911 Calls Data Capstone Project Walkthrough.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 911 Calls Capstone Project"
   ]
  },
  {
   "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": [
    "## Data and Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "** Import numpy and pandas **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import visualization libraries and set %matplotlib inline. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the csv file as a dataframe called df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('911.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the info() of the df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": 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": {
    "collapsed": false
   },
   "source": [
    "** Check the head of df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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": 5,
     "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": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19401.0    6979\n",
       "19464.0    6643\n",
       "19403.0    4854\n",
       "19446.0    4748\n",
       "19406.0    3174\n",
       "Name: zip, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 7,
   "metadata": {
    "collapsed": false
   },
   "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": 7,
     "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? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['title'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 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.** \n",
    "\n",
    "**For example, if the title column value is EMS: BACK PAINS/INJURY , the Reason column value would be EMS. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = df['title'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EMS'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.split(':')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])"
   ]
  },
  {
   "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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of 911 calls by Reason. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x201eaba17f0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kaeLYWYNjKrBq2PaGiNhZj0WSdio75RwHsBroGbY9KTM3jcUTP/HYw2PxNNqGJx57GA6e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\ng+AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201eab35400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Reason',data=df,palette='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "** 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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['timeStamp'].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** You should have seen that these timestamps are still strings. Use [pd.to_datetime](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html) to convert the column from strings to DateTime objects. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['timeStamp'] = pd.to_datetime(df['timeStamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.tslib.Timestamp"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['timeStamp'].iloc[0])"
   ]
  },
  {
   "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": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = df['timeStamp'].iloc[0]\n",
    "time.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Hour'] = df['timeStamp'].apply(lambda time: time.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Month'] = df['timeStamp'].apply(lambda time: time.month)\n",
    "df['Day of Week'] = df['timeStamp'].apply(lambda time: time.dayofweek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>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>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>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>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>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>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>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  Month  Day of Week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12            3  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12            3  \n",
       "2                    HAWS AVE  1   Fire    17     12            3  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    17     12            3  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12            3  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "** 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",
    "    dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['Day of Week'] = df['Day of Week'].map(dmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>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>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>12</td>\n",
       "      <td>Thu</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>12</td>\n",
       "      <td>Thu</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>12</td>\n",
       "      <td>Thu</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>12</td>\n",
       "      <td>Thu</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>12</td>\n",
       "      <td>Thu</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  Month Day of Week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12         Thu  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12         Thu  \n",
       "2                    HAWS AVE  1   Fire    17     12         Thu  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    17     12         Thu  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12         Thu  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "** 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": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x201e80b9438>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IJIWEiIhEyvWzmzLKzGYAw4A9gR7AKuAQ4Bl3/3Y2a2spM9sPWAG8BuQB9cAi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\nSCSFhIiIRPr/NRNY42T/aHUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201e80bdf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Day of Week', data=df, hue='Reason', palette='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x201eabcbeb8>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9Wh3Rbl+qsb6azmnAq0oVJUnSbNHSWsfNMnNLZn4FOHbKzpIkzXGt7jp+U9PdHuCPgS1F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\nSpJUkEErSVJBBq0kSQUZtJIkFfT/AWjfrpc3NykZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201eabcbb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Month',data=df,hue='Reason',palette='flag')\n",
    "\n",
    "# To relocate the legend\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "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... **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 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": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "byMonth = df.groupby('Month').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth.head()"
   ]
  },
  {
   "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": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x201e9de0828>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gcDgoK8nipcMXOHmugxWLM4JdkkQYhcIIFuc6uXlVDq9VN/FK+XlWL0oPdkkR69r1Pg6c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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201e9d98278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byMonth['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": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x201e9df9748>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/G7c7vWu11188jQvOHscfXtds21NLNBbnzY8OsW1fHV+5YgbTxhec8vnB4MBn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\nhrAkVfQFtU8POgXAdAsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201e9df9f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Month',y='twp',data=byMonth.reset_index())"
   ]
  },
  {
   "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": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Date']=df['timeStamp'].apply(lambda t: t.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": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P/MWZZf7bX73EkdNzRCMh9owNsMvZo6zb+329EGIf1NNSPLv7dzgCtdC8sKtU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201e9f2f0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Date').count()['twp'].plot()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "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": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eoCorFKinDp3HY/unQl1ODe6Zz6WjOHmugCcOTOHcXAWHT3tLKWfse3wsn3As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\nMzSObkEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201ea1fc630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='Traffic'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('Traffic')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3oma7RVwuPlnqOouPd/EF2YISLr7BkCvV8Cu33ou7Hj7a+j1F6z3/7vMefrym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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201e9f7a080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='Fire'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('Fire')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ET3J8pIhGy+PtRCRprB9ID7Z7BMW+axgdVHyhB1oIBzJNjlvvPIlPffNYamX5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\noEREJJeUoEREJJeUoEREJJf+HxzrGFmJAozgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201ea2c2f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='EMS'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('EMS')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "____\n",
    "** 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](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html) method. Reference the solutions if you get stuck on this!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "\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",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayHour = df.groupby(by=['Day of Week','Hour']).count()['Reason'].unstack()\n",
    "dayHour.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "** Now create a HeatMap using this new DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x201ea2fa2b0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1MUsrjkTEo4ED62NX4GqqeY7nFu+ZJEmSBsq4iWNEJHAfVZL4UeDizFzZVsckSZJmpQ5X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\nTakScevQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201e9ec4b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(dayHour,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "** Now create a clustermap using this DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x201ea2bb630>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hS5IkqSDDliRJUkGGLUmSpIIMW5IkSQVNDroDkiRJ/RARr996WWaeVLpdw5Yk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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201ea2bbf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(dayHour,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now repeat these same plots and operations, for a DataFrame that shows the Month as the column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "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",
       "  </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"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayMonth = df.groupby(by=['Day of Week','Month']).count()['Reason'].unstack()\n",
    "dayMonth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x201ea74a908>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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45G1BKUmSVNIAO5TOoZQkSVIjdiglSZIKcshbkiRJzQxwyHtsNMDNNSVJklSO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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201ea7406a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(dayMonth,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x201ea2aed68>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PsyoobAEA28lXu/vLsywobAEA28kVVfXeJBclWUqS7j5zZEFhCwDYTv5i1gW9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\nIGELAGAgYQsAYCBhCwBgoP8PY/fXVfd5iREAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201ea2aea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(dayMonth,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}