08 Capstone Project - 911 Calls Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 911 Calls Capstone Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/911.jpg\"/>"
   ]
  },
  {
   "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": [
    "**Import 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": [
    "**Import 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('whitegrid')\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": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/911.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the info() of the df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   lat        99492 non-null  float64\n",
      " 1   lng        99492 non-null  float64\n",
      " 2   desc       99492 non-null  object \n",
      " 3   zip        86637 non-null  float64\n",
      " 4   title      99492 non-null  object \n",
      " 5   timeStamp  99492 non-null  object \n",
      " 6   twp        99449 non-null  object \n",
      " 7   addr       98973 non-null  object \n",
      " 8   e          99492 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the head of df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
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       "    }\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",
       "  </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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19401.0    6979\n",
       "19464.0    6643\n",
       "19403.0    4854\n",
       "19446.0    4748\n",
       "19406.0    3174\n",
       "           ... \n",
       "77316.0       1\n",
       "19457.0       1\n",
       "19139.0       1\n",
       "19119.0       1\n",
       "17752.0       1\n",
       "Name: zip, Length: 104, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "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": 8,
   "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": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].nunique()"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EMS: BACK PAINS/INJURY'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EMS: BACK PAINS/INJURY'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df['title'].iloc[0]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['EMS', ' BACK PAINS/INJURY']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.split(':')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EMS'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.split(':')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0      REINDEER CT & DEAD END  1    EMS  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS  \n",
       "2                    HAWS AVE  1   Fire  \n",
       "3          AIRY ST & SWEDE ST  1    EMS  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now use seaborn to create a countplot of 911 calls by Reason.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8574785390>"
      ]
     },
     "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='Reason',data=df,palette='viridis')"
   ]
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   lat        99492 non-null  float64\n",
      " 1   lng        99492 non-null  float64\n",
      " 2   desc       99492 non-null  object \n",
      " 3   zip        86637 non-null  float64\n",
      " 4   title      99492 non-null  object \n",
      " 5   timeStamp  99492 non-null  object \n",
      " 6   twp        99449 non-null  object \n",
      " 7   addr       98973 non-null  object \n",
      " 8   e          99492 non-null  int64  \n",
      " 9   Reason     99492 non-null  object \n",
      "dtypes: float64(3), int64(1), object(6)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2015-12-10 17:40:00'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timeStamp'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['timeStamp'] = pd.to_datetime(df['timeStamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = df['timeStamp'].iloc[0]\n",
    "time.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-12-10 17:40:00')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17\n",
       "1    17\n",
       "2    17\n",
       "3    17\n",
       "4    17\n",
       "Name: Hour, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hour'] = df['timeStamp'].apply(lambda time: time.hour)\n",
    "\n",
    "df['Hour'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12\n",
       "1    12\n",
       "2    12\n",
       "3    12\n",
       "4    12\n",
       "Name: Month, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Month'] = df['timeStamp'].apply(lambda time: time.month)\n",
    "\n",
    "df['Month'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    3\n",
       "2    3\n",
       "3    3\n",
       "4    3\n",
       "Name: DayofWeek, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DayofWeek'] = df['timeStamp'].apply(lambda time: time.dayofweek)\n",
    "\n",
    "df['DayofWeek'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>DayofWeek</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  DayofWeek  \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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DayofWeek'] = df['DayofWeek'].map(dmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>DayofWeek</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",
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       "      <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 DayofWeek  \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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8574be1828>"
      ]
     },
     "execution_count": 35,
     "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='DayofWeek',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x85750799e8>"
      ]
     },
     "execution_count": 36,
     "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='DayofWeek',data=df,hue='Reason',palette='viridis')\n",
    "\n",
    "# To relocate the legend\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x85747a3b00>"
      ]
     },
     "execution_count": 37,
     "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=df,hue='Reason',palette='viridis')\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",
    "**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": 38,
   "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>DayofWeek</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  DayofWeek  \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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth = df.groupby('Month').count()\n",
    "\n",
    "byMonth"
   ]
  },
  {
   "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8575827940>"
      ]
     },
     "execution_count": 39,
     "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": [
    "# Could be any column\n",
    "byMonth['lat'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8575881c50>"
      ]
     },
     "execution_count": 40,
     "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=df,palette='viridis')"
   ]
  },
  {
   "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x8575900e10>"
      ]
     },
     "execution_count": 41,
     "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='twp',data=byMonth.reset_index())  # Reset indext to Month "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a new column called 'Date' that contains the date from the timeStamp column. You'll need to use apply along with the .date() method.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-12-10 17:40:00')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = df['timeStamp'].iloc[0]\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2015, 12, 10)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date']=df['timeStamp'].apply(lambda t: t.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>DayofWeek</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 DayofWeek        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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.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": 46,
   "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>DayofWeek</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2015-12-10</th>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>100</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>113</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "      <td>333</td>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "      <td>395</td>\n",
       "      <td>391</td>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-12</th>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>333</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>401</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>319</td>\n",
       "      <td>319</td>\n",
       "      <td>319</td>\n",
       "      <td>280</td>\n",
       "      <td>319</td>\n",
       "      <td>319</td>\n",
       "      <td>319</td>\n",
       "      <td>317</td>\n",
       "      <td>319</td>\n",
       "      <td>319</td>\n",
       "      <td>319</td>\n",
       "      <td>319</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>447</td>\n",
       "      <td>447</td>\n",
       "      <td>447</td>\n",
       "      <td>387</td>\n",
       "      <td>447</td>\n",
       "      <td>447</td>\n",
       "      <td>446</td>\n",
       "      <td>445</td>\n",
       "      <td>447</td>\n",
       "      <td>447</td>\n",
       "      <td>447</td>\n",
       "      <td>447</td>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            lat  lng  desc  zip  title  timeStamp  twp  addr    e  Reason  \\\n",
       "Date                                                                        \n",
       "2015-12-10  115  115   115  100    115        115  115   113  115     115   \n",
       "2015-12-11  396  396   396  333    396        396  395   391  396     396   \n",
       "2015-12-12  403  403   403  333    403        403  403   401  403     403   \n",
       "2015-12-13  319  319   319  280    319        319  319   317  319     319   \n",
       "2015-12-14  447  447   447  387    447        447  446   445  447     447   \n",
       "\n",
       "            Hour  Month  DayofWeek  \n",
       "Date                                \n",
       "2015-12-10   115    115        115  \n",
       "2015-12-11   396    396        396  \n",
       "2015-12-12   403    403        403  \n",
       "2015-12-13   319    319        319  \n",
       "2015-12-14   447    447        447  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Date').count().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "df.groupby('Date').count()['twp'].plot()\n",
    "\n",
    "plt.tight_layout()  # To not overlaping the labels"
   ]
  },
  {
   "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": 48,
   "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": [
    "df[df['Reason']=='Traffic'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('Traffic')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": [
    "df[df['Reason']=='Fire'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('Fire')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": [
    "df[df['Reason']=='EMS'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('EMS')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now let's move on to creating  heatmaps with seaborn and our data. We'll first need to restructure the dataframe so that the columns become the Hours and the Index becomes the Day of the Week. There are lots of ways to do this, but I would recommend trying to combine groupby with an [unstack](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": 51,
   "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></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>Month</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>Hour</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 rowspan=\"5\" valign=\"top\">Fri</th>\n",
       "      <th>0</th>\n",
       "      <td>275</td>\n",
       "      <td>275</td>\n",
       "      <td>275</td>\n",
       "      <td>248</td>\n",
       "      <td>275</td>\n",
       "      <td>275</td>\n",
       "      <td>275</td>\n",
       "      <td>275</td>\n",
       "      <td>275</td>\n",
       "      <td>275</td>\n",
       "      <td>275</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>200</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>232</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>165</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>164</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>184</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                lat  lng  desc  zip  title  timeStamp  twp  addr    e  Reason  \\\n",
       "DayofWeek Hour                                                                  \n",
       "Fri       0     275  275   275  248    275        275  275   275  275     275   \n",
       "          1     235  235   235  200    235        235  235   232  235     235   \n",
       "          2     191  191   191  165    191        191  191   191  191     191   \n",
       "          3     175  175   175  164    175        175  175   175  175     175   \n",
       "          4     201  201   201  184    201        201  201   201  201     201   \n",
       "\n",
       "                Month  Date  \n",
       "DayofWeek Hour               \n",
       "Fri       0       275   275  \n",
       "          1       235   235  \n",
       "          2       191   191  \n",
       "          3       175   175  \n",
       "          4       201   201  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayHour = df.groupby(by=['DayofWeek','Hour']).count()\n",
    "\n",
    "dayHour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DayofWeek  Hour\n",
       "Fri        0       275\n",
       "           1       235\n",
       "           2       191\n",
       "           3       175\n",
       "           4       201\n",
       "                  ... \n",
       "Wed        19      686\n",
       "           20      668\n",
       "           21      575\n",
       "           22      490\n",
       "           23      335\n",
       "Name: Reason, Length: 168, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayHour = df.groupby(by=['DayofWeek','Hour']).count()['Reason']\n",
    "\n",
    "dayHour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>DayofWeek</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>275</td>\n",
       "      <td>235</td>\n",
       "      <td>191</td>\n",
       "      <td>175</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>372</td>\n",
       "      <td>598</td>\n",
       "      <td>742</td>\n",
       "      <td>752</td>\n",
       "      <td>...</td>\n",
       "      <td>932</td>\n",
       "      <td>980</td>\n",
       "      <td>1039</td>\n",
       "      <td>980</td>\n",
       "      <td>820</td>\n",
       "      <td>696</td>\n",
       "      <td>667</td>\n",
       "      <td>559</td>\n",
       "      <td>514</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>282</td>\n",
       "      <td>221</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>204</td>\n",
       "      <td>267</td>\n",
       "      <td>397</td>\n",
       "      <td>653</td>\n",
       "      <td>819</td>\n",
       "      <td>786</td>\n",
       "      <td>...</td>\n",
       "      <td>869</td>\n",
       "      <td>913</td>\n",
       "      <td>989</td>\n",
       "      <td>997</td>\n",
       "      <td>885</td>\n",
       "      <td>746</td>\n",
       "      <td>613</td>\n",
       "      <td>497</td>\n",
       "      <td>472</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>375</td>\n",
       "      <td>301</td>\n",
       "      <td>263</td>\n",
       "      <td>260</td>\n",
       "      <td>224</td>\n",
       "      <td>231</td>\n",
       "      <td>257</td>\n",
       "      <td>391</td>\n",
       "      <td>459</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>789</td>\n",
       "      <td>796</td>\n",
       "      <td>848</td>\n",
       "      <td>757</td>\n",
       "      <td>778</td>\n",
       "      <td>696</td>\n",
       "      <td>628</td>\n",
       "      <td>572</td>\n",
       "      <td>506</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>383</td>\n",
       "      <td>306</td>\n",
       "      <td>286</td>\n",
       "      <td>268</td>\n",
       "      <td>242</td>\n",
       "      <td>240</td>\n",
       "      <td>300</td>\n",
       "      <td>402</td>\n",
       "      <td>483</td>\n",
       "      <td>620</td>\n",
       "      <td>...</td>\n",
       "      <td>684</td>\n",
       "      <td>691</td>\n",
       "      <td>663</td>\n",
       "      <td>714</td>\n",
       "      <td>670</td>\n",
       "      <td>655</td>\n",
       "      <td>537</td>\n",
       "      <td>461</td>\n",
       "      <td>415</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>278</td>\n",
       "      <td>202</td>\n",
       "      <td>233</td>\n",
       "      <td>159</td>\n",
       "      <td>182</td>\n",
       "      <td>203</td>\n",
       "      <td>362</td>\n",
       "      <td>570</td>\n",
       "      <td>777</td>\n",
       "      <td>828</td>\n",
       "      <td>...</td>\n",
       "      <td>876</td>\n",
       "      <td>969</td>\n",
       "      <td>935</td>\n",
       "      <td>1013</td>\n",
       "      <td>810</td>\n",
       "      <td>698</td>\n",
       "      <td>617</td>\n",
       "      <td>553</td>\n",
       "      <td>424</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>269</td>\n",
       "      <td>240</td>\n",
       "      <td>186</td>\n",
       "      <td>170</td>\n",
       "      <td>209</td>\n",
       "      <td>239</td>\n",
       "      <td>415</td>\n",
       "      <td>655</td>\n",
       "      <td>889</td>\n",
       "      <td>880</td>\n",
       "      <td>...</td>\n",
       "      <td>943</td>\n",
       "      <td>938</td>\n",
       "      <td>1026</td>\n",
       "      <td>1019</td>\n",
       "      <td>905</td>\n",
       "      <td>731</td>\n",
       "      <td>647</td>\n",
       "      <td>571</td>\n",
       "      <td>462</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>250</td>\n",
       "      <td>216</td>\n",
       "      <td>189</td>\n",
       "      <td>209</td>\n",
       "      <td>156</td>\n",
       "      <td>255</td>\n",
       "      <td>410</td>\n",
       "      <td>701</td>\n",
       "      <td>875</td>\n",
       "      <td>808</td>\n",
       "      <td>...</td>\n",
       "      <td>904</td>\n",
       "      <td>867</td>\n",
       "      <td>990</td>\n",
       "      <td>1037</td>\n",
       "      <td>894</td>\n",
       "      <td>686</td>\n",
       "      <td>668</td>\n",
       "      <td>575</td>\n",
       "      <td>490</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Hour        0    1    2    3    4    5    6    7    8    9   ...   14   15  \\\n",
       "DayofWeek                                                    ...             \n",
       "Fri        275  235  191  175  201  194  372  598  742  752  ...  932  980   \n",
       "Mon        282  221  201  194  204  267  397  653  819  786  ...  869  913   \n",
       "Sat        375  301  263  260  224  231  257  391  459  640  ...  789  796   \n",
       "Sun        383  306  286  268  242  240  300  402  483  620  ...  684  691   \n",
       "Thu        278  202  233  159  182  203  362  570  777  828  ...  876  969   \n",
       "Tue        269  240  186  170  209  239  415  655  889  880  ...  943  938   \n",
       "Wed        250  216  189  209  156  255  410  701  875  808  ...  904  867   \n",
       "\n",
       "Hour         16    17   18   19   20   21   22   23  \n",
       "DayofWeek                                            \n",
       "Fri        1039   980  820  696  667  559  514  474  \n",
       "Mon         989   997  885  746  613  497  472  325  \n",
       "Sat         848   757  778  696  628  572  506  467  \n",
       "Sun         663   714  670  655  537  461  415  330  \n",
       "Thu         935  1013  810  698  617  553  424  354  \n",
       "Tue        1026  1019  905  731  647  571  462  274  \n",
       "Wed         990  1037  894  686  668  575  490  335  \n",
       "\n",
       "[7 rows x 24 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayHour = df.groupby(by=['DayofWeek','Hour']).count()['Reason'].unstack()\n",
    "\n",
    "dayHour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a HeatMap using this new DataFrame.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x85761f46d8>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gTTOFmJm1qp5KR+7bpkiaCBwKHEbaRm034GJgakRMIM2fmSRpR+CsLO5Y4MKsp6Nh/fWBHydpC+DNwPuBGZLuB/47Ir7f38klbQ90VZ3vkWYqaWZWlgK7UI4FfgdcA4wFPg1MJrXCAeYCx5Dm0SyIiDXAGklLgPE0sUhgv33gWZfJHEkPkb4CnAR8HthkApf0TVK/+WOkT54K6dPJzKxldDcwjFDSFGBK1aGZETEzu78tsAdwAmnf4OuAzojo7aRZAYwjJfdlVefoPd6w/vrAPw1MAPYD7iFdvDw12yatPwcDe0VEC05ANTNLGmmBZ8l6Zh9PLwXui4i1QEhaTepG6TUGeJbUJT2mzvGG9dcC7wKmA09GxAMNnntJ9vpVzVTMzGwwFDiV/nbgbEkXAzsBo4GfS5oYEfOA44BbgDuB6ZK6gFGkGe4LmymwvwT+DuC/gCslfYCqRawiYnE/r90deDjr3wGoRIS7UMyspRQ1CCUi5kg6nJSgO4EzgQdJkyFHAouAqyOiW9IMYH4Wd35ErG6mzP4S+Czgq4DY8GtDBTiqn9ee1EyFzMwGU5GLWUXEP9Y5fESduFmk/Dog/Y1CuQS4RNLkrMBGnFzn2D83eA4zs1K9ZCfyVLlD0q+BXUkbOpwWEXf385renWU7gAPIv/a4mdmg6W7j7Q3yJvCvAf8QEfdK2h/4BmkQep8i4lvVjyXNba6KZmbl6XmpLmZVpTMi7gWIiHskre/vBZL2rnq4M+mipplZS+kZAi3wdZJOIF01PZx8U+q/RbrY+XLS+MhzmqqhmVmJKkMggX8I+DJwEfAH0vTQuiQdQNr0+A2kGUmXAFuSFncxM2sp7TzTMG8CfxdwRkQ8mSN2OnByRKyVNI00eH0JaR2A65qrpplZOYZCC/w54FpJj5Na19dXze+v1RkRv5W0MzA6In4DIKmdP+jM7CWq3wt6LSzX0L6IuDQiDiMtYvUB0gzLL0jaehPnfAtwE7ywKcSYOrFmZptVhY7ct1aTqwWeJer3AR8kLbpydvban7DxLKObJC0gLeLyNkmvJPWDX1VUpc3MitLGexrn7kL5NXAF8N6I+FPvwWxM+AYi4kuSrgOeiIilvQk8Iq4ppMZmZgUaCsMI967u85a0U0Q8HhHn1wuOiEVV9/8I5Fl+FoB71z6RN5SHnvtr/0GZMSO2zB37yPL8dega3tjgmi1G5I9//Zb5h86/8/n8lxgO+sCK3LGdO+6YO5YRef+coPvBP+c/LzD80Nfnjt367bvmP/Gjuf806djvDbljxyzL/zc0bK8DcscCjPr+xbljn5//p/6DMq9clv/vomvV6Nyxt2yRP0GO7hiRO7YobTyPJ3cC/4KkM0hDAbcEFgOvKa1WZmaDZH1H+7bA865PchxpHZQfkNaubaz5ZGbWoioN3FpN3gS+NNu/bUxELCG1ws3M2l5PA7dWk7cL5VFJpwHPSbqItKebmVnbe0mPQpE0HngSeDewgLQZ5/tKrpeZ2aAochSKpLt5ccPiB0kz02eTemAWAmdGRI+kycDppHlE0yJiTjPlbbILRdK7gcuAB0hjuZeR1kF5dTOFmZm1mqL6wLM9LomIidntVOBiYGpETCDtjTBJ0o7AWaQluY8FLswmOzasvxb42cAREfFcVSVnk9Y0+UkzBZqZtZL1xTXAXwdsKelnpNx6HnAgcGv2/FzgGKAbWJBdV1yT7Rs8njTfpiH9XcRcX528ASJiRVYBM7O2V+AolFWkVVuPBT5MGrXXUTWHZgUwjnQNcVnV63qPN6y/FnhfF169PZqZvSQ0chFT0hRgStWhmRHRu+H7YmBJlrAXS1pKaoH3GkNaimQ5G64N1Xu8Yf0l8NdIurLmWAewXzOFmZm1mkaGB2bJemYfT58GvBY4I1uNdSzwM0kTI2IeaT7NLcCdwPSsz3wUaW7Nwmbq3l8Cf08fxy9tpjAzs1ZT4Pju7wCzJd1O6nE5DXgKmCVpJLAIuDoiuiXNIO1w1gmcHxGrmylwkwk8Im7d1PNmZu2uUtBFzIhYC7y/zlO1K7YSEbOAWQMtM//qQ2ZmL0HtvKGDE7iZDWmtuMZJXqUkcEn/VHssIv65jLLMzAbiJT2Vvkm9C3V3AAfgYYdm1qJacZGqvEpJ4BHxrerHkuaWUY6Z2UA5gdeQtHfVw52A/FvLmJkNom53oWykugW+GvhUSeWYmQ2IW+A1IuLIMs5rZlY0j0KpkY1C+ShVQywjYucyyjIzG4ieNk7hZXWhnAjsERHPl3R+M7NCuAtlY08A60o6t5lZYdq3/V1wAq9auXAH4G5JC8l+PhFRb40AM7PNqsANHQZd0S3wo0l7Z5qZtQX3gb/o917B0MzaSfum7+IT+F6SLqj3REScV3BZZmYD5ouYL1oFRMHnNDMrjbtQXvSXiLi84HOamZWmnXdoLzqB31Xw+czMSlV0C1zS9qRc+LekyYyzSV3tC4EzI6JH0mTg9Oz5aRExp5myCk3gETHgNU/W9+T/PNx65Fa5Y/+66pncscM7h+WO3WpkV+5YgDEjtswdu3NH/nPvudNTuWOHHTwhdyzr8g/nrzy9NH8dXrFL/joAVBr4R/b8ytyhHXvsk78Ky/P/jIfvc1j+83Y3tidM5/j989fj93/OHTt6qzW5Y7tW5f/b3KYyKnfsz9fm/xsqSpHpW9II0lpQvZMYLwamRsQ8SZcCkyT9EjgLOAjoAm6XdGNE5P8FZLxOt5kNaT0N3HL4MmnT98eyxwcCvSPz5pKGWh8MLIiINRGxDFgCjG+m7t5SzcyGtEoDbXBJU4ApVYdmRsTM7LlTgCcj4gZJ52bPd0REbwErgHHAWGBZ1Tl6jzfMCdzMhrRGhhFmyXpmH0+fBlQkHQ3sD3wP2L7q+THAs8Dy7H7t8YY5gZvZkNZdUC94RBzee1/SPODDwL9JmhgR84DjgFuAO4HpkrqAUcC+pAucDXMCN7MhreRx4OcAsySNBBYBV0dEt6QZwHzSdcjzI2J1Myd3AjezIa2MmZgRMbHq4RF1np8FzBpoOU7gZjakNXIRs9U4gZvZkOa1UMzM2pRb4GZmbWp9I7N8W4wTuJkNae2bvp3AzWyI83KyZmZtyn3gdUh6M7AXcAewuNmB6mZmZfIolBrZtmq7kqaIrgXOBU4qoywzs4HobuMUXtZysm+KiA8CK7Mdel5RUjlmZgNS8HKyg6qsLpTh2UItFUnDaO9di8zsJaziYYQb+QppS6HtSH3gXympHDOzAfEolBoR8WNJNwGvAh6MiPx7UZmZDaJW7BrJq6yLmN+lany8JCLitDLKMjMbCA8j3NiPsv93AAcAO5dUjpnZgHRX2rcNXlYXyg1VD6+X9LMyyjEzG6j2Td/ldaEcU/VwJ2CHMsoxMxsod6FkJF0VEe9lw0k7q0mbfZqZtZyiRqFkQ6ZnASINnT6V1I08m3RNcCFwZkT0SJoMnA6sB6ZFxJxmyiy6Bb4dQEScWvB5zcxKUeA48BMBIuIwSROBi0kJfGpEzJN0KTBJ0i+Bs4CDgC7gdkk3RsSaRgssOoG/MptGv5GIOK/gsszMBqyoFnhEXCuptyW9B/BX4K3ArdmxucAxpNb5gixhr5G0BBgP/LrRMotO4KuAKPicZmalaWQUiqQpwJSqQzMjYmbvg4hYL+ly4B3Au4ATIqL3E2IFMA4YCyyrOkfv8YYVncD/kq19YmbWFhppf2fJemY/MSdL+gxpFvoWVU+NAZ4Flmf3a483rOjFrO4q+HxmZqXqoZL7timSPiDp3OzhKtIIxf/N+sMBjgPmA3cCEyR1SRpHWrV1YTN1L7QFHhGfKvJ8ZmZlK3AtlP8CvivpNmAE8HFgETBL0sjs/tUR0S1pBimZdwLnN7tfQsvtyLO6Z13u2FXr87/njo6O3LFbd43OHfvUquW5YwG2ednY3LHdDfxhbbXT2vyVWJv/YnfPQw83cN71uUMr6/L/ngGG7blX/uAnH89fj7UN/Lt59uncoT075F9BuXO7PfLXAaCBOq95PP/f0Mgt8//+OvP/c2JsAzNldhrRVFfwgBQ1CiUingPeU+epI+rEziINORyQlkvgZmaDqZ03dHACN7MhzeuBm5m1Ka8HbmbWptwCNzNrU26Bm5m1Ka9GaGbWpryhg5lZm+pxH7iZWXtyF4qZWZtyC9zMrE25BW5m1qbcAjcza1M9le7NXYWmOYGb2ZDmiTwZSbfQxwYXEXFUkWWZmRXBU+lf9OHs/58HrgUWAAcDJxRcjplZIdwCz0REAEjaISL+Mzt8jaSPFVmOmVlRimqBSxoBXAbsCYwCpgF/AGaTeiYWAmdGRI+kycDpwHpgWkTMqXfO/pTWBy7pQ6S93w4l7Q9nZtZyCpxK//fA0oj4gKRtgLuBe4CpETFP0qXAJEm/BM4CDgK6gNsl3RgR+bfKypSVwP8vcA7wTuA+4L0llWNmNiAF9oH/GLi66vF64EDg1uzxXOAYoBtYkCXsNZKWAOOBXzdaYCkJPCL+IukGYDFwB6nCZmYtp6g+8IhYCSBpDCmRTwW+HBG9BawAxgFjgWVVL+093rBSErikC4BdgX2BtcC5wElllGVmNhCNtMAlTQGmVB2aGREzq57fDbgG+GZEXCnpX6tixwDPAsuz+7XHG1ZWF8qbIuJwSbdExOWSPlJSOWZmA9LITMyIxTOBmfWek7QD8DPgoxHx8+zw3ZImRsQ84DjgFtK1wemSukgXO/clXeBsWFkJfHhWuYqkYbgLxcxaVIF94OcBLwM+J+lz2bGzgRmSRgKLgKsjolvSDGA+0AmcHxGrmymw6Ik8r42I3wEXA3cB25H6wL9SZDlmZkUpahRKRJxNSti1jqgTOwuYNdAyi26B/4ekXUlXXb8IPAg8GBFPFVyOmVkh2nkxq84iTxYRE4HXAN8H9gMuAH4oaWqR5ZiZFaXSwH+tptAEDpCNbbwL+G12Gw4cUHQ5ZmZF6KlUct9aTdF94J8E3gpsDdwEzAE+GxHriizHzKwoXszqRf8EXA9cCNzqxG1mra7Hu9K/YDtgAnA8cIGkx0nTR/8nIh4puCwzswFzCzyTtbhvzm5IegtpbOQ3gGFFlmVmVoT2Td/QUeSnj6SDSC3wCcA+wL2kvvAb3QI3MytW0V0oXwJuIK2De3fVIi5mZlawQlvgZmY2eAofB25mZoPDCdzMrE05gZuZtSkncDOzNtXyCVxS6XWUNCpHzBbZmr55zrd9A2V3Stol7/uUtK2kjj6eG5u33DqvHSlpixxxdcs2s8HXkqNQJO1FWlP8INLGoJ3A74BPRMTiAZz3RODrwDrSIupXZcdvjoijamJfQVrH/C+k/e2+TdqY4uyImFMTu3dNUd8DPghQr76SvhMRH5L0BuAHwFLStkqnRcSvamJPBXYjrStzJbAa2BI4IyJuqoldBXwsIr6T42exN2m1yLXAjKzOw4Fze38uVbGvJE3G2hfYmbRY2QPAJyPiL/2VZWblKGtHnoH6NimR3NF7QNIhwHeBwwZw3vOBvwE6gB9L6oqIy7PHtb4LfB7Yk5TA9yYlz7mkZFrtJmAV8Fh2LgHfIk3yOoqNvSL7/3TguIi4X9LOwA/ZePH3M4CJwHXA2yJicRb7k6zcavcCfyPpZuCLEXErfZsF/AtpM9U5wOtI+/LdBFxVE/sN4Kys7ENIC5ZdC3wnu28vIZImAUeT/jaeJe0cc/VA5nVI2g74LPA88JWIWJod/3xEfLEmtgN4G/BXIEgNqW7gvIj4a7N1eClq1QTeVZ28ASLiV5LqBku6hbS3XLUOoBIRh1YdWxsRT2evmQTcLOkR6s+mHZ4lwFslHRkRT2SvW18n9iDgUuCSiLgx2wv0yP7fJt0RcX/2/h7roxtlXUQ8J2kFqdXbG1uvzs9HxEezGbHnSvoGKSE/EBEz6ry/m7J/LBdExJ+z91dvAbJxvd8kst/DBRHxOUkv6+uNtXsSkHRxRHyyj+feHRE/ljQa+AKwP+lbybTencmrYl9BmpU8L6v7gcDvST/zZTWxVwIf7/1by1HHt5K+Tc4jfWPdOnt/G816lvR+4E3AaOAp0uzo6+vEfYP0jXcuabf0MaS9HI8F/qEmdkrt63tVb/Sb+R5ps9/hwG2Sjo+Ih6mzWw3wH72IKG8AAAYMSURBVFk9dwS2ITWGVpAadif2VeZQ1KoJ/F5Jl5FWNlxG+iM6nrS+eD2fJbUo30HqcunLQ5IuBj4XESskvZM0c3TrOrEh6dvAlIg4BUDSZ0ldKhsGRjwh6T3AlyW9Psf721rSXcBoSR8idaP8O/BwndjrJP2EtOnpHEk3AG8hW2+mRkdWn/8F/k7SOOBw0jeCWg9J+hHpb2ClpOmkn/XjdWIfkHQp6R/1CcA92c/uuXpvrsEk8APqfwMiIt5fc6i0JCDpF1UPO4B9s28b1DQCAD4C/Bj4GulD9SzgzaTNbuvV+XNZ7J+AqaTfyZVs/O3ljcD1kv4DmL2pD7vsb7OL9LP9ImkTlcdI/w6OrYn9Gul3e1323pcBx0s6LCI+x4b+T0TU/jyvk7SgTjX2yc73fTb8Hdar96jepC7pHuAnkiZS/3f/uoiYkF1zWtjbJSjp9DqxQ1qrJvAzgLeTWgxjgeWkr/nX1AuOiDskfR8YHxF1YzKnAX9P9gcWEX+SdCRwbp3YycCJEVG91uSjpP7ienVYD3xc0in0c3E4Ig7ILpy+jtT10kPq49+o7zoiLpJ0BOkf5SPA9sCMiPjvOqeeXfPaZcBPs1utk0kfiouBlcAnsrqcVif2VNLP4xjSjtqXAa8H3tfHW2wkCVxN6kr6SB/nqlZmEvg66b2fTfpg+iFwUj/1eXVE9H4gLco+1Gp1R8Q8SedHRG+L9Z7sA7/WQ6RGyBeB32Yt8rmkb1DLa2L3jojDs28av4+Ib2bvr96ejPtX/T6ul/TTiDhR0u11YjslTYiI+b0HJB1OaulvICI+KWkfYG5E/LrOuaoN790zNyJ+IelC0gfKVvWCsw+XBZKOzh6/io2/ZQ95LXkR09qbpPmkr/K1SeCfs233auO/CiyIiB/3c97bgDOzjbOR9F7gTGCriDigJnY+aTORBZJ2j4hHsiQwOyLe1Mf59ydd2P0EqTus3vULJD1K+sb0VuDTEXF31m31tYg4rCb2WuAKYFfgadKH6fHAyRFxTE3sCxfTs+6id5G6ofaOiNfWxP6S1H2zLalRcRjpG8ZVte9P0h2kaxh3SJpAWiH0VFLi/Zua2FeSumMOIH0w9gB3A1N7f+418duQfv4PVx0ble3MVR33OuCrwHuruiP/PvuZbVMTuy8wPSLeWXXsJ8BFEfHL2joMZS0/jNDa0inApyQ9KunPkv4EnENqxW8kIj7eX/LOnEXaOHuH7HVXkbot9qgTe3pWh46qPuF/Bz7d18kj4h7gA8BFpLXt+3Ii6VvhYmB81lX1deCjdWInkxL9GaTumz8Ak6jpSsq80DcfEU9GxCUR8Xe1yTvzkew97kX6ELsVuBH4TB+xX1dan/9C0reMk0hdO7X2I/XprwU+FRG7R8QkUhfQBpRGdf0G+Hn2Ydprbp3z7p7V9Re9sRFxBel6QK1XAQdKWlIVO4n0Tc2quAVum1UDF6A3dY7Omq6ugdapEzggu5YwpEj6Fel6RSepn//yiLi83oX5LPZ4slFdOWIbOW+u2KGuVfvArY31kZSBuhcE816A3uR5gUPzxtb7YKgXn/Uvb/RB0si5C4gd1DqQRmo9k72uv5FajYzqavS8eWOHNCdwK0PupNzABeiGzttgbJnnbrfYRkZqtULskOYuFCuFpE8DS3Ik5dLO22gdyjp3O8VKGk4aqfWfEbEqO7YDaWLdx1stdqhzAjcza1MehWJm1qacwM3M2pQTuG1WkiYqTemvPnZRNqPVzDbBCdzMrE15GKG1LEn/TloPB+DKiPiapNnAjyLieklvAd4XEadIehi4D1jkkQo2VDiBWys4StK8qsd7Af9KWjf9ENLf6e1K65z3ZTfS7MmlpdXSrMU4gVsruDkiXljZUNJFwBbA/GxJ1XXZ9Or9al5XvQrhU07eNtS4D9xa1fNk3SeSRpCmyt9P2hVppyymegXCwtZCMWsXTuDWqlYCD2bLpv6KtJvPb0gr+n1C0k3ALpuzgmabm2dimpm1KbfAzczalBO4mVmbcgI3M2tTTuBmZm3KCdzMrE05gZuZtSkncDOzNuUEbmbWpv4/pz/RDOT2VDIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dayHour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x85764ae9e8>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "sns.heatmap(dayHour,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a clustermap using this DataFrame.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x85761d1518>"
      ]
     },
     "execution_count": 56,
     "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(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": 57,
   "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>DayofWeek</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",
       "DayofWeek                                                      \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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayMonth = df.groupby(by=['DayofWeek','Month']).count()['Reason'].unstack()\n",
    "dayMonth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x85765a1438>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(dayMonth,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x8576f3c3c8>"
      ]
     },
     "execution_count": 59,
     "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(dayMonth,cmap='viridis')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}