07 - Data Capstone Project 1 - 911 Calls.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 911 Calls Capstone Project\n",
    "\n",
    "Solutions by Salih Kilicli"
   ]
  },
  {
   "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",
    "\n",
    "Just go along with this notebook and try to complete the instructions or answer the questions in bold using your Python and Data Science skills!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data and Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing numpy and pandas**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing visualization libraries and set %matplotlib inline.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style='darkgrid', palette='husl')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Read the csv file and give the dataframe a name**\n",
    "\n",
    "*NOTE: I prefer abbreviation of the name of data but I will refer to it as 'df' from now on*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "emerg = pd.read_csv('911.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the first a couple rows of the data using df.head(count)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40.253473</td>\n",
       "      <td>-75.283245</td>\n",
       "      <td>CANNON AVE &amp; W 9TH ST;  LANSDALE; Station 345;...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: HEAD INJURY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LANSDALE</td>\n",
       "      <td>CANNON AVE &amp; W 9TH ST</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",
       "5  40.253473 -75.283245  CANNON AVE & W 9TH ST;  LANSDALE; Station 345;...   \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",
       "5  19446.0         EMS: HEAD INJURY  2015-12-10 17:40:01           LANSDALE   \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  \n",
       "5       CANNON AVE & W 9TH ST  1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emerg.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use df.info to get more information about the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": [
    "emerg.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the head of df **"
   ]
  },
  {
   "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": {},
   "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": [
    "top = emerg['zip'].value_counts()\n",
    "top.head(5) # = top.iloc[:5] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What are the top 5 townships (twp) for 911 calls?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": [
    "top = emerg['twp'].value_counts()\n",
    "top.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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emerg['title'].nunique() # = len(emerg['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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     EMS\n",
       "1     EMS\n",
       "2    Fire\n",
       "3     EMS\n",
       "4     EMS\n",
       "5     EMS\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Reason = emerg['title'].apply(lambda col: col.split(':')[0]) # Here first (index=0) element is the reason\n",
    "emerg['Reason'] = Reason\n",
    "Reason.head(6)"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Reason.value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now use seaborn to create a countplot of 911 calls by Reason.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '911 Emergency Calls')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Reason', data = emerg)\n",
    "plt.title('911 Emergency Calls')"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(emerg['timeStamp'][0]) # = type(emerg['timeStamp'].loc[0]) = type(emerg['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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-12-10 17:40:00\n",
       "1   2015-12-10 17:40:00\n",
       "2   2015-12-10 17:40:00\n",
       "Name: timeStamp, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emerg['timeStamp'] = pd.to_datetime(emerg['timeStamp'])\n",
    "emerg['timeStamp'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emerg['timeStamp'][0].hour # = emerg['timeStamp'].loc[0].hour = emerg['timeStamp'].iloc[0].hour"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = emerg['timeStamp']\n",
    "emerg['Hour'] = time.apply(lambda t: t.hour)\n",
    "emerg['Month'] = time.apply(lambda t: t.month)\n",
    "emerg['Day of Week'] = time.apply(lambda t: t.dayofweek)\n",
    "emerg.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}\n",
    "emerg['Day of Week'] = emerg['Day of Week'].map(dmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Thu\n",
       "1    Thu\n",
       "2    Thu\n",
       "3    Thu\n",
       "4    Thu\n",
       "Name: Day of Week, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emerg['Day of Week'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a252db6d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Day of Week', data=emerg, hue='Reason', palette='viridis')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # in order to move the legend out of the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a227061d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Month', data=emerg, hue='Reason', palette='rainbow')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
   ]
  },
  {
   "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 groupby 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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "      <td>10212</td>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "      <td>11777</td>\n",
       "      <td>11732</td>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "      <td>10633</td>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "      <td>12133</td>\n",
       "      <td>12088</td>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "      <td>7832</td>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "      <td>9073</td>\n",
       "      <td>9025</td>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7969</td>\n",
       "      <td>7969</td>\n",
       "      <td>7969</td>\n",
       "      <td>6907</td>\n",
       "      <td>7969</td>\n",
       "      <td>7969</td>\n",
       "      <td>7963</td>\n",
       "      <td>7916</td>\n",
       "      <td>7969</td>\n",
       "      <td>7969</td>\n",
       "      <td>7969</td>\n",
       "      <td>7969</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",
       "6      11786  11786  11786  10212  11786      11786  11777  11732  11786   \n",
       "7      12137  12137  12137  10633  12137      12137  12133  12088  12137   \n",
       "8       9078   9078   9078   7832   9078       9078   9073   9025   9078   \n",
       "12      7969   7969   7969   6907   7969       7969   7963   7916   7969   \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  \n",
       "6       11786  11786        11786  \n",
       "7       12137  12137        12137  \n",
       "8        9078   9078         9078  \n",
       "12       7969   7969         7969  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth = emerg.groupby('Month').count()\n",
    "byMonth.head(10)"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22697450>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "byMonth['title'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22935dd0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Month', data=emerg, palette='rainbow')"
   ]
  },
  {
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>1</th>\n",
       "      <td>2</td>\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>2</th>\n",
       "      <td>3</td>\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>3</th>\n",
       "      <td>4</td>\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>4</th>\n",
       "      <td>5</td>\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": [
       "   Month    lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
       "0      1  13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
       "1      2  11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
       "2      3  11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
       "3      4  11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
       "4      5  11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
       "\n",
       "   Reason   Hour  Day of Week  \n",
       "0   13205  13205        13205  \n",
       "1   11467  11467        11467  \n",
       "2   11101  11101        11101  \n",
       "3   11326  11326        11326  \n",
       "4   11423  11423        11423  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = byMonth.reset_index() # Reset the index column\n",
    "data1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a23780b50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Month', y='desc', data=data1)"
   ]
  },
  {
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Date</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",
       "      <td>2015-12-10</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",
       "      <td>2015-12-10</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",
       "      <td>2015-12-10</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",
       "\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",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week        Date  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12         Thu  2015-12-10  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12         Thu  2015-12-10  \n",
       "2                    HAWS AVE  1   Fire    17     12         Thu  2015-12-10  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emerg['Date'] = emerg['timeStamp'].apply(lambda t: t.date())\n",
    "emerg.head(3)"
   ]
  },
  {
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "byDate = emerg.groupby('Date').count()\n",
    "byDate['desc'].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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cause = ['Traffic', 'Fire', 'EMS']\n",
    "cmap  = ['viridis', 'plasma', 'inferno']\n",
    "pkind = ['area', 'area', 'area']\n",
    "for i in range(3):\n",
    "    plt.figure(i)\n",
    "    reason = emerg[emerg['Reason']==cause[i]].groupby('Date').count()\n",
    "    reason['desc'].plot(kind=pkind[i], colormap=cmap[i])\n",
    "    plt.title(cause[i])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 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",
       "\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",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = emerg.groupby(['Day of Week','Hour']).count()['desc'].unstack(level=-1)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a HeatMap using this new DataFrame.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12003b810>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(df, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a clustermap using this DataFrame.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1a25c7b150>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df, 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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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",
       "  </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"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = emerg.groupby(['Day of Week','Month']).count()['desc'].unstack(level=-1)\n",
    "df1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a25b063d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(df1, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1a25b06410>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df1, cmap='viridis')"
   ]
  }
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