analysis/madrid-metro-graph-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import gc\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading graph edges and nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../csv_files/metro_edges_no_duplicated_edges_no_cycles_speed_networkx.csv') as f:\n",
    "    f.readline()\n",
    "    # Source,Target,Weight,edge_name,edge_color,travel_seconds,longitude_Source,latitude_Source,longitude_Target,latitude_Target,distance_meters,speed_ms\n",
    "    g = nx.parse_edgelist(f, delimiter=',', nodetype=int, data=(('Weight', float), ('edge_name', str), ('edge_color', str), ('travel_seconds', float), ('longitude_Source', float), ('latitude_Source', float), ('longitude_Target', float), ('latitude_Target', float), ('distance_meters', float), ('speed_ms', float) ), create_using = nx.MultiGraph())\n",
    "    \n",
    "with open('../csv_files/metro_gephi_nodes_coordinates.csv') as f:\n",
    "    reader = csv.DictReader(f)\n",
    "\n",
    "    node_latitudes = {}\n",
    "    node_longitudes = {}\n",
    "    node_names = {}\n",
    "    \n",
    "    for row in reader:\n",
    "        node_latitudes[ int(row['Id']) ] = float(row['latitude'])\n",
    "        node_longitudes[ int(row['Id']) ] = float(row['longitude'])\n",
    "        node_names[ int(row['Id']) ] = row['Label']\n",
    "        \n",
    "nx.set_node_attributes(g, name = 'latitude', values = node_latitudes)\n",
    "nx.set_node_attributes(g, name = 'longitude', values = node_longitudes)\n",
    "nx.set_node_attributes(g, name = 'name', values = node_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_n_stations_by_attribute(graph, attr_name, n, ascending = False):\n",
    "    return pd.DataFrame.from_records(map(lambda x: x[1], list(graph.nodes(data=True)) ))[['name', attr_name]].sort_values(attr_name, ascending = ascending)[:(n+1)].reset_index(drop=True).shift()[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 35 stations with more neighbour stations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AVENIDA DE AMERICA</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PLAZA DE CASTILLA</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALONSO MARTINEZ</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SOL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DIEGO DE LEON</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OPERA</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PRINCIPE PIO</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ARGÜELLES</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NUEVOS MINISTERIOS</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CUATRO CAMINOS</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CHAMARTIN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SAINZ DE BARANDA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MAR DE CRISTAL</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>VENTAS</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GUZMAN EL BUENO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PUEBLO NUEVO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CALLAO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NUÑEZ DE BALBOA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CANAL</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SAN BERNARDO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>OPORTO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PLAZA DE ESPAÑA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PRINCIPE DE VERGARA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>GOYA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MANUEL BECERRA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LEGAZPI</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>GRAN VIA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BILBAO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>GREGORIO MARAÑON</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>COLOMBIA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>TRIBUNAL</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PACIFICO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PUERTA DEL SUR</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>MONCLOA</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>PLAZA ELIPTICA</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  degree\n",
       "1    AVENIDA DE AMERICA     8.0\n",
       "2     PLAZA DE CASTILLA     6.0\n",
       "3       ALONSO MARTINEZ     6.0\n",
       "4                   SOL     6.0\n",
       "5         DIEGO DE LEON     6.0\n",
       "6                 OPERA     5.0\n",
       "7          PRINCIPE PIO     5.0\n",
       "8             ARGÜELLES     5.0\n",
       "9    NUEVOS MINISTERIOS     5.0\n",
       "10       CUATRO CAMINOS     5.0\n",
       "11            CHAMARTIN     4.0\n",
       "12     SAINZ DE BARANDA     4.0\n",
       "13       MAR DE CRISTAL     4.0\n",
       "14               VENTAS     4.0\n",
       "15      GUZMAN EL BUENO     4.0\n",
       "16         PUEBLO NUEVO     4.0\n",
       "17               CALLAO     4.0\n",
       "18      NUÑEZ DE BALBOA     4.0\n",
       "19                CANAL     4.0\n",
       "20         SAN BERNARDO     4.0\n",
       "21               OPORTO     4.0\n",
       "22      PLAZA DE ESPAÑA     4.0\n",
       "23  PRINCIPE DE VERGARA     4.0\n",
       "24                 GOYA     4.0\n",
       "25       MANUEL BECERRA     4.0\n",
       "26              LEGAZPI     4.0\n",
       "27             GRAN VIA     4.0\n",
       "28               BILBAO     4.0\n",
       "29     GREGORIO MARAÑON     4.0\n",
       "30             COLOMBIA     4.0\n",
       "31             TRIBUNAL     4.0\n",
       "32             PACIFICO     4.0\n",
       "33       PUERTA DEL SUR     3.0\n",
       "34              MONCLOA     3.0\n",
       "35       PLAZA ELIPTICA     3.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.set_node_attributes(g, name = 'degree', values = dict(g.degree))\n",
    "\n",
    "stations_and_their_neighbours = top_n_stations_by_attribute(g, 'degree', len(g.edges()))\n",
    "\n",
    "stations_and_their_neighbours[:35]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neighbours' count histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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56dba6t6MDAAAMHy7OQ3yS0meddL1i5J8YnL5g0meluTbk9zce9/svd+V5ItJHjnLQQEA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\nQEFiDQAAoKD/C2z/GTr4k4y2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0fba5ab588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.plt.figure(figsize=(15, 10))\n",
    "sns.distplot(stations_and_their_neighbours['degree'], kde=False, rug=False, bins = np.arange(8)-0.5, hist_kws=dict(edgecolor='k', linewidth=2))\n",
    "sns.plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degree</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count\n",
       "degree       \n",
       "1.0        10\n",
       "2.0       194\n",
       "3.0         4\n",
       "4.0        22\n",
       "5.0         5\n",
       "6.0         4\n",
       "8.0         1"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_and_their_neighbours.groupby('degree').count().rename(columns={'name': 'count'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the stations are connected to two other stations. \n",
    "\n",
    "There are 10 Metro stations which are connected to only 1 stations: these stations are the most vulnerable because they strongly depend on other stations to remain connected to the Madrid Metro network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating stations importance using Closeness Centrality: This metric indicates how long it will take for information (people) from a node (station) \"u\" will take to reach other nodes (stations) in the network. We can determine which is the closest node (station) to any other node (station) in the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(g, name = 'closeness_centrality', values = nx.closeness_centrality(g, distance = 'travel_seconds'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 20 most important (according to Closeness Centrality algorithm) Metro stations are shown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>closeness_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GREGORIO MARAÑON</td>\n",
       "      <td>0.000384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALONSO MARTINEZ</td>\n",
       "      <td>0.000383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TRIBUNAL</td>\n",
       "      <td>0.000376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AVENIDA DE AMERICA</td>\n",
       "      <td>0.000374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PLAZA DE ESPAÑA</td>\n",
       "      <td>0.000369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NUÑEZ DE BALBOA</td>\n",
       "      <td>0.000367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NUEVOS MINISTERIOS</td>\n",
       "      <td>0.000367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RUBEN DARIO</td>\n",
       "      <td>0.000366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BILBAO</td>\n",
       "      <td>0.000365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DIEGO DE LEON</td>\n",
       "      <td>0.000364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SOL</td>\n",
       "      <td>0.000361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PRINCIPE PIO</td>\n",
       "      <td>0.000360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>COLON</td>\n",
       "      <td>0.000360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CHUECA</td>\n",
       "      <td>0.000359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PRINCIPE DE VERGARA</td>\n",
       "      <td>0.000359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>GRAN VIA</td>\n",
       "      <td>0.000359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CALLAO</td>\n",
       "      <td>0.000358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>REPUBLICA ARGENTINA</td>\n",
       "      <td>0.000357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ALONSO CANO</td>\n",
       "      <td>0.000356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MANUEL BECERRA</td>\n",
       "      <td>0.000356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  closeness_centrality\n",
       "1      GREGORIO MARAÑON              0.000384\n",
       "2       ALONSO MARTINEZ              0.000383\n",
       "3              TRIBUNAL              0.000376\n",
       "4    AVENIDA DE AMERICA              0.000374\n",
       "5       PLAZA DE ESPAÑA              0.000369\n",
       "6       NUÑEZ DE BALBOA              0.000367\n",
       "7    NUEVOS MINISTERIOS              0.000367\n",
       "8           RUBEN DARIO              0.000366\n",
       "9                BILBAO              0.000365\n",
       "10        DIEGO DE LEON              0.000364\n",
       "11                  SOL              0.000361\n",
       "12         PRINCIPE PIO              0.000360\n",
       "13                COLON              0.000360\n",
       "14               CHUECA              0.000359\n",
       "15  PRINCIPE DE VERGARA              0.000359\n",
       "16             GRAN VIA              0.000359\n",
       "17               CALLAO              0.000358\n",
       "18  REPUBLICA ARGENTINA              0.000357\n",
       "19          ALONSO CANO              0.000356\n",
       "20       MANUEL BECERRA              0.000356"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n_stations_by_attribute(g, 'closeness_centrality', 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another metric to have in mind: Betweenness Centrality. This metric indicates how often a node (station) is found on a shortest path between two nodes (stations) in the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(g, name = 'betweenness_centrality', values = nx.betweenness_centrality(g, normalized = True, weight = 'Weight'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 20 most important (according to Betweenness Centrality algorithm) Metro stations are shown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>betweenness_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GREGORIO MARAÑON</td>\n",
       "      <td>0.321421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PRINCIPE PIO</td>\n",
       "      <td>0.311290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NUEVOS MINISTERIOS</td>\n",
       "      <td>0.305922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALONSO MARTINEZ</td>\n",
       "      <td>0.296078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AVENIDA DE AMERICA</td>\n",
       "      <td>0.281850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CASA DE CAMPO</td>\n",
       "      <td>0.255851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LAGO</td>\n",
       "      <td>0.246326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BATAN</td>\n",
       "      <td>0.240864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TRIBUNAL</td>\n",
       "      <td>0.230387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>COLONIA JARDIN</td>\n",
       "      <td>0.225907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AVIACION ESPAÑOLA</td>\n",
       "      <td>0.219665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CUATRO VIENTOS</td>\n",
       "      <td>0.213354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SAINZ DE BARANDA</td>\n",
       "      <td>0.211491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>JOAQUIN VILUMBRALES</td>\n",
       "      <td>0.206974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PUERTA DEL SUR</td>\n",
       "      <td>0.203469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PLAZA DE ESPAÑA</td>\n",
       "      <td>0.187532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PLAZA DE CASTILLA</td>\n",
       "      <td>0.170173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PACIFICO</td>\n",
       "      <td>0.167422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SOL</td>\n",
       "      <td>0.166040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SANTIAGO BERNABEU</td>\n",
       "      <td>0.165030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  betweenness_centrality\n",
       "1      GREGORIO MARAÑON                0.321421\n",
       "2          PRINCIPE PIO                0.311290\n",
       "3    NUEVOS MINISTERIOS                0.305922\n",
       "4       ALONSO MARTINEZ                0.296078\n",
       "5    AVENIDA DE AMERICA                0.281850\n",
       "6         CASA DE CAMPO                0.255851\n",
       "7                  LAGO                0.246326\n",
       "8                 BATAN                0.240864\n",
       "9              TRIBUNAL                0.230387\n",
       "10       COLONIA JARDIN                0.225907\n",
       "11    AVIACION ESPAÑOLA                0.219665\n",
       "12       CUATRO VIENTOS                0.213354\n",
       "13     SAINZ DE BARANDA                0.211491\n",
       "14  JOAQUIN VILUMBRALES                0.206974\n",
       "15       PUERTA DEL SUR                0.203469\n",
       "16      PLAZA DE ESPAÑA                0.187532\n",
       "17    PLAZA DE CASTILLA                0.170173\n",
       "18             PACIFICO                0.167422\n",
       "19                  SOL                0.166040\n",
       "20    SANTIAGO BERNABEU                0.165030"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n_stations_by_attribute(g, 'betweenness_centrality', 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems Gregorio Marañón is quite important again, and thanks to the Betweenness Centrality we can say Gregorio Marañón is the Metro station that controls the most the Metro network because more information (people) can pass through it than on any other node (station)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's calculate the articulation points: a node (Metro Station) is an articulation point if the graph gets disconnected (some Metro Stations will stop being connected somehow to other Metro Stations) by removing that node (station)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_nodes = g.node_dict_factory(g.nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96 articulation stations out of 241 stations (39.834 %) \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['ABRANTES',\n",
       " 'AEROPUERTO T1 T2 T3',\n",
       " 'ALMENDRALES',\n",
       " 'ALSACIA',\n",
       " 'ALTO DEL ARENAL',\n",
       " 'ANTONIO MACHADO',\n",
       " 'ARTILLEROS',\n",
       " 'ASCAO',\n",
       " 'AVDA. DE LA ILUSTRACION',\n",
       " 'AVENIDA DE GUADALAJARA',\n",
       " 'AVIACION ESPAÑOLA',\n",
       " 'BARAJAS',\n",
       " 'BARRIO DEL PILAR',\n",
       " 'BARRIO DEL PUERTO',\n",
       " 'BAUNATAL',\n",
       " 'BEGOÑA',\n",
       " 'BUENOS AIRES',\n",
       " 'CANILLEJAS',\n",
       " 'CARABANCHEL ALTO',\n",
       " 'CASA DE CAMPO',\n",
       " 'CHAMARTIN',\n",
       " 'CIUDAD DE LOS ANGELES',\n",
       " 'CIUDAD LINEAL',\n",
       " 'COLONIA JARDIN',\n",
       " 'CONGOSTO',\n",
       " 'COSLADA CENTRAL',\n",
       " 'CUATRO VIENTOS',\n",
       " 'EL CAPRICHO',\n",
       " 'ESTADIO METROPOLITANO',\n",
       " 'ESTRELLA',\n",
       " 'FERIA DE MADRID',\n",
       " 'FRANCOS RODRIGUEZ',\n",
       " 'FUENCARRAL',\n",
       " 'GARCIA NOBLEJAS',\n",
       " 'GUZMAN EL BUENO',\n",
       " 'HENARES',\n",
       " 'HERRERA ORIA',\n",
       " 'HOSPITAL 12 DE OCTUBRE',\n",
       " 'JARAMA',\n",
       " 'JOAQUIN VILUMBRALES',\n",
       " 'LA ALMUDENA',\n",
       " 'LA ELIPA',\n",
       " 'LA GAVIA',\n",
       " 'LA GRANJA',\n",
       " 'LA MORALEJA',\n",
       " 'LA PESETA',\n",
       " 'LA POVEDA',\n",
       " 'LA RAMBLA',\n",
       " 'LACOMA',\n",
       " 'LAS MUSAS',\n",
       " 'LAS SUERTES',\n",
       " 'LAS TABLAS',\n",
       " 'LEGAZPI',\n",
       " 'MANUEL DE FALLA',\n",
       " 'MAR DE CRISTAL',\n",
       " 'MARQUES DE LA VALDAVIA',\n",
       " 'MIGUEL HERNANDEZ',\n",
       " 'MIRASIERRA',\n",
       " 'MONTECARMELO',\n",
       " 'NUEVA NUMANCIA',\n",
       " 'PACIFICO',\n",
       " 'PAN BENDITO',\n",
       " 'PAVONES',\n",
       " 'PEÑAGRANDE',\n",
       " 'PLAZA DE CASTILLA',\n",
       " 'PLAZA ELIPTICA',\n",
       " 'PORTAZGO',\n",
       " 'PUEBLO NUEVO',\n",
       " 'PUENTE DE VALLECAS',\n",
       " 'PUERTA DE ARGANDA',\n",
       " 'PUERTA DEL SUR',\n",
       " 'REYES CATOLICOS',\n",
       " 'RIVAS FUTURA',\n",
       " 'RIVAS URBANIZACIONES',\n",
       " 'RIVAS VACIAMADRID',\n",
       " 'RONDA DE LA COMUNICACION',\n",
       " 'SAINZ DE BARANDA',\n",
       " 'SAN BLAS',\n",
       " 'SAN CIPRIANO',\n",
       " 'SAN CRISTOBAL',\n",
       " 'SAN FERMIN-ORCASUR',\n",
       " 'SAN FERNANDO',\n",
       " 'SAN FRANCISCO',\n",
       " 'SIERRA DE GUADALUPE',\n",
       " 'SIMANCAS',\n",
       " 'SUANZES',\n",
       " 'TORRE ARIAS',\n",
       " 'TRES OLIVOS',\n",
       " 'VALDEBERNARDO',\n",
       " 'VALDEZARZA',\n",
       " 'VENTAS',\n",
       " 'VENTILLA',\n",
       " 'VICALVARO',\n",
       " 'VILLA DE VALLECAS',\n",
       " 'VILLAVERDE BAJO-CRUCE',\n",
       " 'VINATEROS']"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "articulation_stations = list(nx.articulation_points(nx.Graph(g)))\n",
    "\n",
    "print('%i articulation stations out of %i stations (%0.3f %%) \\n' % (len(articulation_stations), len(dict_nodes.keys()), (len(articulation_stations) * 100)/len(dict_nodes.keys()) ) )\n",
    "aux_list = []\n",
    "for i in articulation_stations:\n",
    "    aux_list.append(dict_nodes[i]['name'])\n",
    "    \n",
    "aux_list.sort()\n",
    "aux_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see that was a long list. However, there are Metro Stations which are more critical than others. For example, if Puerta del Sur would stop working, none of the Metro Sur (Line 12) stations could reach any Madrid Metro Station because Puerta del Sur is the only one station that connects the Line 12 with the rest of the Metro network.\n",
    "\n",
    "So in order to know which articulation stations are the most critical, I'm gonna count the number of connected components (the sets of connected Metro Stations) and the number of nodes (stations) each component has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "art_points_dict = {}\n",
    "\n",
    "for i in articulation_stations:\n",
    "    g_copy = g.copy()\n",
    "    \n",
    "    g_copy.remove_node(i)\n",
    "    \n",
    "    art_points_dict[dict_nodes[i]['name']] = {'n_ccs': nx.number_connected_components(g_copy)}\n",
    "#     print(dict_nodes[i]['name'])\n",
    "#     print('')\n",
    "#     print('Connected components if that node (station) is removed: %i' % art_points_dict[dict_nodes[i]['name']]['n_ccs'])\n",
    "    cc_i = 1\n",
    "    n_nodes_ccs = []\n",
    "    for cc in nx.connected_components(g_copy): \n",
    "        art_points_dict[dict_nodes[i]['name']]['nodes_cc_' + str(cc_i)] = len(cc)\n",
    "#         print('Nodes (stations) in connected component %i: %i' % (cc_i, art_points_dict[dict_nodes[i]['name']]['nodes_cc_' + str(cc_i)]))        \n",
    "        n_nodes_ccs.append(art_points_dict[dict_nodes[i]['name']]['nodes_cc_' + str(cc_i)])\n",
    "        cc_i += 1\n",
    "        \n",
    "    n_nodes_ccs.sort(reverse = True)\n",
    "    art_points_dict[dict_nodes[i]['name']]['n_isolated_nodes'] = sum(n_nodes_ccs[1:])\n",
    "    \n",
    "#     print('Number of isolated nodes (stations): %i' % art_points_dict[dict_nodes[i]['name']]['n_isolated_nodes'])\n",
    "        \n",
    "#     print('-------------------------------------------------------------------------------------------')\n",
    "    \n",
    "    del g_copy\n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "art_points_info_df = pd.DataFrame.from_dict(art_points_dict, orient='index')[['n_ccs', 'n_isolated_nodes', 'nodes_cc_1', 'nodes_cc_2', 'nodes_cc_3']] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 20 most critical articulation Metro stations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_ccs</th>\n",
       "      <th>n_isolated_nodes</th>\n",
       "      <th>nodes_cc_1</th>\n",
       "      <th>nodes_cc_2</th>\n",
       "      <th>nodes_cc_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CASA DE CAMPO</th>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>208</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLONIA JARDIN</th>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>209</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVIACION ESPAÑOLA</th>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>210</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUATRO VIENTOS</th>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>211</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOAQUIN VILUMBRALES</th>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>212</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUERTA DEL SUR</th>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>213</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUEBLO NUEVO</th>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>221</td>\n",
       "      <td>6</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAINZ DE BARANDA</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>227</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAMARTIN</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>227</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PACIFICO</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>228</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASCAO</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>228</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESTRELLA</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>228</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEGOÑA</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>228</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARCIA NOBLEJAS</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>229</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUENCARRAL</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>229</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUENTE DE VALLECAS</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>229</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VINATEROS</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>229</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRES OLIVOS</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>230</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NUEVA NUMANCIA</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>230</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARTILLEROS</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>230</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     n_ccs  n_isolated_nodes  nodes_cc_1  nodes_cc_2  \\\n",
       "CASA DE CAMPO            2                32         208          32   \n",
       "COLONIA JARDIN           2                31         209          31   \n",
       "AVIACION ESPAÑOLA        2                30         210          30   \n",
       "CUATRO VIENTOS           2                29         211          29   \n",
       "JOAQUIN VILUMBRALES      2                28         212          28   \n",
       "PUERTA DEL SUR           2                27         213          27   \n",
       "PUEBLO NUEVO             3                19         221           6   \n",
       "SAINZ DE BARANDA         2                13         227          13   \n",
       "CHAMARTIN                2                13         227          13   \n",
       "PACIFICO                 2                12         228          12   \n",
       "ASCAO                    2                12         228          12   \n",
       "ESTRELLA                 2                12         228          12   \n",
       "BEGOÑA                   2                12         228          12   \n",
       "GARCIA NOBLEJAS          2                11         229          11   \n",
       "FUENCARRAL               2                11         229          11   \n",
       "PUENTE DE VALLECAS       2                11         229          11   \n",
       "VINATEROS                2                11         229          11   \n",
       "TRES OLIVOS              2                10         230          10   \n",
       "NUEVA NUMANCIA           2                10         230          10   \n",
       "ARTILLEROS               2                10         230          10   \n",
       "\n",
       "                     nodes_cc_3  \n",
       "CASA DE CAMPO               NaN  \n",
       "COLONIA JARDIN              NaN  \n",
       "AVIACION ESPAÑOLA           NaN  \n",
       "CUATRO VIENTOS              NaN  \n",
       "JOAQUIN VILUMBRALES         NaN  \n",
       "PUERTA DEL SUR              NaN  \n",
       "PUEBLO NUEVO               13.0  \n",
       "SAINZ DE BARANDA            NaN  \n",
       "CHAMARTIN                   NaN  \n",
       "PACIFICO                    NaN  \n",
       "ASCAO                       NaN  \n",
       "ESTRELLA                    NaN  \n",
       "BEGOÑA                      NaN  \n",
       "GARCIA NOBLEJAS             NaN  \n",
       "FUENCARRAL                  NaN  \n",
       "PUENTE DE VALLECAS          NaN  \n",
       "VINATEROS                   NaN  \n",
       "TRES OLIVOS                 NaN  \n",
       "NUEVA NUMANCIA              NaN  \n",
       "ARTILLEROS                  NaN  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "art_points_info_df.sort_values('n_isolated_nodes', ascending = False)[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, Casa de Campo is the most critical Metro Station because removing it...\n",
    "* Would leave the whole Metro Sur network disconnected from the Madrid Metro network\n",
    "* If we had into accont Metro Ligero stations, the rest of Madrid Metro network couldn't reach any Metro Ligero station since Casa de Campo is the only one connection.\n",
    "* Joaquín Vilumbrales, Cuatro Vientos, Aviación Española and Colonia Jardín would get disconnected from the rest of the Madrid Metro network too.\n",
    "* Without taking into account Metro Ligero stations, Móstoles, Alcorcón, Fuenlabrada, Getafe and Leganés would get disconnected from Madrid via Metro (cities/towns would get disconnected from Madrid).\n",
    "\n",
    "Of course, any station between Cuatro Vientos and Puerta del Sur (both included) are extremely critical and would had similar effects of those written above.\n",
    "\n",
    "Pueblo Nuevo is another critical station to have in mind, since is the only one that connects 6 Line 5 stations (Ciudad Lineal, Suanzes, Torre Arias, Canillejas, El Capricho and Alameda Osuna) and 13 Line 7 stations (Ascao, García Noblejas, Simancas, San Blas, Las Musas, Estadio Metropolitano, Barrio del Puerto, La Rambla, San Fernando, Jarama, Henares and Hospital del Henares) to the rest of the Madrid Metro network. Line 7 connects Madrid with Coslada and San Fernando de Henares, so again cities would get disconnected from Madrid. Pueblo Nuevo case is special because it generates 3 connected componets.\n",
    "\n",
    "Similar conclusions can be drawn in the cases of Sainz de Baranda and Chamartín: again there would be a disconnection between Madrid and other towns (not to mention many Madrid wards would get disconnected from the resto of Madrid Metro network too).\n",
    "\n",
    "Though Plaza Elíptica doesn't appear on the Top 20 list, it's a critical station too because it's the only station that connects Line 11 with the rest of Madrid Metro network.\n",
    "\n",
    "My final conclusion on this topic is that towns and cities (some Madrid wards too, specially those in the most outer Madrid areas) are very weakly connected to the rest of the Metro network having many critical unique points of failure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eigenvector centrality: a node (station) importance is based on how important its neighbours are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(g, name = 'eigenvector_centrality', values = nx.eigenvector_centrality_numpy(g, weight = 'Weight', max_iter = 1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 20 most important Metro stations based on Eigenvector Centrality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>eigenvector_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AVENIDA DE AMERICA</td>\n",
       "      <td>0.465889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DIEGO DE LEON</td>\n",
       "      <td>0.453658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NUÑEZ DE BALBOA</td>\n",
       "      <td>0.270761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MANUEL BECERRA</td>\n",
       "      <td>0.215611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALONSO MARTINEZ</td>\n",
       "      <td>0.182170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GREGORIO MARAÑON</td>\n",
       "      <td>0.182113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>VENTAS</td>\n",
       "      <td>0.171162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LISTA</td>\n",
       "      <td>0.160603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CALLAO</td>\n",
       "      <td>0.156859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SOL</td>\n",
       "      <td>0.154792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GOYA</td>\n",
       "      <td>0.152170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TRIBUNAL</td>\n",
       "      <td>0.145724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GRAN VIA</td>\n",
       "      <td>0.139216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>REPUBLICA ARGENTINA</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>OPERA</td>\n",
       "      <td>0.127104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PRINCIPE DE VERGARA</td>\n",
       "      <td>0.125234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CARTAGENA</td>\n",
       "      <td>0.124670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PLAZA DE ESPAÑA</td>\n",
       "      <td>0.124460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CRUZ DEL RAYO</td>\n",
       "      <td>0.116158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BILBAO</td>\n",
       "      <td>0.115849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  eigenvector_centrality\n",
       "1    AVENIDA DE AMERICA                0.465889\n",
       "2         DIEGO DE LEON                0.453658\n",
       "3       NUÑEZ DE BALBOA                0.270761\n",
       "4        MANUEL BECERRA                0.215611\n",
       "5       ALONSO MARTINEZ                0.182170\n",
       "6      GREGORIO MARAÑON                0.182113\n",
       "7                VENTAS                0.171162\n",
       "8                 LISTA                0.160603\n",
       "9                CALLAO                0.156859\n",
       "10                  SOL                0.154792\n",
       "11                 GOYA                0.152170\n",
       "12             TRIBUNAL                0.145724\n",
       "13             GRAN VIA                0.139216\n",
       "14  REPUBLICA ARGENTINA                0.133333\n",
       "15                OPERA                0.127104\n",
       "16  PRINCIPE DE VERGARA                0.125234\n",
       "17            CARTAGENA                0.124670\n",
       "18      PLAZA DE ESPAÑA                0.124460\n",
       "19        CRUZ DEL RAYO                0.116158\n",
       "20               BILBAO                0.115849"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n_stations_by_attribute(g, 'eigenvector_centrality', 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks interesting: as you can see, Gregorio Marañón is not the most important station according to this centrality measure. Avenida América it's important persé and it's connected to other important stations like Diego de León (2th position), Núñez de Balboa (3th position), Manuel Becerra (4th position) and Gregorio Marañón (6th position).\n",
    "\n",
    "I can see there are two important stations in different areas: one is Avenida América and the other one is Alonso Martínez, many Metro lines pass through those stations (lines 6, 4, 7 and 9 pass through Avenida América and lines 10, 5 and 4 pass through Alonso Martinez). Both stations are connected by Gregorio Marañón.\n",
    "\n",
    "Though the data doesn't take this into account, Noviciado and Plaza España are connected underground: the problem is they aren't directly connected by railways, you need to walk through the stations. So we could think about Plaza España and Noviciado as an important unique station that joins up to 3 lines (line 2, line 3 and line 10)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get some info about edges speeds and distances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "aux_edges_dict = {}\n",
    "\n",
    "i = 0\n",
    "for edge in list(g.edges(data = True)):\n",
    "    edge[2]['Source'] = node_names[edge[0]]\n",
    "    edge[2]['Target'] = node_names[edge[1]]\n",
    "    aux_edges_dict[i] = edge[2]\n",
    "    i += 1\n",
    "    \n",
    "edges_pd = pd.DataFrame.from_dict(aux_edges_dict, orient='index')[['Source', 'Target', 'edge_name', 'distance_meters', 'travel_seconds', 'speed_ms', 'Weight', 'edge_color', 'longitude_Source', 'latitude_Source', 'longitude_Target', 'latitude_Target']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 20 further edges (connections between stations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>edge_name</th>\n",
       "      <th>distance_meters</th>\n",
       "      <th>travel_seconds</th>\n",
       "      <th>speed_ms</th>\n",
       "      <th>Weight</th>\n",
       "      <th>edge_color</th>\n",
       "      <th>longitude_Source</th>\n",
       "      <th>latitude_Source</th>\n",
       "      <th>longitude_Target</th>\n",
       "      <th>latitude_Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>PUERTA DE ARGANDA</td>\n",
       "      <td>RIVAS URBANIZACIONES</td>\n",
       "      <td>9</td>\n",
       "      <td>5640.865434</td>\n",
       "      <td>656.0</td>\n",
       "      <td>8.598880</td>\n",
       "      <td>0.001524</td>\n",
       "      <td>A60084</td>\n",
       "      <td>-3.59598</td>\n",
       "      <td>40.40132</td>\n",
       "      <td>-3.54728</td>\n",
       "      <td>40.36677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>FERIA DE MADRID</td>\n",
       "      <td>AEROPUERTO T1 T2 T3</td>\n",
       "      <td>8</td>\n",
       "      <td>3992.241735</td>\n",
       "      <td>492.0</td>\n",
       "      <td>8.114312</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>EC82B1</td>\n",
       "      <td>-3.61620</td>\n",
       "      <td>40.46389</td>\n",
       "      <td>-3.56954</td>\n",
       "      <td>40.46864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>RIVAS VACIAMADRID</td>\n",
       "      <td>LA POVEDA</td>\n",
       "      <td>9</td>\n",
       "      <td>3811.419717</td>\n",
       "      <td>573.0</td>\n",
       "      <td>6.651692</td>\n",
       "      <td>0.001745</td>\n",
       "      <td>A60084</td>\n",
       "      <td>-3.47745</td>\n",
       "      <td>40.31902</td>\n",
       "      <td>-3.52060</td>\n",
       "      <td>40.32837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>RIVAS URBANIZACIONES</td>\n",
       "      <td>RIVAS FUTURA</td>\n",
       "      <td>9</td>\n",
       "      <td>3409.393651</td>\n",
       "      <td>522.0</td>\n",
       "      <td>6.531405</td>\n",
       "      <td>0.001916</td>\n",
       "      <td>A60084</td>\n",
       "      <td>-3.52479</td>\n",
       "      <td>40.34134</td>\n",
       "      <td>-3.54728</td>\n",
       "      <td>40.36677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>CUATRO VIENTOS</td>\n",
       "      <td>JOAQUIN VILUMBRALES</td>\n",
       "      <td>10</td>\n",
       "      <td>3368.128567</td>\n",
       "      <td>535.0</td>\n",
       "      <td>6.295567</td>\n",
       "      <td>0.001869</td>\n",
       "      <td>005AA9</td>\n",
       "      <td>-3.79152</td>\n",
       "      <td>40.37771</td>\n",
       "      <td>-3.80720</td>\n",
       "      <td>40.34985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>PUERTA DEL SUR</td>\n",
       "      <td>SAN NICASIO</td>\n",
       "      <td>12</td>\n",
       "      <td>3240.105573</td>\n",
       "      <td>317.0</td>\n",
       "      <td>10.221153</td>\n",
       "      <td>0.003155</td>\n",
       "      <td>A49800</td>\n",
       "      <td>-3.81211</td>\n",
       "      <td>40.34524</td>\n",
       "      <td>-3.77587</td>\n",
       "      <td>40.33616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>LA POVEDA</td>\n",
       "      <td>ARGANDA DEL REY</td>\n",
       "      <td>9</td>\n",
       "      <td>3062.375720</td>\n",
       "      <td>533.0</td>\n",
       "      <td>5.745545</td>\n",
       "      <td>0.001876</td>\n",
       "      <td>A60084</td>\n",
       "      <td>-3.47745</td>\n",
       "      <td>40.31902</td>\n",
       "      <td>-3.44752</td>\n",
       "      <td>40.30367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>ESTADIO METROPOLITANO</td>\n",
       "      <td>BARRIO DEL PUERTO</td>\n",
       "      <td>7</td>\n",
       "      <td>2892.155719</td>\n",
       "      <td>608.0</td>\n",
       "      <td>4.756835</td>\n",
       "      <td>0.001645</td>\n",
       "      <td>EE7518</td>\n",
       "      <td>-3.60015</td>\n",
       "      <td>40.43339</td>\n",
       "      <td>-3.56919</td>\n",
       "      <td>40.42250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>COLOMBIA</td>\n",
       "      <td>PINAR DEL REY</td>\n",
       "      <td>8</td>\n",
       "      <td>2716.031916</td>\n",
       "      <td>402.0</td>\n",
       "      <td>6.756298</td>\n",
       "      <td>0.002488</td>\n",
       "      <td>EC82B1</td>\n",
       "      <td>-3.67682</td>\n",
       "      <td>40.45634</td>\n",
       "      <td>-3.64867</td>\n",
       "      <td>40.46800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>MANUELA MALASAÑA</td>\n",
       "      <td>LORANCA</td>\n",
       "      <td>12</td>\n",
       "      <td>2618.338612</td>\n",
       "      <td>494.0</td>\n",
       "      <td>5.300281</td>\n",
       "      <td>0.002024</td>\n",
       "      <td>A49800</td>\n",
       "      <td>-3.86402</td>\n",
       "      <td>40.30903</td>\n",
       "      <td>-3.83768</td>\n",
       "      <td>40.29681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>PARQUE DE LOS ESTADOS</td>\n",
       "      <td>ARROYO CULEBRO</td>\n",
       "      <td>12</td>\n",
       "      <td>2572.449756</td>\n",
       "      <td>480.0</td>\n",
       "      <td>5.359270</td>\n",
       "      <td>0.002083</td>\n",
       "      <td>A49800</td>\n",
       "      <td>-3.75682</td>\n",
       "      <td>40.28874</td>\n",
       "      <td>-3.78697</td>\n",
       "      <td>40.28684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>PARQUE OESTE</td>\n",
       "      <td>UNIVERSIDAD REY JUAN CARLOS</td>\n",
       "      <td>12</td>\n",
       "      <td>2279.566278</td>\n",
       "      <td>469.0</td>\n",
       "      <td>4.860482</td>\n",
       "      <td>0.002132</td>\n",
       "      <td>A49800</td>\n",
       "      <td>-3.84934</td>\n",
       "      <td>40.34589</td>\n",
       "      <td>-3.87218</td>\n",
       "      <td>40.33512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>MONTECARMELO</td>\n",
       "      <td>LAS TABLAS</td>\n",
       "      <td>10</td>\n",
       "      <td>2256.090029</td>\n",
       "      <td>387.0</td>\n",
       "      <td>5.829690</td>\n",
       "      <td>0.002584</td>\n",
       "      <td>005AA9</td>\n",
       "      <td>-3.69575</td>\n",
       "      <td>40.50525</td>\n",
       "      <td>-3.66944</td>\n",
       "      <td>40.50833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>LORANCA</td>\n",
       "      <td>HOSPITAL DE FUENLABRADA</td>\n",
       "      <td>12</td>\n",
       "      <td>2184.816777</td>\n",
       "      <td>440.0</td>\n",
       "      <td>4.965493</td>\n",
       "      <td>0.002273</td>\n",
       "      <td>A49800</td>\n",
       "      <td>-3.81642</td>\n",
       "      <td>40.28576</td>\n",
       "      <td>-3.83768</td>\n",
       "      <td>40.29681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>LA GRANJA</td>\n",
       "      <td>LA MORALEJA</td>\n",
       "      <td>10</td>\n",
       "      <td>2010.504111</td>\n",
       "      <td>349.0</td>\n",
       "      <td>5.760757</td>\n",
       "      <td>0.002865</td>\n",
       "      <td>005AA9</td>\n",
       "      <td>-3.63556</td>\n",
       "      <td>40.53196</td>\n",
       "      <td>-3.65859</td>\n",
       "      <td>40.52760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>BARAJAS</td>\n",
       "      <td>AEROPUERTO T4</td>\n",
       "      <td>8</td>\n",
       "      <td>1995.702003</td>\n",
       "      <td>338.0</td>\n",
       "      <td>5.904444</td>\n",
       "      <td>0.002959</td>\n",
       "      <td>EC82B1</td>\n",
       "      <td>-3.59325</td>\n",
       "      <td>40.49177</td>\n",
       "      <td>-3.58253</td>\n",
       "      <td>40.47577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>MAR DE CRISTAL</td>\n",
       "      <td>FERIA DE MADRID</td>\n",
       "      <td>8</td>\n",
       "      <td>1974.282182</td>\n",
       "      <td>348.0</td>\n",
       "      <td>5.673225</td>\n",
       "      <td>0.002874</td>\n",
       "      <td>EC82B1</td>\n",
       "      <td>-3.63832</td>\n",
       "      <td>40.46943</td>\n",
       "      <td>-3.61620</td>\n",
       "      <td>40.46389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>LA PESETA</td>\n",
       "      <td>LA FORTUNA</td>\n",
       "      <td>11</td>\n",
       "      <td>1909.310159</td>\n",
       "      <td>455.0</td>\n",
       "      <td>4.196286</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>009B3A</td>\n",
       "      <td>-3.75690</td>\n",
       "      <td>40.36422</td>\n",
       "      <td>-3.77783</td>\n",
       "      <td>40.35795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>LAGO</td>\n",
       "      <td>BATAN</td>\n",
       "      <td>10</td>\n",
       "      <td>1761.439527</td>\n",
       "      <td>369.0</td>\n",
       "      <td>4.773549</td>\n",
       "      <td>0.002710</td>\n",
       "      <td>005AA9</td>\n",
       "      <td>-3.75311</td>\n",
       "      <td>40.40786</td>\n",
       "      <td>-3.73563</td>\n",
       "      <td>40.41641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>FUENCARRAL</td>\n",
       "      <td>BEGOÑA</td>\n",
       "      <td>10</td>\n",
       "      <td>1734.221107</td>\n",
       "      <td>316.0</td>\n",
       "      <td>5.488041</td>\n",
       "      <td>0.003165</td>\n",
       "      <td>005AA9</td>\n",
       "      <td>-3.68585</td>\n",
       "      <td>40.48041</td>\n",
       "      <td>-3.69283</td>\n",
       "      <td>40.49509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Source                       Target edge_name  \\\n",
       "196      PUERTA DE ARGANDA         RIVAS URBANIZACIONES         9   \n",
       "181        FERIA DE MADRID          AEROPUERTO T1 T2 T3         8   \n",
       "198      RIVAS VACIAMADRID                    LA POVEDA         9   \n",
       "197   RIVAS URBANIZACIONES                 RIVAS FUTURA         9   \n",
       "208         CUATRO VIENTOS          JOAQUIN VILUMBRALES        10   \n",
       "211         PUERTA DEL SUR                  SAN NICASIO        12   \n",
       "200              LA POVEDA              ARGANDA DEL REY         9   \n",
       "265  ESTADIO METROPOLITANO            BARRIO DEL PUERTO         7   \n",
       "179               COLOMBIA                PINAR DEL REY         8   \n",
       "221       MANUELA MALASAÑA                      LORANCA        12   \n",
       "226  PARQUE DE LOS ESTADOS               ARROYO CULEBRO        12   \n",
       "216           PARQUE OESTE  UNIVERSIDAD REY JUAN CARLOS        12   \n",
       "256           MONTECARMELO                   LAS TABLAS        10   \n",
       "222                LORANCA      HOSPITAL DE FUENLABRADA        12   \n",
       "259              LA GRANJA                  LA MORALEJA        10   \n",
       "183                BARAJAS                AEROPUERTO T4         8   \n",
       "111         MAR DE CRISTAL              FERIA DE MADRID         8   \n",
       "243              LA PESETA                   LA FORTUNA        11   \n",
       "205                   LAGO                        BATAN        10   \n",
       "202             FUENCARRAL                       BEGOÑA        10   \n",
       "\n",
       "     distance_meters  travel_seconds   speed_ms    Weight edge_color  \\\n",
       "196      5640.865434           656.0   8.598880  0.001524     A60084   \n",
       "181      3992.241735           492.0   8.114312  0.002033     EC82B1   \n",
       "198      3811.419717           573.0   6.651692  0.001745     A60084   \n",
       "197      3409.393651           522.0   6.531405  0.001916     A60084   \n",
       "208      3368.128567           535.0   6.295567  0.001869     005AA9   \n",
       "211      3240.105573           317.0  10.221153  0.003155     A49800   \n",
       "200      3062.375720           533.0   5.745545  0.001876     A60084   \n",
       "265      2892.155719           608.0   4.756835  0.001645     EE7518   \n",
       "179      2716.031916           402.0   6.756298  0.002488     EC82B1   \n",
       "221      2618.338612           494.0   5.300281  0.002024     A49800   \n",
       "226      2572.449756           480.0   5.359270  0.002083     A49800   \n",
       "216      2279.566278           469.0   4.860482  0.002132     A49800   \n",
       "256      2256.090029           387.0   5.829690  0.002584     005AA9   \n",
       "222      2184.816777           440.0   4.965493  0.002273     A49800   \n",
       "259      2010.504111           349.0   5.760757  0.002865     005AA9   \n",
       "183      1995.702003           338.0   5.904444  0.002959     EC82B1   \n",
       "111      1974.282182           348.0   5.673225  0.002874     EC82B1   \n",
       "243      1909.310159           455.0   4.196286  0.002198     009B3A   \n",
       "205      1761.439527           369.0   4.773549  0.002710     005AA9   \n",
       "202      1734.221107           316.0   5.488041  0.003165     005AA9   \n",
       "\n",
       "     longitude_Source  latitude_Source  longitude_Target  latitude_Target  \n",
       "196          -3.59598         40.40132          -3.54728         40.36677  \n",
       "181          -3.61620         40.46389          -3.56954         40.46864  \n",
       "198          -3.47745         40.31902          -3.52060         40.32837  \n",
       "197          -3.52479         40.34134          -3.54728         40.36677  \n",
       "208          -3.79152         40.37771          -3.80720         40.34985  \n",
       "211          -3.81211         40.34524          -3.77587         40.33616  \n",
       "200          -3.47745         40.31902          -3.44752         40.30367  \n",
       "265          -3.60015         40.43339          -3.56919         40.42250  \n",
       "179          -3.67682         40.45634          -3.64867         40.46800  \n",
       "221          -3.86402         40.30903          -3.83768         40.29681  \n",
       "226          -3.75682         40.28874          -3.78697         40.28684  \n",
       "216          -3.84934         40.34589          -3.87218         40.33512  \n",
       "256          -3.69575         40.50525          -3.66944         40.50833  \n",
       "222          -3.81642         40.28576          -3.83768         40.29681  \n",
       "259          -3.63556         40.53196          -3.65859         40.52760  \n",
       "183          -3.59325         40.49177          -3.58253         40.47577  \n",
       "111          -3.63832         40.46943          -3.61620         40.46389  \n",
       "243          -3.75690         40.36422          -3.77783         40.35795  \n",
       "205          -3.75311         40.40786          -3.73563         40.41641  \n",
       "202          -3.68585         40.48041          -3.69283         40.49509  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_pd.sort_values('distance_meters', ascending = False)[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 20 closest edges (connections between stations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>edge_name</th>\n",
       "      <th>distance_meters</th>\n",
       "      <th>travel_seconds</th>\n",
       "      <th>speed_ms</th>\n",
       "      <th>Weight</th>\n",
       "      <th>edge_color</th>\n",
       "      <th>longitude_Source</th>\n",
       "      <th>latitude_Source</th>\n",
       "      <th>longitude_Target</th>\n",
       "      <th>latitude_Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BILBAO</td>\n",
       "      <td>SAN BERNARDO</td>\n",
       "      <td>4</td>\n",
       "      <td>306.395820</td>\n",
       "      <td>213.0</td>\n",
       "      <td>1.438478</td>\n",
       "      <td>0.004695</td>\n",
       "      <td>B65518</td>\n",
       "      <td>-3.70218</td>\n",
       "      <td>40.42906</td>\n",
       "      <td>-3.70557</td>\n",
       "      <td>40.43001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>SERRANO</td>\n",
       "      <td>VELAZQUEZ</td>\n",
       "      <td>4</td>\n",
       "      <td>317.955028</td>\n",
       "      <td>209.0</td>\n",
       "      <td>1.521316</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>B65518</td>\n",
       "      <td>-3.68663</td>\n",
       "      <td>40.42543</td>\n",
       "      <td>-3.68292</td>\n",
       "      <td>40.42503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GRAN VIA</td>\n",
       "      <td>CALLAO</td>\n",
       "      <td>5</td>\n",
       "      <td>327.909468</td>\n",
       "      <td>215.0</td>\n",
       "      <td>1.525160</td>\n",
       "      <td>0.004651</td>\n",
       "      <td>8FD400</td>\n",
       "      <td>-3.70566</td>\n",
       "      <td>40.42014</td>\n",
       "      <td>-3.70180</td>\n",
       "      <td>40.42001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BILBAO</td>\n",
       "      <td>TRIBUNAL</td>\n",
       "      <td>1</td>\n",
       "      <td>331.608956</td>\n",
       "      <td>269.0</td>\n",
       "      <td>1.232747</td>\n",
       "      <td>0.003717</td>\n",
       "      <td>2DBEF0</td>\n",
       "      <td>-3.70218</td>\n",
       "      <td>40.42906</td>\n",
       "      <td>-3.70110</td>\n",
       "      <td>40.42619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>DELICIAS</td>\n",
       "      <td>PALOS DE LA FRONTERA</td>\n",
       "      <td>3</td>\n",
       "      <td>352.013487</td>\n",
       "      <td>218.0</td>\n",
       "      <td>1.614741</td>\n",
       "      <td>0.004587</td>\n",
       "      <td>FFD000</td>\n",
       "      <td>-3.69423</td>\n",
       "      <td>40.40307</td>\n",
       "      <td>-3.69420</td>\n",
       "      <td>40.39990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ATOCHA</td>\n",
       "      <td>ATOCHA-RENFE</td>\n",
       "      <td>1</td>\n",
       "      <td>364.237439</td>\n",
       "      <td>243.0</td>\n",
       "      <td>1.498920</td>\n",
       "      <td>0.004115</td>\n",
       "      <td>2DBEF0</td>\n",
       "      <td>-3.69249</td>\n",
       "      <td>40.40885</td>\n",
       "      <td>-3.68938</td>\n",
       "      <td>40.40659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SOL</td>\n",
       "      <td>SEVILLA</td>\n",
       "      <td>2</td>\n",
       "      <td>364.289816</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.821449</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>ED1C24</td>\n",
       "      <td>-3.69925</td>\n",
       "      <td>40.41805</td>\n",
       "      <td>-3.70326</td>\n",
       "      <td>40.41688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>GRAN VIA</td>\n",
       "      <td>SOL</td>\n",
       "      <td>1</td>\n",
       "      <td>368.990941</td>\n",
       "      <td>245.0</td>\n",
       "      <td>1.506085</td>\n",
       "      <td>0.004082</td>\n",
       "      <td>2DBEF0</td>\n",
       "      <td>-3.70180</td>\n",
       "      <td>40.42001</td>\n",
       "      <td>-3.70326</td>\n",
       "      <td>40.41688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>SAN BERNARDO</td>\n",
       "      <td>QUEVEDO</td>\n",
       "      <td>2</td>\n",
       "      <td>371.653102</td>\n",
       "      <td>191.0</td>\n",
       "      <td>1.945828</td>\n",
       "      <td>0.005236</td>\n",
       "      <td>ED1C24</td>\n",
       "      <td>-3.70557</td>\n",
       "      <td>40.43001</td>\n",
       "      <td>-3.70433</td>\n",
       "      <td>40.43322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>COLON</td>\n",
       "      <td>SERRANO</td>\n",
       "      <td>4</td>\n",
       "      <td>371.695283</td>\n",
       "      <td>230.0</td>\n",
       "      <td>1.616066</td>\n",
       "      <td>0.004348</td>\n",
       "      <td>B65518</td>\n",
       "      <td>-3.68663</td>\n",
       "      <td>40.42543</td>\n",
       "      <td>-3.69101</td>\n",
       "      <td>40.42542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ALVARADO</td>\n",
       "      <td>CUATRO CAMINOS</td>\n",
       "      <td>1</td>\n",
       "      <td>377.282957</td>\n",
       "      <td>263.0</td>\n",
       "      <td>1.434536</td>\n",
       "      <td>0.003802</td>\n",
       "      <td>2DBEF0</td>\n",
       "      <td>-3.70397</td>\n",
       "      <td>40.44697</td>\n",
       "      <td>-3.70331</td>\n",
       "      <td>40.45033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>OPERA</td>\n",
       "      <td>SANTO DOMINGO</td>\n",
       "      <td>2</td>\n",
       "      <td>379.468025</td>\n",
       "      <td>213.0</td>\n",
       "      <td>1.781540</td>\n",
       "      <td>0.004695</td>\n",
       "      <td>ED1C24</td>\n",
       "      <td>-3.70942</td>\n",
       "      <td>40.41809</td>\n",
       "      <td>-3.70796</td>\n",
       "      <td>40.42132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>BANCO DE ESPAÑA</td>\n",
       "      <td>SEVILLA</td>\n",
       "      <td>2</td>\n",
       "      <td>385.778936</td>\n",
       "      <td>217.0</td>\n",
       "      <td>1.777783</td>\n",
       "      <td>0.004608</td>\n",
       "      <td>ED1C24</td>\n",
       "      <td>-3.69925</td>\n",
       "      <td>40.41805</td>\n",
       "      <td>-3.69497</td>\n",
       "      <td>40.41922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>OPERA</td>\n",
       "      <td>CALLAO</td>\n",
       "      <td>5</td>\n",
       "      <td>391.981598</td>\n",
       "      <td>238.0</td>\n",
       "      <td>1.646982</td>\n",
       "      <td>0.004202</td>\n",
       "      <td>8FD400</td>\n",
       "      <td>-3.70566</td>\n",
       "      <td>40.42014</td>\n",
       "      <td>-3.70942</td>\n",
       "      <td>40.41809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>SANTO DOMINGO</td>\n",
       "      <td>NOVICIADO</td>\n",
       "      <td>2</td>\n",
       "      <td>393.547832</td>\n",
       "      <td>204.0</td>\n",
       "      <td>1.929156</td>\n",
       "      <td>0.004902</td>\n",
       "      <td>ED1C24</td>\n",
       "      <td>-3.70796</td>\n",
       "      <td>40.42132</td>\n",
       "      <td>-3.70742</td>\n",
       "      <td>40.42484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>DIEGO DE LEON</td>\n",
       "      <td>AVENIDA DE AMERICA</td>\n",
       "      <td>4</td>\n",
       "      <td>398.674236</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1.711048</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>B65518</td>\n",
       "      <td>-3.67664</td>\n",
       "      <td>40.43803</td>\n",
       "      <td>-3.67495</td>\n",
       "      <td>40.43468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>DIEGO DE LEON</td>\n",
       "      <td>AVENIDA DE AMERICA</td>\n",
       "      <td>6</td>\n",
       "      <td>398.674236</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1.711048</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>98989B</td>\n",
       "      <td>-3.67664</td>\n",
       "      <td>40.43803</td>\n",
       "      <td>-3.67495</td>\n",
       "      <td>40.43468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>GOYA</td>\n",
       "      <td>PRINCIPE DE VERGARA</td>\n",
       "      <td>2</td>\n",
       "      <td>399.509780</td>\n",
       "      <td>211.0</td>\n",
       "      <td>1.893411</td>\n",
       "      <td>0.004739</td>\n",
       "      <td>ED1C24</td>\n",
       "      <td>-3.67591</td>\n",
       "      <td>40.42455</td>\n",
       "      <td>-3.68012</td>\n",
       "      <td>40.42294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>EMBAJADORES</td>\n",
       "      <td>LAVAPIES</td>\n",
       "      <td>3</td>\n",
       "      <td>404.595473</td>\n",
       "      <td>214.0</td>\n",
       "      <td>1.890633</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>FFD000</td>\n",
       "      <td>-3.70268</td>\n",
       "      <td>40.40513</td>\n",
       "      <td>-3.70090</td>\n",
       "      <td>40.40851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>SIERRA DE GUADALUPE</td>\n",
       "      <td>VILLA DE VALLECAS</td>\n",
       "      <td>1</td>\n",
       "      <td>406.387157</td>\n",
       "      <td>274.0</td>\n",
       "      <td>1.483165</td>\n",
       "      <td>0.003650</td>\n",
       "      <td>2DBEF0</td>\n",
       "      <td>-3.62472</td>\n",
       "      <td>40.38216</td>\n",
       "      <td>-3.62130</td>\n",
       "      <td>40.37960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Source                Target edge_name  distance_meters  \\\n",
       "18               BILBAO          SAN BERNARDO         4       306.395820   \n",
       "92              SERRANO             VELAZQUEZ         4       317.955028   \n",
       "22             GRAN VIA                CALLAO         5       327.909468   \n",
       "17               BILBAO              TRIBUNAL         1       331.608956   \n",
       "77             DELICIAS  PALOS DE LA FRONTERA         3       352.013487   \n",
       "32               ATOCHA          ATOCHA-RENFE         1       364.237439   \n",
       "26                  SOL               SEVILLA         2       364.289816   \n",
       "23             GRAN VIA                   SOL         1       368.990941   \n",
       "67         SAN BERNARDO               QUEVEDO         2       371.653102   \n",
       "91                COLON               SERRANO         4       371.695283   \n",
       "9              ALVARADO        CUATRO CAMINOS         1       377.282957   \n",
       "64                OPERA         SANTO DOMINGO         2       379.468025   \n",
       "61      BANCO DE ESPAÑA               SEVILLA         2       385.778936   \n",
       "62                OPERA                CALLAO         5       391.981598   \n",
       "66        SANTO DOMINGO             NOVICIADO         2       393.547832   \n",
       "94        DIEGO DE LEON    AVENIDA DE AMERICA         4       398.674236   \n",
       "95        DIEGO DE LEON    AVENIDA DE AMERICA         6       398.674236   \n",
       "56                 GOYA   PRINCIPE DE VERGARA         2       399.509780   \n",
       "79          EMBAJADORES              LAVAPIES         3       404.595473   \n",
       "44  SIERRA DE GUADALUPE     VILLA DE VALLECAS         1       406.387157   \n",
       "\n",
       "    travel_seconds  speed_ms    Weight edge_color  longitude_Source  \\\n",
       "18           213.0  1.438478  0.004695     B65518          -3.70218   \n",
       "92           209.0  1.521316  0.004785     B65518          -3.68663   \n",
       "22           215.0  1.525160  0.004651     8FD400          -3.70566   \n",
       "17           269.0  1.232747  0.003717     2DBEF0          -3.70218   \n",
       "77           218.0  1.614741  0.004587     FFD000          -3.69423   \n",
       "32           243.0  1.498920  0.004115     2DBEF0          -3.69249   \n",
       "26           200.0  1.821449  0.005000     ED1C24          -3.69925   \n",
       "23           245.0  1.506085  0.004082     2DBEF0          -3.70180   \n",
       "67           191.0  1.945828  0.005236     ED1C24          -3.70557   \n",
       "91           230.0  1.616066  0.004348     B65518          -3.68663   \n",
       "9            263.0  1.434536  0.003802     2DBEF0          -3.70397   \n",
       "64           213.0  1.781540  0.004695     ED1C24          -3.70942   \n",
       "61           217.0  1.777783  0.004608     ED1C24          -3.69925   \n",
       "62           238.0  1.646982  0.004202     8FD400          -3.70566   \n",
       "66           204.0  1.929156  0.004902     ED1C24          -3.70796   \n",
       "94           233.0  1.711048  0.004292     B65518          -3.67664   \n",
       "95           233.0  1.711048  0.004292     98989B          -3.67664   \n",
       "56           211.0  1.893411  0.004739     ED1C24          -3.67591   \n",
       "79           214.0  1.890633  0.004673     FFD000          -3.70268   \n",
       "44           274.0  1.483165  0.003650     2DBEF0          -3.62472   \n",
       "\n",
       "    latitude_Source  longitude_Target  latitude_Target  \n",
       "18         40.42906          -3.70557         40.43001  \n",
       "92         40.42543          -3.68292         40.42503  \n",
       "22         40.42014          -3.70180         40.42001  \n",
       "17         40.42906          -3.70110         40.42619  \n",
       "77         40.40307          -3.69420         40.39990  \n",
       "32         40.40885          -3.68938         40.40659  \n",
       "26         40.41805          -3.70326         40.41688  \n",
       "23         40.42001          -3.70326         40.41688  \n",
       "67         40.43001          -3.70433         40.43322  \n",
       "91         40.42543          -3.69101         40.42542  \n",
       "9          40.44697          -3.70331         40.45033  \n",
       "64         40.41809          -3.70796         40.42132  \n",
       "61         40.41805          -3.69497         40.41922  \n",
       "62         40.42014          -3.70942         40.41809  \n",
       "66         40.42132          -3.70742         40.42484  \n",
       "94         40.43803          -3.67495         40.43468  \n",
       "95         40.43803          -3.67495         40.43468  \n",
       "56         40.42455          -3.68012         40.42294  \n",
       "79         40.40513          -3.70090         40.40851  \n",
       "44         40.38216          -3.62130         40.37960  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_pd.sort_values('distance_meters')[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploring Metro lines distances and travel times. First let's take a look at distances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2090.554160</td>\n",
       "      <td>1012.626402</td>\n",
       "      <td>892.001535</td>\n",
       "      <td>1531.809875</td>\n",
       "      <td>1974.282182</td>\n",
       "      <td>2355.866960</td>\n",
       "      <td>3992.241735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1308.175857</td>\n",
       "      <td>647.051661</td>\n",
       "      <td>688.576920</td>\n",
       "      <td>913.412813</td>\n",
       "      <td>1068.429811</td>\n",
       "      <td>1248.166272</td>\n",
       "      <td>3240.105573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1305.867872</td>\n",
       "      <td>1197.888734</td>\n",
       "      <td>521.874851</td>\n",
       "      <td>734.917837</td>\n",
       "      <td>852.736464</td>\n",
       "      <td>1136.665113</td>\n",
       "      <td>5640.865434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30.0</td>\n",
       "      <td>1196.303907</td>\n",
       "      <td>603.975131</td>\n",
       "      <td>452.139405</td>\n",
       "      <td>823.668233</td>\n",
       "      <td>998.498537</td>\n",
       "      <td>1395.535527</td>\n",
       "      <td>3368.128567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1023.297599</td>\n",
       "      <td>481.567731</td>\n",
       "      <td>488.703237</td>\n",
       "      <td>802.324197</td>\n",
       "      <td>936.687495</td>\n",
       "      <td>1067.317455</td>\n",
       "      <td>1909.310159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>1.0</td>\n",
       "      <td>982.474455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>982.474455</td>\n",
       "      <td>982.474455</td>\n",
       "      <td>982.474455</td>\n",
       "      <td>982.474455</td>\n",
       "      <td>982.474455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29.0</td>\n",
       "      <td>856.971367</td>\n",
       "      <td>447.237946</td>\n",
       "      <td>407.781924</td>\n",
       "      <td>644.993945</td>\n",
       "      <td>778.963681</td>\n",
       "      <td>931.551413</td>\n",
       "      <td>2892.155719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28.0</td>\n",
       "      <td>807.836514</td>\n",
       "      <td>201.114395</td>\n",
       "      <td>398.674236</td>\n",
       "      <td>690.055645</td>\n",
       "      <td>776.155292</td>\n",
       "      <td>955.202393</td>\n",
       "      <td>1134.044482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.0</td>\n",
       "      <td>770.150140</td>\n",
       "      <td>338.648770</td>\n",
       "      <td>352.013487</td>\n",
       "      <td>451.634963</td>\n",
       "      <td>752.897124</td>\n",
       "      <td>973.579449</td>\n",
       "      <td>1598.601497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>698.599916</td>\n",
       "      <td>233.507344</td>\n",
       "      <td>327.909468</td>\n",
       "      <td>520.261183</td>\n",
       "      <td>680.924522</td>\n",
       "      <td>838.516313</td>\n",
       "      <td>1281.811521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.0</td>\n",
       "      <td>688.115018</td>\n",
       "      <td>318.001062</td>\n",
       "      <td>364.289816</td>\n",
       "      <td>396.528806</td>\n",
       "      <td>595.169517</td>\n",
       "      <td>854.392727</td>\n",
       "      <td>1452.072937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.0</td>\n",
       "      <td>645.793039</td>\n",
       "      <td>250.922358</td>\n",
       "      <td>331.608956</td>\n",
       "      <td>493.155046</td>\n",
       "      <td>603.481091</td>\n",
       "      <td>754.733699</td>\n",
       "      <td>1413.201461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.0</td>\n",
       "      <td>617.701692</td>\n",
       "      <td>199.764084</td>\n",
       "      <td>306.395820</td>\n",
       "      <td>491.979808</td>\n",
       "      <td>583.518703</td>\n",
       "      <td>729.953542</td>\n",
       "      <td>992.863412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count         mean          std         min          25%  \\\n",
       "edge_name                                                             \n",
       "8            7.0  2090.554160  1012.626402  892.001535  1531.809875   \n",
       "12          28.0  1308.175857   647.051661  688.576920   913.412813   \n",
       "9           28.0  1305.867872  1197.888734  521.874851   734.917837   \n",
       "10          30.0  1196.303907   603.975131  452.139405   823.668233   \n",
       "11           6.0  1023.297599   481.567731  488.703237   802.324197   \n",
       "R            1.0   982.474455          NaN  982.474455   982.474455   \n",
       "7           29.0   856.971367   447.237946  407.781924   644.993945   \n",
       "6           28.0   807.836514   201.114395  398.674236   690.055645   \n",
       "3           17.0   770.150140   338.648770  352.013487   451.634963   \n",
       "5           31.0   698.599916   233.507344  327.909468   520.261183   \n",
       "2           19.0   688.115018   318.001062  364.289816   396.528806   \n",
       "1           32.0   645.793039   250.922358  331.608956   493.155046   \n",
       "4           22.0   617.701692   199.764084  306.395820   491.979808   \n",
       "\n",
       "                   50%          75%          max  \n",
       "edge_name                                         \n",
       "8          1974.282182  2355.866960  3992.241735  \n",
       "12         1068.429811  1248.166272  3240.105573  \n",
       "9           852.736464  1136.665113  5640.865434  \n",
       "10          998.498537  1395.535527  3368.128567  \n",
       "11          936.687495  1067.317455  1909.310159  \n",
       "R           982.474455   982.474455   982.474455  \n",
       "7           778.963681   931.551413  2892.155719  \n",
       "6           776.155292   955.202393  1134.044482  \n",
       "3           752.897124   973.579449  1598.601497  \n",
       "5           680.924522   838.516313  1281.811521  \n",
       "2           595.169517   854.392727  1452.072937  \n",
       "1           603.481091   754.733699  1413.201461  \n",
       "4           583.518703   729.953542   992.863412  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_dist_grouped = edges_pd[['edge_name', 'distance_meters']].groupby(by = 'edge_name').describe().iloc[[0, 4, 5, 6, 7, 8, 9, 10, 11, 1, 2, 3, 12],:]\n",
    "edges_dist_grouped['distance_meters'].sort_values('mean', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "metro_colors = ['#2DBEF0', '#ED1C24', '#FFD000', '#B65518', '#8FD400', '#98989B', '#EE7518', '#EC82B1', '#A60084', '#005AA9', '#009B3A', '#A49800', '#FFFFFF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "metro_order = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', 'R']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZnuSZSU50VRQAAABrZ7nB77xa6/OS/Pf0QuCFSb65q6IAAABYO8sNfnf1/1+T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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9e00b0470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "sns.boxplot(data=edges_pd, x = 'edge_name', y = 'distance_meters', order = metro_order, palette = metro_colors).set_title('Distance by Metro Line (geodesic, meters)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean distance per edge (stations connections) per line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">distance_meters</th>\n",
       "      <th>dist_per_edge</th>\n",
       "      <th>dist_per_edge_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2090.554160</td>\n",
       "      <td>1012.626402</td>\n",
       "      <td>892.001535</td>\n",
       "      <td>1531.809875</td>\n",
       "      <td>1974.282182</td>\n",
       "      <td>2355.866960</td>\n",
       "      <td>3992.241735</td>\n",
       "      <td>348.425693</td>\n",
       "      <td>168.771067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1023.297599</td>\n",
       "      <td>481.567731</td>\n",
       "      <td>488.703237</td>\n",
       "      <td>802.324197</td>\n",
       "      <td>936.687495</td>\n",
       "      <td>1067.317455</td>\n",
       "      <td>1909.310159</td>\n",
       "      <td>204.659520</td>\n",
       "      <td>96.313546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1308.175857</td>\n",
       "      <td>647.051661</td>\n",
       "      <td>688.576920</td>\n",
       "      <td>913.412813</td>\n",
       "      <td>1068.429811</td>\n",
       "      <td>1248.166272</td>\n",
       "      <td>3240.105573</td>\n",
       "      <td>48.450958</td>\n",
       "      <td>23.964876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1305.867872</td>\n",
       "      <td>1197.888734</td>\n",
       "      <td>521.874851</td>\n",
       "      <td>734.917837</td>\n",
       "      <td>852.736464</td>\n",
       "      <td>1136.665113</td>\n",
       "      <td>5640.865434</td>\n",
       "      <td>48.365477</td>\n",
       "      <td>44.366249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.0</td>\n",
       "      <td>770.150140</td>\n",
       "      <td>338.648770</td>\n",
       "      <td>352.013487</td>\n",
       "      <td>451.634963</td>\n",
       "      <td>752.897124</td>\n",
       "      <td>973.579449</td>\n",
       "      <td>1598.601497</td>\n",
       "      <td>48.134384</td>\n",
       "      <td>21.165548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30.0</td>\n",
       "      <td>1196.303907</td>\n",
       "      <td>603.975131</td>\n",
       "      <td>452.139405</td>\n",
       "      <td>823.668233</td>\n",
       "      <td>998.498537</td>\n",
       "      <td>1395.535527</td>\n",
       "      <td>3368.128567</td>\n",
       "      <td>41.251859</td>\n",
       "      <td>20.826729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.0</td>\n",
       "      <td>688.115018</td>\n",
       "      <td>318.001062</td>\n",
       "      <td>364.289816</td>\n",
       "      <td>396.528806</td>\n",
       "      <td>595.169517</td>\n",
       "      <td>854.392727</td>\n",
       "      <td>1452.072937</td>\n",
       "      <td>38.228612</td>\n",
       "      <td>17.666726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29.0</td>\n",
       "      <td>856.971367</td>\n",
       "      <td>447.237946</td>\n",
       "      <td>407.781924</td>\n",
       "      <td>644.993945</td>\n",
       "      <td>778.963681</td>\n",
       "      <td>931.551413</td>\n",
       "      <td>2892.155719</td>\n",
       "      <td>30.606120</td>\n",
       "      <td>15.972784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28.0</td>\n",
       "      <td>807.836514</td>\n",
       "      <td>201.114395</td>\n",
       "      <td>398.674236</td>\n",
       "      <td>690.055645</td>\n",
       "      <td>776.155292</td>\n",
       "      <td>955.202393</td>\n",
       "      <td>1134.044482</td>\n",
       "      <td>29.919871</td>\n",
       "      <td>7.448681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.0</td>\n",
       "      <td>617.701692</td>\n",
       "      <td>199.764084</td>\n",
       "      <td>306.395820</td>\n",
       "      <td>491.979808</td>\n",
       "      <td>583.518703</td>\n",
       "      <td>729.953542</td>\n",
       "      <td>992.863412</td>\n",
       "      <td>29.414366</td>\n",
       "      <td>9.512575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>698.599916</td>\n",
       "      <td>233.507344</td>\n",
       "      <td>327.909468</td>\n",
       "      <td>520.261183</td>\n",
       "      <td>680.924522</td>\n",
       "      <td>838.516313</td>\n",
       "      <td>1281.811521</td>\n",
       "      <td>23.286664</td>\n",
       "      <td>7.783578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.0</td>\n",
       "      <td>645.793039</td>\n",
       "      <td>250.922358</td>\n",
       "      <td>331.608956</td>\n",
       "      <td>493.155046</td>\n",
       "      <td>603.481091</td>\n",
       "      <td>754.733699</td>\n",
       "      <td>1413.201461</td>\n",
       "      <td>20.832034</td>\n",
       "      <td>8.094270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          distance_meters                                                     \\\n",
       "                    count         mean          std         min          25%   \n",
       "edge_name                                                                      \n",
       "8                     7.0  2090.554160  1012.626402  892.001535  1531.809875   \n",
       "11                    6.0  1023.297599   481.567731  488.703237   802.324197   \n",
       "12                   28.0  1308.175857   647.051661  688.576920   913.412813   \n",
       "9                    28.0  1305.867872  1197.888734  521.874851   734.917837   \n",
       "3                    17.0   770.150140   338.648770  352.013487   451.634963   \n",
       "10                   30.0  1196.303907   603.975131  452.139405   823.668233   \n",
       "2                    19.0   688.115018   318.001062  364.289816   396.528806   \n",
       "7                    29.0   856.971367   447.237946  407.781924   644.993945   \n",
       "6                    28.0   807.836514   201.114395  398.674236   690.055645   \n",
       "4                    22.0   617.701692   199.764084  306.395820   491.979808   \n",
       "5                    31.0   698.599916   233.507344  327.909468   520.261183   \n",
       "1                    32.0   645.793039   250.922358  331.608956   493.155046   \n",
       "\n",
       "                                                 dist_per_edge  \\\n",
       "                   50%          75%          max                 \n",
       "edge_name                                                        \n",
       "8          1974.282182  2355.866960  3992.241735    348.425693   \n",
       "11          936.687495  1067.317455  1909.310159    204.659520   \n",
       "12         1068.429811  1248.166272  3240.105573     48.450958   \n",
       "9           852.736464  1136.665113  5640.865434     48.365477   \n",
       "3           752.897124   973.579449  1598.601497     48.134384   \n",
       "10          998.498537  1395.535527  3368.128567     41.251859   \n",
       "2           595.169517   854.392727  1452.072937     38.228612   \n",
       "7           778.963681   931.551413  2892.155719     30.606120   \n",
       "6           776.155292   955.202393  1134.044482     29.919871   \n",
       "4           583.518703   729.953542   992.863412     29.414366   \n",
       "5           680.924522   838.516313  1281.811521     23.286664   \n",
       "1           603.481091   754.733699  1413.201461     20.832034   \n",
       "\n",
       "          dist_per_edge_std  \n",
       "                             \n",
       "edge_name                    \n",
       "8                168.771067  \n",
       "11                96.313546  \n",
       "12                23.964876  \n",
       "9                 44.366249  \n",
       "3                 21.165548  \n",
       "10                20.826729  \n",
       "2                 17.666726  \n",
       "7                 15.972784  \n",
       "6                  7.448681  \n",
       "4                  9.512575  \n",
       "5                  7.783578  \n",
       "1                  8.094270  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_dist_grouped = edges_dist_grouped.assign(dist_per_edge = edges_dist_grouped['distance_meters']['mean'] / (edges_dist_grouped['distance_meters']['count'] - 1) )\n",
    "edges_dist_grouped = edges_dist_grouped.assign(dist_per_edge_std = edges_dist_grouped['distance_meters']['std'] / (edges_dist_grouped['distance_meters']['count'] - 1) )\n",
    "edges_dist_grouped.sort_values('dist_per_edge', ascending = False).iloc[1:,:] # I removed the first line because it was useless (R line, only two stations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's clear line 8 is the line with the highest mean distance between stations. \n",
    "\n",
    "To have in mind the number of stations and the variability of the values, I plotted a box-plot and calculated the mean distance between stations by edge (a pair of stations connected).\n",
    "\n",
    "This last part reveals that line 8 is again the line whose stations are further from each other. We can find line 11 in the second place, with a mean distance per edge (pair of connected stations) of 204.66 meters (+/- 96.31 meters)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look now at travel times (in seconds):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28.0</td>\n",
       "      <td>359.357143</td>\n",
       "      <td>51.330602</td>\n",
       "      <td>306.0</td>\n",
       "      <td>327.25</td>\n",
       "      <td>346.5</td>\n",
       "      <td>372.00</td>\n",
       "      <td>494.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>348.142857</td>\n",
       "      <td>79.980057</td>\n",
       "      <td>246.0</td>\n",
       "      <td>305.50</td>\n",
       "      <td>338.0</td>\n",
       "      <td>375.00</td>\n",
       "      <td>492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28.0</td>\n",
       "      <td>340.535714</td>\n",
       "      <td>104.540080</td>\n",
       "      <td>248.0</td>\n",
       "      <td>276.75</td>\n",
       "      <td>296.0</td>\n",
       "      <td>365.75</td>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.0</td>\n",
       "      <td>331.000000</td>\n",
       "      <td>62.973010</td>\n",
       "      <td>278.0</td>\n",
       "      <td>303.75</td>\n",
       "      <td>311.0</td>\n",
       "      <td>325.00</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29.0</td>\n",
       "      <td>311.206897</td>\n",
       "      <td>87.650434</td>\n",
       "      <td>218.0</td>\n",
       "      <td>254.00</td>\n",
       "      <td>275.0</td>\n",
       "      <td>346.00</td>\n",
       "      <td>608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.0</td>\n",
       "      <td>293.968750</td>\n",
       "      <td>37.722659</td>\n",
       "      <td>243.0</td>\n",
       "      <td>273.25</td>\n",
       "      <td>283.5</td>\n",
       "      <td>305.00</td>\n",
       "      <td>402.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30.0</td>\n",
       "      <td>288.333333</td>\n",
       "      <td>64.380514</td>\n",
       "      <td>215.0</td>\n",
       "      <td>249.50</td>\n",
       "      <td>272.0</td>\n",
       "      <td>305.50</td>\n",
       "      <td>535.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>1.0</td>\n",
       "      <td>286.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.0</td>\n",
       "      <td>286.00</td>\n",
       "      <td>286.0</td>\n",
       "      <td>286.00</td>\n",
       "      <td>286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28.0</td>\n",
       "      <td>285.732143</td>\n",
       "      <td>62.409413</td>\n",
       "      <td>205.0</td>\n",
       "      <td>250.25</td>\n",
       "      <td>265.0</td>\n",
       "      <td>290.00</td>\n",
       "      <td>470.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>273.193548</td>\n",
       "      <td>32.588770</td>\n",
       "      <td>215.0</td>\n",
       "      <td>252.50</td>\n",
       "      <td>269.0</td>\n",
       "      <td>280.00</td>\n",
       "      <td>377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.0</td>\n",
       "      <td>257.705882</td>\n",
       "      <td>50.671447</td>\n",
       "      <td>205.0</td>\n",
       "      <td>215.00</td>\n",
       "      <td>257.0</td>\n",
       "      <td>284.00</td>\n",
       "      <td>398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.0</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>38.961519</td>\n",
       "      <td>209.0</td>\n",
       "      <td>229.25</td>\n",
       "      <td>246.5</td>\n",
       "      <td>276.00</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.0</td>\n",
       "      <td>249.631579</td>\n",
       "      <td>44.491211</td>\n",
       "      <td>191.0</td>\n",
       "      <td>215.00</td>\n",
       "      <td>242.0</td>\n",
       "      <td>277.50</td>\n",
       "      <td>356.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean         std    min     25%    50%     75%    max\n",
       "edge_name                                                                    \n",
       "12          28.0  359.357143   51.330602  306.0  327.25  346.5  372.00  494.0\n",
       "8            7.0  348.142857   79.980057  246.0  305.50  338.0  375.00  492.0\n",
       "9           28.0  340.535714  104.540080  248.0  276.75  296.0  365.75  656.0\n",
       "11           6.0  331.000000   62.973010  278.0  303.75  311.0  325.00  455.0\n",
       "7           29.0  311.206897   87.650434  218.0  254.00  275.0  346.00  608.0\n",
       "1           32.0  293.968750   37.722659  243.0  273.25  283.5  305.00  402.0\n",
       "10          30.0  288.333333   64.380514  215.0  249.50  272.0  305.50  535.0\n",
       "R            1.0  286.000000         NaN  286.0  286.00  286.0  286.00  286.0\n",
       "6           28.0  285.732143   62.409413  205.0  250.25  265.0  290.00  470.5\n",
       "5           31.0  273.193548   32.588770  215.0  252.50  269.0  280.00  377.0\n",
       "3           17.0  257.705882   50.671447  205.0  215.00  257.0  284.00  398.0\n",
       "4           22.0  256.000000   38.961519  209.0  229.25  246.5  276.00  359.0\n",
       "2           19.0  249.631579   44.491211  191.0  215.00  242.0  277.50  356.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_time_grouped = edges_pd[['edge_name', 'travel_seconds']].groupby(by = 'edge_name').describe().iloc[[0, 4, 5, 6, 7, 8, 9, 10, 11, 1, 2, 3, 12],:]\n",
    "edges_time_grouped['travel_seconds'].sort_values('mean', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7Oze0Ghie173u9f9q+7rrXn+GZwNtdeLEySTr93eB41/fxw+MhjN90NTPdM03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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9e01da8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "sns.boxplot(data=edges_pd, x = 'edge_name', y = 'travel_seconds', order = metro_order, palette = metro_colors).set_title('Travel time by Metro Line (seconds)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Surprise\": Line 12 is the line with the worst mean time travel between stations. It's even higher in the chart than line 8 (remember we said above line 8 is the line with the highest mean distance between stations). \n",
    "\n",
    "It happens kind of the same with other lines. I suggest you to take the time to compare the positions of some Metro lines in the mean distances chart and in the travel times' chart."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe claiming that line 12 is the worst one only by looking at this plot might not be the most rigorous thing to do. Let's do an hypothesis test.\n",
    "\n",
    "Please correct me if I'm wrong, but I think the H0 would be this: Line 12 median travel time == the rest of lines median times. \n",
    "And H1 would be: Line 12 median travel time > the rest of lines median times. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.553960657127611e-26"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if data is normally distributed with a significance level (alpha) of 0.05.\n",
    "# H0: the data is normally distributed\n",
    "\n",
    "import scipy\n",
    "\n",
    "alpha = 0.05\n",
    "\n",
    "statistic, pvalue = scipy.stats.normaltest(edges_pd[['travel_seconds']])\n",
    "\n",
    "pvalue[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because p-value < alpha, the null hypothesis can be rejected: The data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test if variances are equal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Line 12 and Line 9\n",
      "p-value: 0.143899\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 1\n",
      "p-value: 0.384931\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 10\n",
      "p-value: 0.542089\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 6\n",
      "p-value: 0.742608\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 2\n",
      "p-value: 0.975118\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 4\n",
      "p-value: 0.654346\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 5\n",
      "p-value: 0.183422\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 3\n",
      "p-value: 0.653806\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 7\n",
      "p-value: 0.103938\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 8\n",
      "p-value: 0.256217\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 11\n",
      "p-value: 0.942123\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for i in metro_lines_to_use:\n",
    "    _, pvalue = scipy.stats.levene(edges_pd[edges_pd.edge_name == '12'][['travel_seconds']],\n",
    "                             edges_pd[edges_pd.edge_name == i][['travel_seconds']])\n",
    "    \n",
    "    print('Line 12 and Line %s' % i)\n",
    "    print('p-value: %f' % pvalue)\n",
    "    print('p-value < %f ?: %r' % (alpha, pvalue[0] < alpha))\n",
    "    print('-----------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That means we cannot perform some types of hypothesis tests because some of them assume the data is normally distributed (among other conditions, like the equality of variances among groups. We tested that on the previous chunk and for every pair of groups we cannot reject nor accept H0 (that variances are equal)).\n",
    "\n",
    "Thus, we're gonna apply a non-parametric method that allows to perform hypothesis test with more relaxed conditions. \n",
    "\n",
    "In this case we did a Mann-Whitney test to check if medians between groups are equal (H0) or Line 12 median is greater than other group median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metro_lines_to_use = edges_pd.edge_name.unique()[:-1]\n",
    "metro_lines_to_use = np.delete(metro_lines_to_use, 8) # Removing 'R' line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Line 12 and Line 9\n",
      "p-value: 0.001466\n",
      "p-value < 0.050000 ?: True\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 1\n",
      "p-value: 0.000000\n",
      "p-value < 0.050000 ?: True\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 10\n",
      "p-value: 0.000001\n",
      "p-value < 0.050000 ?: True\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 6\n",
      "p-value: 0.000001\n",
      "p-value < 0.050000 ?: True\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 2\n",
      "p-value: 0.000000\n",
      "p-value < 0.050000 ?: True\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 4\n",
      "p-value: 0.000000\n",
      "p-value < 0.050000 ?: True\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 5\n",
      "p-value: 0.000000\n",
      "p-value < 0.050000 ?: True\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 3\n",
      "p-value: 0.000000\n",
      "p-value < 0.050000 ?: True\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 7\n",
      "p-value: 0.000389\n",
      "p-value < 0.050000 ?: True\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 8\n",
      "p-value: 0.288776\n",
      "p-value < 0.050000 ?: False\n",
      "-----------------------------------------------\n",
      "Line 12 and Line 11\n",
      "p-value: 0.014189\n",
      "p-value < 0.050000 ?: True\n",
      "-----------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for i in metro_lines_to_use:\n",
    "    _, pvalue = scipy.stats.mannwhitneyu(edges_pd[edges_pd.edge_name == '12'][['travel_seconds']],\n",
    "                             edges_pd[edges_pd.edge_name == i][['travel_seconds']], \n",
    "                             use_continuity=False, alternative='greater')\n",
    "    \n",
    "    print('Line 12 and Line %s' % i)\n",
    "    print('p-value: %f' % pvalue)\n",
    "    print('p-value < %f ?: %r' % (alpha, pvalue < alpha))\n",
    "    print('-----------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the Line 12 and Line 8 case. The null hypothesis cannot be rejected in favor of the alternative hypothesis. If you see the box-plot, it's hard to say if the medians are equal or not, they are too close but we can't be sure. This test proves then that we can't say Line 12 has the worst travel times. However, I must say that, from my experience, using Metro Sur (Line 12) can be a real pain in the ass because it's too slow and I can't say the same about Line 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm gonna plot speed (meters / second) vs distance to check the linear correlation between them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9d1e5cf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XoWa4LEmSJEmSdFiN6FpezYBZUttZrGVjMcyWJUmSJEmSXloju5ZXM2CW1FZmqoFZN/Nb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og7yuhUnt6BJL0DCc4nK8Petjo/Pdub6NnezD3NoWt+3uWtMC6kc7z46hmCXo2cUfnanwz0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\nF46tORb2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9d1d672b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "sns.lmplot(x = 'distance_meters', y = 'speed_ms', col = 'edge_name', data = edges_pd, col_wrap = 4, size = 5, \n",
    "           hue = 'edge_name', \n",
    "           hue_order = metro_order,\n",
    "           palette = metro_colors, \n",
    "           col_order = metro_order)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ymHDiUOYAAAAw6YRCQdm2LcfJmY6CUQqFQtq+/Qnt2LFVsVgsv+52u/X446v0yCOPqqGh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\nTWT3NLT+Pho/voFZs44bcr+50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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9d2153f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "sns.regplot(data=edges_pd, x = 'distance_meters', y = 'speed_ms', fit_reg = True, scatter = True, scatter_kws={'facecolors': metro_colors}, line_kws={'color': 'black'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems to me (and please correct me if I'm wrong) that if you look at every Metro Line individually, for almost every point, the following statement applies: the further two stations are the faster the Metro is (except in some cases for lines 6 and 7).\n",
    "\n",
    "But if we plot all the observations, the previous statement doesn't apply, specially in those cases we can observe better on the plot (those outside the main cloud at the bottom left).\n",
    "\n",
    "For non-compliant cases from lines 6 and 7 there's maybe a reason, but I'm not a train expert so if you are a Madrid Metro expert I'd be glad to hear your opinion."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [intro]",
   "language": "python",
   "name": "Python [intro]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}