    545\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minclude_fields\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 547\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpyogrio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_bytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Proprietaire\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pyogrio\\geopandas.py:261\u001b[0m, in \u001b[0;36mread_dataframe\u001b[1;34m(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, fid_as_index, use_arrow, on_invalid, arrow_to_pandas_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m    256\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m use_arrow:\n\u001b[0;32m    257\u001b[0m     \u001b[38;5;66;03m# For arrow, datetimes are read as is.\u001b[39;00m\n\u001b[0;32m    258\u001b[0m     \u001b[38;5;66;03m# For numpy IO, datetimes are read as string values to preserve timezone info\u001b[39;00m\n\u001b[0;32m    259\u001b[0m     \u001b[38;5;66;03m# as numpy does not directly support timezones.\u001b[39;00m\n\u001b[0;32m    260\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatetime_as_string\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 261\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mread_func\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlayer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    266\u001b[0m \u001b[43m    \u001b[49m\u001b[43mread_geometry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mread_geometry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgdal_force_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    268\u001b[0m \u001b[43m    \u001b[49m\u001b[43mskip_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    269\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    270\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    271\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    272\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    273\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    274\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    275\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql_dialect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msql_dialect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    276\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_fids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfid_as_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    277\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    278\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    280\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_arrow:\n\u001b[0;32m    281\u001b[0m     meta, table \u001b[38;5;241m=\u001b[39m result\n",
      "File \u001b[1;32mc:\\Users\\Proprietaire\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pyogrio\\raw.py:196\u001b[0m, in \u001b[0;36mread\u001b[1;34m(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, return_fids, datetime_as_string, **kwargs)\u001b[0m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Read OGR data source into numpy arrays.\u001b[39;00m\n\u001b[0;32m     57\u001b[0m \n\u001b[0;32m     58\u001b[0m \u001b[38;5;124;03mIMPORTANT: non-linear geometry types (e.g., MultiSurface) are converted\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    191\u001b[0m \n\u001b[0;32m    192\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    194\u001b[0m dataset_kwargs \u001b[38;5;241m=\u001b[39m _preprocess_options_key_value(kwargs) \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[1;32m--> 196\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mogr_read\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    197\u001b[0m \u001b[43m    \u001b[49m\u001b[43mget_vsi_path_or_buffer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_buffer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    198\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlayer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    199\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    200\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    201\u001b[0m \u001b[43m    \u001b[49m\u001b[43mread_geometry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mread_geometry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    202\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    203\u001b[0m \u001b[43m    \u001b[49m\u001b[43mskip_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    204\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_features\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    205\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    206\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    207\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_mask_to_wkb\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    208\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    209\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    210\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql_dialect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msql_dialect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    211\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_fids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_fids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    212\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdataset_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataset_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    213\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdatetime_as_string\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdatetime_as_string\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    214\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Proprietaire\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pyogrio\\_io.pyx:1239\u001b[0m, in \u001b[0;36mpyogrio._io.ogr_read\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\Proprietaire\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pyogrio\\_io.pyx:219\u001b[0m, in \u001b[0;36mpyogrio._io.ogr_open\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mDataSourceError\u001b[0m: data\\charging_station.csv: No such file or directory"
     ]
    }
   ],
   "source": [
    "#Reading data from CSV :\n",
    "link_to_csv = r\"data\\charging_station.csv\"\n",
    "gdf_1  = gpd.read_file(link_to_csv, encoding = \"utf-8\", sep = \";\")\n",
    "gdf_1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11764, 19)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['X', 'Y', 'nom_amenageur', 'siren_amenageur', 'contact_amenageur',\n",
       "       'nom_operateur', 'contact_operateur', 'telephone_operateur',\n",
       "       'nom_enseigne', 'id_station_itinerance', 'id_station_local',\n",
       "       'nom_station', 'implantation_station', 'adresse_station',\n",
       "       'code_insee_commune', 'coordonneesXY', 'nbre_pdc', 'id_pdc_itinerance',\n",
       "       'id_pdc_local', 'puissance_nominale', 'prise_type_ef', 'prise_type_2',\n",
       "       'prise_type_combo_ccs', 'prise_type_chademo', 'prise_type_autre',\n",
       "       'gratuit', 'paiement_acte', 'paiement_cb', 'paiement_autre',\n",
       "       'tarification', 'condition_acces', 'reservation', 'horaires',\n",
       "       'accessibilite_pmr', 'restriction_gabarit', 'station_deux_roues',\n",
       "       'raccordement', 'num_pdl', 'date_mise_en_service', 'observations',\n",
       "       'date_maj'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14870 entries, 0 to 14869\n",
      "Data columns (total 41 columns):\n",
      " #   Column                 Non-Null Count  Dtype \n",
      "---  ------                 --------------  ----- \n",
      " 0   X                      14870 non-null  object\n",
      " 1   Y                      14870 non-null  object\n",
      " 2   nom_amenageur          14870 non-null  object\n",
      " 3   siren_amenageur        14870 non-null  object\n",
      " 4   contact_amenageur      14870 non-null  object\n",
      " 5   nom_operateur          14870 non-null  object\n",
      " 6   contact_operateur      14870 non-null  object\n",
      " 7   telephone_operateur    14870 non-null  object\n",
      " 8   nom_enseigne           14870 non-null  object\n",
      " 9   id_station_itinerance  14870 non-null  object\n",
      " 10  id_station_local       14870 non-null  object\n",
      " 11  nom_station            14870 non-null  object\n",
      " 12  implantation_station   14870 non-null  object\n",
      " 13  adresse_station        14870 non-null  object\n",
      " 14  code_insee_commune     14870 non-null  object\n",
      " 15  coordonneesXY          14870 non-null  object\n",
      " 16  nbre_pdc               14870 non-null  object\n",
      " 17  id_pdc_itinerance      14870 non-null  object\n",
      " 18  id_pdc_local           14870 non-null  object\n",
      " 19  puissance_nominale     14870 non-null  object\n",
      " 20  prise_type_ef          14870 non-null  object\n",
      " 21  prise_type_2           14870 non-null  object\n",
      " 22  prise_type_combo_ccs   14870 non-null  object\n",
      " 23  prise_type_chademo     14870 non-null  object\n",
      " 24  prise_type_autre       14870 non-null  object\n",
      " 25  gratuit                14870 non-null  object\n",
      " 26  paiement_acte          14870 non-null  object\n",
      " 27  paiement_cb            14870 non-null  object\n",
      " 28  paiement_autre         14870 non-null  object\n",
      " 29  tarification           14870 non-null  object\n",
      " 30  condition_acces        14870 non-null  object\n",
      " 31  reservation            14870 non-null  object\n",
      " 32  horaires               14870 non-null  object\n",
      " 33  accessibilite_pmr      14870 non-null  object\n",
      " 34  restriction_gabarit    14870 non-null  object\n",
      " 35  station_deux_roues     14870 non-null  object\n",
      " 36  raccordement           14870 non-null  object\n",
      " 37  num_pdl                14870 non-null  object\n",
      " 38  date_mise_en_service   14870 non-null  object\n",
      " 39  observations           14870 non-null  object\n",
      " 40  date_maj               14870 non-null  object\n",
      "dtypes: object(41)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "gdf_1.info()"
   ]
  }
 ],
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