3_cleaning_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook 3/4: Data cleaning (Repository and geographical coordinates)\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of contents\n",
    "1. [Purpose of this notebook](#1)\n",
    "2. [Repository of all LDAs, ZDLs and ZDEs](#2)\n",
    "    1. [Presentation](#2A)\n",
    "    1. [Exploration](#2B)\n",
    "    2. [Conclusion](#2C)  \n",
    "3. [Positions of all rail stations, by ZDL](#3)\n",
    "    1. [Presentation](#3A)\n",
    "    2. [Exploration](#3B)\n",
    "    3. [Cleaning](#3C)\n",
    "        1. [Filter metro stations](#3Ca)\n",
    "        2. [Match station names with other dataframes](#3Cb)\n",
    "        3. [Rename & filter columns](#3Cd)\n",
    "        4. [Create LAT (latitude) and LONG (longitude) columns](#3Ce)\n",
    "    4. [Export to csv](#3D)   \n",
    "4. [Map of Paris metro network](#4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Purpose of this notebook <a name=\"1\"></a>\n",
    "\n",
    "This notebook follows the exploration of the number of checkins and hourly profiles per metro station for the year 2018 (see notebook 2/4). For more general information about the data, please refer to notebook 1/4.\n",
    "\n",
    "In order to enrich our understanding of the data at hand and to produce maps as part of our future analysis, this notebook will look into the following datasets:\n",
    "- Repository of all LDAs, ZDLs and ZDEs\n",
    "- Geographical data of all rail stations, by ZDL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Repository of all LDAs, ZDLs and ZDEs <a name=\"2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.A. Presentation <a name=\"2A\"></a>\n",
    "\n",
    "The STIF provides numerous datasets with data about lines and stops.\n",
    "\n",
    "[This dataset](https://opendata.stif.info/explore/dataset/referentiel-arret-tc-idf/information/) gives all the IDs for LDAs, ZDLs and ZDEs, by ZDE.\n",
    "\n",
    "This page also includes documentation about this repository.\n",
    "\n",
    "Note that the STIF provides a similar repository of all transport lines.\n",
    "\n",
    "The datasets containing the number of checkins and hourly profiles for 2018 only include the LDA IDs and their related commercial name. This dataset can be useful to check if a STOP_NAME is valid, or to fill missing values in the ID_REFA_LDA column, by matching the names in the STOP_NAME column with those of the LDA_NOM column. It may also allow us to cross analyze datasets which do not include the same IDs.\n",
    "\n",
    "#### Identification codes (see notebook 1/4):\n",
    "\n",
    "The STIF assigns 3 levels of identification to each stop:\n",
    "\n",
    "- LDA (Lieu D'Arrêt): Designates a place where vehicules from different lines can stop.\n",
    "- ZDL (Zone De Lieu): Designates an area within an LDA that regroups several ZDEs with the same operating name.\n",
    "- ZDE (Zone D'Embarquement): Designates a precise spot where people can get in and out of a vehicule (ex: metro platform).\n",
    "\n",
    "The STIF also allocates internal codes to identify carriers (CODE_STIF_TRNS), network types (CODE_STIF_RES), and stops (CODE_STIF_ARRET).\n",
    "\n",
    "Although I could not find the exhaustive list of codes and their meanings, I've identified that the CODE_STIF_RES 110 corresponds to the metro network in Paris. Our analysis focuses on Parisian metro stations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.B. Exploration <a name=\"2B\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read file\n",
    "\n",
    "ref_stops = pd.read_csv('../../datasets/referentiel-arret-tc-idf.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZDEr_ID_REF_A</th>\n",
       "      <th>ZDEr_NOM</th>\n",
       "      <th>ZDEr_ID_TYPE_ARRET</th>\n",
       "      <th>ZDEr_LIBELLE_TYPE_ARRET</th>\n",
       "      <th>ZDEr_X_Y</th>\n",
       "      <th>ZDLr_ID_REF_A</th>\n",
       "      <th>ZDLr_NOM</th>\n",
       "      <th>ZDLr_ID_TYPE_ARRET</th>\n",
       "      <th>ZDLr_LIBELLE_TYPE_ARRET</th>\n",
       "      <th>LDA_ID_REF_A</th>\n",
       "      <th>LDA_NOM</th>\n",
       "      <th>LDA_ID_TYPE_ARRET</th>\n",
       "      <th>LDA_LIBELLE_TYPE_ARRET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36757</td>\n",
       "      <td>Saint-Exupéry</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "      <td>640837,6863829</td>\n",
       "      <td>50507</td>\n",
       "      <td>Saint-Exupéry</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "      <td>70831</td>\n",
       "      <td>Saint-Exupéry</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39355</td>\n",
       "      <td>La Paix</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "      <td>641034,6863697</td>\n",
       "      <td>50508</td>\n",
       "      <td>La Paix</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "      <td>70820</td>\n",
       "      <td>La Paix</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39358</td>\n",
       "      <td>Victorien Sardou</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "      <td>641128,6863107</td>\n",
       "      <td>50510</td>\n",
       "      <td>Victorien Sardou</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "      <td>70796</td>\n",
       "      <td>Victorien Sardou</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27653</td>\n",
       "      <td>Aristide Briand</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "      <td>658849,6869841</td>\n",
       "      <td>50518</td>\n",
       "      <td>Aristide Briand / Centre Culturel</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "      <td>72619</td>\n",
       "      <td>Aristide Briand / Centre Culturel</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19289</td>\n",
       "      <td>Bois d'Amour</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "      <td>660488.75,6868398.5</td>\n",
       "      <td>50520</td>\n",
       "      <td>Jean Jaurès / Bois d'Amour</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "      <td>72527</td>\n",
       "      <td>Jean Jaurès / Bois d'Amour</td>\n",
       "      <td>5</td>\n",
       "      <td>Arrêt de bus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ZDEr_ID_REF_A          ZDEr_NOM  ZDEr_ID_TYPE_ARRET  \\\n",
       "0          36757     Saint-Exupéry                   5   \n",
       "1          39355           La Paix                   5   \n",
       "2          39358  Victorien Sardou                   5   \n",
       "3          27653   Aristide Briand                   5   \n",
       "4          19289      Bois d'Amour                   5   \n",
       "\n",
       "  ZDEr_LIBELLE_TYPE_ARRET             ZDEr_X_Y  ZDLr_ID_REF_A  \\\n",
       "0            Arrêt de bus       640837,6863829          50507   \n",
       "1            Arrêt de bus       641034,6863697          50508   \n",
       "2            Arrêt de bus       641128,6863107          50510   \n",
       "3            Arrêt de bus       658849,6869841          50518   \n",
       "4            Arrêt de bus  660488.75,6868398.5          50520   \n",
       "\n",
       "                            ZDLr_NOM  ZDLr_ID_TYPE_ARRET  \\\n",
       "0                      Saint-Exupéry                   5   \n",
       "1                            La Paix                   5   \n",
       "2                   Victorien Sardou                   5   \n",
       "3  Aristide Briand / Centre Culturel                   5   \n",
       "4         Jean Jaurès / Bois d'Amour                   5   \n",
       "\n",
       "  ZDLr_LIBELLE_TYPE_ARRET  LDA_ID_REF_A                            LDA_NOM  \\\n",
       "0            Arrêt de bus         70831                      Saint-Exupéry   \n",
       "1            Arrêt de bus         70820                            La Paix   \n",
       "2            Arrêt de bus         70796                   Victorien Sardou   \n",
       "3            Arrêt de bus         72619  Aristide Briand / Centre Culturel   \n",
       "4            Arrêt de bus         72527         Jean Jaurès / Bois d'Amour   \n",
       "\n",
       "   LDA_ID_TYPE_ARRET LDA_LIBELLE_TYPE_ARRET  \n",
       "0                  5           Arrêt de bus  \n",
       "1                  5           Arrêt de bus  \n",
       "2                  5           Arrêt de bus  \n",
       "3                  5           Arrêt de bus  \n",
       "4                  5           Arrêt de bus  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore dataframe\n",
    "\n",
    "ref_stops.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset does not include geographical data. The ZDEr_X_Y column looks like it contains coordinates, but these values do not correspond to the latitude and longitude of this region. This dataset does not contain the CODE_STIF_RES either, which we use in the other datasets to filter Parisian metro stations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39925 entries, 0 to 39924\n",
      "Data columns (total 13 columns):\n",
      "ZDEr_ID_REF_A              39925 non-null int64\n",
      "ZDEr_NOM                   39925 non-null object\n",
      "ZDEr_ID_TYPE_ARRET         39925 non-null int64\n",
      "ZDEr_LIBELLE_TYPE_ARRET    39925 non-null object\n",
      "ZDEr_X_Y                   39925 non-null object\n",
      "ZDLr_ID_REF_A              39925 non-null int64\n",
      "ZDLr_NOM                   39925 non-null object\n",
      "ZDLr_ID_TYPE_ARRET         39925 non-null int64\n",
      "ZDLr_LIBELLE_TYPE_ARRET    39925 non-null object\n",
      "LDA_ID_REF_A               39925 non-null int64\n",
      "LDA_NOM                    39925 non-null object\n",
      "LDA_ID_TYPE_ARRET          39925 non-null int64\n",
      "LDA_LIBELLE_TYPE_ARRET     39925 non-null object\n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "ref_stops.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ZDEr_ID_REF_A              39925\n",
       "ZDEr_NOM                   14500\n",
       "ZDEr_ID_TYPE_ARRET             4\n",
       "ZDEr_LIBELLE_TYPE_ARRET        4\n",
       "ZDEr_X_Y                   39763\n",
       "ZDLr_ID_REF_A              18412\n",
       "ZDLr_NOM                   13001\n",
       "ZDLr_ID_TYPE_ARRET             4\n",
       "ZDLr_LIBELLE_TYPE_ARRET        4\n",
       "LDA_ID_REF_A               15361\n",
       "LDA_NOM                    11379\n",
       "LDA_ID_TYPE_ARRET              4\n",
       "LDA_LIBELLE_TYPE_ARRET         4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_stops.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row corresponds to a unique ZDE ID, but several ZDE IDs can share the same commercial name. There are no missing values.\n",
    "\n",
    "Data types are consistent. There appears to be no need to clean this dataset, but rather to explore it a little bit further.\n",
    "\n",
    "There seem to be 4 types of stops, whether accross ZDEs, ZDLs or LDAs. Let's look into it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    36695\n",
       "1     2049\n",
       "2      768\n",
       "6      413\n",
       "Name: ZDEr_ID_TYPE_ARRET, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_stops['ZDEr_ID_TYPE_ARRET'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Arrêt de bus            36695\n",
       "Station ferrée / Val     2049\n",
       "Station de métro          768\n",
       "Arrêt de tram             413\n",
       "Name: ZDEr_LIBELLE_TYPE_ARRET, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_stops['ZDEr_LIBELLE_TYPE_ARRET'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stop types (see notebook 1/4)\n",
    "\n",
    "There are 4 types of stops:\n",
    "\n",
    "- 5: Arrêt de bus (=bus stop)\n",
    "- 1: Station ferrée / Val (=rail station)\n",
    "- 2: Station de métro (=metro station)\n",
    "- 6: Arrêt de tram (=tram stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ZDE_val_counts = ref_stops['ZDEr_ID_TYPE_ARRET'].value_counts()\n",
    "ZDL_val_counts = ref_stops['ZDLr_ID_TYPE_ARRET'].value_counts()\n",
    "LDA_val_counts = ref_stops['LDA_ID_TYPE_ARRET'].value_counts()\n",
    "\n",
    "ZDE_counts = pd.DataFrame(data=ZDE_val_counts.values, index=ZDE_val_counts.index, columns = ['ZDE'])\n",
    "ZDL_counts = pd.DataFrame(data=ZDL_val_counts.values, index=ZDL_val_counts.index, columns = ['ZDL'])\n",
    "LDA_counts = pd.DataFrame(data=LDA_val_counts.values, index=LDA_val_counts.index, columns = ['LDA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31819,  5065,  2208,   833])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_stops['LDA_ID_TYPE_ARRET'].value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36695</td>\n",
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       "      <th>1</th>\n",
       "      <td>2049</td>\n",
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       "      <td>768</td>\n",
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       "      <th>6</th>\n",
       "      <td>413</td>\n",
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      ],
      "text/plain": [
       "     ZDE\n",
       "5  36695\n",
       "1   2049\n",
       "2    768\n",
       "6    413"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ZDE_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZDE</th>\n",
       "      <th>ZDL</th>\n",
       "      <th>LDA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36695</td>\n",
       "      <td>36691</td>\n",
       "      <td>31819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2049</td>\n",
       "      <td>2059</td>\n",
       "      <td>5065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>768</td>\n",
       "      <td>762</td>\n",
       "      <td>2208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     ZDE    ZDL    LDA\n",
       "5  36695  36691  31819\n",
       "1   2049   2059   5065\n",
       "2    768    762   2208\n",
       "6    413    413    833"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids_counts = pd.concat([ZDE_counts, ZDL_counts, LDA_counts], axis=1)\n",
    "\n",
    "ids_counts "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of value counts per type of stops is not the same accross all of the ID columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbo_ids = ref_stops.groupby(['LDA_ID_TYPE_ARRET', 'ZDLr_ID_TYPE_ARRET', 'ZDEr_ID_TYPE_ARRET'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>LDA_ID_REF_A</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDA_ID_TYPE_ARRET</th>\n",
       "      <th>ZDLr_ID_TYPE_ARRET</th>\n",
       "      <th>ZDEr_ID_TYPE_ARRET</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>2049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>5</th>\n",
       "      <td>2884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>6</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>5</th>\n",
       "      <td>1471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>6</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>5</th>\n",
       "      <th>5</th>\n",
       "      <td>31819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6</th>\n",
       "      <th>5</th>\n",
       "      <th>5</th>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>6</th>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         LDA_ID_REF_A\n",
       "LDA_ID_TYPE_ARRET ZDLr_ID_TYPE_ARRET ZDEr_ID_TYPE_ARRET              \n",
       "1                 1                  1                           2049\n",
       "                                     2                              6\n",
       "                                     5                              4\n",
       "                  2                  2                             78\n",
       "                  5                  5                           2884\n",
       "                  6                  6                             44\n",
       "2                 2                  2                            684\n",
       "                  5                  5                           1471\n",
       "                  6                  6                             53\n",
       "5                 5                  5                          31819\n",
       "6                 5                  5                            517\n",
       "                  6                  6                            316"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_ids_counts = pd.DataFrame(gbo_ids.count()['LDA_ID_REF_A'])\n",
    "\n",
    "gp_ids_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A single LDA which is categorised as train station, for instance, might include several ZDLs among which some are train stations, others are metro stations, and so on (see gp_ids_counts dataframe). This explains why the number of value counts per type of stops is not the same accross all of the ID columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.C. Conclusion <a name=\"2C\"></a>\n",
    "\n",
    "This dataset does not need to be cleaned since we will use it in a fairly superficial way. \n",
    "\n",
    "There are 3 identification levels: LDA, ZDL and ZDE. Each LDA can contain several ZDLS that themselves contain a number of ZDEs. Each of these IDs has 4 stop types: metro, rail, bus and tram."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # 3. Positions of all rail stations, by ZDL <a name=\"3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.A. Presentation <a name=\"3A\"></a>\n",
    "\n",
    "This dataset provides geographic information about all rail, metro, tram and funicular stops in Ile de France:\n",
    "- https://opendata.stif.info/explore/dataset/emplacement-des-gares-idf-data-generalisee/information/\n",
    "\n",
    "Note that the STIF provides similar data grouped by transport line.\n",
    "\n",
    "This dataset should be particularly useful in order to produce maps.\n",
    "\n",
    "As a reminder, here are the steps we identified in order to clean it:\n",
    "\n",
    "> Filter metro stations\n",
    "\n",
    "> Match station names of geo_stops with that of other dataframes\n",
    "\n",
    "> Update column names and drop useless columns\n",
    "\n",
    "> Create latitude and longitude columns\n",
    " \n",
    "> Export to .csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read file\n",
    "\n",
    "geo_stops = pd.read_csv('../../datasets/emplacement-des-gares-idf-data-generalisee.csv', sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.B. Exploration <a name=\"3B\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Geo Point</th>\n",
       "      <th>Geo Shape</th>\n",
       "      <th>id_ref_zdl</th>\n",
       "      <th>nom_long</th>\n",
       "      <th>label</th>\n",
       "      <th>idrefliga</th>\n",
       "      <th>idrefligc</th>\n",
       "      <th>res_com</th>\n",
       "      <th>mode_</th>\n",
       "      <th>fer</th>\n",
       "      <th>...</th>\n",
       "      <th>terrer</th>\n",
       "      <th>termetro</th>\n",
       "      <th>tertram</th>\n",
       "      <th>ternavette</th>\n",
       "      <th>terval</th>\n",
       "      <th>exploitant</th>\n",
       "      <th>principal</th>\n",
       "      <th>idf</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.8463569889, 2.41947990037</td>\n",
       "      <td>{\"type\": \"Point\", \"coordinates\": [2.4194799003...</td>\n",
       "      <td>47247</td>\n",
       "      <td>SAINT-MANDE</td>\n",
       "      <td>Saint-Mandé</td>\n",
       "      <td>A01534</td>\n",
       "      <td>C01371</td>\n",
       "      <td>M1</td>\n",
       "      <td>Metro</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>RATP</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>657397.0779</td>\n",
       "      <td>6.860858e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.8662858046, 2.32294341224</td>\n",
       "      <td>{\"type\": \"Point\", \"coordinates\": [2.3229434122...</td>\n",
       "      <td>45676</td>\n",
       "      <td>CONCORDE</td>\n",
       "      <td>Concorde</td>\n",
       "      <td>A01534 / A01541 / A01545</td>\n",
       "      <td>C01371 / C01378 / C01382</td>\n",
       "      <td>M1 / M8 / M12</td>\n",
       "      <td>Metro</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>RATP</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>650331.6676</td>\n",
       "      <td>6.863130e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48.8828686476, 2.34413063372</td>\n",
       "      <td>{\"type\": \"Point\", \"coordinates\": [2.3441306337...</td>\n",
       "      <td>42210</td>\n",
       "      <td>ANVERS</td>\n",
       "      <td>Anvers</td>\n",
       "      <td>A01535</td>\n",
       "      <td>C01372</td>\n",
       "      <td>M2</td>\n",
       "      <td>Metro</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>RATP</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>651901.2249</td>\n",
       "      <td>6.864961e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.9078125468, 2.45435282652</td>\n",
       "      <td>{\"type\": \"Point\", \"coordinates\": [2.4543528265...</td>\n",
       "      <td>47334</td>\n",
       "      <td>JEAN ROSTAND</td>\n",
       "      <td>Jean Rostand</td>\n",
       "      <td>A01191</td>\n",
       "      <td>C01389</td>\n",
       "      <td>T1</td>\n",
       "      <td>Tramway</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>RATP</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>660003.4947</td>\n",
       "      <td>6.867673e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48.8930946286, 2.48791098763</td>\n",
       "      <td>{\"type\": \"Point\", \"coordinates\": [2.4879109876...</td>\n",
       "      <td>44603</td>\n",
       "      <td>LA REMISE A JORELLE</td>\n",
       "      <td>La Remise à Jorelle</td>\n",
       "      <td>A01761</td>\n",
       "      <td>C01843</td>\n",
       "      <td>T4</td>\n",
       "      <td>Tramway</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SNCF</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>662452.6934</td>\n",
       "      <td>6.866020e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Geo Point  \\\n",
       "0  48.8463569889, 2.41947990037   \n",
       "1  48.8662858046, 2.32294341224   \n",
       "2  48.8828686476, 2.34413063372   \n",
       "3  48.9078125468, 2.45435282652   \n",
       "4  48.8930946286, 2.48791098763   \n",
       "\n",
       "                                           Geo Shape  id_ref_zdl  \\\n",
       "0  {\"type\": \"Point\", \"coordinates\": [2.4194799003...       47247   \n",
       "1  {\"type\": \"Point\", \"coordinates\": [2.3229434122...       45676   \n",
       "2  {\"type\": \"Point\", \"coordinates\": [2.3441306337...       42210   \n",
       "3  {\"type\": \"Point\", \"coordinates\": [2.4543528265...       47334   \n",
       "4  {\"type\": \"Point\", \"coordinates\": [2.4879109876...       44603   \n",
       "\n",
       "              nom_long                label                 idrefliga  \\\n",
       "0          SAINT-MANDE          Saint-Mandé                    A01534   \n",
       "1             CONCORDE             Concorde  A01534 / A01541 / A01545   \n",
       "2               ANVERS               Anvers                    A01535   \n",
       "3         JEAN ROSTAND         Jean Rostand                    A01191   \n",
       "4  LA REMISE A JORELLE  La Remise à Jorelle                    A01761   \n",
       "\n",
       "                  idrefligc        res_com    mode_  fer      ...       \\\n",
       "0                    C01371             M1    Metro    0      ...        \n",
       "1  C01371 / C01378 / C01382  M1 / M8 / M12    Metro    0      ...        \n",
       "2                    C01372             M2    Metro    0      ...        \n",
       "3                    C01389             T1  Tramway    0      ...        \n",
       "4                    C01843             T4  Tramway    0      ...        \n",
       "\n",
       "   terrer  termetro  tertram  ternavette  terval  exploitant principal idf  \\\n",
       "0       0         0        0           0       0        RATP         0   1   \n",
       "1       0         0        0           0       0        RATP         0   1   \n",
       "2       0         0        0           0       0        RATP         0   1   \n",
       "3       0         0        0           0       0        RATP         0   1   \n",
       "4       0         0        0           0       0        SNCF         0   1   \n",
       "\n",
       "             x             y  \n",
       "0  657397.0779  6.860858e+06  \n",
       "1  650331.6676  6.863130e+06  \n",
       "2  651901.2249  6.864961e+06  \n",
       "3  660003.4947  6.867673e+06  \n",
       "4  662452.6934  6.866020e+06  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore dataframe\n",
    "\n",
    "geo_stops.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 923 entries, 0 to 922\n",
      "Data columns (total 28 columns):\n",
      "Geo Point     923 non-null object\n",
      "Geo Shape     923 non-null object\n",
      "id_ref_zdl    923 non-null int64\n",
      "nom_long      923 non-null object\n",
      "label         923 non-null object\n",
      "idrefliga     890 non-null object\n",
      "idrefligc     847 non-null object\n",
      "res_com       923 non-null object\n",
      "mode_         923 non-null object\n",
      "fer           923 non-null int64\n",
      "train         923 non-null int64\n",
      "rer           923 non-null int64\n",
      "metro         923 non-null int64\n",
      "tramway       923 non-null int64\n",
      "navette       923 non-null int64\n",
      "val           923 non-null int64\n",
      "terfer        923 non-null object\n",
      "tertrain      923 non-null object\n",
      "terrer        923 non-null object\n",
      "termetro      923 non-null object\n",
      "tertram       923 non-null object\n",
      "ternavette    923 non-null object\n",
      "terval        923 non-null object\n",
      "exploitant    923 non-null object\n",
      "principal     923 non-null int64\n",
      "idf           923 non-null int64\n",
      "x             923 non-null float64\n",
      "y             923 non-null float64\n",
      "dtypes: float64(2), int64(10), object(16)\n",
      "memory usage: 202.0+ KB\n"
     ]
    }
   ],
   "source": [
    "geo_stops.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "923"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_stops['id_ref_zdl'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset contains the geo coordinates of all stations of the rail network in Ile de France, by ZDL. It contains 923 unique ZDLs (corresponding to the total number of rows). As we saw earlier, there are multiple ZDLs for one LDA.\n",
    "\n",
    "The datasets we are analyzing (metro_nb and metro_hp) only contain LDAs (301 unique values).\n",
    "\n",
    "In order to match the data from metro_nb and metro_hp and that of geo_stops, we need to filter geo_stops by metro stations and to make sure that we can match their LDAs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.C. Cleaning <a name=\"3C\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.C.a. Filter metro stations <a name=\"3Ca\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Metro', 'Tramway', 'Train', 'Navette', 'RER', 'Metro / Tramway',\n",
       "       'Train / Tramway', 'Train / RER', 'RER / Tramway', 'RER / Metro',\n",
       "       'Train / Metro', 'RER / Navette', 'Train / RER / Tramway',\n",
       "       'Train / RER / Metro', 'Navette / Tramway',\n",
       "       'Train / RER / Metro / Tramway', 'RER / Metro / Tramway'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Array of all the mode types\n",
    "\n",
    "geo_stops['mode_'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Metro',\n",
       " 'Metro / Tramway',\n",
       " 'RER / Metro',\n",
       " 'Train / Metro',\n",
       " 'Train / RER / Metro',\n",
       " 'Train / RER / Metro / Tramway',\n",
       " 'RER / Metro / Tramway']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of mode types containing the word metro\n",
    "\n",
    "metro = geo_stops.loc[geo_stops['mode_'].str.contains('Metro'),'mode_'].unique().tolist()\n",
    "\n",
    "metro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# geo_stops filtered by metro (mode_ contains the word 'metro')\n",
    "\n",
    "geo_metro = geo_stops.loc[geo_stops['mode_'].isin(metro)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "302"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of ZDL IDs (rows) in geo_metro\n",
    "\n",
    "len(geo_metro['id_ref_zdl'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "806"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of ZDL IDs for metro stations\n",
    "\n",
    "zdl_metro = geo_metro['id_ref_zdl'].unique().tolist()\n",
    "\n",
    "# Number of ZDEs (rows of ref_stops) matching zdl_metro\n",
    "\n",
    "len(ref_stops[ref_stops['ZDLr_ID_REF_A'].isin(zdl_metro)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is good news, when we filtered geo_stops, we obtained a dataframe that has 302 rows, which is very close to the unique number of ID_REFA_LDA in metro_nb and metro_hp (301). \n",
    "\n",
    "Let's find the extra value in geo_metro, and see if the names ('nom_long') in geo_metro match those of metro_nb ('STOP_NAME')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.C.b Match station names in geo_metro with names in metro_nb and metro_hp <a name=\"3Cb\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "metro_nb = pd.read_csv('../../datasets/metro_nb.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_metro_nb_names = np.sort(metro_nb['STOP_NAME'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_geo_names = np.sort(geo_metro['nom_long'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLUNY-LA-SORBONNE\n",
      "CRETEIL-POINTE DU LAC\n",
      "FRONT POPULAIRE\n",
      "LES COURTILLES-ASNIERES-GENNEVILLIERS\n",
      "MAIRIE DE MONTROUGE\n",
      "MALAKOFF-RUE ÉTIENNE DOLET\n",
      "SAINT-MANDE\n"
     ]
    }
   ],
   "source": [
    "# Names in geo_metro that are not in metro_nb (& metro_hp)\n",
    "\n",
    "for name in sorted_geo_names:\n",
    "    if name in sorted_metro_nb_names:\n",
    "        continue\n",
    "    else:\n",
    "        print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRETEIL-P. LAC\n",
      "FR. POPULAIRE\n",
      "LES COURTILLES\n",
      "M. MONTROUGE\n",
      "MALAKOFF-RUE ETIENNE DOLET\n",
      "SAINT-MANDE-TOURELLE\n"
     ]
    }
   ],
   "source": [
    "# Names in metro_nb (& metro_hp) that are not in geo_metro\n",
    "\n",
    "for name in sorted_metro_nb_names:\n",
    "    if name in sorted_geo_names:\n",
    "        continue\n",
    "    else:\n",
    "        print(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the extra row in geo_metro corresponds to CLUNY-LA-SORBONNE. After checking the original data of metro_nb and metro_hp, it looks like CLUNY-LA-SORBONNE is missing, no record of the number of checkins for that metro station was provided. We will have to do without it.\n",
    "\n",
    "Some other names do not match, but we can replace them so that they match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary of names to update:\n",
    "\n",
    "names = {'CRETEIL-POINTE DU LAC':'CRETEIL-P. LAC',\n",
    "         'FRONT POPULAIRE':'FR. POPULAIRE',\n",
    "         'LES COURTILLES-ASNIERES-GENNEVILLIERS':'LES COURTILLES',\n",
    "         'MAIRIE DE MONTROUGE':'M. MONTROUGE',\n",
    "         'MALAKOFF-RUE ÉTIENNE DOLET':'MALAKOFF-RUE ETIENNE DOLET',\n",
    "         'SAINT-MANDE':'SAINT-MANDE-TOURELLE'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace names in geo_metro\n",
    "\n",
    "geo_metro['nom_long'] = geo_metro['nom_long'].replace(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Geo Point', 'Geo Shape', 'id_ref_zdl', 'nom_long', 'label',\n",
       "       'idrefliga', 'idrefligc', 'res_com', 'mode_', 'fer', 'train', 'rer',\n",
       "       'metro', 'tramway', 'navette', 'val', 'terfer', 'tertrain', 'terrer',\n",
       "       'termetro', 'tertram', 'ternavette', 'terval', 'exploitant',\n",
       "       'principal', 'idf', 'x', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_metro.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.C.c. Rename & filter columns <a name=\"3Cc\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary with old names (keys) and new names (values)\n",
    "col_names = {'Geo Point':'LAT_LONG', \n",
    "             'id_ref_zdl':'ID_ZDL', \n",
    "             'nom_long':'STOP_NAME', \n",
    "             'res_com':'LINES'}\n",
    "\n",
    "# Rename columns\n",
    "geo_metro.rename(columns=col_names, inplace=True)\n",
    "\n",
    "# List of columns to drop\n",
    "col_todrop = ['Geo Shape', 'label', 'idrefliga', 'idrefligc', 'mode_', 'fer', 'train', 'rer',\n",
    "              'metro', 'tramway', 'navette', 'val', 'terfer', 'tertrain', 'terrer',\n",
    "              'termetro', 'tertram', 'ternavette', 'terval', 'exploitant','principal', 'idf', 'x', 'y']\n",
    "\n",
    "# Drop columns\n",
    "geo_metro.drop(columns=col_todrop, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>LAT_LONG</th>\n",
       "      <th>ID_ZDL</th>\n",
       "      <th>STOP_NAME</th>\n",
       "      <th>LINES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.8463569889, 2.41947990037</td>\n",
       "      <td>47247</td>\n",
       "      <td>SAINT-MANDE-TOURELLE</td>\n",
       "      <td>M1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.8662858046, 2.32294341224</td>\n",
       "      <td>45676</td>\n",
       "      <td>CONCORDE</td>\n",
       "      <td>M1 / M8 / M12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48.8828686476, 2.34413063372</td>\n",
       "      <td>42210</td>\n",
       "      <td>ANVERS</td>\n",
       "      <td>M2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48.8822269008, 2.32127492677</td>\n",
       "      <td>44662</td>\n",
       "      <td>ROME</td>\n",
       "      <td>M2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>48.8804494561, 2.30945116799</td>\n",
       "      <td>44146</td>\n",
       "      <td>MONCEAU</td>\n",
       "      <td>M2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        LAT_LONG  ID_ZDL             STOP_NAME          LINES\n",
       "0   48.8463569889, 2.41947990037   47247  SAINT-MANDE-TOURELLE             M1\n",
       "1   48.8662858046, 2.32294341224   45676              CONCORDE  M1 / M8 / M12\n",
       "2   48.8828686476, 2.34413063372   42210                ANVERS             M2\n",
       "11  48.8822269008, 2.32127492677   44662                  ROME             M2\n",
       "12  48.8804494561, 2.30945116799   44146               MONCEAU             M2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_metro.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 302 entries, 0 to 891\n",
      "Data columns (total 4 columns):\n",
      "LAT_LONG     302 non-null object\n",
      "ID_ZDL       302 non-null int64\n",
      "STOP_NAME    302 non-null object\n",
      "LINES        302 non-null object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 11.8+ KB\n"
     ]
    }
   ],
   "source": [
    "geo_metro.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3.C.d. Create LAT (latitude) and LONG (longitude) columns <a name=\"3Cd\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_metro[\"LAT\"] = geo_metro[\"LAT_LONG\"].str.split(\",\").str.get(0)\n",
    "geo_metro[\"LONG\"] = geo_metro[\"LAT_LONG\"].str.split(\",\").str.get(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>LAT_LONG</th>\n",
       "      <th>ID_ZDL</th>\n",
       "      <th>STOP_NAME</th>\n",
       "      <th>LINES</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LONG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.8463569889, 2.41947990037</td>\n",
       "      <td>47247</td>\n",
       "      <td>SAINT-MANDE-TOURELLE</td>\n",
       "      <td>M1</td>\n",
       "      <td>48.8463569889</td>\n",
       "      <td>2.41947990037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.8662858046, 2.32294341224</td>\n",
       "      <td>45676</td>\n",
       "      <td>CONCORDE</td>\n",
       "      <td>M1 / M8 / M12</td>\n",
       "      <td>48.8662858046</td>\n",
       "      <td>2.32294341224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48.8828686476, 2.34413063372</td>\n",
       "      <td>42210</td>\n",
       "      <td>ANVERS</td>\n",
       "      <td>M2</td>\n",
       "      <td>48.8828686476</td>\n",
       "      <td>2.34413063372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48.8822269008, 2.32127492677</td>\n",
       "      <td>44662</td>\n",
       "      <td>ROME</td>\n",
       "      <td>M2</td>\n",
       "      <td>48.8822269008</td>\n",
       "      <td>2.32127492677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>48.8804494561, 2.30945116799</td>\n",
       "      <td>44146</td>\n",
       "      <td>MONCEAU</td>\n",
       "      <td>M2</td>\n",
       "      <td>48.8804494561</td>\n",
       "      <td>2.30945116799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        LAT_LONG  ID_ZDL             STOP_NAME          LINES  \\\n",
       "0   48.8463569889, 2.41947990037   47247  SAINT-MANDE-TOURELLE             M1   \n",
       "1   48.8662858046, 2.32294341224   45676              CONCORDE  M1 / M8 / M12   \n",
       "2   48.8828686476, 2.34413063372   42210                ANVERS             M2   \n",
       "11  48.8822269008, 2.32127492677   44662                  ROME             M2   \n",
       "12  48.8804494561, 2.30945116799   44146               MONCEAU             M2   \n",
       "\n",
       "              LAT            LONG  \n",
       "0   48.8463569889   2.41947990037  \n",
       "1   48.8662858046   2.32294341224  \n",
       "2   48.8828686476   2.34413063372  \n",
       "11  48.8822269008   2.32127492677  \n",
       "12  48.8804494561   2.30945116799  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_metro.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.D. Export to .csv <a name=\"3D\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_metro.to_csv('../../datasets/geo_metro.csv', index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Map of Paris metro network <a name=\"4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import HTML\n",
    "from ipyleaflet import Map, basemaps, basemap_to_tiles, CircleMarker, Icon, Popup, Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1f7356fed374573a68a0eb834190fa0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Map</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Map(basemap={'url': 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19, 'attribution': 'Map data (c) <a href=\"https://openstreetmap.org\">OpenStreetMap</a> contributors'}, center=[48.855115, 2.352448], layers=(TileLayer(base=True, max_zoom=19, min_zoom=1, options=['attribution', 'detect_retina', 'max_zoom', 'min_zoom', 'tile_size']), CircleMarker(color='#000951', fill_color='#000951', fill_opacity=0.7, location=['48.8463569889', ' 2.41947990037'], opacity=0.7, options=['class_name', 'color', 'dash_array', 'fill', 'fill_color', 'fill_opacity', 'line_cap', 'line_join', 'opacity', 'pointer_events', 'radius', 'stroke', 'weight'], popup=HTML(value='SAINT-MANDE-TOURELLE'), radius=6, stroke=False), CircleMarker(color='#000951', fill_color='#000951', fill_opacity=0.7, location=['48.8662858046', ' 2.32294341224'], opacity=0.7, options=['class_name', 'color', 'dash_array', 'fill', 'fill_color', 'fill_opacity', 'line_cap', 'line_join', 'opacity', 'pointer_events', 'radius', 'stroke', 'weight'], popup=HTML(value='CONCORDE'), radius=6, stroke=False), CircleMarker(color='#000951', fill_color='#000951', fill_opacity=0.7, location=['48.8828686476', ' 2.34413063372'], opacity=0.7, options=['class_name', 'color', 'dash_array', 'fill', 'fill_color', 'fill_opacity', 'line_cap', 'line_join', 'opacity', 'pointer_events', 'radius', 'stroke', 'weight'], popup=HTML(value='ANVERS'), radius=6, stroke=False), CircleMarker(color='#000951', fill_color='#000951', fill_opacity=0.7, location=['48.8822269008', ' 2.32127492677'], opacity=0.7, options=['class_name', 'color', 'dash_array', 'fill', 'fill_color', 'fill_opacity', 'line_cap', 'line_join', 'opacity', 'pointer_events', 'radius', 'stroke', 'weight'], popup=HTML(value='ROME'), radius=6, stroke=False), CircleMarker(color='#000951', fill_color='#000951', fill_opacity=0.7, location=['48.8804494561', ' 2.30945116799'], opacity=0.7, options=['class_name', 'color', 'dash_array', 'fill', 'fill_color', 'fill_opacity', 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options=['class_name', 'color', 'dash_array', 'fill', 'fill_color', 'fill_opacity', 'line_cap', 'line_join', 'opacity', 'pointer_events', 'radius', 'stroke', 'weight'], popup=HTML(value='MAISONS-ALFORT-STADE'), radius=6, stroke=False), CircleMarker(color='#000951', fill_color='#000951', fill_opacity=0.7, location=['48.8379612135', ' 2.25712222985'], opacity=0.7, options=['class_name', 'color', 'dash_array', 'fill', 'fill_color', 'fill_opacity', 'line_cap', 'line_join', 'opacity', 'pointer_events', 'radius', 'stroke', 'weight'], popup=HTML(value='PORTE DE SAINT-CLOUD'), radius=6, stroke=False), CircleMarker(color='#000951', fill_color='#000951', fill_opacity=0.7, location=['48.8429386959', ' 2.26003290221'], opacity=0.7, options=['class_name', 'color', 'dash_array', 'fill', 'fill_color', 'fill_opacity', 'line_cap', 'line_join', 'opacity', 'pointer_events', 'radius', 'stroke', 'weight'], popup=HTML(value='EXELMANS'), radius=6, stroke=False), CircleMarker(color='#000951', fill_color='#000951', fill_opacity=0.7, location=['48.8525040906', ' 2.26817674944'], opacity=0.7, options=['class_name', 'color', 'dash_array', 'fill', 'fill_color', 'fill_opacity', 'line_cap', 'line_join', 'opacity', 'pointer_events', 'radius', 'stroke', 'weight'], popup=HTML(value='JASMIN'), radius=6, stroke=False)), options=['attribution_control', 'basemap', 'bounce_at_zoom_limits', 'box_zoom', 'center', 'close_popup_on_click', 'double_click_zoom', 'dragging', 'inertia', 'inertia_deceleration', 'inertia_max_speed', 'interpolation', 'keyboard', 'keyboard_pan_offset', 'keyboard_zoom_offset', 'max_zoom', 'min_zoom', 'scroll_wheel_zoom', 'tap', 'tap_tolerance', 'touch_zoom', 'world_copy_jump', 'zoom', 'zoom_animation_threshold', 'zoom_control', 'zoom_start'])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "center=(48.855115,2.352448)\n",
    "\n",
    "m = Map(\n",
    "    center=center,\n",
    "    zoom=12\n",
    ")\n",
    "\n",
    "\n",
    "for x in geo_metro.iterrows():\n",
    "    lat = x[1].LAT\n",
    "    long = x[1].LONG\n",
    "    \n",
    "    n = x[1].STOP_NAME\n",
    "    \n",
    "    message = HTML()\n",
    "    \n",
    "    message.value = n\n",
    "    \n",
    "    circle_marker = CircleMarker(location=(lat, long), draggable=False)\n",
    "    circle_marker.radius = 6\n",
    "    circle_marker.stroke = False\n",
    "    circle_marker.color = '#000951'\n",
    "    circle_marker.opacity = 0.7\n",
    "    circle_marker.fill_color = '#000951'\n",
    "    circle_marker.fill_opacity = 0.7\n",
    "    m.add_layer(circle_marker);\n",
    "    \n",
    "    circle_marker.popup = message\n",
    "\n",
    "m"
   ]
  }
 ],
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