1.1-CTA_data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "# https://www.transitchicago.com/data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "l_stops_raw = pd.read_csv(\"cta_data/CTA_-_System_Information_-_List_of__L__Stops_-_Map.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STOP_ID</th>\n",
       "      <th>STOP_NAME</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>MAP_ID</th>\n",
       "      <th>ADA</th>\n",
       "      <th>RED</th>\n",
       "      <th>BLUE</th>\n",
       "      <th>G</th>\n",
       "      <th>BRN</th>\n",
       "      <th>P</th>\n",
       "      <th>Pexp</th>\n",
       "      <th>Y</th>\n",
       "      <th>Pnk</th>\n",
       "      <th>O</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30162</td>\n",
       "      <td>18th (54th/Cermak-bound)</td>\n",
       "      <td>18th</td>\n",
       "      <td>40830</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>41.857908</td>\n",
       "      <td>-87.669147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30161</td>\n",
       "      <td>18th (Loop-bound)</td>\n",
       "      <td>18th</td>\n",
       "      <td>40830</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>41.857908</td>\n",
       "      <td>-87.669147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30022</td>\n",
       "      <td>35th/Archer (Loop-bound)</td>\n",
       "      <td>35th/Archer</td>\n",
       "      <td>40120</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>41.829353</td>\n",
       "      <td>-87.680622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30023</td>\n",
       "      <td>35th/Archer (Midway-bound)</td>\n",
       "      <td>35th/Archer</td>\n",
       "      <td>40120</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>41.829353</td>\n",
       "      <td>-87.680622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30214</td>\n",
       "      <td>35-Bronzeville-IIT (63rd-bound)</td>\n",
       "      <td>35th-Bronzeville-IIT</td>\n",
       "      <td>41120</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>41.831677</td>\n",
       "      <td>-87.625826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>30129</td>\n",
       "      <td>Western (O'Hare Branch) (O'Hare-bound)</td>\n",
       "      <td>Western</td>\n",
       "      <td>40670</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>41.916157</td>\n",
       "      <td>-87.687364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>30385</td>\n",
       "      <td>Wilson (Loop-bound)</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>40540</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>41.964273</td>\n",
       "      <td>-87.657588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>30106</td>\n",
       "      <td>Wilson (95th-bound)</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>40540</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>41.964273</td>\n",
       "      <td>-87.657588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>30105</td>\n",
       "      <td>Wilson (Howard-bound)</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>40540</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>41.964273</td>\n",
       "      <td>-87.657588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>30386</td>\n",
       "      <td>Wilson (Linden-bound)</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>40540</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>41.964273</td>\n",
       "      <td>-87.657588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     STOP_ID                               STOP_NAME          STATION_NAME  \\\n",
       "0      30162                18th (54th/Cermak-bound)                  18th   \n",
       "1      30161                       18th (Loop-bound)                  18th   \n",
       "2      30022                35th/Archer (Loop-bound)           35th/Archer   \n",
       "3      30023              35th/Archer (Midway-bound)           35th/Archer   \n",
       "4      30214         35-Bronzeville-IIT (63rd-bound)  35th-Bronzeville-IIT   \n",
       "..       ...                                     ...                   ...   \n",
       "295    30129  Western (O'Hare Branch) (O'Hare-bound)               Western   \n",
       "296    30385                     Wilson (Loop-bound)                Wilson   \n",
       "297    30106                     Wilson (95th-bound)                Wilson   \n",
       "298    30105                   Wilson (Howard-bound)                Wilson   \n",
       "299    30386                   Wilson (Linden-bound)                Wilson   \n",
       "\n",
       "     MAP_ID   ADA    RED   BLUE      G    BRN      P   Pexp      Y    Pnk  \\\n",
       "0     40830  True  False  False  False  False  False  False  False   True   \n",
       "1     40830  True  False  False  False  False  False  False  False   True   \n",
       "2     40120  True  False  False  False  False  False  False  False  False   \n",
       "3     40120  True  False  False  False  False  False  False  False  False   \n",
       "4     41120  True  False  False   True  False  False  False  False  False   \n",
       "..      ...   ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "295   40670  True  False   True  False  False  False  False  False  False   \n",
       "296   40540  True  False  False  False  False  False   True  False  False   \n",
       "297   40540  True   True  False  False  False  False  False  False  False   \n",
       "298   40540  True   True  False  False  False  False  False  False  False   \n",
       "299   40540  True  False  False  False  False  False   True  False  False   \n",
       "\n",
       "         O        lat       long  \n",
       "0    False  41.857908 -87.669147  \n",
       "1    False  41.857908 -87.669147  \n",
       "2     True  41.829353 -87.680622  \n",
       "3     True  41.829353 -87.680622  \n",
       "4    False  41.831677 -87.625826  \n",
       "..     ...        ...        ...  \n",
       "295  False  41.916157 -87.687364  \n",
       "296  False  41.964273 -87.657588  \n",
       "297  False  41.964273 -87.657588  \n",
       "298  False  41.964273 -87.657588  \n",
       "299  False  41.964273 -87.657588  \n",
       "\n",
       "[300 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert coords to a readable dict\n",
    "locations = l_stops_raw.Location.apply(lambda x: x[1:-1].split(\",\"))\n",
    "coords = pd.DataFrame(locations.tolist(), columns=['lat', 'long']).astype('float')\n",
    "\n",
    "l_stops = l_stops_raw.drop(['Location'], axis=1)\n",
    "l_stops = l_stops.join(coords)\n",
    "\n",
    "l_stops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_coords = {}\n",
    "for i in coords.index:\n",
    "    node_coords[l_stops['STOP_ID'][i]] = (coords.iloc[i].long, \n",
    "                      coords.iloc[i].lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in stop colors\n",
    "stop_colors = l_stops[['STOP_ID', 'RED', 'BLUE', 'G', 'BRN', 'P', 'Pexp', 'Y','Pnk', 'O']]\n",
    "\n",
    "colors_dict = {}\n",
    "for i in stop_colors.index:\n",
    "    colors_list = []\n",
    "    for c in stop_colors.columns.astype('str'):\n",
    "        if stop_colors[c][i] == True:\n",
    "            colors_list.append(c)\n",
    "    if len(colors_list) == 0:\n",
    "        colors_list.append('BLK')\n",
    "    colors_dict[l_stops['STOP_ID'][i]] = colors_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "\n",
    "G.add_nodes_from(node_coords.keys())\n",
    "color_conversion = {'RED':'red', 'BLUE':'blue', 'G':'green', 'BRN':'brown', 'P':'purple', 'Pexp':'purple', 'Y':'yellow','Pnk':'pink', 'O':'orange', 'BLK':'k'}\n",
    "for n in G.nodes:\n",
    "    G.nodes[n]['pos'] = node_coords[n]\n",
    "    G.nodes[n]['colors'] = colors_dict[n]\n",
    "    G.nodes[n]['node_color'] = color_conversion[colors_dict[n][0]]\n",
    "#     print(G.nodes[n]['node_color'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=150)\n",
    "# [G.nodes[i]['color'] for i in G.nodes]\n",
    "nx.draw(G, node_coords, ax=ax, alpha=0.4, node_size=10, node_color=[G.nodes[i]['node_color'] for i in G.nodes])\n",
    "plt.axis('equal')\n",
    "plt.grid(which='both')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:alyssa] *",
   "language": "python",
   "name": "conda-env-alyssa-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}