doc/Single_station_all_day_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single Station Analysis - Historic\n",
    "Building on the [API Exploration Notebook](API_exploration.ipynb) and the [Filtering Observed Arrivals](Filtering Observed Arrivals.ipynb) notebook. Let's explore a different approach for analyzing the data. Note that, I modified the [api scraper](../ttc_api_scraper.py) to only retrieve the soonest time from the next subway API. This should (hopefully) help with some of the issues we were previously having. I made a new database and ran the API for a few hours on Sunday polling only St. George station (station_id == 10) at a poll frequency of once every 10 seconds. I will post the data online so that others can try it out.\n",
    "\n",
    "Created by Rami on May 6/2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "from psycopg2 import connect\n",
    "import configparser\n",
    "import pandas as pd\n",
    "import pandas.io.sql as pandasql\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.widgets import Slider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    con.close()\n",
    "except:\n",
    "    print(\"No existing connection... moving on\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONFIG = configparser.ConfigParser(interpolation=None)\n",
    "CONFIG.read('../db.cfg')\n",
    "dbset = CONFIG['DBSETTINGS']\n",
    "con = connect(**dbset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieving data from the database\n",
    "Let's start by getting data from our database by joining along ```requestid``` to simplify things for us we're only going to look at Southbound trains for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = '''SELECT requestid, stationid, lineid, create_date, request_date, station_char, subwayline, system_message_type, \n",
    "            timint, traindirection, trainid, train_message\n",
    "FROM requests\n",
    "INNER JOIN ntas_data USING (requestid)\n",
    "WHERE request_date >= '2017-06-14'::DATE + interval '6 hours 5 minutes' \n",
    "AND request_date <  '2017-06-14'::DATE + interval '29 hours'\n",
    "AND stationid = 10\n",
    "AND traindirection = 'South'\n",
    "ORDER BY request_date\n",
    "''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "stg_south = pandasql.read_sql(sql, con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>requestid</th>\n",
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       "      <td>2017-06-14 06:05:04</td>\n",
       "      <td>2017-06-14 06:05:01.866256</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>11.552266</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
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       "      <th>2</th>\n",
       "      <td>5167447</td>\n",
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       "      <td>1</td>\n",
       "      <td>2017-06-14 06:05:04</td>\n",
       "      <td>2017-06-14 06:05:01.866256</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>16.417125</td>\n",
       "      <td>South</td>\n",
       "      <td>838</td>\n",
       "      <td>Delayed</td>\n",
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       "      <th>3</th>\n",
       "      <td>5167515</td>\n",
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       "      <td>1</td>\n",
       "      <td>2017-06-14 06:06:06</td>\n",
       "      <td>2017-06-14 06:06:02.215526</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>11.040278</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
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       "      <th>4</th>\n",
       "      <td>5167515</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:06:06</td>\n",
       "      <td>2017-06-14 06:06:02.215526</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>15.478819</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5167515</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:06:06</td>\n",
       "      <td>2017-06-14 06:06:02.215526</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>16.417125</td>\n",
       "      <td>South</td>\n",
       "      <td>838</td>\n",
       "      <td>Delayed</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5167583</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:07:06</td>\n",
       "      <td>2017-06-14 06:07:02.313463</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>10.073480</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5167583</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:07:06</td>\n",
       "      <td>2017-06-14 06:07:02.313463</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>14.007009</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5167583</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:07:06</td>\n",
       "      <td>2017-06-14 06:07:02.313463</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>16.417125</td>\n",
       "      <td>South</td>\n",
       "      <td>838</td>\n",
       "      <td>Delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5167651</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:08:10</td>\n",
       "      <td>2017-06-14 06:08:06.800012</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.233515</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5167651</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:08:10</td>\n",
       "      <td>2017-06-14 06:08:06.800012</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>9.587593</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5167651</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:08:10</td>\n",
       "      <td>2017-06-14 06:08:06.800012</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>13.501595</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5167719</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:09:05</td>\n",
       "      <td>2017-06-14 06:09:02.082338</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.512351</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5167719</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:09:05</td>\n",
       "      <td>2017-06-14 06:09:02.082338</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>8.684744</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5167719</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:09:05</td>\n",
       "      <td>2017-06-14 06:09:02.082338</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>12.688393</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5167787</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:10:05</td>\n",
       "      <td>2017-06-14 06:10:02.076938</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.729031</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5167787</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:10:05</td>\n",
       "      <td>2017-06-14 06:10:02.076938</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>7.898435</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5167787</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:10:05</td>\n",
       "      <td>2017-06-14 06:10:02.076938</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>11.552266</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5167855</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:11:09</td>\n",
       "      <td>2017-06-14 06:11:07.520184</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.534529</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5167855</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:11:09</td>\n",
       "      <td>2017-06-14 06:11:07.520184</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>7.039214</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5167855</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:11:09</td>\n",
       "      <td>2017-06-14 06:11:07.520184</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>10.556781</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5167923</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:12:09</td>\n",
       "      <td>2017-06-14 06:12:05.343393</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.664551</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5167923</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:12:09</td>\n",
       "      <td>2017-06-14 06:12:05.343393</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>6.199715</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5167923</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:12:09</td>\n",
       "      <td>2017-06-14 06:12:05.343393</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>9.885819</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5167991</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:13:06</td>\n",
       "      <td>2017-06-14 06:13:02.211966</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.957729</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5167991</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:13:06</td>\n",
       "      <td>2017-06-14 06:13:02.211966</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.696542</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5167991</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:13:06</td>\n",
       "      <td>2017-06-14 06:13:02.211966</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>9.587593</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5168059</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:14:06</td>\n",
       "      <td>2017-06-14 06:14:02.165433</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5168059</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:14:06</td>\n",
       "      <td>2017-06-14 06:14:02.165433</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.978515</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5168059</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:14:06</td>\n",
       "      <td>2017-06-14 06:14:02.165433</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>8.684744</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>5248023</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:50:14</td>\n",
       "      <td>2017-06-15 01:50:02.422329</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.580080</td>\n",
       "      <td>South</td>\n",
       "      <td>838</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3556</th>\n",
       "      <td>5248023</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:50:14</td>\n",
       "      <td>2017-06-15 01:50:02.422329</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.309362</td>\n",
       "      <td>South</td>\n",
       "      <td>152</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3557</th>\n",
       "      <td>5248023</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:50:14</td>\n",
       "      <td>2017-06-15 01:50:02.422329</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.318331</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3558</th>\n",
       "      <td>5248091</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:51:14</td>\n",
       "      <td>2017-06-15 01:51:02.415042</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>838</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3559</th>\n",
       "      <td>5248091</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:51:14</td>\n",
       "      <td>2017-06-15 01:51:02.415042</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.422056</td>\n",
       "      <td>South</td>\n",
       "      <td>152</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3560</th>\n",
       "      <td>5248091</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:51:14</td>\n",
       "      <td>2017-06-15 01:51:02.415042</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.564362</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3561</th>\n",
       "      <td>5248159</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:52:12</td>\n",
       "      <td>2017-06-15 01:52:02.404154</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.159522</td>\n",
       "      <td>South</td>\n",
       "      <td>152</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3562</th>\n",
       "      <td>5248159</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:52:12</td>\n",
       "      <td>2017-06-15 01:52:02.404154</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.422056</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3563</th>\n",
       "      <td>5248159</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:52:12</td>\n",
       "      <td>2017-06-15 01:52:02.404154</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>8.723740</td>\n",
       "      <td>South</td>\n",
       "      <td>874</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3564</th>\n",
       "      <td>5248227</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:53:10</td>\n",
       "      <td>2017-06-15 01:53:01.534627</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.283633</td>\n",
       "      <td>South</td>\n",
       "      <td>152</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3565</th>\n",
       "      <td>5248227</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:53:10</td>\n",
       "      <td>2017-06-15 01:53:01.534627</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.371189</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3566</th>\n",
       "      <td>5248227</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:53:10</td>\n",
       "      <td>2017-06-15 01:53:01.534627</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>7.572967</td>\n",
       "      <td>South</td>\n",
       "      <td>874</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>5248295</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:54:14</td>\n",
       "      <td>2017-06-15 01:54:02.234853</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>152</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3568</th>\n",
       "      <td>5248295</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:54:14</td>\n",
       "      <td>2017-06-15 01:54:02.234853</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.736318</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3569</th>\n",
       "      <td>5248295</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:54:14</td>\n",
       "      <td>2017-06-15 01:54:02.234853</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>6.747313</td>\n",
       "      <td>South</td>\n",
       "      <td>874</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3570</th>\n",
       "      <td>5248363</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:55:14</td>\n",
       "      <td>2017-06-15 01:55:01.750195</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.365478</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3571</th>\n",
       "      <td>5248363</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:55:14</td>\n",
       "      <td>2017-06-15 01:55:01.750195</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.940287</td>\n",
       "      <td>South</td>\n",
       "      <td>874</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3572</th>\n",
       "      <td>5248363</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:55:14</td>\n",
       "      <td>2017-06-15 01:55:01.750195</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>14.831384</td>\n",
       "      <td>South</td>\n",
       "      <td>672</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3573</th>\n",
       "      <td>5248431</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:56:13</td>\n",
       "      <td>2017-06-15 01:56:02.426207</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3574</th>\n",
       "      <td>5248431</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:56:13</td>\n",
       "      <td>2017-06-15 01:56:02.426207</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.645776</td>\n",
       "      <td>South</td>\n",
       "      <td>874</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3575</th>\n",
       "      <td>5248431</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:56:13</td>\n",
       "      <td>2017-06-15 01:56:02.426207</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>13.427176</td>\n",
       "      <td>South</td>\n",
       "      <td>672</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3576</th>\n",
       "      <td>5248499</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:57:11</td>\n",
       "      <td>2017-06-15 01:57:01.466266</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3577</th>\n",
       "      <td>5248499</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:57:11</td>\n",
       "      <td>2017-06-15 01:57:01.466266</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.013598</td>\n",
       "      <td>South</td>\n",
       "      <td>874</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3578</th>\n",
       "      <td>5248499</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:57:11</td>\n",
       "      <td>2017-06-15 01:57:01.466266</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>12.408482</td>\n",
       "      <td>South</td>\n",
       "      <td>672</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3579</th>\n",
       "      <td>5248567</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:58:14</td>\n",
       "      <td>2017-06-15 01:58:02.208761</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>Delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3580</th>\n",
       "      <td>5248567</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:58:14</td>\n",
       "      <td>2017-06-15 01:58:02.208761</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.318331</td>\n",
       "      <td>South</td>\n",
       "      <td>874</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3581</th>\n",
       "      <td>5248567</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:58:14</td>\n",
       "      <td>2017-06-15 01:58:02.208761</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>11.535980</td>\n",
       "      <td>South</td>\n",
       "      <td>672</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3582</th>\n",
       "      <td>5248635</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:59:14</td>\n",
       "      <td>2017-06-15 01:59:01.926096</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>Delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3583</th>\n",
       "      <td>5248635</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:59:14</td>\n",
       "      <td>2017-06-15 01:59:01.926096</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.309362</td>\n",
       "      <td>South</td>\n",
       "      <td>874</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3584</th>\n",
       "      <td>5248635</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:59:14</td>\n",
       "      <td>2017-06-15 01:59:01.926096</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>10.582762</td>\n",
       "      <td>South</td>\n",
       "      <td>672</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3585 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      requestid  stationid  lineid         create_date  \\\n",
       "0       5167447         10       1 2017-06-14 06:05:04   \n",
       "1       5167447         10       1 2017-06-14 06:05:04   \n",
       "2       5167447         10       1 2017-06-14 06:05:04   \n",
       "3       5167515         10       1 2017-06-14 06:06:06   \n",
       "4       5167515         10       1 2017-06-14 06:06:06   \n",
       "5       5167515         10       1 2017-06-14 06:06:06   \n",
       "6       5167583         10       1 2017-06-14 06:07:06   \n",
       "7       5167583         10       1 2017-06-14 06:07:06   \n",
       "8       5167583         10       1 2017-06-14 06:07:06   \n",
       "9       5167651         10       1 2017-06-14 06:08:10   \n",
       "10      5167651         10       1 2017-06-14 06:08:10   \n",
       "11      5167651         10       1 2017-06-14 06:08:10   \n",
       "12      5167719         10       1 2017-06-14 06:09:05   \n",
       "13      5167719         10       1 2017-06-14 06:09:05   \n",
       "14      5167719         10       1 2017-06-14 06:09:05   \n",
       "15      5167787         10       1 2017-06-14 06:10:05   \n",
       "16      5167787         10       1 2017-06-14 06:10:05   \n",
       "17      5167787         10       1 2017-06-14 06:10:05   \n",
       "18      5167855         10       1 2017-06-14 06:11:09   \n",
       "19      5167855         10       1 2017-06-14 06:11:09   \n",
       "20      5167855         10       1 2017-06-14 06:11:09   \n",
       "21      5167923         10       1 2017-06-14 06:12:09   \n",
       "22      5167923         10       1 2017-06-14 06:12:09   \n",
       "23      5167923         10       1 2017-06-14 06:12:09   \n",
       "24      5167991         10       1 2017-06-14 06:13:06   \n",
       "25      5167991         10       1 2017-06-14 06:13:06   \n",
       "26      5167991         10       1 2017-06-14 06:13:06   \n",
       "27      5168059         10       1 2017-06-14 06:14:06   \n",
       "28      5168059         10       1 2017-06-14 06:14:06   \n",
       "29      5168059         10       1 2017-06-14 06:14:06   \n",
       "...         ...        ...     ...                 ...   \n",
       "3555    5248023         10       1 2017-06-15 01:50:14   \n",
       "3556    5248023         10       1 2017-06-15 01:50:14   \n",
       "3557    5248023         10       1 2017-06-15 01:50:14   \n",
       "3558    5248091         10       1 2017-06-15 01:51:14   \n",
       "3559    5248091         10       1 2017-06-15 01:51:14   \n",
       "3560    5248091         10       1 2017-06-15 01:51:14   \n",
       "3561    5248159         10       1 2017-06-15 01:52:12   \n",
       "3562    5248159         10       1 2017-06-15 01:52:12   \n",
       "3563    5248159         10       1 2017-06-15 01:52:12   \n",
       "3564    5248227         10       1 2017-06-15 01:53:10   \n",
       "3565    5248227         10       1 2017-06-15 01:53:10   \n",
       "3566    5248227         10       1 2017-06-15 01:53:10   \n",
       "3567    5248295         10       1 2017-06-15 01:54:14   \n",
       "3568    5248295         10       1 2017-06-15 01:54:14   \n",
       "3569    5248295         10       1 2017-06-15 01:54:14   \n",
       "3570    5248363         10       1 2017-06-15 01:55:14   \n",
       "3571    5248363         10       1 2017-06-15 01:55:14   \n",
       "3572    5248363         10       1 2017-06-15 01:55:14   \n",
       "3573    5248431         10       1 2017-06-15 01:56:13   \n",
       "3574    5248431         10       1 2017-06-15 01:56:13   \n",
       "3575    5248431         10       1 2017-06-15 01:56:13   \n",
       "3576    5248499         10       1 2017-06-15 01:57:11   \n",
       "3577    5248499         10       1 2017-06-15 01:57:11   \n",
       "3578    5248499         10       1 2017-06-15 01:57:11   \n",
       "3579    5248567         10       1 2017-06-15 01:58:14   \n",
       "3580    5248567         10       1 2017-06-15 01:58:14   \n",
       "3581    5248567         10       1 2017-06-15 01:58:14   \n",
       "3582    5248635         10       1 2017-06-15 01:59:14   \n",
       "3583    5248635         10       1 2017-06-15 01:59:14   \n",
       "3584    5248635         10       1 2017-06-15 01:59:14   \n",
       "\n",
       "                   request_date station_char subwayline system_message_type  \\\n",
       "0    2017-06-14 06:05:01.866256         SGU2        YUS              Normal   \n",
       "1    2017-06-14 06:05:01.866256         SGU2        YUS              Normal   \n",
       "2    2017-06-14 06:05:01.866256         SGU2        YUS              Normal   \n",
       "3    2017-06-14 06:06:02.215526         SGU2        YUS              Normal   \n",
       "4    2017-06-14 06:06:02.215526         SGU2        YUS              Normal   \n",
       "5    2017-06-14 06:06:02.215526         SGU2        YUS              Normal   \n",
       "6    2017-06-14 06:07:02.313463         SGU2        YUS              Normal   \n",
       "7    2017-06-14 06:07:02.313463         SGU2        YUS              Normal   \n",
       "8    2017-06-14 06:07:02.313463         SGU2        YUS              Normal   \n",
       "9    2017-06-14 06:08:06.800012         SGU2        YUS              Normal   \n",
       "10   2017-06-14 06:08:06.800012         SGU2        YUS              Normal   \n",
       "11   2017-06-14 06:08:06.800012         SGU2        YUS              Normal   \n",
       "12   2017-06-14 06:09:02.082338         SGU2        YUS              Normal   \n",
       "13   2017-06-14 06:09:02.082338         SGU2        YUS              Normal   \n",
       "14   2017-06-14 06:09:02.082338         SGU2        YUS              Normal   \n",
       "15   2017-06-14 06:10:02.076938         SGU2        YUS              Normal   \n",
       "16   2017-06-14 06:10:02.076938         SGU2        YUS              Normal   \n",
       "17   2017-06-14 06:10:02.076938         SGU2        YUS              Normal   \n",
       "18   2017-06-14 06:11:07.520184         SGU2        YUS              Normal   \n",
       "19   2017-06-14 06:11:07.520184         SGU2        YUS              Normal   \n",
       "20   2017-06-14 06:11:07.520184         SGU2        YUS              Normal   \n",
       "21   2017-06-14 06:12:05.343393         SGU2        YUS              Normal   \n",
       "22   2017-06-14 06:12:05.343393         SGU2        YUS              Normal   \n",
       "23   2017-06-14 06:12:05.343393         SGU2        YUS              Normal   \n",
       "24   2017-06-14 06:13:02.211966         SGU2        YUS              Normal   \n",
       "25   2017-06-14 06:13:02.211966         SGU2        YUS              Normal   \n",
       "26   2017-06-14 06:13:02.211966         SGU2        YUS              Normal   \n",
       "27   2017-06-14 06:14:02.165433         SGU2        YUS              Normal   \n",
       "28   2017-06-14 06:14:02.165433         SGU2        YUS              Normal   \n",
       "29   2017-06-14 06:14:02.165433         SGU2        YUS              Normal   \n",
       "...                         ...          ...        ...                 ...   \n",
       "3555 2017-06-15 01:50:02.422329         SGU2        YUS              Normal   \n",
       "3556 2017-06-15 01:50:02.422329         SGU2        YUS              Normal   \n",
       "3557 2017-06-15 01:50:02.422329         SGU2        YUS              Normal   \n",
       "3558 2017-06-15 01:51:02.415042         SGU2        YUS              Normal   \n",
       "3559 2017-06-15 01:51:02.415042         SGU2        YUS              Normal   \n",
       "3560 2017-06-15 01:51:02.415042         SGU2        YUS              Normal   \n",
       "3561 2017-06-15 01:52:02.404154         SGU2        YUS              Normal   \n",
       "3562 2017-06-15 01:52:02.404154         SGU2        YUS              Normal   \n",
       "3563 2017-06-15 01:52:02.404154         SGU2        YUS              Normal   \n",
       "3564 2017-06-15 01:53:01.534627         SGU2        YUS              Normal   \n",
       "3565 2017-06-15 01:53:01.534627         SGU2        YUS              Normal   \n",
       "3566 2017-06-15 01:53:01.534627         SGU2        YUS              Normal   \n",
       "3567 2017-06-15 01:54:02.234853         SGU2        YUS              Normal   \n",
       "3568 2017-06-15 01:54:02.234853         SGU2        YUS              Normal   \n",
       "3569 2017-06-15 01:54:02.234853         SGU2        YUS              Normal   \n",
       "3570 2017-06-15 01:55:01.750195         SGU2        YUS              Normal   \n",
       "3571 2017-06-15 01:55:01.750195         SGU2        YUS              Normal   \n",
       "3572 2017-06-15 01:55:01.750195         SGU2        YUS              Normal   \n",
       "3573 2017-06-15 01:56:02.426207         SGU2        YUS              Normal   \n",
       "3574 2017-06-15 01:56:02.426207         SGU2        YUS              Normal   \n",
       "3575 2017-06-15 01:56:02.426207         SGU2        YUS              Normal   \n",
       "3576 2017-06-15 01:57:01.466266         SGU2        YUS              Normal   \n",
       "3577 2017-06-15 01:57:01.466266         SGU2        YUS              Normal   \n",
       "3578 2017-06-15 01:57:01.466266         SGU2        YUS              Normal   \n",
       "3579 2017-06-15 01:58:02.208761         SGU2        YUS              Normal   \n",
       "3580 2017-06-15 01:58:02.208761         SGU2        YUS              Normal   \n",
       "3581 2017-06-15 01:58:02.208761         SGU2        YUS              Normal   \n",
       "3582 2017-06-15 01:59:01.926096         SGU2        YUS              Normal   \n",
       "3583 2017-06-15 01:59:01.926096         SGU2        YUS              Normal   \n",
       "3584 2017-06-15 01:59:01.926096         SGU2        YUS              Normal   \n",
       "\n",
       "         timint traindirection  trainid train_message  \n",
       "0      6.199715          South      137      Arriving  \n",
       "1     11.552266          South      131      Arriving  \n",
       "2     16.417125          South      838       Delayed  \n",
       "3     11.040278          South      131      Arriving  \n",
       "4     15.478819          South      141      Arriving  \n",
       "5     16.417125          South      838       Delayed  \n",
       "6     10.073480          South      131      Arriving  \n",
       "7     14.007009          South      141      Arriving  \n",
       "8     16.417125          South      838       Delayed  \n",
       "9      5.233515          South      137      Arriving  \n",
       "10     9.587593          South      131      Arriving  \n",
       "11    13.501595          South      141      Arriving  \n",
       "12     4.512351          South      137      Arriving  \n",
       "13     8.684744          South      131      Arriving  \n",
       "14    12.688393          South      141      Arriving  \n",
       "15     3.729031          South      137      Arriving  \n",
       "16     7.898435          South      131      Arriving  \n",
       "17    11.552266          South      141      Arriving  \n",
       "18     2.534529          South      137      Arriving  \n",
       "19     7.039214          South      131      Arriving  \n",
       "20    10.556781          South      141      Arriving  \n",
       "21     1.664551          South      137      Arriving  \n",
       "22     6.199715          South      131      Arriving  \n",
       "23     9.885819          South      141      Arriving  \n",
       "24     0.957729          South      137      Arriving  \n",
       "25     5.696542          South      131      Arriving  \n",
       "26     9.587593          South      141      Arriving  \n",
       "27     0.000000          South      137     AtStation  \n",
       "28     4.978515          South      131      Arriving  \n",
       "29     8.684744          South      141      Arriving  \n",
       "...         ...            ...      ...           ...  \n",
       "3555   0.580080          South      838      Arriving  \n",
       "3556   3.309362          South      152      Arriving  \n",
       "3557   4.318331          South      809      Arriving  \n",
       "3558   0.000000          South      838     AtStation  \n",
       "3559   2.422056          South      152      Arriving  \n",
       "3560   3.564362          South      809      Arriving  \n",
       "3561   1.159522          South      152      Arriving  \n",
       "3562   2.422056          South      809      Arriving  \n",
       "3563   8.723740          South      874      Arriving  \n",
       "3564   0.283633          South      152      Arriving  \n",
       "3565   1.371189          South      809      Arriving  \n",
       "3566   7.572967          South      874      Arriving  \n",
       "3567   0.000000          South      152     AtStation  \n",
       "3568   0.736318          South      809      Arriving  \n",
       "3569   6.747313          South      874      Arriving  \n",
       "3570   0.365478          South      809      Arriving  \n",
       "3571   5.940287          South      874      Arriving  \n",
       "3572  14.831384          South      672      Arriving  \n",
       "3573   0.000000          South      809     AtStation  \n",
       "3574   5.645776          South      874      Arriving  \n",
       "3575  13.427176          South      672      Arriving  \n",
       "3576   0.000000          South      809     AtStation  \n",
       "3577   5.013598          South      874      Arriving  \n",
       "3578  12.408482          South      672      Arriving  \n",
       "3579   0.000000          South      809       Delayed  \n",
       "3580   4.318331          South      874      Arriving  \n",
       "3581  11.535980          South      672      Arriving  \n",
       "3582   0.000000          South      809       Delayed  \n",
       "3583   3.309362          South      874      Arriving  \n",
       "3584  10.582762          South      672      Arriving  \n",
       "\n",
       "[3585 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stg_south"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "stg_south_resamp = stg_south[stg_south.index % 3 == 0]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "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>requestid</th>\n",
       "      <th>stationid</th>\n",
       "      <th>lineid</th>\n",
       "      <th>create_date</th>\n",
       "      <th>request_date</th>\n",
       "      <th>station_char</th>\n",
       "      <th>subwayline</th>\n",
       "      <th>system_message_type</th>\n",
       "      <th>timint</th>\n",
       "      <th>traindirection</th>\n",
       "      <th>trainid</th>\n",
       "      <th>train_message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5167447</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:05:04</td>\n",
       "      <td>2017-06-14 06:05:01.866256</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>6.199715</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5167515</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:06:06</td>\n",
       "      <td>2017-06-14 06:06:02.215526</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>11.040278</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5167583</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:07:06</td>\n",
       "      <td>2017-06-14 06:07:02.313463</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>10.073480</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5167651</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:08:10</td>\n",
       "      <td>2017-06-14 06:08:06.800012</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.233515</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5167719</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:09:05</td>\n",
       "      <td>2017-06-14 06:09:02.082338</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.512351</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5167787</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:10:05</td>\n",
       "      <td>2017-06-14 06:10:02.076938</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.729031</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5167855</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:11:09</td>\n",
       "      <td>2017-06-14 06:11:07.520184</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.534529</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5167923</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:12:09</td>\n",
       "      <td>2017-06-14 06:12:05.343393</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.664551</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5167991</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:13:06</td>\n",
       "      <td>2017-06-14 06:13:02.211966</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.957729</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5168059</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:14:06</td>\n",
       "      <td>2017-06-14 06:14:02.165433</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>137</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5168127</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:15:05</td>\n",
       "      <td>2017-06-14 06:15:01.616962</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.257351</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5168195</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:16:06</td>\n",
       "      <td>2017-06-14 06:16:02.414494</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.474031</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5168263</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:17:05</td>\n",
       "      <td>2017-06-14 06:17:01.729177</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.534529</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5168331</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:18:05</td>\n",
       "      <td>2017-06-14 06:18:01.698790</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.212729</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5168399</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:19:05</td>\n",
       "      <td>2017-06-14 06:19:02.135651</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.614202</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5168467</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:20:05</td>\n",
       "      <td>2017-06-14 06:20:02.325968</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>131</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5168535</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:21:08</td>\n",
       "      <td>2017-06-14 06:21:05.104026</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.957929</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5168603</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:22:05</td>\n",
       "      <td>2017-06-14 06:22:01.576576</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.116040</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>5168671</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:23:05</td>\n",
       "      <td>2017-06-14 06:23:02.122965</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.534529</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>5168739</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:24:05</td>\n",
       "      <td>2017-06-14 06:24:01.856485</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.212729</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5168807</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:25:05</td>\n",
       "      <td>2017-06-14 06:25:01.844635</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.637988</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>5168875</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:26:02</td>\n",
       "      <td>2017-06-14 06:26:02.081188</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>141</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5168943</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:27:06</td>\n",
       "      <td>2017-06-14 06:27:02.254455</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.534529</td>\n",
       "      <td>South</td>\n",
       "      <td>143</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5169011</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:28:03</td>\n",
       "      <td>2017-06-14 06:28:01.867800</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.891656</td>\n",
       "      <td>South</td>\n",
       "      <td>143</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>5169079</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:29:06</td>\n",
       "      <td>2017-06-14 06:29:02.253810</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.957729</td>\n",
       "      <td>South</td>\n",
       "      <td>143</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>5169147</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:30:05</td>\n",
       "      <td>2017-06-14 06:30:02.382857</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>143</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>5169215</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:31:09</td>\n",
       "      <td>2017-06-14 06:31:05.005267</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.512351</td>\n",
       "      <td>South</td>\n",
       "      <td>146</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5169283</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:32:10</td>\n",
       "      <td>2017-06-14 06:32:06.909556</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.957929</td>\n",
       "      <td>South</td>\n",
       "      <td>146</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5169351</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:33:05</td>\n",
       "      <td>2017-06-14 06:33:01.905890</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.474031</td>\n",
       "      <td>South</td>\n",
       "      <td>146</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>5169419</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-14 06:34:06</td>\n",
       "      <td>2017-06-14 06:34:02.278264</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.534529</td>\n",
       "      <td>South</td>\n",
       "      <td>146</td>\n",
       "      <td>Arriving</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3495</th>\n",
       "      <td>5246663</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:30:13</td>\n",
       "      <td>2017-06-15 01:30:01.828001</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.422056</td>\n",
       "      <td>South</td>\n",
       "      <td>144</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3498</th>\n",
       "      <td>5246731</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:31:13</td>\n",
       "      <td>2017-06-15 01:31:02.178199</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.371189</td>\n",
       "      <td>South</td>\n",
       "      <td>144</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3501</th>\n",
       "      <td>5246799</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:32:12</td>\n",
       "      <td>2017-06-15 01:32:02.046908</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.602544</td>\n",
       "      <td>South</td>\n",
       "      <td>144</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3504</th>\n",
       "      <td>5246867</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:33:13</td>\n",
       "      <td>2017-06-15 01:33:02.294897</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>144</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3507</th>\n",
       "      <td>5246935</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:34:13</td>\n",
       "      <td>2017-06-15 01:34:01.938788</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.564362</td>\n",
       "      <td>South</td>\n",
       "      <td>145</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3510</th>\n",
       "      <td>5247003</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:35:13</td>\n",
       "      <td>2017-06-15 01:35:02.411738</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.422056</td>\n",
       "      <td>South</td>\n",
       "      <td>145</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3513</th>\n",
       "      <td>5247071</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:36:14</td>\n",
       "      <td>2017-06-15 01:36:02.403219</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.371189</td>\n",
       "      <td>South</td>\n",
       "      <td>145</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3516</th>\n",
       "      <td>5247139</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:37:14</td>\n",
       "      <td>2017-06-15 01:37:02.001731</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.580080</td>\n",
       "      <td>South</td>\n",
       "      <td>145</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3519</th>\n",
       "      <td>5247207</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:38:14</td>\n",
       "      <td>2017-06-15 01:38:02.308221</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>145</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3522</th>\n",
       "      <td>5247275</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:39:15</td>\n",
       "      <td>2017-06-15 01:39:04.755527</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.063331</td>\n",
       "      <td>South</td>\n",
       "      <td>148</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3525</th>\n",
       "      <td>5247343</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:40:14</td>\n",
       "      <td>2017-06-15 01:40:02.072266</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.564362</td>\n",
       "      <td>South</td>\n",
       "      <td>148</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3528</th>\n",
       "      <td>5247411</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:41:12</td>\n",
       "      <td>2017-06-15 01:41:02.430344</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.422056</td>\n",
       "      <td>South</td>\n",
       "      <td>148</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>5247479</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:42:14</td>\n",
       "      <td>2017-06-15 01:42:01.965104</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.586242</td>\n",
       "      <td>South</td>\n",
       "      <td>148</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534</th>\n",
       "      <td>5247547</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:43:13</td>\n",
       "      <td>2017-06-15 01:43:01.768641</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.159522</td>\n",
       "      <td>South</td>\n",
       "      <td>148</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3537</th>\n",
       "      <td>5247615</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:44:16</td>\n",
       "      <td>2017-06-15 01:44:05.130549</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.283633</td>\n",
       "      <td>South</td>\n",
       "      <td>148</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540</th>\n",
       "      <td>5247683</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:45:13</td>\n",
       "      <td>2017-06-15 01:45:01.639589</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>148</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3543</th>\n",
       "      <td>5247751</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:46:13</td>\n",
       "      <td>2017-06-15 01:46:02.034260</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.422056</td>\n",
       "      <td>South</td>\n",
       "      <td>151</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3546</th>\n",
       "      <td>5247819</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:47:14</td>\n",
       "      <td>2017-06-15 01:47:02.358010</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.586242</td>\n",
       "      <td>South</td>\n",
       "      <td>151</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3549</th>\n",
       "      <td>5247887</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:48:14</td>\n",
       "      <td>2017-06-15 01:48:02.263229</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.736318</td>\n",
       "      <td>South</td>\n",
       "      <td>151</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3552</th>\n",
       "      <td>5247955</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:49:12</td>\n",
       "      <td>2017-06-15 01:49:02.243198</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>151</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>5248023</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:50:14</td>\n",
       "      <td>2017-06-15 01:50:02.422329</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.580080</td>\n",
       "      <td>South</td>\n",
       "      <td>838</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3558</th>\n",
       "      <td>5248091</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:51:14</td>\n",
       "      <td>2017-06-15 01:51:02.415042</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>838</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3561</th>\n",
       "      <td>5248159</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:52:12</td>\n",
       "      <td>2017-06-15 01:52:02.404154</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.159522</td>\n",
       "      <td>South</td>\n",
       "      <td>152</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3564</th>\n",
       "      <td>5248227</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:53:10</td>\n",
       "      <td>2017-06-15 01:53:01.534627</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.283633</td>\n",
       "      <td>South</td>\n",
       "      <td>152</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>5248295</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:54:14</td>\n",
       "      <td>2017-06-15 01:54:02.234853</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>152</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3570</th>\n",
       "      <td>5248363</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:55:14</td>\n",
       "      <td>2017-06-15 01:55:01.750195</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.365478</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>Arriving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3573</th>\n",
       "      <td>5248431</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:56:13</td>\n",
       "      <td>2017-06-15 01:56:02.426207</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3576</th>\n",
       "      <td>5248499</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:57:11</td>\n",
       "      <td>2017-06-15 01:57:01.466266</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>AtStation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3579</th>\n",
       "      <td>5248567</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:58:14</td>\n",
       "      <td>2017-06-15 01:58:02.208761</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>Delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3582</th>\n",
       "      <td>5248635</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-15 01:59:14</td>\n",
       "      <td>2017-06-15 01:59:01.926096</td>\n",
       "      <td>SGU2</td>\n",
       "      <td>YUS</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>South</td>\n",
       "      <td>809</td>\n",
       "      <td>Delayed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1195 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      requestid  stationid  lineid         create_date  \\\n",
       "0       5167447         10       1 2017-06-14 06:05:04   \n",
       "3       5167515         10       1 2017-06-14 06:06:06   \n",
       "6       5167583         10       1 2017-06-14 06:07:06   \n",
       "9       5167651         10       1 2017-06-14 06:08:10   \n",
       "12      5167719         10       1 2017-06-14 06:09:05   \n",
       "15      5167787         10       1 2017-06-14 06:10:05   \n",
       "18      5167855         10       1 2017-06-14 06:11:09   \n",
       "21      5167923         10       1 2017-06-14 06:12:09   \n",
       "24      5167991         10       1 2017-06-14 06:13:06   \n",
       "27      5168059         10       1 2017-06-14 06:14:06   \n",
       "30      5168127         10       1 2017-06-14 06:15:05   \n",
       "33      5168195         10       1 2017-06-14 06:16:06   \n",
       "36      5168263         10       1 2017-06-14 06:17:05   \n",
       "39      5168331         10       1 2017-06-14 06:18:05   \n",
       "42      5168399         10       1 2017-06-14 06:19:05   \n",
       "45      5168467         10       1 2017-06-14 06:20:05   \n",
       "48      5168535         10       1 2017-06-14 06:21:08   \n",
       "51      5168603         10       1 2017-06-14 06:22:05   \n",
       "54      5168671         10       1 2017-06-14 06:23:05   \n",
       "57      5168739         10       1 2017-06-14 06:24:05   \n",
       "60      5168807         10       1 2017-06-14 06:25:05   \n",
       "63      5168875         10       1 2017-06-14 06:26:02   \n",
       "66      5168943         10       1 2017-06-14 06:27:06   \n",
       "69      5169011         10       1 2017-06-14 06:28:03   \n",
       "72      5169079         10       1 2017-06-14 06:29:06   \n",
       "75      5169147         10       1 2017-06-14 06:30:05   \n",
       "78      5169215         10       1 2017-06-14 06:31:09   \n",
       "81      5169283         10       1 2017-06-14 06:32:10   \n",
       "84      5169351         10       1 2017-06-14 06:33:05   \n",
       "87      5169419         10       1 2017-06-14 06:34:06   \n",
       "...         ...        ...     ...                 ...   \n",
       "3495    5246663         10       1 2017-06-15 01:30:13   \n",
       "3498    5246731         10       1 2017-06-15 01:31:13   \n",
       "3501    5246799         10       1 2017-06-15 01:32:12   \n",
       "3504    5246867         10       1 2017-06-15 01:33:13   \n",
       "3507    5246935         10       1 2017-06-15 01:34:13   \n",
       "3510    5247003         10       1 2017-06-15 01:35:13   \n",
       "3513    5247071         10       1 2017-06-15 01:36:14   \n",
       "3516    5247139         10       1 2017-06-15 01:37:14   \n",
       "3519    5247207         10       1 2017-06-15 01:38:14   \n",
       "3522    5247275         10       1 2017-06-15 01:39:15   \n",
       "3525    5247343         10       1 2017-06-15 01:40:14   \n",
       "3528    5247411         10       1 2017-06-15 01:41:12   \n",
       "3531    5247479         10       1 2017-06-15 01:42:14   \n",
       "3534    5247547         10       1 2017-06-15 01:43:13   \n",
       "3537    5247615         10       1 2017-06-15 01:44:16   \n",
       "3540    5247683         10       1 2017-06-15 01:45:13   \n",
       "3543    5247751         10       1 2017-06-15 01:46:13   \n",
       "3546    5247819         10       1 2017-06-15 01:47:14   \n",
       "3549    5247887         10       1 2017-06-15 01:48:14   \n",
       "3552    5247955         10       1 2017-06-15 01:49:12   \n",
       "3555    5248023         10       1 2017-06-15 01:50:14   \n",
       "3558    5248091         10       1 2017-06-15 01:51:14   \n",
       "3561    5248159         10       1 2017-06-15 01:52:12   \n",
       "3564    5248227         10       1 2017-06-15 01:53:10   \n",
       "3567    5248295         10       1 2017-06-15 01:54:14   \n",
       "3570    5248363         10       1 2017-06-15 01:55:14   \n",
       "3573    5248431         10       1 2017-06-15 01:56:13   \n",
       "3576    5248499         10       1 2017-06-15 01:57:11   \n",
       "3579    5248567         10       1 2017-06-15 01:58:14   \n",
       "3582    5248635         10       1 2017-06-15 01:59:14   \n",
       "\n",
       "                   request_date station_char subwayline system_message_type  \\\n",
       "0    2017-06-14 06:05:01.866256         SGU2        YUS              Normal   \n",
       "3    2017-06-14 06:06:02.215526         SGU2        YUS              Normal   \n",
       "6    2017-06-14 06:07:02.313463         SGU2        YUS              Normal   \n",
       "9    2017-06-14 06:08:06.800012         SGU2        YUS              Normal   \n",
       "12   2017-06-14 06:09:02.082338         SGU2        YUS              Normal   \n",
       "15   2017-06-14 06:10:02.076938         SGU2        YUS              Normal   \n",
       "18   2017-06-14 06:11:07.520184         SGU2        YUS              Normal   \n",
       "21   2017-06-14 06:12:05.343393         SGU2        YUS              Normal   \n",
       "24   2017-06-14 06:13:02.211966         SGU2        YUS              Normal   \n",
       "27   2017-06-14 06:14:02.165433         SGU2        YUS              Normal   \n",
       "30   2017-06-14 06:15:01.616962         SGU2        YUS              Normal   \n",
       "33   2017-06-14 06:16:02.414494         SGU2        YUS              Normal   \n",
       "36   2017-06-14 06:17:01.729177         SGU2        YUS              Normal   \n",
       "39   2017-06-14 06:18:01.698790         SGU2        YUS              Normal   \n",
       "42   2017-06-14 06:19:02.135651         SGU2        YUS              Normal   \n",
       "45   2017-06-14 06:20:02.325968         SGU2        YUS              Normal   \n",
       "48   2017-06-14 06:21:05.104026         SGU2        YUS              Normal   \n",
       "51   2017-06-14 06:22:01.576576         SGU2        YUS              Normal   \n",
       "54   2017-06-14 06:23:02.122965         SGU2        YUS              Normal   \n",
       "57   2017-06-14 06:24:01.856485         SGU2        YUS              Normal   \n",
       "60   2017-06-14 06:25:01.844635         SGU2        YUS              Normal   \n",
       "63   2017-06-14 06:26:02.081188         SGU2        YUS              Normal   \n",
       "66   2017-06-14 06:27:02.254455         SGU2        YUS              Normal   \n",
       "69   2017-06-14 06:28:01.867800         SGU2        YUS              Normal   \n",
       "72   2017-06-14 06:29:02.253810         SGU2        YUS              Normal   \n",
       "75   2017-06-14 06:30:02.382857         SGU2        YUS              Normal   \n",
       "78   2017-06-14 06:31:05.005267         SGU2        YUS              Normal   \n",
       "81   2017-06-14 06:32:06.909556         SGU2        YUS              Normal   \n",
       "84   2017-06-14 06:33:01.905890         SGU2        YUS              Normal   \n",
       "87   2017-06-14 06:34:02.278264         SGU2        YUS              Normal   \n",
       "...                         ...          ...        ...                 ...   \n",
       "3495 2017-06-15 01:30:01.828001         SGU2        YUS              Normal   \n",
       "3498 2017-06-15 01:31:02.178199         SGU2        YUS              Normal   \n",
       "3501 2017-06-15 01:32:02.046908         SGU2        YUS              Normal   \n",
       "3504 2017-06-15 01:33:02.294897         SGU2        YUS              Normal   \n",
       "3507 2017-06-15 01:34:01.938788         SGU2        YUS              Normal   \n",
       "3510 2017-06-15 01:35:02.411738         SGU2        YUS              Normal   \n",
       "3513 2017-06-15 01:36:02.403219         SGU2        YUS              Normal   \n",
       "3516 2017-06-15 01:37:02.001731         SGU2        YUS              Normal   \n",
       "3519 2017-06-15 01:38:02.308221         SGU2        YUS              Normal   \n",
       "3522 2017-06-15 01:39:04.755527         SGU2        YUS              Normal   \n",
       "3525 2017-06-15 01:40:02.072266         SGU2        YUS              Normal   \n",
       "3528 2017-06-15 01:41:02.430344         SGU2        YUS              Normal   \n",
       "3531 2017-06-15 01:42:01.965104         SGU2        YUS              Normal   \n",
       "3534 2017-06-15 01:43:01.768641         SGU2        YUS              Normal   \n",
       "3537 2017-06-15 01:44:05.130549         SGU2        YUS              Normal   \n",
       "3540 2017-06-15 01:45:01.639589         SGU2        YUS              Normal   \n",
       "3543 2017-06-15 01:46:02.034260         SGU2        YUS              Normal   \n",
       "3546 2017-06-15 01:47:02.358010         SGU2        YUS              Normal   \n",
       "3549 2017-06-15 01:48:02.263229         SGU2        YUS              Normal   \n",
       "3552 2017-06-15 01:49:02.243198         SGU2        YUS              Normal   \n",
       "3555 2017-06-15 01:50:02.422329         SGU2        YUS              Normal   \n",
       "3558 2017-06-15 01:51:02.415042         SGU2        YUS              Normal   \n",
       "3561 2017-06-15 01:52:02.404154         SGU2        YUS              Normal   \n",
       "3564 2017-06-15 01:53:01.534627         SGU2        YUS              Normal   \n",
       "3567 2017-06-15 01:54:02.234853         SGU2        YUS              Normal   \n",
       "3570 2017-06-15 01:55:01.750195         SGU2        YUS              Normal   \n",
       "3573 2017-06-15 01:56:02.426207         SGU2        YUS              Normal   \n",
       "3576 2017-06-15 01:57:01.466266         SGU2        YUS              Normal   \n",
       "3579 2017-06-15 01:58:02.208761         SGU2        YUS              Normal   \n",
       "3582 2017-06-15 01:59:01.926096         SGU2        YUS              Normal   \n",
       "\n",
       "         timint traindirection  trainid train_message  \n",
       "0      6.199715          South      137      Arriving  \n",
       "3     11.040278          South      131      Arriving  \n",
       "6     10.073480          South      131      Arriving  \n",
       "9      5.233515          South      137      Arriving  \n",
       "12     4.512351          South      137      Arriving  \n",
       "15     3.729031          South      137      Arriving  \n",
       "18     2.534529          South      137      Arriving  \n",
       "21     1.664551          South      137      Arriving  \n",
       "24     0.957729          South      137      Arriving  \n",
       "27     0.000000          South      137     AtStation  \n",
       "30     4.257351          South      131      Arriving  \n",
       "33     3.474031          South      131      Arriving  \n",
       "36     2.534529          South      131      Arriving  \n",
       "39     1.212729          South      131      Arriving  \n",
       "42     0.614202          South      131      Arriving  \n",
       "45     0.000000          South      131     AtStation  \n",
       "48     3.957929          South      141      Arriving  \n",
       "51     3.116040          South      141      Arriving  \n",
       "54     2.534529          South      141      Arriving  \n",
       "57     1.212729          South      141      Arriving  \n",
       "60     0.637988          South      141      Arriving  \n",
       "63     0.000000          South      141     AtStation  \n",
       "66     2.534529          South      143      Arriving  \n",
       "69     1.891656          South      143      Arriving  \n",
       "72     0.957729          South      143      Arriving  \n",
       "75     0.000000          South      143     AtStation  \n",
       "78     4.512351          South      146      Arriving  \n",
       "81     3.957929          South      146      Arriving  \n",
       "84     3.474031          South      146      Arriving  \n",
       "87     2.534529          South      146      Arriving  \n",
       "...         ...            ...      ...           ...  \n",
       "3495   2.422056          South      144      Arriving  \n",
       "3498   1.371189          South      144      Arriving  \n",
       "3501   0.602544          South      144      Arriving  \n",
       "3504   0.000000          South      144     AtStation  \n",
       "3507   3.564362          South      145      Arriving  \n",
       "3510   2.422056          South      145      Arriving  \n",
       "3513   1.371189          South      145      Arriving  \n",
       "3516   0.580080          South      145      Arriving  \n",
       "3519   0.000000          South      145     AtStation  \n",
       "3522   4.063331          South      148      Arriving  \n",
       "3525   3.564362          South      148      Arriving  \n",
       "3528   2.422056          South      148      Arriving  \n",
       "3531   1.586242          South      148      Arriving  \n",
       "3534   1.159522          South      148      Arriving  \n",
       "3537   0.283633          South      148      Arriving  \n",
       "3540   0.000000          South      148     AtStation  \n",
       "3543   2.422056          South      151      Arriving  \n",
       "3546   1.586242          South      151      Arriving  \n",
       "3549   0.736318          South      151      Arriving  \n",
       "3552   0.000000          South      151     AtStation  \n",
       "3555   0.580080          South      838      Arriving  \n",
       "3558   0.000000          South      838     AtStation  \n",
       "3561   1.159522          South      152      Arriving  \n",
       "3564   0.283633          South      152      Arriving  \n",
       "3567   0.000000          South      152     AtStation  \n",
       "3570   0.365478          South      809      Arriving  \n",
       "3573   0.000000          South      809     AtStation  \n",
       "3576   0.000000          South      809     AtStation  \n",
       "3579   0.000000          South      809       Delayed  \n",
       "3582   0.000000          South      809       Delayed  \n",
       "\n",
       "[1195 rows x 12 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stg_south_resamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extracting some useful information\n",
    "Now we need to process the data to extract some useful information from the raw ntas_data. To do this we're going to go row by row through the table shown above to get arrival times, departure times and wait times.\n",
    "\n",
    "```arrival_times``` are the times at which a train arrives at St. George station  \n",
    "```departure_times``` are the times at which a train leaves St. George station  \n",
    "```all_wait_times``` are all the reported wait times from every API call (which in this case is every 10 seconds)  \n",
    "```expected_wait_times``` are the expected wait times immediately after a train has departed the station. They represent the worst case wait times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrival_times = []\n",
    "departure_times = []\n",
    "all_wait_times = []\n",
    "all_time_stamps = []\n",
    "expected_wait_times = []\n",
    "prev_arrival_train_id = -1\n",
    "\n",
    "for index, row in stg_south_resamp.iterrows():\n",
    "    if index == 0:\n",
    "        prev_departure_train_id = row['trainid']\n",
    "    all_wait_times.append(row['timint'])\n",
    "    all_time_stamps.append(row['create_date'])\n",
    "    if (row['trainid'] != prev_arrival_train_id):\n",
    "        arrival_times.append(row['create_date'])\n",
    "        prev_arrival_train_id = row['trainid']\n",
    "    #elif (row['trainid'] != prev_departure_train_id):\n",
    "        departure_times.append(row['create_date'])\n",
    "        expected_wait_times.append(row['timint'])\n",
    "        #prev_departure_train_id = row['trainid']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at all the reported wait times. While this is somewhat interesting, it doesn't tell us very much"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.plot(all_time_stamps,all_wait_times)\n",
    "plt.xlabel('Time')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.ylabel('Wait Time (mins)')\n",
    "plt.title('All reported wait times at St. George')\n",
    "plt.savefig('all_wait_times.png', dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1194\n"
     ]
    }
   ],
   "source": [
    "def timeToArrival(all_time_stamps,all_wait_times,arrival_times):\n",
    "    actual_wait_times = []\n",
    "    i = 0\n",
    "    k = 0\n",
    "    arrival_time = arrival_times[i]\n",
    "    for time in all_time_stamps:\n",
    "        if (all_wait_times[k] == 0):\n",
    "            actual_wait_times.append(arrival_times[0]-arrival_times[0])\n",
    "            k+=1\n",
    "            continue\n",
    "        while ((arrival_time - time).total_seconds() < 0):\n",
    "            i+=1\n",
    "            if (i > (len(arrival_times) -1)):\n",
    "                break\n",
    "            arrival_time = arrival_times[i]\n",
    "            \n",
    "        actual_wait_times.append(arrival_time - time)\n",
    "        k+=1\n",
    "    return actual_wait_times\n",
    "\n",
    "print(len(all_time_stamps[0:-1]))\n",
    "actual_wait_times_all = timeToArrival(all_time_stamps,all_wait_times,arrival_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sliding_window_filter(input_mat,window_size,overlap):\n",
    "    average_time = []\n",
    "    max_time = []\n",
    "    for i in range(0,len(input_mat)-window_size,overlap):\n",
    "        window = input_mat[i:(i+window_size)]\n",
    "        average_time.append(np.mean(window))\n",
    "        max_time.append(np.mean(window))\n",
    "    return average_time #, max_time\n",
    "\n",
    "window_size = 30\n",
    "overlap = 25\n",
    "#average_time, max_time = sliding_window_filter(all_wait_times,window_size, overlap)\n",
    "#times = all_time_stamps[0:len(all_time_stamps)-window_size:overlap]\n",
    "\n",
    "#times = all_time_stamps[0:len(actual_wait_times_all)]\n",
    "times = all_time_stamps[0:len(all_time_stamps)-window_size:overlap]\n",
    "plt.plot(times,np.floor(sliding_window_filter(convert_timedelta_to_mins(actual_wait_times_all),window_size,overlap)))\n",
    "\n",
    "#average_time, max_time = sliding_window_filter(convert_timedelta_to_mins(actual_wait_times_all),window_size, overlap)\n",
    "plt.plot(times,np.ceil(sliding_window_filter(all_wait_times,window_size,overlap)))\n",
    "plt.xlabel('Time')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.ylabel('Wait Time (mins)')\n",
    "plt.title('All reported wait times at St. George')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "class sliding_figure:\n",
    "    import matplotlib\n",
    "    import matplotlib.pyplot as plt\n",
    "    from matplotlib.widgets import Slider\n",
    "    \n",
    "    def __init__(self,all_time_stamps,all_wait_times):\n",
    "        self.fig, self.ax = plt.subplots()\n",
    "        plt.subplots_adjust(bottom=0.25)\n",
    "        \n",
    "        self.t = all_time_stamps;\n",
    "        self.s = all_wait_times;\n",
    "        self.l, = plt.plot(self.t,self.s)\n",
    "\n",
    "        self.y_min = 0.0;\n",
    "        self.y_max = max(self.s)\n",
    "\n",
    "        plt.axis([self.t[0], self.t[100], self.y_min, self.y_max])\n",
    "        x_dt = self.t[100] - self.t[0]\n",
    "\n",
    "        self.axcolor = 'lightgoldenrodyellow'\n",
    "        self.axpos = plt.axes([0.2, 0.1, 0.65, 0.03], facecolor=axcolor)\n",
    "        self.spos = Slider(self.axpos, 'Pos', matplotlib.dates.date2num(self.t[0]), matplotlib.dates.date2num(self.t[-1]))\n",
    "        \n",
    "        #self.showPlot()\n",
    "        \n",
    "        # pretty date names\n",
    "        plt.gcf().autofmt_xdate()\n",
    "        \n",
    "        self.plt = plt\n",
    "        #self.showPlot()\n",
    "    \n",
    "    def update(self,val):\n",
    "        pos = self.spos.val\n",
    "        self.xmax_time = matplotlib.dates.num2date(pos) + x_dt\n",
    "        self.xmin_time = pos\n",
    "        self.ax.axis([self.xmin_time, self.xmax_time, self.y_min, self.y_max])\n",
    "        fig.canvas.draw_idle()\n",
    "        \n",
    "    def showPlot(self):\n",
    "        self.spos.on_changed(self.update)\n",
    "        self.plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "wait_times_figure = sliding_figure(all_time_stamps, all_wait_times)\n",
    "wait_times_figure.showPlot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Headway analysis\n",
    "By looking at the difference in arrival times at St. Geore we can determine the headway (aka. the time between trains) as the approach St. George station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_delta(times):\n",
    "    delta_times = []\n",
    "    for n in range(0,len(times)-1):\n",
    "        time_diff = times[n+1] - times[n]\n",
    "        delta_times.append(time_diff/np.timedelta64(1, 's'))\n",
    "    return delta_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_times = time_delta(arrival_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#delta_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69e1e1a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(arrival_times[:-1],np.multiply(delta_times,1/60.0))\n",
    "plt.xlabel('Time')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.ylabel('Headway (mins)')\n",
    "plt.title('Headway between trains as they approach St. George')\n",
    "plt.savefig('headway.png', dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyzing time spent at the station\n",
    "We can also look at how long trains spend at the station by looking at the difference between the departure and arrival times. St. George station is an interchange station, as such, trains do tend to spend longer here than at intermediary station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_at_station = np.subtract(departure_times[:],arrival_times[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#time_at_station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_timedelta_to_mins(mat):\n",
    "    result = []\n",
    "    for element in mat:\n",
    "        result.append((element/np.timedelta64(1, 'm')))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_at_station_mins = convert_timedelta_to_mins(time_at_station)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Duration of time that trains spend at St. George Station')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69f41bfa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(departure_times,time_at_station_mins)\n",
    "plt.xlabel('Time')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.ylabel('Duration of time at station (mins)')\n",
    "plt.title('Duration of time that trains spend at St. George Station')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Expected wait times\n",
    "The expected wait times represent the worst-case wait reported wait time immediately after the previous train has left the station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#expected_wait_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69e2faecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(arrival_times,expected_wait_times)\n",
    "plt.ylabel('Expected Wait Time (mins)')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.xlabel('Time')\n",
    "plt.title('Worst-case expected wait times for next train at St. George')\n",
    "plt.savefig('expected_wait_times.png', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Actual wait time\n",
    "It's instructive if we can look at the actual worst-case wait time and compare this to the expected worst case wait time. In this case, we will also consider the actual worst-case wait time as the time between when a train departs and the next train arrives (i.e the difference between the arrival time and the previous departed time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_wait_times = np.subtract(arrival_times[1:],arrival_times[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_wait_times_mins = convert_timedelta_to_mins(actual_wait_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "346\n"
     ]
    }
   ],
   "source": [
    "plt.plot(arrival_times[1:],actual_wait_times_mins,color = 'C1')\n",
    "plt.xlabel('Time')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.ylabel('Actual wait time (mins)')\n",
    "plt.title('Worst-case actual wait times for next train at St. George')\n",
    "plt.savefig('actual_wait_times.png', dpi=500)\n",
    "plt.show()\n",
    "print(len(actual_wait_times_mins))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "window_size = 15\n",
    "overlap = 14\n",
    "average_time, max_time = sliding_window_filter(actual_wait_times_mins ,window_size, overlap)\n",
    "print(len(average_time))\n",
    "times = arrival_times[0:len(all_time_stamps)-window_size:overlap]\n",
    "print(len(times))\n",
    "plt.plot(times[1:],average_time)\n",
    "plt.plot(times[1:],max_time)\n",
    "plt.xlabel('Time')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.ylabel('Wait Time (mins)')\n",
    "plt.title('All reported wait times at St. George')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing actual and expected wait times\n",
    "Now let's put everything together and compare the actual and expected wait times. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "335\n",
      "335\n",
      "335\n",
      "334\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "datetime.time(7, 10)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(expected_wait_times))\n",
    "print(len(arrival_times))\n",
    "print(len(arrival_times))\n",
    "print(len(actual_wait_times_mins))\n",
    "type(arrival_times[1])\n",
    "\n",
    "arrival_times_pdt = []\n",
    "for item in arrival_times:\n",
    "    arrival_times_pdt.append(datetime.time(item.to_pydatetime().hour,item.to_pydatetime().minute))\n",
    "arrival_times_pdt[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(arrival_times,expected_wait_times)\n",
    "plt.plot(arrival_times[1:],np.floor(actual_wait_times_mins[:]))\n",
    "#plt.legend(['Expected Wait for Next Train','Actual Wait Time for Next Train'],\n",
    "#           bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.xlabel('Time')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.ylabel('Wait Time (mins)')\n",
    "plt.title('Comparing actual and expected wait times at St. George')\n",
    "lgd = plt.legend(['Expected Wait for Next Train','Actual Wait Time for Next Train'],\n",
    "           bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.savefig('actual_and_expected_wait_times.png', bbox_extra_artists=(lgd,), bbox_inches='tight', dpi=700)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n",
      "29\n"
     ]
    }
   ],
   "source": [
    "window_size = 15\n",
    "overlap = 12\n",
    "average_time = sliding_window_filter(actual_wait_times_mins,window_size, overlap)\n",
    "print(len(average_time))\n",
    "times = arrival_times[0:len(all_time_stamps)-window_size:overlap]\n",
    "print(len(times))\n",
    "plt.plot(times[1:],np.floor(average_time))\n",
    "\n",
    "average_time = sliding_window_filter(np.ceil(expected_wait_times),window_size, overlap)\n",
    "plt.plot(times[1:],np.floor(average_time))\n",
    "plt.xlabel('Time')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.ylabel('Wait Time (mins)')\n",
    "plt.title('All reported wait times at St. George')\n",
    "plt.show()\n",
    "lgd = plt.legend(['Actual Wait for Next Train','Expected Wait Time for Next Train'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot all the reported wait times too! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Comparing actual and expected wait times at St. George')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69e39fa5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(departure_times,expected_wait_times)\n",
    "plt.plot(departure_times[1:],actual_wait_times_mins)\n",
    "plt.plot(all_time_stamps,all_wait_times)\n",
    "plt.legend(['Expected Wait for Next Train','Actual Wait Time for Next Train','All Reported Wait Times'],\n",
    "           bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.xlabel('Time')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.ylabel('Wait Time (mins)')\n",
    "plt.title('Comparing actual and expected wait times at St. George')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look at how long trains spend at St. George"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcaf99a1400>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcafb26df28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(all_time_stamps,all_wait_times)\n",
    "plt.plot(arrival_times[:],time_at_station_mins)\n",
    "plt.title('Durtion of time trains spend at St.George')\n",
    "plt.xlabel('Time')\n",
    "plt.xticks(fontsize=10, rotation=90)\n",
    "plt.ylabel('Time (mins)')\n",
    "plt.legend(['All Reported Wait Times','Time train spends at station (mins)'],\n",
    "           bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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