fire_dispatch_eda_inctimes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os \n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "import psycopg2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, I explore the sample fire dispatch data provided to HACK OR Emergency Response Project Team. After looking at the distribution of timedesc_id types, I explore the time deltas between response steps for a given incident (ie time between dispatch to on-scene)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# walk root diretory to find and load .env file w/ AWS host, username and password\n",
    "load_dotenv(find_dotenv())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# connect to postgres\n",
    "\n",
    "try:\n",
    "    conn = psycopg2.connect(database=os.environ.get(\"database\"), user=os.environ.get(\"user\"), \n",
    "                            password = os.environ.get(\"password\"), \n",
    "                            host=os.environ.get(\"host\"), port=os.environ.get(\"port\"))\n",
    "    \n",
    "except psycopg2.Error as e:\n",
    "    print(\"I am unable to connect to the database\")\n",
    "    print(e)\n",
    "    print(e.pgcode)\n",
    "    print(e.pgerror)\n",
    "    print(traceback.format_exc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "def pquery(QUERY):\n",
    "    '''\n",
    "    takes SQL query string, opens a cursor, and executes query in psql\n",
    "    '''\n",
    "    \n",
    "    cur = conn.cursor()\n",
    "    \n",
    "    try:\n",
    "        print(\"SQL QUERY = \"+QUERY)\n",
    "        cur.execute(\"SET statement_timeout = 0\")\n",
    "        cur.execute(QUERY)\n",
    "        # Extract the column names and insert them in header\n",
    "        col_names = []\n",
    "        for elt in cur.description:\n",
    "            col_names.append(elt[0])    \n",
    "    \n",
    "        D = cur.fetchall() #convert query result to list\n",
    "        #pprint(D)\n",
    "        #conn.close()\n",
    "        # Create the dataframe, passing in the list of col_names extracted from the description\n",
    "        return pd.DataFrame(D, columns=col_names)\n",
    "\n",
    "    except Exception as e:\n",
    "        print(e.pgerror)\n",
    "    \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# look at inctimes table first\n",
    "QUERY1='''\n",
    "SELECT inctimes_id, timedesc_id, incident_id, realtime,\n",
    " EXTRACT(DOW FROM realtime) AS dow,\n",
    " EXTRACT(MONTH FROM realtime) AS month,\n",
    " EXTRACT(YEAR FROM realtime) AS year\n",
    "FROM inctimes;\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SQL QUERY = \n",
      "SELECT inctimes_id, timedesc_id, incident_id, realtime,\n",
      " EXTRACT(DOW FROM realtime) AS dow,\n",
      " EXTRACT(MONTH FROM realtime) AS month,\n",
      " EXTRACT(YEAR FROM realtime) AS year\n",
      "FROM inctimes;\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df1 = pquery(QUERY1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1637845 entries, 0 to 1637844\n",
      "Data columns (total 7 columns):\n",
      "inctimes_id    1637845 non-null int64\n",
      "timedesc_id    1637845 non-null int64\n",
      "incident_id    1637845 non-null int64\n",
      "realtime       1637845 non-null datetime64[ns]\n",
      "dow            1637845 non-null float64\n",
      "month          1637845 non-null float64\n",
      "year           1637845 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(3), int64(3)\n",
      "memory usage: 87.5 MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "    inctimes_id  timedesc_id  incident_id            realtime  dow  month  \\\n",
       "0      16517853            1      1102989 2014-09-22 08:33:03  1.0    9.0   \n",
       "1      16517854            2      1102989 2014-09-22 08:34:16  1.0    9.0   \n",
       "2      16517855            3      1102989 2014-09-22 08:34:33  1.0    9.0   \n",
       "3      16519204            3      1102989 2014-09-22 08:34:33  1.0    9.0   \n",
       "4      16926180            3      1102989 2015-01-19 10:29:50  1.0    1.0   \n",
       "5      16517856            4      1102989 2014-09-22 08:35:41  1.0    9.0   \n",
       "6      16519205            4      1102989 2014-09-22 08:35:41  1.0    9.0   \n",
       "7      16517857            5      1102989 2014-09-22 08:42:43  1.0    9.0   \n",
       "8      16519206            5      1102989 2014-09-22 08:42:43  1.0    9.0   \n",
       "9      16517858            7      1102989 2014-09-22 08:56:03  1.0    9.0   \n",
       "10     16519207           12      1102989 2014-09-22 08:56:03  1.0    9.0   \n",
       "11     16926181           12      1102989 2015-01-19 10:29:55  1.0    1.0   \n",
       "12     16518089            0      1102990 2014-09-22 08:49:54  1.0    9.0   \n",
       "13     16518106            1      1102990 2014-09-22 08:50:08  1.0    9.0   \n",
       "14     16518107            2      1102990 2014-09-22 08:50:56  1.0    9.0   \n",
       "\n",
       "      year  \n",
       "0   2014.0  \n",
       "1   2014.0  \n",
       "2   2014.0  \n",
       "3   2014.0  \n",
       "4   2015.0  \n",
       "5   2014.0  \n",
       "6   2014.0  \n",
       "7   2014.0  \n",
       "8   2014.0  \n",
       "9   2014.0  \n",
       "10  2014.0  \n",
       "11  2015.0  \n",
       "12  2014.0  \n",
       "13  2014.0  \n",
       "14  2014.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2      True\n",
       "3      True\n",
       "4      True\n",
       "5      True\n",
       "6      True\n",
       "7      True\n",
       "8      True\n",
       "9     False\n",
       "10     True\n",
       "11     True\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "15     True\n",
       "16     True\n",
       "17     True\n",
       "18     True\n",
       "19     True\n",
       "20     True\n",
       "21     True\n",
       "22     True\n",
       "23    False\n",
       "24    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's audit the data for duplicates where inctimes_id and timedesc_id are the same. \n",
    "# This code shows all rows that have a duplicate pair of incident_id, timedesc_id\n",
    "\n",
    "df1.duplicated(['incident_id','timedesc_id'],keep = False).head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <td>2015-01-19 10:29:55</td>\n",
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       "      <td>2014-09-22 08:51:13</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2014.0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16517985</td>\n",
       "      <td>4</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:53:25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16518129</td>\n",
       "      <td>4</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:51:51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16518124</td>\n",
       "      <td>5</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:56:04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16518130</td>\n",
       "      <td>5</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:56:04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16517986</td>\n",
       "      <td>12</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:58:14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16518210</td>\n",
       "      <td>12</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 09:49:39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16518134</td>\n",
       "      <td>3</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:54:25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16518137</td>\n",
       "      <td>3</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:54:25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16518453</td>\n",
       "      <td>3</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:54:25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>16518135</td>\n",
       "      <td>4</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:54:32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>16518138</td>\n",
       "      <td>4</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:54:32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16518454</td>\n",
       "      <td>4</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:55:32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>16518136</td>\n",
       "      <td>5</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:58:55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>16518139</td>\n",
       "      <td>5</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:58:55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>16518169</td>\n",
       "      <td>12</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 09:45:10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>16518516</td>\n",
       "      <td>12</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:59:58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>16517851</td>\n",
       "      <td>3</td>\n",
       "      <td>1102992</td>\n",
       "      <td>2014-09-22 08:51:38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637799</th>\n",
       "      <td>18667943</td>\n",
       "      <td>12</td>\n",
       "      <td>1210384</td>\n",
       "      <td>2016-01-01 03:53:39</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637800</th>\n",
       "      <td>18669135</td>\n",
       "      <td>13</td>\n",
       "      <td>1210384</td>\n",
       "      <td>2016-01-01 03:04:52</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637801</th>\n",
       "      <td>18668840</td>\n",
       "      <td>13</td>\n",
       "      <td>1210384</td>\n",
       "      <td>2016-01-01 03:02:26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637805</th>\n",
       "      <td>18667841</td>\n",
       "      <td>3</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:03:11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637806</th>\n",
       "      <td>18669136</td>\n",
       "      <td>3</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:04:54</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637807</th>\n",
       "      <td>18668916</td>\n",
       "      <td>3</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:03:11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637808</th>\n",
       "      <td>18667847</td>\n",
       "      <td>3</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:03:11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637809</th>\n",
       "      <td>18667842</td>\n",
       "      <td>4</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:03:50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637810</th>\n",
       "      <td>18669137</td>\n",
       "      <td>4</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:04:54</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637811</th>\n",
       "      <td>18667848</td>\n",
       "      <td>4</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:03:50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637812</th>\n",
       "      <td>18667853</td>\n",
       "      <td>5</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:06:40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637813</th>\n",
       "      <td>18669138</td>\n",
       "      <td>5</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:06:40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637814</th>\n",
       "      <td>18667857</td>\n",
       "      <td>5</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:13:08</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637816</th>\n",
       "      <td>18669139</td>\n",
       "      <td>12</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:14:39</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637817</th>\n",
       "      <td>18668917</td>\n",
       "      <td>12</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:04:37</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637818</th>\n",
       "      <td>18667858</td>\n",
       "      <td>12</td>\n",
       "      <td>1210385</td>\n",
       "      <td>2016-01-01 03:16:39</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637822</th>\n",
       "      <td>18667867</td>\n",
       "      <td>3</td>\n",
       "      <td>1210386</td>\n",
       "      <td>2016-01-01 03:11:11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637823</th>\n",
       "      <td>18667870</td>\n",
       "      <td>3</td>\n",
       "      <td>1210386</td>\n",
       "      <td>2016-01-01 03:11:11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637824</th>\n",
       "      <td>18668368</td>\n",
       "      <td>3</td>\n",
       "      <td>1210386</td>\n",
       "      <td>2016-01-01 03:11:11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637825</th>\n",
       "      <td>18667868</td>\n",
       "      <td>4</td>\n",
       "      <td>1210386</td>\n",
       "      <td>2016-01-01 03:11:46</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637826</th>\n",
       "      <td>18667871</td>\n",
       "      <td>4</td>\n",
       "      <td>1210386</td>\n",
       "      <td>2016-01-01 03:11:46</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637827</th>\n",
       "      <td>18668369</td>\n",
       "      <td>4</td>\n",
       "      <td>1210386</td>\n",
       "      <td>2016-01-01 03:12:51</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637828</th>\n",
       "      <td>18667869</td>\n",
       "      <td>5</td>\n",
       "      <td>1210386</td>\n",
       "      <td>2016-01-01 03:13:52</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637829</th>\n",
       "      <td>18667872</td>\n",
       "      <td>5</td>\n",
       "      <td>1210386</td>\n",
       "      <td>2016-01-01 03:13:52</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637834</th>\n",
       "      <td>18668010</td>\n",
       "      <td>12</td>\n",
       "      <td>1210386</td>\n",
       "      <td>2016-01-01 04:10:49</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637835</th>\n",
       "      <td>18668370</td>\n",
       "      <td>12</td>\n",
       "      <td>1210386</td>\n",
       "      <td>2016-01-01 03:18:40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637838</th>\n",
       "      <td>18667885</td>\n",
       "      <td>3</td>\n",
       "      <td>1210387</td>\n",
       "      <td>2015-12-31 18:31:05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637839</th>\n",
       "      <td>18667889</td>\n",
       "      <td>3</td>\n",
       "      <td>1210387</td>\n",
       "      <td>2015-12-31 18:31:05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637841</th>\n",
       "      <td>18667887</td>\n",
       "      <td>5</td>\n",
       "      <td>1210387</td>\n",
       "      <td>2015-12-31 21:19:09</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637842</th>\n",
       "      <td>18667890</td>\n",
       "      <td>5</td>\n",
       "      <td>1210387</td>\n",
       "      <td>2015-12-31 21:19:09</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1059286 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         inctimes_id  timedesc_id  incident_id            realtime  dow  \\\n",
       "2           16517855            3      1102989 2014-09-22 08:34:33  1.0   \n",
       "3           16519204            3      1102989 2014-09-22 08:34:33  1.0   \n",
       "4           16926180            3      1102989 2015-01-19 10:29:50  1.0   \n",
       "5           16517856            4      1102989 2014-09-22 08:35:41  1.0   \n",
       "6           16519205            4      1102989 2014-09-22 08:35:41  1.0   \n",
       "7           16517857            5      1102989 2014-09-22 08:42:43  1.0   \n",
       "8           16519206            5      1102989 2014-09-22 08:42:43  1.0   \n",
       "10          16519207           12      1102989 2014-09-22 08:56:03  1.0   \n",
       "11          16926181           12      1102989 2015-01-19 10:29:55  1.0   \n",
       "15          16518108            3      1102990 2014-09-22 08:51:13  1.0   \n",
       "16          16517984            3      1102990 2014-09-22 08:51:13  1.0   \n",
       "17          16518128            3      1102990 2014-09-22 08:51:13  1.0   \n",
       "18          16518109            4      1102990 2014-09-22 08:51:51  1.0   \n",
       "19          16517985            4      1102990 2014-09-22 08:53:25  1.0   \n",
       "20          16518129            4      1102990 2014-09-22 08:51:51  1.0   \n",
       "21          16518124            5      1102990 2014-09-22 08:56:04  1.0   \n",
       "22          16518130            5      1102990 2014-09-22 08:56:04  1.0   \n",
       "26          16517986           12      1102990 2014-09-22 08:58:14  1.0   \n",
       "27          16518210           12      1102990 2014-09-22 09:49:39  1.0   \n",
       "31          16518134            3      1102991 2014-09-22 08:54:25  1.0   \n",
       "32          16518137            3      1102991 2014-09-22 08:54:25  1.0   \n",
       "33          16518453            3      1102991 2014-09-22 08:54:25  1.0   \n",
       "34          16518135            4      1102991 2014-09-22 08:54:32  1.0   \n",
       "35          16518138            4      1102991 2014-09-22 08:54:32  1.0   \n",
       "36          16518454            4      1102991 2014-09-22 08:55:32  1.0   \n",
       "37          16518136            5      1102991 2014-09-22 08:58:55  1.0   \n",
       "38          16518139            5      1102991 2014-09-22 08:58:55  1.0   \n",
       "43          16518169           12      1102991 2014-09-22 09:45:10  1.0   \n",
       "44          16518516           12      1102991 2014-09-22 08:59:58  1.0   \n",
       "47          16517851            3      1102992 2014-09-22 08:51:38  1.0   \n",
       "...              ...          ...          ...                 ...  ...   \n",
       "1637799     18667943           12      1210384 2016-01-01 03:53:39  5.0   \n",
       "1637800     18669135           13      1210384 2016-01-01 03:04:52  5.0   \n",
       "1637801     18668840           13      1210384 2016-01-01 03:02:26  5.0   \n",
       "1637805     18667841            3      1210385 2016-01-01 03:03:11  5.0   \n",
       "1637806     18669136            3      1210385 2016-01-01 03:04:54  5.0   \n",
       "1637807     18668916            3      1210385 2016-01-01 03:03:11  5.0   \n",
       "1637808     18667847            3      1210385 2016-01-01 03:03:11  5.0   \n",
       "1637809     18667842            4      1210385 2016-01-01 03:03:50  5.0   \n",
       "1637810     18669137            4      1210385 2016-01-01 03:04:54  5.0   \n",
       "1637811     18667848            4      1210385 2016-01-01 03:03:50  5.0   \n",
       "1637812     18667853            5      1210385 2016-01-01 03:06:40  5.0   \n",
       "1637813     18669138            5      1210385 2016-01-01 03:06:40  5.0   \n",
       "1637814     18667857            5      1210385 2016-01-01 03:13:08  5.0   \n",
       "1637816     18669139           12      1210385 2016-01-01 03:14:39  5.0   \n",
       "1637817     18668917           12      1210385 2016-01-01 03:04:37  5.0   \n",
       "1637818     18667858           12      1210385 2016-01-01 03:16:39  5.0   \n",
       "1637822     18667867            3      1210386 2016-01-01 03:11:11  5.0   \n",
       "1637823     18667870            3      1210386 2016-01-01 03:11:11  5.0   \n",
       "1637824     18668368            3      1210386 2016-01-01 03:11:11  5.0   \n",
       "1637825     18667868            4      1210386 2016-01-01 03:11:46  5.0   \n",
       "1637826     18667871            4      1210386 2016-01-01 03:11:46  5.0   \n",
       "1637827     18668369            4      1210386 2016-01-01 03:12:51  5.0   \n",
       "1637828     18667869            5      1210386 2016-01-01 03:13:52  5.0   \n",
       "1637829     18667872            5      1210386 2016-01-01 03:13:52  5.0   \n",
       "1637834     18668010           12      1210386 2016-01-01 04:10:49  5.0   \n",
       "1637835     18668370           12      1210386 2016-01-01 03:18:40  5.0   \n",
       "1637838     18667885            3      1210387 2015-12-31 18:31:05  4.0   \n",
       "1637839     18667889            3      1210387 2015-12-31 18:31:05  4.0   \n",
       "1637841     18667887            5      1210387 2015-12-31 21:19:09  4.0   \n",
       "1637842     18667890            5      1210387 2015-12-31 21:19:09  4.0   \n",
       "\n",
       "         month    year  \n",
       "2          9.0  2014.0  \n",
       "3          9.0  2014.0  \n",
       "4          1.0  2015.0  \n",
       "5          9.0  2014.0  \n",
       "6          9.0  2014.0  \n",
       "7          9.0  2014.0  \n",
       "8          9.0  2014.0  \n",
       "10         9.0  2014.0  \n",
       "11         1.0  2015.0  \n",
       "15         9.0  2014.0  \n",
       "16         9.0  2014.0  \n",
       "17         9.0  2014.0  \n",
       "18         9.0  2014.0  \n",
       "19         9.0  2014.0  \n",
       "20         9.0  2014.0  \n",
       "21         9.0  2014.0  \n",
       "22         9.0  2014.0  \n",
       "26         9.0  2014.0  \n",
       "27         9.0  2014.0  \n",
       "31         9.0  2014.0  \n",
       "32         9.0  2014.0  \n",
       "33         9.0  2014.0  \n",
       "34         9.0  2014.0  \n",
       "35         9.0  2014.0  \n",
       "36         9.0  2014.0  \n",
       "37         9.0  2014.0  \n",
       "38         9.0  2014.0  \n",
       "43         9.0  2014.0  \n",
       "44         9.0  2014.0  \n",
       "47         9.0  2014.0  \n",
       "...        ...     ...  \n",
       "1637799    1.0  2016.0  \n",
       "1637800    1.0  2016.0  \n",
       "1637801    1.0  2016.0  \n",
       "1637805    1.0  2016.0  \n",
       "1637806    1.0  2016.0  \n",
       "1637807    1.0  2016.0  \n",
       "1637808    1.0  2016.0  \n",
       "1637809    1.0  2016.0  \n",
       "1637810    1.0  2016.0  \n",
       "1637811    1.0  2016.0  \n",
       "1637812    1.0  2016.0  \n",
       "1637813    1.0  2016.0  \n",
       "1637814    1.0  2016.0  \n",
       "1637816    1.0  2016.0  \n",
       "1637817    1.0  2016.0  \n",
       "1637818    1.0  2016.0  \n",
       "1637822    1.0  2016.0  \n",
       "1637823    1.0  2016.0  \n",
       "1637824    1.0  2016.0  \n",
       "1637825    1.0  2016.0  \n",
       "1637826    1.0  2016.0  \n",
       "1637827    1.0  2016.0  \n",
       "1637828    1.0  2016.0  \n",
       "1637829    1.0  2016.0  \n",
       "1637834    1.0  2016.0  \n",
       "1637835    1.0  2016.0  \n",
       "1637838   12.0  2015.0  \n",
       "1637839   12.0  2015.0  \n",
       "1637841   12.0  2015.0  \n",
       "1637842   12.0  2015.0  \n",
       "\n",
       "[1059286 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print only rows where a duplicate incident_id/timedesc_id pair exists\n",
    "df1[df1.duplicated(['incident_id','timedesc_id'],keep = False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the duplicates above, it seems that the first row is more likely to be the \"correct\" one. Some events have duplicate timestamps for timedesc_id that are not even in the same year as the first entry, which seems unlikely. For purposes if this analysis, I will treat the first instance of the timedesc_id as correct and ignore the other duplicates. **However, this merits further discussion with the data people at PF&R and more investigation should be made into why duplicates occur and what they might mean.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>inctimes_id</th>\n",
       "      <th>timedesc_id</th>\n",
       "      <th>incident_id</th>\n",
       "      <th>realtime</th>\n",
       "      <th>dow</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16517853</td>\n",
       "      <td>1</td>\n",
       "      <td>1102989</td>\n",
       "      <td>2014-09-22 08:33:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16517854</td>\n",
       "      <td>2</td>\n",
       "      <td>1102989</td>\n",
       "      <td>2014-09-22 08:34:16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16517855</td>\n",
       "      <td>3</td>\n",
       "      <td>1102989</td>\n",
       "      <td>2014-09-22 08:34:33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16517856</td>\n",
       "      <td>4</td>\n",
       "      <td>1102989</td>\n",
       "      <td>2014-09-22 08:35:41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16517857</td>\n",
       "      <td>5</td>\n",
       "      <td>1102989</td>\n",
       "      <td>2014-09-22 08:42:43</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16517858</td>\n",
       "      <td>7</td>\n",
       "      <td>1102989</td>\n",
       "      <td>2014-09-22 08:56:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16519207</td>\n",
       "      <td>12</td>\n",
       "      <td>1102989</td>\n",
       "      <td>2014-09-22 08:56:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16518089</td>\n",
       "      <td>0</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:49:54</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16518106</td>\n",
       "      <td>1</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:50:08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16518107</td>\n",
       "      <td>2</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:50:56</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16518108</td>\n",
       "      <td>3</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:51:13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16518109</td>\n",
       "      <td>4</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:51:51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16518124</td>\n",
       "      <td>5</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:56:04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16518207</td>\n",
       "      <td>7</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 09:49:42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16518208</td>\n",
       "      <td>9</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 09:12:47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16518209</td>\n",
       "      <td>10</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 09:24:50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16517986</td>\n",
       "      <td>12</td>\n",
       "      <td>1102990</td>\n",
       "      <td>2014-09-22 08:58:14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16518131</td>\n",
       "      <td>0</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:53:04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16518132</td>\n",
       "      <td>1</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:53:14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>16518133</td>\n",
       "      <td>2</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:54:04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16518134</td>\n",
       "      <td>3</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:54:25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>16518135</td>\n",
       "      <td>4</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:54:32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>16518136</td>\n",
       "      <td>5</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:58:55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>16518125</td>\n",
       "      <td>7</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 09:45:12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>16518140</td>\n",
       "      <td>8</td>\n",
       "      <td>1102991</td>\n",
       "      <td>2014-09-22 08:59:27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    inctimes_id  timedesc_id  incident_id            realtime  dow  month  \\\n",
       "0      16517853            1      1102989 2014-09-22 08:33:03  1.0    9.0   \n",
       "1      16517854            2      1102989 2014-09-22 08:34:16  1.0    9.0   \n",
       "2      16517855            3      1102989 2014-09-22 08:34:33  1.0    9.0   \n",
       "5      16517856            4      1102989 2014-09-22 08:35:41  1.0    9.0   \n",
       "7      16517857            5      1102989 2014-09-22 08:42:43  1.0    9.0   \n",
       "9      16517858            7      1102989 2014-09-22 08:56:03  1.0    9.0   \n",
       "10     16519207           12      1102989 2014-09-22 08:56:03  1.0    9.0   \n",
       "12     16518089            0      1102990 2014-09-22 08:49:54  1.0    9.0   \n",
       "13     16518106            1      1102990 2014-09-22 08:50:08  1.0    9.0   \n",
       "14     16518107            2      1102990 2014-09-22 08:50:56  1.0    9.0   \n",
       "15     16518108            3      1102990 2014-09-22 08:51:13  1.0    9.0   \n",
       "18     16518109            4      1102990 2014-09-22 08:51:51  1.0    9.0   \n",
       "21     16518124            5      1102990 2014-09-22 08:56:04  1.0    9.0   \n",
       "23     16518207            7      1102990 2014-09-22 09:49:42  1.0    9.0   \n",
       "24     16518208            9      1102990 2014-09-22 09:12:47  1.0    9.0   \n",
       "25     16518209           10      1102990 2014-09-22 09:24:50  1.0    9.0   \n",
       "26     16517986           12      1102990 2014-09-22 08:58:14  1.0    9.0   \n",
       "28     16518131            0      1102991 2014-09-22 08:53:04  1.0    9.0   \n",
       "29     16518132            1      1102991 2014-09-22 08:53:14  1.0    9.0   \n",
       "30     16518133            2      1102991 2014-09-22 08:54:04  1.0    9.0   \n",
       "31     16518134            3      1102991 2014-09-22 08:54:25  1.0    9.0   \n",
       "34     16518135            4      1102991 2014-09-22 08:54:32  1.0    9.0   \n",
       "37     16518136            5      1102991 2014-09-22 08:58:55  1.0    9.0   \n",
       "39     16518125            7      1102991 2014-09-22 09:45:12  1.0    9.0   \n",
       "40     16518140            8      1102991 2014-09-22 08:59:27  1.0    9.0   \n",
       "\n",
       "      year  \n",
       "0   2014.0  \n",
       "1   2014.0  \n",
       "2   2014.0  \n",
       "5   2014.0  \n",
       "7   2014.0  \n",
       "9   2014.0  \n",
       "10  2014.0  \n",
       "12  2014.0  \n",
       "13  2014.0  \n",
       "14  2014.0  \n",
       "15  2014.0  \n",
       "18  2014.0  \n",
       "21  2014.0  \n",
       "23  2014.0  \n",
       "24  2014.0  \n",
       "25  2014.0  \n",
       "26  2014.0  \n",
       "28  2014.0  \n",
       "29  2014.0  \n",
       "30  2014.0  \n",
       "31  2014.0  \n",
       "34  2014.0  \n",
       "37  2014.0  \n",
       "39  2014.0  \n",
       "40  2014.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this code does that (showing only first 25 rows)\n",
    "df1[df1.duplicated(['incident_id','timedesc_id'],keep = 'first') == False].head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_nd = df1[df1.duplicated(['incident_id','timedesc_id'],keep = 'first') == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timedesc_id\n",
       "0      62106\n",
       "1     107268\n",
       "2     107269\n",
       "3     107271\n",
       "4     105971\n",
       "5     101551\n",
       "7     107241\n",
       "8      42712\n",
       "9      56890\n",
       "10     57321\n",
       "11         2\n",
       "12    105770\n",
       "13      7149\n",
       "Name: timedesc_id, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after eliminating duplicates, what does the distribution of timedesc_ids look like?\n",
    "df1_nd.groupby('timedesc_id')['timedesc_id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>timedesc_id</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>62106</td>\n",
       "      <td>107268</td>\n",
       "      <td>107269</td>\n",
       "      <td>107271</td>\n",
       "      <td>105971</td>\n",
       "      <td>101551</td>\n",
       "      <td>107241</td>\n",
       "      <td>42712</td>\n",
       "      <td>56890</td>\n",
       "      <td>57321</td>\n",
       "      <td>2</td>\n",
       "      <td>105770</td>\n",
       "      <td>7149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "timedesc_id     0       1       2       3       4       5       7      8   \\\n",
       "row_0                                                                       \n",
       "count        62106  107268  107269  107271  105971  101551  107241  42712   \n",
       "\n",
       "timedesc_id     9      10  11      12    13  \n",
       "row_0                                        \n",
       "count        56890  57321   2  105770  7149  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after eliminating duplicates, what does the distribution of timedesc_ids look like?\n",
    "pd.crosstab(index='count', columns=df1_nd['timedesc_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>timedesc_id</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.064125</td>\n",
       "      <td>0.110754</td>\n",
       "      <td>0.110755</td>\n",
       "      <td>0.110758</td>\n",
       "      <td>0.109415</td>\n",
       "      <td>0.104852</td>\n",
       "      <td>0.110727</td>\n",
       "      <td>0.0441</td>\n",
       "      <td>0.058739</td>\n",
       "      <td>0.059184</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.109208</td>\n",
       "      <td>0.007381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "timedesc_id        0         1         2         3         4         5   \\\n",
       "row_0                                                                     \n",
       "count        0.064125  0.110754  0.110755  0.110758  0.109415  0.104852   \n",
       "\n",
       "timedesc_id        7       8         9         10        11        12  \\\n",
       "row_0                                                                   \n",
       "count        0.110727  0.0441  0.058739  0.059184  0.000002  0.109208   \n",
       "\n",
       "timedesc_id        13  \n",
       "row_0                  \n",
       "count        0.007381  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same crosstab data as a %\n",
    "tab_norm = pd.crosstab(index='count', columns=df1_nd['timedesc_id']).apply(lambda r: r/r.sum(), axis=1)\n",
    "tab_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe325ddb0f0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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+n0RODt12GETpXgfw+dxZ2S5N0hYYbiSpGZ1LStnz+1dTv3Yt9WtWUdC9Jx89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\nGG4kSVKsGG4kSVKs/H8pPljUxb7UEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe30758e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab_norm.plot.barh(stacked = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the summary statisics and stacked barplot, (and after removal of duplicates), we see some possibly interesting things regarding counts of each timedesc_id\n",
    "* there are no timedesc_id events labeled 6 (Fire under control)\n",
    "* timedesc_id events 1, 2, 3, 4, 5, 7, 12 all have fairly similar frequency of occurence ~10-11%\n",
    "* why does 11 (depart desination) occur so infrequently?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I look at distribution of response times. For sake of brevity, I initially look only at the time deltas between timedesc_id 3-5 (dispatch to on-scene) and 3-10 (disptach to arrive-destination)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# this function calculates time deltas for each incident id in the df\n",
    "# iterating over the groupby object was very helpful\n",
    "\n",
    "def time_delta(dataframe):\n",
    "    '''caluclates the time delta between two different timedesc_ids for all groups'''\n",
    "    count = 0\n",
    "    group_dict = {}\n",
    "    for name, group in dataframe.groupby('incident_id'):\n",
    "        delta_dict = {}\n",
    "        #print(name)\n",
    "        #print(group)\n",
    "        df_t = group.set_index('timedesc_id') # reset the group df to index on timedesc_id\n",
    "\n",
    "        try:\n",
    "            delta_dict['delta3-5'] = df_t.loc[5,'realtime']-df_t.loc[3,'realtime']\n",
    "            delta_dict['dow'] = df_t.loc[3,'dow']\n",
    "            delta_dict['month'] = df_t.loc[3,'month']\n",
    "            \n",
    "        except KeyError:\n",
    "            delta_dict['delta3-5'] = None\n",
    "            \n",
    "        try:\n",
    "            delta_dict['delta3-10'] =  df_t.loc[10,'realtime']-df_t.loc[3,'realtime']\n",
    "            \n",
    "        except KeyError:\n",
    "            delta_dict['delta3-10'] = None   \n",
    "        \n",
    "        group_dict[name] = delta_dict        \n",
    "\n",
    "    return group_dict\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>dow</th>\n",
       "      <th>delta3-10</th>\n",
       "      <th>delta3-5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incident_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102989</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:08:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102990</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:33:37</td>\n",
       "      <td>00:04:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102991</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:25:31</td>\n",
       "      <td>00:04:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102992</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:07:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102993</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:03:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102994</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:47:55</td>\n",
       "      <td>00:04:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102995</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:03:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102996</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:04:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102997</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:04:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102998</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:41:58</td>\n",
       "      <td>00:03:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102999</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:06:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103000</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:06:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103001</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:18:08</td>\n",
       "      <td>00:05:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103002</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:06:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103003</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:32:16</td>\n",
       "      <td>00:04:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103004</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:24:34</td>\n",
       "      <td>00:06:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103005</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:53:43</td>\n",
       "      <td>00:02:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103006</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:10:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103007</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:27:20</td>\n",
       "      <td>00:05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103008</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:32:07</td>\n",
       "      <td>00:01:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103009</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:35:42</td>\n",
       "      <td>00:06:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103010</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:41:10</td>\n",
       "      <td>00:04:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103011</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:04:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103012</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:35:21</td>\n",
       "      <td>00:04:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103013</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:24:43</td>\n",
       "      <td>00:04:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103014</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>01:07:58</td>\n",
       "      <td>00:05:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103016</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:02:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103017</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:30:32</td>\n",
       "      <td>00:03:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103018</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:04:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103059</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:01:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103060</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:36:43</td>\n",
       "      <td>00:03:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103061</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:43:28</td>\n",
       "      <td>00:04:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103062</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103063</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:06:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103064</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:02:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103065</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:51:43</td>\n",
       "      <td>00:04:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103066</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103067</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:53:22</td>\n",
       "      <td>00:05:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103068</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:05:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103069</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:33:32</td>\n",
       "      <td>00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103070</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:04:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103071</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:29:57</td>\n",
       "      <td>00:05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103072</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:39:14</td>\n",
       "      <td>00:02:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103073</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:05:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103074</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:46:08</td>\n",
       "      <td>00:03:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103075</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:52:48</td>\n",
       "      <td>00:03:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103076</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:04:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103077</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:05:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103078</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:32:46</td>\n",
       "      <td>00:03:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103079</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:43:21</td>\n",
       "      <td>00:03:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103080</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:04:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103081</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:07:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103082</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:30:08</td>\n",
       "      <td>00:02:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103083</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:34:35</td>\n",
       "      <td>00:05:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103084</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:24:21</td>\n",
       "      <td>00:00:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103085</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:05:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103086</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:02:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103087</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:06:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103088</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:37:39</td>\n",
       "      <td>00:05:18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             month  dow  delta3-10  delta3-5\n",
       "incident_id                                 \n",
       "1102989        9.0  1.0        NaT  00:08:10\n",
       "1102990        9.0  1.0   00:33:37  00:04:51\n",
       "1102991        9.0  1.0   00:25:31  00:04:30\n",
       "1102992        9.0  1.0        NaT  00:07:04\n",
       "1102993        9.0  1.0        NaT  00:03:10\n",
       "1102994        9.0  1.0   00:47:55  00:04:30\n",
       "1102995        9.0  1.0        NaT  00:03:10\n",
       "1102996        9.0  1.0        NaT  00:04:07\n",
       "1102997        9.0  1.0        NaT  00:04:37\n",
       "1102998        9.0  1.0   00:41:58  00:03:38\n",
       "1102999        9.0  1.0        NaT  00:06:35\n",
       "1103000        9.0  1.0        NaT  00:06:23\n",
       "1103001        9.0  1.0   00:18:08  00:05:39\n",
       "1103002        9.0  1.0        NaT  00:06:59\n",
       "1103003        9.0  1.0   00:32:16  00:04:15\n",
       "1103004        9.0  1.0   00:24:34  00:06:57\n",
       "1103005        9.0  1.0   00:53:43  00:02:59\n",
       "1103006        9.0  1.0        NaT  00:10:12\n",
       "1103007        9.0  1.0   00:27:20  00:05:02\n",
       "1103008        9.0  1.0   00:32:07  00:01:49\n",
       "1103009        9.0  1.0   00:35:42  00:06:19\n",
       "1103010        9.0  1.0   00:41:10  00:04:35\n",
       "1103011        9.0  1.0        NaT  00:04:24\n",
       "1103012        9.0  1.0   00:35:21  00:04:40\n",
       "1103013        9.0  1.0   00:24:43  00:04:13\n",
       "1103014        9.0  1.0   01:07:58  00:05:21\n",
       "1103015        NaN  NaN        NaT       NaT\n",
       "1103016        9.0  1.0        NaT  00:02:37\n",
       "1103017        9.0  1.0   00:30:32  00:03:16\n",
       "1103018        9.0  1.0        NaT  00:04:41\n",
       "...            ...  ...        ...       ...\n",
       "1103059        9.0  1.0        NaT  00:01:51\n",
       "1103060        9.0  1.0   00:36:43  00:03:03\n",
       "1103061        9.0  1.0   00:43:28  00:04:17\n",
       "1103062        9.0  1.0        NaT  00:00:00\n",
       "1103063        9.0  1.0        NaT  00:06:47\n",
       "1103064        9.0  1.0        NaT  00:02:56\n",
       "1103065        9.0  1.0   00:51:43  00:04:19\n",
       "1103066        9.0  1.0        NaT  00:00:55\n",
       "1103067        9.0  1.0   00:53:22  00:05:36\n",
       "1103068        9.0  1.0        NaT  00:05:12\n",
       "1103069        9.0  1.0   00:33:32  00:00:00\n",
       "1103070        9.0  1.0        NaT  00:04:02\n",
       "1103071        9.0  1.0   00:29:57  00:05:24\n",
       "1103072        9.0  1.0   00:39:14  00:02:33\n",
       "1103073        9.0  1.0        NaT  00:05:50\n",
       "1103074        9.0  1.0   00:46:08  00:03:42\n",
       "1103075        9.0  1.0   00:52:48  00:03:58\n",
       "1103076        9.0  1.0        NaT  00:04:25\n",
       "1103077        9.0  1.0        NaT  00:05:42\n",
       "1103078        9.0  1.0   00:32:46  00:03:11\n",
       "1103079        9.0  1.0   00:43:21  00:03:41\n",
       "1103080        9.0  1.0        NaT  00:04:28\n",
       "1103081        9.0  1.0        NaT  00:07:03\n",
       "1103082        9.0  1.0   00:30:08  00:02:16\n",
       "1103083        9.0  1.0   00:34:35  00:05:26\n",
       "1103084        9.0  1.0   00:24:21  00:00:50\n",
       "1103085        9.0  1.0        NaT  00:05:19\n",
       "1103086        9.0  1.0        NaT  00:02:14\n",
       "1103087        9.0  1.0        NaT  00:06:45\n",
       "1103088        9.0  1.0   00:37:39  00:05:18\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use DataFrame.from_dict method to convert the dictionary object returned in the time_delta function to \n",
    "# a pandas df. Note using argument: orient = 'index' makes the dictionary keys into column headers.\n",
    "\n",
    "#print(time_delta(df1_nd))\n",
    "time_delta_df = pd.DataFrame.from_dict(time_delta(df1_nd), orient = 'index')\n",
    "time_delta_df.index.name = 'incident_id'\n",
    "time_delta_df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>dow</th>\n",
       "      <th>delta3-10</th>\n",
       "      <th>delta3-5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>101551.000000</td>\n",
       "      <td>101551.000000</td>\n",
       "      <td>57321</td>\n",
       "      <td>101551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.471034</td>\n",
       "      <td>3.039419</td>\n",
       "      <td>0 days 00:37:39.478166</td>\n",
       "      <td>0 days 00:05:49.396332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.484812</td>\n",
       "      <td>1.987889</td>\n",
       "      <td>0 days 00:11:50.205399</td>\n",
       "      <td>0 days 02:36:51.456424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1 days +23:39:35</td>\n",
       "      <td>-1 days +14:33:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0 days 00:29:24</td>\n",
       "      <td>0 days 00:03:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0 days 00:36:19</td>\n",
       "      <td>0 days 00:04:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0 days 00:44:11</td>\n",
       "      <td>0 days 00:05:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0 days 05:51:26</td>\n",
       "      <td>32 days 06:23:57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               month            dow               delta3-10  \\\n",
       "count  101551.000000  101551.000000                   57321   \n",
       "mean        7.471034       3.039419  0 days 00:37:39.478166   \n",
       "std         3.484812       1.987889  0 days 00:11:50.205399   \n",
       "min         1.000000       0.000000       -1 days +23:39:35   \n",
       "25%         5.000000       1.000000         0 days 00:29:24   \n",
       "50%         8.000000       3.000000         0 days 00:36:19   \n",
       "75%        11.000000       5.000000         0 days 00:44:11   \n",
       "max        12.000000       6.000000         0 days 05:51:26   \n",
       "\n",
       "                     delta3-5  \n",
       "count                  101551  \n",
       "mean   0 days 00:05:49.396332  \n",
       "std    0 days 02:36:51.456424  \n",
       "min         -1 days +14:33:55  \n",
       "25%           0 days 00:03:35  \n",
       "50%           0 days 00:04:39  \n",
       "75%           0 days 00:05:56  \n",
       "max          32 days 06:23:57  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_delta_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "incident_id\n",
      "1102989      8.166667\n",
      "1102990      4.850000\n",
      "1102991      4.500000\n",
      "1102992      7.066667\n",
      "1102993      3.166667\n",
      "1102994      4.500000\n",
      "1102995      3.166667\n",
      "1102996      4.116667\n",
      "1102997      4.616667\n",
      "1102998      3.633333\n",
      "1102999      6.583333\n",
      "1103000      6.383333\n",
      "1103001      5.650000\n",
      "1103002      6.983333\n",
      "1103003      4.250000\n",
      "1103004      6.950000\n",
      "1103005      2.983333\n",
      "1103006     10.200000\n",
      "1103007      5.033333\n",
      "1103008      1.816667\n",
      "1103009      6.316667\n",
      "1103010      4.583333\n",
      "1103011      4.400000\n",
      "1103012      4.666667\n",
      "1103013      4.216667\n",
      "1103014      5.350000\n",
      "1103016      2.616667\n",
      "1103017      3.266667\n",
      "1103018      4.683333\n",
      "1103019      7.700000\n",
      "              ...    \n",
      "1210353      4.733333\n",
      "1210354      4.950000\n",
      "1210355      5.533333\n",
      "1210356      6.016667\n",
      "1210357      4.200000\n",
      "1210358      3.166667\n",
      "1210359      2.083333\n",
      "1210360     12.066667\n",
      "1210361     14.283333\n",
      "1210362      3.233333\n",
      "1210363     16.233333\n",
      "1210364      4.416667\n",
      "1210365      5.566667\n",
      "1210366      7.333333\n",
      "1210367      3.883333\n",
      "1210368      5.783333\n",
      "1210369      3.600000\n",
      "1210372      4.833333\n",
      "1210373      6.300000\n",
      "1210374      2.683333\n",
      "1210375      4.516667\n",
      "1210378      4.550000\n",
      "1210379      5.150000\n",
      "1210380      3.566667\n",
      "1210381      5.550000\n",
      "1210383      4.450000\n",
      "1210384      4.200000\n",
      "1210385      3.483333\n",
      "1210386      2.683333\n",
      "1210387    168.066667\n",
      "Name: delta3-5, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "I noticed there are some negative timedelta values which are obviously impossible and should be thrown out.\n",
    "In addition, pandas cannot plot histgram by just using: time_delta_df.plot()\n",
    "So we need to do some timedelta conversion to a more easily plotted numerical type.\n",
    "My approach was to convert the deltas to decimal expression of minutes. This link was helpful:\n",
    "http://www.datasciencebytes.com/bytes/2015/05/16/pandas-timedelta-histograms-unit-conversion-and-overflow-danger/\n",
    "'''\n",
    "print(time_delta_df['delta3-5'].dropna() / pd.Timedelta(minutes=1)) # note that I dropped missing values from the time_delta_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# remove null and negative values\n",
    "# this link was helpful: http://stackoverflow.com/questions/24214941/python-pandas-dataframe-filter-negative-values\n",
    "clean3_5 = time_delta_df[time_delta_df.loc[:,'delta3-5'] > \n",
    "                         pd.Timedelta(0)].loc[:,'delta3-5'].dropna()/pd.Timedelta(minutes=1)\n",
    "clean3_10 = time_delta_df[time_delta_df.loc[:,'delta3-10'] > \n",
    "                         pd.Timedelta(0)].loc[:,'delta3-10'].dropna()/pd.Timedelta(minutes=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    99614.000000\n",
       "mean         5.948698\n",
       "std        158.360530\n",
       "min          0.016667\n",
       "25%          3.650000\n",
       "50%          4.700000\n",
       "75%          5.966667\n",
       "max      46463.950000\n",
       "Name: delta3-5, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean3_5.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe307562160>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AZcC7GjMzs58yFMAWwK+A44CjM/P0pjRXUoKid1LGZXoNsFdm3tDpHZYkSb2pZ1qQMvNS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\nSZKkGgMkSZKkmv8POUD+ALiyz5YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe307560ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clean3_5.hist(bins=range(0,30,1))\n",
    "plt.xlabel('Minutes elapsed from timedesc_id 3 to 5 (Dispatched to On-scene)')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe320892f28>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D2wM/q9I/D1iX8ugQgMuBT0XEU2r93HYAHgBuqKXZqW3ZO1TfD8mCBQuZP398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\npal0SvW5VUW8A/A+4PXAvZT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe32308ff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clean3_10.hist(bins=range(0,250,5))\n",
    "plt.xlabel('Minutes elapsed from timedesc_id 3 to 10 (Dispatched to Arrive Destination)')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    57316.000000\n",
       "mean        37.662468\n",
       "std         11.827266\n",
       "min          0.416667\n",
       "25%         29.400000\n",
       "50%         36.316667\n",
       "75%         44.183333\n",
       "max        351.433333\n",
       "Name: delta3-10, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean3_10.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the histogram and summary statistics above, we can see that the mean response time between dispatch and on-scene is around 7.5 minutes.\n",
    "\n",
    "For dispatch to arrival destination, the mean time delta is much longer, at 37.8 minutes. \n",
    "\n",
    "Both distributions appear normal, and are right skewed which is somewhat expected since outliers cannot exist below 0.\n",
    "\n",
    "**_1/10/17 Next, if warranted, I can rebuild a summary dataframe with additional time deltas, not just between events 3-5 or 3-10. Not all timedelta pairs can be calculated for each incident id due to missing data, however. We can also look at the data tables to see types of dispatches and what their makeup is._**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, boxplots to look at response time in different months, years and by other possibly interesting criteria would be good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>dow</th>\n",
       "      <th>delta3-10</th>\n",
       "      <th>delta3-5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incident_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102989</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:08:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102990</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:33:37</td>\n",
       "      <td>00:04:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102991</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:25:31</td>\n",
       "      <td>00:04:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102992</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:07:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102993</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:03:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102994</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:47:55</td>\n",
       "      <td>00:04:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102995</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:03:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102996</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:04:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102997</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:04:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102998</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:41:58</td>\n",
       "      <td>00:03:38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             month  dow  delta3-10  delta3-5\n",
       "incident_id                                 \n",
       "1102989        9.0  1.0        NaT  00:08:10\n",
       "1102990        9.0  1.0   00:33:37  00:04:51\n",
       "1102991        9.0  1.0   00:25:31  00:04:30\n",
       "1102992        9.0  1.0        NaT  00:07:04\n",
       "1102993        9.0  1.0        NaT  00:03:10\n",
       "1102994        9.0  1.0   00:47:55  00:04:30\n",
       "1102995        9.0  1.0        NaT  00:03:10\n",
       "1102996        9.0  1.0        NaT  00:04:07\n",
       "1102997        9.0  1.0        NaT  00:04:37\n",
       "1102998        9.0  1.0   00:41:58  00:03:38"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_delta_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>dow</th>\n",
       "      <th>delta3-10</th>\n",
       "      <th>delta3-5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incident_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102990</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:33:37</td>\n",
       "      <td>00:04:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102991</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:25:31</td>\n",
       "      <td>00:04:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102994</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:47:55</td>\n",
       "      <td>00:04:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102998</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:41:58</td>\n",
       "      <td>00:03:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103001</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:18:08</td>\n",
       "      <td>00:05:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103003</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:32:16</td>\n",
       "      <td>00:04:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103004</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:24:34</td>\n",
       "      <td>00:06:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103005</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:53:43</td>\n",
       "      <td>00:02:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103007</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:27:20</td>\n",
       "      <td>00:05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103008</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00:32:07</td>\n",
       "      <td>00:01:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             month  dow  delta3-10  delta3-5\n",
       "incident_id                                 \n",
       "1102990        9.0  1.0   00:33:37  00:04:51\n",
       "1102991        9.0  1.0   00:25:31  00:04:30\n",
       "1102994        9.0  1.0   00:47:55  00:04:30\n",
       "1102998        9.0  1.0   00:41:58  00:03:38\n",
       "1103001        9.0  1.0   00:18:08  00:05:39\n",
       "1103003        9.0  1.0   00:32:16  00:04:15\n",
       "1103004        9.0  1.0   00:24:34  00:06:57\n",
       "1103005        9.0  1.0   00:53:43  00:02:59\n",
       "1103007        9.0  1.0   00:27:20  00:05:02\n",
       "1103008        9.0  1.0   00:32:07  00:01:49"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean time_delta_df of negative times values and Nulls\n",
    "time_delta_clean310 = time_delta_df[time_delta_df.loc[:,'delta3-10'] > \n",
    "                         pd.Timedelta(0)]\n",
    "\n",
    "time_delta_clean310.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>dow</th>\n",
       "      <th>delta3-10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dow</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">0.0</th>\n",
       "      <th>count</th>\n",
       "      <td>7801.0</td>\n",
       "      <td>7801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:36:20.069862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:11:08.100805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:05:36</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:28:37</td>\n",
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       "      <th>50%</th>\n",
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       "      <td>0 days 00:35:11</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:42:26</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 02:27:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1.0</th>\n",
       "      <th>count</th>\n",
       "      <td>8271.0</td>\n",
       "      <td>8271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0 days 00:37:52.637286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:11:38.116841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0 days 00:06:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0 days 00:29:39.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0 days 00:36:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0 days 00:44:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0 days 02:10:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">2.0</th>\n",
       "      <th>count</th>\n",
       "      <td>8087.0</td>\n",
       "      <td>8087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0 days 00:37:50.843823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:11:53.737977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0 days 00:00:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0 days 00:29:25.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0 days 00:36:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0 days 00:44:33.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0 days 02:33:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">3.0</th>\n",
       "      <th>count</th>\n",
       "      <td>8218.0</td>\n",
       "      <td>8218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0 days 00:38:04.652591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:12:03.930203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0 days 00:07:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0 days 00:29:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0 days 00:36:36.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0 days 00:44:47.750000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0 days 03:34:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">4.0</th>\n",
       "      <th>count</th>\n",
       "      <td>8378.0</td>\n",
       "      <td>8378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0 days 00:38:19.788135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:12:24.902209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0 days 00:06:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0 days 00:29:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0 days 00:36:49.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0 days 00:44:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0 days 05:51:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">5.0</th>\n",
       "      <th>count</th>\n",
       "      <td>8385.0</td>\n",
       "      <td>8385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0 days 00:38:31.425641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:12:07.559420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0 days 00:10:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0 days 00:29:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0 days 00:37:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0 days 00:45:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0 days 02:53:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">6.0</th>\n",
       "      <th>count</th>\n",
       "      <td>8159.0</td>\n",
       "      <td>8159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0 days 00:36:33.169138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0 days 00:11:12.930008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0 days 00:02:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0 days 00:28:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0 days 00:35:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0 days 00:42:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0 days 02:59:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              dow               delta3-10\n",
       "dow                                      \n",
       "0.0 count  7801.0                    7801\n",
       "    mean      0.0  0 days 00:36:20.069862\n",
       "    std       0.0  0 days 00:11:08.100805\n",
       "    min       0.0         0 days 00:05:36\n",
       "    25%       0.0         0 days 00:28:37\n",
       "    50%       0.0         0 days 00:35:11\n",
       "    75%       0.0         0 days 00:42:26\n",
       "    max       0.0         0 days 02:27:25\n",
       "1.0 count  8271.0                    8271\n",
       "    mean      1.0  0 days 00:37:52.637286\n",
       "    std       0.0  0 days 00:11:38.116841\n",
       "    min       1.0         0 days 00:06:47\n",
       "    25%       1.0  0 days 00:29:39.500000\n",
       "    50%       1.0         0 days 00:36:38\n",
       "    75%       1.0         0 days 00:44:29\n",
       "    max       1.0         0 days 02:10:54\n",
       "2.0 count  8087.0                    8087\n",
       "    mean      2.0  0 days 00:37:50.843823\n",
       "    std       0.0  0 days 00:11:53.737977\n",
       "    min       2.0         0 days 00:00:25\n",
       "    25%       2.0  0 days 00:29:25.500000\n",
       "    50%       2.0         0 days 00:36:30\n",
       "    75%       2.0  0 days 00:44:33.500000\n",
       "    max       2.0         0 days 02:33:05\n",
       "3.0 count  8218.0                    8218\n",
       "    mean      3.0  0 days 00:38:04.652591\n",
       "    std       0.0  0 days 00:12:03.930203\n",
       "    min       3.0         0 days 00:07:08\n",
       "    25%       3.0         0 days 00:29:39\n",
       "    50%       3.0  0 days 00:36:36.500000\n",
       "    75%       3.0  0 days 00:44:47.750000\n",
       "    max       3.0         0 days 03:34:43\n",
       "4.0 count  8378.0                    8378\n",
       "    mean      4.0  0 days 00:38:19.788135\n",
       "    std       0.0  0 days 00:12:24.902209\n",
       "    min       4.0         0 days 00:06:26\n",
       "    25%       4.0         0 days 00:29:47\n",
       "    50%       4.0  0 days 00:36:49.500000\n",
       "    75%       4.0         0 days 00:44:56\n",
       "    max       4.0         0 days 05:51:26\n",
       "5.0 count  8385.0                    8385\n",
       "    mean      5.0  0 days 00:38:31.425641\n",
       "    std       0.0  0 days 00:12:07.559420\n",
       "    min       5.0         0 days 00:10:35\n",
       "    25%       5.0         0 days 00:29:58\n",
       "    50%       5.0         0 days 00:37:02\n",
       "    75%       5.0         0 days 00:45:17\n",
       "    max       5.0         0 days 02:53:48\n",
       "6.0 count  8159.0                    8159\n",
       "    mean      6.0  0 days 00:36:33.169138\n",
       "    std       0.0  0 days 00:11:12.930008\n",
       "    min       6.0         0 days 00:02:49\n",
       "    25%       6.0         0 days 00:28:37\n",
       "    50%       6.0         0 days 00:35:22\n",
       "    75%       6.0         0 days 00:42:54\n",
       "    max       6.0         0 days 02:59:17"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary stats for 3-10 response time of each day of week\n",
    "time_delta_clean310.groupby('dow')['dow', 'delta3-10'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# try mapping numbers to strings for days of the week\n",
    "# this was helpful: http://stackoverflow.com/questions/20250771/remap-values-in-pandas-column-with-a-dict\n",
    "dow_map = {0.0:'sunday',\n",
    "           1.0:'monday', \n",
    "           2.0:'tuesday', \n",
    "           3.0:'wednesday', \n",
    "           4.0:'thursday', \n",
    "           5.0:'friday', \n",
    "           6.0:'saturday'}\n",
    "\n",
    "td310_mapped = time_delta_clean310.replace({'dow':dow_map}).groupby('dow')['dow', 'delta3-10']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WAAAL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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe320a72cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot of 3-10 response time for each day of week\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(14,6))\n",
    "bplot = sns.boxplot(x='dow', y='delta3-10', data=time_delta_clean310.replace({'dow':dow_map}), whis=[5,95])\n",
    "title = 'Distribution of 3-10 Reponse Times For Each Day of the Week'\n",
    "bplot.set_title(title, fontsize=15)\n",
    "bplot.set_xlabel('Day of Week', fontsize=12)\n",
    "bplot.set_ylabel('Response Time (3-10)', fontsize=12)\n",
    "bplot.tick_params(axis='both', labelsize=8)\n",
    "sns.despine(left=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>delta3-10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">apr</th>\n",
       "      <th>count</th>\n",
       "      <td>3708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0 days 00:37:28.330097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0 days 00:12:11.662228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0 days 00:11:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0 days 00:29:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0 days 00:35:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0 days 00:43:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0 days 02:53:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">aug</th>\n",
       "      <th>count</th>\n",
       "      <td>3997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0 days 00:36:46.291218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0 days 00:11:39.312274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1 days +23:58:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0 days 00:28:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0 days 00:35:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0 days 00:43:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0 days 02:28:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">dec</th>\n",
       "      <th>count</th>\n",
       "      <td>7581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0 days 00:38:19.201160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0 days 00:12:03.003995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0 days 00:07:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0 days 00:29:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0 days 00:36:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0 days 00:44:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0 days 02:33:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">feb</th>\n",
       "      <th>count</th>\n",
       "      <td>3421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0 days 00:37:15.664425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0 days 00:11:56.607197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0 days 00:09:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0 days 00:28:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0 days 00:36:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">may</th>\n",
       "      <th>std</th>\n",
       "      <td>0 days 00:11:37.640856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0 days 00:06:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0 days 00:29:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0 days 00:36:28.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0 days 00:44:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0 days 01:41:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">nov</th>\n",
       "      <th>count</th>\n",
       "      <td>7019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0 days 00:38:00.895284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0 days 00:12:27.459203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1 days +23:39:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0 days 00:29:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0 days 00:36:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0 days 00:44:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0 days 05:51:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">oct</th>\n",
       "      <th>count</th>\n",
       "      <td>7494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0 days 00:37:41.408993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0 days 00:11:34.161586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0 days 00:00:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0 days 00:29:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0 days 00:36:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0 days 00:44:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0 days 02:11:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">sep</th>\n",
       "      <th>count</th>\n",
       "      <td>4697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0 days 00:37:45.379604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0 days 00:11:42.001603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0 days 00:09:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0 days 00:29:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0 days 00:36:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0 days 00:44:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0 days 01:59:34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          delta3-10\n",
       "month                              \n",
       "apr   count                    3708\n",
       "      mean   0 days 00:37:28.330097\n",
       "      std    0 days 00:12:11.662228\n",
       "      min           0 days 00:11:54\n",
       "      25%           0 days 00:29:12\n",
       "      50%           0 days 00:35:59\n",
       "      75%           0 days 00:43:46\n",
       "      max           0 days 02:53:48\n",
       "aug   count                    3997\n",
       "      mean   0 days 00:36:46.291218\n",
       "      std    0 days 00:11:39.312274\n",
       "      min         -1 days +23:58:33\n",
       "      25%           0 days 00:28:31\n",
       "      50%           0 days 00:35:18\n",
       "      75%           0 days 00:43:09\n",
       "      max           0 days 02:28:39\n",
       "dec   count                    7581\n",
       "      mean   0 days 00:38:19.201160\n",
       "      std    0 days 00:12:03.003995\n",
       "      min           0 days 00:07:08\n",
       "      25%           0 days 00:29:59\n",
       "      50%           0 days 00:36:52\n",
       "      75%           0 days 00:44:53\n",
       "      max           0 days 02:33:05\n",
       "feb   count                    3421\n",
       "      mean   0 days 00:37:15.664425\n",
       "      std    0 days 00:11:56.607197\n",
       "      min           0 days 00:09:25\n",
       "      25%           0 days 00:28:58\n",
       "      50%           0 days 00:36:13\n",
       "...                             ...\n",
       "may   std    0 days 00:11:37.640856\n",
       "      min           0 days 00:06:47\n",
       "      25%           0 days 00:29:17\n",
       "      50%    0 days 00:36:28.500000\n",
       "      75%           0 days 00:44:00\n",
       "      max           0 days 01:41:18\n",
       "nov   count                    7019\n",
       "      mean   0 days 00:38:00.895284\n",
       "      std    0 days 00:12:27.459203\n",
       "      min         -1 days +23:39:35\n",
       "      25%           0 days 00:29:37\n",
       "      50%           0 days 00:36:51\n",
       "      75%           0 days 00:44:36\n",
       "      max           0 days 05:51:26\n",
       "oct   count                    7494\n",
       "      mean   0 days 00:37:41.408993\n",
       "      std    0 days 00:11:34.161586\n",
       "      min           0 days 00:00:25\n",
       "      25%           0 days 00:29:27\n",
       "      50%           0 days 00:36:19\n",
       "      75%           0 days 00:44:17\n",
       "      max           0 days 02:11:28\n",
       "sep   count                    4697\n",
       "      mean   0 days 00:37:45.379604\n",
       "      std    0 days 00:11:42.001603\n",
       "      min           0 days 00:09:45\n",
       "      25%           0 days 00:29:37\n",
       "      50%           0 days 00:36:09\n",
       "      75%           0 days 00:44:28\n",
       "      max           0 days 01:59:34\n",
       "\n",
       "[96 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary stats of 3-10 response time for each month of the year\n",
    "month_map = {1.0:'jan',2.0:'feb',3.0:'mar',4.0:'apr',5.0:'may',6.0:'jun',\n",
    "             7.0:'jul',8.0:'aug',9.0:'sep', 10.0:'oct', 11.0:'nov', 12.0:'dec'}\n",
    "\n",
    "time_delta_df.replace({'month':month_map}).groupby('month')['month', 'delta3-10'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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B81plGIbbGGg7d+7Uhx9+qDJlyqhhw4Y6fPiwoqOj1bNnT3333Xd67LHHXJYPDw93jjlU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\n+23VqlVLRYoUcba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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe320a394e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot of 3-10 response time by month\n",
    "# decided not to map by month since it affects order of months in boxplot\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(14,6))\n",
    "gb_month310 = time_delta_df.groupby('month')['month', 'delta3-10']\n",
    "bplot = sns.boxplot(x='month', y='delta3-10', data=time_delta_df, whis=[5,95])\n",
    "title = 'Distribution of 3-10 Reponse Times For Each Month of the Year'\n",
    "bplot.set_title(title, fontsize=15)\n",
    "bplot.set_xlabel('Month', fontsize=12)\n",
    "bplot.set_ylabel('Response Time (3-10)', fontsize=12)\n",
    "bplot.tick_params(axis='both', labelsize=8)\n",
    "sns.despine(left=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# show descriptions of dispatch events as a quick reference\n",
    "QUERY2='''SELECT * FROM timedesc;\n",
    "  '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SQL QUERY = SELECT * FROM timedesc;\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "incident_types = pquery(QUERY2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timedesc_id</th>\n",
       "      <th>description</th>\n",
       "      <th>id_911</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Received                                      ...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Initiated                                     ...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Entered                                       ...</td>\n",
       "      <td>ENTRY:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Dispatched                                    ...</td>\n",
       "      <td>DISP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Enroute                                       ...</td>\n",
       "      <td>ENROUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>On Scene                                      ...</td>\n",
       "      <td>ONSCNE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Fire Under Control                            ...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Close                                         ...</td>\n",
       "      <td>CLOSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>With Patient                                  ...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Depart Scene                                  ...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Arrive Destination                            ...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Depart Destination                            ...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Clear                                         ...</td>\n",
       "      <td>CLEAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>In Quarters                                   ...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Reopen                                        ...</td>\n",
       "      <td>REOPEN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    timedesc_id                                        description  id_911\n",
       "0             0  Received                                      ...    None\n",
       "1             1  Initiated                                     ...    None\n",
       "2             2  Entered                                       ...  ENTRY:\n",
       "3             3  Dispatched                                    ...  DISP  \n",
       "4             4  Enroute                                       ...  ENROUT\n",
       "5             5  On Scene                                      ...  ONSCNE\n",
       "6             6  Fire Under Control                            ...    None\n",
       "7             7  Close                                         ...  CLOSE \n",
       "8             8  With Patient                                  ...    None\n",
       "9             9  Depart Scene                                  ...    None\n",
       "10           10  Arrive Destination                            ...    None\n",
       "11           11  Depart Destination                            ...    None\n",
       "12           12  Clear                                         ...  CLEAR \n",
       "13           13  In Quarters                                   ...    None\n",
       "14           14  Reopen                                        ...  REOPEN"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incident_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  }
 ],
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