911 calls

911 calls.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: 911 Calls "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this capstone project we will be analyzing some 911 call data from [Kaggle](https://www.kaggle.com/mchirico/montcoalert). The data contains the following fields:\n",
    "\n",
    "* lat : String variable, Latitude\n",
    "* lng: String variable, Longitude\n",
    "* desc: String variable, Description of the Emergency Call\n",
    "* zip: String variable, Zipcode\n",
    "* title: String variable, Title\n",
    "* timeStamp: String variable, YYYY-MM-DD HH:MM:SS\n",
    "* twp: String variable, Township\n",
    "* addr: String variable, Address\n",
    "* e: String variable, Dummy variable (always 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Importing all the required libraries.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Loading the data file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "scrolled": 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>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  \n",
       "3          AIRY ST & SWEDE ST  1  \n",
       "4    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('911.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Viewing the column details**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 9 columns):\n",
      "lat          99492 non-null float64\n",
      "lng          99492 non-null float64\n",
      "desc         99492 non-null object\n",
      "zip          86637 non-null float64\n",
      "title        99492 non-null object\n",
      "timeStamp    99492 non-null object\n",
      "twp          99449 non-null object\n",
      "addr         98973 non-null object\n",
      "e            99492 non-null int64\n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Checking the data inside the columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "scrolled": 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>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  \n",
       "3          AIRY ST & SWEDE ST  1  \n",
       "4    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 zipcodes for 911 calls? **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "df['zip'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **What are the top 5 townships (twp) for 911 calls?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LOWER MERION    8443\n",
       "ABINGTON        5977\n",
       "NORRISTOWN      5890\n",
       "UPPER MERION    5227\n",
       "CHELTENHAM      4575\n",
       "Name: twp, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['twp'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Take a look at the 'title' column, how many unique title codes are there? **\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['title'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Creating new features**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**\n",
    "In the titles column there are \"Reasons/Departments\" specified before the title code. These are EMS, Fire, and Traffic. Use .apply() with a custom lambda expression to create a new column called \"Reason\" that contains this string value. **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For example, if the title column value is EMS: BACK PAINS/INJURY , the Reason column value would be EMS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df['Reason'] = df['title'].apply( lambda x : x.split(':')[0].strip() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What is the most common Reason for a 911 call based off of this new column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Data Visualisation**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**\n",
    "Now use seaborn to create a countplot of 911 calls by Reason. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f1de08ba58>"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SihgckqQiBockqYjBIUkqYnBIkooYHJKkIgaHJKmIwSFJKmJwSJKKGBySpCIGhySpiMEh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\n5P8BLBJbfHkd/UUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1dd7cbf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Reason', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now let us begin to focus on time information. What is the data type of the objects in the timeStamp column?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['timeStamp'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**You should have seen that these timestamps are still strings. Use pd.to_datetime to convert the column from strings to DateTime objects. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df['timeStamp'] = pd.to_datetime(df['timeStamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** You can now grab specific attributes from a Datetime object by calling them. For example:**\n",
    "\n",
    "    time = df['timeStamp'].iloc[0]\n",
    "    time.hour\n",
    "\n",
    "**You can use Jupyter's tab method to explore the various attributes you can call. Now that the timestamp column are actually DateTime objects, use .apply() to create 3 new columns called Hour, Month, and Day of Week. You will create these columns based off of the timeStamp column, reference the solutions if you get stuck on this step.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true,
    "scrolled": 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>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>hour</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  hour  Hour  Month  Day of Week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17    17     12            3  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17    17     12            3  \n",
       "2                    HAWS AVE  1   Fire    17    17     12            3  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    17    17     12            3  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    17    17     12            3  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hour'] = df['timeStamp'].apply(lambda x: x.hour)\n",
    "df['Month'] = df['timeStamp'].apply(lambda x: x.month)\n",
    "df['Day of Week'] = df['timeStamp'].apply(lambda x: x.dayofweek)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**\n",
    "Notice how the Day of Week is an integer 0-6. Use the .map() with this dictionary to map the actual string names to the day of the week:** \n",
    "\n",
    "\n",
    "** dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'} **\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df['Day of Week'] = df['Day of Week'].apply(lambda x: dmap[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**\n",
    "Now use seaborn to create a countplot of the Day of Week column with the hue based off of the Reason column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f1db395a20>"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SEccCxwOPA2dn5pURsQNwBTAH2AgcmZk9TdUrSdJQmuxp/ieAzJwHLAHOAS4ClmTmfMrA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\nTf8P+SL/jX+zwGYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1db233518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = sns.countplot(x='Day of Week', hue = 'Reason', data = df)\n",
    "plt.legend(bbox_to_anchor = (1,1),borderaxespad = 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f1df10f748>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1ddd76a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Month', hue = 'Reason', data= df)\n",
    "plt.legend(bbox_to_anchor = (1.25,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Did you notice something strange about the Plot?**\n",
    "\n",
    "_____\n",
    "\n",
    "** You should have noticed it was missing some Months, let's see if we can maybe fill in this information by plotting the information in another way, possibly a simple line plot that fills in the missing months, in order to do this, we'll need to do some work with pandas... **\n",
    "\n",
    "** Now create a gropuby object called byMonth, where you group the DataFrame by the month column and use the count() method for aggregation. Use the head() method on this returned DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": true,
    "scrolled": 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>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</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>1</th>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>11527</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13203</td>\n",
       "      <td>13096</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>9930</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11465</td>\n",
       "      <td>11396</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>9755</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11092</td>\n",
       "      <td>11059</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>9895</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11323</td>\n",
       "      <td>11283</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>9946</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11420</td>\n",
       "      <td>11378</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
       "Month                                                                      \n",
       "1      13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
       "2      11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
       "3      11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
       "4      11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
       "5      11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
       "\n",
       "       Reason   Hour  Day of Week  \n",
       "Month                              \n",
       "1       13205  13205        13205  \n",
       "2       11467  11467        11467  \n",
       "3       11101  11101        11101  \n",
       "4       11326  11326        11326  \n",
       "5       11423  11423        11423  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_month = df.groupby('Month').count()\n",
    "missing_month.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a simple plot off of the dataframe indicating the count of calls per month.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f1df381a90>"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lOaxb8cEpLgNdVqqm6BTnKBRuYM2yZJakxFFe2URzxxX0Yzn/rvYNsvd4I68cqKetuxcX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1df16b080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_month['lat'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now see if you can use seaborn's lmplot() to create a linear fit on the number of calls per month. Keep in mind you may need to reset the index to a column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f1df3916a0>"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4rO6Ccyd0FUd+fhaONN8qvajI16+Pd3WRj6vnjuvXx+zIqBF5DA9HqKoLEAyl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\nEMIikoCFEMIikoCFEMIikoCFEMIi/z9Yo2cXJ97JqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1dfaf56a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_month.reset_index(inplace=True)\n",
    "sns.lmplot(x='Month', y='twp', data = missing_month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a new column called 'Date' that contains the date from the timeStamp column. You'll need to use apply along with the .date() method. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#df['timestamp'].apply(lambda x: x.time)\n",
    "df['Date'] = df['timeStamp'].apply(lambda x: x.date())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now groupby this Date column with the count() aggregate and create a plot of counts of 911 calls.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f1df513860>"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cPTvnK5+JKbPQM5dy30TcZCZX4sT5nCN7DWeiaJp1vrGI9RDnFyv8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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1df527b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date_col = df.groupby('Date').count()\n",
    "date_col.reset_index()\n",
    "date_col['twp'].plot()\n",
    "# In a single line\n",
    "#df.groupby('Date').count().reset_index()['twp'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now recreate this plot but create 3 separate plots with each plot representing a Reason for the 911 call**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1f1e12db908>"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KmjoAzAn9jPpOmKZO6JQn15apA/gGgG+yf0twWfjFAH7EPvsOgNcDsAA8oOt6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\nAwWnbxnBWy7f6kY0hThKg8RMkSVI8Cb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bl6fNixDrGHUKkU0J/ZT6MW2vl08z+aVPZQI0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\nFaKkLiJSIUrqIiIV8n+6qBmtI6llGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1e1272cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason'] == 'EMS'].groupby('Date').count()['lat'].plot().set_title('EMS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1f1e140ff98>"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oOzSE2s0qPG4hnuuJ1BdWaqJ58GR9/QYhmkPpGFknUmmnaH479gtANhXFtCx3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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1df670128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason'] == 'Traffic'].groupby('Date').count()['lat'].plot().set_title('Traffic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1f1e19657f0>"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oqXvll5Dcsee9Yg823b4wXk+9bfIRF5OrrhNPvd8LpU8fX8bPunhxRxUyxKUm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\neVvGMbtYQCyiYu+FG1kc/4aJGBZzZWc7OtvY0e8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cdU+8+392gyRJePULd7f/omDdExD5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1e15492e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason'] == 'Fire'].groupby('Date').count()['lat'].plot().set_title('Fire')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now let's move on to creating heatmaps with seaborn and our data. We'll first need to restructure the dataframe so that the columns become the Hours and the Index becomes the Day of the Week. There are lots of ways to do this, but I would recommend trying to combine groupby with an unstack method.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Hour</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>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day of Week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>275</td>\n",
       "      <td>235</td>\n",
       "      <td>191</td>\n",
       "      <td>175</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>372</td>\n",
       "      <td>598</td>\n",
       "      <td>742</td>\n",
       "      <td>752</td>\n",
       "      <td>...</td>\n",
       "      <td>932</td>\n",
       "      <td>980</td>\n",
       "      <td>1039</td>\n",
       "      <td>980</td>\n",
       "      <td>820</td>\n",
       "      <td>696</td>\n",
       "      <td>667</td>\n",
       "      <td>559</td>\n",
       "      <td>514</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>282</td>\n",
       "      <td>221</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>204</td>\n",
       "      <td>267</td>\n",
       "      <td>397</td>\n",
       "      <td>653</td>\n",
       "      <td>819</td>\n",
       "      <td>786</td>\n",
       "      <td>...</td>\n",
       "      <td>869</td>\n",
       "      <td>913</td>\n",
       "      <td>989</td>\n",
       "      <td>997</td>\n",
       "      <td>885</td>\n",
       "      <td>746</td>\n",
       "      <td>613</td>\n",
       "      <td>497</td>\n",
       "      <td>472</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>375</td>\n",
       "      <td>301</td>\n",
       "      <td>263</td>\n",
       "      <td>260</td>\n",
       "      <td>224</td>\n",
       "      <td>231</td>\n",
       "      <td>257</td>\n",
       "      <td>391</td>\n",
       "      <td>459</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>789</td>\n",
       "      <td>796</td>\n",
       "      <td>848</td>\n",
       "      <td>757</td>\n",
       "      <td>778</td>\n",
       "      <td>696</td>\n",
       "      <td>628</td>\n",
       "      <td>572</td>\n",
       "      <td>506</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>383</td>\n",
       "      <td>306</td>\n",
       "      <td>286</td>\n",
       "      <td>268</td>\n",
       "      <td>242</td>\n",
       "      <td>240</td>\n",
       "      <td>300</td>\n",
       "      <td>402</td>\n",
       "      <td>483</td>\n",
       "      <td>620</td>\n",
       "      <td>...</td>\n",
       "      <td>684</td>\n",
       "      <td>691</td>\n",
       "      <td>663</td>\n",
       "      <td>714</td>\n",
       "      <td>670</td>\n",
       "      <td>655</td>\n",
       "      <td>537</td>\n",
       "      <td>461</td>\n",
       "      <td>415</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>278</td>\n",
       "      <td>202</td>\n",
       "      <td>233</td>\n",
       "      <td>159</td>\n",
       "      <td>182</td>\n",
       "      <td>203</td>\n",
       "      <td>362</td>\n",
       "      <td>570</td>\n",
       "      <td>777</td>\n",
       "      <td>828</td>\n",
       "      <td>...</td>\n",
       "      <td>876</td>\n",
       "      <td>969</td>\n",
       "      <td>935</td>\n",
       "      <td>1013</td>\n",
       "      <td>810</td>\n",
       "      <td>698</td>\n",
       "      <td>617</td>\n",
       "      <td>553</td>\n",
       "      <td>424</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>269</td>\n",
       "      <td>240</td>\n",
       "      <td>186</td>\n",
       "      <td>170</td>\n",
       "      <td>209</td>\n",
       "      <td>239</td>\n",
       "      <td>415</td>\n",
       "      <td>655</td>\n",
       "      <td>889</td>\n",
       "      <td>880</td>\n",
       "      <td>...</td>\n",
       "      <td>943</td>\n",
       "      <td>938</td>\n",
       "      <td>1026</td>\n",
       "      <td>1019</td>\n",
       "      <td>905</td>\n",
       "      <td>731</td>\n",
       "      <td>647</td>\n",
       "      <td>571</td>\n",
       "      <td>462</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>250</td>\n",
       "      <td>216</td>\n",
       "      <td>189</td>\n",
       "      <td>209</td>\n",
       "      <td>156</td>\n",
       "      <td>255</td>\n",
       "      <td>410</td>\n",
       "      <td>701</td>\n",
       "      <td>875</td>\n",
       "      <td>808</td>\n",
       "      <td>...</td>\n",
       "      <td>904</td>\n",
       "      <td>867</td>\n",
       "      <td>990</td>\n",
       "      <td>1037</td>\n",
       "      <td>894</td>\n",
       "      <td>686</td>\n",
       "      <td>668</td>\n",
       "      <td>575</td>\n",
       "      <td>490</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Hour          0    1    2    3    4    5    6    7    8    9  ...    14   15  \\\n",
       "Day of Week                                                   ...              \n",
       "Fri          275  235  191  175  201  194  372  598  742  752 ...   932  980   \n",
       "Mon          282  221  201  194  204  267  397  653  819  786 ...   869  913   \n",
       "Sat          375  301  263  260  224  231  257  391  459  640 ...   789  796   \n",
       "Sun          383  306  286  268  242  240  300  402  483  620 ...   684  691   \n",
       "Thu          278  202  233  159  182  203  362  570  777  828 ...   876  969   \n",
       "Tue          269  240  186  170  209  239  415  655  889  880 ...   943  938   \n",
       "Wed          250  216  189  209  156  255  410  701  875  808 ...   904  867   \n",
       "\n",
       "Hour           16    17   18   19   20   21   22   23  \n",
       "Day of Week                                            \n",
       "Fri          1039   980  820  696  667  559  514  474  \n",
       "Mon           989   997  885  746  613  497  472  325  \n",
       "Sat           848   757  778  696  628  572  506  467  \n",
       "Sun           663   714  670  655  537  461  415  330  \n",
       "Thu           935  1013  810  698  617  553  424  354  \n",
       "Tue          1026  1019  905  731  647  571  462  274  \n",
       "Wed           990  1037  894  686  668  575  490  335  \n",
       "\n",
       "[7 rows x 24 columns]"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub1 = df.groupby(['Day of Week','Hour']).count()['Reason'].unstack()\n",
    "df_sub1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a HeatMap using this new DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f1e8ca5be0>"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a2WY2kZAgTomox+7AoWZ2t5ldnqyOVC12OrCVmd0KfBS4MyW231nARe7+TErs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\nDvwfBQ9UlEQ3RaUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1e92373c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_sub1, cmap = 'coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a clustermap using this DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook.py:136: MatplotlibDeprecationWarning: The axisbg attribute was deprecated in version 2.0. Use facecolor instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1f1e776ee48>"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u06BVwLJlyzrdBUmSNEZE7Dfq6m7AXqXbNGhJkqR+MXpEqwG8sXSDBi1JktQX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\nkiRJhRi0JEmSCjFoSZIkFfL/AbvZ7RtecsBOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1e776ee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df_sub1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**\n",
    "Now repeat these same plots and operations, for a DataFrame that shows the Month as the column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day of Week</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>Fri</th>\n",
       "      <td>1970</td>\n",
       "      <td>1581</td>\n",
       "      <td>1525</td>\n",
       "      <td>1958</td>\n",
       "      <td>1730</td>\n",
       "      <td>1649</td>\n",
       "      <td>2045</td>\n",
       "      <td>1310</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>1727</td>\n",
       "      <td>1964</td>\n",
       "      <td>1535</td>\n",
       "      <td>1598</td>\n",
       "      <td>1779</td>\n",
       "      <td>1617</td>\n",
       "      <td>1692</td>\n",
       "      <td>1511</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2291</td>\n",
       "      <td>1441</td>\n",
       "      <td>1266</td>\n",
       "      <td>1734</td>\n",
       "      <td>1444</td>\n",
       "      <td>1388</td>\n",
       "      <td>1695</td>\n",
       "      <td>1099</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>1960</td>\n",
       "      <td>1229</td>\n",
       "      <td>1102</td>\n",
       "      <td>1488</td>\n",
       "      <td>1424</td>\n",
       "      <td>1333</td>\n",
       "      <td>1672</td>\n",
       "      <td>1021</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>1584</td>\n",
       "      <td>1596</td>\n",
       "      <td>1900</td>\n",
       "      <td>1601</td>\n",
       "      <td>1590</td>\n",
       "      <td>2065</td>\n",
       "      <td>1646</td>\n",
       "      <td>1230</td>\n",
       "      <td>1266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>1973</td>\n",
       "      <td>1753</td>\n",
       "      <td>1884</td>\n",
       "      <td>1430</td>\n",
       "      <td>1918</td>\n",
       "      <td>1676</td>\n",
       "      <td>1670</td>\n",
       "      <td>1612</td>\n",
       "      <td>1234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>1700</td>\n",
       "      <td>1903</td>\n",
       "      <td>1889</td>\n",
       "      <td>1517</td>\n",
       "      <td>1538</td>\n",
       "      <td>2058</td>\n",
       "      <td>1717</td>\n",
       "      <td>1295</td>\n",
       "      <td>1262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month          1     2     3     4     5     6     7     8     12\n",
       "Day of Week                                                      \n",
       "Fri          1970  1581  1525  1958  1730  1649  2045  1310  1065\n",
       "Mon          1727  1964  1535  1598  1779  1617  1692  1511  1257\n",
       "Sat          2291  1441  1266  1734  1444  1388  1695  1099   978\n",
       "Sun          1960  1229  1102  1488  1424  1333  1672  1021   907\n",
       "Thu          1584  1596  1900  1601  1590  2065  1646  1230  1266\n",
       "Tue          1973  1753  1884  1430  1918  1676  1670  1612  1234\n",
       "Wed          1700  1903  1889  1517  1538  2058  1717  1295  1262"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub2 = df.groupby(['Day of Week','Month']).count()['Reason'].unstack()\n",
    "df_sub2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a HeatMap using this new DataFrame.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f1e88f73c8>"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ShpDPloKzge8BvwksBR4Bzq8yKEmSpF51k9xskZlnAc9n5jOZeQzwsorjkiRJFel0+nsM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\nUqOY3EiSpEYxuZEkSY3y/wF6i/mw1hWyJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1e88f0278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "sns.heatmap(df_sub2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a clustermap using this DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook.py:136: MatplotlibDeprecationWarning: The axisbg attribute was deprecated in version 2.0. Use facecolor instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1f1e692c278>"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Bh4s3aEjWpIkqV/8CbAcOJRmyDqmdIcGLUmS1C8awKPAz4E7gEWlOzRoSZKk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\nklSIQUuSJKkQg5YkSVIh/x9Fq29Vxn4MYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1e692c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df_sub2, cmap = 'coolwarm')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}