ML-ttc-script.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f3c88982",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "\n",
    "#reading each xlsx and changing columns to match to then concat\n",
    "df2014 = pd.read_excel('data/ttc-streetcar-delay-data-2014.xlsx')\n",
    "df2015 = pd.read_excel('data/ttc-streetcar-delay-data-2015.xlsx')\n",
    "df2016 = pd.read_excel('data/ttc-streetcar-delay-data-2016.xlsx')\n",
    "df2017 = pd.read_excel('data/ttc-streetcar-delay-data-2017.xlsx')\n",
    "df2018 = pd.read_excel('data/ttc-streetcar-delay-data-2018.xlsx')\n",
    "df2019 = pd.read_excel('data/ttc-streetcar-delay-data-2019.xlsx')\n",
    "df2020 = pd.read_excel('data/ttc-streetcar-delay-data-2020_final.xlsx').rename(columns={'Delay':'Min Delay',\n",
    "                                                                                  'Gap':'Min Gap'})\n",
    "df2021 = pd.read_excel('data/ttc-streetcar-delay-data-jan-2021-to-august-2021.xlsx').rename(columns={'Bound':'Direction',\n",
    "                                                                                               'Date':'Report Date',\n",
    "                                                                                               'Line':'Route'})\n",
    "\n",
    "df2014['Time'] = pd.to_datetime(df2014['Time'], format='%H:%M:%S').dt.time\n",
    "df2015['Time'] = pd.to_datetime(df2015['Time'], format='%H:%M:%S').dt.time\n",
    "df2016['Time'] = pd.to_datetime(df2016['Time'], format='%H:%M:%S').dt.time\n",
    "df2017['Time'] = pd.to_datetime(df2017['Time'], format='%H:%M:%S').dt.time\n",
    "df2018['Time'] = pd.to_datetime(df2018['Time'], format='%H:%M:%S').dt.time\n",
    "df2019['Time'] = pd.to_datetime(df2019['Time'], format='%H:%M:%S').dt.time\n",
    "df2020['Time'] = pd.to_datetime(df2020['Time'], format='%H:%M:%S').dt.time\n",
    "df2021['Time'] = pd.to_datetime(df2021['Time'], format='%H:%M:%S').dt.time\n",
    "\n",
    "frames = [df2014, df2015, df2016, df2017, df2018, df2019, df2020, df2021]\n",
    "df_all = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d7e842db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Report_Date</th>\n",
       "      <th>Route</th>\n",
       "      <th>Time</th>\n",
       "      <th>Day</th>\n",
       "      <th>Location</th>\n",
       "      <th>Incident</th>\n",
       "      <th>Min_Delay</th>\n",
       "      <th>Min_Gap</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Vehicle</th>\n",
       "      <th>Round_Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>505</td>\n",
       "      <td>06:31:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Dundas and Roncesvalles</td>\n",
       "      <td>Late Leaving Garage</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>E/B</td>\n",
       "      <td>4018.0</td>\n",
       "      <td>06:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>12:43:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>King and Shaw</td>\n",
       "      <td>Utilized Off Route</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>E/B</td>\n",
       "      <td>4128.0</td>\n",
       "      <td>12:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>501</td>\n",
       "      <td>14:01:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Kingston road and Bingham</td>\n",
       "      <td>Held By</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W/B</td>\n",
       "      <td>4016.0</td>\n",
       "      <td>14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>14:22:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>King St. and Roncesvalles Ave.</td>\n",
       "      <td>Investigation</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>W/B</td>\n",
       "      <td>4175.0</td>\n",
       "      <td>14:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>16:42:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>King and Bathurst</td>\n",
       "      <td>Utilized Off Route</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>E/B</td>\n",
       "      <td>4080.0</td>\n",
       "      <td>16:30:00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>501</td>\n",
       "      <td>22:43:00</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>QUEEN AND BROADVIEW</td>\n",
       "      <td>Emergency Services</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4579.0</td>\n",
       "      <td>22:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>504</td>\n",
       "      <td>00:15:00</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>KING AND SPADINA</td>\n",
       "      <td>Mechanical</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4461.0</td>\n",
       "      <td>00:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>510</td>\n",
       "      <td>00:41:00</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>SPADINA STATION</td>\n",
       "      <td>Emergency Services</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4603.0</td>\n",
       "      <td>00:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>501</td>\n",
       "      <td>00:46:00</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>QUEEN AND CHURCH</td>\n",
       "      <td>Held By</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4432.0</td>\n",
       "      <td>01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>510</td>\n",
       "      <td>01:23:00</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>SPADINA AND COLLEGE</td>\n",
       "      <td>Cleaning</td>\n",
       "      <td>12.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4403.0</td>\n",
       "      <td>01:30:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9080 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Report_Date Route      Time       Day                        Location  \\\n",
       "0     2014-01-02   505  06:31:00  Thursday         Dundas and Roncesvalles   \n",
       "1     2014-01-02   504  12:43:00  Thursday                   King and Shaw   \n",
       "2     2014-01-02   501  14:01:00  Thursday       Kingston road and Bingham   \n",
       "3     2014-01-02   504  14:22:00  Thursday  King St. and Roncesvalles Ave.   \n",
       "4     2014-01-02   504  16:42:00  Thursday               King and Bathurst   \n",
       "...          ...   ...       ...       ...                             ...   \n",
       "1013  2021-01-31   501  22:43:00    Sunday             QUEEN AND BROADVIEW   \n",
       "1014  2021-01-31   504  00:15:00    Sunday                KING AND SPADINA   \n",
       "1015  2021-01-31   510  00:41:00    Sunday                 SPADINA STATION   \n",
       "1016  2021-01-31   501  00:46:00    Sunday                QUEEN AND CHURCH   \n",
       "1017  2021-01-31   510  01:23:00    Sunday             SPADINA AND COLLEGE   \n",
       "\n",
       "                 Incident  Min_Delay  Min_Gap Direction  Vehicle Round_Time  \n",
       "0     Late Leaving Garage        4.0      8.0       E/B   4018.0   06:30:00  \n",
       "1      Utilized Off Route       20.0     22.0       E/B   4128.0   12:30:00  \n",
       "2                 Held By       13.0     19.0       W/B   4016.0   14:00:00  \n",
       "3           Investigation        7.0     11.0       W/B   4175.0   14:30:00  \n",
       "4      Utilized Off Route        3.0      6.0       E/B   4080.0   16:30:00  \n",
       "...                   ...        ...      ...       ...      ...        ...  \n",
       "1013   Emergency Services       11.0     21.0         W   4579.0   22:30:00  \n",
       "1014           Mechanical        3.0      8.0         W   4461.0   00:30:00  \n",
       "1015   Emergency Services       20.0     30.0       NaN   4603.0   00:30:00  \n",
       "1016              Held By       13.0     23.0         W   4432.0   01:00:00  \n",
       "1017             Cleaning       12.0     24.0         N   4403.0   01:30:00  \n",
       "\n",
       "[9080 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = df_all.rename(columns={'Report Date':'Report_Date','Min Delay':'Min_Delay','Min Gap':'Min_Gap',\n",
    "                       'Round Time':'Round_Time'})\n",
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78488a54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_all.Location.value_counts().nlargest(50).plot(kind='barh', figsize=(10,15))\n",
    "ax.set_xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "27fb14d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Incident\n",
       "Mechanical                  33239.0\n",
       "Held By                     19725.0\n",
       "Investigation               16716.0\n",
       "Late Leaving Garage          8298.0\n",
       "Emergency Services           6086.0\n",
       "General Delay                4695.0\n",
       "Diversion                    3196.0\n",
       "Operations                   2353.0\n",
       "Utilized Off Route           2100.0\n",
       "Cleaning                     1448.0\n",
       "Security                      784.0\n",
       "Overhead                      440.0\n",
       "Overhead - Pantograph         195.0\n",
       "Collision - TTC Involved      173.0\n",
       "Late                          119.0\n",
       "Rail/Switches                  50.0\n",
       "Name: Min_Delay, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.groupby('Incident')['Min_Delay'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1434a91f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Round_Time\n",
       "06:00:00               5701.0\n",
       "05:30:00               4551.0\n",
       "06:30:00               4159.0\n",
       "07:00:00               3412.0\n",
       "09:30:00               3293.0\n",
       "08:00:00               3065.0\n",
       "14:30:00               2920.0\n",
       "13:30:00               2899.0\n",
       "17:00:00               2841.0\n",
       "19:00:00               2746.0\n",
       "15:00:00               2727.0\n",
       "13:00:00               2587.0\n",
       "18:30:00               2494.0\n",
       "16:30:00               2490.0\n",
       "16:00:00               2472.0\n",
       "08:30:00               2308.0\n",
       "07:30:00               2273.0\n",
       "10:30:00               2198.0\n",
       "14:00:00               2180.0\n",
       "15:30:00               2124.0\n",
       "17:30:00               2115.0\n",
       "12:00:00               2100.0\n",
       "20:00:00               2007.0\n",
       "23:00:00               1961.0\n",
       "09:00:00               1949.0\n",
       "21:00:00               1934.0\n",
       "19:30:00               1878.0\n",
       "10:00:00               1828.0\n",
       "20:30:00               1778.0\n",
       "12:30:00               1663.0\n",
       "18:00:00               1614.0\n",
       "11:30:00               1568.0\n",
       "05:00:00               1552.0\n",
       "01:30:00               1503.0\n",
       "11:00:00               1470.0\n",
       "22:30:00               1393.0\n",
       "21:30:00               1354.0\n",
       "22:00:00               1348.0\n",
       "03:30:00               1189.0\n",
       "02:00:00               1176.0\n",
       "00:30:00               1061.0\n",
       "02:30:00               1059.0\n",
       "23:30:00               1013.0\n",
       "01:00:00                902.0\n",
       "04:30:00                668.0\n",
       "04:00:00                665.0\n",
       "1900-01-01 00:00:00     520.0\n",
       "03:00:00                509.0\n",
       "00:00:00                400.0\n",
       "Name: Min_Delay, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all['Round_Time'] = df_all['Round_Time'].astype(str)\n",
    "df_all.groupby('Round_Time')['Min_Delay'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96c65712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location\n",
       "Leslie Barns                             3579.0\n",
       "Russell Yard                             1255.0\n",
       "Queen and Roncesvalles                    928.0\n",
       "Queen and Connaught                       776.0\n",
       "Dundas West Station                       747.0\n",
       "Queen and Broadview                       678.0\n",
       "Roncesvalles Yard                         602.0\n",
       "King and Dufferin                         558.0\n",
       "Roncesvalles and Queen                    544.0\n",
       "Roncy Yard                                542.0\n",
       "Broadview Station                         534.0\n",
       "king and spadina eb, kingand yirk wb      529.0\n",
       "Union Station                             513.0\n",
       "Spadina @ Dundas                          506.0\n",
       "Neville Loop                              496.0\n",
       "Humber Loop                               495.0\n",
       "St. Clair and Dufferin                    492.0\n",
       "Bathurst and Fleet                        488.0\n",
       "Queens Quay and Spadina                   462.0\n",
       "Oakwood to Gunns Loop both directions     458.0\n",
       "Dundas and Victoria                       438.0\n",
       "SPADINA AND QUEEN'S QU                    429.0\n",
       "King and Spadina                          406.0\n",
       "Main Station                              370.0\n",
       "DUNDAS WEST STATION                       359.0\n",
       "Bingham Loop                              358.0\n",
       "Elmer and Queen                           352.0\n",
       "CNE Loop                                  335.0\n",
       "Queen at Roncesvalles                     333.0\n",
       "Queen at Boulton                          324.0\n",
       "Broadview and Queen                       318.0\n",
       "lakeshore at Royal York                   313.0\n",
       "Roncesvalles Carhouse.                    312.0\n",
       "Broadview and Dundas                      301.0\n",
       "Broadview station                         294.0\n",
       "Dundas west station                       292.0\n",
       "Roncesvalles and Howard Park              291.0\n",
       "Coxwell and Gerrard                       290.0\n",
       "LOWER GERRARD AND COXW                    285.0\n",
       "WOODBINE AND KINGSTON                     285.0\n",
       "Queen at Connaught                        284.0\n",
       "Lakeshore/Symons                          284.0\n",
       "Broadview and Dunds                       283.0\n",
       "Broadview and Gerrard                     277.0\n",
       "BROADVIEW STATION                         272.0\n",
       "Spadina Station                           265.0\n",
       "Spadina and Queen                         259.0\n",
       "Queen's Quay and Bay St.                  254.0\n",
       "College and Spadina                       252.0\n",
       "High Park Loop                            245.0\n",
       "Name: Min_Delay, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.groupby('Location')['Min_Delay'].sum().sort_values(ascending=False).nlargest(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "270f3172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day\n",
       "Tuesday      16406.0\n",
       "Friday       15378.0\n",
       "Wednesday    15295.0\n",
       "Thursday     15217.0\n",
       "Monday       12875.0\n",
       "Saturday     12517.0\n",
       "Sunday       11929.0\n",
       "Name: Min_Delay, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.groupby('Day')['Min_Delay'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "575d8137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean is 2033.0 and standard deviation is 1047.77\n"
     ]
    }
   ],
   "source": [
    "time_delay = df_all.groupby('Round_Time')['Min_Delay'].sum()\n",
    "std = time_delay.std()\n",
    "mean = time_delay.mean()\n",
    "print('The mean is %s and standard deviation is %g' %(mean, std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e83c7fa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Min_Delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th>Incident</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Friday</th>\n",
       "      <th>Mechanical</th>\n",
       "      <td>5188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investigation</th>\n",
       "      <td>2565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Held By</th>\n",
       "      <td>2173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Late Leaving Garage</th>\n",
       "      <td>1667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emergency Services</th>\n",
       "      <td>1145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Monday</th>\n",
       "      <th>Mechanical</th>\n",
       "      <td>5073.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Held By</th>\n",
       "      <td>2696.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investigation</th>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Late Leaving Garage</th>\n",
       "      <td>851.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Delay</th>\n",
       "      <td>703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Saturday</th>\n",
       "      <th>Mechanical</th>\n",
       "      <td>3942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Held By</th>\n",
       "      <td>2190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investigation</th>\n",
       "      <td>2149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diversion</th>\n",
       "      <td>823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Delay</th>\n",
       "      <td>743.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Sunday</th>\n",
       "      <th>Mechanical</th>\n",
       "      <td>3083.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investigation</th>\n",
       "      <td>2318.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Held By</th>\n",
       "      <td>2305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diversion</th>\n",
       "      <td>1235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Late Leaving Garage</th>\n",
       "      <td>1018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Thursday</th>\n",
       "      <th>Mechanical</th>\n",
       "      <td>4878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Held By</th>\n",
       "      <td>3216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investigation</th>\n",
       "      <td>2897.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Late Leaving Garage</th>\n",
       "      <td>2095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Delay</th>\n",
       "      <td>534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Tuesday</th>\n",
       "      <th>Mechanical</th>\n",
       "      <td>5790.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Held By</th>\n",
       "      <td>3409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investigation</th>\n",
       "      <td>2868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emergency Services</th>\n",
       "      <td>1340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Late Leaving Garage</th>\n",
       "      <td>1151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Wednesday</th>\n",
       "      <th>Mechanical</th>\n",
       "      <td>5285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Held By</th>\n",
       "      <td>3736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investigation</th>\n",
       "      <td>1919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emergency Services</th>\n",
       "      <td>1055.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Delay</th>\n",
       "      <td>880.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Min_Delay\n",
       "Day       Incident                      \n",
       "Friday    Mechanical              5188.0\n",
       "          Investigation           2565.0\n",
       "          Held By                 2173.0\n",
       "          Late Leaving Garage     1667.0\n",
       "          Emergency Services      1145.0\n",
       "Monday    Mechanical              5073.0\n",
       "          Held By                 2696.0\n",
       "          Investigation           2000.0\n",
       "          Late Leaving Garage      851.0\n",
       "          General Delay            703.0\n",
       "Saturday  Mechanical              3942.0\n",
       "          Held By                 2190.0\n",
       "          Investigation           2149.0\n",
       "          Diversion                823.0\n",
       "          General Delay            743.0\n",
       "Sunday    Mechanical              3083.0\n",
       "          Investigation           2318.0\n",
       "          Held By                 2305.0\n",
       "          Diversion               1235.0\n",
       "          Late Leaving Garage     1018.0\n",
       "Thursday  Mechanical              4878.0\n",
       "          Held By                 3216.0\n",
       "          Investigation           2897.0\n",
       "          Late Leaving Garage     2095.0\n",
       "          General Delay            534.0\n",
       "Tuesday   Mechanical              5790.0\n",
       "          Held By                 3409.0\n",
       "          Investigation           2868.0\n",
       "          Emergency Services      1340.0\n",
       "          Late Leaving Garage     1151.0\n",
       "Wednesday Mechanical              5285.0\n",
       "          Held By                 3736.0\n",
       "          Investigation           1919.0\n",
       "          Emergency Services      1055.0\n",
       "          General Delay            880.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg = df_all.groupby(['Day','Incident']).agg({'Min_Delay':sum})\n",
    "g = df_agg['Min_Delay'].groupby('Day', group_keys=False)\n",
    "res = g.apply(lambda x: x.sort_values(ascending=False).head(5))\n",
    "pd.DataFrame(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2953e79d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Report_Date</th>\n",
       "      <th>Route</th>\n",
       "      <th>Time</th>\n",
       "      <th>Day</th>\n",
       "      <th>Location</th>\n",
       "      <th>Incident</th>\n",
       "      <th>Min_Delay</th>\n",
       "      <th>Min_Gap</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Vehicle</th>\n",
       "      <th>Round_Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>505</td>\n",
       "      <td>06:31:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>dundas_and_roncesvalles</td>\n",
       "      <td>late_leaving_garage</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>E/B</td>\n",
       "      <td>4018.0</td>\n",
       "      <td>06:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>12:43:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>king_and_shaw</td>\n",
       "      <td>utilized_off_route</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>E/B</td>\n",
       "      <td>4128.0</td>\n",
       "      <td>12:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>501</td>\n",
       "      <td>14:01:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>kingston_road_and_bingham</td>\n",
       "      <td>held_by</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W/B</td>\n",
       "      <td>4016.0</td>\n",
       "      <td>14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>14:22:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>king_st._and_roncesvalles_ave.</td>\n",
       "      <td>investigation</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>W/B</td>\n",
       "      <td>4175.0</td>\n",
       "      <td>14:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>16:42:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>king_and_bathurst</td>\n",
       "      <td>utilized_off_route</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>E/B</td>\n",
       "      <td>4080.0</td>\n",
       "      <td>16:30:00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>501</td>\n",
       "      <td>22:43:00</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>queen_and_broadview</td>\n",
       "      <td>emergency_services</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4579.0</td>\n",
       "      <td>22:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>504</td>\n",
       "      <td>00:15:00</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>king_and_spadina</td>\n",
       "      <td>mechanical</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4461.0</td>\n",
       "      <td>00:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>510</td>\n",
       "      <td>00:41:00</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>spadina_station</td>\n",
       "      <td>emergency_services</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4603.0</td>\n",
       "      <td>00:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>501</td>\n",
       "      <td>00:46:00</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>queen_and_church</td>\n",
       "      <td>held_by</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4432.0</td>\n",
       "      <td>01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>510</td>\n",
       "      <td>01:23:00</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>spadina_and_college</td>\n",
       "      <td>cleaning</td>\n",
       "      <td>12.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4403.0</td>\n",
       "      <td>01:30:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9080 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Report_Date Route      Time       Day                        Location  \\\n",
       "0     2014-01-02   505  06:31:00  Thursday         dundas_and_roncesvalles   \n",
       "1     2014-01-02   504  12:43:00  Thursday                   king_and_shaw   \n",
       "2     2014-01-02   501  14:01:00  Thursday       kingston_road_and_bingham   \n",
       "3     2014-01-02   504  14:22:00  Thursday  king_st._and_roncesvalles_ave.   \n",
       "4     2014-01-02   504  16:42:00  Thursday               king_and_bathurst   \n",
       "...          ...   ...       ...       ...                             ...   \n",
       "1013  2021-01-31   501  22:43:00    Sunday             queen_and_broadview   \n",
       "1014  2021-01-31   504  00:15:00    Sunday                king_and_spadina   \n",
       "1015  2021-01-31   510  00:41:00    Sunday                 spadina_station   \n",
       "1016  2021-01-31   501  00:46:00    Sunday                queen_and_church   \n",
       "1017  2021-01-31   510  01:23:00    Sunday             spadina_and_college   \n",
       "\n",
       "                 Incident  Min_Delay  Min_Gap Direction  Vehicle Round_Time  \n",
       "0     late_leaving_garage        4.0      8.0       E/B   4018.0   06:30:00  \n",
       "1      utilized_off_route       20.0     22.0       E/B   4128.0   12:30:00  \n",
       "2                 held_by       13.0     19.0       W/B   4016.0   14:00:00  \n",
       "3           investigation        7.0     11.0       W/B   4175.0   14:30:00  \n",
       "4      utilized_off_route        3.0      6.0       E/B   4080.0   16:30:00  \n",
       "...                   ...        ...      ...       ...      ...        ...  \n",
       "1013   emergency_services       11.0     21.0         W   4579.0   22:30:00  \n",
       "1014           mechanical        3.0      8.0         W   4461.0   00:30:00  \n",
       "1015   emergency_services       20.0     30.0       NaN   4603.0   00:30:00  \n",
       "1016              held_by       13.0     23.0         W   4432.0   01:00:00  \n",
       "1017             cleaning       12.0     24.0         N   4403.0   01:30:00  \n",
       "\n",
       "[9080 rows x 11 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_all\n",
    "\n",
    "def clean_text(text):\n",
    "    text = text.lower()\n",
    "    text = text.replace(\" \", \"_\")\n",
    "    return text\n",
    "\n",
    "df['Location'] = df['Location'].astype(str).apply(clean_text)\n",
    "df['Incident'] = df['Incident'].astype(str).apply(clean_text)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a53f50fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['late_leaving_garage', 'utilized_off_route', 'held_by',\n",
       "       'investigation', 'mechanical', 'general_delay',\n",
       "       'emergency_services', 'diversion', 'overhead_-_pantograph',\n",
       "       'operations', 'cleaning', 'late', 'security', 'overhead',\n",
       "       'collision_-_ttc_involved', 'rail/switches'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "#np.set_printoptions(threshold=10)\n",
    "np.set_printoptions(threshold=sys.maxsize)\n",
    "\n",
    "column_values = df[[\"Incident\"]].values.ravel()\n",
    "unique_values =  pd.unique(column_values)\n",
    "unique_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d1b6f53b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all.to_csv('ttc-data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5a3ed57d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Report_Date</th>\n",
       "      <th>Route</th>\n",
       "      <th>Time</th>\n",
       "      <th>Day</th>\n",
       "      <th>Location</th>\n",
       "      <th>Incident</th>\n",
       "      <th>Min_Delay</th>\n",
       "      <th>Min_Gap</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Vehicle</th>\n",
       "      <th>Round_Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>505</td>\n",
       "      <td>06:31:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>dundas_and_roncesvalles</td>\n",
       "      <td>late_leaving_garage</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>E/B</td>\n",
       "      <td>4018.0</td>\n",
       "      <td>06:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>12:43:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>king_and_shaw</td>\n",
       "      <td>utilized_off_route</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>E/B</td>\n",
       "      <td>4128.0</td>\n",
       "      <td>12:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>501</td>\n",
       "      <td>14:01:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>kingston_road_and_bingham</td>\n",
       "      <td>held_by</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W/B</td>\n",
       "      <td>4016.0</td>\n",
       "      <td>14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>14:22:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>king_st._and_roncesvalles_ave.</td>\n",
       "      <td>investigation</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>W/B</td>\n",
       "      <td>4175.0</td>\n",
       "      <td>14:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>504</td>\n",
       "      <td>16:42:00</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>king_and_bathurst</td>\n",
       "      <td>utilized_off_route</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>E/B</td>\n",
       "      <td>4080.0</td>\n",
       "      <td>16:30:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Report_Date Route      Time       Day                        Location  \\\n",
       "0  2014-01-02   505  06:31:00  Thursday         dundas_and_roncesvalles   \n",
       "1  2014-01-02   504  12:43:00  Thursday                   king_and_shaw   \n",
       "2  2014-01-02   501  14:01:00  Thursday       kingston_road_and_bingham   \n",
       "3  2014-01-02   504  14:22:00  Thursday  king_st._and_roncesvalles_ave.   \n",
       "4  2014-01-02   504  16:42:00  Thursday               king_and_bathurst   \n",
       "\n",
       "              Incident  Min_Delay  Min_Gap Direction  Vehicle Round_Time  \n",
       "0  late_leaving_garage        4.0      8.0       E/B   4018.0   06:30:00  \n",
       "1   utilized_off_route       20.0     22.0       E/B   4128.0   12:30:00  \n",
       "2              held_by       13.0     19.0       W/B   4016.0   14:00:00  \n",
       "3        investigation        7.0     11.0       W/B   4175.0   14:30:00  \n",
       "4   utilized_off_route        3.0      6.0       E/B   4080.0   16:30:00  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "057dc428",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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