Creating hourly CTA transit data from daily CTA transit data .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 matplotlib as mpl\n",
    "from matplotlib import cm\n",
    "import matplotlib.dates as mdates\n",
    "import datetime\n",
    "import holidays\n",
    "import folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly import __version__\n",
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot\n",
    "init_notebook_mode(connected=True)\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import hourly pickups by Chicago Side\n",
    "df_sides = pd.read_csv(\"hour_pickups_sides_final.csv\")\n",
    "df_sides[\"StartTime\"] = pd.to_datetime(df_sides[\"StartTime\"], \n",
    "                                       format=\"%d/%m/%Y %H:%M\")\n",
    "# set index\n",
    "df_sides.set_index(\"StartTime\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FNS</th>\n",
       "      <th>Ohara</th>\n",
       "      <th>NWS</th>\n",
       "      <th>NS</th>\n",
       "      <th>WS</th>\n",
       "      <th>CEN</th>\n",
       "      <th>SS</th>\n",
       "      <th>SWS</th>\n",
       "      <th>FSWS</th>\n",
       "      <th>FSES</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>season</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StartTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2018-11-01 00:00:00</th>\n",
       "      <td>70</td>\n",
       "      <td>15</td>\n",
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       "      <td>134</td>\n",
       "      <td>141</td>\n",
       "      <td>48</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 01:00:00</th>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>200</td>\n",
       "      <td>94</td>\n",
       "      <td>103</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     FNS  Ohara  NWS   NS   WS  CEN  SS  SWS  FSWS  FSES  \\\n",
       "StartTime                                                                  \n",
       "2018-11-01 00:00:00   70     15   20  174  134  141  48   25    17    12   \n",
       "2018-11-01 01:00:00   46      5    9  200   94  103  22   16    17     7   \n",
       "\n",
       "                     is_holiday  is_weekend  season  Total  \n",
       "StartTime                                                   \n",
       "2018-11-01 00:00:00           0           0       2    656  \n",
       "2018-11-01 01:00:00           0           0       2    519  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sides.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chicago Transit Authority (CTA) station turnstile data can help to guestimate train frequency. There are two types of subway entrances that have different demand patterns\n",
    "\n",
    "- Residential neighborhoods\n",
    "- Commercial neighborhoods\n",
    "\n",
    "Step1: count trains in each neighborhood.\n",
    "\n",
    "Step2: simulate CTA train frequency based on turnstile entry data\n",
    "- multiple business demand with number of stations_frequency of step\n",
    "- you get a dataframewith simulated CTA train frequency by neighborhood.\n",
    "Step3: add transit data to Chicago Side\n",
    "Step4: go trhough each neighborhood with CTA data.\n",
    "\n",
    "for i in neighborhood.keys():\n",
    "\n",
    "    predictions = LSTMpredict()\n",
    "    rmse = rmse(predictions, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cta = pd.read_csv(\"cta daily ridership by station nov2018-2019.csv\")\n",
    "df_cta[\"date\"] = pd.to_datetime(df_cta[\"date\"], format=\"%d/%m/%Y\")\n",
    "df_cta.set_index(\"date\", inplace=True)\n",
    "df_cta = df_cta[[\"stationname\", \"rides\"]]\n",
    "# df_cta.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stationname</th>\n",
       "      <th>rides</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>Austin-Forest Park</td>\n",
       "      <td>2248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>Austin-Forest Park</td>\n",
       "      <td>1983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>Austin-Forest Park</td>\n",
       "      <td>837.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>Austin-Forest Park</td>\n",
       "      <td>604.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>Austin-Forest Park</td>\n",
       "      <td>2161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>Washington/Wabash</td>\n",
       "      <td>7871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-28</th>\n",
       "      <td>Washington/Wabash</td>\n",
       "      <td>4474.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29</th>\n",
       "      <td>Washington/Wabash</td>\n",
       "      <td>3750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>Washington/Wabash</td>\n",
       "      <td>7885.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>Washington/Wabash</td>\n",
       "      <td>6064.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61010 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   stationname   rides\n",
       "date                                  \n",
       "2018-11-01  Austin-Forest Park  2248.0\n",
       "2018-11-02  Austin-Forest Park  1983.0\n",
       "2018-11-03  Austin-Forest Park   837.0\n",
       "2018-11-04  Austin-Forest Park   604.0\n",
       "2018-11-05  Austin-Forest Park  2161.0\n",
       "...                        ...     ...\n",
       "2019-12-27   Washington/Wabash  7871.0\n",
       "2019-12-28   Washington/Wabash  4474.0\n",
       "2019-12-29   Washington/Wabash  3750.0\n",
       "2019-12-30   Washington/Wabash  7885.0\n",
       "2019-12-31   Washington/Wabash  6064.0\n",
       "\n",
       "[61010 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cta_ridership_dic = {}\n",
    "\n",
    "for i in df_cta.stationname.unique():\n",
    "    rides = df_cta[\"rides\"][df_cta[\"stationname\"] == i]\n",
    "    cta_ridership_dic[i] = rides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Austin-Forest Park', 'Harlem-Lake', 'Pulaski-Lake', 'Quincy/Wells', 'Davis', \"Belmont-O'Hare\", 'Jackson/Dearborn', 'Sheridan', 'Damen-Brown', 'Morse', '35th/Archer', '51st', 'Dempster-Skokie', 'Pulaski-Cermak', 'LaSalle/Van Buren', 'Ashland-Lake', 'Oak Park-Forest Park', 'Sox-35th-Dan Ryan', 'Randolph/Wabash', 'Damen-Cermak', 'Western-Forest Park', 'Cumberland', '79th', 'Kedzie-Homan-Forest Park', 'State/Lake', 'Main', 'Central-Lake', 'Ashland/63rd', 'Indiana', 'Western-Orange', 'Division/Milwaukee', 'Grand/State', 'Berwyn', 'UIC-Halsted', 'Southport', 'Washington/Dearborn', 'Clark/Lake', 'Forest Park', 'Noyes', 'Cicero-Cermak', 'Clinton-Forest Park', 'California-Cermak', '95th/Dan Ryan', 'Merchandise Mart', 'Racine', 'Cicero-Lake', 'Grand/Milwaukee', 'Garfield-South Elevated', 'Foster', 'Diversey', 'Wilson', \"Irving Park-O'Hare\", 'Jackson/State', 'California/Milwaukee', '54th/Cermak', 'Damen/Milwaukee', 'Kostner', 'Ridgeland', 'Clark/Division', 'North/Clybourn', 'Armitage', 'Western/Milwaukee', 'Adams/Wabash', 'Dempster', 'Laramie', 'Chicago/Franklin', 'East 63rd-Cottage Grove', 'Washington/Wells', 'Western-Cermak', \"Harlem-O'Hare\", 'Granville', 'Lawrence', 'Central Park', 'Monroe/Dearborn', 'Sedgwick', 'Medical Center', 'Rosemont', '18th', 'South Boulevard', 'Library', 'Francisco', 'Thorndale', \"O'Hare Airport\", 'Howard', '63rd-Dan Ryan', 'Pulaski-Forest Park', 'Pulaski-Orange', 'Midway Airport', 'Halsted/63rd', 'Cicero-Forest Park', 'Harlem-Forest Park', '69th', 'Cermak-Chinatown', 'Rockwell', 'Logan Square', 'Polk', 'Kedzie-Cermak', 'Linden', 'Ashland-Orange', 'Kedzie-Lake', '47th-South Elevated', 'Monroe/State', '35-Bronzeville-IIT', 'Halsted-Orange', 'King Drive', 'Kedzie-Midway', 'Clinton-Lake', 'Garfield-Dan Ryan', 'Kedzie-Brown', 'Jarvis', 'Argyle', 'Wellington', 'Fullerton', '47th-Dan Ryan', \"Addison-O'Hare\", 'Central-Evanston', 'Austin-Lake', '43rd', 'Jefferson Park', 'Kimball', 'Loyola', 'Paulina', 'Belmont-North Main', \"Montrose-O'Hare\", 'LaSalle', 'Oak Park-Lake', 'California-Lake', 'Bryn Mawr', 'Roosevelt', 'Chicago/Milwaukee', 'Addison-North Main', '87th', 'Addison-Brown', 'Chicago/State', 'Irving Park-Brown', 'Western-Brown', 'Harrison', 'Montrose-Brown', 'Morgan-Lake', 'Lake/State', 'Conservatory', 'Oakton-Skokie', 'Cermak-McCormick Place', 'Washington/Wabash'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cta_ridership_dic.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Austin-Forest Park</th>\n",
       "      <th>Harlem-Lake</th>\n",
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       "      <th>2018-11-01</th>\n",
       "      <td>2248.0</td>\n",
       "      <td>4021.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>3734.0</td>\n",
       "      <td>5110.0</td>\n",
       "      <td>8130.0</td>\n",
       "      <td>5480.0</td>\n",
       "      <td>3138.0</td>\n",
       "      <td>4813.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3656.0</td>\n",
       "      <td>4287.0</td>\n",
       "      <td>4806.0</td>\n",
       "      <td>2857.0</td>\n",
       "      <td>3737.0</td>\n",
       "      <td>22982.0</td>\n",
       "      <td>974.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>1809.0</td>\n",
       "      <td>10805.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>1983.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>7627.0</td>\n",
       "      <td>3971.0</td>\n",
       "      <td>4945.0</td>\n",
       "      <td>7110.0</td>\n",
       "      <td>5281.0</td>\n",
       "      <td>2822.0</td>\n",
       "      <td>4688.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3243.0</td>\n",
       "      <td>4074.0</td>\n",
       "      <td>4041.0</td>\n",
       "      <td>2723.0</td>\n",
       "      <td>3706.0</td>\n",
       "      <td>22354.0</td>\n",
       "      <td>933.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>2301.0</td>\n",
       "      <td>10869.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 144 columns</p>\n",
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      ],
      "text/plain": [
       "            Austin-Forest Park  Harlem-Lake  Pulaski-Lake  Quincy/Wells  \\\n",
       "date                                                                      \n",
       "2018-11-01              2248.0       4021.0        1575.0        8530.0   \n",
       "2018-11-02              1983.0       3850.0        1442.0        7627.0   \n",
       "\n",
       "             Davis  Belmont-O'Hare  Jackson/Dearborn  Sheridan  Damen-Brown  \\\n",
       "date                                                                          \n",
       "2018-11-01  3734.0          5110.0            8130.0    5480.0       3138.0   \n",
       "2018-11-02  3971.0          4945.0            7110.0    5281.0       2822.0   \n",
       "\n",
       "             Morse  ...  Irving Park-Brown  Western-Brown  Harrison  \\\n",
       "date                ...                                               \n",
       "2018-11-01  4813.0  ...             3656.0         4287.0    4806.0   \n",
       "2018-11-02  4688.0  ...             3243.0         4074.0    4041.0   \n",
       "\n",
       "            Montrose-Brown  Morgan-Lake  Lake/State  Conservatory  \\\n",
       "date                                                                \n",
       "2018-11-01          2857.0       3737.0     22982.0         974.0   \n",
       "2018-11-02          2723.0       3706.0     22354.0         933.0   \n",
       "\n",
       "            Oakton-Skokie  Cermak-McCormick Place  Washington/Wabash  \n",
       "date                                                                  \n",
       "2018-11-01         1140.0                  1809.0            10805.0  \n",
       "2018-11-02         1002.0                  2301.0            10869.0  \n",
       "\n",
       "[2 rows x 144 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_cta_rides = pd.DataFrame(cta_ridership_dic)\n",
    "# daily_cta_rides.to_csv(\"daily_cta_rides.csv\")\n",
    "daily_cta_rides.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 types of areas: business and residential.\n",
    "2 types of days: business days and weekends.\n",
    "Total 4 types of demand patterns:\n",
    "- business @ business days\n",
    "- business @ weekends\n",
    "- residential @ business days\n",
    "- residential @ weekends\n",
    "\n",
    "Following subway ridership patterns derived from Chen et al. (2009), business areas have 2 daily commute ridership peaks. Residential areas have later afternoon commute peak.\n",
    "\n",
    "Chen, C., Chen. J. & Barry, J. (2009). Diurnal pattern of transit ridership: a case study of the New York City\n",
    "subway system. Journal of Transport Geography. [online]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "business_business_days = {0:0.017, 1:0.017, 2:0.008, 3:0.008, 4:0.004, \n",
    "                          5:0.004, 6:0.008, 7:0.021, 8:0.051, 9:0.042,\n",
    "                          10:0.042, 11:0.038, 12:0.034, 13:0.038, 14:0.051, \n",
    "                          15:0.068, 16:0.084, 17:0.11, 18:0.101, 19:0.084,\n",
    "                          20:0.059, 21:0.051, 22:0.034, 23:0.025}\n",
    "\n",
    "business_weekend_days = {0:0.015, 1:0.015, 2:0.008, 3:0.008, 4:0.008, 5:0.008, \n",
    "                         6:0.015, 7:0.023, 8:0.038, 9:0.046, 10:0.054, \n",
    "                         11:0.077, 12:0.092, 13:0.092, 14:0.092, 15:0.092,\n",
    "                         16:0.062, 17:0.054, 18:0.054, 19:0.054, 20:0.031, \n",
    "                         21:0.031, 22:0.015, 23:0.015}\n",
    "\n",
    "residential_business_days = {0:0.011, 1:0.011, 2:0.005, 3:0.005, 4:0.005,\n",
    "                                5:0.005, 6:0.22, 7:0.054, 8:0.098, 9:0.098,\n",
    "                                10:0.087, 11:0.065, 12:0.043, 13:0.033, 14:0.033,\n",
    "                                15:0.033, 16:0.065, 17:0.098, 18:0.087, \n",
    "                                19:0.054, 20:0.033, 21:0.022, 22:0.022, 23:0.011}\n",
    "\n",
    "residential_weekend_days = {0:0.014, 1:0.007, 2:0.007, 3:0.007, 4:0.007,\n",
    "                            5:0.007, 6:0.014, 7:0.02, 8:0.027, 9:0.034,\n",
    "                            10:0.041, 11:0.068, 12:0.082, 13:0.054, 14:0.041,\n",
    "                            15:0.054, 16:0.082, 17:0.109, 18:0.102, \n",
    "                            19:0.082, 20:0.054, 21:0.041, 22:0.027, 23:0.02}\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Business district stations: Downtown inset\n",
    "- Clinton-Lake, Clark/Lake, State/Lake, Washington/Wabash, Adams/Wabash, Library, LaSalle/Van Buren, Laselle, Quincy/Wells, Washington/Wells, Jackson/State, Jackson/Dearborn, Monroe/Dearborn, Monroe/State, Washington/Wells, Washington/Dearborn, State/Lake, Grand/State"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business district stations: Create hourly values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "business @ business days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_business = pd.read_csv(\"daily_cta_rides_businessstations.csv\")\n",
    "daily_cta_rides_business[\"date\"] = pd.to_datetime(daily_cta_rides_business[\"date\"], format=\"%d/%m/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_business[\"day_of_week\"] = pd.DatetimeIndex(daily_cta_rides_business[\"date\"]).dayofweek\n",
    "daily_cta_rides_business.set_index(\"date\", inplace=True)\n",
    "weekend = daily_cta_rides_business[\"day_of_week\"] > 4\n",
    "normal = daily_cta_rides_business[\"day_of_week\"] < 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "            Clinton-Lake  Clark/Lake  State/Lake  Washington/Wabash  \\\n",
       "date                                                                  \n",
       "2018-11-01          4821       22774       13711              10805   \n",
       "2018-11-02          4512       21978       12562              10869   \n",
       "\n",
       "            Adams/Wabash  Library  LaSalle/Van Buren  LaSalle  Quincy/Wells  \\\n",
       "date                                                                          \n",
       "2018-11-01          8504     4194               3258     3488          8530   \n",
       "2018-11-02          7407     3223               2886     3095          7627   \n",
       "\n",
       "            Washington/Wells  Jackson/Dearborn  Jackson/State  \\\n",
       "date                                                            \n",
       "2018-11-01              8903              8130          11336   \n",
       "2018-11-02              8062              7110           8328   \n",
       "\n",
       "            Monroe/Dearborn  Monroe/State  Washington/Dearborn  Lake/State  \\\n",
       "date                                                                         \n",
       "2018-11-01             8755         11051                14335       22982   \n",
       "2018-11-02             8470         10683                14299       22354   \n",
       "\n",
       "            Grand/State  day_of_week  \n",
       "date                                  \n",
       "2018-11-01        11189            3  \n",
       "2018-11-02        12147            4  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_cta_rides_business.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_business_normal = daily_cta_rides_business[normal]\n",
    "daily_cta_rides_business_normal = daily_cta_rides_business_normal.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            Clinton-Lake  Clark/Lake  State/Lake  Washington/Wabash  \\\n",
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       "2018-11-02          4512       21978       12562              10869   \n",
       "\n",
       "            Adams/Wabash  Library  LaSalle/Van Buren  LaSalle  Quincy/Wells  \\\n",
       "date                                                                          \n",
       "2018-11-01          8504     4194               3258     3488          8530   \n",
       "2018-11-02          7407     3223               2886     3095          7627   \n",
       "\n",
       "            Washington/Wells  Jackson/Dearborn  Jackson/State  \\\n",
       "date                                                            \n",
       "2018-11-01              8903              8130          11336   \n",
       "2018-11-02              8062              7110           8328   \n",
       "\n",
       "            Monroe/Dearborn  Monroe/State  Washington/Dearborn  Lake/State  \\\n",
       "date                                                                         \n",
       "2018-11-01             8755         11051                14335       22982   \n",
       "2018-11-02             8470         10683                14299       22354   \n",
       "\n",
       "            Grand/State  \n",
       "date                     \n",
       "2018-11-01        11189  \n",
       "2018-11-02        12147  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_cta_rides_business_normal.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bdist_bday_dist = [business_business_days[key] for key in business_business_days]\n",
    "datetime_bb_index = pd.to_datetime([datetime.datetime(i.year, i.month, i.day, key) for i in daily_cta_rides_business_normal.index for key in business_business_days])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_business_normal_dic = {}\n",
    "\n",
    "for station in daily_cta_rides_business_normal.columns:\n",
    "    hourly_transit = np.outer(daily_cta_rides_business_normal[station], bdist_bday_dist).ravel()\n",
    "    daily_cta_rides_business_normal_dic[station] = hourly_transit\n",
    "\n",
    "df_hourly_cta_rides_business_normal = pd.DataFrame(daily_cta_rides_business_normal_dic, index=datetime_bb_index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>192.712</td>\n",
       "      <td>148.835</td>\n",
       "      <td>187.867</td>\n",
       "      <td>243.695</td>\n",
       "      <td>390.694</td>\n",
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       "      <td>390.694</td>\n",
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       "      <td>26.064</td>\n",
       "      <td>27.904</td>\n",
       "      <td>68.24</td>\n",
       "      <td>71.224</td>\n",
       "      <td>65.04</td>\n",
       "      <td>90.688</td>\n",
       "      <td>70.040</td>\n",
       "      <td>88.408</td>\n",
       "      <td>114.680</td>\n",
       "      <td>183.856</td>\n",
       "      <td>89.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 03:00:00</th>\n",
       "      <td>38.568</td>\n",
       "      <td>182.192</td>\n",
       "      <td>109.688</td>\n",
       "      <td>86.440</td>\n",
       "      <td>68.032</td>\n",
       "      <td>33.552</td>\n",
       "      <td>26.064</td>\n",
       "      <td>27.904</td>\n",
       "      <td>68.24</td>\n",
       "      <td>71.224</td>\n",
       "      <td>65.04</td>\n",
       "      <td>90.688</td>\n",
       "      <td>70.040</td>\n",
       "      <td>88.408</td>\n",
       "      <td>114.680</td>\n",
       "      <td>183.856</td>\n",
       "      <td>89.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 04:00:00</th>\n",
       "      <td>19.284</td>\n",
       "      <td>91.096</td>\n",
       "      <td>54.844</td>\n",
       "      <td>43.220</td>\n",
       "      <td>34.016</td>\n",
       "      <td>16.776</td>\n",
       "      <td>13.032</td>\n",
       "      <td>13.952</td>\n",
       "      <td>34.12</td>\n",
       "      <td>35.612</td>\n",
       "      <td>32.52</td>\n",
       "      <td>45.344</td>\n",
       "      <td>35.020</td>\n",
       "      <td>44.204</td>\n",
       "      <td>57.340</td>\n",
       "      <td>91.928</td>\n",
       "      <td>44.756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Clinton-Lake  Clark/Lake  State/Lake  Washington/Wabash  \\\n",
       "2018-11-01 00:00:00        81.957     387.158     233.087            183.685   \n",
       "2018-11-01 01:00:00        81.957     387.158     233.087            183.685   \n",
       "2018-11-01 02:00:00        38.568     182.192     109.688             86.440   \n",
       "2018-11-01 03:00:00        38.568     182.192     109.688             86.440   \n",
       "2018-11-01 04:00:00        19.284      91.096      54.844             43.220   \n",
       "\n",
       "                     Adams/Wabash  Library  LaSalle/Van Buren  LaSalle  \\\n",
       "2018-11-01 00:00:00       144.568   71.298             55.386   59.296   \n",
       "2018-11-01 01:00:00       144.568   71.298             55.386   59.296   \n",
       "2018-11-01 02:00:00        68.032   33.552             26.064   27.904   \n",
       "2018-11-01 03:00:00        68.032   33.552             26.064   27.904   \n",
       "2018-11-01 04:00:00        34.016   16.776             13.032   13.952   \n",
       "\n",
       "                     Quincy/Wells  Washington/Wells  Jackson/Dearborn  \\\n",
       "2018-11-01 00:00:00        145.01           151.351            138.21   \n",
       "2018-11-01 01:00:00        145.01           151.351            138.21   \n",
       "2018-11-01 02:00:00         68.24            71.224             65.04   \n",
       "2018-11-01 03:00:00         68.24            71.224             65.04   \n",
       "2018-11-01 04:00:00         34.12            35.612             32.52   \n",
       "\n",
       "                     Jackson/State  Monroe/Dearborn  Monroe/State  \\\n",
       "2018-11-01 00:00:00        192.712          148.835       187.867   \n",
       "2018-11-01 01:00:00        192.712          148.835       187.867   \n",
       "2018-11-01 02:00:00         90.688           70.040        88.408   \n",
       "2018-11-01 03:00:00         90.688           70.040        88.408   \n",
       "2018-11-01 04:00:00         45.344           35.020        44.204   \n",
       "\n",
       "                     Washington/Dearborn  Lake/State  Grand/State  \n",
       "2018-11-01 00:00:00              243.695     390.694      190.213  \n",
       "2018-11-01 01:00:00              243.695     390.694      190.213  \n",
       "2018-11-01 02:00:00              114.680     183.856       89.512  \n",
       "2018-11-01 03:00:00              114.680     183.856       89.512  \n",
       "2018-11-01 04:00:00               57.340      91.928       44.756  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hourly_cta_rides_business_normal.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "business @ weekend days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_business_weekend = daily_cta_rides_business[weekend]\n",
    "daily_cta_rides_business_weekend = daily_cta_rides_business_weekend.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Clinton-Lake</th>\n",
       "      <th>Clark/Lake</th>\n",
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       "      <th>Lake/State</th>\n",
       "      <th>Grand/State</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>1403</td>\n",
       "      <td>7258</td>\n",
       "      <td>8905</td>\n",
       "      <td>7237</td>\n",
       "      <td>5156</td>\n",
       "      <td>1901</td>\n",
       "      <td>539</td>\n",
       "      <td>1267</td>\n",
       "      <td>1573</td>\n",
       "      <td>1390</td>\n",
       "      <td>3046</td>\n",
       "      <td>2651</td>\n",
       "      <td>2254</td>\n",
       "      <td>2697</td>\n",
       "      <td>5271</td>\n",
       "      <td>9845</td>\n",
       "      <td>7073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>886</td>\n",
       "      <td>5795</td>\n",
       "      <td>6356</td>\n",
       "      <td>5540</td>\n",
       "      <td>4858</td>\n",
       "      <td>1331</td>\n",
       "      <td>389</td>\n",
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       "      <td>2351</td>\n",
       "      <td>2408</td>\n",
       "      <td>3697</td>\n",
       "      <td>6175</td>\n",
       "      <td>4280</td>\n",
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      "text/plain": [
       "            Clinton-Lake  Clark/Lake  State/Lake  Washington/Wabash  \\\n",
       "date                                                                  \n",
       "2018-11-03          1403        7258        8905               7237   \n",
       "2018-11-04           886        5795        6356               5540   \n",
       "\n",
       "            Adams/Wabash  Library  LaSalle/Van Buren  LaSalle  Quincy/Wells  \\\n",
       "date                                                                          \n",
       "2018-11-03          5156     1901                539     1267          1573   \n",
       "2018-11-04          4858     1331                389     1130          1330   \n",
       "\n",
       "            Washington/Wells  Jackson/Dearborn  Jackson/State  \\\n",
       "date                                                            \n",
       "2018-11-03              1390              3046           2651   \n",
       "2018-11-04               931              2640           1948   \n",
       "\n",
       "            Monroe/Dearborn  Monroe/State  Washington/Dearborn  Lake/State  \\\n",
       "date                                                                         \n",
       "2018-11-03             2254          2697                 5271        9845   \n",
       "2018-11-04             2351          2408                 3697        6175   \n",
       "\n",
       "            Grand/State  \n",
       "date                     \n",
       "2018-11-03         7073  \n",
       "2018-11-04         4280  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_cta_rides_business_weekend.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "bdist_weekenday_dist = [business_weekend_days[key] for key in business_weekend_days]\n",
    "datetime_bw_index = pd.to_datetime([datetime.datetime(i.year, i.month, i.day, key) for i in daily_cta_rides_business_weekend.index for key in business_weekend_days])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_business_weekend_dic = {}\n",
    "\n",
    "for station in daily_cta_rides_business_weekend.columns:\n",
    "    hourly_transit = np.outer(daily_cta_rides_business_weekend[station], bdist_weekenday_dist).ravel()\n",
    "    daily_cta_rides_business_weekend_dic[station] = hourly_transit\n",
    "\n",
    "df_hourly_cta_rides_business_weekend = pd.DataFrame(daily_cta_rides_business_weekend_dic, index=datetime_bw_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-03 00:00:00</th>\n",
       "      <td>21.045</td>\n",
       "      <td>108.87</td>\n",
       "      <td>133.575</td>\n",
       "      <td>108.555</td>\n",
       "      <td>77.34</td>\n",
       "      <td>28.515</td>\n",
       "      <td>8.085</td>\n",
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       "      <td>23.595</td>\n",
       "      <td>20.85</td>\n",
       "      <td>45.69</td>\n",
       "      <td>39.765</td>\n",
       "      <td>33.81</td>\n",
       "      <td>40.455</td>\n",
       "      <td>79.065</td>\n",
       "      <td>147.675</td>\n",
       "      <td>106.095</td>\n",
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       "    <tr>\n",
       "      <th>2018-11-03 01:00:00</th>\n",
       "      <td>21.045</td>\n",
       "      <td>108.87</td>\n",
       "      <td>133.575</td>\n",
       "      <td>108.555</td>\n",
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       "      <td>45.69</td>\n",
       "      <td>39.765</td>\n",
       "      <td>33.81</td>\n",
       "      <td>40.455</td>\n",
       "      <td>79.065</td>\n",
       "      <td>147.675</td>\n",
       "      <td>106.095</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                     Clinton-Lake  Clark/Lake  State/Lake  Washington/Wabash  \\\n",
       "2018-11-03 00:00:00        21.045      108.87     133.575            108.555   \n",
       "2018-11-03 01:00:00        21.045      108.87     133.575            108.555   \n",
       "\n",
       "                     Adams/Wabash  Library  LaSalle/Van Buren  LaSalle  \\\n",
       "2018-11-03 00:00:00         77.34   28.515              8.085   19.005   \n",
       "2018-11-03 01:00:00         77.34   28.515              8.085   19.005   \n",
       "\n",
       "                     Quincy/Wells  Washington/Wells  Jackson/Dearborn  \\\n",
       "2018-11-03 00:00:00        23.595             20.85             45.69   \n",
       "2018-11-03 01:00:00        23.595             20.85             45.69   \n",
       "\n",
       "                     Jackson/State  Monroe/Dearborn  Monroe/State  \\\n",
       "2018-11-03 00:00:00         39.765            33.81        40.455   \n",
       "2018-11-03 01:00:00         39.765            33.81        40.455   \n",
       "\n",
       "                     Washington/Dearborn  Lake/State  Grand/State  \n",
       "2018-11-03 00:00:00               79.065     147.675      106.095  \n",
       "2018-11-03 01:00:00               79.065     147.675      106.095  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hourly_cta_rides_business_weekend.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9fba37e6d0>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3a4e90>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3cd1d0>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3cd390>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3cd550>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3cd710>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3cd950>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3cdb10>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3cdcd0>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3cd750>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fbba18690>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3d0190>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3d0350>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3d0510>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3d06d0>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3d0890>,\n",
       " <matplotlib.lines.Line2D at 0x7f9fba3d0a50>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(25,10))\n",
    "ax.plot(df_hourly_cta_rides_business_normal.iloc[:48])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Residential stations: Create hourly values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "residential @ business days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_residential = pd.read_csv(\"daily_cta_rides_residential.csv\")\n",
    "daily_cta_rides_residential[\"date\"] = pd.to_datetime(daily_cta_rides_residential[\"date\"], format=\"%d/%m/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_residential[\"day_of_week\"] = pd.DatetimeIndex(daily_cta_rides_residential[\"date\"]).dayofweek\n",
    "daily_cta_rides_residential.set_index(\"date\", inplace=True)\n",
    "weekend = daily_cta_rides_residential[\"day_of_week\"] > 4\n",
    "normal = daily_cta_rides_residential[\"day_of_week\"] < 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Austin-Forest Park</th>\n",
       "      <th>Harlem-Lake</th>\n",
       "      <th>Pulaski-Lake</th>\n",
       "      <th>Davis</th>\n",
       "      <th>Belmont-O'Hare</th>\n",
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       "      <th>Damen-Brown</th>\n",
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       "      <th>51st</th>\n",
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       "      <th>Irving Park-Brown</th>\n",
       "      <th>Western-Brown</th>\n",
       "      <th>Harrison</th>\n",
       "      <th>Montrose-Brown</th>\n",
       "      <th>Morgan-Lake</th>\n",
       "      <th>Conservatory</th>\n",
       "      <th>Oakton-Skokie</th>\n",
       "      <th>Cermak-McCormick Place</th>\n",
       "      <th>day_of_week</th>\n",
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       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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>2018-11-01</th>\n",
       "      <td>2248</td>\n",
       "      <td>4021</td>\n",
       "      <td>1575</td>\n",
       "      <td>3734</td>\n",
       "      <td>5110</td>\n",
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       "      <td>4806</td>\n",
       "      <td>2857</td>\n",
       "      <td>3737</td>\n",
       "      <td>974</td>\n",
       "      <td>1140</td>\n",
       "      <td>1809</td>\n",
       "      <td>3</td>\n",
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       "      <th>2018-11-02</th>\n",
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       "      <td>3971</td>\n",
       "      <td>4945</td>\n",
       "      <td>5281</td>\n",
       "      <td>2822</td>\n",
       "      <td>4688</td>\n",
       "      <td>3059</td>\n",
       "      <td>1792</td>\n",
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       "      <td>15844</td>\n",
       "      <td>3243</td>\n",
       "      <td>4074</td>\n",
       "      <td>4041</td>\n",
       "      <td>2723</td>\n",
       "      <td>3706</td>\n",
       "      <td>933</td>\n",
       "      <td>1002</td>\n",
       "      <td>2301</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 127 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Austin-Forest Park  Harlem-Lake  Pulaski-Lake  Davis  \\\n",
       "date                                                               \n",
       "2018-11-01                2248         4021          1575   3734   \n",
       "2018-11-02                1983         3850          1442   3971   \n",
       "\n",
       "            Belmont-O'Hare  Sheridan  Damen-Brown  Morse  35th/Archer  51st  \\\n",
       "date                                                                          \n",
       "2018-11-01            5110      5480         3138   4813         3431  1880   \n",
       "2018-11-02            4945      5281         2822   4688         3059  1792   \n",
       "\n",
       "            ...  Chicago/State  Irving Park-Brown  Western-Brown  Harrison  \\\n",
       "date        ...                                                              \n",
       "2018-11-01  ...          15046               3656           4287      4806   \n",
       "2018-11-02  ...          15844               3243           4074      4041   \n",
       "\n",
       "            Montrose-Brown  Morgan-Lake  Conservatory  Oakton-Skokie  \\\n",
       "date                                                                   \n",
       "2018-11-01            2857         3737           974           1140   \n",
       "2018-11-02            2723         3706           933           1002   \n",
       "\n",
       "            Cermak-McCormick Place  day_of_week  \n",
       "date                                             \n",
       "2018-11-01                    1809            3  \n",
       "2018-11-02                    2301            4  \n",
       "\n",
       "[2 rows x 127 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_cta_rides_residential.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_residential_normal = daily_cta_rides_residential[normal]\n",
    "daily_cta_rides_residential_normal = daily_cta_rides_residential_normal.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3737</td>\n",
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       "      <td>3706</td>\n",
       "      <td>933</td>\n",
       "      <td>1002</td>\n",
       "      <td>2301</td>\n",
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       "<p>2 rows × 126 columns</p>\n",
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      ],
      "text/plain": [
       "            Austin-Forest Park  Harlem-Lake  Pulaski-Lake  Davis  \\\n",
       "date                                                               \n",
       "2018-11-01                2248         4021          1575   3734   \n",
       "2018-11-02                1983         3850          1442   3971   \n",
       "\n",
       "            Belmont-O'Hare  Sheridan  Damen-Brown  Morse  35th/Archer  51st  \\\n",
       "date                                                                          \n",
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       "2018-11-02            4945      5281         2822   4688         3059  1792   \n",
       "\n",
       "            ...  Addison-Brown  Chicago/State  Irving Park-Brown  \\\n",
       "date        ...                                                    \n",
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       "2018-11-02  ...           2310          15844               3243   \n",
       "\n",
       "            Western-Brown  Harrison  Montrose-Brown  Morgan-Lake  \\\n",
       "date                                                               \n",
       "2018-11-01           4287      4806            2857         3737   \n",
       "2018-11-02           4074      4041            2723         3706   \n",
       "\n",
       "            Conservatory  Oakton-Skokie  Cermak-McCormick Place  \n",
       "date                                                             \n",
       "2018-11-01           974           1140                    1809  \n",
       "2018-11-02           933           1002                    2301  \n",
       "\n",
       "[2 rows x 126 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_cta_rides_residential_normal.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdist_bday_dist = [residential_business_days[key] for key in residential_business_days]\n",
    "datetime_rb_index = pd.to_datetime([datetime.datetime(i.year, i.month, i.day, key) for i in daily_cta_rides_residential_normal.index for key in residential_business_days])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_residential_normal_dic = {}\n",
    "\n",
    "for station in daily_cta_rides_residential_normal.columns:\n",
    "    hourly_transit = np.outer(daily_cta_rides_residential_normal[station], rdist_bday_dist).ravel()\n",
    "    daily_cta_rides_residential_normal_dic[station] = hourly_transit\n",
    "\n",
    "df_hourly_cta_rides_residential_normal = pd.DataFrame(daily_cta_rides_residential_normal_dic, index=datetime_rb_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2018-11-01 00:00:00</th>\n",
       "      <td>24.728</td>\n",
       "      <td>44.231</td>\n",
       "      <td>17.325</td>\n",
       "      <td>41.074</td>\n",
       "      <td>56.21</td>\n",
       "      <td>60.28</td>\n",
       "      <td>34.518</td>\n",
       "      <td>52.943</td>\n",
       "      <td>37.741</td>\n",
       "      <td>20.68</td>\n",
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       "      <th>2018-11-01 01:00:00</th>\n",
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       "      <th>2018-11-01 02:00:00</th>\n",
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       "      <td>7.875</td>\n",
       "      <td>18.670</td>\n",
       "      <td>25.55</td>\n",
       "      <td>27.40</td>\n",
       "      <td>15.690</td>\n",
       "      <td>24.065</td>\n",
       "      <td>17.155</td>\n",
       "      <td>9.40</td>\n",
       "      <td>...</td>\n",
       "      <td>14.170</td>\n",
       "      <td>75.230</td>\n",
       "      <td>18.280</td>\n",
       "      <td>21.435</td>\n",
       "      <td>24.030</td>\n",
       "      <td>14.285</td>\n",
       "      <td>18.685</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5.70</td>\n",
       "      <td>9.045</td>\n",
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       "    <tr>\n",
       "      <th>2018-11-01 03:00:00</th>\n",
       "      <td>11.240</td>\n",
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       "      <td>9.40</td>\n",
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       "      <td>14.170</td>\n",
       "      <td>75.230</td>\n",
       "      <td>18.280</td>\n",
       "      <td>21.435</td>\n",
       "      <td>24.030</td>\n",
       "      <td>14.285</td>\n",
       "      <td>18.685</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5.70</td>\n",
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       "    <tr>\n",
       "      <th>2018-11-01 04:00:00</th>\n",
       "      <td>11.240</td>\n",
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       "      <td>7.875</td>\n",
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       "      <td>18.685</td>\n",
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       "      <td>9.045</td>\n",
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       "<p>5 rows × 126 columns</p>\n",
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      ],
      "text/plain": [
       "                     Austin-Forest Park  Harlem-Lake  Pulaski-Lake   Davis  \\\n",
       "2018-11-01 00:00:00              24.728       44.231        17.325  41.074   \n",
       "2018-11-01 01:00:00              24.728       44.231        17.325  41.074   \n",
       "2018-11-01 02:00:00              11.240       20.105         7.875  18.670   \n",
       "2018-11-01 03:00:00              11.240       20.105         7.875  18.670   \n",
       "2018-11-01 04:00:00              11.240       20.105         7.875  18.670   \n",
       "\n",
       "                     Belmont-O'Hare  Sheridan  Damen-Brown   Morse  \\\n",
       "2018-11-01 00:00:00           56.21     60.28       34.518  52.943   \n",
       "2018-11-01 01:00:00           56.21     60.28       34.518  52.943   \n",
       "2018-11-01 02:00:00           25.55     27.40       15.690  24.065   \n",
       "2018-11-01 03:00:00           25.55     27.40       15.690  24.065   \n",
       "2018-11-01 04:00:00           25.55     27.40       15.690  24.065   \n",
       "\n",
       "                     35th/Archer   51st  ...  Addison-Brown  Chicago/State  \\\n",
       "2018-11-01 00:00:00       37.741  20.68  ...         31.174        165.506   \n",
       "2018-11-01 01:00:00       37.741  20.68  ...         31.174        165.506   \n",
       "2018-11-01 02:00:00       17.155   9.40  ...         14.170         75.230   \n",
       "2018-11-01 03:00:00       17.155   9.40  ...         14.170         75.230   \n",
       "2018-11-01 04:00:00       17.155   9.40  ...         14.170         75.230   \n",
       "\n",
       "                     Irving Park-Brown  Western-Brown  Harrison  \\\n",
       "2018-11-01 00:00:00             40.216         47.157    52.866   \n",
       "2018-11-01 01:00:00             40.216         47.157    52.866   \n",
       "2018-11-01 02:00:00             18.280         21.435    24.030   \n",
       "2018-11-01 03:00:00             18.280         21.435    24.030   \n",
       "2018-11-01 04:00:00             18.280         21.435    24.030   \n",
       "\n",
       "                     Montrose-Brown  Morgan-Lake  Conservatory  Oakton-Skokie  \\\n",
       "2018-11-01 00:00:00          31.427       41.107        10.714          12.54   \n",
       "2018-11-01 01:00:00          31.427       41.107        10.714          12.54   \n",
       "2018-11-01 02:00:00          14.285       18.685         4.870           5.70   \n",
       "2018-11-01 03:00:00          14.285       18.685         4.870           5.70   \n",
       "2018-11-01 04:00:00          14.285       18.685         4.870           5.70   \n",
       "\n",
       "                     Cermak-McCormick Place  \n",
       "2018-11-01 00:00:00                  19.899  \n",
       "2018-11-01 01:00:00                  19.899  \n",
       "2018-11-01 02:00:00                   9.045  \n",
       "2018-11-01 03:00:00                   9.045  \n",
       "2018-11-01 04:00:00                   9.045  \n",
       "\n",
       "[5 rows x 126 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hourly_cta_rides_residential_normal.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "residential @ weekend days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_residential_weekend = daily_cta_rides_residential[weekend]\n",
    "daily_cta_rides_residential_weekend = daily_cta_rides_residential_weekend.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Austin-Forest Park</th>\n",
       "      <th>Harlem-Lake</th>\n",
       "      <th>Pulaski-Lake</th>\n",
       "      <th>Davis</th>\n",
       "      <th>Belmont-O'Hare</th>\n",
       "      <th>Sheridan</th>\n",
       "      <th>Damen-Brown</th>\n",
       "      <th>Morse</th>\n",
       "      <th>35th/Archer</th>\n",
       "      <th>51st</th>\n",
       "      <th>...</th>\n",
       "      <th>Addison-Brown</th>\n",
       "      <th>Chicago/State</th>\n",
       "      <th>Irving Park-Brown</th>\n",
       "      <th>Western-Brown</th>\n",
       "      <th>Harrison</th>\n",
       "      <th>Montrose-Brown</th>\n",
       "      <th>Morgan-Lake</th>\n",
       "      <th>Conservatory</th>\n",
       "      <th>Oakton-Skokie</th>\n",
       "      <th>Cermak-McCormick Place</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>837</td>\n",
       "      <td>2308</td>\n",
       "      <td>1000</td>\n",
       "      <td>3038</td>\n",
       "      <td>2661</td>\n",
       "      <td>3466</td>\n",
       "      <td>1523</td>\n",
       "      <td>3457</td>\n",
       "      <td>1595</td>\n",
       "      <td>837</td>\n",
       "      <td>...</td>\n",
       "      <td>1100</td>\n",
       "      <td>8520</td>\n",
       "      <td>1683</td>\n",
       "      <td>2566</td>\n",
       "      <td>2745</td>\n",
       "      <td>1420</td>\n",
       "      <td>2116</td>\n",
       "      <td>757</td>\n",
       "      <td>438</td>\n",
       "      <td>1864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>604</td>\n",
       "      <td>1443</td>\n",
       "      <td>693</td>\n",
       "      <td>1580</td>\n",
       "      <td>1936</td>\n",
       "      <td>2082</td>\n",
       "      <td>1006</td>\n",
       "      <td>2515</td>\n",
       "      <td>1035</td>\n",
       "      <td>567</td>\n",
       "      <td>...</td>\n",
       "      <td>712</td>\n",
       "      <td>4754</td>\n",
       "      <td>979</td>\n",
       "      <td>1715</td>\n",
       "      <td>1749</td>\n",
       "      <td>933</td>\n",
       "      <td>1362</td>\n",
       "      <td>463</td>\n",
       "      <td>224</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Austin-Forest Park  Harlem-Lake  Pulaski-Lake  Davis  \\\n",
       "date                                                               \n",
       "2018-11-03                 837         2308          1000   3038   \n",
       "2018-11-04                 604         1443           693   1580   \n",
       "\n",
       "            Belmont-O'Hare  Sheridan  Damen-Brown  Morse  35th/Archer  51st  \\\n",
       "date                                                                          \n",
       "2018-11-03            2661      3466         1523   3457         1595   837   \n",
       "2018-11-04            1936      2082         1006   2515         1035   567   \n",
       "\n",
       "            ...  Addison-Brown  Chicago/State  Irving Park-Brown  \\\n",
       "date        ...                                                    \n",
       "2018-11-03  ...           1100           8520               1683   \n",
       "2018-11-04  ...            712           4754                979   \n",
       "\n",
       "            Western-Brown  Harrison  Montrose-Brown  Morgan-Lake  \\\n",
       "date                                                               \n",
       "2018-11-03           2566      2745            1420         2116   \n",
       "2018-11-04           1715      1749             933         1362   \n",
       "\n",
       "            Conservatory  Oakton-Skokie  Cermak-McCormick Place  \n",
       "date                                                             \n",
       "2018-11-03           757            438                    1864  \n",
       "2018-11-04           463            224                     803  \n",
       "\n",
       "[2 rows x 126 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_cta_rides_residential_weekend.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdist_wday_dist = [residential_weekend_days[key] for key in residential_weekend_days]\n",
    "datetime_rw_index = pd.to_datetime([datetime.datetime(i.year, i.month, i.day, key) for i in daily_cta_rides_residential_weekend.index for key in residential_weekend_days])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_cta_rides_residential_weekend_dic = {}\n",
    "\n",
    "for station in daily_cta_rides_residential_weekend.columns:\n",
    "    hourly_transit = np.outer(daily_cta_rides_residential_weekend[station], rdist_wday_dist).ravel()\n",
    "    daily_cta_rides_residential_weekend_dic[station] = hourly_transit\n",
    "\n",
    "df_hourly_cta_rides_residential_weekend = pd.DataFrame(daily_cta_rides_residential_weekend_dic, index=datetime_rw_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>51st</th>\n",
       "      <th>...</th>\n",
       "      <th>Addison-Brown</th>\n",
       "      <th>Chicago/State</th>\n",
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       "      <th>Harrison</th>\n",
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       "      <th>Oakton-Skokie</th>\n",
       "      <th>Cermak-McCormick Place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-03 00:00:00</th>\n",
       "      <td>11.718</td>\n",
       "      <td>32.312</td>\n",
       "      <td>14.0</td>\n",
       "      <td>42.532</td>\n",
       "      <td>37.254</td>\n",
       "      <td>48.524</td>\n",
       "      <td>21.322</td>\n",
       "      <td>48.398</td>\n",
       "      <td>22.330</td>\n",
       "      <td>11.718</td>\n",
       "      <td>...</td>\n",
       "      <td>15.4</td>\n",
       "      <td>119.28</td>\n",
       "      <td>23.562</td>\n",
       "      <td>35.924</td>\n",
       "      <td>38.430</td>\n",
       "      <td>19.88</td>\n",
       "      <td>29.624</td>\n",
       "      <td>10.598</td>\n",
       "      <td>6.132</td>\n",
       "      <td>26.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 01:00:00</th>\n",
       "      <td>5.859</td>\n",
       "      <td>16.156</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.266</td>\n",
       "      <td>18.627</td>\n",
       "      <td>24.262</td>\n",
       "      <td>10.661</td>\n",
       "      <td>24.199</td>\n",
       "      <td>11.165</td>\n",
       "      <td>5.859</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>59.64</td>\n",
       "      <td>11.781</td>\n",
       "      <td>17.962</td>\n",
       "      <td>19.215</td>\n",
       "      <td>9.94</td>\n",
       "      <td>14.812</td>\n",
       "      <td>5.299</td>\n",
       "      <td>3.066</td>\n",
       "      <td>13.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 02:00:00</th>\n",
       "      <td>5.859</td>\n",
       "      <td>16.156</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.266</td>\n",
       "      <td>18.627</td>\n",
       "      <td>24.262</td>\n",
       "      <td>10.661</td>\n",
       "      <td>24.199</td>\n",
       "      <td>11.165</td>\n",
       "      <td>5.859</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>59.64</td>\n",
       "      <td>11.781</td>\n",
       "      <td>17.962</td>\n",
       "      <td>19.215</td>\n",
       "      <td>9.94</td>\n",
       "      <td>14.812</td>\n",
       "      <td>5.299</td>\n",
       "      <td>3.066</td>\n",
       "      <td>13.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 03:00:00</th>\n",
       "      <td>5.859</td>\n",
       "      <td>16.156</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.266</td>\n",
       "      <td>18.627</td>\n",
       "      <td>24.262</td>\n",
       "      <td>10.661</td>\n",
       "      <td>24.199</td>\n",
       "      <td>11.165</td>\n",
       "      <td>5.859</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>59.64</td>\n",
       "      <td>11.781</td>\n",
       "      <td>17.962</td>\n",
       "      <td>19.215</td>\n",
       "      <td>9.94</td>\n",
       "      <td>14.812</td>\n",
       "      <td>5.299</td>\n",
       "      <td>3.066</td>\n",
       "      <td>13.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 04:00:00</th>\n",
       "      <td>5.859</td>\n",
       "      <td>16.156</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.266</td>\n",
       "      <td>18.627</td>\n",
       "      <td>24.262</td>\n",
       "      <td>10.661</td>\n",
       "      <td>24.199</td>\n",
       "      <td>11.165</td>\n",
       "      <td>5.859</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>59.64</td>\n",
       "      <td>11.781</td>\n",
       "      <td>17.962</td>\n",
       "      <td>19.215</td>\n",
       "      <td>9.94</td>\n",
       "      <td>14.812</td>\n",
       "      <td>5.299</td>\n",
       "      <td>3.066</td>\n",
       "      <td>13.048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Austin-Forest Park  Harlem-Lake  Pulaski-Lake   Davis  \\\n",
       "2018-11-03 00:00:00              11.718       32.312          14.0  42.532   \n",
       "2018-11-03 01:00:00               5.859       16.156           7.0  21.266   \n",
       "2018-11-03 02:00:00               5.859       16.156           7.0  21.266   \n",
       "2018-11-03 03:00:00               5.859       16.156           7.0  21.266   \n",
       "2018-11-03 04:00:00               5.859       16.156           7.0  21.266   \n",
       "\n",
       "                     Belmont-O'Hare  Sheridan  Damen-Brown   Morse  \\\n",
       "2018-11-03 00:00:00          37.254    48.524       21.322  48.398   \n",
       "2018-11-03 01:00:00          18.627    24.262       10.661  24.199   \n",
       "2018-11-03 02:00:00          18.627    24.262       10.661  24.199   \n",
       "2018-11-03 03:00:00          18.627    24.262       10.661  24.199   \n",
       "2018-11-03 04:00:00          18.627    24.262       10.661  24.199   \n",
       "\n",
       "                     35th/Archer    51st  ...  Addison-Brown  Chicago/State  \\\n",
       "2018-11-03 00:00:00       22.330  11.718  ...           15.4         119.28   \n",
       "2018-11-03 01:00:00       11.165   5.859  ...            7.7          59.64   \n",
       "2018-11-03 02:00:00       11.165   5.859  ...            7.7          59.64   \n",
       "2018-11-03 03:00:00       11.165   5.859  ...            7.7          59.64   \n",
       "2018-11-03 04:00:00       11.165   5.859  ...            7.7          59.64   \n",
       "\n",
       "                     Irving Park-Brown  Western-Brown  Harrison  \\\n",
       "2018-11-03 00:00:00             23.562         35.924    38.430   \n",
       "2018-11-03 01:00:00             11.781         17.962    19.215   \n",
       "2018-11-03 02:00:00             11.781         17.962    19.215   \n",
       "2018-11-03 03:00:00             11.781         17.962    19.215   \n",
       "2018-11-03 04:00:00             11.781         17.962    19.215   \n",
       "\n",
       "                     Montrose-Brown  Morgan-Lake  Conservatory  Oakton-Skokie  \\\n",
       "2018-11-03 00:00:00           19.88       29.624        10.598          6.132   \n",
       "2018-11-03 01:00:00            9.94       14.812         5.299          3.066   \n",
       "2018-11-03 02:00:00            9.94       14.812         5.299          3.066   \n",
       "2018-11-03 03:00:00            9.94       14.812         5.299          3.066   \n",
       "2018-11-03 04:00:00            9.94       14.812         5.299          3.066   \n",
       "\n",
       "                     Cermak-McCormick Place  \n",
       "2018-11-03 00:00:00                  26.096  \n",
       "2018-11-03 01:00:00                  13.048  \n",
       "2018-11-03 02:00:00                  13.048  \n",
       "2018-11-03 03:00:00                  13.048  \n",
       "2018-11-03 04:00:00                  13.048  \n",
       "\n",
       "[5 rows x 126 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hourly_cta_rides_residential_weekend.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concat businss district dfs and residential dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Clark/Lake</th>\n",
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       "      <th>Adams/Wabash</th>\n",
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       "      <th>LaSalle</th>\n",
       "      <th>Quincy/Wells</th>\n",
       "      <th>Washington/Wells</th>\n",
       "      <th>Jackson/Dearborn</th>\n",
       "      <th>Jackson/State</th>\n",
       "      <th>Monroe/Dearborn</th>\n",
       "      <th>Monroe/State</th>\n",
       "      <th>Washington/Dearborn</th>\n",
       "      <th>Lake/State</th>\n",
       "      <th>Grand/State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:00</th>\n",
       "      <td>81.957</td>\n",
       "      <td>387.158</td>\n",
       "      <td>233.087</td>\n",
       "      <td>183.685</td>\n",
       "      <td>144.568</td>\n",
       "      <td>71.298</td>\n",
       "      <td>55.386</td>\n",
       "      <td>59.296</td>\n",
       "      <td>145.010</td>\n",
       "      <td>151.351</td>\n",
       "      <td>138.21</td>\n",
       "      <td>192.712</td>\n",
       "      <td>148.835</td>\n",
       "      <td>187.867</td>\n",
       "      <td>243.695</td>\n",
       "      <td>390.694</td>\n",
       "      <td>190.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 01:00:00</th>\n",
       "      <td>81.957</td>\n",
       "      <td>387.158</td>\n",
       "      <td>233.087</td>\n",
       "      <td>183.685</td>\n",
       "      <td>144.568</td>\n",
       "      <td>71.298</td>\n",
       "      <td>55.386</td>\n",
       "      <td>59.296</td>\n",
       "      <td>145.010</td>\n",
       "      <td>151.351</td>\n",
       "      <td>138.21</td>\n",
       "      <td>192.712</td>\n",
       "      <td>148.835</td>\n",
       "      <td>187.867</td>\n",
       "      <td>243.695</td>\n",
       "      <td>390.694</td>\n",
       "      <td>190.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 02:00:00</th>\n",
       "      <td>38.568</td>\n",
       "      <td>182.192</td>\n",
       "      <td>109.688</td>\n",
       "      <td>86.440</td>\n",
       "      <td>68.032</td>\n",
       "      <td>33.552</td>\n",
       "      <td>26.064</td>\n",
       "      <td>27.904</td>\n",
       "      <td>68.240</td>\n",
       "      <td>71.224</td>\n",
       "      <td>65.04</td>\n",
       "      <td>90.688</td>\n",
       "      <td>70.040</td>\n",
       "      <td>88.408</td>\n",
       "      <td>114.680</td>\n",
       "      <td>183.856</td>\n",
       "      <td>89.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 03:00:00</th>\n",
       "      <td>38.568</td>\n",
       "      <td>182.192</td>\n",
       "      <td>109.688</td>\n",
       "      <td>86.440</td>\n",
       "      <td>68.032</td>\n",
       "      <td>33.552</td>\n",
       "      <td>26.064</td>\n",
       "      <td>27.904</td>\n",
       "      <td>68.240</td>\n",
       "      <td>71.224</td>\n",
       "      <td>65.04</td>\n",
       "      <td>90.688</td>\n",
       "      <td>70.040</td>\n",
       "      <td>88.408</td>\n",
       "      <td>114.680</td>\n",
       "      <td>183.856</td>\n",
       "      <td>89.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 04:00:00</th>\n",
       "      <td>19.284</td>\n",
       "      <td>91.096</td>\n",
       "      <td>54.844</td>\n",
       "      <td>43.220</td>\n",
       "      <td>34.016</td>\n",
       "      <td>16.776</td>\n",
       "      <td>13.032</td>\n",
       "      <td>13.952</td>\n",
       "      <td>34.120</td>\n",
       "      <td>35.612</td>\n",
       "      <td>32.52</td>\n",
       "      <td>45.344</td>\n",
       "      <td>35.020</td>\n",
       "      <td>44.204</td>\n",
       "      <td>57.340</td>\n",
       "      <td>91.928</td>\n",
       "      <td>44.756</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 19:00:00</th>\n",
       "      <td>38.394</td>\n",
       "      <td>198.180</td>\n",
       "      <td>240.624</td>\n",
       "      <td>202.500</td>\n",
       "      <td>88.722</td>\n",
       "      <td>44.172</td>\n",
       "      <td>15.606</td>\n",
       "      <td>34.182</td>\n",
       "      <td>57.024</td>\n",
       "      <td>38.340</td>\n",
       "      <td>73.44</td>\n",
       "      <td>93.636</td>\n",
       "      <td>69.174</td>\n",
       "      <td>127.278</td>\n",
       "      <td>232.902</td>\n",
       "      <td>409.374</td>\n",
       "      <td>362.664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 20:00:00</th>\n",
       "      <td>22.041</td>\n",
       "      <td>113.770</td>\n",
       "      <td>138.136</td>\n",
       "      <td>116.250</td>\n",
       "      <td>50.933</td>\n",
       "      <td>25.358</td>\n",
       "      <td>8.959</td>\n",
       "      <td>19.623</td>\n",
       "      <td>32.736</td>\n",
       "      <td>22.010</td>\n",
       "      <td>42.16</td>\n",
       "      <td>53.754</td>\n",
       "      <td>39.711</td>\n",
       "      <td>73.067</td>\n",
       "      <td>133.703</td>\n",
       "      <td>235.011</td>\n",
       "      <td>208.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 21:00:00</th>\n",
       "      <td>22.041</td>\n",
       "      <td>113.770</td>\n",
       "      <td>138.136</td>\n",
       "      <td>116.250</td>\n",
       "      <td>50.933</td>\n",
       "      <td>25.358</td>\n",
       "      <td>8.959</td>\n",
       "      <td>19.623</td>\n",
       "      <td>32.736</td>\n",
       "      <td>22.010</td>\n",
       "      <td>42.16</td>\n",
       "      <td>53.754</td>\n",
       "      <td>39.711</td>\n",
       "      <td>73.067</td>\n",
       "      <td>133.703</td>\n",
       "      <td>235.011</td>\n",
       "      <td>208.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 22:00:00</th>\n",
       "      <td>10.665</td>\n",
       "      <td>55.050</td>\n",
       "      <td>66.840</td>\n",
       "      <td>56.250</td>\n",
       "      <td>24.645</td>\n",
       "      <td>12.270</td>\n",
       "      <td>4.335</td>\n",
       "      <td>9.495</td>\n",
       "      <td>15.840</td>\n",
       "      <td>10.650</td>\n",
       "      <td>20.40</td>\n",
       "      <td>26.010</td>\n",
       "      <td>19.215</td>\n",
       "      <td>35.355</td>\n",
       "      <td>64.695</td>\n",
       "      <td>113.715</td>\n",
       "      <td>100.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 23:00:00</th>\n",
       "      <td>10.665</td>\n",
       "      <td>55.050</td>\n",
       "      <td>66.840</td>\n",
       "      <td>56.250</td>\n",
       "      <td>24.645</td>\n",
       "      <td>12.270</td>\n",
       "      <td>4.335</td>\n",
       "      <td>9.495</td>\n",
       "      <td>15.840</td>\n",
       "      <td>10.650</td>\n",
       "      <td>20.40</td>\n",
       "      <td>26.010</td>\n",
       "      <td>19.215</td>\n",
       "      <td>35.355</td>\n",
       "      <td>64.695</td>\n",
       "      <td>113.715</td>\n",
       "      <td>100.740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10224 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Clinton-Lake  Clark/Lake  State/Lake  Washington/Wabash  \\\n",
       "2018-11-01 00:00:00        81.957     387.158     233.087            183.685   \n",
       "2018-11-01 01:00:00        81.957     387.158     233.087            183.685   \n",
       "2018-11-01 02:00:00        38.568     182.192     109.688             86.440   \n",
       "2018-11-01 03:00:00        38.568     182.192     109.688             86.440   \n",
       "2018-11-01 04:00:00        19.284      91.096      54.844             43.220   \n",
       "...                           ...         ...         ...                ...   \n",
       "2019-12-29 19:00:00        38.394     198.180     240.624            202.500   \n",
       "2019-12-29 20:00:00        22.041     113.770     138.136            116.250   \n",
       "2019-12-29 21:00:00        22.041     113.770     138.136            116.250   \n",
       "2019-12-29 22:00:00        10.665      55.050      66.840             56.250   \n",
       "2019-12-29 23:00:00        10.665      55.050      66.840             56.250   \n",
       "\n",
       "                     Adams/Wabash  Library  LaSalle/Van Buren  LaSalle  \\\n",
       "2018-11-01 00:00:00       144.568   71.298             55.386   59.296   \n",
       "2018-11-01 01:00:00       144.568   71.298             55.386   59.296   \n",
       "2018-11-01 02:00:00        68.032   33.552             26.064   27.904   \n",
       "2018-11-01 03:00:00        68.032   33.552             26.064   27.904   \n",
       "2018-11-01 04:00:00        34.016   16.776             13.032   13.952   \n",
       "...                           ...      ...                ...      ...   \n",
       "2019-12-29 19:00:00        88.722   44.172             15.606   34.182   \n",
       "2019-12-29 20:00:00        50.933   25.358              8.959   19.623   \n",
       "2019-12-29 21:00:00        50.933   25.358              8.959   19.623   \n",
       "2019-12-29 22:00:00        24.645   12.270              4.335    9.495   \n",
       "2019-12-29 23:00:00        24.645   12.270              4.335    9.495   \n",
       "\n",
       "                     Quincy/Wells  Washington/Wells  Jackson/Dearborn  \\\n",
       "2018-11-01 00:00:00       145.010           151.351            138.21   \n",
       "2018-11-01 01:00:00       145.010           151.351            138.21   \n",
       "2018-11-01 02:00:00        68.240            71.224             65.04   \n",
       "2018-11-01 03:00:00        68.240            71.224             65.04   \n",
       "2018-11-01 04:00:00        34.120            35.612             32.52   \n",
       "...                           ...               ...               ...   \n",
       "2019-12-29 19:00:00        57.024            38.340             73.44   \n",
       "2019-12-29 20:00:00        32.736            22.010             42.16   \n",
       "2019-12-29 21:00:00        32.736            22.010             42.16   \n",
       "2019-12-29 22:00:00        15.840            10.650             20.40   \n",
       "2019-12-29 23:00:00        15.840            10.650             20.40   \n",
       "\n",
       "                     Jackson/State  Monroe/Dearborn  Monroe/State  \\\n",
       "2018-11-01 00:00:00        192.712          148.835       187.867   \n",
       "2018-11-01 01:00:00        192.712          148.835       187.867   \n",
       "2018-11-01 02:00:00         90.688           70.040        88.408   \n",
       "2018-11-01 03:00:00         90.688           70.040        88.408   \n",
       "2018-11-01 04:00:00         45.344           35.020        44.204   \n",
       "...                            ...              ...           ...   \n",
       "2019-12-29 19:00:00         93.636           69.174       127.278   \n",
       "2019-12-29 20:00:00         53.754           39.711        73.067   \n",
       "2019-12-29 21:00:00         53.754           39.711        73.067   \n",
       "2019-12-29 22:00:00         26.010           19.215        35.355   \n",
       "2019-12-29 23:00:00         26.010           19.215        35.355   \n",
       "\n",
       "                     Washington/Dearborn  Lake/State  Grand/State  \n",
       "2018-11-01 00:00:00              243.695     390.694      190.213  \n",
       "2018-11-01 01:00:00              243.695     390.694      190.213  \n",
       "2018-11-01 02:00:00              114.680     183.856       89.512  \n",
       "2018-11-01 03:00:00              114.680     183.856       89.512  \n",
       "2018-11-01 04:00:00               57.340      91.928       44.756  \n",
       "...                                  ...         ...          ...  \n",
       "2019-12-29 19:00:00              232.902     409.374      362.664  \n",
       "2019-12-29 20:00:00              133.703     235.011      208.196  \n",
       "2019-12-29 21:00:00              133.703     235.011      208.196  \n",
       "2019-12-29 22:00:00               64.695     113.715      100.740  \n",
       "2019-12-29 23:00:00               64.695     113.715      100.740  \n",
       "\n",
       "[10224 rows x 17 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total_hour_business = pd.concat([df_hourly_cta_rides_business_normal,\n",
    "                                    df_hourly_cta_rides_business_weekend], axis=0)\n",
    "df_total_hour_business"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_total_hour_residential = pd.concat([df_hourly_cta_rides_residential_normal,\n",
    "                                       df_hourly_cta_rides_residential_weekend], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Austin-Forest Park</th>\n",
       "      <th>Harlem-Lake</th>\n",
       "      <th>Pulaski-Lake</th>\n",
       "      <th>Davis</th>\n",
       "      <th>Belmont-O'Hare</th>\n",
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       "      <th>...</th>\n",
       "      <th>Addison-Brown</th>\n",
       "      <th>Chicago/State</th>\n",
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       "      <th>Morgan-Lake</th>\n",
       "      <th>Conservatory</th>\n",
       "      <th>Oakton-Skokie</th>\n",
       "      <th>Cermak-McCormick Place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:00</th>\n",
       "      <td>24.728</td>\n",
       "      <td>44.231</td>\n",
       "      <td>17.325</td>\n",
       "      <td>41.074</td>\n",
       "      <td>56.210</td>\n",
       "      <td>60.280</td>\n",
       "      <td>34.518</td>\n",
       "      <td>52.943</td>\n",
       "      <td>37.741</td>\n",
       "      <td>20.680</td>\n",
       "      <td>...</td>\n",
       "      <td>31.174</td>\n",
       "      <td>165.506</td>\n",
       "      <td>40.216</td>\n",
       "      <td>47.157</td>\n",
       "      <td>52.866</td>\n",
       "      <td>31.427</td>\n",
       "      <td>41.107</td>\n",
       "      <td>10.714</td>\n",
       "      <td>12.540</td>\n",
       "      <td>19.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 01:00:00</th>\n",
       "      <td>24.728</td>\n",
       "      <td>44.231</td>\n",
       "      <td>17.325</td>\n",
       "      <td>41.074</td>\n",
       "      <td>56.210</td>\n",
       "      <td>60.280</td>\n",
       "      <td>34.518</td>\n",
       "      <td>52.943</td>\n",
       "      <td>37.741</td>\n",
       "      <td>20.680</td>\n",
       "      <td>...</td>\n",
       "      <td>31.174</td>\n",
       "      <td>165.506</td>\n",
       "      <td>40.216</td>\n",
       "      <td>47.157</td>\n",
       "      <td>52.866</td>\n",
       "      <td>31.427</td>\n",
       "      <td>41.107</td>\n",
       "      <td>10.714</td>\n",
       "      <td>12.540</td>\n",
       "      <td>19.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 02:00:00</th>\n",
       "      <td>11.240</td>\n",
       "      <td>20.105</td>\n",
       "      <td>7.875</td>\n",
       "      <td>18.670</td>\n",
       "      <td>25.550</td>\n",
       "      <td>27.400</td>\n",
       "      <td>15.690</td>\n",
       "      <td>24.065</td>\n",
       "      <td>17.155</td>\n",
       "      <td>9.400</td>\n",
       "      <td>...</td>\n",
       "      <td>14.170</td>\n",
       "      <td>75.230</td>\n",
       "      <td>18.280</td>\n",
       "      <td>21.435</td>\n",
       "      <td>24.030</td>\n",
       "      <td>14.285</td>\n",
       "      <td>18.685</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5.700</td>\n",
       "      <td>9.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 03:00:00</th>\n",
       "      <td>11.240</td>\n",
       "      <td>20.105</td>\n",
       "      <td>7.875</td>\n",
       "      <td>18.670</td>\n",
       "      <td>25.550</td>\n",
       "      <td>27.400</td>\n",
       "      <td>15.690</td>\n",
       "      <td>24.065</td>\n",
       "      <td>17.155</td>\n",
       "      <td>9.400</td>\n",
       "      <td>...</td>\n",
       "      <td>14.170</td>\n",
       "      <td>75.230</td>\n",
       "      <td>18.280</td>\n",
       "      <td>21.435</td>\n",
       "      <td>24.030</td>\n",
       "      <td>14.285</td>\n",
       "      <td>18.685</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5.700</td>\n",
       "      <td>9.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 04:00:00</th>\n",
       "      <td>11.240</td>\n",
       "      <td>20.105</td>\n",
       "      <td>7.875</td>\n",
       "      <td>18.670</td>\n",
       "      <td>25.550</td>\n",
       "      <td>27.400</td>\n",
       "      <td>15.690</td>\n",
       "      <td>24.065</td>\n",
       "      <td>17.155</td>\n",
       "      <td>9.400</td>\n",
       "      <td>...</td>\n",
       "      <td>14.170</td>\n",
       "      <td>75.230</td>\n",
       "      <td>18.280</td>\n",
       "      <td>21.435</td>\n",
       "      <td>24.030</td>\n",
       "      <td>14.285</td>\n",
       "      <td>18.685</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5.700</td>\n",
       "      <td>9.045</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 19:00:00</th>\n",
       "      <td>40.344</td>\n",
       "      <td>110.126</td>\n",
       "      <td>50.266</td>\n",
       "      <td>111.930</td>\n",
       "      <td>147.682</td>\n",
       "      <td>138.990</td>\n",
       "      <td>64.124</td>\n",
       "      <td>161.130</td>\n",
       "      <td>77.326</td>\n",
       "      <td>26.568</td>\n",
       "      <td>...</td>\n",
       "      <td>49.036</td>\n",
       "      <td>596.550</td>\n",
       "      <td>64.370</td>\n",
       "      <td>117.424</td>\n",
       "      <td>109.060</td>\n",
       "      <td>62.074</td>\n",
       "      <td>115.374</td>\n",
       "      <td>39.852</td>\n",
       "      <td>18.450</td>\n",
       "      <td>46.822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 20:00:00</th>\n",
       "      <td>26.568</td>\n",
       "      <td>72.522</td>\n",
       "      <td>33.102</td>\n",
       "      <td>73.710</td>\n",
       "      <td>97.254</td>\n",
       "      <td>91.530</td>\n",
       "      <td>42.228</td>\n",
       "      <td>106.110</td>\n",
       "      <td>50.922</td>\n",
       "      <td>17.496</td>\n",
       "      <td>...</td>\n",
       "      <td>32.292</td>\n",
       "      <td>392.850</td>\n",
       "      <td>42.390</td>\n",
       "      <td>77.328</td>\n",
       "      <td>71.820</td>\n",
       "      <td>40.878</td>\n",
       "      <td>75.978</td>\n",
       "      <td>26.244</td>\n",
       "      <td>12.150</td>\n",
       "      <td>30.834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 21:00:00</th>\n",
       "      <td>20.172</td>\n",
       "      <td>55.063</td>\n",
       "      <td>25.133</td>\n",
       "      <td>55.965</td>\n",
       "      <td>73.841</td>\n",
       "      <td>69.495</td>\n",
       "      <td>32.062</td>\n",
       "      <td>80.565</td>\n",
       "      <td>38.663</td>\n",
       "      <td>13.284</td>\n",
       "      <td>...</td>\n",
       "      <td>24.518</td>\n",
       "      <td>298.275</td>\n",
       "      <td>32.185</td>\n",
       "      <td>58.712</td>\n",
       "      <td>54.530</td>\n",
       "      <td>31.037</td>\n",
       "      <td>57.687</td>\n",
       "      <td>19.926</td>\n",
       "      <td>9.225</td>\n",
       "      <td>23.411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 22:00:00</th>\n",
       "      <td>13.284</td>\n",
       "      <td>36.261</td>\n",
       "      <td>16.551</td>\n",
       "      <td>36.855</td>\n",
       "      <td>48.627</td>\n",
       "      <td>45.765</td>\n",
       "      <td>21.114</td>\n",
       "      <td>53.055</td>\n",
       "      <td>25.461</td>\n",
       "      <td>8.748</td>\n",
       "      <td>...</td>\n",
       "      <td>16.146</td>\n",
       "      <td>196.425</td>\n",
       "      <td>21.195</td>\n",
       "      <td>38.664</td>\n",
       "      <td>35.910</td>\n",
       "      <td>20.439</td>\n",
       "      <td>37.989</td>\n",
       "      <td>13.122</td>\n",
       "      <td>6.075</td>\n",
       "      <td>15.417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 23:00:00</th>\n",
       "      <td>9.840</td>\n",
       "      <td>26.860</td>\n",
       "      <td>12.260</td>\n",
       "      <td>27.300</td>\n",
       "      <td>36.020</td>\n",
       "      <td>33.900</td>\n",
       "      <td>15.640</td>\n",
       "      <td>39.300</td>\n",
       "      <td>18.860</td>\n",
       "      <td>6.480</td>\n",
       "      <td>...</td>\n",
       "      <td>11.960</td>\n",
       "      <td>145.500</td>\n",
       "      <td>15.700</td>\n",
       "      <td>28.640</td>\n",
       "      <td>26.600</td>\n",
       "      <td>15.140</td>\n",
       "      <td>28.140</td>\n",
       "      <td>9.720</td>\n",
       "      <td>4.500</td>\n",
       "      <td>11.420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10224 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Austin-Forest Park  Harlem-Lake  Pulaski-Lake    Davis  \\\n",
       "2018-11-01 00:00:00              24.728       44.231        17.325   41.074   \n",
       "2018-11-01 01:00:00              24.728       44.231        17.325   41.074   \n",
       "2018-11-01 02:00:00              11.240       20.105         7.875   18.670   \n",
       "2018-11-01 03:00:00              11.240       20.105         7.875   18.670   \n",
       "2018-11-01 04:00:00              11.240       20.105         7.875   18.670   \n",
       "...                                 ...          ...           ...      ...   \n",
       "2019-12-29 19:00:00              40.344      110.126        50.266  111.930   \n",
       "2019-12-29 20:00:00              26.568       72.522        33.102   73.710   \n",
       "2019-12-29 21:00:00              20.172       55.063        25.133   55.965   \n",
       "2019-12-29 22:00:00              13.284       36.261        16.551   36.855   \n",
       "2019-12-29 23:00:00               9.840       26.860        12.260   27.300   \n",
       "\n",
       "                     Belmont-O'Hare  Sheridan  Damen-Brown    Morse  \\\n",
       "2018-11-01 00:00:00          56.210    60.280       34.518   52.943   \n",
       "2018-11-01 01:00:00          56.210    60.280       34.518   52.943   \n",
       "2018-11-01 02:00:00          25.550    27.400       15.690   24.065   \n",
       "2018-11-01 03:00:00          25.550    27.400       15.690   24.065   \n",
       "2018-11-01 04:00:00          25.550    27.400       15.690   24.065   \n",
       "...                             ...       ...          ...      ...   \n",
       "2019-12-29 19:00:00         147.682   138.990       64.124  161.130   \n",
       "2019-12-29 20:00:00          97.254    91.530       42.228  106.110   \n",
       "2019-12-29 21:00:00          73.841    69.495       32.062   80.565   \n",
       "2019-12-29 22:00:00          48.627    45.765       21.114   53.055   \n",
       "2019-12-29 23:00:00          36.020    33.900       15.640   39.300   \n",
       "\n",
       "                     35th/Archer    51st  ...  Addison-Brown  Chicago/State  \\\n",
       "2018-11-01 00:00:00       37.741  20.680  ...         31.174        165.506   \n",
       "2018-11-01 01:00:00       37.741  20.680  ...         31.174        165.506   \n",
       "2018-11-01 02:00:00       17.155   9.400  ...         14.170         75.230   \n",
       "2018-11-01 03:00:00       17.155   9.400  ...         14.170         75.230   \n",
       "2018-11-01 04:00:00       17.155   9.400  ...         14.170         75.230   \n",
       "...                          ...     ...  ...            ...            ...   \n",
       "2019-12-29 19:00:00       77.326  26.568  ...         49.036        596.550   \n",
       "2019-12-29 20:00:00       50.922  17.496  ...         32.292        392.850   \n",
       "2019-12-29 21:00:00       38.663  13.284  ...         24.518        298.275   \n",
       "2019-12-29 22:00:00       25.461   8.748  ...         16.146        196.425   \n",
       "2019-12-29 23:00:00       18.860   6.480  ...         11.960        145.500   \n",
       "\n",
       "                     Irving Park-Brown  Western-Brown  Harrison  \\\n",
       "2018-11-01 00:00:00             40.216         47.157    52.866   \n",
       "2018-11-01 01:00:00             40.216         47.157    52.866   \n",
       "2018-11-01 02:00:00             18.280         21.435    24.030   \n",
       "2018-11-01 03:00:00             18.280         21.435    24.030   \n",
       "2018-11-01 04:00:00             18.280         21.435    24.030   \n",
       "...                                ...            ...       ...   \n",
       "2019-12-29 19:00:00             64.370        117.424   109.060   \n",
       "2019-12-29 20:00:00             42.390         77.328    71.820   \n",
       "2019-12-29 21:00:00             32.185         58.712    54.530   \n",
       "2019-12-29 22:00:00             21.195         38.664    35.910   \n",
       "2019-12-29 23:00:00             15.700         28.640    26.600   \n",
       "\n",
       "                     Montrose-Brown  Morgan-Lake  Conservatory  Oakton-Skokie  \\\n",
       "2018-11-01 00:00:00          31.427       41.107        10.714         12.540   \n",
       "2018-11-01 01:00:00          31.427       41.107        10.714         12.540   \n",
       "2018-11-01 02:00:00          14.285       18.685         4.870          5.700   \n",
       "2018-11-01 03:00:00          14.285       18.685         4.870          5.700   \n",
       "2018-11-01 04:00:00          14.285       18.685         4.870          5.700   \n",
       "...                             ...          ...           ...            ...   \n",
       "2019-12-29 19:00:00          62.074      115.374        39.852         18.450   \n",
       "2019-12-29 20:00:00          40.878       75.978        26.244         12.150   \n",
       "2019-12-29 21:00:00          31.037       57.687        19.926          9.225   \n",
       "2019-12-29 22:00:00          20.439       37.989        13.122          6.075   \n",
       "2019-12-29 23:00:00          15.140       28.140         9.720          4.500   \n",
       "\n",
       "                     Cermak-McCormick Place  \n",
       "2018-11-01 00:00:00                  19.899  \n",
       "2018-11-01 01:00:00                  19.899  \n",
       "2018-11-01 02:00:00                   9.045  \n",
       "2018-11-01 03:00:00                   9.045  \n",
       "2018-11-01 04:00:00                   9.045  \n",
       "...                                     ...  \n",
       "2019-12-29 19:00:00                  46.822  \n",
       "2019-12-29 20:00:00                  30.834  \n",
       "2019-12-29 21:00:00                  23.411  \n",
       "2019-12-29 22:00:00                  15.417  \n",
       "2019-12-29 23:00:00                  11.420  \n",
       "\n",
       "[10224 rows x 126 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total_hour_residential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_total_hour_business.to_csv(\"hourly CTA transit residential areas.csv\")\n",
    "df_total_hour_residential.to_csv(\"hourly CTA transit business areas.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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